[2024-02-17 17:03:01,924 INFO train.py line 128 87073] => Loading config ... [2024-02-17 17:03:01,924 INFO train.py line 130 87073] Save path: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme [2024-02-17 17:03:03,341 INFO train.py line 131 87073] Config: weight = None resume = False evaluate = True test_only = False seed = 36123202 save_path = 'exp/s3dis/semseg-pt-v3m1-1-ppt-extreme' num_worker = 48 batch_size = 24 batch_size_val = None batch_size_test = None epoch = 100 eval_epoch = 100 sync_bn = False enable_amp = True empty_cache = False find_unused_parameters = True mix_prob = 0.8 param_dicts = [dict(keyword='block', lr=0.0005)] hooks = [ dict(type='CheckpointLoader'), dict(type='IterationTimer', warmup_iter=2), dict(type='InformationWriter'), dict(type='SemSegEvaluator'), dict(type='CheckpointSaver', save_freq=None), dict(type='PreciseEvaluator', test_last=False) ] train = dict(type='MultiDatasetTrainer') test = dict(type='SemSegTester', verbose=True) model = dict( type='PPT-v1m1', backbone=dict( type='PT-v3m1', in_channels=6, order=('z', 'z-trans', 'hilbert', 'hilbert-trans'), stride=(2, 2, 2, 2), enc_depths=(2, 2, 2, 6, 2), enc_channels=(32, 64, 128, 256, 384), enc_num_head=(2, 4, 8, 16, 24), enc_patch_size=(128, 128, 128, 128, 128), dec_depths=(2, 2, 2, 2), dec_channels=(64, 64, 128, 256), dec_num_head=(4, 4, 8, 16), dec_patch_size=(128, 128, 128, 128), mlp_ratio=4, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, drop_path=0.3, shuffle_orders=True, pre_norm=True, enable_rpe=True, enable_flash=False, upcast_attention=True, upcast_softmax=True, cls_mode=False, pdnorm_bn=True, pdnorm_ln=True, pdnorm_decouple=True, pdnorm_adaptive=False, pdnorm_affine=True, pdnorm_conditions=('ScanNet', 'S3DIS', 'Structured3D')), criteria=[ dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1), dict( type='LovaszLoss', mode='multiclass', loss_weight=1.0, ignore_index=-1) ], backbone_out_channels=64, context_channels=256, conditions=('Structured3D', 'ScanNet', 'S3DIS'), template='[x]', clip_model='ViT-B/16', class_name=('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'bookcase', 'picture', 'counter', 'desk', 'shelves', 'curtain', 'dresser', 'pillow', 'mirror', 'ceiling', 'refrigerator', 'television', 'shower curtain', 'nightstand', 'toilet', 'sink', 'lamp', 'bathtub', 'garbagebin', 'board', 'beam', 'column', 'clutter', 'otherstructure', 'otherfurniture', 'otherprop'), valid_index=((0, 1, 2, 3, 4, 5, 6, 7, 8, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 33, 34, 35), (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 20, 22, 24, 25, 27, 34), (0, 1, 4, 5, 6, 7, 8, 10, 19, 29, 30, 31, 32)), backbone_mode=False) optimizer = dict(type='AdamW', lr=0.005, weight_decay=0.05) scheduler = dict( type='OneCycleLR', max_lr=[0.005, 0.0005], pct_start=0.05, anneal_strategy='cos', div_factor=10.0, final_div_factor=1000.0) data = dict( num_classes=13, ignore_index=-1, names=[ 'ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door', 'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter' ], train=dict( type='ConcatDataset', datasets=[ dict( type='Structured3DDataset', split=['train', 'val', 'test'], data_root='data/structured3d', transform=[ dict(type='CenterShift', apply_z=True), dict( type='RandomDropout', dropout_ratio=0.2, dropout_application_ratio=0.2), dict( type='RandomRotate', angle=[-1, 1], axis='z', center=[0, 0, 0], p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='x', p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='y', p=0.5), dict(type='RandomScale', scale=[0.9, 1.1]), dict(type='RandomFlip', p=0.5), dict(type='RandomJitter', sigma=0.005, clip=0.02), dict( type='ChromaticAutoContrast', p=0.2, blend_factor=None), dict(type='ChromaticTranslation', p=0.95, ratio=0.05), dict(type='ChromaticJitter', p=0.95, std=0.05), dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_grid_coord=True), dict(type='SphereCrop', sample_rate=0.8, mode='random'), dict(type='SphereCrop', point_max=204800, mode='random'), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='Add', keys_dict=dict(condition='Structured3D')), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment', 'condition'), feat_keys=('color', 'normal')) ], test_mode=False, loop=4), dict( type='ScanNetDataset', split='train', data_root='data/scannet', transform=[ dict(type='CenterShift', apply_z=True), dict( type='RandomDropout', dropout_ratio=0.2, dropout_application_ratio=0.2), dict( type='RandomRotate', angle=[-1, 1], axis='z', center=[0, 0, 0], p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='x', p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='y', p=0.5), dict(type='RandomScale', scale=[0.9, 1.1]), dict(type='RandomFlip', p=0.5), dict(type='RandomJitter', sigma=0.005, clip=0.02), dict( type='ChromaticAutoContrast', p=0.2, blend_factor=None), dict(type='ChromaticTranslation', p=0.95, ratio=0.05), dict(type='ChromaticJitter', p=0.95, std=0.05), dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_grid_coord=True), dict(type='SphereCrop', point_max=102400, mode='random'), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='Add', keys_dict=dict(condition='ScanNet')), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment', 'condition'), feat_keys=('color', 'normal')) ], test_mode=False, loop=2), dict( type='S3DISDataset', split=('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6'), data_root='data/s3dis', transform=[ dict(type='CenterShift', apply_z=True), dict( type='RandomDropout', dropout_ratio=0.2, dropout_application_ratio=0.2), dict( type='RandomRotate', angle=[-1, 1], axis='z', center=[0, 0, 0], p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='x', p=0.5), dict( type='RandomRotate', angle=[-0.015625, 0.015625], axis='y', p=0.5), dict(type='RandomScale', scale=[0.9, 1.1]), dict(type='RandomFlip', p=0.5), dict(type='RandomJitter', sigma=0.005, clip=0.02), dict( type='ChromaticAutoContrast', p=0.2, blend_factor=None), dict(type='ChromaticTranslation', p=0.95, ratio=0.05), dict(type='ChromaticJitter', p=0.95, std=0.05), dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_grid_coord=True), dict(type='SphereCrop', sample_rate=0.6, mode='random'), dict(type='SphereCrop', point_max=204800, mode='random'), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='Add', keys_dict=dict(condition='S3DIS')), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment', 'condition'), feat_keys=('color', 'normal')) ], test_mode=False, loop=1) ], loop=1), val=dict( type='S3DISDataset', split='Area_5', data_root='data/s3dis', transform=[ dict(type='CenterShift', apply_z=True), dict( type='Copy', keys_dict=dict(coord='origin_coord', segment='origin_segment')), dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='train', return_grid_coord=True), dict(type='CenterShift', apply_z=False), dict(type='NormalizeColor'), dict(type='ToTensor'), dict(type='Add', keys_dict=dict(condition='S3DIS')), dict( type='Collect', keys=('coord', 'grid_coord', 'origin_coord', 'segment', 'origin_segment', 'condition'), offset_keys_dict=dict( offset='coord', origin_offset='origin_coord'), feat_keys=('color', 'normal')) ], test_mode=False), test=dict( type='S3DISDataset', split='Area_5', data_root='data/s3dis', transform=[ dict(type='CenterShift', apply_z=True), dict(type='NormalizeColor') ], test_mode=True, test_cfg=dict( voxelize=dict( type='GridSample', grid_size=0.02, hash_type='fnv', mode='test', keys=('coord', 'color', 'normal'), return_grid_coord=True), crop=None, post_transform=[ dict(type='CenterShift', apply_z=False), dict(type='Add', keys_dict=dict(condition='S3DIS')), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'index', 'condition'), feat_keys=('color', 'normal')) ], aug_transform=[[{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [0.95, 0.95] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [0.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomRotateTargetAngle', 'angle': [1.5], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }, { 'type': 'RandomScale', 'scale': [1.05, 1.05] }], [{ 'type': 'RandomFlip', 'p': 1 }]]))) num_worker_per_gpu = 6 batch_size_per_gpu = 3 batch_size_val_per_gpu = 1 batch_size_test_per_gpu = 1 [2024-02-17 17:03:03,341 INFO train.py line 132 87073] => Building model ... [2024-02-17 17:03:10,632 INFO train.py line 209 87073] Num params: 37029820 [2024-02-17 17:03:11,071 INFO train.py line 134 87073] => Building writer ... [2024-02-17 17:03:11,076 INFO train.py line 219 87073] Tensorboard writer logging dir: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme [2024-02-17 17:03:11,076 INFO train.py line 136 87073] => Building train dataset & dataloader ... [2024-02-17 17:03:30,789 INFO defaults.py line 58 87073] Totally 21391 x 4 samples in ['train', 'val', 'test'] set. [2024-02-17 17:03:30,845 INFO scannet.py line 72 87073] Totally 1201 x 2 samples in train set. [2024-02-17 17:03:30,864 INFO s3dis.py line 55 87073] Totally 204 x 1 samples in ('Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6') set. [2024-02-17 17:03:30,880 INFO defaults.py line 149 87073] Totally 88170 x 1 samples in the concat set. [2024-02-17 17:03:30,884 INFO train.py line 138 87073] => Building val dataset & dataloader ... [2024-02-17 17:03:30,886 INFO s3dis.py line 55 87073] Totally 68 x 1 samples in Area_5 set. [2024-02-17 17:03:30,886 INFO train.py line 140 87073] => Building optimize, scheduler, scaler(amp) ... [2024-02-17 17:03:30,892 INFO optimizer.py line 54 87073] Params Group 1 - lr: 0.005; Params: ['module.logit_scale', 'module.backbone.embedding.stem.conv.weight', 'module.backbone.embedding.stem.norm.norm.0.weight', 'module.backbone.embedding.stem.norm.norm.0.bias', 'module.backbone.embedding.stem.norm.norm.1.weight', 'module.backbone.embedding.stem.norm.norm.1.bias', 'module.backbone.embedding.stem.norm.norm.2.weight', 'module.backbone.embedding.stem.norm.norm.2.bias', 'module.backbone.enc.enc1.down.proj.weight', 'module.backbone.enc.enc1.down.proj.bias', 'module.backbone.enc.enc1.down.norm.0.norm.0.weight', 'module.backbone.enc.enc1.down.norm.0.norm.0.bias', 'module.backbone.enc.enc1.down.norm.0.norm.1.weight', 'module.backbone.enc.enc1.down.norm.0.norm.1.bias', 'module.backbone.enc.enc1.down.norm.0.norm.2.weight', 'module.backbone.enc.enc1.down.norm.0.norm.2.bias', 'module.backbone.enc.enc2.down.proj.weight', 'module.backbone.enc.enc2.down.proj.bias', 'module.backbone.enc.enc2.down.norm.0.norm.0.weight', 'module.backbone.enc.enc2.down.norm.0.norm.0.bias', 'module.backbone.enc.enc2.down.norm.0.norm.1.weight', 'module.backbone.enc.enc2.down.norm.0.norm.1.bias', 'module.backbone.enc.enc2.down.norm.0.norm.2.weight', 'module.backbone.enc.enc2.down.norm.0.norm.2.bias', 'module.backbone.enc.enc3.down.proj.weight', 'module.backbone.enc.enc3.down.proj.bias', 'module.backbone.enc.enc3.down.norm.0.norm.0.weight', 'module.backbone.enc.enc3.down.norm.0.norm.0.bias', 'module.backbone.enc.enc3.down.norm.0.norm.1.weight', 'module.backbone.enc.enc3.down.norm.0.norm.1.bias', 'module.backbone.enc.enc3.down.norm.0.norm.2.weight', 'module.backbone.enc.enc3.down.norm.0.norm.2.bias', 'module.backbone.enc.enc4.down.proj.weight', 'module.backbone.enc.enc4.down.proj.bias', 'module.backbone.enc.enc4.down.norm.0.norm.0.weight', 'module.backbone.enc.enc4.down.norm.0.norm.0.bias', 'module.backbone.enc.enc4.down.norm.0.norm.1.weight', 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'module.backbone.dec.dec0.up.proj.1.norm.2.weight', 'module.backbone.dec.dec0.up.proj.1.norm.2.bias', 'module.backbone.dec.dec0.up.proj_skip.0.weight', 'module.backbone.dec.dec0.up.proj_skip.0.bias', 'module.backbone.dec.dec0.up.proj_skip.1.norm.0.weight', 'module.backbone.dec.dec0.up.proj_skip.1.norm.0.bias', 'module.backbone.dec.dec0.up.proj_skip.1.norm.1.weight', 'module.backbone.dec.dec0.up.proj_skip.1.norm.1.bias', 'module.backbone.dec.dec0.up.proj_skip.1.norm.2.weight', 'module.backbone.dec.dec0.up.proj_skip.1.norm.2.bias', 'module.embedding_table.weight', 'module.proj_head.weight', 'module.proj_head.bias']. [2024-02-17 17:03:30,892 INFO optimizer.py line 54 87073] Params Group 2 - lr: 0.0005; Params: ['module.backbone.enc.enc0.block0.cpe.0.weight', 'module.backbone.enc.enc0.block0.cpe.0.bias', 'module.backbone.enc.enc0.block0.cpe.1.weight', 'module.backbone.enc.enc0.block0.cpe.1.bias', 'module.backbone.enc.enc0.block0.cpe.2.norm.0.weight', 'module.backbone.enc.enc0.block0.cpe.2.norm.0.bias', 'module.backbone.enc.enc0.block0.cpe.2.norm.1.weight', 'module.backbone.enc.enc0.block0.cpe.2.norm.1.bias', 'module.backbone.enc.enc0.block0.cpe.2.norm.2.weight', 'module.backbone.enc.enc0.block0.cpe.2.norm.2.bias', 'module.backbone.enc.enc0.block0.norm1.0.norm.0.weight', 'module.backbone.enc.enc0.block0.norm1.0.norm.0.bias', 'module.backbone.enc.enc0.block0.norm1.0.norm.1.weight', 'module.backbone.enc.enc0.block0.norm1.0.norm.1.bias', 'module.backbone.enc.enc0.block0.norm1.0.norm.2.weight', 'module.backbone.enc.enc0.block0.norm1.0.norm.2.bias', 'module.backbone.enc.enc0.block0.attn.qkv.weight', 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'module.backbone.dec.dec0.block0.mlp.0.fc2.weight', 'module.backbone.dec.dec0.block0.mlp.0.fc2.bias', 'module.backbone.dec.dec0.block1.cpe.0.weight', 'module.backbone.dec.dec0.block1.cpe.0.bias', 'module.backbone.dec.dec0.block1.cpe.1.weight', 'module.backbone.dec.dec0.block1.cpe.1.bias', 'module.backbone.dec.dec0.block1.cpe.2.norm.0.weight', 'module.backbone.dec.dec0.block1.cpe.2.norm.0.bias', 'module.backbone.dec.dec0.block1.cpe.2.norm.1.weight', 'module.backbone.dec.dec0.block1.cpe.2.norm.1.bias', 'module.backbone.dec.dec0.block1.cpe.2.norm.2.weight', 'module.backbone.dec.dec0.block1.cpe.2.norm.2.bias', 'module.backbone.dec.dec0.block1.norm1.0.norm.0.weight', 'module.backbone.dec.dec0.block1.norm1.0.norm.0.bias', 'module.backbone.dec.dec0.block1.norm1.0.norm.1.weight', 'module.backbone.dec.dec0.block1.norm1.0.norm.1.bias', 'module.backbone.dec.dec0.block1.norm1.0.norm.2.weight', 'module.backbone.dec.dec0.block1.norm1.0.norm.2.bias', 'module.backbone.dec.dec0.block1.attn.qkv.weight', 'module.backbone.dec.dec0.block1.attn.qkv.bias', 'module.backbone.dec.dec0.block1.attn.proj.weight', 'module.backbone.dec.dec0.block1.attn.proj.bias', 'module.backbone.dec.dec0.block1.attn.rpe.rpe_table', 'module.backbone.dec.dec0.block1.norm2.0.norm.0.weight', 'module.backbone.dec.dec0.block1.norm2.0.norm.0.bias', 'module.backbone.dec.dec0.block1.norm2.0.norm.1.weight', 'module.backbone.dec.dec0.block1.norm2.0.norm.1.bias', 'module.backbone.dec.dec0.block1.norm2.0.norm.2.weight', 'module.backbone.dec.dec0.block1.norm2.0.norm.2.bias', 'module.backbone.dec.dec0.block1.mlp.0.fc1.weight', 'module.backbone.dec.dec0.block1.mlp.0.fc1.bias', 'module.backbone.dec.dec0.block1.mlp.0.fc2.weight', 'module.backbone.dec.dec0.block1.mlp.0.fc2.bias']. [2024-02-17 17:03:30,894 INFO train.py line 144 87073] => Building hooks ... [2024-02-17 17:03:30,894 INFO misc.py line 213 87073] => Loading checkpoint & weight ... [2024-02-17 17:03:30,894 INFO misc.py line 250 87073] No weight found at: None [2024-02-17 17:03:30,894 INFO train.py line 151 87073] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>> [2024-02-17 17:05:48,593 INFO misc.py line 119 87073] Train: [1/100][1/1557] Data 122.400 (122.400) Batch 137.693 (137.693) Remain 5955:11:05 loss: 5.7008 Lr: 0.00050 [2024-02-17 17:05:49,966 INFO misc.py line 119 87073] Train: [1/100][2/1557] Data 0.006 (0.006) Batch 1.374 (1.374) Remain 59:25:32 loss: 5.8885 Lr: 0.00050 [2024-02-17 17:05:50,957 INFO misc.py line 119 87073] Train: [1/100][3/1557] Data 0.003 (0.003) Batch 0.989 (0.989) Remain 42:47:36 loss: 5.7171 Lr: 0.00050 [2024-02-17 17:05:52,079 INFO misc.py line 119 87073] Train: [1/100][4/1557] Data 0.005 (0.005) Batch 1.122 (1.122) Remain 48:31:59 loss: 4.3736 Lr: 0.00050 [2024-02-17 17:06:07,795 INFO misc.py line 119 87073] Train: [1/100][5/1557] Data 10.336 (5.170) Batch 15.711 (8.417) Remain 364:00:09 loss: 4.8739 Lr: 0.00050 [2024-02-17 17:06:08,931 INFO misc.py line 119 87073] Train: [1/100][6/1557] Data 0.012 (3.451) Batch 1.142 (5.992) Remain 259:07:40 loss: 3.5893 Lr: 0.00050 [2024-02-17 17:06:10,566 INFO misc.py line 119 87073] Train: [1/100][7/1557] Data 0.004 (2.589) Batch 1.636 (4.903) Remain 212:01:47 loss: 5.1104 Lr: 0.00050 [2024-02-17 17:06:11,670 INFO misc.py line 119 87073] Train: [1/100][8/1557] Data 0.004 (2.072) Batch 1.096 (4.141) Remain 179:06:03 loss: 3.1082 Lr: 0.00050 [2024-02-17 17:06:12,525 INFO misc.py line 119 87073] Train: [1/100][9/1557] Data 0.011 (1.729) Batch 0.863 (3.595) Remain 155:28:20 loss: 3.4082 Lr: 0.00050 [2024-02-17 17:06:13,398 INFO misc.py line 119 87073] Train: [1/100][10/1557] Data 0.004 (1.482) Batch 0.872 (3.206) Remain 138:38:55 loss: 3.4691 Lr: 0.00050 [2024-02-17 17:06:14,380 INFO misc.py line 119 87073] Train: [1/100][11/1557] Data 0.005 (1.298) Batch 0.975 (2.927) Remain 126:35:20 loss: 2.5147 Lr: 0.00050 [2024-02-17 17:06:15,094 INFO misc.py line 119 87073] Train: 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(2.004) Remain 86:38:53 loss: 2.2631 Lr: 0.00050 [2024-02-17 17:06:21,807 INFO misc.py line 119 87073] Train: [1/100][19/1557] Data 0.004 (0.652) Batch 0.796 (1.928) Remain 83:22:57 loss: 2.7428 Lr: 0.00050 [2024-02-17 17:06:22,547 INFO misc.py line 119 87073] Train: [1/100][20/1557] Data 0.005 (0.614) Batch 0.728 (1.858) Remain 80:19:46 loss: 2.7650 Lr: 0.00050 [2024-02-17 17:06:23,802 INFO misc.py line 119 87073] Train: [1/100][21/1557] Data 0.016 (0.581) Batch 1.254 (1.824) Remain 78:52:43 loss: 3.4904 Lr: 0.00050 [2024-02-17 17:06:24,812 INFO misc.py line 119 87073] Train: [1/100][22/1557] Data 0.017 (0.551) Batch 1.022 (1.782) Remain 77:03:13 loss: 2.4191 Lr: 0.00050 [2024-02-17 17:06:25,657 INFO misc.py line 119 87073] Train: [1/100][23/1557] Data 0.006 (0.524) Batch 0.846 (1.735) Remain 75:01:45 loss: 2.6098 Lr: 0.00050 [2024-02-17 17:06:26,637 INFO misc.py line 119 87073] Train: [1/100][24/1557] Data 0.005 (0.499) Batch 0.980 (1.699) Remain 73:28:24 loss: 2.7167 Lr: 0.00050 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line 119 87073] Train: [1/100][1290/1557] Data 0.011 (0.071) Batch 0.830 (1.127) Remain 48:20:37 loss: 1.7121 Lr: 0.00080 [2024-02-17 17:30:02,648 INFO misc.py line 119 87073] Train: [1/100][1291/1557] Data 0.004 (0.071) Batch 1.099 (1.127) Remain 48:20:32 loss: 1.5513 Lr: 0.00080 [2024-02-17 17:30:03,614 INFO misc.py line 119 87073] Train: [1/100][1292/1557] Data 0.004 (0.071) Batch 0.966 (1.127) Remain 48:20:12 loss: 1.5203 Lr: 0.00080 [2024-02-17 17:30:04,370 INFO misc.py line 119 87073] Train: [1/100][1293/1557] Data 0.004 (0.071) Batch 0.749 (1.127) Remain 48:19:25 loss: 1.4254 Lr: 0.00080 [2024-02-17 17:30:05,173 INFO misc.py line 119 87073] Train: [1/100][1294/1557] Data 0.011 (0.071) Batch 0.810 (1.126) Remain 48:18:46 loss: 0.8022 Lr: 0.00080 [2024-02-17 17:30:15,327 INFO misc.py line 119 87073] Train: [1/100][1295/1557] Data 2.860 (0.073) Batch 10.154 (1.133) Remain 48:36:44 loss: 0.8954 Lr: 0.00080 [2024-02-17 17:30:16,261 INFO misc.py line 119 87073] Train: 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loss: 1.1495 Lr: 0.00081 [2024-02-17 17:30:28,753 INFO misc.py line 119 87073] Train: [1/100][1309/1557] Data 0.015 (0.072) Batch 1.152 (1.132) Remain 48:31:38 loss: 0.9622 Lr: 0.00081 [2024-02-17 17:30:29,711 INFO misc.py line 119 87073] Train: [1/100][1310/1557] Data 0.014 (0.072) Batch 0.967 (1.131) Remain 48:31:18 loss: 1.2701 Lr: 0.00081 [2024-02-17 17:30:30,633 INFO misc.py line 119 87073] Train: [1/100][1311/1557] Data 0.005 (0.072) Batch 0.921 (1.131) Remain 48:30:52 loss: 0.8959 Lr: 0.00081 [2024-02-17 17:30:31,520 INFO misc.py line 119 87073] Train: [1/100][1312/1557] Data 0.006 (0.072) Batch 0.881 (1.131) Remain 48:30:21 loss: 1.2328 Lr: 0.00081 [2024-02-17 17:30:32,417 INFO misc.py line 119 87073] Train: [1/100][1313/1557] Data 0.012 (0.072) Batch 0.906 (1.131) Remain 48:29:53 loss: 1.4739 Lr: 0.00081 [2024-02-17 17:30:33,197 INFO misc.py line 119 87073] Train: [1/100][1314/1557] Data 0.004 (0.072) Batch 0.780 (1.131) Remain 48:29:11 loss: 0.8326 Lr: 0.00081 [2024-02-17 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line 119 87073] Train: [1/100][1346/1557] Data 0.004 (0.070) Batch 0.936 (1.126) Remain 48:17:21 loss: 1.7075 Lr: 0.00082 [2024-02-17 17:31:04,392 INFO misc.py line 119 87073] Train: [1/100][1347/1557] Data 0.003 (0.070) Batch 0.868 (1.126) Remain 48:16:50 loss: 0.9770 Lr: 0.00082 [2024-02-17 17:31:05,431 INFO misc.py line 119 87073] Train: [1/100][1348/1557] Data 0.017 (0.070) Batch 1.043 (1.126) Remain 48:16:39 loss: 1.0106 Lr: 0.00082 [2024-02-17 17:31:06,189 INFO misc.py line 119 87073] Train: [1/100][1349/1557] Data 0.012 (0.070) Batch 0.766 (1.126) Remain 48:15:57 loss: 0.8887 Lr: 0.00082 [2024-02-17 17:31:06,971 INFO misc.py line 119 87073] Train: [1/100][1350/1557] Data 0.004 (0.070) Batch 0.776 (1.125) Remain 48:15:16 loss: 1.5523 Lr: 0.00082 [2024-02-17 17:31:17,850 INFO misc.py line 119 87073] Train: [1/100][1351/1557] Data 2.340 (0.072) Batch 10.886 (1.133) Remain 48:33:52 loss: 0.8802 Lr: 0.00083 [2024-02-17 17:31:18,762 INFO misc.py line 119 87073] Train: 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Batch 1.178 (1.132) Remain 48:31:21 loss: 1.0506 Lr: 0.00083 [2024-02-17 17:31:25,568 INFO misc.py line 119 87073] Train: [1/100][1359/1557] Data 0.020 (0.072) Batch 1.036 (1.132) Remain 48:31:09 loss: 0.8443 Lr: 0.00083 [2024-02-17 17:31:26,587 INFO misc.py line 119 87073] Train: [1/100][1360/1557] Data 0.013 (0.072) Batch 1.022 (1.132) Remain 48:30:55 loss: 1.5101 Lr: 0.00083 [2024-02-17 17:31:27,521 INFO misc.py line 119 87073] Train: [1/100][1361/1557] Data 0.010 (0.071) Batch 0.936 (1.131) Remain 48:30:32 loss: 1.4734 Lr: 0.00083 [2024-02-17 17:31:28,525 INFO misc.py line 119 87073] Train: [1/100][1362/1557] Data 0.008 (0.071) Batch 1.008 (1.131) Remain 48:30:17 loss: 1.1204 Lr: 0.00083 [2024-02-17 17:31:29,279 INFO misc.py line 119 87073] Train: [1/100][1363/1557] Data 0.004 (0.071) Batch 0.753 (1.131) Remain 48:29:33 loss: 1.2752 Lr: 0.00083 [2024-02-17 17:31:30,031 INFO misc.py line 119 87073] Train: [1/100][1364/1557] Data 0.004 (0.071) Batch 0.740 (1.131) Remain 48:28:47 loss: 1.1146 Lr: 0.00083 [2024-02-17 17:31:31,208 INFO misc.py line 119 87073] Train: [1/100][1365/1557] Data 0.016 (0.071) Batch 1.176 (1.131) Remain 48:28:51 loss: 0.9608 Lr: 0.00083 [2024-02-17 17:31:32,176 INFO misc.py line 119 87073] Train: [1/100][1366/1557] Data 0.017 (0.071) Batch 0.982 (1.131) Remain 48:28:33 loss: 0.8942 Lr: 0.00083 [2024-02-17 17:31:33,189 INFO misc.py line 119 87073] Train: [1/100][1367/1557] Data 0.003 (0.071) Batch 1.013 (1.131) Remain 48:28:19 loss: 1.0473 Lr: 0.00083 [2024-02-17 17:31:34,177 INFO misc.py line 119 87073] Train: [1/100][1368/1557] Data 0.003 (0.071) Batch 0.988 (1.131) Remain 48:28:02 loss: 1.1333 Lr: 0.00083 [2024-02-17 17:31:35,177 INFO misc.py line 119 87073] Train: [1/100][1369/1557] Data 0.004 (0.071) Batch 0.999 (1.130) Remain 48:27:46 loss: 1.0951 Lr: 0.00083 [2024-02-17 17:31:35,867 INFO misc.py line 119 87073] Train: [1/100][1370/1557] Data 0.004 (0.071) Batch 0.680 (1.130) Remain 48:26:54 loss: 1.2489 Lr: 0.00083 [2024-02-17 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loss: 1.3021 Lr: 0.00089 [2024-02-17 17:33:36,872 INFO misc.py line 119 87073] Train: [1/100][1477/1557] Data 0.005 (0.071) Batch 1.251 (1.130) Remain 48:25:02 loss: 0.6508 Lr: 0.00089 [2024-02-17 17:33:37,712 INFO misc.py line 119 87073] Train: [1/100][1478/1557] Data 0.004 (0.071) Batch 0.841 (1.130) Remain 48:24:31 loss: 1.2253 Lr: 0.00089 [2024-02-17 17:33:38,711 INFO misc.py line 119 87073] Train: [1/100][1479/1557] Data 0.003 (0.071) Batch 0.986 (1.130) Remain 48:24:15 loss: 1.0964 Lr: 0.00089 [2024-02-17 17:33:39,636 INFO misc.py line 119 87073] Train: [1/100][1480/1557] Data 0.017 (0.071) Batch 0.935 (1.130) Remain 48:23:53 loss: 1.5006 Lr: 0.00089 [2024-02-17 17:33:40,524 INFO misc.py line 119 87073] Train: [1/100][1481/1557] Data 0.006 (0.071) Batch 0.890 (1.130) Remain 48:23:27 loss: 0.8207 Lr: 0.00089 [2024-02-17 17:33:41,272 INFO misc.py line 119 87073] Train: [1/100][1482/1557] Data 0.003 (0.071) Batch 0.739 (1.129) Remain 48:22:45 loss: 1.1595 Lr: 0.00089 [2024-02-17 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87073] Train: [1/100][1489/1557] Data 0.003 (0.070) Batch 0.752 (1.129) Remain 48:20:38 loss: 0.9586 Lr: 0.00089 [2024-02-17 17:33:48,879 INFO misc.py line 119 87073] Train: [1/100][1490/1557] Data 0.012 (0.070) Batch 0.860 (1.128) Remain 48:20:09 loss: 1.4418 Lr: 0.00089 [2024-02-17 17:33:50,140 INFO misc.py line 119 87073] Train: [1/100][1491/1557] Data 0.004 (0.070) Batch 1.249 (1.128) Remain 48:20:20 loss: 0.8757 Lr: 0.00089 [2024-02-17 17:33:50,979 INFO misc.py line 119 87073] Train: [1/100][1492/1557] Data 0.016 (0.070) Batch 0.850 (1.128) Remain 48:19:50 loss: 1.4982 Lr: 0.00089 [2024-02-17 17:33:52,039 INFO misc.py line 119 87073] Train: [1/100][1493/1557] Data 0.005 (0.070) Batch 1.061 (1.128) Remain 48:19:42 loss: 1.2068 Lr: 0.00090 [2024-02-17 17:33:53,181 INFO misc.py line 119 87073] Train: [1/100][1494/1557] Data 0.004 (0.070) Batch 1.142 (1.128) Remain 48:19:43 loss: 1.0381 Lr: 0.00090 [2024-02-17 17:33:54,021 INFO misc.py line 119 87073] Train: [1/100][1495/1557] Data 0.004 (0.070) Batch 0.839 (1.128) Remain 48:19:11 loss: 0.8125 Lr: 0.00090 [2024-02-17 17:33:54,757 INFO misc.py line 119 87073] Train: [1/100][1496/1557] Data 0.005 (0.070) Batch 0.725 (1.128) Remain 48:18:29 loss: 1.5035 Lr: 0.00090 [2024-02-17 17:33:55,531 INFO misc.py line 119 87073] Train: [1/100][1497/1557] Data 0.016 (0.070) Batch 0.786 (1.128) Remain 48:17:52 loss: 1.2693 Lr: 0.00090 [2024-02-17 17:33:56,797 INFO misc.py line 119 87073] Train: [1/100][1498/1557] Data 0.003 (0.070) Batch 1.233 (1.128) Remain 48:18:02 loss: 0.8988 Lr: 0.00090 [2024-02-17 17:33:57,818 INFO misc.py line 119 87073] Train: [1/100][1499/1557] Data 0.037 (0.070) Batch 1.041 (1.128) Remain 48:17:52 loss: 1.2062 Lr: 0.00090 [2024-02-17 17:33:58,739 INFO misc.py line 119 87073] Train: [1/100][1500/1557] Data 0.017 (0.070) Batch 0.934 (1.127) Remain 48:17:31 loss: 0.8315 Lr: 0.00090 [2024-02-17 17:33:59,751 INFO misc.py line 119 87073] Train: [1/100][1501/1557] Data 0.004 (0.070) Batch 1.012 (1.127) Remain 48:17:18 loss: 1.1132 Lr: 0.00090 [2024-02-17 17:34:00,748 INFO misc.py line 119 87073] Train: [1/100][1502/1557] Data 0.004 (0.070) Batch 0.997 (1.127) Remain 48:17:03 loss: 1.2311 Lr: 0.00090 [2024-02-17 17:34:01,480 INFO misc.py line 119 87073] Train: [1/100][1503/1557] Data 0.004 (0.070) Batch 0.733 (1.127) Remain 48:16:22 loss: 0.9617 Lr: 0.00090 [2024-02-17 17:34:02,178 INFO misc.py line 119 87073] Train: [1/100][1504/1557] Data 0.004 (0.070) Batch 0.688 (1.127) Remain 48:15:35 loss: 1.2796 Lr: 0.00090 [2024-02-17 17:34:03,365 INFO misc.py line 119 87073] Train: [1/100][1505/1557] Data 0.013 (0.070) Batch 1.183 (1.127) Remain 48:15:40 loss: 0.9254 Lr: 0.00090 [2024-02-17 17:34:04,292 INFO misc.py line 119 87073] Train: [1/100][1506/1557] Data 0.017 (0.070) Batch 0.940 (1.127) Remain 48:15:20 loss: 1.2885 Lr: 0.00090 [2024-02-17 17:34:05,453 INFO misc.py line 119 87073] Train: [1/100][1507/1557] Data 0.004 (0.070) Batch 1.161 (1.127) Remain 48:15:22 loss: 0.7014 Lr: 0.00090 [2024-02-17 17:34:06,441 INFO misc.py line 119 87073] Train: [1/100][1508/1557] Data 0.003 (0.070) Batch 0.987 (1.127) Remain 48:15:07 loss: 1.5252 Lr: 0.00090 [2024-02-17 17:34:07,413 INFO misc.py line 119 87073] Train: [1/100][1509/1557] Data 0.004 (0.069) Batch 0.972 (1.126) Remain 48:14:50 loss: 1.2864 Lr: 0.00090 [2024-02-17 17:34:08,148 INFO misc.py line 119 87073] Train: [1/100][1510/1557] Data 0.004 (0.069) Batch 0.725 (1.126) Remain 48:14:08 loss: 1.5186 Lr: 0.00090 [2024-02-17 17:34:09,027 INFO misc.py line 119 87073] Train: [1/100][1511/1557] Data 0.014 (0.069) Batch 0.890 (1.126) Remain 48:13:42 loss: 1.1792 Lr: 0.00090 [2024-02-17 17:34:10,362 INFO misc.py line 119 87073] Train: [1/100][1512/1557] Data 0.003 (0.069) Batch 1.325 (1.126) Remain 48:14:02 loss: 0.8163 Lr: 0.00091 [2024-02-17 17:34:11,267 INFO misc.py line 119 87073] Train: [1/100][1513/1557] Data 0.013 (0.069) Batch 0.913 (1.126) Remain 48:13:39 loss: 0.9770 Lr: 0.00091 [2024-02-17 17:34:12,308 INFO misc.py line 119 87073] Train: [1/100][1514/1557] Data 0.006 (0.069) Batch 1.040 (1.126) Remain 48:13:29 loss: 1.5511 Lr: 0.00091 [2024-02-17 17:34:13,342 INFO misc.py line 119 87073] Train: [1/100][1515/1557] Data 0.007 (0.069) Batch 1.035 (1.126) Remain 48:13:18 loss: 1.1084 Lr: 0.00091 [2024-02-17 17:34:14,271 INFO misc.py line 119 87073] Train: [1/100][1516/1557] Data 0.005 (0.069) Batch 0.931 (1.126) Remain 48:12:57 loss: 0.8967 Lr: 0.00091 [2024-02-17 17:34:15,079 INFO misc.py line 119 87073] Train: [1/100][1517/1557] Data 0.003 (0.069) Batch 0.808 (1.126) Remain 48:12:24 loss: 1.2617 Lr: 0.00091 [2024-02-17 17:34:15,957 INFO misc.py line 119 87073] Train: [1/100][1518/1557] Data 0.003 (0.069) Batch 0.873 (1.125) Remain 48:11:57 loss: 1.2044 Lr: 0.00091 [2024-02-17 17:34:26,158 INFO misc.py line 119 87073] Train: [1/100][1519/1557] Data 2.334 (0.071) Batch 10.206 (1.131) Remain 48:27:20 loss: 0.9694 Lr: 0.00091 [2024-02-17 17:34:27,038 INFO misc.py line 119 87073] Train: [1/100][1520/1557] Data 0.004 (0.071) Batch 0.879 (1.131) Remain 48:26:53 loss: 0.9822 Lr: 0.00091 [2024-02-17 17:34:27,836 INFO misc.py line 119 87073] Train: [1/100][1521/1557] Data 0.004 (0.070) Batch 0.790 (1.131) Remain 48:26:17 loss: 1.0674 Lr: 0.00091 [2024-02-17 17:34:28,839 INFO misc.py line 119 87073] Train: [1/100][1522/1557] Data 0.012 (0.070) Batch 1.011 (1.131) Remain 48:26:04 loss: 1.0328 Lr: 0.00091 [2024-02-17 17:34:29,882 INFO misc.py line 119 87073] Train: [1/100][1523/1557] Data 0.005 (0.070) Batch 1.036 (1.131) Remain 48:25:53 loss: 1.0101 Lr: 0.00091 [2024-02-17 17:34:30,636 INFO misc.py line 119 87073] Train: [1/100][1524/1557] Data 0.012 (0.070) Batch 0.762 (1.131) Remain 48:25:14 loss: 0.9004 Lr: 0.00091 [2024-02-17 17:34:31,380 INFO misc.py line 119 87073] Train: [1/100][1525/1557] Data 0.004 (0.070) Batch 0.742 (1.130) Remain 48:24:34 loss: 1.4483 Lr: 0.00091 [2024-02-17 17:34:32,539 INFO misc.py line 119 87073] Train: [1/100][1526/1557] Data 0.005 (0.070) Batch 1.159 (1.130) Remain 48:24:36 loss: 0.9352 Lr: 0.00091 [2024-02-17 17:34:33,415 INFO misc.py line 119 87073] Train: [1/100][1527/1557] Data 0.006 (0.070) Batch 0.879 (1.130) Remain 48:24:09 loss: 0.7434 Lr: 0.00091 [2024-02-17 17:34:34,297 INFO misc.py line 119 87073] Train: [1/100][1528/1557] Data 0.003 (0.070) Batch 0.882 (1.130) Remain 48:23:43 loss: 0.8245 Lr: 0.00091 [2024-02-17 17:34:35,175 INFO misc.py line 119 87073] Train: [1/100][1529/1557] Data 0.004 (0.070) Batch 0.877 (1.130) Remain 48:23:16 loss: 0.8913 Lr: 0.00091 [2024-02-17 17:34:36,074 INFO misc.py line 119 87073] Train: [1/100][1530/1557] Data 0.003 (0.070) Batch 0.900 (1.130) Remain 48:22:52 loss: 1.1735 Lr: 0.00091 [2024-02-17 17:34:36,825 INFO misc.py line 119 87073] Train: [1/100][1531/1557] Data 0.003 (0.070) Batch 0.751 (1.129) Remain 48:22:12 loss: 0.8397 Lr: 0.00091 [2024-02-17 17:34:37,605 INFO misc.py line 119 87073] Train: [1/100][1532/1557] Data 0.004 (0.070) Batch 0.778 (1.129) Remain 48:21:36 loss: 1.1044 Lr: 0.00092 [2024-02-17 17:34:38,788 INFO misc.py line 119 87073] Train: [1/100][1533/1557] Data 0.005 (0.070) Batch 1.184 (1.129) Remain 48:21:40 loss: 0.6883 Lr: 0.00092 [2024-02-17 17:34:39,769 INFO misc.py line 119 87073] Train: [1/100][1534/1557] Data 0.005 (0.070) Batch 0.981 (1.129) Remain 48:21:24 loss: 0.9982 Lr: 0.00092 [2024-02-17 17:34:40,698 INFO misc.py line 119 87073] Train: [1/100][1535/1557] Data 0.004 (0.070) Batch 0.929 (1.129) Remain 48:21:03 loss: 1.0783 Lr: 0.00092 [2024-02-17 17:34:41,613 INFO misc.py line 119 87073] Train: [1/100][1536/1557] Data 0.004 (0.070) Batch 0.916 (1.129) Remain 48:20:40 loss: 0.9584 Lr: 0.00092 [2024-02-17 17:34:42,906 INFO misc.py line 119 87073] Train: [1/100][1537/1557] Data 0.004 (0.070) Batch 1.293 (1.129) Remain 48:20:56 loss: 1.4405 Lr: 0.00092 [2024-02-17 17:34:43,648 INFO misc.py line 119 87073] Train: [1/100][1538/1557] Data 0.004 (0.070) Batch 0.742 (1.129) Remain 48:20:16 loss: 1.2774 Lr: 0.00092 [2024-02-17 17:34:44,393 INFO misc.py line 119 87073] Train: [1/100][1539/1557] Data 0.003 (0.070) Batch 0.742 (1.129) Remain 48:19:36 loss: 1.3466 Lr: 0.00092 [2024-02-17 17:34:45,647 INFO misc.py line 119 87073] Train: [1/100][1540/1557] Data 0.007 (0.070) Batch 1.256 (1.129) Remain 48:19:47 loss: 0.9208 Lr: 0.00092 [2024-02-17 17:34:46,675 INFO misc.py line 119 87073] Train: [1/100][1541/1557] Data 0.005 (0.070) Batch 1.027 (1.129) Remain 48:19:36 loss: 1.2891 Lr: 0.00092 [2024-02-17 17:34:47,466 INFO misc.py line 119 87073] Train: [1/100][1542/1557] Data 0.006 (0.070) Batch 0.793 (1.128) Remain 48:19:01 loss: 1.1427 Lr: 0.00092 [2024-02-17 17:34:48,349 INFO misc.py line 119 87073] Train: [1/100][1543/1557] Data 0.004 (0.070) Batch 0.884 (1.128) Remain 48:18:36 loss: 1.2737 Lr: 0.00092 [2024-02-17 17:34:49,278 INFO misc.py line 119 87073] Train: [1/100][1544/1557] Data 0.004 (0.070) Batch 0.928 (1.128) Remain 48:18:14 loss: 1.0923 Lr: 0.00092 [2024-02-17 17:34:50,029 INFO misc.py line 119 87073] Train: [1/100][1545/1557] Data 0.004 (0.069) Batch 0.736 (1.128) Remain 48:17:34 loss: 0.9753 Lr: 0.00092 [2024-02-17 17:34:50,789 INFO misc.py line 119 87073] Train: [1/100][1546/1557] Data 0.019 (0.069) Batch 0.775 (1.128) Remain 48:16:58 loss: 1.5022 Lr: 0.00092 [2024-02-17 17:34:52,087 INFO misc.py line 119 87073] Train: [1/100][1547/1557] Data 0.004 (0.069) Batch 1.284 (1.128) Remain 48:17:12 loss: 1.1031 Lr: 0.00092 [2024-02-17 17:34:53,148 INFO misc.py line 119 87073] Train: [1/100][1548/1557] Data 0.018 (0.069) Batch 1.066 (1.128) Remain 48:17:05 loss: 1.1200 Lr: 0.00092 [2024-02-17 17:34:54,403 INFO misc.py line 119 87073] Train: [1/100][1549/1557] Data 0.013 (0.069) Batch 1.255 (1.128) Remain 48:17:17 loss: 0.8595 Lr: 0.00092 [2024-02-17 17:34:55,328 INFO misc.py line 119 87073] Train: [1/100][1550/1557] Data 0.012 (0.069) Batch 0.933 (1.128) Remain 48:16:56 loss: 1.5067 Lr: 0.00093 [2024-02-17 17:34:56,417 INFO misc.py line 119 87073] Train: [1/100][1551/1557] Data 0.004 (0.069) Batch 1.090 (1.128) Remain 48:16:51 loss: 1.3910 Lr: 0.00093 [2024-02-17 17:34:57,176 INFO misc.py line 119 87073] Train: [1/100][1552/1557] Data 0.004 (0.069) Batch 0.758 (1.127) Remain 48:16:13 loss: 1.3912 Lr: 0.00093 [2024-02-17 17:34:57,948 INFO misc.py line 119 87073] Train: [1/100][1553/1557] Data 0.005 (0.069) Batch 0.759 (1.127) Remain 48:15:36 loss: 0.9038 Lr: 0.00093 [2024-02-17 17:34:59,131 INFO misc.py line 119 87073] Train: [1/100][1554/1557] Data 0.017 (0.069) Batch 1.183 (1.127) Remain 48:15:40 loss: 0.9052 Lr: 0.00093 [2024-02-17 17:34:59,936 INFO misc.py line 119 87073] Train: [1/100][1555/1557] Data 0.017 (0.069) Batch 0.819 (1.127) Remain 48:15:08 loss: 1.0423 Lr: 0.00093 [2024-02-17 17:35:00,684 INFO misc.py line 119 87073] Train: [1/100][1556/1557] Data 0.004 (0.069) Batch 0.748 (1.127) Remain 48:14:30 loss: 1.5692 Lr: 0.00093 [2024-02-17 17:35:01,567 INFO misc.py line 119 87073] Train: [1/100][1557/1557] Data 0.004 (0.069) Batch 0.873 (1.127) Remain 48:14:03 loss: 1.1711 Lr: 0.00093 [2024-02-17 17:35:01,567 INFO misc.py line 136 87073] Train result: loss: 1.5296 [2024-02-17 17:35:01,567 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 17:35:33,195 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 1.0702 [2024-02-17 17:35:34,126 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8066 [2024-02-17 17:35:36,253 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 1.1559 [2024-02-17 17:35:38,460 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.8769 [2024-02-17 17:35:43,394 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 1.0881 [2024-02-17 17:35:44,096 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.4353 [2024-02-17 17:35:45,357 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 1.1361 [2024-02-17 17:35:48,308 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 1.0535 [2024-02-17 17:35:50,116 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 1.1220 [2024-02-17 17:35:51,837 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.4847/0.5650/0.8138. [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.8970/0.9644 [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9667/0.9845 [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.7360/0.9646 [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0002/0.0045 [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.0332/0.0380 [2024-02-17 17:35:51,838 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.2241/0.2561 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.1743/0.1971 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.6393/0.8776 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8288/0.8526 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.5235/0.6727 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.4992/0.5330 [2024-02-17 17:35:51,839 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.3576/0.4102 [2024-02-17 17:35:51,840 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.4217/0.5892 [2024-02-17 17:35:51,840 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 17:35:51,842 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.4847 [2024-02-17 17:35:51,843 INFO misc.py line 165 87073] Currently Best mIoU: 0.4847 [2024-02-17 17:35:51,843 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 17:36:03,104 INFO misc.py line 119 87073] Train: [2/100][1/1557] Data 1.643 (1.643) Batch 2.578 (2.578) Remain 110:22:10 loss: 1.2232 Lr: 0.00093 [2024-02-17 17:36:04,046 INFO misc.py line 119 87073] Train: [2/100][2/1557] Data 0.010 (0.010) Batch 0.944 (0.944) Remain 40:24:09 loss: 1.1813 Lr: 0.00093 [2024-02-17 17:36:05,056 INFO misc.py line 119 87073] Train: [2/100][3/1557] Data 0.008 (0.008) Batch 1.000 (1.000) Remain 42:50:02 loss: 0.4511 Lr: 0.00093 [2024-02-17 17:36:05,825 INFO misc.py line 119 87073] Train: [2/100][4/1557] Data 0.016 (0.016) Batch 0.777 (0.777) Remain 33:14:59 loss: 1.2166 Lr: 0.00093 [2024-02-17 17:36:06,582 INFO misc.py line 119 87073] Train: [2/100][5/1557] Data 0.011 (0.014) Batch 0.760 (0.768) Remain 32:53:51 loss: 1.0316 Lr: 0.00093 [2024-02-17 17:36:07,307 INFO misc.py line 119 87073] Train: [2/100][6/1557] Data 0.007 (0.011) Batch 0.719 (0.752) Remain 32:11:22 loss: 1.0942 Lr: 0.00093 [2024-02-17 17:36:08,509 INFO misc.py line 119 87073] Train: [2/100][7/1557] Data 0.012 (0.011) Batch 1.207 (0.866) Remain 37:03:57 loss: 0.8647 Lr: 0.00093 [2024-02-17 17:36:09,543 INFO misc.py line 119 87073] Train: [2/100][8/1557] Data 0.006 (0.010) Batch 1.024 (0.897) Remain 38:25:20 loss: 0.8687 Lr: 0.00093 [2024-02-17 17:36:10,518 INFO misc.py line 119 87073] Train: [2/100][9/1557] Data 0.017 (0.011) Batch 0.987 (0.912) Remain 39:03:43 loss: 1.0933 Lr: 0.00093 [2024-02-17 17:36:11,437 INFO misc.py line 119 87073] Train: [2/100][10/1557] Data 0.004 (0.010) Batch 0.918 (0.913) Remain 39:05:43 loss: 1.3819 Lr: 0.00093 [2024-02-17 17:36:12,424 INFO misc.py line 119 87073] Train: [2/100][11/1557] Data 0.005 (0.010) Batch 0.987 (0.922) Remain 39:29:30 loss: 1.2781 Lr: 0.00093 [2024-02-17 17:36:13,164 INFO misc.py line 119 87073] Train: [2/100][12/1557] Data 0.005 (0.009) Batch 0.740 (0.902) Remain 38:37:31 loss: 1.3874 Lr: 0.00094 [2024-02-17 17:36:13,938 INFO misc.py line 119 87073] Train: [2/100][13/1557] Data 0.004 (0.009) Batch 0.773 (0.889) Remain 38:04:17 loss: 1.1292 Lr: 0.00094 [2024-02-17 17:36:15,024 INFO misc.py line 119 87073] Train: [2/100][14/1557] Data 0.007 (0.008) Batch 1.074 (0.906) Remain 38:47:30 loss: 1.0573 Lr: 0.00094 [2024-02-17 17:36:16,070 INFO misc.py line 119 87073] Train: [2/100][15/1557] Data 0.017 (0.009) Batch 1.043 (0.917) Remain 39:16:47 loss: 0.9252 Lr: 0.00094 [2024-02-17 17:36:17,091 INFO misc.py line 119 87073] Train: [2/100][16/1557] Data 0.020 (0.010) Batch 1.021 (0.925) Remain 39:37:13 loss: 1.2300 Lr: 0.00094 [2024-02-17 17:36:18,229 INFO misc.py line 119 87073] Train: [2/100][17/1557] Data 0.021 (0.011) Batch 1.136 (0.940) Remain 40:15:47 loss: 2.0107 Lr: 0.00094 [2024-02-17 17:36:19,287 INFO misc.py line 119 87073] Train: [2/100][18/1557] Data 0.023 (0.012) Batch 1.067 (0.949) Remain 40:37:30 loss: 1.2487 Lr: 0.00094 [2024-02-17 17:36:20,028 INFO misc.py line 119 87073] Train: [2/100][19/1557] Data 0.014 (0.012) Batch 0.751 (0.937) Remain 40:05:39 loss: 1.1399 Lr: 0.00094 [2024-02-17 17:36:20,795 INFO misc.py line 119 87073] Train: [2/100][20/1557] Data 0.004 (0.011) Batch 0.765 (0.926) Remain 39:39:41 loss: 0.5776 Lr: 0.00094 [2024-02-17 17:36:28,515 INFO misc.py line 119 87073] Train: [2/100][21/1557] Data 6.474 (0.370) Batch 7.723 (1.304) Remain 55:49:33 loss: 1.3355 Lr: 0.00094 [2024-02-17 17:36:29,468 INFO misc.py line 119 87073] Train: [2/100][22/1557] Data 0.003 (0.351) Batch 0.951 (1.285) Remain 55:01:52 loss: 1.3467 Lr: 0.00094 [2024-02-17 17:36:30,445 INFO misc.py line 119 87073] Train: [2/100][23/1557] Data 0.004 (0.334) Batch 0.978 (1.270) Remain 54:22:19 loss: 1.1603 Lr: 0.00094 [2024-02-17 17:36:31,381 INFO misc.py line 119 87073] Train: [2/100][24/1557] Data 0.004 (0.318) Batch 0.926 (1.254) Remain 53:40:09 loss: 0.8755 Lr: 0.00094 [2024-02-17 17:36:32,452 INFO misc.py line 119 87073] Train: [2/100][25/1557] Data 0.016 (0.304) Batch 1.067 (1.245) Remain 53:18:19 loss: 0.9174 Lr: 0.00094 [2024-02-17 17:36:33,218 INFO misc.py line 119 87073] Train: [2/100][26/1557] Data 0.019 (0.292) Batch 0.782 (1.225) Remain 52:26:32 loss: 1.1742 Lr: 0.00094 [2024-02-17 17:36:33,944 INFO misc.py line 119 87073] Train: [2/100][27/1557] Data 0.004 (0.280) Batch 0.722 (1.204) Remain 51:32:39 loss: 1.7833 Lr: 0.00094 [2024-02-17 17:36:34,930 INFO misc.py line 119 87073] Train: [2/100][28/1557] Data 0.007 (0.269) Batch 0.989 (1.195) Remain 51:10:30 loss: 0.8894 Lr: 0.00094 [2024-02-17 17:36:35,835 INFO misc.py line 119 87073] Train: [2/100][29/1557] Data 0.005 (0.259) Batch 0.906 (1.184) Remain 50:41:51 loss: 0.9649 Lr: 0.00094 [2024-02-17 17:36:36,660 INFO misc.py line 119 87073] Train: [2/100][30/1557] Data 0.004 (0.249) Batch 0.820 (1.171) Remain 50:07:09 loss: 0.7151 Lr: 0.00094 [2024-02-17 17:36:37,479 INFO misc.py line 119 87073] Train: [2/100][31/1557] Data 0.010 (0.241) Batch 0.824 (1.158) Remain 49:35:22 loss: 1.0774 Lr: 0.00095 [2024-02-17 17:36:38,381 INFO misc.py line 119 87073] Train: [2/100][32/1557] Data 0.005 (0.233) Batch 0.901 (1.150) Remain 49:12:34 loss: 1.6229 Lr: 0.00095 [2024-02-17 17:36:39,144 INFO misc.py line 119 87073] Train: [2/100][33/1557] Data 0.005 (0.225) Batch 0.756 (1.136) Remain 48:38:49 loss: 0.7287 Lr: 0.00095 [2024-02-17 17:36:39,907 INFO misc.py line 119 87073] Train: [2/100][34/1557] Data 0.013 (0.218) Batch 0.770 (1.125) Remain 48:08:25 loss: 1.1871 Lr: 0.00095 [2024-02-17 17:36:41,211 INFO misc.py line 119 87073] Train: [2/100][35/1557] Data 0.006 (0.212) Batch 1.304 (1.130) Remain 48:22:49 loss: 0.9072 Lr: 0.00095 [2024-02-17 17:36:42,121 INFO misc.py line 119 87073] Train: [2/100][36/1557] Data 0.005 (0.205) Batch 0.909 (1.123) Remain 48:05:33 loss: 0.7102 Lr: 0.00095 [2024-02-17 17:36:43,071 INFO misc.py line 119 87073] Train: [2/100][37/1557] Data 0.008 (0.200) Batch 0.952 (1.118) Remain 47:52:36 loss: 1.6403 Lr: 0.00095 [2024-02-17 17:36:44,321 INFO misc.py line 119 87073] Train: [2/100][38/1557] Data 0.004 (0.194) Batch 1.241 (1.122) Remain 48:01:35 loss: 0.8297 Lr: 0.00095 [2024-02-17 17:36:45,365 INFO misc.py line 119 87073] Train: [2/100][39/1557] Data 0.013 (0.189) Batch 1.051 (1.120) Remain 47:56:31 loss: 1.2847 Lr: 0.00095 [2024-02-17 17:36:46,234 INFO misc.py line 119 87073] Train: [2/100][40/1557] Data 0.007 (0.184) Batch 0.871 (1.113) Remain 47:39:13 loss: 0.9739 Lr: 0.00095 [2024-02-17 17:36:46,989 INFO misc.py line 119 87073] Train: [2/100][41/1557] Data 0.004 (0.179) Batch 0.756 (1.104) Remain 47:15:02 loss: 1.0598 Lr: 0.00095 [2024-02-17 17:36:48,163 INFO misc.py line 119 87073] Train: [2/100][42/1557] Data 0.003 (0.175) Batch 1.160 (1.105) Remain 47:18:44 loss: 0.9450 Lr: 0.00095 [2024-02-17 17:36:49,161 INFO misc.py line 119 87073] Train: [2/100][43/1557] Data 0.017 (0.171) Batch 1.006 (1.103) Remain 47:12:22 loss: 0.9059 Lr: 0.00095 [2024-02-17 17:36:50,154 INFO misc.py line 119 87073] Train: [2/100][44/1557] Data 0.009 (0.167) Batch 0.997 (1.100) Remain 47:05:45 loss: 1.4127 Lr: 0.00095 [2024-02-17 17:36:51,136 INFO misc.py line 119 87073] Train: [2/100][45/1557] Data 0.005 (0.163) Batch 0.981 (1.097) Remain 46:58:24 loss: 0.8961 Lr: 0.00095 [2024-02-17 17:36:51,914 INFO misc.py line 119 87073] Train: [2/100][46/1557] Data 0.007 (0.159) Batch 0.780 (1.090) Remain 46:39:28 loss: 1.5388 Lr: 0.00095 [2024-02-17 17:36:52,643 INFO misc.py line 119 87073] Train: [2/100][47/1557] Data 0.004 (0.156) Batch 0.719 (1.082) Remain 46:17:49 loss: 0.9532 Lr: 0.00095 [2024-02-17 17:36:53,371 INFO misc.py line 119 87073] Train: [2/100][48/1557] Data 0.013 (0.153) Batch 0.738 (1.074) Remain 45:58:11 loss: 1.7958 Lr: 0.00095 [2024-02-17 17:36:54,596 INFO misc.py line 119 87073] Train: [2/100][49/1557] Data 0.003 (0.149) Batch 1.224 (1.077) Remain 46:06:33 loss: 1.1009 Lr: 0.00096 [2024-02-17 17:36:55,834 INFO misc.py line 119 87073] Train: [2/100][50/1557] Data 0.004 (0.146) Batch 1.226 (1.080) Remain 46:14:39 loss: 1.6974 Lr: 0.00096 [2024-02-17 17:36:56,712 INFO misc.py line 119 87073] Train: [2/100][51/1557] Data 0.016 (0.144) Batch 0.889 (1.076) Remain 46:04:24 loss: 1.8856 Lr: 0.00096 [2024-02-17 17:36:57,764 INFO misc.py line 119 87073] Train: [2/100][52/1557] Data 0.005 (0.141) Batch 1.050 (1.076) Remain 46:03:01 loss: 0.9859 Lr: 0.00096 [2024-02-17 17:36:58,780 INFO misc.py line 119 87073] Train: [2/100][53/1557] Data 0.007 (0.138) Batch 1.018 (1.075) Remain 46:00:02 loss: 1.5163 Lr: 0.00096 [2024-02-17 17:36:59,426 INFO misc.py line 119 87073] Train: [2/100][54/1557] Data 0.003 (0.135) Batch 0.646 (1.066) Remain 45:38:26 loss: 1.0636 Lr: 0.00096 [2024-02-17 17:37:00,161 INFO misc.py line 119 87073] Train: [2/100][55/1557] Data 0.004 (0.133) Batch 0.733 (1.060) Remain 45:21:57 loss: 1.0431 Lr: 0.00096 [2024-02-17 17:37:01,364 INFO misc.py line 119 87073] Train: [2/100][56/1557] Data 0.007 (0.131) Batch 1.204 (1.063) Remain 45:28:56 loss: 0.6458 Lr: 0.00096 [2024-02-17 17:37:02,300 INFO misc.py line 119 87073] Train: [2/100][57/1557] Data 0.005 (0.128) Batch 0.936 (1.060) Remain 45:22:52 loss: 0.9041 Lr: 0.00096 [2024-02-17 17:37:03,222 INFO misc.py line 119 87073] Train: [2/100][58/1557] Data 0.005 (0.126) Batch 0.923 (1.058) Remain 45:16:25 loss: 1.6039 Lr: 0.00096 [2024-02-17 17:37:04,057 INFO misc.py line 119 87073] Train: [2/100][59/1557] Data 0.005 (0.124) Batch 0.836 (1.054) Remain 45:06:13 loss: 1.0709 Lr: 0.00096 [2024-02-17 17:37:04,859 INFO misc.py line 119 87073] Train: [2/100][60/1557] Data 0.004 (0.122) Batch 0.799 (1.049) Remain 44:54:44 loss: 4.4359 Lr: 0.00096 [2024-02-17 17:37:05,645 INFO misc.py line 119 87073] Train: [2/100][61/1557] Data 0.006 (0.120) Batch 0.789 (1.045) Remain 44:43:11 loss: 1.1722 Lr: 0.00096 [2024-02-17 17:37:06,421 INFO misc.py line 119 87073] Train: [2/100][62/1557] Data 0.005 (0.118) Batch 0.776 (1.040) Remain 44:31:27 loss: 1.5025 Lr: 0.00096 [2024-02-17 17:37:18,402 INFO misc.py line 119 87073] Train: [2/100][63/1557] Data 4.901 (0.198) Batch 11.982 (1.223) Remain 52:19:45 loss: 1.1545 Lr: 0.00096 [2024-02-17 17:37:19,647 INFO misc.py line 119 87073] Train: [2/100][64/1557] Data 0.004 (0.194) Batch 1.239 (1.223) Remain 52:20:25 loss: 1.5591 Lr: 0.00096 [2024-02-17 17:37:20,545 INFO misc.py line 119 87073] Train: [2/100][65/1557] Data 0.009 (0.191) Batch 0.903 (1.218) Remain 52:07:08 loss: 0.9901 Lr: 0.00096 [2024-02-17 17:37:21,315 INFO misc.py line 119 87073] Train: [2/100][66/1557] Data 0.004 (0.188) Batch 0.771 (1.211) Remain 51:48:55 loss: 0.8770 Lr: 0.00096 [2024-02-17 17:37:22,352 INFO misc.py line 119 87073] Train: [2/100][67/1557] Data 0.003 (0.185) Batch 1.032 (1.208) Remain 51:41:44 loss: 1.3285 Lr: 0.00097 [2024-02-17 17:37:23,124 INFO misc.py line 119 87073] Train: [2/100][68/1557] Data 0.012 (0.183) Batch 0.777 (1.201) Remain 51:24:41 loss: 1.4362 Lr: 0.00097 [2024-02-17 17:37:23,890 INFO misc.py line 119 87073] Train: [2/100][69/1557] Data 0.003 (0.180) Batch 0.753 (1.194) Remain 51:07:13 loss: 1.4303 Lr: 0.00097 [2024-02-17 17:37:24,949 INFO misc.py line 119 87073] Train: [2/100][70/1557] Data 0.016 (0.178) Batch 1.057 (1.192) Remain 51:01:56 loss: 1.0436 Lr: 0.00097 [2024-02-17 17:37:25,928 INFO misc.py line 119 87073] Train: [2/100][71/1557] Data 0.018 (0.175) Batch 0.994 (1.189) Remain 50:54:25 loss: 1.4113 Lr: 0.00097 [2024-02-17 17:37:27,084 INFO misc.py line 119 87073] Train: [2/100][72/1557] Data 0.004 (0.173) Batch 1.156 (1.189) Remain 50:53:09 loss: 0.8754 Lr: 0.00097 [2024-02-17 17:37:28,069 INFO misc.py line 119 87073] Train: [2/100][73/1557] Data 0.004 (0.170) Batch 0.983 (1.186) Remain 50:45:35 loss: 1.0888 Lr: 0.00097 [2024-02-17 17:37:29,094 INFO misc.py line 119 87073] Train: [2/100][74/1557] Data 0.005 (0.168) Batch 1.026 (1.184) Remain 50:39:46 loss: 0.9643 Lr: 0.00097 [2024-02-17 17:37:29,867 INFO misc.py line 119 87073] Train: [2/100][75/1557] Data 0.004 (0.166) Batch 0.772 (1.178) Remain 50:25:04 loss: 1.2479 Lr: 0.00097 [2024-02-17 17:37:30,599 INFO misc.py line 119 87073] Train: [2/100][76/1557] Data 0.004 (0.164) Batch 0.730 (1.172) Remain 50:09:17 loss: 1.0415 Lr: 0.00097 [2024-02-17 17:37:32,200 INFO misc.py line 119 87073] Train: [2/100][77/1557] Data 0.365 (0.166) Batch 1.598 (1.178) Remain 50:24:03 loss: 1.9050 Lr: 0.00097 [2024-02-17 17:37:33,066 INFO misc.py line 119 87073] Train: [2/100][78/1557] Data 0.010 (0.164) Batch 0.872 (1.174) Remain 50:13:33 loss: 1.1454 Lr: 0.00097 [2024-02-17 17:37:34,057 INFO misc.py line 119 87073] Train: [2/100][79/1557] Data 0.005 (0.162) Batch 0.991 (1.171) Remain 50:07:22 loss: 1.2318 Lr: 0.00097 [2024-02-17 17:37:35,212 INFO misc.py line 119 87073] Train: [2/100][80/1557] Data 0.004 (0.160) Batch 1.156 (1.171) Remain 50:06:50 loss: 1.1419 Lr: 0.00097 [2024-02-17 17:37:36,144 INFO misc.py line 119 87073] Train: [2/100][81/1557] Data 0.003 (0.158) Batch 0.932 (1.168) Remain 49:58:57 loss: 1.0122 Lr: 0.00097 [2024-02-17 17:37:36,836 INFO misc.py line 119 87073] Train: [2/100][82/1557] Data 0.003 (0.156) Batch 0.683 (1.162) Remain 49:43:11 loss: 1.4714 Lr: 0.00097 [2024-02-17 17:37:37,546 INFO misc.py line 119 87073] Train: [2/100][83/1557] Data 0.012 (0.154) Batch 0.717 (1.156) Remain 49:28:53 loss: 1.3165 Lr: 0.00097 [2024-02-17 17:37:38,579 INFO misc.py line 119 87073] Train: [2/100][84/1557] Data 0.004 (0.152) Batch 1.028 (1.155) Remain 49:24:48 loss: 0.8270 Lr: 0.00097 [2024-02-17 17:37:39,526 INFO misc.py line 119 87073] Train: [2/100][85/1557] Data 0.010 (0.151) Batch 0.953 (1.152) Remain 49:18:28 loss: 1.4275 Lr: 0.00098 [2024-02-17 17:37:40,600 INFO misc.py line 119 87073] Train: [2/100][86/1557] Data 0.004 (0.149) Batch 1.074 (1.151) Remain 49:16:01 loss: 0.8235 Lr: 0.00098 [2024-02-17 17:37:41,712 INFO misc.py line 119 87073] Train: [2/100][87/1557] Data 0.004 (0.147) Batch 1.112 (1.151) Remain 49:14:49 loss: 1.0622 Lr: 0.00098 [2024-02-17 17:37:42,625 INFO misc.py line 119 87073] Train: [2/100][88/1557] Data 0.004 (0.146) Batch 0.913 (1.148) Remain 49:07:36 loss: 0.7211 Lr: 0.00098 [2024-02-17 17:37:43,415 INFO misc.py line 119 87073] Train: [2/100][89/1557] Data 0.003 (0.144) Batch 0.785 (1.144) Remain 48:56:46 loss: 0.7445 Lr: 0.00098 [2024-02-17 17:37:44,170 INFO misc.py line 119 87073] Train: [2/100][90/1557] Data 0.008 (0.142) Batch 0.760 (1.139) Remain 48:45:24 loss: 1.1190 Lr: 0.00098 [2024-02-17 17:37:45,428 INFO misc.py line 119 87073] Train: [2/100][91/1557] Data 0.003 (0.141) Batch 1.250 (1.141) Remain 48:48:37 loss: 0.9413 Lr: 0.00098 [2024-02-17 17:37:46,410 INFO misc.py line 119 87073] Train: [2/100][92/1557] Data 0.012 (0.139) Batch 0.989 (1.139) Remain 48:44:14 loss: 1.2648 Lr: 0.00098 [2024-02-17 17:37:47,448 INFO misc.py line 119 87073] Train: [2/100][93/1557] Data 0.004 (0.138) Batch 1.038 (1.138) Remain 48:41:20 loss: 1.2289 Lr: 0.00098 [2024-02-17 17:37:48,358 INFO misc.py line 119 87073] Train: [2/100][94/1557] Data 0.005 (0.136) Batch 0.911 (1.135) Remain 48:34:55 loss: 1.2927 Lr: 0.00098 [2024-02-17 17:37:49,234 INFO misc.py line 119 87073] Train: [2/100][95/1557] Data 0.004 (0.135) Batch 0.873 (1.132) Remain 48:27:35 loss: 1.6111 Lr: 0.00098 [2024-02-17 17:37:49,986 INFO misc.py line 119 87073] Train: [2/100][96/1557] Data 0.006 (0.133) Batch 0.755 (1.128) Remain 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Train: [2/100][109/1557] Data 0.008 (0.118) Batch 0.991 (1.106) Remain 47:18:14 loss: 1.2705 Lr: 0.00099 [2024-02-17 17:38:02,943 INFO misc.py line 119 87073] Train: [2/100][110/1557] Data 0.003 (0.117) Batch 0.709 (1.102) Remain 47:08:42 loss: 1.1396 Lr: 0.00099 [2024-02-17 17:38:03,789 INFO misc.py line 119 87073] Train: [2/100][111/1557] Data 0.004 (0.116) Batch 0.837 (1.099) Remain 47:02:23 loss: 1.2001 Lr: 0.00099 [2024-02-17 17:38:05,057 INFO misc.py line 119 87073] Train: [2/100][112/1557] Data 0.013 (0.115) Batch 1.266 (1.101) Remain 47:06:17 loss: 0.6168 Lr: 0.00099 [2024-02-17 17:38:05,968 INFO misc.py line 119 87073] Train: [2/100][113/1557] Data 0.015 (0.114) Batch 0.922 (1.099) Remain 47:02:06 loss: 1.1844 Lr: 0.00099 [2024-02-17 17:38:07,028 INFO misc.py line 119 87073] Train: [2/100][114/1557] Data 0.003 (0.113) Batch 1.061 (1.099) Remain 47:01:11 loss: 0.7334 Lr: 0.00099 [2024-02-17 17:38:08,002 INFO misc.py line 119 87073] Train: [2/100][115/1557] Data 0.004 (0.112) Batch 0.971 (1.098) Remain 46:58:15 loss: 1.4704 Lr: 0.00099 [2024-02-17 17:38:08,807 INFO misc.py line 119 87073] Train: [2/100][116/1557] Data 0.006 (0.111) Batch 0.807 (1.095) Remain 46:51:37 loss: 1.1885 Lr: 0.00099 [2024-02-17 17:38:09,619 INFO misc.py line 119 87073] Train: [2/100][117/1557] Data 0.005 (0.110) Batch 0.811 (1.093) Remain 46:45:12 loss: 1.9267 Lr: 0.00099 [2024-02-17 17:38:10,362 INFO misc.py line 119 87073] Train: [2/100][118/1557] Data 0.005 (0.109) Batch 0.744 (1.090) Remain 46:37:24 loss: 0.9856 Lr: 0.00099 [2024-02-17 17:38:22,757 INFO misc.py line 119 87073] Train: [2/100][119/1557] Data 4.061 (0.143) Batch 12.395 (1.187) Remain 50:47:33 loss: 1.1795 Lr: 0.00099 [2024-02-17 17:38:23,735 INFO misc.py line 119 87073] Train: [2/100][120/1557] Data 0.004 (0.142) Batch 0.979 (1.185) Remain 50:42:58 loss: 1.1299 Lr: 0.00099 [2024-02-17 17:38:24,790 INFO misc.py line 119 87073] Train: [2/100][121/1557] Data 0.004 (0.141) Batch 1.054 (1.184) Remain 50:40:06 loss: 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INFO misc.py line 119 87073] Train: [2/100][128/1557] Data 0.008 (0.133) Batch 1.025 (1.170) Remain 50:03:09 loss: 0.9435 Lr: 0.00100 [2024-02-17 17:38:32,232 INFO misc.py line 119 87073] Train: [2/100][129/1557] Data 0.004 (0.132) Batch 0.944 (1.168) Remain 49:58:32 loss: 1.0686 Lr: 0.00100 [2024-02-17 17:38:33,442 INFO misc.py line 119 87073] Train: [2/100][130/1557] Data 0.004 (0.131) Batch 1.207 (1.168) Remain 49:59:18 loss: 0.9410 Lr: 0.00100 [2024-02-17 17:38:34,200 INFO misc.py line 119 87073] Train: [2/100][131/1557] Data 0.007 (0.130) Batch 0.761 (1.165) Remain 49:51:07 loss: 1.1834 Lr: 0.00100 [2024-02-17 17:38:34,982 INFO misc.py line 119 87073] Train: [2/100][132/1557] Data 0.004 (0.129) Batch 0.778 (1.162) Remain 49:43:24 loss: 1.6278 Lr: 0.00100 [2024-02-17 17:38:36,289 INFO misc.py line 119 87073] Train: [2/100][133/1557] Data 0.007 (0.128) Batch 1.308 (1.163) Remain 49:46:15 loss: 0.8897 Lr: 0.00100 [2024-02-17 17:38:37,395 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [2/100][153/1557] Data 0.003 (0.112) Batch 0.693 (1.128) Remain 48:15:19 loss: 1.1091 Lr: 0.00101 [2024-02-17 17:38:55,413 INFO misc.py line 119 87073] Train: [2/100][154/1557] Data 0.010 (0.111) Batch 1.143 (1.128) Remain 48:15:32 loss: 1.1410 Lr: 0.00101 [2024-02-17 17:38:56,354 INFO misc.py line 119 87073] Train: [2/100][155/1557] Data 0.012 (0.111) Batch 0.950 (1.127) Remain 48:12:30 loss: 0.8321 Lr: 0.00101 [2024-02-17 17:38:57,343 INFO misc.py line 119 87073] Train: [2/100][156/1557] Data 0.003 (0.110) Batch 0.990 (1.126) Remain 48:10:11 loss: 0.7375 Lr: 0.00102 [2024-02-17 17:38:58,201 INFO misc.py line 119 87073] Train: [2/100][157/1557] Data 0.003 (0.109) Batch 0.856 (1.124) Remain 48:05:39 loss: 0.9729 Lr: 0.00102 [2024-02-17 17:38:59,122 INFO misc.py line 119 87073] Train: [2/100][158/1557] Data 0.007 (0.109) Batch 0.917 (1.123) Remain 48:02:12 loss: 1.5617 Lr: 0.00102 [2024-02-17 17:38:59,847 INFO misc.py line 119 87073] Train: [2/100][159/1557] Data 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Train: [2/100][454/1557] Data 0.023 (0.102) Batch 0.790 (1.145) Remain 48:52:56 loss: 0.8288 Lr: 0.00120 [2024-02-17 17:44:53,682 INFO misc.py line 119 87073] Train: [2/100][455/1557] Data 4.334 (0.111) Batch 12.233 (1.170) Remain 49:55:45 loss: 1.0180 Lr: 0.00120 [2024-02-17 17:44:54,685 INFO misc.py line 119 87073] Train: [2/100][456/1557] Data 0.003 (0.111) Batch 1.003 (1.169) Remain 49:54:48 loss: 1.2445 Lr: 0.00120 [2024-02-17 17:44:55,627 INFO misc.py line 119 87073] Train: [2/100][457/1557] Data 0.003 (0.111) Batch 0.943 (1.169) Remain 49:53:30 loss: 1.2760 Lr: 0.00120 [2024-02-17 17:44:56,617 INFO misc.py line 119 87073] Train: [2/100][458/1557] Data 0.003 (0.110) Batch 0.982 (1.168) Remain 49:52:26 loss: 1.0916 Lr: 0.00120 [2024-02-17 17:44:57,605 INFO misc.py line 119 87073] Train: [2/100][459/1557] Data 0.011 (0.110) Batch 0.995 (1.168) Remain 49:51:26 loss: 1.0260 Lr: 0.00120 [2024-02-17 17:44:58,298 INFO misc.py line 119 87073] Train: [2/100][460/1557] Data 0.003 (0.110) 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Batch 0.813 (1.151) Remain 48:57:02 loss: 0.8565 Lr: 0.00163 [2024-02-17 17:56:10,170 INFO misc.py line 119 87073] Train: [2/100][1050/1557] Data 0.004 (0.097) Batch 1.091 (1.151) Remain 48:56:52 loss: 0.9872 Lr: 0.00163 [2024-02-17 17:56:11,020 INFO misc.py line 119 87073] Train: [2/100][1051/1557] Data 0.010 (0.097) Batch 0.857 (1.151) Remain 48:56:08 loss: 0.8022 Lr: 0.00163 [2024-02-17 17:56:12,023 INFO misc.py line 119 87073] Train: [2/100][1052/1557] Data 0.004 (0.097) Batch 1.004 (1.151) Remain 48:55:46 loss: 0.6724 Lr: 0.00163 [2024-02-17 17:56:13,044 INFO misc.py line 119 87073] Train: [2/100][1053/1557] Data 0.003 (0.097) Batch 1.021 (1.150) Remain 48:55:26 loss: 0.8000 Lr: 0.00164 [2024-02-17 17:56:13,993 INFO misc.py line 119 87073] Train: [2/100][1054/1557] Data 0.003 (0.097) Batch 0.949 (1.150) Remain 48:54:55 loss: 1.0600 Lr: 0.00164 [2024-02-17 17:56:16,685 INFO misc.py line 119 87073] Train: [2/100][1055/1557] Data 1.977 (0.099) Batch 2.692 (1.152) Remain 48:58:38 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17:56:23,218 INFO misc.py line 119 87073] Train: [2/100][1062/1557] Data 0.008 (0.098) Batch 0.724 (1.150) Remain 48:54:49 loss: 1.2917 Lr: 0.00164 [2024-02-17 17:56:24,008 INFO misc.py line 119 87073] Train: [2/100][1063/1557] Data 0.004 (0.098) Batch 0.783 (1.150) Remain 48:53:55 loss: 1.1817 Lr: 0.00164 [2024-02-17 17:56:25,240 INFO misc.py line 119 87073] Train: [2/100][1064/1557] Data 0.010 (0.098) Batch 1.232 (1.150) Remain 48:54:06 loss: 0.7476 Lr: 0.00164 [2024-02-17 17:56:26,107 INFO misc.py line 119 87073] Train: [2/100][1065/1557] Data 0.011 (0.098) Batch 0.875 (1.150) Remain 48:53:25 loss: 1.1290 Lr: 0.00164 [2024-02-17 17:56:27,049 INFO misc.py line 119 87073] Train: [2/100][1066/1557] Data 0.003 (0.098) Batch 0.942 (1.150) Remain 48:52:54 loss: 1.1340 Lr: 0.00165 [2024-02-17 17:56:27,988 INFO misc.py line 119 87073] Train: [2/100][1067/1557] Data 0.004 (0.097) Batch 0.939 (1.149) Remain 48:52:22 loss: 0.7749 Lr: 0.00165 [2024-02-17 17:56:28,978 INFO misc.py line 119 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Batch 0.720 (1.150) Remain 48:53:42 loss: 0.6939 Lr: 0.00168 [2024-02-17 17:57:13,764 INFO misc.py line 119 87073] Train: [2/100][1106/1557] Data 0.006 (0.098) Batch 1.207 (1.150) Remain 48:53:49 loss: 1.0075 Lr: 0.00168 [2024-02-17 17:57:14,706 INFO misc.py line 119 87073] Train: [2/100][1107/1557] Data 0.006 (0.098) Batch 0.944 (1.150) Remain 48:53:19 loss: 0.7998 Lr: 0.00168 [2024-02-17 17:57:15,697 INFO misc.py line 119 87073] Train: [2/100][1108/1557] Data 0.004 (0.098) Batch 0.991 (1.150) Remain 48:52:56 loss: 0.8690 Lr: 0.00168 [2024-02-17 17:57:16,698 INFO misc.py line 119 87073] Train: [2/100][1109/1557] Data 0.004 (0.098) Batch 1.002 (1.150) Remain 48:52:34 loss: 1.0255 Lr: 0.00168 [2024-02-17 17:57:17,751 INFO misc.py line 119 87073] Train: [2/100][1110/1557] Data 0.004 (0.098) Batch 1.053 (1.150) Remain 48:52:20 loss: 0.9425 Lr: 0.00168 [2024-02-17 17:57:18,499 INFO misc.py line 119 87073] Train: [2/100][1111/1557] Data 0.004 (0.097) Batch 0.748 (1.149) Remain 48:51:23 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Batch 0.659 (1.151) Remain 48:53:15 loss: 1.4383 Lr: 0.00177 [2024-02-17 17:59:23,289 INFO misc.py line 119 87073] Train: [2/100][1218/1557] Data 0.010 (0.098) Batch 1.104 (1.151) Remain 48:53:08 loss: 0.8037 Lr: 0.00177 [2024-02-17 17:59:24,086 INFO misc.py line 119 87073] Train: [2/100][1219/1557] Data 0.009 (0.098) Batch 0.802 (1.151) Remain 48:52:23 loss: 2.0104 Lr: 0.00177 [2024-02-17 17:59:24,950 INFO misc.py line 119 87073] Train: [2/100][1220/1557] Data 0.004 (0.098) Batch 0.864 (1.150) Remain 48:51:45 loss: 1.4368 Lr: 0.00177 [2024-02-17 17:59:25,904 INFO misc.py line 119 87073] Train: [2/100][1221/1557] Data 0.004 (0.098) Batch 0.951 (1.150) Remain 48:51:19 loss: 0.8767 Lr: 0.00177 [2024-02-17 17:59:26,809 INFO misc.py line 119 87073] Train: [2/100][1222/1557] Data 0.006 (0.097) Batch 0.908 (1.150) Remain 48:50:48 loss: 0.5900 Lr: 0.00177 [2024-02-17 17:59:27,538 INFO misc.py line 119 87073] Train: [2/100][1223/1557] Data 0.003 (0.097) Batch 0.728 (1.150) Remain 48:49:54 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Batch 0.772 (1.151) Remain 48:51:25 loss: 0.7587 Lr: 0.00186 [2024-02-17 18:01:32,511 INFO misc.py line 119 87073] Train: [2/100][1330/1557] Data 0.010 (0.098) Batch 1.263 (1.151) Remain 48:51:36 loss: 0.9563 Lr: 0.00186 [2024-02-17 18:01:33,353 INFO misc.py line 119 87073] Train: [2/100][1331/1557] Data 0.013 (0.098) Batch 0.851 (1.151) Remain 48:51:01 loss: 0.8235 Lr: 0.00186 [2024-02-17 18:01:34,256 INFO misc.py line 119 87073] Train: [2/100][1332/1557] Data 0.003 (0.098) Batch 0.903 (1.151) Remain 48:50:31 loss: 0.9335 Lr: 0.00186 [2024-02-17 18:01:35,176 INFO misc.py line 119 87073] Train: [2/100][1333/1557] Data 0.003 (0.098) Batch 0.915 (1.150) Remain 48:50:03 loss: 0.9704 Lr: 0.00186 [2024-02-17 18:01:36,127 INFO misc.py line 119 87073] Train: [2/100][1334/1557] Data 0.009 (0.098) Batch 0.957 (1.150) Remain 48:49:39 loss: 1.1338 Lr: 0.00186 [2024-02-17 18:01:36,820 INFO misc.py line 119 87073] Train: [2/100][1335/1557] Data 0.005 (0.098) Batch 0.692 (1.150) Remain 48:48:46 loss: 0.8611 Lr: 0.00186 [2024-02-17 18:01:37,631 INFO misc.py line 119 87073] Train: [2/100][1336/1557] Data 0.003 (0.098) Batch 0.806 (1.150) Remain 48:48:05 loss: 1.1788 Lr: 0.00187 [2024-02-17 18:01:38,901 INFO misc.py line 119 87073] Train: [2/100][1337/1557] Data 0.008 (0.098) Batch 1.268 (1.150) Remain 48:48:17 loss: 0.9286 Lr: 0.00187 [2024-02-17 18:01:39,912 INFO misc.py line 119 87073] Train: [2/100][1338/1557] Data 0.011 (0.098) Batch 1.007 (1.150) Remain 48:48:00 loss: 1.4279 Lr: 0.00187 [2024-02-17 18:01:40,791 INFO misc.py line 119 87073] Train: [2/100][1339/1557] Data 0.015 (0.098) Batch 0.891 (1.150) Remain 48:47:29 loss: 1.8579 Lr: 0.00187 [2024-02-17 18:01:41,636 INFO misc.py line 119 87073] Train: [2/100][1340/1557] Data 0.003 (0.098) Batch 0.845 (1.149) Remain 48:46:53 loss: 0.3463 Lr: 0.00187 [2024-02-17 18:01:42,836 INFO misc.py line 119 87073] Train: [2/100][1341/1557] Data 0.003 (0.098) Batch 1.191 (1.149) Remain 48:46:57 loss: 0.8446 Lr: 0.00187 [2024-02-17 18:01:43,572 INFO misc.py line 119 87073] Train: [2/100][1342/1557] Data 0.014 (0.097) Batch 0.746 (1.149) Remain 48:46:10 loss: 1.5666 Lr: 0.00187 [2024-02-17 18:01:44,307 INFO misc.py line 119 87073] Train: [2/100][1343/1557] Data 0.003 (0.097) Batch 0.728 (1.149) Remain 48:45:20 loss: 0.9645 Lr: 0.00187 [2024-02-17 18:01:45,547 INFO misc.py line 119 87073] Train: [2/100][1344/1557] Data 0.010 (0.097) Batch 1.238 (1.149) Remain 48:45:29 loss: 0.3444 Lr: 0.00187 [2024-02-17 18:01:46,606 INFO misc.py line 119 87073] Train: [2/100][1345/1557] Data 0.012 (0.097) Batch 1.056 (1.149) Remain 48:45:18 loss: 0.7537 Lr: 0.00187 [2024-02-17 18:01:47,492 INFO misc.py line 119 87073] Train: [2/100][1346/1557] Data 0.015 (0.097) Batch 0.899 (1.149) Remain 48:44:48 loss: 0.9958 Lr: 0.00187 [2024-02-17 18:01:48,568 INFO misc.py line 119 87073] Train: [2/100][1347/1557] Data 0.002 (0.097) Batch 1.076 (1.148) Remain 48:44:39 loss: 0.6887 Lr: 0.00187 [2024-02-17 18:01:49,466 INFO misc.py line 119 87073] Train: [2/100][1348/1557] Data 0.003 (0.097) Batch 0.898 (1.148) Remain 48:44:09 loss: 0.9909 Lr: 0.00188 [2024-02-17 18:01:50,237 INFO misc.py line 119 87073] Train: [2/100][1349/1557] Data 0.004 (0.097) Batch 0.766 (1.148) Remain 48:43:25 loss: 0.9539 Lr: 0.00188 [2024-02-17 18:01:51,015 INFO misc.py line 119 87073] Train: [2/100][1350/1557] Data 0.007 (0.097) Batch 0.783 (1.148) Remain 48:42:42 loss: 0.9592 Lr: 0.00188 [2024-02-17 18:02:03,249 INFO misc.py line 119 87073] Train: [2/100][1351/1557] Data 4.349 (0.100) Batch 12.234 (1.156) Remain 49:03:37 loss: 0.6981 Lr: 0.00188 [2024-02-17 18:02:04,128 INFO misc.py line 119 87073] Train: [2/100][1352/1557] Data 0.004 (0.100) Batch 0.880 (1.156) Remain 49:03:05 loss: 1.1932 Lr: 0.00188 [2024-02-17 18:02:05,198 INFO misc.py line 119 87073] Train: [2/100][1353/1557] Data 0.003 (0.100) Batch 1.064 (1.156) Remain 49:02:53 loss: 0.9754 Lr: 0.00188 [2024-02-17 18:02:06,138 INFO misc.py line 119 87073] Train: [2/100][1354/1557] Data 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[2024-02-17 18:02:18,277 INFO misc.py line 119 87073] Train: [2/100][1367/1557] Data 0.004 (0.099) Batch 1.014 (1.153) Remain 48:56:51 loss: 0.6433 Lr: 0.00189 [2024-02-17 18:02:19,263 INFO misc.py line 119 87073] Train: [2/100][1368/1557] Data 0.003 (0.099) Batch 0.985 (1.153) Remain 48:56:31 loss: 1.1284 Lr: 0.00189 [2024-02-17 18:02:20,472 INFO misc.py line 119 87073] Train: [2/100][1369/1557] Data 0.004 (0.099) Batch 1.209 (1.153) Remain 48:56:36 loss: 1.0585 Lr: 0.00189 [2024-02-17 18:02:21,155 INFO misc.py line 119 87073] Train: [2/100][1370/1557] Data 0.004 (0.099) Batch 0.683 (1.153) Remain 48:55:42 loss: 1.1855 Lr: 0.00189 [2024-02-17 18:02:21,892 INFO misc.py line 119 87073] Train: [2/100][1371/1557] Data 0.003 (0.099) Batch 0.725 (1.153) Remain 48:54:53 loss: 0.8793 Lr: 0.00189 [2024-02-17 18:02:22,885 INFO misc.py line 119 87073] Train: [2/100][1372/1557] Data 0.016 (0.099) Batch 1.004 (1.153) Remain 48:54:35 loss: 0.7303 Lr: 0.00190 [2024-02-17 18:02:23,857 INFO misc.py line 119 87073] Train: [2/100][1373/1557] Data 0.004 (0.099) Batch 0.974 (1.152) Remain 48:54:14 loss: 0.9265 Lr: 0.00190 [2024-02-17 18:02:24,858 INFO misc.py line 119 87073] Train: [2/100][1374/1557] Data 0.003 (0.098) Batch 0.999 (1.152) Remain 48:53:56 loss: 0.9129 Lr: 0.00190 [2024-02-17 18:02:25,801 INFO misc.py line 119 87073] Train: [2/100][1375/1557] Data 0.004 (0.098) Batch 0.944 (1.152) Remain 48:53:32 loss: 0.8822 Lr: 0.00190 [2024-02-17 18:02:26,621 INFO misc.py line 119 87073] Train: [2/100][1376/1557] Data 0.005 (0.098) Batch 0.817 (1.152) Remain 48:52:53 loss: 0.9817 Lr: 0.00190 [2024-02-17 18:02:27,426 INFO misc.py line 119 87073] Train: [2/100][1377/1557] Data 0.007 (0.098) Batch 0.807 (1.152) Remain 48:52:14 loss: 1.2120 Lr: 0.00190 [2024-02-17 18:02:28,134 INFO misc.py line 119 87073] Train: [2/100][1378/1557] Data 0.004 (0.098) Batch 0.707 (1.151) Remain 48:51:23 loss: 1.0261 Lr: 0.00190 [2024-02-17 18:02:29,277 INFO misc.py line 119 87073] Train: 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Batch 0.779 (1.150) Remain 48:48:41 loss: 0.6750 Lr: 0.00191 [2024-02-17 18:02:36,013 INFO misc.py line 119 87073] Train: [2/100][1386/1557] Data 0.004 (0.098) Batch 1.215 (1.150) Remain 48:48:46 loss: 0.8334 Lr: 0.00191 [2024-02-17 18:02:36,827 INFO misc.py line 119 87073] Train: [2/100][1387/1557] Data 0.009 (0.098) Batch 0.818 (1.150) Remain 48:48:09 loss: 0.6542 Lr: 0.00191 [2024-02-17 18:02:37,874 INFO misc.py line 119 87073] Train: [2/100][1388/1557] Data 0.006 (0.098) Batch 1.049 (1.150) Remain 48:47:56 loss: 0.8496 Lr: 0.00191 [2024-02-17 18:02:38,815 INFO misc.py line 119 87073] Train: [2/100][1389/1557] Data 0.003 (0.097) Batch 0.940 (1.150) Remain 48:47:32 loss: 1.2006 Lr: 0.00191 [2024-02-17 18:02:39,737 INFO misc.py line 119 87073] Train: [2/100][1390/1557] Data 0.004 (0.097) Batch 0.923 (1.150) Remain 48:47:06 loss: 0.7123 Lr: 0.00191 [2024-02-17 18:02:40,461 INFO misc.py line 119 87073] Train: [2/100][1391/1557] Data 0.003 (0.097) Batch 0.712 (1.149) Remain 48:46:17 loss: 0.6668 Lr: 0.00191 [2024-02-17 18:02:41,174 INFO misc.py line 119 87073] Train: [2/100][1392/1557] Data 0.015 (0.097) Batch 0.725 (1.149) Remain 48:45:29 loss: 1.1400 Lr: 0.00191 [2024-02-17 18:02:42,376 INFO misc.py line 119 87073] Train: [2/100][1393/1557] Data 0.003 (0.097) Batch 1.201 (1.149) Remain 48:45:33 loss: 0.8073 Lr: 0.00191 [2024-02-17 18:02:43,351 INFO misc.py line 119 87073] Train: [2/100][1394/1557] Data 0.003 (0.097) Batch 0.975 (1.149) Remain 48:45:13 loss: 0.7531 Lr: 0.00191 [2024-02-17 18:02:44,313 INFO misc.py line 119 87073] Train: [2/100][1395/1557] Data 0.004 (0.097) Batch 0.963 (1.149) Remain 48:44:51 loss: 0.8378 Lr: 0.00192 [2024-02-17 18:02:45,255 INFO misc.py line 119 87073] Train: [2/100][1396/1557] Data 0.003 (0.097) Batch 0.942 (1.149) Remain 48:44:28 loss: 1.1289 Lr: 0.00192 [2024-02-17 18:02:46,154 INFO misc.py line 119 87073] Train: [2/100][1397/1557] Data 0.003 (0.097) Batch 0.883 (1.149) Remain 48:43:57 loss: 0.5912 Lr: 0.00192 [2024-02-17 18:02:46,886 INFO misc.py line 119 87073] Train: [2/100][1398/1557] Data 0.018 (0.097) Batch 0.746 (1.148) Remain 48:43:12 loss: 0.9506 Lr: 0.00192 [2024-02-17 18:02:47,735 INFO misc.py line 119 87073] Train: [2/100][1399/1557] Data 0.005 (0.097) Batch 0.838 (1.148) Remain 48:42:37 loss: 0.7660 Lr: 0.00192 [2024-02-17 18:02:48,979 INFO misc.py line 119 87073] Train: [2/100][1400/1557] Data 0.016 (0.097) Batch 1.240 (1.148) Remain 48:42:46 loss: 0.4432 Lr: 0.00192 [2024-02-17 18:02:49,905 INFO misc.py line 119 87073] Train: [2/100][1401/1557] Data 0.020 (0.097) Batch 0.942 (1.148) Remain 48:42:22 loss: 0.6602 Lr: 0.00192 [2024-02-17 18:02:50,840 INFO misc.py line 119 87073] Train: [2/100][1402/1557] Data 0.004 (0.097) Batch 0.935 (1.148) Remain 48:41:58 loss: 1.0551 Lr: 0.00192 [2024-02-17 18:02:51,685 INFO misc.py line 119 87073] Train: [2/100][1403/1557] Data 0.004 (0.097) Batch 0.844 (1.148) Remain 48:41:24 loss: 1.3292 Lr: 0.00192 [2024-02-17 18:02:52,755 INFO misc.py line 119 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Batch 0.746 (1.150) Remain 48:47:59 loss: 0.9257 Lr: 0.00195 [2024-02-17 18:03:40,559 INFO misc.py line 119 87073] Train: [2/100][1442/1557] Data 0.004 (0.098) Batch 1.119 (1.150) Remain 48:47:55 loss: 1.2414 Lr: 0.00195 [2024-02-17 18:03:41,536 INFO misc.py line 119 87073] Train: [2/100][1443/1557] Data 0.012 (0.098) Batch 0.987 (1.150) Remain 48:47:36 loss: 1.1270 Lr: 0.00196 [2024-02-17 18:03:42,605 INFO misc.py line 119 87073] Train: [2/100][1444/1557] Data 0.003 (0.098) Batch 1.069 (1.150) Remain 48:47:27 loss: 1.2551 Lr: 0.00196 [2024-02-17 18:03:43,543 INFO misc.py line 119 87073] Train: [2/100][1445/1557] Data 0.003 (0.098) Batch 0.938 (1.150) Remain 48:47:03 loss: 1.1165 Lr: 0.00196 [2024-02-17 18:03:44,572 INFO misc.py line 119 87073] Train: [2/100][1446/1557] Data 0.004 (0.098) Batch 1.029 (1.150) Remain 48:46:49 loss: 1.0569 Lr: 0.00196 [2024-02-17 18:03:45,288 INFO misc.py line 119 87073] Train: [2/100][1447/1557] Data 0.003 (0.098) Batch 0.707 (1.150) Remain 48:46:01 loss: 1.0446 Lr: 0.00196 [2024-02-17 18:03:46,036 INFO misc.py line 119 87073] Train: [2/100][1448/1557] Data 0.013 (0.098) Batch 0.757 (1.149) Remain 48:45:18 loss: 1.1843 Lr: 0.00196 [2024-02-17 18:03:47,170 INFO misc.py line 119 87073] Train: [2/100][1449/1557] Data 0.003 (0.098) Batch 1.133 (1.149) Remain 48:45:15 loss: 0.7895 Lr: 0.00196 [2024-02-17 18:03:48,209 INFO misc.py line 119 87073] Train: [2/100][1450/1557] Data 0.005 (0.098) Batch 1.039 (1.149) Remain 48:45:03 loss: 0.8544 Lr: 0.00196 [2024-02-17 18:03:49,111 INFO misc.py line 119 87073] Train: [2/100][1451/1557] Data 0.005 (0.098) Batch 0.904 (1.149) Remain 48:44:36 loss: 1.5041 Lr: 0.00196 [2024-02-17 18:03:50,132 INFO misc.py line 119 87073] Train: [2/100][1452/1557] Data 0.003 (0.098) Batch 1.020 (1.149) Remain 48:44:21 loss: 1.4394 Lr: 0.00196 [2024-02-17 18:03:50,934 INFO misc.py line 119 87073] Train: [2/100][1453/1557] Data 0.004 (0.098) Batch 0.798 (1.149) Remain 48:43:43 loss: 0.9365 Lr: 0.00196 [2024-02-17 18:03:51,570 INFO misc.py line 119 87073] Train: [2/100][1454/1557] Data 0.007 (0.098) Batch 0.641 (1.149) Remain 48:42:48 loss: 0.9295 Lr: 0.00197 [2024-02-17 18:03:52,335 INFO misc.py line 119 87073] Train: [2/100][1455/1557] Data 0.003 (0.098) Batch 0.759 (1.148) Remain 48:42:06 loss: 0.6567 Lr: 0.00197 [2024-02-17 18:03:53,553 INFO misc.py line 119 87073] Train: [2/100][1456/1557] Data 0.009 (0.098) Batch 1.215 (1.148) Remain 48:42:12 loss: 0.5861 Lr: 0.00197 [2024-02-17 18:03:54,506 INFO misc.py line 119 87073] Train: [2/100][1457/1557] Data 0.013 (0.097) Batch 0.962 (1.148) Remain 48:41:51 loss: 1.2687 Lr: 0.00197 [2024-02-17 18:03:55,443 INFO misc.py line 119 87073] Train: [2/100][1458/1557] Data 0.003 (0.097) Batch 0.937 (1.148) Remain 48:41:28 loss: 0.7445 Lr: 0.00197 [2024-02-17 18:03:56,560 INFO misc.py line 119 87073] Train: [2/100][1459/1557] Data 0.004 (0.097) Batch 1.117 (1.148) Remain 48:41:23 loss: 1.3518 Lr: 0.00197 [2024-02-17 18:03:57,541 INFO misc.py line 119 87073] Train: [2/100][1460/1557] Data 0.003 (0.097) Batch 0.981 (1.148) Remain 48:41:05 loss: 0.6735 Lr: 0.00197 [2024-02-17 18:03:58,295 INFO misc.py line 119 87073] Train: [2/100][1461/1557] Data 0.003 (0.097) Batch 0.754 (1.148) Remain 48:40:22 loss: 1.0129 Lr: 0.00197 [2024-02-17 18:03:59,087 INFO misc.py line 119 87073] Train: [2/100][1462/1557] Data 0.003 (0.097) Batch 0.785 (1.147) Remain 48:39:43 loss: 1.2916 Lr: 0.00197 [2024-02-17 18:04:10,553 INFO misc.py line 119 87073] Train: [2/100][1463/1557] Data 3.687 (0.100) Batch 11.473 (1.154) Remain 48:57:42 loss: 0.8828 Lr: 0.00197 [2024-02-17 18:04:11,520 INFO misc.py line 119 87073] Train: [2/100][1464/1557] Data 0.004 (0.100) Batch 0.965 (1.154) Remain 48:57:21 loss: 0.8672 Lr: 0.00197 [2024-02-17 18:04:12,626 INFO misc.py line 119 87073] Train: [2/100][1465/1557] Data 0.007 (0.099) Batch 1.108 (1.154) Remain 48:57:15 loss: 0.9197 Lr: 0.00197 [2024-02-17 18:04:13,546 INFO misc.py line 119 87073] Train: [2/100][1466/1557] Data 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[2024-02-17 18:04:25,282 INFO misc.py line 119 87073] Train: [2/100][1479/1557] Data 0.004 (0.099) Batch 0.989 (1.152) Remain 48:50:56 loss: 0.5478 Lr: 0.00199 [2024-02-17 18:04:26,285 INFO misc.py line 119 87073] Train: [2/100][1480/1557] Data 0.004 (0.099) Batch 1.004 (1.152) Remain 48:50:40 loss: 0.8884 Lr: 0.00199 [2024-02-17 18:04:27,183 INFO misc.py line 119 87073] Train: [2/100][1481/1557] Data 0.004 (0.098) Batch 0.898 (1.152) Remain 48:50:12 loss: 0.7924 Lr: 0.00199 [2024-02-17 18:04:27,849 INFO misc.py line 119 87073] Train: [2/100][1482/1557] Data 0.004 (0.098) Batch 0.658 (1.151) Remain 48:49:20 loss: 1.1141 Lr: 0.00199 [2024-02-17 18:04:28,577 INFO misc.py line 119 87073] Train: [2/100][1483/1557] Data 0.012 (0.098) Batch 0.734 (1.151) Remain 48:48:36 loss: 1.3645 Lr: 0.00199 [2024-02-17 18:04:29,598 INFO misc.py line 119 87073] Train: [2/100][1484/1557] Data 0.004 (0.098) Batch 1.011 (1.151) Remain 48:48:21 loss: 0.6566 Lr: 0.00199 [2024-02-17 18:04:30,530 INFO misc.py line 119 87073] Train: [2/100][1485/1557] Data 0.015 (0.098) Batch 0.943 (1.151) Remain 48:47:58 loss: 0.6159 Lr: 0.00199 [2024-02-17 18:04:31,357 INFO misc.py line 119 87073] Train: [2/100][1486/1557] Data 0.004 (0.098) Batch 0.827 (1.151) Remain 48:47:24 loss: 1.3092 Lr: 0.00199 [2024-02-17 18:04:32,277 INFO misc.py line 119 87073] Train: [2/100][1487/1557] Data 0.003 (0.098) Batch 0.914 (1.150) Remain 48:46:58 loss: 0.9457 Lr: 0.00199 [2024-02-17 18:04:33,186 INFO misc.py line 119 87073] Train: [2/100][1488/1557] Data 0.010 (0.098) Batch 0.916 (1.150) Remain 48:46:33 loss: 0.7600 Lr: 0.00199 [2024-02-17 18:04:33,940 INFO misc.py line 119 87073] Train: [2/100][1489/1557] Data 0.003 (0.098) Batch 0.753 (1.150) Remain 48:45:51 loss: 1.2373 Lr: 0.00199 [2024-02-17 18:04:34,714 INFO misc.py line 119 87073] Train: [2/100][1490/1557] Data 0.004 (0.098) Batch 0.761 (1.150) Remain 48:45:10 loss: 0.8930 Lr: 0.00200 [2024-02-17 18:04:36,018 INFO misc.py line 119 87073] Train: 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Batch 0.728 (1.149) Remain 48:42:58 loss: 0.9343 Lr: 0.00200 [2024-02-17 18:04:42,694 INFO misc.py line 119 87073] Train: [2/100][1498/1557] Data 0.015 (0.097) Batch 1.145 (1.149) Remain 48:42:57 loss: 0.8084 Lr: 0.00200 [2024-02-17 18:04:43,627 INFO misc.py line 119 87073] Train: [2/100][1499/1557] Data 0.013 (0.097) Batch 0.943 (1.149) Remain 48:42:34 loss: 1.1952 Lr: 0.00200 [2024-02-17 18:04:44,474 INFO misc.py line 119 87073] Train: [2/100][1500/1557] Data 0.003 (0.097) Batch 0.847 (1.149) Remain 48:42:02 loss: 0.9058 Lr: 0.00200 [2024-02-17 18:04:45,401 INFO misc.py line 119 87073] Train: [2/100][1501/1557] Data 0.003 (0.097) Batch 0.927 (1.148) Remain 48:41:39 loss: 1.3466 Lr: 0.00201 [2024-02-17 18:04:46,275 INFO misc.py line 119 87073] Train: [2/100][1502/1557] Data 0.003 (0.097) Batch 0.874 (1.148) Remain 48:41:10 loss: 1.2976 Lr: 0.00201 [2024-02-17 18:04:46,979 INFO misc.py line 119 87073] Train: [2/100][1503/1557] Data 0.003 (0.097) Batch 0.703 (1.148) Remain 48:40:23 loss: 1.1181 Lr: 0.00201 [2024-02-17 18:04:47,723 INFO misc.py line 119 87073] Train: [2/100][1504/1557] Data 0.003 (0.097) Batch 0.742 (1.148) Remain 48:39:41 loss: 0.7761 Lr: 0.00201 [2024-02-17 18:04:48,977 INFO misc.py line 119 87073] Train: [2/100][1505/1557] Data 0.004 (0.097) Batch 1.252 (1.148) Remain 48:39:50 loss: 1.1072 Lr: 0.00201 [2024-02-17 18:04:49,976 INFO misc.py line 119 87073] Train: [2/100][1506/1557] Data 0.006 (0.097) Batch 1.002 (1.148) Remain 48:39:34 loss: 1.0141 Lr: 0.00201 [2024-02-17 18:04:50,942 INFO misc.py line 119 87073] Train: [2/100][1507/1557] Data 0.004 (0.097) Batch 0.966 (1.148) Remain 48:39:15 loss: 1.0405 Lr: 0.00201 [2024-02-17 18:04:51,997 INFO misc.py line 119 87073] Train: [2/100][1508/1557] Data 0.004 (0.097) Batch 1.055 (1.147) Remain 48:39:04 loss: 0.9678 Lr: 0.00201 [2024-02-17 18:04:52,917 INFO misc.py line 119 87073] Train: [2/100][1509/1557] Data 0.003 (0.097) Batch 0.920 (1.147) Remain 48:38:40 loss: 0.7415 Lr: 0.00201 [2024-02-17 18:04:53,620 INFO misc.py line 119 87073] Train: [2/100][1510/1557] Data 0.004 (0.097) Batch 0.702 (1.147) Remain 48:37:54 loss: 1.0531 Lr: 0.00201 [2024-02-17 18:04:54,392 INFO misc.py line 119 87073] Train: [2/100][1511/1557] Data 0.004 (0.097) Batch 0.773 (1.147) Remain 48:37:15 loss: 1.1201 Lr: 0.00201 [2024-02-17 18:04:55,574 INFO misc.py line 119 87073] Train: [2/100][1512/1557] Data 0.003 (0.097) Batch 1.182 (1.147) Remain 48:37:17 loss: 0.5621 Lr: 0.00201 [2024-02-17 18:04:56,575 INFO misc.py line 119 87073] Train: [2/100][1513/1557] Data 0.003 (0.096) Batch 0.999 (1.147) Remain 48:37:01 loss: 1.4127 Lr: 0.00202 [2024-02-17 18:04:57,597 INFO misc.py line 119 87073] Train: [2/100][1514/1557] Data 0.005 (0.096) Batch 1.024 (1.147) Remain 48:36:48 loss: 1.0580 Lr: 0.00202 [2024-02-17 18:04:58,520 INFO misc.py line 119 87073] Train: [2/100][1515/1557] Data 0.004 (0.096) Batch 0.923 (1.146) Remain 48:36:24 loss: 0.5853 Lr: 0.00202 [2024-02-17 18:04:59,475 INFO misc.py line 119 87073] Train: [2/100][1516/1557] Data 0.003 (0.096) Batch 0.948 (1.146) Remain 48:36:03 loss: 0.6281 Lr: 0.00202 [2024-02-17 18:05:00,193 INFO misc.py line 119 87073] Train: [2/100][1517/1557] Data 0.010 (0.096) Batch 0.725 (1.146) Remain 48:35:19 loss: 1.6109 Lr: 0.00202 [2024-02-17 18:05:00,918 INFO misc.py line 119 87073] Train: [2/100][1518/1557] Data 0.003 (0.096) Batch 0.717 (1.146) Remain 48:34:35 loss: 0.7755 Lr: 0.00202 [2024-02-17 18:05:12,982 INFO misc.py line 119 87073] Train: [2/100][1519/1557] Data 4.724 (0.099) Batch 12.072 (1.153) Remain 48:52:54 loss: 0.9224 Lr: 0.00202 [2024-02-17 18:05:13,759 INFO misc.py line 119 87073] Train: [2/100][1520/1557] Data 0.004 (0.099) Batch 0.777 (1.153) Remain 48:52:15 loss: 1.3595 Lr: 0.00202 [2024-02-17 18:05:14,656 INFO misc.py line 119 87073] Train: [2/100][1521/1557] Data 0.003 (0.099) Batch 0.883 (1.153) Remain 48:51:46 loss: 1.2963 Lr: 0.00202 [2024-02-17 18:05:15,707 INFO misc.py line 119 87073] Train: [2/100][1522/1557] Data 0.018 (0.099) Batch 1.053 (1.153) Remain 48:51:35 loss: 0.5842 Lr: 0.00202 [2024-02-17 18:05:16,633 INFO misc.py line 119 87073] Train: [2/100][1523/1557] Data 0.016 (0.099) Batch 0.938 (1.152) Remain 48:51:13 loss: 1.4915 Lr: 0.00202 [2024-02-17 18:05:17,384 INFO misc.py line 119 87073] Train: [2/100][1524/1557] Data 0.004 (0.099) Batch 0.750 (1.152) Remain 48:50:31 loss: 0.9935 Lr: 0.00202 [2024-02-17 18:05:18,137 INFO misc.py line 119 87073] Train: [2/100][1525/1557] Data 0.004 (0.099) Batch 0.747 (1.152) Remain 48:49:49 loss: 1.0887 Lr: 0.00203 [2024-02-17 18:05:19,235 INFO misc.py line 119 87073] Train: [2/100][1526/1557] Data 0.010 (0.099) Batch 1.100 (1.152) Remain 48:49:43 loss: 0.9322 Lr: 0.00203 [2024-02-17 18:05:20,129 INFO misc.py line 119 87073] Train: [2/100][1527/1557] Data 0.009 (0.099) Batch 0.898 (1.152) Remain 48:49:16 loss: 1.3451 Lr: 0.00203 [2024-02-17 18:05:21,201 INFO misc.py line 119 87073] Train: [2/100][1528/1557] Data 0.006 (0.099) Batch 1.073 (1.152) Remain 48:49:07 loss: 1.2274 Lr: 0.00203 [2024-02-17 18:05:22,343 INFO misc.py line 119 87073] Train: [2/100][1529/1557] Data 0.003 (0.099) Batch 1.142 (1.152) Remain 48:49:05 loss: 0.8981 Lr: 0.00203 [2024-02-17 18:05:23,264 INFO misc.py line 119 87073] Train: [2/100][1530/1557] Data 0.003 (0.099) Batch 0.920 (1.151) Remain 48:48:41 loss: 1.0085 Lr: 0.00203 [2024-02-17 18:05:24,022 INFO misc.py line 119 87073] Train: [2/100][1531/1557] Data 0.004 (0.099) Batch 0.751 (1.151) Remain 48:48:00 loss: 1.0125 Lr: 0.00203 [2024-02-17 18:05:24,748 INFO misc.py line 119 87073] Train: [2/100][1532/1557] Data 0.012 (0.098) Batch 0.734 (1.151) Remain 48:47:17 loss: 1.1363 Lr: 0.00203 [2024-02-17 18:05:26,060 INFO misc.py line 119 87073] Train: [2/100][1533/1557] Data 0.004 (0.098) Batch 1.298 (1.151) Remain 48:47:31 loss: 0.7769 Lr: 0.00203 [2024-02-17 18:05:27,087 INFO misc.py line 119 87073] Train: [2/100][1534/1557] Data 0.017 (0.098) Batch 1.025 (1.151) Remain 48:47:17 loss: 1.0055 Lr: 0.00203 [2024-02-17 18:05:28,037 INFO misc.py line 119 87073] Train: [2/100][1535/1557] Data 0.019 (0.098) Batch 0.964 (1.151) Remain 48:46:57 loss: 1.4276 Lr: 0.00203 [2024-02-17 18:05:29,057 INFO misc.py line 119 87073] Train: [2/100][1536/1557] Data 0.006 (0.098) Batch 1.022 (1.151) Remain 48:46:43 loss: 0.8623 Lr: 0.00204 [2024-02-17 18:05:29,927 INFO misc.py line 119 87073] Train: [2/100][1537/1557] Data 0.003 (0.098) Batch 0.869 (1.151) Remain 48:46:14 loss: 0.5223 Lr: 0.00204 [2024-02-17 18:05:30,704 INFO misc.py line 119 87073] Train: [2/100][1538/1557] Data 0.005 (0.098) Batch 0.769 (1.150) Remain 48:45:35 loss: 0.8927 Lr: 0.00204 [2024-02-17 18:05:31,435 INFO misc.py line 119 87073] Train: [2/100][1539/1557] Data 0.012 (0.098) Batch 0.739 (1.150) Remain 48:44:53 loss: 1.3230 Lr: 0.00204 [2024-02-17 18:05:32,482 INFO misc.py line 119 87073] Train: [2/100][1540/1557] Data 0.004 (0.098) Batch 1.042 (1.150) Remain 48:44:41 loss: 0.8921 Lr: 0.00204 [2024-02-17 18:05:33,605 INFO misc.py line 119 87073] Train: [2/100][1541/1557] Data 0.009 (0.098) Batch 1.121 (1.150) Remain 48:44:37 loss: 0.8797 Lr: 0.00204 [2024-02-17 18:05:34,544 INFO misc.py line 119 87073] Train: [2/100][1542/1557] Data 0.010 (0.098) Batch 0.946 (1.150) Remain 48:44:16 loss: 0.9210 Lr: 0.00204 [2024-02-17 18:05:35,595 INFO misc.py line 119 87073] Train: [2/100][1543/1557] Data 0.004 (0.098) Batch 1.051 (1.150) Remain 48:44:05 loss: 0.6490 Lr: 0.00204 [2024-02-17 18:05:36,712 INFO misc.py line 119 87073] Train: [2/100][1544/1557] Data 0.004 (0.098) Batch 1.117 (1.150) Remain 48:44:00 loss: 1.4952 Lr: 0.00204 [2024-02-17 18:05:37,371 INFO misc.py line 119 87073] Train: [2/100][1545/1557] Data 0.005 (0.098) Batch 0.659 (1.149) Remain 48:43:11 loss: 1.1310 Lr: 0.00204 [2024-02-17 18:05:38,102 INFO misc.py line 119 87073] Train: [2/100][1546/1557] Data 0.004 (0.098) Batch 0.725 (1.149) Remain 48:42:27 loss: 0.8954 Lr: 0.00204 [2024-02-17 18:05:39,373 INFO misc.py line 119 87073] Train: [2/100][1547/1557] Data 0.009 (0.098) Batch 1.268 (1.149) Remain 48:42:38 loss: 0.7273 Lr: 0.00204 [2024-02-17 18:05:40,275 INFO misc.py line 119 87073] Train: [2/100][1548/1557] Data 0.013 (0.098) Batch 0.911 (1.149) Remain 48:42:13 loss: 0.7170 Lr: 0.00205 [2024-02-17 18:05:41,285 INFO misc.py line 119 87073] Train: [2/100][1549/1557] Data 0.004 (0.097) Batch 1.010 (1.149) Remain 48:41:59 loss: 1.2451 Lr: 0.00205 [2024-02-17 18:05:41,924 INFO misc.py line 119 87073] Train: [2/100][1550/1557] Data 0.004 (0.097) Batch 0.633 (1.149) Remain 48:41:06 loss: 0.6481 Lr: 0.00205 [2024-02-17 18:05:42,998 INFO misc.py line 119 87073] Train: [2/100][1551/1557] Data 0.009 (0.097) Batch 1.069 (1.149) Remain 48:40:57 loss: 0.9513 Lr: 0.00205 [2024-02-17 18:05:43,781 INFO misc.py line 119 87073] Train: [2/100][1552/1557] Data 0.015 (0.097) Batch 0.795 (1.148) Remain 48:40:21 loss: 1.1385 Lr: 0.00205 [2024-02-17 18:05:44,556 INFO misc.py line 119 87073] Train: [2/100][1553/1557] Data 0.003 (0.097) Batch 0.764 (1.148) Remain 48:39:42 loss: 0.9959 Lr: 0.00205 [2024-02-17 18:05:45,743 INFO misc.py line 119 87073] Train: [2/100][1554/1557] Data 0.015 (0.097) Batch 1.189 (1.148) Remain 48:39:45 loss: 0.9044 Lr: 0.00205 [2024-02-17 18:05:46,675 INFO misc.py line 119 87073] Train: [2/100][1555/1557] Data 0.012 (0.097) Batch 0.941 (1.148) Remain 48:39:24 loss: 1.1814 Lr: 0.00205 [2024-02-17 18:05:47,776 INFO misc.py line 119 87073] Train: [2/100][1556/1557] Data 0.004 (0.097) Batch 1.101 (1.148) Remain 48:39:18 loss: 1.0843 Lr: 0.00205 [2024-02-17 18:05:48,572 INFO misc.py line 119 87073] Train: [2/100][1557/1557] Data 0.004 (0.097) Batch 0.796 (1.148) Remain 48:38:42 loss: 1.1255 Lr: 0.00205 [2024-02-17 18:05:48,573 INFO misc.py line 136 87073] Train result: loss: 1.0559 [2024-02-17 18:05:48,573 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 18:06:20,293 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.9539 [2024-02-17 18:06:21,081 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9243 [2024-02-17 18:06:23,206 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.9094 [2024-02-17 18:06:25,417 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.7272 [2024-02-17 18:06:30,362 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.7198 [2024-02-17 18:06:31,060 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2775 [2024-02-17 18:06:32,320 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.8682 [2024-02-17 18:06:35,272 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.7123 [2024-02-17 18:06:37,085 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.7922 [2024-02-17 18:06:38,747 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.5643/0.7031/0.8430. [2024-02-17 18:06:38,747 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.8856/0.9222 [2024-02-17 18:06:38,747 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9697/0.9918 [2024-02-17 18:06:38,747 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8202/0.9308 [2024-02-17 18:06:38,747 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2072/0.2604 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5785/0.6455 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.3317/0.5915 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.6426/0.8904 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8516/0.9360 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6296/0.9143 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.5928/0.6737 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.3995/0.8500 [2024-02-17 18:06:38,748 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.4272/0.5336 [2024-02-17 18:06:38,748 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 18:06:38,754 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.5643 [2024-02-17 18:06:38,754 INFO misc.py line 165 87073] Currently Best mIoU: 0.5643 [2024-02-17 18:06:38,754 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 18:06:48,737 INFO misc.py line 119 87073] Train: [3/100][1/1557] Data 1.018 (1.018) Batch 1.618 (1.618) Remain 68:35:19 loss: 1.0205 Lr: 0.00205 [2024-02-17 18:06:49,857 INFO misc.py line 119 87073] Train: [3/100][2/1557] Data 0.006 (0.006) Batch 1.118 (1.118) Remain 47:22:06 loss: 1.1202 Lr: 0.00206 [2024-02-17 18:06:50,735 INFO misc.py line 119 87073] Train: [3/100][3/1557] Data 0.009 (0.009) Batch 0.884 (0.884) Remain 37:27:01 loss: 1.1360 Lr: 0.00206 [2024-02-17 18:06:51,633 INFO misc.py line 119 87073] Train: [3/100][4/1557] Data 0.004 (0.004) Batch 0.896 (0.896) Remain 37:58:23 loss: 1.1017 Lr: 0.00206 [2024-02-17 18:06:52,419 INFO misc.py line 119 87073] Train: [3/100][5/1557] Data 0.005 (0.004) Batch 0.779 (0.837) Remain 35:29:43 loss: 1.4821 Lr: 0.00206 [2024-02-17 18:06:53,257 INFO misc.py line 119 87073] Train: [3/100][6/1557] Data 0.012 (0.007) Batch 0.847 (0.841) Remain 35:37:47 loss: 1.3624 Lr: 0.00206 [2024-02-17 18:06:54,467 INFO misc.py line 119 87073] Train: [3/100][7/1557] Data 0.003 (0.006) Batch 1.209 (0.933) Remain 39:31:57 loss: 0.6355 Lr: 0.00206 [2024-02-17 18:06:55,611 INFO misc.py line 119 87073] Train: [3/100][8/1557] Data 0.004 (0.006) Batch 1.145 (0.975) Remain 41:19:39 loss: 1.1868 Lr: 0.00206 [2024-02-17 18:06:56,571 INFO misc.py line 119 87073] Train: [3/100][9/1557] Data 0.003 (0.005) Batch 0.960 (0.973) Remain 41:13:05 loss: 1.3026 Lr: 0.00206 [2024-02-17 18:06:57,511 INFO misc.py line 119 87073] Train: [3/100][10/1557] Data 0.003 (0.005) Batch 0.939 (0.968) Remain 41:01:03 loss: 1.0951 Lr: 0.00206 [2024-02-17 18:06:58,502 INFO misc.py line 119 87073] Train: [3/100][11/1557] Data 0.004 (0.005) Batch 0.990 (0.971) Remain 41:08:13 loss: 1.2918 Lr: 0.00206 [2024-02-17 18:06:59,189 INFO misc.py line 119 87073] Train: [3/100][12/1557] Data 0.005 (0.005) Batch 0.689 (0.939) Remain 39:48:29 loss: 0.7673 Lr: 0.00206 [2024-02-17 18:06:59,955 INFO misc.py line 119 87073] Train: [3/100][13/1557] Data 0.003 (0.005) Batch 0.764 (0.922) Remain 39:03:57 loss: 0.9573 Lr: 0.00206 [2024-02-17 18:07:01,217 INFO misc.py line 119 87073] Train: [3/100][14/1557] Data 0.005 (0.005) Batch 1.256 (0.952) Remain 40:21:13 loss: 0.9154 Lr: 0.00207 [2024-02-17 18:07:02,164 INFO misc.py line 119 87073] Train: [3/100][15/1557] Data 0.011 (0.005) Batch 0.955 (0.952) Remain 40:21:43 loss: 1.2047 Lr: 0.00207 [2024-02-17 18:07:03,209 INFO misc.py line 119 87073] Train: [3/100][16/1557] Data 0.004 (0.005) Batch 1.045 (0.959) Remain 40:39:45 loss: 0.9876 Lr: 0.00207 [2024-02-17 18:07:04,255 INFO misc.py line 119 87073] Train: [3/100][17/1557] Data 0.003 (0.005) Batch 1.046 (0.966) Remain 40:55:28 loss: 0.7767 Lr: 0.00207 [2024-02-17 18:07:05,503 INFO misc.py line 119 87073] Train: [3/100][18/1557] Data 0.003 (0.005) Batch 1.241 (0.984) Remain 41:42:11 loss: 1.5616 Lr: 0.00207 [2024-02-17 18:07:06,197 INFO misc.py line 119 87073] Train: [3/100][19/1557] Data 0.010 (0.005) Batch 0.701 (0.966) Remain 40:57:08 loss: 0.8867 Lr: 0.00207 [2024-02-17 18:07:06,977 INFO misc.py line 119 87073] Train: [3/100][20/1557] Data 0.003 (0.005) Batch 0.776 (0.955) Remain 40:28:35 loss: 1.1941 Lr: 0.00207 [2024-02-17 18:07:08,263 INFO misc.py line 119 87073] Train: [3/100][21/1557] Data 0.008 (0.005) Batch 1.289 (0.974) Remain 41:15:44 loss: 0.7326 Lr: 0.00207 [2024-02-17 18:07:09,244 INFO misc.py line 119 87073] Train: [3/100][22/1557] Data 0.005 (0.005) Batch 0.982 (0.974) Remain 41:16:49 loss: 1.4950 Lr: 0.00207 [2024-02-17 18:07:10,188 INFO misc.py line 119 87073] Train: [3/100][23/1557] Data 0.004 (0.005) Batch 0.945 (0.973) Remain 41:13:07 loss: 1.0079 Lr: 0.00207 [2024-02-17 18:07:11,156 INFO misc.py line 119 87073] Train: [3/100][24/1557] Data 0.003 (0.005) Batch 0.968 (0.972) Remain 41:12:31 loss: 1.0679 Lr: 0.00207 [2024-02-17 18:07:12,121 INFO misc.py line 119 87073] Train: [3/100][25/1557] Data 0.003 (0.005) Batch 0.963 (0.972) Remain 41:11:28 loss: 1.4544 Lr: 0.00207 [2024-02-17 18:07:12,832 INFO misc.py line 119 87073] Train: [3/100][26/1557] Data 0.005 (0.005) Batch 0.710 (0.961) Remain 40:42:31 loss: 0.9161 Lr: 0.00208 [2024-02-17 18:07:13,523 INFO misc.py line 119 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Train: [3/100][109/1557] Data 0.004 (0.049) Batch 0.958 (1.017) Remain 43:04:25 loss: 1.4135 Lr: 0.00215 [2024-02-17 18:08:39,313 INFO misc.py line 119 87073] Train: [3/100][110/1557] Data 0.007 (0.048) Batch 0.779 (1.015) Remain 42:58:44 loss: 1.2600 Lr: 0.00215 [2024-02-17 18:08:40,035 INFO misc.py line 119 87073] Train: [3/100][111/1557] Data 0.003 (0.048) Batch 0.720 (1.012) Remain 42:51:47 loss: 1.1259 Lr: 0.00215 [2024-02-17 18:08:41,169 INFO misc.py line 119 87073] Train: [3/100][112/1557] Data 0.004 (0.048) Batch 1.126 (1.013) Remain 42:54:26 loss: 0.7609 Lr: 0.00215 [2024-02-17 18:08:42,282 INFO misc.py line 119 87073] Train: [3/100][113/1557] Data 0.012 (0.047) Batch 1.112 (1.014) Remain 42:56:42 loss: 0.6496 Lr: 0.00215 [2024-02-17 18:08:43,214 INFO misc.py line 119 87073] Train: [3/100][114/1557] Data 0.013 (0.047) Batch 0.942 (1.013) Remain 42:55:01 loss: 0.9486 Lr: 0.00215 [2024-02-17 18:08:44,167 INFO misc.py line 119 87073] Train: [3/100][115/1557] Data 0.005 (0.047) 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INFO misc.py line 119 87073] Train: [3/100][128/1557] Data 0.003 (0.080) Batch 0.898 (1.070) Remain 45:18:20 loss: 0.9838 Lr: 0.00216 [2024-02-17 18:09:05,628 INFO misc.py line 119 87073] Train: [3/100][129/1557] Data 0.003 (0.079) Batch 1.167 (1.071) Remain 45:20:16 loss: 0.8631 Lr: 0.00217 [2024-02-17 18:09:06,668 INFO misc.py line 119 87073] Train: [3/100][130/1557] Data 0.003 (0.079) Batch 1.040 (1.070) Remain 45:19:38 loss: 0.8681 Lr: 0.00217 [2024-02-17 18:09:07,415 INFO misc.py line 119 87073] Train: [3/100][131/1557] Data 0.003 (0.078) Batch 0.747 (1.068) Remain 45:13:11 loss: 1.1096 Lr: 0.00217 [2024-02-17 18:09:08,164 INFO misc.py line 119 87073] Train: [3/100][132/1557] Data 0.004 (0.077) Batch 0.742 (1.065) Remain 45:06:45 loss: 1.5327 Lr: 0.00217 [2024-02-17 18:09:09,421 INFO misc.py line 119 87073] Train: [3/100][133/1557] Data 0.010 (0.077) Batch 1.260 (1.067) Remain 45:10:32 loss: 0.5525 Lr: 0.00217 [2024-02-17 18:09:10,253 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [3/100][153/1557] Data 0.003 (0.068) Batch 0.749 (1.047) Remain 44:18:58 loss: 1.2225 Lr: 0.00219 [2024-02-17 18:09:28,839 INFO misc.py line 119 87073] Train: [3/100][154/1557] Data 0.008 (0.067) Batch 1.105 (1.047) Remain 44:19:56 loss: 0.5454 Lr: 0.00219 [2024-02-17 18:09:29,782 INFO misc.py line 119 87073] Train: [3/100][155/1557] Data 0.009 (0.067) Batch 0.948 (1.046) Remain 44:18:16 loss: 1.0203 Lr: 0.00219 [2024-02-17 18:09:30,797 INFO misc.py line 119 87073] Train: [3/100][156/1557] Data 0.004 (0.066) Batch 1.015 (1.046) Remain 44:17:44 loss: 0.6505 Lr: 0.00219 [2024-02-17 18:09:31,656 INFO misc.py line 119 87073] Train: [3/100][157/1557] Data 0.003 (0.066) Batch 0.859 (1.045) Remain 44:14:38 loss: 0.7760 Lr: 0.00219 [2024-02-17 18:09:32,608 INFO misc.py line 119 87073] Train: [3/100][158/1557] Data 0.003 (0.066) Batch 0.952 (1.044) Remain 44:13:05 loss: 0.8055 Lr: 0.00219 [2024-02-17 18:09:33,305 INFO misc.py line 119 87073] Train: [3/100][159/1557] Data 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Train: [3/100][178/1557] Data 0.003 (0.088) Batch 0.894 (1.085) Remain 45:55:21 loss: 0.6986 Lr: 0.00221 [2024-02-17 18:10:01,508 INFO misc.py line 119 87073] Train: [3/100][179/1557] Data 0.011 (0.087) Batch 0.944 (1.084) Remain 45:53:18 loss: 0.9579 Lr: 0.00221 [2024-02-17 18:10:04,214 INFO misc.py line 119 87073] Train: [3/100][180/1557] Data 1.461 (0.095) Batch 2.706 (1.093) Remain 46:16:34 loss: 1.2497 Lr: 0.00221 [2024-02-17 18:10:05,037 INFO misc.py line 119 87073] Train: [3/100][181/1557] Data 0.003 (0.094) Batch 0.814 (1.092) Remain 46:12:34 loss: 1.0949 Lr: 0.00221 [2024-02-17 18:10:06,254 INFO misc.py line 119 87073] Train: [3/100][182/1557] Data 0.012 (0.094) Batch 1.216 (1.092) Remain 46:14:19 loss: 0.8615 Lr: 0.00221 [2024-02-17 18:10:07,188 INFO misc.py line 119 87073] Train: [3/100][183/1557] Data 0.013 (0.094) Batch 0.944 (1.091) Remain 46:12:12 loss: 0.8153 Lr: 0.00221 [2024-02-17 18:10:08,131 INFO misc.py line 119 87073] Train: [3/100][184/1557] Data 0.003 (0.093) 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Train: [3/100][247/1557] Data 0.015 (0.089) Batch 1.271 (1.089) Remain 46:05:06 loss: 1.0070 Lr: 0.00227 [2024-02-17 18:11:17,339 INFO misc.py line 119 87073] Train: [3/100][248/1557] Data 0.012 (0.089) Batch 0.872 (1.088) Remain 46:02:50 loss: 0.9541 Lr: 0.00227 [2024-02-17 18:11:18,399 INFO misc.py line 119 87073] Train: [3/100][249/1557] Data 0.004 (0.088) Batch 1.058 (1.088) Remain 46:02:30 loss: 0.8106 Lr: 0.00227 [2024-02-17 18:11:19,156 INFO misc.py line 119 87073] Train: [3/100][250/1557] Data 0.005 (0.088) Batch 0.758 (1.087) Remain 45:59:06 loss: 0.6340 Lr: 0.00227 [2024-02-17 18:11:19,888 INFO misc.py line 119 87073] Train: [3/100][251/1557] Data 0.004 (0.088) Batch 0.726 (1.085) Remain 45:55:23 loss: 1.1199 Lr: 0.00227 [2024-02-17 18:11:21,016 INFO misc.py line 119 87073] Train: [3/100][252/1557] Data 0.010 (0.087) Batch 1.129 (1.085) Remain 45:55:49 loss: 0.6688 Lr: 0.00227 [2024-02-17 18:11:21,919 INFO misc.py line 119 87073] Train: [3/100][253/1557] Data 0.008 (0.087) 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Train: [3/100][454/1557] Data 0.003 (0.089) Batch 0.730 (1.082) Remain 45:44:19 loss: 1.0034 Lr: 0.00245 [2024-02-17 18:15:09,165 INFO misc.py line 119 87073] Train: [3/100][455/1557] Data 4.954 (0.100) Batch 10.291 (1.103) Remain 46:35:57 loss: 0.3764 Lr: 0.00246 [2024-02-17 18:15:10,363 INFO misc.py line 119 87073] Train: [3/100][456/1557] Data 0.003 (0.100) Batch 1.198 (1.103) Remain 46:36:28 loss: 0.9214 Lr: 0.00246 [2024-02-17 18:15:11,299 INFO misc.py line 119 87073] Train: [3/100][457/1557] Data 0.003 (0.100) Batch 0.935 (1.103) Remain 46:35:31 loss: 1.0929 Lr: 0.00246 [2024-02-17 18:15:12,231 INFO misc.py line 119 87073] Train: [3/100][458/1557] Data 0.004 (0.100) Batch 0.932 (1.102) Remain 46:34:32 loss: 1.3600 Lr: 0.00246 [2024-02-17 18:15:13,045 INFO misc.py line 119 87073] Train: [3/100][459/1557] Data 0.004 (0.099) Batch 0.805 (1.102) Remain 46:32:52 loss: 0.7465 Lr: 0.00246 [2024-02-17 18:15:13,837 INFO misc.py line 119 87073] Train: [3/100][460/1557] Data 0.013 (0.099) 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Train: [3/100][799/1557] Data 0.010 (0.105) Batch 0.939 (1.104) Remain 46:32:09 loss: 1.1649 Lr: 0.00277 [2024-02-17 18:21:30,400 INFO misc.py line 119 87073] Train: [3/100][800/1557] Data 0.008 (0.105) Batch 1.105 (1.104) Remain 46:32:08 loss: 1.3280 Lr: 0.00277 [2024-02-17 18:21:31,541 INFO misc.py line 119 87073] Train: [3/100][801/1557] Data 0.007 (0.105) Batch 1.141 (1.104) Remain 46:32:14 loss: 1.1893 Lr: 0.00277 [2024-02-17 18:21:32,348 INFO misc.py line 119 87073] Train: [3/100][802/1557] Data 0.007 (0.104) Batch 0.807 (1.103) Remain 46:31:16 loss: 0.6518 Lr: 0.00277 [2024-02-17 18:21:33,026 INFO misc.py line 119 87073] Train: [3/100][803/1557] Data 0.007 (0.104) Batch 0.674 (1.103) Remain 46:29:54 loss: 1.1793 Lr: 0.00277 [2024-02-17 18:21:33,747 INFO misc.py line 119 87073] Train: [3/100][804/1557] Data 0.010 (0.104) Batch 0.727 (1.102) Remain 46:28:41 loss: 0.9680 Lr: 0.00277 [2024-02-17 18:21:35,051 INFO misc.py line 119 87073] Train: [3/100][805/1557] Data 0.004 (0.104) 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Train: [3/100][868/1557] Data 0.004 (0.103) Batch 1.241 (1.101) Remain 46:25:12 loss: 0.4700 Lr: 0.00283 [2024-02-17 18:22:44,573 INFO misc.py line 119 87073] Train: [3/100][869/1557] Data 0.004 (0.103) Batch 1.067 (1.101) Remain 46:25:05 loss: 0.8774 Lr: 0.00283 [2024-02-17 18:22:45,525 INFO misc.py line 119 87073] Train: [3/100][870/1557] Data 0.004 (0.103) Batch 0.948 (1.101) Remain 46:24:37 loss: 1.0818 Lr: 0.00283 [2024-02-17 18:22:46,667 INFO misc.py line 119 87073] Train: [3/100][871/1557] Data 0.008 (0.103) Batch 1.144 (1.101) Remain 46:24:43 loss: 1.0787 Lr: 0.00283 [2024-02-17 18:22:47,665 INFO misc.py line 119 87073] Train: [3/100][872/1557] Data 0.006 (0.103) Batch 0.997 (1.101) Remain 46:24:24 loss: 1.1181 Lr: 0.00283 [2024-02-17 18:22:48,436 INFO misc.py line 119 87073] Train: [3/100][873/1557] Data 0.007 (0.103) Batch 0.773 (1.101) Remain 46:23:25 loss: 1.2405 Lr: 0.00283 [2024-02-17 18:22:49,193 INFO misc.py line 119 87073] Train: [3/100][874/1557] Data 0.004 (0.102) 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Batch 1.249 (1.103) Remain 46:24:11 loss: 0.4661 Lr: 0.00304 [2024-02-17 18:27:00,312 INFO misc.py line 119 87073] Train: [3/100][1100/1557] Data 0.009 (0.105) Batch 0.965 (1.103) Remain 46:23:51 loss: 1.3217 Lr: 0.00304 [2024-02-17 18:27:01,283 INFO misc.py line 119 87073] Train: [3/100][1101/1557] Data 0.006 (0.105) Batch 0.971 (1.102) Remain 46:23:32 loss: 0.4578 Lr: 0.00304 [2024-02-17 18:27:02,201 INFO misc.py line 119 87073] Train: [3/100][1102/1557] Data 0.005 (0.105) Batch 0.919 (1.102) Remain 46:23:05 loss: 1.1609 Lr: 0.00304 [2024-02-17 18:27:03,120 INFO misc.py line 119 87073] Train: [3/100][1103/1557] Data 0.004 (0.105) Batch 0.916 (1.102) Remain 46:22:38 loss: 0.7052 Lr: 0.00304 [2024-02-17 18:27:03,901 INFO misc.py line 119 87073] Train: [3/100][1104/1557] Data 0.007 (0.105) Batch 0.784 (1.102) Remain 46:21:53 loss: 0.9035 Lr: 0.00304 [2024-02-17 18:27:04,744 INFO misc.py line 119 87073] Train: [3/100][1105/1557] Data 0.004 (0.105) Batch 0.841 (1.102) Remain 46:21:17 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line 119 87073] Train: [3/100][1143/1557] Data 0.005 (0.106) Batch 1.065 (1.104) Remain 46:26:43 loss: 1.1864 Lr: 0.00308 [2024-02-17 18:27:50,252 INFO misc.py line 119 87073] Train: [3/100][1144/1557] Data 0.004 (0.106) Batch 0.874 (1.104) Remain 46:26:11 loss: 0.9777 Lr: 0.00308 [2024-02-17 18:27:51,162 INFO misc.py line 119 87073] Train: [3/100][1145/1557] Data 0.004 (0.106) Batch 0.909 (1.104) Remain 46:25:45 loss: 0.7099 Lr: 0.00308 [2024-02-17 18:27:51,905 INFO misc.py line 119 87073] Train: [3/100][1146/1557] Data 0.005 (0.106) Batch 0.744 (1.103) Remain 46:24:56 loss: 0.9306 Lr: 0.00308 [2024-02-17 18:27:52,649 INFO misc.py line 119 87073] Train: [3/100][1147/1557] Data 0.004 (0.106) Batch 0.744 (1.103) Remain 46:24:07 loss: 0.8842 Lr: 0.00308 [2024-02-17 18:27:53,765 INFO misc.py line 119 87073] Train: [3/100][1148/1557] Data 0.003 (0.105) Batch 1.115 (1.103) Remain 46:24:08 loss: 0.6694 Lr: 0.00308 [2024-02-17 18:27:54,587 INFO misc.py line 119 87073] Train: 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Batch 1.229 (1.102) Remain 46:20:53 loss: 0.5599 Lr: 0.00309 [2024-02-17 18:28:01,199 INFO misc.py line 119 87073] Train: [3/100][1156/1557] Data 0.007 (0.105) Batch 1.126 (1.102) Remain 46:20:55 loss: 0.5642 Lr: 0.00309 [2024-02-17 18:28:02,077 INFO misc.py line 119 87073] Train: [3/100][1157/1557] Data 0.009 (0.105) Batch 0.885 (1.102) Remain 46:20:26 loss: 0.5786 Lr: 0.00309 [2024-02-17 18:28:02,901 INFO misc.py line 119 87073] Train: [3/100][1158/1557] Data 0.003 (0.105) Batch 0.824 (1.101) Remain 46:19:48 loss: 1.1163 Lr: 0.00309 [2024-02-17 18:28:03,902 INFO misc.py line 119 87073] Train: [3/100][1159/1557] Data 0.003 (0.105) Batch 0.999 (1.101) Remain 46:19:34 loss: 0.8032 Lr: 0.00309 [2024-02-17 18:28:04,665 INFO misc.py line 119 87073] Train: [3/100][1160/1557] Data 0.006 (0.104) Batch 0.766 (1.101) Remain 46:18:49 loss: 1.2895 Lr: 0.00309 [2024-02-17 18:28:05,423 INFO misc.py line 119 87073] Train: [3/100][1161/1557] Data 0.003 (0.104) Batch 0.745 (1.101) Remain 46:18:01 loss: 0.8631 Lr: 0.00309 [2024-02-17 18:28:06,387 INFO misc.py line 119 87073] Train: [3/100][1162/1557] Data 0.015 (0.104) Batch 0.977 (1.101) Remain 46:17:44 loss: 0.5981 Lr: 0.00309 [2024-02-17 18:28:07,226 INFO misc.py line 119 87073] Train: [3/100][1163/1557] Data 0.003 (0.104) Batch 0.839 (1.100) Remain 46:17:09 loss: 1.1392 Lr: 0.00310 [2024-02-17 18:28:08,249 INFO misc.py line 119 87073] Train: [3/100][1164/1557] Data 0.003 (0.104) Batch 1.016 (1.100) Remain 46:16:56 loss: 1.1960 Lr: 0.00310 [2024-02-17 18:28:09,282 INFO misc.py line 119 87073] Train: [3/100][1165/1557] Data 0.010 (0.104) Batch 1.029 (1.100) Remain 46:16:46 loss: 0.6470 Lr: 0.00310 [2024-02-17 18:28:10,309 INFO misc.py line 119 87073] Train: [3/100][1166/1557] Data 0.014 (0.104) Batch 1.034 (1.100) Remain 46:16:36 loss: 1.0484 Lr: 0.00310 [2024-02-17 18:28:11,091 INFO misc.py line 119 87073] Train: [3/100][1167/1557] Data 0.007 (0.104) Batch 0.785 (1.100) Remain 46:15:54 loss: 0.7968 Lr: 0.00310 [2024-02-17 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line 119 87073] Train: [3/100][1199/1557] Data 0.015 (0.106) Batch 0.929 (1.102) Remain 46:20:50 loss: 0.6470 Lr: 0.00313 [2024-02-17 18:28:49,848 INFO misc.py line 119 87073] Train: [3/100][1200/1557] Data 0.003 (0.105) Batch 0.942 (1.102) Remain 46:20:29 loss: 0.7609 Lr: 0.00313 [2024-02-17 18:28:50,915 INFO misc.py line 119 87073] Train: [3/100][1201/1557] Data 0.003 (0.105) Batch 1.067 (1.102) Remain 46:20:23 loss: 0.5919 Lr: 0.00313 [2024-02-17 18:28:51,570 INFO misc.py line 119 87073] Train: [3/100][1202/1557] Data 0.003 (0.105) Batch 0.646 (1.102) Remain 46:19:25 loss: 0.9984 Lr: 0.00313 [2024-02-17 18:28:52,319 INFO misc.py line 119 87073] Train: [3/100][1203/1557] Data 0.013 (0.105) Batch 0.759 (1.101) Remain 46:18:40 loss: 0.9572 Lr: 0.00313 [2024-02-17 18:28:53,471 INFO misc.py line 119 87073] Train: [3/100][1204/1557] Data 0.003 (0.105) Batch 1.151 (1.101) Remain 46:18:45 loss: 0.6346 Lr: 0.00313 [2024-02-17 18:28:54,415 INFO misc.py line 119 87073] Train: 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Batch 1.222 (1.100) Remain 46:15:52 loss: 0.5159 Lr: 0.00314 [2024-02-17 18:29:00,889 INFO misc.py line 119 87073] Train: [3/100][1212/1557] Data 0.015 (0.104) Batch 1.023 (1.100) Remain 46:15:41 loss: 0.6818 Lr: 0.00314 [2024-02-17 18:29:01,785 INFO misc.py line 119 87073] Train: [3/100][1213/1557] Data 0.014 (0.104) Batch 0.906 (1.100) Remain 46:15:16 loss: 1.4020 Lr: 0.00314 [2024-02-17 18:29:02,729 INFO misc.py line 119 87073] Train: [3/100][1214/1557] Data 0.003 (0.104) Batch 0.944 (1.100) Remain 46:14:55 loss: 0.9199 Lr: 0.00314 [2024-02-17 18:29:03,621 INFO misc.py line 119 87073] Train: [3/100][1215/1557] Data 0.003 (0.104) Batch 0.891 (1.100) Remain 46:14:28 loss: 0.8548 Lr: 0.00314 [2024-02-17 18:29:04,390 INFO misc.py line 119 87073] Train: [3/100][1216/1557] Data 0.003 (0.104) Batch 0.758 (1.099) Remain 46:13:44 loss: 0.5839 Lr: 0.00314 [2024-02-17 18:29:05,158 INFO misc.py line 119 87073] Train: [3/100][1217/1557] Data 0.015 (0.104) Batch 0.780 (1.099) Remain 46:13:03 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Batch 1.260 (1.102) Remain 46:16:46 loss: 0.5055 Lr: 0.00329 [2024-02-17 18:32:07,875 INFO misc.py line 119 87073] Train: [3/100][1380/1557] Data 0.031 (0.105) Batch 0.999 (1.102) Remain 46:16:34 loss: 1.1113 Lr: 0.00329 [2024-02-17 18:32:08,809 INFO misc.py line 119 87073] Train: [3/100][1381/1557] Data 0.003 (0.105) Batch 0.934 (1.102) Remain 46:16:14 loss: 0.7915 Lr: 0.00329 [2024-02-17 18:32:09,872 INFO misc.py line 119 87073] Train: [3/100][1382/1557] Data 0.003 (0.104) Batch 1.062 (1.102) Remain 46:16:09 loss: 0.5540 Lr: 0.00329 [2024-02-17 18:32:11,026 INFO misc.py line 119 87073] Train: [3/100][1383/1557] Data 0.003 (0.104) Batch 1.154 (1.102) Remain 46:16:13 loss: 0.9661 Lr: 0.00329 [2024-02-17 18:32:11,784 INFO misc.py line 119 87073] Train: [3/100][1384/1557] Data 0.003 (0.104) Batch 0.758 (1.101) Remain 46:15:35 loss: 0.7640 Lr: 0.00329 [2024-02-17 18:32:12,542 INFO misc.py line 119 87073] Train: [3/100][1385/1557] Data 0.003 (0.104) Batch 0.744 (1.101) Remain 46:14:54 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line 119 87073] Train: [3/100][1423/1557] Data 0.003 (0.106) Batch 1.057 (1.104) Remain 46:21:57 loss: 0.8324 Lr: 0.00333 [2024-02-17 18:32:59,654 INFO misc.py line 119 87073] Train: [3/100][1424/1557] Data 0.004 (0.106) Batch 0.919 (1.104) Remain 46:21:36 loss: 0.5553 Lr: 0.00333 [2024-02-17 18:33:00,695 INFO misc.py line 119 87073] Train: [3/100][1425/1557] Data 0.003 (0.106) Batch 1.042 (1.104) Remain 46:21:29 loss: 0.7181 Lr: 0.00333 [2024-02-17 18:33:01,398 INFO misc.py line 119 87073] Train: [3/100][1426/1557] Data 0.003 (0.106) Batch 0.693 (1.104) Remain 46:20:44 loss: 0.6606 Lr: 0.00333 [2024-02-17 18:33:02,073 INFO misc.py line 119 87073] Train: [3/100][1427/1557] Data 0.012 (0.106) Batch 0.685 (1.103) Remain 46:19:58 loss: 0.8084 Lr: 0.00333 [2024-02-17 18:33:03,208 INFO misc.py line 119 87073] Train: [3/100][1428/1557] Data 0.003 (0.106) Batch 1.128 (1.103) Remain 46:20:00 loss: 0.6474 Lr: 0.00333 [2024-02-17 18:33:04,214 INFO misc.py line 119 87073] Train: 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Batch 1.344 (1.103) Remain 46:18:36 loss: 0.4322 Lr: 0.00334 [2024-02-17 18:33:11,021 INFO misc.py line 119 87073] Train: [3/100][1436/1557] Data 0.008 (0.105) Batch 0.818 (1.103) Remain 46:18:05 loss: 0.9327 Lr: 0.00334 [2024-02-17 18:33:12,009 INFO misc.py line 119 87073] Train: [3/100][1437/1557] Data 0.004 (0.105) Batch 0.987 (1.103) Remain 46:17:51 loss: 1.0802 Lr: 0.00334 [2024-02-17 18:33:12,964 INFO misc.py line 119 87073] Train: [3/100][1438/1557] Data 0.003 (0.105) Batch 0.955 (1.103) Remain 46:17:35 loss: 0.9404 Lr: 0.00334 [2024-02-17 18:33:13,977 INFO misc.py line 119 87073] Train: [3/100][1439/1557] Data 0.004 (0.105) Batch 1.013 (1.103) Remain 46:17:24 loss: 0.5939 Lr: 0.00334 [2024-02-17 18:33:14,657 INFO misc.py line 119 87073] Train: [3/100][1440/1557] Data 0.004 (0.105) Batch 0.679 (1.102) Remain 46:16:38 loss: 0.6956 Lr: 0.00334 [2024-02-17 18:33:15,381 INFO misc.py line 119 87073] Train: [3/100][1441/1557] Data 0.004 (0.105) Batch 0.724 (1.102) Remain 46:15:58 loss: 1.0810 Lr: 0.00334 [2024-02-17 18:33:16,424 INFO misc.py line 119 87073] Train: [3/100][1442/1557] Data 0.004 (0.105) Batch 1.043 (1.102) Remain 46:15:50 loss: 0.8670 Lr: 0.00334 [2024-02-17 18:33:17,277 INFO misc.py line 119 87073] Train: [3/100][1443/1557] Data 0.004 (0.105) Batch 0.854 (1.102) Remain 46:15:23 loss: 0.9901 Lr: 0.00334 [2024-02-17 18:33:18,312 INFO misc.py line 119 87073] Train: [3/100][1444/1557] Data 0.004 (0.105) Batch 1.036 (1.102) Remain 46:15:15 loss: 0.6834 Lr: 0.00334 [2024-02-17 18:33:19,165 INFO misc.py line 119 87073] Train: [3/100][1445/1557] Data 0.003 (0.105) Batch 0.853 (1.102) Remain 46:14:48 loss: 0.6253 Lr: 0.00335 [2024-02-17 18:33:20,104 INFO misc.py line 119 87073] Train: [3/100][1446/1557] Data 0.004 (0.104) Batch 0.933 (1.101) Remain 46:14:29 loss: 0.6729 Lr: 0.00335 [2024-02-17 18:33:20,782 INFO misc.py line 119 87073] Train: [3/100][1447/1557] Data 0.010 (0.104) Batch 0.683 (1.101) Remain 46:13:44 loss: 0.8305 Lr: 0.00335 [2024-02-17 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line 119 87073] Train: [3/100][1479/1557] Data 0.003 (0.106) Batch 1.036 (1.104) Remain 46:20:18 loss: 0.7825 Lr: 0.00338 [2024-02-17 18:34:01,195 INFO misc.py line 119 87073] Train: [3/100][1480/1557] Data 0.004 (0.106) Batch 0.989 (1.104) Remain 46:20:05 loss: 1.4751 Lr: 0.00338 [2024-02-17 18:34:02,196 INFO misc.py line 119 87073] Train: [3/100][1481/1557] Data 0.003 (0.106) Batch 1.001 (1.104) Remain 46:19:53 loss: 1.0082 Lr: 0.00338 [2024-02-17 18:34:02,911 INFO misc.py line 119 87073] Train: [3/100][1482/1557] Data 0.003 (0.106) Batch 0.715 (1.104) Remain 46:19:13 loss: 0.9615 Lr: 0.00338 [2024-02-17 18:34:03,692 INFO misc.py line 119 87073] Train: [3/100][1483/1557] Data 0.003 (0.106) Batch 0.767 (1.103) Remain 46:18:37 loss: 1.0072 Lr: 0.00338 [2024-02-17 18:34:04,844 INFO misc.py line 119 87073] Train: [3/100][1484/1557] Data 0.016 (0.106) Batch 1.151 (1.103) Remain 46:18:41 loss: 0.6064 Lr: 0.00338 [2024-02-17 18:34:05,781 INFO misc.py line 119 87073] Train: 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Batch 1.275 (1.103) Remain 46:16:34 loss: 0.5572 Lr: 0.00339 [2024-02-17 18:34:12,432 INFO misc.py line 119 87073] Train: [3/100][1492/1557] Data 0.017 (0.105) Batch 1.045 (1.103) Remain 46:16:27 loss: 0.9093 Lr: 0.00339 [2024-02-17 18:34:13,404 INFO misc.py line 119 87073] Train: [3/100][1493/1557] Data 0.014 (0.105) Batch 0.981 (1.102) Remain 46:16:13 loss: 0.7516 Lr: 0.00339 [2024-02-17 18:34:14,524 INFO misc.py line 119 87073] Train: [3/100][1494/1557] Data 0.004 (0.105) Batch 1.122 (1.102) Remain 46:16:14 loss: 1.1798 Lr: 0.00339 [2024-02-17 18:34:15,572 INFO misc.py line 119 87073] Train: [3/100][1495/1557] Data 0.002 (0.105) Batch 1.047 (1.102) Remain 46:16:07 loss: 1.0502 Lr: 0.00339 [2024-02-17 18:34:16,282 INFO misc.py line 119 87073] Train: [3/100][1496/1557] Data 0.004 (0.105) Batch 0.711 (1.102) Remain 46:15:27 loss: 0.9024 Lr: 0.00339 [2024-02-17 18:34:17,069 INFO misc.py line 119 87073] Train: [3/100][1497/1557] Data 0.003 (0.105) Batch 0.776 (1.102) Remain 46:14:53 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0.004 (0.104) Batch 0.916 (1.100) Remain 46:08:49 loss: 1.0495 Lr: 0.00341 [2024-02-17 18:34:35,341 INFO misc.py line 119 87073] Train: [3/100][1517/1557] Data 0.004 (0.104) Batch 0.778 (1.099) Remain 46:08:16 loss: 1.2334 Lr: 0.00341 [2024-02-17 18:34:36,083 INFO misc.py line 119 87073] Train: [3/100][1518/1557] Data 0.006 (0.103) Batch 0.744 (1.099) Remain 46:07:39 loss: 0.6375 Lr: 0.00341 [2024-02-17 18:34:46,067 INFO misc.py line 119 87073] Train: [3/100][1519/1557] Data 5.200 (0.107) Batch 9.983 (1.105) Remain 46:22:23 loss: 0.4873 Lr: 0.00341 [2024-02-17 18:34:46,990 INFO misc.py line 119 87073] Train: [3/100][1520/1557] Data 0.007 (0.107) Batch 0.924 (1.105) Remain 46:22:04 loss: 0.7042 Lr: 0.00341 [2024-02-17 18:34:47,926 INFO misc.py line 119 87073] Train: [3/100][1521/1557] Data 0.005 (0.107) Batch 0.935 (1.105) Remain 46:21:46 loss: 1.0292 Lr: 0.00341 [2024-02-17 18:34:48,786 INFO misc.py line 119 87073] Train: [3/100][1522/1557] Data 0.006 (0.107) Batch 0.861 (1.105) Remain 46:21:21 loss: 0.8849 Lr: 0.00341 [2024-02-17 18:34:49,602 INFO misc.py line 119 87073] Train: [3/100][1523/1557] Data 0.004 (0.107) Batch 0.814 (1.105) Remain 46:20:51 loss: 0.4647 Lr: 0.00341 [2024-02-17 18:34:50,381 INFO misc.py line 119 87073] Train: [3/100][1524/1557] Data 0.006 (0.106) Batch 0.779 (1.104) Remain 46:20:17 loss: 0.8140 Lr: 0.00341 [2024-02-17 18:34:51,156 INFO misc.py line 119 87073] Train: [3/100][1525/1557] Data 0.006 (0.106) Batch 0.777 (1.104) Remain 46:19:44 loss: 1.1243 Lr: 0.00342 [2024-02-17 18:34:52,498 INFO misc.py line 119 87073] Train: [3/100][1526/1557] Data 0.003 (0.106) Batch 1.333 (1.104) Remain 46:20:05 loss: 0.6228 Lr: 0.00342 [2024-02-17 18:34:53,455 INFO misc.py line 119 87073] Train: [3/100][1527/1557] Data 0.012 (0.106) Batch 0.965 (1.104) Remain 46:19:50 loss: 1.1089 Lr: 0.00342 [2024-02-17 18:34:54,424 INFO misc.py line 119 87073] Train: [3/100][1528/1557] Data 0.004 (0.106) Batch 0.970 (1.104) Remain 46:19:36 loss: 1.0999 Lr: 0.00342 [2024-02-17 18:34:55,477 INFO misc.py line 119 87073] Train: [3/100][1529/1557] Data 0.003 (0.106) Batch 1.052 (1.104) Remain 46:19:30 loss: 1.7784 Lr: 0.00342 [2024-02-17 18:34:56,507 INFO misc.py line 119 87073] Train: [3/100][1530/1557] Data 0.004 (0.106) Batch 1.031 (1.104) Remain 46:19:21 loss: 0.6968 Lr: 0.00342 [2024-02-17 18:34:57,222 INFO misc.py line 119 87073] Train: [3/100][1531/1557] Data 0.004 (0.106) Batch 0.713 (1.104) Remain 46:18:42 loss: 0.8909 Lr: 0.00342 [2024-02-17 18:34:57,955 INFO misc.py line 119 87073] Train: [3/100][1532/1557] Data 0.006 (0.106) Batch 0.730 (1.103) Remain 46:18:04 loss: 1.3492 Lr: 0.00342 [2024-02-17 18:34:59,205 INFO misc.py line 119 87073] Train: [3/100][1533/1557] Data 0.009 (0.106) Batch 1.250 (1.104) Remain 46:18:17 loss: 0.4475 Lr: 0.00342 [2024-02-17 18:35:00,095 INFO misc.py line 119 87073] Train: [3/100][1534/1557] Data 0.008 (0.106) Batch 0.890 (1.103) Remain 46:17:55 loss: 0.9418 Lr: 0.00342 [2024-02-17 18:35:01,161 INFO misc.py line 119 87073] Train: [3/100][1535/1557] Data 0.009 (0.106) Batch 1.067 (1.103) Remain 46:17:50 loss: 0.6113 Lr: 0.00342 [2024-02-17 18:35:02,170 INFO misc.py line 119 87073] Train: [3/100][1536/1557] Data 0.009 (0.106) Batch 1.012 (1.103) Remain 46:17:40 loss: 0.9874 Lr: 0.00343 [2024-02-17 18:35:03,045 INFO misc.py line 119 87073] Train: [3/100][1537/1557] Data 0.004 (0.106) Batch 0.875 (1.103) Remain 46:17:16 loss: 1.9679 Lr: 0.00343 [2024-02-17 18:35:03,796 INFO misc.py line 119 87073] Train: [3/100][1538/1557] Data 0.004 (0.106) Batch 0.746 (1.103) Remain 46:16:40 loss: 1.0694 Lr: 0.00343 [2024-02-17 18:35:04,537 INFO misc.py line 119 87073] Train: [3/100][1539/1557] Data 0.008 (0.106) Batch 0.744 (1.103) Remain 46:16:04 loss: 1.1479 Lr: 0.00343 [2024-02-17 18:35:05,694 INFO misc.py line 119 87073] Train: [3/100][1540/1557] Data 0.005 (0.105) Batch 1.157 (1.103) Remain 46:16:08 loss: 0.4866 Lr: 0.00343 [2024-02-17 18:35:06,807 INFO misc.py line 119 87073] Train: [3/100][1541/1557] Data 0.004 (0.105) Batch 1.113 (1.103) Remain 46:16:08 loss: 0.8008 Lr: 0.00343 [2024-02-17 18:35:07,845 INFO misc.py line 119 87073] Train: [3/100][1542/1557] Data 0.004 (0.105) Batch 1.038 (1.103) Remain 46:16:01 loss: 1.0968 Lr: 0.00343 [2024-02-17 18:35:08,903 INFO misc.py line 119 87073] Train: [3/100][1543/1557] Data 0.005 (0.105) Batch 1.058 (1.103) Remain 46:15:55 loss: 1.2570 Lr: 0.00343 [2024-02-17 18:35:09,865 INFO misc.py line 119 87073] Train: [3/100][1544/1557] Data 0.004 (0.105) Batch 0.963 (1.103) Remain 46:15:40 loss: 0.9167 Lr: 0.00343 [2024-02-17 18:35:10,593 INFO misc.py line 119 87073] Train: [3/100][1545/1557] Data 0.004 (0.105) Batch 0.707 (1.102) Remain 46:15:00 loss: 1.0890 Lr: 0.00343 [2024-02-17 18:35:11,272 INFO misc.py line 119 87073] Train: [3/100][1546/1557] Data 0.026 (0.105) Batch 0.700 (1.102) Remain 46:14:20 loss: 0.6278 Lr: 0.00343 [2024-02-17 18:35:12,529 INFO misc.py line 119 87073] Train: [3/100][1547/1557] Data 0.004 (0.105) Batch 1.257 (1.102) Remain 46:14:34 loss: 0.5980 Lr: 0.00343 [2024-02-17 18:35:13,426 INFO misc.py line 119 87073] Train: [3/100][1548/1557] Data 0.005 (0.105) Batch 0.898 (1.102) Remain 46:14:13 loss: 0.7727 Lr: 0.00344 [2024-02-17 18:35:14,287 INFO misc.py line 119 87073] Train: [3/100][1549/1557] Data 0.004 (0.105) Batch 0.861 (1.102) Remain 46:13:48 loss: 0.7674 Lr: 0.00344 [2024-02-17 18:35:15,196 INFO misc.py line 119 87073] Train: [3/100][1550/1557] Data 0.004 (0.105) Batch 0.909 (1.102) Remain 46:13:28 loss: 0.6386 Lr: 0.00344 [2024-02-17 18:35:16,175 INFO misc.py line 119 87073] Train: [3/100][1551/1557] Data 0.004 (0.105) Batch 0.979 (1.102) Remain 46:13:15 loss: 0.6238 Lr: 0.00344 [2024-02-17 18:35:16,837 INFO misc.py line 119 87073] Train: [3/100][1552/1557] Data 0.006 (0.105) Batch 0.661 (1.101) Remain 46:12:31 loss: 0.9737 Lr: 0.00344 [2024-02-17 18:35:17,606 INFO misc.py line 119 87073] Train: [3/100][1553/1557] Data 0.005 (0.105) Batch 0.766 (1.101) Remain 46:11:57 loss: 1.1313 Lr: 0.00344 [2024-02-17 18:35:18,673 INFO misc.py line 119 87073] Train: [3/100][1554/1557] Data 0.008 (0.105) Batch 1.067 (1.101) Remain 46:11:53 loss: 0.5752 Lr: 0.00344 [2024-02-17 18:35:19,683 INFO misc.py line 119 87073] Train: [3/100][1555/1557] Data 0.008 (0.104) Batch 1.011 (1.101) Remain 46:11:43 loss: 0.7690 Lr: 0.00344 [2024-02-17 18:35:20,810 INFO misc.py line 119 87073] Train: [3/100][1556/1557] Data 0.008 (0.104) Batch 1.122 (1.101) Remain 46:11:44 loss: 0.4339 Lr: 0.00344 [2024-02-17 18:35:21,637 INFO misc.py line 119 87073] Train: [3/100][1557/1557] Data 0.012 (0.104) Batch 0.835 (1.101) Remain 46:11:17 loss: 0.9241 Lr: 0.00344 [2024-02-17 18:35:21,638 INFO misc.py line 136 87073] Train result: loss: 0.9289 [2024-02-17 18:35:21,638 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 18:35:53,088 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8514 [2024-02-17 18:35:53,868 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 1.1394 [2024-02-17 18:35:55,995 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.7018 [2024-02-17 18:35:58,202 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4013 [2024-02-17 18:36:03,142 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.4544 [2024-02-17 18:36:03,845 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2124 [2024-02-17 18:36:05,107 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4287 [2024-02-17 18:36:08,065 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4100 [2024-02-17 18:36:09,874 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.6787 [2024-02-17 18:36:11,799 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6313/0.7472/0.8565. [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.8265/0.8336 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9775/0.9880 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8293/0.9001 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0045/0.1333 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3262/0.5010 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5988/0.6235 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.4943/0.6986 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.7747/0.8973 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8657/0.9356 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7302/0.8087 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7097/0.8297 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6180/0.8332 [2024-02-17 18:36:11,800 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.4518/0.7311 [2024-02-17 18:36:11,801 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 18:36:11,803 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6313 [2024-02-17 18:36:11,803 INFO misc.py line 165 87073] Currently Best mIoU: 0.6313 [2024-02-17 18:36:11,803 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 18:36:25,827 INFO misc.py line 119 87073] Train: [4/100][1/1557] Data 1.054 (1.054) Batch 1.954 (1.954) Remain 81:59:11 loss: 1.1122 Lr: 0.00344 [2024-02-17 18:36:26,734 INFO misc.py line 119 87073] Train: [4/100][2/1557] Data 0.004 (0.004) Batch 0.906 (0.906) Remain 38:01:25 loss: 0.7934 Lr: 0.00344 [2024-02-17 18:36:27,692 INFO misc.py line 119 87073] Train: [4/100][3/1557] Data 0.005 (0.005) Batch 0.959 (0.959) Remain 40:13:03 loss: 1.0235 Lr: 0.00345 [2024-02-17 18:36:28,704 INFO misc.py line 119 87073] Train: [4/100][4/1557] Data 0.004 (0.004) Batch 1.012 (1.012) Remain 42:26:25 loss: 0.9143 Lr: 0.00345 [2024-02-17 18:36:29,492 INFO misc.py line 119 87073] Train: [4/100][5/1557] Data 0.005 (0.004) Batch 0.788 (0.900) Remain 37:44:18 loss: 0.5493 Lr: 0.00345 [2024-02-17 18:36:30,303 INFO misc.py line 119 87073] Train: [4/100][6/1557] Data 0.005 (0.004) Batch 0.810 (0.870) Remain 36:29:22 loss: 0.6406 Lr: 0.00345 [2024-02-17 18:36:31,517 INFO misc.py line 119 87073] Train: [4/100][7/1557] Data 0.007 (0.005) Batch 1.213 (0.956) Remain 40:05:10 loss: 0.5104 Lr: 0.00345 [2024-02-17 18:36:32,394 INFO misc.py line 119 87073] Train: [4/100][8/1557] Data 0.008 (0.006) Batch 0.877 (0.940) Remain 39:25:32 loss: 0.8873 Lr: 0.00345 [2024-02-17 18:36:33,492 INFO misc.py line 119 87073] Train: [4/100][9/1557] Data 0.008 (0.006) Batch 1.101 (0.967) Remain 40:33:01 loss: 1.3342 Lr: 0.00345 [2024-02-17 18:36:34,416 INFO misc.py line 119 87073] Train: [4/100][10/1557] Data 0.004 (0.006) Batch 0.924 (0.960) Remain 40:17:31 loss: 0.8152 Lr: 0.00345 [2024-02-17 18:36:35,384 INFO misc.py line 119 87073] Train: [4/100][11/1557] Data 0.005 (0.006) Batch 0.969 (0.962) Remain 40:20:15 loss: 0.8842 Lr: 0.00345 [2024-02-17 18:36:36,169 INFO misc.py line 119 87073] Train: [4/100][12/1557] Data 0.003 (0.005) Batch 0.785 (0.942) Remain 39:30:51 loss: 0.9205 Lr: 0.00345 [2024-02-17 18:36:36,966 INFO misc.py line 119 87073] Train: [4/100][13/1557] Data 0.003 (0.005) Batch 0.795 (0.927) Remain 38:53:58 loss: 0.8355 Lr: 0.00345 [2024-02-17 18:36:38,201 INFO misc.py line 119 87073] Train: [4/100][14/1557] Data 0.004 (0.005) Batch 1.227 (0.955) Remain 40:02:32 loss: 0.7835 Lr: 0.00346 [2024-02-17 18:36:39,236 INFO misc.py line 119 87073] Train: [4/100][15/1557] Data 0.013 (0.006) Batch 1.031 (0.961) Remain 40:18:35 loss: 0.8853 Lr: 0.00346 [2024-02-17 18:36:40,232 INFO misc.py line 119 87073] Train: [4/100][16/1557] Data 0.017 (0.007) Batch 1.006 (0.964) Remain 40:27:16 loss: 1.1172 Lr: 0.00346 [2024-02-17 18:36:41,064 INFO misc.py line 119 87073] Train: [4/100][17/1557] Data 0.008 (0.007) Batch 0.829 (0.955) Remain 40:02:57 loss: 0.6978 Lr: 0.00346 [2024-02-17 18:36:42,113 INFO misc.py line 119 87073] Train: [4/100][18/1557] Data 0.010 (0.007) Batch 1.054 (0.961) Remain 40:19:40 loss: 0.6524 Lr: 0.00346 [2024-02-17 18:36:42,843 INFO misc.py line 119 87073] Train: [4/100][19/1557] Data 0.004 (0.007) Batch 0.730 (0.947) Remain 39:43:14 loss: 0.9658 Lr: 0.00346 [2024-02-17 18:36:43,572 INFO misc.py line 119 87073] Train: [4/100][20/1557] Data 0.004 (0.007) Batch 0.729 (0.934) Remain 39:11:00 loss: 0.7743 Lr: 0.00346 [2024-02-17 18:36:44,822 INFO misc.py line 119 87073] Train: [4/100][21/1557] Data 0.004 (0.006) Batch 1.236 (0.951) Remain 39:53:14 loss: 1.1931 Lr: 0.00346 [2024-02-17 18:36:45,848 INFO misc.py line 119 87073] Train: [4/100][22/1557] Data 0.018 (0.007) Batch 1.032 (0.955) Remain 40:03:56 loss: 0.9941 Lr: 0.00346 [2024-02-17 18:36:46,890 INFO misc.py line 119 87073] Train: [4/100][23/1557] Data 0.012 (0.007) Batch 1.049 (0.960) Remain 40:15:44 loss: 0.4110 Lr: 0.00346 [2024-02-17 18:36:48,089 INFO misc.py line 119 87073] Train: [4/100][24/1557] Data 0.005 (0.007) Batch 1.186 (0.971) Remain 40:42:47 loss: 0.5718 Lr: 0.00346 [2024-02-17 18:36:49,011 INFO misc.py line 119 87073] Train: [4/100][25/1557] Data 0.018 (0.008) Batch 0.933 (0.969) Remain 40:38:29 loss: 0.9659 Lr: 0.00346 [2024-02-17 18:36:49,786 INFO misc.py line 119 87073] Train: [4/100][26/1557] Data 0.007 (0.008) Batch 0.778 (0.961) Remain 40:17:37 loss: 1.4373 Lr: 0.00347 [2024-02-17 18:36:50,523 INFO misc.py line 119 87073] Train: [4/100][27/1557] Data 0.004 (0.007) Batch 0.736 (0.951) Remain 39:54:04 loss: 0.7950 Lr: 0.00347 [2024-02-17 18:36:51,838 INFO misc.py line 119 87073] Train: [4/100][28/1557] Data 0.004 (0.007) Batch 1.315 (0.966) Remain 40:30:38 loss: 0.8076 Lr: 0.00347 [2024-02-17 18:36:52,761 INFO misc.py line 119 87073] Train: [4/100][29/1557] Data 0.005 (0.007) Batch 0.924 (0.964) Remain 40:26:34 loss: 0.9534 Lr: 0.00347 [2024-02-17 18:36:53,851 INFO misc.py line 119 87073] Train: [4/100][30/1557] Data 0.003 (0.007) Batch 1.090 (0.969) Remain 40:38:19 loss: 0.9561 Lr: 0.00347 [2024-02-17 18:36:54,961 INFO misc.py line 119 87073] Train: [4/100][31/1557] Data 0.003 (0.007) Batch 1.108 (0.974) Remain 40:50:49 loss: 1.4992 Lr: 0.00347 [2024-02-17 18:36:55,994 INFO misc.py line 119 87073] Train: [4/100][32/1557] Data 0.005 (0.007) Batch 1.033 (0.976) Remain 40:55:58 loss: 1.0028 Lr: 0.00347 [2024-02-17 18:36:56,645 INFO misc.py line 119 87073] Train: [4/100][33/1557] Data 0.004 (0.007) Batch 0.652 (0.965) Remain 40:28:46 loss: 0.8473 Lr: 0.00347 [2024-02-17 18:36:57,370 INFO misc.py line 119 87073] Train: [4/100][34/1557] Data 0.004 (0.007) Batch 0.720 (0.957) Remain 40:08:51 loss: 0.9675 Lr: 0.00347 [2024-02-17 18:36:58,584 INFO misc.py line 119 87073] Train: [4/100][35/1557] Data 0.008 (0.007) Batch 1.216 (0.965) Remain 40:29:11 loss: 1.3534 Lr: 0.00347 [2024-02-17 18:36:59,490 INFO misc.py line 119 87073] Train: [4/100][36/1557] Data 0.007 (0.007) Batch 0.910 (0.964) Remain 40:24:55 loss: 0.9115 Lr: 0.00347 [2024-02-17 18:37:00,572 INFO misc.py line 119 87073] Train: [4/100][37/1557] Data 0.003 (0.007) Batch 1.080 (0.967) Remain 40:33:30 loss: 0.7281 Lr: 0.00348 [2024-02-17 18:37:01,531 INFO misc.py line 119 87073] Train: [4/100][38/1557] Data 0.007 (0.007) Batch 0.959 (0.967) Remain 40:32:55 loss: 0.9104 Lr: 0.00348 [2024-02-17 18:37:02,468 INFO misc.py line 119 87073] Train: [4/100][39/1557] Data 0.005 (0.007) Batch 0.938 (0.966) Remain 40:30:54 loss: 1.4485 Lr: 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line 119 87073] Train: [4/100][222/1557] Data 0.005 (0.109) Batch 0.750 (1.095) Remain 45:51:34 loss: 1.1657 Lr: 0.00363 [2024-02-17 18:40:28,146 INFO misc.py line 119 87073] Train: [4/100][223/1557] Data 0.005 (0.108) Batch 0.705 (1.093) Remain 45:47:06 loss: 1.5539 Lr: 0.00363 [2024-02-17 18:40:29,350 INFO misc.py line 119 87073] Train: [4/100][224/1557] Data 0.003 (0.108) Batch 1.204 (1.093) Remain 45:48:21 loss: 0.4000 Lr: 0.00363 [2024-02-17 18:40:30,383 INFO misc.py line 119 87073] Train: [4/100][225/1557] Data 0.003 (0.107) Batch 1.033 (1.093) Remain 45:47:39 loss: 0.7632 Lr: 0.00363 [2024-02-17 18:40:31,518 INFO misc.py line 119 87073] Train: [4/100][226/1557] Data 0.003 (0.107) Batch 1.135 (1.093) Remain 45:48:06 loss: 1.0018 Lr: 0.00364 [2024-02-17 18:40:32,466 INFO misc.py line 119 87073] Train: [4/100][227/1557] Data 0.004 (0.106) Batch 0.948 (1.093) Remain 45:46:27 loss: 0.7672 Lr: 0.00364 [2024-02-17 18:40:33,409 INFO misc.py line 119 87073] Train: [4/100][228/1557] Data 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Train: [4/100][247/1557] Data 0.005 (0.122) Batch 0.858 (1.117) Remain 46:46:56 loss: 1.0570 Lr: 0.00365 [2024-02-17 18:41:01,228 INFO misc.py line 119 87073] Train: [4/100][248/1557] Data 0.006 (0.122) Batch 0.998 (1.116) Remain 46:45:41 loss: 0.6564 Lr: 0.00365 [2024-02-17 18:41:02,077 INFO misc.py line 119 87073] Train: [4/100][249/1557] Data 0.006 (0.121) Batch 0.849 (1.115) Remain 46:42:56 loss: 0.7893 Lr: 0.00365 [2024-02-17 18:41:02,888 INFO misc.py line 119 87073] Train: [4/100][250/1557] Data 0.007 (0.121) Batch 0.813 (1.114) Remain 46:39:50 loss: 0.7324 Lr: 0.00366 [2024-02-17 18:41:03,544 INFO misc.py line 119 87073] Train: [4/100][251/1557] Data 0.003 (0.120) Batch 0.654 (1.112) Remain 46:35:09 loss: 0.6475 Lr: 0.00366 [2024-02-17 18:41:04,789 INFO misc.py line 119 87073] Train: [4/100][252/1557] Data 0.005 (0.120) Batch 1.247 (1.113) Remain 46:36:30 loss: 0.7341 Lr: 0.00366 [2024-02-17 18:41:05,595 INFO misc.py line 119 87073] Train: [4/100][253/1557] Data 0.005 (0.119) 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line 119 87073] Train: [4/100][291/1557] Data 0.012 (0.126) Batch 0.987 (1.122) Remain 46:57:44 loss: 0.6252 Lr: 0.00369 [2024-02-17 18:41:51,484 INFO misc.py line 119 87073] Train: [4/100][292/1557] Data 0.003 (0.126) Batch 0.776 (1.120) Remain 46:54:43 loss: 1.4892 Lr: 0.00369 [2024-02-17 18:41:52,198 INFO misc.py line 119 87073] Train: [4/100][293/1557] Data 0.004 (0.125) Batch 0.713 (1.119) Remain 46:51:10 loss: 0.5480 Lr: 0.00369 [2024-02-17 18:41:53,510 INFO misc.py line 119 87073] Train: [4/100][294/1557] Data 0.005 (0.125) Batch 1.314 (1.120) Remain 46:52:50 loss: 0.7288 Lr: 0.00369 [2024-02-17 18:41:54,714 INFO misc.py line 119 87073] Train: [4/100][295/1557] Data 0.004 (0.124) Batch 1.198 (1.120) Remain 46:53:29 loss: 0.5071 Lr: 0.00369 [2024-02-17 18:41:55,725 INFO misc.py line 119 87073] Train: [4/100][296/1557] Data 0.009 (0.124) Batch 1.014 (1.120) Remain 46:52:33 loss: 0.7810 Lr: 0.00369 [2024-02-17 18:41:56,686 INFO misc.py line 119 87073] Train: [4/100][297/1557] Data 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Train: [4/100][316/1557] Data 0.010 (0.116) Batch 1.106 (1.109) Remain 46:25:35 loss: 1.0264 Lr: 0.00371 [2024-02-17 18:42:15,744 INFO misc.py line 119 87073] Train: [4/100][317/1557] Data 0.012 (0.116) Batch 0.944 (1.108) Remain 46:24:15 loss: 0.7275 Lr: 0.00371 [2024-02-17 18:42:16,763 INFO misc.py line 119 87073] Train: [4/100][318/1557] Data 0.004 (0.116) Batch 1.019 (1.108) Remain 46:23:31 loss: 0.6349 Lr: 0.00371 [2024-02-17 18:42:17,561 INFO misc.py line 119 87073] Train: [4/100][319/1557] Data 0.004 (0.115) Batch 0.797 (1.107) Remain 46:21:02 loss: 1.0643 Lr: 0.00371 [2024-02-17 18:42:18,338 INFO misc.py line 119 87073] Train: [4/100][320/1557] Data 0.004 (0.115) Batch 0.767 (1.106) Remain 46:18:19 loss: 0.8522 Lr: 0.00371 [2024-02-17 18:42:19,024 INFO misc.py line 119 87073] Train: [4/100][321/1557] Data 0.014 (0.115) Batch 0.696 (1.105) Remain 46:15:04 loss: 0.7824 Lr: 0.00371 [2024-02-17 18:42:20,208 INFO misc.py line 119 87073] Train: [4/100][322/1557] Data 0.004 (0.114) 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INFO misc.py line 119 87073] Train: [4/100][335/1557] Data 0.014 (0.110) Batch 0.749 (1.098) Remain 45:57:51 loss: 1.7581 Lr: 0.00373 [2024-02-17 18:42:33,435 INFO misc.py line 119 87073] Train: [4/100][336/1557] Data 0.005 (0.110) Batch 1.185 (1.098) Remain 45:58:29 loss: 0.6588 Lr: 0.00373 [2024-02-17 18:42:34,314 INFO misc.py line 119 87073] Train: [4/100][337/1557] Data 0.004 (0.110) Batch 0.879 (1.098) Remain 45:56:49 loss: 0.8674 Lr: 0.00373 [2024-02-17 18:42:35,344 INFO misc.py line 119 87073] Train: [4/100][338/1557] Data 0.004 (0.109) Batch 1.028 (1.097) Remain 45:56:17 loss: 0.8256 Lr: 0.00373 [2024-02-17 18:42:36,557 INFO misc.py line 119 87073] Train: [4/100][339/1557] Data 0.006 (0.109) Batch 1.206 (1.098) Remain 45:57:05 loss: 0.9155 Lr: 0.00373 [2024-02-17 18:42:37,507 INFO misc.py line 119 87073] Train: [4/100][340/1557] Data 0.013 (0.109) Batch 0.959 (1.097) Remain 45:56:01 loss: 0.7785 Lr: 0.00373 [2024-02-17 18:42:38,212 INFO misc.py line 119 87073] Train: 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Train: [4/100][385/1557] Data 0.014 (0.118) Batch 1.141 (1.109) Remain 46:23:38 loss: 0.4210 Lr: 0.00377 [2024-02-17 18:43:32,057 INFO misc.py line 119 87073] Train: [4/100][386/1557] Data 0.014 (0.118) Batch 0.841 (1.108) Remain 46:21:52 loss: 0.7122 Lr: 0.00377 [2024-02-17 18:43:32,996 INFO misc.py line 119 87073] Train: [4/100][387/1557] Data 0.004 (0.117) Batch 0.940 (1.108) Remain 46:20:45 loss: 0.7214 Lr: 0.00377 [2024-02-17 18:43:33,992 INFO misc.py line 119 87073] Train: [4/100][388/1557] Data 0.003 (0.117) Batch 0.995 (1.107) Remain 46:20:00 loss: 0.9292 Lr: 0.00377 [2024-02-17 18:43:35,009 INFO misc.py line 119 87073] Train: [4/100][389/1557] Data 0.004 (0.117) Batch 1.018 (1.107) Remain 46:19:24 loss: 0.9454 Lr: 0.00377 [2024-02-17 18:43:35,712 INFO misc.py line 119 87073] Train: [4/100][390/1557] Data 0.003 (0.116) Batch 0.695 (1.106) Remain 46:16:42 loss: 0.4643 Lr: 0.00377 [2024-02-17 18:43:36,459 INFO misc.py line 119 87073] Train: [4/100][391/1557] Data 0.011 (0.116) Batch 0.755 (1.105) Remain 46:14:25 loss: 0.9781 Lr: 0.00377 [2024-02-17 18:43:37,566 INFO misc.py line 119 87073] Train: [4/100][392/1557] Data 0.003 (0.116) Batch 1.106 (1.105) Remain 46:14:24 loss: 0.6100 Lr: 0.00377 [2024-02-17 18:43:38,763 INFO misc.py line 119 87073] Train: [4/100][393/1557] Data 0.003 (0.116) Batch 1.195 (1.105) Remain 46:14:58 loss: 0.8696 Lr: 0.00377 [2024-02-17 18:43:39,530 INFO misc.py line 119 87073] Train: [4/100][394/1557] Data 0.007 (0.115) Batch 0.770 (1.104) Remain 46:12:47 loss: 0.6085 Lr: 0.00377 [2024-02-17 18:43:40,448 INFO misc.py line 119 87073] Train: [4/100][395/1557] Data 0.003 (0.115) Batch 0.918 (1.104) Remain 46:11:35 loss: 1.0077 Lr: 0.00377 [2024-02-17 18:43:41,443 INFO misc.py line 119 87073] Train: [4/100][396/1557] Data 0.003 (0.115) Batch 0.987 (1.104) Remain 46:10:49 loss: 3.5985 Lr: 0.00378 [2024-02-17 18:43:42,202 INFO misc.py line 119 87073] Train: [4/100][397/1557] Data 0.011 (0.115) Batch 0.766 (1.103) Remain 46:08:39 loss: 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Batch 0.746 (1.123) Remain 46:40:13 loss: 1.1966 Lr: 0.00451 [2024-02-17 19:03:24,131 INFO misc.py line 119 87073] Train: [4/100][1442/1557] Data 0.015 (0.127) Batch 1.293 (1.123) Remain 46:40:29 loss: 0.7989 Lr: 0.00451 [2024-02-17 19:03:25,193 INFO misc.py line 119 87073] Train: [4/100][1443/1557] Data 0.023 (0.127) Batch 1.065 (1.123) Remain 46:40:22 loss: 0.4484 Lr: 0.00451 [2024-02-17 19:03:26,145 INFO misc.py line 119 87073] Train: [4/100][1444/1557] Data 0.019 (0.126) Batch 0.968 (1.123) Remain 46:40:05 loss: 1.2174 Lr: 0.00451 [2024-02-17 19:03:27,137 INFO misc.py line 119 87073] Train: [4/100][1445/1557] Data 0.003 (0.126) Batch 0.992 (1.123) Remain 46:39:50 loss: 0.7908 Lr: 0.00451 [2024-02-17 19:03:28,038 INFO misc.py line 119 87073] Train: [4/100][1446/1557] Data 0.004 (0.126) Batch 0.901 (1.123) Remain 46:39:26 loss: 0.4960 Lr: 0.00451 [2024-02-17 19:03:28,788 INFO misc.py line 119 87073] Train: [4/100][1447/1557] Data 0.004 (0.126) Batch 0.739 (1.123) Remain 46:38:45 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[2024-02-17 19:05:10,066 INFO misc.py line 119 87073] Train: [4/100][1535/1557] Data 0.006 (0.128) Batch 0.866 (1.124) Remain 46:41:10 loss: 1.0134 Lr: 0.00456 [2024-02-17 19:05:11,185 INFO misc.py line 119 87073] Train: [4/100][1536/1557] Data 0.006 (0.128) Batch 1.117 (1.124) Remain 46:41:08 loss: 0.9090 Lr: 0.00456 [2024-02-17 19:05:12,227 INFO misc.py line 119 87073] Train: [4/100][1537/1557] Data 0.007 (0.128) Batch 1.044 (1.124) Remain 46:40:59 loss: 0.6498 Lr: 0.00456 [2024-02-17 19:05:12,937 INFO misc.py line 119 87073] Train: [4/100][1538/1557] Data 0.006 (0.128) Batch 0.711 (1.124) Remain 46:40:18 loss: 0.7497 Lr: 0.00456 [2024-02-17 19:05:13,682 INFO misc.py line 119 87073] Train: [4/100][1539/1557] Data 0.005 (0.128) Batch 0.745 (1.124) Remain 46:39:40 loss: 1.1717 Lr: 0.00456 [2024-02-17 19:05:15,009 INFO misc.py line 119 87073] Train: [4/100][1540/1557] Data 0.005 (0.128) Batch 1.327 (1.124) Remain 46:39:58 loss: 0.9060 Lr: 0.00456 [2024-02-17 19:05:16,053 INFO misc.py line 119 87073] Train: [4/100][1541/1557] Data 0.004 (0.128) Batch 1.044 (1.124) Remain 46:39:50 loss: 1.0088 Lr: 0.00456 [2024-02-17 19:05:17,011 INFO misc.py line 119 87073] Train: [4/100][1542/1557] Data 0.004 (0.128) Batch 0.958 (1.124) Remain 46:39:32 loss: 0.8060 Lr: 0.00456 [2024-02-17 19:05:17,857 INFO misc.py line 119 87073] Train: [4/100][1543/1557] Data 0.004 (0.128) Batch 0.846 (1.123) Remain 46:39:04 loss: 1.0017 Lr: 0.00456 [2024-02-17 19:05:18,934 INFO misc.py line 119 87073] Train: [4/100][1544/1557] Data 0.005 (0.128) Batch 1.069 (1.123) Remain 46:38:58 loss: 0.6687 Lr: 0.00456 [2024-02-17 19:05:19,679 INFO misc.py line 119 87073] Train: [4/100][1545/1557] Data 0.011 (0.128) Batch 0.752 (1.123) Remain 46:38:21 loss: 0.8117 Lr: 0.00456 [2024-02-17 19:05:20,378 INFO misc.py line 119 87073] Train: [4/100][1546/1557] Data 0.004 (0.128) Batch 0.690 (1.123) Remain 46:37:38 loss: 0.7039 Lr: 0.00456 [2024-02-17 19:05:21,600 INFO misc.py line 119 87073] Train: [4/100][1547/1557] Data 0.015 (0.128) Batch 1.223 (1.123) Remain 46:37:46 loss: 1.2322 Lr: 0.00456 [2024-02-17 19:05:22,657 INFO misc.py line 119 87073] Train: [4/100][1548/1557] Data 0.013 (0.127) Batch 1.061 (1.123) Remain 46:37:39 loss: 1.3805 Lr: 0.00456 [2024-02-17 19:05:23,743 INFO misc.py line 119 87073] Train: [4/100][1549/1557] Data 0.007 (0.127) Batch 1.075 (1.123) Remain 46:37:33 loss: 1.0923 Lr: 0.00456 [2024-02-17 19:05:24,630 INFO misc.py line 119 87073] Train: [4/100][1550/1557] Data 0.019 (0.127) Batch 0.902 (1.123) Remain 46:37:11 loss: 0.7056 Lr: 0.00457 [2024-02-17 19:05:25,489 INFO misc.py line 119 87073] Train: [4/100][1551/1557] Data 0.004 (0.127) Batch 0.858 (1.123) Remain 46:36:44 loss: 1.5225 Lr: 0.00457 [2024-02-17 19:05:26,274 INFO misc.py line 119 87073] Train: [4/100][1552/1557] Data 0.005 (0.127) Batch 0.776 (1.122) Remain 46:36:10 loss: 0.9340 Lr: 0.00457 [2024-02-17 19:05:27,080 INFO misc.py line 119 87073] Train: [4/100][1553/1557] Data 0.014 (0.127) Batch 0.816 (1.122) Remain 46:35:39 loss: 1.4397 Lr: 0.00457 [2024-02-17 19:05:28,267 INFO misc.py line 119 87073] Train: [4/100][1554/1557] Data 0.004 (0.127) Batch 1.188 (1.122) Remain 46:35:44 loss: 0.5468 Lr: 0.00457 [2024-02-17 19:05:29,102 INFO misc.py line 119 87073] Train: [4/100][1555/1557] Data 0.004 (0.127) Batch 0.834 (1.122) Remain 46:35:15 loss: 1.1541 Lr: 0.00457 [2024-02-17 19:05:30,353 INFO misc.py line 119 87073] Train: [4/100][1556/1557] Data 0.005 (0.127) Batch 1.239 (1.122) Remain 46:35:25 loss: 0.7572 Lr: 0.00457 [2024-02-17 19:05:31,252 INFO misc.py line 119 87073] Train: [4/100][1557/1557] Data 0.016 (0.127) Batch 0.909 (1.122) Remain 46:35:04 loss: 0.9946 Lr: 0.00457 [2024-02-17 19:05:31,253 INFO misc.py line 136 87073] Train result: loss: 0.8636 [2024-02-17 19:05:31,253 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 19:06:02,877 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8995 [2024-02-17 19:06:03,655 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8104 [2024-02-17 19:06:05,777 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.7010 [2024-02-17 19:06:07,982 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3584 [2024-02-17 19:06:12,920 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3425 [2024-02-17 19:06:13,617 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.4093 [2024-02-17 19:06:14,878 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4867 [2024-02-17 19:06:17,831 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4429 [2024-02-17 19:06:19,640 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.6093 [2024-02-17 19:06:21,166 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6365/0.7204/0.8777. [2024-02-17 19:06:21,166 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.8821/0.9780 [2024-02-17 19:06:21,166 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9749/0.9832 [2024-02-17 19:06:21,166 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8386/0.9522 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3034/0.3613 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5891/0.7406 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6206/0.8850 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.7677/0.8877 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8848/0.9441 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6307/0.6865 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.6786/0.7296 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6391/0.6605 [2024-02-17 19:06:21,167 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.4652/0.5569 [2024-02-17 19:06:21,167 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 19:06:21,172 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6365 [2024-02-17 19:06:21,172 INFO misc.py line 165 87073] Currently Best mIoU: 0.6365 [2024-02-17 19:06:21,172 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 19:06:30,882 INFO misc.py line 119 87073] Train: [5/100][1/1557] Data 1.286 (1.286) Batch 2.038 (2.038) Remain 84:36:37 loss: 1.0713 Lr: 0.00457 [2024-02-17 19:06:31,658 INFO misc.py line 119 87073] Train: [5/100][2/1557] Data 0.010 (0.010) Batch 0.778 (0.778) Remain 32:19:05 loss: 1.1261 Lr: 0.00457 [2024-02-17 19:06:32,535 INFO misc.py line 119 87073] Train: [5/100][3/1557] Data 0.006 (0.006) Batch 0.876 (0.876) Remain 36:21:30 loss: 0.4419 Lr: 0.00457 [2024-02-17 19:06:33,445 INFO misc.py line 119 87073] Train: [5/100][4/1557] Data 0.006 (0.006) Batch 0.911 (0.911) Remain 37:49:05 loss: 0.2994 Lr: 0.00457 [2024-02-17 19:06:34,189 INFO misc.py line 119 87073] Train: [5/100][5/1557] Data 0.006 (0.006) Batch 0.742 (0.826) Remain 34:18:09 loss: 0.8639 Lr: 0.00457 [2024-02-17 19:06:34,985 INFO misc.py line 119 87073] Train: [5/100][6/1557] Data 0.008 (0.007) Batch 0.800 (0.818) Remain 33:56:36 loss: 0.8183 Lr: 0.00457 [2024-02-17 19:06:37,556 INFO misc.py line 119 87073] Train: [5/100][7/1557] Data 1.421 (0.360) Batch 2.571 (1.256) Remain 52:08:36 loss: 0.5114 Lr: 0.00457 [2024-02-17 19:06:38,349 INFO misc.py line 119 87073] Train: [5/100][8/1557] Data 0.004 (0.289) Batch 0.793 (1.163) Remain 48:17:50 loss: 0.6595 Lr: 0.00457 [2024-02-17 19:06:39,275 INFO misc.py line 119 87073] Train: [5/100][9/1557] Data 0.004 (0.241) Batch 0.922 (1.123) Remain 46:37:47 loss: 0.8032 Lr: 0.00457 [2024-02-17 19:06:40,078 INFO misc.py line 119 87073] Train: [5/100][10/1557] Data 0.009 (0.208) Batch 0.806 (1.078) Remain 44:44:49 loss: 1.3274 Lr: 0.00457 [2024-02-17 19:06:41,025 INFO misc.py line 119 87073] Train: [5/100][11/1557] Data 0.005 (0.183) Batch 0.947 (1.061) Remain 44:03:58 loss: 0.9171 Lr: 0.00457 [2024-02-17 19:06:41,786 INFO misc.py line 119 87073] Train: [5/100][12/1557] Data 0.006 (0.163) Batch 0.756 (1.027) Remain 42:39:18 loss: 1.0408 Lr: 0.00458 [2024-02-17 19:06:42,527 INFO misc.py line 119 87073] Train: [5/100][13/1557] Data 0.010 (0.148) Batch 0.747 (0.999) Remain 41:29:21 loss: 0.7370 Lr: 0.00458 [2024-02-17 19:06:43,649 INFO misc.py line 119 87073] Train: [5/100][14/1557] Data 0.005 (0.135) Batch 1.123 (1.011) Remain 41:57:16 loss: 0.4565 Lr: 0.00458 [2024-02-17 19:06:44,614 INFO misc.py line 119 87073] Train: [5/100][15/1557] Data 0.004 (0.124) Batch 0.965 (1.007) Remain 41:47:43 loss: 0.6778 Lr: 0.00458 [2024-02-17 19:06:45,586 INFO misc.py line 119 87073] Train: [5/100][16/1557] Data 0.005 (0.115) Batch 0.973 (1.004) Remain 41:41:11 loss: 1.1374 Lr: 0.00458 [2024-02-17 19:06:46,504 INFO misc.py line 119 87073] Train: [5/100][17/1557] Data 0.003 (0.107) Batch 0.917 (0.998) Remain 41:25:36 loss: 0.7670 Lr: 0.00458 [2024-02-17 19:06:47,300 INFO misc.py line 119 87073] Train: [5/100][18/1557] Data 0.006 (0.100) Batch 0.792 (0.984) Remain 40:51:26 loss: 0.9056 Lr: 0.00458 [2024-02-17 19:06:48,108 INFO misc.py line 119 87073] Train: [5/100][19/1557] Data 0.010 (0.094) Batch 0.812 (0.973) Remain 40:24:36 loss: 1.2872 Lr: 0.00458 [2024-02-17 19:06:48,885 INFO misc.py line 119 87073] Train: [5/100][20/1557] Data 0.005 (0.089) Batch 0.777 (0.962) Remain 39:55:47 loss: 0.9003 Lr: 0.00458 [2024-02-17 19:06:50,204 INFO misc.py line 119 87073] Train: [5/100][21/1557] Data 0.005 (0.085) Batch 1.314 (0.981) Remain 40:44:30 loss: 0.5794 Lr: 0.00458 [2024-02-17 19:06:51,187 INFO misc.py line 119 87073] Train: [5/100][22/1557] Data 0.010 (0.081) Batch 0.989 (0.982) Remain 40:45:29 loss: 0.8831 Lr: 0.00458 [2024-02-17 19:06:52,104 INFO misc.py line 119 87073] Train: [5/100][23/1557] Data 0.004 (0.077) Batch 0.914 (0.978) Remain 40:37:05 loss: 0.9085 Lr: 0.00458 [2024-02-17 19:06:53,068 INFO misc.py line 119 87073] Train: [5/100][24/1557] Data 0.010 (0.074) Batch 0.961 (0.978) Remain 40:35:02 loss: 0.7828 Lr: 0.00458 [2024-02-17 19:06:53,977 INFO misc.py line 119 87073] Train: [5/100][25/1557] Data 0.009 (0.071) Batch 0.913 (0.975) Remain 40:27:41 loss: 1.2430 Lr: 0.00458 [2024-02-17 19:06:54,730 INFO misc.py line 119 87073] Train: [5/100][26/1557] Data 0.007 (0.068) Batch 0.754 (0.965) Remain 40:03:44 loss: 0.7035 Lr: 0.00458 [2024-02-17 19:06:55,473 INFO misc.py line 119 87073] Train: [5/100][27/1557] Data 0.005 (0.065) Batch 0.734 (0.955) Remain 39:39:46 loss: 0.6564 Lr: 0.00458 [2024-02-17 19:06:56,706 INFO misc.py line 119 87073] Train: [5/100][28/1557] Data 0.013 (0.063) Batch 1.229 (0.966) Remain 40:07:00 loss: 0.6468 Lr: 0.00458 [2024-02-17 19:06:57,805 INFO misc.py line 119 87073] Train: [5/100][29/1557] Data 0.018 (0.061) Batch 1.104 (0.972) Remain 40:20:11 loss: 0.7313 Lr: 0.00458 [2024-02-17 19:06:58,865 INFO misc.py line 119 87073] Train: [5/100][30/1557] Data 0.013 (0.060) Batch 1.057 (0.975) Remain 40:28:03 loss: 0.7915 Lr: 0.00459 [2024-02-17 19:06:59,936 INFO misc.py line 119 87073] Train: [5/100][31/1557] Data 0.016 (0.058) Batch 1.080 (0.979) Remain 40:37:21 loss: 0.5938 Lr: 0.00459 [2024-02-17 19:07:00,842 INFO misc.py line 119 87073] Train: [5/100][32/1557] Data 0.007 (0.056) Batch 0.909 (0.976) Remain 40:31:20 loss: 1.7192 Lr: 0.00459 [2024-02-17 19:07:01,612 INFO misc.py line 119 87073] Train: [5/100][33/1557] Data 0.003 (0.055) Batch 0.770 (0.969) Remain 40:14:14 loss: 1.0065 Lr: 0.00459 [2024-02-17 19:07:02,367 INFO misc.py line 119 87073] Train: [5/100][34/1557] Data 0.003 (0.053) Batch 0.743 (0.962) Remain 39:56:01 loss: 0.6579 Lr: 0.00459 [2024-02-17 19:07:03,415 INFO misc.py line 119 87073] Train: [5/100][35/1557] Data 0.015 (0.052) Batch 1.051 (0.965) Remain 40:02:58 loss: 0.6537 Lr: 0.00459 [2024-02-17 19:07:04,404 INFO misc.py line 119 87073] Train: [5/100][36/1557] Data 0.013 (0.051) Batch 0.998 (0.966) Remain 40:05:26 loss: 0.8030 Lr: 0.00459 [2024-02-17 19:07:05,457 INFO misc.py line 119 87073] Train: [5/100][37/1557] Data 0.003 (0.049) Batch 1.047 (0.968) Remain 40:11:21 loss: 0.7948 Lr: 0.00459 [2024-02-17 19:07:06,425 INFO misc.py line 119 87073] Train: [5/100][38/1557] Data 0.009 (0.048) Batch 0.974 (0.968) Remain 40:11:46 loss: 0.7890 Lr: 0.00459 [2024-02-17 19:07:07,420 INFO misc.py line 119 87073] Train: [5/100][39/1557] Data 0.004 (0.047) Batch 0.995 (0.969) Remain 40:13:37 loss: 0.9684 Lr: 0.00459 [2024-02-17 19:07:08,184 INFO misc.py line 119 87073] Train: [5/100][40/1557] Data 0.003 (0.046) Batch 0.753 (0.963) Remain 39:59:03 loss: 0.8038 Lr: 0.00459 [2024-02-17 19:07:08,941 INFO misc.py line 119 87073] Train: [5/100][41/1557] Data 0.014 (0.045) Batch 0.766 (0.958) Remain 39:46:08 loss: 0.8990 Lr: 0.00459 [2024-02-17 19:07:10,202 INFO misc.py line 119 87073] Train: [5/100][42/1557] Data 0.004 (0.044) Batch 1.253 (0.966) Remain 40:04:57 loss: 0.4908 Lr: 0.00459 [2024-02-17 19:07:11,214 INFO misc.py line 119 87073] Train: [5/100][43/1557] Data 0.013 (0.043) Batch 1.010 (0.967) Remain 40:07:43 loss: 0.6890 Lr: 0.00459 [2024-02-17 19:07:12,061 INFO misc.py line 119 87073] Train: [5/100][44/1557] Data 0.014 (0.042) Batch 0.858 (0.964) Remain 40:01:07 loss: 0.8018 Lr: 0.00459 [2024-02-17 19:07:12,908 INFO misc.py line 119 87073] Train: [5/100][45/1557] Data 0.004 (0.041) Batch 0.847 (0.961) Remain 39:54:08 loss: 0.5881 Lr: 0.00459 [2024-02-17 19:07:13,949 INFO misc.py line 119 87073] Train: [5/100][46/1557] Data 0.004 (0.041) Batch 1.026 (0.963) Remain 39:57:53 loss: 0.9186 Lr: 0.00459 [2024-02-17 19:07:14,719 INFO misc.py line 119 87073] Train: [5/100][47/1557] Data 0.017 (0.040) Batch 0.784 (0.959) Remain 39:47:46 loss: 0.7859 Lr: 0.00459 [2024-02-17 19:07:15,503 INFO misc.py line 119 87073] Train: [5/100][48/1557] Data 0.004 (0.039) Batch 0.782 (0.955) Remain 39:37:59 loss: 1.1460 Lr: 0.00459 [2024-02-17 19:07:16,845 INFO misc.py line 119 87073] Train: [5/100][49/1557] Data 0.004 (0.038) Batch 1.334 (0.963) Remain 39:58:30 loss: 0.9060 Lr: 0.00459 [2024-02-17 19:07:17,763 INFO misc.py line 119 87073] Train: [5/100][50/1557] Data 0.013 (0.038) Batch 0.926 (0.962) Remain 39:56:30 loss: 1.2867 Lr: 0.00460 [2024-02-17 19:07:18,690 INFO misc.py line 119 87073] Train: [5/100][51/1557] Data 0.006 (0.037) Batch 0.929 (0.962) Remain 39:54:44 loss: 0.6157 Lr: 0.00460 [2024-02-17 19:07:19,505 INFO misc.py line 119 87073] Train: [5/100][52/1557] Data 0.004 (0.037) Batch 0.814 (0.959) Remain 39:47:11 loss: 0.4605 Lr: 0.00460 [2024-02-17 19:07:20,504 INFO misc.py line 119 87073] Train: [5/100][53/1557] Data 0.006 (0.036) Batch 0.993 (0.959) Remain 39:48:54 loss: 0.7479 Lr: 0.00460 [2024-02-17 19:07:21,279 INFO misc.py line 119 87073] Train: [5/100][54/1557] Data 0.011 (0.035) Batch 0.782 (0.956) Remain 39:40:14 loss: 0.7322 Lr: 0.00460 [2024-02-17 19:07:22,022 INFO misc.py line 119 87073] Train: [5/100][55/1557] Data 0.004 (0.035) Batch 0.734 (0.952) Remain 39:29:35 loss: 0.6793 Lr: 0.00460 [2024-02-17 19:07:23,307 INFO misc.py line 119 87073] Train: [5/100][56/1557] Data 0.012 (0.034) Batch 1.286 (0.958) Remain 39:45:16 loss: 0.9154 Lr: 0.00460 [2024-02-17 19:07:24,296 INFO misc.py line 119 87073] Train: [5/100][57/1557] Data 0.013 (0.034) Batch 0.998 (0.959) Remain 39:47:07 loss: 1.0840 Lr: 0.00460 [2024-02-17 19:07:25,408 INFO misc.py line 119 87073] Train: [5/100][58/1557] Data 0.003 (0.033) Batch 1.109 (0.961) Remain 39:53:54 loss: 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Train: [5/100][454/1557] Data 0.003 (0.136) Batch 0.803 (1.120) Remain 46:21:00 loss: 0.7798 Lr: 0.00478 [2024-02-17 19:15:09,743 INFO misc.py line 119 87073] Train: [5/100][455/1557] Data 8.284 (0.154) Batch 12.208 (1.144) Remain 47:21:55 loss: 0.7407 Lr: 0.00478 [2024-02-17 19:15:10,793 INFO misc.py line 119 87073] Train: [5/100][456/1557] Data 0.004 (0.154) Batch 1.050 (1.144) Remain 47:21:23 loss: 1.8207 Lr: 0.00478 [2024-02-17 19:15:11,865 INFO misc.py line 119 87073] Train: [5/100][457/1557] Data 0.003 (0.153) Batch 1.073 (1.144) Remain 47:20:58 loss: 1.2290 Lr: 0.00478 [2024-02-17 19:15:12,924 INFO misc.py line 119 87073] Train: [5/100][458/1557] Data 0.003 (0.153) Batch 1.059 (1.144) Remain 47:20:29 loss: 0.4701 Lr: 0.00478 [2024-02-17 19:15:14,139 INFO misc.py line 119 87073] Train: [5/100][459/1557] Data 0.003 (0.153) Batch 1.201 (1.144) Remain 47:20:47 loss: 0.5596 Lr: 0.00478 [2024-02-17 19:15:14,938 INFO misc.py line 119 87073] Train: [5/100][460/1557] Data 0.018 (0.152) 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Train: [5/100][523/1557] Data 0.004 (0.151) Batch 0.717 (1.139) Remain 47:07:47 loss: 0.8401 Lr: 0.00481 [2024-02-17 19:16:25,607 INFO misc.py line 119 87073] Train: [5/100][524/1557] Data 0.012 (0.151) Batch 0.742 (1.138) Remain 47:05:52 loss: 1.1069 Lr: 0.00481 [2024-02-17 19:16:26,876 INFO misc.py line 119 87073] Train: [5/100][525/1557] Data 0.004 (0.151) Batch 1.268 (1.139) Remain 47:06:28 loss: 0.2607 Lr: 0.00481 [2024-02-17 19:16:27,674 INFO misc.py line 119 87073] Train: [5/100][526/1557] Data 0.005 (0.150) Batch 0.799 (1.138) Remain 47:04:50 loss: 0.4018 Lr: 0.00481 [2024-02-17 19:16:28,699 INFO misc.py line 119 87073] Train: [5/100][527/1557] Data 0.005 (0.150) Batch 1.016 (1.138) Remain 47:04:14 loss: 0.9965 Lr: 0.00481 [2024-02-17 19:16:29,721 INFO misc.py line 119 87073] Train: [5/100][528/1557] Data 0.014 (0.150) Batch 1.024 (1.137) Remain 47:03:41 loss: 0.7281 Lr: 0.00481 [2024-02-17 19:16:30,859 INFO misc.py line 119 87073] Train: [5/100][529/1557] Data 0.012 (0.149) 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Train: [5/100][592/1557] Data 0.005 (0.149) Batch 0.974 (1.137) Remain 47:02:14 loss: 0.8402 Lr: 0.00483 [2024-02-17 19:17:43,216 INFO misc.py line 119 87073] Train: [5/100][593/1557] Data 0.007 (0.149) Batch 0.759 (1.137) Remain 47:00:37 loss: 0.9587 Lr: 0.00483 [2024-02-17 19:17:43,935 INFO misc.py line 119 87073] Train: [5/100][594/1557] Data 0.006 (0.149) Batch 0.720 (1.136) Remain 46:58:51 loss: 0.7697 Lr: 0.00483 [2024-02-17 19:17:44,987 INFO misc.py line 119 87073] Train: [5/100][595/1557] Data 0.005 (0.148) Batch 1.043 (1.136) Remain 46:58:27 loss: 0.7952 Lr: 0.00483 [2024-02-17 19:17:45,843 INFO misc.py line 119 87073] Train: [5/100][596/1557] Data 0.014 (0.148) Batch 0.865 (1.135) Remain 46:57:17 loss: 0.6701 Lr: 0.00483 [2024-02-17 19:17:46,818 INFO misc.py line 119 87073] Train: [5/100][597/1557] Data 0.005 (0.148) Batch 0.976 (1.135) Remain 46:56:36 loss: 0.9371 Lr: 0.00483 [2024-02-17 19:17:47,806 INFO misc.py line 119 87073] Train: [5/100][598/1557] Data 0.004 (0.148) 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Batch 0.774 (1.132) Remain 46:37:29 loss: 0.9049 Lr: 0.00497 [2024-02-17 19:28:24,394 INFO misc.py line 119 87073] Train: [5/100][1162/1557] Data 0.005 (0.152) Batch 1.287 (1.132) Remain 46:37:48 loss: 0.2488 Lr: 0.00497 [2024-02-17 19:28:25,466 INFO misc.py line 119 87073] Train: [5/100][1163/1557] Data 0.017 (0.152) Batch 1.074 (1.132) Remain 46:37:40 loss: 0.5810 Lr: 0.00497 [2024-02-17 19:28:26,549 INFO misc.py line 119 87073] Train: [5/100][1164/1557] Data 0.015 (0.152) Batch 1.086 (1.132) Remain 46:37:33 loss: 1.1198 Lr: 0.00497 [2024-02-17 19:28:27,436 INFO misc.py line 119 87073] Train: [5/100][1165/1557] Data 0.011 (0.152) Batch 0.895 (1.132) Remain 46:37:01 loss: 0.6528 Lr: 0.00497 [2024-02-17 19:28:28,472 INFO misc.py line 119 87073] Train: [5/100][1166/1557] Data 0.004 (0.152) Batch 1.035 (1.132) Remain 46:36:48 loss: 0.6530 Lr: 0.00497 [2024-02-17 19:28:29,213 INFO misc.py line 119 87073] Train: [5/100][1167/1557] Data 0.004 (0.151) Batch 0.742 (1.131) Remain 46:35:57 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87073] Train: [5/100][1236/1557] Data 0.024 (0.150) Batch 0.946 (1.131) Remain 46:33:28 loss: 0.5914 Lr: 0.00498 [2024-02-17 19:29:47,413 INFO misc.py line 119 87073] Train: [5/100][1237/1557] Data 0.004 (0.150) Batch 0.737 (1.130) Remain 46:32:40 loss: 1.0163 Lr: 0.00498 [2024-02-17 19:29:48,132 INFO misc.py line 119 87073] Train: [5/100][1238/1557] Data 0.004 (0.150) Batch 0.707 (1.130) Remain 46:31:48 loss: 0.9805 Lr: 0.00498 [2024-02-17 19:29:58,328 INFO misc.py line 119 87073] Train: [5/100][1239/1557] Data 8.975 (0.157) Batch 10.202 (1.137) Remain 46:49:55 loss: 0.6934 Lr: 0.00498 [2024-02-17 19:29:59,259 INFO misc.py line 119 87073] Train: [5/100][1240/1557] Data 0.010 (0.157) Batch 0.934 (1.137) Remain 46:49:29 loss: 0.7440 Lr: 0.00498 [2024-02-17 19:30:00,335 INFO misc.py line 119 87073] Train: [5/100][1241/1557] Data 0.007 (0.157) Batch 1.079 (1.137) Remain 46:49:21 loss: 0.7236 Lr: 0.00498 [2024-02-17 19:30:01,339 INFO misc.py line 119 87073] Train: [5/100][1242/1557] Data 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[2024-02-17 19:30:13,096 INFO misc.py line 119 87073] Train: [5/100][1255/1557] Data 0.004 (0.155) Batch 0.990 (1.135) Remain 46:42:51 loss: 0.5576 Lr: 0.00498 [2024-02-17 19:30:14,126 INFO misc.py line 119 87073] Train: [5/100][1256/1557] Data 0.003 (0.155) Batch 1.026 (1.135) Remain 46:42:37 loss: 0.8314 Lr: 0.00498 [2024-02-17 19:30:15,048 INFO misc.py line 119 87073] Train: [5/100][1257/1557] Data 0.008 (0.155) Batch 0.925 (1.134) Remain 46:42:12 loss: 0.7600 Lr: 0.00498 [2024-02-17 19:30:15,977 INFO misc.py line 119 87073] Train: [5/100][1258/1557] Data 0.004 (0.155) Batch 0.930 (1.134) Remain 46:41:46 loss: 0.6781 Lr: 0.00498 [2024-02-17 19:30:16,715 INFO misc.py line 119 87073] Train: [5/100][1259/1557] Data 0.003 (0.155) Batch 0.737 (1.134) Remain 46:40:58 loss: 0.6224 Lr: 0.00498 [2024-02-17 19:30:17,904 INFO misc.py line 119 87073] Train: [5/100][1260/1557] Data 0.004 (0.155) Batch 1.179 (1.134) Remain 46:41:02 loss: 0.6108 Lr: 0.00498 [2024-02-17 19:30:18,965 INFO misc.py 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Batch 0.770 (1.132) Remain 46:35:05 loss: 1.0799 Lr: 0.00498 [2024-02-17 19:30:31,016 INFO misc.py line 119 87073] Train: [5/100][1274/1557] Data 0.005 (0.153) Batch 1.308 (1.132) Remain 46:35:24 loss: 0.3771 Lr: 0.00499 [2024-02-17 19:30:31,845 INFO misc.py line 119 87073] Train: [5/100][1275/1557] Data 0.014 (0.153) Batch 0.840 (1.132) Remain 46:34:49 loss: 0.5418 Lr: 0.00499 [2024-02-17 19:30:32,793 INFO misc.py line 119 87073] Train: [5/100][1276/1557] Data 0.003 (0.153) Batch 0.947 (1.131) Remain 46:34:27 loss: 0.6886 Lr: 0.00499 [2024-02-17 19:30:33,762 INFO misc.py line 119 87073] Train: [5/100][1277/1557] Data 0.004 (0.153) Batch 0.970 (1.131) Remain 46:34:07 loss: 0.5407 Lr: 0.00499 [2024-02-17 19:30:34,737 INFO misc.py line 119 87073] Train: [5/100][1278/1557] Data 0.003 (0.153) Batch 0.952 (1.131) Remain 46:33:45 loss: 0.5798 Lr: 0.00499 [2024-02-17 19:30:35,520 INFO misc.py line 119 87073] Train: [5/100][1279/1557] Data 0.027 (0.153) Batch 0.806 (1.131) Remain 46:33:06 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Batch 0.713 (1.130) Remain 46:29:55 loss: 0.8796 Lr: 0.00499 [2024-02-17 19:32:36,106 INFO misc.py line 119 87073] Train: [5/100][1386/1557] Data 0.004 (0.153) Batch 1.370 (1.131) Remain 46:30:20 loss: 0.3737 Lr: 0.00499 [2024-02-17 19:32:37,026 INFO misc.py line 119 87073] Train: [5/100][1387/1557] Data 0.009 (0.153) Batch 0.924 (1.130) Remain 46:29:57 loss: 1.1435 Lr: 0.00499 [2024-02-17 19:32:38,007 INFO misc.py line 119 87073] Train: [5/100][1388/1557] Data 0.004 (0.153) Batch 0.981 (1.130) Remain 46:29:39 loss: 0.6792 Lr: 0.00499 [2024-02-17 19:32:38,996 INFO misc.py line 119 87073] Train: [5/100][1389/1557] Data 0.005 (0.153) Batch 0.988 (1.130) Remain 46:29:23 loss: 1.0087 Lr: 0.00499 [2024-02-17 19:32:39,936 INFO misc.py line 119 87073] Train: [5/100][1390/1557] Data 0.006 (0.153) Batch 0.940 (1.130) Remain 46:29:02 loss: 0.5397 Lr: 0.00499 [2024-02-17 19:32:40,693 INFO misc.py line 119 87073] Train: [5/100][1391/1557] Data 0.005 (0.153) Batch 0.748 (1.130) Remain 46:28:20 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19:32:47,688 INFO misc.py line 119 87073] Train: [5/100][1398/1557] Data 0.004 (0.152) Batch 0.762 (1.129) Remain 46:26:35 loss: 0.7662 Lr: 0.00500 [2024-02-17 19:32:48,412 INFO misc.py line 119 87073] Train: [5/100][1399/1557] Data 0.016 (0.152) Batch 0.735 (1.129) Remain 46:25:52 loss: 0.9307 Lr: 0.00500 [2024-02-17 19:32:49,625 INFO misc.py line 119 87073] Train: [5/100][1400/1557] Data 0.005 (0.152) Batch 1.213 (1.129) Remain 46:26:00 loss: 0.5122 Lr: 0.00500 [2024-02-17 19:32:50,699 INFO misc.py line 119 87073] Train: [5/100][1401/1557] Data 0.005 (0.152) Batch 1.074 (1.129) Remain 46:25:53 loss: 0.4968 Lr: 0.00500 [2024-02-17 19:32:51,780 INFO misc.py line 119 87073] Train: [5/100][1402/1557] Data 0.006 (0.152) Batch 1.080 (1.129) Remain 46:25:46 loss: 0.8770 Lr: 0.00500 [2024-02-17 19:32:52,848 INFO misc.py line 119 87073] Train: [5/100][1403/1557] Data 0.007 (0.151) Batch 1.069 (1.129) Remain 46:25:39 loss: 0.9101 Lr: 0.00500 [2024-02-17 19:32:53,948 INFO misc.py line 119 87073] Train: [5/100][1404/1557] Data 0.006 (0.151) Batch 1.100 (1.129) Remain 46:25:35 loss: 1.0281 Lr: 0.00500 [2024-02-17 19:32:56,639 INFO misc.py line 119 87073] Train: [5/100][1405/1557] Data 1.154 (0.152) Batch 2.689 (1.130) Remain 46:28:18 loss: 0.5817 Lr: 0.00500 [2024-02-17 19:32:57,515 INFO misc.py line 119 87073] Train: [5/100][1406/1557] Data 0.008 (0.152) Batch 0.877 (1.130) Remain 46:27:51 loss: 0.6854 Lr: 0.00500 [2024-02-17 19:33:08,855 INFO misc.py line 119 87073] Train: [5/100][1407/1557] Data 9.515 (0.159) Batch 11.340 (1.137) Remain 46:45:46 loss: 0.4884 Lr: 0.00500 [2024-02-17 19:33:09,951 INFO misc.py line 119 87073] Train: [5/100][1408/1557] Data 0.005 (0.159) Batch 1.090 (1.137) Remain 46:45:40 loss: 0.5584 Lr: 0.00500 [2024-02-17 19:33:10,918 INFO misc.py line 119 87073] Train: [5/100][1409/1557] Data 0.010 (0.158) Batch 0.972 (1.137) Remain 46:45:22 loss: 1.2370 Lr: 0.00500 [2024-02-17 19:33:11,928 INFO misc.py line 119 87073] Train: [5/100][1410/1557] Data 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line 119 87073] Train: [5/100][1429/1557] Data 0.017 (0.156) Batch 0.988 (1.134) Remain 46:37:53 loss: 0.9121 Lr: 0.00500 [2024-02-17 19:33:30,612 INFO misc.py line 119 87073] Train: [5/100][1430/1557] Data 0.004 (0.156) Batch 1.060 (1.134) Remain 46:37:44 loss: 1.5576 Lr: 0.00500 [2024-02-17 19:33:31,643 INFO misc.py line 119 87073] Train: [5/100][1431/1557] Data 0.004 (0.156) Batch 1.031 (1.134) Remain 46:37:33 loss: 0.7457 Lr: 0.00500 [2024-02-17 19:33:32,530 INFO misc.py line 119 87073] Train: [5/100][1432/1557] Data 0.004 (0.156) Batch 0.886 (1.134) Remain 46:37:06 loss: 0.9425 Lr: 0.00500 [2024-02-17 19:33:33,347 INFO misc.py line 119 87073] Train: [5/100][1433/1557] Data 0.006 (0.156) Batch 0.818 (1.133) Remain 46:36:32 loss: 0.8443 Lr: 0.00500 [2024-02-17 19:33:34,052 INFO misc.py line 119 87073] Train: [5/100][1434/1557] Data 0.005 (0.156) Batch 0.706 (1.133) Remain 46:35:47 loss: 0.5632 Lr: 0.00500 [2024-02-17 19:33:35,121 INFO misc.py line 119 87073] Train: 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Batch 0.779 (1.132) Remain 46:33:27 loss: 0.7296 Lr: 0.00500 [2024-02-17 19:33:41,993 INFO misc.py line 119 87073] Train: [5/100][1442/1557] Data 0.013 (0.155) Batch 1.278 (1.132) Remain 46:33:41 loss: 0.3307 Lr: 0.00500 [2024-02-17 19:33:42,959 INFO misc.py line 119 87073] Train: [5/100][1443/1557] Data 0.013 (0.155) Batch 0.975 (1.132) Remain 46:33:24 loss: 0.8152 Lr: 0.00500 [2024-02-17 19:33:43,884 INFO misc.py line 119 87073] Train: [5/100][1444/1557] Data 0.004 (0.155) Batch 0.924 (1.132) Remain 46:33:01 loss: 0.7394 Lr: 0.00500 [2024-02-17 19:33:44,791 INFO misc.py line 119 87073] Train: [5/100][1445/1557] Data 0.005 (0.155) Batch 0.906 (1.132) Remain 46:32:37 loss: 0.7999 Lr: 0.00500 [2024-02-17 19:33:45,757 INFO misc.py line 119 87073] Train: [5/100][1446/1557] Data 0.007 (0.155) Batch 0.953 (1.132) Remain 46:32:17 loss: 0.8386 Lr: 0.00500 [2024-02-17 19:33:46,502 INFO misc.py line 119 87073] Train: [5/100][1447/1557] Data 0.019 (0.154) Batch 0.760 (1.132) Remain 46:31:38 loss: 0.9956 Lr: 0.00500 [2024-02-17 19:33:47,331 INFO misc.py line 119 87073] Train: [5/100][1448/1557] Data 0.004 (0.154) Batch 0.816 (1.131) Remain 46:31:05 loss: 1.0437 Lr: 0.00500 [2024-02-17 19:33:48,655 INFO misc.py line 119 87073] Train: [5/100][1449/1557] Data 0.016 (0.154) Batch 1.319 (1.131) Remain 46:31:23 loss: 0.6519 Lr: 0.00500 [2024-02-17 19:33:49,959 INFO misc.py line 119 87073] Train: [5/100][1450/1557] Data 0.021 (0.154) Batch 1.315 (1.132) Remain 46:31:40 loss: 0.7443 Lr: 0.00500 [2024-02-17 19:33:50,987 INFO misc.py line 119 87073] Train: [5/100][1451/1557] Data 0.011 (0.154) Batch 1.025 (1.132) Remain 46:31:28 loss: 0.9444 Lr: 0.00500 [2024-02-17 19:33:51,885 INFO misc.py line 119 87073] Train: [5/100][1452/1557] Data 0.014 (0.154) Batch 0.908 (1.131) Remain 46:31:04 loss: 0.6788 Lr: 0.00500 [2024-02-17 19:33:52,751 INFO misc.py line 119 87073] Train: [5/100][1453/1557] Data 0.004 (0.154) Batch 0.866 (1.131) Remain 46:30:36 loss: 0.8001 Lr: 0.00500 [2024-02-17 19:33:53,526 INFO misc.py line 119 87073] Train: [5/100][1454/1557] Data 0.004 (0.154) Batch 0.765 (1.131) Remain 46:29:58 loss: 0.6767 Lr: 0.00500 [2024-02-17 19:33:54,275 INFO misc.py line 119 87073] Train: [5/100][1455/1557] Data 0.015 (0.154) Batch 0.760 (1.131) Remain 46:29:19 loss: 0.6437 Lr: 0.00500 [2024-02-17 19:33:55,546 INFO misc.py line 119 87073] Train: [5/100][1456/1557] Data 0.004 (0.154) Batch 1.255 (1.131) Remain 46:29:30 loss: 0.4020 Lr: 0.00500 [2024-02-17 19:33:56,432 INFO misc.py line 119 87073] Train: [5/100][1457/1557] Data 0.020 (0.153) Batch 0.902 (1.131) Remain 46:29:06 loss: 1.4527 Lr: 0.00500 [2024-02-17 19:33:57,258 INFO misc.py line 119 87073] Train: [5/100][1458/1557] Data 0.004 (0.153) Batch 0.823 (1.130) Remain 46:28:33 loss: 0.5454 Lr: 0.00500 [2024-02-17 19:33:58,257 INFO misc.py line 119 87073] Train: [5/100][1459/1557] Data 0.007 (0.153) Batch 0.994 (1.130) Remain 46:28:18 loss: 0.8584 Lr: 0.00500 [2024-02-17 19:33:59,155 INFO misc.py line 119 87073] Train: [5/100][1460/1557] Data 0.011 (0.153) Batch 0.904 (1.130) Remain 46:27:54 loss: 0.6420 Lr: 0.00500 [2024-02-17 19:33:59,850 INFO misc.py line 119 87073] Train: [5/100][1461/1557] Data 0.006 (0.153) Batch 0.695 (1.130) Remain 46:27:09 loss: 0.8208 Lr: 0.00500 [2024-02-17 19:34:00,628 INFO misc.py line 119 87073] Train: [5/100][1462/1557] Data 0.005 (0.153) Batch 0.763 (1.130) Remain 46:26:31 loss: 0.7835 Lr: 0.00500 [2024-02-17 19:34:11,882 INFO misc.py line 119 87073] Train: [5/100][1463/1557] Data 8.598 (0.159) Batch 11.270 (1.137) Remain 46:43:37 loss: 0.5141 Lr: 0.00500 [2024-02-17 19:34:12,816 INFO misc.py line 119 87073] Train: [5/100][1464/1557] Data 0.004 (0.159) Batch 0.933 (1.136) Remain 46:43:16 loss: 1.1703 Lr: 0.00500 [2024-02-17 19:34:13,927 INFO misc.py line 119 87073] Train: [5/100][1465/1557] Data 0.005 (0.159) Batch 1.112 (1.136) Remain 46:43:12 loss: 1.1230 Lr: 0.00500 [2024-02-17 19:34:14,782 INFO misc.py line 119 87073] Train: [5/100][1466/1557] Data 0.004 (0.158) Batch 0.854 (1.136) Remain 46:42:42 loss: 0.6519 Lr: 0.00500 [2024-02-17 19:34:15,689 INFO misc.py line 119 87073] Train: [5/100][1467/1557] Data 0.006 (0.158) Batch 0.905 (1.136) Remain 46:42:18 loss: 0.5809 Lr: 0.00500 [2024-02-17 19:34:16,459 INFO misc.py line 119 87073] Train: [5/100][1468/1557] Data 0.007 (0.158) Batch 0.771 (1.136) Remain 46:41:40 loss: 0.6547 Lr: 0.00500 [2024-02-17 19:34:17,192 INFO misc.py line 119 87073] Train: [5/100][1469/1557] Data 0.005 (0.158) Batch 0.721 (1.136) Remain 46:40:57 loss: 0.7388 Lr: 0.00500 [2024-02-17 19:34:18,392 INFO misc.py line 119 87073] Train: [5/100][1470/1557] Data 0.017 (0.158) Batch 1.210 (1.136) Remain 46:41:03 loss: 0.2961 Lr: 0.00500 [2024-02-17 19:34:19,175 INFO misc.py line 119 87073] Train: [5/100][1471/1557] Data 0.007 (0.158) Batch 0.785 (1.135) Remain 46:40:27 loss: 0.8957 Lr: 0.00500 [2024-02-17 19:34:20,158 INFO misc.py line 119 87073] Train: [5/100][1472/1557] Data 0.005 (0.158) Batch 0.983 (1.135) Remain 46:40:10 loss: 0.9931 Lr: 0.00500 [2024-02-17 19:34:21,440 INFO misc.py line 119 87073] Train: [5/100][1473/1557] Data 0.005 (0.158) Batch 1.270 (1.135) Remain 46:40:23 loss: 0.7495 Lr: 0.00500 [2024-02-17 19:34:22,392 INFO misc.py line 119 87073] Train: [5/100][1474/1557] Data 0.018 (0.158) Batch 0.964 (1.135) Remain 46:40:04 loss: 0.5836 Lr: 0.00500 [2024-02-17 19:34:23,136 INFO misc.py line 119 87073] Train: [5/100][1475/1557] Data 0.005 (0.157) Batch 0.745 (1.135) Remain 46:39:24 loss: 0.7536 Lr: 0.00500 [2024-02-17 19:34:23,885 INFO misc.py line 119 87073] Train: [5/100][1476/1557] Data 0.004 (0.157) Batch 0.735 (1.135) Remain 46:38:43 loss: 0.7947 Lr: 0.00500 [2024-02-17 19:34:25,182 INFO misc.py line 119 87073] Train: [5/100][1477/1557] Data 0.018 (0.157) Batch 1.300 (1.135) Remain 46:38:58 loss: 0.4678 Lr: 0.00500 [2024-02-17 19:34:26,107 INFO misc.py line 119 87073] Train: [5/100][1478/1557] Data 0.015 (0.157) Batch 0.936 (1.135) Remain 46:38:37 loss: 0.9352 Lr: 0.00500 [2024-02-17 19:34:27,304 INFO misc.py line 119 87073] Train: [5/100][1479/1557] Data 0.005 (0.157) Batch 1.195 (1.135) Remain 46:38:42 loss: 0.5071 Lr: 0.00500 [2024-02-17 19:34:28,549 INFO misc.py line 119 87073] Train: [5/100][1480/1557] Data 0.006 (0.157) Batch 1.235 (1.135) Remain 46:38:51 loss: 0.9588 Lr: 0.00500 [2024-02-17 19:34:29,512 INFO misc.py line 119 87073] Train: [5/100][1481/1557] Data 0.016 (0.157) Batch 0.974 (1.135) Remain 46:38:34 loss: 0.9476 Lr: 0.00500 [2024-02-17 19:34:30,231 INFO misc.py line 119 87073] Train: [5/100][1482/1557] Data 0.004 (0.157) Batch 0.720 (1.134) Remain 46:37:51 loss: 1.2870 Lr: 0.00500 [2024-02-17 19:34:30,994 INFO misc.py line 119 87073] Train: [5/100][1483/1557] Data 0.004 (0.157) Batch 0.753 (1.134) Remain 46:37:12 loss: 0.6466 Lr: 0.00500 [2024-02-17 19:34:32,299 INFO misc.py line 119 87073] Train: [5/100][1484/1557] Data 0.013 (0.157) Batch 1.302 (1.134) Remain 46:37:28 loss: 0.4177 Lr: 0.00500 [2024-02-17 19:34:33,436 INFO misc.py line 119 87073] Train: [5/100][1485/1557] Data 0.016 (0.156) Batch 1.144 (1.134) Remain 46:37:27 loss: 0.7394 Lr: 0.00500 [2024-02-17 19:34:34,319 INFO misc.py line 119 87073] Train: [5/100][1486/1557] Data 0.010 (0.156) Batch 0.887 (1.134) Remain 46:37:02 loss: 0.3995 Lr: 0.00500 [2024-02-17 19:34:35,415 INFO misc.py line 119 87073] Train: [5/100][1487/1557] Data 0.005 (0.156) Batch 1.097 (1.134) Remain 46:36:57 loss: 0.6189 Lr: 0.00500 [2024-02-17 19:34:36,416 INFO misc.py line 119 87073] Train: [5/100][1488/1557] Data 0.004 (0.156) Batch 1.001 (1.134) Remain 46:36:42 loss: 0.3732 Lr: 0.00500 [2024-02-17 19:34:37,274 INFO misc.py line 119 87073] Train: [5/100][1489/1557] Data 0.004 (0.156) Batch 0.857 (1.134) Remain 46:36:14 loss: 0.6466 Lr: 0.00500 [2024-02-17 19:34:38,087 INFO misc.py line 119 87073] Train: [5/100][1490/1557] Data 0.004 (0.156) Batch 0.804 (1.134) Remain 46:35:40 loss: 0.5897 Lr: 0.00500 [2024-02-17 19:34:39,117 INFO misc.py line 119 87073] Train: 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Batch 0.780 (1.132) Remain 46:32:54 loss: 0.8176 Lr: 0.00500 [2024-02-17 19:34:45,695 INFO misc.py line 119 87073] Train: [5/100][1498/1557] Data 0.009 (0.155) Batch 1.263 (1.133) Remain 46:33:06 loss: 0.3077 Lr: 0.00500 [2024-02-17 19:34:46,757 INFO misc.py line 119 87073] Train: [5/100][1499/1557] Data 0.018 (0.155) Batch 1.067 (1.132) Remain 46:32:58 loss: 0.8385 Lr: 0.00500 [2024-02-17 19:34:47,986 INFO misc.py line 119 87073] Train: [5/100][1500/1557] Data 0.012 (0.155) Batch 1.222 (1.133) Remain 46:33:06 loss: 0.7515 Lr: 0.00500 [2024-02-17 19:34:49,017 INFO misc.py line 119 87073] Train: [5/100][1501/1557] Data 0.020 (0.155) Batch 1.034 (1.132) Remain 46:32:55 loss: 0.2637 Lr: 0.00500 [2024-02-17 19:34:49,987 INFO misc.py line 119 87073] Train: [5/100][1502/1557] Data 0.017 (0.155) Batch 0.981 (1.132) Remain 46:32:39 loss: 1.3081 Lr: 0.00500 [2024-02-17 19:34:50,754 INFO misc.py line 119 87073] Train: [5/100][1503/1557] Data 0.005 (0.155) Batch 0.768 (1.132) Remain 46:32:02 loss: 0.9259 Lr: 0.00500 [2024-02-17 19:34:51,504 INFO misc.py line 119 87073] Train: [5/100][1504/1557] Data 0.004 (0.155) Batch 0.745 (1.132) Remain 46:31:23 loss: 0.6257 Lr: 0.00500 [2024-02-17 19:34:52,800 INFO misc.py line 119 87073] Train: [5/100][1505/1557] Data 0.008 (0.155) Batch 1.286 (1.132) Remain 46:31:37 loss: 0.4505 Lr: 0.00500 [2024-02-17 19:34:53,824 INFO misc.py line 119 87073] Train: [5/100][1506/1557] Data 0.018 (0.154) Batch 1.031 (1.132) Remain 46:31:26 loss: 0.6443 Lr: 0.00500 [2024-02-17 19:34:54,833 INFO misc.py line 119 87073] Train: [5/100][1507/1557] Data 0.011 (0.154) Batch 1.002 (1.132) Remain 46:31:12 loss: 1.1257 Lr: 0.00500 [2024-02-17 19:34:55,791 INFO misc.py line 119 87073] Train: [5/100][1508/1557] Data 0.018 (0.154) Batch 0.972 (1.132) Remain 46:30:55 loss: 0.8178 Lr: 0.00500 [2024-02-17 19:34:57,109 INFO misc.py line 119 87073] Train: [5/100][1509/1557] Data 0.004 (0.154) Batch 1.317 (1.132) Remain 46:31:12 loss: 1.3431 Lr: 0.00500 [2024-02-17 19:34:57,844 INFO misc.py line 119 87073] Train: [5/100][1510/1557] Data 0.006 (0.154) Batch 0.736 (1.132) Remain 46:30:32 loss: 0.4376 Lr: 0.00500 [2024-02-17 19:34:58,627 INFO misc.py line 119 87073] Train: [5/100][1511/1557] Data 0.004 (0.154) Batch 0.774 (1.131) Remain 46:29:56 loss: 0.8532 Lr: 0.00500 [2024-02-17 19:34:59,937 INFO misc.py line 119 87073] Train: [5/100][1512/1557] Data 0.013 (0.154) Batch 1.306 (1.131) Remain 46:30:12 loss: 0.5431 Lr: 0.00500 [2024-02-17 19:35:00,851 INFO misc.py line 119 87073] Train: [5/100][1513/1557] Data 0.018 (0.154) Batch 0.927 (1.131) Remain 46:29:51 loss: 0.9509 Lr: 0.00500 [2024-02-17 19:35:01,919 INFO misc.py line 119 87073] Train: [5/100][1514/1557] Data 0.006 (0.154) Batch 1.068 (1.131) Remain 46:29:43 loss: 0.6128 Lr: 0.00500 [2024-02-17 19:35:02,819 INFO misc.py line 119 87073] Train: [5/100][1515/1557] Data 0.004 (0.154) Batch 0.901 (1.131) Remain 46:29:20 loss: 0.8580 Lr: 0.00500 [2024-02-17 19:35:03,813 INFO misc.py line 119 87073] Train: [5/100][1516/1557] Data 0.004 (0.153) Batch 0.994 (1.131) Remain 46:29:05 loss: 0.5863 Lr: 0.00500 [2024-02-17 19:35:04,609 INFO misc.py line 119 87073] Train: [5/100][1517/1557] Data 0.004 (0.153) Batch 0.790 (1.131) Remain 46:28:31 loss: 0.7466 Lr: 0.00500 [2024-02-17 19:35:05,379 INFO misc.py line 119 87073] Train: [5/100][1518/1557] Data 0.010 (0.153) Batch 0.776 (1.131) Remain 46:27:55 loss: 0.8847 Lr: 0.00500 [2024-02-17 19:35:17,895 INFO misc.py line 119 87073] Train: [5/100][1519/1557] Data 8.047 (0.158) Batch 12.515 (1.138) Remain 46:46:25 loss: 0.7053 Lr: 0.00500 [2024-02-17 19:35:18,821 INFO misc.py line 119 87073] Train: [5/100][1520/1557] Data 0.005 (0.158) Batch 0.926 (1.138) Remain 46:46:03 loss: 1.0760 Lr: 0.00500 [2024-02-17 19:35:19,659 INFO misc.py line 119 87073] Train: [5/100][1521/1557] Data 0.003 (0.158) Batch 0.831 (1.138) Remain 46:45:32 loss: 0.3738 Lr: 0.00500 [2024-02-17 19:35:20,607 INFO misc.py line 119 87073] Train: [5/100][1522/1557] Data 0.012 (0.158) Batch 0.955 (1.138) Remain 46:45:13 loss: 1.0864 Lr: 0.00500 [2024-02-17 19:35:21,735 INFO misc.py line 119 87073] Train: [5/100][1523/1557] Data 0.004 (0.158) Batch 1.128 (1.138) Remain 46:45:11 loss: 0.5863 Lr: 0.00500 [2024-02-17 19:35:22,494 INFO misc.py line 119 87073] Train: [5/100][1524/1557] Data 0.005 (0.158) Batch 0.759 (1.137) Remain 46:44:33 loss: 1.0094 Lr: 0.00500 [2024-02-17 19:35:23,243 INFO misc.py line 119 87073] Train: [5/100][1525/1557] Data 0.004 (0.158) Batch 0.738 (1.137) Remain 46:43:53 loss: 0.6743 Lr: 0.00500 [2024-02-17 19:35:24,444 INFO misc.py line 119 87073] Train: [5/100][1526/1557] Data 0.015 (0.158) Batch 1.198 (1.137) Remain 46:43:58 loss: 0.4383 Lr: 0.00500 [2024-02-17 19:35:25,522 INFO misc.py line 119 87073] Train: [5/100][1527/1557] Data 0.018 (0.158) Batch 1.081 (1.137) Remain 46:43:51 loss: 0.5886 Lr: 0.00500 [2024-02-17 19:35:26,499 INFO misc.py line 119 87073] Train: [5/100][1528/1557] Data 0.015 (0.158) Batch 0.989 (1.137) Remain 46:43:36 loss: 1.1816 Lr: 0.00500 [2024-02-17 19:35:27,312 INFO misc.py line 119 87073] Train: [5/100][1529/1557] Data 0.003 (0.158) Batch 0.813 (1.137) Remain 46:43:03 loss: 0.4888 Lr: 0.00500 [2024-02-17 19:35:28,424 INFO misc.py line 119 87073] Train: [5/100][1530/1557] Data 0.004 (0.157) Batch 1.103 (1.137) Remain 46:42:59 loss: 0.5545 Lr: 0.00500 [2024-02-17 19:35:29,225 INFO misc.py line 119 87073] Train: [5/100][1531/1557] Data 0.013 (0.157) Batch 0.809 (1.137) Remain 46:42:26 loss: 0.7794 Lr: 0.00500 [2024-02-17 19:35:29,988 INFO misc.py line 119 87073] Train: [5/100][1532/1557] Data 0.005 (0.157) Batch 0.764 (1.136) Remain 46:41:49 loss: 0.8282 Lr: 0.00500 [2024-02-17 19:35:31,291 INFO misc.py line 119 87073] Train: [5/100][1533/1557] Data 0.004 (0.157) Batch 1.292 (1.136) Remain 46:42:02 loss: 0.2950 Lr: 0.00500 [2024-02-17 19:35:32,174 INFO misc.py line 119 87073] Train: [5/100][1534/1557] Data 0.015 (0.157) Batch 0.895 (1.136) Remain 46:41:38 loss: 1.5658 Lr: 0.00500 [2024-02-17 19:35:33,220 INFO misc.py line 119 87073] Train: [5/100][1535/1557] Data 0.004 (0.157) Batch 1.046 (1.136) Remain 46:41:28 loss: 0.7262 Lr: 0.00500 [2024-02-17 19:35:34,429 INFO misc.py line 119 87073] Train: [5/100][1536/1557] Data 0.004 (0.157) Batch 1.194 (1.136) Remain 46:41:32 loss: 0.8681 Lr: 0.00500 [2024-02-17 19:35:35,190 INFO misc.py line 119 87073] Train: [5/100][1537/1557] Data 0.020 (0.157) Batch 0.777 (1.136) Remain 46:40:57 loss: 0.5675 Lr: 0.00500 [2024-02-17 19:35:35,950 INFO misc.py line 119 87073] Train: [5/100][1538/1557] Data 0.004 (0.157) Batch 0.747 (1.136) Remain 46:40:18 loss: 0.9434 Lr: 0.00500 [2024-02-17 19:35:36,685 INFO misc.py line 119 87073] Train: [5/100][1539/1557] Data 0.016 (0.157) Batch 0.747 (1.136) Remain 46:39:39 loss: 0.7519 Lr: 0.00500 [2024-02-17 19:35:37,912 INFO misc.py line 119 87073] Train: [5/100][1540/1557] Data 0.004 (0.156) Batch 1.227 (1.136) Remain 46:39:47 loss: 0.3256 Lr: 0.00500 [2024-02-17 19:35:38,707 INFO misc.py line 119 87073] Train: [5/100][1541/1557] Data 0.003 (0.156) Batch 0.795 (1.135) Remain 46:39:13 loss: 0.5885 Lr: 0.00500 [2024-02-17 19:35:39,740 INFO misc.py line 119 87073] Train: [5/100][1542/1557] Data 0.003 (0.156) Batch 1.020 (1.135) Remain 46:39:01 loss: 1.2622 Lr: 0.00500 [2024-02-17 19:35:40,694 INFO misc.py line 119 87073] Train: [5/100][1543/1557] Data 0.017 (0.156) Batch 0.967 (1.135) Remain 46:38:44 loss: 0.8958 Lr: 0.00500 [2024-02-17 19:35:41,689 INFO misc.py line 119 87073] Train: [5/100][1544/1557] Data 0.006 (0.156) Batch 0.996 (1.135) Remain 46:38:29 loss: 0.9156 Lr: 0.00500 [2024-02-17 19:35:42,432 INFO misc.py line 119 87073] Train: [5/100][1545/1557] Data 0.003 (0.156) Batch 0.743 (1.135) Remain 46:37:50 loss: 1.0129 Lr: 0.00500 [2024-02-17 19:35:43,173 INFO misc.py line 119 87073] Train: [5/100][1546/1557] Data 0.003 (0.156) Batch 0.726 (1.135) Remain 46:37:10 loss: 0.7939 Lr: 0.00500 [2024-02-17 19:35:44,277 INFO misc.py line 119 87073] Train: [5/100][1547/1557] Data 0.018 (0.156) Batch 1.101 (1.135) Remain 46:37:06 loss: 0.5437 Lr: 0.00500 [2024-02-17 19:35:45,239 INFO misc.py line 119 87073] Train: [5/100][1548/1557] Data 0.020 (0.156) Batch 0.979 (1.134) Remain 46:36:50 loss: 0.5451 Lr: 0.00500 [2024-02-17 19:35:46,196 INFO misc.py line 119 87073] Train: [5/100][1549/1557] Data 0.004 (0.156) Batch 0.957 (1.134) Remain 46:36:32 loss: 2.3940 Lr: 0.00500 [2024-02-17 19:35:47,099 INFO misc.py line 119 87073] Train: [5/100][1550/1557] Data 0.004 (0.155) Batch 0.903 (1.134) Remain 46:36:08 loss: 0.4934 Lr: 0.00500 [2024-02-17 19:35:48,034 INFO misc.py line 119 87073] Train: [5/100][1551/1557] Data 0.005 (0.155) Batch 0.917 (1.134) Remain 46:35:46 loss: 0.6728 Lr: 0.00500 [2024-02-17 19:35:48,721 INFO misc.py line 119 87073] Train: [5/100][1552/1557] Data 0.022 (0.155) Batch 0.705 (1.134) Remain 46:35:04 loss: 0.8635 Lr: 0.00500 [2024-02-17 19:35:49,479 INFO misc.py line 119 87073] Train: [5/100][1553/1557] Data 0.004 (0.155) Batch 0.745 (1.134) Remain 46:34:26 loss: 0.6541 Lr: 0.00500 [2024-02-17 19:35:50,756 INFO misc.py line 119 87073] Train: [5/100][1554/1557] Data 0.017 (0.155) Batch 1.276 (1.134) Remain 46:34:39 loss: 0.3822 Lr: 0.00500 [2024-02-17 19:35:51,716 INFO misc.py line 119 87073] Train: [5/100][1555/1557] Data 0.018 (0.155) Batch 0.974 (1.133) Remain 46:34:22 loss: 0.9777 Lr: 0.00500 [2024-02-17 19:35:52,561 INFO misc.py line 119 87073] Train: [5/100][1556/1557] Data 0.004 (0.155) Batch 0.845 (1.133) Remain 46:33:54 loss: 0.7353 Lr: 0.00500 [2024-02-17 19:35:53,415 INFO misc.py line 119 87073] Train: [5/100][1557/1557] Data 0.004 (0.155) Batch 0.837 (1.133) Remain 46:33:24 loss: 1.0797 Lr: 0.00500 [2024-02-17 19:35:53,416 INFO misc.py line 136 87073] Train result: loss: 0.7978 [2024-02-17 19:35:53,416 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 19:36:24,630 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6551 [2024-02-17 19:36:25,410 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9451 [2024-02-17 19:36:27,535 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5383 [2024-02-17 19:36:29,744 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3580 [2024-02-17 19:36:34,684 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.5288 [2024-02-17 19:36:35,384 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1268 [2024-02-17 19:36:36,645 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.5767 [2024-02-17 19:36:39,596 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3240 [2024-02-17 19:36:41,404 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.6432 [2024-02-17 19:36:43,115 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6616/0.7544/0.8858. [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9059/0.9133 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9758/0.9928 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8332/0.9451 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0033/0.0354 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3601/0.5016 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5995/0.6230 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.4990/0.6573 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8236/0.8936 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8930/0.9472 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7935/0.8631 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7311/0.8264 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6152/0.8700 [2024-02-17 19:36:43,116 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5672/0.7387 [2024-02-17 19:36:43,117 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 19:36:43,120 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6616 [2024-02-17 19:36:43,120 INFO misc.py line 165 87073] Currently Best mIoU: 0.6616 [2024-02-17 19:36:43,120 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 19:36:55,208 INFO misc.py line 119 87073] Train: [6/100][1/1557] Data 1.251 (1.251) Batch 1.849 (1.849) Remain 75:57:27 loss: 1.0697 Lr: 0.00500 [2024-02-17 19:36:56,134 INFO misc.py line 119 87073] Train: [6/100][2/1557] Data 0.005 (0.005) Batch 0.922 (0.922) Remain 37:52:16 loss: 0.5420 Lr: 0.00500 [2024-02-17 19:36:57,120 INFO misc.py line 119 87073] Train: [6/100][3/1557] Data 0.009 (0.009) Batch 0.991 (0.991) Remain 40:43:26 loss: 0.7875 Lr: 0.00500 [2024-02-17 19:36:58,080 INFO misc.py line 119 87073] Train: [6/100][4/1557] Data 0.004 (0.004) Batch 0.960 (0.960) Remain 39:27:06 loss: 0.8418 Lr: 0.00500 [2024-02-17 19:36:58,870 INFO misc.py line 119 87073] Train: [6/100][5/1557] Data 0.004 (0.004) Batch 0.788 (0.874) Remain 35:55:14 loss: 0.7820 Lr: 0.00500 [2024-02-17 19:36:59,658 INFO misc.py line 119 87073] Train: [6/100][6/1557] Data 0.006 (0.005) Batch 0.788 (0.845) Remain 34:44:10 loss: 0.5768 Lr: 0.00500 [2024-02-17 19:37:00,914 INFO misc.py line 119 87073] Train: [6/100][7/1557] Data 0.005 (0.005) Batch 1.253 (0.947) Remain 38:55:33 loss: 1.4535 Lr: 0.00500 [2024-02-17 19:37:01,924 INFO misc.py line 119 87073] Train: [6/100][8/1557] Data 0.009 (0.006) Batch 1.003 (0.959) Remain 39:23:05 loss: 1.1199 Lr: 0.00500 [2024-02-17 19:37:02,813 INFO misc.py line 119 87073] Train: [6/100][9/1557] Data 0.015 (0.007) Batch 0.900 (0.949) Remain 38:58:58 loss: 0.8313 Lr: 0.00500 [2024-02-17 19:37:03,859 INFO misc.py line 119 87073] Train: [6/100][10/1557] Data 0.004 (0.007) Batch 1.045 (0.963) Remain 39:32:49 loss: 0.8629 Lr: 0.00500 [2024-02-17 19:37:04,726 INFO misc.py line 119 87073] Train: [6/100][11/1557] Data 0.006 (0.007) Batch 0.868 (0.951) Remain 39:03:49 loss: 1.0069 Lr: 0.00500 [2024-02-17 19:37:05,496 INFO misc.py line 119 87073] Train: [6/100][12/1557] Data 0.004 (0.006) Batch 0.764 (0.930) Remain 38:12:38 loss: 0.6479 Lr: 0.00500 [2024-02-17 19:37:06,248 INFO misc.py line 119 87073] Train: [6/100][13/1557] Data 0.009 (0.007) Batch 0.753 (0.912) Remain 37:28:57 loss: 0.9852 Lr: 0.00500 [2024-02-17 19:37:07,427 INFO misc.py line 119 87073] Train: [6/100][14/1557] Data 0.009 (0.007) Batch 1.182 (0.937) Remain 38:29:26 loss: 0.6545 Lr: 0.00500 [2024-02-17 19:37:08,323 INFO misc.py line 119 87073] Train: [6/100][15/1557] Data 0.005 (0.007) Batch 0.896 (0.933) Remain 38:21:00 loss: 1.0021 Lr: 0.00500 [2024-02-17 19:37:09,221 INFO misc.py line 119 87073] Train: [6/100][16/1557] Data 0.006 (0.007) Batch 0.898 (0.931) Remain 38:14:14 loss: 0.6522 Lr: 0.00500 [2024-02-17 19:37:10,122 INFO misc.py line 119 87073] Train: [6/100][17/1557] Data 0.006 (0.007) Batch 0.902 (0.929) Remain 38:09:05 loss: 1.1390 Lr: 0.00500 [2024-02-17 19:37:11,161 INFO misc.py line 119 87073] Train: [6/100][18/1557] Data 0.004 (0.006) Batch 1.037 (0.936) Remain 38:26:49 loss: 0.9078 Lr: 0.00500 [2024-02-17 19:37:11,898 INFO misc.py line 119 87073] Train: [6/100][19/1557] Data 0.007 (0.006) Batch 0.740 (0.924) Remain 37:56:36 loss: 0.6516 Lr: 0.00500 [2024-02-17 19:37:12,683 INFO misc.py line 119 87073] Train: [6/100][20/1557] Data 0.005 (0.006) Batch 0.785 (0.915) Remain 37:36:25 loss: 0.4154 Lr: 0.00500 [2024-02-17 19:37:13,943 INFO misc.py line 119 87073] Train: [6/100][21/1557] Data 0.005 (0.006) Batch 1.253 (0.934) Remain 38:22:38 loss: 0.7324 Lr: 0.00500 [2024-02-17 19:37:15,088 INFO misc.py line 119 87073] Train: [6/100][22/1557] Data 0.012 (0.007) Batch 1.143 (0.945) Remain 38:49:43 loss: 0.7271 Lr: 0.00500 [2024-02-17 19:37:16,152 INFO misc.py line 119 87073] Train: [6/100][23/1557] Data 0.014 (0.007) Batch 1.068 (0.951) Remain 39:04:47 loss: 0.8004 Lr: 0.00500 [2024-02-17 19:37:17,025 INFO misc.py line 119 87073] Train: [6/100][24/1557] Data 0.011 (0.007) Batch 0.879 (0.948) Remain 38:56:14 loss: 1.1232 Lr: 0.00500 [2024-02-17 19:37:18,003 INFO misc.py line 119 87073] Train: [6/100][25/1557] Data 0.004 (0.007) Batch 0.978 (0.949) Remain 38:59:37 loss: 0.6429 Lr: 0.00500 [2024-02-17 19:37:18,752 INFO misc.py line 119 87073] Train: [6/100][26/1557] Data 0.004 (0.007) Batch 0.747 (0.940) Remain 38:37:55 loss: 0.9951 Lr: 0.00500 [2024-02-17 19:37:19,511 INFO misc.py line 119 87073] Train: [6/100][27/1557] Data 0.007 (0.007) Batch 0.759 (0.933) Remain 38:19:15 loss: 0.9220 Lr: 0.00500 [2024-02-17 19:37:20,709 INFO misc.py line 119 87073] Train: [6/100][28/1557] Data 0.007 (0.007) Batch 1.193 (0.943) Remain 38:44:53 loss: 0.4703 Lr: 0.00500 [2024-02-17 19:37:21,878 INFO misc.py line 119 87073] Train: [6/100][29/1557] Data 0.012 (0.007) Batch 1.164 (0.952) Remain 39:05:46 loss: 0.5385 Lr: 0.00500 [2024-02-17 19:37:22,762 INFO misc.py line 119 87073] Train: [6/100][30/1557] Data 0.017 (0.007) Batch 0.897 (0.950) Remain 39:00:44 loss: 0.8403 Lr: 0.00500 [2024-02-17 19:37:23,899 INFO misc.py line 119 87073] Train: [6/100][31/1557] Data 0.004 (0.007) Batch 1.137 (0.956) Remain 39:17:13 loss: 0.7783 Lr: 0.00500 [2024-02-17 19:37:24,833 INFO misc.py line 119 87073] Train: [6/100][32/1557] Data 0.005 (0.007) Batch 0.934 (0.956) Remain 39:15:18 loss: 0.6879 Lr: 0.00500 [2024-02-17 19:37:25,590 INFO misc.py line 119 87073] Train: [6/100][33/1557] Data 0.005 (0.007) Batch 0.756 (0.949) Remain 38:58:53 loss: 0.8175 Lr: 0.00500 [2024-02-17 19:37:26,352 INFO misc.py line 119 87073] Train: [6/100][34/1557] Data 0.005 (0.007) Batch 0.754 (0.943) Remain 38:43:24 loss: 0.6357 Lr: 0.00500 [2024-02-17 19:37:27,663 INFO misc.py line 119 87073] Train: [6/100][35/1557] Data 0.012 (0.007) Batch 1.310 (0.954) Remain 39:11:39 loss: 0.6480 Lr: 0.00500 [2024-02-17 19:37:28,795 INFO misc.py line 119 87073] Train: [6/100][36/1557] Data 0.014 (0.007) Batch 1.133 (0.960) Remain 39:25:00 loss: 1.2149 Lr: 0.00500 [2024-02-17 19:37:29,938 INFO misc.py line 119 87073] Train: [6/100][37/1557] Data 0.013 (0.008) Batch 1.141 (0.965) Remain 39:38:10 loss: 1.0675 Lr: 0.00500 [2024-02-17 19:37:30,992 INFO misc.py line 119 87073] Train: [6/100][38/1557] Data 0.015 (0.008) Batch 1.056 (0.968) Remain 39:44:32 loss: 0.4957 Lr: 0.00500 [2024-02-17 19:37:32,068 INFO misc.py line 119 87073] Train: [6/100][39/1557] Data 0.013 (0.008) Batch 1.078 (0.971) Remain 39:52:05 loss: 1.5076 Lr: 0.00500 [2024-02-17 19:37:32,846 INFO misc.py line 119 87073] Train: [6/100][40/1557] Data 0.011 (0.008) Batch 0.784 (0.966) Remain 39:39:39 loss: 0.6423 Lr: 0.00500 [2024-02-17 19:37:33,647 INFO misc.py line 119 87073] Train: [6/100][41/1557] Data 0.005 (0.008) Batch 0.801 (0.961) Remain 39:28:59 loss: 0.8400 Lr: 0.00500 [2024-02-17 19:37:34,823 INFO misc.py line 119 87073] Train: [6/100][42/1557] Data 0.004 (0.008) Batch 1.168 (0.967) Remain 39:42:03 loss: 0.6847 Lr: 0.00500 [2024-02-17 19:37:35,719 INFO misc.py line 119 87073] Train: [6/100][43/1557] Data 0.012 (0.008) Batch 0.903 (0.965) Remain 39:38:08 loss: 0.7533 Lr: 0.00500 [2024-02-17 19:37:36,723 INFO misc.py line 119 87073] Train: [6/100][44/1557] Data 0.006 (0.008) Batch 1.005 (0.966) Remain 39:40:31 loss: 0.7186 Lr: 0.00500 [2024-02-17 19:37:37,709 INFO misc.py line 119 87073] Train: [6/100][45/1557] Data 0.005 (0.008) Batch 0.986 (0.966) Remain 39:41:42 loss: 0.6172 Lr: 0.00500 [2024-02-17 19:37:39,004 INFO misc.py line 119 87073] Train: [6/100][46/1557] Data 0.004 (0.008) Batch 1.282 (0.974) Remain 39:59:47 loss: 0.8709 Lr: 0.00500 [2024-02-17 19:37:39,740 INFO misc.py line 119 87073] Train: [6/100][47/1557] Data 0.016 (0.008) Batch 0.748 (0.969) Remain 39:47:08 loss: 0.5926 Lr: 0.00500 [2024-02-17 19:37:40,491 INFO misc.py line 119 87073] Train: [6/100][48/1557] Data 0.004 (0.008) Batch 0.742 (0.964) Remain 39:34:42 loss: 0.4682 Lr: 0.00500 [2024-02-17 19:37:41,815 INFO misc.py line 119 87073] Train: [6/100][49/1557] Data 0.014 (0.008) Batch 1.320 (0.971) Remain 39:53:46 loss: 0.4729 Lr: 0.00500 [2024-02-17 19:37:42,960 INFO misc.py line 119 87073] Train: [6/100][50/1557] Data 0.018 (0.008) Batch 1.145 (0.975) Remain 40:02:50 loss: 0.6606 Lr: 0.00500 [2024-02-17 19:37:43,778 INFO misc.py line 119 87073] Train: [6/100][51/1557] Data 0.018 (0.008) Batch 0.832 (0.972) Remain 39:55:30 loss: 1.2470 Lr: 0.00500 [2024-02-17 19:37:44,952 INFO misc.py line 119 87073] Train: [6/100][52/1557] Data 0.004 (0.008) Batch 1.173 (0.976) Remain 40:05:36 loss: 0.3225 Lr: 0.00500 [2024-02-17 19:37:45,801 INFO misc.py line 119 87073] Train: [6/100][53/1557] Data 0.004 (0.008) Batch 0.849 (0.974) Remain 39:59:19 loss: 1.0169 Lr: 0.00500 [2024-02-17 19:37:46,581 INFO misc.py line 119 87073] Train: [6/100][54/1557] Data 0.006 (0.008) Batch 0.769 (0.970) Remain 39:49:25 loss: 1.2810 Lr: 0.00500 [2024-02-17 19:37:47,370 INFO misc.py line 119 87073] Train: [6/100][55/1557] Data 0.016 (0.008) Batch 0.800 (0.966) Remain 39:41:22 loss: 0.7605 Lr: 0.00500 [2024-02-17 19:37:48,451 INFO misc.py line 119 87073] Train: [6/100][56/1557] Data 0.005 (0.008) Batch 1.080 (0.968) Remain 39:46:39 loss: 0.6236 Lr: 0.00500 [2024-02-17 19:37:49,242 INFO misc.py line 119 87073] Train: [6/100][57/1557] Data 0.005 (0.008) Batch 0.791 (0.965) Remain 39:38:32 loss: 1.0017 Lr: 0.00500 [2024-02-17 19:37:50,188 INFO misc.py line 119 87073] Train: [6/100][58/1557] Data 0.005 (0.008) Batch 0.938 (0.965) Remain 39:37:19 loss: 1.2875 Lr: 0.00500 [2024-02-17 19:37:51,090 INFO misc.py line 119 87073] Train: [6/100][59/1557] Data 0.012 (0.008) Batch 0.910 (0.964) Remain 39:34:53 loss: 0.6639 Lr: 0.00500 [2024-02-17 19:37:52,215 INFO misc.py line 119 87073] Train: [6/100][60/1557] Data 0.005 (0.008) Batch 1.126 (0.967) Remain 39:41:53 loss: 1.1884 Lr: 0.00500 [2024-02-17 19:37:52,901 INFO misc.py line 119 87073] Train: [6/100][61/1557] Data 0.004 (0.008) Batch 0.685 (0.962) Remain 39:29:56 loss: 0.9977 Lr: 0.00500 [2024-02-17 19:37:53,602 INFO misc.py line 119 87073] Train: [6/100][62/1557] Data 0.004 (0.008) Batch 0.690 (0.957) Remain 39:18:33 loss: 0.8525 Lr: 0.00500 [2024-02-17 19:38:00,737 INFO misc.py line 119 87073] Train: [6/100][63/1557] Data 4.491 (0.083) Batch 7.137 (1.060) Remain 43:32:20 loss: 1.0153 Lr: 0.00500 [2024-02-17 19:38:01,677 INFO misc.py line 119 87073] Train: [6/100][64/1557] Data 0.014 (0.082) Batch 0.950 (1.058) Remain 43:27:52 loss: 0.9456 Lr: 0.00500 [2024-02-17 19:38:02,579 INFO misc.py line 119 87073] Train: [6/100][65/1557] Data 0.004 (0.080) Batch 0.902 (1.056) Remain 43:21:37 loss: 0.7445 Lr: 0.00500 [2024-02-17 19:38:03,639 INFO misc.py line 119 87073] Train: [6/100][66/1557] Data 0.004 (0.079) Batch 1.061 (1.056) Remain 43:21:47 loss: 0.7335 Lr: 0.00500 [2024-02-17 19:38:04,782 INFO misc.py line 119 87073] Train: [6/100][67/1557] Data 0.004 (0.078) Batch 1.143 (1.057) Remain 43:25:07 loss: 0.6170 Lr: 0.00500 [2024-02-17 19:38:05,528 INFO misc.py line 119 87073] Train: [6/100][68/1557] Data 0.004 (0.077) Batch 0.747 (1.052) Remain 43:13:20 loss: 1.0744 Lr: 0.00500 [2024-02-17 19:38:06,261 INFO misc.py line 119 87073] Train: [6/100][69/1557] Data 0.003 (0.076) Batch 0.722 (1.047) Remain 43:00:59 loss: 0.9980 Lr: 0.00500 [2024-02-17 19:38:07,441 INFO misc.py line 119 87073] Train: [6/100][70/1557] Data 0.013 (0.075) Batch 1.173 (1.049) Remain 43:05:36 loss: 0.9150 Lr: 0.00500 [2024-02-17 19:38:08,297 INFO misc.py line 119 87073] Train: [6/100][71/1557] Data 0.021 (0.074) Batch 0.871 (1.047) Remain 42:59:07 loss: 0.5810 Lr: 0.00500 [2024-02-17 19:38:09,127 INFO misc.py line 119 87073] Train: [6/100][72/1557] Data 0.006 (0.073) Batch 0.829 (1.044) Remain 42:51:20 loss: 1.0429 Lr: 0.00500 [2024-02-17 19:38:10,082 INFO misc.py line 119 87073] Train: [6/100][73/1557] Data 0.007 (0.072) Batch 0.953 (1.042) Remain 42:48:07 loss: 0.7405 Lr: 0.00500 [2024-02-17 19:38:10,996 INFO misc.py line 119 87073] Train: [6/100][74/1557] Data 0.009 (0.071) Batch 0.917 (1.040) Remain 42:43:46 loss: 1.1617 Lr: 0.00500 [2024-02-17 19:38:11,762 INFO misc.py line 119 87073] Train: [6/100][75/1557] Data 0.006 (0.070) Batch 0.767 (1.037) Remain 42:34:24 loss: 0.8250 Lr: 0.00500 [2024-02-17 19:38:12,513 INFO misc.py line 119 87073] Train: [6/100][76/1557] Data 0.005 (0.069) Batch 0.737 (1.033) Remain 42:24:17 loss: 1.0287 Lr: 0.00500 [2024-02-17 19:38:13,777 INFO misc.py line 119 87073] Train: [6/100][77/1557] Data 0.017 (0.069) Batch 1.266 (1.036) Remain 42:32:02 loss: 0.5349 Lr: 0.00500 [2024-02-17 19:38:14,921 INFO misc.py line 119 87073] Train: [6/100][78/1557] Data 0.015 (0.068) Batch 1.143 (1.037) Remain 42:35:33 loss: 0.7658 Lr: 0.00500 [2024-02-17 19:38:15,838 INFO misc.py line 119 87073] Train: [6/100][79/1557] Data 0.016 (0.067) Batch 0.929 (1.036) Remain 42:32:02 loss: 0.5494 Lr: 0.00500 [2024-02-17 19:38:16,733 INFO misc.py line 119 87073] Train: [6/100][80/1557] Data 0.004 (0.066) Batch 0.894 (1.034) Remain 42:27:30 loss: 1.1748 Lr: 0.00500 [2024-02-17 19:38:17,572 INFO misc.py line 119 87073] Train: [6/100][81/1557] Data 0.004 (0.066) Batch 0.840 (1.031) Remain 42:21:21 loss: 1.3469 Lr: 0.00500 [2024-02-17 19:38:18,419 INFO misc.py line 119 87073] Train: [6/100][82/1557] Data 0.004 (0.065) Batch 0.840 (1.029) Remain 42:15:23 loss: 0.7277 Lr: 0.00500 [2024-02-17 19:38:19,233 INFO misc.py line 119 87073] Train: [6/100][83/1557] Data 0.010 (0.064) Batch 0.819 (1.026) Remain 42:08:53 loss: 0.8915 Lr: 0.00500 [2024-02-17 19:38:20,480 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Train: [6/100][385/1557] Data 0.007 (0.087) Batch 1.206 (1.058) Remain 43:22:30 loss: 0.4137 Lr: 0.00500 [2024-02-17 19:43:42,498 INFO misc.py line 119 87073] Train: [6/100][386/1557] Data 0.006 (0.086) Batch 1.050 (1.058) Remain 43:22:25 loss: 1.1220 Lr: 0.00500 [2024-02-17 19:43:43,647 INFO misc.py line 119 87073] Train: [6/100][387/1557] Data 0.012 (0.086) Batch 1.148 (1.059) Remain 43:22:59 loss: 0.6503 Lr: 0.00500 [2024-02-17 19:43:44,602 INFO misc.py line 119 87073] Train: [6/100][388/1557] Data 0.012 (0.086) Batch 0.963 (1.058) Remain 43:22:21 loss: 0.7850 Lr: 0.00500 [2024-02-17 19:43:45,635 INFO misc.py line 119 87073] Train: [6/100][389/1557] Data 0.005 (0.086) Batch 1.033 (1.058) Remain 43:22:10 loss: 0.5653 Lr: 0.00500 [2024-02-17 19:43:46,422 INFO misc.py line 119 87073] Train: [6/100][390/1557] Data 0.004 (0.086) Batch 0.787 (1.058) Remain 43:20:26 loss: 0.5684 Lr: 0.00500 [2024-02-17 19:43:47,226 INFO misc.py line 119 87073] Train: [6/100][391/1557] Data 0.004 (0.085) 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Train: [6/100][730/1557] Data 0.005 (0.089) Batch 0.929 (1.068) Remain 43:40:56 loss: 0.3605 Lr: 0.00500 [2024-02-17 19:49:54,981 INFO misc.py line 119 87073] Train: [6/100][731/1557] Data 0.005 (0.089) Batch 1.112 (1.068) Remain 43:41:04 loss: 1.1354 Lr: 0.00500 [2024-02-17 19:49:55,931 INFO misc.py line 119 87073] Train: [6/100][732/1557] Data 0.005 (0.089) Batch 0.951 (1.068) Remain 43:40:39 loss: 0.2051 Lr: 0.00500 [2024-02-17 19:49:56,703 INFO misc.py line 119 87073] Train: [6/100][733/1557] Data 0.004 (0.089) Batch 0.770 (1.068) Remain 43:39:38 loss: 0.8230 Lr: 0.00500 [2024-02-17 19:49:57,456 INFO misc.py line 119 87073] Train: [6/100][734/1557] Data 0.007 (0.089) Batch 0.755 (1.067) Remain 43:38:34 loss: 0.5202 Lr: 0.00500 [2024-02-17 19:50:05,684 INFO misc.py line 119 87073] Train: [6/100][735/1557] Data 4.506 (0.095) Batch 8.215 (1.077) Remain 44:02:30 loss: 0.5940 Lr: 0.00500 [2024-02-17 19:50:06,790 INFO misc.py line 119 87073] Train: [6/100][736/1557] Data 0.017 (0.095) 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Train: [6/100][799/1557] Data 0.009 (0.094) Batch 0.860 (1.075) Remain 43:57:03 loss: 0.2809 Lr: 0.00500 [2024-02-17 19:51:14,186 INFO misc.py line 119 87073] Train: [6/100][800/1557] Data 0.004 (0.094) Batch 0.967 (1.075) Remain 43:56:41 loss: 1.0242 Lr: 0.00500 [2024-02-17 19:51:15,043 INFO misc.py line 119 87073] Train: [6/100][801/1557] Data 0.004 (0.093) Batch 0.857 (1.075) Remain 43:56:00 loss: 0.6995 Lr: 0.00500 [2024-02-17 19:51:16,216 INFO misc.py line 119 87073] Train: [6/100][802/1557] Data 0.005 (0.093) Batch 1.163 (1.075) Remain 43:56:15 loss: 2.1659 Lr: 0.00500 [2024-02-17 19:51:16,968 INFO misc.py line 119 87073] Train: [6/100][803/1557] Data 0.013 (0.093) Batch 0.762 (1.075) Remain 43:55:17 loss: 1.1168 Lr: 0.00500 [2024-02-17 19:51:17,823 INFO misc.py line 119 87073] Train: [6/100][804/1557] Data 0.004 (0.093) Batch 0.842 (1.075) Remain 43:54:33 loss: 0.4732 Lr: 0.00500 [2024-02-17 19:51:19,051 INFO misc.py line 119 87073] Train: [6/100][805/1557] Data 0.017 (0.093) 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line 119 87073] Train: [6/100][1479/1557] Data 0.008 (0.094) Batch 0.921 (1.073) Remain 43:39:56 loss: 0.4715 Lr: 0.00500 [2024-02-17 20:03:22,555 INFO misc.py line 119 87073] Train: [6/100][1480/1557] Data 0.003 (0.094) Batch 0.968 (1.073) Remain 43:39:45 loss: 1.0562 Lr: 0.00500 [2024-02-17 20:03:23,477 INFO misc.py line 119 87073] Train: [6/100][1481/1557] Data 0.004 (0.094) Batch 0.921 (1.073) Remain 43:39:29 loss: 0.7396 Lr: 0.00500 [2024-02-17 20:03:24,265 INFO misc.py line 119 87073] Train: [6/100][1482/1557] Data 0.004 (0.094) Batch 0.784 (1.073) Remain 43:38:59 loss: 0.9281 Lr: 0.00500 [2024-02-17 20:03:25,035 INFO misc.py line 119 87073] Train: [6/100][1483/1557] Data 0.009 (0.094) Batch 0.774 (1.073) Remain 43:38:28 loss: 0.5086 Lr: 0.00500 [2024-02-17 20:03:26,195 INFO misc.py line 119 87073] Train: [6/100][1484/1557] Data 0.005 (0.094) Batch 1.156 (1.073) Remain 43:38:35 loss: 0.4812 Lr: 0.00500 [2024-02-17 20:03:27,236 INFO misc.py line 119 87073] Train: 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Batch 1.246 (1.072) Remain 43:37:05 loss: 0.4095 Lr: 0.00500 [2024-02-17 20:03:33,822 INFO misc.py line 119 87073] Train: [6/100][1492/1557] Data 0.014 (0.093) Batch 0.962 (1.072) Remain 43:36:53 loss: 0.7205 Lr: 0.00500 [2024-02-17 20:03:34,825 INFO misc.py line 119 87073] Train: [6/100][1493/1557] Data 0.006 (0.093) Batch 1.004 (1.072) Remain 43:36:45 loss: 0.8832 Lr: 0.00500 [2024-02-17 20:03:35,721 INFO misc.py line 119 87073] Train: [6/100][1494/1557] Data 0.004 (0.093) Batch 0.893 (1.072) Remain 43:36:27 loss: 0.9060 Lr: 0.00500 [2024-02-17 20:03:36,514 INFO misc.py line 119 87073] Train: [6/100][1495/1557] Data 0.007 (0.093) Batch 0.786 (1.072) Remain 43:35:58 loss: 0.5714 Lr: 0.00500 [2024-02-17 20:03:37,232 INFO misc.py line 119 87073] Train: [6/100][1496/1557] Data 0.014 (0.093) Batch 0.728 (1.072) Remain 43:35:23 loss: 0.6594 Lr: 0.00500 [2024-02-17 20:03:37,950 INFO misc.py line 119 87073] Train: [6/100][1497/1557] Data 0.004 (0.093) Batch 0.708 (1.071) Remain 43:34:46 loss: 0.7111 Lr: 0.00500 [2024-02-17 20:03:39,164 INFO misc.py line 119 87073] Train: [6/100][1498/1557] Data 0.014 (0.093) Batch 1.214 (1.072) Remain 43:34:59 loss: 0.6131 Lr: 0.00500 [2024-02-17 20:03:40,127 INFO misc.py line 119 87073] Train: [6/100][1499/1557] Data 0.013 (0.093) Batch 0.972 (1.072) Remain 43:34:48 loss: 0.5501 Lr: 0.00500 [2024-02-17 20:03:40,985 INFO misc.py line 119 87073] Train: [6/100][1500/1557] Data 0.004 (0.093) Batch 0.858 (1.071) Remain 43:34:26 loss: 0.7739 Lr: 0.00500 [2024-02-17 20:03:42,056 INFO misc.py line 119 87073] Train: [6/100][1501/1557] Data 0.005 (0.093) Batch 1.070 (1.071) Remain 43:34:25 loss: 0.9332 Lr: 0.00500 [2024-02-17 20:03:42,917 INFO misc.py line 119 87073] Train: [6/100][1502/1557] Data 0.005 (0.092) Batch 0.863 (1.071) Remain 43:34:04 loss: 0.7234 Lr: 0.00500 [2024-02-17 20:03:43,656 INFO misc.py line 119 87073] Train: [6/100][1503/1557] Data 0.004 (0.092) Batch 0.737 (1.071) Remain 43:33:30 loss: 0.8796 Lr: 0.00500 [2024-02-17 20:03:44,561 INFO misc.py line 119 87073] Train: [6/100][1504/1557] Data 0.006 (0.092) Batch 0.905 (1.071) Remain 43:33:13 loss: 0.3195 Lr: 0.00500 [2024-02-17 20:03:45,829 INFO misc.py line 119 87073] Train: [6/100][1505/1557] Data 0.005 (0.092) Batch 1.269 (1.071) Remain 43:33:31 loss: 0.2568 Lr: 0.00500 [2024-02-17 20:03:46,869 INFO misc.py line 119 87073] Train: [6/100][1506/1557] Data 0.004 (0.092) Batch 1.020 (1.071) Remain 43:33:25 loss: 0.7467 Lr: 0.00500 [2024-02-17 20:03:47,769 INFO misc.py line 119 87073] Train: [6/100][1507/1557] Data 0.024 (0.092) Batch 0.920 (1.071) Remain 43:33:09 loss: 0.5730 Lr: 0.00500 [2024-02-17 20:03:48,750 INFO misc.py line 119 87073] Train: [6/100][1508/1557] Data 0.005 (0.092) Batch 0.981 (1.071) Remain 43:32:59 loss: 0.6651 Lr: 0.00500 [2024-02-17 20:03:49,603 INFO misc.py line 119 87073] Train: [6/100][1509/1557] Data 0.004 (0.092) Batch 0.851 (1.071) Remain 43:32:37 loss: 0.8559 Lr: 0.00500 [2024-02-17 20:03:50,322 INFO misc.py line 119 87073] Train: [6/100][1510/1557] Data 0.005 (0.092) Batch 0.718 (1.070) Remain 43:32:01 loss: 0.7178 Lr: 0.00500 [2024-02-17 20:03:51,102 INFO misc.py line 119 87073] Train: [6/100][1511/1557] Data 0.007 (0.092) Batch 0.783 (1.070) Remain 43:31:32 loss: 0.5732 Lr: 0.00500 [2024-02-17 20:03:52,185 INFO misc.py line 119 87073] Train: [6/100][1512/1557] Data 0.004 (0.092) Batch 1.083 (1.070) Remain 43:31:33 loss: 0.6663 Lr: 0.00500 [2024-02-17 20:03:53,110 INFO misc.py line 119 87073] Train: [6/100][1513/1557] Data 0.004 (0.092) Batch 0.925 (1.070) Remain 43:31:17 loss: 0.6341 Lr: 0.00500 [2024-02-17 20:03:54,096 INFO misc.py line 119 87073] Train: [6/100][1514/1557] Data 0.004 (0.092) Batch 0.985 (1.070) Remain 43:31:08 loss: 0.9198 Lr: 0.00500 [2024-02-17 20:03:55,003 INFO misc.py line 119 87073] Train: [6/100][1515/1557] Data 0.005 (0.092) Batch 0.901 (1.070) Remain 43:30:51 loss: 0.5754 Lr: 0.00500 [2024-02-17 20:03:56,070 INFO misc.py line 119 87073] Train: [6/100][1516/1557] Data 0.010 (0.092) Batch 1.066 (1.070) Remain 43:30:49 loss: 0.8440 Lr: 0.00500 [2024-02-17 20:03:56,809 INFO misc.py line 119 87073] Train: [6/100][1517/1557] Data 0.011 (0.092) Batch 0.746 (1.070) Remain 43:30:17 loss: 0.6859 Lr: 0.00500 [2024-02-17 20:03:57,593 INFO misc.py line 119 87073] Train: [6/100][1518/1557] Data 0.004 (0.092) Batch 0.782 (1.070) Remain 43:29:48 loss: 0.6297 Lr: 0.00500 [2024-02-17 20:04:05,646 INFO misc.py line 119 87073] Train: [6/100][1519/1557] Data 4.090 (0.094) Batch 8.056 (1.074) Remain 43:41:02 loss: 0.7352 Lr: 0.00500 [2024-02-17 20:04:06,793 INFO misc.py line 119 87073] Train: [6/100][1520/1557] Data 0.004 (0.094) Batch 1.147 (1.074) Remain 43:41:07 loss: 0.4745 Lr: 0.00500 [2024-02-17 20:04:07,804 INFO misc.py line 119 87073] Train: [6/100][1521/1557] Data 0.004 (0.094) Batch 1.008 (1.074) Remain 43:41:00 loss: 0.7901 Lr: 0.00500 [2024-02-17 20:04:08,742 INFO misc.py line 119 87073] Train: [6/100][1522/1557] Data 0.007 (0.094) Batch 0.942 (1.074) Remain 43:40:46 loss: 0.9279 Lr: 0.00500 [2024-02-17 20:04:09,854 INFO misc.py line 119 87073] Train: [6/100][1523/1557] Data 0.004 (0.094) Batch 1.112 (1.074) Remain 43:40:49 loss: 0.8895 Lr: 0.00500 [2024-02-17 20:04:10,518 INFO misc.py line 119 87073] Train: [6/100][1524/1557] Data 0.004 (0.094) Batch 0.664 (1.074) Remain 43:40:08 loss: 0.7205 Lr: 0.00500 [2024-02-17 20:04:11,160 INFO misc.py line 119 87073] Train: [6/100][1525/1557] Data 0.004 (0.094) Batch 0.641 (1.074) Remain 43:39:25 loss: 0.6993 Lr: 0.00500 [2024-02-17 20:04:12,411 INFO misc.py line 119 87073] Train: [6/100][1526/1557] Data 0.005 (0.094) Batch 1.243 (1.074) Remain 43:39:41 loss: 0.3948 Lr: 0.00500 [2024-02-17 20:04:13,417 INFO misc.py line 119 87073] Train: [6/100][1527/1557] Data 0.013 (0.094) Batch 1.014 (1.074) Remain 43:39:34 loss: 0.7201 Lr: 0.00500 [2024-02-17 20:04:14,269 INFO misc.py line 119 87073] Train: [6/100][1528/1557] Data 0.005 (0.094) Batch 0.850 (1.074) Remain 43:39:11 loss: 0.4772 Lr: 0.00500 [2024-02-17 20:04:15,305 INFO misc.py line 119 87073] Train: [6/100][1529/1557] Data 0.006 (0.094) Batch 1.039 (1.074) Remain 43:39:07 loss: 0.9502 Lr: 0.00500 [2024-02-17 20:04:16,285 INFO misc.py line 119 87073] Train: [6/100][1530/1557] Data 0.004 (0.094) Batch 0.980 (1.073) Remain 43:38:57 loss: 0.9464 Lr: 0.00500 [2024-02-17 20:04:17,132 INFO misc.py line 119 87073] Train: [6/100][1531/1557] Data 0.004 (0.094) Batch 0.845 (1.073) Remain 43:38:34 loss: 0.8693 Lr: 0.00500 [2024-02-17 20:04:17,942 INFO misc.py line 119 87073] Train: [6/100][1532/1557] Data 0.005 (0.093) Batch 0.807 (1.073) Remain 43:38:07 loss: 1.0456 Lr: 0.00500 [2024-02-17 20:04:19,201 INFO misc.py line 119 87073] Train: [6/100][1533/1557] Data 0.009 (0.093) Batch 1.249 (1.073) Remain 43:38:23 loss: 0.6392 Lr: 0.00500 [2024-02-17 20:04:20,136 INFO misc.py line 119 87073] Train: [6/100][1534/1557] Data 0.018 (0.093) Batch 0.949 (1.073) Remain 43:38:10 loss: 0.9154 Lr: 0.00500 [2024-02-17 20:04:21,121 INFO misc.py line 119 87073] Train: [6/100][1535/1557] Data 0.004 (0.093) Batch 0.986 (1.073) Remain 43:38:01 loss: 0.5364 Lr: 0.00500 [2024-02-17 20:04:22,100 INFO misc.py line 119 87073] Train: [6/100][1536/1557] Data 0.003 (0.093) Batch 0.979 (1.073) Remain 43:37:51 loss: 0.9266 Lr: 0.00500 [2024-02-17 20:04:23,107 INFO misc.py line 119 87073] Train: [6/100][1537/1557] Data 0.004 (0.093) Batch 1.006 (1.073) Remain 43:37:43 loss: 0.7736 Lr: 0.00500 [2024-02-17 20:04:23,880 INFO misc.py line 119 87073] Train: [6/100][1538/1557] Data 0.004 (0.093) Batch 0.766 (1.073) Remain 43:37:13 loss: 0.7016 Lr: 0.00500 [2024-02-17 20:04:24,613 INFO misc.py line 119 87073] Train: [6/100][1539/1557] Data 0.011 (0.093) Batch 0.741 (1.073) Remain 43:36:40 loss: 0.5866 Lr: 0.00500 [2024-02-17 20:04:25,818 INFO misc.py line 119 87073] Train: [6/100][1540/1557] Data 0.004 (0.093) Batch 1.205 (1.073) Remain 43:36:52 loss: 0.2972 Lr: 0.00500 [2024-02-17 20:04:26,732 INFO misc.py line 119 87073] Train: [6/100][1541/1557] Data 0.004 (0.093) Batch 0.914 (1.073) Remain 43:36:35 loss: 1.0078 Lr: 0.00500 [2024-02-17 20:04:27,655 INFO misc.py line 119 87073] Train: [6/100][1542/1557] Data 0.004 (0.093) Batch 0.914 (1.072) Remain 43:36:19 loss: 1.0156 Lr: 0.00500 [2024-02-17 20:04:28,670 INFO misc.py line 119 87073] Train: [6/100][1543/1557] Data 0.013 (0.093) Batch 1.023 (1.072) Remain 43:36:13 loss: 0.8013 Lr: 0.00500 [2024-02-17 20:04:29,795 INFO misc.py line 119 87073] Train: [6/100][1544/1557] Data 0.005 (0.093) Batch 1.118 (1.072) Remain 43:36:17 loss: 0.6970 Lr: 0.00500 [2024-02-17 20:04:30,538 INFO misc.py line 119 87073] Train: [6/100][1545/1557] Data 0.012 (0.093) Batch 0.751 (1.072) Remain 43:35:45 loss: 0.4937 Lr: 0.00500 [2024-02-17 20:04:31,309 INFO misc.py line 119 87073] Train: [6/100][1546/1557] Data 0.004 (0.093) Batch 0.762 (1.072) Remain 43:35:15 loss: 0.4776 Lr: 0.00500 [2024-02-17 20:04:32,517 INFO misc.py line 119 87073] Train: [6/100][1547/1557] Data 0.013 (0.093) Batch 1.217 (1.072) Remain 43:35:27 loss: 0.4082 Lr: 0.00500 [2024-02-17 20:04:33,498 INFO misc.py line 119 87073] Train: [6/100][1548/1557] Data 0.005 (0.093) Batch 0.981 (1.072) Remain 43:35:18 loss: 0.4301 Lr: 0.00500 [2024-02-17 20:04:34,514 INFO misc.py line 119 87073] Train: [6/100][1549/1557] Data 0.004 (0.093) Batch 1.016 (1.072) Remain 43:35:11 loss: 0.7393 Lr: 0.00500 [2024-02-17 20:04:35,666 INFO misc.py line 119 87073] Train: [6/100][1550/1557] Data 0.004 (0.092) Batch 1.153 (1.072) Remain 43:35:18 loss: 0.5835 Lr: 0.00500 [2024-02-17 20:04:36,603 INFO misc.py line 119 87073] Train: [6/100][1551/1557] Data 0.004 (0.092) Batch 0.936 (1.072) Remain 43:35:04 loss: 0.5829 Lr: 0.00500 [2024-02-17 20:04:37,349 INFO misc.py line 119 87073] Train: [6/100][1552/1557] Data 0.004 (0.092) Batch 0.736 (1.072) Remain 43:34:31 loss: 1.1462 Lr: 0.00500 [2024-02-17 20:04:38,111 INFO misc.py line 119 87073] Train: [6/100][1553/1557] Data 0.015 (0.092) Batch 0.772 (1.072) Remain 43:34:02 loss: 1.0934 Lr: 0.00500 [2024-02-17 20:04:39,406 INFO misc.py line 119 87073] Train: [6/100][1554/1557] Data 0.004 (0.092) Batch 1.285 (1.072) Remain 43:34:21 loss: 0.5682 Lr: 0.00500 [2024-02-17 20:04:40,329 INFO misc.py line 119 87073] Train: [6/100][1555/1557] Data 0.014 (0.092) Batch 0.933 (1.072) Remain 43:34:07 loss: 0.6181 Lr: 0.00500 [2024-02-17 20:04:41,293 INFO misc.py line 119 87073] Train: [6/100][1556/1557] Data 0.004 (0.092) Batch 0.963 (1.072) Remain 43:33:55 loss: 0.6519 Lr: 0.00500 [2024-02-17 20:04:42,301 INFO misc.py line 119 87073] Train: [6/100][1557/1557] Data 0.004 (0.092) Batch 1.008 (1.072) Remain 43:33:48 loss: 0.7697 Lr: 0.00500 [2024-02-17 20:04:42,302 INFO misc.py line 136 87073] Train result: loss: 0.7476 [2024-02-17 20:04:42,302 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 20:05:13,164 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8602 [2024-02-17 20:05:13,940 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7926 [2024-02-17 20:05:16,067 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5283 [2024-02-17 20:05:18,273 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.5019 [2024-02-17 20:05:23,210 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.4243 [2024-02-17 20:05:23,909 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1659 [2024-02-17 20:05:25,169 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4192 [2024-02-17 20:05:28,121 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4955 [2024-02-17 20:05:29,930 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.4375 [2024-02-17 20:05:31,616 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6557/0.7286/0.8916. [2024-02-17 20:05:31,616 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9148/0.9385 [2024-02-17 20:05:31,616 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9772/0.9905 [2024-02-17 20:05:31,616 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8570/0.9558 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3147/0.3394 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6198/0.6388 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.5451/0.6402 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.7829/0.8703 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8198/0.8395 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7350/0.8308 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7369/0.8224 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6650/0.8097 [2024-02-17 20:05:31,617 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5556/0.7962 [2024-02-17 20:05:31,618 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 20:05:31,621 INFO misc.py line 165 87073] Currently Best mIoU: 0.6616 [2024-02-17 20:05:31,621 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 20:05:39,025 INFO misc.py line 119 87073] Train: [7/100][1/1557] Data 1.901 (1.901) Batch 2.676 (2.676) Remain 108:46:56 loss: 0.7718 Lr: 0.00500 [2024-02-17 20:05:40,034 INFO misc.py line 119 87073] Train: [7/100][2/1557] Data 0.007 (0.007) Batch 1.010 (1.010) Remain 41:03:07 loss: 0.4363 Lr: 0.00500 [2024-02-17 20:05:41,174 INFO misc.py line 119 87073] Train: [7/100][3/1557] Data 0.006 (0.006) Batch 1.131 (1.131) Remain 45:59:09 loss: 0.6408 Lr: 0.00500 [2024-02-17 20:05:42,107 INFO misc.py line 119 87073] Train: [7/100][4/1557] Data 0.015 (0.015) Batch 0.940 (0.940) Remain 38:14:01 loss: 0.6901 Lr: 0.00500 [2024-02-17 20:05:42,887 INFO misc.py line 119 87073] Train: [7/100][5/1557] Data 0.007 (0.011) Batch 0.782 (0.861) Remain 35:00:25 loss: 0.8485 Lr: 0.00500 [2024-02-17 20:05:43,675 INFO misc.py line 119 87073] Train: [7/100][6/1557] Data 0.004 (0.009) Batch 0.782 (0.835) Remain 33:55:53 loss: 0.8512 Lr: 0.00500 [2024-02-17 20:05:45,075 INFO misc.py line 119 87073] Train: [7/100][7/1557] Data 0.010 (0.009) Batch 1.400 (0.976) Remain 39:40:50 loss: 0.4670 Lr: 0.00500 [2024-02-17 20:05:46,088 INFO misc.py line 119 87073] Train: [7/100][8/1557] Data 0.010 (0.009) Batch 1.020 (0.985) Remain 40:02:13 loss: 0.6367 Lr: 0.00500 [2024-02-17 20:05:47,053 INFO misc.py line 119 87073] Train: [7/100][9/1557] Data 0.003 (0.008) Batch 0.963 (0.981) Remain 39:53:30 loss: 0.8980 Lr: 0.00500 [2024-02-17 20:05:47,974 INFO misc.py line 119 87073] Train: [7/100][10/1557] Data 0.005 (0.008) Batch 0.922 (0.973) Remain 39:32:45 loss: 0.6475 Lr: 0.00500 [2024-02-17 20:05:48,799 INFO misc.py line 119 87073] Train: [7/100][11/1557] Data 0.004 (0.007) Batch 0.825 (0.954) Remain 38:47:35 loss: 0.5764 Lr: 0.00500 [2024-02-17 20:05:49,607 INFO misc.py line 119 87073] Train: [7/100][12/1557] Data 0.004 (0.007) Batch 0.809 (0.938) Remain 38:08:07 loss: 1.0172 Lr: 0.00500 [2024-02-17 20:05:50,366 INFO misc.py line 119 87073] Train: [7/100][13/1557] Data 0.004 (0.007) Batch 0.759 (0.920) Remain 37:24:18 loss: 0.9126 Lr: 0.00500 [2024-02-17 20:05:51,634 INFO misc.py line 119 87073] Train: [7/100][14/1557] Data 0.005 (0.006) Batch 1.255 (0.951) Remain 38:38:26 loss: 0.3041 Lr: 0.00500 [2024-02-17 20:05:52,515 INFO misc.py line 119 87073] Train: [7/100][15/1557] Data 0.018 (0.007) Batch 0.895 (0.946) Remain 38:27:04 loss: 0.5730 Lr: 0.00500 [2024-02-17 20:05:53,526 INFO misc.py line 119 87073] Train: [7/100][16/1557] Data 0.004 (0.007) Batch 1.011 (0.951) Remain 38:39:16 loss: 0.6989 Lr: 0.00500 [2024-02-17 20:05:54,612 INFO misc.py line 119 87073] Train: [7/100][17/1557] Data 0.004 (0.007) Batch 1.087 (0.961) Remain 39:02:55 loss: 0.7846 Lr: 0.00500 [2024-02-17 20:05:55,544 INFO misc.py line 119 87073] Train: [7/100][18/1557] Data 0.004 (0.007) Batch 0.931 (0.959) Remain 38:58:03 loss: 1.1021 Lr: 0.00500 [2024-02-17 20:05:56,283 INFO misc.py line 119 87073] Train: [7/100][19/1557] Data 0.005 (0.007) Batch 0.739 (0.945) Remain 38:24:33 loss: 0.9243 Lr: 0.00500 [2024-02-17 20:05:57,188 INFO misc.py line 119 87073] Train: [7/100][20/1557] Data 0.005 (0.006) Batch 0.902 (0.942) Remain 38:18:25 loss: 0.8935 Lr: 0.00500 [2024-02-17 20:05:58,367 INFO misc.py line 119 87073] Train: [7/100][21/1557] Data 0.007 (0.006) Batch 1.183 (0.956) Remain 38:50:56 loss: 0.3470 Lr: 0.00500 [2024-02-17 20:05:59,350 INFO misc.py line 119 87073] Train: [7/100][22/1557] Data 0.004 (0.006) Batch 0.983 (0.957) Remain 38:54:23 loss: 0.6824 Lr: 0.00500 [2024-02-17 20:06:00,334 INFO misc.py line 119 87073] Train: [7/100][23/1557] Data 0.004 (0.006) Batch 0.984 (0.958) Remain 38:57:36 loss: 0.6044 Lr: 0.00500 [2024-02-17 20:06:01,357 INFO misc.py line 119 87073] Train: [7/100][24/1557] Data 0.004 (0.006) Batch 1.024 (0.962) Remain 39:05:11 loss: 0.9486 Lr: 0.00500 [2024-02-17 20:06:02,377 INFO misc.py line 119 87073] Train: [7/100][25/1557] Data 0.004 (0.006) Batch 1.019 (0.964) Remain 39:11:35 loss: 0.4648 Lr: 0.00500 [2024-02-17 20:06:03,141 INFO misc.py line 119 87073] Train: [7/100][26/1557] Data 0.004 (0.006) Batch 0.764 (0.956) Remain 38:50:20 loss: 0.5812 Lr: 0.00500 [2024-02-17 20:06:04,003 INFO misc.py line 119 87073] Train: [7/100][27/1557] Data 0.004 (0.006) Batch 0.854 (0.951) Remain 38:40:00 loss: 0.9525 Lr: 0.00500 [2024-02-17 20:06:05,311 INFO misc.py line 119 87073] Train: [7/100][28/1557] Data 0.012 (0.006) Batch 1.299 (0.965) Remain 39:13:53 loss: 0.3183 Lr: 0.00500 [2024-02-17 20:06:06,209 INFO misc.py line 119 87073] Train: [7/100][29/1557] Data 0.021 (0.007) Batch 0.916 (0.963) Remain 39:09:13 loss: 0.7765 Lr: 0.00500 [2024-02-17 20:06:07,215 INFO misc.py line 119 87073] Train: [7/100][30/1557] Data 0.004 (0.007) Batch 1.006 (0.965) Remain 39:13:05 loss: 0.5500 Lr: 0.00500 [2024-02-17 20:06:08,061 INFO misc.py line 119 87073] Train: [7/100][31/1557] Data 0.004 (0.006) Batch 0.844 (0.961) Remain 39:02:34 loss: 0.8152 Lr: 0.00500 [2024-02-17 20:06:09,118 INFO misc.py line 119 87073] Train: [7/100][32/1557] Data 0.005 (0.006) Batch 1.052 (0.964) Remain 39:10:13 loss: 0.6072 Lr: 0.00500 [2024-02-17 20:06:09,904 INFO misc.py line 119 87073] Train: [7/100][33/1557] Data 0.011 (0.007) Batch 0.792 (0.958) Remain 38:56:16 loss: 0.8223 Lr: 0.00500 [2024-02-17 20:06:10,710 INFO misc.py line 119 87073] Train: [7/100][34/1557] Data 0.004 (0.006) Batch 0.805 (0.953) Remain 38:44:15 loss: 0.9380 Lr: 0.00500 [2024-02-17 20:06:12,019 INFO misc.py line 119 87073] Train: [7/100][35/1557] Data 0.005 (0.006) Batch 1.307 (0.964) Remain 39:11:13 loss: 0.3650 Lr: 0.00500 [2024-02-17 20:06:12,992 INFO misc.py line 119 87073] Train: [7/100][36/1557] Data 0.007 (0.006) Batch 0.974 (0.964) Remain 39:11:57 loss: 1.2834 Lr: 0.00500 [2024-02-17 20:06:13,821 INFO misc.py line 119 87073] Train: [7/100][37/1557] Data 0.007 (0.006) Batch 0.831 (0.961) Remain 39:02:21 loss: 0.8024 Lr: 0.00500 [2024-02-17 20:06:14,783 INFO misc.py line 119 87073] Train: [7/100][38/1557] Data 0.004 (0.006) Batch 0.951 (0.960) Remain 39:01:40 loss: 0.6239 Lr: 0.00500 [2024-02-17 20:06:15,600 INFO misc.py line 119 87073] Train: [7/100][39/1557] Data 0.016 (0.007) Batch 0.827 (0.957) Remain 38:52:37 loss: 0.6597 Lr: 0.00500 [2024-02-17 20:06:16,329 INFO misc.py line 119 87073] Train: [7/100][40/1557] Data 0.005 (0.007) Batch 0.730 (0.950) Remain 38:37:41 loss: 0.5272 Lr: 0.00500 [2024-02-17 20:06:17,076 INFO misc.py line 119 87073] Train: [7/100][41/1557] Data 0.004 (0.007) Batch 0.739 (0.945) Remain 38:24:05 loss: 0.6635 Lr: 0.00500 [2024-02-17 20:06:18,165 INFO misc.py line 119 87073] Train: [7/100][42/1557] Data 0.011 (0.007) Batch 1.091 (0.949) Remain 38:33:11 loss: 0.2956 Lr: 0.00500 [2024-02-17 20:06:19,015 INFO misc.py line 119 87073] Train: [7/100][43/1557] Data 0.010 (0.007) Batch 0.856 (0.946) Remain 38:27:30 loss: 0.5748 Lr: 0.00500 [2024-02-17 20:06:20,074 INFO misc.py line 119 87073] Train: [7/100][44/1557] Data 0.006 (0.007) Batch 1.060 (0.949) Remain 38:34:15 loss: 1.6001 Lr: 0.00500 [2024-02-17 20:06:21,094 INFO misc.py line 119 87073] Train: [7/100][45/1557] Data 0.004 (0.007) Batch 1.020 (0.951) Remain 38:38:21 loss: 0.4104 Lr: 0.00500 [2024-02-17 20:06:21,918 INFO misc.py line 119 87073] Train: [7/100][46/1557] Data 0.004 (0.007) Batch 0.824 (0.948) Remain 38:31:07 loss: 0.4700 Lr: 0.00500 [2024-02-17 20:06:22,682 INFO misc.py line 119 87073] Train: [7/100][47/1557] Data 0.004 (0.007) Batch 0.763 (0.944) Remain 38:20:52 loss: 0.7920 Lr: 0.00500 [2024-02-17 20:06:23,452 INFO misc.py line 119 87073] Train: [7/100][48/1557] Data 0.006 (0.007) Batch 0.771 (0.940) Remain 38:11:29 loss: 0.8898 Lr: 0.00500 [2024-02-17 20:06:24,570 INFO misc.py line 119 87073] Train: [7/100][49/1557] Data 0.004 (0.006) Batch 1.118 (0.944) Remain 38:20:56 loss: 0.6016 Lr: 0.00500 [2024-02-17 20:06:25,530 INFO misc.py line 119 87073] Train: [7/100][50/1557] Data 0.004 (0.006) Batch 0.960 (0.944) Remain 38:21:47 loss: 0.8550 Lr: 0.00500 [2024-02-17 20:06:26,477 INFO misc.py line 119 87073] Train: [7/100][51/1557] Data 0.004 (0.006) Batch 0.947 (0.944) Remain 38:21:56 loss: 0.4869 Lr: 0.00500 [2024-02-17 20:06:27,662 INFO misc.py line 119 87073] Train: [7/100][52/1557] Data 0.004 (0.006) Batch 1.186 (0.949) Remain 38:33:56 loss: 0.6974 Lr: 0.00500 [2024-02-17 20:06:28,531 INFO misc.py line 119 87073] Train: [7/100][53/1557] Data 0.004 (0.006) Batch 0.868 (0.947) Remain 38:29:59 loss: 0.5415 Lr: 0.00500 [2024-02-17 20:06:29,302 INFO misc.py line 119 87073] Train: [7/100][54/1557] Data 0.004 (0.006) Batch 0.760 (0.944) Remain 38:20:59 loss: 0.9100 Lr: 0.00500 [2024-02-17 20:06:30,078 INFO misc.py line 119 87073] Train: [7/100][55/1557] Data 0.015 (0.006) Batch 0.785 (0.941) Remain 38:13:33 loss: 0.6630 Lr: 0.00500 [2024-02-17 20:06:31,324 INFO misc.py line 119 87073] Train: [7/100][56/1557] Data 0.006 (0.006) Batch 1.234 (0.946) Remain 38:27:02 loss: 0.6618 Lr: 0.00500 [2024-02-17 20:06:32,314 INFO misc.py line 119 87073] Train: [7/100][57/1557] Data 0.019 (0.007) Batch 1.002 (0.947) Remain 38:29:31 loss: 1.1922 Lr: 0.00500 [2024-02-17 20:06:33,248 INFO misc.py line 119 87073] Train: [7/100][58/1557] Data 0.006 (0.007) Batch 0.937 (0.947) Remain 38:29:03 loss: 0.7695 Lr: 0.00500 [2024-02-17 20:06:34,112 INFO misc.py line 119 87073] Train: [7/100][59/1557] Data 0.004 (0.007) Batch 0.864 (0.945) Remain 38:25:25 loss: 0.6150 Lr: 0.00500 [2024-02-17 20:06:35,092 INFO misc.py line 119 87073] Train: [7/100][60/1557] Data 0.003 (0.006) Batch 0.971 (0.946) Remain 38:26:29 loss: 0.3222 Lr: 0.00500 [2024-02-17 20:06:35,847 INFO misc.py line 119 87073] Train: [7/100][61/1557] Data 0.013 (0.007) Batch 0.764 (0.943) Remain 38:18:48 loss: 0.7834 Lr: 0.00500 [2024-02-17 20:06:36,638 INFO misc.py line 119 87073] Train: [7/100][62/1557] Data 0.004 (0.007) Batch 0.774 (0.940) Remain 38:11:49 loss: 0.4866 Lr: 0.00500 [2024-02-17 20:06:46,614 INFO misc.py line 119 87073] Train: [7/100][63/1557] Data 4.379 (0.079) Batch 9.993 (1.091) Remain 44:19:41 loss: 0.6411 Lr: 0.00500 [2024-02-17 20:06:47,427 INFO misc.py line 119 87073] Train: [7/100][64/1557] Data 0.005 (0.078) Batch 0.813 (1.086) Remain 44:08:33 loss: 0.2742 Lr: 0.00500 [2024-02-17 20:06:48,318 INFO misc.py line 119 87073] Train: [7/100][65/1557] Data 0.005 (0.077) Batch 0.887 (1.083) Remain 44:00:41 loss: 1.1585 Lr: 0.00500 [2024-02-17 20:06:49,218 INFO misc.py line 119 87073] Train: [7/100][66/1557] Data 0.008 (0.076) Batch 0.904 (1.080) Remain 43:53:45 loss: 0.6544 Lr: 0.00500 [2024-02-17 20:06:50,130 INFO misc.py line 119 87073] Train: [7/100][67/1557] Data 0.005 (0.075) Batch 0.912 (1.078) Remain 43:47:19 loss: 0.9712 Lr: 0.00500 [2024-02-17 20:06:50,854 INFO misc.py line 119 87073] Train: [7/100][68/1557] Data 0.005 (0.074) Batch 0.713 (1.072) Remain 43:33:39 loss: 0.3502 Lr: 0.00500 [2024-02-17 20:06:51,612 INFO misc.py line 119 87073] Train: [7/100][69/1557] Data 0.015 (0.073) Batch 0.770 (1.067) Remain 43:22:27 loss: 0.8142 Lr: 0.00500 [2024-02-17 20:06:52,799 INFO misc.py line 119 87073] Train: [7/100][70/1557] Data 0.004 (0.072) Batch 1.186 (1.069) Remain 43:26:46 loss: 0.7303 Lr: 0.00500 [2024-02-17 20:06:53,818 INFO misc.py line 119 87073] Train: [7/100][71/1557] Data 0.005 (0.071) Batch 1.020 (1.068) Remain 43:24:59 loss: 0.7899 Lr: 0.00500 [2024-02-17 20:06:54,769 INFO misc.py line 119 87073] Train: [7/100][72/1557] Data 0.004 (0.070) Batch 0.949 (1.067) Remain 43:20:45 loss: 0.6506 Lr: 0.00500 [2024-02-17 20:06:55,724 INFO misc.py line 119 87073] Train: [7/100][73/1557] Data 0.005 (0.069) Batch 0.947 (1.065) Remain 43:16:34 loss: 0.3579 Lr: 0.00500 [2024-02-17 20:06:56,594 INFO misc.py line 119 87073] Train: [7/100][74/1557] Data 0.013 (0.068) Batch 0.879 (1.062) Remain 43:10:10 loss: 0.9234 Lr: 0.00500 [2024-02-17 20:06:57,394 INFO misc.py line 119 87073] Train: [7/100][75/1557] Data 0.004 (0.067) Batch 0.800 (1.059) Remain 43:01:16 loss: 0.7933 Lr: 0.00500 [2024-02-17 20:06:58,120 INFO misc.py line 119 87073] Train: [7/100][76/1557] Data 0.003 (0.066) Batch 0.725 (1.054) Remain 42:50:06 loss: 1.0043 Lr: 0.00500 [2024-02-17 20:06:59,417 INFO misc.py line 119 87073] Train: [7/100][77/1557] Data 0.005 (0.066) Batch 1.292 (1.057) Remain 42:57:56 loss: 0.3473 Lr: 0.00500 [2024-02-17 20:07:00,498 INFO misc.py line 119 87073] Train: [7/100][78/1557] Data 0.009 (0.065) Batch 1.086 (1.058) Remain 42:58:50 loss: 0.7754 Lr: 0.00500 [2024-02-17 20:07:01,512 INFO misc.py line 119 87073] Train: [7/100][79/1557] Data 0.005 (0.064) Batch 0.996 (1.057) Remain 42:56:51 loss: 0.8966 Lr: 0.00500 [2024-02-17 20:07:02,400 INFO misc.py line 119 87073] Train: [7/100][80/1557] Data 0.023 (0.063) Batch 0.907 (1.055) Remain 42:52:04 loss: 0.4417 Lr: 0.00500 [2024-02-17 20:07:03,473 INFO misc.py line 119 87073] Train: [7/100][81/1557] Data 0.004 (0.063) Batch 1.072 (1.055) Remain 42:52:34 loss: 1.3199 Lr: 0.00500 [2024-02-17 20:07:04,207 INFO misc.py line 119 87073] Train: [7/100][82/1557] Data 0.006 (0.062) Batch 0.735 (1.051) Remain 42:42:41 loss: 0.9044 Lr: 0.00500 [2024-02-17 20:07:04,998 INFO misc.py line 119 87073] Train: [7/100][83/1557] Data 0.004 (0.061) Batch 0.779 (1.048) Remain 42:34:23 loss: 0.4308 Lr: 0.00500 [2024-02-17 20:07:06,296 INFO misc.py line 119 87073] Train: [7/100][84/1557] Data 0.015 (0.061) Batch 1.298 (1.051) Remain 42:41:53 loss: 0.3173 Lr: 0.00500 [2024-02-17 20:07:07,391 INFO misc.py line 119 87073] Train: [7/100][85/1557] Data 0.017 (0.060) Batch 1.099 (1.051) Remain 42:43:19 loss: 0.8667 Lr: 0.00500 [2024-02-17 20:07:08,266 INFO misc.py line 119 87073] Train: [7/100][86/1557] Data 0.012 (0.060) Batch 0.883 (1.049) Remain 42:38:20 loss: 0.2553 Lr: 0.00500 [2024-02-17 20:07:09,112 INFO misc.py line 119 87073] Train: [7/100][87/1557] Data 0.004 (0.059) Batch 0.845 (1.047) Remain 42:32:22 loss: 0.7630 Lr: 0.00500 [2024-02-17 20:07:10,001 INFO misc.py line 119 87073] Train: [7/100][88/1557] Data 0.006 (0.058) Batch 0.887 (1.045) Remain 42:27:46 loss: 0.9375 Lr: 0.00500 [2024-02-17 20:07:10,729 INFO misc.py line 119 87073] Train: [7/100][89/1557] Data 0.008 (0.058) Batch 0.730 (1.041) Remain 42:18:50 loss: 0.5616 Lr: 0.00500 [2024-02-17 20:07:11,487 INFO misc.py line 119 87073] Train: [7/100][90/1557] Data 0.005 (0.057) Batch 0.752 (1.038) Remain 42:10:42 loss: 0.7235 Lr: 0.00500 [2024-02-17 20:07:12,759 INFO misc.py line 119 87073] Train: [7/100][91/1557] Data 0.011 (0.057) Batch 1.275 (1.041) Remain 42:17:15 loss: 0.3808 Lr: 0.00500 [2024-02-17 20:07:13,764 INFO misc.py line 119 87073] Train: [7/100][92/1557] Data 0.008 (0.056) Batch 1.008 (1.040) Remain 42:16:20 loss: 0.5702 Lr: 0.00500 [2024-02-17 20:07:14,594 INFO misc.py line 119 87073] Train: [7/100][93/1557] Data 0.006 (0.055) Batch 0.831 (1.038) Remain 42:10:38 loss: 0.6059 Lr: 0.00500 [2024-02-17 20:07:15,506 INFO misc.py line 119 87073] Train: [7/100][94/1557] Data 0.004 (0.055) Batch 0.912 (1.037) Remain 42:07:15 loss: 1.1171 Lr: 0.00500 [2024-02-17 20:07:16,391 INFO misc.py line 119 87073] Train: [7/100][95/1557] Data 0.003 (0.054) Batch 0.876 (1.035) Remain 42:03:00 loss: 1.2013 Lr: 0.00500 [2024-02-17 20:07:17,077 INFO misc.py line 119 87073] Train: [7/100][96/1557] Data 0.012 (0.054) Batch 0.695 (1.031) Remain 41:54:03 loss: 1.1021 Lr: 0.00500 [2024-02-17 20:07:17,859 INFO misc.py line 119 87073] Train: [7/100][97/1557] Data 0.003 (0.053) Batch 0.772 (1.029) Remain 41:47:19 loss: 0.9916 Lr: 0.00500 [2024-02-17 20:07:18,958 INFO misc.py line 119 87073] Train: [7/100][98/1557] Data 0.013 (0.053) Batch 1.098 (1.029) Remain 41:49:05 loss: 0.3798 Lr: 0.00500 [2024-02-17 20:07:20,072 INFO misc.py line 119 87073] Train: [7/100][99/1557] Data 0.014 (0.053) Batch 1.124 (1.030) Remain 41:51:27 loss: 1.0746 Lr: 0.00500 [2024-02-17 20:07:21,065 INFO misc.py line 119 87073] Train: [7/100][100/1557] Data 0.005 (0.052) Batch 0.992 (1.030) Remain 41:50:29 loss: 0.5313 Lr: 0.00500 [2024-02-17 20:07:22,172 INFO misc.py line 119 87073] Train: [7/100][101/1557] Data 0.006 (0.052) Batch 1.106 (1.031) Remain 41:52:22 loss: 0.9143 Lr: 0.00500 [2024-02-17 20:07:23,140 INFO misc.py line 119 87073] Train: [7/100][102/1557] Data 0.007 (0.051) Batch 0.971 (1.030) Remain 41:50:52 loss: 0.9678 Lr: 0.00500 [2024-02-17 20:07:23,885 INFO misc.py line 119 87073] Train: [7/100][103/1557] Data 0.004 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Train: [7/100][467/1557] Data 0.003 (0.083) Batch 0.770 (1.118) Remain 45:19:34 loss: 0.9527 Lr: 0.00500 [2024-02-17 20:14:20,898 INFO misc.py line 119 87073] Train: [7/100][468/1557] Data 0.004 (0.083) Batch 0.761 (1.118) Remain 45:17:41 loss: 1.1061 Lr: 0.00500 [2024-02-17 20:14:22,082 INFO misc.py line 119 87073] Train: [7/100][469/1557] Data 0.007 (0.082) Batch 1.183 (1.118) Remain 45:18:00 loss: 0.3131 Lr: 0.00500 [2024-02-17 20:14:23,012 INFO misc.py line 119 87073] Train: [7/100][470/1557] Data 0.008 (0.082) Batch 0.934 (1.117) Remain 45:17:01 loss: 0.6802 Lr: 0.00500 [2024-02-17 20:14:24,004 INFO misc.py line 119 87073] Train: [7/100][471/1557] Data 0.004 (0.082) Batch 0.992 (1.117) Remain 45:16:21 loss: 0.7735 Lr: 0.00500 [2024-02-17 20:14:25,025 INFO misc.py line 119 87073] Train: [7/100][472/1557] Data 0.004 (0.082) Batch 1.021 (1.117) Remain 45:15:50 loss: 0.3551 Lr: 0.00500 [2024-02-17 20:14:25,961 INFO misc.py line 119 87073] Train: [7/100][473/1557] Data 0.004 (0.082) 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Train: [7/100][536/1557] Data 0.004 (0.084) Batch 1.078 (1.115) Remain 45:10:53 loss: 0.3705 Lr: 0.00500 [2024-02-17 20:15:36,587 INFO misc.py line 119 87073] Train: [7/100][537/1557] Data 0.005 (0.084) Batch 0.898 (1.115) Remain 45:09:53 loss: 1.0343 Lr: 0.00500 [2024-02-17 20:15:37,290 INFO misc.py line 119 87073] Train: [7/100][538/1557] Data 0.004 (0.084) Batch 0.702 (1.114) Remain 45:07:59 loss: 0.5728 Lr: 0.00500 [2024-02-17 20:15:38,502 INFO misc.py line 119 87073] Train: [7/100][539/1557] Data 0.006 (0.083) Batch 1.213 (1.114) Remain 45:08:25 loss: 0.5411 Lr: 0.00500 [2024-02-17 20:15:39,550 INFO misc.py line 119 87073] Train: [7/100][540/1557] Data 0.005 (0.083) Batch 1.046 (1.114) Remain 45:08:05 loss: 1.2220 Lr: 0.00500 [2024-02-17 20:15:40,521 INFO misc.py line 119 87073] Train: [7/100][541/1557] Data 0.007 (0.083) Batch 0.972 (1.114) Remain 45:07:26 loss: 0.7732 Lr: 0.00500 [2024-02-17 20:15:41,469 INFO misc.py line 119 87073] Train: [7/100][542/1557] Data 0.005 (0.083) 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line 119 87073] Train: [7/100][1218/1557] Data 0.021 (0.082) Batch 1.115 (1.114) Remain 44:53:43 loss: 0.3441 Lr: 0.00500 [2024-02-17 20:28:15,119 INFO misc.py line 119 87073] Train: [7/100][1219/1557] Data 0.007 (0.082) Batch 0.964 (1.113) Remain 44:53:24 loss: 0.7964 Lr: 0.00500 [2024-02-17 20:28:16,066 INFO misc.py line 119 87073] Train: [7/100][1220/1557] Data 0.006 (0.082) Batch 0.947 (1.113) Remain 44:53:03 loss: 0.6466 Lr: 0.00500 [2024-02-17 20:28:16,992 INFO misc.py line 119 87073] Train: [7/100][1221/1557] Data 0.005 (0.082) Batch 0.926 (1.113) Remain 44:52:40 loss: 0.7149 Lr: 0.00500 [2024-02-17 20:28:18,028 INFO misc.py line 119 87073] Train: [7/100][1222/1557] Data 0.005 (0.082) Batch 1.031 (1.113) Remain 44:52:29 loss: 0.5992 Lr: 0.00500 [2024-02-17 20:28:18,715 INFO misc.py line 119 87073] Train: [7/100][1223/1557] Data 0.010 (0.082) Batch 0.693 (1.113) Remain 44:51:38 loss: 0.7148 Lr: 0.00500 [2024-02-17 20:28:19,441 INFO misc.py line 119 87073] Train: 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Batch 2.304 (1.113) Remain 44:52:46 loss: 0.6594 Lr: 0.00500 [2024-02-17 20:28:27,939 INFO misc.py line 119 87073] Train: [7/100][1231/1557] Data 0.005 (0.082) Batch 0.790 (1.113) Remain 44:52:07 loss: 0.9973 Lr: 0.00500 [2024-02-17 20:28:29,212 INFO misc.py line 119 87073] Train: [7/100][1232/1557] Data 0.005 (0.082) Batch 1.265 (1.113) Remain 44:52:24 loss: 0.5561 Lr: 0.00500 [2024-02-17 20:28:30,399 INFO misc.py line 119 87073] Train: [7/100][1233/1557] Data 0.013 (0.082) Batch 1.185 (1.113) Remain 44:52:31 loss: 0.9812 Lr: 0.00500 [2024-02-17 20:28:31,498 INFO misc.py line 119 87073] Train: [7/100][1234/1557] Data 0.016 (0.082) Batch 1.105 (1.113) Remain 44:52:29 loss: 0.6707 Lr: 0.00500 [2024-02-17 20:28:32,484 INFO misc.py line 119 87073] Train: [7/100][1235/1557] Data 0.009 (0.082) Batch 0.991 (1.113) Remain 44:52:13 loss: 0.8950 Lr: 0.00500 [2024-02-17 20:28:33,399 INFO misc.py line 119 87073] Train: [7/100][1236/1557] Data 0.004 (0.082) Batch 0.913 (1.113) Remain 44:51:49 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line 119 87073] Train: [7/100][1274/1557] Data 0.004 (0.083) Batch 1.047 (1.116) Remain 44:58:37 loss: 0.2989 Lr: 0.00500 [2024-02-17 20:29:20,589 INFO misc.py line 119 87073] Train: [7/100][1275/1557] Data 0.008 (0.083) Batch 0.956 (1.116) Remain 44:58:17 loss: 1.2029 Lr: 0.00500 [2024-02-17 20:29:21,599 INFO misc.py line 119 87073] Train: [7/100][1276/1557] Data 0.005 (0.082) Batch 1.012 (1.116) Remain 44:58:04 loss: 0.6174 Lr: 0.00500 [2024-02-17 20:29:22,495 INFO misc.py line 119 87073] Train: [7/100][1277/1557] Data 0.004 (0.082) Batch 0.895 (1.116) Remain 44:57:38 loss: 0.6197 Lr: 0.00500 [2024-02-17 20:29:23,534 INFO misc.py line 119 87073] Train: [7/100][1278/1557] Data 0.004 (0.082) Batch 1.027 (1.116) Remain 44:57:27 loss: 0.8252 Lr: 0.00500 [2024-02-17 20:29:24,322 INFO misc.py line 119 87073] Train: [7/100][1279/1557] Data 0.016 (0.082) Batch 0.800 (1.115) Remain 44:56:50 loss: 1.0140 Lr: 0.00500 [2024-02-17 20:29:25,084 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [7/100][1498/1557] Data 0.013 (0.084) Batch 1.042 (1.116) Remain 44:53:53 loss: 0.3978 Lr: 0.00499 [2024-02-17 20:33:30,245 INFO misc.py line 119 87073] Train: [7/100][1499/1557] Data 0.014 (0.084) Batch 0.968 (1.116) Remain 44:53:38 loss: 0.6371 Lr: 0.00499 [2024-02-17 20:33:31,196 INFO misc.py line 119 87073] Train: [7/100][1500/1557] Data 0.004 (0.084) Batch 0.951 (1.116) Remain 44:53:21 loss: 1.2166 Lr: 0.00499 [2024-02-17 20:33:32,286 INFO misc.py line 119 87073] Train: [7/100][1501/1557] Data 0.005 (0.083) Batch 1.090 (1.116) Remain 44:53:17 loss: 0.6677 Lr: 0.00499 [2024-02-17 20:33:33,599 INFO misc.py line 119 87073] Train: [7/100][1502/1557] Data 0.003 (0.083) Batch 1.313 (1.116) Remain 44:53:35 loss: 0.9169 Lr: 0.00499 [2024-02-17 20:33:34,517 INFO misc.py line 119 87073] Train: [7/100][1503/1557] Data 0.004 (0.083) Batch 0.917 (1.116) Remain 44:53:15 loss: 0.5493 Lr: 0.00499 [2024-02-17 20:33:35,264 INFO misc.py line 119 87073] Train: 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Batch 0.742 (1.115) Remain 44:51:11 loss: 0.8979 Lr: 0.00499 [2024-02-17 20:33:41,950 INFO misc.py line 119 87073] Train: [7/100][1511/1557] Data 0.005 (0.083) Batch 0.813 (1.115) Remain 44:50:41 loss: 0.4869 Lr: 0.00499 [2024-02-17 20:33:43,200 INFO misc.py line 119 87073] Train: [7/100][1512/1557] Data 0.018 (0.083) Batch 1.254 (1.115) Remain 44:50:54 loss: 0.4912 Lr: 0.00499 [2024-02-17 20:33:44,097 INFO misc.py line 119 87073] Train: [7/100][1513/1557] Data 0.015 (0.083) Batch 0.907 (1.115) Remain 44:50:33 loss: 0.4837 Lr: 0.00499 [2024-02-17 20:33:45,108 INFO misc.py line 119 87073] Train: [7/100][1514/1557] Data 0.004 (0.083) Batch 1.010 (1.114) Remain 44:50:21 loss: 0.7697 Lr: 0.00499 [2024-02-17 20:33:46,094 INFO misc.py line 119 87073] Train: [7/100][1515/1557] Data 0.006 (0.083) Batch 0.982 (1.114) Remain 44:50:08 loss: 0.3380 Lr: 0.00499 [2024-02-17 20:33:47,121 INFO misc.py line 119 87073] Train: [7/100][1516/1557] Data 0.009 (0.083) Batch 1.030 (1.114) Remain 44:49:58 loss: 1.1165 Lr: 0.00499 [2024-02-17 20:33:47,878 INFO misc.py line 119 87073] Train: [7/100][1517/1557] Data 0.006 (0.083) Batch 0.759 (1.114) Remain 44:49:23 loss: 0.9137 Lr: 0.00499 [2024-02-17 20:33:48,602 INFO misc.py line 119 87073] Train: [7/100][1518/1557] Data 0.005 (0.083) Batch 0.718 (1.114) Remain 44:48:44 loss: 0.9101 Lr: 0.00499 [2024-02-17 20:34:00,197 INFO misc.py line 119 87073] Train: [7/100][1519/1557] Data 4.612 (0.086) Batch 11.602 (1.121) Remain 45:05:25 loss: 0.4593 Lr: 0.00499 [2024-02-17 20:34:01,095 INFO misc.py line 119 87073] Train: [7/100][1520/1557] Data 0.004 (0.086) Batch 0.898 (1.121) Remain 45:05:03 loss: 0.7627 Lr: 0.00499 [2024-02-17 20:34:02,027 INFO misc.py line 119 87073] Train: [7/100][1521/1557] Data 0.005 (0.085) Batch 0.931 (1.120) Remain 45:04:44 loss: 0.7231 Lr: 0.00499 [2024-02-17 20:34:02,989 INFO misc.py line 119 87073] Train: [7/100][1522/1557] Data 0.005 (0.085) Batch 0.963 (1.120) Remain 45:04:27 loss: 0.7866 Lr: 0.00499 [2024-02-17 20:34:03,867 INFO misc.py line 119 87073] Train: [7/100][1523/1557] Data 0.004 (0.085) Batch 0.878 (1.120) Remain 45:04:03 loss: 0.4575 Lr: 0.00499 [2024-02-17 20:34:04,608 INFO misc.py line 119 87073] Train: [7/100][1524/1557] Data 0.004 (0.085) Batch 0.734 (1.120) Remain 45:03:25 loss: 0.5996 Lr: 0.00499 [2024-02-17 20:34:05,391 INFO misc.py line 119 87073] Train: [7/100][1525/1557] Data 0.010 (0.085) Batch 0.789 (1.120) Remain 45:02:53 loss: 0.4208 Lr: 0.00499 [2024-02-17 20:34:06,640 INFO misc.py line 119 87073] Train: [7/100][1526/1557] Data 0.004 (0.085) Batch 1.236 (1.120) Remain 45:03:03 loss: 0.2111 Lr: 0.00499 [2024-02-17 20:34:07,491 INFO misc.py line 119 87073] Train: [7/100][1527/1557] Data 0.017 (0.085) Batch 0.863 (1.120) Remain 45:02:37 loss: 0.7092 Lr: 0.00499 [2024-02-17 20:34:08,414 INFO misc.py line 119 87073] Train: [7/100][1528/1557] Data 0.005 (0.085) Batch 0.923 (1.120) Remain 45:02:17 loss: 0.4477 Lr: 0.00499 [2024-02-17 20:34:09,340 INFO misc.py line 119 87073] Train: [7/100][1529/1557] Data 0.004 (0.085) Batch 0.924 (1.119) Remain 45:01:58 loss: 0.8468 Lr: 0.00499 [2024-02-17 20:34:10,335 INFO misc.py line 119 87073] Train: [7/100][1530/1557] Data 0.008 (0.085) Batch 0.998 (1.119) Remain 45:01:45 loss: 0.4472 Lr: 0.00499 [2024-02-17 20:34:11,087 INFO misc.py line 119 87073] Train: [7/100][1531/1557] Data 0.004 (0.085) Batch 0.753 (1.119) Remain 45:01:09 loss: 0.4286 Lr: 0.00499 [2024-02-17 20:34:11,865 INFO misc.py line 119 87073] Train: [7/100][1532/1557] Data 0.004 (0.085) Batch 0.772 (1.119) Remain 45:00:35 loss: 0.8934 Lr: 0.00499 [2024-02-17 20:34:13,111 INFO misc.py line 119 87073] Train: [7/100][1533/1557] Data 0.010 (0.085) Batch 1.243 (1.119) Remain 45:00:46 loss: 0.1359 Lr: 0.00499 [2024-02-17 20:34:13,950 INFO misc.py line 119 87073] Train: [7/100][1534/1557] Data 0.013 (0.085) Batch 0.847 (1.119) Remain 45:00:19 loss: 0.9595 Lr: 0.00499 [2024-02-17 20:34:14,933 INFO misc.py line 119 87073] Train: [7/100][1535/1557] Data 0.004 (0.085) Batch 0.984 (1.119) Remain 45:00:05 loss: 0.7138 Lr: 0.00499 [2024-02-17 20:34:16,045 INFO misc.py line 119 87073] Train: [7/100][1536/1557] Data 0.004 (0.085) Batch 1.111 (1.119) Remain 45:00:03 loss: 0.7811 Lr: 0.00499 [2024-02-17 20:34:16,963 INFO misc.py line 119 87073] Train: [7/100][1537/1557] Data 0.005 (0.085) Batch 0.915 (1.119) Remain 44:59:43 loss: 0.6317 Lr: 0.00499 [2024-02-17 20:34:17,675 INFO misc.py line 119 87073] Train: [7/100][1538/1557] Data 0.007 (0.085) Batch 0.709 (1.118) Remain 44:59:03 loss: 1.1310 Lr: 0.00499 [2024-02-17 20:34:18,342 INFO misc.py line 119 87073] Train: [7/100][1539/1557] Data 0.011 (0.085) Batch 0.673 (1.118) Remain 44:58:20 loss: 0.5434 Lr: 0.00499 [2024-02-17 20:34:19,606 INFO misc.py line 119 87073] Train: [7/100][1540/1557] Data 0.005 (0.085) Batch 1.248 (1.118) Remain 44:58:31 loss: 1.1372 Lr: 0.00499 [2024-02-17 20:34:20,542 INFO misc.py line 119 87073] Train: [7/100][1541/1557] Data 0.021 (0.084) Batch 0.952 (1.118) Remain 44:58:15 loss: 0.4046 Lr: 0.00499 [2024-02-17 20:34:21,691 INFO misc.py line 119 87073] Train: [7/100][1542/1557] Data 0.004 (0.084) Batch 1.149 (1.118) Remain 44:58:16 loss: 0.6439 Lr: 0.00499 [2024-02-17 20:34:22,586 INFO misc.py line 119 87073] Train: [7/100][1543/1557] Data 0.004 (0.084) Batch 0.896 (1.118) Remain 44:57:54 loss: 0.4861 Lr: 0.00499 [2024-02-17 20:34:23,541 INFO misc.py line 119 87073] Train: [7/100][1544/1557] Data 0.004 (0.084) Batch 0.944 (1.118) Remain 44:57:37 loss: 0.8089 Lr: 0.00499 [2024-02-17 20:34:24,250 INFO misc.py line 119 87073] Train: [7/100][1545/1557] Data 0.014 (0.084) Batch 0.720 (1.117) Remain 44:56:58 loss: 0.6119 Lr: 0.00499 [2024-02-17 20:34:25,004 INFO misc.py line 119 87073] Train: [7/100][1546/1557] Data 0.004 (0.084) Batch 0.739 (1.117) Remain 44:56:22 loss: 0.8719 Lr: 0.00499 [2024-02-17 20:34:26,225 INFO misc.py line 119 87073] Train: [7/100][1547/1557] Data 0.020 (0.084) Batch 1.223 (1.117) Remain 44:56:31 loss: 0.2716 Lr: 0.00499 [2024-02-17 20:34:27,254 INFO misc.py line 119 87073] Train: [7/100][1548/1557] Data 0.017 (0.084) Batch 1.031 (1.117) Remain 44:56:21 loss: 0.8451 Lr: 0.00499 [2024-02-17 20:34:28,094 INFO misc.py line 119 87073] Train: [7/100][1549/1557] Data 0.016 (0.084) Batch 0.852 (1.117) Remain 44:55:55 loss: 0.4969 Lr: 0.00499 [2024-02-17 20:34:29,194 INFO misc.py line 119 87073] Train: [7/100][1550/1557] Data 0.004 (0.084) Batch 1.100 (1.117) Remain 44:55:53 loss: 0.7538 Lr: 0.00499 [2024-02-17 20:34:30,286 INFO misc.py line 119 87073] Train: [7/100][1551/1557] Data 0.004 (0.084) Batch 1.093 (1.117) Remain 44:55:49 loss: 0.2410 Lr: 0.00499 [2024-02-17 20:34:30,960 INFO misc.py line 119 87073] Train: [7/100][1552/1557] Data 0.003 (0.084) Batch 0.673 (1.117) Remain 44:55:07 loss: 0.4890 Lr: 0.00499 [2024-02-17 20:34:31,695 INFO misc.py line 119 87073] Train: [7/100][1553/1557] Data 0.005 (0.084) Batch 0.723 (1.116) Remain 44:54:29 loss: 0.5383 Lr: 0.00499 [2024-02-17 20:34:32,739 INFO misc.py line 119 87073] Train: [7/100][1554/1557] Data 0.016 (0.084) Batch 1.048 (1.116) Remain 44:54:21 loss: 0.5161 Lr: 0.00499 [2024-02-17 20:34:33,747 INFO misc.py line 119 87073] Train: [7/100][1555/1557] Data 0.013 (0.084) Batch 1.008 (1.116) Remain 44:54:10 loss: 0.8580 Lr: 0.00499 [2024-02-17 20:34:34,891 INFO misc.py line 119 87073] Train: [7/100][1556/1557] Data 0.013 (0.084) Batch 1.139 (1.116) Remain 44:54:11 loss: 0.7089 Lr: 0.00499 [2024-02-17 20:34:35,859 INFO misc.py line 119 87073] Train: [7/100][1557/1557] Data 0.018 (0.084) Batch 0.982 (1.116) Remain 44:53:57 loss: 0.2559 Lr: 0.00499 [2024-02-17 20:34:35,860 INFO misc.py line 136 87073] Train result: loss: 0.7024 [2024-02-17 20:34:35,861 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 20:35:06,769 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8110 [2024-02-17 20:35:07,546 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6737 [2024-02-17 20:35:09,670 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4168 [2024-02-17 20:35:11,878 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2700 [2024-02-17 20:35:16,823 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2659 [2024-02-17 20:35:17,522 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1298 [2024-02-17 20:35:18,782 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3901 [2024-02-17 20:35:21,733 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3190 [2024-02-17 20:35:23,541 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.5591 [2024-02-17 20:35:25,197 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6864/0.7570/0.9021. [2024-02-17 20:35:25,197 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9371/0.9587 [2024-02-17 20:35:25,197 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9802/0.9886 [2024-02-17 20:35:25,197 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8624/0.9455 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2990/0.3433 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6533/0.6932 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6295/0.7996 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8295/0.8916 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9169/0.9579 [2024-02-17 20:35:25,198 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7600/0.7892 [2024-02-17 20:35:25,199 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7324/0.8254 [2024-02-17 20:35:25,199 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7401/0.8585 [2024-02-17 20:35:25,199 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5826/0.7901 [2024-02-17 20:35:25,199 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 20:35:25,202 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6864 [2024-02-17 20:35:25,202 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 20:35:25,202 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 20:35:35,495 INFO misc.py line 119 87073] Train: [8/100][1/1557] Data 1.222 (1.222) Batch 1.825 (1.825) Remain 73:25:31 loss: 0.5597 Lr: 0.00499 [2024-02-17 20:35:36,472 INFO misc.py line 119 87073] Train: [8/100][2/1557] Data 0.007 (0.007) Batch 0.974 (0.974) Remain 39:09:45 loss: 0.3087 Lr: 0.00499 [2024-02-17 20:35:37,352 INFO misc.py line 119 87073] Train: [8/100][3/1557] Data 0.010 (0.010) Batch 0.884 (0.884) Remain 35:32:53 loss: 0.4539 Lr: 0.00499 [2024-02-17 20:35:38,164 INFO misc.py line 119 87073] Train: [8/100][4/1557] Data 0.006 (0.006) Batch 0.811 (0.811) Remain 32:37:50 loss: 0.8306 Lr: 0.00499 [2024-02-17 20:35:38,991 INFO misc.py line 119 87073] Train: [8/100][5/1557] Data 0.007 (0.006) Batch 0.824 (0.818) Remain 32:53:35 loss: 0.5675 Lr: 0.00499 [2024-02-17 20:35:39,735 INFO misc.py line 119 87073] Train: [8/100][6/1557] Data 0.009 (0.007) Batch 0.749 (0.795) Remain 31:58:28 loss: 0.3405 Lr: 0.00499 [2024-02-17 20:35:42,144 INFO misc.py line 119 87073] Train: [8/100][7/1557] Data 1.424 (0.361) Batch 2.408 (1.198) Remain 48:11:49 loss: 0.5850 Lr: 0.00499 [2024-02-17 20:35:43,141 INFO misc.py line 119 87073] Train: [8/100][8/1557] Data 0.004 (0.290) Batch 0.997 (1.158) Remain 46:34:44 loss: 0.7636 Lr: 0.00499 [2024-02-17 20:35:44,080 INFO misc.py line 119 87073] Train: [8/100][9/1557] Data 0.004 (0.242) Batch 0.937 (1.121) Remain 45:05:58 loss: 0.6881 Lr: 0.00499 [2024-02-17 20:35:44,947 INFO misc.py line 119 87073] Train: [8/100][10/1557] Data 0.006 (0.208) Batch 0.869 (1.085) Remain 43:38:52 loss: 0.5130 Lr: 0.00499 [2024-02-17 20:35:45,826 INFO misc.py line 119 87073] Train: [8/100][11/1557] Data 0.004 (0.183) Batch 0.878 (1.059) Remain 42:36:15 loss: 0.6059 Lr: 0.00499 [2024-02-17 20:35:46,608 INFO misc.py line 119 87073] Train: [8/100][12/1557] Data 0.006 (0.163) Batch 0.782 (1.028) Remain 41:21:47 loss: 0.6571 Lr: 0.00499 [2024-02-17 20:35:47,325 INFO misc.py line 119 87073] Train: [8/100][13/1557] Data 0.005 (0.147) Batch 0.718 (0.997) Remain 40:06:48 loss: 0.7395 Lr: 0.00499 [2024-02-17 20:35:49,505 INFO misc.py line 119 87073] Train: [8/100][14/1557] Data 0.005 (0.134) Batch 2.179 (1.105) Remain 44:25:54 loss: 0.4404 Lr: 0.00499 [2024-02-17 20:35:50,514 INFO misc.py line 119 87073] Train: [8/100][15/1557] Data 0.006 (0.124) Batch 1.001 (1.096) Remain 44:04:56 loss: 0.6639 Lr: 0.00499 [2024-02-17 20:35:51,337 INFO misc.py line 119 87073] Train: [8/100][16/1557] Data 0.015 (0.115) Batch 0.835 (1.076) Remain 43:16:24 loss: 0.3965 Lr: 0.00499 [2024-02-17 20:35:52,226 INFO misc.py line 119 87073] Train: [8/100][17/1557] Data 0.004 (0.107) Batch 0.889 (1.063) Remain 42:44:07 loss: 0.3002 Lr: 0.00499 [2024-02-17 20:35:53,089 INFO misc.py line 119 87073] Train: [8/100][18/1557] Data 0.004 (0.101) Batch 0.858 (1.049) Remain 42:11:14 loss: 0.6344 Lr: 0.00499 [2024-02-17 20:35:53,930 INFO misc.py line 119 87073] Train: [8/100][19/1557] Data 0.008 (0.095) Batch 0.845 (1.036) Remain 41:40:29 loss: 0.6281 Lr: 0.00499 [2024-02-17 20:35:54,668 INFO misc.py line 119 87073] Train: [8/100][20/1557] Data 0.003 (0.089) Batch 0.736 (1.019) Remain 40:57:47 loss: 0.6734 Lr: 0.00499 [2024-02-17 20:35:55,869 INFO misc.py line 119 87073] Train: [8/100][21/1557] Data 0.006 (0.085) Batch 1.197 (1.028) Remain 41:21:42 loss: 0.4128 Lr: 0.00499 [2024-02-17 20:35:56,771 INFO misc.py line 119 87073] Train: [8/100][22/1557] Data 0.010 (0.081) Batch 0.908 (1.022) Remain 41:06:23 loss: 0.8185 Lr: 0.00499 [2024-02-17 20:35:57,892 INFO misc.py line 119 87073] Train: [8/100][23/1557] Data 0.004 (0.077) Batch 1.121 (1.027) Remain 41:18:16 loss: 0.9192 Lr: 0.00499 [2024-02-17 20:35:58,803 INFO misc.py line 119 87073] Train: [8/100][24/1557] Data 0.004 (0.073) Batch 0.912 (1.022) Remain 41:05:03 loss: 0.4291 Lr: 0.00499 [2024-02-17 20:35:59,889 INFO misc.py line 119 87073] Train: [8/100][25/1557] Data 0.004 (0.070) Batch 1.086 (1.025) Remain 41:12:04 loss: 0.6146 Lr: 0.00499 [2024-02-17 20:36:00,632 INFO misc.py line 119 87073] Train: [8/100][26/1557] Data 0.004 (0.067) Batch 0.743 (1.012) Remain 40:42:30 loss: 0.7673 Lr: 0.00499 [2024-02-17 20:36:01,383 INFO misc.py line 119 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INFO misc.py line 119 87073] Train: [8/100][128/1557] Data 0.004 (0.128) Batch 1.039 (1.157) Remain 46:28:35 loss: 0.4426 Lr: 0.00499 [2024-02-17 20:38:02,839 INFO misc.py line 119 87073] Train: [8/100][129/1557] Data 0.005 (0.127) Batch 0.925 (1.155) Remain 46:24:08 loss: 0.3475 Lr: 0.00499 [2024-02-17 20:38:03,796 INFO misc.py line 119 87073] Train: [8/100][130/1557] Data 0.003 (0.127) Batch 0.956 (1.153) Remain 46:20:21 loss: 0.6346 Lr: 0.00499 [2024-02-17 20:38:04,586 INFO misc.py line 119 87073] Train: [8/100][131/1557] Data 0.004 (0.126) Batch 0.785 (1.150) Remain 46:13:24 loss: 0.9031 Lr: 0.00499 [2024-02-17 20:38:05,448 INFO misc.py line 119 87073] Train: [8/100][132/1557] Data 0.009 (0.125) Batch 0.868 (1.148) Remain 46:08:07 loss: 0.8699 Lr: 0.00499 [2024-02-17 20:38:06,652 INFO misc.py line 119 87073] Train: [8/100][133/1557] Data 0.003 (0.124) Batch 1.196 (1.148) Remain 46:08:58 loss: 0.3660 Lr: 0.00499 [2024-02-17 20:38:07,543 INFO misc.py line 119 87073] Train: 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Train: [8/100][178/1557] Data 0.008 (0.133) Batch 0.907 (1.145) Remain 46:00:28 loss: 0.3498 Lr: 0.00499 [2024-02-17 20:38:58,525 INFO misc.py line 119 87073] Train: [8/100][179/1557] Data 0.004 (0.132) Batch 0.757 (1.143) Remain 45:55:08 loss: 0.6012 Lr: 0.00499 [2024-02-17 20:39:01,061 INFO misc.py line 119 87073] Train: [8/100][180/1557] Data 1.256 (0.139) Batch 2.536 (1.151) Remain 46:14:04 loss: 0.5219 Lr: 0.00499 [2024-02-17 20:39:01,778 INFO misc.py line 119 87073] Train: [8/100][181/1557] Data 0.006 (0.138) Batch 0.717 (1.148) Remain 46:08:11 loss: 0.4207 Lr: 0.00499 [2024-02-17 20:39:08,319 INFO misc.py line 119 87073] Train: [8/100][182/1557] Data 0.004 (0.137) Batch 6.541 (1.179) Remain 47:20:47 loss: 0.5171 Lr: 0.00499 [2024-02-17 20:39:09,323 INFO misc.py line 119 87073] Train: [8/100][183/1557] Data 0.005 (0.136) Batch 1.004 (1.178) Remain 47:18:26 loss: 0.7904 Lr: 0.00499 [2024-02-17 20:39:10,443 INFO misc.py line 119 87073] Train: [8/100][184/1557] Data 0.003 (0.136) 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0.007 (0.136) Batch 0.990 (1.200) Remain 47:45:19 loss: 0.8192 Lr: 0.00499 [2024-02-17 21:05:53,502 INFO misc.py line 119 87073] Train: [8/100][1517/1557] Data 0.003 (0.136) Batch 0.779 (1.200) Remain 47:44:38 loss: 0.9555 Lr: 0.00499 [2024-02-17 21:05:54,270 INFO misc.py line 119 87073] Train: [8/100][1518/1557] Data 0.011 (0.136) Batch 0.775 (1.199) Remain 47:43:57 loss: 0.3681 Lr: 0.00499 [2024-02-17 21:06:03,906 INFO misc.py line 119 87073] Train: [8/100][1519/1557] Data 6.870 (0.140) Batch 9.637 (1.205) Remain 47:57:13 loss: 0.3058 Lr: 0.00499 [2024-02-17 21:06:04,884 INFO misc.py line 119 87073] Train: [8/100][1520/1557] Data 0.004 (0.140) Batch 0.973 (1.205) Remain 47:56:50 loss: 0.7394 Lr: 0.00499 [2024-02-17 21:06:05,932 INFO misc.py line 119 87073] Train: [8/100][1521/1557] Data 0.009 (0.140) Batch 1.053 (1.205) Remain 47:56:34 loss: 0.9025 Lr: 0.00499 [2024-02-17 21:06:06,918 INFO misc.py line 119 87073] Train: [8/100][1522/1557] Data 0.004 (0.140) Batch 0.986 (1.204) Remain 47:56:12 loss: 0.3260 Lr: 0.00499 [2024-02-17 21:06:07,779 INFO misc.py line 119 87073] Train: [8/100][1523/1557] Data 0.004 (0.140) Batch 0.861 (1.204) Remain 47:55:39 loss: 0.6740 Lr: 0.00499 [2024-02-17 21:06:08,502 INFO misc.py line 119 87073] Train: [8/100][1524/1557] Data 0.003 (0.140) Batch 0.714 (1.204) Remain 47:54:51 loss: 0.4501 Lr: 0.00499 [2024-02-17 21:06:09,185 INFO misc.py line 119 87073] Train: [8/100][1525/1557] Data 0.013 (0.140) Batch 0.689 (1.204) Remain 47:54:02 loss: 0.5522 Lr: 0.00499 [2024-02-17 21:06:16,155 INFO misc.py line 119 87073] Train: [8/100][1526/1557] Data 0.006 (0.140) Batch 6.973 (1.207) Remain 48:03:03 loss: 0.6212 Lr: 0.00499 [2024-02-17 21:06:17,080 INFO misc.py line 119 87073] Train: [8/100][1527/1557] Data 0.005 (0.140) Batch 0.925 (1.207) Remain 48:02:36 loss: 0.6793 Lr: 0.00499 [2024-02-17 21:06:17,903 INFO misc.py line 119 87073] Train: [8/100][1528/1557] Data 0.004 (0.140) Batch 0.824 (1.207) Remain 48:01:58 loss: 0.4636 Lr: 0.00499 [2024-02-17 21:06:18,941 INFO misc.py line 119 87073] Train: [8/100][1529/1557] Data 0.003 (0.140) Batch 1.027 (1.207) Remain 48:01:40 loss: 0.4942 Lr: 0.00499 [2024-02-17 21:06:19,916 INFO misc.py line 119 87073] Train: [8/100][1530/1557] Data 0.013 (0.140) Batch 0.985 (1.207) Remain 48:01:18 loss: 0.6172 Lr: 0.00499 [2024-02-17 21:06:20,690 INFO misc.py line 119 87073] Train: [8/100][1531/1557] Data 0.004 (0.139) Batch 0.774 (1.206) Remain 48:00:36 loss: 0.6208 Lr: 0.00499 [2024-02-17 21:06:21,534 INFO misc.py line 119 87073] Train: [8/100][1532/1557] Data 0.004 (0.139) Batch 0.835 (1.206) Remain 48:00:00 loss: 0.6249 Lr: 0.00499 [2024-02-17 21:06:22,709 INFO misc.py line 119 87073] Train: [8/100][1533/1557] Data 0.013 (0.139) Batch 1.180 (1.206) Remain 47:59:57 loss: 0.4175 Lr: 0.00499 [2024-02-17 21:06:23,793 INFO misc.py line 119 87073] Train: [8/100][1534/1557] Data 0.009 (0.139) Batch 1.064 (1.206) Remain 47:59:42 loss: 0.7492 Lr: 0.00499 [2024-02-17 21:06:24,818 INFO misc.py line 119 87073] Train: [8/100][1535/1557] Data 0.029 (0.139) Batch 1.035 (1.206) Remain 47:59:25 loss: 1.0640 Lr: 0.00499 [2024-02-17 21:06:25,851 INFO misc.py line 119 87073] Train: [8/100][1536/1557] Data 0.019 (0.139) Batch 1.035 (1.206) Remain 47:59:08 loss: 0.2338 Lr: 0.00499 [2024-02-17 21:06:26,858 INFO misc.py line 119 87073] Train: [8/100][1537/1557] Data 0.017 (0.139) Batch 1.011 (1.206) Remain 47:58:48 loss: 1.3512 Lr: 0.00499 [2024-02-17 21:06:27,600 INFO misc.py line 119 87073] Train: [8/100][1538/1557] Data 0.013 (0.139) Batch 0.751 (1.205) Remain 47:58:05 loss: 0.4761 Lr: 0.00499 [2024-02-17 21:06:28,414 INFO misc.py line 119 87073] Train: [8/100][1539/1557] Data 0.004 (0.139) Batch 0.804 (1.205) Remain 47:57:26 loss: 0.9272 Lr: 0.00499 [2024-02-17 21:06:29,546 INFO misc.py line 119 87073] Train: [8/100][1540/1557] Data 0.014 (0.139) Batch 1.131 (1.205) Remain 47:57:18 loss: 0.2739 Lr: 0.00499 [2024-02-17 21:06:30,600 INFO misc.py line 119 87073] Train: [8/100][1541/1557] Data 0.014 (0.139) Batch 1.055 (1.205) Remain 47:57:03 loss: 1.0351 Lr: 0.00499 [2024-02-17 21:06:31,485 INFO misc.py line 119 87073] Train: [8/100][1542/1557] Data 0.013 (0.139) Batch 0.895 (1.205) Remain 47:56:33 loss: 0.8462 Lr: 0.00499 [2024-02-17 21:06:32,563 INFO misc.py line 119 87073] Train: [8/100][1543/1557] Data 0.004 (0.138) Batch 1.078 (1.205) Remain 47:56:20 loss: 0.6794 Lr: 0.00499 [2024-02-17 21:06:33,487 INFO misc.py line 119 87073] Train: [8/100][1544/1557] Data 0.004 (0.138) Batch 0.923 (1.204) Remain 47:55:52 loss: 0.5322 Lr: 0.00499 [2024-02-17 21:06:34,225 INFO misc.py line 119 87073] Train: [8/100][1545/1557] Data 0.004 (0.138) Batch 0.727 (1.204) Remain 47:55:07 loss: 0.3772 Lr: 0.00499 [2024-02-17 21:06:34,994 INFO misc.py line 119 87073] Train: [8/100][1546/1557] Data 0.016 (0.138) Batch 0.781 (1.204) Remain 47:54:26 loss: 0.7121 Lr: 0.00499 [2024-02-17 21:06:36,281 INFO misc.py line 119 87073] Train: [8/100][1547/1557] Data 0.003 (0.138) Batch 1.274 (1.204) Remain 47:54:32 loss: 0.5338 Lr: 0.00499 [2024-02-17 21:06:37,291 INFO misc.py line 119 87073] Train: [8/100][1548/1557] Data 0.017 (0.138) Batch 1.011 (1.204) Remain 47:54:13 loss: 0.7608 Lr: 0.00499 [2024-02-17 21:06:38,377 INFO misc.py line 119 87073] Train: [8/100][1549/1557] Data 0.016 (0.138) Batch 1.088 (1.204) Remain 47:54:01 loss: 0.7870 Lr: 0.00499 [2024-02-17 21:06:39,258 INFO misc.py line 119 87073] Train: [8/100][1550/1557] Data 0.014 (0.138) Batch 0.891 (1.204) Remain 47:53:30 loss: 0.7375 Lr: 0.00499 [2024-02-17 21:06:40,190 INFO misc.py line 119 87073] Train: [8/100][1551/1557] Data 0.003 (0.138) Batch 0.932 (1.203) Remain 47:53:04 loss: 0.5317 Lr: 0.00499 [2024-02-17 21:06:40,920 INFO misc.py line 119 87073] Train: [8/100][1552/1557] Data 0.003 (0.138) Batch 0.722 (1.203) Remain 47:52:18 loss: 0.6158 Lr: 0.00499 [2024-02-17 21:06:41,644 INFO misc.py line 119 87073] Train: [8/100][1553/1557] Data 0.012 (0.138) Batch 0.731 (1.203) Remain 47:51:34 loss: 0.4772 Lr: 0.00499 [2024-02-17 21:06:42,941 INFO misc.py line 119 87073] Train: [8/100][1554/1557] Data 0.004 (0.138) Batch 1.284 (1.203) Remain 47:51:40 loss: 0.3892 Lr: 0.00499 [2024-02-17 21:06:43,836 INFO misc.py line 119 87073] Train: [8/100][1555/1557] Data 0.018 (0.137) Batch 0.909 (1.203) Remain 47:51:11 loss: 0.7048 Lr: 0.00499 [2024-02-17 21:06:44,663 INFO misc.py line 119 87073] Train: [8/100][1556/1557] Data 0.004 (0.137) Batch 0.828 (1.202) Remain 47:50:36 loss: 0.9952 Lr: 0.00499 [2024-02-17 21:06:45,979 INFO misc.py line 119 87073] Train: [8/100][1557/1557] Data 0.004 (0.137) Batch 1.316 (1.202) Remain 47:50:45 loss: 0.4394 Lr: 0.00499 [2024-02-17 21:06:45,980 INFO misc.py line 136 87073] Train result: loss: 0.6548 [2024-02-17 21:06:45,980 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 21:07:16,745 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6549 [2024-02-17 21:07:17,520 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9874 [2024-02-17 21:07:19,646 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5488 [2024-02-17 21:07:21,852 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4094 [2024-02-17 21:07:26,802 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.4890 [2024-02-17 21:07:27,501 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1901 [2024-02-17 21:07:28,761 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3862 [2024-02-17 21:07:31,717 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4671 [2024-02-17 21:07:33,540 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.5246 [2024-02-17 21:07:35,116 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6416/0.7376/0.8892. [2024-02-17 21:07:35,117 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9348/0.9520 [2024-02-17 21:07:35,117 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9775/0.9830 [2024-02-17 21:07:35,117 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8293/0.9610 [2024-02-17 21:07:35,117 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 21:07:35,117 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2942/0.3149 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.4852/0.4911 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.5310/0.6638 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8370/0.9176 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9055/0.9688 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7564/0.9197 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7512/0.8521 [2024-02-17 21:07:35,118 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.4651/0.8715 [2024-02-17 21:07:35,119 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5737/0.6941 [2024-02-17 21:07:35,119 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 21:07:35,122 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 21:07:35,122 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 21:07:42,509 INFO misc.py line 119 87073] Train: [9/100][1/1557] Data 1.518 (1.518) Batch 2.427 (2.427) Remain 96:33:10 loss: 0.6742 Lr: 0.00499 [2024-02-17 21:07:43,383 INFO misc.py line 119 87073] Train: [9/100][2/1557] Data 0.007 (0.007) Batch 0.872 (0.872) Remain 34:42:17 loss: 0.9852 Lr: 0.00499 [2024-02-17 21:07:44,447 INFO misc.py line 119 87073] Train: [9/100][3/1557] Data 0.008 (0.008) Batch 1.065 (1.065) Remain 42:21:27 loss: 0.5066 Lr: 0.00499 [2024-02-17 21:07:45,568 INFO misc.py line 119 87073] Train: [9/100][4/1557] Data 0.006 (0.006) Batch 1.119 (1.119) Remain 44:30:58 loss: 0.5203 Lr: 0.00499 [2024-02-17 21:07:46,364 INFO misc.py line 119 87073] Train: [9/100][5/1557] Data 0.009 (0.007) Batch 0.801 (0.960) Remain 38:11:04 loss: 0.9185 Lr: 0.00499 [2024-02-17 21:07:47,132 INFO misc.py line 119 87073] Train: [9/100][6/1557] Data 0.003 (0.006) Batch 0.767 (0.896) Remain 35:38:01 loss: 0.9389 Lr: 0.00499 [2024-02-17 21:07:48,202 INFO misc.py line 119 87073] Train: [9/100][7/1557] Data 0.005 (0.006) Batch 1.070 (0.939) Remain 37:21:48 loss: 0.6461 Lr: 0.00499 [2024-02-17 21:07:49,262 INFO misc.py line 119 87073] Train: [9/100][8/1557] Data 0.005 (0.006) Batch 1.049 (0.961) Remain 38:14:18 loss: 0.9635 Lr: 0.00499 [2024-02-17 21:07:50,199 INFO misc.py line 119 87073] Train: [9/100][9/1557] Data 0.015 (0.007) Batch 0.949 (0.959) Remain 38:09:26 loss: 0.3909 Lr: 0.00499 [2024-02-17 21:07:51,155 INFO misc.py line 119 87073] Train: [9/100][10/1557] Data 0.004 (0.007) Batch 0.956 (0.959) Remain 38:08:22 loss: 0.6009 Lr: 0.00499 [2024-02-17 21:07:52,374 INFO misc.py line 119 87073] Train: [9/100][11/1557] Data 0.003 (0.006) Batch 1.218 (0.991) Remain 39:25:53 loss: 0.9948 Lr: 0.00499 [2024-02-17 21:07:53,040 INFO misc.py line 119 87073] Train: [9/100][12/1557] Data 0.004 (0.006) Batch 0.666 (0.955) Remain 37:59:43 loss: 0.7162 Lr: 0.00499 [2024-02-17 21:07:53,751 INFO misc.py line 119 87073] Train: [9/100][13/1557] Data 0.004 (0.006) Batch 0.704 (0.930) Remain 36:59:52 loss: 0.8134 Lr: 0.00499 [2024-02-17 21:07:55,022 INFO misc.py line 119 87073] Train: [9/100][14/1557] Data 0.010 (0.006) Batch 1.267 (0.961) Remain 38:13:01 loss: 0.2978 Lr: 0.00499 [2024-02-17 21:07:55,986 INFO misc.py line 119 87073] Train: [9/100][15/1557] Data 0.014 (0.007) Batch 0.974 (0.962) Remain 38:15:43 loss: 0.7769 Lr: 0.00499 [2024-02-17 21:07:56,840 INFO misc.py line 119 87073] Train: [9/100][16/1557] Data 0.004 (0.007) Batch 0.853 (0.953) Remain 37:55:43 loss: 0.7812 Lr: 0.00499 [2024-02-17 21:07:57,631 INFO misc.py line 119 87073] Train: [9/100][17/1557] Data 0.005 (0.006) Batch 0.791 (0.942) Remain 37:28:03 loss: 0.4944 Lr: 0.00499 [2024-02-17 21:07:58,519 INFO misc.py line 119 87073] Train: [9/100][18/1557] Data 0.005 (0.006) Batch 0.888 (0.938) Remain 37:19:31 loss: 0.5548 Lr: 0.00499 [2024-02-17 21:07:59,245 INFO misc.py line 119 87073] Train: [9/100][19/1557] Data 0.004 (0.006) Batch 0.727 (0.925) Remain 36:48:00 loss: 0.6765 Lr: 0.00499 [2024-02-17 21:07:59,988 INFO misc.py line 119 87073] Train: [9/100][20/1557] Data 0.004 (0.006) Batch 0.735 (0.914) Remain 36:21:17 loss: 0.4701 Lr: 0.00499 [2024-02-17 21:08:03,555 INFO misc.py line 119 87073] Train: [9/100][21/1557] Data 2.405 (0.139) Batch 3.576 (1.062) Remain 42:14:17 loss: 0.2284 Lr: 0.00499 [2024-02-17 21:08:04,370 INFO misc.py line 119 87073] Train: [9/100][22/1557] Data 0.003 (0.132) Batch 0.814 (1.049) Remain 41:43:10 loss: 0.5323 Lr: 0.00499 [2024-02-17 21:08:05,177 INFO misc.py line 119 87073] Train: [9/100][23/1557] Data 0.005 (0.126) Batch 0.802 (1.036) Remain 41:13:40 loss: 0.7426 Lr: 0.00499 [2024-02-17 21:08:06,282 INFO misc.py line 119 87073] Train: [9/100][24/1557] Data 0.010 (0.120) Batch 1.104 (1.040) Remain 41:21:21 loss: 0.2570 Lr: 0.00499 [2024-02-17 21:08:07,231 INFO misc.py line 119 87073] Train: [9/100][25/1557] Data 0.011 (0.115) Batch 0.956 (1.036) Remain 41:12:15 loss: 0.4731 Lr: 0.00499 [2024-02-17 21:08:07,956 INFO misc.py line 119 87073] Train: [9/100][26/1557] Data 0.004 (0.111) Batch 0.725 (1.022) Remain 40:39:57 loss: 0.5779 Lr: 0.00499 [2024-02-17 21:08:08,652 INFO misc.py line 119 87073] Train: [9/100][27/1557] Data 0.004 (0.106) Batch 0.696 (1.009) Remain 40:07:31 loss: 0.7019 Lr: 0.00499 [2024-02-17 21:08:09,794 INFO misc.py line 119 87073] Train: [9/100][28/1557] Data 0.004 (0.102) Batch 1.131 (1.013) Remain 40:19:09 loss: 0.2538 Lr: 0.00499 [2024-02-17 21:08:10,760 INFO misc.py line 119 87073] Train: [9/100][29/1557] Data 0.015 (0.099) Batch 0.977 (1.012) Remain 40:15:45 loss: 0.8564 Lr: 0.00499 [2024-02-17 21:08:11,663 INFO misc.py line 119 87073] Train: [9/100][30/1557] Data 0.004 (0.095) Batch 0.903 (1.008) Remain 40:06:05 loss: 0.6436 Lr: 0.00499 [2024-02-17 21:08:12,557 INFO misc.py line 119 87073] Train: [9/100][31/1557] Data 0.005 (0.092) Batch 0.890 (1.004) Remain 39:55:59 loss: 0.6369 Lr: 0.00499 [2024-02-17 21:08:13,434 INFO misc.py line 119 87073] Train: [9/100][32/1557] Data 0.009 (0.089) Batch 0.882 (1.000) Remain 39:45:57 loss: 0.8991 Lr: 0.00499 [2024-02-17 21:08:14,106 INFO misc.py line 119 87073] Train: [9/100][33/1557] Data 0.003 (0.086) Batch 0.672 (0.989) Remain 39:19:52 loss: 0.6977 Lr: 0.00499 [2024-02-17 21:08:14,864 INFO misc.py line 119 87073] Train: [9/100][34/1557] Data 0.004 (0.084) Batch 0.758 (0.981) Remain 39:02:03 loss: 0.7023 Lr: 0.00499 [2024-02-17 21:08:15,994 INFO misc.py line 119 87073] Train: [9/100][35/1557] Data 0.004 (0.081) Batch 1.131 (0.986) Remain 39:13:10 loss: 0.4686 Lr: 0.00499 [2024-02-17 21:08:16,964 INFO misc.py line 119 87073] Train: [9/100][36/1557] Data 0.005 (0.079) Batch 0.970 (0.985) Remain 39:12:01 loss: 0.6981 Lr: 0.00499 [2024-02-17 21:08:17,978 INFO misc.py line 119 87073] Train: [9/100][37/1557] Data 0.004 (0.077) Batch 1.014 (0.986) Remain 39:14:00 loss: 0.7863 Lr: 0.00499 [2024-02-17 21:08:18,925 INFO misc.py line 119 87073] Train: [9/100][38/1557] Data 0.004 (0.074) Batch 0.946 (0.985) Remain 39:11:13 loss: 0.8196 Lr: 0.00499 [2024-02-17 21:08:19,868 INFO misc.py line 119 87073] Train: [9/100][39/1557] Data 0.006 (0.073) Batch 0.945 (0.984) Remain 39:08:31 loss: 0.5756 Lr: 0.00499 [2024-02-17 21:08:20,641 INFO misc.py line 119 87073] Train: [9/100][40/1557] Data 0.003 (0.071) Batch 0.772 (0.978) Remain 38:54:49 loss: 1.0186 Lr: 0.00499 [2024-02-17 21:08:21,467 INFO misc.py line 119 87073] Train: [9/100][41/1557] Data 0.005 (0.069) Batch 0.826 (0.974) Remain 38:45:16 loss: 0.5185 Lr: 0.00499 [2024-02-17 21:08:22,746 INFO misc.py line 119 87073] Train: [9/100][42/1557] Data 0.003 (0.067) Batch 1.268 (0.982) Remain 39:03:14 loss: 0.3233 Lr: 0.00499 [2024-02-17 21:08:23,840 INFO misc.py line 119 87073] Train: [9/100][43/1557] Data 0.015 (0.066) Batch 1.092 (0.985) Remain 39:09:48 loss: 0.9545 Lr: 0.00499 [2024-02-17 21:08:24,624 INFO misc.py line 119 87073] Train: [9/100][44/1557] Data 0.017 (0.065) Batch 0.797 (0.980) Remain 38:58:50 loss: 0.4010 Lr: 0.00499 [2024-02-17 21:08:25,487 INFO misc.py line 119 87073] Train: [9/100][45/1557] Data 0.005 (0.063) Batch 0.863 (0.977) Remain 38:52:11 loss: 0.7301 Lr: 0.00499 [2024-02-17 21:08:26,529 INFO misc.py line 119 87073] Train: [9/100][46/1557] Data 0.004 (0.062) Batch 1.032 (0.978) Remain 38:55:14 loss: 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Train: [9/100][693/1557] Data 0.003 (0.105) Batch 1.255 (1.110) Remain 43:57:56 loss: 0.3570 Lr: 0.00498 [2024-02-17 21:20:31,583 INFO misc.py line 119 87073] Train: [9/100][694/1557] Data 0.011 (0.104) Batch 1.007 (1.110) Remain 43:57:33 loss: 0.7629 Lr: 0.00498 [2024-02-17 21:20:32,450 INFO misc.py line 119 87073] Train: [9/100][695/1557] Data 0.017 (0.104) Batch 0.879 (1.110) Remain 43:56:45 loss: 1.0417 Lr: 0.00498 [2024-02-17 21:20:33,359 INFO misc.py line 119 87073] Train: [9/100][696/1557] Data 0.004 (0.104) Batch 0.910 (1.110) Remain 43:56:03 loss: 0.8073 Lr: 0.00498 [2024-02-17 21:20:34,498 INFO misc.py line 119 87073] Train: [9/100][697/1557] Data 0.004 (0.104) Batch 1.139 (1.110) Remain 43:56:08 loss: 0.4895 Lr: 0.00498 [2024-02-17 21:20:35,257 INFO misc.py line 119 87073] Train: [9/100][698/1557] Data 0.004 (0.104) Batch 0.759 (1.109) Remain 43:54:54 loss: 0.5742 Lr: 0.00498 [2024-02-17 21:20:35,991 INFO misc.py line 119 87073] Train: [9/100][699/1557] Data 0.004 (0.104) 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Train: [9/100][762/1557] Data 0.008 (0.104) Batch 0.792 (1.109) Remain 43:53:38 loss: 0.9322 Lr: 0.00498 [2024-02-17 21:21:47,349 INFO misc.py line 119 87073] Train: [9/100][763/1557] Data 0.004 (0.104) Batch 1.140 (1.109) Remain 43:53:42 loss: 0.4898 Lr: 0.00498 [2024-02-17 21:21:48,200 INFO misc.py line 119 87073] Train: [9/100][764/1557] Data 0.004 (0.103) Batch 0.851 (1.109) Remain 43:52:53 loss: 0.2897 Lr: 0.00498 [2024-02-17 21:21:49,125 INFO misc.py line 119 87073] Train: [9/100][765/1557] Data 0.005 (0.103) Batch 0.921 (1.108) Remain 43:52:17 loss: 0.9848 Lr: 0.00498 [2024-02-17 21:21:50,248 INFO misc.py line 119 87073] Train: [9/100][766/1557] Data 0.010 (0.103) Batch 1.111 (1.108) Remain 43:52:16 loss: 0.6780 Lr: 0.00498 [2024-02-17 21:21:51,216 INFO misc.py line 119 87073] Train: [9/100][767/1557] Data 0.021 (0.103) Batch 0.985 (1.108) Remain 43:51:52 loss: 0.6849 Lr: 0.00498 [2024-02-17 21:21:51,969 INFO misc.py line 119 87073] Train: [9/100][768/1557] Data 0.004 (0.103) 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43:35:59 loss: 0.8003 Lr: 0.00498 [2024-02-17 21:36:02,244 INFO misc.py line 119 87073] Train: [9/100][1536/1557] Data 0.004 (0.103) Batch 0.929 (1.107) Remain 43:35:41 loss: 0.9649 Lr: 0.00498 [2024-02-17 21:36:03,436 INFO misc.py line 119 87073] Train: [9/100][1537/1557] Data 0.005 (0.103) Batch 1.192 (1.108) Remain 43:35:48 loss: 0.4198 Lr: 0.00498 [2024-02-17 21:36:04,195 INFO misc.py line 119 87073] Train: [9/100][1538/1557] Data 0.004 (0.102) Batch 0.747 (1.107) Remain 43:35:13 loss: 0.5437 Lr: 0.00498 [2024-02-17 21:36:04,911 INFO misc.py line 119 87073] Train: [9/100][1539/1557] Data 0.016 (0.102) Batch 0.729 (1.107) Remain 43:34:37 loss: 0.7250 Lr: 0.00498 [2024-02-17 21:36:06,012 INFO misc.py line 119 87073] Train: [9/100][1540/1557] Data 0.004 (0.102) Batch 1.095 (1.107) Remain 43:34:35 loss: 0.2694 Lr: 0.00498 [2024-02-17 21:36:06,925 INFO misc.py line 119 87073] Train: [9/100][1541/1557] Data 0.010 (0.102) Batch 0.916 (1.107) Remain 43:34:16 loss: 1.1638 Lr: 0.00498 [2024-02-17 21:36:07,870 INFO misc.py line 119 87073] Train: [9/100][1542/1557] Data 0.007 (0.102) Batch 0.947 (1.107) Remain 43:34:00 loss: 0.5545 Lr: 0.00498 [2024-02-17 21:36:08,880 INFO misc.py line 119 87073] Train: [9/100][1543/1557] Data 0.005 (0.102) Batch 1.010 (1.107) Remain 43:33:50 loss: 0.7727 Lr: 0.00498 [2024-02-17 21:36:09,725 INFO misc.py line 119 87073] Train: [9/100][1544/1557] Data 0.005 (0.102) Batch 0.842 (1.107) Remain 43:33:25 loss: 0.7906 Lr: 0.00498 [2024-02-17 21:36:10,490 INFO misc.py line 119 87073] Train: [9/100][1545/1557] Data 0.007 (0.102) Batch 0.769 (1.106) Remain 43:32:53 loss: 0.4325 Lr: 0.00498 [2024-02-17 21:36:11,270 INFO misc.py line 119 87073] Train: [9/100][1546/1557] Data 0.004 (0.102) Batch 0.769 (1.106) Remain 43:32:21 loss: 0.4383 Lr: 0.00498 [2024-02-17 21:36:12,473 INFO misc.py line 119 87073] Train: [9/100][1547/1557] Data 0.015 (0.102) Batch 1.208 (1.106) Remain 43:32:29 loss: 0.5143 Lr: 0.00498 [2024-02-17 21:36:13,370 INFO misc.py line 119 87073] Train: [9/100][1548/1557] Data 0.010 (0.102) Batch 0.902 (1.106) Remain 43:32:09 loss: 0.3303 Lr: 0.00498 [2024-02-17 21:36:14,272 INFO misc.py line 119 87073] Train: [9/100][1549/1557] Data 0.004 (0.102) Batch 0.902 (1.106) Remain 43:31:49 loss: 0.9690 Lr: 0.00498 [2024-02-17 21:36:15,285 INFO misc.py line 119 87073] Train: [9/100][1550/1557] Data 0.005 (0.102) Batch 1.013 (1.106) Remain 43:31:40 loss: 0.4495 Lr: 0.00498 [2024-02-17 21:36:16,295 INFO misc.py line 119 87073] Train: [9/100][1551/1557] Data 0.004 (0.102) Batch 1.010 (1.106) Remain 43:31:30 loss: 0.5218 Lr: 0.00498 [2024-02-17 21:36:17,076 INFO misc.py line 119 87073] Train: [9/100][1552/1557] Data 0.004 (0.102) Batch 0.772 (1.106) Remain 43:30:58 loss: 0.6485 Lr: 0.00498 [2024-02-17 21:36:17,753 INFO misc.py line 119 87073] Train: [9/100][1553/1557] Data 0.014 (0.102) Batch 0.686 (1.105) Remain 43:30:19 loss: 0.4643 Lr: 0.00498 [2024-02-17 21:36:19,064 INFO misc.py line 119 87073] Train: [9/100][1554/1557] Data 0.004 (0.101) Batch 1.307 (1.105) Remain 43:30:36 loss: 0.2568 Lr: 0.00498 [2024-02-17 21:36:19,987 INFO misc.py line 119 87073] Train: [9/100][1555/1557] Data 0.008 (0.101) Batch 0.928 (1.105) Remain 43:30:19 loss: 0.6264 Lr: 0.00498 [2024-02-17 21:36:21,218 INFO misc.py line 119 87073] Train: [9/100][1556/1557] Data 0.004 (0.101) Batch 1.223 (1.105) Remain 43:30:28 loss: 0.9273 Lr: 0.00498 [2024-02-17 21:36:22,194 INFO misc.py line 119 87073] Train: [9/100][1557/1557] Data 0.013 (0.101) Batch 0.984 (1.105) Remain 43:30:16 loss: 0.5470 Lr: 0.00498 [2024-02-17 21:36:22,194 INFO misc.py line 136 87073] Train result: loss: 0.6341 [2024-02-17 21:36:22,194 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 21:36:50,290 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7856 [2024-02-17 21:36:51,069 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8773 [2024-02-17 21:36:53,198 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4837 [2024-02-17 21:36:55,406 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4484 [2024-02-17 21:37:00,362 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2863 [2024-02-17 21:37:01,059 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2338 [2024-02-17 21:37:02,320 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3988 [2024-02-17 21:37:05,274 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2965 [2024-02-17 21:37:07,084 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.4313 [2024-02-17 21:37:08,548 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6744/0.7453/0.9010. [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9298/0.9591 [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9800/0.9885 [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8468/0.9608 [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4093/0.5166 [2024-02-17 21:37:08,548 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6422/0.6602 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.5969/0.6385 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8138/0.9350 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9111/0.9521 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6406/0.6531 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7576/0.8796 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6640/0.8677 [2024-02-17 21:37:08,549 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5749/0.6783 [2024-02-17 21:37:08,549 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 21:37:08,553 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 21:37:08,553 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 21:37:16,320 INFO misc.py line 119 87073] Train: [10/100][1/1557] Data 1.411 (1.411) Batch 2.205 (2.205) Remain 86:48:07 loss: 0.3010 Lr: 0.00498 [2024-02-17 21:37:17,128 INFO misc.py line 119 87073] Train: [10/100][2/1557] Data 0.005 (0.005) Batch 0.803 (0.803) Remain 31:36:25 loss: 0.3467 Lr: 0.00498 [2024-02-17 21:37:18,044 INFO misc.py line 119 87073] Train: [10/100][3/1557] Data 0.010 (0.010) Batch 0.919 (0.919) Remain 36:11:05 loss: 0.5423 Lr: 0.00498 [2024-02-17 21:37:19,046 INFO misc.py line 119 87073] Train: [10/100][4/1557] Data 0.007 (0.007) Batch 1.005 (1.005) Remain 39:33:02 loss: 0.4972 Lr: 0.00498 [2024-02-17 21:37:19,876 INFO misc.py line 119 87073] Train: [10/100][5/1557] Data 0.004 (0.006) Batch 0.830 (0.917) Remain 36:05:59 loss: 0.4576 Lr: 0.00498 [2024-02-17 21:37:20,667 INFO misc.py line 119 87073] Train: [10/100][6/1557] Data 0.004 (0.005) Batch 0.791 (0.875) Remain 34:26:37 loss: 0.4970 Lr: 0.00498 [2024-02-17 21:37:21,684 INFO misc.py line 119 87073] Train: [10/100][7/1557] Data 0.004 (0.005) Batch 1.016 (0.910) Remain 35:49:59 loss: 0.3943 Lr: 0.00498 [2024-02-17 21:37:22,655 INFO misc.py line 119 87073] Train: [10/100][8/1557] Data 0.004 (0.005) Batch 0.970 (0.922) Remain 36:18:13 loss: 0.9770 Lr: 0.00498 [2024-02-17 21:37:23,651 INFO misc.py line 119 87073] Train: [10/100][9/1557] Data 0.005 (0.005) Batch 0.998 (0.935) Remain 36:47:50 loss: 0.9177 Lr: 0.00498 [2024-02-17 21:37:24,462 INFO misc.py line 119 87073] Train: [10/100][10/1557] Data 0.004 (0.005) Batch 0.810 (0.917) Remain 36:05:34 loss: 0.6022 Lr: 0.00498 [2024-02-17 21:37:25,473 INFO misc.py line 119 87073] Train: [10/100][11/1557] Data 0.005 (0.005) Batch 1.011 (0.929) Remain 36:33:12 loss: 0.6329 Lr: 0.00498 [2024-02-17 21:37:26,199 INFO misc.py line 119 87073] Train: [10/100][12/1557] Data 0.005 (0.005) Batch 0.726 (0.906) Remain 35:40:00 loss: 0.3670 Lr: 0.00498 [2024-02-17 21:37:26,929 INFO misc.py line 119 87073] Train: [10/100][13/1557] Data 0.004 (0.005) Batch 0.730 (0.889) Remain 34:58:21 loss: 1.1886 Lr: 0.00498 [2024-02-17 21:37:35,693 INFO misc.py line 119 87073] Train: [10/100][14/1557] Data 0.004 (0.005) Batch 8.764 (1.605) Remain 63:08:51 loss: 0.5898 Lr: 0.00498 [2024-02-17 21:37:36,610 INFO misc.py line 119 87073] Train: [10/100][15/1557] Data 0.004 (0.004) Batch 0.917 (1.547) Remain 60:53:33 loss: 0.4676 Lr: 0.00498 [2024-02-17 21:37:37,655 INFO misc.py line 119 87073] Train: [10/100][16/1557] Data 0.004 (0.004) Batch 1.045 (1.509) Remain 59:22:22 loss: 0.7614 Lr: 0.00498 [2024-02-17 21:37:38,562 INFO misc.py line 119 87073] Train: [10/100][17/1557] Data 0.004 (0.004) Batch 0.906 (1.466) Remain 57:40:44 loss: 0.3941 Lr: 0.00498 [2024-02-17 21:37:39,526 INFO misc.py line 119 87073] Train: [10/100][18/1557] Data 0.005 (0.004) Batch 0.965 (1.432) Remain 56:21:51 loss: 1.1467 Lr: 0.00498 [2024-02-17 21:37:40,387 INFO misc.py line 119 87073] Train: [10/100][19/1557] Data 0.003 (0.004) Batch 0.855 (1.396) Remain 54:56:42 loss: 0.3730 Lr: 0.00498 [2024-02-17 21:37:41,156 INFO misc.py line 119 87073] Train: [10/100][20/1557] Data 0.009 (0.005) Batch 0.774 (1.360) Remain 53:30:12 loss: 0.3651 Lr: 0.00498 [2024-02-17 21:37:42,301 INFO misc.py line 119 87073] Train: [10/100][21/1557] Data 0.005 (0.005) Batch 1.145 (1.348) Remain 53:01:59 loss: 0.6791 Lr: 0.00498 [2024-02-17 21:37:43,663 INFO misc.py line 119 87073] Train: [10/100][22/1557] Data 0.005 (0.005) Batch 1.343 (1.347) Remain 53:01:23 loss: 1.1029 Lr: 0.00498 [2024-02-17 21:37:44,608 INFO misc.py line 119 87073] Train: [10/100][23/1557] Data 0.024 (0.006) Batch 0.966 (1.328) Remain 52:16:18 loss: 0.2919 Lr: 0.00498 [2024-02-17 21:37:45,627 INFO misc.py line 119 87073] Train: [10/100][24/1557] Data 0.003 (0.006) Batch 1.017 (1.313) Remain 51:41:13 loss: 0.6596 Lr: 0.00498 [2024-02-17 21:37:46,674 INFO misc.py line 119 87073] Train: [10/100][25/1557] Data 0.006 (0.006) Batch 1.050 (1.302) Remain 51:12:53 loss: 0.2910 Lr: 0.00498 [2024-02-17 21:37:47,411 INFO misc.py line 119 87073] Train: [10/100][26/1557] Data 0.003 (0.005) Batch 0.736 (1.277) Remain 50:14:51 loss: 0.6580 Lr: 0.00498 [2024-02-17 21:37:48,158 INFO misc.py line 119 87073] Train: [10/100][27/1557] Data 0.004 (0.005) Batch 0.743 (1.255) Remain 49:22:18 loss: 0.4343 Lr: 0.00498 [2024-02-17 21:37:49,326 INFO misc.py line 119 87073] Train: [10/100][28/1557] Data 0.008 (0.005) Batch 1.164 (1.251) Remain 49:13:46 loss: 0.5472 Lr: 0.00498 [2024-02-17 21:37:50,290 INFO misc.py line 119 87073] Train: [10/100][29/1557] Data 0.011 (0.006) Batch 0.969 (1.240) Remain 48:48:06 loss: 0.4523 Lr: 0.00498 [2024-02-17 21:37:51,322 INFO misc.py line 119 87073] Train: [10/100][30/1557] Data 0.007 (0.006) Batch 1.034 (1.233) Remain 48:30:04 loss: 1.0987 Lr: 0.00498 [2024-02-17 21:37:52,203 INFO misc.py line 119 87073] Train: [10/100][31/1557] Data 0.004 (0.006) Batch 0.881 (1.220) Remain 48:00:22 loss: 1.0536 Lr: 0.00498 [2024-02-17 21:37:53,126 INFO misc.py line 119 87073] Train: [10/100][32/1557] Data 0.005 (0.006) Batch 0.910 (1.209) Remain 47:35:06 loss: 0.3568 Lr: 0.00498 [2024-02-17 21:37:53,891 INFO misc.py line 119 87073] Train: [10/100][33/1557] Data 0.019 (0.006) Batch 0.779 (1.195) Remain 47:01:14 loss: 0.5763 Lr: 0.00498 [2024-02-17 21:37:54,690 INFO misc.py line 119 87073] Train: [10/100][34/1557] Data 0.003 (0.006) Batch 0.797 (1.182) Remain 46:30:54 loss: 0.9601 Lr: 0.00498 [2024-02-17 21:37:55,926 INFO misc.py line 119 87073] Train: [10/100][35/1557] Data 0.005 (0.006) Batch 1.227 (1.184) Remain 46:34:10 loss: 0.4792 Lr: 0.00498 [2024-02-17 21:37:57,067 INFO misc.py line 119 87073] Train: [10/100][36/1557] Data 0.015 (0.006) Batch 1.147 (1.182) Remain 46:31:33 loss: 0.7895 Lr: 0.00498 [2024-02-17 21:37:58,071 INFO misc.py line 119 87073] Train: [10/100][37/1557] Data 0.008 (0.006) Batch 0.996 (1.177) Remain 46:18:35 loss: 0.6195 Lr: 0.00498 [2024-02-17 21:37:59,035 INFO misc.py line 119 87073] Train: [10/100][38/1557] Data 0.017 (0.007) Batch 0.977 (1.171) Remain 46:05:03 loss: 0.5831 Lr: 0.00498 [2024-02-17 21:37:59,907 INFO misc.py line 119 87073] Train: [10/100][39/1557] Data 0.005 (0.007) Batch 0.871 (1.163) Remain 45:45:22 loss: 0.3660 Lr: 0.00498 [2024-02-17 21:38:00,652 INFO misc.py line 119 87073] Train: [10/100][40/1557] Data 0.006 (0.007) Batch 0.744 (1.152) Remain 45:18:37 loss: 0.4674 Lr: 0.00498 [2024-02-17 21:38:01,397 INFO misc.py line 119 87073] Train: [10/100][41/1557] Data 0.006 (0.007) Batch 0.747 (1.141) Remain 44:53:29 loss: 0.7332 Lr: 0.00498 [2024-02-17 21:38:02,684 INFO misc.py line 119 87073] Train: [10/100][42/1557] Data 0.004 (0.006) Batch 1.275 (1.144) Remain 45:01:35 loss: 0.5369 Lr: 0.00498 [2024-02-17 21:38:03,725 INFO misc.py line 119 87073] Train: [10/100][43/1557] Data 0.015 (0.007) Batch 1.041 (1.142) Remain 44:55:27 loss: 0.6748 Lr: 0.00498 [2024-02-17 21:38:04,781 INFO misc.py line 119 87073] Train: [10/100][44/1557] Data 0.015 (0.007) Batch 1.067 (1.140) Remain 44:51:08 loss: 0.3960 Lr: 0.00498 [2024-02-17 21:38:05,780 INFO misc.py line 119 87073] Train: [10/100][45/1557] Data 0.005 (0.007) Batch 0.993 (1.136) Remain 44:42:51 loss: 0.5858 Lr: 0.00498 [2024-02-17 21:38:06,688 INFO misc.py line 119 87073] Train: [10/100][46/1557] Data 0.011 (0.007) Batch 0.915 (1.131) Remain 44:30:39 loss: 0.5961 Lr: 0.00498 [2024-02-17 21:38:07,396 INFO misc.py line 119 87073] Train: [10/100][47/1557] Data 0.005 (0.007) Batch 0.707 (1.122) Remain 44:07:51 loss: 0.4640 Lr: 0.00498 [2024-02-17 21:38:08,126 INFO misc.py line 119 87073] Train: [10/100][48/1557] Data 0.006 (0.007) Batch 0.728 (1.113) Remain 43:47:12 loss: 0.8374 Lr: 0.00498 [2024-02-17 21:38:09,407 INFO misc.py line 119 87073] Train: [10/100][49/1557] Data 0.006 (0.007) Batch 1.276 (1.116) Remain 43:55:32 loss: 0.4565 Lr: 0.00498 [2024-02-17 21:38:10,382 INFO misc.py line 119 87073] Train: [10/100][50/1557] Data 0.013 (0.007) Batch 0.984 (1.114) Remain 43:48:51 loss: 0.7260 Lr: 0.00498 [2024-02-17 21:38:11,608 INFO misc.py line 119 87073] Train: [10/100][51/1557] Data 0.004 (0.007) Batch 1.219 (1.116) Remain 43:54:00 loss: 0.9401 Lr: 0.00498 [2024-02-17 21:38:12,550 INFO misc.py line 119 87073] Train: [10/100][52/1557] Data 0.012 (0.007) Batch 0.948 (1.112) Remain 43:45:54 loss: 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INFO misc.py line 119 87073] Train: [10/100][59/1557] Data 0.004 (0.007) Batch 0.967 (1.093) Remain 42:59:30 loss: 0.6956 Lr: 0.00498 [2024-02-17 21:38:20,373 INFO misc.py line 119 87073] Train: [10/100][60/1557] Data 0.005 (0.007) Batch 1.135 (1.094) Remain 43:01:14 loss: 0.4001 Lr: 0.00498 [2024-02-17 21:38:21,162 INFO misc.py line 119 87073] Train: [10/100][61/1557] Data 0.003 (0.007) Batch 0.788 (1.088) Remain 42:48:47 loss: 0.8640 Lr: 0.00498 [2024-02-17 21:38:21,924 INFO misc.py line 119 87073] Train: [10/100][62/1557] Data 0.004 (0.007) Batch 0.755 (1.083) Remain 42:35:27 loss: 0.6809 Lr: 0.00498 [2024-02-17 21:38:29,009 INFO misc.py line 119 87073] Train: [10/100][63/1557] Data 4.372 (0.080) Batch 7.092 (1.183) Remain 46:31:51 loss: 0.6998 Lr: 0.00498 [2024-02-17 21:38:30,008 INFO misc.py line 119 87073] Train: [10/100][64/1557] Data 0.004 (0.078) Batch 0.988 (1.180) Remain 46:24:18 loss: 0.4933 Lr: 0.00498 [2024-02-17 21:38:31,010 INFO misc.py line 119 87073] Train: 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Batch 1.237 (1.243) Remain 48:49:57 loss: 0.9383 Lr: 0.00498 [2024-02-17 21:43:14,586 INFO misc.py line 119 87073] Train: [10/100][290/1557] Data 0.014 (0.099) Batch 0.965 (1.242) Remain 48:47:39 loss: 0.5846 Lr: 0.00498 [2024-02-17 21:43:15,622 INFO misc.py line 119 87073] Train: [10/100][291/1557] Data 0.004 (0.099) Batch 1.036 (1.242) Remain 48:45:56 loss: 0.7756 Lr: 0.00498 [2024-02-17 21:43:16,401 INFO misc.py line 119 87073] Train: [10/100][292/1557] Data 0.005 (0.098) Batch 0.779 (1.240) Remain 48:42:09 loss: 0.4519 Lr: 0.00498 [2024-02-17 21:43:17,227 INFO misc.py line 119 87073] Train: [10/100][293/1557] Data 0.004 (0.098) Batch 0.813 (1.239) Remain 48:38:39 loss: 0.7673 Lr: 0.00498 [2024-02-17 21:43:29,765 INFO misc.py line 119 87073] Train: [10/100][294/1557] Data 0.017 (0.098) Batch 12.551 (1.277) Remain 50:10:15 loss: 0.4630 Lr: 0.00498 [2024-02-17 21:43:30,683 INFO misc.py line 119 87073] Train: [10/100][295/1557] Data 0.005 (0.097) Batch 0.919 (1.276) Remain 50:07:20 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line 119 87073] Train: [10/100][333/1557] Data 0.005 (0.087) Batch 0.990 (1.238) Remain 48:36:09 loss: 0.5559 Lr: 0.00498 [2024-02-17 21:44:07,243 INFO misc.py line 119 87073] Train: [10/100][334/1557] Data 0.005 (0.087) Batch 0.723 (1.236) Remain 48:32:28 loss: 0.4968 Lr: 0.00498 [2024-02-17 21:44:07,984 INFO misc.py line 119 87073] Train: [10/100][335/1557] Data 0.003 (0.086) Batch 0.739 (1.235) Remain 48:28:55 loss: 0.5715 Lr: 0.00498 [2024-02-17 21:44:09,205 INFO misc.py line 119 87073] Train: [10/100][336/1557] Data 0.005 (0.086) Batch 1.221 (1.235) Remain 48:28:48 loss: 0.2863 Lr: 0.00498 [2024-02-17 21:44:10,187 INFO misc.py line 119 87073] Train: [10/100][337/1557] Data 0.006 (0.086) Batch 0.983 (1.234) Remain 48:27:00 loss: 0.9809 Lr: 0.00498 [2024-02-17 21:44:11,321 INFO misc.py line 119 87073] Train: [10/100][338/1557] Data 0.004 (0.086) Batch 1.135 (1.234) Remain 48:26:17 loss: 1.0066 Lr: 0.00498 [2024-02-17 21:44:12,197 INFO misc.py line 119 87073] Train: 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loss: 0.5557 Lr: 0.00498 [2024-02-17 21:44:38,866 INFO misc.py line 119 87073] Train: [10/100][352/1557] Data 0.003 (0.097) Batch 0.894 (1.263) Remain 49:35:20 loss: 0.6533 Lr: 0.00498 [2024-02-17 21:44:39,686 INFO misc.py line 119 87073] Train: [10/100][353/1557] Data 0.006 (0.096) Batch 0.822 (1.262) Remain 49:32:20 loss: 0.5776 Lr: 0.00498 [2024-02-17 21:44:40,699 INFO misc.py line 119 87073] Train: [10/100][354/1557] Data 0.004 (0.096) Batch 1.012 (1.261) Remain 49:30:38 loss: 0.8780 Lr: 0.00498 [2024-02-17 21:44:43,230 INFO misc.py line 119 87073] Train: [10/100][355/1557] Data 1.198 (0.099) Batch 2.532 (1.265) Remain 49:39:07 loss: 0.4098 Lr: 0.00498 [2024-02-17 21:44:44,016 INFO misc.py line 119 87073] Train: [10/100][356/1557] Data 0.003 (0.099) Batch 0.775 (1.263) Remain 49:35:50 loss: 0.6958 Lr: 0.00498 [2024-02-17 21:44:45,245 INFO misc.py line 119 87073] Train: [10/100][357/1557] Data 0.015 (0.099) Batch 1.231 (1.263) Remain 49:35:36 loss: 0.5042 Lr: 0.00498 [2024-02-17 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[2024-02-17 21:45:09,408 INFO misc.py line 119 87073] Train: [10/100][383/1557] Data 0.004 (0.092) Batch 0.773 (1.240) Remain 48:41:18 loss: 0.2920 Lr: 0.00498 [2024-02-17 21:45:10,113 INFO misc.py line 119 87073] Train: [10/100][384/1557] Data 0.005 (0.092) Batch 0.702 (1.239) Remain 48:37:57 loss: 0.7378 Lr: 0.00498 [2024-02-17 21:45:11,377 INFO misc.py line 119 87073] Train: [10/100][385/1557] Data 0.007 (0.092) Batch 1.268 (1.239) Remain 48:38:06 loss: 0.9176 Lr: 0.00498 [2024-02-17 21:45:12,360 INFO misc.py line 119 87073] Train: [10/100][386/1557] Data 0.003 (0.092) Batch 0.984 (1.238) Remain 48:36:31 loss: 0.8117 Lr: 0.00498 [2024-02-17 21:45:13,601 INFO misc.py line 119 87073] Train: [10/100][387/1557] Data 0.004 (0.091) Batch 1.231 (1.238) Remain 48:36:27 loss: 0.6649 Lr: 0.00498 [2024-02-17 21:45:14,564 INFO misc.py line 119 87073] Train: [10/100][388/1557] Data 0.013 (0.091) Batch 0.972 (1.238) Remain 48:34:48 loss: 0.6141 Lr: 0.00498 [2024-02-17 21:45:15,426 INFO misc.py 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Batch 0.957 (1.239) Remain 48:37:38 loss: 0.4759 Lr: 0.00498 [2024-02-17 21:45:32,233 INFO misc.py line 119 87073] Train: [10/100][402/1557] Data 0.007 (0.101) Batch 1.054 (1.239) Remain 48:36:31 loss: 0.3432 Lr: 0.00498 [2024-02-17 21:45:33,296 INFO misc.py line 119 87073] Train: [10/100][403/1557] Data 0.005 (0.100) Batch 1.065 (1.238) Remain 48:35:28 loss: 0.7762 Lr: 0.00498 [2024-02-17 21:45:34,187 INFO misc.py line 119 87073] Train: [10/100][404/1557] Data 0.003 (0.100) Batch 0.889 (1.237) Remain 48:33:24 loss: 0.4231 Lr: 0.00498 [2024-02-17 21:45:34,925 INFO misc.py line 119 87073] Train: [10/100][405/1557] Data 0.005 (0.100) Batch 0.738 (1.236) Remain 48:30:28 loss: 0.4175 Lr: 0.00498 [2024-02-17 21:45:49,371 INFO misc.py line 119 87073] Train: [10/100][406/1557] Data 0.004 (0.100) Batch 14.447 (1.269) Remain 49:47:38 loss: 0.3129 Lr: 0.00498 [2024-02-17 21:45:50,250 INFO misc.py line 119 87073] Train: [10/100][407/1557] Data 0.003 (0.099) Batch 0.880 (1.268) Remain 49:45:20 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Remain 49:34:46 loss: 0.3689 Lr: 0.00497 [2024-02-17 22:04:10,541 INFO misc.py line 119 87073] Train: [10/100][1272/1557] Data 0.013 (0.102) Batch 0.711 (1.271) Remain 49:33:42 loss: 0.3875 Lr: 0.00497 [2024-02-17 22:04:11,316 INFO misc.py line 119 87073] Train: [10/100][1273/1557] Data 0.004 (0.101) Batch 0.773 (1.270) Remain 49:32:46 loss: 0.6942 Lr: 0.00497 [2024-02-17 22:04:12,615 INFO misc.py line 119 87073] Train: [10/100][1274/1557] Data 0.005 (0.101) Batch 1.292 (1.270) Remain 49:32:47 loss: 0.3662 Lr: 0.00497 [2024-02-17 22:04:13,594 INFO misc.py line 119 87073] Train: [10/100][1275/1557] Data 0.013 (0.101) Batch 0.987 (1.270) Remain 49:32:15 loss: 0.7430 Lr: 0.00497 [2024-02-17 22:04:14,628 INFO misc.py line 119 87073] Train: [10/100][1276/1557] Data 0.004 (0.101) Batch 1.033 (1.270) Remain 49:31:47 loss: 0.5046 Lr: 0.00497 [2024-02-17 22:04:15,770 INFO misc.py line 119 87073] Train: [10/100][1277/1557] Data 0.005 (0.101) Batch 1.143 (1.270) Remain 49:31:32 loss: 0.3095 Lr: 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Remain 49:39:39 loss: 0.5325 Lr: 0.00497 [2024-02-17 22:06:13,242 INFO misc.py line 119 87073] Train: [10/100][1365/1557] Data 0.004 (0.102) Batch 1.208 (1.274) Remain 49:39:31 loss: 0.5044 Lr: 0.00497 [2024-02-17 22:06:14,307 INFO misc.py line 119 87073] Train: [10/100][1366/1557] Data 0.004 (0.102) Batch 1.065 (1.274) Remain 49:39:08 loss: 0.7085 Lr: 0.00497 [2024-02-17 22:06:15,154 INFO misc.py line 119 87073] Train: [10/100][1367/1557] Data 0.004 (0.102) Batch 0.848 (1.274) Remain 49:38:23 loss: 1.1450 Lr: 0.00497 [2024-02-17 22:06:16,104 INFO misc.py line 119 87073] Train: [10/100][1368/1557] Data 0.004 (0.102) Batch 0.940 (1.273) Remain 49:37:47 loss: 0.6460 Lr: 0.00497 [2024-02-17 22:06:17,033 INFO misc.py line 119 87073] Train: [10/100][1369/1557] Data 0.014 (0.102) Batch 0.939 (1.273) Remain 49:37:12 loss: 0.5910 Lr: 0.00497 [2024-02-17 22:06:17,774 INFO misc.py line 119 87073] Train: [10/100][1370/1557] Data 0.004 (0.102) Batch 0.741 (1.273) Remain 49:36:16 loss: 0.6764 Lr: 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Train: [10/100][1383/1557] Data 0.005 (0.101) Batch 0.839 (1.269) Remain 49:28:14 loss: 1.0023 Lr: 0.00497 [2024-02-17 22:06:30,514 INFO misc.py line 119 87073] Train: [10/100][1384/1557] Data 0.005 (0.101) Batch 0.774 (1.269) Remain 49:27:22 loss: 0.6809 Lr: 0.00497 [2024-02-17 22:06:31,292 INFO misc.py line 119 87073] Train: [10/100][1385/1557] Data 0.005 (0.101) Batch 0.779 (1.269) Remain 49:26:31 loss: 0.5601 Lr: 0.00497 [2024-02-17 22:06:32,571 INFO misc.py line 119 87073] Train: [10/100][1386/1557] Data 0.004 (0.101) Batch 1.274 (1.269) Remain 49:26:30 loss: 0.4916 Lr: 0.00497 [2024-02-17 22:06:33,746 INFO misc.py line 119 87073] Train: [10/100][1387/1557] Data 0.010 (0.101) Batch 1.166 (1.269) Remain 49:26:18 loss: 0.5588 Lr: 0.00497 [2024-02-17 22:06:34,686 INFO misc.py line 119 87073] Train: [10/100][1388/1557] Data 0.018 (0.101) Batch 0.954 (1.268) Remain 49:25:45 loss: 0.5846 Lr: 0.00497 [2024-02-17 22:06:35,734 INFO misc.py line 119 87073] Train: [10/100][1389/1557] Data 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Remain 49:35:43 loss: 0.5562 Lr: 0.00497 [2024-02-17 22:07:30,155 INFO misc.py line 119 87073] Train: [10/100][1427/1557] Data 0.004 (0.103) Batch 0.715 (1.273) Remain 49:34:47 loss: 0.4673 Lr: 0.00497 [2024-02-17 22:07:31,279 INFO misc.py line 119 87073] Train: [10/100][1428/1557] Data 0.005 (0.103) Batch 1.118 (1.272) Remain 49:34:31 loss: 0.4579 Lr: 0.00497 [2024-02-17 22:07:32,381 INFO misc.py line 119 87073] Train: [10/100][1429/1557] Data 0.011 (0.103) Batch 1.107 (1.272) Remain 49:34:13 loss: 0.4782 Lr: 0.00497 [2024-02-17 22:07:33,151 INFO misc.py line 119 87073] Train: [10/100][1430/1557] Data 0.005 (0.102) Batch 0.772 (1.272) Remain 49:33:23 loss: 0.5260 Lr: 0.00497 [2024-02-17 22:07:33,915 INFO misc.py line 119 87073] Train: [10/100][1431/1557] Data 0.004 (0.102) Batch 0.764 (1.272) Remain 49:32:32 loss: 0.8644 Lr: 0.00497 [2024-02-17 22:07:34,971 INFO misc.py line 119 87073] Train: [10/100][1432/1557] Data 0.004 (0.102) Batch 1.054 (1.271) Remain 49:32:09 loss: 0.5697 Lr: 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Remain 49:38:07 loss: 0.4906 Lr: 0.00497 [2024-02-17 22:08:51,500 INFO misc.py line 119 87073] Train: [10/100][1489/1557] Data 0.009 (0.102) Batch 0.786 (1.274) Remain 49:37:19 loss: 0.6782 Lr: 0.00497 [2024-02-17 22:08:52,281 INFO misc.py line 119 87073] Train: [10/100][1490/1557] Data 0.004 (0.102) Batch 0.781 (1.274) Remain 49:36:31 loss: 0.4018 Lr: 0.00497 [2024-02-17 22:08:53,583 INFO misc.py line 119 87073] Train: [10/100][1491/1557] Data 0.004 (0.102) Batch 1.301 (1.274) Remain 49:36:33 loss: 0.1626 Lr: 0.00497 [2024-02-17 22:08:54,528 INFO misc.py line 119 87073] Train: [10/100][1492/1557] Data 0.006 (0.102) Batch 0.945 (1.274) Remain 49:36:01 loss: 0.9499 Lr: 0.00497 [2024-02-17 22:08:55,695 INFO misc.py line 119 87073] Train: [10/100][1493/1557] Data 0.005 (0.102) Batch 1.168 (1.274) Remain 49:35:49 loss: 0.4540 Lr: 0.00497 [2024-02-17 22:08:56,689 INFO misc.py line 119 87073] Train: [10/100][1494/1557] Data 0.004 (0.102) Batch 0.994 (1.273) Remain 49:35:22 loss: 0.7416 Lr: 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0.013 (0.102) Batch 1.082 (1.274) Remain 49:36:10 loss: 0.6945 Lr: 0.00497 [2024-02-17 22:10:02,376 INFO misc.py line 119 87073] Train: [10/100][1545/1557] Data 0.015 (0.102) Batch 0.787 (1.274) Remain 49:35:24 loss: 0.5715 Lr: 0.00497 [2024-02-17 22:10:03,040 INFO misc.py line 119 87073] Train: [10/100][1546/1557] Data 0.005 (0.102) Batch 0.658 (1.273) Remain 49:34:27 loss: 0.3721 Lr: 0.00497 [2024-02-17 22:10:04,287 INFO misc.py line 119 87073] Train: [10/100][1547/1557] Data 0.013 (0.102) Batch 1.248 (1.273) Remain 49:34:23 loss: 0.2396 Lr: 0.00497 [2024-02-17 22:10:05,153 INFO misc.py line 119 87073] Train: [10/100][1548/1557] Data 0.012 (0.102) Batch 0.872 (1.273) Remain 49:33:46 loss: 1.1357 Lr: 0.00497 [2024-02-17 22:10:06,026 INFO misc.py line 119 87073] Train: [10/100][1549/1557] Data 0.005 (0.102) Batch 0.871 (1.273) Remain 49:33:08 loss: 0.5079 Lr: 0.00497 [2024-02-17 22:10:06,865 INFO misc.py line 119 87073] Train: [10/100][1550/1557] Data 0.007 (0.102) Batch 0.839 (1.273) Remain 49:32:27 loss: 0.6735 Lr: 0.00497 [2024-02-17 22:10:08,008 INFO misc.py line 119 87073] Train: [10/100][1551/1557] Data 0.007 (0.101) Batch 1.143 (1.273) Remain 49:32:14 loss: 0.7372 Lr: 0.00497 [2024-02-17 22:10:08,790 INFO misc.py line 119 87073] Train: [10/100][1552/1557] Data 0.007 (0.101) Batch 0.786 (1.272) Remain 49:31:29 loss: 1.0031 Lr: 0.00497 [2024-02-17 22:10:09,532 INFO misc.py line 119 87073] Train: [10/100][1553/1557] Data 0.004 (0.101) Batch 0.733 (1.272) Remain 49:30:39 loss: 0.3970 Lr: 0.00497 [2024-02-17 22:10:10,768 INFO misc.py line 119 87073] Train: [10/100][1554/1557] Data 0.013 (0.101) Batch 1.237 (1.272) Remain 49:30:35 loss: 0.2976 Lr: 0.00497 [2024-02-17 22:10:11,775 INFO misc.py line 119 87073] Train: [10/100][1555/1557] Data 0.012 (0.101) Batch 1.014 (1.272) Remain 49:30:10 loss: 0.5607 Lr: 0.00497 [2024-02-17 22:10:12,712 INFO misc.py line 119 87073] Train: [10/100][1556/1557] Data 0.005 (0.101) Batch 0.938 (1.272) Remain 49:29:39 loss: 0.5263 Lr: 0.00497 [2024-02-17 22:10:13,730 INFO misc.py line 119 87073] Train: [10/100][1557/1557] Data 0.004 (0.101) Batch 1.018 (1.271) Remain 49:29:14 loss: 0.4712 Lr: 0.00497 [2024-02-17 22:10:13,730 INFO misc.py line 136 87073] Train result: loss: 0.6158 [2024-02-17 22:10:13,731 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 22:10:43,914 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8692 [2024-02-17 22:10:44,693 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8993 [2024-02-17 22:10:46,821 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5134 [2024-02-17 22:10:49,028 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4067 [2024-02-17 22:10:53,973 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3439 [2024-02-17 22:10:54,670 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1471 [2024-02-17 22:10:55,931 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4771 [2024-02-17 22:10:58,887 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3637 [2024-02-17 22:11:00,698 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3441 [2024-02-17 22:11:02,412 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6850/0.7464/0.8987. [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9298/0.9584 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9800/0.9864 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8288/0.9817 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3091/0.3313 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5756/0.5810 [2024-02-17 22:11:02,413 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6759/0.8078 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8283/0.8972 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8941/0.9655 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8079/0.8984 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7576/0.8521 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7481/0.7976 [2024-02-17 22:11:02,414 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5692/0.6463 [2024-02-17 22:11:02,414 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 22:11:02,418 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 22:11:02,418 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 22:11:10,857 INFO misc.py line 119 87073] Train: [11/100][1/1557] Data 1.434 (1.434) Batch 2.409 (2.409) Remain 93:45:50 loss: 0.4282 Lr: 0.00497 [2024-02-17 22:11:11,787 INFO misc.py line 119 87073] Train: [11/100][2/1557] Data 0.009 (0.009) Batch 0.926 (0.926) Remain 36:01:40 loss: 0.9087 Lr: 0.00497 [2024-02-17 22:11:12,746 INFO misc.py line 119 87073] Train: [11/100][3/1557] Data 0.013 (0.013) Batch 0.965 (0.965) Remain 37:34:15 loss: 0.6471 Lr: 0.00497 [2024-02-17 22:11:13,734 INFO misc.py line 119 87073] Train: [11/100][4/1557] Data 0.006 (0.006) Batch 0.989 (0.989) Remain 38:30:48 loss: 0.5317 Lr: 0.00497 [2024-02-17 22:11:14,525 INFO misc.py line 119 87073] Train: [11/100][5/1557] Data 0.004 (0.005) Batch 0.789 (0.889) Remain 34:36:57 loss: 0.3967 Lr: 0.00497 [2024-02-17 22:11:15,293 INFO misc.py line 119 87073] Train: [11/100][6/1557] Data 0.005 (0.005) Batch 0.769 (0.849) Remain 33:03:05 loss: 0.5374 Lr: 0.00497 [2024-02-17 22:11:16,740 INFO misc.py line 119 87073] Train: [11/100][7/1557] Data 0.314 (0.082) Batch 1.447 (0.999) Remain 38:51:59 loss: 0.3501 Lr: 0.00497 [2024-02-17 22:11:17,635 INFO misc.py line 119 87073] Train: [11/100][8/1557] Data 0.005 (0.067) Batch 0.894 (0.978) Remain 38:03:10 loss: 0.3599 Lr: 0.00497 [2024-02-17 22:11:18,693 INFO misc.py line 119 87073] Train: [11/100][9/1557] Data 0.006 (0.057) Batch 1.060 (0.991) Remain 38:35:19 loss: 0.7638 Lr: 0.00497 [2024-02-17 22:11:19,546 INFO misc.py line 119 87073] Train: [11/100][10/1557] Data 0.004 (0.049) Batch 0.852 (0.971) Remain 37:48:42 loss: 0.5119 Lr: 0.00497 [2024-02-17 22:11:20,730 INFO misc.py line 119 87073] Train: [11/100][11/1557] Data 0.006 (0.044) Batch 1.176 (0.997) Remain 38:48:24 loss: 0.8237 Lr: 0.00497 [2024-02-17 22:11:21,495 INFO misc.py line 119 87073] Train: [11/100][12/1557] Data 0.013 (0.040) Batch 0.775 (0.972) Remain 37:50:43 loss: 0.7254 Lr: 0.00497 [2024-02-17 22:11:22,276 INFO misc.py line 119 87073] Train: [11/100][13/1557] Data 0.003 (0.037) Batch 0.771 (0.952) Remain 37:03:39 loss: 0.4646 Lr: 0.00497 [2024-02-17 22:11:25,778 INFO misc.py line 119 87073] Train: [11/100][14/1557] Data 2.224 (0.235) Batch 3.512 (1.185) Remain 46:06:58 loss: 0.1762 Lr: 0.00497 [2024-02-17 22:11:26,699 INFO misc.py line 119 87073] Train: [11/100][15/1557] Data 0.004 (0.216) Batch 0.920 (1.163) Remain 45:15:27 loss: 0.6366 Lr: 0.00497 [2024-02-17 22:11:27,644 INFO misc.py line 119 87073] Train: [11/100][16/1557] Data 0.005 (0.200) Batch 0.946 (1.146) Remain 44:36:25 loss: 1.0537 Lr: 0.00497 [2024-02-17 22:11:28,596 INFO misc.py line 119 87073] Train: [11/100][17/1557] Data 0.004 (0.186) Batch 0.944 (1.132) Remain 44:02:37 loss: 1.2447 Lr: 0.00497 [2024-02-17 22:11:29,540 INFO misc.py line 119 87073] Train: [11/100][18/1557] Data 0.013 (0.174) Batch 0.952 (1.120) Remain 43:34:38 loss: 0.8919 Lr: 0.00497 [2024-02-17 22:11:30,345 INFO misc.py line 119 87073] Train: [11/100][19/1557] Data 0.003 (0.164) Batch 0.805 (1.100) Remain 42:48:40 loss: 0.4119 Lr: 0.00497 [2024-02-17 22:11:31,088 INFO misc.py line 119 87073] Train: [11/100][20/1557] Data 0.004 (0.154) Batch 0.729 (1.078) Remain 41:57:44 loss: 0.5033 Lr: 0.00497 [2024-02-17 22:11:32,407 INFO misc.py line 119 87073] Train: [11/100][21/1557] Data 0.017 (0.147) Batch 1.322 (1.092) Remain 42:29:24 loss: 0.4913 Lr: 0.00497 [2024-02-17 22:11:33,351 INFO misc.py line 119 87073] Train: [11/100][22/1557] Data 0.015 (0.140) Batch 0.955 (1.085) Remain 42:12:32 loss: 0.8482 Lr: 0.00497 [2024-02-17 22:11:34,170 INFO misc.py line 119 87073] Train: [11/100][23/1557] Data 0.004 (0.133) Batch 0.818 (1.071) Remain 41:41:24 loss: 0.6298 Lr: 0.00497 [2024-02-17 22:11:35,022 INFO misc.py line 119 87073] Train: [11/100][24/1557] Data 0.005 (0.127) Batch 0.850 (1.061) Remain 41:16:46 loss: 0.5652 Lr: 0.00497 [2024-02-17 22:11:36,170 INFO misc.py line 119 87073] Train: [11/100][25/1557] Data 0.007 (0.121) Batch 1.145 (1.065) Remain 41:25:44 loss: 0.5206 Lr: 0.00497 [2024-02-17 22:11:36,945 INFO misc.py line 119 87073] Train: [11/100][26/1557] Data 0.010 (0.116) Batch 0.779 (1.052) Remain 40:56:46 loss: 0.4067 Lr: 0.00497 [2024-02-17 22:11:37,716 INFO misc.py line 119 87073] Train: [11/100][27/1557] Data 0.004 (0.112) Batch 0.767 (1.040) Remain 40:29:00 loss: 0.8413 Lr: 0.00497 [2024-02-17 22:11:39,022 INFO misc.py line 119 87073] Train: [11/100][28/1557] Data 0.009 (0.108) Batch 1.302 (1.051) Remain 40:53:27 loss: 0.1903 Lr: 0.00497 [2024-02-17 22:11:40,003 INFO misc.py line 119 87073] Train: [11/100][29/1557] Data 0.013 (0.104) Batch 0.990 (1.048) Remain 40:48:01 loss: 0.4514 Lr: 0.00497 [2024-02-17 22:11:40,898 INFO misc.py line 119 87073] Train: [11/100][30/1557] Data 0.004 (0.100) Batch 0.894 (1.043) Remain 40:34:38 loss: 0.4308 Lr: 0.00497 [2024-02-17 22:11:41,794 INFO misc.py line 119 87073] Train: [11/100][31/1557] Data 0.006 (0.097) Batch 0.896 (1.037) Remain 40:22:22 loss: 0.7284 Lr: 0.00497 [2024-02-17 22:11:42,775 INFO misc.py line 119 87073] Train: [11/100][32/1557] Data 0.005 (0.094) Batch 0.982 (1.036) Remain 40:17:52 loss: 0.6762 Lr: 0.00497 [2024-02-17 22:11:43,542 INFO misc.py line 119 87073] Train: [11/100][33/1557] Data 0.004 (0.091) Batch 0.766 (1.027) Remain 39:56:51 loss: 0.9798 Lr: 0.00497 [2024-02-17 22:11:44,303 INFO misc.py line 119 87073] Train: [11/100][34/1557] Data 0.005 (0.088) Batch 0.752 (1.018) Remain 39:36:11 loss: 0.5862 Lr: 0.00497 [2024-02-17 22:11:45,521 INFO misc.py line 119 87073] Train: [11/100][35/1557] Data 0.014 (0.086) Batch 1.217 (1.024) Remain 39:50:43 loss: 0.2134 Lr: 0.00497 [2024-02-17 22:11:46,352 INFO misc.py line 119 87073] Train: [11/100][36/1557] Data 0.014 (0.084) Batch 0.842 (1.018) Remain 39:37:50 loss: 0.4962 Lr: 0.00497 [2024-02-17 22:11:47,638 INFO misc.py line 119 87073] Train: [11/100][37/1557] Data 0.004 (0.081) Batch 1.281 (1.026) Remain 39:55:53 loss: 0.7962 Lr: 0.00497 [2024-02-17 22:11:48,628 INFO misc.py line 119 87073] Train: [11/100][38/1557] Data 0.009 (0.079) Batch 0.995 (1.025) Remain 39:53:46 loss: 0.8736 Lr: 0.00497 [2024-02-17 22:11:49,438 INFO misc.py line 119 87073] Train: [11/100][39/1557] Data 0.004 (0.077) Batch 0.807 (1.019) Remain 39:39:38 loss: 0.4821 Lr: 0.00497 [2024-02-17 22:11:50,141 INFO misc.py line 119 87073] Train: [11/100][40/1557] Data 0.007 (0.075) Batch 0.701 (1.011) Remain 39:19:32 loss: 0.9048 Lr: 0.00497 [2024-02-17 22:11:50,900 INFO misc.py line 119 87073] Train: [11/100][41/1557] Data 0.008 (0.073) Batch 0.759 (1.004) Remain 39:04:03 loss: 0.4698 Lr: 0.00497 [2024-02-17 22:11:52,069 INFO misc.py line 119 87073] Train: [11/100][42/1557] Data 0.008 (0.072) Batch 1.171 (1.008) Remain 39:14:02 loss: 0.2918 Lr: 0.00497 [2024-02-17 22:11:53,014 INFO misc.py line 119 87073] Train: [11/100][43/1557] Data 0.006 (0.070) Batch 0.948 (1.007) Remain 39:10:30 loss: 0.6797 Lr: 0.00497 [2024-02-17 22:11:53,971 INFO misc.py line 119 87073] Train: [11/100][44/1557] Data 0.003 (0.068) Batch 0.957 (1.006) Remain 39:07:37 loss: 0.8718 Lr: 0.00497 [2024-02-17 22:11:54,918 INFO misc.py line 119 87073] Train: [11/100][45/1557] Data 0.004 (0.067) Batch 0.943 (1.004) Remain 39:04:08 loss: 0.9953 Lr: 0.00497 [2024-02-17 22:11:56,008 INFO misc.py line 119 87073] Train: [11/100][46/1557] Data 0.008 (0.066) Batch 1.088 (1.006) Remain 39:08:40 loss: 1.8955 Lr: 0.00497 [2024-02-17 22:11:56,730 INFO misc.py line 119 87073] Train: [11/100][47/1557] Data 0.010 (0.064) Batch 0.727 (1.000) Remain 38:53:53 loss: 1.0036 Lr: 0.00497 [2024-02-17 22:11:57,521 INFO misc.py line 119 87073] Train: [11/100][48/1557] Data 0.004 (0.063) Batch 0.780 (0.995) Remain 38:42:27 loss: 0.5444 Lr: 0.00497 [2024-02-17 22:11:58,837 INFO misc.py line 119 87073] Train: [11/100][49/1557] Data 0.015 (0.062) Batch 1.322 (1.002) Remain 38:59:03 loss: 0.3757 Lr: 0.00497 [2024-02-17 22:11:59,672 INFO misc.py line 119 87073] Train: [11/100][50/1557] Data 0.009 (0.061) Batch 0.841 (0.998) Remain 38:51:04 loss: 0.8399 Lr: 0.00497 [2024-02-17 22:12:00,870 INFO misc.py line 119 87073] Train: [11/100][51/1557] Data 0.003 (0.060) Batch 1.187 (1.002) Remain 39:00:14 loss: 0.6637 Lr: 0.00497 [2024-02-17 22:12:01,797 INFO misc.py line 119 87073] Train: [11/100][52/1557] Data 0.014 (0.059) Batch 0.936 (1.001) Remain 38:57:04 loss: 0.7668 Lr: 0.00497 [2024-02-17 22:12:02,691 INFO misc.py line 119 87073] Train: [11/100][53/1557] Data 0.004 (0.058) Batch 0.895 (0.999) Remain 38:52:05 loss: 0.7231 Lr: 0.00497 [2024-02-17 22:12:03,443 INFO misc.py line 119 87073] Train: [11/100][54/1557] Data 0.004 (0.057) Batch 0.748 (0.994) Remain 38:40:34 loss: 0.9902 Lr: 0.00497 [2024-02-17 22:12:04,153 INFO misc.py line 119 87073] Train: [11/100][55/1557] Data 0.008 (0.056) Batch 0.714 (0.989) Remain 38:28:00 loss: 0.4015 Lr: 0.00497 [2024-02-17 22:12:05,208 INFO misc.py line 119 87073] Train: [11/100][56/1557] Data 0.004 (0.055) Batch 1.055 (0.990) Remain 38:30:55 loss: 0.4704 Lr: 0.00497 [2024-02-17 22:12:06,021 INFO misc.py line 119 87073] Train: [11/100][57/1557] Data 0.004 (0.054) Batch 0.807 (0.986) Remain 38:22:58 loss: 0.4935 Lr: 0.00497 [2024-02-17 22:12:07,025 INFO misc.py line 119 87073] Train: [11/100][58/1557] Data 0.010 (0.053) Batch 1.006 (0.987) Remain 38:23:48 loss: 0.4104 Lr: 0.00497 [2024-02-17 22:12:08,021 INFO misc.py line 119 87073] Train: [11/100][59/1557] Data 0.008 (0.052) Batch 0.998 (0.987) Remain 38:24:14 loss: 0.6272 Lr: 0.00497 [2024-02-17 22:12:08,868 INFO misc.py line 119 87073] Train: [11/100][60/1557] Data 0.006 (0.051) Batch 0.849 (0.985) Remain 38:18:34 loss: 0.4838 Lr: 0.00497 [2024-02-17 22:12:09,617 INFO misc.py line 119 87073] Train: [11/100][61/1557] Data 0.004 (0.050) Batch 0.747 (0.981) Remain 38:09:00 loss: 0.6280 Lr: 0.00497 [2024-02-17 22:12:10,389 INFO misc.py line 119 87073] Train: [11/100][62/1557] Data 0.006 (0.050) Batch 0.769 (0.977) Remain 38:00:38 loss: 0.5857 Lr: 0.00497 [2024-02-17 22:12:19,724 INFO misc.py line 119 87073] Train: [11/100][63/1557] Data 7.973 (0.182) Batch 9.333 (1.116) Remain 43:25:44 loss: 0.4042 Lr: 0.00497 [2024-02-17 22:12:20,689 INFO misc.py line 119 87073] Train: [11/100][64/1557] Data 0.011 (0.179) Batch 0.971 (1.114) Remain 43:20:10 loss: 0.3866 Lr: 0.00497 [2024-02-17 22:12:21,711 INFO misc.py line 119 87073] Train: 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Batch 1.021 (1.150) Remain 44:41:16 loss: 0.3885 Lr: 0.00496 [2024-02-17 22:15:38,399 INFO misc.py line 119 87073] Train: [11/100][234/1557] Data 0.013 (0.188) Batch 1.156 (1.150) Remain 44:41:18 loss: 0.8095 Lr: 0.00496 [2024-02-17 22:15:39,355 INFO misc.py line 119 87073] Train: [11/100][235/1557] Data 0.010 (0.187) Batch 0.961 (1.149) Remain 44:39:23 loss: 0.6216 Lr: 0.00496 [2024-02-17 22:15:40,185 INFO misc.py line 119 87073] Train: [11/100][236/1557] Data 0.004 (0.186) Batch 0.830 (1.148) Remain 44:36:11 loss: 0.5496 Lr: 0.00496 [2024-02-17 22:15:41,020 INFO misc.py line 119 87073] Train: [11/100][237/1557] Data 0.004 (0.185) Batch 0.836 (1.146) Remain 44:33:03 loss: 0.7070 Lr: 0.00496 [2024-02-17 22:15:42,607 INFO misc.py line 119 87073] Train: [11/100][238/1557] Data 0.369 (0.186) Batch 1.581 (1.148) Remain 44:37:20 loss: 0.3283 Lr: 0.00496 [2024-02-17 22:15:43,602 INFO misc.py line 119 87073] Train: [11/100][239/1557] Data 0.010 (0.185) Batch 1.001 (1.148) Remain 44:35:52 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Batch 0.941 (1.149) Remain 44:34:36 loss: 0.5859 Lr: 0.00496 [2024-02-17 22:21:00,121 INFO misc.py line 119 87073] Train: [11/100][514/1557] Data 0.005 (0.186) Batch 1.169 (1.149) Remain 44:34:40 loss: 0.5096 Lr: 0.00496 [2024-02-17 22:21:01,239 INFO misc.py line 119 87073] Train: [11/100][515/1557] Data 0.014 (0.186) Batch 1.124 (1.149) Remain 44:34:32 loss: 0.8115 Lr: 0.00496 [2024-02-17 22:21:02,024 INFO misc.py line 119 87073] Train: [11/100][516/1557] Data 0.007 (0.186) Batch 0.788 (1.149) Remain 44:32:53 loss: 0.3107 Lr: 0.00496 [2024-02-17 22:21:02,774 INFO misc.py line 119 87073] Train: [11/100][517/1557] Data 0.004 (0.185) Batch 0.747 (1.148) Remain 44:31:02 loss: 0.5050 Lr: 0.00496 [2024-02-17 22:21:07,090 INFO misc.py line 119 87073] Train: [11/100][518/1557] Data 3.041 (0.191) Batch 4.319 (1.154) Remain 44:45:21 loss: 0.2208 Lr: 0.00496 [2024-02-17 22:21:08,029 INFO misc.py line 119 87073] Train: [11/100][519/1557] Data 0.004 (0.190) Batch 0.940 (1.154) Remain 44:44:22 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[2024-02-17 22:39:14,991 INFO misc.py line 119 87073] Train: [11/100][1464/1557] Data 0.004 (0.187) Batch 1.117 (1.151) Remain 44:21:04 loss: 0.6621 Lr: 0.00495 [2024-02-17 22:39:15,981 INFO misc.py line 119 87073] Train: [11/100][1465/1557] Data 0.004 (0.187) Batch 0.989 (1.151) Remain 44:20:47 loss: 0.7544 Lr: 0.00495 [2024-02-17 22:39:16,899 INFO misc.py line 119 87073] Train: [11/100][1466/1557] Data 0.006 (0.187) Batch 0.919 (1.151) Remain 44:20:24 loss: 0.5412 Lr: 0.00495 [2024-02-17 22:39:17,707 INFO misc.py line 119 87073] Train: [11/100][1467/1557] Data 0.005 (0.187) Batch 0.805 (1.151) Remain 44:19:50 loss: 0.4113 Lr: 0.00495 [2024-02-17 22:39:18,500 INFO misc.py line 119 87073] Train: [11/100][1468/1557] Data 0.008 (0.187) Batch 0.796 (1.151) Remain 44:19:16 loss: 0.3496 Lr: 0.00495 [2024-02-17 22:39:19,246 INFO misc.py line 119 87073] Train: [11/100][1469/1557] Data 0.004 (0.187) Batch 0.747 (1.150) Remain 44:18:36 loss: 0.6949 Lr: 0.00495 [2024-02-17 22:39:21,595 INFO misc.py line 119 87073] Train: [11/100][1470/1557] Data 1.198 (0.187) Batch 2.348 (1.151) Remain 44:20:28 loss: 0.2139 Lr: 0.00495 [2024-02-17 22:39:22,513 INFO misc.py line 119 87073] Train: [11/100][1471/1557] Data 0.004 (0.187) Batch 0.914 (1.151) Remain 44:20:05 loss: 0.5727 Lr: 0.00495 [2024-02-17 22:39:23,511 INFO misc.py line 119 87073] Train: [11/100][1472/1557] Data 0.008 (0.187) Batch 1.003 (1.151) Remain 44:19:50 loss: 0.8101 Lr: 0.00495 [2024-02-17 22:39:24,507 INFO misc.py line 119 87073] Train: [11/100][1473/1557] Data 0.004 (0.187) Batch 0.983 (1.151) Remain 44:19:33 loss: 0.5729 Lr: 0.00495 [2024-02-17 22:39:25,523 INFO misc.py line 119 87073] Train: [11/100][1474/1557] Data 0.017 (0.187) Batch 1.020 (1.151) Remain 44:19:19 loss: 0.5646 Lr: 0.00495 [2024-02-17 22:39:26,322 INFO misc.py line 119 87073] Train: [11/100][1475/1557] Data 0.013 (0.187) Batch 0.807 (1.151) Remain 44:18:46 loss: 0.6392 Lr: 0.00495 [2024-02-17 22:39:27,045 INFO misc.py line 119 87073] Train: 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(0.186) Batch 0.711 (1.150) Remain 44:16:45 loss: 0.4233 Lr: 0.00495 [2024-02-17 22:39:33,947 INFO misc.py line 119 87073] Train: [11/100][1483/1557] Data 0.004 (0.186) Batch 0.758 (1.149) Remain 44:16:08 loss: 0.4675 Lr: 0.00495 [2024-02-17 22:39:35,205 INFO misc.py line 119 87073] Train: [11/100][1484/1557] Data 0.013 (0.186) Batch 1.255 (1.150) Remain 44:16:16 loss: 0.2293 Lr: 0.00495 [2024-02-17 22:39:36,185 INFO misc.py line 119 87073] Train: [11/100][1485/1557] Data 0.016 (0.185) Batch 0.993 (1.149) Remain 44:16:01 loss: 0.4324 Lr: 0.00495 [2024-02-17 22:39:37,135 INFO misc.py line 119 87073] Train: [11/100][1486/1557] Data 0.004 (0.185) Batch 0.947 (1.149) Remain 44:15:40 loss: 0.5442 Lr: 0.00495 [2024-02-17 22:39:38,148 INFO misc.py line 119 87073] Train: [11/100][1487/1557] Data 0.007 (0.185) Batch 1.013 (1.149) Remain 44:15:27 loss: 0.4547 Lr: 0.00495 [2024-02-17 22:39:39,158 INFO misc.py line 119 87073] Train: [11/100][1488/1557] Data 0.006 (0.185) Batch 1.012 (1.149) Remain 44:15:13 loss: 0.5591 Lr: 0.00495 [2024-02-17 22:39:39,867 INFO misc.py line 119 87073] Train: [11/100][1489/1557] Data 0.004 (0.185) Batch 0.710 (1.149) Remain 44:14:31 loss: 0.4686 Lr: 0.00495 [2024-02-17 22:39:40,639 INFO misc.py line 119 87073] Train: [11/100][1490/1557] Data 0.003 (0.185) Batch 0.766 (1.149) Remain 44:13:54 loss: 0.8503 Lr: 0.00495 [2024-02-17 22:39:41,830 INFO misc.py line 119 87073] Train: [11/100][1491/1557] Data 0.010 (0.185) Batch 1.192 (1.149) Remain 44:13:57 loss: 0.2944 Lr: 0.00495 [2024-02-17 22:39:42,796 INFO misc.py line 119 87073] Train: [11/100][1492/1557] Data 0.008 (0.185) Batch 0.969 (1.148) Remain 44:13:39 loss: 0.5966 Lr: 0.00495 [2024-02-17 22:39:43,712 INFO misc.py line 119 87073] Train: [11/100][1493/1557] Data 0.007 (0.184) Batch 0.918 (1.148) Remain 44:13:16 loss: 0.4596 Lr: 0.00495 [2024-02-17 22:39:44,712 INFO misc.py line 119 87073] Train: [11/100][1494/1557] Data 0.005 (0.184) Batch 1.000 (1.148) Remain 44:13:01 loss: 0.9275 Lr: 0.00495 [2024-02-17 22:39:45,604 INFO misc.py line 119 87073] Train: [11/100][1495/1557] Data 0.005 (0.184) Batch 0.891 (1.148) Remain 44:12:36 loss: 0.6692 Lr: 0.00495 [2024-02-17 22:39:46,368 INFO misc.py line 119 87073] Train: [11/100][1496/1557] Data 0.006 (0.184) Batch 0.765 (1.148) Remain 44:11:59 loss: 0.5300 Lr: 0.00495 [2024-02-17 22:39:47,122 INFO misc.py line 119 87073] Train: [11/100][1497/1557] Data 0.005 (0.184) Batch 0.755 (1.148) Remain 44:11:22 loss: 0.4794 Lr: 0.00495 [2024-02-17 22:39:48,209 INFO misc.py line 119 87073] Train: [11/100][1498/1557] Data 0.004 (0.184) Batch 1.073 (1.147) Remain 44:11:14 loss: 0.2892 Lr: 0.00495 [2024-02-17 22:39:49,347 INFO misc.py line 119 87073] Train: [11/100][1499/1557] Data 0.019 (0.184) Batch 1.142 (1.147) Remain 44:11:12 loss: 0.5433 Lr: 0.00495 [2024-02-17 22:39:50,142 INFO misc.py line 119 87073] Train: [11/100][1500/1557] Data 0.014 (0.184) Batch 0.805 (1.147) Remain 44:10:39 loss: 0.6014 Lr: 0.00495 [2024-02-17 22:39:51,307 INFO misc.py line 119 87073] Train: [11/100][1501/1557] Data 0.005 (0.184) Batch 1.165 (1.147) Remain 44:10:40 loss: 0.7147 Lr: 0.00495 [2024-02-17 22:39:52,141 INFO misc.py line 119 87073] Train: [11/100][1502/1557] Data 0.004 (0.183) Batch 0.831 (1.147) Remain 44:10:09 loss: 0.7627 Lr: 0.00495 [2024-02-17 22:39:52,897 INFO misc.py line 119 87073] Train: [11/100][1503/1557] Data 0.008 (0.183) Batch 0.758 (1.147) Remain 44:09:32 loss: 0.4991 Lr: 0.00495 [2024-02-17 22:39:53,632 INFO misc.py line 119 87073] Train: [11/100][1504/1557] Data 0.006 (0.183) Batch 0.737 (1.146) Remain 44:08:53 loss: 0.4100 Lr: 0.00495 [2024-02-17 22:39:54,943 INFO misc.py line 119 87073] Train: [11/100][1505/1557] Data 0.004 (0.183) Batch 1.303 (1.147) Remain 44:09:06 loss: 0.5540 Lr: 0.00495 [2024-02-17 22:39:55,997 INFO misc.py line 119 87073] Train: [11/100][1506/1557] Data 0.012 (0.183) Batch 1.056 (1.147) Remain 44:08:57 loss: 0.6749 Lr: 0.00495 [2024-02-17 22:39:56,956 INFO misc.py line 119 87073] Train: 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(0.182) Batch 0.944 (1.146) Remain 44:06:34 loss: 0.6784 Lr: 0.00495 [2024-02-17 22:40:03,368 INFO misc.py line 119 87073] Train: [11/100][1514/1557] Data 0.005 (0.182) Batch 0.814 (1.145) Remain 44:06:03 loss: 0.4211 Lr: 0.00495 [2024-02-17 22:40:04,322 INFO misc.py line 119 87073] Train: [11/100][1515/1557] Data 0.004 (0.182) Batch 0.944 (1.145) Remain 44:05:43 loss: 0.7952 Lr: 0.00495 [2024-02-17 22:40:05,327 INFO misc.py line 119 87073] Train: [11/100][1516/1557] Data 0.014 (0.182) Batch 1.015 (1.145) Remain 44:05:30 loss: 0.3014 Lr: 0.00495 [2024-02-17 22:40:06,063 INFO misc.py line 119 87073] Train: [11/100][1517/1557] Data 0.005 (0.182) Batch 0.736 (1.145) Remain 44:04:52 loss: 0.4262 Lr: 0.00495 [2024-02-17 22:40:06,731 INFO misc.py line 119 87073] Train: [11/100][1518/1557] Data 0.005 (0.182) Batch 0.660 (1.145) Remain 44:04:06 loss: 0.6647 Lr: 0.00495 [2024-02-17 22:40:17,083 INFO misc.py line 119 87073] Train: [11/100][1519/1557] Data 7.206 (0.186) Batch 10.358 (1.151) Remain 44:18:07 loss: 0.3287 Lr: 0.00495 [2024-02-17 22:40:18,086 INFO misc.py line 119 87073] Train: [11/100][1520/1557] Data 0.008 (0.186) Batch 1.005 (1.151) Remain 44:17:53 loss: 0.9135 Lr: 0.00495 [2024-02-17 22:40:18,940 INFO misc.py line 119 87073] Train: [11/100][1521/1557] Data 0.004 (0.186) Batch 0.854 (1.150) Remain 44:17:25 loss: 0.3436 Lr: 0.00495 [2024-02-17 22:40:19,919 INFO misc.py line 119 87073] Train: [11/100][1522/1557] Data 0.005 (0.186) Batch 0.969 (1.150) Remain 44:17:07 loss: 0.7392 Lr: 0.00495 [2024-02-17 22:40:20,958 INFO misc.py line 119 87073] Train: [11/100][1523/1557] Data 0.014 (0.186) Batch 1.042 (1.150) Remain 44:16:56 loss: 0.6859 Lr: 0.00495 [2024-02-17 22:40:21,735 INFO misc.py line 119 87073] Train: [11/100][1524/1557] Data 0.011 (0.186) Batch 0.784 (1.150) Remain 44:16:22 loss: 0.4876 Lr: 0.00495 [2024-02-17 22:40:22,500 INFO misc.py line 119 87073] Train: [11/100][1525/1557] Data 0.004 (0.185) Batch 0.762 (1.150) Remain 44:15:45 loss: 0.5213 Lr: 0.00495 [2024-02-17 22:40:26,348 INFO misc.py line 119 87073] Train: [11/100][1526/1557] Data 2.631 (0.187) Batch 3.849 (1.151) Remain 44:19:50 loss: 0.1982 Lr: 0.00495 [2024-02-17 22:40:27,258 INFO misc.py line 119 87073] Train: [11/100][1527/1557] Data 0.006 (0.187) Batch 0.910 (1.151) Remain 44:19:27 loss: 0.8566 Lr: 0.00495 [2024-02-17 22:40:28,180 INFO misc.py line 119 87073] Train: [11/100][1528/1557] Data 0.005 (0.187) Batch 0.923 (1.151) Remain 44:19:05 loss: 0.7789 Lr: 0.00495 [2024-02-17 22:40:29,147 INFO misc.py line 119 87073] Train: [11/100][1529/1557] Data 0.004 (0.187) Batch 0.967 (1.151) Remain 44:18:47 loss: 0.4928 Lr: 0.00495 [2024-02-17 22:40:30,040 INFO misc.py line 119 87073] Train: [11/100][1530/1557] Data 0.005 (0.187) Batch 0.892 (1.151) Remain 44:18:22 loss: 0.8493 Lr: 0.00495 [2024-02-17 22:40:30,751 INFO misc.py line 119 87073] Train: [11/100][1531/1557] Data 0.005 (0.186) Batch 0.711 (1.151) Remain 44:17:41 loss: 0.3002 Lr: 0.00495 [2024-02-17 22:40:31,511 INFO misc.py line 119 87073] Train: [11/100][1532/1557] Data 0.006 (0.186) Batch 0.759 (1.150) Remain 44:17:04 loss: 0.4943 Lr: 0.00495 [2024-02-17 22:40:32,828 INFO misc.py line 119 87073] Train: [11/100][1533/1557] Data 0.006 (0.186) Batch 1.312 (1.150) Remain 44:17:18 loss: 0.4454 Lr: 0.00495 [2024-02-17 22:40:33,737 INFO misc.py line 119 87073] Train: [11/100][1534/1557] Data 0.011 (0.186) Batch 0.915 (1.150) Remain 44:16:55 loss: 0.7139 Lr: 0.00495 [2024-02-17 22:40:35,007 INFO misc.py line 119 87073] Train: [11/100][1535/1557] Data 0.005 (0.186) Batch 1.270 (1.150) Remain 44:17:05 loss: 0.4727 Lr: 0.00495 [2024-02-17 22:40:35,876 INFO misc.py line 119 87073] Train: [11/100][1536/1557] Data 0.006 (0.186) Batch 0.869 (1.150) Remain 44:16:39 loss: 1.0175 Lr: 0.00495 [2024-02-17 22:40:36,827 INFO misc.py line 119 87073] Train: [11/100][1537/1557] Data 0.005 (0.186) Batch 0.951 (1.150) Remain 44:16:19 loss: 0.9235 Lr: 0.00495 [2024-02-17 22:40:37,795 INFO misc.py line 119 87073] Train: [11/100][1538/1557] Data 0.004 (0.186) Batch 0.967 (1.150) Remain 44:16:02 loss: 0.3834 Lr: 0.00495 [2024-02-17 22:40:38,513 INFO misc.py line 119 87073] Train: [11/100][1539/1557] Data 0.005 (0.186) Batch 0.709 (1.150) Remain 44:15:21 loss: 0.3597 Lr: 0.00495 [2024-02-17 22:40:39,734 INFO misc.py line 119 87073] Train: [11/100][1540/1557] Data 0.014 (0.185) Batch 1.225 (1.150) Remain 44:15:27 loss: 0.2680 Lr: 0.00495 [2024-02-17 22:40:40,593 INFO misc.py line 119 87073] Train: [11/100][1541/1557] Data 0.010 (0.185) Batch 0.865 (1.149) Remain 44:15:00 loss: 0.3873 Lr: 0.00495 [2024-02-17 22:40:41,798 INFO misc.py line 119 87073] Train: [11/100][1542/1557] Data 0.004 (0.185) Batch 1.197 (1.149) Remain 44:15:03 loss: 0.4643 Lr: 0.00495 [2024-02-17 22:40:42,829 INFO misc.py line 119 87073] Train: [11/100][1543/1557] Data 0.012 (0.185) Batch 1.033 (1.149) Remain 44:14:51 loss: 0.5231 Lr: 0.00495 [2024-02-17 22:40:43,705 INFO misc.py line 119 87073] Train: [11/100][1544/1557] Data 0.010 (0.185) Batch 0.882 (1.149) Remain 44:14:26 loss: 0.7743 Lr: 0.00495 [2024-02-17 22:40:44,449 INFO misc.py line 119 87073] Train: [11/100][1545/1557] Data 0.005 (0.185) Batch 0.744 (1.149) Remain 44:13:49 loss: 0.6369 Lr: 0.00495 [2024-02-17 22:40:45,130 INFO misc.py line 119 87073] Train: [11/100][1546/1557] Data 0.004 (0.185) Batch 0.658 (1.149) Remain 44:13:03 loss: 0.5279 Lr: 0.00495 [2024-02-17 22:40:46,308 INFO misc.py line 119 87073] Train: [11/100][1547/1557] Data 0.027 (0.185) Batch 1.190 (1.149) Remain 44:13:06 loss: 0.2759 Lr: 0.00495 [2024-02-17 22:40:47,259 INFO misc.py line 119 87073] Train: [11/100][1548/1557] Data 0.015 (0.185) Batch 0.962 (1.149) Remain 44:12:48 loss: 0.8929 Lr: 0.00495 [2024-02-17 22:40:48,232 INFO misc.py line 119 87073] Train: [11/100][1549/1557] Data 0.004 (0.184) Batch 0.973 (1.148) Remain 44:12:31 loss: 0.2935 Lr: 0.00495 [2024-02-17 22:40:49,182 INFO misc.py line 119 87073] Train: [11/100][1550/1557] Data 0.004 (0.184) Batch 0.950 (1.148) Remain 44:12:12 loss: 0.4148 Lr: 0.00495 [2024-02-17 22:40:50,145 INFO misc.py line 119 87073] Train: [11/100][1551/1557] Data 0.004 (0.184) Batch 0.962 (1.148) Remain 44:11:54 loss: 0.2250 Lr: 0.00495 [2024-02-17 22:40:50,999 INFO misc.py line 119 87073] Train: [11/100][1552/1557] Data 0.005 (0.184) Batch 0.833 (1.148) Remain 44:11:25 loss: 1.1181 Lr: 0.00495 [2024-02-17 22:40:51,781 INFO misc.py line 119 87073] Train: [11/100][1553/1557] Data 0.026 (0.184) Batch 0.803 (1.148) Remain 44:10:53 loss: 0.5455 Lr: 0.00495 [2024-02-17 22:40:52,942 INFO misc.py line 119 87073] Train: [11/100][1554/1557] Data 0.005 (0.184) Batch 1.163 (1.148) Remain 44:10:53 loss: 0.3424 Lr: 0.00495 [2024-02-17 22:40:53,872 INFO misc.py line 119 87073] Train: [11/100][1555/1557] Data 0.004 (0.184) Batch 0.930 (1.148) Remain 44:10:32 loss: 0.5893 Lr: 0.00495 [2024-02-17 22:40:54,707 INFO misc.py line 119 87073] Train: [11/100][1556/1557] Data 0.004 (0.184) Batch 0.823 (1.147) Remain 44:10:02 loss: 0.7535 Lr: 0.00495 [2024-02-17 22:40:55,626 INFO misc.py line 119 87073] Train: [11/100][1557/1557] Data 0.016 (0.184) Batch 0.930 (1.147) Remain 44:09:42 loss: 0.5214 Lr: 0.00495 [2024-02-17 22:40:55,626 INFO misc.py line 136 87073] Train result: loss: 0.5879 [2024-02-17 22:40:55,627 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 22:41:26,050 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7416 [2024-02-17 22:41:26,831 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6526 [2024-02-17 22:41:28,960 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4794 [2024-02-17 22:41:31,167 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4589 [2024-02-17 22:41:36,110 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3072 [2024-02-17 22:41:36,809 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1670 [2024-02-17 22:41:38,069 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3600 [2024-02-17 22:41:41,021 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2650 [2024-02-17 22:41:42,829 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3338 [2024-02-17 22:41:44,644 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6761/0.7393/0.8986. [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9282/0.9456 [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9795/0.9853 [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8430/0.9657 [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4379/0.5304 [2024-02-17 22:41:44,644 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6474/0.6619 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.5600/0.6134 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.7794/0.9263 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8980/0.9538 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6395/0.6516 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7586/0.8639 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7376/0.8068 [2024-02-17 22:41:44,645 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5807/0.7061 [2024-02-17 22:41:44,645 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 22:41:44,647 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 22:41:44,647 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 22:41:52,383 INFO misc.py line 119 87073] Train: [12/100][1/1557] Data 1.012 (1.012) Batch 1.703 (1.703) Remain 65:33:10 loss: 0.4934 Lr: 0.00495 [2024-02-17 22:41:53,591 INFO misc.py line 119 87073] Train: [12/100][2/1557] Data 0.171 (0.171) Batch 1.209 (1.209) Remain 46:32:11 loss: 0.6737 Lr: 0.00495 [2024-02-17 22:41:54,699 INFO misc.py line 119 87073] Train: [12/100][3/1557] Data 0.007 (0.007) Batch 1.107 (1.107) Remain 42:36:46 loss: 0.5676 Lr: 0.00495 [2024-02-17 22:41:55,505 INFO misc.py line 119 87073] Train: [12/100][4/1557] Data 0.007 (0.007) Batch 0.807 (0.807) Remain 31:03:30 loss: 0.2677 Lr: 0.00495 [2024-02-17 22:41:56,276 INFO misc.py line 119 87073] Train: [12/100][5/1557] Data 0.006 (0.007) Batch 0.773 (0.790) Remain 30:24:13 loss: 0.4782 Lr: 0.00495 [2024-02-17 22:41:57,074 INFO misc.py line 119 87073] Train: [12/100][6/1557] Data 0.004 (0.006) Batch 0.789 (0.790) Remain 30:23:22 loss: 0.6293 Lr: 0.00495 [2024-02-17 22:42:06,891 INFO misc.py line 119 87073] Train: [12/100][7/1557] Data 0.013 (0.007) Batch 9.826 (3.049) Remain 117:20:46 loss: 0.3175 Lr: 0.00495 [2024-02-17 22:42:07,861 INFO misc.py line 119 87073] Train: [12/100][8/1557] Data 0.004 (0.007) Batch 0.970 (2.633) Remain 101:20:24 loss: 1.1992 Lr: 0.00495 [2024-02-17 22:42:08,937 INFO misc.py line 119 87073] Train: [12/100][9/1557] Data 0.005 (0.006) Batch 1.077 (2.374) Remain 91:21:33 loss: 0.6644 Lr: 0.00495 [2024-02-17 22:42:09,837 INFO misc.py line 119 87073] Train: [12/100][10/1557] Data 0.004 (0.006) Batch 0.898 (2.163) Remain 83:14:50 loss: 0.5137 Lr: 0.00495 [2024-02-17 22:42:11,172 INFO misc.py line 119 87073] Train: [12/100][11/1557] Data 0.005 (0.006) Batch 1.335 (2.059) Remain 79:15:54 loss: 0.7279 Lr: 0.00495 [2024-02-17 22:42:11,929 INFO misc.py line 119 87073] Train: [12/100][12/1557] Data 0.005 (0.006) Batch 0.758 (1.915) Remain 73:41:51 loss: 0.5876 Lr: 0.00495 [2024-02-17 22:42:12,708 INFO misc.py line 119 87073] Train: [12/100][13/1557] Data 0.004 (0.006) Batch 0.776 (1.801) Remain 69:18:54 loss: 0.5164 Lr: 0.00495 [2024-02-17 22:42:14,053 INFO misc.py line 119 87073] Train: [12/100][14/1557] Data 0.007 (0.006) Batch 1.345 (1.759) Remain 67:43:05 loss: 0.2648 Lr: 0.00495 [2024-02-17 22:42:14,922 INFO misc.py line 119 87073] Train: [12/100][15/1557] Data 0.007 (0.006) Batch 0.871 (1.685) Remain 64:52:09 loss: 0.8673 Lr: 0.00495 [2024-02-17 22:42:15,843 INFO misc.py line 119 87073] Train: [12/100][16/1557] Data 0.005 (0.006) Batch 0.921 (1.627) Remain 62:36:23 loss: 0.7194 Lr: 0.00495 [2024-02-17 22:42:16,912 INFO misc.py line 119 87073] Train: [12/100][17/1557] Data 0.005 (0.006) Batch 1.069 (1.587) Remain 61:04:22 loss: 0.6979 Lr: 0.00495 [2024-02-17 22:42:17,908 INFO misc.py line 119 87073] Train: [12/100][18/1557] Data 0.005 (0.006) Batch 0.996 (1.547) Remain 59:33:24 loss: 0.7849 Lr: 0.00495 [2024-02-17 22:42:18,661 INFO misc.py line 119 87073] Train: [12/100][19/1557] Data 0.004 (0.005) Batch 0.753 (1.498) Remain 57:38:40 loss: 0.5348 Lr: 0.00495 [2024-02-17 22:42:19,445 INFO misc.py line 119 87073] Train: [12/100][20/1557] Data 0.005 (0.005) Batch 0.777 (1.455) Remain 56:00:41 loss: 0.2599 Lr: 0.00495 [2024-02-17 22:42:20,655 INFO misc.py line 119 87073] Train: 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0.720 (1.297) Remain 49:55:12 loss: 0.5037 Lr: 0.00495 [2024-02-17 22:42:27,034 INFO misc.py line 119 87073] Train: [12/100][28/1557] Data 0.022 (0.007) Batch 1.196 (1.293) Remain 49:45:50 loss: 0.1891 Lr: 0.00495 [2024-02-17 22:42:28,108 INFO misc.py line 119 87073] Train: [12/100][29/1557] Data 0.014 (0.007) Batch 1.069 (1.284) Remain 49:25:53 loss: 0.4204 Lr: 0.00495 [2024-02-17 22:42:29,312 INFO misc.py line 119 87073] Train: [12/100][30/1557] Data 0.020 (0.008) Batch 1.207 (1.282) Remain 49:19:15 loss: 0.2509 Lr: 0.00495 [2024-02-17 22:42:30,250 INFO misc.py line 119 87073] Train: [12/100][31/1557] Data 0.017 (0.008) Batch 0.951 (1.270) Remain 48:51:57 loss: 0.4068 Lr: 0.00495 [2024-02-17 22:42:31,190 INFO misc.py line 119 87073] Train: [12/100][32/1557] Data 0.005 (0.008) Batch 0.940 (1.258) Remain 48:25:39 loss: 0.3011 Lr: 0.00495 [2024-02-17 22:42:31,983 INFO misc.py line 119 87073] Train: [12/100][33/1557] Data 0.003 (0.008) Batch 0.784 (1.243) Remain 47:49:05 loss: 0.5067 Lr: 0.00495 [2024-02-17 22:42:32,801 INFO misc.py line 119 87073] Train: [12/100][34/1557] Data 0.013 (0.008) Batch 0.827 (1.229) Remain 47:18:05 loss: 0.5573 Lr: 0.00495 [2024-02-17 22:42:33,936 INFO misc.py line 119 87073] Train: [12/100][35/1557] Data 0.005 (0.008) Batch 1.135 (1.226) Remain 47:11:17 loss: 0.3288 Lr: 0.00495 [2024-02-17 22:42:34,886 INFO misc.py line 119 87073] Train: [12/100][36/1557] Data 0.004 (0.008) Batch 0.951 (1.218) Remain 46:52:01 loss: 0.7191 Lr: 0.00495 [2024-02-17 22:42:35,760 INFO misc.py line 119 87073] Train: [12/100][37/1557] Data 0.003 (0.008) Batch 0.874 (1.208) Remain 46:28:37 loss: 0.7178 Lr: 0.00495 [2024-02-17 22:42:36,641 INFO misc.py line 119 87073] Train: [12/100][38/1557] Data 0.004 (0.007) Batch 0.875 (1.198) Remain 46:06:38 loss: 1.1003 Lr: 0.00495 [2024-02-17 22:42:37,562 INFO misc.py line 119 87073] Train: [12/100][39/1557] Data 0.011 (0.008) Batch 0.925 (1.191) Remain 45:49:06 loss: 0.4610 Lr: 0.00495 [2024-02-17 22:42:38,346 INFO misc.py line 119 87073] Train: [12/100][40/1557] Data 0.006 (0.007) Batch 0.787 (1.180) Remain 45:23:53 loss: 0.5756 Lr: 0.00495 [2024-02-17 22:42:39,149 INFO misc.py line 119 87073] Train: [12/100][41/1557] Data 0.003 (0.007) Batch 0.791 (1.170) Remain 45:00:16 loss: 0.9661 Lr: 0.00495 [2024-02-17 22:42:40,384 INFO misc.py line 119 87073] Train: [12/100][42/1557] Data 0.015 (0.008) Batch 1.238 (1.171) Remain 45:04:16 loss: 0.3322 Lr: 0.00495 [2024-02-17 22:42:41,504 INFO misc.py line 119 87073] Train: [12/100][43/1557] Data 0.013 (0.008) Batch 1.111 (1.170) Remain 45:00:48 loss: 0.6036 Lr: 0.00495 [2024-02-17 22:42:42,346 INFO misc.py line 119 87073] Train: [12/100][44/1557] Data 0.021 (0.008) Batch 0.859 (1.162) Remain 44:43:17 loss: 0.7848 Lr: 0.00495 [2024-02-17 22:42:43,288 INFO misc.py line 119 87073] Train: [12/100][45/1557] Data 0.004 (0.008) Batch 0.941 (1.157) Remain 44:31:08 loss: 0.4068 Lr: 0.00495 [2024-02-17 22:42:44,229 INFO misc.py line 119 87073] Train: [12/100][46/1557] Data 0.004 (0.008) Batch 0.942 (1.152) Remain 44:19:34 loss: 0.9988 Lr: 0.00495 [2024-02-17 22:42:44,953 INFO misc.py line 119 87073] Train: [12/100][47/1557] Data 0.003 (0.008) Batch 0.715 (1.142) Remain 43:56:37 loss: 0.7796 Lr: 0.00495 [2024-02-17 22:42:45,730 INFO misc.py line 119 87073] Train: [12/100][48/1557] Data 0.012 (0.008) Batch 0.785 (1.134) Remain 43:38:18 loss: 0.7684 Lr: 0.00495 [2024-02-17 22:42:46,876 INFO misc.py line 119 87073] Train: [12/100][49/1557] Data 0.004 (0.008) Batch 1.146 (1.134) Remain 43:38:53 loss: 0.3065 Lr: 0.00495 [2024-02-17 22:42:47,651 INFO misc.py line 119 87073] Train: [12/100][50/1557] Data 0.004 (0.008) Batch 0.774 (1.127) Remain 43:21:11 loss: 0.5686 Lr: 0.00495 [2024-02-17 22:42:48,658 INFO misc.py line 119 87073] Train: [12/100][51/1557] Data 0.004 (0.008) Batch 0.996 (1.124) Remain 43:14:53 loss: 0.3679 Lr: 0.00495 [2024-02-17 22:42:49,790 INFO misc.py line 119 87073] Train: [12/100][52/1557] Data 0.016 (0.008) Batch 1.133 (1.124) Remain 43:15:16 loss: 0.8180 Lr: 0.00495 [2024-02-17 22:42:50,627 INFO misc.py line 119 87073] Train: [12/100][53/1557] Data 0.015 (0.008) Batch 0.848 (1.119) Remain 43:02:29 loss: 0.4247 Lr: 0.00495 [2024-02-17 22:42:51,346 INFO misc.py line 119 87073] Train: [12/100][54/1557] Data 0.005 (0.008) Batch 0.720 (1.111) Remain 42:44:25 loss: 0.7603 Lr: 0.00495 [2024-02-17 22:42:51,987 INFO misc.py line 119 87073] Train: [12/100][55/1557] Data 0.004 (0.008) Batch 0.640 (1.102) Remain 42:23:30 loss: 0.6376 Lr: 0.00495 [2024-02-17 22:42:53,200 INFO misc.py line 119 87073] Train: [12/100][56/1557] Data 0.004 (0.008) Batch 1.195 (1.104) Remain 42:27:34 loss: 0.3778 Lr: 0.00495 [2024-02-17 22:42:54,080 INFO misc.py line 119 87073] Train: [12/100][57/1557] Data 0.022 (0.008) Batch 0.898 (1.100) Remain 42:18:45 loss: 0.6780 Lr: 0.00495 [2024-02-17 22:42:54,903 INFO misc.py line 119 87073] Train: [12/100][58/1557] Data 0.004 (0.008) Batch 0.824 (1.095) Remain 42:07:09 loss: 0.7070 Lr: 0.00495 [2024-02-17 22:42:55,846 INFO misc.py line 119 87073] Train: [12/100][59/1557] Data 0.003 (0.008) Batch 0.931 (1.092) Remain 42:00:23 loss: 0.4818 Lr: 0.00495 [2024-02-17 22:42:56,712 INFO misc.py line 119 87073] Train: [12/100][60/1557] Data 0.016 (0.008) Batch 0.875 (1.088) Remain 41:51:36 loss: 0.5789 Lr: 0.00495 [2024-02-17 22:42:57,413 INFO misc.py line 119 87073] Train: [12/100][61/1557] Data 0.005 (0.008) Batch 0.702 (1.081) Remain 41:36:13 loss: 0.8483 Lr: 0.00495 [2024-02-17 22:42:58,173 INFO misc.py line 119 87073] Train: [12/100][62/1557] Data 0.004 (0.008) Batch 0.755 (1.076) Remain 41:23:27 loss: 0.6077 Lr: 0.00495 [2024-02-17 22:43:16,694 INFO misc.py line 119 87073] Train: [12/100][63/1557] Data 4.734 (0.087) Batch 18.525 (1.367) Remain 52:34:48 loss: 0.2401 Lr: 0.00495 [2024-02-17 22:43:17,602 INFO misc.py line 119 87073] Train: [12/100][64/1557] Data 0.006 (0.085) Batch 0.909 (1.359) Remain 52:17:28 loss: 0.5458 Lr: 0.00495 [2024-02-17 22:43:18,654 INFO misc.py line 119 87073] Train: [12/100][65/1557] Data 0.004 (0.084) Batch 1.051 (1.354) Remain 52:05:59 loss: 0.5976 Lr: 0.00495 [2024-02-17 22:43:19,472 INFO misc.py line 119 87073] Train: [12/100][66/1557] Data 0.005 (0.083) Batch 0.819 (1.346) Remain 51:46:21 loss: 0.7657 Lr: 0.00495 [2024-02-17 22:43:20,420 INFO misc.py line 119 87073] Train: [12/100][67/1557] Data 0.004 (0.081) Batch 0.940 (1.339) Remain 51:31:41 loss: 0.4144 Lr: 0.00495 [2024-02-17 22:43:21,125 INFO misc.py line 119 87073] Train: [12/100][68/1557] Data 0.012 (0.080) Batch 0.713 (1.330) Remain 51:09:26 loss: 0.6867 Lr: 0.00495 [2024-02-17 22:43:21,916 INFO misc.py line 119 87073] Train: [12/100][69/1557] Data 0.003 (0.079) Batch 0.778 (1.321) Remain 50:50:08 loss: 0.5806 Lr: 0.00495 [2024-02-17 22:43:23,300 INFO misc.py line 119 87073] Train: [12/100][70/1557] Data 0.016 (0.078) Batch 1.385 (1.322) Remain 50:52:18 loss: 0.5034 Lr: 0.00495 [2024-02-17 22:43:24,203 INFO misc.py line 119 87073] Train: [12/100][71/1557] Data 0.016 (0.077) Batch 0.912 (1.316) Remain 50:38:22 loss: 0.6764 Lr: 0.00495 [2024-02-17 22:43:25,094 INFO misc.py line 119 87073] Train: [12/100][72/1557] Data 0.006 (0.076) Batch 0.891 (1.310) Remain 50:24:08 loss: 0.7764 Lr: 0.00495 [2024-02-17 22:43:25,908 INFO misc.py line 119 87073] Train: [12/100][73/1557] Data 0.005 (0.075) Batch 0.812 (1.303) Remain 50:07:42 loss: 0.4348 Lr: 0.00495 [2024-02-17 22:43:26,768 INFO misc.py line 119 87073] Train: [12/100][74/1557] Data 0.007 (0.074) Batch 0.863 (1.297) Remain 49:53:23 loss: 0.6487 Lr: 0.00495 [2024-02-17 22:43:27,558 INFO misc.py line 119 87073] Train: [12/100][75/1557] Data 0.004 (0.073) Batch 0.790 (1.290) Remain 49:37:06 loss: 0.7442 Lr: 0.00495 [2024-02-17 22:43:28,304 INFO misc.py line 119 87073] Train: [12/100][76/1557] Data 0.004 (0.072) Batch 0.746 (1.282) Remain 49:19:53 loss: 0.4942 Lr: 0.00495 [2024-02-17 22:43:29,569 INFO misc.py line 119 87073] Train: [12/100][77/1557] Data 0.005 (0.072) Batch 1.263 (1.282) Remain 49:19:15 loss: 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INFO misc.py line 119 87073] Train: [12/100][84/1557] Data 0.007 (0.066) Batch 1.162 (1.251) Remain 48:07:37 loss: 0.3170 Lr: 0.00495 [2024-02-17 22:43:36,979 INFO misc.py line 119 87073] Train: [12/100][85/1557] Data 0.007 (0.065) Batch 0.946 (1.247) Remain 47:59:01 loss: 0.6307 Lr: 0.00495 [2024-02-17 22:43:37,995 INFO misc.py line 119 87073] Train: [12/100][86/1557] Data 0.004 (0.064) Batch 1.017 (1.245) Remain 47:52:35 loss: 0.4042 Lr: 0.00495 [2024-02-17 22:43:38,939 INFO misc.py line 119 87073] Train: [12/100][87/1557] Data 0.004 (0.064) Batch 0.944 (1.241) Remain 47:44:19 loss: 0.2403 Lr: 0.00495 [2024-02-17 22:43:39,890 INFO misc.py line 119 87073] Train: [12/100][88/1557] Data 0.004 (0.063) Batch 0.951 (1.238) Remain 47:36:25 loss: 1.1378 Lr: 0.00495 [2024-02-17 22:43:40,665 INFO misc.py line 119 87073] Train: [12/100][89/1557] Data 0.004 (0.062) Batch 0.760 (1.232) Remain 47:23:35 loss: 0.3532 Lr: 0.00495 [2024-02-17 22:43:41,409 INFO misc.py line 119 87073] Train: 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Train: [12/100][1290/1557] Data 0.004 (0.090) Batch 0.889 (1.276) Remain 48:40:16 loss: 0.3514 Lr: 0.00494 [2024-02-17 23:09:18,373 INFO misc.py line 119 87073] Train: [12/100][1291/1557] Data 0.004 (0.090) Batch 1.055 (1.276) Remain 48:39:51 loss: 0.6368 Lr: 0.00494 [2024-02-17 23:09:19,315 INFO misc.py line 119 87073] Train: [12/100][1292/1557] Data 0.005 (0.090) Batch 0.944 (1.276) Remain 48:39:14 loss: 0.7764 Lr: 0.00494 [2024-02-17 23:09:20,089 INFO misc.py line 119 87073] Train: [12/100][1293/1557] Data 0.003 (0.090) Batch 0.772 (1.275) Remain 48:38:19 loss: 0.5844 Lr: 0.00494 [2024-02-17 23:09:20,858 INFO misc.py line 119 87073] Train: [12/100][1294/1557] Data 0.005 (0.090) Batch 0.767 (1.275) Remain 48:37:24 loss: 0.6164 Lr: 0.00494 [2024-02-17 23:09:40,881 INFO misc.py line 119 87073] Train: [12/100][1295/1557] Data 4.374 (0.093) Batch 20.025 (1.290) Remain 49:10:35 loss: 0.2331 Lr: 0.00494 [2024-02-17 23:09:41,816 INFO misc.py line 119 87073] Train: [12/100][1296/1557] Data 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Remain 49:06:33 loss: 0.2700 Lr: 0.00494 [2024-02-17 23:09:48,768 INFO misc.py line 119 87073] Train: [12/100][1303/1557] Data 0.017 (0.092) Batch 1.050 (1.288) Remain 49:06:07 loss: 0.4312 Lr: 0.00494 [2024-02-17 23:09:49,710 INFO misc.py line 119 87073] Train: [12/100][1304/1557] Data 0.018 (0.092) Batch 0.955 (1.287) Remain 49:05:30 loss: 1.0724 Lr: 0.00494 [2024-02-17 23:09:50,617 INFO misc.py line 119 87073] Train: [12/100][1305/1557] Data 0.005 (0.092) Batch 0.909 (1.287) Remain 49:04:49 loss: 0.5425 Lr: 0.00494 [2024-02-17 23:09:51,610 INFO misc.py line 119 87073] Train: [12/100][1306/1557] Data 0.004 (0.092) Batch 0.988 (1.287) Remain 49:04:16 loss: 0.7606 Lr: 0.00494 [2024-02-17 23:09:52,346 INFO misc.py line 119 87073] Train: [12/100][1307/1557] Data 0.008 (0.092) Batch 0.736 (1.287) Remain 49:03:17 loss: 0.6292 Lr: 0.00494 [2024-02-17 23:09:53,120 INFO misc.py line 119 87073] Train: [12/100][1308/1557] Data 0.008 (0.092) Batch 0.777 (1.286) Remain 49:02:22 loss: 0.5241 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1315/1557] Data 0.005 (0.092) Batch 0.723 (1.284) Remain 48:58:19 loss: 0.9856 Lr: 0.00494 [2024-02-17 23:10:01,000 INFO misc.py line 119 87073] Train: [12/100][1316/1557] Data 0.008 (0.092) Batch 1.110 (1.284) Remain 48:58:00 loss: 0.2254 Lr: 0.00494 [2024-02-17 23:10:01,956 INFO misc.py line 119 87073] Train: [12/100][1317/1557] Data 0.009 (0.092) Batch 0.957 (1.284) Remain 48:57:24 loss: 0.6068 Lr: 0.00494 [2024-02-17 23:10:02,979 INFO misc.py line 119 87073] Train: [12/100][1318/1557] Data 0.009 (0.092) Batch 1.024 (1.284) Remain 48:56:56 loss: 0.6183 Lr: 0.00494 [2024-02-17 23:10:03,928 INFO misc.py line 119 87073] Train: [12/100][1319/1557] Data 0.006 (0.091) Batch 0.950 (1.284) Remain 48:56:20 loss: 0.5204 Lr: 0.00494 [2024-02-17 23:10:04,695 INFO misc.py line 119 87073] Train: [12/100][1320/1557] Data 0.004 (0.091) Batch 0.765 (1.283) Remain 48:55:24 loss: 0.2050 Lr: 0.00494 [2024-02-17 23:10:05,477 INFO misc.py line 119 87073] Train: [12/100][1321/1557] Data 0.010 (0.091) Batch 0.785 (1.283) Remain 48:54:31 loss: 0.4774 Lr: 0.00494 [2024-02-17 23:10:06,197 INFO misc.py line 119 87073] Train: [12/100][1322/1557] Data 0.004 (0.091) Batch 0.720 (1.282) Remain 48:53:31 loss: 0.8606 Lr: 0.00494 [2024-02-17 23:10:07,327 INFO misc.py line 119 87073] Train: [12/100][1323/1557] Data 0.004 (0.091) Batch 1.116 (1.282) Remain 48:53:13 loss: 0.3698 Lr: 0.00494 [2024-02-17 23:10:08,302 INFO misc.py line 119 87073] Train: [12/100][1324/1557] Data 0.019 (0.091) Batch 0.990 (1.282) Remain 48:52:41 loss: 0.4235 Lr: 0.00494 [2024-02-17 23:10:09,289 INFO misc.py line 119 87073] Train: [12/100][1325/1557] Data 0.004 (0.091) Batch 0.986 (1.282) Remain 48:52:09 loss: 1.1104 Lr: 0.00494 [2024-02-17 23:10:10,302 INFO misc.py line 119 87073] Train: [12/100][1326/1557] Data 0.004 (0.091) Batch 1.013 (1.282) Remain 48:51:40 loss: 0.9403 Lr: 0.00494 [2024-02-17 23:10:11,278 INFO misc.py line 119 87073] Train: [12/100][1327/1557] Data 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Remain 48:47:13 loss: 0.3255 Lr: 0.00494 [2024-02-17 23:10:17,728 INFO misc.py line 119 87073] Train: [12/100][1334/1557] Data 0.006 (0.090) Batch 0.934 (1.279) Remain 48:46:37 loss: 0.5098 Lr: 0.00494 [2024-02-17 23:10:18,423 INFO misc.py line 119 87073] Train: [12/100][1335/1557] Data 0.020 (0.090) Batch 0.709 (1.279) Remain 48:45:36 loss: 0.3921 Lr: 0.00494 [2024-02-17 23:10:19,187 INFO misc.py line 119 87073] Train: [12/100][1336/1557] Data 0.006 (0.090) Batch 0.754 (1.279) Remain 48:44:41 loss: 0.7893 Lr: 0.00494 [2024-02-17 23:10:20,330 INFO misc.py line 119 87073] Train: [12/100][1337/1557] Data 0.016 (0.090) Batch 1.142 (1.279) Remain 48:44:26 loss: 0.1257 Lr: 0.00494 [2024-02-17 23:10:21,447 INFO misc.py line 119 87073] Train: [12/100][1338/1557] Data 0.017 (0.090) Batch 1.128 (1.278) Remain 48:44:09 loss: 0.8430 Lr: 0.00494 [2024-02-17 23:10:22,442 INFO misc.py line 119 87073] Train: [12/100][1339/1557] Data 0.007 (0.090) Batch 0.995 (1.278) Remain 48:43:39 loss: 0.6753 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1346/1557] Data 0.011 (0.090) Batch 1.146 (1.277) Remain 48:39:42 loss: 0.4666 Lr: 0.00494 [2024-02-17 23:10:30,012 INFO misc.py line 119 87073] Train: [12/100][1347/1557] Data 0.017 (0.090) Batch 0.851 (1.276) Remain 48:38:57 loss: 0.8446 Lr: 0.00494 [2024-02-17 23:10:30,990 INFO misc.py line 119 87073] Train: [12/100][1348/1557] Data 0.004 (0.090) Batch 0.979 (1.276) Remain 48:38:26 loss: 0.8000 Lr: 0.00494 [2024-02-17 23:10:31,757 INFO misc.py line 119 87073] Train: [12/100][1349/1557] Data 0.004 (0.090) Batch 0.768 (1.276) Remain 48:37:33 loss: 0.8666 Lr: 0.00494 [2024-02-17 23:10:32,447 INFO misc.py line 119 87073] Train: [12/100][1350/1557] Data 0.004 (0.090) Batch 0.679 (1.275) Remain 48:36:31 loss: 0.8329 Lr: 0.00494 [2024-02-17 23:10:51,687 INFO misc.py line 119 87073] Train: [12/100][1351/1557] Data 4.106 (0.093) Batch 19.248 (1.289) Remain 49:06:59 loss: 0.2773 Lr: 0.00494 [2024-02-17 23:10:52,523 INFO misc.py line 119 87073] Train: [12/100][1352/1557] Data 0.007 (0.092) Batch 0.838 (1.288) Remain 49:06:12 loss: 0.5838 Lr: 0.00494 [2024-02-17 23:10:53,553 INFO misc.py line 119 87073] Train: [12/100][1353/1557] Data 0.004 (0.092) Batch 1.030 (1.288) Remain 49:05:44 loss: 0.7300 Lr: 0.00494 [2024-02-17 23:10:54,651 INFO misc.py line 119 87073] Train: [12/100][1354/1557] Data 0.005 (0.092) Batch 1.094 (1.288) Remain 49:05:23 loss: 0.7277 Lr: 0.00494 [2024-02-17 23:10:55,478 INFO misc.py line 119 87073] Train: [12/100][1355/1557] Data 0.008 (0.092) Batch 0.824 (1.288) Remain 49:04:35 loss: 0.7861 Lr: 0.00494 [2024-02-17 23:10:56,407 INFO misc.py line 119 87073] Train: [12/100][1356/1557] Data 0.011 (0.092) Batch 0.935 (1.287) Remain 49:03:58 loss: 0.3285 Lr: 0.00494 [2024-02-17 23:10:57,045 INFO misc.py line 119 87073] Train: [12/100][1357/1557] Data 0.005 (0.092) Batch 0.626 (1.287) Remain 49:02:50 loss: 0.7386 Lr: 0.00494 [2024-02-17 23:10:58,382 INFO misc.py line 119 87073] Train: [12/100][1358/1557] Data 0.016 (0.092) Batch 1.342 (1.287) Remain 49:02:54 loss: 0.2919 Lr: 0.00494 [2024-02-17 23:10:59,277 INFO misc.py line 119 87073] Train: [12/100][1359/1557] Data 0.012 (0.092) Batch 0.900 (1.287) Remain 49:02:13 loss: 1.0602 Lr: 0.00494 [2024-02-17 23:11:00,322 INFO misc.py line 119 87073] Train: [12/100][1360/1557] Data 0.014 (0.092) Batch 1.049 (1.286) Remain 49:01:48 loss: 0.5276 Lr: 0.00494 [2024-02-17 23:11:01,250 INFO misc.py line 119 87073] Train: [12/100][1361/1557] Data 0.004 (0.092) Batch 0.926 (1.286) Remain 49:01:10 loss: 0.8738 Lr: 0.00494 [2024-02-17 23:11:02,305 INFO misc.py line 119 87073] Train: [12/100][1362/1557] Data 0.006 (0.092) Batch 1.057 (1.286) Remain 49:00:46 loss: 0.4360 Lr: 0.00494 [2024-02-17 23:11:03,050 INFO misc.py line 119 87073] Train: [12/100][1363/1557] Data 0.004 (0.092) Batch 0.735 (1.286) Remain 48:59:49 loss: 0.8234 Lr: 0.00494 [2024-02-17 23:11:03,744 INFO misc.py line 119 87073] Train: [12/100][1364/1557] Data 0.014 (0.092) Batch 0.702 (1.285) Remain 48:58:49 loss: 0.3582 Lr: 0.00494 [2024-02-17 23:11:05,000 INFO misc.py line 119 87073] Train: [12/100][1365/1557] Data 0.006 (0.092) Batch 1.257 (1.285) Remain 48:58:45 loss: 0.4744 Lr: 0.00494 [2024-02-17 23:11:05,953 INFO misc.py line 119 87073] Train: [12/100][1366/1557] Data 0.005 (0.092) Batch 0.953 (1.285) Remain 48:58:10 loss: 0.6804 Lr: 0.00494 [2024-02-17 23:11:06,964 INFO misc.py line 119 87073] Train: [12/100][1367/1557] Data 0.004 (0.092) Batch 1.012 (1.285) Remain 48:57:41 loss: 0.4675 Lr: 0.00494 [2024-02-17 23:11:08,003 INFO misc.py line 119 87073] Train: [12/100][1368/1557] Data 0.004 (0.091) Batch 1.039 (1.284) Remain 48:57:15 loss: 0.6285 Lr: 0.00494 [2024-02-17 23:11:08,922 INFO misc.py line 119 87073] Train: [12/100][1369/1557] Data 0.004 (0.091) Batch 0.918 (1.284) Remain 48:56:37 loss: 0.3139 Lr: 0.00494 [2024-02-17 23:11:09,638 INFO misc.py line 119 87073] Train: [12/100][1370/1557] Data 0.004 (0.091) Batch 0.707 (1.284) Remain 48:55:38 loss: 0.3316 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1377/1557] Data 0.006 (0.091) Batch 0.712 (1.282) Remain 48:51:06 loss: 0.5266 Lr: 0.00494 [2024-02-17 23:11:16,767 INFO misc.py line 119 87073] Train: [12/100][1378/1557] Data 0.016 (0.091) Batch 0.782 (1.282) Remain 48:50:15 loss: 0.4596 Lr: 0.00494 [2024-02-17 23:11:17,835 INFO misc.py line 119 87073] Train: [12/100][1379/1557] Data 0.005 (0.091) Batch 1.068 (1.281) Remain 48:49:53 loss: 0.4784 Lr: 0.00494 [2024-02-17 23:11:18,788 INFO misc.py line 119 87073] Train: [12/100][1380/1557] Data 0.005 (0.091) Batch 0.954 (1.281) Remain 48:49:19 loss: 0.5900 Lr: 0.00494 [2024-02-17 23:11:19,604 INFO misc.py line 119 87073] Train: [12/100][1381/1557] Data 0.004 (0.091) Batch 0.814 (1.281) Remain 48:48:31 loss: 0.6318 Lr: 0.00494 [2024-02-17 23:11:20,625 INFO misc.py line 119 87073] Train: [12/100][1382/1557] Data 0.006 (0.091) Batch 1.016 (1.281) Remain 48:48:03 loss: 0.5691 Lr: 0.00494 [2024-02-17 23:11:21,531 INFO misc.py line 119 87073] Train: [12/100][1383/1557] Data 0.011 (0.091) Batch 0.913 (1.280) Remain 48:47:26 loss: 0.7328 Lr: 0.00494 [2024-02-17 23:11:22,246 INFO misc.py line 119 87073] Train: [12/100][1384/1557] Data 0.004 (0.090) Batch 0.715 (1.280) Remain 48:46:28 loss: 0.3020 Lr: 0.00494 [2024-02-17 23:11:23,000 INFO misc.py line 119 87073] Train: [12/100][1385/1557] Data 0.004 (0.090) Batch 0.745 (1.280) Remain 48:45:34 loss: 0.8412 Lr: 0.00494 [2024-02-17 23:11:24,138 INFO misc.py line 119 87073] Train: [12/100][1386/1557] Data 0.013 (0.090) Batch 1.142 (1.279) Remain 48:45:19 loss: 0.2791 Lr: 0.00494 [2024-02-17 23:11:25,258 INFO misc.py line 119 87073] Train: [12/100][1387/1557] Data 0.009 (0.090) Batch 1.117 (1.279) Remain 48:45:01 loss: 0.5614 Lr: 0.00494 [2024-02-17 23:11:26,128 INFO misc.py line 119 87073] Train: [12/100][1388/1557] Data 0.012 (0.090) Batch 0.877 (1.279) Remain 48:44:20 loss: 1.0221 Lr: 0.00494 [2024-02-17 23:11:27,092 INFO misc.py line 119 87073] Train: [12/100][1389/1557] Data 0.005 (0.090) Batch 0.965 (1.279) Remain 48:43:48 loss: 0.5171 Lr: 0.00494 [2024-02-17 23:11:28,165 INFO misc.py line 119 87073] Train: [12/100][1390/1557] Data 0.004 (0.090) Batch 1.071 (1.279) Remain 48:43:26 loss: 0.3441 Lr: 0.00494 [2024-02-17 23:11:28,951 INFO misc.py line 119 87073] Train: [12/100][1391/1557] Data 0.006 (0.090) Batch 0.787 (1.278) Remain 48:42:36 loss: 0.4033 Lr: 0.00494 [2024-02-17 23:11:29,641 INFO misc.py line 119 87073] Train: [12/100][1392/1557] Data 0.005 (0.090) Batch 0.684 (1.278) Remain 48:41:36 loss: 0.7641 Lr: 0.00494 [2024-02-17 23:11:30,825 INFO misc.py line 119 87073] Train: [12/100][1393/1557] Data 0.010 (0.090) Batch 1.179 (1.278) Remain 48:41:25 loss: 0.2836 Lr: 0.00494 [2024-02-17 23:11:31,742 INFO misc.py line 119 87073] Train: [12/100][1394/1557] Data 0.016 (0.090) Batch 0.925 (1.278) Remain 48:40:49 loss: 0.4595 Lr: 0.00494 [2024-02-17 23:11:32,583 INFO misc.py line 119 87073] Train: [12/100][1395/1557] Data 0.010 (0.090) Batch 0.845 (1.277) Remain 48:40:05 loss: 1.0071 Lr: 0.00494 [2024-02-17 23:11:33,532 INFO misc.py line 119 87073] Train: [12/100][1396/1557] Data 0.005 (0.090) Batch 0.947 (1.277) Remain 48:39:31 loss: 0.5334 Lr: 0.00494 [2024-02-17 23:11:34,452 INFO misc.py line 119 87073] Train: [12/100][1397/1557] Data 0.006 (0.090) Batch 0.923 (1.277) Remain 48:38:55 loss: 0.4834 Lr: 0.00494 [2024-02-17 23:11:35,166 INFO misc.py line 119 87073] Train: [12/100][1398/1557] Data 0.004 (0.090) Batch 0.714 (1.276) Remain 48:37:59 loss: 0.4748 Lr: 0.00494 [2024-02-17 23:11:35,987 INFO misc.py line 119 87073] Train: [12/100][1399/1557] Data 0.003 (0.090) Batch 0.816 (1.276) Remain 48:37:12 loss: 0.8345 Lr: 0.00494 [2024-02-17 23:11:37,209 INFO misc.py line 119 87073] Train: [12/100][1400/1557] Data 0.009 (0.090) Batch 1.219 (1.276) Remain 48:37:05 loss: 0.2439 Lr: 0.00494 [2024-02-17 23:11:38,135 INFO misc.py line 119 87073] Train: [12/100][1401/1557] Data 0.012 (0.089) Batch 0.933 (1.276) Remain 48:36:30 loss: 0.3324 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1408/1557] Data 0.005 (0.093) Batch 1.142 (1.288) Remain 49:04:16 loss: 0.4056 Lr: 0.00494 [2024-02-17 23:12:05,138 INFO misc.py line 119 87073] Train: [12/100][1409/1557] Data 0.004 (0.093) Batch 0.918 (1.288) Remain 49:03:39 loss: 1.2571 Lr: 0.00494 [2024-02-17 23:12:06,121 INFO misc.py line 119 87073] Train: [12/100][1410/1557] Data 0.004 (0.093) Batch 0.984 (1.287) Remain 49:03:08 loss: 0.5706 Lr: 0.00494 [2024-02-17 23:12:07,144 INFO misc.py line 119 87073] Train: [12/100][1411/1557] Data 0.003 (0.093) Batch 1.024 (1.287) Remain 49:02:41 loss: 0.5308 Lr: 0.00494 [2024-02-17 23:12:07,899 INFO misc.py line 119 87073] Train: [12/100][1412/1557] Data 0.002 (0.093) Batch 0.750 (1.287) Remain 49:01:47 loss: 0.6695 Lr: 0.00494 [2024-02-17 23:12:08,644 INFO misc.py line 119 87073] Train: [12/100][1413/1557] Data 0.008 (0.093) Batch 0.745 (1.286) Remain 49:00:53 loss: 0.4736 Lr: 0.00494 [2024-02-17 23:12:09,961 INFO misc.py line 119 87073] Train: [12/100][1414/1557] Data 0.008 (0.093) Batch 1.314 (1.287) Remain 49:00:55 loss: 0.2631 Lr: 0.00494 [2024-02-17 23:12:11,070 INFO misc.py line 119 87073] Train: [12/100][1415/1557] Data 0.012 (0.093) Batch 1.108 (1.286) Remain 49:00:36 loss: 0.4982 Lr: 0.00494 [2024-02-17 23:12:12,144 INFO misc.py line 119 87073] Train: [12/100][1416/1557] Data 0.013 (0.093) Batch 1.077 (1.286) Remain 49:00:14 loss: 0.7681 Lr: 0.00494 [2024-02-17 23:12:13,073 INFO misc.py line 119 87073] Train: [12/100][1417/1557] Data 0.010 (0.092) Batch 0.932 (1.286) Remain 48:59:39 loss: 0.7648 Lr: 0.00494 [2024-02-17 23:12:14,029 INFO misc.py line 119 87073] Train: [12/100][1418/1557] Data 0.008 (0.092) Batch 0.958 (1.286) Remain 48:59:06 loss: 0.4563 Lr: 0.00494 [2024-02-17 23:12:14,785 INFO misc.py line 119 87073] Train: [12/100][1419/1557] Data 0.005 (0.092) Batch 0.750 (1.285) Remain 48:58:13 loss: 0.6364 Lr: 0.00494 [2024-02-17 23:12:15,567 INFO misc.py line 119 87073] Train: [12/100][1420/1557] Data 0.012 (0.092) Batch 0.790 (1.285) Remain 48:57:23 loss: 0.5095 Lr: 0.00494 [2024-02-17 23:12:16,853 INFO misc.py line 119 87073] Train: [12/100][1421/1557] Data 0.004 (0.092) Batch 1.282 (1.285) Remain 48:57:22 loss: 0.3179 Lr: 0.00494 [2024-02-17 23:12:17,952 INFO misc.py line 119 87073] Train: [12/100][1422/1557] Data 0.007 (0.092) Batch 1.102 (1.285) Remain 48:57:03 loss: 0.7716 Lr: 0.00494 [2024-02-17 23:12:18,875 INFO misc.py line 119 87073] Train: [12/100][1423/1557] Data 0.006 (0.092) Batch 0.923 (1.285) Remain 48:56:27 loss: 0.6701 Lr: 0.00494 [2024-02-17 23:12:19,816 INFO misc.py line 119 87073] Train: [12/100][1424/1557] Data 0.004 (0.092) Batch 0.941 (1.284) Remain 48:55:52 loss: 0.5589 Lr: 0.00494 [2024-02-17 23:12:20,675 INFO misc.py line 119 87073] Train: [12/100][1425/1557] Data 0.004 (0.092) Batch 0.858 (1.284) Remain 48:55:10 loss: 0.6703 Lr: 0.00494 [2024-02-17 23:12:21,483 INFO misc.py line 119 87073] Train: [12/100][1426/1557] Data 0.005 (0.092) Batch 0.809 (1.284) Remain 48:54:23 loss: 1.0079 Lr: 0.00493 [2024-02-17 23:12:22,235 INFO misc.py line 119 87073] Train: [12/100][1427/1557] Data 0.004 (0.092) Batch 0.752 (1.283) Remain 48:53:30 loss: 0.5403 Lr: 0.00493 [2024-02-17 23:12:23,381 INFO misc.py line 119 87073] Train: [12/100][1428/1557] Data 0.004 (0.092) Batch 1.145 (1.283) Remain 48:53:16 loss: 0.6059 Lr: 0.00493 [2024-02-17 23:12:24,463 INFO misc.py line 119 87073] Train: [12/100][1429/1557] Data 0.004 (0.092) Batch 1.083 (1.283) Remain 48:52:55 loss: 0.9106 Lr: 0.00493 [2024-02-17 23:12:25,443 INFO misc.py line 119 87073] Train: [12/100][1430/1557] Data 0.003 (0.092) Batch 0.980 (1.283) Remain 48:52:25 loss: 0.7186 Lr: 0.00493 [2024-02-17 23:12:26,349 INFO misc.py line 119 87073] Train: [12/100][1431/1557] Data 0.003 (0.092) Batch 0.907 (1.283) Remain 48:51:47 loss: 0.4144 Lr: 0.00493 [2024-02-17 23:12:27,383 INFO misc.py line 119 87073] Train: [12/100][1432/1557] Data 0.005 (0.092) Batch 1.007 (1.282) Remain 48:51:19 loss: 0.4316 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1439/1557] Data 0.004 (0.091) Batch 0.969 (1.281) Remain 48:47:48 loss: 0.7455 Lr: 0.00493 [2024-02-17 23:12:34,968 INFO misc.py line 119 87073] Train: [12/100][1440/1557] Data 0.004 (0.091) Batch 0.753 (1.281) Remain 48:46:56 loss: 0.7896 Lr: 0.00493 [2024-02-17 23:12:35,737 INFO misc.py line 119 87073] Train: [12/100][1441/1557] Data 0.007 (0.091) Batch 0.770 (1.280) Remain 48:46:06 loss: 0.9811 Lr: 0.00493 [2024-02-17 23:12:36,931 INFO misc.py line 119 87073] Train: [12/100][1442/1557] Data 0.004 (0.091) Batch 1.193 (1.280) Remain 48:45:57 loss: 0.2915 Lr: 0.00493 [2024-02-17 23:12:37,880 INFO misc.py line 119 87073] Train: [12/100][1443/1557] Data 0.006 (0.091) Batch 0.949 (1.280) Remain 48:45:24 loss: 0.9241 Lr: 0.00493 [2024-02-17 23:12:38,878 INFO misc.py line 119 87073] Train: [12/100][1444/1557] Data 0.010 (0.091) Batch 0.999 (1.280) Remain 48:44:56 loss: 0.8111 Lr: 0.00493 [2024-02-17 23:12:39,951 INFO misc.py line 119 87073] Train: [12/100][1445/1557] Data 0.009 (0.091) Batch 1.073 (1.280) Remain 48:44:35 loss: 0.2976 Lr: 0.00493 [2024-02-17 23:12:40,851 INFO misc.py line 119 87073] Train: [12/100][1446/1557] Data 0.004 (0.091) Batch 0.900 (1.279) Remain 48:43:58 loss: 0.2959 Lr: 0.00493 [2024-02-17 23:12:41,589 INFO misc.py line 119 87073] Train: [12/100][1447/1557] Data 0.004 (0.091) Batch 0.738 (1.279) Remain 48:43:05 loss: 0.5149 Lr: 0.00493 [2024-02-17 23:12:42,392 INFO misc.py line 119 87073] Train: [12/100][1448/1557] Data 0.005 (0.091) Batch 0.802 (1.279) Remain 48:42:18 loss: 0.5273 Lr: 0.00493 [2024-02-17 23:12:43,534 INFO misc.py line 119 87073] Train: [12/100][1449/1557] Data 0.005 (0.091) Batch 1.141 (1.279) Remain 48:42:04 loss: 0.4906 Lr: 0.00493 [2024-02-17 23:12:44,712 INFO misc.py line 119 87073] Train: [12/100][1450/1557] Data 0.006 (0.091) Batch 1.178 (1.279) Remain 48:41:53 loss: 0.8120 Lr: 0.00493 [2024-02-17 23:12:45,671 INFO misc.py line 119 87073] Train: [12/100][1451/1557] Data 0.005 (0.090) Batch 0.960 (1.278) Remain 48:41:22 loss: 0.4529 Lr: 0.00493 [2024-02-17 23:12:46,495 INFO misc.py line 119 87073] Train: [12/100][1452/1557] Data 0.004 (0.090) Batch 0.823 (1.278) Remain 48:40:38 loss: 0.3513 Lr: 0.00493 [2024-02-17 23:12:47,539 INFO misc.py line 119 87073] Train: [12/100][1453/1557] Data 0.008 (0.090) Batch 1.042 (1.278) Remain 48:40:14 loss: 0.6102 Lr: 0.00493 [2024-02-17 23:12:48,286 INFO misc.py line 119 87073] Train: [12/100][1454/1557] Data 0.007 (0.090) Batch 0.749 (1.277) Remain 48:39:23 loss: 0.6361 Lr: 0.00493 [2024-02-17 23:12:48,990 INFO misc.py line 119 87073] Train: [12/100][1455/1557] Data 0.007 (0.090) Batch 0.703 (1.277) Remain 48:38:27 loss: 0.5486 Lr: 0.00493 [2024-02-17 23:12:50,131 INFO misc.py line 119 87073] Train: [12/100][1456/1557] Data 0.005 (0.090) Batch 1.141 (1.277) Remain 48:38:13 loss: 0.1858 Lr: 0.00493 [2024-02-17 23:12:51,100 INFO misc.py line 119 87073] Train: [12/100][1457/1557] Data 0.006 (0.090) Batch 0.971 (1.277) Remain 48:37:43 loss: 0.7249 Lr: 0.00493 [2024-02-17 23:12:52,085 INFO misc.py line 119 87073] Train: [12/100][1458/1557] Data 0.005 (0.090) Batch 0.984 (1.277) Remain 48:37:14 loss: 0.5777 Lr: 0.00493 [2024-02-17 23:12:52,980 INFO misc.py line 119 87073] Train: [12/100][1459/1557] Data 0.006 (0.090) Batch 0.894 (1.276) Remain 48:36:37 loss: 0.5010 Lr: 0.00493 [2024-02-17 23:12:53,893 INFO misc.py line 119 87073] Train: [12/100][1460/1557] Data 0.007 (0.090) Batch 0.913 (1.276) Remain 48:36:01 loss: 0.6680 Lr: 0.00493 [2024-02-17 23:12:54,562 INFO misc.py line 119 87073] Train: [12/100][1461/1557] Data 0.007 (0.090) Batch 0.672 (1.276) Remain 48:35:03 loss: 0.9037 Lr: 0.00493 [2024-02-17 23:12:55,323 INFO misc.py line 119 87073] Train: [12/100][1462/1557] Data 0.004 (0.090) Batch 0.761 (1.275) Remain 48:34:13 loss: 0.8024 Lr: 0.00493 [2024-02-17 23:13:15,021 INFO misc.py line 119 87073] Train: [12/100][1463/1557] Data 4.556 (0.093) Batch 19.696 (1.288) Remain 49:03:02 loss: 0.2119 Lr: 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INFO misc.py line 119 87073] Train: [12/100][1470/1557] Data 0.006 (0.092) Batch 1.336 (1.286) Remain 48:58:47 loss: 0.3534 Lr: 0.00493 [2024-02-17 23:13:22,361 INFO misc.py line 119 87073] Train: [12/100][1471/1557] Data 0.006 (0.092) Batch 0.955 (1.286) Remain 48:58:15 loss: 0.7258 Lr: 0.00493 [2024-02-17 23:13:23,327 INFO misc.py line 119 87073] Train: [12/100][1472/1557] Data 0.004 (0.092) Batch 0.966 (1.286) Remain 48:57:44 loss: 1.0052 Lr: 0.00493 [2024-02-17 23:13:24,336 INFO misc.py line 119 87073] Train: [12/100][1473/1557] Data 0.004 (0.092) Batch 1.008 (1.285) Remain 48:57:17 loss: 1.1020 Lr: 0.00493 [2024-02-17 23:13:25,297 INFO misc.py line 119 87073] Train: [12/100][1474/1557] Data 0.006 (0.092) Batch 0.962 (1.285) Remain 48:56:45 loss: 1.0063 Lr: 0.00493 [2024-02-17 23:13:26,110 INFO misc.py line 119 87073] Train: [12/100][1475/1557] Data 0.005 (0.092) Batch 0.811 (1.285) Remain 48:56:00 loss: 0.4812 Lr: 0.00493 [2024-02-17 23:13:26,862 INFO misc.py line 119 87073] Train: [12/100][1476/1557] Data 0.006 (0.092) Batch 0.754 (1.285) Remain 48:55:09 loss: 0.4222 Lr: 0.00493 [2024-02-17 23:13:28,101 INFO misc.py line 119 87073] Train: [12/100][1477/1557] Data 0.004 (0.092) Batch 1.238 (1.285) Remain 48:55:04 loss: 0.3245 Lr: 0.00493 [2024-02-17 23:13:29,256 INFO misc.py line 119 87073] Train: [12/100][1478/1557] Data 0.004 (0.092) Batch 1.154 (1.284) Remain 48:54:50 loss: 1.0269 Lr: 0.00493 [2024-02-17 23:13:30,171 INFO misc.py line 119 87073] Train: [12/100][1479/1557] Data 0.005 (0.092) Batch 0.916 (1.284) Remain 48:54:15 loss: 0.7274 Lr: 0.00493 [2024-02-17 23:13:31,060 INFO misc.py line 119 87073] Train: [12/100][1480/1557] Data 0.005 (0.092) Batch 0.889 (1.284) Remain 48:53:37 loss: 0.6265 Lr: 0.00493 [2024-02-17 23:13:32,108 INFO misc.py line 119 87073] Train: [12/100][1481/1557] Data 0.004 (0.092) Batch 1.047 (1.284) Remain 48:53:14 loss: 0.6074 Lr: 0.00493 [2024-02-17 23:13:32,935 INFO misc.py line 119 87073] Train: [12/100][1482/1557] Data 0.006 (0.092) Batch 0.828 (1.283) Remain 48:52:30 loss: 0.7095 Lr: 0.00493 [2024-02-17 23:13:33,711 INFO misc.py line 119 87073] Train: [12/100][1483/1557] Data 0.004 (0.092) Batch 0.775 (1.283) Remain 48:51:42 loss: 0.4631 Lr: 0.00493 [2024-02-17 23:13:34,952 INFO misc.py line 119 87073] Train: [12/100][1484/1557] Data 0.006 (0.092) Batch 1.239 (1.283) Remain 48:51:36 loss: 0.3611 Lr: 0.00493 [2024-02-17 23:13:35,895 INFO misc.py line 119 87073] Train: [12/100][1485/1557] Data 0.007 (0.092) Batch 0.946 (1.283) Remain 48:51:04 loss: 0.7820 Lr: 0.00493 [2024-02-17 23:13:36,920 INFO misc.py line 119 87073] Train: [12/100][1486/1557] Data 0.004 (0.092) Batch 1.025 (1.283) Remain 48:50:39 loss: 0.5830 Lr: 0.00493 [2024-02-17 23:13:37,918 INFO misc.py line 119 87073] Train: [12/100][1487/1557] Data 0.004 (0.091) Batch 0.998 (1.282) Remain 48:50:11 loss: 1.2880 Lr: 0.00493 [2024-02-17 23:13:38,785 INFO misc.py line 119 87073] Train: [12/100][1488/1557] Data 0.004 (0.091) Batch 0.867 (1.282) Remain 48:49:32 loss: 0.4434 Lr: 0.00493 [2024-02-17 23:13:39,586 INFO misc.py line 119 87073] Train: [12/100][1489/1557] Data 0.005 (0.091) Batch 0.799 (1.282) Remain 48:48:46 loss: 0.4642 Lr: 0.00493 [2024-02-17 23:13:40,319 INFO misc.py line 119 87073] Train: [12/100][1490/1557] Data 0.006 (0.091) Batch 0.734 (1.282) Remain 48:47:54 loss: 0.4046 Lr: 0.00493 [2024-02-17 23:13:41,459 INFO misc.py line 119 87073] Train: [12/100][1491/1557] Data 0.006 (0.091) Batch 1.141 (1.281) Remain 48:47:40 loss: 0.2037 Lr: 0.00493 [2024-02-17 23:13:42,375 INFO misc.py line 119 87073] Train: [12/100][1492/1557] Data 0.004 (0.091) Batch 0.916 (1.281) Remain 48:47:05 loss: 0.2823 Lr: 0.00493 [2024-02-17 23:13:43,449 INFO misc.py line 119 87073] Train: [12/100][1493/1557] Data 0.005 (0.091) Batch 1.074 (1.281) Remain 48:46:44 loss: 1.1472 Lr: 0.00493 [2024-02-17 23:13:44,484 INFO misc.py line 119 87073] Train: [12/100][1494/1557] Data 0.005 (0.091) Batch 1.035 (1.281) Remain 48:46:20 loss: 0.6145 Lr: 0.00493 [2024-02-17 23:13:45,305 INFO misc.py line 119 87073] Train: [12/100][1495/1557] Data 0.005 (0.091) Batch 0.821 (1.281) Remain 48:45:37 loss: 0.4843 Lr: 0.00493 [2024-02-17 23:13:46,171 INFO misc.py line 119 87073] Train: [12/100][1496/1557] Data 0.004 (0.091) Batch 0.867 (1.280) Remain 48:44:58 loss: 0.6445 Lr: 0.00493 [2024-02-17 23:13:46,884 INFO misc.py line 119 87073] Train: [12/100][1497/1557] Data 0.004 (0.091) Batch 0.710 (1.280) Remain 48:44:04 loss: 0.6506 Lr: 0.00493 [2024-02-17 23:13:48,055 INFO misc.py line 119 87073] Train: [12/100][1498/1557] Data 0.006 (0.091) Batch 1.170 (1.280) Remain 48:43:53 loss: 0.7303 Lr: 0.00493 [2024-02-17 23:13:48,983 INFO misc.py line 119 87073] Train: [12/100][1499/1557] Data 0.008 (0.091) Batch 0.930 (1.280) Remain 48:43:19 loss: 0.5114 Lr: 0.00493 [2024-02-17 23:13:49,980 INFO misc.py line 119 87073] Train: [12/100][1500/1557] Data 0.006 (0.091) Batch 0.998 (1.279) Remain 48:42:52 loss: 0.6944 Lr: 0.00493 [2024-02-17 23:13:50,898 INFO misc.py line 119 87073] Train: [12/100][1501/1557] Data 0.005 (0.091) Batch 0.918 (1.279) Remain 48:42:18 loss: 0.8109 Lr: 0.00493 [2024-02-17 23:13:51,876 INFO misc.py line 119 87073] Train: [12/100][1502/1557] Data 0.004 (0.091) Batch 0.977 (1.279) Remain 48:41:49 loss: 0.7817 Lr: 0.00493 [2024-02-17 23:13:52,615 INFO misc.py line 119 87073] Train: [12/100][1503/1557] Data 0.005 (0.091) Batch 0.739 (1.279) Remain 48:40:58 loss: 0.5637 Lr: 0.00493 [2024-02-17 23:13:53,390 INFO misc.py line 119 87073] Train: [12/100][1504/1557] Data 0.005 (0.090) Batch 0.744 (1.278) Remain 48:40:08 loss: 0.5955 Lr: 0.00493 [2024-02-17 23:13:54,540 INFO misc.py line 119 87073] Train: [12/100][1505/1557] Data 0.037 (0.090) Batch 1.181 (1.278) Remain 48:39:58 loss: 0.2503 Lr: 0.00493 [2024-02-17 23:13:55,519 INFO misc.py line 119 87073] Train: [12/100][1506/1557] Data 0.006 (0.090) Batch 0.980 (1.278) Remain 48:39:30 loss: 0.2844 Lr: 0.00493 [2024-02-17 23:13:56,430 INFO misc.py line 119 87073] Train: [12/100][1507/1557] Data 0.005 (0.090) Batch 0.911 (1.278) Remain 48:38:55 loss: 0.7016 Lr: 0.00493 [2024-02-17 23:13:57,405 INFO misc.py line 119 87073] Train: [12/100][1508/1557] Data 0.005 (0.090) Batch 0.975 (1.278) Remain 48:38:26 loss: 0.3828 Lr: 0.00493 [2024-02-17 23:13:58,330 INFO misc.py line 119 87073] Train: [12/100][1509/1557] Data 0.004 (0.090) Batch 0.921 (1.277) Remain 48:37:52 loss: 0.4550 Lr: 0.00493 [2024-02-17 23:13:59,009 INFO misc.py line 119 87073] Train: [12/100][1510/1557] Data 0.010 (0.090) Batch 0.684 (1.277) Remain 48:36:57 loss: 0.4751 Lr: 0.00493 [2024-02-17 23:13:59,761 INFO misc.py line 119 87073] Train: [12/100][1511/1557] Data 0.004 (0.090) Batch 0.745 (1.277) Remain 48:36:08 loss: 0.7831 Lr: 0.00493 [2024-02-17 23:14:01,012 INFO misc.py line 119 87073] Train: [12/100][1512/1557] Data 0.010 (0.090) Batch 1.248 (1.277) Remain 48:36:04 loss: 0.2811 Lr: 0.00493 [2024-02-17 23:14:01,936 INFO misc.py line 119 87073] Train: [12/100][1513/1557] Data 0.014 (0.090) Batch 0.934 (1.276) Remain 48:35:31 loss: 0.7929 Lr: 0.00493 [2024-02-17 23:14:02,906 INFO misc.py line 119 87073] Train: [12/100][1514/1557] Data 0.003 (0.090) Batch 0.969 (1.276) Remain 48:35:02 loss: 0.4369 Lr: 0.00493 [2024-02-17 23:14:03,919 INFO misc.py line 119 87073] Train: [12/100][1515/1557] Data 0.004 (0.090) Batch 1.013 (1.276) Remain 48:34:37 loss: 0.5885 Lr: 0.00493 [2024-02-17 23:14:04,841 INFO misc.py line 119 87073] Train: [12/100][1516/1557] Data 0.004 (0.090) Batch 0.920 (1.276) Remain 48:34:04 loss: 0.6839 Lr: 0.00493 [2024-02-17 23:14:05,565 INFO misc.py line 119 87073] Train: [12/100][1517/1557] Data 0.008 (0.090) Batch 0.722 (1.275) Remain 48:33:12 loss: 0.6284 Lr: 0.00493 [2024-02-17 23:14:06,342 INFO misc.py line 119 87073] Train: [12/100][1518/1557] Data 0.008 (0.090) Batch 0.781 (1.275) Remain 48:32:26 loss: 0.4608 Lr: 0.00493 [2024-02-17 23:14:25,039 INFO misc.py line 119 87073] Train: [12/100][1519/1557] Data 5.129 (0.093) Batch 18.694 (1.287) Remain 48:58:40 loss: 0.3810 Lr: 0.00493 [2024-02-17 23:14:25,875 INFO misc.py line 119 87073] Train: [12/100][1520/1557] Data 0.008 (0.093) Batch 0.838 (1.286) Remain 48:57:58 loss: 0.6756 Lr: 0.00493 [2024-02-17 23:14:26,851 INFO misc.py line 119 87073] Train: [12/100][1521/1557] Data 0.005 (0.093) Batch 0.977 (1.286) Remain 48:57:29 loss: 0.7641 Lr: 0.00493 [2024-02-17 23:14:27,749 INFO misc.py line 119 87073] Train: [12/100][1522/1557] Data 0.004 (0.093) Batch 0.896 (1.286) Remain 48:56:52 loss: 0.5274 Lr: 0.00493 [2024-02-17 23:14:28,687 INFO misc.py line 119 87073] Train: [12/100][1523/1557] Data 0.006 (0.093) Batch 0.934 (1.286) Remain 48:56:19 loss: 0.4952 Lr: 0.00493 [2024-02-17 23:14:29,434 INFO misc.py line 119 87073] Train: [12/100][1524/1557] Data 0.010 (0.093) Batch 0.753 (1.285) Remain 48:55:30 loss: 0.6656 Lr: 0.00493 [2024-02-17 23:14:30,156 INFO misc.py line 119 87073] Train: [12/100][1525/1557] Data 0.003 (0.093) Batch 0.711 (1.285) Remain 48:54:37 loss: 0.3555 Lr: 0.00493 [2024-02-17 23:14:31,467 INFO misc.py line 119 87073] Train: [12/100][1526/1557] Data 0.014 (0.093) Batch 1.319 (1.285) Remain 48:54:39 loss: 0.4161 Lr: 0.00493 [2024-02-17 23:14:32,528 INFO misc.py line 119 87073] Train: [12/100][1527/1557] Data 0.006 (0.093) Batch 1.051 (1.285) Remain 48:54:17 loss: 0.1268 Lr: 0.00493 [2024-02-17 23:14:33,781 INFO misc.py line 119 87073] Train: [12/100][1528/1557] Data 0.015 (0.093) Batch 1.254 (1.285) Remain 48:54:13 loss: 0.9061 Lr: 0.00493 [2024-02-17 23:14:35,000 INFO misc.py line 119 87073] Train: [12/100][1529/1557] Data 0.015 (0.093) Batch 1.218 (1.285) Remain 48:54:05 loss: 0.6400 Lr: 0.00493 [2024-02-17 23:14:36,072 INFO misc.py line 119 87073] Train: [12/100][1530/1557] Data 0.016 (0.092) Batch 1.072 (1.284) Remain 48:53:45 loss: 0.2866 Lr: 0.00493 [2024-02-17 23:14:36,781 INFO misc.py line 119 87073] Train: [12/100][1531/1557] Data 0.016 (0.092) Batch 0.721 (1.284) Remain 48:52:53 loss: 0.5165 Lr: 0.00493 [2024-02-17 23:14:37,458 INFO misc.py line 119 87073] Train: [12/100][1532/1557] Data 0.004 (0.092) Batch 0.673 (1.284) Remain 48:51:57 loss: 0.7059 Lr: 0.00493 [2024-02-17 23:14:38,726 INFO misc.py line 119 87073] Train: [12/100][1533/1557] Data 0.007 (0.092) Batch 1.263 (1.284) Remain 48:51:54 loss: 0.4172 Lr: 0.00493 [2024-02-17 23:14:39,664 INFO misc.py line 119 87073] Train: [12/100][1534/1557] Data 0.012 (0.092) Batch 0.947 (1.283) Remain 48:51:22 loss: 0.8374 Lr: 0.00493 [2024-02-17 23:14:40,781 INFO misc.py line 119 87073] Train: [12/100][1535/1557] Data 0.003 (0.092) Batch 1.116 (1.283) Remain 48:51:06 loss: 0.5456 Lr: 0.00493 [2024-02-17 23:14:41,658 INFO misc.py line 119 87073] Train: [12/100][1536/1557] Data 0.004 (0.092) Batch 0.877 (1.283) Remain 48:50:29 loss: 0.7158 Lr: 0.00493 [2024-02-17 23:14:42,484 INFO misc.py line 119 87073] Train: [12/100][1537/1557] Data 0.004 (0.092) Batch 0.823 (1.283) Remain 48:49:46 loss: 0.5719 Lr: 0.00493 [2024-02-17 23:14:43,236 INFO misc.py line 119 87073] Train: [12/100][1538/1557] Data 0.007 (0.092) Batch 0.756 (1.282) Remain 48:48:58 loss: 1.0636 Lr: 0.00493 [2024-02-17 23:14:43,899 INFO misc.py line 119 87073] Train: [12/100][1539/1557] Data 0.004 (0.092) Batch 0.652 (1.282) Remain 48:48:00 loss: 0.4063 Lr: 0.00493 [2024-02-17 23:14:45,151 INFO misc.py line 119 87073] Train: [12/100][1540/1557] Data 0.015 (0.092) Batch 1.255 (1.282) Remain 48:47:57 loss: 0.2122 Lr: 0.00493 [2024-02-17 23:14:46,135 INFO misc.py line 119 87073] Train: [12/100][1541/1557] Data 0.011 (0.092) Batch 0.990 (1.282) Remain 48:47:29 loss: 0.4584 Lr: 0.00493 [2024-02-17 23:14:46,964 INFO misc.py line 119 87073] Train: [12/100][1542/1557] Data 0.005 (0.092) Batch 0.829 (1.282) Remain 48:46:48 loss: 1.1344 Lr: 0.00493 [2024-02-17 23:14:47,865 INFO misc.py line 119 87073] Train: [12/100][1543/1557] Data 0.005 (0.092) Batch 0.895 (1.281) Remain 48:46:12 loss: 0.7525 Lr: 0.00493 [2024-02-17 23:14:48,840 INFO misc.py line 119 87073] Train: [12/100][1544/1557] Data 0.011 (0.092) Batch 0.981 (1.281) Remain 48:45:44 loss: 0.7194 Lr: 0.00493 [2024-02-17 23:14:49,584 INFO misc.py line 119 87073] Train: [12/100][1545/1557] Data 0.005 (0.092) Batch 0.744 (1.281) Remain 48:44:55 loss: 0.3296 Lr: 0.00493 [2024-02-17 23:14:50,302 INFO misc.py line 119 87073] Train: [12/100][1546/1557] Data 0.005 (0.092) Batch 0.706 (1.280) Remain 48:44:03 loss: 0.6429 Lr: 0.00493 [2024-02-17 23:14:51,400 INFO misc.py line 119 87073] Train: [12/100][1547/1557] Data 0.017 (0.092) Batch 1.106 (1.280) Remain 48:43:46 loss: 0.2371 Lr: 0.00493 [2024-02-17 23:14:52,421 INFO misc.py line 119 87073] Train: [12/100][1548/1557] Data 0.009 (0.091) Batch 1.016 (1.280) Remain 48:43:21 loss: 0.6507 Lr: 0.00493 [2024-02-17 23:14:53,274 INFO misc.py line 119 87073] Train: [12/100][1549/1557] Data 0.014 (0.091) Batch 0.861 (1.280) Remain 48:42:43 loss: 0.8064 Lr: 0.00493 [2024-02-17 23:14:54,268 INFO misc.py line 119 87073] Train: [12/100][1550/1557] Data 0.005 (0.091) Batch 0.995 (1.280) Remain 48:42:16 loss: 0.5642 Lr: 0.00493 [2024-02-17 23:14:55,476 INFO misc.py line 119 87073] Train: [12/100][1551/1557] Data 0.005 (0.091) Batch 1.199 (1.280) Remain 48:42:08 loss: 0.5194 Lr: 0.00493 [2024-02-17 23:14:56,218 INFO misc.py line 119 87073] Train: [12/100][1552/1557] Data 0.013 (0.091) Batch 0.750 (1.279) Remain 48:41:20 loss: 0.3686 Lr: 0.00493 [2024-02-17 23:14:56,970 INFO misc.py line 119 87073] Train: [12/100][1553/1557] Data 0.004 (0.091) Batch 0.743 (1.279) Remain 48:40:31 loss: 0.4711 Lr: 0.00493 [2024-02-17 23:14:58,060 INFO misc.py line 119 87073] Train: [12/100][1554/1557] Data 0.014 (0.091) Batch 1.091 (1.279) Remain 48:40:13 loss: 0.3412 Lr: 0.00493 [2024-02-17 23:14:59,083 INFO misc.py line 119 87073] Train: [12/100][1555/1557] Data 0.012 (0.091) Batch 1.024 (1.279) Remain 48:39:50 loss: 0.4684 Lr: 0.00493 [2024-02-17 23:14:59,989 INFO misc.py line 119 87073] Train: [12/100][1556/1557] Data 0.013 (0.091) Batch 0.912 (1.278) Remain 48:39:16 loss: 0.7648 Lr: 0.00493 [2024-02-17 23:15:00,766 INFO misc.py line 119 87073] Train: [12/100][1557/1557] Data 0.006 (0.091) Batch 0.778 (1.278) Remain 48:38:31 loss: 0.6493 Lr: 0.00493 [2024-02-17 23:15:00,767 INFO misc.py line 136 87073] Train result: loss: 0.5806 [2024-02-17 23:15:00,767 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 23:15:30,790 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5095 [2024-02-17 23:15:31,568 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5879 [2024-02-17 23:15:33,692 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.6519 [2024-02-17 23:15:35,898 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3920 [2024-02-17 23:15:40,845 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3374 [2024-02-17 23:15:41,544 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1278 [2024-02-17 23:15:42,806 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3458 [2024-02-17 23:15:45,765 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3440 [2024-02-17 23:15:47,573 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.5190 [2024-02-17 23:15:48,899 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6795/0.7634/0.9022. [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9294/0.9586 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9808/0.9884 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8659/0.9544 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3282/0.3488 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6559/0.6785 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6421/0.7639 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8219/0.9290 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9177/0.9626 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8424/0.9023 [2024-02-17 23:15:48,899 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7569/0.8882 [2024-02-17 23:15:48,900 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.5235/0.8678 [2024-02-17 23:15:48,900 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5683/0.6823 [2024-02-17 23:15:48,900 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 23:15:48,902 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 23:15:48,902 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 23:15:56,877 INFO misc.py line 119 87073] Train: [13/100][1/1557] Data 1.009 (1.009) Batch 1.703 (1.703) Remain 64:49:03 loss: 0.5758 Lr: 0.00493 [2024-02-17 23:15:57,850 INFO misc.py line 119 87073] Train: [13/100][2/1557] Data 0.011 (0.011) Batch 0.978 (0.978) Remain 37:13:32 loss: 0.5667 Lr: 0.00493 [2024-02-17 23:15:58,945 INFO misc.py line 119 87073] Train: [13/100][3/1557] Data 0.006 (0.006) Batch 1.094 (1.094) Remain 41:37:26 loss: 0.6053 Lr: 0.00493 [2024-02-17 23:15:59,977 INFO misc.py line 119 87073] Train: [13/100][4/1557] Data 0.007 (0.007) Batch 1.033 (1.033) Remain 39:19:25 loss: 0.3503 Lr: 0.00493 [2024-02-17 23:16:00,772 INFO misc.py line 119 87073] Train: [13/100][5/1557] Data 0.006 (0.006) Batch 0.795 (0.914) Remain 34:47:21 loss: 0.6750 Lr: 0.00493 [2024-02-17 23:16:01,486 INFO misc.py line 119 87073] Train: [13/100][6/1557] Data 0.005 (0.006) Batch 0.713 (0.847) Remain 32:13:59 loss: 0.4778 Lr: 0.00493 [2024-02-17 23:16:02,918 INFO misc.py line 119 87073] Train: [13/100][7/1557] Data 0.006 (0.006) Batch 1.434 (0.994) Remain 37:48:58 loss: 0.2123 Lr: 0.00493 [2024-02-17 23:16:03,842 INFO misc.py line 119 87073] Train: [13/100][8/1557] Data 0.005 (0.005) Batch 0.923 (0.980) Remain 37:16:44 loss: 0.3066 Lr: 0.00493 [2024-02-17 23:16:04,855 INFO misc.py line 119 87073] Train: [13/100][9/1557] Data 0.006 (0.005) Batch 1.012 (0.985) Remain 37:29:00 loss: 0.9988 Lr: 0.00493 [2024-02-17 23:16:05,814 INFO misc.py line 119 87073] Train: [13/100][10/1557] Data 0.006 (0.006) Batch 0.961 (0.982) Remain 37:21:12 loss: 0.5901 Lr: 0.00493 [2024-02-17 23:16:06,774 INFO misc.py line 119 87073] Train: [13/100][11/1557] Data 0.004 (0.005) Batch 0.960 (0.979) Remain 37:14:57 loss: 0.2514 Lr: 0.00493 [2024-02-17 23:16:07,480 INFO misc.py line 119 87073] Train: [13/100][12/1557] Data 0.005 (0.005) Batch 0.705 (0.948) Remain 36:05:34 loss: 0.2393 Lr: 0.00493 [2024-02-17 23:16:08,241 INFO misc.py line 119 87073] Train: [13/100][13/1557] Data 0.005 (0.005) Batch 0.763 (0.930) Remain 35:23:09 loss: 0.6138 Lr: 0.00493 [2024-02-17 23:16:09,471 INFO misc.py line 119 87073] Train: [13/100][14/1557] Data 0.004 (0.005) Batch 1.229 (0.957) Remain 36:25:19 loss: 0.5372 Lr: 0.00493 [2024-02-17 23:16:10,643 INFO misc.py line 119 87073] Train: [13/100][15/1557] Data 0.004 (0.005) Batch 1.170 (0.975) Remain 37:05:49 loss: 0.9441 Lr: 0.00493 [2024-02-17 23:16:11,557 INFO misc.py line 119 87073] Train: [13/100][16/1557] Data 0.006 (0.005) Batch 0.915 (0.970) Remain 36:55:22 loss: 0.6407 Lr: 0.00493 [2024-02-17 23:16:12,545 INFO misc.py line 119 87073] Train: [13/100][17/1557] Data 0.005 (0.005) Batch 0.987 (0.971) Remain 36:58:09 loss: 0.7010 Lr: 0.00493 [2024-02-17 23:16:13,479 INFO misc.py line 119 87073] Train: [13/100][18/1557] Data 0.005 (0.005) Batch 0.935 (0.969) Remain 36:52:38 loss: 0.3417 Lr: 0.00493 [2024-02-17 23:16:14,255 INFO misc.py line 119 87073] Train: [13/100][19/1557] Data 0.004 (0.005) Batch 0.769 (0.957) Remain 36:24:03 loss: 0.5029 Lr: 0.00493 [2024-02-17 23:16:15,035 INFO misc.py line 119 87073] Train: [13/100][20/1557] Data 0.011 (0.005) Batch 0.787 (0.947) Remain 36:01:17 loss: 0.7009 Lr: 0.00493 [2024-02-17 23:16:16,177 INFO misc.py line 119 87073] Train: [13/100][21/1557] Data 0.004 (0.005) Batch 1.142 (0.957) Remain 36:26:06 loss: 0.1961 Lr: 0.00493 [2024-02-17 23:16:17,167 INFO misc.py line 119 87073] Train: [13/100][22/1557] Data 0.004 (0.005) Batch 0.989 (0.959) Remain 36:29:52 loss: 0.7830 Lr: 0.00493 [2024-02-17 23:16:18,059 INFO misc.py line 119 87073] Train: [13/100][23/1557] Data 0.005 (0.005) Batch 0.893 (0.956) Remain 36:22:17 loss: 0.4967 Lr: 0.00493 [2024-02-17 23:16:19,140 INFO misc.py line 119 87073] Train: [13/100][24/1557] Data 0.004 (0.005) Batch 1.082 (0.962) Remain 36:35:58 loss: 0.4045 Lr: 0.00493 [2024-02-17 23:16:20,083 INFO misc.py line 119 87073] Train: [13/100][25/1557] Data 0.003 (0.005) Batch 0.942 (0.961) Remain 36:33:54 loss: 0.5414 Lr: 0.00493 [2024-02-17 23:16:20,775 INFO misc.py line 119 87073] Train: [13/100][26/1557] Data 0.004 (0.005) Batch 0.681 (0.949) Remain 36:06:07 loss: 0.4251 Lr: 0.00493 [2024-02-17 23:16:21,556 INFO misc.py line 119 87073] Train: [13/100][27/1557] Data 0.015 (0.005) Batch 0.792 (0.942) Remain 35:51:10 loss: 0.2691 Lr: 0.00493 [2024-02-17 23:16:22,757 INFO misc.py line 119 87073] Train: [13/100][28/1557] Data 0.003 (0.005) Batch 1.200 (0.953) Remain 36:14:44 loss: 0.3538 Lr: 0.00493 [2024-02-17 23:16:23,712 INFO misc.py line 119 87073] Train: [13/100][29/1557] Data 0.004 (0.005) Batch 0.956 (0.953) Remain 36:14:59 loss: 0.6236 Lr: 0.00493 [2024-02-17 23:16:24,579 INFO misc.py line 119 87073] Train: [13/100][30/1557] Data 0.004 (0.005) Batch 0.867 (0.949) Remain 36:07:43 loss: 0.5785 Lr: 0.00493 [2024-02-17 23:16:25,463 INFO misc.py line 119 87073] Train: [13/100][31/1557] Data 0.004 (0.005) Batch 0.877 (0.947) Remain 36:01:49 loss: 0.5852 Lr: 0.00493 [2024-02-17 23:16:26,557 INFO misc.py line 119 87073] Train: [13/100][32/1557] Data 0.011 (0.005) Batch 1.097 (0.952) Remain 36:13:36 loss: 0.6160 Lr: 0.00493 [2024-02-17 23:16:27,329 INFO misc.py line 119 87073] Train: [13/100][33/1557] Data 0.008 (0.005) Batch 0.775 (0.946) Remain 36:00:08 loss: 0.5955 Lr: 0.00493 [2024-02-17 23:16:28,008 INFO misc.py line 119 87073] Train: [13/100][34/1557] Data 0.005 (0.005) Batch 0.669 (0.937) Remain 35:39:45 loss: 0.6039 Lr: 0.00493 [2024-02-17 23:16:29,209 INFO misc.py line 119 87073] Train: [13/100][35/1557] Data 0.014 (0.006) Batch 1.200 (0.945) Remain 35:58:30 loss: 0.2768 Lr: 0.00493 [2024-02-17 23:16:30,217 INFO misc.py line 119 87073] Train: [13/100][36/1557] Data 0.014 (0.006) Batch 1.006 (0.947) Remain 36:02:39 loss: 0.9063 Lr: 0.00493 [2024-02-17 23:16:31,156 INFO misc.py line 119 87073] Train: [13/100][37/1557] Data 0.018 (0.006) Batch 0.951 (0.947) Remain 36:02:54 loss: 0.5781 Lr: 0.00493 [2024-02-17 23:16:32,177 INFO misc.py line 119 87073] Train: [13/100][38/1557] Data 0.005 (0.006) Batch 1.022 (0.950) Remain 36:07:46 loss: 0.5020 Lr: 0.00493 [2024-02-17 23:16:33,376 INFO misc.py line 119 87073] Train: [13/100][39/1557] Data 0.004 (0.006) Batch 1.198 (0.956) Remain 36:23:32 loss: 0.6041 Lr: 0.00493 [2024-02-17 23:16:34,028 INFO misc.py line 119 87073] Train: [13/100][40/1557] Data 0.004 (0.006) Batch 0.651 (0.948) Remain 36:04:42 loss: 0.6244 Lr: 0.00493 [2024-02-17 23:16:34,771 INFO misc.py line 119 87073] Train: [13/100][41/1557] Data 0.005 (0.006) Batch 0.742 (0.943) Remain 35:52:16 loss: 0.6175 Lr: 0.00493 [2024-02-17 23:16:36,072 INFO misc.py line 119 87073] Train: [13/100][42/1557] Data 0.006 (0.006) Batch 1.301 (0.952) Remain 36:13:14 loss: 0.4513 Lr: 0.00493 [2024-02-17 23:16:37,009 INFO misc.py line 119 87073] Train: [13/100][43/1557] Data 0.006 (0.006) Batch 0.938 (0.952) Remain 36:12:26 loss: 0.7152 Lr: 0.00493 [2024-02-17 23:16:37,908 INFO misc.py line 119 87073] Train: [13/100][44/1557] Data 0.005 (0.006) Batch 0.899 (0.950) Remain 36:09:29 loss: 0.5563 Lr: 0.00493 [2024-02-17 23:16:38,695 INFO misc.py line 119 87073] Train: [13/100][45/1557] Data 0.005 (0.006) Batch 0.786 (0.946) Remain 36:00:33 loss: 0.7714 Lr: 0.00493 [2024-02-17 23:16:39,557 INFO misc.py line 119 87073] Train: [13/100][46/1557] Data 0.005 (0.006) Batch 0.863 (0.944) Remain 35:56:06 loss: 0.4238 Lr: 0.00493 [2024-02-17 23:16:40,329 INFO misc.py line 119 87073] Train: [13/100][47/1557] Data 0.005 (0.006) Batch 0.772 (0.941) Remain 35:47:10 loss: 0.6570 Lr: 0.00493 [2024-02-17 23:16:41,019 INFO misc.py line 119 87073] Train: [13/100][48/1557] Data 0.004 (0.006) Batch 0.689 (0.935) Remain 35:34:24 loss: 0.4381 Lr: 0.00493 [2024-02-17 23:16:42,236 INFO misc.py line 119 87073] Train: [13/100][49/1557] Data 0.005 (0.006) Batch 1.215 (0.941) Remain 35:48:18 loss: 0.3161 Lr: 0.00493 [2024-02-17 23:16:43,192 INFO misc.py line 119 87073] Train: [13/100][50/1557] Data 0.007 (0.006) Batch 0.959 (0.941) Remain 35:49:09 loss: 0.4731 Lr: 0.00493 [2024-02-17 23:16:44,093 INFO misc.py line 119 87073] Train: [13/100][51/1557] Data 0.004 (0.006) Batch 0.899 (0.941) Remain 35:47:08 loss: 0.4229 Lr: 0.00493 [2024-02-17 23:16:44,992 INFO misc.py line 119 87073] Train: [13/100][52/1557] Data 0.005 (0.006) Batch 0.899 (0.940) Remain 35:45:10 loss: 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INFO misc.py line 119 87073] Train: [13/100][59/1557] Data 0.005 (0.006) Batch 0.845 (0.937) Remain 35:38:17 loss: 0.3855 Lr: 0.00493 [2024-02-17 23:16:52,302 INFO misc.py line 119 87073] Train: [13/100][60/1557] Data 0.003 (0.006) Batch 0.887 (0.936) Remain 35:36:17 loss: 0.4132 Lr: 0.00493 [2024-02-17 23:16:53,123 INFO misc.py line 119 87073] Train: [13/100][61/1557] Data 0.017 (0.006) Batch 0.832 (0.934) Remain 35:32:11 loss: 0.6379 Lr: 0.00493 [2024-02-17 23:16:53,856 INFO misc.py line 119 87073] Train: [13/100][62/1557] Data 0.005 (0.006) Batch 0.733 (0.931) Remain 35:24:23 loss: 0.6865 Lr: 0.00493 [2024-02-17 23:17:03,934 INFO misc.py line 119 87073] Train: [13/100][63/1557] Data 4.522 (0.081) Batch 10.068 (1.083) Remain 41:11:59 loss: 0.3597 Lr: 0.00493 [2024-02-17 23:17:05,218 INFO misc.py line 119 87073] Train: [13/100][64/1557] Data 0.015 (0.080) Batch 1.288 (1.086) Remain 41:19:39 loss: 0.4820 Lr: 0.00493 [2024-02-17 23:17:06,126 INFO misc.py line 119 87073] Train: 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0.883 (1.068) Remain 40:37:31 loss: 0.9747 Lr: 0.00493 [2024-02-17 23:17:12,384 INFO misc.py line 119 87073] Train: [13/100][72/1557] Data 0.005 (0.072) Batch 0.818 (1.064) Remain 40:29:13 loss: 0.5212 Lr: 0.00493 [2024-02-17 23:17:13,327 INFO misc.py line 119 87073] Train: [13/100][73/1557] Data 0.006 (0.071) Batch 0.936 (1.063) Remain 40:25:02 loss: 0.7225 Lr: 0.00493 [2024-02-17 23:17:14,258 INFO misc.py line 119 87073] Train: [13/100][74/1557] Data 0.013 (0.070) Batch 0.939 (1.061) Remain 40:21:03 loss: 0.6063 Lr: 0.00493 [2024-02-17 23:17:15,004 INFO misc.py line 119 87073] Train: [13/100][75/1557] Data 0.004 (0.069) Batch 0.745 (1.056) Remain 40:11:01 loss: 0.3952 Lr: 0.00493 [2024-02-17 23:17:15,704 INFO misc.py line 119 87073] Train: [13/100][76/1557] Data 0.005 (0.068) Batch 0.698 (1.051) Remain 39:59:48 loss: 0.4852 Lr: 0.00493 [2024-02-17 23:17:16,773 INFO misc.py line 119 87073] Train: [13/100][77/1557] Data 0.007 (0.067) Batch 1.071 (1.052) Remain 40:00:23 loss: 0.1923 Lr: 0.00493 [2024-02-17 23:17:17,853 INFO misc.py line 119 87073] Train: [13/100][78/1557] Data 0.006 (0.067) Batch 1.078 (1.052) Remain 40:01:09 loss: 0.6893 Lr: 0.00493 [2024-02-17 23:17:19,154 INFO misc.py line 119 87073] Train: [13/100][79/1557] Data 0.007 (0.066) Batch 1.293 (1.055) Remain 40:08:23 loss: 0.4449 Lr: 0.00493 [2024-02-17 23:17:20,010 INFO misc.py line 119 87073] Train: [13/100][80/1557] Data 0.015 (0.065) Batch 0.867 (1.053) Remain 40:02:46 loss: 0.9580 Lr: 0.00493 [2024-02-17 23:17:21,083 INFO misc.py line 119 87073] Train: [13/100][81/1557] Data 0.005 (0.064) Batch 1.074 (1.053) Remain 40:03:23 loss: 0.9489 Lr: 0.00493 [2024-02-17 23:17:21,821 INFO misc.py line 119 87073] Train: [13/100][82/1557] Data 0.003 (0.064) Batch 0.736 (1.049) Remain 39:54:13 loss: 0.7409 Lr: 0.00493 [2024-02-17 23:17:22,562 INFO misc.py line 119 87073] Train: [13/100][83/1557] Data 0.006 (0.063) Batch 0.735 (1.045) Remain 39:45:14 loss: 0.7142 Lr: 0.00493 [2024-02-17 23:17:23,751 INFO misc.py line 119 87073] Train: [13/100][84/1557] Data 0.011 (0.062) Batch 1.184 (1.047) Remain 39:49:08 loss: 0.3886 Lr: 0.00493 [2024-02-17 23:17:24,774 INFO misc.py line 119 87073] Train: [13/100][85/1557] Data 0.016 (0.062) Batch 1.025 (1.047) Remain 39:48:31 loss: 0.8739 Lr: 0.00493 [2024-02-17 23:17:25,731 INFO misc.py line 119 87073] Train: [13/100][86/1557] Data 0.014 (0.061) Batch 0.967 (1.046) Remain 39:46:19 loss: 0.3722 Lr: 0.00493 [2024-02-17 23:17:26,654 INFO misc.py line 119 87073] Train: [13/100][87/1557] Data 0.004 (0.060) Batch 0.923 (1.044) Remain 39:42:58 loss: 0.4761 Lr: 0.00493 [2024-02-17 23:17:27,738 INFO misc.py line 119 87073] Train: [13/100][88/1557] Data 0.003 (0.060) Batch 1.084 (1.045) Remain 39:44:00 loss: 0.8680 Lr: 0.00493 [2024-02-17 23:17:28,468 INFO misc.py line 119 87073] Train: [13/100][89/1557] Data 0.005 (0.059) Batch 0.729 (1.041) Remain 39:35:37 loss: 0.3962 Lr: 0.00493 [2024-02-17 23:17:29,261 INFO misc.py line 119 87073] Train: [13/100][90/1557] Data 0.005 (0.058) Batch 0.793 (1.038) Remain 39:29:05 loss: 0.7438 Lr: 0.00493 [2024-02-17 23:17:30,525 INFO misc.py line 119 87073] Train: [13/100][91/1557] Data 0.005 (0.058) Batch 1.262 (1.041) Remain 39:34:52 loss: 0.2984 Lr: 0.00493 [2024-02-17 23:17:31,410 INFO misc.py line 119 87073] Train: [13/100][92/1557] Data 0.007 (0.057) Batch 0.888 (1.039) Remain 39:30:57 loss: 0.4065 Lr: 0.00493 [2024-02-17 23:17:32,240 INFO misc.py line 119 87073] Train: [13/100][93/1557] Data 0.004 (0.057) Batch 0.830 (1.037) Remain 39:25:38 loss: 0.4216 Lr: 0.00493 [2024-02-17 23:17:33,301 INFO misc.py line 119 87073] Train: [13/100][94/1557] Data 0.004 (0.056) Batch 1.060 (1.037) Remain 39:26:13 loss: 0.8213 Lr: 0.00493 [2024-02-17 23:17:34,265 INFO misc.py line 119 87073] Train: [13/100][95/1557] Data 0.005 (0.056) Batch 0.965 (1.036) Remain 39:24:25 loss: 0.4616 Lr: 0.00493 [2024-02-17 23:17:35,065 INFO misc.py line 119 87073] Train: [13/100][96/1557] Data 0.004 (0.055) Batch 0.800 (1.034) Remain 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Batch 0.910 (1.121) Remain 42:24:07 loss: 1.1034 Lr: 0.00492 [2024-02-17 23:30:45,296 INFO misc.py line 119 87073] Train: [13/100][794/1557] Data 0.004 (0.086) Batch 1.101 (1.121) Remain 42:24:03 loss: 0.7327 Lr: 0.00492 [2024-02-17 23:30:46,348 INFO misc.py line 119 87073] Train: [13/100][795/1557] Data 0.004 (0.086) Batch 1.051 (1.120) Remain 42:23:49 loss: 0.4649 Lr: 0.00492 [2024-02-17 23:30:47,084 INFO misc.py line 119 87073] Train: [13/100][796/1557] Data 0.005 (0.086) Batch 0.736 (1.120) Remain 42:22:42 loss: 0.5559 Lr: 0.00492 [2024-02-17 23:30:47,878 INFO misc.py line 119 87073] Train: [13/100][797/1557] Data 0.004 (0.086) Batch 0.792 (1.120) Remain 42:21:45 loss: 0.4569 Lr: 0.00492 [2024-02-17 23:30:49,126 INFO misc.py line 119 87073] Train: [13/100][798/1557] Data 0.007 (0.085) Batch 1.248 (1.120) Remain 42:22:06 loss: 0.6204 Lr: 0.00492 [2024-02-17 23:30:50,013 INFO misc.py line 119 87073] Train: [13/100][799/1557] Data 0.006 (0.085) Batch 0.889 (1.119) Remain 42:21:25 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Train: [13/100][1259/1557] Data 0.003 (0.086) Batch 0.695 (1.122) Remain 42:17:40 loss: 0.8046 Lr: 0.00492 [2024-02-17 23:39:28,850 INFO misc.py line 119 87073] Train: [13/100][1260/1557] Data 0.017 (0.086) Batch 1.200 (1.122) Remain 42:17:47 loss: 0.2715 Lr: 0.00492 [2024-02-17 23:39:29,673 INFO misc.py line 119 87073] Train: [13/100][1261/1557] Data 0.021 (0.086) Batch 0.840 (1.121) Remain 42:17:16 loss: 0.1885 Lr: 0.00492 [2024-02-17 23:39:30,684 INFO misc.py line 119 87073] Train: [13/100][1262/1557] Data 0.004 (0.086) Batch 1.012 (1.121) Remain 42:17:03 loss: 0.7702 Lr: 0.00492 [2024-02-17 23:39:32,021 INFO misc.py line 119 87073] Train: [13/100][1263/1557] Data 0.004 (0.086) Batch 1.324 (1.121) Remain 42:17:24 loss: 0.6145 Lr: 0.00492 [2024-02-17 23:39:32,962 INFO misc.py line 119 87073] Train: [13/100][1264/1557] Data 0.016 (0.086) Batch 0.954 (1.121) Remain 42:17:04 loss: 0.4689 Lr: 0.00492 [2024-02-17 23:39:33,614 INFO misc.py line 119 87073] Train: [13/100][1265/1557] Data 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Remain 42:21:29 loss: 0.2644 Lr: 0.00492 [2024-02-17 23:40:19,390 INFO misc.py line 119 87073] Train: [13/100][1303/1557] Data 0.009 (0.086) Batch 0.872 (1.123) Remain 42:21:02 loss: 0.3965 Lr: 0.00492 [2024-02-17 23:40:20,616 INFO misc.py line 119 87073] Train: [13/100][1304/1557] Data 0.004 (0.086) Batch 1.225 (1.123) Remain 42:21:12 loss: 0.4322 Lr: 0.00492 [2024-02-17 23:40:21,532 INFO misc.py line 119 87073] Train: [13/100][1305/1557] Data 0.005 (0.086) Batch 0.916 (1.123) Remain 42:20:49 loss: 0.5538 Lr: 0.00492 [2024-02-17 23:40:22,560 INFO misc.py line 119 87073] Train: [13/100][1306/1557] Data 0.004 (0.086) Batch 1.029 (1.123) Remain 42:20:38 loss: 0.7309 Lr: 0.00492 [2024-02-17 23:40:23,322 INFO misc.py line 119 87073] Train: [13/100][1307/1557] Data 0.004 (0.086) Batch 0.761 (1.123) Remain 42:19:59 loss: 0.6764 Lr: 0.00492 [2024-02-17 23:40:24,093 INFO misc.py line 119 87073] Train: [13/100][1308/1557] Data 0.004 (0.086) Batch 0.771 (1.123) Remain 42:19:21 loss: 0.6472 Lr: 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Remain 42:19:41 loss: 0.4164 Lr: 0.00492 [2024-02-17 23:41:28,936 INFO misc.py line 119 87073] Train: [13/100][1365/1557] Data 0.003 (0.086) Batch 1.143 (1.123) Remain 42:19:41 loss: 0.4312 Lr: 0.00492 [2024-02-17 23:41:30,027 INFO misc.py line 119 87073] Train: [13/100][1366/1557] Data 0.007 (0.086) Batch 1.090 (1.123) Remain 42:19:37 loss: 0.7696 Lr: 0.00492 [2024-02-17 23:41:30,963 INFO misc.py line 119 87073] Train: [13/100][1367/1557] Data 0.008 (0.085) Batch 0.940 (1.123) Remain 42:19:18 loss: 0.7975 Lr: 0.00492 [2024-02-17 23:41:31,881 INFO misc.py line 119 87073] Train: [13/100][1368/1557] Data 0.004 (0.085) Batch 0.918 (1.123) Remain 42:18:56 loss: 0.5149 Lr: 0.00492 [2024-02-17 23:41:33,147 INFO misc.py line 119 87073] Train: [13/100][1369/1557] Data 0.004 (0.085) Batch 1.265 (1.123) Remain 42:19:09 loss: 0.7316 Lr: 0.00492 [2024-02-17 23:41:33,840 INFO misc.py line 119 87073] Train: [13/100][1370/1557] Data 0.005 (0.085) Batch 0.692 (1.123) Remain 42:18:25 loss: 0.4940 Lr: 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Remain 42:11:34 loss: 0.3848 Lr: 0.00492 [2024-02-17 23:41:58,895 INFO misc.py line 119 87073] Train: [13/100][1396/1557] Data 0.004 (0.084) Batch 0.918 (1.120) Remain 42:11:13 loss: 0.6210 Lr: 0.00492 [2024-02-17 23:41:59,839 INFO misc.py line 119 87073] Train: [13/100][1397/1557] Data 0.005 (0.084) Batch 0.944 (1.120) Remain 42:10:55 loss: 0.4389 Lr: 0.00492 [2024-02-17 23:42:00,617 INFO misc.py line 119 87073] Train: [13/100][1398/1557] Data 0.004 (0.084) Batch 0.773 (1.119) Remain 42:10:20 loss: 0.6616 Lr: 0.00492 [2024-02-17 23:42:01,335 INFO misc.py line 119 87073] Train: [13/100][1399/1557] Data 0.009 (0.084) Batch 0.722 (1.119) Remain 42:09:41 loss: 0.6198 Lr: 0.00492 [2024-02-17 23:42:02,560 INFO misc.py line 119 87073] Train: [13/100][1400/1557] Data 0.005 (0.084) Batch 1.225 (1.119) Remain 42:09:50 loss: 0.2937 Lr: 0.00492 [2024-02-17 23:42:03,668 INFO misc.py line 119 87073] Train: [13/100][1401/1557] Data 0.006 (0.084) Batch 1.110 (1.119) Remain 42:09:48 loss: 1.6021 Lr: 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Train: [13/100][1414/1557] Data 0.011 (0.087) Batch 1.271 (1.126) Remain 42:24:10 loss: 0.3955 Lr: 0.00492 [2024-02-17 23:42:28,273 INFO misc.py line 119 87073] Train: [13/100][1415/1557] Data 0.008 (0.087) Batch 0.926 (1.126) Remain 42:23:50 loss: 0.5817 Lr: 0.00492 [2024-02-17 23:42:29,450 INFO misc.py line 119 87073] Train: [13/100][1416/1557] Data 0.005 (0.087) Batch 1.173 (1.126) Remain 42:23:53 loss: 0.6652 Lr: 0.00492 [2024-02-17 23:42:30,589 INFO misc.py line 119 87073] Train: [13/100][1417/1557] Data 0.008 (0.087) Batch 1.096 (1.126) Remain 42:23:49 loss: 0.6412 Lr: 0.00492 [2024-02-17 23:42:31,460 INFO misc.py line 119 87073] Train: [13/100][1418/1557] Data 0.052 (0.087) Batch 0.920 (1.125) Remain 42:23:29 loss: 0.5532 Lr: 0.00492 [2024-02-17 23:42:32,208 INFO misc.py line 119 87073] Train: [13/100][1419/1557] Data 0.003 (0.087) Batch 0.748 (1.125) Remain 42:22:51 loss: 0.7544 Lr: 0.00492 [2024-02-17 23:42:33,016 INFO misc.py line 119 87073] Train: [13/100][1420/1557] Data 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Remain 42:20:27 loss: 0.2955 Lr: 0.00491 [2024-02-17 23:42:39,428 INFO misc.py line 119 87073] Train: [13/100][1427/1557] Data 0.003 (0.086) Batch 0.763 (1.124) Remain 42:19:52 loss: 0.3694 Lr: 0.00491 [2024-02-17 23:42:40,619 INFO misc.py line 119 87073] Train: [13/100][1428/1557] Data 0.014 (0.086) Batch 1.188 (1.124) Remain 42:19:57 loss: 0.3897 Lr: 0.00491 [2024-02-17 23:42:41,478 INFO misc.py line 119 87073] Train: [13/100][1429/1557] Data 0.017 (0.086) Batch 0.872 (1.124) Remain 42:19:32 loss: 0.8368 Lr: 0.00491 [2024-02-17 23:42:42,518 INFO misc.py line 119 87073] Train: [13/100][1430/1557] Data 0.004 (0.086) Batch 1.038 (1.124) Remain 42:19:22 loss: 0.4964 Lr: 0.00491 [2024-02-17 23:42:43,501 INFO misc.py line 119 87073] Train: [13/100][1431/1557] Data 0.005 (0.086) Batch 0.984 (1.124) Remain 42:19:08 loss: 0.5164 Lr: 0.00491 [2024-02-17 23:42:44,392 INFO misc.py line 119 87073] Train: [13/100][1432/1557] Data 0.003 (0.086) Batch 0.891 (1.123) Remain 42:18:45 loss: 0.4718 Lr: 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Train: [13/100][1445/1557] Data 0.004 (0.085) Batch 1.046 (1.122) Remain 42:15:23 loss: 0.3915 Lr: 0.00491 [2024-02-17 23:42:57,922 INFO misc.py line 119 87073] Train: [13/100][1446/1557] Data 0.004 (0.085) Batch 0.913 (1.122) Remain 42:15:02 loss: 0.6851 Lr: 0.00491 [2024-02-17 23:42:58,686 INFO misc.py line 119 87073] Train: [13/100][1447/1557] Data 0.006 (0.085) Batch 0.757 (1.122) Remain 42:14:27 loss: 0.7764 Lr: 0.00491 [2024-02-17 23:42:59,394 INFO misc.py line 119 87073] Train: [13/100][1448/1557] Data 0.013 (0.085) Batch 0.717 (1.121) Remain 42:13:48 loss: 0.4662 Lr: 0.00491 [2024-02-17 23:43:00,590 INFO misc.py line 119 87073] Train: [13/100][1449/1557] Data 0.003 (0.085) Batch 1.193 (1.121) Remain 42:13:53 loss: 0.1430 Lr: 0.00491 [2024-02-17 23:43:01,521 INFO misc.py line 119 87073] Train: [13/100][1450/1557] Data 0.006 (0.085) Batch 0.934 (1.121) Remain 42:13:35 loss: 0.5787 Lr: 0.00491 [2024-02-17 23:43:02,522 INFO misc.py line 119 87073] Train: [13/100][1451/1557] Data 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Remain 42:11:12 loss: 0.6275 Lr: 0.00491 [2024-02-17 23:43:08,928 INFO misc.py line 119 87073] Train: [13/100][1458/1557] Data 0.007 (0.085) Batch 1.010 (1.120) Remain 42:11:00 loss: 0.6383 Lr: 0.00491 [2024-02-17 23:43:09,773 INFO misc.py line 119 87073] Train: [13/100][1459/1557] Data 0.004 (0.084) Batch 0.843 (1.120) Remain 42:10:33 loss: 0.5836 Lr: 0.00491 [2024-02-17 23:43:10,779 INFO misc.py line 119 87073] Train: [13/100][1460/1557] Data 0.005 (0.084) Batch 1.005 (1.120) Remain 42:10:21 loss: 0.3471 Lr: 0.00491 [2024-02-17 23:43:11,560 INFO misc.py line 119 87073] Train: [13/100][1461/1557] Data 0.007 (0.084) Batch 0.781 (1.120) Remain 42:09:49 loss: 0.5144 Lr: 0.00491 [2024-02-17 23:43:12,354 INFO misc.py line 119 87073] Train: [13/100][1462/1557] Data 0.006 (0.084) Batch 0.795 (1.120) Remain 42:09:17 loss: 0.5377 Lr: 0.00491 [2024-02-17 23:43:21,862 INFO misc.py line 119 87073] Train: [13/100][1463/1557] Data 4.243 (0.087) Batch 9.507 (1.125) Remain 42:22:15 loss: 0.2728 Lr: 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Remain 42:14:30 loss: 0.4937 Lr: 0.00491 [2024-02-17 23:43:45,991 INFO misc.py line 119 87073] Train: [13/100][1489/1557] Data 0.018 (0.086) Batch 0.786 (1.122) Remain 42:13:58 loss: 0.3904 Lr: 0.00491 [2024-02-17 23:43:46,769 INFO misc.py line 119 87073] Train: [13/100][1490/1557] Data 0.006 (0.086) Batch 0.780 (1.122) Remain 42:13:26 loss: 0.3985 Lr: 0.00491 [2024-02-17 23:43:47,964 INFO misc.py line 119 87073] Train: [13/100][1491/1557] Data 0.005 (0.086) Batch 1.184 (1.122) Remain 42:13:30 loss: 0.4091 Lr: 0.00491 [2024-02-17 23:43:49,099 INFO misc.py line 119 87073] Train: [13/100][1492/1557] Data 0.016 (0.086) Batch 1.139 (1.122) Remain 42:13:31 loss: 0.4380 Lr: 0.00491 [2024-02-17 23:43:49,971 INFO misc.py line 119 87073] Train: [13/100][1493/1557] Data 0.012 (0.086) Batch 0.881 (1.121) Remain 42:13:08 loss: 0.7520 Lr: 0.00491 [2024-02-17 23:43:50,941 INFO misc.py line 119 87073] Train: [13/100][1494/1557] Data 0.003 (0.085) Batch 0.969 (1.121) Remain 42:12:53 loss: 0.6513 Lr: 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INFO misc.py line 119 87073] Train: [13/100][1501/1557] Data 0.003 (0.085) Batch 1.001 (1.121) Remain 42:11:24 loss: 1.3518 Lr: 0.00491 [2024-02-17 23:43:59,021 INFO misc.py line 119 87073] Train: [13/100][1502/1557] Data 0.004 (0.085) Batch 1.120 (1.121) Remain 42:11:23 loss: 0.5717 Lr: 0.00491 [2024-02-17 23:43:59,807 INFO misc.py line 119 87073] Train: [13/100][1503/1557] Data 0.004 (0.085) Batch 0.786 (1.121) Remain 42:10:52 loss: 0.2958 Lr: 0.00491 [2024-02-17 23:44:00,535 INFO misc.py line 119 87073] Train: [13/100][1504/1557] Data 0.004 (0.085) Batch 0.723 (1.120) Remain 42:10:15 loss: 0.6103 Lr: 0.00491 [2024-02-17 23:44:01,782 INFO misc.py line 119 87073] Train: [13/100][1505/1557] Data 0.009 (0.085) Batch 1.242 (1.120) Remain 42:10:25 loss: 0.1666 Lr: 0.00491 [2024-02-17 23:44:02,706 INFO misc.py line 119 87073] Train: [13/100][1506/1557] Data 0.014 (0.085) Batch 0.932 (1.120) Remain 42:10:07 loss: 0.8216 Lr: 0.00491 [2024-02-17 23:44:03,699 INFO misc.py line 119 87073] Train: [13/100][1507/1557] Data 0.006 (0.085) Batch 0.995 (1.120) Remain 42:09:54 loss: 0.7045 Lr: 0.00491 [2024-02-17 23:44:04,859 INFO misc.py line 119 87073] Train: [13/100][1508/1557] Data 0.003 (0.085) Batch 1.160 (1.120) Remain 42:09:57 loss: 0.6424 Lr: 0.00491 [2024-02-17 23:44:05,973 INFO misc.py line 119 87073] Train: [13/100][1509/1557] Data 0.004 (0.085) Batch 1.111 (1.120) Remain 42:09:55 loss: 0.8106 Lr: 0.00491 [2024-02-17 23:44:06,730 INFO misc.py line 119 87073] Train: [13/100][1510/1557] Data 0.006 (0.085) Batch 0.759 (1.120) Remain 42:09:21 loss: 0.2823 Lr: 0.00491 [2024-02-17 23:44:07,438 INFO misc.py line 119 87073] Train: [13/100][1511/1557] Data 0.004 (0.085) Batch 0.700 (1.120) Remain 42:08:42 loss: 0.7677 Lr: 0.00491 [2024-02-17 23:44:08,622 INFO misc.py line 119 87073] Train: [13/100][1512/1557] Data 0.012 (0.085) Batch 1.180 (1.120) Remain 42:08:47 loss: 0.2741 Lr: 0.00491 [2024-02-17 23:44:09,894 INFO misc.py line 119 87073] Train: [13/100][1513/1557] Data 0.016 (0.085) Batch 1.275 (1.120) Remain 42:08:59 loss: 0.4202 Lr: 0.00491 [2024-02-17 23:44:10,804 INFO misc.py line 119 87073] Train: [13/100][1514/1557] Data 0.012 (0.084) Batch 0.919 (1.120) Remain 42:08:40 loss: 0.5452 Lr: 0.00491 [2024-02-17 23:44:11,836 INFO misc.py line 119 87073] Train: [13/100][1515/1557] Data 0.004 (0.084) Batch 1.032 (1.120) Remain 42:08:31 loss: 0.5720 Lr: 0.00491 [2024-02-17 23:44:12,918 INFO misc.py line 119 87073] Train: [13/100][1516/1557] Data 0.004 (0.084) Batch 1.083 (1.120) Remain 42:08:27 loss: 0.6070 Lr: 0.00491 [2024-02-17 23:44:13,726 INFO misc.py line 119 87073] Train: [13/100][1517/1557] Data 0.003 (0.084) Batch 0.808 (1.119) Remain 42:07:58 loss: 0.4440 Lr: 0.00491 [2024-02-17 23:44:14,543 INFO misc.py line 119 87073] Train: [13/100][1518/1557] Data 0.004 (0.084) Batch 0.808 (1.119) Remain 42:07:29 loss: 0.3000 Lr: 0.00491 [2024-02-17 23:44:24,862 INFO misc.py line 119 87073] Train: [13/100][1519/1557] Data 3.891 (0.087) Batch 10.328 (1.125) Remain 42:21:11 loss: 0.2377 Lr: 0.00491 [2024-02-17 23:44:25,826 INFO misc.py line 119 87073] Train: [13/100][1520/1557] Data 0.004 (0.087) Batch 0.964 (1.125) Remain 42:20:55 loss: 0.7755 Lr: 0.00491 [2024-02-17 23:44:26,707 INFO misc.py line 119 87073] Train: [13/100][1521/1557] Data 0.005 (0.087) Batch 0.882 (1.125) Remain 42:20:32 loss: 0.7094 Lr: 0.00491 [2024-02-17 23:44:27,862 INFO misc.py line 119 87073] Train: [13/100][1522/1557] Data 0.005 (0.087) Batch 1.146 (1.125) Remain 42:20:33 loss: 0.6235 Lr: 0.00491 [2024-02-17 23:44:28,896 INFO misc.py line 119 87073] Train: [13/100][1523/1557] Data 0.014 (0.087) Batch 1.035 (1.125) Remain 42:20:24 loss: 1.2114 Lr: 0.00491 [2024-02-17 23:44:29,670 INFO misc.py line 119 87073] Train: [13/100][1524/1557] Data 0.013 (0.086) Batch 0.783 (1.125) Remain 42:19:52 loss: 0.4555 Lr: 0.00491 [2024-02-17 23:44:30,455 INFO misc.py line 119 87073] Train: [13/100][1525/1557] Data 0.004 (0.086) Batch 0.785 (1.125) Remain 42:19:21 loss: 0.3834 Lr: 0.00491 [2024-02-17 23:44:31,695 INFO misc.py line 119 87073] Train: [13/100][1526/1557] Data 0.004 (0.086) Batch 1.231 (1.125) Remain 42:19:29 loss: 0.3735 Lr: 0.00491 [2024-02-17 23:44:33,150 INFO misc.py line 119 87073] Train: [13/100][1527/1557] Data 0.013 (0.086) Batch 1.451 (1.125) Remain 42:19:57 loss: 0.7146 Lr: 0.00491 [2024-02-17 23:44:34,276 INFO misc.py line 119 87073] Train: [13/100][1528/1557] Data 0.017 (0.086) Batch 1.128 (1.125) Remain 42:19:56 loss: 0.3974 Lr: 0.00491 [2024-02-17 23:44:35,152 INFO misc.py line 119 87073] Train: [13/100][1529/1557] Data 0.014 (0.086) Batch 0.886 (1.125) Remain 42:19:34 loss: 0.7435 Lr: 0.00491 [2024-02-17 23:44:35,976 INFO misc.py line 119 87073] Train: [13/100][1530/1557] Data 0.004 (0.086) Batch 0.825 (1.124) Remain 42:19:06 loss: 1.4196 Lr: 0.00491 [2024-02-17 23:44:36,741 INFO misc.py line 119 87073] Train: [13/100][1531/1557] Data 0.003 (0.086) Batch 0.757 (1.124) Remain 42:18:33 loss: 0.7161 Lr: 0.00491 [2024-02-17 23:44:37,517 INFO misc.py line 119 87073] Train: [13/100][1532/1557] Data 0.011 (0.086) Batch 0.781 (1.124) Remain 42:18:01 loss: 0.5321 Lr: 0.00491 [2024-02-17 23:44:38,583 INFO misc.py line 119 87073] Train: [13/100][1533/1557] Data 0.006 (0.086) Batch 1.067 (1.124) Remain 42:17:55 loss: 0.2255 Lr: 0.00491 [2024-02-17 23:44:39,480 INFO misc.py line 119 87073] Train: [13/100][1534/1557] Data 0.005 (0.086) Batch 0.898 (1.124) Remain 42:17:34 loss: 0.5006 Lr: 0.00491 [2024-02-17 23:44:40,440 INFO misc.py line 119 87073] Train: [13/100][1535/1557] Data 0.004 (0.086) Batch 0.953 (1.124) Remain 42:17:18 loss: 0.4622 Lr: 0.00491 [2024-02-17 23:44:41,426 INFO misc.py line 119 87073] Train: [13/100][1536/1557] Data 0.012 (0.086) Batch 0.991 (1.124) Remain 42:17:05 loss: 0.5442 Lr: 0.00491 [2024-02-17 23:44:42,250 INFO misc.py line 119 87073] Train: [13/100][1537/1557] Data 0.006 (0.086) Batch 0.825 (1.123) Remain 42:16:37 loss: 0.3769 Lr: 0.00491 [2024-02-17 23:44:43,004 INFO misc.py line 119 87073] Train: [13/100][1538/1557] Data 0.004 (0.086) Batch 0.741 (1.123) Remain 42:16:02 loss: 0.5926 Lr: 0.00491 [2024-02-17 23:44:43,711 INFO misc.py line 119 87073] Train: [13/100][1539/1557] Data 0.017 (0.086) Batch 0.720 (1.123) Remain 42:15:26 loss: 0.2666 Lr: 0.00491 [2024-02-17 23:44:44,783 INFO misc.py line 119 87073] Train: [13/100][1540/1557] Data 0.003 (0.086) Batch 1.062 (1.123) Remain 42:15:19 loss: 0.3340 Lr: 0.00491 [2024-02-17 23:44:45,854 INFO misc.py line 119 87073] Train: [13/100][1541/1557] Data 0.014 (0.086) Batch 1.072 (1.123) Remain 42:15:14 loss: 0.6499 Lr: 0.00491 [2024-02-17 23:44:46,765 INFO misc.py line 119 87073] Train: [13/100][1542/1557] Data 0.013 (0.086) Batch 0.920 (1.123) Remain 42:14:55 loss: 0.7134 Lr: 0.00491 [2024-02-17 23:44:47,646 INFO misc.py line 119 87073] Train: [13/100][1543/1557] Data 0.004 (0.086) Batch 0.881 (1.123) Remain 42:14:32 loss: 0.9822 Lr: 0.00491 [2024-02-17 23:44:48,650 INFO misc.py line 119 87073] Train: [13/100][1544/1557] Data 0.004 (0.085) Batch 1.004 (1.122) Remain 42:14:21 loss: 0.7641 Lr: 0.00491 [2024-02-17 23:44:49,507 INFO misc.py line 119 87073] Train: [13/100][1545/1557] Data 0.004 (0.085) Batch 0.857 (1.122) Remain 42:13:56 loss: 0.4313 Lr: 0.00491 [2024-02-17 23:44:50,337 INFO misc.py line 119 87073] Train: [13/100][1546/1557] Data 0.004 (0.085) Batch 0.819 (1.122) Remain 42:13:29 loss: 0.9118 Lr: 0.00491 [2024-02-17 23:44:51,552 INFO misc.py line 119 87073] Train: [13/100][1547/1557] Data 0.015 (0.085) Batch 1.212 (1.122) Remain 42:13:35 loss: 0.4372 Lr: 0.00491 [2024-02-17 23:44:52,516 INFO misc.py line 119 87073] Train: [13/100][1548/1557] Data 0.018 (0.085) Batch 0.978 (1.122) Remain 42:13:22 loss: 0.6553 Lr: 0.00491 [2024-02-17 23:44:53,554 INFO misc.py line 119 87073] Train: [13/100][1549/1557] Data 0.004 (0.085) Batch 1.038 (1.122) Remain 42:13:13 loss: 0.6587 Lr: 0.00491 [2024-02-17 23:44:54,524 INFO misc.py line 119 87073] Train: [13/100][1550/1557] Data 0.004 (0.085) Batch 0.970 (1.122) Remain 42:12:59 loss: 0.5815 Lr: 0.00491 [2024-02-17 23:44:55,513 INFO misc.py line 119 87073] Train: [13/100][1551/1557] Data 0.004 (0.085) Batch 0.990 (1.122) Remain 42:12:46 loss: 1.2484 Lr: 0.00491 [2024-02-17 23:44:56,300 INFO misc.py line 119 87073] Train: [13/100][1552/1557] Data 0.004 (0.085) Batch 0.775 (1.122) Remain 42:12:14 loss: 0.5292 Lr: 0.00491 [2024-02-17 23:44:56,997 INFO misc.py line 119 87073] Train: [13/100][1553/1557] Data 0.016 (0.085) Batch 0.709 (1.121) Remain 42:11:37 loss: 0.4876 Lr: 0.00491 [2024-02-17 23:44:58,301 INFO misc.py line 119 87073] Train: [13/100][1554/1557] Data 0.004 (0.085) Batch 1.304 (1.121) Remain 42:11:52 loss: 0.3920 Lr: 0.00491 [2024-02-17 23:44:59,200 INFO misc.py line 119 87073] Train: [13/100][1555/1557] Data 0.004 (0.085) Batch 0.897 (1.121) Remain 42:11:31 loss: 0.5383 Lr: 0.00491 [2024-02-17 23:45:00,092 INFO misc.py line 119 87073] Train: [13/100][1556/1557] Data 0.005 (0.085) Batch 0.893 (1.121) Remain 42:11:10 loss: 0.4559 Lr: 0.00491 [2024-02-17 23:45:01,162 INFO misc.py line 119 87073] Train: [13/100][1557/1557] Data 0.004 (0.085) Batch 1.055 (1.121) Remain 42:11:04 loss: 0.3250 Lr: 0.00491 [2024-02-17 23:45:01,163 INFO misc.py line 136 87073] Train result: loss: 0.5518 [2024-02-17 23:45:01,163 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-17 23:45:32,905 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7066 [2024-02-17 23:45:33,682 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7261 [2024-02-17 23:45:35,807 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.6055 [2024-02-17 23:45:38,014 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4903 [2024-02-17 23:45:42,962 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3736 [2024-02-17 23:45:43,663 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1559 [2024-02-17 23:45:44,925 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3259 [2024-02-17 23:45:47,883 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2946 [2024-02-17 23:45:49,693 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.4476 [2024-02-17 23:45:51,477 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6806/0.7855/0.8994. [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9317/0.9546 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9817/0.9884 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8433/0.9407 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0173/0.3961 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4482/0.6791 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6221/0.6293 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6247/0.7042 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8246/0.8811 [2024-02-17 23:45:51,477 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9145/0.9546 [2024-02-17 23:45:51,478 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6069/0.6189 [2024-02-17 23:45:51,478 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7606/0.8642 [2024-02-17 23:45:51,478 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6736/0.8637 [2024-02-17 23:45:51,478 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5986/0.7369 [2024-02-17 23:45:51,478 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-17 23:45:51,480 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-17 23:45:51,480 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-17 23:45:59,605 INFO misc.py line 119 87073] Train: [14/100][1/1557] Data 1.492 (1.492) Batch 2.228 (2.228) Remain 83:49:03 loss: 0.3957 Lr: 0.00491 [2024-02-17 23:46:00,566 INFO misc.py line 119 87073] Train: [14/100][2/1557] Data 0.007 (0.007) Batch 0.962 (0.962) Remain 36:12:30 loss: 0.4448 Lr: 0.00491 [2024-02-17 23:46:01,493 INFO misc.py line 119 87073] Train: [14/100][3/1557] Data 0.006 (0.006) Batch 0.927 (0.927) Remain 34:52:13 loss: 0.9069 Lr: 0.00491 [2024-02-17 23:46:02,409 INFO misc.py line 119 87073] Train: [14/100][4/1557] Data 0.006 (0.006) Batch 0.917 (0.917) Remain 34:30:14 loss: 0.7757 Lr: 0.00491 [2024-02-17 23:46:03,222 INFO misc.py line 119 87073] Train: [14/100][5/1557] Data 0.005 (0.005) Batch 0.809 (0.863) Remain 32:28:15 loss: 0.2634 Lr: 0.00491 [2024-02-17 23:46:03,967 INFO misc.py line 119 87073] Train: [14/100][6/1557] Data 0.009 (0.006) Batch 0.748 (0.825) Remain 31:02:04 loss: 0.6358 Lr: 0.00491 [2024-02-17 23:46:19,568 INFO misc.py line 119 87073] Train: [14/100][7/1557] Data 0.005 (0.006) Batch 15.602 (4.519) Remain 170:02:02 loss: 0.3293 Lr: 0.00491 [2024-02-17 23:46:20,553 INFO misc.py line 119 87073] Train: [14/100][8/1557] Data 0.004 (0.006) Batch 0.985 (3.812) Remain 143:26:17 loss: 0.4064 Lr: 0.00491 [2024-02-17 23:46:21,540 INFO misc.py line 119 87073] Train: [14/100][9/1557] Data 0.004 (0.005) Batch 0.987 (3.341) Remain 125:43:12 loss: 0.4285 Lr: 0.00491 [2024-02-17 23:46:22,519 INFO misc.py line 119 87073] Train: [14/100][10/1557] Data 0.003 (0.005) Batch 0.979 (3.004) Remain 113:01:20 loss: 0.3951 Lr: 0.00491 [2024-02-17 23:46:23,362 INFO misc.py line 119 87073] Train: [14/100][11/1557] Data 0.004 (0.005) Batch 0.843 (2.734) Remain 102:51:27 loss: 0.6099 Lr: 0.00491 [2024-02-17 23:46:24,116 INFO misc.py line 119 87073] Train: [14/100][12/1557] Data 0.004 (0.005) Batch 0.752 (2.514) Remain 94:34:21 loss: 0.9134 Lr: 0.00491 [2024-02-17 23:46:24,876 INFO misc.py line 119 87073] Train: [14/100][13/1557] Data 0.006 (0.005) Batch 0.762 (2.338) Remain 87:58:56 loss: 0.7275 Lr: 0.00491 [2024-02-17 23:46:26,093 INFO misc.py line 119 87073] Train: [14/100][14/1557] Data 0.004 (0.005) Batch 1.215 (2.236) Remain 84:08:25 loss: 0.2272 Lr: 0.00491 [2024-02-17 23:46:27,075 INFO misc.py line 119 87073] Train: [14/100][15/1557] Data 0.006 (0.005) Batch 0.984 (2.132) Remain 80:12:43 loss: 0.3720 Lr: 0.00491 [2024-02-17 23:46:27,986 INFO misc.py line 119 87073] Train: [14/100][16/1557] Data 0.004 (0.005) Batch 0.910 (2.038) Remain 76:40:33 loss: 0.7618 Lr: 0.00491 [2024-02-17 23:46:29,067 INFO misc.py line 119 87073] Train: [14/100][17/1557] Data 0.004 (0.005) Batch 1.082 (1.970) Remain 74:06:18 loss: 0.5363 Lr: 0.00491 [2024-02-17 23:46:30,341 INFO misc.py line 119 87073] Train: [14/100][18/1557] Data 0.004 (0.005) Batch 1.261 (1.922) Remain 72:19:37 loss: 0.3581 Lr: 0.00491 [2024-02-17 23:46:31,034 INFO misc.py line 119 87073] Train: [14/100][19/1557] Data 0.016 (0.005) Batch 0.706 (1.846) Remain 69:27:55 loss: 0.4665 Lr: 0.00491 [2024-02-17 23:46:31,778 INFO misc.py line 119 87073] Train: [14/100][20/1557] Data 0.004 (0.005) Batch 0.737 (1.781) Remain 67:00:35 loss: 0.4742 Lr: 0.00491 [2024-02-17 23:46:32,991 INFO misc.py line 119 87073] Train: 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Train: [14/100][65/1557] Data 0.005 (0.100) Batch 1.113 (1.516) Remain 57:01:56 loss: 0.5394 Lr: 0.00491 [2024-02-17 23:47:36,313 INFO misc.py line 119 87073] Train: [14/100][66/1557] Data 0.004 (0.099) Batch 0.803 (1.505) Remain 56:36:20 loss: 0.7667 Lr: 0.00491 [2024-02-17 23:47:37,312 INFO misc.py line 119 87073] Train: [14/100][67/1557] Data 0.004 (0.097) Batch 0.987 (1.497) Remain 56:18:02 loss: 0.4276 Lr: 0.00491 [2024-02-17 23:47:38,064 INFO misc.py line 119 87073] Train: [14/100][68/1557] Data 0.017 (0.096) Batch 0.764 (1.486) Remain 55:52:34 loss: 0.3974 Lr: 0.00491 [2024-02-17 23:47:38,914 INFO misc.py line 119 87073] Train: [14/100][69/1557] Data 0.004 (0.095) Batch 0.850 (1.476) Remain 55:30:49 loss: 0.4751 Lr: 0.00491 [2024-02-17 23:47:40,164 INFO misc.py line 119 87073] Train: [14/100][70/1557] Data 0.004 (0.093) Batch 1.244 (1.473) Remain 55:22:57 loss: 0.3148 Lr: 0.00491 [2024-02-17 23:47:41,227 INFO misc.py line 119 87073] Train: [14/100][71/1557] Data 0.010 (0.092) 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Batch 1.012 (1.359) Remain 50:59:12 loss: 0.4920 Lr: 0.00491 [2024-02-17 23:53:46,963 INFO misc.py line 119 87073] Train: [14/100][346/1557] Data 0.004 (0.124) Batch 0.862 (1.357) Remain 50:55:55 loss: 0.4967 Lr: 0.00491 [2024-02-17 23:53:47,964 INFO misc.py line 119 87073] Train: [14/100][347/1557] Data 0.004 (0.124) Batch 0.991 (1.356) Remain 50:53:30 loss: 0.4909 Lr: 0.00491 [2024-02-17 23:53:48,688 INFO misc.py line 119 87073] Train: [14/100][348/1557] Data 0.015 (0.124) Batch 0.734 (1.354) Remain 50:49:25 loss: 0.7663 Lr: 0.00491 [2024-02-17 23:53:49,418 INFO misc.py line 119 87073] Train: [14/100][349/1557] Data 0.005 (0.123) Batch 0.726 (1.352) Remain 50:45:18 loss: 0.7976 Lr: 0.00491 [2024-02-17 23:53:50,692 INFO misc.py line 119 87073] Train: [14/100][350/1557] Data 0.010 (0.123) Batch 1.271 (1.352) Remain 50:44:45 loss: 0.3039 Lr: 0.00491 [2024-02-17 23:53:51,644 INFO misc.py line 119 87073] Train: [14/100][351/1557] Data 0.012 (0.123) Batch 0.959 (1.351) Remain 50:42:11 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Train: [14/100][1259/1557] Data 0.004 (0.129) Batch 0.805 (1.333) Remain 49:42:00 loss: 0.4573 Lr: 0.00489 [2024-02-18 00:13:57,343 INFO misc.py line 119 87073] Train: [14/100][1260/1557] Data 0.007 (0.129) Batch 1.295 (1.333) Remain 49:41:54 loss: 0.3643 Lr: 0.00489 [2024-02-18 00:13:58,146 INFO misc.py line 119 87073] Train: [14/100][1261/1557] Data 0.015 (0.129) Batch 0.814 (1.333) Remain 49:40:57 loss: 0.6868 Lr: 0.00489 [2024-02-18 00:13:59,088 INFO misc.py line 119 87073] Train: [14/100][1262/1557] Data 0.005 (0.129) Batch 0.943 (1.332) Remain 49:40:14 loss: 0.5631 Lr: 0.00489 [2024-02-18 00:14:00,003 INFO misc.py line 119 87073] Train: [14/100][1263/1557] Data 0.005 (0.128) Batch 0.909 (1.332) Remain 49:39:28 loss: 0.6641 Lr: 0.00489 [2024-02-18 00:14:01,111 INFO misc.py line 119 87073] Train: [14/100][1264/1557] Data 0.011 (0.128) Batch 1.108 (1.332) Remain 49:39:03 loss: 0.4468 Lr: 0.00489 [2024-02-18 00:14:01,907 INFO misc.py line 119 87073] Train: [14/100][1265/1557] Data 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Remain 49:34:08 loss: 0.6096 Lr: 0.00489 [2024-02-18 00:14:08,519 INFO misc.py line 119 87073] Train: [14/100][1272/1557] Data 0.003 (0.128) Batch 0.788 (1.329) Remain 49:33:09 loss: 0.4255 Lr: 0.00489 [2024-02-18 00:14:09,352 INFO misc.py line 119 87073] Train: [14/100][1273/1557] Data 0.005 (0.127) Batch 0.834 (1.329) Remain 49:32:16 loss: 0.3150 Lr: 0.00489 [2024-02-18 00:14:10,497 INFO misc.py line 119 87073] Train: [14/100][1274/1557] Data 0.005 (0.127) Batch 1.141 (1.329) Remain 49:31:54 loss: 0.3964 Lr: 0.00489 [2024-02-18 00:14:11,450 INFO misc.py line 119 87073] Train: [14/100][1275/1557] Data 0.009 (0.127) Batch 0.956 (1.329) Remain 49:31:14 loss: 0.7621 Lr: 0.00489 [2024-02-18 00:14:12,525 INFO misc.py line 119 87073] Train: [14/100][1276/1557] Data 0.005 (0.127) Batch 1.075 (1.328) Remain 49:30:46 loss: 0.9341 Lr: 0.00489 [2024-02-18 00:14:13,620 INFO misc.py line 119 87073] Train: [14/100][1277/1557] Data 0.005 (0.127) Batch 1.095 (1.328) Remain 49:30:20 loss: 0.8350 Lr: 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Train: [14/100][1290/1557] Data 0.006 (0.126) Batch 0.987 (1.324) Remain 49:21:36 loss: 0.6145 Lr: 0.00489 [2024-02-18 00:14:26,921 INFO misc.py line 119 87073] Train: [14/100][1291/1557] Data 0.006 (0.126) Batch 0.891 (1.324) Remain 49:20:50 loss: 0.2636 Lr: 0.00489 [2024-02-18 00:14:27,834 INFO misc.py line 119 87073] Train: [14/100][1292/1557] Data 0.005 (0.126) Batch 0.913 (1.324) Remain 49:20:06 loss: 0.5570 Lr: 0.00489 [2024-02-18 00:14:28,871 INFO misc.py line 119 87073] Train: [14/100][1293/1557] Data 0.006 (0.126) Batch 1.027 (1.324) Remain 49:19:33 loss: 0.4720 Lr: 0.00489 [2024-02-18 00:14:29,767 INFO misc.py line 119 87073] Train: [14/100][1294/1557] Data 0.015 (0.125) Batch 0.905 (1.323) Remain 49:18:49 loss: 0.3781 Lr: 0.00489 [2024-02-18 00:14:49,894 INFO misc.py line 119 87073] Train: [14/100][1295/1557] Data 6.382 (0.130) Batch 20.128 (1.338) Remain 49:51:20 loss: 0.3358 Lr: 0.00489 [2024-02-18 00:14:50,772 INFO misc.py line 119 87073] Train: [14/100][1296/1557] Data 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Remain 49:46:43 loss: 0.1723 Lr: 0.00489 [2024-02-18 00:14:57,606 INFO misc.py line 119 87073] Train: [14/100][1303/1557] Data 0.008 (0.130) Batch 0.944 (1.335) Remain 49:46:01 loss: 0.6078 Lr: 0.00489 [2024-02-18 00:14:58,513 INFO misc.py line 119 87073] Train: [14/100][1304/1557] Data 0.004 (0.129) Batch 0.906 (1.335) Remain 49:45:15 loss: 0.4019 Lr: 0.00489 [2024-02-18 00:14:59,405 INFO misc.py line 119 87073] Train: [14/100][1305/1557] Data 0.004 (0.129) Batch 0.890 (1.335) Remain 49:44:28 loss: 0.4043 Lr: 0.00489 [2024-02-18 00:15:00,298 INFO misc.py line 119 87073] Train: [14/100][1306/1557] Data 0.006 (0.129) Batch 0.892 (1.334) Remain 49:43:41 loss: 0.6687 Lr: 0.00489 [2024-02-18 00:15:00,975 INFO misc.py line 119 87073] Train: [14/100][1307/1557] Data 0.008 (0.129) Batch 0.680 (1.334) Remain 49:42:33 loss: 0.5088 Lr: 0.00489 [2024-02-18 00:15:01,780 INFO misc.py line 119 87073] Train: [14/100][1308/1557] Data 0.006 (0.129) Batch 0.803 (1.334) Remain 49:41:37 loss: 0.9044 Lr: 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INFO misc.py line 119 87073] Train: [14/100][1315/1557] Data 0.005 (0.128) Batch 0.781 (1.331) Remain 49:36:37 loss: 0.6110 Lr: 0.00489 [2024-02-18 00:15:09,620 INFO misc.py line 119 87073] Train: [14/100][1316/1557] Data 0.010 (0.128) Batch 1.345 (1.331) Remain 49:36:37 loss: 0.2277 Lr: 0.00489 [2024-02-18 00:15:10,601 INFO misc.py line 119 87073] Train: [14/100][1317/1557] Data 0.005 (0.128) Batch 0.982 (1.331) Remain 49:36:00 loss: 0.4453 Lr: 0.00489 [2024-02-18 00:15:11,438 INFO misc.py line 119 87073] Train: [14/100][1318/1557] Data 0.005 (0.128) Batch 0.837 (1.331) Remain 49:35:08 loss: 0.3856 Lr: 0.00489 [2024-02-18 00:15:12,313 INFO misc.py line 119 87073] Train: [14/100][1319/1557] Data 0.004 (0.128) Batch 0.872 (1.330) Remain 49:34:20 loss: 0.6731 Lr: 0.00489 [2024-02-18 00:15:13,220 INFO misc.py line 119 87073] Train: [14/100][1320/1557] Data 0.008 (0.128) Batch 0.909 (1.330) Remain 49:33:36 loss: 1.0505 Lr: 0.00489 [2024-02-18 00:15:14,012 INFO misc.py line 119 87073] Train: [14/100][1321/1557] Data 0.005 (0.128) Batch 0.793 (1.330) Remain 49:32:40 loss: 0.1971 Lr: 0.00489 [2024-02-18 00:15:14,780 INFO misc.py line 119 87073] Train: [14/100][1322/1557] Data 0.004 (0.128) Batch 0.768 (1.329) Remain 49:31:42 loss: 0.6322 Lr: 0.00489 [2024-02-18 00:15:16,060 INFO misc.py line 119 87073] Train: [14/100][1323/1557] Data 0.005 (0.128) Batch 1.278 (1.329) Remain 49:31:35 loss: 0.3699 Lr: 0.00489 [2024-02-18 00:15:17,227 INFO misc.py line 119 87073] Train: [14/100][1324/1557] Data 0.007 (0.128) Batch 1.157 (1.329) Remain 49:31:16 loss: 0.4299 Lr: 0.00489 [2024-02-18 00:15:18,126 INFO misc.py line 119 87073] Train: [14/100][1325/1557] Data 0.018 (0.128) Batch 0.911 (1.329) Remain 49:30:32 loss: 0.8908 Lr: 0.00489 [2024-02-18 00:15:19,044 INFO misc.py line 119 87073] Train: [14/100][1326/1557] Data 0.005 (0.127) Batch 0.919 (1.328) Remain 49:29:50 loss: 0.6147 Lr: 0.00489 [2024-02-18 00:15:19,982 INFO misc.py line 119 87073] Train: [14/100][1327/1557] Data 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Remain 49:24:37 loss: 0.6647 Lr: 0.00489 [2024-02-18 00:15:26,291 INFO misc.py line 119 87073] Train: [14/100][1334/1557] Data 0.007 (0.127) Batch 0.947 (1.326) Remain 49:23:58 loss: 0.4992 Lr: 0.00489 [2024-02-18 00:15:27,057 INFO misc.py line 119 87073] Train: [14/100][1335/1557] Data 0.008 (0.127) Batch 0.767 (1.325) Remain 49:23:00 loss: 0.4104 Lr: 0.00489 [2024-02-18 00:15:27,836 INFO misc.py line 119 87073] Train: [14/100][1336/1557] Data 0.008 (0.127) Batch 0.781 (1.325) Remain 49:22:04 loss: 0.3596 Lr: 0.00489 [2024-02-18 00:15:28,972 INFO misc.py line 119 87073] Train: [14/100][1337/1557] Data 0.005 (0.126) Batch 1.135 (1.325) Remain 49:21:44 loss: 0.3020 Lr: 0.00489 [2024-02-18 00:15:29,876 INFO misc.py line 119 87073] Train: [14/100][1338/1557] Data 0.006 (0.126) Batch 0.905 (1.325) Remain 49:21:00 loss: 1.3038 Lr: 0.00489 [2024-02-18 00:15:30,750 INFO misc.py line 119 87073] Train: [14/100][1339/1557] Data 0.005 (0.126) Batch 0.876 (1.324) Remain 49:20:14 loss: 0.8179 Lr: 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Train: [14/100][1352/1557] Data 0.005 (0.130) Batch 0.827 (1.337) Remain 49:48:28 loss: 0.3598 Lr: 0.00489 [2024-02-18 00:16:06,313 INFO misc.py line 119 87073] Train: [14/100][1353/1557] Data 0.005 (0.130) Batch 1.123 (1.337) Remain 49:48:06 loss: 0.1624 Lr: 0.00489 [2024-02-18 00:16:07,221 INFO misc.py line 119 87073] Train: [14/100][1354/1557] Data 0.008 (0.130) Batch 0.912 (1.337) Remain 49:47:22 loss: 0.6062 Lr: 0.00489 [2024-02-18 00:16:08,177 INFO misc.py line 119 87073] Train: [14/100][1355/1557] Data 0.005 (0.130) Batch 0.955 (1.336) Remain 49:46:43 loss: 0.3755 Lr: 0.00489 [2024-02-18 00:16:08,944 INFO misc.py line 119 87073] Train: [14/100][1356/1557] Data 0.006 (0.130) Batch 0.761 (1.336) Remain 49:45:45 loss: 0.6607 Lr: 0.00489 [2024-02-18 00:16:09,733 INFO misc.py line 119 87073] Train: [14/100][1357/1557] Data 0.011 (0.130) Batch 0.795 (1.335) Remain 49:44:50 loss: 0.2075 Lr: 0.00489 [2024-02-18 00:16:10,924 INFO misc.py line 119 87073] Train: [14/100][1358/1557] Data 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Remain 49:39:54 loss: 0.5509 Lr: 0.00489 [2024-02-18 00:16:17,422 INFO misc.py line 119 87073] Train: [14/100][1365/1557] Data 0.005 (0.129) Batch 1.240 (1.333) Remain 49:39:44 loss: 0.3218 Lr: 0.00489 [2024-02-18 00:16:18,490 INFO misc.py line 119 87073] Train: [14/100][1366/1557] Data 0.010 (0.129) Batch 1.065 (1.333) Remain 49:39:16 loss: 0.8320 Lr: 0.00489 [2024-02-18 00:16:19,566 INFO misc.py line 119 87073] Train: [14/100][1367/1557] Data 0.014 (0.129) Batch 1.066 (1.333) Remain 49:38:48 loss: 0.9764 Lr: 0.00489 [2024-02-18 00:16:20,483 INFO misc.py line 119 87073] Train: [14/100][1368/1557] Data 0.023 (0.129) Batch 0.937 (1.333) Remain 49:38:08 loss: 0.5155 Lr: 0.00489 [2024-02-18 00:16:21,672 INFO misc.py line 119 87073] Train: [14/100][1369/1557] Data 0.004 (0.129) Batch 1.183 (1.332) Remain 49:37:52 loss: 0.6450 Lr: 0.00489 [2024-02-18 00:16:22,460 INFO misc.py line 119 87073] Train: [14/100][1370/1557] Data 0.010 (0.129) Batch 0.793 (1.332) Remain 49:36:58 loss: 0.8598 Lr: 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Train: [14/100][1383/1557] Data 0.013 (0.128) Batch 0.935 (1.329) Remain 49:29:55 loss: 0.4350 Lr: 0.00489 [2024-02-18 00:16:36,350 INFO misc.py line 119 87073] Train: [14/100][1384/1557] Data 0.005 (0.127) Batch 0.747 (1.329) Remain 49:28:57 loss: 0.4090 Lr: 0.00489 [2024-02-18 00:16:37,124 INFO misc.py line 119 87073] Train: [14/100][1385/1557] Data 0.006 (0.127) Batch 0.773 (1.328) Remain 49:28:02 loss: 0.3812 Lr: 0.00489 [2024-02-18 00:16:38,224 INFO misc.py line 119 87073] Train: [14/100][1386/1557] Data 0.007 (0.127) Batch 1.093 (1.328) Remain 49:27:38 loss: 0.4406 Lr: 0.00489 [2024-02-18 00:16:39,129 INFO misc.py line 119 87073] Train: [14/100][1387/1557] Data 0.013 (0.127) Batch 0.914 (1.328) Remain 49:26:56 loss: 0.3826 Lr: 0.00489 [2024-02-18 00:16:40,187 INFO misc.py line 119 87073] Train: [14/100][1388/1557] Data 0.004 (0.127) Batch 1.058 (1.328) Remain 49:26:29 loss: 0.4608 Lr: 0.00489 [2024-02-18 00:16:40,941 INFO misc.py line 119 87073] Train: [14/100][1389/1557] Data 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Remain 49:21:05 loss: 0.7174 Lr: 0.00489 [2024-02-18 00:16:47,067 INFO misc.py line 119 87073] Train: [14/100][1396/1557] Data 0.004 (0.126) Batch 0.853 (1.325) Remain 49:20:18 loss: 0.5422 Lr: 0.00489 [2024-02-18 00:16:47,950 INFO misc.py line 119 87073] Train: [14/100][1397/1557] Data 0.006 (0.126) Batch 0.882 (1.325) Remain 49:19:34 loss: 0.7373 Lr: 0.00489 [2024-02-18 00:16:48,707 INFO misc.py line 119 87073] Train: [14/100][1398/1557] Data 0.005 (0.126) Batch 0.758 (1.324) Remain 49:18:39 loss: 0.6651 Lr: 0.00489 [2024-02-18 00:16:49,447 INFO misc.py line 119 87073] Train: [14/100][1399/1557] Data 0.004 (0.126) Batch 0.738 (1.324) Remain 49:17:41 loss: 0.6775 Lr: 0.00489 [2024-02-18 00:16:50,724 INFO misc.py line 119 87073] Train: [14/100][1400/1557] Data 0.006 (0.126) Batch 1.267 (1.324) Remain 49:17:34 loss: 0.2253 Lr: 0.00489 [2024-02-18 00:16:51,629 INFO misc.py line 119 87073] Train: [14/100][1401/1557] Data 0.015 (0.126) Batch 0.917 (1.323) Remain 49:16:54 loss: 0.7090 Lr: 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Train: [14/100][1414/1557] Data 0.013 (0.130) Batch 1.337 (1.335) Remain 49:42:34 loss: 0.2352 Lr: 0.00489 [2024-02-18 00:17:26,176 INFO misc.py line 119 87073] Train: [14/100][1415/1557] Data 0.010 (0.130) Batch 0.954 (1.335) Remain 49:41:56 loss: 1.2248 Lr: 0.00489 [2024-02-18 00:17:27,006 INFO misc.py line 119 87073] Train: [14/100][1416/1557] Data 0.004 (0.130) Batch 0.830 (1.334) Remain 49:41:07 loss: 0.7690 Lr: 0.00489 [2024-02-18 00:17:27,945 INFO misc.py line 119 87073] Train: [14/100][1417/1557] Data 0.004 (0.130) Batch 0.938 (1.334) Remain 49:40:28 loss: 0.2637 Lr: 0.00489 [2024-02-18 00:17:28,911 INFO misc.py line 119 87073] Train: [14/100][1418/1557] Data 0.005 (0.130) Batch 0.967 (1.334) Remain 49:39:52 loss: 0.4364 Lr: 0.00489 [2024-02-18 00:17:29,627 INFO misc.py line 119 87073] Train: [14/100][1419/1557] Data 0.003 (0.130) Batch 0.716 (1.333) Remain 49:38:52 loss: 0.4373 Lr: 0.00489 [2024-02-18 00:17:30,387 INFO misc.py line 119 87073] Train: [14/100][1420/1557] Data 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Train: [14/100][1445/1557] Data 0.016 (0.128) Batch 1.211 (1.326) Remain 49:22:18 loss: 0.7192 Lr: 0.00489 [2024-02-18 00:17:54,862 INFO misc.py line 119 87073] Train: [14/100][1446/1557] Data 0.012 (0.128) Batch 0.890 (1.326) Remain 49:21:36 loss: 0.4210 Lr: 0.00489 [2024-02-18 00:17:55,617 INFO misc.py line 119 87073] Train: [14/100][1447/1557] Data 0.005 (0.128) Batch 0.754 (1.326) Remain 49:20:42 loss: 0.2679 Lr: 0.00489 [2024-02-18 00:17:56,412 INFO misc.py line 119 87073] Train: [14/100][1448/1557] Data 0.006 (0.127) Batch 0.795 (1.325) Remain 49:19:51 loss: 0.3074 Lr: 0.00489 [2024-02-18 00:17:57,471 INFO misc.py line 119 87073] Train: [14/100][1449/1557] Data 0.005 (0.127) Batch 1.058 (1.325) Remain 49:19:25 loss: 0.3616 Lr: 0.00489 [2024-02-18 00:17:58,385 INFO misc.py line 119 87073] Train: [14/100][1450/1557] Data 0.006 (0.127) Batch 0.915 (1.325) Remain 49:18:46 loss: 0.6783 Lr: 0.00489 [2024-02-18 00:17:59,339 INFO misc.py line 119 87073] Train: [14/100][1451/1557] Data 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Remain 49:26:31 loss: 0.8174 Lr: 0.00489 [2024-02-18 00:18:55,239 INFO misc.py line 119 87073] Train: [14/100][1489/1557] Data 0.005 (0.129) Batch 0.801 (1.328) Remain 49:25:42 loss: 0.9796 Lr: 0.00489 [2024-02-18 00:18:55,998 INFO misc.py line 119 87073] Train: [14/100][1490/1557] Data 0.004 (0.129) Batch 0.752 (1.328) Remain 49:24:49 loss: 0.4936 Lr: 0.00489 [2024-02-18 00:18:57,243 INFO misc.py line 119 87073] Train: [14/100][1491/1557] Data 0.011 (0.129) Batch 1.247 (1.328) Remain 49:24:40 loss: 0.3417 Lr: 0.00489 [2024-02-18 00:18:58,332 INFO misc.py line 119 87073] Train: [14/100][1492/1557] Data 0.010 (0.129) Batch 1.077 (1.328) Remain 49:24:16 loss: 0.8234 Lr: 0.00489 [2024-02-18 00:18:59,208 INFO misc.py line 119 87073] Train: [14/100][1493/1557] Data 0.021 (0.128) Batch 0.894 (1.327) Remain 49:23:36 loss: 0.9618 Lr: 0.00489 [2024-02-18 00:19:00,244 INFO misc.py line 119 87073] Train: [14/100][1494/1557] Data 0.004 (0.128) Batch 1.034 (1.327) Remain 49:23:08 loss: 0.4383 Lr: 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INFO misc.py line 119 87073] Train: [14/100][1501/1557] Data 0.004 (0.128) Batch 1.012 (1.325) Remain 49:18:57 loss: 0.2479 Lr: 0.00489 [2024-02-18 00:19:07,896 INFO misc.py line 119 87073] Train: [14/100][1502/1557] Data 0.012 (0.128) Batch 1.064 (1.325) Remain 49:18:33 loss: 0.3396 Lr: 0.00489 [2024-02-18 00:19:08,664 INFO misc.py line 119 87073] Train: [14/100][1503/1557] Data 0.007 (0.128) Batch 0.770 (1.325) Remain 49:17:42 loss: 0.6166 Lr: 0.00489 [2024-02-18 00:19:09,441 INFO misc.py line 119 87073] Train: [14/100][1504/1557] Data 0.005 (0.128) Batch 0.769 (1.324) Remain 49:16:51 loss: 0.4746 Lr: 0.00489 [2024-02-18 00:19:10,477 INFO misc.py line 119 87073] Train: [14/100][1505/1557] Data 0.013 (0.127) Batch 1.036 (1.324) Remain 49:16:24 loss: 0.2128 Lr: 0.00489 [2024-02-18 00:19:11,462 INFO misc.py line 119 87073] Train: [14/100][1506/1557] Data 0.013 (0.127) Batch 0.995 (1.324) Remain 49:15:53 loss: 0.6394 Lr: 0.00489 [2024-02-18 00:19:12,498 INFO misc.py line 119 87073] Train: [14/100][1507/1557] Data 0.004 (0.127) Batch 1.031 (1.324) Remain 49:15:25 loss: 0.9078 Lr: 0.00489 [2024-02-18 00:19:13,420 INFO misc.py line 119 87073] Train: [14/100][1508/1557] Data 0.009 (0.127) Batch 0.926 (1.324) Remain 49:14:49 loss: 0.7305 Lr: 0.00489 [2024-02-18 00:19:14,319 INFO misc.py line 119 87073] Train: [14/100][1509/1557] Data 0.005 (0.127) Batch 0.899 (1.323) Remain 49:14:10 loss: 0.4620 Lr: 0.00489 [2024-02-18 00:19:15,104 INFO misc.py line 119 87073] Train: [14/100][1510/1557] Data 0.005 (0.127) Batch 0.782 (1.323) Remain 49:13:20 loss: 0.3940 Lr: 0.00489 [2024-02-18 00:19:15,903 INFO misc.py line 119 87073] Train: [14/100][1511/1557] Data 0.009 (0.127) Batch 0.802 (1.323) Remain 49:12:33 loss: 0.6544 Lr: 0.00489 [2024-02-18 00:19:17,152 INFO misc.py line 119 87073] Train: [14/100][1512/1557] Data 0.005 (0.127) Batch 1.249 (1.323) Remain 49:12:25 loss: 0.3192 Lr: 0.00489 [2024-02-18 00:19:17,946 INFO misc.py line 119 87073] Train: [14/100][1513/1557] Data 0.006 (0.127) Batch 0.794 (1.322) Remain 49:11:36 loss: 0.4277 Lr: 0.00489 [2024-02-18 00:19:19,065 INFO misc.py line 119 87073] Train: [14/100][1514/1557] Data 0.006 (0.127) Batch 1.121 (1.322) Remain 49:11:17 loss: 0.3261 Lr: 0.00489 [2024-02-18 00:19:20,215 INFO misc.py line 119 87073] Train: [14/100][1515/1557] Data 0.004 (0.127) Batch 1.148 (1.322) Remain 49:11:01 loss: 1.1506 Lr: 0.00489 [2024-02-18 00:19:21,332 INFO misc.py line 119 87073] Train: [14/100][1516/1557] Data 0.005 (0.127) Batch 1.119 (1.322) Remain 49:10:41 loss: 0.3433 Lr: 0.00489 [2024-02-18 00:19:22,106 INFO misc.py line 119 87073] Train: [14/100][1517/1557] Data 0.003 (0.127) Batch 0.773 (1.321) Remain 49:09:52 loss: 0.6400 Lr: 0.00489 [2024-02-18 00:19:22,842 INFO misc.py line 119 87073] Train: [14/100][1518/1557] Data 0.004 (0.126) Batch 0.729 (1.321) Remain 49:08:58 loss: 0.4398 Lr: 0.00489 [2024-02-18 00:19:44,679 INFO misc.py line 119 87073] Train: [14/100][1519/1557] Data 6.326 (0.131) Batch 21.844 (1.335) Remain 49:39:10 loss: 0.3269 Lr: 0.00489 [2024-02-18 00:19:45,655 INFO misc.py line 119 87073] Train: [14/100][1520/1557] Data 0.004 (0.130) Batch 0.976 (1.334) Remain 49:38:37 loss: 0.6446 Lr: 0.00489 [2024-02-18 00:19:46,612 INFO misc.py line 119 87073] Train: [14/100][1521/1557] Data 0.003 (0.130) Batch 0.956 (1.334) Remain 49:38:02 loss: 0.5781 Lr: 0.00489 [2024-02-18 00:19:47,574 INFO misc.py line 119 87073] Train: [14/100][1522/1557] Data 0.004 (0.130) Batch 0.961 (1.334) Remain 49:37:28 loss: 0.3887 Lr: 0.00489 [2024-02-18 00:19:48,599 INFO misc.py line 119 87073] Train: [14/100][1523/1557] Data 0.006 (0.130) Batch 1.019 (1.334) Remain 49:36:59 loss: 0.3748 Lr: 0.00489 [2024-02-18 00:19:49,399 INFO misc.py line 119 87073] Train: [14/100][1524/1557] Data 0.012 (0.130) Batch 0.807 (1.333) Remain 49:36:11 loss: 0.5293 Lr: 0.00489 [2024-02-18 00:19:50,158 INFO misc.py line 119 87073] Train: [14/100][1525/1557] Data 0.004 (0.130) Batch 0.759 (1.333) Remain 49:35:19 loss: 0.4624 Lr: 0.00489 [2024-02-18 00:19:51,342 INFO misc.py line 119 87073] Train: [14/100][1526/1557] Data 0.004 (0.130) Batch 1.184 (1.333) Remain 49:35:05 loss: 0.3028 Lr: 0.00489 [2024-02-18 00:19:52,319 INFO misc.py line 119 87073] Train: [14/100][1527/1557] Data 0.005 (0.130) Batch 0.977 (1.333) Remain 49:34:32 loss: 0.6421 Lr: 0.00489 [2024-02-18 00:19:53,233 INFO misc.py line 119 87073] Train: [14/100][1528/1557] Data 0.004 (0.130) Batch 0.913 (1.332) Remain 49:33:54 loss: 0.6069 Lr: 0.00489 [2024-02-18 00:19:54,135 INFO misc.py line 119 87073] Train: [14/100][1529/1557] Data 0.006 (0.130) Batch 0.902 (1.332) Remain 49:33:15 loss: 0.3803 Lr: 0.00489 [2024-02-18 00:19:55,072 INFO misc.py line 119 87073] Train: [14/100][1530/1557] Data 0.005 (0.130) Batch 0.932 (1.332) Remain 49:32:38 loss: 0.6472 Lr: 0.00489 [2024-02-18 00:19:55,840 INFO misc.py line 119 87073] Train: [14/100][1531/1557] Data 0.010 (0.130) Batch 0.775 (1.331) Remain 49:31:48 loss: 0.7447 Lr: 0.00489 [2024-02-18 00:19:56,613 INFO misc.py line 119 87073] Train: [14/100][1532/1557] Data 0.004 (0.129) Batch 0.771 (1.331) Remain 49:30:58 loss: 0.4075 Lr: 0.00489 [2024-02-18 00:19:57,784 INFO misc.py line 119 87073] Train: [14/100][1533/1557] Data 0.006 (0.129) Batch 1.164 (1.331) Remain 49:30:42 loss: 0.3954 Lr: 0.00489 [2024-02-18 00:19:58,661 INFO misc.py line 119 87073] Train: [14/100][1534/1557] Data 0.013 (0.129) Batch 0.887 (1.331) Remain 49:30:02 loss: 0.8249 Lr: 0.00489 [2024-02-18 00:19:59,551 INFO misc.py line 119 87073] Train: [14/100][1535/1557] Data 0.003 (0.129) Batch 0.890 (1.330) Remain 49:29:22 loss: 1.0009 Lr: 0.00489 [2024-02-18 00:20:00,456 INFO misc.py line 119 87073] Train: [14/100][1536/1557] Data 0.004 (0.129) Batch 0.899 (1.330) Remain 49:28:43 loss: 0.4335 Lr: 0.00489 [2024-02-18 00:20:01,499 INFO misc.py line 119 87073] Train: [14/100][1537/1557] Data 0.010 (0.129) Batch 1.039 (1.330) Remain 49:28:16 loss: 0.6284 Lr: 0.00489 [2024-02-18 00:20:02,183 INFO misc.py line 119 87073] Train: [14/100][1538/1557] Data 0.014 (0.129) Batch 0.694 (1.329) Remain 49:27:19 loss: 0.4389 Lr: 0.00489 [2024-02-18 00:20:02,902 INFO misc.py line 119 87073] Train: [14/100][1539/1557] Data 0.004 (0.129) Batch 0.711 (1.329) Remain 49:26:24 loss: 0.5707 Lr: 0.00489 [2024-02-18 00:20:04,190 INFO misc.py line 119 87073] Train: [14/100][1540/1557] Data 0.011 (0.129) Batch 1.290 (1.329) Remain 49:26:19 loss: 0.2047 Lr: 0.00489 [2024-02-18 00:20:05,146 INFO misc.py line 119 87073] Train: [14/100][1541/1557] Data 0.009 (0.129) Batch 0.960 (1.329) Remain 49:25:46 loss: 0.4206 Lr: 0.00489 [2024-02-18 00:20:06,079 INFO misc.py line 119 87073] Train: [14/100][1542/1557] Data 0.005 (0.129) Batch 0.935 (1.329) Remain 49:25:10 loss: 0.6292 Lr: 0.00489 [2024-02-18 00:20:07,111 INFO misc.py line 119 87073] Train: [14/100][1543/1557] Data 0.004 (0.129) Batch 1.031 (1.328) Remain 49:24:43 loss: 0.8263 Lr: 0.00489 [2024-02-18 00:20:07,965 INFO misc.py line 119 87073] Train: [14/100][1544/1557] Data 0.004 (0.129) Batch 0.845 (1.328) Remain 49:24:00 loss: 1.2088 Lr: 0.00489 [2024-02-18 00:20:08,683 INFO misc.py line 119 87073] Train: [14/100][1545/1557] Data 0.013 (0.128) Batch 0.727 (1.328) Remain 49:23:06 loss: 0.8598 Lr: 0.00489 [2024-02-18 00:20:09,442 INFO misc.py line 119 87073] Train: [14/100][1546/1557] Data 0.003 (0.128) Batch 0.750 (1.327) Remain 49:22:15 loss: 0.4295 Lr: 0.00489 [2024-02-18 00:20:10,686 INFO misc.py line 119 87073] Train: [14/100][1547/1557] Data 0.013 (0.128) Batch 1.237 (1.327) Remain 49:22:06 loss: 0.3149 Lr: 0.00489 [2024-02-18 00:20:11,817 INFO misc.py line 119 87073] Train: [14/100][1548/1557] Data 0.021 (0.128) Batch 1.132 (1.327) Remain 49:21:47 loss: 0.7163 Lr: 0.00489 [2024-02-18 00:20:12,851 INFO misc.py line 119 87073] Train: [14/100][1549/1557] Data 0.019 (0.128) Batch 1.043 (1.327) Remain 49:21:21 loss: 0.5659 Lr: 0.00489 [2024-02-18 00:20:13,795 INFO misc.py line 119 87073] Train: [14/100][1550/1557] Data 0.011 (0.128) Batch 0.951 (1.327) Remain 49:20:48 loss: 0.5654 Lr: 0.00489 [2024-02-18 00:20:14,723 INFO misc.py line 119 87073] Train: [14/100][1551/1557] Data 0.004 (0.128) Batch 0.928 (1.326) Remain 49:20:12 loss: 0.4972 Lr: 0.00489 [2024-02-18 00:20:15,504 INFO misc.py line 119 87073] Train: [14/100][1552/1557] Data 0.004 (0.128) Batch 0.768 (1.326) Remain 49:19:22 loss: 0.8570 Lr: 0.00489 [2024-02-18 00:20:16,271 INFO misc.py line 119 87073] Train: [14/100][1553/1557] Data 0.016 (0.128) Batch 0.780 (1.326) Remain 49:18:34 loss: 0.6552 Lr: 0.00489 [2024-02-18 00:20:17,344 INFO misc.py line 119 87073] Train: [14/100][1554/1557] Data 0.004 (0.128) Batch 1.073 (1.325) Remain 49:18:10 loss: 0.2221 Lr: 0.00489 [2024-02-18 00:20:18,276 INFO misc.py line 119 87073] Train: [14/100][1555/1557] Data 0.004 (0.128) Batch 0.932 (1.325) Remain 49:17:35 loss: 1.3443 Lr: 0.00489 [2024-02-18 00:20:19,225 INFO misc.py line 119 87073] Train: [14/100][1556/1557] Data 0.004 (0.128) Batch 0.950 (1.325) Remain 49:17:01 loss: 0.4103 Lr: 0.00489 [2024-02-18 00:20:20,224 INFO misc.py line 119 87073] Train: [14/100][1557/1557] Data 0.003 (0.128) Batch 0.989 (1.325) Remain 49:16:31 loss: 0.4736 Lr: 0.00489 [2024-02-18 00:20:20,225 INFO misc.py line 136 87073] Train result: loss: 0.5390 [2024-02-18 00:20:20,225 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 00:20:50,217 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7521 [2024-02-18 00:20:51,001 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8171 [2024-02-18 00:20:53,130 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5326 [2024-02-18 00:20:55,339 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3119 [2024-02-18 00:21:00,293 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2136 [2024-02-18 00:21:00,991 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1525 [2024-02-18 00:21:02,252 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3585 [2024-02-18 00:21:05,207 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2934 [2024-02-18 00:21:07,019 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3293 [2024-02-18 00:21:08,432 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6710/0.8017/0.8964. [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9081/0.9375 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9673/0.9706 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8501/0.9421 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0015/0.0285 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4827/0.6745 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6712/0.6991 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6988/0.8659 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.7914/0.9463 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9151/0.9707 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.5545/0.9707 [2024-02-18 00:21:08,432 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7630/0.8502 [2024-02-18 00:21:08,433 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.5520/0.9063 [2024-02-18 00:21:08,433 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5673/0.6595 [2024-02-18 00:21:08,433 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 00:21:08,435 INFO misc.py line 165 87073] Currently Best mIoU: 0.6864 [2024-02-18 00:21:08,435 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 00:21:16,053 INFO misc.py line 119 87073] Train: [15/100][1/1557] Data 1.436 (1.436) Batch 2.228 (2.228) Remain 82:51:52 loss: 0.5769 Lr: 0.00489 [2024-02-18 00:21:16,998 INFO misc.py line 119 87073] Train: [15/100][2/1557] Data 0.007 (0.007) Batch 0.946 (0.946) Remain 35:11:57 loss: 0.4047 Lr: 0.00489 [2024-02-18 00:21:18,026 INFO misc.py line 119 87073] Train: [15/100][3/1557] Data 0.004 (0.004) Batch 1.021 (1.021) Remain 37:58:39 loss: 0.7629 Lr: 0.00489 [2024-02-18 00:21:18,987 INFO misc.py line 119 87073] Train: [15/100][4/1557] Data 0.013 (0.013) Batch 0.967 (0.967) Remain 35:58:35 loss: 0.2895 Lr: 0.00489 [2024-02-18 00:21:19,740 INFO misc.py line 119 87073] Train: [15/100][5/1557] Data 0.005 (0.009) Batch 0.754 (0.861) Remain 32:00:31 loss: 0.6364 Lr: 0.00489 [2024-02-18 00:21:20,577 INFO misc.py line 119 87073] Train: [15/100][6/1557] Data 0.004 (0.008) Batch 0.825 (0.849) Remain 31:33:59 loss: 0.5954 Lr: 0.00489 [2024-02-18 00:21:22,339 INFO misc.py line 119 87073] Train: [15/100][7/1557] Data 0.017 (0.010) Batch 1.771 (1.079) Remain 40:08:28 loss: 0.3976 Lr: 0.00489 [2024-02-18 00:21:23,420 INFO misc.py line 119 87073] Train: [15/100][8/1557] Data 0.007 (0.009) Batch 1.069 (1.077) Remain 40:03:58 loss: 0.4835 Lr: 0.00489 [2024-02-18 00:21:24,331 INFO misc.py line 119 87073] Train: [15/100][9/1557] Data 0.019 (0.011) Batch 0.926 (1.052) Remain 39:07:48 loss: 0.9013 Lr: 0.00489 [2024-02-18 00:21:25,322 INFO misc.py line 119 87073] Train: [15/100][10/1557] Data 0.003 (0.010) Batch 0.991 (1.043) Remain 38:48:21 loss: 0.2996 Lr: 0.00489 [2024-02-18 00:21:26,253 INFO misc.py line 119 87073] Train: [15/100][11/1557] Data 0.003 (0.009) Batch 0.930 (1.029) Remain 38:16:37 loss: 0.2886 Lr: 0.00489 [2024-02-18 00:21:27,021 INFO misc.py line 119 87073] Train: [15/100][12/1557] Data 0.005 (0.009) Batch 0.757 (0.999) Remain 37:09:02 loss: 0.7047 Lr: 0.00489 [2024-02-18 00:21:27,754 INFO misc.py line 119 87073] Train: [15/100][13/1557] Data 0.016 (0.009) Batch 0.745 (0.974) Remain 36:12:23 loss: 0.4890 Lr: 0.00489 [2024-02-18 00:21:28,965 INFO misc.py line 119 87073] Train: [15/100][14/1557] Data 0.004 (0.009) Batch 1.210 (0.995) Remain 37:00:23 loss: 0.6599 Lr: 0.00489 [2024-02-18 00:21:30,102 INFO misc.py line 119 87073] Train: [15/100][15/1557] Data 0.005 (0.008) Batch 1.138 (1.007) Remain 37:26:53 loss: 0.6375 Lr: 0.00489 [2024-02-18 00:21:31,085 INFO misc.py line 119 87073] Train: [15/100][16/1557] Data 0.004 (0.008) Batch 0.983 (1.005) Remain 37:22:42 loss: 0.2821 Lr: 0.00489 [2024-02-18 00:21:31,972 INFO misc.py line 119 87073] Train: [15/100][17/1557] Data 0.005 (0.008) Batch 0.888 (0.997) Remain 37:03:58 loss: 0.4043 Lr: 0.00489 [2024-02-18 00:21:32,963 INFO misc.py line 119 87073] Train: [15/100][18/1557] Data 0.004 (0.008) Batch 0.984 (0.996) Remain 37:02:02 loss: 0.4703 Lr: 0.00489 [2024-02-18 00:21:33,732 INFO misc.py line 119 87073] Train: [15/100][19/1557] Data 0.011 (0.008) Batch 0.776 (0.982) Remain 36:31:24 loss: 0.7903 Lr: 0.00489 [2024-02-18 00:21:34,481 INFO misc.py line 119 87073] Train: [15/100][20/1557] Data 0.004 (0.008) Batch 0.749 (0.968) Remain 36:00:48 loss: 0.3135 Lr: 0.00489 [2024-02-18 00:21:35,667 INFO misc.py line 119 87073] Train: [15/100][21/1557] Data 0.003 (0.007) Batch 1.173 (0.980) Remain 36:26:10 loss: 0.1974 Lr: 0.00489 [2024-02-18 00:21:36,432 INFO misc.py line 119 87073] Train: [15/100][22/1557] Data 0.016 (0.008) Batch 0.778 (0.969) Remain 36:02:29 loss: 0.5435 Lr: 0.00489 [2024-02-18 00:21:37,374 INFO misc.py line 119 87073] Train: [15/100][23/1557] Data 0.003 (0.008) Batch 0.941 (0.968) Remain 35:59:18 loss: 0.4385 Lr: 0.00489 [2024-02-18 00:21:38,181 INFO misc.py line 119 87073] Train: [15/100][24/1557] Data 0.004 (0.007) Batch 0.789 (0.959) Remain 35:40:14 loss: 0.6457 Lr: 0.00489 [2024-02-18 00:21:39,088 INFO misc.py line 119 87073] Train: [15/100][25/1557] Data 0.023 (0.008) Batch 0.926 (0.958) Remain 35:36:54 loss: 0.7981 Lr: 0.00489 [2024-02-18 00:21:39,812 INFO misc.py line 119 87073] Train: [15/100][26/1557] Data 0.004 (0.008) Batch 0.723 (0.948) Remain 35:14:10 loss: 0.3639 Lr: 0.00489 [2024-02-18 00:21:40,573 INFO misc.py line 119 87073] Train: [15/100][27/1557] Data 0.004 (0.008) Batch 0.751 (0.939) Remain 34:55:53 loss: 0.5993 Lr: 0.00489 [2024-02-18 00:21:41,901 INFO misc.py line 119 87073] Train: [15/100][28/1557] Data 0.014 (0.008) Batch 1.325 (0.955) Remain 35:30:18 loss: 0.4034 Lr: 0.00489 [2024-02-18 00:21:42,739 INFO misc.py line 119 87073] Train: [15/100][29/1557] Data 0.018 (0.008) Batch 0.852 (0.951) Remain 35:21:26 loss: 0.4748 Lr: 0.00489 [2024-02-18 00:21:43,728 INFO misc.py line 119 87073] Train: [15/100][30/1557] Data 0.004 (0.008) Batch 0.988 (0.952) Remain 35:24:30 loss: 1.0014 Lr: 0.00489 [2024-02-18 00:21:44,750 INFO misc.py line 119 87073] Train: [15/100][31/1557] Data 0.004 (0.008) Batch 1.023 (0.955) Remain 35:30:05 loss: 0.5812 Lr: 0.00489 [2024-02-18 00:21:45,668 INFO misc.py line 119 87073] Train: [15/100][32/1557] Data 0.003 (0.008) Batch 0.918 (0.953) Remain 35:27:14 loss: 0.4907 Lr: 0.00489 [2024-02-18 00:21:46,476 INFO misc.py line 119 87073] Train: [15/100][33/1557] Data 0.004 (0.008) Batch 0.797 (0.948) Remain 35:15:35 loss: 0.3836 Lr: 0.00489 [2024-02-18 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[2024-02-18 00:49:27,448 INFO misc.py line 119 87073] Train: [15/100][1526/1557] Data 0.005 (0.051) Batch 1.163 (1.109) Remain 40:47:21 loss: 0.3099 Lr: 0.00487 [2024-02-18 00:49:28,411 INFO misc.py line 119 87073] Train: [15/100][1527/1557] Data 0.006 (0.051) Batch 0.964 (1.109) Remain 40:47:07 loss: 0.7233 Lr: 0.00487 [2024-02-18 00:49:29,638 INFO misc.py line 119 87073] Train: [15/100][1528/1557] Data 0.005 (0.051) Batch 1.218 (1.109) Remain 40:47:15 loss: 0.4340 Lr: 0.00487 [2024-02-18 00:49:30,645 INFO misc.py line 119 87073] Train: [15/100][1529/1557] Data 0.012 (0.051) Batch 1.006 (1.109) Remain 40:47:05 loss: 0.2241 Lr: 0.00487 [2024-02-18 00:49:31,642 INFO misc.py line 119 87073] Train: [15/100][1530/1557] Data 0.013 (0.051) Batch 1.007 (1.109) Remain 40:46:55 loss: 0.5030 Lr: 0.00487 [2024-02-18 00:49:32,410 INFO misc.py line 119 87073] Train: [15/100][1531/1557] Data 0.004 (0.051) Batch 0.767 (1.109) Remain 40:46:25 loss: 0.3096 Lr: 0.00487 [2024-02-18 00:49:33,165 INFO misc.py line 119 87073] Train: [15/100][1532/1557] Data 0.005 (0.051) Batch 0.751 (1.109) Remain 40:45:53 loss: 0.7613 Lr: 0.00487 [2024-02-18 00:49:34,295 INFO misc.py line 119 87073] Train: [15/100][1533/1557] Data 0.008 (0.051) Batch 1.125 (1.109) Remain 40:45:53 loss: 0.2144 Lr: 0.00487 [2024-02-18 00:49:35,281 INFO misc.py line 119 87073] Train: [15/100][1534/1557] Data 0.012 (0.051) Batch 0.995 (1.109) Remain 40:45:42 loss: 0.2501 Lr: 0.00487 [2024-02-18 00:49:36,198 INFO misc.py line 119 87073] Train: [15/100][1535/1557] Data 0.004 (0.051) Batch 0.917 (1.108) Remain 40:45:24 loss: 0.6085 Lr: 0.00487 [2024-02-18 00:49:37,261 INFO misc.py line 119 87073] Train: [15/100][1536/1557] Data 0.004 (0.051) Batch 1.063 (1.108) Remain 40:45:19 loss: 0.7352 Lr: 0.00487 [2024-02-18 00:49:38,111 INFO misc.py line 119 87073] Train: [15/100][1537/1557] Data 0.004 (0.051) Batch 0.850 (1.108) Remain 40:44:56 loss: 0.4455 Lr: 0.00487 [2024-02-18 00:49:38,868 INFO misc.py line 119 87073] Train: [15/100][1538/1557] Data 0.004 (0.051) Batch 0.748 (1.108) Remain 40:44:24 loss: 1.0684 Lr: 0.00487 [2024-02-18 00:49:39,558 INFO misc.py line 119 87073] Train: [15/100][1539/1557] Data 0.013 (0.051) Batch 0.698 (1.108) Remain 40:43:47 loss: 0.3206 Lr: 0.00487 [2024-02-18 00:49:40,930 INFO misc.py line 119 87073] Train: [15/100][1540/1557] Data 0.004 (0.051) Batch 1.362 (1.108) Remain 40:44:08 loss: 0.2321 Lr: 0.00487 [2024-02-18 00:49:41,811 INFO misc.py line 119 87073] Train: [15/100][1541/1557] Data 0.014 (0.051) Batch 0.891 (1.108) Remain 40:43:48 loss: 1.0950 Lr: 0.00486 [2024-02-18 00:49:42,870 INFO misc.py line 119 87073] Train: [15/100][1542/1557] Data 0.003 (0.051) Batch 1.059 (1.108) Remain 40:43:43 loss: 0.6777 Lr: 0.00486 [2024-02-18 00:49:44,039 INFO misc.py line 119 87073] Train: [15/100][1543/1557] Data 0.004 (0.051) Batch 1.153 (1.108) Remain 40:43:46 loss: 0.5892 Lr: 0.00486 [2024-02-18 00:49:44,956 INFO misc.py line 119 87073] Train: [15/100][1544/1557] Data 0.020 (0.051) Batch 0.933 (1.108) Remain 40:43:30 loss: 0.3211 Lr: 0.00486 [2024-02-18 00:49:45,724 INFO misc.py line 119 87073] Train: [15/100][1545/1557] Data 0.004 (0.051) Batch 0.768 (1.107) Remain 40:42:59 loss: 0.4134 Lr: 0.00486 [2024-02-18 00:49:46,461 INFO misc.py line 119 87073] Train: [15/100][1546/1557] Data 0.005 (0.051) Batch 0.728 (1.107) Remain 40:42:26 loss: 0.4663 Lr: 0.00486 [2024-02-18 00:49:47,767 INFO misc.py line 119 87073] Train: [15/100][1547/1557] Data 0.013 (0.051) Batch 1.308 (1.107) Remain 40:42:42 loss: 0.5436 Lr: 0.00486 [2024-02-18 00:49:48,781 INFO misc.py line 119 87073] Train: [15/100][1548/1557] Data 0.011 (0.050) Batch 1.013 (1.107) Remain 40:42:33 loss: 0.6349 Lr: 0.00486 [2024-02-18 00:49:49,721 INFO misc.py line 119 87073] Train: [15/100][1549/1557] Data 0.013 (0.050) Batch 0.948 (1.107) Remain 40:42:18 loss: 0.5289 Lr: 0.00486 [2024-02-18 00:49:50,761 INFO misc.py line 119 87073] Train: [15/100][1550/1557] Data 0.005 (0.050) Batch 1.041 (1.107) Remain 40:42:11 loss: 0.4968 Lr: 0.00486 [2024-02-18 00:49:51,771 INFO misc.py line 119 87073] Train: [15/100][1551/1557] Data 0.004 (0.050) Batch 1.010 (1.107) Remain 40:42:02 loss: 0.7548 Lr: 0.00486 [2024-02-18 00:49:52,461 INFO misc.py line 119 87073] Train: [15/100][1552/1557] Data 0.004 (0.050) Batch 0.690 (1.107) Remain 40:41:25 loss: 0.2376 Lr: 0.00486 [2024-02-18 00:49:53,247 INFO misc.py line 119 87073] Train: [15/100][1553/1557] Data 0.004 (0.050) Batch 0.770 (1.107) Remain 40:40:55 loss: 0.8094 Lr: 0.00486 [2024-02-18 00:49:54,381 INFO misc.py line 119 87073] Train: [15/100][1554/1557] Data 0.021 (0.050) Batch 1.141 (1.107) Remain 40:40:57 loss: 0.1981 Lr: 0.00486 [2024-02-18 00:49:55,238 INFO misc.py line 119 87073] Train: [15/100][1555/1557] Data 0.014 (0.050) Batch 0.866 (1.106) Remain 40:40:35 loss: 0.4125 Lr: 0.00486 [2024-02-18 00:49:56,153 INFO misc.py line 119 87073] Train: [15/100][1556/1557] Data 0.004 (0.050) Batch 0.916 (1.106) Remain 40:40:18 loss: 0.4618 Lr: 0.00486 [2024-02-18 00:49:57,143 INFO misc.py line 119 87073] Train: [15/100][1557/1557] Data 0.003 (0.050) Batch 0.990 (1.106) Remain 40:40:07 loss: 0.4122 Lr: 0.00486 [2024-02-18 00:49:57,144 INFO misc.py line 136 87073] Train result: loss: 0.5427 [2024-02-18 00:49:57,144 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 00:50:27,853 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8164 [2024-02-18 00:50:28,630 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8656 [2024-02-18 00:50:30,755 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4507 [2024-02-18 00:50:32,961 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3047 [2024-02-18 00:50:37,906 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2085 [2024-02-18 00:50:38,607 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2219 [2024-02-18 00:50:39,869 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2757 [2024-02-18 00:50:42,826 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2981 [2024-02-18 00:50:44,641 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2659 [2024-02-18 00:50:46,175 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6970/0.7878/0.9045. [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9149/0.9632 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9818/0.9891 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8610/0.9662 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0026/0.0482 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4509/0.5621 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6453/0.6692 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7392/0.9017 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8310/0.9215 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9143/0.9537 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7328/0.9636 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7727/0.8787 [2024-02-18 00:50:46,175 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6736/0.8346 [2024-02-18 00:50:46,176 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5409/0.5899 [2024-02-18 00:50:46,176 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 00:50:46,178 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6970 [2024-02-18 00:50:46,178 INFO misc.py line 165 87073] Currently Best mIoU: 0.6970 [2024-02-18 00:50:46,178 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 00:50:57,079 INFO misc.py line 119 87073] Train: [16/100][1/1557] Data 1.384 (1.384) Batch 2.142 (2.142) Remain 78:44:47 loss: 0.4139 Lr: 0.00486 [2024-02-18 00:50:57,950 INFO misc.py line 119 87073] Train: [16/100][2/1557] Data 0.007 (0.007) Batch 0.871 (0.871) Remain 32:00:09 loss: 1.0574 Lr: 0.00486 [2024-02-18 00:50:58,913 INFO misc.py line 119 87073] Train: [16/100][3/1557] Data 0.006 (0.006) Batch 0.965 (0.965) Remain 35:27:38 loss: 0.5625 Lr: 0.00486 [2024-02-18 00:50:59,987 INFO misc.py line 119 87073] Train: [16/100][4/1557] Data 0.005 (0.005) Batch 1.075 (1.075) Remain 39:31:05 loss: 0.5974 Lr: 0.00486 [2024-02-18 00:51:00,756 INFO misc.py line 119 87073] Train: [16/100][5/1557] Data 0.004 (0.005) Batch 0.769 (0.922) Remain 33:54:01 loss: 0.4002 Lr: 0.00486 [2024-02-18 00:51:01,527 INFO misc.py line 119 87073] Train: [16/100][6/1557] Data 0.004 (0.004) Batch 0.759 (0.868) Remain 31:54:21 loss: 0.8645 Lr: 0.00486 [2024-02-18 00:51:11,863 INFO misc.py line 119 87073] Train: [16/100][7/1557] Data 9.302 (2.329) Batch 10.341 (3.236) Remain 118:57:45 loss: 0.4927 Lr: 0.00486 [2024-02-18 00:51:12,855 INFO misc.py line 119 87073] Train: [16/100][8/1557] Data 0.011 (1.865) Batch 0.998 (2.789) Remain 102:30:33 loss: 0.6789 Lr: 0.00486 [2024-02-18 00:51:13,843 INFO misc.py line 119 87073] Train: [16/100][9/1557] Data 0.004 (1.555) Batch 0.987 (2.488) Remain 91:28:24 loss: 0.5622 Lr: 0.00486 [2024-02-18 00:51:14,816 INFO misc.py line 119 87073] Train: [16/100][10/1557] Data 0.005 (1.334) Batch 0.964 (2.271) Remain 83:28:04 loss: 0.4901 Lr: 0.00486 [2024-02-18 00:51:15,681 INFO misc.py line 119 87073] Train: [16/100][11/1557] Data 0.014 (1.169) Batch 0.871 (2.096) Remain 77:02:11 loss: 0.5407 Lr: 0.00486 [2024-02-18 00:51:16,415 INFO misc.py line 119 87073] Train: [16/100][12/1557] Data 0.008 (1.040) Batch 0.738 (1.945) Remain 71:29:27 loss: 0.5791 Lr: 0.00486 [2024-02-18 00:51:17,306 INFO misc.py line 119 87073] Train: [16/100][13/1557] Data 0.003 (0.936) Batch 0.887 (1.839) Remain 67:36:06 loss: 0.5369 Lr: 0.00486 [2024-02-18 00:51:18,417 INFO misc.py line 119 87073] Train: [16/100][14/1557] Data 0.008 (0.852) Batch 1.112 (1.773) Remain 65:10:16 loss: 0.2868 Lr: 0.00486 [2024-02-18 00:51:19,336 INFO misc.py line 119 87073] Train: [16/100][15/1557] Data 0.007 (0.781) Batch 0.921 (1.702) Remain 62:33:38 loss: 0.7426 Lr: 0.00486 [2024-02-18 00:51:20,297 INFO misc.py line 119 87073] Train: [16/100][16/1557] Data 0.005 (0.722) Batch 0.962 (1.645) Remain 60:28:08 loss: 0.6476 Lr: 0.00486 [2024-02-18 00:51:21,299 INFO misc.py line 119 87073] Train: [16/100][17/1557] Data 0.003 (0.670) Batch 1.000 (1.599) Remain 58:46:34 loss: 0.6148 Lr: 0.00486 [2024-02-18 00:51:22,177 INFO misc.py line 119 87073] Train: [16/100][18/1557] Data 0.005 (0.626) Batch 0.877 (1.551) Remain 57:00:24 loss: 0.4832 Lr: 0.00486 [2024-02-18 00:51:22,961 INFO misc.py line 119 87073] Train: [16/100][19/1557] Data 0.006 (0.587) Batch 0.775 (1.502) Remain 55:13:27 loss: 0.4430 Lr: 0.00486 [2024-02-18 00:51:23,678 INFO misc.py line 119 87073] Train: [16/100][20/1557] Data 0.015 (0.554) Batch 0.727 (1.457) Remain 53:32:51 loss: 0.4999 Lr: 0.00486 [2024-02-18 00:51:24,936 INFO misc.py line 119 87073] Train: [16/100][21/1557] Data 0.004 (0.523) Batch 1.258 (1.446) Remain 53:08:29 loss: 0.2251 Lr: 0.00486 [2024-02-18 00:51:25,918 INFO misc.py line 119 87073] Train: [16/100][22/1557] Data 0.004 (0.496) Batch 0.982 (1.421) Remain 52:14:38 loss: 0.6220 Lr: 0.00486 [2024-02-18 00:51:26,974 INFO misc.py line 119 87073] Train: [16/100][23/1557] Data 0.004 (0.471) Batch 1.057 (1.403) Remain 51:34:23 loss: 0.5057 Lr: 0.00486 [2024-02-18 00:51:27,816 INFO misc.py line 119 87073] Train: [16/100][24/1557] Data 0.004 (0.449) Batch 0.841 (1.376) Remain 50:35:23 loss: 0.3799 Lr: 0.00486 [2024-02-18 00:51:28,820 INFO misc.py line 119 87073] Train: [16/100][25/1557] Data 0.004 (0.429) Batch 0.994 (1.359) Remain 49:57:03 loss: 0.8415 Lr: 0.00486 [2024-02-18 00:51:29,631 INFO misc.py line 119 87073] Train: [16/100][26/1557] Data 0.014 (0.411) Batch 0.821 (1.336) Remain 49:05:24 loss: 0.6619 Lr: 0.00486 [2024-02-18 00:51:30,396 INFO misc.py line 119 87073] Train: [16/100][27/1557] Data 0.004 (0.394) Batch 0.765 (1.312) Remain 48:12:58 loss: 0.4202 Lr: 0.00486 [2024-02-18 00:51:31,560 INFO misc.py line 119 87073] Train: [16/100][28/1557] Data 0.003 (0.378) Batch 1.154 (1.306) Remain 47:59:04 loss: 0.3240 Lr: 0.00486 [2024-02-18 00:51:32,474 INFO misc.py line 119 87073] Train: [16/100][29/1557] Data 0.014 (0.364) Batch 0.924 (1.291) Remain 47:26:40 loss: 1.2268 Lr: 0.00486 [2024-02-18 00:51:33,396 INFO misc.py line 119 87073] Train: [16/100][30/1557] Data 0.004 (0.351) Batch 0.922 (1.277) Remain 46:56:31 loss: 0.4157 Lr: 0.00486 [2024-02-18 00:51:34,402 INFO misc.py line 119 87073] Train: [16/100][31/1557] Data 0.004 (0.338) Batch 1.006 (1.268) Remain 46:35:08 loss: 0.5313 Lr: 0.00486 [2024-02-18 00:51:35,422 INFO misc.py line 119 87073] Train: [16/100][32/1557] Data 0.004 (0.327) Batch 1.019 (1.259) Remain 46:16:14 loss: 0.7536 Lr: 0.00486 [2024-02-18 00:51:36,174 INFO misc.py line 119 87073] Train: [16/100][33/1557] Data 0.004 (0.316) Batch 0.753 (1.242) Remain 45:39:01 loss: 0.3644 Lr: 0.00486 [2024-02-18 00:51:36,967 INFO misc.py line 119 87073] Train: [16/100][34/1557] Data 0.003 (0.306) Batch 0.784 (1.227) Remain 45:06:25 loss: 0.4325 Lr: 0.00486 [2024-02-18 00:51:38,154 INFO misc.py line 119 87073] Train: [16/100][35/1557] Data 0.013 (0.297) Batch 1.150 (1.225) Remain 45:01:03 loss: 0.1667 Lr: 0.00486 [2024-02-18 00:51:39,126 INFO misc.py line 119 87073] Train: [16/100][36/1557] Data 0.050 (0.289) Batch 1.019 (1.219) Remain 44:47:15 loss: 0.5231 Lr: 0.00486 [2024-02-18 00:51:40,037 INFO misc.py line 119 87073] Train: [16/100][37/1557] Data 0.004 (0.281) Batch 0.911 (1.210) Remain 44:27:15 loss: 0.2828 Lr: 0.00486 [2024-02-18 00:51:40,980 INFO misc.py line 119 87073] Train: [16/100][38/1557] Data 0.004 (0.273) Batch 0.942 (1.202) Remain 44:10:21 loss: 0.7042 Lr: 0.00486 [2024-02-18 00:51:41,912 INFO misc.py line 119 87073] Train: [16/100][39/1557] Data 0.005 (0.266) Batch 0.933 (1.194) Remain 43:53:51 loss: 0.5182 Lr: 0.00486 [2024-02-18 00:51:42,640 INFO misc.py line 119 87073] Train: [16/100][40/1557] Data 0.004 (0.259) Batch 0.729 (1.182) Remain 43:26:06 loss: 0.4656 Lr: 0.00486 [2024-02-18 00:51:43,427 INFO misc.py line 119 87073] Train: [16/100][41/1557] Data 0.003 (0.252) Batch 0.781 (1.171) Remain 43:02:48 loss: 0.3087 Lr: 0.00486 [2024-02-18 00:51:44,561 INFO misc.py line 119 87073] Train: [16/100][42/1557] Data 0.009 (0.246) Batch 1.132 (1.170) Remain 43:00:33 loss: 0.8699 Lr: 0.00486 [2024-02-18 00:51:45,399 INFO misc.py line 119 87073] Train: [16/100][43/1557] Data 0.012 (0.240) Batch 0.846 (1.162) Remain 42:42:39 loss: 0.7122 Lr: 0.00486 [2024-02-18 00:51:46,434 INFO misc.py line 119 87073] Train: [16/100][44/1557] Data 0.004 (0.234) Batch 1.034 (1.159) Remain 42:35:44 loss: 0.3812 Lr: 0.00486 [2024-02-18 00:51:47,397 INFO misc.py line 119 87073] Train: [16/100][45/1557] Data 0.005 (0.229) Batch 0.963 (1.154) Remain 42:25:27 loss: 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INFO misc.py line 119 87073] Train: [16/100][52/1557] Data 0.004 (0.197) Batch 0.979 (1.131) Remain 41:33:57 loss: 0.4849 Lr: 0.00486 [2024-02-18 00:51:55,296 INFO misc.py line 119 87073] Train: [16/100][53/1557] Data 0.005 (0.193) Batch 0.960 (1.128) Remain 41:26:23 loss: 0.4101 Lr: 0.00486 [2024-02-18 00:51:56,041 INFO misc.py line 119 87073] Train: [16/100][54/1557] Data 0.004 (0.189) Batch 0.745 (1.120) Remain 41:09:49 loss: 0.7271 Lr: 0.00486 [2024-02-18 00:51:56,756 INFO misc.py line 119 87073] Train: [16/100][55/1557] Data 0.004 (0.186) Batch 0.716 (1.112) Remain 40:52:39 loss: 0.4860 Lr: 0.00486 [2024-02-18 00:51:58,034 INFO misc.py line 119 87073] Train: [16/100][56/1557] Data 0.003 (0.182) Batch 1.277 (1.116) Remain 40:59:30 loss: 0.4010 Lr: 0.00486 [2024-02-18 00:51:58,975 INFO misc.py line 119 87073] Train: [16/100][57/1557] Data 0.004 (0.179) Batch 0.941 (1.112) Remain 40:52:21 loss: 1.0247 Lr: 0.00486 [2024-02-18 00:51:59,870 INFO misc.py line 119 87073] Train: 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Train: [16/100][102/1557] Data 0.004 (0.274) Batch 0.975 (1.205) Remain 44:15:15 loss: 0.2960 Lr: 0.00486 [2024-02-18 00:52:58,907 INFO misc.py line 119 87073] Train: [16/100][103/1557] Data 0.003 (0.272) Batch 0.728 (1.200) Remain 44:04:43 loss: 0.9191 Lr: 0.00486 [2024-02-18 00:52:59,722 INFO misc.py line 119 87073] Train: [16/100][104/1557] Data 0.004 (0.269) Batch 0.811 (1.196) Remain 43:56:12 loss: 0.5900 Lr: 0.00486 [2024-02-18 00:53:01,058 INFO misc.py line 119 87073] Train: [16/100][105/1557] Data 0.008 (0.267) Batch 1.328 (1.197) Remain 43:59:02 loss: 0.2892 Lr: 0.00486 [2024-02-18 00:53:02,148 INFO misc.py line 119 87073] Train: [16/100][106/1557] Data 0.016 (0.264) Batch 1.098 (1.196) Remain 43:56:53 loss: 0.3908 Lr: 0.00486 [2024-02-18 00:53:03,095 INFO misc.py line 119 87073] Train: [16/100][107/1557] Data 0.009 (0.262) Batch 0.951 (1.194) Remain 43:51:40 loss: 0.4947 Lr: 0.00486 [2024-02-18 00:53:04,037 INFO misc.py line 119 87073] Train: [16/100][108/1557] Data 0.005 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line 119 87073] Train: [16/100][127/1557] Data 0.013 (0.352) Batch 0.834 (1.281) Remain 47:02:45 loss: 1.0259 Lr: 0.00486 [2024-02-18 00:53:38,677 INFO misc.py line 119 87073] Train: [16/100][128/1557] Data 0.005 (0.349) Batch 0.927 (1.278) Remain 46:56:29 loss: 0.4059 Lr: 0.00486 [2024-02-18 00:53:39,789 INFO misc.py line 119 87073] Train: [16/100][129/1557] Data 0.004 (0.346) Batch 1.111 (1.277) Remain 46:53:32 loss: 0.4666 Lr: 0.00486 [2024-02-18 00:53:40,714 INFO misc.py line 119 87073] Train: [16/100][130/1557] Data 0.004 (0.344) Batch 0.924 (1.274) Remain 46:47:24 loss: 0.7047 Lr: 0.00486 [2024-02-18 00:53:41,493 INFO misc.py line 119 87073] Train: [16/100][131/1557] Data 0.005 (0.341) Batch 0.780 (1.270) Remain 46:38:52 loss: 0.4953 Lr: 0.00486 [2024-02-18 00:53:42,260 INFO misc.py line 119 87073] Train: [16/100][132/1557] Data 0.005 (0.338) Batch 0.766 (1.266) Remain 46:30:14 loss: 0.4067 Lr: 0.00486 [2024-02-18 00:53:43,596 INFO misc.py line 119 87073] Train: 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Batch 0.776 (1.249) Remain 45:52:27 loss: 0.3687 Lr: 0.00486 [2024-02-18 00:53:49,979 INFO misc.py line 119 87073] Train: [16/100][140/1557] Data 0.006 (0.319) Batch 1.169 (1.249) Remain 45:51:09 loss: 0.2103 Lr: 0.00486 [2024-02-18 00:53:50,960 INFO misc.py line 119 87073] Train: [16/100][141/1557] Data 0.015 (0.317) Batch 0.992 (1.247) Remain 45:47:01 loss: 0.6939 Lr: 0.00486 [2024-02-18 00:53:51,960 INFO misc.py line 119 87073] Train: [16/100][142/1557] Data 0.004 (0.314) Batch 1.001 (1.245) Remain 45:43:06 loss: 0.8606 Lr: 0.00486 [2024-02-18 00:53:52,894 INFO misc.py line 119 87073] Train: [16/100][143/1557] Data 0.004 (0.312) Batch 0.933 (1.243) Remain 45:38:10 loss: 0.7151 Lr: 0.00486 [2024-02-18 00:53:53,865 INFO misc.py line 119 87073] Train: [16/100][144/1557] Data 0.004 (0.310) Batch 0.959 (1.241) Remain 45:33:43 loss: 0.6518 Lr: 0.00486 [2024-02-18 00:53:54,634 INFO misc.py line 119 87073] Train: [16/100][145/1557] Data 0.015 (0.308) Batch 0.779 (1.237) Remain 45:26:32 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line 119 87073] Train: [16/100][183/1557] Data 0.008 (0.343) Batch 1.236 (1.281) Remain 47:02:05 loss: 0.3136 Lr: 0.00486 [2024-02-18 00:54:50,365 INFO misc.py line 119 87073] Train: [16/100][184/1557] Data 0.017 (0.341) Batch 0.838 (1.279) Remain 46:56:40 loss: 0.7919 Lr: 0.00486 [2024-02-18 00:54:51,209 INFO misc.py line 119 87073] Train: [16/100][185/1557] Data 0.004 (0.339) Batch 0.843 (1.276) Remain 46:51:22 loss: 0.7273 Lr: 0.00486 [2024-02-18 00:54:52,149 INFO misc.py line 119 87073] Train: [16/100][186/1557] Data 0.005 (0.338) Batch 0.934 (1.274) Remain 46:47:13 loss: 0.3803 Lr: 0.00486 [2024-02-18 00:54:52,892 INFO misc.py line 119 87073] Train: [16/100][187/1557] Data 0.011 (0.336) Batch 0.751 (1.272) Remain 46:40:56 loss: 0.7188 Lr: 0.00486 [2024-02-18 00:54:53,680 INFO misc.py line 119 87073] Train: [16/100][188/1557] Data 0.003 (0.334) Batch 0.782 (1.269) Remain 46:35:05 loss: 0.7050 Lr: 0.00486 [2024-02-18 00:54:55,022 INFO misc.py line 119 87073] Train: 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Batch 0.817 (1.259) Remain 46:13:28 loss: 0.2684 Lr: 0.00486 [2024-02-18 00:55:01,858 INFO misc.py line 119 87073] Train: [16/100][196/1557] Data 0.004 (0.321) Batch 1.169 (1.259) Remain 46:12:25 loss: 0.3444 Lr: 0.00486 [2024-02-18 00:55:02,812 INFO misc.py line 119 87073] Train: [16/100][197/1557] Data 0.006 (0.319) Batch 0.957 (1.257) Remain 46:08:58 loss: 0.3919 Lr: 0.00486 [2024-02-18 00:55:03,742 INFO misc.py line 119 87073] Train: [16/100][198/1557] Data 0.004 (0.317) Batch 0.930 (1.256) Remain 46:05:15 loss: 0.3921 Lr: 0.00486 [2024-02-18 00:55:04,795 INFO misc.py line 119 87073] Train: [16/100][199/1557] Data 0.004 (0.316) Batch 1.052 (1.254) Remain 46:02:56 loss: 0.2933 Lr: 0.00486 [2024-02-18 00:55:06,133 INFO misc.py line 119 87073] Train: [16/100][200/1557] Data 0.005 (0.314) Batch 1.338 (1.255) Remain 46:03:51 loss: 0.6786 Lr: 0.00486 [2024-02-18 00:55:06,927 INFO misc.py line 119 87073] Train: [16/100][201/1557] Data 0.005 (0.313) Batch 0.794 (1.253) Remain 45:58:42 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Batch 0.754 (1.250) Remain 45:51:21 loss: 0.4921 Lr: 0.00486 [2024-02-18 00:57:20,169 INFO misc.py line 119 87073] Train: [16/100][308/1557] Data 0.005 (0.311) Batch 1.171 (1.250) Remain 45:50:46 loss: 0.2003 Lr: 0.00486 [2024-02-18 00:57:21,050 INFO misc.py line 119 87073] Train: [16/100][309/1557] Data 0.011 (0.310) Batch 0.888 (1.249) Remain 45:48:08 loss: 0.3130 Lr: 0.00486 [2024-02-18 00:57:22,013 INFO misc.py line 119 87073] Train: [16/100][310/1557] Data 0.006 (0.309) Batch 0.964 (1.248) Remain 45:46:04 loss: 0.9928 Lr: 0.00486 [2024-02-18 00:57:22,999 INFO misc.py line 119 87073] Train: [16/100][311/1557] Data 0.004 (0.308) Batch 0.985 (1.247) Remain 45:44:10 loss: 0.7384 Lr: 0.00486 [2024-02-18 00:57:24,043 INFO misc.py line 119 87073] Train: [16/100][312/1557] Data 0.004 (0.307) Batch 1.044 (1.246) Remain 45:42:42 loss: 1.0541 Lr: 0.00486 [2024-02-18 00:57:24,784 INFO misc.py line 119 87073] Train: [16/100][313/1557] Data 0.005 (0.306) Batch 0.742 (1.245) Remain 45:39:06 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Batch 0.825 (1.257) Remain 46:05:09 loss: 0.7288 Lr: 0.00486 [2024-02-18 00:58:32,599 INFO misc.py line 119 87073] Train: [16/100][364/1557] Data 0.005 (0.316) Batch 1.144 (1.257) Remain 46:04:26 loss: 0.2713 Lr: 0.00486 [2024-02-18 00:58:33,681 INFO misc.py line 119 87073] Train: [16/100][365/1557] Data 0.004 (0.315) Batch 1.081 (1.256) Remain 46:03:21 loss: 0.5182 Lr: 0.00486 [2024-02-18 00:58:34,567 INFO misc.py line 119 87073] Train: [16/100][366/1557] Data 0.006 (0.314) Batch 0.885 (1.255) Remain 46:01:05 loss: 0.4420 Lr: 0.00486 [2024-02-18 00:58:35,594 INFO misc.py line 119 87073] Train: [16/100][367/1557] Data 0.007 (0.313) Batch 1.028 (1.255) Remain 45:59:41 loss: 0.3098 Lr: 0.00486 [2024-02-18 00:58:36,623 INFO misc.py line 119 87073] Train: [16/100][368/1557] Data 0.006 (0.312) Batch 1.026 (1.254) Remain 45:58:17 loss: 0.3934 Lr: 0.00486 [2024-02-18 00:58:37,414 INFO misc.py line 119 87073] Train: [16/100][369/1557] Data 0.009 (0.312) Batch 0.793 (1.253) Remain 45:55:29 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Batch 0.764 (1.258) Remain 46:04:10 loss: 0.7198 Lr: 0.00486 [2024-02-18 01:00:53,745 INFO misc.py line 119 87073] Train: [16/100][476/1557] Data 0.005 (0.316) Batch 1.195 (1.258) Remain 46:03:52 loss: 0.2359 Lr: 0.00486 [2024-02-18 01:00:54,666 INFO misc.py line 119 87073] Train: [16/100][477/1557] Data 0.014 (0.316) Batch 0.929 (1.257) Remain 46:02:19 loss: 0.8063 Lr: 0.00486 [2024-02-18 01:00:55,639 INFO misc.py line 119 87073] Train: [16/100][478/1557] Data 0.006 (0.315) Batch 0.974 (1.256) Remain 46:00:59 loss: 0.4588 Lr: 0.00486 [2024-02-18 01:00:56,490 INFO misc.py line 119 87073] Train: [16/100][479/1557] Data 0.005 (0.314) Batch 0.853 (1.255) Remain 45:59:06 loss: 0.6123 Lr: 0.00486 [2024-02-18 01:00:57,427 INFO misc.py line 119 87073] Train: [16/100][480/1557] Data 0.006 (0.314) Batch 0.927 (1.255) Remain 45:57:34 loss: 0.6000 Lr: 0.00486 [2024-02-18 01:00:58,188 INFO misc.py line 119 87073] Train: [16/100][481/1557] Data 0.013 (0.313) Batch 0.769 (1.254) Remain 45:55:19 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Batch 0.777 (1.255) Remain 45:56:40 loss: 0.5655 Lr: 0.00485 [2024-02-18 01:03:13,325 INFO misc.py line 119 87073] Train: [16/100][588/1557] Data 0.005 (0.313) Batch 1.283 (1.255) Remain 45:56:45 loss: 0.1610 Lr: 0.00485 [2024-02-18 01:03:14,421 INFO misc.py line 119 87073] Train: [16/100][589/1557] Data 0.017 (0.313) Batch 1.100 (1.255) Remain 45:56:09 loss: 0.4702 Lr: 0.00485 [2024-02-18 01:03:15,395 INFO misc.py line 119 87073] Train: [16/100][590/1557] Data 0.014 (0.312) Batch 0.983 (1.255) Remain 45:55:06 loss: 0.8557 Lr: 0.00485 [2024-02-18 01:03:16,437 INFO misc.py line 119 87073] Train: [16/100][591/1557] Data 0.005 (0.312) Batch 1.043 (1.254) Remain 45:54:18 loss: 0.6021 Lr: 0.00485 [2024-02-18 01:03:17,484 INFO misc.py line 119 87073] Train: [16/100][592/1557] Data 0.003 (0.311) Batch 1.046 (1.254) Remain 45:53:30 loss: 0.7960 Lr: 0.00485 [2024-02-18 01:03:18,264 INFO misc.py line 119 87073] Train: [16/100][593/1557] Data 0.004 (0.311) Batch 0.780 (1.253) Remain 45:51:43 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Batch 0.730 (1.252) Remain 45:47:41 loss: 0.3384 Lr: 0.00485 [2024-02-18 01:04:21,308 INFO misc.py line 119 87073] Train: [16/100][644/1557] Data 0.010 (0.311) Batch 1.244 (1.252) Remain 45:47:38 loss: 0.2291 Lr: 0.00485 [2024-02-18 01:04:22,424 INFO misc.py line 119 87073] Train: [16/100][645/1557] Data 0.018 (0.310) Batch 1.104 (1.252) Remain 45:47:06 loss: 0.4286 Lr: 0.00485 [2024-02-18 01:04:23,257 INFO misc.py line 119 87073] Train: [16/100][646/1557] Data 0.031 (0.310) Batch 0.859 (1.251) Remain 45:45:45 loss: 0.8057 Lr: 0.00485 [2024-02-18 01:04:24,428 INFO misc.py line 119 87073] Train: [16/100][647/1557] Data 0.004 (0.309) Batch 1.172 (1.251) Remain 45:45:27 loss: 0.7464 Lr: 0.00485 [2024-02-18 01:04:25,406 INFO misc.py line 119 87073] Train: [16/100][648/1557] Data 0.004 (0.309) Batch 0.979 (1.250) Remain 45:44:30 loss: 0.3723 Lr: 0.00485 [2024-02-18 01:04:26,156 INFO misc.py line 119 87073] Train: [16/100][649/1557] Data 0.004 (0.308) Batch 0.750 (1.250) Remain 45:42:47 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line 119 87073] Train: [16/100][687/1557] Data 0.019 (0.316) Batch 0.933 (1.256) Remain 45:55:39 loss: 0.7423 Lr: 0.00485 [2024-02-18 01:05:18,836 INFO misc.py line 119 87073] Train: [16/100][688/1557] Data 0.004 (0.315) Batch 0.935 (1.255) Remain 45:54:36 loss: 0.4586 Lr: 0.00485 [2024-02-18 01:05:19,752 INFO misc.py line 119 87073] Train: [16/100][689/1557] Data 0.005 (0.315) Batch 0.915 (1.255) Remain 45:53:30 loss: 0.5683 Lr: 0.00485 [2024-02-18 01:05:20,746 INFO misc.py line 119 87073] Train: [16/100][690/1557] Data 0.006 (0.315) Batch 0.988 (1.254) Remain 45:52:38 loss: 0.5955 Lr: 0.00485 [2024-02-18 01:05:21,491 INFO misc.py line 119 87073] Train: [16/100][691/1557] Data 0.011 (0.314) Batch 0.752 (1.254) Remain 45:51:00 loss: 0.3849 Lr: 0.00485 [2024-02-18 01:05:22,278 INFO misc.py line 119 87073] Train: [16/100][692/1557] Data 0.004 (0.314) Batch 0.787 (1.253) Remain 45:49:30 loss: 0.3663 Lr: 0.00485 [2024-02-18 01:05:23,554 INFO misc.py line 119 87073] Train: 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Batch 0.757 (1.250) Remain 45:41:33 loss: 0.4009 Lr: 0.00485 [2024-02-18 01:05:29,841 INFO misc.py line 119 87073] Train: [16/100][700/1557] Data 0.014 (0.310) Batch 1.254 (1.250) Remain 45:41:32 loss: 0.3697 Lr: 0.00485 [2024-02-18 01:05:30,831 INFO misc.py line 119 87073] Train: [16/100][701/1557] Data 0.019 (0.310) Batch 1.003 (1.249) Remain 45:40:44 loss: 0.5787 Lr: 0.00485 [2024-02-18 01:05:31,763 INFO misc.py line 119 87073] Train: [16/100][702/1557] Data 0.006 (0.309) Batch 0.933 (1.249) Remain 45:39:44 loss: 0.6787 Lr: 0.00485 [2024-02-18 01:05:32,696 INFO misc.py line 119 87073] Train: [16/100][703/1557] Data 0.005 (0.309) Batch 0.933 (1.248) Remain 45:38:43 loss: 0.4484 Lr: 0.00485 [2024-02-18 01:05:33,761 INFO misc.py line 119 87073] Train: [16/100][704/1557] Data 0.004 (0.308) Batch 1.065 (1.248) Remain 45:38:07 loss: 0.9606 Lr: 0.00485 [2024-02-18 01:05:36,218 INFO misc.py line 119 87073] Train: [16/100][705/1557] Data 0.792 (0.309) Batch 2.455 (1.250) Remain 45:41:52 loss: 0.2282 Lr: 0.00485 [2024-02-18 01:05:36,971 INFO misc.py line 119 87073] Train: [16/100][706/1557] Data 0.007 (0.309) Batch 0.756 (1.249) Remain 45:40:19 loss: 0.6779 Lr: 0.00485 [2024-02-18 01:05:38,089 INFO misc.py line 119 87073] Train: [16/100][707/1557] Data 0.004 (0.308) Batch 1.116 (1.249) Remain 45:39:53 loss: 0.1668 Lr: 0.00485 [2024-02-18 01:05:39,007 INFO misc.py line 119 87073] Train: [16/100][708/1557] Data 0.006 (0.308) Batch 0.920 (1.248) Remain 45:38:50 loss: 0.4961 Lr: 0.00485 [2024-02-18 01:05:39,863 INFO misc.py line 119 87073] Train: [16/100][709/1557] Data 0.004 (0.307) Batch 0.855 (1.248) Remain 45:37:35 loss: 0.3252 Lr: 0.00485 [2024-02-18 01:05:40,802 INFO misc.py line 119 87073] Train: [16/100][710/1557] Data 0.004 (0.307) Batch 0.933 (1.247) Remain 45:36:35 loss: 0.5449 Lr: 0.00485 [2024-02-18 01:05:41,886 INFO misc.py line 119 87073] Train: [16/100][711/1557] Data 0.011 (0.307) Batch 1.090 (1.247) Remain 45:36:05 loss: 0.4985 Lr: 0.00485 [2024-02-18 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line 119 87073] Train: [16/100][743/1557] Data 0.004 (0.318) Batch 0.922 (1.258) Remain 45:58:41 loss: 0.4211 Lr: 0.00485 [2024-02-18 01:06:30,683 INFO misc.py line 119 87073] Train: [16/100][744/1557] Data 0.004 (0.318) Batch 1.037 (1.257) Remain 45:58:01 loss: 0.5516 Lr: 0.00485 [2024-02-18 01:06:31,678 INFO misc.py line 119 87073] Train: [16/100][745/1557] Data 0.005 (0.317) Batch 0.996 (1.257) Remain 45:57:13 loss: 0.2914 Lr: 0.00485 [2024-02-18 01:06:32,566 INFO misc.py line 119 87073] Train: [16/100][746/1557] Data 0.004 (0.317) Batch 0.886 (1.257) Remain 45:56:06 loss: 0.4126 Lr: 0.00485 [2024-02-18 01:06:33,268 INFO misc.py line 119 87073] Train: [16/100][747/1557] Data 0.009 (0.316) Batch 0.702 (1.256) Remain 45:54:27 loss: 0.2797 Lr: 0.00485 [2024-02-18 01:06:34,036 INFO misc.py line 119 87073] Train: [16/100][748/1557] Data 0.006 (0.316) Batch 0.764 (1.255) Remain 45:52:59 loss: 0.8039 Lr: 0.00485 [2024-02-18 01:06:35,314 INFO misc.py line 119 87073] Train: 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Batch 0.766 (1.252) Remain 45:46:25 loss: 0.4213 Lr: 0.00485 [2024-02-18 01:06:41,807 INFO misc.py line 119 87073] Train: [16/100][756/1557] Data 0.018 (0.313) Batch 1.178 (1.252) Remain 45:46:11 loss: 0.3260 Lr: 0.00485 [2024-02-18 01:06:42,732 INFO misc.py line 119 87073] Train: [16/100][757/1557] Data 0.016 (0.312) Batch 0.937 (1.252) Remain 45:45:14 loss: 0.1839 Lr: 0.00485 [2024-02-18 01:06:43,882 INFO misc.py line 119 87073] Train: [16/100][758/1557] Data 0.004 (0.312) Batch 1.150 (1.252) Remain 45:44:56 loss: 0.9075 Lr: 0.00485 [2024-02-18 01:06:44,738 INFO misc.py line 119 87073] Train: [16/100][759/1557] Data 0.005 (0.312) Batch 0.856 (1.251) Remain 45:43:46 loss: 0.4338 Lr: 0.00485 [2024-02-18 01:06:45,615 INFO misc.py line 119 87073] Train: [16/100][760/1557] Data 0.004 (0.311) Batch 0.877 (1.251) Remain 45:42:39 loss: 0.5490 Lr: 0.00485 [2024-02-18 01:06:46,412 INFO misc.py line 119 87073] Train: [16/100][761/1557] Data 0.004 (0.311) Batch 0.793 (1.250) Remain 45:41:19 loss: 0.3197 Lr: 0.00485 [2024-02-18 01:06:47,289 INFO misc.py line 119 87073] Train: [16/100][762/1557] Data 0.008 (0.310) Batch 0.880 (1.250) Remain 45:40:13 loss: 0.6381 Lr: 0.00485 [2024-02-18 01:06:48,344 INFO misc.py line 119 87073] Train: [16/100][763/1557] Data 0.004 (0.310) Batch 1.055 (1.249) Remain 45:39:38 loss: 0.1865 Lr: 0.00485 [2024-02-18 01:06:49,248 INFO misc.py line 119 87073] Train: [16/100][764/1557] Data 0.004 (0.310) Batch 0.904 (1.249) Remain 45:38:37 loss: 0.7493 Lr: 0.00485 [2024-02-18 01:06:50,252 INFO misc.py line 119 87073] Train: [16/100][765/1557] Data 0.004 (0.309) Batch 1.005 (1.248) Remain 45:37:54 loss: 0.4002 Lr: 0.00485 [2024-02-18 01:06:51,113 INFO misc.py line 119 87073] Train: [16/100][766/1557] Data 0.004 (0.309) Batch 0.857 (1.248) Remain 45:36:45 loss: 0.4585 Lr: 0.00485 [2024-02-18 01:06:52,295 INFO misc.py line 119 87073] Train: [16/100][767/1557] Data 0.009 (0.308) Batch 1.179 (1.248) Remain 45:36:32 loss: 0.7289 Lr: 0.00485 [2024-02-18 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Batch 0.813 (1.253) Remain 45:44:44 loss: 0.8141 Lr: 0.00485 [2024-02-18 01:10:12,929 INFO misc.py line 119 87073] Train: [16/100][924/1557] Data 0.004 (0.311) Batch 1.168 (1.253) Remain 45:44:30 loss: 0.1881 Lr: 0.00485 [2024-02-18 01:10:13,896 INFO misc.py line 119 87073] Train: [16/100][925/1557] Data 0.004 (0.310) Batch 0.967 (1.253) Remain 45:43:48 loss: 0.8576 Lr: 0.00485 [2024-02-18 01:10:15,060 INFO misc.py line 119 87073] Train: [16/100][926/1557] Data 0.004 (0.310) Batch 1.164 (1.253) Remain 45:43:34 loss: 0.5891 Lr: 0.00485 [2024-02-18 01:10:15,955 INFO misc.py line 119 87073] Train: [16/100][927/1557] Data 0.003 (0.310) Batch 0.893 (1.252) Remain 45:42:42 loss: 1.1440 Lr: 0.00485 [2024-02-18 01:10:16,865 INFO misc.py line 119 87073] Train: [16/100][928/1557] Data 0.007 (0.309) Batch 0.911 (1.252) Remain 45:41:52 loss: 0.5934 Lr: 0.00485 [2024-02-18 01:10:17,634 INFO misc.py line 119 87073] Train: [16/100][929/1557] Data 0.006 (0.309) Batch 0.769 (1.251) Remain 45:40:43 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line 119 87073] Train: [16/100][967/1557] Data 0.014 (0.313) Batch 0.805 (1.255) Remain 45:48:10 loss: 0.2117 Lr: 0.00485 [2024-02-18 01:11:09,759 INFO misc.py line 119 87073] Train: [16/100][968/1557] Data 0.005 (0.312) Batch 0.942 (1.255) Remain 45:47:26 loss: 0.8999 Lr: 0.00485 [2024-02-18 01:11:10,683 INFO misc.py line 119 87073] Train: [16/100][969/1557] Data 0.004 (0.312) Batch 0.920 (1.254) Remain 45:46:40 loss: 0.5226 Lr: 0.00485 [2024-02-18 01:11:11,764 INFO misc.py line 119 87073] Train: [16/100][970/1557] Data 0.008 (0.312) Batch 1.074 (1.254) Remain 45:46:14 loss: 0.1146 Lr: 0.00485 [2024-02-18 01:11:12,526 INFO misc.py line 119 87073] Train: [16/100][971/1557] Data 0.015 (0.312) Batch 0.773 (1.254) Remain 45:45:07 loss: 0.5592 Lr: 0.00485 [2024-02-18 01:11:13,248 INFO misc.py line 119 87073] Train: [16/100][972/1557] Data 0.004 (0.311) Batch 0.712 (1.253) Remain 45:43:53 loss: 0.6090 Lr: 0.00485 [2024-02-18 01:11:14,625 INFO misc.py line 119 87073] Train: 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Data 0.007 (0.302) Batch 1.257 (1.244) Remain 45:23:01 loss: 0.3920 Lr: 0.00485 [2024-02-18 01:11:44,994 INFO misc.py line 119 87073] Train: [16/100][1005/1557] Data 0.012 (0.301) Batch 0.885 (1.244) Remain 45:22:13 loss: 0.5829 Lr: 0.00485 [2024-02-18 01:11:45,731 INFO misc.py line 119 87073] Train: [16/100][1006/1557] Data 0.005 (0.301) Batch 0.737 (1.243) Remain 45:21:05 loss: 0.1792 Lr: 0.00485 [2024-02-18 01:11:46,532 INFO misc.py line 119 87073] Train: [16/100][1007/1557] Data 0.004 (0.301) Batch 0.800 (1.243) Remain 45:20:06 loss: 0.4860 Lr: 0.00485 [2024-02-18 01:11:47,796 INFO misc.py line 119 87073] Train: [16/100][1008/1557] Data 0.005 (0.300) Batch 1.262 (1.243) Remain 45:20:08 loss: 0.2532 Lr: 0.00485 [2024-02-18 01:11:48,906 INFO misc.py line 119 87073] Train: [16/100][1009/1557] Data 0.006 (0.300) Batch 1.112 (1.243) Remain 45:19:49 loss: 0.6334 Lr: 0.00485 [2024-02-18 01:11:49,910 INFO misc.py line 119 87073] Train: [16/100][1010/1557] Data 0.005 (0.300) Batch 0.997 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Data 0.009 (0.310) Batch 0.696 (1.252) Remain 45:40:04 loss: 0.7501 Lr: 0.00485 [2024-02-18 01:12:32,214 INFO misc.py line 119 87073] Train: [16/100][1036/1557] Data 0.004 (0.310) Batch 1.205 (1.252) Remain 45:39:56 loss: 0.2826 Lr: 0.00485 [2024-02-18 01:12:33,140 INFO misc.py line 119 87073] Train: [16/100][1037/1557] Data 0.004 (0.310) Batch 0.925 (1.252) Remain 45:39:14 loss: 0.8898 Lr: 0.00485 [2024-02-18 01:12:34,132 INFO misc.py line 119 87073] Train: [16/100][1038/1557] Data 0.005 (0.310) Batch 0.993 (1.251) Remain 45:38:39 loss: 0.4771 Lr: 0.00485 [2024-02-18 01:12:35,031 INFO misc.py line 119 87073] Train: [16/100][1039/1557] Data 0.004 (0.309) Batch 0.897 (1.251) Remain 45:37:53 loss: 0.2466 Lr: 0.00485 [2024-02-18 01:12:35,991 INFO misc.py line 119 87073] Train: [16/100][1040/1557] Data 0.006 (0.309) Batch 0.953 (1.251) Remain 45:37:14 loss: 0.4221 Lr: 0.00485 [2024-02-18 01:12:36,739 INFO misc.py line 119 87073] Train: [16/100][1041/1557] Data 0.012 (0.309) Batch 0.757 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Data 0.006 (0.302) Batch 0.892 (1.245) Remain 45:23:33 loss: 0.4051 Lr: 0.00485 [2024-02-18 01:13:03,071 INFO misc.py line 119 87073] Train: [16/100][1067/1557] Data 0.004 (0.302) Batch 0.951 (1.245) Remain 45:22:56 loss: 0.5719 Lr: 0.00485 [2024-02-18 01:13:04,077 INFO misc.py line 119 87073] Train: [16/100][1068/1557] Data 0.006 (0.302) Batch 1.009 (1.244) Remain 45:22:25 loss: 0.8117 Lr: 0.00485 [2024-02-18 01:13:04,847 INFO misc.py line 119 87073] Train: [16/100][1069/1557] Data 0.004 (0.301) Batch 0.769 (1.244) Remain 45:21:26 loss: 0.5780 Lr: 0.00485 [2024-02-18 01:13:05,530 INFO misc.py line 119 87073] Train: [16/100][1070/1557] Data 0.004 (0.301) Batch 0.680 (1.243) Remain 45:20:15 loss: 0.6199 Lr: 0.00485 [2024-02-18 01:13:23,062 INFO misc.py line 119 87073] Train: [16/100][1071/1557] Data 16.540 (0.316) Batch 17.536 (1.259) Remain 45:53:36 loss: 0.3764 Lr: 0.00485 [2024-02-18 01:13:24,404 INFO misc.py line 119 87073] Train: [16/100][1072/1557] Data 0.004 (0.316) Batch 1.330 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Data 0.003 (0.309) Batch 0.742 (1.252) Remain 45:38:16 loss: 0.2185 Lr: 0.00485 [2024-02-18 01:13:49,141 INFO misc.py line 119 87073] Train: [16/100][1098/1557] Data 0.005 (0.309) Batch 0.745 (1.251) Remain 45:37:14 loss: 0.7161 Lr: 0.00485 [2024-02-18 01:13:50,360 INFO misc.py line 119 87073] Train: [16/100][1099/1557] Data 0.013 (0.308) Batch 1.216 (1.251) Remain 45:37:09 loss: 0.1926 Lr: 0.00485 [2024-02-18 01:13:51,228 INFO misc.py line 119 87073] Train: [16/100][1100/1557] Data 0.017 (0.308) Batch 0.881 (1.251) Remain 45:36:23 loss: 0.4350 Lr: 0.00485 [2024-02-18 01:13:52,185 INFO misc.py line 119 87073] Train: [16/100][1101/1557] Data 0.004 (0.308) Batch 0.956 (1.251) Remain 45:35:47 loss: 0.7642 Lr: 0.00485 [2024-02-18 01:13:53,058 INFO misc.py line 119 87073] Train: [16/100][1102/1557] Data 0.004 (0.308) Batch 0.863 (1.250) Remain 45:34:59 loss: 0.6003 Lr: 0.00485 [2024-02-18 01:13:54,082 INFO misc.py line 119 87073] Train: [16/100][1103/1557] Data 0.014 (0.307) Batch 1.020 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[2024-02-18 01:14:52,117 INFO misc.py line 119 87073] Train: [16/100][1147/1557] Data 0.004 (0.311) Batch 0.689 (1.253) Remain 45:39:24 loss: 0.5986 Lr: 0.00484 [2024-02-18 01:14:53,343 INFO misc.py line 119 87073] Train: [16/100][1148/1557] Data 0.005 (0.310) Batch 1.225 (1.253) Remain 45:39:20 loss: 0.2506 Lr: 0.00484 [2024-02-18 01:14:54,438 INFO misc.py line 119 87073] Train: [16/100][1149/1557] Data 0.007 (0.310) Batch 1.088 (1.253) Remain 45:39:00 loss: 0.7622 Lr: 0.00484 [2024-02-18 01:14:55,603 INFO misc.py line 119 87073] Train: [16/100][1150/1557] Data 0.014 (0.310) Batch 1.159 (1.253) Remain 45:38:48 loss: 0.2555 Lr: 0.00484 [2024-02-18 01:14:56,478 INFO misc.py line 119 87073] Train: [16/100][1151/1557] Data 0.020 (0.310) Batch 0.891 (1.252) Remain 45:38:05 loss: 0.3678 Lr: 0.00484 [2024-02-18 01:14:57,349 INFO misc.py line 119 87073] Train: [16/100][1152/1557] Data 0.004 (0.309) Batch 0.871 (1.252) Remain 45:37:20 loss: 0.8631 Lr: 0.00484 [2024-02-18 01:14:58,114 INFO 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INFO misc.py line 119 87073] Train: [16/100][1246/1557] Data 0.008 (0.314) Batch 1.107 (1.256) Remain 45:45:00 loss: 0.1342 Lr: 0.00484 [2024-02-18 01:17:01,367 INFO misc.py line 119 87073] Train: [16/100][1247/1557] Data 0.012 (0.314) Batch 0.861 (1.256) Remain 45:44:17 loss: 0.2968 Lr: 0.00484 [2024-02-18 01:17:02,264 INFO misc.py line 119 87073] Train: [16/100][1248/1557] Data 0.003 (0.313) Batch 0.898 (1.256) Remain 45:43:38 loss: 0.6955 Lr: 0.00484 [2024-02-18 01:17:03,356 INFO misc.py line 119 87073] Train: [16/100][1249/1557] Data 0.003 (0.313) Batch 1.091 (1.256) Remain 45:43:20 loss: 0.5731 Lr: 0.00484 [2024-02-18 01:17:04,212 INFO misc.py line 119 87073] Train: [16/100][1250/1557] Data 0.004 (0.313) Batch 0.854 (1.255) Remain 45:42:36 loss: 0.5153 Lr: 0.00484 [2024-02-18 01:17:05,017 INFO misc.py line 119 87073] Train: [16/100][1251/1557] Data 0.006 (0.313) Batch 0.803 (1.255) Remain 45:41:48 loss: 0.4617 Lr: 0.00484 [2024-02-18 01:17:05,780 INFO misc.py line 119 87073] Train: [16/100][1252/1557] Data 0.008 (0.312) Batch 0.765 (1.254) Remain 45:40:55 loss: 0.5220 Lr: 0.00484 [2024-02-18 01:17:07,097 INFO misc.py line 119 87073] Train: [16/100][1253/1557] Data 0.006 (0.312) Batch 1.309 (1.255) Remain 45:40:59 loss: 0.3705 Lr: 0.00484 [2024-02-18 01:17:08,098 INFO misc.py line 119 87073] Train: [16/100][1254/1557] Data 0.014 (0.312) Batch 1.003 (1.254) Remain 45:40:32 loss: 1.6044 Lr: 0.00484 [2024-02-18 01:17:09,119 INFO misc.py line 119 87073] Train: [16/100][1255/1557] Data 0.012 (0.312) Batch 1.020 (1.254) Remain 45:40:06 loss: 0.2968 Lr: 0.00484 [2024-02-18 01:17:10,022 INFO misc.py line 119 87073] Train: [16/100][1256/1557] Data 0.013 (0.311) Batch 0.913 (1.254) Remain 45:39:29 loss: 0.5347 Lr: 0.00484 [2024-02-18 01:17:11,021 INFO misc.py line 119 87073] Train: [16/100][1257/1557] Data 0.004 (0.311) Batch 0.999 (1.254) Remain 45:39:01 loss: 0.8332 Lr: 0.00484 [2024-02-18 01:17:11,774 INFO misc.py line 119 87073] Train: [16/100][1258/1557] Data 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Train: [16/100][1283/1557] Data 0.009 (0.305) Batch 1.001 (1.247) Remain 45:24:44 loss: 0.4737 Lr: 0.00484 [2024-02-18 01:17:36,830 INFO misc.py line 119 87073] Train: [16/100][1284/1557] Data 0.005 (0.305) Batch 1.226 (1.247) Remain 45:24:40 loss: 0.4995 Lr: 0.00484 [2024-02-18 01:17:37,780 INFO misc.py line 119 87073] Train: [16/100][1285/1557] Data 0.044 (0.305) Batch 0.992 (1.247) Remain 45:24:13 loss: 0.6921 Lr: 0.00484 [2024-02-18 01:17:38,553 INFO misc.py line 119 87073] Train: [16/100][1286/1557] Data 0.003 (0.304) Batch 0.770 (1.247) Remain 45:23:23 loss: 0.2646 Lr: 0.00484 [2024-02-18 01:17:39,375 INFO misc.py line 119 87073] Train: [16/100][1287/1557] Data 0.006 (0.304) Batch 0.820 (1.246) Remain 45:22:38 loss: 0.5767 Lr: 0.00484 [2024-02-18 01:17:40,615 INFO misc.py line 119 87073] Train: [16/100][1288/1557] Data 0.007 (0.304) Batch 1.232 (1.246) Remain 45:22:36 loss: 0.3841 Lr: 0.00484 [2024-02-18 01:17:41,591 INFO misc.py line 119 87073] Train: [16/100][1289/1557] Data 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87073] Train: [16/100][1314/1557] Data 0.004 (0.311) Batch 0.759 (1.254) Remain 45:38:19 loss: 0.6111 Lr: 0.00484 [2024-02-18 01:18:23,548 INFO misc.py line 119 87073] Train: [16/100][1315/1557] Data 0.013 (0.311) Batch 0.769 (1.254) Remain 45:37:30 loss: 0.8063 Lr: 0.00484 [2024-02-18 01:18:24,659 INFO misc.py line 119 87073] Train: [16/100][1316/1557] Data 0.004 (0.310) Batch 1.110 (1.253) Remain 45:37:14 loss: 0.2381 Lr: 0.00484 [2024-02-18 01:18:25,407 INFO misc.py line 119 87073] Train: [16/100][1317/1557] Data 0.004 (0.310) Batch 0.748 (1.253) Remain 45:36:23 loss: 0.3785 Lr: 0.00484 [2024-02-18 01:18:26,316 INFO misc.py line 119 87073] Train: [16/100][1318/1557] Data 0.004 (0.310) Batch 0.903 (1.253) Remain 45:35:46 loss: 0.8132 Lr: 0.00484 [2024-02-18 01:18:27,654 INFO misc.py line 119 87073] Train: [16/100][1319/1557] Data 0.011 (0.310) Batch 1.337 (1.253) Remain 45:35:54 loss: 0.4832 Lr: 0.00484 [2024-02-18 01:18:28,727 INFO misc.py line 119 87073] Train: [16/100][1320/1557] Data 0.012 (0.310) Batch 1.069 (1.253) Remain 45:35:34 loss: 0.5430 Lr: 0.00484 [2024-02-18 01:18:29,483 INFO misc.py line 119 87073] Train: [16/100][1321/1557] Data 0.014 (0.309) Batch 0.768 (1.252) Remain 45:34:45 loss: 0.5229 Lr: 0.00484 [2024-02-18 01:18:30,190 INFO misc.py line 119 87073] Train: [16/100][1322/1557] Data 0.004 (0.309) Batch 0.697 (1.252) Remain 45:33:48 loss: 0.6816 Lr: 0.00484 [2024-02-18 01:18:31,261 INFO misc.py line 119 87073] Train: [16/100][1323/1557] Data 0.013 (0.309) Batch 1.064 (1.252) Remain 45:33:28 loss: 0.4158 Lr: 0.00484 [2024-02-18 01:18:32,132 INFO misc.py line 119 87073] Train: [16/100][1324/1557] Data 0.021 (0.309) Batch 0.887 (1.251) Remain 45:32:51 loss: 0.9474 Lr: 0.00484 [2024-02-18 01:18:33,019 INFO misc.py line 119 87073] Train: [16/100][1325/1557] Data 0.003 (0.308) Batch 0.887 (1.251) Remain 45:32:14 loss: 0.4704 Lr: 0.00484 [2024-02-18 01:18:33,998 INFO misc.py line 119 87073] Train: [16/100][1326/1557] Data 0.004 (0.308) Batch 0.978 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Data 15.887 (0.314) Batch 16.963 (1.257) Remain 45:44:53 loss: 0.3232 Lr: 0.00484 [2024-02-18 01:19:14,603 INFO misc.py line 119 87073] Train: [16/100][1352/1557] Data 0.004 (0.314) Batch 0.900 (1.257) Remain 45:44:17 loss: 0.7824 Lr: 0.00484 [2024-02-18 01:19:15,533 INFO misc.py line 119 87073] Train: [16/100][1353/1557] Data 0.004 (0.314) Batch 0.928 (1.257) Remain 45:43:44 loss: 1.3093 Lr: 0.00484 [2024-02-18 01:19:16,776 INFO misc.py line 119 87073] Train: [16/100][1354/1557] Data 0.005 (0.314) Batch 1.236 (1.257) Remain 45:43:41 loss: 0.8027 Lr: 0.00484 [2024-02-18 01:19:17,703 INFO misc.py line 119 87073] Train: [16/100][1355/1557] Data 0.012 (0.313) Batch 0.936 (1.257) Remain 45:43:08 loss: 0.3239 Lr: 0.00484 [2024-02-18 01:19:18,466 INFO misc.py line 119 87073] Train: [16/100][1356/1557] Data 0.004 (0.313) Batch 0.762 (1.256) Remain 45:42:19 loss: 0.4299 Lr: 0.00484 [2024-02-18 01:19:19,254 INFO misc.py line 119 87073] Train: [16/100][1357/1557] Data 0.004 (0.313) Batch 0.785 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87073] Train: [16/100][1376/1557] Data 0.016 (0.309) Batch 0.941 (1.252) Remain 45:32:12 loss: 0.7693 Lr: 0.00484 [2024-02-18 01:19:38,221 INFO misc.py line 119 87073] Train: [16/100][1377/1557] Data 0.003 (0.309) Batch 0.736 (1.251) Remain 45:31:22 loss: 0.4866 Lr: 0.00484 [2024-02-18 01:19:38,949 INFO misc.py line 119 87073] Train: [16/100][1378/1557] Data 0.004 (0.308) Batch 0.720 (1.251) Remain 45:30:30 loss: 0.4792 Lr: 0.00484 [2024-02-18 01:19:40,063 INFO misc.py line 119 87073] Train: [16/100][1379/1557] Data 0.011 (0.308) Batch 1.109 (1.251) Remain 45:30:15 loss: 0.4938 Lr: 0.00484 [2024-02-18 01:19:40,951 INFO misc.py line 119 87073] Train: [16/100][1380/1557] Data 0.016 (0.308) Batch 0.894 (1.251) Remain 45:29:40 loss: 1.0903 Lr: 0.00484 [2024-02-18 01:19:41,949 INFO misc.py line 119 87073] Train: [16/100][1381/1557] Data 0.010 (0.308) Batch 1.004 (1.250) Remain 45:29:15 loss: 0.4888 Lr: 0.00484 [2024-02-18 01:19:42,913 INFO misc.py line 119 87073] Train: [16/100][1382/1557] Data 0.004 (0.308) Batch 0.964 (1.250) Remain 45:28:47 loss: 0.5795 Lr: 0.00484 [2024-02-18 01:19:43,759 INFO misc.py line 119 87073] Train: [16/100][1383/1557] Data 0.004 (0.307) Batch 0.845 (1.250) Remain 45:28:07 loss: 0.8061 Lr: 0.00484 [2024-02-18 01:19:44,517 INFO misc.py line 119 87073] Train: [16/100][1384/1557] Data 0.005 (0.307) Batch 0.758 (1.250) Remain 45:27:19 loss: 0.3836 Lr: 0.00484 [2024-02-18 01:19:45,196 INFO misc.py line 119 87073] Train: [16/100][1385/1557] Data 0.006 (0.307) Batch 0.678 (1.249) Remain 45:26:24 loss: 0.7409 Lr: 0.00484 [2024-02-18 01:19:46,273 INFO misc.py line 119 87073] Train: [16/100][1386/1557] Data 0.007 (0.307) Batch 1.071 (1.249) Remain 45:26:06 loss: 0.4114 Lr: 0.00484 [2024-02-18 01:19:47,141 INFO misc.py line 119 87073] Train: [16/100][1387/1557] Data 0.013 (0.306) Batch 0.877 (1.249) Remain 45:25:29 loss: 0.6360 Lr: 0.00484 [2024-02-18 01:19:48,216 INFO misc.py line 119 87073] Train: [16/100][1388/1557] Data 0.004 (0.306) Batch 1.074 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01:20:01,071 INFO misc.py line 119 87073] Train: [16/100][1401/1557] Data 0.017 (0.303) Batch 1.142 (1.246) Remain 45:19:39 loss: 0.4748 Lr: 0.00484 [2024-02-18 01:20:01,962 INFO misc.py line 119 87073] Train: [16/100][1402/1557] Data 0.005 (0.303) Batch 0.892 (1.246) Remain 45:19:04 loss: 0.7680 Lr: 0.00484 [2024-02-18 01:20:03,065 INFO misc.py line 119 87073] Train: [16/100][1403/1557] Data 0.004 (0.303) Batch 1.103 (1.246) Remain 45:18:50 loss: 0.4779 Lr: 0.00484 [2024-02-18 01:20:03,957 INFO misc.py line 119 87073] Train: [16/100][1404/1557] Data 0.004 (0.303) Batch 0.892 (1.246) Remain 45:18:16 loss: 0.8598 Lr: 0.00484 [2024-02-18 01:20:06,615 INFO misc.py line 119 87073] Train: [16/100][1405/1557] Data 0.914 (0.303) Batch 2.652 (1.247) Remain 45:20:26 loss: 0.3139 Lr: 0.00484 [2024-02-18 01:20:07,421 INFO misc.py line 119 87073] Train: [16/100][1406/1557] Data 0.010 (0.303) Batch 0.812 (1.246) Remain 45:19:44 loss: 0.7823 Lr: 0.00484 [2024-02-18 01:20:24,617 INFO misc.py line 119 87073] Train: [16/100][1407/1557] Data 16.080 (0.314) Batch 17.195 (1.258) Remain 45:44:30 loss: 0.3704 Lr: 0.00484 [2024-02-18 01:20:25,584 INFO misc.py line 119 87073] Train: [16/100][1408/1557] Data 0.004 (0.314) Batch 0.967 (1.257) Remain 45:44:02 loss: 0.5415 Lr: 0.00484 [2024-02-18 01:20:26,487 INFO misc.py line 119 87073] Train: [16/100][1409/1557] Data 0.005 (0.314) Batch 0.902 (1.257) Remain 45:43:27 loss: 0.6175 Lr: 0.00484 [2024-02-18 01:20:27,436 INFO misc.py line 119 87073] Train: [16/100][1410/1557] Data 0.006 (0.314) Batch 0.950 (1.257) Remain 45:42:58 loss: 0.4532 Lr: 0.00484 [2024-02-18 01:20:28,263 INFO misc.py line 119 87073] Train: [16/100][1411/1557] Data 0.005 (0.313) Batch 0.827 (1.257) Remain 45:42:16 loss: 0.5381 Lr: 0.00484 [2024-02-18 01:20:29,044 INFO misc.py line 119 87073] Train: [16/100][1412/1557] Data 0.004 (0.313) Batch 0.773 (1.256) Remain 45:41:30 loss: 0.7459 Lr: 0.00484 [2024-02-18 01:20:29,848 INFO misc.py line 119 87073] Train: 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[2024-02-18 01:20:47,901 INFO misc.py line 119 87073] Train: [16/100][1432/1557] Data 0.004 (0.309) Batch 0.767 (1.252) Remain 45:31:31 loss: 0.4896 Lr: 0.00484 [2024-02-18 01:20:48,679 INFO misc.py line 119 87073] Train: [16/100][1433/1557] Data 0.007 (0.309) Batch 0.780 (1.252) Remain 45:30:47 loss: 0.4207 Lr: 0.00484 [2024-02-18 01:20:49,474 INFO misc.py line 119 87073] Train: [16/100][1434/1557] Data 0.004 (0.308) Batch 0.795 (1.251) Remain 45:30:04 loss: 0.2879 Lr: 0.00484 [2024-02-18 01:20:50,675 INFO misc.py line 119 87073] Train: [16/100][1435/1557] Data 0.004 (0.308) Batch 1.187 (1.251) Remain 45:29:57 loss: 0.2808 Lr: 0.00484 [2024-02-18 01:20:51,699 INFO misc.py line 119 87073] Train: [16/100][1436/1557] Data 0.017 (0.308) Batch 1.036 (1.251) Remain 45:29:36 loss: 0.6300 Lr: 0.00484 [2024-02-18 01:20:52,654 INFO misc.py line 119 87073] Train: [16/100][1437/1557] Data 0.006 (0.308) Batch 0.956 (1.251) Remain 45:29:08 loss: 0.3554 Lr: 0.00484 [2024-02-18 01:20:53,594 INFO 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45:20:16 loss: 0.2813 Lr: 0.00484 [2024-02-18 01:21:11,732 INFO misc.py line 119 87073] Train: [16/100][1457/1557] Data 0.012 (0.304) Batch 0.954 (1.247) Remain 45:19:48 loss: 0.5279 Lr: 0.00484 [2024-02-18 01:21:12,660 INFO misc.py line 119 87073] Train: [16/100][1458/1557] Data 0.004 (0.303) Batch 0.926 (1.247) Remain 45:19:18 loss: 0.5619 Lr: 0.00484 [2024-02-18 01:21:13,505 INFO misc.py line 119 87073] Train: [16/100][1459/1557] Data 0.007 (0.303) Batch 0.843 (1.246) Remain 45:18:40 loss: 0.4932 Lr: 0.00484 [2024-02-18 01:21:14,538 INFO misc.py line 119 87073] Train: [16/100][1460/1557] Data 0.009 (0.303) Batch 1.032 (1.246) Remain 45:18:20 loss: 0.5728 Lr: 0.00484 [2024-02-18 01:21:15,295 INFO misc.py line 119 87073] Train: [16/100][1461/1557] Data 0.010 (0.303) Batch 0.763 (1.246) Remain 45:17:35 loss: 0.3098 Lr: 0.00484 [2024-02-18 01:21:16,115 INFO misc.py line 119 87073] Train: [16/100][1462/1557] Data 0.004 (0.303) Batch 0.819 (1.246) Remain 45:16:56 loss: 0.5829 Lr: 0.00484 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misc.py line 119 87073] Train: [16/100][1469/1557] Data 0.011 (0.312) Batch 0.784 (1.255) Remain 45:37:46 loss: 0.3696 Lr: 0.00484 [2024-02-18 01:21:40,100 INFO misc.py line 119 87073] Train: [16/100][1470/1557] Data 0.006 (0.312) Batch 1.157 (1.255) Remain 45:37:36 loss: 0.3763 Lr: 0.00484 [2024-02-18 01:21:41,304 INFO misc.py line 119 87073] Train: [16/100][1471/1557] Data 0.006 (0.312) Batch 1.199 (1.255) Remain 45:37:30 loss: 0.6907 Lr: 0.00484 [2024-02-18 01:21:42,308 INFO misc.py line 119 87073] Train: [16/100][1472/1557] Data 0.011 (0.312) Batch 1.011 (1.255) Remain 45:37:07 loss: 1.0262 Lr: 0.00484 [2024-02-18 01:21:43,324 INFO misc.py line 119 87073] Train: [16/100][1473/1557] Data 0.005 (0.312) Batch 1.010 (1.255) Remain 45:36:44 loss: 0.3648 Lr: 0.00484 [2024-02-18 01:21:44,293 INFO misc.py line 119 87073] Train: [16/100][1474/1557] Data 0.011 (0.311) Batch 0.976 (1.255) Remain 45:36:18 loss: 0.5207 Lr: 0.00484 [2024-02-18 01:21:45,073 INFO misc.py line 119 87073] Train: 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[2024-02-18 01:22:02,639 INFO misc.py line 119 87073] Train: [16/100][1494/1557] Data 0.005 (0.307) Batch 1.021 (1.250) Remain 45:26:01 loss: 0.4310 Lr: 0.00484 [2024-02-18 01:22:03,617 INFO misc.py line 119 87073] Train: [16/100][1495/1557] Data 0.005 (0.307) Batch 0.980 (1.250) Remain 45:25:36 loss: 0.8383 Lr: 0.00484 [2024-02-18 01:22:04,398 INFO misc.py line 119 87073] Train: [16/100][1496/1557] Data 0.004 (0.307) Batch 0.780 (1.249) Remain 45:24:53 loss: 0.5509 Lr: 0.00484 [2024-02-18 01:22:05,116 INFO misc.py line 119 87073] Train: [16/100][1497/1557] Data 0.004 (0.307) Batch 0.710 (1.249) Remain 45:24:05 loss: 0.7211 Lr: 0.00484 [2024-02-18 01:22:06,331 INFO misc.py line 119 87073] Train: [16/100][1498/1557] Data 0.012 (0.307) Batch 1.212 (1.249) Remain 45:24:01 loss: 0.4764 Lr: 0.00484 [2024-02-18 01:22:07,245 INFO misc.py line 119 87073] Train: [16/100][1499/1557] Data 0.015 (0.306) Batch 0.924 (1.249) Remain 45:23:31 loss: 0.5799 Lr: 0.00484 [2024-02-18 01:22:08,164 INFO misc.py line 119 87073] Train: [16/100][1500/1557] Data 0.006 (0.306) Batch 0.920 (1.249) Remain 45:23:01 loss: 0.4574 Lr: 0.00484 [2024-02-18 01:22:09,303 INFO misc.py line 119 87073] Train: [16/100][1501/1557] Data 0.003 (0.306) Batch 1.138 (1.249) Remain 45:22:50 loss: 0.6869 Lr: 0.00484 [2024-02-18 01:22:10,180 INFO misc.py line 119 87073] Train: [16/100][1502/1557] Data 0.005 (0.306) Batch 0.878 (1.248) Remain 45:22:16 loss: 0.5497 Lr: 0.00484 [2024-02-18 01:22:10,892 INFO misc.py line 119 87073] Train: [16/100][1503/1557] Data 0.003 (0.306) Batch 0.701 (1.248) Remain 45:21:27 loss: 0.7653 Lr: 0.00484 [2024-02-18 01:22:11,668 INFO misc.py line 119 87073] Train: [16/100][1504/1557] Data 0.014 (0.305) Batch 0.786 (1.248) Remain 45:20:46 loss: 0.6386 Lr: 0.00484 [2024-02-18 01:22:13,001 INFO misc.py line 119 87073] Train: [16/100][1505/1557] Data 0.004 (0.305) Batch 1.318 (1.248) Remain 45:20:51 loss: 0.1904 Lr: 0.00484 [2024-02-18 01:22:13,938 INFO misc.py line 119 87073] Train: 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0.00484 [2024-02-18 01:22:48,301 INFO misc.py line 119 87073] Train: [16/100][1525/1557] Data 0.006 (0.312) Batch 0.722 (1.255) Remain 45:35:15 loss: 0.5185 Lr: 0.00484 [2024-02-18 01:22:49,451 INFO misc.py line 119 87073] Train: [16/100][1526/1557] Data 0.015 (0.312) Batch 1.157 (1.254) Remain 45:35:06 loss: 0.1768 Lr: 0.00484 [2024-02-18 01:22:50,395 INFO misc.py line 119 87073] Train: [16/100][1527/1557] Data 0.008 (0.312) Batch 0.946 (1.254) Remain 45:34:38 loss: 0.5649 Lr: 0.00484 [2024-02-18 01:22:51,423 INFO misc.py line 119 87073] Train: [16/100][1528/1557] Data 0.005 (0.312) Batch 1.029 (1.254) Remain 45:34:17 loss: 0.4694 Lr: 0.00484 [2024-02-18 01:22:52,473 INFO misc.py line 119 87073] Train: [16/100][1529/1557] Data 0.006 (0.312) Batch 1.051 (1.254) Remain 45:33:59 loss: 0.4441 Lr: 0.00484 [2024-02-18 01:22:53,570 INFO misc.py line 119 87073] Train: [16/100][1530/1557] Data 0.004 (0.311) Batch 1.097 (1.254) Remain 45:33:44 loss: 0.3170 Lr: 0.00484 [2024-02-18 01:22:54,294 INFO misc.py line 119 87073] Train: [16/100][1531/1557] Data 0.003 (0.311) Batch 0.725 (1.254) Remain 45:32:58 loss: 0.4782 Lr: 0.00484 [2024-02-18 01:22:55,066 INFO misc.py line 119 87073] Train: [16/100][1532/1557] Data 0.003 (0.311) Batch 0.766 (1.253) Remain 45:32:15 loss: 0.3972 Lr: 0.00484 [2024-02-18 01:22:56,424 INFO misc.py line 119 87073] Train: [16/100][1533/1557] Data 0.009 (0.311) Batch 1.359 (1.253) Remain 45:32:22 loss: 0.2615 Lr: 0.00484 [2024-02-18 01:22:57,361 INFO misc.py line 119 87073] Train: [16/100][1534/1557] Data 0.008 (0.311) Batch 0.942 (1.253) Remain 45:31:55 loss: 0.5341 Lr: 0.00484 [2024-02-18 01:22:58,480 INFO misc.py line 119 87073] Train: [16/100][1535/1557] Data 0.004 (0.310) Batch 1.118 (1.253) Remain 45:31:42 loss: 0.3399 Lr: 0.00484 [2024-02-18 01:22:59,351 INFO misc.py line 119 87073] Train: [16/100][1536/1557] Data 0.004 (0.310) Batch 0.871 (1.253) Remain 45:31:08 loss: 0.1362 Lr: 0.00484 [2024-02-18 01:23:00,207 INFO misc.py line 119 87073] Train: [16/100][1537/1557] Data 0.005 (0.310) Batch 0.848 (1.252) Remain 45:30:32 loss: 0.5420 Lr: 0.00484 [2024-02-18 01:23:00,974 INFO misc.py line 119 87073] Train: [16/100][1538/1557] Data 0.012 (0.310) Batch 0.774 (1.252) Remain 45:29:50 loss: 0.6228 Lr: 0.00484 [2024-02-18 01:23:01,734 INFO misc.py line 119 87073] Train: [16/100][1539/1557] Data 0.005 (0.310) Batch 0.756 (1.252) Remain 45:29:07 loss: 0.4835 Lr: 0.00484 [2024-02-18 01:23:02,945 INFO misc.py line 119 87073] Train: [16/100][1540/1557] Data 0.010 (0.309) Batch 1.210 (1.252) Remain 45:29:02 loss: 0.2608 Lr: 0.00484 [2024-02-18 01:23:03,810 INFO misc.py line 119 87073] Train: [16/100][1541/1557] Data 0.010 (0.309) Batch 0.872 (1.252) Remain 45:28:28 loss: 0.4859 Lr: 0.00484 [2024-02-18 01:23:04,866 INFO misc.py line 119 87073] Train: [16/100][1542/1557] Data 0.004 (0.309) Batch 1.055 (1.251) Remain 45:28:10 loss: 0.3199 Lr: 0.00484 [2024-02-18 01:23:06,050 INFO misc.py line 119 87073] Train: [16/100][1543/1557] Data 0.004 (0.309) Batch 1.168 (1.251) Remain 45:28:02 loss: 0.4436 Lr: 0.00484 [2024-02-18 01:23:07,013 INFO misc.py line 119 87073] Train: [16/100][1544/1557] Data 0.020 (0.309) Batch 0.980 (1.251) Remain 45:27:38 loss: 0.6610 Lr: 0.00484 [2024-02-18 01:23:07,773 INFO misc.py line 119 87073] Train: [16/100][1545/1557] Data 0.004 (0.308) Batch 0.759 (1.251) Remain 45:26:55 loss: 0.2364 Lr: 0.00484 [2024-02-18 01:23:08,560 INFO misc.py line 119 87073] Train: [16/100][1546/1557] Data 0.004 (0.308) Batch 0.776 (1.251) Remain 45:26:13 loss: 0.6826 Lr: 0.00484 [2024-02-18 01:23:09,677 INFO misc.py line 119 87073] Train: [16/100][1547/1557] Data 0.015 (0.308) Batch 1.118 (1.250) Remain 45:26:01 loss: 0.2388 Lr: 0.00484 [2024-02-18 01:23:10,582 INFO misc.py line 119 87073] Train: [16/100][1548/1557] Data 0.014 (0.308) Batch 0.915 (1.250) Remain 45:25:31 loss: 0.7941 Lr: 0.00484 [2024-02-18 01:23:11,532 INFO misc.py line 119 87073] Train: [16/100][1549/1557] Data 0.004 (0.308) Batch 0.950 (1.250) Remain 45:25:04 loss: 0.6221 Lr: 0.00484 [2024-02-18 01:23:12,568 INFO misc.py line 119 87073] Train: [16/100][1550/1557] Data 0.004 (0.307) Batch 1.034 (1.250) Remain 45:24:45 loss: 1.0223 Lr: 0.00484 [2024-02-18 01:23:13,549 INFO misc.py line 119 87073] Train: [16/100][1551/1557] Data 0.005 (0.307) Batch 0.982 (1.250) Remain 45:24:21 loss: 0.4016 Lr: 0.00484 [2024-02-18 01:23:14,341 INFO misc.py line 119 87073] Train: [16/100][1552/1557] Data 0.004 (0.307) Batch 0.779 (1.249) Remain 45:23:40 loss: 0.2787 Lr: 0.00484 [2024-02-18 01:23:15,073 INFO misc.py line 119 87073] Train: [16/100][1553/1557] Data 0.017 (0.307) Batch 0.745 (1.249) Remain 45:22:56 loss: 0.8264 Lr: 0.00484 [2024-02-18 01:23:16,192 INFO misc.py line 119 87073] Train: [16/100][1554/1557] Data 0.003 (0.307) Batch 1.118 (1.249) Remain 45:22:44 loss: 0.3888 Lr: 0.00484 [2024-02-18 01:23:17,206 INFO misc.py line 119 87073] Train: [16/100][1555/1557] Data 0.004 (0.306) Batch 1.015 (1.249) Remain 45:22:23 loss: 0.5435 Lr: 0.00484 [2024-02-18 01:23:18,254 INFO misc.py line 119 87073] Train: [16/100][1556/1557] Data 0.004 (0.306) Batch 1.048 (1.249) Remain 45:22:05 loss: 0.5526 Lr: 0.00484 [2024-02-18 01:23:19,178 INFO misc.py line 119 87073] Train: [16/100][1557/1557] Data 0.004 (0.306) Batch 0.924 (1.249) Remain 45:21:36 loss: 0.4811 Lr: 0.00484 [2024-02-18 01:23:19,179 INFO misc.py line 136 87073] Train result: loss: 0.5206 [2024-02-18 01:23:19,179 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 01:23:48,628 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6689 [2024-02-18 01:23:49,407 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7198 [2024-02-18 01:23:51,531 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4961 [2024-02-18 01:23:53,738 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3561 [2024-02-18 01:23:58,688 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3102 [2024-02-18 01:23:59,388 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0981 [2024-02-18 01:24:00,649 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3671 [2024-02-18 01:24:03,605 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4459 [2024-02-18 01:24:05,414 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3254 [2024-02-18 01:24:07,087 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6967/0.7637/0.9038. [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9369/0.9696 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9782/0.9820 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8496/0.9643 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3366/0.3814 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5983/0.6057 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7759/0.8255 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8139/0.9256 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9086/0.9683 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8256/0.8662 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7409/0.8695 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7181/0.8955 [2024-02-18 01:24:07,087 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5746/0.6749 [2024-02-18 01:24:07,088 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 01:24:07,091 INFO misc.py line 165 87073] Currently Best mIoU: 0.6970 [2024-02-18 01:24:07,091 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 01:24:15,150 INFO misc.py line 119 87073] Train: [17/100][1/1557] Data 1.632 (1.632) Batch 2.625 (2.625) Remain 95:22:37 loss: 0.8546 Lr: 0.00484 [2024-02-18 01:24:16,284 INFO misc.py line 119 87073] Train: [17/100][2/1557] Data 0.005 (0.005) Batch 1.133 (1.133) Remain 41:09:20 loss: 1.0505 Lr: 0.00484 [2024-02-18 01:24:17,287 INFO misc.py line 119 87073] Train: [17/100][3/1557] Data 0.006 (0.006) Batch 1.005 (1.005) Remain 36:30:11 loss: 0.6619 Lr: 0.00484 [2024-02-18 01:24:18,333 INFO misc.py line 119 87073] Train: [17/100][4/1557] Data 0.004 (0.004) Batch 1.046 (1.046) Remain 38:01:03 loss: 0.4534 Lr: 0.00484 [2024-02-18 01:24:19,139 INFO misc.py line 119 87073] Train: [17/100][5/1557] Data 0.004 (0.004) Batch 0.805 (0.926) Remain 33:37:47 loss: 0.4004 Lr: 0.00484 [2024-02-18 01:24:19,966 INFO misc.py line 119 87073] Train: [17/100][6/1557] Data 0.005 (0.004) Batch 0.823 (0.892) Remain 32:23:28 loss: 0.3516 Lr: 0.00484 [2024-02-18 01:24:21,062 INFO misc.py line 119 87073] Train: [17/100][7/1557] Data 0.007 (0.005) Batch 1.093 (0.942) Remain 34:13:12 loss: 0.3811 Lr: 0.00484 [2024-02-18 01:24:21,944 INFO misc.py line 119 87073] Train: [17/100][8/1557] Data 0.011 (0.006) Batch 0.889 (0.931) Remain 33:50:02 loss: 0.5070 Lr: 0.00484 [2024-02-18 01:24:22,905 INFO misc.py line 119 87073] Train: [17/100][9/1557] Data 0.004 (0.006) Batch 0.962 (0.936) Remain 34:01:04 loss: 0.8721 Lr: 0.00484 [2024-02-18 01:24:23,753 INFO misc.py line 119 87073] Train: [17/100][10/1557] Data 0.004 (0.005) Batch 0.847 (0.924) Remain 33:33:15 loss: 0.5587 Lr: 0.00484 [2024-02-18 01:24:24,598 INFO misc.py line 119 87073] Train: [17/100][11/1557] Data 0.004 (0.005) Batch 0.843 (0.914) Remain 33:11:17 loss: 0.6088 Lr: 0.00484 [2024-02-18 01:24:25,415 INFO misc.py line 119 87073] Train: [17/100][12/1557] Data 0.006 (0.005) Batch 0.817 (0.903) Remain 32:47:59 loss: 0.4657 Lr: 0.00484 [2024-02-18 01:24:26,139 INFO misc.py line 119 87073] Train: [17/100][13/1557] Data 0.005 (0.005) Batch 0.726 (0.885) Remain 32:09:20 loss: 0.3971 Lr: 0.00484 [2024-02-18 01:24:27,284 INFO misc.py line 119 87073] Train: [17/100][14/1557] Data 0.004 (0.005) Batch 1.133 (0.908) Remain 32:58:29 loss: 0.3515 Lr: 0.00484 [2024-02-18 01:24:28,350 INFO misc.py line 119 87073] Train: [17/100][15/1557] Data 0.015 (0.006) Batch 1.068 (0.921) Remain 33:27:31 loss: 0.5840 Lr: 0.00484 [2024-02-18 01:24:29,382 INFO misc.py line 119 87073] Train: [17/100][16/1557] Data 0.013 (0.007) Batch 1.039 (0.930) Remain 33:47:17 loss: 0.4173 Lr: 0.00484 [2024-02-18 01:24:30,487 INFO misc.py line 119 87073] Train: [17/100][17/1557] Data 0.007 (0.007) Batch 1.107 (0.943) Remain 34:14:44 loss: 0.6396 Lr: 0.00484 [2024-02-18 01:24:31,506 INFO misc.py line 119 87073] Train: [17/100][18/1557] Data 0.006 (0.007) Batch 1.020 (0.948) Remain 34:25:57 loss: 0.5298 Lr: 0.00484 [2024-02-18 01:24:32,277 INFO misc.py line 119 87073] Train: [17/100][19/1557] Data 0.004 (0.006) Batch 0.772 (0.937) Remain 34:01:55 loss: 0.4346 Lr: 0.00484 [2024-02-18 01:24:32,972 INFO misc.py line 119 87073] Train: [17/100][20/1557] Data 0.004 (0.006) Batch 0.686 (0.922) Remain 33:29:48 loss: 0.6399 Lr: 0.00484 [2024-02-18 01:24:34,254 INFO misc.py line 119 87073] Train: [17/100][21/1557] Data 0.012 (0.007) Batch 1.279 (0.942) Remain 34:13:02 loss: 0.4443 Lr: 0.00484 [2024-02-18 01:24:35,110 INFO misc.py line 119 87073] Train: [17/100][22/1557] Data 0.015 (0.007) Batch 0.866 (0.938) Remain 34:04:20 loss: 0.6215 Lr: 0.00484 [2024-02-18 01:24:36,063 INFO misc.py line 119 87073] Train: [17/100][23/1557] Data 0.005 (0.007) Batch 0.951 (0.939) Remain 34:05:43 loss: 0.3276 Lr: 0.00484 [2024-02-18 01:24:36,913 INFO misc.py line 119 87073] Train: [17/100][24/1557] Data 0.006 (0.007) Batch 0.852 (0.935) Remain 33:56:42 loss: 0.4885 Lr: 0.00484 [2024-02-18 01:24:37,855 INFO misc.py line 119 87073] Train: [17/100][25/1557] Data 0.004 (0.007) Batch 0.936 (0.935) Remain 33:56:51 loss: 0.5621 Lr: 0.00484 [2024-02-18 01:24:38,590 INFO misc.py line 119 87073] Train: [17/100][26/1557] Data 0.011 (0.007) Batch 0.742 (0.926) Remain 33:38:33 loss: 0.6179 Lr: 0.00484 [2024-02-18 01:24:39,368 INFO misc.py line 119 87073] Train: [17/100][27/1557] Data 0.004 (0.007) Batch 0.767 (0.920) Remain 33:24:05 loss: 0.3654 Lr: 0.00484 [2024-02-18 01:24:40,559 INFO misc.py line 119 87073] Train: [17/100][28/1557] Data 0.014 (0.007) Batch 1.190 (0.930) Remain 33:47:41 loss: 0.1966 Lr: 0.00484 [2024-02-18 01:24:41,443 INFO misc.py line 119 87073] Train: [17/100][29/1557] Data 0.015 (0.007) Batch 0.894 (0.929) Remain 33:44:36 loss: 0.4488 Lr: 0.00484 [2024-02-18 01:24:42,316 INFO misc.py line 119 87073] Train: [17/100][30/1557] Data 0.005 (0.007) Batch 0.873 (0.927) Remain 33:40:03 loss: 0.4557 Lr: 0.00484 [2024-02-18 01:24:43,429 INFO misc.py line 119 87073] Train: [17/100][31/1557] Data 0.005 (0.007) Batch 1.112 (0.934) Remain 33:54:27 loss: 0.4863 Lr: 0.00484 [2024-02-18 01:24:44,633 INFO misc.py line 119 87073] Train: [17/100][32/1557] Data 0.007 (0.007) Batch 1.194 (0.943) Remain 34:13:59 loss: 0.6577 Lr: 0.00484 [2024-02-18 01:24:45,413 INFO misc.py line 119 87073] Train: [17/100][33/1557] Data 0.017 (0.008) Batch 0.793 (0.938) Remain 34:03:07 loss: 0.9513 Lr: 0.00484 [2024-02-18 01:24:46,181 INFO misc.py line 119 87073] Train: [17/100][34/1557] Data 0.004 (0.007) Batch 0.767 (0.932) Remain 33:51:07 loss: 0.4839 Lr: 0.00484 [2024-02-18 01:24:47,383 INFO misc.py line 119 87073] Train: [17/100][35/1557] Data 0.004 (0.007) Batch 1.193 (0.940) Remain 34:08:52 loss: 0.4361 Lr: 0.00484 [2024-02-18 01:24:48,355 INFO misc.py line 119 87073] Train: [17/100][36/1557] Data 0.014 (0.008) Batch 0.980 (0.941) Remain 34:11:29 loss: 0.6744 Lr: 0.00484 [2024-02-18 01:24:49,393 INFO misc.py line 119 87073] Train: [17/100][37/1557] Data 0.005 (0.008) Batch 1.040 (0.944) Remain 34:17:48 loss: 0.7232 Lr: 0.00484 [2024-02-18 01:24:50,259 INFO misc.py line 119 87073] Train: [17/100][38/1557] Data 0.004 (0.007) Batch 0.865 (0.942) Remain 34:12:51 loss: 0.7544 Lr: 0.00484 [2024-02-18 01:24:51,437 INFO misc.py line 119 87073] Train: [17/100][39/1557] Data 0.005 (0.007) Batch 1.179 (0.949) Remain 34:27:10 loss: 0.2662 Lr: 0.00484 [2024-02-18 01:24:52,151 INFO misc.py line 119 87073] Train: [17/100][40/1557] Data 0.004 (0.007) Batch 0.713 (0.942) Remain 34:13:17 loss: 0.4138 Lr: 0.00484 [2024-02-18 01:24:52,892 INFO misc.py line 119 87073] Train: [17/100][41/1557] Data 0.005 (0.007) Batch 0.738 (0.937) Remain 34:01:33 loss: 0.2415 Lr: 0.00484 [2024-02-18 01:24:54,211 INFO misc.py line 119 87073] Train: [17/100][42/1557] Data 0.008 (0.007) Batch 1.319 (0.947) Remain 34:22:54 loss: 0.4332 Lr: 0.00484 [2024-02-18 01:24:55,116 INFO misc.py line 119 87073] Train: [17/100][43/1557] Data 0.008 (0.007) Batch 0.907 (0.946) Remain 34:20:44 loss: 0.3917 Lr: 0.00484 [2024-02-18 01:24:56,094 INFO misc.py line 119 87073] Train: [17/100][44/1557] Data 0.007 (0.007) Batch 0.980 (0.947) Remain 34:22:32 loss: 0.4378 Lr: 0.00484 [2024-02-18 01:24:57,243 INFO misc.py line 119 87073] Train: [17/100][45/1557] Data 0.004 (0.007) Batch 1.148 (0.951) Remain 34:32:58 loss: 0.5748 Lr: 0.00484 [2024-02-18 01:24:58,151 INFO misc.py line 119 87073] Train: [17/100][46/1557] Data 0.004 (0.007) Batch 0.909 (0.950) Remain 34:30:47 loss: 0.5653 Lr: 0.00484 [2024-02-18 01:24:58,953 INFO misc.py line 119 87073] Train: [17/100][47/1557] Data 0.003 (0.007) Batch 0.800 (0.947) Remain 34:23:21 loss: 0.2609 Lr: 0.00484 [2024-02-18 01:24:59,698 INFO misc.py line 119 87073] Train: [17/100][48/1557] Data 0.005 (0.007) Batch 0.746 (0.942) Remain 34:13:36 loss: 0.5051 Lr: 0.00484 [2024-02-18 01:25:00,858 INFO misc.py line 119 87073] Train: [17/100][49/1557] Data 0.004 (0.007) Batch 1.160 (0.947) Remain 34:23:54 loss: 0.4253 Lr: 0.00484 [2024-02-18 01:25:01,762 INFO misc.py line 119 87073] Train: [17/100][50/1557] Data 0.004 (0.007) Batch 0.904 (0.946) Remain 34:21:54 loss: 0.9408 Lr: 0.00484 [2024-02-18 01:25:02,605 INFO misc.py line 119 87073] Train: [17/100][51/1557] Data 0.003 (0.007) Batch 0.828 (0.944) Remain 34:16:31 loss: 0.3672 Lr: 0.00484 [2024-02-18 01:25:03,652 INFO misc.py line 119 87073] Train: [17/100][52/1557] Data 0.019 (0.007) Batch 1.057 (0.946) Remain 34:21:32 loss: 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INFO misc.py line 119 87073] Train: [17/100][59/1557] Data 0.003 (0.007) Batch 1.014 (0.946) Remain 34:21:40 loss: 0.3767 Lr: 0.00484 [2024-02-18 01:25:11,291 INFO misc.py line 119 87073] Train: [17/100][60/1557] Data 0.003 (0.007) Batch 1.015 (0.947) Remain 34:24:17 loss: 0.4678 Lr: 0.00484 [2024-02-18 01:25:12,072 INFO misc.py line 119 87073] Train: [17/100][61/1557] Data 0.004 (0.007) Batch 0.780 (0.945) Remain 34:17:58 loss: 0.3712 Lr: 0.00484 [2024-02-18 01:25:12,815 INFO misc.py line 119 87073] Train: [17/100][62/1557] Data 0.005 (0.007) Batch 0.744 (0.941) Remain 34:10:31 loss: 0.7579 Lr: 0.00484 [2024-02-18 01:25:20,143 INFO misc.py line 119 87073] Train: [17/100][63/1557] Data 4.643 (0.084) Batch 7.321 (1.047) Remain 38:02:11 loss: 0.1982 Lr: 0.00484 [2024-02-18 01:25:21,310 INFO misc.py line 119 87073] Train: [17/100][64/1557] Data 0.012 (0.083) Batch 1.172 (1.050) Remain 38:06:36 loss: 0.7608 Lr: 0.00484 [2024-02-18 01:25:22,243 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [17/100][109/1557] Data 0.004 (0.051) Batch 1.143 (1.011) Remain 36:41:18 loss: 0.5286 Lr: 0.00483 [2024-02-18 01:26:05,222 INFO misc.py line 119 87073] Train: [17/100][110/1557] Data 0.005 (0.050) Batch 0.799 (1.009) Remain 36:36:59 loss: 0.3814 Lr: 0.00483 [2024-02-18 01:26:05,942 INFO misc.py line 119 87073] Train: [17/100][111/1557] Data 0.004 (0.050) Batch 0.711 (1.006) Remain 36:30:58 loss: 0.4516 Lr: 0.00483 [2024-02-18 01:26:07,209 INFO misc.py line 119 87073] Train: [17/100][112/1557] Data 0.013 (0.050) Batch 1.263 (1.008) Remain 36:36:04 loss: 0.3911 Lr: 0.00483 [2024-02-18 01:26:08,229 INFO misc.py line 119 87073] Train: [17/100][113/1557] Data 0.017 (0.049) Batch 1.020 (1.008) Remain 36:36:18 loss: 0.7435 Lr: 0.00483 [2024-02-18 01:26:09,109 INFO misc.py line 119 87073] Train: [17/100][114/1557] Data 0.017 (0.049) Batch 0.891 (1.007) Remain 36:33:59 loss: 0.3531 Lr: 0.00483 [2024-02-18 01:26:09,996 INFO misc.py line 119 87073] Train: 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Batch 0.971 (1.054) Remain 38:14:26 loss: 0.6098 Lr: 0.00483 [2024-02-18 01:26:22,513 INFO misc.py line 119 87073] Train: [17/100][122/1557] Data 0.004 (0.080) Batch 0.904 (1.052) Remain 38:11:41 loss: 0.9139 Lr: 0.00483 [2024-02-18 01:26:23,489 INFO misc.py line 119 87073] Train: [17/100][123/1557] Data 0.005 (0.080) Batch 0.975 (1.052) Remain 38:10:16 loss: 0.7067 Lr: 0.00483 [2024-02-18 01:26:24,361 INFO misc.py line 119 87073] Train: [17/100][124/1557] Data 0.005 (0.079) Batch 0.873 (1.050) Remain 38:07:03 loss: 0.7929 Lr: 0.00483 [2024-02-18 01:26:25,110 INFO misc.py line 119 87073] Train: [17/100][125/1557] Data 0.004 (0.078) Batch 0.749 (1.048) Remain 38:01:39 loss: 0.6902 Lr: 0.00483 [2024-02-18 01:26:26,242 INFO misc.py line 119 87073] Train: [17/100][126/1557] Data 0.003 (0.078) Batch 1.132 (1.048) Remain 38:03:07 loss: 0.4374 Lr: 0.00483 [2024-02-18 01:26:27,163 INFO misc.py line 119 87073] Train: [17/100][127/1557] Data 0.005 (0.077) Batch 0.921 (1.047) Remain 38:00:52 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line 119 87073] Train: [17/100][277/1557] Data 0.008 (0.081) Batch 0.919 (1.047) Remain 37:57:46 loss: 0.3478 Lr: 0.00483 [2024-02-18 01:29:04,948 INFO misc.py line 119 87073] Train: [17/100][278/1557] Data 0.003 (0.081) Batch 0.737 (1.046) Remain 37:55:18 loss: 0.7136 Lr: 0.00483 [2024-02-18 01:29:05,684 INFO misc.py line 119 87073] Train: [17/100][279/1557] Data 0.003 (0.081) Batch 0.733 (1.045) Remain 37:52:49 loss: 0.4723 Lr: 0.00483 [2024-02-18 01:29:06,891 INFO misc.py line 119 87073] Train: [17/100][280/1557] Data 0.007 (0.081) Batch 1.202 (1.045) Remain 37:54:01 loss: 0.2592 Lr: 0.00483 [2024-02-18 01:29:07,869 INFO misc.py line 119 87073] Train: [17/100][281/1557] Data 0.012 (0.080) Batch 0.987 (1.045) Remain 37:53:33 loss: 0.3811 Lr: 0.00483 [2024-02-18 01:29:08,781 INFO misc.py line 119 87073] Train: [17/100][282/1557] Data 0.004 (0.080) Batch 0.910 (1.045) Remain 37:52:29 loss: 0.6731 Lr: 0.00483 [2024-02-18 01:29:09,674 INFO misc.py line 119 87073] Train: 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Batch 0.924 (1.064) Remain 38:33:40 loss: 0.9826 Lr: 0.00483 [2024-02-18 01:29:22,509 INFO misc.py line 119 87073] Train: [17/100][290/1557] Data 0.005 (0.095) Batch 0.982 (1.063) Remain 38:33:02 loss: 0.4251 Lr: 0.00483 [2024-02-18 01:29:23,389 INFO misc.py line 119 87073] Train: [17/100][291/1557] Data 0.007 (0.095) Batch 0.882 (1.063) Remain 38:31:38 loss: 1.0529 Lr: 0.00483 [2024-02-18 01:29:24,140 INFO misc.py line 119 87073] Train: [17/100][292/1557] Data 0.005 (0.094) Batch 0.751 (1.062) Remain 38:29:16 loss: 0.4676 Lr: 0.00483 [2024-02-18 01:29:24,905 INFO misc.py line 119 87073] Train: [17/100][293/1557] Data 0.005 (0.094) Batch 0.764 (1.061) Remain 38:27:01 loss: 0.5557 Lr: 0.00483 [2024-02-18 01:29:26,051 INFO misc.py line 119 87073] Train: [17/100][294/1557] Data 0.007 (0.094) Batch 1.143 (1.061) Remain 38:27:37 loss: 0.5106 Lr: 0.00483 [2024-02-18 01:29:26,979 INFO misc.py line 119 87073] Train: [17/100][295/1557] Data 0.010 (0.093) Batch 0.932 (1.061) Remain 38:26:39 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line 119 87073] Train: [17/100][557/1557] Data 0.018 (0.086) Batch 0.946 (1.065) Remain 38:32:13 loss: 0.4839 Lr: 0.00483 [2024-02-18 01:34:08,222 INFO misc.py line 119 87073] Train: [17/100][558/1557] Data 0.004 (0.086) Batch 0.764 (1.065) Remain 38:31:01 loss: 1.0534 Lr: 0.00483 [2024-02-18 01:34:09,013 INFO misc.py line 119 87073] Train: [17/100][559/1557] Data 0.004 (0.086) Batch 0.778 (1.064) Remain 38:29:53 loss: 0.3539 Lr: 0.00483 [2024-02-18 01:34:10,260 INFO misc.py line 119 87073] Train: [17/100][560/1557] Data 0.016 (0.086) Batch 1.243 (1.065) Remain 38:30:34 loss: 0.1829 Lr: 0.00483 [2024-02-18 01:34:11,276 INFO misc.py line 119 87073] Train: [17/100][561/1557] Data 0.020 (0.086) Batch 1.019 (1.064) Remain 38:30:22 loss: 0.3734 Lr: 0.00483 [2024-02-18 01:34:12,320 INFO misc.py line 119 87073] Train: [17/100][562/1557] Data 0.017 (0.086) Batch 1.047 (1.064) Remain 38:30:17 loss: 0.3901 Lr: 0.00483 [2024-02-18 01:34:13,347 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [17/100][837/1557] Data 0.006 (0.086) Batch 1.278 (1.069) Remain 38:35:26 loss: 0.3558 Lr: 0.00482 [2024-02-18 01:39:09,665 INFO misc.py line 119 87073] Train: [17/100][838/1557] Data 0.008 (0.086) Batch 0.772 (1.069) Remain 38:34:39 loss: 0.3514 Lr: 0.00482 [2024-02-18 01:39:10,433 INFO misc.py line 119 87073] Train: [17/100][839/1557] Data 0.005 (0.086) Batch 0.760 (1.068) Remain 38:33:50 loss: 0.7880 Lr: 0.00482 [2024-02-18 01:39:11,702 INFO misc.py line 119 87073] Train: [17/100][840/1557] Data 0.013 (0.086) Batch 1.277 (1.069) Remain 38:34:21 loss: 0.2090 Lr: 0.00482 [2024-02-18 01:39:12,619 INFO misc.py line 119 87073] Train: [17/100][841/1557] Data 0.005 (0.086) Batch 0.917 (1.068) Remain 38:33:56 loss: 0.5757 Lr: 0.00482 [2024-02-18 01:39:13,496 INFO misc.py line 119 87073] Train: [17/100][842/1557] Data 0.006 (0.086) Batch 0.876 (1.068) Remain 38:33:25 loss: 0.4317 Lr: 0.00482 [2024-02-18 01:39:14,637 INFO misc.py line 119 87073] Train: 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Batch 0.894 (1.074) Remain 38:43:25 loss: 0.7577 Lr: 0.00482 [2024-02-18 01:41:26,858 INFO misc.py line 119 87073] Train: [17/100][962/1557] Data 0.005 (0.091) Batch 0.890 (1.074) Remain 38:42:59 loss: 0.4959 Lr: 0.00482 [2024-02-18 01:41:27,747 INFO misc.py line 119 87073] Train: [17/100][963/1557] Data 0.005 (0.091) Batch 0.887 (1.073) Remain 38:42:32 loss: 0.5918 Lr: 0.00482 [2024-02-18 01:41:28,496 INFO misc.py line 119 87073] Train: [17/100][964/1557] Data 0.007 (0.091) Batch 0.752 (1.073) Remain 38:41:48 loss: 0.3913 Lr: 0.00482 [2024-02-18 01:41:29,274 INFO misc.py line 119 87073] Train: [17/100][965/1557] Data 0.004 (0.091) Batch 0.767 (1.073) Remain 38:41:06 loss: 0.4130 Lr: 0.00482 [2024-02-18 01:41:30,475 INFO misc.py line 119 87073] Train: [17/100][966/1557] Data 0.015 (0.091) Batch 1.197 (1.073) Remain 38:41:21 loss: 0.5085 Lr: 0.00482 [2024-02-18 01:41:31,443 INFO misc.py line 119 87073] Train: [17/100][967/1557] Data 0.019 (0.091) Batch 0.982 (1.073) Remain 38:41:08 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[2024-02-18 01:43:45,638 INFO misc.py line 119 87073] Train: [17/100][1092/1557] Data 0.005 (0.091) Batch 1.046 (1.073) Remain 38:39:04 loss: 0.3117 Lr: 0.00482 [2024-02-18 01:43:46,699 INFO misc.py line 119 87073] Train: [17/100][1093/1557] Data 0.013 (0.091) Batch 1.061 (1.073) Remain 38:39:02 loss: 0.6622 Lr: 0.00482 [2024-02-18 01:43:47,724 INFO misc.py line 119 87073] Train: [17/100][1094/1557] Data 0.014 (0.091) Batch 1.013 (1.073) Remain 38:38:54 loss: 0.4778 Lr: 0.00482 [2024-02-18 01:43:48,783 INFO misc.py line 119 87073] Train: [17/100][1095/1557] Data 0.025 (0.091) Batch 1.078 (1.073) Remain 38:38:53 loss: 0.2019 Lr: 0.00482 [2024-02-18 01:43:49,751 INFO misc.py line 119 87073] Train: [17/100][1096/1557] Data 0.008 (0.091) Batch 0.970 (1.073) Remain 38:38:40 loss: 0.7397 Lr: 0.00482 [2024-02-18 01:43:50,551 INFO misc.py line 119 87073] Train: [17/100][1097/1557] Data 0.005 (0.091) Batch 0.801 (1.072) Remain 38:38:07 loss: 0.2898 Lr: 0.00482 [2024-02-18 01:43:51,249 INFO 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[2024-02-18 01:46:34,282 INFO misc.py line 119 87073] Train: [17/100][1247/1557] Data 0.009 (0.092) Batch 1.019 (1.075) Remain 38:40:24 loss: 0.3931 Lr: 0.00481 [2024-02-18 01:46:35,074 INFO misc.py line 119 87073] Train: [17/100][1248/1557] Data 0.005 (0.092) Batch 0.793 (1.075) Remain 38:39:54 loss: 0.5789 Lr: 0.00481 [2024-02-18 01:46:36,043 INFO misc.py line 119 87073] Train: [17/100][1249/1557] Data 0.003 (0.092) Batch 0.969 (1.074) Remain 38:39:42 loss: 0.6002 Lr: 0.00481 [2024-02-18 01:46:37,025 INFO misc.py line 119 87073] Train: [17/100][1250/1557] Data 0.003 (0.092) Batch 0.982 (1.074) Remain 38:39:31 loss: 0.4536 Lr: 0.00481 [2024-02-18 01:46:37,771 INFO misc.py line 119 87073] Train: [17/100][1251/1557] Data 0.004 (0.092) Batch 0.746 (1.074) Remain 38:38:56 loss: 0.6377 Lr: 0.00481 [2024-02-18 01:46:38,523 INFO misc.py line 119 87073] Train: [17/100][1252/1557] Data 0.003 (0.092) Batch 0.751 (1.074) Remain 38:38:21 loss: 0.3673 Lr: 0.00481 [2024-02-18 01:46:39,817 INFO 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misc.py line 119 87073] Train: [17/100][1532/1557] Data 0.005 (0.091) Batch 0.796 (1.073) Remain 38:32:05 loss: 0.6816 Lr: 0.00481 [2024-02-18 01:51:39,547 INFO misc.py line 119 87073] Train: [17/100][1533/1557] Data 0.004 (0.091) Batch 1.237 (1.073) Remain 38:32:17 loss: 0.1433 Lr: 0.00481 [2024-02-18 01:51:40,585 INFO misc.py line 119 87073] Train: [17/100][1534/1557] Data 0.011 (0.091) Batch 1.040 (1.073) Remain 38:32:13 loss: 0.8185 Lr: 0.00481 [2024-02-18 01:51:41,438 INFO misc.py line 119 87073] Train: [17/100][1535/1557] Data 0.010 (0.091) Batch 0.858 (1.073) Remain 38:31:54 loss: 0.3548 Lr: 0.00481 [2024-02-18 01:51:42,369 INFO misc.py line 119 87073] Train: [17/100][1536/1557] Data 0.004 (0.091) Batch 0.931 (1.073) Remain 38:31:41 loss: 0.5287 Lr: 0.00481 [2024-02-18 01:51:43,283 INFO misc.py line 119 87073] Train: [17/100][1537/1557] Data 0.005 (0.091) Batch 0.914 (1.073) Remain 38:31:27 loss: 0.3296 Lr: 0.00481 [2024-02-18 01:51:44,142 INFO misc.py line 119 87073] Train: [17/100][1538/1557] Data 0.005 (0.091) Batch 0.858 (1.073) Remain 38:31:08 loss: 0.5568 Lr: 0.00481 [2024-02-18 01:51:44,910 INFO misc.py line 119 87073] Train: [17/100][1539/1557] Data 0.005 (0.091) Batch 0.760 (1.073) Remain 38:30:40 loss: 0.3716 Lr: 0.00481 [2024-02-18 01:51:46,090 INFO misc.py line 119 87073] Train: [17/100][1540/1557] Data 0.012 (0.091) Batch 1.187 (1.073) Remain 38:30:49 loss: 0.3296 Lr: 0.00481 [2024-02-18 01:51:47,111 INFO misc.py line 119 87073] Train: [17/100][1541/1557] Data 0.005 (0.091) Batch 1.014 (1.073) Remain 38:30:43 loss: 0.7483 Lr: 0.00481 [2024-02-18 01:51:47,935 INFO misc.py line 119 87073] Train: [17/100][1542/1557] Data 0.012 (0.091) Batch 0.831 (1.073) Remain 38:30:21 loss: 0.5030 Lr: 0.00481 [2024-02-18 01:51:48,799 INFO misc.py line 119 87073] Train: [17/100][1543/1557] Data 0.006 (0.091) Batch 0.865 (1.072) Remain 38:30:03 loss: 0.5397 Lr: 0.00481 [2024-02-18 01:51:49,830 INFO misc.py line 119 87073] Train: [17/100][1544/1557] Data 0.005 (0.091) Batch 1.030 (1.072) Remain 38:29:58 loss: 0.4693 Lr: 0.00481 [2024-02-18 01:51:50,583 INFO misc.py line 119 87073] Train: [17/100][1545/1557] Data 0.007 (0.091) Batch 0.755 (1.072) Remain 38:29:30 loss: 0.5366 Lr: 0.00481 [2024-02-18 01:51:51,276 INFO misc.py line 119 87073] Train: [17/100][1546/1557] Data 0.005 (0.091) Batch 0.689 (1.072) Remain 38:28:57 loss: 0.4462 Lr: 0.00481 [2024-02-18 01:51:52,480 INFO misc.py line 119 87073] Train: [17/100][1547/1557] Data 0.010 (0.091) Batch 1.203 (1.072) Remain 38:29:07 loss: 0.5181 Lr: 0.00481 [2024-02-18 01:51:53,199 INFO misc.py line 119 87073] Train: [17/100][1548/1557] Data 0.009 (0.090) Batch 0.724 (1.072) Remain 38:28:37 loss: 0.3708 Lr: 0.00481 [2024-02-18 01:51:54,074 INFO misc.py line 119 87073] Train: [17/100][1549/1557] Data 0.005 (0.090) Batch 0.873 (1.072) Remain 38:28:19 loss: 0.8358 Lr: 0.00481 [2024-02-18 01:51:55,106 INFO misc.py line 119 87073] Train: [17/100][1550/1557] Data 0.006 (0.090) Batch 1.029 (1.072) Remain 38:28:15 loss: 0.2735 Lr: 0.00481 [2024-02-18 01:51:56,111 INFO misc.py line 119 87073] Train: [17/100][1551/1557] Data 0.009 (0.090) Batch 1.007 (1.072) Remain 38:28:08 loss: 0.5337 Lr: 0.00481 [2024-02-18 01:51:56,864 INFO misc.py line 119 87073] Train: [17/100][1552/1557] Data 0.008 (0.090) Batch 0.755 (1.071) Remain 38:27:41 loss: 0.7692 Lr: 0.00481 [2024-02-18 01:51:57,619 INFO misc.py line 119 87073] Train: [17/100][1553/1557] Data 0.005 (0.090) Batch 0.748 (1.071) Remain 38:27:13 loss: 0.5885 Lr: 0.00481 [2024-02-18 01:51:58,874 INFO misc.py line 119 87073] Train: [17/100][1554/1557] Data 0.012 (0.090) Batch 1.255 (1.071) Remain 38:27:27 loss: 0.3152 Lr: 0.00481 [2024-02-18 01:52:00,253 INFO misc.py line 119 87073] Train: [17/100][1555/1557] Data 0.012 (0.090) Batch 1.384 (1.071) Remain 38:27:52 loss: 0.3339 Lr: 0.00481 [2024-02-18 01:52:01,322 INFO misc.py line 119 87073] Train: [17/100][1556/1557] Data 0.007 (0.090) Batch 1.068 (1.071) Remain 38:27:51 loss: 0.7077 Lr: 0.00481 [2024-02-18 01:52:02,303 INFO misc.py line 119 87073] Train: [17/100][1557/1557] Data 0.009 (0.090) Batch 0.984 (1.071) Remain 38:27:42 loss: 0.6506 Lr: 0.00481 [2024-02-18 01:52:02,322 INFO misc.py line 136 87073] Train result: loss: 0.5380 [2024-02-18 01:52:02,323 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 01:52:33,731 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.9705 [2024-02-18 01:52:34,507 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6555 [2024-02-18 01:52:36,634 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4299 [2024-02-18 01:52:38,839 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3622 [2024-02-18 01:52:43,785 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2692 [2024-02-18 01:52:44,482 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1309 [2024-02-18 01:52:45,746 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3120 [2024-02-18 01:52:48,699 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3875 [2024-02-18 01:52:50,511 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3155 [2024-02-18 01:52:52,268 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6976/0.7701/0.9006. [2024-02-18 01:52:52,268 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9180/0.9365 [2024-02-18 01:52:52,268 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9781/0.9833 [2024-02-18 01:52:52,268 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8431/0.9720 [2024-02-18 01:52:52,268 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 01:52:52,268 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3179/0.4245 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6532/0.6832 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7822/0.9003 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8170/0.9278 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9166/0.9576 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8324/0.8880 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7460/0.8145 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6815/0.8274 [2024-02-18 01:52:52,269 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5833/0.6955 [2024-02-18 01:52:52,270 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 01:52:52,270 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.6976 [2024-02-18 01:52:52,270 INFO misc.py line 165 87073] Currently Best mIoU: 0.6976 [2024-02-18 01:52:52,270 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 01:53:03,474 INFO misc.py line 119 87073] Train: [18/100][1/1557] Data 1.140 (1.140) Batch 2.163 (2.163) Remain 77:37:54 loss: 0.6807 Lr: 0.00481 [2024-02-18 01:53:04,476 INFO misc.py line 119 87073] Train: [18/100][2/1557] Data 0.005 (0.005) Batch 1.001 (1.001) Remain 35:56:47 loss: 0.5063 Lr: 0.00481 [2024-02-18 01:53:05,273 INFO misc.py line 119 87073] Train: [18/100][3/1557] Data 0.006 (0.006) Batch 0.792 (0.792) Remain 28:25:17 loss: 0.6250 Lr: 0.00481 [2024-02-18 01:53:06,382 INFO misc.py line 119 87073] Train: [18/100][4/1557] Data 0.010 (0.010) Batch 1.115 (1.115) Remain 40:00:24 loss: 0.2266 Lr: 0.00481 [2024-02-18 01:53:07,270 INFO misc.py line 119 87073] Train: [18/100][5/1557] Data 0.005 (0.008) Batch 0.889 (1.002) Remain 35:57:18 loss: 0.5267 Lr: 0.00481 [2024-02-18 01:53:08,018 INFO misc.py line 119 87073] Train: [18/100][6/1557] Data 0.004 (0.007) Batch 0.747 (0.917) Remain 32:54:20 loss: 0.7586 Lr: 0.00481 [2024-02-18 01:53:09,142 INFO misc.py line 119 87073] Train: [18/100][7/1557] Data 0.007 (0.007) Batch 1.117 (0.967) Remain 34:42:25 loss: 0.2168 Lr: 0.00481 [2024-02-18 01:53:10,296 INFO misc.py line 119 87073] Train: [18/100][8/1557] Data 0.012 (0.008) Batch 1.157 (1.005) Remain 36:04:21 loss: 0.8324 Lr: 0.00481 [2024-02-18 01:53:11,262 INFO misc.py line 119 87073] Train: [18/100][9/1557] Data 0.008 (0.008) Batch 0.968 (0.999) Remain 35:51:10 loss: 0.4326 Lr: 0.00481 [2024-02-18 01:53:12,231 INFO misc.py line 119 87073] Train: [18/100][10/1557] Data 0.006 (0.008) Batch 0.971 (0.995) Remain 35:42:43 loss: 0.2973 Lr: 0.00481 [2024-02-18 01:53:13,159 INFO misc.py line 119 87073] Train: [18/100][11/1557] Data 0.004 (0.007) Batch 0.927 (0.986) Remain 35:24:29 loss: 0.6495 Lr: 0.00481 [2024-02-18 01:53:13,963 INFO misc.py line 119 87073] Train: [18/100][12/1557] Data 0.004 (0.007) Batch 0.795 (0.965) Remain 34:38:36 loss: 0.5738 Lr: 0.00481 [2024-02-18 01:53:14,755 INFO misc.py line 119 87073] Train: [18/100][13/1557] Data 0.014 (0.008) Batch 0.802 (0.949) Remain 34:03:24 loss: 0.4627 Lr: 0.00481 [2024-02-18 01:53:15,897 INFO misc.py line 119 87073] Train: [18/100][14/1557] Data 0.004 (0.007) Batch 1.141 (0.966) Remain 34:41:01 loss: 0.2014 Lr: 0.00481 [2024-02-18 01:53:16,965 INFO misc.py line 119 87073] Train: [18/100][15/1557] Data 0.004 (0.007) Batch 1.068 (0.975) Remain 34:59:15 loss: 0.5203 Lr: 0.00481 [2024-02-18 01:53:17,851 INFO misc.py line 119 87073] Train: [18/100][16/1557] Data 0.005 (0.007) Batch 0.886 (0.968) Remain 34:44:35 loss: 0.6482 Lr: 0.00481 [2024-02-18 01:53:18,717 INFO misc.py line 119 87073] Train: [18/100][17/1557] Data 0.005 (0.007) Batch 0.862 (0.960) Remain 34:28:12 loss: 0.5300 Lr: 0.00481 [2024-02-18 01:53:19,847 INFO misc.py line 119 87073] Train: [18/100][18/1557] Data 0.010 (0.007) Batch 1.133 (0.972) Remain 34:52:59 loss: 0.3931 Lr: 0.00481 [2024-02-18 01:53:20,549 INFO misc.py line 119 87073] Train: [18/100][19/1557] Data 0.006 (0.007) Batch 0.704 (0.955) Remain 34:16:51 loss: 0.3089 Lr: 0.00481 [2024-02-18 01:53:21,311 INFO misc.py line 119 87073] Train: [18/100][20/1557] Data 0.004 (0.007) Batch 0.755 (0.943) Remain 33:51:26 loss: 0.5037 Lr: 0.00481 [2024-02-18 01:53:22,503 INFO misc.py line 119 87073] Train: [18/100][21/1557] Data 0.012 (0.007) Batch 1.194 (0.957) Remain 34:21:26 loss: 0.2904 Lr: 0.00481 [2024-02-18 01:53:23,425 INFO misc.py line 119 87073] Train: [18/100][22/1557] Data 0.009 (0.007) Batch 0.927 (0.956) Remain 34:18:00 loss: 0.7248 Lr: 0.00481 [2024-02-18 01:53:24,433 INFO misc.py line 119 87073] Train: [18/100][23/1557] Data 0.004 (0.007) Batch 1.006 (0.958) Remain 34:23:26 loss: 0.6016 Lr: 0.00481 [2024-02-18 01:53:25,258 INFO misc.py line 119 87073] Train: [18/100][24/1557] Data 0.007 (0.007) Batch 0.826 (0.952) Remain 34:09:50 loss: 0.4363 Lr: 0.00481 [2024-02-18 01:53:26,368 INFO misc.py line 119 87073] Train: [18/100][25/1557] Data 0.006 (0.007) Batch 1.045 (0.956) Remain 34:18:53 loss: 0.2223 Lr: 0.00481 [2024-02-18 01:53:27,089 INFO misc.py line 119 87073] Train: [18/100][26/1557] Data 0.071 (0.010) Batch 0.787 (0.949) Remain 34:03:00 loss: 0.3588 Lr: 0.00481 [2024-02-18 01:53:27,749 INFO misc.py line 119 87073] Train: [18/100][27/1557] Data 0.005 (0.009) Batch 0.652 (0.936) Remain 33:36:23 loss: 0.4878 Lr: 0.00481 [2024-02-18 01:53:28,903 INFO misc.py line 119 87073] Train: [18/100][28/1557] Data 0.013 (0.010) Batch 1.143 (0.945) Remain 33:54:11 loss: 0.4714 Lr: 0.00481 [2024-02-18 01:53:29,810 INFO misc.py line 119 87073] Train: [18/100][29/1557] Data 0.024 (0.010) Batch 0.927 (0.944) Remain 33:52:41 loss: 0.5802 Lr: 0.00481 [2024-02-18 01:53:30,917 INFO misc.py line 119 87073] Train: [18/100][30/1557] Data 0.005 (0.010) Batch 1.106 (0.950) Remain 34:05:34 loss: 0.4329 Lr: 0.00481 [2024-02-18 01:53:31,857 INFO misc.py line 119 87073] Train: [18/100][31/1557] Data 0.006 (0.010) Batch 0.941 (0.950) Remain 34:04:53 loss: 0.3989 Lr: 0.00481 [2024-02-18 01:53:32,951 INFO misc.py line 119 87073] Train: [18/100][32/1557] Data 0.005 (0.010) Batch 1.094 (0.955) Remain 34:15:35 loss: 0.5309 Lr: 0.00481 [2024-02-18 01:53:33,703 INFO misc.py line 119 87073] Train: [18/100][33/1557] Data 0.003 (0.009) Batch 0.751 (0.948) Remain 34:00:59 loss: 0.7501 Lr: 0.00481 [2024-02-18 01:53:34,458 INFO misc.py line 119 87073] Train: [18/100][34/1557] Data 0.005 (0.009) Batch 0.747 (0.941) Remain 33:47:00 loss: 0.4107 Lr: 0.00481 [2024-02-18 01:53:35,789 INFO misc.py line 119 87073] Train: [18/100][35/1557] Data 0.014 (0.009) Batch 1.334 (0.954) Remain 34:13:26 loss: 0.4474 Lr: 0.00481 [2024-02-18 01:53:36,625 INFO misc.py line 119 87073] Train: [18/100][36/1557] Data 0.009 (0.009) Batch 0.841 (0.950) Remain 34:06:03 loss: 0.4696 Lr: 0.00481 [2024-02-18 01:53:37,439 INFO misc.py line 119 87073] Train: [18/100][37/1557] Data 0.010 (0.009) Batch 0.813 (0.946) Remain 33:57:22 loss: 0.4502 Lr: 0.00481 [2024-02-18 01:53:38,578 INFO misc.py line 119 87073] Train: [18/100][38/1557] Data 0.006 (0.009) Batch 1.132 (0.952) Remain 34:08:47 loss: 0.4111 Lr: 0.00481 [2024-02-18 01:53:39,677 INFO misc.py line 119 87073] Train: [18/100][39/1557] Data 0.013 (0.009) Batch 1.103 (0.956) Remain 34:17:48 loss: 0.8009 Lr: 0.00481 [2024-02-18 01:53:40,440 INFO misc.py line 119 87073] Train: [18/100][40/1557] Data 0.010 (0.009) Batch 0.765 (0.951) Remain 34:06:42 loss: 0.4549 Lr: 0.00481 [2024-02-18 01:53:41,171 INFO misc.py line 119 87073] Train: [18/100][41/1557] Data 0.007 (0.009) Batch 0.728 (0.945) Remain 33:54:04 loss: 0.3831 Lr: 0.00481 [2024-02-18 01:53:42,429 INFO misc.py line 119 87073] Train: [18/100][42/1557] Data 0.010 (0.009) Batch 1.256 (0.953) Remain 34:11:13 loss: 0.3441 Lr: 0.00481 [2024-02-18 01:53:43,458 INFO misc.py line 119 87073] Train: [18/100][43/1557] Data 0.014 (0.010) Batch 1.029 (0.955) Remain 34:15:18 loss: 0.4103 Lr: 0.00481 [2024-02-18 01:53:44,519 INFO misc.py line 119 87073] Train: [18/100][44/1557] Data 0.012 (0.010) Batch 1.049 (0.957) Remain 34:20:14 loss: 0.5975 Lr: 0.00481 [2024-02-18 01:53:45,432 INFO misc.py line 119 87073] Train: [18/100][45/1557] Data 0.025 (0.010) Batch 0.933 (0.956) Remain 34:19:01 loss: 0.3377 Lr: 0.00481 [2024-02-18 01:53:46,383 INFO misc.py line 119 87073] Train: [18/100][46/1557] Data 0.005 (0.010) Batch 0.949 (0.956) Remain 34:18:37 loss: 0.8454 Lr: 0.00481 [2024-02-18 01:53:47,128 INFO misc.py line 119 87073] Train: [18/100][47/1557] Data 0.009 (0.010) Batch 0.738 (0.951) Remain 34:07:57 loss: 0.9297 Lr: 0.00481 [2024-02-18 01:53:47,901 INFO misc.py line 119 87073] Train: [18/100][48/1557] Data 0.013 (0.010) Batch 0.779 (0.947) Remain 33:59:42 loss: 0.2315 Lr: 0.00480 [2024-02-18 01:53:49,163 INFO misc.py line 119 87073] Train: [18/100][49/1557] Data 0.007 (0.010) Batch 1.262 (0.954) Remain 34:14:26 loss: 0.3804 Lr: 0.00480 [2024-02-18 01:53:50,102 INFO misc.py line 119 87073] Train: [18/100][50/1557] Data 0.007 (0.010) Batch 0.942 (0.954) Remain 34:13:51 loss: 0.4721 Lr: 0.00480 [2024-02-18 01:53:51,099 INFO misc.py line 119 87073] Train: [18/100][51/1557] Data 0.003 (0.010) Batch 0.995 (0.955) Remain 34:15:41 loss: 0.4182 Lr: 0.00480 [2024-02-18 01:53:52,151 INFO misc.py line 119 87073] Train: [18/100][52/1557] Data 0.006 (0.010) Batch 1.053 (0.957) Remain 34:19:59 loss: 0.3567 Lr: 0.00480 [2024-02-18 01:53:53,243 INFO misc.py line 119 87073] Train: [18/100][53/1557] Data 0.005 (0.009) Batch 1.092 (0.960) Remain 34:25:48 loss: 0.2704 Lr: 0.00480 [2024-02-18 01:53:54,054 INFO misc.py line 119 87073] Train: [18/100][54/1557] Data 0.004 (0.009) Batch 0.810 (0.957) Remain 34:19:29 loss: 0.8102 Lr: 0.00480 [2024-02-18 01:53:54,862 INFO misc.py line 119 87073] Train: [18/100][55/1557] Data 0.004 (0.009) Batch 0.802 (0.954) Remain 34:13:04 loss: 0.3501 Lr: 0.00480 [2024-02-18 01:53:56,073 INFO misc.py line 119 87073] Train: [18/100][56/1557] Data 0.011 (0.009) Batch 1.214 (0.959) Remain 34:23:39 loss: 0.3412 Lr: 0.00480 [2024-02-18 01:53:56,950 INFO misc.py line 119 87073] Train: [18/100][57/1557] Data 0.008 (0.009) Batch 0.880 (0.957) Remain 34:20:31 loss: 0.4649 Lr: 0.00480 [2024-02-18 01:53:57,931 INFO misc.py line 119 87073] Train: 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Batch 0.756 (1.046) Remain 37:30:11 loss: 0.4377 Lr: 0.00480 [2024-02-18 01:56:27,352 INFO misc.py line 119 87073] Train: [18/100][196/1557] Data 0.022 (0.075) Batch 1.188 (1.047) Remain 37:31:44 loss: 0.2263 Lr: 0.00480 [2024-02-18 01:56:28,225 INFO misc.py line 119 87073] Train: [18/100][197/1557] Data 0.010 (0.074) Batch 0.878 (1.046) Remain 37:29:51 loss: 0.8140 Lr: 0.00480 [2024-02-18 01:56:29,052 INFO misc.py line 119 87073] Train: [18/100][198/1557] Data 0.005 (0.074) Batch 0.826 (1.045) Remain 37:27:24 loss: 0.8382 Lr: 0.00480 [2024-02-18 01:56:30,120 INFO misc.py line 119 87073] Train: [18/100][199/1557] Data 0.006 (0.074) Batch 1.067 (1.045) Remain 37:27:38 loss: 0.2670 Lr: 0.00480 [2024-02-18 01:56:31,098 INFO misc.py line 119 87073] Train: [18/100][200/1557] Data 0.007 (0.073) Batch 0.981 (1.045) Remain 37:26:55 loss: 0.5177 Lr: 0.00480 [2024-02-18 01:56:31,884 INFO misc.py line 119 87073] Train: [18/100][201/1557] Data 0.004 (0.073) Batch 0.785 (1.044) Remain 37:24:04 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line 119 87073] Train: [18/100][239/1557] Data 0.009 (0.078) Batch 0.920 (1.049) Remain 37:35:17 loss: 0.4183 Lr: 0.00480 [2024-02-18 01:57:13,880 INFO misc.py line 119 87073] Train: [18/100][240/1557] Data 0.005 (0.078) Batch 1.038 (1.049) Remain 37:35:10 loss: 0.5379 Lr: 0.00480 [2024-02-18 01:57:14,801 INFO misc.py line 119 87073] Train: [18/100][241/1557] Data 0.005 (0.077) Batch 0.923 (1.048) Remain 37:34:01 loss: 0.5200 Lr: 0.00480 [2024-02-18 01:57:15,807 INFO misc.py line 119 87073] Train: [18/100][242/1557] Data 0.004 (0.077) Batch 1.004 (1.048) Remain 37:33:36 loss: 0.3207 Lr: 0.00480 [2024-02-18 01:57:16,498 INFO misc.py line 119 87073] Train: [18/100][243/1557] Data 0.005 (0.077) Batch 0.691 (1.047) Remain 37:30:23 loss: 0.4161 Lr: 0.00480 [2024-02-18 01:57:17,218 INFO misc.py line 119 87073] Train: [18/100][244/1557] Data 0.005 (0.076) Batch 0.721 (1.045) Remain 37:27:27 loss: 0.3245 Lr: 0.00480 [2024-02-18 01:57:18,475 INFO misc.py line 119 87073] Train: 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loss: 0.5055 Lr: 0.00480 [2024-02-18 01:57:30,348 INFO misc.py line 119 87073] Train: [18/100][258/1557] Data 0.005 (0.073) Batch 0.648 (1.040) Remain 37:14:28 loss: 0.5791 Lr: 0.00480 [2024-02-18 01:57:31,701 INFO misc.py line 119 87073] Train: [18/100][259/1557] Data 0.009 (0.072) Batch 1.341 (1.041) Remain 37:16:59 loss: 0.4373 Lr: 0.00480 [2024-02-18 01:57:32,783 INFO misc.py line 119 87073] Train: [18/100][260/1557] Data 0.021 (0.072) Batch 1.093 (1.041) Remain 37:17:25 loss: 0.6626 Lr: 0.00480 [2024-02-18 01:57:33,946 INFO misc.py line 119 87073] Train: [18/100][261/1557] Data 0.009 (0.072) Batch 1.153 (1.041) Remain 37:18:20 loss: 0.3365 Lr: 0.00480 [2024-02-18 01:57:34,873 INFO misc.py line 119 87073] Train: [18/100][262/1557] Data 0.021 (0.072) Batch 0.943 (1.041) Remain 37:17:30 loss: 0.3050 Lr: 0.00480 [2024-02-18 01:57:36,108 INFO misc.py line 119 87073] Train: [18/100][263/1557] Data 0.004 (0.071) Batch 1.227 (1.042) Remain 37:19:01 loss: 0.4375 Lr: 0.00480 [2024-02-18 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[2024-02-18 01:58:06,175 INFO misc.py line 119 87073] Train: [18/100][289/1557] Data 0.012 (0.077) Batch 1.089 (1.052) Remain 37:41:01 loss: 0.5982 Lr: 0.00480 [2024-02-18 01:58:06,996 INFO misc.py line 119 87073] Train: [18/100][290/1557] Data 0.007 (0.077) Batch 0.824 (1.051) Remain 37:39:18 loss: 0.5504 Lr: 0.00480 [2024-02-18 01:58:07,987 INFO misc.py line 119 87073] Train: [18/100][291/1557] Data 0.005 (0.076) Batch 0.991 (1.051) Remain 37:38:50 loss: 0.5388 Lr: 0.00480 [2024-02-18 01:58:08,758 INFO misc.py line 119 87073] Train: [18/100][292/1557] Data 0.004 (0.076) Batch 0.770 (1.050) Remain 37:36:43 loss: 0.5082 Lr: 0.00480 [2024-02-18 01:58:09,508 INFO misc.py line 119 87073] Train: [18/100][293/1557] Data 0.004 (0.076) Batch 0.745 (1.049) Remain 37:34:27 loss: 0.5694 Lr: 0.00480 [2024-02-18 01:58:10,603 INFO misc.py line 119 87073] Train: [18/100][294/1557] Data 0.009 (0.076) Batch 1.096 (1.049) Remain 37:34:47 loss: 0.2468 Lr: 0.00480 [2024-02-18 01:58:11,588 INFO misc.py line 119 87073] Train: [18/100][295/1557] Data 0.009 (0.075) Batch 0.988 (1.049) Remain 37:34:19 loss: 0.6407 Lr: 0.00480 [2024-02-18 01:58:12,572 INFO misc.py line 119 87073] Train: [18/100][296/1557] Data 0.004 (0.075) Batch 0.985 (1.049) Remain 37:33:49 loss: 0.4645 Lr: 0.00480 [2024-02-18 01:58:13,606 INFO misc.py line 119 87073] Train: [18/100][297/1557] Data 0.003 (0.075) Batch 1.029 (1.049) Remain 37:33:40 loss: 0.4997 Lr: 0.00480 [2024-02-18 01:58:14,442 INFO misc.py line 119 87073] Train: [18/100][298/1557] Data 0.011 (0.075) Batch 0.838 (1.048) Remain 37:32:06 loss: 0.2607 Lr: 0.00480 [2024-02-18 01:58:15,206 INFO misc.py line 119 87073] Train: [18/100][299/1557] Data 0.007 (0.074) Batch 0.751 (1.047) Remain 37:29:56 loss: 0.3650 Lr: 0.00480 [2024-02-18 01:58:16,086 INFO misc.py line 119 87073] Train: [18/100][300/1557] Data 0.019 (0.074) Batch 0.895 (1.047) Remain 37:28:49 loss: 0.5693 Lr: 0.00480 [2024-02-18 01:58:17,190 INFO misc.py line 119 87073] Train: 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Batch 0.752 (1.044) Remain 37:23:05 loss: 0.4276 Lr: 0.00480 [2024-02-18 01:58:23,773 INFO misc.py line 119 87073] Train: [18/100][308/1557] Data 0.004 (0.072) Batch 1.142 (1.044) Remain 37:23:46 loss: 0.2776 Lr: 0.00480 [2024-02-18 01:58:24,859 INFO misc.py line 119 87073] Train: [18/100][309/1557] Data 0.018 (0.072) Batch 1.098 (1.044) Remain 37:24:07 loss: 0.7176 Lr: 0.00480 [2024-02-18 01:58:25,820 INFO misc.py line 119 87073] Train: [18/100][310/1557] Data 0.006 (0.072) Batch 0.963 (1.044) Remain 37:23:32 loss: 0.4517 Lr: 0.00480 [2024-02-18 01:58:26,663 INFO misc.py line 119 87073] Train: [18/100][311/1557] Data 0.005 (0.072) Batch 0.840 (1.043) Remain 37:22:05 loss: 0.4972 Lr: 0.00480 [2024-02-18 01:58:27,564 INFO misc.py line 119 87073] Train: [18/100][312/1557] Data 0.008 (0.072) Batch 0.902 (1.043) Remain 37:21:05 loss: 0.2578 Lr: 0.00480 [2024-02-18 01:58:28,309 INFO misc.py line 119 87073] Train: [18/100][313/1557] Data 0.006 (0.071) Batch 0.745 (1.042) Remain 37:19:00 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87073] Train: [18/100][326/1557] Data 0.006 (0.069) Batch 1.022 (1.040) Remain 37:13:55 loss: 0.4772 Lr: 0.00480 [2024-02-18 01:58:41,858 INFO misc.py line 119 87073] Train: [18/100][327/1557] Data 0.010 (0.069) Batch 0.733 (1.039) Remain 37:11:52 loss: 0.5748 Lr: 0.00480 [2024-02-18 01:58:42,623 INFO misc.py line 119 87073] Train: [18/100][328/1557] Data 0.006 (0.068) Batch 0.757 (1.038) Remain 37:09:59 loss: 0.3766 Lr: 0.00480 [2024-02-18 01:58:43,798 INFO misc.py line 119 87073] Train: [18/100][329/1557] Data 0.014 (0.068) Batch 1.174 (1.038) Remain 37:10:52 loss: 0.2742 Lr: 0.00480 [2024-02-18 01:58:44,762 INFO misc.py line 119 87073] Train: [18/100][330/1557] Data 0.014 (0.068) Batch 0.974 (1.038) Remain 37:10:26 loss: 0.5524 Lr: 0.00480 [2024-02-18 01:58:45,770 INFO misc.py line 119 87073] Train: [18/100][331/1557] Data 0.005 (0.068) Batch 1.006 (1.038) Remain 37:10:12 loss: 0.4314 Lr: 0.00480 [2024-02-18 01:58:46,982 INFO misc.py line 119 87073] Train: [18/100][332/1557] Data 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Batch 0.754 (1.048) Remain 37:29:25 loss: 0.7085 Lr: 0.00480 [2024-02-18 02:01:21,149 INFO misc.py line 119 87073] Train: [18/100][476/1557] Data 0.004 (0.073) Batch 1.114 (1.048) Remain 37:29:42 loss: 0.6473 Lr: 0.00480 [2024-02-18 02:01:22,203 INFO misc.py line 119 87073] Train: [18/100][477/1557] Data 0.006 (0.073) Batch 1.055 (1.048) Remain 37:29:43 loss: 0.5770 Lr: 0.00480 [2024-02-18 02:01:23,147 INFO misc.py line 119 87073] Train: [18/100][478/1557] Data 0.005 (0.073) Batch 0.944 (1.048) Remain 37:29:14 loss: 0.5148 Lr: 0.00480 [2024-02-18 02:01:24,298 INFO misc.py line 119 87073] Train: [18/100][479/1557] Data 0.004 (0.073) Batch 1.151 (1.048) Remain 37:29:41 loss: 0.9336 Lr: 0.00480 [2024-02-18 02:01:25,236 INFO misc.py line 119 87073] Train: [18/100][480/1557] Data 0.005 (0.073) Batch 0.937 (1.048) Remain 37:29:10 loss: 0.5363 Lr: 0.00480 [2024-02-18 02:01:26,040 INFO misc.py line 119 87073] Train: [18/100][481/1557] Data 0.005 (0.073) Batch 0.802 (1.048) Remain 37:28:02 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line 119 87073] Train: [18/100][519/1557] Data 0.014 (0.075) Batch 0.911 (1.050) Remain 37:32:49 loss: 0.6179 Lr: 0.00480 [2024-02-18 02:02:08,043 INFO misc.py line 119 87073] Train: [18/100][520/1557] Data 0.004 (0.075) Batch 0.888 (1.050) Remain 37:32:07 loss: 0.9305 Lr: 0.00480 [2024-02-18 02:02:08,952 INFO misc.py line 119 87073] Train: [18/100][521/1557] Data 0.004 (0.075) Batch 0.900 (1.050) Remain 37:31:29 loss: 0.4946 Lr: 0.00480 [2024-02-18 02:02:09,738 INFO misc.py line 119 87073] Train: [18/100][522/1557] Data 0.013 (0.074) Batch 0.794 (1.049) Remain 37:30:25 loss: 0.3203 Lr: 0.00480 [2024-02-18 02:02:10,482 INFO misc.py line 119 87073] Train: [18/100][523/1557] Data 0.004 (0.074) Batch 0.745 (1.048) Remain 37:29:08 loss: 0.3196 Lr: 0.00480 [2024-02-18 02:02:11,215 INFO misc.py line 119 87073] Train: [18/100][524/1557] Data 0.003 (0.074) Batch 0.722 (1.048) Remain 37:27:47 loss: 0.5277 Lr: 0.00480 [2024-02-18 02:02:12,393 INFO misc.py line 119 87073] Train: 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Batch 0.743 (1.049) Remain 37:31:10 loss: 0.6129 Lr: 0.00479 [2024-02-18 02:02:20,581 INFO misc.py line 119 87073] Train: [18/100][532/1557] Data 0.009 (0.076) Batch 1.161 (1.050) Remain 37:31:36 loss: 0.2024 Lr: 0.00479 [2024-02-18 02:02:21,477 INFO misc.py line 119 87073] Train: [18/100][533/1557] Data 0.021 (0.076) Batch 0.913 (1.049) Remain 37:31:02 loss: 0.6026 Lr: 0.00479 [2024-02-18 02:02:22,683 INFO misc.py line 119 87073] Train: [18/100][534/1557] Data 0.005 (0.076) Batch 1.205 (1.050) Remain 37:31:38 loss: 0.1708 Lr: 0.00479 [2024-02-18 02:02:23,695 INFO misc.py line 119 87073] Train: [18/100][535/1557] Data 0.005 (0.076) Batch 1.007 (1.050) Remain 37:31:27 loss: 0.7428 Lr: 0.00479 [2024-02-18 02:02:24,615 INFO misc.py line 119 87073] Train: [18/100][536/1557] Data 0.010 (0.076) Batch 0.925 (1.049) Remain 37:30:56 loss: 0.6655 Lr: 0.00479 [2024-02-18 02:02:25,333 INFO misc.py line 119 87073] Train: [18/100][537/1557] Data 0.005 (0.076) Batch 0.719 (1.049) Remain 37:29:35 loss: 0.6454 Lr: 0.00479 [2024-02-18 02:02:26,113 INFO misc.py line 119 87073] Train: [18/100][538/1557] Data 0.004 (0.075) Batch 0.778 (1.048) Remain 37:28:29 loss: 0.3178 Lr: 0.00479 [2024-02-18 02:02:27,451 INFO misc.py line 119 87073] Train: [18/100][539/1557] Data 0.005 (0.075) Batch 1.315 (1.049) Remain 37:29:32 loss: 0.4120 Lr: 0.00479 [2024-02-18 02:02:28,579 INFO misc.py line 119 87073] Train: [18/100][540/1557] Data 0.028 (0.075) Batch 1.145 (1.049) Remain 37:29:54 loss: 0.3563 Lr: 0.00479 [2024-02-18 02:02:29,572 INFO misc.py line 119 87073] Train: [18/100][541/1557] Data 0.012 (0.075) Batch 0.999 (1.049) Remain 37:29:41 loss: 0.6092 Lr: 0.00479 [2024-02-18 02:02:30,554 INFO misc.py line 119 87073] Train: [18/100][542/1557] Data 0.006 (0.075) Batch 0.984 (1.049) Remain 37:29:25 loss: 0.3929 Lr: 0.00479 [2024-02-18 02:02:31,581 INFO misc.py line 119 87073] Train: [18/100][543/1557] Data 0.004 (0.075) Batch 1.027 (1.049) Remain 37:29:18 loss: 0.4503 Lr: 0.00479 [2024-02-18 02:02:32,296 INFO misc.py line 119 87073] Train: [18/100][544/1557] Data 0.004 (0.075) Batch 0.715 (1.048) Remain 37:27:58 loss: 0.2067 Lr: 0.00479 [2024-02-18 02:02:33,058 INFO misc.py line 119 87073] Train: [18/100][545/1557] Data 0.004 (0.075) Batch 0.749 (1.048) Remain 37:26:46 loss: 0.4839 Lr: 0.00479 [2024-02-18 02:02:34,352 INFO misc.py line 119 87073] Train: [18/100][546/1557] Data 0.016 (0.074) Batch 1.293 (1.048) Remain 37:27:43 loss: 0.2656 Lr: 0.00479 [2024-02-18 02:02:35,249 INFO misc.py line 119 87073] Train: [18/100][547/1557] Data 0.018 (0.074) Batch 0.911 (1.048) Remain 37:27:09 loss: 0.7777 Lr: 0.00479 [2024-02-18 02:02:36,382 INFO misc.py line 119 87073] Train: [18/100][548/1557] Data 0.004 (0.074) Batch 1.133 (1.048) Remain 37:27:28 loss: 0.5028 Lr: 0.00479 [2024-02-18 02:02:37,261 INFO misc.py line 119 87073] Train: [18/100][549/1557] Data 0.003 (0.074) Batch 0.876 (1.048) Remain 37:26:47 loss: 0.4520 Lr: 0.00479 [2024-02-18 02:02:38,375 INFO misc.py line 119 87073] Train: [18/100][550/1557] Data 0.007 (0.074) Batch 1.116 (1.048) Remain 37:27:02 loss: 0.4652 Lr: 0.00479 [2024-02-18 02:02:39,108 INFO misc.py line 119 87073] Train: [18/100][551/1557] Data 0.006 (0.074) Batch 0.734 (1.047) Remain 37:25:47 loss: 0.5070 Lr: 0.00479 [2024-02-18 02:02:39,824 INFO misc.py line 119 87073] Train: [18/100][552/1557] Data 0.004 (0.074) Batch 0.705 (1.047) Remain 37:24:26 loss: 0.4847 Lr: 0.00479 [2024-02-18 02:02:41,037 INFO misc.py line 119 87073] Train: [18/100][553/1557] Data 0.013 (0.074) Batch 1.220 (1.047) Remain 37:25:06 loss: 0.5641 Lr: 0.00479 [2024-02-18 02:02:41,965 INFO misc.py line 119 87073] Train: [18/100][554/1557] Data 0.006 (0.073) Batch 0.930 (1.047) Remain 37:24:38 loss: 0.2234 Lr: 0.00479 [2024-02-18 02:02:42,890 INFO misc.py line 119 87073] Train: [18/100][555/1557] Data 0.005 (0.073) Batch 0.924 (1.046) Remain 37:24:08 loss: 0.8975 Lr: 0.00479 [2024-02-18 02:02:43,938 INFO misc.py line 119 87073] Train: [18/100][556/1557] Data 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[2024-02-18 02:03:01,632 INFO misc.py line 119 87073] Train: [18/100][569/1557] Data 0.003 (0.078) Batch 0.953 (1.054) Remain 37:39:24 loss: 0.5784 Lr: 0.00479 [2024-02-18 02:03:02,703 INFO misc.py line 119 87073] Train: [18/100][570/1557] Data 0.005 (0.078) Batch 1.071 (1.054) Remain 37:39:27 loss: 0.7855 Lr: 0.00479 [2024-02-18 02:03:03,678 INFO misc.py line 119 87073] Train: [18/100][571/1557] Data 0.003 (0.078) Batch 0.975 (1.054) Remain 37:39:08 loss: 0.5327 Lr: 0.00479 [2024-02-18 02:03:04,442 INFO misc.py line 119 87073] Train: [18/100][572/1557] Data 0.004 (0.078) Batch 0.764 (1.053) Remain 37:38:01 loss: 0.5339 Lr: 0.00479 [2024-02-18 02:03:05,219 INFO misc.py line 119 87073] Train: [18/100][573/1557] Data 0.003 (0.078) Batch 0.771 (1.053) Remain 37:36:57 loss: 0.3101 Lr: 0.00479 [2024-02-18 02:03:06,400 INFO misc.py line 119 87073] Train: [18/100][574/1557] Data 0.009 (0.077) Batch 1.177 (1.053) Remain 37:37:24 loss: 0.1743 Lr: 0.00479 [2024-02-18 02:03:07,472 INFO misc.py line 119 87073] Train: [18/100][575/1557] Data 0.013 (0.077) Batch 1.075 (1.053) Remain 37:37:28 loss: 0.5759 Lr: 0.00479 [2024-02-18 02:03:08,490 INFO misc.py line 119 87073] Train: [18/100][576/1557] Data 0.009 (0.077) Batch 1.018 (1.053) Remain 37:37:19 loss: 0.8296 Lr: 0.00479 [2024-02-18 02:03:09,469 INFO misc.py line 119 87073] Train: [18/100][577/1557] Data 0.010 (0.077) Batch 0.985 (1.053) Remain 37:37:03 loss: 0.6452 Lr: 0.00479 [2024-02-18 02:03:10,313 INFO misc.py line 119 87073] Train: [18/100][578/1557] Data 0.004 (0.077) Batch 0.844 (1.052) Remain 37:36:15 loss: 0.4794 Lr: 0.00479 [2024-02-18 02:03:11,109 INFO misc.py line 119 87073] Train: [18/100][579/1557] Data 0.004 (0.077) Batch 0.791 (1.052) Remain 37:35:16 loss: 0.6864 Lr: 0.00479 [2024-02-18 02:03:11,920 INFO misc.py line 119 87073] Train: [18/100][580/1557] Data 0.008 (0.077) Batch 0.816 (1.051) Remain 37:34:22 loss: 0.4497 Lr: 0.00479 [2024-02-18 02:03:13,059 INFO misc.py line 119 87073] Train: 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Batch 0.788 (1.050) Remain 37:30:51 loss: 0.5984 Lr: 0.00479 [2024-02-18 02:03:19,485 INFO misc.py line 119 87073] Train: [18/100][588/1557] Data 0.011 (0.076) Batch 1.121 (1.050) Remain 37:31:06 loss: 0.3734 Lr: 0.00479 [2024-02-18 02:03:20,504 INFO misc.py line 119 87073] Train: [18/100][589/1557] Data 0.010 (0.076) Batch 1.024 (1.050) Remain 37:30:59 loss: 1.0853 Lr: 0.00479 [2024-02-18 02:03:21,550 INFO misc.py line 119 87073] Train: [18/100][590/1557] Data 0.006 (0.075) Batch 1.046 (1.050) Remain 37:30:57 loss: 0.2676 Lr: 0.00479 [2024-02-18 02:03:22,527 INFO misc.py line 119 87073] Train: [18/100][591/1557] Data 0.006 (0.075) Batch 0.979 (1.050) Remain 37:30:41 loss: 0.5596 Lr: 0.00479 [2024-02-18 02:03:23,502 INFO misc.py line 119 87073] Train: [18/100][592/1557] Data 0.003 (0.075) Batch 0.975 (1.050) Remain 37:30:23 loss: 0.2836 Lr: 0.00479 [2024-02-18 02:03:24,191 INFO misc.py line 119 87073] Train: [18/100][593/1557] Data 0.004 (0.075) Batch 0.688 (1.049) Remain 37:29:04 loss: 0.5195 Lr: 0.00479 [2024-02-18 02:03:24,917 INFO misc.py line 119 87073] Train: [18/100][594/1557] Data 0.004 (0.075) Batch 0.726 (1.048) Remain 37:27:52 loss: 0.4559 Lr: 0.00479 [2024-02-18 02:03:26,206 INFO misc.py line 119 87073] Train: [18/100][595/1557] Data 0.003 (0.075) Batch 1.289 (1.049) Remain 37:28:44 loss: 0.4706 Lr: 0.00479 [2024-02-18 02:03:27,273 INFO misc.py line 119 87073] Train: [18/100][596/1557] Data 0.004 (0.075) Batch 1.049 (1.049) Remain 37:28:42 loss: 0.9279 Lr: 0.00479 [2024-02-18 02:03:28,294 INFO misc.py line 119 87073] Train: [18/100][597/1557] Data 0.021 (0.075) Batch 1.027 (1.049) Remain 37:28:37 loss: 0.6643 Lr: 0.00479 [2024-02-18 02:03:29,416 INFO misc.py line 119 87073] Train: [18/100][598/1557] Data 0.016 (0.075) Batch 1.129 (1.049) Remain 37:28:53 loss: 0.7146 Lr: 0.00479 [2024-02-18 02:03:30,294 INFO misc.py line 119 87073] Train: [18/100][599/1557] Data 0.010 (0.074) Batch 0.883 (1.049) Remain 37:28:16 loss: 0.6104 Lr: 0.00479 [2024-02-18 02:03:31,075 INFO misc.py line 119 87073] Train: [18/100][600/1557] Data 0.004 (0.074) Batch 0.781 (1.048) Remain 37:27:17 loss: 0.5626 Lr: 0.00479 [2024-02-18 02:03:31,788 INFO misc.py line 119 87073] Train: [18/100][601/1557] Data 0.004 (0.074) Batch 0.707 (1.048) Remain 37:26:03 loss: 0.4236 Lr: 0.00479 [2024-02-18 02:03:32,995 INFO misc.py line 119 87073] Train: [18/100][602/1557] Data 0.009 (0.074) Batch 1.208 (1.048) Remain 37:26:36 loss: 0.3424 Lr: 0.00479 [2024-02-18 02:03:33,892 INFO misc.py line 119 87073] Train: [18/100][603/1557] Data 0.009 (0.074) Batch 0.899 (1.048) Remain 37:26:03 loss: 0.7362 Lr: 0.00479 [2024-02-18 02:03:34,851 INFO misc.py line 119 87073] Train: [18/100][604/1557] Data 0.007 (0.074) Batch 0.960 (1.048) Remain 37:25:43 loss: 0.5902 Lr: 0.00479 [2024-02-18 02:03:35,913 INFO misc.py line 119 87073] Train: [18/100][605/1557] Data 0.007 (0.074) Batch 1.063 (1.048) Remain 37:25:46 loss: 0.5391 Lr: 0.00479 [2024-02-18 02:03:36,790 INFO misc.py line 119 87073] Train: [18/100][606/1557] Data 0.006 (0.074) Batch 0.878 (1.047) Remain 37:25:08 loss: 0.6920 Lr: 0.00479 [2024-02-18 02:03:37,566 INFO misc.py line 119 87073] Train: [18/100][607/1557] Data 0.004 (0.074) Batch 0.764 (1.047) Remain 37:24:07 loss: 0.3591 Lr: 0.00479 [2024-02-18 02:03:38,273 INFO misc.py line 119 87073] Train: [18/100][608/1557] Data 0.016 (0.073) Batch 0.719 (1.046) Remain 37:22:56 loss: 0.3771 Lr: 0.00479 [2024-02-18 02:03:39,365 INFO misc.py line 119 87073] Train: [18/100][609/1557] Data 0.003 (0.073) Batch 1.092 (1.046) Remain 37:23:05 loss: 0.3008 Lr: 0.00479 [2024-02-18 02:03:40,282 INFO misc.py line 119 87073] Train: [18/100][610/1557] Data 0.004 (0.073) Batch 0.917 (1.046) Remain 37:22:36 loss: 0.9350 Lr: 0.00479 [2024-02-18 02:03:41,049 INFO misc.py line 119 87073] Train: [18/100][611/1557] Data 0.004 (0.073) Batch 0.760 (1.046) Remain 37:21:35 loss: 0.4469 Lr: 0.00479 [2024-02-18 02:03:42,251 INFO misc.py line 119 87073] Train: [18/100][612/1557] Data 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line 119 87073] Train: [18/100][799/1557] Data 0.012 (0.078) Batch 1.067 (1.052) Remain 37:32:28 loss: 1.1347 Lr: 0.00479 [2024-02-18 02:07:03,742 INFO misc.py line 119 87073] Train: [18/100][800/1557] Data 0.006 (0.078) Batch 0.846 (1.052) Remain 37:31:53 loss: 0.3114 Lr: 0.00479 [2024-02-18 02:07:04,877 INFO misc.py line 119 87073] Train: [18/100][801/1557] Data 0.006 (0.077) Batch 1.133 (1.052) Remain 37:32:05 loss: 0.6647 Lr: 0.00479 [2024-02-18 02:07:05,774 INFO misc.py line 119 87073] Train: [18/100][802/1557] Data 0.008 (0.077) Batch 0.899 (1.052) Remain 37:31:40 loss: 0.3255 Lr: 0.00479 [2024-02-18 02:07:06,575 INFO misc.py line 119 87073] Train: [18/100][803/1557] Data 0.004 (0.077) Batch 0.791 (1.052) Remain 37:30:57 loss: 0.3840 Lr: 0.00479 [2024-02-18 02:07:07,368 INFO misc.py line 119 87073] Train: [18/100][804/1557] Data 0.015 (0.077) Batch 0.804 (1.051) Remain 37:30:16 loss: 0.5259 Lr: 0.00479 [2024-02-18 02:07:08,610 INFO misc.py line 119 87073] Train: 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Batch 0.805 (1.051) Remain 37:28:26 loss: 0.5511 Lr: 0.00479 [2024-02-18 02:07:15,243 INFO misc.py line 119 87073] Train: [18/100][812/1557] Data 0.007 (0.076) Batch 1.158 (1.051) Remain 37:28:42 loss: 0.1112 Lr: 0.00479 [2024-02-18 02:07:16,261 INFO misc.py line 119 87073] Train: [18/100][813/1557] Data 0.009 (0.076) Batch 1.017 (1.051) Remain 37:28:36 loss: 0.5218 Lr: 0.00479 [2024-02-18 02:07:17,160 INFO misc.py line 119 87073] Train: [18/100][814/1557] Data 0.012 (0.076) Batch 0.904 (1.050) Remain 37:28:11 loss: 0.6370 Lr: 0.00479 [2024-02-18 02:07:18,127 INFO misc.py line 119 87073] Train: [18/100][815/1557] Data 0.005 (0.076) Batch 0.967 (1.050) Remain 37:27:57 loss: 0.3259 Lr: 0.00479 [2024-02-18 02:07:19,055 INFO misc.py line 119 87073] Train: [18/100][816/1557] Data 0.004 (0.076) Batch 0.925 (1.050) Remain 37:27:36 loss: 0.6990 Lr: 0.00479 [2024-02-18 02:07:19,875 INFO misc.py line 119 87073] Train: [18/100][817/1557] Data 0.009 (0.076) Batch 0.823 (1.050) Remain 37:26:59 loss: 0.6783 Lr: 0.00479 [2024-02-18 02:07:20,617 INFO misc.py line 119 87073] Train: [18/100][818/1557] Data 0.005 (0.076) Batch 0.740 (1.050) Remain 37:26:09 loss: 0.6313 Lr: 0.00479 [2024-02-18 02:07:21,891 INFO misc.py line 119 87073] Train: [18/100][819/1557] Data 0.006 (0.076) Batch 1.274 (1.050) Remain 37:26:44 loss: 0.4950 Lr: 0.00479 [2024-02-18 02:07:22,719 INFO misc.py line 119 87073] Train: [18/100][820/1557] Data 0.008 (0.076) Batch 0.829 (1.050) Remain 37:26:08 loss: 0.4812 Lr: 0.00479 [2024-02-18 02:07:23,482 INFO misc.py line 119 87073] Train: [18/100][821/1557] Data 0.006 (0.076) Batch 0.764 (1.049) Remain 37:25:22 loss: 0.2428 Lr: 0.00479 [2024-02-18 02:07:24,536 INFO misc.py line 119 87073] Train: [18/100][822/1557] Data 0.006 (0.076) Batch 1.054 (1.049) Remain 37:25:22 loss: 0.3182 Lr: 0.00479 [2024-02-18 02:07:25,489 INFO misc.py line 119 87073] Train: [18/100][823/1557] Data 0.005 (0.076) Batch 0.951 (1.049) Remain 37:25:05 loss: 1.3767 Lr: 0.00479 [2024-02-18 02:07:26,237 INFO misc.py line 119 87073] Train: [18/100][824/1557] Data 0.007 (0.075) Batch 0.750 (1.049) Remain 37:24:17 loss: 0.3085 Lr: 0.00479 [2024-02-18 02:07:26,985 INFO misc.py line 119 87073] Train: [18/100][825/1557] Data 0.006 (0.075) Batch 0.743 (1.048) Remain 37:23:29 loss: 0.6196 Lr: 0.00479 [2024-02-18 02:07:28,298 INFO misc.py line 119 87073] Train: [18/100][826/1557] Data 0.010 (0.075) Batch 1.307 (1.049) Remain 37:24:08 loss: 0.1368 Lr: 0.00479 [2024-02-18 02:07:29,383 INFO misc.py line 119 87073] Train: [18/100][827/1557] Data 0.017 (0.075) Batch 1.086 (1.049) Remain 37:24:13 loss: 0.8537 Lr: 0.00479 [2024-02-18 02:07:30,265 INFO misc.py line 119 87073] Train: [18/100][828/1557] Data 0.016 (0.075) Batch 0.892 (1.048) Remain 37:23:47 loss: 0.7954 Lr: 0.00479 [2024-02-18 02:07:31,214 INFO misc.py line 119 87073] Train: [18/100][829/1557] Data 0.007 (0.075) Batch 0.949 (1.048) Remain 37:23:31 loss: 0.6411 Lr: 0.00479 [2024-02-18 02:07:32,150 INFO misc.py line 119 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[2024-02-18 02:07:54,666 INFO misc.py line 119 87073] Train: [18/100][849/1557] Data 0.005 (0.077) Batch 0.840 (1.051) Remain 37:29:27 loss: 0.4781 Lr: 0.00479 [2024-02-18 02:07:55,786 INFO misc.py line 119 87073] Train: [18/100][850/1557] Data 0.006 (0.077) Batch 1.122 (1.051) Remain 37:29:36 loss: 0.2494 Lr: 0.00479 [2024-02-18 02:07:56,605 INFO misc.py line 119 87073] Train: [18/100][851/1557] Data 0.004 (0.077) Batch 0.818 (1.051) Remain 37:29:00 loss: 0.2263 Lr: 0.00479 [2024-02-18 02:07:57,358 INFO misc.py line 119 87073] Train: [18/100][852/1557] Data 0.005 (0.077) Batch 0.744 (1.051) Remain 37:28:13 loss: 0.4524 Lr: 0.00479 [2024-02-18 02:07:58,124 INFO misc.py line 119 87073] Train: [18/100][853/1557] Data 0.014 (0.077) Batch 0.776 (1.050) Remain 37:27:30 loss: 0.4852 Lr: 0.00479 [2024-02-18 02:07:59,304 INFO misc.py line 119 87073] Train: [18/100][854/1557] Data 0.004 (0.077) Batch 1.177 (1.051) Remain 37:27:48 loss: 0.3073 Lr: 0.00479 [2024-02-18 02:08:00,117 INFO misc.py line 119 87073] Train: [18/100][855/1557] Data 0.007 (0.077) Batch 0.814 (1.050) Remain 37:27:11 loss: 0.6874 Lr: 0.00479 [2024-02-18 02:08:01,323 INFO misc.py line 119 87073] Train: [18/100][856/1557] Data 0.006 (0.077) Batch 1.204 (1.050) Remain 37:27:34 loss: 1.2413 Lr: 0.00479 [2024-02-18 02:08:02,500 INFO misc.py line 119 87073] Train: [18/100][857/1557] Data 0.009 (0.077) Batch 1.179 (1.051) Remain 37:27:52 loss: 0.8661 Lr: 0.00479 [2024-02-18 02:08:03,418 INFO misc.py line 119 87073] Train: [18/100][858/1557] Data 0.006 (0.077) Batch 0.918 (1.050) Remain 37:27:31 loss: 0.3433 Lr: 0.00479 [2024-02-18 02:08:04,152 INFO misc.py line 119 87073] Train: [18/100][859/1557] Data 0.006 (0.076) Batch 0.735 (1.050) Remain 37:26:42 loss: 0.6295 Lr: 0.00479 [2024-02-18 02:08:04,923 INFO misc.py line 119 87073] Train: [18/100][860/1557] Data 0.005 (0.076) Batch 0.762 (1.050) Remain 37:25:58 loss: 0.3418 Lr: 0.00479 [2024-02-18 02:08:06,085 INFO misc.py line 119 87073] Train: 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Batch 0.734 (1.049) Remain 37:23:19 loss: 0.3749 Lr: 0.00479 [2024-02-18 02:08:12,455 INFO misc.py line 119 87073] Train: [18/100][868/1557] Data 0.015 (0.076) Batch 1.205 (1.049) Remain 37:23:41 loss: 0.6013 Lr: 0.00479 [2024-02-18 02:08:13,325 INFO misc.py line 119 87073] Train: [18/100][869/1557] Data 0.013 (0.076) Batch 0.879 (1.049) Remain 37:23:15 loss: 0.3386 Lr: 0.00479 [2024-02-18 02:08:14,261 INFO misc.py line 119 87073] Train: [18/100][870/1557] Data 0.005 (0.076) Batch 0.935 (1.048) Remain 37:22:57 loss: 0.9113 Lr: 0.00479 [2024-02-18 02:08:15,135 INFO misc.py line 119 87073] Train: [18/100][871/1557] Data 0.007 (0.075) Batch 0.873 (1.048) Remain 37:22:30 loss: 0.5451 Lr: 0.00479 [2024-02-18 02:08:16,298 INFO misc.py line 119 87073] Train: [18/100][872/1557] Data 0.007 (0.075) Batch 1.165 (1.048) Remain 37:22:46 loss: 0.5263 Lr: 0.00479 [2024-02-18 02:08:17,034 INFO misc.py line 119 87073] Train: [18/100][873/1557] Data 0.005 (0.075) Batch 0.735 (1.048) Remain 37:21:59 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87073] Train: [18/100][886/1557] Data 0.004 (0.075) Batch 0.979 (1.049) Remain 37:23:37 loss: 0.5210 Lr: 0.00479 [2024-02-18 02:08:32,206 INFO misc.py line 119 87073] Train: [18/100][887/1557] Data 0.004 (0.075) Batch 0.785 (1.049) Remain 37:22:57 loss: 0.3768 Lr: 0.00479 [2024-02-18 02:08:32,962 INFO misc.py line 119 87073] Train: [18/100][888/1557] Data 0.005 (0.075) Batch 0.756 (1.048) Remain 37:22:14 loss: 0.6075 Lr: 0.00479 [2024-02-18 02:08:34,106 INFO misc.py line 119 87073] Train: [18/100][889/1557] Data 0.004 (0.075) Batch 1.135 (1.048) Remain 37:22:25 loss: 0.5734 Lr: 0.00479 [2024-02-18 02:08:35,301 INFO misc.py line 119 87073] Train: [18/100][890/1557] Data 0.012 (0.075) Batch 1.196 (1.049) Remain 37:22:46 loss: 0.4267 Lr: 0.00479 [2024-02-18 02:08:36,612 INFO misc.py line 119 87073] Train: [18/100][891/1557] Data 0.013 (0.075) Batch 1.316 (1.049) Remain 37:23:23 loss: 0.7600 Lr: 0.00479 [2024-02-18 02:08:37,557 INFO misc.py line 119 87073] Train: [18/100][892/1557] Data 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87073] Train: [18/100][998/1557] Data 0.004 (0.075) Batch 0.890 (1.050) Remain 37:24:35 loss: 0.5828 Lr: 0.00478 [2024-02-18 02:10:31,024 INFO misc.py line 119 87073] Train: [18/100][999/1557] Data 0.005 (0.075) Batch 0.764 (1.050) Remain 37:23:57 loss: 0.4257 Lr: 0.00478 [2024-02-18 02:10:31,763 INFO misc.py line 119 87073] Train: [18/100][1000/1557] Data 0.007 (0.075) Batch 0.740 (1.050) Remain 37:23:16 loss: 0.6032 Lr: 0.00478 [2024-02-18 02:10:32,924 INFO misc.py line 119 87073] Train: [18/100][1001/1557] Data 0.006 (0.075) Batch 1.162 (1.050) Remain 37:23:29 loss: 0.5566 Lr: 0.00478 [2024-02-18 02:10:34,008 INFO misc.py line 119 87073] Train: [18/100][1002/1557] Data 0.006 (0.075) Batch 1.084 (1.050) Remain 37:23:33 loss: 0.3474 Lr: 0.00478 [2024-02-18 02:10:34,875 INFO misc.py line 119 87073] Train: [18/100][1003/1557] Data 0.005 (0.075) Batch 0.866 (1.050) Remain 37:23:08 loss: 0.4692 Lr: 0.00478 [2024-02-18 02:10:35,971 INFO misc.py line 119 87073] Train: [18/100][1004/1557] Data 0.008 (0.075) Batch 1.092 (1.050) Remain 37:23:12 loss: 0.5066 Lr: 0.00478 [2024-02-18 02:10:37,149 INFO misc.py line 119 87073] Train: [18/100][1005/1557] Data 0.011 (0.074) Batch 1.169 (1.050) Remain 37:23:27 loss: 0.4825 Lr: 0.00478 [2024-02-18 02:10:37,909 INFO misc.py line 119 87073] Train: [18/100][1006/1557] Data 0.019 (0.074) Batch 0.774 (1.049) Remain 37:22:50 loss: 0.6562 Lr: 0.00478 [2024-02-18 02:10:38,673 INFO misc.py line 119 87073] Train: [18/100][1007/1557] Data 0.006 (0.074) Batch 0.758 (1.049) Remain 37:22:12 loss: 0.5334 Lr: 0.00478 [2024-02-18 02:10:39,842 INFO misc.py line 119 87073] Train: [18/100][1008/1557] Data 0.011 (0.074) Batch 1.165 (1.049) Remain 37:22:26 loss: 0.2867 Lr: 0.00478 [2024-02-18 02:10:40,713 INFO misc.py line 119 87073] Train: [18/100][1009/1557] Data 0.015 (0.074) Batch 0.880 (1.049) Remain 37:22:03 loss: 0.7930 Lr: 0.00478 [2024-02-18 02:10:41,858 INFO misc.py line 119 87073] Train: [18/100][1010/1557] Data 0.006 (0.074) Batch 1.146 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02:10:59,420 INFO misc.py line 119 87073] Train: [18/100][1023/1557] Data 0.011 (0.077) Batch 0.970 (1.053) Remain 37:30:14 loss: 0.5385 Lr: 0.00478 [2024-02-18 02:11:00,359 INFO misc.py line 119 87073] Train: [18/100][1024/1557] Data 0.007 (0.076) Batch 0.941 (1.053) Remain 37:29:58 loss: 0.5327 Lr: 0.00478 [2024-02-18 02:11:01,389 INFO misc.py line 119 87073] Train: [18/100][1025/1557] Data 0.005 (0.076) Batch 1.030 (1.053) Remain 37:29:54 loss: 0.2476 Lr: 0.00478 [2024-02-18 02:11:02,247 INFO misc.py line 119 87073] Train: [18/100][1026/1557] Data 0.005 (0.076) Batch 0.858 (1.053) Remain 37:29:29 loss: 0.3147 Lr: 0.00478 [2024-02-18 02:11:03,003 INFO misc.py line 119 87073] Train: [18/100][1027/1557] Data 0.005 (0.076) Batch 0.754 (1.052) Remain 37:28:51 loss: 0.4205 Lr: 0.00478 [2024-02-18 02:11:03,762 INFO misc.py line 119 87073] Train: [18/100][1028/1557] Data 0.007 (0.076) Batch 0.761 (1.052) Remain 37:28:13 loss: 0.4894 Lr: 0.00478 [2024-02-18 02:11:04,872 INFO misc.py line 119 87073] Train: [18/100][1029/1557] Data 0.004 (0.076) Batch 1.111 (1.052) Remain 37:28:19 loss: 0.2001 Lr: 0.00478 [2024-02-18 02:11:05,855 INFO misc.py line 119 87073] Train: [18/100][1030/1557] Data 0.004 (0.076) Batch 0.982 (1.052) Remain 37:28:10 loss: 0.6317 Lr: 0.00478 [2024-02-18 02:11:06,720 INFO misc.py line 119 87073] Train: [18/100][1031/1557] Data 0.004 (0.076) Batch 0.865 (1.052) Remain 37:27:45 loss: 0.4965 Lr: 0.00478 [2024-02-18 02:11:07,855 INFO misc.py line 119 87073] Train: [18/100][1032/1557] Data 0.004 (0.076) Batch 1.134 (1.052) Remain 37:27:54 loss: 1.0555 Lr: 0.00478 [2024-02-18 02:11:08,820 INFO misc.py line 119 87073] Train: [18/100][1033/1557] Data 0.006 (0.076) Batch 0.967 (1.052) Remain 37:27:43 loss: 0.2913 Lr: 0.00478 [2024-02-18 02:11:09,597 INFO misc.py line 119 87073] Train: [18/100][1034/1557] Data 0.004 (0.076) Batch 0.776 (1.052) Remain 37:27:07 loss: 0.6019 Lr: 0.00478 [2024-02-18 02:11:10,376 INFO misc.py line 119 87073] Train: [18/100][1035/1557] Data 0.005 (0.076) Batch 0.773 (1.051) Remain 37:26:32 loss: 0.3857 Lr: 0.00478 [2024-02-18 02:11:11,525 INFO misc.py line 119 87073] Train: [18/100][1036/1557] Data 0.011 (0.076) Batch 1.145 (1.052) Remain 37:26:42 loss: 0.3351 Lr: 0.00478 [2024-02-18 02:11:12,479 INFO misc.py line 119 87073] Train: [18/100][1037/1557] Data 0.014 (0.076) Batch 0.964 (1.051) Remain 37:26:30 loss: 0.5468 Lr: 0.00478 [2024-02-18 02:11:13,445 INFO misc.py line 119 87073] Train: [18/100][1038/1557] Data 0.007 (0.076) Batch 0.967 (1.051) Remain 37:26:19 loss: 0.8168 Lr: 0.00478 [2024-02-18 02:11:14,304 INFO misc.py line 119 87073] Train: [18/100][1039/1557] Data 0.004 (0.075) Batch 0.859 (1.051) Remain 37:25:54 loss: 0.5605 Lr: 0.00478 [2024-02-18 02:11:15,445 INFO misc.py line 119 87073] Train: [18/100][1040/1557] Data 0.004 (0.075) Batch 1.138 (1.051) Remain 37:26:04 loss: 0.4172 Lr: 0.00478 [2024-02-18 02:11:16,186 INFO misc.py line 119 87073] Train: [18/100][1041/1557] Data 0.007 (0.075) Batch 0.742 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02:11:28,797 INFO misc.py line 119 87073] Train: [18/100][1054/1557] Data 0.010 (0.074) Batch 1.025 (1.050) Remain 37:23:03 loss: 0.3468 Lr: 0.00478 [2024-02-18 02:11:31,580 INFO misc.py line 119 87073] Train: [18/100][1055/1557] Data 0.823 (0.075) Batch 2.782 (1.052) Remain 37:26:32 loss: 0.4873 Lr: 0.00478 [2024-02-18 02:11:32,340 INFO misc.py line 119 87073] Train: [18/100][1056/1557] Data 0.007 (0.075) Batch 0.758 (1.051) Remain 37:25:56 loss: 0.6377 Lr: 0.00478 [2024-02-18 02:11:33,494 INFO misc.py line 119 87073] Train: [18/100][1057/1557] Data 0.007 (0.075) Batch 1.156 (1.051) Remain 37:26:07 loss: 0.4097 Lr: 0.00478 [2024-02-18 02:11:34,399 INFO misc.py line 119 87073] Train: [18/100][1058/1557] Data 0.007 (0.075) Batch 0.905 (1.051) Remain 37:25:49 loss: 0.5814 Lr: 0.00478 [2024-02-18 02:11:35,241 INFO misc.py line 119 87073] Train: [18/100][1059/1557] Data 0.006 (0.075) Batch 0.842 (1.051) Remain 37:25:22 loss: 0.6867 Lr: 0.00478 [2024-02-18 02:11:36,129 INFO misc.py line 119 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Data 0.008 (0.074) Batch 0.998 (1.050) Remain 37:23:21 loss: 0.7696 Lr: 0.00478 [2024-02-18 02:11:42,590 INFO misc.py line 119 87073] Train: [18/100][1067/1557] Data 0.040 (0.074) Batch 0.934 (1.050) Remain 37:23:06 loss: 0.9082 Lr: 0.00478 [2024-02-18 02:11:43,563 INFO misc.py line 119 87073] Train: [18/100][1068/1557] Data 0.006 (0.074) Batch 0.974 (1.050) Remain 37:22:56 loss: 0.8674 Lr: 0.00478 [2024-02-18 02:11:44,346 INFO misc.py line 119 87073] Train: [18/100][1069/1557] Data 0.005 (0.074) Batch 0.782 (1.050) Remain 37:22:22 loss: 0.2233 Lr: 0.00478 [2024-02-18 02:11:45,121 INFO misc.py line 119 87073] Train: [18/100][1070/1557] Data 0.007 (0.074) Batch 0.773 (1.050) Remain 37:21:48 loss: 0.4056 Lr: 0.00478 [2024-02-18 02:11:52,140 INFO misc.py line 119 87073] Train: [18/100][1071/1557] Data 3.968 (0.078) Batch 7.022 (1.055) Remain 37:33:44 loss: 0.2817 Lr: 0.00478 [2024-02-18 02:11:53,244 INFO misc.py line 119 87073] Train: [18/100][1072/1557] Data 0.006 (0.078) Batch 1.105 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87073] Train: [18/100][1091/1557] Data 0.009 (0.077) Batch 0.768 (1.053) Remain 37:28:28 loss: 0.4629 Lr: 0.00478 [2024-02-18 02:12:11,942 INFO misc.py line 119 87073] Train: [18/100][1092/1557] Data 0.016 (0.077) Batch 1.193 (1.053) Remain 37:28:43 loss: 0.2792 Lr: 0.00478 [2024-02-18 02:12:12,805 INFO misc.py line 119 87073] Train: [18/100][1093/1557] Data 0.015 (0.076) Batch 0.872 (1.053) Remain 37:28:21 loss: 0.9541 Lr: 0.00478 [2024-02-18 02:12:13,774 INFO misc.py line 119 87073] Train: [18/100][1094/1557] Data 0.005 (0.076) Batch 0.970 (1.053) Remain 37:28:10 loss: 0.7717 Lr: 0.00478 [2024-02-18 02:12:14,747 INFO misc.py line 119 87073] Train: [18/100][1095/1557] Data 0.004 (0.076) Batch 0.973 (1.053) Remain 37:28:00 loss: 0.3616 Lr: 0.00478 [2024-02-18 02:12:15,621 INFO misc.py line 119 87073] Train: [18/100][1096/1557] Data 0.004 (0.076) Batch 0.874 (1.052) Remain 37:27:38 loss: 0.6267 Lr: 0.00478 [2024-02-18 02:12:16,361 INFO misc.py line 119 87073] Train: [18/100][1097/1557] Data 0.003 (0.076) Batch 0.734 (1.052) Remain 37:27:00 loss: 0.4793 Lr: 0.00478 [2024-02-18 02:12:17,130 INFO misc.py line 119 87073] Train: [18/100][1098/1557] Data 0.009 (0.076) Batch 0.774 (1.052) Remain 37:26:26 loss: 0.2304 Lr: 0.00478 [2024-02-18 02:12:18,431 INFO misc.py line 119 87073] Train: [18/100][1099/1557] Data 0.004 (0.076) Batch 1.299 (1.052) Remain 37:26:54 loss: 0.2433 Lr: 0.00478 [2024-02-18 02:12:19,435 INFO misc.py line 119 87073] Train: [18/100][1100/1557] Data 0.006 (0.076) Batch 1.005 (1.052) Remain 37:26:47 loss: 0.7113 Lr: 0.00478 [2024-02-18 02:12:20,419 INFO misc.py line 119 87073] Train: [18/100][1101/1557] Data 0.005 (0.076) Batch 0.984 (1.052) Remain 37:26:38 loss: 0.5583 Lr: 0.00478 [2024-02-18 02:12:21,437 INFO misc.py line 119 87073] Train: [18/100][1102/1557] Data 0.004 (0.076) Batch 1.016 (1.052) Remain 37:26:33 loss: 0.6253 Lr: 0.00478 [2024-02-18 02:12:22,621 INFO misc.py line 119 87073] Train: [18/100][1103/1557] Data 0.007 (0.076) Batch 1.185 (1.052) Remain 37:26:48 loss: 0.4715 Lr: 0.00478 [2024-02-18 02:12:23,407 INFO misc.py line 119 87073] Train: [18/100][1104/1557] Data 0.005 (0.076) Batch 0.787 (1.052) Remain 37:26:16 loss: 0.9308 Lr: 0.00478 [2024-02-18 02:12:24,181 INFO misc.py line 119 87073] Train: [18/100][1105/1557] Data 0.004 (0.076) Batch 0.773 (1.052) Remain 37:25:42 loss: 0.4074 Lr: 0.00478 [2024-02-18 02:12:25,522 INFO misc.py line 119 87073] Train: [18/100][1106/1557] Data 0.006 (0.076) Batch 1.338 (1.052) Remain 37:26:14 loss: 0.3368 Lr: 0.00478 [2024-02-18 02:12:26,460 INFO misc.py line 119 87073] Train: [18/100][1107/1557] Data 0.010 (0.076) Batch 0.943 (1.052) Remain 37:26:01 loss: 0.4977 Lr: 0.00478 [2024-02-18 02:12:27,375 INFO misc.py line 119 87073] Train: [18/100][1108/1557] Data 0.005 (0.075) Batch 0.915 (1.052) Remain 37:25:44 loss: 0.4781 Lr: 0.00478 [2024-02-18 02:12:28,383 INFO misc.py line 119 87073] Train: [18/100][1109/1557] Data 0.005 (0.075) Batch 1.008 (1.052) Remain 37:25:38 loss: 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02:12:35,011 INFO misc.py line 119 87073] Train: [18/100][1116/1557] Data 0.004 (0.075) Batch 1.045 (1.051) Remain 37:24:05 loss: 0.3096 Lr: 0.00478 [2024-02-18 02:12:35,888 INFO misc.py line 119 87073] Train: [18/100][1117/1557] Data 0.008 (0.075) Batch 0.879 (1.051) Remain 37:23:45 loss: 0.4116 Lr: 0.00478 [2024-02-18 02:12:36,651 INFO misc.py line 119 87073] Train: [18/100][1118/1557] Data 0.006 (0.075) Batch 0.764 (1.051) Remain 37:23:11 loss: 0.4132 Lr: 0.00478 [2024-02-18 02:12:37,347 INFO misc.py line 119 87073] Train: [18/100][1119/1557] Data 0.005 (0.075) Batch 0.695 (1.050) Remain 37:22:29 loss: 0.4071 Lr: 0.00478 [2024-02-18 02:12:38,567 INFO misc.py line 119 87073] Train: [18/100][1120/1557] Data 0.005 (0.075) Batch 1.215 (1.050) Remain 37:22:47 loss: 0.2323 Lr: 0.00478 [2024-02-18 02:12:39,519 INFO misc.py line 119 87073] Train: [18/100][1121/1557] Data 0.012 (0.075) Batch 0.958 (1.050) Remain 37:22:35 loss: 0.6270 Lr: 0.00478 [2024-02-18 02:12:40,624 INFO misc.py line 119 87073] Train: [18/100][1122/1557] Data 0.006 (0.075) Batch 1.105 (1.050) Remain 37:22:40 loss: 0.6665 Lr: 0.00478 [2024-02-18 02:12:41,693 INFO misc.py line 119 87073] Train: [18/100][1123/1557] Data 0.006 (0.075) Batch 1.069 (1.050) Remain 37:22:41 loss: 1.1997 Lr: 0.00478 [2024-02-18 02:12:42,649 INFO misc.py line 119 87073] Train: [18/100][1124/1557] Data 0.006 (0.075) Batch 0.958 (1.050) Remain 37:22:30 loss: 0.7444 Lr: 0.00478 [2024-02-18 02:12:43,400 INFO misc.py line 119 87073] Train: [18/100][1125/1557] Data 0.004 (0.074) Batch 0.750 (1.050) Remain 37:21:54 loss: 0.6950 Lr: 0.00478 [2024-02-18 02:12:44,137 INFO misc.py line 119 87073] Train: [18/100][1126/1557] Data 0.005 (0.074) Batch 0.729 (1.050) Remain 37:21:17 loss: 0.5648 Lr: 0.00478 [2024-02-18 02:12:51,126 INFO misc.py line 119 87073] Train: [18/100][1127/1557] Data 3.982 (0.078) Batch 6.996 (1.055) Remain 37:32:33 loss: 0.2751 Lr: 0.00478 [2024-02-18 02:12:52,194 INFO misc.py line 119 87073] Train: [18/100][1128/1557] Data 0.006 (0.078) Batch 1.070 (1.055) Remain 37:32:34 loss: 0.7695 Lr: 0.00478 [2024-02-18 02:12:53,047 INFO misc.py line 119 87073] Train: [18/100][1129/1557] Data 0.004 (0.078) Batch 0.852 (1.055) Remain 37:32:10 loss: 0.7732 Lr: 0.00478 [2024-02-18 02:12:53,921 INFO misc.py line 119 87073] Train: [18/100][1130/1557] Data 0.005 (0.078) Batch 0.873 (1.055) Remain 37:31:48 loss: 0.4942 Lr: 0.00478 [2024-02-18 02:12:54,993 INFO misc.py line 119 87073] Train: [18/100][1131/1557] Data 0.007 (0.078) Batch 1.061 (1.055) Remain 37:31:48 loss: 0.4262 Lr: 0.00478 [2024-02-18 02:12:55,741 INFO misc.py line 119 87073] Train: [18/100][1132/1557] Data 0.017 (0.078) Batch 0.761 (1.054) Remain 37:31:13 loss: 0.3294 Lr: 0.00478 [2024-02-18 02:12:56,432 INFO misc.py line 119 87073] Train: [18/100][1133/1557] Data 0.004 (0.077) Batch 0.684 (1.054) Remain 37:30:30 loss: 0.4024 Lr: 0.00478 [2024-02-18 02:12:57,547 INFO misc.py line 119 87073] Train: [18/100][1134/1557] Data 0.011 (0.077) Batch 1.119 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02:13:09,720 INFO misc.py line 119 87073] Train: [18/100][1147/1557] Data 0.006 (0.077) Batch 0.764 (1.053) Remain 37:27:31 loss: 0.3356 Lr: 0.00478 [2024-02-18 02:13:10,851 INFO misc.py line 119 87073] Train: [18/100][1148/1557] Data 0.013 (0.077) Batch 1.130 (1.053) Remain 37:27:38 loss: 0.1902 Lr: 0.00478 [2024-02-18 02:13:11,952 INFO misc.py line 119 87073] Train: [18/100][1149/1557] Data 0.014 (0.077) Batch 1.101 (1.053) Remain 37:27:42 loss: 0.5040 Lr: 0.00478 [2024-02-18 02:13:12,816 INFO misc.py line 119 87073] Train: [18/100][1150/1557] Data 0.015 (0.076) Batch 0.874 (1.053) Remain 37:27:21 loss: 0.6397 Lr: 0.00478 [2024-02-18 02:13:13,904 INFO misc.py line 119 87073] Train: [18/100][1151/1557] Data 0.005 (0.076) Batch 1.088 (1.053) Remain 37:27:24 loss: 0.7495 Lr: 0.00478 [2024-02-18 02:13:14,873 INFO misc.py line 119 87073] Train: [18/100][1152/1557] Data 0.005 (0.076) Batch 0.968 (1.053) Remain 37:27:14 loss: 0.2402 Lr: 0.00478 [2024-02-18 02:13:15,636 INFO misc.py line 119 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Data 0.006 (0.076) Batch 1.009 (1.052) Remain 37:26:13 loss: 0.2299 Lr: 0.00478 [2024-02-18 02:13:22,614 INFO misc.py line 119 87073] Train: [18/100][1160/1557] Data 0.003 (0.076) Batch 0.856 (1.052) Remain 37:25:50 loss: 0.6493 Lr: 0.00478 [2024-02-18 02:13:23,444 INFO misc.py line 119 87073] Train: [18/100][1161/1557] Data 0.004 (0.076) Batch 0.779 (1.052) Remain 37:25:19 loss: 0.5492 Lr: 0.00478 [2024-02-18 02:13:24,713 INFO misc.py line 119 87073] Train: [18/100][1162/1557] Data 0.054 (0.076) Batch 1.315 (1.052) Remain 37:25:47 loss: 0.0802 Lr: 0.00478 [2024-02-18 02:13:25,677 INFO misc.py line 119 87073] Train: [18/100][1163/1557] Data 0.009 (0.076) Batch 0.966 (1.052) Remain 37:25:36 loss: 0.4825 Lr: 0.00478 [2024-02-18 02:13:26,623 INFO misc.py line 119 87073] Train: [18/100][1164/1557] Data 0.006 (0.076) Batch 0.947 (1.052) Remain 37:25:24 loss: 0.7134 Lr: 0.00478 [2024-02-18 02:13:27,717 INFO misc.py line 119 87073] Train: [18/100][1165/1557] Data 0.005 (0.076) Batch 1.094 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87073] Train: [18/100][1184/1557] Data 0.017 (0.077) Batch 0.989 (1.055) Remain 37:30:28 loss: 0.2146 Lr: 0.00478 [2024-02-18 02:13:51,527 INFO misc.py line 119 87073] Train: [18/100][1185/1557] Data 0.006 (0.077) Batch 0.861 (1.054) Remain 37:30:06 loss: 0.2504 Lr: 0.00478 [2024-02-18 02:13:52,474 INFO misc.py line 119 87073] Train: [18/100][1186/1557] Data 0.006 (0.077) Batch 0.947 (1.054) Remain 37:29:54 loss: 0.6457 Lr: 0.00478 [2024-02-18 02:13:53,405 INFO misc.py line 119 87073] Train: [18/100][1187/1557] Data 0.004 (0.077) Batch 0.931 (1.054) Remain 37:29:39 loss: 0.3373 Lr: 0.00478 [2024-02-18 02:13:54,161 INFO misc.py line 119 87073] Train: [18/100][1188/1557] Data 0.005 (0.077) Batch 0.751 (1.054) Remain 37:29:05 loss: 0.3432 Lr: 0.00478 [2024-02-18 02:13:54,805 INFO misc.py line 119 87073] Train: [18/100][1189/1557] Data 0.011 (0.077) Batch 0.650 (1.054) Remain 37:28:21 loss: 0.2701 Lr: 0.00478 [2024-02-18 02:13:56,015 INFO misc.py line 119 87073] Train: [18/100][1190/1557] Data 0.004 (0.077) Batch 1.209 (1.054) Remain 37:28:36 loss: 0.3626 Lr: 0.00478 [2024-02-18 02:13:56,826 INFO misc.py line 119 87073] Train: [18/100][1191/1557] Data 0.005 (0.077) Batch 0.812 (1.053) Remain 37:28:09 loss: 0.3820 Lr: 0.00478 [2024-02-18 02:13:57,754 INFO misc.py line 119 87073] Train: [18/100][1192/1557] Data 0.005 (0.077) Batch 0.929 (1.053) Remain 37:27:55 loss: 0.6632 Lr: 0.00478 [2024-02-18 02:13:58,725 INFO misc.py line 119 87073] Train: [18/100][1193/1557] Data 0.004 (0.077) Batch 0.962 (1.053) Remain 37:27:44 loss: 0.4968 Lr: 0.00478 [2024-02-18 02:13:59,679 INFO misc.py line 119 87073] Train: [18/100][1194/1557] Data 0.013 (0.077) Batch 0.962 (1.053) Remain 37:27:33 loss: 0.8429 Lr: 0.00478 [2024-02-18 02:14:00,442 INFO misc.py line 119 87073] Train: [18/100][1195/1557] Data 0.004 (0.077) Batch 0.763 (1.053) Remain 37:27:01 loss: 0.3441 Lr: 0.00478 [2024-02-18 02:14:01,226 INFO misc.py line 119 87073] Train: [18/100][1196/1557] Data 0.005 (0.077) Batch 0.778 (1.053) Remain 37:26:30 loss: 0.4106 Lr: 0.00478 [2024-02-18 02:14:02,372 INFO misc.py line 119 87073] Train: [18/100][1197/1557] Data 0.011 (0.077) Batch 1.150 (1.053) Remain 37:26:40 loss: 0.2046 Lr: 0.00478 [2024-02-18 02:14:03,340 INFO misc.py line 119 87073] Train: [18/100][1198/1557] Data 0.007 (0.077) Batch 0.970 (1.053) Remain 37:26:30 loss: 0.5834 Lr: 0.00478 [2024-02-18 02:14:04,320 INFO misc.py line 119 87073] Train: [18/100][1199/1557] Data 0.005 (0.077) Batch 0.981 (1.053) Remain 37:26:21 loss: 0.8766 Lr: 0.00478 [2024-02-18 02:14:05,197 INFO misc.py line 119 87073] Train: [18/100][1200/1557] Data 0.004 (0.077) Batch 0.877 (1.053) Remain 37:26:01 loss: 0.6230 Lr: 0.00478 [2024-02-18 02:14:06,598 INFO misc.py line 119 87073] Train: [18/100][1201/1557] Data 0.004 (0.076) Batch 1.398 (1.053) Remain 37:26:37 loss: 0.6575 Lr: 0.00478 [2024-02-18 02:14:07,331 INFO misc.py line 119 87073] Train: [18/100][1202/1557] Data 0.007 (0.076) Batch 0.736 (1.053) Remain 37:26:02 loss: 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02:14:13,748 INFO misc.py line 119 87073] Train: [18/100][1209/1557] Data 0.005 (0.076) Batch 0.733 (1.052) Remain 37:24:13 loss: 0.4303 Lr: 0.00478 [2024-02-18 02:14:14,494 INFO misc.py line 119 87073] Train: [18/100][1210/1557] Data 0.009 (0.076) Batch 0.750 (1.052) Remain 37:23:40 loss: 0.3906 Lr: 0.00478 [2024-02-18 02:14:15,867 INFO misc.py line 119 87073] Train: [18/100][1211/1557] Data 0.006 (0.076) Batch 1.373 (1.052) Remain 37:24:13 loss: 0.4227 Lr: 0.00478 [2024-02-18 02:14:16,954 INFO misc.py line 119 87073] Train: [18/100][1212/1557] Data 0.006 (0.076) Batch 1.082 (1.052) Remain 37:24:15 loss: 1.0099 Lr: 0.00478 [2024-02-18 02:14:17,790 INFO misc.py line 119 87073] Train: [18/100][1213/1557] Data 0.011 (0.076) Batch 0.843 (1.052) Remain 37:23:52 loss: 0.9352 Lr: 0.00478 [2024-02-18 02:14:18,829 INFO misc.py line 119 87073] Train: [18/100][1214/1557] Data 0.003 (0.076) Batch 1.039 (1.052) Remain 37:23:50 loss: 0.4023 Lr: 0.00478 [2024-02-18 02:14:19,809 INFO misc.py line 119 87073] Train: [18/100][1215/1557] Data 0.004 (0.076) Batch 0.980 (1.052) Remain 37:23:41 loss: 0.9330 Lr: 0.00478 [2024-02-18 02:14:20,550 INFO misc.py line 119 87073] Train: [18/100][1216/1557] Data 0.003 (0.076) Batch 0.741 (1.051) Remain 37:23:07 loss: 0.5195 Lr: 0.00478 [2024-02-18 02:14:21,317 INFO misc.py line 119 87073] Train: [18/100][1217/1557] Data 0.004 (0.076) Batch 0.767 (1.051) Remain 37:22:36 loss: 0.2562 Lr: 0.00478 [2024-02-18 02:14:22,554 INFO misc.py line 119 87073] Train: [18/100][1218/1557] Data 0.004 (0.075) Batch 1.229 (1.051) Remain 37:22:54 loss: 0.2059 Lr: 0.00478 [2024-02-18 02:14:23,376 INFO misc.py line 119 87073] Train: [18/100][1219/1557] Data 0.011 (0.075) Batch 0.828 (1.051) Remain 37:22:29 loss: 0.7882 Lr: 0.00478 [2024-02-18 02:14:24,280 INFO misc.py line 119 87073] Train: [18/100][1220/1557] Data 0.005 (0.075) Batch 0.905 (1.051) Remain 37:22:13 loss: 0.5489 Lr: 0.00478 [2024-02-18 02:14:25,238 INFO misc.py line 119 87073] Train: [18/100][1221/1557] Data 0.005 (0.075) Batch 0.954 (1.051) Remain 37:22:02 loss: 0.7331 Lr: 0.00478 [2024-02-18 02:14:26,161 INFO misc.py line 119 87073] Train: [18/100][1222/1557] Data 0.010 (0.075) Batch 0.926 (1.051) Remain 37:21:48 loss: 0.7860 Lr: 0.00478 [2024-02-18 02:14:26,893 INFO misc.py line 119 87073] Train: [18/100][1223/1557] Data 0.005 (0.075) Batch 0.733 (1.051) Remain 37:21:13 loss: 0.3321 Lr: 0.00478 [2024-02-18 02:14:27,634 INFO misc.py line 119 87073] Train: [18/100][1224/1557] Data 0.005 (0.075) Batch 0.742 (1.050) Remain 37:20:40 loss: 0.3245 Lr: 0.00478 [2024-02-18 02:14:28,815 INFO misc.py line 119 87073] Train: [18/100][1225/1557] Data 0.005 (0.075) Batch 1.178 (1.050) Remain 37:20:52 loss: 0.4032 Lr: 0.00478 [2024-02-18 02:14:29,845 INFO misc.py line 119 87073] Train: [18/100][1226/1557] Data 0.008 (0.075) Batch 1.032 (1.050) Remain 37:20:49 loss: 0.5416 Lr: 0.00478 [2024-02-18 02:14:30,691 INFO misc.py line 119 87073] Train: [18/100][1227/1557] Data 0.006 (0.075) Batch 0.847 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02:14:50,248 INFO misc.py line 119 87073] Train: [18/100][1240/1557] Data 0.015 (0.078) Batch 0.988 (1.055) Remain 37:30:24 loss: 1.1232 Lr: 0.00478 [2024-02-18 02:14:51,373 INFO misc.py line 119 87073] Train: [18/100][1241/1557] Data 0.006 (0.078) Batch 1.125 (1.055) Remain 37:30:30 loss: 0.4648 Lr: 0.00478 [2024-02-18 02:14:52,540 INFO misc.py line 119 87073] Train: [18/100][1242/1557] Data 0.005 (0.078) Batch 1.168 (1.055) Remain 37:30:41 loss: 0.7547 Lr: 0.00478 [2024-02-18 02:14:53,402 INFO misc.py line 119 87073] Train: [18/100][1243/1557] Data 0.006 (0.078) Batch 0.857 (1.055) Remain 37:30:19 loss: 0.3800 Lr: 0.00478 [2024-02-18 02:14:54,208 INFO misc.py line 119 87073] Train: [18/100][1244/1557] Data 0.014 (0.078) Batch 0.812 (1.055) Remain 37:29:53 loss: 0.2050 Lr: 0.00478 [2024-02-18 02:14:54,975 INFO misc.py line 119 87073] Train: [18/100][1245/1557] Data 0.004 (0.078) Batch 0.763 (1.055) Remain 37:29:22 loss: 0.4229 Lr: 0.00478 [2024-02-18 02:14:56,103 INFO misc.py line 119 87073] Train: [18/100][1246/1557] Data 0.008 (0.077) Batch 1.123 (1.055) Remain 37:29:28 loss: 0.2340 Lr: 0.00478 [2024-02-18 02:14:57,042 INFO misc.py line 119 87073] Train: [18/100][1247/1557] Data 0.013 (0.077) Batch 0.948 (1.054) Remain 37:29:16 loss: 1.0041 Lr: 0.00478 [2024-02-18 02:14:58,117 INFO misc.py line 119 87073] Train: [18/100][1248/1557] Data 0.005 (0.077) Batch 1.075 (1.054) Remain 37:29:17 loss: 0.3672 Lr: 0.00478 [2024-02-18 02:14:59,041 INFO misc.py line 119 87073] Train: [18/100][1249/1557] Data 0.005 (0.077) Batch 0.923 (1.054) Remain 37:29:02 loss: 0.3697 Lr: 0.00478 [2024-02-18 02:14:59,891 INFO misc.py line 119 87073] Train: [18/100][1250/1557] Data 0.006 (0.077) Batch 0.851 (1.054) Remain 37:28:41 loss: 0.4876 Lr: 0.00478 [2024-02-18 02:15:00,623 INFO misc.py line 119 87073] Train: [18/100][1251/1557] Data 0.004 (0.077) Batch 0.730 (1.054) Remain 37:28:06 loss: 0.4634 Lr: 0.00478 [2024-02-18 02:15:01,296 INFO misc.py line 119 87073] Train: [18/100][1252/1557] Data 0.007 (0.077) Batch 0.662 (1.054) Remain 37:27:25 loss: 0.4315 Lr: 0.00478 [2024-02-18 02:15:02,465 INFO misc.py line 119 87073] Train: [18/100][1253/1557] Data 0.017 (0.077) Batch 1.177 (1.054) Remain 37:27:37 loss: 0.2931 Lr: 0.00478 [2024-02-18 02:15:03,416 INFO misc.py line 119 87073] Train: [18/100][1254/1557] Data 0.009 (0.077) Batch 0.956 (1.054) Remain 37:27:26 loss: 0.2402 Lr: 0.00478 [2024-02-18 02:15:04,358 INFO misc.py line 119 87073] Train: [18/100][1255/1557] Data 0.004 (0.077) Batch 0.942 (1.054) Remain 37:27:13 loss: 0.3903 Lr: 0.00478 [2024-02-18 02:15:05,392 INFO misc.py line 119 87073] Train: [18/100][1256/1557] Data 0.004 (0.077) Batch 1.035 (1.054) Remain 37:27:10 loss: 0.6556 Lr: 0.00478 [2024-02-18 02:15:06,355 INFO misc.py line 119 87073] Train: [18/100][1257/1557] Data 0.003 (0.077) Batch 0.962 (1.053) Remain 37:27:00 loss: 0.7369 Lr: 0.00478 [2024-02-18 02:15:07,151 INFO misc.py line 119 87073] Train: [18/100][1258/1557] Data 0.005 (0.077) Batch 0.792 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02:15:19,810 INFO misc.py line 119 87073] Train: [18/100][1271/1557] Data 0.005 (0.076) Batch 0.850 (1.052) Remain 37:24:34 loss: 0.3723 Lr: 0.00478 [2024-02-18 02:15:20,573 INFO misc.py line 119 87073] Train: [18/100][1272/1557] Data 0.004 (0.076) Batch 0.763 (1.052) Remain 37:24:04 loss: 0.4332 Lr: 0.00478 [2024-02-18 02:15:21,325 INFO misc.py line 119 87073] Train: [18/100][1273/1557] Data 0.007 (0.076) Batch 0.752 (1.052) Remain 37:23:33 loss: 0.2634 Lr: 0.00478 [2024-02-18 02:15:22,676 INFO misc.py line 119 87073] Train: [18/100][1274/1557] Data 0.005 (0.076) Batch 1.348 (1.052) Remain 37:24:01 loss: 0.3918 Lr: 0.00478 [2024-02-18 02:15:23,623 INFO misc.py line 119 87073] Train: [18/100][1275/1557] Data 0.008 (0.076) Batch 0.950 (1.052) Remain 37:23:50 loss: 0.8337 Lr: 0.00478 [2024-02-18 02:15:24,632 INFO misc.py line 119 87073] Train: [18/100][1276/1557] Data 0.006 (0.076) Batch 1.011 (1.052) Remain 37:23:45 loss: 0.4556 Lr: 0.00478 [2024-02-18 02:15:25,551 INFO misc.py line 119 87073] Train: [18/100][1277/1557] Data 0.004 (0.076) Batch 0.918 (1.052) Remain 37:23:30 loss: 0.3061 Lr: 0.00478 [2024-02-18 02:15:26,535 INFO misc.py line 119 87073] Train: [18/100][1278/1557] Data 0.004 (0.076) Batch 0.967 (1.052) Remain 37:23:21 loss: 0.3111 Lr: 0.00478 [2024-02-18 02:15:27,311 INFO misc.py line 119 87073] Train: [18/100][1279/1557] Data 0.021 (0.076) Batch 0.792 (1.052) Remain 37:22:54 loss: 0.5416 Lr: 0.00478 [2024-02-18 02:15:28,069 INFO misc.py line 119 87073] Train: [18/100][1280/1557] Data 0.004 (0.076) Batch 0.758 (1.052) Remain 37:22:23 loss: 0.4174 Lr: 0.00478 [2024-02-18 02:15:29,253 INFO misc.py line 119 87073] Train: [18/100][1281/1557] Data 0.004 (0.076) Batch 1.177 (1.052) Remain 37:22:35 loss: 0.4591 Lr: 0.00478 [2024-02-18 02:15:30,215 INFO misc.py line 119 87073] Train: [18/100][1282/1557] Data 0.012 (0.076) Batch 0.967 (1.052) Remain 37:22:25 loss: 0.4205 Lr: 0.00478 [2024-02-18 02:15:31,130 INFO misc.py line 119 87073] Train: [18/100][1283/1557] Data 0.006 (0.075) Batch 0.916 (1.051) Remain 37:22:11 loss: 0.8644 Lr: 0.00478 [2024-02-18 02:15:31,962 INFO misc.py line 119 87073] Train: [18/100][1284/1557] Data 0.004 (0.075) Batch 0.822 (1.051) Remain 37:21:47 loss: 0.4503 Lr: 0.00478 [2024-02-18 02:15:32,919 INFO misc.py line 119 87073] Train: [18/100][1285/1557] Data 0.014 (0.075) Batch 0.967 (1.051) Remain 37:21:37 loss: 0.5040 Lr: 0.00478 [2024-02-18 02:15:33,649 INFO misc.py line 119 87073] Train: [18/100][1286/1557] Data 0.006 (0.075) Batch 0.730 (1.051) Remain 37:21:04 loss: 0.6717 Lr: 0.00478 [2024-02-18 02:15:34,402 INFO misc.py line 119 87073] Train: [18/100][1287/1557] Data 0.005 (0.075) Batch 0.746 (1.051) Remain 37:20:33 loss: 0.7080 Lr: 0.00478 [2024-02-18 02:15:35,583 INFO misc.py line 119 87073] Train: [18/100][1288/1557] Data 0.011 (0.075) Batch 1.185 (1.051) Remain 37:20:45 loss: 0.3242 Lr: 0.00478 [2024-02-18 02:15:36,440 INFO misc.py line 119 87073] Train: [18/100][1289/1557] Data 0.007 (0.075) Batch 0.861 (1.051) Remain 37:20:25 loss: 0.8502 Lr: 0.00478 [2024-02-18 02:15:37,254 INFO misc.py line 119 87073] Train: [18/100][1290/1557] Data 0.005 (0.075) Batch 0.812 (1.050) Remain 37:20:00 loss: 0.3118 Lr: 0.00478 [2024-02-18 02:15:38,149 INFO misc.py line 119 87073] Train: [18/100][1291/1557] Data 0.006 (0.075) Batch 0.890 (1.050) Remain 37:19:43 loss: 0.5471 Lr: 0.00478 [2024-02-18 02:15:39,058 INFO misc.py line 119 87073] Train: [18/100][1292/1557] Data 0.011 (0.075) Batch 0.915 (1.050) Remain 37:19:29 loss: 0.7688 Lr: 0.00478 [2024-02-18 02:15:39,743 INFO misc.py line 119 87073] Train: [18/100][1293/1557] Data 0.004 (0.075) Batch 0.685 (1.050) Remain 37:18:52 loss: 0.6036 Lr: 0.00478 [2024-02-18 02:15:40,472 INFO misc.py line 119 87073] Train: [18/100][1294/1557] Data 0.005 (0.075) Batch 0.726 (1.050) Remain 37:18:19 loss: 0.4831 Lr: 0.00478 [2024-02-18 02:15:46,861 INFO misc.py line 119 87073] Train: [18/100][1295/1557] Data 3.962 (0.078) Batch 6.392 (1.054) Remain 37:27:07 loss: 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02:15:53,306 INFO misc.py line 119 87073] Train: [18/100][1302/1557] Data 0.008 (0.077) Batch 1.160 (1.053) Remain 37:25:27 loss: 0.4091 Lr: 0.00478 [2024-02-18 02:15:54,230 INFO misc.py line 119 87073] Train: [18/100][1303/1557] Data 0.007 (0.077) Batch 0.926 (1.053) Remain 37:25:14 loss: 1.0281 Lr: 0.00478 [2024-02-18 02:15:55,215 INFO misc.py line 119 87073] Train: [18/100][1304/1557] Data 0.005 (0.077) Batch 0.987 (1.053) Remain 37:25:06 loss: 0.5607 Lr: 0.00478 [2024-02-18 02:15:56,340 INFO misc.py line 119 87073] Train: [18/100][1305/1557] Data 0.003 (0.077) Batch 1.124 (1.053) Remain 37:25:12 loss: 0.4423 Lr: 0.00478 [2024-02-18 02:15:57,279 INFO misc.py line 119 87073] Train: [18/100][1306/1557] Data 0.003 (0.077) Batch 0.937 (1.053) Remain 37:24:59 loss: 0.3816 Lr: 0.00478 [2024-02-18 02:15:58,046 INFO misc.py line 119 87073] Train: [18/100][1307/1557] Data 0.006 (0.077) Batch 0.767 (1.053) Remain 37:24:30 loss: 0.4152 Lr: 0.00478 [2024-02-18 02:15:58,727 INFO misc.py line 119 87073] Train: [18/100][1308/1557] Data 0.007 (0.077) Batch 0.681 (1.052) Remain 37:23:53 loss: 0.4412 Lr: 0.00478 [2024-02-18 02:15:59,871 INFO misc.py line 119 87073] Train: [18/100][1309/1557] Data 0.006 (0.077) Batch 1.141 (1.053) Remain 37:24:00 loss: 0.1911 Lr: 0.00478 [2024-02-18 02:16:00,910 INFO misc.py line 119 87073] Train: [18/100][1310/1557] Data 0.010 (0.077) Batch 1.035 (1.053) Remain 37:23:58 loss: 0.6062 Lr: 0.00478 [2024-02-18 02:16:01,673 INFO misc.py line 119 87073] Train: [18/100][1311/1557] Data 0.013 (0.077) Batch 0.771 (1.052) Remain 37:23:29 loss: 0.4807 Lr: 0.00478 [2024-02-18 02:16:02,573 INFO misc.py line 119 87073] Train: [18/100][1312/1557] Data 0.005 (0.077) Batch 0.901 (1.052) Remain 37:23:13 loss: 0.4709 Lr: 0.00478 [2024-02-18 02:16:03,381 INFO misc.py line 119 87073] Train: [18/100][1313/1557] Data 0.005 (0.077) Batch 0.806 (1.052) Remain 37:22:48 loss: 0.3100 Lr: 0.00478 [2024-02-18 02:16:04,152 INFO misc.py line 119 87073] Train: [18/100][1314/1557] Data 0.008 (0.077) Batch 0.771 (1.052) Remain 37:22:20 loss: 0.4595 Lr: 0.00478 [2024-02-18 02:16:04,892 INFO misc.py line 119 87073] Train: [18/100][1315/1557] Data 0.008 (0.077) Batch 0.740 (1.052) Remain 37:21:48 loss: 0.1836 Lr: 0.00478 [2024-02-18 02:16:06,014 INFO misc.py line 119 87073] Train: [18/100][1316/1557] Data 0.006 (0.077) Batch 1.122 (1.052) Remain 37:21:54 loss: 0.3151 Lr: 0.00478 [2024-02-18 02:16:07,050 INFO misc.py line 119 87073] Train: [18/100][1317/1557] Data 0.007 (0.077) Batch 1.037 (1.052) Remain 37:21:51 loss: 0.8760 Lr: 0.00478 [2024-02-18 02:16:08,155 INFO misc.py line 119 87073] Train: [18/100][1318/1557] Data 0.006 (0.077) Batch 1.105 (1.052) Remain 37:21:56 loss: 1.0227 Lr: 0.00478 [2024-02-18 02:16:09,030 INFO misc.py line 119 87073] Train: [18/100][1319/1557] Data 0.006 (0.077) Batch 0.876 (1.051) Remain 37:21:38 loss: 0.3256 Lr: 0.00478 [2024-02-18 02:16:10,064 INFO misc.py line 119 87073] Train: [18/100][1320/1557] Data 0.006 (0.077) Batch 1.034 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02:16:22,529 INFO misc.py line 119 87073] Train: [18/100][1333/1557] Data 0.004 (0.076) Batch 0.996 (1.051) Remain 37:19:25 loss: 0.5596 Lr: 0.00478 [2024-02-18 02:16:23,526 INFO misc.py line 119 87073] Train: [18/100][1334/1557] Data 0.005 (0.076) Batch 0.996 (1.051) Remain 37:19:19 loss: 0.2399 Lr: 0.00478 [2024-02-18 02:16:24,379 INFO misc.py line 119 87073] Train: [18/100][1335/1557] Data 0.006 (0.076) Batch 0.848 (1.050) Remain 37:18:59 loss: 0.3447 Lr: 0.00478 [2024-02-18 02:16:25,172 INFO misc.py line 119 87073] Train: [18/100][1336/1557] Data 0.011 (0.076) Batch 0.798 (1.050) Remain 37:18:33 loss: 0.4060 Lr: 0.00478 [2024-02-18 02:16:26,499 INFO misc.py line 119 87073] Train: [18/100][1337/1557] Data 0.006 (0.076) Batch 1.327 (1.050) Remain 37:18:59 loss: 0.3778 Lr: 0.00478 [2024-02-18 02:16:27,483 INFO misc.py line 119 87073] Train: [18/100][1338/1557] Data 0.005 (0.076) Batch 0.984 (1.050) Remain 37:18:51 loss: 0.5186 Lr: 0.00478 [2024-02-18 02:16:28,409 INFO misc.py line 119 87073] Train: [18/100][1339/1557] Data 0.005 (0.076) Batch 0.926 (1.050) Remain 37:18:38 loss: 0.3003 Lr: 0.00478 [2024-02-18 02:16:29,243 INFO misc.py line 119 87073] Train: [18/100][1340/1557] Data 0.005 (0.075) Batch 0.833 (1.050) Remain 37:18:17 loss: 0.7248 Lr: 0.00478 [2024-02-18 02:16:30,317 INFO misc.py line 119 87073] Train: [18/100][1341/1557] Data 0.007 (0.075) Batch 1.065 (1.050) Remain 37:18:17 loss: 0.6962 Lr: 0.00478 [2024-02-18 02:16:31,125 INFO misc.py line 119 87073] Train: [18/100][1342/1557] Data 0.015 (0.075) Batch 0.818 (1.050) Remain 37:17:54 loss: 0.5518 Lr: 0.00478 [2024-02-18 02:16:31,851 INFO misc.py line 119 87073] Train: [18/100][1343/1557] Data 0.005 (0.075) Batch 0.727 (1.050) Remain 37:17:22 loss: 0.6737 Lr: 0.00478 [2024-02-18 02:16:33,185 INFO misc.py line 119 87073] Train: [18/100][1344/1557] Data 0.004 (0.075) Batch 1.327 (1.050) Remain 37:17:47 loss: 0.3914 Lr: 0.00478 [2024-02-18 02:16:34,074 INFO misc.py line 119 87073] Train: [18/100][1345/1557] Data 0.011 (0.075) Batch 0.896 (1.050) Remain 37:17:32 loss: 0.2104 Lr: 0.00478 [2024-02-18 02:16:35,047 INFO misc.py line 119 87073] Train: [18/100][1346/1557] Data 0.004 (0.075) Batch 0.972 (1.050) Remain 37:17:23 loss: 0.3299 Lr: 0.00478 [2024-02-18 02:16:36,055 INFO misc.py line 119 87073] Train: [18/100][1347/1557] Data 0.005 (0.075) Batch 1.009 (1.050) Remain 37:17:18 loss: 0.3711 Lr: 0.00478 [2024-02-18 02:16:36,911 INFO misc.py line 119 87073] Train: [18/100][1348/1557] Data 0.005 (0.075) Batch 0.856 (1.050) Remain 37:16:59 loss: 0.7930 Lr: 0.00478 [2024-02-18 02:16:37,692 INFO misc.py line 119 87073] Train: [18/100][1349/1557] Data 0.004 (0.075) Batch 0.770 (1.049) Remain 37:16:31 loss: 0.4193 Lr: 0.00478 [2024-02-18 02:16:38,417 INFO misc.py line 119 87073] Train: [18/100][1350/1557] Data 0.016 (0.075) Batch 0.734 (1.049) Remain 37:16:00 loss: 0.6159 Lr: 0.00478 [2024-02-18 02:16:44,992 INFO misc.py line 119 87073] Train: [18/100][1351/1557] Data 3.622 (0.078) Batch 6.576 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02:16:57,120 INFO misc.py line 119 87073] Train: [18/100][1364/1557] Data 0.005 (0.077) Batch 0.731 (1.052) Remain 37:22:03 loss: 0.3803 Lr: 0.00478 [2024-02-18 02:16:58,322 INFO misc.py line 119 87073] Train: [18/100][1365/1557] Data 0.009 (0.077) Batch 1.170 (1.052) Remain 37:22:13 loss: 0.3176 Lr: 0.00478 [2024-02-18 02:16:59,190 INFO misc.py line 119 87073] Train: [18/100][1366/1557] Data 0.039 (0.077) Batch 0.902 (1.052) Remain 37:21:58 loss: 0.3605 Lr: 0.00478 [2024-02-18 02:17:00,148 INFO misc.py line 119 87073] Train: [18/100][1367/1557] Data 0.004 (0.077) Batch 0.959 (1.052) Remain 37:21:48 loss: 0.4229 Lr: 0.00478 [2024-02-18 02:17:01,186 INFO misc.py line 119 87073] Train: [18/100][1368/1557] Data 0.004 (0.077) Batch 1.039 (1.052) Remain 37:21:45 loss: 1.0032 Lr: 0.00478 [2024-02-18 02:17:02,173 INFO misc.py line 119 87073] Train: [18/100][1369/1557] Data 0.004 (0.077) Batch 0.986 (1.052) Remain 37:21:38 loss: 0.5234 Lr: 0.00478 [2024-02-18 02:17:02,906 INFO misc.py line 119 87073] Train: [18/100][1370/1557] Data 0.004 (0.077) Batch 0.731 (1.052) Remain 37:21:07 loss: 0.7507 Lr: 0.00478 [2024-02-18 02:17:03,655 INFO misc.py line 119 87073] Train: [18/100][1371/1557] Data 0.006 (0.077) Batch 0.749 (1.051) Remain 37:20:38 loss: 0.6201 Lr: 0.00478 [2024-02-18 02:17:04,919 INFO misc.py line 119 87073] Train: [18/100][1372/1557] Data 0.005 (0.077) Batch 1.265 (1.052) Remain 37:20:57 loss: 0.2309 Lr: 0.00478 [2024-02-18 02:17:06,019 INFO misc.py line 119 87073] Train: [18/100][1373/1557] Data 0.005 (0.076) Batch 1.097 (1.052) Remain 37:21:00 loss: 0.4764 Lr: 0.00478 [2024-02-18 02:17:07,096 INFO misc.py line 119 87073] Train: [18/100][1374/1557] Data 0.009 (0.076) Batch 1.080 (1.052) Remain 37:21:01 loss: 0.8491 Lr: 0.00478 [2024-02-18 02:17:07,987 INFO misc.py line 119 87073] Train: [18/100][1375/1557] Data 0.006 (0.076) Batch 0.892 (1.052) Remain 37:20:46 loss: 0.7725 Lr: 0.00478 [2024-02-18 02:17:08,868 INFO misc.py line 119 87073] Train: [18/100][1376/1557] Data 0.004 (0.076) Batch 0.879 (1.051) Remain 37:20:28 loss: 0.7606 Lr: 0.00478 [2024-02-18 02:17:09,572 INFO misc.py line 119 87073] Train: [18/100][1377/1557] Data 0.006 (0.076) Batch 0.704 (1.051) Remain 37:19:55 loss: 0.5649 Lr: 0.00478 [2024-02-18 02:17:10,333 INFO misc.py line 119 87073] Train: [18/100][1378/1557] Data 0.006 (0.076) Batch 0.763 (1.051) Remain 37:19:27 loss: 0.6623 Lr: 0.00478 [2024-02-18 02:17:11,668 INFO misc.py line 119 87073] Train: [18/100][1379/1557] Data 0.005 (0.076) Batch 1.313 (1.051) Remain 37:19:50 loss: 0.5280 Lr: 0.00478 [2024-02-18 02:17:12,459 INFO misc.py line 119 87073] Train: [18/100][1380/1557] Data 0.027 (0.076) Batch 0.812 (1.051) Remain 37:19:27 loss: 1.2023 Lr: 0.00478 [2024-02-18 02:17:13,481 INFO misc.py line 119 87073] Train: [18/100][1381/1557] Data 0.006 (0.076) Batch 1.024 (1.051) Remain 37:19:24 loss: 0.8372 Lr: 0.00478 [2024-02-18 02:17:14,403 INFO misc.py line 119 87073] Train: [18/100][1382/1557] Data 0.005 (0.076) Batch 0.921 (1.051) Remain 37:19:10 loss: 0.2397 Lr: 0.00478 [2024-02-18 02:17:15,261 INFO misc.py line 119 87073] Train: [18/100][1383/1557] Data 0.006 (0.076) Batch 0.858 (1.051) Remain 37:18:51 loss: 0.2751 Lr: 0.00478 [2024-02-18 02:17:16,029 INFO misc.py line 119 87073] Train: [18/100][1384/1557] Data 0.007 (0.076) Batch 0.769 (1.051) Remain 37:18:24 loss: 0.4804 Lr: 0.00478 [2024-02-18 02:17:16,800 INFO misc.py line 119 87073] Train: [18/100][1385/1557] Data 0.005 (0.076) Batch 0.771 (1.050) Remain 37:17:57 loss: 0.4256 Lr: 0.00478 [2024-02-18 02:17:18,082 INFO misc.py line 119 87073] Train: [18/100][1386/1557] Data 0.005 (0.076) Batch 1.283 (1.050) Remain 37:18:18 loss: 0.5698 Lr: 0.00478 [2024-02-18 02:17:19,149 INFO misc.py line 119 87073] Train: [18/100][1387/1557] Data 0.005 (0.076) Batch 1.059 (1.050) Remain 37:18:18 loss: 0.4285 Lr: 0.00478 [2024-02-18 02:17:20,244 INFO misc.py line 119 87073] Train: [18/100][1388/1557] Data 0.013 (0.076) Batch 1.103 (1.051) Remain 37:18:21 loss: 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02:17:26,636 INFO misc.py line 119 87073] Train: [18/100][1395/1557] Data 0.006 (0.075) Batch 0.917 (1.050) Remain 37:16:46 loss: 0.4653 Lr: 0.00478 [2024-02-18 02:17:27,701 INFO misc.py line 119 87073] Train: [18/100][1396/1557] Data 0.004 (0.075) Batch 1.065 (1.050) Remain 37:16:46 loss: 0.7433 Lr: 0.00478 [2024-02-18 02:17:28,594 INFO misc.py line 119 87073] Train: [18/100][1397/1557] Data 0.005 (0.075) Batch 0.892 (1.050) Remain 37:16:31 loss: 0.3992 Lr: 0.00478 [2024-02-18 02:17:29,344 INFO misc.py line 119 87073] Train: [18/100][1398/1557] Data 0.005 (0.075) Batch 0.742 (1.050) Remain 37:16:01 loss: 0.4898 Lr: 0.00478 [2024-02-18 02:17:30,109 INFO misc.py line 119 87073] Train: [18/100][1399/1557] Data 0.014 (0.075) Batch 0.774 (1.049) Remain 37:15:35 loss: 0.5014 Lr: 0.00478 [2024-02-18 02:17:31,337 INFO misc.py line 119 87073] Train: [18/100][1400/1557] Data 0.005 (0.075) Batch 1.228 (1.049) Remain 37:15:50 loss: 0.4493 Lr: 0.00478 [2024-02-18 02:17:32,276 INFO misc.py line 119 87073] Train: [18/100][1401/1557] Data 0.004 (0.075) Batch 0.940 (1.049) Remain 37:15:39 loss: 0.4363 Lr: 0.00478 [2024-02-18 02:17:33,265 INFO misc.py line 119 87073] Train: [18/100][1402/1557] Data 0.004 (0.075) Batch 0.988 (1.049) Remain 37:15:33 loss: 0.5203 Lr: 0.00478 [2024-02-18 02:17:34,291 INFO misc.py line 119 87073] Train: [18/100][1403/1557] Data 0.007 (0.075) Batch 1.027 (1.049) Remain 37:15:30 loss: 0.5160 Lr: 0.00478 [2024-02-18 02:17:35,575 INFO misc.py line 119 87073] Train: [18/100][1404/1557] Data 0.004 (0.075) Batch 1.277 (1.049) Remain 37:15:49 loss: 0.8073 Lr: 0.00478 [2024-02-18 02:17:37,977 INFO misc.py line 119 87073] Train: [18/100][1405/1557] Data 1.155 (0.076) Batch 2.394 (1.050) Remain 37:17:51 loss: 0.4452 Lr: 0.00478 [2024-02-18 02:17:38,747 INFO misc.py line 119 87073] Train: [18/100][1406/1557] Data 0.021 (0.076) Batch 0.786 (1.050) Remain 37:17:26 loss: 0.6861 Lr: 0.00478 [2024-02-18 02:17:44,928 INFO misc.py line 119 87073] Train: [18/100][1407/1557] Data 3.341 (0.078) Batch 6.180 (1.054) Remain 37:25:12 loss: 0.3973 Lr: 0.00478 [2024-02-18 02:17:45,921 INFO misc.py line 119 87073] Train: [18/100][1408/1557] Data 0.006 (0.078) Batch 0.993 (1.054) Remain 37:25:05 loss: 1.2441 Lr: 0.00478 [2024-02-18 02:17:46,870 INFO misc.py line 119 87073] Train: [18/100][1409/1557] Data 0.005 (0.078) Batch 0.948 (1.054) Remain 37:24:54 loss: 0.6275 Lr: 0.00478 [2024-02-18 02:17:47,731 INFO misc.py line 119 87073] Train: [18/100][1410/1557] Data 0.006 (0.078) Batch 0.862 (1.054) Remain 37:24:36 loss: 0.6456 Lr: 0.00478 [2024-02-18 02:17:48,696 INFO misc.py line 119 87073] Train: [18/100][1411/1557] Data 0.004 (0.078) Batch 0.961 (1.054) Remain 37:24:26 loss: 0.4312 Lr: 0.00478 [2024-02-18 02:17:49,490 INFO misc.py line 119 87073] Train: [18/100][1412/1557] Data 0.008 (0.078) Batch 0.797 (1.053) Remain 37:24:02 loss: 0.4147 Lr: 0.00478 [2024-02-18 02:17:50,261 INFO misc.py line 119 87073] Train: [18/100][1413/1557] Data 0.005 (0.078) Batch 0.772 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02:18:02,306 INFO misc.py line 119 87073] Train: [18/100][1426/1557] Data 0.006 (0.077) Batch 0.734 (1.052) Remain 37:20:54 loss: 0.4606 Lr: 0.00478 [2024-02-18 02:18:02,976 INFO misc.py line 119 87073] Train: [18/100][1427/1557] Data 0.007 (0.077) Batch 0.670 (1.052) Remain 37:20:18 loss: 0.5580 Lr: 0.00478 [2024-02-18 02:18:04,142 INFO misc.py line 119 87073] Train: [18/100][1428/1557] Data 0.006 (0.077) Batch 1.165 (1.052) Remain 37:20:28 loss: 0.2176 Lr: 0.00478 [2024-02-18 02:18:05,156 INFO misc.py line 119 87073] Train: [18/100][1429/1557] Data 0.008 (0.077) Batch 1.015 (1.052) Remain 37:20:23 loss: 0.3726 Lr: 0.00478 [2024-02-18 02:18:06,350 INFO misc.py line 119 87073] Train: [18/100][1430/1557] Data 0.006 (0.077) Batch 1.195 (1.052) Remain 37:20:35 loss: 0.2037 Lr: 0.00478 [2024-02-18 02:18:07,248 INFO misc.py line 119 87073] Train: [18/100][1431/1557] Data 0.005 (0.077) Batch 0.899 (1.052) Remain 37:20:20 loss: 0.5304 Lr: 0.00478 [2024-02-18 02:18:08,331 INFO misc.py line 119 87073] Train: [18/100][1432/1557] Data 0.004 (0.077) Batch 1.081 (1.052) Remain 37:20:22 loss: 0.5199 Lr: 0.00478 [2024-02-18 02:18:09,114 INFO misc.py line 119 87073] Train: [18/100][1433/1557] Data 0.009 (0.077) Batch 0.786 (1.052) Remain 37:19:57 loss: 0.4946 Lr: 0.00478 [2024-02-18 02:18:09,895 INFO misc.py line 119 87073] Train: [18/100][1434/1557] Data 0.003 (0.077) Batch 0.778 (1.051) Remain 37:19:31 loss: 0.3213 Lr: 0.00478 [2024-02-18 02:18:11,237 INFO misc.py line 119 87073] Train: [18/100][1435/1557] Data 0.006 (0.077) Batch 1.341 (1.052) Remain 37:19:56 loss: 0.3593 Lr: 0.00478 [2024-02-18 02:18:12,394 INFO misc.py line 119 87073] Train: [18/100][1436/1557] Data 0.008 (0.077) Batch 1.149 (1.052) Remain 37:20:04 loss: 0.7026 Lr: 0.00478 [2024-02-18 02:18:13,417 INFO misc.py line 119 87073] Train: [18/100][1437/1557] Data 0.016 (0.077) Batch 1.034 (1.052) Remain 37:20:01 loss: 0.5994 Lr: 0.00478 [2024-02-18 02:18:14,492 INFO misc.py line 119 87073] Train: [18/100][1438/1557] Data 0.006 (0.076) Batch 1.064 (1.052) Remain 37:20:01 loss: 0.5912 Lr: 0.00478 [2024-02-18 02:18:15,618 INFO misc.py line 119 87073] Train: [18/100][1439/1557] Data 0.015 (0.076) Batch 1.125 (1.052) Remain 37:20:07 loss: 0.3991 Lr: 0.00478 [2024-02-18 02:18:16,370 INFO misc.py line 119 87073] Train: [18/100][1440/1557] Data 0.018 (0.076) Batch 0.764 (1.052) Remain 37:19:40 loss: 0.2378 Lr: 0.00478 [2024-02-18 02:18:17,103 INFO misc.py line 119 87073] Train: [18/100][1441/1557] Data 0.005 (0.076) Batch 0.725 (1.051) Remain 37:19:10 loss: 0.3679 Lr: 0.00478 [2024-02-18 02:18:18,422 INFO misc.py line 119 87073] Train: [18/100][1442/1557] Data 0.013 (0.076) Batch 1.316 (1.052) Remain 37:19:32 loss: 0.1853 Lr: 0.00478 [2024-02-18 02:18:19,302 INFO misc.py line 119 87073] Train: [18/100][1443/1557] Data 0.016 (0.076) Batch 0.891 (1.051) Remain 37:19:17 loss: 0.5482 Lr: 0.00478 [2024-02-18 02:18:20,656 INFO misc.py line 119 87073] Train: [18/100][1444/1557] Data 0.006 (0.076) Batch 1.352 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Data 0.006 (0.077) Batch 0.707 (1.053) Remain 37:21:42 loss: 0.3743 Lr: 0.00477 [2024-02-18 02:18:49,821 INFO misc.py line 119 87073] Train: [18/100][1470/1557] Data 0.011 (0.077) Batch 1.195 (1.053) Remain 37:21:53 loss: 0.1406 Lr: 0.00477 [2024-02-18 02:18:50,734 INFO misc.py line 119 87073] Train: [18/100][1471/1557] Data 0.016 (0.077) Batch 0.927 (1.053) Remain 37:21:41 loss: 0.6587 Lr: 0.00477 [2024-02-18 02:18:51,644 INFO misc.py line 119 87073] Train: [18/100][1472/1557] Data 0.003 (0.077) Batch 0.910 (1.053) Remain 37:21:28 loss: 0.9589 Lr: 0.00477 [2024-02-18 02:18:52,610 INFO misc.py line 119 87073] Train: [18/100][1473/1557] Data 0.004 (0.077) Batch 0.966 (1.053) Remain 37:21:19 loss: 0.7277 Lr: 0.00477 [2024-02-18 02:18:53,707 INFO misc.py line 119 87073] Train: [18/100][1474/1557] Data 0.003 (0.077) Batch 1.093 (1.053) Remain 37:21:22 loss: 0.5457 Lr: 0.00477 [2024-02-18 02:18:54,474 INFO misc.py line 119 87073] Train: [18/100][1475/1557] Data 0.007 (0.077) Batch 0.771 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Data 0.013 (0.076) Batch 1.043 (1.051) Remain 37:16:43 loss: 0.8608 Lr: 0.00477 [2024-02-18 02:19:19,250 INFO misc.py line 119 87073] Train: [18/100][1501/1557] Data 0.013 (0.076) Batch 1.118 (1.051) Remain 37:16:47 loss: 0.5250 Lr: 0.00477 [2024-02-18 02:19:20,227 INFO misc.py line 119 87073] Train: [18/100][1502/1557] Data 0.013 (0.076) Batch 0.985 (1.051) Remain 37:16:41 loss: 0.8638 Lr: 0.00477 [2024-02-18 02:19:20,972 INFO misc.py line 119 87073] Train: [18/100][1503/1557] Data 0.004 (0.076) Batch 0.744 (1.050) Remain 37:16:13 loss: 0.9199 Lr: 0.00477 [2024-02-18 02:19:21,969 INFO misc.py line 119 87073] Train: [18/100][1504/1557] Data 0.007 (0.076) Batch 0.998 (1.050) Remain 37:16:08 loss: 0.3702 Lr: 0.00477 [2024-02-18 02:19:23,015 INFO misc.py line 119 87073] Train: [18/100][1505/1557] Data 0.004 (0.076) Batch 1.041 (1.050) Remain 37:16:06 loss: 0.5452 Lr: 0.00477 [2024-02-18 02:19:23,964 INFO misc.py line 119 87073] Train: [18/100][1506/1557] Data 0.010 (0.076) Batch 0.953 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02:19:41,429 INFO misc.py line 119 87073] Train: [18/100][1519/1557] Data 3.660 (0.077) Batch 6.303 (1.053) Remain 37:21:04 loss: 0.2381 Lr: 0.00477 [2024-02-18 02:19:42,462 INFO misc.py line 119 87073] Train: [18/100][1520/1557] Data 0.007 (0.077) Batch 1.032 (1.053) Remain 37:21:01 loss: 1.0924 Lr: 0.00477 [2024-02-18 02:19:43,427 INFO misc.py line 119 87073] Train: [18/100][1521/1557] Data 0.008 (0.077) Batch 0.968 (1.053) Remain 37:20:53 loss: 0.3463 Lr: 0.00477 [2024-02-18 02:19:44,495 INFO misc.py line 119 87073] Train: [18/100][1522/1557] Data 0.004 (0.077) Batch 1.069 (1.053) Remain 37:20:53 loss: 0.5092 Lr: 0.00477 [2024-02-18 02:19:45,625 INFO misc.py line 119 87073] Train: [18/100][1523/1557] Data 0.004 (0.077) Batch 1.129 (1.053) Remain 37:20:59 loss: 0.5426 Lr: 0.00477 [2024-02-18 02:19:46,392 INFO misc.py line 119 87073] Train: [18/100][1524/1557] Data 0.005 (0.077) Batch 0.767 (1.053) Remain 37:20:34 loss: 0.4139 Lr: 0.00477 [2024-02-18 02:19:47,128 INFO misc.py line 119 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Data 0.009 (0.077) Batch 0.784 (1.052) Remain 37:19:58 loss: 0.8327 Lr: 0.00477 [2024-02-18 02:19:54,170 INFO misc.py line 119 87073] Train: [18/100][1532/1557] Data 0.004 (0.077) Batch 0.747 (1.052) Remain 37:19:31 loss: 0.5862 Lr: 0.00477 [2024-02-18 02:19:55,366 INFO misc.py line 119 87073] Train: [18/100][1533/1557] Data 0.005 (0.077) Batch 1.191 (1.052) Remain 37:19:42 loss: 0.3417 Lr: 0.00477 [2024-02-18 02:19:56,268 INFO misc.py line 119 87073] Train: [18/100][1534/1557] Data 0.010 (0.077) Batch 0.907 (1.052) Remain 37:19:29 loss: 0.3098 Lr: 0.00477 [2024-02-18 02:19:57,195 INFO misc.py line 119 87073] Train: [18/100][1535/1557] Data 0.005 (0.077) Batch 0.928 (1.052) Remain 37:19:17 loss: 0.7341 Lr: 0.00477 [2024-02-18 02:19:58,064 INFO misc.py line 119 87073] Train: [18/100][1536/1557] Data 0.005 (0.077) Batch 0.869 (1.052) Remain 37:19:01 loss: 0.4812 Lr: 0.00477 [2024-02-18 02:19:59,074 INFO misc.py line 119 87073] Train: [18/100][1537/1557] Data 0.005 (0.077) Batch 0.998 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0.3355 Lr: 0.00477 [2024-02-18 02:20:05,709 INFO misc.py line 119 87073] Train: [18/100][1544/1557] Data 0.005 (0.076) Batch 1.166 (1.052) Remain 37:17:49 loss: 0.3267 Lr: 0.00477 [2024-02-18 02:20:06,453 INFO misc.py line 119 87073] Train: [18/100][1545/1557] Data 0.005 (0.076) Batch 0.744 (1.051) Remain 37:17:22 loss: 0.2340 Lr: 0.00477 [2024-02-18 02:20:07,224 INFO misc.py line 119 87073] Train: [18/100][1546/1557] Data 0.005 (0.076) Batch 0.767 (1.051) Remain 37:16:58 loss: 0.4107 Lr: 0.00477 [2024-02-18 02:20:08,537 INFO misc.py line 119 87073] Train: [18/100][1547/1557] Data 0.010 (0.076) Batch 1.316 (1.051) Remain 37:17:18 loss: 0.4530 Lr: 0.00477 [2024-02-18 02:20:09,672 INFO misc.py line 119 87073] Train: [18/100][1548/1557] Data 0.008 (0.076) Batch 1.132 (1.051) Remain 37:17:24 loss: 0.3095 Lr: 0.00477 [2024-02-18 02:20:10,568 INFO misc.py line 119 87073] Train: [18/100][1549/1557] Data 0.009 (0.076) Batch 0.898 (1.051) Remain 37:17:10 loss: 0.6103 Lr: 0.00477 [2024-02-18 02:20:11,535 INFO misc.py line 119 87073] Train: [18/100][1550/1557] Data 0.006 (0.076) Batch 0.968 (1.051) Remain 37:17:02 loss: 0.7782 Lr: 0.00477 [2024-02-18 02:20:12,548 INFO misc.py line 119 87073] Train: [18/100][1551/1557] Data 0.006 (0.076) Batch 1.012 (1.051) Remain 37:16:58 loss: 0.5468 Lr: 0.00477 [2024-02-18 02:20:13,300 INFO misc.py line 119 87073] Train: [18/100][1552/1557] Data 0.007 (0.076) Batch 0.754 (1.051) Remain 37:16:33 loss: 0.4243 Lr: 0.00477 [2024-02-18 02:20:14,032 INFO misc.py line 119 87073] Train: [18/100][1553/1557] Data 0.005 (0.076) Batch 0.732 (1.051) Remain 37:16:05 loss: 0.4498 Lr: 0.00477 [2024-02-18 02:20:15,259 INFO misc.py line 119 87073] Train: [18/100][1554/1557] Data 0.005 (0.076) Batch 1.222 (1.051) Remain 37:16:18 loss: 0.2584 Lr: 0.00477 [2024-02-18 02:20:16,223 INFO misc.py line 119 87073] Train: [18/100][1555/1557] Data 0.010 (0.076) Batch 0.969 (1.051) Remain 37:16:10 loss: 0.4591 Lr: 0.00477 [2024-02-18 02:20:17,237 INFO misc.py line 119 87073] Train: [18/100][1556/1557] Data 0.005 (0.076) Batch 1.012 (1.051) Remain 37:16:06 loss: 0.5803 Lr: 0.00477 [2024-02-18 02:20:18,162 INFO misc.py line 119 87073] Train: [18/100][1557/1557] Data 0.008 (0.076) Batch 0.926 (1.051) Remain 37:15:55 loss: 1.3440 Lr: 0.00477 [2024-02-18 02:20:18,163 INFO misc.py line 136 87073] Train result: loss: 0.5013 [2024-02-18 02:20:18,163 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 02:20:47,285 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6149 [2024-02-18 02:20:48,063 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7861 [2024-02-18 02:20:50,189 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5678 [2024-02-18 02:20:52,395 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3457 [2024-02-18 02:20:57,343 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2334 [2024-02-18 02:20:58,040 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0864 [2024-02-18 02:20:59,302 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3521 [2024-02-18 02:21:02,259 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.5373 [2024-02-18 02:21:04,068 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2743 [2024-02-18 02:21:05,715 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6678/0.7519/0.8965. [2024-02-18 02:21:05,715 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9107/0.9545 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9799/0.9851 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8429/0.9717 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0095/0.0972 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3035/0.3192 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5735/0.5839 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6339/0.7912 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8325/0.9065 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9142/0.9625 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7594/0.8015 [2024-02-18 02:21:05,716 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7731/0.8748 [2024-02-18 02:21:05,717 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.5939/0.8954 [2024-02-18 02:21:05,717 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5542/0.6318 [2024-02-18 02:21:05,717 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 02:21:05,723 INFO misc.py line 165 87073] Currently Best mIoU: 0.6976 [2024-02-18 02:21:05,723 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 02:21:13,270 INFO misc.py line 119 87073] Train: [19/100][1/1557] Data 1.396 (1.396) Batch 2.161 (2.161) Remain 76:39:07 loss: 0.2425 Lr: 0.00477 [2024-02-18 02:21:14,196 INFO misc.py line 119 87073] Train: [19/100][2/1557] Data 0.004 (0.004) Batch 0.926 (0.926) Remain 32:50:26 loss: 0.4527 Lr: 0.00477 [2024-02-18 02:21:15,166 INFO misc.py line 119 87073] Train: [19/100][3/1557] Data 0.005 (0.005) Batch 0.969 (0.969) Remain 34:20:55 loss: 0.3485 Lr: 0.00477 [2024-02-18 02:21:16,099 INFO misc.py line 119 87073] Train: [19/100][4/1557] Data 0.006 (0.006) Batch 0.926 (0.926) Remain 32:50:18 loss: 0.6019 Lr: 0.00477 [2024-02-18 02:21:16,834 INFO misc.py line 119 87073] Train: [19/100][5/1557] Data 0.013 (0.010) Batch 0.740 (0.833) Remain 29:32:28 loss: 0.4732 Lr: 0.00477 [2024-02-18 02:21:17,642 INFO misc.py line 119 87073] Train: [19/100][6/1557] Data 0.009 (0.009) Batch 0.810 (0.825) Remain 29:16:24 loss: 0.3020 Lr: 0.00477 [2024-02-18 02:21:21,488 INFO misc.py line 119 87073] Train: [19/100][7/1557] Data 0.371 (0.100) Batch 3.847 (1.581) Remain 56:03:43 loss: 0.2775 Lr: 0.00477 [2024-02-18 02:21:22,445 INFO misc.py line 119 87073] Train: [19/100][8/1557] Data 0.005 (0.081) Batch 0.958 (1.456) Remain 51:38:31 loss: 0.8153 Lr: 0.00477 [2024-02-18 02:21:23,628 INFO misc.py line 119 87073] Train: [19/100][9/1557] Data 0.004 (0.068) Batch 1.182 (1.411) Remain 50:01:22 loss: 0.3971 Lr: 0.00477 [2024-02-18 02:21:24,557 INFO misc.py line 119 87073] Train: [19/100][10/1557] Data 0.004 (0.059) Batch 0.928 (1.342) Remain 47:34:43 loss: 0.5590 Lr: 0.00477 [2024-02-18 02:21:25,545 INFO misc.py line 119 87073] Train: [19/100][11/1557] Data 0.005 (0.052) Batch 0.988 (1.297) Remain 46:00:42 loss: 0.6893 Lr: 0.00477 [2024-02-18 02:21:26,297 INFO misc.py line 119 87073] Train: [19/100][12/1557] Data 0.004 (0.047) Batch 0.724 (1.234) Remain 43:45:10 loss: 0.2336 Lr: 0.00477 [2024-02-18 02:21:27,030 INFO misc.py line 119 87073] Train: [19/100][13/1557] Data 0.031 (0.045) Batch 0.760 (1.186) Remain 42:04:26 loss: 0.4901 Lr: 0.00477 [2024-02-18 02:21:28,237 INFO misc.py line 119 87073] Train: [19/100][14/1557] Data 0.005 (0.042) Batch 1.207 (1.188) Remain 42:08:19 loss: 0.5072 Lr: 0.00477 [2024-02-18 02:21:29,134 INFO misc.py line 119 87073] Train: [19/100][15/1557] Data 0.005 (0.038) Batch 0.896 (1.164) Remain 41:16:29 loss: 0.6390 Lr: 0.00477 [2024-02-18 02:21:30,156 INFO misc.py line 119 87073] Train: [19/100][16/1557] Data 0.006 (0.036) Batch 0.992 (1.151) Remain 40:48:23 loss: 0.7428 Lr: 0.00477 [2024-02-18 02:21:31,236 INFO misc.py line 119 87073] Train: [19/100][17/1557] Data 0.035 (0.036) Batch 1.110 (1.148) Remain 40:42:10 loss: 0.6076 Lr: 0.00477 [2024-02-18 02:21:32,130 INFO misc.py line 119 87073] Train: [19/100][18/1557] Data 0.006 (0.034) Batch 0.894 (1.131) Remain 40:06:04 loss: 0.6834 Lr: 0.00477 [2024-02-18 02:21:32,926 INFO misc.py line 119 87073] Train: [19/100][19/1557] Data 0.006 (0.032) Batch 0.794 (1.110) Remain 39:21:15 loss: 0.7257 Lr: 0.00477 [2024-02-18 02:21:33,679 INFO misc.py line 119 87073] Train: [19/100][20/1557] Data 0.009 (0.031) Batch 0.756 (1.089) Remain 38:37:00 loss: 0.2315 Lr: 0.00477 [2024-02-18 02:21:34,925 INFO misc.py line 119 87073] Train: [19/100][21/1557] Data 0.005 (0.029) Batch 1.246 (1.098) Remain 38:55:32 loss: 0.3388 Lr: 0.00477 [2024-02-18 02:21:35,891 INFO misc.py line 119 87073] Train: [19/100][22/1557] Data 0.005 (0.028) Batch 0.966 (1.091) Remain 38:40:45 loss: 0.3671 Lr: 0.00477 [2024-02-18 02:21:37,008 INFO misc.py line 119 87073] Train: [19/100][23/1557] Data 0.005 (0.027) Batch 1.116 (1.092) Remain 38:43:27 loss: 1.1661 Lr: 0.00477 [2024-02-18 02:21:38,219 INFO misc.py line 119 87073] Train: [19/100][24/1557] Data 0.006 (0.026) Batch 1.199 (1.097) Remain 38:54:14 loss: 0.8176 Lr: 0.00477 [2024-02-18 02:21:39,425 INFO misc.py line 119 87073] Train: [19/100][25/1557] Data 0.018 (0.026) Batch 1.008 (1.093) Remain 38:45:34 loss: 0.7584 Lr: 0.00477 [2024-02-18 02:21:40,153 INFO misc.py line 119 87073] Train: [19/100][26/1557] Data 0.218 (0.034) Batch 0.939 (1.086) Remain 38:31:16 loss: 0.6301 Lr: 0.00477 [2024-02-18 02:21:40,945 INFO misc.py line 119 87073] Train: [19/100][27/1557] Data 0.006 (0.033) Batch 0.793 (1.074) Remain 38:05:15 loss: 0.4432 Lr: 0.00477 [2024-02-18 02:21:42,087 INFO misc.py line 119 87073] Train: [19/100][28/1557] Data 0.004 (0.032) Batch 1.127 (1.076) Remain 38:09:43 loss: 0.3107 Lr: 0.00477 [2024-02-18 02:21:43,300 INFO misc.py line 119 87073] Train: [19/100][29/1557] Data 0.020 (0.031) Batch 1.218 (1.082) Remain 38:21:19 loss: 0.6251 Lr: 0.00477 [2024-02-18 02:21:44,388 INFO misc.py line 119 87073] Train: [19/100][30/1557] Data 0.014 (0.031) Batch 1.092 (1.082) Remain 38:22:07 loss: 0.3225 Lr: 0.00477 [2024-02-18 02:21:45,421 INFO misc.py line 119 87073] Train: [19/100][31/1557] Data 0.012 (0.030) Batch 1.027 (1.080) Remain 38:17:55 loss: 0.8831 Lr: 0.00477 [2024-02-18 02:21:46,539 INFO misc.py line 119 87073] Train: [19/100][32/1557] Data 0.017 (0.029) Batch 1.128 (1.082) Remain 38:21:25 loss: 0.4304 Lr: 0.00477 [2024-02-18 02:21:47,300 INFO misc.py line 119 87073] Train: [19/100][33/1557] Data 0.006 (0.029) Batch 0.761 (1.071) Remain 37:58:38 loss: 0.2495 Lr: 0.00477 [2024-02-18 02:21:47,973 INFO misc.py line 119 87073] Train: [19/100][34/1557] Data 0.006 (0.028) Batch 0.666 (1.058) Remain 37:30:51 loss: 0.3401 Lr: 0.00477 [2024-02-18 02:21:49,324 INFO misc.py line 119 87073] Train: [19/100][35/1557] Data 0.012 (0.027) Batch 1.354 (1.067) Remain 37:50:28 loss: 0.5378 Lr: 0.00477 [2024-02-18 02:21:50,295 INFO misc.py line 119 87073] Train: [19/100][36/1557] Data 0.009 (0.027) Batch 0.975 (1.064) Remain 37:44:29 loss: 0.4459 Lr: 0.00477 [2024-02-18 02:21:51,205 INFO misc.py line 119 87073] Train: [19/100][37/1557] Data 0.006 (0.026) Batch 0.911 (1.060) Remain 37:34:52 loss: 0.6457 Lr: 0.00477 [2024-02-18 02:21:52,337 INFO misc.py line 119 87073] Train: [19/100][38/1557] Data 0.006 (0.026) Batch 1.133 (1.062) Remain 37:39:17 loss: 0.3537 Lr: 0.00477 [2024-02-18 02:21:53,443 INFO misc.py line 119 87073] Train: [19/100][39/1557] Data 0.004 (0.025) Batch 1.102 (1.063) Remain 37:41:37 loss: 0.4840 Lr: 0.00477 [2024-02-18 02:21:54,212 INFO misc.py line 119 87073] Train: [19/100][40/1557] Data 0.010 (0.025) Batch 0.771 (1.055) Remain 37:24:48 loss: 0.7527 Lr: 0.00477 [2024-02-18 02:21:54,954 INFO misc.py line 119 87073] Train: [19/100][41/1557] Data 0.006 (0.024) Batch 0.731 (1.047) Remain 37:06:38 loss: 0.5363 Lr: 0.00477 [2024-02-18 02:21:56,065 INFO misc.py line 119 87073] Train: [19/100][42/1557] Data 0.017 (0.024) Batch 1.121 (1.049) Remain 37:10:40 loss: 0.3048 Lr: 0.00477 [2024-02-18 02:21:57,217 INFO misc.py line 119 87073] Train: [19/100][43/1557] Data 0.007 (0.024) Batch 1.149 (1.051) Remain 37:15:58 loss: 0.4061 Lr: 0.00477 [2024-02-18 02:21:58,122 INFO misc.py line 119 87073] Train: [19/100][44/1557] Data 0.011 (0.023) Batch 0.911 (1.048) Remain 37:08:40 loss: 0.7542 Lr: 0.00477 [2024-02-18 02:21:59,206 INFO misc.py line 119 87073] Train: [19/100][45/1557] Data 0.005 (0.023) Batch 1.084 (1.049) Remain 37:10:28 loss: 0.5032 Lr: 0.00477 [2024-02-18 02:22:00,277 INFO misc.py line 119 87073] Train: [19/100][46/1557] Data 0.006 (0.022) Batch 1.069 (1.049) Remain 37:11:28 loss: 0.3480 Lr: 0.00477 [2024-02-18 02:22:01,045 INFO misc.py line 119 87073] Train: [19/100][47/1557] Data 0.008 (0.022) Batch 0.770 (1.043) Remain 36:57:56 loss: 0.6912 Lr: 0.00477 [2024-02-18 02:22:01,866 INFO misc.py line 119 87073] Train: [19/100][48/1557] Data 0.007 (0.022) Batch 0.809 (1.038) Remain 36:46:53 loss: 0.5144 Lr: 0.00477 [2024-02-18 02:22:03,122 INFO misc.py line 119 87073] Train: [19/100][49/1557] Data 0.017 (0.022) Batch 1.258 (1.042) Remain 36:57:04 loss: 0.3952 Lr: 0.00477 [2024-02-18 02:22:04,138 INFO misc.py line 119 87073] Train: [19/100][50/1557] Data 0.015 (0.021) Batch 1.026 (1.042) Remain 36:56:18 loss: 0.3164 Lr: 0.00477 [2024-02-18 02:22:04,990 INFO misc.py line 119 87073] Train: [19/100][51/1557] Data 0.006 (0.021) Batch 0.851 (1.038) Remain 36:47:50 loss: 0.6858 Lr: 0.00477 [2024-02-18 02:22:06,250 INFO misc.py line 119 87073] Train: [19/100][52/1557] Data 0.006 (0.021) Batch 1.259 (1.043) Remain 36:57:26 loss: 0.2003 Lr: 0.00477 [2024-02-18 02:22:07,218 INFO misc.py line 119 87073] Train: [19/100][53/1557] Data 0.007 (0.021) Batch 0.971 (1.041) Remain 36:54:22 loss: 0.5622 Lr: 0.00477 [2024-02-18 02:22:08,006 INFO misc.py line 119 87073] Train: [19/100][54/1557] Data 0.004 (0.020) Batch 0.788 (1.036) Remain 36:43:48 loss: 0.6289 Lr: 0.00477 [2024-02-18 02:22:08,764 INFO misc.py line 119 87073] Train: [19/100][55/1557] Data 0.004 (0.020) Batch 0.753 (1.031) Remain 36:32:12 loss: 0.3476 Lr: 0.00477 [2024-02-18 02:22:10,046 INFO misc.py line 119 87073] Train: [19/100][56/1557] Data 0.008 (0.020) Batch 1.248 (1.035) Remain 36:40:54 loss: 0.4391 Lr: 0.00477 [2024-02-18 02:22:10,915 INFO misc.py line 119 87073] Train: [19/100][57/1557] Data 0.042 (0.020) Batch 0.906 (1.032) Remain 36:35:47 loss: 0.6007 Lr: 0.00477 [2024-02-18 02:22:11,870 INFO misc.py line 119 87073] Train: [19/100][58/1557] Data 0.007 (0.020) Batch 0.955 (1.031) Remain 36:32:48 loss: 1.1182 Lr: 0.00477 [2024-02-18 02:22:12,836 INFO misc.py line 119 87073] Train: [19/100][59/1557] Data 0.007 (0.020) Batch 0.967 (1.030) Remain 36:30:21 loss: 0.4049 Lr: 0.00477 [2024-02-18 02:22:13,898 INFO misc.py line 119 87073] Train: [19/100][60/1557] Data 0.005 (0.019) Batch 1.055 (1.030) Remain 36:31:16 loss: 0.9210 Lr: 0.00477 [2024-02-18 02:22:14,571 INFO misc.py line 119 87073] Train: [19/100][61/1557] Data 0.012 (0.019) Batch 0.680 (1.024) Remain 36:18:25 loss: 0.3739 Lr: 0.00477 [2024-02-18 02:22:15,341 INFO misc.py line 119 87073] Train: [19/100][62/1557] Data 0.004 (0.019) Batch 0.770 (1.020) Remain 36:09:13 loss: 0.7430 Lr: 0.00477 [2024-02-18 02:22:28,609 INFO misc.py line 119 87073] Train: [19/100][63/1557] Data 6.364 (0.125) Batch 13.269 (1.224) Remain 43:23:24 loss: 0.4351 Lr: 0.00477 [2024-02-18 02:22:29,694 INFO misc.py line 119 87073] Train: [19/100][64/1557] Data 0.004 (0.123) Batch 1.086 (1.222) Remain 43:18:33 loss: 0.1706 Lr: 0.00477 [2024-02-18 02:22:30,645 INFO misc.py line 119 87073] Train: 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Batch 1.028 (1.187) Remain 42:02:54 loss: 0.8036 Lr: 0.00477 [2024-02-18 02:23:36,139 INFO misc.py line 119 87073] Train: [19/100][122/1557] Data 0.006 (0.122) Batch 0.936 (1.185) Remain 41:58:24 loss: 0.3678 Lr: 0.00477 [2024-02-18 02:23:37,169 INFO misc.py line 119 87073] Train: [19/100][123/1557] Data 0.004 (0.121) Batch 1.030 (1.183) Remain 41:55:39 loss: 0.5958 Lr: 0.00477 [2024-02-18 02:23:37,908 INFO misc.py line 119 87073] Train: [19/100][124/1557] Data 0.004 (0.120) Batch 0.739 (1.180) Remain 41:47:49 loss: 0.2669 Lr: 0.00477 [2024-02-18 02:23:38,745 INFO misc.py line 119 87073] Train: [19/100][125/1557] Data 0.005 (0.119) Batch 0.836 (1.177) Remain 41:41:49 loss: 0.4046 Lr: 0.00477 [2024-02-18 02:23:40,011 INFO misc.py line 119 87073] Train: [19/100][126/1557] Data 0.005 (0.118) Batch 1.249 (1.177) Remain 41:43:03 loss: 0.5127 Lr: 0.00477 [2024-02-18 02:23:41,102 INFO misc.py line 119 87073] Train: [19/100][127/1557] Data 0.022 (0.117) Batch 1.095 (1.177) Remain 41:41:37 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Batch 0.813 (1.172) Remain 41:28:18 loss: 0.3097 Lr: 0.00477 [2024-02-18 02:25:45,526 INFO misc.py line 119 87073] Train: [19/100][234/1557] Data 0.007 (0.129) Batch 0.914 (1.170) Remain 41:25:55 loss: 0.4258 Lr: 0.00477 [2024-02-18 02:25:46,452 INFO misc.py line 119 87073] Train: [19/100][235/1557] Data 0.005 (0.129) Batch 0.925 (1.169) Remain 41:23:39 loss: 0.4506 Lr: 0.00477 [2024-02-18 02:25:47,138 INFO misc.py line 119 87073] Train: [19/100][236/1557] Data 0.004 (0.128) Batch 0.675 (1.167) Remain 41:19:07 loss: 0.6399 Lr: 0.00477 [2024-02-18 02:25:47,906 INFO misc.py line 119 87073] Train: [19/100][237/1557] Data 0.016 (0.128) Batch 0.780 (1.166) Remain 41:15:35 loss: 0.4549 Lr: 0.00477 [2024-02-18 02:25:49,243 INFO misc.py line 119 87073] Train: [19/100][238/1557] Data 0.003 (0.127) Batch 1.332 (1.166) Remain 41:17:04 loss: 0.4102 Lr: 0.00477 [2024-02-18 02:25:50,012 INFO misc.py line 119 87073] Train: [19/100][239/1557] Data 0.010 (0.127) Batch 0.772 (1.165) Remain 41:13:30 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Batch 1.023 (1.160) Remain 41:00:47 loss: 0.5519 Lr: 0.00477 [2024-02-18 02:27:52,651 INFO misc.py line 119 87073] Train: [19/100][346/1557] Data 0.005 (0.128) Batch 0.910 (1.159) Remain 40:59:13 loss: 0.6124 Lr: 0.00477 [2024-02-18 02:27:53,654 INFO misc.py line 119 87073] Train: [19/100][347/1557] Data 0.006 (0.128) Batch 1.004 (1.158) Remain 40:58:14 loss: 0.5578 Lr: 0.00477 [2024-02-18 02:27:54,380 INFO misc.py line 119 87073] Train: [19/100][348/1557] Data 0.005 (0.127) Batch 0.723 (1.157) Remain 40:55:33 loss: 0.3619 Lr: 0.00477 [2024-02-18 02:27:55,091 INFO misc.py line 119 87073] Train: [19/100][349/1557] Data 0.007 (0.127) Batch 0.711 (1.156) Remain 40:52:47 loss: 0.6393 Lr: 0.00477 [2024-02-18 02:27:56,328 INFO misc.py line 119 87073] Train: [19/100][350/1557] Data 0.008 (0.127) Batch 1.240 (1.156) Remain 40:53:17 loss: 0.5085 Lr: 0.00476 [2024-02-18 02:27:57,317 INFO misc.py line 119 87073] Train: [19/100][351/1557] Data 0.005 (0.126) Batch 0.990 (1.156) Remain 40:52:15 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line 119 87073] Train: [19/100][501/1557] Data 0.004 (0.119) Batch 1.147 (1.146) Remain 40:30:02 loss: 1.1286 Lr: 0.00476 [2024-02-18 02:30:46,867 INFO misc.py line 119 87073] Train: [19/100][502/1557] Data 0.004 (0.119) Batch 0.747 (1.146) Remain 40:28:19 loss: 0.5358 Lr: 0.00476 [2024-02-18 02:30:47,722 INFO misc.py line 119 87073] Train: [19/100][503/1557] Data 0.005 (0.119) Batch 0.825 (1.145) Remain 40:26:57 loss: 0.4374 Lr: 0.00476 [2024-02-18 02:30:49,030 INFO misc.py line 119 87073] Train: [19/100][504/1557] Data 0.034 (0.119) Batch 1.332 (1.145) Remain 40:27:43 loss: 0.5058 Lr: 0.00476 [2024-02-18 02:30:50,147 INFO misc.py line 119 87073] Train: [19/100][505/1557] Data 0.010 (0.118) Batch 1.112 (1.145) Remain 40:27:33 loss: 0.6532 Lr: 0.00476 [2024-02-18 02:30:51,180 INFO misc.py line 119 87073] Train: [19/100][506/1557] Data 0.015 (0.118) Batch 1.039 (1.145) Remain 40:27:06 loss: 0.2395 Lr: 0.00476 [2024-02-18 02:30:52,201 INFO misc.py line 119 87073] Train: 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Batch 1.217 (1.164) Remain 41:07:38 loss: 0.4072 Lr: 0.00476 [2024-02-18 02:31:10,137 INFO misc.py line 119 87073] Train: [19/100][514/1557] Data 0.005 (0.129) Batch 1.158 (1.164) Remain 41:07:35 loss: 0.4052 Lr: 0.00476 [2024-02-18 02:31:11,032 INFO misc.py line 119 87073] Train: [19/100][515/1557] Data 0.004 (0.128) Batch 0.894 (1.164) Remain 41:06:27 loss: 0.3986 Lr: 0.00476 [2024-02-18 02:31:11,823 INFO misc.py line 119 87073] Train: [19/100][516/1557] Data 0.004 (0.128) Batch 0.775 (1.163) Remain 41:04:49 loss: 0.5333 Lr: 0.00476 [2024-02-18 02:31:12,567 INFO misc.py line 119 87073] Train: [19/100][517/1557] Data 0.020 (0.128) Batch 0.760 (1.162) Remain 41:03:08 loss: 0.4022 Lr: 0.00476 [2024-02-18 02:31:13,871 INFO misc.py line 119 87073] Train: [19/100][518/1557] Data 0.005 (0.128) Batch 1.299 (1.163) Remain 41:03:41 loss: 0.3520 Lr: 0.00476 [2024-02-18 02:31:14,855 INFO misc.py line 119 87073] Train: [19/100][519/1557] Data 0.011 (0.128) Batch 0.991 (1.162) Remain 41:02:58 loss: 0.6698 Lr: 0.00476 [2024-02-18 02:31:15,839 INFO misc.py line 119 87073] Train: [19/100][520/1557] Data 0.004 (0.127) Batch 0.984 (1.162) Remain 41:02:13 loss: 0.9236 Lr: 0.00476 [2024-02-18 02:31:16,676 INFO misc.py line 119 87073] Train: [19/100][521/1557] Data 0.004 (0.127) Batch 0.837 (1.161) Remain 41:00:52 loss: 0.5772 Lr: 0.00476 [2024-02-18 02:31:17,691 INFO misc.py line 119 87073] Train: [19/100][522/1557] Data 0.004 (0.127) Batch 1.007 (1.161) Remain 41:00:13 loss: 0.4752 Lr: 0.00476 [2024-02-18 02:31:18,470 INFO misc.py line 119 87073] Train: [19/100][523/1557] Data 0.012 (0.127) Batch 0.787 (1.160) Remain 40:58:40 loss: 0.5057 Lr: 0.00476 [2024-02-18 02:31:19,167 INFO misc.py line 119 87073] Train: [19/100][524/1557] Data 0.003 (0.126) Batch 0.691 (1.159) Remain 40:56:44 loss: 0.5155 Lr: 0.00476 [2024-02-18 02:31:20,385 INFO misc.py line 119 87073] Train: [19/100][525/1557] Data 0.010 (0.126) Batch 1.160 (1.159) Remain 40:56:43 loss: 0.3005 Lr: 0.00476 [2024-02-18 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87073] Train: [19/100][588/1557] Data 0.005 (0.127) Batch 1.121 (1.156) Remain 40:49:07 loss: 0.4096 Lr: 0.00476 [2024-02-18 02:32:32,559 INFO misc.py line 119 87073] Train: [19/100][589/1557] Data 0.005 (0.127) Batch 0.962 (1.156) Remain 40:48:24 loss: 0.4258 Lr: 0.00476 [2024-02-18 02:32:33,503 INFO misc.py line 119 87073] Train: [19/100][590/1557] Data 0.007 (0.126) Batch 0.947 (1.156) Remain 40:47:38 loss: 0.5426 Lr: 0.00476 [2024-02-18 02:32:34,452 INFO misc.py line 119 87073] Train: [19/100][591/1557] Data 0.004 (0.126) Batch 0.948 (1.155) Remain 40:46:52 loss: 0.4270 Lr: 0.00476 [2024-02-18 02:32:35,448 INFO misc.py line 119 87073] Train: [19/100][592/1557] Data 0.006 (0.126) Batch 0.994 (1.155) Remain 40:46:15 loss: 0.5126 Lr: 0.00476 [2024-02-18 02:32:36,189 INFO misc.py line 119 87073] Train: [19/100][593/1557] Data 0.009 (0.126) Batch 0.744 (1.154) Remain 40:44:46 loss: 0.6071 Lr: 0.00476 [2024-02-18 02:32:36,944 INFO misc.py line 119 87073] Train: [19/100][594/1557] Data 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[2024-02-18 02:32:49,244 INFO misc.py line 119 87073] Train: [19/100][607/1557] Data 0.003 (0.123) Batch 0.672 (1.149) Remain 40:33:36 loss: 0.5963 Lr: 0.00476 [2024-02-18 02:32:50,009 INFO misc.py line 119 87073] Train: [19/100][608/1557] Data 0.005 (0.123) Batch 0.766 (1.148) Remain 40:32:15 loss: 0.4014 Lr: 0.00476 [2024-02-18 02:32:51,277 INFO misc.py line 119 87073] Train: [19/100][609/1557] Data 0.004 (0.123) Batch 1.262 (1.149) Remain 40:32:37 loss: 0.4008 Lr: 0.00476 [2024-02-18 02:32:52,126 INFO misc.py line 119 87073] Train: [19/100][610/1557] Data 0.012 (0.122) Batch 0.855 (1.148) Remain 40:31:35 loss: 0.3232 Lr: 0.00476 [2024-02-18 02:32:53,110 INFO misc.py line 119 87073] Train: [19/100][611/1557] Data 0.005 (0.122) Batch 0.981 (1.148) Remain 40:30:59 loss: 0.5596 Lr: 0.00476 [2024-02-18 02:32:53,992 INFO misc.py line 119 87073] Train: [19/100][612/1557] Data 0.007 (0.122) Batch 0.881 (1.147) Remain 40:30:02 loss: 0.5578 Lr: 0.00476 [2024-02-18 02:32:54,949 INFO misc.py line 119 87073] Train: [19/100][613/1557] Data 0.010 (0.122) Batch 0.960 (1.147) Remain 40:29:22 loss: 0.7669 Lr: 0.00476 [2024-02-18 02:32:55,711 INFO misc.py line 119 87073] Train: [19/100][614/1557] Data 0.006 (0.122) Batch 0.762 (1.147) Remain 40:28:00 loss: 0.4029 Lr: 0.00476 [2024-02-18 02:32:56,439 INFO misc.py line 119 87073] Train: [19/100][615/1557] Data 0.005 (0.121) Batch 0.726 (1.146) Remain 40:26:32 loss: 0.2905 Lr: 0.00476 [2024-02-18 02:32:57,637 INFO misc.py line 119 87073] Train: [19/100][616/1557] Data 0.007 (0.121) Batch 1.193 (1.146) Remain 40:26:41 loss: 0.6695 Lr: 0.00476 [2024-02-18 02:32:58,596 INFO misc.py line 119 87073] Train: [19/100][617/1557] Data 0.011 (0.121) Batch 0.965 (1.146) Remain 40:26:02 loss: 0.4544 Lr: 0.00476 [2024-02-18 02:32:59,578 INFO misc.py line 119 87073] Train: [19/100][618/1557] Data 0.007 (0.121) Batch 0.982 (1.145) Remain 40:25:27 loss: 0.9917 Lr: 0.00476 [2024-02-18 02:33:00,541 INFO misc.py line 119 87073] Train: 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Batch 0.936 (1.159) Remain 40:55:03 loss: 0.5004 Lr: 0.00476 [2024-02-18 02:33:17,267 INFO misc.py line 119 87073] Train: [19/100][626/1557] Data 0.004 (0.129) Batch 0.931 (1.159) Remain 40:54:15 loss: 0.4263 Lr: 0.00476 [2024-02-18 02:33:18,277 INFO misc.py line 119 87073] Train: [19/100][627/1557] Data 0.013 (0.129) Batch 1.012 (1.159) Remain 40:53:44 loss: 0.3191 Lr: 0.00476 [2024-02-18 02:33:19,081 INFO misc.py line 119 87073] Train: [19/100][628/1557] Data 0.011 (0.129) Batch 0.811 (1.158) Remain 40:52:32 loss: 0.3093 Lr: 0.00476 [2024-02-18 02:33:19,835 INFO misc.py line 119 87073] Train: [19/100][629/1557] Data 0.004 (0.129) Batch 0.753 (1.158) Remain 40:51:09 loss: 0.3400 Lr: 0.00476 [2024-02-18 02:33:21,171 INFO misc.py line 119 87073] Train: [19/100][630/1557] Data 0.004 (0.129) Batch 1.335 (1.158) Remain 40:51:44 loss: 0.5664 Lr: 0.00476 [2024-02-18 02:33:22,077 INFO misc.py line 119 87073] Train: [19/100][631/1557] Data 0.007 (0.128) Batch 0.908 (1.158) Remain 40:50:52 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Batch 0.936 (1.159) Remain 40:53:38 loss: 0.6299 Lr: 0.00476 [2024-02-18 02:34:22,030 INFO misc.py line 119 87073] Train: [19/100][682/1557] Data 0.005 (0.130) Batch 0.877 (1.159) Remain 40:52:44 loss: 0.4408 Lr: 0.00476 [2024-02-18 02:34:23,056 INFO misc.py line 119 87073] Train: [19/100][683/1557] Data 0.010 (0.130) Batch 1.024 (1.159) Remain 40:52:18 loss: 0.4833 Lr: 0.00476 [2024-02-18 02:34:23,815 INFO misc.py line 119 87073] Train: [19/100][684/1557] Data 0.012 (0.129) Batch 0.766 (1.158) Remain 40:51:03 loss: 0.7000 Lr: 0.00476 [2024-02-18 02:34:24,520 INFO misc.py line 119 87073] Train: [19/100][685/1557] Data 0.004 (0.129) Batch 0.704 (1.157) Remain 40:49:37 loss: 0.7479 Lr: 0.00476 [2024-02-18 02:34:25,836 INFO misc.py line 119 87073] Train: [19/100][686/1557] Data 0.006 (0.129) Batch 1.318 (1.158) Remain 40:50:06 loss: 0.3632 Lr: 0.00476 [2024-02-18 02:34:26,900 INFO misc.py line 119 87073] Train: [19/100][687/1557] Data 0.004 (0.129) Batch 1.056 (1.157) Remain 40:49:46 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Batch 0.928 (1.161) Remain 40:55:48 loss: 0.4284 Lr: 0.00475 [2024-02-18 02:36:33,611 INFO misc.py line 119 87073] Train: [19/100][794/1557] Data 0.008 (0.130) Batch 1.004 (1.161) Remain 40:55:22 loss: 0.7085 Lr: 0.00475 [2024-02-18 02:36:34,669 INFO misc.py line 119 87073] Train: [19/100][795/1557] Data 0.004 (0.130) Batch 1.057 (1.161) Remain 40:55:04 loss: 0.3034 Lr: 0.00475 [2024-02-18 02:36:35,335 INFO misc.py line 119 87073] Train: [19/100][796/1557] Data 0.005 (0.130) Batch 0.666 (1.160) Remain 40:53:44 loss: 0.5207 Lr: 0.00475 [2024-02-18 02:36:36,130 INFO misc.py line 119 87073] Train: [19/100][797/1557] Data 0.004 (0.129) Batch 0.793 (1.160) Remain 40:52:44 loss: 0.3720 Lr: 0.00475 [2024-02-18 02:36:37,373 INFO misc.py line 119 87073] Train: [19/100][798/1557] Data 0.006 (0.129) Batch 1.236 (1.160) Remain 40:52:55 loss: 0.3493 Lr: 0.00475 [2024-02-18 02:36:38,426 INFO misc.py line 119 87073] Train: [19/100][799/1557] Data 0.014 (0.129) Batch 1.061 (1.160) Remain 40:52:38 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Batch 1.199 (1.162) Remain 40:54:04 loss: 0.5547 Lr: 0.00475 [2024-02-18 02:38:43,831 INFO misc.py line 119 87073] Train: [19/100][906/1557] Data 0.003 (0.130) Batch 0.975 (1.161) Remain 40:53:36 loss: 0.8757 Lr: 0.00475 [2024-02-18 02:38:44,913 INFO misc.py line 119 87073] Train: [19/100][907/1557] Data 0.004 (0.130) Batch 1.082 (1.161) Remain 40:53:24 loss: 0.5565 Lr: 0.00475 [2024-02-18 02:38:45,661 INFO misc.py line 119 87073] Train: [19/100][908/1557] Data 0.003 (0.130) Batch 0.747 (1.161) Remain 40:52:25 loss: 0.2138 Lr: 0.00475 [2024-02-18 02:38:46,362 INFO misc.py line 119 87073] Train: [19/100][909/1557] Data 0.005 (0.130) Batch 0.698 (1.160) Remain 40:51:19 loss: 0.3196 Lr: 0.00475 [2024-02-18 02:38:47,552 INFO misc.py line 119 87073] Train: [19/100][910/1557] Data 0.007 (0.130) Batch 1.189 (1.160) Remain 40:51:22 loss: 0.6361 Lr: 0.00475 [2024-02-18 02:38:48,599 INFO misc.py line 119 87073] Train: [19/100][911/1557] Data 0.009 (0.129) Batch 1.042 (1.160) Remain 40:51:04 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Remain 40:47:54 loss: 0.3357 Lr: 0.00474 [2024-02-18 02:45:13,074 INFO misc.py line 119 87073] Train: [19/100][1241/1557] Data 0.004 (0.130) Batch 0.917 (1.161) Remain 40:47:28 loss: 0.6464 Lr: 0.00474 [2024-02-18 02:45:14,193 INFO misc.py line 119 87073] Train: [19/100][1242/1557] Data 0.005 (0.130) Batch 1.120 (1.161) Remain 40:47:23 loss: 0.5191 Lr: 0.00474 [2024-02-18 02:45:15,143 INFO misc.py line 119 87073] Train: [19/100][1243/1557] Data 0.004 (0.130) Batch 0.950 (1.161) Remain 40:47:00 loss: 0.8839 Lr: 0.00474 [2024-02-18 02:45:15,927 INFO misc.py line 119 87073] Train: [19/100][1244/1557] Data 0.005 (0.130) Batch 0.784 (1.161) Remain 40:46:20 loss: 0.6939 Lr: 0.00474 [2024-02-18 02:45:16,659 INFO misc.py line 119 87073] Train: [19/100][1245/1557] Data 0.004 (0.130) Batch 0.731 (1.161) Remain 40:45:35 loss: 0.3142 Lr: 0.00474 [2024-02-18 02:45:18,078 INFO misc.py line 119 87073] Train: [19/100][1246/1557] Data 0.005 (0.130) Batch 1.345 (1.161) Remain 40:45:53 loss: 0.3596 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1253/1557] Data 0.003 (0.129) Batch 1.297 (1.160) Remain 40:43:31 loss: 0.1894 Lr: 0.00474 [2024-02-18 02:45:25,815 INFO misc.py line 119 87073] Train: [19/100][1254/1557] Data 0.016 (0.129) Batch 1.011 (1.160) Remain 40:43:15 loss: 0.3482 Lr: 0.00474 [2024-02-18 02:45:26,718 INFO misc.py line 119 87073] Train: [19/100][1255/1557] Data 0.007 (0.129) Batch 0.905 (1.159) Remain 40:42:48 loss: 0.5366 Lr: 0.00474 [2024-02-18 02:45:27,830 INFO misc.py line 119 87073] Train: [19/100][1256/1557] Data 0.004 (0.129) Batch 1.111 (1.159) Remain 40:42:42 loss: 0.5078 Lr: 0.00474 [2024-02-18 02:45:28,946 INFO misc.py line 119 87073] Train: [19/100][1257/1557] Data 0.006 (0.129) Batch 1.117 (1.159) Remain 40:42:36 loss: 0.7435 Lr: 0.00474 [2024-02-18 02:45:29,746 INFO misc.py line 119 87073] Train: [19/100][1258/1557] Data 0.004 (0.128) Batch 0.801 (1.159) Remain 40:41:59 loss: 0.3523 Lr: 0.00474 [2024-02-18 02:45:30,426 INFO misc.py line 119 87073] Train: [19/100][1259/1557] Data 0.003 (0.128) Batch 0.667 (1.159) Remain 40:41:08 loss: 0.3859 Lr: 0.00474 [2024-02-18 02:45:31,599 INFO misc.py line 119 87073] Train: [19/100][1260/1557] Data 0.016 (0.128) Batch 1.169 (1.159) Remain 40:41:08 loss: 0.1886 Lr: 0.00474 [2024-02-18 02:45:32,490 INFO misc.py line 119 87073] Train: [19/100][1261/1557] Data 0.020 (0.128) Batch 0.908 (1.158) Remain 40:40:42 loss: 0.3991 Lr: 0.00474 [2024-02-18 02:45:33,355 INFO misc.py line 119 87073] Train: [19/100][1262/1557] Data 0.004 (0.128) Batch 0.864 (1.158) Remain 40:40:11 loss: 0.4254 Lr: 0.00474 [2024-02-18 02:45:34,269 INFO misc.py line 119 87073] Train: [19/100][1263/1557] Data 0.004 (0.128) Batch 0.906 (1.158) Remain 40:39:45 loss: 0.6184 Lr: 0.00474 [2024-02-18 02:45:35,283 INFO misc.py line 119 87073] Train: [19/100][1264/1557] Data 0.012 (0.128) Batch 1.012 (1.158) Remain 40:39:29 loss: 0.3241 Lr: 0.00474 [2024-02-18 02:45:36,039 INFO misc.py line 119 87073] Train: [19/100][1265/1557] Data 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Remain 40:37:17 loss: 0.4715 Lr: 0.00474 [2024-02-18 02:45:42,889 INFO misc.py line 119 87073] Train: [19/100][1272/1557] Data 0.004 (0.127) Batch 0.751 (1.157) Remain 40:36:36 loss: 0.5177 Lr: 0.00474 [2024-02-18 02:45:43,688 INFO misc.py line 119 87073] Train: [19/100][1273/1557] Data 0.004 (0.127) Batch 0.788 (1.156) Remain 40:35:58 loss: 0.5333 Lr: 0.00474 [2024-02-18 02:45:44,824 INFO misc.py line 119 87073] Train: [19/100][1274/1557] Data 0.015 (0.127) Batch 1.140 (1.156) Remain 40:35:55 loss: 0.3562 Lr: 0.00474 [2024-02-18 02:45:45,749 INFO misc.py line 119 87073] Train: [19/100][1275/1557] Data 0.010 (0.127) Batch 0.931 (1.156) Remain 40:35:31 loss: 0.7050 Lr: 0.00474 [2024-02-18 02:45:46,556 INFO misc.py line 119 87073] Train: [19/100][1276/1557] Data 0.004 (0.127) Batch 0.804 (1.156) Remain 40:34:55 loss: 0.7591 Lr: 0.00474 [2024-02-18 02:45:47,533 INFO misc.py line 119 87073] Train: [19/100][1277/1557] Data 0.006 (0.127) Batch 0.978 (1.156) Remain 40:34:37 loss: 0.7726 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1284/1557] Data 0.005 (0.126) Batch 0.948 (1.154) Remain 40:31:24 loss: 0.5414 Lr: 0.00474 [2024-02-18 02:45:54,652 INFO misc.py line 119 87073] Train: [19/100][1285/1557] Data 0.005 (0.126) Batch 0.902 (1.154) Remain 40:30:58 loss: 0.5825 Lr: 0.00474 [2024-02-18 02:45:55,364 INFO misc.py line 119 87073] Train: [19/100][1286/1557] Data 0.006 (0.126) Batch 0.714 (1.154) Remain 40:30:13 loss: 0.3772 Lr: 0.00474 [2024-02-18 02:45:56,159 INFO misc.py line 119 87073] Train: [19/100][1287/1557] Data 0.003 (0.126) Batch 0.789 (1.153) Remain 40:29:36 loss: 0.6934 Lr: 0.00474 [2024-02-18 02:45:57,403 INFO misc.py line 119 87073] Train: [19/100][1288/1557] Data 0.010 (0.126) Batch 1.249 (1.153) Remain 40:29:44 loss: 0.5900 Lr: 0.00474 [2024-02-18 02:45:58,285 INFO misc.py line 119 87073] Train: [19/100][1289/1557] Data 0.009 (0.126) Batch 0.882 (1.153) Remain 40:29:17 loss: 0.4405 Lr: 0.00474 [2024-02-18 02:45:59,330 INFO misc.py line 119 87073] Train: [19/100][1290/1557] Data 0.005 (0.125) Batch 1.046 (1.153) Remain 40:29:05 loss: 0.6986 Lr: 0.00474 [2024-02-18 02:46:00,406 INFO misc.py line 119 87073] Train: [19/100][1291/1557] Data 0.003 (0.125) Batch 1.075 (1.153) Remain 40:28:56 loss: 0.9038 Lr: 0.00474 [2024-02-18 02:46:01,318 INFO misc.py line 119 87073] Train: [19/100][1292/1557] Data 0.003 (0.125) Batch 0.912 (1.153) Remain 40:28:31 loss: 0.6393 Lr: 0.00474 [2024-02-18 02:46:02,095 INFO misc.py line 119 87073] Train: [19/100][1293/1557] Data 0.004 (0.125) Batch 0.766 (1.153) Remain 40:27:52 loss: 0.5908 Lr: 0.00474 [2024-02-18 02:46:02,867 INFO misc.py line 119 87073] Train: [19/100][1294/1557] Data 0.015 (0.125) Batch 0.782 (1.152) Remain 40:27:15 loss: 0.4731 Lr: 0.00474 [2024-02-18 02:46:15,237 INFO misc.py line 119 87073] Train: [19/100][1295/1557] Data 7.134 (0.130) Batch 12.370 (1.161) Remain 40:45:31 loss: 0.3530 Lr: 0.00474 [2024-02-18 02:46:16,275 INFO misc.py line 119 87073] Train: [19/100][1296/1557] Data 0.004 (0.130) Batch 1.039 (1.161) Remain 40:45:18 loss: 0.4128 Lr: 0.00474 [2024-02-18 02:46:17,207 INFO misc.py line 119 87073] Train: [19/100][1297/1557] Data 0.003 (0.130) Batch 0.931 (1.161) Remain 40:44:54 loss: 0.4238 Lr: 0.00474 [2024-02-18 02:46:18,154 INFO misc.py line 119 87073] Train: [19/100][1298/1557] Data 0.004 (0.130) Batch 0.938 (1.161) Remain 40:44:31 loss: 0.8202 Lr: 0.00474 [2024-02-18 02:46:19,285 INFO misc.py line 119 87073] Train: [19/100][1299/1557] Data 0.013 (0.130) Batch 1.138 (1.161) Remain 40:44:28 loss: 0.8397 Lr: 0.00474 [2024-02-18 02:46:20,077 INFO misc.py line 119 87073] Train: [19/100][1300/1557] Data 0.006 (0.130) Batch 0.793 (1.160) Remain 40:43:51 loss: 0.7116 Lr: 0.00474 [2024-02-18 02:46:20,888 INFO misc.py line 119 87073] Train: [19/100][1301/1557] Data 0.004 (0.130) Batch 0.810 (1.160) Remain 40:43:16 loss: 0.3801 Lr: 0.00474 [2024-02-18 02:46:22,204 INFO misc.py line 119 87073] Train: [19/100][1302/1557] Data 0.006 (0.130) Batch 1.306 (1.160) Remain 40:43:29 loss: 0.4189 Lr: 0.00474 [2024-02-18 02:46:23,059 INFO misc.py line 119 87073] Train: [19/100][1303/1557] Data 0.016 (0.130) Batch 0.866 (1.160) Remain 40:42:59 loss: 0.2706 Lr: 0.00474 [2024-02-18 02:46:24,007 INFO misc.py line 119 87073] Train: [19/100][1304/1557] Data 0.004 (0.130) Batch 0.949 (1.160) Remain 40:42:37 loss: 0.5602 Lr: 0.00474 [2024-02-18 02:46:24,895 INFO misc.py line 119 87073] Train: [19/100][1305/1557] Data 0.004 (0.130) Batch 0.886 (1.160) Remain 40:42:10 loss: 0.9694 Lr: 0.00474 [2024-02-18 02:46:26,005 INFO misc.py line 119 87073] Train: [19/100][1306/1557] Data 0.007 (0.129) Batch 1.103 (1.159) Remain 40:42:03 loss: 0.3942 Lr: 0.00474 [2024-02-18 02:46:26,673 INFO misc.py line 119 87073] Train: [19/100][1307/1557] Data 0.013 (0.129) Batch 0.674 (1.159) Remain 40:41:15 loss: 0.5941 Lr: 0.00474 [2024-02-18 02:46:27,424 INFO misc.py line 119 87073] Train: [19/100][1308/1557] Data 0.008 (0.129) Batch 0.753 (1.159) Remain 40:40:34 loss: 0.3093 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1315/1557] Data 0.005 (0.129) Batch 0.776 (1.158) Remain 40:37:59 loss: 0.3229 Lr: 0.00474 [2024-02-18 02:46:35,218 INFO misc.py line 119 87073] Train: [19/100][1316/1557] Data 0.007 (0.129) Batch 1.215 (1.158) Remain 40:38:03 loss: 0.2226 Lr: 0.00474 [2024-02-18 02:46:36,038 INFO misc.py line 119 87073] Train: [19/100][1317/1557] Data 0.006 (0.128) Batch 0.820 (1.157) Remain 40:37:29 loss: 0.4208 Lr: 0.00474 [2024-02-18 02:46:37,113 INFO misc.py line 119 87073] Train: [19/100][1318/1557] Data 0.006 (0.128) Batch 1.075 (1.157) Remain 40:37:20 loss: 0.6048 Lr: 0.00474 [2024-02-18 02:46:38,165 INFO misc.py line 119 87073] Train: [19/100][1319/1557] Data 0.006 (0.128) Batch 1.054 (1.157) Remain 40:37:09 loss: 0.4888 Lr: 0.00474 [2024-02-18 02:46:39,160 INFO misc.py line 119 87073] Train: [19/100][1320/1557] Data 0.004 (0.128) Batch 0.993 (1.157) Remain 40:36:52 loss: 0.5509 Lr: 0.00474 [2024-02-18 02:46:39,837 INFO misc.py line 119 87073] Train: [19/100][1321/1557] Data 0.006 (0.128) Batch 0.679 (1.157) Remain 40:36:05 loss: 0.4734 Lr: 0.00474 [2024-02-18 02:46:40,604 INFO misc.py line 119 87073] Train: [19/100][1322/1557] Data 0.004 (0.128) Batch 0.765 (1.157) Remain 40:35:26 loss: 0.3810 Lr: 0.00474 [2024-02-18 02:46:42,000 INFO misc.py line 119 87073] Train: [19/100][1323/1557] Data 0.006 (0.128) Batch 1.394 (1.157) Remain 40:35:48 loss: 0.4106 Lr: 0.00474 [2024-02-18 02:46:42,961 INFO misc.py line 119 87073] Train: [19/100][1324/1557] Data 0.009 (0.128) Batch 0.965 (1.157) Remain 40:35:29 loss: 0.5353 Lr: 0.00474 [2024-02-18 02:46:43,863 INFO misc.py line 119 87073] Train: [19/100][1325/1557] Data 0.005 (0.128) Batch 0.901 (1.156) Remain 40:35:03 loss: 0.9479 Lr: 0.00474 [2024-02-18 02:46:44,887 INFO misc.py line 119 87073] Train: [19/100][1326/1557] Data 0.006 (0.128) Batch 1.021 (1.156) Remain 40:34:49 loss: 0.6537 Lr: 0.00474 [2024-02-18 02:46:45,852 INFO misc.py line 119 87073] Train: [19/100][1327/1557] Data 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Remain 40:32:11 loss: 0.8677 Lr: 0.00474 [2024-02-18 02:46:52,329 INFO misc.py line 119 87073] Train: [19/100][1334/1557] Data 0.006 (0.127) Batch 0.928 (1.155) Remain 40:31:48 loss: 0.7075 Lr: 0.00474 [2024-02-18 02:46:53,120 INFO misc.py line 119 87073] Train: [19/100][1335/1557] Data 0.006 (0.127) Batch 0.783 (1.155) Remain 40:31:12 loss: 0.6010 Lr: 0.00474 [2024-02-18 02:46:53,875 INFO misc.py line 119 87073] Train: [19/100][1336/1557] Data 0.014 (0.127) Batch 0.764 (1.154) Remain 40:30:34 loss: 0.3625 Lr: 0.00474 [2024-02-18 02:46:55,168 INFO misc.py line 119 87073] Train: [19/100][1337/1557] Data 0.004 (0.127) Batch 1.278 (1.154) Remain 40:30:44 loss: 0.4383 Lr: 0.00474 [2024-02-18 02:46:56,127 INFO misc.py line 119 87073] Train: [19/100][1338/1557] Data 0.020 (0.127) Batch 0.974 (1.154) Remain 40:30:26 loss: 0.4719 Lr: 0.00474 [2024-02-18 02:46:57,037 INFO misc.py line 119 87073] Train: [19/100][1339/1557] Data 0.004 (0.126) Batch 0.910 (1.154) Remain 40:30:02 loss: 0.6322 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1346/1557] Data 0.005 (0.126) Batch 0.965 (1.153) Remain 40:27:32 loss: 0.6570 Lr: 0.00474 [2024-02-18 02:47:04,480 INFO misc.py line 119 87073] Train: [19/100][1347/1557] Data 0.005 (0.126) Batch 0.872 (1.153) Remain 40:27:04 loss: 0.2758 Lr: 0.00474 [2024-02-18 02:47:05,391 INFO misc.py line 119 87073] Train: [19/100][1348/1557] Data 0.005 (0.126) Batch 0.907 (1.153) Remain 40:26:40 loss: 0.9038 Lr: 0.00474 [2024-02-18 02:47:06,133 INFO misc.py line 119 87073] Train: [19/100][1349/1557] Data 0.012 (0.126) Batch 0.744 (1.152) Remain 40:26:01 loss: 0.3656 Lr: 0.00474 [2024-02-18 02:47:07,004 INFO misc.py line 119 87073] Train: [19/100][1350/1557] Data 0.007 (0.125) Batch 0.862 (1.152) Remain 40:25:32 loss: 0.4014 Lr: 0.00474 [2024-02-18 02:47:19,191 INFO misc.py line 119 87073] Train: [19/100][1351/1557] Data 7.188 (0.131) Batch 12.198 (1.160) Remain 40:42:46 loss: 0.4014 Lr: 0.00474 [2024-02-18 02:47:20,070 INFO misc.py line 119 87073] Train: [19/100][1352/1557] Data 0.005 (0.131) Batch 0.879 (1.160) Remain 40:42:19 loss: 0.4600 Lr: 0.00474 [2024-02-18 02:47:21,023 INFO misc.py line 119 87073] Train: [19/100][1353/1557] Data 0.004 (0.130) Batch 0.953 (1.160) Remain 40:41:58 loss: 0.8925 Lr: 0.00474 [2024-02-18 02:47:22,000 INFO misc.py line 119 87073] Train: [19/100][1354/1557] Data 0.004 (0.130) Batch 0.973 (1.160) Remain 40:41:40 loss: 0.1442 Lr: 0.00474 [2024-02-18 02:47:22,944 INFO misc.py line 119 87073] Train: [19/100][1355/1557] Data 0.008 (0.130) Batch 0.947 (1.160) Remain 40:41:19 loss: 0.6432 Lr: 0.00474 [2024-02-18 02:47:23,680 INFO misc.py line 119 87073] Train: [19/100][1356/1557] Data 0.006 (0.130) Batch 0.734 (1.159) Remain 40:40:38 loss: 0.7233 Lr: 0.00474 [2024-02-18 02:47:24,472 INFO misc.py line 119 87073] Train: [19/100][1357/1557] Data 0.009 (0.130) Batch 0.793 (1.159) Remain 40:40:02 loss: 0.4871 Lr: 0.00474 [2024-02-18 02:47:25,812 INFO misc.py line 119 87073] Train: [19/100][1358/1557] Data 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Remain 40:37:35 loss: 0.5294 Lr: 0.00474 [2024-02-18 02:47:32,371 INFO misc.py line 119 87073] Train: [19/100][1365/1557] Data 0.008 (0.129) Batch 1.277 (1.158) Remain 40:37:45 loss: 0.2088 Lr: 0.00474 [2024-02-18 02:47:33,471 INFO misc.py line 119 87073] Train: [19/100][1366/1557] Data 0.011 (0.129) Batch 1.106 (1.158) Remain 40:37:39 loss: 0.3488 Lr: 0.00474 [2024-02-18 02:47:34,353 INFO misc.py line 119 87073] Train: [19/100][1367/1557] Data 0.007 (0.129) Batch 0.883 (1.158) Remain 40:37:13 loss: 0.5640 Lr: 0.00474 [2024-02-18 02:47:35,201 INFO misc.py line 119 87073] Train: [19/100][1368/1557] Data 0.005 (0.129) Batch 0.845 (1.158) Remain 40:36:42 loss: 0.5492 Lr: 0.00474 [2024-02-18 02:47:36,180 INFO misc.py line 119 87073] Train: [19/100][1369/1557] Data 0.008 (0.129) Batch 0.982 (1.157) Remain 40:36:25 loss: 0.5337 Lr: 0.00474 [2024-02-18 02:47:36,919 INFO misc.py line 119 87073] Train: [19/100][1370/1557] Data 0.006 (0.129) Batch 0.739 (1.157) Remain 40:35:45 loss: 0.2485 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1377/1557] Data 0.004 (0.128) Batch 0.743 (1.156) Remain 40:33:11 loss: 0.6887 Lr: 0.00474 [2024-02-18 02:47:44,221 INFO misc.py line 119 87073] Train: [19/100][1378/1557] Data 0.016 (0.128) Batch 0.791 (1.156) Remain 40:32:36 loss: 0.5492 Lr: 0.00474 [2024-02-18 02:47:45,512 INFO misc.py line 119 87073] Train: [19/100][1379/1557] Data 0.004 (0.128) Batch 1.285 (1.156) Remain 40:32:47 loss: 0.4225 Lr: 0.00474 [2024-02-18 02:47:46,452 INFO misc.py line 119 87073] Train: [19/100][1380/1557] Data 0.011 (0.128) Batch 0.946 (1.156) Remain 40:32:27 loss: 1.0479 Lr: 0.00474 [2024-02-18 02:47:47,395 INFO misc.py line 119 87073] Train: [19/100][1381/1557] Data 0.006 (0.128) Batch 0.944 (1.155) Remain 40:32:06 loss: 0.5051 Lr: 0.00474 [2024-02-18 02:47:48,328 INFO misc.py line 119 87073] Train: [19/100][1382/1557] Data 0.004 (0.128) Batch 0.932 (1.155) Remain 40:31:45 loss: 0.5063 Lr: 0.00474 [2024-02-18 02:47:49,299 INFO misc.py line 119 87073] Train: [19/100][1383/1557] Data 0.005 (0.128) Batch 0.963 (1.155) Remain 40:31:26 loss: 0.7775 Lr: 0.00474 [2024-02-18 02:47:50,075 INFO misc.py line 119 87073] Train: [19/100][1384/1557] Data 0.012 (0.128) Batch 0.785 (1.155) Remain 40:30:51 loss: 0.8315 Lr: 0.00474 [2024-02-18 02:47:50,808 INFO misc.py line 119 87073] Train: [19/100][1385/1557] Data 0.003 (0.128) Batch 0.726 (1.155) Remain 40:30:11 loss: 0.5117 Lr: 0.00474 [2024-02-18 02:47:52,029 INFO misc.py line 119 87073] Train: [19/100][1386/1557] Data 0.010 (0.128) Batch 1.224 (1.155) Remain 40:30:16 loss: 0.1648 Lr: 0.00474 [2024-02-18 02:47:53,052 INFO misc.py line 119 87073] Train: [19/100][1387/1557] Data 0.009 (0.127) Batch 1.014 (1.155) Remain 40:30:02 loss: 0.5385 Lr: 0.00474 [2024-02-18 02:47:53,977 INFO misc.py line 119 87073] Train: [19/100][1388/1557] Data 0.017 (0.127) Batch 0.932 (1.154) Remain 40:29:40 loss: 0.4125 Lr: 0.00474 [2024-02-18 02:47:54,862 INFO misc.py line 119 87073] Train: [19/100][1389/1557] Data 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Remain 40:27:03 loss: 0.4928 Lr: 0.00474 [2024-02-18 02:48:01,498 INFO misc.py line 119 87073] Train: [19/100][1396/1557] Data 0.008 (0.127) Batch 1.092 (1.153) Remain 40:26:56 loss: 0.9734 Lr: 0.00474 [2024-02-18 02:48:02,533 INFO misc.py line 119 87073] Train: [19/100][1397/1557] Data 0.004 (0.127) Batch 1.035 (1.153) Remain 40:26:44 loss: 0.3734 Lr: 0.00474 [2024-02-18 02:48:03,298 INFO misc.py line 119 87073] Train: [19/100][1398/1557] Data 0.005 (0.127) Batch 0.764 (1.153) Remain 40:26:08 loss: 0.4163 Lr: 0.00474 [2024-02-18 02:48:03,976 INFO misc.py line 119 87073] Train: [19/100][1399/1557] Data 0.006 (0.126) Batch 0.678 (1.152) Remain 40:25:24 loss: 0.7300 Lr: 0.00474 [2024-02-18 02:48:05,270 INFO misc.py line 119 87073] Train: [19/100][1400/1557] Data 0.006 (0.126) Batch 1.291 (1.153) Remain 40:25:35 loss: 0.4426 Lr: 0.00474 [2024-02-18 02:48:06,107 INFO misc.py line 119 87073] Train: [19/100][1401/1557] Data 0.011 (0.126) Batch 0.841 (1.152) Remain 40:25:06 loss: 0.4541 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1408/1557] Data 0.006 (0.131) Batch 1.000 (1.160) Remain 40:41:11 loss: 0.7372 Lr: 0.00474 [2024-02-18 02:48:25,927 INFO misc.py line 119 87073] Train: [19/100][1409/1557] Data 0.004 (0.131) Batch 0.921 (1.160) Remain 40:40:49 loss: 0.7153 Lr: 0.00474 [2024-02-18 02:48:26,906 INFO misc.py line 119 87073] Train: [19/100][1410/1557] Data 0.006 (0.131) Batch 0.981 (1.160) Remain 40:40:31 loss: 0.2324 Lr: 0.00474 [2024-02-18 02:48:27,947 INFO misc.py line 119 87073] Train: [19/100][1411/1557] Data 0.004 (0.131) Batch 1.038 (1.160) Remain 40:40:19 loss: 0.5570 Lr: 0.00474 [2024-02-18 02:48:28,718 INFO misc.py line 119 87073] Train: [19/100][1412/1557] Data 0.007 (0.130) Batch 0.773 (1.159) Remain 40:39:44 loss: 0.4113 Lr: 0.00474 [2024-02-18 02:48:29,486 INFO misc.py line 119 87073] Train: [19/100][1413/1557] Data 0.004 (0.130) Batch 0.767 (1.159) Remain 40:39:07 loss: 0.6981 Lr: 0.00474 [2024-02-18 02:48:30,739 INFO misc.py line 119 87073] Train: [19/100][1414/1557] Data 0.007 (0.130) Batch 1.253 (1.159) Remain 40:39:15 loss: 0.2881 Lr: 0.00474 [2024-02-18 02:48:31,594 INFO misc.py line 119 87073] Train: [19/100][1415/1557] Data 0.005 (0.130) Batch 0.856 (1.159) Remain 40:38:46 loss: 0.5171 Lr: 0.00474 [2024-02-18 02:48:32,628 INFO misc.py line 119 87073] Train: [19/100][1416/1557] Data 0.003 (0.130) Batch 1.033 (1.159) Remain 40:38:34 loss: 0.8956 Lr: 0.00474 [2024-02-18 02:48:33,507 INFO misc.py line 119 87073] Train: [19/100][1417/1557] Data 0.005 (0.130) Batch 0.880 (1.159) Remain 40:38:08 loss: 0.2668 Lr: 0.00474 [2024-02-18 02:48:34,505 INFO misc.py line 119 87073] Train: [19/100][1418/1557] Data 0.004 (0.130) Batch 0.991 (1.159) Remain 40:37:52 loss: 0.4581 Lr: 0.00474 [2024-02-18 02:48:35,292 INFO misc.py line 119 87073] Train: [19/100][1419/1557] Data 0.011 (0.130) Batch 0.793 (1.158) Remain 40:37:18 loss: 0.3986 Lr: 0.00474 [2024-02-18 02:48:36,029 INFO misc.py line 119 87073] Train: [19/100][1420/1557] Data 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Remain 40:35:28 loss: 0.4595 Lr: 0.00474 [2024-02-18 02:48:42,998 INFO misc.py line 119 87073] Train: [19/100][1427/1557] Data 0.004 (0.129) Batch 0.748 (1.157) Remain 40:34:50 loss: 0.4407 Lr: 0.00474 [2024-02-18 02:48:44,095 INFO misc.py line 119 87073] Train: [19/100][1428/1557] Data 0.004 (0.129) Batch 1.092 (1.157) Remain 40:34:43 loss: 0.1490 Lr: 0.00474 [2024-02-18 02:48:44,983 INFO misc.py line 119 87073] Train: [19/100][1429/1557] Data 0.009 (0.129) Batch 0.893 (1.157) Remain 40:34:19 loss: 0.4538 Lr: 0.00474 [2024-02-18 02:48:45,919 INFO misc.py line 119 87073] Train: [19/100][1430/1557] Data 0.004 (0.129) Batch 0.936 (1.157) Remain 40:33:58 loss: 0.4211 Lr: 0.00474 [2024-02-18 02:48:46,888 INFO misc.py line 119 87073] Train: [19/100][1431/1557] Data 0.004 (0.129) Batch 0.966 (1.157) Remain 40:33:40 loss: 0.5143 Lr: 0.00474 [2024-02-18 02:48:47,869 INFO misc.py line 119 87073] Train: [19/100][1432/1557] Data 0.008 (0.129) Batch 0.981 (1.157) Remain 40:33:24 loss: 0.4813 Lr: 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INFO misc.py line 119 87073] Train: [19/100][1439/1557] Data 0.004 (0.128) Batch 1.066 (1.156) Remain 40:31:20 loss: 0.5905 Lr: 0.00474 [2024-02-18 02:48:55,423 INFO misc.py line 119 87073] Train: [19/100][1440/1557] Data 0.007 (0.128) Batch 0.776 (1.155) Remain 40:30:45 loss: 0.8197 Lr: 0.00474 [2024-02-18 02:48:56,144 INFO misc.py line 119 87073] Train: [19/100][1441/1557] Data 0.004 (0.128) Batch 0.717 (1.155) Remain 40:30:06 loss: 0.4633 Lr: 0.00474 [2024-02-18 02:48:57,269 INFO misc.py line 119 87073] Train: [19/100][1442/1557] Data 0.009 (0.128) Batch 1.120 (1.155) Remain 40:30:01 loss: 0.2757 Lr: 0.00474 [2024-02-18 02:48:58,245 INFO misc.py line 119 87073] Train: [19/100][1443/1557] Data 0.013 (0.128) Batch 0.985 (1.155) Remain 40:29:45 loss: 0.9752 Lr: 0.00474 [2024-02-18 02:48:59,252 INFO misc.py line 119 87073] Train: [19/100][1444/1557] Data 0.004 (0.128) Batch 1.008 (1.155) Remain 40:29:31 loss: 0.5114 Lr: 0.00474 [2024-02-18 02:49:00,157 INFO misc.py line 119 87073] Train: [19/100][1445/1557] Data 0.003 (0.128) Batch 0.904 (1.155) Remain 40:29:08 loss: 0.6390 Lr: 0.00474 [2024-02-18 02:49:01,077 INFO misc.py line 119 87073] Train: [19/100][1446/1557] Data 0.004 (0.127) Batch 0.918 (1.154) Remain 40:28:46 loss: 0.6325 Lr: 0.00474 [2024-02-18 02:49:01,838 INFO misc.py line 119 87073] Train: [19/100][1447/1557] Data 0.007 (0.127) Batch 0.762 (1.154) Remain 40:28:11 loss: 0.3684 Lr: 0.00474 [2024-02-18 02:49:02,613 INFO misc.py line 119 87073] Train: [19/100][1448/1557] Data 0.006 (0.127) Batch 0.776 (1.154) Remain 40:27:37 loss: 0.3124 Lr: 0.00474 [2024-02-18 02:49:03,956 INFO misc.py line 119 87073] Train: [19/100][1449/1557] Data 0.004 (0.127) Batch 1.336 (1.154) Remain 40:27:51 loss: 0.2777 Lr: 0.00474 [2024-02-18 02:49:04,863 INFO misc.py line 119 87073] Train: [19/100][1450/1557] Data 0.012 (0.127) Batch 0.913 (1.154) Remain 40:27:29 loss: 0.5510 Lr: 0.00474 [2024-02-18 02:49:05,722 INFO misc.py line 119 87073] Train: [19/100][1451/1557] Data 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Remain 40:25:21 loss: 0.2432 Lr: 0.00474 [2024-02-18 02:49:12,455 INFO misc.py line 119 87073] Train: [19/100][1458/1557] Data 0.006 (0.126) Batch 0.895 (1.153) Remain 40:24:58 loss: 0.3594 Lr: 0.00474 [2024-02-18 02:49:13,412 INFO misc.py line 119 87073] Train: [19/100][1459/1557] Data 0.004 (0.126) Batch 0.958 (1.153) Remain 40:24:40 loss: 0.2696 Lr: 0.00474 [2024-02-18 02:49:14,555 INFO misc.py line 119 87073] Train: [19/100][1460/1557] Data 0.005 (0.126) Batch 1.140 (1.153) Remain 40:24:38 loss: 0.7018 Lr: 0.00474 [2024-02-18 02:49:15,204 INFO misc.py line 119 87073] Train: [19/100][1461/1557] Data 0.007 (0.126) Batch 0.651 (1.152) Remain 40:23:53 loss: 0.5546 Lr: 0.00474 [2024-02-18 02:49:15,964 INFO misc.py line 119 87073] Train: [19/100][1462/1557] Data 0.004 (0.126) Batch 0.757 (1.152) Remain 40:23:18 loss: 0.5588 Lr: 0.00474 [2024-02-18 02:49:28,895 INFO misc.py line 119 87073] Train: [19/100][1463/1557] Data 7.186 (0.131) Batch 12.935 (1.160) Remain 40:40:15 loss: 0.4991 Lr: 0.00474 [2024-02-18 02:49:29,910 INFO misc.py line 119 87073] Train: [19/100][1464/1557] Data 0.004 (0.131) Batch 1.015 (1.160) Remain 40:40:01 loss: 0.5471 Lr: 0.00474 [2024-02-18 02:49:30,897 INFO misc.py line 119 87073] Train: [19/100][1465/1557] Data 0.004 (0.131) Batch 0.986 (1.160) Remain 40:39:45 loss: 0.5311 Lr: 0.00474 [2024-02-18 02:49:31,870 INFO misc.py line 119 87073] Train: [19/100][1466/1557] Data 0.005 (0.131) Batch 0.974 (1.160) Remain 40:39:28 loss: 0.9313 Lr: 0.00474 [2024-02-18 02:49:32,886 INFO misc.py line 119 87073] Train: [19/100][1467/1557] Data 0.005 (0.131) Batch 1.015 (1.160) Remain 40:39:14 loss: 0.6520 Lr: 0.00474 [2024-02-18 02:49:33,622 INFO misc.py line 119 87073] Train: [19/100][1468/1557] Data 0.005 (0.131) Batch 0.736 (1.159) Remain 40:38:37 loss: 0.5947 Lr: 0.00474 [2024-02-18 02:49:34,385 INFO misc.py line 119 87073] Train: [19/100][1469/1557] Data 0.006 (0.130) Batch 0.764 (1.159) Remain 40:38:02 loss: 0.5535 Lr: 0.00474 [2024-02-18 02:49:35,655 INFO misc.py line 119 87073] Train: [19/100][1470/1557] Data 0.004 (0.130) Batch 1.260 (1.159) Remain 40:38:09 loss: 0.4633 Lr: 0.00474 [2024-02-18 02:49:36,669 INFO misc.py line 119 87073] Train: [19/100][1471/1557] Data 0.014 (0.130) Batch 1.024 (1.159) Remain 40:37:56 loss: 0.4165 Lr: 0.00474 [2024-02-18 02:49:37,498 INFO misc.py line 119 87073] Train: [19/100][1472/1557] Data 0.005 (0.130) Batch 0.830 (1.159) Remain 40:37:27 loss: 0.4692 Lr: 0.00474 [2024-02-18 02:49:38,491 INFO misc.py line 119 87073] Train: [19/100][1473/1557] Data 0.005 (0.130) Batch 0.993 (1.159) Remain 40:37:11 loss: 0.5520 Lr: 0.00474 [2024-02-18 02:49:39,527 INFO misc.py line 119 87073] Train: [19/100][1474/1557] Data 0.004 (0.130) Batch 1.035 (1.159) Remain 40:37:00 loss: 0.1797 Lr: 0.00474 [2024-02-18 02:49:40,347 INFO misc.py line 119 87073] Train: [19/100][1475/1557] Data 0.005 (0.130) Batch 0.819 (1.158) Remain 40:36:29 loss: 0.7613 Lr: 0.00474 [2024-02-18 02:49:41,192 INFO misc.py line 119 87073] Train: [19/100][1476/1557] Data 0.006 (0.130) Batch 0.833 (1.158) Remain 40:36:00 loss: 0.6666 Lr: 0.00474 [2024-02-18 02:49:42,453 INFO misc.py line 119 87073] Train: [19/100][1477/1557] Data 0.018 (0.130) Batch 1.273 (1.158) Remain 40:36:09 loss: 0.4756 Lr: 0.00474 [2024-02-18 02:49:43,427 INFO misc.py line 119 87073] Train: [19/100][1478/1557] Data 0.006 (0.130) Batch 0.976 (1.158) Remain 40:35:52 loss: 0.4596 Lr: 0.00474 [2024-02-18 02:49:44,379 INFO misc.py line 119 87073] Train: [19/100][1479/1557] Data 0.004 (0.130) Batch 0.953 (1.158) Remain 40:35:34 loss: 0.9351 Lr: 0.00474 [2024-02-18 02:49:45,593 INFO misc.py line 119 87073] Train: [19/100][1480/1557] Data 0.004 (0.130) Batch 1.213 (1.158) Remain 40:35:37 loss: 0.6650 Lr: 0.00474 [2024-02-18 02:49:46,617 INFO misc.py line 119 87073] Train: [19/100][1481/1557] Data 0.004 (0.129) Batch 1.025 (1.158) Remain 40:35:25 loss: 0.2535 Lr: 0.00474 [2024-02-18 02:49:47,301 INFO misc.py line 119 87073] Train: [19/100][1482/1557] Data 0.004 (0.129) Batch 0.683 (1.158) Remain 40:34:43 loss: 0.6047 Lr: 0.00474 [2024-02-18 02:49:48,060 INFO misc.py line 119 87073] Train: [19/100][1483/1557] Data 0.005 (0.129) Batch 0.758 (1.157) Remain 40:34:08 loss: 0.5038 Lr: 0.00474 [2024-02-18 02:49:49,213 INFO misc.py line 119 87073] Train: [19/100][1484/1557] Data 0.004 (0.129) Batch 1.152 (1.157) Remain 40:34:06 loss: 0.3644 Lr: 0.00474 [2024-02-18 02:49:50,035 INFO misc.py line 119 87073] Train: [19/100][1485/1557] Data 0.005 (0.129) Batch 0.821 (1.157) Remain 40:33:36 loss: 0.4895 Lr: 0.00474 [2024-02-18 02:49:51,053 INFO misc.py line 119 87073] Train: [19/100][1486/1557] Data 0.006 (0.129) Batch 1.020 (1.157) Remain 40:33:23 loss: 0.9173 Lr: 0.00474 [2024-02-18 02:49:52,056 INFO misc.py line 119 87073] Train: [19/100][1487/1557] Data 0.005 (0.129) Batch 1.001 (1.157) Remain 40:33:09 loss: 0.6918 Lr: 0.00474 [2024-02-18 02:49:53,080 INFO misc.py line 119 87073] Train: [19/100][1488/1557] Data 0.007 (0.129) Batch 1.026 (1.157) Remain 40:32:57 loss: 0.3034 Lr: 0.00474 [2024-02-18 02:49:53,859 INFO misc.py line 119 87073] Train: [19/100][1489/1557] Data 0.005 (0.129) Batch 0.777 (1.157) Remain 40:32:23 loss: 0.4511 Lr: 0.00474 [2024-02-18 02:49:54,656 INFO misc.py line 119 87073] Train: [19/100][1490/1557] Data 0.007 (0.129) Batch 0.790 (1.156) Remain 40:31:51 loss: 0.4768 Lr: 0.00474 [2024-02-18 02:49:55,956 INFO misc.py line 119 87073] Train: [19/100][1491/1557] Data 0.014 (0.129) Batch 1.307 (1.156) Remain 40:32:03 loss: 0.2799 Lr: 0.00474 [2024-02-18 02:49:56,962 INFO misc.py line 119 87073] Train: [19/100][1492/1557] Data 0.006 (0.129) Batch 1.001 (1.156) Remain 40:31:48 loss: 0.5350 Lr: 0.00474 [2024-02-18 02:49:58,008 INFO misc.py line 119 87073] Train: [19/100][1493/1557] Data 0.012 (0.128) Batch 1.037 (1.156) Remain 40:31:37 loss: 0.7089 Lr: 0.00474 [2024-02-18 02:49:58,903 INFO misc.py line 119 87073] Train: [19/100][1494/1557] Data 0.020 (0.128) Batch 0.911 (1.156) Remain 40:31:15 loss: 0.8354 Lr: 0.00474 [2024-02-18 02:49:59,839 INFO misc.py line 119 87073] Train: [19/100][1495/1557] Data 0.005 (0.128) Batch 0.936 (1.156) Remain 40:30:55 loss: 0.3720 Lr: 0.00474 [2024-02-18 02:50:00,579 INFO misc.py line 119 87073] Train: [19/100][1496/1557] Data 0.005 (0.128) Batch 0.740 (1.156) Remain 40:30:19 loss: 0.4081 Lr: 0.00474 [2024-02-18 02:50:01,354 INFO misc.py line 119 87073] Train: [19/100][1497/1557] Data 0.004 (0.128) Batch 0.773 (1.155) Remain 40:29:46 loss: 0.3541 Lr: 0.00474 [2024-02-18 02:50:02,554 INFO misc.py line 119 87073] Train: [19/100][1498/1557] Data 0.005 (0.128) Batch 1.201 (1.155) Remain 40:29:48 loss: 0.3101 Lr: 0.00474 [2024-02-18 02:50:03,465 INFO misc.py line 119 87073] Train: [19/100][1499/1557] Data 0.005 (0.128) Batch 0.912 (1.155) Remain 40:29:27 loss: 0.3673 Lr: 0.00474 [2024-02-18 02:50:04,365 INFO misc.py line 119 87073] Train: [19/100][1500/1557] Data 0.004 (0.128) Batch 0.891 (1.155) Remain 40:29:03 loss: 0.9924 Lr: 0.00474 [2024-02-18 02:50:05,472 INFO misc.py line 119 87073] Train: [19/100][1501/1557] Data 0.013 (0.128) Batch 1.112 (1.155) Remain 40:28:58 loss: 0.7589 Lr: 0.00474 [2024-02-18 02:50:06,307 INFO misc.py line 119 87073] Train: [19/100][1502/1557] Data 0.009 (0.128) Batch 0.840 (1.155) Remain 40:28:31 loss: 0.5358 Lr: 0.00474 [2024-02-18 02:50:07,138 INFO misc.py line 119 87073] Train: [19/100][1503/1557] Data 0.004 (0.128) Batch 0.830 (1.155) Remain 40:28:02 loss: 0.3395 Lr: 0.00474 [2024-02-18 02:50:07,928 INFO misc.py line 119 87073] Train: [19/100][1504/1557] Data 0.004 (0.128) Batch 0.788 (1.154) Remain 40:27:30 loss: 0.5804 Lr: 0.00474 [2024-02-18 02:50:09,179 INFO misc.py line 119 87073] Train: [19/100][1505/1557] Data 0.006 (0.128) Batch 1.245 (1.154) Remain 40:27:37 loss: 0.4726 Lr: 0.00474 [2024-02-18 02:50:10,186 INFO misc.py line 119 87073] Train: [19/100][1506/1557] Data 0.012 (0.127) Batch 1.003 (1.154) Remain 40:27:23 loss: 0.5412 Lr: 0.00474 [2024-02-18 02:50:11,048 INFO misc.py line 119 87073] Train: [19/100][1507/1557] Data 0.016 (0.127) Batch 0.873 (1.154) Remain 40:26:58 loss: 0.6955 Lr: 0.00474 [2024-02-18 02:50:12,129 INFO misc.py line 119 87073] Train: [19/100][1508/1557] Data 0.005 (0.127) Batch 1.081 (1.154) Remain 40:26:51 loss: 0.4111 Lr: 0.00474 [2024-02-18 02:50:13,084 INFO misc.py line 119 87073] Train: [19/100][1509/1557] Data 0.005 (0.127) Batch 0.955 (1.154) Remain 40:26:33 loss: 0.2871 Lr: 0.00474 [2024-02-18 02:50:13,896 INFO misc.py line 119 87073] Train: [19/100][1510/1557] Data 0.004 (0.127) Batch 0.794 (1.154) Remain 40:26:02 loss: 0.6201 Lr: 0.00474 [2024-02-18 02:50:14,703 INFO misc.py line 119 87073] Train: [19/100][1511/1557] Data 0.022 (0.127) Batch 0.826 (1.154) Remain 40:25:33 loss: 0.5395 Lr: 0.00474 [2024-02-18 02:50:15,899 INFO misc.py line 119 87073] Train: [19/100][1512/1557] Data 0.003 (0.127) Batch 1.195 (1.154) Remain 40:25:36 loss: 0.2769 Lr: 0.00474 [2024-02-18 02:50:16,677 INFO misc.py line 119 87073] Train: [19/100][1513/1557] Data 0.005 (0.127) Batch 0.778 (1.153) Remain 40:25:03 loss: 0.6822 Lr: 0.00474 [2024-02-18 02:50:17,841 INFO misc.py line 119 87073] Train: [19/100][1514/1557] Data 0.004 (0.127) Batch 1.162 (1.153) Remain 40:25:03 loss: 0.6695 Lr: 0.00474 [2024-02-18 02:50:18,821 INFO misc.py line 119 87073] Train: [19/100][1515/1557] Data 0.009 (0.127) Batch 0.982 (1.153) Remain 40:24:47 loss: 0.2550 Lr: 0.00474 [2024-02-18 02:50:19,714 INFO misc.py line 119 87073] Train: [19/100][1516/1557] Data 0.005 (0.127) Batch 0.893 (1.153) Remain 40:24:24 loss: 0.4600 Lr: 0.00474 [2024-02-18 02:50:20,481 INFO misc.py line 119 87073] Train: [19/100][1517/1557] Data 0.005 (0.127) Batch 0.764 (1.153) Remain 40:23:51 loss: 0.4335 Lr: 0.00474 [2024-02-18 02:50:21,240 INFO misc.py line 119 87073] Train: [19/100][1518/1557] Data 0.008 (0.126) Batch 0.764 (1.153) Remain 40:23:17 loss: 0.3983 Lr: 0.00474 [2024-02-18 02:50:33,592 INFO misc.py line 119 87073] Train: [19/100][1519/1557] Data 6.557 (0.131) Batch 12.352 (1.160) Remain 40:38:48 loss: 0.2797 Lr: 0.00474 [2024-02-18 02:50:34,490 INFO misc.py line 119 87073] Train: [19/100][1520/1557] Data 0.005 (0.131) Batch 0.898 (1.160) Remain 40:38:25 loss: 0.6741 Lr: 0.00474 [2024-02-18 02:50:35,538 INFO misc.py line 119 87073] Train: [19/100][1521/1557] Data 0.004 (0.131) Batch 1.048 (1.160) Remain 40:38:14 loss: 0.6408 Lr: 0.00474 [2024-02-18 02:50:36,575 INFO misc.py line 119 87073] Train: [19/100][1522/1557] Data 0.004 (0.130) Batch 1.036 (1.160) Remain 40:38:03 loss: 0.1928 Lr: 0.00474 [2024-02-18 02:50:37,414 INFO misc.py line 119 87073] Train: [19/100][1523/1557] Data 0.004 (0.130) Batch 0.820 (1.159) Remain 40:37:34 loss: 0.5126 Lr: 0.00474 [2024-02-18 02:50:38,150 INFO misc.py line 119 87073] Train: [19/100][1524/1557] Data 0.024 (0.130) Batch 0.755 (1.159) Remain 40:36:59 loss: 0.2749 Lr: 0.00474 [2024-02-18 02:50:39,009 INFO misc.py line 119 87073] Train: [19/100][1525/1557] Data 0.005 (0.130) Batch 0.860 (1.159) Remain 40:36:33 loss: 0.4899 Lr: 0.00474 [2024-02-18 02:50:40,249 INFO misc.py line 119 87073] Train: [19/100][1526/1557] Data 0.004 (0.130) Batch 1.231 (1.159) Remain 40:36:38 loss: 0.4281 Lr: 0.00474 [2024-02-18 02:50:41,227 INFO misc.py line 119 87073] Train: [19/100][1527/1557] Data 0.013 (0.130) Batch 0.987 (1.159) Remain 40:36:22 loss: 0.5946 Lr: 0.00474 [2024-02-18 02:50:42,136 INFO misc.py line 119 87073] Train: [19/100][1528/1557] Data 0.004 (0.130) Batch 0.909 (1.159) Remain 40:36:01 loss: 0.5884 Lr: 0.00474 [2024-02-18 02:50:43,245 INFO misc.py line 119 87073] Train: [19/100][1529/1557] Data 0.004 (0.130) Batch 1.109 (1.159) Remain 40:35:55 loss: 0.4406 Lr: 0.00474 [2024-02-18 02:50:44,201 INFO misc.py line 119 87073] Train: [19/100][1530/1557] Data 0.004 (0.130) Batch 0.956 (1.159) Remain 40:35:37 loss: 1.0870 Lr: 0.00474 [2024-02-18 02:50:44,938 INFO misc.py line 119 87073] Train: [19/100][1531/1557] Data 0.005 (0.130) Batch 0.731 (1.158) Remain 40:35:01 loss: 1.1949 Lr: 0.00474 [2024-02-18 02:50:45,737 INFO misc.py line 119 87073] Train: [19/100][1532/1557] Data 0.011 (0.130) Batch 0.806 (1.158) Remain 40:34:31 loss: 0.3400 Lr: 0.00474 [2024-02-18 02:50:46,967 INFO misc.py line 119 87073] Train: [19/100][1533/1557] Data 0.004 (0.130) Batch 1.228 (1.158) Remain 40:34:35 loss: 0.2370 Lr: 0.00474 [2024-02-18 02:50:47,879 INFO misc.py line 119 87073] Train: [19/100][1534/1557] Data 0.006 (0.130) Batch 0.912 (1.158) Remain 40:34:14 loss: 0.9382 Lr: 0.00474 [2024-02-18 02:50:48,689 INFO misc.py line 119 87073] Train: [19/100][1535/1557] Data 0.008 (0.129) Batch 0.809 (1.158) Remain 40:33:44 loss: 0.5945 Lr: 0.00474 [2024-02-18 02:50:49,755 INFO misc.py line 119 87073] Train: [19/100][1536/1557] Data 0.007 (0.129) Batch 1.058 (1.158) Remain 40:33:35 loss: 0.1871 Lr: 0.00474 [2024-02-18 02:50:50,895 INFO misc.py line 119 87073] Train: [19/100][1537/1557] Data 0.015 (0.129) Batch 1.150 (1.158) Remain 40:33:33 loss: 0.7457 Lr: 0.00474 [2024-02-18 02:50:51,615 INFO misc.py line 119 87073] Train: [19/100][1538/1557] Data 0.006 (0.129) Batch 0.721 (1.157) Remain 40:32:56 loss: 0.3759 Lr: 0.00474 [2024-02-18 02:50:52,423 INFO misc.py line 119 87073] Train: [19/100][1539/1557] Data 0.004 (0.129) Batch 0.800 (1.157) Remain 40:32:25 loss: 0.4762 Lr: 0.00474 [2024-02-18 02:50:53,569 INFO misc.py line 119 87073] Train: [19/100][1540/1557] Data 0.012 (0.129) Batch 1.153 (1.157) Remain 40:32:24 loss: 0.1637 Lr: 0.00474 [2024-02-18 02:50:54,707 INFO misc.py line 119 87073] Train: [19/100][1541/1557] Data 0.006 (0.129) Batch 1.136 (1.157) Remain 40:32:21 loss: 0.6367 Lr: 0.00474 [2024-02-18 02:50:55,708 INFO misc.py line 119 87073] Train: [19/100][1542/1557] Data 0.007 (0.129) Batch 1.001 (1.157) Remain 40:32:07 loss: 0.4139 Lr: 0.00474 [2024-02-18 02:50:56,712 INFO misc.py line 119 87073] Train: [19/100][1543/1557] Data 0.007 (0.129) Batch 1.006 (1.157) Remain 40:31:53 loss: 0.5168 Lr: 0.00474 [2024-02-18 02:50:57,845 INFO misc.py line 119 87073] Train: [19/100][1544/1557] Data 0.006 (0.129) Batch 1.130 (1.157) Remain 40:31:50 loss: 0.7685 Lr: 0.00474 [2024-02-18 02:50:58,571 INFO misc.py line 119 87073] Train: [19/100][1545/1557] Data 0.010 (0.129) Batch 0.731 (1.157) Remain 40:31:14 loss: 0.4103 Lr: 0.00474 [2024-02-18 02:50:59,296 INFO misc.py line 119 87073] Train: [19/100][1546/1557] Data 0.004 (0.129) Batch 0.725 (1.156) Remain 40:30:38 loss: 0.2414 Lr: 0.00474 [2024-02-18 02:51:00,630 INFO misc.py line 119 87073] Train: [19/100][1547/1557] Data 0.005 (0.128) Batch 1.332 (1.156) Remain 40:30:51 loss: 0.5561 Lr: 0.00474 [2024-02-18 02:51:01,530 INFO misc.py line 119 87073] Train: [19/100][1548/1557] Data 0.007 (0.128) Batch 0.902 (1.156) Remain 40:30:29 loss: 0.4922 Lr: 0.00474 [2024-02-18 02:51:02,515 INFO misc.py line 119 87073] Train: [19/100][1549/1557] Data 0.004 (0.128) Batch 0.985 (1.156) Remain 40:30:14 loss: 0.4586 Lr: 0.00474 [2024-02-18 02:51:03,597 INFO misc.py line 119 87073] Train: [19/100][1550/1557] Data 0.004 (0.128) Batch 1.081 (1.156) Remain 40:30:07 loss: 0.5030 Lr: 0.00474 [2024-02-18 02:51:04,429 INFO misc.py line 119 87073] Train: [19/100][1551/1557] Data 0.005 (0.128) Batch 0.832 (1.156) Remain 40:29:39 loss: 0.2503 Lr: 0.00474 [2024-02-18 02:51:05,163 INFO misc.py line 119 87073] Train: [19/100][1552/1557] Data 0.004 (0.128) Batch 0.728 (1.156) Remain 40:29:03 loss: 0.5391 Lr: 0.00474 [2024-02-18 02:51:05,890 INFO misc.py line 119 87073] Train: [19/100][1553/1557] Data 0.012 (0.128) Batch 0.734 (1.155) Remain 40:28:27 loss: 0.4023 Lr: 0.00474 [2024-02-18 02:51:06,950 INFO misc.py line 119 87073] Train: [19/100][1554/1557] Data 0.004 (0.128) Batch 1.060 (1.155) Remain 40:28:19 loss: 0.3661 Lr: 0.00474 [2024-02-18 02:51:07,915 INFO misc.py line 119 87073] Train: [19/100][1555/1557] Data 0.005 (0.128) Batch 0.965 (1.155) Remain 40:28:02 loss: 0.2288 Lr: 0.00474 [2024-02-18 02:51:08,811 INFO misc.py line 119 87073] Train: [19/100][1556/1557] Data 0.004 (0.128) Batch 0.897 (1.155) Remain 40:27:40 loss: 0.8771 Lr: 0.00474 [2024-02-18 02:51:09,862 INFO misc.py line 119 87073] Train: [19/100][1557/1557] Data 0.004 (0.128) Batch 1.050 (1.155) Remain 40:27:30 loss: 0.8753 Lr: 0.00474 [2024-02-18 02:51:09,863 INFO misc.py line 136 87073] Train result: loss: 0.5103 [2024-02-18 02:51:09,863 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 02:51:40,363 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8197 [2024-02-18 02:51:41,139 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8407 [2024-02-18 02:51:43,265 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.7007 [2024-02-18 02:51:45,473 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3743 [2024-02-18 02:51:50,418 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3080 [2024-02-18 02:51:51,116 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0852 [2024-02-18 02:51:52,380 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2934 [2024-02-18 02:51:55,340 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4296 [2024-02-18 02:51:57,147 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.4413 [2024-02-18 02:51:58,674 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6949/0.7612/0.9066. [2024-02-18 02:51:58,674 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9280/0.9682 [2024-02-18 02:51:58,674 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9816/0.9920 [2024-02-18 02:51:58,674 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8572/0.9668 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2610/0.2865 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6338/0.6515 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7393/0.8783 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8148/0.9313 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9237/0.9538 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7852/0.8447 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7591/0.8545 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7507/0.8807 [2024-02-18 02:51:58,675 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5991/0.6877 [2024-02-18 02:51:58,676 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 02:51:58,679 INFO misc.py line 165 87073] Currently Best mIoU: 0.6976 [2024-02-18 02:51:58,679 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 02:52:05,597 INFO misc.py line 119 87073] Train: [20/100][1/1557] Data 2.206 (2.206) Batch 3.163 (3.163) Remain 110:48:38 loss: 1.1555 Lr: 0.00474 [2024-02-18 02:52:06,766 INFO misc.py line 119 87073] Train: [20/100][2/1557] Data 0.010 (0.010) Batch 1.173 (1.173) Remain 41:05:40 loss: 1.1560 Lr: 0.00474 [2024-02-18 02:52:07,857 INFO misc.py line 119 87073] Train: [20/100][3/1557] Data 0.005 (0.005) Batch 1.090 (1.090) Remain 38:10:51 loss: 0.2626 Lr: 0.00474 [2024-02-18 02:52:08,703 INFO misc.py line 119 87073] Train: [20/100][4/1557] Data 0.007 (0.007) Batch 0.847 (0.847) Remain 29:41:11 loss: 0.6496 Lr: 0.00474 [2024-02-18 02:52:09,501 INFO misc.py line 119 87073] Train: [20/100][5/1557] Data 0.005 (0.006) Batch 0.798 (0.823) Remain 28:49:14 loss: 0.5046 Lr: 0.00474 [2024-02-18 02:52:10,259 INFO misc.py line 119 87073] Train: [20/100][6/1557] Data 0.005 (0.006) Batch 0.752 (0.799) Remain 27:59:39 loss: 0.2158 Lr: 0.00474 [2024-02-18 02:52:11,435 INFO misc.py line 119 87073] Train: [20/100][7/1557] Data 0.010 (0.007) Batch 1.183 (0.895) Remain 31:21:07 loss: 0.3270 Lr: 0.00474 [2024-02-18 02:52:12,523 INFO misc.py line 119 87073] Train: [20/100][8/1557] Data 0.005 (0.006) Batch 1.078 (0.932) Remain 32:38:08 loss: 0.7614 Lr: 0.00474 [2024-02-18 02:52:13,630 INFO misc.py line 119 87073] Train: [20/100][9/1557] Data 0.014 (0.008) Batch 1.109 (0.961) Remain 33:40:20 loss: 0.7513 Lr: 0.00474 [2024-02-18 02:52:14,619 INFO misc.py line 119 87073] Train: [20/100][10/1557] Data 0.012 (0.008) Batch 0.997 (0.966) Remain 33:50:58 loss: 0.6651 Lr: 0.00474 [2024-02-18 02:52:15,447 INFO misc.py line 119 87073] Train: [20/100][11/1557] Data 0.003 (0.008) Batch 0.825 (0.949) Remain 33:13:56 loss: 0.7026 Lr: 0.00474 [2024-02-18 02:52:16,248 INFO misc.py line 119 87073] Train: [20/100][12/1557] Data 0.008 (0.008) Batch 0.801 (0.932) Remain 32:39:26 loss: 0.7178 Lr: 0.00474 [2024-02-18 02:52:17,099 INFO misc.py line 119 87073] Train: [20/100][13/1557] Data 0.008 (0.008) Batch 0.853 (0.924) Remain 32:22:50 loss: 0.5280 Lr: 0.00474 [2024-02-18 02:52:18,411 INFO misc.py line 119 87073] Train: [20/100][14/1557] Data 0.004 (0.007) Batch 1.298 (0.958) Remain 33:34:07 loss: 0.4280 Lr: 0.00474 [2024-02-18 02:52:19,384 INFO misc.py line 119 87073] Train: [20/100][15/1557] Data 0.018 (0.008) Batch 0.987 (0.961) Remain 33:39:10 loss: 0.7472 Lr: 0.00474 [2024-02-18 02:52:20,528 INFO misc.py line 119 87073] Train: [20/100][16/1557] Data 0.005 (0.008) Batch 1.145 (0.975) Remain 34:08:52 loss: 0.6525 Lr: 0.00474 [2024-02-18 02:52:21,382 INFO misc.py line 119 87073] Train: [20/100][17/1557] Data 0.005 (0.008) Batch 0.853 (0.966) Remain 33:50:35 loss: 0.5757 Lr: 0.00474 [2024-02-18 02:52:22,462 INFO misc.py line 119 87073] Train: [20/100][18/1557] Data 0.004 (0.007) Batch 1.080 (0.974) Remain 34:06:29 loss: 0.5787 Lr: 0.00474 [2024-02-18 02:52:23,162 INFO misc.py line 119 87073] Train: [20/100][19/1557] Data 0.005 (0.007) Batch 0.701 (0.957) Remain 33:30:36 loss: 0.4015 Lr: 0.00474 [2024-02-18 02:52:23,914 INFO misc.py line 119 87073] Train: [20/100][20/1557] Data 0.004 (0.007) Batch 0.745 (0.944) Remain 33:04:23 loss: 0.3912 Lr: 0.00474 [2024-02-18 02:52:25,226 INFO misc.py line 119 87073] Train: [20/100][21/1557] Data 0.012 (0.007) Batch 1.311 (0.965) Remain 33:47:11 loss: 0.4450 Lr: 0.00474 [2024-02-18 02:52:26,283 INFO misc.py line 119 87073] Train: [20/100][22/1557] Data 0.013 (0.008) Batch 1.058 (0.970) Remain 33:57:30 loss: 0.7305 Lr: 0.00474 [2024-02-18 02:52:27,424 INFO misc.py line 119 87073] Train: [20/100][23/1557] Data 0.012 (0.008) Batch 1.145 (0.978) Remain 34:15:53 loss: 0.7953 Lr: 0.00474 [2024-02-18 02:52:28,445 INFO misc.py line 119 87073] Train: [20/100][24/1557] Data 0.008 (0.008) Batch 1.013 (0.980) Remain 34:19:23 loss: 0.6476 Lr: 0.00474 [2024-02-18 02:52:29,453 INFO misc.py line 119 87073] Train: [20/100][25/1557] Data 0.016 (0.008) Batch 1.011 (0.981) Remain 34:22:19 loss: 0.5229 Lr: 0.00474 [2024-02-18 02:52:30,205 INFO misc.py line 119 87073] Train: [20/100][26/1557] Data 0.013 (0.008) Batch 0.759 (0.972) Remain 34:02:01 loss: 0.3075 Lr: 0.00474 [2024-02-18 02:52:31,022 INFO misc.py line 119 87073] Train: [20/100][27/1557] Data 0.005 (0.008) Batch 0.807 (0.965) Remain 33:47:33 loss: 0.2241 Lr: 0.00474 [2024-02-18 02:52:32,153 INFO misc.py line 119 87073] Train: [20/100][28/1557] Data 0.016 (0.009) Batch 1.140 (0.972) Remain 34:02:18 loss: 0.2929 Lr: 0.00474 [2024-02-18 02:52:33,164 INFO misc.py line 119 87073] Train: [20/100][29/1557] Data 0.006 (0.009) Batch 1.005 (0.973) Remain 34:05:00 loss: 0.5531 Lr: 0.00474 [2024-02-18 02:52:34,177 INFO misc.py line 119 87073] Train: [20/100][30/1557] Data 0.012 (0.009) Batch 1.021 (0.975) Remain 34:08:41 loss: 0.3670 Lr: 0.00474 [2024-02-18 02:52:35,144 INFO misc.py line 119 87073] Train: [20/100][31/1557] Data 0.005 (0.009) Batch 0.964 (0.975) Remain 34:07:53 loss: 0.7085 Lr: 0.00474 [2024-02-18 02:52:36,045 INFO misc.py line 119 87073] Train: [20/100][32/1557] Data 0.007 (0.008) Batch 0.901 (0.972) Remain 34:02:33 loss: 0.6402 Lr: 0.00474 [2024-02-18 02:52:36,821 INFO misc.py line 119 87073] Train: [20/100][33/1557] Data 0.010 (0.009) Batch 0.776 (0.965) Remain 33:48:47 loss: 0.3846 Lr: 0.00474 [2024-02-18 02:52:37,551 INFO misc.py line 119 87073] Train: [20/100][34/1557] Data 0.007 (0.008) Batch 0.731 (0.958) Remain 33:32:53 loss: 0.4849 Lr: 0.00474 [2024-02-18 02:52:38,719 INFO misc.py line 119 87073] Train: [20/100][35/1557] Data 0.006 (0.008) Batch 1.165 (0.964) Remain 33:46:29 loss: 0.2418 Lr: 0.00474 [2024-02-18 02:52:39,765 INFO misc.py line 119 87073] Train: [20/100][36/1557] Data 0.013 (0.009) Batch 1.045 (0.967) Remain 33:51:36 loss: 0.6498 Lr: 0.00474 [2024-02-18 02:52:40,623 INFO misc.py line 119 87073] Train: [20/100][37/1557] Data 0.010 (0.009) Batch 0.862 (0.964) Remain 33:45:05 loss: 0.6387 Lr: 0.00474 [2024-02-18 02:52:41,661 INFO misc.py line 119 87073] Train: [20/100][38/1557] Data 0.007 (0.009) Batch 1.034 (0.966) Remain 33:49:17 loss: 0.1706 Lr: 0.00474 [2024-02-18 02:52:42,500 INFO misc.py line 119 87073] Train: [20/100][39/1557] Data 0.010 (0.009) Batch 0.843 (0.962) Remain 33:42:05 loss: 0.1992 Lr: 0.00474 [2024-02-18 02:52:43,222 INFO misc.py line 119 87073] Train: [20/100][40/1557] Data 0.007 (0.009) Batch 0.724 (0.956) Remain 33:28:31 loss: 0.3938 Lr: 0.00474 [2024-02-18 02:52:44,011 INFO misc.py line 119 87073] Train: [20/100][41/1557] Data 0.006 (0.008) Batch 0.788 (0.951) Remain 33:19:12 loss: 0.3873 Lr: 0.00474 [2024-02-18 02:52:45,384 INFO misc.py line 119 87073] Train: [20/100][42/1557] Data 0.006 (0.008) Batch 1.372 (0.962) Remain 33:41:50 loss: 0.4642 Lr: 0.00474 [2024-02-18 02:52:46,332 INFO misc.py line 119 87073] Train: [20/100][43/1557] Data 0.007 (0.008) Batch 0.950 (0.962) Remain 33:41:09 loss: 0.6963 Lr: 0.00474 [2024-02-18 02:52:47,319 INFO misc.py line 119 87073] Train: [20/100][44/1557] Data 0.007 (0.008) Batch 0.987 (0.963) Remain 33:42:26 loss: 0.4939 Lr: 0.00474 [2024-02-18 02:52:48,380 INFO misc.py line 119 87073] Train: [20/100][45/1557] Data 0.006 (0.008) Batch 1.060 (0.965) Remain 33:47:19 loss: 0.2958 Lr: 0.00474 [2024-02-18 02:52:49,298 INFO misc.py line 119 87073] Train: [20/100][46/1557] Data 0.007 (0.008) Batch 0.920 (0.964) Remain 33:45:07 loss: 0.7421 Lr: 0.00474 [2024-02-18 02:52:50,045 INFO misc.py line 119 87073] Train: [20/100][47/1557] Data 0.005 (0.008) Batch 0.747 (0.959) Remain 33:34:43 loss: 0.4319 Lr: 0.00474 [2024-02-18 02:52:50,800 INFO misc.py line 119 87073] Train: [20/100][48/1557] Data 0.004 (0.008) Batch 0.744 (0.954) Remain 33:24:42 loss: 0.3267 Lr: 0.00474 [2024-02-18 02:52:52,062 INFO misc.py line 119 87073] Train: [20/100][49/1557] Data 0.014 (0.008) Batch 1.272 (0.961) Remain 33:39:11 loss: 0.2152 Lr: 0.00474 [2024-02-18 02:52:53,252 INFO misc.py line 119 87073] Train: [20/100][50/1557] Data 0.005 (0.008) Batch 1.187 (0.966) Remain 33:49:17 loss: 0.8342 Lr: 0.00474 [2024-02-18 02:52:54,122 INFO misc.py line 119 87073] Train: [20/100][51/1557] Data 0.008 (0.008) Batch 0.874 (0.964) Remain 33:45:13 loss: 0.6013 Lr: 0.00474 [2024-02-18 02:52:55,033 INFO misc.py line 119 87073] Train: [20/100][52/1557] Data 0.006 (0.008) Batch 0.912 (0.963) Remain 33:42:59 loss: 0.6592 Lr: 0.00474 [2024-02-18 02:52:56,079 INFO misc.py line 119 87073] Train: [20/100][53/1557] Data 0.004 (0.008) Batch 1.042 (0.964) Remain 33:46:17 loss: 0.3282 Lr: 0.00474 [2024-02-18 02:52:56,869 INFO misc.py line 119 87073] Train: [20/100][54/1557] Data 0.008 (0.008) Batch 0.792 (0.961) Remain 33:39:09 loss: 0.4412 Lr: 0.00474 [2024-02-18 02:52:57,597 INFO misc.py line 119 87073] Train: [20/100][55/1557] Data 0.007 (0.008) Batch 0.728 (0.957) Remain 33:29:42 loss: 0.3225 Lr: 0.00474 [2024-02-18 02:52:58,905 INFO misc.py line 119 87073] Train: [20/100][56/1557] Data 0.007 (0.008) Batch 1.298 (0.963) Remain 33:43:13 loss: 0.3565 Lr: 0.00474 [2024-02-18 02:52:59,904 INFO misc.py line 119 87073] Train: [20/100][57/1557] Data 0.017 (0.008) Batch 1.011 (0.964) Remain 33:45:03 loss: 0.7779 Lr: 0.00474 [2024-02-18 02:53:00,782 INFO misc.py line 119 87073] Train: [20/100][58/1557] Data 0.006 (0.008) Batch 0.879 (0.962) Remain 33:41:47 loss: 0.4807 Lr: 0.00474 [2024-02-18 02:53:01,718 INFO misc.py line 119 87073] Train: [20/100][59/1557] Data 0.004 (0.008) Batch 0.935 (0.962) Remain 33:40:44 loss: 0.4336 Lr: 0.00474 [2024-02-18 02:53:02,673 INFO misc.py line 119 87073] Train: [20/100][60/1557] Data 0.007 (0.008) Batch 0.955 (0.962) Remain 33:40:29 loss: 0.3623 Lr: 0.00474 [2024-02-18 02:53:03,370 INFO misc.py line 119 87073] Train: [20/100][61/1557] Data 0.006 (0.008) Batch 0.695 (0.957) Remain 33:30:48 loss: 0.5757 Lr: 0.00474 [2024-02-18 02:53:04,124 INFO misc.py line 119 87073] Train: [20/100][62/1557] Data 0.007 (0.008) Batch 0.756 (0.954) Remain 33:23:38 loss: 0.6128 Lr: 0.00474 [2024-02-18 02:53:12,483 INFO misc.py line 119 87073] Train: [20/100][63/1557] Data 4.367 (0.081) Batch 8.324 (1.077) Remain 37:41:40 loss: 0.2391 Lr: 0.00474 [2024-02-18 02:53:13,410 INFO misc.py line 119 87073] Train: [20/100][64/1557] Data 0.040 (0.080) Batch 0.962 (1.075) Remain 37:37:42 loss: 0.5868 Lr: 0.00474 [2024-02-18 02:53:14,368 INFO misc.py line 119 87073] Train: [20/100][65/1557] Data 0.006 (0.079) Batch 0.958 (1.073) Remain 37:33:44 loss: 0.6188 Lr: 0.00474 [2024-02-18 02:53:15,315 INFO misc.py line 119 87073] Train: [20/100][66/1557] Data 0.006 (0.078) Batch 0.948 (1.071) Remain 37:29:34 loss: 0.4773 Lr: 0.00474 [2024-02-18 02:53:16,155 INFO misc.py line 119 87073] Train: [20/100][67/1557] Data 0.005 (0.076) Batch 0.838 (1.067) Remain 37:21:54 loss: 0.8947 Lr: 0.00474 [2024-02-18 02:53:16,882 INFO misc.py line 119 87073] Train: [20/100][68/1557] Data 0.007 (0.075) Batch 0.729 (1.062) Remain 37:10:57 loss: 0.4983 Lr: 0.00474 [2024-02-18 02:53:17,703 INFO misc.py line 119 87073] Train: [20/100][69/1557] Data 0.005 (0.074) Batch 0.815 (1.058) Remain 37:03:04 loss: 0.5296 Lr: 0.00474 [2024-02-18 02:53:19,007 INFO misc.py line 119 87073] Train: [20/100][70/1557] Data 0.011 (0.073) Batch 1.300 (1.062) Remain 37:10:38 loss: 0.2523 Lr: 0.00474 [2024-02-18 02:53:20,051 INFO misc.py line 119 87073] Train: [20/100][71/1557] Data 0.015 (0.073) Batch 1.044 (1.062) Remain 37:10:04 loss: 0.7692 Lr: 0.00474 [2024-02-18 02:53:21,030 INFO misc.py line 119 87073] Train: [20/100][72/1557] Data 0.014 (0.072) Batch 0.989 (1.060) Remain 37:07:50 loss: 0.6721 Lr: 0.00474 [2024-02-18 02:53:22,035 INFO misc.py line 119 87073] Train: [20/100][73/1557] Data 0.005 (0.071) Batch 1.004 (1.060) Remain 37:06:06 loss: 0.6798 Lr: 0.00474 [2024-02-18 02:53:23,075 INFO misc.py line 119 87073] Train: [20/100][74/1557] Data 0.006 (0.070) Batch 1.042 (1.059) Remain 37:05:35 loss: 0.6017 Lr: 0.00474 [2024-02-18 02:53:23,795 INFO misc.py line 119 87073] Train: [20/100][75/1557] Data 0.004 (0.069) Batch 0.720 (1.055) Remain 36:55:39 loss: 0.2608 Lr: 0.00474 [2024-02-18 02:53:24,591 INFO misc.py line 119 87073] Train: [20/100][76/1557] Data 0.004 (0.068) Batch 0.795 (1.051) Remain 36:48:09 loss: 0.4751 Lr: 0.00474 [2024-02-18 02:53:25,847 INFO misc.py line 119 87073] Train: [20/100][77/1557] Data 0.005 (0.067) Batch 1.255 (1.054) Remain 36:53:55 loss: 0.3766 Lr: 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line 119 87073] Train: [20/100][84/1557] Data 0.005 (0.062) Batch 1.199 (1.046) Remain 36:37:40 loss: 0.2137 Lr: 0.00474 [2024-02-18 02:53:33,722 INFO misc.py line 119 87073] Train: [20/100][85/1557] Data 0.015 (0.061) Batch 1.116 (1.047) Remain 36:39:26 loss: 0.4009 Lr: 0.00474 [2024-02-18 02:53:34,675 INFO misc.py line 119 87073] Train: [20/100][86/1557] Data 0.010 (0.061) Batch 0.958 (1.046) Remain 36:37:10 loss: 0.7820 Lr: 0.00474 [2024-02-18 02:53:35,595 INFO misc.py line 119 87073] Train: [20/100][87/1557] Data 0.005 (0.060) Batch 0.920 (1.045) Remain 36:34:00 loss: 0.2050 Lr: 0.00474 [2024-02-18 02:53:36,708 INFO misc.py line 119 87073] Train: [20/100][88/1557] Data 0.005 (0.059) Batch 1.111 (1.045) Remain 36:35:38 loss: 0.7495 Lr: 0.00474 [2024-02-18 02:53:37,446 INFO misc.py line 119 87073] Train: [20/100][89/1557] Data 0.007 (0.059) Batch 0.738 (1.042) Remain 36:28:07 loss: 0.6317 Lr: 0.00474 [2024-02-18 02:53:38,220 INFO misc.py line 119 87073] Train: [20/100][90/1557] Data 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[2024-02-18 03:19:17,854 INFO misc.py line 119 87073] Train: [20/100][1495/1557] Data 0.005 (0.097) Batch 0.910 (1.092) Remain 37:49:07 loss: 0.6509 Lr: 0.00470 [2024-02-18 03:19:18,659 INFO misc.py line 119 87073] Train: [20/100][1496/1557] Data 0.009 (0.097) Batch 0.810 (1.092) Remain 37:48:43 loss: 0.5094 Lr: 0.00470 [2024-02-18 03:19:19,426 INFO misc.py line 119 87073] Train: [20/100][1497/1557] Data 0.005 (0.097) Batch 0.768 (1.092) Remain 37:48:15 loss: 0.5087 Lr: 0.00470 [2024-02-18 03:19:20,740 INFO misc.py line 119 87073] Train: [20/100][1498/1557] Data 0.004 (0.097) Batch 1.314 (1.092) Remain 37:48:32 loss: 0.5027 Lr: 0.00470 [2024-02-18 03:19:21,661 INFO misc.py line 119 87073] Train: [20/100][1499/1557] Data 0.004 (0.097) Batch 0.920 (1.092) Remain 37:48:17 loss: 0.4608 Lr: 0.00470 [2024-02-18 03:19:22,662 INFO misc.py line 119 87073] Train: [20/100][1500/1557] Data 0.005 (0.097) Batch 1.001 (1.092) Remain 37:48:08 loss: 0.5853 Lr: 0.00470 [2024-02-18 03:19:23,638 INFO 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(0.096) Batch 1.064 (1.091) Remain 37:45:59 loss: 0.4597 Lr: 0.00470 [2024-02-18 03:19:36,373 INFO misc.py line 119 87073] Train: [20/100][1514/1557] Data 0.011 (0.096) Batch 0.903 (1.091) Remain 37:45:43 loss: 0.3009 Lr: 0.00470 [2024-02-18 03:19:37,160 INFO misc.py line 119 87073] Train: [20/100][1515/1557] Data 0.004 (0.096) Batch 0.785 (1.091) Remain 37:45:16 loss: 0.4602 Lr: 0.00470 [2024-02-18 03:19:38,054 INFO misc.py line 119 87073] Train: [20/100][1516/1557] Data 0.006 (0.096) Batch 0.893 (1.091) Remain 37:44:59 loss: 0.2265 Lr: 0.00470 [2024-02-18 03:19:38,849 INFO misc.py line 119 87073] Train: [20/100][1517/1557] Data 0.006 (0.096) Batch 0.797 (1.090) Remain 37:44:34 loss: 0.5739 Lr: 0.00470 [2024-02-18 03:19:39,655 INFO misc.py line 119 87073] Train: [20/100][1518/1557] Data 0.006 (0.096) Batch 0.807 (1.090) Remain 37:44:09 loss: 0.7074 Lr: 0.00470 [2024-02-18 03:19:48,844 INFO misc.py line 119 87073] Train: [20/100][1519/1557] Data 5.183 (0.099) Batch 9.188 (1.096) Remain 37:55:14 loss: 0.1959 Lr: 0.00470 [2024-02-18 03:19:49,819 INFO misc.py line 119 87073] Train: [20/100][1520/1557] Data 0.005 (0.099) Batch 0.976 (1.096) Remain 37:55:03 loss: 0.5429 Lr: 0.00470 [2024-02-18 03:19:50,701 INFO misc.py line 119 87073] Train: [20/100][1521/1557] Data 0.005 (0.099) Batch 0.882 (1.095) Remain 37:54:44 loss: 0.4477 Lr: 0.00470 [2024-02-18 03:19:51,630 INFO misc.py line 119 87073] Train: [20/100][1522/1557] Data 0.004 (0.099) Batch 0.924 (1.095) Remain 37:54:29 loss: 0.4304 Lr: 0.00470 [2024-02-18 03:19:52,538 INFO misc.py line 119 87073] Train: [20/100][1523/1557] Data 0.010 (0.099) Batch 0.911 (1.095) Remain 37:54:13 loss: 0.5313 Lr: 0.00470 [2024-02-18 03:19:53,304 INFO misc.py line 119 87073] Train: [20/100][1524/1557] Data 0.006 (0.099) Batch 0.767 (1.095) Remain 37:53:45 loss: 0.4359 Lr: 0.00470 [2024-02-18 03:19:54,163 INFO misc.py line 119 87073] Train: [20/100][1525/1557] Data 0.006 (0.099) Batch 0.860 (1.095) Remain 37:53:24 loss: 0.6100 Lr: 0.00470 [2024-02-18 03:19:55,379 INFO misc.py line 119 87073] Train: [20/100][1526/1557] Data 0.005 (0.099) Batch 1.203 (1.095) Remain 37:53:32 loss: 0.6056 Lr: 0.00470 [2024-02-18 03:19:56,433 INFO misc.py line 119 87073] Train: [20/100][1527/1557] Data 0.017 (0.099) Batch 1.063 (1.095) Remain 37:53:28 loss: 0.4953 Lr: 0.00470 [2024-02-18 03:19:57,267 INFO misc.py line 119 87073] Train: [20/100][1528/1557] Data 0.008 (0.099) Batch 0.838 (1.095) Remain 37:53:06 loss: 0.8337 Lr: 0.00470 [2024-02-18 03:19:58,289 INFO misc.py line 119 87073] Train: [20/100][1529/1557] Data 0.005 (0.099) Batch 1.023 (1.095) Remain 37:52:59 loss: 0.5264 Lr: 0.00470 [2024-02-18 03:19:59,215 INFO misc.py line 119 87073] Train: [20/100][1530/1557] Data 0.004 (0.099) Batch 0.926 (1.095) Remain 37:52:45 loss: 0.6186 Lr: 0.00470 [2024-02-18 03:19:59,991 INFO misc.py line 119 87073] Train: [20/100][1531/1557] Data 0.004 (0.099) Batch 0.773 (1.094) Remain 37:52:17 loss: 0.3476 Lr: 0.00470 [2024-02-18 03:20:00,803 INFO misc.py line 119 87073] Train: [20/100][1532/1557] Data 0.006 (0.099) Batch 0.814 (1.094) Remain 37:51:53 loss: 0.4361 Lr: 0.00470 [2024-02-18 03:20:02,186 INFO misc.py line 119 87073] Train: [20/100][1533/1557] Data 0.004 (0.099) Batch 1.381 (1.094) Remain 37:52:16 loss: 0.2917 Lr: 0.00470 [2024-02-18 03:20:03,104 INFO misc.py line 119 87073] Train: [20/100][1534/1557] Data 0.006 (0.098) Batch 0.919 (1.094) Remain 37:52:00 loss: 0.6590 Lr: 0.00470 [2024-02-18 03:20:04,131 INFO misc.py line 119 87073] Train: [20/100][1535/1557] Data 0.005 (0.098) Batch 1.027 (1.094) Remain 37:51:54 loss: 0.4472 Lr: 0.00470 [2024-02-18 03:20:05,059 INFO misc.py line 119 87073] Train: [20/100][1536/1557] Data 0.005 (0.098) Batch 0.928 (1.094) Remain 37:51:39 loss: 0.5258 Lr: 0.00470 [2024-02-18 03:20:05,948 INFO misc.py line 119 87073] Train: [20/100][1537/1557] Data 0.004 (0.098) Batch 0.889 (1.094) Remain 37:51:21 loss: 0.3089 Lr: 0.00470 [2024-02-18 03:20:06,641 INFO misc.py line 119 87073] Train: [20/100][1538/1557] Data 0.004 (0.098) Batch 0.689 (1.094) Remain 37:50:47 loss: 0.3992 Lr: 0.00470 [2024-02-18 03:20:07,413 INFO misc.py line 119 87073] Train: [20/100][1539/1557] Data 0.007 (0.098) Batch 0.775 (1.093) Remain 37:50:21 loss: 0.4688 Lr: 0.00470 [2024-02-18 03:20:08,573 INFO misc.py line 119 87073] Train: [20/100][1540/1557] Data 0.005 (0.098) Batch 1.161 (1.094) Remain 37:50:25 loss: 0.2975 Lr: 0.00470 [2024-02-18 03:20:09,525 INFO misc.py line 119 87073] Train: [20/100][1541/1557] Data 0.004 (0.098) Batch 0.950 (1.093) Remain 37:50:12 loss: 0.6539 Lr: 0.00470 [2024-02-18 03:20:10,558 INFO misc.py line 119 87073] Train: [20/100][1542/1557] Data 0.005 (0.098) Batch 1.034 (1.093) Remain 37:50:06 loss: 0.4257 Lr: 0.00470 [2024-02-18 03:20:11,597 INFO misc.py line 119 87073] Train: [20/100][1543/1557] Data 0.004 (0.098) Batch 1.039 (1.093) Remain 37:50:01 loss: 0.4849 Lr: 0.00470 [2024-02-18 03:20:12,540 INFO misc.py line 119 87073] Train: [20/100][1544/1557] Data 0.004 (0.098) Batch 0.943 (1.093) Remain 37:49:48 loss: 0.4643 Lr: 0.00470 [2024-02-18 03:20:13,213 INFO misc.py line 119 87073] Train: [20/100][1545/1557] Data 0.005 (0.098) Batch 0.666 (1.093) Remain 37:49:12 loss: 0.4310 Lr: 0.00470 [2024-02-18 03:20:13,958 INFO misc.py line 119 87073] Train: [20/100][1546/1557] Data 0.012 (0.098) Batch 0.752 (1.093) Remain 37:48:43 loss: 0.7777 Lr: 0.00470 [2024-02-18 03:20:15,094 INFO misc.py line 119 87073] Train: [20/100][1547/1557] Data 0.005 (0.098) Batch 1.135 (1.093) Remain 37:48:46 loss: 0.1647 Lr: 0.00470 [2024-02-18 03:20:15,825 INFO misc.py line 119 87073] Train: [20/100][1548/1557] Data 0.005 (0.098) Batch 0.729 (1.093) Remain 37:48:15 loss: 0.5430 Lr: 0.00470 [2024-02-18 03:20:16,673 INFO misc.py line 119 87073] Train: [20/100][1549/1557] Data 0.008 (0.098) Batch 0.847 (1.092) Remain 37:47:54 loss: 0.6200 Lr: 0.00470 [2024-02-18 03:20:17,592 INFO misc.py line 119 87073] Train: [20/100][1550/1557] Data 0.009 (0.098) Batch 0.923 (1.092) Remain 37:47:40 loss: 0.4108 Lr: 0.00470 [2024-02-18 03:20:18,699 INFO misc.py line 119 87073] Train: [20/100][1551/1557] Data 0.004 (0.097) Batch 1.107 (1.092) Remain 37:47:40 loss: 0.5983 Lr: 0.00470 [2024-02-18 03:20:19,446 INFO misc.py line 119 87073] Train: [20/100][1552/1557] Data 0.006 (0.097) Batch 0.747 (1.092) Remain 37:47:11 loss: 0.8387 Lr: 0.00470 [2024-02-18 03:20:20,237 INFO misc.py line 119 87073] Train: [20/100][1553/1557] Data 0.004 (0.097) Batch 0.791 (1.092) Remain 37:46:46 loss: 0.3377 Lr: 0.00470 [2024-02-18 03:20:21,533 INFO misc.py line 119 87073] Train: [20/100][1554/1557] Data 0.004 (0.097) Batch 1.286 (1.092) Remain 37:47:00 loss: 0.5515 Lr: 0.00470 [2024-02-18 03:20:22,570 INFO misc.py line 119 87073] Train: [20/100][1555/1557] Data 0.015 (0.097) Batch 1.038 (1.092) Remain 37:46:55 loss: 0.8669 Lr: 0.00470 [2024-02-18 03:20:23,605 INFO misc.py line 119 87073] Train: [20/100][1556/1557] Data 0.013 (0.097) Batch 1.027 (1.092) Remain 37:46:48 loss: 0.3220 Lr: 0.00470 [2024-02-18 03:20:24,706 INFO misc.py line 119 87073] Train: [20/100][1557/1557] Data 0.022 (0.097) Batch 1.109 (1.092) Remain 37:46:49 loss: 0.8185 Lr: 0.00470 [2024-02-18 03:20:24,707 INFO misc.py line 136 87073] Train result: loss: 0.4978 [2024-02-18 03:20:24,707 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 03:20:56,208 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5959 [2024-02-18 03:20:56,990 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4840 [2024-02-18 03:20:59,119 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5570 [2024-02-18 03:21:01,327 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4366 [2024-02-18 03:21:06,275 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2918 [2024-02-18 03:21:06,973 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0907 [2024-02-18 03:21:08,233 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2932 [2024-02-18 03:21:11,185 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2827 [2024-02-18 03:21:12,994 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3026 [2024-02-18 03:21:14,783 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7180/0.7847/0.9118. [2024-02-18 03:21:14,783 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9262/0.9655 [2024-02-18 03:21:14,783 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9815/0.9926 [2024-02-18 03:21:14,783 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8616/0.9722 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0230/0.1493 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4281/0.4910 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6922/0.7162 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7159/0.8325 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8247/0.8940 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9126/0.9695 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7764/0.8014 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7729/0.8760 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8183/0.8678 [2024-02-18 03:21:14,784 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6002/0.6735 [2024-02-18 03:21:14,785 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 03:21:14,787 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7180 [2024-02-18 03:21:14,787 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 03:21:14,787 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 03:21:25,149 INFO misc.py line 119 87073] Train: [21/100][1/1557] Data 1.196 (1.196) Batch 1.917 (1.917) Remain 66:19:50 loss: 0.2480 Lr: 0.00470 [2024-02-18 03:21:26,199 INFO misc.py line 119 87073] Train: [21/100][2/1557] Data 0.004 (0.004) Batch 1.049 (1.049) Remain 36:17:03 loss: 0.7456 Lr: 0.00470 [2024-02-18 03:21:27,198 INFO misc.py line 119 87073] Train: [21/100][3/1557] Data 0.005 (0.005) Batch 0.998 (0.998) Remain 34:31:58 loss: 0.6608 Lr: 0.00470 [2024-02-18 03:21:28,035 INFO misc.py line 119 87073] Train: [21/100][4/1557] Data 0.008 (0.008) Batch 0.837 (0.837) Remain 28:58:05 loss: 0.7330 Lr: 0.00470 [2024-02-18 03:21:28,825 INFO misc.py line 119 87073] Train: [21/100][5/1557] Data 0.006 (0.007) Batch 0.789 (0.813) Remain 28:07:56 loss: 0.3532 Lr: 0.00470 [2024-02-18 03:21:29,576 INFO misc.py line 119 87073] Train: [21/100][6/1557] Data 0.006 (0.007) Batch 0.747 (0.791) Remain 27:22:26 loss: 0.4534 Lr: 0.00470 [2024-02-18 03:21:30,690 INFO misc.py line 119 87073] Train: [21/100][7/1557] Data 0.010 (0.008) Batch 1.119 (0.873) Remain 30:12:22 loss: 0.2783 Lr: 0.00470 [2024-02-18 03:21:31,536 INFO misc.py line 119 87073] Train: [21/100][8/1557] Data 0.006 (0.007) Batch 0.847 (0.868) Remain 30:01:21 loss: 0.3736 Lr: 0.00470 [2024-02-18 03:21:32,507 INFO misc.py line 119 87073] Train: [21/100][9/1557] Data 0.005 (0.007) Batch 0.971 (0.885) Remain 30:37:06 loss: 0.7304 Lr: 0.00470 [2024-02-18 03:21:33,364 INFO misc.py line 119 87073] Train: [21/100][10/1557] Data 0.005 (0.007) Batch 0.858 (0.881) Remain 30:29:04 loss: 0.2644 Lr: 0.00470 [2024-02-18 03:21:34,330 INFO misc.py line 119 87073] Train: [21/100][11/1557] Data 0.005 (0.006) Batch 0.956 (0.890) Remain 30:48:24 loss: 0.6448 Lr: 0.00470 [2024-02-18 03:21:35,105 INFO misc.py line 119 87073] Train: [21/100][12/1557] Data 0.015 (0.007) Batch 0.784 (0.879) Remain 30:23:53 loss: 0.3237 Lr: 0.00470 [2024-02-18 03:21:35,884 INFO misc.py line 119 87073] Train: [21/100][13/1557] Data 0.006 (0.007) Batch 0.779 (0.869) Remain 30:03:09 loss: 0.3517 Lr: 0.00470 [2024-02-18 03:21:36,868 INFO misc.py line 119 87073] Train: [21/100][14/1557] Data 0.006 (0.007) Batch 0.982 (0.879) Remain 30:24:32 loss: 0.2805 Lr: 0.00470 [2024-02-18 03:21:37,880 INFO misc.py line 119 87073] Train: [21/100][15/1557] Data 0.008 (0.007) Batch 1.011 (0.890) Remain 30:47:20 loss: 0.3764 Lr: 0.00470 [2024-02-18 03:21:38,875 INFO misc.py line 119 87073] Train: [21/100][16/1557] Data 0.008 (0.007) Batch 0.995 (0.898) Remain 31:04:05 loss: 0.4931 Lr: 0.00470 [2024-02-18 03:21:39,750 INFO misc.py line 119 87073] Train: [21/100][17/1557] Data 0.008 (0.007) Batch 0.879 (0.897) Remain 31:01:12 loss: 0.7802 Lr: 0.00470 [2024-02-18 03:21:40,683 INFO misc.py line 119 87073] Train: [21/100][18/1557] Data 0.005 (0.007) Batch 0.931 (0.899) Remain 31:05:59 loss: 0.2731 Lr: 0.00470 [2024-02-18 03:21:41,451 INFO misc.py line 119 87073] Train: [21/100][19/1557] Data 0.006 (0.007) Batch 0.766 (0.891) Remain 30:48:46 loss: 0.4261 Lr: 0.00470 [2024-02-18 03:21:42,178 INFO misc.py line 119 87073] Train: [21/100][20/1557] Data 0.008 (0.007) Batch 0.730 (0.881) Remain 30:29:06 loss: 0.2460 Lr: 0.00470 [2024-02-18 03:21:43,586 INFO misc.py line 119 87073] Train: [21/100][21/1557] Data 0.146 (0.015) Batch 1.408 (0.910) Remain 31:29:51 loss: 0.3589 Lr: 0.00470 [2024-02-18 03:21:44,439 INFO misc.py line 119 87073] Train: [21/100][22/1557] Data 0.006 (0.014) Batch 0.853 (0.907) Remain 31:23:36 loss: 0.2050 Lr: 0.00470 [2024-02-18 03:21:45,474 INFO misc.py line 119 87073] Train: [21/100][23/1557] Data 0.005 (0.014) Batch 1.035 (0.914) Remain 31:36:48 loss: 0.5720 Lr: 0.00470 [2024-02-18 03:21:46,368 INFO misc.py line 119 87073] Train: [21/100][24/1557] Data 0.005 (0.013) Batch 0.893 (0.913) Remain 31:34:43 loss: 0.3428 Lr: 0.00470 [2024-02-18 03:21:47,348 INFO misc.py line 119 87073] Train: [21/100][25/1557] Data 0.006 (0.013) Batch 0.980 (0.916) Remain 31:41:03 loss: 0.8221 Lr: 0.00470 [2024-02-18 03:21:48,116 INFO misc.py line 119 87073] Train: [21/100][26/1557] Data 0.006 (0.013) Batch 0.769 (0.910) Remain 31:27:46 loss: 0.3494 Lr: 0.00470 [2024-02-18 03:21:48,891 INFO misc.py line 119 87073] Train: [21/100][27/1557] Data 0.005 (0.012) Batch 0.771 (0.904) Remain 31:15:44 loss: 0.2805 Lr: 0.00470 [2024-02-18 03:21:50,062 INFO misc.py line 119 87073] Train: [21/100][28/1557] Data 0.009 (0.012) Batch 1.172 (0.914) Remain 31:38:00 loss: 0.3309 Lr: 0.00470 [2024-02-18 03:21:50,940 INFO misc.py line 119 87073] Train: [21/100][29/1557] Data 0.008 (0.012) Batch 0.881 (0.913) Remain 31:35:19 loss: 0.5090 Lr: 0.00470 [2024-02-18 03:21:51,868 INFO misc.py line 119 87073] Train: [21/100][30/1557] Data 0.006 (0.012) Batch 0.928 (0.914) Remain 31:36:28 loss: 0.7082 Lr: 0.00470 [2024-02-18 03:21:52,804 INFO misc.py line 119 87073] Train: [21/100][31/1557] Data 0.004 (0.012) Batch 0.936 (0.915) Remain 31:38:07 loss: 0.5453 Lr: 0.00470 [2024-02-18 03:21:53,729 INFO misc.py line 119 87073] Train: [21/100][32/1557] Data 0.004 (0.011) Batch 0.924 (0.915) Remain 31:38:44 loss: 1.0293 Lr: 0.00470 [2024-02-18 03:21:54,492 INFO misc.py line 119 87073] Train: [21/100][33/1557] Data 0.006 (0.011) Batch 0.765 (0.910) Remain 31:28:23 loss: 0.4989 Lr: 0.00470 [2024-02-18 03:21:55,267 INFO misc.py line 119 87073] Train: [21/100][34/1557] Data 0.004 (0.011) Batch 0.771 (0.905) Remain 31:19:04 loss: 0.4741 Lr: 0.00470 [2024-02-18 03:21:56,577 INFO misc.py line 119 87073] Train: [21/100][35/1557] Data 0.008 (0.011) Batch 1.307 (0.918) Remain 31:45:05 loss: 0.1523 Lr: 0.00470 [2024-02-18 03:21:57,481 INFO misc.py line 119 87073] Train: [21/100][36/1557] Data 0.012 (0.011) Batch 0.911 (0.918) Remain 31:44:36 loss: 0.6328 Lr: 0.00470 [2024-02-18 03:21:58,398 INFO misc.py line 119 87073] Train: [21/100][37/1557] Data 0.005 (0.011) Batch 0.917 (0.918) Remain 31:44:33 loss: 0.7263 Lr: 0.00470 [2024-02-18 03:21:59,514 INFO misc.py line 119 87073] Train: [21/100][38/1557] Data 0.004 (0.011) Batch 1.117 (0.923) Remain 31:56:20 loss: 0.4158 Lr: 0.00470 [2024-02-18 03:22:00,423 INFO misc.py line 119 87073] Train: [21/100][39/1557] Data 0.004 (0.010) Batch 0.908 (0.923) Remain 31:55:25 loss: 0.1944 Lr: 0.00470 [2024-02-18 03:22:01,149 INFO misc.py line 119 87073] Train: [21/100][40/1557] Data 0.004 (0.010) Batch 0.724 (0.918) Remain 31:44:13 loss: 0.2950 Lr: 0.00470 [2024-02-18 03:22:01,861 INFO misc.py line 119 87073] Train: [21/100][41/1557] Data 0.008 (0.010) Batch 0.713 (0.912) Remain 31:33:02 loss: 0.3282 Lr: 0.00470 [2024-02-18 03:22:03,090 INFO misc.py line 119 87073] Train: [21/100][42/1557] Data 0.006 (0.010) Batch 1.230 (0.920) Remain 31:49:54 loss: 0.4046 Lr: 0.00470 [2024-02-18 03:22:04,083 INFO misc.py line 119 87073] Train: [21/100][43/1557] Data 0.006 (0.010) Batch 0.995 (0.922) Remain 31:53:45 loss: 0.6387 Lr: 0.00470 [2024-02-18 03:22:05,113 INFO misc.py line 119 87073] Train: [21/100][44/1557] Data 0.003 (0.010) Batch 1.030 (0.925) Remain 31:59:12 loss: 0.8168 Lr: 0.00470 [2024-02-18 03:22:06,126 INFO misc.py line 119 87073] Train: [21/100][45/1557] Data 0.004 (0.010) Batch 1.012 (0.927) Remain 32:03:30 loss: 0.2799 Lr: 0.00470 [2024-02-18 03:22:07,028 INFO misc.py line 119 87073] Train: [21/100][46/1557] Data 0.005 (0.010) Batch 0.900 (0.926) Remain 32:02:11 loss: 0.5939 Lr: 0.00470 [2024-02-18 03:22:07,796 INFO misc.py line 119 87073] Train: [21/100][47/1557] Data 0.007 (0.009) Batch 0.769 (0.923) Remain 31:54:46 loss: 0.4142 Lr: 0.00470 [2024-02-18 03:22:08,589 INFO misc.py line 119 87073] Train: [21/100][48/1557] Data 0.005 (0.009) Batch 0.792 (0.920) Remain 31:48:44 loss: 0.1697 Lr: 0.00470 [2024-02-18 03:22:09,873 INFO misc.py line 119 87073] Train: [21/100][49/1557] Data 0.007 (0.009) Batch 1.285 (0.928) Remain 32:05:10 loss: 0.3111 Lr: 0.00470 [2024-02-18 03:22:10,800 INFO misc.py line 119 87073] Train: [21/100][50/1557] Data 0.006 (0.009) Batch 0.928 (0.928) Remain 32:05:10 loss: 0.3179 Lr: 0.00470 [2024-02-18 03:22:11,813 INFO misc.py line 119 87073] Train: [21/100][51/1557] Data 0.004 (0.009) Batch 1.012 (0.929) Remain 32:08:49 loss: 0.6468 Lr: 0.00470 [2024-02-18 03:22:12,935 INFO misc.py line 119 87073] Train: [21/100][52/1557] Data 0.005 (0.009) Batch 1.121 (0.933) Remain 32:16:54 loss: 0.6868 Lr: 0.00470 [2024-02-18 03:22:14,141 INFO misc.py line 119 87073] Train: [21/100][53/1557] Data 0.006 (0.009) Batch 1.204 (0.939) Remain 32:28:08 loss: 0.3584 Lr: 0.00470 [2024-02-18 03:22:14,929 INFO misc.py line 119 87073] Train: [21/100][54/1557] Data 0.009 (0.009) Batch 0.790 (0.936) Remain 32:22:04 loss: 0.3604 Lr: 0.00470 [2024-02-18 03:22:15,633 INFO misc.py line 119 87073] Train: [21/100][55/1557] Data 0.007 (0.009) Batch 0.705 (0.931) Remain 32:12:50 loss: 0.6838 Lr: 0.00470 [2024-02-18 03:22:16,817 INFO misc.py line 119 87073] Train: [21/100][56/1557] Data 0.005 (0.009) Batch 1.185 (0.936) Remain 32:22:44 loss: 0.4958 Lr: 0.00470 [2024-02-18 03:22:17,817 INFO misc.py line 119 87073] Train: [21/100][57/1557] Data 0.006 (0.009) Batch 0.995 (0.937) Remain 32:24:58 loss: 0.2272 Lr: 0.00470 [2024-02-18 03:22:18,897 INFO misc.py line 119 87073] Train: [21/100][58/1557] Data 0.009 (0.009) Batch 1.085 (0.940) Remain 32:30:31 loss: 0.2703 Lr: 0.00470 [2024-02-18 03:22:20,005 INFO misc.py line 119 87073] Train: [21/100][59/1557] Data 0.005 (0.009) Batch 1.101 (0.943) Remain 32:36:27 loss: 0.4215 Lr: 0.00470 [2024-02-18 03:22:20,962 INFO misc.py line 119 87073] Train: [21/100][60/1557] Data 0.012 (0.009) Batch 0.966 (0.943) Remain 32:37:16 loss: 0.3983 Lr: 0.00470 [2024-02-18 03:22:21,730 INFO misc.py line 119 87073] Train: [21/100][61/1557] Data 0.004 (0.009) Batch 0.768 (0.940) Remain 32:30:58 loss: 0.3199 Lr: 0.00470 [2024-02-18 03:22:22,560 INFO misc.py line 119 87073] Train: [21/100][62/1557] Data 0.005 (0.009) Batch 0.813 (0.938) Remain 32:26:28 loss: 0.7859 Lr: 0.00470 [2024-02-18 03:22:29,054 INFO misc.py line 119 87073] Train: [21/100][63/1557] Data 5.682 (0.103) Batch 6.504 (1.031) Remain 35:38:57 loss: 0.2292 Lr: 0.00470 [2024-02-18 03:22:30,116 INFO misc.py line 119 87073] Train: [21/100][64/1557] Data 0.012 (0.102) Batch 1.068 (1.031) Remain 35:40:12 loss: 0.4971 Lr: 0.00470 [2024-02-18 03:22:31,091 INFO misc.py line 119 87073] Train: [21/100][65/1557] Data 0.005 (0.100) Batch 0.976 (1.031) Remain 35:38:19 loss: 0.6454 Lr: 0.00470 [2024-02-18 03:22:31,939 INFO misc.py line 119 87073] Train: [21/100][66/1557] Data 0.004 (0.099) Batch 0.849 (1.028) Remain 35:32:18 loss: 0.3989 Lr: 0.00470 [2024-02-18 03:22:32,979 INFO misc.py line 119 87073] Train: [21/100][67/1557] Data 0.004 (0.097) Batch 1.039 (1.028) Remain 35:32:40 loss: 0.4784 Lr: 0.00470 [2024-02-18 03:22:33,779 INFO misc.py line 119 87073] Train: [21/100][68/1557] Data 0.005 (0.096) Batch 0.799 (1.024) Remain 35:25:20 loss: 0.3853 Lr: 0.00470 [2024-02-18 03:22:34,582 INFO misc.py line 119 87073] Train: [21/100][69/1557] Data 0.006 (0.094) Batch 0.802 (1.021) Remain 35:18:20 loss: 0.6975 Lr: 0.00470 [2024-02-18 03:22:35,605 INFO misc.py line 119 87073] Train: [21/100][70/1557] Data 0.006 (0.093) Batch 1.015 (1.021) Remain 35:18:09 loss: 0.1633 Lr: 0.00470 [2024-02-18 03:22:36,650 INFO misc.py line 119 87073] Train: [21/100][71/1557] Data 0.014 (0.092) Batch 1.053 (1.021) Remain 35:19:06 loss: 0.3317 Lr: 0.00470 [2024-02-18 03:22:37,455 INFO misc.py line 119 87073] Train: [21/100][72/1557] Data 0.006 (0.091) Batch 0.807 (1.018) Remain 35:12:38 loss: 0.5267 Lr: 0.00470 [2024-02-18 03:22:38,341 INFO misc.py line 119 87073] Train: [21/100][73/1557] Data 0.004 (0.089) Batch 0.884 (1.016) Remain 35:08:39 loss: 0.4820 Lr: 0.00470 [2024-02-18 03:22:39,522 INFO misc.py line 119 87073] Train: [21/100][74/1557] Data 0.006 (0.088) Batch 1.182 (1.019) Remain 35:13:28 loss: 1.3787 Lr: 0.00470 [2024-02-18 03:22:40,311 INFO misc.py line 119 87073] Train: [21/100][75/1557] Data 0.006 (0.087) Batch 0.791 (1.015) Remain 35:06:53 loss: 0.4723 Lr: 0.00470 [2024-02-18 03:22:41,090 INFO misc.py line 119 87073] Train: [21/100][76/1557] Data 0.004 (0.086) Batch 0.771 (1.012) Remain 34:59:55 loss: 0.4469 Lr: 0.00470 [2024-02-18 03:22:45,255 INFO misc.py line 119 87073] Train: [21/100][77/1557] Data 2.916 (0.124) Batch 4.170 (1.055) Remain 36:28:26 loss: 0.1885 Lr: 0.00470 [2024-02-18 03:22:46,469 INFO misc.py line 119 87073] Train: [21/100][78/1557] Data 0.007 (0.123) Batch 1.216 (1.057) Remain 36:32:52 loss: 0.2108 Lr: 0.00470 [2024-02-18 03:22:47,501 INFO misc.py line 119 87073] Train: [21/100][79/1557] Data 0.006 (0.121) Batch 1.028 (1.057) Remain 36:32:03 loss: 0.3658 Lr: 0.00470 [2024-02-18 03:22:48,573 INFO misc.py line 119 87073] Train: [21/100][80/1557] Data 0.009 (0.120) Batch 1.072 (1.057) Remain 36:32:28 loss: 1.1800 Lr: 0.00470 [2024-02-18 03:22:49,647 INFO misc.py line 119 87073] Train: [21/100][81/1557] Data 0.008 (0.118) Batch 1.078 (1.057) Remain 36:33:00 loss: 0.6688 Lr: 0.00470 [2024-02-18 03:22:50,441 INFO misc.py line 119 87073] Train: [21/100][82/1557] Data 0.004 (0.117) Batch 0.794 (1.054) Remain 36:26:04 loss: 0.7935 Lr: 0.00470 [2024-02-18 03:22:51,174 INFO misc.py line 119 87073] Train: [21/100][83/1557] Data 0.005 (0.115) Batch 0.733 (1.050) Remain 36:17:44 loss: 0.4735 Lr: 0.00470 [2024-02-18 03:22:52,405 INFO misc.py line 119 87073] Train: [21/100][84/1557] Data 0.005 (0.114) Batch 1.222 (1.052) Remain 36:22:08 loss: 0.4451 Lr: 0.00470 [2024-02-18 03:22:53,462 INFO misc.py line 119 87073] Train: [21/100][85/1557] Data 0.014 (0.113) Batch 1.066 (1.052) Remain 36:22:27 loss: 0.2048 Lr: 0.00470 [2024-02-18 03:22:54,417 INFO misc.py line 119 87073] Train: [21/100][86/1557] Data 0.005 (0.112) Batch 0.956 (1.051) Remain 36:20:03 loss: 1.0020 Lr: 0.00470 [2024-02-18 03:22:55,380 INFO misc.py line 119 87073] Train: [21/100][87/1557] Data 0.005 (0.110) Batch 0.963 (1.050) Remain 36:17:51 loss: 0.6717 Lr: 0.00470 [2024-02-18 03:22:56,399 INFO misc.py line 119 87073] Train: [21/100][88/1557] Data 0.006 (0.109) Batch 1.019 (1.049) Remain 36:17:06 loss: 0.3523 Lr: 0.00470 [2024-02-18 03:22:57,105 INFO misc.py line 119 87073] Train: [21/100][89/1557] Data 0.004 (0.108) Batch 0.705 (1.045) Remain 36:08:46 loss: 0.5558 Lr: 0.00470 [2024-02-18 03:22:57,848 INFO misc.py line 119 87073] Train: [21/100][90/1557] Data 0.005 (0.107) Batch 0.737 (1.042) Remain 36:01:23 loss: 0.3150 Lr: 0.00470 [2024-02-18 03:22:59,190 INFO misc.py line 119 87073] Train: [21/100][91/1557] Data 0.011 (0.106) Batch 1.344 (1.045) Remain 36:08:30 loss: 0.1844 Lr: 0.00470 [2024-02-18 03:23:00,158 INFO misc.py line 119 87073] Train: [21/100][92/1557] Data 0.010 (0.104) Batch 0.972 (1.044) Remain 36:06:46 loss: 0.4668 Lr: 0.00470 [2024-02-18 03:23:01,010 INFO misc.py line 119 87073] Train: [21/100][93/1557] Data 0.005 (0.103) Batch 0.851 (1.042) Remain 36:02:17 loss: 0.2248 Lr: 0.00470 [2024-02-18 03:23:02,166 INFO misc.py line 119 87073] Train: [21/100][94/1557] Data 0.008 (0.102) Batch 1.157 (1.044) Remain 36:04:54 loss: 0.1571 Lr: 0.00470 [2024-02-18 03:23:03,136 INFO misc.py line 119 87073] Train: [21/100][95/1557] Data 0.007 (0.101) Batch 0.971 (1.043) Remain 36:03:14 loss: 0.4761 Lr: 0.00470 [2024-02-18 03:23:03,894 INFO misc.py line 119 87073] Train: [21/100][96/1557] Data 0.005 (0.100) Batch 0.756 (1.040) Remain 35:56:50 loss: 0.6708 Lr: 0.00470 [2024-02-18 03:23:04,675 INFO misc.py line 119 87073] Train: [21/100][97/1557] Data 0.006 (0.099) Batch 0.780 (1.037) Remain 35:51:06 loss: 0.6546 Lr: 0.00470 [2024-02-18 03:23:05,893 INFO misc.py line 119 87073] Train: [21/100][98/1557] Data 0.005 (0.098) Batch 1.220 (1.039) Remain 35:55:04 loss: 0.2808 Lr: 0.00470 [2024-02-18 03:23:06,922 INFO misc.py line 119 87073] Train: [21/100][99/1557] Data 0.005 (0.097) Batch 1.018 (1.039) Remain 35:54:36 loss: 0.7117 Lr: 0.00470 [2024-02-18 03:23:07,886 INFO misc.py line 119 87073] Train: [21/100][100/1557] Data 0.016 (0.096) Batch 0.975 (1.038) Remain 35:53:13 loss: 0.3138 Lr: 0.00470 [2024-02-18 03:23:08,741 INFO misc.py line 119 87073] Train: [21/100][101/1557] Data 0.005 (0.095) Batch 0.855 (1.036) Remain 35:49:19 loss: 0.8110 Lr: 0.00470 [2024-02-18 03:23:09,939 INFO misc.py line 119 87073] Train: [21/100][102/1557] Data 0.006 (0.095) Batch 1.197 (1.038) Remain 35:52:41 loss: 0.2151 Lr: 0.00470 [2024-02-18 03:23:10,739 INFO misc.py line 119 87073] Train: [21/100][103/1557] Data 0.006 (0.094) Batch 0.800 (1.035) Remain 35:47:44 loss: 0.6083 Lr: 0.00470 [2024-02-18 03:23:11,434 INFO misc.py line 119 87073] Train: [21/100][104/1557] Data 0.006 (0.093) Batch 0.695 (1.032) Remain 35:40:44 loss: 0.4749 Lr: 0.00470 [2024-02-18 03:23:12,682 INFO misc.py line 119 87073] Train: [21/100][105/1557] Data 0.005 (0.092) Batch 1.246 (1.034) Remain 35:45:04 loss: 0.1911 Lr: 0.00470 [2024-02-18 03:23:13,792 INFO misc.py line 119 87073] Train: [21/100][106/1557] Data 0.007 (0.091) Batch 1.113 (1.035) Remain 35:46:38 loss: 0.2893 Lr: 0.00470 [2024-02-18 03:23:14,785 INFO misc.py line 119 87073] Train: [21/100][107/1557] Data 0.004 (0.090) Batch 0.992 (1.035) Remain 35:45:46 loss: 0.6640 Lr: 0.00470 [2024-02-18 03:23:15,917 INFO misc.py line 119 87073] Train: [21/100][108/1557] Data 0.004 (0.089) Batch 1.124 (1.035) Remain 35:47:31 loss: 0.6768 Lr: 0.00470 [2024-02-18 03:23:16,809 INFO misc.py line 119 87073] Train: [21/100][109/1557] Data 0.012 (0.089) Batch 0.899 (1.034) Remain 35:44:50 loss: 0.3267 Lr: 0.00470 [2024-02-18 03:23:17,612 INFO misc.py line 119 87073] Train: [21/100][110/1557] Data 0.006 (0.088) Batch 0.802 (1.032) Remain 35:40:19 loss: 0.7295 Lr: 0.00470 [2024-02-18 03:23:18,315 INFO misc.py line 119 87073] Train: [21/100][111/1557] Data 0.007 (0.087) Batch 0.706 (1.029) Remain 35:34:02 loss: 0.4022 Lr: 0.00470 [2024-02-18 03:23:19,485 INFO misc.py line 119 87073] Train: [21/100][112/1557] Data 0.004 (0.086) Batch 1.168 (1.030) Remain 35:36:41 loss: 0.3074 Lr: 0.00470 [2024-02-18 03:23:20,439 INFO misc.py line 119 87073] Train: [21/100][113/1557] Data 0.005 (0.086) Batch 0.955 (1.029) Remain 35:35:14 loss: 0.4312 Lr: 0.00470 [2024-02-18 03:23:21,401 INFO misc.py line 119 87073] Train: [21/100][114/1557] Data 0.005 (0.085) Batch 0.962 (1.029) Remain 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line 119 87073] Train: [21/100][127/1557] Data 0.005 (0.124) Batch 1.001 (1.063) Remain 36:43:51 loss: 0.5336 Lr: 0.00470 [2024-02-18 03:23:40,030 INFO misc.py line 119 87073] Train: [21/100][128/1557] Data 0.016 (0.123) Batch 1.041 (1.062) Remain 36:43:28 loss: 0.2867 Lr: 0.00470 [2024-02-18 03:23:41,038 INFO misc.py line 119 87073] Train: [21/100][129/1557] Data 0.025 (0.122) Batch 1.024 (1.062) Remain 36:42:48 loss: 0.4483 Lr: 0.00470 [2024-02-18 03:23:42,054 INFO misc.py line 119 87073] Train: [21/100][130/1557] Data 0.010 (0.121) Batch 1.015 (1.062) Remain 36:42:01 loss: 0.5504 Lr: 0.00470 [2024-02-18 03:23:42,838 INFO misc.py line 119 87073] Train: [21/100][131/1557] Data 0.011 (0.120) Batch 0.791 (1.060) Remain 36:37:37 loss: 0.4836 Lr: 0.00470 [2024-02-18 03:23:43,716 INFO misc.py line 119 87073] Train: [21/100][132/1557] Data 0.004 (0.119) Batch 0.878 (1.058) Remain 36:34:40 loss: 0.6530 Lr: 0.00470 [2024-02-18 03:23:46,658 INFO misc.py line 119 87073] Train: 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Batch 0.743 (1.066) Remain 36:50:07 loss: 0.7153 Lr: 0.00470 [2024-02-18 03:23:53,332 INFO misc.py line 119 87073] Train: [21/100][140/1557] Data 0.006 (0.125) Batch 1.181 (1.067) Remain 36:51:51 loss: 0.3183 Lr: 0.00470 [2024-02-18 03:23:54,216 INFO misc.py line 119 87073] Train: [21/100][141/1557] Data 0.010 (0.124) Batch 0.888 (1.065) Remain 36:49:09 loss: 0.4110 Lr: 0.00470 [2024-02-18 03:23:55,284 INFO misc.py line 119 87073] Train: [21/100][142/1557] Data 0.005 (0.123) Batch 1.068 (1.065) Remain 36:49:10 loss: 0.6822 Lr: 0.00470 [2024-02-18 03:23:56,281 INFO misc.py line 119 87073] Train: [21/100][143/1557] Data 0.005 (0.122) Batch 0.999 (1.065) Remain 36:48:11 loss: 0.3065 Lr: 0.00470 [2024-02-18 03:23:57,299 INFO misc.py line 119 87073] Train: [21/100][144/1557] Data 0.003 (0.121) Batch 1.016 (1.065) Remain 36:47:26 loss: 0.6992 Lr: 0.00470 [2024-02-18 03:23:58,076 INFO misc.py line 119 87073] Train: [21/100][145/1557] Data 0.005 (0.120) Batch 0.778 (1.063) Remain 36:43:14 loss: 0.3597 Lr: 0.00470 [2024-02-18 03:23:58,845 INFO misc.py line 119 87073] Train: [21/100][146/1557] Data 0.005 (0.120) Batch 0.765 (1.060) Remain 36:38:54 loss: 0.4688 Lr: 0.00470 [2024-02-18 03:24:00,116 INFO misc.py line 119 87073] Train: [21/100][147/1557] Data 0.009 (0.119) Batch 1.272 (1.062) Remain 36:41:55 loss: 0.1824 Lr: 0.00470 [2024-02-18 03:24:01,157 INFO misc.py line 119 87073] Train: [21/100][148/1557] Data 0.008 (0.118) Batch 1.037 (1.062) Remain 36:41:33 loss: 1.0069 Lr: 0.00470 [2024-02-18 03:24:02,123 INFO misc.py line 119 87073] Train: [21/100][149/1557] Data 0.013 (0.117) Batch 0.975 (1.061) Remain 36:40:17 loss: 0.8532 Lr: 0.00470 [2024-02-18 03:24:03,135 INFO misc.py line 119 87073] Train: [21/100][150/1557] Data 0.003 (0.117) Batch 1.012 (1.061) Remain 36:39:35 loss: 0.5513 Lr: 0.00470 [2024-02-18 03:24:04,291 INFO misc.py line 119 87073] Train: [21/100][151/1557] Data 0.003 (0.116) Batch 1.155 (1.061) Remain 36:40:53 loss: 0.6534 Lr: 0.00470 [2024-02-18 03:24:05,053 INFO misc.py line 119 87073] Train: [21/100][152/1557] Data 0.005 (0.115) Batch 0.763 (1.059) Remain 36:36:42 loss: 0.2679 Lr: 0.00470 [2024-02-18 03:24:05,776 INFO misc.py line 119 87073] Train: [21/100][153/1557] Data 0.003 (0.114) Batch 0.709 (1.057) Remain 36:31:51 loss: 0.2897 Lr: 0.00470 [2024-02-18 03:24:07,083 INFO misc.py line 119 87073] Train: [21/100][154/1557] Data 0.017 (0.114) Batch 1.314 (1.059) Remain 36:35:22 loss: 0.2889 Lr: 0.00470 [2024-02-18 03:24:07,991 INFO misc.py line 119 87073] Train: [21/100][155/1557] Data 0.011 (0.113) Batch 0.911 (1.058) Remain 36:33:20 loss: 0.6588 Lr: 0.00470 [2024-02-18 03:24:08,931 INFO misc.py line 119 87073] Train: [21/100][156/1557] Data 0.010 (0.112) Batch 0.943 (1.057) Remain 36:31:46 loss: 0.5409 Lr: 0.00470 [2024-02-18 03:24:09,847 INFO misc.py line 119 87073] Train: [21/100][157/1557] Data 0.004 (0.112) Batch 0.916 (1.056) Remain 36:29:50 loss: 0.7218 Lr: 0.00470 [2024-02-18 03:24:10,769 INFO misc.py line 119 87073] Train: [21/100][158/1557] Data 0.004 (0.111) Batch 0.917 (1.055) Remain 36:27:57 loss: 0.5402 Lr: 0.00470 [2024-02-18 03:24:11,602 INFO misc.py line 119 87073] Train: [21/100][159/1557] Data 0.009 (0.110) Batch 0.838 (1.054) Remain 36:25:03 loss: 0.4252 Lr: 0.00470 [2024-02-18 03:24:12,384 INFO misc.py line 119 87073] Train: [21/100][160/1557] Data 0.007 (0.110) Batch 0.782 (1.052) Remain 36:21:26 loss: 0.5082 Lr: 0.00469 [2024-02-18 03:24:13,661 INFO misc.py line 119 87073] Train: [21/100][161/1557] Data 0.005 (0.109) Batch 1.274 (1.054) Remain 36:24:20 loss: 0.4567 Lr: 0.00469 [2024-02-18 03:24:14,596 INFO misc.py line 119 87073] Train: [21/100][162/1557] Data 0.008 (0.108) Batch 0.939 (1.053) Remain 36:22:49 loss: 0.4120 Lr: 0.00469 [2024-02-18 03:24:15,616 INFO misc.py line 119 87073] Train: [21/100][163/1557] Data 0.003 (0.108) Batch 1.020 (1.053) Remain 36:22:23 loss: 0.5905 Lr: 0.00469 [2024-02-18 03:24:16,698 INFO misc.py line 119 87073] Train: [21/100][164/1557] Data 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line 119 87073] Train: [21/100][183/1557] Data 0.005 (0.130) Batch 1.015 (1.081) Remain 37:21:01 loss: 0.6670 Lr: 0.00469 [2024-02-18 03:24:42,733 INFO misc.py line 119 87073] Train: [21/100][184/1557] Data 0.005 (0.129) Batch 0.943 (1.080) Remain 37:19:25 loss: 0.4882 Lr: 0.00469 [2024-02-18 03:24:43,933 INFO misc.py line 119 87073] Train: [21/100][185/1557] Data 0.005 (0.128) Batch 1.199 (1.081) Remain 37:20:45 loss: 1.3636 Lr: 0.00469 [2024-02-18 03:24:45,030 INFO misc.py line 119 87073] Train: [21/100][186/1557] Data 0.004 (0.128) Batch 1.096 (1.081) Remain 37:20:54 loss: 0.6021 Lr: 0.00469 [2024-02-18 03:24:45,815 INFO misc.py line 119 87073] Train: [21/100][187/1557] Data 0.005 (0.127) Batch 0.786 (1.079) Remain 37:17:34 loss: 0.1868 Lr: 0.00469 [2024-02-18 03:24:46,573 INFO misc.py line 119 87073] Train: [21/100][188/1557] Data 0.004 (0.126) Batch 0.751 (1.078) Remain 37:13:52 loss: 0.2991 Lr: 0.00469 [2024-02-18 03:24:48,787 INFO misc.py line 119 87073] Train: 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[2024-02-18 03:25:37,710 INFO misc.py line 119 87073] Train: [21/100][233/1557] Data 0.005 (0.136) Batch 1.012 (1.089) Remain 37:36:54 loss: 0.9270 Lr: 0.00469 [2024-02-18 03:25:38,702 INFO misc.py line 119 87073] Train: [21/100][234/1557] Data 0.006 (0.135) Batch 0.993 (1.089) Remain 37:36:01 loss: 0.3618 Lr: 0.00469 [2024-02-18 03:25:39,782 INFO misc.py line 119 87073] Train: [21/100][235/1557] Data 0.005 (0.135) Batch 1.080 (1.089) Remain 37:35:55 loss: 0.6245 Lr: 0.00469 [2024-02-18 03:25:40,529 INFO misc.py line 119 87073] Train: [21/100][236/1557] Data 0.006 (0.134) Batch 0.747 (1.087) Remain 37:32:52 loss: 0.5778 Lr: 0.00469 [2024-02-18 03:25:41,327 INFO misc.py line 119 87073] Train: [21/100][237/1557] Data 0.005 (0.133) Batch 0.798 (1.086) Remain 37:30:17 loss: 0.5944 Lr: 0.00469 [2024-02-18 03:25:42,316 INFO misc.py line 119 87073] Train: [21/100][238/1557] Data 0.005 (0.133) Batch 0.988 (1.086) Remain 37:29:24 loss: 0.2740 Lr: 0.00469 [2024-02-18 03:25:43,299 INFO misc.py line 119 87073] Train: [21/100][239/1557] Data 0.007 (0.132) Batch 0.984 (1.085) Remain 37:28:29 loss: 0.5995 Lr: 0.00469 [2024-02-18 03:25:44,180 INFO misc.py line 119 87073] Train: [21/100][240/1557] Data 0.005 (0.132) Batch 0.882 (1.084) Remain 37:26:42 loss: 0.9160 Lr: 0.00469 [2024-02-18 03:25:45,184 INFO misc.py line 119 87073] Train: [21/100][241/1557] Data 0.004 (0.131) Batch 1.001 (1.084) Remain 37:25:57 loss: 0.3984 Lr: 0.00469 [2024-02-18 03:25:46,517 INFO misc.py line 119 87073] Train: [21/100][242/1557] Data 0.006 (0.131) Batch 1.333 (1.085) Remain 37:28:06 loss: 0.3102 Lr: 0.00469 [2024-02-18 03:25:47,325 INFO misc.py line 119 87073] Train: [21/100][243/1557] Data 0.008 (0.130) Batch 0.809 (1.084) Remain 37:25:42 loss: 0.5221 Lr: 0.00469 [2024-02-18 03:25:48,122 INFO misc.py line 119 87073] Train: [21/100][244/1557] Data 0.006 (0.130) Batch 0.797 (1.083) Remain 37:23:13 loss: 0.4364 Lr: 0.00469 [2024-02-18 03:25:50,208 INFO misc.py line 119 87073] Train: 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Batch 0.804 (1.083) Remain 37:23:29 loss: 0.4433 Lr: 0.00469 [2024-02-18 03:25:56,940 INFO misc.py line 119 87073] Train: [21/100][252/1557] Data 0.005 (0.129) Batch 1.191 (1.083) Remain 37:24:22 loss: 0.3559 Lr: 0.00469 [2024-02-18 03:25:57,991 INFO misc.py line 119 87073] Train: [21/100][253/1557] Data 0.007 (0.128) Batch 1.045 (1.083) Remain 37:24:01 loss: 0.8459 Lr: 0.00469 [2024-02-18 03:25:58,907 INFO misc.py line 119 87073] Train: [21/100][254/1557] Data 0.014 (0.128) Batch 0.922 (1.082) Remain 37:22:40 loss: 0.4234 Lr: 0.00469 [2024-02-18 03:25:59,966 INFO misc.py line 119 87073] Train: [21/100][255/1557] Data 0.007 (0.127) Batch 1.061 (1.082) Remain 37:22:29 loss: 0.7302 Lr: 0.00469 [2024-02-18 03:26:01,010 INFO misc.py line 119 87073] Train: [21/100][256/1557] Data 0.006 (0.127) Batch 1.043 (1.082) Remain 37:22:08 loss: 0.5805 Lr: 0.00469 [2024-02-18 03:26:01,790 INFO misc.py line 119 87073] Train: [21/100][257/1557] Data 0.006 (0.127) Batch 0.782 (1.081) Remain 37:19:40 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Batch 0.804 (1.096) Remain 37:45:57 loss: 0.6210 Lr: 0.00469 [2024-02-18 03:30:05,526 INFO misc.py line 119 87073] Train: [21/100][476/1557] Data 0.011 (0.143) Batch 1.165 (1.096) Remain 37:46:14 loss: 0.1692 Lr: 0.00469 [2024-02-18 03:30:06,408 INFO misc.py line 119 87073] Train: [21/100][477/1557] Data 0.007 (0.142) Batch 0.883 (1.095) Remain 37:45:17 loss: 0.1564 Lr: 0.00469 [2024-02-18 03:30:07,367 INFO misc.py line 119 87073] Train: [21/100][478/1557] Data 0.006 (0.142) Batch 0.962 (1.095) Remain 37:44:41 loss: 0.4980 Lr: 0.00469 [2024-02-18 03:30:08,383 INFO misc.py line 119 87073] Train: [21/100][479/1557] Data 0.003 (0.142) Batch 1.015 (1.095) Remain 37:44:19 loss: 0.8459 Lr: 0.00469 [2024-02-18 03:30:09,362 INFO misc.py line 119 87073] Train: [21/100][480/1557] Data 0.004 (0.142) Batch 0.978 (1.095) Remain 37:43:48 loss: 0.5140 Lr: 0.00469 [2024-02-18 03:30:10,121 INFO misc.py line 119 87073] Train: [21/100][481/1557] Data 0.006 (0.141) Batch 0.759 (1.094) Remain 37:42:19 loss: 0.7127 Lr: 0.00469 [2024-02-18 03:30:11,035 INFO misc.py line 119 87073] Train: [21/100][482/1557] Data 0.006 (0.141) Batch 0.914 (1.094) Remain 37:41:32 loss: 0.3223 Lr: 0.00469 [2024-02-18 03:30:12,305 INFO misc.py line 119 87073] Train: [21/100][483/1557] Data 0.005 (0.141) Batch 1.270 (1.094) Remain 37:42:16 loss: 0.1934 Lr: 0.00469 [2024-02-18 03:30:13,146 INFO misc.py line 119 87073] Train: [21/100][484/1557] Data 0.005 (0.140) Batch 0.842 (1.093) Remain 37:41:10 loss: 0.6063 Lr: 0.00469 [2024-02-18 03:30:14,191 INFO misc.py line 119 87073] Train: [21/100][485/1557] Data 0.004 (0.140) Batch 1.044 (1.093) Remain 37:40:56 loss: 0.3871 Lr: 0.00469 [2024-02-18 03:30:15,095 INFO misc.py line 119 87073] Train: [21/100][486/1557] Data 0.005 (0.140) Batch 0.905 (1.093) Remain 37:40:07 loss: 0.6672 Lr: 0.00469 [2024-02-18 03:30:16,168 INFO misc.py line 119 87073] Train: [21/100][487/1557] Data 0.004 (0.140) Batch 1.072 (1.093) Remain 37:40:01 loss: 0.8618 Lr: 0.00469 [2024-02-18 03:30:16,908 INFO misc.py line 119 87073] Train: [21/100][488/1557] Data 0.004 (0.139) Batch 0.738 (1.092) Remain 37:38:29 loss: 0.3479 Lr: 0.00469 [2024-02-18 03:30:17,650 INFO misc.py line 119 87073] Train: [21/100][489/1557] Data 0.006 (0.139) Batch 0.743 (1.091) Remain 37:36:59 loss: 0.6867 Lr: 0.00469 [2024-02-18 03:30:18,859 INFO misc.py line 119 87073] Train: [21/100][490/1557] Data 0.005 (0.139) Batch 1.201 (1.092) Remain 37:37:25 loss: 0.2970 Lr: 0.00469 [2024-02-18 03:30:19,704 INFO misc.py line 119 87073] Train: [21/100][491/1557] Data 0.013 (0.138) Batch 0.853 (1.091) Remain 37:36:24 loss: 0.6342 Lr: 0.00469 [2024-02-18 03:30:20,659 INFO misc.py line 119 87073] Train: [21/100][492/1557] Data 0.005 (0.138) Batch 0.955 (1.091) Remain 37:35:48 loss: 0.3894 Lr: 0.00469 [2024-02-18 03:30:21,717 INFO misc.py line 119 87073] Train: [21/100][493/1557] Data 0.005 (0.138) Batch 1.057 (1.091) Remain 37:35:38 loss: 0.6521 Lr: 0.00469 [2024-02-18 03:30:22,725 INFO misc.py line 119 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[2024-02-18 03:30:46,689 INFO misc.py line 119 87073] Train: [21/100][513/1557] Data 0.007 (0.144) Batch 0.903 (1.097) Remain 37:48:04 loss: 0.4356 Lr: 0.00469 [2024-02-18 03:30:47,730 INFO misc.py line 119 87073] Train: [21/100][514/1557] Data 0.006 (0.144) Batch 1.036 (1.097) Remain 37:47:48 loss: 0.4708 Lr: 0.00469 [2024-02-18 03:30:48,850 INFO misc.py line 119 87073] Train: [21/100][515/1557] Data 0.010 (0.144) Batch 1.116 (1.097) Remain 37:47:52 loss: 0.5989 Lr: 0.00469 [2024-02-18 03:30:49,606 INFO misc.py line 119 87073] Train: [21/100][516/1557] Data 0.014 (0.144) Batch 0.765 (1.096) Remain 37:46:31 loss: 0.3463 Lr: 0.00469 [2024-02-18 03:30:50,384 INFO misc.py line 119 87073] Train: [21/100][517/1557] Data 0.004 (0.143) Batch 0.775 (1.096) Remain 37:45:12 loss: 0.5933 Lr: 0.00469 [2024-02-18 03:30:51,369 INFO misc.py line 119 87073] Train: [21/100][518/1557] Data 0.007 (0.143) Batch 0.988 (1.095) Remain 37:44:45 loss: 0.1095 Lr: 0.00469 [2024-02-18 03:30:52,317 INFO misc.py line 119 87073] Train: [21/100][519/1557] Data 0.005 (0.143) Batch 0.947 (1.095) Remain 37:44:08 loss: 0.6110 Lr: 0.00469 [2024-02-18 03:30:53,225 INFO misc.py line 119 87073] Train: [21/100][520/1557] Data 0.005 (0.143) Batch 0.908 (1.095) Remain 37:43:22 loss: 0.7300 Lr: 0.00469 [2024-02-18 03:30:54,231 INFO misc.py line 119 87073] Train: [21/100][521/1557] Data 0.005 (0.142) Batch 1.005 (1.095) Remain 37:42:59 loss: 0.4148 Lr: 0.00469 [2024-02-18 03:30:55,219 INFO misc.py line 119 87073] Train: [21/100][522/1557] Data 0.009 (0.142) Batch 0.990 (1.094) Remain 37:42:33 loss: 0.4473 Lr: 0.00469 [2024-02-18 03:30:55,959 INFO misc.py line 119 87073] Train: [21/100][523/1557] Data 0.005 (0.142) Batch 0.740 (1.094) Remain 37:41:07 loss: 0.2704 Lr: 0.00469 [2024-02-18 03:30:56,771 INFO misc.py line 119 87073] Train: [21/100][524/1557] Data 0.006 (0.142) Batch 0.810 (1.093) Remain 37:39:59 loss: 0.5424 Lr: 0.00469 [2024-02-18 03:30:59,280 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [21/100][743/1557] Data 0.006 (0.143) Batch 0.894 (1.095) Remain 37:40:35 loss: 0.9180 Lr: 0.00468 [2024-02-18 03:34:58,879 INFO misc.py line 119 87073] Train: [21/100][744/1557] Data 0.003 (0.142) Batch 1.049 (1.095) Remain 37:40:26 loss: 0.8313 Lr: 0.00468 [2024-02-18 03:34:59,795 INFO misc.py line 119 87073] Train: [21/100][745/1557] Data 0.003 (0.142) Batch 0.913 (1.095) Remain 37:39:54 loss: 0.4531 Lr: 0.00468 [2024-02-18 03:35:00,702 INFO misc.py line 119 87073] Train: [21/100][746/1557] Data 0.006 (0.142) Batch 0.908 (1.095) Remain 37:39:22 loss: 0.6028 Lr: 0.00468 [2024-02-18 03:35:01,495 INFO misc.py line 119 87073] Train: [21/100][747/1557] Data 0.006 (0.142) Batch 0.793 (1.094) Remain 37:38:31 loss: 0.8139 Lr: 0.00468 [2024-02-18 03:35:02,578 INFO misc.py line 119 87073] Train: [21/100][748/1557] Data 0.005 (0.142) Batch 1.083 (1.094) Remain 37:38:28 loss: 0.7524 Lr: 0.00468 [2024-02-18 03:35:07,086 INFO misc.py line 119 87073] Train: 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loss: 0.8083 Lr: 0.00468 [2024-02-18 03:35:19,696 INFO misc.py line 119 87073] Train: [21/100][762/1557] Data 0.006 (0.143) Batch 0.802 (1.097) Remain 37:43:05 loss: 0.7621 Lr: 0.00468 [2024-02-18 03:35:20,914 INFO misc.py line 119 87073] Train: [21/100][763/1557] Data 0.006 (0.143) Batch 1.219 (1.097) Remain 37:43:24 loss: 0.8398 Lr: 0.00468 [2024-02-18 03:35:21,812 INFO misc.py line 119 87073] Train: [21/100][764/1557] Data 0.005 (0.143) Batch 0.896 (1.097) Remain 37:42:50 loss: 0.4165 Lr: 0.00468 [2024-02-18 03:35:22,843 INFO misc.py line 119 87073] Train: [21/100][765/1557] Data 0.007 (0.143) Batch 1.033 (1.097) Remain 37:42:39 loss: 0.8405 Lr: 0.00468 [2024-02-18 03:35:23,794 INFO misc.py line 119 87073] Train: [21/100][766/1557] Data 0.006 (0.143) Batch 0.949 (1.096) Remain 37:42:14 loss: 1.1820 Lr: 0.00468 [2024-02-18 03:35:24,748 INFO misc.py line 119 87073] Train: [21/100][767/1557] Data 0.007 (0.142) Batch 0.955 (1.096) Remain 37:41:50 loss: 0.4967 Lr: 0.00468 [2024-02-18 03:35:25,518 INFO misc.py line 119 87073] Train: [21/100][768/1557] Data 0.006 (0.142) Batch 0.770 (1.096) Remain 37:40:56 loss: 0.8300 Lr: 0.00468 [2024-02-18 03:35:26,261 INFO misc.py line 119 87073] Train: [21/100][769/1557] Data 0.005 (0.142) Batch 0.744 (1.095) Remain 37:39:58 loss: 0.7754 Lr: 0.00468 [2024-02-18 03:35:27,522 INFO misc.py line 119 87073] Train: [21/100][770/1557] Data 0.004 (0.142) Batch 1.255 (1.096) Remain 37:40:23 loss: 3.2896 Lr: 0.00468 [2024-02-18 03:35:28,474 INFO misc.py line 119 87073] Train: [21/100][771/1557] Data 0.010 (0.142) Batch 0.956 (1.095) Remain 37:39:59 loss: 1.1779 Lr: 0.00468 [2024-02-18 03:35:29,516 INFO misc.py line 119 87073] Train: [21/100][772/1557] Data 0.005 (0.142) Batch 1.043 (1.095) Remain 37:39:50 loss: 0.4499 Lr: 0.00468 [2024-02-18 03:35:30,347 INFO misc.py line 119 87073] Train: [21/100][773/1557] Data 0.005 (0.141) Batch 0.830 (1.095) Remain 37:39:06 loss: 0.7431 Lr: 0.00468 [2024-02-18 03:35:31,454 INFO misc.py line 119 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[2024-02-18 03:35:55,540 INFO misc.py line 119 87073] Train: [21/100][793/1557] Data 0.019 (0.146) Batch 1.069 (1.099) Remain 37:47:20 loss: 0.6985 Lr: 0.00468 [2024-02-18 03:35:56,520 INFO misc.py line 119 87073] Train: [21/100][794/1557] Data 0.008 (0.145) Batch 0.981 (1.099) Remain 37:47:00 loss: 0.4215 Lr: 0.00468 [2024-02-18 03:35:57,454 INFO misc.py line 119 87073] Train: [21/100][795/1557] Data 0.008 (0.145) Batch 0.935 (1.099) Remain 37:46:33 loss: 0.7858 Lr: 0.00468 [2024-02-18 03:35:58,210 INFO misc.py line 119 87073] Train: [21/100][796/1557] Data 0.006 (0.145) Batch 0.755 (1.098) Remain 37:45:38 loss: 0.6137 Lr: 0.00468 [2024-02-18 03:35:58,994 INFO misc.py line 119 87073] Train: [21/100][797/1557] Data 0.008 (0.145) Batch 0.787 (1.098) Remain 37:44:49 loss: 0.6729 Lr: 0.00468 [2024-02-18 03:36:00,018 INFO misc.py line 119 87073] Train: [21/100][798/1557] Data 0.005 (0.145) Batch 1.022 (1.098) Remain 37:44:36 loss: 0.4249 Lr: 0.00468 [2024-02-18 03:36:01,209 INFO misc.py line 119 87073] Train: [21/100][799/1557] Data 0.007 (0.145) Batch 1.192 (1.098) Remain 37:44:49 loss: 0.4696 Lr: 0.00468 [2024-02-18 03:36:02,219 INFO misc.py line 119 87073] Train: [21/100][800/1557] Data 0.005 (0.144) Batch 1.012 (1.098) Remain 37:44:35 loss: 0.4550 Lr: 0.00468 [2024-02-18 03:36:03,329 INFO misc.py line 119 87073] Train: [21/100][801/1557] Data 0.003 (0.144) Batch 1.108 (1.098) Remain 37:44:35 loss: 0.6622 Lr: 0.00468 [2024-02-18 03:36:04,292 INFO misc.py line 119 87073] Train: [21/100][802/1557] Data 0.006 (0.144) Batch 0.965 (1.098) Remain 37:44:14 loss: 0.5009 Lr: 0.00468 [2024-02-18 03:36:05,097 INFO misc.py line 119 87073] Train: [21/100][803/1557] Data 0.004 (0.144) Batch 0.805 (1.097) Remain 37:43:27 loss: 0.8224 Lr: 0.00468 [2024-02-18 03:36:05,826 INFO misc.py line 119 87073] Train: [21/100][804/1557] Data 0.004 (0.144) Batch 0.725 (1.097) Remain 37:42:28 loss: 0.6697 Lr: 0.00468 [2024-02-18 03:36:09,658 INFO misc.py line 119 87073] Train: 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Data 0.007 (0.144) Batch 1.057 (1.097) Remain 37:39:56 loss: 0.2594 Lr: 0.00467 [2024-02-18 03:39:46,658 INFO misc.py line 119 87073] Train: [21/100][1005/1557] Data 0.010 (0.144) Batch 0.912 (1.097) Remain 37:39:32 loss: 0.5214 Lr: 0.00467 [2024-02-18 03:39:47,430 INFO misc.py line 119 87073] Train: [21/100][1006/1557] Data 0.005 (0.143) Batch 0.771 (1.097) Remain 37:38:51 loss: 0.6271 Lr: 0.00467 [2024-02-18 03:39:48,231 INFO misc.py line 119 87073] Train: [21/100][1007/1557] Data 0.006 (0.143) Batch 0.802 (1.097) Remain 37:38:13 loss: 0.4931 Lr: 0.00467 [2024-02-18 03:39:49,499 INFO misc.py line 119 87073] Train: [21/100][1008/1557] Data 0.004 (0.143) Batch 1.266 (1.097) Remain 37:38:33 loss: 0.4329 Lr: 0.00467 [2024-02-18 03:39:50,632 INFO misc.py line 119 87073] Train: [21/100][1009/1557] Data 0.007 (0.143) Batch 1.135 (1.097) Remain 37:38:37 loss: 0.4277 Lr: 0.00467 [2024-02-18 03:39:51,593 INFO misc.py line 119 87073] Train: [21/100][1010/1557] Data 0.005 (0.143) Batch 0.960 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Data 0.005 (0.143) Batch 1.108 (1.097) Remain 37:38:53 loss: 0.2866 Lr: 0.00467 [2024-02-18 03:40:54,902 INFO misc.py line 119 87073] Train: [21/100][1067/1557] Data 0.007 (0.143) Batch 1.070 (1.097) Remain 37:38:48 loss: 0.4339 Lr: 0.00467 [2024-02-18 03:40:55,885 INFO misc.py line 119 87073] Train: [21/100][1068/1557] Data 0.007 (0.143) Batch 0.985 (1.097) Remain 37:38:34 loss: 0.4552 Lr: 0.00467 [2024-02-18 03:40:56,665 INFO misc.py line 119 87073] Train: [21/100][1069/1557] Data 0.005 (0.143) Batch 0.780 (1.097) Remain 37:37:56 loss: 0.5229 Lr: 0.00467 [2024-02-18 03:40:57,371 INFO misc.py line 119 87073] Train: [21/100][1070/1557] Data 0.005 (0.143) Batch 0.704 (1.097) Remain 37:37:10 loss: 0.2034 Lr: 0.00467 [2024-02-18 03:41:04,313 INFO misc.py line 119 87073] Train: [21/100][1071/1557] Data 5.364 (0.148) Batch 6.944 (1.102) Remain 37:48:25 loss: 0.2861 Lr: 0.00467 [2024-02-18 03:41:05,429 INFO misc.py line 119 87073] Train: [21/100][1072/1557] Data 0.007 (0.148) Batch 1.115 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Data 0.005 (0.145) Batch 0.764 (1.099) Remain 37:40:34 loss: 0.4751 Lr: 0.00467 [2024-02-18 03:41:29,866 INFO misc.py line 119 87073] Train: [21/100][1098/1557] Data 0.006 (0.145) Batch 0.813 (1.098) Remain 37:40:01 loss: 0.3080 Lr: 0.00467 [2024-02-18 03:41:31,081 INFO misc.py line 119 87073] Train: [21/100][1099/1557] Data 0.004 (0.145) Batch 1.213 (1.098) Remain 37:40:13 loss: 0.2290 Lr: 0.00467 [2024-02-18 03:41:32,156 INFO misc.py line 119 87073] Train: [21/100][1100/1557] Data 0.005 (0.145) Batch 1.068 (1.098) Remain 37:40:08 loss: 0.4162 Lr: 0.00467 [2024-02-18 03:41:32,997 INFO misc.py line 119 87073] Train: [21/100][1101/1557] Data 0.013 (0.144) Batch 0.850 (1.098) Remain 37:39:39 loss: 0.4349 Lr: 0.00467 [2024-02-18 03:41:33,979 INFO misc.py line 119 87073] Train: [21/100][1102/1557] Data 0.004 (0.144) Batch 0.982 (1.098) Remain 37:39:25 loss: 0.5249 Lr: 0.00467 [2024-02-18 03:41:34,995 INFO misc.py line 119 87073] Train: [21/100][1103/1557] Data 0.003 (0.144) Batch 1.016 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Data 0.010 (0.146) Batch 1.147 (1.100) Remain 37:42:27 loss: 0.5425 Lr: 0.00467 [2024-02-18 03:42:05,304 INFO misc.py line 119 87073] Train: [21/100][1129/1557] Data 0.008 (0.146) Batch 0.859 (1.100) Remain 37:41:59 loss: 0.4759 Lr: 0.00467 [2024-02-18 03:42:06,345 INFO misc.py line 119 87073] Train: [21/100][1130/1557] Data 0.005 (0.146) Batch 1.042 (1.100) Remain 37:41:52 loss: 0.5873 Lr: 0.00467 [2024-02-18 03:42:07,307 INFO misc.py line 119 87073] Train: [21/100][1131/1557] Data 0.004 (0.146) Batch 0.959 (1.099) Remain 37:41:35 loss: 0.3262 Lr: 0.00467 [2024-02-18 03:42:08,064 INFO misc.py line 119 87073] Train: [21/100][1132/1557] Data 0.008 (0.145) Batch 0.759 (1.099) Remain 37:40:57 loss: 0.5251 Lr: 0.00467 [2024-02-18 03:42:08,796 INFO misc.py line 119 87073] Train: [21/100][1133/1557] Data 0.005 (0.145) Batch 0.730 (1.099) Remain 37:40:16 loss: 0.3536 Lr: 0.00467 [2024-02-18 03:42:09,874 INFO misc.py line 119 87073] Train: [21/100][1134/1557] Data 0.007 (0.145) Batch 1.075 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Data 0.007 (0.146) Batch 1.040 (1.100) Remain 37:41:13 loss: 0.2552 Lr: 0.00467 [2024-02-18 03:43:13,570 INFO misc.py line 119 87073] Train: [21/100][1191/1557] Data 0.010 (0.146) Batch 0.991 (1.100) Remain 37:41:01 loss: 0.3269 Lr: 0.00467 [2024-02-18 03:43:14,425 INFO misc.py line 119 87073] Train: [21/100][1192/1557] Data 0.008 (0.146) Batch 0.857 (1.099) Remain 37:40:34 loss: 0.4122 Lr: 0.00467 [2024-02-18 03:43:15,415 INFO misc.py line 119 87073] Train: [21/100][1193/1557] Data 0.006 (0.146) Batch 0.989 (1.099) Remain 37:40:22 loss: 0.8362 Lr: 0.00467 [2024-02-18 03:43:16,462 INFO misc.py line 119 87073] Train: [21/100][1194/1557] Data 0.007 (0.145) Batch 1.049 (1.099) Remain 37:40:15 loss: 0.6578 Lr: 0.00467 [2024-02-18 03:43:17,237 INFO misc.py line 119 87073] Train: [21/100][1195/1557] Data 0.004 (0.145) Batch 0.774 (1.099) Remain 37:39:41 loss: 0.2722 Lr: 0.00467 [2024-02-18 03:43:18,026 INFO misc.py line 119 87073] Train: [21/100][1196/1557] Data 0.005 (0.145) Batch 0.788 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Data 0.007 (0.143) Batch 1.125 (1.097) Remain 37:35:18 loss: 0.2235 Lr: 0.00467 [2024-02-18 03:43:44,531 INFO misc.py line 119 87073] Train: [21/100][1222/1557] Data 0.006 (0.143) Batch 1.027 (1.097) Remain 37:35:10 loss: 0.2292 Lr: 0.00467 [2024-02-18 03:43:45,328 INFO misc.py line 119 87073] Train: [21/100][1223/1557] Data 0.004 (0.143) Batch 0.796 (1.097) Remain 37:34:39 loss: 0.2542 Lr: 0.00467 [2024-02-18 03:43:46,076 INFO misc.py line 119 87073] Train: [21/100][1224/1557] Data 0.004 (0.143) Batch 0.735 (1.097) Remain 37:34:01 loss: 0.3427 Lr: 0.00467 [2024-02-18 03:43:47,281 INFO misc.py line 119 87073] Train: [21/100][1225/1557] Data 0.016 (0.143) Batch 1.205 (1.097) Remain 37:34:11 loss: 0.4095 Lr: 0.00467 [2024-02-18 03:43:48,114 INFO misc.py line 119 87073] Train: [21/100][1226/1557] Data 0.017 (0.143) Batch 0.846 (1.096) Remain 37:33:45 loss: 0.4626 Lr: 0.00467 [2024-02-18 03:43:49,136 INFO misc.py line 119 87073] Train: [21/100][1227/1557] Data 0.003 (0.142) Batch 1.022 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Data 0.009 (0.145) Batch 0.799 (1.099) Remain 37:38:33 loss: 0.5831 Lr: 0.00467 [2024-02-18 03:44:21,444 INFO misc.py line 119 87073] Train: [21/100][1253/1557] Data 0.342 (0.145) Batch 1.605 (1.099) Remain 37:39:22 loss: 0.3598 Lr: 0.00467 [2024-02-18 03:44:22,401 INFO misc.py line 119 87073] Train: [21/100][1254/1557] Data 0.012 (0.145) Batch 0.964 (1.099) Remain 37:39:07 loss: 1.0999 Lr: 0.00467 [2024-02-18 03:44:23,426 INFO misc.py line 119 87073] Train: [21/100][1255/1557] Data 0.006 (0.144) Batch 1.026 (1.099) Remain 37:38:59 loss: 0.4663 Lr: 0.00467 [2024-02-18 03:44:24,410 INFO misc.py line 119 87073] Train: [21/100][1256/1557] Data 0.004 (0.144) Batch 0.983 (1.099) Remain 37:38:47 loss: 0.6602 Lr: 0.00467 [2024-02-18 03:44:25,367 INFO misc.py line 119 87073] Train: [21/100][1257/1557] Data 0.005 (0.144) Batch 0.957 (1.099) Remain 37:38:32 loss: 0.2938 Lr: 0.00467 [2024-02-18 03:44:26,111 INFO misc.py line 119 87073] Train: [21/100][1258/1557] Data 0.004 (0.144) Batch 0.744 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Data 0.004 (0.141) Batch 1.007 (1.096) Remain 37:31:41 loss: 0.3115 Lr: 0.00467 [2024-02-18 03:44:51,121 INFO misc.py line 119 87073] Train: [21/100][1284/1557] Data 0.004 (0.141) Batch 1.143 (1.096) Remain 37:31:45 loss: 0.9311 Lr: 0.00467 [2024-02-18 03:44:52,093 INFO misc.py line 119 87073] Train: [21/100][1285/1557] Data 0.005 (0.141) Batch 0.973 (1.096) Remain 37:31:32 loss: 0.4244 Lr: 0.00467 [2024-02-18 03:44:52,870 INFO misc.py line 119 87073] Train: [21/100][1286/1557] Data 0.003 (0.141) Batch 0.775 (1.096) Remain 37:31:00 loss: 0.3037 Lr: 0.00467 [2024-02-18 03:44:53,661 INFO misc.py line 119 87073] Train: [21/100][1287/1557] Data 0.004 (0.141) Batch 0.785 (1.095) Remain 37:30:29 loss: 0.4654 Lr: 0.00467 [2024-02-18 03:44:54,870 INFO misc.py line 119 87073] Train: [21/100][1288/1557] Data 0.011 (0.141) Batch 1.201 (1.095) Remain 37:30:38 loss: 0.3715 Lr: 0.00467 [2024-02-18 03:44:55,941 INFO misc.py line 119 87073] Train: [21/100][1289/1557] Data 0.019 (0.141) Batch 1.075 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Data 0.004 (0.143) Batch 0.733 (1.097) Remain 37:34:06 loss: 0.4763 Lr: 0.00467 [2024-02-18 03:45:26,611 INFO misc.py line 119 87073] Train: [21/100][1315/1557] Data 0.005 (0.143) Batch 0.751 (1.097) Remain 37:33:33 loss: 0.3053 Lr: 0.00466 [2024-02-18 03:45:27,794 INFO misc.py line 119 87073] Train: [21/100][1316/1557] Data 0.007 (0.143) Batch 1.170 (1.097) Remain 37:33:39 loss: 0.1882 Lr: 0.00466 [2024-02-18 03:45:28,726 INFO misc.py line 119 87073] Train: [21/100][1317/1557] Data 0.021 (0.143) Batch 0.948 (1.097) Remain 37:33:23 loss: 0.7083 Lr: 0.00466 [2024-02-18 03:45:29,642 INFO misc.py line 119 87073] Train: [21/100][1318/1557] Data 0.004 (0.143) Batch 0.917 (1.097) Remain 37:33:06 loss: 0.6906 Lr: 0.00466 [2024-02-18 03:45:30,580 INFO misc.py line 119 87073] Train: [21/100][1319/1557] Data 0.004 (0.142) Batch 0.935 (1.097) Remain 37:32:49 loss: 0.5082 Lr: 0.00466 [2024-02-18 03:45:31,776 INFO misc.py line 119 87073] Train: [21/100][1320/1557] Data 0.006 (0.142) Batch 1.190 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87073] Train: [21/100][1339/1557] Data 0.005 (0.140) Batch 0.886 (1.095) Remain 37:28:02 loss: 0.7029 Lr: 0.00466 [2024-02-18 03:45:50,566 INFO misc.py line 119 87073] Train: [21/100][1340/1557] Data 0.007 (0.140) Batch 0.934 (1.095) Remain 37:27:46 loss: 0.4771 Lr: 0.00466 [2024-02-18 03:45:51,565 INFO misc.py line 119 87073] Train: [21/100][1341/1557] Data 0.004 (0.140) Batch 0.998 (1.094) Remain 37:27:36 loss: 0.6133 Lr: 0.00466 [2024-02-18 03:45:52,337 INFO misc.py line 119 87073] Train: [21/100][1342/1557] Data 0.005 (0.140) Batch 0.772 (1.094) Remain 37:27:05 loss: 0.3014 Lr: 0.00466 [2024-02-18 03:45:53,103 INFO misc.py line 119 87073] Train: [21/100][1343/1557] Data 0.005 (0.140) Batch 0.763 (1.094) Remain 37:26:33 loss: 0.8449 Lr: 0.00466 [2024-02-18 03:45:54,348 INFO misc.py line 119 87073] Train: [21/100][1344/1557] Data 0.008 (0.140) Batch 1.235 (1.094) Remain 37:26:45 loss: 0.2536 Lr: 0.00466 [2024-02-18 03:45:55,454 INFO misc.py line 119 87073] Train: [21/100][1345/1557] Data 0.019 (0.140) Batch 1.113 (1.094) Remain 37:26:46 loss: 0.6487 Lr: 0.00466 [2024-02-18 03:45:56,360 INFO misc.py line 119 87073] Train: [21/100][1346/1557] Data 0.011 (0.140) Batch 0.912 (1.094) Remain 37:26:28 loss: 0.4275 Lr: 0.00466 [2024-02-18 03:45:57,347 INFO misc.py line 119 87073] Train: [21/100][1347/1557] Data 0.006 (0.140) Batch 0.988 (1.094) Remain 37:26:17 loss: 0.5829 Lr: 0.00466 [2024-02-18 03:45:58,359 INFO misc.py line 119 87073] Train: [21/100][1348/1557] Data 0.005 (0.139) Batch 1.005 (1.094) Remain 37:26:08 loss: 0.7095 Lr: 0.00466 [2024-02-18 03:45:59,097 INFO misc.py line 119 87073] Train: [21/100][1349/1557] Data 0.013 (0.139) Batch 0.740 (1.094) Remain 37:25:35 loss: 0.3207 Lr: 0.00466 [2024-02-18 03:45:59,871 INFO misc.py line 119 87073] Train: [21/100][1350/1557] Data 0.009 (0.139) Batch 0.779 (1.093) Remain 37:25:05 loss: 0.5535 Lr: 0.00466 [2024-02-18 03:46:06,915 INFO misc.py line 119 87073] Train: [21/100][1351/1557] Data 6.221 (0.144) Batch 7.043 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87073] Train: [21/100][1370/1557] Data 0.007 (0.144) Batch 0.764 (1.097) Remain 37:33:10 loss: 0.2816 Lr: 0.00466 [2024-02-18 03:46:28,170 INFO misc.py line 119 87073] Train: [21/100][1371/1557] Data 0.005 (0.144) Batch 0.796 (1.097) Remain 37:32:42 loss: 0.4662 Lr: 0.00466 [2024-02-18 03:46:29,353 INFO misc.py line 119 87073] Train: [21/100][1372/1557] Data 0.013 (0.144) Batch 1.188 (1.097) Remain 37:32:49 loss: 0.2073 Lr: 0.00466 [2024-02-18 03:46:30,320 INFO misc.py line 119 87073] Train: [21/100][1373/1557] Data 0.008 (0.144) Batch 0.969 (1.097) Remain 37:32:36 loss: 1.1432 Lr: 0.00466 [2024-02-18 03:46:31,564 INFO misc.py line 119 87073] Train: [21/100][1374/1557] Data 0.005 (0.143) Batch 1.244 (1.097) Remain 37:32:48 loss: 0.3600 Lr: 0.00466 [2024-02-18 03:46:32,539 INFO misc.py line 119 87073] Train: [21/100][1375/1557] Data 0.006 (0.143) Batch 0.975 (1.097) Remain 37:32:36 loss: 0.4132 Lr: 0.00466 [2024-02-18 03:46:33,650 INFO misc.py line 119 87073] Train: [21/100][1376/1557] Data 0.005 (0.143) Batch 1.111 (1.097) Remain 37:32:37 loss: 0.5879 Lr: 0.00466 [2024-02-18 03:46:34,389 INFO misc.py line 119 87073] Train: [21/100][1377/1557] Data 0.004 (0.143) Batch 0.738 (1.097) Remain 37:32:03 loss: 0.3861 Lr: 0.00466 [2024-02-18 03:46:35,099 INFO misc.py line 119 87073] Train: [21/100][1378/1557] Data 0.005 (0.143) Batch 0.701 (1.097) Remain 37:31:27 loss: 0.3545 Lr: 0.00466 [2024-02-18 03:46:36,431 INFO misc.py line 119 87073] Train: [21/100][1379/1557] Data 0.014 (0.143) Batch 1.332 (1.097) Remain 37:31:47 loss: 0.1829 Lr: 0.00466 [2024-02-18 03:46:37,357 INFO misc.py line 119 87073] Train: [21/100][1380/1557] Data 0.013 (0.143) Batch 0.935 (1.097) Remain 37:31:31 loss: 0.9042 Lr: 0.00466 [2024-02-18 03:46:38,294 INFO misc.py line 119 87073] Train: [21/100][1381/1557] Data 0.004 (0.143) Batch 0.937 (1.097) Remain 37:31:16 loss: 0.2650 Lr: 0.00466 [2024-02-18 03:46:39,138 INFO misc.py line 119 87073] Train: [21/100][1382/1557] Data 0.005 (0.143) Batch 0.842 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Data 5.439 (0.145) Batch 6.563 (1.099) Remain 37:36:13 loss: 0.3020 Lr: 0.00466 [2024-02-18 03:47:11,402 INFO misc.py line 119 87073] Train: [21/100][1408/1557] Data 0.007 (0.145) Batch 0.887 (1.099) Remain 37:35:53 loss: 0.5680 Lr: 0.00466 [2024-02-18 03:47:12,295 INFO misc.py line 119 87073] Train: [21/100][1409/1557] Data 0.005 (0.145) Batch 0.893 (1.099) Remain 37:35:34 loss: 0.4921 Lr: 0.00466 [2024-02-18 03:47:13,252 INFO misc.py line 119 87073] Train: [21/100][1410/1557] Data 0.007 (0.144) Batch 0.957 (1.099) Remain 37:35:20 loss: 0.8969 Lr: 0.00466 [2024-02-18 03:47:14,222 INFO misc.py line 119 87073] Train: [21/100][1411/1557] Data 0.006 (0.144) Batch 0.971 (1.099) Remain 37:35:08 loss: 0.3526 Lr: 0.00466 [2024-02-18 03:47:15,060 INFO misc.py line 119 87073] Train: [21/100][1412/1557] Data 0.003 (0.144) Batch 0.837 (1.099) Remain 37:34:44 loss: 0.2213 Lr: 0.00466 [2024-02-18 03:47:15,777 INFO misc.py line 119 87073] Train: [21/100][1413/1557] Data 0.005 (0.144) Batch 0.712 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03:47:30,703 INFO misc.py line 119 87073] Train: [21/100][1426/1557] Data 0.006 (0.145) Batch 0.755 (1.099) Remain 37:34:51 loss: 0.2274 Lr: 0.00466 [2024-02-18 03:47:31,482 INFO misc.py line 119 87073] Train: [21/100][1427/1557] Data 0.004 (0.144) Batch 0.769 (1.099) Remain 37:34:22 loss: 0.4236 Lr: 0.00466 [2024-02-18 03:47:32,691 INFO misc.py line 119 87073] Train: [21/100][1428/1557] Data 0.015 (0.144) Batch 1.219 (1.099) Remain 37:34:31 loss: 0.2031 Lr: 0.00466 [2024-02-18 03:47:33,908 INFO misc.py line 119 87073] Train: [21/100][1429/1557] Data 0.005 (0.144) Batch 1.214 (1.099) Remain 37:34:40 loss: 0.7814 Lr: 0.00466 [2024-02-18 03:47:34,865 INFO misc.py line 119 87073] Train: [21/100][1430/1557] Data 0.008 (0.144) Batch 0.961 (1.099) Remain 37:34:27 loss: 0.2559 Lr: 0.00466 [2024-02-18 03:47:35,965 INFO misc.py line 119 87073] Train: [21/100][1431/1557] Data 0.005 (0.144) Batch 1.100 (1.099) Remain 37:34:26 loss: 0.4275 Lr: 0.00466 [2024-02-18 03:47:36,878 INFO misc.py line 119 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Data 0.005 (0.143) Batch 0.828 (1.098) Remain 37:32:43 loss: 0.4465 Lr: 0.00466 [2024-02-18 03:47:43,468 INFO misc.py line 119 87073] Train: [21/100][1439/1557] Data 0.005 (0.143) Batch 0.917 (1.098) Remain 37:32:27 loss: 0.5605 Lr: 0.00466 [2024-02-18 03:47:44,221 INFO misc.py line 119 87073] Train: [21/100][1440/1557] Data 0.006 (0.143) Batch 0.755 (1.097) Remain 37:31:56 loss: 0.4143 Lr: 0.00466 [2024-02-18 03:47:44,897 INFO misc.py line 119 87073] Train: [21/100][1441/1557] Data 0.004 (0.143) Batch 0.676 (1.097) Remain 37:31:19 loss: 0.3442 Lr: 0.00466 [2024-02-18 03:47:46,116 INFO misc.py line 119 87073] Train: [21/100][1442/1557] Data 0.004 (0.143) Batch 1.211 (1.097) Remain 37:31:28 loss: 0.2338 Lr: 0.00466 [2024-02-18 03:47:47,315 INFO misc.py line 119 87073] Train: [21/100][1443/1557] Data 0.012 (0.143) Batch 1.201 (1.097) Remain 37:31:36 loss: 0.5114 Lr: 0.00466 [2024-02-18 03:47:48,293 INFO misc.py line 119 87073] Train: [21/100][1444/1557] Data 0.010 (0.143) Batch 0.980 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87073] Train: [21/100][1463/1557] Data 6.099 (0.145) Batch 6.863 (1.099) Remain 37:35:22 loss: 0.2290 Lr: 0.00466 [2024-02-18 03:48:13,173 INFO misc.py line 119 87073] Train: [21/100][1464/1557] Data 0.007 (0.145) Batch 0.967 (1.099) Remain 37:35:10 loss: 0.3210 Lr: 0.00466 [2024-02-18 03:48:14,184 INFO misc.py line 119 87073] Train: [21/100][1465/1557] Data 0.004 (0.145) Batch 1.012 (1.099) Remain 37:35:02 loss: 0.3543 Lr: 0.00466 [2024-02-18 03:48:15,142 INFO misc.py line 119 87073] Train: [21/100][1466/1557] Data 0.005 (0.145) Batch 0.958 (1.099) Remain 37:34:49 loss: 0.5251 Lr: 0.00466 [2024-02-18 03:48:16,136 INFO misc.py line 119 87073] Train: [21/100][1467/1557] Data 0.004 (0.145) Batch 0.993 (1.099) Remain 37:34:39 loss: 0.4180 Lr: 0.00466 [2024-02-18 03:48:16,886 INFO misc.py line 119 87073] Train: [21/100][1468/1557] Data 0.005 (0.145) Batch 0.743 (1.099) Remain 37:34:08 loss: 0.3364 Lr: 0.00466 [2024-02-18 03:48:17,688 INFO misc.py line 119 87073] Train: [21/100][1469/1557] Data 0.011 (0.145) Batch 0.808 (1.099) Remain 37:33:42 loss: 0.5398 Lr: 0.00466 [2024-02-18 03:48:18,726 INFO misc.py line 119 87073] Train: [21/100][1470/1557] Data 0.005 (0.145) Batch 1.037 (1.099) Remain 37:33:36 loss: 0.3255 Lr: 0.00466 [2024-02-18 03:48:19,764 INFO misc.py line 119 87073] Train: [21/100][1471/1557] Data 0.006 (0.144) Batch 1.040 (1.098) Remain 37:33:30 loss: 1.2781 Lr: 0.00466 [2024-02-18 03:48:20,544 INFO misc.py line 119 87073] Train: [21/100][1472/1557] Data 0.004 (0.144) Batch 0.780 (1.098) Remain 37:33:02 loss: 0.7775 Lr: 0.00466 [2024-02-18 03:48:21,489 INFO misc.py line 119 87073] Train: [21/100][1473/1557] Data 0.005 (0.144) Batch 0.941 (1.098) Remain 37:32:48 loss: 0.7917 Lr: 0.00466 [2024-02-18 03:48:22,412 INFO misc.py line 119 87073] Train: [21/100][1474/1557] Data 0.009 (0.144) Batch 0.925 (1.098) Remain 37:32:32 loss: 0.5247 Lr: 0.00466 [2024-02-18 03:48:23,213 INFO misc.py line 119 87073] Train: [21/100][1475/1557] Data 0.007 (0.144) Batch 0.804 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03:48:37,764 INFO misc.py line 119 87073] Train: [21/100][1488/1557] Data 0.005 (0.144) Batch 1.003 (1.098) Remain 37:32:15 loss: 0.4171 Lr: 0.00466 [2024-02-18 03:48:38,489 INFO misc.py line 119 87073] Train: [21/100][1489/1557] Data 0.004 (0.144) Batch 0.725 (1.098) Remain 37:31:43 loss: 0.7173 Lr: 0.00466 [2024-02-18 03:48:39,274 INFO misc.py line 119 87073] Train: [21/100][1490/1557] Data 0.004 (0.144) Batch 0.784 (1.098) Remain 37:31:16 loss: 0.2178 Lr: 0.00466 [2024-02-18 03:48:40,575 INFO misc.py line 119 87073] Train: [21/100][1491/1557] Data 0.005 (0.144) Batch 1.299 (1.098) Remain 37:31:32 loss: 0.1628 Lr: 0.00466 [2024-02-18 03:48:41,540 INFO misc.py line 119 87073] Train: [21/100][1492/1557] Data 0.007 (0.144) Batch 0.967 (1.098) Remain 37:31:20 loss: 0.7161 Lr: 0.00466 [2024-02-18 03:48:42,454 INFO misc.py line 119 87073] Train: [21/100][1493/1557] Data 0.006 (0.144) Batch 0.916 (1.097) Remain 37:31:04 loss: 0.3314 Lr: 0.00466 [2024-02-18 03:48:43,653 INFO misc.py line 119 87073] Train: [21/100][1494/1557] Data 0.004 (0.144) Batch 1.198 (1.098) Remain 37:31:11 loss: 0.7113 Lr: 0.00466 [2024-02-18 03:48:44,510 INFO misc.py line 119 87073] Train: [21/100][1495/1557] Data 0.005 (0.144) Batch 0.857 (1.097) Remain 37:30:50 loss: 0.5365 Lr: 0.00466 [2024-02-18 03:48:45,318 INFO misc.py line 119 87073] Train: [21/100][1496/1557] Data 0.006 (0.143) Batch 0.801 (1.097) Remain 37:30:25 loss: 0.6205 Lr: 0.00466 [2024-02-18 03:48:46,126 INFO misc.py line 119 87073] Train: [21/100][1497/1557] Data 0.012 (0.143) Batch 0.815 (1.097) Remain 37:30:00 loss: 0.5973 Lr: 0.00466 [2024-02-18 03:48:47,380 INFO misc.py line 119 87073] Train: [21/100][1498/1557] Data 0.005 (0.143) Batch 1.254 (1.097) Remain 37:30:12 loss: 0.2547 Lr: 0.00466 [2024-02-18 03:48:48,386 INFO misc.py line 119 87073] Train: [21/100][1499/1557] Data 0.005 (0.143) Batch 0.998 (1.097) Remain 37:30:03 loss: 0.4542 Lr: 0.00466 [2024-02-18 03:48:49,439 INFO misc.py line 119 87073] Train: [21/100][1500/1557] Data 0.013 (0.143) Batch 1.058 (1.097) Remain 37:29:58 loss: 0.6860 Lr: 0.00466 [2024-02-18 03:48:50,409 INFO misc.py line 119 87073] Train: [21/100][1501/1557] Data 0.008 (0.143) Batch 0.972 (1.097) Remain 37:29:47 loss: 0.8141 Lr: 0.00466 [2024-02-18 03:48:51,472 INFO misc.py line 119 87073] Train: [21/100][1502/1557] Data 0.006 (0.143) Batch 1.065 (1.097) Remain 37:29:43 loss: 0.7843 Lr: 0.00466 [2024-02-18 03:48:52,235 INFO misc.py line 119 87073] Train: [21/100][1503/1557] Data 0.004 (0.143) Batch 0.763 (1.097) Remain 37:29:15 loss: 0.4852 Lr: 0.00466 [2024-02-18 03:48:52,959 INFO misc.py line 119 87073] Train: [21/100][1504/1557] Data 0.004 (0.143) Batch 0.718 (1.096) Remain 37:28:43 loss: 0.6604 Lr: 0.00466 [2024-02-18 03:48:54,200 INFO misc.py line 119 87073] Train: [21/100][1505/1557] Data 0.012 (0.143) Batch 1.237 (1.097) Remain 37:28:53 loss: 0.2922 Lr: 0.00466 [2024-02-18 03:48:55,063 INFO misc.py line 119 87073] Train: [21/100][1506/1557] Data 0.015 (0.143) Batch 0.874 (1.096) Remain 37:28:34 loss: 0.3850 Lr: 0.00466 [2024-02-18 03:48:56,114 INFO misc.py line 119 87073] Train: [21/100][1507/1557] Data 0.004 (0.142) Batch 1.051 (1.096) Remain 37:28:29 loss: 0.5452 Lr: 0.00466 [2024-02-18 03:48:57,094 INFO misc.py line 119 87073] Train: [21/100][1508/1557] Data 0.004 (0.142) Batch 0.981 (1.096) Remain 37:28:18 loss: 0.4866 Lr: 0.00466 [2024-02-18 03:48:57,992 INFO misc.py line 119 87073] Train: [21/100][1509/1557] Data 0.004 (0.142) Batch 0.897 (1.096) Remain 37:28:01 loss: 0.6232 Lr: 0.00466 [2024-02-18 03:48:58,717 INFO misc.py line 119 87073] Train: [21/100][1510/1557] Data 0.005 (0.142) Batch 0.724 (1.096) Remain 37:27:30 loss: 0.4547 Lr: 0.00466 [2024-02-18 03:48:59,455 INFO misc.py line 119 87073] Train: [21/100][1511/1557] Data 0.005 (0.142) Batch 0.740 (1.096) Remain 37:26:59 loss: 0.2325 Lr: 0.00466 [2024-02-18 03:49:00,641 INFO misc.py line 119 87073] Train: [21/100][1512/1557] Data 0.003 (0.142) Batch 1.184 (1.096) Remain 37:27:06 loss: 0.2754 Lr: 0.00466 [2024-02-18 03:49:01,615 INFO misc.py line 119 87073] Train: [21/100][1513/1557] Data 0.006 (0.142) Batch 0.974 (1.096) Remain 37:26:55 loss: 0.6387 Lr: 0.00466 [2024-02-18 03:49:02,719 INFO misc.py line 119 87073] Train: [21/100][1514/1557] Data 0.005 (0.142) Batch 1.106 (1.096) Remain 37:26:54 loss: 0.3895 Lr: 0.00466 [2024-02-18 03:49:03,714 INFO misc.py line 119 87073] Train: [21/100][1515/1557] Data 0.004 (0.142) Batch 0.992 (1.096) Remain 37:26:45 loss: 0.4933 Lr: 0.00466 [2024-02-18 03:49:04,633 INFO misc.py line 119 87073] Train: [21/100][1516/1557] Data 0.008 (0.142) Batch 0.921 (1.095) Remain 37:26:29 loss: 0.5282 Lr: 0.00466 [2024-02-18 03:49:05,403 INFO misc.py line 119 87073] Train: [21/100][1517/1557] Data 0.005 (0.142) Batch 0.763 (1.095) Remain 37:26:01 loss: 0.3497 Lr: 0.00466 [2024-02-18 03:49:06,184 INFO misc.py line 119 87073] Train: [21/100][1518/1557] Data 0.012 (0.141) Batch 0.788 (1.095) Remain 37:25:35 loss: 0.5754 Lr: 0.00466 [2024-02-18 03:49:13,313 INFO misc.py line 119 87073] Train: [21/100][1519/1557] Data 6.308 (0.146) Batch 7.129 (1.099) Remain 37:33:44 loss: 0.1490 Lr: 0.00466 [2024-02-18 03:49:14,330 INFO misc.py line 119 87073] Train: [21/100][1520/1557] Data 0.005 (0.145) Batch 1.017 (1.099) Remain 37:33:36 loss: 0.7831 Lr: 0.00466 [2024-02-18 03:49:15,321 INFO misc.py line 119 87073] Train: [21/100][1521/1557] Data 0.005 (0.145) Batch 0.991 (1.099) Remain 37:33:26 loss: 0.1732 Lr: 0.00466 [2024-02-18 03:49:16,322 INFO misc.py line 119 87073] Train: [21/100][1522/1557] Data 0.005 (0.145) Batch 1.002 (1.099) Remain 37:33:17 loss: 0.2293 Lr: 0.00466 [2024-02-18 03:49:17,330 INFO misc.py line 119 87073] Train: [21/100][1523/1557] Data 0.004 (0.145) Batch 1.009 (1.099) Remain 37:33:09 loss: 0.4555 Lr: 0.00466 [2024-02-18 03:49:18,025 INFO misc.py line 119 87073] Train: [21/100][1524/1557] Data 0.004 (0.145) Batch 0.694 (1.099) Remain 37:32:35 loss: 0.5512 Lr: 0.00466 [2024-02-18 03:49:18,825 INFO misc.py line 119 87073] Train: [21/100][1525/1557] Data 0.005 (0.145) Batch 0.801 (1.098) Remain 37:32:10 loss: 0.6652 Lr: 0.00466 [2024-02-18 03:49:19,864 INFO misc.py line 119 87073] Train: [21/100][1526/1557] Data 0.004 (0.145) Batch 1.031 (1.098) Remain 37:32:03 loss: 0.3085 Lr: 0.00466 [2024-02-18 03:49:20,863 INFO misc.py line 119 87073] Train: [21/100][1527/1557] Data 0.012 (0.145) Batch 1.006 (1.098) Remain 37:31:55 loss: 0.6056 Lr: 0.00466 [2024-02-18 03:49:21,774 INFO misc.py line 119 87073] Train: [21/100][1528/1557] Data 0.006 (0.145) Batch 0.911 (1.098) Remain 37:31:39 loss: 1.0915 Lr: 0.00466 [2024-02-18 03:49:22,793 INFO misc.py line 119 87073] Train: [21/100][1529/1557] Data 0.007 (0.145) Batch 1.021 (1.098) Remain 37:31:31 loss: 0.5125 Lr: 0.00466 [2024-02-18 03:49:23,946 INFO misc.py line 119 87073] Train: [21/100][1530/1557] Data 0.003 (0.145) Batch 1.154 (1.098) Remain 37:31:35 loss: 0.5197 Lr: 0.00466 [2024-02-18 03:49:24,721 INFO misc.py line 119 87073] Train: [21/100][1531/1557] Data 0.004 (0.144) Batch 0.773 (1.098) Remain 37:31:07 loss: 0.6423 Lr: 0.00466 [2024-02-18 03:49:25,569 INFO misc.py line 119 87073] Train: [21/100][1532/1557] Data 0.005 (0.144) Batch 0.841 (1.098) Remain 37:30:46 loss: 0.3449 Lr: 0.00466 [2024-02-18 03:49:27,823 INFO misc.py line 119 87073] Train: [21/100][1533/1557] Data 1.072 (0.145) Batch 2.261 (1.098) Remain 37:32:18 loss: 0.1844 Lr: 0.00466 [2024-02-18 03:49:28,805 INFO misc.py line 119 87073] Train: [21/100][1534/1557] Data 0.006 (0.145) Batch 0.983 (1.098) Remain 37:32:08 loss: 0.3685 Lr: 0.00466 [2024-02-18 03:49:29,681 INFO misc.py line 119 87073] Train: [21/100][1535/1557] Data 0.003 (0.145) Batch 0.874 (1.098) Remain 37:31:49 loss: 0.7557 Lr: 0.00466 [2024-02-18 03:49:30,544 INFO misc.py line 119 87073] Train: [21/100][1536/1557] Data 0.006 (0.145) Batch 0.857 (1.098) Remain 37:31:28 loss: 0.3377 Lr: 0.00466 [2024-02-18 03:49:31,720 INFO misc.py line 119 87073] Train: [21/100][1537/1557] Data 0.012 (0.145) Batch 1.169 (1.098) Remain 37:31:33 loss: 0.6910 Lr: 0.00466 [2024-02-18 03:49:32,451 INFO misc.py line 119 87073] Train: [21/100][1538/1557] Data 0.019 (0.144) Batch 0.745 (1.098) Remain 37:31:03 loss: 0.3974 Lr: 0.00466 [2024-02-18 03:49:33,194 INFO misc.py line 119 87073] Train: [21/100][1539/1557] Data 0.005 (0.144) Batch 0.742 (1.098) Remain 37:30:34 loss: 0.7494 Lr: 0.00466 [2024-02-18 03:49:34,347 INFO misc.py line 119 87073] Train: [21/100][1540/1557] Data 0.006 (0.144) Batch 1.149 (1.098) Remain 37:30:37 loss: 0.4093 Lr: 0.00466 [2024-02-18 03:49:35,365 INFO misc.py line 119 87073] Train: [21/100][1541/1557] Data 0.010 (0.144) Batch 1.017 (1.098) Remain 37:30:29 loss: 0.7190 Lr: 0.00466 [2024-02-18 03:49:36,370 INFO misc.py line 119 87073] Train: [21/100][1542/1557] Data 0.012 (0.144) Batch 1.003 (1.098) Remain 37:30:20 loss: 0.2965 Lr: 0.00466 [2024-02-18 03:49:37,252 INFO misc.py line 119 87073] Train: [21/100][1543/1557] Data 0.013 (0.144) Batch 0.891 (1.097) Remain 37:30:03 loss: 0.5238 Lr: 0.00466 [2024-02-18 03:49:38,184 INFO misc.py line 119 87073] Train: [21/100][1544/1557] Data 0.003 (0.144) Batch 0.931 (1.097) Remain 37:29:48 loss: 0.5308 Lr: 0.00466 [2024-02-18 03:49:38,953 INFO misc.py line 119 87073] Train: [21/100][1545/1557] Data 0.005 (0.144) Batch 0.770 (1.097) Remain 37:29:21 loss: 0.3753 Lr: 0.00466 [2024-02-18 03:49:39,720 INFO misc.py line 119 87073] Train: [21/100][1546/1557] Data 0.004 (0.144) Batch 0.761 (1.097) Remain 37:28:53 loss: 0.5237 Lr: 0.00466 [2024-02-18 03:49:40,893 INFO misc.py line 119 87073] Train: [21/100][1547/1557] Data 0.011 (0.144) Batch 1.171 (1.097) Remain 37:28:58 loss: 0.2193 Lr: 0.00466 [2024-02-18 03:49:41,786 INFO misc.py line 119 87073] Train: [21/100][1548/1557] Data 0.013 (0.144) Batch 0.902 (1.097) Remain 37:28:41 loss: 0.7649 Lr: 0.00466 [2024-02-18 03:49:42,734 INFO misc.py line 119 87073] Train: [21/100][1549/1557] Data 0.004 (0.144) Batch 0.948 (1.097) Remain 37:28:28 loss: 0.5436 Lr: 0.00466 [2024-02-18 03:49:43,716 INFO misc.py line 119 87073] Train: [21/100][1550/1557] Data 0.005 (0.143) Batch 0.982 (1.097) Remain 37:28:18 loss: 0.5301 Lr: 0.00466 [2024-02-18 03:49:44,771 INFO misc.py line 119 87073] Train: [21/100][1551/1557] Data 0.005 (0.143) Batch 1.055 (1.097) Remain 37:28:14 loss: 0.1730 Lr: 0.00466 [2024-02-18 03:49:45,488 INFO misc.py line 119 87073] Train: [21/100][1552/1557] Data 0.004 (0.143) Batch 0.717 (1.096) Remain 37:27:43 loss: 0.3491 Lr: 0.00466 [2024-02-18 03:49:46,175 INFO misc.py line 119 87073] Train: [21/100][1553/1557] Data 0.004 (0.143) Batch 0.686 (1.096) Remain 37:27:09 loss: 0.2045 Lr: 0.00466 [2024-02-18 03:49:47,355 INFO misc.py line 119 87073] Train: [21/100][1554/1557] Data 0.004 (0.143) Batch 1.175 (1.096) Remain 37:27:14 loss: 0.2789 Lr: 0.00466 [2024-02-18 03:49:48,251 INFO misc.py line 119 87073] Train: [21/100][1555/1557] Data 0.009 (0.143) Batch 0.901 (1.096) Remain 37:26:58 loss: 0.1083 Lr: 0.00466 [2024-02-18 03:49:49,096 INFO misc.py line 119 87073] Train: [21/100][1556/1557] Data 0.005 (0.143) Batch 0.844 (1.096) Remain 37:26:37 loss: 0.6250 Lr: 0.00466 [2024-02-18 03:49:50,067 INFO misc.py line 119 87073] Train: [21/100][1557/1557] Data 0.006 (0.143) Batch 0.957 (1.096) Remain 37:26:24 loss: 0.6326 Lr: 0.00466 [2024-02-18 03:49:50,068 INFO misc.py line 136 87073] Train result: loss: 0.5036 [2024-02-18 03:49:50,069 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 03:50:20,935 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6109 [2024-02-18 03:50:21,715 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4288 [2024-02-18 03:50:23,841 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3817 [2024-02-18 03:50:26,057 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3171 [2024-02-18 03:50:31,002 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2372 [2024-02-18 03:50:31,699 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0817 [2024-02-18 03:50:32,962 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3244 [2024-02-18 03:50:35,914 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3360 [2024-02-18 03:50:37,723 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2581 [2024-02-18 03:50:39,326 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7166/0.7850/0.9120. [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9176/0.9408 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9815/0.9888 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8566/0.9612 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0007/0.0041 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4195/0.4483 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7068/0.7383 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8190/0.9081 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8413/0.9111 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9105/0.9483 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8123/0.8436 [2024-02-18 03:50:39,326 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7874/0.9007 [2024-02-18 03:50:39,327 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6533/0.9001 [2024-02-18 03:50:39,327 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6091/0.7110 [2024-02-18 03:50:39,327 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 03:50:39,329 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 03:50:39,329 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 03:50:46,182 INFO misc.py line 119 87073] Train: [22/100][1/1557] Data 1.405 (1.405) Batch 2.238 (2.238) Remain 76:27:00 loss: 0.7320 Lr: 0.00466 [2024-02-18 03:50:47,128 INFO misc.py line 119 87073] Train: [22/100][2/1557] Data 0.005 (0.005) Batch 0.942 (0.942) Remain 32:11:06 loss: 0.6788 Lr: 0.00466 [2024-02-18 03:50:48,025 INFO misc.py line 119 87073] Train: [22/100][3/1557] Data 0.010 (0.010) Batch 0.902 (0.902) Remain 30:48:25 loss: 0.3806 Lr: 0.00466 [2024-02-18 03:50:48,986 INFO misc.py line 119 87073] Train: [22/100][4/1557] Data 0.005 (0.005) Batch 0.960 (0.960) Remain 32:48:05 loss: 0.3624 Lr: 0.00466 [2024-02-18 03:50:49,764 INFO misc.py line 119 87073] Train: [22/100][5/1557] Data 0.005 (0.005) Batch 0.778 (0.869) Remain 29:41:48 loss: 0.7269 Lr: 0.00466 [2024-02-18 03:50:50,568 INFO misc.py line 119 87073] Train: [22/100][6/1557] Data 0.005 (0.005) Batch 0.804 (0.847) Remain 28:57:00 loss: 0.7047 Lr: 0.00466 [2024-02-18 03:50:51,820 INFO misc.py line 119 87073] Train: [22/100][7/1557] Data 0.005 (0.005) Batch 1.251 (0.948) Remain 32:23:46 loss: 0.2415 Lr: 0.00466 [2024-02-18 03:50:52,834 INFO misc.py line 119 87073] Train: [22/100][8/1557] Data 0.006 (0.005) Batch 1.014 (0.961) Remain 32:50:38 loss: 1.4382 Lr: 0.00466 [2024-02-18 03:50:53,814 INFO misc.py line 119 87073] Train: [22/100][9/1557] Data 0.007 (0.005) Batch 0.982 (0.965) Remain 32:57:40 loss: 0.3186 Lr: 0.00466 [2024-02-18 03:50:54,817 INFO misc.py line 119 87073] Train: [22/100][10/1557] Data 0.005 (0.005) Batch 1.003 (0.970) Remain 33:08:49 loss: 0.4020 Lr: 0.00466 [2024-02-18 03:50:55,852 INFO misc.py line 119 87073] Train: [22/100][11/1557] Data 0.004 (0.005) Batch 1.035 (0.978) Remain 33:25:24 loss: 0.6084 Lr: 0.00466 [2024-02-18 03:50:56,590 INFO misc.py line 119 87073] Train: [22/100][12/1557] Data 0.005 (0.005) Batch 0.739 (0.952) Remain 32:30:47 loss: 0.5091 Lr: 0.00466 [2024-02-18 03:50:57,436 INFO misc.py line 119 87073] Train: [22/100][13/1557] Data 0.004 (0.005) Batch 0.843 (0.941) Remain 32:08:30 loss: 0.4404 Lr: 0.00466 [2024-02-18 03:50:58,668 INFO misc.py line 119 87073] Train: [22/100][14/1557] Data 0.007 (0.005) Batch 1.235 (0.968) Remain 33:03:16 loss: 0.3018 Lr: 0.00466 [2024-02-18 03:50:59,658 INFO misc.py line 119 87073] Train: [22/100][15/1557] Data 0.004 (0.005) Batch 0.990 (0.969) Remain 33:07:05 loss: 0.5210 Lr: 0.00466 [2024-02-18 03:51:00,650 INFO misc.py line 119 87073] Train: [22/100][16/1557] Data 0.003 (0.005) Batch 0.992 (0.971) Remain 33:10:36 loss: 0.4723 Lr: 0.00466 [2024-02-18 03:51:01,609 INFO misc.py line 119 87073] Train: [22/100][17/1557] Data 0.005 (0.005) Batch 0.959 (0.970) Remain 33:08:47 loss: 0.4230 Lr: 0.00466 [2024-02-18 03:51:02,670 INFO misc.py line 119 87073] Train: [22/100][18/1557] Data 0.004 (0.005) Batch 1.060 (0.976) Remain 33:21:03 loss: 0.4168 Lr: 0.00466 [2024-02-18 03:51:03,451 INFO misc.py line 119 87073] Train: [22/100][19/1557] Data 0.006 (0.005) Batch 0.778 (0.964) Remain 32:55:39 loss: 0.3577 Lr: 0.00466 [2024-02-18 03:51:04,274 INFO misc.py line 119 87073] Train: [22/100][20/1557] Data 0.009 (0.005) Batch 0.824 (0.956) Remain 32:38:46 loss: 0.3732 Lr: 0.00466 [2024-02-18 03:51:05,568 INFO misc.py line 119 87073] Train: [22/100][21/1557] Data 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Train: [22/100][40/1557] Data 0.006 (0.005) Batch 0.777 (0.951) Remain 32:28:38 loss: 0.3595 Lr: 0.00466 [2024-02-18 03:51:23,991 INFO misc.py line 119 87073] Train: [22/100][41/1557] Data 0.003 (0.005) Batch 0.784 (0.946) Remain 32:19:38 loss: 0.3672 Lr: 0.00466 [2024-02-18 03:51:25,145 INFO misc.py line 119 87073] Train: [22/100][42/1557] Data 0.005 (0.005) Batch 1.151 (0.952) Remain 32:30:20 loss: 0.3837 Lr: 0.00466 [2024-02-18 03:51:26,189 INFO misc.py line 119 87073] Train: [22/100][43/1557] Data 0.008 (0.005) Batch 1.042 (0.954) Remain 32:34:56 loss: 0.1396 Lr: 0.00466 [2024-02-18 03:51:27,174 INFO misc.py line 119 87073] Train: [22/100][44/1557] Data 0.011 (0.005) Batch 0.992 (0.955) Remain 32:36:47 loss: 0.5072 Lr: 0.00466 [2024-02-18 03:51:28,242 INFO misc.py line 119 87073] Train: [22/100][45/1557] Data 0.005 (0.005) Batch 1.068 (0.958) Remain 32:42:16 loss: 0.4899 Lr: 0.00466 [2024-02-18 03:51:29,144 INFO misc.py line 119 87073] Train: [22/100][46/1557] Data 0.004 (0.005) 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[2024-02-18 04:14:55,245 INFO misc.py line 119 87073] Train: [22/100][1340/1557] Data 0.005 (0.077) Batch 1.010 (1.082) Remain 36:34:52 loss: 0.6008 Lr: 0.00462 [2024-02-18 04:14:56,086 INFO misc.py line 119 87073] Train: [22/100][1341/1557] Data 0.003 (0.077) Batch 0.841 (1.082) Remain 36:34:29 loss: 0.8607 Lr: 0.00462 [2024-02-18 04:14:56,818 INFO misc.py line 119 87073] Train: [22/100][1342/1557] Data 0.003 (0.077) Batch 0.725 (1.082) Remain 36:33:55 loss: 0.5434 Lr: 0.00462 [2024-02-18 04:14:57,581 INFO misc.py line 119 87073] Train: [22/100][1343/1557] Data 0.010 (0.077) Batch 0.769 (1.082) Remain 36:33:26 loss: 0.3647 Lr: 0.00462 [2024-02-18 04:14:58,825 INFO misc.py line 119 87073] Train: [22/100][1344/1557] Data 0.003 (0.077) Batch 1.244 (1.082) Remain 36:33:40 loss: 0.2178 Lr: 0.00462 [2024-02-18 04:14:59,704 INFO misc.py line 119 87073] Train: [22/100][1345/1557] Data 0.003 (0.077) Batch 0.879 (1.082) Remain 36:33:20 loss: 0.6376 Lr: 0.00462 [2024-02-18 04:15:00,562 INFO 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[2024-02-18 04:16:01,747 INFO misc.py line 119 87073] Train: [22/100][1402/1557] Data 0.003 (0.076) Batch 1.001 (1.082) Remain 36:32:52 loss: 0.3139 Lr: 0.00462 [2024-02-18 04:16:02,601 INFO misc.py line 119 87073] Train: [22/100][1403/1557] Data 0.003 (0.076) Batch 0.854 (1.082) Remain 36:32:31 loss: 0.6317 Lr: 0.00462 [2024-02-18 04:16:03,580 INFO misc.py line 119 87073] Train: [22/100][1404/1557] Data 0.004 (0.076) Batch 0.970 (1.082) Remain 36:32:20 loss: 0.2538 Lr: 0.00462 [2024-02-18 04:16:06,416 INFO misc.py line 119 87073] Train: [22/100][1405/1557] Data 1.160 (0.077) Batch 2.845 (1.083) Remain 36:34:52 loss: 0.7213 Lr: 0.00462 [2024-02-18 04:16:07,137 INFO misc.py line 119 87073] Train: [22/100][1406/1557] Data 0.003 (0.077) Batch 0.716 (1.083) Remain 36:34:19 loss: 0.6078 Lr: 0.00462 [2024-02-18 04:16:14,796 INFO misc.py line 119 87073] Train: [22/100][1407/1557] Data 3.220 (0.079) Batch 7.653 (1.087) Remain 36:43:47 loss: 0.2742 Lr: 0.00462 [2024-02-18 04:16:15,908 INFO 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[2024-02-18 04:17:45,746 INFO misc.py line 119 87073] Train: [22/100][1495/1557] Data 0.003 (0.078) Batch 0.944 (1.084) Remain 36:35:46 loss: 0.6664 Lr: 0.00462 [2024-02-18 04:17:46,505 INFO misc.py line 119 87073] Train: [22/100][1496/1557] Data 0.004 (0.078) Batch 0.754 (1.084) Remain 36:35:18 loss: 0.2765 Lr: 0.00462 [2024-02-18 04:17:47,261 INFO misc.py line 119 87073] Train: [22/100][1497/1557] Data 0.009 (0.078) Batch 0.761 (1.084) Remain 36:34:51 loss: 0.3301 Lr: 0.00462 [2024-02-18 04:17:48,383 INFO misc.py line 119 87073] Train: [22/100][1498/1557] Data 0.003 (0.078) Batch 1.122 (1.084) Remain 36:34:53 loss: 0.3863 Lr: 0.00462 [2024-02-18 04:17:49,504 INFO misc.py line 119 87073] Train: [22/100][1499/1557] Data 0.004 (0.078) Batch 1.121 (1.084) Remain 36:34:55 loss: 0.7141 Lr: 0.00462 [2024-02-18 04:17:50,475 INFO misc.py line 119 87073] Train: [22/100][1500/1557] Data 0.004 (0.078) Batch 0.972 (1.084) Remain 36:34:44 loss: 0.5147 Lr: 0.00462 [2024-02-18 04:17:51,317 INFO misc.py line 119 87073] Train: [22/100][1501/1557] Data 0.003 (0.078) Batch 0.841 (1.084) Remain 36:34:24 loss: 0.5195 Lr: 0.00462 [2024-02-18 04:17:52,251 INFO misc.py line 119 87073] Train: [22/100][1502/1557] Data 0.004 (0.078) Batch 0.932 (1.084) Remain 36:34:10 loss: 0.4730 Lr: 0.00462 [2024-02-18 04:17:52,990 INFO misc.py line 119 87073] Train: [22/100][1503/1557] Data 0.007 (0.077) Batch 0.743 (1.083) Remain 36:33:41 loss: 0.4657 Lr: 0.00462 [2024-02-18 04:17:53,771 INFO misc.py line 119 87073] Train: [22/100][1504/1557] Data 0.003 (0.077) Batch 0.761 (1.083) Remain 36:33:14 loss: 0.5188 Lr: 0.00462 [2024-02-18 04:17:55,047 INFO misc.py line 119 87073] Train: [22/100][1505/1557] Data 0.022 (0.077) Batch 1.289 (1.083) Remain 36:33:30 loss: 0.5951 Lr: 0.00462 [2024-02-18 04:17:55,993 INFO misc.py line 119 87073] Train: [22/100][1506/1557] Data 0.009 (0.077) Batch 0.952 (1.083) Remain 36:33:18 loss: 0.3055 Lr: 0.00462 [2024-02-18 04:17:56,975 INFO misc.py line 119 87073] Train: 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(0.077) Batch 1.008 (1.083) Remain 36:31:55 loss: 0.7286 Lr: 0.00462 [2024-02-18 04:18:03,599 INFO misc.py line 119 87073] Train: [22/100][1514/1557] Data 0.012 (0.077) Batch 0.964 (1.082) Remain 36:31:44 loss: 0.9114 Lr: 0.00462 [2024-02-18 04:18:04,505 INFO misc.py line 119 87073] Train: [22/100][1515/1557] Data 0.003 (0.077) Batch 0.906 (1.082) Remain 36:31:29 loss: 0.4195 Lr: 0.00462 [2024-02-18 04:18:05,597 INFO misc.py line 119 87073] Train: [22/100][1516/1557] Data 0.003 (0.077) Batch 1.088 (1.082) Remain 36:31:29 loss: 0.3179 Lr: 0.00462 [2024-02-18 04:18:06,331 INFO misc.py line 119 87073] Train: [22/100][1517/1557] Data 0.008 (0.077) Batch 0.737 (1.082) Remain 36:31:00 loss: 0.4787 Lr: 0.00462 [2024-02-18 04:18:07,127 INFO misc.py line 119 87073] Train: [22/100][1518/1557] Data 0.004 (0.077) Batch 0.792 (1.082) Remain 36:30:35 loss: 0.4599 Lr: 0.00462 [2024-02-18 04:18:14,728 INFO misc.py line 119 87073] Train: [22/100][1519/1557] Data 3.903 (0.079) Batch 7.597 (1.086) Remain 36:39:16 loss: 0.3965 Lr: 0.00462 [2024-02-18 04:18:15,634 INFO misc.py line 119 87073] Train: [22/100][1520/1557] Data 0.012 (0.079) Batch 0.915 (1.086) Remain 36:39:02 loss: 0.6228 Lr: 0.00462 [2024-02-18 04:18:16,487 INFO misc.py line 119 87073] Train: [22/100][1521/1557] Data 0.004 (0.079) Batch 0.852 (1.086) Remain 36:38:42 loss: 0.5082 Lr: 0.00462 [2024-02-18 04:18:17,470 INFO misc.py line 119 87073] Train: [22/100][1522/1557] Data 0.004 (0.079) Batch 0.983 (1.086) Remain 36:38:32 loss: 0.1258 Lr: 0.00462 [2024-02-18 04:18:18,385 INFO misc.py line 119 87073] Train: [22/100][1523/1557] Data 0.004 (0.079) Batch 0.916 (1.086) Remain 36:38:18 loss: 0.8062 Lr: 0.00462 [2024-02-18 04:18:19,163 INFO misc.py line 119 87073] Train: [22/100][1524/1557] Data 0.002 (0.079) Batch 0.778 (1.086) Remain 36:37:52 loss: 0.4847 Lr: 0.00462 [2024-02-18 04:18:20,000 INFO misc.py line 119 87073] Train: [22/100][1525/1557] Data 0.003 (0.079) Batch 0.830 (1.085) Remain 36:37:31 loss: 0.4513 Lr: 0.00462 [2024-02-18 04:18:21,213 INFO misc.py line 119 87073] Train: [22/100][1526/1557] Data 0.010 (0.079) Batch 1.212 (1.085) Remain 36:37:40 loss: 0.2463 Lr: 0.00462 [2024-02-18 04:18:21,981 INFO misc.py line 119 87073] Train: [22/100][1527/1557] Data 0.012 (0.079) Batch 0.776 (1.085) Remain 36:37:14 loss: 0.2954 Lr: 0.00462 [2024-02-18 04:18:22,966 INFO misc.py line 119 87073] Train: [22/100][1528/1557] Data 0.003 (0.079) Batch 0.985 (1.085) Remain 36:37:05 loss: 0.3321 Lr: 0.00462 [2024-02-18 04:18:23,938 INFO misc.py line 119 87073] Train: [22/100][1529/1557] Data 0.004 (0.079) Batch 0.970 (1.085) Remain 36:36:55 loss: 0.3011 Lr: 0.00462 [2024-02-18 04:18:25,099 INFO misc.py line 119 87073] Train: [22/100][1530/1557] Data 0.005 (0.079) Batch 1.162 (1.085) Remain 36:37:00 loss: 0.7146 Lr: 0.00462 [2024-02-18 04:18:25,889 INFO misc.py line 119 87073] Train: [22/100][1531/1557] Data 0.003 (0.079) Batch 0.790 (1.085) Remain 36:36:35 loss: 0.4063 Lr: 0.00462 [2024-02-18 04:18:26,666 INFO misc.py line 119 87073] Train: [22/100][1532/1557] Data 0.003 (0.079) Batch 0.773 (1.085) Remain 36:36:09 loss: 0.6965 Lr: 0.00462 [2024-02-18 04:18:27,966 INFO misc.py line 119 87073] Train: [22/100][1533/1557] Data 0.007 (0.079) Batch 1.295 (1.085) Remain 36:36:25 loss: 0.1734 Lr: 0.00462 [2024-02-18 04:18:29,053 INFO misc.py line 119 87073] Train: [22/100][1534/1557] Data 0.013 (0.079) Batch 1.086 (1.085) Remain 36:36:24 loss: 0.5862 Lr: 0.00462 [2024-02-18 04:18:29,887 INFO misc.py line 119 87073] Train: [22/100][1535/1557] Data 0.013 (0.079) Batch 0.844 (1.085) Remain 36:36:04 loss: 0.3719 Lr: 0.00462 [2024-02-18 04:18:30,921 INFO misc.py line 119 87073] Train: [22/100][1536/1557] Data 0.003 (0.078) Batch 1.034 (1.085) Remain 36:35:59 loss: 0.2516 Lr: 0.00462 [2024-02-18 04:18:31,877 INFO misc.py line 119 87073] Train: [22/100][1537/1557] Data 0.004 (0.078) Batch 0.955 (1.085) Remain 36:35:47 loss: 0.2876 Lr: 0.00462 [2024-02-18 04:18:32,651 INFO misc.py line 119 87073] Train: [22/100][1538/1557] Data 0.004 (0.078) Batch 0.770 (1.084) Remain 36:35:21 loss: 0.4991 Lr: 0.00462 [2024-02-18 04:18:33,498 INFO misc.py line 119 87073] Train: [22/100][1539/1557] Data 0.009 (0.078) Batch 0.852 (1.084) Remain 36:35:02 loss: 0.3162 Lr: 0.00462 [2024-02-18 04:18:34,727 INFO misc.py line 119 87073] Train: [22/100][1540/1557] Data 0.004 (0.078) Batch 1.224 (1.084) Remain 36:35:12 loss: 0.4349 Lr: 0.00462 [2024-02-18 04:18:35,778 INFO misc.py line 119 87073] Train: [22/100][1541/1557] Data 0.008 (0.078) Batch 1.050 (1.084) Remain 36:35:08 loss: 0.4441 Lr: 0.00462 [2024-02-18 04:18:36,709 INFO misc.py line 119 87073] Train: [22/100][1542/1557] Data 0.010 (0.078) Batch 0.937 (1.084) Remain 36:34:55 loss: 0.3859 Lr: 0.00462 [2024-02-18 04:18:37,701 INFO misc.py line 119 87073] Train: [22/100][1543/1557] Data 0.004 (0.078) Batch 0.991 (1.084) Remain 36:34:47 loss: 0.7860 Lr: 0.00462 [2024-02-18 04:18:38,737 INFO misc.py line 119 87073] Train: [22/100][1544/1557] Data 0.004 (0.078) Batch 1.037 (1.084) Remain 36:34:42 loss: 0.2693 Lr: 0.00462 [2024-02-18 04:18:39,496 INFO misc.py line 119 87073] Train: [22/100][1545/1557] Data 0.004 (0.078) Batch 0.759 (1.084) Remain 36:34:15 loss: 0.4844 Lr: 0.00462 [2024-02-18 04:18:40,263 INFO misc.py line 119 87073] Train: [22/100][1546/1557] Data 0.004 (0.078) Batch 0.763 (1.084) Remain 36:33:49 loss: 0.5277 Lr: 0.00462 [2024-02-18 04:18:41,297 INFO misc.py line 119 87073] Train: [22/100][1547/1557] Data 0.008 (0.078) Batch 1.032 (1.084) Remain 36:33:44 loss: 0.2751 Lr: 0.00462 [2024-02-18 04:18:42,224 INFO misc.py line 119 87073] Train: [22/100][1548/1557] Data 0.010 (0.078) Batch 0.934 (1.084) Remain 36:33:31 loss: 0.4920 Lr: 0.00462 [2024-02-18 04:18:43,322 INFO misc.py line 119 87073] Train: [22/100][1549/1557] Data 0.004 (0.078) Batch 1.098 (1.084) Remain 36:33:31 loss: 0.6395 Lr: 0.00462 [2024-02-18 04:18:44,250 INFO misc.py line 119 87073] Train: [22/100][1550/1557] Data 0.004 (0.078) Batch 0.928 (1.084) Remain 36:33:17 loss: 0.7829 Lr: 0.00462 [2024-02-18 04:18:45,285 INFO misc.py line 119 87073] Train: [22/100][1551/1557] Data 0.004 (0.078) Batch 1.035 (1.083) Remain 36:33:13 loss: 0.4883 Lr: 0.00462 [2024-02-18 04:18:46,024 INFO misc.py line 119 87073] Train: [22/100][1552/1557] Data 0.004 (0.078) Batch 0.728 (1.083) Remain 36:32:44 loss: 0.4378 Lr: 0.00462 [2024-02-18 04:18:46,691 INFO misc.py line 119 87073] Train: [22/100][1553/1557] Data 0.016 (0.078) Batch 0.678 (1.083) Remain 36:32:11 loss: 0.6260 Lr: 0.00462 [2024-02-18 04:18:47,794 INFO misc.py line 119 87073] Train: [22/100][1554/1557] Data 0.003 (0.078) Batch 1.092 (1.083) Remain 36:32:10 loss: 0.2502 Lr: 0.00462 [2024-02-18 04:18:48,963 INFO misc.py line 119 87073] Train: [22/100][1555/1557] Data 0.016 (0.078) Batch 1.167 (1.083) Remain 36:32:16 loss: 0.3449 Lr: 0.00462 [2024-02-18 04:18:49,904 INFO misc.py line 119 87073] Train: [22/100][1556/1557] Data 0.017 (0.078) Batch 0.955 (1.083) Remain 36:32:05 loss: 0.3720 Lr: 0.00462 [2024-02-18 04:18:50,809 INFO misc.py line 119 87073] Train: [22/100][1557/1557] Data 0.004 (0.078) Batch 0.904 (1.083) Remain 36:31:50 loss: 0.2961 Lr: 0.00462 [2024-02-18 04:18:50,809 INFO misc.py line 136 87073] Train result: loss: 0.4872 [2024-02-18 04:18:50,809 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 04:19:21,669 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7046 [2024-02-18 04:19:22,447 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5750 [2024-02-18 04:19:24,580 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4807 [2024-02-18 04:19:26,786 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3728 [2024-02-18 04:19:31,741 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2576 [2024-02-18 04:19:32,439 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1230 [2024-02-18 04:19:33,702 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2515 [2024-02-18 04:19:36,659 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3730 [2024-02-18 04:19:38,468 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2832 [2024-02-18 04:19:39,739 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7024/0.7814/0.9066. [2024-02-18 04:19:39,739 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9333/0.9724 [2024-02-18 04:19:39,739 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9782/0.9841 [2024-02-18 04:19:39,739 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8598/0.9693 [2024-02-18 04:19:39,739 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0324/0.1708 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4089/0.4589 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7038/0.7231 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6781/0.7290 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8049/0.9187 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9297/0.9678 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8326/0.8520 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7342/0.8332 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6381/0.8774 [2024-02-18 04:19:39,740 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5969/0.7014 [2024-02-18 04:19:39,740 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 04:19:39,742 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 04:19:39,742 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 04:19:46,707 INFO misc.py line 119 87073] Train: [23/100][1/1557] Data 1.436 (1.436) Batch 2.119 (2.119) Remain 71:28:24 loss: 0.5303 Lr: 0.00462 [2024-02-18 04:19:47,649 INFO misc.py line 119 87073] Train: [23/100][2/1557] Data 0.007 (0.007) Batch 0.944 (0.944) Remain 31:50:48 loss: 0.2890 Lr: 0.00462 [2024-02-18 04:19:48,637 INFO misc.py line 119 87073] Train: [23/100][3/1557] Data 0.004 (0.004) Batch 0.989 (0.989) Remain 33:21:08 loss: 0.4343 Lr: 0.00462 [2024-02-18 04:19:49,612 INFO misc.py line 119 87073] Train: [23/100][4/1557] Data 0.004 (0.004) Batch 0.974 (0.974) Remain 32:52:01 loss: 0.4720 Lr: 0.00462 [2024-02-18 04:19:50,412 INFO misc.py line 119 87073] Train: [23/100][5/1557] Data 0.004 (0.004) Batch 0.799 (0.887) Remain 29:54:54 loss: 0.3031 Lr: 0.00462 [2024-02-18 04:19:51,187 INFO misc.py line 119 87073] Train: [23/100][6/1557] Data 0.005 (0.004) Batch 0.762 (0.845) Remain 28:30:51 loss: 0.3992 Lr: 0.00462 [2024-02-18 04:19:54,574 INFO misc.py line 119 87073] Train: [23/100][7/1557] Data 0.017 (0.007) Batch 3.401 (1.484) Remain 50:04:02 loss: 0.2884 Lr: 0.00462 [2024-02-18 04:19:55,683 INFO misc.py line 119 87073] Train: [23/100][8/1557] Data 0.003 (0.006) Batch 1.109 (1.409) Remain 47:31:58 loss: 0.6240 Lr: 0.00462 [2024-02-18 04:19:56,749 INFO misc.py line 119 87073] Train: [23/100][9/1557] Data 0.003 (0.006) Batch 1.066 (1.352) Remain 45:36:16 loss: 0.4270 Lr: 0.00462 [2024-02-18 04:19:57,662 INFO misc.py line 119 87073] Train: [23/100][10/1557] Data 0.003 (0.005) Batch 0.913 (1.289) Remain 43:29:18 loss: 0.3921 Lr: 0.00462 [2024-02-18 04:19:58,601 INFO misc.py line 119 87073] Train: [23/100][11/1557] Data 0.004 (0.005) Batch 0.931 (1.244) Remain 41:58:38 loss: 0.6973 Lr: 0.00462 [2024-02-18 04:19:59,378 INFO misc.py line 119 87073] Train: [23/100][12/1557] Data 0.012 (0.006) Batch 0.785 (1.193) Remain 40:15:24 loss: 0.5162 Lr: 0.00462 [2024-02-18 04:20:00,179 INFO misc.py line 119 87073] Train: [23/100][13/1557] Data 0.004 (0.006) Batch 0.789 (1.153) Remain 38:53:35 loss: 0.2590 Lr: 0.00462 [2024-02-18 04:20:01,353 INFO misc.py line 119 87073] Train: [23/100][14/1557] Data 0.015 (0.007) Batch 1.175 (1.155) Remain 38:57:39 loss: 0.1868 Lr: 0.00462 [2024-02-18 04:20:02,311 INFO misc.py line 119 87073] Train: [23/100][15/1557] Data 0.014 (0.007) Batch 0.968 (1.139) Remain 38:26:03 loss: 0.1393 Lr: 0.00462 [2024-02-18 04:20:03,410 INFO misc.py line 119 87073] Train: [23/100][16/1557] Data 0.004 (0.007) Batch 1.099 (1.136) Remain 38:19:46 loss: 0.5142 Lr: 0.00462 [2024-02-18 04:20:04,312 INFO misc.py line 119 87073] Train: [23/100][17/1557] Data 0.004 (0.007) Batch 0.900 (1.119) Remain 37:45:37 loss: 0.8332 Lr: 0.00462 [2024-02-18 04:20:05,372 INFO misc.py line 119 87073] Train: [23/100][18/1557] Data 0.007 (0.007) Batch 1.060 (1.115) Remain 37:37:31 loss: 0.4511 Lr: 0.00462 [2024-02-18 04:20:06,101 INFO misc.py line 119 87073] Train: [23/100][19/1557] Data 0.006 (0.007) Batch 0.730 (1.091) Remain 36:48:47 loss: 0.5825 Lr: 0.00462 [2024-02-18 04:20:06,872 INFO misc.py line 119 87073] Train: [23/100][20/1557] Data 0.004 (0.007) Batch 0.763 (1.072) Remain 36:09:41 loss: 0.5902 Lr: 0.00462 [2024-02-18 04:20:08,844 INFO misc.py line 119 87073] Train: [23/100][21/1557] Data 0.012 (0.007) Batch 1.980 (1.123) Remain 37:51:47 loss: 0.2217 Lr: 0.00462 [2024-02-18 04:20:09,852 INFO misc.py line 119 87073] Train: [23/100][22/1557] Data 0.004 (0.007) Batch 1.009 (1.117) Remain 37:39:39 loss: 0.3396 Lr: 0.00462 [2024-02-18 04:20:10,726 INFO misc.py line 119 87073] Train: [23/100][23/1557] Data 0.004 (0.007) Batch 0.873 (1.104) Remain 37:15:01 loss: 0.5277 Lr: 0.00461 [2024-02-18 04:20:11,868 INFO misc.py line 119 87073] Train: [23/100][24/1557] Data 0.004 (0.006) Batch 1.140 (1.106) Remain 37:18:25 loss: 0.5922 Lr: 0.00461 [2024-02-18 04:20:12,787 INFO misc.py line 119 87073] Train: [23/100][25/1557] Data 0.007 (0.006) Batch 0.918 (1.098) Remain 37:01:07 loss: 0.4753 Lr: 0.00461 [2024-02-18 04:20:13,549 INFO misc.py line 119 87073] Train: [23/100][26/1557] Data 0.008 (0.006) Batch 0.764 (1.083) Remain 36:31:47 loss: 0.7319 Lr: 0.00461 [2024-02-18 04:20:14,325 INFO misc.py line 119 87073] Train: [23/100][27/1557] Data 0.005 (0.006) Batch 0.768 (1.070) Remain 36:05:12 loss: 0.3699 Lr: 0.00461 [2024-02-18 04:20:15,592 INFO misc.py line 119 87073] Train: [23/100][28/1557] Data 0.012 (0.007) Batch 1.266 (1.078) Remain 36:21:03 loss: 0.2141 Lr: 0.00461 [2024-02-18 04:20:16,501 INFO misc.py line 119 87073] Train: [23/100][29/1557] Data 0.014 (0.007) Batch 0.918 (1.072) Remain 36:08:36 loss: 0.7881 Lr: 0.00461 [2024-02-18 04:20:17,500 INFO misc.py line 119 87073] Train: [23/100][30/1557] Data 0.005 (0.007) Batch 1.000 (1.069) Remain 36:03:11 loss: 0.7077 Lr: 0.00461 [2024-02-18 04:20:18,536 INFO misc.py line 119 87073] Train: [23/100][31/1557] Data 0.004 (0.007) Batch 1.035 (1.068) Remain 36:00:45 loss: 0.7125 Lr: 0.00461 [2024-02-18 04:20:19,577 INFO misc.py line 119 87073] Train: [23/100][32/1557] Data 0.004 (0.007) Batch 1.041 (1.067) Remain 35:58:50 loss: 0.3311 Lr: 0.00461 [2024-02-18 04:20:20,278 INFO misc.py line 119 87073] Train: [23/100][33/1557] Data 0.005 (0.007) Batch 0.702 (1.055) Remain 35:34:10 loss: 0.4546 Lr: 0.00461 [2024-02-18 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Train: [23/100][40/1557] Data 0.003 (0.007) Batch 0.785 (1.034) Remain 34:53:08 loss: 0.5813 Lr: 0.00461 [2024-02-18 04:20:27,669 INFO misc.py line 119 87073] Train: [23/100][41/1557] Data 0.003 (0.007) Batch 0.755 (1.027) Remain 34:38:14 loss: 0.5851 Lr: 0.00461 [2024-02-18 04:20:28,763 INFO misc.py line 119 87073] Train: [23/100][42/1557] Data 0.005 (0.007) Batch 1.093 (1.029) Remain 34:41:37 loss: 0.2874 Lr: 0.00461 [2024-02-18 04:20:29,667 INFO misc.py line 119 87073] Train: [23/100][43/1557] Data 0.007 (0.007) Batch 0.907 (1.026) Remain 34:35:27 loss: 0.5632 Lr: 0.00461 [2024-02-18 04:20:30,655 INFO misc.py line 119 87073] Train: [23/100][44/1557] Data 0.004 (0.007) Batch 0.988 (1.025) Remain 34:33:34 loss: 0.6395 Lr: 0.00461 [2024-02-18 04:20:31,907 INFO misc.py line 119 87073] Train: [23/100][45/1557] Data 0.004 (0.006) Batch 1.246 (1.030) Remain 34:44:12 loss: 0.8440 Lr: 0.00461 [2024-02-18 04:20:32,982 INFO misc.py line 119 87073] Train: [23/100][46/1557] Data 0.011 (0.007) Batch 1.077 (1.031) Remain 34:46:24 loss: 0.6342 Lr: 0.00461 [2024-02-18 04:20:33,685 INFO misc.py line 119 87073] Train: [23/100][47/1557] Data 0.009 (0.007) Batch 0.704 (1.024) Remain 34:31:19 loss: 0.3388 Lr: 0.00461 [2024-02-18 04:20:34,496 INFO misc.py line 119 87073] Train: [23/100][48/1557] Data 0.008 (0.007) Batch 0.812 (1.019) Remain 34:21:48 loss: 0.4672 Lr: 0.00461 [2024-02-18 04:20:35,742 INFO misc.py line 119 87073] Train: [23/100][49/1557] Data 0.005 (0.007) Batch 1.240 (1.024) Remain 34:31:31 loss: 0.2614 Lr: 0.00461 [2024-02-18 04:20:36,693 INFO misc.py line 119 87073] Train: [23/100][50/1557] Data 0.012 (0.007) Batch 0.958 (1.022) Remain 34:28:39 loss: 0.5556 Lr: 0.00461 [2024-02-18 04:20:37,699 INFO misc.py line 119 87073] Train: [23/100][51/1557] Data 0.005 (0.007) Batch 1.004 (1.022) Remain 34:27:53 loss: 0.6395 Lr: 0.00461 [2024-02-18 04:20:38,712 INFO misc.py line 119 87073] Train: [23/100][52/1557] Data 0.007 (0.007) Batch 1.014 (1.022) Remain 34:27:32 loss: 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Batch 0.939 (1.164) Remain 39:14:05 loss: 0.5928 Lr: 0.00461 [2024-02-18 04:22:06,875 INFO misc.py line 119 87073] Train: [23/100][122/1557] Data 0.007 (0.106) Batch 0.862 (1.162) Remain 39:08:56 loss: 1.0500 Lr: 0.00461 [2024-02-18 04:22:07,833 INFO misc.py line 119 87073] Train: [23/100][123/1557] Data 0.004 (0.105) Batch 0.958 (1.160) Remain 39:05:29 loss: 0.5989 Lr: 0.00461 [2024-02-18 04:22:08,646 INFO misc.py line 119 87073] Train: [23/100][124/1557] Data 0.005 (0.104) Batch 0.812 (1.157) Remain 38:59:39 loss: 0.2034 Lr: 0.00461 [2024-02-18 04:22:09,535 INFO misc.py line 119 87073] Train: [23/100][125/1557] Data 0.004 (0.103) Batch 0.883 (1.155) Remain 38:55:06 loss: 0.4298 Lr: 0.00461 [2024-02-18 04:22:10,745 INFO misc.py line 119 87073] Train: [23/100][126/1557] Data 0.010 (0.102) Batch 1.209 (1.155) Remain 38:55:59 loss: 0.2651 Lr: 0.00461 [2024-02-18 04:22:11,794 INFO misc.py line 119 87073] Train: [23/100][127/1557] Data 0.012 (0.102) Batch 1.044 (1.154) Remain 38:54:08 loss: 0.7534 Lr: 0.00461 [2024-02-18 04:22:12,658 INFO misc.py line 119 87073] Train: [23/100][128/1557] Data 0.018 (0.101) Batch 0.876 (1.152) Remain 38:49:37 loss: 0.5292 Lr: 0.00461 [2024-02-18 04:22:13,735 INFO misc.py line 119 87073] Train: [23/100][129/1557] Data 0.004 (0.100) Batch 1.077 (1.152) Remain 38:48:24 loss: 0.5117 Lr: 0.00461 [2024-02-18 04:22:14,753 INFO misc.py line 119 87073] Train: [23/100][130/1557] Data 0.004 (0.099) Batch 1.016 (1.150) Remain 38:46:13 loss: 0.5157 Lr: 0.00461 [2024-02-18 04:22:15,523 INFO misc.py line 119 87073] Train: [23/100][131/1557] Data 0.008 (0.099) Batch 0.771 (1.148) Remain 38:40:12 loss: 0.9367 Lr: 0.00461 [2024-02-18 04:22:16,242 INFO misc.py line 119 87073] Train: [23/100][132/1557] Data 0.006 (0.098) Batch 0.711 (1.144) Remain 38:33:20 loss: 0.2971 Lr: 0.00461 [2024-02-18 04:22:17,507 INFO misc.py line 119 87073] Train: [23/100][133/1557] Data 0.016 (0.097) Batch 1.273 (1.145) Remain 38:35:19 loss: 0.1362 Lr: 0.00461 [2024-02-18 04:22:18,348 INFO misc.py line 119 87073] Train: [23/100][134/1557] Data 0.007 (0.097) Batch 0.842 (1.143) Remain 38:30:37 loss: 0.4659 Lr: 0.00461 [2024-02-18 04:22:19,313 INFO misc.py line 119 87073] Train: [23/100][135/1557] Data 0.006 (0.096) Batch 0.966 (1.141) Remain 38:27:53 loss: 0.3703 Lr: 0.00461 [2024-02-18 04:22:20,264 INFO misc.py line 119 87073] Train: [23/100][136/1557] Data 0.004 (0.095) Batch 0.952 (1.140) Remain 38:24:59 loss: 0.5073 Lr: 0.00461 [2024-02-18 04:22:21,214 INFO misc.py line 119 87073] Train: [23/100][137/1557] Data 0.004 (0.095) Batch 0.941 (1.139) Remain 38:21:58 loss: 0.4513 Lr: 0.00461 [2024-02-18 04:22:22,037 INFO misc.py line 119 87073] Train: [23/100][138/1557] Data 0.012 (0.094) Batch 0.832 (1.136) Remain 38:17:21 loss: 0.4521 Lr: 0.00461 [2024-02-18 04:22:22,762 INFO misc.py line 119 87073] Train: [23/100][139/1557] Data 0.004 (0.093) Batch 0.725 (1.133) Remain 38:11:13 loss: 1.1139 Lr: 0.00461 [2024-02-18 04:22:24,065 INFO misc.py line 119 87073] Train: [23/100][140/1557] Data 0.005 (0.093) Batch 1.301 (1.134) Remain 38:13:41 loss: 0.3139 Lr: 0.00461 [2024-02-18 04:22:25,085 INFO misc.py line 119 87073] Train: [23/100][141/1557] Data 0.006 (0.092) Batch 1.021 (1.134) Remain 38:11:59 loss: 0.6841 Lr: 0.00461 [2024-02-18 04:22:26,021 INFO misc.py line 119 87073] Train: [23/100][142/1557] Data 0.004 (0.091) Batch 0.937 (1.132) Remain 38:09:06 loss: 0.1621 Lr: 0.00461 [2024-02-18 04:22:27,154 INFO misc.py line 119 87073] Train: [23/100][143/1557] Data 0.004 (0.091) Batch 1.133 (1.132) Remain 38:09:06 loss: 0.4588 Lr: 0.00461 [2024-02-18 04:22:28,014 INFO misc.py line 119 87073] Train: [23/100][144/1557] Data 0.005 (0.090) Batch 0.860 (1.130) Remain 38:05:11 loss: 0.3978 Lr: 0.00461 [2024-02-18 04:22:28,732 INFO misc.py line 119 87073] Train: [23/100][145/1557] Data 0.004 (0.090) Batch 0.715 (1.127) Remain 37:59:14 loss: 0.5570 Lr: 0.00461 [2024-02-18 04:22:29,486 INFO misc.py line 119 87073] Train: [23/100][146/1557] Data 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37:44:28 loss: 0.5343 Lr: 0.00461 [2024-02-18 04:22:36,327 INFO misc.py line 119 87073] Train: [23/100][153/1557] Data 0.014 (0.085) Batch 0.785 (1.118) Remain 37:39:56 loss: 0.4190 Lr: 0.00461 [2024-02-18 04:22:37,519 INFO misc.py line 119 87073] Train: [23/100][154/1557] Data 0.004 (0.085) Batch 1.191 (1.118) Remain 37:40:54 loss: 0.3446 Lr: 0.00461 [2024-02-18 04:22:38,286 INFO misc.py line 119 87073] Train: [23/100][155/1557] Data 0.006 (0.084) Batch 0.766 (1.116) Remain 37:36:12 loss: 0.4997 Lr: 0.00461 [2024-02-18 04:22:39,412 INFO misc.py line 119 87073] Train: [23/100][156/1557] Data 0.006 (0.084) Batch 1.124 (1.116) Remain 37:36:17 loss: 0.9088 Lr: 0.00461 [2024-02-18 04:22:40,362 INFO misc.py line 119 87073] Train: [23/100][157/1557] Data 0.008 (0.083) Batch 0.953 (1.115) Remain 37:34:07 loss: 0.6193 Lr: 0.00461 [2024-02-18 04:22:41,450 INFO misc.py line 119 87073] Train: [23/100][158/1557] Data 0.006 (0.083) Batch 1.089 (1.115) Remain 37:33:46 loss: 0.9465 Lr: 0.00461 [2024-02-18 04:22:42,194 INFO misc.py line 119 87073] Train: [23/100][159/1557] Data 0.004 (0.082) Batch 0.742 (1.113) Remain 37:28:54 loss: 0.5066 Lr: 0.00461 [2024-02-18 04:22:42,985 INFO misc.py line 119 87073] Train: [23/100][160/1557] Data 0.006 (0.082) Batch 0.792 (1.110) Remain 37:24:46 loss: 0.9219 Lr: 0.00461 [2024-02-18 04:22:44,263 INFO misc.py line 119 87073] Train: [23/100][161/1557] Data 0.005 (0.081) Batch 1.274 (1.112) Remain 37:26:50 loss: 0.2923 Lr: 0.00461 [2024-02-18 04:22:45,290 INFO misc.py line 119 87073] Train: [23/100][162/1557] Data 0.009 (0.081) Batch 1.022 (1.111) Remain 37:25:41 loss: 0.5318 Lr: 0.00461 [2024-02-18 04:22:46,362 INFO misc.py line 119 87073] Train: [23/100][163/1557] Data 0.013 (0.080) Batch 1.065 (1.111) Remain 37:25:06 loss: 1.4310 Lr: 0.00461 [2024-02-18 04:22:47,338 INFO misc.py line 119 87073] Train: [23/100][164/1557] Data 0.020 (0.080) Batch 0.992 (1.110) Remain 37:23:35 loss: 0.2992 Lr: 0.00461 [2024-02-18 04:22:48,437 INFO misc.py line 119 87073] Train: [23/100][165/1557] Data 0.005 (0.080) Batch 1.097 (1.110) Remain 37:23:24 loss: 0.6080 Lr: 0.00461 [2024-02-18 04:22:49,154 INFO misc.py line 119 87073] Train: [23/100][166/1557] Data 0.007 (0.079) Batch 0.718 (1.107) Remain 37:18:31 loss: 0.4701 Lr: 0.00461 [2024-02-18 04:22:49,884 INFO misc.py line 119 87073] Train: [23/100][167/1557] Data 0.005 (0.079) Batch 0.713 (1.105) Remain 37:13:38 loss: 0.3392 Lr: 0.00461 [2024-02-18 04:22:51,057 INFO misc.py line 119 87073] Train: [23/100][168/1557] Data 0.023 (0.078) Batch 1.184 (1.106) Remain 37:14:35 loss: 0.3231 Lr: 0.00461 [2024-02-18 04:22:52,103 INFO misc.py line 119 87073] Train: [23/100][169/1557] Data 0.012 (0.078) Batch 1.043 (1.105) Remain 37:13:48 loss: 0.3993 Lr: 0.00461 [2024-02-18 04:22:53,046 INFO misc.py line 119 87073] Train: [23/100][170/1557] Data 0.015 (0.078) Batch 0.952 (1.104) Remain 37:11:56 loss: 0.9102 Lr: 0.00461 [2024-02-18 04:22:53,955 INFO misc.py line 119 87073] Train: 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Batch 1.036 (1.162) Remain 39:08:22 loss: 1.0097 Lr: 0.00461 [2024-02-18 04:23:11,809 INFO misc.py line 119 87073] Train: [23/100][178/1557] Data 0.004 (0.109) Batch 0.999 (1.161) Remain 39:06:28 loss: 0.5683 Lr: 0.00461 [2024-02-18 04:23:12,920 INFO misc.py line 119 87073] Train: [23/100][179/1557] Data 0.005 (0.108) Batch 1.112 (1.161) Remain 39:05:53 loss: 0.3957 Lr: 0.00461 [2024-02-18 04:23:15,911 INFO misc.py line 119 87073] Train: [23/100][180/1557] Data 0.723 (0.112) Batch 2.992 (1.171) Remain 39:26:46 loss: 0.2997 Lr: 0.00461 [2024-02-18 04:23:16,701 INFO misc.py line 119 87073] Train: [23/100][181/1557] Data 0.003 (0.111) Batch 0.790 (1.169) Remain 39:22:25 loss: 0.4835 Lr: 0.00461 [2024-02-18 04:23:18,013 INFO misc.py line 119 87073] Train: [23/100][182/1557] Data 0.004 (0.110) Batch 1.284 (1.170) Remain 39:23:42 loss: 0.1601 Lr: 0.00461 [2024-02-18 04:23:19,254 INFO misc.py line 119 87073] Train: [23/100][183/1557] Data 0.032 (0.110) Batch 1.268 (1.170) Remain 39:24:47 loss: 0.2495 Lr: 0.00461 [2024-02-18 04:23:20,149 INFO misc.py line 119 87073] Train: [23/100][184/1557] Data 0.005 (0.109) Batch 0.894 (1.169) Remain 39:21:41 loss: 0.4462 Lr: 0.00461 [2024-02-18 04:23:21,209 INFO misc.py line 119 87073] Train: [23/100][185/1557] Data 0.006 (0.109) Batch 1.062 (1.168) Remain 39:20:29 loss: 0.5688 Lr: 0.00461 [2024-02-18 04:23:22,107 INFO misc.py line 119 87073] Train: [23/100][186/1557] Data 0.004 (0.108) Batch 0.898 (1.166) Remain 39:17:29 loss: 0.4218 Lr: 0.00461 [2024-02-18 04:23:22,931 INFO misc.py line 119 87073] Train: [23/100][187/1557] Data 0.004 (0.108) Batch 0.816 (1.165) Remain 39:13:37 loss: 0.4947 Lr: 0.00461 [2024-02-18 04:23:23,685 INFO misc.py line 119 87073] Train: [23/100][188/1557] Data 0.012 (0.107) Batch 0.762 (1.162) Remain 39:09:12 loss: 0.3149 Lr: 0.00461 [2024-02-18 04:23:24,831 INFO misc.py line 119 87073] Train: [23/100][189/1557] Data 0.004 (0.107) Batch 1.145 (1.162) Remain 39:09:00 loss: 0.3025 Lr: 0.00461 [2024-02-18 04:23:25,786 INFO misc.py line 119 87073] Train: [23/100][190/1557] Data 0.004 (0.106) Batch 0.956 (1.161) Remain 39:06:45 loss: 0.3777 Lr: 0.00461 [2024-02-18 04:23:26,843 INFO misc.py line 119 87073] Train: [23/100][191/1557] Data 0.004 (0.106) Batch 1.055 (1.161) Remain 39:05:35 loss: 0.3998 Lr: 0.00461 [2024-02-18 04:23:27,659 INFO misc.py line 119 87073] Train: [23/100][192/1557] Data 0.006 (0.105) Batch 0.818 (1.159) Remain 39:01:54 loss: 0.6841 Lr: 0.00461 [2024-02-18 04:23:28,696 INFO misc.py line 119 87073] Train: [23/100][193/1557] Data 0.004 (0.104) Batch 1.032 (1.158) Remain 39:00:32 loss: 0.4103 Lr: 0.00461 [2024-02-18 04:23:29,362 INFO misc.py line 119 87073] Train: [23/100][194/1557] Data 0.009 (0.104) Batch 0.670 (1.156) Remain 38:55:21 loss: 0.7467 Lr: 0.00461 [2024-02-18 04:23:30,117 INFO misc.py line 119 87073] Train: [23/100][195/1557] Data 0.004 (0.103) Batch 0.753 (1.154) Remain 38:51:05 loss: 0.4883 Lr: 0.00461 [2024-02-18 04:23:31,406 INFO misc.py line 119 87073] Train: [23/100][196/1557] Data 0.007 (0.103) Batch 1.285 (1.154) Remain 38:52:27 loss: 0.3560 Lr: 0.00461 [2024-02-18 04:23:32,286 INFO misc.py line 119 87073] Train: [23/100][197/1557] Data 0.010 (0.102) Batch 0.886 (1.153) Remain 38:49:38 loss: 0.5900 Lr: 0.00461 [2024-02-18 04:23:33,273 INFO misc.py line 119 87073] Train: [23/100][198/1557] Data 0.004 (0.102) Batch 0.987 (1.152) Remain 38:47:54 loss: 0.7246 Lr: 0.00461 [2024-02-18 04:23:34,325 INFO misc.py line 119 87073] Train: [23/100][199/1557] Data 0.004 (0.101) Batch 1.052 (1.151) Remain 38:46:51 loss: 0.4650 Lr: 0.00461 [2024-02-18 04:23:35,293 INFO misc.py line 119 87073] Train: [23/100][200/1557] Data 0.005 (0.101) Batch 0.968 (1.151) Remain 38:44:57 loss: 0.5314 Lr: 0.00461 [2024-02-18 04:23:36,084 INFO misc.py line 119 87073] Train: [23/100][201/1557] Data 0.004 (0.101) Batch 0.791 (1.149) Remain 38:41:16 loss: 0.4692 Lr: 0.00461 [2024-02-18 04:23:36,886 INFO misc.py line 119 87073] Train: [23/100][202/1557] Data 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[2024-02-18 04:23:50,065 INFO misc.py line 119 87073] Train: [23/100][215/1557] Data 0.007 (0.094) Batch 0.763 (1.139) Remain 38:20:51 loss: 0.6071 Lr: 0.00461 [2024-02-18 04:23:50,815 INFO misc.py line 119 87073] Train: [23/100][216/1557] Data 0.016 (0.094) Batch 0.761 (1.137) Remain 38:17:16 loss: 0.1751 Lr: 0.00461 [2024-02-18 04:23:52,135 INFO misc.py line 119 87073] Train: [23/100][217/1557] Data 0.004 (0.094) Batch 1.311 (1.138) Remain 38:18:53 loss: 0.5121 Lr: 0.00461 [2024-02-18 04:23:52,973 INFO misc.py line 119 87073] Train: [23/100][218/1557] Data 0.013 (0.093) Batch 0.847 (1.136) Remain 38:16:08 loss: 0.5771 Lr: 0.00461 [2024-02-18 04:23:53,931 INFO misc.py line 119 87073] Train: [23/100][219/1557] Data 0.003 (0.093) Batch 0.959 (1.136) Remain 38:14:27 loss: 0.4681 Lr: 0.00461 [2024-02-18 04:23:54,864 INFO misc.py line 119 87073] Train: [23/100][220/1557] Data 0.003 (0.092) Batch 0.932 (1.135) Remain 38:12:32 loss: 0.3475 Lr: 0.00461 [2024-02-18 04:23:55,780 INFO misc.py line 119 87073] Train: [23/100][221/1557] Data 0.005 (0.092) Batch 0.908 (1.134) Remain 38:10:25 loss: 0.5791 Lr: 0.00461 [2024-02-18 04:23:56,503 INFO misc.py line 119 87073] Train: [23/100][222/1557] Data 0.013 (0.092) Batch 0.732 (1.132) Remain 38:06:41 loss: 0.6706 Lr: 0.00461 [2024-02-18 04:23:57,257 INFO misc.py line 119 87073] Train: [23/100][223/1557] Data 0.004 (0.091) Batch 0.746 (1.130) Remain 38:03:08 loss: 0.6600 Lr: 0.00461 [2024-02-18 04:23:58,402 INFO misc.py line 119 87073] Train: [23/100][224/1557] Data 0.012 (0.091) Batch 1.151 (1.130) Remain 38:03:18 loss: 0.3860 Lr: 0.00461 [2024-02-18 04:23:59,362 INFO misc.py line 119 87073] Train: [23/100][225/1557] Data 0.006 (0.090) Batch 0.961 (1.129) Remain 38:01:45 loss: 0.6536 Lr: 0.00461 [2024-02-18 04:24:00,434 INFO misc.py line 119 87073] Train: [23/100][226/1557] Data 0.004 (0.090) Batch 1.072 (1.129) Remain 38:01:12 loss: 0.5522 Lr: 0.00461 [2024-02-18 04:24:01,567 INFO misc.py line 119 87073] Train: 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Batch 0.982 (1.168) Remain 39:18:41 loss: 0.2324 Lr: 0.00461 [2024-02-18 04:24:18,114 INFO misc.py line 119 87073] Train: [23/100][234/1557] Data 0.007 (0.111) Batch 0.939 (1.167) Remain 39:16:40 loss: 0.3833 Lr: 0.00461 [2024-02-18 04:24:19,098 INFO misc.py line 119 87073] Train: [23/100][235/1557] Data 0.004 (0.110) Batch 0.985 (1.166) Remain 39:15:04 loss: 0.3440 Lr: 0.00461 [2024-02-18 04:24:19,867 INFO misc.py line 119 87073] Train: [23/100][236/1557] Data 0.004 (0.110) Batch 0.770 (1.164) Remain 39:11:37 loss: 0.5483 Lr: 0.00461 [2024-02-18 04:24:20,638 INFO misc.py line 119 87073] Train: [23/100][237/1557] Data 0.004 (0.109) Batch 0.769 (1.162) Remain 39:08:11 loss: 0.4201 Lr: 0.00461 [2024-02-18 04:24:21,908 INFO misc.py line 119 87073] Train: [23/100][238/1557] Data 0.005 (0.109) Batch 1.269 (1.163) Remain 39:09:05 loss: 0.1306 Lr: 0.00461 [2024-02-18 04:24:22,880 INFO misc.py line 119 87073] Train: [23/100][239/1557] Data 0.007 (0.108) Batch 0.975 (1.162) Remain 39:07:27 loss: 0.4819 Lr: 0.00461 [2024-02-18 04:24:23,704 INFO misc.py line 119 87073] Train: [23/100][240/1557] Data 0.004 (0.108) Batch 0.822 (1.161) Remain 39:04:32 loss: 0.2845 Lr: 0.00461 [2024-02-18 04:24:24,813 INFO misc.py line 119 87073] Train: [23/100][241/1557] Data 0.006 (0.108) Batch 1.108 (1.160) Remain 39:04:05 loss: 0.3864 Lr: 0.00461 [2024-02-18 04:24:25,750 INFO misc.py line 119 87073] Train: [23/100][242/1557] Data 0.006 (0.107) Batch 0.937 (1.159) Remain 39:02:10 loss: 0.2800 Lr: 0.00461 [2024-02-18 04:24:26,490 INFO misc.py line 119 87073] Train: [23/100][243/1557] Data 0.006 (0.107) Batch 0.741 (1.158) Remain 38:58:38 loss: 0.3384 Lr: 0.00461 [2024-02-18 04:24:27,242 INFO misc.py line 119 87073] Train: [23/100][244/1557] Data 0.005 (0.106) Batch 0.747 (1.156) Remain 38:55:10 loss: 0.3534 Lr: 0.00461 [2024-02-18 04:24:28,399 INFO misc.py line 119 87073] Train: [23/100][245/1557] Data 0.010 (0.106) Batch 1.159 (1.156) Remain 38:55:11 loss: 0.2320 Lr: 0.00461 [2024-02-18 04:24:29,368 INFO misc.py line 119 87073] Train: [23/100][246/1557] Data 0.008 (0.105) Batch 0.972 (1.155) Remain 38:53:38 loss: 0.6658 Lr: 0.00461 [2024-02-18 04:24:30,180 INFO misc.py line 119 87073] Train: [23/100][247/1557] Data 0.004 (0.105) Batch 0.812 (1.154) Remain 38:50:46 loss: 0.6186 Lr: 0.00461 [2024-02-18 04:24:31,024 INFO misc.py line 119 87073] Train: [23/100][248/1557] Data 0.006 (0.105) Batch 0.838 (1.153) Remain 38:48:09 loss: 0.4646 Lr: 0.00461 [2024-02-18 04:24:31,864 INFO misc.py line 119 87073] Train: [23/100][249/1557] Data 0.011 (0.104) Batch 0.845 (1.151) Remain 38:45:36 loss: 0.5472 Lr: 0.00461 [2024-02-18 04:24:32,704 INFO misc.py line 119 87073] Train: [23/100][250/1557] Data 0.005 (0.104) Batch 0.838 (1.150) Remain 38:43:01 loss: 0.3964 Lr: 0.00461 [2024-02-18 04:24:33,470 INFO misc.py line 119 87073] Train: [23/100][251/1557] Data 0.007 (0.103) Batch 0.764 (1.148) Remain 38:39:51 loss: 0.4737 Lr: 0.00461 [2024-02-18 04:24:34,761 INFO misc.py line 119 87073] Train: [23/100][252/1557] Data 0.008 (0.103) Batch 1.295 (1.149) Remain 38:41:01 loss: 0.2269 Lr: 0.00461 [2024-02-18 04:24:35,641 INFO misc.py line 119 87073] Train: [23/100][253/1557] Data 0.006 (0.103) Batch 0.880 (1.148) Remain 38:38:50 loss: 0.5191 Lr: 0.00461 [2024-02-18 04:24:36,618 INFO misc.py line 119 87073] Train: [23/100][254/1557] Data 0.007 (0.102) Batch 0.978 (1.147) Remain 38:37:27 loss: 0.8740 Lr: 0.00461 [2024-02-18 04:24:37,692 INFO misc.py line 119 87073] Train: [23/100][255/1557] Data 0.004 (0.102) Batch 1.074 (1.147) Remain 38:36:50 loss: 0.2281 Lr: 0.00461 [2024-02-18 04:24:38,695 INFO misc.py line 119 87073] Train: [23/100][256/1557] Data 0.004 (0.102) Batch 1.002 (1.146) Remain 38:35:40 loss: 0.4645 Lr: 0.00461 [2024-02-18 04:24:39,478 INFO misc.py line 119 87073] Train: [23/100][257/1557] Data 0.006 (0.101) Batch 0.784 (1.145) Remain 38:32:45 loss: 0.4650 Lr: 0.00461 [2024-02-18 04:24:40,195 INFO misc.py line 119 87073] Train: [23/100][258/1557] Data 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line 119 87073] Train: [23/100][277/1557] Data 0.004 (0.094) Batch 0.906 (1.132) Remain 38:05:13 loss: 1.1661 Lr: 0.00461 [2024-02-18 04:24:59,418 INFO misc.py line 119 87073] Train: [23/100][278/1557] Data 0.006 (0.094) Batch 0.724 (1.130) Remain 38:02:13 loss: 0.4720 Lr: 0.00461 [2024-02-18 04:25:00,149 INFO misc.py line 119 87073] Train: [23/100][279/1557] Data 0.004 (0.094) Batch 0.728 (1.129) Remain 37:59:15 loss: 0.5413 Lr: 0.00461 [2024-02-18 04:25:01,428 INFO misc.py line 119 87073] Train: [23/100][280/1557] Data 0.007 (0.093) Batch 1.278 (1.129) Remain 38:00:19 loss: 0.4788 Lr: 0.00461 [2024-02-18 04:25:02,310 INFO misc.py line 119 87073] Train: [23/100][281/1557] Data 0.007 (0.093) Batch 0.884 (1.128) Remain 37:58:32 loss: 0.2179 Lr: 0.00461 [2024-02-18 04:25:03,254 INFO misc.py line 119 87073] Train: [23/100][282/1557] Data 0.004 (0.093) Batch 0.945 (1.128) Remain 37:57:11 loss: 0.3069 Lr: 0.00461 [2024-02-18 04:25:04,129 INFO misc.py line 119 87073] Train: 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Batch 1.085 (1.160) Remain 39:02:00 loss: 0.7273 Lr: 0.00461 [2024-02-18 04:25:21,359 INFO misc.py line 119 87073] Train: [23/100][290/1557] Data 0.005 (0.110) Batch 1.010 (1.159) Remain 39:00:55 loss: 0.4705 Lr: 0.00461 [2024-02-18 04:25:22,265 INFO misc.py line 119 87073] Train: [23/100][291/1557] Data 0.006 (0.110) Batch 0.907 (1.158) Remain 38:59:08 loss: 0.3641 Lr: 0.00461 [2024-02-18 04:25:23,011 INFO misc.py line 119 87073] Train: [23/100][292/1557] Data 0.006 (0.109) Batch 0.747 (1.157) Remain 38:56:14 loss: 0.4865 Lr: 0.00461 [2024-02-18 04:25:23,740 INFO misc.py line 119 87073] Train: [23/100][293/1557] Data 0.005 (0.109) Batch 0.729 (1.156) Remain 38:53:14 loss: 0.3138 Lr: 0.00461 [2024-02-18 04:25:25,003 INFO misc.py line 119 87073] Train: [23/100][294/1557] Data 0.004 (0.109) Batch 1.261 (1.156) Remain 38:53:57 loss: 0.2250 Lr: 0.00461 [2024-02-18 04:25:25,959 INFO misc.py line 119 87073] Train: [23/100][295/1557] Data 0.006 (0.108) Batch 0.957 (1.155) Remain 38:52:34 loss: 0.8049 Lr: 0.00461 [2024-02-18 04:25:27,136 INFO misc.py line 119 87073] Train: [23/100][296/1557] Data 0.006 (0.108) Batch 1.177 (1.155) Remain 38:52:41 loss: 0.6057 Lr: 0.00461 [2024-02-18 04:25:28,147 INFO misc.py line 119 87073] Train: [23/100][297/1557] Data 0.005 (0.108) Batch 1.011 (1.155) Remain 38:51:41 loss: 0.4325 Lr: 0.00461 [2024-02-18 04:25:29,137 INFO misc.py line 119 87073] Train: [23/100][298/1557] Data 0.005 (0.107) Batch 0.991 (1.154) Remain 38:50:32 loss: 0.5851 Lr: 0.00461 [2024-02-18 04:25:29,924 INFO misc.py line 119 87073] Train: [23/100][299/1557] Data 0.004 (0.107) Batch 0.786 (1.153) Remain 38:48:01 loss: 0.5768 Lr: 0.00461 [2024-02-18 04:25:30,765 INFO misc.py line 119 87073] Train: [23/100][300/1557] Data 0.005 (0.107) Batch 0.836 (1.152) Remain 38:45:50 loss: 0.4834 Lr: 0.00461 [2024-02-18 04:25:32,033 INFO misc.py line 119 87073] Train: [23/100][301/1557] Data 0.010 (0.106) Batch 1.266 (1.152) Remain 38:46:36 loss: 0.3178 Lr: 0.00461 [2024-02-18 04:25:33,156 INFO misc.py line 119 87073] Train: [23/100][302/1557] Data 0.011 (0.106) Batch 1.130 (1.152) Remain 38:46:26 loss: 0.3417 Lr: 0.00461 [2024-02-18 04:25:34,094 INFO misc.py line 119 87073] Train: [23/100][303/1557] Data 0.005 (0.106) Batch 0.937 (1.152) Remain 38:44:57 loss: 0.5784 Lr: 0.00461 [2024-02-18 04:25:35,024 INFO misc.py line 119 87073] Train: [23/100][304/1557] Data 0.006 (0.105) Batch 0.932 (1.151) Remain 38:43:28 loss: 0.5116 Lr: 0.00461 [2024-02-18 04:25:35,881 INFO misc.py line 119 87073] Train: [23/100][305/1557] Data 0.004 (0.105) Batch 0.855 (1.150) Remain 38:41:28 loss: 0.8479 Lr: 0.00461 [2024-02-18 04:25:36,609 INFO misc.py line 119 87073] Train: [23/100][306/1557] Data 0.005 (0.105) Batch 0.729 (1.148) Remain 38:38:39 loss: 0.4912 Lr: 0.00461 [2024-02-18 04:25:37,337 INFO misc.py line 119 87073] Train: [23/100][307/1557] Data 0.005 (0.104) Batch 0.725 (1.147) Remain 38:35:49 loss: 0.2907 Lr: 0.00461 [2024-02-18 04:25:38,643 INFO misc.py line 119 87073] Train: [23/100][308/1557] Data 0.006 (0.104) Batch 1.304 (1.148) Remain 38:36:50 loss: 0.2871 Lr: 0.00461 [2024-02-18 04:25:39,609 INFO misc.py line 119 87073] Train: [23/100][309/1557] Data 0.009 (0.104) Batch 0.970 (1.147) Remain 38:35:39 loss: 0.6017 Lr: 0.00461 [2024-02-18 04:25:40,566 INFO misc.py line 119 87073] Train: [23/100][310/1557] Data 0.005 (0.103) Batch 0.957 (1.146) Remain 38:34:23 loss: 0.4829 Lr: 0.00461 [2024-02-18 04:25:41,603 INFO misc.py line 119 87073] Train: [23/100][311/1557] Data 0.006 (0.103) Batch 1.038 (1.146) Remain 38:33:39 loss: 0.3069 Lr: 0.00461 [2024-02-18 04:25:42,625 INFO misc.py line 119 87073] Train: [23/100][312/1557] Data 0.004 (0.103) Batch 1.023 (1.146) Remain 38:32:49 loss: 0.4646 Lr: 0.00461 [2024-02-18 04:25:43,407 INFO misc.py line 119 87073] Train: [23/100][313/1557] Data 0.004 (0.102) Batch 0.780 (1.144) Remain 38:30:26 loss: 0.4773 Lr: 0.00461 [2024-02-18 04:25:44,114 INFO misc.py line 119 87073] Train: [23/100][314/1557] Data 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38:20:19 loss: 0.3392 Lr: 0.00461 [2024-02-18 04:25:50,620 INFO misc.py line 119 87073] Train: [23/100][321/1557] Data 0.010 (0.100) Batch 0.768 (1.138) Remain 38:17:57 loss: 0.4176 Lr: 0.00461 [2024-02-18 04:25:51,765 INFO misc.py line 119 87073] Train: [23/100][322/1557] Data 0.004 (0.100) Batch 1.144 (1.138) Remain 38:17:58 loss: 0.4475 Lr: 0.00461 [2024-02-18 04:25:52,684 INFO misc.py line 119 87073] Train: [23/100][323/1557] Data 0.007 (0.099) Batch 0.921 (1.138) Remain 38:16:34 loss: 0.5303 Lr: 0.00461 [2024-02-18 04:25:53,825 INFO misc.py line 119 87073] Train: [23/100][324/1557] Data 0.003 (0.099) Batch 1.141 (1.138) Remain 38:16:34 loss: 0.8561 Lr: 0.00461 [2024-02-18 04:25:54,736 INFO misc.py line 119 87073] Train: [23/100][325/1557] Data 0.004 (0.099) Batch 0.909 (1.137) Remain 38:15:07 loss: 0.8253 Lr: 0.00461 [2024-02-18 04:25:55,610 INFO misc.py line 119 87073] Train: [23/100][326/1557] Data 0.005 (0.099) Batch 0.874 (1.136) Remain 38:13:28 loss: 1.0800 Lr: 0.00461 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line 119 87073] Train: [23/100][333/1557] Data 0.005 (0.097) Batch 1.081 (1.133) Remain 38:06:36 loss: 0.6622 Lr: 0.00461 [2024-02-18 04:26:03,190 INFO misc.py line 119 87073] Train: [23/100][334/1557] Data 0.004 (0.096) Batch 0.728 (1.132) Remain 38:04:07 loss: 0.4239 Lr: 0.00461 [2024-02-18 04:26:03,980 INFO misc.py line 119 87073] Train: [23/100][335/1557] Data 0.004 (0.096) Batch 0.784 (1.131) Remain 38:01:59 loss: 1.0248 Lr: 0.00461 [2024-02-18 04:26:05,192 INFO misc.py line 119 87073] Train: [23/100][336/1557] Data 0.010 (0.096) Batch 1.217 (1.131) Remain 38:02:29 loss: 0.5134 Lr: 0.00461 [2024-02-18 04:26:06,202 INFO misc.py line 119 87073] Train: [23/100][337/1557] Data 0.005 (0.095) Batch 0.998 (1.130) Remain 38:01:40 loss: 0.2682 Lr: 0.00461 [2024-02-18 04:26:07,157 INFO misc.py line 119 87073] Train: [23/100][338/1557] Data 0.016 (0.095) Batch 0.967 (1.130) Remain 38:00:40 loss: 0.6559 Lr: 0.00461 [2024-02-18 04:26:08,101 INFO misc.py line 119 87073] Train: 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Batch 0.984 (1.155) Remain 38:50:49 loss: 0.6179 Lr: 0.00461 [2024-02-18 04:26:24,601 INFO misc.py line 119 87073] Train: [23/100][346/1557] Data 0.005 (0.108) Batch 1.014 (1.154) Remain 38:49:58 loss: 0.5137 Lr: 0.00461 [2024-02-18 04:26:25,571 INFO misc.py line 119 87073] Train: [23/100][347/1557] Data 0.006 (0.108) Batch 0.970 (1.154) Remain 38:48:52 loss: 0.2989 Lr: 0.00461 [2024-02-18 04:26:26,338 INFO misc.py line 119 87073] Train: [23/100][348/1557] Data 0.005 (0.107) Batch 0.759 (1.153) Remain 38:46:33 loss: 0.3339 Lr: 0.00461 [2024-02-18 04:26:27,053 INFO misc.py line 119 87073] Train: [23/100][349/1557] Data 0.013 (0.107) Batch 0.721 (1.151) Remain 38:44:00 loss: 0.3334 Lr: 0.00461 [2024-02-18 04:26:28,313 INFO misc.py line 119 87073] Train: [23/100][350/1557] Data 0.006 (0.107) Batch 1.261 (1.152) Remain 38:44:38 loss: 0.1576 Lr: 0.00461 [2024-02-18 04:26:29,547 INFO misc.py line 119 87073] Train: [23/100][351/1557] Data 0.006 (0.106) Batch 1.217 (1.152) Remain 38:44:59 loss: 0.3731 Lr: 0.00461 [2024-02-18 04:26:30,510 INFO misc.py line 119 87073] Train: [23/100][352/1557] Data 0.023 (0.106) Batch 0.981 (1.151) Remain 38:43:59 loss: 1.1110 Lr: 0.00461 [2024-02-18 04:26:31,560 INFO misc.py line 119 87073] Train: [23/100][353/1557] Data 0.004 (0.106) Batch 1.050 (1.151) Remain 38:43:22 loss: 0.6171 Lr: 0.00461 [2024-02-18 04:26:32,407 INFO misc.py line 119 87073] Train: [23/100][354/1557] Data 0.004 (0.106) Batch 0.845 (1.150) Remain 38:41:35 loss: 0.2591 Lr: 0.00461 [2024-02-18 04:26:35,080 INFO misc.py line 119 87073] Train: [23/100][355/1557] Data 1.122 (0.109) Batch 2.674 (1.155) Remain 38:50:19 loss: 0.2776 Lr: 0.00461 [2024-02-18 04:26:35,851 INFO misc.py line 119 87073] Train: [23/100][356/1557] Data 0.004 (0.108) Batch 0.771 (1.154) Remain 38:48:06 loss: 0.6286 Lr: 0.00461 [2024-02-18 04:26:36,966 INFO misc.py line 119 87073] Train: [23/100][357/1557] Data 0.005 (0.108) Batch 1.112 (1.153) Remain 38:47:51 loss: 0.3244 Lr: 0.00461 [2024-02-18 04:26:37,875 INFO misc.py line 119 87073] Train: [23/100][358/1557] Data 0.007 (0.108) Batch 0.911 (1.153) Remain 38:46:27 loss: 0.6662 Lr: 0.00461 [2024-02-18 04:26:39,077 INFO misc.py line 119 87073] Train: [23/100][359/1557] Data 0.005 (0.107) Batch 1.198 (1.153) Remain 38:46:41 loss: 0.5381 Lr: 0.00461 [2024-02-18 04:26:39,952 INFO misc.py line 119 87073] Train: [23/100][360/1557] Data 0.011 (0.107) Batch 0.880 (1.152) Remain 38:45:07 loss: 0.6431 Lr: 0.00461 [2024-02-18 04:26:41,284 INFO misc.py line 119 87073] Train: [23/100][361/1557] Data 0.004 (0.107) Batch 1.329 (1.153) Remain 38:46:06 loss: 0.7065 Lr: 0.00461 [2024-02-18 04:26:42,080 INFO misc.py line 119 87073] Train: [23/100][362/1557] Data 0.008 (0.107) Batch 0.797 (1.152) Remain 38:44:05 loss: 0.3688 Lr: 0.00461 [2024-02-18 04:26:42,820 INFO misc.py line 119 87073] Train: [23/100][363/1557] Data 0.008 (0.106) Batch 0.742 (1.151) Remain 38:41:46 loss: 0.3378 Lr: 0.00461 [2024-02-18 04:26:44,110 INFO misc.py line 119 87073] Train: [23/100][364/1557] Data 0.004 (0.106) Batch 1.280 (1.151) Remain 38:42:28 loss: 0.4146 Lr: 0.00461 [2024-02-18 04:26:45,187 INFO misc.py line 119 87073] Train: [23/100][365/1557] Data 0.015 (0.106) Batch 1.083 (1.151) Remain 38:42:04 loss: 0.5166 Lr: 0.00461 [2024-02-18 04:26:46,168 INFO misc.py line 119 87073] Train: [23/100][366/1557] Data 0.009 (0.105) Batch 0.985 (1.150) Remain 38:41:08 loss: 0.2884 Lr: 0.00461 [2024-02-18 04:26:47,101 INFO misc.py line 119 87073] Train: [23/100][367/1557] Data 0.004 (0.105) Batch 0.933 (1.150) Remain 38:39:54 loss: 0.9458 Lr: 0.00461 [2024-02-18 04:26:48,037 INFO misc.py line 119 87073] Train: [23/100][368/1557] Data 0.004 (0.105) Batch 0.936 (1.149) Remain 38:38:42 loss: 0.1895 Lr: 0.00461 [2024-02-18 04:26:48,749 INFO misc.py line 119 87073] Train: [23/100][369/1557] Data 0.005 (0.105) Batch 0.701 (1.148) Remain 38:36:13 loss: 0.6087 Lr: 0.00461 [2024-02-18 04:26:49,504 INFO misc.py line 119 87073] Train: [23/100][370/1557] Data 0.016 (0.104) Batch 0.768 (1.147) Remain 38:34:06 loss: 0.2974 Lr: 0.00461 [2024-02-18 04:26:50,726 INFO misc.py line 119 87073] Train: [23/100][371/1557] Data 0.004 (0.104) Batch 1.220 (1.147) Remain 38:34:29 loss: 0.3546 Lr: 0.00461 [2024-02-18 04:26:51,624 INFO misc.py line 119 87073] Train: [23/100][372/1557] Data 0.005 (0.104) Batch 0.900 (1.146) Remain 38:33:07 loss: 0.2202 Lr: 0.00461 [2024-02-18 04:26:52,612 INFO misc.py line 119 87073] Train: [23/100][373/1557] Data 0.004 (0.104) Batch 0.987 (1.146) Remain 38:32:14 loss: 0.3053 Lr: 0.00461 [2024-02-18 04:26:53,449 INFO misc.py line 119 87073] Train: [23/100][374/1557] Data 0.005 (0.103) Batch 0.838 (1.145) Remain 38:30:32 loss: 0.3990 Lr: 0.00460 [2024-02-18 04:26:54,411 INFO misc.py line 119 87073] Train: [23/100][375/1557] Data 0.005 (0.103) Batch 0.961 (1.145) Remain 38:29:31 loss: 0.4759 Lr: 0.00460 [2024-02-18 04:26:55,172 INFO misc.py line 119 87073] Train: [23/100][376/1557] Data 0.006 (0.103) Batch 0.762 (1.144) Remain 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[2024-02-18 04:27:01,580 INFO misc.py line 119 87073] Train: [23/100][383/1557] Data 0.003 (0.101) Batch 0.739 (1.139) Remain 38:18:47 loss: 0.2606 Lr: 0.00460 [2024-02-18 04:27:02,361 INFO misc.py line 119 87073] Train: [23/100][384/1557] Data 0.010 (0.101) Batch 0.784 (1.138) Remain 38:16:53 loss: 0.6405 Lr: 0.00460 [2024-02-18 04:27:03,587 INFO misc.py line 119 87073] Train: [23/100][385/1557] Data 0.005 (0.101) Batch 1.222 (1.139) Remain 38:17:19 loss: 0.2911 Lr: 0.00460 [2024-02-18 04:27:04,817 INFO misc.py line 119 87073] Train: [23/100][386/1557] Data 0.010 (0.100) Batch 1.222 (1.139) Remain 38:17:44 loss: 0.6059 Lr: 0.00460 [2024-02-18 04:27:05,805 INFO misc.py line 119 87073] Train: [23/100][387/1557] Data 0.018 (0.100) Batch 0.999 (1.138) Remain 38:16:59 loss: 0.7686 Lr: 0.00460 [2024-02-18 04:27:06,701 INFO misc.py line 119 87073] Train: [23/100][388/1557] Data 0.007 (0.100) Batch 0.894 (1.138) Remain 38:15:41 loss: 0.6455 Lr: 0.00460 [2024-02-18 04:27:07,957 INFO misc.py line 119 87073] Train: [23/100][389/1557] Data 0.009 (0.100) Batch 1.259 (1.138) Remain 38:16:17 loss: 0.5700 Lr: 0.00460 [2024-02-18 04:27:08,728 INFO misc.py line 119 87073] Train: [23/100][390/1557] Data 0.007 (0.099) Batch 0.772 (1.137) Remain 38:14:22 loss: 0.3771 Lr: 0.00460 [2024-02-18 04:27:09,483 INFO misc.py line 119 87073] Train: [23/100][391/1557] Data 0.005 (0.099) Batch 0.755 (1.136) Remain 38:12:21 loss: 0.5046 Lr: 0.00460 [2024-02-18 04:27:10,677 INFO misc.py line 119 87073] Train: [23/100][392/1557] Data 0.005 (0.099) Batch 1.195 (1.136) Remain 38:12:39 loss: 0.4550 Lr: 0.00460 [2024-02-18 04:27:11,536 INFO misc.py line 119 87073] Train: [23/100][393/1557] Data 0.006 (0.099) Batch 0.860 (1.136) Remain 38:11:11 loss: 0.5313 Lr: 0.00460 [2024-02-18 04:27:12,637 INFO misc.py line 119 87073] Train: [23/100][394/1557] Data 0.004 (0.098) Batch 1.101 (1.136) Remain 38:10:59 loss: 0.7069 Lr: 0.00460 [2024-02-18 04:27:13,650 INFO misc.py line 119 87073] Train: 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Batch 0.929 (1.155) Remain 38:50:21 loss: 0.6406 Lr: 0.00460 [2024-02-18 04:27:29,428 INFO misc.py line 119 87073] Train: [23/100][402/1557] Data 0.006 (0.111) Batch 1.052 (1.155) Remain 38:49:48 loss: 0.4239 Lr: 0.00460 [2024-02-18 04:27:30,412 INFO misc.py line 119 87073] Train: [23/100][403/1557] Data 0.005 (0.111) Batch 0.984 (1.154) Remain 38:48:55 loss: 0.3540 Lr: 0.00460 [2024-02-18 04:27:31,130 INFO misc.py line 119 87073] Train: [23/100][404/1557] Data 0.006 (0.110) Batch 0.719 (1.153) Remain 38:46:43 loss: 0.2881 Lr: 0.00460 [2024-02-18 04:27:31,944 INFO misc.py line 119 87073] Train: [23/100][405/1557] Data 0.004 (0.110) Batch 0.807 (1.152) Remain 38:44:57 loss: 0.3649 Lr: 0.00460 [2024-02-18 04:27:33,152 INFO misc.py line 119 87073] Train: [23/100][406/1557] Data 0.011 (0.110) Batch 1.210 (1.153) Remain 38:45:14 loss: 0.1907 Lr: 0.00460 [2024-02-18 04:27:34,083 INFO misc.py line 119 87073] Train: [23/100][407/1557] Data 0.009 (0.110) Batch 0.936 (1.152) Remain 38:44:07 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line 119 87073] Train: [23/100][445/1557] Data 0.005 (0.101) Batch 0.864 (1.135) Remain 38:09:52 loss: 0.5596 Lr: 0.00460 [2024-02-18 04:28:11,331 INFO misc.py line 119 87073] Train: [23/100][446/1557] Data 0.004 (0.101) Batch 0.816 (1.135) Remain 38:08:23 loss: 0.2901 Lr: 0.00460 [2024-02-18 04:28:12,086 INFO misc.py line 119 87073] Train: [23/100][447/1557] Data 0.005 (0.100) Batch 0.756 (1.134) Remain 38:06:39 loss: 0.4188 Lr: 0.00460 [2024-02-18 04:28:13,305 INFO misc.py line 119 87073] Train: [23/100][448/1557] Data 0.005 (0.100) Batch 1.219 (1.134) Remain 38:07:01 loss: 0.2118 Lr: 0.00460 [2024-02-18 04:28:14,268 INFO misc.py line 119 87073] Train: [23/100][449/1557] Data 0.005 (0.100) Batch 0.963 (1.134) Remain 38:06:14 loss: 0.3017 Lr: 0.00460 [2024-02-18 04:28:15,324 INFO misc.py line 119 87073] Train: [23/100][450/1557] Data 0.004 (0.100) Batch 1.056 (1.134) Remain 38:05:51 loss: 0.7492 Lr: 0.00460 [2024-02-18 04:28:16,314 INFO misc.py line 119 87073] Train: 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87073] Train: [23/100][700/1557] Data 0.007 (0.108) Batch 1.339 (1.141) Remain 38:16:27 loss: 0.1953 Lr: 0.00460 [2024-02-18 04:33:04,861 INFO misc.py line 119 87073] Train: [23/100][701/1557] Data 0.006 (0.107) Batch 0.850 (1.141) Remain 38:15:36 loss: 0.5711 Lr: 0.00460 [2024-02-18 04:33:06,034 INFO misc.py line 119 87073] Train: [23/100][702/1557] Data 0.004 (0.107) Batch 1.165 (1.141) Remain 38:15:39 loss: 0.4592 Lr: 0.00460 [2024-02-18 04:33:07,017 INFO misc.py line 119 87073] Train: [23/100][703/1557] Data 0.012 (0.107) Batch 0.991 (1.141) Remain 38:15:12 loss: 0.4606 Lr: 0.00460 [2024-02-18 04:33:08,032 INFO misc.py line 119 87073] Train: [23/100][704/1557] Data 0.004 (0.107) Batch 1.009 (1.140) Remain 38:14:48 loss: 0.4843 Lr: 0.00460 [2024-02-18 04:33:10,561 INFO misc.py line 119 87073] Train: [23/100][705/1557] Data 0.742 (0.108) Batch 2.534 (1.142) Remain 38:18:47 loss: 0.3144 Lr: 0.00460 [2024-02-18 04:33:11,356 INFO misc.py line 119 87073] Train: [23/100][706/1557] Data 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line 119 87073] Train: [23/100][725/1557] Data 0.005 (0.105) Batch 0.961 (1.138) Remain 38:08:42 loss: 0.3777 Lr: 0.00459 [2024-02-18 04:33:30,707 INFO misc.py line 119 87073] Train: [23/100][726/1557] Data 0.004 (0.105) Batch 0.772 (1.137) Remain 38:07:40 loss: 0.5238 Lr: 0.00459 [2024-02-18 04:33:31,486 INFO misc.py line 119 87073] Train: [23/100][727/1557] Data 0.009 (0.105) Batch 0.785 (1.137) Remain 38:06:40 loss: 0.2688 Lr: 0.00459 [2024-02-18 04:33:32,638 INFO misc.py line 119 87073] Train: [23/100][728/1557] Data 0.004 (0.105) Batch 1.151 (1.137) Remain 38:06:41 loss: 0.2066 Lr: 0.00459 [2024-02-18 04:33:33,595 INFO misc.py line 119 87073] Train: [23/100][729/1557] Data 0.005 (0.104) Batch 0.959 (1.136) Remain 38:06:11 loss: 0.3973 Lr: 0.00459 [2024-02-18 04:33:34,560 INFO misc.py line 119 87073] Train: [23/100][730/1557] Data 0.004 (0.104) Batch 0.963 (1.136) Remain 38:05:41 loss: 0.6750 Lr: 0.00459 [2024-02-18 04:33:35,553 INFO misc.py line 119 87073] Train: 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Batch 0.954 (1.147) Remain 38:27:40 loss: 0.3750 Lr: 0.00459 [2024-02-18 04:33:51,503 INFO misc.py line 119 87073] Train: [23/100][738/1557] Data 0.004 (0.111) Batch 0.919 (1.147) Remain 38:27:02 loss: 0.1866 Lr: 0.00459 [2024-02-18 04:33:52,333 INFO misc.py line 119 87073] Train: [23/100][739/1557] Data 0.003 (0.111) Batch 0.827 (1.146) Remain 38:26:08 loss: 0.5604 Lr: 0.00459 [2024-02-18 04:33:53,093 INFO misc.py line 119 87073] Train: [23/100][740/1557] Data 0.007 (0.111) Batch 0.762 (1.146) Remain 38:25:04 loss: 0.7882 Lr: 0.00459 [2024-02-18 04:33:53,904 INFO misc.py line 119 87073] Train: [23/100][741/1557] Data 0.003 (0.111) Batch 0.811 (1.145) Remain 38:24:08 loss: 0.3923 Lr: 0.00459 [2024-02-18 04:33:55,168 INFO misc.py line 119 87073] Train: [23/100][742/1557] Data 0.004 (0.111) Batch 1.255 (1.145) Remain 38:24:25 loss: 0.1609 Lr: 0.00459 [2024-02-18 04:33:56,036 INFO misc.py line 119 87073] Train: [23/100][743/1557] Data 0.013 (0.111) Batch 0.876 (1.145) Remain 38:23:40 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Batch 1.042 (1.145) Remain 38:20:39 loss: 0.5096 Lr: 0.00459 [2024-02-18 04:35:57,995 INFO misc.py line 119 87073] Train: [23/100][850/1557] Data 0.005 (0.111) Batch 0.993 (1.144) Remain 38:20:16 loss: 0.4857 Lr: 0.00459 [2024-02-18 04:35:59,102 INFO misc.py line 119 87073] Train: [23/100][851/1557] Data 0.005 (0.111) Batch 1.108 (1.144) Remain 38:20:10 loss: 0.4673 Lr: 0.00459 [2024-02-18 04:35:59,860 INFO misc.py line 119 87073] Train: [23/100][852/1557] Data 0.005 (0.111) Batch 0.759 (1.144) Remain 38:19:14 loss: 0.5811 Lr: 0.00459 [2024-02-18 04:36:00,650 INFO misc.py line 119 87073] Train: [23/100][853/1557] Data 0.003 (0.111) Batch 0.779 (1.144) Remain 38:18:21 loss: 0.4587 Lr: 0.00459 [2024-02-18 04:36:01,895 INFO misc.py line 119 87073] Train: [23/100][854/1557] Data 0.015 (0.111) Batch 1.247 (1.144) Remain 38:18:35 loss: 0.2434 Lr: 0.00459 [2024-02-18 04:36:02,989 INFO misc.py line 119 87073] Train: [23/100][855/1557] Data 0.014 (0.111) Batch 1.098 (1.144) Remain 38:18:27 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Batch 0.871 (1.146) Remain 38:21:19 loss: 0.1407 Lr: 0.00459 [2024-02-18 04:37:02,778 INFO misc.py line 119 87073] Train: [23/100][906/1557] Data 0.004 (0.112) Batch 0.897 (1.145) Remain 38:20:45 loss: 0.5361 Lr: 0.00459 [2024-02-18 04:37:03,789 INFO misc.py line 119 87073] Train: [23/100][907/1557] Data 0.005 (0.112) Batch 1.012 (1.145) Remain 38:20:26 loss: 0.5660 Lr: 0.00459 [2024-02-18 04:37:04,548 INFO misc.py line 119 87073] Train: [23/100][908/1557] Data 0.004 (0.112) Batch 0.758 (1.145) Remain 38:19:33 loss: 0.5741 Lr: 0.00459 [2024-02-18 04:37:05,322 INFO misc.py line 119 87073] Train: [23/100][909/1557] Data 0.005 (0.112) Batch 0.773 (1.144) Remain 38:18:43 loss: 0.4428 Lr: 0.00459 [2024-02-18 04:37:06,574 INFO misc.py line 119 87073] Train: [23/100][910/1557] Data 0.006 (0.112) Batch 1.237 (1.144) Remain 38:18:54 loss: 0.1579 Lr: 0.00459 [2024-02-18 04:37:07,494 INFO misc.py line 119 87073] Train: [23/100][911/1557] Data 0.021 (0.111) Batch 0.937 (1.144) Remain 38:18:25 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[2024-02-18 04:37:38,217 INFO misc.py line 119 87073] Train: [23/100][943/1557] Data 0.005 (0.108) Batch 0.736 (1.138) Remain 38:05:13 loss: 0.4166 Lr: 0.00459 [2024-02-18 04:37:39,052 INFO misc.py line 119 87073] Train: [23/100][944/1557] Data 0.006 (0.108) Batch 0.831 (1.138) Remain 38:04:33 loss: 0.2294 Lr: 0.00459 [2024-02-18 04:37:40,289 INFO misc.py line 119 87073] Train: [23/100][945/1557] Data 0.009 (0.108) Batch 1.237 (1.138) Remain 38:04:44 loss: 0.3479 Lr: 0.00459 [2024-02-18 04:37:41,297 INFO misc.py line 119 87073] Train: [23/100][946/1557] Data 0.010 (0.108) Batch 1.008 (1.137) Remain 38:04:27 loss: 0.6846 Lr: 0.00459 [2024-02-18 04:37:42,165 INFO misc.py line 119 87073] Train: [23/100][947/1557] Data 0.010 (0.108) Batch 0.873 (1.137) Remain 38:03:52 loss: 1.0051 Lr: 0.00459 [2024-02-18 04:37:43,159 INFO misc.py line 119 87073] Train: [23/100][948/1557] Data 0.005 (0.107) Batch 0.994 (1.137) Remain 38:03:33 loss: 0.7178 Lr: 0.00459 [2024-02-18 04:37:44,178 INFO misc.py line 119 87073] Train: [23/100][949/1557] Data 0.004 (0.107) Batch 1.020 (1.137) Remain 38:03:17 loss: 0.3484 Lr: 0.00459 [2024-02-18 04:37:44,980 INFO misc.py line 119 87073] Train: [23/100][950/1557] Data 0.004 (0.107) Batch 0.801 (1.137) Remain 38:02:33 loss: 0.2002 Lr: 0.00459 [2024-02-18 04:37:45,757 INFO misc.py line 119 87073] Train: [23/100][951/1557] Data 0.004 (0.107) Batch 0.776 (1.136) Remain 38:01:46 loss: 0.5267 Lr: 0.00459 [2024-02-18 04:37:46,840 INFO misc.py line 119 87073] Train: [23/100][952/1557] Data 0.004 (0.107) Batch 1.079 (1.136) Remain 38:01:37 loss: 0.2829 Lr: 0.00459 [2024-02-18 04:37:47,863 INFO misc.py line 119 87073] Train: [23/100][953/1557] Data 0.008 (0.107) Batch 1.026 (1.136) Remain 38:01:22 loss: 0.4742 Lr: 0.00459 [2024-02-18 04:37:48,879 INFO misc.py line 119 87073] Train: [23/100][954/1557] Data 0.006 (0.107) Batch 1.017 (1.136) Remain 38:01:06 loss: 0.6788 Lr: 0.00459 [2024-02-18 04:37:49,895 INFO misc.py line 119 87073] Train: 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Batch 0.846 (1.145) Remain 38:19:16 loss: 0.7979 Lr: 0.00459 [2024-02-18 04:38:06,620 INFO misc.py line 119 87073] Train: [23/100][962/1557] Data 0.005 (0.112) Batch 1.056 (1.145) Remain 38:19:03 loss: 0.5288 Lr: 0.00459 [2024-02-18 04:38:07,542 INFO misc.py line 119 87073] Train: [23/100][963/1557] Data 0.011 (0.112) Batch 0.927 (1.145) Remain 38:18:35 loss: 0.7346 Lr: 0.00459 [2024-02-18 04:38:08,358 INFO misc.py line 119 87073] Train: [23/100][964/1557] Data 0.006 (0.111) Batch 0.816 (1.144) Remain 38:17:53 loss: 0.4303 Lr: 0.00459 [2024-02-18 04:38:09,142 INFO misc.py line 119 87073] Train: [23/100][965/1557] Data 0.006 (0.111) Batch 0.772 (1.144) Remain 38:17:05 loss: 0.5757 Lr: 0.00459 [2024-02-18 04:38:10,360 INFO misc.py line 119 87073] Train: [23/100][966/1557] Data 0.018 (0.111) Batch 1.220 (1.144) Remain 38:17:13 loss: 0.3078 Lr: 0.00459 [2024-02-18 04:38:11,270 INFO misc.py line 119 87073] Train: [23/100][967/1557] Data 0.016 (0.111) Batch 0.922 (1.144) Remain 38:16:44 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87073] Train: [23/100][980/1557] Data 0.004 (0.110) Batch 1.365 (1.141) Remain 38:10:59 loss: 0.3113 Lr: 0.00459 [2024-02-18 04:38:24,490 INFO misc.py line 119 87073] Train: [23/100][981/1557] Data 0.016 (0.110) Batch 1.017 (1.141) Remain 38:10:43 loss: 1.2260 Lr: 0.00459 [2024-02-18 04:38:25,464 INFO misc.py line 119 87073] Train: [23/100][982/1557] Data 0.015 (0.109) Batch 0.985 (1.141) Remain 38:10:23 loss: 0.6734 Lr: 0.00459 [2024-02-18 04:38:26,398 INFO misc.py line 119 87073] Train: [23/100][983/1557] Data 0.003 (0.109) Batch 0.933 (1.141) Remain 38:09:56 loss: 0.4835 Lr: 0.00459 [2024-02-18 04:38:27,363 INFO misc.py line 119 87073] Train: [23/100][984/1557] Data 0.005 (0.109) Batch 0.965 (1.140) Remain 38:09:33 loss: 0.4231 Lr: 0.00459 [2024-02-18 04:38:28,140 INFO misc.py line 119 87073] Train: [23/100][985/1557] Data 0.004 (0.109) Batch 0.770 (1.140) Remain 38:08:47 loss: 0.2410 Lr: 0.00459 [2024-02-18 04:38:28,966 INFO misc.py line 119 87073] Train: [23/100][986/1557] Data 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Remain 38:16:57 loss: 0.2718 Lr: 0.00458 [2024-02-18 04:43:27,820 INFO misc.py line 119 87073] Train: [23/100][1241/1557] Data 0.008 (0.112) Batch 0.927 (1.146) Remain 38:16:34 loss: 0.2974 Lr: 0.00458 [2024-02-18 04:43:28,808 INFO misc.py line 119 87073] Train: [23/100][1242/1557] Data 0.025 (0.112) Batch 1.009 (1.146) Remain 38:16:20 loss: 0.2788 Lr: 0.00458 [2024-02-18 04:43:29,836 INFO misc.py line 119 87073] Train: [23/100][1243/1557] Data 0.005 (0.112) Batch 1.028 (1.146) Remain 38:16:07 loss: 0.7009 Lr: 0.00458 [2024-02-18 04:43:30,566 INFO misc.py line 119 87073] Train: [23/100][1244/1557] Data 0.005 (0.112) Batch 0.728 (1.146) Remain 38:15:26 loss: 0.5741 Lr: 0.00458 [2024-02-18 04:43:31,312 INFO misc.py line 119 87073] Train: [23/100][1245/1557] Data 0.006 (0.112) Batch 0.735 (1.145) Remain 38:14:45 loss: 0.2709 Lr: 0.00458 [2024-02-18 04:43:32,457 INFO misc.py line 119 87073] Train: [23/100][1246/1557] Data 0.017 (0.112) Batch 1.152 (1.145) Remain 38:14:44 loss: 0.2450 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1253/1557] Data 0.005 (0.111) Batch 1.356 (1.144) Remain 38:12:20 loss: 0.2209 Lr: 0.00458 [2024-02-18 04:43:39,888 INFO misc.py line 119 87073] Train: [23/100][1254/1557] Data 0.004 (0.111) Batch 0.830 (1.144) Remain 38:11:49 loss: 0.4896 Lr: 0.00458 [2024-02-18 04:43:40,898 INFO misc.py line 119 87073] Train: [23/100][1255/1557] Data 0.005 (0.111) Batch 1.010 (1.144) Remain 38:11:35 loss: 0.7961 Lr: 0.00458 [2024-02-18 04:43:41,851 INFO misc.py line 119 87073] Train: [23/100][1256/1557] Data 0.005 (0.111) Batch 0.952 (1.144) Remain 38:11:16 loss: 0.1002 Lr: 0.00458 [2024-02-18 04:43:42,845 INFO misc.py line 119 87073] Train: [23/100][1257/1557] Data 0.005 (0.111) Batch 0.993 (1.144) Remain 38:11:00 loss: 0.7958 Lr: 0.00458 [2024-02-18 04:43:43,601 INFO misc.py line 119 87073] Train: [23/100][1258/1557] Data 0.006 (0.111) Batch 0.757 (1.143) Remain 38:10:22 loss: 0.5489 Lr: 0.00458 [2024-02-18 04:43:44,372 INFO misc.py line 119 87073] Train: [23/100][1259/1557] Data 0.005 (0.111) Batch 0.767 (1.143) Remain 38:09:45 loss: 0.3181 Lr: 0.00458 [2024-02-18 04:43:45,636 INFO misc.py line 119 87073] Train: [23/100][1260/1557] Data 0.009 (0.111) Batch 1.263 (1.143) Remain 38:09:55 loss: 0.2577 Lr: 0.00458 [2024-02-18 04:43:46,506 INFO misc.py line 119 87073] Train: [23/100][1261/1557] Data 0.010 (0.111) Batch 0.876 (1.143) Remain 38:09:28 loss: 0.3586 Lr: 0.00458 [2024-02-18 04:43:47,529 INFO misc.py line 119 87073] Train: [23/100][1262/1557] Data 0.004 (0.111) Batch 1.023 (1.143) Remain 38:09:16 loss: 0.8345 Lr: 0.00458 [2024-02-18 04:43:48,451 INFO misc.py line 119 87073] Train: [23/100][1263/1557] Data 0.004 (0.110) Batch 0.921 (1.143) Remain 38:08:53 loss: 0.5083 Lr: 0.00458 [2024-02-18 04:43:49,554 INFO misc.py line 119 87073] Train: [23/100][1264/1557] Data 0.005 (0.110) Batch 1.103 (1.143) Remain 38:08:48 loss: 0.3228 Lr: 0.00458 [2024-02-18 04:43:50,337 INFO misc.py line 119 87073] Train: [23/100][1265/1557] Data 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Remain 38:06:41 loss: 0.4037 Lr: 0.00458 [2024-02-18 04:43:57,008 INFO misc.py line 119 87073] Train: [23/100][1272/1557] Data 0.006 (0.110) Batch 0.721 (1.141) Remain 38:06:00 loss: 0.3856 Lr: 0.00458 [2024-02-18 04:43:57,793 INFO misc.py line 119 87073] Train: [23/100][1273/1557] Data 0.004 (0.110) Batch 0.768 (1.141) Remain 38:05:23 loss: 0.4172 Lr: 0.00458 [2024-02-18 04:43:58,944 INFO misc.py line 119 87073] Train: [23/100][1274/1557] Data 0.020 (0.110) Batch 1.166 (1.141) Remain 38:05:24 loss: 0.4192 Lr: 0.00458 [2024-02-18 04:43:59,843 INFO misc.py line 119 87073] Train: [23/100][1275/1557] Data 0.006 (0.110) Batch 0.898 (1.141) Remain 38:05:00 loss: 0.6329 Lr: 0.00458 [2024-02-18 04:44:00,735 INFO misc.py line 119 87073] Train: [23/100][1276/1557] Data 0.006 (0.109) Batch 0.894 (1.141) Remain 38:04:36 loss: 0.6743 Lr: 0.00458 [2024-02-18 04:44:01,729 INFO misc.py line 119 87073] Train: [23/100][1277/1557] Data 0.004 (0.109) Batch 0.993 (1.141) Remain 38:04:21 loss: 0.8222 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1284/1557] Data 0.005 (0.109) Batch 0.990 (1.139) Remain 38:01:56 loss: 0.3100 Lr: 0.00458 [2024-02-18 04:44:09,145 INFO misc.py line 119 87073] Train: [23/100][1285/1557] Data 0.004 (0.109) Batch 0.895 (1.139) Remain 38:01:32 loss: 0.6720 Lr: 0.00458 [2024-02-18 04:44:09,964 INFO misc.py line 119 87073] Train: [23/100][1286/1557] Data 0.005 (0.109) Batch 0.811 (1.139) Remain 38:01:00 loss: 0.9843 Lr: 0.00458 [2024-02-18 04:44:10,736 INFO misc.py line 119 87073] Train: [23/100][1287/1557] Data 0.014 (0.109) Batch 0.781 (1.139) Remain 38:00:25 loss: 0.2525 Lr: 0.00458 [2024-02-18 04:44:12,001 INFO misc.py line 119 87073] Train: [23/100][1288/1557] Data 0.004 (0.108) Batch 1.260 (1.139) Remain 38:00:35 loss: 0.5213 Lr: 0.00458 [2024-02-18 04:44:13,216 INFO misc.py line 119 87073] Train: [23/100][1289/1557] Data 0.010 (0.108) Batch 1.212 (1.139) Remain 38:00:41 loss: 0.8294 Lr: 0.00458 [2024-02-18 04:44:14,095 INFO misc.py line 119 87073] Train: [23/100][1290/1557] Data 0.013 (0.108) Batch 0.888 (1.139) Remain 38:00:16 loss: 0.6273 Lr: 0.00458 [2024-02-18 04:44:15,180 INFO misc.py line 119 87073] Train: [23/100][1291/1557] Data 0.004 (0.108) Batch 1.085 (1.139) Remain 38:00:10 loss: 0.9078 Lr: 0.00458 [2024-02-18 04:44:16,128 INFO misc.py line 119 87073] Train: [23/100][1292/1557] Data 0.004 (0.108) Batch 0.947 (1.138) Remain 37:59:51 loss: 0.5331 Lr: 0.00458 [2024-02-18 04:44:16,884 INFO misc.py line 119 87073] Train: [23/100][1293/1557] Data 0.005 (0.108) Batch 0.741 (1.138) Remain 37:59:13 loss: 0.4619 Lr: 0.00458 [2024-02-18 04:44:17,662 INFO misc.py line 119 87073] Train: [23/100][1294/1557] Data 0.019 (0.108) Batch 0.793 (1.138) Remain 37:58:40 loss: 0.3330 Lr: 0.00458 [2024-02-18 04:44:28,481 INFO misc.py line 119 87073] Train: [23/100][1295/1557] Data 6.242 (0.113) Batch 10.819 (1.145) Remain 38:13:39 loss: 0.3175 Lr: 0.00458 [2024-02-18 04:44:29,548 INFO misc.py line 119 87073] Train: [23/100][1296/1557] Data 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Remain 38:11:39 loss: 0.1570 Lr: 0.00458 [2024-02-18 04:44:36,343 INFO misc.py line 119 87073] Train: [23/100][1303/1557] Data 0.004 (0.112) Batch 1.049 (1.144) Remain 38:11:29 loss: 0.4002 Lr: 0.00458 [2024-02-18 04:44:37,549 INFO misc.py line 119 87073] Train: [23/100][1304/1557] Data 0.006 (0.112) Batch 1.202 (1.144) Remain 38:11:34 loss: 0.4441 Lr: 0.00458 [2024-02-18 04:44:38,478 INFO misc.py line 119 87073] Train: [23/100][1305/1557] Data 0.010 (0.112) Batch 0.936 (1.144) Remain 38:11:13 loss: 0.1431 Lr: 0.00458 [2024-02-18 04:44:39,670 INFO misc.py line 119 87073] Train: [23/100][1306/1557] Data 0.003 (0.112) Batch 1.184 (1.144) Remain 38:11:16 loss: 0.6376 Lr: 0.00458 [2024-02-18 04:44:40,432 INFO misc.py line 119 87073] Train: [23/100][1307/1557] Data 0.011 (0.112) Batch 0.769 (1.144) Remain 38:10:40 loss: 0.2747 Lr: 0.00458 [2024-02-18 04:44:41,147 INFO misc.py line 119 87073] Train: [23/100][1308/1557] Data 0.004 (0.112) Batch 0.712 (1.144) Remain 38:09:59 loss: 0.2016 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1315/1557] Data 0.013 (0.111) Batch 0.802 (1.143) Remain 38:07:35 loss: 0.4855 Lr: 0.00458 [2024-02-18 04:44:48,950 INFO misc.py line 119 87073] Train: [23/100][1316/1557] Data 0.006 (0.111) Batch 1.275 (1.143) Remain 38:07:46 loss: 0.3963 Lr: 0.00458 [2024-02-18 04:44:50,143 INFO misc.py line 119 87073] Train: [23/100][1317/1557] Data 0.010 (0.111) Batch 1.188 (1.143) Remain 38:07:49 loss: 0.6282 Lr: 0.00458 [2024-02-18 04:44:51,098 INFO misc.py line 119 87073] Train: [23/100][1318/1557] Data 0.015 (0.111) Batch 0.966 (1.143) Remain 38:07:32 loss: 0.5943 Lr: 0.00458 [2024-02-18 04:44:52,057 INFO misc.py line 119 87073] Train: [23/100][1319/1557] Data 0.004 (0.111) Batch 0.958 (1.142) Remain 38:07:14 loss: 0.7897 Lr: 0.00458 [2024-02-18 04:44:52,892 INFO misc.py line 119 87073] Train: [23/100][1320/1557] Data 0.005 (0.111) Batch 0.836 (1.142) Remain 38:06:45 loss: 0.3796 Lr: 0.00458 [2024-02-18 04:44:53,688 INFO misc.py line 119 87073] Train: [23/100][1321/1557] Data 0.005 (0.111) Batch 0.793 (1.142) Remain 38:06:12 loss: 0.3651 Lr: 0.00458 [2024-02-18 04:44:54,481 INFO misc.py line 119 87073] Train: [23/100][1322/1557] Data 0.007 (0.111) Batch 0.795 (1.142) Remain 38:05:39 loss: 0.1965 Lr: 0.00458 [2024-02-18 04:44:55,657 INFO misc.py line 119 87073] Train: [23/100][1323/1557] Data 0.004 (0.110) Batch 1.176 (1.142) Remain 38:05:41 loss: 0.1565 Lr: 0.00458 [2024-02-18 04:44:56,669 INFO misc.py line 119 87073] Train: [23/100][1324/1557] Data 0.004 (0.110) Batch 1.011 (1.142) Remain 38:05:28 loss: 0.2497 Lr: 0.00458 [2024-02-18 04:44:57,672 INFO misc.py line 119 87073] Train: [23/100][1325/1557] Data 0.006 (0.110) Batch 1.002 (1.141) Remain 38:05:15 loss: 0.4745 Lr: 0.00458 [2024-02-18 04:44:58,605 INFO misc.py line 119 87073] Train: [23/100][1326/1557] Data 0.006 (0.110) Batch 0.935 (1.141) Remain 38:04:55 loss: 1.2081 Lr: 0.00458 [2024-02-18 04:44:59,486 INFO misc.py line 119 87073] Train: [23/100][1327/1557] Data 0.004 (0.110) Batch 0.882 (1.141) Remain 38:04:30 loss: 0.4591 Lr: 0.00458 [2024-02-18 04:45:00,240 INFO misc.py line 119 87073] Train: [23/100][1328/1557] Data 0.004 (0.110) Batch 0.752 (1.141) Remain 38:03:54 loss: 0.4653 Lr: 0.00458 [2024-02-18 04:45:01,245 INFO misc.py line 119 87073] Train: [23/100][1329/1557] Data 0.005 (0.110) Batch 1.003 (1.141) Remain 38:03:40 loss: 0.3317 Lr: 0.00458 [2024-02-18 04:45:02,367 INFO misc.py line 119 87073] Train: [23/100][1330/1557] Data 0.007 (0.110) Batch 1.117 (1.141) Remain 38:03:37 loss: 0.3088 Lr: 0.00458 [2024-02-18 04:45:03,354 INFO misc.py line 119 87073] Train: [23/100][1331/1557] Data 0.012 (0.110) Batch 0.994 (1.141) Remain 38:03:22 loss: 0.8508 Lr: 0.00458 [2024-02-18 04:45:04,252 INFO misc.py line 119 87073] Train: [23/100][1332/1557] Data 0.005 (0.110) Batch 0.899 (1.140) Remain 38:02:59 loss: 0.5382 Lr: 0.00458 [2024-02-18 04:45:05,149 INFO misc.py line 119 87073] Train: [23/100][1333/1557] Data 0.005 (0.110) Batch 0.897 (1.140) Remain 38:02:36 loss: 0.6707 Lr: 0.00458 [2024-02-18 04:45:06,158 INFO misc.py line 119 87073] Train: [23/100][1334/1557] Data 0.005 (0.110) Batch 1.006 (1.140) Remain 38:02:23 loss: 0.3146 Lr: 0.00458 [2024-02-18 04:45:06,917 INFO misc.py line 119 87073] Train: [23/100][1335/1557] Data 0.008 (0.110) Batch 0.760 (1.140) Remain 38:01:47 loss: 0.2433 Lr: 0.00458 [2024-02-18 04:45:07,711 INFO misc.py line 119 87073] Train: [23/100][1336/1557] Data 0.007 (0.109) Batch 0.796 (1.140) Remain 38:01:15 loss: 0.1773 Lr: 0.00458 [2024-02-18 04:45:09,047 INFO misc.py line 119 87073] Train: [23/100][1337/1557] Data 0.005 (0.109) Batch 1.336 (1.140) Remain 38:01:32 loss: 0.3354 Lr: 0.00458 [2024-02-18 04:45:10,036 INFO misc.py line 119 87073] Train: [23/100][1338/1557] Data 0.006 (0.109) Batch 0.990 (1.140) Remain 38:01:17 loss: 0.5905 Lr: 0.00458 [2024-02-18 04:45:11,080 INFO misc.py line 119 87073] Train: [23/100][1339/1557] Data 0.005 (0.109) Batch 1.044 (1.140) Remain 38:01:07 loss: 0.2449 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1346/1557] Data 0.004 (0.109) Batch 0.806 (1.138) Remain 37:58:26 loss: 0.6227 Lr: 0.00458 [2024-02-18 04:45:18,668 INFO misc.py line 119 87073] Train: [23/100][1347/1557] Data 0.009 (0.109) Batch 1.318 (1.138) Remain 37:58:41 loss: 0.3486 Lr: 0.00458 [2024-02-18 04:45:19,556 INFO misc.py line 119 87073] Train: [23/100][1348/1557] Data 0.014 (0.109) Batch 0.899 (1.138) Remain 37:58:18 loss: 0.2089 Lr: 0.00458 [2024-02-18 04:45:20,353 INFO misc.py line 119 87073] Train: [23/100][1349/1557] Data 0.004 (0.108) Batch 0.794 (1.138) Remain 37:57:47 loss: 0.3010 Lr: 0.00458 [2024-02-18 04:45:21,154 INFO misc.py line 119 87073] Train: [23/100][1350/1557] Data 0.006 (0.108) Batch 0.794 (1.138) Remain 37:57:15 loss: 0.4348 Lr: 0.00458 [2024-02-18 04:45:32,838 INFO misc.py line 119 87073] Train: [23/100][1351/1557] Data 6.462 (0.113) Batch 11.693 (1.146) Remain 38:12:54 loss: 0.2633 Lr: 0.00458 [2024-02-18 04:45:33,892 INFO misc.py line 119 87073] Train: [23/100][1352/1557] Data 0.005 (0.113) Batch 1.053 (1.145) Remain 38:12:45 loss: 1.0438 Lr: 0.00458 [2024-02-18 04:45:34,929 INFO misc.py line 119 87073] Train: [23/100][1353/1557] Data 0.006 (0.113) Batch 1.037 (1.145) Remain 38:12:34 loss: 0.6157 Lr: 0.00458 [2024-02-18 04:45:35,913 INFO misc.py line 119 87073] Train: [23/100][1354/1557] Data 0.005 (0.113) Batch 0.984 (1.145) Remain 38:12:18 loss: 0.9955 Lr: 0.00458 [2024-02-18 04:45:36,820 INFO misc.py line 119 87073] Train: [23/100][1355/1557] Data 0.006 (0.113) Batch 0.907 (1.145) Remain 38:11:56 loss: 0.3836 Lr: 0.00458 [2024-02-18 04:45:37,577 INFO misc.py line 119 87073] Train: [23/100][1356/1557] Data 0.004 (0.113) Batch 0.748 (1.145) Remain 38:11:20 loss: 0.5420 Lr: 0.00458 [2024-02-18 04:45:38,303 INFO misc.py line 119 87073] Train: [23/100][1357/1557] Data 0.014 (0.113) Batch 0.736 (1.145) Remain 38:10:42 loss: 0.3602 Lr: 0.00458 [2024-02-18 04:45:39,537 INFO misc.py line 119 87073] Train: [23/100][1358/1557] Data 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Remain 38:08:30 loss: 0.7793 Lr: 0.00458 [2024-02-18 04:45:46,054 INFO misc.py line 119 87073] Train: [23/100][1365/1557] Data 0.021 (0.112) Batch 1.145 (1.143) Remain 38:08:29 loss: 0.4118 Lr: 0.00458 [2024-02-18 04:45:47,008 INFO misc.py line 119 87073] Train: [23/100][1366/1557] Data 0.009 (0.112) Batch 0.959 (1.143) Remain 38:08:11 loss: 0.7122 Lr: 0.00458 [2024-02-18 04:45:47,826 INFO misc.py line 119 87073] Train: [23/100][1367/1557] Data 0.004 (0.112) Batch 0.817 (1.143) Remain 38:07:42 loss: 0.4964 Lr: 0.00458 [2024-02-18 04:45:48,949 INFO misc.py line 119 87073] Train: [23/100][1368/1557] Data 0.005 (0.112) Batch 1.121 (1.143) Remain 38:07:38 loss: 0.9356 Lr: 0.00458 [2024-02-18 04:45:49,870 INFO misc.py line 119 87073] Train: [23/100][1369/1557] Data 0.007 (0.112) Batch 0.921 (1.143) Remain 38:07:18 loss: 0.3814 Lr: 0.00458 [2024-02-18 04:45:50,641 INFO misc.py line 119 87073] Train: [23/100][1370/1557] Data 0.007 (0.112) Batch 0.773 (1.143) Remain 38:06:44 loss: 0.3793 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1377/1557] Data 0.004 (0.111) Batch 0.748 (1.142) Remain 38:04:57 loss: 0.5523 Lr: 0.00458 [2024-02-18 04:45:58,226 INFO misc.py line 119 87073] Train: [23/100][1378/1557] Data 0.005 (0.111) Batch 0.725 (1.142) Remain 38:04:19 loss: 0.5198 Lr: 0.00458 [2024-02-18 04:45:59,362 INFO misc.py line 119 87073] Train: [23/100][1379/1557] Data 0.004 (0.111) Batch 1.137 (1.142) Remain 38:04:18 loss: 0.1509 Lr: 0.00458 [2024-02-18 04:46:00,364 INFO misc.py line 119 87073] Train: [23/100][1380/1557] Data 0.003 (0.111) Batch 1.002 (1.141) Remain 38:04:04 loss: 0.8619 Lr: 0.00458 [2024-02-18 04:46:01,365 INFO misc.py line 119 87073] Train: [23/100][1381/1557] Data 0.004 (0.111) Batch 1.000 (1.141) Remain 38:03:51 loss: 0.4786 Lr: 0.00458 [2024-02-18 04:46:02,325 INFO misc.py line 119 87073] Train: [23/100][1382/1557] Data 0.004 (0.111) Batch 0.960 (1.141) Remain 38:03:34 loss: 0.2712 Lr: 0.00458 [2024-02-18 04:46:03,397 INFO misc.py line 119 87073] Train: [23/100][1383/1557] Data 0.004 (0.111) Batch 1.071 (1.141) Remain 38:03:27 loss: 0.6503 Lr: 0.00458 [2024-02-18 04:46:04,124 INFO misc.py line 119 87073] Train: [23/100][1384/1557] Data 0.005 (0.111) Batch 0.727 (1.141) Remain 38:02:50 loss: 0.3721 Lr: 0.00458 [2024-02-18 04:46:04,899 INFO misc.py line 119 87073] Train: [23/100][1385/1557] Data 0.005 (0.110) Batch 0.767 (1.141) Remain 38:02:16 loss: 0.8322 Lr: 0.00458 [2024-02-18 04:46:06,072 INFO misc.py line 119 87073] Train: [23/100][1386/1557] Data 0.013 (0.110) Batch 1.169 (1.141) Remain 38:02:17 loss: 0.4862 Lr: 0.00458 [2024-02-18 04:46:07,024 INFO misc.py line 119 87073] Train: [23/100][1387/1557] Data 0.018 (0.110) Batch 0.966 (1.140) Remain 38:02:01 loss: 0.5138 Lr: 0.00458 [2024-02-18 04:46:08,151 INFO misc.py line 119 87073] Train: [23/100][1388/1557] Data 0.004 (0.110) Batch 1.123 (1.140) Remain 38:01:58 loss: 0.6244 Lr: 0.00458 [2024-02-18 04:46:08,992 INFO misc.py line 119 87073] Train: [23/100][1389/1557] Data 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Remain 37:59:58 loss: 0.2463 Lr: 0.00458 [2024-02-18 04:46:15,793 INFO misc.py line 119 87073] Train: [23/100][1396/1557] Data 0.004 (0.110) Batch 0.968 (1.139) Remain 37:59:42 loss: 0.3345 Lr: 0.00458 [2024-02-18 04:46:16,708 INFO misc.py line 119 87073] Train: [23/100][1397/1557] Data 0.004 (0.110) Batch 0.913 (1.139) Remain 37:59:21 loss: 0.3854 Lr: 0.00458 [2024-02-18 04:46:17,477 INFO misc.py line 119 87073] Train: [23/100][1398/1557] Data 0.005 (0.110) Batch 0.771 (1.139) Remain 37:58:48 loss: 0.2910 Lr: 0.00458 [2024-02-18 04:46:18,229 INFO misc.py line 119 87073] Train: [23/100][1399/1557] Data 0.005 (0.109) Batch 0.742 (1.139) Remain 37:58:13 loss: 0.4899 Lr: 0.00458 [2024-02-18 04:46:19,298 INFO misc.py line 119 87073] Train: [23/100][1400/1557] Data 0.014 (0.109) Batch 1.072 (1.139) Remain 37:58:06 loss: 0.2955 Lr: 0.00458 [2024-02-18 04:46:20,434 INFO misc.py line 119 87073] Train: [23/100][1401/1557] Data 0.010 (0.109) Batch 1.134 (1.139) Remain 37:58:05 loss: 0.2767 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1408/1557] Data 0.005 (0.114) Batch 1.090 (1.147) Remain 38:14:16 loss: 0.4056 Lr: 0.00457 [2024-02-18 04:46:40,831 INFO misc.py line 119 87073] Train: [23/100][1409/1557] Data 0.005 (0.114) Batch 0.973 (1.147) Remain 38:14:00 loss: 0.4366 Lr: 0.00457 [2024-02-18 04:46:41,824 INFO misc.py line 119 87073] Train: [23/100][1410/1557] Data 0.005 (0.114) Batch 0.993 (1.147) Remain 38:13:46 loss: 0.8187 Lr: 0.00457 [2024-02-18 04:46:42,692 INFO misc.py line 119 87073] Train: [23/100][1411/1557] Data 0.004 (0.113) Batch 0.867 (1.146) Remain 38:13:21 loss: 0.3772 Lr: 0.00457 [2024-02-18 04:46:43,472 INFO misc.py line 119 87073] Train: [23/100][1412/1557] Data 0.006 (0.113) Batch 0.780 (1.146) Remain 38:12:48 loss: 0.4682 Lr: 0.00457 [2024-02-18 04:46:44,253 INFO misc.py line 119 87073] Train: [23/100][1413/1557] Data 0.005 (0.113) Batch 0.781 (1.146) Remain 38:12:16 loss: 0.2340 Lr: 0.00457 [2024-02-18 04:46:45,526 INFO misc.py line 119 87073] Train: [23/100][1414/1557] Data 0.005 (0.113) Batch 1.258 (1.146) Remain 38:12:25 loss: 0.1412 Lr: 0.00457 [2024-02-18 04:46:46,488 INFO misc.py line 119 87073] Train: [23/100][1415/1557] Data 0.021 (0.113) Batch 0.977 (1.146) Remain 38:12:09 loss: 0.8197 Lr: 0.00457 [2024-02-18 04:46:47,415 INFO misc.py line 119 87073] Train: [23/100][1416/1557] Data 0.004 (0.113) Batch 0.926 (1.146) Remain 38:11:49 loss: 1.7197 Lr: 0.00457 [2024-02-18 04:46:48,480 INFO misc.py line 119 87073] Train: [23/100][1417/1557] Data 0.006 (0.113) Batch 1.064 (1.146) Remain 38:11:41 loss: 0.4916 Lr: 0.00457 [2024-02-18 04:46:49,415 INFO misc.py line 119 87073] Train: [23/100][1418/1557] Data 0.006 (0.113) Batch 0.938 (1.145) Remain 38:11:22 loss: 0.4747 Lr: 0.00457 [2024-02-18 04:46:50,128 INFO misc.py line 119 87073] Train: [23/100][1419/1557] Data 0.003 (0.113) Batch 0.706 (1.145) Remain 38:10:44 loss: 0.5614 Lr: 0.00457 [2024-02-18 04:46:50,918 INFO misc.py line 119 87073] Train: [23/100][1420/1557] Data 0.010 (0.113) Batch 0.794 (1.145) Remain 38:10:13 loss: 0.3018 Lr: 0.00457 [2024-02-18 04:46:52,074 INFO misc.py line 119 87073] Train: [23/100][1421/1557] Data 0.006 (0.113) Batch 1.158 (1.145) Remain 38:10:13 loss: 0.1865 Lr: 0.00457 [2024-02-18 04:46:53,264 INFO misc.py line 119 87073] Train: [23/100][1422/1557] Data 0.004 (0.113) Batch 1.182 (1.145) Remain 38:10:15 loss: 0.3576 Lr: 0.00457 [2024-02-18 04:46:54,156 INFO misc.py line 119 87073] Train: [23/100][1423/1557] Data 0.013 (0.113) Batch 0.900 (1.145) Remain 38:09:53 loss: 0.3720 Lr: 0.00457 [2024-02-18 04:46:55,218 INFO misc.py line 119 87073] Train: [23/100][1424/1557] Data 0.005 (0.113) Batch 1.060 (1.145) Remain 38:09:45 loss: 0.4404 Lr: 0.00457 [2024-02-18 04:46:56,251 INFO misc.py line 119 87073] Train: [23/100][1425/1557] Data 0.007 (0.112) Batch 1.035 (1.145) Remain 38:09:35 loss: 0.3962 Lr: 0.00457 [2024-02-18 04:46:57,036 INFO misc.py line 119 87073] Train: [23/100][1426/1557] Data 0.004 (0.112) Batch 0.785 (1.144) Remain 38:09:03 loss: 0.4637 Lr: 0.00457 [2024-02-18 04:46:57,816 INFO misc.py line 119 87073] Train: [23/100][1427/1557] Data 0.004 (0.112) Batch 0.779 (1.144) Remain 38:08:31 loss: 0.2476 Lr: 0.00457 [2024-02-18 04:46:59,052 INFO misc.py line 119 87073] Train: [23/100][1428/1557] Data 0.005 (0.112) Batch 1.235 (1.144) Remain 38:08:38 loss: 0.1936 Lr: 0.00457 [2024-02-18 04:46:59,971 INFO misc.py line 119 87073] Train: [23/100][1429/1557] Data 0.006 (0.112) Batch 0.920 (1.144) Remain 38:08:18 loss: 0.4794 Lr: 0.00457 [2024-02-18 04:47:01,111 INFO misc.py line 119 87073] Train: [23/100][1430/1557] Data 0.009 (0.112) Batch 1.141 (1.144) Remain 38:08:16 loss: 0.6556 Lr: 0.00457 [2024-02-18 04:47:02,017 INFO misc.py line 119 87073] Train: [23/100][1431/1557] Data 0.004 (0.112) Batch 0.906 (1.144) Remain 38:07:55 loss: 0.8973 Lr: 0.00457 [2024-02-18 04:47:02,966 INFO misc.py line 119 87073] Train: [23/100][1432/1557] Data 0.003 (0.112) Batch 0.938 (1.144) Remain 38:07:37 loss: 0.4744 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1439/1557] Data 0.007 (0.111) Batch 0.881 (1.143) Remain 38:05:08 loss: 0.3204 Lr: 0.00457 [2024-02-18 04:47:10,057 INFO misc.py line 119 87073] Train: [23/100][1440/1557] Data 0.006 (0.111) Batch 0.776 (1.142) Remain 38:04:37 loss: 0.8695 Lr: 0.00457 [2024-02-18 04:47:10,815 INFO misc.py line 119 87073] Train: [23/100][1441/1557] Data 0.005 (0.111) Batch 0.755 (1.142) Remain 38:04:03 loss: 0.3690 Lr: 0.00457 [2024-02-18 04:47:11,999 INFO misc.py line 119 87073] Train: [23/100][1442/1557] Data 0.007 (0.111) Batch 1.169 (1.142) Remain 38:04:04 loss: 0.3106 Lr: 0.00457 [2024-02-18 04:47:12,927 INFO misc.py line 119 87073] Train: [23/100][1443/1557] Data 0.024 (0.111) Batch 0.945 (1.142) Remain 38:03:47 loss: 0.4296 Lr: 0.00457 [2024-02-18 04:47:13,801 INFO misc.py line 119 87073] Train: [23/100][1444/1557] Data 0.005 (0.111) Batch 0.875 (1.142) Remain 38:03:23 loss: 0.9466 Lr: 0.00457 [2024-02-18 04:47:14,770 INFO misc.py line 119 87073] Train: [23/100][1445/1557] Data 0.005 (0.111) Batch 0.966 (1.142) Remain 38:03:08 loss: 0.7806 Lr: 0.00457 [2024-02-18 04:47:15,540 INFO misc.py line 119 87073] Train: [23/100][1446/1557] Data 0.007 (0.111) Batch 0.773 (1.141) Remain 38:02:36 loss: 0.7197 Lr: 0.00457 [2024-02-18 04:47:16,259 INFO misc.py line 119 87073] Train: [23/100][1447/1557] Data 0.004 (0.111) Batch 0.719 (1.141) Remain 38:02:00 loss: 0.5323 Lr: 0.00457 [2024-02-18 04:47:17,056 INFO misc.py line 119 87073] Train: [23/100][1448/1557] Data 0.004 (0.111) Batch 0.798 (1.141) Remain 38:01:30 loss: 0.4823 Lr: 0.00457 [2024-02-18 04:47:18,353 INFO misc.py line 119 87073] Train: [23/100][1449/1557] Data 0.003 (0.111) Batch 1.282 (1.141) Remain 38:01:40 loss: 0.3146 Lr: 0.00457 [2024-02-18 04:47:19,278 INFO misc.py line 119 87073] Train: [23/100][1450/1557] Data 0.018 (0.111) Batch 0.938 (1.141) Remain 38:01:23 loss: 0.1597 Lr: 0.00457 [2024-02-18 04:47:20,453 INFO misc.py line 119 87073] Train: [23/100][1451/1557] Data 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Remain 37:59:26 loss: 0.9824 Lr: 0.00457 [2024-02-18 04:47:26,909 INFO misc.py line 119 87073] Train: [23/100][1458/1557] Data 0.003 (0.110) Batch 0.962 (1.140) Remain 37:59:10 loss: 0.3816 Lr: 0.00457 [2024-02-18 04:47:27,995 INFO misc.py line 119 87073] Train: [23/100][1459/1557] Data 0.005 (0.110) Batch 1.080 (1.140) Remain 37:59:04 loss: 0.6154 Lr: 0.00457 [2024-02-18 04:47:28,908 INFO misc.py line 119 87073] Train: [23/100][1460/1557] Data 0.010 (0.110) Batch 0.916 (1.140) Remain 37:58:45 loss: 0.5419 Lr: 0.00457 [2024-02-18 04:47:29,718 INFO misc.py line 119 87073] Train: [23/100][1461/1557] Data 0.007 (0.110) Batch 0.812 (1.139) Remain 37:58:17 loss: 0.8728 Lr: 0.00457 [2024-02-18 04:47:30,561 INFO misc.py line 119 87073] Train: [23/100][1462/1557] Data 0.006 (0.110) Batch 0.831 (1.139) Remain 37:57:50 loss: 0.4931 Lr: 0.00457 [2024-02-18 04:47:42,484 INFO misc.py line 119 87073] Train: [23/100][1463/1557] Data 6.645 (0.114) Batch 11.935 (1.146) Remain 38:12:36 loss: 0.5147 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1470/1557] Data 0.004 (0.114) Batch 1.255 (1.146) Remain 38:10:48 loss: 0.1066 Lr: 0.00457 [2024-02-18 04:47:50,257 INFO misc.py line 119 87073] Train: [23/100][1471/1557] Data 0.007 (0.114) Batch 0.974 (1.146) Remain 38:10:33 loss: 0.4208 Lr: 0.00457 [2024-02-18 04:47:51,216 INFO misc.py line 119 87073] Train: [23/100][1472/1557] Data 0.004 (0.114) Batch 0.959 (1.145) Remain 38:10:16 loss: 0.4002 Lr: 0.00457 [2024-02-18 04:47:52,094 INFO misc.py line 119 87073] Train: [23/100][1473/1557] Data 0.005 (0.114) Batch 0.879 (1.145) Remain 38:09:53 loss: 0.5415 Lr: 0.00457 [2024-02-18 04:47:52,968 INFO misc.py line 119 87073] Train: [23/100][1474/1557] Data 0.003 (0.113) Batch 0.870 (1.145) Remain 38:09:30 loss: 0.3681 Lr: 0.00457 [2024-02-18 04:47:53,780 INFO misc.py line 119 87073] Train: [23/100][1475/1557] Data 0.007 (0.113) Batch 0.814 (1.145) Remain 38:09:02 loss: 0.3008 Lr: 0.00457 [2024-02-18 04:47:54,595 INFO misc.py line 119 87073] Train: [23/100][1476/1557] Data 0.005 (0.113) Batch 0.815 (1.145) Remain 38:08:34 loss: 0.5180 Lr: 0.00457 [2024-02-18 04:47:55,751 INFO misc.py line 119 87073] Train: [23/100][1477/1557] Data 0.006 (0.113) Batch 1.145 (1.145) Remain 38:08:33 loss: 0.1384 Lr: 0.00457 [2024-02-18 04:47:56,874 INFO misc.py line 119 87073] Train: [23/100][1478/1557] Data 0.017 (0.113) Batch 1.119 (1.145) Remain 38:08:29 loss: 0.2804 Lr: 0.00457 [2024-02-18 04:47:57,947 INFO misc.py line 119 87073] Train: [23/100][1479/1557] Data 0.021 (0.113) Batch 1.082 (1.145) Remain 38:08:23 loss: 0.5738 Lr: 0.00457 [2024-02-18 04:47:58,937 INFO misc.py line 119 87073] Train: [23/100][1480/1557] Data 0.011 (0.113) Batch 0.995 (1.144) Remain 38:08:10 loss: 0.5188 Lr: 0.00457 [2024-02-18 04:47:59,790 INFO misc.py line 119 87073] Train: [23/100][1481/1557] Data 0.007 (0.113) Batch 0.855 (1.144) Remain 38:07:45 loss: 0.4051 Lr: 0.00457 [2024-02-18 04:48:00,557 INFO misc.py line 119 87073] Train: [23/100][1482/1557] Data 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Remain 38:05:36 loss: 0.3577 Lr: 0.00457 [2024-02-18 04:48:07,071 INFO misc.py line 119 87073] Train: [23/100][1489/1557] Data 0.005 (0.112) Batch 0.769 (1.143) Remain 38:05:05 loss: 0.7133 Lr: 0.00457 [2024-02-18 04:48:07,838 INFO misc.py line 119 87073] Train: [23/100][1490/1557] Data 0.007 (0.112) Batch 0.769 (1.143) Remain 38:04:33 loss: 0.3724 Lr: 0.00457 [2024-02-18 04:48:09,112 INFO misc.py line 119 87073] Train: [23/100][1491/1557] Data 0.005 (0.112) Batch 1.272 (1.143) Remain 38:04:43 loss: 0.2977 Lr: 0.00457 [2024-02-18 04:48:09,982 INFO misc.py line 119 87073] Train: [23/100][1492/1557] Data 0.007 (0.112) Batch 0.871 (1.143) Remain 38:04:20 loss: 0.2244 Lr: 0.00457 [2024-02-18 04:48:10,956 INFO misc.py line 119 87073] Train: [23/100][1493/1557] Data 0.005 (0.112) Batch 0.974 (1.142) Remain 38:04:05 loss: 0.3715 Lr: 0.00457 [2024-02-18 04:48:11,773 INFO misc.py line 119 87073] Train: [23/100][1494/1557] Data 0.005 (0.112) Batch 0.818 (1.142) Remain 38:03:38 loss: 0.6394 Lr: 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INFO misc.py line 119 87073] Train: [23/100][1501/1557] Data 0.006 (0.112) Batch 0.925 (1.141) Remain 38:01:27 loss: 0.5309 Lr: 0.00457 [2024-02-18 04:48:19,142 INFO misc.py line 119 87073] Train: [23/100][1502/1557] Data 0.004 (0.111) Batch 0.900 (1.141) Remain 38:01:07 loss: 0.5272 Lr: 0.00457 [2024-02-18 04:48:19,896 INFO misc.py line 119 87073] Train: [23/100][1503/1557] Data 0.009 (0.111) Batch 0.759 (1.141) Remain 38:00:35 loss: 0.4265 Lr: 0.00457 [2024-02-18 04:48:20,592 INFO misc.py line 119 87073] Train: [23/100][1504/1557] Data 0.004 (0.111) Batch 0.689 (1.141) Remain 37:59:58 loss: 0.4434 Lr: 0.00457 [2024-02-18 04:48:21,785 INFO misc.py line 119 87073] Train: [23/100][1505/1557] Data 0.011 (0.111) Batch 1.191 (1.141) Remain 38:00:01 loss: 0.3069 Lr: 0.00457 [2024-02-18 04:48:22,816 INFO misc.py line 119 87073] Train: [23/100][1506/1557] Data 0.013 (0.111) Batch 1.037 (1.141) Remain 37:59:51 loss: 0.4726 Lr: 0.00457 [2024-02-18 04:48:23,861 INFO misc.py line 119 87073] Train: [23/100][1507/1557] Data 0.007 (0.111) Batch 1.047 (1.140) Remain 37:59:43 loss: 0.4490 Lr: 0.00457 [2024-02-18 04:48:24,989 INFO misc.py line 119 87073] Train: [23/100][1508/1557] Data 0.004 (0.111) Batch 1.125 (1.140) Remain 37:59:40 loss: 0.6068 Lr: 0.00457 [2024-02-18 04:48:26,132 INFO misc.py line 119 87073] Train: [23/100][1509/1557] Data 0.007 (0.111) Batch 1.141 (1.140) Remain 37:59:39 loss: 0.4513 Lr: 0.00457 [2024-02-18 04:48:26,905 INFO misc.py line 119 87073] Train: [23/100][1510/1557] Data 0.008 (0.111) Batch 0.777 (1.140) Remain 37:59:09 loss: 0.2741 Lr: 0.00457 [2024-02-18 04:48:27,651 INFO misc.py line 119 87073] Train: [23/100][1511/1557] Data 0.005 (0.111) Batch 0.746 (1.140) Remain 37:58:37 loss: 0.3050 Lr: 0.00457 [2024-02-18 04:48:28,899 INFO misc.py line 119 87073] Train: [23/100][1512/1557] Data 0.005 (0.111) Batch 1.245 (1.140) Remain 37:58:44 loss: 0.1875 Lr: 0.00457 [2024-02-18 04:48:29,772 INFO misc.py line 119 87073] Train: [23/100][1513/1557] Data 0.009 (0.111) Batch 0.877 (1.140) Remain 37:58:22 loss: 0.6387 Lr: 0.00457 [2024-02-18 04:48:30,675 INFO misc.py line 119 87073] Train: [23/100][1514/1557] Data 0.005 (0.111) Batch 0.904 (1.140) Remain 37:58:02 loss: 0.5825 Lr: 0.00457 [2024-02-18 04:48:31,733 INFO misc.py line 119 87073] Train: [23/100][1515/1557] Data 0.004 (0.111) Batch 1.052 (1.140) Remain 37:57:54 loss: 0.6542 Lr: 0.00457 [2024-02-18 04:48:32,670 INFO misc.py line 119 87073] Train: [23/100][1516/1557] Data 0.009 (0.110) Batch 0.944 (1.139) Remain 37:57:37 loss: 0.3552 Lr: 0.00457 [2024-02-18 04:48:33,475 INFO misc.py line 119 87073] Train: [23/100][1517/1557] Data 0.003 (0.110) Batch 0.804 (1.139) Remain 37:57:09 loss: 0.3546 Lr: 0.00457 [2024-02-18 04:48:34,243 INFO misc.py line 119 87073] Train: [23/100][1518/1557] Data 0.005 (0.110) Batch 0.765 (1.139) Remain 37:56:39 loss: 0.2315 Lr: 0.00457 [2024-02-18 04:48:45,592 INFO misc.py line 119 87073] Train: [23/100][1519/1557] Data 6.382 (0.114) Batch 11.352 (1.146) Remain 38:10:05 loss: 0.2813 Lr: 0.00457 [2024-02-18 04:48:46,536 INFO misc.py line 119 87073] Train: [23/100][1520/1557] Data 0.005 (0.114) Batch 0.945 (1.146) Remain 38:09:48 loss: 0.5348 Lr: 0.00457 [2024-02-18 04:48:47,619 INFO misc.py line 119 87073] Train: [23/100][1521/1557] Data 0.004 (0.114) Batch 1.083 (1.146) Remain 38:09:42 loss: 0.1064 Lr: 0.00457 [2024-02-18 04:48:48,519 INFO misc.py line 119 87073] Train: [23/100][1522/1557] Data 0.004 (0.114) Batch 0.900 (1.145) Remain 38:09:22 loss: 0.1962 Lr: 0.00457 [2024-02-18 04:48:49,450 INFO misc.py line 119 87073] Train: [23/100][1523/1557] Data 0.004 (0.114) Batch 0.930 (1.145) Remain 38:09:04 loss: 0.6343 Lr: 0.00457 [2024-02-18 04:48:50,263 INFO misc.py line 119 87073] Train: [23/100][1524/1557] Data 0.005 (0.114) Batch 0.813 (1.145) Remain 38:08:36 loss: 0.4082 Lr: 0.00457 [2024-02-18 04:48:51,124 INFO misc.py line 119 87073] Train: [23/100][1525/1557] Data 0.004 (0.114) Batch 0.861 (1.145) Remain 38:08:13 loss: 0.4283 Lr: 0.00457 [2024-02-18 04:48:52,393 INFO misc.py line 119 87073] Train: [23/100][1526/1557] Data 0.004 (0.114) Batch 1.261 (1.145) Remain 38:08:21 loss: 0.2167 Lr: 0.00457 [2024-02-18 04:48:53,293 INFO misc.py line 119 87073] Train: [23/100][1527/1557] Data 0.013 (0.114) Batch 0.908 (1.145) Remain 38:08:01 loss: 0.5702 Lr: 0.00457 [2024-02-18 04:48:54,263 INFO misc.py line 119 87073] Train: [23/100][1528/1557] Data 0.004 (0.114) Batch 0.970 (1.145) Remain 38:07:46 loss: 0.2760 Lr: 0.00457 [2024-02-18 04:48:55,465 INFO misc.py line 119 87073] Train: [23/100][1529/1557] Data 0.004 (0.114) Batch 1.202 (1.145) Remain 38:07:49 loss: 0.8162 Lr: 0.00457 [2024-02-18 04:48:56,420 INFO misc.py line 119 87073] Train: [23/100][1530/1557] Data 0.004 (0.114) Batch 0.955 (1.145) Remain 38:07:33 loss: 0.3408 Lr: 0.00457 [2024-02-18 04:48:57,156 INFO misc.py line 119 87073] Train: [23/100][1531/1557] Data 0.004 (0.114) Batch 0.731 (1.144) Remain 38:07:00 loss: 0.4779 Lr: 0.00457 [2024-02-18 04:48:57,858 INFO misc.py line 119 87073] Train: [23/100][1532/1557] Data 0.008 (0.114) Batch 0.706 (1.144) Remain 38:06:24 loss: 0.3450 Lr: 0.00457 [2024-02-18 04:48:59,049 INFO misc.py line 119 87073] Train: [23/100][1533/1557] Data 0.003 (0.113) Batch 1.190 (1.144) Remain 38:06:27 loss: 0.2742 Lr: 0.00457 [2024-02-18 04:48:59,980 INFO misc.py line 119 87073] Train: [23/100][1534/1557] Data 0.005 (0.113) Batch 0.932 (1.144) Remain 38:06:09 loss: 0.4110 Lr: 0.00457 [2024-02-18 04:49:00,881 INFO misc.py line 119 87073] Train: [23/100][1535/1557] Data 0.004 (0.113) Batch 0.900 (1.144) Remain 38:05:49 loss: 0.5101 Lr: 0.00457 [2024-02-18 04:49:01,913 INFO misc.py line 119 87073] Train: [23/100][1536/1557] Data 0.005 (0.113) Batch 1.032 (1.144) Remain 38:05:39 loss: 0.4305 Lr: 0.00457 [2024-02-18 04:49:02,800 INFO misc.py line 119 87073] Train: [23/100][1537/1557] Data 0.005 (0.113) Batch 0.886 (1.144) Remain 38:05:18 loss: 0.4684 Lr: 0.00457 [2024-02-18 04:49:03,562 INFO misc.py line 119 87073] Train: [23/100][1538/1557] Data 0.005 (0.113) Batch 0.762 (1.143) Remain 38:04:47 loss: 0.4345 Lr: 0.00457 [2024-02-18 04:49:04,308 INFO misc.py line 119 87073] Train: [23/100][1539/1557] Data 0.005 (0.113) Batch 0.742 (1.143) Remain 38:04:14 loss: 0.6238 Lr: 0.00457 [2024-02-18 04:49:05,574 INFO misc.py line 119 87073] Train: [23/100][1540/1557] Data 0.009 (0.113) Batch 1.261 (1.143) Remain 38:04:22 loss: 0.2303 Lr: 0.00457 [2024-02-18 04:49:06,494 INFO misc.py line 119 87073] Train: [23/100][1541/1557] Data 0.015 (0.113) Batch 0.931 (1.143) Remain 38:04:05 loss: 1.0183 Lr: 0.00457 [2024-02-18 04:49:07,319 INFO misc.py line 119 87073] Train: [23/100][1542/1557] Data 0.004 (0.113) Batch 0.825 (1.143) Remain 38:03:39 loss: 0.6125 Lr: 0.00457 [2024-02-18 04:49:08,319 INFO misc.py line 119 87073] Train: [23/100][1543/1557] Data 0.004 (0.113) Batch 0.996 (1.143) Remain 38:03:26 loss: 0.8419 Lr: 0.00457 [2024-02-18 04:49:09,108 INFO misc.py line 119 87073] Train: [23/100][1544/1557] Data 0.008 (0.113) Batch 0.793 (1.142) Remain 38:02:58 loss: 0.6007 Lr: 0.00457 [2024-02-18 04:49:09,839 INFO misc.py line 119 87073] Train: [23/100][1545/1557] Data 0.004 (0.113) Batch 0.723 (1.142) Remain 38:02:24 loss: 0.4992 Lr: 0.00457 [2024-02-18 04:49:10,613 INFO misc.py line 119 87073] Train: [23/100][1546/1557] Data 0.013 (0.113) Batch 0.783 (1.142) Remain 38:01:55 loss: 0.2227 Lr: 0.00457 [2024-02-18 04:49:11,809 INFO misc.py line 119 87073] Train: [23/100][1547/1557] Data 0.004 (0.113) Batch 1.195 (1.142) Remain 38:01:58 loss: 0.2420 Lr: 0.00457 [2024-02-18 04:49:12,699 INFO misc.py line 119 87073] Train: [23/100][1548/1557] Data 0.004 (0.112) Batch 0.891 (1.142) Remain 38:01:37 loss: 0.5987 Lr: 0.00457 [2024-02-18 04:49:13,709 INFO misc.py line 119 87073] Train: [23/100][1549/1557] Data 0.004 (0.112) Batch 0.999 (1.142) Remain 38:01:25 loss: 0.6950 Lr: 0.00457 [2024-02-18 04:49:14,649 INFO misc.py line 119 87073] Train: [23/100][1550/1557] Data 0.014 (0.112) Batch 0.951 (1.142) Remain 38:01:09 loss: 0.1819 Lr: 0.00457 [2024-02-18 04:49:15,453 INFO misc.py line 119 87073] Train: [23/100][1551/1557] Data 0.004 (0.112) Batch 0.804 (1.141) Remain 38:00:42 loss: 0.3162 Lr: 0.00457 [2024-02-18 04:49:16,183 INFO misc.py line 119 87073] Train: [23/100][1552/1557] Data 0.004 (0.112) Batch 0.722 (1.141) Remain 38:00:08 loss: 0.5768 Lr: 0.00457 [2024-02-18 04:49:16,971 INFO misc.py line 119 87073] Train: [23/100][1553/1557] Data 0.012 (0.112) Batch 0.796 (1.141) Remain 37:59:40 loss: 0.4494 Lr: 0.00457 [2024-02-18 04:49:18,166 INFO misc.py line 119 87073] Train: [23/100][1554/1557] Data 0.003 (0.112) Batch 1.193 (1.141) Remain 37:59:43 loss: 0.2906 Lr: 0.00457 [2024-02-18 04:49:19,143 INFO misc.py line 119 87073] Train: [23/100][1555/1557] Data 0.006 (0.112) Batch 0.978 (1.141) Remain 37:59:30 loss: 0.3747 Lr: 0.00457 [2024-02-18 04:49:20,028 INFO misc.py line 119 87073] Train: [23/100][1556/1557] Data 0.004 (0.112) Batch 0.886 (1.141) Remain 37:59:09 loss: 0.5231 Lr: 0.00457 [2024-02-18 04:49:21,114 INFO misc.py line 119 87073] Train: [23/100][1557/1557] Data 0.004 (0.112) Batch 1.077 (1.141) Remain 37:59:03 loss: 0.5321 Lr: 0.00457 [2024-02-18 04:49:21,114 INFO misc.py line 136 87073] Train result: loss: 0.4763 [2024-02-18 04:49:21,115 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 04:49:50,134 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7871 [2024-02-18 04:49:50,916 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8339 [2024-02-18 04:49:53,041 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4220 [2024-02-18 04:49:55,248 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4120 [2024-02-18 04:50:00,204 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3010 [2024-02-18 04:50:00,901 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.5317 [2024-02-18 04:50:02,162 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2868 [2024-02-18 04:50:05,113 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4195 [2024-02-18 04:50:06,921 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2390 [2024-02-18 04:50:08,609 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6752/0.7392/0.8962. [2024-02-18 04:50:08,609 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.8980/0.9742 [2024-02-18 04:50:08,609 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9809/0.9912 [2024-02-18 04:50:08,610 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8464/0.9701 [2024-02-18 04:50:08,610 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 04:50:08,610 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3365/0.3621 [2024-02-18 04:50:08,610 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6344/0.6555 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6599/0.7824 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8536/0.9208 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9027/0.9556 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7566/0.8064 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7751/0.8751 [2024-02-18 04:50:08,611 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6465/0.7576 [2024-02-18 04:50:08,612 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.4864/0.5588 [2024-02-18 04:50:08,612 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 04:50:08,613 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 04:50:08,613 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 04:50:15,632 INFO misc.py line 119 87073] Train: [24/100][1/1557] Data 1.456 (1.456) Batch 2.168 (2.168) Remain 72:12:29 loss: 0.4126 Lr: 0.00457 [2024-02-18 04:50:16,754 INFO misc.py line 119 87073] Train: [24/100][2/1557] Data 0.005 (0.005) Batch 1.109 (1.109) Remain 36:55:25 loss: 1.1149 Lr: 0.00457 [2024-02-18 04:50:17,637 INFO misc.py line 119 87073] Train: [24/100][3/1557] Data 0.019 (0.019) Batch 0.897 (0.897) Remain 29:53:03 loss: 0.1069 Lr: 0.00457 [2024-02-18 04:50:18,556 INFO misc.py line 119 87073] Train: [24/100][4/1557] Data 0.004 (0.004) Batch 0.918 (0.918) Remain 30:33:20 loss: 0.5775 Lr: 0.00457 [2024-02-18 04:50:19,371 INFO misc.py line 119 87073] Train: [24/100][5/1557] Data 0.006 (0.005) Batch 0.806 (0.862) Remain 28:42:02 loss: 0.4746 Lr: 0.00457 [2024-02-18 04:50:20,146 INFO misc.py line 119 87073] Train: [24/100][6/1557] Data 0.014 (0.008) Batch 0.785 (0.836) Remain 27:50:31 loss: 0.4217 Lr: 0.00457 [2024-02-18 04:50:22,658 INFO misc.py line 119 87073] Train: [24/100][7/1557] Data 0.036 (0.015) Batch 2.510 (1.255) Remain 41:46:41 loss: 0.4010 Lr: 0.00457 [2024-02-18 04:50:23,766 INFO misc.py line 119 87073] Train: [24/100][8/1557] Data 0.006 (0.013) Batch 1.098 (1.223) Remain 40:44:06 loss: 0.6392 Lr: 0.00457 [2024-02-18 04:50:24,851 INFO misc.py line 119 87073] Train: [24/100][9/1557] Data 0.016 (0.014) Batch 1.091 (1.201) Remain 40:00:08 loss: 0.7236 Lr: 0.00457 [2024-02-18 04:50:25,881 INFO misc.py line 119 87073] Train: [24/100][10/1557] Data 0.010 (0.013) Batch 1.021 (1.176) Remain 39:08:40 loss: 0.3938 Lr: 0.00457 [2024-02-18 04:50:26,930 INFO misc.py line 119 87073] Train: [24/100][11/1557] Data 0.019 (0.014) Batch 1.054 (1.160) Remain 38:38:16 loss: 0.6103 Lr: 0.00457 [2024-02-18 04:50:27,684 INFO misc.py line 119 87073] Train: [24/100][12/1557] Data 0.014 (0.014) Batch 0.763 (1.116) Remain 37:10:08 loss: 0.9976 Lr: 0.00457 [2024-02-18 04:50:28,465 INFO misc.py line 119 87073] Train: [24/100][13/1557] Data 0.005 (0.013) Batch 0.776 (1.082) Remain 36:02:13 loss: 0.2732 Lr: 0.00457 [2024-02-18 04:50:29,690 INFO misc.py line 119 87073] Train: [24/100][14/1557] Data 0.009 (0.013) Batch 1.228 (1.096) Remain 36:28:46 loss: 0.3290 Lr: 0.00457 [2024-02-18 04:50:30,579 INFO misc.py line 119 87073] Train: [24/100][15/1557] Data 0.006 (0.012) Batch 0.891 (1.078) Remain 35:54:37 loss: 0.7581 Lr: 0.00457 [2024-02-18 04:50:31,583 INFO misc.py line 119 87073] Train: [24/100][16/1557] Data 0.004 (0.012) Batch 1.004 (1.073) Remain 35:43:13 loss: 0.4824 Lr: 0.00457 [2024-02-18 04:50:32,471 INFO misc.py line 119 87073] Train: [24/100][17/1557] Data 0.004 (0.011) Batch 0.888 (1.060) Remain 35:16:47 loss: 0.3488 Lr: 0.00457 [2024-02-18 04:50:33,369 INFO misc.py line 119 87073] Train: [24/100][18/1557] Data 0.005 (0.011) Batch 0.898 (1.049) Remain 34:55:19 loss: 0.5709 Lr: 0.00457 [2024-02-18 04:50:34,162 INFO misc.py line 119 87073] Train: [24/100][19/1557] Data 0.004 (0.010) Batch 0.791 (1.033) Remain 34:23:09 loss: 0.3372 Lr: 0.00457 [2024-02-18 04:50:35,006 INFO misc.py line 119 87073] Train: [24/100][20/1557] Data 0.006 (0.010) Batch 0.845 (1.022) Remain 34:01:01 loss: 0.5865 Lr: 0.00457 [2024-02-18 04:50:36,340 INFO misc.py line 119 87073] Train: [24/100][21/1557] Data 0.004 (0.010) Batch 1.325 (1.038) Remain 34:34:39 loss: 0.2888 Lr: 0.00457 [2024-02-18 04:50:37,427 INFO misc.py line 119 87073] Train: [24/100][22/1557] Data 0.014 (0.010) Batch 1.077 (1.040) Remain 34:38:40 loss: 0.2514 Lr: 0.00457 [2024-02-18 04:50:38,514 INFO misc.py line 119 87073] Train: [24/100][23/1557] Data 0.028 (0.011) Batch 1.095 (1.043) Remain 34:44:04 loss: 0.3384 Lr: 0.00457 [2024-02-18 04:50:39,441 INFO misc.py line 119 87073] Train: [24/100][24/1557] Data 0.017 (0.011) Batch 0.940 (1.038) Remain 34:34:16 loss: 0.5786 Lr: 0.00457 [2024-02-18 04:50:40,401 INFO misc.py line 119 87073] Train: [24/100][25/1557] Data 0.004 (0.011) Batch 0.959 (1.035) Remain 34:27:05 loss: 0.2907 Lr: 0.00457 [2024-02-18 04:50:41,171 INFO misc.py line 119 87073] Train: [24/100][26/1557] Data 0.004 (0.010) Batch 0.770 (1.023) Remain 34:04:03 loss: 0.3903 Lr: 0.00457 [2024-02-18 04:50:41,954 INFO misc.py line 119 87073] Train: [24/100][27/1557] Data 0.004 (0.010) Batch 0.777 (1.013) Remain 33:43:32 loss: 0.4732 Lr: 0.00457 [2024-02-18 04:50:43,308 INFO misc.py line 119 87073] Train: [24/100][28/1557] Data 0.010 (0.010) Batch 1.353 (1.027) Remain 34:10:44 loss: 0.2218 Lr: 0.00457 [2024-02-18 04:50:44,329 INFO misc.py line 119 87073] Train: [24/100][29/1557] Data 0.011 (0.010) Batch 1.023 (1.026) Remain 34:10:27 loss: 0.6948 Lr: 0.00457 [2024-02-18 04:50:45,312 INFO misc.py line 119 87073] Train: [24/100][30/1557] Data 0.010 (0.010) Batch 0.987 (1.025) Remain 34:07:30 loss: 0.8157 Lr: 0.00457 [2024-02-18 04:50:46,415 INFO misc.py line 119 87073] Train: [24/100][31/1557] Data 0.005 (0.010) Batch 1.102 (1.028) Remain 34:13:00 loss: 0.8347 Lr: 0.00457 [2024-02-18 04:50:47,389 INFO misc.py line 119 87073] Train: [24/100][32/1557] Data 0.006 (0.010) Batch 0.976 (1.026) Remain 34:09:23 loss: 0.3626 Lr: 0.00457 [2024-02-18 04:50:48,051 INFO misc.py line 119 87073] Train: [24/100][33/1557] Data 0.004 (0.010) Batch 0.662 (1.014) Remain 33:45:09 loss: 0.3475 Lr: 0.00457 [2024-02-18 04:50:48,907 INFO misc.py line 119 87073] Train: [24/100][34/1557] Data 0.004 (0.009) Batch 0.856 (1.009) Remain 33:34:57 loss: 0.4856 Lr: 0.00457 [2024-02-18 04:50:49,957 INFO misc.py line 119 87073] Train: [24/100][35/1557] Data 0.003 (0.009) Batch 1.039 (1.010) Remain 33:36:48 loss: 0.3294 Lr: 0.00457 [2024-02-18 04:50:51,101 INFO misc.py line 119 87073] Train: [24/100][36/1557] Data 0.015 (0.009) Batch 1.144 (1.014) Remain 33:44:55 loss: 0.4678 Lr: 0.00457 [2024-02-18 04:50:51,990 INFO misc.py line 119 87073] Train: [24/100][37/1557] Data 0.016 (0.010) Batch 0.899 (1.010) Remain 33:38:11 loss: 0.5659 Lr: 0.00457 [2024-02-18 04:50:53,307 INFO misc.py line 119 87073] Train: [24/100][38/1557] Data 0.005 (0.009) Batch 1.311 (1.019) Remain 33:55:20 loss: 0.7323 Lr: 0.00457 [2024-02-18 04:50:54,412 INFO misc.py line 119 87073] Train: [24/100][39/1557] Data 0.011 (0.009) Batch 1.101 (1.021) Remain 33:59:52 loss: 0.3197 Lr: 0.00457 [2024-02-18 04:50:55,136 INFO misc.py line 119 87073] Train: [24/100][40/1557] Data 0.016 (0.010) Batch 0.735 (1.013) Remain 33:44:23 loss: 0.5309 Lr: 0.00457 [2024-02-18 04:50:55,898 INFO misc.py line 119 87073] Train: [24/100][41/1557] Data 0.004 (0.009) Batch 0.759 (1.007) Remain 33:31:00 loss: 0.5315 Lr: 0.00457 [2024-02-18 04:50:56,997 INFO misc.py line 119 87073] Train: [24/100][42/1557] Data 0.007 (0.009) Batch 1.097 (1.009) Remain 33:35:35 loss: 0.2281 Lr: 0.00457 [2024-02-18 04:50:57,864 INFO misc.py line 119 87073] Train: [24/100][43/1557] Data 0.009 (0.009) Batch 0.872 (1.006) Remain 33:28:45 loss: 0.2920 Lr: 0.00457 [2024-02-18 04:50:58,864 INFO misc.py line 119 87073] Train: [24/100][44/1557] Data 0.005 (0.009) Batch 0.999 (1.005) Remain 33:28:23 loss: 0.6031 Lr: 0.00457 [2024-02-18 04:50:59,748 INFO misc.py line 119 87073] Train: [24/100][45/1557] Data 0.006 (0.009) Batch 0.884 (1.003) Remain 33:22:35 loss: 0.3681 Lr: 0.00457 [2024-02-18 04:51:00,704 INFO misc.py line 119 87073] Train: [24/100][46/1557] Data 0.005 (0.009) 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line 119 87073] Train: [24/100][109/1557] Data 0.004 (0.071) Batch 0.909 (1.080) Remain 35:56:28 loss: 0.5554 Lr: 0.00457 [2024-02-18 04:52:12,911 INFO misc.py line 119 87073] Train: [24/100][110/1557] Data 0.005 (0.070) Batch 0.768 (1.077) Remain 35:50:38 loss: 0.3917 Lr: 0.00457 [2024-02-18 04:52:13,693 INFO misc.py line 119 87073] Train: [24/100][111/1557] Data 0.005 (0.070) Batch 0.782 (1.075) Remain 35:45:09 loss: 0.2466 Lr: 0.00457 [2024-02-18 04:52:14,923 INFO misc.py line 119 87073] Train: [24/100][112/1557] Data 0.006 (0.069) Batch 1.228 (1.076) Remain 35:47:56 loss: 0.1910 Lr: 0.00457 [2024-02-18 04:52:15,823 INFO misc.py line 119 87073] Train: [24/100][113/1557] Data 0.009 (0.069) Batch 0.905 (1.074) Remain 35:44:49 loss: 0.9640 Lr: 0.00457 [2024-02-18 04:52:16,661 INFO misc.py line 119 87073] Train: [24/100][114/1557] Data 0.003 (0.068) Batch 0.836 (1.072) Remain 35:40:30 loss: 0.6810 Lr: 0.00457 [2024-02-18 04:52:17,860 INFO misc.py line 119 87073] Train: 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Batch 1.104 (1.160) Remain 38:34:33 loss: 0.4969 Lr: 0.00457 [2024-02-18 04:52:35,350 INFO misc.py line 119 87073] Train: [24/100][122/1557] Data 0.005 (0.121) Batch 0.888 (1.157) Remain 38:29:58 loss: 0.3703 Lr: 0.00457 [2024-02-18 04:52:36,245 INFO misc.py line 119 87073] Train: [24/100][123/1557] Data 0.005 (0.120) Batch 0.895 (1.155) Remain 38:25:36 loss: 0.4659 Lr: 0.00457 [2024-02-18 04:52:36,963 INFO misc.py line 119 87073] Train: [24/100][124/1557] Data 0.006 (0.120) Batch 0.719 (1.151) Remain 38:18:23 loss: 0.4152 Lr: 0.00457 [2024-02-18 04:52:37,675 INFO misc.py line 119 87073] Train: [24/100][125/1557] Data 0.004 (0.119) Batch 0.705 (1.148) Remain 38:11:04 loss: 0.3224 Lr: 0.00457 [2024-02-18 04:52:38,950 INFO misc.py line 119 87073] Train: [24/100][126/1557] Data 0.010 (0.118) Batch 1.271 (1.149) Remain 38:13:03 loss: 0.2071 Lr: 0.00457 [2024-02-18 04:52:39,826 INFO misc.py line 119 87073] Train: [24/100][127/1557] Data 0.014 (0.117) Batch 0.884 (1.147) Remain 38:08:46 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87073] Train: [24/100][140/1557] Data 0.024 (0.106) Batch 1.324 (1.131) Remain 37:36:59 loss: 0.1847 Lr: 0.00457 [2024-02-18 04:52:53,527 INFO misc.py line 119 87073] Train: [24/100][141/1557] Data 0.013 (0.106) Batch 0.961 (1.130) Remain 37:34:31 loss: 0.2380 Lr: 0.00457 [2024-02-18 04:52:54,625 INFO misc.py line 119 87073] Train: [24/100][142/1557] Data 0.004 (0.105) Batch 1.098 (1.129) Remain 37:34:02 loss: 0.4559 Lr: 0.00457 [2024-02-18 04:52:55,536 INFO misc.py line 119 87073] Train: [24/100][143/1557] Data 0.004 (0.104) Batch 0.912 (1.128) Remain 37:30:55 loss: 0.7408 Lr: 0.00457 [2024-02-18 04:52:56,463 INFO misc.py line 119 87073] Train: [24/100][144/1557] Data 0.004 (0.104) Batch 0.892 (1.126) Remain 37:27:34 loss: 0.9206 Lr: 0.00457 [2024-02-18 04:52:57,211 INFO misc.py line 119 87073] Train: [24/100][145/1557] Data 0.038 (0.103) Batch 0.781 (1.124) Remain 37:22:41 loss: 0.5250 Lr: 0.00457 [2024-02-18 04:52:57,964 INFO misc.py line 119 87073] Train: [24/100][146/1557] Data 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line 119 87073] Train: [24/100][165/1557] Data 0.009 (0.091) Batch 1.037 (1.105) Remain 36:44:27 loss: 0.8577 Lr: 0.00457 [2024-02-18 04:53:17,386 INFO misc.py line 119 87073] Train: [24/100][166/1557] Data 0.005 (0.091) Batch 0.775 (1.103) Remain 36:40:23 loss: 0.3728 Lr: 0.00457 [2024-02-18 04:53:18,163 INFO misc.py line 119 87073] Train: [24/100][167/1557] Data 0.005 (0.090) Batch 0.777 (1.101) Remain 36:36:24 loss: 0.3507 Lr: 0.00457 [2024-02-18 04:53:19,432 INFO misc.py line 119 87073] Train: [24/100][168/1557] Data 0.005 (0.090) Batch 1.257 (1.102) Remain 36:38:16 loss: 0.4329 Lr: 0.00457 [2024-02-18 04:53:20,363 INFO misc.py line 119 87073] Train: [24/100][169/1557] Data 0.018 (0.089) Batch 0.944 (1.101) Remain 36:36:21 loss: 0.3373 Lr: 0.00457 [2024-02-18 04:53:21,546 INFO misc.py line 119 87073] Train: [24/100][170/1557] Data 0.005 (0.089) Batch 1.177 (1.101) Remain 36:37:15 loss: 1.6192 Lr: 0.00457 [2024-02-18 04:53:22,585 INFO misc.py line 119 87073] Train: 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Batch 0.946 (1.154) Remain 38:23:26 loss: 0.8448 Lr: 0.00457 [2024-02-18 04:53:39,611 INFO misc.py line 119 87073] Train: [24/100][178/1557] Data 0.004 (0.124) Batch 1.092 (1.154) Remain 38:22:42 loss: 0.4958 Lr: 0.00457 [2024-02-18 04:53:40,496 INFO misc.py line 119 87073] Train: [24/100][179/1557] Data 0.004 (0.123) Batch 0.886 (1.153) Remain 38:19:38 loss: 0.4151 Lr: 0.00457 [2024-02-18 04:53:43,166 INFO misc.py line 119 87073] Train: [24/100][180/1557] Data 1.489 (0.131) Batch 2.668 (1.161) Remain 38:36:42 loss: 0.5013 Lr: 0.00457 [2024-02-18 04:53:44,112 INFO misc.py line 119 87073] Train: [24/100][181/1557] Data 0.006 (0.130) Batch 0.947 (1.160) Remain 38:34:17 loss: 0.4608 Lr: 0.00457 [2024-02-18 04:53:45,279 INFO misc.py line 119 87073] Train: [24/100][182/1557] Data 0.004 (0.130) Batch 1.166 (1.160) Remain 38:34:19 loss: 0.2113 Lr: 0.00457 [2024-02-18 04:53:46,377 INFO misc.py line 119 87073] Train: [24/100][183/1557] Data 0.006 (0.129) Batch 1.100 (1.160) Remain 38:33:38 loss: 0.3598 Lr: 0.00457 [2024-02-18 04:53:47,492 INFO misc.py line 119 87073] Train: [24/100][184/1557] Data 0.005 (0.128) Batch 1.115 (1.159) Remain 38:33:07 loss: 0.8595 Lr: 0.00456 [2024-02-18 04:53:48,434 INFO misc.py line 119 87073] Train: [24/100][185/1557] Data 0.005 (0.128) Batch 0.941 (1.158) Remain 38:30:43 loss: 0.4855 Lr: 0.00456 [2024-02-18 04:53:49,263 INFO misc.py line 119 87073] Train: [24/100][186/1557] Data 0.004 (0.127) Batch 0.829 (1.156) Remain 38:27:07 loss: 0.4567 Lr: 0.00456 [2024-02-18 04:53:50,051 INFO misc.py line 119 87073] Train: [24/100][187/1557] Data 0.004 (0.126) Batch 0.788 (1.154) Remain 38:23:06 loss: 0.3140 Lr: 0.00456 [2024-02-18 04:53:50,833 INFO misc.py line 119 87073] Train: [24/100][188/1557] Data 0.005 (0.126) Batch 0.783 (1.152) Remain 38:19:04 loss: 0.4513 Lr: 0.00456 [2024-02-18 04:53:52,107 INFO misc.py line 119 87073] Train: [24/100][189/1557] Data 0.003 (0.125) Batch 1.272 (1.153) Remain 38:20:20 loss: 0.2458 Lr: 0.00456 [2024-02-18 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87073] Train: [24/100][196/1557] Data 0.004 (0.121) Batch 1.329 (1.149) Remain 38:12:25 loss: 0.1172 Lr: 0.00456 [2024-02-18 04:54:00,406 INFO misc.py line 119 87073] Train: [24/100][197/1557] Data 0.007 (0.120) Batch 0.977 (1.148) Remain 38:10:38 loss: 0.6379 Lr: 0.00456 [2024-02-18 04:54:01,201 INFO misc.py line 119 87073] Train: [24/100][198/1557] Data 0.009 (0.119) Batch 0.798 (1.146) Remain 38:07:02 loss: 0.6890 Lr: 0.00456 [2024-02-18 04:54:02,288 INFO misc.py line 119 87073] Train: [24/100][199/1557] Data 0.006 (0.119) Batch 1.082 (1.146) Remain 38:06:21 loss: 1.2082 Lr: 0.00456 [2024-02-18 04:54:03,335 INFO misc.py line 119 87073] Train: [24/100][200/1557] Data 0.012 (0.118) Batch 1.051 (1.146) Remain 38:05:22 loss: 0.5321 Lr: 0.00456 [2024-02-18 04:54:04,091 INFO misc.py line 119 87073] Train: [24/100][201/1557] Data 0.008 (0.118) Batch 0.759 (1.144) Remain 38:01:27 loss: 0.3497 Lr: 0.00456 [2024-02-18 04:54:04,882 INFO misc.py line 119 87073] Train: [24/100][202/1557] Data 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[2024-02-18 04:54:17,205 INFO misc.py line 119 87073] Train: [24/100][215/1557] Data 0.004 (0.110) Batch 0.794 (1.130) Remain 37:33:55 loss: 0.4994 Lr: 0.00456 [2024-02-18 04:54:17,962 INFO misc.py line 119 87073] Train: [24/100][216/1557] Data 0.004 (0.110) Batch 0.757 (1.128) Remain 37:30:24 loss: 0.7417 Lr: 0.00456 [2024-02-18 04:54:19,281 INFO misc.py line 119 87073] Train: [24/100][217/1557] Data 0.004 (0.109) Batch 1.318 (1.129) Remain 37:32:09 loss: 0.3188 Lr: 0.00456 [2024-02-18 04:54:20,222 INFO misc.py line 119 87073] Train: [24/100][218/1557] Data 0.006 (0.109) Batch 0.943 (1.128) Remain 37:30:25 loss: 0.3080 Lr: 0.00456 [2024-02-18 04:54:21,256 INFO misc.py line 119 87073] Train: [24/100][219/1557] Data 0.003 (0.108) Batch 1.034 (1.128) Remain 37:29:31 loss: 0.4484 Lr: 0.00456 [2024-02-18 04:54:22,277 INFO misc.py line 119 87073] Train: [24/100][220/1557] Data 0.004 (0.108) Batch 1.020 (1.127) Remain 37:28:30 loss: 0.6971 Lr: 0.00456 [2024-02-18 04:54:23,202 INFO misc.py line 119 87073] Train: [24/100][221/1557] Data 0.004 (0.107) Batch 0.923 (1.126) Remain 37:26:37 loss: 0.8125 Lr: 0.00456 [2024-02-18 04:54:23,960 INFO misc.py line 119 87073] Train: [24/100][222/1557] Data 0.008 (0.107) Batch 0.760 (1.125) Remain 37:23:16 loss: 0.5109 Lr: 0.00456 [2024-02-18 04:54:24,750 INFO misc.py line 119 87073] Train: [24/100][223/1557] Data 0.006 (0.107) Batch 0.791 (1.123) Remain 37:20:13 loss: 0.4776 Lr: 0.00456 [2024-02-18 04:54:25,914 INFO misc.py line 119 87073] Train: [24/100][224/1557] Data 0.005 (0.106) Batch 1.163 (1.123) Remain 37:20:33 loss: 0.1905 Lr: 0.00456 [2024-02-18 04:54:26,783 INFO misc.py line 119 87073] Train: [24/100][225/1557] Data 0.005 (0.106) Batch 0.868 (1.122) Remain 37:18:14 loss: 0.5987 Lr: 0.00456 [2024-02-18 04:54:27,709 INFO misc.py line 119 87073] Train: [24/100][226/1557] Data 0.007 (0.105) Batch 0.927 (1.121) Remain 37:16:28 loss: 0.4307 Lr: 0.00456 [2024-02-18 04:54:28,667 INFO misc.py line 119 87073] Train: 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Batch 1.035 (1.161) Remain 38:35:38 loss: 0.3310 Lr: 0.00456 [2024-02-18 04:54:45,722 INFO misc.py line 119 87073] Train: [24/100][234/1557] Data 0.005 (0.131) Batch 1.021 (1.161) Remain 38:34:24 loss: 0.7058 Lr: 0.00456 [2024-02-18 04:54:46,647 INFO misc.py line 119 87073] Train: [24/100][235/1557] Data 0.004 (0.130) Batch 0.924 (1.160) Remain 38:32:21 loss: 0.3993 Lr: 0.00456 [2024-02-18 04:54:47,373 INFO misc.py line 119 87073] Train: [24/100][236/1557] Data 0.004 (0.130) Batch 0.726 (1.158) Remain 38:28:37 loss: 0.2996 Lr: 0.00456 [2024-02-18 04:54:48,140 INFO misc.py line 119 87073] Train: [24/100][237/1557] Data 0.005 (0.129) Batch 0.767 (1.156) Remain 38:25:16 loss: 0.3193 Lr: 0.00456 [2024-02-18 04:54:49,397 INFO misc.py line 119 87073] Train: [24/100][238/1557] Data 0.004 (0.129) Batch 1.247 (1.156) Remain 38:26:01 loss: 0.1770 Lr: 0.00456 [2024-02-18 04:54:50,526 INFO misc.py line 119 87073] Train: [24/100][239/1557] Data 0.014 (0.128) Batch 1.130 (1.156) Remain 38:25:47 loss: 0.3000 Lr: 0.00456 [2024-02-18 04:54:51,419 INFO misc.py line 119 87073] Train: [24/100][240/1557] Data 0.013 (0.128) Batch 0.901 (1.155) Remain 38:23:37 loss: 0.5951 Lr: 0.00456 [2024-02-18 04:54:52,466 INFO misc.py line 119 87073] Train: [24/100][241/1557] Data 0.005 (0.127) Batch 1.046 (1.155) Remain 38:22:41 loss: 0.5216 Lr: 0.00456 [2024-02-18 04:54:53,477 INFO misc.py line 119 87073] Train: [24/100][242/1557] Data 0.007 (0.127) Batch 1.012 (1.154) Remain 38:21:28 loss: 0.5165 Lr: 0.00456 [2024-02-18 04:54:54,218 INFO misc.py line 119 87073] Train: [24/100][243/1557] Data 0.005 (0.126) Batch 0.742 (1.152) Remain 38:18:01 loss: 0.7202 Lr: 0.00456 [2024-02-18 04:54:55,038 INFO misc.py line 119 87073] Train: [24/100][244/1557] Data 0.004 (0.126) Batch 0.809 (1.151) Remain 38:15:10 loss: 0.2551 Lr: 0.00456 [2024-02-18 04:54:56,286 INFO misc.py line 119 87073] Train: [24/100][245/1557] Data 0.015 (0.125) Batch 1.256 (1.151) Remain 38:16:00 loss: 0.1801 Lr: 0.00456 [2024-02-18 04:54:57,314 INFO misc.py line 119 87073] Train: [24/100][246/1557] Data 0.008 (0.125) Batch 1.025 (1.151) Remain 38:14:57 loss: 0.4601 Lr: 0.00456 [2024-02-18 04:54:58,327 INFO misc.py line 119 87073] Train: [24/100][247/1557] Data 0.012 (0.124) Batch 1.017 (1.150) Remain 38:13:50 loss: 0.6639 Lr: 0.00456 [2024-02-18 04:54:59,181 INFO misc.py line 119 87073] Train: [24/100][248/1557] Data 0.007 (0.124) Batch 0.856 (1.149) Remain 38:11:25 loss: 0.3619 Lr: 0.00456 [2024-02-18 04:55:00,071 INFO misc.py line 119 87073] Train: [24/100][249/1557] Data 0.005 (0.123) Batch 0.890 (1.148) Remain 38:09:18 loss: 0.7803 Lr: 0.00456 [2024-02-18 04:55:00,781 INFO misc.py line 119 87073] Train: [24/100][250/1557] Data 0.005 (0.123) Batch 0.710 (1.146) Remain 38:05:45 loss: 0.5331 Lr: 0.00456 [2024-02-18 04:55:01,524 INFO misc.py line 119 87073] Train: [24/100][251/1557] Data 0.005 (0.123) Batch 0.742 (1.145) Remain 38:02:29 loss: 0.5627 Lr: 0.00456 [2024-02-18 04:55:02,765 INFO misc.py line 119 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[2024-02-18 04:55:20,796 INFO misc.py line 119 87073] Train: [24/100][271/1557] Data 0.005 (0.114) Batch 0.743 (1.131) Remain 37:35:09 loss: 0.2072 Lr: 0.00456 [2024-02-18 04:55:21,544 INFO misc.py line 119 87073] Train: [24/100][272/1557] Data 0.006 (0.114) Batch 0.748 (1.130) Remain 37:32:18 loss: 0.3415 Lr: 0.00456 [2024-02-18 04:55:22,826 INFO misc.py line 119 87073] Train: [24/100][273/1557] Data 0.006 (0.113) Batch 1.273 (1.130) Remain 37:33:20 loss: 0.4488 Lr: 0.00456 [2024-02-18 04:55:23,871 INFO misc.py line 119 87073] Train: [24/100][274/1557] Data 0.014 (0.113) Batch 1.048 (1.130) Remain 37:32:43 loss: 0.4437 Lr: 0.00456 [2024-02-18 04:55:24,786 INFO misc.py line 119 87073] Train: [24/100][275/1557] Data 0.013 (0.112) Batch 0.921 (1.129) Remain 37:31:10 loss: 0.3062 Lr: 0.00456 [2024-02-18 04:55:25,867 INFO misc.py line 119 87073] Train: [24/100][276/1557] Data 0.005 (0.112) Batch 1.081 (1.129) Remain 37:30:48 loss: 0.4550 Lr: 0.00456 [2024-02-18 04:55:26,809 INFO misc.py line 119 87073] Train: [24/100][277/1557] Data 0.005 (0.112) Batch 0.941 (1.128) Remain 37:29:24 loss: 0.6013 Lr: 0.00456 [2024-02-18 04:55:27,574 INFO misc.py line 119 87073] Train: [24/100][278/1557] Data 0.006 (0.111) Batch 0.766 (1.127) Remain 37:26:46 loss: 0.2674 Lr: 0.00456 [2024-02-18 04:55:28,287 INFO misc.py line 119 87073] Train: [24/100][279/1557] Data 0.005 (0.111) Batch 0.709 (1.126) Remain 37:23:43 loss: 0.3895 Lr: 0.00456 [2024-02-18 04:55:29,439 INFO misc.py line 119 87073] Train: [24/100][280/1557] Data 0.008 (0.111) Batch 1.155 (1.126) Remain 37:23:55 loss: 0.2681 Lr: 0.00456 [2024-02-18 04:55:30,360 INFO misc.py line 119 87073] Train: [24/100][281/1557] Data 0.006 (0.110) Batch 0.920 (1.125) Remain 37:22:26 loss: 0.7532 Lr: 0.00456 [2024-02-18 04:55:31,419 INFO misc.py line 119 87073] Train: [24/100][282/1557] Data 0.007 (0.110) Batch 1.062 (1.125) Remain 37:21:57 loss: 0.2463 Lr: 0.00456 [2024-02-18 04:55:32,328 INFO misc.py line 119 87073] Train: 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Batch 1.008 (1.156) Remain 38:24:40 loss: 0.3447 Lr: 0.00456 [2024-02-18 04:55:49,325 INFO misc.py line 119 87073] Train: [24/100][290/1557] Data 0.004 (0.129) Batch 1.016 (1.156) Remain 38:23:40 loss: 0.5102 Lr: 0.00456 [2024-02-18 04:55:50,272 INFO misc.py line 119 87073] Train: [24/100][291/1557] Data 0.004 (0.129) Batch 0.948 (1.155) Remain 38:22:13 loss: 0.5163 Lr: 0.00456 [2024-02-18 04:55:50,996 INFO misc.py line 119 87073] Train: [24/100][292/1557] Data 0.003 (0.129) Batch 0.712 (1.153) Remain 38:19:09 loss: 0.5033 Lr: 0.00456 [2024-02-18 04:55:51,770 INFO misc.py line 119 87073] Train: [24/100][293/1557] Data 0.015 (0.128) Batch 0.780 (1.152) Remain 38:16:34 loss: 0.3136 Lr: 0.00456 [2024-02-18 04:55:53,096 INFO misc.py line 119 87073] Train: [24/100][294/1557] Data 0.008 (0.128) Batch 1.322 (1.153) Remain 38:17:42 loss: 0.2574 Lr: 0.00456 [2024-02-18 04:55:54,261 INFO misc.py line 119 87073] Train: [24/100][295/1557] Data 0.013 (0.127) Batch 1.168 (1.153) Remain 38:17:48 loss: 0.6146 Lr: 0.00456 [2024-02-18 04:55:55,302 INFO misc.py line 119 87073] Train: [24/100][296/1557] Data 0.009 (0.127) Batch 1.035 (1.152) Remain 38:16:58 loss: 0.2735 Lr: 0.00456 [2024-02-18 04:55:56,149 INFO misc.py line 119 87073] Train: [24/100][297/1557] Data 0.015 (0.127) Batch 0.858 (1.151) Remain 38:14:57 loss: 0.5270 Lr: 0.00456 [2024-02-18 04:55:57,103 INFO misc.py line 119 87073] Train: [24/100][298/1557] Data 0.004 (0.126) Batch 0.955 (1.151) Remain 38:13:36 loss: 0.3921 Lr: 0.00456 [2024-02-18 04:55:57,896 INFO misc.py line 119 87073] Train: [24/100][299/1557] Data 0.004 (0.126) Batch 0.793 (1.150) Remain 38:11:11 loss: 0.4554 Lr: 0.00456 [2024-02-18 04:55:58,634 INFO misc.py line 119 87073] Train: [24/100][300/1557] Data 0.004 (0.125) Batch 0.737 (1.148) Remain 38:08:24 loss: 0.2176 Lr: 0.00456 [2024-02-18 04:55:59,930 INFO misc.py line 119 87073] Train: [24/100][301/1557] Data 0.005 (0.125) Batch 1.290 (1.149) Remain 38:09:19 loss: 0.1882 Lr: 0.00456 [2024-02-18 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Batch 0.832 (1.155) Remain 38:20:55 loss: 0.6069 Lr: 0.00456 [2024-02-18 04:57:58,426 INFO misc.py line 119 87073] Train: [24/100][402/1557] Data 0.003 (0.132) Batch 0.940 (1.155) Remain 38:19:50 loss: 0.9630 Lr: 0.00456 [2024-02-18 04:57:59,472 INFO misc.py line 119 87073] Train: [24/100][403/1557] Data 0.005 (0.132) Batch 1.046 (1.155) Remain 38:19:16 loss: 0.5146 Lr: 0.00456 [2024-02-18 04:58:00,247 INFO misc.py line 119 87073] Train: [24/100][404/1557] Data 0.005 (0.131) Batch 0.775 (1.154) Remain 38:17:22 loss: 0.4995 Lr: 0.00456 [2024-02-18 04:58:01,036 INFO misc.py line 119 87073] Train: [24/100][405/1557] Data 0.004 (0.131) Batch 0.789 (1.153) Remain 38:15:32 loss: 0.4132 Lr: 0.00456 [2024-02-18 04:58:02,204 INFO misc.py line 119 87073] Train: [24/100][406/1557] Data 0.005 (0.131) Batch 1.168 (1.153) Remain 38:15:36 loss: 0.2301 Lr: 0.00456 [2024-02-18 04:58:03,525 INFO misc.py line 119 87073] Train: [24/100][407/1557] Data 0.005 (0.130) Batch 1.314 (1.153) Remain 38:16:22 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Batch 0.868 (1.154) Remain 38:17:30 loss: 0.4514 Lr: 0.00456 [2024-02-18 04:59:02,735 INFO misc.py line 119 87073] Train: [24/100][458/1557] Data 0.004 (0.132) Batch 1.083 (1.154) Remain 38:17:10 loss: 0.5813 Lr: 0.00456 [2024-02-18 04:59:03,682 INFO misc.py line 119 87073] Train: [24/100][459/1557] Data 0.005 (0.132) Batch 0.948 (1.154) Remain 38:16:15 loss: 0.5682 Lr: 0.00456 [2024-02-18 04:59:04,441 INFO misc.py line 119 87073] Train: [24/100][460/1557] Data 0.003 (0.131) Batch 0.758 (1.153) Remain 38:14:30 loss: 0.2886 Lr: 0.00456 [2024-02-18 04:59:05,227 INFO misc.py line 119 87073] Train: [24/100][461/1557] Data 0.005 (0.131) Batch 0.786 (1.152) Remain 38:12:53 loss: 0.3257 Lr: 0.00456 [2024-02-18 04:59:06,478 INFO misc.py line 119 87073] Train: [24/100][462/1557] Data 0.004 (0.131) Batch 1.250 (1.152) Remain 38:13:18 loss: 0.2583 Lr: 0.00456 [2024-02-18 04:59:07,497 INFO misc.py line 119 87073] Train: [24/100][463/1557] Data 0.005 (0.131) Batch 1.010 (1.152) Remain 38:12:40 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line 119 87073] Train: [24/100][501/1557] Data 0.004 (0.121) Batch 1.113 (1.138) Remain 37:44:13 loss: 0.1638 Lr: 0.00456 [2024-02-18 04:59:45,023 INFO misc.py line 119 87073] Train: [24/100][502/1557] Data 0.004 (0.121) Batch 0.704 (1.137) Remain 37:42:28 loss: 0.7106 Lr: 0.00456 [2024-02-18 04:59:45,808 INFO misc.py line 119 87073] Train: [24/100][503/1557] Data 0.004 (0.121) Batch 0.773 (1.136) Remain 37:41:00 loss: 0.3410 Lr: 0.00456 [2024-02-18 04:59:47,104 INFO misc.py line 119 87073] Train: [24/100][504/1557] Data 0.016 (0.121) Batch 1.302 (1.137) Remain 37:41:38 loss: 0.2638 Lr: 0.00456 [2024-02-18 04:59:48,250 INFO misc.py line 119 87073] Train: [24/100][505/1557] Data 0.011 (0.120) Batch 1.144 (1.137) Remain 37:41:38 loss: 0.5792 Lr: 0.00456 [2024-02-18 04:59:49,440 INFO misc.py line 119 87073] Train: [24/100][506/1557] Data 0.013 (0.120) Batch 1.184 (1.137) Remain 37:41:49 loss: 0.4264 Lr: 0.00456 [2024-02-18 04:59:50,333 INFO misc.py line 119 87073] Train: 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Batch 0.847 (1.152) Remain 38:11:47 loss: 0.2095 Lr: 0.00456 [2024-02-18 05:00:06,011 INFO misc.py line 119 87073] Train: [24/100][514/1557] Data 0.004 (0.130) Batch 0.908 (1.151) Remain 38:10:49 loss: 0.6148 Lr: 0.00456 [2024-02-18 05:00:06,839 INFO misc.py line 119 87073] Train: [24/100][515/1557] Data 0.006 (0.130) Batch 0.830 (1.151) Remain 38:09:33 loss: 0.5133 Lr: 0.00456 [2024-02-18 05:00:07,650 INFO misc.py line 119 87073] Train: [24/100][516/1557] Data 0.004 (0.130) Batch 0.809 (1.150) Remain 38:08:12 loss: 0.3476 Lr: 0.00456 [2024-02-18 05:00:08,429 INFO misc.py line 119 87073] Train: [24/100][517/1557] Data 0.006 (0.130) Batch 0.773 (1.149) Remain 38:06:44 loss: 0.4697 Lr: 0.00455 [2024-02-18 05:00:09,756 INFO misc.py line 119 87073] Train: [24/100][518/1557] Data 0.012 (0.129) Batch 1.333 (1.150) Remain 38:07:25 loss: 0.2080 Lr: 0.00455 [2024-02-18 05:00:10,629 INFO misc.py line 119 87073] Train: [24/100][519/1557] Data 0.006 (0.129) Batch 0.874 (1.149) Remain 38:06:20 loss: 0.2874 Lr: 0.00455 [2024-02-18 05:00:11,659 INFO misc.py line 119 87073] Train: [24/100][520/1557] Data 0.005 (0.129) Batch 1.030 (1.149) Remain 38:05:52 loss: 0.1883 Lr: 0.00455 [2024-02-18 05:00:12,515 INFO misc.py line 119 87073] Train: [24/100][521/1557] Data 0.004 (0.129) Batch 0.855 (1.148) Remain 38:04:43 loss: 0.5307 Lr: 0.00455 [2024-02-18 05:00:13,485 INFO misc.py line 119 87073] Train: [24/100][522/1557] Data 0.005 (0.128) Batch 0.964 (1.148) Remain 38:03:59 loss: 0.6324 Lr: 0.00455 [2024-02-18 05:00:14,195 INFO misc.py line 119 87073] Train: [24/100][523/1557] Data 0.011 (0.128) Batch 0.716 (1.147) Remain 38:02:19 loss: 0.4529 Lr: 0.00455 [2024-02-18 05:00:14,929 INFO misc.py line 119 87073] Train: [24/100][524/1557] Data 0.005 (0.128) Batch 0.734 (1.146) Remain 38:00:43 loss: 0.4774 Lr: 0.00455 [2024-02-18 05:00:16,229 INFO misc.py line 119 87073] Train: [24/100][525/1557] Data 0.006 (0.128) Batch 1.300 (1.147) Remain 38:01:17 loss: 0.1877 Lr: 0.00455 [2024-02-18 05:00:17,098 INFO misc.py line 119 87073] Train: [24/100][526/1557] Data 0.006 (0.128) Batch 0.869 (1.146) Remain 38:00:12 loss: 0.3909 Lr: 0.00455 [2024-02-18 05:00:18,076 INFO misc.py line 119 87073] Train: [24/100][527/1557] Data 0.006 (0.127) Batch 0.979 (1.146) Remain 37:59:33 loss: 0.3655 Lr: 0.00455 [2024-02-18 05:00:19,001 INFO misc.py line 119 87073] Train: [24/100][528/1557] Data 0.005 (0.127) Batch 0.926 (1.145) Remain 37:58:42 loss: 0.6951 Lr: 0.00455 [2024-02-18 05:00:19,967 INFO misc.py line 119 87073] Train: [24/100][529/1557] Data 0.003 (0.127) Batch 0.966 (1.145) Remain 37:58:00 loss: 0.4481 Lr: 0.00455 [2024-02-18 05:00:22,771 INFO misc.py line 119 87073] Train: [24/100][530/1557] Data 1.748 (0.130) Batch 2.803 (1.148) Remain 38:04:14 loss: 0.6330 Lr: 0.00455 [2024-02-18 05:00:23,564 INFO misc.py line 119 87073] Train: [24/100][531/1557] Data 0.005 (0.130) Batch 0.783 (1.148) Remain 38:02:51 loss: 0.4695 Lr: 0.00455 [2024-02-18 05:00:24,811 INFO misc.py line 119 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line 119 87073] Train: [24/100][557/1557] Data 0.006 (0.124) Batch 0.804 (1.138) Remain 37:43:54 loss: 0.2509 Lr: 0.00455 [2024-02-18 05:00:48,944 INFO misc.py line 119 87073] Train: [24/100][558/1557] Data 0.004 (0.124) Batch 0.689 (1.137) Remain 37:42:16 loss: 0.4427 Lr: 0.00455 [2024-02-18 05:00:49,660 INFO misc.py line 119 87073] Train: [24/100][559/1557] Data 0.007 (0.123) Batch 0.719 (1.137) Remain 37:40:45 loss: 0.5213 Lr: 0.00455 [2024-02-18 05:00:50,850 INFO misc.py line 119 87073] Train: [24/100][560/1557] Data 0.004 (0.123) Batch 1.190 (1.137) Remain 37:40:56 loss: 0.6121 Lr: 0.00455 [2024-02-18 05:00:51,690 INFO misc.py line 119 87073] Train: [24/100][561/1557] Data 0.004 (0.123) Batch 0.839 (1.136) Remain 37:39:51 loss: 0.3914 Lr: 0.00455 [2024-02-18 05:00:52,681 INFO misc.py line 119 87073] Train: [24/100][562/1557] Data 0.007 (0.123) Batch 0.991 (1.136) Remain 37:39:19 loss: 0.3613 Lr: 0.00455 [2024-02-18 05:00:53,707 INFO misc.py line 119 87073] Train: 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Batch 0.991 (1.152) Remain 38:11:52 loss: 0.4755 Lr: 0.00455 [2024-02-18 05:01:11,235 INFO misc.py line 119 87073] Train: [24/100][570/1557] Data 0.004 (0.133) Batch 1.284 (1.153) Remain 38:12:18 loss: 0.3329 Lr: 0.00455 [2024-02-18 05:01:12,021 INFO misc.py line 119 87073] Train: [24/100][571/1557] Data 0.020 (0.133) Batch 0.801 (1.152) Remain 38:11:03 loss: 0.3757 Lr: 0.00455 [2024-02-18 05:01:12,894 INFO misc.py line 119 87073] Train: [24/100][572/1557] Data 0.004 (0.132) Batch 0.874 (1.152) Remain 38:10:04 loss: 0.4362 Lr: 0.00455 [2024-02-18 05:01:13,688 INFO misc.py line 119 87073] Train: [24/100][573/1557] Data 0.005 (0.132) Batch 0.793 (1.151) Remain 38:08:48 loss: 0.4463 Lr: 0.00455 [2024-02-18 05:01:14,915 INFO misc.py line 119 87073] Train: [24/100][574/1557] Data 0.006 (0.132) Batch 1.222 (1.151) Remain 38:09:01 loss: 0.2602 Lr: 0.00455 [2024-02-18 05:01:15,769 INFO misc.py line 119 87073] Train: [24/100][575/1557] Data 0.010 (0.132) Batch 0.859 (1.151) Remain 38:07:59 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Batch 0.954 (1.150) Remain 38:03:59 loss: 0.7139 Lr: 0.00455 [2024-02-18 05:04:22,712 INFO misc.py line 119 87073] Train: [24/100][738/1557] Data 0.007 (0.135) Batch 0.881 (1.150) Remain 38:03:14 loss: 0.2640 Lr: 0.00455 [2024-02-18 05:04:23,785 INFO misc.py line 119 87073] Train: [24/100][739/1557] Data 0.005 (0.134) Batch 1.070 (1.150) Remain 38:03:00 loss: 0.5213 Lr: 0.00455 [2024-02-18 05:04:24,555 INFO misc.py line 119 87073] Train: [24/100][740/1557] Data 0.008 (0.134) Batch 0.774 (1.149) Remain 38:01:58 loss: 0.4905 Lr: 0.00455 [2024-02-18 05:04:25,340 INFO misc.py line 119 87073] Train: [24/100][741/1557] Data 0.004 (0.134) Batch 0.785 (1.149) Remain 38:00:58 loss: 0.4691 Lr: 0.00455 [2024-02-18 05:04:26,591 INFO misc.py line 119 87073] Train: [24/100][742/1557] Data 0.004 (0.134) Batch 1.238 (1.149) Remain 38:01:12 loss: 0.3172 Lr: 0.00455 [2024-02-18 05:04:27,594 INFO misc.py line 119 87073] Train: [24/100][743/1557] Data 0.016 (0.134) Batch 1.014 (1.149) Remain 38:00:49 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Remain 37:52:20 loss: 0.2552 Lr: 0.00453 [2024-02-18 05:13:59,970 INFO misc.py line 119 87073] Train: [24/100][1241/1557] Data 0.004 (0.136) Batch 0.879 (1.149) Remain 37:51:53 loss: 0.4875 Lr: 0.00453 [2024-02-18 05:14:00,991 INFO misc.py line 119 87073] Train: [24/100][1242/1557] Data 0.008 (0.135) Batch 1.020 (1.149) Remain 37:51:40 loss: 0.6452 Lr: 0.00453 [2024-02-18 05:14:01,917 INFO misc.py line 119 87073] Train: [24/100][1243/1557] Data 0.009 (0.135) Batch 0.930 (1.149) Remain 37:51:18 loss: 0.3649 Lr: 0.00453 [2024-02-18 05:14:02,695 INFO misc.py line 119 87073] Train: [24/100][1244/1557] Data 0.004 (0.135) Batch 0.778 (1.148) Remain 37:50:41 loss: 0.3368 Lr: 0.00453 [2024-02-18 05:14:03,523 INFO misc.py line 119 87073] Train: [24/100][1245/1557] Data 0.005 (0.135) Batch 0.825 (1.148) Remain 37:50:09 loss: 0.5913 Lr: 0.00453 [2024-02-18 05:14:04,741 INFO misc.py line 119 87073] Train: [24/100][1246/1557] Data 0.008 (0.135) Batch 1.216 (1.148) Remain 37:50:14 loss: 0.2171 Lr: 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Train: [24/100][1259/1557] Data 0.010 (0.134) Batch 0.722 (1.146) Remain 37:45:34 loss: 0.2618 Lr: 0.00453 [2024-02-18 05:14:18,139 INFO misc.py line 119 87073] Train: [24/100][1260/1557] Data 0.004 (0.134) Batch 1.283 (1.146) Remain 37:45:46 loss: 0.1562 Lr: 0.00453 [2024-02-18 05:14:19,118 INFO misc.py line 119 87073] Train: [24/100][1261/1557] Data 0.008 (0.134) Batch 0.983 (1.146) Remain 37:45:29 loss: 0.5209 Lr: 0.00453 [2024-02-18 05:14:20,011 INFO misc.py line 119 87073] Train: [24/100][1262/1557] Data 0.004 (0.133) Batch 0.893 (1.146) Remain 37:45:04 loss: 0.5603 Lr: 0.00453 [2024-02-18 05:14:20,895 INFO misc.py line 119 87073] Train: [24/100][1263/1557] Data 0.005 (0.133) Batch 0.884 (1.145) Remain 37:44:38 loss: 0.8623 Lr: 0.00453 [2024-02-18 05:14:22,125 INFO misc.py line 119 87073] Train: [24/100][1264/1557] Data 0.004 (0.133) Batch 1.224 (1.146) Remain 37:44:45 loss: 0.2659 Lr: 0.00453 [2024-02-18 05:14:22,900 INFO misc.py line 119 87073] Train: [24/100][1265/1557] Data 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Remain 37:42:00 loss: 0.7127 Lr: 0.00453 [2024-02-18 05:14:29,237 INFO misc.py line 119 87073] Train: [24/100][1272/1557] Data 0.009 (0.132) Batch 0.773 (1.144) Remain 37:41:24 loss: 0.3723 Lr: 0.00453 [2024-02-18 05:14:30,013 INFO misc.py line 119 87073] Train: [24/100][1273/1557] Data 0.004 (0.132) Batch 0.773 (1.144) Remain 37:40:49 loss: 0.3494 Lr: 0.00453 [2024-02-18 05:14:31,106 INFO misc.py line 119 87073] Train: [24/100][1274/1557] Data 0.007 (0.132) Batch 1.096 (1.144) Remain 37:40:43 loss: 0.3983 Lr: 0.00453 [2024-02-18 05:14:31,943 INFO misc.py line 119 87073] Train: [24/100][1275/1557] Data 0.004 (0.132) Batch 0.838 (1.143) Remain 37:40:13 loss: 0.9771 Lr: 0.00453 [2024-02-18 05:14:32,880 INFO misc.py line 119 87073] Train: [24/100][1276/1557] Data 0.004 (0.132) Batch 0.936 (1.143) Remain 37:39:53 loss: 0.6967 Lr: 0.00453 [2024-02-18 05:14:33,779 INFO misc.py line 119 87073] Train: [24/100][1277/1557] Data 0.004 (0.132) Batch 0.899 (1.143) Remain 37:39:29 loss: 0.7278 Lr: 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INFO misc.py line 119 87073] Train: [24/100][1284/1557] Data 0.007 (0.131) Batch 1.124 (1.142) Remain 37:37:51 loss: 0.5447 Lr: 0.00453 [2024-02-18 05:14:41,681 INFO misc.py line 119 87073] Train: [24/100][1285/1557] Data 0.007 (0.131) Batch 0.877 (1.142) Remain 37:37:25 loss: 0.8542 Lr: 0.00453 [2024-02-18 05:14:42,396 INFO misc.py line 119 87073] Train: [24/100][1286/1557] Data 0.006 (0.131) Batch 0.715 (1.142) Remain 37:36:44 loss: 0.5607 Lr: 0.00453 [2024-02-18 05:14:43,199 INFO misc.py line 119 87073] Train: [24/100][1287/1557] Data 0.005 (0.131) Batch 0.803 (1.141) Remain 37:36:12 loss: 0.2669 Lr: 0.00453 [2024-02-18 05:14:44,444 INFO misc.py line 119 87073] Train: [24/100][1288/1557] Data 0.005 (0.131) Batch 1.246 (1.141) Remain 37:36:20 loss: 0.3514 Lr: 0.00453 [2024-02-18 05:14:45,444 INFO misc.py line 119 87073] Train: [24/100][1289/1557] Data 0.004 (0.131) Batch 0.998 (1.141) Remain 37:36:06 loss: 0.3497 Lr: 0.00453 [2024-02-18 05:14:46,331 INFO misc.py line 119 87073] Train: [24/100][1290/1557] Data 0.005 (0.131) Batch 0.889 (1.141) Remain 37:35:42 loss: 0.6360 Lr: 0.00453 [2024-02-18 05:14:47,378 INFO misc.py line 119 87073] Train: [24/100][1291/1557] Data 0.004 (0.131) Batch 1.045 (1.141) Remain 37:35:32 loss: 0.3994 Lr: 0.00453 [2024-02-18 05:14:48,445 INFO misc.py line 119 87073] Train: [24/100][1292/1557] Data 0.006 (0.130) Batch 1.069 (1.141) Remain 37:35:24 loss: 0.3257 Lr: 0.00453 [2024-02-18 05:14:49,207 INFO misc.py line 119 87073] Train: [24/100][1293/1557] Data 0.004 (0.130) Batch 0.761 (1.141) Remain 37:34:48 loss: 0.5260 Lr: 0.00453 [2024-02-18 05:14:49,953 INFO misc.py line 119 87073] Train: [24/100][1294/1557] Data 0.003 (0.130) Batch 0.745 (1.140) Remain 37:34:10 loss: 0.3218 Lr: 0.00453 [2024-02-18 05:15:01,247 INFO misc.py line 119 87073] Train: [24/100][1295/1557] Data 7.158 (0.136) Batch 11.295 (1.148) Remain 37:49:41 loss: 0.2676 Lr: 0.00453 [2024-02-18 05:15:02,132 INFO misc.py line 119 87073] Train: [24/100][1296/1557] Data 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Remain 37:47:07 loss: 0.1670 Lr: 0.00453 [2024-02-18 05:15:08,681 INFO misc.py line 119 87073] Train: [24/100][1303/1557] Data 0.009 (0.135) Batch 0.999 (1.147) Remain 37:46:52 loss: 0.4959 Lr: 0.00453 [2024-02-18 05:15:09,703 INFO misc.py line 119 87073] Train: [24/100][1304/1557] Data 0.005 (0.135) Batch 1.018 (1.147) Remain 37:46:39 loss: 0.4212 Lr: 0.00453 [2024-02-18 05:15:10,714 INFO misc.py line 119 87073] Train: [24/100][1305/1557] Data 0.011 (0.135) Batch 1.013 (1.147) Remain 37:46:26 loss: 0.2500 Lr: 0.00453 [2024-02-18 05:15:11,630 INFO misc.py line 119 87073] Train: [24/100][1306/1557] Data 0.007 (0.135) Batch 0.918 (1.147) Remain 37:46:04 loss: 0.6634 Lr: 0.00453 [2024-02-18 05:15:12,417 INFO misc.py line 119 87073] Train: [24/100][1307/1557] Data 0.005 (0.134) Batch 0.788 (1.146) Remain 37:45:30 loss: 0.7566 Lr: 0.00453 [2024-02-18 05:15:13,240 INFO misc.py line 119 87073] Train: [24/100][1308/1557] Data 0.004 (0.134) Batch 0.821 (1.146) Remain 37:44:59 loss: 0.4103 Lr: 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INFO misc.py line 119 87073] Train: [24/100][1315/1557] Data 0.006 (0.134) Batch 0.795 (1.145) Remain 37:42:26 loss: 0.2351 Lr: 0.00453 [2024-02-18 05:15:20,951 INFO misc.py line 119 87073] Train: [24/100][1316/1557] Data 0.008 (0.134) Batch 1.294 (1.145) Remain 37:42:39 loss: 0.1830 Lr: 0.00453 [2024-02-18 05:15:22,080 INFO misc.py line 119 87073] Train: [24/100][1317/1557] Data 0.006 (0.134) Batch 1.130 (1.145) Remain 37:42:36 loss: 0.6781 Lr: 0.00453 [2024-02-18 05:15:22,977 INFO misc.py line 119 87073] Train: [24/100][1318/1557] Data 0.004 (0.133) Batch 0.897 (1.145) Remain 37:42:13 loss: 0.2632 Lr: 0.00453 [2024-02-18 05:15:24,042 INFO misc.py line 119 87073] Train: [24/100][1319/1557] Data 0.004 (0.133) Batch 1.063 (1.145) Remain 37:42:04 loss: 0.3290 Lr: 0.00453 [2024-02-18 05:15:24,971 INFO misc.py line 119 87073] Train: [24/100][1320/1557] Data 0.006 (0.133) Batch 0.931 (1.145) Remain 37:41:44 loss: 0.1271 Lr: 0.00453 [2024-02-18 05:15:25,716 INFO misc.py line 119 87073] Train: [24/100][1321/1557] Data 0.004 (0.133) Batch 0.744 (1.144) Remain 37:41:07 loss: 0.4983 Lr: 0.00453 [2024-02-18 05:15:26,445 INFO misc.py line 119 87073] Train: [24/100][1322/1557] Data 0.006 (0.133) Batch 0.730 (1.144) Remain 37:40:28 loss: 0.4000 Lr: 0.00453 [2024-02-18 05:15:27,581 INFO misc.py line 119 87073] Train: [24/100][1323/1557] Data 0.004 (0.133) Batch 1.135 (1.144) Remain 37:40:26 loss: 0.0852 Lr: 0.00453 [2024-02-18 05:15:28,507 INFO misc.py line 119 87073] Train: [24/100][1324/1557] Data 0.005 (0.133) Batch 0.927 (1.144) Remain 37:40:06 loss: 0.7807 Lr: 0.00453 [2024-02-18 05:15:29,517 INFO misc.py line 119 87073] Train: [24/100][1325/1557] Data 0.005 (0.133) Batch 1.010 (1.144) Remain 37:39:53 loss: 0.5787 Lr: 0.00453 [2024-02-18 05:15:30,474 INFO misc.py line 119 87073] Train: [24/100][1326/1557] Data 0.004 (0.133) Batch 0.958 (1.143) Remain 37:39:35 loss: 0.5979 Lr: 0.00453 [2024-02-18 05:15:31,435 INFO misc.py line 119 87073] Train: [24/100][1327/1557] Data 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Remain 37:36:59 loss: 0.4499 Lr: 0.00453 [2024-02-18 05:15:37,636 INFO misc.py line 119 87073] Train: [24/100][1334/1557] Data 0.009 (0.132) Batch 0.814 (1.142) Remain 37:36:29 loss: 0.5833 Lr: 0.00453 [2024-02-18 05:15:38,416 INFO misc.py line 119 87073] Train: [24/100][1335/1557] Data 0.004 (0.132) Batch 0.780 (1.142) Remain 37:35:55 loss: 0.2244 Lr: 0.00453 [2024-02-18 05:15:39,219 INFO misc.py line 119 87073] Train: [24/100][1336/1557] Data 0.004 (0.132) Batch 0.793 (1.141) Remain 37:35:23 loss: 0.3205 Lr: 0.00453 [2024-02-18 05:15:40,496 INFO misc.py line 119 87073] Train: [24/100][1337/1557] Data 0.014 (0.132) Batch 1.277 (1.142) Remain 37:35:34 loss: 0.2318 Lr: 0.00453 [2024-02-18 05:15:41,515 INFO misc.py line 119 87073] Train: [24/100][1338/1557] Data 0.014 (0.132) Batch 1.018 (1.141) Remain 37:35:22 loss: 1.0795 Lr: 0.00453 [2024-02-18 05:15:42,563 INFO misc.py line 119 87073] Train: [24/100][1339/1557] Data 0.015 (0.131) Batch 1.056 (1.141) Remain 37:35:13 loss: 0.4666 Lr: 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Train: [24/100][1352/1557] Data 0.011 (0.136) Batch 0.873 (1.146) Remain 37:43:50 loss: 0.7185 Lr: 0.00453 [2024-02-18 05:16:04,546 INFO misc.py line 119 87073] Train: [24/100][1353/1557] Data 0.005 (0.136) Batch 1.098 (1.146) Remain 37:43:45 loss: 0.3094 Lr: 0.00453 [2024-02-18 05:16:05,600 INFO misc.py line 119 87073] Train: [24/100][1354/1557] Data 0.004 (0.135) Batch 1.054 (1.146) Remain 37:43:36 loss: 0.5365 Lr: 0.00453 [2024-02-18 05:16:06,406 INFO misc.py line 119 87073] Train: [24/100][1355/1557] Data 0.004 (0.135) Batch 0.806 (1.146) Remain 37:43:05 loss: 0.5220 Lr: 0.00453 [2024-02-18 05:16:07,283 INFO misc.py line 119 87073] Train: [24/100][1356/1557] Data 0.004 (0.135) Batch 0.875 (1.145) Remain 37:42:40 loss: 0.4572 Lr: 0.00453 [2024-02-18 05:16:08,072 INFO misc.py line 119 87073] Train: [24/100][1357/1557] Data 0.005 (0.135) Batch 0.790 (1.145) Remain 37:42:08 loss: 0.4148 Lr: 0.00453 [2024-02-18 05:16:09,261 INFO misc.py line 119 87073] Train: [24/100][1358/1557] Data 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Remain 37:40:05 loss: 0.3931 Lr: 0.00453 [2024-02-18 05:16:16,005 INFO misc.py line 119 87073] Train: [24/100][1365/1557] Data 0.004 (0.134) Batch 1.230 (1.144) Remain 37:40:12 loss: 0.1892 Lr: 0.00453 [2024-02-18 05:16:16,963 INFO misc.py line 119 87073] Train: [24/100][1366/1557] Data 0.004 (0.134) Batch 0.956 (1.144) Remain 37:39:54 loss: 0.7859 Lr: 0.00453 [2024-02-18 05:16:18,088 INFO misc.py line 119 87073] Train: [24/100][1367/1557] Data 0.005 (0.134) Batch 1.126 (1.144) Remain 37:39:51 loss: 0.5358 Lr: 0.00453 [2024-02-18 05:16:19,147 INFO misc.py line 119 87073] Train: [24/100][1368/1557] Data 0.004 (0.134) Batch 1.059 (1.144) Remain 37:39:43 loss: 0.4932 Lr: 0.00453 [2024-02-18 05:16:20,078 INFO misc.py line 119 87073] Train: [24/100][1369/1557] Data 0.004 (0.134) Batch 0.931 (1.144) Remain 37:39:23 loss: 0.3072 Lr: 0.00453 [2024-02-18 05:16:20,810 INFO misc.py line 119 87073] Train: [24/100][1370/1557] Data 0.004 (0.134) Batch 0.732 (1.144) Remain 37:38:46 loss: 0.7133 Lr: 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Train: [24/100][1383/1557] Data 0.004 (0.133) Batch 0.982 (1.142) Remain 37:34:58 loss: 0.4064 Lr: 0.00453 [2024-02-18 05:16:33,996 INFO misc.py line 119 87073] Train: [24/100][1384/1557] Data 0.007 (0.133) Batch 0.800 (1.141) Remain 37:34:28 loss: 0.8846 Lr: 0.00453 [2024-02-18 05:16:34,699 INFO misc.py line 119 87073] Train: [24/100][1385/1557] Data 0.011 (0.133) Batch 0.706 (1.141) Remain 37:33:49 loss: 0.6444 Lr: 0.00453 [2024-02-18 05:16:35,908 INFO misc.py line 119 87073] Train: [24/100][1386/1557] Data 0.004 (0.132) Batch 1.208 (1.141) Remain 37:33:54 loss: 0.1565 Lr: 0.00453 [2024-02-18 05:16:36,985 INFO misc.py line 119 87073] Train: [24/100][1387/1557] Data 0.004 (0.132) Batch 1.078 (1.141) Remain 37:33:48 loss: 0.7753 Lr: 0.00453 [2024-02-18 05:16:37,924 INFO misc.py line 119 87073] Train: [24/100][1388/1557] Data 0.004 (0.132) Batch 0.939 (1.141) Remain 37:33:29 loss: 1.0169 Lr: 0.00453 [2024-02-18 05:16:38,914 INFO misc.py line 119 87073] Train: [24/100][1389/1557] Data 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Remain 37:31:57 loss: 0.7190 Lr: 0.00453 [2024-02-18 05:16:45,729 INFO misc.py line 119 87073] Train: [24/100][1396/1557] Data 0.003 (0.132) Batch 0.807 (1.140) Remain 37:31:27 loss: 0.6862 Lr: 0.00453 [2024-02-18 05:16:46,631 INFO misc.py line 119 87073] Train: [24/100][1397/1557] Data 0.003 (0.131) Batch 0.893 (1.140) Remain 37:31:05 loss: 0.4598 Lr: 0.00453 [2024-02-18 05:16:47,318 INFO misc.py line 119 87073] Train: [24/100][1398/1557] Data 0.011 (0.131) Batch 0.695 (1.140) Remain 37:30:26 loss: 0.2282 Lr: 0.00453 [2024-02-18 05:16:48,031 INFO misc.py line 119 87073] Train: [24/100][1399/1557] Data 0.004 (0.131) Batch 0.707 (1.139) Remain 37:29:49 loss: 0.7498 Lr: 0.00453 [2024-02-18 05:16:49,207 INFO misc.py line 119 87073] Train: [24/100][1400/1557] Data 0.010 (0.131) Batch 1.175 (1.139) Remain 37:29:50 loss: 0.2144 Lr: 0.00453 [2024-02-18 05:16:50,291 INFO misc.py line 119 87073] Train: [24/100][1401/1557] Data 0.010 (0.131) Batch 1.084 (1.139) Remain 37:29:45 loss: 0.7345 Lr: 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INFO misc.py line 119 87073] Train: [24/100][1408/1557] Data 0.010 (0.136) Batch 0.908 (1.146) Remain 37:42:46 loss: 0.8897 Lr: 0.00453 [2024-02-18 05:17:08,661 INFO misc.py line 119 87073] Train: [24/100][1409/1557] Data 0.005 (0.136) Batch 1.042 (1.146) Remain 37:42:36 loss: 0.6367 Lr: 0.00453 [2024-02-18 05:17:09,724 INFO misc.py line 119 87073] Train: [24/100][1410/1557] Data 0.004 (0.136) Batch 1.063 (1.146) Remain 37:42:28 loss: 0.5088 Lr: 0.00453 [2024-02-18 05:17:10,797 INFO misc.py line 119 87073] Train: [24/100][1411/1557] Data 0.003 (0.136) Batch 1.073 (1.146) Remain 37:42:21 loss: 0.6340 Lr: 0.00453 [2024-02-18 05:17:11,581 INFO misc.py line 119 87073] Train: [24/100][1412/1557] Data 0.004 (0.136) Batch 0.784 (1.145) Remain 37:41:49 loss: 0.3355 Lr: 0.00453 [2024-02-18 05:17:12,375 INFO misc.py line 119 87073] Train: [24/100][1413/1557] Data 0.004 (0.136) Batch 0.791 (1.145) Remain 37:41:18 loss: 0.3706 Lr: 0.00453 [2024-02-18 05:17:13,652 INFO misc.py line 119 87073] Train: [24/100][1414/1557] Data 0.007 (0.136) Batch 1.275 (1.145) Remain 37:41:28 loss: 0.1877 Lr: 0.00453 [2024-02-18 05:17:14,634 INFO misc.py line 119 87073] Train: [24/100][1415/1557] Data 0.009 (0.136) Batch 0.987 (1.145) Remain 37:41:13 loss: 0.5390 Lr: 0.00453 [2024-02-18 05:17:15,525 INFO misc.py line 119 87073] Train: [24/100][1416/1557] Data 0.004 (0.136) Batch 0.892 (1.145) Remain 37:40:51 loss: 0.3805 Lr: 0.00453 [2024-02-18 05:17:16,434 INFO misc.py line 119 87073] Train: [24/100][1417/1557] Data 0.004 (0.136) Batch 0.908 (1.145) Remain 37:40:30 loss: 0.5331 Lr: 0.00453 [2024-02-18 05:17:17,293 INFO misc.py line 119 87073] Train: [24/100][1418/1557] Data 0.005 (0.136) Batch 0.859 (1.145) Remain 37:40:05 loss: 0.3901 Lr: 0.00453 [2024-02-18 05:17:18,124 INFO misc.py line 119 87073] Train: [24/100][1419/1557] Data 0.003 (0.135) Batch 0.829 (1.144) Remain 37:39:38 loss: 0.2135 Lr: 0.00453 [2024-02-18 05:17:18,972 INFO misc.py line 119 87073] Train: [24/100][1420/1557] Data 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Remain 37:37:32 loss: 0.2669 Lr: 0.00453 [2024-02-18 05:17:25,518 INFO misc.py line 119 87073] Train: [24/100][1427/1557] Data 0.005 (0.135) Batch 0.787 (1.143) Remain 37:37:02 loss: 0.5768 Lr: 0.00453 [2024-02-18 05:17:26,794 INFO misc.py line 119 87073] Train: [24/100][1428/1557] Data 0.009 (0.135) Batch 1.275 (1.143) Remain 37:37:11 loss: 0.1671 Lr: 0.00453 [2024-02-18 05:17:27,734 INFO misc.py line 119 87073] Train: [24/100][1429/1557] Data 0.010 (0.135) Batch 0.945 (1.143) Remain 37:36:54 loss: 0.4830 Lr: 0.00453 [2024-02-18 05:17:28,671 INFO misc.py line 119 87073] Train: [24/100][1430/1557] Data 0.005 (0.134) Batch 0.936 (1.143) Remain 37:36:36 loss: 0.7265 Lr: 0.00453 [2024-02-18 05:17:29,693 INFO misc.py line 119 87073] Train: [24/100][1431/1557] Data 0.005 (0.134) Batch 1.023 (1.143) Remain 37:36:24 loss: 0.2438 Lr: 0.00453 [2024-02-18 05:17:30,735 INFO misc.py line 119 87073] Train: [24/100][1432/1557] Data 0.004 (0.134) Batch 1.042 (1.143) Remain 37:36:15 loss: 0.3266 Lr: 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INFO misc.py line 119 87073] Train: [24/100][1439/1557] Data 0.004 (0.134) Batch 0.945 (1.142) Remain 37:34:18 loss: 0.3988 Lr: 0.00453 [2024-02-18 05:17:38,192 INFO misc.py line 119 87073] Train: [24/100][1440/1557] Data 0.005 (0.134) Batch 0.774 (1.142) Remain 37:33:46 loss: 0.4575 Lr: 0.00453 [2024-02-18 05:17:38,973 INFO misc.py line 119 87073] Train: [24/100][1441/1557] Data 0.009 (0.133) Batch 0.785 (1.141) Remain 37:33:16 loss: 0.4675 Lr: 0.00453 [2024-02-18 05:17:40,085 INFO misc.py line 119 87073] Train: [24/100][1442/1557] Data 0.005 (0.133) Batch 1.114 (1.141) Remain 37:33:12 loss: 0.1305 Lr: 0.00453 [2024-02-18 05:17:40,977 INFO misc.py line 119 87073] Train: [24/100][1443/1557] Data 0.004 (0.133) Batch 0.891 (1.141) Remain 37:32:51 loss: 0.4119 Lr: 0.00453 [2024-02-18 05:17:41,936 INFO misc.py line 119 87073] Train: [24/100][1444/1557] Data 0.006 (0.133) Batch 0.961 (1.141) Remain 37:32:35 loss: 0.3292 Lr: 0.00453 [2024-02-18 05:17:42,890 INFO misc.py line 119 87073] Train: [24/100][1445/1557] Data 0.003 (0.133) Batch 0.947 (1.141) Remain 37:32:18 loss: 0.3304 Lr: 0.00453 [2024-02-18 05:17:43,907 INFO misc.py line 119 87073] Train: [24/100][1446/1557] Data 0.010 (0.133) Batch 1.023 (1.141) Remain 37:32:07 loss: 0.3039 Lr: 0.00453 [2024-02-18 05:17:44,632 INFO misc.py line 119 87073] Train: [24/100][1447/1557] Data 0.004 (0.133) Batch 0.725 (1.141) Remain 37:31:32 loss: 0.2707 Lr: 0.00453 [2024-02-18 05:17:45,394 INFO misc.py line 119 87073] Train: [24/100][1448/1557] Data 0.004 (0.133) Batch 0.760 (1.140) Remain 37:30:59 loss: 0.5247 Lr: 0.00453 [2024-02-18 05:17:46,640 INFO misc.py line 119 87073] Train: [24/100][1449/1557] Data 0.005 (0.133) Batch 1.243 (1.140) Remain 37:31:07 loss: 0.4730 Lr: 0.00453 [2024-02-18 05:17:47,696 INFO misc.py line 119 87073] Train: [24/100][1450/1557] Data 0.009 (0.133) Batch 1.055 (1.140) Remain 37:30:58 loss: 0.5776 Lr: 0.00453 [2024-02-18 05:17:48,707 INFO misc.py line 119 87073] Train: [24/100][1451/1557] Data 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Remain 37:28:50 loss: 0.6929 Lr: 0.00453 [2024-02-18 05:17:55,273 INFO misc.py line 119 87073] Train: [24/100][1458/1557] Data 0.006 (0.132) Batch 1.082 (1.139) Remain 37:28:44 loss: 0.3590 Lr: 0.00453 [2024-02-18 05:17:56,262 INFO misc.py line 119 87073] Train: [24/100][1459/1557] Data 0.004 (0.132) Batch 0.988 (1.139) Remain 37:28:31 loss: 0.7366 Lr: 0.00453 [2024-02-18 05:17:57,063 INFO misc.py line 119 87073] Train: [24/100][1460/1557] Data 0.005 (0.132) Batch 0.801 (1.139) Remain 37:28:02 loss: 0.5488 Lr: 0.00453 [2024-02-18 05:17:57,768 INFO misc.py line 119 87073] Train: [24/100][1461/1557] Data 0.004 (0.132) Batch 0.704 (1.139) Remain 37:27:25 loss: 0.3703 Lr: 0.00453 [2024-02-18 05:17:58,545 INFO misc.py line 119 87073] Train: [24/100][1462/1557] Data 0.005 (0.132) Batch 0.778 (1.138) Remain 37:26:55 loss: 0.1723 Lr: 0.00453 [2024-02-18 05:18:09,004 INFO misc.py line 119 87073] Train: [24/100][1463/1557] Data 6.309 (0.136) Batch 10.456 (1.145) Remain 37:39:30 loss: 0.2941 Lr: 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Train: [24/100][1476/1557] Data 0.006 (0.135) Batch 0.739 (1.143) Remain 37:35:16 loss: 0.5012 Lr: 0.00453 [2024-02-18 05:18:22,232 INFO misc.py line 119 87073] Train: [24/100][1477/1557] Data 0.006 (0.135) Batch 1.309 (1.143) Remain 37:35:28 loss: 0.2284 Lr: 0.00453 [2024-02-18 05:18:23,270 INFO misc.py line 119 87073] Train: [24/100][1478/1557] Data 0.016 (0.135) Batch 1.042 (1.143) Remain 37:35:19 loss: 0.7217 Lr: 0.00453 [2024-02-18 05:18:24,180 INFO misc.py line 119 87073] Train: [24/100][1479/1557] Data 0.013 (0.134) Batch 0.917 (1.143) Remain 37:35:00 loss: 0.6326 Lr: 0.00453 [2024-02-18 05:18:25,144 INFO misc.py line 119 87073] Train: [24/100][1480/1557] Data 0.005 (0.134) Batch 0.964 (1.143) Remain 37:34:44 loss: 0.5659 Lr: 0.00453 [2024-02-18 05:18:26,518 INFO misc.py line 119 87073] Train: [24/100][1481/1557] Data 0.004 (0.134) Batch 1.364 (1.143) Remain 37:35:01 loss: 0.5461 Lr: 0.00453 [2024-02-18 05:18:27,286 INFO misc.py line 119 87073] Train: [24/100][1482/1557] Data 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Remain 37:33:39 loss: 0.4909 Lr: 0.00453 [2024-02-18 05:18:34,281 INFO misc.py line 119 87073] Train: [24/100][1489/1557] Data 0.005 (0.134) Batch 0.695 (1.142) Remain 37:33:03 loss: 0.3911 Lr: 0.00453 [2024-02-18 05:18:35,053 INFO misc.py line 119 87073] Train: [24/100][1490/1557] Data 0.004 (0.134) Batch 0.764 (1.141) Remain 37:32:32 loss: 0.3811 Lr: 0.00453 [2024-02-18 05:18:36,261 INFO misc.py line 119 87073] Train: [24/100][1491/1557] Data 0.012 (0.133) Batch 1.208 (1.142) Remain 37:32:36 loss: 0.1258 Lr: 0.00453 [2024-02-18 05:18:37,182 INFO misc.py line 119 87073] Train: [24/100][1492/1557] Data 0.013 (0.133) Batch 0.928 (1.141) Remain 37:32:18 loss: 0.4160 Lr: 0.00453 [2024-02-18 05:18:38,112 INFO misc.py line 119 87073] Train: [24/100][1493/1557] Data 0.005 (0.133) Batch 0.931 (1.141) Remain 37:32:00 loss: 0.3968 Lr: 0.00453 [2024-02-18 05:18:39,001 INFO misc.py line 119 87073] Train: [24/100][1494/1557] Data 0.004 (0.133) Batch 0.889 (1.141) Remain 37:31:38 loss: 0.6462 Lr: 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INFO misc.py line 119 87073] Train: [24/100][1501/1557] Data 0.006 (0.133) Batch 0.910 (1.140) Remain 37:29:36 loss: 0.5700 Lr: 0.00452 [2024-02-18 05:18:46,378 INFO misc.py line 119 87073] Train: [24/100][1502/1557] Data 0.005 (0.133) Batch 0.835 (1.140) Remain 37:29:11 loss: 0.3145 Lr: 0.00452 [2024-02-18 05:18:47,171 INFO misc.py line 119 87073] Train: [24/100][1503/1557] Data 0.004 (0.132) Batch 0.787 (1.140) Remain 37:28:42 loss: 0.4903 Lr: 0.00452 [2024-02-18 05:18:47,867 INFO misc.py line 119 87073] Train: [24/100][1504/1557] Data 0.011 (0.132) Batch 0.701 (1.139) Remain 37:28:06 loss: 0.3769 Lr: 0.00452 [2024-02-18 05:18:49,171 INFO misc.py line 119 87073] Train: [24/100][1505/1557] Data 0.006 (0.132) Batch 1.303 (1.139) Remain 37:28:18 loss: 0.3965 Lr: 0.00452 [2024-02-18 05:18:50,207 INFO misc.py line 119 87073] Train: [24/100][1506/1557] Data 0.006 (0.132) Batch 1.037 (1.139) Remain 37:28:09 loss: 0.4806 Lr: 0.00452 [2024-02-18 05:18:51,130 INFO misc.py line 119 87073] Train: [24/100][1507/1557] Data 0.005 (0.132) Batch 0.924 (1.139) Remain 37:27:51 loss: 0.4576 Lr: 0.00452 [2024-02-18 05:18:52,004 INFO misc.py line 119 87073] Train: [24/100][1508/1557] Data 0.005 (0.132) Batch 0.873 (1.139) Remain 37:27:29 loss: 0.1422 Lr: 0.00452 [2024-02-18 05:18:52,792 INFO misc.py line 119 87073] Train: [24/100][1509/1557] Data 0.005 (0.132) Batch 0.788 (1.139) Remain 37:27:00 loss: 0.6762 Lr: 0.00452 [2024-02-18 05:18:53,532 INFO misc.py line 119 87073] Train: [24/100][1510/1557] Data 0.005 (0.132) Batch 0.742 (1.139) Remain 37:26:28 loss: 0.3296 Lr: 0.00452 [2024-02-18 05:18:54,291 INFO misc.py line 119 87073] Train: [24/100][1511/1557] Data 0.003 (0.132) Batch 0.750 (1.138) Remain 37:25:56 loss: 0.1795 Lr: 0.00452 [2024-02-18 05:18:55,496 INFO misc.py line 119 87073] Train: [24/100][1512/1557] Data 0.012 (0.132) Batch 1.207 (1.138) Remain 37:26:00 loss: 0.1863 Lr: 0.00452 [2024-02-18 05:18:56,577 INFO misc.py line 119 87073] Train: [24/100][1513/1557] Data 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Remain 37:37:20 loss: 0.4243 Lr: 0.00452 [2024-02-18 05:19:13,269 INFO misc.py line 119 87073] Train: [24/100][1520/1557] Data 0.005 (0.135) Batch 0.997 (1.144) Remain 37:37:08 loss: 0.8868 Lr: 0.00452 [2024-02-18 05:19:14,313 INFO misc.py line 119 87073] Train: [24/100][1521/1557] Data 0.004 (0.135) Batch 1.045 (1.144) Remain 37:36:59 loss: 0.5225 Lr: 0.00452 [2024-02-18 05:19:15,158 INFO misc.py line 119 87073] Train: [24/100][1522/1557] Data 0.004 (0.135) Batch 0.844 (1.144) Remain 37:36:34 loss: 0.4548 Lr: 0.00452 [2024-02-18 05:19:16,082 INFO misc.py line 119 87073] Train: [24/100][1523/1557] Data 0.005 (0.135) Batch 0.921 (1.144) Remain 37:36:16 loss: 0.3137 Lr: 0.00452 [2024-02-18 05:19:16,810 INFO misc.py line 119 87073] Train: [24/100][1524/1557] Data 0.007 (0.135) Batch 0.731 (1.143) Remain 37:35:43 loss: 0.2954 Lr: 0.00452 [2024-02-18 05:19:17,544 INFO misc.py line 119 87073] Train: [24/100][1525/1557] Data 0.004 (0.135) Batch 0.728 (1.143) Remain 37:35:09 loss: 0.2418 Lr: 0.00452 [2024-02-18 05:19:18,786 INFO misc.py line 119 87073] Train: [24/100][1526/1557] Data 0.011 (0.135) Batch 1.247 (1.143) Remain 37:35:16 loss: 0.2672 Lr: 0.00452 [2024-02-18 05:19:19,740 INFO misc.py line 119 87073] Train: [24/100][1527/1557] Data 0.005 (0.135) Batch 0.954 (1.143) Remain 37:35:00 loss: 0.8761 Lr: 0.00452 [2024-02-18 05:19:20,721 INFO misc.py line 119 87073] Train: [24/100][1528/1557] Data 0.005 (0.135) Batch 0.981 (1.143) Remain 37:34:46 loss: 0.7359 Lr: 0.00452 [2024-02-18 05:19:21,659 INFO misc.py line 119 87073] Train: [24/100][1529/1557] Data 0.006 (0.135) Batch 0.939 (1.143) Remain 37:34:29 loss: 0.4930 Lr: 0.00452 [2024-02-18 05:19:22,636 INFO misc.py line 119 87073] Train: [24/100][1530/1557] Data 0.004 (0.135) Batch 0.977 (1.143) Remain 37:34:15 loss: 0.2994 Lr: 0.00452 [2024-02-18 05:19:23,432 INFO misc.py line 119 87073] Train: [24/100][1531/1557] Data 0.005 (0.134) Batch 0.795 (1.143) Remain 37:33:47 loss: 0.5202 Lr: 0.00452 [2024-02-18 05:19:24,231 INFO misc.py line 119 87073] Train: [24/100][1532/1557] Data 0.006 (0.134) Batch 0.800 (1.142) Remain 37:33:20 loss: 0.4429 Lr: 0.00452 [2024-02-18 05:19:25,487 INFO misc.py line 119 87073] Train: [24/100][1533/1557] Data 0.005 (0.134) Batch 1.255 (1.142) Remain 37:33:27 loss: 0.2013 Lr: 0.00452 [2024-02-18 05:19:26,518 INFO misc.py line 119 87073] Train: [24/100][1534/1557] Data 0.007 (0.134) Batch 1.032 (1.142) Remain 37:33:18 loss: 0.6954 Lr: 0.00452 [2024-02-18 05:19:27,416 INFO misc.py line 119 87073] Train: [24/100][1535/1557] Data 0.005 (0.134) Batch 0.899 (1.142) Remain 37:32:58 loss: 0.2536 Lr: 0.00452 [2024-02-18 05:19:28,474 INFO misc.py line 119 87073] Train: [24/100][1536/1557] Data 0.004 (0.134) Batch 1.058 (1.142) Remain 37:32:50 loss: 0.5161 Lr: 0.00452 [2024-02-18 05:19:29,596 INFO misc.py line 119 87073] Train: [24/100][1537/1557] Data 0.004 (0.134) Batch 1.121 (1.142) Remain 37:32:47 loss: 0.5744 Lr: 0.00452 [2024-02-18 05:19:30,357 INFO misc.py line 119 87073] Train: [24/100][1538/1557] Data 0.005 (0.134) Batch 0.761 (1.142) Remain 37:32:17 loss: 1.0218 Lr: 0.00452 [2024-02-18 05:19:31,133 INFO misc.py line 119 87073] Train: [24/100][1539/1557] Data 0.005 (0.134) Batch 0.776 (1.142) Remain 37:31:47 loss: 0.4505 Lr: 0.00452 [2024-02-18 05:19:32,416 INFO misc.py line 119 87073] Train: [24/100][1540/1557] Data 0.004 (0.134) Batch 1.281 (1.142) Remain 37:31:57 loss: 0.1759 Lr: 0.00452 [2024-02-18 05:19:33,253 INFO misc.py line 119 87073] Train: [24/100][1541/1557] Data 0.006 (0.134) Batch 0.839 (1.141) Remain 37:31:33 loss: 0.5234 Lr: 0.00452 [2024-02-18 05:19:34,243 INFO misc.py line 119 87073] Train: [24/100][1542/1557] Data 0.003 (0.133) Batch 0.990 (1.141) Remain 37:31:20 loss: 0.2074 Lr: 0.00452 [2024-02-18 05:19:35,248 INFO misc.py line 119 87073] Train: [24/100][1543/1557] Data 0.003 (0.133) Batch 1.004 (1.141) Remain 37:31:08 loss: 0.6120 Lr: 0.00452 [2024-02-18 05:19:36,143 INFO misc.py line 119 87073] Train: [24/100][1544/1557] Data 0.004 (0.133) Batch 0.895 (1.141) Remain 37:30:48 loss: 0.7668 Lr: 0.00452 [2024-02-18 05:19:36,885 INFO misc.py line 119 87073] Train: [24/100][1545/1557] Data 0.005 (0.133) Batch 0.741 (1.141) Remain 37:30:16 loss: 0.6902 Lr: 0.00452 [2024-02-18 05:19:37,624 INFO misc.py line 119 87073] Train: [24/100][1546/1557] Data 0.006 (0.133) Batch 0.741 (1.141) Remain 37:29:44 loss: 0.5648 Lr: 0.00452 [2024-02-18 05:19:38,767 INFO misc.py line 119 87073] Train: [24/100][1547/1557] Data 0.003 (0.133) Batch 1.141 (1.141) Remain 37:29:43 loss: 0.3180 Lr: 0.00452 [2024-02-18 05:19:39,659 INFO misc.py line 119 87073] Train: [24/100][1548/1557] Data 0.005 (0.133) Batch 0.892 (1.140) Remain 37:29:23 loss: 0.4472 Lr: 0.00452 [2024-02-18 05:19:40,774 INFO misc.py line 119 87073] Train: [24/100][1549/1557] Data 0.005 (0.133) Batch 1.113 (1.140) Remain 37:29:20 loss: 0.9352 Lr: 0.00452 [2024-02-18 05:19:41,816 INFO misc.py line 119 87073] Train: [24/100][1550/1557] Data 0.007 (0.133) Batch 1.042 (1.140) Remain 37:29:11 loss: 0.3778 Lr: 0.00452 [2024-02-18 05:19:42,744 INFO misc.py line 119 87073] Train: [24/100][1551/1557] Data 0.006 (0.133) Batch 0.930 (1.140) Remain 37:28:54 loss: 0.6552 Lr: 0.00452 [2024-02-18 05:19:43,455 INFO misc.py line 119 87073] Train: [24/100][1552/1557] Data 0.004 (0.133) Batch 0.711 (1.140) Remain 37:28:20 loss: 0.5298 Lr: 0.00452 [2024-02-18 05:19:44,245 INFO misc.py line 119 87073] Train: [24/100][1553/1557] Data 0.004 (0.133) Batch 0.790 (1.140) Remain 37:27:52 loss: 0.2987 Lr: 0.00452 [2024-02-18 05:19:45,360 INFO misc.py line 119 87073] Train: [24/100][1554/1557] Data 0.004 (0.132) Batch 1.114 (1.140) Remain 37:27:49 loss: 0.3110 Lr: 0.00452 [2024-02-18 05:19:46,404 INFO misc.py line 119 87073] Train: [24/100][1555/1557] Data 0.005 (0.132) Batch 1.044 (1.140) Remain 37:27:41 loss: 0.5984 Lr: 0.00452 [2024-02-18 05:19:47,705 INFO misc.py line 119 87073] Train: [24/100][1556/1557] Data 0.005 (0.132) Batch 1.301 (1.140) Remain 37:27:52 loss: 0.7125 Lr: 0.00452 [2024-02-18 05:19:48,650 INFO misc.py line 119 87073] Train: [24/100][1557/1557] Data 0.005 (0.132) Batch 0.945 (1.140) Remain 37:27:36 loss: 0.4759 Lr: 0.00452 [2024-02-18 05:19:48,651 INFO misc.py line 136 87073] Train result: loss: 0.4762 [2024-02-18 05:19:48,651 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 05:20:14,957 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7811 [2024-02-18 05:20:15,734 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6426 [2024-02-18 05:20:17,861 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3509 [2024-02-18 05:20:20,071 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3247 [2024-02-18 05:20:25,009 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2421 [2024-02-18 05:20:25,709 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1086 [2024-02-18 05:20:26,970 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2621 [2024-02-18 05:20:29,921 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3295 [2024-02-18 05:20:31,730 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2655 [2024-02-18 05:20:33,345 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7015/0.7681/0.9019. [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9204/0.9545 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9822/0.9892 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8534/0.9678 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3584/0.4021 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6477/0.6722 [2024-02-18 05:20:33,345 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8031/0.8869 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8003/0.9350 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9192/0.9614 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7933/0.8367 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7153/0.7790 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7497/0.8802 [2024-02-18 05:20:33,346 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5764/0.7206 [2024-02-18 05:20:33,346 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 05:20:33,348 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 05:20:33,348 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 05:20:41,277 INFO misc.py line 119 87073] Train: [25/100][1/1557] Data 1.439 (1.439) Batch 2.163 (2.163) Remain 71:04:58 loss: 0.3029 Lr: 0.00452 [2024-02-18 05:20:42,334 INFO misc.py line 119 87073] Train: [25/100][2/1557] Data 0.006 (0.006) Batch 1.058 (1.058) Remain 34:45:59 loss: 0.4860 Lr: 0.00452 [2024-02-18 05:20:43,235 INFO misc.py line 119 87073] Train: [25/100][3/1557] Data 0.005 (0.005) Batch 0.901 (0.901) Remain 29:37:43 loss: 0.4129 Lr: 0.00452 [2024-02-18 05:20:44,239 INFO misc.py line 119 87073] Train: [25/100][4/1557] Data 0.006 (0.006) Batch 1.003 (1.003) Remain 32:58:58 loss: 0.7879 Lr: 0.00452 [2024-02-18 05:20:45,016 INFO misc.py line 119 87073] Train: [25/100][5/1557] Data 0.006 (0.006) Batch 0.777 (0.890) Remain 29:16:02 loss: 1.2380 Lr: 0.00452 [2024-02-18 05:20:45,782 INFO misc.py line 119 87073] Train: [25/100][6/1557] Data 0.005 (0.006) Batch 0.764 (0.848) Remain 27:52:57 loss: 0.4342 Lr: 0.00452 [2024-02-18 05:20:54,017 INFO misc.py line 119 87073] Train: [25/100][7/1557] Data 0.007 (0.006) Batch 8.238 (2.696) Remain 88:36:15 loss: 0.2205 Lr: 0.00452 [2024-02-18 05:20:54,772 INFO misc.py line 119 87073] Train: [25/100][8/1557] Data 0.004 (0.006) Batch 0.744 (2.305) Remain 75:46:32 loss: 0.5373 Lr: 0.00452 [2024-02-18 05:20:55,979 INFO misc.py line 119 87073] Train: [25/100][9/1557] Data 0.014 (0.007) Batch 1.210 (2.123) Remain 69:46:26 loss: 0.3262 Lr: 0.00452 [2024-02-18 05:20:56,969 INFO misc.py line 119 87073] Train: [25/100][10/1557] Data 0.011 (0.008) Batch 0.998 (1.962) Remain 64:29:24 loss: 0.5948 Lr: 0.00452 [2024-02-18 05:20:57,862 INFO misc.py line 119 87073] Train: [25/100][11/1557] Data 0.004 (0.007) Batch 0.892 (1.828) Remain 60:05:34 loss: 0.4480 Lr: 0.00452 [2024-02-18 05:20:58,620 INFO misc.py line 119 87073] Train: [25/100][12/1557] Data 0.005 (0.007) Batch 0.758 (1.709) Remain 56:11:02 loss: 0.2065 Lr: 0.00452 [2024-02-18 05:20:59,391 INFO misc.py line 119 87073] Train: [25/100][13/1557] Data 0.004 (0.007) Batch 0.764 (1.615) Remain 53:04:35 loss: 0.4042 Lr: 0.00452 [2024-02-18 05:21:00,580 INFO misc.py line 119 87073] Train: [25/100][14/1557] Data 0.011 (0.007) Batch 1.191 (1.576) Remain 51:48:36 loss: 0.3531 Lr: 0.00452 [2024-02-18 05:21:01,470 INFO misc.py line 119 87073] Train: [25/100][15/1557] Data 0.009 (0.007) Batch 0.894 (1.520) Remain 49:56:30 loss: 0.3574 Lr: 0.00452 [2024-02-18 05:21:02,412 INFO misc.py line 119 87073] Train: [25/100][16/1557] Data 0.005 (0.007) Batch 0.943 (1.475) Remain 48:28:58 loss: 0.6352 Lr: 0.00452 [2024-02-18 05:21:03,465 INFO misc.py line 119 87073] Train: [25/100][17/1557] Data 0.005 (0.007) Batch 1.054 (1.445) Remain 47:29:38 loss: 0.8264 Lr: 0.00452 [2024-02-18 05:21:04,288 INFO misc.py line 119 87073] Train: [25/100][18/1557] Data 0.003 (0.007) Batch 0.823 (1.404) Remain 46:07:46 loss: 0.3268 Lr: 0.00452 [2024-02-18 05:21:05,073 INFO misc.py line 119 87073] Train: [25/100][19/1557] Data 0.004 (0.006) Batch 0.781 (1.365) Remain 44:51:01 loss: 0.4035 Lr: 0.00452 [2024-02-18 05:21:05,845 INFO misc.py line 119 87073] Train: [25/100][20/1557] Data 0.007 (0.007) Batch 0.774 (1.330) Remain 43:42:31 loss: 0.4391 Lr: 0.00452 [2024-02-18 05:21:07,117 INFO misc.py line 119 87073] Train: [25/100][21/1557] Data 0.005 (0.006) Batch 1.267 (1.326) Remain 43:35:37 loss: 0.2642 Lr: 0.00452 [2024-02-18 05:21:08,142 INFO misc.py line 119 87073] Train: [25/100][22/1557] Data 0.010 (0.007) Batch 1.031 (1.311) Remain 43:04:55 loss: 0.5154 Lr: 0.00452 [2024-02-18 05:21:09,196 INFO misc.py line 119 87073] Train: [25/100][23/1557] Data 0.004 (0.006) Batch 1.043 (1.298) Remain 42:38:27 loss: 0.2297 Lr: 0.00452 [2024-02-18 05:21:09,993 INFO misc.py line 119 87073] Train: [25/100][24/1557] Data 0.015 (0.007) Batch 0.809 (1.274) Remain 41:52:34 loss: 0.2761 Lr: 0.00452 [2024-02-18 05:21:10,869 INFO misc.py line 119 87073] Train: [25/100][25/1557] Data 0.003 (0.007) Batch 0.876 (1.256) Remain 41:16:49 loss: 0.7400 Lr: 0.00452 [2024-02-18 05:21:11,689 INFO misc.py line 119 87073] Train: [25/100][26/1557] Data 0.004 (0.007) Batch 0.817 (1.237) Remain 40:39:07 loss: 0.4434 Lr: 0.00452 [2024-02-18 05:21:12,469 INFO misc.py line 119 87073] Train: [25/100][27/1557] Data 0.008 (0.007) Batch 0.783 (1.218) Remain 40:01:47 loss: 0.4909 Lr: 0.00452 [2024-02-18 05:21:13,603 INFO misc.py line 119 87073] Train: [25/100][28/1557] Data 0.004 (0.007) Batch 1.134 (1.215) Remain 39:55:09 loss: 0.1982 Lr: 0.00452 [2024-02-18 05:21:14,495 INFO misc.py line 119 87073] Train: [25/100][29/1557] Data 0.004 (0.006) Batch 0.892 (1.202) Remain 39:30:41 loss: 0.6845 Lr: 0.00452 [2024-02-18 05:21:15,528 INFO misc.py line 119 87073] Train: [25/100][30/1557] Data 0.003 (0.006) Batch 1.033 (1.196) Remain 39:18:17 loss: 0.2223 Lr: 0.00452 [2024-02-18 05:21:16,522 INFO misc.py line 119 87073] Train: [25/100][31/1557] Data 0.003 (0.006) Batch 0.994 (1.189) Remain 39:04:02 loss: 0.7036 Lr: 0.00452 [2024-02-18 05:21:17,479 INFO misc.py line 119 87073] Train: [25/100][32/1557] Data 0.004 (0.006) Batch 0.952 (1.181) Remain 38:47:55 loss: 0.4388 Lr: 0.00452 [2024-02-18 05:21:18,218 INFO misc.py line 119 87073] Train: [25/100][33/1557] Data 0.008 (0.006) Batch 0.743 (1.166) Remain 38:19:07 loss: 0.2066 Lr: 0.00452 [2024-02-18 05:21:19,009 INFO misc.py line 119 87073] Train: [25/100][34/1557] Data 0.005 (0.006) Batch 0.786 (1.154) Remain 37:54:54 loss: 0.2552 Lr: 0.00452 [2024-02-18 05:21:20,363 INFO misc.py line 119 87073] Train: [25/100][35/1557] Data 0.010 (0.006) Batch 1.350 (1.160) Remain 38:07:00 loss: 0.3127 Lr: 0.00452 [2024-02-18 05:21:21,396 INFO misc.py line 119 87073] Train: [25/100][36/1557] Data 0.014 (0.007) Batch 1.043 (1.156) Remain 38:00:00 loss: 0.4300 Lr: 0.00452 [2024-02-18 05:21:22,559 INFO misc.py line 119 87073] Train: [25/100][37/1557] Data 0.004 (0.006) Batch 1.155 (1.156) Remain 37:59:53 loss: 0.5093 Lr: 0.00452 [2024-02-18 05:21:23,485 INFO misc.py line 119 87073] Train: [25/100][38/1557] Data 0.012 (0.007) Batch 0.934 (1.150) Remain 37:47:20 loss: 0.5144 Lr: 0.00452 [2024-02-18 05:21:24,437 INFO misc.py line 119 87073] Train: [25/100][39/1557] Data 0.004 (0.007) Batch 0.952 (1.145) Remain 37:36:28 loss: 0.7353 Lr: 0.00452 [2024-02-18 05:21:25,215 INFO misc.py line 119 87073] Train: [25/100][40/1557] Data 0.003 (0.006) Batch 0.777 (1.135) Remain 37:16:53 loss: 0.4843 Lr: 0.00452 [2024-02-18 05:21:25,966 INFO misc.py line 119 87073] Train: [25/100][41/1557] Data 0.004 (0.006) Batch 0.741 (1.124) Remain 36:56:27 loss: 0.2465 Lr: 0.00452 [2024-02-18 05:21:27,211 INFO misc.py line 119 87073] Train: [25/100][42/1557] Data 0.014 (0.007) Batch 1.246 (1.127) Remain 37:02:34 loss: 0.2965 Lr: 0.00452 [2024-02-18 05:21:28,199 INFO misc.py line 119 87073] Train: [25/100][43/1557] Data 0.013 (0.007) Batch 0.998 (1.124) Remain 36:56:11 loss: 0.3653 Lr: 0.00452 [2024-02-18 05:21:29,078 INFO misc.py line 119 87073] Train: [25/100][44/1557] Data 0.004 (0.007) Batch 0.877 (1.118) Remain 36:44:18 loss: 0.3299 Lr: 0.00452 [2024-02-18 05:21:29,918 INFO misc.py line 119 87073] Train: [25/100][45/1557] Data 0.006 (0.007) Batch 0.840 (1.111) Remain 36:31:14 loss: 0.3085 Lr: 0.00452 [2024-02-18 05:21:30,819 INFO misc.py line 119 87073] Train: [25/100][46/1557] Data 0.006 (0.007) Batch 0.903 (1.107) Remain 36:21:40 loss: 0.7583 Lr: 0.00452 [2024-02-18 05:21:31,619 INFO misc.py line 119 87073] Train: [25/100][47/1557] Data 0.003 (0.007) Batch 0.800 (1.100) Remain 36:07:53 loss: 1.1466 Lr: 0.00452 [2024-02-18 05:21:32,424 INFO misc.py line 119 87073] Train: [25/100][48/1557] Data 0.003 (0.006) Batch 0.799 (1.093) Remain 35:54:42 loss: 0.2626 Lr: 0.00452 [2024-02-18 05:21:33,597 INFO misc.py line 119 87073] Train: [25/100][49/1557] Data 0.009 (0.007) Batch 1.169 (1.095) Remain 35:57:56 loss: 0.1940 Lr: 0.00452 [2024-02-18 05:21:34,653 INFO misc.py line 119 87073] Train: [25/100][50/1557] Data 0.014 (0.007) Batch 1.054 (1.094) Remain 35:56:13 loss: 0.3488 Lr: 0.00452 [2024-02-18 05:21:35,516 INFO misc.py line 119 87073] Train: [25/100][51/1557] Data 0.016 (0.007) Batch 0.874 (1.089) Remain 35:47:10 loss: 0.5408 Lr: 0.00452 [2024-02-18 05:21:36,606 INFO misc.py line 119 87073] Train: [25/100][52/1557] Data 0.005 (0.007) Batch 1.089 (1.089) Remain 35:47:09 loss: 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Remain 40:16:59 loss: 0.4586 Lr: 0.00448 [2024-02-18 05:46:16,224 INFO misc.py line 119 87073] Train: [25/100][1241/1557] Data 0.006 (0.073) Batch 0.951 (1.238) Remain 40:16:31 loss: 0.2405 Lr: 0.00448 [2024-02-18 05:46:17,135 INFO misc.py line 119 87073] Train: [25/100][1242/1557] Data 0.004 (0.073) Batch 0.911 (1.238) Remain 40:15:58 loss: 0.5469 Lr: 0.00448 [2024-02-18 05:46:18,098 INFO misc.py line 119 87073] Train: [25/100][1243/1557] Data 0.004 (0.073) Batch 0.962 (1.238) Remain 40:15:31 loss: 0.4109 Lr: 0.00448 [2024-02-18 05:46:18,894 INFO misc.py line 119 87073] Train: [25/100][1244/1557] Data 0.004 (0.073) Batch 0.785 (1.237) Remain 40:14:47 loss: 0.3302 Lr: 0.00448 [2024-02-18 05:46:19,700 INFO misc.py line 119 87073] Train: [25/100][1245/1557] Data 0.016 (0.073) Batch 0.817 (1.237) Remain 40:14:06 loss: 0.3565 Lr: 0.00448 [2024-02-18 05:46:20,858 INFO misc.py line 119 87073] Train: [25/100][1246/1557] Data 0.005 (0.073) Batch 1.158 (1.237) Remain 40:13:58 loss: 0.2549 Lr: 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Train: [25/100][1259/1557] Data 0.003 (0.072) Batch 0.812 (1.234) Remain 40:07:01 loss: 1.0985 Lr: 0.00448 [2024-02-18 05:46:33,789 INFO misc.py line 119 87073] Train: [25/100][1260/1557] Data 0.006 (0.072) Batch 1.138 (1.234) Remain 40:06:51 loss: 0.2578 Lr: 0.00448 [2024-02-18 05:46:34,628 INFO misc.py line 119 87073] Train: [25/100][1261/1557] Data 0.011 (0.072) Batch 0.845 (1.233) Remain 40:06:14 loss: 0.3347 Lr: 0.00448 [2024-02-18 05:46:35,755 INFO misc.py line 119 87073] Train: [25/100][1262/1557] Data 0.006 (0.072) Batch 1.129 (1.233) Remain 40:06:03 loss: 1.2357 Lr: 0.00448 [2024-02-18 05:46:36,560 INFO misc.py line 119 87073] Train: [25/100][1263/1557] Data 0.004 (0.072) Batch 0.806 (1.233) Remain 40:05:22 loss: 0.6585 Lr: 0.00448 [2024-02-18 05:46:37,544 INFO misc.py line 119 87073] Train: [25/100][1264/1557] Data 0.004 (0.072) Batch 0.984 (1.233) Remain 40:04:57 loss: 0.3481 Lr: 0.00448 [2024-02-18 05:46:38,327 INFO misc.py line 119 87073] Train: [25/100][1265/1557] Data 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Remain 40:02:27 loss: 0.3861 Lr: 0.00448 [2024-02-18 05:46:45,437 INFO misc.py line 119 87073] Train: [25/100][1272/1557] Data 0.012 (0.071) Batch 0.797 (1.231) Remain 40:01:46 loss: 0.9055 Lr: 0.00448 [2024-02-18 05:46:46,197 INFO misc.py line 119 87073] Train: [25/100][1273/1557] Data 0.005 (0.071) Batch 0.761 (1.231) Remain 40:01:01 loss: 0.4412 Lr: 0.00448 [2024-02-18 05:46:47,359 INFO misc.py line 119 87073] Train: [25/100][1274/1557] Data 0.003 (0.071) Batch 1.155 (1.231) Remain 40:00:53 loss: 0.1701 Lr: 0.00448 [2024-02-18 05:46:48,391 INFO misc.py line 119 87073] Train: [25/100][1275/1557] Data 0.012 (0.071) Batch 1.031 (1.230) Remain 40:00:33 loss: 0.5903 Lr: 0.00448 [2024-02-18 05:46:49,314 INFO misc.py line 119 87073] Train: [25/100][1276/1557] Data 0.013 (0.071) Batch 0.932 (1.230) Remain 40:00:05 loss: 0.3315 Lr: 0.00448 [2024-02-18 05:46:50,266 INFO misc.py line 119 87073] Train: [25/100][1277/1557] Data 0.004 (0.071) Batch 0.952 (1.230) Remain 39:59:38 loss: 0.4762 Lr: 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INFO misc.py line 119 87073] Train: [25/100][1284/1557] Data 0.004 (0.071) Batch 1.139 (1.229) Remain 39:56:40 loss: 0.4022 Lr: 0.00448 [2024-02-18 05:46:57,957 INFO misc.py line 119 87073] Train: [25/100][1285/1557] Data 0.003 (0.071) Batch 0.935 (1.228) Remain 39:56:12 loss: 0.6956 Lr: 0.00448 [2024-02-18 05:46:58,738 INFO misc.py line 119 87073] Train: [25/100][1286/1557] Data 0.003 (0.071) Batch 0.773 (1.228) Remain 39:55:29 loss: 0.5600 Lr: 0.00448 [2024-02-18 05:46:59,481 INFO misc.py line 119 87073] Train: [25/100][1287/1557] Data 0.011 (0.071) Batch 0.751 (1.228) Remain 39:54:44 loss: 0.1912 Lr: 0.00448 [2024-02-18 05:47:00,633 INFO misc.py line 119 87073] Train: [25/100][1288/1557] Data 0.003 (0.071) Batch 1.150 (1.228) Remain 39:54:36 loss: 0.2894 Lr: 0.00448 [2024-02-18 05:47:01,620 INFO misc.py line 119 87073] Train: [25/100][1289/1557] Data 0.006 (0.071) Batch 0.989 (1.227) Remain 39:54:13 loss: 0.8586 Lr: 0.00448 [2024-02-18 05:47:02,705 INFO misc.py line 119 87073] Train: [25/100][1290/1557] Data 0.004 (0.071) Batch 1.085 (1.227) Remain 39:53:59 loss: 0.6345 Lr: 0.00448 [2024-02-18 05:47:03,463 INFO misc.py line 119 87073] Train: [25/100][1291/1557] Data 0.004 (0.071) Batch 0.758 (1.227) Remain 39:53:15 loss: 0.6017 Lr: 0.00448 [2024-02-18 05:47:04,405 INFO misc.py line 119 87073] Train: [25/100][1292/1557] Data 0.004 (0.070) Batch 0.938 (1.227) Remain 39:52:48 loss: 0.2654 Lr: 0.00448 [2024-02-18 05:47:05,195 INFO misc.py line 119 87073] Train: [25/100][1293/1557] Data 0.007 (0.070) Batch 0.794 (1.226) Remain 39:52:07 loss: 0.3915 Lr: 0.00448 [2024-02-18 05:47:05,889 INFO misc.py line 119 87073] Train: [25/100][1294/1557] Data 0.003 (0.070) Batch 0.683 (1.226) Remain 39:51:17 loss: 0.2744 Lr: 0.00448 [2024-02-18 05:47:22,398 INFO misc.py line 119 87073] Train: [25/100][1295/1557] Data 3.604 (0.073) Batch 16.518 (1.238) Remain 40:14:21 loss: 0.1564 Lr: 0.00448 [2024-02-18 05:47:23,354 INFO misc.py line 119 87073] Train: [25/100][1296/1557] Data 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Remain 40:11:17 loss: 0.2765 Lr: 0.00448 [2024-02-18 05:47:29,978 INFO misc.py line 119 87073] Train: [25/100][1303/1557] Data 0.006 (0.073) Batch 0.865 (1.236) Remain 40:10:42 loss: 0.4791 Lr: 0.00448 [2024-02-18 05:47:30,913 INFO misc.py line 119 87073] Train: [25/100][1304/1557] Data 0.004 (0.073) Batch 0.936 (1.236) Remain 40:10:14 loss: 0.7824 Lr: 0.00448 [2024-02-18 05:47:31,881 INFO misc.py line 119 87073] Train: [25/100][1305/1557] Data 0.003 (0.073) Batch 0.967 (1.236) Remain 40:09:48 loss: 0.3993 Lr: 0.00448 [2024-02-18 05:47:32,941 INFO misc.py line 119 87073] Train: [25/100][1306/1557] Data 0.003 (0.073) Batch 1.060 (1.235) Remain 40:09:31 loss: 0.5494 Lr: 0.00448 [2024-02-18 05:47:33,627 INFO misc.py line 119 87073] Train: [25/100][1307/1557] Data 0.003 (0.072) Batch 0.677 (1.235) Remain 40:08:40 loss: 0.1753 Lr: 0.00448 [2024-02-18 05:47:34,424 INFO misc.py line 119 87073] Train: [25/100][1308/1557] Data 0.012 (0.072) Batch 0.806 (1.235) Remain 40:08:00 loss: 0.3842 Lr: 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INFO misc.py line 119 87073] Train: [25/100][1315/1557] Data 0.004 (0.072) Batch 0.776 (1.233) Remain 40:04:53 loss: 0.5630 Lr: 0.00448 [2024-02-18 05:47:42,203 INFO misc.py line 119 87073] Train: [25/100][1316/1557] Data 0.004 (0.072) Batch 1.139 (1.233) Remain 40:04:44 loss: 0.3019 Lr: 0.00448 [2024-02-18 05:47:43,161 INFO misc.py line 119 87073] Train: [25/100][1317/1557] Data 0.003 (0.072) Batch 0.958 (1.233) Remain 40:04:18 loss: 0.2793 Lr: 0.00448 [2024-02-18 05:47:44,082 INFO misc.py line 119 87073] Train: [25/100][1318/1557] Data 0.003 (0.072) Batch 0.921 (1.233) Remain 40:03:49 loss: 0.3460 Lr: 0.00448 [2024-02-18 05:47:45,246 INFO misc.py line 119 87073] Train: [25/100][1319/1557] Data 0.004 (0.072) Batch 1.164 (1.233) Remain 40:03:41 loss: 1.0910 Lr: 0.00448 [2024-02-18 05:47:46,181 INFO misc.py line 119 87073] Train: [25/100][1320/1557] Data 0.004 (0.072) Batch 0.934 (1.232) Remain 40:03:14 loss: 0.7575 Lr: 0.00448 [2024-02-18 05:47:46,965 INFO misc.py line 119 87073] Train: [25/100][1321/1557] Data 0.005 (0.072) Batch 0.784 (1.232) Remain 40:02:33 loss: 0.2321 Lr: 0.00448 [2024-02-18 05:47:47,754 INFO misc.py line 119 87073] Train: [25/100][1322/1557] Data 0.005 (0.072) Batch 0.782 (1.232) Remain 40:01:52 loss: 0.4726 Lr: 0.00448 [2024-02-18 05:47:49,022 INFO misc.py line 119 87073] Train: [25/100][1323/1557] Data 0.012 (0.072) Batch 1.266 (1.232) Remain 40:01:53 loss: 0.3083 Lr: 0.00448 [2024-02-18 05:47:49,884 INFO misc.py line 119 87073] Train: [25/100][1324/1557] Data 0.014 (0.072) Batch 0.872 (1.231) Remain 40:01:20 loss: 1.2515 Lr: 0.00448 [2024-02-18 05:47:50,743 INFO misc.py line 119 87073] Train: [25/100][1325/1557] Data 0.003 (0.072) Batch 0.858 (1.231) Remain 40:00:46 loss: 0.4012 Lr: 0.00448 [2024-02-18 05:47:51,811 INFO misc.py line 119 87073] Train: [25/100][1326/1557] Data 0.004 (0.072) Batch 1.059 (1.231) Remain 40:00:30 loss: 0.7520 Lr: 0.00448 [2024-02-18 05:47:52,696 INFO misc.py line 119 87073] Train: [25/100][1327/1557] Data 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Remain 39:57:24 loss: 0.1632 Lr: 0.00448 [2024-02-18 05:47:59,384 INFO misc.py line 119 87073] Train: [25/100][1334/1557] Data 0.003 (0.071) Batch 0.981 (1.229) Remain 39:57:01 loss: 0.5619 Lr: 0.00448 [2024-02-18 05:48:00,144 INFO misc.py line 119 87073] Train: [25/100][1335/1557] Data 0.004 (0.071) Batch 0.760 (1.229) Remain 39:56:18 loss: 0.4129 Lr: 0.00448 [2024-02-18 05:48:00,828 INFO misc.py line 119 87073] Train: [25/100][1336/1557] Data 0.004 (0.071) Batch 0.675 (1.228) Remain 39:55:28 loss: 0.2185 Lr: 0.00448 [2024-02-18 05:48:02,043 INFO misc.py line 119 87073] Train: [25/100][1337/1557] Data 0.012 (0.071) Batch 1.216 (1.228) Remain 39:55:26 loss: 0.2149 Lr: 0.00448 [2024-02-18 05:48:03,086 INFO misc.py line 119 87073] Train: [25/100][1338/1557] Data 0.011 (0.071) Batch 1.050 (1.228) Remain 39:55:09 loss: 0.5603 Lr: 0.00448 [2024-02-18 05:48:04,074 INFO misc.py line 119 87073] Train: [25/100][1339/1557] Data 0.005 (0.071) Batch 0.989 (1.228) Remain 39:54:47 loss: 0.2081 Lr: 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Train: [25/100][1352/1557] Data 0.004 (0.073) Batch 0.847 (1.237) Remain 40:12:17 loss: 0.6732 Lr: 0.00448 [2024-02-18 05:48:33,283 INFO misc.py line 119 87073] Train: [25/100][1353/1557] Data 0.008 (0.073) Batch 0.955 (1.237) Remain 40:11:51 loss: 0.8507 Lr: 0.00448 [2024-02-18 05:48:34,157 INFO misc.py line 119 87073] Train: [25/100][1354/1557] Data 0.004 (0.073) Batch 0.873 (1.237) Remain 40:11:18 loss: 0.4078 Lr: 0.00448 [2024-02-18 05:48:35,097 INFO misc.py line 119 87073] Train: [25/100][1355/1557] Data 0.005 (0.073) Batch 0.935 (1.237) Remain 40:10:51 loss: 0.3738 Lr: 0.00448 [2024-02-18 05:48:35,929 INFO misc.py line 119 87073] Train: [25/100][1356/1557] Data 0.011 (0.072) Batch 0.838 (1.236) Remain 40:10:15 loss: 0.4084 Lr: 0.00448 [2024-02-18 05:48:36,787 INFO misc.py line 119 87073] Train: [25/100][1357/1557] Data 0.004 (0.072) Batch 0.859 (1.236) Remain 40:09:41 loss: 0.3388 Lr: 0.00448 [2024-02-18 05:48:37,997 INFO misc.py line 119 87073] Train: [25/100][1358/1557] Data 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Remain 40:06:46 loss: 0.3983 Lr: 0.00448 [2024-02-18 05:48:44,734 INFO misc.py line 119 87073] Train: [25/100][1365/1557] Data 0.004 (0.072) Batch 1.234 (1.235) Remain 40:06:45 loss: 0.1417 Lr: 0.00448 [2024-02-18 05:48:45,654 INFO misc.py line 119 87073] Train: [25/100][1366/1557] Data 0.005 (0.072) Batch 0.920 (1.234) Remain 40:06:16 loss: 0.3754 Lr: 0.00448 [2024-02-18 05:48:46,679 INFO misc.py line 119 87073] Train: [25/100][1367/1557] Data 0.003 (0.072) Batch 1.024 (1.234) Remain 40:05:57 loss: 0.5229 Lr: 0.00448 [2024-02-18 05:48:47,587 INFO misc.py line 119 87073] Train: [25/100][1368/1557] Data 0.006 (0.072) Batch 0.910 (1.234) Remain 40:05:28 loss: 0.4166 Lr: 0.00448 [2024-02-18 05:48:48,456 INFO misc.py line 119 87073] Train: [25/100][1369/1557] Data 0.004 (0.072) Batch 0.869 (1.234) Remain 40:04:55 loss: 0.5363 Lr: 0.00448 [2024-02-18 05:48:49,236 INFO misc.py line 119 87073] Train: [25/100][1370/1557] Data 0.003 (0.072) Batch 0.767 (1.233) Remain 40:04:14 loss: 0.2225 Lr: 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Train: [25/100][1383/1557] Data 0.005 (0.071) Batch 1.010 (1.231) Remain 39:58:28 loss: 0.3124 Lr: 0.00448 [2024-02-18 05:49:02,098 INFO misc.py line 119 87073] Train: [25/100][1384/1557] Data 0.009 (0.071) Batch 0.735 (1.230) Remain 39:57:45 loss: 0.4593 Lr: 0.00448 [2024-02-18 05:49:02,851 INFO misc.py line 119 87073] Train: [25/100][1385/1557] Data 0.004 (0.071) Batch 0.751 (1.230) Remain 39:57:03 loss: 0.2026 Lr: 0.00448 [2024-02-18 05:49:04,055 INFO misc.py line 119 87073] Train: [25/100][1386/1557] Data 0.005 (0.071) Batch 1.205 (1.230) Remain 39:57:00 loss: 0.1630 Lr: 0.00448 [2024-02-18 05:49:04,933 INFO misc.py line 119 87073] Train: [25/100][1387/1557] Data 0.005 (0.071) Batch 0.877 (1.230) Remain 39:56:29 loss: 0.6377 Lr: 0.00448 [2024-02-18 05:49:05,810 INFO misc.py line 119 87073] Train: [25/100][1388/1557] Data 0.006 (0.071) Batch 0.878 (1.229) Remain 39:55:58 loss: 0.4175 Lr: 0.00448 [2024-02-18 05:49:06,625 INFO misc.py line 119 87073] Train: [25/100][1389/1557] Data 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Remain 39:52:44 loss: 0.6293 Lr: 0.00448 [2024-02-18 05:49:12,999 INFO misc.py line 119 87073] Train: [25/100][1396/1557] Data 0.004 (0.071) Batch 0.790 (1.227) Remain 39:52:06 loss: 0.5403 Lr: 0.00448 [2024-02-18 05:49:13,945 INFO misc.py line 119 87073] Train: [25/100][1397/1557] Data 0.006 (0.071) Batch 0.948 (1.227) Remain 39:51:41 loss: 0.6576 Lr: 0.00448 [2024-02-18 05:49:14,647 INFO misc.py line 119 87073] Train: [25/100][1398/1557] Data 0.003 (0.070) Batch 0.701 (1.227) Remain 39:50:56 loss: 0.8165 Lr: 0.00448 [2024-02-18 05:49:15,424 INFO misc.py line 119 87073] Train: [25/100][1399/1557] Data 0.005 (0.070) Batch 0.771 (1.226) Remain 39:50:17 loss: 0.3570 Lr: 0.00448 [2024-02-18 05:49:16,587 INFO misc.py line 119 87073] Train: [25/100][1400/1557] Data 0.010 (0.070) Batch 1.165 (1.226) Remain 39:50:10 loss: 0.2086 Lr: 0.00448 [2024-02-18 05:49:17,558 INFO misc.py line 119 87073] Train: [25/100][1401/1557] Data 0.009 (0.070) Batch 0.977 (1.226) Remain 39:49:48 loss: 0.4920 Lr: 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INFO misc.py line 119 87073] Train: [25/100][1408/1557] Data 0.005 (0.073) Batch 1.034 (1.238) Remain 40:12:33 loss: 0.1985 Lr: 0.00448 [2024-02-18 05:49:43,518 INFO misc.py line 119 87073] Train: [25/100][1409/1557] Data 0.012 (0.073) Batch 0.867 (1.238) Remain 40:12:01 loss: 0.4946 Lr: 0.00448 [2024-02-18 05:49:44,404 INFO misc.py line 119 87073] Train: [25/100][1410/1557] Data 0.005 (0.073) Batch 0.886 (1.238) Remain 40:11:31 loss: 0.3365 Lr: 0.00448 [2024-02-18 05:49:45,295 INFO misc.py line 119 87073] Train: [25/100][1411/1557] Data 0.003 (0.073) Batch 0.886 (1.237) Remain 40:11:00 loss: 0.7366 Lr: 0.00448 [2024-02-18 05:49:46,067 INFO misc.py line 119 87073] Train: [25/100][1412/1557] Data 0.009 (0.073) Batch 0.778 (1.237) Remain 40:10:21 loss: 0.1820 Lr: 0.00448 [2024-02-18 05:49:46,814 INFO misc.py line 119 87073] Train: [25/100][1413/1557] Data 0.004 (0.073) Batch 0.747 (1.237) Remain 40:09:39 loss: 0.1858 Lr: 0.00448 [2024-02-18 05:49:48,090 INFO misc.py line 119 87073] Train: [25/100][1414/1557] Data 0.004 (0.073) Batch 1.265 (1.237) Remain 40:09:40 loss: 0.1693 Lr: 0.00448 [2024-02-18 05:49:49,026 INFO misc.py line 119 87073] Train: [25/100][1415/1557] Data 0.015 (0.073) Batch 0.947 (1.236) Remain 40:09:15 loss: 0.5681 Lr: 0.00448 [2024-02-18 05:49:50,124 INFO misc.py line 119 87073] Train: [25/100][1416/1557] Data 0.003 (0.073) Batch 1.097 (1.236) Remain 40:09:02 loss: 0.4734 Lr: 0.00448 [2024-02-18 05:49:51,061 INFO misc.py line 119 87073] Train: [25/100][1417/1557] Data 0.004 (0.072) Batch 0.938 (1.236) Remain 40:08:36 loss: 0.4638 Lr: 0.00448 [2024-02-18 05:49:52,030 INFO misc.py line 119 87073] Train: [25/100][1418/1557] Data 0.003 (0.072) Batch 0.969 (1.236) Remain 40:08:13 loss: 0.3333 Lr: 0.00448 [2024-02-18 05:49:52,929 INFO misc.py line 119 87073] Train: [25/100][1419/1557] Data 0.003 (0.072) Batch 0.898 (1.236) Remain 40:07:44 loss: 0.3202 Lr: 0.00448 [2024-02-18 05:49:53,698 INFO misc.py line 119 87073] Train: [25/100][1420/1557] Data 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Remain 40:04:32 loss: 0.5121 Lr: 0.00448 [2024-02-18 05:50:00,129 INFO misc.py line 119 87073] Train: [25/100][1427/1557] Data 0.005 (0.072) Batch 0.765 (1.234) Remain 40:03:53 loss: 0.3990 Lr: 0.00448 [2024-02-18 05:50:01,312 INFO misc.py line 119 87073] Train: [25/100][1428/1557] Data 0.017 (0.072) Batch 1.187 (1.234) Remain 40:03:48 loss: 0.1205 Lr: 0.00448 [2024-02-18 05:50:02,167 INFO misc.py line 119 87073] Train: [25/100][1429/1557] Data 0.013 (0.072) Batch 0.864 (1.233) Remain 40:03:16 loss: 0.2876 Lr: 0.00448 [2024-02-18 05:50:03,088 INFO misc.py line 119 87073] Train: [25/100][1430/1557] Data 0.004 (0.072) Batch 0.920 (1.233) Remain 40:02:49 loss: 0.3384 Lr: 0.00448 [2024-02-18 05:50:04,052 INFO misc.py line 119 87073] Train: [25/100][1431/1557] Data 0.004 (0.072) Batch 0.959 (1.233) Remain 40:02:25 loss: 0.4832 Lr: 0.00448 [2024-02-18 05:50:05,279 INFO misc.py line 119 87073] Train: [25/100][1432/1557] Data 0.009 (0.072) Batch 1.226 (1.233) Remain 40:02:24 loss: 0.4000 Lr: 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Train: [25/100][1445/1557] Data 0.004 (0.071) Batch 1.022 (1.230) Remain 39:56:56 loss: 0.6048 Lr: 0.00448 [2024-02-18 05:50:18,410 INFO misc.py line 119 87073] Train: [25/100][1446/1557] Data 0.004 (0.071) Batch 0.954 (1.230) Remain 39:56:32 loss: 0.4735 Lr: 0.00448 [2024-02-18 05:50:19,350 INFO misc.py line 119 87073] Train: [25/100][1447/1557] Data 0.003 (0.071) Batch 0.940 (1.230) Remain 39:56:07 loss: 0.5424 Lr: 0.00448 [2024-02-18 05:50:20,143 INFO misc.py line 119 87073] Train: [25/100][1448/1557] Data 0.004 (0.071) Batch 0.789 (1.230) Remain 39:55:31 loss: 0.4191 Lr: 0.00448 [2024-02-18 05:50:21,419 INFO misc.py line 119 87073] Train: [25/100][1449/1557] Data 0.008 (0.071) Batch 1.270 (1.230) Remain 39:55:33 loss: 0.1608 Lr: 0.00448 [2024-02-18 05:50:22,358 INFO misc.py line 119 87073] Train: [25/100][1450/1557] Data 0.015 (0.071) Batch 0.949 (1.230) Remain 39:55:09 loss: 0.3714 Lr: 0.00448 [2024-02-18 05:50:23,329 INFO misc.py line 119 87073] Train: [25/100][1451/1557] Data 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Remain 39:51:59 loss: 0.2338 Lr: 0.00448 [2024-02-18 05:50:29,571 INFO misc.py line 119 87073] Train: [25/100][1458/1557] Data 0.004 (0.071) Batch 0.859 (1.228) Remain 39:51:28 loss: 0.8794 Lr: 0.00448 [2024-02-18 05:50:30,578 INFO misc.py line 119 87073] Train: [25/100][1459/1557] Data 0.003 (0.071) Batch 0.999 (1.228) Remain 39:51:09 loss: 0.3658 Lr: 0.00448 [2024-02-18 05:50:31,442 INFO misc.py line 119 87073] Train: [25/100][1460/1557] Data 0.012 (0.071) Batch 0.872 (1.227) Remain 39:50:39 loss: 0.2601 Lr: 0.00448 [2024-02-18 05:50:32,210 INFO misc.py line 119 87073] Train: [25/100][1461/1557] Data 0.004 (0.071) Batch 0.768 (1.227) Remain 39:50:01 loss: 0.3559 Lr: 0.00448 [2024-02-18 05:50:33,036 INFO misc.py line 119 87073] Train: [25/100][1462/1557] Data 0.004 (0.070) Batch 0.822 (1.227) Remain 39:49:27 loss: 0.4133 Lr: 0.00448 [2024-02-18 05:50:49,732 INFO misc.py line 119 87073] Train: [25/100][1463/1557] Data 4.058 (0.073) Batch 16.700 (1.237) Remain 40:10:04 loss: 0.1663 Lr: 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Train: [25/100][1476/1557] Data 0.005 (0.073) Batch 0.808 (1.235) Remain 40:04:54 loss: 0.3818 Lr: 0.00448 [2024-02-18 05:51:03,264 INFO misc.py line 119 87073] Train: [25/100][1477/1557] Data 0.004 (0.073) Batch 1.151 (1.235) Remain 40:04:46 loss: 0.1653 Lr: 0.00448 [2024-02-18 05:51:04,094 INFO misc.py line 119 87073] Train: [25/100][1478/1557] Data 0.011 (0.073) Batch 0.838 (1.234) Remain 40:04:13 loss: 0.2442 Lr: 0.00448 [2024-02-18 05:51:05,026 INFO misc.py line 119 87073] Train: [25/100][1479/1557] Data 0.003 (0.072) Batch 0.932 (1.234) Remain 40:03:48 loss: 0.4093 Lr: 0.00448 [2024-02-18 05:51:06,011 INFO misc.py line 119 87073] Train: [25/100][1480/1557] Data 0.004 (0.072) Batch 0.985 (1.234) Remain 40:03:27 loss: 0.7975 Lr: 0.00448 [2024-02-18 05:51:07,119 INFO misc.py line 119 87073] Train: [25/100][1481/1557] Data 0.003 (0.072) Batch 1.098 (1.234) Remain 40:03:15 loss: 0.4566 Lr: 0.00448 [2024-02-18 05:51:07,789 INFO misc.py line 119 87073] Train: [25/100][1482/1557] Data 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Remain 40:00:29 loss: 0.3700 Lr: 0.00448 [2024-02-18 05:51:14,507 INFO misc.py line 119 87073] Train: [25/100][1489/1557] Data 0.004 (0.072) Batch 0.766 (1.232) Remain 39:59:51 loss: 0.2234 Lr: 0.00448 [2024-02-18 05:51:15,290 INFO misc.py line 119 87073] Train: [25/100][1490/1557] Data 0.004 (0.072) Batch 0.772 (1.232) Remain 39:59:13 loss: 0.4545 Lr: 0.00448 [2024-02-18 05:51:16,541 INFO misc.py line 119 87073] Train: [25/100][1491/1557] Data 0.014 (0.072) Batch 1.243 (1.232) Remain 39:59:13 loss: 0.2981 Lr: 0.00448 [2024-02-18 05:51:17,634 INFO misc.py line 119 87073] Train: [25/100][1492/1557] Data 0.023 (0.072) Batch 1.103 (1.232) Remain 39:59:02 loss: 0.5579 Lr: 0.00448 [2024-02-18 05:51:18,608 INFO misc.py line 119 87073] Train: [25/100][1493/1557] Data 0.012 (0.072) Batch 0.982 (1.232) Remain 39:58:41 loss: 0.3743 Lr: 0.00448 [2024-02-18 05:51:19,425 INFO misc.py line 119 87073] Train: [25/100][1494/1557] Data 0.005 (0.072) Batch 0.818 (1.232) Remain 39:58:07 loss: 0.2828 Lr: 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INFO misc.py line 119 87073] Train: [25/100][1501/1557] Data 0.004 (0.072) Batch 0.802 (1.230) Remain 39:55:32 loss: 0.6750 Lr: 0.00448 [2024-02-18 05:51:27,110 INFO misc.py line 119 87073] Train: [25/100][1502/1557] Data 0.014 (0.071) Batch 0.949 (1.230) Remain 39:55:08 loss: 0.6581 Lr: 0.00448 [2024-02-18 05:51:27,862 INFO misc.py line 119 87073] Train: [25/100][1503/1557] Data 0.004 (0.071) Batch 0.750 (1.230) Remain 39:54:30 loss: 0.3890 Lr: 0.00448 [2024-02-18 05:51:28,581 INFO misc.py line 119 87073] Train: [25/100][1504/1557] Data 0.006 (0.071) Batch 0.720 (1.229) Remain 39:53:49 loss: 0.5171 Lr: 0.00448 [2024-02-18 05:51:29,847 INFO misc.py line 119 87073] Train: [25/100][1505/1557] Data 0.005 (0.071) Batch 1.260 (1.229) Remain 39:53:50 loss: 0.1923 Lr: 0.00448 [2024-02-18 05:51:30,710 INFO misc.py line 119 87073] Train: [25/100][1506/1557] Data 0.011 (0.071) Batch 0.870 (1.229) Remain 39:53:21 loss: 0.9003 Lr: 0.00448 [2024-02-18 05:51:31,617 INFO misc.py line 119 87073] Train: [25/100][1507/1557] Data 0.004 (0.071) Batch 0.905 (1.229) Remain 39:52:54 loss: 0.5572 Lr: 0.00448 [2024-02-18 05:51:32,537 INFO misc.py line 119 87073] Train: [25/100][1508/1557] Data 0.006 (0.071) Batch 0.915 (1.229) Remain 39:52:29 loss: 0.2836 Lr: 0.00447 [2024-02-18 05:51:33,485 INFO misc.py line 119 87073] Train: [25/100][1509/1557] Data 0.012 (0.071) Batch 0.956 (1.229) Remain 39:52:06 loss: 0.3290 Lr: 0.00447 [2024-02-18 05:51:34,256 INFO misc.py line 119 87073] Train: [25/100][1510/1557] Data 0.004 (0.071) Batch 0.770 (1.228) Remain 39:51:30 loss: 0.4403 Lr: 0.00447 [2024-02-18 05:51:34,958 INFO misc.py line 119 87073] Train: [25/100][1511/1557] Data 0.004 (0.071) Batch 0.692 (1.228) Remain 39:50:47 loss: 0.2099 Lr: 0.00447 [2024-02-18 05:51:36,208 INFO misc.py line 119 87073] Train: [25/100][1512/1557] Data 0.013 (0.071) Batch 1.252 (1.228) Remain 39:50:48 loss: 0.2829 Lr: 0.00447 [2024-02-18 05:51:37,237 INFO misc.py line 119 87073] Train: [25/100][1513/1557] Data 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Remain 40:08:32 loss: 0.1512 Lr: 0.00447 [2024-02-18 05:51:59,522 INFO misc.py line 119 87073] Train: [25/100][1520/1557] Data 0.006 (0.073) Batch 0.797 (1.237) Remain 40:07:57 loss: 0.7407 Lr: 0.00447 [2024-02-18 05:52:00,544 INFO misc.py line 119 87073] Train: [25/100][1521/1557] Data 0.010 (0.073) Batch 1.027 (1.237) Remain 40:07:39 loss: 0.6832 Lr: 0.00447 [2024-02-18 05:52:01,504 INFO misc.py line 119 87073] Train: [25/100][1522/1557] Data 0.006 (0.073) Batch 0.960 (1.237) Remain 40:07:17 loss: 0.1768 Lr: 0.00447 [2024-02-18 05:52:02,590 INFO misc.py line 119 87073] Train: [25/100][1523/1557] Data 0.005 (0.073) Batch 1.086 (1.236) Remain 40:07:04 loss: 0.5727 Lr: 0.00447 [2024-02-18 05:52:03,351 INFO misc.py line 119 87073] Train: [25/100][1524/1557] Data 0.005 (0.073) Batch 0.762 (1.236) Remain 40:06:26 loss: 0.3633 Lr: 0.00447 [2024-02-18 05:52:04,150 INFO misc.py line 119 87073] Train: [25/100][1525/1557] Data 0.004 (0.072) Batch 0.799 (1.236) Remain 40:05:52 loss: 0.2957 Lr: 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Train: [25/100][1538/1557] Data 0.004 (0.072) Batch 0.791 (1.234) Remain 40:01:28 loss: 0.2859 Lr: 0.00447 [2024-02-18 05:52:17,759 INFO misc.py line 119 87073] Train: [25/100][1539/1557] Data 0.004 (0.072) Batch 0.784 (1.233) Remain 40:00:52 loss: 0.3539 Lr: 0.00447 [2024-02-18 05:52:18,934 INFO misc.py line 119 87073] Train: [25/100][1540/1557] Data 0.017 (0.072) Batch 1.179 (1.233) Remain 40:00:47 loss: 0.1959 Lr: 0.00447 [2024-02-18 05:52:20,004 INFO misc.py line 119 87073] Train: [25/100][1541/1557] Data 0.014 (0.072) Batch 1.067 (1.233) Remain 40:00:33 loss: 0.3952 Lr: 0.00447 [2024-02-18 05:52:20,981 INFO misc.py line 119 87073] Train: [25/100][1542/1557] Data 0.017 (0.072) Batch 0.990 (1.233) Remain 40:00:13 loss: 0.4794 Lr: 0.00447 [2024-02-18 05:52:21,828 INFO misc.py line 119 87073] Train: [25/100][1543/1557] Data 0.004 (0.072) Batch 0.847 (1.233) Remain 39:59:43 loss: 0.4084 Lr: 0.00447 [2024-02-18 05:52:22,857 INFO misc.py line 119 87073] Train: [25/100][1544/1557] Data 0.004 (0.072) Batch 1.017 (1.233) Remain 39:59:25 loss: 0.7902 Lr: 0.00447 [2024-02-18 05:52:23,632 INFO misc.py line 119 87073] Train: [25/100][1545/1557] Data 0.015 (0.072) Batch 0.787 (1.232) Remain 39:58:50 loss: 0.2404 Lr: 0.00447 [2024-02-18 05:52:24,395 INFO misc.py line 119 87073] Train: [25/100][1546/1557] Data 0.004 (0.072) Batch 0.751 (1.232) Remain 39:58:13 loss: 0.4232 Lr: 0.00447 [2024-02-18 05:52:25,691 INFO misc.py line 119 87073] Train: [25/100][1547/1557] Data 0.015 (0.072) Batch 1.305 (1.232) Remain 39:58:17 loss: 0.4579 Lr: 0.00447 [2024-02-18 05:52:26,808 INFO misc.py line 119 87073] Train: [25/100][1548/1557] Data 0.006 (0.072) Batch 1.108 (1.232) Remain 39:58:06 loss: 0.8167 Lr: 0.00447 [2024-02-18 05:52:27,867 INFO misc.py line 119 87073] Train: [25/100][1549/1557] Data 0.015 (0.071) Batch 1.059 (1.232) Remain 39:57:52 loss: 0.4004 Lr: 0.00447 [2024-02-18 05:52:28,681 INFO misc.py line 119 87073] Train: [25/100][1550/1557] Data 0.016 (0.071) Batch 0.826 (1.232) Remain 39:57:20 loss: 0.6972 Lr: 0.00447 [2024-02-18 05:52:29,693 INFO misc.py line 119 87073] Train: [25/100][1551/1557] Data 0.004 (0.071) Batch 1.011 (1.232) Remain 39:57:02 loss: 0.3021 Lr: 0.00447 [2024-02-18 05:52:30,428 INFO misc.py line 119 87073] Train: [25/100][1552/1557] Data 0.005 (0.071) Batch 0.735 (1.231) Remain 39:56:24 loss: 1.0178 Lr: 0.00447 [2024-02-18 05:52:31,200 INFO misc.py line 119 87073] Train: [25/100][1553/1557] Data 0.004 (0.071) Batch 0.763 (1.231) Remain 39:55:47 loss: 0.2245 Lr: 0.00447 [2024-02-18 05:52:32,391 INFO misc.py line 119 87073] Train: [25/100][1554/1557] Data 0.014 (0.071) Batch 1.189 (1.231) Remain 39:55:43 loss: 0.2596 Lr: 0.00447 [2024-02-18 05:52:33,379 INFO misc.py line 119 87073] Train: [25/100][1555/1557] Data 0.015 (0.071) Batch 0.999 (1.231) Remain 39:55:24 loss: 0.6395 Lr: 0.00447 [2024-02-18 05:52:34,253 INFO misc.py line 119 87073] Train: [25/100][1556/1557] Data 0.004 (0.071) Batch 0.875 (1.231) Remain 39:54:56 loss: 0.4843 Lr: 0.00447 [2024-02-18 05:52:35,137 INFO misc.py line 119 87073] Train: [25/100][1557/1557] Data 0.004 (0.071) Batch 0.880 (1.230) Remain 39:54:29 loss: 0.5846 Lr: 0.00447 [2024-02-18 05:52:35,138 INFO misc.py line 136 87073] Train result: loss: 0.4609 [2024-02-18 05:52:35,138 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 05:53:00,795 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7006 [2024-02-18 05:53:01,575 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3887 [2024-02-18 05:53:03,699 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4880 [2024-02-18 05:53:05,908 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4215 [2024-02-18 05:53:10,856 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3647 [2024-02-18 05:53:11,557 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1146 [2024-02-18 05:53:12,819 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3949 [2024-02-18 05:53:15,775 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3384 [2024-02-18 05:53:17,585 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.4066 [2024-02-18 05:53:19,116 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6880/0.7500/0.9040. [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9253/0.9708 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9817/0.9891 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8416/0.9687 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4215/0.5058 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6990/0.7217 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6032/0.6181 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8364/0.9185 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.8995/0.9614 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6779/0.6963 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7827/0.8797 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7136/0.8723 [2024-02-18 05:53:19,117 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5611/0.6477 [2024-02-18 05:53:19,118 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 05:53:19,119 INFO misc.py line 165 87073] Currently Best mIoU: 0.7180 [2024-02-18 05:53:19,119 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 05:53:26,400 INFO misc.py line 119 87073] Train: [26/100][1/1557] Data 1.708 (1.708) Batch 2.498 (2.498) Remain 81:02:34 loss: 0.4351 Lr: 0.00447 [2024-02-18 05:53:27,319 INFO misc.py line 119 87073] Train: [26/100][2/1557] Data 0.006 (0.006) Batch 0.919 (0.919) Remain 29:48:15 loss: 0.7007 Lr: 0.00447 [2024-02-18 05:53:28,284 INFO misc.py line 119 87073] Train: [26/100][3/1557] Data 0.006 (0.006) Batch 0.963 (0.963) Remain 31:14:57 loss: 0.5453 Lr: 0.00447 [2024-02-18 05:53:29,126 INFO misc.py line 119 87073] Train: [26/100][4/1557] Data 0.007 (0.007) Batch 0.844 (0.844) Remain 27:21:47 loss: 0.4066 Lr: 0.00447 [2024-02-18 05:53:29,900 INFO misc.py line 119 87073] Train: [26/100][5/1557] Data 0.006 (0.007) Batch 0.775 (0.809) Remain 26:14:34 loss: 0.3726 Lr: 0.00447 [2024-02-18 05:53:30,667 INFO misc.py line 119 87073] Train: [26/100][6/1557] Data 0.004 (0.006) Batch 0.766 (0.795) Remain 25:46:54 loss: 0.4229 Lr: 0.00447 [2024-02-18 05:53:31,767 INFO misc.py line 119 87073] Train: [26/100][7/1557] Data 0.006 (0.006) Batch 1.100 (0.871) Remain 28:15:17 loss: 0.4485 Lr: 0.00447 [2024-02-18 05:53:32,798 INFO misc.py line 119 87073] Train: [26/100][8/1557] Data 0.006 (0.006) Batch 1.032 (0.903) Remain 29:17:43 loss: 0.4787 Lr: 0.00447 [2024-02-18 05:53:33,811 INFO misc.py line 119 87073] Train: [26/100][9/1557] Data 0.005 (0.006) Batch 1.014 (0.922) Remain 29:53:46 loss: 0.5595 Lr: 0.00447 [2024-02-18 05:53:34,647 INFO misc.py line 119 87073] Train: [26/100][10/1557] Data 0.003 (0.005) Batch 0.836 (0.909) Remain 29:29:50 loss: 0.4563 Lr: 0.00447 [2024-02-18 05:53:35,538 INFO misc.py line 119 87073] Train: [26/100][11/1557] Data 0.004 (0.005) Batch 0.889 (0.907) Remain 29:24:48 loss: 0.5889 Lr: 0.00447 [2024-02-18 05:53:36,327 INFO misc.py line 119 87073] Train: [26/100][12/1557] Data 0.006 (0.005) Batch 0.791 (0.894) Remain 28:59:42 loss: 0.3793 Lr: 0.00447 [2024-02-18 05:53:37,107 INFO misc.py line 119 87073] Train: [26/100][13/1557] Data 0.003 (0.005) Batch 0.780 (0.883) Remain 28:37:30 loss: 0.3622 Lr: 0.00447 [2024-02-18 05:53:38,391 INFO misc.py line 119 87073] Train: [26/100][14/1557] Data 0.004 (0.005) Batch 1.275 (0.918) Remain 29:46:57 loss: 0.2004 Lr: 0.00447 [2024-02-18 05:53:39,466 INFO misc.py line 119 87073] Train: [26/100][15/1557] Data 0.013 (0.006) Batch 1.075 (0.931) Remain 30:12:25 loss: 0.8908 Lr: 0.00447 [2024-02-18 05:53:40,283 INFO misc.py line 119 87073] Train: [26/100][16/1557] Data 0.012 (0.006) Batch 0.826 (0.923) Remain 29:56:34 loss: 0.3048 Lr: 0.00447 [2024-02-18 05:53:41,085 INFO misc.py line 119 87073] Train: [26/100][17/1557] Data 0.004 (0.006) Batch 0.800 (0.914) Remain 29:39:25 loss: 0.4969 Lr: 0.00447 [2024-02-18 05:53:42,005 INFO misc.py line 119 87073] Train: [26/100][18/1557] Data 0.006 (0.006) Batch 0.915 (0.914) Remain 29:39:32 loss: 0.7240 Lr: 0.00447 [2024-02-18 05:53:42,813 INFO misc.py line 119 87073] Train: [26/100][19/1557] Data 0.010 (0.006) Batch 0.814 (0.908) Remain 29:27:19 loss: 0.2730 Lr: 0.00447 [2024-02-18 05:53:43,547 INFO misc.py line 119 87073] Train: [26/100][20/1557] Data 0.004 (0.006) Batch 0.734 (0.898) Remain 29:07:25 loss: 0.4762 Lr: 0.00447 [2024-02-18 05:53:44,880 INFO misc.py line 119 87073] Train: [26/100][21/1557] Data 0.004 (0.006) Batch 1.328 (0.922) Remain 29:53:52 loss: 0.2938 Lr: 0.00447 [2024-02-18 05:53:45,917 INFO misc.py line 119 87073] Train: [26/100][22/1557] Data 0.009 (0.006) Batch 1.041 (0.928) Remain 30:06:06 loss: 0.4694 Lr: 0.00447 [2024-02-18 05:53:46,875 INFO misc.py line 119 87073] Train: [26/100][23/1557] Data 0.005 (0.006) Batch 0.953 (0.929) Remain 30:08:27 loss: 0.2640 Lr: 0.00447 [2024-02-18 05:53:47,856 INFO misc.py line 119 87073] Train: [26/100][24/1557] Data 0.011 (0.006) Batch 0.986 (0.932) Remain 30:13:39 loss: 0.5639 Lr: 0.00447 [2024-02-18 05:53:48,715 INFO misc.py line 119 87073] Train: [26/100][25/1557] Data 0.005 (0.006) Batch 0.860 (0.929) Remain 30:07:16 loss: 0.6061 Lr: 0.00447 [2024-02-18 05:53:49,452 INFO misc.py line 119 87073] Train: [26/100][26/1557] Data 0.004 (0.006) Batch 0.736 (0.920) Remain 29:50:55 loss: 0.4047 Lr: 0.00447 [2024-02-18 05:53:50,228 INFO misc.py line 119 87073] Train: [26/100][27/1557] Data 0.006 (0.006) Batch 0.778 (0.914) Remain 29:39:21 loss: 0.2065 Lr: 0.00447 [2024-02-18 05:53:51,413 INFO misc.py line 119 87073] Train: [26/100][28/1557] Data 0.004 (0.006) Batch 1.185 (0.925) Remain 30:00:22 loss: 0.1731 Lr: 0.00447 [2024-02-18 05:53:52,327 INFO misc.py line 119 87073] Train: [26/100][29/1557] Data 0.004 (0.006) Batch 0.914 (0.925) Remain 29:59:32 loss: 0.3423 Lr: 0.00447 [2024-02-18 05:53:53,300 INFO misc.py line 119 87073] Train: [26/100][30/1557] Data 0.004 (0.006) Batch 0.973 (0.927) Remain 30:02:59 loss: 0.1992 Lr: 0.00447 [2024-02-18 05:53:54,162 INFO misc.py line 119 87073] Train: [26/100][31/1557] Data 0.004 (0.006) Batch 0.862 (0.924) Remain 29:58:29 loss: 0.5215 Lr: 0.00447 [2024-02-18 05:53:55,046 INFO misc.py line 119 87073] Train: [26/100][32/1557] Data 0.004 (0.006) Batch 0.882 (0.923) Remain 29:55:39 loss: 0.2349 Lr: 0.00447 [2024-02-18 05:53:55,859 INFO misc.py line 119 87073] Train: [26/100][33/1557] Data 0.005 (0.006) Batch 0.814 (0.919) Remain 29:48:32 loss: 0.5958 Lr: 0.00447 [2024-02-18 05:53:56,572 INFO misc.py line 119 87073] Train: [26/100][34/1557] Data 0.004 (0.006) Batch 0.713 (0.913) Remain 29:35:34 loss: 0.3114 Lr: 0.00447 [2024-02-18 05:53:57,794 INFO misc.py line 119 87073] Train: [26/100][35/1557] Data 0.005 (0.006) Batch 1.215 (0.922) Remain 29:53:55 loss: 0.1073 Lr: 0.00447 [2024-02-18 05:53:58,685 INFO misc.py line 119 87073] Train: [26/100][36/1557] Data 0.013 (0.006) Batch 0.900 (0.921) Remain 29:52:35 loss: 0.4419 Lr: 0.00447 [2024-02-18 05:53:59,731 INFO misc.py line 119 87073] Train: [26/100][37/1557] Data 0.003 (0.006) Batch 1.046 (0.925) Remain 29:59:42 loss: 0.4835 Lr: 0.00447 [2024-02-18 05:54:00,748 INFO misc.py line 119 87073] Train: [26/100][38/1557] Data 0.004 (0.006) Batch 1.016 (0.928) Remain 30:04:45 loss: 0.7175 Lr: 0.00447 [2024-02-18 05:54:01,611 INFO misc.py line 119 87073] Train: [26/100][39/1557] Data 0.004 (0.006) Batch 0.863 (0.926) Remain 30:01:16 loss: 0.2180 Lr: 0.00447 [2024-02-18 05:54:02,463 INFO misc.py line 119 87073] Train: [26/100][40/1557] Data 0.003 (0.006) Batch 0.852 (0.924) Remain 29:57:22 loss: 0.6409 Lr: 0.00447 [2024-02-18 05:54:03,197 INFO misc.py line 119 87073] Train: [26/100][41/1557] Data 0.004 (0.006) Batch 0.726 (0.919) Remain 29:47:12 loss: 0.6202 Lr: 0.00447 [2024-02-18 05:54:04,386 INFO misc.py line 119 87073] Train: [26/100][42/1557] Data 0.012 (0.006) Batch 1.189 (0.926) Remain 30:00:41 loss: 0.2936 Lr: 0.00447 [2024-02-18 05:54:05,315 INFO misc.py line 119 87073] Train: [26/100][43/1557] Data 0.012 (0.006) Batch 0.936 (0.926) Remain 30:01:12 loss: 0.6409 Lr: 0.00447 [2024-02-18 05:54:06,261 INFO misc.py line 119 87073] Train: [26/100][44/1557] Data 0.006 (0.006) Batch 0.947 (0.926) Remain 30:02:12 loss: 0.5823 Lr: 0.00447 [2024-02-18 05:54:07,330 INFO misc.py line 119 87073] Train: [26/100][45/1557] Data 0.004 (0.006) Batch 1.069 (0.930) Remain 30:08:49 loss: 0.1846 Lr: 0.00447 [2024-02-18 05:54:08,316 INFO misc.py line 119 87073] Train: [26/100][46/1557] Data 0.003 (0.006) Batch 0.986 (0.931) Remain 30:11:20 loss: 0.5115 Lr: 0.00447 [2024-02-18 05:54:09,060 INFO misc.py line 119 87073] Train: [26/100][47/1557] Data 0.003 (0.006) Batch 0.742 (0.927) Remain 30:02:59 loss: 0.3898 Lr: 0.00447 [2024-02-18 05:54:09,790 INFO misc.py line 119 87073] Train: [26/100][48/1557] Data 0.005 (0.006) Batch 0.728 (0.922) Remain 29:54:23 loss: 0.5336 Lr: 0.00447 [2024-02-18 05:54:10,946 INFO misc.py line 119 87073] Train: [26/100][49/1557] Data 0.007 (0.006) Batch 1.148 (0.927) Remain 30:03:55 loss: 0.3169 Lr: 0.00447 [2024-02-18 05:54:11,809 INFO misc.py line 119 87073] Train: [26/100][50/1557] Data 0.015 (0.006) Batch 0.873 (0.926) Remain 30:01:39 loss: 0.4588 Lr: 0.00447 [2024-02-18 05:54:12,791 INFO misc.py line 119 87073] Train: [26/100][51/1557] Data 0.005 (0.006) Batch 0.982 (0.927) Remain 30:03:55 loss: 0.5129 Lr: 0.00447 [2024-02-18 05:54:13,999 INFO misc.py line 119 87073] Train: [26/100][52/1557] Data 0.004 (0.006) Batch 1.207 (0.933) Remain 30:15:00 loss: 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INFO misc.py line 119 87073] Train: [26/100][59/1557] Data 0.006 (0.006) Batch 1.109 (0.936) Remain 30:21:21 loss: 0.3068 Lr: 0.00447 [2024-02-18 05:54:21,689 INFO misc.py line 119 87073] Train: [26/100][60/1557] Data 0.004 (0.006) Batch 0.973 (0.937) Remain 30:22:36 loss: 0.5495 Lr: 0.00447 [2024-02-18 05:54:22,459 INFO misc.py line 119 87073] Train: [26/100][61/1557] Data 0.006 (0.006) Batch 0.771 (0.934) Remain 30:17:01 loss: 0.2232 Lr: 0.00447 [2024-02-18 05:54:23,253 INFO misc.py line 119 87073] Train: [26/100][62/1557] Data 0.005 (0.006) Batch 0.789 (0.932) Remain 30:12:12 loss: 0.7028 Lr: 0.00447 [2024-02-18 05:54:31,950 INFO misc.py line 119 87073] Train: [26/100][63/1557] Data 4.871 (0.087) Batch 8.703 (1.061) Remain 34:24:08 loss: 0.5933 Lr: 0.00447 [2024-02-18 05:54:32,902 INFO misc.py line 119 87073] Train: [26/100][64/1557] Data 0.004 (0.086) Batch 0.951 (1.059) Remain 34:20:36 loss: 0.4777 Lr: 0.00447 [2024-02-18 05:54:33,896 INFO misc.py line 119 87073] Train: 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Batch 0.963 (1.082) Remain 35:02:28 loss: 0.5104 Lr: 0.00447 [2024-02-18 05:56:37,526 INFO misc.py line 119 87073] Train: [26/100][178/1557] Data 0.003 (0.093) Batch 0.984 (1.081) Remain 35:01:22 loss: 0.3559 Lr: 0.00447 [2024-02-18 05:56:38,297 INFO misc.py line 119 87073] Train: [26/100][179/1557] Data 0.012 (0.092) Batch 0.780 (1.080) Remain 34:58:01 loss: 0.2606 Lr: 0.00447 [2024-02-18 05:56:40,604 INFO misc.py line 119 87073] Train: [26/100][180/1557] Data 0.955 (0.097) Batch 2.306 (1.087) Remain 35:11:28 loss: 0.2157 Lr: 0.00447 [2024-02-18 05:56:41,341 INFO misc.py line 119 87073] Train: [26/100][181/1557] Data 0.004 (0.096) Batch 0.737 (1.085) Remain 35:07:38 loss: 0.2352 Lr: 0.00447 [2024-02-18 05:56:42,621 INFO misc.py line 119 87073] Train: [26/100][182/1557] Data 0.003 (0.096) Batch 1.270 (1.086) Remain 35:09:38 loss: 0.3165 Lr: 0.00447 [2024-02-18 05:56:43,540 INFO misc.py line 119 87073] Train: [26/100][183/1557] Data 0.013 (0.096) Batch 0.928 (1.085) Remain 35:07:54 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Batch 1.021 (1.091) Remain 35:18:46 loss: 0.4278 Lr: 0.00447 [2024-02-18 05:57:40,235 INFO misc.py line 119 87073] Train: [26/100][234/1557] Data 0.005 (0.100) Batch 1.063 (1.091) Remain 35:18:31 loss: 0.5945 Lr: 0.00447 [2024-02-18 05:57:41,178 INFO misc.py line 119 87073] Train: [26/100][235/1557] Data 0.005 (0.099) Batch 0.945 (1.090) Remain 35:17:17 loss: 0.7879 Lr: 0.00447 [2024-02-18 05:57:41,982 INFO misc.py line 119 87073] Train: [26/100][236/1557] Data 0.003 (0.099) Batch 0.803 (1.089) Remain 35:14:52 loss: 0.2917 Lr: 0.00447 [2024-02-18 05:57:42,753 INFO misc.py line 119 87073] Train: [26/100][237/1557] Data 0.004 (0.099) Batch 0.766 (1.087) Remain 35:12:10 loss: 0.5806 Lr: 0.00447 [2024-02-18 05:57:43,971 INFO misc.py line 119 87073] Train: [26/100][238/1557] Data 0.009 (0.098) Batch 1.222 (1.088) Remain 35:13:16 loss: 0.2185 Lr: 0.00447 [2024-02-18 05:57:45,027 INFO misc.py line 119 87073] Train: [26/100][239/1557] Data 0.006 (0.098) Batch 1.052 (1.088) Remain 35:12:57 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line 119 87073] Train: [26/100][669/1557] Data 0.004 (0.096) Batch 0.861 (1.090) Remain 35:10:11 loss: 0.6832 Lr: 0.00445 [2024-02-18 06:05:35,283 INFO misc.py line 119 87073] Train: [26/100][670/1557] Data 0.012 (0.096) Batch 0.741 (1.090) Remain 35:09:09 loss: 0.4996 Lr: 0.00445 [2024-02-18 06:05:36,049 INFO misc.py line 119 87073] Train: [26/100][671/1557] Data 0.004 (0.095) Batch 0.761 (1.089) Remain 35:08:11 loss: 0.3619 Lr: 0.00445 [2024-02-18 06:05:37,221 INFO misc.py line 119 87073] Train: [26/100][672/1557] Data 0.008 (0.095) Batch 1.172 (1.090) Remain 35:08:24 loss: 0.2092 Lr: 0.00445 [2024-02-18 06:05:38,116 INFO misc.py line 119 87073] Train: [26/100][673/1557] Data 0.008 (0.095) Batch 0.899 (1.089) Remain 35:07:50 loss: 0.7879 Lr: 0.00445 [2024-02-18 06:05:39,204 INFO misc.py line 119 87073] Train: [26/100][674/1557] Data 0.005 (0.095) Batch 1.089 (1.089) Remain 35:07:49 loss: 0.9455 Lr: 0.00445 [2024-02-18 06:05:40,134 INFO misc.py line 119 87073] Train: 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Batch 0.920 (1.100) Remain 35:28:56 loss: 0.3947 Lr: 0.00445 [2024-02-18 06:05:55,200 INFO misc.py line 119 87073] Train: [26/100][682/1557] Data 0.005 (0.101) Batch 0.919 (1.100) Remain 35:28:24 loss: 0.2977 Lr: 0.00445 [2024-02-18 06:05:56,193 INFO misc.py line 119 87073] Train: [26/100][683/1557] Data 0.009 (0.101) Batch 0.996 (1.100) Remain 35:28:05 loss: 0.6207 Lr: 0.00445 [2024-02-18 06:05:56,962 INFO misc.py line 119 87073] Train: [26/100][684/1557] Data 0.005 (0.101) Batch 0.769 (1.099) Remain 35:27:08 loss: 0.4813 Lr: 0.00445 [2024-02-18 06:05:57,687 INFO misc.py line 119 87073] Train: [26/100][685/1557] Data 0.006 (0.101) Batch 0.721 (1.099) Remain 35:26:02 loss: 0.3797 Lr: 0.00445 [2024-02-18 06:05:58,894 INFO misc.py line 119 87073] Train: [26/100][686/1557] Data 0.009 (0.101) Batch 1.203 (1.099) Remain 35:26:19 loss: 0.2107 Lr: 0.00445 [2024-02-18 06:05:59,840 INFO misc.py line 119 87073] Train: [26/100][687/1557] Data 0.013 (0.101) Batch 0.956 (1.099) Remain 35:25:53 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35:14:50 loss: 0.4472 Lr: 0.00442 [2024-02-18 06:20:09,086 INFO misc.py line 119 87073] Train: [26/100][1458/1557] Data 0.011 (0.102) Batch 0.889 (1.100) Remain 35:14:32 loss: 0.5609 Lr: 0.00442 [2024-02-18 06:20:10,122 INFO misc.py line 119 87073] Train: [26/100][1459/1557] Data 0.004 (0.102) Batch 1.035 (1.100) Remain 35:14:26 loss: 0.9540 Lr: 0.00442 [2024-02-18 06:20:11,072 INFO misc.py line 119 87073] Train: [26/100][1460/1557] Data 0.005 (0.102) Batch 0.949 (1.100) Remain 35:14:13 loss: 0.5488 Lr: 0.00442 [2024-02-18 06:20:11,834 INFO misc.py line 119 87073] Train: [26/100][1461/1557] Data 0.006 (0.102) Batch 0.760 (1.100) Remain 35:13:45 loss: 0.6494 Lr: 0.00442 [2024-02-18 06:20:12,571 INFO misc.py line 119 87073] Train: [26/100][1462/1557] Data 0.008 (0.102) Batch 0.741 (1.100) Remain 35:13:15 loss: 0.6494 Lr: 0.00442 [2024-02-18 06:20:21,369 INFO misc.py line 119 87073] Train: [26/100][1463/1557] Data 5.002 (0.105) Batch 8.795 (1.105) Remain 35:23:22 loss: 0.2926 Lr: 0.00442 [2024-02-18 06:20:22,401 INFO misc.py line 119 87073] Train: [26/100][1464/1557] Data 0.007 (0.105) Batch 1.034 (1.105) Remain 35:23:15 loss: 0.8246 Lr: 0.00442 [2024-02-18 06:20:23,316 INFO misc.py line 119 87073] Train: [26/100][1465/1557] Data 0.005 (0.105) Batch 0.914 (1.105) Remain 35:22:59 loss: 0.4664 Lr: 0.00442 [2024-02-18 06:20:24,340 INFO misc.py line 119 87073] Train: [26/100][1466/1557] Data 0.005 (0.105) Batch 1.023 (1.105) Remain 35:22:52 loss: 0.6746 Lr: 0.00442 [2024-02-18 06:20:25,359 INFO misc.py line 119 87073] Train: [26/100][1467/1557] Data 0.007 (0.105) Batch 1.019 (1.105) Remain 35:22:44 loss: 0.6295 Lr: 0.00442 [2024-02-18 06:20:26,074 INFO misc.py line 119 87073] Train: [26/100][1468/1557] Data 0.007 (0.105) Batch 0.715 (1.104) Remain 35:22:12 loss: 0.3753 Lr: 0.00442 [2024-02-18 06:20:26,848 INFO misc.py line 119 87073] Train: [26/100][1469/1557] Data 0.008 (0.105) Batch 0.775 (1.104) Remain 35:21:45 loss: 0.3014 Lr: 0.00442 [2024-02-18 06:20:28,143 INFO misc.py line 119 87073] Train: [26/100][1470/1557] Data 0.006 (0.105) Batch 1.294 (1.104) Remain 35:21:59 loss: 0.3758 Lr: 0.00442 [2024-02-18 06:20:29,054 INFO misc.py line 119 87073] Train: [26/100][1471/1557] Data 0.008 (0.105) Batch 0.912 (1.104) Remain 35:21:43 loss: 0.2008 Lr: 0.00442 [2024-02-18 06:20:30,191 INFO misc.py line 119 87073] Train: [26/100][1472/1557] Data 0.005 (0.105) Batch 1.138 (1.104) Remain 35:21:44 loss: 0.5074 Lr: 0.00442 [2024-02-18 06:20:31,025 INFO misc.py line 119 87073] Train: [26/100][1473/1557] Data 0.004 (0.105) Batch 0.834 (1.104) Remain 35:21:22 loss: 0.7720 Lr: 0.00442 [2024-02-18 06:20:31,936 INFO misc.py line 119 87073] Train: [26/100][1474/1557] Data 0.005 (0.105) Batch 0.910 (1.104) Remain 35:21:06 loss: 0.4221 Lr: 0.00442 [2024-02-18 06:20:32,628 INFO misc.py line 119 87073] Train: [26/100][1475/1557] Data 0.006 (0.105) Batch 0.692 (1.103) Remain 35:20:32 loss: 0.2171 Lr: 0.00442 [2024-02-18 06:20:33,439 INFO misc.py line 119 87073] Train: 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(0.104) Batch 0.735 (1.103) Remain 35:19:06 loss: 0.1565 Lr: 0.00442 [2024-02-18 06:20:40,109 INFO misc.py line 119 87073] Train: [26/100][1483/1557] Data 0.009 (0.104) Batch 0.766 (1.103) Remain 35:18:39 loss: 0.3514 Lr: 0.00442 [2024-02-18 06:20:41,216 INFO misc.py line 119 87073] Train: [26/100][1484/1557] Data 0.004 (0.104) Batch 1.107 (1.103) Remain 35:18:38 loss: 0.2402 Lr: 0.00442 [2024-02-18 06:20:42,231 INFO misc.py line 119 87073] Train: [26/100][1485/1557] Data 0.004 (0.104) Batch 1.015 (1.103) Remain 35:18:30 loss: 0.6018 Lr: 0.00442 [2024-02-18 06:20:43,283 INFO misc.py line 119 87073] Train: [26/100][1486/1557] Data 0.005 (0.104) Batch 1.052 (1.102) Remain 35:18:25 loss: 0.3965 Lr: 0.00442 [2024-02-18 06:20:44,162 INFO misc.py line 119 87073] Train: [26/100][1487/1557] Data 0.004 (0.104) Batch 0.880 (1.102) Remain 35:18:06 loss: 0.8065 Lr: 0.00442 [2024-02-18 06:20:45,315 INFO misc.py line 119 87073] Train: [26/100][1488/1557] Data 0.003 (0.104) Batch 1.152 (1.102) Remain 35:18:09 loss: 0.4040 Lr: 0.00442 [2024-02-18 06:20:46,091 INFO misc.py line 119 87073] Train: [26/100][1489/1557] Data 0.003 (0.104) Batch 0.775 (1.102) Remain 35:17:43 loss: 0.2855 Lr: 0.00442 [2024-02-18 06:20:46,884 INFO misc.py line 119 87073] Train: [26/100][1490/1557] Data 0.005 (0.104) Batch 0.794 (1.102) Remain 35:17:18 loss: 0.3196 Lr: 0.00442 [2024-02-18 06:20:48,232 INFO misc.py line 119 87073] Train: [26/100][1491/1557] Data 0.004 (0.103) Batch 1.339 (1.102) Remain 35:17:35 loss: 0.1728 Lr: 0.00442 [2024-02-18 06:20:49,189 INFO misc.py line 119 87073] Train: [26/100][1492/1557] Data 0.013 (0.103) Batch 0.967 (1.102) Remain 35:17:23 loss: 0.3103 Lr: 0.00442 [2024-02-18 06:20:50,213 INFO misc.py line 119 87073] Train: [26/100][1493/1557] Data 0.004 (0.103) Batch 1.023 (1.102) Remain 35:17:16 loss: 0.9798 Lr: 0.00442 [2024-02-18 06:20:51,136 INFO misc.py line 119 87073] Train: [26/100][1494/1557] Data 0.004 (0.103) Batch 0.923 (1.102) Remain 35:17:01 loss: 0.5175 Lr: 0.00442 [2024-02-18 06:20:52,054 INFO misc.py line 119 87073] Train: [26/100][1495/1557] Data 0.004 (0.103) Batch 0.914 (1.102) Remain 35:16:46 loss: 0.5539 Lr: 0.00442 [2024-02-18 06:20:52,839 INFO misc.py line 119 87073] Train: [26/100][1496/1557] Data 0.008 (0.103) Batch 0.788 (1.102) Remain 35:16:20 loss: 0.4621 Lr: 0.00442 [2024-02-18 06:20:53,592 INFO misc.py line 119 87073] Train: [26/100][1497/1557] Data 0.009 (0.103) Batch 0.746 (1.101) Remain 35:15:52 loss: 0.4027 Lr: 0.00442 [2024-02-18 06:20:54,851 INFO misc.py line 119 87073] Train: [26/100][1498/1557] Data 0.012 (0.103) Batch 1.260 (1.101) Remain 35:16:03 loss: 0.1785 Lr: 0.00442 [2024-02-18 06:20:55,904 INFO misc.py line 119 87073] Train: [26/100][1499/1557] Data 0.011 (0.103) Batch 1.053 (1.101) Remain 35:15:58 loss: 0.5011 Lr: 0.00442 [2024-02-18 06:20:56,824 INFO misc.py line 119 87073] Train: [26/100][1500/1557] Data 0.011 (0.103) Batch 0.927 (1.101) Remain 35:15:44 loss: 0.3854 Lr: 0.00442 [2024-02-18 06:20:57,958 INFO misc.py line 119 87073] Train: [26/100][1501/1557] Data 0.004 (0.103) Batch 1.133 (1.101) Remain 35:15:45 loss: 0.7938 Lr: 0.00442 [2024-02-18 06:20:58,988 INFO misc.py line 119 87073] Train: [26/100][1502/1557] Data 0.005 (0.103) Batch 1.031 (1.101) Remain 35:15:39 loss: 0.6119 Lr: 0.00442 [2024-02-18 06:20:59,764 INFO misc.py line 119 87073] Train: [26/100][1503/1557] Data 0.004 (0.103) Batch 0.775 (1.101) Remain 35:15:12 loss: 0.2644 Lr: 0.00442 [2024-02-18 06:21:00,513 INFO misc.py line 119 87073] Train: [26/100][1504/1557] Data 0.005 (0.103) Batch 0.746 (1.101) Remain 35:14:44 loss: 0.3119 Lr: 0.00442 [2024-02-18 06:21:01,664 INFO misc.py line 119 87073] Train: [26/100][1505/1557] Data 0.008 (0.103) Batch 1.151 (1.101) Remain 35:14:47 loss: 0.2102 Lr: 0.00442 [2024-02-18 06:21:02,557 INFO misc.py line 119 87073] Train: [26/100][1506/1557] Data 0.008 (0.103) Batch 0.896 (1.101) Remain 35:14:30 loss: 0.3264 Lr: 0.00442 [2024-02-18 06:21:03,447 INFO misc.py line 119 87073] Train: [26/100][1507/1557] Data 0.004 (0.102) Batch 0.890 (1.101) Remain 35:14:13 loss: 0.5685 Lr: 0.00442 [2024-02-18 06:21:04,387 INFO misc.py line 119 87073] Train: [26/100][1508/1557] Data 0.004 (0.102) Batch 0.937 (1.100) Remain 35:13:59 loss: 0.7907 Lr: 0.00442 [2024-02-18 06:21:05,341 INFO misc.py line 119 87073] Train: [26/100][1509/1557] Data 0.007 (0.102) Batch 0.956 (1.100) Remain 35:13:47 loss: 0.8318 Lr: 0.00442 [2024-02-18 06:21:06,068 INFO misc.py line 119 87073] Train: [26/100][1510/1557] Data 0.006 (0.102) Batch 0.728 (1.100) Remain 35:13:17 loss: 0.2103 Lr: 0.00442 [2024-02-18 06:21:06,754 INFO misc.py line 119 87073] Train: [26/100][1511/1557] Data 0.004 (0.102) Batch 0.684 (1.100) Remain 35:12:44 loss: 0.5405 Lr: 0.00442 [2024-02-18 06:21:07,962 INFO misc.py line 119 87073] Train: [26/100][1512/1557] Data 0.006 (0.102) Batch 1.207 (1.100) Remain 35:12:52 loss: 0.2882 Lr: 0.00442 [2024-02-18 06:21:08,945 INFO misc.py line 119 87073] Train: [26/100][1513/1557] Data 0.007 (0.102) Batch 0.985 (1.100) Remain 35:12:42 loss: 0.5030 Lr: 0.00442 [2024-02-18 06:21:10,107 INFO misc.py line 119 87073] Train: [26/100][1514/1557] Data 0.007 (0.102) Batch 1.161 (1.100) Remain 35:12:45 loss: 0.2744 Lr: 0.00442 [2024-02-18 06:21:10,963 INFO misc.py line 119 87073] Train: [26/100][1515/1557] Data 0.006 (0.102) Batch 0.856 (1.100) Remain 35:12:25 loss: 0.4348 Lr: 0.00442 [2024-02-18 06:21:11,978 INFO misc.py line 119 87073] Train: [26/100][1516/1557] Data 0.007 (0.102) Batch 1.014 (1.100) Remain 35:12:18 loss: 0.3910 Lr: 0.00442 [2024-02-18 06:21:12,662 INFO misc.py line 119 87073] Train: [26/100][1517/1557] Data 0.008 (0.102) Batch 0.687 (1.099) Remain 35:11:45 loss: 0.2330 Lr: 0.00442 [2024-02-18 06:21:13,408 INFO misc.py line 119 87073] Train: [26/100][1518/1557] Data 0.005 (0.102) Batch 0.742 (1.099) Remain 35:11:17 loss: 0.5956 Lr: 0.00442 [2024-02-18 06:21:23,151 INFO misc.py line 119 87073] Train: [26/100][1519/1557] Data 5.220 (0.105) Batch 9.748 (1.105) Remain 35:22:14 loss: 0.3727 Lr: 0.00442 [2024-02-18 06:21:24,060 INFO misc.py line 119 87073] Train: [26/100][1520/1557] Data 0.004 (0.105) Batch 0.908 (1.105) Remain 35:21:58 loss: 0.6091 Lr: 0.00442 [2024-02-18 06:21:24,969 INFO misc.py line 119 87073] Train: [26/100][1521/1557] Data 0.004 (0.105) Batch 0.906 (1.105) Remain 35:21:41 loss: 0.5108 Lr: 0.00442 [2024-02-18 06:21:26,146 INFO misc.py line 119 87073] Train: [26/100][1522/1557] Data 0.009 (0.105) Batch 1.178 (1.105) Remain 35:21:46 loss: 0.6939 Lr: 0.00442 [2024-02-18 06:21:27,126 INFO misc.py line 119 87073] Train: [26/100][1523/1557] Data 0.007 (0.105) Batch 0.980 (1.104) Remain 35:21:35 loss: 0.5079 Lr: 0.00442 [2024-02-18 06:21:27,869 INFO misc.py line 119 87073] Train: [26/100][1524/1557] Data 0.008 (0.105) Batch 0.744 (1.104) Remain 35:21:07 loss: 0.4785 Lr: 0.00442 [2024-02-18 06:21:28,569 INFO misc.py line 119 87073] Train: [26/100][1525/1557] Data 0.005 (0.105) Batch 0.695 (1.104) Remain 35:20:35 loss: 0.4682 Lr: 0.00442 [2024-02-18 06:21:29,794 INFO misc.py line 119 87073] Train: [26/100][1526/1557] Data 0.011 (0.105) Batch 1.220 (1.104) Remain 35:20:42 loss: 0.1913 Lr: 0.00442 [2024-02-18 06:21:30,670 INFO misc.py line 119 87073] Train: [26/100][1527/1557] Data 0.015 (0.105) Batch 0.888 (1.104) Remain 35:20:25 loss: 0.7756 Lr: 0.00442 [2024-02-18 06:21:31,624 INFO misc.py line 119 87073] Train: [26/100][1528/1557] Data 0.003 (0.105) Batch 0.954 (1.104) Remain 35:20:12 loss: 0.5869 Lr: 0.00442 [2024-02-18 06:21:32,604 INFO misc.py line 119 87073] Train: [26/100][1529/1557] Data 0.004 (0.104) Batch 0.980 (1.104) Remain 35:20:02 loss: 0.6197 Lr: 0.00442 [2024-02-18 06:21:33,571 INFO misc.py line 119 87073] Train: [26/100][1530/1557] Data 0.003 (0.104) Batch 0.964 (1.104) Remain 35:19:50 loss: 0.6353 Lr: 0.00442 [2024-02-18 06:21:34,327 INFO misc.py line 119 87073] Train: [26/100][1531/1557] Data 0.006 (0.104) Batch 0.754 (1.103) Remain 35:19:23 loss: 0.6622 Lr: 0.00442 [2024-02-18 06:21:35,107 INFO misc.py line 119 87073] Train: [26/100][1532/1557] Data 0.008 (0.104) Batch 0.783 (1.103) Remain 35:18:58 loss: 0.2921 Lr: 0.00442 [2024-02-18 06:21:36,435 INFO misc.py line 119 87073] Train: [26/100][1533/1557] Data 0.006 (0.104) Batch 1.327 (1.103) Remain 35:19:13 loss: 0.4195 Lr: 0.00442 [2024-02-18 06:21:37,392 INFO misc.py line 119 87073] Train: [26/100][1534/1557] Data 0.006 (0.104) Batch 0.957 (1.103) Remain 35:19:01 loss: 0.6046 Lr: 0.00442 [2024-02-18 06:21:38,402 INFO misc.py line 119 87073] Train: [26/100][1535/1557] Data 0.007 (0.104) Batch 1.012 (1.103) Remain 35:18:53 loss: 0.3435 Lr: 0.00442 [2024-02-18 06:21:39,391 INFO misc.py line 119 87073] Train: [26/100][1536/1557] Data 0.006 (0.104) Batch 0.989 (1.103) Remain 35:18:44 loss: 0.9561 Lr: 0.00442 [2024-02-18 06:21:40,306 INFO misc.py line 119 87073] Train: [26/100][1537/1557] Data 0.005 (0.104) Batch 0.915 (1.103) Remain 35:18:28 loss: 0.2169 Lr: 0.00442 [2024-02-18 06:21:41,072 INFO misc.py line 119 87073] Train: [26/100][1538/1557] Data 0.004 (0.104) Batch 0.756 (1.103) Remain 35:18:01 loss: 0.4241 Lr: 0.00442 [2024-02-18 06:21:41,830 INFO misc.py line 119 87073] Train: [26/100][1539/1557] Data 0.013 (0.104) Batch 0.768 (1.103) Remain 35:17:35 loss: 0.4408 Lr: 0.00442 [2024-02-18 06:21:43,116 INFO misc.py line 119 87073] Train: [26/100][1540/1557] Data 0.003 (0.104) Batch 1.280 (1.103) Remain 35:17:47 loss: 0.1563 Lr: 0.00442 [2024-02-18 06:21:44,229 INFO misc.py line 119 87073] Train: [26/100][1541/1557] Data 0.010 (0.104) Batch 1.107 (1.103) Remain 35:17:46 loss: 0.3825 Lr: 0.00442 [2024-02-18 06:21:45,337 INFO misc.py line 119 87073] Train: [26/100][1542/1557] Data 0.016 (0.104) Batch 1.108 (1.103) Remain 35:17:46 loss: 0.5416 Lr: 0.00442 [2024-02-18 06:21:46,312 INFO misc.py line 119 87073] Train: [26/100][1543/1557] Data 0.016 (0.104) Batch 0.987 (1.103) Remain 35:17:36 loss: 0.3485 Lr: 0.00442 [2024-02-18 06:21:47,280 INFO misc.py line 119 87073] Train: [26/100][1544/1557] Data 0.004 (0.104) Batch 0.968 (1.103) Remain 35:17:25 loss: 0.4422 Lr: 0.00442 [2024-02-18 06:21:47,992 INFO misc.py line 119 87073] Train: [26/100][1545/1557] Data 0.004 (0.103) Batch 0.711 (1.102) Remain 35:16:55 loss: 0.3760 Lr: 0.00442 [2024-02-18 06:21:48,730 INFO misc.py line 119 87073] Train: [26/100][1546/1557] Data 0.003 (0.103) Batch 0.728 (1.102) Remain 35:16:25 loss: 0.4060 Lr: 0.00442 [2024-02-18 06:21:50,019 INFO misc.py line 119 87073] Train: [26/100][1547/1557] Data 0.014 (0.103) Batch 1.290 (1.102) Remain 35:16:38 loss: 0.2512 Lr: 0.00442 [2024-02-18 06:21:50,932 INFO misc.py line 119 87073] Train: [26/100][1548/1557] Data 0.013 (0.103) Batch 0.923 (1.102) Remain 35:16:24 loss: 0.1871 Lr: 0.00442 [2024-02-18 06:21:51,781 INFO misc.py line 119 87073] Train: [26/100][1549/1557] Data 0.003 (0.103) Batch 0.849 (1.102) Remain 35:16:04 loss: 0.3869 Lr: 0.00442 [2024-02-18 06:21:52,633 INFO misc.py line 119 87073] Train: [26/100][1550/1557] Data 0.003 (0.103) Batch 0.840 (1.102) Remain 35:15:43 loss: 0.5981 Lr: 0.00442 [2024-02-18 06:21:53,874 INFO misc.py line 119 87073] Train: [26/100][1551/1557] Data 0.016 (0.103) Batch 1.243 (1.102) Remain 35:15:53 loss: 0.2320 Lr: 0.00442 [2024-02-18 06:21:54,636 INFO misc.py line 119 87073] Train: [26/100][1552/1557] Data 0.014 (0.103) Batch 0.773 (1.102) Remain 35:15:27 loss: 0.2851 Lr: 0.00442 [2024-02-18 06:21:55,365 INFO misc.py line 119 87073] Train: [26/100][1553/1557] Data 0.003 (0.103) Batch 0.717 (1.101) Remain 35:14:57 loss: 0.3665 Lr: 0.00442 [2024-02-18 06:21:56,587 INFO misc.py line 119 87073] Train: [26/100][1554/1557] Data 0.014 (0.103) Batch 1.221 (1.101) Remain 35:15:05 loss: 0.2906 Lr: 0.00442 [2024-02-18 06:21:57,598 INFO misc.py line 119 87073] Train: [26/100][1555/1557] Data 0.016 (0.103) Batch 1.011 (1.101) Remain 35:14:57 loss: 0.2278 Lr: 0.00442 [2024-02-18 06:21:58,662 INFO misc.py line 119 87073] Train: [26/100][1556/1557] Data 0.017 (0.103) Batch 1.067 (1.101) Remain 35:14:54 loss: 0.6614 Lr: 0.00442 [2024-02-18 06:21:59,678 INFO misc.py line 119 87073] Train: [26/100][1557/1557] Data 0.013 (0.103) Batch 1.015 (1.101) Remain 35:14:46 loss: 0.2429 Lr: 0.00442 [2024-02-18 06:21:59,679 INFO misc.py line 136 87073] Train result: loss: 0.4492 [2024-02-18 06:21:59,679 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 06:22:24,169 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5824 [2024-02-18 06:22:24,972 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2801 [2024-02-18 06:22:27,788 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.7372 [2024-02-18 06:22:30,000 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3216 [2024-02-18 06:22:34,949 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2752 [2024-02-18 06:22:35,650 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1185 [2024-02-18 06:22:36,912 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3084 [2024-02-18 06:22:39,863 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2894 [2024-02-18 06:22:41,675 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3095 [2024-02-18 06:22:43,155 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7277/0.8157/0.9103. [2024-02-18 06:22:43,155 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9294/0.9673 [2024-02-18 06:22:43,155 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9820/0.9909 [2024-02-18 06:22:43,155 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8651/0.9545 [2024-02-18 06:22:43,155 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0111/0.2701 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4836/0.6672 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7330/0.7573 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7985/0.8523 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8277/0.9312 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9175/0.9465 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8634/0.9056 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7623/0.8676 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7051/0.8370 [2024-02-18 06:22:43,156 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5809/0.6568 [2024-02-18 06:22:43,157 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 06:22:43,160 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7277 [2024-02-18 06:22:43,160 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 06:22:43,160 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 06:22:54,411 INFO misc.py line 119 87073] Train: [27/100][1/1557] Data 1.227 (1.227) Batch 2.104 (2.104) Remain 67:21:11 loss: 0.1976 Lr: 0.00442 [2024-02-18 06:22:55,314 INFO misc.py line 119 87073] Train: [27/100][2/1557] Data 0.008 (0.008) Batch 0.902 (0.902) Remain 28:51:36 loss: 0.5384 Lr: 0.00442 [2024-02-18 06:22:56,215 INFO misc.py line 119 87073] Train: [27/100][3/1557] Data 0.007 (0.007) Batch 0.903 (0.903) Remain 28:54:16 loss: 1.5548 Lr: 0.00442 [2024-02-18 06:22:57,442 INFO misc.py line 119 87073] Train: [27/100][4/1557] Data 0.005 (0.005) Batch 1.226 (1.226) Remain 39:13:54 loss: 0.7487 Lr: 0.00442 [2024-02-18 06:22:58,224 INFO misc.py line 119 87073] Train: [27/100][5/1557] Data 0.007 (0.006) Batch 0.781 (1.003) Remain 32:06:32 loss: 0.2604 Lr: 0.00442 [2024-02-18 06:22:59,011 INFO misc.py line 119 87073] Train: [27/100][6/1557] Data 0.007 (0.006) Batch 0.790 (0.932) Remain 29:50:02 loss: 0.5970 Lr: 0.00442 [2024-02-18 06:23:00,207 INFO misc.py line 119 87073] Train: [27/100][7/1557] Data 0.004 (0.006) Batch 1.195 (0.998) Remain 31:56:08 loss: 0.2206 Lr: 0.00442 [2024-02-18 06:23:01,132 INFO misc.py line 119 87073] Train: [27/100][8/1557] Data 0.007 (0.006) Batch 0.924 (0.983) Remain 31:27:45 loss: 0.4116 Lr: 0.00442 [2024-02-18 06:23:01,969 INFO misc.py line 119 87073] Train: [27/100][9/1557] Data 0.012 (0.007) Batch 0.838 (0.959) Remain 30:41:20 loss: 0.6724 Lr: 0.00442 [2024-02-18 06:23:02,955 INFO misc.py line 119 87073] Train: [27/100][10/1557] Data 0.004 (0.006) Batch 0.983 (0.962) Remain 30:47:49 loss: 0.5377 Lr: 0.00442 [2024-02-18 06:23:03,937 INFO misc.py line 119 87073] Train: [27/100][11/1557] Data 0.008 (0.007) Batch 0.985 (0.965) Remain 30:53:14 loss: 0.7517 Lr: 0.00442 [2024-02-18 06:23:04,742 INFO misc.py line 119 87073] Train: [27/100][12/1557] Data 0.005 (0.006) Batch 0.805 (0.947) Remain 30:19:06 loss: 0.5192 Lr: 0.00442 [2024-02-18 06:23:05,487 INFO misc.py line 119 87073] Train: [27/100][13/1557] Data 0.006 (0.006) Batch 0.745 (0.927) Remain 29:40:12 loss: 0.4103 Lr: 0.00442 [2024-02-18 06:23:06,724 INFO misc.py line 119 87073] Train: [27/100][14/1557] Data 0.005 (0.006) Batch 1.232 (0.955) Remain 30:33:29 loss: 0.1864 Lr: 0.00442 [2024-02-18 06:23:07,728 INFO misc.py line 119 87073] Train: [27/100][15/1557] Data 0.012 (0.007) Batch 1.010 (0.959) Remain 30:42:14 loss: 0.6501 Lr: 0.00442 [2024-02-18 06:23:08,708 INFO misc.py line 119 87073] Train: [27/100][16/1557] Data 0.005 (0.007) Batch 0.979 (0.961) Remain 30:45:10 loss: 0.1619 Lr: 0.00442 [2024-02-18 06:23:09,762 INFO misc.py line 119 87073] Train: [27/100][17/1557] Data 0.004 (0.006) Batch 1.054 (0.968) Remain 30:57:53 loss: 0.5726 Lr: 0.00442 [2024-02-18 06:23:10,627 INFO misc.py line 119 87073] Train: [27/100][18/1557] Data 0.005 (0.006) Batch 0.865 (0.961) Remain 30:44:44 loss: 0.1760 Lr: 0.00442 [2024-02-18 06:23:11,384 INFO misc.py line 119 87073] Train: [27/100][19/1557] Data 0.005 (0.006) Batch 0.754 (0.948) Remain 30:19:55 loss: 0.3725 Lr: 0.00442 [2024-02-18 06:23:12,134 INFO misc.py line 119 87073] Train: [27/100][20/1557] Data 0.007 (0.006) Batch 0.754 (0.936) Remain 29:58:01 loss: 0.5789 Lr: 0.00442 [2024-02-18 06:23:13,400 INFO misc.py line 119 87073] Train: [27/100][21/1557] Data 0.004 (0.006) Batch 1.265 (0.955) Remain 30:33:01 loss: 0.1943 Lr: 0.00442 [2024-02-18 06:23:14,426 INFO misc.py line 119 87073] Train: [27/100][22/1557] Data 0.005 (0.006) Batch 1.023 (0.958) Remain 30:39:52 loss: 0.6271 Lr: 0.00442 [2024-02-18 06:23:15,318 INFO misc.py line 119 87073] Train: [27/100][23/1557] Data 0.008 (0.006) Batch 0.893 (0.955) Remain 30:33:38 loss: 0.3907 Lr: 0.00442 [2024-02-18 06:23:16,254 INFO misc.py line 119 87073] Train: [27/100][24/1557] Data 0.006 (0.006) Batch 0.939 (0.954) Remain 30:32:07 loss: 0.4356 Lr: 0.00442 [2024-02-18 06:23:17,019 INFO misc.py line 119 87073] Train: [27/100][25/1557] Data 0.004 (0.006) Batch 0.763 (0.946) Remain 30:15:26 loss: 0.4213 Lr: 0.00442 [2024-02-18 06:23:17,731 INFO misc.py line 119 87073] Train: [27/100][26/1557] Data 0.006 (0.006) Batch 0.714 (0.936) Remain 29:56:03 loss: 0.7735 Lr: 0.00442 [2024-02-18 06:23:18,463 INFO misc.py line 119 87073] Train: [27/100][27/1557] Data 0.004 (0.006) Batch 0.729 (0.927) Remain 29:39:29 loss: 0.3911 Lr: 0.00442 [2024-02-18 06:23:19,597 INFO misc.py line 119 87073] Train: [27/100][28/1557] Data 0.009 (0.006) Batch 1.137 (0.935) Remain 29:55:36 loss: 0.4393 Lr: 0.00442 [2024-02-18 06:23:20,612 INFO misc.py line 119 87073] Train: [27/100][29/1557] Data 0.004 (0.006) Batch 1.014 (0.938) Remain 30:01:25 loss: 0.1566 Lr: 0.00442 [2024-02-18 06:23:21,518 INFO misc.py line 119 87073] Train: [27/100][30/1557] Data 0.005 (0.006) Batch 0.905 (0.937) Remain 29:59:03 loss: 0.6739 Lr: 0.00442 [2024-02-18 06:23:22,588 INFO misc.py line 119 87073] Train: [27/100][31/1557] Data 0.006 (0.006) Batch 1.072 (0.942) Remain 30:08:16 loss: 0.9243 Lr: 0.00442 [2024-02-18 06:23:23,468 INFO misc.py line 119 87073] Train: [27/100][32/1557] Data 0.005 (0.006) Batch 0.878 (0.940) Remain 30:04:03 loss: 0.5524 Lr: 0.00442 [2024-02-18 06:23:24,264 INFO misc.py line 119 87073] Train: [27/100][33/1557] Data 0.005 (0.006) Batch 0.796 (0.935) Remain 29:54:53 loss: 0.5951 Lr: 0.00442 [2024-02-18 06:23:25,060 INFO misc.py line 119 87073] Train: [27/100][34/1557] Data 0.004 (0.006) Batch 0.797 (0.931) Remain 29:46:19 loss: 0.2665 Lr: 0.00442 [2024-02-18 06:23:26,254 INFO misc.py line 119 87073] Train: [27/100][35/1557] Data 0.004 (0.006) Batch 1.193 (0.939) Remain 30:02:03 loss: 0.2078 Lr: 0.00442 [2024-02-18 06:23:27,188 INFO misc.py line 119 87073] Train: [27/100][36/1557] Data 0.005 (0.006) Batch 0.934 (0.939) Remain 30:01:48 loss: 0.7123 Lr: 0.00442 [2024-02-18 06:23:28,227 INFO misc.py line 119 87073] Train: [27/100][37/1557] Data 0.005 (0.006) Batch 1.038 (0.942) Remain 30:07:25 loss: 0.3417 Lr: 0.00442 [2024-02-18 06:23:29,143 INFO misc.py line 119 87073] Train: [27/100][38/1557] Data 0.005 (0.006) Batch 0.917 (0.941) Remain 30:06:02 loss: 0.4964 Lr: 0.00442 [2024-02-18 06:23:30,139 INFO misc.py line 119 87073] Train: [27/100][39/1557] Data 0.004 (0.006) Batch 0.994 (0.942) Remain 30:08:52 loss: 0.3482 Lr: 0.00442 [2024-02-18 06:23:30,921 INFO misc.py line 119 87073] Train: [27/100][40/1557] Data 0.007 (0.006) Batch 0.784 (0.938) Remain 30:00:38 loss: 0.3351 Lr: 0.00442 [2024-02-18 06:23:31,622 INFO misc.py line 119 87073] Train: [27/100][41/1557] Data 0.005 (0.006) Batch 0.691 (0.932) Remain 29:48:08 loss: 0.3543 Lr: 0.00442 [2024-02-18 06:23:32,807 INFO misc.py line 119 87073] Train: [27/100][42/1557] Data 0.014 (0.006) Batch 1.188 (0.938) Remain 30:00:44 loss: 0.3931 Lr: 0.00442 [2024-02-18 06:23:33,959 INFO misc.py line 119 87073] Train: [27/100][43/1557] Data 0.012 (0.006) Batch 1.146 (0.943) Remain 30:10:42 loss: 0.5535 Lr: 0.00442 [2024-02-18 06:23:34,880 INFO misc.py line 119 87073] Train: [27/100][44/1557] Data 0.018 (0.006) Batch 0.933 (0.943) Remain 30:10:13 loss: 0.6734 Lr: 0.00442 [2024-02-18 06:23:35,786 INFO misc.py line 119 87073] Train: [27/100][45/1557] Data 0.006 (0.006) Batch 0.904 (0.942) Remain 30:08:25 loss: 1.2744 Lr: 0.00442 [2024-02-18 06:23:36,738 INFO misc.py line 119 87073] Train: [27/100][46/1557] Data 0.007 (0.006) Batch 0.955 (0.942) Remain 30:08:57 loss: 0.3843 Lr: 0.00442 [2024-02-18 06:23:37,452 INFO misc.py line 119 87073] Train: [27/100][47/1557] Data 0.005 (0.006) Batch 0.713 (0.937) Remain 29:58:57 loss: 0.3403 Lr: 0.00442 [2024-02-18 06:23:38,241 INFO misc.py line 119 87073] Train: [27/100][48/1557] Data 0.006 (0.006) Batch 0.788 (0.934) Remain 29:52:33 loss: 0.4549 Lr: 0.00442 [2024-02-18 06:23:39,482 INFO misc.py line 119 87073] Train: [27/100][49/1557] Data 0.006 (0.006) Batch 1.241 (0.941) Remain 30:05:21 loss: 0.3084 Lr: 0.00442 [2024-02-18 06:23:40,542 INFO misc.py line 119 87073] Train: [27/100][50/1557] Data 0.007 (0.006) Batch 1.060 (0.943) Remain 30:10:12 loss: 0.3245 Lr: 0.00442 [2024-02-18 06:23:41,593 INFO misc.py line 119 87073] Train: [27/100][51/1557] Data 0.007 (0.006) Batch 1.054 (0.945) Remain 30:14:37 loss: 0.5450 Lr: 0.00442 [2024-02-18 06:23:42,664 INFO misc.py line 119 87073] Train: [27/100][52/1557] Data 0.004 (0.006) Batch 1.063 (0.948) Remain 30:19:13 loss: 0.2813 Lr: 0.00442 [2024-02-18 06:23:43,631 INFO misc.py line 119 87073] Train: [27/100][53/1557] Data 0.011 (0.006) Batch 0.973 (0.948) Remain 30:20:10 loss: 0.4405 Lr: 0.00442 [2024-02-18 06:23:44,493 INFO misc.py line 119 87073] Train: [27/100][54/1557] Data 0.006 (0.006) Batch 0.860 (0.947) Remain 30:16:51 loss: 0.4579 Lr: 0.00442 [2024-02-18 06:23:45,280 INFO misc.py line 119 87073] Train: [27/100][55/1557] Data 0.008 (0.006) Batch 0.789 (0.944) Remain 30:11:01 loss: 0.5879 Lr: 0.00442 [2024-02-18 06:23:46,654 INFO misc.py line 119 87073] Train: [27/100][56/1557] Data 0.005 (0.006) Batch 1.369 (0.952) Remain 30:26:25 loss: 0.1272 Lr: 0.00442 [2024-02-18 06:23:48,055 INFO misc.py line 119 87073] Train: [27/100][57/1557] Data 0.011 (0.007) Batch 1.396 (0.960) Remain 30:42:12 loss: 0.2861 Lr: 0.00442 [2024-02-18 06:23:49,141 INFO misc.py line 119 87073] Train: [27/100][58/1557] Data 0.016 (0.007) Batch 1.088 (0.962) Remain 30:46:40 loss: 0.3572 Lr: 0.00442 [2024-02-18 06:23:50,407 INFO misc.py line 119 87073] Train: [27/100][59/1557] Data 0.013 (0.007) Batch 1.274 (0.968) Remain 30:57:20 loss: 0.5185 Lr: 0.00442 [2024-02-18 06:23:51,181 INFO misc.py line 119 87073] Train: [27/100][60/1557] Data 0.006 (0.007) Batch 0.775 (0.964) Remain 30:50:49 loss: 0.4411 Lr: 0.00442 [2024-02-18 06:23:51,928 INFO misc.py line 119 87073] Train: [27/100][61/1557] Data 0.004 (0.007) Batch 0.747 (0.961) Remain 30:43:36 loss: 0.5381 Lr: 0.00442 [2024-02-18 06:23:52,763 INFO misc.py line 119 87073] Train: [27/100][62/1557] Data 0.005 (0.007) Batch 0.836 (0.958) Remain 30:39:32 loss: 0.5582 Lr: 0.00442 [2024-02-18 06:24:04,449 INFO misc.py line 119 87073] Train: [27/100][63/1557] Data 4.657 (0.084) Batch 11.685 (1.137) Remain 36:22:38 loss: 0.3182 Lr: 0.00442 [2024-02-18 06:24:05,437 INFO misc.py line 119 87073] Train: [27/100][64/1557] Data 0.005 (0.083) Batch 0.988 (1.135) Remain 36:17:55 loss: 0.3586 Lr: 0.00442 [2024-02-18 06:24:06,374 INFO misc.py line 119 87073] Train: [27/100][65/1557] Data 0.005 (0.082) Batch 0.932 (1.132) Remain 36:11:38 loss: 0.5796 Lr: 0.00442 [2024-02-18 06:24:07,446 INFO misc.py line 119 87073] Train: [27/100][66/1557] Data 0.010 (0.081) Batch 1.077 (1.131) Remain 36:09:58 loss: 0.7588 Lr: 0.00442 [2024-02-18 06:24:08,480 INFO misc.py line 119 87073] Train: [27/100][67/1557] Data 0.004 (0.079) Batch 1.034 (1.129) Remain 36:07:03 loss: 0.4203 Lr: 0.00442 [2024-02-18 06:24:09,268 INFO misc.py line 119 87073] Train: [27/100][68/1557] Data 0.004 (0.078) Batch 0.787 (1.124) Remain 35:56:56 loss: 0.7944 Lr: 0.00442 [2024-02-18 06:24:10,050 INFO misc.py line 119 87073] Train: [27/100][69/1557] Data 0.005 (0.077) Batch 0.774 (1.119) Remain 35:46:45 loss: 0.6245 Lr: 0.00442 [2024-02-18 06:24:11,338 INFO misc.py line 119 87073] Train: [27/100][70/1557] Data 0.012 (0.076) Batch 1.285 (1.121) Remain 35:51:30 loss: 0.2356 Lr: 0.00442 [2024-02-18 06:24:12,184 INFO misc.py line 119 87073] Train: [27/100][71/1557] Data 0.015 (0.075) Batch 0.857 (1.117) Remain 35:44:01 loss: 0.3077 Lr: 0.00442 [2024-02-18 06:24:13,116 INFO misc.py line 119 87073] Train: [27/100][72/1557] Data 0.004 (0.074) Batch 0.931 (1.114) Remain 35:38:49 loss: 1.0596 Lr: 0.00442 [2024-02-18 06:24:14,289 INFO misc.py line 119 87073] Train: [27/100][73/1557] Data 0.007 (0.073) Batch 1.174 (1.115) Remain 35:40:27 loss: 0.3964 Lr: 0.00442 [2024-02-18 06:24:15,253 INFO misc.py line 119 87073] Train: [27/100][74/1557] Data 0.004 (0.072) Batch 0.965 (1.113) Remain 35:36:21 loss: 1.0286 Lr: 0.00442 [2024-02-18 06:24:15,972 INFO misc.py line 119 87073] Train: [27/100][75/1557] Data 0.004 (0.071) Batch 0.718 (1.108) Remain 35:25:48 loss: 0.5124 Lr: 0.00442 [2024-02-18 06:24:16,697 INFO misc.py line 119 87073] Train: [27/100][76/1557] Data 0.004 (0.070) Batch 0.721 (1.102) Remain 35:15:37 loss: 0.6591 Lr: 0.00442 [2024-02-18 06:24:17,992 INFO misc.py line 119 87073] Train: [27/100][77/1557] Data 0.008 (0.070) Batch 1.286 (1.105) Remain 35:20:21 loss: 0.2025 Lr: 0.00442 [2024-02-18 06:24:18,773 INFO misc.py line 119 87073] Train: [27/100][78/1557] Data 0.018 (0.069) Batch 0.795 (1.101) Remain 35:12:24 loss: 0.5531 Lr: 0.00442 [2024-02-18 06:24:19,830 INFO misc.py line 119 87073] Train: [27/100][79/1557] Data 0.004 (0.068) Batch 1.057 (1.100) Remain 35:11:16 loss: 0.3519 Lr: 0.00442 [2024-02-18 06:24:20,848 INFO misc.py line 119 87073] Train: [27/100][80/1557] Data 0.004 (0.067) Batch 1.017 (1.099) Remain 35:09:12 loss: 0.4278 Lr: 0.00442 [2024-02-18 06:24:21,714 INFO misc.py line 119 87073] Train: [27/100][81/1557] Data 0.004 (0.066) Batch 0.866 (1.096) Remain 35:03:27 loss: 0.6342 Lr: 0.00442 [2024-02-18 06:24:22,506 INFO misc.py line 119 87073] Train: [27/100][82/1557] Data 0.004 (0.066) Batch 0.792 (1.092) Remain 34:56:02 loss: 0.5127 Lr: 0.00442 [2024-02-18 06:24:23,301 INFO misc.py line 119 87073] Train: [27/100][83/1557] Data 0.004 (0.065) Batch 0.793 (1.089) Remain 34:48:51 loss: 0.5045 Lr: 0.00442 [2024-02-18 06:24:24,475 INFO misc.py line 119 87073] Train: [27/100][84/1557] Data 0.005 (0.064) Batch 1.176 (1.090) Remain 34:50:54 loss: 0.2080 Lr: 0.00442 [2024-02-18 06:24:25,484 INFO misc.py line 119 87073] Train: [27/100][85/1557] Data 0.004 (0.063) Batch 1.009 (1.089) Remain 34:48:59 loss: 0.3914 Lr: 0.00442 [2024-02-18 06:24:26,494 INFO misc.py line 119 87073] Train: [27/100][86/1557] Data 0.004 (0.063) Batch 1.011 (1.088) Remain 34:47:10 loss: 0.7144 Lr: 0.00442 [2024-02-18 06:24:27,593 INFO misc.py line 119 87073] Train: [27/100][87/1557] Data 0.004 (0.062) Batch 1.098 (1.088) Remain 34:47:22 loss: 0.9923 Lr: 0.00442 [2024-02-18 06:24:28,477 INFO misc.py line 119 87073] Train: [27/100][88/1557] Data 0.005 (0.061) Batch 0.885 (1.085) Remain 34:42:47 loss: 0.8831 Lr: 0.00442 [2024-02-18 06:24:29,287 INFO misc.py line 119 87073] Train: [27/100][89/1557] Data 0.004 (0.061) Batch 0.804 (1.082) Remain 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06:24:35,875 INFO misc.py line 119 87073] Train: [27/100][96/1557] Data 0.005 (0.056) Batch 0.865 (1.072) Remain 34:16:07 loss: 0.8288 Lr: 0.00442 [2024-02-18 06:24:36,705 INFO misc.py line 119 87073] Train: [27/100][97/1557] Data 0.004 (0.056) Batch 0.824 (1.069) Remain 34:11:02 loss: 0.2830 Lr: 0.00442 [2024-02-18 06:24:37,957 INFO misc.py line 119 87073] Train: [27/100][98/1557] Data 0.010 (0.055) Batch 1.252 (1.071) Remain 34:14:43 loss: 0.2862 Lr: 0.00442 [2024-02-18 06:24:38,917 INFO misc.py line 119 87073] Train: [27/100][99/1557] Data 0.010 (0.055) Batch 0.965 (1.070) Remain 34:12:35 loss: 0.0571 Lr: 0.00442 [2024-02-18 06:24:39,877 INFO misc.py line 119 87073] Train: [27/100][100/1557] Data 0.005 (0.054) Batch 0.959 (1.069) Remain 34:10:23 loss: 0.8326 Lr: 0.00442 [2024-02-18 06:24:40,830 INFO misc.py line 119 87073] Train: [27/100][101/1557] Data 0.005 (0.054) Batch 0.954 (1.068) Remain 34:08:07 loss: 0.7445 Lr: 0.00442 [2024-02-18 06:24:41,666 INFO misc.py line 119 87073] Train: [27/100][102/1557] Data 0.004 (0.053) Batch 0.833 (1.065) Remain 34:03:34 loss: 0.9977 Lr: 0.00442 [2024-02-18 06:24:42,438 INFO misc.py line 119 87073] Train: [27/100][103/1557] Data 0.008 (0.053) Batch 0.774 (1.062) Remain 33:57:57 loss: 0.7083 Lr: 0.00442 [2024-02-18 06:24:43,149 INFO misc.py line 119 87073] Train: [27/100][104/1557] Data 0.005 (0.052) Batch 0.710 (1.059) Remain 33:51:15 loss: 0.3620 Lr: 0.00442 [2024-02-18 06:24:44,400 INFO misc.py line 119 87073] Train: [27/100][105/1557] Data 0.006 (0.052) Batch 1.248 (1.061) Remain 33:54:48 loss: 0.2354 Lr: 0.00442 [2024-02-18 06:24:45,337 INFO misc.py line 119 87073] Train: [27/100][106/1557] Data 0.008 (0.052) Batch 0.941 (1.059) Remain 33:52:33 loss: 0.3917 Lr: 0.00442 [2024-02-18 06:24:46,317 INFO misc.py line 119 87073] Train: [27/100][107/1557] Data 0.004 (0.051) Batch 0.982 (1.059) Remain 33:51:06 loss: 0.5348 Lr: 0.00442 [2024-02-18 06:24:47,352 INFO misc.py line 119 87073] Train: [27/100][108/1557] Data 0.003 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line 119 87073] Train: [27/100][127/1557] Data 0.013 (0.080) Batch 0.994 (1.119) Remain 35:46:00 loss: 0.2687 Lr: 0.00442 [2024-02-18 06:25:16,014 INFO misc.py line 119 87073] Train: [27/100][128/1557] Data 0.005 (0.080) Batch 1.071 (1.118) Remain 35:45:14 loss: 0.5784 Lr: 0.00442 [2024-02-18 06:25:16,891 INFO misc.py line 119 87073] Train: [27/100][129/1557] Data 0.005 (0.079) Batch 0.878 (1.116) Remain 35:41:34 loss: 0.3932 Lr: 0.00442 [2024-02-18 06:25:17,991 INFO misc.py line 119 87073] Train: [27/100][130/1557] Data 0.004 (0.079) Batch 1.099 (1.116) Remain 35:41:16 loss: 0.4713 Lr: 0.00442 [2024-02-18 06:25:18,791 INFO misc.py line 119 87073] Train: [27/100][131/1557] Data 0.005 (0.078) Batch 0.801 (1.114) Remain 35:36:32 loss: 0.5702 Lr: 0.00442 [2024-02-18 06:25:19,472 INFO misc.py line 119 87073] Train: [27/100][132/1557] Data 0.004 (0.077) Batch 0.672 (1.110) Remain 35:29:57 loss: 0.3070 Lr: 0.00442 [2024-02-18 06:25:20,801 INFO misc.py line 119 87073] Train: 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Batch 0.696 (1.101) Remain 35:12:26 loss: 0.3106 Lr: 0.00442 [2024-02-18 06:25:27,169 INFO misc.py line 119 87073] Train: [27/100][140/1557] Data 0.004 (0.073) Batch 1.158 (1.102) Remain 35:13:12 loss: 0.1502 Lr: 0.00442 [2024-02-18 06:25:28,145 INFO misc.py line 119 87073] Train: [27/100][141/1557] Data 0.011 (0.073) Batch 0.984 (1.101) Remain 35:11:33 loss: 0.4122 Lr: 0.00442 [2024-02-18 06:25:29,328 INFO misc.py line 119 87073] Train: [27/100][142/1557] Data 0.004 (0.072) Batch 1.183 (1.102) Remain 35:12:40 loss: 0.3844 Lr: 0.00442 [2024-02-18 06:25:30,354 INFO misc.py line 119 87073] Train: [27/100][143/1557] Data 0.005 (0.072) Batch 1.025 (1.101) Remain 35:11:36 loss: 0.4501 Lr: 0.00442 [2024-02-18 06:25:31,601 INFO misc.py line 119 87073] Train: [27/100][144/1557] Data 0.005 (0.072) Batch 1.238 (1.102) Remain 35:13:26 loss: 0.3409 Lr: 0.00442 [2024-02-18 06:25:32,349 INFO misc.py line 119 87073] Train: [27/100][145/1557] Data 0.015 (0.071) Batch 0.759 (1.100) Remain 35:08:47 loss: 0.3299 Lr: 0.00442 [2024-02-18 06:25:33,038 INFO misc.py line 119 87073] Train: [27/100][146/1557] Data 0.004 (0.071) Batch 0.689 (1.097) Remain 35:03:15 loss: 0.6174 Lr: 0.00442 [2024-02-18 06:25:34,257 INFO misc.py line 119 87073] Train: [27/100][147/1557] Data 0.004 (0.070) Batch 1.212 (1.097) Remain 35:04:46 loss: 0.1437 Lr: 0.00442 [2024-02-18 06:25:35,251 INFO misc.py line 119 87073] Train: [27/100][148/1557] Data 0.011 (0.070) Batch 1.000 (1.097) Remain 35:03:28 loss: 0.3483 Lr: 0.00442 [2024-02-18 06:25:36,173 INFO misc.py line 119 87073] Train: [27/100][149/1557] Data 0.006 (0.069) Batch 0.923 (1.096) Remain 35:01:09 loss: 0.3841 Lr: 0.00442 [2024-02-18 06:25:37,209 INFO misc.py line 119 87073] Train: [27/100][150/1557] Data 0.005 (0.069) Batch 1.036 (1.095) Remain 35:00:21 loss: 0.4132 Lr: 0.00442 [2024-02-18 06:25:38,411 INFO misc.py line 119 87073] Train: [27/100][151/1557] Data 0.004 (0.068) Batch 1.202 (1.096) Remain 35:01:43 loss: 1.0519 Lr: 0.00442 [2024-02-18 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87073] Train: [27/100][158/1557] Data 0.004 (0.066) Batch 1.018 (1.090) Remain 34:50:39 loss: 0.5822 Lr: 0.00442 [2024-02-18 06:25:45,909 INFO misc.py line 119 87073] Train: [27/100][159/1557] Data 0.005 (0.065) Batch 0.712 (1.088) Remain 34:45:58 loss: 0.2632 Lr: 0.00442 [2024-02-18 06:25:46,593 INFO misc.py line 119 87073] Train: [27/100][160/1557] Data 0.004 (0.065) Batch 0.682 (1.085) Remain 34:41:00 loss: 0.5128 Lr: 0.00442 [2024-02-18 06:25:47,827 INFO misc.py line 119 87073] Train: [27/100][161/1557] Data 0.006 (0.065) Batch 1.234 (1.086) Remain 34:42:47 loss: 0.3393 Lr: 0.00442 [2024-02-18 06:25:48,827 INFO misc.py line 119 87073] Train: [27/100][162/1557] Data 0.007 (0.064) Batch 1.002 (1.086) Remain 34:41:45 loss: 0.1569 Lr: 0.00442 [2024-02-18 06:25:49,709 INFO misc.py line 119 87073] Train: [27/100][163/1557] Data 0.005 (0.064) Batch 0.881 (1.084) Remain 34:39:17 loss: 0.8278 Lr: 0.00442 [2024-02-18 06:25:50,922 INFO misc.py line 119 87073] Train: [27/100][164/1557] Data 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Batch 0.786 (1.117) Remain 35:41:04 loss: 0.4821 Lr: 0.00441 [2024-02-18 06:26:31,833 INFO misc.py line 119 87073] Train: [27/100][196/1557] Data 0.005 (0.089) Batch 1.179 (1.117) Remain 35:41:40 loss: 0.1231 Lr: 0.00441 [2024-02-18 06:26:32,734 INFO misc.py line 119 87073] Train: [27/100][197/1557] Data 0.006 (0.088) Batch 0.902 (1.116) Remain 35:39:31 loss: 0.6643 Lr: 0.00441 [2024-02-18 06:26:33,745 INFO misc.py line 119 87073] Train: [27/100][198/1557] Data 0.006 (0.088) Batch 1.010 (1.116) Remain 35:38:27 loss: 0.6007 Lr: 0.00441 [2024-02-18 06:26:34,718 INFO misc.py line 119 87073] Train: [27/100][199/1557] Data 0.007 (0.088) Batch 0.975 (1.115) Remain 35:37:04 loss: 0.5320 Lr: 0.00441 [2024-02-18 06:26:35,590 INFO misc.py line 119 87073] Train: [27/100][200/1557] Data 0.004 (0.087) Batch 0.871 (1.114) Remain 35:34:40 loss: 0.5420 Lr: 0.00441 [2024-02-18 06:26:36,308 INFO misc.py line 119 87073] Train: [27/100][201/1557] Data 0.006 (0.087) Batch 0.719 (1.112) Remain 35:30:50 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line 119 87073] Train: [27/100][239/1557] Data 0.014 (0.095) Batch 0.867 (1.130) Remain 36:04:59 loss: 0.6751 Lr: 0.00441 [2024-02-18 06:27:23,783 INFO misc.py line 119 87073] Train: [27/100][240/1557] Data 0.004 (0.095) Batch 0.943 (1.129) Remain 36:03:27 loss: 0.3697 Lr: 0.00441 [2024-02-18 06:27:24,786 INFO misc.py line 119 87073] Train: [27/100][241/1557] Data 0.004 (0.095) Batch 1.001 (1.128) Remain 36:02:25 loss: 0.4669 Lr: 0.00441 [2024-02-18 06:27:25,665 INFO misc.py line 119 87073] Train: [27/100][242/1557] Data 0.005 (0.094) Batch 0.878 (1.127) Remain 36:00:23 loss: 0.7813 Lr: 0.00441 [2024-02-18 06:27:26,467 INFO misc.py line 119 87073] Train: [27/100][243/1557] Data 0.007 (0.094) Batch 0.795 (1.126) Remain 35:57:43 loss: 0.3948 Lr: 0.00441 [2024-02-18 06:27:27,228 INFO misc.py line 119 87073] Train: [27/100][244/1557] Data 0.013 (0.094) Batch 0.768 (1.125) Remain 35:54:51 loss: 0.4982 Lr: 0.00441 [2024-02-18 06:27:28,505 INFO misc.py line 119 87073] Train: 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Batch 0.739 (1.121) Remain 35:47:59 loss: 0.5061 Lr: 0.00441 [2024-02-18 06:27:35,363 INFO misc.py line 119 87073] Train: [27/100][252/1557] Data 0.004 (0.091) Batch 1.126 (1.121) Remain 35:48:01 loss: 0.2502 Lr: 0.00441 [2024-02-18 06:27:36,118 INFO misc.py line 119 87073] Train: [27/100][253/1557] Data 0.014 (0.091) Batch 0.765 (1.120) Remain 35:45:16 loss: 0.7115 Lr: 0.00441 [2024-02-18 06:27:37,133 INFO misc.py line 119 87073] Train: [27/100][254/1557] Data 0.004 (0.090) Batch 1.015 (1.119) Remain 35:44:26 loss: 0.6572 Lr: 0.00441 [2024-02-18 06:27:38,136 INFO misc.py line 119 87073] Train: [27/100][255/1557] Data 0.004 (0.090) Batch 1.004 (1.119) Remain 35:43:33 loss: 0.5919 Lr: 0.00441 [2024-02-18 06:27:38,941 INFO misc.py line 119 87073] Train: [27/100][256/1557] Data 0.004 (0.089) Batch 0.804 (1.117) Remain 35:41:09 loss: 0.1998 Lr: 0.00441 [2024-02-18 06:27:39,721 INFO misc.py line 119 87073] Train: [27/100][257/1557] Data 0.006 (0.089) Batch 0.773 (1.116) Remain 35:38:31 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Batch 0.695 (1.128) Remain 36:01:16 loss: 0.2585 Lr: 0.00441 [2024-02-18 06:28:40,431 INFO misc.py line 119 87073] Train: [27/100][308/1557] Data 0.005 (0.092) Batch 1.154 (1.129) Remain 36:01:24 loss: 0.1946 Lr: 0.00441 [2024-02-18 06:28:41,270 INFO misc.py line 119 87073] Train: [27/100][309/1557] Data 0.005 (0.092) Batch 0.838 (1.128) Remain 35:59:34 loss: 0.1807 Lr: 0.00441 [2024-02-18 06:28:42,217 INFO misc.py line 119 87073] Train: [27/100][310/1557] Data 0.005 (0.091) Batch 0.949 (1.127) Remain 35:58:26 loss: 0.1354 Lr: 0.00441 [2024-02-18 06:28:43,206 INFO misc.py line 119 87073] Train: [27/100][311/1557] Data 0.004 (0.091) Batch 0.989 (1.127) Remain 35:57:33 loss: 0.5068 Lr: 0.00441 [2024-02-18 06:28:44,061 INFO misc.py line 119 87073] Train: [27/100][312/1557] Data 0.004 (0.091) Batch 0.844 (1.126) Remain 35:55:47 loss: 0.6501 Lr: 0.00441 [2024-02-18 06:28:44,786 INFO misc.py line 119 87073] Train: [27/100][313/1557] Data 0.014 (0.090) Batch 0.734 (1.124) Remain 35:53:21 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line 119 87073] Train: [27/100][687/1557] Data 0.006 (0.098) Batch 0.914 (1.130) Remain 35:57:56 loss: 0.1509 Lr: 0.00440 [2024-02-18 06:35:50,348 INFO misc.py line 119 87073] Train: [27/100][688/1557] Data 0.005 (0.098) Batch 0.874 (1.130) Remain 35:57:12 loss: 0.3134 Lr: 0.00440 [2024-02-18 06:35:51,196 INFO misc.py line 119 87073] Train: [27/100][689/1557] Data 0.003 (0.098) Batch 0.845 (1.130) Remain 35:56:23 loss: 0.7666 Lr: 0.00440 [2024-02-18 06:35:52,354 INFO misc.py line 119 87073] Train: [27/100][690/1557] Data 0.007 (0.098) Batch 1.157 (1.130) Remain 35:56:27 loss: 0.5707 Lr: 0.00440 [2024-02-18 06:35:53,100 INFO misc.py line 119 87073] Train: [27/100][691/1557] Data 0.008 (0.098) Batch 0.751 (1.129) Remain 35:55:23 loss: 0.3818 Lr: 0.00440 [2024-02-18 06:35:53,840 INFO misc.py line 119 87073] Train: [27/100][692/1557] Data 0.003 (0.098) Batch 0.738 (1.129) Remain 35:54:17 loss: 0.6600 Lr: 0.00440 [2024-02-18 06:35:55,136 INFO misc.py line 119 87073] Train: 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Batch 0.771 (1.127) Remain 35:51:12 loss: 0.8251 Lr: 0.00440 [2024-02-18 06:36:01,785 INFO misc.py line 119 87073] Train: [27/100][700/1557] Data 0.004 (0.097) Batch 1.117 (1.127) Remain 35:51:09 loss: 0.1317 Lr: 0.00440 [2024-02-18 06:36:02,848 INFO misc.py line 119 87073] Train: [27/100][701/1557] Data 0.004 (0.096) Batch 0.859 (1.127) Remain 35:50:24 loss: 0.4389 Lr: 0.00440 [2024-02-18 06:36:03,727 INFO misc.py line 119 87073] Train: [27/100][702/1557] Data 0.209 (0.097) Batch 1.084 (1.127) Remain 35:50:16 loss: 0.9486 Lr: 0.00440 [2024-02-18 06:36:04,627 INFO misc.py line 119 87073] Train: [27/100][703/1557] Data 0.004 (0.096) Batch 0.896 (1.126) Remain 35:49:37 loss: 0.4144 Lr: 0.00440 [2024-02-18 06:36:05,499 INFO misc.py line 119 87073] Train: [27/100][704/1557] Data 0.009 (0.096) Batch 0.874 (1.126) Remain 35:48:55 loss: 0.5563 Lr: 0.00440 [2024-02-18 06:36:08,116 INFO misc.py line 119 87073] Train: [27/100][705/1557] Data 1.765 (0.099) Batch 2.617 (1.128) Remain 35:52:57 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Batch 0.778 (1.131) Remain 35:56:35 loss: 0.5104 Lr: 0.00439 [2024-02-18 06:38:11,204 INFO misc.py line 119 87073] Train: [27/100][812/1557] Data 0.005 (0.099) Batch 1.133 (1.131) Remain 35:56:34 loss: 0.1516 Lr: 0.00439 [2024-02-18 06:38:12,155 INFO misc.py line 119 87073] Train: [27/100][813/1557] Data 0.005 (0.098) Batch 0.952 (1.131) Remain 35:56:07 loss: 0.5081 Lr: 0.00439 [2024-02-18 06:38:13,151 INFO misc.py line 119 87073] Train: [27/100][814/1557] Data 0.004 (0.098) Batch 0.996 (1.131) Remain 35:55:47 loss: 0.6178 Lr: 0.00439 [2024-02-18 06:38:14,121 INFO misc.py line 119 87073] Train: [27/100][815/1557] Data 0.005 (0.098) Batch 0.968 (1.130) Remain 35:55:23 loss: 0.3795 Lr: 0.00439 [2024-02-18 06:38:15,211 INFO misc.py line 119 87073] Train: [27/100][816/1557] Data 0.005 (0.098) Batch 1.084 (1.130) Remain 35:55:16 loss: 0.3588 Lr: 0.00439 [2024-02-18 06:38:15,994 INFO misc.py line 119 87073] Train: [27/100][817/1557] Data 0.012 (0.098) Batch 0.790 (1.130) Remain 35:54:27 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Batch 1.037 (1.130) Remain 35:54:03 loss: 0.6505 Lr: 0.00439 [2024-02-18 06:39:13,879 INFO misc.py line 119 87073] Train: [27/100][868/1557] Data 0.004 (0.098) Batch 1.140 (1.130) Remain 35:54:03 loss: 0.2778 Lr: 0.00439 [2024-02-18 06:39:15,012 INFO misc.py line 119 87073] Train: [27/100][869/1557] Data 0.008 (0.098) Batch 1.134 (1.130) Remain 35:54:02 loss: 0.4263 Lr: 0.00439 [2024-02-18 06:39:16,048 INFO misc.py line 119 87073] Train: [27/100][870/1557] Data 0.008 (0.098) Batch 1.037 (1.130) Remain 35:53:49 loss: 0.4331 Lr: 0.00439 [2024-02-18 06:39:16,975 INFO misc.py line 119 87073] Train: [27/100][871/1557] Data 0.007 (0.097) Batch 0.929 (1.130) Remain 35:53:21 loss: 0.3228 Lr: 0.00439 [2024-02-18 06:39:17,893 INFO misc.py line 119 87073] Train: [27/100][872/1557] Data 0.003 (0.097) Batch 0.917 (1.130) Remain 35:52:52 loss: 0.2480 Lr: 0.00439 [2024-02-18 06:39:18,655 INFO misc.py line 119 87073] Train: [27/100][873/1557] Data 0.004 (0.097) Batch 0.754 (1.129) Remain 35:52:01 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Batch 0.947 (1.132) Remain 35:55:41 loss: 0.6840 Lr: 0.00439 [2024-02-18 06:40:18,489 INFO misc.py line 119 87073] Train: [27/100][924/1557] Data 0.004 (0.099) Batch 1.156 (1.132) Remain 35:55:43 loss: 0.2143 Lr: 0.00439 [2024-02-18 06:40:19,366 INFO misc.py line 119 87073] Train: [27/100][925/1557] Data 0.005 (0.099) Batch 0.879 (1.131) Remain 35:55:11 loss: 0.1920 Lr: 0.00439 [2024-02-18 06:40:20,514 INFO misc.py line 119 87073] Train: [27/100][926/1557] Data 0.003 (0.099) Batch 1.145 (1.131) Remain 35:55:11 loss: 0.5138 Lr: 0.00439 [2024-02-18 06:40:21,485 INFO misc.py line 119 87073] Train: [27/100][927/1557] Data 0.005 (0.099) Batch 0.974 (1.131) Remain 35:54:50 loss: 0.5077 Lr: 0.00439 [2024-02-18 06:40:22,656 INFO misc.py line 119 87073] Train: [27/100][928/1557] Data 0.003 (0.099) Batch 1.169 (1.131) Remain 35:54:54 loss: 0.5479 Lr: 0.00439 [2024-02-18 06:40:23,458 INFO misc.py line 119 87073] Train: [27/100][929/1557] Data 0.006 (0.099) Batch 0.805 (1.131) Remain 35:54:13 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Data 0.014 (0.099) Batch 0.697 (1.131) Remain 35:52:57 loss: 0.4432 Lr: 0.00439 [2024-02-18 06:42:25,013 INFO misc.py line 119 87073] Train: [27/100][1036/1557] Data 0.004 (0.099) Batch 1.268 (1.131) Remain 35:53:11 loss: 0.2927 Lr: 0.00439 [2024-02-18 06:42:25,974 INFO misc.py line 119 87073] Train: [27/100][1037/1557] Data 0.013 (0.099) Batch 0.971 (1.131) Remain 35:52:52 loss: 0.6741 Lr: 0.00439 [2024-02-18 06:42:27,010 INFO misc.py line 119 87073] Train: [27/100][1038/1557] Data 0.003 (0.099) Batch 1.036 (1.131) Remain 35:52:40 loss: 0.5231 Lr: 0.00439 [2024-02-18 06:42:27,922 INFO misc.py line 119 87073] Train: [27/100][1039/1557] Data 0.003 (0.099) Batch 0.910 (1.131) Remain 35:52:15 loss: 0.7065 Lr: 0.00439 [2024-02-18 06:42:28,863 INFO misc.py line 119 87073] Train: [27/100][1040/1557] Data 0.006 (0.099) Batch 0.937 (1.131) Remain 35:51:52 loss: 0.5245 Lr: 0.00439 [2024-02-18 06:42:29,596 INFO misc.py line 119 87073] Train: [27/100][1041/1557] Data 0.010 (0.099) Batch 0.739 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06:42:42,073 INFO misc.py line 119 87073] Train: [27/100][1054/1557] Data 0.003 (0.098) Batch 0.979 (1.128) Remain 35:46:51 loss: 0.4525 Lr: 0.00439 [2024-02-18 06:42:44,587 INFO misc.py line 119 87073] Train: [27/100][1055/1557] Data 1.757 (0.099) Batch 2.521 (1.130) Remain 35:49:21 loss: 0.3460 Lr: 0.00439 [2024-02-18 06:42:45,340 INFO misc.py line 119 87073] Train: [27/100][1056/1557] Data 0.004 (0.099) Batch 0.753 (1.129) Remain 35:48:39 loss: 0.4844 Lr: 0.00439 [2024-02-18 06:42:46,573 INFO misc.py line 119 87073] Train: [27/100][1057/1557] Data 0.004 (0.099) Batch 1.232 (1.129) Remain 35:48:50 loss: 0.2611 Lr: 0.00439 [2024-02-18 06:42:47,487 INFO misc.py line 119 87073] Train: [27/100][1058/1557] Data 0.004 (0.099) Batch 0.914 (1.129) Remain 35:48:25 loss: 0.7219 Lr: 0.00439 [2024-02-18 06:42:48,364 INFO misc.py line 119 87073] Train: [27/100][1059/1557] Data 0.004 (0.099) Batch 0.872 (1.129) Remain 35:47:56 loss: 0.4236 Lr: 0.00439 [2024-02-18 06:42:49,400 INFO misc.py line 119 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Data 0.017 (0.098) Batch 0.996 (1.128) Remain 35:46:12 loss: 0.5524 Lr: 0.00438 [2024-02-18 06:42:56,397 INFO misc.py line 119 87073] Train: [27/100][1067/1557] Data 0.004 (0.098) Batch 1.030 (1.128) Remain 35:46:01 loss: 0.6030 Lr: 0.00438 [2024-02-18 06:42:57,279 INFO misc.py line 119 87073] Train: [27/100][1068/1557] Data 0.005 (0.098) Batch 0.882 (1.128) Remain 35:45:33 loss: 0.3623 Lr: 0.00438 [2024-02-18 06:42:58,026 INFO misc.py line 119 87073] Train: [27/100][1069/1557] Data 0.005 (0.098) Batch 0.747 (1.127) Remain 35:44:51 loss: 0.5526 Lr: 0.00438 [2024-02-18 06:42:58,797 INFO misc.py line 119 87073] Train: [27/100][1070/1557] Data 0.005 (0.098) Batch 0.772 (1.127) Remain 35:44:12 loss: 0.2940 Lr: 0.00438 [2024-02-18 06:43:09,157 INFO misc.py line 119 87073] Train: [27/100][1071/1557] Data 4.423 (0.102) Batch 10.360 (1.136) Remain 36:00:38 loss: 0.3388 Lr: 0.00438 [2024-02-18 06:43:10,036 INFO misc.py line 119 87073] Train: [27/100][1072/1557] Data 0.004 (0.102) Batch 0.869 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Data 0.004 (0.100) Batch 0.738 (1.131) Remain 35:51:48 loss: 0.5666 Lr: 0.00438 [2024-02-18 06:43:34,700 INFO misc.py line 119 87073] Train: [27/100][1098/1557] Data 0.004 (0.100) Batch 0.797 (1.131) Remain 35:51:12 loss: 0.5441 Lr: 0.00438 [2024-02-18 06:43:35,968 INFO misc.py line 119 87073] Train: [27/100][1099/1557] Data 0.015 (0.100) Batch 1.267 (1.131) Remain 35:51:25 loss: 0.2143 Lr: 0.00438 [2024-02-18 06:43:37,113 INFO misc.py line 119 87073] Train: [27/100][1100/1557] Data 0.015 (0.100) Batch 1.143 (1.131) Remain 35:51:25 loss: 0.2419 Lr: 0.00438 [2024-02-18 06:43:38,138 INFO misc.py line 119 87073] Train: [27/100][1101/1557] Data 0.017 (0.099) Batch 1.035 (1.131) Remain 35:51:14 loss: 0.4814 Lr: 0.00438 [2024-02-18 06:43:39,058 INFO misc.py line 119 87073] Train: [27/100][1102/1557] Data 0.007 (0.099) Batch 0.923 (1.131) Remain 35:50:51 loss: 0.4604 Lr: 0.00438 [2024-02-18 06:43:39,983 INFO misc.py line 119 87073] Train: [27/100][1103/1557] Data 0.004 (0.099) Batch 0.925 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Data 0.004 (0.102) Batch 1.049 (1.136) Remain 35:59:30 loss: 0.5386 Lr: 0.00438 [2024-02-18 06:44:14,785 INFO misc.py line 119 87073] Train: [27/100][1129/1557] Data 0.007 (0.102) Batch 0.920 (1.135) Remain 35:59:07 loss: 0.5024 Lr: 0.00438 [2024-02-18 06:44:15,645 INFO misc.py line 119 87073] Train: [27/100][1130/1557] Data 0.004 (0.102) Batch 0.860 (1.135) Remain 35:58:38 loss: 0.8820 Lr: 0.00438 [2024-02-18 06:44:16,782 INFO misc.py line 119 87073] Train: [27/100][1131/1557] Data 0.004 (0.101) Batch 1.127 (1.135) Remain 35:58:36 loss: 0.5628 Lr: 0.00438 [2024-02-18 06:44:17,566 INFO misc.py line 119 87073] Train: [27/100][1132/1557] Data 0.014 (0.101) Batch 0.793 (1.135) Remain 35:58:00 loss: 0.2939 Lr: 0.00438 [2024-02-18 06:44:18,353 INFO misc.py line 119 87073] Train: [27/100][1133/1557] Data 0.004 (0.101) Batch 0.787 (1.135) Remain 35:57:24 loss: 0.5376 Lr: 0.00438 [2024-02-18 06:44:19,609 INFO misc.py line 119 87073] Train: [27/100][1134/1557] Data 0.005 (0.101) Batch 1.245 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Data 0.005 (0.099) Batch 0.776 (1.131) Remain 35:49:12 loss: 0.2513 Lr: 0.00438 [2024-02-18 06:44:43,905 INFO misc.py line 119 87073] Train: [27/100][1160/1557] Data 0.004 (0.099) Batch 0.741 (1.130) Remain 35:48:32 loss: 0.5557 Lr: 0.00438 [2024-02-18 06:44:44,676 INFO misc.py line 119 87073] Train: [27/100][1161/1557] Data 0.005 (0.099) Batch 0.771 (1.130) Remain 35:47:56 loss: 0.4998 Lr: 0.00438 [2024-02-18 06:44:45,861 INFO misc.py line 119 87073] Train: [27/100][1162/1557] Data 0.004 (0.099) Batch 1.183 (1.130) Remain 35:48:00 loss: 0.2198 Lr: 0.00438 [2024-02-18 06:44:46,870 INFO misc.py line 119 87073] Train: [27/100][1163/1557] Data 0.006 (0.099) Batch 1.011 (1.130) Remain 35:47:47 loss: 0.7127 Lr: 0.00438 [2024-02-18 06:44:47,882 INFO misc.py line 119 87073] Train: [27/100][1164/1557] Data 0.005 (0.099) Batch 1.005 (1.130) Remain 35:47:34 loss: 0.6842 Lr: 0.00438 [2024-02-18 06:44:48,746 INFO misc.py line 119 87073] Train: [27/100][1165/1557] Data 0.011 (0.099) Batch 0.871 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Remain 35:55:21 loss: 0.1863 Lr: 0.00438 [2024-02-18 06:47:23,712 INFO misc.py line 119 87073] Train: [27/100][1296/1557] Data 0.007 (0.102) Batch 0.864 (1.135) Remain 35:54:56 loss: 0.4697 Lr: 0.00438 [2024-02-18 06:47:24,588 INFO misc.py line 119 87073] Train: [27/100][1297/1557] Data 0.005 (0.102) Batch 0.876 (1.135) Remain 35:54:32 loss: 0.5115 Lr: 0.00438 [2024-02-18 06:47:25,578 INFO misc.py line 119 87073] Train: [27/100][1298/1557] Data 0.006 (0.102) Batch 0.991 (1.135) Remain 35:54:18 loss: 0.5195 Lr: 0.00438 [2024-02-18 06:47:26,529 INFO misc.py line 119 87073] Train: [27/100][1299/1557] Data 0.005 (0.102) Batch 0.951 (1.134) Remain 35:54:01 loss: 0.4093 Lr: 0.00438 [2024-02-18 06:47:27,223 INFO misc.py line 119 87073] Train: [27/100][1300/1557] Data 0.005 (0.102) Batch 0.687 (1.134) Remain 35:53:20 loss: 0.6090 Lr: 0.00438 [2024-02-18 06:47:27,995 INFO misc.py line 119 87073] Train: [27/100][1301/1557] Data 0.012 (0.102) Batch 0.780 (1.134) Remain 35:52:48 loss: 0.3656 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1308/1557] Data 0.004 (0.101) Batch 0.757 (1.133) Remain 35:51:00 loss: 0.7063 Lr: 0.00438 [2024-02-18 06:47:36,017 INFO misc.py line 119 87073] Train: [27/100][1309/1557] Data 0.008 (0.101) Batch 1.219 (1.133) Remain 35:51:07 loss: 0.1837 Lr: 0.00438 [2024-02-18 06:47:37,105 INFO misc.py line 119 87073] Train: [27/100][1310/1557] Data 0.011 (0.101) Batch 1.093 (1.133) Remain 35:51:02 loss: 0.2846 Lr: 0.00438 [2024-02-18 06:47:38,178 INFO misc.py line 119 87073] Train: [27/100][1311/1557] Data 0.006 (0.101) Batch 1.067 (1.133) Remain 35:50:55 loss: 0.3612 Lr: 0.00438 [2024-02-18 06:47:39,122 INFO misc.py line 119 87073] Train: [27/100][1312/1557] Data 0.012 (0.101) Batch 0.953 (1.133) Remain 35:50:38 loss: 0.6975 Lr: 0.00438 [2024-02-18 06:47:40,022 INFO misc.py line 119 87073] Train: [27/100][1313/1557] Data 0.003 (0.101) Batch 0.900 (1.133) Remain 35:50:17 loss: 0.4320 Lr: 0.00438 [2024-02-18 06:47:40,789 INFO misc.py line 119 87073] Train: [27/100][1314/1557] Data 0.004 (0.101) Batch 0.767 (1.132) Remain 35:49:44 loss: 0.7806 Lr: 0.00438 [2024-02-18 06:47:41,507 INFO misc.py line 119 87073] Train: [27/100][1315/1557] Data 0.003 (0.101) Batch 0.717 (1.132) Remain 35:49:07 loss: 0.4354 Lr: 0.00438 [2024-02-18 06:47:42,759 INFO misc.py line 119 87073] Train: [27/100][1316/1557] Data 0.004 (0.101) Batch 1.252 (1.132) Remain 35:49:16 loss: 0.1749 Lr: 0.00438 [2024-02-18 06:47:43,845 INFO misc.py line 119 87073] Train: [27/100][1317/1557] Data 0.005 (0.101) Batch 1.087 (1.132) Remain 35:49:11 loss: 0.3878 Lr: 0.00438 [2024-02-18 06:47:44,771 INFO misc.py line 119 87073] Train: [27/100][1318/1557] Data 0.004 (0.101) Batch 0.926 (1.132) Remain 35:48:52 loss: 0.3226 Lr: 0.00438 [2024-02-18 06:47:45,673 INFO misc.py line 119 87073] Train: [27/100][1319/1557] Data 0.005 (0.100) Batch 0.901 (1.132) Remain 35:48:31 loss: 0.7897 Lr: 0.00438 [2024-02-18 06:47:46,698 INFO misc.py line 119 87073] Train: [27/100][1320/1557] Data 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Remain 35:47:00 loss: 0.4116 Lr: 0.00438 [2024-02-18 06:47:53,661 INFO misc.py line 119 87073] Train: [27/100][1327/1557] Data 0.005 (0.100) Batch 1.029 (1.131) Remain 35:46:50 loss: 0.5671 Lr: 0.00438 [2024-02-18 06:47:54,437 INFO misc.py line 119 87073] Train: [27/100][1328/1557] Data 0.006 (0.100) Batch 0.777 (1.131) Remain 35:46:19 loss: 0.3107 Lr: 0.00438 [2024-02-18 06:47:55,162 INFO misc.py line 119 87073] Train: [27/100][1329/1557] Data 0.004 (0.100) Batch 0.721 (1.130) Remain 35:45:42 loss: 0.3468 Lr: 0.00438 [2024-02-18 06:47:56,421 INFO misc.py line 119 87073] Train: [27/100][1330/1557] Data 0.009 (0.100) Batch 1.253 (1.131) Remain 35:45:52 loss: 0.2147 Lr: 0.00438 [2024-02-18 06:47:57,305 INFO misc.py line 119 87073] Train: [27/100][1331/1557] Data 0.015 (0.100) Batch 0.894 (1.130) Remain 35:45:30 loss: 0.6351 Lr: 0.00438 [2024-02-18 06:47:58,289 INFO misc.py line 119 87073] Train: [27/100][1332/1557] Data 0.005 (0.100) Batch 0.983 (1.130) Remain 35:45:16 loss: 0.3108 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1339/1557] Data 0.006 (0.099) Batch 0.880 (1.129) Remain 35:43:12 loss: 0.7047 Lr: 0.00438 [2024-02-18 06:48:05,748 INFO misc.py line 119 87073] Train: [27/100][1340/1557] Data 0.004 (0.099) Batch 0.910 (1.129) Remain 35:42:53 loss: 0.3693 Lr: 0.00438 [2024-02-18 06:48:06,663 INFO misc.py line 119 87073] Train: [27/100][1341/1557] Data 0.005 (0.099) Batch 0.917 (1.129) Remain 35:42:33 loss: 0.6360 Lr: 0.00438 [2024-02-18 06:48:07,479 INFO misc.py line 119 87073] Train: [27/100][1342/1557] Data 0.004 (0.099) Batch 0.815 (1.129) Remain 35:42:06 loss: 0.4882 Lr: 0.00438 [2024-02-18 06:48:08,274 INFO misc.py line 119 87073] Train: [27/100][1343/1557] Data 0.005 (0.099) Batch 0.794 (1.128) Remain 35:41:36 loss: 0.4976 Lr: 0.00438 [2024-02-18 06:48:09,663 INFO misc.py line 119 87073] Train: [27/100][1344/1557] Data 0.005 (0.099) Batch 1.382 (1.129) Remain 35:41:56 loss: 0.2190 Lr: 0.00438 [2024-02-18 06:48:10,492 INFO misc.py line 119 87073] Train: [27/100][1345/1557] Data 0.012 (0.099) Batch 0.838 (1.128) Remain 35:41:31 loss: 0.5517 Lr: 0.00438 [2024-02-18 06:48:11,473 INFO misc.py line 119 87073] Train: [27/100][1346/1557] Data 0.004 (0.099) Batch 0.980 (1.128) Remain 35:41:17 loss: 0.5192 Lr: 0.00438 [2024-02-18 06:48:12,321 INFO misc.py line 119 87073] Train: [27/100][1347/1557] Data 0.005 (0.098) Batch 0.847 (1.128) Remain 35:40:52 loss: 0.4374 Lr: 0.00438 [2024-02-18 06:48:13,528 INFO misc.py line 119 87073] Train: [27/100][1348/1557] Data 0.006 (0.098) Batch 1.206 (1.128) Remain 35:40:57 loss: 0.7140 Lr: 0.00438 [2024-02-18 06:48:14,288 INFO misc.py line 119 87073] Train: [27/100][1349/1557] Data 0.007 (0.098) Batch 0.763 (1.128) Remain 35:40:25 loss: 0.1380 Lr: 0.00438 [2024-02-18 06:48:15,040 INFO misc.py line 119 87073] Train: [27/100][1350/1557] Data 0.004 (0.098) Batch 0.749 (1.128) Remain 35:39:52 loss: 0.3147 Lr: 0.00437 [2024-02-18 06:48:26,723 INFO misc.py line 119 87073] Train: [27/100][1351/1557] Data 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Remain 35:52:39 loss: 0.2550 Lr: 0.00437 [2024-02-18 06:48:33,499 INFO misc.py line 119 87073] Train: [27/100][1358/1557] Data 0.015 (0.102) Batch 1.348 (1.135) Remain 35:52:56 loss: 0.1446 Lr: 0.00437 [2024-02-18 06:48:34,457 INFO misc.py line 119 87073] Train: [27/100][1359/1557] Data 0.012 (0.102) Batch 0.966 (1.134) Remain 35:52:41 loss: 0.8236 Lr: 0.00437 [2024-02-18 06:48:35,249 INFO misc.py line 119 87073] Train: [27/100][1360/1557] Data 0.005 (0.102) Batch 0.793 (1.134) Remain 35:52:11 loss: 0.6298 Lr: 0.00437 [2024-02-18 06:48:36,234 INFO misc.py line 119 87073] Train: [27/100][1361/1557] Data 0.004 (0.101) Batch 0.978 (1.134) Remain 35:51:56 loss: 0.4986 Lr: 0.00437 [2024-02-18 06:48:37,220 INFO misc.py line 119 87073] Train: [27/100][1362/1557] Data 0.011 (0.101) Batch 0.993 (1.134) Remain 35:51:44 loss: 0.4035 Lr: 0.00437 [2024-02-18 06:48:37,962 INFO misc.py line 119 87073] Train: [27/100][1363/1557] Data 0.005 (0.101) Batch 0.741 (1.134) Remain 35:51:10 loss: 1.4311 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1370/1557] Data 0.005 (0.101) Batch 0.762 (1.132) Remain 35:48:51 loss: 0.2914 Lr: 0.00437 [2024-02-18 06:48:45,013 INFO misc.py line 119 87073] Train: [27/100][1371/1557] Data 0.005 (0.101) Batch 0.689 (1.132) Remain 35:48:13 loss: 0.2238 Lr: 0.00437 [2024-02-18 06:48:46,099 INFO misc.py line 119 87073] Train: [27/100][1372/1557] Data 0.005 (0.101) Batch 1.086 (1.132) Remain 35:48:08 loss: 0.3345 Lr: 0.00437 [2024-02-18 06:48:47,028 INFO misc.py line 119 87073] Train: [27/100][1373/1557] Data 0.005 (0.101) Batch 0.930 (1.132) Remain 35:47:50 loss: 0.7640 Lr: 0.00437 [2024-02-18 06:48:48,028 INFO misc.py line 119 87073] Train: [27/100][1374/1557] Data 0.004 (0.101) Batch 1.000 (1.132) Remain 35:47:37 loss: 0.5292 Lr: 0.00437 [2024-02-18 06:48:49,110 INFO misc.py line 119 87073] Train: [27/100][1375/1557] Data 0.004 (0.100) Batch 1.082 (1.132) Remain 35:47:32 loss: 0.7824 Lr: 0.00437 [2024-02-18 06:48:50,112 INFO misc.py line 119 87073] Train: [27/100][1376/1557] Data 0.004 (0.100) Batch 1.002 (1.132) Remain 35:47:20 loss: 0.4115 Lr: 0.00437 [2024-02-18 06:48:50,921 INFO misc.py line 119 87073] Train: [27/100][1377/1557] Data 0.004 (0.100) Batch 0.809 (1.132) Remain 35:46:52 loss: 0.3931 Lr: 0.00437 [2024-02-18 06:48:51,628 INFO misc.py line 119 87073] Train: [27/100][1378/1557] Data 0.003 (0.100) Batch 0.697 (1.131) Remain 35:46:15 loss: 0.3113 Lr: 0.00437 [2024-02-18 06:48:52,884 INFO misc.py line 119 87073] Train: [27/100][1379/1557] Data 0.014 (0.100) Batch 1.255 (1.131) Remain 35:46:25 loss: 0.2057 Lr: 0.00437 [2024-02-18 06:48:53,786 INFO misc.py line 119 87073] Train: [27/100][1380/1557] Data 0.014 (0.100) Batch 0.913 (1.131) Remain 35:46:05 loss: 0.2893 Lr: 0.00437 [2024-02-18 06:48:54,734 INFO misc.py line 119 87073] Train: [27/100][1381/1557] Data 0.004 (0.100) Batch 0.949 (1.131) Remain 35:45:49 loss: 0.5898 Lr: 0.00437 [2024-02-18 06:48:55,617 INFO misc.py line 119 87073] Train: [27/100][1382/1557] Data 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Remain 35:43:28 loss: 0.5181 Lr: 0.00437 [2024-02-18 06:49:01,967 INFO misc.py line 119 87073] Train: [27/100][1389/1557] Data 0.003 (0.100) Batch 0.932 (1.130) Remain 35:43:11 loss: 0.3798 Lr: 0.00437 [2024-02-18 06:49:02,944 INFO misc.py line 119 87073] Train: [27/100][1390/1557] Data 0.004 (0.099) Batch 0.975 (1.130) Remain 35:42:57 loss: 0.7358 Lr: 0.00437 [2024-02-18 06:49:03,741 INFO misc.py line 119 87073] Train: [27/100][1391/1557] Data 0.006 (0.099) Batch 0.798 (1.129) Remain 35:42:29 loss: 0.4147 Lr: 0.00437 [2024-02-18 06:49:04,488 INFO misc.py line 119 87073] Train: [27/100][1392/1557] Data 0.005 (0.099) Batch 0.746 (1.129) Remain 35:41:56 loss: 0.4432 Lr: 0.00437 [2024-02-18 06:49:05,736 INFO misc.py line 119 87073] Train: [27/100][1393/1557] Data 0.005 (0.099) Batch 1.242 (1.129) Remain 35:42:04 loss: 0.3113 Lr: 0.00437 [2024-02-18 06:49:06,680 INFO misc.py line 119 87073] Train: [27/100][1394/1557] Data 0.011 (0.099) Batch 0.949 (1.129) Remain 35:41:49 loss: 0.6163 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1401/1557] Data 0.014 (0.099) Batch 1.096 (1.128) Remain 35:40:17 loss: 0.2756 Lr: 0.00437 [2024-02-18 06:49:14,459 INFO misc.py line 119 87073] Train: [27/100][1402/1557] Data 0.017 (0.099) Batch 0.908 (1.128) Remain 35:39:58 loss: 0.2533 Lr: 0.00437 [2024-02-18 06:49:15,472 INFO misc.py line 119 87073] Train: [27/100][1403/1557] Data 0.005 (0.099) Batch 1.014 (1.128) Remain 35:39:47 loss: 0.4519 Lr: 0.00437 [2024-02-18 06:49:16,328 INFO misc.py line 119 87073] Train: [27/100][1404/1557] Data 0.004 (0.099) Batch 0.856 (1.128) Remain 35:39:24 loss: 0.3977 Lr: 0.00437 [2024-02-18 06:49:18,831 INFO misc.py line 119 87073] Train: [27/100][1405/1557] Data 1.483 (0.100) Batch 2.498 (1.129) Remain 35:41:14 loss: 0.2388 Lr: 0.00437 [2024-02-18 06:49:19,597 INFO misc.py line 119 87073] Train: [27/100][1406/1557] Data 0.009 (0.099) Batch 0.763 (1.129) Remain 35:40:43 loss: 0.2774 Lr: 0.00437 [2024-02-18 06:49:30,223 INFO misc.py line 119 87073] Train: [27/100][1407/1557] Data 5.349 (0.103) Batch 10.633 (1.135) Remain 35:53:33 loss: 0.3985 Lr: 0.00437 [2024-02-18 06:49:31,187 INFO misc.py line 119 87073] Train: [27/100][1408/1557] Data 0.005 (0.103) Batch 0.957 (1.135) Remain 35:53:17 loss: 0.5419 Lr: 0.00437 [2024-02-18 06:49:32,192 INFO misc.py line 119 87073] Train: [27/100][1409/1557] Data 0.012 (0.103) Batch 1.011 (1.135) Remain 35:53:06 loss: 0.5443 Lr: 0.00437 [2024-02-18 06:49:33,280 INFO misc.py line 119 87073] Train: [27/100][1410/1557] Data 0.006 (0.103) Batch 1.089 (1.135) Remain 35:53:01 loss: 0.4808 Lr: 0.00437 [2024-02-18 06:49:34,445 INFO misc.py line 119 87073] Train: [27/100][1411/1557] Data 0.005 (0.103) Batch 1.165 (1.135) Remain 35:53:02 loss: 0.3892 Lr: 0.00437 [2024-02-18 06:49:35,265 INFO misc.py line 119 87073] Train: [27/100][1412/1557] Data 0.005 (0.103) Batch 0.821 (1.135) Remain 35:52:36 loss: 0.3576 Lr: 0.00437 [2024-02-18 06:49:36,031 INFO misc.py line 119 87073] Train: [27/100][1413/1557] Data 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Remain 35:50:39 loss: 0.5239 Lr: 0.00437 [2024-02-18 06:49:42,612 INFO misc.py line 119 87073] Train: [27/100][1420/1557] Data 0.004 (0.102) Batch 0.763 (1.134) Remain 35:50:08 loss: 0.4283 Lr: 0.00437 [2024-02-18 06:49:43,818 INFO misc.py line 119 87073] Train: [27/100][1421/1557] Data 0.005 (0.102) Batch 1.200 (1.134) Remain 35:50:12 loss: 0.1472 Lr: 0.00437 [2024-02-18 06:49:44,655 INFO misc.py line 119 87073] Train: [27/100][1422/1557] Data 0.011 (0.102) Batch 0.842 (1.133) Remain 35:49:47 loss: 0.7464 Lr: 0.00437 [2024-02-18 06:49:45,678 INFO misc.py line 119 87073] Train: [27/100][1423/1557] Data 0.005 (0.102) Batch 1.025 (1.133) Remain 35:49:37 loss: 0.4303 Lr: 0.00437 [2024-02-18 06:49:46,546 INFO misc.py line 119 87073] Train: [27/100][1424/1557] Data 0.004 (0.102) Batch 0.866 (1.133) Remain 35:49:15 loss: 0.3249 Lr: 0.00437 [2024-02-18 06:49:47,484 INFO misc.py line 119 87073] Train: [27/100][1425/1557] Data 0.005 (0.102) Batch 0.931 (1.133) Remain 35:48:58 loss: 0.5091 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1432/1557] Data 0.004 (0.102) Batch 0.923 (1.132) Remain 35:47:09 loss: 0.1226 Lr: 0.00437 [2024-02-18 06:49:54,926 INFO misc.py line 119 87073] Train: [27/100][1433/1557] Data 0.003 (0.101) Batch 0.774 (1.132) Remain 35:46:40 loss: 0.3181 Lr: 0.00437 [2024-02-18 06:49:55,691 INFO misc.py line 119 87073] Train: [27/100][1434/1557] Data 0.007 (0.101) Batch 0.769 (1.132) Remain 35:46:10 loss: 0.1710 Lr: 0.00437 [2024-02-18 06:49:56,905 INFO misc.py line 119 87073] Train: [27/100][1435/1557] Data 0.003 (0.101) Batch 1.214 (1.132) Remain 35:46:15 loss: 0.2092 Lr: 0.00437 [2024-02-18 06:49:57,842 INFO misc.py line 119 87073] Train: [27/100][1436/1557] Data 0.005 (0.101) Batch 0.936 (1.132) Remain 35:45:58 loss: 0.4373 Lr: 0.00437 [2024-02-18 06:49:58,989 INFO misc.py line 119 87073] Train: [27/100][1437/1557] Data 0.006 (0.101) Batch 1.147 (1.132) Remain 35:45:59 loss: 0.3583 Lr: 0.00437 [2024-02-18 06:49:59,866 INFO misc.py line 119 87073] Train: [27/100][1438/1557] Data 0.005 (0.101) Batch 0.878 (1.131) Remain 35:45:37 loss: 0.7493 Lr: 0.00437 [2024-02-18 06:50:00,913 INFO misc.py line 119 87073] Train: [27/100][1439/1557] Data 0.006 (0.101) Batch 1.047 (1.131) Remain 35:45:29 loss: 0.5317 Lr: 0.00437 [2024-02-18 06:50:01,692 INFO misc.py line 119 87073] Train: [27/100][1440/1557] Data 0.005 (0.101) Batch 0.780 (1.131) Remain 35:45:00 loss: 0.1651 Lr: 0.00437 [2024-02-18 06:50:02,431 INFO misc.py line 119 87073] Train: [27/100][1441/1557] Data 0.004 (0.101) Batch 0.739 (1.131) Remain 35:44:28 loss: 0.2906 Lr: 0.00437 [2024-02-18 06:50:03,562 INFO misc.py line 119 87073] Train: [27/100][1442/1557] Data 0.003 (0.101) Batch 1.125 (1.131) Remain 35:44:27 loss: 0.1288 Lr: 0.00437 [2024-02-18 06:50:04,703 INFO misc.py line 119 87073] Train: [27/100][1443/1557] Data 0.010 (0.101) Batch 1.146 (1.131) Remain 35:44:27 loss: 0.6959 Lr: 0.00437 [2024-02-18 06:50:05,586 INFO misc.py line 119 87073] Train: [27/100][1444/1557] Data 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Remain 35:42:30 loss: 1.4200 Lr: 0.00437 [2024-02-18 06:50:12,105 INFO misc.py line 119 87073] Train: [27/100][1451/1557] Data 0.004 (0.100) Batch 0.876 (1.130) Remain 35:42:09 loss: 0.3217 Lr: 0.00437 [2024-02-18 06:50:13,076 INFO misc.py line 119 87073] Train: [27/100][1452/1557] Data 0.004 (0.100) Batch 0.969 (1.130) Remain 35:41:55 loss: 0.6757 Lr: 0.00437 [2024-02-18 06:50:14,064 INFO misc.py line 119 87073] Train: [27/100][1453/1557] Data 0.005 (0.100) Batch 0.990 (1.130) Remain 35:41:43 loss: 0.6447 Lr: 0.00437 [2024-02-18 06:50:14,951 INFO misc.py line 119 87073] Train: [27/100][1454/1557] Data 0.004 (0.100) Batch 0.886 (1.129) Remain 35:41:22 loss: 0.4140 Lr: 0.00437 [2024-02-18 06:50:15,730 INFO misc.py line 119 87073] Train: [27/100][1455/1557] Data 0.005 (0.100) Batch 0.778 (1.129) Remain 35:40:54 loss: 0.3736 Lr: 0.00437 [2024-02-18 06:50:17,065 INFO misc.py line 119 87073] Train: [27/100][1456/1557] Data 0.005 (0.100) Batch 1.334 (1.129) Remain 35:41:09 loss: 0.1449 Lr: 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INFO misc.py line 119 87073] Train: [27/100][1463/1557] Data 5.344 (0.103) Batch 11.443 (1.135) Remain 35:52:32 loss: 0.3188 Lr: 0.00437 [2024-02-18 06:50:34,783 INFO misc.py line 119 87073] Train: [27/100][1464/1557] Data 0.004 (0.103) Batch 0.936 (1.135) Remain 35:52:16 loss: 0.3773 Lr: 0.00437 [2024-02-18 06:50:35,897 INFO misc.py line 119 87073] Train: [27/100][1465/1557] Data 0.008 (0.103) Batch 1.113 (1.135) Remain 35:52:13 loss: 0.4111 Lr: 0.00437 [2024-02-18 06:50:36,967 INFO misc.py line 119 87073] Train: [27/100][1466/1557] Data 0.010 (0.103) Batch 1.066 (1.135) Remain 35:52:06 loss: 0.5684 Lr: 0.00437 [2024-02-18 06:50:37,924 INFO misc.py line 119 87073] Train: [27/100][1467/1557] Data 0.014 (0.103) Batch 0.964 (1.135) Remain 35:51:52 loss: 0.2721 Lr: 0.00437 [2024-02-18 06:50:38,720 INFO misc.py line 119 87073] Train: [27/100][1468/1557] Data 0.006 (0.103) Batch 0.797 (1.135) Remain 35:51:25 loss: 0.4173 Lr: 0.00437 [2024-02-18 06:50:39,469 INFO misc.py line 119 87073] Train: [27/100][1469/1557] Data 0.006 (0.103) Batch 0.745 (1.135) Remain 35:50:53 loss: 0.5145 Lr: 0.00437 [2024-02-18 06:50:40,657 INFO misc.py line 119 87073] Train: [27/100][1470/1557] Data 0.009 (0.103) Batch 1.192 (1.135) Remain 35:50:56 loss: 0.1365 Lr: 0.00437 [2024-02-18 06:50:41,573 INFO misc.py line 119 87073] Train: [27/100][1471/1557] Data 0.004 (0.103) Batch 0.917 (1.134) Remain 35:50:39 loss: 0.3516 Lr: 0.00437 [2024-02-18 06:50:42,705 INFO misc.py line 119 87073] Train: [27/100][1472/1557] Data 0.003 (0.103) Batch 1.132 (1.134) Remain 35:50:37 loss: 0.6440 Lr: 0.00437 [2024-02-18 06:50:43,938 INFO misc.py line 119 87073] Train: [27/100][1473/1557] Data 0.004 (0.102) Batch 1.223 (1.134) Remain 35:50:43 loss: 0.6871 Lr: 0.00437 [2024-02-18 06:50:44,924 INFO misc.py line 119 87073] Train: [27/100][1474/1557] Data 0.014 (0.102) Batch 0.996 (1.134) Remain 35:50:31 loss: 0.9730 Lr: 0.00437 [2024-02-18 06:50:45,646 INFO misc.py line 119 87073] Train: [27/100][1475/1557] Data 0.004 (0.102) Batch 0.722 (1.134) Remain 35:49:58 loss: 0.5350 Lr: 0.00437 [2024-02-18 06:50:46,375 INFO misc.py line 119 87073] Train: [27/100][1476/1557] Data 0.004 (0.102) Batch 0.724 (1.134) Remain 35:49:25 loss: 0.4997 Lr: 0.00437 [2024-02-18 06:50:47,656 INFO misc.py line 119 87073] Train: [27/100][1477/1557] Data 0.009 (0.102) Batch 1.283 (1.134) Remain 35:49:36 loss: 0.3592 Lr: 0.00437 [2024-02-18 06:50:48,511 INFO misc.py line 119 87073] Train: [27/100][1478/1557] Data 0.007 (0.102) Batch 0.857 (1.134) Remain 35:49:13 loss: 0.4056 Lr: 0.00437 [2024-02-18 06:50:49,472 INFO misc.py line 119 87073] Train: [27/100][1479/1557] Data 0.004 (0.102) Batch 0.963 (1.134) Remain 35:48:59 loss: 1.0948 Lr: 0.00437 [2024-02-18 06:50:50,420 INFO misc.py line 119 87073] Train: [27/100][1480/1557] Data 0.003 (0.102) Batch 0.947 (1.134) Remain 35:48:43 loss: 0.2895 Lr: 0.00437 [2024-02-18 06:50:51,381 INFO misc.py line 119 87073] Train: [27/100][1481/1557] Data 0.004 (0.102) Batch 0.961 (1.133) Remain 35:48:29 loss: 0.3429 Lr: 0.00437 [2024-02-18 06:50:52,165 INFO misc.py line 119 87073] Train: [27/100][1482/1557] Data 0.004 (0.102) Batch 0.775 (1.133) Remain 35:48:00 loss: 0.2471 Lr: 0.00437 [2024-02-18 06:50:52,964 INFO misc.py line 119 87073] Train: [27/100][1483/1557] Data 0.014 (0.102) Batch 0.806 (1.133) Remain 35:47:34 loss: 0.6178 Lr: 0.00437 [2024-02-18 06:50:54,136 INFO misc.py line 119 87073] Train: [27/100][1484/1557] Data 0.006 (0.102) Batch 1.173 (1.133) Remain 35:47:36 loss: 0.4569 Lr: 0.00437 [2024-02-18 06:50:55,334 INFO misc.py line 119 87073] Train: [27/100][1485/1557] Data 0.005 (0.102) Batch 1.189 (1.133) Remain 35:47:39 loss: 0.5653 Lr: 0.00437 [2024-02-18 06:50:56,183 INFO misc.py line 119 87073] Train: [27/100][1486/1557] Data 0.015 (0.102) Batch 0.859 (1.133) Remain 35:47:17 loss: 0.4535 Lr: 0.00437 [2024-02-18 06:50:57,199 INFO misc.py line 119 87073] Train: [27/100][1487/1557] Data 0.003 (0.102) Batch 1.017 (1.133) Remain 35:47:07 loss: 0.4127 Lr: 0.00437 [2024-02-18 06:50:58,121 INFO misc.py line 119 87073] Train: [27/100][1488/1557] Data 0.004 (0.102) Batch 0.920 (1.133) Remain 35:46:49 loss: 0.4329 Lr: 0.00437 [2024-02-18 06:50:58,898 INFO misc.py line 119 87073] Train: [27/100][1489/1557] Data 0.004 (0.101) Batch 0.778 (1.132) Remain 35:46:21 loss: 0.8973 Lr: 0.00437 [2024-02-18 06:50:59,594 INFO misc.py line 119 87073] Train: [27/100][1490/1557] Data 0.004 (0.101) Batch 0.695 (1.132) Remain 35:45:47 loss: 0.4554 Lr: 0.00437 [2024-02-18 06:51:00,866 INFO misc.py line 119 87073] Train: [27/100][1491/1557] Data 0.005 (0.101) Batch 1.271 (1.132) Remain 35:45:56 loss: 0.2169 Lr: 0.00437 [2024-02-18 06:51:01,900 INFO misc.py line 119 87073] Train: [27/100][1492/1557] Data 0.006 (0.101) Batch 1.037 (1.132) Remain 35:45:48 loss: 0.3254 Lr: 0.00437 [2024-02-18 06:51:02,678 INFO misc.py line 119 87073] Train: [27/100][1493/1557] Data 0.003 (0.101) Batch 0.775 (1.132) Remain 35:45:19 loss: 0.5426 Lr: 0.00437 [2024-02-18 06:51:03,531 INFO misc.py line 119 87073] Train: [27/100][1494/1557] Data 0.007 (0.101) Batch 0.850 (1.132) Remain 35:44:57 loss: 0.6528 Lr: 0.00437 [2024-02-18 06:51:04,428 INFO misc.py line 119 87073] Train: [27/100][1495/1557] Data 0.010 (0.101) Batch 0.903 (1.132) Remain 35:44:38 loss: 0.1983 Lr: 0.00437 [2024-02-18 06:51:05,324 INFO misc.py line 119 87073] Train: [27/100][1496/1557] Data 0.004 (0.101) Batch 0.895 (1.131) Remain 35:44:19 loss: 0.7642 Lr: 0.00437 [2024-02-18 06:51:06,088 INFO misc.py line 119 87073] Train: [27/100][1497/1557] Data 0.004 (0.101) Batch 0.761 (1.131) Remain 35:43:50 loss: 0.4642 Lr: 0.00437 [2024-02-18 06:51:07,232 INFO misc.py line 119 87073] Train: [27/100][1498/1557] Data 0.007 (0.101) Batch 1.147 (1.131) Remain 35:43:50 loss: 0.2784 Lr: 0.00437 [2024-02-18 06:51:08,079 INFO misc.py line 119 87073] Train: [27/100][1499/1557] Data 0.004 (0.101) Batch 0.847 (1.131) Remain 35:43:27 loss: 0.5937 Lr: 0.00437 [2024-02-18 06:51:09,141 INFO misc.py line 119 87073] Train: [27/100][1500/1557] Data 0.005 (0.101) Batch 1.063 (1.131) Remain 35:43:21 loss: 0.4686 Lr: 0.00437 [2024-02-18 06:51:10,033 INFO misc.py line 119 87073] Train: [27/100][1501/1557] Data 0.004 (0.101) Batch 0.892 (1.131) Remain 35:43:01 loss: 1.4250 Lr: 0.00437 [2024-02-18 06:51:10,912 INFO misc.py line 119 87073] Train: [27/100][1502/1557] Data 0.004 (0.101) Batch 0.877 (1.131) Remain 35:42:41 loss: 1.5132 Lr: 0.00437 [2024-02-18 06:51:11,695 INFO misc.py line 119 87073] Train: [27/100][1503/1557] Data 0.007 (0.101) Batch 0.784 (1.130) Remain 35:42:14 loss: 0.7086 Lr: 0.00437 [2024-02-18 06:51:12,359 INFO misc.py line 119 87073] Train: [27/100][1504/1557] Data 0.005 (0.100) Batch 0.664 (1.130) Remain 35:41:37 loss: 0.2781 Lr: 0.00437 [2024-02-18 06:51:13,605 INFO misc.py line 119 87073] Train: [27/100][1505/1557] Data 0.005 (0.100) Batch 1.240 (1.130) Remain 35:41:44 loss: 0.2011 Lr: 0.00437 [2024-02-18 06:51:14,836 INFO misc.py line 119 87073] Train: [27/100][1506/1557] Data 0.012 (0.100) Batch 1.238 (1.130) Remain 35:41:51 loss: 0.6103 Lr: 0.00437 [2024-02-18 06:51:15,805 INFO misc.py line 119 87073] Train: [27/100][1507/1557] Data 0.006 (0.100) Batch 0.970 (1.130) Remain 35:41:38 loss: 0.4199 Lr: 0.00437 [2024-02-18 06:51:16,756 INFO misc.py line 119 87073] Train: [27/100][1508/1557] Data 0.004 (0.100) Batch 0.951 (1.130) Remain 35:41:23 loss: 0.5026 Lr: 0.00437 [2024-02-18 06:51:17,584 INFO misc.py line 119 87073] Train: [27/100][1509/1557] Data 0.005 (0.100) Batch 0.828 (1.130) Remain 35:40:59 loss: 0.5228 Lr: 0.00437 [2024-02-18 06:51:18,291 INFO misc.py line 119 87073] Train: [27/100][1510/1557] Data 0.004 (0.100) Batch 0.697 (1.129) Remain 35:40:26 loss: 0.4777 Lr: 0.00437 [2024-02-18 06:51:19,095 INFO misc.py line 119 87073] Train: [27/100][1511/1557] Data 0.015 (0.100) Batch 0.813 (1.129) Remain 35:40:01 loss: 0.3914 Lr: 0.00437 [2024-02-18 06:51:20,477 INFO misc.py line 119 87073] Train: [27/100][1512/1557] Data 0.005 (0.100) Batch 1.374 (1.129) Remain 35:40:18 loss: 0.1278 Lr: 0.00437 [2024-02-18 06:51:21,384 INFO misc.py line 119 87073] Train: [27/100][1513/1557] Data 0.013 (0.100) Batch 0.916 (1.129) Remain 35:40:01 loss: 0.2922 Lr: 0.00437 [2024-02-18 06:51:22,470 INFO misc.py line 119 87073] Train: [27/100][1514/1557] Data 0.004 (0.100) Batch 1.085 (1.129) Remain 35:39:56 loss: 0.7898 Lr: 0.00437 [2024-02-18 06:51:23,287 INFO misc.py line 119 87073] Train: [27/100][1515/1557] Data 0.006 (0.100) Batch 0.817 (1.129) Remain 35:39:32 loss: 0.4326 Lr: 0.00437 [2024-02-18 06:51:24,429 INFO misc.py line 119 87073] Train: [27/100][1516/1557] Data 0.006 (0.100) Batch 1.143 (1.129) Remain 35:39:32 loss: 0.6206 Lr: 0.00437 [2024-02-18 06:51:25,193 INFO misc.py line 119 87073] Train: [27/100][1517/1557] Data 0.005 (0.100) Batch 0.765 (1.129) Remain 35:39:03 loss: 0.4173 Lr: 0.00437 [2024-02-18 06:51:25,980 INFO misc.py line 119 87073] Train: [27/100][1518/1557] Data 0.004 (0.100) Batch 0.778 (1.129) Remain 35:38:36 loss: 0.6070 Lr: 0.00437 [2024-02-18 06:51:36,973 INFO misc.py line 119 87073] Train: [27/100][1519/1557] Data 5.009 (0.103) Batch 11.002 (1.135) Remain 35:50:55 loss: 0.2791 Lr: 0.00437 [2024-02-18 06:51:37,933 INFO misc.py line 119 87073] Train: [27/100][1520/1557] Data 0.004 (0.103) Batch 0.950 (1.135) Remain 35:50:40 loss: 0.6622 Lr: 0.00437 [2024-02-18 06:51:39,061 INFO misc.py line 119 87073] Train: [27/100][1521/1557] Data 0.014 (0.103) Batch 1.134 (1.135) Remain 35:50:39 loss: 0.3094 Lr: 0.00437 [2024-02-18 06:51:39,916 INFO misc.py line 119 87073] Train: [27/100][1522/1557] Data 0.008 (0.103) Batch 0.857 (1.135) Remain 35:50:17 loss: 0.3517 Lr: 0.00437 [2024-02-18 06:51:40,751 INFO misc.py line 119 87073] Train: [27/100][1523/1557] Data 0.007 (0.103) Batch 0.837 (1.135) Remain 35:49:53 loss: 0.7603 Lr: 0.00437 [2024-02-18 06:51:41,510 INFO misc.py line 119 87073] Train: [27/100][1524/1557] Data 0.004 (0.103) Batch 0.754 (1.134) Remain 35:49:24 loss: 0.1333 Lr: 0.00437 [2024-02-18 06:51:42,225 INFO misc.py line 119 87073] Train: [27/100][1525/1557] Data 0.008 (0.102) Batch 0.719 (1.134) Remain 35:48:52 loss: 0.3781 Lr: 0.00437 [2024-02-18 06:51:43,450 INFO misc.py line 119 87073] Train: [27/100][1526/1557] Data 0.004 (0.102) Batch 1.225 (1.134) Remain 35:48:57 loss: 0.1737 Lr: 0.00437 [2024-02-18 06:51:44,255 INFO misc.py line 119 87073] Train: [27/100][1527/1557] Data 0.004 (0.102) Batch 0.805 (1.134) Remain 35:48:32 loss: 0.8578 Lr: 0.00437 [2024-02-18 06:51:45,100 INFO misc.py line 119 87073] Train: [27/100][1528/1557] Data 0.004 (0.102) Batch 0.843 (1.134) Remain 35:48:09 loss: 0.4566 Lr: 0.00437 [2024-02-18 06:51:45,999 INFO misc.py line 119 87073] Train: [27/100][1529/1557] Data 0.005 (0.102) Batch 0.899 (1.134) Remain 35:47:50 loss: 0.3727 Lr: 0.00437 [2024-02-18 06:51:46,720 INFO misc.py line 119 87073] Train: [27/100][1530/1557] Data 0.006 (0.102) Batch 0.722 (1.133) Remain 35:47:19 loss: 0.5258 Lr: 0.00437 [2024-02-18 06:51:47,629 INFO misc.py line 119 87073] Train: [27/100][1531/1557] Data 0.004 (0.102) Batch 0.909 (1.133) Remain 35:47:01 loss: 0.4202 Lr: 0.00437 [2024-02-18 06:51:48,347 INFO misc.py line 119 87073] Train: [27/100][1532/1557] Data 0.004 (0.102) Batch 0.719 (1.133) Remain 35:46:29 loss: 0.2453 Lr: 0.00437 [2024-02-18 06:51:49,587 INFO misc.py line 119 87073] Train: [27/100][1533/1557] Data 0.003 (0.102) Batch 1.240 (1.133) Remain 35:46:36 loss: 0.3846 Lr: 0.00437 [2024-02-18 06:51:50,416 INFO misc.py line 119 87073] Train: [27/100][1534/1557] Data 0.004 (0.102) Batch 0.827 (1.133) Remain 35:46:12 loss: 0.6534 Lr: 0.00437 [2024-02-18 06:51:51,526 INFO misc.py line 119 87073] Train: [27/100][1535/1557] Data 0.006 (0.102) Batch 1.105 (1.133) Remain 35:46:09 loss: 0.7424 Lr: 0.00437 [2024-02-18 06:51:52,542 INFO misc.py line 119 87073] Train: [27/100][1536/1557] Data 0.011 (0.102) Batch 1.013 (1.133) Remain 35:45:59 loss: 0.9153 Lr: 0.00437 [2024-02-18 06:51:53,483 INFO misc.py line 119 87073] Train: [27/100][1537/1557] Data 0.013 (0.102) Batch 0.950 (1.133) Remain 35:45:44 loss: 0.2024 Lr: 0.00437 [2024-02-18 06:51:54,168 INFO misc.py line 119 87073] Train: [27/100][1538/1557] Data 0.005 (0.102) Batch 0.686 (1.132) Remain 35:45:10 loss: 1.1539 Lr: 0.00437 [2024-02-18 06:51:54,854 INFO misc.py line 119 87073] Train: [27/100][1539/1557] Data 0.004 (0.102) Batch 0.676 (1.132) Remain 35:44:35 loss: 0.3390 Lr: 0.00437 [2024-02-18 06:51:56,149 INFO misc.py line 119 87073] Train: [27/100][1540/1557] Data 0.014 (0.102) Batch 1.297 (1.132) Remain 35:44:46 loss: 0.2052 Lr: 0.00437 [2024-02-18 06:51:57,098 INFO misc.py line 119 87073] Train: [27/100][1541/1557] Data 0.012 (0.101) Batch 0.958 (1.132) Remain 35:44:32 loss: 0.3258 Lr: 0.00437 [2024-02-18 06:51:58,068 INFO misc.py line 119 87073] Train: [27/100][1542/1557] Data 0.004 (0.101) Batch 0.970 (1.132) Remain 35:44:19 loss: 0.3870 Lr: 0.00437 [2024-02-18 06:51:58,879 INFO misc.py line 119 87073] Train: [27/100][1543/1557] Data 0.003 (0.101) Batch 0.810 (1.132) Remain 35:43:54 loss: 0.4588 Lr: 0.00437 [2024-02-18 06:51:59,740 INFO misc.py line 119 87073] Train: [27/100][1544/1557] Data 0.004 (0.101) Batch 0.852 (1.131) Remain 35:43:32 loss: 0.3560 Lr: 0.00437 [2024-02-18 06:52:00,507 INFO misc.py line 119 87073] Train: [27/100][1545/1557] Data 0.012 (0.101) Batch 0.776 (1.131) Remain 35:43:05 loss: 0.3404 Lr: 0.00437 [2024-02-18 06:52:01,182 INFO misc.py line 119 87073] Train: [27/100][1546/1557] Data 0.004 (0.101) Batch 0.666 (1.131) Remain 35:42:29 loss: 0.2722 Lr: 0.00437 [2024-02-18 06:52:02,421 INFO misc.py line 119 87073] Train: [27/100][1547/1557] Data 0.013 (0.101) Batch 1.239 (1.131) Remain 35:42:36 loss: 0.2330 Lr: 0.00437 [2024-02-18 06:52:03,248 INFO misc.py line 119 87073] Train: [27/100][1548/1557] Data 0.012 (0.101) Batch 0.837 (1.131) Remain 35:42:13 loss: 0.6302 Lr: 0.00437 [2024-02-18 06:52:04,132 INFO misc.py line 119 87073] Train: [27/100][1549/1557] Data 0.003 (0.101) Batch 0.884 (1.131) Remain 35:41:54 loss: 0.2975 Lr: 0.00437 [2024-02-18 06:52:05,265 INFO misc.py line 119 87073] Train: [27/100][1550/1557] Data 0.004 (0.101) Batch 1.124 (1.131) Remain 35:41:53 loss: 0.4135 Lr: 0.00437 [2024-02-18 06:52:06,258 INFO misc.py line 119 87073] Train: [27/100][1551/1557] Data 0.013 (0.101) Batch 1.002 (1.131) Remain 35:41:42 loss: 0.3417 Lr: 0.00437 [2024-02-18 06:52:07,039 INFO misc.py line 119 87073] Train: [27/100][1552/1557] Data 0.003 (0.101) Batch 0.780 (1.130) Remain 35:41:15 loss: 0.4559 Lr: 0.00437 [2024-02-18 06:52:07,769 INFO misc.py line 119 87073] Train: [27/100][1553/1557] Data 0.004 (0.101) Batch 0.720 (1.130) Remain 35:40:44 loss: 0.5086 Lr: 0.00437 [2024-02-18 06:52:08,892 INFO misc.py line 119 87073] Train: [27/100][1554/1557] Data 0.014 (0.101) Batch 1.124 (1.130) Remain 35:40:42 loss: 0.3038 Lr: 0.00437 [2024-02-18 06:52:09,824 INFO misc.py line 119 87073] Train: [27/100][1555/1557] Data 0.014 (0.101) Batch 0.942 (1.130) Remain 35:40:27 loss: 0.2879 Lr: 0.00437 [2024-02-18 06:52:10,852 INFO misc.py line 119 87073] Train: [27/100][1556/1557] Data 0.004 (0.101) Batch 1.028 (1.130) Remain 35:40:19 loss: 0.2997 Lr: 0.00437 [2024-02-18 06:52:11,774 INFO misc.py line 119 87073] Train: [27/100][1557/1557] Data 0.003 (0.101) Batch 0.922 (1.130) Remain 35:40:03 loss: 0.3328 Lr: 0.00437 [2024-02-18 06:52:11,775 INFO misc.py line 136 87073] Train result: loss: 0.4659 [2024-02-18 06:52:11,775 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 06:52:36,331 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5060 [2024-02-18 06:52:37,114 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9476 [2024-02-18 06:52:39,754 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.7836 [2024-02-18 06:52:41,961 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3255 [2024-02-18 06:52:46,907 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2761 [2024-02-18 06:52:47,606 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1948 [2024-02-18 06:52:48,866 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2879 [2024-02-18 06:52:51,821 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2704 [2024-02-18 06:52:53,632 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.5893 [2024-02-18 06:52:55,183 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6903/0.7635/0.9020. [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9219/0.9757 [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9807/0.9864 [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8460/0.9759 [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3783/0.4541 [2024-02-18 06:52:55,183 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6017/0.6087 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7095/0.7605 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8367/0.9197 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9243/0.9686 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8507/0.9021 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7720/0.8963 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6201/0.9098 [2024-02-18 06:52:55,184 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5323/0.5678 [2024-02-18 06:52:55,184 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 06:52:55,188 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 06:52:55,188 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 06:53:02,273 INFO misc.py line 119 87073] Train: [28/100][1/1557] Data 1.577 (1.577) Batch 2.275 (2.275) Remain 71:49:44 loss: 0.2348 Lr: 0.00437 [2024-02-18 06:53:03,358 INFO misc.py line 119 87073] Train: [28/100][2/1557] Data 0.006 (0.006) Batch 1.085 (1.085) Remain 34:15:03 loss: 0.6091 Lr: 0.00437 [2024-02-18 06:53:04,356 INFO misc.py line 119 87073] Train: [28/100][3/1557] Data 0.006 (0.006) Batch 0.989 (0.989) Remain 31:14:16 loss: 0.2863 Lr: 0.00437 [2024-02-18 06:53:05,220 INFO misc.py line 119 87073] Train: [28/100][4/1557] Data 0.015 (0.015) Batch 0.875 (0.875) Remain 27:37:07 loss: 0.3785 Lr: 0.00437 [2024-02-18 06:53:05,945 INFO misc.py line 119 87073] Train: [28/100][5/1557] Data 0.004 (0.010) Batch 0.722 (0.799) Remain 25:12:40 loss: 0.2235 Lr: 0.00437 [2024-02-18 06:53:06,757 INFO misc.py line 119 87073] Train: [28/100][6/1557] Data 0.006 (0.009) Batch 0.808 (0.802) Remain 25:18:41 loss: 0.4496 Lr: 0.00437 [2024-02-18 06:53:17,459 INFO misc.py line 119 87073] Train: [28/100][7/1557] Data 0.010 (0.009) Batch 10.707 (3.278) Remain 103:29:15 loss: 0.2827 Lr: 0.00437 [2024-02-18 06:53:18,404 INFO misc.py line 119 87073] Train: [28/100][8/1557] Data 0.006 (0.008) Batch 0.946 (2.812) Remain 88:45:55 loss: 0.5315 Lr: 0.00437 [2024-02-18 06:53:19,329 INFO misc.py line 119 87073] Train: [28/100][9/1557] Data 0.003 (0.008) Batch 0.917 (2.496) Remain 78:47:41 loss: 0.4193 Lr: 0.00437 [2024-02-18 06:53:20,231 INFO misc.py line 119 87073] Train: [28/100][10/1557] Data 0.012 (0.008) Batch 0.910 (2.269) Remain 71:38:24 loss: 0.2715 Lr: 0.00437 [2024-02-18 06:53:21,255 INFO misc.py line 119 87073] Train: [28/100][11/1557] Data 0.005 (0.008) Batch 1.022 (2.113) Remain 66:43:02 loss: 0.2814 Lr: 0.00437 [2024-02-18 06:53:22,021 INFO misc.py line 119 87073] Train: [28/100][12/1557] Data 0.006 (0.008) Batch 0.767 (1.964) Remain 61:59:37 loss: 0.4983 Lr: 0.00437 [2024-02-18 06:53:22,843 INFO misc.py line 119 87073] Train: [28/100][13/1557] Data 0.005 (0.007) Batch 0.821 (1.849) Remain 58:23:11 loss: 0.3479 Lr: 0.00437 [2024-02-18 06:53:24,145 INFO misc.py line 119 87073] Train: [28/100][14/1557] Data 0.006 (0.007) Batch 1.297 (1.799) Remain 56:48:02 loss: 0.3215 Lr: 0.00437 [2024-02-18 06:53:25,205 INFO misc.py line 119 87073] Train: [28/100][15/1557] Data 0.011 (0.007) Batch 1.055 (1.737) Remain 54:50:36 loss: 0.3480 Lr: 0.00437 [2024-02-18 06:53:26,354 INFO misc.py line 119 87073] Train: [28/100][16/1557] Data 0.015 (0.008) Batch 1.160 (1.693) Remain 53:26:25 loss: 0.2474 Lr: 0.00437 [2024-02-18 06:53:27,324 INFO misc.py line 119 87073] Train: [28/100][17/1557] Data 0.004 (0.008) Batch 0.971 (1.641) Remain 51:48:45 loss: 0.5262 Lr: 0.00437 [2024-02-18 06:53:28,254 INFO misc.py line 119 87073] Train: [28/100][18/1557] Data 0.003 (0.007) Batch 0.930 (1.594) Remain 50:18:52 loss: 0.4295 Lr: 0.00437 [2024-02-18 06:53:29,032 INFO misc.py line 119 87073] Train: [28/100][19/1557] Data 0.004 (0.007) Batch 0.778 (1.543) Remain 48:42:14 loss: 0.3083 Lr: 0.00437 [2024-02-18 06:53:29,804 INFO misc.py line 119 87073] Train: [28/100][20/1557] Data 0.004 (0.007) Batch 0.765 (1.497) Remain 47:15:31 loss: 0.1577 Lr: 0.00437 [2024-02-18 06:53:31,020 INFO misc.py line 119 87073] Train: [28/100][21/1557] Data 0.011 (0.007) Batch 1.214 (1.481) Remain 46:45:42 loss: 0.2002 Lr: 0.00437 [2024-02-18 06:53:31,954 INFO misc.py line 119 87073] Train: [28/100][22/1557] Data 0.013 (0.008) Batch 0.944 (1.453) Remain 45:52:05 loss: 0.4552 Lr: 0.00437 [2024-02-18 06:53:32,944 INFO misc.py line 119 87073] Train: [28/100][23/1557] Data 0.004 (0.007) Batch 0.988 (1.430) Remain 45:08:02 loss: 0.4257 Lr: 0.00437 [2024-02-18 06:53:33,970 INFO misc.py line 119 87073] Train: [28/100][24/1557] Data 0.005 (0.007) Batch 1.027 (1.411) Remain 44:31:42 loss: 0.6237 Lr: 0.00437 [2024-02-18 06:53:34,819 INFO misc.py line 119 87073] Train: [28/100][25/1557] Data 0.004 (0.007) Batch 0.850 (1.385) Remain 43:43:24 loss: 0.6124 Lr: 0.00437 [2024-02-18 06:53:35,669 INFO misc.py line 119 87073] Train: [28/100][26/1557] Data 0.003 (0.007) Batch 0.850 (1.362) Remain 42:59:17 loss: 0.1882 Lr: 0.00437 [2024-02-18 06:53:36,370 INFO misc.py line 119 87073] Train: [28/100][27/1557] Data 0.003 (0.007) Batch 0.699 (1.334) Remain 42:06:56 loss: 0.5184 Lr: 0.00437 [2024-02-18 06:53:37,634 INFO misc.py line 119 87073] Train: [28/100][28/1557] Data 0.005 (0.007) Batch 1.264 (1.331) Remain 42:01:35 loss: 0.2935 Lr: 0.00437 [2024-02-18 06:53:38,488 INFO misc.py line 119 87073] Train: [28/100][29/1557] Data 0.006 (0.007) Batch 0.857 (1.313) Remain 41:27:00 loss: 0.4877 Lr: 0.00437 [2024-02-18 06:53:39,368 INFO misc.py line 119 87073] Train: [28/100][30/1557] Data 0.003 (0.007) Batch 0.879 (1.297) Remain 40:56:31 loss: 0.5582 Lr: 0.00437 [2024-02-18 06:53:40,402 INFO misc.py line 119 87073] Train: [28/100][31/1557] Data 0.004 (0.006) Batch 1.030 (1.288) Remain 40:38:27 loss: 0.2921 Lr: 0.00437 [2024-02-18 06:53:41,250 INFO misc.py line 119 87073] Train: [28/100][32/1557] Data 0.008 (0.007) Batch 0.852 (1.273) Remain 40:09:58 loss: 0.5621 Lr: 0.00437 [2024-02-18 06:53:42,033 INFO misc.py line 119 87073] Train: [28/100][33/1557] Data 0.005 (0.006) Batch 0.783 (1.256) Remain 39:39:02 loss: 0.2003 Lr: 0.00437 [2024-02-18 06:53:42,742 INFO misc.py line 119 87073] Train: [28/100][34/1557] Data 0.004 (0.006) Batch 0.709 (1.239) Remain 39:05:35 loss: 0.2693 Lr: 0.00437 [2024-02-18 06:53:44,038 INFO misc.py line 119 87073] Train: [28/100][35/1557] Data 0.004 (0.006) Batch 1.294 (1.240) Remain 39:08:52 loss: 0.3095 Lr: 0.00437 [2024-02-18 06:53:45,080 INFO misc.py line 119 87073] Train: [28/100][36/1557] Data 0.005 (0.006) Batch 1.044 (1.234) Remain 38:57:35 loss: 0.4193 Lr: 0.00437 [2024-02-18 06:53:45,960 INFO misc.py line 119 87073] Train: [28/100][37/1557] Data 0.004 (0.006) Batch 0.880 (1.224) Remain 38:37:49 loss: 0.2243 Lr: 0.00437 [2024-02-18 06:53:46,934 INFO misc.py line 119 87073] Train: [28/100][38/1557] Data 0.004 (0.006) Batch 0.972 (1.217) Remain 38:24:11 loss: 0.2687 Lr: 0.00437 [2024-02-18 06:53:48,001 INFO misc.py line 119 87073] Train: [28/100][39/1557] Data 0.005 (0.006) Batch 1.068 (1.213) Remain 38:16:21 loss: 0.5025 Lr: 0.00437 [2024-02-18 06:53:48,773 INFO misc.py line 119 87073] Train: [28/100][40/1557] Data 0.004 (0.006) Batch 0.772 (1.201) Remain 37:53:46 loss: 0.2059 Lr: 0.00437 [2024-02-18 06:53:49,617 INFO misc.py line 119 87073] Train: [28/100][41/1557] Data 0.004 (0.006) Batch 0.844 (1.191) Remain 37:35:58 loss: 0.3965 Lr: 0.00437 [2024-02-18 06:53:50,779 INFO misc.py line 119 87073] Train: [28/100][42/1557] Data 0.004 (0.006) Batch 1.158 (1.190) Remain 37:34:19 loss: 0.2647 Lr: 0.00437 [2024-02-18 06:53:51,783 INFO misc.py line 119 87073] Train: [28/100][43/1557] Data 0.009 (0.006) Batch 1.008 (1.186) Remain 37:25:39 loss: 0.3340 Lr: 0.00437 [2024-02-18 06:53:52,839 INFO misc.py line 119 87073] Train: [28/100][44/1557] Data 0.005 (0.006) Batch 1.051 (1.183) Remain 37:19:24 loss: 0.6336 Lr: 0.00437 [2024-02-18 06:53:54,162 INFO misc.py line 119 87073] Train: [28/100][45/1557] Data 0.010 (0.006) Batch 1.323 (1.186) Remain 37:25:42 loss: 0.5050 Lr: 0.00437 [2024-02-18 06:53:55,028 INFO misc.py line 119 87073] Train: [28/100][46/1557] Data 0.010 (0.006) Batch 0.872 (1.179) Remain 37:11:52 loss: 0.4534 Lr: 0.00437 [2024-02-18 06:53:55,819 INFO misc.py line 119 87073] Train: [28/100][47/1557] Data 0.003 (0.006) Batch 0.791 (1.170) Remain 36:55:11 loss: 0.5375 Lr: 0.00437 [2024-02-18 06:53:56,603 INFO misc.py line 119 87073] Train: [28/100][48/1557] Data 0.003 (0.006) Batch 0.783 (1.161) Remain 36:38:52 loss: 0.5334 Lr: 0.00437 [2024-02-18 06:53:57,876 INFO misc.py line 119 87073] Train: [28/100][49/1557] Data 0.005 (0.006) Batch 1.273 (1.164) Remain 36:43:26 loss: 0.1910 Lr: 0.00437 [2024-02-18 06:53:58,711 INFO misc.py line 119 87073] Train: [28/100][50/1557] Data 0.005 (0.006) Batch 0.835 (1.157) Remain 36:30:11 loss: 0.1453 Lr: 0.00437 [2024-02-18 06:53:59,622 INFO misc.py line 119 87073] Train: [28/100][51/1557] Data 0.005 (0.006) Batch 0.912 (1.152) Remain 36:20:31 loss: 0.4126 Lr: 0.00437 [2024-02-18 06:54:00,589 INFO misc.py line 119 87073] Train: [28/100][52/1557] Data 0.004 (0.006) Batch 0.966 (1.148) Remain 36:13:19 loss: 0.3579 Lr: 0.00437 [2024-02-18 06:54:01,495 INFO misc.py line 119 87073] Train: [28/100][53/1557] Data 0.006 (0.006) Batch 0.898 (1.143) Remain 36:03:50 loss: 0.6193 Lr: 0.00437 [2024-02-18 06:54:02,227 INFO misc.py line 119 87073] Train: [28/100][54/1557] Data 0.013 (0.006) Batch 0.740 (1.135) Remain 35:48:50 loss: 0.2247 Lr: 0.00437 [2024-02-18 06:54:03,015 INFO misc.py line 119 87073] Train: [28/100][55/1557] Data 0.006 (0.006) Batch 0.779 (1.128) Remain 35:35:52 loss: 0.6941 Lr: 0.00437 [2024-02-18 06:54:04,379 INFO misc.py line 119 87073] Train: [28/100][56/1557] Data 0.015 (0.006) Batch 1.357 (1.132) Remain 35:44:01 loss: 0.2680 Lr: 0.00437 [2024-02-18 06:54:05,558 INFO misc.py line 119 87073] Train: [28/100][57/1557] Data 0.022 (0.007) Batch 1.195 (1.134) Remain 35:46:13 loss: 0.5851 Lr: 0.00437 [2024-02-18 06:54:06,688 INFO misc.py line 119 87073] Train: [28/100][58/1557] Data 0.006 (0.007) Batch 1.119 (1.133) Remain 35:45:42 loss: 0.7555 Lr: 0.00437 [2024-02-18 06:54:07,710 INFO misc.py line 119 87073] Train: [28/100][59/1557] Data 0.016 (0.007) Batch 1.033 (1.131) Remain 35:42:17 loss: 0.3761 Lr: 0.00437 [2024-02-18 06:54:08,564 INFO misc.py line 119 87073] Train: [28/100][60/1557] Data 0.005 (0.007) Batch 0.855 (1.127) Remain 35:33:06 loss: 0.3601 Lr: 0.00437 [2024-02-18 06:54:09,217 INFO misc.py line 119 87073] Train: [28/100][61/1557] Data 0.004 (0.007) Batch 0.652 (1.118) Remain 35:17:36 loss: 0.2820 Lr: 0.00437 [2024-02-18 06:54:09,941 INFO misc.py line 119 87073] Train: [28/100][62/1557] Data 0.004 (0.007) Batch 0.724 (1.112) Remain 35:04:54 loss: 0.2229 Lr: 0.00437 [2024-02-18 06:54:25,082 INFO misc.py line 119 87073] Train: [28/100][63/1557] Data 2.412 (0.047) Batch 15.141 (1.346) Remain 42:27:35 loss: 0.2070 Lr: 0.00437 [2024-02-18 06:54:26,119 INFO misc.py line 119 87073] Train: [28/100][64/1557] Data 0.005 (0.046) Batch 1.032 (1.340) Remain 42:17:49 loss: 0.3567 Lr: 0.00437 [2024-02-18 06:54:27,173 INFO misc.py line 119 87073] Train: [28/100][65/1557] Data 0.010 (0.045) Batch 1.059 (1.336) Remain 42:09:12 loss: 0.6018 Lr: 0.00437 [2024-02-18 06:54:28,157 INFO misc.py line 119 87073] Train: [28/100][66/1557] Data 0.005 (0.045) Batch 0.984 (1.330) Remain 41:58:37 loss: 0.6361 Lr: 0.00437 [2024-02-18 06:54:28,998 INFO misc.py line 119 87073] Train: [28/100][67/1557] Data 0.005 (0.044) Batch 0.841 (1.323) Remain 41:44:07 loss: 0.3281 Lr: 0.00437 [2024-02-18 06:54:29,802 INFO misc.py line 119 87073] Train: [28/100][68/1557] Data 0.005 (0.044) Batch 0.805 (1.315) Remain 41:29:01 loss: 0.2373 Lr: 0.00437 [2024-02-18 06:54:30,590 INFO misc.py line 119 87073] Train: [28/100][69/1557] Data 0.004 (0.043) Batch 0.782 (1.307) Remain 41:13:42 loss: 0.5551 Lr: 0.00437 [2024-02-18 06:54:31,881 INFO misc.py line 119 87073] Train: [28/100][70/1557] Data 0.010 (0.042) Batch 1.285 (1.306) Remain 41:13:04 loss: 0.2280 Lr: 0.00437 [2024-02-18 06:54:32,882 INFO misc.py line 119 87073] Train: [28/100][71/1557] Data 0.016 (0.042) Batch 1.004 (1.302) Remain 41:04:37 loss: 0.4978 Lr: 0.00437 [2024-02-18 06:54:33,868 INFO misc.py line 119 87073] Train: [28/100][72/1557] Data 0.014 (0.042) Batch 0.994 (1.297) Remain 40:56:10 loss: 0.6675 Lr: 0.00437 [2024-02-18 06:54:34,990 INFO misc.py line 119 87073] Train: [28/100][73/1557] Data 0.005 (0.041) Batch 1.116 (1.295) Remain 40:51:15 loss: 0.1745 Lr: 0.00437 [2024-02-18 06:54:36,015 INFO misc.py line 119 87073] Train: [28/100][74/1557] Data 0.011 (0.041) Batch 1.021 (1.291) Remain 40:43:55 loss: 0.2435 Lr: 0.00437 [2024-02-18 06:54:36,707 INFO misc.py line 119 87073] Train: [28/100][75/1557] Data 0.016 (0.040) Batch 0.703 (1.283) Remain 40:28:26 loss: 0.3922 Lr: 0.00437 [2024-02-18 06:54:37,415 INFO misc.py line 119 87073] Train: [28/100][76/1557] Data 0.004 (0.040) Batch 0.707 (1.275) Remain 40:13:29 loss: 0.6361 Lr: 0.00437 [2024-02-18 06:54:38,585 INFO misc.py line 119 87073] Train: [28/100][77/1557] Data 0.005 (0.039) Batch 1.162 (1.273) Remain 40:10:35 loss: 0.1259 Lr: 0.00436 [2024-02-18 06:54:39,580 INFO misc.py line 119 87073] Train: [28/100][78/1557] Data 0.013 (0.039) Batch 1.000 (1.270) Remain 40:03:40 loss: 0.7177 Lr: 0.00436 [2024-02-18 06:54:40,536 INFO misc.py line 119 87073] Train: [28/100][79/1557] Data 0.007 (0.039) Batch 0.959 (1.266) Remain 39:55:55 loss: 0.2776 Lr: 0.00436 [2024-02-18 06:54:41,568 INFO misc.py line 119 87073] Train: [28/100][80/1557] Data 0.004 (0.038) Batch 1.032 (1.263) Remain 39:50:09 loss: 0.0962 Lr: 0.00436 [2024-02-18 06:54:42,508 INFO misc.py line 119 87073] Train: [28/100][81/1557] Data 0.004 (0.038) Batch 0.939 (1.258) Remain 39:42:17 loss: 0.6558 Lr: 0.00436 [2024-02-18 06:54:43,236 INFO misc.py line 119 87073] Train: [28/100][82/1557] Data 0.005 (0.037) Batch 0.727 (1.252) Remain 39:29:31 loss: 0.5010 Lr: 0.00436 [2024-02-18 06:54:44,059 INFO misc.py line 119 87073] Train: [28/100][83/1557] Data 0.006 (0.037) Batch 0.823 (1.246) Remain 39:19:21 loss: 0.2797 Lr: 0.00436 [2024-02-18 06:54:45,389 INFO misc.py line 119 87073] Train: [28/100][84/1557] Data 0.006 (0.037) Batch 1.328 (1.247) Remain 39:21:14 loss: 0.2612 Lr: 0.00436 [2024-02-18 06:54:46,363 INFO misc.py line 119 87073] Train: [28/100][85/1557] Data 0.008 (0.036) Batch 0.977 (1.244) Remain 39:14:59 loss: 0.7330 Lr: 0.00436 [2024-02-18 06:54:47,270 INFO misc.py line 119 87073] Train: [28/100][86/1557] Data 0.005 (0.036) Batch 0.907 (1.240) Remain 39:07:16 loss: 0.4625 Lr: 0.00436 [2024-02-18 06:54:48,205 INFO misc.py line 119 87073] Train: [28/100][87/1557] Data 0.005 (0.035) Batch 0.928 (1.236) Remain 39:00:13 loss: 0.4392 Lr: 0.00436 [2024-02-18 06:54:49,116 INFO misc.py line 119 87073] Train: [28/100][88/1557] Data 0.012 (0.035) Batch 0.919 (1.233) Remain 38:53:08 loss: 0.7035 Lr: 0.00436 [2024-02-18 06:54:49,893 INFO misc.py line 119 87073] Train: [28/100][89/1557] Data 0.004 (0.035) Batch 0.775 (1.227) Remain 38:43:02 loss: 0.6557 Lr: 0.00436 [2024-02-18 06:54:50,680 INFO misc.py line 119 87073] Train: 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Remain 38:45:00 loss: 0.3918 Lr: 0.00432 [2024-02-18 07:18:40,232 INFO misc.py line 119 87073] Train: [28/100][1241/1557] Data 0.004 (0.061) Batch 0.910 (1.241) Remain 38:44:29 loss: 0.2914 Lr: 0.00432 [2024-02-18 07:18:41,203 INFO misc.py line 119 87073] Train: [28/100][1242/1557] Data 0.014 (0.061) Batch 0.982 (1.240) Remain 38:44:04 loss: 0.6380 Lr: 0.00432 [2024-02-18 07:18:42,132 INFO misc.py line 119 87073] Train: [28/100][1243/1557] Data 0.004 (0.061) Batch 0.927 (1.240) Remain 38:43:34 loss: 0.6896 Lr: 0.00432 [2024-02-18 07:18:42,918 INFO misc.py line 119 87073] Train: [28/100][1244/1557] Data 0.005 (0.061) Batch 0.775 (1.240) Remain 38:42:51 loss: 0.5069 Lr: 0.00432 [2024-02-18 07:18:43,642 INFO misc.py line 119 87073] Train: [28/100][1245/1557] Data 0.016 (0.061) Batch 0.737 (1.239) Remain 38:42:04 loss: 0.1998 Lr: 0.00432 [2024-02-18 07:18:44,884 INFO misc.py line 119 87073] Train: [28/100][1246/1557] Data 0.003 (0.061) Batch 1.241 (1.239) Remain 38:42:03 loss: 0.2147 Lr: 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Train: [28/100][1259/1557] Data 0.004 (0.060) Batch 0.714 (1.236) Remain 38:36:13 loss: 0.4056 Lr: 0.00432 [2024-02-18 07:18:58,545 INFO misc.py line 119 87073] Train: [28/100][1260/1557] Data 0.008 (0.060) Batch 1.270 (1.236) Remain 38:36:15 loss: 0.1749 Lr: 0.00432 [2024-02-18 07:18:59,518 INFO misc.py line 119 87073] Train: [28/100][1261/1557] Data 0.015 (0.060) Batch 0.984 (1.236) Remain 38:35:51 loss: 0.2534 Lr: 0.00432 [2024-02-18 07:19:00,270 INFO misc.py line 119 87073] Train: [28/100][1262/1557] Data 0.004 (0.060) Batch 0.752 (1.236) Remain 38:35:07 loss: 0.6162 Lr: 0.00432 [2024-02-18 07:19:01,303 INFO misc.py line 119 87073] Train: [28/100][1263/1557] Data 0.004 (0.060) Batch 1.033 (1.236) Remain 38:34:47 loss: 0.6487 Lr: 0.00432 [2024-02-18 07:19:02,336 INFO misc.py line 119 87073] Train: [28/100][1264/1557] Data 0.004 (0.060) Batch 1.034 (1.236) Remain 38:34:28 loss: 1.4490 Lr: 0.00432 [2024-02-18 07:19:03,075 INFO misc.py line 119 87073] Train: [28/100][1265/1557] Data 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Remain 38:31:40 loss: 0.5630 Lr: 0.00432 [2024-02-18 07:19:10,030 INFO misc.py line 119 87073] Train: [28/100][1272/1557] Data 0.005 (0.060) Batch 0.841 (1.234) Remain 38:31:04 loss: 0.4969 Lr: 0.00432 [2024-02-18 07:19:10,816 INFO misc.py line 119 87073] Train: [28/100][1273/1557] Data 0.007 (0.060) Batch 0.787 (1.233) Remain 38:30:23 loss: 0.2946 Lr: 0.00432 [2024-02-18 07:19:12,037 INFO misc.py line 119 87073] Train: [28/100][1274/1557] Data 0.005 (0.059) Batch 1.215 (1.233) Remain 38:30:20 loss: 0.2371 Lr: 0.00432 [2024-02-18 07:19:13,160 INFO misc.py line 119 87073] Train: [28/100][1275/1557] Data 0.011 (0.059) Batch 0.926 (1.233) Remain 38:29:52 loss: 0.3885 Lr: 0.00432 [2024-02-18 07:19:13,857 INFO misc.py line 119 87073] Train: [28/100][1276/1557] Data 0.208 (0.060) Batch 0.898 (1.233) Remain 38:29:21 loss: 0.5842 Lr: 0.00432 [2024-02-18 07:19:14,819 INFO misc.py line 119 87073] Train: [28/100][1277/1557] Data 0.007 (0.060) Batch 0.964 (1.233) Remain 38:28:56 loss: 0.5792 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1284/1557] Data 0.005 (0.059) Batch 0.993 (1.231) Remain 38:26:09 loss: 0.4545 Lr: 0.00432 [2024-02-18 07:19:22,713 INFO misc.py line 119 87073] Train: [28/100][1285/1557] Data 0.003 (0.059) Batch 1.077 (1.231) Remain 38:25:54 loss: 0.4403 Lr: 0.00432 [2024-02-18 07:19:23,349 INFO misc.py line 119 87073] Train: [28/100][1286/1557] Data 0.004 (0.059) Batch 0.635 (1.231) Remain 38:25:00 loss: 0.2910 Lr: 0.00432 [2024-02-18 07:19:24,088 INFO misc.py line 119 87073] Train: [28/100][1287/1557] Data 0.005 (0.059) Batch 0.739 (1.230) Remain 38:24:16 loss: 0.3694 Lr: 0.00432 [2024-02-18 07:19:25,478 INFO misc.py line 119 87073] Train: [28/100][1288/1557] Data 0.005 (0.059) Batch 1.383 (1.230) Remain 38:24:28 loss: 0.2548 Lr: 0.00432 [2024-02-18 07:19:26,527 INFO misc.py line 119 87073] Train: [28/100][1289/1557] Data 0.013 (0.059) Batch 1.043 (1.230) Remain 38:24:11 loss: 0.3829 Lr: 0.00432 [2024-02-18 07:19:27,581 INFO misc.py line 119 87073] Train: [28/100][1290/1557] Data 0.018 (0.059) Batch 1.060 (1.230) Remain 38:23:55 loss: 0.5540 Lr: 0.00432 [2024-02-18 07:19:28,664 INFO misc.py line 119 87073] Train: [28/100][1291/1557] Data 0.012 (0.059) Batch 1.083 (1.230) Remain 38:23:40 loss: 0.3886 Lr: 0.00432 [2024-02-18 07:19:29,580 INFO misc.py line 119 87073] Train: [28/100][1292/1557] Data 0.012 (0.059) Batch 0.922 (1.230) Remain 38:23:12 loss: 0.3639 Lr: 0.00432 [2024-02-18 07:19:30,373 INFO misc.py line 119 87073] Train: [28/100][1293/1557] Data 0.005 (0.059) Batch 0.795 (1.229) Remain 38:22:33 loss: 0.3493 Lr: 0.00432 [2024-02-18 07:19:31,195 INFO misc.py line 119 87073] Train: [28/100][1294/1557] Data 0.004 (0.059) Batch 0.822 (1.229) Remain 38:21:57 loss: 0.6413 Lr: 0.00432 [2024-02-18 07:19:47,719 INFO misc.py line 119 87073] Train: [28/100][1295/1557] Data 3.021 (0.061) Batch 16.524 (1.241) Remain 38:44:06 loss: 0.1387 Lr: 0.00432 [2024-02-18 07:19:48,685 INFO misc.py line 119 87073] Train: [28/100][1296/1557] Data 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Remain 38:40:32 loss: 0.1680 Lr: 0.00432 [2024-02-18 07:19:55,129 INFO misc.py line 119 87073] Train: [28/100][1303/1557] Data 0.006 (0.061) Batch 1.075 (1.239) Remain 38:40:17 loss: 0.7216 Lr: 0.00432 [2024-02-18 07:19:56,276 INFO misc.py line 119 87073] Train: [28/100][1304/1557] Data 0.016 (0.061) Batch 1.156 (1.239) Remain 38:40:09 loss: 0.6905 Lr: 0.00432 [2024-02-18 07:19:57,194 INFO misc.py line 119 87073] Train: [28/100][1305/1557] Data 0.007 (0.061) Batch 0.920 (1.239) Remain 38:39:40 loss: 0.2217 Lr: 0.00432 [2024-02-18 07:19:58,167 INFO misc.py line 119 87073] Train: [28/100][1306/1557] Data 0.005 (0.061) Batch 0.974 (1.239) Remain 38:39:16 loss: 0.3294 Lr: 0.00432 [2024-02-18 07:19:58,905 INFO misc.py line 119 87073] Train: [28/100][1307/1557] Data 0.005 (0.061) Batch 0.739 (1.238) Remain 38:38:31 loss: 0.2729 Lr: 0.00432 [2024-02-18 07:19:59,682 INFO misc.py line 119 87073] Train: [28/100][1308/1557] Data 0.005 (0.061) Batch 0.769 (1.238) Remain 38:37:50 loss: 0.4830 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1315/1557] Data 0.005 (0.060) Batch 0.767 (1.236) Remain 38:34:27 loss: 0.4929 Lr: 0.00432 [2024-02-18 07:20:07,307 INFO misc.py line 119 87073] Train: [28/100][1316/1557] Data 0.005 (0.060) Batch 1.232 (1.236) Remain 38:34:25 loss: 0.2895 Lr: 0.00432 [2024-02-18 07:20:08,329 INFO misc.py line 119 87073] Train: [28/100][1317/1557] Data 0.012 (0.060) Batch 1.024 (1.236) Remain 38:34:06 loss: 0.4498 Lr: 0.00432 [2024-02-18 07:20:09,333 INFO misc.py line 119 87073] Train: [28/100][1318/1557] Data 0.010 (0.060) Batch 1.009 (1.236) Remain 38:33:45 loss: 0.3813 Lr: 0.00432 [2024-02-18 07:20:10,245 INFO misc.py line 119 87073] Train: [28/100][1319/1557] Data 0.005 (0.060) Batch 0.913 (1.235) Remain 38:33:16 loss: 0.6855 Lr: 0.00432 [2024-02-18 07:20:11,060 INFO misc.py line 119 87073] Train: [28/100][1320/1557] Data 0.004 (0.060) Batch 0.813 (1.235) Remain 38:32:39 loss: 0.3146 Lr: 0.00432 [2024-02-18 07:20:11,853 INFO misc.py line 119 87073] Train: [28/100][1321/1557] Data 0.006 (0.060) Batch 0.794 (1.235) Remain 38:32:00 loss: 0.3456 Lr: 0.00432 [2024-02-18 07:20:12,581 INFO misc.py line 119 87073] Train: [28/100][1322/1557] Data 0.005 (0.060) Batch 0.729 (1.234) Remain 38:31:16 loss: 0.4001 Lr: 0.00432 [2024-02-18 07:20:13,868 INFO misc.py line 119 87073] Train: [28/100][1323/1557] Data 0.004 (0.060) Batch 1.284 (1.234) Remain 38:31:19 loss: 0.4732 Lr: 0.00432 [2024-02-18 07:20:14,785 INFO misc.py line 119 87073] Train: [28/100][1324/1557] Data 0.007 (0.060) Batch 0.918 (1.234) Remain 38:30:51 loss: 0.7208 Lr: 0.00432 [2024-02-18 07:20:15,903 INFO misc.py line 119 87073] Train: [28/100][1325/1557] Data 0.007 (0.060) Batch 1.120 (1.234) Remain 38:30:40 loss: 0.2894 Lr: 0.00432 [2024-02-18 07:20:16,747 INFO misc.py line 119 87073] Train: [28/100][1326/1557] Data 0.005 (0.060) Batch 0.845 (1.234) Remain 38:30:05 loss: 0.2466 Lr: 0.00432 [2024-02-18 07:20:17,718 INFO misc.py line 119 87073] Train: [28/100][1327/1557] Data 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Remain 38:27:01 loss: 0.2606 Lr: 0.00432 [2024-02-18 07:20:24,326 INFO misc.py line 119 87073] Train: [28/100][1334/1557] Data 0.007 (0.060) Batch 1.019 (1.232) Remain 38:26:42 loss: 0.4147 Lr: 0.00432 [2024-02-18 07:20:25,099 INFO misc.py line 119 87073] Train: [28/100][1335/1557] Data 0.005 (0.059) Batch 0.772 (1.232) Remain 38:26:02 loss: 0.7024 Lr: 0.00432 [2024-02-18 07:20:25,868 INFO misc.py line 119 87073] Train: [28/100][1336/1557] Data 0.005 (0.059) Batch 0.765 (1.231) Remain 38:25:21 loss: 0.3894 Lr: 0.00432 [2024-02-18 07:20:27,051 INFO misc.py line 119 87073] Train: [28/100][1337/1557] Data 0.010 (0.059) Batch 1.187 (1.231) Remain 38:25:16 loss: 0.1415 Lr: 0.00432 [2024-02-18 07:20:28,067 INFO misc.py line 119 87073] Train: [28/100][1338/1557] Data 0.006 (0.059) Batch 1.007 (1.231) Remain 38:24:56 loss: 0.6725 Lr: 0.00432 [2024-02-18 07:20:29,053 INFO misc.py line 119 87073] Train: [28/100][1339/1557] Data 0.015 (0.059) Batch 0.994 (1.231) Remain 38:24:35 loss: 0.3250 Lr: 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Train: [28/100][1352/1557] Data 0.007 (0.061) Batch 0.929 (1.239) Remain 38:39:24 loss: 0.5686 Lr: 0.00432 [2024-02-18 07:20:56,855 INFO misc.py line 119 87073] Train: [28/100][1353/1557] Data 0.006 (0.061) Batch 0.928 (1.239) Remain 38:38:57 loss: 0.8022 Lr: 0.00432 [2024-02-18 07:20:57,788 INFO misc.py line 119 87073] Train: [28/100][1354/1557] Data 0.007 (0.061) Batch 0.935 (1.239) Remain 38:38:30 loss: 0.3489 Lr: 0.00432 [2024-02-18 07:20:58,835 INFO misc.py line 119 87073] Train: [28/100][1355/1557] Data 0.005 (0.061) Batch 1.048 (1.239) Remain 38:38:13 loss: 0.5751 Lr: 0.00432 [2024-02-18 07:20:59,578 INFO misc.py line 119 87073] Train: [28/100][1356/1557] Data 0.004 (0.061) Batch 0.743 (1.238) Remain 38:37:31 loss: 0.2217 Lr: 0.00432 [2024-02-18 07:21:00,342 INFO misc.py line 119 87073] Train: [28/100][1357/1557] Data 0.004 (0.061) Batch 0.761 (1.238) Remain 38:36:50 loss: 0.4607 Lr: 0.00432 [2024-02-18 07:21:01,666 INFO misc.py line 119 87073] Train: [28/100][1358/1557] Data 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Remain 38:34:30 loss: 0.5621 Lr: 0.00432 [2024-02-18 07:21:08,670 INFO misc.py line 119 87073] Train: [28/100][1365/1557] Data 0.004 (0.061) Batch 1.251 (1.237) Remain 38:34:30 loss: 0.2948 Lr: 0.00432 [2024-02-18 07:21:09,659 INFO misc.py line 119 87073] Train: [28/100][1366/1557] Data 0.007 (0.061) Batch 0.991 (1.236) Remain 38:34:09 loss: 0.6160 Lr: 0.00432 [2024-02-18 07:21:10,708 INFO misc.py line 119 87073] Train: [28/100][1367/1557] Data 0.005 (0.061) Batch 1.050 (1.236) Remain 38:33:52 loss: 0.4054 Lr: 0.00432 [2024-02-18 07:21:11,761 INFO misc.py line 119 87073] Train: [28/100][1368/1557] Data 0.006 (0.061) Batch 1.053 (1.236) Remain 38:33:36 loss: 0.8100 Lr: 0.00432 [2024-02-18 07:21:12,955 INFO misc.py line 119 87073] Train: [28/100][1369/1557] Data 0.004 (0.060) Batch 1.195 (1.236) Remain 38:33:31 loss: 0.3788 Lr: 0.00432 [2024-02-18 07:21:13,656 INFO misc.py line 119 87073] Train: [28/100][1370/1557] Data 0.004 (0.060) Batch 0.700 (1.236) Remain 38:32:46 loss: 0.4473 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1377/1557] Data 0.004 (0.060) Batch 0.716 (1.234) Remain 38:29:53 loss: 0.4561 Lr: 0.00432 [2024-02-18 07:21:21,042 INFO misc.py line 119 87073] Train: [28/100][1378/1557] Data 0.003 (0.060) Batch 0.741 (1.234) Remain 38:29:12 loss: 0.3229 Lr: 0.00432 [2024-02-18 07:21:22,380 INFO misc.py line 119 87073] Train: [28/100][1379/1557] Data 0.005 (0.060) Batch 1.335 (1.234) Remain 38:29:19 loss: 0.3030 Lr: 0.00432 [2024-02-18 07:21:23,274 INFO misc.py line 119 87073] Train: [28/100][1380/1557] Data 0.009 (0.060) Batch 0.899 (1.234) Remain 38:28:50 loss: 0.9550 Lr: 0.00432 [2024-02-18 07:21:24,210 INFO misc.py line 119 87073] Train: [28/100][1381/1557] Data 0.004 (0.060) Batch 0.936 (1.234) Remain 38:28:25 loss: 0.8694 Lr: 0.00432 [2024-02-18 07:21:25,251 INFO misc.py line 119 87073] Train: [28/100][1382/1557] Data 0.004 (0.060) Batch 1.041 (1.233) Remain 38:28:08 loss: 0.4867 Lr: 0.00432 [2024-02-18 07:21:26,153 INFO misc.py line 119 87073] Train: [28/100][1383/1557] Data 0.004 (0.060) Batch 0.903 (1.233) Remain 38:27:40 loss: 0.6615 Lr: 0.00432 [2024-02-18 07:21:26,917 INFO misc.py line 119 87073] Train: [28/100][1384/1557] Data 0.003 (0.060) Batch 0.759 (1.233) Remain 38:27:00 loss: 0.3231 Lr: 0.00432 [2024-02-18 07:21:27,732 INFO misc.py line 119 87073] Train: [28/100][1385/1557] Data 0.008 (0.060) Batch 0.818 (1.233) Remain 38:26:25 loss: 0.4839 Lr: 0.00432 [2024-02-18 07:21:28,958 INFO misc.py line 119 87073] Train: [28/100][1386/1557] Data 0.005 (0.060) Batch 1.223 (1.233) Remain 38:26:23 loss: 0.2510 Lr: 0.00432 [2024-02-18 07:21:29,867 INFO misc.py line 119 87073] Train: [28/100][1387/1557] Data 0.008 (0.060) Batch 0.913 (1.232) Remain 38:25:56 loss: 0.4905 Lr: 0.00432 [2024-02-18 07:21:30,815 INFO misc.py line 119 87073] Train: [28/100][1388/1557] Data 0.004 (0.060) Batch 0.948 (1.232) Remain 38:25:32 loss: 0.4918 Lr: 0.00432 [2024-02-18 07:21:31,710 INFO misc.py line 119 87073] Train: [28/100][1389/1557] Data 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Remain 38:22:46 loss: 0.2789 Lr: 0.00432 [2024-02-18 07:21:38,439 INFO misc.py line 119 87073] Train: [28/100][1396/1557] Data 0.004 (0.059) Batch 0.948 (1.231) Remain 38:22:22 loss: 0.6332 Lr: 0.00432 [2024-02-18 07:21:39,888 INFO misc.py line 119 87073] Train: [28/100][1397/1557] Data 0.004 (0.059) Batch 1.447 (1.231) Remain 38:22:38 loss: 0.6083 Lr: 0.00432 [2024-02-18 07:21:40,616 INFO misc.py line 119 87073] Train: [28/100][1398/1557] Data 0.005 (0.059) Batch 0.729 (1.230) Remain 38:21:57 loss: 0.3964 Lr: 0.00432 [2024-02-18 07:21:41,367 INFO misc.py line 119 87073] Train: [28/100][1399/1557] Data 0.004 (0.059) Batch 0.749 (1.230) Remain 38:21:17 loss: 0.2862 Lr: 0.00432 [2024-02-18 07:21:42,673 INFO misc.py line 119 87073] Train: [28/100][1400/1557] Data 0.006 (0.059) Batch 1.303 (1.230) Remain 38:21:21 loss: 0.2072 Lr: 0.00432 [2024-02-18 07:21:43,677 INFO misc.py line 119 87073] Train: [28/100][1401/1557] Data 0.010 (0.059) Batch 1.008 (1.230) Remain 38:21:02 loss: 0.5564 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1408/1557] Data 0.004 (0.061) Batch 0.912 (1.240) Remain 38:40:01 loss: 0.5302 Lr: 0.00432 [2024-02-18 07:22:07,525 INFO misc.py line 119 87073] Train: [28/100][1409/1557] Data 0.006 (0.061) Batch 0.872 (1.240) Remain 38:39:31 loss: 0.2230 Lr: 0.00432 [2024-02-18 07:22:08,530 INFO misc.py line 119 87073] Train: [28/100][1410/1557] Data 0.007 (0.061) Batch 1.006 (1.240) Remain 38:39:11 loss: 0.6434 Lr: 0.00432 [2024-02-18 07:22:09,497 INFO misc.py line 119 87073] Train: [28/100][1411/1557] Data 0.006 (0.061) Batch 0.969 (1.239) Remain 38:38:48 loss: 1.2014 Lr: 0.00432 [2024-02-18 07:22:10,296 INFO misc.py line 119 87073] Train: [28/100][1412/1557] Data 0.004 (0.061) Batch 0.799 (1.239) Remain 38:38:12 loss: 0.3687 Lr: 0.00432 [2024-02-18 07:22:11,116 INFO misc.py line 119 87073] Train: [28/100][1413/1557] Data 0.004 (0.061) Batch 0.816 (1.239) Remain 38:37:37 loss: 0.4889 Lr: 0.00432 [2024-02-18 07:22:12,396 INFO misc.py line 119 87073] Train: [28/100][1414/1557] Data 0.008 (0.061) Batch 1.272 (1.239) Remain 38:37:38 loss: 0.3023 Lr: 0.00432 [2024-02-18 07:22:13,293 INFO misc.py line 119 87073] Train: [28/100][1415/1557] Data 0.017 (0.061) Batch 0.909 (1.239) Remain 38:37:11 loss: 0.3193 Lr: 0.00432 [2024-02-18 07:22:14,205 INFO misc.py line 119 87073] Train: [28/100][1416/1557] Data 0.006 (0.061) Batch 0.912 (1.238) Remain 38:36:43 loss: 0.4640 Lr: 0.00432 [2024-02-18 07:22:15,125 INFO misc.py line 119 87073] Train: [28/100][1417/1557] Data 0.004 (0.061) Batch 0.912 (1.238) Remain 38:36:16 loss: 0.7439 Lr: 0.00432 [2024-02-18 07:22:15,935 INFO misc.py line 119 87073] Train: [28/100][1418/1557] Data 0.012 (0.061) Batch 0.816 (1.238) Remain 38:35:42 loss: 0.2251 Lr: 0.00432 [2024-02-18 07:22:16,730 INFO misc.py line 119 87073] Train: [28/100][1419/1557] Data 0.007 (0.061) Batch 0.796 (1.238) Remain 38:35:05 loss: 0.3054 Lr: 0.00432 [2024-02-18 07:22:17,469 INFO misc.py line 119 87073] Train: [28/100][1420/1557] Data 0.005 (0.061) Batch 0.732 (1.237) Remain 38:34:24 loss: 0.1607 Lr: 0.00432 [2024-02-18 07:22:18,577 INFO misc.py line 119 87073] Train: [28/100][1421/1557] Data 0.011 (0.061) Batch 1.111 (1.237) Remain 38:34:13 loss: 0.1456 Lr: 0.00432 [2024-02-18 07:22:19,611 INFO misc.py line 119 87073] Train: [28/100][1422/1557] Data 0.011 (0.061) Batch 1.035 (1.237) Remain 38:33:56 loss: 0.5826 Lr: 0.00432 [2024-02-18 07:22:20,559 INFO misc.py line 119 87073] Train: [28/100][1423/1557] Data 0.007 (0.061) Batch 0.951 (1.237) Remain 38:33:32 loss: 0.5309 Lr: 0.00432 [2024-02-18 07:22:21,507 INFO misc.py line 119 87073] Train: [28/100][1424/1557] Data 0.005 (0.061) Batch 0.949 (1.237) Remain 38:33:08 loss: 0.7124 Lr: 0.00432 [2024-02-18 07:22:22,449 INFO misc.py line 119 87073] Train: [28/100][1425/1557] Data 0.005 (0.061) Batch 0.941 (1.236) Remain 38:32:43 loss: 0.4307 Lr: 0.00432 [2024-02-18 07:22:23,238 INFO misc.py line 119 87073] Train: [28/100][1426/1557] Data 0.005 (0.061) Batch 0.789 (1.236) Remain 38:32:07 loss: 0.3078 Lr: 0.00432 [2024-02-18 07:22:24,017 INFO misc.py line 119 87073] Train: [28/100][1427/1557] Data 0.005 (0.061) Batch 0.779 (1.236) Remain 38:31:29 loss: 0.2473 Lr: 0.00432 [2024-02-18 07:22:25,269 INFO misc.py line 119 87073] Train: [28/100][1428/1557] Data 0.005 (0.061) Batch 1.244 (1.236) Remain 38:31:29 loss: 0.3768 Lr: 0.00432 [2024-02-18 07:22:26,231 INFO misc.py line 119 87073] Train: [28/100][1429/1557] Data 0.013 (0.061) Batch 0.970 (1.236) Remain 38:31:07 loss: 0.5890 Lr: 0.00432 [2024-02-18 07:22:27,166 INFO misc.py line 119 87073] Train: [28/100][1430/1557] Data 0.005 (0.061) Batch 0.935 (1.235) Remain 38:30:42 loss: 0.5013 Lr: 0.00432 [2024-02-18 07:22:28,180 INFO misc.py line 119 87073] Train: [28/100][1431/1557] Data 0.005 (0.061) Batch 1.014 (1.235) Remain 38:30:23 loss: 0.8570 Lr: 0.00432 [2024-02-18 07:22:29,200 INFO misc.py line 119 87073] Train: [28/100][1432/1557] Data 0.005 (0.061) Batch 1.021 (1.235) Remain 38:30:05 loss: 0.4302 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1439/1557] Data 0.006 (0.060) Batch 1.048 (1.234) Remain 38:27:33 loss: 0.6572 Lr: 0.00432 [2024-02-18 07:22:36,835 INFO misc.py line 119 87073] Train: [28/100][1440/1557] Data 0.004 (0.060) Batch 0.820 (1.233) Remain 38:27:00 loss: 0.1418 Lr: 0.00432 [2024-02-18 07:22:37,676 INFO misc.py line 119 87073] Train: [28/100][1441/1557] Data 0.004 (0.060) Batch 0.837 (1.233) Remain 38:26:28 loss: 0.3732 Lr: 0.00432 [2024-02-18 07:22:38,841 INFO misc.py line 119 87073] Train: [28/100][1442/1557] Data 0.008 (0.060) Batch 1.162 (1.233) Remain 38:26:21 loss: 0.2669 Lr: 0.00432 [2024-02-18 07:22:39,927 INFO misc.py line 119 87073] Train: [28/100][1443/1557] Data 0.011 (0.060) Batch 1.082 (1.233) Remain 38:26:08 loss: 0.4915 Lr: 0.00432 [2024-02-18 07:22:40,903 INFO misc.py line 119 87073] Train: [28/100][1444/1557] Data 0.015 (0.060) Batch 0.986 (1.233) Remain 38:25:48 loss: 0.6089 Lr: 0.00432 [2024-02-18 07:22:41,838 INFO misc.py line 119 87073] Train: [28/100][1445/1557] Data 0.005 (0.060) Batch 0.936 (1.233) Remain 38:25:23 loss: 0.4723 Lr: 0.00432 [2024-02-18 07:22:42,987 INFO misc.py line 119 87073] Train: [28/100][1446/1557] Data 0.004 (0.060) Batch 1.149 (1.233) Remain 38:25:15 loss: 1.0075 Lr: 0.00432 [2024-02-18 07:22:43,830 INFO misc.py line 119 87073] Train: [28/100][1447/1557] Data 0.004 (0.060) Batch 0.840 (1.232) Remain 38:24:44 loss: 0.2895 Lr: 0.00432 [2024-02-18 07:22:44,618 INFO misc.py line 119 87073] Train: [28/100][1448/1557] Data 0.007 (0.060) Batch 0.784 (1.232) Remain 38:24:08 loss: 0.3576 Lr: 0.00432 [2024-02-18 07:22:45,879 INFO misc.py line 119 87073] Train: [28/100][1449/1557] Data 0.011 (0.060) Batch 1.262 (1.232) Remain 38:24:09 loss: 0.3955 Lr: 0.00432 [2024-02-18 07:22:46,852 INFO misc.py line 119 87073] Train: [28/100][1450/1557] Data 0.010 (0.060) Batch 0.979 (1.232) Remain 38:23:48 loss: 0.3083 Lr: 0.00432 [2024-02-18 07:22:47,756 INFO misc.py line 119 87073] Train: [28/100][1451/1557] Data 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Remain 38:21:18 loss: 0.7933 Lr: 0.00432 [2024-02-18 07:22:54,551 INFO misc.py line 119 87073] Train: [28/100][1458/1557] Data 0.003 (0.060) Batch 0.901 (1.230) Remain 38:20:52 loss: 1.0183 Lr: 0.00432 [2024-02-18 07:22:55,464 INFO misc.py line 119 87073] Train: [28/100][1459/1557] Data 0.004 (0.060) Batch 0.912 (1.230) Remain 38:20:26 loss: 0.5360 Lr: 0.00432 [2024-02-18 07:22:56,470 INFO misc.py line 119 87073] Train: [28/100][1460/1557] Data 0.005 (0.060) Batch 0.999 (1.230) Remain 38:20:07 loss: 0.5254 Lr: 0.00432 [2024-02-18 07:22:57,131 INFO misc.py line 119 87073] Train: [28/100][1461/1557] Data 0.012 (0.060) Batch 0.668 (1.230) Remain 38:19:22 loss: 0.3070 Lr: 0.00432 [2024-02-18 07:22:57,853 INFO misc.py line 119 87073] Train: [28/100][1462/1557] Data 0.005 (0.059) Batch 0.714 (1.229) Remain 38:18:42 loss: 0.4234 Lr: 0.00432 [2024-02-18 07:23:13,697 INFO misc.py line 119 87073] Train: [28/100][1463/1557] Data 2.944 (0.061) Batch 15.853 (1.239) Remain 38:37:24 loss: 0.2139 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1470/1557] Data 0.006 (0.061) Batch 1.252 (1.238) Remain 38:35:39 loss: 0.3095 Lr: 0.00431 [2024-02-18 07:23:22,114 INFO misc.py line 119 87073] Train: [28/100][1471/1557] Data 0.005 (0.061) Batch 0.995 (1.238) Remain 38:35:19 loss: 1.2122 Lr: 0.00431 [2024-02-18 07:23:23,022 INFO misc.py line 119 87073] Train: [28/100][1472/1557] Data 0.013 (0.061) Batch 0.916 (1.238) Remain 38:34:53 loss: 0.6274 Lr: 0.00431 [2024-02-18 07:23:24,074 INFO misc.py line 119 87073] Train: [28/100][1473/1557] Data 0.005 (0.061) Batch 1.052 (1.238) Remain 38:34:38 loss: 0.3642 Lr: 0.00431 [2024-02-18 07:23:25,077 INFO misc.py line 119 87073] Train: [28/100][1474/1557] Data 0.005 (0.061) Batch 1.002 (1.238) Remain 38:34:19 loss: 0.3967 Lr: 0.00431 [2024-02-18 07:23:25,759 INFO misc.py line 119 87073] Train: [28/100][1475/1557] Data 0.005 (0.061) Batch 0.683 (1.237) Remain 38:33:35 loss: 0.3226 Lr: 0.00431 [2024-02-18 07:23:26,426 INFO misc.py line 119 87073] Train: [28/100][1476/1557] Data 0.004 (0.061) Batch 0.664 (1.237) Remain 38:32:50 loss: 0.4862 Lr: 0.00431 [2024-02-18 07:23:27,617 INFO misc.py line 119 87073] Train: [28/100][1477/1557] Data 0.007 (0.061) Batch 1.194 (1.237) Remain 38:32:46 loss: 0.1546 Lr: 0.00431 [2024-02-18 07:23:28,481 INFO misc.py line 119 87073] Train: [28/100][1478/1557] Data 0.004 (0.061) Batch 0.863 (1.237) Remain 38:32:16 loss: 0.9209 Lr: 0.00431 [2024-02-18 07:23:29,589 INFO misc.py line 119 87073] Train: [28/100][1479/1557] Data 0.007 (0.061) Batch 1.108 (1.237) Remain 38:32:05 loss: 0.3641 Lr: 0.00431 [2024-02-18 07:23:30,572 INFO misc.py line 119 87073] Train: [28/100][1480/1557] Data 0.005 (0.061) Batch 0.983 (1.236) Remain 38:31:45 loss: 0.4641 Lr: 0.00431 [2024-02-18 07:23:31,567 INFO misc.py line 119 87073] Train: [28/100][1481/1557] Data 0.005 (0.061) Batch 0.997 (1.236) Remain 38:31:25 loss: 0.4248 Lr: 0.00431 [2024-02-18 07:23:32,267 INFO misc.py line 119 87073] Train: [28/100][1482/1557] Data 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Remain 38:29:04 loss: 0.5051 Lr: 0.00431 [2024-02-18 07:23:39,238 INFO misc.py line 119 87073] Train: [28/100][1489/1557] Data 0.005 (0.060) Batch 0.777 (1.235) Remain 38:28:28 loss: 0.3719 Lr: 0.00431 [2024-02-18 07:23:40,005 INFO misc.py line 119 87073] Train: [28/100][1490/1557] Data 0.005 (0.060) Batch 0.766 (1.234) Remain 38:27:51 loss: 0.4556 Lr: 0.00431 [2024-02-18 07:23:41,260 INFO misc.py line 119 87073] Train: [28/100][1491/1557] Data 0.004 (0.060) Batch 1.255 (1.234) Remain 38:27:51 loss: 0.7543 Lr: 0.00431 [2024-02-18 07:23:42,202 INFO misc.py line 119 87073] Train: [28/100][1492/1557] Data 0.004 (0.060) Batch 0.942 (1.234) Remain 38:27:28 loss: 0.4158 Lr: 0.00431 [2024-02-18 07:23:43,102 INFO misc.py line 119 87073] Train: [28/100][1493/1557] Data 0.005 (0.060) Batch 0.900 (1.234) Remain 38:27:02 loss: 1.1772 Lr: 0.00431 [2024-02-18 07:23:44,118 INFO misc.py line 119 87073] Train: [28/100][1494/1557] Data 0.004 (0.060) Batch 1.016 (1.234) Remain 38:26:44 loss: 0.6422 Lr: 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INFO misc.py line 119 87073] Train: [28/100][1501/1557] Data 0.004 (0.060) Batch 0.972 (1.232) Remain 38:23:33 loss: 0.4568 Lr: 0.00431 [2024-02-18 07:23:51,402 INFO misc.py line 119 87073] Train: [28/100][1502/1557] Data 0.005 (0.060) Batch 1.077 (1.232) Remain 38:23:20 loss: 0.3758 Lr: 0.00431 [2024-02-18 07:23:52,152 INFO misc.py line 119 87073] Train: [28/100][1503/1557] Data 0.007 (0.060) Batch 0.753 (1.232) Remain 38:22:43 loss: 0.4364 Lr: 0.00431 [2024-02-18 07:23:52,936 INFO misc.py line 119 87073] Train: [28/100][1504/1557] Data 0.004 (0.060) Batch 0.775 (1.232) Remain 38:22:08 loss: 0.3513 Lr: 0.00431 [2024-02-18 07:23:54,273 INFO misc.py line 119 87073] Train: [28/100][1505/1557] Data 0.012 (0.060) Batch 1.344 (1.232) Remain 38:22:15 loss: 0.3109 Lr: 0.00431 [2024-02-18 07:23:55,318 INFO misc.py line 119 87073] Train: [28/100][1506/1557] Data 0.005 (0.060) Batch 1.036 (1.232) Remain 38:21:59 loss: 0.4863 Lr: 0.00431 [2024-02-18 07:23:56,093 INFO misc.py line 119 87073] Train: [28/100][1507/1557] Data 0.015 (0.060) Batch 0.785 (1.231) Remain 38:21:25 loss: 0.5442 Lr: 0.00431 [2024-02-18 07:23:56,903 INFO misc.py line 119 87073] Train: [28/100][1508/1557] Data 0.006 (0.060) Batch 0.811 (1.231) Remain 38:20:52 loss: 0.3330 Lr: 0.00431 [2024-02-18 07:23:57,918 INFO misc.py line 119 87073] Train: [28/100][1509/1557] Data 0.003 (0.060) Batch 1.013 (1.231) Remain 38:20:35 loss: 0.5474 Lr: 0.00431 [2024-02-18 07:23:58,668 INFO misc.py line 119 87073] Train: [28/100][1510/1557] Data 0.007 (0.060) Batch 0.752 (1.230) Remain 38:19:58 loss: 0.6111 Lr: 0.00431 [2024-02-18 07:23:59,437 INFO misc.py line 119 87073] Train: [28/100][1511/1557] Data 0.005 (0.060) Batch 0.768 (1.230) Remain 38:19:22 loss: 0.4842 Lr: 0.00431 [2024-02-18 07:24:00,742 INFO misc.py line 119 87073] Train: [28/100][1512/1557] Data 0.005 (0.060) Batch 1.300 (1.230) Remain 38:19:26 loss: 0.4368 Lr: 0.00431 [2024-02-18 07:24:01,652 INFO misc.py line 119 87073] Train: [28/100][1513/1557] Data 0.010 (0.060) Batch 0.916 (1.230) Remain 38:19:02 loss: 0.6578 Lr: 0.00431 [2024-02-18 07:24:02,601 INFO misc.py line 119 87073] Train: [28/100][1514/1557] Data 0.004 (0.060) Batch 0.948 (1.230) Remain 38:18:40 loss: 0.5928 Lr: 0.00431 [2024-02-18 07:24:03,623 INFO misc.py line 119 87073] Train: [28/100][1515/1557] Data 0.005 (0.060) Batch 1.023 (1.230) Remain 38:18:23 loss: 0.1902 Lr: 0.00431 [2024-02-18 07:24:04,734 INFO misc.py line 119 87073] Train: [28/100][1516/1557] Data 0.003 (0.060) Batch 1.110 (1.230) Remain 38:18:13 loss: 0.6340 Lr: 0.00431 [2024-02-18 07:24:05,525 INFO misc.py line 119 87073] Train: [28/100][1517/1557] Data 0.005 (0.059) Batch 0.791 (1.229) Remain 38:17:39 loss: 0.6133 Lr: 0.00431 [2024-02-18 07:24:06,274 INFO misc.py line 119 87073] Train: [28/100][1518/1557] Data 0.005 (0.059) Batch 0.747 (1.229) Remain 38:17:02 loss: 0.3696 Lr: 0.00431 [2024-02-18 07:24:21,844 INFO misc.py line 119 87073] Train: [28/100][1519/1557] Data 2.944 (0.061) Batch 15.572 (1.238) Remain 38:34:42 loss: 0.1367 Lr: 0.00431 [2024-02-18 07:24:22,739 INFO misc.py line 119 87073] Train: [28/100][1520/1557] Data 0.004 (0.061) Batch 0.895 (1.238) Remain 38:34:15 loss: 1.2548 Lr: 0.00431 [2024-02-18 07:24:23,706 INFO misc.py line 119 87073] Train: [28/100][1521/1557] Data 0.004 (0.061) Batch 0.967 (1.238) Remain 38:33:54 loss: 0.3896 Lr: 0.00431 [2024-02-18 07:24:24,684 INFO misc.py line 119 87073] Train: [28/100][1522/1557] Data 0.004 (0.061) Batch 0.978 (1.238) Remain 38:33:34 loss: 0.8614 Lr: 0.00431 [2024-02-18 07:24:25,468 INFO misc.py line 119 87073] Train: [28/100][1523/1557] Data 0.004 (0.061) Batch 0.782 (1.238) Remain 38:32:59 loss: 0.7008 Lr: 0.00431 [2024-02-18 07:24:26,276 INFO misc.py line 119 87073] Train: [28/100][1524/1557] Data 0.006 (0.061) Batch 0.809 (1.237) Remain 38:32:26 loss: 0.7050 Lr: 0.00431 [2024-02-18 07:24:27,022 INFO misc.py line 119 87073] Train: [28/100][1525/1557] Data 0.005 (0.061) Batch 0.746 (1.237) Remain 38:31:49 loss: 0.3307 Lr: 0.00431 [2024-02-18 07:24:28,260 INFO misc.py line 119 87073] Train: [28/100][1526/1557] Data 0.005 (0.061) Batch 1.238 (1.237) Remain 38:31:48 loss: 0.1786 Lr: 0.00431 [2024-02-18 07:24:29,197 INFO misc.py line 119 87073] Train: [28/100][1527/1557] Data 0.004 (0.061) Batch 0.938 (1.237) Remain 38:31:24 loss: 0.3762 Lr: 0.00431 [2024-02-18 07:24:30,161 INFO misc.py line 119 87073] Train: [28/100][1528/1557] Data 0.004 (0.061) Batch 0.964 (1.237) Remain 38:31:03 loss: 0.4623 Lr: 0.00431 [2024-02-18 07:24:31,129 INFO misc.py line 119 87073] Train: [28/100][1529/1557] Data 0.003 (0.061) Batch 0.968 (1.236) Remain 38:30:42 loss: 0.6334 Lr: 0.00431 [2024-02-18 07:24:31,990 INFO misc.py line 119 87073] Train: [28/100][1530/1557] Data 0.003 (0.061) Batch 0.859 (1.236) Remain 38:30:13 loss: 0.5656 Lr: 0.00431 [2024-02-18 07:24:32,755 INFO misc.py line 119 87073] Train: [28/100][1531/1557] Data 0.006 (0.061) Batch 0.766 (1.236) Remain 38:29:37 loss: 0.2583 Lr: 0.00431 [2024-02-18 07:24:33,547 INFO misc.py line 119 87073] Train: [28/100][1532/1557] Data 0.005 (0.061) Batch 0.792 (1.236) Remain 38:29:04 loss: 0.2907 Lr: 0.00431 [2024-02-18 07:24:34,763 INFO misc.py line 119 87073] Train: [28/100][1533/1557] Data 0.004 (0.061) Batch 1.216 (1.236) Remain 38:29:01 loss: 0.1780 Lr: 0.00431 [2024-02-18 07:24:35,754 INFO misc.py line 119 87073] Train: [28/100][1534/1557] Data 0.005 (0.061) Batch 0.992 (1.235) Remain 38:28:42 loss: 0.6491 Lr: 0.00431 [2024-02-18 07:24:36,662 INFO misc.py line 119 87073] Train: [28/100][1535/1557] Data 0.005 (0.061) Batch 0.908 (1.235) Remain 38:28:17 loss: 0.4240 Lr: 0.00431 [2024-02-18 07:24:37,516 INFO misc.py line 119 87073] Train: [28/100][1536/1557] Data 0.004 (0.061) Batch 0.848 (1.235) Remain 38:27:47 loss: 0.3790 Lr: 0.00431 [2024-02-18 07:24:38,647 INFO misc.py line 119 87073] Train: [28/100][1537/1557] Data 0.010 (0.061) Batch 1.125 (1.235) Remain 38:27:38 loss: 0.5520 Lr: 0.00431 [2024-02-18 07:24:39,533 INFO misc.py line 119 87073] Train: [28/100][1538/1557] Data 0.017 (0.061) Batch 0.898 (1.235) Remain 38:27:12 loss: 0.3886 Lr: 0.00431 [2024-02-18 07:24:40,288 INFO misc.py line 119 87073] Train: [28/100][1539/1557] Data 0.005 (0.061) Batch 0.755 (1.234) Remain 38:26:36 loss: 0.3338 Lr: 0.00431 [2024-02-18 07:24:41,563 INFO misc.py line 119 87073] Train: [28/100][1540/1557] Data 0.004 (0.061) Batch 1.275 (1.234) Remain 38:26:37 loss: 0.2647 Lr: 0.00431 [2024-02-18 07:24:42,545 INFO misc.py line 119 87073] Train: [28/100][1541/1557] Data 0.005 (0.061) Batch 0.982 (1.234) Remain 38:26:18 loss: 0.4902 Lr: 0.00431 [2024-02-18 07:24:43,622 INFO misc.py line 119 87073] Train: [28/100][1542/1557] Data 0.005 (0.060) Batch 1.077 (1.234) Remain 38:26:05 loss: 0.1899 Lr: 0.00431 [2024-02-18 07:24:44,546 INFO misc.py line 119 87073] Train: [28/100][1543/1557] Data 0.005 (0.060) Batch 0.925 (1.234) Remain 38:25:41 loss: 0.7736 Lr: 0.00431 [2024-02-18 07:24:45,487 INFO misc.py line 119 87073] Train: [28/100][1544/1557] Data 0.004 (0.060) Batch 0.941 (1.234) Remain 38:25:19 loss: 0.4377 Lr: 0.00431 [2024-02-18 07:24:46,237 INFO misc.py line 119 87073] Train: [28/100][1545/1557] Data 0.004 (0.060) Batch 0.746 (1.233) Remain 38:24:42 loss: 0.2835 Lr: 0.00431 [2024-02-18 07:24:46,999 INFO misc.py line 119 87073] Train: [28/100][1546/1557] Data 0.008 (0.060) Batch 0.766 (1.233) Remain 38:24:07 loss: 0.3214 Lr: 0.00431 [2024-02-18 07:24:48,285 INFO misc.py line 119 87073] Train: [28/100][1547/1557] Data 0.004 (0.060) Batch 1.285 (1.233) Remain 38:24:09 loss: 0.3716 Lr: 0.00431 [2024-02-18 07:24:49,136 INFO misc.py line 119 87073] Train: [28/100][1548/1557] Data 0.005 (0.060) Batch 0.851 (1.233) Remain 38:23:40 loss: 0.2574 Lr: 0.00431 [2024-02-18 07:24:50,027 INFO misc.py line 119 87073] Train: [28/100][1549/1557] Data 0.004 (0.060) Batch 0.889 (1.233) Remain 38:23:14 loss: 0.5705 Lr: 0.00431 [2024-02-18 07:24:50,993 INFO misc.py line 119 87073] Train: [28/100][1550/1557] Data 0.007 (0.060) Batch 0.966 (1.232) Remain 38:22:54 loss: 0.3596 Lr: 0.00431 [2024-02-18 07:24:52,047 INFO misc.py line 119 87073] Train: [28/100][1551/1557] Data 0.008 (0.060) Batch 1.050 (1.232) Remain 38:22:39 loss: 0.5582 Lr: 0.00431 [2024-02-18 07:24:52,800 INFO misc.py line 119 87073] Train: [28/100][1552/1557] Data 0.011 (0.060) Batch 0.759 (1.232) Remain 38:22:04 loss: 0.3615 Lr: 0.00431 [2024-02-18 07:24:53,578 INFO misc.py line 119 87073] Train: [28/100][1553/1557] Data 0.004 (0.060) Batch 0.770 (1.232) Remain 38:21:29 loss: 0.3906 Lr: 0.00431 [2024-02-18 07:24:54,729 INFO misc.py line 119 87073] Train: [28/100][1554/1557] Data 0.012 (0.060) Batch 1.153 (1.232) Remain 38:21:22 loss: 0.2298 Lr: 0.00431 [2024-02-18 07:24:55,576 INFO misc.py line 119 87073] Train: [28/100][1555/1557] Data 0.010 (0.060) Batch 0.853 (1.231) Remain 38:20:54 loss: 0.7236 Lr: 0.00431 [2024-02-18 07:24:56,526 INFO misc.py line 119 87073] Train: [28/100][1556/1557] Data 0.004 (0.060) Batch 0.950 (1.231) Remain 38:20:32 loss: 0.2414 Lr: 0.00431 [2024-02-18 07:24:57,419 INFO misc.py line 119 87073] Train: [28/100][1557/1557] Data 0.004 (0.060) Batch 0.893 (1.231) Remain 38:20:06 loss: 0.3651 Lr: 0.00431 [2024-02-18 07:24:57,420 INFO misc.py line 136 87073] Train result: loss: 0.4529 [2024-02-18 07:24:57,420 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 07:25:22,942 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7234 [2024-02-18 07:25:23,723 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6328 [2024-02-18 07:25:26,146 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4587 [2024-02-18 07:25:28,354 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4248 [2024-02-18 07:25:33,301 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3951 [2024-02-18 07:25:34,003 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0782 [2024-02-18 07:25:35,268 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4803 [2024-02-18 07:25:38,229 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3502 [2024-02-18 07:25:40,039 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2655 [2024-02-18 07:25:41,473 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7050/0.7716/0.9079. [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9244/0.9653 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9808/0.9882 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8624/0.9662 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3833/0.5067 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7059/0.7398 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.6923/0.7602 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8352/0.8920 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9198/0.9692 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7709/0.8088 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7591/0.8656 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7453/0.8883 [2024-02-18 07:25:41,474 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5852/0.6806 [2024-02-18 07:25:41,475 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 07:25:41,476 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 07:25:41,476 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 07:25:48,717 INFO misc.py line 119 87073] Train: [29/100][1/1557] Data 1.561 (1.561) Batch 2.278 (2.278) Remain 70:56:48 loss: 0.5802 Lr: 0.00431 [2024-02-18 07:25:49,649 INFO misc.py line 119 87073] Train: [29/100][2/1557] Data 0.006 (0.006) Batch 0.931 (0.931) Remain 28:59:03 loss: 0.4304 Lr: 0.00431 [2024-02-18 07:25:50,634 INFO misc.py line 119 87073] Train: [29/100][3/1557] Data 0.007 (0.007) Batch 0.987 (0.987) Remain 30:43:48 loss: 1.0953 Lr: 0.00431 [2024-02-18 07:25:51,656 INFO misc.py line 119 87073] Train: [29/100][4/1557] Data 0.005 (0.005) Batch 1.022 (1.022) Remain 31:50:17 loss: 0.3745 Lr: 0.00431 [2024-02-18 07:25:52,431 INFO misc.py line 119 87073] Train: [29/100][5/1557] Data 0.005 (0.005) Batch 0.775 (0.899) Remain 27:58:49 loss: 0.4624 Lr: 0.00431 [2024-02-18 07:25:53,143 INFO misc.py line 119 87073] Train: [29/100][6/1557] Data 0.004 (0.005) Batch 0.712 (0.836) Remain 26:02:35 loss: 0.5398 Lr: 0.00431 [2024-02-18 07:26:06,965 INFO misc.py line 119 87073] Train: [29/100][7/1557] Data 12.878 (3.223) Batch 13.823 (4.083) Remain 127:08:10 loss: 0.2050 Lr: 0.00431 [2024-02-18 07:26:07,839 INFO misc.py line 119 87073] Train: [29/100][8/1557] Data 0.004 (2.579) Batch 0.863 (3.439) Remain 107:05:06 loss: 0.6327 Lr: 0.00431 [2024-02-18 07:26:08,820 INFO misc.py line 119 87073] Train: [29/100][9/1557] Data 0.015 (2.152) Batch 0.991 (3.031) Remain 94:22:47 loss: 0.5599 Lr: 0.00431 [2024-02-18 07:26:09,873 INFO misc.py line 119 87073] Train: [29/100][10/1557] Data 0.005 (1.845) Batch 1.053 (2.749) Remain 85:34:51 loss: 1.3569 Lr: 0.00431 [2024-02-18 07:26:10,841 INFO misc.py line 119 87073] Train: [29/100][11/1557] Data 0.005 (1.615) Batch 0.968 (2.526) Remain 78:38:59 loss: 0.2596 Lr: 0.00431 [2024-02-18 07:26:11,629 INFO misc.py line 119 87073] Train: [29/100][12/1557] Data 0.004 (1.436) Batch 0.777 (2.332) Remain 72:35:50 loss: 0.3131 Lr: 0.00431 [2024-02-18 07:26:12,376 INFO misc.py line 119 87073] Train: [29/100][13/1557] Data 0.016 (1.294) Batch 0.759 (2.174) Remain 67:42:06 loss: 0.3584 Lr: 0.00431 [2024-02-18 07:26:13,506 INFO misc.py line 119 87073] Train: [29/100][14/1557] Data 0.003 (1.177) Batch 1.130 (2.079) Remain 64:44:38 loss: 0.3042 Lr: 0.00431 [2024-02-18 07:26:14,476 INFO misc.py line 119 87073] Train: [29/100][15/1557] Data 0.004 (1.079) Batch 0.970 (1.987) Remain 61:51:48 loss: 0.4180 Lr: 0.00431 [2024-02-18 07:26:15,564 INFO misc.py line 119 87073] Train: [29/100][16/1557] Data 0.004 (0.996) Batch 1.089 (1.918) Remain 59:42:41 loss: 0.4794 Lr: 0.00431 [2024-02-18 07:26:16,408 INFO misc.py line 119 87073] Train: [29/100][17/1557] Data 0.003 (0.926) Batch 0.842 (1.841) Remain 57:19:07 loss: 0.6401 Lr: 0.00431 [2024-02-18 07:26:17,393 INFO misc.py line 119 87073] Train: [29/100][18/1557] Data 0.005 (0.864) Batch 0.985 (1.784) Remain 55:32:26 loss: 0.2203 Lr: 0.00431 [2024-02-18 07:26:18,179 INFO misc.py line 119 87073] Train: [29/100][19/1557] Data 0.006 (0.811) Batch 0.788 (1.722) Remain 53:36:10 loss: 0.3689 Lr: 0.00431 [2024-02-18 07:26:18,919 INFO misc.py line 119 87073] Train: [29/100][20/1557] Data 0.004 (0.763) Batch 0.734 (1.664) Remain 51:47:39 loss: 0.3491 Lr: 0.00431 [2024-02-18 07:26:20,192 INFO misc.py line 119 87073] Train: 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Train: [29/100][65/1557] Data 0.009 (0.528) Batch 1.043 (1.476) Remain 45:56:52 loss: 0.8654 Lr: 0.00431 [2024-02-18 07:27:22,998 INFO misc.py line 119 87073] Train: [29/100][66/1557] Data 0.004 (0.520) Batch 0.829 (1.466) Remain 45:37:40 loss: 1.1772 Lr: 0.00431 [2024-02-18 07:27:24,178 INFO misc.py line 119 87073] Train: [29/100][67/1557] Data 0.003 (0.512) Batch 1.181 (1.462) Remain 45:29:18 loss: 0.5305 Lr: 0.00431 [2024-02-18 07:27:24,951 INFO misc.py line 119 87073] Train: [29/100][68/1557] Data 0.003 (0.504) Batch 0.771 (1.451) Remain 45:09:26 loss: 0.5460 Lr: 0.00431 [2024-02-18 07:27:25,736 INFO misc.py line 119 87073] Train: [29/100][69/1557] Data 0.005 (0.497) Batch 0.785 (1.441) Remain 44:50:34 loss: 0.3539 Lr: 0.00431 [2024-02-18 07:27:26,932 INFO misc.py line 119 87073] Train: [29/100][70/1557] Data 0.005 (0.489) Batch 1.190 (1.437) Remain 44:43:34 loss: 0.2185 Lr: 0.00431 [2024-02-18 07:27:27,828 INFO misc.py line 119 87073] Train: [29/100][71/1557] Data 0.011 (0.482) 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Batch 1.151 (1.346) Remain 41:51:08 loss: 0.5950 Lr: 0.00431 [2024-02-18 07:29:45,681 INFO misc.py line 119 87073] Train: [29/100][178/1557] Data 0.013 (0.401) Batch 0.821 (1.343) Remain 41:45:30 loss: 0.5139 Lr: 0.00431 [2024-02-18 07:29:46,658 INFO misc.py line 119 87073] Train: [29/100][179/1557] Data 0.005 (0.399) Batch 0.977 (1.341) Remain 41:41:36 loss: 0.6383 Lr: 0.00431 [2024-02-18 07:29:49,215 INFO misc.py line 119 87073] Train: [29/100][180/1557] Data 0.637 (0.400) Batch 2.556 (1.348) Remain 41:54:23 loss: 0.6381 Lr: 0.00431 [2024-02-18 07:29:49,919 INFO misc.py line 119 87073] Train: [29/100][181/1557] Data 0.005 (0.398) Batch 0.702 (1.344) Remain 41:47:36 loss: 0.3441 Lr: 0.00431 [2024-02-18 07:29:51,213 INFO misc.py line 119 87073] Train: [29/100][182/1557] Data 0.007 (0.396) Batch 1.289 (1.344) Remain 41:47:00 loss: 0.4281 Lr: 0.00431 [2024-02-18 07:29:52,113 INFO misc.py line 119 87073] Train: [29/100][183/1557] Data 0.011 (0.394) Batch 0.906 (1.342) Remain 41:42:26 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87073] Train: [29/100][252/1557] Data 0.009 (0.365) Batch 1.046 (1.312) Remain 40:45:33 loss: 0.2979 Lr: 0.00430 [2024-02-18 07:31:18,293 INFO misc.py line 119 87073] Train: [29/100][253/1557] Data 0.010 (0.363) Batch 1.001 (1.311) Remain 40:43:13 loss: 0.5728 Lr: 0.00430 [2024-02-18 07:31:19,224 INFO misc.py line 119 87073] Train: [29/100][254/1557] Data 0.011 (0.362) Batch 0.938 (1.309) Remain 40:40:25 loss: 0.5991 Lr: 0.00430 [2024-02-18 07:31:20,309 INFO misc.py line 119 87073] Train: [29/100][255/1557] Data 0.003 (0.360) Batch 1.085 (1.308) Remain 40:38:45 loss: 1.1511 Lr: 0.00430 [2024-02-18 07:31:21,233 INFO misc.py line 119 87073] Train: [29/100][256/1557] Data 0.004 (0.359) Batch 0.921 (1.307) Remain 40:35:52 loss: 0.6335 Lr: 0.00430 [2024-02-18 07:31:21,982 INFO misc.py line 119 87073] Train: [29/100][257/1557] Data 0.007 (0.358) Batch 0.752 (1.305) Remain 40:31:46 loss: 0.3388 Lr: 0.00430 [2024-02-18 07:31:22,707 INFO misc.py line 119 87073] Train: [29/100][258/1557] Data 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Batch 1.053 (1.322) Remain 41:03:17 loss: 0.9285 Lr: 0.00430 [2024-02-18 07:33:23,779 INFO misc.py line 119 87073] Train: [29/100][346/1557] Data 0.013 (0.379) Batch 0.862 (1.321) Remain 41:00:46 loss: 0.0626 Lr: 0.00430 [2024-02-18 07:33:24,695 INFO misc.py line 119 87073] Train: [29/100][347/1557] Data 0.004 (0.378) Batch 0.916 (1.320) Remain 40:58:33 loss: 0.2403 Lr: 0.00430 [2024-02-18 07:33:25,414 INFO misc.py line 119 87073] Train: [29/100][348/1557] Data 0.004 (0.377) Batch 0.708 (1.318) Remain 40:55:13 loss: 0.4027 Lr: 0.00430 [2024-02-18 07:33:26,142 INFO misc.py line 119 87073] Train: [29/100][349/1557] Data 0.015 (0.376) Batch 0.739 (1.316) Remain 40:52:05 loss: 0.4061 Lr: 0.00430 [2024-02-18 07:33:27,444 INFO misc.py line 119 87073] Train: [29/100][350/1557] Data 0.003 (0.375) Batch 1.291 (1.316) Remain 40:51:55 loss: 0.1837 Lr: 0.00430 [2024-02-18 07:33:28,403 INFO misc.py line 119 87073] Train: [29/100][351/1557] Data 0.016 (0.374) Batch 0.971 (1.315) Remain 40:50:03 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Batch 1.019 (1.308) Remain 40:33:55 loss: 0.3402 Lr: 0.00429 [2024-02-18 07:35:45,522 INFO misc.py line 119 87073] Train: [29/100][458/1557] Data 0.014 (0.367) Batch 1.039 (1.307) Remain 40:32:48 loss: 0.5001 Lr: 0.00429 [2024-02-18 07:35:46,764 INFO misc.py line 119 87073] Train: [29/100][459/1557] Data 0.014 (0.366) Batch 1.244 (1.307) Remain 40:32:31 loss: 0.4382 Lr: 0.00429 [2024-02-18 07:35:47,559 INFO misc.py line 119 87073] Train: [29/100][460/1557] Data 0.013 (0.366) Batch 0.804 (1.306) Remain 40:30:27 loss: 0.2445 Lr: 0.00429 [2024-02-18 07:35:48,223 INFO misc.py line 119 87073] Train: [29/100][461/1557] Data 0.004 (0.365) Batch 0.656 (1.305) Remain 40:27:47 loss: 0.5990 Lr: 0.00429 [2024-02-18 07:35:49,462 INFO misc.py line 119 87073] Train: [29/100][462/1557] Data 0.012 (0.364) Batch 1.240 (1.305) Remain 40:27:30 loss: 0.1578 Lr: 0.00429 [2024-02-18 07:35:50,330 INFO misc.py line 119 87073] Train: [29/100][463/1557] Data 0.011 (0.363) Batch 0.875 (1.304) Remain 40:25:44 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Batch 0.913 (1.308) Remain 40:32:38 loss: 0.6006 Lr: 0.00429 [2024-02-18 07:36:58,852 INFO misc.py line 119 87073] Train: [29/100][514/1557] Data 0.013 (0.368) Batch 1.139 (1.308) Remain 40:32:00 loss: 0.4410 Lr: 0.00429 [2024-02-18 07:36:59,780 INFO misc.py line 119 87073] Train: [29/100][515/1557] Data 0.013 (0.367) Batch 0.936 (1.307) Remain 40:30:38 loss: 1.3423 Lr: 0.00429 [2024-02-18 07:37:00,490 INFO misc.py line 119 87073] Train: [29/100][516/1557] Data 0.005 (0.366) Batch 0.711 (1.306) Remain 40:28:27 loss: 0.3333 Lr: 0.00429 [2024-02-18 07:37:01,249 INFO misc.py line 119 87073] Train: [29/100][517/1557] Data 0.004 (0.366) Batch 0.751 (1.305) Remain 40:26:25 loss: 0.2087 Lr: 0.00429 [2024-02-18 07:37:02,516 INFO misc.py line 119 87073] Train: [29/100][518/1557] Data 0.011 (0.365) Batch 1.266 (1.305) Remain 40:26:16 loss: 0.1953 Lr: 0.00429 [2024-02-18 07:37:03,329 INFO misc.py line 119 87073] Train: [29/100][519/1557] Data 0.012 (0.364) Batch 0.821 (1.304) Remain 40:24:30 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Batch 1.118 (1.310) Remain 40:35:10 loss: 0.5250 Lr: 0.00429 [2024-02-18 07:38:13,028 INFO misc.py line 119 87073] Train: [29/100][570/1557] Data 0.005 (0.368) Batch 0.938 (1.309) Remain 40:33:55 loss: 0.5820 Lr: 0.00429 [2024-02-18 07:38:14,089 INFO misc.py line 119 87073] Train: [29/100][571/1557] Data 0.003 (0.367) Batch 1.061 (1.309) Remain 40:33:05 loss: 0.6627 Lr: 0.00429 [2024-02-18 07:38:14,867 INFO misc.py line 119 87073] Train: [29/100][572/1557] Data 0.004 (0.367) Batch 0.778 (1.308) Remain 40:31:20 loss: 0.3997 Lr: 0.00429 [2024-02-18 07:38:15,663 INFO misc.py line 119 87073] Train: [29/100][573/1557] Data 0.004 (0.366) Batch 0.794 (1.307) Remain 40:29:38 loss: 0.3260 Lr: 0.00429 [2024-02-18 07:38:16,926 INFO misc.py line 119 87073] Train: [29/100][574/1557] Data 0.006 (0.365) Batch 1.264 (1.307) Remain 40:29:28 loss: 0.2480 Lr: 0.00429 [2024-02-18 07:38:17,788 INFO misc.py line 119 87073] Train: [29/100][575/1557] Data 0.004 (0.365) Batch 0.861 (1.306) Remain 40:28:00 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Batch 1.031 (1.304) Remain 40:21:09 loss: 0.7764 Lr: 0.00428 [2024-02-18 07:41:49,072 INFO misc.py line 119 87073] Train: [29/100][738/1557] Data 0.009 (0.363) Batch 0.992 (1.304) Remain 40:20:20 loss: 0.5316 Lr: 0.00428 [2024-02-18 07:41:50,269 INFO misc.py line 119 87073] Train: [29/100][739/1557] Data 0.003 (0.362) Batch 1.189 (1.304) Remain 40:20:02 loss: 0.7080 Lr: 0.00428 [2024-02-18 07:41:51,054 INFO misc.py line 119 87073] Train: [29/100][740/1557] Data 0.012 (0.362) Batch 0.793 (1.303) Remain 40:18:43 loss: 0.3139 Lr: 0.00428 [2024-02-18 07:41:51,766 INFO misc.py line 119 87073] Train: [29/100][741/1557] Data 0.004 (0.361) Batch 0.710 (1.302) Remain 40:17:12 loss: 0.5307 Lr: 0.00428 [2024-02-18 07:41:52,984 INFO misc.py line 119 87073] Train: [29/100][742/1557] Data 0.007 (0.361) Batch 1.211 (1.302) Remain 40:16:57 loss: 0.1655 Lr: 0.00428 [2024-02-18 07:41:53,876 INFO misc.py line 119 87073] Train: [29/100][743/1557] Data 0.013 (0.360) Batch 0.899 (1.302) Remain 40:15:55 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Batch 0.990 (1.303) Remain 40:16:35 loss: 0.6734 Lr: 0.00428 [2024-02-18 07:43:00,798 INFO misc.py line 119 87073] Train: [29/100][794/1557] Data 0.004 (0.362) Batch 1.090 (1.302) Remain 40:16:03 loss: 0.2077 Lr: 0.00428 [2024-02-18 07:43:01,913 INFO misc.py line 119 87073] Train: [29/100][795/1557] Data 0.013 (0.361) Batch 1.118 (1.302) Remain 40:15:36 loss: 0.5866 Lr: 0.00428 [2024-02-18 07:43:02,664 INFO misc.py line 119 87073] Train: [29/100][796/1557] Data 0.011 (0.361) Batch 0.757 (1.301) Remain 40:14:18 loss: 0.2025 Lr: 0.00428 [2024-02-18 07:43:03,378 INFO misc.py line 119 87073] Train: [29/100][797/1557] Data 0.004 (0.360) Batch 0.709 (1.301) Remain 40:12:54 loss: 0.3285 Lr: 0.00428 [2024-02-18 07:43:04,568 INFO misc.py line 119 87073] Train: [29/100][798/1557] Data 0.009 (0.360) Batch 1.190 (1.301) Remain 40:12:37 loss: 0.2334 Lr: 0.00428 [2024-02-18 07:43:05,521 INFO misc.py line 119 87073] Train: [29/100][799/1557] Data 0.009 (0.359) Batch 0.958 (1.300) Remain 40:11:48 loss: 0.2061 Lr: 0.00428 [2024-02-18 07:43:06,676 INFO misc.py line 119 87073] Train: [29/100][800/1557] Data 0.003 (0.359) Batch 1.146 (1.300) Remain 40:11:25 loss: 0.1896 Lr: 0.00428 [2024-02-18 07:43:07,524 INFO misc.py line 119 87073] Train: [29/100][801/1557] Data 0.012 (0.359) Batch 0.856 (1.299) Remain 40:10:22 loss: 0.2473 Lr: 0.00428 [2024-02-18 07:43:08,439 INFO misc.py line 119 87073] Train: [29/100][802/1557] Data 0.004 (0.358) Batch 0.916 (1.299) Remain 40:09:27 loss: 0.7886 Lr: 0.00428 [2024-02-18 07:43:09,280 INFO misc.py line 119 87073] Train: [29/100][803/1557] Data 0.003 (0.358) Batch 0.832 (1.298) Remain 40:08:21 loss: 0.2421 Lr: 0.00428 [2024-02-18 07:43:10,045 INFO misc.py line 119 87073] Train: [29/100][804/1557] Data 0.012 (0.357) Batch 0.773 (1.298) Remain 40:07:07 loss: 0.2719 Lr: 0.00428 [2024-02-18 07:43:11,300 INFO misc.py line 119 87073] Train: [29/100][805/1557] Data 0.004 (0.357) Batch 1.254 (1.298) Remain 40:07:00 loss: 0.3091 Lr: 0.00428 [2024-02-18 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[2024-02-18 07:43:35,749 INFO misc.py line 119 87073] Train: [29/100][831/1557] Data 0.004 (0.346) Batch 0.797 (1.286) Remain 39:45:37 loss: 0.5417 Lr: 0.00428 [2024-02-18 07:43:36,560 INFO misc.py line 119 87073] Train: [29/100][832/1557] Data 0.012 (0.345) Batch 0.818 (1.286) Remain 39:44:33 loss: 0.3615 Lr: 0.00428 [2024-02-18 07:43:37,791 INFO misc.py line 119 87073] Train: [29/100][833/1557] Data 0.005 (0.345) Batch 1.223 (1.286) Remain 39:44:23 loss: 0.2088 Lr: 0.00428 [2024-02-18 07:43:38,649 INFO misc.py line 119 87073] Train: [29/100][834/1557] Data 0.013 (0.345) Batch 0.867 (1.285) Remain 39:43:25 loss: 0.5904 Lr: 0.00428 [2024-02-18 07:43:39,602 INFO misc.py line 119 87073] Train: [29/100][835/1557] Data 0.004 (0.344) Batch 0.952 (1.285) Remain 39:42:40 loss: 0.7185 Lr: 0.00428 [2024-02-18 07:43:40,519 INFO misc.py line 119 87073] Train: [29/100][836/1557] Data 0.005 (0.344) Batch 0.918 (1.284) Remain 39:41:49 loss: 0.4564 Lr: 0.00428 [2024-02-18 07:43:41,441 INFO misc.py 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Batch 0.974 (1.300) Remain 40:10:26 loss: 0.4497 Lr: 0.00428 [2024-02-18 07:44:11,447 INFO misc.py line 119 87073] Train: [29/100][850/1557] Data 0.005 (0.360) Batch 1.045 (1.300) Remain 40:09:52 loss: 0.6667 Lr: 0.00428 [2024-02-18 07:44:12,427 INFO misc.py line 119 87073] Train: [29/100][851/1557] Data 0.006 (0.359) Batch 0.982 (1.299) Remain 40:09:09 loss: 0.4093 Lr: 0.00428 [2024-02-18 07:44:13,129 INFO misc.py line 119 87073] Train: [29/100][852/1557] Data 0.004 (0.359) Batch 0.701 (1.299) Remain 40:07:49 loss: 0.6528 Lr: 0.00428 [2024-02-18 07:44:13,862 INFO misc.py line 119 87073] Train: [29/100][853/1557] Data 0.005 (0.359) Batch 0.727 (1.298) Remain 40:06:33 loss: 0.3019 Lr: 0.00428 [2024-02-18 07:44:15,109 INFO misc.py line 119 87073] Train: [29/100][854/1557] Data 0.011 (0.358) Batch 1.247 (1.298) Remain 40:06:25 loss: 0.2085 Lr: 0.00428 [2024-02-18 07:44:16,028 INFO misc.py line 119 87073] Train: [29/100][855/1557] Data 0.012 (0.358) Batch 0.924 (1.297) Remain 40:05:35 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Batch 0.920 (1.300) Remain 40:08:52 loss: 0.2257 Lr: 0.00428 [2024-02-18 07:45:24,081 INFO misc.py line 119 87073] Train: [29/100][906/1557] Data 0.003 (0.359) Batch 1.061 (1.299) Remain 40:08:21 loss: 0.8616 Lr: 0.00428 [2024-02-18 07:45:24,816 INFO misc.py line 119 87073] Train: [29/100][907/1557] Data 0.004 (0.359) Batch 0.731 (1.299) Remain 40:07:10 loss: 0.3505 Lr: 0.00428 [2024-02-18 07:45:25,616 INFO misc.py line 119 87073] Train: [29/100][908/1557] Data 0.008 (0.358) Batch 0.794 (1.298) Remain 40:06:07 loss: 0.4021 Lr: 0.00428 [2024-02-18 07:45:26,368 INFO misc.py line 119 87073] Train: [29/100][909/1557] Data 0.014 (0.358) Batch 0.761 (1.298) Remain 40:04:59 loss: 0.4952 Lr: 0.00428 [2024-02-18 07:45:27,591 INFO misc.py line 119 87073] Train: [29/100][910/1557] Data 0.004 (0.357) Batch 1.224 (1.298) Remain 40:04:49 loss: 0.2505 Lr: 0.00428 [2024-02-18 07:45:28,540 INFO misc.py line 119 87073] Train: [29/100][911/1557] Data 0.004 (0.357) Batch 0.949 (1.297) Remain 40:04:05 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line 119 87073] Train: [29/100][949/1557] Data 0.004 (0.343) Batch 0.964 (1.283) Remain 39:36:39 loss: 0.2977 Lr: 0.00428 [2024-02-18 07:46:04,982 INFO misc.py line 119 87073] Train: [29/100][950/1557] Data 0.008 (0.343) Batch 0.739 (1.282) Remain 39:35:33 loss: 0.3092 Lr: 0.00428 [2024-02-18 07:46:05,721 INFO misc.py line 119 87073] Train: [29/100][951/1557] Data 0.003 (0.342) Batch 0.730 (1.282) Remain 39:34:27 loss: 0.5692 Lr: 0.00428 [2024-02-18 07:46:06,901 INFO misc.py line 119 87073] Train: [29/100][952/1557] Data 0.012 (0.342) Batch 1.178 (1.282) Remain 39:34:14 loss: 0.3496 Lr: 0.00428 [2024-02-18 07:46:07,775 INFO misc.py line 119 87073] Train: [29/100][953/1557] Data 0.014 (0.341) Batch 0.883 (1.281) Remain 39:33:26 loss: 0.3986 Lr: 0.00428 [2024-02-18 07:46:08,778 INFO misc.py line 119 87073] Train: [29/100][954/1557] Data 0.004 (0.341) Batch 1.004 (1.281) Remain 39:32:52 loss: 0.6157 Lr: 0.00428 [2024-02-18 07:46:09,753 INFO misc.py line 119 87073] Train: 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Batch 0.897 (1.296) Remain 40:01:33 loss: 0.2327 Lr: 0.00428 [2024-02-18 07:46:33,608 INFO misc.py line 119 87073] Train: [29/100][962/1557] Data 0.004 (0.357) Batch 0.957 (1.296) Remain 40:00:52 loss: 0.5892 Lr: 0.00428 [2024-02-18 07:46:34,584 INFO misc.py line 119 87073] Train: [29/100][963/1557] Data 0.006 (0.357) Batch 0.977 (1.296) Remain 40:00:14 loss: 0.7835 Lr: 0.00428 [2024-02-18 07:46:35,348 INFO misc.py line 119 87073] Train: [29/100][964/1557] Data 0.004 (0.356) Batch 0.760 (1.295) Remain 39:59:11 loss: 0.4265 Lr: 0.00428 [2024-02-18 07:46:36,119 INFO misc.py line 119 87073] Train: [29/100][965/1557] Data 0.008 (0.356) Batch 0.775 (1.295) Remain 39:58:09 loss: 0.6235 Lr: 0.00428 [2024-02-18 07:46:37,323 INFO misc.py line 119 87073] Train: [29/100][966/1557] Data 0.003 (0.355) Batch 1.205 (1.295) Remain 39:57:57 loss: 0.3032 Lr: 0.00428 [2024-02-18 07:46:38,229 INFO misc.py line 119 87073] Train: [29/100][967/1557] Data 0.004 (0.355) Batch 0.905 (1.294) Remain 39:57:11 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87073] Train: [29/100][980/1557] Data 0.004 (0.350) Batch 1.111 (1.290) Remain 39:48:37 loss: 0.1730 Lr: 0.00428 [2024-02-18 07:46:51,630 INFO misc.py line 119 87073] Train: [29/100][981/1557] Data 0.005 (0.350) Batch 0.949 (1.289) Remain 39:47:57 loss: 0.4887 Lr: 0.00428 [2024-02-18 07:46:52,524 INFO misc.py line 119 87073] Train: [29/100][982/1557] Data 0.004 (0.350) Batch 0.895 (1.289) Remain 39:47:11 loss: 0.2505 Lr: 0.00428 [2024-02-18 07:46:53,404 INFO misc.py line 119 87073] Train: [29/100][983/1557] Data 0.004 (0.349) Batch 0.878 (1.289) Remain 39:46:23 loss: 0.0851 Lr: 0.00428 [2024-02-18 07:46:54,237 INFO misc.py line 119 87073] Train: [29/100][984/1557] Data 0.005 (0.349) Batch 0.835 (1.288) Remain 39:45:30 loss: 0.5856 Lr: 0.00428 [2024-02-18 07:46:55,024 INFO misc.py line 119 87073] Train: [29/100][985/1557] Data 0.004 (0.349) Batch 0.787 (1.288) Remain 39:44:32 loss: 0.3357 Lr: 0.00428 [2024-02-18 07:46:55,699 INFO misc.py line 119 87073] Train: [29/100][986/1557] Data 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(0.357) Batch 0.891 (1.296) Remain 39:59:11 loss: 0.3814 Lr: 0.00427 [2024-02-18 07:47:45,525 INFO misc.py line 119 87073] Train: [29/100][1018/1557] Data 0.004 (0.357) Batch 0.900 (1.295) Remain 39:58:27 loss: 0.5848 Lr: 0.00427 [2024-02-18 07:47:46,500 INFO misc.py line 119 87073] Train: [29/100][1019/1557] Data 0.006 (0.356) Batch 0.976 (1.295) Remain 39:57:50 loss: 0.7534 Lr: 0.00427 [2024-02-18 07:47:47,238 INFO misc.py line 119 87073] Train: [29/100][1020/1557] Data 0.004 (0.356) Batch 0.732 (1.295) Remain 39:56:48 loss: 0.4558 Lr: 0.00427 [2024-02-18 07:47:48,118 INFO misc.py line 119 87073] Train: [29/100][1021/1557] Data 0.010 (0.356) Batch 0.886 (1.294) Remain 39:56:02 loss: 0.6411 Lr: 0.00427 [2024-02-18 07:47:49,413 INFO misc.py line 119 87073] Train: [29/100][1022/1557] Data 0.003 (0.355) Batch 1.285 (1.294) Remain 39:55:59 loss: 0.4735 Lr: 0.00427 [2024-02-18 07:47:50,316 INFO misc.py line 119 87073] Train: [29/100][1023/1557] Data 0.015 (0.355) Batch 0.913 (1.294) Remain 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[2024-02-18 07:48:28,629 INFO misc.py line 119 87073] Train: [29/100][1061/1557] Data 0.004 (0.343) Batch 1.175 (1.284) Remain 39:35:28 loss: 0.3583 Lr: 0.00427 [2024-02-18 07:48:29,405 INFO misc.py line 119 87073] Train: [29/100][1062/1557] Data 0.004 (0.343) Batch 0.776 (1.283) Remain 39:34:34 loss: 0.2849 Lr: 0.00427 [2024-02-18 07:48:30,155 INFO misc.py line 119 87073] Train: [29/100][1063/1557] Data 0.004 (0.343) Batch 0.742 (1.283) Remain 39:33:36 loss: 0.3945 Lr: 0.00427 [2024-02-18 07:48:31,387 INFO misc.py line 119 87073] Train: [29/100][1064/1557] Data 0.013 (0.342) Batch 1.228 (1.283) Remain 39:33:29 loss: 0.2988 Lr: 0.00427 [2024-02-18 07:48:32,422 INFO misc.py line 119 87073] Train: [29/100][1065/1557] Data 0.016 (0.342) Batch 1.035 (1.282) Remain 39:33:02 loss: 0.3116 Lr: 0.00427 [2024-02-18 07:48:33,385 INFO misc.py line 119 87073] Train: [29/100][1066/1557] Data 0.016 (0.342) Batch 0.975 (1.282) Remain 39:32:29 loss: 0.4918 Lr: 0.00427 [2024-02-18 07:48:34,441 INFO 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[2024-02-18 07:49:18,043 INFO misc.py line 119 87073] Train: [29/100][1092/1557] Data 0.007 (0.352) Batch 1.111 (1.292) Remain 39:51:09 loss: 0.1555 Lr: 0.00427 [2024-02-18 07:49:18,994 INFO misc.py line 119 87073] Train: [29/100][1093/1557] Data 0.013 (0.352) Batch 0.961 (1.292) Remain 39:50:34 loss: 0.3064 Lr: 0.00427 [2024-02-18 07:49:19,917 INFO misc.py line 119 87073] Train: [29/100][1094/1557] Data 0.003 (0.351) Batch 0.922 (1.292) Remain 39:49:55 loss: 0.5884 Lr: 0.00427 [2024-02-18 07:49:20,810 INFO misc.py line 119 87073] Train: [29/100][1095/1557] Data 0.005 (0.351) Batch 0.893 (1.291) Remain 39:49:13 loss: 0.8645 Lr: 0.00427 [2024-02-18 07:49:21,823 INFO misc.py line 119 87073] Train: [29/100][1096/1557] Data 0.004 (0.351) Batch 1.008 (1.291) Remain 39:48:43 loss: 0.6814 Lr: 0.00427 [2024-02-18 07:49:22,580 INFO misc.py line 119 87073] Train: [29/100][1097/1557] Data 0.009 (0.350) Batch 0.760 (1.291) Remain 39:47:48 loss: 0.1989 Lr: 0.00427 [2024-02-18 07:49:23,373 INFO 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[2024-02-18 07:49:48,120 INFO misc.py line 119 87073] Train: [29/100][1123/1557] Data 0.003 (0.342) Batch 1.026 (1.283) Remain 39:34:00 loss: 0.5285 Lr: 0.00427 [2024-02-18 07:49:49,135 INFO misc.py line 119 87073] Train: [29/100][1124/1557] Data 0.004 (0.342) Batch 1.014 (1.283) Remain 39:33:32 loss: 0.3810 Lr: 0.00427 [2024-02-18 07:49:49,876 INFO misc.py line 119 87073] Train: [29/100][1125/1557] Data 0.005 (0.342) Batch 0.742 (1.283) Remain 39:32:37 loss: 0.1595 Lr: 0.00427 [2024-02-18 07:49:50,565 INFO misc.py line 119 87073] Train: [29/100][1126/1557] Data 0.003 (0.341) Batch 0.681 (1.282) Remain 39:31:37 loss: 0.5864 Lr: 0.00427 [2024-02-18 07:50:10,633 INFO misc.py line 119 87073] Train: [29/100][1127/1557] Data 19.124 (0.358) Batch 20.074 (1.299) Remain 40:02:31 loss: 0.2061 Lr: 0.00427 [2024-02-18 07:50:11,552 INFO misc.py line 119 87073] Train: [29/100][1128/1557] Data 0.006 (0.358) Batch 0.916 (1.299) Remain 40:01:52 loss: 0.5123 Lr: 0.00427 [2024-02-18 07:50:12,494 INFO 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[2024-02-18 07:50:36,947 INFO misc.py line 119 87073] Train: [29/100][1154/1557] Data 0.004 (0.350) Batch 0.741 (1.291) Remain 39:47:52 loss: 0.3284 Lr: 0.00427 [2024-02-18 07:50:38,305 INFO misc.py line 119 87073] Train: [29/100][1155/1557] Data 0.003 (0.350) Batch 1.348 (1.291) Remain 39:47:56 loss: 0.1805 Lr: 0.00427 [2024-02-18 07:50:39,176 INFO misc.py line 119 87073] Train: [29/100][1156/1557] Data 0.013 (0.349) Batch 0.880 (1.291) Remain 39:47:15 loss: 0.3738 Lr: 0.00427 [2024-02-18 07:50:40,283 INFO misc.py line 119 87073] Train: [29/100][1157/1557] Data 0.006 (0.349) Batch 1.107 (1.291) Remain 39:46:56 loss: 0.5020 Lr: 0.00427 [2024-02-18 07:50:41,149 INFO misc.py line 119 87073] Train: [29/100][1158/1557] Data 0.004 (0.349) Batch 0.866 (1.290) Remain 39:46:14 loss: 0.3685 Lr: 0.00427 [2024-02-18 07:50:42,123 INFO misc.py line 119 87073] Train: [29/100][1159/1557] Data 0.004 (0.348) Batch 0.972 (1.290) Remain 39:45:42 loss: 0.9366 Lr: 0.00427 [2024-02-18 07:50:42,861 INFO 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Train: [29/100][1197/1557] Data 0.012 (0.351) Batch 1.232 (1.292) Remain 39:48:45 loss: 0.2055 Lr: 0.00427 [2024-02-18 07:51:34,686 INFO misc.py line 119 87073] Train: [29/100][1198/1557] Data 0.010 (0.351) Batch 1.031 (1.292) Remain 39:48:20 loss: 0.5878 Lr: 0.00427 [2024-02-18 07:51:35,930 INFO misc.py line 119 87073] Train: [29/100][1199/1557] Data 0.012 (0.351) Batch 1.243 (1.292) Remain 39:48:14 loss: 0.5170 Lr: 0.00427 [2024-02-18 07:51:36,912 INFO misc.py line 119 87073] Train: [29/100][1200/1557] Data 0.013 (0.350) Batch 0.992 (1.292) Remain 39:47:45 loss: 0.7163 Lr: 0.00427 [2024-02-18 07:51:37,809 INFO misc.py line 119 87073] Train: [29/100][1201/1557] Data 0.004 (0.350) Batch 0.896 (1.291) Remain 39:47:07 loss: 0.7002 Lr: 0.00427 [2024-02-18 07:51:38,464 INFO misc.py line 119 87073] Train: [29/100][1202/1557] Data 0.004 (0.350) Batch 0.647 (1.291) Remain 39:46:06 loss: 0.5252 Lr: 0.00427 [2024-02-18 07:51:39,247 INFO misc.py line 119 87073] Train: [29/100][1203/1557] Data 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Remain 39:41:56 loss: 0.5074 Lr: 0.00427 [2024-02-18 07:51:45,645 INFO misc.py line 119 87073] Train: [29/100][1210/1557] Data 0.005 (0.347) Batch 0.759 (1.288) Remain 39:41:06 loss: 0.3503 Lr: 0.00427 [2024-02-18 07:51:46,906 INFO misc.py line 119 87073] Train: [29/100][1211/1557] Data 0.011 (0.347) Batch 1.260 (1.288) Remain 39:41:03 loss: 0.1596 Lr: 0.00427 [2024-02-18 07:51:47,861 INFO misc.py line 119 87073] Train: [29/100][1212/1557] Data 0.013 (0.347) Batch 0.964 (1.288) Remain 39:40:32 loss: 0.5348 Lr: 0.00427 [2024-02-18 07:51:48,924 INFO misc.py line 119 87073] Train: [29/100][1213/1557] Data 0.003 (0.347) Batch 1.063 (1.288) Remain 39:40:10 loss: 0.7506 Lr: 0.00427 [2024-02-18 07:51:50,078 INFO misc.py line 119 87073] Train: [29/100][1214/1557] Data 0.003 (0.346) Batch 1.154 (1.288) Remain 39:39:56 loss: 0.4751 Lr: 0.00427 [2024-02-18 07:51:51,158 INFO misc.py line 119 87073] Train: [29/100][1215/1557] Data 0.004 (0.346) Batch 1.079 (1.288) Remain 39:39:36 loss: 0.7395 Lr: 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INFO misc.py line 119 87073] Train: [29/100][1222/1557] Data 0.004 (0.344) Batch 1.030 (1.286) Remain 39:35:58 loss: 0.4754 Lr: 0.00427 [2024-02-18 07:51:58,572 INFO misc.py line 119 87073] Train: [29/100][1223/1557] Data 0.004 (0.344) Batch 0.694 (1.285) Remain 39:35:03 loss: 0.5065 Lr: 0.00427 [2024-02-18 07:51:59,376 INFO misc.py line 119 87073] Train: [29/100][1224/1557] Data 0.004 (0.344) Batch 0.794 (1.285) Remain 39:34:17 loss: 0.3945 Lr: 0.00427 [2024-02-18 07:52:00,709 INFO misc.py line 119 87073] Train: [29/100][1225/1557] Data 0.014 (0.343) Batch 1.338 (1.285) Remain 39:34:21 loss: 0.2001 Lr: 0.00427 [2024-02-18 07:52:01,641 INFO misc.py line 119 87073] Train: [29/100][1226/1557] Data 0.009 (0.343) Batch 0.938 (1.285) Remain 39:33:48 loss: 0.4696 Lr: 0.00427 [2024-02-18 07:52:02,636 INFO misc.py line 119 87073] Train: [29/100][1227/1557] Data 0.004 (0.343) Batch 0.995 (1.284) Remain 39:33:20 loss: 0.2880 Lr: 0.00427 [2024-02-18 07:52:03,607 INFO misc.py line 119 87073] Train: [29/100][1228/1557] Data 0.003 (0.342) Batch 0.970 (1.284) Remain 39:32:51 loss: 0.2988 Lr: 0.00427 [2024-02-18 07:52:04,575 INFO misc.py line 119 87073] Train: [29/100][1229/1557] Data 0.004 (0.342) Batch 0.969 (1.284) Remain 39:32:21 loss: 0.4400 Lr: 0.00427 [2024-02-18 07:52:07,342 INFO misc.py line 119 87073] Train: [29/100][1230/1557] Data 0.824 (0.343) Batch 2.766 (1.285) Remain 39:34:34 loss: 0.7063 Lr: 0.00427 [2024-02-18 07:52:08,021 INFO misc.py line 119 87073] Train: [29/100][1231/1557] Data 0.005 (0.342) Batch 0.680 (1.285) Remain 39:33:38 loss: 0.4275 Lr: 0.00427 [2024-02-18 07:52:09,236 INFO misc.py line 119 87073] Train: [29/100][1232/1557] Data 0.003 (0.342) Batch 1.210 (1.284) Remain 39:33:30 loss: 0.3766 Lr: 0.00427 [2024-02-18 07:52:10,204 INFO misc.py line 119 87073] Train: [29/100][1233/1557] Data 0.009 (0.342) Batch 0.974 (1.284) Remain 39:33:00 loss: 0.5637 Lr: 0.00427 [2024-02-18 07:52:11,224 INFO misc.py line 119 87073] Train: [29/100][1234/1557] Data 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Data 0.004 (0.348) Batch 0.810 (1.291) Remain 39:45:06 loss: 0.2702 Lr: 0.00426 [2024-02-18 07:53:00,779 INFO misc.py line 119 87073] Train: [29/100][1266/1557] Data 0.012 (0.348) Batch 0.746 (1.291) Remain 39:44:17 loss: 0.2869 Lr: 0.00426 [2024-02-18 07:53:02,035 INFO misc.py line 119 87073] Train: [29/100][1267/1557] Data 0.004 (0.348) Batch 1.255 (1.291) Remain 39:44:13 loss: 0.1765 Lr: 0.00426 [2024-02-18 07:53:02,894 INFO misc.py line 119 87073] Train: [29/100][1268/1557] Data 0.005 (0.347) Batch 0.861 (1.290) Remain 39:43:34 loss: 0.3108 Lr: 0.00426 [2024-02-18 07:53:03,749 INFO misc.py line 119 87073] Train: [29/100][1269/1557] Data 0.004 (0.347) Batch 0.847 (1.290) Remain 39:42:54 loss: 0.3503 Lr: 0.00426 [2024-02-18 07:53:04,941 INFO misc.py line 119 87073] Train: [29/100][1270/1557] Data 0.012 (0.347) Batch 1.192 (1.290) Remain 39:42:44 loss: 0.3008 Lr: 0.00426 [2024-02-18 07:53:06,000 INFO misc.py line 119 87073] Train: [29/100][1271/1557] Data 0.012 (0.347) Batch 1.062 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(0.345) Batch 1.006 (1.288) Remain 39:38:35 loss: 0.4497 Lr: 0.00426 [2024-02-18 07:54:25,189 INFO misc.py line 119 87073] Train: [29/100][1334/1557] Data 0.003 (0.345) Batch 1.008 (1.288) Remain 39:38:10 loss: 0.6694 Lr: 0.00426 [2024-02-18 07:54:25,981 INFO misc.py line 119 87073] Train: [29/100][1335/1557] Data 0.004 (0.344) Batch 0.785 (1.288) Remain 39:37:27 loss: 0.6170 Lr: 0.00426 [2024-02-18 07:54:26,684 INFO misc.py line 119 87073] Train: [29/100][1336/1557] Data 0.010 (0.344) Batch 0.710 (1.287) Remain 39:36:38 loss: 0.7292 Lr: 0.00426 [2024-02-18 07:54:28,047 INFO misc.py line 119 87073] Train: [29/100][1337/1557] Data 0.004 (0.344) Batch 1.352 (1.287) Remain 39:36:42 loss: 0.3187 Lr: 0.00426 [2024-02-18 07:54:29,066 INFO misc.py line 119 87073] Train: [29/100][1338/1557] Data 0.015 (0.344) Batch 1.021 (1.287) Remain 39:36:18 loss: 0.3923 Lr: 0.00426 [2024-02-18 07:54:29,975 INFO misc.py line 119 87073] Train: [29/100][1339/1557] Data 0.013 (0.343) Batch 0.917 (1.287) Remain 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[2024-02-18 07:54:36,646 INFO misc.py line 119 87073] Train: [29/100][1346/1557] Data 0.004 (0.342) Batch 0.962 (1.285) Remain 39:32:25 loss: 0.6595 Lr: 0.00426 [2024-02-18 07:54:37,585 INFO misc.py line 119 87073] Train: [29/100][1347/1557] Data 0.004 (0.341) Batch 0.938 (1.285) Remain 39:31:55 loss: 0.2315 Lr: 0.00426 [2024-02-18 07:54:38,615 INFO misc.py line 119 87073] Train: [29/100][1348/1557] Data 0.005 (0.341) Batch 1.031 (1.285) Remain 39:31:32 loss: 0.3920 Lr: 0.00426 [2024-02-18 07:54:39,401 INFO misc.py line 119 87073] Train: [29/100][1349/1557] Data 0.004 (0.341) Batch 0.786 (1.284) Remain 39:30:50 loss: 0.2605 Lr: 0.00426 [2024-02-18 07:54:40,268 INFO misc.py line 119 87073] Train: [29/100][1350/1557] Data 0.004 (0.341) Batch 0.867 (1.284) Remain 39:30:15 loss: 0.2951 Lr: 0.00426 [2024-02-18 07:55:00,469 INFO misc.py line 119 87073] Train: [29/100][1351/1557] Data 19.282 (0.355) Batch 20.202 (1.298) Remain 39:56:08 loss: 0.1192 Lr: 0.00426 [2024-02-18 07:55:01,393 INFO misc.py line 119 87073] Train: [29/100][1352/1557] Data 0.004 (0.354) Batch 0.924 (1.298) Remain 39:55:36 loss: 0.4006 Lr: 0.00426 [2024-02-18 07:55:02,384 INFO misc.py line 119 87073] Train: [29/100][1353/1557] Data 0.004 (0.354) Batch 0.990 (1.298) Remain 39:55:09 loss: 0.2361 Lr: 0.00426 [2024-02-18 07:55:03,380 INFO misc.py line 119 87073] Train: [29/100][1354/1557] Data 0.005 (0.354) Batch 0.997 (1.297) Remain 39:54:43 loss: 0.7847 Lr: 0.00426 [2024-02-18 07:55:04,485 INFO misc.py line 119 87073] Train: [29/100][1355/1557] Data 0.004 (0.354) Batch 1.105 (1.297) Remain 39:54:26 loss: 0.3875 Lr: 0.00426 [2024-02-18 07:55:05,270 INFO misc.py line 119 87073] Train: [29/100][1356/1557] Data 0.004 (0.353) Batch 0.785 (1.297) Remain 39:53:43 loss: 0.5653 Lr: 0.00426 [2024-02-18 07:55:06,058 INFO misc.py line 119 87073] Train: [29/100][1357/1557] Data 0.004 (0.353) Batch 0.789 (1.296) Remain 39:53:00 loss: 0.4451 Lr: 0.00426 [2024-02-18 07:55:07,373 INFO misc.py line 119 87073] Train: 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[2024-02-18 07:55:24,957 INFO misc.py line 119 87073] Train: [29/100][1377/1557] Data 0.006 (0.348) Batch 0.658 (1.291) Remain 39:43:07 loss: 0.4880 Lr: 0.00426 [2024-02-18 07:55:25,752 INFO misc.py line 119 87073] Train: [29/100][1378/1557] Data 0.006 (0.348) Batch 0.788 (1.291) Remain 39:42:25 loss: 0.5224 Lr: 0.00426 [2024-02-18 07:55:26,987 INFO misc.py line 119 87073] Train: [29/100][1379/1557] Data 0.013 (0.348) Batch 1.233 (1.291) Remain 39:42:19 loss: 0.1983 Lr: 0.00426 [2024-02-18 07:55:27,950 INFO misc.py line 119 87073] Train: [29/100][1380/1557] Data 0.015 (0.347) Batch 0.975 (1.291) Remain 39:41:53 loss: 0.4024 Lr: 0.00426 [2024-02-18 07:55:29,122 INFO misc.py line 119 87073] Train: [29/100][1381/1557] Data 0.003 (0.347) Batch 1.159 (1.291) Remain 39:41:41 loss: 0.4959 Lr: 0.00426 [2024-02-18 07:55:30,103 INFO misc.py line 119 87073] Train: [29/100][1382/1557] Data 0.016 (0.347) Batch 0.995 (1.290) Remain 39:41:16 loss: 0.4411 Lr: 0.00426 [2024-02-18 07:55:31,190 INFO 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INFO misc.py line 119 87073] Train: [29/100][1414/1557] Data 0.004 (0.353) Batch 1.242 (1.297) Remain 39:52:42 loss: 0.1515 Lr: 0.00426 [2024-02-18 07:56:21,682 INFO misc.py line 119 87073] Train: [29/100][1415/1557] Data 0.016 (0.353) Batch 1.001 (1.297) Remain 39:52:17 loss: 0.2929 Lr: 0.00426 [2024-02-18 07:56:22,693 INFO misc.py line 119 87073] Train: [29/100][1416/1557] Data 0.016 (0.352) Batch 1.010 (1.297) Remain 39:51:54 loss: 0.2846 Lr: 0.00426 [2024-02-18 07:56:23,750 INFO misc.py line 119 87073] Train: [29/100][1417/1557] Data 0.017 (0.352) Batch 1.059 (1.296) Remain 39:51:34 loss: 0.4915 Lr: 0.00426 [2024-02-18 07:56:24,663 INFO misc.py line 119 87073] Train: [29/100][1418/1557] Data 0.014 (0.352) Batch 0.924 (1.296) Remain 39:51:03 loss: 0.7969 Lr: 0.00426 [2024-02-18 07:56:25,433 INFO misc.py line 119 87073] Train: [29/100][1419/1557] Data 0.004 (0.352) Batch 0.770 (1.296) Remain 39:50:21 loss: 0.2689 Lr: 0.00426 [2024-02-18 07:56:26,207 INFO misc.py line 119 87073] Train: [29/100][1420/1557] Data 0.004 (0.351) Batch 0.767 (1.295) Remain 39:49:38 loss: 0.4234 Lr: 0.00426 [2024-02-18 07:56:27,456 INFO misc.py line 119 87073] Train: [29/100][1421/1557] Data 0.011 (0.351) Batch 1.249 (1.295) Remain 39:49:33 loss: 0.1931 Lr: 0.00426 [2024-02-18 07:56:28,339 INFO misc.py line 119 87073] Train: [29/100][1422/1557] Data 0.011 (0.351) Batch 0.889 (1.295) Remain 39:49:00 loss: 0.7099 Lr: 0.00426 [2024-02-18 07:56:29,388 INFO misc.py line 119 87073] Train: [29/100][1423/1557] Data 0.005 (0.351) Batch 1.051 (1.295) Remain 39:48:40 loss: 1.0194 Lr: 0.00426 [2024-02-18 07:56:30,430 INFO misc.py line 119 87073] Train: [29/100][1424/1557] Data 0.004 (0.350) Batch 1.042 (1.295) Remain 39:48:19 loss: 0.7790 Lr: 0.00426 [2024-02-18 07:56:31,427 INFO misc.py line 119 87073] Train: [29/100][1425/1557] Data 0.004 (0.350) Batch 0.997 (1.295) Remain 39:47:54 loss: 0.4860 Lr: 0.00426 [2024-02-18 07:56:32,167 INFO misc.py line 119 87073] Train: [29/100][1426/1557] Data 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Remain 39:44:25 loss: 0.3947 Lr: 0.00426 [2024-02-18 07:56:38,699 INFO misc.py line 119 87073] Train: [29/100][1433/1557] Data 0.010 (0.348) Batch 0.797 (1.292) Remain 39:43:45 loss: 0.3747 Lr: 0.00426 [2024-02-18 07:56:39,446 INFO misc.py line 119 87073] Train: [29/100][1434/1557] Data 0.004 (0.348) Batch 0.746 (1.292) Remain 39:43:02 loss: 0.5543 Lr: 0.00426 [2024-02-18 07:56:40,725 INFO misc.py line 119 87073] Train: [29/100][1435/1557] Data 0.004 (0.348) Batch 1.278 (1.292) Remain 39:43:00 loss: 0.2543 Lr: 0.00426 [2024-02-18 07:56:41,664 INFO misc.py line 119 87073] Train: [29/100][1436/1557] Data 0.005 (0.348) Batch 0.939 (1.292) Remain 39:42:31 loss: 0.1347 Lr: 0.00426 [2024-02-18 07:56:42,659 INFO misc.py line 119 87073] Train: [29/100][1437/1557] Data 0.005 (0.347) Batch 0.997 (1.292) Remain 39:42:07 loss: 0.3764 Lr: 0.00426 [2024-02-18 07:56:43,524 INFO misc.py line 119 87073] Train: [29/100][1438/1557] Data 0.003 (0.347) Batch 0.864 (1.291) Remain 39:41:33 loss: 0.7827 Lr: 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INFO misc.py line 119 87073] Train: [29/100][1445/1557] Data 0.007 (0.345) Batch 0.991 (1.290) Remain 39:38:16 loss: 0.3507 Lr: 0.00426 [2024-02-18 07:56:51,088 INFO misc.py line 119 87073] Train: [29/100][1446/1557] Data 0.010 (0.345) Batch 0.966 (1.289) Remain 39:37:50 loss: 0.4239 Lr: 0.00426 [2024-02-18 07:56:51,856 INFO misc.py line 119 87073] Train: [29/100][1447/1557] Data 0.004 (0.345) Batch 0.767 (1.289) Remain 39:37:09 loss: 0.4494 Lr: 0.00426 [2024-02-18 07:56:52,647 INFO misc.py line 119 87073] Train: [29/100][1448/1557] Data 0.004 (0.345) Batch 0.779 (1.289) Remain 39:36:29 loss: 0.3489 Lr: 0.00426 [2024-02-18 07:56:53,958 INFO misc.py line 119 87073] Train: [29/100][1449/1557] Data 0.016 (0.344) Batch 1.314 (1.289) Remain 39:36:29 loss: 0.3160 Lr: 0.00426 [2024-02-18 07:56:54,870 INFO misc.py line 119 87073] Train: [29/100][1450/1557] Data 0.013 (0.344) Batch 0.921 (1.288) Remain 39:36:00 loss: 0.5140 Lr: 0.00426 [2024-02-18 07:56:55,939 INFO misc.py line 119 87073] Train: [29/100][1451/1557] Data 0.004 (0.344) Batch 1.069 (1.288) Remain 39:35:42 loss: 0.3904 Lr: 0.00426 [2024-02-18 07:56:56,851 INFO misc.py line 119 87073] Train: [29/100][1452/1557] Data 0.004 (0.344) Batch 0.912 (1.288) Remain 39:35:12 loss: 0.4121 Lr: 0.00426 [2024-02-18 07:56:57,853 INFO misc.py line 119 87073] Train: [29/100][1453/1557] Data 0.004 (0.344) Batch 1.001 (1.288) Remain 39:34:49 loss: 0.1627 Lr: 0.00426 [2024-02-18 07:56:58,560 INFO misc.py line 119 87073] Train: [29/100][1454/1557] Data 0.004 (0.343) Batch 0.697 (1.287) Remain 39:34:02 loss: 0.3873 Lr: 0.00426 [2024-02-18 07:56:59,321 INFO misc.py line 119 87073] Train: [29/100][1455/1557] Data 0.013 (0.343) Batch 0.770 (1.287) Remain 39:33:22 loss: 0.2806 Lr: 0.00426 [2024-02-18 07:57:00,528 INFO misc.py line 119 87073] Train: [29/100][1456/1557] Data 0.004 (0.343) Batch 1.208 (1.287) Remain 39:33:14 loss: 0.5800 Lr: 0.00426 [2024-02-18 07:57:01,574 INFO misc.py line 119 87073] Train: [29/100][1457/1557] Data 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(1.298) Remain 39:53:10 loss: 0.2005 Lr: 0.00426 [2024-02-18 07:57:26,322 INFO misc.py line 119 87073] Train: [29/100][1464/1557] Data 0.004 (0.354) Batch 0.889 (1.298) Remain 39:52:37 loss: 0.7833 Lr: 0.00426 [2024-02-18 07:57:27,285 INFO misc.py line 119 87073] Train: [29/100][1465/1557] Data 0.010 (0.353) Batch 0.967 (1.297) Remain 39:52:11 loss: 0.4634 Lr: 0.00426 [2024-02-18 07:57:28,231 INFO misc.py line 119 87073] Train: [29/100][1466/1557] Data 0.005 (0.353) Batch 0.945 (1.297) Remain 39:51:43 loss: 0.5032 Lr: 0.00426 [2024-02-18 07:57:29,144 INFO misc.py line 119 87073] Train: [29/100][1467/1557] Data 0.007 (0.353) Batch 0.914 (1.297) Remain 39:51:13 loss: 0.8733 Lr: 0.00426 [2024-02-18 07:57:29,932 INFO misc.py line 119 87073] Train: [29/100][1468/1557] Data 0.004 (0.353) Batch 0.782 (1.296) Remain 39:50:33 loss: 0.5628 Lr: 0.00426 [2024-02-18 07:57:30,717 INFO misc.py line 119 87073] Train: [29/100][1469/1557] Data 0.010 (0.353) Batch 0.792 (1.296) Remain 39:49:53 loss: 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07:57:37,226 INFO misc.py line 119 87073] Train: [29/100][1476/1557] Data 0.004 (0.351) Batch 0.681 (1.294) Remain 39:46:31 loss: 0.3755 Lr: 0.00426 [2024-02-18 07:57:38,443 INFO misc.py line 119 87073] Train: [29/100][1477/1557] Data 0.007 (0.351) Batch 1.220 (1.294) Remain 39:46:25 loss: 0.2018 Lr: 0.00426 [2024-02-18 07:57:39,537 INFO misc.py line 119 87073] Train: [29/100][1478/1557] Data 0.005 (0.350) Batch 1.090 (1.294) Remain 39:46:08 loss: 0.3893 Lr: 0.00426 [2024-02-18 07:57:40,552 INFO misc.py line 119 87073] Train: [29/100][1479/1557] Data 0.010 (0.350) Batch 1.015 (1.294) Remain 39:45:46 loss: 0.5134 Lr: 0.00426 [2024-02-18 07:57:41,438 INFO misc.py line 119 87073] Train: [29/100][1480/1557] Data 0.010 (0.350) Batch 0.891 (1.294) Remain 39:45:14 loss: 0.8490 Lr: 0.00426 [2024-02-18 07:57:42,467 INFO misc.py line 119 87073] Train: [29/100][1481/1557] Data 0.005 (0.350) Batch 1.027 (1.294) Remain 39:44:53 loss: 0.5306 Lr: 0.00426 [2024-02-18 07:57:43,171 INFO misc.py line 119 87073] Train: [29/100][1482/1557] Data 0.006 (0.349) Batch 0.707 (1.293) Remain 39:44:08 loss: 0.3031 Lr: 0.00426 [2024-02-18 07:57:43,893 INFO misc.py line 119 87073] Train: [29/100][1483/1557] Data 0.003 (0.349) Batch 0.711 (1.293) Remain 39:43:23 loss: 0.5364 Lr: 0.00426 [2024-02-18 07:57:44,948 INFO misc.py line 119 87073] Train: [29/100][1484/1557] Data 0.014 (0.349) Batch 1.056 (1.293) Remain 39:43:04 loss: 0.1728 Lr: 0.00426 [2024-02-18 07:57:46,209 INFO misc.py line 119 87073] Train: [29/100][1485/1557] Data 0.013 (0.349) Batch 1.258 (1.293) Remain 39:43:00 loss: 0.4433 Lr: 0.00426 [2024-02-18 07:57:47,494 INFO misc.py line 119 87073] Train: [29/100][1486/1557] Data 0.016 (0.349) Batch 1.293 (1.293) Remain 39:42:59 loss: 0.2946 Lr: 0.00426 [2024-02-18 07:57:48,555 INFO misc.py line 119 87073] Train: [29/100][1487/1557] Data 0.008 (0.348) Batch 1.059 (1.292) Remain 39:42:40 loss: 0.6171 Lr: 0.00426 [2024-02-18 07:57:49,487 INFO misc.py line 119 87073] Train: [29/100][1488/1557] Data 0.009 (0.348) Batch 0.937 (1.292) Remain 39:42:12 loss: 0.6609 Lr: 0.00426 [2024-02-18 07:57:50,225 INFO misc.py line 119 87073] Train: [29/100][1489/1557] Data 0.005 (0.348) Batch 0.739 (1.292) Remain 39:41:30 loss: 0.4175 Lr: 0.00426 [2024-02-18 07:57:51,019 INFO misc.py line 119 87073] Train: [29/100][1490/1557] Data 0.003 (0.348) Batch 0.790 (1.291) Remain 39:40:51 loss: 0.3651 Lr: 0.00426 [2024-02-18 07:57:52,373 INFO misc.py line 119 87073] Train: [29/100][1491/1557] Data 0.008 (0.347) Batch 1.347 (1.291) Remain 39:40:54 loss: 0.2085 Lr: 0.00426 [2024-02-18 07:57:53,201 INFO misc.py line 119 87073] Train: [29/100][1492/1557] Data 0.015 (0.347) Batch 0.836 (1.291) Remain 39:40:19 loss: 0.2770 Lr: 0.00426 [2024-02-18 07:57:54,306 INFO misc.py line 119 87073] Train: [29/100][1493/1557] Data 0.006 (0.347) Batch 1.107 (1.291) Remain 39:40:04 loss: 0.4997 Lr: 0.00426 [2024-02-18 07:57:55,120 INFO misc.py line 119 87073] Train: [29/100][1494/1557] Data 0.003 (0.347) Batch 0.813 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07:58:08,024 INFO misc.py line 119 87073] Train: [29/100][1507/1557] Data 0.003 (0.344) Batch 0.945 (1.288) Remain 39:34:26 loss: 0.3476 Lr: 0.00426 [2024-02-18 07:58:09,007 INFO misc.py line 119 87073] Train: [29/100][1508/1557] Data 0.003 (0.344) Batch 0.974 (1.288) Remain 39:34:01 loss: 0.4150 Lr: 0.00426 [2024-02-18 07:58:10,044 INFO misc.py line 119 87073] Train: [29/100][1509/1557] Data 0.013 (0.343) Batch 1.037 (1.288) Remain 39:33:42 loss: 0.5656 Lr: 0.00426 [2024-02-18 07:58:10,791 INFO misc.py line 119 87073] Train: [29/100][1510/1557] Data 0.012 (0.343) Batch 0.755 (1.287) Remain 39:33:01 loss: 0.3966 Lr: 0.00426 [2024-02-18 07:58:11,461 INFO misc.py line 119 87073] Train: [29/100][1511/1557] Data 0.004 (0.343) Batch 0.666 (1.287) Remain 39:32:15 loss: 0.5406 Lr: 0.00426 [2024-02-18 07:58:12,716 INFO misc.py line 119 87073] Train: [29/100][1512/1557] Data 0.008 (0.343) Batch 1.249 (1.287) Remain 39:32:10 loss: 0.3650 Lr: 0.00426 [2024-02-18 07:58:13,683 INFO misc.py line 119 87073] Train: [29/100][1513/1557] Data 0.014 (0.342) Batch 0.978 (1.287) Remain 39:31:46 loss: 0.5784 Lr: 0.00426 [2024-02-18 07:58:14,649 INFO misc.py line 119 87073] Train: [29/100][1514/1557] Data 0.004 (0.342) Batch 0.966 (1.287) Remain 39:31:22 loss: 0.4586 Lr: 0.00426 [2024-02-18 07:58:15,473 INFO misc.py line 119 87073] Train: [29/100][1515/1557] Data 0.004 (0.342) Batch 0.820 (1.286) Remain 39:30:46 loss: 0.7950 Lr: 0.00426 [2024-02-18 07:58:16,510 INFO misc.py line 119 87073] Train: [29/100][1516/1557] Data 0.008 (0.342) Batch 1.036 (1.286) Remain 39:30:27 loss: 0.8143 Lr: 0.00426 [2024-02-18 07:58:17,242 INFO misc.py line 119 87073] Train: [29/100][1517/1557] Data 0.009 (0.342) Batch 0.737 (1.286) Remain 39:29:45 loss: 0.2235 Lr: 0.00426 [2024-02-18 07:58:18,004 INFO misc.py line 119 87073] Train: [29/100][1518/1557] Data 0.004 (0.341) Batch 0.752 (1.285) Remain 39:29:05 loss: 0.4904 Lr: 0.00426 [2024-02-18 07:58:38,101 INFO misc.py line 119 87073] Train: [29/100][1519/1557] Data 19.132 (0.354) Batch 20.105 (1.298) Remain 39:51:57 loss: 0.1423 Lr: 0.00426 [2024-02-18 07:58:39,044 INFO misc.py line 119 87073] Train: [29/100][1520/1557] Data 0.006 (0.354) Batch 0.936 (1.298) Remain 39:51:29 loss: 0.6815 Lr: 0.00426 [2024-02-18 07:58:40,218 INFO misc.py line 119 87073] Train: [29/100][1521/1557] Data 0.014 (0.353) Batch 1.175 (1.297) Remain 39:51:19 loss: 0.5292 Lr: 0.00426 [2024-02-18 07:58:41,309 INFO misc.py line 119 87073] Train: [29/100][1522/1557] Data 0.012 (0.353) Batch 1.091 (1.297) Remain 39:51:02 loss: 0.6184 Lr: 0.00426 [2024-02-18 07:58:42,321 INFO misc.py line 119 87073] Train: [29/100][1523/1557] Data 0.011 (0.353) Batch 1.010 (1.297) Remain 39:50:40 loss: 0.7443 Lr: 0.00426 [2024-02-18 07:58:43,049 INFO misc.py line 119 87073] Train: [29/100][1524/1557] Data 0.013 (0.353) Batch 0.738 (1.297) Remain 39:49:58 loss: 0.1639 Lr: 0.00426 [2024-02-18 07:58:43,940 INFO misc.py line 119 87073] Train: [29/100][1525/1557] Data 0.004 (0.352) Batch 0.883 (1.297) Remain 39:49:27 loss: 0.3740 Lr: 0.00426 [2024-02-18 07:58:45,190 INFO misc.py line 119 87073] Train: [29/100][1526/1557] Data 0.011 (0.352) Batch 1.253 (1.296) Remain 39:49:22 loss: 0.1927 Lr: 0.00425 [2024-02-18 07:58:46,122 INFO misc.py line 119 87073] Train: [29/100][1527/1557] Data 0.008 (0.352) Batch 0.936 (1.296) Remain 39:48:55 loss: 0.7308 Lr: 0.00425 [2024-02-18 07:58:47,023 INFO misc.py line 119 87073] Train: [29/100][1528/1557] Data 0.003 (0.352) Batch 0.900 (1.296) Remain 39:48:25 loss: 0.4668 Lr: 0.00425 [2024-02-18 07:58:48,067 INFO misc.py line 119 87073] Train: [29/100][1529/1557] Data 0.004 (0.351) Batch 1.045 (1.296) Remain 39:48:05 loss: 0.4115 Lr: 0.00425 [2024-02-18 07:58:49,109 INFO misc.py line 119 87073] Train: [29/100][1530/1557] Data 0.004 (0.351) Batch 1.042 (1.296) Remain 39:47:46 loss: 0.5837 Lr: 0.00425 [2024-02-18 07:58:49,908 INFO misc.py line 119 87073] Train: [29/100][1531/1557] Data 0.003 (0.351) Batch 0.793 (1.295) Remain 39:47:08 loss: 0.3083 Lr: 0.00425 [2024-02-18 07:58:50,679 INFO misc.py line 119 87073] Train: [29/100][1532/1557] Data 0.010 (0.351) Batch 0.778 (1.295) Remain 39:46:29 loss: 0.4134 Lr: 0.00425 [2024-02-18 07:58:52,026 INFO misc.py line 119 87073] Train: [29/100][1533/1557] Data 0.004 (0.351) Batch 1.335 (1.295) Remain 39:46:31 loss: 0.2237 Lr: 0.00425 [2024-02-18 07:58:53,073 INFO misc.py line 119 87073] Train: [29/100][1534/1557] Data 0.015 (0.350) Batch 1.051 (1.295) Remain 39:46:12 loss: 0.6547 Lr: 0.00425 [2024-02-18 07:58:53,877 INFO misc.py line 119 87073] Train: [29/100][1535/1557] Data 0.012 (0.350) Batch 0.812 (1.295) Remain 39:45:36 loss: 0.2604 Lr: 0.00425 [2024-02-18 07:58:54,953 INFO misc.py line 119 87073] Train: [29/100][1536/1557] Data 0.004 (0.350) Batch 1.076 (1.294) Remain 39:45:19 loss: 0.2401 Lr: 0.00425 [2024-02-18 07:58:55,869 INFO misc.py line 119 87073] Train: [29/100][1537/1557] Data 0.004 (0.350) Batch 0.916 (1.294) Remain 39:44:50 loss: 0.4630 Lr: 0.00425 [2024-02-18 07:58:56,627 INFO misc.py line 119 87073] Train: [29/100][1538/1557] Data 0.004 (0.349) Batch 0.748 (1.294) Remain 39:44:10 loss: 0.4147 Lr: 0.00425 [2024-02-18 07:58:57,345 INFO misc.py line 119 87073] Train: [29/100][1539/1557] Data 0.014 (0.349) Batch 0.728 (1.293) Remain 39:43:28 loss: 0.4629 Lr: 0.00425 [2024-02-18 07:58:58,433 INFO misc.py line 119 87073] Train: [29/100][1540/1557] Data 0.004 (0.349) Batch 1.088 (1.293) Remain 39:43:12 loss: 0.1776 Lr: 0.00425 [2024-02-18 07:58:59,348 INFO misc.py line 119 87073] Train: [29/100][1541/1557] Data 0.004 (0.349) Batch 0.915 (1.293) Remain 39:42:43 loss: 0.4686 Lr: 0.00425 [2024-02-18 07:59:00,392 INFO misc.py line 119 87073] Train: [29/100][1542/1557] Data 0.004 (0.349) Batch 1.038 (1.293) Remain 39:42:23 loss: 0.3079 Lr: 0.00425 [2024-02-18 07:59:01,234 INFO misc.py line 119 87073] Train: [29/100][1543/1557] Data 0.010 (0.348) Batch 0.847 (1.293) Remain 39:41:50 loss: 0.4850 Lr: 0.00425 [2024-02-18 07:59:02,351 INFO misc.py line 119 87073] Train: [29/100][1544/1557] Data 0.005 (0.348) Batch 1.118 (1.292) Remain 39:41:36 loss: 0.5783 Lr: 0.00425 [2024-02-18 07:59:03,078 INFO misc.py line 119 87073] Train: [29/100][1545/1557] Data 0.004 (0.348) Batch 0.727 (1.292) Remain 39:40:55 loss: 0.6345 Lr: 0.00425 [2024-02-18 07:59:03,848 INFO misc.py line 119 87073] Train: [29/100][1546/1557] Data 0.004 (0.348) Batch 0.761 (1.292) Remain 39:40:15 loss: 0.5232 Lr: 0.00425 [2024-02-18 07:59:05,152 INFO misc.py line 119 87073] Train: [29/100][1547/1557] Data 0.013 (0.347) Batch 1.307 (1.292) Remain 39:40:15 loss: 0.2497 Lr: 0.00425 [2024-02-18 07:59:06,078 INFO misc.py line 119 87073] Train: [29/100][1548/1557] Data 0.010 (0.347) Batch 0.932 (1.292) Remain 39:39:48 loss: 0.4272 Lr: 0.00425 [2024-02-18 07:59:06,938 INFO misc.py line 119 87073] Train: [29/100][1549/1557] Data 0.005 (0.347) Batch 0.861 (1.291) Remain 39:39:16 loss: 0.4059 Lr: 0.00425 [2024-02-18 07:59:07,966 INFO misc.py line 119 87073] Train: [29/100][1550/1557] Data 0.004 (0.347) Batch 1.019 (1.291) Remain 39:38:55 loss: 0.2855 Lr: 0.00425 [2024-02-18 07:59:08,852 INFO misc.py line 119 87073] Train: [29/100][1551/1557] Data 0.013 (0.347) Batch 0.894 (1.291) Remain 39:38:25 loss: 0.3466 Lr: 0.00425 [2024-02-18 07:59:09,572 INFO misc.py line 119 87073] Train: [29/100][1552/1557] Data 0.004 (0.346) Batch 0.720 (1.290) Remain 39:37:43 loss: 0.5534 Lr: 0.00425 [2024-02-18 07:59:10,304 INFO misc.py line 119 87073] Train: [29/100][1553/1557] Data 0.004 (0.346) Batch 0.724 (1.290) Remain 39:37:02 loss: 0.3709 Lr: 0.00425 [2024-02-18 07:59:11,568 INFO misc.py line 119 87073] Train: [29/100][1554/1557] Data 0.012 (0.346) Batch 1.266 (1.290) Remain 39:36:59 loss: 0.0986 Lr: 0.00425 [2024-02-18 07:59:12,516 INFO misc.py line 119 87073] Train: [29/100][1555/1557] Data 0.011 (0.346) Batch 0.955 (1.290) Remain 39:36:33 loss: 0.4180 Lr: 0.00425 [2024-02-18 07:59:13,462 INFO misc.py line 119 87073] Train: [29/100][1556/1557] Data 0.004 (0.345) Batch 0.945 (1.290) Remain 39:36:08 loss: 0.4645 Lr: 0.00425 [2024-02-18 07:59:14,323 INFO misc.py line 119 87073] Train: [29/100][1557/1557] Data 0.005 (0.345) Batch 0.862 (1.289) Remain 39:35:36 loss: 0.2833 Lr: 0.00425 [2024-02-18 07:59:14,324 INFO misc.py line 136 87073] Train result: loss: 0.4519 [2024-02-18 07:59:14,324 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 07:59:39,070 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7812 [2024-02-18 07:59:39,847 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4859 [2024-02-18 07:59:41,972 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4140 [2024-02-18 07:59:44,179 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2742 [2024-02-18 07:59:49,115 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2294 [2024-02-18 07:59:49,814 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0869 [2024-02-18 07:59:51,074 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3555 [2024-02-18 07:59:54,028 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3091 [2024-02-18 07:59:55,837 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2498 [2024-02-18 07:59:57,563 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7225/0.7787/0.9152. [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9228/0.9680 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9823/0.9904 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8675/0.9694 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0002/0.0021 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3700/0.4074 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7104/0.7607 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8566/0.9362 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8358/0.9205 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9190/0.9599 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7725/0.8029 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7940/0.8920 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7767/0.8548 [2024-02-18 07:59:57,564 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5846/0.6583 [2024-02-18 07:59:57,565 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 07:59:57,567 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 07:59:57,567 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 08:00:04,854 INFO misc.py line 119 87073] Train: [30/100][1/1557] Data 1.472 (1.472) Batch 2.172 (2.172) Remain 66:42:10 loss: 0.2873 Lr: 0.00425 [2024-02-18 08:00:05,986 INFO misc.py line 119 87073] Train: [30/100][2/1557] Data 0.005 (0.005) Batch 1.131 (1.131) Remain 34:44:14 loss: 0.4834 Lr: 0.00425 [2024-02-18 08:00:07,024 INFO misc.py line 119 87073] Train: [30/100][3/1557] Data 0.007 (0.007) Batch 1.033 (1.033) Remain 31:43:04 loss: 0.4409 Lr: 0.00425 [2024-02-18 08:00:08,114 INFO misc.py line 119 87073] Train: [30/100][4/1557] Data 0.011 (0.011) Batch 1.093 (1.093) Remain 33:33:12 loss: 0.6529 Lr: 0.00425 [2024-02-18 08:00:08,887 INFO misc.py line 119 87073] Train: [30/100][5/1557] Data 0.008 (0.010) Batch 0.776 (0.934) Remain 28:41:26 loss: 0.5724 Lr: 0.00425 [2024-02-18 08:00:09,607 INFO misc.py line 119 87073] Train: [30/100][6/1557] Data 0.005 (0.008) Batch 0.721 (0.863) Remain 26:30:28 loss: 0.5110 Lr: 0.00425 [2024-02-18 08:00:10,826 INFO misc.py line 119 87073] Train: [30/100][7/1557] Data 0.003 (0.007) Batch 1.218 (0.952) Remain 29:13:59 loss: 0.2321 Lr: 0.00425 [2024-02-18 08:00:11,857 INFO misc.py line 119 87073] Train: [30/100][8/1557] Data 0.004 (0.006) Batch 1.031 (0.968) Remain 29:42:57 loss: 0.3603 Lr: 0.00425 [2024-02-18 08:00:12,870 INFO misc.py line 119 87073] Train: [30/100][9/1557] Data 0.004 (0.006) Batch 1.008 (0.975) Remain 29:55:25 loss: 0.2915 Lr: 0.00425 [2024-02-18 08:00:13,717 INFO misc.py line 119 87073] Train: [30/100][10/1557] Data 0.009 (0.006) Batch 0.853 (0.957) Remain 29:23:25 loss: 0.5858 Lr: 0.00425 [2024-02-18 08:00:14,791 INFO misc.py line 119 87073] Train: [30/100][11/1557] Data 0.003 (0.006) Batch 1.073 (0.972) Remain 29:50:04 loss: 0.3134 Lr: 0.00425 [2024-02-18 08:00:15,575 INFO misc.py line 119 87073] Train: [30/100][12/1557] Data 0.004 (0.006) Batch 0.784 (0.951) Remain 29:11:41 loss: 0.2240 Lr: 0.00425 [2024-02-18 08:00:16,387 INFO misc.py line 119 87073] Train: [30/100][13/1557] Data 0.004 (0.006) Batch 0.801 (0.936) Remain 28:44:09 loss: 0.4537 Lr: 0.00425 [2024-02-18 08:00:19,094 INFO misc.py line 119 87073] Train: [30/100][14/1557] Data 0.015 (0.006) Batch 2.717 (1.098) Remain 33:42:30 loss: 0.3312 Lr: 0.00425 [2024-02-18 08:00:20,005 INFO misc.py line 119 87073] Train: [30/100][15/1557] Data 0.004 (0.006) Batch 0.904 (1.082) Remain 33:12:48 loss: 0.6107 Lr: 0.00425 [2024-02-18 08:00:21,081 INFO misc.py line 119 87073] Train: [30/100][16/1557] Data 0.010 (0.007) Batch 1.076 (1.081) Remain 33:11:58 loss: 0.5086 Lr: 0.00425 [2024-02-18 08:00:21,981 INFO misc.py line 119 87073] Train: [30/100][17/1557] Data 0.010 (0.007) Batch 0.906 (1.069) Remain 32:48:55 loss: 0.4689 Lr: 0.00425 [2024-02-18 08:00:22,939 INFO misc.py line 119 87073] Train: [30/100][18/1557] Data 0.005 (0.007) Batch 0.958 (1.061) Remain 32:35:14 loss: 0.7775 Lr: 0.00425 [2024-02-18 08:00:23,699 INFO misc.py line 119 87073] Train: [30/100][19/1557] Data 0.004 (0.006) Batch 0.760 (1.043) Remain 32:00:33 loss: 0.4236 Lr: 0.00425 [2024-02-18 08:00:24,421 INFO misc.py line 119 87073] Train: [30/100][20/1557] Data 0.004 (0.006) Batch 0.713 (1.023) Remain 31:24:47 loss: 0.5094 Lr: 0.00425 [2024-02-18 08:00:25,614 INFO misc.py line 119 87073] Train: [30/100][21/1557] Data 0.013 (0.007) Batch 1.196 (1.033) Remain 31:42:26 loss: 0.2108 Lr: 0.00425 [2024-02-18 08:00:26,465 INFO misc.py line 119 87073] Train: [30/100][22/1557] Data 0.011 (0.007) Batch 0.858 (1.024) Remain 31:25:26 loss: 0.5833 Lr: 0.00425 [2024-02-18 08:00:27,371 INFO misc.py line 119 87073] Train: [30/100][23/1557] Data 0.004 (0.007) Batch 0.905 (1.018) Remain 31:14:31 loss: 0.4203 Lr: 0.00425 [2024-02-18 08:00:28,378 INFO misc.py line 119 87073] Train: [30/100][24/1557] Data 0.005 (0.007) Batch 1.000 (1.017) Remain 31:12:56 loss: 0.7297 Lr: 0.00425 [2024-02-18 08:00:29,161 INFO misc.py line 119 87073] Train: [30/100][25/1557] Data 0.012 (0.007) Batch 0.791 (1.007) Remain 30:54:00 loss: 0.8755 Lr: 0.00425 [2024-02-18 08:00:29,874 INFO misc.py line 119 87073] Train: [30/100][26/1557] Data 0.005 (0.007) Batch 0.713 (0.994) Remain 30:30:28 loss: 0.8764 Lr: 0.00425 [2024-02-18 08:00:30,649 INFO misc.py line 119 87073] Train: [30/100][27/1557] Data 0.004 (0.007) Batch 0.772 (0.984) Remain 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line 119 87073] Train: [30/100][165/1557] Data 0.005 (0.079) Batch 0.987 (1.058) Remain 32:25:41 loss: 0.5568 Lr: 0.00425 [2024-02-18 08:02:59,009 INFO misc.py line 119 87073] Train: [30/100][166/1557] Data 0.004 (0.078) Batch 0.659 (1.055) Remain 32:21:09 loss: 0.3191 Lr: 0.00425 [2024-02-18 08:02:59,759 INFO misc.py line 119 87073] Train: [30/100][167/1557] Data 0.003 (0.078) Batch 0.743 (1.053) Remain 32:17:38 loss: 0.3660 Lr: 0.00425 [2024-02-18 08:03:00,823 INFO misc.py line 119 87073] Train: [30/100][168/1557] Data 0.010 (0.078) Batch 1.065 (1.053) Remain 32:17:45 loss: 0.2848 Lr: 0.00425 [2024-02-18 08:03:02,018 INFO misc.py line 119 87073] Train: [30/100][169/1557] Data 0.010 (0.077) Batch 1.191 (1.054) Remain 32:19:16 loss: 0.4393 Lr: 0.00425 [2024-02-18 08:03:02,966 INFO misc.py line 119 87073] Train: [30/100][170/1557] Data 0.014 (0.077) Batch 0.957 (1.054) Remain 32:18:11 loss: 0.4884 Lr: 0.00425 [2024-02-18 08:03:03,795 INFO misc.py line 119 87073] Train: 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[2024-02-18 08:28:31,243 INFO misc.py line 119 87073] Train: [30/100][1526/1557] Data 0.004 (0.117) Batch 4.172 (1.119) Remain 33:53:13 loss: 0.3086 Lr: 0.00420 [2024-02-18 08:28:32,139 INFO misc.py line 119 87073] Train: [30/100][1527/1557] Data 0.005 (0.116) Batch 0.885 (1.119) Remain 33:52:55 loss: 0.6717 Lr: 0.00420 [2024-02-18 08:28:33,164 INFO misc.py line 119 87073] Train: [30/100][1528/1557] Data 0.014 (0.116) Batch 1.035 (1.119) Remain 33:52:48 loss: 0.9732 Lr: 0.00420 [2024-02-18 08:28:33,991 INFO misc.py line 119 87073] Train: [30/100][1529/1557] Data 0.005 (0.116) Batch 0.828 (1.119) Remain 33:52:26 loss: 0.3891 Lr: 0.00420 [2024-02-18 08:28:35,183 INFO misc.py line 119 87073] Train: [30/100][1530/1557] Data 0.004 (0.116) Batch 1.191 (1.119) Remain 33:52:30 loss: 1.3034 Lr: 0.00420 [2024-02-18 08:28:35,967 INFO misc.py line 119 87073] Train: [30/100][1531/1557] Data 0.005 (0.116) Batch 0.785 (1.118) Remain 33:52:05 loss: 0.2505 Lr: 0.00420 [2024-02-18 08:28:36,753 INFO misc.py line 119 87073] Train: [30/100][1532/1557] Data 0.005 (0.116) Batch 0.787 (1.118) Remain 33:51:40 loss: 0.2677 Lr: 0.00419 [2024-02-18 08:28:37,911 INFO misc.py line 119 87073] Train: [30/100][1533/1557] Data 0.004 (0.116) Batch 1.155 (1.118) Remain 33:51:42 loss: 0.1708 Lr: 0.00419 [2024-02-18 08:28:38,873 INFO misc.py line 119 87073] Train: [30/100][1534/1557] Data 0.006 (0.116) Batch 0.964 (1.118) Remain 33:51:30 loss: 0.8166 Lr: 0.00419 [2024-02-18 08:28:40,045 INFO misc.py line 119 87073] Train: [30/100][1535/1557] Data 0.004 (0.116) Batch 1.170 (1.118) Remain 33:51:32 loss: 0.2955 Lr: 0.00419 [2024-02-18 08:28:41,008 INFO misc.py line 119 87073] Train: [30/100][1536/1557] Data 0.007 (0.116) Batch 0.965 (1.118) Remain 33:51:20 loss: 0.1304 Lr: 0.00419 [2024-02-18 08:28:41,863 INFO misc.py line 119 87073] Train: [30/100][1537/1557] Data 0.004 (0.116) Batch 0.854 (1.118) Remain 33:51:01 loss: 0.4395 Lr: 0.00419 [2024-02-18 08:28:42,638 INFO misc.py line 119 87073] Train: [30/100][1538/1557] Data 0.005 (0.116) Batch 0.769 (1.118) Remain 33:50:35 loss: 0.5034 Lr: 0.00419 [2024-02-18 08:28:43,338 INFO misc.py line 119 87073] Train: [30/100][1539/1557] Data 0.011 (0.116) Batch 0.707 (1.117) Remain 33:50:04 loss: 0.3416 Lr: 0.00419 [2024-02-18 08:28:44,512 INFO misc.py line 119 87073] Train: [30/100][1540/1557] Data 0.004 (0.116) Batch 1.172 (1.117) Remain 33:50:07 loss: 0.3065 Lr: 0.00419 [2024-02-18 08:28:45,377 INFO misc.py line 119 87073] Train: [30/100][1541/1557] Data 0.006 (0.115) Batch 0.866 (1.117) Remain 33:49:48 loss: 0.1736 Lr: 0.00419 [2024-02-18 08:28:46,395 INFO misc.py line 119 87073] Train: [30/100][1542/1557] Data 0.005 (0.115) Batch 1.019 (1.117) Remain 33:49:40 loss: 0.7472 Lr: 0.00419 [2024-02-18 08:28:47,453 INFO misc.py line 119 87073] Train: [30/100][1543/1557] Data 0.004 (0.115) Batch 1.057 (1.117) Remain 33:49:35 loss: 0.1821 Lr: 0.00419 [2024-02-18 08:28:48,506 INFO misc.py line 119 87073] Train: [30/100][1544/1557] Data 0.005 (0.115) Batch 1.054 (1.117) Remain 33:49:29 loss: 0.2529 Lr: 0.00419 [2024-02-18 08:28:49,260 INFO misc.py line 119 87073] Train: [30/100][1545/1557] Data 0.005 (0.115) Batch 0.755 (1.117) Remain 33:49:02 loss: 0.4588 Lr: 0.00419 [2024-02-18 08:28:50,016 INFO misc.py line 119 87073] Train: [30/100][1546/1557] Data 0.003 (0.115) Batch 0.745 (1.117) Remain 33:48:35 loss: 0.3895 Lr: 0.00419 [2024-02-18 08:28:51,341 INFO misc.py line 119 87073] Train: [30/100][1547/1557] Data 0.015 (0.115) Batch 1.328 (1.117) Remain 33:48:49 loss: 0.3082 Lr: 0.00419 [2024-02-18 08:28:52,291 INFO misc.py line 119 87073] Train: [30/100][1548/1557] Data 0.013 (0.115) Batch 0.959 (1.117) Remain 33:48:36 loss: 0.5178 Lr: 0.00419 [2024-02-18 08:28:53,293 INFO misc.py line 119 87073] Train: [30/100][1549/1557] Data 0.004 (0.115) Batch 1.002 (1.117) Remain 33:48:27 loss: 0.5111 Lr: 0.00419 [2024-02-18 08:28:54,135 INFO misc.py line 119 87073] Train: [30/100][1550/1557] Data 0.004 (0.115) Batch 0.842 (1.116) Remain 33:48:07 loss: 0.8166 Lr: 0.00419 [2024-02-18 08:28:55,277 INFO misc.py line 119 87073] Train: [30/100][1551/1557] Data 0.004 (0.115) Batch 1.130 (1.116) Remain 33:48:07 loss: 0.4120 Lr: 0.00419 [2024-02-18 08:28:56,065 INFO misc.py line 119 87073] Train: [30/100][1552/1557] Data 0.016 (0.115) Batch 0.800 (1.116) Remain 33:47:43 loss: 0.3472 Lr: 0.00419 [2024-02-18 08:28:56,869 INFO misc.py line 119 87073] Train: [30/100][1553/1557] Data 0.004 (0.115) Batch 0.804 (1.116) Remain 33:47:20 loss: 0.4103 Lr: 0.00419 [2024-02-18 08:28:58,077 INFO misc.py line 119 87073] Train: [30/100][1554/1557] Data 0.004 (0.115) Batch 1.201 (1.116) Remain 33:47:25 loss: 0.4135 Lr: 0.00419 [2024-02-18 08:28:59,053 INFO misc.py line 119 87073] Train: [30/100][1555/1557] Data 0.011 (0.114) Batch 0.983 (1.116) Remain 33:47:15 loss: 0.4353 Lr: 0.00419 [2024-02-18 08:28:59,867 INFO misc.py line 119 87073] Train: [30/100][1556/1557] Data 0.003 (0.114) Batch 0.814 (1.116) Remain 33:46:52 loss: 0.3748 Lr: 0.00419 [2024-02-18 08:29:00,815 INFO misc.py line 119 87073] Train: [30/100][1557/1557] Data 0.003 (0.114) Batch 0.946 (1.116) Remain 33:46:39 loss: 0.7140 Lr: 0.00419 [2024-02-18 08:29:00,816 INFO misc.py line 136 87073] Train result: loss: 0.4281 [2024-02-18 08:29:00,816 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 08:29:28,581 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7030 [2024-02-18 08:29:29,357 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9142 [2024-02-18 08:29:31,482 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5114 [2024-02-18 08:29:33,691 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2593 [2024-02-18 08:29:38,644 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2367 [2024-02-18 08:29:39,343 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1873 [2024-02-18 08:29:40,602 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2884 [2024-02-18 08:29:43,556 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4036 [2024-02-18 08:29:45,364 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2426 [2024-02-18 08:29:47,008 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6986/0.7904/0.9033. [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9130/0.9565 [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9804/0.9910 [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8662/0.9653 [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0130/0.4106 [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3961/0.4677 [2024-02-18 08:29:47,009 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6538/0.6727 [2024-02-18 08:29:47,010 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7141/0.8451 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8381/0.8844 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9122/0.9489 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7425/0.7663 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7751/0.8740 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7231/0.8390 [2024-02-18 08:29:47,014 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5547/0.6541 [2024-02-18 08:29:47,015 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 08:29:47,018 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 08:29:47,019 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 08:29:53,710 INFO misc.py line 119 87073] Train: [31/100][1/1557] Data 1.237 (1.237) Batch 2.078 (2.078) Remain 62:53:53 loss: 0.4618 Lr: 0.00419 [2024-02-18 08:29:54,732 INFO misc.py line 119 87073] Train: [31/100][2/1557] Data 0.013 (0.013) Batch 1.013 (1.013) Remain 30:40:31 loss: 0.3003 Lr: 0.00419 [2024-02-18 08:29:55,664 INFO misc.py line 119 87073] Train: [31/100][3/1557] Data 0.020 (0.020) Batch 0.947 (0.947) Remain 28:39:52 loss: 0.5933 Lr: 0.00419 [2024-02-18 08:29:56,728 INFO misc.py line 119 87073] Train: [31/100][4/1557] Data 0.006 (0.006) Batch 1.064 (1.064) Remain 32:12:48 loss: 0.3708 Lr: 0.00419 [2024-02-18 08:29:57,457 INFO misc.py line 119 87073] Train: [31/100][5/1557] Data 0.005 (0.005) Batch 0.728 (0.896) Remain 27:07:45 loss: 0.4731 Lr: 0.00419 [2024-02-18 08:29:58,267 INFO misc.py line 119 87073] Train: [31/100][6/1557] Data 0.006 (0.005) Batch 0.810 (0.868) Remain 26:15:51 loss: 0.4829 Lr: 0.00419 [2024-02-18 08:29:59,582 INFO misc.py line 119 87073] Train: [31/100][7/1557] Data 0.004 (0.005) Batch 1.305 (0.977) Remain 29:34:19 loss: 0.3461 Lr: 0.00419 [2024-02-18 08:30:00,690 INFO misc.py line 119 87073] Train: [31/100][8/1557] Data 0.015 (0.007) Batch 1.118 (1.005) Remain 30:25:42 loss: 0.3921 Lr: 0.00419 [2024-02-18 08:30:01,643 INFO misc.py line 119 87073] Train: [31/100][9/1557] Data 0.005 (0.007) Batch 0.954 (0.997) Remain 30:10:15 loss: 0.3507 Lr: 0.00419 [2024-02-18 08:30:02,625 INFO misc.py line 119 87073] Train: [31/100][10/1557] Data 0.004 (0.006) Batch 0.982 (0.995) Remain 30:06:26 loss: 0.5242 Lr: 0.00419 [2024-02-18 08:30:03,461 INFO misc.py line 119 87073] Train: [31/100][11/1557] Data 0.003 (0.006) Batch 0.836 (0.975) Remain 29:30:18 loss: 0.6337 Lr: 0.00419 [2024-02-18 08:30:04,219 INFO misc.py line 119 87073] Train: [31/100][12/1557] Data 0.005 (0.006) Batch 0.758 (0.951) Remain 28:46:32 loss: 0.4398 Lr: 0.00419 [2024-02-18 08:30:04,923 INFO misc.py line 119 87073] Train: [31/100][13/1557] Data 0.005 (0.006) Batch 0.704 (0.926) Remain 28:01:47 loss: 0.5586 Lr: 0.00419 [2024-02-18 08:30:06,273 INFO misc.py line 119 87073] Train: [31/100][14/1557] Data 0.003 (0.005) Batch 1.345 (0.964) Remain 29:11:02 loss: 0.3583 Lr: 0.00419 [2024-02-18 08:30:07,088 INFO misc.py line 119 87073] Train: [31/100][15/1557] Data 0.009 (0.006) Batch 0.820 (0.952) Remain 28:49:11 loss: 0.6976 Lr: 0.00419 [2024-02-18 08:30:07,987 INFO misc.py line 119 87073] Train: [31/100][16/1557] Data 0.004 (0.006) Batch 0.899 (0.948) Remain 28:41:43 loss: 0.1090 Lr: 0.00419 [2024-02-18 08:30:08,892 INFO misc.py line 119 87073] Train: [31/100][17/1557] Data 0.004 (0.005) Batch 0.899 (0.944) Remain 28:35:24 loss: 0.3396 Lr: 0.00419 [2024-02-18 08:30:10,074 INFO misc.py line 119 87073] Train: [31/100][18/1557] Data 0.010 (0.006) Batch 1.179 (0.960) Remain 29:03:46 loss: 0.2022 Lr: 0.00419 [2024-02-18 08:30:10,828 INFO misc.py line 119 87073] Train: [31/100][19/1557] Data 0.013 (0.006) Batch 0.761 (0.948) Remain 28:41:11 loss: 0.6803 Lr: 0.00419 [2024-02-18 08:30:11,543 INFO misc.py line 119 87073] Train: [31/100][20/1557] Data 0.005 (0.006) Batch 0.716 (0.934) Remain 28:16:24 loss: 0.5038 Lr: 0.00419 [2024-02-18 08:30:12,868 INFO misc.py line 119 87073] Train: [31/100][21/1557] Data 0.005 (0.006) Batch 1.325 (0.956) Remain 28:55:52 loss: 0.3007 Lr: 0.00419 [2024-02-18 08:30:13,779 INFO misc.py line 119 87073] Train: [31/100][22/1557] Data 0.004 (0.006) Batch 0.911 (0.953) Remain 28:51:34 loss: 0.3680 Lr: 0.00419 [2024-02-18 08:30:14,694 INFO misc.py line 119 87073] Train: [31/100][23/1557] Data 0.004 (0.006) Batch 0.916 (0.952) Remain 28:48:07 loss: 0.5644 Lr: 0.00419 [2024-02-18 08:30:15,662 INFO misc.py line 119 87073] Train: [31/100][24/1557] Data 0.004 (0.006) Batch 0.968 (0.952) Remain 28:49:32 loss: 0.0790 Lr: 0.00419 [2024-02-18 08:30:16,498 INFO misc.py line 119 87073] Train: [31/100][25/1557] Data 0.004 (0.006) Batch 0.835 (0.947) Remain 28:39:51 loss: 0.2275 Lr: 0.00419 [2024-02-18 08:30:17,280 INFO misc.py line 119 87073] Train: [31/100][26/1557] Data 0.005 (0.006) Batch 0.782 (0.940) Remain 28:26:50 loss: 0.4209 Lr: 0.00419 [2024-02-18 08:30:18,024 INFO misc.py line 119 87073] Train: [31/100][27/1557] Data 0.004 (0.006) Batch 0.738 (0.931) Remain 28:11:33 loss: 0.3250 Lr: 0.00419 [2024-02-18 08:30:19,206 INFO misc.py line 119 87073] Train: [31/100][28/1557] Data 0.009 (0.006) Batch 1.183 (0.942) Remain 28:29:51 loss: 0.2708 Lr: 0.00419 [2024-02-18 08:30:20,212 INFO misc.py line 119 87073] Train: [31/100][29/1557] Data 0.009 (0.006) Batch 1.000 (0.944) Remain 28:33:54 loss: 0.6471 Lr: 0.00419 [2024-02-18 08:30:21,198 INFO misc.py line 119 87073] Train: [31/100][30/1557] Data 0.014 (0.006) Batch 0.997 (0.946) Remain 28:37:26 loss: 0.5340 Lr: 0.00419 [2024-02-18 08:30:21,994 INFO misc.py line 119 87073] Train: [31/100][31/1557] Data 0.004 (0.006) Batch 0.796 (0.940) Remain 28:27:41 loss: 0.1877 Lr: 0.00419 [2024-02-18 08:30:23,071 INFO misc.py line 119 87073] Train: [31/100][32/1557] Data 0.004 (0.006) Batch 1.077 (0.945) Remain 28:36:13 loss: 0.5566 Lr: 0.00419 [2024-02-18 08:30:23,805 INFO misc.py line 119 87073] Train: [31/100][33/1557] Data 0.004 (0.006) Batch 0.734 (0.938) Remain 28:23:24 loss: 0.4846 Lr: 0.00419 [2024-02-18 08:30:24,606 INFO misc.py line 119 87073] Train: [31/100][34/1557] Data 0.005 (0.006) Batch 0.801 (0.934) Remain 28:15:22 loss: 0.6849 Lr: 0.00419 [2024-02-18 08:30:25,888 INFO misc.py line 119 87073] Train: [31/100][35/1557] Data 0.005 (0.006) Batch 1.281 (0.944) Remain 28:35:03 loss: 0.2407 Lr: 0.00419 [2024-02-18 08:30:26,888 INFO misc.py line 119 87073] Train: [31/100][36/1557] Data 0.005 (0.006) Batch 1.002 (0.946) Remain 28:38:12 loss: 0.4874 Lr: 0.00419 [2024-02-18 08:30:27,837 INFO misc.py line 119 87073] Train: [31/100][37/1557] Data 0.004 (0.006) Batch 0.949 (0.946) Remain 28:38:19 loss: 0.3650 Lr: 0.00419 [2024-02-18 08:30:28,763 INFO misc.py line 119 87073] Train: [31/100][38/1557] Data 0.004 (0.006) Batch 0.926 (0.946) Remain 28:37:17 loss: 0.9465 Lr: 0.00419 [2024-02-18 08:30:29,803 INFO misc.py line 119 87073] Train: [31/100][39/1557] Data 0.003 (0.006) Batch 1.040 (0.948) Remain 28:42:00 loss: 0.6909 Lr: 0.00419 [2024-02-18 08:30:30,695 INFO misc.py line 119 87073] Train: [31/100][40/1557] Data 0.004 (0.006) Batch 0.892 (0.947) Remain 28:39:12 loss: 0.5654 Lr: 0.00419 [2024-02-18 08:30:31,395 INFO misc.py line 119 87073] Train: [31/100][41/1557] Data 0.004 (0.006) Batch 0.698 (0.940) Remain 28:27:19 loss: 0.3317 Lr: 0.00419 [2024-02-18 08:30:32,482 INFO misc.py line 119 87073] Train: [31/100][42/1557] Data 0.007 (0.006) Batch 1.083 (0.944) Remain 28:33:56 loss: 0.1710 Lr: 0.00419 [2024-02-18 08:30:33,486 INFO misc.py line 119 87073] Train: [31/100][43/1557] Data 0.010 (0.006) Batch 1.007 (0.945) Remain 28:36:46 loss: 0.5848 Lr: 0.00419 [2024-02-18 08:30:34,357 INFO misc.py line 119 87073] Train: [31/100][44/1557] Data 0.008 (0.006) Batch 0.874 (0.944) Remain 28:33:35 loss: 0.8927 Lr: 0.00419 [2024-02-18 08:30:35,278 INFO misc.py line 119 87073] Train: [31/100][45/1557] Data 0.006 (0.006) Batch 0.922 (0.943) Remain 28:32:36 loss: 0.6815 Lr: 0.00419 [2024-02-18 08:30:36,293 INFO misc.py line 119 87073] Train: [31/100][46/1557] Data 0.004 (0.006) 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Batch 0.898 (1.094) Remain 33:04:25 loss: 0.5679 Lr: 0.00419 [2024-02-18 08:33:07,251 INFO misc.py line 119 87073] Train: [31/100][178/1557] Data 0.008 (0.073) Batch 1.192 (1.095) Remain 33:05:24 loss: 0.5613 Lr: 0.00419 [2024-02-18 08:33:08,131 INFO misc.py line 119 87073] Train: [31/100][179/1557] Data 0.006 (0.072) Batch 0.881 (1.094) Remain 33:03:11 loss: 0.2173 Lr: 0.00419 [2024-02-18 08:33:10,789 INFO misc.py line 119 87073] Train: [31/100][180/1557] Data 1.911 (0.083) Batch 2.657 (1.102) Remain 33:19:12 loss: 0.1989 Lr: 0.00419 [2024-02-18 08:33:11,585 INFO misc.py line 119 87073] Train: [31/100][181/1557] Data 0.005 (0.082) Batch 0.788 (1.101) Remain 33:15:58 loss: 0.4384 Lr: 0.00419 [2024-02-18 08:33:13,063 INFO misc.py line 119 87073] Train: [31/100][182/1557] Data 0.012 (0.082) Batch 1.480 (1.103) Remain 33:19:48 loss: 0.1878 Lr: 0.00419 [2024-02-18 08:33:13,992 INFO misc.py line 119 87073] Train: [31/100][183/1557] Data 0.011 (0.081) Batch 0.936 (1.102) Remain 33:18:06 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87073] Train: [31/100][196/1557] Data 0.006 (0.076) Batch 1.130 (1.091) Remain 32:57:20 loss: 0.2330 Lr: 0.00419 [2024-02-18 08:33:27,023 INFO misc.py line 119 87073] Train: [31/100][197/1557] Data 0.016 (0.076) Batch 0.892 (1.089) Remain 32:55:27 loss: 0.6262 Lr: 0.00419 [2024-02-18 08:33:27,849 INFO misc.py line 119 87073] Train: [31/100][198/1557] Data 0.004 (0.076) Batch 0.827 (1.088) Remain 32:53:00 loss: 0.4495 Lr: 0.00419 [2024-02-18 08:33:28,703 INFO misc.py line 119 87073] Train: [31/100][199/1557] Data 0.004 (0.075) Batch 0.853 (1.087) Remain 32:50:48 loss: 0.4237 Lr: 0.00419 [2024-02-18 08:33:29,661 INFO misc.py line 119 87073] Train: [31/100][200/1557] Data 0.005 (0.075) Batch 0.958 (1.086) Remain 32:49:36 loss: 0.3743 Lr: 0.00419 [2024-02-18 08:33:30,439 INFO misc.py line 119 87073] Train: [31/100][201/1557] Data 0.004 (0.075) Batch 0.778 (1.085) Remain 32:46:46 loss: 0.5685 Lr: 0.00419 [2024-02-18 08:33:31,201 INFO misc.py line 119 87073] Train: [31/100][202/1557] Data 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line 119 87073] Train: [31/100][221/1557] Data 0.005 (0.068) Batch 1.081 (1.074) Remain 32:27:36 loss: 0.4216 Lr: 0.00419 [2024-02-18 08:33:50,636 INFO misc.py line 119 87073] Train: [31/100][222/1557] Data 0.005 (0.068) Batch 0.761 (1.073) Remain 32:24:59 loss: 0.4780 Lr: 0.00419 [2024-02-18 08:33:51,404 INFO misc.py line 119 87073] Train: [31/100][223/1557] Data 0.005 (0.068) Batch 0.767 (1.072) Remain 32:22:27 loss: 0.6358 Lr: 0.00419 [2024-02-18 08:33:52,738 INFO misc.py line 119 87073] Train: [31/100][224/1557] Data 0.007 (0.068) Batch 1.334 (1.073) Remain 32:24:35 loss: 0.4818 Lr: 0.00419 [2024-02-18 08:33:53,627 INFO misc.py line 119 87073] Train: [31/100][225/1557] Data 0.007 (0.067) Batch 0.891 (1.072) Remain 32:23:05 loss: 0.3399 Lr: 0.00419 [2024-02-18 08:33:54,499 INFO misc.py line 119 87073] Train: [31/100][226/1557] Data 0.005 (0.067) Batch 0.874 (1.071) Remain 32:21:27 loss: 0.4071 Lr: 0.00419 [2024-02-18 08:33:55,672 INFO misc.py line 119 87073] Train: 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Batch 0.869 (1.099) Remain 33:11:29 loss: 0.6047 Lr: 0.00418 [2024-02-18 08:34:09,297 INFO misc.py line 119 87073] Train: [31/100][234/1557] Data 0.005 (0.081) Batch 0.933 (1.098) Remain 33:10:10 loss: 0.0827 Lr: 0.00418 [2024-02-18 08:34:10,283 INFO misc.py line 119 87073] Train: [31/100][235/1557] Data 0.006 (0.081) Batch 0.989 (1.097) Remain 33:09:18 loss: 0.5469 Lr: 0.00418 [2024-02-18 08:34:11,045 INFO misc.py line 119 87073] Train: [31/100][236/1557] Data 0.004 (0.081) Batch 0.759 (1.096) Remain 33:06:39 loss: 0.4881 Lr: 0.00418 [2024-02-18 08:34:11,843 INFO misc.py line 119 87073] Train: [31/100][237/1557] Data 0.007 (0.080) Batch 0.800 (1.095) Remain 33:04:20 loss: 0.4996 Lr: 0.00418 [2024-02-18 08:34:14,175 INFO misc.py line 119 87073] Train: [31/100][238/1557] Data 0.004 (0.080) Batch 2.333 (1.100) Remain 33:13:52 loss: 0.3637 Lr: 0.00418 [2024-02-18 08:34:15,076 INFO misc.py line 119 87073] Train: [31/100][239/1557] Data 0.004 (0.080) Batch 0.900 (1.099) Remain 33:12:19 loss: 0.3939 Lr: 0.00418 [2024-02-18 08:34:16,146 INFO misc.py line 119 87073] Train: [31/100][240/1557] Data 0.006 (0.079) Batch 1.069 (1.099) Remain 33:12:04 loss: 0.5931 Lr: 0.00418 [2024-02-18 08:34:17,180 INFO misc.py line 119 87073] Train: [31/100][241/1557] Data 0.006 (0.079) Batch 1.035 (1.099) Remain 33:11:33 loss: 0.3700 Lr: 0.00418 [2024-02-18 08:34:18,366 INFO misc.py line 119 87073] Train: [31/100][242/1557] Data 0.006 (0.079) Batch 1.186 (1.099) Remain 33:12:12 loss: 0.6007 Lr: 0.00418 [2024-02-18 08:34:19,094 INFO misc.py line 119 87073] Train: [31/100][243/1557] Data 0.005 (0.079) Batch 0.729 (1.098) Remain 33:09:23 loss: 0.3861 Lr: 0.00418 [2024-02-18 08:34:19,815 INFO misc.py line 119 87073] Train: [31/100][244/1557] Data 0.004 (0.078) Batch 0.714 (1.096) Remain 33:06:29 loss: 0.5135 Lr: 0.00418 [2024-02-18 08:34:21,095 INFO misc.py line 119 87073] Train: [31/100][245/1557] Data 0.010 (0.078) Batch 1.278 (1.097) Remain 33:07:49 loss: 0.1361 Lr: 0.00418 [2024-02-18 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[2024-02-18 08:34:45,521 INFO misc.py line 119 87073] Train: [31/100][271/1557] Data 0.004 (0.071) Batch 0.700 (1.082) Remain 32:39:45 loss: 0.3366 Lr: 0.00418 [2024-02-18 08:34:46,254 INFO misc.py line 119 87073] Train: [31/100][272/1557] Data 0.004 (0.071) Batch 0.732 (1.080) Remain 32:37:23 loss: 0.3610 Lr: 0.00418 [2024-02-18 08:34:47,407 INFO misc.py line 119 87073] Train: [31/100][273/1557] Data 0.005 (0.071) Batch 1.153 (1.081) Remain 32:37:51 loss: 0.5432 Lr: 0.00418 [2024-02-18 08:34:48,353 INFO misc.py line 119 87073] Train: [31/100][274/1557] Data 0.005 (0.070) Batch 0.945 (1.080) Remain 32:36:56 loss: 0.2488 Lr: 0.00418 [2024-02-18 08:34:49,338 INFO misc.py line 119 87073] Train: [31/100][275/1557] Data 0.005 (0.070) Batch 0.985 (1.080) Remain 32:36:17 loss: 0.2976 Lr: 0.00418 [2024-02-18 08:34:50,270 INFO misc.py line 119 87073] Train: [31/100][276/1557] Data 0.005 (0.070) Batch 0.932 (1.079) Remain 32:35:17 loss: 0.3871 Lr: 0.00418 [2024-02-18 08:34:51,301 INFO misc.py line 119 87073] Train: [31/100][277/1557] Data 0.004 (0.070) Batch 1.024 (1.079) Remain 32:34:54 loss: 1.1836 Lr: 0.00418 [2024-02-18 08:34:52,041 INFO misc.py line 119 87073] Train: [31/100][278/1557] Data 0.012 (0.069) Batch 0.748 (1.078) Remain 32:32:42 loss: 0.7123 Lr: 0.00418 [2024-02-18 08:34:52,782 INFO misc.py line 119 87073] Train: [31/100][279/1557] Data 0.004 (0.069) Batch 0.732 (1.076) Remain 32:30:25 loss: 0.5249 Lr: 0.00418 [2024-02-18 08:34:54,080 INFO misc.py line 119 87073] Train: [31/100][280/1557] Data 0.013 (0.069) Batch 1.300 (1.077) Remain 32:31:52 loss: 0.2310 Lr: 0.00418 [2024-02-18 08:34:54,955 INFO misc.py line 119 87073] Train: [31/100][281/1557] Data 0.011 (0.069) Batch 0.882 (1.077) Remain 32:30:34 loss: 0.6272 Lr: 0.00418 [2024-02-18 08:34:55,914 INFO misc.py line 119 87073] Train: [31/100][282/1557] Data 0.005 (0.069) Batch 0.959 (1.076) Remain 32:29:47 loss: 0.4371 Lr: 0.00418 [2024-02-18 08:34:56,827 INFO misc.py line 119 87073] Train: 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Batch 1.017 (1.098) Remain 33:08:31 loss: 0.5694 Lr: 0.00418 [2024-02-18 08:35:10,594 INFO misc.py line 119 87073] Train: [31/100][290/1557] Data 0.003 (0.080) Batch 1.014 (1.097) Remain 33:07:58 loss: 0.3645 Lr: 0.00418 [2024-02-18 08:35:11,730 INFO misc.py line 119 87073] Train: [31/100][291/1557] Data 0.004 (0.080) Batch 1.137 (1.097) Remain 33:08:11 loss: 0.4827 Lr: 0.00418 [2024-02-18 08:35:12,475 INFO misc.py line 119 87073] Train: [31/100][292/1557] Data 0.004 (0.080) Batch 0.745 (1.096) Remain 33:05:58 loss: 0.4795 Lr: 0.00418 [2024-02-18 08:35:13,232 INFO misc.py line 119 87073] Train: [31/100][293/1557] Data 0.003 (0.080) Batch 0.752 (1.095) Remain 33:03:48 loss: 0.2681 Lr: 0.00418 [2024-02-18 08:35:14,803 INFO misc.py line 119 87073] Train: [31/100][294/1557] Data 0.008 (0.079) Batch 1.566 (1.097) Remain 33:06:43 loss: 0.4699 Lr: 0.00418 [2024-02-18 08:35:15,750 INFO misc.py line 119 87073] Train: [31/100][295/1557] Data 0.012 (0.079) Batch 0.956 (1.096) Remain 33:05:49 loss: 0.8261 Lr: 0.00418 [2024-02-18 08:35:16,696 INFO misc.py line 119 87073] Train: [31/100][296/1557] Data 0.004 (0.079) Batch 0.944 (1.096) Remain 33:04:52 loss: 0.3226 Lr: 0.00418 [2024-02-18 08:35:17,599 INFO misc.py line 119 87073] Train: [31/100][297/1557] Data 0.006 (0.079) Batch 0.904 (1.095) Remain 33:03:40 loss: 0.4283 Lr: 0.00418 [2024-02-18 08:35:18,501 INFO misc.py line 119 87073] Train: [31/100][298/1557] Data 0.005 (0.078) Batch 0.901 (1.094) Remain 33:02:27 loss: 0.1325 Lr: 0.00418 [2024-02-18 08:35:19,295 INFO misc.py line 119 87073] Train: [31/100][299/1557] Data 0.007 (0.078) Batch 0.795 (1.093) Remain 33:00:36 loss: 0.1672 Lr: 0.00418 [2024-02-18 08:35:20,085 INFO misc.py line 119 87073] Train: [31/100][300/1557] Data 0.005 (0.078) Batch 0.790 (1.092) Remain 32:58:44 loss: 0.5037 Lr: 0.00418 [2024-02-18 08:35:21,346 INFO misc.py line 119 87073] Train: [31/100][301/1557] Data 0.004 (0.078) Batch 1.247 (1.093) Remain 32:59:40 loss: 0.2240 Lr: 0.00418 [2024-02-18 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87073] Train: [31/100][308/1557] Data 0.009 (0.076) Batch 1.134 (1.090) Remain 32:53:48 loss: 0.2064 Lr: 0.00418 [2024-02-18 08:35:29,084 INFO misc.py line 119 87073] Train: [31/100][309/1557] Data 0.014 (0.076) Batch 1.060 (1.090) Remain 32:53:36 loss: 0.3930 Lr: 0.00418 [2024-02-18 08:35:30,300 INFO misc.py line 119 87073] Train: [31/100][310/1557] Data 0.014 (0.076) Batch 1.222 (1.090) Remain 32:54:22 loss: 0.5475 Lr: 0.00418 [2024-02-18 08:35:31,484 INFO misc.py line 119 87073] Train: [31/100][311/1557] Data 0.006 (0.075) Batch 1.187 (1.090) Remain 32:54:55 loss: 0.4969 Lr: 0.00418 [2024-02-18 08:35:32,383 INFO misc.py line 119 87073] Train: [31/100][312/1557] Data 0.004 (0.075) Batch 0.900 (1.090) Remain 32:53:47 loss: 0.8695 Lr: 0.00418 [2024-02-18 08:35:33,122 INFO misc.py line 119 87073] Train: [31/100][313/1557] Data 0.003 (0.075) Batch 0.737 (1.089) Remain 32:51:42 loss: 0.3449 Lr: 0.00418 [2024-02-18 08:35:33,840 INFO misc.py line 119 87073] Train: [31/100][314/1557] Data 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line 119 87073] Train: [31/100][501/1557] Data 0.004 (0.075) Batch 0.926 (1.088) Remain 32:47:34 loss: 0.6552 Lr: 0.00417 [2024-02-18 08:38:58,341 INFO misc.py line 119 87073] Train: [31/100][502/1557] Data 0.003 (0.074) Batch 0.767 (1.088) Remain 32:46:23 loss: 0.1890 Lr: 0.00417 [2024-02-18 08:38:59,150 INFO misc.py line 119 87073] Train: [31/100][503/1557] Data 0.005 (0.074) Batch 0.809 (1.087) Remain 32:45:21 loss: 0.3995 Lr: 0.00417 [2024-02-18 08:39:00,436 INFO misc.py line 119 87073] Train: [31/100][504/1557] Data 0.005 (0.074) Batch 1.279 (1.087) Remain 32:46:02 loss: 0.2512 Lr: 0.00417 [2024-02-18 08:39:01,473 INFO misc.py line 119 87073] Train: [31/100][505/1557] Data 0.012 (0.074) Batch 1.043 (1.087) Remain 32:45:51 loss: 0.8024 Lr: 0.00417 [2024-02-18 08:39:02,516 INFO misc.py line 119 87073] Train: [31/100][506/1557] Data 0.006 (0.074) Batch 1.033 (1.087) Remain 32:45:39 loss: 0.2482 Lr: 0.00417 [2024-02-18 08:39:03,566 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [31/100][557/1557] Data 0.004 (0.077) Batch 0.744 (1.093) Remain 32:55:50 loss: 0.4721 Lr: 0.00417 [2024-02-18 08:40:02,100 INFO misc.py line 119 87073] Train: [31/100][558/1557] Data 0.007 (0.077) Batch 0.742 (1.093) Remain 32:54:40 loss: 1.2188 Lr: 0.00417 [2024-02-18 08:40:02,876 INFO misc.py line 119 87073] Train: [31/100][559/1557] Data 0.005 (0.077) Batch 0.777 (1.092) Remain 32:53:38 loss: 0.5198 Lr: 0.00417 [2024-02-18 08:40:04,184 INFO misc.py line 119 87073] Train: [31/100][560/1557] Data 0.004 (0.077) Batch 1.307 (1.092) Remain 32:54:19 loss: 0.4141 Lr: 0.00417 [2024-02-18 08:40:05,145 INFO misc.py line 119 87073] Train: [31/100][561/1557] Data 0.006 (0.076) Batch 0.962 (1.092) Remain 32:53:52 loss: 0.4103 Lr: 0.00417 [2024-02-18 08:40:06,140 INFO misc.py line 119 87073] Train: [31/100][562/1557] Data 0.004 (0.076) Batch 0.995 (1.092) Remain 32:53:32 loss: 0.3960 Lr: 0.00417 [2024-02-18 08:40:07,127 INFO misc.py line 119 87073] Train: 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[2024-02-18 08:58:04,403 INFO misc.py line 119 87073] Train: [31/100][1526/1557] Data 0.008 (0.082) Batch 1.623 (1.109) Remain 33:05:58 loss: 0.3214 Lr: 0.00413 [2024-02-18 08:58:05,247 INFO misc.py line 119 87073] Train: [31/100][1527/1557] Data 0.005 (0.082) Batch 0.841 (1.109) Remain 33:05:38 loss: 0.6352 Lr: 0.00413 [2024-02-18 08:58:06,183 INFO misc.py line 119 87073] Train: [31/100][1528/1557] Data 0.008 (0.082) Batch 0.940 (1.109) Remain 33:05:25 loss: 0.5452 Lr: 0.00413 [2024-02-18 08:58:07,198 INFO misc.py line 119 87073] Train: [31/100][1529/1557] Data 0.004 (0.082) Batch 1.012 (1.108) Remain 33:05:17 loss: 0.7781 Lr: 0.00413 [2024-02-18 08:58:08,184 INFO misc.py line 119 87073] Train: [31/100][1530/1557] Data 0.008 (0.082) Batch 0.989 (1.108) Remain 33:05:07 loss: 0.6976 Lr: 0.00413 [2024-02-18 08:58:08,971 INFO misc.py line 119 87073] Train: [31/100][1531/1557] Data 0.004 (0.082) Batch 0.786 (1.108) Remain 33:04:44 loss: 0.3650 Lr: 0.00413 [2024-02-18 08:58:09,736 INFO misc.py line 119 87073] Train: [31/100][1532/1557] Data 0.005 (0.082) Batch 0.761 (1.108) Remain 33:04:18 loss: 0.3033 Lr: 0.00413 [2024-02-18 08:58:10,985 INFO misc.py line 119 87073] Train: [31/100][1533/1557] Data 0.008 (0.082) Batch 1.243 (1.108) Remain 33:04:27 loss: 0.1489 Lr: 0.00413 [2024-02-18 08:58:12,027 INFO misc.py line 119 87073] Train: [31/100][1534/1557] Data 0.016 (0.082) Batch 1.042 (1.108) Remain 33:04:21 loss: 0.4857 Lr: 0.00413 [2024-02-18 08:58:12,946 INFO misc.py line 119 87073] Train: [31/100][1535/1557] Data 0.014 (0.081) Batch 0.929 (1.108) Remain 33:04:07 loss: 0.7517 Lr: 0.00413 [2024-02-18 08:58:13,882 INFO misc.py line 119 87073] Train: [31/100][1536/1557] Data 0.004 (0.081) Batch 0.937 (1.108) Remain 33:03:54 loss: 0.3454 Lr: 0.00413 [2024-02-18 08:58:14,853 INFO misc.py line 119 87073] Train: [31/100][1537/1557] Data 0.003 (0.081) Batch 0.967 (1.108) Remain 33:03:43 loss: 0.2522 Lr: 0.00413 [2024-02-18 08:58:15,605 INFO misc.py line 119 87073] Train: [31/100][1538/1557] Data 0.007 (0.081) Batch 0.752 (1.107) Remain 33:03:17 loss: 0.3810 Lr: 0.00413 [2024-02-18 08:58:16,351 INFO misc.py line 119 87073] Train: [31/100][1539/1557] Data 0.007 (0.081) Batch 0.749 (1.107) Remain 33:02:51 loss: 0.3305 Lr: 0.00413 [2024-02-18 08:58:17,488 INFO misc.py line 119 87073] Train: [31/100][1540/1557] Data 0.004 (0.081) Batch 1.137 (1.107) Remain 33:02:52 loss: 0.2655 Lr: 0.00413 [2024-02-18 08:58:18,497 INFO misc.py line 119 87073] Train: [31/100][1541/1557] Data 0.003 (0.081) Batch 1.008 (1.107) Remain 33:02:44 loss: 0.3960 Lr: 0.00413 [2024-02-18 08:58:19,467 INFO misc.py line 119 87073] Train: [31/100][1542/1557] Data 0.003 (0.081) Batch 0.970 (1.107) Remain 33:02:33 loss: 0.3751 Lr: 0.00413 [2024-02-18 08:58:20,450 INFO misc.py line 119 87073] Train: [31/100][1543/1557] Data 0.003 (0.081) Batch 0.984 (1.107) Remain 33:02:24 loss: 0.2425 Lr: 0.00413 [2024-02-18 08:58:21,441 INFO misc.py line 119 87073] Train: [31/100][1544/1557] Data 0.003 (0.081) Batch 0.989 (1.107) Remain 33:02:14 loss: 0.6268 Lr: 0.00413 [2024-02-18 08:58:22,223 INFO misc.py line 119 87073] Train: [31/100][1545/1557] Data 0.004 (0.081) Batch 0.773 (1.107) Remain 33:01:50 loss: 0.4035 Lr: 0.00413 [2024-02-18 08:58:22,964 INFO misc.py line 119 87073] Train: [31/100][1546/1557] Data 0.013 (0.081) Batch 0.751 (1.106) Remain 33:01:24 loss: 0.4768 Lr: 0.00413 [2024-02-18 08:58:24,217 INFO misc.py line 119 87073] Train: [31/100][1547/1557] Data 0.004 (0.081) Batch 1.253 (1.107) Remain 33:01:33 loss: 0.2056 Lr: 0.00413 [2024-02-18 08:58:25,335 INFO misc.py line 119 87073] Train: [31/100][1548/1557] Data 0.004 (0.081) Batch 1.119 (1.107) Remain 33:01:33 loss: 0.5039 Lr: 0.00413 [2024-02-18 08:58:26,298 INFO misc.py line 119 87073] Train: [31/100][1549/1557] Data 0.004 (0.081) Batch 0.962 (1.106) Remain 33:01:22 loss: 0.5677 Lr: 0.00413 [2024-02-18 08:58:27,092 INFO misc.py line 119 87073] Train: [31/100][1550/1557] Data 0.004 (0.081) Batch 0.795 (1.106) Remain 33:00:59 loss: 0.3980 Lr: 0.00413 [2024-02-18 08:58:27,975 INFO misc.py line 119 87073] Train: [31/100][1551/1557] Data 0.004 (0.081) Batch 0.873 (1.106) Remain 33:00:42 loss: 0.2606 Lr: 0.00413 [2024-02-18 08:58:28,691 INFO misc.py line 119 87073] Train: [31/100][1552/1557] Data 0.014 (0.081) Batch 0.726 (1.106) Remain 33:00:14 loss: 0.2286 Lr: 0.00413 [2024-02-18 08:58:29,493 INFO misc.py line 119 87073] Train: [31/100][1553/1557] Data 0.004 (0.081) Batch 0.793 (1.106) Remain 32:59:51 loss: 0.5458 Lr: 0.00413 [2024-02-18 08:58:30,529 INFO misc.py line 119 87073] Train: [31/100][1554/1557] Data 0.013 (0.081) Batch 1.035 (1.106) Remain 32:59:45 loss: 0.2146 Lr: 0.00413 [2024-02-18 08:58:31,383 INFO misc.py line 119 87073] Train: [31/100][1555/1557] Data 0.013 (0.081) Batch 0.863 (1.105) Remain 32:59:27 loss: 0.2244 Lr: 0.00413 [2024-02-18 08:58:32,310 INFO misc.py line 119 87073] Train: [31/100][1556/1557] Data 0.004 (0.080) Batch 0.927 (1.105) Remain 32:59:14 loss: 0.5987 Lr: 0.00413 [2024-02-18 08:58:33,223 INFO misc.py line 119 87073] Train: [31/100][1557/1557] Data 0.003 (0.080) Batch 0.914 (1.105) Remain 32:59:00 loss: 0.2828 Lr: 0.00413 [2024-02-18 08:58:33,224 INFO misc.py line 136 87073] Train result: loss: 0.4317 [2024-02-18 08:58:33,224 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 08:59:01,347 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7191 [2024-02-18 08:59:02,126 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8524 [2024-02-18 08:59:04,250 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4120 [2024-02-18 08:59:06,456 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2921 [2024-02-18 08:59:11,389 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2722 [2024-02-18 08:59:12,088 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2327 [2024-02-18 08:59:13,348 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3618 [2024-02-18 08:59:16,302 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3138 [2024-02-18 08:59:18,109 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2431 [2024-02-18 08:59:19,616 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6876/0.7542/0.9040. [2024-02-18 08:59:19,616 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9129/0.9797 [2024-02-18 08:59:19,616 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9831/0.9911 [2024-02-18 08:59:19,616 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8635/0.9673 [2024-02-18 08:59:19,616 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 08:59:19,616 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3748/0.3997 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5970/0.6110 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7453/0.9191 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8406/0.9211 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9082/0.9482 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7426/0.7671 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7805/0.9012 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6819/0.8304 [2024-02-18 08:59:19,617 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5086/0.5694 [2024-02-18 08:59:19,617 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 08:59:19,619 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 08:59:19,619 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 08:59:26,477 INFO misc.py line 119 87073] Train: [32/100][1/1557] Data 1.298 (1.298) Batch 2.045 (2.045) Remain 61:01:29 loss: 0.7450 Lr: 0.00413 [2024-02-18 08:59:27,486 INFO misc.py line 119 87073] Train: [32/100][2/1557] Data 0.004 (0.004) Batch 1.001 (1.001) Remain 29:52:41 loss: 0.6300 Lr: 0.00413 [2024-02-18 08:59:28,445 INFO misc.py line 119 87073] Train: [32/100][3/1557] Data 0.012 (0.012) Batch 0.967 (0.967) Remain 28:51:18 loss: 0.2917 Lr: 0.00413 [2024-02-18 08:59:29,604 INFO misc.py line 119 87073] Train: [32/100][4/1557] Data 0.004 (0.004) Batch 1.159 (1.159) Remain 34:35:49 loss: 0.5215 Lr: 0.00413 [2024-02-18 08:59:30,378 INFO misc.py line 119 87073] Train: [32/100][5/1557] Data 0.003 (0.004) Batch 0.773 (0.966) Remain 28:50:09 loss: 0.8058 Lr: 0.00413 [2024-02-18 08:59:31,076 INFO misc.py line 119 87073] Train: [32/100][6/1557] Data 0.005 (0.004) Batch 0.699 (0.877) Remain 26:10:18 loss: 0.5279 Lr: 0.00413 [2024-02-18 08:59:43,243 INFO misc.py line 119 87073] Train: [32/100][7/1557] Data 0.004 (0.004) Batch 12.167 (3.700) Remain 110:23:43 loss: 0.1290 Lr: 0.00413 [2024-02-18 08:59:44,125 INFO misc.py line 119 87073] Train: [32/100][8/1557] Data 0.004 (0.004) Batch 0.882 (3.136) Remain 93:34:38 loss: 0.6952 Lr: 0.00413 [2024-02-18 08:59:45,453 INFO misc.py line 119 87073] Train: [32/100][9/1557] Data 0.004 (0.004) Batch 1.326 (2.834) Remain 84:34:21 loss: 0.4421 Lr: 0.00413 [2024-02-18 08:59:46,410 INFO misc.py line 119 87073] Train: [32/100][10/1557] Data 0.007 (0.004) Batch 0.958 (2.566) Remain 76:34:27 loss: 0.5628 Lr: 0.00413 [2024-02-18 08:59:47,420 INFO misc.py line 119 87073] Train: [32/100][11/1557] Data 0.006 (0.005) Batch 1.010 (2.372) Remain 70:46:08 loss: 0.2861 Lr: 0.00413 [2024-02-18 08:59:48,211 INFO misc.py line 119 87073] Train: [32/100][12/1557] Data 0.006 (0.005) Batch 0.790 (2.196) Remain 65:31:28 loss: 0.3276 Lr: 0.00413 [2024-02-18 08:59:49,013 INFO misc.py line 119 87073] Train: [32/100][13/1557] Data 0.008 (0.005) Batch 0.802 (2.057) Remain 61:21:52 loss: 0.5406 Lr: 0.00413 [2024-02-18 08:59:50,159 INFO misc.py line 119 87073] Train: [32/100][14/1557] Data 0.008 (0.005) Batch 1.147 (1.974) Remain 58:53:45 loss: 0.1591 Lr: 0.00413 [2024-02-18 08:59:50,989 INFO misc.py line 119 87073] Train: [32/100][15/1557] Data 0.007 (0.006) Batch 0.829 (1.878) Remain 56:02:52 loss: 0.3759 Lr: 0.00413 [2024-02-18 08:59:52,005 INFO misc.py line 119 87073] Train: [32/100][16/1557] Data 0.009 (0.006) Batch 1.018 (1.812) Remain 54:04:21 loss: 0.8153 Lr: 0.00413 [2024-02-18 08:59:52,979 INFO misc.py line 119 87073] Train: [32/100][17/1557] Data 0.005 (0.006) Batch 0.973 (1.752) Remain 52:16:59 loss: 0.7058 Lr: 0.00413 [2024-02-18 08:59:53,914 INFO misc.py line 119 87073] Train: [32/100][18/1557] Data 0.007 (0.006) Batch 0.936 (1.698) Remain 50:39:31 loss: 0.7854 Lr: 0.00413 [2024-02-18 08:59:54,595 INFO misc.py line 119 87073] Train: [32/100][19/1557] Data 0.007 (0.006) Batch 0.680 (1.634) Remain 48:45:39 loss: 0.1956 Lr: 0.00413 [2024-02-18 08:59:55,390 INFO misc.py line 119 87073] Train: [32/100][20/1557] Data 0.007 (0.006) Batch 0.796 (1.585) Remain 47:17:20 loss: 0.6159 Lr: 0.00413 [2024-02-18 08:59:56,516 INFO misc.py line 119 87073] Train: [32/100][21/1557] Data 0.006 (0.006) Batch 1.125 (1.559) Remain 46:31:33 loss: 0.2706 Lr: 0.00413 [2024-02-18 08:59:57,560 INFO misc.py line 119 87073] Train: [32/100][22/1557] Data 0.007 (0.006) Batch 1.045 (1.532) Remain 45:43:03 loss: 0.2251 Lr: 0.00413 [2024-02-18 08:59:58,578 INFO misc.py line 119 87073] Train: [32/100][23/1557] Data 0.005 (0.006) Batch 1.019 (1.507) Remain 44:57:07 loss: 0.5482 Lr: 0.00413 [2024-02-18 08:59:59,489 INFO misc.py line 119 87073] Train: [32/100][24/1557] Data 0.005 (0.006) Batch 0.911 (1.478) Remain 44:06:21 loss: 0.8301 Lr: 0.00413 [2024-02-18 09:00:00,289 INFO misc.py line 119 87073] Train: [32/100][25/1557] Data 0.003 (0.006) Batch 0.794 (1.447) Remain 43:10:38 loss: 0.3070 Lr: 0.00413 [2024-02-18 09:00:01,052 INFO misc.py line 119 87073] Train: [32/100][26/1557] Data 0.011 (0.006) Batch 0.767 (1.418) Remain 42:17:42 loss: 0.4673 Lr: 0.00413 [2024-02-18 09:00:01,836 INFO misc.py line 119 87073] Train: [32/100][27/1557] Data 0.007 (0.006) Batch 0.785 (1.391) Remain 41:30:31 loss: 0.3535 Lr: 0.00413 [2024-02-18 09:00:03,128 INFO misc.py line 119 87073] Train: [32/100][28/1557] Data 0.005 (0.006) Batch 1.289 (1.387) Remain 41:23:13 loss: 0.1790 Lr: 0.00413 [2024-02-18 09:00:04,235 INFO misc.py line 119 87073] Train: [32/100][29/1557] Data 0.007 (0.006) Batch 1.109 (1.376) Remain 41:04:00 loss: 0.6555 Lr: 0.00413 [2024-02-18 09:00:05,269 INFO misc.py line 119 87073] Train: [32/100][30/1557] Data 0.007 (0.006) Batch 1.034 (1.364) Remain 40:41:16 loss: 0.5911 Lr: 0.00413 [2024-02-18 09:00:06,224 INFO misc.py line 119 87073] Train: [32/100][31/1557] Data 0.006 (0.006) Batch 0.953 (1.349) Remain 40:14:58 loss: 0.2626 Lr: 0.00413 [2024-02-18 09:00:07,093 INFO misc.py line 119 87073] Train: [32/100][32/1557] Data 0.009 (0.006) Batch 0.870 (1.333) Remain 39:45:22 loss: 0.1704 Lr: 0.00413 [2024-02-18 09:00:07,885 INFO misc.py line 119 87073] Train: [32/100][33/1557] Data 0.007 (0.006) Batch 0.794 (1.315) Remain 39:13:12 loss: 0.5248 Lr: 0.00413 [2024-02-18 09:00:08,659 INFO misc.py line 119 87073] Train: [32/100][34/1557] Data 0.007 (0.006) Batch 0.774 (1.297) Remain 38:41:56 loss: 0.3491 Lr: 0.00413 [2024-02-18 09:00:10,117 INFO misc.py line 119 87073] Train: [32/100][35/1557] Data 0.005 (0.006) Batch 1.251 (1.296) Remain 38:39:18 loss: 0.2091 Lr: 0.00413 [2024-02-18 09:00:10,908 INFO misc.py line 119 87073] Train: [32/100][36/1557] Data 0.212 (0.012) Batch 0.997 (1.287) Remain 38:23:05 loss: 0.2106 Lr: 0.00413 [2024-02-18 09:00:11,876 INFO misc.py line 119 87073] Train: [32/100][37/1557] Data 0.007 (0.012) Batch 0.968 (1.277) Remain 38:06:18 loss: 0.7681 Lr: 0.00413 [2024-02-18 09:00:12,926 INFO misc.py line 119 87073] Train: [32/100][38/1557] Data 0.007 (0.012) Batch 1.048 (1.271) Remain 37:54:35 loss: 1.1513 Lr: 0.00413 [2024-02-18 09:00:13,733 INFO misc.py line 119 87073] Train: [32/100][39/1557] Data 0.008 (0.012) Batch 0.807 (1.258) Remain 37:31:30 loss: 0.2206 Lr: 0.00413 [2024-02-18 09:00:14,529 INFO misc.py line 119 87073] Train: [32/100][40/1557] Data 0.009 (0.012) Batch 0.798 (1.245) Remain 37:09:15 loss: 0.4447 Lr: 0.00413 [2024-02-18 09:00:15,269 INFO misc.py line 119 87073] Train: [32/100][41/1557] Data 0.006 (0.012) Batch 0.739 (1.232) Remain 36:45:24 loss: 0.2472 Lr: 0.00413 [2024-02-18 09:00:16,398 INFO misc.py line 119 87073] Train: [32/100][42/1557] Data 0.005 (0.012) Batch 1.131 (1.230) Remain 36:40:44 loss: 0.2348 Lr: 0.00413 [2024-02-18 09:00:17,221 INFO misc.py line 119 87073] Train: [32/100][43/1557] Data 0.004 (0.011) Batch 0.823 (1.219) Remain 36:22:32 loss: 0.3992 Lr: 0.00413 [2024-02-18 09:00:18,367 INFO misc.py line 119 87073] Train: [32/100][44/1557] Data 0.004 (0.011) Batch 1.145 (1.218) Remain 36:19:15 loss: 0.5549 Lr: 0.00413 [2024-02-18 09:00:19,413 INFO misc.py line 119 87073] Train: [32/100][45/1557] Data 0.006 (0.011) Batch 1.043 (1.213) Remain 36:11:46 loss: 0.6332 Lr: 0.00413 [2024-02-18 09:00:20,462 INFO misc.py line 119 87073] Train: [32/100][46/1557] Data 0.009 (0.011) Batch 1.053 (1.210) Remain 36:05:04 loss: 0.5861 Lr: 0.00413 [2024-02-18 09:00:21,242 INFO misc.py line 119 87073] Train: [32/100][47/1557] Data 0.004 (0.011) Batch 0.780 (1.200) Remain 35:47:33 loss: 0.2018 Lr: 0.00413 [2024-02-18 09:00:21,984 INFO misc.py line 119 87073] Train: [32/100][48/1557] Data 0.004 (0.011) Batch 0.742 (1.190) Remain 35:29:21 loss: 0.4254 Lr: 0.00413 [2024-02-18 09:00:23,237 INFO misc.py line 119 87073] Train: [32/100][49/1557] Data 0.004 (0.011) Batch 1.251 (1.191) Remain 35:31:43 loss: 0.2096 Lr: 0.00413 [2024-02-18 09:00:24,139 INFO misc.py line 119 87073] Train: [32/100][50/1557] Data 0.006 (0.010) Batch 0.904 (1.185) Remain 35:20:45 loss: 0.7283 Lr: 0.00413 [2024-02-18 09:00:25,054 INFO misc.py line 119 87073] Train: [32/100][51/1557] Data 0.004 (0.010) Batch 0.912 (1.179) Remain 35:10:34 loss: 1.3627 Lr: 0.00413 [2024-02-18 09:00:25,981 INFO misc.py line 119 87073] Train: [32/100][52/1557] Data 0.007 (0.010) Batch 0.928 (1.174) Remain 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Batch 1.344 (1.313) Remain 39:02:39 loss: 0.5193 Lr: 0.00412 [2024-02-18 09:08:11,784 INFO misc.py line 119 87073] Train: [32/100][402/1557] Data 0.007 (0.074) Batch 0.663 (1.312) Remain 38:59:43 loss: 0.4589 Lr: 0.00412 [2024-02-18 09:08:12,686 INFO misc.py line 119 87073] Train: [32/100][403/1557] Data 0.007 (0.074) Batch 0.904 (1.311) Remain 38:57:53 loss: 0.3451 Lr: 0.00412 [2024-02-18 09:08:13,455 INFO misc.py line 119 87073] Train: [32/100][404/1557] Data 0.005 (0.074) Batch 0.766 (1.309) Remain 38:55:26 loss: 0.4390 Lr: 0.00412 [2024-02-18 09:08:14,193 INFO misc.py line 119 87073] Train: [32/100][405/1557] Data 0.008 (0.074) Batch 0.739 (1.308) Remain 38:52:53 loss: 0.2158 Lr: 0.00412 [2024-02-18 09:08:15,423 INFO misc.py line 119 87073] Train: [32/100][406/1557] Data 0.007 (0.074) Batch 1.232 (1.308) Remain 38:52:32 loss: 0.0999 Lr: 0.00412 [2024-02-18 09:08:16,405 INFO misc.py line 119 87073] Train: [32/100][407/1557] Data 0.005 (0.073) Batch 0.983 (1.307) Remain 38:51:04 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Batch 1.067 (1.299) Remain 38:32:57 loss: 0.5305 Lr: 0.00411 [2024-02-18 09:11:44,461 INFO misc.py line 119 87073] Train: [32/100][570/1557] Data 0.010 (0.074) Batch 0.985 (1.298) Remain 38:31:56 loss: 0.6315 Lr: 0.00411 [2024-02-18 09:11:45,302 INFO misc.py line 119 87073] Train: [32/100][571/1557] Data 0.006 (0.074) Batch 0.842 (1.297) Remain 38:30:29 loss: 0.1739 Lr: 0.00411 [2024-02-18 09:11:46,103 INFO misc.py line 119 87073] Train: [32/100][572/1557] Data 0.006 (0.073) Batch 0.797 (1.296) Remain 38:28:54 loss: 0.5337 Lr: 0.00411 [2024-02-18 09:11:46,908 INFO misc.py line 119 87073] Train: [32/100][573/1557] Data 0.010 (0.073) Batch 0.809 (1.296) Remain 38:27:21 loss: 0.4511 Lr: 0.00411 [2024-02-18 09:11:48,140 INFO misc.py line 119 87073] Train: [32/100][574/1557] Data 0.006 (0.073) Batch 1.226 (1.295) Remain 38:27:07 loss: 0.1427 Lr: 0.00411 [2024-02-18 09:11:49,118 INFO misc.py line 119 87073] Train: [32/100][575/1557] Data 0.010 (0.073) Batch 0.984 (1.295) Remain 38:26:08 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Batch 1.104 (1.292) Remain 38:13:34 loss: 0.3349 Lr: 0.00409 [2024-02-18 09:20:07,701 INFO misc.py line 119 87073] Train: [32/100][962/1557] Data 0.005 (0.072) Batch 1.039 (1.292) Remain 38:13:05 loss: 0.4699 Lr: 0.00409 [2024-02-18 09:20:08,685 INFO misc.py line 119 87073] Train: [32/100][963/1557] Data 0.005 (0.072) Batch 0.984 (1.292) Remain 38:12:30 loss: 0.1013 Lr: 0.00409 [2024-02-18 09:20:09,450 INFO misc.py line 119 87073] Train: [32/100][964/1557] Data 0.004 (0.071) Batch 0.763 (1.291) Remain 38:11:30 loss: 0.4867 Lr: 0.00409 [2024-02-18 09:20:10,154 INFO misc.py line 119 87073] Train: [32/100][965/1557] Data 0.007 (0.071) Batch 0.700 (1.291) Remain 38:10:23 loss: 0.5057 Lr: 0.00409 [2024-02-18 09:20:11,364 INFO misc.py line 119 87073] Train: [32/100][966/1557] Data 0.011 (0.071) Batch 1.213 (1.291) Remain 38:10:13 loss: 0.1637 Lr: 0.00409 [2024-02-18 09:20:12,221 INFO misc.py line 119 87073] Train: [32/100][967/1557] Data 0.007 (0.071) Batch 0.857 (1.290) Remain 38:09:24 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Remain 38:08:02 loss: 0.3741 Lr: 0.00408 [2024-02-18 09:26:08,712 INFO misc.py line 119 87073] Train: [32/100][1241/1557] Data 0.012 (0.072) Batch 1.110 (1.293) Remain 38:07:45 loss: 0.6503 Lr: 0.00408 [2024-02-18 09:26:09,674 INFO misc.py line 119 87073] Train: [32/100][1242/1557] Data 0.009 (0.072) Batch 0.966 (1.292) Remain 38:07:16 loss: 0.5317 Lr: 0.00408 [2024-02-18 09:26:10,608 INFO misc.py line 119 87073] Train: [32/100][1243/1557] Data 0.005 (0.071) Batch 0.935 (1.292) Remain 38:06:44 loss: 0.6384 Lr: 0.00408 [2024-02-18 09:26:11,281 INFO misc.py line 119 87073] Train: [32/100][1244/1557] Data 0.003 (0.071) Batch 0.670 (1.292) Remain 38:05:49 loss: 0.2032 Lr: 0.00408 [2024-02-18 09:26:12,087 INFO misc.py line 119 87073] Train: [32/100][1245/1557] Data 0.006 (0.071) Batch 0.807 (1.291) Remain 38:05:07 loss: 0.3631 Lr: 0.00408 [2024-02-18 09:26:13,283 INFO misc.py line 119 87073] Train: [32/100][1246/1557] Data 0.005 (0.071) Batch 1.197 (1.291) Remain 38:04:57 loss: 0.1479 Lr: 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Train: [32/100][1259/1557] Data 0.008 (0.071) Batch 0.791 (1.287) Remain 37:57:41 loss: 0.2765 Lr: 0.00408 [2024-02-18 09:26:26,386 INFO misc.py line 119 87073] Train: [32/100][1260/1557] Data 0.006 (0.071) Batch 1.278 (1.287) Remain 37:57:39 loss: 0.1553 Lr: 0.00408 [2024-02-18 09:26:27,296 INFO misc.py line 119 87073] Train: [32/100][1261/1557] Data 0.007 (0.071) Batch 0.914 (1.287) Remain 37:57:06 loss: 0.6106 Lr: 0.00408 [2024-02-18 09:26:28,350 INFO misc.py line 119 87073] Train: [32/100][1262/1557] Data 0.004 (0.070) Batch 1.052 (1.287) Remain 37:56:45 loss: 0.6797 Lr: 0.00408 [2024-02-18 09:26:29,309 INFO misc.py line 119 87073] Train: [32/100][1263/1557] Data 0.006 (0.070) Batch 0.959 (1.286) Remain 37:56:16 loss: 1.0579 Lr: 0.00408 [2024-02-18 09:26:30,254 INFO misc.py line 119 87073] Train: [32/100][1264/1557] Data 0.005 (0.070) Batch 0.946 (1.286) Remain 37:55:46 loss: 0.2509 Lr: 0.00408 [2024-02-18 09:26:31,070 INFO misc.py line 119 87073] Train: [32/100][1265/1557] Data 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Remain 37:52:30 loss: 0.3574 Lr: 0.00408 [2024-02-18 09:26:37,817 INFO misc.py line 119 87073] Train: [32/100][1272/1557] Data 0.004 (0.070) Batch 0.790 (1.284) Remain 37:51:47 loss: 0.2935 Lr: 0.00408 [2024-02-18 09:26:38,622 INFO misc.py line 119 87073] Train: [32/100][1273/1557] Data 0.012 (0.070) Batch 0.812 (1.284) Remain 37:51:07 loss: 0.3004 Lr: 0.00408 [2024-02-18 09:26:39,832 INFO misc.py line 119 87073] Train: [32/100][1274/1557] Data 0.005 (0.070) Batch 1.207 (1.284) Remain 37:50:59 loss: 0.2634 Lr: 0.00408 [2024-02-18 09:26:40,730 INFO misc.py line 119 87073] Train: [32/100][1275/1557] Data 0.007 (0.070) Batch 0.901 (1.283) Remain 37:50:26 loss: 0.3714 Lr: 0.00408 [2024-02-18 09:26:41,759 INFO misc.py line 119 87073] Train: [32/100][1276/1557] Data 0.004 (0.070) Batch 1.029 (1.283) Remain 37:50:03 loss: 0.7586 Lr: 0.00408 [2024-02-18 09:26:42,786 INFO misc.py line 119 87073] Train: [32/100][1277/1557] Data 0.004 (0.070) Batch 1.024 (1.283) Remain 37:49:40 loss: 0.5181 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1284/1557] Data 0.005 (0.069) Batch 0.978 (1.281) Remain 37:46:29 loss: 0.5628 Lr: 0.00408 [2024-02-18 09:26:50,510 INFO misc.py line 119 87073] Train: [32/100][1285/1557] Data 0.004 (0.069) Batch 0.948 (1.281) Remain 37:46:00 loss: 0.0895 Lr: 0.00408 [2024-02-18 09:26:51,246 INFO misc.py line 119 87073] Train: [32/100][1286/1557] Data 0.006 (0.069) Batch 0.733 (1.280) Remain 37:45:13 loss: 0.2986 Lr: 0.00408 [2024-02-18 09:26:51,942 INFO misc.py line 119 87073] Train: [32/100][1287/1557] Data 0.007 (0.069) Batch 0.700 (1.280) Remain 37:44:24 loss: 0.5325 Lr: 0.00408 [2024-02-18 09:26:53,119 INFO misc.py line 119 87073] Train: [32/100][1288/1557] Data 0.004 (0.069) Batch 1.171 (1.280) Remain 37:44:14 loss: 0.2579 Lr: 0.00408 [2024-02-18 09:26:54,114 INFO misc.py line 119 87073] Train: [32/100][1289/1557] Data 0.011 (0.069) Batch 0.999 (1.280) Remain 37:43:49 loss: 0.8005 Lr: 0.00408 [2024-02-18 09:26:55,073 INFO misc.py line 119 87073] Train: [32/100][1290/1557] Data 0.006 (0.069) Batch 0.958 (1.279) Remain 37:43:22 loss: 0.3635 Lr: 0.00408 [2024-02-18 09:26:55,991 INFO misc.py line 119 87073] Train: [32/100][1291/1557] Data 0.007 (0.069) Batch 0.919 (1.279) Remain 37:42:51 loss: 0.3973 Lr: 0.00408 [2024-02-18 09:26:56,951 INFO misc.py line 119 87073] Train: [32/100][1292/1557] Data 0.006 (0.069) Batch 0.960 (1.279) Remain 37:42:23 loss: 0.4016 Lr: 0.00408 [2024-02-18 09:26:57,757 INFO misc.py line 119 87073] Train: [32/100][1293/1557] Data 0.006 (0.069) Batch 0.805 (1.279) Remain 37:41:43 loss: 0.5520 Lr: 0.00408 [2024-02-18 09:26:58,462 INFO misc.py line 119 87073] Train: [32/100][1294/1557] Data 0.006 (0.069) Batch 0.707 (1.278) Remain 37:40:55 loss: 0.2793 Lr: 0.00408 [2024-02-18 09:27:20,072 INFO misc.py line 119 87073] Train: [32/100][1295/1557] Data 2.738 (0.071) Batch 21.609 (1.294) Remain 38:08:44 loss: 0.1811 Lr: 0.00408 [2024-02-18 09:27:20,928 INFO misc.py line 119 87073] Train: [32/100][1296/1557] Data 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Remain 38:05:12 loss: 0.1772 Lr: 0.00408 [2024-02-18 09:27:27,659 INFO misc.py line 119 87073] Train: [32/100][1303/1557] Data 0.007 (0.071) Batch 1.002 (1.292) Remain 38:04:47 loss: 0.1682 Lr: 0.00408 [2024-02-18 09:27:28,624 INFO misc.py line 119 87073] Train: [32/100][1304/1557] Data 0.007 (0.071) Batch 0.968 (1.291) Remain 38:04:20 loss: 0.7628 Lr: 0.00408 [2024-02-18 09:27:29,558 INFO misc.py line 119 87073] Train: [32/100][1305/1557] Data 0.004 (0.070) Batch 0.933 (1.291) Remain 38:03:49 loss: 1.0462 Lr: 0.00408 [2024-02-18 09:27:30,517 INFO misc.py line 119 87073] Train: [32/100][1306/1557] Data 0.005 (0.070) Batch 0.960 (1.291) Remain 38:03:21 loss: 0.7799 Lr: 0.00408 [2024-02-18 09:27:31,561 INFO misc.py line 119 87073] Train: [32/100][1307/1557] Data 0.004 (0.070) Batch 1.043 (1.291) Remain 38:03:00 loss: 0.1706 Lr: 0.00408 [2024-02-18 09:27:32,526 INFO misc.py line 119 87073] Train: [32/100][1308/1557] Data 0.005 (0.070) Batch 0.965 (1.290) Remain 38:02:32 loss: 0.3140 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1315/1557] Data 0.006 (0.070) Batch 0.762 (1.289) Remain 37:59:04 loss: 0.5077 Lr: 0.00408 [2024-02-18 09:27:40,408 INFO misc.py line 119 87073] Train: [32/100][1316/1557] Data 0.005 (0.070) Batch 1.303 (1.289) Remain 37:59:04 loss: 0.1517 Lr: 0.00408 [2024-02-18 09:27:41,314 INFO misc.py line 119 87073] Train: [32/100][1317/1557] Data 0.006 (0.070) Batch 0.906 (1.288) Remain 37:58:32 loss: 0.3480 Lr: 0.00408 [2024-02-18 09:27:42,287 INFO misc.py line 119 87073] Train: [32/100][1318/1557] Data 0.006 (0.070) Batch 0.975 (1.288) Remain 37:58:05 loss: 0.5877 Lr: 0.00408 [2024-02-18 09:27:43,242 INFO misc.py line 119 87073] Train: [32/100][1319/1557] Data 0.004 (0.070) Batch 0.954 (1.288) Remain 37:57:37 loss: 0.4374 Lr: 0.00408 [2024-02-18 09:27:44,115 INFO misc.py line 119 87073] Train: [32/100][1320/1557] Data 0.004 (0.070) Batch 0.873 (1.288) Remain 37:57:02 loss: 0.4363 Lr: 0.00408 [2024-02-18 09:27:44,863 INFO misc.py line 119 87073] Train: [32/100][1321/1557] Data 0.004 (0.070) Batch 0.746 (1.287) Remain 37:56:17 loss: 0.3237 Lr: 0.00408 [2024-02-18 09:27:45,599 INFO misc.py line 119 87073] Train: [32/100][1322/1557] Data 0.006 (0.070) Batch 0.739 (1.287) Remain 37:55:32 loss: 0.1722 Lr: 0.00408 [2024-02-18 09:27:46,905 INFO misc.py line 119 87073] Train: [32/100][1323/1557] Data 0.003 (0.070) Batch 1.299 (1.287) Remain 37:55:32 loss: 0.1382 Lr: 0.00408 [2024-02-18 09:27:47,777 INFO misc.py line 119 87073] Train: [32/100][1324/1557] Data 0.010 (0.070) Batch 0.879 (1.286) Remain 37:54:58 loss: 1.2237 Lr: 0.00408 [2024-02-18 09:27:48,635 INFO misc.py line 119 87073] Train: [32/100][1325/1557] Data 0.003 (0.069) Batch 0.857 (1.286) Remain 37:54:22 loss: 0.4421 Lr: 0.00408 [2024-02-18 09:27:49,499 INFO misc.py line 119 87073] Train: [32/100][1326/1557] Data 0.004 (0.069) Batch 0.861 (1.286) Remain 37:53:47 loss: 0.3181 Lr: 0.00408 [2024-02-18 09:27:50,480 INFO misc.py line 119 87073] Train: [32/100][1327/1557] Data 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Remain 37:50:34 loss: 0.4201 Lr: 0.00408 [2024-02-18 09:27:57,113 INFO misc.py line 119 87073] Train: [32/100][1334/1557] Data 0.004 (0.069) Batch 0.920 (1.284) Remain 37:50:04 loss: 0.2989 Lr: 0.00408 [2024-02-18 09:27:57,876 INFO misc.py line 119 87073] Train: [32/100][1335/1557] Data 0.003 (0.069) Batch 0.764 (1.283) Remain 37:49:21 loss: 0.3206 Lr: 0.00408 [2024-02-18 09:27:58,640 INFO misc.py line 119 87073] Train: [32/100][1336/1557] Data 0.003 (0.069) Batch 0.761 (1.283) Remain 37:48:38 loss: 0.3594 Lr: 0.00408 [2024-02-18 09:27:59,953 INFO misc.py line 119 87073] Train: [32/100][1337/1557] Data 0.006 (0.069) Batch 1.312 (1.283) Remain 37:48:39 loss: 0.2242 Lr: 0.00408 [2024-02-18 09:28:01,048 INFO misc.py line 119 87073] Train: [32/100][1338/1557] Data 0.008 (0.069) Batch 1.096 (1.283) Remain 37:48:23 loss: 0.2485 Lr: 0.00408 [2024-02-18 09:28:02,210 INFO misc.py line 119 87073] Train: [32/100][1339/1557] Data 0.006 (0.069) Batch 1.164 (1.283) Remain 37:48:12 loss: 0.5669 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1346/1557] Data 0.006 (0.068) Batch 0.983 (1.281) Remain 37:45:15 loss: 0.5952 Lr: 0.00408 [2024-02-18 09:28:09,943 INFO misc.py line 119 87073] Train: [32/100][1347/1557] Data 0.003 (0.068) Batch 0.882 (1.281) Remain 37:44:42 loss: 0.5624 Lr: 0.00408 [2024-02-18 09:28:10,971 INFO misc.py line 119 87073] Train: [32/100][1348/1557] Data 0.004 (0.068) Batch 1.023 (1.281) Remain 37:44:21 loss: 0.3632 Lr: 0.00408 [2024-02-18 09:28:11,767 INFO misc.py line 119 87073] Train: [32/100][1349/1557] Data 0.008 (0.068) Batch 0.801 (1.280) Remain 37:43:42 loss: 0.2328 Lr: 0.00408 [2024-02-18 09:28:12,487 INFO misc.py line 119 87073] Train: [32/100][1350/1557] Data 0.003 (0.068) Batch 0.719 (1.280) Remain 37:42:56 loss: 0.4091 Lr: 0.00408 [2024-02-18 09:28:31,849 INFO misc.py line 119 87073] Train: [32/100][1351/1557] Data 2.739 (0.070) Batch 19.363 (1.293) Remain 38:06:38 loss: 0.1165 Lr: 0.00408 [2024-02-18 09:28:32,752 INFO misc.py line 119 87073] Train: [32/100][1352/1557] Data 0.006 (0.070) Batch 0.903 (1.293) Remain 38:06:06 loss: 0.1974 Lr: 0.00408 [2024-02-18 09:28:33,655 INFO misc.py line 119 87073] Train: [32/100][1353/1557] Data 0.004 (0.070) Batch 0.903 (1.293) Remain 38:05:34 loss: 0.5954 Lr: 0.00408 [2024-02-18 09:28:34,607 INFO misc.py line 119 87073] Train: [32/100][1354/1557] Data 0.005 (0.070) Batch 0.953 (1.292) Remain 38:05:06 loss: 0.3202 Lr: 0.00408 [2024-02-18 09:28:35,678 INFO misc.py line 119 87073] Train: [32/100][1355/1557] Data 0.004 (0.070) Batch 1.071 (1.292) Remain 38:04:47 loss: 0.6030 Lr: 0.00408 [2024-02-18 09:28:36,428 INFO misc.py line 119 87073] Train: [32/100][1356/1557] Data 0.003 (0.070) Batch 0.750 (1.292) Remain 38:04:03 loss: 0.3700 Lr: 0.00408 [2024-02-18 09:28:37,199 INFO misc.py line 119 87073] Train: [32/100][1357/1557] Data 0.004 (0.070) Batch 0.771 (1.292) Remain 38:03:21 loss: 0.3106 Lr: 0.00408 [2024-02-18 09:28:38,428 INFO misc.py line 119 87073] Train: [32/100][1358/1557] Data 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Remain 38:00:19 loss: 0.4671 Lr: 0.00408 [2024-02-18 09:28:45,092 INFO misc.py line 119 87073] Train: [32/100][1365/1557] Data 0.004 (0.070) Batch 1.077 (1.290) Remain 38:00:01 loss: 0.1326 Lr: 0.00408 [2024-02-18 09:28:46,018 INFO misc.py line 119 87073] Train: [32/100][1366/1557] Data 0.005 (0.070) Batch 0.926 (1.289) Remain 37:59:31 loss: 0.7392 Lr: 0.00408 [2024-02-18 09:28:47,090 INFO misc.py line 119 87073] Train: [32/100][1367/1557] Data 0.004 (0.069) Batch 1.071 (1.289) Remain 37:59:13 loss: 0.4120 Lr: 0.00408 [2024-02-18 09:28:48,056 INFO misc.py line 119 87073] Train: [32/100][1368/1557] Data 0.003 (0.069) Batch 0.967 (1.289) Remain 37:58:47 loss: 0.7966 Lr: 0.00408 [2024-02-18 09:28:49,007 INFO misc.py line 119 87073] Train: [32/100][1369/1557] Data 0.003 (0.069) Batch 0.951 (1.289) Remain 37:58:19 loss: 0.1222 Lr: 0.00408 [2024-02-18 09:28:49,769 INFO misc.py line 119 87073] Train: [32/100][1370/1557] Data 0.003 (0.069) Batch 0.755 (1.288) Remain 37:57:37 loss: 0.2931 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1377/1557] Data 0.003 (0.069) Batch 0.665 (1.287) Remain 37:54:30 loss: 0.7682 Lr: 0.00408 [2024-02-18 09:28:57,126 INFO misc.py line 119 87073] Train: [32/100][1378/1557] Data 0.003 (0.069) Batch 0.639 (1.286) Remain 37:53:39 loss: 0.2736 Lr: 0.00408 [2024-02-18 09:28:58,367 INFO misc.py line 119 87073] Train: [32/100][1379/1557] Data 0.003 (0.069) Batch 1.240 (1.286) Remain 37:53:34 loss: 0.1504 Lr: 0.00408 [2024-02-18 09:28:59,305 INFO misc.py line 119 87073] Train: [32/100][1380/1557] Data 0.005 (0.069) Batch 0.939 (1.286) Remain 37:53:06 loss: 0.3815 Lr: 0.00408 [2024-02-18 09:29:00,234 INFO misc.py line 119 87073] Train: [32/100][1381/1557] Data 0.004 (0.069) Batch 0.929 (1.286) Remain 37:52:38 loss: 0.5160 Lr: 0.00408 [2024-02-18 09:29:01,178 INFO misc.py line 119 87073] Train: [32/100][1382/1557] Data 0.004 (0.069) Batch 0.945 (1.286) Remain 37:52:10 loss: 0.4241 Lr: 0.00408 [2024-02-18 09:29:02,125 INFO misc.py line 119 87073] Train: [32/100][1383/1557] Data 0.003 (0.069) Batch 0.944 (1.285) Remain 37:51:43 loss: 0.2141 Lr: 0.00408 [2024-02-18 09:29:02,833 INFO misc.py line 119 87073] Train: [32/100][1384/1557] Data 0.005 (0.069) Batch 0.711 (1.285) Remain 37:50:57 loss: 0.2237 Lr: 0.00408 [2024-02-18 09:29:03,621 INFO misc.py line 119 87073] Train: [32/100][1385/1557] Data 0.003 (0.069) Batch 0.788 (1.284) Remain 37:50:18 loss: 0.5038 Lr: 0.00408 [2024-02-18 09:29:04,831 INFO misc.py line 119 87073] Train: [32/100][1386/1557] Data 0.003 (0.069) Batch 1.210 (1.284) Remain 37:50:11 loss: 0.2187 Lr: 0.00408 [2024-02-18 09:29:05,661 INFO misc.py line 119 87073] Train: [32/100][1387/1557] Data 0.004 (0.069) Batch 0.830 (1.284) Remain 37:49:35 loss: 0.9711 Lr: 0.00408 [2024-02-18 09:29:06,806 INFO misc.py line 119 87073] Train: [32/100][1388/1557] Data 0.004 (0.069) Batch 1.144 (1.284) Remain 37:49:23 loss: 0.4930 Lr: 0.00408 [2024-02-18 09:29:07,810 INFO misc.py line 119 87073] Train: [32/100][1389/1557] Data 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Remain 37:46:30 loss: 0.7111 Lr: 0.00408 [2024-02-18 09:29:14,508 INFO misc.py line 119 87073] Train: [32/100][1396/1557] Data 0.003 (0.068) Batch 0.863 (1.282) Remain 37:45:57 loss: 0.5208 Lr: 0.00408 [2024-02-18 09:29:15,477 INFO misc.py line 119 87073] Train: [32/100][1397/1557] Data 0.004 (0.068) Batch 0.969 (1.282) Remain 37:45:32 loss: 0.2690 Lr: 0.00408 [2024-02-18 09:29:16,254 INFO misc.py line 119 87073] Train: [32/100][1398/1557] Data 0.003 (0.068) Batch 0.777 (1.282) Remain 37:44:52 loss: 0.4584 Lr: 0.00408 [2024-02-18 09:29:17,029 INFO misc.py line 119 87073] Train: [32/100][1399/1557] Data 0.003 (0.068) Batch 0.775 (1.281) Remain 37:44:12 loss: 0.4740 Lr: 0.00408 [2024-02-18 09:29:18,194 INFO misc.py line 119 87073] Train: [32/100][1400/1557] Data 0.004 (0.068) Batch 1.165 (1.281) Remain 37:44:02 loss: 0.3846 Lr: 0.00408 [2024-02-18 09:29:19,227 INFO misc.py line 119 87073] Train: [32/100][1401/1557] Data 0.003 (0.068) Batch 1.026 (1.281) Remain 37:43:41 loss: 0.1675 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1408/1557] Data 0.003 (0.071) Batch 0.915 (1.293) Remain 38:04:20 loss: 0.5399 Lr: 0.00407 [2024-02-18 09:29:45,621 INFO misc.py line 119 87073] Train: [32/100][1409/1557] Data 0.009 (0.071) Batch 0.909 (1.292) Remain 38:03:49 loss: 0.8752 Lr: 0.00407 [2024-02-18 09:29:46,543 INFO misc.py line 119 87073] Train: [32/100][1410/1557] Data 0.004 (0.071) Batch 0.922 (1.292) Remain 38:03:20 loss: 0.2139 Lr: 0.00407 [2024-02-18 09:29:47,560 INFO misc.py line 119 87073] Train: [32/100][1411/1557] Data 0.003 (0.071) Batch 1.017 (1.292) Remain 38:02:58 loss: 0.5151 Lr: 0.00407 [2024-02-18 09:29:48,310 INFO misc.py line 119 87073] Train: [32/100][1412/1557] Data 0.003 (0.071) Batch 0.742 (1.292) Remain 38:02:15 loss: 0.2812 Lr: 0.00407 [2024-02-18 09:29:49,081 INFO misc.py line 119 87073] Train: [32/100][1413/1557] Data 0.010 (0.071) Batch 0.778 (1.291) Remain 38:01:36 loss: 0.4918 Lr: 0.00407 [2024-02-18 09:29:50,242 INFO misc.py line 119 87073] Train: [32/100][1414/1557] Data 0.004 (0.071) Batch 1.162 (1.291) Remain 38:01:25 loss: 0.2591 Lr: 0.00407 [2024-02-18 09:29:51,204 INFO misc.py line 119 87073] Train: [32/100][1415/1557] Data 0.003 (0.071) Batch 0.960 (1.291) Remain 38:00:58 loss: 0.3565 Lr: 0.00407 [2024-02-18 09:29:52,271 INFO misc.py line 119 87073] Train: [32/100][1416/1557] Data 0.005 (0.071) Batch 1.068 (1.291) Remain 38:00:40 loss: 0.3039 Lr: 0.00407 [2024-02-18 09:29:53,190 INFO misc.py line 119 87073] Train: [32/100][1417/1557] Data 0.004 (0.071) Batch 0.919 (1.290) Remain 38:00:11 loss: 0.3790 Lr: 0.00407 [2024-02-18 09:29:54,040 INFO misc.py line 119 87073] Train: [32/100][1418/1557] Data 0.006 (0.071) Batch 0.842 (1.290) Remain 37:59:36 loss: 0.2400 Lr: 0.00407 [2024-02-18 09:29:54,689 INFO misc.py line 119 87073] Train: [32/100][1419/1557] Data 0.012 (0.071) Batch 0.657 (1.290) Remain 37:58:48 loss: 0.1733 Lr: 0.00407 [2024-02-18 09:29:55,489 INFO misc.py line 119 87073] Train: [32/100][1420/1557] Data 0.003 (0.071) Batch 0.794 (1.289) Remain 37:58:09 loss: 0.2129 Lr: 0.00407 [2024-02-18 09:29:56,582 INFO misc.py line 119 87073] Train: [32/100][1421/1557] Data 0.010 (0.071) Batch 1.089 (1.289) Remain 37:57:53 loss: 0.2229 Lr: 0.00407 [2024-02-18 09:29:57,587 INFO misc.py line 119 87073] Train: [32/100][1422/1557] Data 0.013 (0.071) Batch 1.008 (1.289) Remain 37:57:31 loss: 0.3345 Lr: 0.00407 [2024-02-18 09:29:58,546 INFO misc.py line 119 87073] Train: [32/100][1423/1557] Data 0.010 (0.071) Batch 0.966 (1.289) Remain 37:57:05 loss: 0.4875 Lr: 0.00407 [2024-02-18 09:29:59,582 INFO misc.py line 119 87073] Train: [32/100][1424/1557] Data 0.003 (0.071) Batch 1.035 (1.289) Remain 37:56:45 loss: 0.4270 Lr: 0.00407 [2024-02-18 09:30:00,470 INFO misc.py line 119 87073] Train: [32/100][1425/1557] Data 0.004 (0.071) Batch 0.889 (1.288) Remain 37:56:14 loss: 0.3035 Lr: 0.00407 [2024-02-18 09:30:01,252 INFO misc.py line 119 87073] Train: [32/100][1426/1557] Data 0.004 (0.070) Batch 0.775 (1.288) Remain 37:55:34 loss: 0.2519 Lr: 0.00407 [2024-02-18 09:30:02,012 INFO misc.py line 119 87073] Train: [32/100][1427/1557] Data 0.011 (0.070) Batch 0.768 (1.288) Remain 37:54:54 loss: 0.3652 Lr: 0.00407 [2024-02-18 09:30:03,283 INFO misc.py line 119 87073] Train: [32/100][1428/1557] Data 0.002 (0.070) Batch 1.264 (1.288) Remain 37:54:51 loss: 0.3888 Lr: 0.00407 [2024-02-18 09:30:04,210 INFO misc.py line 119 87073] Train: [32/100][1429/1557] Data 0.010 (0.070) Batch 0.934 (1.287) Remain 37:54:24 loss: 0.3760 Lr: 0.00407 [2024-02-18 09:30:05,096 INFO misc.py line 119 87073] Train: [32/100][1430/1557] Data 0.003 (0.070) Batch 0.887 (1.287) Remain 37:53:53 loss: 0.5031 Lr: 0.00407 [2024-02-18 09:30:06,045 INFO misc.py line 119 87073] Train: [32/100][1431/1557] Data 0.003 (0.070) Batch 0.942 (1.287) Remain 37:53:26 loss: 0.6405 Lr: 0.00407 [2024-02-18 09:30:07,117 INFO misc.py line 119 87073] Train: [32/100][1432/1557] Data 0.010 (0.070) Batch 1.069 (1.287) Remain 37:53:08 loss: 0.3360 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1439/1557] Data 0.003 (0.070) Batch 1.046 (1.285) Remain 37:49:58 loss: 0.7366 Lr: 0.00407 [2024-02-18 09:30:14,452 INFO misc.py line 119 87073] Train: [32/100][1440/1557] Data 0.003 (0.070) Batch 0.800 (1.285) Remain 37:49:21 loss: 0.3879 Lr: 0.00407 [2024-02-18 09:30:15,242 INFO misc.py line 119 87073] Train: [32/100][1441/1557] Data 0.003 (0.070) Batch 0.784 (1.284) Remain 37:48:42 loss: 0.4422 Lr: 0.00407 [2024-02-18 09:30:16,485 INFO misc.py line 119 87073] Train: [32/100][1442/1557] Data 0.009 (0.070) Batch 1.242 (1.284) Remain 37:48:38 loss: 0.2270 Lr: 0.00407 [2024-02-18 09:30:17,389 INFO misc.py line 119 87073] Train: [32/100][1443/1557] Data 0.010 (0.070) Batch 0.912 (1.284) Remain 37:48:09 loss: 0.5136 Lr: 0.00407 [2024-02-18 09:30:18,182 INFO misc.py line 119 87073] Train: [32/100][1444/1557] Data 0.003 (0.070) Batch 0.792 (1.284) Remain 37:47:32 loss: 0.4054 Lr: 0.00407 [2024-02-18 09:30:19,198 INFO misc.py line 119 87073] Train: [32/100][1445/1557] Data 0.003 (0.070) Batch 1.010 (1.283) Remain 37:47:10 loss: 0.3081 Lr: 0.00407 [2024-02-18 09:30:20,221 INFO misc.py line 119 87073] Train: [32/100][1446/1557] Data 0.009 (0.070) Batch 1.023 (1.283) Remain 37:46:50 loss: 0.5906 Lr: 0.00407 [2024-02-18 09:30:21,027 INFO misc.py line 119 87073] Train: [32/100][1447/1557] Data 0.009 (0.070) Batch 0.811 (1.283) Remain 37:46:14 loss: 0.3272 Lr: 0.00407 [2024-02-18 09:30:21,798 INFO misc.py line 119 87073] Train: [32/100][1448/1557] Data 0.003 (0.069) Batch 0.772 (1.283) Remain 37:45:35 loss: 0.5862 Lr: 0.00407 [2024-02-18 09:30:23,062 INFO misc.py line 119 87073] Train: [32/100][1449/1557] Data 0.003 (0.069) Batch 1.259 (1.283) Remain 37:45:32 loss: 0.4262 Lr: 0.00407 [2024-02-18 09:30:23,916 INFO misc.py line 119 87073] Train: [32/100][1450/1557] Data 0.009 (0.069) Batch 0.859 (1.282) Remain 37:45:00 loss: 0.7384 Lr: 0.00407 [2024-02-18 09:30:24,820 INFO misc.py line 119 87073] Train: [32/100][1451/1557] Data 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Remain 37:42:00 loss: 0.2976 Lr: 0.00407 [2024-02-18 09:30:31,375 INFO misc.py line 119 87073] Train: [32/100][1458/1557] Data 0.003 (0.069) Batch 0.835 (1.280) Remain 37:41:26 loss: 0.5553 Lr: 0.00407 [2024-02-18 09:30:32,297 INFO misc.py line 119 87073] Train: [32/100][1459/1557] Data 0.004 (0.069) Batch 0.919 (1.280) Remain 37:40:58 loss: 0.7926 Lr: 0.00407 [2024-02-18 09:30:33,184 INFO misc.py line 119 87073] Train: [32/100][1460/1557] Data 0.006 (0.069) Batch 0.890 (1.280) Remain 37:40:29 loss: 0.4132 Lr: 0.00407 [2024-02-18 09:30:33,961 INFO misc.py line 119 87073] Train: [32/100][1461/1557] Data 0.004 (0.069) Batch 0.776 (1.280) Remain 37:39:51 loss: 0.6705 Lr: 0.00407 [2024-02-18 09:30:34,639 INFO misc.py line 119 87073] Train: [32/100][1462/1557] Data 0.005 (0.069) Batch 0.670 (1.279) Remain 37:39:05 loss: 0.5717 Lr: 0.00407 [2024-02-18 09:30:56,810 INFO misc.py line 119 87073] Train: [32/100][1463/1557] Data 3.215 (0.071) Batch 22.180 (1.293) Remain 38:04:21 loss: 0.3280 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1470/1557] Data 0.012 (0.071) Batch 1.203 (1.292) Remain 38:01:19 loss: 0.1262 Lr: 0.00407 [2024-02-18 09:31:04,638 INFO misc.py line 119 87073] Train: [32/100][1471/1557] Data 0.011 (0.071) Batch 1.160 (1.292) Remain 38:01:08 loss: 1.3311 Lr: 0.00407 [2024-02-18 09:31:05,561 INFO misc.py line 119 87073] Train: [32/100][1472/1557] Data 0.007 (0.071) Batch 0.926 (1.291) Remain 38:00:41 loss: 0.2925 Lr: 0.00407 [2024-02-18 09:31:06,517 INFO misc.py line 119 87073] Train: [32/100][1473/1557] Data 0.003 (0.071) Batch 0.957 (1.291) Remain 38:00:15 loss: 0.3039 Lr: 0.00407 [2024-02-18 09:31:07,486 INFO misc.py line 119 87073] Train: [32/100][1474/1557] Data 0.003 (0.071) Batch 0.969 (1.291) Remain 37:59:51 loss: 0.4942 Lr: 0.00407 [2024-02-18 09:31:08,246 INFO misc.py line 119 87073] Train: [32/100][1475/1557] Data 0.004 (0.071) Batch 0.758 (1.291) Remain 37:59:11 loss: 0.8763 Lr: 0.00407 [2024-02-18 09:31:08,985 INFO misc.py line 119 87073] Train: [32/100][1476/1557] Data 0.004 (0.070) Batch 0.740 (1.290) Remain 37:58:30 loss: 0.2619 Lr: 0.00407 [2024-02-18 09:31:10,159 INFO misc.py line 119 87073] Train: [32/100][1477/1557] Data 0.003 (0.070) Batch 1.174 (1.290) Remain 37:58:21 loss: 0.1928 Lr: 0.00407 [2024-02-18 09:31:11,111 INFO misc.py line 119 87073] Train: [32/100][1478/1557] Data 0.004 (0.070) Batch 0.952 (1.290) Remain 37:57:55 loss: 0.4302 Lr: 0.00407 [2024-02-18 09:31:11,919 INFO misc.py line 119 87073] Train: [32/100][1479/1557] Data 0.004 (0.070) Batch 0.808 (1.290) Remain 37:57:19 loss: 0.1351 Lr: 0.00407 [2024-02-18 09:31:12,938 INFO misc.py line 119 87073] Train: [32/100][1480/1557] Data 0.004 (0.070) Batch 1.016 (1.289) Remain 37:56:58 loss: 0.5334 Lr: 0.00407 [2024-02-18 09:31:13,823 INFO misc.py line 119 87073] Train: [32/100][1481/1557] Data 0.007 (0.070) Batch 0.888 (1.289) Remain 37:56:28 loss: 0.3320 Lr: 0.00407 [2024-02-18 09:31:14,600 INFO misc.py line 119 87073] Train: [32/100][1482/1557] Data 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Remain 37:53:45 loss: 0.2587 Lr: 0.00407 [2024-02-18 09:31:21,441 INFO misc.py line 119 87073] Train: [32/100][1489/1557] Data 0.007 (0.070) Batch 0.753 (1.287) Remain 37:53:06 loss: 0.3538 Lr: 0.00407 [2024-02-18 09:31:22,206 INFO misc.py line 119 87073] Train: [32/100][1490/1557] Data 0.006 (0.070) Batch 0.764 (1.287) Remain 37:52:27 loss: 0.4938 Lr: 0.00407 [2024-02-18 09:31:23,581 INFO misc.py line 119 87073] Train: [32/100][1491/1557] Data 0.009 (0.070) Batch 1.370 (1.287) Remain 37:52:32 loss: 0.3114 Lr: 0.00407 [2024-02-18 09:31:24,624 INFO misc.py line 119 87073] Train: [32/100][1492/1557] Data 0.012 (0.070) Batch 1.047 (1.287) Remain 37:52:13 loss: 0.4207 Lr: 0.00407 [2024-02-18 09:31:25,538 INFO misc.py line 119 87073] Train: [32/100][1493/1557] Data 0.009 (0.070) Batch 0.918 (1.287) Remain 37:51:46 loss: 0.6262 Lr: 0.00407 [2024-02-18 09:31:26,634 INFO misc.py line 119 87073] Train: [32/100][1494/1557] Data 0.005 (0.070) Batch 1.097 (1.287) Remain 37:51:31 loss: 0.5132 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1501/1557] Data 0.004 (0.069) Batch 0.918 (1.285) Remain 37:48:21 loss: 0.4416 Lr: 0.00407 [2024-02-18 09:31:33,879 INFO misc.py line 119 87073] Train: [32/100][1502/1557] Data 0.013 (0.069) Batch 0.800 (1.284) Remain 37:47:45 loss: 0.4580 Lr: 0.00407 [2024-02-18 09:31:34,693 INFO misc.py line 119 87073] Train: [32/100][1503/1557] Data 0.005 (0.069) Batch 0.813 (1.284) Remain 37:47:11 loss: 0.3010 Lr: 0.00407 [2024-02-18 09:31:35,488 INFO misc.py line 119 87073] Train: [32/100][1504/1557] Data 0.007 (0.069) Batch 0.796 (1.284) Remain 37:46:35 loss: 0.3521 Lr: 0.00407 [2024-02-18 09:31:36,756 INFO misc.py line 119 87073] Train: [32/100][1505/1557] Data 0.005 (0.069) Batch 1.268 (1.284) Remain 37:46:33 loss: 0.1873 Lr: 0.00407 [2024-02-18 09:31:37,759 INFO misc.py line 119 87073] Train: [32/100][1506/1557] Data 0.005 (0.069) Batch 1.002 (1.284) Remain 37:46:11 loss: 0.1970 Lr: 0.00407 [2024-02-18 09:31:38,773 INFO misc.py line 119 87073] Train: [32/100][1507/1557] Data 0.006 (0.069) Batch 1.008 (1.283) Remain 37:45:51 loss: 0.2697 Lr: 0.00407 [2024-02-18 09:31:39,754 INFO misc.py line 119 87073] Train: [32/100][1508/1557] Data 0.012 (0.069) Batch 0.989 (1.283) Remain 37:45:29 loss: 0.4015 Lr: 0.00407 [2024-02-18 09:31:40,678 INFO misc.py line 119 87073] Train: [32/100][1509/1557] Data 0.004 (0.069) Batch 0.925 (1.283) Remain 37:45:02 loss: 0.3234 Lr: 0.00407 [2024-02-18 09:31:41,454 INFO misc.py line 119 87073] Train: [32/100][1510/1557] Data 0.003 (0.069) Batch 0.766 (1.283) Remain 37:44:25 loss: 0.2916 Lr: 0.00407 [2024-02-18 09:31:42,149 INFO misc.py line 119 87073] Train: [32/100][1511/1557] Data 0.012 (0.069) Batch 0.704 (1.282) Remain 37:43:43 loss: 0.3455 Lr: 0.00407 [2024-02-18 09:31:43,310 INFO misc.py line 119 87073] Train: [32/100][1512/1557] Data 0.003 (0.069) Batch 1.152 (1.282) Remain 37:43:32 loss: 0.3021 Lr: 0.00407 [2024-02-18 09:31:44,146 INFO misc.py line 119 87073] Train: [32/100][1513/1557] Data 0.013 (0.069) Batch 0.846 (1.282) Remain 37:43:00 loss: 0.6686 Lr: 0.00407 [2024-02-18 09:31:45,178 INFO misc.py line 119 87073] Train: [32/100][1514/1557] Data 0.003 (0.069) Batch 1.031 (1.282) Remain 37:42:41 loss: 0.6899 Lr: 0.00407 [2024-02-18 09:31:46,167 INFO misc.py line 119 87073] Train: [32/100][1515/1557] Data 0.005 (0.069) Batch 0.989 (1.282) Remain 37:42:20 loss: 0.3403 Lr: 0.00407 [2024-02-18 09:31:47,214 INFO misc.py line 119 87073] Train: [32/100][1516/1557] Data 0.005 (0.069) Batch 1.047 (1.281) Remain 37:42:02 loss: 0.3181 Lr: 0.00407 [2024-02-18 09:31:47,971 INFO misc.py line 119 87073] Train: [32/100][1517/1557] Data 0.004 (0.069) Batch 0.757 (1.281) Remain 37:41:24 loss: 0.4348 Lr: 0.00407 [2024-02-18 09:31:48,707 INFO misc.py line 119 87073] Train: [32/100][1518/1557] Data 0.004 (0.069) Batch 0.732 (1.281) Remain 37:40:44 loss: 0.3478 Lr: 0.00407 [2024-02-18 09:32:11,026 INFO misc.py line 119 87073] Train: [32/100][1519/1557] Data 2.974 (0.071) Batch 22.323 (1.295) Remain 38:05:13 loss: 0.1021 Lr: 0.00407 [2024-02-18 09:32:12,133 INFO misc.py line 119 87073] Train: [32/100][1520/1557] Data 0.005 (0.071) Batch 1.099 (1.294) Remain 38:04:58 loss: 0.3266 Lr: 0.00407 [2024-02-18 09:32:13,095 INFO misc.py line 119 87073] Train: [32/100][1521/1557] Data 0.012 (0.071) Batch 0.971 (1.294) Remain 38:04:34 loss: 0.1882 Lr: 0.00407 [2024-02-18 09:32:14,003 INFO misc.py line 119 87073] Train: [32/100][1522/1557] Data 0.004 (0.070) Batch 0.907 (1.294) Remain 38:04:06 loss: 0.3646 Lr: 0.00407 [2024-02-18 09:32:14,904 INFO misc.py line 119 87073] Train: [32/100][1523/1557] Data 0.004 (0.070) Batch 0.902 (1.294) Remain 38:03:37 loss: 0.3335 Lr: 0.00407 [2024-02-18 09:32:15,673 INFO misc.py line 119 87073] Train: [32/100][1524/1557] Data 0.004 (0.070) Batch 0.761 (1.293) Remain 38:02:59 loss: 0.1255 Lr: 0.00407 [2024-02-18 09:32:16,384 INFO misc.py line 119 87073] Train: [32/100][1525/1557] Data 0.012 (0.070) Batch 0.719 (1.293) Remain 38:02:18 loss: 0.4068 Lr: 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INFO misc.py line 119 87073] Train: [32/100][1532/1557] Data 0.003 (0.070) Batch 0.756 (1.291) Remain 37:59:20 loss: 0.3646 Lr: 0.00407 [2024-02-18 09:32:24,163 INFO misc.py line 119 87073] Train: [32/100][1533/1557] Data 0.003 (0.070) Batch 1.160 (1.291) Remain 37:59:09 loss: 0.1334 Lr: 0.00407 [2024-02-18 09:32:25,151 INFO misc.py line 119 87073] Train: [32/100][1534/1557] Data 0.013 (0.070) Batch 0.997 (1.291) Remain 37:58:48 loss: 0.3764 Lr: 0.00407 [2024-02-18 09:32:26,062 INFO misc.py line 119 87073] Train: [32/100][1535/1557] Data 0.003 (0.070) Batch 0.910 (1.291) Remain 37:58:20 loss: 0.6112 Lr: 0.00407 [2024-02-18 09:32:27,048 INFO misc.py line 119 87073] Train: [32/100][1536/1557] Data 0.004 (0.070) Batch 0.986 (1.291) Remain 37:57:58 loss: 0.4035 Lr: 0.00407 [2024-02-18 09:32:28,020 INFO misc.py line 119 87073] Train: [32/100][1537/1557] Data 0.004 (0.070) Batch 0.973 (1.290) Remain 37:57:34 loss: 0.2647 Lr: 0.00407 [2024-02-18 09:32:28,785 INFO misc.py line 119 87073] Train: [32/100][1538/1557] Data 0.004 (0.070) Batch 0.765 (1.290) Remain 37:56:57 loss: 0.5367 Lr: 0.00407 [2024-02-18 09:32:29,561 INFO misc.py line 119 87073] Train: [32/100][1539/1557] Data 0.003 (0.070) Batch 0.772 (1.290) Remain 37:56:20 loss: 0.5562 Lr: 0.00407 [2024-02-18 09:32:30,812 INFO misc.py line 119 87073] Train: [32/100][1540/1557] Data 0.007 (0.070) Batch 1.244 (1.290) Remain 37:56:15 loss: 0.1343 Lr: 0.00407 [2024-02-18 09:32:31,817 INFO misc.py line 119 87073] Train: [32/100][1541/1557] Data 0.015 (0.070) Batch 1.006 (1.290) Remain 37:55:55 loss: 1.2257 Lr: 0.00407 [2024-02-18 09:32:32,616 INFO misc.py line 119 87073] Train: [32/100][1542/1557] Data 0.014 (0.070) Batch 0.809 (1.289) Remain 37:55:20 loss: 0.5946 Lr: 0.00407 [2024-02-18 09:32:33,665 INFO misc.py line 119 87073] Train: [32/100][1543/1557] Data 0.003 (0.070) Batch 1.049 (1.289) Remain 37:55:02 loss: 0.4564 Lr: 0.00407 [2024-02-18 09:32:34,728 INFO misc.py line 119 87073] Train: [32/100][1544/1557] Data 0.003 (0.070) Batch 1.063 (1.289) Remain 37:54:46 loss: 0.6699 Lr: 0.00407 [2024-02-18 09:32:35,470 INFO misc.py line 119 87073] Train: [32/100][1545/1557] Data 0.003 (0.070) Batch 0.742 (1.289) Remain 37:54:07 loss: 0.3245 Lr: 0.00407 [2024-02-18 09:32:36,175 INFO misc.py line 119 87073] Train: [32/100][1546/1557] Data 0.003 (0.069) Batch 0.695 (1.288) Remain 37:53:25 loss: 0.4530 Lr: 0.00407 [2024-02-18 09:32:37,463 INFO misc.py line 119 87073] Train: [32/100][1547/1557] Data 0.014 (0.069) Batch 1.288 (1.288) Remain 37:53:24 loss: 0.2068 Lr: 0.00407 [2024-02-18 09:32:38,379 INFO misc.py line 119 87073] Train: [32/100][1548/1557] Data 0.012 (0.069) Batch 0.926 (1.288) Remain 37:52:57 loss: 0.4421 Lr: 0.00407 [2024-02-18 09:32:39,226 INFO misc.py line 119 87073] Train: [32/100][1549/1557] Data 0.003 (0.069) Batch 0.846 (1.288) Remain 37:52:26 loss: 0.4434 Lr: 0.00407 [2024-02-18 09:32:40,103 INFO misc.py line 119 87073] Train: [32/100][1550/1557] Data 0.003 (0.069) Batch 0.869 (1.287) Remain 37:51:56 loss: 0.5355 Lr: 0.00407 [2024-02-18 09:32:40,972 INFO misc.py line 119 87073] Train: [32/100][1551/1557] Data 0.012 (0.069) Batch 0.876 (1.287) Remain 37:51:27 loss: 0.5253 Lr: 0.00407 [2024-02-18 09:32:41,676 INFO misc.py line 119 87073] Train: [32/100][1552/1557] Data 0.004 (0.069) Batch 0.705 (1.287) Remain 37:50:45 loss: 0.4707 Lr: 0.00407 [2024-02-18 09:32:42,352 INFO misc.py line 119 87073] Train: [32/100][1553/1557] Data 0.003 (0.069) Batch 0.668 (1.286) Remain 37:50:02 loss: 0.2173 Lr: 0.00407 [2024-02-18 09:32:43,588 INFO misc.py line 119 87073] Train: [32/100][1554/1557] Data 0.012 (0.069) Batch 1.233 (1.286) Remain 37:49:57 loss: 0.2038 Lr: 0.00407 [2024-02-18 09:32:44,580 INFO misc.py line 119 87073] Train: [32/100][1555/1557] Data 0.015 (0.069) Batch 1.001 (1.286) Remain 37:49:36 loss: 0.5822 Lr: 0.00407 [2024-02-18 09:32:45,338 INFO misc.py line 119 87073] Train: [32/100][1556/1557] Data 0.005 (0.069) Batch 0.761 (1.286) Remain 37:48:59 loss: 0.3821 Lr: 0.00407 [2024-02-18 09:32:46,343 INFO misc.py line 119 87073] Train: [32/100][1557/1557] Data 0.003 (0.069) Batch 1.005 (1.286) Remain 37:48:39 loss: 0.8439 Lr: 0.00407 [2024-02-18 09:32:46,343 INFO misc.py line 136 87073] Train result: loss: 0.4313 [2024-02-18 09:32:46,344 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 09:33:13,291 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 1.0035 [2024-02-18 09:33:14,067 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4194 [2024-02-18 09:33:16,195 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3787 [2024-02-18 09:33:18,402 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3242 [2024-02-18 09:33:23,347 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2796 [2024-02-18 09:33:24,047 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0871 [2024-02-18 09:33:25,308 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3045 [2024-02-18 09:33:28,263 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2945 [2024-02-18 09:33:30,070 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2886 [2024-02-18 09:33:31,697 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7142/0.7688/0.9139. [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9365/0.9723 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9821/0.9900 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8657/0.9773 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0003/0.0026 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3500/0.3823 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6750/0.6995 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8099/0.8855 [2024-02-18 09:33:31,697 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8178/0.8999 [2024-02-18 09:33:31,698 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9250/0.9686 [2024-02-18 09:33:31,698 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7979/0.8306 [2024-02-18 09:33:31,698 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7795/0.8678 [2024-02-18 09:33:31,698 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7446/0.8247 [2024-02-18 09:33:31,698 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6005/0.6929 [2024-02-18 09:33:31,702 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 09:33:31,703 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 09:33:31,703 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 09:33:38,229 INFO misc.py line 119 87073] Train: [33/100][1/1557] Data 1.400 (1.400) Batch 2.208 (2.208) Remain 64:56:14 loss: 0.5032 Lr: 0.00407 [2024-02-18 09:33:39,199 INFO misc.py line 119 87073] Train: [33/100][2/1557] Data 0.005 (0.005) Batch 0.970 (0.970) Remain 28:31:09 loss: 0.2674 Lr: 0.00407 [2024-02-18 09:33:40,093 INFO misc.py line 119 87073] Train: [33/100][3/1557] Data 0.005 (0.005) Batch 0.895 (0.895) Remain 26:18:50 loss: 0.4264 Lr: 0.00407 [2024-02-18 09:33:40,988 INFO misc.py line 119 87073] Train: [33/100][4/1557] Data 0.005 (0.005) Batch 0.895 (0.895) Remain 26:19:03 loss: 0.3618 Lr: 0.00407 [2024-02-18 09:33:41,712 INFO misc.py line 119 87073] Train: [33/100][5/1557] Data 0.007 (0.006) Batch 0.722 (0.809) Remain 23:46:39 loss: 0.7653 Lr: 0.00407 [2024-02-18 09:33:42,450 INFO misc.py line 119 87073] Train: [33/100][6/1557] Data 0.005 (0.006) Batch 0.741 (0.786) Remain 23:07:11 loss: 0.3455 Lr: 0.00407 [2024-02-18 09:33:43,856 INFO misc.py line 119 87073] Train: [33/100][7/1557] Data 0.265 (0.071) Batch 1.398 (0.939) Remain 27:37:05 loss: 0.2922 Lr: 0.00407 [2024-02-18 09:33:44,848 INFO misc.py line 119 87073] Train: [33/100][8/1557] Data 0.011 (0.059) Batch 0.999 (0.951) Remain 27:58:03 loss: 0.4410 Lr: 0.00407 [2024-02-18 09:33:45,819 INFO misc.py line 119 87073] Train: [33/100][9/1557] Data 0.004 (0.050) Batch 0.971 (0.954) Remain 28:04:02 loss: 0.8089 Lr: 0.00407 [2024-02-18 09:33:46,885 INFO misc.py line 119 87073] Train: [33/100][10/1557] Data 0.004 (0.043) Batch 1.065 (0.970) Remain 28:31:57 loss: 0.2789 Lr: 0.00407 [2024-02-18 09:33:47,912 INFO misc.py line 119 87073] Train: [33/100][11/1557] Data 0.004 (0.038) Batch 1.026 (0.977) Remain 28:44:11 loss: 0.6075 Lr: 0.00407 [2024-02-18 09:33:48,677 INFO misc.py line 119 87073] Train: [33/100][12/1557] Data 0.006 (0.035) Batch 0.766 (0.954) Remain 28:02:41 loss: 0.4653 Lr: 0.00407 [2024-02-18 09:33:49,436 INFO misc.py line 119 87073] Train: [33/100][13/1557] Data 0.005 (0.032) Batch 0.759 (0.934) Remain 27:28:23 loss: 0.3094 Lr: 0.00407 [2024-02-18 09:33:50,663 INFO misc.py line 119 87073] Train: [33/100][14/1557] Data 0.005 (0.029) Batch 1.226 (0.961) Remain 28:15:12 loss: 0.1772 Lr: 0.00407 [2024-02-18 09:33:51,650 INFO misc.py line 119 87073] Train: [33/100][15/1557] Data 0.006 (0.027) Batch 0.988 (0.963) Remain 28:19:13 loss: 0.6410 Lr: 0.00407 [2024-02-18 09:33:52,541 INFO misc.py line 119 87073] Train: [33/100][16/1557] Data 0.004 (0.026) Batch 0.892 (0.958) Remain 28:09:30 loss: 0.3644 Lr: 0.00407 [2024-02-18 09:33:53,463 INFO misc.py line 119 87073] Train: [33/100][17/1557] Data 0.003 (0.024) Batch 0.920 (0.955) Remain 28:04:45 loss: 0.6945 Lr: 0.00407 [2024-02-18 09:33:54,470 INFO misc.py line 119 87073] Train: [33/100][18/1557] Data 0.006 (0.023) Batch 1.009 (0.959) Remain 28:11:08 loss: 0.2342 Lr: 0.00407 [2024-02-18 09:33:55,228 INFO misc.py line 119 87073] Train: [33/100][19/1557] Data 0.003 (0.021) Batch 0.758 (0.946) Remain 27:49:00 loss: 0.4612 Lr: 0.00407 [2024-02-18 09:33:55,985 INFO misc.py line 119 87073] Train: [33/100][20/1557] Data 0.003 (0.020) Batch 0.755 (0.935) Remain 27:29:12 loss: 0.3717 Lr: 0.00407 [2024-02-18 09:33:57,277 INFO misc.py line 119 87073] Train: [33/100][21/1557] Data 0.005 (0.019) Batch 1.292 (0.955) Remain 28:04:13 loss: 0.3471 Lr: 0.00407 [2024-02-18 09:33:58,297 INFO misc.py line 119 87073] Train: [33/100][22/1557] Data 0.004 (0.019) Batch 1.018 (0.958) Remain 28:10:08 loss: 0.9530 Lr: 0.00407 [2024-02-18 09:33:59,367 INFO misc.py line 119 87073] Train: [33/100][23/1557] Data 0.005 (0.018) Batch 1.072 (0.964) Remain 28:20:12 loss: 0.4404 Lr: 0.00407 [2024-02-18 09:34:00,232 INFO misc.py line 119 87073] Train: [33/100][24/1557] Data 0.004 (0.017) Batch 0.864 (0.959) Remain 28:11:48 loss: 0.6634 Lr: 0.00407 [2024-02-18 09:34:01,082 INFO misc.py line 119 87073] Train: [33/100][25/1557] Data 0.005 (0.017) Batch 0.851 (0.954) Remain 28:03:07 loss: 0.3959 Lr: 0.00407 [2024-02-18 09:34:01,873 INFO misc.py line 119 87073] Train: [33/100][26/1557] Data 0.005 (0.016) Batch 0.788 (0.947) Remain 27:50:23 loss: 0.5658 Lr: 0.00407 [2024-02-18 09:34:02,690 INFO misc.py line 119 87073] Train: [33/100][27/1557] Data 0.008 (0.016) Batch 0.818 (0.941) Remain 27:40:52 loss: 0.1743 Lr: 0.00407 [2024-02-18 09:34:03,796 INFO misc.py line 119 87073] Train: [33/100][28/1557] Data 0.007 (0.016) Batch 1.106 (0.948) Remain 27:52:28 loss: 0.3274 Lr: 0.00407 [2024-02-18 09:34:04,857 INFO misc.py line 119 87073] Train: [33/100][29/1557] Data 0.007 (0.015) Batch 1.062 (0.952) Remain 28:00:10 loss: 0.2656 Lr: 0.00407 [2024-02-18 09:34:05,887 INFO misc.py line 119 87073] Train: [33/100][30/1557] Data 0.006 (0.015) Batch 1.031 (0.955) Remain 28:05:16 loss: 0.4684 Lr: 0.00407 [2024-02-18 09:34:06,706 INFO misc.py line 119 87073] Train: [33/100][31/1557] Data 0.004 (0.015) Batch 0.819 (0.950) Remain 27:56:39 loss: 0.7028 Lr: 0.00407 [2024-02-18 09:34:07,568 INFO misc.py line 119 87073] Train: [33/100][32/1557] Data 0.005 (0.014) Batch 0.859 (0.947) Remain 27:51:03 loss: 0.7219 Lr: 0.00407 [2024-02-18 09:34:08,343 INFO misc.py line 119 87073] Train: [33/100][33/1557] Data 0.011 (0.014) Batch 0.776 (0.942) Remain 27:40:57 loss: 0.5023 Lr: 0.00407 [2024-02-18 09:34:09,076 INFO misc.py line 119 87073] Train: [33/100][34/1557] Data 0.008 (0.014) Batch 0.737 (0.935) Remain 27:29:17 loss: 0.4529 Lr: 0.00407 [2024-02-18 09:34:10,296 INFO misc.py line 119 87073] Train: [33/100][35/1557] Data 0.003 (0.014) Batch 1.220 (0.944) Remain 27:44:57 loss: 0.0889 Lr: 0.00407 [2024-02-18 09:34:11,212 INFO misc.py line 119 87073] Train: [33/100][36/1557] Data 0.004 (0.013) Batch 0.917 (0.943) Remain 27:43:29 loss: 0.8834 Lr: 0.00407 [2024-02-18 09:34:12,082 INFO misc.py line 119 87073] Train: [33/100][37/1557] Data 0.004 (0.013) Batch 0.870 (0.941) Remain 27:39:41 loss: 0.2291 Lr: 0.00407 [2024-02-18 09:34:13,068 INFO misc.py line 119 87073] Train: [33/100][38/1557] Data 0.003 (0.013) Batch 0.982 (0.942) Remain 27:41:43 loss: 0.7683 Lr: 0.00407 [2024-02-18 09:34:13,940 INFO misc.py line 119 87073] Train: [33/100][39/1557] Data 0.007 (0.013) Batch 0.875 (0.940) Remain 27:38:26 loss: 0.4568 Lr: 0.00407 [2024-02-18 09:34:14,798 INFO misc.py line 119 87073] Train: [33/100][40/1557] Data 0.004 (0.012) Batch 0.858 (0.938) Remain 27:34:30 loss: 0.2533 Lr: 0.00407 [2024-02-18 09:34:15,523 INFO misc.py line 119 87073] Train: [33/100][41/1557] Data 0.005 (0.012) Batch 0.724 (0.932) Remain 27:24:35 loss: 0.4416 Lr: 0.00407 [2024-02-18 09:34:16,571 INFO misc.py line 119 87073] Train: [33/100][42/1557] Data 0.005 (0.012) Batch 1.048 (0.935) Remain 27:29:48 loss: 0.3483 Lr: 0.00407 [2024-02-18 09:34:17,589 INFO misc.py line 119 87073] Train: [33/100][43/1557] Data 0.005 (0.012) Batch 1.013 (0.937) Remain 27:33:12 loss: 0.3542 Lr: 0.00407 [2024-02-18 09:34:18,590 INFO misc.py line 119 87073] Train: [33/100][44/1557] Data 0.011 (0.012) Batch 1.003 (0.939) Remain 27:36:01 loss: 0.6994 Lr: 0.00407 [2024-02-18 09:34:19,666 INFO misc.py line 119 87073] Train: [33/100][45/1557] Data 0.007 (0.012) Batch 1.075 (0.942) Remain 27:41:44 loss: 0.6061 Lr: 0.00407 [2024-02-18 09:34:20,729 INFO misc.py line 119 87073] Train: [33/100][46/1557] Data 0.008 (0.012) Batch 1.060 (0.945) Remain 27:46:34 loss: 0.1610 Lr: 0.00407 [2024-02-18 09:34:21,476 INFO misc.py line 119 87073] Train: [33/100][47/1557] Data 0.012 (0.012) Batch 0.755 (0.941) Remain 27:38:56 loss: 0.4224 Lr: 0.00407 [2024-02-18 09:34:22,261 INFO misc.py line 119 87073] Train: [33/100][48/1557] Data 0.003 (0.011) Batch 0.776 (0.937) Remain 27:32:28 loss: 0.8793 Lr: 0.00407 [2024-02-18 09:34:23,451 INFO misc.py line 119 87073] Train: [33/100][49/1557] Data 0.012 (0.011) Batch 1.190 (0.942) Remain 27:42:11 loss: 0.5708 Lr: 0.00407 [2024-02-18 09:34:24,478 INFO misc.py line 119 87073] Train: [33/100][50/1557] Data 0.011 (0.011) Batch 1.033 (0.944) Remain 27:45:34 loss: 0.2429 Lr: 0.00407 [2024-02-18 09:34:25,440 INFO misc.py line 119 87073] Train: [33/100][51/1557] Data 0.005 (0.011) Batch 0.964 (0.945) Remain 27:46:17 loss: 0.4473 Lr: 0.00407 [2024-02-18 09:34:26,379 INFO misc.py line 119 87073] Train: [33/100][52/1557] Data 0.003 (0.011) Batch 0.936 (0.945) Remain 27:45:57 loss: 0.2756 Lr: 0.00407 [2024-02-18 09:34:27,267 INFO misc.py line 119 87073] Train: [33/100][53/1557] Data 0.007 (0.011) Batch 0.891 (0.943) Remain 27:44:02 loss: 0.4894 Lr: 0.00407 [2024-02-18 09:34:28,068 INFO misc.py line 119 87073] Train: [33/100][54/1557] Data 0.004 (0.011) Batch 0.800 (0.941) Remain 27:39:04 loss: 0.3799 Lr: 0.00407 [2024-02-18 09:34:28,865 INFO misc.py line 119 87073] Train: [33/100][55/1557] Data 0.005 (0.011) Batch 0.796 (0.938) Remain 27:34:08 loss: 0.3636 Lr: 0.00407 [2024-02-18 09:34:30,140 INFO misc.py line 119 87073] Train: [33/100][56/1557] Data 0.006 (0.011) Batch 1.277 (0.944) Remain 27:45:23 loss: 0.1532 Lr: 0.00407 [2024-02-18 09:34:31,096 INFO misc.py line 119 87073] Train: [33/100][57/1557] Data 0.004 (0.011) Batch 0.955 (0.944) Remain 27:45:44 loss: 0.4374 Lr: 0.00407 [2024-02-18 09:34:32,159 INFO misc.py line 119 87073] Train: [33/100][58/1557] Data 0.008 (0.011) Batch 1.059 (0.947) Remain 27:49:22 loss: 0.8346 Lr: 0.00407 [2024-02-18 09:34:33,249 INFO misc.py line 119 87073] Train: [33/100][59/1557] Data 0.010 (0.011) Batch 1.093 (0.949) Remain 27:53:59 loss: 0.3612 Lr: 0.00407 [2024-02-18 09:34:34,311 INFO misc.py line 119 87073] Train: [33/100][60/1557] Data 0.007 (0.010) Batch 1.062 (0.951) Remain 27:57:27 loss: 0.4337 Lr: 0.00407 [2024-02-18 09:34:35,099 INFO misc.py line 119 87073] Train: [33/100][61/1557] Data 0.006 (0.010) Batch 0.788 (0.948) Remain 27:52:29 loss: 0.2695 Lr: 0.00407 [2024-02-18 09:34:35,872 INFO misc.py line 119 87073] Train: [33/100][62/1557] Data 0.007 (0.010) Batch 0.774 (0.945) Remain 27:47:15 loss: 0.6645 Lr: 0.00407 [2024-02-18 09:34:43,353 INFO misc.py line 119 87073] Train: [33/100][63/1557] Data 6.394 (0.117) Batch 7.481 (1.054) Remain 30:59:20 loss: 0.2573 Lr: 0.00407 [2024-02-18 09:34:44,304 INFO misc.py line 119 87073] Train: [33/100][64/1557] Data 0.007 (0.115) Batch 0.952 (1.053) Remain 30:56:21 loss: 0.3852 Lr: 0.00407 [2024-02-18 09:34:45,255 INFO misc.py line 119 87073] Train: 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Batch 0.906 (1.049) Remain 30:49:26 loss: 0.6578 Lr: 0.00406 [2024-02-18 09:35:44,928 INFO misc.py line 119 87073] Train: [33/100][122/1557] Data 0.007 (0.115) Batch 1.019 (1.049) Remain 30:48:58 loss: 0.2078 Lr: 0.00406 [2024-02-18 09:35:45,883 INFO misc.py line 119 87073] Train: [33/100][123/1557] Data 0.005 (0.114) Batch 0.956 (1.048) Remain 30:47:35 loss: 0.4946 Lr: 0.00406 [2024-02-18 09:35:46,621 INFO misc.py line 119 87073] Train: [33/100][124/1557] Data 0.004 (0.113) Batch 0.738 (1.046) Remain 30:43:03 loss: 0.4190 Lr: 0.00406 [2024-02-18 09:35:47,395 INFO misc.py line 119 87073] Train: [33/100][125/1557] Data 0.004 (0.112) Batch 0.767 (1.043) Remain 30:39:00 loss: 0.3514 Lr: 0.00406 [2024-02-18 09:35:48,713 INFO misc.py line 119 87073] Train: [33/100][126/1557] Data 0.012 (0.111) Batch 1.319 (1.046) Remain 30:42:56 loss: 0.1728 Lr: 0.00406 [2024-02-18 09:35:49,547 INFO misc.py line 119 87073] Train: [33/100][127/1557] Data 0.011 (0.110) Batch 0.840 (1.044) Remain 30:39:59 loss: 0.6473 Lr: 0.00406 [2024-02-18 09:35:50,591 INFO misc.py line 119 87073] Train: [33/100][128/1557] Data 0.005 (0.110) Batch 1.045 (1.044) Remain 30:39:59 loss: 0.5618 Lr: 0.00406 [2024-02-18 09:35:51,514 INFO misc.py line 119 87073] Train: [33/100][129/1557] Data 0.004 (0.109) Batch 0.924 (1.043) Remain 30:38:17 loss: 0.6850 Lr: 0.00406 [2024-02-18 09:35:52,381 INFO misc.py line 119 87073] Train: [33/100][130/1557] Data 0.003 (0.108) Batch 0.867 (1.042) Remain 30:35:49 loss: 0.5666 Lr: 0.00406 [2024-02-18 09:35:53,185 INFO misc.py line 119 87073] Train: [33/100][131/1557] Data 0.004 (0.107) Batch 0.797 (1.040) Remain 30:32:26 loss: 0.4006 Lr: 0.00406 [2024-02-18 09:35:53,976 INFO misc.py line 119 87073] Train: [33/100][132/1557] Data 0.009 (0.106) Batch 0.796 (1.038) Remain 30:29:06 loss: 0.4619 Lr: 0.00406 [2024-02-18 09:35:55,210 INFO misc.py line 119 87073] Train: [33/100][133/1557] Data 0.005 (0.106) Batch 1.234 (1.039) Remain 30:31:44 loss: 0.1938 Lr: 0.00406 [2024-02-18 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87073] Train: [33/100][140/1557] Data 0.010 (0.100) Batch 1.133 (1.034) Remain 30:22:00 loss: 0.1716 Lr: 0.00406 [2024-02-18 09:36:02,779 INFO misc.py line 119 87073] Train: [33/100][141/1557] Data 0.004 (0.100) Batch 1.036 (1.034) Remain 30:22:01 loss: 0.2189 Lr: 0.00406 [2024-02-18 09:36:03,728 INFO misc.py line 119 87073] Train: [33/100][142/1557] Data 0.009 (0.099) Batch 0.955 (1.033) Remain 30:21:00 loss: 0.9504 Lr: 0.00406 [2024-02-18 09:36:04,589 INFO misc.py line 119 87073] Train: [33/100][143/1557] Data 0.004 (0.098) Batch 0.860 (1.032) Remain 30:18:48 loss: 0.2793 Lr: 0.00406 [2024-02-18 09:36:05,553 INFO misc.py line 119 87073] Train: [33/100][144/1557] Data 0.004 (0.098) Batch 0.965 (1.032) Remain 30:17:56 loss: 0.4269 Lr: 0.00406 [2024-02-18 09:36:06,298 INFO misc.py line 119 87073] Train: [33/100][145/1557] Data 0.004 (0.097) Batch 0.744 (1.030) Remain 30:14:21 loss: 0.4058 Lr: 0.00406 [2024-02-18 09:36:07,017 INFO misc.py line 119 87073] Train: [33/100][146/1557] Data 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Batch 0.843 (1.062) Remain 31:09:34 loss: 0.2166 Lr: 0.00406 [2024-02-18 09:37:45,264 INFO misc.py line 119 87073] Train: [33/100][234/1557] Data 0.003 (0.122) Batch 0.953 (1.061) Remain 31:08:43 loss: 0.3881 Lr: 0.00406 [2024-02-18 09:37:46,212 INFO misc.py line 119 87073] Train: [33/100][235/1557] Data 0.002 (0.121) Batch 0.948 (1.061) Remain 31:07:50 loss: 0.4328 Lr: 0.00406 [2024-02-18 09:37:46,977 INFO misc.py line 119 87073] Train: [33/100][236/1557] Data 0.003 (0.121) Batch 0.761 (1.060) Remain 31:05:33 loss: 0.2482 Lr: 0.00406 [2024-02-18 09:37:47,782 INFO misc.py line 119 87073] Train: [33/100][237/1557] Data 0.007 (0.120) Batch 0.809 (1.059) Remain 31:03:39 loss: 0.3573 Lr: 0.00406 [2024-02-18 09:37:49,042 INFO misc.py line 119 87073] Train: [33/100][238/1557] Data 0.003 (0.120) Batch 1.251 (1.059) Remain 31:05:04 loss: 0.1642 Lr: 0.00406 [2024-02-18 09:37:50,269 INFO misc.py line 119 87073] Train: [33/100][239/1557] Data 0.012 (0.119) Batch 1.223 (1.060) Remain 31:06:16 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Batch 1.001 (1.058) Remain 31:02:14 loss: 0.3673 Lr: 0.00406 [2024-02-18 09:38:43,877 INFO misc.py line 119 87073] Train: [33/100][290/1557] Data 0.012 (0.120) Batch 1.132 (1.058) Remain 31:02:40 loss: 0.5141 Lr: 0.00406 [2024-02-18 09:38:44,962 INFO misc.py line 119 87073] Train: [33/100][291/1557] Data 0.006 (0.120) Batch 1.086 (1.059) Remain 31:02:49 loss: 0.5730 Lr: 0.00406 [2024-02-18 09:38:45,732 INFO misc.py line 119 87073] Train: [33/100][292/1557] Data 0.003 (0.119) Batch 0.769 (1.058) Remain 31:01:02 loss: 0.3840 Lr: 0.00406 [2024-02-18 09:38:46,453 INFO misc.py line 119 87073] Train: [33/100][293/1557] Data 0.004 (0.119) Batch 0.719 (1.056) Remain 30:58:58 loss: 0.3077 Lr: 0.00406 [2024-02-18 09:38:47,707 INFO misc.py line 119 87073] Train: [33/100][294/1557] Data 0.007 (0.118) Batch 1.247 (1.057) Remain 31:00:06 loss: 0.1590 Lr: 0.00406 [2024-02-18 09:38:48,604 INFO misc.py line 119 87073] Train: [33/100][295/1557] Data 0.014 (0.118) Batch 0.905 (1.057) Remain 30:59:10 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[2024-02-18 09:39:18,253 INFO misc.py line 119 87073] Train: [33/100][327/1557] Data 0.007 (0.107) Batch 0.765 (1.044) Remain 30:36:01 loss: 0.4180 Lr: 0.00406 [2024-02-18 09:39:19,024 INFO misc.py line 119 87073] Train: [33/100][328/1557] Data 0.004 (0.107) Batch 0.765 (1.043) Remain 30:34:30 loss: 0.5639 Lr: 0.00406 [2024-02-18 09:39:20,189 INFO misc.py line 119 87073] Train: [33/100][329/1557] Data 0.011 (0.106) Batch 1.167 (1.043) Remain 30:35:09 loss: 0.2026 Lr: 0.00406 [2024-02-18 09:39:21,096 INFO misc.py line 119 87073] Train: [33/100][330/1557] Data 0.008 (0.106) Batch 0.911 (1.043) Remain 30:34:25 loss: 0.3679 Lr: 0.00406 [2024-02-18 09:39:22,305 INFO misc.py line 119 87073] Train: [33/100][331/1557] Data 0.005 (0.106) Batch 1.201 (1.043) Remain 30:35:15 loss: 0.4468 Lr: 0.00406 [2024-02-18 09:39:23,286 INFO misc.py line 119 87073] Train: [33/100][332/1557] Data 0.012 (0.106) Batch 0.988 (1.043) Remain 30:34:56 loss: 0.5371 Lr: 0.00406 [2024-02-18 09:39:24,169 INFO misc.py 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line 119 87073] Train: [33/100][389/1557] Data 0.007 (0.108) Batch 0.833 (1.049) Remain 30:44:28 loss: 0.3606 Lr: 0.00405 [2024-02-18 09:40:25,877 INFO misc.py line 119 87073] Train: [33/100][390/1557] Data 0.004 (0.107) Batch 0.820 (1.049) Remain 30:43:25 loss: 0.4084 Lr: 0.00405 [2024-02-18 09:40:26,592 INFO misc.py line 119 87073] Train: [33/100][391/1557] Data 0.006 (0.107) Batch 0.716 (1.048) Remain 30:41:53 loss: 0.5754 Lr: 0.00405 [2024-02-18 09:40:27,751 INFO misc.py line 119 87073] Train: [33/100][392/1557] Data 0.005 (0.107) Batch 1.159 (1.048) Remain 30:42:23 loss: 0.1565 Lr: 0.00405 [2024-02-18 09:40:28,831 INFO misc.py line 119 87073] Train: [33/100][393/1557] Data 0.005 (0.107) Batch 1.080 (1.048) Remain 30:42:30 loss: 0.3467 Lr: 0.00405 [2024-02-18 09:40:29,816 INFO misc.py line 119 87073] Train: [33/100][394/1557] Data 0.005 (0.106) Batch 0.986 (1.048) Remain 30:42:12 loss: 0.4761 Lr: 0.00405 [2024-02-18 09:40:30,799 INFO misc.py line 119 87073] Train: 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Batch 0.963 (1.061) Remain 31:05:46 loss: 0.6542 Lr: 0.00405 [2024-02-18 09:40:43,443 INFO misc.py line 119 87073] Train: [33/100][402/1557] Data 0.008 (0.120) Batch 0.930 (1.061) Remain 31:05:10 loss: 0.4540 Lr: 0.00405 [2024-02-18 09:40:44,325 INFO misc.py line 119 87073] Train: [33/100][403/1557] Data 0.004 (0.120) Batch 0.880 (1.061) Remain 31:04:21 loss: 0.4255 Lr: 0.00405 [2024-02-18 09:40:45,115 INFO misc.py line 119 87073] Train: [33/100][404/1557] Data 0.007 (0.119) Batch 0.791 (1.060) Remain 31:03:09 loss: 0.4684 Lr: 0.00405 [2024-02-18 09:40:45,881 INFO misc.py line 119 87073] Train: [33/100][405/1557] Data 0.006 (0.119) Batch 0.767 (1.059) Remain 31:01:51 loss: 0.3304 Lr: 0.00405 [2024-02-18 09:40:47,124 INFO misc.py line 119 87073] Train: [33/100][406/1557] Data 0.004 (0.119) Batch 1.242 (1.060) Remain 31:02:38 loss: 0.3407 Lr: 0.00405 [2024-02-18 09:40:48,053 INFO misc.py line 119 87073] Train: [33/100][407/1557] Data 0.005 (0.118) Batch 0.930 (1.059) Remain 31:02:03 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line 119 87073] Train: [33/100][501/1557] Data 0.005 (0.108) Batch 1.110 (1.049) Remain 30:41:33 loss: 0.5356 Lr: 0.00405 [2024-02-18 09:42:23,024 INFO misc.py line 119 87073] Train: [33/100][502/1557] Data 0.005 (0.108) Batch 0.738 (1.048) Remain 30:40:27 loss: 0.2571 Lr: 0.00405 [2024-02-18 09:42:23,757 INFO misc.py line 119 87073] Train: [33/100][503/1557] Data 0.005 (0.108) Batch 0.729 (1.047) Remain 30:39:18 loss: 0.6043 Lr: 0.00405 [2024-02-18 09:42:24,982 INFO misc.py line 119 87073] Train: [33/100][504/1557] Data 0.009 (0.107) Batch 1.224 (1.048) Remain 30:39:55 loss: 0.1625 Lr: 0.00405 [2024-02-18 09:42:26,103 INFO misc.py line 119 87073] Train: [33/100][505/1557] Data 0.009 (0.107) Batch 1.121 (1.048) Remain 30:40:09 loss: 0.5113 Lr: 0.00405 [2024-02-18 09:42:27,131 INFO misc.py line 119 87073] Train: [33/100][506/1557] Data 0.010 (0.107) Batch 1.023 (1.048) Remain 30:40:03 loss: 0.4932 Lr: 0.00405 [2024-02-18 09:42:28,100 INFO misc.py line 119 87073] Train: 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Batch 1.083 (1.057) Remain 30:55:37 loss: 0.4443 Lr: 0.00405 [2024-02-18 09:42:39,907 INFO misc.py line 119 87073] Train: [33/100][514/1557] Data 0.004 (0.116) Batch 0.891 (1.056) Remain 30:55:02 loss: 0.6525 Lr: 0.00405 [2024-02-18 09:42:40,989 INFO misc.py line 119 87073] Train: [33/100][515/1557] Data 0.005 (0.115) Batch 1.083 (1.056) Remain 30:55:07 loss: 0.4268 Lr: 0.00405 [2024-02-18 09:42:41,785 INFO misc.py line 119 87073] Train: [33/100][516/1557] Data 0.003 (0.115) Batch 0.794 (1.056) Remain 30:54:12 loss: 0.2893 Lr: 0.00405 [2024-02-18 09:42:42,545 INFO misc.py line 119 87073] Train: [33/100][517/1557] Data 0.004 (0.115) Batch 0.756 (1.055) Remain 30:53:09 loss: 0.4028 Lr: 0.00405 [2024-02-18 09:42:43,761 INFO misc.py line 119 87073] Train: [33/100][518/1557] Data 0.009 (0.115) Batch 1.214 (1.056) Remain 30:53:41 loss: 0.1903 Lr: 0.00405 [2024-02-18 09:42:44,813 INFO misc.py line 119 87073] Train: [33/100][519/1557] Data 0.010 (0.115) Batch 1.048 (1.056) Remain 30:53:38 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[2024-02-18 09:44:16,472 INFO misc.py line 119 87073] Train: [33/100][607/1557] Data 0.006 (0.111) Batch 0.711 (1.054) Remain 30:48:31 loss: 0.4472 Lr: 0.00404 [2024-02-18 09:44:17,239 INFO misc.py line 119 87073] Train: [33/100][608/1557] Data 0.008 (0.110) Batch 0.772 (1.053) Remain 30:47:41 loss: 0.1484 Lr: 0.00404 [2024-02-18 09:44:18,384 INFO misc.py line 119 87073] Train: [33/100][609/1557] Data 0.004 (0.110) Batch 1.145 (1.053) Remain 30:47:55 loss: 0.2410 Lr: 0.00404 [2024-02-18 09:44:19,269 INFO misc.py line 119 87073] Train: [33/100][610/1557] Data 0.004 (0.110) Batch 0.885 (1.053) Remain 30:47:25 loss: 0.3729 Lr: 0.00404 [2024-02-18 09:44:20,331 INFO misc.py line 119 87073] Train: [33/100][611/1557] Data 0.004 (0.110) Batch 1.055 (1.053) Remain 30:47:24 loss: 0.8863 Lr: 0.00404 [2024-02-18 09:44:21,155 INFO misc.py line 119 87073] Train: [33/100][612/1557] Data 0.012 (0.110) Batch 0.831 (1.053) Remain 30:46:45 loss: 0.3437 Lr: 0.00404 [2024-02-18 09:44:22,310 INFO misc.py 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Batch 1.045 (1.060) Remain 30:58:39 loss: 0.4234 Lr: 0.00404 [2024-02-18 09:44:40,046 INFO misc.py line 119 87073] Train: [33/100][626/1557] Data 0.003 (0.116) Batch 0.907 (1.059) Remain 30:58:12 loss: 0.2998 Lr: 0.00404 [2024-02-18 09:44:41,026 INFO misc.py line 119 87073] Train: [33/100][627/1557] Data 0.004 (0.116) Batch 0.981 (1.059) Remain 30:57:58 loss: 0.4871 Lr: 0.00404 [2024-02-18 09:44:41,852 INFO misc.py line 119 87073] Train: [33/100][628/1557] Data 0.003 (0.116) Batch 0.825 (1.059) Remain 30:57:17 loss: 0.4488 Lr: 0.00404 [2024-02-18 09:44:42,637 INFO misc.py line 119 87073] Train: [33/100][629/1557] Data 0.004 (0.115) Batch 0.778 (1.058) Remain 30:56:29 loss: 0.3185 Lr: 0.00404 [2024-02-18 09:44:43,885 INFO misc.py line 119 87073] Train: [33/100][630/1557] Data 0.010 (0.115) Batch 1.246 (1.059) Remain 30:57:00 loss: 0.1796 Lr: 0.00404 [2024-02-18 09:44:44,805 INFO misc.py line 119 87073] Train: [33/100][631/1557] Data 0.013 (0.115) Batch 0.930 (1.058) Remain 30:56:37 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Batch 0.919 (1.058) Remain 30:54:40 loss: 0.4861 Lr: 0.00404 [2024-02-18 09:45:38,321 INFO misc.py line 119 87073] Train: [33/100][682/1557] Data 0.003 (0.116) Batch 1.004 (1.058) Remain 30:54:31 loss: 0.2094 Lr: 0.00404 [2024-02-18 09:45:39,257 INFO misc.py line 119 87073] Train: [33/100][683/1557] Data 0.005 (0.115) Batch 0.936 (1.058) Remain 30:54:11 loss: 0.3616 Lr: 0.00404 [2024-02-18 09:45:40,044 INFO misc.py line 119 87073] Train: [33/100][684/1557] Data 0.004 (0.115) Batch 0.787 (1.057) Remain 30:53:28 loss: 0.3010 Lr: 0.00404 [2024-02-18 09:45:40,847 INFO misc.py line 119 87073] Train: [33/100][685/1557] Data 0.003 (0.115) Batch 0.798 (1.057) Remain 30:52:47 loss: 0.1749 Lr: 0.00404 [2024-02-18 09:45:42,132 INFO misc.py line 119 87073] Train: [33/100][686/1557] Data 0.009 (0.115) Batch 1.284 (1.057) Remain 30:53:21 loss: 0.2102 Lr: 0.00404 [2024-02-18 09:45:43,368 INFO misc.py line 119 87073] Train: [33/100][687/1557] Data 0.011 (0.115) Batch 1.231 (1.057) Remain 30:53:46 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Batch 0.866 (1.060) Remain 30:58:14 loss: 0.7340 Lr: 0.00404 [2024-02-18 09:46:39,524 INFO misc.py line 119 87073] Train: [33/100][738/1557] Data 0.007 (0.117) Batch 1.064 (1.060) Remain 30:58:13 loss: 0.5467 Lr: 0.00404 [2024-02-18 09:46:40,397 INFO misc.py line 119 87073] Train: [33/100][739/1557] Data 0.004 (0.116) Batch 0.874 (1.060) Remain 30:57:45 loss: 0.0890 Lr: 0.00404 [2024-02-18 09:46:41,169 INFO misc.py line 119 87073] Train: [33/100][740/1557] Data 0.003 (0.116) Batch 0.772 (1.060) Remain 30:57:03 loss: 0.3202 Lr: 0.00404 [2024-02-18 09:46:41,868 INFO misc.py line 119 87073] Train: [33/100][741/1557] Data 0.003 (0.116) Batch 0.697 (1.059) Remain 30:56:10 loss: 0.3094 Lr: 0.00404 [2024-02-18 09:46:43,227 INFO misc.py line 119 87073] Train: [33/100][742/1557] Data 0.005 (0.116) Batch 1.349 (1.060) Remain 30:56:50 loss: 0.1612 Lr: 0.00404 [2024-02-18 09:46:44,170 INFO misc.py line 119 87073] Train: [33/100][743/1557] Data 0.016 (0.116) Batch 0.956 (1.060) Remain 30:56:35 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Batch 1.049 (1.062) Remain 30:59:19 loss: 0.5713 Lr: 0.00404 [2024-02-18 09:47:39,754 INFO misc.py line 119 87073] Train: [33/100][794/1557] Data 0.005 (0.116) Batch 0.971 (1.062) Remain 30:59:06 loss: 0.4445 Lr: 0.00404 [2024-02-18 09:47:40,916 INFO misc.py line 119 87073] Train: [33/100][795/1557] Data 0.005 (0.116) Batch 1.163 (1.062) Remain 30:59:18 loss: 0.4250 Lr: 0.00404 [2024-02-18 09:47:41,703 INFO misc.py line 119 87073] Train: [33/100][796/1557] Data 0.005 (0.116) Batch 0.786 (1.061) Remain 30:58:40 loss: 0.1930 Lr: 0.00404 [2024-02-18 09:47:42,485 INFO misc.py line 119 87073] Train: [33/100][797/1557] Data 0.005 (0.116) Batch 0.781 (1.061) Remain 30:58:02 loss: 0.3987 Lr: 0.00404 [2024-02-18 09:47:43,718 INFO misc.py line 119 87073] Train: [33/100][798/1557] Data 0.006 (0.116) Batch 1.229 (1.061) Remain 30:58:23 loss: 0.1954 Lr: 0.00404 [2024-02-18 09:47:44,741 INFO misc.py line 119 87073] Train: [33/100][799/1557] Data 0.010 (0.116) Batch 1.026 (1.061) Remain 30:58:18 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Batch 1.169 (1.061) Remain 30:56:51 loss: 0.6875 Lr: 0.00403 [2024-02-18 09:48:38,440 INFO misc.py line 119 87073] Train: [33/100][850/1557] Data 0.004 (0.116) Batch 0.918 (1.061) Remain 30:56:32 loss: 0.4523 Lr: 0.00403 [2024-02-18 09:48:39,277 INFO misc.py line 119 87073] Train: [33/100][851/1557] Data 0.004 (0.115) Batch 0.836 (1.060) Remain 30:56:03 loss: 0.3609 Lr: 0.00403 [2024-02-18 09:48:39,998 INFO misc.py line 119 87073] Train: [33/100][852/1557] Data 0.005 (0.115) Batch 0.722 (1.060) Remain 30:55:20 loss: 0.2305 Lr: 0.00403 [2024-02-18 09:48:40,744 INFO misc.py line 119 87073] Train: [33/100][853/1557] Data 0.003 (0.115) Batch 0.740 (1.060) Remain 30:54:40 loss: 0.7201 Lr: 0.00403 [2024-02-18 09:48:42,051 INFO misc.py line 119 87073] Train: [33/100][854/1557] Data 0.010 (0.115) Batch 1.304 (1.060) Remain 30:55:09 loss: 0.1460 Lr: 0.00403 [2024-02-18 09:48:43,024 INFO misc.py line 119 87073] Train: [33/100][855/1557] Data 0.013 (0.115) Batch 0.981 (1.060) Remain 30:54:58 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Batch 0.950 (1.063) Remain 30:59:19 loss: 0.2864 Lr: 0.00403 [2024-02-18 09:49:39,732 INFO misc.py line 119 87073] Train: [33/100][906/1557] Data 0.009 (0.117) Batch 1.026 (1.063) Remain 30:59:13 loss: 0.6085 Lr: 0.00403 [2024-02-18 09:49:40,593 INFO misc.py line 119 87073] Train: [33/100][907/1557] Data 0.004 (0.116) Batch 0.860 (1.062) Remain 30:58:49 loss: 0.3969 Lr: 0.00403 [2024-02-18 09:49:41,365 INFO misc.py line 119 87073] Train: [33/100][908/1557] Data 0.005 (0.116) Batch 0.773 (1.062) Remain 30:58:14 loss: 0.2122 Lr: 0.00403 [2024-02-18 09:49:42,181 INFO misc.py line 119 87073] Train: [33/100][909/1557] Data 0.003 (0.116) Batch 0.813 (1.062) Remain 30:57:44 loss: 0.4145 Lr: 0.00403 [2024-02-18 09:49:43,415 INFO misc.py line 119 87073] Train: [33/100][910/1557] Data 0.007 (0.116) Batch 1.232 (1.062) Remain 30:58:03 loss: 0.2010 Lr: 0.00403 [2024-02-18 09:49:44,344 INFO misc.py line 119 87073] Train: [33/100][911/1557] Data 0.009 (0.116) Batch 0.933 (1.062) Remain 30:57:47 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Batch 1.059 (1.062) Remain 30:57:24 loss: 1.0056 Lr: 0.00403 [2024-02-18 09:50:38,657 INFO misc.py line 119 87073] Train: [33/100][962/1557] Data 0.006 (0.116) Batch 0.943 (1.062) Remain 30:57:10 loss: 0.6755 Lr: 0.00403 [2024-02-18 09:50:39,603 INFO misc.py line 119 87073] Train: [33/100][963/1557] Data 0.003 (0.116) Batch 0.946 (1.062) Remain 30:56:56 loss: 0.4125 Lr: 0.00403 [2024-02-18 09:50:40,375 INFO misc.py line 119 87073] Train: [33/100][964/1557] Data 0.004 (0.116) Batch 0.770 (1.062) Remain 30:56:23 loss: 0.1909 Lr: 0.00403 [2024-02-18 09:50:41,072 INFO misc.py line 119 87073] Train: [33/100][965/1557] Data 0.007 (0.116) Batch 0.694 (1.061) Remain 30:55:42 loss: 0.3174 Lr: 0.00403 [2024-02-18 09:50:42,332 INFO misc.py line 119 87073] Train: [33/100][966/1557] Data 0.009 (0.115) Batch 1.254 (1.062) Remain 30:56:02 loss: 0.2350 Lr: 0.00403 [2024-02-18 09:50:43,573 INFO misc.py line 119 87073] Train: [33/100][967/1557] Data 0.015 (0.115) Batch 1.244 (1.062) Remain 30:56:20 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[2024-02-18 09:52:21,362 INFO misc.py line 119 87073] Train: [33/100][1061/1557] Data 0.004 (0.112) Batch 0.951 (1.060) Remain 30:51:22 loss: 0.5522 Lr: 0.00402 [2024-02-18 09:52:22,114 INFO misc.py line 119 87073] Train: [33/100][1062/1557] Data 0.006 (0.112) Batch 0.751 (1.060) Remain 30:50:51 loss: 0.3852 Lr: 0.00402 [2024-02-18 09:52:22,863 INFO misc.py line 119 87073] Train: [33/100][1063/1557] Data 0.005 (0.112) Batch 0.748 (1.059) Remain 30:50:19 loss: 0.5174 Lr: 0.00402 [2024-02-18 09:52:24,154 INFO misc.py line 119 87073] Train: [33/100][1064/1557] Data 0.006 (0.112) Batch 1.292 (1.059) Remain 30:50:41 loss: 0.1417 Lr: 0.00402 [2024-02-18 09:52:25,081 INFO misc.py line 119 87073] Train: [33/100][1065/1557] Data 0.005 (0.112) Batch 0.928 (1.059) Remain 30:50:27 loss: 0.7417 Lr: 0.00402 [2024-02-18 09:52:25,987 INFO misc.py line 119 87073] Train: [33/100][1066/1557] Data 0.004 (0.112) Batch 0.907 (1.059) Remain 30:50:11 loss: 0.1862 Lr: 0.00402 [2024-02-18 09:52:26,944 INFO 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[2024-02-18 09:53:25,688 INFO misc.py line 119 87073] Train: [33/100][1123/1557] Data 0.003 (0.112) Batch 1.042 (1.059) Remain 30:48:07 loss: 0.3344 Lr: 0.00402 [2024-02-18 09:53:26,714 INFO misc.py line 119 87073] Train: [33/100][1124/1557] Data 0.005 (0.111) Batch 1.025 (1.059) Remain 30:48:03 loss: 0.3332 Lr: 0.00402 [2024-02-18 09:53:27,499 INFO misc.py line 119 87073] Train: [33/100][1125/1557] Data 0.006 (0.111) Batch 0.786 (1.058) Remain 30:47:37 loss: 0.3540 Lr: 0.00402 [2024-02-18 09:53:28,308 INFO misc.py line 119 87073] Train: [33/100][1126/1557] Data 0.005 (0.111) Batch 0.808 (1.058) Remain 30:47:12 loss: 0.2496 Lr: 0.00402 [2024-02-18 09:53:34,955 INFO misc.py line 119 87073] Train: [33/100][1127/1557] Data 5.548 (0.116) Batch 6.645 (1.063) Remain 30:55:52 loss: 0.1553 Lr: 0.00402 [2024-02-18 09:53:35,961 INFO misc.py line 119 87073] Train: [33/100][1128/1557] Data 0.008 (0.116) Batch 0.999 (1.063) Remain 30:55:45 loss: 0.5171 Lr: 0.00402 [2024-02-18 09:53:36,842 INFO 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[2024-02-18 09:55:42,615 INFO misc.py line 119 87073] Train: [33/100][1247/1557] Data 0.015 (0.116) Batch 0.906 (1.063) Remain 30:53:52 loss: 0.5438 Lr: 0.00402 [2024-02-18 09:55:43,578 INFO misc.py line 119 87073] Train: [33/100][1248/1557] Data 0.005 (0.116) Batch 0.964 (1.063) Remain 30:53:43 loss: 0.4750 Lr: 0.00402 [2024-02-18 09:55:44,478 INFO misc.py line 119 87073] Train: [33/100][1249/1557] Data 0.004 (0.116) Batch 0.901 (1.063) Remain 30:53:28 loss: 0.6771 Lr: 0.00402 [2024-02-18 09:55:45,548 INFO misc.py line 119 87073] Train: [33/100][1250/1557] Data 0.003 (0.116) Batch 1.068 (1.063) Remain 30:53:28 loss: 0.4181 Lr: 0.00402 [2024-02-18 09:55:46,322 INFO misc.py line 119 87073] Train: [33/100][1251/1557] Data 0.004 (0.116) Batch 0.775 (1.063) Remain 30:53:03 loss: 0.2292 Lr: 0.00402 [2024-02-18 09:55:47,121 INFO misc.py line 119 87073] Train: [33/100][1252/1557] Data 0.005 (0.115) Batch 0.797 (1.062) Remain 30:52:39 loss: 0.1775 Lr: 0.00402 [2024-02-18 09:55:48,424 INFO 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30:45:01 loss: 0.4905 Lr: 0.00401 [2024-02-18 09:59:22,632 INFO misc.py line 119 87073] Train: [33/100][1458/1557] Data 0.003 (0.113) Batch 1.054 (1.060) Remain 30:44:59 loss: 0.4470 Lr: 0.00401 [2024-02-18 09:59:23,602 INFO misc.py line 119 87073] Train: [33/100][1459/1557] Data 0.009 (0.113) Batch 0.974 (1.060) Remain 30:44:52 loss: 0.5950 Lr: 0.00401 [2024-02-18 09:59:24,645 INFO misc.py line 119 87073] Train: [33/100][1460/1557] Data 0.004 (0.112) Batch 1.041 (1.060) Remain 30:44:50 loss: 0.5150 Lr: 0.00401 [2024-02-18 09:59:25,389 INFO misc.py line 119 87073] Train: [33/100][1461/1557] Data 0.006 (0.112) Batch 0.745 (1.060) Remain 30:44:26 loss: 0.3176 Lr: 0.00401 [2024-02-18 09:59:26,206 INFO misc.py line 119 87073] Train: [33/100][1462/1557] Data 0.004 (0.112) Batch 0.817 (1.060) Remain 30:44:08 loss: 0.9100 Lr: 0.00401 [2024-02-18 09:59:33,515 INFO misc.py line 119 87073] Train: [33/100][1463/1557] Data 6.150 (0.116) Batch 7.309 (1.064) Remain 30:51:33 loss: 0.1671 Lr: 0.00401 [2024-02-18 09:59:34,627 INFO misc.py line 119 87073] Train: [33/100][1464/1557] Data 0.005 (0.116) Batch 1.112 (1.064) Remain 30:51:36 loss: 0.6692 Lr: 0.00401 [2024-02-18 09:59:35,587 INFO misc.py line 119 87073] Train: [33/100][1465/1557] Data 0.005 (0.116) Batch 0.960 (1.064) Remain 30:51:27 loss: 0.4758 Lr: 0.00401 [2024-02-18 09:59:36,416 INFO misc.py line 119 87073] Train: [33/100][1466/1557] Data 0.005 (0.116) Batch 0.829 (1.064) Remain 30:51:09 loss: 0.5993 Lr: 0.00401 [2024-02-18 09:59:37,407 INFO misc.py line 119 87073] Train: [33/100][1467/1557] Data 0.005 (0.116) Batch 0.990 (1.064) Remain 30:51:03 loss: 0.3527 Lr: 0.00401 [2024-02-18 09:59:38,141 INFO misc.py line 119 87073] Train: [33/100][1468/1557] Data 0.007 (0.116) Batch 0.735 (1.064) Remain 30:50:39 loss: 0.5618 Lr: 0.00401 [2024-02-18 09:59:38,896 INFO misc.py line 119 87073] Train: [33/100][1469/1557] Data 0.004 (0.116) Batch 0.755 (1.063) Remain 30:50:16 loss: 0.1695 Lr: 0.00401 [2024-02-18 09:59:40,230 INFO misc.py line 119 87073] Train: [33/100][1470/1557] Data 0.005 (0.116) Batch 1.330 (1.063) Remain 30:50:34 loss: 0.1402 Lr: 0.00401 [2024-02-18 09:59:41,053 INFO misc.py line 119 87073] Train: [33/100][1471/1557] Data 0.008 (0.116) Batch 0.825 (1.063) Remain 30:50:16 loss: 0.5635 Lr: 0.00401 [2024-02-18 09:59:41,988 INFO misc.py line 119 87073] Train: [33/100][1472/1557] Data 0.006 (0.116) Batch 0.938 (1.063) Remain 30:50:06 loss: 0.6390 Lr: 0.00401 [2024-02-18 09:59:42,808 INFO misc.py line 119 87073] Train: [33/100][1473/1557] Data 0.004 (0.116) Batch 0.819 (1.063) Remain 30:49:47 loss: 0.2766 Lr: 0.00401 [2024-02-18 09:59:43,732 INFO misc.py line 119 87073] Train: [33/100][1474/1557] Data 0.004 (0.116) Batch 0.918 (1.063) Remain 30:49:36 loss: 0.5855 Lr: 0.00401 [2024-02-18 09:59:44,500 INFO misc.py line 119 87073] Train: [33/100][1475/1557] Data 0.010 (0.116) Batch 0.774 (1.063) Remain 30:49:14 loss: 0.3808 Lr: 0.00401 [2024-02-18 09:59:45,283 INFO misc.py line 119 87073] Train: [33/100][1476/1557] Data 0.004 (0.115) Batch 0.784 (1.063) Remain 30:48:53 loss: 0.2999 Lr: 0.00401 [2024-02-18 09:59:46,562 INFO misc.py line 119 87073] Train: [33/100][1477/1557] Data 0.004 (0.115) Batch 1.275 (1.063) Remain 30:49:07 loss: 0.1826 Lr: 0.00401 [2024-02-18 09:59:47,457 INFO misc.py line 119 87073] Train: [33/100][1478/1557] Data 0.007 (0.115) Batch 0.898 (1.063) Remain 30:48:55 loss: 0.4292 Lr: 0.00401 [2024-02-18 09:59:48,453 INFO misc.py line 119 87073] Train: [33/100][1479/1557] Data 0.005 (0.115) Batch 0.996 (1.063) Remain 30:48:49 loss: 0.4716 Lr: 0.00401 [2024-02-18 09:59:49,381 INFO misc.py line 119 87073] Train: [33/100][1480/1557] Data 0.004 (0.115) Batch 0.927 (1.062) Remain 30:48:38 loss: 0.4287 Lr: 0.00401 [2024-02-18 09:59:50,245 INFO misc.py line 119 87073] Train: [33/100][1481/1557] Data 0.004 (0.115) Batch 0.859 (1.062) Remain 30:48:23 loss: 0.6445 Lr: 0.00401 [2024-02-18 09:59:50,963 INFO misc.py line 119 87073] Train: [33/100][1482/1557] Data 0.009 (0.115) Batch 0.723 (1.062) Remain 30:47:58 loss: 0.2976 Lr: 0.00401 [2024-02-18 09:59:51,763 INFO misc.py line 119 87073] Train: [33/100][1483/1557] Data 0.004 (0.115) Batch 0.799 (1.062) Remain 30:47:38 loss: 0.1511 Lr: 0.00401 [2024-02-18 09:59:52,957 INFO misc.py line 119 87073] Train: [33/100][1484/1557] Data 0.006 (0.115) Batch 1.194 (1.062) Remain 30:47:46 loss: 0.1691 Lr: 0.00401 [2024-02-18 09:59:53,935 INFO misc.py line 119 87073] Train: [33/100][1485/1557] Data 0.005 (0.115) Batch 0.979 (1.062) Remain 30:47:40 loss: 0.4802 Lr: 0.00401 [2024-02-18 09:59:55,082 INFO misc.py line 119 87073] Train: [33/100][1486/1557] Data 0.005 (0.115) Batch 1.148 (1.062) Remain 30:47:44 loss: 0.6202 Lr: 0.00401 [2024-02-18 09:59:55,884 INFO misc.py line 119 87073] Train: [33/100][1487/1557] Data 0.004 (0.115) Batch 0.800 (1.062) Remain 30:47:25 loss: 0.9899 Lr: 0.00401 [2024-02-18 09:59:56,845 INFO misc.py line 119 87073] Train: [33/100][1488/1557] Data 0.007 (0.115) Batch 0.962 (1.062) Remain 30:47:17 loss: 0.3884 Lr: 0.00401 [2024-02-18 09:59:57,606 INFO misc.py line 119 87073] Train: [33/100][1489/1557] Data 0.006 (0.115) Batch 0.761 (1.062) Remain 30:46:55 loss: 0.1932 Lr: 0.00401 [2024-02-18 09:59:58,294 INFO misc.py line 119 87073] Train: [33/100][1490/1557] Data 0.004 (0.114) Batch 0.685 (1.061) Remain 30:46:27 loss: 0.1328 Lr: 0.00401 [2024-02-18 09:59:59,547 INFO misc.py line 119 87073] Train: [33/100][1491/1557] Data 0.009 (0.114) Batch 1.246 (1.061) Remain 30:46:39 loss: 0.2490 Lr: 0.00401 [2024-02-18 10:00:00,574 INFO misc.py line 119 87073] Train: [33/100][1492/1557] Data 0.014 (0.114) Batch 1.028 (1.061) Remain 30:46:36 loss: 0.5013 Lr: 0.00401 [2024-02-18 10:00:01,502 INFO misc.py line 119 87073] Train: [33/100][1493/1557] Data 0.014 (0.114) Batch 0.938 (1.061) Remain 30:46:26 loss: 0.4028 Lr: 0.00401 [2024-02-18 10:00:02,516 INFO misc.py line 119 87073] Train: [33/100][1494/1557] Data 0.004 (0.114) Batch 1.014 (1.061) Remain 30:46:22 loss: 0.4878 Lr: 0.00401 [2024-02-18 10:00:03,531 INFO misc.py line 119 87073] Train: [33/100][1495/1557] Data 0.004 (0.114) Batch 1.012 (1.061) Remain 30:46:17 loss: 0.2294 Lr: 0.00401 [2024-02-18 10:00:04,299 INFO misc.py line 119 87073] Train: [33/100][1496/1557] Data 0.007 (0.114) Batch 0.770 (1.061) Remain 30:45:56 loss: 0.2414 Lr: 0.00401 [2024-02-18 10:00:05,023 INFO misc.py line 119 87073] Train: [33/100][1497/1557] Data 0.005 (0.114) Batch 0.724 (1.061) Remain 30:45:31 loss: 0.4414 Lr: 0.00401 [2024-02-18 10:00:06,126 INFO misc.py line 119 87073] Train: [33/100][1498/1557] Data 0.005 (0.114) Batch 1.093 (1.061) Remain 30:45:32 loss: 0.3724 Lr: 0.00401 [2024-02-18 10:00:06,922 INFO misc.py line 119 87073] Train: [33/100][1499/1557] Data 0.015 (0.114) Batch 0.806 (1.061) Remain 30:45:13 loss: 0.4517 Lr: 0.00401 [2024-02-18 10:00:08,026 INFO misc.py line 119 87073] Train: [33/100][1500/1557] Data 0.004 (0.114) Batch 1.105 (1.061) Remain 30:45:15 loss: 0.7814 Lr: 0.00401 [2024-02-18 10:00:08,818 INFO misc.py line 119 87073] Train: [33/100][1501/1557] Data 0.004 (0.114) Batch 0.790 (1.061) Remain 30:44:56 loss: 0.4155 Lr: 0.00401 [2024-02-18 10:00:09,775 INFO misc.py line 119 87073] Train: [33/100][1502/1557] Data 0.005 (0.114) Batch 0.949 (1.060) Remain 30:44:47 loss: 0.6109 Lr: 0.00401 [2024-02-18 10:00:10,556 INFO misc.py line 119 87073] Train: [33/100][1503/1557] Data 0.013 (0.114) Batch 0.788 (1.060) Remain 30:44:27 loss: 0.6209 Lr: 0.00401 [2024-02-18 10:00:11,296 INFO misc.py line 119 87073] Train: [33/100][1504/1557] Data 0.005 (0.113) Batch 0.742 (1.060) Remain 30:44:04 loss: 0.3104 Lr: 0.00401 [2024-02-18 10:00:12,495 INFO misc.py line 119 87073] Train: [33/100][1505/1557] Data 0.004 (0.113) Batch 1.175 (1.060) Remain 30:44:11 loss: 0.3875 Lr: 0.00401 [2024-02-18 10:00:13,411 INFO misc.py line 119 87073] Train: [33/100][1506/1557] Data 0.027 (0.113) Batch 0.939 (1.060) Remain 30:44:01 loss: 0.4899 Lr: 0.00401 [2024-02-18 10:00:14,405 INFO misc.py line 119 87073] Train: [33/100][1507/1557] Data 0.004 (0.113) Batch 0.995 (1.060) Remain 30:43:55 loss: 0.4463 Lr: 0.00401 [2024-02-18 10:00:15,293 INFO misc.py line 119 87073] Train: [33/100][1508/1557] Data 0.004 (0.113) Batch 0.887 (1.060) Remain 30:43:42 loss: 0.4358 Lr: 0.00401 [2024-02-18 10:00:16,297 INFO misc.py line 119 87073] Train: [33/100][1509/1557] Data 0.004 (0.113) Batch 1.003 (1.060) Remain 30:43:37 loss: 0.4949 Lr: 0.00401 [2024-02-18 10:00:17,019 INFO misc.py line 119 87073] Train: [33/100][1510/1557] Data 0.005 (0.113) Batch 0.723 (1.060) Remain 30:43:13 loss: 0.4312 Lr: 0.00401 [2024-02-18 10:00:17,791 INFO misc.py line 119 87073] Train: [33/100][1511/1557] Data 0.004 (0.113) Batch 0.766 (1.059) Remain 30:42:52 loss: 0.2437 Lr: 0.00401 [2024-02-18 10:00:18,976 INFO misc.py line 119 87073] Train: [33/100][1512/1557] Data 0.012 (0.113) Batch 1.182 (1.060) Remain 30:42:59 loss: 0.1556 Lr: 0.00401 [2024-02-18 10:00:19,796 INFO misc.py line 119 87073] Train: [33/100][1513/1557] Data 0.014 (0.113) Batch 0.829 (1.059) Remain 30:42:42 loss: 0.6685 Lr: 0.00401 [2024-02-18 10:00:20,674 INFO misc.py line 119 87073] Train: [33/100][1514/1557] Data 0.004 (0.113) Batch 0.879 (1.059) Remain 30:42:29 loss: 0.6545 Lr: 0.00401 [2024-02-18 10:00:21,570 INFO misc.py line 119 87073] Train: [33/100][1515/1557] Data 0.004 (0.113) Batch 0.892 (1.059) Remain 30:42:16 loss: 0.4109 Lr: 0.00401 [2024-02-18 10:00:22,470 INFO misc.py line 119 87073] Train: [33/100][1516/1557] Data 0.007 (0.113) Batch 0.904 (1.059) Remain 30:42:04 loss: 0.1475 Lr: 0.00401 [2024-02-18 10:00:23,275 INFO misc.py line 119 87073] Train: [33/100][1517/1557] Data 0.004 (0.113) Batch 0.804 (1.059) Remain 30:41:46 loss: 0.5582 Lr: 0.00401 [2024-02-18 10:00:24,023 INFO misc.py line 119 87073] Train: [33/100][1518/1557] Data 0.004 (0.112) Batch 0.740 (1.059) Remain 30:41:22 loss: 0.3238 Lr: 0.00401 [2024-02-18 10:00:31,154 INFO misc.py line 119 87073] Train: [33/100][1519/1557] Data 5.989 (0.116) Batch 7.140 (1.063) Remain 30:48:20 loss: 0.1673 Lr: 0.00401 [2024-02-18 10:00:32,162 INFO misc.py line 119 87073] Train: [33/100][1520/1557] Data 0.004 (0.116) Batch 1.008 (1.063) Remain 30:48:15 loss: 0.1353 Lr: 0.00401 [2024-02-18 10:00:33,323 INFO misc.py line 119 87073] Train: [33/100][1521/1557] Data 0.004 (0.116) Batch 1.161 (1.063) Remain 30:48:21 loss: 0.4913 Lr: 0.00401 [2024-02-18 10:00:34,410 INFO misc.py line 119 87073] Train: [33/100][1522/1557] Data 0.005 (0.116) Batch 1.086 (1.063) Remain 30:48:21 loss: 0.3664 Lr: 0.00401 [2024-02-18 10:00:35,290 INFO misc.py line 119 87073] Train: [33/100][1523/1557] Data 0.005 (0.116) Batch 0.880 (1.063) Remain 30:48:08 loss: 0.4189 Lr: 0.00401 [2024-02-18 10:00:36,006 INFO misc.py line 119 87073] Train: [33/100][1524/1557] Data 0.004 (0.116) Batch 0.717 (1.062) Remain 30:47:43 loss: 0.3050 Lr: 0.00401 [2024-02-18 10:00:36,764 INFO misc.py line 119 87073] Train: [33/100][1525/1557] Data 0.004 (0.116) Batch 0.749 (1.062) Remain 30:47:21 loss: 0.4758 Lr: 0.00401 [2024-02-18 10:00:37,995 INFO misc.py line 119 87073] Train: [33/100][1526/1557] Data 0.013 (0.116) Batch 1.238 (1.062) Remain 30:47:32 loss: 0.2440 Lr: 0.00400 [2024-02-18 10:00:38,872 INFO misc.py line 119 87073] Train: [33/100][1527/1557] Data 0.005 (0.116) Batch 0.879 (1.062) Remain 30:47:18 loss: 0.2315 Lr: 0.00400 [2024-02-18 10:00:40,039 INFO misc.py line 119 87073] Train: [33/100][1528/1557] Data 0.004 (0.116) Batch 1.163 (1.062) Remain 30:47:24 loss: 0.2384 Lr: 0.00400 [2024-02-18 10:00:41,119 INFO misc.py line 119 87073] Train: [33/100][1529/1557] Data 0.008 (0.116) Batch 1.083 (1.062) Remain 30:47:24 loss: 0.4187 Lr: 0.00400 [2024-02-18 10:00:42,105 INFO misc.py line 119 87073] Train: [33/100][1530/1557] Data 0.005 (0.116) Batch 0.985 (1.062) Remain 30:47:18 loss: 0.2274 Lr: 0.00400 [2024-02-18 10:00:42,841 INFO misc.py line 119 87073] Train: [33/100][1531/1557] Data 0.006 (0.115) Batch 0.737 (1.062) Remain 30:46:55 loss: 0.2743 Lr: 0.00400 [2024-02-18 10:00:43,622 INFO misc.py line 119 87073] Train: [33/100][1532/1557] Data 0.004 (0.115) Batch 0.774 (1.062) Remain 30:46:34 loss: 0.5131 Lr: 0.00400 [2024-02-18 10:00:44,916 INFO misc.py line 119 87073] Train: [33/100][1533/1557] Data 0.011 (0.115) Batch 1.295 (1.062) Remain 30:46:49 loss: 0.2644 Lr: 0.00400 [2024-02-18 10:00:46,122 INFO misc.py line 119 87073] Train: [33/100][1534/1557] Data 0.010 (0.115) Batch 1.206 (1.062) Remain 30:46:57 loss: 0.1203 Lr: 0.00400 [2024-02-18 10:00:46,967 INFO misc.py line 119 87073] Train: [33/100][1535/1557] Data 0.010 (0.115) Batch 0.848 (1.062) Remain 30:46:42 loss: 0.4021 Lr: 0.00400 [2024-02-18 10:00:48,042 INFO misc.py line 119 87073] Train: [33/100][1536/1557] Data 0.007 (0.115) Batch 1.077 (1.062) Remain 30:46:42 loss: 0.6144 Lr: 0.00400 [2024-02-18 10:00:49,086 INFO misc.py line 119 87073] Train: [33/100][1537/1557] Data 0.005 (0.115) Batch 1.042 (1.062) Remain 30:46:39 loss: 0.3096 Lr: 0.00400 [2024-02-18 10:00:49,798 INFO misc.py line 119 87073] Train: [33/100][1538/1557] Data 0.007 (0.115) Batch 0.714 (1.062) Remain 30:46:15 loss: 0.3275 Lr: 0.00400 [2024-02-18 10:00:50,508 INFO misc.py line 119 87073] Train: [33/100][1539/1557] Data 0.005 (0.115) Batch 0.710 (1.061) Remain 30:45:50 loss: 0.3504 Lr: 0.00400 [2024-02-18 10:00:51,591 INFO misc.py line 119 87073] Train: [33/100][1540/1557] Data 0.005 (0.115) Batch 1.083 (1.061) Remain 30:45:50 loss: 0.1556 Lr: 0.00400 [2024-02-18 10:00:52,411 INFO misc.py line 119 87073] Train: [33/100][1541/1557] Data 0.005 (0.115) Batch 0.820 (1.061) Remain 30:45:33 loss: 0.7682 Lr: 0.00400 [2024-02-18 10:00:53,504 INFO misc.py line 119 87073] Train: [33/100][1542/1557] Data 0.004 (0.115) Batch 1.093 (1.061) Remain 30:45:34 loss: 0.3761 Lr: 0.00400 [2024-02-18 10:00:54,439 INFO misc.py line 119 87073] Train: [33/100][1543/1557] Data 0.004 (0.115) Batch 0.935 (1.061) Remain 30:45:24 loss: 0.4514 Lr: 0.00400 [2024-02-18 10:00:55,355 INFO misc.py line 119 87073] Train: [33/100][1544/1557] Data 0.004 (0.115) Batch 0.916 (1.061) Remain 30:45:13 loss: 0.2134 Lr: 0.00400 [2024-02-18 10:00:56,104 INFO misc.py line 119 87073] Train: [33/100][1545/1557] Data 0.004 (0.114) Batch 0.747 (1.061) Remain 30:44:51 loss: 0.3312 Lr: 0.00400 [2024-02-18 10:00:56,846 INFO misc.py line 119 87073] Train: [33/100][1546/1557] Data 0.008 (0.114) Batch 0.745 (1.061) Remain 30:44:28 loss: 0.2332 Lr: 0.00400 [2024-02-18 10:00:58,105 INFO misc.py line 119 87073] Train: [33/100][1547/1557] Data 0.004 (0.114) Batch 1.259 (1.061) Remain 30:44:41 loss: 0.1800 Lr: 0.00400 [2024-02-18 10:00:59,038 INFO misc.py line 119 87073] Train: [33/100][1548/1557] Data 0.004 (0.114) Batch 0.933 (1.061) Remain 30:44:31 loss: 0.6362 Lr: 0.00400 [2024-02-18 10:01:00,231 INFO misc.py line 119 87073] Train: [33/100][1549/1557] Data 0.004 (0.114) Batch 1.193 (1.061) Remain 30:44:39 loss: 1.3436 Lr: 0.00400 [2024-02-18 10:01:01,186 INFO misc.py line 119 87073] Train: [33/100][1550/1557] Data 0.004 (0.114) Batch 0.955 (1.061) Remain 30:44:31 loss: 0.5229 Lr: 0.00400 [2024-02-18 10:01:02,179 INFO misc.py line 119 87073] Train: [33/100][1551/1557] Data 0.004 (0.114) Batch 0.992 (1.061) Remain 30:44:25 loss: 0.5865 Lr: 0.00400 [2024-02-18 10:01:02,933 INFO misc.py line 119 87073] Train: [33/100][1552/1557] Data 0.004 (0.114) Batch 0.755 (1.061) Remain 30:44:03 loss: 0.4928 Lr: 0.00400 [2024-02-18 10:01:03,659 INFO misc.py line 119 87073] Train: [33/100][1553/1557] Data 0.004 (0.114) Batch 0.726 (1.060) Remain 30:43:40 loss: 0.4793 Lr: 0.00400 [2024-02-18 10:01:04,690 INFO misc.py line 119 87073] Train: [33/100][1554/1557] Data 0.004 (0.114) Batch 1.025 (1.060) Remain 30:43:36 loss: 0.2397 Lr: 0.00400 [2024-02-18 10:01:05,461 INFO misc.py line 119 87073] Train: [33/100][1555/1557] Data 0.010 (0.114) Batch 0.777 (1.060) Remain 30:43:16 loss: 0.4406 Lr: 0.00400 [2024-02-18 10:01:06,355 INFO misc.py line 119 87073] Train: [33/100][1556/1557] Data 0.004 (0.114) Batch 0.894 (1.060) Remain 30:43:04 loss: 0.7221 Lr: 0.00400 [2024-02-18 10:01:07,539 INFO misc.py line 119 87073] Train: [33/100][1557/1557] Data 0.004 (0.114) Batch 1.175 (1.060) Remain 30:43:11 loss: 0.4777 Lr: 0.00400 [2024-02-18 10:01:07,540 INFO misc.py line 136 87073] Train result: loss: 0.4196 [2024-02-18 10:01:07,540 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 10:01:34,548 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 1.0319 [2024-02-18 10:01:35,326 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7939 [2024-02-18 10:01:37,455 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4449 [2024-02-18 10:01:39,661 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.5736 [2024-02-18 10:01:44,605 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3440 [2024-02-18 10:01:45,305 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1195 [2024-02-18 10:01:46,567 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3112 [2024-02-18 10:01:49,522 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.5362 [2024-02-18 10:01:51,333 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2760 [2024-02-18 10:01:52,882 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6879/0.7596/0.8962. [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9224/0.9628 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9754/0.9821 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8234/0.9823 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0163/0.2610 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2580/0.2700 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6218/0.6298 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7187/0.7910 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8068/0.9145 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9174/0.9481 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8385/0.8725 [2024-02-18 10:01:52,882 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7617/0.8329 [2024-02-18 10:01:52,883 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7243/0.7947 [2024-02-18 10:01:52,883 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5574/0.6332 [2024-02-18 10:01:52,883 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 10:01:52,885 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 10:01:52,885 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 10:02:01,475 INFO misc.py line 119 87073] Train: [34/100][1/1557] Data 1.694 (1.694) Batch 2.643 (2.643) Remain 76:34:41 loss: 0.2913 Lr: 0.00400 [2024-02-18 10:02:02,433 INFO misc.py line 119 87073] Train: [34/100][2/1557] Data 0.005 (0.005) Batch 0.959 (0.959) Remain 27:46:33 loss: 0.2275 Lr: 0.00400 [2024-02-18 10:02:03,439 INFO misc.py line 119 87073] Train: [34/100][3/1557] Data 0.005 (0.005) Batch 1.000 (1.000) Remain 28:58:23 loss: 0.7683 Lr: 0.00400 [2024-02-18 10:02:04,413 INFO misc.py line 119 87073] Train: [34/100][4/1557] Data 0.010 (0.010) Batch 0.981 (0.981) Remain 28:25:36 loss: 0.4014 Lr: 0.00400 [2024-02-18 10:02:05,171 INFO misc.py line 119 87073] Train: [34/100][5/1557] Data 0.003 (0.007) Batch 0.757 (0.869) Remain 25:10:28 loss: 0.3180 Lr: 0.00400 [2024-02-18 10:02:05,978 INFO misc.py line 119 87073] Train: [34/100][6/1557] Data 0.005 (0.006) Batch 0.808 (0.849) Remain 24:35:12 loss: 0.4513 Lr: 0.00400 [2024-02-18 10:02:07,829 INFO misc.py line 119 87073] Train: [34/100][7/1557] Data 0.006 (0.006) Batch 1.844 (1.098) Remain 31:48:03 loss: 0.2444 Lr: 0.00400 [2024-02-18 10:02:08,838 INFO misc.py line 119 87073] Train: [34/100][8/1557] Data 0.011 (0.007) Batch 1.005 (1.079) Remain 31:15:43 loss: 0.8288 Lr: 0.00400 [2024-02-18 10:02:09,725 INFO misc.py line 119 87073] Train: [34/100][9/1557] Data 0.015 (0.008) Batch 0.897 (1.049) Remain 30:23:00 loss: 0.8286 Lr: 0.00400 [2024-02-18 10:02:10,624 INFO misc.py line 119 87073] Train: [34/100][10/1557] Data 0.005 (0.008) Batch 0.900 (1.027) Remain 29:46:00 loss: 0.5680 Lr: 0.00400 [2024-02-18 10:02:11,698 INFO misc.py line 119 87073] Train: [34/100][11/1557] Data 0.005 (0.008) Batch 1.074 (1.033) Remain 29:56:08 loss: 0.7500 Lr: 0.00400 [2024-02-18 10:02:12,399 INFO misc.py line 119 87073] Train: [34/100][12/1557] Data 0.004 (0.007) Batch 0.700 (0.996) Remain 28:51:46 loss: 0.3798 Lr: 0.00400 [2024-02-18 10:02:13,176 INFO misc.py line 119 87073] Train: [34/100][13/1557] Data 0.005 (0.007) Batch 0.773 (0.974) Remain 28:12:54 loss: 0.4342 Lr: 0.00400 [2024-02-18 10:02:14,382 INFO misc.py line 119 87073] Train: [34/100][14/1557] Data 0.009 (0.007) Batch 1.205 (0.995) Remain 28:49:21 loss: 0.3639 Lr: 0.00400 [2024-02-18 10:02:15,407 INFO misc.py line 119 87073] Train: [34/100][15/1557] Data 0.011 (0.008) Batch 1.025 (0.997) Remain 28:53:39 loss: 0.2787 Lr: 0.00400 [2024-02-18 10:02:16,410 INFO misc.py line 119 87073] Train: [34/100][16/1557] Data 0.012 (0.008) Batch 0.999 (0.997) Remain 28:53:48 loss: 0.3748 Lr: 0.00400 [2024-02-18 10:02:17,401 INFO misc.py line 119 87073] Train: [34/100][17/1557] Data 0.015 (0.008) Batch 1.001 (0.998) Remain 28:54:17 loss: 0.7048 Lr: 0.00400 [2024-02-18 10:02:18,353 INFO misc.py line 119 87073] Train: [34/100][18/1557] Data 0.006 (0.008) Batch 0.952 (0.995) Remain 28:48:58 loss: 0.2517 Lr: 0.00400 [2024-02-18 10:02:19,095 INFO misc.py line 119 87073] Train: [34/100][19/1557] Data 0.005 (0.008) Batch 0.744 (0.979) Remain 28:21:44 loss: 0.2706 Lr: 0.00400 [2024-02-18 10:02:19,905 INFO misc.py line 119 87073] Train: [34/100][20/1557] Data 0.004 (0.008) Batch 0.802 (0.969) Remain 28:03:38 loss: 0.4462 Lr: 0.00400 [2024-02-18 10:02:21,030 INFO misc.py line 119 87073] Train: [34/100][21/1557] Data 0.012 (0.008) Batch 1.126 (0.977) Remain 28:18:48 loss: 0.2602 Lr: 0.00400 [2024-02-18 10:02:22,028 INFO misc.py line 119 87073] Train: [34/100][22/1557] Data 0.011 (0.008) Batch 1.001 (0.979) Remain 28:20:56 loss: 0.9099 Lr: 0.00400 [2024-02-18 10:02:23,050 INFO misc.py line 119 87073] Train: [34/100][23/1557] Data 0.007 (0.008) Batch 1.016 (0.980) Remain 28:24:12 loss: 0.1958 Lr: 0.00400 [2024-02-18 10:02:23,891 INFO misc.py line 119 87073] Train: [34/100][24/1557] Data 0.014 (0.008) Batch 0.850 (0.974) Remain 28:13:22 loss: 0.6893 Lr: 0.00400 [2024-02-18 10:02:24,877 INFO misc.py line 119 87073] Train: [34/100][25/1557] Data 0.005 (0.008) Batch 0.986 (0.975) Remain 28:14:19 loss: 1.0363 Lr: 0.00400 [2024-02-18 10:02:25,645 INFO misc.py line 119 87073] Train: [34/100][26/1557] Data 0.005 (0.008) Batch 0.768 (0.966) Remain 27:58:41 loss: 0.2440 Lr: 0.00400 [2024-02-18 10:02:26,415 INFO misc.py line 119 87073] Train: [34/100][27/1557] Data 0.005 (0.008) Batch 0.767 (0.957) Remain 27:44:16 loss: 0.3218 Lr: 0.00400 [2024-02-18 10:02:27,695 INFO misc.py line 119 87073] Train: [34/100][28/1557] Data 0.007 (0.008) Batch 1.277 (0.970) Remain 28:06:29 loss: 0.5349 Lr: 0.00400 [2024-02-18 10:02:28,477 INFO misc.py line 119 87073] Train: [34/100][29/1557] Data 0.011 (0.008) Batch 0.788 (0.963) Remain 27:54:15 loss: 0.3111 Lr: 0.00400 [2024-02-18 10:02:29,420 INFO misc.py line 119 87073] Train: [34/100][30/1557] Data 0.005 (0.008) Batch 0.943 (0.962) Remain 27:52:57 loss: 0.5263 Lr: 0.00400 [2024-02-18 10:02:30,325 INFO misc.py line 119 87073] Train: [34/100][31/1557] Data 0.004 (0.008) Batch 0.899 (0.960) Remain 27:49:00 loss: 0.9613 Lr: 0.00400 [2024-02-18 10:02:31,377 INFO misc.py line 119 87073] Train: [34/100][32/1557] Data 0.010 (0.008) Batch 1.050 (0.963) Remain 27:54:22 loss: 0.3563 Lr: 0.00400 [2024-02-18 10:02:32,157 INFO misc.py line 119 87073] Train: [34/100][33/1557] Data 0.012 (0.008) Batch 0.789 (0.958) Remain 27:44:13 loss: 0.8337 Lr: 0.00400 [2024-02-18 10:02:32,953 INFO misc.py line 119 87073] Train: [34/100][34/1557] Data 0.004 (0.008) Batch 0.795 (0.952) Remain 27:35:08 loss: 0.3339 Lr: 0.00400 [2024-02-18 10:02:34,139 INFO misc.py line 119 87073] Train: [34/100][35/1557] Data 0.004 (0.008) Batch 1.177 (0.959) Remain 27:47:20 loss: 0.2636 Lr: 0.00400 [2024-02-18 10:02:35,368 INFO misc.py line 119 87073] Train: [34/100][36/1557] Data 0.013 (0.008) Batch 1.230 (0.968) Remain 28:01:33 loss: 0.1049 Lr: 0.00400 [2024-02-18 10:02:36,298 INFO misc.py line 119 87073] Train: [34/100][37/1557] Data 0.011 (0.008) Batch 0.938 (0.967) Remain 28:00:03 loss: 0.3759 Lr: 0.00400 [2024-02-18 10:02:37,391 INFO misc.py line 119 87073] Train: [34/100][38/1557] Data 0.004 (0.008) Batch 1.092 (0.970) Remain 28:06:16 loss: 0.6684 Lr: 0.00400 [2024-02-18 10:02:38,409 INFO misc.py line 119 87073] Train: [34/100][39/1557] Data 0.005 (0.008) Batch 1.017 (0.972) Remain 28:08:31 loss: 0.3090 Lr: 0.00400 [2024-02-18 10:02:39,180 INFO misc.py line 119 87073] Train: [34/100][40/1557] Data 0.006 (0.008) Batch 0.771 (0.966) Remain 27:59:06 loss: 0.4487 Lr: 0.00400 [2024-02-18 10:02:39,940 INFO misc.py line 119 87073] Train: [34/100][41/1557] Data 0.005 (0.008) Batch 0.761 (0.961) Remain 27:49:43 loss: 0.5257 Lr: 0.00400 [2024-02-18 10:02:41,104 INFO misc.py line 119 87073] Train: [34/100][42/1557] Data 0.004 (0.008) Batch 1.153 (0.966) Remain 27:58:17 loss: 0.1982 Lr: 0.00400 [2024-02-18 10:02:42,123 INFO misc.py line 119 87073] Train: [34/100][43/1557] Data 0.014 (0.008) Batch 1.018 (0.967) Remain 28:00:33 loss: 0.2104 Lr: 0.00400 [2024-02-18 10:02:43,077 INFO misc.py line 119 87073] Train: [34/100][44/1557] Data 0.016 (0.008) Batch 0.965 (0.967) Remain 28:00:28 loss: 0.3932 Lr: 0.00400 [2024-02-18 10:02:44,151 INFO misc.py line 119 87073] Train: [34/100][45/1557] Data 0.004 (0.008) Batch 1.074 (0.969) Remain 28:04:52 loss: 0.3712 Lr: 0.00400 [2024-02-18 10:02:45,026 INFO misc.py line 119 87073] Train: [34/100][46/1557] Data 0.004 (0.008) Batch 0.874 (0.967) Remain 28:01:00 loss: 0.4533 Lr: 0.00400 [2024-02-18 10:02:45,694 INFO misc.py line 119 87073] Train: [34/100][47/1557] Data 0.005 (0.008) Batch 0.667 (0.960) Remain 27:49:07 loss: 0.2952 Lr: 0.00400 [2024-02-18 10:02:46,465 INFO misc.py line 119 87073] Train: [34/100][48/1557] Data 0.006 (0.008) Batch 0.773 (0.956) Remain 27:41:53 loss: 0.6625 Lr: 0.00400 [2024-02-18 10:02:47,779 INFO misc.py line 119 87073] Train: [34/100][49/1557] Data 0.004 (0.008) Batch 1.304 (0.964) Remain 27:55:00 loss: 0.1865 Lr: 0.00400 [2024-02-18 10:02:48,746 INFO misc.py line 119 87073] Train: [34/100][50/1557] Data 0.013 (0.008) Batch 0.975 (0.964) Remain 27:55:24 loss: 0.4648 Lr: 0.00400 [2024-02-18 10:02:49,615 INFO misc.py line 119 87073] Train: [34/100][51/1557] Data 0.006 (0.008) Batch 0.870 (0.962) Remain 27:51:59 loss: 0.6536 Lr: 0.00400 [2024-02-18 10:02:50,541 INFO misc.py line 119 87073] Train: [34/100][52/1557] Data 0.004 (0.008) Batch 0.919 (0.961) Remain 27:50:26 loss: 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INFO misc.py line 119 87073] Train: [34/100][59/1557] Data 0.004 (0.008) Batch 1.097 (0.962) Remain 27:51:52 loss: 0.5007 Lr: 0.00400 [2024-02-18 10:02:58,290 INFO misc.py line 119 87073] Train: [34/100][60/1557] Data 0.005 (0.008) Batch 0.978 (0.962) Remain 27:52:20 loss: 0.5624 Lr: 0.00400 [2024-02-18 10:02:59,041 INFO misc.py line 119 87073] Train: [34/100][61/1557] Data 0.004 (0.008) Batch 0.751 (0.959) Remain 27:45:58 loss: 0.4309 Lr: 0.00400 [2024-02-18 10:02:59,816 INFO misc.py line 119 87073] Train: [34/100][62/1557] Data 0.004 (0.007) Batch 0.773 (0.956) Remain 27:40:30 loss: 0.9493 Lr: 0.00400 [2024-02-18 10:03:09,870 INFO misc.py line 119 87073] Train: [34/100][63/1557] Data 7.226 (0.128) Batch 10.056 (1.107) Remain 32:04:02 loss: 0.2818 Lr: 0.00400 [2024-02-18 10:03:10,894 INFO misc.py line 119 87073] Train: [34/100][64/1557] Data 0.004 (0.126) Batch 1.023 (1.106) Remain 32:01:37 loss: 0.5742 Lr: 0.00400 [2024-02-18 10:03:12,009 INFO misc.py line 119 87073] Train: [34/100][65/1557] Data 0.004 (0.124) Batch 1.114 (1.106) Remain 32:01:50 loss: 0.4129 Lr: 0.00400 [2024-02-18 10:03:12,984 INFO misc.py line 119 87073] Train: [34/100][66/1557] Data 0.006 (0.122) Batch 0.975 (1.104) Remain 31:58:13 loss: 0.3566 Lr: 0.00400 [2024-02-18 10:03:13,958 INFO misc.py line 119 87073] Train: [34/100][67/1557] Data 0.004 (0.120) Batch 0.975 (1.102) Remain 31:54:41 loss: 0.7148 Lr: 0.00400 [2024-02-18 10:03:14,696 INFO misc.py line 119 87073] Train: [34/100][68/1557] Data 0.005 (0.118) Batch 0.732 (1.096) Remain 31:44:47 loss: 0.3680 Lr: 0.00400 [2024-02-18 10:03:15,472 INFO misc.py line 119 87073] Train: [34/100][69/1557] Data 0.010 (0.117) Batch 0.781 (1.091) Remain 31:36:28 loss: 0.2094 Lr: 0.00400 [2024-02-18 10:03:16,637 INFO misc.py line 119 87073] Train: [34/100][70/1557] Data 0.005 (0.115) Batch 1.165 (1.093) Remain 31:38:22 loss: 0.3019 Lr: 0.00400 [2024-02-18 10:03:17,719 INFO misc.py line 119 87073] Train: [34/100][71/1557] Data 0.004 (0.113) Batch 1.081 (1.092) Remain 31:38:04 loss: 0.5622 Lr: 0.00400 [2024-02-18 10:03:18,600 INFO misc.py line 119 87073] Train: [34/100][72/1557] Data 0.005 (0.112) Batch 0.882 (1.089) Remain 31:32:45 loss: 0.3952 Lr: 0.00400 [2024-02-18 10:03:19,803 INFO misc.py line 119 87073] Train: [34/100][73/1557] Data 0.004 (0.110) Batch 1.202 (1.091) Remain 31:35:32 loss: 1.3711 Lr: 0.00400 [2024-02-18 10:03:20,954 INFO misc.py line 119 87073] Train: [34/100][74/1557] Data 0.004 (0.109) Batch 1.151 (1.092) Remain 31:36:59 loss: 0.3012 Lr: 0.00400 [2024-02-18 10:03:21,690 INFO misc.py line 119 87073] Train: [34/100][75/1557] Data 0.004 (0.107) Batch 0.737 (1.087) Remain 31:28:25 loss: 0.2646 Lr: 0.00400 [2024-02-18 10:03:22,511 INFO misc.py line 119 87073] Train: [34/100][76/1557] Data 0.003 (0.106) Batch 0.820 (1.083) Remain 31:22:02 loss: 0.3383 Lr: 0.00400 [2024-02-18 10:03:23,639 INFO misc.py line 119 87073] Train: [34/100][77/1557] Data 0.004 (0.104) Batch 1.126 (1.084) Remain 31:23:00 loss: 0.2182 Lr: 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line 119 87073] Train: [34/100][84/1557] Data 0.007 (0.096) Batch 1.205 (1.070) Remain 30:59:07 loss: 0.2476 Lr: 0.00400 [2024-02-18 10:03:31,130 INFO misc.py line 119 87073] Train: [34/100][85/1557] Data 0.011 (0.095) Batch 1.015 (1.069) Remain 30:57:55 loss: 0.5239 Lr: 0.00400 [2024-02-18 10:03:32,337 INFO misc.py line 119 87073] Train: [34/100][86/1557] Data 0.005 (0.094) Batch 1.202 (1.071) Remain 31:00:40 loss: 0.3997 Lr: 0.00400 [2024-02-18 10:03:33,471 INFO misc.py line 119 87073] Train: [34/100][87/1557] Data 0.010 (0.093) Batch 1.134 (1.072) Remain 31:01:57 loss: 0.5130 Lr: 0.00400 [2024-02-18 10:03:34,528 INFO misc.py line 119 87073] Train: [34/100][88/1557] Data 0.010 (0.092) Batch 1.055 (1.072) Remain 31:01:35 loss: 0.7000 Lr: 0.00400 [2024-02-18 10:03:35,326 INFO misc.py line 119 87073] Train: [34/100][89/1557] Data 0.012 (0.091) Batch 0.805 (1.069) Remain 30:56:11 loss: 0.2037 Lr: 0.00400 [2024-02-18 10:03:36,071 INFO misc.py line 119 87073] Train: [34/100][90/1557] Data 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Batch 1.070 (1.107) Remain 32:02:28 loss: 0.8822 Lr: 0.00400 [2024-02-18 10:04:14,899 INFO misc.py line 119 87073] Train: [34/100][122/1557] Data 0.004 (0.131) Batch 0.840 (1.105) Remain 31:58:32 loss: 0.8343 Lr: 0.00400 [2024-02-18 10:04:15,826 INFO misc.py line 119 87073] Train: [34/100][123/1557] Data 0.004 (0.130) Batch 0.918 (1.103) Remain 31:55:49 loss: 0.4062 Lr: 0.00400 [2024-02-18 10:04:16,590 INFO misc.py line 119 87073] Train: [34/100][124/1557] Data 0.013 (0.129) Batch 0.772 (1.100) Remain 31:51:02 loss: 0.3903 Lr: 0.00400 [2024-02-18 10:04:17,285 INFO misc.py line 119 87073] Train: [34/100][125/1557] Data 0.005 (0.128) Batch 0.691 (1.097) Remain 31:45:11 loss: 0.4655 Lr: 0.00400 [2024-02-18 10:04:18,424 INFO misc.py line 119 87073] Train: [34/100][126/1557] Data 0.009 (0.127) Batch 1.140 (1.097) Remain 31:45:47 loss: 0.3459 Lr: 0.00400 [2024-02-18 10:04:19,314 INFO misc.py line 119 87073] Train: [34/100][127/1557] Data 0.008 (0.126) Batch 0.894 (1.096) Remain 31:42:55 loss: 0.3389 Lr: 0.00400 [2024-02-18 10:04:20,216 INFO misc.py line 119 87073] Train: [34/100][128/1557] Data 0.003 (0.125) Batch 0.902 (1.094) Remain 31:40:12 loss: 0.1891 Lr: 0.00400 [2024-02-18 10:04:21,287 INFO misc.py line 119 87073] Train: [34/100][129/1557] Data 0.004 (0.124) Batch 1.071 (1.094) Remain 31:39:52 loss: 0.4359 Lr: 0.00400 [2024-02-18 10:04:22,327 INFO misc.py line 119 87073] Train: [34/100][130/1557] Data 0.004 (0.123) Batch 1.040 (1.094) Remain 31:39:07 loss: 0.3100 Lr: 0.00400 [2024-02-18 10:04:23,108 INFO misc.py line 119 87073] Train: [34/100][131/1557] Data 0.004 (0.122) Batch 0.781 (1.091) Remain 31:34:51 loss: 0.3495 Lr: 0.00400 [2024-02-18 10:04:23,889 INFO misc.py line 119 87073] Train: [34/100][132/1557] Data 0.004 (0.121) Batch 0.776 (1.089) Remain 31:30:35 loss: 0.3467 Lr: 0.00400 [2024-02-18 10:04:24,987 INFO misc.py line 119 87073] Train: [34/100][133/1557] Data 0.008 (0.120) Batch 1.092 (1.089) Remain 31:30:37 loss: 0.3020 Lr: 0.00400 [2024-02-18 10:04:26,022 INFO misc.py line 119 87073] Train: [34/100][134/1557] Data 0.015 (0.119) Batch 1.045 (1.088) Remain 31:30:01 loss: 0.6219 Lr: 0.00400 [2024-02-18 10:04:26,933 INFO misc.py line 119 87073] Train: [34/100][135/1557] Data 0.004 (0.118) Batch 0.912 (1.087) Remain 31:27:41 loss: 0.2577 Lr: 0.00400 [2024-02-18 10:04:27,909 INFO misc.py line 119 87073] Train: [34/100][136/1557] Data 0.003 (0.118) Batch 0.976 (1.086) Remain 31:26:13 loss: 0.5666 Lr: 0.00400 [2024-02-18 10:04:28,794 INFO misc.py line 119 87073] Train: [34/100][137/1557] Data 0.003 (0.117) Batch 0.885 (1.085) Remain 31:23:35 loss: 0.3847 Lr: 0.00400 [2024-02-18 10:04:29,424 INFO misc.py line 119 87073] Train: [34/100][138/1557] Data 0.003 (0.116) Batch 0.619 (1.081) Remain 31:17:35 loss: 0.2658 Lr: 0.00400 [2024-02-18 10:04:30,207 INFO misc.py line 119 87073] Train: [34/100][139/1557] Data 0.015 (0.115) Batch 0.794 (1.079) Remain 31:13:54 loss: 0.3871 Lr: 0.00400 [2024-02-18 10:04:31,469 INFO misc.py line 119 87073] Train: [34/100][140/1557] Data 0.003 (0.114) Batch 1.246 (1.080) Remain 31:15:59 loss: 0.5169 Lr: 0.00400 [2024-02-18 10:04:32,504 INFO misc.py line 119 87073] Train: [34/100][141/1557] Data 0.020 (0.114) Batch 1.050 (1.080) Remain 31:15:35 loss: 0.2927 Lr: 0.00400 [2024-02-18 10:04:33,514 INFO misc.py line 119 87073] Train: [34/100][142/1557] Data 0.005 (0.113) Batch 1.002 (1.080) Remain 31:14:35 loss: 0.4803 Lr: 0.00400 [2024-02-18 10:04:34,433 INFO misc.py line 119 87073] Train: [34/100][143/1557] Data 0.013 (0.112) Batch 0.928 (1.079) Remain 31:12:41 loss: 0.5935 Lr: 0.00400 [2024-02-18 10:04:35,582 INFO misc.py line 119 87073] Train: [34/100][144/1557] Data 0.003 (0.111) Batch 1.148 (1.079) Remain 31:13:31 loss: 0.3269 Lr: 0.00400 [2024-02-18 10:04:36,352 INFO misc.py line 119 87073] Train: [34/100][145/1557] Data 0.004 (0.111) Batch 0.771 (1.077) Remain 31:09:44 loss: 0.2780 Lr: 0.00400 [2024-02-18 10:04:37,162 INFO misc.py line 119 87073] Train: [34/100][146/1557] Data 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line 119 87073] Train: [34/100][165/1557] Data 0.006 (0.098) Batch 1.129 (1.065) Remain 30:48:51 loss: 0.6151 Lr: 0.00400 [2024-02-18 10:04:56,753 INFO misc.py line 119 87073] Train: [34/100][166/1557] Data 0.003 (0.097) Batch 0.779 (1.063) Remain 30:45:47 loss: 0.5251 Lr: 0.00400 [2024-02-18 10:04:57,447 INFO misc.py line 119 87073] Train: [34/100][167/1557] Data 0.003 (0.097) Batch 0.685 (1.061) Remain 30:41:46 loss: 0.3390 Lr: 0.00400 [2024-02-18 10:04:58,793 INFO misc.py line 119 87073] Train: [34/100][168/1557] Data 0.012 (0.096) Batch 1.344 (1.063) Remain 30:44:43 loss: 0.3360 Lr: 0.00400 [2024-02-18 10:04:59,842 INFO misc.py line 119 87073] Train: [34/100][169/1557] Data 0.015 (0.096) Batch 1.049 (1.063) Remain 30:44:34 loss: 0.5189 Lr: 0.00400 [2024-02-18 10:05:01,089 INFO misc.py line 119 87073] Train: [34/100][170/1557] Data 0.014 (0.095) Batch 1.254 (1.064) Remain 30:46:32 loss: 0.4281 Lr: 0.00400 [2024-02-18 10:05:02,295 INFO misc.py line 119 87073] Train: 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Batch 1.125 (1.115) Remain 32:15:25 loss: 0.3589 Lr: 0.00400 [2024-02-18 10:05:18,433 INFO misc.py line 119 87073] Train: [34/100][178/1557] Data 0.004 (0.140) Batch 0.978 (1.114) Remain 32:14:02 loss: 0.2383 Lr: 0.00400 [2024-02-18 10:05:19,391 INFO misc.py line 119 87073] Train: [34/100][179/1557] Data 0.004 (0.139) Batch 0.959 (1.113) Remain 32:12:29 loss: 0.7361 Lr: 0.00400 [2024-02-18 10:05:22,136 INFO misc.py line 119 87073] Train: [34/100][180/1557] Data 1.939 (0.149) Batch 2.743 (1.123) Remain 32:28:27 loss: 0.3449 Lr: 0.00400 [2024-02-18 10:05:22,907 INFO misc.py line 119 87073] Train: [34/100][181/1557] Data 0.006 (0.149) Batch 0.773 (1.121) Remain 32:25:01 loss: 0.3221 Lr: 0.00400 [2024-02-18 10:05:24,119 INFO misc.py line 119 87073] Train: [34/100][182/1557] Data 0.004 (0.148) Batch 1.212 (1.121) Remain 32:25:53 loss: 0.2496 Lr: 0.00400 [2024-02-18 10:05:25,122 INFO misc.py line 119 87073] Train: [34/100][183/1557] Data 0.004 (0.147) Batch 0.996 (1.120) Remain 32:24:39 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line 119 87073] Train: [34/100][221/1557] Data 0.004 (0.122) Batch 1.113 (1.087) Remain 31:25:40 loss: 0.8716 Lr: 0.00399 [2024-02-18 10:06:01,060 INFO misc.py line 119 87073] Train: [34/100][222/1557] Data 0.008 (0.122) Batch 0.692 (1.085) Remain 31:22:31 loss: 0.4300 Lr: 0.00399 [2024-02-18 10:06:01,905 INFO misc.py line 119 87073] Train: [34/100][223/1557] Data 0.004 (0.121) Batch 0.837 (1.084) Remain 31:20:32 loss: 0.5919 Lr: 0.00399 [2024-02-18 10:06:03,199 INFO misc.py line 119 87073] Train: [34/100][224/1557] Data 0.011 (0.121) Batch 1.298 (1.085) Remain 31:22:12 loss: 0.2784 Lr: 0.00399 [2024-02-18 10:06:04,056 INFO misc.py line 119 87073] Train: [34/100][225/1557] Data 0.007 (0.120) Batch 0.860 (1.084) Remain 31:20:26 loss: 0.4453 Lr: 0.00399 [2024-02-18 10:06:04,977 INFO misc.py line 119 87073] Train: [34/100][226/1557] Data 0.004 (0.120) Batch 0.922 (1.083) Remain 31:19:09 loss: 0.2420 Lr: 0.00399 [2024-02-18 10:06:06,103 INFO misc.py line 119 87073] Train: 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Batch 0.998 (1.118) Remain 32:19:43 loss: 0.5761 Lr: 0.00399 [2024-02-18 10:06:21,714 INFO misc.py line 119 87073] Train: [34/100][234/1557] Data 0.004 (0.148) Batch 1.106 (1.118) Remain 32:19:37 loss: 0.3890 Lr: 0.00399 [2024-02-18 10:06:22,504 INFO misc.py line 119 87073] Train: [34/100][235/1557] Data 0.004 (0.148) Batch 0.790 (1.117) Remain 32:17:08 loss: 0.6082 Lr: 0.00399 [2024-02-18 10:06:23,238 INFO misc.py line 119 87073] Train: [34/100][236/1557] Data 0.005 (0.147) Batch 0.728 (1.115) Remain 32:14:14 loss: 0.3259 Lr: 0.00399 [2024-02-18 10:06:24,033 INFO misc.py line 119 87073] Train: [34/100][237/1557] Data 0.011 (0.147) Batch 0.800 (1.114) Remain 32:11:52 loss: 0.7421 Lr: 0.00399 [2024-02-18 10:06:25,250 INFO misc.py line 119 87073] Train: [34/100][238/1557] Data 0.005 (0.146) Batch 1.217 (1.114) Remain 32:12:37 loss: 0.4867 Lr: 0.00399 [2024-02-18 10:06:26,350 INFO misc.py line 119 87073] Train: [34/100][239/1557] Data 0.006 (0.145) Batch 1.101 (1.114) Remain 32:12:30 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87073] Train: [34/100][252/1557] Data 0.004 (0.138) Batch 1.215 (1.107) Remain 32:00:01 loss: 0.2370 Lr: 0.00399 [2024-02-18 10:06:39,927 INFO misc.py line 119 87073] Train: [34/100][253/1557] Data 0.004 (0.138) Batch 0.852 (1.106) Remain 31:58:14 loss: 0.6083 Lr: 0.00399 [2024-02-18 10:06:40,765 INFO misc.py line 119 87073] Train: [34/100][254/1557] Data 0.005 (0.137) Batch 0.836 (1.105) Remain 31:56:21 loss: 0.6083 Lr: 0.00399 [2024-02-18 10:06:41,767 INFO misc.py line 119 87073] Train: [34/100][255/1557] Data 0.007 (0.137) Batch 1.003 (1.104) Remain 31:55:38 loss: 0.1306 Lr: 0.00399 [2024-02-18 10:06:42,720 INFO misc.py line 119 87073] Train: [34/100][256/1557] Data 0.005 (0.136) Batch 0.954 (1.104) Remain 31:54:35 loss: 0.3002 Lr: 0.00399 [2024-02-18 10:06:43,423 INFO misc.py line 119 87073] Train: [34/100][257/1557] Data 0.004 (0.136) Batch 0.703 (1.102) Remain 31:51:50 loss: 0.3028 Lr: 0.00399 [2024-02-18 10:06:44,198 INFO misc.py line 119 87073] Train: [34/100][258/1557] Data 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31:55:57 loss: 0.4544 Lr: 0.00394 [2024-02-18 10:29:44,342 INFO misc.py line 119 87073] Train: [34/100][1489/1557] Data 0.005 (0.150) Batch 0.779 (1.118) Remain 31:55:33 loss: 0.1828 Lr: 0.00394 [2024-02-18 10:29:45,071 INFO misc.py line 119 87073] Train: [34/100][1490/1557] Data 0.005 (0.150) Batch 0.723 (1.117) Remain 31:55:04 loss: 0.3320 Lr: 0.00394 [2024-02-18 10:29:46,426 INFO misc.py line 119 87073] Train: [34/100][1491/1557] Data 0.011 (0.149) Batch 1.357 (1.118) Remain 31:55:20 loss: 0.3175 Lr: 0.00394 [2024-02-18 10:29:47,659 INFO misc.py line 119 87073] Train: [34/100][1492/1557] Data 0.008 (0.149) Batch 1.236 (1.118) Remain 31:55:27 loss: 0.3126 Lr: 0.00394 [2024-02-18 10:29:48,581 INFO misc.py line 119 87073] Train: [34/100][1493/1557] Data 0.006 (0.149) Batch 0.924 (1.118) Remain 31:55:12 loss: 0.1753 Lr: 0.00394 [2024-02-18 10:29:49,692 INFO misc.py line 119 87073] Train: [34/100][1494/1557] Data 0.004 (0.149) Batch 1.111 (1.118) Remain 31:55:11 loss: 0.5287 Lr: 0.00394 [2024-02-18 10:29:50,692 INFO misc.py line 119 87073] Train: [34/100][1495/1557] Data 0.005 (0.149) Batch 1.000 (1.117) Remain 31:55:02 loss: 0.5800 Lr: 0.00394 [2024-02-18 10:29:51,407 INFO misc.py line 119 87073] Train: [34/100][1496/1557] Data 0.005 (0.149) Batch 0.715 (1.117) Remain 31:54:33 loss: 0.4351 Lr: 0.00394 [2024-02-18 10:29:52,143 INFO misc.py line 119 87073] Train: [34/100][1497/1557] Data 0.005 (0.149) Batch 0.737 (1.117) Remain 31:54:05 loss: 0.3932 Lr: 0.00394 [2024-02-18 10:29:53,321 INFO misc.py line 119 87073] Train: [34/100][1498/1557] Data 0.004 (0.149) Batch 1.177 (1.117) Remain 31:54:08 loss: 0.1479 Lr: 0.00394 [2024-02-18 10:29:54,426 INFO misc.py line 119 87073] Train: [34/100][1499/1557] Data 0.006 (0.149) Batch 1.093 (1.117) Remain 31:54:06 loss: 0.3202 Lr: 0.00394 [2024-02-18 10:29:55,236 INFO misc.py line 119 87073] Train: [34/100][1500/1557] Data 0.018 (0.149) Batch 0.823 (1.117) Remain 31:53:44 loss: 0.9045 Lr: 0.00394 [2024-02-18 10:29:56,164 INFO misc.py line 119 87073] Train: [34/100][1501/1557] Data 0.004 (0.148) Batch 0.928 (1.117) Remain 31:53:30 loss: 0.5153 Lr: 0.00394 [2024-02-18 10:29:57,167 INFO misc.py line 119 87073] Train: [34/100][1502/1557] Data 0.005 (0.148) Batch 1.003 (1.117) Remain 31:53:21 loss: 0.6988 Lr: 0.00394 [2024-02-18 10:29:57,912 INFO misc.py line 119 87073] Train: [34/100][1503/1557] Data 0.005 (0.148) Batch 0.745 (1.116) Remain 31:52:55 loss: 0.2389 Lr: 0.00394 [2024-02-18 10:29:58,689 INFO misc.py line 119 87073] Train: [34/100][1504/1557] Data 0.004 (0.148) Batch 0.777 (1.116) Remain 31:52:30 loss: 0.2585 Lr: 0.00394 [2024-02-18 10:29:59,927 INFO misc.py line 119 87073] Train: [34/100][1505/1557] Data 0.004 (0.148) Batch 1.227 (1.116) Remain 31:52:37 loss: 0.1759 Lr: 0.00394 [2024-02-18 10:30:00,807 INFO misc.py line 119 87073] Train: [34/100][1506/1557] Data 0.016 (0.148) Batch 0.892 (1.116) Remain 31:52:20 loss: 0.4236 Lr: 0.00394 [2024-02-18 10:30:01,801 INFO misc.py line 119 87073] Train: 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(0.147) Batch 1.138 (1.116) Remain 31:51:20 loss: 0.8011 Lr: 0.00394 [2024-02-18 10:30:08,824 INFO misc.py line 119 87073] Train: [34/100][1514/1557] Data 0.011 (0.147) Batch 0.979 (1.115) Remain 31:51:09 loss: 0.5670 Lr: 0.00394 [2024-02-18 10:30:09,858 INFO misc.py line 119 87073] Train: [34/100][1515/1557] Data 0.006 (0.147) Batch 1.034 (1.115) Remain 31:51:03 loss: 0.4743 Lr: 0.00394 [2024-02-18 10:30:10,727 INFO misc.py line 119 87073] Train: [34/100][1516/1557] Data 0.005 (0.147) Batch 0.868 (1.115) Remain 31:50:45 loss: 0.5598 Lr: 0.00394 [2024-02-18 10:30:11,426 INFO misc.py line 119 87073] Train: [34/100][1517/1557] Data 0.006 (0.147) Batch 0.690 (1.115) Remain 31:50:15 loss: 0.3182 Lr: 0.00394 [2024-02-18 10:30:12,173 INFO misc.py line 119 87073] Train: [34/100][1518/1557] Data 0.016 (0.147) Batch 0.758 (1.115) Remain 31:49:50 loss: 0.4164 Lr: 0.00394 [2024-02-18 10:30:22,172 INFO misc.py line 119 87073] Train: [34/100][1519/1557] Data 6.967 (0.151) Batch 9.989 (1.121) Remain 31:59:50 loss: 0.2292 Lr: 0.00394 [2024-02-18 10:30:23,083 INFO misc.py line 119 87073] Train: [34/100][1520/1557] Data 0.014 (0.151) Batch 0.920 (1.120) Remain 31:59:35 loss: 0.2592 Lr: 0.00394 [2024-02-18 10:30:24,099 INFO misc.py line 119 87073] Train: [34/100][1521/1557] Data 0.006 (0.151) Batch 1.016 (1.120) Remain 31:59:27 loss: 0.4201 Lr: 0.00394 [2024-02-18 10:30:25,058 INFO misc.py line 119 87073] Train: [34/100][1522/1557] Data 0.005 (0.151) Batch 0.959 (1.120) Remain 31:59:15 loss: 0.3247 Lr: 0.00394 [2024-02-18 10:30:26,012 INFO misc.py line 119 87073] Train: [34/100][1523/1557] Data 0.005 (0.151) Batch 0.955 (1.120) Remain 31:59:03 loss: 0.6869 Lr: 0.00394 [2024-02-18 10:30:26,765 INFO misc.py line 119 87073] Train: [34/100][1524/1557] Data 0.004 (0.151) Batch 0.749 (1.120) Remain 31:58:37 loss: 0.3073 Lr: 0.00394 [2024-02-18 10:30:27,495 INFO misc.py line 119 87073] Train: [34/100][1525/1557] Data 0.008 (0.151) Batch 0.733 (1.120) Remain 31:58:09 loss: 0.2471 Lr: 0.00394 [2024-02-18 10:30:28,640 INFO misc.py line 119 87073] Train: [34/100][1526/1557] Data 0.005 (0.151) Batch 1.146 (1.120) Remain 31:58:10 loss: 0.3275 Lr: 0.00394 [2024-02-18 10:30:29,725 INFO misc.py line 119 87073] Train: [34/100][1527/1557] Data 0.004 (0.151) Batch 1.084 (1.120) Remain 31:58:07 loss: 0.9609 Lr: 0.00394 [2024-02-18 10:30:30,637 INFO misc.py line 119 87073] Train: [34/100][1528/1557] Data 0.005 (0.151) Batch 0.913 (1.119) Remain 31:57:52 loss: 0.5311 Lr: 0.00394 [2024-02-18 10:30:31,552 INFO misc.py line 119 87073] Train: [34/100][1529/1557] Data 0.005 (0.150) Batch 0.910 (1.119) Remain 31:57:36 loss: 0.4623 Lr: 0.00394 [2024-02-18 10:30:32,384 INFO misc.py line 119 87073] Train: [34/100][1530/1557] Data 0.009 (0.150) Batch 0.837 (1.119) Remain 31:57:16 loss: 1.0307 Lr: 0.00394 [2024-02-18 10:30:33,179 INFO misc.py line 119 87073] Train: [34/100][1531/1557] Data 0.005 (0.150) Batch 0.796 (1.119) Remain 31:56:53 loss: 0.3756 Lr: 0.00394 [2024-02-18 10:30:33,983 INFO misc.py line 119 87073] Train: [34/100][1532/1557] Data 0.003 (0.150) Batch 0.799 (1.119) Remain 31:56:31 loss: 0.2663 Lr: 0.00394 [2024-02-18 10:30:35,180 INFO misc.py line 119 87073] Train: [34/100][1533/1557] Data 0.009 (0.150) Batch 1.195 (1.119) Remain 31:56:35 loss: 0.1765 Lr: 0.00394 [2024-02-18 10:30:36,115 INFO misc.py line 119 87073] Train: [34/100][1534/1557] Data 0.009 (0.150) Batch 0.941 (1.119) Remain 31:56:22 loss: 0.8057 Lr: 0.00394 [2024-02-18 10:30:37,132 INFO misc.py line 119 87073] Train: [34/100][1535/1557] Data 0.003 (0.150) Batch 1.016 (1.119) Remain 31:56:14 loss: 0.3269 Lr: 0.00394 [2024-02-18 10:30:38,088 INFO misc.py line 119 87073] Train: [34/100][1536/1557] Data 0.004 (0.150) Batch 0.957 (1.118) Remain 31:56:02 loss: 0.2327 Lr: 0.00394 [2024-02-18 10:30:39,030 INFO misc.py line 119 87073] Train: [34/100][1537/1557] Data 0.004 (0.150) Batch 0.941 (1.118) Remain 31:55:49 loss: 0.3126 Lr: 0.00394 [2024-02-18 10:30:39,686 INFO misc.py line 119 87073] Train: [34/100][1538/1557] Data 0.004 (0.150) Batch 0.650 (1.118) Remain 31:55:16 loss: 0.2035 Lr: 0.00394 [2024-02-18 10:30:40,446 INFO misc.py line 119 87073] Train: [34/100][1539/1557] Data 0.010 (0.149) Batch 0.766 (1.118) Remain 31:54:52 loss: 0.4484 Lr: 0.00394 [2024-02-18 10:30:41,706 INFO misc.py line 119 87073] Train: [34/100][1540/1557] Data 0.004 (0.149) Batch 1.253 (1.118) Remain 31:55:00 loss: 0.2015 Lr: 0.00394 [2024-02-18 10:30:42,865 INFO misc.py line 119 87073] Train: [34/100][1541/1557] Data 0.011 (0.149) Batch 1.158 (1.118) Remain 31:55:01 loss: 0.4574 Lr: 0.00394 [2024-02-18 10:30:43,854 INFO misc.py line 119 87073] Train: [34/100][1542/1557] Data 0.012 (0.149) Batch 0.997 (1.118) Remain 31:54:52 loss: 0.1774 Lr: 0.00394 [2024-02-18 10:30:44,837 INFO misc.py line 119 87073] Train: [34/100][1543/1557] Data 0.004 (0.149) Batch 0.983 (1.118) Remain 31:54:42 loss: 0.3806 Lr: 0.00394 [2024-02-18 10:30:45,850 INFO misc.py line 119 87073] Train: [34/100][1544/1557] Data 0.004 (0.149) Batch 1.013 (1.118) Remain 31:54:34 loss: 0.5083 Lr: 0.00394 [2024-02-18 10:30:46,580 INFO misc.py line 119 87073] Train: [34/100][1545/1557] Data 0.004 (0.149) Batch 0.730 (1.117) Remain 31:54:07 loss: 0.4900 Lr: 0.00394 [2024-02-18 10:30:47,405 INFO misc.py line 119 87073] Train: [34/100][1546/1557] Data 0.004 (0.149) Batch 0.819 (1.117) Remain 31:53:46 loss: 0.3478 Lr: 0.00394 [2024-02-18 10:30:48,694 INFO misc.py line 119 87073] Train: [34/100][1547/1557] Data 0.011 (0.149) Batch 1.284 (1.117) Remain 31:53:56 loss: 0.1701 Lr: 0.00394 [2024-02-18 10:30:49,639 INFO misc.py line 119 87073] Train: [34/100][1548/1557] Data 0.015 (0.149) Batch 0.957 (1.117) Remain 31:53:44 loss: 0.3742 Lr: 0.00394 [2024-02-18 10:30:50,622 INFO misc.py line 119 87073] Train: [34/100][1549/1557] Data 0.004 (0.149) Batch 0.984 (1.117) Remain 31:53:34 loss: 0.2884 Lr: 0.00394 [2024-02-18 10:30:51,770 INFO misc.py line 119 87073] Train: [34/100][1550/1557] Data 0.004 (0.148) Batch 1.147 (1.117) Remain 31:53:35 loss: 0.5349 Lr: 0.00394 [2024-02-18 10:30:52,806 INFO misc.py line 119 87073] Train: [34/100][1551/1557] Data 0.003 (0.148) Batch 1.037 (1.117) Remain 31:53:28 loss: 0.2414 Lr: 0.00394 [2024-02-18 10:30:53,562 INFO misc.py line 119 87073] Train: [34/100][1552/1557] Data 0.003 (0.148) Batch 0.756 (1.117) Remain 31:53:03 loss: 0.3307 Lr: 0.00394 [2024-02-18 10:30:54,328 INFO misc.py line 119 87073] Train: [34/100][1553/1557] Data 0.004 (0.148) Batch 0.751 (1.117) Remain 31:52:38 loss: 0.5090 Lr: 0.00394 [2024-02-18 10:30:55,558 INFO misc.py line 119 87073] Train: [34/100][1554/1557] Data 0.018 (0.148) Batch 1.220 (1.117) Remain 31:52:44 loss: 0.2803 Lr: 0.00394 [2024-02-18 10:30:56,408 INFO misc.py line 119 87073] Train: [34/100][1555/1557] Data 0.028 (0.148) Batch 0.874 (1.117) Remain 31:52:26 loss: 0.3304 Lr: 0.00394 [2024-02-18 10:30:57,369 INFO misc.py line 119 87073] Train: [34/100][1556/1557] Data 0.004 (0.148) Batch 0.961 (1.117) Remain 31:52:15 loss: 0.6144 Lr: 0.00394 [2024-02-18 10:30:58,288 INFO misc.py line 119 87073] Train: [34/100][1557/1557] Data 0.004 (0.148) Batch 0.919 (1.116) Remain 31:52:01 loss: 0.7762 Lr: 0.00394 [2024-02-18 10:30:58,289 INFO misc.py line 136 87073] Train result: loss: 0.4174 [2024-02-18 10:30:58,289 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 10:31:27,068 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8632 [2024-02-18 10:31:27,845 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6498 [2024-02-18 10:31:29,969 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4295 [2024-02-18 10:31:32,177 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3239 [2024-02-18 10:31:37,121 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2271 [2024-02-18 10:31:37,818 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0852 [2024-02-18 10:31:39,080 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4213 [2024-02-18 10:31:42,033 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3584 [2024-02-18 10:31:43,841 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2283 [2024-02-18 10:31:45,358 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7136/0.7828/0.9078. [2024-02-18 10:31:45,358 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9343/0.9718 [2024-02-18 10:31:45,358 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9809/0.9874 [2024-02-18 10:31:45,358 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8706/0.9678 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0037/0.0379 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4198/0.4689 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6843/0.7218 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7735/0.8885 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8353/0.9083 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9187/0.9583 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8494/0.9208 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7273/0.8056 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7144/0.8357 [2024-02-18 10:31:45,359 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5642/0.7036 [2024-02-18 10:31:45,359 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 10:31:45,361 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 10:31:45,361 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 10:31:51,871 INFO misc.py line 119 87073] Train: [35/100][1/1557] Data 1.307 (1.307) Batch 2.049 (2.049) Remain 58:29:40 loss: 0.3363 Lr: 0.00394 [2024-02-18 10:31:52,971 INFO misc.py line 119 87073] Train: [35/100][2/1557] Data 0.008 (0.008) Batch 1.093 (1.093) Remain 31:12:16 loss: 0.4984 Lr: 0.00394 [2024-02-18 10:31:53,921 INFO misc.py line 119 87073] Train: [35/100][3/1557] Data 0.015 (0.015) Batch 0.960 (0.960) Remain 27:24:14 loss: 0.2719 Lr: 0.00394 [2024-02-18 10:31:54,929 INFO misc.py line 119 87073] Train: [35/100][4/1557] Data 0.004 (0.004) Batch 1.005 (1.005) Remain 28:41:53 loss: 0.2718 Lr: 0.00394 [2024-02-18 10:31:55,661 INFO misc.py line 119 87073] Train: [35/100][5/1557] Data 0.008 (0.006) Batch 0.734 (0.870) Remain 24:49:18 loss: 0.2360 Lr: 0.00394 [2024-02-18 10:31:56,355 INFO misc.py line 119 87073] Train: [35/100][6/1557] Data 0.005 (0.006) Batch 0.693 (0.811) Remain 23:08:30 loss: 0.4863 Lr: 0.00394 [2024-02-18 10:31:57,642 INFO misc.py line 119 87073] Train: [35/100][7/1557] Data 0.006 (0.006) Batch 1.280 (0.928) Remain 26:29:13 loss: 0.1773 Lr: 0.00394 [2024-02-18 10:31:58,605 INFO misc.py line 119 87073] Train: [35/100][8/1557] Data 0.012 (0.007) Batch 0.971 (0.937) Remain 26:43:56 loss: 0.8248 Lr: 0.00394 [2024-02-18 10:31:59,623 INFO misc.py line 119 87073] Train: [35/100][9/1557] Data 0.006 (0.007) Batch 1.019 (0.950) Remain 27:07:24 loss: 0.4075 Lr: 0.00394 [2024-02-18 10:32:00,540 INFO misc.py line 119 87073] Train: [35/100][10/1557] Data 0.005 (0.006) Batch 0.916 (0.945) Remain 26:59:03 loss: 0.5830 Lr: 0.00394 [2024-02-18 10:32:01,466 INFO misc.py line 119 87073] Train: [35/100][11/1557] Data 0.006 (0.006) Batch 0.916 (0.942) Remain 26:52:50 loss: 0.8405 Lr: 0.00394 [2024-02-18 10:32:02,241 INFO misc.py line 119 87073] Train: [35/100][12/1557] Data 0.014 (0.007) Batch 0.785 (0.924) Remain 26:23:04 loss: 0.2883 Lr: 0.00394 [2024-02-18 10:32:03,008 INFO misc.py line 119 87073] Train: [35/100][13/1557] Data 0.004 (0.007) Batch 0.767 (0.909) Remain 25:56:02 loss: 0.2407 Lr: 0.00394 [2024-02-18 10:32:04,278 INFO misc.py line 119 87073] Train: [35/100][14/1557] Data 0.005 (0.007) Batch 1.259 (0.940) Remain 26:50:32 loss: 0.2822 Lr: 0.00394 [2024-02-18 10:32:05,350 INFO misc.py line 119 87073] Train: [35/100][15/1557] Data 0.016 (0.007) Batch 1.075 (0.952) Remain 27:09:43 loss: 0.4853 Lr: 0.00394 [2024-02-18 10:32:06,440 INFO misc.py line 119 87073] Train: [35/100][16/1557] Data 0.013 (0.008) Batch 1.088 (0.962) Remain 27:27:41 loss: 0.5123 Lr: 0.00394 [2024-02-18 10:32:07,362 INFO misc.py line 119 87073] Train: [35/100][17/1557] Data 0.014 (0.008) Batch 0.932 (0.960) Remain 27:23:58 loss: 0.3436 Lr: 0.00394 [2024-02-18 10:32:08,316 INFO misc.py line 119 87073] Train: [35/100][18/1557] Data 0.004 (0.008) Batch 0.953 (0.960) Remain 27:23:10 loss: 0.4517 Lr: 0.00394 [2024-02-18 10:32:09,092 INFO misc.py line 119 87073] Train: [35/100][19/1557] Data 0.006 (0.008) Batch 0.776 (0.948) Remain 27:03:28 loss: 0.3379 Lr: 0.00394 [2024-02-18 10:32:09,923 INFO misc.py line 119 87073] Train: [35/100][20/1557] Data 0.005 (0.008) Batch 0.833 (0.941) Remain 26:51:50 loss: 0.2963 Lr: 0.00394 [2024-02-18 10:32:11,188 INFO misc.py line 119 87073] Train: [35/100][21/1557] Data 0.003 (0.007) Batch 1.259 (0.959) Remain 27:22:01 loss: 0.1622 Lr: 0.00394 [2024-02-18 10:32:12,247 INFO misc.py line 119 87073] Train: [35/100][22/1557] Data 0.011 (0.008) Batch 1.051 (0.964) Remain 27:30:19 loss: 0.4674 Lr: 0.00394 [2024-02-18 10:32:13,304 INFO misc.py line 119 87073] Train: [35/100][23/1557] Data 0.018 (0.008) Batch 1.059 (0.969) Remain 27:38:26 loss: 1.3894 Lr: 0.00394 [2024-02-18 10:32:14,226 INFO misc.py line 119 87073] Train: [35/100][24/1557] Data 0.017 (0.009) Batch 0.933 (0.967) Remain 27:35:31 loss: 0.4996 Lr: 0.00394 [2024-02-18 10:32:15,220 INFO misc.py line 119 87073] Train: [35/100][25/1557] Data 0.005 (0.008) Batch 0.995 (0.968) Remain 27:37:43 loss: 0.3818 Lr: 0.00394 [2024-02-18 10:32:15,977 INFO misc.py line 119 87073] Train: [35/100][26/1557] Data 0.003 (0.008) Batch 0.755 (0.959) Remain 27:21:52 loss: 0.3370 Lr: 0.00394 [2024-02-18 10:32:16,711 INFO misc.py line 119 87073] Train: [35/100][27/1557] Data 0.006 (0.008) Batch 0.734 (0.950) Remain 27:05:50 loss: 0.3582 Lr: 0.00394 [2024-02-18 10:32:17,867 INFO misc.py line 119 87073] Train: [35/100][28/1557] Data 0.005 (0.008) Batch 1.145 (0.957) Remain 27:19:12 loss: 0.2245 Lr: 0.00394 [2024-02-18 10:32:18,970 INFO misc.py line 119 87073] Train: [35/100][29/1557] Data 0.015 (0.008) Batch 1.101 (0.963) Remain 27:28:38 loss: 0.3055 Lr: 0.00394 [2024-02-18 10:32:19,999 INFO misc.py line 119 87073] Train: [35/100][30/1557] Data 0.018 (0.009) Batch 1.031 (0.965) Remain 27:32:57 loss: 0.3074 Lr: 0.00394 [2024-02-18 10:32:21,004 INFO misc.py line 119 87073] Train: [35/100][31/1557] Data 0.016 (0.009) Batch 1.002 (0.967) Remain 27:35:10 loss: 0.3648 Lr: 0.00394 [2024-02-18 10:32:21,853 INFO misc.py line 119 87073] Train: [35/100][32/1557] Data 0.019 (0.009) Batch 0.864 (0.963) Remain 27:29:04 loss: 0.3287 Lr: 0.00394 [2024-02-18 10:32:22,657 INFO misc.py line 119 87073] Train: [35/100][33/1557] Data 0.005 (0.009) Batch 0.804 (0.958) Remain 27:19:58 loss: 0.2586 Lr: 0.00394 [2024-02-18 10:32:23,382 INFO misc.py line 119 87073] Train: [35/100][34/1557] Data 0.004 (0.009) Batch 0.716 (0.950) Remain 27:06:36 loss: 0.1945 Lr: 0.00394 [2024-02-18 10:32:24,580 INFO misc.py line 119 87073] Train: [35/100][35/1557] Data 0.014 (0.009) Batch 1.196 (0.958) Remain 27:19:46 loss: 0.2723 Lr: 0.00394 [2024-02-18 10:32:25,668 INFO misc.py line 119 87073] Train: [35/100][36/1557] Data 0.015 (0.009) Batch 1.096 (0.962) Remain 27:26:55 loss: 0.2285 Lr: 0.00394 [2024-02-18 10:32:26,551 INFO misc.py line 119 87073] Train: [35/100][37/1557] Data 0.007 (0.009) Batch 0.886 (0.960) Remain 27:23:04 loss: 0.7972 Lr: 0.00394 [2024-02-18 10:32:27,642 INFO misc.py line 119 87073] Train: [35/100][38/1557] Data 0.004 (0.009) Batch 1.091 (0.963) Remain 27:29:30 loss: 0.2138 Lr: 0.00394 [2024-02-18 10:32:28,633 INFO misc.py line 119 87073] Train: [35/100][39/1557] Data 0.004 (0.009) Batch 0.991 (0.964) Remain 27:30:46 loss: 0.7413 Lr: 0.00394 [2024-02-18 10:32:29,357 INFO misc.py line 119 87073] Train: [35/100][40/1557] Data 0.005 (0.009) Batch 0.723 (0.958) Remain 27:19:35 loss: 0.4460 Lr: 0.00394 [2024-02-18 10:32:30,076 INFO misc.py line 119 87073] Train: [35/100][41/1557] Data 0.005 (0.009) Batch 0.719 (0.951) Remain 27:08:49 loss: 0.2286 Lr: 0.00394 [2024-02-18 10:32:31,194 INFO misc.py line 119 87073] Train: [35/100][42/1557] Data 0.005 (0.009) Batch 1.112 (0.956) Remain 27:15:50 loss: 0.2542 Lr: 0.00394 [2024-02-18 10:32:32,262 INFO misc.py line 119 87073] Train: [35/100][43/1557] Data 0.012 (0.009) Batch 1.073 (0.958) Remain 27:20:51 loss: 0.2436 Lr: 0.00393 [2024-02-18 10:32:33,265 INFO misc.py line 119 87073] Train: [35/100][44/1557] Data 0.006 (0.009) Batch 0.995 (0.959) Remain 27:22:23 loss: 0.4702 Lr: 0.00393 [2024-02-18 10:32:34,136 INFO misc.py line 119 87073] Train: [35/100][45/1557] Data 0.014 (0.009) Batch 0.881 (0.957) Remain 27:19:10 loss: 0.5176 Lr: 0.00393 [2024-02-18 10:32:35,179 INFO misc.py line 119 87073] Train: [35/100][46/1557] Data 0.005 (0.009) Batch 1.044 (0.959) Remain 27:22:34 loss: 0.2622 Lr: 0.00393 [2024-02-18 10:32:35,932 INFO misc.py line 119 87073] Train: [35/100][47/1557] Data 0.004 (0.009) Batch 0.753 (0.955) Remain 27:14:31 loss: 0.4271 Lr: 0.00393 [2024-02-18 10:32:36,724 INFO misc.py line 119 87073] Train: [35/100][48/1557] Data 0.004 (0.008) Batch 0.783 (0.951) Remain 27:07:58 loss: 0.4934 Lr: 0.00393 [2024-02-18 10:32:38,036 INFO misc.py line 119 87073] Train: [35/100][49/1557] Data 0.012 (0.008) Batch 1.320 (0.959) Remain 27:21:40 loss: 0.2670 Lr: 0.00393 [2024-02-18 10:32:38,966 INFO misc.py line 119 87073] Train: [35/100][50/1557] Data 0.005 (0.008) Batch 0.931 (0.958) Remain 27:20:37 loss: 0.6355 Lr: 0.00393 [2024-02-18 10:32:39,821 INFO misc.py line 119 87073] Train: [35/100][51/1557] Data 0.004 (0.008) Batch 0.853 (0.956) Remain 27:16:51 loss: 0.2056 Lr: 0.00393 [2024-02-18 10:32:40,649 INFO misc.py line 119 87073] Train: [35/100][52/1557] Data 0.007 (0.008) Batch 0.830 (0.954) Remain 27:12:27 loss: 0.5619 Lr: 0.00393 [2024-02-18 10:32:41,651 INFO misc.py line 119 87073] Train: [35/100][53/1557] Data 0.005 (0.008) Batch 0.999 (0.955) Remain 27:13:59 loss: 0.2592 Lr: 0.00393 [2024-02-18 10:32:42,355 INFO misc.py line 119 87073] Train: [35/100][54/1557] Data 0.007 (0.008) Batch 0.707 (0.950) Remain 27:05:40 loss: 0.4297 Lr: 0.00393 [2024-02-18 10:32:43,186 INFO misc.py line 119 87073] Train: [35/100][55/1557] Data 0.004 (0.008) Batch 0.825 (0.947) Remain 27:01:33 loss: 0.5404 Lr: 0.00393 [2024-02-18 10:32:44,421 INFO misc.py line 119 87073] Train: [35/100][56/1557] Data 0.010 (0.008) Batch 1.233 (0.953) Remain 27:10:46 loss: 0.4919 Lr: 0.00393 [2024-02-18 10:32:45,478 INFO misc.py line 119 87073] Train: [35/100][57/1557] Data 0.011 (0.008) Batch 1.065 (0.955) Remain 27:14:18 loss: 0.3072 Lr: 0.00393 [2024-02-18 10:32:46,850 INFO misc.py line 119 87073] Train: [35/100][58/1557] Data 0.004 (0.008) Batch 1.362 (0.962) Remain 27:26:57 loss: 0.7705 Lr: 0.00393 [2024-02-18 10:32:47,786 INFO misc.py line 119 87073] Train: [35/100][59/1557] Data 0.014 (0.008) Batch 0.944 (0.962) Remain 27:26:23 loss: 0.4995 Lr: 0.00393 [2024-02-18 10:32:48,850 INFO misc.py line 119 87073] Train: [35/100][60/1557] Data 0.006 (0.008) Batch 1.065 (0.964) Remain 27:29:28 loss: 0.4108 Lr: 0.00393 [2024-02-18 10:32:49,609 INFO misc.py line 119 87073] Train: [35/100][61/1557] Data 0.005 (0.008) Batch 0.758 (0.960) Remain 27:23:24 loss: 0.3995 Lr: 0.00393 [2024-02-18 10:32:50,398 INFO misc.py line 119 87073] Train: [35/100][62/1557] Data 0.005 (0.008) Batch 0.786 (0.957) Remain 27:18:20 loss: 0.5196 Lr: 0.00393 [2024-02-18 10:32:58,883 INFO misc.py line 119 87073] Train: [35/100][63/1557] Data 4.896 (0.090) Batch 8.478 (1.083) Remain 30:52:52 loss: 0.4309 Lr: 0.00393 [2024-02-18 10:32:59,904 INFO misc.py line 119 87073] Train: [35/100][64/1557] Data 0.015 (0.088) Batch 1.026 (1.082) Remain 30:51:16 loss: 0.2316 Lr: 0.00393 [2024-02-18 10:33:00,765 INFO misc.py line 119 87073] Train: [35/100][65/1557] Data 0.011 (0.087) Batch 0.864 (1.078) Remain 30:45:14 loss: 1.0321 Lr: 0.00393 [2024-02-18 10:33:01,826 INFO misc.py line 119 87073] Train: [35/100][66/1557] Data 0.010 (0.086) Batch 1.064 (1.078) Remain 30:44:49 loss: 0.4925 Lr: 0.00393 [2024-02-18 10:33:02,797 INFO misc.py line 119 87073] Train: [35/100][67/1557] Data 0.004 (0.085) Batch 0.972 (1.076) Remain 30:41:58 loss: 0.6637 Lr: 0.00393 [2024-02-18 10:33:03,443 INFO misc.py line 119 87073] Train: [35/100][68/1557] Data 0.004 (0.083) Batch 0.646 (1.070) Remain 30:30:38 loss: 0.3642 Lr: 0.00393 [2024-02-18 10:33:04,201 INFO misc.py line 119 87073] Train: [35/100][69/1557] Data 0.004 (0.082) Batch 0.751 (1.065) Remain 30:22:20 loss: 0.2697 Lr: 0.00393 [2024-02-18 10:33:05,486 INFO misc.py line 119 87073] Train: [35/100][70/1557] Data 0.011 (0.081) Batch 1.282 (1.068) Remain 30:27:52 loss: 0.3122 Lr: 0.00393 [2024-02-18 10:33:06,328 INFO misc.py line 119 87073] Train: [35/100][71/1557] Data 0.014 (0.080) Batch 0.852 (1.065) Remain 30:22:25 loss: 0.7134 Lr: 0.00393 [2024-02-18 10:33:07,145 INFO misc.py line 119 87073] Train: [35/100][72/1557] Data 0.004 (0.079) Batch 0.815 (1.061) Remain 30:16:13 loss: 0.3356 Lr: 0.00393 [2024-02-18 10:33:08,264 INFO misc.py line 119 87073] Train: [35/100][73/1557] Data 0.005 (0.078) Batch 1.120 (1.062) Remain 30:17:38 loss: 0.5917 Lr: 0.00393 [2024-02-18 10:33:09,304 INFO misc.py line 119 87073] Train: [35/100][74/1557] Data 0.005 (0.077) Batch 1.040 (1.062) Remain 30:17:05 loss: 0.1718 Lr: 0.00393 [2024-02-18 10:33:10,056 INFO misc.py line 119 87073] Train: [35/100][75/1557] Data 0.005 (0.076) Batch 0.751 (1.057) Remain 30:09:41 loss: 0.3614 Lr: 0.00393 [2024-02-18 10:33:10,834 INFO misc.py line 119 87073] Train: [35/100][76/1557] Data 0.006 (0.075) Batch 0.776 (1.054) Remain 30:03:04 loss: 0.5924 Lr: 0.00393 [2024-02-18 10:33:12,066 INFO misc.py line 119 87073] Train: [35/100][77/1557] Data 0.008 (0.074) Batch 1.227 (1.056) Remain 30:07:03 loss: 0.1533 Lr: 0.00393 [2024-02-18 10:33:13,077 INFO misc.py line 119 87073] Train: [35/100][78/1557] Data 0.013 (0.073) Batch 1.012 (1.055) Remain 30:06:03 loss: 0.3775 Lr: 0.00393 [2024-02-18 10:33:14,000 INFO misc.py line 119 87073] Train: [35/100][79/1557] Data 0.011 (0.072) Batch 0.930 (1.054) Remain 30:03:13 loss: 0.5992 Lr: 0.00393 [2024-02-18 10:33:14,928 INFO misc.py line 119 87073] Train: [35/100][80/1557] Data 0.005 (0.072) Batch 0.929 (1.052) Remain 30:00:25 loss: 0.3740 Lr: 0.00393 [2024-02-18 10:33:15,921 INFO misc.py line 119 87073] Train: [35/100][81/1557] Data 0.004 (0.071) Batch 0.992 (1.051) Remain 29:59:05 loss: 0.3938 Lr: 0.00393 [2024-02-18 10:33:16,767 INFO misc.py line 119 87073] Train: [35/100][82/1557] Data 0.004 (0.070) Batch 0.836 (1.049) Remain 29:54:24 loss: 0.5127 Lr: 0.00393 [2024-02-18 10:33:17,557 INFO misc.py line 119 87073] Train: [35/100][83/1557] Data 0.014 (0.069) Batch 0.800 (1.045) Remain 29:49:05 loss: 0.6239 Lr: 0.00393 [2024-02-18 10:33:18,689 INFO misc.py line 119 87073] Train: [35/100][84/1557] Data 0.004 (0.068) Batch 1.132 (1.047) Remain 29:50:53 loss: 0.2015 Lr: 0.00393 [2024-02-18 10:33:19,593 INFO misc.py line 119 87073] Train: [35/100][85/1557] Data 0.004 (0.068) Batch 0.904 (1.045) Remain 29:47:54 loss: 0.2858 Lr: 0.00393 [2024-02-18 10:33:20,641 INFO misc.py line 119 87073] Train: [35/100][86/1557] Data 0.004 (0.067) Batch 1.048 (1.045) Remain 29:47:56 loss: 0.3153 Lr: 0.00393 [2024-02-18 10:33:21,567 INFO misc.py line 119 87073] Train: [35/100][87/1557] Data 0.005 (0.066) Batch 0.926 (1.043) Remain 29:45:30 loss: 0.5614 Lr: 0.00393 [2024-02-18 10:33:22,477 INFO misc.py line 119 87073] Train: [35/100][88/1557] Data 0.005 (0.065) Batch 0.909 (1.042) Remain 29:42:47 loss: 0.3968 Lr: 0.00393 [2024-02-18 10:33:23,288 INFO misc.py line 119 87073] Train: [35/100][89/1557] Data 0.005 (0.065) Batch 0.812 (1.039) Remain 29:38:11 loss: 0.2077 Lr: 0.00393 [2024-02-18 10:33:24,035 INFO misc.py line 119 87073] Train: [35/100][90/1557] Data 0.005 (0.064) Batch 0.748 (1.036) Remain 29:32:26 loss: 0.4169 Lr: 0.00393 [2024-02-18 10:33:25,219 INFO misc.py line 119 87073] Train: [35/100][91/1557] Data 0.004 (0.063) Batch 1.179 (1.037) Remain 29:35:11 loss: 0.2017 Lr: 0.00393 [2024-02-18 10:33:26,369 INFO misc.py line 119 87073] Train: [35/100][92/1557] Data 0.009 (0.063) Batch 1.141 (1.039) Remain 29:37:10 loss: 0.3503 Lr: 0.00393 [2024-02-18 10:33:27,241 INFO misc.py line 119 87073] Train: [35/100][93/1557] Data 0.018 (0.062) Batch 0.886 (1.037) Remain 29:34:15 loss: 0.4491 Lr: 0.00393 [2024-02-18 10:33:28,150 INFO misc.py line 119 87073] Train: [35/100][94/1557] Data 0.004 (0.061) Batch 0.908 (1.035) Remain 29:31:49 loss: 0.4035 Lr: 0.00393 [2024-02-18 10:33:29,012 INFO misc.py line 119 87073] Train: [35/100][95/1557] Data 0.005 (0.061) Batch 0.857 (1.034) Remain 29:28:28 loss: 0.6413 Lr: 0.00393 [2024-02-18 10:33:29,765 INFO misc.py line 119 87073] Train: [35/100][96/1557] Data 0.011 (0.060) Batch 0.759 (1.031) Remain 29:23:24 loss: 0.3252 Lr: 0.00393 [2024-02-18 10:33:30,503 INFO misc.py line 119 87073] Train: [35/100][97/1557] Data 0.004 (0.060) Batch 0.731 (1.027) Remain 29:17:55 loss: 0.4257 Lr: 0.00393 [2024-02-18 10:33:31,632 INFO misc.py line 119 87073] Train: [35/100][98/1557] Data 0.012 (0.059) Batch 1.124 (1.028) Remain 29:19:39 loss: 0.1972 Lr: 0.00393 [2024-02-18 10:33:32,638 INFO misc.py line 119 87073] Train: [35/100][99/1557] Data 0.016 (0.059) Batch 1.007 (1.028) Remain 29:19:15 loss: 0.4471 Lr: 0.00393 [2024-02-18 10:33:33,679 INFO misc.py line 119 87073] Train: [35/100][100/1557] Data 0.015 (0.058) Batch 1.045 (1.028) Remain 29:19:31 loss: 0.3828 Lr: 0.00393 [2024-02-18 10:33:34,801 INFO misc.py line 119 87073] Train: [35/100][101/1557] Data 0.012 (0.058) Batch 1.127 (1.029) Remain 29:21:14 loss: 0.6815 Lr: 0.00393 [2024-02-18 10:33:35,844 INFO misc.py line 119 87073] Train: [35/100][102/1557] Data 0.008 (0.057) Batch 1.037 (1.029) Remain 29:21:20 loss: 0.5270 Lr: 0.00393 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Batch 0.911 (1.077) Remain 30:41:47 loss: 0.0772 Lr: 0.00393 [2024-02-18 10:34:01,903 INFO misc.py line 119 87073] Train: [35/100][122/1557] Data 0.010 (0.093) Batch 0.938 (1.075) Remain 30:39:46 loss: 0.4274 Lr: 0.00393 [2024-02-18 10:34:02,939 INFO misc.py line 119 87073] Train: [35/100][123/1557] Data 0.004 (0.092) Batch 1.035 (1.075) Remain 30:39:11 loss: 0.2133 Lr: 0.00393 [2024-02-18 10:34:03,689 INFO misc.py line 119 87073] Train: [35/100][124/1557] Data 0.004 (0.092) Batch 0.750 (1.072) Remain 30:34:34 loss: 0.2713 Lr: 0.00393 [2024-02-18 10:34:04,472 INFO misc.py line 119 87073] Train: [35/100][125/1557] Data 0.004 (0.091) Batch 0.782 (1.070) Remain 30:30:29 loss: 0.3950 Lr: 0.00393 [2024-02-18 10:34:05,765 INFO misc.py line 119 87073] Train: [35/100][126/1557] Data 0.005 (0.090) Batch 1.289 (1.072) Remain 30:33:30 loss: 0.3289 Lr: 0.00393 [2024-02-18 10:34:06,677 INFO misc.py line 119 87073] Train: [35/100][127/1557] Data 0.009 (0.090) Batch 0.918 (1.071) Remain 30:31:22 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Batch 0.991 (1.076) Remain 30:38:39 loss: 0.6089 Lr: 0.00393 [2024-02-18 10:36:02,438 INFO misc.py line 119 87073] Train: [35/100][234/1557] Data 0.003 (0.100) Batch 1.040 (1.076) Remain 30:38:21 loss: 0.5819 Lr: 0.00393 [2024-02-18 10:36:03,388 INFO misc.py line 119 87073] Train: [35/100][235/1557] Data 0.005 (0.100) Batch 0.947 (1.075) Remain 30:37:24 loss: 0.4035 Lr: 0.00393 [2024-02-18 10:36:04,175 INFO misc.py line 119 87073] Train: [35/100][236/1557] Data 0.009 (0.099) Batch 0.791 (1.074) Remain 30:35:17 loss: 0.2382 Lr: 0.00393 [2024-02-18 10:36:04,957 INFO misc.py line 119 87073] Train: [35/100][237/1557] Data 0.004 (0.099) Batch 0.781 (1.073) Remain 30:33:08 loss: 0.1041 Lr: 0.00393 [2024-02-18 10:36:06,270 INFO misc.py line 119 87073] Train: [35/100][238/1557] Data 0.005 (0.099) Batch 1.299 (1.074) Remain 30:34:45 loss: 0.2193 Lr: 0.00393 [2024-02-18 10:36:07,341 INFO misc.py line 119 87073] Train: [35/100][239/1557] Data 0.020 (0.098) Batch 1.087 (1.074) Remain 30:34:50 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line 119 87073] Train: [35/100][277/1557] Data 0.004 (0.085) Batch 0.974 (1.060) Remain 30:10:02 loss: 0.3494 Lr: 0.00392 [2024-02-18 10:36:45,060 INFO misc.py line 119 87073] Train: [35/100][278/1557] Data 0.007 (0.085) Batch 0.783 (1.059) Remain 30:08:18 loss: 0.5169 Lr: 0.00392 [2024-02-18 10:36:45,754 INFO misc.py line 119 87073] Train: [35/100][279/1557] Data 0.004 (0.085) Batch 0.694 (1.057) Remain 30:06:01 loss: 0.2811 Lr: 0.00392 [2024-02-18 10:36:47,070 INFO misc.py line 119 87073] Train: [35/100][280/1557] Data 0.004 (0.085) Batch 1.311 (1.058) Remain 30:07:34 loss: 0.4080 Lr: 0.00392 [2024-02-18 10:36:48,073 INFO misc.py line 119 87073] Train: [35/100][281/1557] Data 0.008 (0.084) Batch 0.998 (1.058) Remain 30:07:11 loss: 0.6798 Lr: 0.00392 [2024-02-18 10:36:49,201 INFO misc.py line 119 87073] Train: [35/100][282/1557] Data 0.014 (0.084) Batch 1.124 (1.058) Remain 30:07:34 loss: 0.5490 Lr: 0.00392 [2024-02-18 10:36:50,218 INFO misc.py line 119 87073] Train: 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Batch 1.073 (1.082) Remain 30:48:21 loss: 0.3184 Lr: 0.00392 [2024-02-18 10:37:04,417 INFO misc.py line 119 87073] Train: [35/100][290/1557] Data 0.007 (0.100) Batch 0.971 (1.082) Remain 30:47:41 loss: 0.5045 Lr: 0.00392 [2024-02-18 10:37:05,252 INFO misc.py line 119 87073] Train: [35/100][291/1557] Data 0.004 (0.100) Batch 0.834 (1.081) Remain 30:46:11 loss: 0.2590 Lr: 0.00392 [2024-02-18 10:37:06,026 INFO misc.py line 119 87073] Train: [35/100][292/1557] Data 0.004 (0.100) Batch 0.768 (1.080) Remain 30:44:19 loss: 0.6997 Lr: 0.00392 [2024-02-18 10:37:06,772 INFO misc.py line 119 87073] Train: [35/100][293/1557] Data 0.010 (0.099) Batch 0.753 (1.079) Remain 30:42:23 loss: 0.2939 Lr: 0.00392 [2024-02-18 10:37:08,039 INFO misc.py line 119 87073] Train: [35/100][294/1557] Data 0.003 (0.099) Batch 1.267 (1.079) Remain 30:43:28 loss: 0.3486 Lr: 0.00392 [2024-02-18 10:37:09,004 INFO misc.py line 119 87073] Train: [35/100][295/1557] Data 0.004 (0.099) Batch 0.963 (1.079) Remain 30:42:46 loss: 0.9086 Lr: 0.00392 [2024-02-18 10:37:10,050 INFO misc.py line 119 87073] Train: [35/100][296/1557] Data 0.005 (0.098) Batch 1.047 (1.079) Remain 30:42:34 loss: 0.3840 Lr: 0.00392 [2024-02-18 10:37:10,929 INFO misc.py line 119 87073] Train: [35/100][297/1557] Data 0.004 (0.098) Batch 0.879 (1.078) Remain 30:41:23 loss: 0.5541 Lr: 0.00392 [2024-02-18 10:37:11,788 INFO misc.py line 119 87073] Train: [35/100][298/1557] Data 0.004 (0.098) Batch 0.859 (1.078) Remain 30:40:06 loss: 0.1426 Lr: 0.00392 [2024-02-18 10:37:12,525 INFO misc.py line 119 87073] Train: [35/100][299/1557] Data 0.004 (0.098) Batch 0.735 (1.076) Remain 30:38:06 loss: 0.3508 Lr: 0.00392 [2024-02-18 10:37:13,303 INFO misc.py line 119 87073] Train: [35/100][300/1557] Data 0.006 (0.097) Batch 0.776 (1.075) Remain 30:36:21 loss: 0.3643 Lr: 0.00392 [2024-02-18 10:37:14,524 INFO misc.py line 119 87073] Train: [35/100][301/1557] Data 0.008 (0.097) Batch 1.220 (1.076) Remain 30:37:10 loss: 0.1690 Lr: 0.00392 [2024-02-18 10:37:15,538 INFO misc.py line 119 87073] Train: [35/100][302/1557] Data 0.009 (0.097) Batch 1.019 (1.076) Remain 30:36:49 loss: 0.3814 Lr: 0.00392 [2024-02-18 10:37:16,703 INFO misc.py line 119 87073] Train: [35/100][303/1557] Data 0.005 (0.096) Batch 1.164 (1.076) Remain 30:37:19 loss: 0.3388 Lr: 0.00392 [2024-02-18 10:37:17,876 INFO misc.py line 119 87073] Train: [35/100][304/1557] Data 0.005 (0.096) Batch 1.172 (1.076) Remain 30:37:50 loss: 0.2876 Lr: 0.00392 [2024-02-18 10:37:18,808 INFO misc.py line 119 87073] Train: [35/100][305/1557] Data 0.006 (0.096) Batch 0.934 (1.076) Remain 30:37:01 loss: 0.1825 Lr: 0.00392 [2024-02-18 10:37:19,569 INFO misc.py line 119 87073] Train: [35/100][306/1557] Data 0.004 (0.095) Batch 0.761 (1.075) Remain 30:35:13 loss: 0.4123 Lr: 0.00392 [2024-02-18 10:37:20,362 INFO misc.py line 119 87073] Train: [35/100][307/1557] Data 0.005 (0.095) Batch 0.791 (1.074) Remain 30:33:37 loss: 0.4184 Lr: 0.00392 [2024-02-18 10:37:21,524 INFO misc.py line 119 87073] Train: [35/100][308/1557] Data 0.006 (0.095) Batch 1.163 (1.074) Remain 30:34:06 loss: 0.1667 Lr: 0.00392 [2024-02-18 10:37:22,561 INFO misc.py line 119 87073] Train: [35/100][309/1557] Data 0.005 (0.095) Batch 1.032 (1.074) Remain 30:33:50 loss: 0.5603 Lr: 0.00392 [2024-02-18 10:37:23,486 INFO misc.py line 119 87073] Train: [35/100][310/1557] Data 0.010 (0.094) Batch 0.931 (1.073) Remain 30:33:02 loss: 0.5326 Lr: 0.00392 [2024-02-18 10:37:24,387 INFO misc.py line 119 87073] Train: [35/100][311/1557] Data 0.004 (0.094) Batch 0.900 (1.073) Remain 30:32:03 loss: 0.2966 Lr: 0.00392 [2024-02-18 10:37:25,286 INFO misc.py line 119 87073] Train: [35/100][312/1557] Data 0.005 (0.094) Batch 0.900 (1.072) Remain 30:31:04 loss: 0.3275 Lr: 0.00392 [2024-02-18 10:37:25,977 INFO misc.py line 119 87073] Train: [35/100][313/1557] Data 0.005 (0.093) Batch 0.692 (1.071) Remain 30:28:57 loss: 0.4423 Lr: 0.00392 [2024-02-18 10:37:26,762 INFO misc.py line 119 87073] Train: [35/100][314/1557] Data 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[2024-02-18 10:37:39,425 INFO misc.py line 119 87073] Train: [35/100][327/1557] Data 0.006 (0.090) Batch 0.738 (1.066) Remain 30:20:33 loss: 0.3734 Lr: 0.00392 [2024-02-18 10:37:40,154 INFO misc.py line 119 87073] Train: [35/100][328/1557] Data 0.004 (0.089) Batch 0.728 (1.065) Remain 30:18:45 loss: 0.2357 Lr: 0.00392 [2024-02-18 10:37:41,420 INFO misc.py line 119 87073] Train: [35/100][329/1557] Data 0.006 (0.089) Batch 1.264 (1.066) Remain 30:19:46 loss: 0.4722 Lr: 0.00392 [2024-02-18 10:37:42,518 INFO misc.py line 119 87073] Train: [35/100][330/1557] Data 0.008 (0.089) Batch 1.099 (1.066) Remain 30:19:56 loss: 1.2331 Lr: 0.00392 [2024-02-18 10:37:43,483 INFO misc.py line 119 87073] Train: [35/100][331/1557] Data 0.006 (0.089) Batch 0.966 (1.066) Remain 30:19:23 loss: 0.1505 Lr: 0.00392 [2024-02-18 10:37:44,346 INFO misc.py line 119 87073] Train: [35/100][332/1557] Data 0.005 (0.088) Batch 0.864 (1.065) Remain 30:18:20 loss: 0.5686 Lr: 0.00392 [2024-02-18 10:37:45,278 INFO misc.py line 119 87073] Train: [35/100][333/1557] Data 0.005 (0.088) Batch 0.925 (1.065) Remain 30:17:35 loss: 0.8592 Lr: 0.00392 [2024-02-18 10:37:45,966 INFO misc.py line 119 87073] Train: [35/100][334/1557] Data 0.012 (0.088) Batch 0.694 (1.064) Remain 30:15:39 loss: 0.5136 Lr: 0.00392 [2024-02-18 10:37:46,660 INFO misc.py line 119 87073] Train: [35/100][335/1557] Data 0.005 (0.088) Batch 0.696 (1.062) Remain 30:13:45 loss: 0.4659 Lr: 0.00392 [2024-02-18 10:37:47,981 INFO misc.py line 119 87073] Train: [35/100][336/1557] Data 0.004 (0.087) Batch 1.319 (1.063) Remain 30:15:02 loss: 0.2975 Lr: 0.00392 [2024-02-18 10:37:49,034 INFO misc.py line 119 87073] Train: [35/100][337/1557] Data 0.006 (0.087) Batch 1.055 (1.063) Remain 30:14:59 loss: 0.1712 Lr: 0.00392 [2024-02-18 10:37:50,019 INFO misc.py line 119 87073] Train: [35/100][338/1557] Data 0.005 (0.087) Batch 0.981 (1.063) Remain 30:14:32 loss: 0.2384 Lr: 0.00392 [2024-02-18 10:37:50,921 INFO misc.py line 119 87073] Train: 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Batch 0.980 (1.083) Remain 30:47:53 loss: 0.4286 Lr: 0.00392 [2024-02-18 10:38:05,146 INFO misc.py line 119 87073] Train: [35/100][346/1557] Data 0.004 (0.100) Batch 0.985 (1.082) Remain 30:47:23 loss: 0.2748 Lr: 0.00392 [2024-02-18 10:38:06,066 INFO misc.py line 119 87073] Train: [35/100][347/1557] Data 0.005 (0.100) Batch 0.921 (1.082) Remain 30:46:33 loss: 0.7693 Lr: 0.00392 [2024-02-18 10:38:06,781 INFO misc.py line 119 87073] Train: [35/100][348/1557] Data 0.004 (0.100) Batch 0.714 (1.081) Remain 30:44:43 loss: 0.3178 Lr: 0.00392 [2024-02-18 10:38:07,519 INFO misc.py line 119 87073] Train: [35/100][349/1557] Data 0.005 (0.100) Batch 0.737 (1.080) Remain 30:43:00 loss: 0.4014 Lr: 0.00392 [2024-02-18 10:38:08,778 INFO misc.py line 119 87073] Train: [35/100][350/1557] Data 0.005 (0.099) Batch 1.259 (1.080) Remain 30:43:52 loss: 0.3491 Lr: 0.00392 [2024-02-18 10:38:09,656 INFO misc.py line 119 87073] Train: [35/100][351/1557] Data 0.006 (0.099) Batch 0.880 (1.080) Remain 30:42:52 loss: 0.7617 Lr: 0.00392 [2024-02-18 10:38:10,780 INFO misc.py line 119 87073] Train: [35/100][352/1557] Data 0.004 (0.099) Batch 1.125 (1.080) Remain 30:43:04 loss: 0.8396 Lr: 0.00392 [2024-02-18 10:38:11,868 INFO misc.py line 119 87073] Train: [35/100][353/1557] Data 0.004 (0.099) Batch 1.086 (1.080) Remain 30:43:05 loss: 0.3633 Lr: 0.00392 [2024-02-18 10:38:12,753 INFO misc.py line 119 87073] Train: [35/100][354/1557] Data 0.005 (0.098) Batch 0.887 (1.079) Remain 30:42:08 loss: 0.5750 Lr: 0.00392 [2024-02-18 10:38:15,253 INFO misc.py line 119 87073] Train: [35/100][355/1557] Data 1.384 (0.102) Batch 2.498 (1.083) Remain 30:48:59 loss: 0.3619 Lr: 0.00392 [2024-02-18 10:38:15,978 INFO misc.py line 119 87073] Train: [35/100][356/1557] Data 0.006 (0.102) Batch 0.717 (1.082) Remain 30:47:12 loss: 0.3132 Lr: 0.00392 [2024-02-18 10:38:17,206 INFO misc.py line 119 87073] Train: [35/100][357/1557] Data 0.013 (0.101) Batch 1.232 (1.083) Remain 30:47:54 loss: 0.1397 Lr: 0.00392 [2024-02-18 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87073] Train: [35/100][364/1557] Data 0.007 (0.100) Batch 1.182 (1.080) Remain 30:43:28 loss: 0.3360 Lr: 0.00392 [2024-02-18 10:38:24,954 INFO misc.py line 119 87073] Train: [35/100][365/1557] Data 0.010 (0.099) Batch 1.077 (1.080) Remain 30:43:26 loss: 0.3000 Lr: 0.00392 [2024-02-18 10:38:25,942 INFO misc.py line 119 87073] Train: [35/100][366/1557] Data 0.014 (0.099) Batch 0.996 (1.080) Remain 30:43:01 loss: 0.6105 Lr: 0.00392 [2024-02-18 10:38:26,975 INFO misc.py line 119 87073] Train: [35/100][367/1557] Data 0.005 (0.099) Batch 1.032 (1.080) Remain 30:42:47 loss: 0.4513 Lr: 0.00392 [2024-02-18 10:38:27,888 INFO misc.py line 119 87073] Train: [35/100][368/1557] Data 0.006 (0.099) Batch 0.915 (1.079) Remain 30:41:59 loss: 0.6231 Lr: 0.00392 [2024-02-18 10:38:28,675 INFO misc.py line 119 87073] Train: [35/100][369/1557] Data 0.004 (0.098) Batch 0.779 (1.079) Remain 30:40:34 loss: 0.3353 Lr: 0.00392 [2024-02-18 10:38:29,451 INFO misc.py line 119 87073] Train: [35/100][370/1557] Data 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line 119 87073] Train: [35/100][501/1557] Data 0.004 (0.094) Batch 1.253 (1.073) Remain 30:29:17 loss: 0.3826 Lr: 0.00391 [2024-02-18 10:40:49,171 INFO misc.py line 119 87073] Train: [35/100][502/1557] Data 0.014 (0.094) Batch 0.743 (1.073) Remain 30:28:08 loss: 0.1980 Lr: 0.00391 [2024-02-18 10:40:49,903 INFO misc.py line 119 87073] Train: [35/100][503/1557] Data 0.004 (0.094) Batch 0.722 (1.072) Remain 30:26:55 loss: 0.2386 Lr: 0.00391 [2024-02-18 10:40:51,235 INFO misc.py line 119 87073] Train: [35/100][504/1557] Data 0.014 (0.093) Batch 1.332 (1.072) Remain 30:27:47 loss: 0.2483 Lr: 0.00391 [2024-02-18 10:40:52,191 INFO misc.py line 119 87073] Train: [35/100][505/1557] Data 0.014 (0.093) Batch 0.966 (1.072) Remain 30:27:24 loss: 0.2967 Lr: 0.00391 [2024-02-18 10:40:53,134 INFO misc.py line 119 87073] Train: [35/100][506/1557] Data 0.004 (0.093) Batch 0.943 (1.072) Remain 30:26:57 loss: 0.5640 Lr: 0.00391 [2024-02-18 10:40:54,031 INFO misc.py line 119 87073] Train: 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Batch 0.965 (1.083) Remain 30:45:54 loss: 0.4996 Lr: 0.00391 [2024-02-18 10:41:07,435 INFO misc.py line 119 87073] Train: [35/100][514/1557] Data 0.004 (0.102) Batch 1.089 (1.083) Remain 30:45:54 loss: 0.3330 Lr: 0.00391 [2024-02-18 10:41:08,438 INFO misc.py line 119 87073] Train: [35/100][515/1557] Data 0.006 (0.102) Batch 1.005 (1.083) Remain 30:45:37 loss: 0.3231 Lr: 0.00391 [2024-02-18 10:41:09,238 INFO misc.py line 119 87073] Train: [35/100][516/1557] Data 0.004 (0.101) Batch 0.798 (1.082) Remain 30:44:39 loss: 0.5156 Lr: 0.00391 [2024-02-18 10:41:09,989 INFO misc.py line 119 87073] Train: [35/100][517/1557] Data 0.006 (0.101) Batch 0.740 (1.082) Remain 30:43:30 loss: 0.6028 Lr: 0.00391 [2024-02-18 10:41:11,224 INFO misc.py line 119 87073] Train: [35/100][518/1557] Data 0.016 (0.101) Batch 1.241 (1.082) Remain 30:44:00 loss: 0.5111 Lr: 0.00391 [2024-02-18 10:41:12,340 INFO misc.py line 119 87073] Train: [35/100][519/1557] Data 0.011 (0.101) Batch 1.119 (1.082) Remain 30:44:07 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87073] Train: [35/100][532/1557] Data 0.004 (0.100) Batch 1.153 (1.082) Remain 30:43:52 loss: 0.2406 Lr: 0.00391 [2024-02-18 10:41:27,292 INFO misc.py line 119 87073] Train: [35/100][533/1557] Data 0.005 (0.100) Batch 0.890 (1.082) Remain 30:43:14 loss: 1.1782 Lr: 0.00391 [2024-02-18 10:41:28,228 INFO misc.py line 119 87073] Train: [35/100][534/1557] Data 0.004 (0.100) Batch 0.934 (1.082) Remain 30:42:44 loss: 0.4494 Lr: 0.00391 [2024-02-18 10:41:29,263 INFO misc.py line 119 87073] Train: [35/100][535/1557] Data 0.005 (0.100) Batch 1.032 (1.081) Remain 30:42:34 loss: 0.5159 Lr: 0.00391 [2024-02-18 10:41:30,285 INFO misc.py line 119 87073] Train: [35/100][536/1557] Data 0.008 (0.100) Batch 1.024 (1.081) Remain 30:42:22 loss: 0.2606 Lr: 0.00391 [2024-02-18 10:41:31,045 INFO misc.py line 119 87073] Train: [35/100][537/1557] Data 0.005 (0.100) Batch 0.761 (1.081) Remain 30:41:19 loss: 0.1720 Lr: 0.00391 [2024-02-18 10:41:31,825 INFO misc.py line 119 87073] Train: [35/100][538/1557] Data 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line 119 87073] Train: [35/100][557/1557] Data 0.014 (0.096) Batch 0.959 (1.079) Remain 30:37:12 loss: 0.1650 Lr: 0.00391 [2024-02-18 10:41:52,222 INFO misc.py line 119 87073] Train: [35/100][558/1557] Data 0.004 (0.096) Batch 0.788 (1.078) Remain 30:36:17 loss: 0.3263 Lr: 0.00391 [2024-02-18 10:41:52,952 INFO misc.py line 119 87073] Train: [35/100][559/1557] Data 0.004 (0.096) Batch 0.724 (1.077) Remain 30:35:11 loss: 0.3423 Lr: 0.00391 [2024-02-18 10:41:54,230 INFO misc.py line 119 87073] Train: [35/100][560/1557] Data 0.011 (0.096) Batch 1.271 (1.078) Remain 30:35:45 loss: 0.4267 Lr: 0.00391 [2024-02-18 10:41:55,091 INFO misc.py line 119 87073] Train: [35/100][561/1557] Data 0.017 (0.096) Batch 0.874 (1.077) Remain 30:35:07 loss: 0.2539 Lr: 0.00391 [2024-02-18 10:41:56,057 INFO misc.py line 119 87073] Train: [35/100][562/1557] Data 0.003 (0.096) Batch 0.966 (1.077) Remain 30:34:46 loss: 0.4985 Lr: 0.00391 [2024-02-18 10:41:57,021 INFO misc.py line 119 87073] Train: 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[2024-02-18 10:42:45,132 INFO misc.py line 119 87073] Train: [35/100][607/1557] Data 0.004 (0.098) Batch 0.853 (1.078) Remain 30:35:39 loss: 0.5319 Lr: 0.00391 [2024-02-18 10:42:45,889 INFO misc.py line 119 87073] Train: [35/100][608/1557] Data 0.005 (0.097) Batch 0.757 (1.078) Remain 30:34:44 loss: 0.5399 Lr: 0.00391 [2024-02-18 10:42:47,219 INFO misc.py line 119 87073] Train: [35/100][609/1557] Data 0.005 (0.097) Batch 1.329 (1.078) Remain 30:35:25 loss: 0.3408 Lr: 0.00391 [2024-02-18 10:42:48,357 INFO misc.py line 119 87073] Train: [35/100][610/1557] Data 0.005 (0.097) Batch 1.131 (1.078) Remain 30:35:33 loss: 0.3862 Lr: 0.00391 [2024-02-18 10:42:49,408 INFO misc.py line 119 87073] Train: [35/100][611/1557] Data 0.012 (0.097) Batch 1.058 (1.078) Remain 30:35:28 loss: 0.6905 Lr: 0.00391 [2024-02-18 10:42:50,243 INFO misc.py line 119 87073] Train: [35/100][612/1557] Data 0.005 (0.097) Batch 0.837 (1.078) Remain 30:34:47 loss: 0.2867 Lr: 0.00391 [2024-02-18 10:42:51,237 INFO misc.py line 119 87073] Train: [35/100][613/1557] Data 0.005 (0.097) Batch 0.993 (1.078) Remain 30:34:31 loss: 0.4606 Lr: 0.00391 [2024-02-18 10:42:52,026 INFO misc.py line 119 87073] Train: [35/100][614/1557] Data 0.006 (0.097) Batch 0.788 (1.077) Remain 30:33:42 loss: 0.3079 Lr: 0.00391 [2024-02-18 10:42:52,786 INFO misc.py line 119 87073] Train: [35/100][615/1557] Data 0.006 (0.096) Batch 0.756 (1.077) Remain 30:32:47 loss: 0.2664 Lr: 0.00391 [2024-02-18 10:42:54,128 INFO misc.py line 119 87073] Train: [35/100][616/1557] Data 0.010 (0.096) Batch 1.340 (1.077) Remain 30:33:30 loss: 0.3053 Lr: 0.00391 [2024-02-18 10:42:55,215 INFO misc.py line 119 87073] Train: [35/100][617/1557] Data 0.013 (0.096) Batch 1.088 (1.077) Remain 30:33:31 loss: 0.7731 Lr: 0.00391 [2024-02-18 10:42:56,302 INFO misc.py line 119 87073] Train: [35/100][618/1557] Data 0.011 (0.096) Batch 1.085 (1.077) Remain 30:33:31 loss: 0.5991 Lr: 0.00391 [2024-02-18 10:42:57,336 INFO misc.py line 119 87073] Train: 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Batch 0.893 (1.087) Remain 30:50:01 loss: 0.3468 Lr: 0.00391 [2024-02-18 10:43:10,838 INFO misc.py line 119 87073] Train: [35/100][626/1557] Data 0.004 (0.104) Batch 0.925 (1.087) Remain 30:49:33 loss: 0.2498 Lr: 0.00391 [2024-02-18 10:43:11,863 INFO misc.py line 119 87073] Train: [35/100][627/1557] Data 0.015 (0.103) Batch 1.030 (1.086) Remain 30:49:23 loss: 0.3607 Lr: 0.00391 [2024-02-18 10:43:12,665 INFO misc.py line 119 87073] Train: [35/100][628/1557] Data 0.009 (0.103) Batch 0.807 (1.086) Remain 30:48:36 loss: 0.6014 Lr: 0.00391 [2024-02-18 10:43:13,344 INFO misc.py line 119 87073] Train: [35/100][629/1557] Data 0.004 (0.103) Batch 0.678 (1.085) Remain 30:47:28 loss: 0.2110 Lr: 0.00391 [2024-02-18 10:43:14,636 INFO misc.py line 119 87073] Train: [35/100][630/1557] Data 0.004 (0.103) Batch 1.282 (1.086) Remain 30:47:59 loss: 0.2569 Lr: 0.00391 [2024-02-18 10:43:15,737 INFO misc.py line 119 87073] Train: [35/100][631/1557] Data 0.015 (0.103) Batch 1.063 (1.086) Remain 30:47:54 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Batch 0.872 (1.088) Remain 30:49:15 loss: 0.8515 Lr: 0.00390 [2024-02-18 10:46:14,551 INFO misc.py line 119 87073] Train: [35/100][794/1557] Data 0.006 (0.103) Batch 1.003 (1.088) Remain 30:49:02 loss: 0.4015 Lr: 0.00390 [2024-02-18 10:46:15,588 INFO misc.py line 119 87073] Train: [35/100][795/1557] Data 0.010 (0.103) Batch 1.036 (1.088) Remain 30:48:55 loss: 0.2391 Lr: 0.00390 [2024-02-18 10:46:16,360 INFO misc.py line 119 87073] Train: [35/100][796/1557] Data 0.011 (0.103) Batch 0.778 (1.088) Remain 30:48:14 loss: 0.3233 Lr: 0.00390 [2024-02-18 10:46:17,073 INFO misc.py line 119 87073] Train: [35/100][797/1557] Data 0.005 (0.103) Batch 0.699 (1.087) Remain 30:47:23 loss: 0.1906 Lr: 0.00390 [2024-02-18 10:46:18,396 INFO misc.py line 119 87073] Train: [35/100][798/1557] Data 0.019 (0.103) Batch 1.325 (1.087) Remain 30:47:52 loss: 0.2915 Lr: 0.00390 [2024-02-18 10:46:19,451 INFO misc.py line 119 87073] Train: [35/100][799/1557] Data 0.018 (0.103) Batch 1.057 (1.087) Remain 30:47:47 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line 119 87073] Train: [35/100][837/1557] Data 0.014 (0.098) Batch 0.858 (1.080) Remain 30:34:48 loss: 0.1991 Lr: 0.00390 [2024-02-18 10:46:55,455 INFO misc.py line 119 87073] Train: [35/100][838/1557] Data 0.005 (0.098) Batch 0.731 (1.080) Remain 30:34:05 loss: 0.4740 Lr: 0.00390 [2024-02-18 10:46:56,284 INFO misc.py line 119 87073] Train: [35/100][839/1557] Data 0.005 (0.098) Batch 0.813 (1.079) Remain 30:33:31 loss: 0.4919 Lr: 0.00390 [2024-02-18 10:46:57,610 INFO misc.py line 119 87073] Train: [35/100][840/1557] Data 0.021 (0.098) Batch 1.341 (1.080) Remain 30:34:02 loss: 0.3078 Lr: 0.00390 [2024-02-18 10:46:58,569 INFO misc.py line 119 87073] Train: [35/100][841/1557] Data 0.008 (0.098) Batch 0.961 (1.080) Remain 30:33:46 loss: 0.2749 Lr: 0.00390 [2024-02-18 10:46:59,565 INFO misc.py line 119 87073] Train: [35/100][842/1557] Data 0.004 (0.098) Batch 0.996 (1.079) Remain 30:33:35 loss: 0.4565 Lr: 0.00390 [2024-02-18 10:47:00,907 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [35/100][893/1557] Data 0.004 (0.100) Batch 1.048 (1.082) Remain 30:36:39 loss: 0.6339 Lr: 0.00390 [2024-02-18 10:47:57,463 INFO misc.py line 119 87073] Train: [35/100][894/1557] Data 0.004 (0.100) Batch 0.762 (1.081) Remain 30:36:01 loss: 0.4172 Lr: 0.00390 [2024-02-18 10:47:58,279 INFO misc.py line 119 87073] Train: [35/100][895/1557] Data 0.003 (0.099) Batch 0.806 (1.081) Remain 30:35:28 loss: 0.3446 Lr: 0.00390 [2024-02-18 10:47:59,627 INFO misc.py line 119 87073] Train: [35/100][896/1557] Data 0.013 (0.099) Batch 1.350 (1.081) Remain 30:35:58 loss: 0.3031 Lr: 0.00390 [2024-02-18 10:48:00,720 INFO misc.py line 119 87073] Train: [35/100][897/1557] Data 0.012 (0.099) Batch 1.087 (1.081) Remain 30:35:58 loss: 0.3398 Lr: 0.00390 [2024-02-18 10:48:01,743 INFO misc.py line 119 87073] Train: [35/100][898/1557] Data 0.018 (0.099) Batch 1.027 (1.081) Remain 30:35:50 loss: 0.4491 Lr: 0.00390 [2024-02-18 10:48:02,627 INFO misc.py line 119 87073] Train: 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Batch 1.109 (1.088) Remain 30:47:49 loss: 0.3499 Lr: 0.00390 [2024-02-18 10:48:16,637 INFO misc.py line 119 87073] Train: [35/100][906/1557] Data 0.004 (0.104) Batch 0.903 (1.088) Remain 30:47:27 loss: 0.4455 Lr: 0.00390 [2024-02-18 10:48:17,607 INFO misc.py line 119 87073] Train: [35/100][907/1557] Data 0.004 (0.104) Batch 0.969 (1.088) Remain 30:47:12 loss: 0.3092 Lr: 0.00390 [2024-02-18 10:48:18,392 INFO misc.py line 119 87073] Train: [35/100][908/1557] Data 0.005 (0.104) Batch 0.785 (1.088) Remain 30:46:37 loss: 0.2927 Lr: 0.00390 [2024-02-18 10:48:19,121 INFO misc.py line 119 87073] Train: [35/100][909/1557] Data 0.005 (0.104) Batch 0.729 (1.087) Remain 30:45:56 loss: 0.2796 Lr: 0.00390 [2024-02-18 10:48:20,432 INFO misc.py line 119 87073] Train: [35/100][910/1557] Data 0.005 (0.103) Batch 1.300 (1.088) Remain 30:46:19 loss: 0.2935 Lr: 0.00390 [2024-02-18 10:48:21,573 INFO misc.py line 119 87073] Train: [35/100][911/1557] Data 0.016 (0.103) Batch 1.151 (1.088) Remain 30:46:25 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misc.py line 119 87073] Train: [35/100][1129/1557] Data 0.005 (0.104) Batch 0.933 (1.087) Remain 30:41:49 loss: 0.6329 Lr: 0.00389 [2024-02-18 10:52:19,130 INFO misc.py line 119 87073] Train: [35/100][1130/1557] Data 0.004 (0.104) Batch 0.862 (1.087) Remain 30:41:28 loss: 0.6216 Lr: 0.00389 [2024-02-18 10:52:19,998 INFO misc.py line 119 87073] Train: [35/100][1131/1557] Data 0.004 (0.104) Batch 0.860 (1.087) Remain 30:41:06 loss: 0.6700 Lr: 0.00389 [2024-02-18 10:52:20,809 INFO misc.py line 119 87073] Train: [35/100][1132/1557] Data 0.013 (0.104) Batch 0.819 (1.087) Remain 30:40:41 loss: 0.3173 Lr: 0.00389 [2024-02-18 10:52:21,597 INFO misc.py line 119 87073] Train: [35/100][1133/1557] Data 0.005 (0.104) Batch 0.788 (1.086) Remain 30:40:13 loss: 0.4631 Lr: 0.00389 [2024-02-18 10:52:22,871 INFO misc.py line 119 87073] Train: [35/100][1134/1557] Data 0.004 (0.103) Batch 1.267 (1.087) Remain 30:40:28 loss: 0.1635 Lr: 0.00389 [2024-02-18 10:52:23,890 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [35/100][1222/1557] Data 0.016 (0.101) Batch 1.090 (1.082) Remain 30:31:49 loss: 0.4850 Lr: 0.00388 [2024-02-18 10:53:54,152 INFO misc.py line 119 87073] Train: [35/100][1223/1557] Data 0.015 (0.101) Batch 0.755 (1.082) Remain 30:31:20 loss: 0.2943 Lr: 0.00388 [2024-02-18 10:53:54,891 INFO misc.py line 119 87073] Train: [35/100][1224/1557] Data 0.004 (0.101) Batch 0.727 (1.082) Remain 30:30:50 loss: 0.4561 Lr: 0.00388 [2024-02-18 10:53:56,184 INFO misc.py line 119 87073] Train: [35/100][1225/1557] Data 0.016 (0.100) Batch 1.295 (1.082) Remain 30:31:06 loss: 0.4575 Lr: 0.00388 [2024-02-18 10:53:57,224 INFO misc.py line 119 87073] Train: [35/100][1226/1557] Data 0.015 (0.100) Batch 1.040 (1.082) Remain 30:31:02 loss: 0.5108 Lr: 0.00388 [2024-02-18 10:53:58,083 INFO misc.py line 119 87073] Train: [35/100][1227/1557] Data 0.015 (0.100) Batch 0.870 (1.082) Remain 30:30:43 loss: 0.2564 Lr: 0.00388 [2024-02-18 10:53:59,080 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [35/100][1315/1557] Data 0.005 (0.103) Batch 0.772 (1.085) Remain 30:33:53 loss: 0.3476 Lr: 0.00388 [2024-02-18 10:55:38,127 INFO misc.py line 119 87073] Train: [35/100][1316/1557] Data 0.010 (0.103) Batch 1.147 (1.085) Remain 30:33:57 loss: 0.1982 Lr: 0.00388 [2024-02-18 10:55:38,989 INFO misc.py line 119 87073] Train: [35/100][1317/1557] Data 0.010 (0.103) Batch 0.868 (1.085) Remain 30:33:39 loss: 0.3954 Lr: 0.00388 [2024-02-18 10:55:39,835 INFO misc.py line 119 87073] Train: [35/100][1318/1557] Data 0.005 (0.102) Batch 0.844 (1.084) Remain 30:33:19 loss: 0.2131 Lr: 0.00388 [2024-02-18 10:55:40,745 INFO misc.py line 119 87073] Train: [35/100][1319/1557] Data 0.006 (0.102) Batch 0.912 (1.084) Remain 30:33:05 loss: 0.2273 Lr: 0.00388 [2024-02-18 10:55:41,540 INFO misc.py line 119 87073] Train: [35/100][1320/1557] Data 0.004 (0.102) Batch 0.795 (1.084) Remain 30:32:42 loss: 0.3703 Lr: 0.00388 [2024-02-18 10:55:42,325 INFO misc.py line 119 87073] Train: 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[2024-02-18 10:56:36,585 INFO misc.py line 119 87073] Train: [35/100][1371/1557] Data 0.004 (0.102) Batch 0.660 (1.084) Remain 30:31:28 loss: 0.4660 Lr: 0.00388 [2024-02-18 10:56:37,729 INFO misc.py line 119 87073] Train: [35/100][1372/1557] Data 0.010 (0.102) Batch 1.146 (1.084) Remain 30:31:32 loss: 0.2785 Lr: 0.00388 [2024-02-18 10:56:38,821 INFO misc.py line 119 87073] Train: [35/100][1373/1557] Data 0.008 (0.102) Batch 1.096 (1.084) Remain 30:31:32 loss: 0.1309 Lr: 0.00388 [2024-02-18 10:56:39,772 INFO misc.py line 119 87073] Train: [35/100][1374/1557] Data 0.004 (0.102) Batch 0.951 (1.084) Remain 30:31:21 loss: 0.3249 Lr: 0.00388 [2024-02-18 10:56:40,804 INFO misc.py line 119 87073] Train: [35/100][1375/1557] Data 0.005 (0.102) Batch 1.032 (1.084) Remain 30:31:16 loss: 0.4855 Lr: 0.00388 [2024-02-18 10:56:41,759 INFO misc.py line 119 87073] Train: [35/100][1376/1557] Data 0.005 (0.102) Batch 0.956 (1.084) Remain 30:31:05 loss: 0.6095 Lr: 0.00388 [2024-02-18 10:56:42,467 INFO 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30:30:59 loss: 0.4070 Lr: 0.00387 [2024-02-18 10:58:45,608 INFO misc.py line 119 87073] Train: [35/100][1489/1557] Data 0.004 (0.103) Batch 0.785 (1.085) Remain 30:30:37 loss: 0.3166 Lr: 0.00387 [2024-02-18 10:58:46,349 INFO misc.py line 119 87073] Train: [35/100][1490/1557] Data 0.014 (0.103) Batch 0.749 (1.084) Remain 30:30:13 loss: 0.3047 Lr: 0.00387 [2024-02-18 10:58:47,518 INFO misc.py line 119 87073] Train: [35/100][1491/1557] Data 0.006 (0.102) Batch 1.171 (1.084) Remain 30:30:18 loss: 0.2399 Lr: 0.00387 [2024-02-18 10:58:48,525 INFO misc.py line 119 87073] Train: [35/100][1492/1557] Data 0.005 (0.102) Batch 1.007 (1.084) Remain 30:30:12 loss: 0.3333 Lr: 0.00387 [2024-02-18 10:58:49,366 INFO misc.py line 119 87073] Train: [35/100][1493/1557] Data 0.004 (0.102) Batch 0.840 (1.084) Remain 30:29:54 loss: 0.1797 Lr: 0.00387 [2024-02-18 10:58:50,393 INFO misc.py line 119 87073] Train: [35/100][1494/1557] Data 0.004 (0.102) Batch 1.022 (1.084) Remain 30:29:49 loss: 0.3496 Lr: 0.00387 [2024-02-18 10:58:51,413 INFO misc.py line 119 87073] Train: [35/100][1495/1557] Data 0.010 (0.102) Batch 1.016 (1.084) Remain 30:29:43 loss: 0.5624 Lr: 0.00387 [2024-02-18 10:58:52,155 INFO misc.py line 119 87073] Train: [35/100][1496/1557] Data 0.013 (0.102) Batch 0.751 (1.084) Remain 30:29:20 loss: 0.2916 Lr: 0.00387 [2024-02-18 10:58:52,889 INFO misc.py line 119 87073] Train: [35/100][1497/1557] Data 0.004 (0.102) Batch 0.725 (1.084) Remain 30:28:54 loss: 0.5872 Lr: 0.00387 [2024-02-18 10:58:54,246 INFO misc.py line 119 87073] Train: [35/100][1498/1557] Data 0.013 (0.102) Batch 1.356 (1.084) Remain 30:29:11 loss: 0.1225 Lr: 0.00387 [2024-02-18 10:58:55,291 INFO misc.py line 119 87073] Train: [35/100][1499/1557] Data 0.015 (0.102) Batch 1.044 (1.084) Remain 30:29:08 loss: 0.5352 Lr: 0.00387 [2024-02-18 10:58:56,311 INFO misc.py line 119 87073] Train: [35/100][1500/1557] Data 0.015 (0.102) Batch 1.031 (1.084) Remain 30:29:03 loss: 0.8831 Lr: 0.00387 [2024-02-18 10:58:57,169 INFO misc.py line 119 87073] Train: [35/100][1501/1557] Data 0.005 (0.102) Batch 0.858 (1.084) Remain 30:28:47 loss: 0.3057 Lr: 0.00387 [2024-02-18 10:58:58,169 INFO misc.py line 119 87073] Train: [35/100][1502/1557] Data 0.004 (0.102) Batch 1.000 (1.084) Remain 30:28:40 loss: 0.4203 Lr: 0.00387 [2024-02-18 10:58:58,925 INFO misc.py line 119 87073] Train: [35/100][1503/1557] Data 0.004 (0.102) Batch 0.756 (1.083) Remain 30:28:17 loss: 0.5030 Lr: 0.00387 [2024-02-18 10:58:59,610 INFO misc.py line 119 87073] Train: [35/100][1504/1557] Data 0.004 (0.102) Batch 0.677 (1.083) Remain 30:27:48 loss: 0.2988 Lr: 0.00387 [2024-02-18 10:59:00,920 INFO misc.py line 119 87073] Train: [35/100][1505/1557] Data 0.012 (0.102) Batch 1.309 (1.083) Remain 30:28:02 loss: 0.2689 Lr: 0.00387 [2024-02-18 10:59:01,738 INFO misc.py line 119 87073] Train: [35/100][1506/1557] Data 0.014 (0.102) Batch 0.828 (1.083) Remain 30:27:44 loss: 0.3042 Lr: 0.00387 [2024-02-18 10:59:02,605 INFO misc.py line 119 87073] Train: [35/100][1507/1557] Data 0.004 (0.101) Batch 0.867 (1.083) Remain 30:27:28 loss: 0.6184 Lr: 0.00387 [2024-02-18 10:59:03,515 INFO misc.py line 119 87073] Train: [35/100][1508/1557] Data 0.005 (0.101) Batch 0.902 (1.083) Remain 30:27:15 loss: 0.5014 Lr: 0.00387 [2024-02-18 10:59:04,377 INFO misc.py line 119 87073] Train: [35/100][1509/1557] Data 0.011 (0.101) Batch 0.869 (1.083) Remain 30:27:00 loss: 0.4263 Lr: 0.00387 [2024-02-18 10:59:05,123 INFO misc.py line 119 87073] Train: [35/100][1510/1557] Data 0.006 (0.101) Batch 0.746 (1.082) Remain 30:26:36 loss: 0.4033 Lr: 0.00387 [2024-02-18 10:59:05,920 INFO misc.py line 119 87073] Train: [35/100][1511/1557] Data 0.004 (0.101) Batch 0.794 (1.082) Remain 30:26:16 loss: 0.3909 Lr: 0.00387 [2024-02-18 10:59:07,210 INFO misc.py line 119 87073] Train: [35/100][1512/1557] Data 0.008 (0.101) Batch 1.286 (1.082) Remain 30:26:28 loss: 0.4543 Lr: 0.00387 [2024-02-18 10:59:08,218 INFO misc.py line 119 87073] Train: [35/100][1513/1557] Data 0.012 (0.101) Batch 1.008 (1.082) Remain 30:26:22 loss: 0.4634 Lr: 0.00387 [2024-02-18 10:59:09,112 INFO misc.py line 119 87073] Train: [35/100][1514/1557] Data 0.012 (0.101) Batch 0.902 (1.082) Remain 30:26:09 loss: 0.2518 Lr: 0.00387 [2024-02-18 10:59:10,072 INFO misc.py line 119 87073] Train: [35/100][1515/1557] Data 0.004 (0.101) Batch 0.960 (1.082) Remain 30:26:00 loss: 0.3517 Lr: 0.00387 [2024-02-18 10:59:11,017 INFO misc.py line 119 87073] Train: [35/100][1516/1557] Data 0.004 (0.101) Batch 0.945 (1.082) Remain 30:25:49 loss: 0.5268 Lr: 0.00387 [2024-02-18 10:59:11,793 INFO misc.py line 119 87073] Train: [35/100][1517/1557] Data 0.004 (0.101) Batch 0.765 (1.082) Remain 30:25:27 loss: 0.5343 Lr: 0.00387 [2024-02-18 10:59:12,562 INFO misc.py line 119 87073] Train: [35/100][1518/1557] Data 0.015 (0.101) Batch 0.780 (1.082) Remain 30:25:06 loss: 0.4688 Lr: 0.00387 [2024-02-18 10:59:21,131 INFO misc.py line 119 87073] Train: [35/100][1519/1557] Data 4.963 (0.104) Batch 8.561 (1.087) Remain 30:33:24 loss: 0.1038 Lr: 0.00387 [2024-02-18 10:59:22,045 INFO misc.py line 119 87073] Train: [35/100][1520/1557] Data 0.012 (0.104) Batch 0.923 (1.086) Remain 30:33:12 loss: 0.4164 Lr: 0.00387 [2024-02-18 10:59:22,932 INFO misc.py line 119 87073] Train: [35/100][1521/1557] Data 0.004 (0.104) Batch 0.887 (1.086) Remain 30:32:58 loss: 0.3169 Lr: 0.00387 [2024-02-18 10:59:23,790 INFO misc.py line 119 87073] Train: [35/100][1522/1557] Data 0.004 (0.104) Batch 0.855 (1.086) Remain 30:32:41 loss: 0.3268 Lr: 0.00387 [2024-02-18 10:59:24,690 INFO misc.py line 119 87073] Train: [35/100][1523/1557] Data 0.006 (0.104) Batch 0.898 (1.086) Remain 30:32:28 loss: 0.4814 Lr: 0.00387 [2024-02-18 10:59:25,419 INFO misc.py line 119 87073] Train: [35/100][1524/1557] Data 0.010 (0.104) Batch 0.733 (1.086) Remain 30:32:03 loss: 0.3233 Lr: 0.00387 [2024-02-18 10:59:26,206 INFO misc.py line 119 87073] Train: [35/100][1525/1557] Data 0.004 (0.104) Batch 0.781 (1.086) Remain 30:31:42 loss: 0.3108 Lr: 0.00387 [2024-02-18 10:59:27,472 INFO misc.py line 119 87073] Train: [35/100][1526/1557] Data 0.010 (0.104) Batch 1.264 (1.086) Remain 30:31:53 loss: 0.2307 Lr: 0.00387 [2024-02-18 10:59:28,595 INFO misc.py line 119 87073] Train: [35/100][1527/1557] Data 0.013 (0.103) Batch 1.131 (1.086) Remain 30:31:55 loss: 0.8341 Lr: 0.00387 [2024-02-18 10:59:29,616 INFO misc.py line 119 87073] Train: [35/100][1528/1557] Data 0.005 (0.103) Batch 1.020 (1.086) Remain 30:31:49 loss: 0.2420 Lr: 0.00387 [2024-02-18 10:59:30,581 INFO misc.py line 119 87073] Train: [35/100][1529/1557] Data 0.005 (0.103) Batch 0.965 (1.086) Remain 30:31:40 loss: 0.1767 Lr: 0.00387 [2024-02-18 10:59:31,506 INFO misc.py line 119 87073] Train: [35/100][1530/1557] Data 0.006 (0.103) Batch 0.925 (1.086) Remain 30:31:28 loss: 0.2919 Lr: 0.00387 [2024-02-18 10:59:32,267 INFO misc.py line 119 87073] Train: [35/100][1531/1557] Data 0.005 (0.103) Batch 0.761 (1.085) Remain 30:31:06 loss: 0.4771 Lr: 0.00387 [2024-02-18 10:59:33,061 INFO misc.py line 119 87073] Train: [35/100][1532/1557] Data 0.005 (0.103) Batch 0.794 (1.085) Remain 30:30:45 loss: 0.3827 Lr: 0.00387 [2024-02-18 10:59:34,271 INFO misc.py line 119 87073] Train: [35/100][1533/1557] Data 0.005 (0.103) Batch 1.210 (1.085) Remain 30:30:52 loss: 0.1464 Lr: 0.00387 [2024-02-18 10:59:35,331 INFO misc.py line 119 87073] Train: [35/100][1534/1557] Data 0.005 (0.103) Batch 1.060 (1.085) Remain 30:30:50 loss: 0.5588 Lr: 0.00387 [2024-02-18 10:59:36,241 INFO misc.py line 119 87073] Train: [35/100][1535/1557] Data 0.005 (0.103) Batch 0.909 (1.085) Remain 30:30:37 loss: 0.4413 Lr: 0.00387 [2024-02-18 10:59:37,135 INFO misc.py line 119 87073] Train: [35/100][1536/1557] Data 0.007 (0.103) Batch 0.894 (1.085) Remain 30:30:23 loss: 0.5425 Lr: 0.00387 [2024-02-18 10:59:38,078 INFO misc.py line 119 87073] Train: [35/100][1537/1557] Data 0.006 (0.103) Batch 0.945 (1.085) Remain 30:30:13 loss: 0.3512 Lr: 0.00387 [2024-02-18 10:59:38,779 INFO misc.py line 119 87073] Train: [35/100][1538/1557] Data 0.004 (0.103) Batch 0.700 (1.085) Remain 30:29:47 loss: 0.3035 Lr: 0.00387 [2024-02-18 10:59:39,550 INFO misc.py line 119 87073] Train: [35/100][1539/1557] Data 0.004 (0.103) Batch 0.770 (1.084) Remain 30:29:25 loss: 0.3797 Lr: 0.00387 [2024-02-18 10:59:40,696 INFO misc.py line 119 87073] Train: [35/100][1540/1557] Data 0.005 (0.103) Batch 1.139 (1.084) Remain 30:29:27 loss: 0.1813 Lr: 0.00387 [2024-02-18 10:59:41,561 INFO misc.py line 119 87073] Train: [35/100][1541/1557] Data 0.012 (0.103) Batch 0.872 (1.084) Remain 30:29:12 loss: 0.4768 Lr: 0.00387 [2024-02-18 10:59:42,409 INFO misc.py line 119 87073] Train: [35/100][1542/1557] Data 0.004 (0.103) Batch 0.848 (1.084) Remain 30:28:56 loss: 0.6620 Lr: 0.00387 [2024-02-18 10:59:43,381 INFO misc.py line 119 87073] Train: [35/100][1543/1557] Data 0.004 (0.102) Batch 0.966 (1.084) Remain 30:28:47 loss: 0.3764 Lr: 0.00387 [2024-02-18 10:59:44,315 INFO misc.py line 119 87073] Train: [35/100][1544/1557] Data 0.010 (0.102) Batch 0.941 (1.084) Remain 30:28:36 loss: 0.4736 Lr: 0.00387 [2024-02-18 10:59:45,003 INFO misc.py line 119 87073] Train: [35/100][1545/1557] Data 0.004 (0.102) Batch 0.686 (1.084) Remain 30:28:09 loss: 0.3577 Lr: 0.00387 [2024-02-18 10:59:45,633 INFO misc.py line 119 87073] Train: [35/100][1546/1557] Data 0.005 (0.102) Batch 0.613 (1.083) Remain 30:27:37 loss: 0.2502 Lr: 0.00387 [2024-02-18 10:59:46,826 INFO misc.py line 119 87073] Train: [35/100][1547/1557] Data 0.022 (0.102) Batch 1.189 (1.083) Remain 30:27:43 loss: 0.3380 Lr: 0.00387 [2024-02-18 10:59:47,710 INFO misc.py line 119 87073] Train: [35/100][1548/1557] Data 0.028 (0.102) Batch 0.906 (1.083) Remain 30:27:30 loss: 0.2314 Lr: 0.00387 [2024-02-18 10:59:48,589 INFO misc.py line 119 87073] Train: [35/100][1549/1557] Data 0.005 (0.102) Batch 0.879 (1.083) Remain 30:27:16 loss: 0.3728 Lr: 0.00387 [2024-02-18 10:59:49,464 INFO misc.py line 119 87073] Train: [35/100][1550/1557] Data 0.005 (0.102) Batch 0.858 (1.083) Remain 30:27:00 loss: 0.5417 Lr: 0.00387 [2024-02-18 10:59:50,349 INFO misc.py line 119 87073] Train: [35/100][1551/1557] Data 0.022 (0.102) Batch 0.903 (1.083) Remain 30:26:47 loss: 0.6090 Lr: 0.00387 [2024-02-18 10:59:51,070 INFO misc.py line 119 87073] Train: [35/100][1552/1557] Data 0.004 (0.102) Batch 0.721 (1.083) Remain 30:26:22 loss: 0.5163 Lr: 0.00387 [2024-02-18 10:59:51,878 INFO misc.py line 119 87073] Train: [35/100][1553/1557] Data 0.004 (0.102) Batch 0.796 (1.083) Remain 30:26:03 loss: 0.2076 Lr: 0.00387 [2024-02-18 10:59:53,049 INFO misc.py line 119 87073] Train: [35/100][1554/1557] Data 0.016 (0.102) Batch 1.172 (1.083) Remain 30:26:07 loss: 0.2190 Lr: 0.00387 [2024-02-18 10:59:54,099 INFO misc.py line 119 87073] Train: [35/100][1555/1557] Data 0.015 (0.102) Batch 1.051 (1.083) Remain 30:26:04 loss: 0.6990 Lr: 0.00387 [2024-02-18 10:59:55,037 INFO misc.py line 119 87073] Train: [35/100][1556/1557] Data 0.014 (0.102) Batch 0.948 (1.082) Remain 30:25:54 loss: 0.4200 Lr: 0.00387 [2024-02-18 10:59:56,126 INFO misc.py line 119 87073] Train: [35/100][1557/1557] Data 0.004 (0.102) Batch 1.088 (1.082) Remain 30:25:54 loss: 0.7800 Lr: 0.00387 [2024-02-18 10:59:56,126 INFO misc.py line 136 87073] Train result: loss: 0.4066 [2024-02-18 10:59:56,127 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 11:00:24,168 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8688 [2024-02-18 11:00:24,946 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6978 [2024-02-18 11:00:27,071 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4503 [2024-02-18 11:00:29,277 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4218 [2024-02-18 11:00:34,219 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3237 [2024-02-18 11:00:34,919 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1397 [2024-02-18 11:00:36,180 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2448 [2024-02-18 11:00:39,133 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3734 [2024-02-18 11:00:40,943 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2007 [2024-02-18 11:00:42,489 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7179/0.7712/0.9099. [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9282/0.9493 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9805/0.9859 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8422/0.9692 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3120/0.3607 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6529/0.6689 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8139/0.9008 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8290/0.9066 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9159/0.9661 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8531/0.8823 [2024-02-18 11:00:42,489 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7848/0.8696 [2024-02-18 11:00:42,490 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7773/0.8298 [2024-02-18 11:00:42,490 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6429/0.7363 [2024-02-18 11:00:42,490 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 11:00:42,492 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 11:00:42,492 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 11:00:50,560 INFO misc.py line 119 87073] Train: [36/100][1/1557] Data 2.086 (2.086) Batch 3.161 (3.161) Remain 88:50:58 loss: 0.4946 Lr: 0.00387 [2024-02-18 11:00:51,704 INFO misc.py line 119 87073] Train: [36/100][2/1557] Data 0.007 (0.007) Batch 1.144 (1.144) Remain 32:10:17 loss: 0.2794 Lr: 0.00387 [2024-02-18 11:00:52,671 INFO misc.py line 119 87073] Train: [36/100][3/1557] Data 0.007 (0.007) Batch 0.969 (0.969) Remain 27:14:40 loss: 0.2601 Lr: 0.00387 [2024-02-18 11:00:53,837 INFO misc.py line 119 87073] Train: [36/100][4/1557] Data 0.004 (0.004) Batch 1.165 (1.165) Remain 32:45:26 loss: 0.3276 Lr: 0.00387 [2024-02-18 11:00:54,574 INFO misc.py line 119 87073] Train: [36/100][5/1557] Data 0.005 (0.005) Batch 0.738 (0.952) Remain 26:45:07 loss: 0.3152 Lr: 0.00387 [2024-02-18 11:00:55,360 INFO misc.py line 119 87073] Train: [36/100][6/1557] Data 0.003 (0.004) Batch 0.773 (0.892) Remain 25:04:38 loss: 0.2225 Lr: 0.00387 [2024-02-18 11:00:56,515 INFO misc.py line 119 87073] Train: [36/100][7/1557] Data 0.017 (0.007) Batch 1.163 (0.960) Remain 26:58:57 loss: 0.1258 Lr: 0.00387 [2024-02-18 11:00:57,685 INFO misc.py line 119 87073] Train: [36/100][8/1557] Data 0.009 (0.008) Batch 1.167 (1.001) Remain 28:08:41 loss: 0.3860 Lr: 0.00387 [2024-02-18 11:00:58,643 INFO misc.py line 119 87073] Train: [36/100][9/1557] Data 0.012 (0.008) Batch 0.965 (0.995) Remain 27:58:36 loss: 0.1950 Lr: 0.00387 [2024-02-18 11:00:59,607 INFO misc.py line 119 87073] Train: [36/100][10/1557] Data 0.005 (0.008) Batch 0.964 (0.991) Remain 27:51:06 loss: 0.4018 Lr: 0.00387 [2024-02-18 11:01:00,622 INFO misc.py line 119 87073] Train: [36/100][11/1557] Data 0.005 (0.007) Batch 1.015 (0.994) Remain 27:56:15 loss: 0.6677 Lr: 0.00387 [2024-02-18 11:01:01,371 INFO misc.py line 119 87073] Train: [36/100][12/1557] Data 0.003 (0.007) Batch 0.749 (0.967) Remain 27:10:25 loss: 0.2384 Lr: 0.00387 [2024-02-18 11:01:02,158 INFO misc.py line 119 87073] Train: [36/100][13/1557] Data 0.004 (0.007) Batch 0.780 (0.948) Remain 26:38:57 loss: 0.5040 Lr: 0.00387 [2024-02-18 11:01:06,112 INFO misc.py line 119 87073] Train: [36/100][14/1557] Data 0.010 (0.007) Batch 3.960 (1.222) Remain 34:20:41 loss: 0.4031 Lr: 0.00387 [2024-02-18 11:01:07,212 INFO misc.py line 119 87073] Train: [36/100][15/1557] Data 0.005 (0.007) Batch 1.100 (1.212) Remain 34:03:35 loss: 0.4859 Lr: 0.00387 [2024-02-18 11:01:08,250 INFO misc.py line 119 87073] Train: [36/100][16/1557] Data 0.004 (0.007) Batch 1.033 (1.198) Remain 33:40:21 loss: 0.2597 Lr: 0.00387 [2024-02-18 11:01:09,420 INFO misc.py line 119 87073] Train: [36/100][17/1557] Data 0.009 (0.007) Batch 1.174 (1.196) Remain 33:37:27 loss: 0.7282 Lr: 0.00387 [2024-02-18 11:01:10,421 INFO misc.py line 119 87073] Train: [36/100][18/1557] Data 0.005 (0.007) Batch 0.997 (1.183) Remain 33:14:59 loss: 0.3383 Lr: 0.00387 [2024-02-18 11:01:11,176 INFO misc.py line 119 87073] Train: [36/100][19/1557] Data 0.010 (0.007) Batch 0.761 (1.157) Remain 32:30:27 loss: 0.2731 Lr: 0.00387 [2024-02-18 11:01:12,034 INFO misc.py line 119 87073] Train: [36/100][20/1557] Data 0.005 (0.007) Batch 0.852 (1.139) Remain 32:00:11 loss: 0.2844 Lr: 0.00387 [2024-02-18 11:01:13,280 INFO misc.py line 119 87073] Train: [36/100][21/1557] Data 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Train: [36/100][40/1557] Data 0.010 (0.008) Batch 0.765 (1.056) Remain 29:40:52 loss: 0.5155 Lr: 0.00387 [2024-02-18 11:01:32,470 INFO misc.py line 119 87073] Train: [36/100][41/1557] Data 0.006 (0.008) Batch 0.718 (1.047) Remain 29:25:51 loss: 0.4156 Lr: 0.00387 [2024-02-18 11:01:33,717 INFO misc.py line 119 87073] Train: [36/100][42/1557] Data 0.004 (0.008) Batch 1.239 (1.052) Remain 29:34:08 loss: 0.1419 Lr: 0.00387 [2024-02-18 11:01:34,893 INFO misc.py line 119 87073] Train: [36/100][43/1557] Data 0.013 (0.008) Batch 1.182 (1.055) Remain 29:39:35 loss: 0.4325 Lr: 0.00387 [2024-02-18 11:01:35,811 INFO misc.py line 119 87073] Train: [36/100][44/1557] Data 0.006 (0.008) Batch 0.920 (1.052) Remain 29:34:00 loss: 0.5159 Lr: 0.00387 [2024-02-18 11:01:36,808 INFO misc.py line 119 87073] Train: [36/100][45/1557] Data 0.004 (0.008) Batch 0.996 (1.051) Remain 29:31:45 loss: 0.3458 Lr: 0.00387 [2024-02-18 11:01:37,703 INFO misc.py line 119 87073] Train: [36/100][46/1557] Data 0.005 (0.008) 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loss: 0.7859 Lr: 0.00386 [2024-02-18 11:03:21,502 INFO misc.py line 119 87073] Train: [36/100][128/1557] Data 0.005 (0.083) Batch 0.945 (1.191) Remain 33:25:47 loss: 0.4740 Lr: 0.00386 [2024-02-18 11:03:22,339 INFO misc.py line 119 87073] Train: [36/100][129/1557] Data 0.004 (0.083) Batch 0.833 (1.188) Remain 33:20:59 loss: 0.1649 Lr: 0.00386 [2024-02-18 11:03:23,299 INFO misc.py line 119 87073] Train: [36/100][130/1557] Data 0.007 (0.082) Batch 0.963 (1.186) Remain 33:17:59 loss: 0.4353 Lr: 0.00386 [2024-02-18 11:03:24,119 INFO misc.py line 119 87073] Train: [36/100][131/1557] Data 0.004 (0.082) Batch 0.819 (1.183) Remain 33:13:08 loss: 0.5502 Lr: 0.00386 [2024-02-18 11:03:24,935 INFO misc.py line 119 87073] Train: [36/100][132/1557] Data 0.005 (0.081) Batch 0.817 (1.180) Remain 33:08:20 loss: 0.6034 Lr: 0.00386 [2024-02-18 11:03:26,269 INFO misc.py line 119 87073] Train: [36/100][133/1557] Data 0.004 (0.080) Batch 1.328 (1.181) Remain 33:10:13 loss: 0.3541 Lr: 0.00386 [2024-02-18 11:03:27,165 INFO misc.py line 119 87073] Train: [36/100][134/1557] Data 0.010 (0.080) Batch 0.903 (1.179) Remain 33:06:37 loss: 0.2322 Lr: 0.00386 [2024-02-18 11:03:28,237 INFO misc.py line 119 87073] Train: [36/100][135/1557] Data 0.004 (0.079) Batch 1.071 (1.179) Remain 33:05:14 loss: 0.1612 Lr: 0.00386 [2024-02-18 11:03:29,172 INFO misc.py line 119 87073] Train: [36/100][136/1557] Data 0.004 (0.079) Batch 0.935 (1.177) Remain 33:02:08 loss: 0.3668 Lr: 0.00386 [2024-02-18 11:03:30,077 INFO misc.py line 119 87073] Train: [36/100][137/1557] Data 0.004 (0.078) Batch 0.904 (1.175) Remain 32:58:41 loss: 0.3817 Lr: 0.00386 [2024-02-18 11:03:30,840 INFO misc.py line 119 87073] Train: [36/100][138/1557] Data 0.004 (0.078) Batch 0.760 (1.172) Remain 32:53:29 loss: 0.3156 Lr: 0.00386 [2024-02-18 11:03:31,531 INFO misc.py line 119 87073] Train: [36/100][139/1557] Data 0.007 (0.077) Batch 0.694 (1.168) Remain 32:47:33 loss: 0.3453 Lr: 0.00386 [2024-02-18 11:03:32,804 INFO misc.py line 119 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line 119 87073] Train: [36/100][165/1557] Data 0.004 (0.066) Batch 1.094 (1.135) Remain 31:51:36 loss: 0.4914 Lr: 0.00386 [2024-02-18 11:03:57,362 INFO misc.py line 119 87073] Train: [36/100][166/1557] Data 0.005 (0.065) Batch 0.793 (1.133) Remain 31:48:03 loss: 0.3650 Lr: 0.00386 [2024-02-18 11:03:58,100 INFO misc.py line 119 87073] Train: [36/100][167/1557] Data 0.005 (0.065) Batch 0.738 (1.131) Remain 31:43:58 loss: 0.2017 Lr: 0.00386 [2024-02-18 11:03:59,215 INFO misc.py line 119 87073] Train: [36/100][168/1557] Data 0.005 (0.064) Batch 1.116 (1.131) Remain 31:43:48 loss: 0.1327 Lr: 0.00386 [2024-02-18 11:04:00,168 INFO misc.py line 119 87073] Train: [36/100][169/1557] Data 0.004 (0.064) Batch 0.954 (1.130) Remain 31:42:00 loss: 0.3627 Lr: 0.00386 [2024-02-18 11:04:01,018 INFO misc.py line 119 87073] Train: [36/100][170/1557] Data 0.003 (0.064) Batch 0.849 (1.128) Remain 31:39:09 loss: 0.5312 Lr: 0.00386 [2024-02-18 11:04:02,043 INFO misc.py line 119 87073] Train: 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Batch 0.921 (1.149) Remain 32:14:14 loss: 0.5655 Lr: 0.00386 [2024-02-18 11:04:13,470 INFO misc.py line 119 87073] Train: [36/100][178/1557] Data 0.005 (0.087) Batch 0.917 (1.147) Remain 32:11:59 loss: 0.4099 Lr: 0.00386 [2024-02-18 11:04:14,386 INFO misc.py line 119 87073] Train: [36/100][179/1557] Data 0.007 (0.087) Batch 0.918 (1.146) Remain 32:09:46 loss: 0.2243 Lr: 0.00386 [2024-02-18 11:04:16,930 INFO misc.py line 119 87073] Train: [36/100][180/1557] Data 1.196 (0.093) Batch 2.538 (1.154) Remain 32:22:59 loss: 0.4124 Lr: 0.00386 [2024-02-18 11:04:17,698 INFO misc.py line 119 87073] Train: [36/100][181/1557] Data 0.011 (0.092) Batch 0.773 (1.152) Remain 32:19:22 loss: 0.4513 Lr: 0.00386 [2024-02-18 11:04:26,680 INFO misc.py line 119 87073] Train: [36/100][182/1557] Data 0.007 (0.092) Batch 8.981 (1.196) Remain 33:33:00 loss: 0.3103 Lr: 0.00386 [2024-02-18 11:04:27,625 INFO misc.py line 119 87073] Train: [36/100][183/1557] Data 0.006 (0.091) Batch 0.945 (1.194) Remain 33:30:38 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87073] Train: [36/100][252/1557] Data 0.004 (0.088) Batch 1.311 (1.178) Remain 33:01:27 loss: 0.2202 Lr: 0.00386 [2024-02-18 11:05:47,097 INFO misc.py line 119 87073] Train: [36/100][253/1557] Data 0.014 (0.088) Batch 1.190 (1.178) Remain 33:01:31 loss: 0.2754 Lr: 0.00386 [2024-02-18 11:05:48,025 INFO misc.py line 119 87073] Train: [36/100][254/1557] Data 0.004 (0.087) Batch 0.928 (1.177) Remain 32:59:49 loss: 0.4225 Lr: 0.00386 [2024-02-18 11:05:48,969 INFO misc.py line 119 87073] Train: [36/100][255/1557] Data 0.005 (0.087) Batch 0.942 (1.176) Remain 32:58:14 loss: 0.2369 Lr: 0.00386 [2024-02-18 11:05:49,945 INFO misc.py line 119 87073] Train: [36/100][256/1557] Data 0.007 (0.087) Batch 0.969 (1.175) Remain 32:56:51 loss: 0.6371 Lr: 0.00386 [2024-02-18 11:05:50,671 INFO misc.py line 119 87073] Train: [36/100][257/1557] Data 0.012 (0.086) Batch 0.735 (1.173) Remain 32:53:54 loss: 0.7291 Lr: 0.00386 [2024-02-18 11:05:51,417 INFO misc.py line 119 87073] Train: [36/100][258/1557] Data 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[2024-02-18 11:06:04,171 INFO misc.py line 119 87073] Train: [36/100][271/1557] Data 0.005 (0.082) Batch 0.768 (1.162) Remain 32:35:16 loss: 0.4582 Lr: 0.00386 [2024-02-18 11:06:04,932 INFO misc.py line 119 87073] Train: [36/100][272/1557] Data 0.006 (0.082) Batch 0.754 (1.161) Remain 32:32:42 loss: 0.4444 Lr: 0.00386 [2024-02-18 11:06:05,943 INFO misc.py line 119 87073] Train: [36/100][273/1557] Data 0.012 (0.082) Batch 1.011 (1.160) Remain 32:31:44 loss: 0.1236 Lr: 0.00386 [2024-02-18 11:06:06,870 INFO misc.py line 119 87073] Train: [36/100][274/1557] Data 0.012 (0.081) Batch 0.934 (1.159) Remain 32:30:19 loss: 0.3429 Lr: 0.00386 [2024-02-18 11:06:07,687 INFO misc.py line 119 87073] Train: [36/100][275/1557] Data 0.005 (0.081) Batch 0.818 (1.158) Remain 32:28:11 loss: 0.4186 Lr: 0.00386 [2024-02-18 11:06:08,572 INFO misc.py line 119 87073] Train: [36/100][276/1557] Data 0.004 (0.081) Batch 0.883 (1.157) Remain 32:26:28 loss: 0.3479 Lr: 0.00386 [2024-02-18 11:06:09,508 INFO misc.py line 119 87073] Train: [36/100][277/1557] Data 0.007 (0.081) Batch 0.936 (1.156) Remain 32:25:06 loss: 0.3306 Lr: 0.00386 [2024-02-18 11:06:10,241 INFO misc.py line 119 87073] Train: [36/100][278/1557] Data 0.006 (0.080) Batch 0.736 (1.155) Remain 32:22:30 loss: 0.4047 Lr: 0.00386 [2024-02-18 11:06:11,006 INFO misc.py line 119 87073] Train: [36/100][279/1557] Data 0.004 (0.080) Batch 0.758 (1.153) Remain 32:20:04 loss: 0.1882 Lr: 0.00386 [2024-02-18 11:06:12,197 INFO misc.py line 119 87073] Train: [36/100][280/1557] Data 0.010 (0.080) Batch 1.191 (1.153) Remain 32:20:16 loss: 0.1953 Lr: 0.00386 [2024-02-18 11:06:13,175 INFO misc.py line 119 87073] Train: [36/100][281/1557] Data 0.011 (0.079) Batch 0.984 (1.153) Remain 32:19:14 loss: 1.2072 Lr: 0.00386 [2024-02-18 11:06:14,247 INFO misc.py line 119 87073] Train: [36/100][282/1557] Data 0.003 (0.079) Batch 1.070 (1.153) Remain 32:18:43 loss: 0.6232 Lr: 0.00386 [2024-02-18 11:06:15,178 INFO misc.py line 119 87073] Train: 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Batch 1.056 (1.167) Remain 32:42:07 loss: 0.2539 Lr: 0.00386 [2024-02-18 11:06:27,219 INFO misc.py line 119 87073] Train: [36/100][290/1557] Data 0.029 (0.094) Batch 0.903 (1.166) Remain 32:40:33 loss: 0.2938 Lr: 0.00386 [2024-02-18 11:06:28,103 INFO misc.py line 119 87073] Train: [36/100][291/1557] Data 0.004 (0.094) Batch 0.884 (1.165) Remain 32:38:53 loss: 0.5807 Lr: 0.00386 [2024-02-18 11:06:28,866 INFO misc.py line 119 87073] Train: [36/100][292/1557] Data 0.005 (0.094) Batch 0.762 (1.163) Remain 32:36:32 loss: 0.2442 Lr: 0.00386 [2024-02-18 11:06:29,591 INFO misc.py line 119 87073] Train: [36/100][293/1557] Data 0.005 (0.094) Batch 0.725 (1.162) Remain 32:33:58 loss: 0.4017 Lr: 0.00386 [2024-02-18 11:06:39,181 INFO misc.py line 119 87073] Train: [36/100][294/1557] Data 0.004 (0.093) Batch 9.590 (1.191) Remain 33:22:40 loss: 0.2645 Lr: 0.00386 [2024-02-18 11:06:40,291 INFO misc.py line 119 87073] Train: [36/100][295/1557] Data 0.005 (0.093) Batch 1.110 (1.190) Remain 33:22:10 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Batch 1.106 (1.176) Remain 32:55:11 loss: 0.4281 Lr: 0.00385 [2024-02-18 11:08:41,440 INFO misc.py line 119 87073] Train: [36/100][402/1557] Data 0.005 (0.097) Batch 0.853 (1.175) Remain 32:53:49 loss: 0.6424 Lr: 0.00385 [2024-02-18 11:08:42,345 INFO misc.py line 119 87073] Train: [36/100][403/1557] Data 0.004 (0.097) Batch 0.906 (1.174) Remain 32:52:40 loss: 0.6575 Lr: 0.00385 [2024-02-18 11:08:43,083 INFO misc.py line 119 87073] Train: [36/100][404/1557] Data 0.004 (0.097) Batch 0.738 (1.173) Remain 32:50:49 loss: 0.3911 Lr: 0.00385 [2024-02-18 11:08:43,882 INFO misc.py line 119 87073] Train: [36/100][405/1557] Data 0.004 (0.097) Batch 0.797 (1.172) Remain 32:49:13 loss: 0.2808 Lr: 0.00385 [2024-02-18 11:08:56,796 INFO misc.py line 119 87073] Train: [36/100][406/1557] Data 0.006 (0.096) Batch 12.915 (1.201) Remain 33:38:09 loss: 0.2410 Lr: 0.00385 [2024-02-18 11:08:57,724 INFO misc.py line 119 87073] Train: [36/100][407/1557] Data 0.005 (0.096) Batch 0.921 (1.201) Remain 33:36:58 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Batch 1.001 (1.182) Remain 33:04:56 loss: 0.4636 Lr: 0.00385 [2024-02-18 11:09:50,485 INFO misc.py line 119 87073] Train: [36/100][458/1557] Data 0.006 (0.097) Batch 1.128 (1.182) Remain 33:04:43 loss: 0.3872 Lr: 0.00385 [2024-02-18 11:09:51,479 INFO misc.py line 119 87073] Train: [36/100][459/1557] Data 0.006 (0.097) Batch 0.989 (1.182) Remain 33:03:59 loss: 0.4552 Lr: 0.00385 [2024-02-18 11:09:52,298 INFO misc.py line 119 87073] Train: [36/100][460/1557] Data 0.011 (0.096) Batch 0.824 (1.181) Remain 33:02:39 loss: 0.3699 Lr: 0.00385 [2024-02-18 11:09:53,093 INFO misc.py line 119 87073] Train: [36/100][461/1557] Data 0.004 (0.096) Batch 0.796 (1.180) Remain 33:01:13 loss: 0.3793 Lr: 0.00385 [2024-02-18 11:10:01,979 INFO misc.py line 119 87073] Train: [36/100][462/1557] Data 0.003 (0.096) Batch 8.885 (1.197) Remain 33:29:23 loss: 0.3351 Lr: 0.00385 [2024-02-18 11:10:02,924 INFO misc.py line 119 87073] Train: [36/100][463/1557] Data 0.005 (0.096) Batch 0.942 (1.196) Remain 33:28:26 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Batch 0.913 (1.184) Remain 33:02:05 loss: 0.4662 Lr: 0.00383 [2024-02-18 11:16:29,494 INFO misc.py line 119 87073] Train: [36/100][794/1557] Data 0.004 (0.099) Batch 1.166 (1.184) Remain 33:02:01 loss: 0.3351 Lr: 0.00383 [2024-02-18 11:16:30,513 INFO misc.py line 119 87073] Train: [36/100][795/1557] Data 0.003 (0.098) Batch 1.019 (1.184) Remain 33:01:39 loss: 0.6342 Lr: 0.00383 [2024-02-18 11:16:31,254 INFO misc.py line 119 87073] Train: [36/100][796/1557] Data 0.004 (0.098) Batch 0.740 (1.184) Remain 33:00:42 loss: 0.3638 Lr: 0.00383 [2024-02-18 11:16:32,011 INFO misc.py line 119 87073] Train: [36/100][797/1557] Data 0.005 (0.098) Batch 0.744 (1.183) Remain 32:59:45 loss: 0.3117 Lr: 0.00383 [2024-02-18 11:16:44,301 INFO misc.py line 119 87073] Train: [36/100][798/1557] Data 0.018 (0.098) Batch 12.303 (1.197) Remain 33:23:08 loss: 0.1793 Lr: 0.00383 [2024-02-18 11:16:45,411 INFO misc.py line 119 87073] Train: [36/100][799/1557] Data 0.005 (0.098) Batch 1.110 (1.197) Remain 33:22:56 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Batch 1.224 (1.187) Remain 33:05:10 loss: 0.5072 Lr: 0.00383 [2024-02-18 11:17:37,904 INFO misc.py line 119 87073] Train: [36/100][850/1557] Data 0.005 (0.098) Batch 1.128 (1.187) Remain 33:05:02 loss: 0.6496 Lr: 0.00383 [2024-02-18 11:17:38,938 INFO misc.py line 119 87073] Train: [36/100][851/1557] Data 0.005 (0.098) Batch 1.035 (1.187) Remain 33:04:43 loss: 0.6885 Lr: 0.00383 [2024-02-18 11:17:39,754 INFO misc.py line 119 87073] Train: [36/100][852/1557] Data 0.003 (0.098) Batch 0.815 (1.186) Remain 33:03:58 loss: 0.3529 Lr: 0.00383 [2024-02-18 11:17:40,540 INFO misc.py line 119 87073] Train: [36/100][853/1557] Data 0.005 (0.098) Batch 0.781 (1.186) Remain 33:03:09 loss: 0.5242 Lr: 0.00383 [2024-02-18 11:17:50,366 INFO misc.py line 119 87073] Train: [36/100][854/1557] Data 0.010 (0.098) Batch 9.832 (1.196) Remain 33:20:07 loss: 0.2115 Lr: 0.00383 [2024-02-18 11:17:51,201 INFO misc.py line 119 87073] Train: [36/100][855/1557] Data 0.004 (0.098) Batch 0.829 (1.195) Remain 33:19:23 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Batch 0.959 (1.190) Remain 33:08:48 loss: 0.3473 Lr: 0.00383 [2024-02-18 11:18:46,770 INFO misc.py line 119 87073] Train: [36/100][906/1557] Data 0.005 (0.099) Batch 0.969 (1.189) Remain 33:08:23 loss: 0.3119 Lr: 0.00383 [2024-02-18 11:18:47,675 INFO misc.py line 119 87073] Train: [36/100][907/1557] Data 0.004 (0.099) Batch 0.906 (1.189) Remain 33:07:50 loss: 0.5429 Lr: 0.00383 [2024-02-18 11:18:48,479 INFO misc.py line 119 87073] Train: [36/100][908/1557] Data 0.004 (0.099) Batch 0.802 (1.189) Remain 33:07:06 loss: 0.3770 Lr: 0.00383 [2024-02-18 11:18:49,298 INFO misc.py line 119 87073] Train: [36/100][909/1557] Data 0.005 (0.099) Batch 0.821 (1.188) Remain 33:06:24 loss: 0.4390 Lr: 0.00383 [2024-02-18 11:18:57,489 INFO misc.py line 119 87073] Train: [36/100][910/1557] Data 0.004 (0.099) Batch 8.189 (1.196) Remain 33:19:17 loss: 0.2773 Lr: 0.00383 [2024-02-18 11:18:58,394 INFO misc.py line 119 87073] Train: [36/100][911/1557] Data 0.005 (0.099) Batch 0.904 (1.196) Remain 33:18:44 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line 119 87073] Train: [36/100][949/1557] Data 0.004 (0.095) Batch 0.880 (1.186) Remain 33:01:13 loss: 0.5263 Lr: 0.00383 [2024-02-18 11:19:35,002 INFO misc.py line 119 87073] Train: [36/100][950/1557] Data 0.008 (0.095) Batch 0.659 (1.185) Remain 33:00:16 loss: 0.7139 Lr: 0.00383 [2024-02-18 11:19:35,767 INFO misc.py line 119 87073] Train: [36/100][951/1557] Data 0.005 (0.095) Batch 0.760 (1.185) Remain 32:59:30 loss: 0.3487 Lr: 0.00383 [2024-02-18 11:19:36,936 INFO misc.py line 119 87073] Train: [36/100][952/1557] Data 0.009 (0.095) Batch 1.172 (1.185) Remain 32:59:27 loss: 0.1149 Lr: 0.00383 [2024-02-18 11:19:37,854 INFO misc.py line 119 87073] Train: [36/100][953/1557] Data 0.005 (0.095) Batch 0.920 (1.184) Remain 32:58:58 loss: 0.3147 Lr: 0.00383 [2024-02-18 11:19:38,900 INFO misc.py line 119 87073] Train: [36/100][954/1557] Data 0.003 (0.095) Batch 1.046 (1.184) Remain 32:58:42 loss: 0.3701 Lr: 0.00383 [2024-02-18 11:19:39,969 INFO misc.py line 119 87073] Train: 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Batch 1.038 (1.189) Remain 33:06:17 loss: 0.7299 Lr: 0.00383 [2024-02-18 11:19:52,544 INFO misc.py line 119 87073] Train: [36/100][962/1557] Data 0.004 (0.099) Batch 0.931 (1.189) Remain 33:05:49 loss: 0.3976 Lr: 0.00383 [2024-02-18 11:19:53,477 INFO misc.py line 119 87073] Train: [36/100][963/1557] Data 0.006 (0.099) Batch 0.929 (1.188) Remain 33:05:20 loss: 0.4443 Lr: 0.00383 [2024-02-18 11:19:54,280 INFO misc.py line 119 87073] Train: [36/100][964/1557] Data 0.009 (0.099) Batch 0.802 (1.188) Remain 33:04:39 loss: 0.3734 Lr: 0.00383 [2024-02-18 11:19:55,012 INFO misc.py line 119 87073] Train: [36/100][965/1557] Data 0.010 (0.099) Batch 0.738 (1.187) Remain 33:03:51 loss: 0.7298 Lr: 0.00383 [2024-02-18 11:20:03,801 INFO misc.py line 119 87073] Train: [36/100][966/1557] Data 0.003 (0.099) Batch 8.790 (1.195) Remain 33:17:01 loss: 0.2351 Lr: 0.00383 [2024-02-18 11:20:04,725 INFO misc.py line 119 87073] Train: [36/100][967/1557] Data 0.003 (0.099) Batch 0.923 (1.195) Remain 33:16:31 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33:03:09 loss: 0.1810 Lr: 0.00382 [2024-02-18 11:21:43,635 INFO misc.py line 119 87073] Train: [36/100][1055/1557] Data 1.259 (0.096) Batch 2.264 (1.189) Remain 33:04:50 loss: 0.3749 Lr: 0.00382 [2024-02-18 11:21:44,439 INFO misc.py line 119 87073] Train: [36/100][1056/1557] Data 0.006 (0.096) Batch 0.805 (1.189) Remain 33:04:13 loss: 0.3761 Lr: 0.00382 [2024-02-18 11:21:45,452 INFO misc.py line 119 87073] Train: [36/100][1057/1557] Data 0.005 (0.096) Batch 1.013 (1.189) Remain 33:03:55 loss: 0.1881 Lr: 0.00382 [2024-02-18 11:21:46,281 INFO misc.py line 119 87073] Train: [36/100][1058/1557] Data 0.006 (0.096) Batch 0.830 (1.188) Remain 33:03:20 loss: 0.3339 Lr: 0.00382 [2024-02-18 11:21:47,107 INFO misc.py line 119 87073] Train: [36/100][1059/1557] Data 0.005 (0.096) Batch 0.815 (1.188) Remain 33:02:43 loss: 0.2664 Lr: 0.00382 [2024-02-18 11:21:47,967 INFO misc.py line 119 87073] Train: [36/100][1060/1557] Data 0.015 (0.096) Batch 0.871 (1.188) Remain 33:02:12 loss: 0.5344 Lr: 0.00382 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misc.py line 119 87073] Train: [36/100][1067/1557] Data 0.006 (0.095) Batch 1.147 (1.186) Remain 32:59:38 loss: 0.6476 Lr: 0.00382 [2024-02-18 11:21:55,742 INFO misc.py line 119 87073] Train: [36/100][1068/1557] Data 0.005 (0.095) Batch 1.006 (1.186) Remain 32:59:20 loss: 0.3566 Lr: 0.00382 [2024-02-18 11:21:56,488 INFO misc.py line 119 87073] Train: [36/100][1069/1557] Data 0.003 (0.095) Batch 0.745 (1.186) Remain 32:58:37 loss: 0.2058 Lr: 0.00382 [2024-02-18 11:21:57,375 INFO misc.py line 119 87073] Train: [36/100][1070/1557] Data 0.005 (0.095) Batch 0.886 (1.185) Remain 32:58:08 loss: 0.2085 Lr: 0.00382 [2024-02-18 11:22:04,353 INFO misc.py line 119 87073] Train: [36/100][1071/1557] Data 4.877 (0.100) Batch 6.979 (1.191) Remain 33:07:10 loss: 0.2429 Lr: 0.00382 [2024-02-18 11:22:05,351 INFO misc.py line 119 87073] Train: [36/100][1072/1557] Data 0.006 (0.100) Batch 0.998 (1.191) Remain 33:06:51 loss: 0.2118 Lr: 0.00382 [2024-02-18 11:22:06,361 INFO misc.py line 119 87073] Train: 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[2024-02-18 11:24:57,748 INFO misc.py line 119 87073] Train: [36/100][1216/1557] Data 0.007 (0.097) Batch 0.749 (1.191) Remain 33:05:19 loss: 0.7397 Lr: 0.00381 [2024-02-18 11:24:58,478 INFO misc.py line 119 87073] Train: [36/100][1217/1557] Data 0.005 (0.097) Batch 0.729 (1.191) Remain 33:04:39 loss: 0.3344 Lr: 0.00381 [2024-02-18 11:24:59,830 INFO misc.py line 119 87073] Train: [36/100][1218/1557] Data 0.006 (0.096) Batch 1.349 (1.191) Remain 33:04:51 loss: 0.1544 Lr: 0.00381 [2024-02-18 11:25:00,761 INFO misc.py line 119 87073] Train: [36/100][1219/1557] Data 0.012 (0.096) Batch 0.937 (1.191) Remain 33:04:29 loss: 0.3671 Lr: 0.00381 [2024-02-18 11:25:01,904 INFO misc.py line 119 87073] Train: [36/100][1220/1557] Data 0.004 (0.096) Batch 1.142 (1.191) Remain 33:04:24 loss: 0.4378 Lr: 0.00381 [2024-02-18 11:25:02,838 INFO misc.py line 119 87073] Train: [36/100][1221/1557] Data 0.004 (0.096) Batch 0.933 (1.191) Remain 33:04:01 loss: 0.5142 Lr: 0.00381 [2024-02-18 11:25:03,753 INFO misc.py line 119 87073] Train: [36/100][1222/1557] Data 0.006 (0.096) Batch 0.915 (1.190) Remain 33:03:38 loss: 0.4299 Lr: 0.00381 [2024-02-18 11:25:04,497 INFO misc.py line 119 87073] Train: [36/100][1223/1557] Data 0.005 (0.096) Batch 0.723 (1.190) Remain 33:02:58 loss: 0.2364 Lr: 0.00381 [2024-02-18 11:25:05,279 INFO misc.py line 119 87073] Train: [36/100][1224/1557] Data 0.026 (0.096) Batch 0.782 (1.190) Remain 33:02:24 loss: 0.5021 Lr: 0.00381 [2024-02-18 11:25:06,321 INFO misc.py line 119 87073] Train: [36/100][1225/1557] Data 0.026 (0.096) Batch 1.030 (1.190) Remain 33:02:09 loss: 0.0964 Lr: 0.00381 [2024-02-18 11:25:07,255 INFO misc.py line 119 87073] Train: [36/100][1226/1557] Data 0.039 (0.096) Batch 0.965 (1.189) Remain 33:01:50 loss: 0.3316 Lr: 0.00381 [2024-02-18 11:25:08,254 INFO misc.py line 119 87073] Train: [36/100][1227/1557] Data 0.007 (0.096) Batch 1.000 (1.189) Remain 33:01:33 loss: 0.9975 Lr: 0.00381 [2024-02-18 11:25:09,203 INFO misc.py line 119 87073] Train: 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33:05:40 loss: 0.2695 Lr: 0.00381 [2024-02-18 11:27:20,069 INFO misc.py line 119 87073] Train: [36/100][1334/1557] Data 0.004 (0.097) Batch 0.801 (1.193) Remain 33:05:09 loss: 0.3876 Lr: 0.00381 [2024-02-18 11:27:20,871 INFO misc.py line 119 87073] Train: [36/100][1335/1557] Data 0.004 (0.097) Batch 0.798 (1.192) Remain 33:04:38 loss: 0.3200 Lr: 0.00381 [2024-02-18 11:27:21,658 INFO misc.py line 119 87073] Train: [36/100][1336/1557] Data 0.008 (0.097) Batch 0.790 (1.192) Remain 33:04:07 loss: 0.5115 Lr: 0.00381 [2024-02-18 11:27:22,704 INFO misc.py line 119 87073] Train: [36/100][1337/1557] Data 0.004 (0.097) Batch 1.045 (1.192) Remain 33:03:55 loss: 0.0983 Lr: 0.00381 [2024-02-18 11:27:23,679 INFO misc.py line 119 87073] Train: [36/100][1338/1557] Data 0.005 (0.097) Batch 0.975 (1.192) Remain 33:03:37 loss: 0.6311 Lr: 0.00381 [2024-02-18 11:27:24,493 INFO misc.py line 119 87073] Train: [36/100][1339/1557] Data 0.005 (0.097) Batch 0.815 (1.191) Remain 33:03:08 loss: 0.1562 Lr: 0.00381 [2024-02-18 11:27:25,484 INFO misc.py line 119 87073] Train: [36/100][1340/1557] Data 0.004 (0.097) Batch 0.983 (1.191) Remain 33:02:51 loss: 0.4144 Lr: 0.00381 [2024-02-18 11:27:26,613 INFO misc.py line 119 87073] Train: [36/100][1341/1557] Data 0.012 (0.097) Batch 1.137 (1.191) Remain 33:02:46 loss: 0.4818 Lr: 0.00381 [2024-02-18 11:27:27,363 INFO misc.py line 119 87073] Train: [36/100][1342/1557] Data 0.005 (0.096) Batch 0.750 (1.191) Remain 33:02:12 loss: 0.2793 Lr: 0.00381 [2024-02-18 11:27:28,131 INFO misc.py line 119 87073] Train: [36/100][1343/1557] Data 0.004 (0.096) Batch 0.767 (1.191) Remain 33:01:39 loss: 0.4457 Lr: 0.00381 [2024-02-18 11:27:29,321 INFO misc.py line 119 87073] Train: [36/100][1344/1557] Data 0.005 (0.096) Batch 1.180 (1.191) Remain 33:01:37 loss: 0.3103 Lr: 0.00381 [2024-02-18 11:27:30,401 INFO misc.py line 119 87073] Train: [36/100][1345/1557] Data 0.014 (0.096) Batch 1.081 (1.191) Remain 33:01:28 loss: 0.3899 Lr: 0.00381 [2024-02-18 11:27:31,547 INFO misc.py line 119 87073] Train: [36/100][1346/1557] Data 0.014 (0.096) Batch 1.154 (1.191) Remain 33:01:24 loss: 0.6996 Lr: 0.00381 [2024-02-18 11:27:32,620 INFO misc.py line 119 87073] Train: [36/100][1347/1557] Data 0.005 (0.096) Batch 1.073 (1.190) Remain 33:01:14 loss: 0.3461 Lr: 0.00381 [2024-02-18 11:27:33,638 INFO misc.py line 119 87073] Train: [36/100][1348/1557] Data 0.005 (0.096) Batch 1.010 (1.190) Remain 33:00:59 loss: 0.3481 Lr: 0.00381 [2024-02-18 11:27:34,384 INFO misc.py line 119 87073] Train: [36/100][1349/1557] Data 0.013 (0.096) Batch 0.755 (1.190) Remain 33:00:26 loss: 0.3211 Lr: 0.00381 [2024-02-18 11:27:35,199 INFO misc.py line 119 87073] Train: [36/100][1350/1557] Data 0.005 (0.096) Batch 0.815 (1.190) Remain 32:59:57 loss: 0.4100 Lr: 0.00381 [2024-02-18 11:27:41,720 INFO misc.py line 119 87073] Train: [36/100][1351/1557] Data 5.362 (0.100) Batch 6.522 (1.194) Remain 33:06:31 loss: 0.1854 Lr: 0.00381 [2024-02-18 11:27:42,561 INFO misc.py line 119 87073] Train: 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Remain 33:15:07 loss: 0.2181 Lr: 0.00381 [2024-02-18 11:28:05,848 INFO misc.py line 119 87073] Train: [36/100][1365/1557] Data 0.010 (0.099) Batch 1.354 (1.199) Remain 33:15:17 loss: 0.4649 Lr: 0.00381 [2024-02-18 11:28:06,804 INFO misc.py line 119 87073] Train: [36/100][1366/1557] Data 0.013 (0.099) Batch 0.966 (1.199) Remain 33:14:59 loss: 0.6746 Lr: 0.00381 [2024-02-18 11:28:07,616 INFO misc.py line 119 87073] Train: [36/100][1367/1557] Data 0.003 (0.099) Batch 0.812 (1.199) Remain 33:14:29 loss: 0.5118 Lr: 0.00381 [2024-02-18 11:28:08,521 INFO misc.py line 119 87073] Train: [36/100][1368/1557] Data 0.004 (0.099) Batch 0.896 (1.198) Remain 33:14:06 loss: 0.4346 Lr: 0.00381 [2024-02-18 11:28:09,405 INFO misc.py line 119 87073] Train: [36/100][1369/1557] Data 0.012 (0.099) Batch 0.892 (1.198) Remain 33:13:42 loss: 0.5773 Lr: 0.00381 [2024-02-18 11:28:10,104 INFO misc.py line 119 87073] Train: [36/100][1370/1557] Data 0.004 (0.099) Batch 0.698 (1.198) Remain 33:13:05 loss: 0.8131 Lr: 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INFO misc.py line 119 87073] Train: [36/100][1377/1557] Data 0.004 (0.098) Batch 0.748 (1.197) Remain 33:11:10 loss: 0.3443 Lr: 0.00381 [2024-02-18 11:28:17,863 INFO misc.py line 119 87073] Train: [36/100][1378/1557] Data 0.003 (0.098) Batch 0.828 (1.196) Remain 33:10:42 loss: 0.6889 Lr: 0.00381 [2024-02-18 11:28:19,185 INFO misc.py line 119 87073] Train: [36/100][1379/1557] Data 0.008 (0.098) Batch 1.323 (1.197) Remain 33:10:50 loss: 0.2583 Lr: 0.00381 [2024-02-18 11:28:20,057 INFO misc.py line 119 87073] Train: [36/100][1380/1557] Data 0.008 (0.098) Batch 0.875 (1.196) Remain 33:10:26 loss: 0.3042 Lr: 0.00381 [2024-02-18 11:28:20,948 INFO misc.py line 119 87073] Train: [36/100][1381/1557] Data 0.004 (0.098) Batch 0.891 (1.196) Remain 33:10:02 loss: 0.4201 Lr: 0.00381 [2024-02-18 11:28:21,874 INFO misc.py line 119 87073] Train: [36/100][1382/1557] Data 0.005 (0.098) Batch 0.919 (1.196) Remain 33:09:41 loss: 0.1400 Lr: 0.00381 [2024-02-18 11:28:22,833 INFO misc.py line 119 87073] Train: [36/100][1383/1557] Data 0.011 (0.098) Batch 0.966 (1.196) Remain 33:09:23 loss: 0.2857 Lr: 0.00381 [2024-02-18 11:28:23,618 INFO misc.py line 119 87073] Train: [36/100][1384/1557] Data 0.004 (0.098) Batch 0.784 (1.195) Remain 33:08:53 loss: 0.2533 Lr: 0.00381 [2024-02-18 11:28:24,329 INFO misc.py line 119 87073] Train: [36/100][1385/1557] Data 0.004 (0.098) Batch 0.705 (1.195) Remain 33:08:16 loss: 0.2310 Lr: 0.00381 [2024-02-18 11:28:25,615 INFO misc.py line 119 87073] Train: [36/100][1386/1557] Data 0.010 (0.098) Batch 1.286 (1.195) Remain 33:08:21 loss: 0.1939 Lr: 0.00381 [2024-02-18 11:28:26,484 INFO misc.py line 119 87073] Train: [36/100][1387/1557] Data 0.010 (0.097) Batch 0.875 (1.195) Remain 33:07:57 loss: 0.4355 Lr: 0.00381 [2024-02-18 11:28:27,357 INFO misc.py line 119 87073] Train: [36/100][1388/1557] Data 0.004 (0.097) Batch 0.874 (1.195) Remain 33:07:33 loss: 0.6405 Lr: 0.00381 [2024-02-18 11:28:28,304 INFO misc.py line 119 87073] Train: [36/100][1389/1557] Data 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Remain 33:05:09 loss: 0.4833 Lr: 0.00381 [2024-02-18 11:28:34,719 INFO misc.py line 119 87073] Train: [36/100][1396/1557] Data 0.003 (0.097) Batch 0.890 (1.193) Remain 33:04:46 loss: 0.4230 Lr: 0.00381 [2024-02-18 11:28:35,687 INFO misc.py line 119 87073] Train: [36/100][1397/1557] Data 0.004 (0.097) Batch 0.969 (1.193) Remain 33:04:28 loss: 0.4879 Lr: 0.00381 [2024-02-18 11:28:36,379 INFO misc.py line 119 87073] Train: [36/100][1398/1557] Data 0.004 (0.097) Batch 0.689 (1.193) Remain 33:03:51 loss: 0.2542 Lr: 0.00381 [2024-02-18 11:28:37,138 INFO misc.py line 119 87073] Train: [36/100][1399/1557] Data 0.006 (0.097) Batch 0.761 (1.192) Remain 33:03:19 loss: 0.4716 Lr: 0.00381 [2024-02-18 11:28:38,280 INFO misc.py line 119 87073] Train: [36/100][1400/1557] Data 0.004 (0.097) Batch 1.142 (1.192) Remain 33:03:14 loss: 0.1965 Lr: 0.00381 [2024-02-18 11:28:39,195 INFO misc.py line 119 87073] Train: [36/100][1401/1557] Data 0.005 (0.097) Batch 0.913 (1.192) Remain 33:02:53 loss: 0.3936 Lr: 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INFO misc.py line 119 87073] Train: [36/100][1408/1557] Data 0.028 (0.100) Batch 0.954 (1.196) Remain 33:08:48 loss: 0.4643 Lr: 0.00381 [2024-02-18 11:28:53,458 INFO misc.py line 119 87073] Train: [36/100][1409/1557] Data 0.006 (0.100) Batch 0.814 (1.195) Remain 33:08:19 loss: 1.1840 Lr: 0.00381 [2024-02-18 11:28:54,419 INFO misc.py line 119 87073] Train: [36/100][1410/1557] Data 0.006 (0.100) Batch 0.935 (1.195) Remain 33:08:00 loss: 0.3663 Lr: 0.00381 [2024-02-18 11:28:55,311 INFO misc.py line 119 87073] Train: [36/100][1411/1557] Data 0.031 (0.100) Batch 0.919 (1.195) Remain 33:07:39 loss: 0.1610 Lr: 0.00381 [2024-02-18 11:28:56,052 INFO misc.py line 119 87073] Train: [36/100][1412/1557] Data 0.004 (0.100) Batch 0.741 (1.195) Remain 33:07:05 loss: 0.2951 Lr: 0.00381 [2024-02-18 11:28:56,854 INFO misc.py line 119 87073] Train: [36/100][1413/1557] Data 0.004 (0.100) Batch 0.780 (1.194) Remain 33:06:35 loss: 0.2031 Lr: 0.00381 [2024-02-18 11:29:09,779 INFO misc.py line 119 87073] Train: [36/100][1414/1557] Data 0.026 (0.100) Batch 12.947 (1.203) Remain 33:20:25 loss: 0.4127 Lr: 0.00381 [2024-02-18 11:29:10,757 INFO misc.py line 119 87073] Train: [36/100][1415/1557] Data 0.004 (0.100) Batch 0.975 (1.203) Remain 33:20:08 loss: 1.0013 Lr: 0.00380 [2024-02-18 11:29:11,806 INFO misc.py line 119 87073] Train: [36/100][1416/1557] Data 0.007 (0.100) Batch 1.051 (1.202) Remain 33:19:56 loss: 0.6359 Lr: 0.00380 [2024-02-18 11:29:12,713 INFO misc.py line 119 87073] Train: [36/100][1417/1557] Data 0.004 (0.100) Batch 0.907 (1.202) Remain 33:19:34 loss: 0.2082 Lr: 0.00380 [2024-02-18 11:29:13,659 INFO misc.py line 119 87073] Train: [36/100][1418/1557] Data 0.005 (0.100) Batch 0.947 (1.202) Remain 33:19:14 loss: 0.5135 Lr: 0.00380 [2024-02-18 11:29:14,374 INFO misc.py line 119 87073] Train: [36/100][1419/1557] Data 0.004 (0.100) Batch 0.705 (1.202) Remain 33:18:38 loss: 0.1496 Lr: 0.00380 [2024-02-18 11:29:15,182 INFO misc.py line 119 87073] Train: [36/100][1420/1557] Data 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Remain 33:16:16 loss: 0.3664 Lr: 0.00380 [2024-02-18 11:29:21,611 INFO misc.py line 119 87073] Train: [36/100][1427/1557] Data 0.003 (0.099) Batch 0.743 (1.200) Remain 33:15:43 loss: 0.3371 Lr: 0.00380 [2024-02-18 11:29:22,856 INFO misc.py line 119 87073] Train: [36/100][1428/1557] Data 0.006 (0.099) Batch 1.243 (1.200) Remain 33:15:44 loss: 0.1359 Lr: 0.00380 [2024-02-18 11:29:23,758 INFO misc.py line 119 87073] Train: [36/100][1429/1557] Data 0.009 (0.099) Batch 0.908 (1.200) Remain 33:15:23 loss: 0.5298 Lr: 0.00380 [2024-02-18 11:29:24,794 INFO misc.py line 119 87073] Train: [36/100][1430/1557] Data 0.004 (0.099) Batch 1.035 (1.200) Remain 33:15:10 loss: 0.2728 Lr: 0.00380 [2024-02-18 11:29:25,861 INFO misc.py line 119 87073] Train: [36/100][1431/1557] Data 0.004 (0.099) Batch 1.066 (1.200) Remain 33:14:59 loss: 0.1896 Lr: 0.00380 [2024-02-18 11:29:26,947 INFO misc.py line 119 87073] Train: [36/100][1432/1557] Data 0.005 (0.099) Batch 1.087 (1.200) Remain 33:14:50 loss: 0.2856 Lr: 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INFO misc.py line 119 87073] Train: [36/100][1439/1557] Data 0.015 (0.098) Batch 0.900 (1.199) Remain 33:13:12 loss: 0.7149 Lr: 0.00380 [2024-02-18 11:29:34,795 INFO misc.py line 119 87073] Train: [36/100][1440/1557] Data 0.004 (0.098) Batch 0.744 (1.198) Remain 33:12:39 loss: 0.2566 Lr: 0.00380 [2024-02-18 11:29:35,469 INFO misc.py line 119 87073] Train: [36/100][1441/1557] Data 0.004 (0.098) Batch 0.665 (1.198) Remain 33:12:01 loss: 0.3812 Lr: 0.00380 [2024-02-18 11:29:36,743 INFO misc.py line 119 87073] Train: [36/100][1442/1557] Data 0.012 (0.098) Batch 1.275 (1.198) Remain 33:12:05 loss: 0.1074 Lr: 0.00380 [2024-02-18 11:29:37,683 INFO misc.py line 119 87073] Train: [36/100][1443/1557] Data 0.012 (0.098) Batch 0.948 (1.198) Remain 33:11:47 loss: 0.5681 Lr: 0.00380 [2024-02-18 11:29:38,569 INFO misc.py line 119 87073] Train: [36/100][1444/1557] Data 0.004 (0.098) Batch 0.886 (1.198) Remain 33:11:24 loss: 0.5334 Lr: 0.00380 [2024-02-18 11:29:39,431 INFO misc.py line 119 87073] Train: [36/100][1445/1557] Data 0.003 (0.098) Batch 0.855 (1.197) Remain 33:10:59 loss: 0.5801 Lr: 0.00380 [2024-02-18 11:29:40,518 INFO misc.py line 119 87073] Train: [36/100][1446/1557] Data 0.011 (0.098) Batch 1.082 (1.197) Remain 33:10:50 loss: 0.3257 Lr: 0.00380 [2024-02-18 11:29:41,281 INFO misc.py line 119 87073] Train: [36/100][1447/1557] Data 0.016 (0.098) Batch 0.774 (1.197) Remain 33:10:19 loss: 0.4223 Lr: 0.00380 [2024-02-18 11:29:42,067 INFO misc.py line 119 87073] Train: [36/100][1448/1557] Data 0.004 (0.098) Batch 0.775 (1.197) Remain 33:09:49 loss: 0.2317 Lr: 0.00380 [2024-02-18 11:29:43,092 INFO misc.py line 119 87073] Train: [36/100][1449/1557] Data 0.015 (0.098) Batch 1.028 (1.197) Remain 33:09:36 loss: 0.1532 Lr: 0.00380 [2024-02-18 11:29:44,194 INFO misc.py line 119 87073] Train: [36/100][1450/1557] Data 0.012 (0.098) Batch 1.101 (1.197) Remain 33:09:28 loss: 0.6790 Lr: 0.00380 [2024-02-18 11:29:45,138 INFO misc.py line 119 87073] Train: [36/100][1451/1557] Data 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Remain 33:07:11 loss: 0.5538 Lr: 0.00380 [2024-02-18 11:29:51,736 INFO misc.py line 119 87073] Train: [36/100][1458/1557] Data 0.003 (0.097) Batch 1.055 (1.195) Remain 33:07:00 loss: 0.7938 Lr: 0.00380 [2024-02-18 11:29:52,741 INFO misc.py line 119 87073] Train: [36/100][1459/1557] Data 0.004 (0.097) Batch 1.002 (1.195) Remain 33:06:46 loss: 0.5513 Lr: 0.00380 [2024-02-18 11:29:53,635 INFO misc.py line 119 87073] Train: [36/100][1460/1557] Data 0.005 (0.097) Batch 0.895 (1.195) Remain 33:06:24 loss: 0.5647 Lr: 0.00380 [2024-02-18 11:29:54,447 INFO misc.py line 119 87073] Train: [36/100][1461/1557] Data 0.005 (0.097) Batch 0.804 (1.195) Remain 33:05:56 loss: 0.2593 Lr: 0.00380 [2024-02-18 11:29:55,203 INFO misc.py line 119 87073] Train: [36/100][1462/1557] Data 0.012 (0.097) Batch 0.765 (1.194) Remain 33:05:26 loss: 0.2680 Lr: 0.00380 [2024-02-18 11:30:01,371 INFO misc.py line 119 87073] Train: [36/100][1463/1557] Data 5.130 (0.100) Batch 6.144 (1.198) Remain 33:11:03 loss: 0.1760 Lr: 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INFO misc.py line 119 87073] Train: [36/100][1470/1557] Data 0.014 (0.100) Batch 13.516 (1.205) Remain 33:23:06 loss: 0.3757 Lr: 0.00380 [2024-02-18 11:30:21,391 INFO misc.py line 119 87073] Train: [36/100][1471/1557] Data 0.004 (0.100) Batch 0.894 (1.205) Remain 33:22:43 loss: 0.2418 Lr: 0.00380 [2024-02-18 11:30:22,213 INFO misc.py line 119 87073] Train: [36/100][1472/1557] Data 0.011 (0.100) Batch 0.827 (1.205) Remain 33:22:17 loss: 0.7229 Lr: 0.00380 [2024-02-18 11:30:23,194 INFO misc.py line 119 87073] Train: [36/100][1473/1557] Data 0.006 (0.100) Batch 0.983 (1.204) Remain 33:22:00 loss: 0.1369 Lr: 0.00380 [2024-02-18 11:30:24,124 INFO misc.py line 119 87073] Train: [36/100][1474/1557] Data 0.004 (0.100) Batch 0.929 (1.204) Remain 33:21:40 loss: 0.5411 Lr: 0.00380 [2024-02-18 11:30:24,880 INFO misc.py line 119 87073] Train: [36/100][1475/1557] Data 0.005 (0.100) Batch 0.756 (1.204) Remain 33:21:09 loss: 0.5467 Lr: 0.00380 [2024-02-18 11:30:25,623 INFO misc.py line 119 87073] Train: [36/100][1476/1557] Data 0.005 (0.100) Batch 0.744 (1.204) Remain 33:20:36 loss: 0.2963 Lr: 0.00380 [2024-02-18 11:30:26,953 INFO misc.py line 119 87073] Train: [36/100][1477/1557] Data 0.004 (0.100) Batch 1.311 (1.204) Remain 33:20:43 loss: 0.2482 Lr: 0.00380 [2024-02-18 11:30:27,820 INFO misc.py line 119 87073] Train: [36/100][1478/1557] Data 0.024 (0.099) Batch 0.887 (1.203) Remain 33:20:20 loss: 0.4574 Lr: 0.00380 [2024-02-18 11:30:28,782 INFO misc.py line 119 87073] Train: [36/100][1479/1557] Data 0.003 (0.099) Batch 0.962 (1.203) Remain 33:20:02 loss: 0.6000 Lr: 0.00380 [2024-02-18 11:30:29,736 INFO misc.py line 119 87073] Train: [36/100][1480/1557] Data 0.003 (0.099) Batch 0.954 (1.203) Remain 33:19:44 loss: 0.3030 Lr: 0.00380 [2024-02-18 11:30:30,697 INFO misc.py line 119 87073] Train: [36/100][1481/1557] Data 0.005 (0.099) Batch 0.959 (1.203) Remain 33:19:27 loss: 0.1574 Lr: 0.00380 [2024-02-18 11:30:31,410 INFO misc.py line 119 87073] Train: [36/100][1482/1557] Data 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Remain 33:17:47 loss: 0.5313 Lr: 0.00380 [2024-02-18 11:30:38,504 INFO misc.py line 119 87073] Train: [36/100][1489/1557] Data 0.005 (0.099) Batch 0.740 (1.202) Remain 33:17:15 loss: 0.4762 Lr: 0.00380 [2024-02-18 11:30:39,299 INFO misc.py line 119 87073] Train: [36/100][1490/1557] Data 0.004 (0.099) Batch 0.784 (1.201) Remain 33:16:46 loss: 0.3530 Lr: 0.00380 [2024-02-18 11:30:40,632 INFO misc.py line 119 87073] Train: [36/100][1491/1557] Data 0.014 (0.099) Batch 1.335 (1.202) Remain 33:16:54 loss: 0.4905 Lr: 0.00380 [2024-02-18 11:30:41,615 INFO misc.py line 119 87073] Train: [36/100][1492/1557] Data 0.013 (0.099) Batch 0.992 (1.201) Remain 33:16:38 loss: 0.2790 Lr: 0.00380 [2024-02-18 11:30:42,541 INFO misc.py line 119 87073] Train: [36/100][1493/1557] Data 0.004 (0.099) Batch 0.927 (1.201) Remain 33:16:19 loss: 0.7562 Lr: 0.00380 [2024-02-18 11:30:43,589 INFO misc.py line 119 87073] Train: [36/100][1494/1557] Data 0.003 (0.098) Batch 1.046 (1.201) Remain 33:16:07 loss: 0.5818 Lr: 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INFO misc.py line 119 87073] Train: [36/100][1501/1557] Data 0.004 (0.098) Batch 0.896 (1.200) Remain 33:14:09 loss: 0.4705 Lr: 0.00380 [2024-02-18 11:30:51,275 INFO misc.py line 119 87073] Train: [36/100][1502/1557] Data 0.003 (0.098) Batch 0.924 (1.200) Remain 33:13:50 loss: 0.4813 Lr: 0.00380 [2024-02-18 11:30:52,065 INFO misc.py line 119 87073] Train: [36/100][1503/1557] Data 0.009 (0.098) Batch 0.794 (1.200) Remain 33:13:21 loss: 0.3388 Lr: 0.00380 [2024-02-18 11:30:52,829 INFO misc.py line 119 87073] Train: [36/100][1504/1557] Data 0.004 (0.098) Batch 0.764 (1.199) Remain 33:12:51 loss: 0.2010 Lr: 0.00380 [2024-02-18 11:30:53,877 INFO misc.py line 119 87073] Train: [36/100][1505/1557] Data 0.004 (0.098) Batch 1.038 (1.199) Remain 33:12:39 loss: 0.1257 Lr: 0.00380 [2024-02-18 11:30:54,725 INFO misc.py line 119 87073] Train: [36/100][1506/1557] Data 0.015 (0.098) Batch 0.858 (1.199) Remain 33:12:15 loss: 0.5103 Lr: 0.00380 [2024-02-18 11:30:55,738 INFO misc.py line 119 87073] Train: [36/100][1507/1557] Data 0.006 (0.098) Batch 1.013 (1.199) Remain 33:12:02 loss: 0.6404 Lr: 0.00380 [2024-02-18 11:30:56,761 INFO misc.py line 119 87073] Train: [36/100][1508/1557] Data 0.005 (0.098) Batch 1.023 (1.199) Remain 33:11:49 loss: 0.3628 Lr: 0.00380 [2024-02-18 11:30:57,711 INFO misc.py line 119 87073] Train: [36/100][1509/1557] Data 0.005 (0.098) Batch 0.947 (1.199) Remain 33:11:31 loss: 0.4880 Lr: 0.00380 [2024-02-18 11:30:58,516 INFO misc.py line 119 87073] Train: [36/100][1510/1557] Data 0.008 (0.098) Batch 0.802 (1.198) Remain 33:11:04 loss: 0.4111 Lr: 0.00380 [2024-02-18 11:30:59,290 INFO misc.py line 119 87073] Train: [36/100][1511/1557] Data 0.012 (0.097) Batch 0.782 (1.198) Remain 33:10:35 loss: 0.4751 Lr: 0.00380 [2024-02-18 11:31:00,442 INFO misc.py line 119 87073] Train: [36/100][1512/1557] Data 0.004 (0.097) Batch 1.153 (1.198) Remain 33:10:31 loss: 0.1450 Lr: 0.00380 [2024-02-18 11:31:01,411 INFO misc.py line 119 87073] Train: [36/100][1513/1557] Data 0.004 (0.097) Batch 0.965 (1.198) Remain 33:10:14 loss: 0.5397 Lr: 0.00380 [2024-02-18 11:31:02,394 INFO misc.py line 119 87073] Train: [36/100][1514/1557] Data 0.008 (0.097) Batch 0.983 (1.198) Remain 33:09:59 loss: 0.3983 Lr: 0.00380 [2024-02-18 11:31:03,417 INFO misc.py line 119 87073] Train: [36/100][1515/1557] Data 0.008 (0.097) Batch 1.027 (1.198) Remain 33:09:46 loss: 0.6008 Lr: 0.00380 [2024-02-18 11:31:04,289 INFO misc.py line 119 87073] Train: [36/100][1516/1557] Data 0.003 (0.097) Batch 0.872 (1.197) Remain 33:09:24 loss: 0.4714 Lr: 0.00380 [2024-02-18 11:31:05,021 INFO misc.py line 119 87073] Train: [36/100][1517/1557] Data 0.003 (0.097) Batch 0.731 (1.197) Remain 33:08:52 loss: 0.2411 Lr: 0.00380 [2024-02-18 11:31:05,797 INFO misc.py line 119 87073] Train: [36/100][1518/1557] Data 0.006 (0.097) Batch 0.776 (1.197) Remain 33:08:23 loss: 0.4300 Lr: 0.00380 [2024-02-18 11:31:12,003 INFO misc.py line 119 87073] Train: [36/100][1519/1557] Data 5.097 (0.100) Batch 6.204 (1.200) Remain 33:13:51 loss: 0.2113 Lr: 0.00380 [2024-02-18 11:31:12,880 INFO misc.py line 119 87073] Train: [36/100][1520/1557] Data 0.007 (0.100) Batch 0.878 (1.200) Remain 33:13:29 loss: 0.6204 Lr: 0.00380 [2024-02-18 11:31:13,848 INFO misc.py line 119 87073] Train: [36/100][1521/1557] Data 0.005 (0.100) Batch 0.966 (1.200) Remain 33:13:12 loss: 0.7699 Lr: 0.00380 [2024-02-18 11:31:14,909 INFO misc.py line 119 87073] Train: [36/100][1522/1557] Data 0.007 (0.100) Batch 1.058 (1.200) Remain 33:13:02 loss: 1.3160 Lr: 0.00380 [2024-02-18 11:31:16,191 INFO misc.py line 119 87073] Train: [36/100][1523/1557] Data 0.010 (0.100) Batch 1.278 (1.200) Remain 33:13:06 loss: 0.8563 Lr: 0.00380 [2024-02-18 11:31:16,918 INFO misc.py line 119 87073] Train: [36/100][1524/1557] Data 0.014 (0.100) Batch 0.736 (1.199) Remain 33:12:34 loss: 0.4692 Lr: 0.00380 [2024-02-18 11:31:17,694 INFO misc.py line 119 87073] Train: [36/100][1525/1557] Data 0.005 (0.100) Batch 0.776 (1.199) Remain 33:12:05 loss: 0.3692 Lr: 0.00380 [2024-02-18 11:31:26,437 INFO misc.py line 119 87073] Train: [36/100][1526/1557] Data 0.005 (0.100) Batch 8.744 (1.204) Remain 33:20:18 loss: 0.2381 Lr: 0.00380 [2024-02-18 11:31:27,610 INFO misc.py line 119 87073] Train: [36/100][1527/1557] Data 0.004 (0.100) Batch 1.171 (1.204) Remain 33:20:14 loss: 0.6053 Lr: 0.00380 [2024-02-18 11:31:28,398 INFO misc.py line 119 87073] Train: [36/100][1528/1557] Data 0.005 (0.100) Batch 0.788 (1.204) Remain 33:19:46 loss: 0.2175 Lr: 0.00380 [2024-02-18 11:31:29,488 INFO misc.py line 119 87073] Train: [36/100][1529/1557] Data 0.005 (0.100) Batch 1.091 (1.204) Remain 33:19:37 loss: 0.5312 Lr: 0.00380 [2024-02-18 11:31:30,435 INFO misc.py line 119 87073] Train: [36/100][1530/1557] Data 0.005 (0.100) Batch 0.946 (1.204) Remain 33:19:19 loss: 0.3075 Lr: 0.00380 [2024-02-18 11:31:31,220 INFO misc.py line 119 87073] Train: [36/100][1531/1557] Data 0.005 (0.100) Batch 0.778 (1.203) Remain 33:18:50 loss: 0.5088 Lr: 0.00380 [2024-02-18 11:31:32,002 INFO misc.py line 119 87073] Train: [36/100][1532/1557] Data 0.013 (0.100) Batch 0.790 (1.203) Remain 33:18:22 loss: 0.4721 Lr: 0.00380 [2024-02-18 11:31:33,339 INFO misc.py line 119 87073] Train: [36/100][1533/1557] Data 0.005 (0.099) Batch 1.329 (1.203) Remain 33:18:29 loss: 0.3356 Lr: 0.00380 [2024-02-18 11:31:34,236 INFO misc.py line 119 87073] Train: [36/100][1534/1557] Data 0.013 (0.099) Batch 0.906 (1.203) Remain 33:18:09 loss: 0.6404 Lr: 0.00380 [2024-02-18 11:31:35,206 INFO misc.py line 119 87073] Train: [36/100][1535/1557] Data 0.004 (0.099) Batch 0.970 (1.203) Remain 33:17:52 loss: 0.4046 Lr: 0.00380 [2024-02-18 11:31:36,239 INFO misc.py line 119 87073] Train: [36/100][1536/1557] Data 0.004 (0.099) Batch 1.031 (1.203) Remain 33:17:40 loss: 0.3923 Lr: 0.00380 [2024-02-18 11:31:37,232 INFO misc.py line 119 87073] Train: [36/100][1537/1557] Data 0.005 (0.099) Batch 0.995 (1.202) Remain 33:17:25 loss: 0.5412 Lr: 0.00380 [2024-02-18 11:31:37,952 INFO misc.py line 119 87073] Train: [36/100][1538/1557] Data 0.004 (0.099) Batch 0.719 (1.202) Remain 33:16:53 loss: 0.3508 Lr: 0.00380 [2024-02-18 11:31:38,670 INFO misc.py line 119 87073] Train: [36/100][1539/1557] Data 0.005 (0.099) Batch 0.718 (1.202) Remain 33:16:20 loss: 0.2230 Lr: 0.00380 [2024-02-18 11:31:39,966 INFO misc.py line 119 87073] Train: [36/100][1540/1557] Data 0.005 (0.099) Batch 1.288 (1.202) Remain 33:16:24 loss: 0.1340 Lr: 0.00380 [2024-02-18 11:31:40,996 INFO misc.py line 119 87073] Train: [36/100][1541/1557] Data 0.014 (0.099) Batch 1.039 (1.202) Remain 33:16:13 loss: 0.2169 Lr: 0.00380 [2024-02-18 11:31:42,120 INFO misc.py line 119 87073] Train: [36/100][1542/1557] Data 0.005 (0.099) Batch 1.121 (1.202) Remain 33:16:06 loss: 0.2947 Lr: 0.00380 [2024-02-18 11:31:43,110 INFO misc.py line 119 87073] Train: [36/100][1543/1557] Data 0.007 (0.099) Batch 0.993 (1.202) Remain 33:15:52 loss: 0.2016 Lr: 0.00380 [2024-02-18 11:31:44,129 INFO misc.py line 119 87073] Train: [36/100][1544/1557] Data 0.004 (0.099) Batch 1.012 (1.201) Remain 33:15:38 loss: 0.6496 Lr: 0.00380 [2024-02-18 11:31:44,828 INFO misc.py line 119 87073] Train: [36/100][1545/1557] Data 0.011 (0.099) Batch 0.706 (1.201) Remain 33:15:05 loss: 0.4897 Lr: 0.00380 [2024-02-18 11:31:45,552 INFO misc.py line 119 87073] Train: [36/100][1546/1557] Data 0.004 (0.099) Batch 0.721 (1.201) Remain 33:14:33 loss: 0.3112 Lr: 0.00380 [2024-02-18 11:31:46,848 INFO misc.py line 119 87073] Train: [36/100][1547/1557] Data 0.006 (0.099) Batch 1.295 (1.201) Remain 33:14:38 loss: 0.1965 Lr: 0.00380 [2024-02-18 11:31:47,744 INFO misc.py line 119 87073] Train: [36/100][1548/1557] Data 0.007 (0.099) Batch 0.898 (1.201) Remain 33:14:17 loss: 0.4065 Lr: 0.00380 [2024-02-18 11:31:48,771 INFO misc.py line 119 87073] Train: [36/100][1549/1557] Data 0.005 (0.099) Batch 1.028 (1.201) Remain 33:14:05 loss: 0.4037 Lr: 0.00380 [2024-02-18 11:31:49,831 INFO misc.py line 119 87073] Train: [36/100][1550/1557] Data 0.004 (0.098) Batch 1.059 (1.200) Remain 33:13:54 loss: 0.3628 Lr: 0.00380 [2024-02-18 11:31:50,794 INFO misc.py line 119 87073] Train: [36/100][1551/1557] Data 0.004 (0.098) Batch 0.963 (1.200) Remain 33:13:38 loss: 0.3950 Lr: 0.00380 [2024-02-18 11:31:51,539 INFO misc.py line 119 87073] Train: [36/100][1552/1557] Data 0.004 (0.098) Batch 0.745 (1.200) Remain 33:13:07 loss: 0.2640 Lr: 0.00380 [2024-02-18 11:31:52,328 INFO misc.py line 119 87073] Train: [36/100][1553/1557] Data 0.004 (0.098) Batch 0.775 (1.200) Remain 33:12:39 loss: 0.3676 Lr: 0.00380 [2024-02-18 11:31:53,622 INFO misc.py line 119 87073] Train: [36/100][1554/1557] Data 0.018 (0.098) Batch 1.298 (1.200) Remain 33:12:44 loss: 0.1616 Lr: 0.00380 [2024-02-18 11:31:54,701 INFO misc.py line 119 87073] Train: [36/100][1555/1557] Data 0.013 (0.098) Batch 1.076 (1.200) Remain 33:12:35 loss: 0.7815 Lr: 0.00380 [2024-02-18 11:31:55,581 INFO misc.py line 119 87073] Train: [36/100][1556/1557] Data 0.017 (0.098) Batch 0.890 (1.200) Remain 33:12:14 loss: 0.2644 Lr: 0.00380 [2024-02-18 11:31:56,502 INFO misc.py line 119 87073] Train: [36/100][1557/1557] Data 0.005 (0.098) Batch 0.923 (1.199) Remain 33:11:55 loss: 0.3342 Lr: 0.00380 [2024-02-18 11:31:56,503 INFO misc.py line 136 87073] Train result: loss: 0.4103 [2024-02-18 11:31:56,504 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 11:32:24,286 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6445 [2024-02-18 11:32:25,064 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2251 [2024-02-18 11:32:27,192 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4070 [2024-02-18 11:32:29,401 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3665 [2024-02-18 11:32:34,347 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2087 [2024-02-18 11:32:35,051 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0867 [2024-02-18 11:32:36,311 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3876 [2024-02-18 11:32:39,268 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3412 [2024-02-18 11:32:41,077 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3408 [2024-02-18 11:32:42,576 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7156/0.7723/0.9145. [2024-02-18 11:32:42,576 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9345/0.9752 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9826/0.9898 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8555/0.9766 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3138/0.3469 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7363/0.7993 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8735/0.9032 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8439/0.8974 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9070/0.9765 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7290/0.7656 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7831/0.8680 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7538/0.8897 [2024-02-18 11:32:42,577 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5900/0.6516 [2024-02-18 11:32:42,578 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 11:32:42,579 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 11:32:42,579 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 11:32:49,839 INFO misc.py line 119 87073] Train: [37/100][1/1557] Data 1.755 (1.755) Batch 2.525 (2.525) Remain 69:52:51 loss: 0.1795 Lr: 0.00380 [2024-02-18 11:32:50,711 INFO misc.py line 119 87073] Train: [37/100][2/1557] Data 0.008 (0.008) Batch 0.870 (0.870) Remain 24:05:38 loss: 0.5112 Lr: 0.00380 [2024-02-18 11:32:51,864 INFO misc.py line 119 87073] Train: [37/100][3/1557] Data 0.008 (0.008) Batch 1.135 (1.135) Remain 31:24:32 loss: 0.5722 Lr: 0.00380 [2024-02-18 11:32:52,841 INFO misc.py line 119 87073] Train: [37/100][4/1557] Data 0.026 (0.026) Batch 0.997 (0.997) Remain 27:35:22 loss: 0.4171 Lr: 0.00380 [2024-02-18 11:32:53,660 INFO misc.py line 119 87073] Train: [37/100][5/1557] Data 0.007 (0.016) Batch 0.817 (0.907) Remain 25:05:58 loss: 0.7083 Lr: 0.00380 [2024-02-18 11:32:54,462 INFO misc.py line 119 87073] Train: [37/100][6/1557] Data 0.009 (0.014) Batch 0.800 (0.871) Remain 24:06:41 loss: 0.2198 Lr: 0.00380 [2024-02-18 11:32:55,670 INFO misc.py line 119 87073] Train: [37/100][7/1557] Data 0.011 (0.013) Batch 1.208 (0.955) Remain 26:26:23 loss: 0.1309 Lr: 0.00380 [2024-02-18 11:32:56,623 INFO misc.py line 119 87073] Train: [37/100][8/1557] Data 0.011 (0.013) Batch 0.960 (0.956) Remain 26:28:05 loss: 0.6414 Lr: 0.00380 [2024-02-18 11:32:57,593 INFO misc.py line 119 87073] Train: [37/100][9/1557] Data 0.004 (0.011) Batch 0.968 (0.958) Remain 26:31:23 loss: 0.7895 Lr: 0.00380 [2024-02-18 11:32:58,596 INFO misc.py line 119 87073] Train: [37/100][10/1557] Data 0.006 (0.011) Batch 1.004 (0.965) Remain 26:42:15 loss: 0.5562 Lr: 0.00380 [2024-02-18 11:32:59,582 INFO misc.py line 119 87073] Train: [37/100][11/1557] Data 0.005 (0.010) Batch 0.986 (0.967) Remain 26:46:33 loss: 0.3068 Lr: 0.00380 [2024-02-18 11:33:00,357 INFO misc.py line 119 87073] Train: [37/100][12/1557] Data 0.004 (0.009) Batch 0.775 (0.946) Remain 26:11:07 loss: 0.2266 Lr: 0.00380 [2024-02-18 11:33:01,084 INFO misc.py line 119 87073] Train: [37/100][13/1557] Data 0.004 (0.009) Batch 0.726 (0.924) Remain 25:34:34 loss: 0.1588 Lr: 0.00380 [2024-02-18 11:33:02,274 INFO misc.py line 119 87073] Train: [37/100][14/1557] Data 0.005 (0.008) Batch 1.191 (0.948) Remain 26:14:47 loss: 0.2088 Lr: 0.00380 [2024-02-18 11:33:03,180 INFO misc.py line 119 87073] Train: [37/100][15/1557] Data 0.004 (0.008) Batch 0.903 (0.945) Remain 26:08:27 loss: 0.2233 Lr: 0.00380 [2024-02-18 11:33:04,023 INFO misc.py line 119 87073] Train: [37/100][16/1557] Data 0.008 (0.008) Batch 0.846 (0.937) Remain 25:55:50 loss: 0.2313 Lr: 0.00380 [2024-02-18 11:33:05,016 INFO misc.py line 119 87073] Train: [37/100][17/1557] Data 0.005 (0.008) Batch 0.994 (0.941) Remain 26:02:37 loss: 0.7524 Lr: 0.00380 [2024-02-18 11:33:05,953 INFO misc.py line 119 87073] Train: [37/100][18/1557] Data 0.004 (0.008) Batch 0.937 (0.941) Remain 26:02:08 loss: 0.3550 Lr: 0.00380 [2024-02-18 11:33:06,712 INFO misc.py line 119 87073] Train: [37/100][19/1557] Data 0.003 (0.007) Batch 0.758 (0.929) Remain 25:43:08 loss: 0.2978 Lr: 0.00380 [2024-02-18 11:33:07,481 INFO misc.py line 119 87073] Train: [37/100][20/1557] Data 0.005 (0.007) Batch 0.766 (0.920) Remain 25:27:10 loss: 0.1708 Lr: 0.00380 [2024-02-18 11:33:08,702 INFO misc.py line 119 87073] Train: [37/100][21/1557] Data 0.008 (0.007) Batch 1.218 (0.936) Remain 25:54:40 loss: 0.1964 Lr: 0.00380 [2024-02-18 11:33:09,763 INFO misc.py line 119 87073] Train: [37/100][22/1557] Data 0.011 (0.007) Batch 1.067 (0.943) Remain 26:06:03 loss: 0.1694 Lr: 0.00380 [2024-02-18 11:33:10,743 INFO misc.py line 119 87073] Train: [37/100][23/1557] Data 0.005 (0.007) Batch 0.981 (0.945) Remain 26:09:10 loss: 0.8153 Lr: 0.00380 [2024-02-18 11:33:11,497 INFO misc.py line 119 87073] Train: [37/100][24/1557] Data 0.004 (0.007) Batch 0.753 (0.936) Remain 25:53:57 loss: 0.3200 Lr: 0.00380 [2024-02-18 11:33:12,444 INFO misc.py line 119 87073] Train: [37/100][25/1557] Data 0.006 (0.007) Batch 0.943 (0.936) Remain 25:54:30 loss: 0.2548 Lr: 0.00380 [2024-02-18 11:33:13,218 INFO misc.py line 119 87073] Train: [37/100][26/1557] Data 0.009 (0.007) Batch 0.778 (0.929) Remain 25:43:05 loss: 0.4035 Lr: 0.00380 [2024-02-18 11:33:13,993 INFO misc.py line 119 87073] Train: [37/100][27/1557] Data 0.004 (0.007) Batch 0.768 (0.923) Remain 25:31:56 loss: 0.1846 Lr: 0.00380 [2024-02-18 11:33:15,245 INFO misc.py line 119 87073] Train: [37/100][28/1557] Data 0.012 (0.007) Batch 1.253 (0.936) Remain 25:53:52 loss: 0.0798 Lr: 0.00380 [2024-02-18 11:33:16,444 INFO misc.py line 119 87073] Train: [37/100][29/1557] Data 0.010 (0.007) Batch 1.194 (0.946) Remain 26:10:21 loss: 0.3237 Lr: 0.00380 [2024-02-18 11:33:17,670 INFO misc.py line 119 87073] Train: [37/100][30/1557] Data 0.015 (0.008) Batch 1.234 (0.956) Remain 26:28:04 loss: 0.4873 Lr: 0.00380 [2024-02-18 11:33:18,744 INFO misc.py line 119 87073] Train: [37/100][31/1557] Data 0.007 (0.008) Batch 1.076 (0.961) Remain 26:35:07 loss: 0.2066 Lr: 0.00380 [2024-02-18 11:33:19,663 INFO misc.py line 119 87073] Train: [37/100][32/1557] Data 0.005 (0.007) Batch 0.920 (0.959) Remain 26:32:46 loss: 0.4768 Lr: 0.00380 [2024-02-18 11:33:20,437 INFO misc.py line 119 87073] Train: [37/100][33/1557] Data 0.003 (0.007) Batch 0.773 (0.953) Remain 26:22:25 loss: 0.2064 Lr: 0.00380 [2024-02-18 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Train: [37/100][40/1557] Data 0.011 (0.008) Batch 0.825 (0.959) Remain 26:31:44 loss: 0.4504 Lr: 0.00380 [2024-02-18 11:33:28,072 INFO misc.py line 119 87073] Train: [37/100][41/1557] Data 0.004 (0.008) Batch 0.754 (0.953) Remain 26:22:47 loss: 0.4119 Lr: 0.00380 [2024-02-18 11:33:29,294 INFO misc.py line 119 87073] Train: [37/100][42/1557] Data 0.004 (0.008) Batch 1.208 (0.960) Remain 26:33:36 loss: 0.1837 Lr: 0.00380 [2024-02-18 11:33:30,115 INFO misc.py line 119 87073] Train: [37/100][43/1557] Data 0.018 (0.008) Batch 0.835 (0.957) Remain 26:28:24 loss: 0.4507 Lr: 0.00380 [2024-02-18 11:33:30,937 INFO misc.py line 119 87073] Train: [37/100][44/1557] Data 0.005 (0.008) Batch 0.822 (0.954) Remain 26:22:55 loss: 0.3102 Lr: 0.00380 [2024-02-18 11:33:31,897 INFO misc.py line 119 87073] Train: [37/100][45/1557] Data 0.004 (0.008) Batch 0.955 (0.954) Remain 26:22:56 loss: 0.2358 Lr: 0.00380 [2024-02-18 11:33:32,941 INFO misc.py line 119 87073] Train: [37/100][46/1557] Data 0.009 (0.008) 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INFO misc.py line 119 87073] Train: [37/100][59/1557] Data 0.003 (0.007) Batch 0.927 (0.959) Remain 26:32:16 loss: 0.4248 Lr: 0.00380 [2024-02-18 11:33:46,521 INFO misc.py line 119 87073] Train: [37/100][60/1557] Data 0.005 (0.007) Batch 0.957 (0.959) Remain 26:32:12 loss: 0.2608 Lr: 0.00380 [2024-02-18 11:33:47,281 INFO misc.py line 119 87073] Train: [37/100][61/1557] Data 0.005 (0.007) Batch 0.757 (0.956) Remain 26:26:24 loss: 0.6231 Lr: 0.00380 [2024-02-18 11:33:48,018 INFO misc.py line 119 87073] Train: [37/100][62/1557] Data 0.007 (0.007) Batch 0.739 (0.952) Remain 26:20:16 loss: 0.3300 Lr: 0.00380 [2024-02-18 11:33:56,259 INFO misc.py line 119 87073] Train: [37/100][63/1557] Data 5.626 (0.101) Batch 8.242 (1.074) Remain 29:41:54 loss: 0.1271 Lr: 0.00380 [2024-02-18 11:33:57,217 INFO misc.py line 119 87073] Train: [37/100][64/1557] Data 0.005 (0.099) Batch 0.959 (1.072) Remain 29:38:46 loss: 0.3371 Lr: 0.00380 [2024-02-18 11:33:58,051 INFO misc.py line 119 87073] Train: 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Batch 1.026 (1.076) Remain 29:44:53 loss: 0.2514 Lr: 0.00379 [2024-02-18 11:34:59,725 INFO misc.py line 119 87073] Train: [37/100][122/1557] Data 0.005 (0.103) Batch 0.911 (1.075) Remain 29:42:35 loss: 0.5372 Lr: 0.00379 [2024-02-18 11:35:00,750 INFO misc.py line 119 87073] Train: [37/100][123/1557] Data 0.003 (0.102) Batch 1.024 (1.074) Remain 29:41:52 loss: 0.6511 Lr: 0.00379 [2024-02-18 11:35:01,540 INFO misc.py line 119 87073] Train: [37/100][124/1557] Data 0.005 (0.101) Batch 0.790 (1.072) Remain 29:37:57 loss: 0.3308 Lr: 0.00379 [2024-02-18 11:35:02,323 INFO misc.py line 119 87073] Train: [37/100][125/1557] Data 0.004 (0.100) Batch 0.779 (1.069) Remain 29:33:57 loss: 0.3403 Lr: 0.00379 [2024-02-18 11:35:03,600 INFO misc.py line 119 87073] Train: [37/100][126/1557] Data 0.009 (0.099) Batch 1.277 (1.071) Remain 29:36:43 loss: 0.2404 Lr: 0.00379 [2024-02-18 11:35:04,469 INFO misc.py line 119 87073] Train: [37/100][127/1557] Data 0.010 (0.099) Batch 0.874 (1.070) Remain 29:34:04 loss: 0.3707 Lr: 0.00379 [2024-02-18 11:35:05,392 INFO misc.py line 119 87073] Train: [37/100][128/1557] Data 0.004 (0.098) Batch 0.921 (1.068) Remain 29:32:05 loss: 0.4955 Lr: 0.00379 [2024-02-18 11:35:06,274 INFO misc.py line 119 87073] Train: [37/100][129/1557] Data 0.005 (0.097) Batch 0.882 (1.067) Remain 29:29:37 loss: 0.4170 Lr: 0.00379 [2024-02-18 11:35:07,168 INFO misc.py line 119 87073] Train: [37/100][130/1557] Data 0.005 (0.096) Batch 0.894 (1.066) Remain 29:27:21 loss: 0.5690 Lr: 0.00379 [2024-02-18 11:35:07,930 INFO misc.py line 119 87073] Train: [37/100][131/1557] Data 0.005 (0.096) Batch 0.762 (1.063) Remain 29:23:24 loss: 0.4094 Lr: 0.00379 [2024-02-18 11:35:08,728 INFO misc.py line 119 87073] Train: [37/100][132/1557] Data 0.005 (0.095) Batch 0.799 (1.061) Remain 29:19:59 loss: 0.1995 Lr: 0.00379 [2024-02-18 11:35:10,033 INFO misc.py line 119 87073] Train: [37/100][133/1557] Data 0.005 (0.094) Batch 1.301 (1.063) Remain 29:23:01 loss: 0.1784 Lr: 0.00379 [2024-02-18 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87073] Train: [37/100][140/1557] Data 0.011 (0.090) Batch 1.299 (1.056) Remain 29:11:48 loss: 0.0773 Lr: 0.00379 [2024-02-18 11:35:17,569 INFO misc.py line 119 87073] Train: [37/100][141/1557] Data 0.010 (0.089) Batch 1.004 (1.056) Remain 29:11:10 loss: 0.5645 Lr: 0.00379 [2024-02-18 11:35:18,525 INFO misc.py line 119 87073] Train: [37/100][142/1557] Data 0.016 (0.089) Batch 0.968 (1.055) Remain 29:10:06 loss: 0.5521 Lr: 0.00379 [2024-02-18 11:35:19,450 INFO misc.py line 119 87073] Train: [37/100][143/1557] Data 0.004 (0.088) Batch 0.925 (1.054) Remain 29:08:32 loss: 0.3249 Lr: 0.00379 [2024-02-18 11:35:20,416 INFO misc.py line 119 87073] Train: [37/100][144/1557] Data 0.004 (0.088) Batch 0.965 (1.054) Remain 29:07:28 loss: 0.2259 Lr: 0.00379 [2024-02-18 11:35:21,182 INFO misc.py line 119 87073] Train: [37/100][145/1557] Data 0.004 (0.087) Batch 0.765 (1.052) Remain 29:04:05 loss: 0.3109 Lr: 0.00379 [2024-02-18 11:35:21,946 INFO misc.py line 119 87073] Train: [37/100][146/1557] Data 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line 119 87073] Train: [37/100][165/1557] Data 0.004 (0.077) Batch 0.988 (1.042) Remain 28:48:06 loss: 0.3294 Lr: 0.00379 [2024-02-18 11:35:41,426 INFO misc.py line 119 87073] Train: [37/100][166/1557] Data 0.004 (0.077) Batch 0.738 (1.040) Remain 28:44:59 loss: 0.4715 Lr: 0.00379 [2024-02-18 11:35:42,121 INFO misc.py line 119 87073] Train: [37/100][167/1557] Data 0.006 (0.076) Batch 0.688 (1.038) Remain 28:41:24 loss: 0.6831 Lr: 0.00379 [2024-02-18 11:35:43,350 INFO misc.py line 119 87073] Train: [37/100][168/1557] Data 0.012 (0.076) Batch 1.229 (1.039) Remain 28:43:18 loss: 0.2313 Lr: 0.00379 [2024-02-18 11:35:44,579 INFO misc.py line 119 87073] Train: [37/100][169/1557] Data 0.012 (0.076) Batch 1.233 (1.041) Remain 28:45:13 loss: 0.3635 Lr: 0.00379 [2024-02-18 11:35:45,555 INFO misc.py line 119 87073] Train: [37/100][170/1557] Data 0.008 (0.075) Batch 0.980 (1.040) Remain 28:44:36 loss: 0.8334 Lr: 0.00379 [2024-02-18 11:35:46,384 INFO misc.py line 119 87073] Train: 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Batch 0.889 (1.075) Remain 29:42:50 loss: 0.6203 Lr: 0.00379 [2024-02-18 11:36:00,209 INFO misc.py line 119 87073] Train: [37/100][178/1557] Data 0.005 (0.104) Batch 1.243 (1.076) Remain 29:44:24 loss: 0.4351 Lr: 0.00379 [2024-02-18 11:36:01,168 INFO misc.py line 119 87073] Train: [37/100][179/1557] Data 0.010 (0.104) Batch 0.965 (1.076) Remain 29:43:20 loss: 0.1483 Lr: 0.00379 [2024-02-18 11:36:03,972 INFO misc.py line 119 87073] Train: [37/100][180/1557] Data 1.715 (0.113) Batch 2.802 (1.085) Remain 29:59:29 loss: 0.4901 Lr: 0.00379 [2024-02-18 11:36:04,740 INFO misc.py line 119 87073] Train: [37/100][181/1557] Data 0.006 (0.112) Batch 0.765 (1.084) Remain 29:56:29 loss: 0.7111 Lr: 0.00379 [2024-02-18 11:36:05,958 INFO misc.py line 119 87073] Train: [37/100][182/1557] Data 0.008 (0.112) Batch 1.220 (1.084) Remain 29:57:44 loss: 0.3324 Lr: 0.00379 [2024-02-18 11:36:07,038 INFO misc.py line 119 87073] Train: [37/100][183/1557] Data 0.007 (0.111) Batch 1.082 (1.084) Remain 29:57:41 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line 119 87073] Train: [37/100][221/1557] Data 0.005 (0.093) Batch 1.100 (1.065) Remain 29:24:37 loss: 0.3136 Lr: 0.00379 [2024-02-18 11:36:44,841 INFO misc.py line 119 87073] Train: [37/100][222/1557] Data 0.004 (0.092) Batch 0.855 (1.064) Remain 29:23:01 loss: 0.4748 Lr: 0.00379 [2024-02-18 11:36:45,574 INFO misc.py line 119 87073] Train: [37/100][223/1557] Data 0.005 (0.092) Batch 0.733 (1.062) Remain 29:20:30 loss: 0.4882 Lr: 0.00379 [2024-02-18 11:36:46,735 INFO misc.py line 119 87073] Train: [37/100][224/1557] Data 0.004 (0.092) Batch 1.154 (1.063) Remain 29:21:10 loss: 0.2227 Lr: 0.00379 [2024-02-18 11:36:47,689 INFO misc.py line 119 87073] Train: [37/100][225/1557] Data 0.010 (0.091) Batch 0.961 (1.062) Remain 29:20:24 loss: 0.6711 Lr: 0.00379 [2024-02-18 11:36:48,498 INFO misc.py line 119 87073] Train: [37/100][226/1557] Data 0.005 (0.091) Batch 0.808 (1.061) Remain 29:18:29 loss: 0.5368 Lr: 0.00379 [2024-02-18 11:36:49,349 INFO misc.py line 119 87073] Train: 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Batch 0.888 (1.088) Remain 30:01:54 loss: 0.4292 Lr: 0.00379 [2024-02-18 11:37:03,092 INFO misc.py line 119 87073] Train: [37/100][234/1557] Data 0.008 (0.114) Batch 1.119 (1.088) Remain 30:02:07 loss: 0.4944 Lr: 0.00379 [2024-02-18 11:37:04,137 INFO misc.py line 119 87073] Train: [37/100][235/1557] Data 0.007 (0.113) Batch 1.047 (1.087) Remain 30:01:48 loss: 0.2838 Lr: 0.00379 [2024-02-18 11:37:04,899 INFO misc.py line 119 87073] Train: [37/100][236/1557] Data 0.006 (0.113) Batch 0.764 (1.086) Remain 29:59:29 loss: 0.6605 Lr: 0.00379 [2024-02-18 11:37:05,626 INFO misc.py line 119 87073] Train: [37/100][237/1557] Data 0.004 (0.112) Batch 0.723 (1.085) Remain 29:56:54 loss: 0.3419 Lr: 0.00379 [2024-02-18 11:37:06,825 INFO misc.py line 119 87073] Train: [37/100][238/1557] Data 0.007 (0.112) Batch 1.202 (1.085) Remain 29:57:42 loss: 0.2221 Lr: 0.00379 [2024-02-18 11:37:07,948 INFO misc.py line 119 87073] Train: [37/100][239/1557] Data 0.004 (0.112) Batch 1.116 (1.085) Remain 29:57:54 loss: 0.2973 Lr: 0.00379 [2024-02-18 11:37:08,903 INFO misc.py line 119 87073] Train: [37/100][240/1557] Data 0.011 (0.111) Batch 0.963 (1.085) Remain 29:57:02 loss: 0.3157 Lr: 0.00379 [2024-02-18 11:37:09,870 INFO misc.py line 119 87073] Train: [37/100][241/1557] Data 0.004 (0.111) Batch 0.966 (1.084) Remain 29:56:11 loss: 0.6289 Lr: 0.00379 [2024-02-18 11:37:10,855 INFO misc.py line 119 87073] Train: [37/100][242/1557] Data 0.007 (0.110) Batch 0.986 (1.084) Remain 29:55:29 loss: 0.7041 Lr: 0.00379 [2024-02-18 11:37:11,584 INFO misc.py line 119 87073] Train: [37/100][243/1557] Data 0.005 (0.110) Batch 0.726 (1.082) Remain 29:53:00 loss: 0.3647 Lr: 0.00379 [2024-02-18 11:37:12,407 INFO misc.py line 119 87073] Train: [37/100][244/1557] Data 0.007 (0.109) Batch 0.826 (1.081) Remain 29:51:13 loss: 0.7254 Lr: 0.00379 [2024-02-18 11:37:13,619 INFO misc.py line 119 87073] Train: [37/100][245/1557] Data 0.004 (0.109) Batch 1.200 (1.082) Remain 29:52:01 loss: 0.2413 Lr: 0.00379 [2024-02-18 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line 119 87073] Train: [37/100][277/1557] Data 0.006 (0.097) Batch 0.915 (1.068) Remain 29:28:17 loss: 0.4929 Lr: 0.00379 [2024-02-18 11:37:45,160 INFO misc.py line 119 87073] Train: [37/100][278/1557] Data 0.003 (0.097) Batch 0.760 (1.067) Remain 29:26:25 loss: 0.3252 Lr: 0.00379 [2024-02-18 11:37:45,848 INFO misc.py line 119 87073] Train: [37/100][279/1557] Data 0.014 (0.097) Batch 0.695 (1.065) Remain 29:24:10 loss: 0.2947 Lr: 0.00379 [2024-02-18 11:37:47,056 INFO misc.py line 119 87073] Train: [37/100][280/1557] Data 0.005 (0.096) Batch 1.210 (1.066) Remain 29:25:01 loss: 0.1638 Lr: 0.00379 [2024-02-18 11:37:48,057 INFO misc.py line 119 87073] Train: [37/100][281/1557] Data 0.004 (0.096) Batch 0.996 (1.066) Remain 29:24:35 loss: 0.1953 Lr: 0.00379 [2024-02-18 11:37:49,112 INFO misc.py line 119 87073] Train: [37/100][282/1557] Data 0.009 (0.096) Batch 1.059 (1.065) Remain 29:24:32 loss: 0.2154 Lr: 0.00379 [2024-02-18 11:37:50,062 INFO misc.py line 119 87073] Train: 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Batch 0.927 (1.088) Remain 30:02:18 loss: 0.2374 Lr: 0.00379 [2024-02-18 11:38:04,063 INFO misc.py line 119 87073] Train: [37/100][290/1557] Data 0.006 (0.113) Batch 0.951 (1.088) Remain 30:01:29 loss: 0.1833 Lr: 0.00379 [2024-02-18 11:38:04,964 INFO misc.py line 119 87073] Train: [37/100][291/1557] Data 0.003 (0.113) Batch 0.899 (1.087) Remain 30:00:23 loss: 0.7022 Lr: 0.00379 [2024-02-18 11:38:05,740 INFO misc.py line 119 87073] Train: [37/100][292/1557] Data 0.005 (0.113) Batch 0.777 (1.086) Remain 29:58:35 loss: 0.5780 Lr: 0.00379 [2024-02-18 11:38:06,480 INFO misc.py line 119 87073] Train: [37/100][293/1557] Data 0.004 (0.112) Batch 0.740 (1.085) Remain 29:56:35 loss: 0.2599 Lr: 0.00379 [2024-02-18 11:38:07,747 INFO misc.py line 119 87073] Train: [37/100][294/1557] Data 0.004 (0.112) Batch 1.256 (1.086) Remain 29:57:33 loss: 0.3269 Lr: 0.00379 [2024-02-18 11:38:08,665 INFO misc.py line 119 87073] Train: [37/100][295/1557] Data 0.015 (0.112) Batch 0.928 (1.085) Remain 29:56:38 loss: 0.2061 Lr: 0.00379 [2024-02-18 11:38:09,720 INFO misc.py line 119 87073] Train: [37/100][296/1557] Data 0.004 (0.111) Batch 1.056 (1.085) Remain 29:56:27 loss: 0.4916 Lr: 0.00379 [2024-02-18 11:38:10,590 INFO misc.py line 119 87073] Train: [37/100][297/1557] Data 0.003 (0.111) Batch 0.869 (1.084) Remain 29:55:13 loss: 0.2598 Lr: 0.00379 [2024-02-18 11:38:11,645 INFO misc.py line 119 87073] Train: [37/100][298/1557] Data 0.004 (0.110) Batch 1.049 (1.084) Remain 29:55:00 loss: 0.6703 Lr: 0.00378 [2024-02-18 11:38:12,421 INFO misc.py line 119 87073] Train: [37/100][299/1557] Data 0.011 (0.110) Batch 0.782 (1.083) Remain 29:53:18 loss: 0.2213 Lr: 0.00378 [2024-02-18 11:38:13,213 INFO misc.py line 119 87073] Train: [37/100][300/1557] Data 0.004 (0.110) Batch 0.792 (1.082) Remain 29:51:40 loss: 0.4677 Lr: 0.00378 [2024-02-18 11:38:14,491 INFO misc.py line 119 87073] Train: [37/100][301/1557] Data 0.004 (0.109) Batch 1.269 (1.083) Remain 29:52:41 loss: 0.1647 Lr: 0.00378 [2024-02-18 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Batch 0.907 (1.085) Remain 29:56:24 loss: 0.4928 Lr: 0.00378 [2024-02-18 11:39:03,928 INFO misc.py line 119 87073] Train: [37/100][346/1557] Data 0.007 (0.113) Batch 0.870 (1.085) Remain 29:55:20 loss: 0.4556 Lr: 0.00378 [2024-02-18 11:39:04,898 INFO misc.py line 119 87073] Train: [37/100][347/1557] Data 0.011 (0.113) Batch 0.976 (1.084) Remain 29:54:48 loss: 0.3713 Lr: 0.00378 [2024-02-18 11:39:05,679 INFO misc.py line 119 87073] Train: [37/100][348/1557] Data 0.004 (0.112) Batch 0.780 (1.084) Remain 29:53:19 loss: 0.5851 Lr: 0.00378 [2024-02-18 11:39:06,542 INFO misc.py line 119 87073] Train: [37/100][349/1557] Data 0.004 (0.112) Batch 0.849 (1.083) Remain 29:52:11 loss: 0.4659 Lr: 0.00378 [2024-02-18 11:39:07,840 INFO misc.py line 119 87073] Train: [37/100][350/1557] Data 0.019 (0.112) Batch 1.301 (1.084) Remain 29:53:12 loss: 0.2453 Lr: 0.00378 [2024-02-18 11:39:08,698 INFO misc.py line 119 87073] Train: [37/100][351/1557] Data 0.016 (0.111) Batch 0.869 (1.083) Remain 29:52:10 loss: 0.1526 Lr: 0.00378 [2024-02-18 11:39:09,584 INFO misc.py line 119 87073] Train: [37/100][352/1557] Data 0.005 (0.111) Batch 0.886 (1.082) Remain 29:51:13 loss: 0.4741 Lr: 0.00378 [2024-02-18 11:39:10,413 INFO misc.py line 119 87073] Train: [37/100][353/1557] Data 0.004 (0.111) Batch 0.829 (1.082) Remain 29:50:00 loss: 0.4312 Lr: 0.00378 [2024-02-18 11:39:11,307 INFO misc.py line 119 87073] Train: [37/100][354/1557] Data 0.004 (0.111) Batch 0.895 (1.081) Remain 29:49:06 loss: 0.6155 Lr: 0.00378 [2024-02-18 11:39:13,928 INFO misc.py line 119 87073] Train: [37/100][355/1557] Data 1.143 (0.113) Batch 2.620 (1.085) Remain 29:56:19 loss: 0.2590 Lr: 0.00378 [2024-02-18 11:39:14,695 INFO misc.py line 119 87073] Train: [37/100][356/1557] Data 0.004 (0.113) Batch 0.767 (1.085) Remain 29:54:48 loss: 0.5066 Lr: 0.00378 [2024-02-18 11:39:15,936 INFO misc.py line 119 87073] Train: [37/100][357/1557] Data 0.004 (0.113) Batch 1.241 (1.085) Remain 29:55:31 loss: 0.2412 Lr: 0.00378 [2024-02-18 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[2024-02-18 11:39:40,270 INFO misc.py line 119 87073] Train: [37/100][383/1557] Data 0.004 (0.106) Batch 0.707 (1.075) Remain 29:38:10 loss: 0.2115 Lr: 0.00378 [2024-02-18 11:39:41,032 INFO misc.py line 119 87073] Train: [37/100][384/1557] Data 0.004 (0.105) Batch 0.761 (1.074) Remain 29:36:48 loss: 0.4260 Lr: 0.00378 [2024-02-18 11:39:42,364 INFO misc.py line 119 87073] Train: [37/100][385/1557] Data 0.004 (0.105) Batch 1.329 (1.075) Remain 29:37:53 loss: 0.2900 Lr: 0.00378 [2024-02-18 11:39:43,244 INFO misc.py line 119 87073] Train: [37/100][386/1557] Data 0.007 (0.105) Batch 0.883 (1.074) Remain 29:37:02 loss: 0.4520 Lr: 0.00378 [2024-02-18 11:39:44,203 INFO misc.py line 119 87073] Train: [37/100][387/1557] Data 0.005 (0.104) Batch 0.960 (1.074) Remain 29:36:32 loss: 0.6196 Lr: 0.00378 [2024-02-18 11:39:45,308 INFO misc.py line 119 87073] Train: [37/100][388/1557] Data 0.004 (0.104) Batch 1.105 (1.074) Remain 29:36:38 loss: 0.7331 Lr: 0.00378 [2024-02-18 11:39:46,054 INFO misc.py line 119 87073] Train: [37/100][389/1557] Data 0.004 (0.104) Batch 0.744 (1.073) Remain 29:35:13 loss: 0.5972 Lr: 0.00378 [2024-02-18 11:39:46,808 INFO misc.py line 119 87073] Train: [37/100][390/1557] Data 0.006 (0.104) Batch 0.749 (1.072) Remain 29:33:48 loss: 0.4153 Lr: 0.00378 [2024-02-18 11:39:47,610 INFO misc.py line 119 87073] Train: [37/100][391/1557] Data 0.011 (0.103) Batch 0.808 (1.072) Remain 29:32:39 loss: 0.3002 Lr: 0.00378 [2024-02-18 11:39:48,750 INFO misc.py line 119 87073] Train: [37/100][392/1557] Data 0.005 (0.103) Batch 1.141 (1.072) Remain 29:32:56 loss: 0.1864 Lr: 0.00378 [2024-02-18 11:39:49,687 INFO misc.py line 119 87073] Train: [37/100][393/1557] Data 0.004 (0.103) Batch 0.938 (1.071) Remain 29:32:21 loss: 0.2678 Lr: 0.00378 [2024-02-18 11:39:50,664 INFO misc.py line 119 87073] Train: [37/100][394/1557] Data 0.004 (0.103) Batch 0.976 (1.071) Remain 29:31:56 loss: 0.4801 Lr: 0.00378 [2024-02-18 11:39:51,725 INFO misc.py line 119 87073] Train: 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Batch 0.968 (1.090) Remain 30:03:11 loss: 0.1065 Lr: 0.00378 [2024-02-18 11:40:06,537 INFO misc.py line 119 87073] Train: [37/100][402/1557] Data 0.003 (0.116) Batch 0.822 (1.089) Remain 30:02:04 loss: 0.2713 Lr: 0.00378 [2024-02-18 11:40:07,403 INFO misc.py line 119 87073] Train: [37/100][403/1557] Data 0.007 (0.115) Batch 0.868 (1.089) Remain 30:01:07 loss: 0.5163 Lr: 0.00378 [2024-02-18 11:40:08,188 INFO misc.py line 119 87073] Train: [37/100][404/1557] Data 0.004 (0.115) Batch 0.784 (1.088) Remain 29:59:51 loss: 0.2552 Lr: 0.00378 [2024-02-18 11:40:08,972 INFO misc.py line 119 87073] Train: [37/100][405/1557] Data 0.005 (0.115) Batch 0.785 (1.087) Remain 29:58:35 loss: 0.5778 Lr: 0.00378 [2024-02-18 11:40:10,248 INFO misc.py line 119 87073] Train: [37/100][406/1557] Data 0.004 (0.115) Batch 1.275 (1.088) Remain 29:59:20 loss: 0.3326 Lr: 0.00378 [2024-02-18 11:40:11,224 INFO misc.py line 119 87073] Train: [37/100][407/1557] Data 0.006 (0.114) Batch 0.976 (1.088) Remain 29:58:51 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line 119 87073] Train: [37/100][445/1557] Data 0.006 (0.105) Batch 0.983 (1.076) Remain 29:39:08 loss: 0.5339 Lr: 0.00378 [2024-02-18 11:40:48,200 INFO misc.py line 119 87073] Train: [37/100][446/1557] Data 0.011 (0.105) Batch 0.738 (1.075) Remain 29:37:51 loss: 0.3181 Lr: 0.00378 [2024-02-18 11:40:48,967 INFO misc.py line 119 87073] Train: [37/100][447/1557] Data 0.004 (0.105) Batch 0.765 (1.075) Remain 29:36:41 loss: 0.4523 Lr: 0.00378 [2024-02-18 11:40:50,135 INFO misc.py line 119 87073] Train: [37/100][448/1557] Data 0.005 (0.104) Batch 1.156 (1.075) Remain 29:36:58 loss: 0.1425 Lr: 0.00378 [2024-02-18 11:40:51,075 INFO misc.py line 119 87073] Train: [37/100][449/1557] Data 0.017 (0.104) Batch 0.953 (1.075) Remain 29:36:30 loss: 0.3441 Lr: 0.00378 [2024-02-18 11:40:51,900 INFO misc.py line 119 87073] Train: [37/100][450/1557] Data 0.003 (0.104) Batch 0.824 (1.074) Remain 29:35:33 loss: 0.8035 Lr: 0.00378 [2024-02-18 11:40:52,931 INFO misc.py line 119 87073] Train: 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Batch 1.005 (1.087) Remain 29:56:43 loss: 0.1931 Lr: 0.00378 [2024-02-18 11:41:06,579 INFO misc.py line 119 87073] Train: [37/100][458/1557] Data 0.004 (0.113) Batch 1.295 (1.087) Remain 29:57:27 loss: 0.1228 Lr: 0.00378 [2024-02-18 11:41:07,449 INFO misc.py line 119 87073] Train: [37/100][459/1557] Data 0.027 (0.113) Batch 0.890 (1.087) Remain 29:56:43 loss: 0.3619 Lr: 0.00378 [2024-02-18 11:41:08,244 INFO misc.py line 119 87073] Train: [37/100][460/1557] Data 0.007 (0.113) Batch 0.797 (1.086) Remain 29:55:39 loss: 0.2785 Lr: 0.00378 [2024-02-18 11:41:09,034 INFO misc.py line 119 87073] Train: [37/100][461/1557] Data 0.005 (0.113) Batch 0.791 (1.086) Remain 29:54:34 loss: 0.3992 Lr: 0.00378 [2024-02-18 11:41:10,244 INFO misc.py line 119 87073] Train: [37/100][462/1557] Data 0.003 (0.112) Batch 1.210 (1.086) Remain 29:55:00 loss: 0.1915 Lr: 0.00378 [2024-02-18 11:41:11,236 INFO misc.py line 119 87073] Train: [37/100][463/1557] Data 0.004 (0.112) Batch 0.992 (1.086) Remain 29:54:38 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Batch 0.936 (1.088) Remain 29:58:21 loss: 0.2709 Lr: 0.00378 [2024-02-18 11:42:07,931 INFO misc.py line 119 87073] Train: [37/100][514/1557] Data 0.004 (0.113) Batch 0.984 (1.088) Remain 29:58:00 loss: 0.3234 Lr: 0.00378 [2024-02-18 11:42:08,873 INFO misc.py line 119 87073] Train: [37/100][515/1557] Data 0.010 (0.113) Batch 0.946 (1.088) Remain 29:57:31 loss: 0.2695 Lr: 0.00378 [2024-02-18 11:42:09,632 INFO misc.py line 119 87073] Train: [37/100][516/1557] Data 0.005 (0.112) Batch 0.760 (1.087) Remain 29:56:27 loss: 0.5387 Lr: 0.00378 [2024-02-18 11:42:10,346 INFO misc.py line 119 87073] Train: [37/100][517/1557] Data 0.004 (0.112) Batch 0.706 (1.087) Remain 29:55:12 loss: 0.6923 Lr: 0.00378 [2024-02-18 11:42:11,580 INFO misc.py line 119 87073] Train: [37/100][518/1557] Data 0.012 (0.112) Batch 1.236 (1.087) Remain 29:55:40 loss: 0.3056 Lr: 0.00377 [2024-02-18 11:42:12,534 INFO misc.py line 119 87073] Train: [37/100][519/1557] Data 0.009 (0.112) Batch 0.960 (1.087) Remain 29:55:14 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line 119 87073] Train: [37/100][557/1557] Data 0.003 (0.108) Batch 0.988 (1.081) Remain 29:45:28 loss: 0.7019 Lr: 0.00377 [2024-02-18 11:42:51,535 INFO misc.py line 119 87073] Train: [37/100][558/1557] Data 0.004 (0.107) Batch 0.753 (1.081) Remain 29:44:29 loss: 0.2179 Lr: 0.00377 [2024-02-18 11:42:52,301 INFO misc.py line 119 87073] Train: [37/100][559/1557] Data 0.005 (0.107) Batch 0.766 (1.080) Remain 29:43:32 loss: 0.2459 Lr: 0.00377 [2024-02-18 11:42:53,481 INFO misc.py line 119 87073] Train: [37/100][560/1557] Data 0.005 (0.107) Batch 1.181 (1.080) Remain 29:43:48 loss: 0.1502 Lr: 0.00377 [2024-02-18 11:42:54,532 INFO misc.py line 119 87073] Train: [37/100][561/1557] Data 0.004 (0.107) Batch 1.050 (1.080) Remain 29:43:42 loss: 0.4521 Lr: 0.00377 [2024-02-18 11:42:55,456 INFO misc.py line 119 87073] Train: [37/100][562/1557] Data 0.005 (0.107) Batch 0.925 (1.080) Remain 29:43:13 loss: 0.5167 Lr: 0.00377 [2024-02-18 11:42:56,358 INFO misc.py line 119 87073] Train: 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Batch 0.928 (1.092) Remain 30:03:29 loss: 0.7575 Lr: 0.00377 [2024-02-18 11:43:11,098 INFO misc.py line 119 87073] Train: [37/100][570/1557] Data 0.004 (0.115) Batch 1.098 (1.092) Remain 30:03:29 loss: 0.4507 Lr: 0.00377 [2024-02-18 11:43:12,198 INFO misc.py line 119 87073] Train: [37/100][571/1557] Data 0.004 (0.115) Batch 1.100 (1.092) Remain 30:03:29 loss: 0.5904 Lr: 0.00377 [2024-02-18 11:43:13,065 INFO misc.py line 119 87073] Train: [37/100][572/1557] Data 0.004 (0.115) Batch 0.864 (1.092) Remain 30:02:48 loss: 0.5945 Lr: 0.00377 [2024-02-18 11:43:13,828 INFO misc.py line 119 87073] Train: [37/100][573/1557] Data 0.007 (0.115) Batch 0.758 (1.091) Remain 30:01:49 loss: 0.4503 Lr: 0.00377 [2024-02-18 11:43:15,069 INFO misc.py line 119 87073] Train: [37/100][574/1557] Data 0.012 (0.115) Batch 1.243 (1.091) Remain 30:02:14 loss: 0.2312 Lr: 0.00377 [2024-02-18 11:43:16,012 INFO misc.py line 119 87073] Train: [37/100][575/1557] Data 0.010 (0.114) Batch 0.948 (1.091) Remain 30:01:48 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Batch 1.068 (1.093) Remain 30:03:51 loss: 0.4783 Lr: 0.00377 [2024-02-18 11:44:12,583 INFO misc.py line 119 87073] Train: [37/100][626/1557] Data 0.004 (0.115) Batch 0.892 (1.093) Remain 30:03:18 loss: 0.4405 Lr: 0.00377 [2024-02-18 11:44:13,426 INFO misc.py line 119 87073] Train: [37/100][627/1557] Data 0.007 (0.114) Batch 0.846 (1.092) Remain 30:02:38 loss: 0.7176 Lr: 0.00377 [2024-02-18 11:44:14,201 INFO misc.py line 119 87073] Train: [37/100][628/1557] Data 0.006 (0.114) Batch 0.776 (1.092) Remain 30:01:47 loss: 0.3607 Lr: 0.00377 [2024-02-18 11:44:14,874 INFO misc.py line 119 87073] Train: [37/100][629/1557] Data 0.004 (0.114) Batch 0.672 (1.091) Remain 30:00:39 loss: 0.1432 Lr: 0.00377 [2024-02-18 11:44:16,212 INFO misc.py line 119 87073] Train: [37/100][630/1557] Data 0.004 (0.114) Batch 1.326 (1.091) Remain 30:01:15 loss: 0.1974 Lr: 0.00377 [2024-02-18 11:44:17,321 INFO misc.py line 119 87073] Train: [37/100][631/1557] Data 0.016 (0.114) Batch 1.109 (1.092) Remain 30:01:17 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line 119 87073] Train: [37/100][669/1557] Data 0.004 (0.108) Batch 0.990 (1.084) Remain 29:47:43 loss: 0.0840 Lr: 0.00377 [2024-02-18 11:44:54,393 INFO misc.py line 119 87073] Train: [37/100][670/1557] Data 0.007 (0.108) Batch 0.802 (1.083) Remain 29:47:00 loss: 0.1921 Lr: 0.00377 [2024-02-18 11:44:55,179 INFO misc.py line 119 87073] Train: [37/100][671/1557] Data 0.007 (0.108) Batch 0.787 (1.083) Remain 29:46:15 loss: 0.4073 Lr: 0.00377 [2024-02-18 11:44:56,363 INFO misc.py line 119 87073] Train: [37/100][672/1557] Data 0.006 (0.108) Batch 1.179 (1.083) Remain 29:46:28 loss: 0.1713 Lr: 0.00377 [2024-02-18 11:44:57,302 INFO misc.py line 119 87073] Train: [37/100][673/1557] Data 0.011 (0.107) Batch 0.945 (1.083) Remain 29:46:07 loss: 0.5098 Lr: 0.00377 [2024-02-18 11:44:58,308 INFO misc.py line 119 87073] Train: [37/100][674/1557] Data 0.004 (0.107) Batch 1.006 (1.083) Remain 29:45:55 loss: 0.3315 Lr: 0.00377 [2024-02-18 11:44:59,301 INFO misc.py line 119 87073] Train: 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Batch 0.917 (1.093) Remain 30:02:08 loss: 0.4266 Lr: 0.00377 [2024-02-18 11:45:13,503 INFO misc.py line 119 87073] Train: [37/100][682/1557] Data 0.004 (0.115) Batch 0.889 (1.092) Remain 30:01:38 loss: 1.0548 Lr: 0.00377 [2024-02-18 11:45:14,497 INFO misc.py line 119 87073] Train: [37/100][683/1557] Data 0.009 (0.115) Batch 0.999 (1.092) Remain 30:01:23 loss: 0.6068 Lr: 0.00377 [2024-02-18 11:45:15,247 INFO misc.py line 119 87073] Train: [37/100][684/1557] Data 0.003 (0.115) Batch 0.751 (1.092) Remain 30:00:32 loss: 0.2050 Lr: 0.00377 [2024-02-18 11:45:15,971 INFO misc.py line 119 87073] Train: [37/100][685/1557] Data 0.003 (0.114) Batch 0.716 (1.091) Remain 29:59:37 loss: 0.4589 Lr: 0.00377 [2024-02-18 11:45:17,245 INFO misc.py line 119 87073] Train: [37/100][686/1557] Data 0.011 (0.114) Batch 1.281 (1.091) Remain 30:00:03 loss: 0.2096 Lr: 0.00377 [2024-02-18 11:45:18,358 INFO misc.py line 119 87073] Train: [37/100][687/1557] Data 0.005 (0.114) Batch 1.103 (1.091) Remain 30:00:04 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line 119 87073] Train: [37/100][725/1557] Data 0.005 (0.111) Batch 1.114 (1.086) Remain 29:50:44 loss: 0.4509 Lr: 0.00377 [2024-02-18 11:45:56,865 INFO misc.py line 119 87073] Train: [37/100][726/1557] Data 0.004 (0.111) Batch 0.826 (1.086) Remain 29:50:07 loss: 0.4366 Lr: 0.00377 [2024-02-18 11:45:57,633 INFO misc.py line 119 87073] Train: [37/100][727/1557] Data 0.005 (0.111) Batch 0.765 (1.085) Remain 29:49:22 loss: 0.1419 Lr: 0.00377 [2024-02-18 11:45:58,780 INFO misc.py line 119 87073] Train: [37/100][728/1557] Data 0.008 (0.110) Batch 1.142 (1.085) Remain 29:49:29 loss: 0.1828 Lr: 0.00377 [2024-02-18 11:45:59,747 INFO misc.py line 119 87073] Train: [37/100][729/1557] Data 0.012 (0.110) Batch 0.974 (1.085) Remain 29:49:13 loss: 0.8805 Lr: 0.00377 [2024-02-18 11:46:00,628 INFO misc.py line 119 87073] Train: [37/100][730/1557] Data 0.006 (0.110) Batch 0.883 (1.085) Remain 29:48:44 loss: 0.3888 Lr: 0.00377 [2024-02-18 11:46:01,648 INFO misc.py line 119 87073] Train: 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Batch 0.862 (1.094) Remain 30:03:01 loss: 0.2313 Lr: 0.00376 [2024-02-18 11:46:15,464 INFO misc.py line 119 87073] Train: [37/100][738/1557] Data 0.010 (0.116) Batch 0.826 (1.093) Remain 30:02:24 loss: 0.4881 Lr: 0.00376 [2024-02-18 11:46:16,397 INFO misc.py line 119 87073] Train: [37/100][739/1557] Data 0.004 (0.116) Batch 0.934 (1.093) Remain 30:02:01 loss: 0.3485 Lr: 0.00376 [2024-02-18 11:46:17,185 INFO misc.py line 119 87073] Train: [37/100][740/1557] Data 0.003 (0.116) Batch 0.782 (1.093) Remain 30:01:19 loss: 0.2647 Lr: 0.00376 [2024-02-18 11:46:17,920 INFO misc.py line 119 87073] Train: [37/100][741/1557] Data 0.009 (0.116) Batch 0.741 (1.092) Remain 30:00:30 loss: 0.4046 Lr: 0.00376 [2024-02-18 11:46:19,103 INFO misc.py line 119 87073] Train: [37/100][742/1557] Data 0.003 (0.116) Batch 1.182 (1.092) Remain 30:00:41 loss: 0.2511 Lr: 0.00376 [2024-02-18 11:46:20,212 INFO misc.py line 119 87073] Train: [37/100][743/1557] Data 0.004 (0.116) Batch 1.110 (1.092) Remain 30:00:42 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Batch 0.911 (1.092) Remain 29:57:47 loss: 0.3534 Lr: 0.00376 [2024-02-18 11:48:16,439 INFO misc.py line 119 87073] Train: [37/100][850/1557] Data 0.006 (0.115) Batch 0.939 (1.092) Remain 29:57:28 loss: 0.6917 Lr: 0.00376 [2024-02-18 11:48:17,359 INFO misc.py line 119 87073] Train: [37/100][851/1557] Data 0.005 (0.115) Batch 0.920 (1.091) Remain 29:57:07 loss: 0.7430 Lr: 0.00376 [2024-02-18 11:48:18,115 INFO misc.py line 119 87073] Train: [37/100][852/1557] Data 0.005 (0.114) Batch 0.758 (1.091) Remain 29:56:27 loss: 0.2780 Lr: 0.00376 [2024-02-18 11:48:18,910 INFO misc.py line 119 87073] Train: [37/100][853/1557] Data 0.004 (0.114) Batch 0.790 (1.091) Remain 29:55:51 loss: 0.1969 Lr: 0.00376 [2024-02-18 11:48:20,103 INFO misc.py line 119 87073] Train: [37/100][854/1557] Data 0.008 (0.114) Batch 1.193 (1.091) Remain 29:56:02 loss: 0.2004 Lr: 0.00376 [2024-02-18 11:48:21,181 INFO misc.py line 119 87073] Train: [37/100][855/1557] Data 0.008 (0.114) Batch 1.082 (1.091) Remain 29:56:00 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line 119 87073] Train: [37/100][949/1557] Data 0.007 (0.111) Batch 1.060 (1.086) Remain 29:47:02 loss: 0.3699 Lr: 0.00376 [2024-02-18 11:50:00,313 INFO misc.py line 119 87073] Train: [37/100][950/1557] Data 0.011 (0.111) Batch 0.775 (1.086) Remain 29:46:28 loss: 0.5231 Lr: 0.00376 [2024-02-18 11:50:01,073 INFO misc.py line 119 87073] Train: [37/100][951/1557] Data 0.004 (0.111) Batch 0.758 (1.086) Remain 29:45:53 loss: 0.4768 Lr: 0.00376 [2024-02-18 11:50:02,251 INFO misc.py line 119 87073] Train: [37/100][952/1557] Data 0.004 (0.111) Batch 1.167 (1.086) Remain 29:46:01 loss: 0.2431 Lr: 0.00376 [2024-02-18 11:50:03,264 INFO misc.py line 119 87073] Train: [37/100][953/1557] Data 0.017 (0.111) Batch 1.025 (1.086) Remain 29:45:53 loss: 0.3095 Lr: 0.00376 [2024-02-18 11:50:04,273 INFO misc.py line 119 87073] Train: [37/100][954/1557] Data 0.004 (0.111) Batch 1.002 (1.086) Remain 29:45:43 loss: 0.7573 Lr: 0.00375 [2024-02-18 11:50:05,325 INFO misc.py line 119 87073] Train: 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(0.112) Batch 1.017 (1.090) Remain 29:42:48 loss: 0.2985 Lr: 0.00373 [2024-02-18 12:00:18,853 INFO misc.py line 119 87073] Train: [37/100][1514/1557] Data 0.013 (0.112) Batch 1.069 (1.090) Remain 29:42:46 loss: 0.3877 Lr: 0.00373 [2024-02-18 12:00:19,663 INFO misc.py line 119 87073] Train: [37/100][1515/1557] Data 0.017 (0.112) Batch 0.823 (1.090) Remain 29:42:27 loss: 0.3957 Lr: 0.00373 [2024-02-18 12:00:20,616 INFO misc.py line 119 87073] Train: [37/100][1516/1557] Data 0.003 (0.112) Batch 0.953 (1.090) Remain 29:42:17 loss: 0.5405 Lr: 0.00373 [2024-02-18 12:00:21,406 INFO misc.py line 119 87073] Train: [37/100][1517/1557] Data 0.004 (0.112) Batch 0.780 (1.090) Remain 29:41:56 loss: 0.4720 Lr: 0.00373 [2024-02-18 12:00:22,166 INFO misc.py line 119 87073] Train: [37/100][1518/1557] Data 0.014 (0.112) Batch 0.770 (1.089) Remain 29:41:35 loss: 0.2884 Lr: 0.00373 [2024-02-18 12:00:31,512 INFO misc.py line 119 87073] Train: [37/100][1519/1557] Data 6.093 (0.116) Batch 9.343 (1.095) Remain 29:50:28 loss: 0.1287 Lr: 0.00373 [2024-02-18 12:00:32,439 INFO misc.py line 119 87073] Train: [37/100][1520/1557] Data 0.008 (0.116) Batch 0.929 (1.095) Remain 29:50:16 loss: 0.9755 Lr: 0.00373 [2024-02-18 12:00:33,416 INFO misc.py line 119 87073] Train: [37/100][1521/1557] Data 0.005 (0.116) Batch 0.978 (1.095) Remain 29:50:07 loss: 1.0392 Lr: 0.00373 [2024-02-18 12:00:34,321 INFO misc.py line 119 87073] Train: [37/100][1522/1557] Data 0.004 (0.116) Batch 0.905 (1.094) Remain 29:49:54 loss: 0.3197 Lr: 0.00373 [2024-02-18 12:00:35,272 INFO misc.py line 119 87073] Train: [37/100][1523/1557] Data 0.004 (0.115) Batch 0.949 (1.094) Remain 29:49:43 loss: 0.6427 Lr: 0.00373 [2024-02-18 12:00:36,078 INFO misc.py line 119 87073] Train: [37/100][1524/1557] Data 0.005 (0.115) Batch 0.808 (1.094) Remain 29:49:24 loss: 0.2885 Lr: 0.00373 [2024-02-18 12:00:36,848 INFO misc.py line 119 87073] Train: [37/100][1525/1557] Data 0.004 (0.115) Batch 0.769 (1.094) Remain 29:49:02 loss: 0.3497 Lr: 0.00373 [2024-02-18 12:00:38,121 INFO misc.py line 119 87073] Train: [37/100][1526/1557] Data 0.005 (0.115) Batch 1.272 (1.094) Remain 29:49:12 loss: 0.2169 Lr: 0.00373 [2024-02-18 12:00:39,190 INFO misc.py line 119 87073] Train: [37/100][1527/1557] Data 0.007 (0.115) Batch 1.059 (1.094) Remain 29:49:09 loss: 0.4224 Lr: 0.00373 [2024-02-18 12:00:40,126 INFO misc.py line 119 87073] Train: [37/100][1528/1557] Data 0.016 (0.115) Batch 0.948 (1.094) Remain 29:48:58 loss: 0.9506 Lr: 0.00373 [2024-02-18 12:00:41,233 INFO misc.py line 119 87073] Train: [37/100][1529/1557] Data 0.004 (0.115) Batch 1.107 (1.094) Remain 29:48:58 loss: 0.3706 Lr: 0.00373 [2024-02-18 12:00:42,328 INFO misc.py line 119 87073] Train: [37/100][1530/1557] Data 0.004 (0.115) Batch 1.095 (1.094) Remain 29:48:57 loss: 0.3240 Lr: 0.00373 [2024-02-18 12:00:43,139 INFO misc.py line 119 87073] Train: [37/100][1531/1557] Data 0.004 (0.115) Batch 0.810 (1.094) Remain 29:48:38 loss: 0.4215 Lr: 0.00373 [2024-02-18 12:00:43,922 INFO misc.py line 119 87073] Train: [37/100][1532/1557] Data 0.005 (0.115) Batch 0.778 (1.094) Remain 29:48:16 loss: 0.1776 Lr: 0.00373 [2024-02-18 12:00:45,233 INFO misc.py line 119 87073] Train: [37/100][1533/1557] Data 0.009 (0.115) Batch 1.310 (1.094) Remain 29:48:29 loss: 0.2787 Lr: 0.00373 [2024-02-18 12:00:46,206 INFO misc.py line 119 87073] Train: [37/100][1534/1557] Data 0.011 (0.115) Batch 0.977 (1.094) Remain 29:48:21 loss: 0.1570 Lr: 0.00373 [2024-02-18 12:00:47,155 INFO misc.py line 119 87073] Train: [37/100][1535/1557] Data 0.006 (0.115) Batch 0.951 (1.094) Remain 29:48:10 loss: 0.3684 Lr: 0.00373 [2024-02-18 12:00:48,122 INFO misc.py line 119 87073] Train: [37/100][1536/1557] Data 0.003 (0.115) Batch 0.967 (1.093) Remain 29:48:01 loss: 0.7740 Lr: 0.00373 [2024-02-18 12:00:48,976 INFO misc.py line 119 87073] Train: [37/100][1537/1557] Data 0.004 (0.114) Batch 0.846 (1.093) Remain 29:47:44 loss: 0.7962 Lr: 0.00373 [2024-02-18 12:00:49,636 INFO misc.py line 119 87073] Train: [37/100][1538/1557] Data 0.013 (0.114) Batch 0.668 (1.093) Remain 29:47:16 loss: 0.2362 Lr: 0.00373 [2024-02-18 12:00:50,373 INFO misc.py line 119 87073] Train: [37/100][1539/1557] Data 0.004 (0.114) Batch 0.727 (1.093) Remain 29:46:52 loss: 0.2401 Lr: 0.00373 [2024-02-18 12:00:51,707 INFO misc.py line 119 87073] Train: [37/100][1540/1557] Data 0.014 (0.114) Batch 1.312 (1.093) Remain 29:47:04 loss: 0.3500 Lr: 0.00373 [2024-02-18 12:00:52,934 INFO misc.py line 119 87073] Train: [37/100][1541/1557] Data 0.036 (0.114) Batch 1.249 (1.093) Remain 29:47:13 loss: 0.4431 Lr: 0.00373 [2024-02-18 12:00:53,904 INFO misc.py line 119 87073] Train: [37/100][1542/1557] Data 0.014 (0.114) Batch 0.981 (1.093) Remain 29:47:05 loss: 0.6569 Lr: 0.00373 [2024-02-18 12:00:54,851 INFO misc.py line 119 87073] Train: [37/100][1543/1557] Data 0.003 (0.114) Batch 0.947 (1.093) Remain 29:46:55 loss: 0.5532 Lr: 0.00373 [2024-02-18 12:00:55,774 INFO misc.py line 119 87073] Train: [37/100][1544/1557] Data 0.003 (0.114) Batch 0.923 (1.093) Remain 29:46:43 loss: 0.4276 Lr: 0.00373 [2024-02-18 12:00:56,561 INFO misc.py line 119 87073] Train: [37/100][1545/1557] Data 0.004 (0.114) Batch 0.776 (1.093) Remain 29:46:22 loss: 0.1888 Lr: 0.00373 [2024-02-18 12:00:57,284 INFO misc.py line 119 87073] Train: [37/100][1546/1557] Data 0.015 (0.114) Batch 0.734 (1.092) Remain 29:45:58 loss: 0.3186 Lr: 0.00373 [2024-02-18 12:00:58,430 INFO misc.py line 119 87073] Train: [37/100][1547/1557] Data 0.004 (0.114) Batch 1.146 (1.092) Remain 29:46:00 loss: 0.1051 Lr: 0.00373 [2024-02-18 12:00:59,413 INFO misc.py line 119 87073] Train: [37/100][1548/1557] Data 0.004 (0.114) Batch 0.983 (1.092) Remain 29:45:52 loss: 0.4507 Lr: 0.00373 [2024-02-18 12:01:00,221 INFO misc.py line 119 87073] Train: [37/100][1549/1557] Data 0.004 (0.114) Batch 0.808 (1.092) Remain 29:45:33 loss: 0.3095 Lr: 0.00373 [2024-02-18 12:01:01,112 INFO misc.py line 119 87073] Train: [37/100][1550/1557] Data 0.004 (0.114) Batch 0.883 (1.092) Remain 29:45:18 loss: 1.4694 Lr: 0.00373 [2024-02-18 12:01:02,141 INFO misc.py line 119 87073] Train: [37/100][1551/1557] Data 0.012 (0.114) Batch 1.028 (1.092) Remain 29:45:13 loss: 0.7957 Lr: 0.00373 [2024-02-18 12:01:02,909 INFO misc.py line 119 87073] Train: [37/100][1552/1557] Data 0.013 (0.113) Batch 0.776 (1.092) Remain 29:44:52 loss: 0.3358 Lr: 0.00373 [2024-02-18 12:01:03,639 INFO misc.py line 119 87073] Train: [37/100][1553/1557] Data 0.005 (0.113) Batch 0.722 (1.091) Remain 29:44:28 loss: 0.2982 Lr: 0.00373 [2024-02-18 12:01:04,858 INFO misc.py line 119 87073] Train: [37/100][1554/1557] Data 0.013 (0.113) Batch 1.218 (1.092) Remain 29:44:35 loss: 0.1802 Lr: 0.00373 [2024-02-18 12:01:05,784 INFO misc.py line 119 87073] Train: [37/100][1555/1557] Data 0.015 (0.113) Batch 0.937 (1.091) Remain 29:44:24 loss: 0.5980 Lr: 0.00373 [2024-02-18 12:01:06,620 INFO misc.py line 119 87073] Train: [37/100][1556/1557] Data 0.004 (0.113) Batch 0.837 (1.091) Remain 29:44:06 loss: 0.4626 Lr: 0.00373 [2024-02-18 12:01:07,635 INFO misc.py line 119 87073] Train: [37/100][1557/1557] Data 0.004 (0.113) Batch 1.004 (1.091) Remain 29:44:00 loss: 0.2195 Lr: 0.00373 [2024-02-18 12:01:07,635 INFO misc.py line 136 87073] Train result: loss: 0.4066 [2024-02-18 12:01:07,636 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 12:01:35,810 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7298 [2024-02-18 12:01:36,587 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6799 [2024-02-18 12:01:38,712 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4646 [2024-02-18 12:01:40,918 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4408 [2024-02-18 12:01:45,865 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3683 [2024-02-18 12:01:46,564 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1910 [2024-02-18 12:01:47,826 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2870 [2024-02-18 12:01:50,782 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3918 [2024-02-18 12:01:52,598 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2888 [2024-02-18 12:01:54,255 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7190/0.7786/0.9092. [2024-02-18 12:01:54,255 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9272/0.9701 [2024-02-18 12:01:54,255 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9780/0.9849 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8519/0.9780 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0110/0.1154 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4366/0.5124 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6038/0.6098 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7805/0.8285 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8314/0.9164 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9250/0.9623 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8599/0.9013 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7808/0.8712 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7699/0.8028 [2024-02-18 12:01:54,256 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5914/0.6693 [2024-02-18 12:01:54,257 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 12:01:54,260 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 12:01:54,260 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 12:02:01,427 INFO misc.py line 119 87073] Train: [38/100][1/1557] Data 1.659 (1.659) Batch 2.605 (2.605) Remain 70:58:32 loss: 0.8108 Lr: 0.00373 [2024-02-18 12:02:02,492 INFO misc.py line 119 87073] Train: [38/100][2/1557] Data 0.006 (0.006) Batch 1.064 (1.064) Remain 28:59:28 loss: 0.5305 Lr: 0.00373 [2024-02-18 12:02:03,443 INFO misc.py line 119 87073] Train: [38/100][3/1557] Data 0.007 (0.007) Batch 0.953 (0.953) Remain 25:57:17 loss: 0.9019 Lr: 0.00373 [2024-02-18 12:02:04,429 INFO misc.py line 119 87073] Train: [38/100][4/1557] Data 0.006 (0.006) Batch 0.986 (0.986) Remain 26:51:06 loss: 0.5060 Lr: 0.00373 [2024-02-18 12:02:05,244 INFO misc.py line 119 87073] Train: [38/100][5/1557] Data 0.005 (0.005) Batch 0.815 (0.900) Remain 24:31:46 loss: 0.5944 Lr: 0.00373 [2024-02-18 12:02:06,050 INFO misc.py line 119 87073] Train: [38/100][6/1557] Data 0.005 (0.005) Batch 0.807 (0.869) Remain 23:40:43 loss: 0.1485 Lr: 0.00373 [2024-02-18 12:02:07,226 INFO misc.py line 119 87073] Train: [38/100][7/1557] Data 0.004 (0.005) Batch 1.168 (0.944) Remain 25:42:44 loss: 0.1634 Lr: 0.00373 [2024-02-18 12:02:08,092 INFO misc.py line 119 87073] Train: [38/100][8/1557] Data 0.012 (0.007) Batch 0.874 (0.930) Remain 25:20:05 loss: 0.6242 Lr: 0.00373 [2024-02-18 12:02:09,029 INFO misc.py line 119 87073] Train: [38/100][9/1557] Data 0.005 (0.006) Batch 0.937 (0.931) Remain 25:21:58 loss: 0.4545 Lr: 0.00373 [2024-02-18 12:02:10,000 INFO misc.py line 119 87073] Train: [38/100][10/1557] Data 0.004 (0.006) Batch 0.972 (0.937) Remain 25:31:33 loss: 0.1460 Lr: 0.00373 [2024-02-18 12:02:10,768 INFO misc.py line 119 87073] Train: [38/100][11/1557] Data 0.004 (0.006) Batch 0.762 (0.915) Remain 24:55:45 loss: 0.4261 Lr: 0.00373 [2024-02-18 12:02:11,563 INFO misc.py line 119 87073] Train: [38/100][12/1557] Data 0.009 (0.006) Batch 0.801 (0.902) Remain 24:34:56 loss: 0.5340 Lr: 0.00373 [2024-02-18 12:02:12,356 INFO misc.py line 119 87073] Train: [38/100][13/1557] Data 0.004 (0.006) Batch 0.793 (0.891) Remain 24:17:01 loss: 0.4834 Lr: 0.00373 [2024-02-18 12:02:13,481 INFO misc.py line 119 87073] Train: [38/100][14/1557] Data 0.004 (0.006) Batch 1.115 (0.912) Remain 24:50:15 loss: 0.3129 Lr: 0.00373 [2024-02-18 12:02:14,380 INFO misc.py line 119 87073] Train: [38/100][15/1557] Data 0.013 (0.006) Batch 0.909 (0.911) Remain 24:49:55 loss: 0.4250 Lr: 0.00373 [2024-02-18 12:02:15,252 INFO misc.py line 119 87073] Train: [38/100][16/1557] Data 0.004 (0.006) Batch 0.871 (0.908) Remain 24:44:46 loss: 0.2709 Lr: 0.00373 [2024-02-18 12:02:16,325 INFO misc.py line 119 87073] Train: [38/100][17/1557] Data 0.006 (0.006) Batch 1.072 (0.920) Remain 25:03:53 loss: 0.3336 Lr: 0.00373 [2024-02-18 12:02:17,324 INFO misc.py line 119 87073] Train: [38/100][18/1557] Data 0.005 (0.006) Batch 1.000 (0.925) Remain 25:12:32 loss: 0.5318 Lr: 0.00373 [2024-02-18 12:02:18,106 INFO misc.py line 119 87073] Train: [38/100][19/1557] Data 0.005 (0.006) Batch 0.773 (0.916) Remain 24:56:58 loss: 0.2290 Lr: 0.00373 [2024-02-18 12:02:18,899 INFO misc.py line 119 87073] Train: [38/100][20/1557] Data 0.014 (0.006) Batch 0.803 (0.909) Remain 24:46:05 loss: 0.2557 Lr: 0.00373 [2024-02-18 12:02:20,103 INFO misc.py line 119 87073] Train: [38/100][21/1557] Data 0.004 (0.006) Batch 1.203 (0.926) Remain 25:12:45 loss: 0.5678 Lr: 0.00373 [2024-02-18 12:02:21,142 INFO misc.py line 119 87073] Train: [38/100][22/1557] Data 0.005 (0.006) Batch 1.031 (0.931) Remain 25:21:50 loss: 0.2877 Lr: 0.00373 [2024-02-18 12:02:21,934 INFO misc.py line 119 87073] Train: [38/100][23/1557] Data 0.013 (0.007) Batch 0.800 (0.925) Remain 25:11:08 loss: 0.3710 Lr: 0.00373 [2024-02-18 12:02:22,921 INFO misc.py line 119 87073] Train: [38/100][24/1557] Data 0.005 (0.006) Batch 0.986 (0.927) Remain 25:15:53 loss: 0.6211 Lr: 0.00373 [2024-02-18 12:02:23,861 INFO misc.py line 119 87073] Train: [38/100][25/1557] Data 0.006 (0.006) Batch 0.940 (0.928) Remain 25:16:47 loss: 0.6364 Lr: 0.00373 [2024-02-18 12:02:24,578 INFO misc.py line 119 87073] Train: [38/100][26/1557] Data 0.006 (0.006) Batch 0.708 (0.918) Remain 25:01:07 loss: 0.3273 Lr: 0.00373 [2024-02-18 12:02:25,358 INFO misc.py line 119 87073] Train: [38/100][27/1557] Data 0.015 (0.007) Batch 0.789 (0.913) Remain 24:52:18 loss: 0.3143 Lr: 0.00373 [2024-02-18 12:02:26,458 INFO misc.py line 119 87073] Train: [38/100][28/1557] Data 0.006 (0.007) Batch 1.101 (0.921) Remain 25:04:33 loss: 0.1044 Lr: 0.00373 [2024-02-18 12:02:27,513 INFO misc.py line 119 87073] Train: [38/100][29/1557] Data 0.005 (0.007) Batch 1.055 (0.926) Remain 25:13:01 loss: 0.5976 Lr: 0.00373 [2024-02-18 12:02:28,392 INFO misc.py line 119 87073] Train: [38/100][30/1557] Data 0.005 (0.007) Batch 0.879 (0.924) Remain 25:10:09 loss: 0.2838 Lr: 0.00373 [2024-02-18 12:02:29,335 INFO misc.py line 119 87073] Train: [38/100][31/1557] Data 0.006 (0.007) Batch 0.944 (0.925) Remain 25:11:16 loss: 0.3463 Lr: 0.00373 [2024-02-18 12:02:30,561 INFO misc.py line 119 87073] Train: [38/100][32/1557] Data 0.004 (0.007) Batch 1.217 (0.935) Remain 25:27:43 loss: 0.4020 Lr: 0.00373 [2024-02-18 12:02:31,370 INFO misc.py line 119 87073] Train: [38/100][33/1557] Data 0.014 (0.007) Batch 0.818 (0.931) Remain 25:21:19 loss: 0.2866 Lr: 0.00373 [2024-02-18 12:02:32,142 INFO misc.py line 119 87073] Train: [38/100][34/1557] Data 0.005 (0.007) Batch 0.768 (0.926) Remain 25:12:44 loss: 0.3456 Lr: 0.00373 [2024-02-18 12:02:33,412 INFO misc.py line 119 87073] Train: [38/100][35/1557] Data 0.009 (0.007) Batch 1.274 (0.937) Remain 25:30:30 loss: 0.4860 Lr: 0.00373 [2024-02-18 12:02:34,298 INFO misc.py line 119 87073] Train: [38/100][36/1557] Data 0.006 (0.007) Batch 0.888 (0.935) Remain 25:28:04 loss: 0.4047 Lr: 0.00373 [2024-02-18 12:02:35,237 INFO misc.py line 119 87073] Train: [38/100][37/1557] Data 0.003 (0.007) Batch 0.938 (0.935) Remain 25:28:12 loss: 0.4135 Lr: 0.00373 [2024-02-18 12:02:36,357 INFO misc.py line 119 87073] Train: [38/100][38/1557] Data 0.005 (0.007) Batch 1.120 (0.940) Remain 25:36:50 loss: 0.4432 Lr: 0.00373 [2024-02-18 12:02:37,305 INFO misc.py line 119 87073] Train: [38/100][39/1557] Data 0.003 (0.007) Batch 0.948 (0.941) Remain 25:37:11 loss: 0.3524 Lr: 0.00373 [2024-02-18 12:02:38,047 INFO misc.py line 119 87073] Train: [38/100][40/1557] Data 0.003 (0.006) Batch 0.733 (0.935) Remain 25:27:58 loss: 0.1733 Lr: 0.00373 [2024-02-18 12:02:38,852 INFO misc.py line 119 87073] Train: [38/100][41/1557] Data 0.014 (0.007) Batch 0.814 (0.932) Remain 25:22:45 loss: 0.3870 Lr: 0.00373 [2024-02-18 12:02:40,207 INFO misc.py line 119 87073] Train: [38/100][42/1557] Data 0.004 (0.007) Batch 1.354 (0.943) Remain 25:40:25 loss: 0.4868 Lr: 0.00373 [2024-02-18 12:02:41,225 INFO misc.py line 119 87073] Train: [38/100][43/1557] Data 0.005 (0.007) Batch 1.018 (0.945) Remain 25:43:28 loss: 0.7148 Lr: 0.00373 [2024-02-18 12:02:42,177 INFO misc.py line 119 87073] Train: [38/100][44/1557] Data 0.005 (0.006) Batch 0.954 (0.945) Remain 25:43:49 loss: 0.7135 Lr: 0.00373 [2024-02-18 12:02:43,116 INFO misc.py line 119 87073] Train: [38/100][45/1557] Data 0.003 (0.006) Batch 0.935 (0.945) Remain 25:43:24 loss: 0.5255 Lr: 0.00373 [2024-02-18 12:02:44,130 INFO misc.py line 119 87073] Train: [38/100][46/1557] Data 0.008 (0.006) Batch 1.019 (0.946) Remain 25:46:13 loss: 0.4821 Lr: 0.00373 [2024-02-18 12:02:44,877 INFO misc.py line 119 87073] Train: [38/100][47/1557] Data 0.004 (0.006) Batch 0.745 (0.942) Remain 25:38:44 loss: 0.3237 Lr: 0.00373 [2024-02-18 12:02:45,609 INFO misc.py line 119 87073] Train: [38/100][48/1557] Data 0.006 (0.006) Batch 0.733 (0.937) Remain 25:31:08 loss: 0.4346 Lr: 0.00372 [2024-02-18 12:02:46,741 INFO misc.py line 119 87073] Train: [38/100][49/1557] Data 0.005 (0.006) Batch 1.130 (0.941) Remain 25:37:57 loss: 0.1357 Lr: 0.00372 [2024-02-18 12:02:47,944 INFO misc.py line 119 87073] Train: [38/100][50/1557] Data 0.007 (0.006) Batch 1.202 (0.947) Remain 25:47:00 loss: 0.5417 Lr: 0.00372 [2024-02-18 12:02:49,170 INFO misc.py line 119 87073] Train: [38/100][51/1557] Data 0.008 (0.006) Batch 1.223 (0.952) Remain 25:56:23 loss: 0.7051 Lr: 0.00372 [2024-02-18 12:02:50,076 INFO misc.py line 119 87073] Train: [38/100][52/1557] Data 0.012 (0.007) Batch 0.912 (0.952) Remain 25:55:00 loss: 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INFO misc.py line 119 87073] Train: [38/100][59/1557] Data 0.005 (0.007) Batch 1.005 (0.956) Remain 26:02:25 loss: 0.3006 Lr: 0.00372 [2024-02-18 12:02:58,055 INFO misc.py line 119 87073] Train: [38/100][60/1557] Data 0.005 (0.007) Batch 1.060 (0.958) Remain 26:05:23 loss: 0.2989 Lr: 0.00372 [2024-02-18 12:02:58,894 INFO misc.py line 119 87073] Train: [38/100][61/1557] Data 0.005 (0.007) Batch 0.840 (0.956) Remain 26:02:02 loss: 0.6108 Lr: 0.00372 [2024-02-18 12:02:59,657 INFO misc.py line 119 87073] Train: [38/100][62/1557] Data 0.004 (0.007) Batch 0.762 (0.953) Remain 25:56:38 loss: 0.2237 Lr: 0.00372 [2024-02-18 12:03:08,142 INFO misc.py line 119 87073] Train: [38/100][63/1557] Data 4.605 (0.083) Batch 8.485 (1.078) Remain 29:21:43 loss: 0.2732 Lr: 0.00372 [2024-02-18 12:03:09,216 INFO misc.py line 119 87073] Train: [38/100][64/1557] Data 0.005 (0.082) Batch 1.076 (1.078) Remain 29:21:38 loss: 0.6041 Lr: 0.00372 [2024-02-18 12:03:10,026 INFO misc.py line 119 87073] Train: 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0.005 (0.065) Batch 0.775 (1.038) Remain 28:14:40 loss: 0.3219 Lr: 0.00372 [2024-02-18 12:03:34,918 INFO misc.py line 119 87073] Train: [38/100][91/1557] Data 0.004 (0.064) Batch 1.199 (1.039) Remain 28:17:39 loss: 0.2492 Lr: 0.00372 [2024-02-18 12:03:35,866 INFO misc.py line 119 87073] Train: [38/100][92/1557] Data 0.014 (0.063) Batch 0.958 (1.038) Remain 28:16:08 loss: 0.4354 Lr: 0.00372 [2024-02-18 12:03:36,906 INFO misc.py line 119 87073] Train: [38/100][93/1557] Data 0.004 (0.063) Batch 1.041 (1.038) Remain 28:16:10 loss: 0.3027 Lr: 0.00372 [2024-02-18 12:03:37,703 INFO misc.py line 119 87073] Train: [38/100][94/1557] Data 0.003 (0.062) Batch 0.795 (1.036) Remain 28:11:46 loss: 0.3642 Lr: 0.00372 [2024-02-18 12:03:38,696 INFO misc.py line 119 87073] Train: [38/100][95/1557] Data 0.005 (0.061) Batch 0.986 (1.035) Remain 28:10:53 loss: 0.5949 Lr: 0.00372 [2024-02-18 12:03:39,469 INFO misc.py line 119 87073] Train: [38/100][96/1557] Data 0.012 (0.061) Batch 0.781 (1.033) Remain 28:06:23 loss: 0.4546 Lr: 0.00372 [2024-02-18 12:03:40,209 INFO misc.py line 119 87073] Train: [38/100][97/1557] Data 0.004 (0.060) Batch 0.738 (1.029) Remain 28:01:15 loss: 0.2267 Lr: 0.00372 [2024-02-18 12:03:41,514 INFO misc.py line 119 87073] Train: [38/100][98/1557] Data 0.006 (0.060) Batch 1.307 (1.032) Remain 28:06:00 loss: 0.1073 Lr: 0.00372 [2024-02-18 12:03:42,520 INFO misc.py line 119 87073] Train: [38/100][99/1557] Data 0.005 (0.059) Batch 1.005 (1.032) Remain 28:05:31 loss: 0.6020 Lr: 0.00372 [2024-02-18 12:03:43,394 INFO misc.py line 119 87073] Train: [38/100][100/1557] Data 0.006 (0.059) Batch 0.874 (1.030) Remain 28:02:50 loss: 0.3471 Lr: 0.00372 [2024-02-18 12:03:44,455 INFO misc.py line 119 87073] Train: [38/100][101/1557] Data 0.006 (0.058) Batch 1.061 (1.031) Remain 28:03:20 loss: 0.4285 Lr: 0.00372 [2024-02-18 12:03:45,366 INFO misc.py line 119 87073] Train: [38/100][102/1557] Data 0.005 (0.058) Batch 0.911 (1.030) Remain 28:01:21 loss: 0.2784 Lr: 0.00372 [2024-02-18 12:03:46,120 INFO misc.py line 119 87073] Train: [38/100][103/1557] Data 0.005 (0.057) Batch 0.737 (1.027) Remain 27:56:33 loss: 0.2038 Lr: 0.00372 [2024-02-18 12:03:46,865 INFO misc.py line 119 87073] Train: [38/100][104/1557] Data 0.022 (0.057) Batch 0.763 (1.024) Remain 27:52:16 loss: 0.4112 Lr: 0.00372 [2024-02-18 12:03:48,039 INFO misc.py line 119 87073] Train: [38/100][105/1557] Data 0.004 (0.056) Batch 1.172 (1.025) Remain 27:54:37 loss: 0.2401 Lr: 0.00372 [2024-02-18 12:03:49,266 INFO misc.py line 119 87073] Train: [38/100][106/1557] Data 0.007 (0.056) Batch 1.218 (1.027) Remain 27:57:40 loss: 0.8002 Lr: 0.00372 [2024-02-18 12:03:50,154 INFO misc.py line 119 87073] Train: [38/100][107/1557] Data 0.015 (0.055) Batch 0.899 (1.026) Remain 27:55:38 loss: 0.6026 Lr: 0.00372 [2024-02-18 12:03:51,166 INFO misc.py line 119 87073] Train: [38/100][108/1557] Data 0.004 (0.055) Batch 1.012 (1.026) Remain 27:55:24 loss: 0.4312 Lr: 0.00372 [2024-02-18 12:03:52,172 INFO misc.py line 119 87073] Train: [38/100][109/1557] Data 0.004 (0.054) Batch 1.006 (1.026) Remain 27:55:04 loss: 0.1811 Lr: 0.00372 [2024-02-18 12:03:52,980 INFO misc.py line 119 87073] Train: [38/100][110/1557] Data 0.004 (0.054) Batch 0.808 (1.024) Remain 27:51:44 loss: 0.1995 Lr: 0.00372 [2024-02-18 12:03:53,730 INFO misc.py line 119 87073] Train: [38/100][111/1557] Data 0.005 (0.053) Batch 0.750 (1.021) Remain 27:47:34 loss: 0.5551 Lr: 0.00372 [2024-02-18 12:03:54,905 INFO misc.py line 119 87073] Train: [38/100][112/1557] Data 0.004 (0.053) Batch 1.163 (1.022) Remain 27:49:41 loss: 0.2743 Lr: 0.00372 [2024-02-18 12:03:55,885 INFO misc.py line 119 87073] Train: [38/100][113/1557] Data 0.016 (0.053) Batch 0.991 (1.022) Remain 27:49:12 loss: 0.6496 Lr: 0.00372 [2024-02-18 12:03:56,934 INFO misc.py line 119 87073] Train: [38/100][114/1557] Data 0.004 (0.052) Batch 1.049 (1.022) Remain 27:49:35 loss: 0.7961 Lr: 0.00372 [2024-02-18 12:03:57,827 INFO misc.py line 119 87073] Train: 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Batch 1.061 (1.074) Remain 29:13:47 loss: 0.7816 Lr: 0.00372 [2024-02-18 12:04:11,153 INFO misc.py line 119 87073] Train: [38/100][122/1557] Data 0.003 (0.090) Batch 0.968 (1.073) Remain 29:12:18 loss: 0.3760 Lr: 0.00372 [2024-02-18 12:04:12,076 INFO misc.py line 119 87073] Train: [38/100][123/1557] Data 0.005 (0.090) Batch 0.924 (1.072) Remain 29:10:15 loss: 0.5595 Lr: 0.00372 [2024-02-18 12:04:12,899 INFO misc.py line 119 87073] Train: [38/100][124/1557] Data 0.005 (0.089) Batch 0.821 (1.070) Remain 29:06:51 loss: 0.4006 Lr: 0.00372 [2024-02-18 12:04:13,656 INFO misc.py line 119 87073] Train: [38/100][125/1557] Data 0.007 (0.088) Batch 0.758 (1.067) Remain 29:02:40 loss: 0.4473 Lr: 0.00372 [2024-02-18 12:04:14,851 INFO misc.py line 119 87073] Train: [38/100][126/1557] Data 0.005 (0.088) Batch 1.195 (1.068) Remain 29:04:21 loss: 0.2853 Lr: 0.00372 [2024-02-18 12:04:15,729 INFO misc.py line 119 87073] Train: [38/100][127/1557] Data 0.005 (0.087) Batch 0.879 (1.067) Remain 29:01:50 loss: 0.3223 Lr: 0.00372 [2024-02-18 12:04:16,695 INFO misc.py line 119 87073] Train: [38/100][128/1557] Data 0.004 (0.086) Batch 0.965 (1.066) Remain 29:00:29 loss: 0.7832 Lr: 0.00372 [2024-02-18 12:04:17,564 INFO misc.py line 119 87073] Train: [38/100][129/1557] Data 0.005 (0.086) Batch 0.866 (1.064) Remain 28:57:53 loss: 0.9694 Lr: 0.00372 [2024-02-18 12:04:18,673 INFO misc.py line 119 87073] Train: [38/100][130/1557] Data 0.008 (0.085) Batch 1.113 (1.065) Remain 28:58:29 loss: 0.0693 Lr: 0.00372 [2024-02-18 12:04:19,565 INFO misc.py line 119 87073] Train: [38/100][131/1557] Data 0.004 (0.084) Batch 0.891 (1.063) Remain 28:56:15 loss: 0.3214 Lr: 0.00372 [2024-02-18 12:04:20,419 INFO misc.py line 119 87073] Train: [38/100][132/1557] Data 0.005 (0.084) Batch 0.854 (1.062) Remain 28:53:35 loss: 0.5177 Lr: 0.00372 [2024-02-18 12:04:21,731 INFO misc.py line 119 87073] Train: [38/100][133/1557] Data 0.005 (0.083) Batch 1.293 (1.064) Remain 28:56:28 loss: 0.1060 Lr: 0.00372 [2024-02-18 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Batch 0.897 (1.070) Remain 29:06:19 loss: 0.2094 Lr: 0.00372 [2024-02-18 12:05:10,467 INFO misc.py line 119 87073] Train: [38/100][178/1557] Data 0.006 (0.087) Batch 0.823 (1.069) Remain 29:04:00 loss: 0.8289 Lr: 0.00372 [2024-02-18 12:05:11,464 INFO misc.py line 119 87073] Train: [38/100][179/1557] Data 0.004 (0.086) Batch 0.996 (1.068) Remain 29:03:18 loss: 0.9065 Lr: 0.00372 [2024-02-18 12:05:13,768 INFO misc.py line 119 87073] Train: [38/100][180/1557] Data 1.465 (0.094) Batch 2.305 (1.075) Remain 29:14:41 loss: 0.6393 Lr: 0.00372 [2024-02-18 12:05:14,533 INFO misc.py line 119 87073] Train: [38/100][181/1557] Data 0.012 (0.094) Batch 0.765 (1.074) Remain 29:11:50 loss: 0.2332 Lr: 0.00372 [2024-02-18 12:05:15,691 INFO misc.py line 119 87073] Train: [38/100][182/1557] Data 0.004 (0.093) Batch 1.156 (1.074) Remain 29:12:34 loss: 0.2838 Lr: 0.00372 [2024-02-18 12:05:16,612 INFO misc.py line 119 87073] Train: [38/100][183/1557] Data 0.007 (0.093) Batch 0.921 (1.073) Remain 29:11:09 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line 119 87073] Train: [38/100][221/1557] Data 0.005 (0.078) Batch 0.988 (1.055) Remain 28:40:32 loss: 0.3540 Lr: 0.00372 [2024-02-18 12:05:54,117 INFO misc.py line 119 87073] Train: [38/100][222/1557] Data 0.004 (0.077) Batch 0.723 (1.053) Remain 28:38:03 loss: 0.2227 Lr: 0.00372 [2024-02-18 12:05:54,840 INFO misc.py line 119 87073] Train: [38/100][223/1557] Data 0.011 (0.077) Batch 0.729 (1.052) Remain 28:35:37 loss: 0.2133 Lr: 0.00372 [2024-02-18 12:05:56,031 INFO misc.py line 119 87073] Train: [38/100][224/1557] Data 0.004 (0.077) Batch 1.192 (1.052) Remain 28:36:38 loss: 0.2115 Lr: 0.00372 [2024-02-18 12:05:57,119 INFO misc.py line 119 87073] Train: [38/100][225/1557] Data 0.004 (0.076) Batch 1.087 (1.053) Remain 28:36:52 loss: 0.6416 Lr: 0.00372 [2024-02-18 12:05:58,044 INFO misc.py line 119 87073] Train: [38/100][226/1557] Data 0.005 (0.076) Batch 0.926 (1.052) Remain 28:35:56 loss: 0.4164 Lr: 0.00372 [2024-02-18 12:05:58,949 INFO misc.py line 119 87073] Train: 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Batch 0.894 (1.078) Remain 29:18:16 loss: 0.4472 Lr: 0.00372 [2024-02-18 12:06:12,282 INFO misc.py line 119 87073] Train: [38/100][234/1557] Data 0.008 (0.097) Batch 0.885 (1.077) Remain 29:16:53 loss: 0.2573 Lr: 0.00372 [2024-02-18 12:06:13,199 INFO misc.py line 119 87073] Train: [38/100][235/1557] Data 0.004 (0.097) Batch 0.915 (1.077) Remain 29:15:44 loss: 0.2821 Lr: 0.00372 [2024-02-18 12:06:13,992 INFO misc.py line 119 87073] Train: [38/100][236/1557] Data 0.007 (0.096) Batch 0.795 (1.075) Remain 29:13:45 loss: 0.4385 Lr: 0.00372 [2024-02-18 12:06:14,778 INFO misc.py line 119 87073] Train: [38/100][237/1557] Data 0.005 (0.096) Batch 0.786 (1.074) Remain 29:11:43 loss: 0.1956 Lr: 0.00372 [2024-02-18 12:06:15,989 INFO misc.py line 119 87073] Train: [38/100][238/1557] Data 0.004 (0.095) Batch 1.205 (1.075) Remain 29:12:36 loss: 0.1948 Lr: 0.00372 [2024-02-18 12:06:16,973 INFO misc.py line 119 87073] Train: [38/100][239/1557] Data 0.010 (0.095) Batch 0.988 (1.074) Remain 29:11:59 loss: 0.6283 Lr: 0.00372 [2024-02-18 12:06:18,041 INFO misc.py line 119 87073] Train: [38/100][240/1557] Data 0.006 (0.095) Batch 1.069 (1.074) Remain 29:11:56 loss: 0.9168 Lr: 0.00372 [2024-02-18 12:06:18,845 INFO misc.py line 119 87073] Train: [38/100][241/1557] Data 0.004 (0.094) Batch 0.803 (1.073) Remain 29:10:04 loss: 0.4711 Lr: 0.00372 [2024-02-18 12:06:19,662 INFO misc.py line 119 87073] Train: [38/100][242/1557] Data 0.005 (0.094) Batch 0.811 (1.072) Remain 29:08:15 loss: 0.4111 Lr: 0.00372 [2024-02-18 12:06:20,466 INFO misc.py line 119 87073] Train: [38/100][243/1557] Data 0.011 (0.094) Batch 0.810 (1.071) Remain 29:06:27 loss: 0.1772 Lr: 0.00372 [2024-02-18 12:06:21,182 INFO misc.py line 119 87073] Train: [38/100][244/1557] Data 0.005 (0.093) Batch 0.717 (1.069) Remain 29:04:03 loss: 0.3478 Lr: 0.00372 [2024-02-18 12:06:22,469 INFO misc.py line 119 87073] Train: [38/100][245/1557] Data 0.004 (0.093) Batch 1.281 (1.070) Remain 29:05:27 loss: 0.1841 Lr: 0.00372 [2024-02-18 12:06:23,480 INFO misc.py line 119 87073] Train: [38/100][246/1557] Data 0.010 (0.092) Batch 1.014 (1.070) Remain 29:05:03 loss: 0.5320 Lr: 0.00372 [2024-02-18 12:06:24,292 INFO misc.py line 119 87073] Train: [38/100][247/1557] Data 0.006 (0.092) Batch 0.815 (1.069) Remain 29:03:20 loss: 0.2143 Lr: 0.00372 [2024-02-18 12:06:25,230 INFO misc.py line 119 87073] Train: [38/100][248/1557] Data 0.004 (0.092) Batch 0.938 (1.069) Remain 29:02:27 loss: 0.4809 Lr: 0.00372 [2024-02-18 12:06:26,216 INFO misc.py line 119 87073] Train: [38/100][249/1557] Data 0.004 (0.091) Batch 0.984 (1.068) Remain 29:01:52 loss: 0.4369 Lr: 0.00372 [2024-02-18 12:06:27,002 INFO misc.py line 119 87073] Train: [38/100][250/1557] Data 0.006 (0.091) Batch 0.778 (1.067) Remain 28:59:56 loss: 0.3303 Lr: 0.00372 [2024-02-18 12:06:27,765 INFO misc.py line 119 87073] Train: [38/100][251/1557] Data 0.013 (0.091) Batch 0.773 (1.066) Remain 28:57:59 loss: 0.3078 Lr: 0.00372 [2024-02-18 12:06:28,831 INFO misc.py line 119 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[2024-02-18 12:06:46,728 INFO misc.py line 119 87073] Train: [38/100][271/1557] Data 0.003 (0.084) Batch 0.764 (1.057) Remain 28:43:06 loss: 0.2203 Lr: 0.00371 [2024-02-18 12:06:47,526 INFO misc.py line 119 87073] Train: [38/100][272/1557] Data 0.040 (0.084) Batch 0.834 (1.056) Remain 28:41:43 loss: 0.5116 Lr: 0.00371 [2024-02-18 12:06:48,673 INFO misc.py line 119 87073] Train: [38/100][273/1557] Data 0.003 (0.084) Batch 1.146 (1.056) Remain 28:42:15 loss: 0.1909 Lr: 0.00371 [2024-02-18 12:06:49,611 INFO misc.py line 119 87073] Train: [38/100][274/1557] Data 0.004 (0.084) Batch 0.939 (1.056) Remain 28:41:31 loss: 0.3735 Lr: 0.00371 [2024-02-18 12:06:50,779 INFO misc.py line 119 87073] Train: [38/100][275/1557] Data 0.004 (0.083) Batch 1.168 (1.056) Remain 28:42:11 loss: 0.2316 Lr: 0.00371 [2024-02-18 12:06:51,730 INFO misc.py line 119 87073] Train: [38/100][276/1557] Data 0.004 (0.083) Batch 0.950 (1.056) Remain 28:41:31 loss: 0.1463 Lr: 0.00371 [2024-02-18 12:06:52,576 INFO misc.py line 119 87073] Train: [38/100][277/1557] Data 0.005 (0.083) Batch 0.845 (1.055) Remain 28:40:15 loss: 0.4796 Lr: 0.00371 [2024-02-18 12:06:53,315 INFO misc.py line 119 87073] Train: [38/100][278/1557] Data 0.006 (0.082) Batch 0.740 (1.054) Remain 28:38:22 loss: 0.2555 Lr: 0.00371 [2024-02-18 12:06:54,065 INFO misc.py line 119 87073] Train: [38/100][279/1557] Data 0.006 (0.082) Batch 0.743 (1.053) Remain 28:36:31 loss: 0.2303 Lr: 0.00371 [2024-02-18 12:06:55,162 INFO misc.py line 119 87073] Train: [38/100][280/1557] Data 0.012 (0.082) Batch 1.097 (1.053) Remain 28:36:45 loss: 0.1389 Lr: 0.00371 [2024-02-18 12:06:56,278 INFO misc.py line 119 87073] Train: [38/100][281/1557] Data 0.012 (0.082) Batch 1.116 (1.053) Remain 28:37:06 loss: 0.3524 Lr: 0.00371 [2024-02-18 12:06:57,246 INFO misc.py line 119 87073] Train: [38/100][282/1557] Data 0.011 (0.081) Batch 0.975 (1.053) Remain 28:36:38 loss: 0.4753 Lr: 0.00371 [2024-02-18 12:06:58,222 INFO misc.py line 119 87073] Train: 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Batch 0.911 (1.074) Remain 29:10:35 loss: 0.7875 Lr: 0.00371 [2024-02-18 12:07:11,465 INFO misc.py line 119 87073] Train: [38/100][290/1557] Data 0.006 (0.098) Batch 0.857 (1.073) Remain 29:09:20 loss: 0.1833 Lr: 0.00371 [2024-02-18 12:07:12,302 INFO misc.py line 119 87073] Train: [38/100][291/1557] Data 0.017 (0.097) Batch 0.848 (1.072) Remain 29:08:02 loss: 0.2393 Lr: 0.00371 [2024-02-18 12:07:13,095 INFO misc.py line 119 87073] Train: [38/100][292/1557] Data 0.007 (0.097) Batch 0.795 (1.071) Remain 29:06:27 loss: 0.1793 Lr: 0.00371 [2024-02-18 12:07:13,913 INFO misc.py line 119 87073] Train: [38/100][293/1557] Data 0.005 (0.097) Batch 0.817 (1.071) Remain 29:05:00 loss: 0.2940 Lr: 0.00371 [2024-02-18 12:07:15,088 INFO misc.py line 119 87073] Train: [38/100][294/1557] Data 0.006 (0.096) Batch 1.169 (1.071) Remain 29:05:32 loss: 0.2711 Lr: 0.00371 [2024-02-18 12:07:16,145 INFO misc.py line 119 87073] Train: [38/100][295/1557] Data 0.012 (0.096) Batch 1.057 (1.071) Remain 29:05:27 loss: 0.3231 Lr: 0.00371 [2024-02-18 12:07:17,040 INFO misc.py line 119 87073] Train: [38/100][296/1557] Data 0.011 (0.096) Batch 0.900 (1.070) Remain 29:04:29 loss: 0.6062 Lr: 0.00371 [2024-02-18 12:07:18,004 INFO misc.py line 119 87073] Train: [38/100][297/1557] Data 0.006 (0.095) Batch 0.966 (1.070) Remain 29:03:53 loss: 0.2630 Lr: 0.00371 [2024-02-18 12:07:18,846 INFO misc.py line 119 87073] Train: [38/100][298/1557] Data 0.004 (0.095) Batch 0.842 (1.069) Remain 29:02:36 loss: 0.5306 Lr: 0.00371 [2024-02-18 12:07:19,693 INFO misc.py line 119 87073] Train: [38/100][299/1557] Data 0.004 (0.095) Batch 0.836 (1.068) Remain 29:01:18 loss: 0.5179 Lr: 0.00371 [2024-02-18 12:07:20,451 INFO misc.py line 119 87073] Train: [38/100][300/1557] Data 0.016 (0.095) Batch 0.768 (1.067) Remain 28:59:38 loss: 0.4250 Lr: 0.00371 [2024-02-18 12:07:21,665 INFO misc.py line 119 87073] Train: [38/100][301/1557] Data 0.005 (0.094) Batch 1.216 (1.068) Remain 29:00:25 loss: 0.0925 Lr: 0.00371 [2024-02-18 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87073] Train: [38/100][308/1557] Data 0.004 (0.092) Batch 1.210 (1.065) Remain 28:56:05 loss: 0.1004 Lr: 0.00371 [2024-02-18 12:07:29,271 INFO misc.py line 119 87073] Train: [38/100][309/1557] Data 0.005 (0.092) Batch 0.920 (1.065) Remain 28:55:17 loss: 0.5136 Lr: 0.00371 [2024-02-18 12:07:30,154 INFO misc.py line 119 87073] Train: [38/100][310/1557] Data 0.004 (0.092) Batch 0.880 (1.064) Remain 28:54:18 loss: 0.5555 Lr: 0.00371 [2024-02-18 12:07:31,072 INFO misc.py line 119 87073] Train: [38/100][311/1557] Data 0.007 (0.091) Batch 0.919 (1.064) Remain 28:53:31 loss: 0.4994 Lr: 0.00371 [2024-02-18 12:07:32,097 INFO misc.py line 119 87073] Train: [38/100][312/1557] Data 0.006 (0.091) Batch 1.026 (1.064) Remain 28:53:18 loss: 0.5194 Lr: 0.00371 [2024-02-18 12:07:32,860 INFO misc.py line 119 87073] Train: [38/100][313/1557] Data 0.004 (0.091) Batch 0.763 (1.063) Remain 28:51:42 loss: 0.6453 Lr: 0.00371 [2024-02-18 12:07:33,642 INFO misc.py line 119 87073] Train: [38/100][314/1557] Data 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Batch 0.961 (1.075) Remain 29:09:37 loss: 0.4370 Lr: 0.00371 [2024-02-18 12:10:12,535 INFO misc.py line 119 87073] Train: [38/100][458/1557] Data 0.005 (0.101) Batch 0.942 (1.075) Remain 29:09:07 loss: 0.4014 Lr: 0.00371 [2024-02-18 12:10:13,405 INFO misc.py line 119 87073] Train: [38/100][459/1557] Data 0.007 (0.101) Batch 0.872 (1.074) Remain 29:08:23 loss: 0.4646 Lr: 0.00371 [2024-02-18 12:10:14,211 INFO misc.py line 119 87073] Train: [38/100][460/1557] Data 0.004 (0.101) Batch 0.796 (1.074) Remain 29:07:22 loss: 0.2593 Lr: 0.00371 [2024-02-18 12:10:14,940 INFO misc.py line 119 87073] Train: [38/100][461/1557] Data 0.015 (0.101) Batch 0.740 (1.073) Remain 29:06:10 loss: 0.4433 Lr: 0.00371 [2024-02-18 12:10:16,105 INFO misc.py line 119 87073] Train: [38/100][462/1557] Data 0.003 (0.100) Batch 1.165 (1.073) Remain 29:06:28 loss: 0.4605 Lr: 0.00371 [2024-02-18 12:10:17,009 INFO misc.py line 119 87073] Train: [38/100][463/1557] Data 0.004 (0.100) Batch 0.902 (1.073) Remain 29:05:51 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line 119 87073] Train: [38/100][501/1557] Data 0.004 (0.093) Batch 0.911 (1.063) Remain 28:48:36 loss: 0.3278 Lr: 0.00370 [2024-02-18 12:10:53,459 INFO misc.py line 119 87073] Train: [38/100][502/1557] Data 0.007 (0.093) Batch 0.754 (1.062) Remain 28:47:34 loss: 0.4359 Lr: 0.00370 [2024-02-18 12:10:54,169 INFO misc.py line 119 87073] Train: [38/100][503/1557] Data 0.004 (0.093) Batch 0.710 (1.061) Remain 28:46:24 loss: 0.3526 Lr: 0.00370 [2024-02-18 12:10:55,363 INFO misc.py line 119 87073] Train: [38/100][504/1557] Data 0.005 (0.093) Batch 1.191 (1.062) Remain 28:46:48 loss: 0.5826 Lr: 0.00370 [2024-02-18 12:10:56,605 INFO misc.py line 119 87073] Train: [38/100][505/1557] Data 0.007 (0.092) Batch 1.235 (1.062) Remain 28:47:21 loss: 0.5486 Lr: 0.00370 [2024-02-18 12:10:57,586 INFO misc.py line 119 87073] Train: [38/100][506/1557] Data 0.015 (0.092) Batch 0.992 (1.062) Remain 28:47:06 loss: 0.4706 Lr: 0.00370 [2024-02-18 12:10:58,514 INFO misc.py line 119 87073] Train: 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Batch 0.999 (1.074) Remain 29:06:06 loss: 0.2675 Lr: 0.00370 [2024-02-18 12:11:12,065 INFO misc.py line 119 87073] Train: [38/100][514/1557] Data 0.003 (0.101) Batch 1.031 (1.074) Remain 29:05:57 loss: 0.5738 Lr: 0.00370 [2024-02-18 12:11:12,960 INFO misc.py line 119 87073] Train: [38/100][515/1557] Data 0.025 (0.101) Batch 0.914 (1.073) Remain 29:05:25 loss: 0.4813 Lr: 0.00370 [2024-02-18 12:11:13,892 INFO misc.py line 119 87073] Train: [38/100][516/1557] Data 0.005 (0.100) Batch 0.934 (1.073) Remain 29:04:57 loss: 0.2814 Lr: 0.00370 [2024-02-18 12:11:14,478 INFO misc.py line 119 87073] Train: [38/100][517/1557] Data 0.003 (0.100) Batch 0.585 (1.072) Remain 29:03:24 loss: 0.2759 Lr: 0.00370 [2024-02-18 12:11:15,627 INFO misc.py line 119 87073] Train: [38/100][518/1557] Data 0.004 (0.100) Batch 1.139 (1.072) Remain 29:03:35 loss: 0.1485 Lr: 0.00370 [2024-02-18 12:11:16,717 INFO misc.py line 119 87073] Train: [38/100][519/1557] Data 0.015 (0.100) Batch 1.097 (1.072) Remain 29:03:39 loss: 0.4010 Lr: 0.00370 [2024-02-18 12:11:17,603 INFO misc.py line 119 87073] Train: [38/100][520/1557] Data 0.008 (0.100) Batch 0.889 (1.072) Remain 29:03:03 loss: 0.4909 Lr: 0.00370 [2024-02-18 12:11:18,450 INFO misc.py line 119 87073] Train: [38/100][521/1557] Data 0.005 (0.100) Batch 0.848 (1.071) Remain 29:02:20 loss: 0.2363 Lr: 0.00370 [2024-02-18 12:11:19,491 INFO misc.py line 119 87073] Train: [38/100][522/1557] Data 0.004 (0.099) Batch 1.034 (1.071) Remain 29:02:12 loss: 0.7032 Lr: 0.00370 [2024-02-18 12:11:20,233 INFO misc.py line 119 87073] Train: [38/100][523/1557] Data 0.010 (0.099) Batch 0.746 (1.071) Remain 29:01:10 loss: 0.4825 Lr: 0.00370 [2024-02-18 12:11:21,014 INFO misc.py line 119 87073] Train: [38/100][524/1557] Data 0.006 (0.099) Batch 0.775 (1.070) Remain 29:00:13 loss: 0.4392 Lr: 0.00370 [2024-02-18 12:11:22,232 INFO misc.py line 119 87073] Train: [38/100][525/1557] Data 0.013 (0.099) Batch 1.223 (1.070) Remain 29:00:41 loss: 0.0829 Lr: 0.00370 [2024-02-18 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[2024-02-18 12:11:48,227 INFO misc.py line 119 87073] Train: [38/100][551/1557] Data 0.010 (0.097) Batch 0.719 (1.067) Remain 28:54:47 loss: 0.4478 Lr: 0.00370 [2024-02-18 12:11:48,960 INFO misc.py line 119 87073] Train: [38/100][552/1557] Data 0.003 (0.097) Batch 0.723 (1.066) Remain 28:53:44 loss: 0.6324 Lr: 0.00370 [2024-02-18 12:11:50,243 INFO misc.py line 119 87073] Train: [38/100][553/1557] Data 0.014 (0.097) Batch 1.282 (1.067) Remain 28:54:22 loss: 0.3124 Lr: 0.00370 [2024-02-18 12:11:51,064 INFO misc.py line 119 87073] Train: [38/100][554/1557] Data 0.014 (0.097) Batch 0.831 (1.066) Remain 28:53:39 loss: 0.4040 Lr: 0.00370 [2024-02-18 12:11:52,014 INFO misc.py line 119 87073] Train: [38/100][555/1557] Data 0.005 (0.097) Batch 0.950 (1.066) Remain 28:53:17 loss: 0.5176 Lr: 0.00370 [2024-02-18 12:11:52,931 INFO misc.py line 119 87073] Train: [38/100][556/1557] Data 0.005 (0.096) Batch 0.917 (1.066) Remain 28:52:50 loss: 0.2867 Lr: 0.00370 [2024-02-18 12:11:54,005 INFO misc.py line 119 87073] Train: [38/100][557/1557] Data 0.004 (0.096) Batch 1.068 (1.066) Remain 28:52:49 loss: 0.3047 Lr: 0.00370 [2024-02-18 12:11:54,825 INFO misc.py line 119 87073] Train: [38/100][558/1557] Data 0.011 (0.096) Batch 0.826 (1.066) Remain 28:52:06 loss: 0.4348 Lr: 0.00370 [2024-02-18 12:11:55,651 INFO misc.py line 119 87073] Train: [38/100][559/1557] Data 0.004 (0.096) Batch 0.824 (1.065) Remain 28:51:22 loss: 0.2671 Lr: 0.00370 [2024-02-18 12:11:56,776 INFO misc.py line 119 87073] Train: [38/100][560/1557] Data 0.006 (0.096) Batch 1.120 (1.065) Remain 28:51:31 loss: 0.1775 Lr: 0.00370 [2024-02-18 12:11:57,589 INFO misc.py line 119 87073] Train: [38/100][561/1557] Data 0.012 (0.096) Batch 0.819 (1.065) Remain 28:50:47 loss: 0.3831 Lr: 0.00370 [2024-02-18 12:11:58,516 INFO misc.py line 119 87073] Train: [38/100][562/1557] Data 0.004 (0.095) Batch 0.925 (1.065) Remain 28:50:22 loss: 0.6352 Lr: 0.00370 [2024-02-18 12:11:59,411 INFO misc.py line 119 87073] Train: 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Batch 0.981 (1.074) Remain 29:05:40 loss: 0.4806 Lr: 0.00370 [2024-02-18 12:12:12,381 INFO misc.py line 119 87073] Train: [38/100][570/1557] Data 0.005 (0.103) Batch 1.040 (1.074) Remain 29:05:33 loss: 0.1719 Lr: 0.00370 [2024-02-18 12:12:13,359 INFO misc.py line 119 87073] Train: [38/100][571/1557] Data 0.008 (0.103) Batch 0.981 (1.074) Remain 29:05:16 loss: 0.5125 Lr: 0.00370 [2024-02-18 12:12:14,056 INFO misc.py line 119 87073] Train: [38/100][572/1557] Data 0.005 (0.103) Batch 0.699 (1.073) Remain 29:04:10 loss: 0.1639 Lr: 0.00370 [2024-02-18 12:12:14,754 INFO misc.py line 119 87073] Train: [38/100][573/1557] Data 0.004 (0.102) Batch 0.697 (1.072) Remain 29:03:05 loss: 0.4185 Lr: 0.00370 [2024-02-18 12:12:15,905 INFO misc.py line 119 87073] Train: [38/100][574/1557] Data 0.004 (0.102) Batch 1.150 (1.073) Remain 29:03:17 loss: 0.4425 Lr: 0.00370 [2024-02-18 12:12:16,944 INFO misc.py line 119 87073] Train: [38/100][575/1557] Data 0.005 (0.102) Batch 1.033 (1.073) Remain 29:03:09 loss: 0.1555 Lr: 0.00370 [2024-02-18 12:12:17,900 INFO misc.py line 119 87073] Train: [38/100][576/1557] Data 0.010 (0.102) Batch 0.963 (1.072) Remain 29:02:50 loss: 0.2041 Lr: 0.00370 [2024-02-18 12:12:18,731 INFO misc.py line 119 87073] Train: [38/100][577/1557] Data 0.004 (0.102) Batch 0.831 (1.072) Remain 29:02:08 loss: 0.7846 Lr: 0.00370 [2024-02-18 12:12:19,571 INFO misc.py line 119 87073] Train: [38/100][578/1557] Data 0.004 (0.102) Batch 0.838 (1.072) Remain 29:01:27 loss: 0.4724 Lr: 0.00370 [2024-02-18 12:12:20,352 INFO misc.py line 119 87073] Train: [38/100][579/1557] Data 0.006 (0.101) Batch 0.783 (1.071) Remain 29:00:37 loss: 0.3707 Lr: 0.00370 [2024-02-18 12:12:21,131 INFO misc.py line 119 87073] Train: [38/100][580/1557] Data 0.005 (0.101) Batch 0.778 (1.071) Remain 28:59:46 loss: 0.4932 Lr: 0.00370 [2024-02-18 12:12:24,082 INFO misc.py line 119 87073] Train: [38/100][581/1557] Data 1.928 (0.104) Batch 2.951 (1.074) Remain 29:05:02 loss: 0.1396 Lr: 0.00370 [2024-02-18 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misc.py line 119 87073] Train: [38/100][1408/1557] Data 0.004 (0.116) Batch 1.085 (1.090) Remain 29:16:42 loss: 0.6769 Lr: 0.00366 [2024-02-18 12:27:36,137 INFO misc.py line 119 87073] Train: [38/100][1409/1557] Data 0.005 (0.115) Batch 0.985 (1.090) Remain 29:16:33 loss: 0.5657 Lr: 0.00366 [2024-02-18 12:27:37,190 INFO misc.py line 119 87073] Train: [38/100][1410/1557] Data 0.004 (0.115) Batch 1.053 (1.090) Remain 29:16:30 loss: 0.3925 Lr: 0.00366 [2024-02-18 12:27:38,194 INFO misc.py line 119 87073] Train: [38/100][1411/1557] Data 0.004 (0.115) Batch 1.002 (1.090) Remain 29:16:23 loss: 0.0935 Lr: 0.00366 [2024-02-18 12:27:39,022 INFO misc.py line 119 87073] Train: [38/100][1412/1557] Data 0.006 (0.115) Batch 0.830 (1.090) Remain 29:16:04 loss: 0.6078 Lr: 0.00366 [2024-02-18 12:27:39,832 INFO misc.py line 119 87073] Train: [38/100][1413/1557] Data 0.004 (0.115) Batch 0.803 (1.090) Remain 29:15:43 loss: 0.3966 Lr: 0.00366 [2024-02-18 12:27:41,105 INFO misc.py line 119 87073] Train: 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[2024-02-18 12:29:42,688 INFO misc.py line 119 87073] Train: [38/100][1526/1557] Data 0.006 (0.114) Batch 1.162 (1.089) Remain 29:13:23 loss: 0.1293 Lr: 0.00366 [2024-02-18 12:29:43,806 INFO misc.py line 119 87073] Train: [38/100][1527/1557] Data 0.006 (0.114) Batch 1.110 (1.089) Remain 29:13:23 loss: 0.9820 Lr: 0.00366 [2024-02-18 12:29:44,885 INFO misc.py line 119 87073] Train: [38/100][1528/1557] Data 0.014 (0.114) Batch 1.079 (1.089) Remain 29:13:21 loss: 0.3686 Lr: 0.00366 [2024-02-18 12:29:45,676 INFO misc.py line 119 87073] Train: [38/100][1529/1557] Data 0.014 (0.114) Batch 0.801 (1.089) Remain 29:13:02 loss: 0.4110 Lr: 0.00366 [2024-02-18 12:29:46,552 INFO misc.py line 119 87073] Train: [38/100][1530/1557] Data 0.004 (0.114) Batch 0.876 (1.089) Remain 29:12:47 loss: 0.2727 Lr: 0.00366 [2024-02-18 12:29:47,405 INFO misc.py line 119 87073] Train: [38/100][1531/1557] Data 0.005 (0.113) Batch 0.853 (1.089) Remain 29:12:31 loss: 0.2589 Lr: 0.00366 [2024-02-18 12:29:48,252 INFO misc.py line 119 87073] Train: [38/100][1532/1557] Data 0.004 (0.113) Batch 0.847 (1.089) Remain 29:12:15 loss: 0.3347 Lr: 0.00366 [2024-02-18 12:29:49,625 INFO misc.py line 119 87073] Train: [38/100][1533/1557] Data 0.308 (0.114) Batch 1.364 (1.089) Remain 29:12:31 loss: 0.1255 Lr: 0.00366 [2024-02-18 12:29:50,511 INFO misc.py line 119 87073] Train: [38/100][1534/1557] Data 0.014 (0.113) Batch 0.895 (1.089) Remain 29:12:18 loss: 0.6680 Lr: 0.00366 [2024-02-18 12:29:51,555 INFO misc.py line 119 87073] Train: [38/100][1535/1557] Data 0.004 (0.113) Batch 1.045 (1.089) Remain 29:12:14 loss: 0.5190 Lr: 0.00366 [2024-02-18 12:29:52,471 INFO misc.py line 119 87073] Train: [38/100][1536/1557] Data 0.003 (0.113) Batch 0.916 (1.089) Remain 29:12:02 loss: 0.4083 Lr: 0.00366 [2024-02-18 12:29:53,573 INFO misc.py line 119 87073] Train: [38/100][1537/1557] Data 0.004 (0.113) Batch 1.101 (1.089) Remain 29:12:02 loss: 0.3148 Lr: 0.00366 [2024-02-18 12:29:54,327 INFO misc.py line 119 87073] Train: [38/100][1538/1557] Data 0.004 (0.113) Batch 0.754 (1.089) Remain 29:11:40 loss: 0.6388 Lr: 0.00366 [2024-02-18 12:29:55,107 INFO misc.py line 119 87073] Train: [38/100][1539/1557] Data 0.004 (0.113) Batch 0.770 (1.088) Remain 29:11:18 loss: 0.3714 Lr: 0.00366 [2024-02-18 12:29:56,181 INFO misc.py line 119 87073] Train: [38/100][1540/1557] Data 0.015 (0.113) Batch 1.079 (1.088) Remain 29:11:17 loss: 0.1062 Lr: 0.00366 [2024-02-18 12:29:56,954 INFO misc.py line 119 87073] Train: [38/100][1541/1557] Data 0.009 (0.113) Batch 0.778 (1.088) Remain 29:10:56 loss: 1.0177 Lr: 0.00366 [2024-02-18 12:29:58,127 INFO misc.py line 119 87073] Train: [38/100][1542/1557] Data 0.003 (0.113) Batch 1.173 (1.088) Remain 29:11:00 loss: 0.6690 Lr: 0.00366 [2024-02-18 12:29:59,196 INFO misc.py line 119 87073] Train: [38/100][1543/1557] Data 0.004 (0.113) Batch 1.068 (1.088) Remain 29:10:58 loss: 0.4131 Lr: 0.00366 [2024-02-18 12:30:00,008 INFO misc.py line 119 87073] Train: [38/100][1544/1557] Data 0.005 (0.113) Batch 0.813 (1.088) Remain 29:10:40 loss: 0.3240 Lr: 0.00366 [2024-02-18 12:30:00,758 INFO misc.py line 119 87073] Train: [38/100][1545/1557] Data 0.004 (0.113) Batch 0.741 (1.088) Remain 29:10:17 loss: 0.5342 Lr: 0.00366 [2024-02-18 12:30:01,505 INFO misc.py line 119 87073] Train: [38/100][1546/1557] Data 0.012 (0.113) Batch 0.756 (1.088) Remain 29:09:55 loss: 0.2279 Lr: 0.00366 [2024-02-18 12:30:02,716 INFO misc.py line 119 87073] Train: [38/100][1547/1557] Data 0.004 (0.113) Batch 1.211 (1.088) Remain 29:10:02 loss: 0.1725 Lr: 0.00365 [2024-02-18 12:30:03,637 INFO misc.py line 119 87073] Train: [38/100][1548/1557] Data 0.004 (0.112) Batch 0.921 (1.088) Remain 29:09:50 loss: 0.5582 Lr: 0.00365 [2024-02-18 12:30:04,725 INFO misc.py line 119 87073] Train: [38/100][1549/1557] Data 0.004 (0.112) Batch 1.088 (1.088) Remain 29:09:49 loss: 0.4591 Lr: 0.00365 [2024-02-18 12:30:05,544 INFO misc.py line 119 87073] Train: [38/100][1550/1557] Data 0.004 (0.112) Batch 0.819 (1.087) Remain 29:09:31 loss: 0.4252 Lr: 0.00365 [2024-02-18 12:30:06,560 INFO misc.py line 119 87073] Train: [38/100][1551/1557] Data 0.004 (0.112) Batch 1.005 (1.087) Remain 29:09:25 loss: 0.1961 Lr: 0.00365 [2024-02-18 12:30:07,286 INFO misc.py line 119 87073] Train: [38/100][1552/1557] Data 0.014 (0.112) Batch 0.737 (1.087) Remain 29:09:02 loss: 0.4416 Lr: 0.00365 [2024-02-18 12:30:08,064 INFO misc.py line 119 87073] Train: [38/100][1553/1557] Data 0.004 (0.112) Batch 0.768 (1.087) Remain 29:08:41 loss: 0.4910 Lr: 0.00365 [2024-02-18 12:30:09,397 INFO misc.py line 119 87073] Train: [38/100][1554/1557] Data 0.014 (0.112) Batch 1.334 (1.087) Remain 29:08:55 loss: 0.1576 Lr: 0.00365 [2024-02-18 12:30:10,348 INFO misc.py line 119 87073] Train: [38/100][1555/1557] Data 0.013 (0.112) Batch 0.961 (1.087) Remain 29:08:47 loss: 0.3714 Lr: 0.00365 [2024-02-18 12:30:11,464 INFO misc.py line 119 87073] Train: [38/100][1556/1557] Data 0.004 (0.112) Batch 1.116 (1.087) Remain 29:08:47 loss: 0.6841 Lr: 0.00365 [2024-02-18 12:30:12,408 INFO misc.py line 119 87073] Train: [38/100][1557/1557] Data 0.004 (0.112) Batch 0.943 (1.087) Remain 29:08:37 loss: 0.6692 Lr: 0.00365 [2024-02-18 12:30:12,408 INFO misc.py line 136 87073] Train result: loss: 0.4084 [2024-02-18 12:30:12,408 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 12:30:39,760 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5513 [2024-02-18 12:30:40,538 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9055 [2024-02-18 12:30:42,674 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4093 [2024-02-18 12:30:44,882 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.5214 [2024-02-18 12:30:49,838 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3199 [2024-02-18 12:30:50,537 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1024 [2024-02-18 12:30:51,804 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2384 [2024-02-18 12:30:54,759 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4212 [2024-02-18 12:30:56,567 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2304 [2024-02-18 12:30:58,158 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6951/0.7600/0.9114. [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9222/0.9725 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9809/0.9868 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8684/0.9730 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2499/0.2583 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6474/0.6630 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7399/0.9341 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8324/0.9332 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9096/0.9680 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7687/0.7947 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8008/0.8905 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7234/0.8416 [2024-02-18 12:30:58,158 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5926/0.6639 [2024-02-18 12:30:58,159 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 12:30:58,160 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 12:30:58,160 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 12:31:05,162 INFO misc.py line 119 87073] Train: [39/100][1/1557] Data 2.103 (2.103) Batch 3.001 (3.001) Remain 80:28:35 loss: 0.2767 Lr: 0.00365 [2024-02-18 12:31:06,105 INFO misc.py line 119 87073] Train: [39/100][2/1557] Data 0.010 (0.010) Batch 0.947 (0.947) Remain 25:23:49 loss: 0.4540 Lr: 0.00365 [2024-02-18 12:31:07,114 INFO misc.py line 119 87073] Train: [39/100][3/1557] Data 0.006 (0.006) Batch 0.999 (0.999) Remain 26:46:49 loss: 0.6516 Lr: 0.00365 [2024-02-18 12:31:07,974 INFO misc.py line 119 87073] Train: [39/100][4/1557] Data 0.017 (0.017) Batch 0.872 (0.872) Remain 23:22:55 loss: 0.4097 Lr: 0.00365 [2024-02-18 12:31:08,804 INFO misc.py line 119 87073] Train: [39/100][5/1557] Data 0.004 (0.010) Batch 0.828 (0.850) Remain 22:47:35 loss: 0.4367 Lr: 0.00365 [2024-02-18 12:31:09,627 INFO misc.py line 119 87073] Train: [39/100][6/1557] Data 0.004 (0.008) Batch 0.812 (0.837) Remain 22:27:05 loss: 0.5044 Lr: 0.00365 [2024-02-18 12:31:23,811 INFO misc.py line 119 87073] Train: [39/100][7/1557] Data 13.010 (3.259) Batch 14.196 (4.177) Remain 111:59:45 loss: 0.1807 Lr: 0.00365 [2024-02-18 12:31:24,637 INFO misc.py line 119 87073] Train: [39/100][8/1557] Data 0.005 (2.608) Batch 0.826 (3.507) Remain 94:01:33 loss: 0.3769 Lr: 0.00365 [2024-02-18 12:31:25,441 INFO misc.py line 119 87073] Train: [39/100][9/1557] Data 0.004 (2.174) Batch 0.804 (3.056) Remain 81:56:43 loss: 0.6006 Lr: 0.00365 [2024-02-18 12:31:26,397 INFO misc.py line 119 87073] Train: [39/100][10/1557] Data 0.004 (1.864) Batch 0.956 (2.756) Remain 73:54:03 loss: 0.5028 Lr: 0.00365 [2024-02-18 12:31:27,407 INFO misc.py line 119 87073] Train: [39/100][11/1557] Data 0.005 (1.632) Batch 1.011 (2.538) Remain 68:03:05 loss: 0.8288 Lr: 0.00365 [2024-02-18 12:31:28,147 INFO misc.py line 119 87073] Train: [39/100][12/1557] Data 0.004 (1.451) Batch 0.739 (2.338) Remain 62:41:30 loss: 0.4056 Lr: 0.00365 [2024-02-18 12:31:28,882 INFO misc.py line 119 87073] Train: [39/100][13/1557] Data 0.005 (1.306) Batch 0.734 (2.178) Remain 58:23:27 loss: 0.5709 Lr: 0.00365 [2024-02-18 12:31:30,009 INFO misc.py line 119 87073] Train: [39/100][14/1557] Data 0.005 (1.188) Batch 1.126 (2.082) Remain 55:49:37 loss: 0.3045 Lr: 0.00365 [2024-02-18 12:31:31,034 INFO misc.py line 119 87073] Train: [39/100][15/1557] Data 0.006 (1.089) Batch 1.019 (1.994) Remain 53:27:04 loss: 0.4449 Lr: 0.00365 [2024-02-18 12:31:31,986 INFO misc.py line 119 87073] Train: [39/100][16/1557] Data 0.011 (1.006) Batch 0.959 (1.914) Remain 51:19:03 loss: 0.4187 Lr: 0.00365 [2024-02-18 12:31:33,022 INFO misc.py line 119 87073] Train: [39/100][17/1557] Data 0.004 (0.935) Batch 1.037 (1.851) Remain 49:38:13 loss: 0.7714 Lr: 0.00365 [2024-02-18 12:31:33,771 INFO misc.py line 119 87073] Train: [39/100][18/1557] Data 0.004 (0.873) Batch 0.746 (1.778) Remain 47:39:36 loss: 0.3215 Lr: 0.00365 [2024-02-18 12:31:34,586 INFO misc.py line 119 87073] Train: [39/100][19/1557] Data 0.007 (0.819) Batch 0.817 (1.718) Remain 46:03:01 loss: 0.3820 Lr: 0.00365 [2024-02-18 12:31:35,398 INFO misc.py line 119 87073] Train: [39/100][20/1557] Data 0.004 (0.771) Batch 0.811 (1.664) Remain 44:37:14 loss: 0.1793 Lr: 0.00365 [2024-02-18 12:31:36,500 INFO misc.py line 119 87073] Train: [39/100][21/1557] Data 0.005 (0.728) Batch 1.103 (1.633) Remain 43:47:03 loss: 0.2986 Lr: 0.00365 [2024-02-18 12:31:37,585 INFO misc.py line 119 87073] Train: [39/100][22/1557] Data 0.004 (0.690) Batch 1.084 (1.604) Remain 43:00:33 loss: 0.9534 Lr: 0.00365 [2024-02-18 12:31:38,487 INFO misc.py line 119 87073] Train: [39/100][23/1557] Data 0.005 (0.656) Batch 0.903 (1.569) Remain 42:04:07 loss: 0.6089 Lr: 0.00365 [2024-02-18 12:31:39,471 INFO misc.py line 119 87073] Train: [39/100][24/1557] Data 0.004 (0.625) Batch 0.983 (1.541) Remain 41:19:13 loss: 0.3050 Lr: 0.00365 [2024-02-18 12:31:40,254 INFO misc.py line 119 87073] Train: [39/100][25/1557] Data 0.005 (0.597) Batch 0.783 (1.507) Remain 40:23:46 loss: 0.5275 Lr: 0.00365 [2024-02-18 12:31:40,994 INFO misc.py line 119 87073] Train: [39/100][26/1557] Data 0.004 (0.571) Batch 0.740 (1.474) Remain 39:30:05 loss: 0.4208 Lr: 0.00365 [2024-02-18 12:31:41,783 INFO misc.py line 119 87073] Train: [39/100][27/1557] Data 0.005 (0.547) Batch 0.789 (1.445) Remain 38:44:11 loss: 0.2850 Lr: 0.00365 [2024-02-18 12:31:43,016 INFO misc.py line 119 87073] Train: [39/100][28/1557] Data 0.004 (0.526) Batch 1.233 (1.437) Remain 38:30:32 loss: 0.2055 Lr: 0.00365 [2024-02-18 12:31:43,946 INFO misc.py line 119 87073] Train: [39/100][29/1557] Data 0.004 (0.506) Batch 0.929 (1.417) Remain 37:59:08 loss: 0.7069 Lr: 0.00365 [2024-02-18 12:31:45,080 INFO misc.py line 119 87073] Train: [39/100][30/1557] Data 0.006 (0.487) Batch 1.136 (1.407) Remain 37:42:21 loss: 0.4698 Lr: 0.00365 [2024-02-18 12:31:46,065 INFO misc.py line 119 87073] Train: [39/100][31/1557] Data 0.004 (0.470) Batch 0.985 (1.392) Remain 37:18:07 loss: 0.3492 Lr: 0.00365 [2024-02-18 12:31:47,046 INFO misc.py line 119 87073] Train: [39/100][32/1557] Data 0.003 (0.454) Batch 0.980 (1.377) Remain 36:55:15 loss: 0.3965 Lr: 0.00365 [2024-02-18 12:31:47,817 INFO misc.py line 119 87073] Train: [39/100][33/1557] Data 0.004 (0.439) Batch 0.763 (1.357) Remain 36:22:16 loss: 0.2024 Lr: 0.00365 [2024-02-18 12:31:48,565 INFO misc.py line 119 87073] Train: [39/100][34/1557] Data 0.013 (0.425) Batch 0.757 (1.337) Remain 35:51:08 loss: 0.5406 Lr: 0.00365 [2024-02-18 12:31:49,800 INFO misc.py line 119 87073] Train: [39/100][35/1557] Data 0.003 (0.412) Batch 1.235 (1.334) Remain 35:45:58 loss: 0.2133 Lr: 0.00365 [2024-02-18 12:31:50,721 INFO misc.py line 119 87073] Train: [39/100][36/1557] Data 0.004 (0.399) Batch 0.920 (1.322) Remain 35:25:46 loss: 0.3221 Lr: 0.00365 [2024-02-18 12:31:51,746 INFO misc.py line 119 87073] Train: [39/100][37/1557] Data 0.005 (0.388) Batch 1.026 (1.313) Remain 35:11:44 loss: 0.2251 Lr: 0.00365 [2024-02-18 12:31:52,681 INFO misc.py line 119 87073] Train: [39/100][38/1557] Data 0.004 (0.377) Batch 0.932 (1.302) Remain 34:54:14 loss: 0.5304 Lr: 0.00365 [2024-02-18 12:31:53,565 INFO misc.py line 119 87073] Train: [39/100][39/1557] Data 0.008 (0.367) Batch 0.884 (1.291) Remain 34:35:32 loss: 0.4848 Lr: 0.00365 [2024-02-18 12:31:54,349 INFO misc.py line 119 87073] Train: [39/100][40/1557] Data 0.007 (0.357) Batch 0.785 (1.277) Remain 34:13:33 loss: 0.2257 Lr: 0.00365 [2024-02-18 12:31:55,169 INFO misc.py line 119 87073] Train: [39/100][41/1557] Data 0.005 (0.348) Batch 0.820 (1.265) Remain 33:54:12 loss: 0.4087 Lr: 0.00365 [2024-02-18 12:31:56,468 INFO misc.py line 119 87073] Train: [39/100][42/1557] Data 0.005 (0.339) Batch 1.297 (1.266) Remain 33:55:29 loss: 0.3933 Lr: 0.00365 [2024-02-18 12:31:57,404 INFO misc.py line 119 87073] Train: [39/100][43/1557] Data 0.007 (0.331) Batch 0.939 (1.258) Remain 33:42:20 loss: 0.3022 Lr: 0.00365 [2024-02-18 12:31:58,413 INFO misc.py line 119 87073] Train: [39/100][44/1557] Data 0.003 (0.323) Batch 1.009 (1.251) Remain 33:32:34 loss: 0.2983 Lr: 0.00365 [2024-02-18 12:31:59,434 INFO misc.py line 119 87073] Train: [39/100][45/1557] Data 0.004 (0.315) Batch 1.020 (1.246) Remain 33:23:41 loss: 0.3223 Lr: 0.00365 [2024-02-18 12:32:00,636 INFO misc.py line 119 87073] Train: [39/100][46/1557] Data 0.005 (0.308) Batch 1.199 (1.245) Remain 33:21:54 loss: 0.5507 Lr: 0.00365 [2024-02-18 12:32:01,431 INFO misc.py line 119 87073] Train: [39/100][47/1557] Data 0.008 (0.301) Batch 0.799 (1.235) Remain 33:05:35 loss: 0.3115 Lr: 0.00365 [2024-02-18 12:32:02,168 INFO misc.py line 119 87073] Train: [39/100][48/1557] Data 0.004 (0.294) Batch 0.737 (1.224) Remain 32:47:46 loss: 0.2970 Lr: 0.00365 [2024-02-18 12:32:03,356 INFO misc.py line 119 87073] Train: [39/100][49/1557] Data 0.005 (0.288) Batch 1.183 (1.223) Remain 32:46:20 loss: 0.1589 Lr: 0.00365 [2024-02-18 12:32:04,386 INFO misc.py line 119 87073] Train: [39/100][50/1557] Data 0.009 (0.282) Batch 1.035 (1.219) Remain 32:39:52 loss: 0.4887 Lr: 0.00365 [2024-02-18 12:32:05,507 INFO misc.py line 119 87073] Train: [39/100][51/1557] Data 0.005 (0.276) Batch 1.112 (1.217) Remain 32:36:16 loss: 0.3032 Lr: 0.00365 [2024-02-18 12:32:06,414 INFO misc.py line 119 87073] Train: [39/100][52/1557] Data 0.014 (0.271) Batch 0.917 (1.210) Remain 32:26:25 loss: 0.2933 Lr: 0.00365 [2024-02-18 12:32:07,360 INFO misc.py line 119 87073] Train: [39/100][53/1557] Data 0.005 (0.266) Batch 0.944 (1.205) Remain 32:17:50 loss: 0.3284 Lr: 0.00365 [2024-02-18 12:32:08,055 INFO misc.py line 119 87073] Train: [39/100][54/1557] Data 0.006 (0.261) Batch 0.691 (1.195) Remain 32:01:36 loss: 0.3958 Lr: 0.00365 [2024-02-18 12:32:08,795 INFO misc.py line 119 87073] Train: [39/100][55/1557] Data 0.010 (0.256) Batch 0.746 (1.186) Remain 31:47:41 loss: 0.2550 Lr: 0.00365 [2024-02-18 12:32:10,079 INFO misc.py line 119 87073] Train: [39/100][56/1557] Data 0.004 (0.251) Batch 1.274 (1.188) Remain 31:50:19 loss: 0.1994 Lr: 0.00365 [2024-02-18 12:32:11,046 INFO misc.py line 119 87073] Train: [39/100][57/1557] Data 0.014 (0.247) Batch 0.978 (1.184) Remain 31:44:02 loss: 0.7574 Lr: 0.00365 [2024-02-18 12:32:12,010 INFO misc.py line 119 87073] Train: [39/100][58/1557] Data 0.004 (0.242) Batch 0.963 (1.180) Remain 31:37:32 loss: 0.3705 Lr: 0.00365 [2024-02-18 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Train: [39/100][65/1557] Data 0.005 (0.517) Batch 0.885 (1.451) Remain 38:53:35 loss: 0.3801 Lr: 0.00365 [2024-02-18 12:32:37,979 INFO misc.py line 119 87073] Train: [39/100][66/1557] Data 0.005 (0.509) Batch 0.886 (1.442) Remain 38:39:08 loss: 0.5135 Lr: 0.00365 [2024-02-18 12:32:38,903 INFO misc.py line 119 87073] Train: [39/100][67/1557] Data 0.007 (0.501) Batch 0.926 (1.434) Remain 38:26:09 loss: 0.2356 Lr: 0.00365 [2024-02-18 12:32:39,681 INFO misc.py line 119 87073] Train: [39/100][68/1557] Data 0.005 (0.493) Batch 0.779 (1.424) Remain 38:09:55 loss: 0.4267 Lr: 0.00365 [2024-02-18 12:32:40,447 INFO misc.py line 119 87073] Train: [39/100][69/1557] Data 0.004 (0.486) Batch 0.762 (1.414) Remain 37:53:45 loss: 0.2246 Lr: 0.00365 [2024-02-18 12:32:41,501 INFO misc.py line 119 87073] Train: [39/100][70/1557] Data 0.008 (0.479) Batch 1.049 (1.409) Remain 37:44:57 loss: 0.1990 Lr: 0.00365 [2024-02-18 12:32:42,442 INFO misc.py line 119 87073] Train: [39/100][71/1557] Data 0.013 (0.472) 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87073] Train: [39/100][196/1557] Data 0.004 (0.388) Batch 1.166 (1.321) Remain 35:20:29 loss: 0.3429 Lr: 0.00365 [2024-02-18 12:35:22,916 INFO misc.py line 119 87073] Train: [39/100][197/1557] Data 0.004 (0.386) Batch 0.924 (1.319) Remain 35:17:11 loss: 0.3090 Lr: 0.00365 [2024-02-18 12:35:23,908 INFO misc.py line 119 87073] Train: [39/100][198/1557] Data 0.006 (0.384) Batch 0.993 (1.317) Remain 35:14:29 loss: 0.4495 Lr: 0.00365 [2024-02-18 12:35:24,783 INFO misc.py line 119 87073] Train: [39/100][199/1557] Data 0.004 (0.382) Batch 0.870 (1.315) Remain 35:10:48 loss: 0.3015 Lr: 0.00365 [2024-02-18 12:35:25,732 INFO misc.py line 119 87073] Train: [39/100][200/1557] Data 0.010 (0.380) Batch 0.954 (1.313) Remain 35:07:51 loss: 0.4169 Lr: 0.00365 [2024-02-18 12:35:26,475 INFO misc.py line 119 87073] Train: [39/100][201/1557] Data 0.004 (0.378) Batch 0.743 (1.310) Remain 35:03:12 loss: 0.3662 Lr: 0.00365 [2024-02-18 12:35:27,246 INFO misc.py line 119 87073] Train: [39/100][202/1557] Data 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loss: 0.6439 Lr: 0.00364 [2024-02-18 12:38:51,843 INFO misc.py line 119 87073] Train: [39/100][352/1557] Data 0.006 (0.391) Batch 1.134 (1.332) Remain 35:34:38 loss: 0.7746 Lr: 0.00364 [2024-02-18 12:38:52,857 INFO misc.py line 119 87073] Train: [39/100][353/1557] Data 0.004 (0.390) Batch 1.014 (1.331) Remain 35:33:10 loss: 0.4929 Lr: 0.00364 [2024-02-18 12:38:53,869 INFO misc.py line 119 87073] Train: [39/100][354/1557] Data 0.004 (0.389) Batch 1.011 (1.330) Remain 35:31:41 loss: 0.5198 Lr: 0.00364 [2024-02-18 12:38:56,295 INFO misc.py line 119 87073] Train: [39/100][355/1557] Data 1.523 (0.392) Batch 2.426 (1.333) Remain 35:36:39 loss: 0.5743 Lr: 0.00364 [2024-02-18 12:38:57,022 INFO misc.py line 119 87073] Train: [39/100][356/1557] Data 0.005 (0.391) Batch 0.727 (1.331) Remain 35:33:53 loss: 0.4150 Lr: 0.00364 [2024-02-18 12:38:58,145 INFO misc.py line 119 87073] Train: [39/100][357/1557] Data 0.005 (0.390) Batch 1.123 (1.331) Remain 35:32:55 loss: 0.3653 Lr: 0.00364 [2024-02-18 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Batch 0.946 (1.345) Remain 35:54:53 loss: 0.1524 Lr: 0.00364 [2024-02-18 12:40:03,313 INFO misc.py line 119 87073] Train: [39/100][402/1557] Data 0.010 (0.403) Batch 0.923 (1.344) Remain 35:53:10 loss: 0.4627 Lr: 0.00364 [2024-02-18 12:40:04,325 INFO misc.py line 119 87073] Train: [39/100][403/1557] Data 0.003 (0.402) Batch 1.011 (1.343) Remain 35:51:49 loss: 0.5830 Lr: 0.00364 [2024-02-18 12:40:05,075 INFO misc.py line 119 87073] Train: [39/100][404/1557] Data 0.003 (0.401) Batch 0.750 (1.342) Remain 35:49:25 loss: 0.3298 Lr: 0.00364 [2024-02-18 12:40:05,962 INFO misc.py line 119 87073] Train: [39/100][405/1557] Data 0.004 (0.400) Batch 0.877 (1.340) Remain 35:47:33 loss: 0.3244 Lr: 0.00364 [2024-02-18 12:40:07,030 INFO misc.py line 119 87073] Train: [39/100][406/1557] Data 0.014 (0.399) Batch 1.074 (1.340) Remain 35:46:28 loss: 0.4967 Lr: 0.00364 [2024-02-18 12:40:08,020 INFO misc.py line 119 87073] Train: [39/100][407/1557] Data 0.008 (0.398) Batch 0.992 (1.339) Remain 35:45:04 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Batch 1.002 (1.343) Remain 35:51:06 loss: 0.4313 Lr: 0.00363 [2024-02-18 12:41:18,030 INFO misc.py line 119 87073] Train: [39/100][458/1557] Data 0.006 (0.401) Batch 1.038 (1.343) Remain 35:50:00 loss: 0.3741 Lr: 0.00363 [2024-02-18 12:41:18,939 INFO misc.py line 119 87073] Train: [39/100][459/1557] Data 0.005 (0.401) Batch 0.909 (1.342) Remain 35:48:27 loss: 0.2184 Lr: 0.00363 [2024-02-18 12:41:19,679 INFO misc.py line 119 87073] Train: [39/100][460/1557] Data 0.005 (0.400) Batch 0.740 (1.340) Remain 35:46:20 loss: 0.3643 Lr: 0.00363 [2024-02-18 12:41:20,528 INFO misc.py line 119 87073] Train: [39/100][461/1557] Data 0.005 (0.399) Batch 0.849 (1.339) Remain 35:44:35 loss: 0.4099 Lr: 0.00363 [2024-02-18 12:41:21,721 INFO misc.py line 119 87073] Train: [39/100][462/1557] Data 0.005 (0.398) Batch 1.188 (1.339) Remain 35:44:02 loss: 0.2249 Lr: 0.00363 [2024-02-18 12:41:22,669 INFO misc.py line 119 87073] Train: [39/100][463/1557] Data 0.010 (0.397) Batch 0.952 (1.338) Remain 35:42:40 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87073] Train: [39/100][476/1557] Data 0.011 (0.386) Batch 1.193 (1.328) Remain 35:25:25 loss: 0.1770 Lr: 0.00363 [2024-02-18 12:41:36,131 INFO misc.py line 119 87073] Train: [39/100][477/1557] Data 0.007 (0.386) Batch 1.068 (1.327) Remain 35:24:31 loss: 0.4840 Lr: 0.00363 [2024-02-18 12:41:36,994 INFO misc.py line 119 87073] Train: [39/100][478/1557] Data 0.014 (0.385) Batch 0.874 (1.326) Remain 35:22:58 loss: 0.3980 Lr: 0.00363 [2024-02-18 12:41:37,955 INFO misc.py line 119 87073] Train: [39/100][479/1557] Data 0.004 (0.384) Batch 0.960 (1.325) Remain 35:21:43 loss: 0.6908 Lr: 0.00363 [2024-02-18 12:41:38,930 INFO misc.py line 119 87073] Train: [39/100][480/1557] Data 0.005 (0.383) Batch 0.975 (1.325) Remain 35:20:31 loss: 0.2426 Lr: 0.00363 [2024-02-18 12:41:39,692 INFO misc.py line 119 87073] Train: [39/100][481/1557] Data 0.005 (0.382) Batch 0.760 (1.323) Remain 35:18:36 loss: 0.4145 Lr: 0.00363 [2024-02-18 12:41:40,455 INFO misc.py line 119 87073] Train: [39/100][482/1557] Data 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Batch 0.928 (1.337) Remain 35:39:35 loss: 0.2770 Lr: 0.00363 [2024-02-18 12:42:29,876 INFO misc.py line 119 87073] Train: [39/100][514/1557] Data 0.004 (0.394) Batch 0.927 (1.336) Remain 35:38:16 loss: 0.4777 Lr: 0.00363 [2024-02-18 12:42:30,728 INFO misc.py line 119 87073] Train: [39/100][515/1557] Data 0.006 (0.394) Batch 0.852 (1.335) Remain 35:36:44 loss: 0.3310 Lr: 0.00363 [2024-02-18 12:42:31,553 INFO misc.py line 119 87073] Train: [39/100][516/1557] Data 0.005 (0.393) Batch 0.825 (1.334) Remain 35:35:07 loss: 0.3942 Lr: 0.00363 [2024-02-18 12:42:32,265 INFO misc.py line 119 87073] Train: [39/100][517/1557] Data 0.006 (0.392) Batch 0.713 (1.333) Remain 35:33:10 loss: 0.2517 Lr: 0.00363 [2024-02-18 12:42:33,373 INFO misc.py line 119 87073] Train: [39/100][518/1557] Data 0.004 (0.391) Batch 1.107 (1.333) Remain 35:32:26 loss: 0.1572 Lr: 0.00363 [2024-02-18 12:42:34,350 INFO misc.py line 119 87073] Train: [39/100][519/1557] Data 0.006 (0.391) Batch 0.978 (1.332) Remain 35:31:19 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Batch 0.917 (1.334) Remain 35:34:03 loss: 0.6271 Lr: 0.00363 [2024-02-18 12:43:43,192 INFO misc.py line 119 87073] Train: [39/100][570/1557] Data 0.004 (0.393) Batch 0.887 (1.333) Remain 35:32:46 loss: 0.2429 Lr: 0.00363 [2024-02-18 12:43:44,225 INFO misc.py line 119 87073] Train: [39/100][571/1557] Data 0.012 (0.393) Batch 1.031 (1.333) Remain 35:31:53 loss: 0.2189 Lr: 0.00363 [2024-02-18 12:43:45,011 INFO misc.py line 119 87073] Train: [39/100][572/1557] Data 0.011 (0.392) Batch 0.793 (1.332) Remain 35:30:21 loss: 0.1564 Lr: 0.00363 [2024-02-18 12:43:45,723 INFO misc.py line 119 87073] Train: [39/100][573/1557] Data 0.004 (0.391) Batch 0.710 (1.331) Remain 35:28:35 loss: 0.2261 Lr: 0.00363 [2024-02-18 12:43:46,912 INFO misc.py line 119 87073] Train: [39/100][574/1557] Data 0.005 (0.391) Batch 1.189 (1.331) Remain 35:28:10 loss: 0.2489 Lr: 0.00363 [2024-02-18 12:43:47,724 INFO misc.py line 119 87073] Train: [39/100][575/1557] Data 0.006 (0.390) Batch 0.813 (1.330) Remain 35:26:41 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Batch 1.034 (1.328) Remain 35:21:01 loss: 0.5200 Lr: 0.00362 [2024-02-18 12:46:08,118 INFO misc.py line 119 87073] Train: [39/100][682/1557] Data 0.004 (0.386) Batch 0.853 (1.327) Remain 35:19:52 loss: 0.2992 Lr: 0.00362 [2024-02-18 12:46:09,124 INFO misc.py line 119 87073] Train: [39/100][683/1557] Data 0.006 (0.386) Batch 1.006 (1.326) Remain 35:19:06 loss: 0.3867 Lr: 0.00362 [2024-02-18 12:46:09,886 INFO misc.py line 119 87073] Train: [39/100][684/1557] Data 0.005 (0.385) Batch 0.763 (1.326) Remain 35:17:45 loss: 0.6353 Lr: 0.00362 [2024-02-18 12:46:10,673 INFO misc.py line 119 87073] Train: [39/100][685/1557] Data 0.004 (0.385) Batch 0.784 (1.325) Remain 35:16:28 loss: 0.3945 Lr: 0.00362 [2024-02-18 12:46:11,834 INFO misc.py line 119 87073] Train: [39/100][686/1557] Data 0.007 (0.384) Batch 1.164 (1.325) Remain 35:16:04 loss: 0.3265 Lr: 0.00362 [2024-02-18 12:46:12,814 INFO misc.py line 119 87073] Train: [39/100][687/1557] Data 0.005 (0.383) Batch 0.981 (1.324) Remain 35:15:14 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Batch 0.896 (1.325) Remain 35:12:16 loss: 1.2894 Lr: 0.00361 [2024-02-18 12:51:03,352 INFO misc.py line 119 87073] Train: [39/100][906/1557] Data 0.005 (0.383) Batch 0.830 (1.325) Remain 35:11:22 loss: 0.7187 Lr: 0.00361 [2024-02-18 12:51:04,248 INFO misc.py line 119 87073] Train: [39/100][907/1557] Data 0.004 (0.382) Batch 0.885 (1.324) Remain 35:10:35 loss: 0.3069 Lr: 0.00361 [2024-02-18 12:51:04,938 INFO misc.py line 119 87073] Train: [39/100][908/1557] Data 0.015 (0.382) Batch 0.700 (1.324) Remain 35:09:27 loss: 0.1970 Lr: 0.00361 [2024-02-18 12:51:05,720 INFO misc.py line 119 87073] Train: [39/100][909/1557] Data 0.004 (0.382) Batch 0.775 (1.323) Remain 35:08:28 loss: 0.4798 Lr: 0.00361 [2024-02-18 12:51:06,833 INFO misc.py line 119 87073] Train: [39/100][910/1557] Data 0.011 (0.381) Batch 1.112 (1.323) Remain 35:08:05 loss: 0.2377 Lr: 0.00361 [2024-02-18 12:51:07,790 INFO misc.py line 119 87073] Train: [39/100][911/1557] Data 0.012 (0.381) Batch 0.965 (1.322) Remain 35:07:26 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12:51:14,840 INFO misc.py line 119 87073] Train: [39/100][918/1557] Data 0.015 (0.378) Batch 1.286 (1.320) Remain 35:03:25 loss: 0.5924 Lr: 0.00361 [2024-02-18 12:51:15,723 INFO misc.py line 119 87073] Train: [39/100][919/1557] Data 0.009 (0.377) Batch 0.889 (1.319) Remain 35:02:39 loss: 0.2986 Lr: 0.00361 [2024-02-18 12:51:16,736 INFO misc.py line 119 87073] Train: [39/100][920/1557] Data 0.004 (0.377) Batch 1.012 (1.319) Remain 35:02:06 loss: 0.3537 Lr: 0.00361 [2024-02-18 12:51:17,717 INFO misc.py line 119 87073] Train: [39/100][921/1557] Data 0.004 (0.377) Batch 0.981 (1.319) Remain 35:01:29 loss: 0.4044 Lr: 0.00361 [2024-02-18 12:51:18,513 INFO misc.py line 119 87073] Train: [39/100][922/1557] Data 0.004 (0.376) Batch 0.795 (1.318) Remain 35:00:33 loss: 0.1771 Lr: 0.00361 [2024-02-18 12:51:19,256 INFO misc.py line 119 87073] Train: [39/100][923/1557] Data 0.004 (0.376) Batch 0.732 (1.318) Remain 34:59:31 loss: 0.3410 Lr: 0.00361 [2024-02-18 12:51:20,400 INFO misc.py line 119 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Batch 0.979 (1.323) Remain 35:06:47 loss: 0.3992 Lr: 0.00361 [2024-02-18 12:52:15,073 INFO misc.py line 119 87073] Train: [39/100][962/1557] Data 0.013 (0.380) Batch 0.890 (1.322) Remain 35:06:02 loss: 0.0949 Lr: 0.00361 [2024-02-18 12:52:15,963 INFO misc.py line 119 87073] Train: [39/100][963/1557] Data 0.006 (0.380) Batch 0.891 (1.322) Remain 35:05:18 loss: 0.2376 Lr: 0.00361 [2024-02-18 12:52:16,779 INFO misc.py line 119 87073] Train: [39/100][964/1557] Data 0.006 (0.380) Batch 0.814 (1.321) Remain 35:04:26 loss: 0.2936 Lr: 0.00361 [2024-02-18 12:52:17,518 INFO misc.py line 119 87073] Train: [39/100][965/1557] Data 0.007 (0.379) Batch 0.742 (1.321) Remain 35:03:27 loss: 0.2479 Lr: 0.00361 [2024-02-18 12:52:18,758 INFO misc.py line 119 87073] Train: [39/100][966/1557] Data 0.005 (0.379) Batch 1.240 (1.321) Remain 35:03:18 loss: 0.1674 Lr: 0.00361 [2024-02-18 12:52:19,675 INFO misc.py line 119 87073] Train: [39/100][967/1557] Data 0.004 (0.378) Batch 0.917 (1.320) Remain 35:02:37 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INFO misc.py line 119 87073] Train: [39/100][1191/1557] Data 0.008 (0.376) Batch 0.953 (1.318) Remain 34:53:54 loss: 0.3950 Lr: 0.00360 [2024-02-18 12:57:13,707 INFO misc.py line 119 87073] Train: [39/100][1192/1557] Data 0.004 (0.376) Batch 1.162 (1.318) Remain 34:53:40 loss: 0.5068 Lr: 0.00360 [2024-02-18 12:57:14,982 INFO misc.py line 119 87073] Train: [39/100][1193/1557] Data 0.005 (0.375) Batch 1.273 (1.318) Remain 34:53:35 loss: 0.5857 Lr: 0.00360 [2024-02-18 12:57:15,869 INFO misc.py line 119 87073] Train: [39/100][1194/1557] Data 0.007 (0.375) Batch 0.890 (1.317) Remain 34:53:00 loss: 0.2220 Lr: 0.00360 [2024-02-18 12:57:16,571 INFO misc.py line 119 87073] Train: [39/100][1195/1557] Data 0.004 (0.375) Batch 0.702 (1.317) Remain 34:52:09 loss: 0.3454 Lr: 0.00360 [2024-02-18 12:57:17,332 INFO misc.py line 119 87073] Train: [39/100][1196/1557] Data 0.004 (0.374) Batch 0.755 (1.316) Remain 34:51:23 loss: 0.2538 Lr: 0.00360 [2024-02-18 12:57:18,449 INFO misc.py line 119 87073] Train: [39/100][1197/1557] Data 0.010 (0.374) Batch 1.123 (1.316) Remain 34:51:06 loss: 0.2405 Lr: 0.00360 [2024-02-18 12:57:19,646 INFO misc.py line 119 87073] Train: [39/100][1198/1557] Data 0.005 (0.374) Batch 1.185 (1.316) Remain 34:50:54 loss: 0.6975 Lr: 0.00360 [2024-02-18 12:57:20,669 INFO misc.py line 119 87073] Train: [39/100][1199/1557] Data 0.016 (0.373) Batch 1.023 (1.316) Remain 34:50:30 loss: 0.2086 Lr: 0.00360 [2024-02-18 12:57:21,647 INFO misc.py line 119 87073] Train: [39/100][1200/1557] Data 0.016 (0.373) Batch 0.990 (1.315) Remain 34:50:02 loss: 0.5595 Lr: 0.00360 [2024-02-18 12:57:22,600 INFO misc.py line 119 87073] Train: [39/100][1201/1557] Data 0.004 (0.373) Batch 0.954 (1.315) Remain 34:49:32 loss: 0.1454 Lr: 0.00360 [2024-02-18 12:57:23,376 INFO misc.py line 119 87073] Train: [39/100][1202/1557] Data 0.003 (0.372) Batch 0.776 (1.315) Remain 34:48:48 loss: 0.5444 Lr: 0.00360 [2024-02-18 12:57:24,109 INFO misc.py line 119 87073] Train: [39/100][1203/1557] Data 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Remain 34:45:21 loss: 0.5882 Lr: 0.00360 [2024-02-18 12:57:30,886 INFO misc.py line 119 87073] Train: [39/100][1210/1557] Data 0.005 (0.370) Batch 0.811 (1.312) Remain 34:44:40 loss: 0.3626 Lr: 0.00360 [2024-02-18 12:57:32,187 INFO misc.py line 119 87073] Train: [39/100][1211/1557] Data 0.004 (0.370) Batch 1.291 (1.312) Remain 34:44:37 loss: 0.1430 Lr: 0.00360 [2024-02-18 12:57:33,164 INFO misc.py line 119 87073] Train: [39/100][1212/1557] Data 0.014 (0.369) Batch 0.988 (1.312) Remain 34:44:10 loss: 0.2327 Lr: 0.00360 [2024-02-18 12:57:34,243 INFO misc.py line 119 87073] Train: [39/100][1213/1557] Data 0.003 (0.369) Batch 1.079 (1.312) Remain 34:43:51 loss: 0.1964 Lr: 0.00360 [2024-02-18 12:57:35,093 INFO misc.py line 119 87073] Train: [39/100][1214/1557] Data 0.004 (0.369) Batch 0.848 (1.311) Remain 34:43:13 loss: 0.3380 Lr: 0.00360 [2024-02-18 12:57:35,953 INFO misc.py line 119 87073] Train: [39/100][1215/1557] Data 0.006 (0.369) Batch 0.860 (1.311) Remain 34:42:36 loss: 0.2858 Lr: 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INFO misc.py line 119 87073] Train: [39/100][1222/1557] Data 0.004 (0.366) Batch 0.836 (1.309) Remain 34:39:00 loss: 0.2893 Lr: 0.00360 [2024-02-18 12:57:43,221 INFO misc.py line 119 87073] Train: [39/100][1223/1557] Data 0.005 (0.366) Batch 0.731 (1.308) Remain 34:38:14 loss: 0.1735 Lr: 0.00360 [2024-02-18 12:57:43,994 INFO misc.py line 119 87073] Train: [39/100][1224/1557] Data 0.012 (0.366) Batch 0.780 (1.308) Remain 34:37:31 loss: 0.2066 Lr: 0.00360 [2024-02-18 12:57:45,167 INFO misc.py line 119 87073] Train: [39/100][1225/1557] Data 0.003 (0.366) Batch 1.173 (1.308) Remain 34:37:19 loss: 0.2125 Lr: 0.00360 [2024-02-18 12:57:45,985 INFO misc.py line 119 87073] Train: [39/100][1226/1557] Data 0.004 (0.365) Batch 0.816 (1.307) Remain 34:36:40 loss: 0.2769 Lr: 0.00360 [2024-02-18 12:57:46,968 INFO misc.py line 119 87073] Train: [39/100][1227/1557] Data 0.006 (0.365) Batch 0.984 (1.307) Remain 34:36:13 loss: 0.6509 Lr: 0.00360 [2024-02-18 12:57:47,915 INFO misc.py line 119 87073] Train: [39/100][1228/1557] Data 0.004 (0.365) Batch 0.946 (1.307) Remain 34:35:44 loss: 0.6041 Lr: 0.00360 [2024-02-18 12:57:48,831 INFO misc.py line 119 87073] Train: [39/100][1229/1557] Data 0.005 (0.364) Batch 0.918 (1.306) Remain 34:35:12 loss: 0.5888 Lr: 0.00360 [2024-02-18 12:57:51,244 INFO misc.py line 119 87073] Train: [39/100][1230/1557] Data 1.638 (0.365) Batch 2.412 (1.307) Remain 34:36:37 loss: 0.4257 Lr: 0.00360 [2024-02-18 12:57:51,979 INFO misc.py line 119 87073] Train: [39/100][1231/1557] Data 0.004 (0.365) Batch 0.733 (1.307) Remain 34:35:51 loss: 0.4831 Lr: 0.00360 [2024-02-18 12:57:53,242 INFO misc.py line 119 87073] Train: [39/100][1232/1557] Data 0.006 (0.365) Batch 1.260 (1.307) Remain 34:35:46 loss: 0.2466 Lr: 0.00360 [2024-02-18 12:57:54,178 INFO misc.py line 119 87073] Train: [39/100][1233/1557] Data 0.009 (0.365) Batch 0.942 (1.307) Remain 34:35:16 loss: 0.5536 Lr: 0.00360 [2024-02-18 12:57:54,991 INFO misc.py line 119 87073] Train: [39/100][1234/1557] Data 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(1.320) Remain 34:56:46 loss: 0.8648 Lr: 0.00360 [2024-02-18 12:58:21,247 INFO misc.py line 119 87073] Train: [39/100][1241/1557] Data 0.004 (0.378) Batch 1.065 (1.320) Remain 34:56:25 loss: 0.6186 Lr: 0.00360 [2024-02-18 12:58:22,163 INFO misc.py line 119 87073] Train: [39/100][1242/1557] Data 0.004 (0.378) Batch 0.915 (1.320) Remain 34:55:52 loss: 0.2801 Lr: 0.00360 [2024-02-18 12:58:23,006 INFO misc.py line 119 87073] Train: [39/100][1243/1557] Data 0.004 (0.378) Batch 0.836 (1.319) Remain 34:55:14 loss: 0.2149 Lr: 0.00360 [2024-02-18 12:58:23,756 INFO misc.py line 119 87073] Train: [39/100][1244/1557] Data 0.011 (0.377) Batch 0.758 (1.319) Remain 34:54:29 loss: 0.3058 Lr: 0.00360 [2024-02-18 12:58:24,551 INFO misc.py line 119 87073] Train: [39/100][1245/1557] Data 0.004 (0.377) Batch 0.793 (1.318) Remain 34:53:48 loss: 0.1395 Lr: 0.00360 [2024-02-18 12:58:25,662 INFO misc.py line 119 87073] Train: [39/100][1246/1557] Data 0.005 (0.377) Batch 1.105 (1.318) Remain 34:53:30 loss: 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Data 0.005 (0.371) Batch 0.764 (1.312) Remain 34:43:45 loss: 0.3657 Lr: 0.00359 [2024-02-18 12:58:44,084 INFO misc.py line 119 87073] Train: [39/100][1266/1557] Data 0.004 (0.371) Batch 0.799 (1.312) Remain 34:43:05 loss: 0.2761 Lr: 0.00359 [2024-02-18 12:58:45,349 INFO misc.py line 119 87073] Train: [39/100][1267/1557] Data 0.005 (0.371) Batch 1.261 (1.312) Remain 34:43:00 loss: 0.1303 Lr: 0.00359 [2024-02-18 12:58:46,469 INFO misc.py line 119 87073] Train: [39/100][1268/1557] Data 0.008 (0.370) Batch 1.117 (1.312) Remain 34:42:44 loss: 0.3822 Lr: 0.00359 [2024-02-18 12:58:47,387 INFO misc.py line 119 87073] Train: [39/100][1269/1557] Data 0.011 (0.370) Batch 0.925 (1.311) Remain 34:42:14 loss: 0.7279 Lr: 0.00359 [2024-02-18 12:58:48,405 INFO misc.py line 119 87073] Train: [39/100][1270/1557] Data 0.005 (0.370) Batch 1.018 (1.311) Remain 34:41:50 loss: 0.5682 Lr: 0.00359 [2024-02-18 12:58:49,294 INFO misc.py line 119 87073] Train: [39/100][1271/1557] Data 0.004 (0.369) Batch 0.890 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87073] Train: [39/100][1290/1557] Data 0.013 (0.364) Batch 0.838 (1.306) Remain 34:32:31 loss: 0.4607 Lr: 0.00359 [2024-02-18 12:59:08,450 INFO misc.py line 119 87073] Train: [39/100][1291/1557] Data 0.004 (0.364) Batch 1.031 (1.305) Remain 34:32:09 loss: 0.5144 Lr: 0.00359 [2024-02-18 12:59:09,351 INFO misc.py line 119 87073] Train: [39/100][1292/1557] Data 0.004 (0.364) Batch 0.900 (1.305) Remain 34:31:38 loss: 0.3753 Lr: 0.00359 [2024-02-18 12:59:10,117 INFO misc.py line 119 87073] Train: [39/100][1293/1557] Data 0.005 (0.363) Batch 0.761 (1.305) Remain 34:30:56 loss: 0.2215 Lr: 0.00359 [2024-02-18 12:59:10,887 INFO misc.py line 119 87073] Train: [39/100][1294/1557] Data 0.010 (0.363) Batch 0.775 (1.304) Remain 34:30:16 loss: 0.3383 Lr: 0.00359 [2024-02-18 12:59:31,817 INFO misc.py line 119 87073] Train: [39/100][1295/1557] Data 19.833 (0.378) Batch 20.929 (1.319) Remain 34:54:21 loss: 0.1711 Lr: 0.00359 [2024-02-18 12:59:32,915 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [39/100][1383/1557] Data 0.005 (0.366) Batch 0.900 (1.307) Remain 34:32:20 loss: 0.3796 Lr: 0.00359 [2024-02-18 13:01:11,232 INFO misc.py line 119 87073] Train: [39/100][1384/1557] Data 0.010 (0.366) Batch 0.780 (1.306) Remain 34:31:43 loss: 0.4778 Lr: 0.00359 [2024-02-18 13:01:11,968 INFO misc.py line 119 87073] Train: [39/100][1385/1557] Data 0.004 (0.365) Batch 0.731 (1.306) Remain 34:31:02 loss: 0.2346 Lr: 0.00359 [2024-02-18 13:01:13,209 INFO misc.py line 119 87073] Train: [39/100][1386/1557] Data 0.009 (0.365) Batch 1.238 (1.306) Remain 34:30:56 loss: 0.1723 Lr: 0.00359 [2024-02-18 13:01:14,181 INFO misc.py line 119 87073] Train: [39/100][1387/1557] Data 0.011 (0.365) Batch 0.977 (1.306) Remain 34:30:32 loss: 0.5855 Lr: 0.00359 [2024-02-18 13:01:15,189 INFO misc.py line 119 87073] Train: [39/100][1388/1557] Data 0.006 (0.364) Batch 1.010 (1.305) Remain 34:30:10 loss: 0.2253 Lr: 0.00359 [2024-02-18 13:01:16,145 INFO misc.py line 119 87073] Train: 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INFO misc.py line 119 87073] Train: [39/100][1414/1557] Data 0.014 (0.373) Batch 1.159 (1.314) Remain 34:42:54 loss: 0.1619 Lr: 0.00359 [2024-02-18 13:02:01,866 INFO misc.py line 119 87073] Train: [39/100][1415/1557] Data 0.011 (0.373) Batch 0.897 (1.314) Remain 34:42:25 loss: 0.5561 Lr: 0.00359 [2024-02-18 13:02:02,746 INFO misc.py line 119 87073] Train: [39/100][1416/1557] Data 0.003 (0.372) Batch 0.879 (1.313) Remain 34:41:54 loss: 0.2011 Lr: 0.00359 [2024-02-18 13:02:03,921 INFO misc.py line 119 87073] Train: [39/100][1417/1557] Data 0.004 (0.372) Batch 1.174 (1.313) Remain 34:41:44 loss: 0.6489 Lr: 0.00359 [2024-02-18 13:02:05,152 INFO misc.py line 119 87073] Train: [39/100][1418/1557] Data 0.006 (0.372) Batch 1.232 (1.313) Remain 34:41:37 loss: 0.2157 Lr: 0.00359 [2024-02-18 13:02:05,943 INFO misc.py line 119 87073] Train: [39/100][1419/1557] Data 0.004 (0.372) Batch 0.791 (1.313) Remain 34:41:01 loss: 0.3367 Lr: 0.00359 [2024-02-18 13:02:06,741 INFO misc.py line 119 87073] Train: [39/100][1420/1557] Data 0.004 (0.371) Batch 0.797 (1.312) Remain 34:40:25 loss: 0.1424 Lr: 0.00359 [2024-02-18 13:02:07,854 INFO misc.py line 119 87073] Train: [39/100][1421/1557] Data 0.005 (0.371) Batch 1.113 (1.312) Remain 34:40:10 loss: 0.1179 Lr: 0.00359 [2024-02-18 13:02:08,882 INFO misc.py line 119 87073] Train: [39/100][1422/1557] Data 0.006 (0.371) Batch 1.028 (1.312) Remain 34:39:50 loss: 0.5842 Lr: 0.00359 [2024-02-18 13:02:09,943 INFO misc.py line 119 87073] Train: [39/100][1423/1557] Data 0.005 (0.371) Batch 1.061 (1.312) Remain 34:39:31 loss: 0.7756 Lr: 0.00359 [2024-02-18 13:02:10,992 INFO misc.py line 119 87073] Train: [39/100][1424/1557] Data 0.005 (0.370) Batch 1.046 (1.312) Remain 34:39:12 loss: 0.9388 Lr: 0.00359 [2024-02-18 13:02:11,980 INFO misc.py line 119 87073] Train: [39/100][1425/1557] Data 0.008 (0.370) Batch 0.992 (1.311) Remain 34:38:50 loss: 0.7577 Lr: 0.00359 [2024-02-18 13:02:12,715 INFO misc.py line 119 87073] Train: [39/100][1426/1557] Data 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Remain 34:35:26 loss: 0.4369 Lr: 0.00359 [2024-02-18 13:02:18,984 INFO misc.py line 119 87073] Train: [39/100][1433/1557] Data 0.004 (0.368) Batch 0.752 (1.309) Remain 34:34:47 loss: 0.2916 Lr: 0.00359 [2024-02-18 13:02:19,775 INFO misc.py line 119 87073] Train: [39/100][1434/1557] Data 0.004 (0.368) Batch 0.787 (1.309) Remain 34:34:11 loss: 0.5978 Lr: 0.00359 [2024-02-18 13:02:21,071 INFO misc.py line 119 87073] Train: [39/100][1435/1557] Data 0.008 (0.367) Batch 1.278 (1.309) Remain 34:34:08 loss: 0.1608 Lr: 0.00359 [2024-02-18 13:02:21,965 INFO misc.py line 119 87073] Train: [39/100][1436/1557] Data 0.026 (0.367) Batch 0.915 (1.308) Remain 34:33:40 loss: 0.6341 Lr: 0.00359 [2024-02-18 13:02:22,971 INFO misc.py line 119 87073] Train: [39/100][1437/1557] Data 0.005 (0.367) Batch 1.007 (1.308) Remain 34:33:19 loss: 0.3337 Lr: 0.00359 [2024-02-18 13:02:23,882 INFO misc.py line 119 87073] Train: [39/100][1438/1557] Data 0.004 (0.367) Batch 0.912 (1.308) Remain 34:32:52 loss: 0.4820 Lr: 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INFO misc.py line 119 87073] Train: [39/100][1445/1557] Data 0.005 (0.365) Batch 1.047 (1.306) Remain 34:30:13 loss: 0.6391 Lr: 0.00359 [2024-02-18 13:02:31,602 INFO misc.py line 119 87073] Train: [39/100][1446/1557] Data 0.004 (0.365) Batch 0.834 (1.306) Remain 34:29:40 loss: 0.2334 Lr: 0.00359 [2024-02-18 13:02:32,412 INFO misc.py line 119 87073] Train: [39/100][1447/1557] Data 0.004 (0.365) Batch 0.805 (1.306) Remain 34:29:06 loss: 0.1784 Lr: 0.00359 [2024-02-18 13:02:33,111 INFO misc.py line 119 87073] Train: [39/100][1448/1557] Data 0.010 (0.364) Batch 0.704 (1.305) Remain 34:28:25 loss: 0.2171 Lr: 0.00359 [2024-02-18 13:02:34,372 INFO misc.py line 119 87073] Train: [39/100][1449/1557] Data 0.004 (0.364) Batch 1.260 (1.305) Remain 34:28:21 loss: 0.3199 Lr: 0.00359 [2024-02-18 13:02:35,277 INFO misc.py line 119 87073] Train: [39/100][1450/1557] Data 0.005 (0.364) Batch 0.905 (1.305) Remain 34:27:53 loss: 0.8442 Lr: 0.00359 [2024-02-18 13:02:36,307 INFO misc.py line 119 87073] Train: [39/100][1451/1557] Data 0.005 (0.364) Batch 1.024 (1.305) Remain 34:27:34 loss: 0.6655 Lr: 0.00359 [2024-02-18 13:02:37,513 INFO misc.py line 119 87073] Train: [39/100][1452/1557] Data 0.011 (0.363) Batch 1.203 (1.305) Remain 34:27:26 loss: 0.2465 Lr: 0.00359 [2024-02-18 13:02:38,473 INFO misc.py line 119 87073] Train: [39/100][1453/1557] Data 0.013 (0.363) Batch 0.969 (1.304) Remain 34:27:02 loss: 0.3530 Lr: 0.00359 [2024-02-18 13:02:39,233 INFO misc.py line 119 87073] Train: [39/100][1454/1557] Data 0.004 (0.363) Batch 0.760 (1.304) Remain 34:26:25 loss: 0.5117 Lr: 0.00359 [2024-02-18 13:02:39,989 INFO misc.py line 119 87073] Train: [39/100][1455/1557] Data 0.004 (0.363) Batch 0.748 (1.304) Remain 34:25:48 loss: 0.1164 Lr: 0.00359 [2024-02-18 13:02:41,269 INFO misc.py line 119 87073] Train: [39/100][1456/1557] Data 0.012 (0.362) Batch 1.282 (1.304) Remain 34:25:45 loss: 0.1204 Lr: 0.00359 [2024-02-18 13:02:42,178 INFO misc.py line 119 87073] Train: [39/100][1457/1557] Data 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(1.314) Remain 34:42:26 loss: 0.1519 Lr: 0.00359 [2024-02-18 13:03:06,868 INFO misc.py line 119 87073] Train: [39/100][1464/1557] Data 0.004 (0.373) Batch 0.968 (1.314) Remain 34:42:02 loss: 0.4442 Lr: 0.00359 [2024-02-18 13:03:07,854 INFO misc.py line 119 87073] Train: [39/100][1465/1557] Data 0.004 (0.372) Batch 0.986 (1.314) Remain 34:41:39 loss: 0.3466 Lr: 0.00359 [2024-02-18 13:03:08,877 INFO misc.py line 119 87073] Train: [39/100][1466/1557] Data 0.003 (0.372) Batch 1.023 (1.314) Remain 34:41:19 loss: 0.5873 Lr: 0.00359 [2024-02-18 13:03:09,779 INFO misc.py line 119 87073] Train: [39/100][1467/1557] Data 0.004 (0.372) Batch 0.894 (1.313) Remain 34:40:51 loss: 0.3769 Lr: 0.00358 [2024-02-18 13:03:10,557 INFO misc.py line 119 87073] Train: [39/100][1468/1557] Data 0.012 (0.372) Batch 0.785 (1.313) Remain 34:40:15 loss: 0.3346 Lr: 0.00358 [2024-02-18 13:03:11,303 INFO misc.py line 119 87073] Train: [39/100][1469/1557] Data 0.006 (0.371) Batch 0.746 (1.313) Remain 34:39:37 loss: 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87073] Train: [39/100][1482/1557] Data 0.004 (0.368) Batch 0.762 (1.309) Remain 34:34:20 loss: 0.1707 Lr: 0.00358 [2024-02-18 13:03:24,357 INFO misc.py line 119 87073] Train: [39/100][1483/1557] Data 0.003 (0.368) Batch 0.660 (1.309) Remain 34:33:37 loss: 0.7291 Lr: 0.00358 [2024-02-18 13:03:25,503 INFO misc.py line 119 87073] Train: [39/100][1484/1557] Data 0.005 (0.368) Batch 1.147 (1.309) Remain 34:33:25 loss: 0.4552 Lr: 0.00358 [2024-02-18 13:03:26,566 INFO misc.py line 119 87073] Train: [39/100][1485/1557] Data 0.004 (0.368) Batch 1.061 (1.309) Remain 34:33:08 loss: 0.8703 Lr: 0.00358 [2024-02-18 13:03:27,467 INFO misc.py line 119 87073] Train: [39/100][1486/1557] Data 0.006 (0.367) Batch 0.903 (1.308) Remain 34:32:41 loss: 0.4196 Lr: 0.00358 [2024-02-18 13:03:28,267 INFO misc.py line 119 87073] Train: [39/100][1487/1557] Data 0.004 (0.367) Batch 0.799 (1.308) Remain 34:32:07 loss: 0.4119 Lr: 0.00358 [2024-02-18 13:03:29,156 INFO misc.py line 119 87073] Train: [39/100][1488/1557] Data 0.007 (0.367) Batch 0.888 (1.308) Remain 34:31:38 loss: 0.2871 Lr: 0.00358 [2024-02-18 13:03:30,015 INFO misc.py line 119 87073] Train: [39/100][1489/1557] Data 0.006 (0.367) Batch 0.860 (1.307) Remain 34:31:08 loss: 0.3294 Lr: 0.00358 [2024-02-18 13:03:30,732 INFO misc.py line 119 87073] Train: [39/100][1490/1557] Data 0.006 (0.366) Batch 0.718 (1.307) Remain 34:30:29 loss: 0.2544 Lr: 0.00358 [2024-02-18 13:03:32,001 INFO misc.py line 119 87073] Train: [39/100][1491/1557] Data 0.004 (0.366) Batch 1.269 (1.307) Remain 34:30:26 loss: 0.1166 Lr: 0.00358 [2024-02-18 13:03:33,143 INFO misc.py line 119 87073] Train: [39/100][1492/1557] Data 0.004 (0.366) Batch 1.131 (1.307) Remain 34:30:13 loss: 0.4386 Lr: 0.00358 [2024-02-18 13:03:34,197 INFO misc.py line 119 87073] Train: [39/100][1493/1557] Data 0.015 (0.366) Batch 1.054 (1.307) Remain 34:29:56 loss: 0.1932 Lr: 0.00358 [2024-02-18 13:03:35,089 INFO misc.py line 119 87073] Train: [39/100][1494/1557] Data 0.015 (0.365) Batch 0.902 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13:03:47,553 INFO misc.py line 119 87073] Train: [39/100][1507/1557] Data 0.016 (0.362) Batch 0.900 (1.303) Remain 34:24:26 loss: 0.5031 Lr: 0.00358 [2024-02-18 13:03:48,644 INFO misc.py line 119 87073] Train: [39/100][1508/1557] Data 0.004 (0.362) Batch 1.090 (1.303) Remain 34:24:11 loss: 0.6712 Lr: 0.00358 [2024-02-18 13:03:49,581 INFO misc.py line 119 87073] Train: [39/100][1509/1557] Data 0.005 (0.362) Batch 0.938 (1.303) Remain 34:23:47 loss: 0.4509 Lr: 0.00358 [2024-02-18 13:03:50,313 INFO misc.py line 119 87073] Train: [39/100][1510/1557] Data 0.003 (0.362) Batch 0.723 (1.303) Remain 34:23:09 loss: 0.3176 Lr: 0.00358 [2024-02-18 13:03:51,027 INFO misc.py line 119 87073] Train: [39/100][1511/1557] Data 0.013 (0.361) Batch 0.722 (1.302) Remain 34:22:31 loss: 0.6323 Lr: 0.00358 [2024-02-18 13:03:52,332 INFO misc.py line 119 87073] Train: [39/100][1512/1557] Data 0.004 (0.361) Batch 1.303 (1.302) Remain 34:22:30 loss: 0.1420 Lr: 0.00358 [2024-02-18 13:03:53,249 INFO misc.py line 119 87073] Train: [39/100][1513/1557] Data 0.006 (0.361) Batch 0.918 (1.302) Remain 34:22:05 loss: 0.2948 Lr: 0.00358 [2024-02-18 13:03:54,443 INFO misc.py line 119 87073] Train: [39/100][1514/1557] Data 0.005 (0.361) Batch 1.196 (1.302) Remain 34:21:57 loss: 0.1944 Lr: 0.00358 [2024-02-18 13:03:55,510 INFO misc.py line 119 87073] Train: [39/100][1515/1557] Data 0.004 (0.360) Batch 1.065 (1.302) Remain 34:21:40 loss: 0.0837 Lr: 0.00358 [2024-02-18 13:03:56,486 INFO misc.py line 119 87073] Train: [39/100][1516/1557] Data 0.006 (0.360) Batch 0.976 (1.302) Remain 34:21:19 loss: 0.4534 Lr: 0.00358 [2024-02-18 13:03:57,262 INFO misc.py line 119 87073] Train: [39/100][1517/1557] Data 0.005 (0.360) Batch 0.769 (1.301) Remain 34:20:44 loss: 0.2475 Lr: 0.00358 [2024-02-18 13:03:58,079 INFO misc.py line 119 87073] Train: [39/100][1518/1557] Data 0.011 (0.360) Batch 0.824 (1.301) Remain 34:20:13 loss: 0.2462 Lr: 0.00358 [2024-02-18 13:04:20,035 INFO misc.py line 119 87073] Train: [39/100][1519/1557] Data 20.908 (0.373) Batch 21.956 (1.315) Remain 34:41:46 loss: 0.1723 Lr: 0.00358 [2024-02-18 13:04:21,139 INFO misc.py line 119 87073] Train: [39/100][1520/1557] Data 0.005 (0.373) Batch 1.105 (1.314) Remain 34:41:31 loss: 0.1230 Lr: 0.00358 [2024-02-18 13:04:22,223 INFO misc.py line 119 87073] Train: [39/100][1521/1557] Data 0.003 (0.373) Batch 1.083 (1.314) Remain 34:41:16 loss: 0.4258 Lr: 0.00358 [2024-02-18 13:04:23,241 INFO misc.py line 119 87073] Train: [39/100][1522/1557] Data 0.005 (0.372) Batch 1.018 (1.314) Remain 34:40:56 loss: 0.1631 Lr: 0.00358 [2024-02-18 13:04:24,077 INFO misc.py line 119 87073] Train: [39/100][1523/1557] Data 0.005 (0.372) Batch 0.836 (1.314) Remain 34:40:25 loss: 0.7875 Lr: 0.00358 [2024-02-18 13:04:24,809 INFO misc.py line 119 87073] Train: [39/100][1524/1557] Data 0.006 (0.372) Batch 0.730 (1.313) Remain 34:39:47 loss: 0.4218 Lr: 0.00358 [2024-02-18 13:04:25,623 INFO misc.py line 119 87073] Train: [39/100][1525/1557] Data 0.008 (0.372) Batch 0.815 (1.313) Remain 34:39:14 loss: 0.3162 Lr: 0.00358 [2024-02-18 13:04:26,676 INFO misc.py line 119 87073] Train: [39/100][1526/1557] Data 0.006 (0.372) Batch 1.054 (1.313) Remain 34:38:57 loss: 0.2026 Lr: 0.00358 [2024-02-18 13:04:27,783 INFO misc.py line 119 87073] Train: [39/100][1527/1557] Data 0.005 (0.371) Batch 1.108 (1.313) Remain 34:38:43 loss: 1.0410 Lr: 0.00358 [2024-02-18 13:04:28,871 INFO misc.py line 119 87073] Train: [39/100][1528/1557] Data 0.004 (0.371) Batch 1.086 (1.313) Remain 34:38:27 loss: 0.4434 Lr: 0.00358 [2024-02-18 13:04:29,667 INFO misc.py line 119 87073] Train: [39/100][1529/1557] Data 0.005 (0.371) Batch 0.797 (1.312) Remain 34:37:54 loss: 0.0831 Lr: 0.00358 [2024-02-18 13:04:30,744 INFO misc.py line 119 87073] Train: [39/100][1530/1557] Data 0.003 (0.371) Batch 1.074 (1.312) Remain 34:37:38 loss: 0.4341 Lr: 0.00358 [2024-02-18 13:04:31,510 INFO misc.py line 119 87073] Train: [39/100][1531/1557] Data 0.007 (0.370) Batch 0.769 (1.312) Remain 34:37:03 loss: 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13:04:37,977 INFO misc.py line 119 87073] Train: [39/100][1538/1557] Data 0.005 (0.369) Batch 0.750 (1.310) Remain 34:34:06 loss: 0.3636 Lr: 0.00358 [2024-02-18 13:04:38,701 INFO misc.py line 119 87073] Train: [39/100][1539/1557] Data 0.005 (0.368) Batch 0.725 (1.310) Remain 34:33:28 loss: 0.2706 Lr: 0.00358 [2024-02-18 13:04:39,829 INFO misc.py line 119 87073] Train: [39/100][1540/1557] Data 0.004 (0.368) Batch 1.128 (1.310) Remain 34:33:15 loss: 0.2251 Lr: 0.00358 [2024-02-18 13:04:40,767 INFO misc.py line 119 87073] Train: [39/100][1541/1557] Data 0.004 (0.368) Batch 0.937 (1.309) Remain 34:32:51 loss: 0.2804 Lr: 0.00358 [2024-02-18 13:04:41,664 INFO misc.py line 119 87073] Train: [39/100][1542/1557] Data 0.004 (0.368) Batch 0.897 (1.309) Remain 34:32:24 loss: 0.5029 Lr: 0.00358 [2024-02-18 13:04:42,648 INFO misc.py line 119 87073] Train: [39/100][1543/1557] Data 0.004 (0.367) Batch 0.984 (1.309) Remain 34:32:03 loss: 0.9829 Lr: 0.00358 [2024-02-18 13:04:43,658 INFO misc.py line 119 87073] Train: [39/100][1544/1557] Data 0.005 (0.367) Batch 1.010 (1.309) Remain 34:31:43 loss: 0.7395 Lr: 0.00358 [2024-02-18 13:04:44,336 INFO misc.py line 119 87073] Train: [39/100][1545/1557] Data 0.004 (0.367) Batch 0.678 (1.308) Remain 34:31:03 loss: 0.2550 Lr: 0.00358 [2024-02-18 13:04:45,084 INFO misc.py line 119 87073] Train: [39/100][1546/1557] Data 0.004 (0.367) Batch 0.742 (1.308) Remain 34:30:27 loss: 0.2900 Lr: 0.00358 [2024-02-18 13:04:46,316 INFO misc.py line 119 87073] Train: [39/100][1547/1557] Data 0.011 (0.367) Batch 1.233 (1.308) Remain 34:30:21 loss: 0.1688 Lr: 0.00358 [2024-02-18 13:04:47,142 INFO misc.py line 119 87073] Train: [39/100][1548/1557] Data 0.010 (0.366) Batch 0.831 (1.307) Remain 34:29:50 loss: 0.5354 Lr: 0.00358 [2024-02-18 13:04:48,071 INFO misc.py line 119 87073] Train: [39/100][1549/1557] Data 0.004 (0.366) Batch 0.929 (1.307) Remain 34:29:26 loss: 0.5733 Lr: 0.00358 [2024-02-18 13:04:49,184 INFO misc.py line 119 87073] Train: [39/100][1550/1557] Data 0.004 (0.366) Batch 1.113 (1.307) Remain 34:29:13 loss: 0.3327 Lr: 0.00358 [2024-02-18 13:04:49,948 INFO misc.py line 119 87073] Train: [39/100][1551/1557] Data 0.003 (0.366) Batch 0.764 (1.307) Remain 34:28:38 loss: 0.2975 Lr: 0.00358 [2024-02-18 13:04:50,693 INFO misc.py line 119 87073] Train: [39/100][1552/1557] Data 0.004 (0.365) Batch 0.736 (1.306) Remain 34:28:02 loss: 0.3276 Lr: 0.00358 [2024-02-18 13:04:51,452 INFO misc.py line 119 87073] Train: [39/100][1553/1557] Data 0.013 (0.365) Batch 0.767 (1.306) Remain 34:27:27 loss: 0.2709 Lr: 0.00358 [2024-02-18 13:04:52,695 INFO misc.py line 119 87073] Train: [39/100][1554/1557] Data 0.004 (0.365) Batch 1.244 (1.306) Remain 34:27:22 loss: 0.2020 Lr: 0.00358 [2024-02-18 13:04:53,706 INFO misc.py line 119 87073] Train: [39/100][1555/1557] Data 0.004 (0.365) Batch 1.011 (1.306) Remain 34:27:03 loss: 0.8926 Lr: 0.00358 [2024-02-18 13:04:54,593 INFO misc.py line 119 87073] Train: [39/100][1556/1557] Data 0.004 (0.364) Batch 0.887 (1.306) Remain 34:26:36 loss: 0.3313 Lr: 0.00358 [2024-02-18 13:04:55,534 INFO misc.py line 119 87073] Train: [39/100][1557/1557] Data 0.004 (0.364) Batch 0.935 (1.305) Remain 34:26:12 loss: 0.4320 Lr: 0.00358 [2024-02-18 13:04:55,535 INFO misc.py line 136 87073] Train result: loss: 0.3939 [2024-02-18 13:04:55,535 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 13:05:22,374 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.9063 [2024-02-18 13:05:23,150 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4471 [2024-02-18 13:05:25,278 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4865 [2024-02-18 13:05:27,484 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.5652 [2024-02-18 13:05:32,430 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2422 [2024-02-18 13:05:33,127 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0910 [2024-02-18 13:05:34,387 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2482 [2024-02-18 13:05:37,340 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3681 [2024-02-18 13:05:39,146 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2643 [2024-02-18 13:05:40,838 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7185/0.7738/0.9112. [2024-02-18 13:05:40,838 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9322/0.9767 [2024-02-18 13:05:40,838 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9784/0.9852 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8578/0.9714 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3795/0.4249 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6690/0.6960 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7940/0.8555 [2024-02-18 13:05:40,839 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8481/0.9337 [2024-02-18 13:05:40,840 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9150/0.9525 [2024-02-18 13:05:40,840 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8408/0.8723 [2024-02-18 13:05:40,840 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7623/0.8432 [2024-02-18 13:05:40,840 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7663/0.8493 [2024-02-18 13:05:40,840 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5973/0.6983 [2024-02-18 13:05:40,841 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 13:05:40,844 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 13:05:40,844 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 13:05:49,215 INFO misc.py line 119 87073] Train: [40/100][1/1557] Data 1.747 (1.747) Batch 2.713 (2.713) Remain 71:35:11 loss: 1.0802 Lr: 0.00358 [2024-02-18 13:05:50,163 INFO misc.py line 119 87073] Train: [40/100][2/1557] Data 0.005 (0.005) Batch 0.936 (0.936) Remain 24:41:43 loss: 0.7488 Lr: 0.00358 [2024-02-18 13:05:51,191 INFO misc.py line 119 87073] Train: [40/100][3/1557] Data 0.017 (0.017) Batch 1.039 (1.039) Remain 27:25:09 loss: 0.1328 Lr: 0.00358 [2024-02-18 13:05:52,101 INFO misc.py line 119 87073] Train: [40/100][4/1557] Data 0.005 (0.005) Batch 0.909 (0.909) Remain 23:58:47 loss: 0.3520 Lr: 0.00358 [2024-02-18 13:05:52,812 INFO misc.py line 119 87073] Train: [40/100][5/1557] Data 0.007 (0.006) Batch 0.714 (0.811) Remain 21:24:17 loss: 0.3978 Lr: 0.00358 [2024-02-18 13:05:53,642 INFO misc.py line 119 87073] Train: [40/100][6/1557] Data 0.004 (0.005) Batch 0.826 (0.816) Remain 21:31:45 loss: 0.2941 Lr: 0.00358 [2024-02-18 13:05:54,711 INFO misc.py line 119 87073] Train: [40/100][7/1557] Data 0.009 (0.006) Batch 1.063 (0.878) Remain 23:09:30 loss: 0.1832 Lr: 0.00358 [2024-02-18 13:05:55,744 INFO misc.py line 119 87073] Train: [40/100][8/1557] Data 0.014 (0.008) Batch 1.032 (0.909) Remain 23:58:25 loss: 0.6042 Lr: 0.00358 [2024-02-18 13:05:56,655 INFO misc.py line 119 87073] Train: [40/100][9/1557] Data 0.014 (0.009) Batch 0.920 (0.911) Remain 24:01:26 loss: 0.5757 Lr: 0.00358 [2024-02-18 13:05:57,554 INFO misc.py line 119 87073] Train: [40/100][10/1557] Data 0.005 (0.008) Batch 0.899 (0.909) Remain 23:58:52 loss: 0.6609 Lr: 0.00358 [2024-02-18 13:05:58,573 INFO misc.py line 119 87073] Train: [40/100][11/1557] Data 0.005 (0.008) Batch 1.013 (0.922) Remain 24:19:24 loss: 0.5668 Lr: 0.00358 [2024-02-18 13:05:59,352 INFO misc.py line 119 87073] Train: [40/100][12/1557] Data 0.010 (0.008) Batch 0.787 (0.907) Remain 23:55:33 loss: 0.5648 Lr: 0.00358 [2024-02-18 13:06:00,118 INFO misc.py line 119 87073] Train: [40/100][13/1557] Data 0.003 (0.008) Batch 0.765 (0.893) Remain 23:33:05 loss: 0.5622 Lr: 0.00358 [2024-02-18 13:06:01,400 INFO misc.py line 119 87073] Train: [40/100][14/1557] Data 0.003 (0.007) Batch 1.272 (0.927) Remain 24:27:38 loss: 0.2572 Lr: 0.00358 [2024-02-18 13:06:02,277 INFO misc.py line 119 87073] Train: [40/100][15/1557] Data 0.014 (0.008) Batch 0.887 (0.924) Remain 24:22:18 loss: 0.1108 Lr: 0.00358 [2024-02-18 13:06:03,252 INFO misc.py line 119 87073] Train: [40/100][16/1557] Data 0.004 (0.007) Batch 0.975 (0.928) Remain 24:28:31 loss: 0.3191 Lr: 0.00358 [2024-02-18 13:06:04,250 INFO misc.py line 119 87073] Train: [40/100][17/1557] Data 0.003 (0.007) Batch 0.998 (0.933) Remain 24:36:29 loss: 0.4404 Lr: 0.00358 [2024-02-18 13:06:05,153 INFO misc.py line 119 87073] Train: [40/100][18/1557] Data 0.004 (0.007) Batch 0.902 (0.931) Remain 24:33:15 loss: 0.2504 Lr: 0.00358 [2024-02-18 13:06:05,897 INFO misc.py line 119 87073] Train: [40/100][19/1557] Data 0.004 (0.007) Batch 0.743 (0.919) Remain 24:14:40 loss: 0.3011 Lr: 0.00358 [2024-02-18 13:06:06,626 INFO misc.py line 119 87073] Train: [40/100][20/1557] Data 0.005 (0.007) Batch 0.728 (0.908) Remain 23:56:52 loss: 0.4295 Lr: 0.00358 [2024-02-18 13:06:07,814 INFO misc.py line 119 87073] Train: [40/100][21/1557] Data 0.005 (0.007) Batch 1.188 (0.923) Remain 24:21:31 loss: 0.1601 Lr: 0.00358 [2024-02-18 13:06:08,735 INFO misc.py line 119 87073] Train: [40/100][22/1557] Data 0.005 (0.007) Batch 0.923 (0.923) Remain 24:21:27 loss: 0.4560 Lr: 0.00358 [2024-02-18 13:06:09,943 INFO misc.py line 119 87073] Train: [40/100][23/1557] Data 0.004 (0.006) Batch 1.207 (0.938) Remain 24:43:54 loss: 0.9467 Lr: 0.00358 [2024-02-18 13:06:10,859 INFO misc.py line 119 87073] Train: [40/100][24/1557] Data 0.005 (0.006) Batch 0.915 (0.937) Remain 24:42:12 loss: 0.4871 Lr: 0.00358 [2024-02-18 13:06:11,623 INFO misc.py line 119 87073] Train: [40/100][25/1557] Data 0.005 (0.006) Batch 0.765 (0.929) Remain 24:29:49 loss: 0.3300 Lr: 0.00358 [2024-02-18 13:06:12,458 INFO misc.py line 119 87073] Train: [40/100][26/1557] Data 0.004 (0.006) Batch 0.835 (0.925) Remain 24:23:20 loss: 0.1612 Lr: 0.00358 [2024-02-18 13:06:13,195 INFO misc.py line 119 87073] Train: [40/100][27/1557] Data 0.005 (0.006) Batch 0.737 (0.917) Remain 24:10:57 loss: 0.4592 Lr: 0.00358 [2024-02-18 13:06:14,479 INFO misc.py line 119 87073] Train: [40/100][28/1557] Data 0.004 (0.006) Batch 1.282 (0.931) Remain 24:34:01 loss: 0.2116 Lr: 0.00358 [2024-02-18 13:06:15,449 INFO misc.py line 119 87073] Train: [40/100][29/1557] Data 0.007 (0.006) Batch 0.972 (0.933) Remain 24:36:30 loss: 0.4655 Lr: 0.00358 [2024-02-18 13:06:16,430 INFO misc.py line 119 87073] Train: [40/100][30/1557] Data 0.004 (0.006) Batch 0.981 (0.935) Remain 24:39:19 loss: 0.2861 Lr: 0.00358 [2024-02-18 13:06:17,424 INFO misc.py line 119 87073] Train: [40/100][31/1557] Data 0.004 (0.006) Batch 0.994 (0.937) Remain 24:42:37 loss: 0.1599 Lr: 0.00358 [2024-02-18 13:06:18,377 INFO misc.py line 119 87073] Train: [40/100][32/1557] Data 0.005 (0.006) Batch 0.953 (0.937) Remain 24:43:29 loss: 0.4185 Lr: 0.00358 [2024-02-18 13:06:19,257 INFO misc.py line 119 87073] Train: [40/100][33/1557] Data 0.004 (0.006) Batch 0.879 (0.936) Remain 24:40:23 loss: 0.4285 Lr: 0.00358 [2024-02-18 13:06:19,933 INFO misc.py line 119 87073] Train: [40/100][34/1557] Data 0.006 (0.006) Batch 0.676 (0.927) Remain 24:27:07 loss: 0.2329 Lr: 0.00358 [2024-02-18 13:06:21,231 INFO misc.py line 119 87073] Train: [40/100][35/1557] Data 0.005 (0.006) Batch 1.298 (0.939) Remain 24:45:27 loss: 0.1782 Lr: 0.00358 [2024-02-18 13:06:22,275 INFO misc.py line 119 87073] Train: [40/100][36/1557] Data 0.005 (0.006) Batch 1.044 (0.942) Remain 24:50:28 loss: 0.3608 Lr: 0.00358 [2024-02-18 13:06:23,427 INFO misc.py line 119 87073] Train: [40/100][37/1557] Data 0.005 (0.006) Batch 1.153 (0.948) Remain 25:00:17 loss: 0.5603 Lr: 0.00358 [2024-02-18 13:06:24,305 INFO misc.py line 119 87073] Train: [40/100][38/1557] Data 0.004 (0.006) Batch 0.878 (0.946) Remain 24:57:06 loss: 0.2307 Lr: 0.00358 [2024-02-18 13:06:25,247 INFO misc.py line 119 87073] Train: [40/100][39/1557] Data 0.004 (0.006) Batch 0.940 (0.946) Remain 24:56:49 loss: 0.4511 Lr: 0.00358 [2024-02-18 13:06:26,044 INFO misc.py line 119 87073] Train: [40/100][40/1557] Data 0.005 (0.006) Batch 0.798 (0.942) Remain 24:50:30 loss: 0.2158 Lr: 0.00358 [2024-02-18 13:06:26,827 INFO misc.py line 119 87073] Train: [40/100][41/1557] Data 0.004 (0.006) Batch 0.782 (0.938) Remain 24:43:49 loss: 0.3605 Lr: 0.00358 [2024-02-18 13:06:28,001 INFO misc.py line 119 87073] Train: [40/100][42/1557] Data 0.005 (0.006) Batch 1.164 (0.944) Remain 24:52:59 loss: 0.3658 Lr: 0.00358 [2024-02-18 13:06:29,003 INFO misc.py line 119 87073] Train: [40/100][43/1557] Data 0.015 (0.006) Batch 1.004 (0.945) Remain 24:55:22 loss: 0.2583 Lr: 0.00358 [2024-02-18 13:06:29,962 INFO misc.py line 119 87073] Train: [40/100][44/1557] Data 0.014 (0.006) Batch 0.968 (0.946) Remain 24:56:15 loss: 0.5513 Lr: 0.00358 [2024-02-18 13:06:31,032 INFO misc.py line 119 87073] Train: [40/100][45/1557] Data 0.004 (0.006) Batch 1.070 (0.949) Remain 25:00:55 loss: 0.3352 Lr: 0.00358 [2024-02-18 13:06:32,040 INFO misc.py line 119 87073] Train: [40/100][46/1557] Data 0.004 (0.006) Batch 1.007 (0.950) Remain 25:03:04 loss: 0.4154 Lr: 0.00358 [2024-02-18 13:06:32,772 INFO misc.py line 119 87073] Train: [40/100][47/1557] Data 0.004 (0.006) Batch 0.732 (0.945) Remain 24:55:13 loss: 0.8107 Lr: 0.00358 [2024-02-18 13:06:33,553 INFO misc.py line 119 87073] Train: [40/100][48/1557] Data 0.003 (0.006) Batch 0.773 (0.941) Remain 24:49:10 loss: 0.2725 Lr: 0.00358 [2024-02-18 13:06:34,732 INFO misc.py line 119 87073] Train: [40/100][49/1557] Data 0.012 (0.006) Batch 1.180 (0.946) Remain 24:57:22 loss: 0.2071 Lr: 0.00358 [2024-02-18 13:06:35,888 INFO misc.py line 119 87073] Train: [40/100][50/1557] Data 0.011 (0.006) Batch 1.159 (0.951) Remain 25:04:30 loss: 0.2231 Lr: 0.00358 [2024-02-18 13:06:36,931 INFO misc.py line 119 87073] Train: [40/100][51/1557] Data 0.008 (0.006) Batch 1.042 (0.953) Remain 25:07:30 loss: 0.2521 Lr: 0.00358 [2024-02-18 13:06:37,911 INFO misc.py line 119 87073] Train: [40/100][52/1557] Data 0.009 (0.006) Batch 0.984 (0.953) Remain 25:08:29 loss: 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INFO misc.py line 119 87073] Train: [40/100][59/1557] Data 0.003 (0.006) Batch 0.798 (0.957) Remain 25:14:12 loss: 0.5126 Lr: 0.00358 [2024-02-18 13:06:45,685 INFO misc.py line 119 87073] Train: [40/100][60/1557] Data 0.006 (0.006) Batch 0.894 (0.956) Remain 25:12:25 loss: 0.5571 Lr: 0.00358 [2024-02-18 13:06:46,448 INFO misc.py line 119 87073] Train: [40/100][61/1557] Data 0.004 (0.006) Batch 0.762 (0.953) Remain 25:07:07 loss: 0.2297 Lr: 0.00358 [2024-02-18 13:06:47,211 INFO misc.py line 119 87073] Train: [40/100][62/1557] Data 0.006 (0.006) Batch 0.762 (0.949) Remain 25:01:59 loss: 0.2917 Lr: 0.00358 [2024-02-18 13:06:57,225 INFO misc.py line 119 87073] Train: [40/100][63/1557] Data 5.078 (0.091) Batch 10.016 (1.101) Remain 29:01:00 loss: 0.1588 Lr: 0.00358 [2024-02-18 13:06:58,282 INFO misc.py line 119 87073] Train: [40/100][64/1557] Data 0.004 (0.089) Batch 1.058 (1.100) Remain 28:59:53 loss: 0.7256 Lr: 0.00358 [2024-02-18 13:06:59,199 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [40/100][669/1557] Data 0.005 (0.103) Batch 1.084 (1.108) Remain 29:01:24 loss: 0.2415 Lr: 0.00355 [2024-02-18 13:18:09,833 INFO misc.py line 119 87073] Train: [40/100][670/1557] Data 0.005 (0.103) Batch 0.773 (1.107) Remain 29:00:36 loss: 0.2308 Lr: 0.00355 [2024-02-18 13:18:10,582 INFO misc.py line 119 87073] Train: [40/100][671/1557] Data 0.005 (0.103) Batch 0.742 (1.107) Remain 28:59:43 loss: 0.3307 Lr: 0.00355 [2024-02-18 13:18:11,973 INFO misc.py line 119 87073] Train: [40/100][672/1557] Data 0.012 (0.103) Batch 1.388 (1.107) Remain 29:00:22 loss: 0.1676 Lr: 0.00355 [2024-02-18 13:18:12,950 INFO misc.py line 119 87073] Train: [40/100][673/1557] Data 0.015 (0.103) Batch 0.988 (1.107) Remain 29:00:04 loss: 0.3183 Lr: 0.00355 [2024-02-18 13:18:13,937 INFO misc.py line 119 87073] Train: [40/100][674/1557] Data 0.004 (0.103) Batch 0.987 (1.107) Remain 28:59:46 loss: 0.1613 Lr: 0.00355 [2024-02-18 13:18:14,924 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [40/100][1005/1557] Data 0.009 (0.106) Batch 0.942 (1.112) Remain 29:01:17 loss: 0.3596 Lr: 0.00353 [2024-02-18 13:24:25,949 INFO misc.py line 119 87073] Train: [40/100][1006/1557] Data 0.004 (0.106) Batch 0.735 (1.111) Remain 29:00:41 loss: 0.4028 Lr: 0.00353 [2024-02-18 13:24:26,737 INFO misc.py line 119 87073] Train: [40/100][1007/1557] Data 0.004 (0.106) Batch 0.784 (1.111) Remain 29:00:09 loss: 0.1755 Lr: 0.00353 [2024-02-18 13:24:28,097 INFO misc.py line 119 87073] Train: [40/100][1008/1557] Data 0.008 (0.106) Batch 1.354 (1.111) Remain 29:00:31 loss: 0.2955 Lr: 0.00353 [2024-02-18 13:24:29,215 INFO misc.py line 119 87073] Train: [40/100][1009/1557] Data 0.014 (0.106) Batch 1.119 (1.111) Remain 29:00:30 loss: 0.3809 Lr: 0.00353 [2024-02-18 13:24:30,138 INFO misc.py line 119 87073] Train: [40/100][1010/1557] Data 0.013 (0.106) Batch 0.933 (1.111) Remain 29:00:13 loss: 0.2530 Lr: 0.00353 [2024-02-18 13:24:30,961 INFO misc.py line 119 87073] Train: 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28:52:10 loss: 0.5279 Lr: 0.00351 [2024-02-18 13:32:47,941 INFO misc.py line 119 87073] Train: [40/100][1458/1557] Data 0.015 (0.108) Batch 0.887 (1.111) Remain 28:51:55 loss: 0.6253 Lr: 0.00351 [2024-02-18 13:32:48,839 INFO misc.py line 119 87073] Train: [40/100][1459/1557] Data 0.006 (0.108) Batch 0.898 (1.111) Remain 28:51:40 loss: 0.5005 Lr: 0.00351 [2024-02-18 13:32:49,901 INFO misc.py line 119 87073] Train: [40/100][1460/1557] Data 0.005 (0.108) Batch 1.062 (1.111) Remain 28:51:36 loss: 0.4992 Lr: 0.00351 [2024-02-18 13:32:50,653 INFO misc.py line 119 87073] Train: [40/100][1461/1557] Data 0.004 (0.108) Batch 0.746 (1.111) Remain 28:51:11 loss: 0.3911 Lr: 0.00351 [2024-02-18 13:32:51,443 INFO misc.py line 119 87073] Train: [40/100][1462/1557] Data 0.010 (0.108) Batch 0.796 (1.111) Remain 28:50:50 loss: 0.5511 Lr: 0.00351 [2024-02-18 13:33:00,509 INFO misc.py line 119 87073] Train: [40/100][1463/1557] Data 6.123 (0.112) Batch 9.066 (1.116) Remain 28:59:18 loss: 0.1140 Lr: 0.00351 [2024-02-18 13:33:01,549 INFO misc.py line 119 87073] Train: [40/100][1464/1557] Data 0.004 (0.112) Batch 1.039 (1.116) Remain 28:59:12 loss: 0.1816 Lr: 0.00351 [2024-02-18 13:33:02,472 INFO misc.py line 119 87073] Train: [40/100][1465/1557] Data 0.005 (0.112) Batch 0.922 (1.116) Remain 28:58:59 loss: 0.4875 Lr: 0.00351 [2024-02-18 13:33:03,253 INFO misc.py line 119 87073] Train: [40/100][1466/1557] Data 0.006 (0.111) Batch 0.777 (1.116) Remain 28:58:36 loss: 0.3728 Lr: 0.00351 [2024-02-18 13:33:04,244 INFO misc.py line 119 87073] Train: [40/100][1467/1557] Data 0.010 (0.111) Batch 0.997 (1.115) Remain 28:58:27 loss: 0.1678 Lr: 0.00351 [2024-02-18 13:33:05,012 INFO misc.py line 119 87073] Train: [40/100][1468/1557] Data 0.004 (0.111) Batch 0.766 (1.115) Remain 28:58:04 loss: 0.1927 Lr: 0.00351 [2024-02-18 13:33:05,758 INFO misc.py line 119 87073] Train: [40/100][1469/1557] Data 0.005 (0.111) Batch 0.745 (1.115) Remain 28:57:39 loss: 0.2911 Lr: 0.00351 [2024-02-18 13:33:07,057 INFO misc.py line 119 87073] Train: [40/100][1470/1557] Data 0.006 (0.111) Batch 1.296 (1.115) Remain 28:57:50 loss: 0.2911 Lr: 0.00351 [2024-02-18 13:33:07,992 INFO misc.py line 119 87073] Train: [40/100][1471/1557] Data 0.010 (0.111) Batch 0.939 (1.115) Remain 28:57:37 loss: 0.6373 Lr: 0.00351 [2024-02-18 13:33:08,798 INFO misc.py line 119 87073] Train: [40/100][1472/1557] Data 0.005 (0.111) Batch 0.806 (1.115) Remain 28:57:17 loss: 0.3224 Lr: 0.00351 [2024-02-18 13:33:09,687 INFO misc.py line 119 87073] Train: [40/100][1473/1557] Data 0.005 (0.111) Batch 0.889 (1.115) Remain 28:57:01 loss: 0.2252 Lr: 0.00351 [2024-02-18 13:33:10,649 INFO misc.py line 119 87073] Train: [40/100][1474/1557] Data 0.006 (0.111) Batch 0.962 (1.115) Remain 28:56:50 loss: 0.2644 Lr: 0.00351 [2024-02-18 13:33:11,600 INFO misc.py line 119 87073] Train: [40/100][1475/1557] Data 0.006 (0.111) Batch 0.953 (1.114) Remain 28:56:39 loss: 0.4629 Lr: 0.00351 [2024-02-18 13:33:12,404 INFO misc.py line 119 87073] Train: 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(0.110) Batch 0.762 (1.113) Remain 28:55:03 loss: 0.7356 Lr: 0.00351 [2024-02-18 13:33:18,772 INFO misc.py line 119 87073] Train: [40/100][1483/1557] Data 0.004 (0.110) Batch 0.768 (1.113) Remain 28:54:40 loss: 0.2638 Lr: 0.00351 [2024-02-18 13:33:20,070 INFO misc.py line 119 87073] Train: [40/100][1484/1557] Data 0.005 (0.110) Batch 1.299 (1.113) Remain 28:54:50 loss: 0.2003 Lr: 0.00351 [2024-02-18 13:33:20,918 INFO misc.py line 119 87073] Train: [40/100][1485/1557] Data 0.004 (0.110) Batch 0.847 (1.113) Remain 28:54:32 loss: 0.3816 Lr: 0.00351 [2024-02-18 13:33:21,880 INFO misc.py line 119 87073] Train: [40/100][1486/1557] Data 0.005 (0.110) Batch 0.963 (1.113) Remain 28:54:22 loss: 0.3113 Lr: 0.00351 [2024-02-18 13:33:22,881 INFO misc.py line 119 87073] Train: [40/100][1487/1557] Data 0.004 (0.110) Batch 1.001 (1.113) Remain 28:54:14 loss: 0.6706 Lr: 0.00351 [2024-02-18 13:33:23,752 INFO misc.py line 119 87073] Train: [40/100][1488/1557] Data 0.003 (0.110) Batch 0.871 (1.113) Remain 28:53:57 loss: 0.2705 Lr: 0.00351 [2024-02-18 13:33:24,479 INFO misc.py line 119 87073] Train: [40/100][1489/1557] Data 0.004 (0.110) Batch 0.719 (1.113) Remain 28:53:31 loss: 0.4870 Lr: 0.00351 [2024-02-18 13:33:25,248 INFO misc.py line 119 87073] Train: [40/100][1490/1557] Data 0.012 (0.110) Batch 0.777 (1.112) Remain 28:53:09 loss: 0.4199 Lr: 0.00351 [2024-02-18 13:33:26,451 INFO misc.py line 119 87073] Train: [40/100][1491/1557] Data 0.004 (0.110) Batch 1.201 (1.112) Remain 28:53:14 loss: 0.3017 Lr: 0.00351 [2024-02-18 13:33:27,323 INFO misc.py line 119 87073] Train: [40/100][1492/1557] Data 0.005 (0.110) Batch 0.871 (1.112) Remain 28:52:57 loss: 0.1368 Lr: 0.00351 [2024-02-18 13:33:28,165 INFO misc.py line 119 87073] Train: [40/100][1493/1557] Data 0.008 (0.110) Batch 0.844 (1.112) Remain 28:52:39 loss: 0.3558 Lr: 0.00351 [2024-02-18 13:33:29,022 INFO misc.py line 119 87073] Train: [40/100][1494/1557] Data 0.005 (0.109) Batch 0.858 (1.112) Remain 28:52:22 loss: 0.9566 Lr: 0.00351 [2024-02-18 13:33:29,955 INFO misc.py line 119 87073] Train: [40/100][1495/1557] Data 0.004 (0.109) Batch 0.933 (1.112) Remain 28:52:10 loss: 0.6046 Lr: 0.00351 [2024-02-18 13:33:30,656 INFO misc.py line 119 87073] Train: [40/100][1496/1557] Data 0.004 (0.109) Batch 0.691 (1.111) Remain 28:51:43 loss: 0.4986 Lr: 0.00351 [2024-02-18 13:33:31,435 INFO misc.py line 119 87073] Train: [40/100][1497/1557] Data 0.013 (0.109) Batch 0.788 (1.111) Remain 28:51:21 loss: 0.3234 Lr: 0.00351 [2024-02-18 13:33:32,650 INFO misc.py line 119 87073] Train: [40/100][1498/1557] Data 0.004 (0.109) Batch 1.214 (1.111) Remain 28:51:27 loss: 0.1848 Lr: 0.00351 [2024-02-18 13:33:33,594 INFO misc.py line 119 87073] Train: [40/100][1499/1557] Data 0.005 (0.109) Batch 0.945 (1.111) Remain 28:51:15 loss: 0.3062 Lr: 0.00351 [2024-02-18 13:33:34,521 INFO misc.py line 119 87073] Train: [40/100][1500/1557] Data 0.004 (0.109) Batch 0.927 (1.111) Remain 28:51:02 loss: 0.5190 Lr: 0.00351 [2024-02-18 13:33:35,384 INFO misc.py line 119 87073] Train: [40/100][1501/1557] Data 0.004 (0.109) Batch 0.862 (1.111) Remain 28:50:46 loss: 0.0922 Lr: 0.00351 [2024-02-18 13:33:36,331 INFO misc.py line 119 87073] Train: [40/100][1502/1557] Data 0.005 (0.109) Batch 0.947 (1.111) Remain 28:50:35 loss: 0.2041 Lr: 0.00351 [2024-02-18 13:33:37,072 INFO misc.py line 119 87073] Train: [40/100][1503/1557] Data 0.005 (0.109) Batch 0.741 (1.111) Remain 28:50:10 loss: 0.5090 Lr: 0.00351 [2024-02-18 13:33:37,830 INFO misc.py line 119 87073] Train: [40/100][1504/1557] Data 0.006 (0.109) Batch 0.759 (1.110) Remain 28:49:47 loss: 0.5945 Lr: 0.00351 [2024-02-18 13:33:39,035 INFO misc.py line 119 87073] Train: [40/100][1505/1557] Data 0.005 (0.109) Batch 1.194 (1.110) Remain 28:49:51 loss: 0.1913 Lr: 0.00351 [2024-02-18 13:33:39,885 INFO misc.py line 119 87073] Train: [40/100][1506/1557] Data 0.016 (0.109) Batch 0.862 (1.110) Remain 28:49:35 loss: 0.2333 Lr: 0.00351 [2024-02-18 13:33:40,929 INFO misc.py line 119 87073] Train: [40/100][1507/1557] Data 0.004 (0.109) Batch 1.043 (1.110) Remain 28:49:30 loss: 0.3806 Lr: 0.00351 [2024-02-18 13:33:41,911 INFO misc.py line 119 87073] Train: [40/100][1508/1557] Data 0.006 (0.109) Batch 0.983 (1.110) Remain 28:49:21 loss: 0.1917 Lr: 0.00351 [2024-02-18 13:33:43,079 INFO misc.py line 119 87073] Train: [40/100][1509/1557] Data 0.004 (0.108) Batch 1.169 (1.110) Remain 28:49:23 loss: 0.2840 Lr: 0.00351 [2024-02-18 13:33:43,814 INFO misc.py line 119 87073] Train: [40/100][1510/1557] Data 0.004 (0.108) Batch 0.734 (1.110) Remain 28:48:59 loss: 0.3036 Lr: 0.00351 [2024-02-18 13:33:44,611 INFO misc.py line 119 87073] Train: [40/100][1511/1557] Data 0.004 (0.108) Batch 0.789 (1.110) Remain 28:48:38 loss: 0.2889 Lr: 0.00351 [2024-02-18 13:33:45,965 INFO misc.py line 119 87073] Train: [40/100][1512/1557] Data 0.013 (0.108) Batch 1.357 (1.110) Remain 28:48:52 loss: 0.4626 Lr: 0.00351 [2024-02-18 13:33:46,990 INFO misc.py line 119 87073] Train: [40/100][1513/1557] Data 0.010 (0.108) Batch 1.029 (1.110) Remain 28:48:46 loss: 0.3186 Lr: 0.00351 [2024-02-18 13:33:47,944 INFO misc.py line 119 87073] Train: [40/100][1514/1557] Data 0.006 (0.108) Batch 0.955 (1.110) Remain 28:48:35 loss: 0.5830 Lr: 0.00351 [2024-02-18 13:33:48,900 INFO misc.py line 119 87073] Train: [40/100][1515/1557] Data 0.005 (0.108) Batch 0.957 (1.110) Remain 28:48:24 loss: 0.3660 Lr: 0.00351 [2024-02-18 13:33:49,886 INFO misc.py line 119 87073] Train: [40/100][1516/1557] Data 0.005 (0.108) Batch 0.986 (1.110) Remain 28:48:16 loss: 0.2777 Lr: 0.00351 [2024-02-18 13:33:50,668 INFO misc.py line 119 87073] Train: [40/100][1517/1557] Data 0.004 (0.108) Batch 0.773 (1.109) Remain 28:47:54 loss: 0.4070 Lr: 0.00351 [2024-02-18 13:33:51,549 INFO misc.py line 119 87073] Train: [40/100][1518/1557] Data 0.013 (0.108) Batch 0.889 (1.109) Remain 28:47:39 loss: 0.8915 Lr: 0.00351 [2024-02-18 13:34:00,867 INFO misc.py line 119 87073] Train: [40/100][1519/1557] Data 5.585 (0.111) Batch 9.313 (1.115) Remain 28:56:04 loss: 0.2194 Lr: 0.00351 [2024-02-18 13:34:01,833 INFO misc.py line 119 87073] Train: [40/100][1520/1557] Data 0.010 (0.111) Batch 0.970 (1.114) Remain 28:55:54 loss: 0.3865 Lr: 0.00351 [2024-02-18 13:34:02,715 INFO misc.py line 119 87073] Train: [40/100][1521/1557] Data 0.005 (0.111) Batch 0.882 (1.114) Remain 28:55:38 loss: 0.1815 Lr: 0.00351 [2024-02-18 13:34:03,631 INFO misc.py line 119 87073] Train: [40/100][1522/1557] Data 0.006 (0.111) Batch 0.917 (1.114) Remain 28:55:25 loss: 0.5327 Lr: 0.00351 [2024-02-18 13:34:04,659 INFO misc.py line 119 87073] Train: [40/100][1523/1557] Data 0.005 (0.111) Batch 1.026 (1.114) Remain 28:55:19 loss: 0.5785 Lr: 0.00351 [2024-02-18 13:34:05,416 INFO misc.py line 119 87073] Train: [40/100][1524/1557] Data 0.006 (0.111) Batch 0.758 (1.114) Remain 28:54:56 loss: 0.3481 Lr: 0.00351 [2024-02-18 13:34:06,215 INFO misc.py line 119 87073] Train: [40/100][1525/1557] Data 0.005 (0.111) Batch 0.799 (1.114) Remain 28:54:35 loss: 0.3106 Lr: 0.00351 [2024-02-18 13:34:07,392 INFO misc.py line 119 87073] Train: [40/100][1526/1557] Data 0.006 (0.111) Batch 1.172 (1.114) Remain 28:54:38 loss: 0.2514 Lr: 0.00351 [2024-02-18 13:34:08,427 INFO misc.py line 119 87073] Train: [40/100][1527/1557] Data 0.010 (0.111) Batch 1.040 (1.114) Remain 28:54:32 loss: 0.5019 Lr: 0.00351 [2024-02-18 13:34:09,415 INFO misc.py line 119 87073] Train: [40/100][1528/1557] Data 0.004 (0.111) Batch 0.987 (1.114) Remain 28:54:23 loss: 0.4132 Lr: 0.00351 [2024-02-18 13:34:10,382 INFO misc.py line 119 87073] Train: [40/100][1529/1557] Data 0.005 (0.111) Batch 0.969 (1.113) Remain 28:54:13 loss: 0.3959 Lr: 0.00351 [2024-02-18 13:34:11,519 INFO misc.py line 119 87073] Train: [40/100][1530/1557] Data 0.004 (0.111) Batch 1.135 (1.114) Remain 28:54:13 loss: 0.2588 Lr: 0.00351 [2024-02-18 13:34:12,268 INFO misc.py line 119 87073] Train: [40/100][1531/1557] Data 0.006 (0.111) Batch 0.745 (1.113) Remain 28:53:50 loss: 0.4670 Lr: 0.00351 [2024-02-18 13:34:13,032 INFO misc.py line 119 87073] Train: [40/100][1532/1557] Data 0.010 (0.111) Batch 0.768 (1.113) Remain 28:53:28 loss: 0.1380 Lr: 0.00351 [2024-02-18 13:34:14,175 INFO misc.py line 119 87073] Train: [40/100][1533/1557] Data 0.005 (0.111) Batch 1.130 (1.113) Remain 28:53:27 loss: 0.2162 Lr: 0.00351 [2024-02-18 13:34:15,258 INFO misc.py line 119 87073] Train: [40/100][1534/1557] Data 0.018 (0.110) Batch 1.095 (1.113) Remain 28:53:25 loss: 0.4230 Lr: 0.00351 [2024-02-18 13:34:16,318 INFO misc.py line 119 87073] Train: [40/100][1535/1557] Data 0.006 (0.110) Batch 1.053 (1.113) Remain 28:53:20 loss: 0.2118 Lr: 0.00351 [2024-02-18 13:34:17,296 INFO misc.py line 119 87073] Train: [40/100][1536/1557] Data 0.013 (0.110) Batch 0.987 (1.113) Remain 28:53:12 loss: 0.4683 Lr: 0.00351 [2024-02-18 13:34:18,149 INFO misc.py line 119 87073] Train: [40/100][1537/1557] Data 0.004 (0.110) Batch 0.852 (1.113) Remain 28:52:55 loss: 0.6393 Lr: 0.00351 [2024-02-18 13:34:18,930 INFO misc.py line 119 87073] Train: [40/100][1538/1557] Data 0.004 (0.110) Batch 0.769 (1.113) Remain 28:52:33 loss: 0.3407 Lr: 0.00351 [2024-02-18 13:34:19,692 INFO misc.py line 119 87073] Train: [40/100][1539/1557] Data 0.017 (0.110) Batch 0.775 (1.112) Remain 28:52:11 loss: 0.6190 Lr: 0.00351 [2024-02-18 13:34:20,968 INFO misc.py line 119 87073] Train: [40/100][1540/1557] Data 0.004 (0.110) Batch 1.267 (1.112) Remain 28:52:19 loss: 0.2479 Lr: 0.00351 [2024-02-18 13:34:21,775 INFO misc.py line 119 87073] Train: [40/100][1541/1557] Data 0.013 (0.110) Batch 0.817 (1.112) Remain 28:52:00 loss: 0.6663 Lr: 0.00351 [2024-02-18 13:34:22,653 INFO misc.py line 119 87073] Train: [40/100][1542/1557] Data 0.004 (0.110) Batch 0.877 (1.112) Remain 28:51:45 loss: 0.3914 Lr: 0.00351 [2024-02-18 13:34:23,584 INFO misc.py line 119 87073] Train: [40/100][1543/1557] Data 0.004 (0.110) Batch 0.930 (1.112) Remain 28:51:33 loss: 1.2288 Lr: 0.00351 [2024-02-18 13:34:24,634 INFO misc.py line 119 87073] Train: [40/100][1544/1557] Data 0.006 (0.110) Batch 1.040 (1.112) Remain 28:51:27 loss: 0.7446 Lr: 0.00351 [2024-02-18 13:34:25,406 INFO misc.py line 119 87073] Train: [40/100][1545/1557] Data 0.015 (0.110) Batch 0.782 (1.112) Remain 28:51:06 loss: 0.2419 Lr: 0.00351 [2024-02-18 13:34:26,163 INFO misc.py line 119 87073] Train: [40/100][1546/1557] Data 0.005 (0.110) Batch 0.749 (1.111) Remain 28:50:43 loss: 0.2754 Lr: 0.00351 [2024-02-18 13:34:27,421 INFO misc.py line 119 87073] Train: [40/100][1547/1557] Data 0.013 (0.110) Batch 1.258 (1.112) Remain 28:50:51 loss: 0.1277 Lr: 0.00351 [2024-02-18 13:34:28,357 INFO misc.py line 119 87073] Train: [40/100][1548/1557] Data 0.013 (0.110) Batch 0.944 (1.111) Remain 28:50:40 loss: 0.4100 Lr: 0.00351 [2024-02-18 13:34:29,383 INFO misc.py line 119 87073] Train: [40/100][1549/1557] Data 0.004 (0.109) Batch 1.027 (1.111) Remain 28:50:33 loss: 0.4575 Lr: 0.00351 [2024-02-18 13:34:30,385 INFO misc.py line 119 87073] Train: [40/100][1550/1557] Data 0.005 (0.109) Batch 1.002 (1.111) Remain 28:50:26 loss: 0.2996 Lr: 0.00351 [2024-02-18 13:34:31,168 INFO misc.py line 119 87073] Train: [40/100][1551/1557] Data 0.004 (0.109) Batch 0.783 (1.111) Remain 28:50:05 loss: 0.3434 Lr: 0.00351 [2024-02-18 13:34:31,931 INFO misc.py line 119 87073] Train: [40/100][1552/1557] Data 0.004 (0.109) Batch 0.758 (1.111) Remain 28:49:42 loss: 0.3168 Lr: 0.00351 [2024-02-18 13:34:32,680 INFO misc.py line 119 87073] Train: [40/100][1553/1557] Data 0.010 (0.109) Batch 0.754 (1.111) Remain 28:49:20 loss: 0.3530 Lr: 0.00351 [2024-02-18 13:34:33,865 INFO misc.py line 119 87073] Train: [40/100][1554/1557] Data 0.004 (0.109) Batch 1.185 (1.111) Remain 28:49:23 loss: 0.2178 Lr: 0.00351 [2024-02-18 13:34:34,840 INFO misc.py line 119 87073] Train: [40/100][1555/1557] Data 0.004 (0.109) Batch 0.976 (1.111) Remain 28:49:14 loss: 0.3168 Lr: 0.00351 [2024-02-18 13:34:35,882 INFO misc.py line 119 87073] Train: [40/100][1556/1557] Data 0.004 (0.109) Batch 1.041 (1.111) Remain 28:49:08 loss: 0.7262 Lr: 0.00351 [2024-02-18 13:34:36,699 INFO misc.py line 119 87073] Train: [40/100][1557/1557] Data 0.004 (0.109) Batch 0.817 (1.110) Remain 28:48:50 loss: 0.5350 Lr: 0.00351 [2024-02-18 13:34:36,700 INFO misc.py line 136 87073] Train result: loss: 0.4086 [2024-02-18 13:34:36,700 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 13:35:03,703 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6295 [2024-02-18 13:35:04,482 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5065 [2024-02-18 13:35:06,614 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4454 [2024-02-18 13:35:08,823 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4235 [2024-02-18 13:35:13,770 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3698 [2024-02-18 13:35:14,468 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0871 [2024-02-18 13:35:15,728 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2784 [2024-02-18 13:35:18,687 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2675 [2024-02-18 13:35:20,496 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2218 [2024-02-18 13:35:22,097 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7193/0.7851/0.9137. [2024-02-18 13:35:22,098 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9240/0.9494 [2024-02-18 13:35:22,098 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9809/0.9882 [2024-02-18 13:35:22,098 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8635/0.9681 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.5162/0.5973 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6472/0.6650 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7620/0.8670 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8420/0.9149 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9277/0.9656 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7874/0.8221 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7860/0.8755 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6879/0.8611 [2024-02-18 13:35:22,099 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6262/0.7317 [2024-02-18 13:35:22,100 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 13:35:22,104 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 13:35:22,105 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 13:35:29,743 INFO misc.py line 119 87073] Train: [41/100][1/1557] Data 1.787 (1.787) Batch 2.704 (2.704) Remain 70:09:18 loss: 0.4751 Lr: 0.00351 [2024-02-18 13:35:30,578 INFO misc.py line 119 87073] Train: [41/100][2/1557] Data 0.005 (0.005) Batch 0.835 (0.835) Remain 21:40:35 loss: 0.4406 Lr: 0.00351 [2024-02-18 13:35:31,591 INFO misc.py line 119 87073] Train: [41/100][3/1557] Data 0.005 (0.005) Batch 1.010 (1.010) Remain 26:12:48 loss: 0.5790 Lr: 0.00351 [2024-02-18 13:35:32,677 INFO misc.py line 119 87073] Train: [41/100][4/1557] Data 0.007 (0.007) Batch 1.089 (1.089) Remain 28:15:32 loss: 0.3330 Lr: 0.00351 [2024-02-18 13:35:33,414 INFO misc.py line 119 87073] Train: [41/100][5/1557] Data 0.004 (0.006) Batch 0.737 (0.913) Remain 23:41:34 loss: 0.5926 Lr: 0.00351 [2024-02-18 13:35:34,133 INFO misc.py line 119 87073] Train: [41/100][6/1557] Data 0.004 (0.005) Batch 0.719 (0.848) Remain 22:00:53 loss: 0.4650 Lr: 0.00351 [2024-02-18 13:35:41,634 INFO misc.py line 119 87073] Train: [41/100][7/1557] Data 6.294 (1.578) Batch 7.501 (2.511) Remain 65:10:06 loss: 0.2419 Lr: 0.00351 [2024-02-18 13:35:42,610 INFO misc.py line 119 87073] Train: [41/100][8/1557] Data 0.004 (1.263) Batch 0.976 (2.204) Remain 57:12:02 loss: 0.4135 Lr: 0.00351 [2024-02-18 13:35:43,634 INFO misc.py line 119 87073] Train: [41/100][9/1557] Data 0.004 (1.053) Batch 1.024 (2.008) Remain 52:05:45 loss: 0.4017 Lr: 0.00351 [2024-02-18 13:35:44,625 INFO misc.py line 119 87073] Train: [41/100][10/1557] Data 0.004 (0.903) Batch 0.990 (1.862) Remain 48:19:24 loss: 0.3811 Lr: 0.00351 [2024-02-18 13:35:45,820 INFO misc.py line 119 87073] Train: [41/100][11/1557] Data 0.004 (0.791) Batch 1.196 (1.779) Remain 46:09:40 loss: 0.4425 Lr: 0.00351 [2024-02-18 13:35:46,578 INFO misc.py line 119 87073] Train: [41/100][12/1557] Data 0.004 (0.703) Batch 0.758 (1.666) Remain 43:12:59 loss: 0.1826 Lr: 0.00350 [2024-02-18 13:35:47,338 INFO misc.py line 119 87073] Train: [41/100][13/1557] Data 0.004 (0.633) Batch 0.752 (1.574) Remain 40:50:44 loss: 0.4581 Lr: 0.00350 [2024-02-18 13:35:48,605 INFO misc.py line 119 87073] Train: [41/100][14/1557] Data 0.012 (0.577) Batch 1.263 (1.546) Remain 40:06:36 loss: 0.1505 Lr: 0.00350 [2024-02-18 13:35:49,541 INFO misc.py line 119 87073] Train: [41/100][15/1557] Data 0.016 (0.530) Batch 0.946 (1.496) Remain 38:48:49 loss: 0.1076 Lr: 0.00350 [2024-02-18 13:35:50,490 INFO misc.py line 119 87073] Train: [41/100][16/1557] Data 0.006 (0.490) Batch 0.951 (1.454) Remain 37:43:30 loss: 0.5640 Lr: 0.00350 [2024-02-18 13:35:51,340 INFO misc.py line 119 87073] Train: [41/100][17/1557] Data 0.004 (0.455) Batch 0.850 (1.411) Remain 36:36:16 loss: 0.3569 Lr: 0.00350 [2024-02-18 13:35:52,346 INFO misc.py line 119 87073] Train: [41/100][18/1557] Data 0.004 (0.425) Batch 1.000 (1.383) Remain 35:53:38 loss: 0.5411 Lr: 0.00350 [2024-02-18 13:35:53,127 INFO misc.py line 119 87073] Train: [41/100][19/1557] Data 0.010 (0.399) Batch 0.787 (1.346) Remain 34:55:35 loss: 0.4131 Lr: 0.00350 [2024-02-18 13:35:53,918 INFO misc.py line 119 87073] Train: [41/100][20/1557] Data 0.004 (0.376) Batch 0.791 (1.314) Remain 34:04:43 loss: 0.4320 Lr: 0.00350 [2024-02-18 13:35:55,235 INFO misc.py line 119 87073] Train: [41/100][21/1557] Data 0.004 (0.355) Batch 1.315 (1.314) Remain 34:04:51 loss: 0.1993 Lr: 0.00350 [2024-02-18 13:35:56,145 INFO misc.py line 119 87073] Train: [41/100][22/1557] Data 0.006 (0.337) Batch 0.911 (1.292) Remain 33:31:51 loss: 0.3541 Lr: 0.00350 [2024-02-18 13:35:57,263 INFO misc.py line 119 87073] Train: [41/100][23/1557] Data 0.004 (0.320) Batch 1.119 (1.284) Remain 33:18:22 loss: 0.3627 Lr: 0.00350 [2024-02-18 13:35:58,312 INFO misc.py line 119 87073] Train: [41/100][24/1557] Data 0.004 (0.305) Batch 1.047 (1.273) Remain 33:00:48 loss: 0.4425 Lr: 0.00350 [2024-02-18 13:35:59,547 INFO misc.py line 119 87073] Train: [41/100][25/1557] Data 0.005 (0.291) Batch 1.235 (1.271) Remain 32:58:09 loss: 0.7083 Lr: 0.00350 [2024-02-18 13:36:00,319 INFO misc.py line 119 87073] Train: [41/100][26/1557] Data 0.005 (0.279) Batch 0.773 (1.249) Remain 32:24:25 loss: 0.3403 Lr: 0.00350 [2024-02-18 13:36:00,998 INFO misc.py line 119 87073] Train: [41/100][27/1557] Data 0.004 (0.268) Batch 0.675 (1.225) Remain 31:47:09 loss: 0.4590 Lr: 0.00350 [2024-02-18 13:36:02,051 INFO misc.py line 119 87073] Train: [41/100][28/1557] Data 0.008 (0.257) Batch 1.057 (1.219) Remain 31:36:40 loss: 0.2086 Lr: 0.00350 [2024-02-18 13:36:03,047 INFO misc.py line 119 87073] Train: [41/100][29/1557] Data 0.007 (0.247) Batch 0.995 (1.210) Remain 31:23:16 loss: 0.2129 Lr: 0.00350 [2024-02-18 13:36:04,015 INFO misc.py line 119 87073] Train: [41/100][30/1557] Data 0.006 (0.239) Batch 0.968 (1.201) Remain 31:09:18 loss: 0.3544 Lr: 0.00350 [2024-02-18 13:36:05,013 INFO misc.py line 119 87073] Train: [41/100][31/1557] Data 0.005 (0.230) Batch 0.998 (1.194) Remain 30:58:02 loss: 0.4560 Lr: 0.00350 [2024-02-18 13:36:05,911 INFO misc.py line 119 87073] Train: [41/100][32/1557] Data 0.005 (0.222) Batch 0.897 (1.184) Remain 30:42:06 loss: 0.3384 Lr: 0.00350 [2024-02-18 13:36:06,721 INFO misc.py line 119 87073] Train: [41/100][33/1557] Data 0.005 (0.215) Batch 0.778 (1.170) Remain 30:21:01 loss: 0.2354 Lr: 0.00350 [2024-02-18 13:36:07,512 INFO misc.py line 119 87073] Train: [41/100][34/1557] Data 0.037 (0.209) Batch 0.825 (1.159) Remain 30:03:40 loss: 0.3883 Lr: 0.00350 [2024-02-18 13:36:08,854 INFO misc.py line 119 87073] Train: [41/100][35/1557] Data 0.004 (0.203) Batch 1.342 (1.165) Remain 30:12:32 loss: 0.0957 Lr: 0.00350 [2024-02-18 13:36:09,932 INFO misc.py line 119 87073] Train: [41/100][36/1557] Data 0.004 (0.197) Batch 1.067 (1.162) Remain 30:07:56 loss: 0.6914 Lr: 0.00350 [2024-02-18 13:36:10,766 INFO misc.py line 119 87073] Train: [41/100][37/1557] Data 0.015 (0.192) Batch 0.844 (1.152) Remain 29:53:22 loss: 0.4531 Lr: 0.00350 [2024-02-18 13:36:11,610 INFO misc.py line 119 87073] Train: [41/100][38/1557] Data 0.006 (0.186) Batch 0.843 (1.143) Remain 29:39:37 loss: 0.2835 Lr: 0.00350 [2024-02-18 13:36:12,578 INFO misc.py line 119 87073] Train: [41/100][39/1557] Data 0.006 (0.181) Batch 0.969 (1.139) Remain 29:32:02 loss: 0.4804 Lr: 0.00350 [2024-02-18 13:36:13,384 INFO misc.py line 119 87073] Train: [41/100][40/1557] Data 0.006 (0.177) Batch 0.799 (1.129) Remain 29:17:43 loss: 0.6682 Lr: 0.00350 [2024-02-18 13:36:14,147 INFO misc.py line 119 87073] Train: [41/100][41/1557] Data 0.012 (0.172) Batch 0.771 (1.120) Remain 29:03:02 loss: 0.1988 Lr: 0.00350 [2024-02-18 13:36:15,431 INFO misc.py line 119 87073] Train: [41/100][42/1557] Data 0.004 (0.168) Batch 1.282 (1.124) Remain 29:09:28 loss: 0.1785 Lr: 0.00350 [2024-02-18 13:36:16,438 INFO misc.py line 119 87073] Train: [41/100][43/1557] Data 0.005 (0.164) Batch 1.002 (1.121) Remain 29:04:41 loss: 0.6299 Lr: 0.00350 [2024-02-18 13:36:17,537 INFO misc.py line 119 87073] Train: [41/100][44/1557] Data 0.012 (0.160) Batch 1.103 (1.121) Remain 29:04:00 loss: 0.4992 Lr: 0.00350 [2024-02-18 13:36:18,466 INFO misc.py line 119 87073] Train: [41/100][45/1557] Data 0.007 (0.157) Batch 0.931 (1.116) Remain 28:56:57 loss: 0.2432 Lr: 0.00350 [2024-02-18 13:36:19,548 INFO misc.py line 119 87073] Train: [41/100][46/1557] Data 0.005 (0.153) Batch 1.082 (1.115) Remain 28:55:43 loss: 0.7346 Lr: 0.00350 [2024-02-18 13:36:20,410 INFO misc.py line 119 87073] Train: [41/100][47/1557] Data 0.005 (0.150) Batch 0.863 (1.110) Remain 28:46:46 loss: 0.3021 Lr: 0.00350 [2024-02-18 13:36:21,150 INFO misc.py line 119 87073] Train: [41/100][48/1557] Data 0.003 (0.146) Batch 0.739 (1.101) Remain 28:33:56 loss: 0.3261 Lr: 0.00350 [2024-02-18 13:36:22,344 INFO misc.py line 119 87073] Train: [41/100][49/1557] Data 0.005 (0.143) Batch 1.193 (1.103) Remain 28:37:01 loss: 0.4488 Lr: 0.00350 [2024-02-18 13:36:23,278 INFO misc.py line 119 87073] Train: [41/100][50/1557] Data 0.005 (0.140) Batch 0.935 (1.100) Remain 28:31:26 loss: 0.4496 Lr: 0.00350 [2024-02-18 13:36:24,356 INFO misc.py line 119 87073] Train: [41/100][51/1557] Data 0.005 (0.138) Batch 1.078 (1.099) Remain 28:30:42 loss: 0.3510 Lr: 0.00350 [2024-02-18 13:36:25,339 INFO misc.py line 119 87073] Train: [41/100][52/1557] Data 0.005 (0.135) Batch 0.983 (1.097) Remain 28:26:58 loss: 0.4451 Lr: 0.00350 [2024-02-18 13:36:26,311 INFO misc.py line 119 87073] Train: [41/100][53/1557] Data 0.005 (0.132) Batch 0.973 (1.094) Remain 28:23:06 loss: 0.2137 Lr: 0.00350 [2024-02-18 13:36:27,051 INFO misc.py line 119 87073] Train: [41/100][54/1557] Data 0.004 (0.130) Batch 0.740 (1.088) Remain 28:12:16 loss: 0.2403 Lr: 0.00350 [2024-02-18 13:36:27,854 INFO misc.py line 119 87073] Train: [41/100][55/1557] Data 0.004 (0.127) Batch 0.803 (1.082) Remain 28:03:43 loss: 0.2284 Lr: 0.00350 [2024-02-18 13:36:29,035 INFO misc.py line 119 87073] Train: [41/100][56/1557] Data 0.005 (0.125) Batch 1.177 (1.084) Remain 28:06:29 loss: 0.2184 Lr: 0.00350 [2024-02-18 13:36:30,046 INFO misc.py line 119 87073] Train: [41/100][57/1557] Data 0.009 (0.123) Batch 1.016 (1.083) Remain 28:04:30 loss: 0.8256 Lr: 0.00350 [2024-02-18 13:36:30,959 INFO misc.py line 119 87073] Train: [41/100][58/1557] Data 0.004 (0.121) Batch 0.913 (1.079) Remain 27:59:41 loss: 0.5799 Lr: 0.00350 [2024-02-18 13:36:31,925 INFO misc.py line 119 87073] Train: [41/100][59/1557] Data 0.005 (0.119) Batch 0.964 (1.077) Remain 27:56:28 loss: 0.5166 Lr: 0.00350 [2024-02-18 13:36:32,768 INFO misc.py line 119 87073] Train: [41/100][60/1557] Data 0.006 (0.117) Batch 0.845 (1.073) Remain 27:50:06 loss: 0.1383 Lr: 0.00350 [2024-02-18 13:36:33,548 INFO misc.py line 119 87073] Train: [41/100][61/1557] Data 0.004 (0.115) Batch 0.773 (1.068) Remain 27:42:02 loss: 0.5450 Lr: 0.00350 [2024-02-18 13:36:34,304 INFO misc.py line 119 87073] Train: [41/100][62/1557] Data 0.011 (0.113) Batch 0.762 (1.063) Remain 27:33:56 loss: 0.4257 Lr: 0.00350 [2024-02-18 13:36:47,459 INFO misc.py line 119 87073] Train: [41/100][63/1557] Data 11.808 (0.308) Batch 13.156 (1.265) Remain 32:47:31 loss: 0.2710 Lr: 0.00350 [2024-02-18 13:36:48,371 INFO misc.py line 119 87073] Train: [41/100][64/1557] Data 0.004 (0.303) Batch 0.912 (1.259) Remain 32:38:30 loss: 0.1087 Lr: 0.00350 [2024-02-18 13:36:49,295 INFO misc.py line 119 87073] Train: [41/100][65/1557] Data 0.005 (0.298) Batch 0.924 (1.253) Remain 32:30:04 loss: 0.6080 Lr: 0.00350 [2024-02-18 13:36:50,286 INFO misc.py line 119 87073] Train: [41/100][66/1557] Data 0.006 (0.293) Batch 0.982 (1.249) Remain 32:23:21 loss: 0.3217 Lr: 0.00350 [2024-02-18 13:36:51,244 INFO misc.py line 119 87073] Train: [41/100][67/1557] Data 0.014 (0.289) Batch 0.967 (1.245) Remain 32:16:28 loss: 0.1893 Lr: 0.00350 [2024-02-18 13:36:51,944 INFO misc.py line 119 87073] Train: [41/100][68/1557] Data 0.005 (0.285) Batch 0.701 (1.236) Remain 32:03:26 loss: 0.3111 Lr: 0.00350 [2024-02-18 13:36:52,839 INFO misc.py line 119 87073] Train: [41/100][69/1557] Data 0.005 (0.280) Batch 0.889 (1.231) Remain 31:55:13 loss: 0.4183 Lr: 0.00350 [2024-02-18 13:36:54,125 INFO misc.py line 119 87073] Train: [41/100][70/1557] Data 0.010 (0.276) Batch 1.287 (1.232) Remain 31:56:30 loss: 0.1309 Lr: 0.00350 [2024-02-18 13:36:55,151 INFO misc.py line 119 87073] Train: [41/100][71/1557] Data 0.009 (0.272) Batch 1.029 (1.229) Remain 31:51:51 loss: 0.2889 Lr: 0.00350 [2024-02-18 13:36:56,150 INFO misc.py line 119 87073] Train: [41/100][72/1557] Data 0.006 (0.269) Batch 0.998 (1.226) Remain 31:46:38 loss: 0.3199 Lr: 0.00350 [2024-02-18 13:36:57,393 INFO misc.py line 119 87073] Train: [41/100][73/1557] Data 0.007 (0.265) Batch 1.240 (1.226) Remain 31:46:56 loss: 0.2837 Lr: 0.00350 [2024-02-18 13:36:58,246 INFO misc.py line 119 87073] Train: [41/100][74/1557] Data 0.010 (0.261) Batch 0.857 (1.221) Remain 31:38:50 loss: 0.7731 Lr: 0.00350 [2024-02-18 13:36:58,996 INFO misc.py line 119 87073] Train: [41/100][75/1557] Data 0.006 (0.258) Batch 0.751 (1.214) Remain 31:28:39 loss: 0.6311 Lr: 0.00350 [2024-02-18 13:36:59,770 INFO misc.py line 119 87073] Train: [41/100][76/1557] Data 0.005 (0.254) Batch 0.770 (1.208) Remain 31:19:11 loss: 0.2939 Lr: 0.00350 [2024-02-18 13:37:01,059 INFO misc.py line 119 87073] Train: [41/100][77/1557] Data 0.009 (0.251) Batch 1.290 (1.209) Remain 31:20:53 loss: 0.2364 Lr: 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line 119 87073] Train: [41/100][84/1557] Data 0.005 (0.230) Batch 1.099 (1.186) Remain 30:45:28 loss: 0.1496 Lr: 0.00350 [2024-02-18 13:37:08,656 INFO misc.py line 119 87073] Train: [41/100][85/1557] Data 0.005 (0.227) Batch 0.974 (1.184) Remain 30:41:26 loss: 0.3056 Lr: 0.00350 [2024-02-18 13:37:09,710 INFO misc.py line 119 87073] Train: [41/100][86/1557] Data 0.004 (0.224) Batch 1.054 (1.182) Remain 30:38:59 loss: 0.3921 Lr: 0.00350 [2024-02-18 13:37:10,794 INFO misc.py line 119 87073] Train: [41/100][87/1557] Data 0.004 (0.222) Batch 1.084 (1.181) Remain 30:37:09 loss: 0.5449 Lr: 0.00350 [2024-02-18 13:37:11,916 INFO misc.py line 119 87073] Train: [41/100][88/1557] Data 0.004 (0.219) Batch 1.121 (1.180) Remain 30:36:02 loss: 0.7352 Lr: 0.00350 [2024-02-18 13:37:12,713 INFO misc.py line 119 87073] Train: [41/100][89/1557] Data 0.004 (0.217) Batch 0.796 (1.176) Remain 30:29:03 loss: 0.2877 Lr: 0.00350 [2024-02-18 13:37:13,467 INFO misc.py line 119 87073] Train: [41/100][90/1557] Data 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Batch 1.057 (1.220) Remain 31:36:49 loss: 0.3775 Lr: 0.00350 [2024-02-18 13:37:56,555 INFO misc.py line 119 87073] Train: [41/100][122/1557] Data 0.007 (0.261) Batch 1.025 (1.218) Remain 31:34:15 loss: 0.7374 Lr: 0.00350 [2024-02-18 13:37:57,570 INFO misc.py line 119 87073] Train: [41/100][123/1557] Data 0.005 (0.259) Batch 1.016 (1.217) Remain 31:31:37 loss: 1.3046 Lr: 0.00350 [2024-02-18 13:37:58,295 INFO misc.py line 119 87073] Train: [41/100][124/1557] Data 0.004 (0.257) Batch 0.724 (1.212) Remain 31:25:16 loss: 0.2588 Lr: 0.00350 [2024-02-18 13:37:59,122 INFO misc.py line 119 87073] Train: [41/100][125/1557] Data 0.006 (0.255) Batch 0.822 (1.209) Remain 31:20:16 loss: 0.3438 Lr: 0.00350 [2024-02-18 13:38:00,369 INFO misc.py line 119 87073] Train: [41/100][126/1557] Data 0.010 (0.253) Batch 1.246 (1.210) Remain 31:20:43 loss: 0.2196 Lr: 0.00350 [2024-02-18 13:38:01,286 INFO misc.py line 119 87073] Train: [41/100][127/1557] Data 0.011 (0.251) Batch 0.923 (1.207) Remain 31:17:06 loss: 0.4178 Lr: 0.00350 [2024-02-18 13:38:02,203 INFO misc.py line 119 87073] Train: [41/100][128/1557] Data 0.005 (0.249) Batch 0.915 (1.205) Remain 31:13:27 loss: 0.3845 Lr: 0.00350 [2024-02-18 13:38:03,122 INFO misc.py line 119 87073] Train: [41/100][129/1557] Data 0.006 (0.247) Batch 0.920 (1.203) Remain 31:09:55 loss: 0.2685 Lr: 0.00350 [2024-02-18 13:38:04,081 INFO misc.py line 119 87073] Train: [41/100][130/1557] Data 0.005 (0.245) Batch 0.960 (1.201) Remain 31:06:56 loss: 0.4941 Lr: 0.00350 [2024-02-18 13:38:04,859 INFO misc.py line 119 87073] Train: [41/100][131/1557] Data 0.004 (0.243) Batch 0.776 (1.197) Remain 31:01:45 loss: 0.2570 Lr: 0.00350 [2024-02-18 13:38:05,573 INFO misc.py line 119 87073] Train: [41/100][132/1557] Data 0.006 (0.241) Batch 0.707 (1.194) Remain 30:55:49 loss: 0.1723 Lr: 0.00350 [2024-02-18 13:38:06,873 INFO misc.py line 119 87073] Train: [41/100][133/1557] Data 0.013 (0.240) Batch 1.298 (1.194) Remain 30:57:03 loss: 0.2679 Lr: 0.00350 [2024-02-18 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87073] Train: [41/100][140/1557] Data 0.004 (0.228) Batch 1.097 (1.181) Remain 30:35:29 loss: 0.1527 Lr: 0.00350 [2024-02-18 13:38:14,349 INFO misc.py line 119 87073] Train: [41/100][141/1557] Data 0.004 (0.226) Batch 1.014 (1.179) Remain 30:33:36 loss: 0.2270 Lr: 0.00350 [2024-02-18 13:38:15,390 INFO misc.py line 119 87073] Train: [41/100][142/1557] Data 0.003 (0.224) Batch 1.040 (1.178) Remain 30:32:01 loss: 0.1324 Lr: 0.00350 [2024-02-18 13:38:16,115 INFO misc.py line 119 87073] Train: [41/100][143/1557] Data 0.004 (0.223) Batch 0.726 (1.175) Remain 30:26:58 loss: 0.1917 Lr: 0.00350 [2024-02-18 13:38:17,112 INFO misc.py line 119 87073] Train: [41/100][144/1557] Data 0.004 (0.221) Batch 0.992 (1.174) Remain 30:24:55 loss: 0.1689 Lr: 0.00350 [2024-02-18 13:38:17,870 INFO misc.py line 119 87073] Train: [41/100][145/1557] Data 0.009 (0.220) Batch 0.762 (1.171) Remain 30:20:24 loss: 0.4128 Lr: 0.00350 [2024-02-18 13:38:18,642 INFO misc.py line 119 87073] Train: [41/100][146/1557] Data 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line 119 87073] Train: [41/100][165/1557] Data 0.003 (0.193) Batch 0.926 (1.150) Remain 29:47:02 loss: 0.4186 Lr: 0.00350 [2024-02-18 13:38:38,602 INFO misc.py line 119 87073] Train: [41/100][166/1557] Data 0.008 (0.192) Batch 0.749 (1.147) Remain 29:43:12 loss: 0.4316 Lr: 0.00350 [2024-02-18 13:38:39,379 INFO misc.py line 119 87073] Train: [41/100][167/1557] Data 0.004 (0.191) Batch 0.774 (1.145) Remain 29:39:38 loss: 0.3604 Lr: 0.00350 [2024-02-18 13:38:40,525 INFO misc.py line 119 87073] Train: [41/100][168/1557] Data 0.008 (0.190) Batch 1.148 (1.145) Remain 29:39:39 loss: 0.2703 Lr: 0.00350 [2024-02-18 13:38:41,349 INFO misc.py line 119 87073] Train: [41/100][169/1557] Data 0.006 (0.189) Batch 0.824 (1.143) Remain 29:36:38 loss: 0.7574 Lr: 0.00350 [2024-02-18 13:38:42,378 INFO misc.py line 119 87073] Train: [41/100][170/1557] Data 0.004 (0.188) Batch 1.030 (1.142) Remain 29:35:33 loss: 0.5519 Lr: 0.00350 [2024-02-18 13:38:43,273 INFO misc.py line 119 87073] Train: 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Batch 1.072 (1.201) Remain 31:06:47 loss: 0.2155 Lr: 0.00350 [2024-02-18 13:39:01,586 INFO misc.py line 119 87073] Train: [41/100][178/1557] Data 0.005 (0.246) Batch 0.981 (1.200) Remain 31:04:48 loss: 0.4514 Lr: 0.00350 [2024-02-18 13:39:02,525 INFO misc.py line 119 87073] Train: [41/100][179/1557] Data 0.005 (0.245) Batch 0.939 (1.199) Remain 31:02:29 loss: 0.4199 Lr: 0.00350 [2024-02-18 13:39:04,998 INFO misc.py line 119 87073] Train: [41/100][180/1557] Data 0.953 (0.249) Batch 2.474 (1.206) Remain 31:13:40 loss: 0.5485 Lr: 0.00350 [2024-02-18 13:39:05,712 INFO misc.py line 119 87073] Train: [41/100][181/1557] Data 0.003 (0.247) Batch 0.713 (1.203) Remain 31:09:20 loss: 0.2788 Lr: 0.00350 [2024-02-18 13:39:06,943 INFO misc.py line 119 87073] Train: [41/100][182/1557] Data 0.005 (0.246) Batch 1.231 (1.203) Remain 31:09:33 loss: 0.1167 Lr: 0.00350 [2024-02-18 13:39:07,855 INFO misc.py line 119 87073] Train: [41/100][183/1557] Data 0.005 (0.245) Batch 0.912 (1.201) Remain 31:07:02 loss: 0.2442 Lr: 0.00350 [2024-02-18 13:39:09,126 INFO misc.py line 119 87073] Train: [41/100][184/1557] Data 0.005 (0.243) Batch 1.261 (1.202) Remain 31:07:31 loss: 0.2735 Lr: 0.00350 [2024-02-18 13:39:10,061 INFO misc.py line 119 87073] Train: [41/100][185/1557] Data 0.015 (0.242) Batch 0.946 (1.200) Remain 31:05:19 loss: 0.3895 Lr: 0.00350 [2024-02-18 13:39:11,134 INFO misc.py line 119 87073] Train: [41/100][186/1557] Data 0.004 (0.241) Batch 1.073 (1.200) Remain 31:04:13 loss: 0.6062 Lr: 0.00350 [2024-02-18 13:39:11,893 INFO misc.py line 119 87073] Train: [41/100][187/1557] Data 0.004 (0.240) Batch 0.759 (1.197) Remain 31:00:28 loss: 0.2608 Lr: 0.00350 [2024-02-18 13:39:12,663 INFO misc.py line 119 87073] Train: [41/100][188/1557] Data 0.004 (0.238) Batch 0.770 (1.195) Remain 30:56:52 loss: 0.4593 Lr: 0.00350 [2024-02-18 13:39:13,930 INFO misc.py line 119 87073] Train: [41/100][189/1557] Data 0.004 (0.237) Batch 1.263 (1.195) Remain 30:57:24 loss: 0.2225 Lr: 0.00350 [2024-02-18 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line 119 87073] Train: [41/100][221/1557] Data 0.004 (0.203) Batch 0.931 (1.162) Remain 30:04:15 loss: 0.6465 Lr: 0.00349 [2024-02-18 13:39:45,721 INFO misc.py line 119 87073] Train: [41/100][222/1557] Data 0.005 (0.202) Batch 0.914 (1.160) Remain 30:02:28 loss: 0.1526 Lr: 0.00349 [2024-02-18 13:39:46,431 INFO misc.py line 119 87073] Train: [41/100][223/1557] Data 0.005 (0.201) Batch 0.704 (1.158) Remain 29:59:14 loss: 0.2934 Lr: 0.00349 [2024-02-18 13:39:47,580 INFO misc.py line 119 87073] Train: [41/100][224/1557] Data 0.011 (0.200) Batch 1.148 (1.158) Remain 29:59:08 loss: 0.1149 Lr: 0.00349 [2024-02-18 13:39:48,603 INFO misc.py line 119 87073] Train: [41/100][225/1557] Data 0.011 (0.200) Batch 1.027 (1.158) Remain 29:58:12 loss: 0.6454 Lr: 0.00349 [2024-02-18 13:39:49,572 INFO misc.py line 119 87073] Train: [41/100][226/1557] Data 0.007 (0.199) Batch 0.972 (1.157) Remain 29:56:53 loss: 0.5486 Lr: 0.00349 [2024-02-18 13:39:50,595 INFO misc.py line 119 87073] Train: 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Batch 1.010 (1.201) Remain 31:05:13 loss: 0.4399 Lr: 0.00349 [2024-02-18 13:40:08,757 INFO misc.py line 119 87073] Train: [41/100][234/1557] Data 0.008 (0.242) Batch 0.949 (1.200) Remain 31:03:30 loss: 0.3793 Lr: 0.00349 [2024-02-18 13:40:09,796 INFO misc.py line 119 87073] Train: [41/100][235/1557] Data 0.004 (0.241) Batch 1.031 (1.199) Remain 31:02:21 loss: 0.9007 Lr: 0.00349 [2024-02-18 13:40:10,525 INFO misc.py line 119 87073] Train: [41/100][236/1557] Data 0.013 (0.240) Batch 0.738 (1.197) Remain 30:59:15 loss: 0.4170 Lr: 0.00349 [2024-02-18 13:40:11,291 INFO misc.py line 119 87073] Train: [41/100][237/1557] Data 0.003 (0.239) Batch 0.759 (1.195) Remain 30:56:19 loss: 0.3833 Lr: 0.00349 [2024-02-18 13:40:12,486 INFO misc.py line 119 87073] Train: [41/100][238/1557] Data 0.011 (0.238) Batch 1.197 (1.195) Remain 30:56:19 loss: 0.1279 Lr: 0.00349 [2024-02-18 13:40:13,402 INFO misc.py line 119 87073] Train: [41/100][239/1557] Data 0.009 (0.237) Batch 0.921 (1.194) Remain 30:54:29 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87073] Train: [41/100][252/1557] Data 0.004 (0.225) Batch 1.151 (1.181) Remain 30:33:47 loss: 0.1542 Lr: 0.00349 [2024-02-18 13:40:26,506 INFO misc.py line 119 87073] Train: [41/100][253/1557] Data 0.005 (0.224) Batch 0.858 (1.180) Remain 30:31:45 loss: 0.5193 Lr: 0.00349 [2024-02-18 13:40:27,417 INFO misc.py line 119 87073] Train: [41/100][254/1557] Data 0.007 (0.223) Batch 0.893 (1.179) Remain 30:29:58 loss: 0.5339 Lr: 0.00349 [2024-02-18 13:40:28,605 INFO misc.py line 119 87073] Train: [41/100][255/1557] Data 0.024 (0.222) Batch 1.198 (1.179) Remain 30:30:03 loss: 0.4039 Lr: 0.00349 [2024-02-18 13:40:29,481 INFO misc.py line 119 87073] Train: [41/100][256/1557] Data 0.014 (0.221) Batch 0.886 (1.177) Remain 30:28:15 loss: 3.0945 Lr: 0.00349 [2024-02-18 13:40:30,184 INFO misc.py line 119 87073] Train: [41/100][257/1557] Data 0.004 (0.221) Batch 0.703 (1.176) Remain 30:25:20 loss: 0.5047 Lr: 0.00349 [2024-02-18 13:40:30,950 INFO misc.py line 119 87073] Train: [41/100][258/1557] Data 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13:41:25,165 INFO misc.py line 119 87073] Train: [41/100][302/1557] Data 0.005 (0.228) Batch 0.854 (1.183) Remain 30:35:15 loss: 0.4389 Lr: 0.00349 [2024-02-18 13:41:26,203 INFO misc.py line 119 87073] Train: [41/100][303/1557] Data 0.004 (0.227) Batch 1.036 (1.182) Remain 30:34:28 loss: 0.4256 Lr: 0.00349 [2024-02-18 13:41:27,160 INFO misc.py line 119 87073] Train: [41/100][304/1557] Data 0.005 (0.227) Batch 0.957 (1.181) Remain 30:33:17 loss: 0.3591 Lr: 0.00349 [2024-02-18 13:41:28,247 INFO misc.py line 119 87073] Train: [41/100][305/1557] Data 0.005 (0.226) Batch 1.088 (1.181) Remain 30:32:47 loss: 0.4221 Lr: 0.00349 [2024-02-18 13:41:29,078 INFO misc.py line 119 87073] Train: [41/100][306/1557] Data 0.004 (0.225) Batch 0.830 (1.180) Remain 30:30:58 loss: 0.3574 Lr: 0.00349 [2024-02-18 13:41:29,794 INFO misc.py line 119 87073] Train: [41/100][307/1557] Data 0.005 (0.224) Batch 0.709 (1.178) Remain 30:28:33 loss: 0.2917 Lr: 0.00349 [2024-02-18 13:41:30,898 INFO misc.py line 119 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line 119 87073] Train: [41/100][333/1557] Data 0.004 (0.207) Batch 0.929 (1.163) Remain 30:04:55 loss: 0.5779 Lr: 0.00349 [2024-02-18 13:41:56,275 INFO misc.py line 119 87073] Train: [41/100][334/1557] Data 0.003 (0.207) Batch 0.762 (1.162) Remain 30:03:01 loss: 0.2246 Lr: 0.00349 [2024-02-18 13:41:57,044 INFO misc.py line 119 87073] Train: [41/100][335/1557] Data 0.014 (0.206) Batch 0.780 (1.161) Remain 30:01:12 loss: 0.1987 Lr: 0.00349 [2024-02-18 13:41:58,130 INFO misc.py line 119 87073] Train: [41/100][336/1557] Data 0.003 (0.205) Batch 1.085 (1.161) Remain 30:00:50 loss: 0.2255 Lr: 0.00349 [2024-02-18 13:41:58,995 INFO misc.py line 119 87073] Train: [41/100][337/1557] Data 0.005 (0.205) Batch 0.865 (1.160) Remain 29:59:26 loss: 0.2691 Lr: 0.00349 [2024-02-18 13:41:59,911 INFO misc.py line 119 87073] Train: [41/100][338/1557] Data 0.006 (0.204) Batch 0.908 (1.159) Remain 29:58:15 loss: 0.3019 Lr: 0.00349 [2024-02-18 13:42:00,872 INFO misc.py line 119 87073] Train: 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Batch 0.931 (1.192) Remain 30:49:15 loss: 0.4842 Lr: 0.00349 [2024-02-18 13:42:20,143 INFO misc.py line 119 87073] Train: [41/100][346/1557] Data 0.004 (0.236) Batch 0.852 (1.191) Remain 30:47:42 loss: 0.6825 Lr: 0.00349 [2024-02-18 13:42:20,979 INFO misc.py line 119 87073] Train: [41/100][347/1557] Data 0.004 (0.235) Batch 0.833 (1.190) Remain 30:46:04 loss: 0.7028 Lr: 0.00349 [2024-02-18 13:42:21,640 INFO misc.py line 119 87073] Train: [41/100][348/1557] Data 0.007 (0.234) Batch 0.663 (1.189) Remain 30:43:41 loss: 0.2941 Lr: 0.00349 [2024-02-18 13:42:22,426 INFO misc.py line 119 87073] Train: [41/100][349/1557] Data 0.004 (0.234) Batch 0.781 (1.187) Remain 30:41:50 loss: 0.2546 Lr: 0.00349 [2024-02-18 13:42:23,796 INFO misc.py line 119 87073] Train: [41/100][350/1557] Data 0.009 (0.233) Batch 1.372 (1.188) Remain 30:42:38 loss: 0.1959 Lr: 0.00349 [2024-02-18 13:42:24,703 INFO misc.py line 119 87073] Train: [41/100][351/1557] Data 0.008 (0.232) Batch 0.910 (1.187) Remain 30:41:22 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87073] Train: [41/100][364/1557] Data 0.003 (0.228) Batch 1.141 (1.185) Remain 30:37:12 loss: 0.1623 Lr: 0.00349 [2024-02-18 13:42:40,128 INFO misc.py line 119 87073] Train: [41/100][365/1557] Data 0.006 (0.228) Batch 0.902 (1.184) Remain 30:35:59 loss: 0.6494 Lr: 0.00349 [2024-02-18 13:42:41,031 INFO misc.py line 119 87073] Train: [41/100][366/1557] Data 0.006 (0.227) Batch 0.905 (1.183) Remain 30:34:46 loss: 0.3062 Lr: 0.00349 [2024-02-18 13:42:42,216 INFO misc.py line 119 87073] Train: [41/100][367/1557] Data 0.004 (0.227) Batch 1.177 (1.183) Remain 30:34:43 loss: 0.4666 Lr: 0.00349 [2024-02-18 13:42:43,126 INFO misc.py line 119 87073] Train: [41/100][368/1557] Data 0.013 (0.226) Batch 0.917 (1.182) Remain 30:33:34 loss: 0.1476 Lr: 0.00349 [2024-02-18 13:42:43,931 INFO misc.py line 119 87073] Train: [41/100][369/1557] Data 0.004 (0.225) Batch 0.806 (1.181) Remain 30:31:57 loss: 0.5035 Lr: 0.00349 [2024-02-18 13:42:44,710 INFO misc.py line 119 87073] Train: [41/100][370/1557] Data 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[2024-02-18 13:42:57,612 INFO misc.py line 119 87073] Train: [41/100][383/1557] Data 0.004 (0.217) Batch 0.798 (1.174) Remain 30:20:01 loss: 0.2502 Lr: 0.00349 [2024-02-18 13:42:58,353 INFO misc.py line 119 87073] Train: [41/100][384/1557] Data 0.004 (0.217) Batch 0.736 (1.173) Remain 30:18:13 loss: 0.3126 Lr: 0.00349 [2024-02-18 13:42:59,596 INFO misc.py line 119 87073] Train: [41/100][385/1557] Data 0.009 (0.216) Batch 1.240 (1.173) Remain 30:18:28 loss: 0.1401 Lr: 0.00349 [2024-02-18 13:43:00,515 INFO misc.py line 119 87073] Train: [41/100][386/1557] Data 0.012 (0.216) Batch 0.926 (1.172) Remain 30:17:27 loss: 0.1440 Lr: 0.00349 [2024-02-18 13:43:01,609 INFO misc.py line 119 87073] Train: [41/100][387/1557] Data 0.005 (0.215) Batch 1.095 (1.172) Remain 30:17:08 loss: 0.3716 Lr: 0.00349 [2024-02-18 13:43:02,463 INFO misc.py line 119 87073] Train: [41/100][388/1557] Data 0.003 (0.215) Batch 0.853 (1.171) Remain 30:15:49 loss: 0.3425 Lr: 0.00349 [2024-02-18 13:43:03,441 INFO misc.py line 119 87073] Train: [41/100][389/1557] Data 0.004 (0.214) Batch 0.977 (1.171) Remain 30:15:02 loss: 0.7052 Lr: 0.00349 [2024-02-18 13:43:04,210 INFO misc.py line 119 87073] Train: [41/100][390/1557] Data 0.006 (0.214) Batch 0.769 (1.170) Remain 30:13:24 loss: 0.3927 Lr: 0.00349 [2024-02-18 13:43:04,987 INFO misc.py line 119 87073] Train: [41/100][391/1557] Data 0.006 (0.213) Batch 0.772 (1.169) Remain 30:11:47 loss: 0.4513 Lr: 0.00349 [2024-02-18 13:43:06,132 INFO misc.py line 119 87073] Train: [41/100][392/1557] Data 0.010 (0.212) Batch 1.149 (1.168) Remain 30:11:41 loss: 0.3120 Lr: 0.00349 [2024-02-18 13:43:07,022 INFO misc.py line 119 87073] Train: [41/100][393/1557] Data 0.006 (0.212) Batch 0.893 (1.168) Remain 30:10:34 loss: 0.1545 Lr: 0.00349 [2024-02-18 13:43:07,900 INFO misc.py line 119 87073] Train: [41/100][394/1557] Data 0.004 (0.211) Batch 0.877 (1.167) Remain 30:09:24 loss: 0.2865 Lr: 0.00349 [2024-02-18 13:43:08,886 INFO misc.py line 119 87073] Train: 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Batch 0.888 (1.195) Remain 30:52:01 loss: 0.2066 Lr: 0.00349 [2024-02-18 13:43:28,043 INFO misc.py line 119 87073] Train: [41/100][402/1557] Data 0.004 (0.238) Batch 0.998 (1.194) Remain 30:51:14 loss: 0.4322 Lr: 0.00349 [2024-02-18 13:43:29,224 INFO misc.py line 119 87073] Train: [41/100][403/1557] Data 0.004 (0.237) Batch 1.170 (1.194) Remain 30:51:08 loss: 0.3977 Lr: 0.00349 [2024-02-18 13:43:29,975 INFO misc.py line 119 87073] Train: [41/100][404/1557] Data 0.015 (0.237) Batch 0.762 (1.193) Remain 30:49:26 loss: 0.1474 Lr: 0.00349 [2024-02-18 13:43:30,758 INFO misc.py line 119 87073] Train: [41/100][405/1557] Data 0.004 (0.236) Batch 0.776 (1.192) Remain 30:47:48 loss: 0.2558 Lr: 0.00349 [2024-02-18 13:43:32,049 INFO misc.py line 119 87073] Train: [41/100][406/1557] Data 0.011 (0.236) Batch 1.296 (1.192) Remain 30:48:11 loss: 0.1467 Lr: 0.00349 [2024-02-18 13:43:32,942 INFO misc.py line 119 87073] Train: [41/100][407/1557] Data 0.006 (0.235) Batch 0.895 (1.191) Remain 30:47:02 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13:43:39,798 INFO misc.py line 119 87073] Train: [41/100][414/1557] Data 0.007 (0.231) Batch 1.071 (1.188) Remain 30:41:15 loss: 0.5764 Lr: 0.00349 [2024-02-18 13:43:40,804 INFO misc.py line 119 87073] Train: [41/100][415/1557] Data 0.014 (0.231) Batch 1.003 (1.187) Remain 30:40:32 loss: 0.3534 Lr: 0.00349 [2024-02-18 13:43:41,956 INFO misc.py line 119 87073] Train: [41/100][416/1557] Data 0.016 (0.230) Batch 1.160 (1.187) Remain 30:40:25 loss: 0.3587 Lr: 0.00349 [2024-02-18 13:43:42,882 INFO misc.py line 119 87073] Train: [41/100][417/1557] Data 0.008 (0.230) Batch 0.927 (1.187) Remain 30:39:25 loss: 0.6124 Lr: 0.00349 [2024-02-18 13:43:43,572 INFO misc.py line 119 87073] Train: [41/100][418/1557] Data 0.007 (0.229) Batch 0.693 (1.186) Remain 30:37:34 loss: 0.3024 Lr: 0.00349 [2024-02-18 13:43:44,352 INFO misc.py line 119 87073] Train: [41/100][419/1557] Data 0.004 (0.229) Batch 0.777 (1.185) Remain 30:36:01 loss: 0.4787 Lr: 0.00349 [2024-02-18 13:43:45,447 INFO misc.py line 119 87073] Train: [41/100][420/1557] Data 0.007 (0.228) Batch 1.087 (1.184) Remain 30:35:38 loss: 0.1485 Lr: 0.00349 [2024-02-18 13:43:46,379 INFO misc.py line 119 87073] Train: [41/100][421/1557] Data 0.014 (0.227) Batch 0.943 (1.184) Remain 30:34:43 loss: 0.5589 Lr: 0.00349 [2024-02-18 13:43:47,358 INFO misc.py line 119 87073] Train: [41/100][422/1557] Data 0.004 (0.227) Batch 0.978 (1.183) Remain 30:33:57 loss: 0.3370 Lr: 0.00349 [2024-02-18 13:43:48,471 INFO misc.py line 119 87073] Train: [41/100][423/1557] Data 0.004 (0.226) Batch 1.114 (1.183) Remain 30:33:40 loss: 0.1587 Lr: 0.00348 [2024-02-18 13:43:49,704 INFO misc.py line 119 87073] Train: [41/100][424/1557] Data 0.004 (0.226) Batch 1.232 (1.183) Remain 30:33:50 loss: 0.6472 Lr: 0.00348 [2024-02-18 13:43:50,475 INFO misc.py line 119 87073] Train: [41/100][425/1557] Data 0.005 (0.225) Batch 0.772 (1.182) Remain 30:32:18 loss: 0.4209 Lr: 0.00348 [2024-02-18 13:43:51,307 INFO misc.py line 119 87073] Train: [41/100][426/1557] Data 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[2024-02-18 13:44:03,941 INFO misc.py line 119 87073] Train: [41/100][439/1557] Data 0.005 (0.218) Batch 0.793 (1.175) Remain 30:21:03 loss: 0.4067 Lr: 0.00348 [2024-02-18 13:44:04,715 INFO misc.py line 119 87073] Train: [41/100][440/1557] Data 0.006 (0.218) Batch 0.766 (1.174) Remain 30:19:35 loss: 0.4144 Lr: 0.00348 [2024-02-18 13:44:05,863 INFO misc.py line 119 87073] Train: [41/100][441/1557] Data 0.013 (0.217) Batch 1.153 (1.174) Remain 30:19:29 loss: 0.1722 Lr: 0.00348 [2024-02-18 13:44:06,891 INFO misc.py line 119 87073] Train: [41/100][442/1557] Data 0.010 (0.217) Batch 1.025 (1.174) Remain 30:18:56 loss: 0.6376 Lr: 0.00348 [2024-02-18 13:44:07,867 INFO misc.py line 119 87073] Train: [41/100][443/1557] Data 0.012 (0.216) Batch 0.984 (1.173) Remain 30:18:15 loss: 0.2650 Lr: 0.00348 [2024-02-18 13:44:08,637 INFO misc.py line 119 87073] Train: [41/100][444/1557] Data 0.003 (0.216) Batch 0.768 (1.172) Remain 30:16:49 loss: 0.1035 Lr: 0.00348 [2024-02-18 13:44:09,760 INFO misc.py line 119 87073] Train: [41/100][445/1557] Data 0.005 (0.215) Batch 1.123 (1.172) Remain 30:16:37 loss: 0.2121 Lr: 0.00348 [2024-02-18 13:44:10,498 INFO misc.py line 119 87073] Train: [41/100][446/1557] Data 0.005 (0.215) Batch 0.739 (1.171) Remain 30:15:05 loss: 0.2602 Lr: 0.00348 [2024-02-18 13:44:11,264 INFO misc.py line 119 87073] Train: [41/100][447/1557] Data 0.004 (0.215) Batch 0.757 (1.170) Remain 30:13:37 loss: 0.6136 Lr: 0.00348 [2024-02-18 13:44:12,387 INFO misc.py line 119 87073] Train: [41/100][448/1557] Data 0.013 (0.214) Batch 1.128 (1.170) Remain 30:13:27 loss: 0.1149 Lr: 0.00348 [2024-02-18 13:44:13,401 INFO misc.py line 119 87073] Train: [41/100][449/1557] Data 0.009 (0.214) Batch 1.012 (1.170) Remain 30:12:53 loss: 0.4027 Lr: 0.00348 [2024-02-18 13:44:14,420 INFO misc.py line 119 87073] Train: [41/100][450/1557] Data 0.010 (0.213) Batch 1.015 (1.170) Remain 30:12:19 loss: 0.3329 Lr: 0.00348 [2024-02-18 13:44:15,254 INFO misc.py line 119 87073] Train: 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Batch 1.196 (1.196) Remain 30:52:22 loss: 0.3916 Lr: 0.00348 [2024-02-18 13:44:35,366 INFO misc.py line 119 87073] Train: [41/100][458/1557] Data 0.010 (0.238) Batch 0.996 (1.195) Remain 30:51:40 loss: 0.3736 Lr: 0.00348 [2024-02-18 13:44:36,544 INFO misc.py line 119 87073] Train: [41/100][459/1557] Data 0.004 (0.238) Batch 1.178 (1.195) Remain 30:51:35 loss: 0.3466 Lr: 0.00348 [2024-02-18 13:44:37,322 INFO misc.py line 119 87073] Train: [41/100][460/1557] Data 0.005 (0.237) Batch 0.779 (1.194) Remain 30:50:09 loss: 0.3182 Lr: 0.00348 [2024-02-18 13:44:38,153 INFO misc.py line 119 87073] Train: [41/100][461/1557] Data 0.004 (0.237) Batch 0.831 (1.193) Remain 30:48:54 loss: 0.5245 Lr: 0.00348 [2024-02-18 13:44:39,337 INFO misc.py line 119 87073] Train: [41/100][462/1557] Data 0.004 (0.236) Batch 1.181 (1.193) Remain 30:48:50 loss: 0.1154 Lr: 0.00348 [2024-02-18 13:44:40,343 INFO misc.py line 119 87073] Train: [41/100][463/1557] Data 0.007 (0.236) Batch 1.000 (1.193) Remain 30:48:10 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87073] Train: [41/100][476/1557] Data 0.015 (0.229) Batch 1.163 (1.186) Remain 30:36:32 loss: 0.1593 Lr: 0.00348 [2024-02-18 13:44:53,354 INFO misc.py line 119 87073] Train: [41/100][477/1557] Data 0.015 (0.229) Batch 0.987 (1.185) Remain 30:35:52 loss: 0.3833 Lr: 0.00348 [2024-02-18 13:44:54,214 INFO misc.py line 119 87073] Train: [41/100][478/1557] Data 0.003 (0.228) Batch 0.860 (1.184) Remain 30:34:47 loss: 0.3741 Lr: 0.00348 [2024-02-18 13:44:55,106 INFO misc.py line 119 87073] Train: [41/100][479/1557] Data 0.005 (0.228) Batch 0.884 (1.184) Remain 30:33:47 loss: 0.4280 Lr: 0.00348 [2024-02-18 13:44:56,170 INFO misc.py line 119 87073] Train: [41/100][480/1557] Data 0.013 (0.227) Batch 1.071 (1.184) Remain 30:33:24 loss: 0.3436 Lr: 0.00348 [2024-02-18 13:44:56,959 INFO misc.py line 119 87073] Train: [41/100][481/1557] Data 0.005 (0.227) Batch 0.789 (1.183) Remain 30:32:06 loss: 0.2627 Lr: 0.00348 [2024-02-18 13:44:57,673 INFO misc.py line 119 87073] Train: [41/100][482/1557] Data 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line 119 87073] Train: [41/100][501/1557] Data 0.007 (0.218) Batch 0.958 (1.174) Remain 30:17:51 loss: 0.2668 Lr: 0.00348 [2024-02-18 13:45:16,994 INFO misc.py line 119 87073] Train: [41/100][502/1557] Data 0.003 (0.218) Batch 0.839 (1.173) Remain 30:16:47 loss: 0.2393 Lr: 0.00348 [2024-02-18 13:45:17,773 INFO misc.py line 119 87073] Train: [41/100][503/1557] Data 0.004 (0.217) Batch 0.773 (1.172) Remain 30:15:32 loss: 0.3745 Lr: 0.00348 [2024-02-18 13:45:18,886 INFO misc.py line 119 87073] Train: [41/100][504/1557] Data 0.010 (0.217) Batch 1.113 (1.172) Remain 30:15:19 loss: 0.2232 Lr: 0.00348 [2024-02-18 13:45:19,784 INFO misc.py line 119 87073] Train: [41/100][505/1557] Data 0.009 (0.217) Batch 0.903 (1.172) Remain 30:14:28 loss: 0.3749 Lr: 0.00348 [2024-02-18 13:45:20,582 INFO misc.py line 119 87073] Train: [41/100][506/1557] Data 0.005 (0.216) Batch 0.797 (1.171) Remain 30:13:18 loss: 0.1642 Lr: 0.00348 [2024-02-18 13:45:21,709 INFO misc.py line 119 87073] Train: 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Batch 1.069 (1.193) Remain 30:47:22 loss: 0.5051 Lr: 0.00348 [2024-02-18 13:45:40,908 INFO misc.py line 119 87073] Train: [41/100][514/1557] Data 0.004 (0.237) Batch 0.867 (1.192) Remain 30:46:21 loss: 0.3238 Lr: 0.00348 [2024-02-18 13:45:41,979 INFO misc.py line 119 87073] Train: [41/100][515/1557] Data 0.004 (0.237) Batch 1.071 (1.192) Remain 30:45:58 loss: 0.4078 Lr: 0.00348 [2024-02-18 13:45:42,689 INFO misc.py line 119 87073] Train: [41/100][516/1557] Data 0.004 (0.236) Batch 0.703 (1.191) Remain 30:44:28 loss: 0.3176 Lr: 0.00348 [2024-02-18 13:45:43,399 INFO misc.py line 119 87073] Train: [41/100][517/1557] Data 0.011 (0.236) Batch 0.717 (1.190) Remain 30:43:01 loss: 0.3059 Lr: 0.00348 [2024-02-18 13:45:44,628 INFO misc.py line 119 87073] Train: [41/100][518/1557] Data 0.003 (0.236) Batch 1.229 (1.190) Remain 30:43:07 loss: 0.1673 Lr: 0.00348 [2024-02-18 13:45:45,626 INFO misc.py line 119 87073] Train: [41/100][519/1557] Data 0.004 (0.235) Batch 0.997 (1.190) Remain 30:42:31 loss: 0.2437 Lr: 0.00348 [2024-02-18 13:45:46,654 INFO misc.py line 119 87073] Train: [41/100][520/1557] Data 0.005 (0.235) Batch 1.029 (1.190) Remain 30:42:01 loss: 1.0922 Lr: 0.00348 [2024-02-18 13:45:47,646 INFO misc.py line 119 87073] Train: [41/100][521/1557] Data 0.006 (0.234) Batch 0.991 (1.189) Remain 30:41:24 loss: 0.5635 Lr: 0.00348 [2024-02-18 13:45:48,560 INFO misc.py line 119 87073] Train: [41/100][522/1557] Data 0.005 (0.234) Batch 0.915 (1.189) Remain 30:40:34 loss: 0.5481 Lr: 0.00348 [2024-02-18 13:45:49,276 INFO misc.py line 119 87073] Train: [41/100][523/1557] Data 0.004 (0.233) Batch 0.707 (1.188) Remain 30:39:06 loss: 0.2763 Lr: 0.00348 [2024-02-18 13:45:49,985 INFO misc.py line 119 87073] Train: [41/100][524/1557] Data 0.012 (0.233) Batch 0.718 (1.187) Remain 30:37:41 loss: 0.2342 Lr: 0.00348 [2024-02-18 13:45:51,308 INFO misc.py line 119 87073] Train: [41/100][525/1557] Data 0.004 (0.232) Batch 1.323 (1.187) Remain 30:38:04 loss: 0.3083 Lr: 0.00348 [2024-02-18 13:45:52,154 INFO misc.py line 119 87073] Train: [41/100][526/1557] Data 0.005 (0.232) Batch 0.847 (1.187) Remain 30:37:03 loss: 0.5381 Lr: 0.00348 [2024-02-18 13:45:53,179 INFO misc.py line 119 87073] Train: [41/100][527/1557] Data 0.004 (0.232) Batch 1.024 (1.186) Remain 30:36:33 loss: 0.6880 Lr: 0.00348 [2024-02-18 13:45:54,148 INFO misc.py line 119 87073] Train: [41/100][528/1557] Data 0.005 (0.231) Batch 0.969 (1.186) Remain 30:35:53 loss: 0.2234 Lr: 0.00348 [2024-02-18 13:45:55,150 INFO misc.py line 119 87073] Train: [41/100][529/1557] Data 0.005 (0.231) Batch 1.003 (1.185) Remain 30:35:20 loss: 0.3918 Lr: 0.00348 [2024-02-18 13:45:57,886 INFO misc.py line 119 87073] Train: [41/100][530/1557] Data 1.196 (0.233) Batch 2.734 (1.188) Remain 30:39:52 loss: 0.5460 Lr: 0.00348 [2024-02-18 13:45:58,597 INFO misc.py line 119 87073] Train: [41/100][531/1557] Data 0.005 (0.232) Batch 0.706 (1.188) Remain 30:38:25 loss: 0.1783 Lr: 0.00348 [2024-02-18 13:45:59,807 INFO misc.py line 119 87073] Train: [41/100][532/1557] Data 0.010 (0.232) Batch 1.210 (1.188) Remain 30:38:28 loss: 0.1166 Lr: 0.00348 [2024-02-18 13:46:00,897 INFO misc.py line 119 87073] Train: [41/100][533/1557] Data 0.011 (0.231) Batch 1.096 (1.187) Remain 30:38:11 loss: 0.1452 Lr: 0.00348 [2024-02-18 13:46:02,080 INFO misc.py line 119 87073] Train: [41/100][534/1557] Data 0.005 (0.231) Batch 1.175 (1.187) Remain 30:38:08 loss: 0.6044 Lr: 0.00348 [2024-02-18 13:46:02,950 INFO misc.py line 119 87073] Train: [41/100][535/1557] Data 0.012 (0.230) Batch 0.877 (1.187) Remain 30:37:12 loss: 0.3886 Lr: 0.00348 [2024-02-18 13:46:03,857 INFO misc.py line 119 87073] Train: [41/100][536/1557] Data 0.006 (0.230) Batch 0.908 (1.186) Remain 30:36:23 loss: 0.4435 Lr: 0.00348 [2024-02-18 13:46:04,639 INFO misc.py line 119 87073] Train: [41/100][537/1557] Data 0.004 (0.230) Batch 0.774 (1.185) Remain 30:35:10 loss: 0.2086 Lr: 0.00348 [2024-02-18 13:46:05,367 INFO misc.py line 119 87073] Train: [41/100][538/1557] Data 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line 119 87073] Train: [41/100][557/1557] Data 0.003 (0.222) Batch 0.941 (1.178) Remain 30:23:36 loss: 0.6805 Lr: 0.00348 [2024-02-18 13:46:25,122 INFO misc.py line 119 87073] Train: [41/100][558/1557] Data 0.003 (0.221) Batch 0.774 (1.178) Remain 30:22:27 loss: 0.4332 Lr: 0.00348 [2024-02-18 13:46:25,907 INFO misc.py line 119 87073] Train: [41/100][559/1557] Data 0.012 (0.221) Batch 0.791 (1.177) Remain 30:21:21 loss: 0.3818 Lr: 0.00348 [2024-02-18 13:46:27,010 INFO misc.py line 119 87073] Train: [41/100][560/1557] Data 0.005 (0.220) Batch 1.105 (1.177) Remain 30:21:08 loss: 0.1645 Lr: 0.00348 [2024-02-18 13:46:27,903 INFO misc.py line 119 87073] Train: [41/100][561/1557] Data 0.004 (0.220) Batch 0.893 (1.176) Remain 30:20:19 loss: 0.4828 Lr: 0.00348 [2024-02-18 13:46:28,916 INFO misc.py line 119 87073] Train: [41/100][562/1557] Data 0.004 (0.220) Batch 1.013 (1.176) Remain 30:19:51 loss: 0.4683 Lr: 0.00348 [2024-02-18 13:46:29,853 INFO misc.py line 119 87073] Train: 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Batch 0.937 (1.194) Remain 30:48:26 loss: 0.5336 Lr: 0.00348 [2024-02-18 13:46:48,494 INFO misc.py line 119 87073] Train: [41/100][570/1557] Data 0.006 (0.238) Batch 0.839 (1.194) Remain 30:47:27 loss: 0.7642 Lr: 0.00348 [2024-02-18 13:46:49,358 INFO misc.py line 119 87073] Train: [41/100][571/1557] Data 0.007 (0.238) Batch 0.867 (1.193) Remain 30:46:32 loss: 0.8433 Lr: 0.00348 [2024-02-18 13:46:50,222 INFO misc.py line 119 87073] Train: [41/100][572/1557] Data 0.005 (0.237) Batch 0.862 (1.193) Remain 30:45:37 loss: 0.1852 Lr: 0.00348 [2024-02-18 13:46:50,993 INFO misc.py line 119 87073] Train: [41/100][573/1557] Data 0.006 (0.237) Batch 0.773 (1.192) Remain 30:44:27 loss: 0.2129 Lr: 0.00348 [2024-02-18 13:46:52,336 INFO misc.py line 119 87073] Train: [41/100][574/1557] Data 0.004 (0.237) Batch 1.335 (1.192) Remain 30:44:49 loss: 0.0971 Lr: 0.00348 [2024-02-18 13:46:53,362 INFO misc.py line 119 87073] Train: [41/100][575/1557] Data 0.013 (0.236) Batch 1.029 (1.192) Remain 30:44:22 loss: 0.3937 Lr: 0.00348 [2024-02-18 13:46:54,546 INFO misc.py line 119 87073] Train: [41/100][576/1557] Data 0.010 (0.236) Batch 1.177 (1.192) Remain 30:44:18 loss: 0.5482 Lr: 0.00348 [2024-02-18 13:46:55,394 INFO misc.py line 119 87073] Train: [41/100][577/1557] Data 0.016 (0.235) Batch 0.861 (1.191) Remain 30:43:23 loss: 0.6125 Lr: 0.00348 [2024-02-18 13:46:56,359 INFO misc.py line 119 87073] Train: [41/100][578/1557] Data 0.003 (0.235) Batch 0.963 (1.191) Remain 30:42:45 loss: 0.7339 Lr: 0.00348 [2024-02-18 13:46:57,112 INFO misc.py line 119 87073] Train: [41/100][579/1557] Data 0.005 (0.235) Batch 0.753 (1.190) Remain 30:41:33 loss: 0.1334 Lr: 0.00348 [2024-02-18 13:46:57,914 INFO misc.py line 119 87073] Train: [41/100][580/1557] Data 0.006 (0.234) Batch 0.804 (1.189) Remain 30:40:30 loss: 0.2959 Lr: 0.00348 [2024-02-18 13:46:59,170 INFO misc.py line 119 87073] Train: [41/100][581/1557] Data 0.004 (0.234) Batch 1.245 (1.190) Remain 30:40:38 loss: 0.2082 Lr: 0.00348 [2024-02-18 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87073] Train: [41/100][588/1557] Data 0.007 (0.231) Batch 1.112 (1.186) Remain 30:35:39 loss: 0.2768 Lr: 0.00348 [2024-02-18 13:47:06,760 INFO misc.py line 119 87073] Train: [41/100][589/1557] Data 0.004 (0.231) Batch 1.106 (1.186) Remain 30:35:25 loss: 0.4302 Lr: 0.00348 [2024-02-18 13:47:07,973 INFO misc.py line 119 87073] Train: [41/100][590/1557] Data 0.005 (0.230) Batch 1.206 (1.186) Remain 30:35:27 loss: 0.3856 Lr: 0.00348 [2024-02-18 13:47:08,928 INFO misc.py line 119 87073] Train: [41/100][591/1557] Data 0.012 (0.230) Batch 0.962 (1.186) Remain 30:34:50 loss: 0.4383 Lr: 0.00348 [2024-02-18 13:47:09,874 INFO misc.py line 119 87073] Train: [41/100][592/1557] Data 0.005 (0.230) Batch 0.945 (1.186) Remain 30:34:11 loss: 0.2843 Lr: 0.00348 [2024-02-18 13:47:10,579 INFO misc.py line 119 87073] Train: [41/100][593/1557] Data 0.006 (0.229) Batch 0.697 (1.185) Remain 30:32:53 loss: 0.4737 Lr: 0.00348 [2024-02-18 13:47:11,316 INFO misc.py line 119 87073] Train: [41/100][594/1557] Data 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line 119 87073] Train: [41/100][613/1557] Data 0.004 (0.222) Batch 1.089 (1.178) Remain 30:22:17 loss: 0.3190 Lr: 0.00348 [2024-02-18 13:47:31,033 INFO misc.py line 119 87073] Train: [41/100][614/1557] Data 0.003 (0.221) Batch 0.791 (1.177) Remain 30:21:17 loss: 0.2889 Lr: 0.00348 [2024-02-18 13:47:31,833 INFO misc.py line 119 87073] Train: [41/100][615/1557] Data 0.004 (0.221) Batch 0.790 (1.177) Remain 30:20:17 loss: 0.4844 Lr: 0.00348 [2024-02-18 13:47:32,934 INFO misc.py line 119 87073] Train: [41/100][616/1557] Data 0.014 (0.221) Batch 1.106 (1.177) Remain 30:20:05 loss: 0.1487 Lr: 0.00348 [2024-02-18 13:47:33,913 INFO misc.py line 119 87073] Train: [41/100][617/1557] Data 0.010 (0.220) Batch 0.984 (1.176) Remain 30:19:35 loss: 0.1743 Lr: 0.00348 [2024-02-18 13:47:34,947 INFO misc.py line 119 87073] Train: [41/100][618/1557] Data 0.003 (0.220) Batch 1.035 (1.176) Remain 30:19:13 loss: 0.6190 Lr: 0.00348 [2024-02-18 13:47:35,970 INFO misc.py line 119 87073] Train: 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Batch 0.949 (1.194) Remain 30:46:02 loss: 0.4573 Lr: 0.00348 [2024-02-18 13:47:54,955 INFO misc.py line 119 87073] Train: [41/100][626/1557] Data 0.004 (0.237) Batch 0.929 (1.193) Remain 30:45:22 loss: 0.2986 Lr: 0.00348 [2024-02-18 13:47:56,011 INFO misc.py line 119 87073] Train: [41/100][627/1557] Data 0.004 (0.236) Batch 1.055 (1.193) Remain 30:45:00 loss: 0.3307 Lr: 0.00348 [2024-02-18 13:47:56,797 INFO misc.py line 119 87073] Train: [41/100][628/1557] Data 0.004 (0.236) Batch 0.777 (1.192) Remain 30:43:57 loss: 0.2628 Lr: 0.00347 [2024-02-18 13:47:57,561 INFO misc.py line 119 87073] Train: [41/100][629/1557] Data 0.013 (0.236) Batch 0.773 (1.192) Remain 30:42:54 loss: 0.5749 Lr: 0.00347 [2024-02-18 13:47:58,794 INFO misc.py line 119 87073] Train: [41/100][630/1557] Data 0.004 (0.235) Batch 1.232 (1.192) Remain 30:42:59 loss: 0.1279 Lr: 0.00347 [2024-02-18 13:47:59,639 INFO misc.py line 119 87073] Train: [41/100][631/1557] Data 0.005 (0.235) Batch 0.845 (1.191) Remain 30:42:06 loss: 0.4579 Lr: 0.00347 [2024-02-18 13:48:00,585 INFO misc.py line 119 87073] Train: [41/100][632/1557] Data 0.004 (0.234) Batch 0.944 (1.191) Remain 30:41:29 loss: 0.2418 Lr: 0.00347 [2024-02-18 13:48:01,557 INFO misc.py line 119 87073] Train: [41/100][633/1557] Data 0.006 (0.234) Batch 0.973 (1.190) Remain 30:40:55 loss: 0.4470 Lr: 0.00347 [2024-02-18 13:48:02,464 INFO misc.py line 119 87073] Train: [41/100][634/1557] Data 0.006 (0.234) Batch 0.907 (1.190) Remain 30:40:12 loss: 0.3881 Lr: 0.00347 [2024-02-18 13:48:03,230 INFO misc.py line 119 87073] Train: [41/100][635/1557] Data 0.006 (0.233) Batch 0.758 (1.189) Remain 30:39:08 loss: 0.4089 Lr: 0.00347 [2024-02-18 13:48:04,029 INFO misc.py line 119 87073] Train: [41/100][636/1557] Data 0.014 (0.233) Batch 0.809 (1.189) Remain 30:38:11 loss: 0.4713 Lr: 0.00347 [2024-02-18 13:48:05,394 INFO misc.py line 119 87073] Train: [41/100][637/1557] Data 0.004 (0.233) Batch 1.352 (1.189) Remain 30:38:33 loss: 0.1777 Lr: 0.00347 [2024-02-18 13:48:06,443 INFO misc.py line 119 87073] Train: [41/100][638/1557] Data 0.016 (0.232) Batch 1.056 (1.189) Remain 30:38:13 loss: 0.3432 Lr: 0.00347 [2024-02-18 13:48:07,340 INFO misc.py line 119 87073] Train: [41/100][639/1557] Data 0.011 (0.232) Batch 0.904 (1.188) Remain 30:37:30 loss: 0.3678 Lr: 0.00347 [2024-02-18 13:48:08,415 INFO misc.py line 119 87073] Train: [41/100][640/1557] Data 0.004 (0.232) Batch 1.073 (1.188) Remain 30:37:12 loss: 0.2689 Lr: 0.00347 [2024-02-18 13:48:09,376 INFO misc.py line 119 87073] Train: [41/100][641/1557] Data 0.006 (0.231) Batch 0.962 (1.188) Remain 30:36:38 loss: 0.4012 Lr: 0.00347 [2024-02-18 13:48:10,112 INFO misc.py line 119 87073] Train: [41/100][642/1557] Data 0.004 (0.231) Batch 0.734 (1.187) Remain 30:35:31 loss: 0.1978 Lr: 0.00347 [2024-02-18 13:48:10,889 INFO misc.py line 119 87073] Train: [41/100][643/1557] Data 0.006 (0.230) Batch 0.774 (1.186) Remain 30:34:30 loss: 0.2339 Lr: 0.00347 [2024-02-18 13:48:12,050 INFO misc.py line 119 87073] Train: [41/100][644/1557] Data 0.009 (0.230) Batch 1.154 (1.186) Remain 30:34:24 loss: 0.1660 Lr: 0.00347 [2024-02-18 13:48:12,932 INFO misc.py line 119 87073] Train: [41/100][645/1557] Data 0.016 (0.230) Batch 0.894 (1.186) Remain 30:33:41 loss: 0.6351 Lr: 0.00347 [2024-02-18 13:48:13,845 INFO misc.py line 119 87073] Train: [41/100][646/1557] Data 0.004 (0.229) Batch 0.914 (1.185) Remain 30:33:00 loss: 0.3112 Lr: 0.00347 [2024-02-18 13:48:14,910 INFO misc.py line 119 87073] Train: [41/100][647/1557] Data 0.003 (0.229) Batch 1.058 (1.185) Remain 30:32:41 loss: 0.3398 Lr: 0.00347 [2024-02-18 13:48:15,779 INFO misc.py line 119 87073] Train: [41/100][648/1557] Data 0.010 (0.229) Batch 0.874 (1.185) Remain 30:31:55 loss: 0.3615 Lr: 0.00347 [2024-02-18 13:48:16,531 INFO misc.py line 119 87073] Train: [41/100][649/1557] Data 0.006 (0.228) Batch 0.752 (1.184) Remain 30:30:51 loss: 0.3578 Lr: 0.00347 [2024-02-18 13:48:17,407 INFO misc.py line 119 87073] Train: [41/100][650/1557] Data 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30:27:40 loss: 0.2619 Lr: 0.00347 [2024-02-18 13:48:24,219 INFO misc.py line 119 87073] Train: [41/100][657/1557] Data 0.007 (0.226) Batch 0.686 (1.181) Remain 30:26:28 loss: 0.3012 Lr: 0.00347 [2024-02-18 13:48:25,426 INFO misc.py line 119 87073] Train: [41/100][658/1557] Data 0.010 (0.225) Batch 1.202 (1.181) Remain 30:26:30 loss: 0.1159 Lr: 0.00347 [2024-02-18 13:48:26,409 INFO misc.py line 119 87073] Train: [41/100][659/1557] Data 0.015 (0.225) Batch 0.994 (1.181) Remain 30:26:02 loss: 0.4719 Lr: 0.00347 [2024-02-18 13:48:27,401 INFO misc.py line 119 87073] Train: [41/100][660/1557] Data 0.003 (0.225) Batch 0.992 (1.181) Remain 30:25:34 loss: 0.2847 Lr: 0.00347 [2024-02-18 13:48:28,317 INFO misc.py line 119 87073] Train: [41/100][661/1557] Data 0.004 (0.224) Batch 0.916 (1.180) Remain 30:24:56 loss: 0.4526 Lr: 0.00347 [2024-02-18 13:48:29,273 INFO misc.py line 119 87073] Train: [41/100][662/1557] Data 0.004 (0.224) Batch 0.955 (1.180) Remain 30:24:23 loss: 0.3593 Lr: 0.00347 [2024-02-18 13:48:30,011 INFO misc.py line 119 87073] Train: [41/100][663/1557] Data 0.005 (0.224) Batch 0.738 (1.179) Remain 30:23:19 loss: 0.4645 Lr: 0.00347 [2024-02-18 13:48:30,745 INFO misc.py line 119 87073] Train: [41/100][664/1557] Data 0.005 (0.223) Batch 0.736 (1.179) Remain 30:22:16 loss: 0.1910 Lr: 0.00347 [2024-02-18 13:48:31,920 INFO misc.py line 119 87073] Train: [41/100][665/1557] Data 0.004 (0.223) Batch 1.175 (1.179) Remain 30:22:14 loss: 0.3030 Lr: 0.00347 [2024-02-18 13:48:32,863 INFO misc.py line 119 87073] Train: [41/100][666/1557] Data 0.004 (0.223) Batch 0.944 (1.178) Remain 30:21:40 loss: 0.5035 Lr: 0.00347 [2024-02-18 13:48:33,758 INFO misc.py line 119 87073] Train: [41/100][667/1557] Data 0.004 (0.222) Batch 0.892 (1.178) Remain 30:20:59 loss: 0.3132 Lr: 0.00347 [2024-02-18 13:48:34,704 INFO misc.py line 119 87073] Train: [41/100][668/1557] Data 0.007 (0.222) Batch 0.945 (1.178) Remain 30:20:25 loss: 0.3102 Lr: 0.00347 [2024-02-18 13:48:35,754 INFO misc.py line 119 87073] Train: [41/100][669/1557] Data 0.007 (0.222) Batch 1.051 (1.177) Remain 30:20:07 loss: 0.3356 Lr: 0.00347 [2024-02-18 13:48:36,457 INFO misc.py line 119 87073] Train: [41/100][670/1557] Data 0.006 (0.221) Batch 0.704 (1.177) Remain 30:19:00 loss: 0.3082 Lr: 0.00347 [2024-02-18 13:48:37,211 INFO misc.py line 119 87073] Train: [41/100][671/1557] Data 0.005 (0.221) Batch 0.745 (1.176) Remain 30:17:58 loss: 0.3232 Lr: 0.00347 [2024-02-18 13:48:38,346 INFO misc.py line 119 87073] Train: [41/100][672/1557] Data 0.014 (0.221) Batch 1.136 (1.176) Remain 30:17:52 loss: 0.2197 Lr: 0.00347 [2024-02-18 13:48:39,130 INFO misc.py line 119 87073] Train: [41/100][673/1557] Data 0.012 (0.220) Batch 0.793 (1.175) Remain 30:16:57 loss: 0.2637 Lr: 0.00347 [2024-02-18 13:48:40,123 INFO misc.py line 119 87073] Train: [41/100][674/1557] Data 0.005 (0.220) Batch 0.992 (1.175) Remain 30:16:31 loss: 0.1784 Lr: 0.00347 [2024-02-18 13:48:41,112 INFO misc.py line 119 87073] Train: 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Batch 0.976 (1.190) Remain 30:40:00 loss: 0.1932 Lr: 0.00347 [2024-02-18 13:48:59,538 INFO misc.py line 119 87073] Train: [41/100][682/1557] Data 0.011 (0.235) Batch 0.826 (1.190) Remain 30:39:09 loss: 0.1727 Lr: 0.00347 [2024-02-18 13:49:00,605 INFO misc.py line 119 87073] Train: [41/100][683/1557] Data 0.004 (0.235) Batch 1.067 (1.190) Remain 30:38:51 loss: 0.4563 Lr: 0.00347 [2024-02-18 13:49:01,348 INFO misc.py line 119 87073] Train: [41/100][684/1557] Data 0.004 (0.234) Batch 0.742 (1.189) Remain 30:37:49 loss: 0.2085 Lr: 0.00347 [2024-02-18 13:49:02,117 INFO misc.py line 119 87073] Train: [41/100][685/1557] Data 0.004 (0.234) Batch 0.759 (1.188) Remain 30:36:50 loss: 0.2168 Lr: 0.00347 [2024-02-18 13:49:03,402 INFO misc.py line 119 87073] Train: [41/100][686/1557] Data 0.015 (0.234) Batch 1.286 (1.189) Remain 30:37:02 loss: 0.1095 Lr: 0.00347 [2024-02-18 13:49:04,621 INFO misc.py line 119 87073] Train: [41/100][687/1557] Data 0.015 (0.233) Batch 1.221 (1.189) Remain 30:37:05 loss: 0.2369 Lr: 0.00347 [2024-02-18 13:49:05,571 INFO misc.py line 119 87073] Train: [41/100][688/1557] Data 0.012 (0.233) Batch 0.959 (1.188) Remain 30:36:33 loss: 0.2804 Lr: 0.00347 [2024-02-18 13:49:06,576 INFO misc.py line 119 87073] Train: [41/100][689/1557] Data 0.003 (0.233) Batch 1.004 (1.188) Remain 30:36:06 loss: 0.3071 Lr: 0.00347 [2024-02-18 13:49:07,422 INFO misc.py line 119 87073] Train: [41/100][690/1557] Data 0.004 (0.232) Batch 0.846 (1.188) Remain 30:35:19 loss: 0.6817 Lr: 0.00347 [2024-02-18 13:49:08,162 INFO misc.py line 119 87073] Train: [41/100][691/1557] Data 0.003 (0.232) Batch 0.733 (1.187) Remain 30:34:17 loss: 0.6403 Lr: 0.00347 [2024-02-18 13:49:08,929 INFO misc.py line 119 87073] Train: [41/100][692/1557] Data 0.011 (0.232) Batch 0.774 (1.186) Remain 30:33:20 loss: 0.3353 Lr: 0.00347 [2024-02-18 13:49:10,163 INFO misc.py line 119 87073] Train: [41/100][693/1557] Data 0.004 (0.231) Batch 1.233 (1.186) Remain 30:33:25 loss: 0.1991 Lr: 0.00347 [2024-02-18 13:49:11,203 INFO misc.py line 119 87073] Train: [41/100][694/1557] Data 0.004 (0.231) Batch 1.041 (1.186) Remain 30:33:04 loss: 0.3525 Lr: 0.00347 [2024-02-18 13:49:12,258 INFO misc.py line 119 87073] Train: [41/100][695/1557] Data 0.004 (0.231) Batch 1.055 (1.186) Remain 30:32:46 loss: 0.6231 Lr: 0.00347 [2024-02-18 13:49:13,110 INFO misc.py line 119 87073] Train: [41/100][696/1557] Data 0.004 (0.230) Batch 0.851 (1.185) Remain 30:32:00 loss: 0.3311 Lr: 0.00347 [2024-02-18 13:49:14,085 INFO misc.py line 119 87073] Train: [41/100][697/1557] Data 0.005 (0.230) Batch 0.965 (1.185) Remain 30:31:29 loss: 0.4690 Lr: 0.00347 [2024-02-18 13:49:14,776 INFO misc.py line 119 87073] Train: [41/100][698/1557] Data 0.015 (0.230) Batch 0.702 (1.184) Remain 30:30:23 loss: 0.2847 Lr: 0.00347 [2024-02-18 13:49:15,507 INFO misc.py line 119 87073] Train: [41/100][699/1557] Data 0.004 (0.230) Batch 0.728 (1.184) Remain 30:29:21 loss: 0.2261 Lr: 0.00347 [2024-02-18 13:49:16,560 INFO misc.py line 119 87073] Train: [41/100][700/1557] Data 0.006 (0.229) Batch 1.043 (1.184) Remain 30:29:01 loss: 0.1417 Lr: 0.00347 [2024-02-18 13:49:17,534 INFO misc.py line 119 87073] Train: [41/100][701/1557] Data 0.016 (0.229) Batch 0.987 (1.183) Remain 30:28:34 loss: 0.2991 Lr: 0.00347 [2024-02-18 13:49:18,574 INFO misc.py line 119 87073] Train: [41/100][702/1557] Data 0.004 (0.229) Batch 1.040 (1.183) Remain 30:28:14 loss: 0.7606 Lr: 0.00347 [2024-02-18 13:49:19,447 INFO misc.py line 119 87073] Train: [41/100][703/1557] Data 0.004 (0.228) Batch 0.873 (1.183) Remain 30:27:32 loss: 0.2036 Lr: 0.00347 [2024-02-18 13:49:20,327 INFO misc.py line 119 87073] Train: [41/100][704/1557] Data 0.004 (0.228) Batch 0.880 (1.182) Remain 30:26:50 loss: 0.2602 Lr: 0.00347 [2024-02-18 13:49:23,019 INFO misc.py line 119 87073] Train: [41/100][705/1557] Data 1.433 (0.230) Batch 2.692 (1.184) Remain 30:30:09 loss: 0.4500 Lr: 0.00347 [2024-02-18 13:49:23,726 INFO misc.py line 119 87073] Train: [41/100][706/1557] Data 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[2024-02-18 13:49:36,425 INFO misc.py line 119 87073] Train: [41/100][719/1557] Data 0.014 (0.225) Batch 0.776 (1.180) Remain 30:23:01 loss: 0.5255 Lr: 0.00347 [2024-02-18 13:49:37,202 INFO misc.py line 119 87073] Train: [41/100][720/1557] Data 0.006 (0.225) Batch 0.773 (1.179) Remain 30:22:07 loss: 0.3362 Lr: 0.00347 [2024-02-18 13:49:38,409 INFO misc.py line 119 87073] Train: [41/100][721/1557] Data 0.009 (0.225) Batch 1.205 (1.179) Remain 30:22:09 loss: 0.1888 Lr: 0.00347 [2024-02-18 13:49:39,300 INFO misc.py line 119 87073] Train: [41/100][722/1557] Data 0.011 (0.224) Batch 0.898 (1.179) Remain 30:21:32 loss: 0.8104 Lr: 0.00347 [2024-02-18 13:49:40,242 INFO misc.py line 119 87073] Train: [41/100][723/1557] Data 0.004 (0.224) Batch 0.942 (1.179) Remain 30:21:00 loss: 0.8311 Lr: 0.00347 [2024-02-18 13:49:41,150 INFO misc.py line 119 87073] Train: [41/100][724/1557] Data 0.004 (0.224) Batch 0.906 (1.178) Remain 30:20:24 loss: 0.2792 Lr: 0.00347 [2024-02-18 13:49:42,203 INFO misc.py line 119 87073] Train: [41/100][725/1557] Data 0.006 (0.223) Batch 1.044 (1.178) Remain 30:20:05 loss: 0.4431 Lr: 0.00347 [2024-02-18 13:49:42,987 INFO misc.py line 119 87073] Train: [41/100][726/1557] Data 0.014 (0.223) Batch 0.793 (1.178) Remain 30:19:15 loss: 0.2597 Lr: 0.00347 [2024-02-18 13:49:43,691 INFO misc.py line 119 87073] Train: [41/100][727/1557] Data 0.005 (0.223) Batch 0.700 (1.177) Remain 30:18:13 loss: 0.1912 Lr: 0.00347 [2024-02-18 13:49:44,824 INFO misc.py line 119 87073] Train: [41/100][728/1557] Data 0.009 (0.223) Batch 1.123 (1.177) Remain 30:18:05 loss: 0.2398 Lr: 0.00347 [2024-02-18 13:49:45,731 INFO misc.py line 119 87073] Train: [41/100][729/1557] Data 0.018 (0.222) Batch 0.922 (1.177) Remain 30:17:31 loss: 0.1961 Lr: 0.00347 [2024-02-18 13:49:46,678 INFO misc.py line 119 87073] Train: [41/100][730/1557] Data 0.004 (0.222) Batch 0.946 (1.176) Remain 30:17:00 loss: 0.3930 Lr: 0.00347 [2024-02-18 13:49:47,689 INFO misc.py line 119 87073] Train: 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Batch 1.085 (1.189) Remain 30:36:01 loss: 0.2552 Lr: 0.00347 [2024-02-18 13:50:05,007 INFO misc.py line 119 87073] Train: [41/100][738/1557] Data 0.012 (0.234) Batch 1.000 (1.188) Remain 30:35:36 loss: 0.1936 Lr: 0.00347 [2024-02-18 13:50:05,909 INFO misc.py line 119 87073] Train: [41/100][739/1557] Data 0.004 (0.234) Batch 0.902 (1.188) Remain 30:34:58 loss: 0.6955 Lr: 0.00347 [2024-02-18 13:50:06,679 INFO misc.py line 119 87073] Train: [41/100][740/1557] Data 0.005 (0.233) Batch 0.770 (1.187) Remain 30:34:05 loss: 0.7098 Lr: 0.00347 [2024-02-18 13:50:07,459 INFO misc.py line 119 87073] Train: [41/100][741/1557] Data 0.005 (0.233) Batch 0.774 (1.187) Remain 30:33:12 loss: 0.2498 Lr: 0.00347 [2024-02-18 13:50:08,653 INFO misc.py line 119 87073] Train: [41/100][742/1557] Data 0.011 (0.233) Batch 1.192 (1.187) Remain 30:33:11 loss: 0.1036 Lr: 0.00347 [2024-02-18 13:50:09,606 INFO misc.py line 119 87073] Train: [41/100][743/1557] Data 0.013 (0.233) Batch 0.961 (1.187) Remain 30:32:42 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line 119 87073] Train: [41/100][781/1557] Data 0.017 (0.221) Batch 1.045 (1.176) Remain 30:15:32 loss: 0.5397 Lr: 0.00347 [2024-02-18 13:50:47,198 INFO misc.py line 119 87073] Train: [41/100][782/1557] Data 0.009 (0.221) Batch 0.771 (1.175) Remain 30:14:43 loss: 0.4455 Lr: 0.00347 [2024-02-18 13:50:47,976 INFO misc.py line 119 87073] Train: [41/100][783/1557] Data 0.005 (0.221) Batch 0.779 (1.175) Remain 30:13:55 loss: 0.3683 Lr: 0.00347 [2024-02-18 13:50:49,151 INFO misc.py line 119 87073] Train: [41/100][784/1557] Data 0.004 (0.221) Batch 1.163 (1.175) Remain 30:13:52 loss: 0.2849 Lr: 0.00347 [2024-02-18 13:50:50,274 INFO misc.py line 119 87073] Train: [41/100][785/1557] Data 0.015 (0.220) Batch 1.122 (1.175) Remain 30:13:45 loss: 0.1910 Lr: 0.00347 [2024-02-18 13:50:51,242 INFO misc.py line 119 87073] Train: [41/100][786/1557] Data 0.016 (0.220) Batch 0.979 (1.175) Remain 30:13:20 loss: 0.3042 Lr: 0.00347 [2024-02-18 13:50:52,207 INFO misc.py line 119 87073] Train: 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Batch 0.989 (1.188) Remain 30:34:11 loss: 0.2723 Lr: 0.00347 [2024-02-18 13:51:11,244 INFO misc.py line 119 87073] Train: [41/100][794/1557] Data 0.006 (0.233) Batch 1.047 (1.188) Remain 30:33:53 loss: 0.3483 Lr: 0.00347 [2024-02-18 13:51:12,234 INFO misc.py line 119 87073] Train: [41/100][795/1557] Data 0.003 (0.233) Batch 0.990 (1.188) Remain 30:33:29 loss: 0.3680 Lr: 0.00347 [2024-02-18 13:51:12,971 INFO misc.py line 119 87073] Train: [41/100][796/1557] Data 0.004 (0.233) Batch 0.736 (1.187) Remain 30:32:35 loss: 0.1879 Lr: 0.00347 [2024-02-18 13:51:13,743 INFO misc.py line 119 87073] Train: [41/100][797/1557] Data 0.004 (0.233) Batch 0.771 (1.187) Remain 30:31:45 loss: 0.3179 Lr: 0.00347 [2024-02-18 13:51:15,246 INFO misc.py line 119 87073] Train: [41/100][798/1557] Data 0.006 (0.232) Batch 1.502 (1.187) Remain 30:32:21 loss: 0.1616 Lr: 0.00347 [2024-02-18 13:51:16,209 INFO misc.py line 119 87073] Train: [41/100][799/1557] Data 0.006 (0.232) Batch 0.964 (1.187) Remain 30:31:53 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loss: 0.3748 Lr: 0.00346 [2024-02-18 13:52:23,917 INFO misc.py line 119 87073] Train: [41/100][856/1557] Data 0.013 (0.232) Batch 1.049 (1.187) Remain 30:30:52 loss: 0.6786 Lr: 0.00346 [2024-02-18 13:52:24,888 INFO misc.py line 119 87073] Train: [41/100][857/1557] Data 0.016 (0.231) Batch 0.982 (1.187) Remain 30:30:28 loss: 0.4206 Lr: 0.00346 [2024-02-18 13:52:25,865 INFO misc.py line 119 87073] Train: [41/100][858/1557] Data 0.004 (0.231) Batch 0.977 (1.186) Remain 30:30:05 loss: 0.1595 Lr: 0.00346 [2024-02-18 13:52:26,633 INFO misc.py line 119 87073] Train: [41/100][859/1557] Data 0.004 (0.231) Batch 0.767 (1.186) Remain 30:29:18 loss: 0.1580 Lr: 0.00346 [2024-02-18 13:52:27,402 INFO misc.py line 119 87073] Train: [41/100][860/1557] Data 0.004 (0.231) Batch 0.748 (1.185) Remain 30:28:30 loss: 0.2752 Lr: 0.00346 [2024-02-18 13:52:28,684 INFO misc.py line 119 87073] Train: [41/100][861/1557] Data 0.026 (0.230) Batch 1.294 (1.185) Remain 30:28:40 loss: 0.2016 Lr: 0.00346 [2024-02-18 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line 119 87073] Train: [41/100][893/1557] Data 0.004 (0.224) Batch 0.841 (1.179) Remain 30:18:08 loss: 0.5957 Lr: 0.00346 [2024-02-18 13:53:01,689 INFO misc.py line 119 87073] Train: [41/100][894/1557] Data 0.003 (0.223) Batch 0.787 (1.179) Remain 30:17:26 loss: 0.5008 Lr: 0.00346 [2024-02-18 13:53:02,459 INFO misc.py line 119 87073] Train: [41/100][895/1557] Data 0.013 (0.223) Batch 0.779 (1.178) Remain 30:16:44 loss: 0.3008 Lr: 0.00346 [2024-02-18 13:53:03,616 INFO misc.py line 119 87073] Train: [41/100][896/1557] Data 0.004 (0.223) Batch 1.156 (1.178) Remain 30:16:40 loss: 0.1604 Lr: 0.00346 [2024-02-18 13:53:04,596 INFO misc.py line 119 87073] Train: [41/100][897/1557] Data 0.004 (0.223) Batch 0.980 (1.178) Remain 30:16:19 loss: 0.4927 Lr: 0.00346 [2024-02-18 13:53:05,606 INFO misc.py line 119 87073] Train: [41/100][898/1557] Data 0.004 (0.222) Batch 1.011 (1.178) Remain 30:16:00 loss: 0.5418 Lr: 0.00346 [2024-02-18 13:53:06,744 INFO misc.py line 119 87073] Train: 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Batch 0.931 (1.190) Remain 30:34:49 loss: 0.3002 Lr: 0.00346 [2024-02-18 13:53:25,826 INFO misc.py line 119 87073] Train: [41/100][906/1557] Data 0.006 (0.234) Batch 0.886 (1.190) Remain 30:34:17 loss: 0.1248 Lr: 0.00346 [2024-02-18 13:53:27,058 INFO misc.py line 119 87073] Train: [41/100][907/1557] Data 0.005 (0.234) Batch 1.228 (1.190) Remain 30:34:20 loss: 0.5341 Lr: 0.00346 [2024-02-18 13:53:27,848 INFO misc.py line 119 87073] Train: [41/100][908/1557] Data 0.009 (0.234) Batch 0.795 (1.189) Remain 30:33:38 loss: 0.2844 Lr: 0.00346 [2024-02-18 13:53:28,601 INFO misc.py line 119 87073] Train: [41/100][909/1557] Data 0.006 (0.234) Batch 0.754 (1.189) Remain 30:32:52 loss: 0.3475 Lr: 0.00346 [2024-02-18 13:53:29,890 INFO misc.py line 119 87073] Train: [41/100][910/1557] Data 0.003 (0.233) Batch 1.287 (1.189) Remain 30:33:01 loss: 0.2955 Lr: 0.00346 [2024-02-18 13:53:31,089 INFO misc.py line 119 87073] Train: [41/100][911/1557] Data 0.006 (0.233) Batch 1.196 (1.189) Remain 30:33:01 loss: 0.7126 Lr: 0.00346 [2024-02-18 13:53:32,148 INFO misc.py line 119 87073] Train: [41/100][912/1557] Data 0.009 (0.233) Batch 1.045 (1.189) Remain 30:32:45 loss: 0.3949 Lr: 0.00346 [2024-02-18 13:53:33,030 INFO misc.py line 119 87073] Train: [41/100][913/1557] Data 0.024 (0.233) Batch 0.901 (1.188) Remain 30:32:14 loss: 0.8000 Lr: 0.00346 [2024-02-18 13:53:34,027 INFO misc.py line 119 87073] Train: [41/100][914/1557] Data 0.004 (0.232) Batch 0.997 (1.188) Remain 30:31:54 loss: 0.3149 Lr: 0.00346 [2024-02-18 13:53:34,838 INFO misc.py line 119 87073] Train: [41/100][915/1557] Data 0.004 (0.232) Batch 0.810 (1.188) Remain 30:31:14 loss: 0.3629 Lr: 0.00346 [2024-02-18 13:53:35,634 INFO misc.py line 119 87073] Train: [41/100][916/1557] Data 0.005 (0.232) Batch 0.795 (1.187) Remain 30:30:33 loss: 0.3637 Lr: 0.00346 [2024-02-18 13:53:36,933 INFO misc.py line 119 87073] Train: [41/100][917/1557] Data 0.006 (0.232) Batch 1.298 (1.187) Remain 30:30:43 loss: 0.1748 Lr: 0.00346 [2024-02-18 13:53:37,994 INFO misc.py line 119 87073] Train: [41/100][918/1557] Data 0.008 (0.231) Batch 1.065 (1.187) Remain 30:30:30 loss: 1.0707 Lr: 0.00346 [2024-02-18 13:53:39,071 INFO misc.py line 119 87073] Train: [41/100][919/1557] Data 0.004 (0.231) Batch 1.076 (1.187) Remain 30:30:17 loss: 0.3958 Lr: 0.00346 [2024-02-18 13:53:40,046 INFO misc.py line 119 87073] Train: [41/100][920/1557] Data 0.004 (0.231) Batch 0.976 (1.187) Remain 30:29:55 loss: 0.2145 Lr: 0.00346 [2024-02-18 13:53:41,044 INFO misc.py line 119 87073] Train: [41/100][921/1557] Data 0.004 (0.231) Batch 0.997 (1.187) Remain 30:29:34 loss: 0.2299 Lr: 0.00346 [2024-02-18 13:53:41,929 INFO misc.py line 119 87073] Train: [41/100][922/1557] Data 0.005 (0.230) Batch 0.885 (1.186) Remain 30:29:03 loss: 0.1489 Lr: 0.00346 [2024-02-18 13:53:42,679 INFO misc.py line 119 87073] Train: [41/100][923/1557] Data 0.004 (0.230) Batch 0.750 (1.186) Remain 30:28:18 loss: 0.2855 Lr: 0.00346 [2024-02-18 13:53:43,834 INFO misc.py line 119 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line 119 87073] Train: [41/100][949/1557] Data 0.005 (0.224) Batch 0.990 (1.180) Remain 30:18:37 loss: 0.3720 Lr: 0.00346 [2024-02-18 13:54:08,677 INFO misc.py line 119 87073] Train: [41/100][950/1557] Data 0.006 (0.224) Batch 0.785 (1.180) Remain 30:17:58 loss: 0.3382 Lr: 0.00346 [2024-02-18 13:54:09,503 INFO misc.py line 119 87073] Train: [41/100][951/1557] Data 0.004 (0.223) Batch 0.822 (1.179) Remain 30:17:22 loss: 0.3454 Lr: 0.00346 [2024-02-18 13:54:10,644 INFO misc.py line 119 87073] Train: [41/100][952/1557] Data 0.008 (0.223) Batch 1.141 (1.179) Remain 30:17:17 loss: 0.2647 Lr: 0.00346 [2024-02-18 13:54:11,596 INFO misc.py line 119 87073] Train: [41/100][953/1557] Data 0.008 (0.223) Batch 0.956 (1.179) Remain 30:16:54 loss: 0.4847 Lr: 0.00346 [2024-02-18 13:54:12,563 INFO misc.py line 119 87073] Train: [41/100][954/1557] Data 0.003 (0.223) Batch 0.967 (1.179) Remain 30:16:32 loss: 0.8746 Lr: 0.00346 [2024-02-18 13:54:13,405 INFO misc.py line 119 87073] Train: 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Batch 0.880 (1.189) Remain 30:32:34 loss: 0.6772 Lr: 0.00346 [2024-02-18 13:54:31,922 INFO misc.py line 119 87073] Train: [41/100][962/1557] Data 0.004 (0.233) Batch 1.043 (1.189) Remain 30:32:19 loss: 0.2542 Lr: 0.00346 [2024-02-18 13:54:32,844 INFO misc.py line 119 87073] Train: [41/100][963/1557] Data 0.011 (0.233) Batch 0.927 (1.189) Remain 30:31:53 loss: 0.4424 Lr: 0.00346 [2024-02-18 13:54:33,677 INFO misc.py line 119 87073] Train: [41/100][964/1557] Data 0.006 (0.233) Batch 0.833 (1.188) Remain 30:31:17 loss: 0.2639 Lr: 0.00346 [2024-02-18 13:54:34,471 INFO misc.py line 119 87073] Train: [41/100][965/1557] Data 0.006 (0.233) Batch 0.793 (1.188) Remain 30:30:38 loss: 0.5118 Lr: 0.00346 [2024-02-18 13:54:35,783 INFO misc.py line 119 87073] Train: [41/100][966/1557] Data 0.007 (0.232) Batch 1.314 (1.188) Remain 30:30:49 loss: 0.1351 Lr: 0.00346 [2024-02-18 13:54:36,819 INFO misc.py line 119 87073] Train: [41/100][967/1557] Data 0.007 (0.232) Batch 1.029 (1.188) Remain 30:30:33 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[2024-02-18 13:55:07,510 INFO misc.py line 119 87073] Train: [41/100][999/1557] Data 0.005 (0.225) Batch 0.719 (1.181) Remain 30:18:36 loss: 0.2839 Lr: 0.00346 [2024-02-18 13:55:08,317 INFO misc.py line 119 87073] Train: [41/100][1000/1557] Data 0.005 (0.225) Batch 0.803 (1.180) Remain 30:17:59 loss: 0.3958 Lr: 0.00346 [2024-02-18 13:55:09,535 INFO misc.py line 119 87073] Train: [41/100][1001/1557] Data 0.009 (0.224) Batch 1.216 (1.180) Remain 30:18:02 loss: 0.2754 Lr: 0.00346 [2024-02-18 13:55:10,562 INFO misc.py line 119 87073] Train: [41/100][1002/1557] Data 0.011 (0.224) Batch 1.027 (1.180) Remain 30:17:46 loss: 0.5512 Lr: 0.00346 [2024-02-18 13:55:11,521 INFO misc.py line 119 87073] Train: [41/100][1003/1557] Data 0.010 (0.224) Batch 0.966 (1.180) Remain 30:17:25 loss: 0.5987 Lr: 0.00346 [2024-02-18 13:55:12,606 INFO misc.py line 119 87073] Train: [41/100][1004/1557] Data 0.004 (0.224) Batch 1.085 (1.180) Remain 30:17:15 loss: 0.4789 Lr: 0.00346 [2024-02-18 13:55:13,478 INFO 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(0.234) Batch 0.972 (1.189) Remain 30:31:49 loss: 0.2871 Lr: 0.00346 [2024-02-18 13:55:38,595 INFO misc.py line 119 87073] Train: [41/100][1018/1557] Data 0.005 (0.233) Batch 0.891 (1.189) Remain 30:31:21 loss: 0.5151 Lr: 0.00346 [2024-02-18 13:55:39,513 INFO misc.py line 119 87073] Train: [41/100][1019/1557] Data 0.005 (0.233) Batch 0.916 (1.189) Remain 30:30:55 loss: 0.4134 Lr: 0.00346 [2024-02-18 13:55:40,235 INFO misc.py line 119 87073] Train: [41/100][1020/1557] Data 0.007 (0.233) Batch 0.723 (1.188) Remain 30:30:11 loss: 0.4320 Lr: 0.00346 [2024-02-18 13:55:40,964 INFO misc.py line 119 87073] Train: [41/100][1021/1557] Data 0.005 (0.233) Batch 0.730 (1.188) Remain 30:29:29 loss: 0.3532 Lr: 0.00346 [2024-02-18 13:55:42,305 INFO misc.py line 119 87073] Train: [41/100][1022/1557] Data 0.004 (0.233) Batch 1.331 (1.188) Remain 30:29:40 loss: 0.1812 Lr: 0.00346 [2024-02-18 13:55:43,292 INFO misc.py line 119 87073] Train: [41/100][1023/1557] Data 0.014 (0.232) Batch 0.997 (1.188) Remain 30:29:22 loss: 0.5117 Lr: 0.00346 [2024-02-18 13:55:44,276 INFO misc.py line 119 87073] Train: [41/100][1024/1557] Data 0.003 (0.232) Batch 0.984 (1.188) Remain 30:29:02 loss: 0.4080 Lr: 0.00346 [2024-02-18 13:55:45,303 INFO misc.py line 119 87073] Train: [41/100][1025/1557] Data 0.004 (0.232) Batch 1.027 (1.188) Remain 30:28:46 loss: 0.4509 Lr: 0.00346 [2024-02-18 13:55:46,248 INFO misc.py line 119 87073] Train: [41/100][1026/1557] Data 0.004 (0.232) Batch 0.946 (1.187) Remain 30:28:23 loss: 0.2924 Lr: 0.00346 [2024-02-18 13:55:47,073 INFO misc.py line 119 87073] Train: [41/100][1027/1557] Data 0.004 (0.231) Batch 0.817 (1.187) Remain 30:27:49 loss: 0.2421 Lr: 0.00346 [2024-02-18 13:55:47,840 INFO misc.py line 119 87073] Train: [41/100][1028/1557] Data 0.011 (0.231) Batch 0.775 (1.187) Remain 30:27:11 loss: 0.7392 Lr: 0.00346 [2024-02-18 13:55:49,187 INFO misc.py line 119 87073] Train: [41/100][1029/1557] Data 0.004 (0.231) Batch 1.336 (1.187) Remain 30:27:23 loss: 0.2725 Lr: 0.00346 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misc.py line 119 87073] Train: [41/100][1036/1557] Data 0.005 (0.230) Batch 1.028 (1.185) Remain 30:24:44 loss: 0.2120 Lr: 0.00346 [2024-02-18 13:55:56,920 INFO misc.py line 119 87073] Train: [41/100][1037/1557] Data 0.007 (0.229) Batch 1.110 (1.185) Remain 30:24:36 loss: 0.3215 Lr: 0.00345 [2024-02-18 13:55:57,887 INFO misc.py line 119 87073] Train: [41/100][1038/1557] Data 0.011 (0.229) Batch 0.974 (1.185) Remain 30:24:16 loss: 0.7253 Lr: 0.00345 [2024-02-18 13:55:58,884 INFO misc.py line 119 87073] Train: [41/100][1039/1557] Data 0.003 (0.229) Batch 0.995 (1.185) Remain 30:23:58 loss: 0.8606 Lr: 0.00345 [2024-02-18 13:55:59,784 INFO misc.py line 119 87073] Train: [41/100][1040/1557] Data 0.005 (0.229) Batch 0.900 (1.184) Remain 30:23:32 loss: 0.5341 Lr: 0.00345 [2024-02-18 13:56:00,539 INFO misc.py line 119 87073] Train: [41/100][1041/1557] Data 0.005 (0.228) Batch 0.746 (1.184) Remain 30:22:51 loss: 0.3690 Lr: 0.00345 [2024-02-18 13:56:01,310 INFO misc.py line 119 87073] Train: 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[2024-02-18 13:56:21,784 INFO misc.py line 119 87073] Train: [41/100][1061/1557] Data 0.003 (0.226) Batch 1.229 (1.182) Remain 30:18:56 loss: 0.2768 Lr: 0.00345 [2024-02-18 13:56:22,627 INFO misc.py line 119 87073] Train: [41/100][1062/1557] Data 0.005 (0.226) Batch 0.844 (1.181) Remain 30:18:26 loss: 0.2409 Lr: 0.00345 [2024-02-18 13:56:23,380 INFO misc.py line 119 87073] Train: [41/100][1063/1557] Data 0.004 (0.225) Batch 0.753 (1.181) Remain 30:17:47 loss: 0.2502 Lr: 0.00345 [2024-02-18 13:56:24,480 INFO misc.py line 119 87073] Train: [41/100][1064/1557] Data 0.004 (0.225) Batch 1.087 (1.181) Remain 30:17:38 loss: 0.1273 Lr: 0.00345 [2024-02-18 13:56:25,320 INFO misc.py line 119 87073] Train: [41/100][1065/1557] Data 0.016 (0.225) Batch 0.852 (1.181) Remain 30:17:08 loss: 0.4295 Lr: 0.00345 [2024-02-18 13:56:26,266 INFO misc.py line 119 87073] Train: [41/100][1066/1557] Data 0.004 (0.225) Batch 0.946 (1.180) Remain 30:16:46 loss: 0.4037 Lr: 0.00345 [2024-02-18 13:56:27,182 INFO misc.py line 119 87073] Train: [41/100][1067/1557] Data 0.004 (0.224) Batch 0.916 (1.180) Remain 30:16:22 loss: 0.3390 Lr: 0.00345 [2024-02-18 13:56:28,282 INFO misc.py line 119 87073] Train: [41/100][1068/1557] Data 0.005 (0.224) Batch 1.096 (1.180) Remain 30:16:14 loss: 0.6071 Lr: 0.00345 [2024-02-18 13:56:29,041 INFO misc.py line 119 87073] Train: [41/100][1069/1557] Data 0.008 (0.224) Batch 0.764 (1.180) Remain 30:15:37 loss: 0.4391 Lr: 0.00345 [2024-02-18 13:56:29,826 INFO misc.py line 119 87073] Train: [41/100][1070/1557] Data 0.004 (0.224) Batch 0.784 (1.179) Remain 30:15:01 loss: 0.2984 Lr: 0.00345 [2024-02-18 13:56:43,037 INFO misc.py line 119 87073] Train: [41/100][1071/1557] Data 11.870 (0.235) Batch 13.206 (1.190) Remain 30:32:20 loss: 0.1593 Lr: 0.00345 [2024-02-18 13:56:43,858 INFO misc.py line 119 87073] Train: [41/100][1072/1557] Data 0.009 (0.235) Batch 0.825 (1.190) Remain 30:31:47 loss: 0.0961 Lr: 0.00345 [2024-02-18 13:56:44,812 INFO misc.py line 119 87073] Train: 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30:15:55 loss: 0.6094 Lr: 0.00345 [2024-02-18 13:57:26,441 INFO misc.py line 119 87073] Train: [41/100][1117/1557] Data 0.004 (0.225) Batch 1.061 (1.180) Remain 30:15:44 loss: 0.6120 Lr: 0.00345 [2024-02-18 13:57:27,134 INFO misc.py line 119 87073] Train: [41/100][1118/1557] Data 0.004 (0.225) Batch 0.694 (1.180) Remain 30:15:03 loss: 0.1737 Lr: 0.00345 [2024-02-18 13:57:27,922 INFO misc.py line 119 87073] Train: [41/100][1119/1557] Data 0.003 (0.225) Batch 0.783 (1.180) Remain 30:14:29 loss: 0.5831 Lr: 0.00345 [2024-02-18 13:57:29,039 INFO misc.py line 119 87073] Train: [41/100][1120/1557] Data 0.008 (0.225) Batch 1.119 (1.179) Remain 30:14:23 loss: 0.1682 Lr: 0.00345 [2024-02-18 13:57:29,912 INFO misc.py line 119 87073] Train: [41/100][1121/1557] Data 0.007 (0.225) Batch 0.875 (1.179) Remain 30:13:56 loss: 0.3673 Lr: 0.00345 [2024-02-18 13:57:30,907 INFO misc.py line 119 87073] Train: [41/100][1122/1557] Data 0.006 (0.224) Batch 0.994 (1.179) Remain 30:13:40 loss: 0.5006 Lr: 0.00345 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misc.py line 119 87073] Train: [41/100][1129/1557] Data 0.004 (0.235) Batch 0.907 (1.189) Remain 30:29:09 loss: 0.3043 Lr: 0.00345 [2024-02-18 13:57:51,551 INFO misc.py line 119 87073] Train: [41/100][1130/1557] Data 0.004 (0.234) Batch 0.960 (1.189) Remain 30:28:49 loss: 0.1299 Lr: 0.00345 [2024-02-18 13:57:52,591 INFO misc.py line 119 87073] Train: [41/100][1131/1557] Data 0.007 (0.234) Batch 1.041 (1.189) Remain 30:28:35 loss: 0.6303 Lr: 0.00345 [2024-02-18 13:57:53,319 INFO misc.py line 119 87073] Train: [41/100][1132/1557] Data 0.006 (0.234) Batch 0.728 (1.188) Remain 30:27:56 loss: 0.4302 Lr: 0.00345 [2024-02-18 13:57:54,122 INFO misc.py line 119 87073] Train: [41/100][1133/1557] Data 0.006 (0.234) Batch 0.804 (1.188) Remain 30:27:24 loss: 0.3020 Lr: 0.00345 [2024-02-18 13:57:55,364 INFO misc.py line 119 87073] Train: [41/100][1134/1557] Data 0.004 (0.234) Batch 1.236 (1.188) Remain 30:27:27 loss: 0.1246 Lr: 0.00345 [2024-02-18 13:57:56,296 INFO misc.py line 119 87073] Train: 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INFO misc.py line 119 87073] Train: [41/100][1191/1557] Data 0.004 (0.234) Batch 1.119 (1.188) Remain 30:26:18 loss: 0.6284 Lr: 0.00345 [2024-02-18 13:59:03,963 INFO misc.py line 119 87073] Train: [41/100][1192/1557] Data 0.004 (0.233) Batch 0.899 (1.188) Remain 30:25:54 loss: 0.2331 Lr: 0.00345 [2024-02-18 13:59:04,914 INFO misc.py line 119 87073] Train: [41/100][1193/1557] Data 0.004 (0.233) Batch 0.942 (1.188) Remain 30:25:34 loss: 0.5458 Lr: 0.00345 [2024-02-18 13:59:05,886 INFO misc.py line 119 87073] Train: [41/100][1194/1557] Data 0.013 (0.233) Batch 0.982 (1.187) Remain 30:25:17 loss: 0.4105 Lr: 0.00345 [2024-02-18 13:59:06,645 INFO misc.py line 119 87073] Train: [41/100][1195/1557] Data 0.004 (0.233) Batch 0.759 (1.187) Remain 30:24:42 loss: 0.1942 Lr: 0.00345 [2024-02-18 13:59:07,383 INFO misc.py line 119 87073] Train: [41/100][1196/1557] Data 0.004 (0.233) Batch 0.735 (1.187) Remain 30:24:06 loss: 0.5601 Lr: 0.00345 [2024-02-18 13:59:08,772 INFO misc.py line 119 87073] Train: [41/100][1197/1557] Data 0.006 (0.232) Batch 1.391 (1.187) Remain 30:24:21 loss: 0.3770 Lr: 0.00345 [2024-02-18 13:59:09,657 INFO misc.py line 119 87073] Train: [41/100][1198/1557] Data 0.005 (0.232) Batch 0.886 (1.187) Remain 30:23:56 loss: 0.4122 Lr: 0.00345 [2024-02-18 13:59:10,733 INFO misc.py line 119 87073] Train: [41/100][1199/1557] Data 0.004 (0.232) Batch 1.076 (1.187) Remain 30:23:47 loss: 0.5437 Lr: 0.00345 [2024-02-18 13:59:11,732 INFO misc.py line 119 87073] Train: [41/100][1200/1557] Data 0.004 (0.232) Batch 0.999 (1.186) Remain 30:23:31 loss: 0.4002 Lr: 0.00345 [2024-02-18 13:59:12,686 INFO misc.py line 119 87073] Train: [41/100][1201/1557] Data 0.004 (0.232) Batch 0.954 (1.186) Remain 30:23:12 loss: 0.4091 Lr: 0.00345 [2024-02-18 13:59:13,542 INFO misc.py line 119 87073] Train: [41/100][1202/1557] Data 0.003 (0.231) Batch 0.847 (1.186) Remain 30:22:45 loss: 0.3745 Lr: 0.00345 [2024-02-18 13:59:14,299 INFO misc.py line 119 87073] Train: [41/100][1203/1557] Data 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Remain 30:20:17 loss: 0.4776 Lr: 0.00345 [2024-02-18 13:59:20,749 INFO misc.py line 119 87073] Train: [41/100][1210/1557] Data 0.004 (0.230) Batch 0.732 (1.184) Remain 30:19:41 loss: 0.1914 Lr: 0.00345 [2024-02-18 13:59:22,060 INFO misc.py line 119 87073] Train: [41/100][1211/1557] Data 0.014 (0.230) Batch 1.309 (1.184) Remain 30:19:49 loss: 0.2074 Lr: 0.00345 [2024-02-18 13:59:22,982 INFO misc.py line 119 87073] Train: [41/100][1212/1557] Data 0.015 (0.230) Batch 0.933 (1.184) Remain 30:19:29 loss: 0.3274 Lr: 0.00345 [2024-02-18 13:59:23,862 INFO misc.py line 119 87073] Train: [41/100][1213/1557] Data 0.005 (0.229) Batch 0.882 (1.184) Remain 30:19:05 loss: 0.2287 Lr: 0.00345 [2024-02-18 13:59:24,777 INFO misc.py line 119 87073] Train: [41/100][1214/1557] Data 0.003 (0.229) Batch 0.909 (1.183) Remain 30:18:42 loss: 0.4059 Lr: 0.00345 [2024-02-18 13:59:25,817 INFO misc.py line 119 87073] Train: [41/100][1215/1557] Data 0.010 (0.229) Batch 1.040 (1.183) Remain 30:18:30 loss: 0.7154 Lr: 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INFO misc.py line 119 87073] Train: [41/100][1222/1557] Data 0.010 (0.228) Batch 0.956 (1.182) Remain 30:16:02 loss: 0.4625 Lr: 0.00345 [2024-02-18 13:59:32,930 INFO misc.py line 119 87073] Train: [41/100][1223/1557] Data 0.003 (0.228) Batch 0.692 (1.181) Remain 30:15:23 loss: 0.2122 Lr: 0.00345 [2024-02-18 13:59:33,623 INFO misc.py line 119 87073] Train: [41/100][1224/1557] Data 0.004 (0.227) Batch 0.694 (1.181) Remain 30:14:45 loss: 0.3187 Lr: 0.00345 [2024-02-18 13:59:35,013 INFO misc.py line 119 87073] Train: [41/100][1225/1557] Data 0.004 (0.227) Batch 1.389 (1.181) Remain 30:15:00 loss: 0.1707 Lr: 0.00345 [2024-02-18 13:59:35,924 INFO misc.py line 119 87073] Train: [41/100][1226/1557] Data 0.004 (0.227) Batch 0.912 (1.181) Remain 30:14:38 loss: 0.5081 Lr: 0.00345 [2024-02-18 13:59:36,843 INFO misc.py line 119 87073] Train: [41/100][1227/1557] Data 0.004 (0.227) Batch 0.918 (1.181) Remain 30:14:17 loss: 0.2798 Lr: 0.00345 [2024-02-18 13:59:37,795 INFO misc.py line 119 87073] Train: [41/100][1228/1557] Data 0.004 (0.227) Batch 0.951 (1.181) Remain 30:13:59 loss: 0.2820 Lr: 0.00345 [2024-02-18 13:59:38,834 INFO misc.py line 119 87073] Train: [41/100][1229/1557] Data 0.005 (0.226) Batch 1.030 (1.180) Remain 30:13:46 loss: 0.4028 Lr: 0.00345 [2024-02-18 13:59:40,991 INFO misc.py line 119 87073] Train: [41/100][1230/1557] Data 1.106 (0.227) Batch 2.159 (1.181) Remain 30:14:59 loss: 0.3877 Lr: 0.00345 [2024-02-18 13:59:41,714 INFO misc.py line 119 87073] Train: [41/100][1231/1557] Data 0.012 (0.227) Batch 0.732 (1.181) Remain 30:14:24 loss: 0.3478 Lr: 0.00345 [2024-02-18 13:59:42,857 INFO misc.py line 119 87073] Train: [41/100][1232/1557] Data 0.003 (0.227) Batch 1.141 (1.181) Remain 30:14:20 loss: 0.2029 Lr: 0.00345 [2024-02-18 13:59:43,946 INFO misc.py line 119 87073] Train: [41/100][1233/1557] Data 0.005 (0.227) Batch 1.089 (1.181) Remain 30:14:12 loss: 0.2005 Lr: 0.00345 [2024-02-18 13:59:45,215 INFO misc.py line 119 87073] Train: [41/100][1234/1557] Data 0.006 (0.226) Batch 1.258 (1.181) Remain 30:14:16 loss: 0.5065 Lr: 0.00345 [2024-02-18 13:59:46,186 INFO misc.py line 119 87073] Train: [41/100][1235/1557] Data 0.016 (0.226) Batch 0.983 (1.181) Remain 30:14:00 loss: 0.7265 Lr: 0.00345 [2024-02-18 13:59:47,157 INFO misc.py line 119 87073] Train: [41/100][1236/1557] Data 0.003 (0.226) Batch 0.971 (1.181) Remain 30:13:43 loss: 0.4178 Lr: 0.00345 [2024-02-18 13:59:47,966 INFO misc.py line 119 87073] Train: [41/100][1237/1557] Data 0.005 (0.226) Batch 0.809 (1.180) Remain 30:13:15 loss: 0.2404 Lr: 0.00345 [2024-02-18 13:59:48,749 INFO misc.py line 119 87073] Train: [41/100][1238/1557] Data 0.004 (0.226) Batch 0.782 (1.180) Remain 30:12:44 loss: 0.2513 Lr: 0.00345 [2024-02-18 14:00:01,987 INFO misc.py line 119 87073] Train: [41/100][1239/1557] Data 11.967 (0.235) Batch 13.237 (1.190) Remain 30:27:42 loss: 0.1948 Lr: 0.00345 [2024-02-18 14:00:02,883 INFO misc.py line 119 87073] Train: [41/100][1240/1557] Data 0.006 (0.235) Batch 0.897 (1.189) Remain 30:27:19 loss: 0.8542 Lr: 0.00344 [2024-02-18 14:00:03,798 INFO misc.py line 119 87073] Train: [41/100][1241/1557] Data 0.004 (0.235) Batch 0.909 (1.189) Remain 30:26:57 loss: 0.5681 Lr: 0.00344 [2024-02-18 14:00:04,768 INFO misc.py line 119 87073] Train: [41/100][1242/1557] Data 0.010 (0.235) Batch 0.976 (1.189) Remain 30:26:40 loss: 0.3808 Lr: 0.00344 [2024-02-18 14:00:05,721 INFO misc.py line 119 87073] Train: [41/100][1243/1557] Data 0.004 (0.235) Batch 0.952 (1.189) Remain 30:26:21 loss: 0.2890 Lr: 0.00344 [2024-02-18 14:00:06,491 INFO misc.py line 119 87073] Train: [41/100][1244/1557] Data 0.006 (0.234) Batch 0.771 (1.188) Remain 30:25:49 loss: 0.4964 Lr: 0.00344 [2024-02-18 14:00:07,248 INFO misc.py line 119 87073] Train: [41/100][1245/1557] Data 0.004 (0.234) Batch 0.756 (1.188) Remain 30:25:15 loss: 0.4403 Lr: 0.00344 [2024-02-18 14:00:08,478 INFO misc.py line 119 87073] Train: [41/100][1246/1557] Data 0.005 (0.234) Batch 1.230 (1.188) Remain 30:25:17 loss: 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14:00:15,168 INFO misc.py line 119 87073] Train: [41/100][1253/1557] Data 0.010 (0.233) Batch 1.304 (1.187) Remain 30:23:09 loss: 0.2014 Lr: 0.00344 [2024-02-18 14:00:16,088 INFO misc.py line 119 87073] Train: [41/100][1254/1557] Data 0.007 (0.233) Batch 0.919 (1.187) Remain 30:22:48 loss: 0.2901 Lr: 0.00344 [2024-02-18 14:00:17,058 INFO misc.py line 119 87073] Train: [41/100][1255/1557] Data 0.008 (0.232) Batch 0.973 (1.186) Remain 30:22:31 loss: 0.7736 Lr: 0.00344 [2024-02-18 14:00:17,975 INFO misc.py line 119 87073] Train: [41/100][1256/1557] Data 0.005 (0.232) Batch 0.917 (1.186) Remain 30:22:10 loss: 0.5448 Lr: 0.00344 [2024-02-18 14:00:19,000 INFO misc.py line 119 87073] Train: [41/100][1257/1557] Data 0.004 (0.232) Batch 1.025 (1.186) Remain 30:21:57 loss: 0.3822 Lr: 0.00344 [2024-02-18 14:00:19,810 INFO misc.py line 119 87073] Train: [41/100][1258/1557] Data 0.004 (0.232) Batch 0.808 (1.186) Remain 30:21:28 loss: 0.2517 Lr: 0.00344 [2024-02-18 14:00:20,529 INFO misc.py line 119 87073] Train: [41/100][1259/1557] Data 0.006 (0.232) Batch 0.721 (1.185) Remain 30:20:53 loss: 0.3137 Lr: 0.00344 [2024-02-18 14:00:21,683 INFO misc.py line 119 87073] Train: [41/100][1260/1557] Data 0.004 (0.231) Batch 1.117 (1.185) Remain 30:20:47 loss: 0.2394 Lr: 0.00344 [2024-02-18 14:00:22,840 INFO misc.py line 119 87073] Train: [41/100][1261/1557] Data 0.041 (0.231) Batch 1.182 (1.185) Remain 30:20:45 loss: 0.5507 Lr: 0.00344 [2024-02-18 14:00:23,796 INFO misc.py line 119 87073] Train: [41/100][1262/1557] Data 0.016 (0.231) Batch 0.968 (1.185) Remain 30:20:28 loss: 0.2331 Lr: 0.00344 [2024-02-18 14:00:24,659 INFO misc.py line 119 87073] Train: [41/100][1263/1557] Data 0.004 (0.231) Batch 0.863 (1.185) Remain 30:20:03 loss: 0.3547 Lr: 0.00344 [2024-02-18 14:00:25,695 INFO misc.py line 119 87073] Train: [41/100][1264/1557] Data 0.004 (0.231) Batch 1.030 (1.185) Remain 30:19:51 loss: 0.4862 Lr: 0.00344 [2024-02-18 14:00:26,486 INFO misc.py line 119 87073] Train: [41/100][1265/1557] Data 0.010 (0.231) Batch 0.797 (1.185) Remain 30:19:21 loss: 0.5269 Lr: 0.00344 [2024-02-18 14:00:27,247 INFO misc.py line 119 87073] Train: [41/100][1266/1557] Data 0.006 (0.230) Batch 0.762 (1.184) Remain 30:18:49 loss: 0.2162 Lr: 0.00344 [2024-02-18 14:00:28,583 INFO misc.py line 119 87073] Train: [41/100][1267/1557] Data 0.004 (0.230) Batch 1.331 (1.184) Remain 30:18:59 loss: 0.2418 Lr: 0.00344 [2024-02-18 14:00:29,464 INFO misc.py line 119 87073] Train: [41/100][1268/1557] Data 0.009 (0.230) Batch 0.886 (1.184) Remain 30:18:36 loss: 0.5658 Lr: 0.00344 [2024-02-18 14:00:30,565 INFO misc.py line 119 87073] Train: [41/100][1269/1557] Data 0.004 (0.230) Batch 1.100 (1.184) Remain 30:18:28 loss: 0.4166 Lr: 0.00344 [2024-02-18 14:00:31,363 INFO misc.py line 119 87073] Train: [41/100][1270/1557] Data 0.005 (0.230) Batch 0.797 (1.184) Remain 30:17:59 loss: 0.3434 Lr: 0.00344 [2024-02-18 14:00:32,501 INFO misc.py line 119 87073] Train: [41/100][1271/1557] Data 0.008 (0.230) Batch 1.133 (1.184) Remain 30:17:54 loss: 0.4501 Lr: 0.00344 [2024-02-18 14:00:33,389 INFO misc.py line 119 87073] Train: [41/100][1272/1557] Data 0.011 (0.229) Batch 0.895 (1.183) Remain 30:17:32 loss: 0.3860 Lr: 0.00344 [2024-02-18 14:00:34,125 INFO misc.py line 119 87073] Train: [41/100][1273/1557] Data 0.004 (0.229) Batch 0.736 (1.183) Remain 30:16:58 loss: 0.2378 Lr: 0.00344 [2024-02-18 14:00:35,398 INFO misc.py line 119 87073] Train: [41/100][1274/1557] Data 0.004 (0.229) Batch 1.269 (1.183) Remain 30:17:04 loss: 0.1955 Lr: 0.00344 [2024-02-18 14:00:36,379 INFO misc.py line 119 87073] Train: [41/100][1275/1557] Data 0.007 (0.229) Batch 0.985 (1.183) Remain 30:16:48 loss: 0.5721 Lr: 0.00344 [2024-02-18 14:00:37,308 INFO misc.py line 119 87073] Train: [41/100][1276/1557] Data 0.004 (0.229) Batch 0.929 (1.183) Remain 30:16:28 loss: 0.4364 Lr: 0.00344 [2024-02-18 14:00:38,172 INFO misc.py line 119 87073] Train: [41/100][1277/1557] Data 0.004 (0.228) Batch 0.863 (1.183) Remain 30:16:04 loss: 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14:00:44,769 INFO misc.py line 119 87073] Train: [41/100][1284/1557] Data 0.006 (0.227) Batch 1.045 (1.181) Remain 30:13:55 loss: 0.4157 Lr: 0.00344 [2024-02-18 14:00:45,674 INFO misc.py line 119 87073] Train: [41/100][1285/1557] Data 0.007 (0.227) Batch 0.904 (1.181) Remain 30:13:34 loss: 0.8229 Lr: 0.00344 [2024-02-18 14:00:46,396 INFO misc.py line 119 87073] Train: [41/100][1286/1557] Data 0.006 (0.227) Batch 0.713 (1.181) Remain 30:12:59 loss: 0.6111 Lr: 0.00344 [2024-02-18 14:00:47,191 INFO misc.py line 119 87073] Train: [41/100][1287/1557] Data 0.014 (0.227) Batch 0.806 (1.180) Remain 30:12:31 loss: 0.5193 Lr: 0.00344 [2024-02-18 14:00:48,323 INFO misc.py line 119 87073] Train: [41/100][1288/1557] Data 0.004 (0.227) Batch 1.130 (1.180) Remain 30:12:26 loss: 0.2175 Lr: 0.00344 [2024-02-18 14:00:49,253 INFO misc.py line 119 87073] Train: [41/100][1289/1557] Data 0.007 (0.226) Batch 0.933 (1.180) Remain 30:12:07 loss: 0.5334 Lr: 0.00344 [2024-02-18 14:00:50,131 INFO misc.py line 119 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[2024-02-18 14:02:38,005 INFO misc.py line 119 87073] Train: [41/100][1377/1557] Data 0.011 (0.230) Batch 0.850 (1.184) Remain 30:15:52 loss: 0.2276 Lr: 0.00344 [2024-02-18 14:02:38,783 INFO misc.py line 119 87073] Train: [41/100][1378/1557] Data 0.004 (0.230) Batch 0.778 (1.183) Remain 30:15:23 loss: 0.5371 Lr: 0.00344 [2024-02-18 14:02:40,063 INFO misc.py line 119 87073] Train: [41/100][1379/1557] Data 0.004 (0.230) Batch 1.271 (1.183) Remain 30:15:28 loss: 0.2178 Lr: 0.00344 [2024-02-18 14:02:41,160 INFO misc.py line 119 87073] Train: [41/100][1380/1557] Data 0.013 (0.230) Batch 1.098 (1.183) Remain 30:15:21 loss: 0.3170 Lr: 0.00344 [2024-02-18 14:02:42,192 INFO misc.py line 119 87073] Train: [41/100][1381/1557] Data 0.011 (0.230) Batch 1.029 (1.183) Remain 30:15:09 loss: 0.5850 Lr: 0.00344 [2024-02-18 14:02:43,079 INFO misc.py line 119 87073] Train: [41/100][1382/1557] Data 0.014 (0.229) Batch 0.897 (1.183) Remain 30:14:49 loss: 0.3065 Lr: 0.00344 [2024-02-18 14:02:44,008 INFO 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30:08:54 loss: 0.4594 Lr: 0.00344 [2024-02-18 14:03:01,544 INFO misc.py line 119 87073] Train: [41/100][1402/1557] Data 0.003 (0.226) Batch 1.037 (1.179) Remain 30:08:44 loss: 0.2967 Lr: 0.00344 [2024-02-18 14:03:02,471 INFO misc.py line 119 87073] Train: [41/100][1403/1557] Data 0.004 (0.226) Batch 0.926 (1.179) Remain 30:08:26 loss: 0.2104 Lr: 0.00344 [2024-02-18 14:03:03,343 INFO misc.py line 119 87073] Train: [41/100][1404/1557] Data 0.005 (0.226) Batch 0.873 (1.179) Remain 30:08:05 loss: 0.0958 Lr: 0.00344 [2024-02-18 14:03:06,061 INFO misc.py line 119 87073] Train: [41/100][1405/1557] Data 1.258 (0.227) Batch 2.717 (1.180) Remain 30:09:44 loss: 0.7056 Lr: 0.00344 [2024-02-18 14:03:06,777 INFO misc.py line 119 87073] Train: [41/100][1406/1557] Data 0.006 (0.227) Batch 0.713 (1.180) Remain 30:09:13 loss: 0.3597 Lr: 0.00344 [2024-02-18 14:03:19,765 INFO misc.py line 119 87073] Train: [41/100][1407/1557] Data 11.658 (0.235) Batch 12.990 (1.188) Remain 30:22:05 loss: 0.1796 Lr: 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INFO misc.py line 119 87073] Train: [41/100][1414/1557] Data 0.004 (0.234) Batch 1.205 (1.187) Remain 30:20:13 loss: 0.2213 Lr: 0.00344 [2024-02-18 14:03:27,569 INFO misc.py line 119 87073] Train: [41/100][1415/1557] Data 0.005 (0.233) Batch 1.088 (1.187) Remain 30:20:05 loss: 0.3701 Lr: 0.00344 [2024-02-18 14:03:28,611 INFO misc.py line 119 87073] Train: [41/100][1416/1557] Data 0.005 (0.233) Batch 1.042 (1.187) Remain 30:19:54 loss: 0.9395 Lr: 0.00344 [2024-02-18 14:03:29,726 INFO misc.py line 119 87073] Train: [41/100][1417/1557] Data 0.005 (0.233) Batch 1.116 (1.187) Remain 30:19:49 loss: 0.3278 Lr: 0.00344 [2024-02-18 14:03:30,631 INFO misc.py line 119 87073] Train: [41/100][1418/1557] Data 0.005 (0.233) Batch 0.906 (1.187) Remain 30:19:29 loss: 0.4292 Lr: 0.00344 [2024-02-18 14:03:31,407 INFO misc.py line 119 87073] Train: [41/100][1419/1557] Data 0.004 (0.233) Batch 0.775 (1.186) Remain 30:19:01 loss: 0.5711 Lr: 0.00344 [2024-02-18 14:03:32,159 INFO misc.py line 119 87073] Train: [41/100][1420/1557] Data 0.004 (0.233) Batch 0.753 (1.186) Remain 30:18:32 loss: 0.4101 Lr: 0.00344 [2024-02-18 14:03:33,391 INFO misc.py line 119 87073] Train: [41/100][1421/1557] Data 0.004 (0.232) Batch 1.231 (1.186) Remain 30:18:34 loss: 0.1778 Lr: 0.00344 [2024-02-18 14:03:34,365 INFO misc.py line 119 87073] Train: [41/100][1422/1557] Data 0.005 (0.232) Batch 0.973 (1.186) Remain 30:18:19 loss: 0.3324 Lr: 0.00344 [2024-02-18 14:03:35,281 INFO misc.py line 119 87073] Train: [41/100][1423/1557] Data 0.004 (0.232) Batch 0.917 (1.186) Remain 30:18:00 loss: 0.4957 Lr: 0.00344 [2024-02-18 14:03:36,220 INFO misc.py line 119 87073] Train: [41/100][1424/1557] Data 0.004 (0.232) Batch 0.931 (1.186) Remain 30:17:42 loss: 0.7225 Lr: 0.00344 [2024-02-18 14:03:37,241 INFO misc.py line 119 87073] Train: [41/100][1425/1557] Data 0.011 (0.232) Batch 1.020 (1.185) Remain 30:17:31 loss: 0.5018 Lr: 0.00344 [2024-02-18 14:03:38,056 INFO misc.py line 119 87073] Train: [41/100][1426/1557] Data 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Remain 30:15:28 loss: 0.2583 Lr: 0.00344 [2024-02-18 14:03:44,510 INFO misc.py line 119 87073] Train: [41/100][1433/1557] Data 0.004 (0.231) Batch 0.746 (1.184) Remain 30:14:59 loss: 0.2585 Lr: 0.00344 [2024-02-18 14:03:45,246 INFO misc.py line 119 87073] Train: [41/100][1434/1557] Data 0.006 (0.230) Batch 0.739 (1.184) Remain 30:14:29 loss: 0.3296 Lr: 0.00344 [2024-02-18 14:03:46,545 INFO misc.py line 119 87073] Train: [41/100][1435/1557] Data 0.003 (0.230) Batch 1.287 (1.184) Remain 30:14:35 loss: 0.2177 Lr: 0.00344 [2024-02-18 14:03:47,593 INFO misc.py line 119 87073] Train: [41/100][1436/1557] Data 0.015 (0.230) Batch 1.047 (1.184) Remain 30:14:25 loss: 0.1756 Lr: 0.00344 [2024-02-18 14:03:48,554 INFO misc.py line 119 87073] Train: [41/100][1437/1557] Data 0.017 (0.230) Batch 0.973 (1.183) Remain 30:14:10 loss: 0.3800 Lr: 0.00344 [2024-02-18 14:03:49,563 INFO misc.py line 119 87073] Train: [41/100][1438/1557] Data 0.005 (0.230) Batch 1.010 (1.183) Remain 30:13:58 loss: 0.4200 Lr: 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INFO misc.py line 119 87073] Train: [41/100][1445/1557] Data 0.006 (0.229) Batch 1.053 (1.182) Remain 30:11:59 loss: 0.4987 Lr: 0.00343 [2024-02-18 14:03:57,277 INFO misc.py line 119 87073] Train: [41/100][1446/1557] Data 0.012 (0.229) Batch 1.136 (1.182) Remain 30:11:55 loss: 0.3105 Lr: 0.00343 [2024-02-18 14:03:58,132 INFO misc.py line 119 87073] Train: [41/100][1447/1557] Data 0.033 (0.228) Batch 0.885 (1.182) Remain 30:11:35 loss: 0.5014 Lr: 0.00343 [2024-02-18 14:03:58,859 INFO misc.py line 119 87073] Train: [41/100][1448/1557] Data 0.004 (0.228) Batch 0.726 (1.182) Remain 30:11:04 loss: 0.2888 Lr: 0.00343 [2024-02-18 14:03:59,970 INFO misc.py line 119 87073] Train: [41/100][1449/1557] Data 0.005 (0.228) Batch 1.102 (1.181) Remain 30:10:58 loss: 0.2018 Lr: 0.00343 [2024-02-18 14:04:00,943 INFO misc.py line 119 87073] Train: [41/100][1450/1557] Data 0.013 (0.228) Batch 0.983 (1.181) Remain 30:10:44 loss: 0.2681 Lr: 0.00343 [2024-02-18 14:04:01,823 INFO misc.py line 119 87073] Train: [41/100][1451/1557] Data 0.004 (0.228) Batch 0.880 (1.181) Remain 30:10:24 loss: 0.8063 Lr: 0.00343 [2024-02-18 14:04:02,666 INFO misc.py line 119 87073] Train: [41/100][1452/1557] Data 0.004 (0.228) Batch 0.837 (1.181) Remain 30:10:01 loss: 0.1882 Lr: 0.00343 [2024-02-18 14:04:03,681 INFO misc.py line 119 87073] Train: [41/100][1453/1557] Data 0.010 (0.227) Batch 1.021 (1.181) Remain 30:09:50 loss: 0.2481 Lr: 0.00343 [2024-02-18 14:04:04,406 INFO misc.py line 119 87073] Train: [41/100][1454/1557] Data 0.005 (0.227) Batch 0.724 (1.180) Remain 30:09:20 loss: 0.3349 Lr: 0.00343 [2024-02-18 14:04:05,193 INFO misc.py line 119 87073] Train: [41/100][1455/1557] Data 0.006 (0.227) Batch 0.788 (1.180) Remain 30:08:53 loss: 0.2796 Lr: 0.00343 [2024-02-18 14:04:06,326 INFO misc.py line 119 87073] Train: [41/100][1456/1557] Data 0.006 (0.227) Batch 1.124 (1.180) Remain 30:08:49 loss: 0.2506 Lr: 0.00343 [2024-02-18 14:04:07,276 INFO misc.py line 119 87073] Train: [41/100][1457/1557] Data 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(1.188) Remain 30:20:57 loss: 0.3239 Lr: 0.00343 [2024-02-18 14:04:27,363 INFO misc.py line 119 87073] Train: [41/100][1464/1557] Data 0.006 (0.235) Batch 1.090 (1.188) Remain 30:20:50 loss: 0.5402 Lr: 0.00343 [2024-02-18 14:04:28,205 INFO misc.py line 119 87073] Train: [41/100][1465/1557] Data 0.005 (0.235) Batch 0.842 (1.188) Remain 30:20:27 loss: 0.0958 Lr: 0.00343 [2024-02-18 14:04:29,142 INFO misc.py line 119 87073] Train: [41/100][1466/1557] Data 0.005 (0.235) Batch 0.938 (1.188) Remain 30:20:10 loss: 0.4916 Lr: 0.00343 [2024-02-18 14:04:30,165 INFO misc.py line 119 87073] Train: [41/100][1467/1557] Data 0.005 (0.234) Batch 1.023 (1.188) Remain 30:19:58 loss: 0.3119 Lr: 0.00343 [2024-02-18 14:04:30,878 INFO misc.py line 119 87073] Train: [41/100][1468/1557] Data 0.005 (0.234) Batch 0.714 (1.187) Remain 30:19:27 loss: 0.2780 Lr: 0.00343 [2024-02-18 14:04:31,647 INFO misc.py line 119 87073] Train: [41/100][1469/1557] Data 0.004 (0.234) Batch 0.759 (1.187) Remain 30:18:59 loss: 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14:04:38,251 INFO misc.py line 119 87073] Train: [41/100][1476/1557] Data 0.006 (0.233) Batch 0.775 (1.186) Remain 30:17:05 loss: 0.3208 Lr: 0.00343 [2024-02-18 14:04:39,544 INFO misc.py line 119 87073] Train: [41/100][1477/1557] Data 0.005 (0.233) Batch 1.288 (1.186) Remain 30:17:10 loss: 0.2100 Lr: 0.00343 [2024-02-18 14:04:40,636 INFO misc.py line 119 87073] Train: [41/100][1478/1557] Data 0.011 (0.233) Batch 1.093 (1.186) Remain 30:17:03 loss: 0.3219 Lr: 0.00343 [2024-02-18 14:04:41,616 INFO misc.py line 119 87073] Train: [41/100][1479/1557] Data 0.010 (0.233) Batch 0.986 (1.186) Remain 30:16:50 loss: 0.2540 Lr: 0.00343 [2024-02-18 14:04:42,479 INFO misc.py line 119 87073] Train: [41/100][1480/1557] Data 0.004 (0.232) Batch 0.863 (1.185) Remain 30:16:28 loss: 0.3086 Lr: 0.00343 [2024-02-18 14:04:43,423 INFO misc.py line 119 87073] Train: [41/100][1481/1557] Data 0.005 (0.232) Batch 0.943 (1.185) Remain 30:16:12 loss: 0.2956 Lr: 0.00343 [2024-02-18 14:04:44,149 INFO misc.py line 119 87073] Train: [41/100][1482/1557] Data 0.006 (0.232) Batch 0.727 (1.185) Remain 30:15:42 loss: 0.3536 Lr: 0.00343 [2024-02-18 14:04:44,938 INFO misc.py line 119 87073] Train: [41/100][1483/1557] Data 0.005 (0.232) Batch 0.784 (1.185) Remain 30:15:16 loss: 0.3159 Lr: 0.00343 [2024-02-18 14:04:46,028 INFO misc.py line 119 87073] Train: [41/100][1484/1557] Data 0.010 (0.232) Batch 1.091 (1.185) Remain 30:15:09 loss: 0.1586 Lr: 0.00343 [2024-02-18 14:04:46,910 INFO misc.py line 119 87073] Train: [41/100][1485/1557] Data 0.009 (0.232) Batch 0.887 (1.184) Remain 30:14:50 loss: 0.2450 Lr: 0.00343 [2024-02-18 14:04:47,947 INFO misc.py line 119 87073] Train: [41/100][1486/1557] Data 0.004 (0.231) Batch 1.037 (1.184) Remain 30:14:39 loss: 0.3492 Lr: 0.00343 [2024-02-18 14:04:49,026 INFO misc.py line 119 87073] Train: [41/100][1487/1557] Data 0.004 (0.231) Batch 1.076 (1.184) Remain 30:14:32 loss: 0.2584 Lr: 0.00343 [2024-02-18 14:04:50,198 INFO misc.py line 119 87073] Train: [41/100][1488/1557] Data 0.006 (0.231) Batch 1.164 (1.184) Remain 30:14:29 loss: 0.3491 Lr: 0.00343 [2024-02-18 14:04:50,994 INFO misc.py line 119 87073] Train: [41/100][1489/1557] Data 0.014 (0.231) Batch 0.805 (1.184) Remain 30:14:04 loss: 0.2817 Lr: 0.00343 [2024-02-18 14:04:51,777 INFO misc.py line 119 87073] Train: [41/100][1490/1557] Data 0.005 (0.231) Batch 0.783 (1.184) Remain 30:13:38 loss: 0.3958 Lr: 0.00343 [2024-02-18 14:04:53,089 INFO misc.py line 119 87073] Train: [41/100][1491/1557] Data 0.005 (0.231) Batch 1.309 (1.184) Remain 30:13:45 loss: 0.1650 Lr: 0.00343 [2024-02-18 14:04:54,166 INFO misc.py line 119 87073] Train: [41/100][1492/1557] Data 0.009 (0.231) Batch 1.079 (1.184) Remain 30:13:37 loss: 0.5834 Lr: 0.00343 [2024-02-18 14:04:55,303 INFO misc.py line 119 87073] Train: [41/100][1493/1557] Data 0.007 (0.230) Batch 1.130 (1.184) Remain 30:13:33 loss: 0.4799 Lr: 0.00343 [2024-02-18 14:04:56,233 INFO misc.py line 119 87073] Train: [41/100][1494/1557] Data 0.014 (0.230) Batch 0.938 (1.184) Remain 30:13:17 loss: 0.4178 Lr: 0.00343 [2024-02-18 14:04:57,269 INFO misc.py line 119 87073] Train: [41/100][1495/1557] Data 0.006 (0.230) Batch 1.009 (1.183) Remain 30:13:05 loss: 0.3891 Lr: 0.00343 [2024-02-18 14:04:58,026 INFO misc.py line 119 87073] Train: [41/100][1496/1557] Data 0.032 (0.230) Batch 0.784 (1.183) Remain 30:12:39 loss: 0.1794 Lr: 0.00343 [2024-02-18 14:04:58,823 INFO misc.py line 119 87073] Train: [41/100][1497/1557] Data 0.005 (0.230) Batch 0.788 (1.183) Remain 30:12:13 loss: 0.5086 Lr: 0.00343 [2024-02-18 14:05:00,038 INFO misc.py line 119 87073] Train: [41/100][1498/1557] Data 0.015 (0.230) Batch 1.213 (1.183) Remain 30:12:14 loss: 0.0845 Lr: 0.00343 [2024-02-18 14:05:01,067 INFO misc.py line 119 87073] Train: [41/100][1499/1557] Data 0.017 (0.230) Batch 1.039 (1.183) Remain 30:12:04 loss: 0.8822 Lr: 0.00343 [2024-02-18 14:05:02,044 INFO misc.py line 119 87073] Train: [41/100][1500/1557] Data 0.007 (0.229) Batch 0.980 (1.183) Remain 30:11:50 loss: 0.3300 Lr: 0.00343 [2024-02-18 14:05:03,044 INFO misc.py line 119 87073] Train: [41/100][1501/1557] Data 0.004 (0.229) Batch 1.000 (1.183) Remain 30:11:38 loss: 0.3485 Lr: 0.00343 [2024-02-18 14:05:03,853 INFO misc.py line 119 87073] Train: [41/100][1502/1557] Data 0.004 (0.229) Batch 0.809 (1.182) Remain 30:11:14 loss: 0.6771 Lr: 0.00343 [2024-02-18 14:05:04,601 INFO misc.py line 119 87073] Train: [41/100][1503/1557] Data 0.004 (0.229) Batch 0.741 (1.182) Remain 30:10:46 loss: 0.3638 Lr: 0.00343 [2024-02-18 14:05:05,411 INFO misc.py line 119 87073] Train: [41/100][1504/1557] Data 0.011 (0.229) Batch 0.816 (1.182) Remain 30:10:22 loss: 0.4676 Lr: 0.00343 [2024-02-18 14:05:06,591 INFO misc.py line 119 87073] Train: [41/100][1505/1557] Data 0.005 (0.229) Batch 1.181 (1.182) Remain 30:10:21 loss: 0.1914 Lr: 0.00343 [2024-02-18 14:05:07,607 INFO misc.py line 119 87073] Train: [41/100][1506/1557] Data 0.005 (0.229) Batch 1.016 (1.182) Remain 30:10:09 loss: 0.3650 Lr: 0.00343 [2024-02-18 14:05:08,558 INFO misc.py line 119 87073] Train: [41/100][1507/1557] Data 0.005 (0.228) Batch 0.951 (1.181) Remain 30:09:54 loss: 0.3098 Lr: 0.00343 [2024-02-18 14:05:09,375 INFO misc.py line 119 87073] Train: [41/100][1508/1557] Data 0.004 (0.228) Batch 0.817 (1.181) Remain 30:09:31 loss: 0.6316 Lr: 0.00343 [2024-02-18 14:05:10,357 INFO misc.py line 119 87073] Train: [41/100][1509/1557] Data 0.004 (0.228) Batch 0.975 (1.181) Remain 30:09:17 loss: 0.6226 Lr: 0.00343 [2024-02-18 14:05:11,121 INFO misc.py line 119 87073] Train: [41/100][1510/1557] Data 0.011 (0.228) Batch 0.770 (1.181) Remain 30:08:51 loss: 0.3074 Lr: 0.00343 [2024-02-18 14:05:11,910 INFO misc.py line 119 87073] Train: [41/100][1511/1557] Data 0.004 (0.228) Batch 0.786 (1.181) Remain 30:08:26 loss: 0.6155 Lr: 0.00343 [2024-02-18 14:05:13,016 INFO misc.py line 119 87073] Train: [41/100][1512/1557] Data 0.007 (0.228) Batch 1.098 (1.181) Remain 30:08:19 loss: 0.1600 Lr: 0.00343 [2024-02-18 14:05:14,053 INFO misc.py line 119 87073] Train: [41/100][1513/1557] Data 0.015 (0.228) Batch 1.043 (1.180) Remain 30:08:10 loss: 0.2372 Lr: 0.00343 [2024-02-18 14:05:14,977 INFO misc.py line 119 87073] Train: [41/100][1514/1557] Data 0.009 (0.227) Batch 0.929 (1.180) Remain 30:07:53 loss: 0.2606 Lr: 0.00343 [2024-02-18 14:05:16,057 INFO misc.py line 119 87073] Train: [41/100][1515/1557] Data 0.004 (0.227) Batch 1.079 (1.180) Remain 30:07:46 loss: 0.2275 Lr: 0.00343 [2024-02-18 14:05:16,968 INFO misc.py line 119 87073] Train: [41/100][1516/1557] Data 0.004 (0.227) Batch 0.911 (1.180) Remain 30:07:28 loss: 0.6110 Lr: 0.00343 [2024-02-18 14:05:17,723 INFO misc.py line 119 87073] Train: [41/100][1517/1557] Data 0.004 (0.227) Batch 0.753 (1.180) Remain 30:07:01 loss: 0.2974 Lr: 0.00343 [2024-02-18 14:05:18,468 INFO misc.py line 119 87073] Train: [41/100][1518/1557] Data 0.006 (0.227) Batch 0.734 (1.179) Remain 30:06:33 loss: 0.2957 Lr: 0.00343 [2024-02-18 14:05:33,125 INFO misc.py line 119 87073] Train: [41/100][1519/1557] Data 13.374 (0.235) Batch 14.670 (1.188) Remain 30:20:10 loss: 0.1444 Lr: 0.00343 [2024-02-18 14:05:34,189 INFO misc.py line 119 87073] Train: [41/100][1520/1557] Data 0.005 (0.235) Batch 1.065 (1.188) Remain 30:20:01 loss: 0.3641 Lr: 0.00343 [2024-02-18 14:05:35,130 INFO misc.py line 119 87073] Train: [41/100][1521/1557] Data 0.004 (0.235) Batch 0.939 (1.188) Remain 30:19:45 loss: 0.3366 Lr: 0.00343 [2024-02-18 14:05:36,265 INFO misc.py line 119 87073] Train: [41/100][1522/1557] Data 0.006 (0.235) Batch 1.136 (1.188) Remain 30:19:41 loss: 0.2359 Lr: 0.00343 [2024-02-18 14:05:37,313 INFO misc.py line 119 87073] Train: [41/100][1523/1557] Data 0.004 (0.235) Batch 1.048 (1.188) Remain 30:19:31 loss: 0.1877 Lr: 0.00343 [2024-02-18 14:05:38,012 INFO misc.py line 119 87073] Train: [41/100][1524/1557] Data 0.004 (0.235) Batch 0.698 (1.188) Remain 30:19:00 loss: 0.3931 Lr: 0.00343 [2024-02-18 14:05:38,818 INFO misc.py line 119 87073] Train: [41/100][1525/1557] Data 0.005 (0.235) Batch 0.805 (1.187) Remain 30:18:36 loss: 0.3255 Lr: 0.00343 [2024-02-18 14:05:39,993 INFO misc.py line 119 87073] Train: [41/100][1526/1557] Data 0.006 (0.234) Batch 1.176 (1.187) Remain 30:18:34 loss: 0.1155 Lr: 0.00343 [2024-02-18 14:05:40,894 INFO misc.py line 119 87073] Train: [41/100][1527/1557] Data 0.006 (0.234) Batch 0.901 (1.187) Remain 30:18:15 loss: 0.2157 Lr: 0.00343 [2024-02-18 14:05:41,817 INFO misc.py line 119 87073] Train: [41/100][1528/1557] Data 0.004 (0.234) Batch 0.923 (1.187) Remain 30:17:58 loss: 0.2805 Lr: 0.00343 [2024-02-18 14:05:42,687 INFO misc.py line 119 87073] Train: [41/100][1529/1557] Data 0.004 (0.234) Batch 0.871 (1.187) Remain 30:17:38 loss: 0.5074 Lr: 0.00343 [2024-02-18 14:05:43,532 INFO misc.py line 119 87073] Train: [41/100][1530/1557] Data 0.004 (0.234) Batch 0.839 (1.187) Remain 30:17:16 loss: 0.3877 Lr: 0.00343 [2024-02-18 14:05:44,430 INFO misc.py line 119 87073] Train: [41/100][1531/1557] Data 0.010 (0.234) Batch 0.902 (1.186) Remain 30:16:58 loss: 0.2452 Lr: 0.00343 [2024-02-18 14:05:45,114 INFO misc.py line 119 87073] Train: [41/100][1532/1557] Data 0.005 (0.233) Batch 0.684 (1.186) Remain 30:16:26 loss: 0.2637 Lr: 0.00343 [2024-02-18 14:05:46,411 INFO misc.py line 119 87073] Train: [41/100][1533/1557] Data 0.006 (0.233) Batch 1.291 (1.186) Remain 30:16:31 loss: 0.1856 Lr: 0.00343 [2024-02-18 14:05:47,318 INFO misc.py line 119 87073] Train: [41/100][1534/1557] Data 0.012 (0.233) Batch 0.916 (1.186) Remain 30:16:14 loss: 0.2151 Lr: 0.00343 [2024-02-18 14:05:48,204 INFO misc.py line 119 87073] Train: [41/100][1535/1557] Data 0.003 (0.233) Batch 0.885 (1.186) Remain 30:15:55 loss: 0.3691 Lr: 0.00343 [2024-02-18 14:05:49,068 INFO misc.py line 119 87073] Train: [41/100][1536/1557] Data 0.004 (0.233) Batch 0.864 (1.186) Remain 30:15:34 loss: 0.7304 Lr: 0.00343 [2024-02-18 14:05:50,132 INFO misc.py line 119 87073] Train: [41/100][1537/1557] Data 0.004 (0.233) Batch 1.052 (1.185) Remain 30:15:25 loss: 0.2054 Lr: 0.00343 [2024-02-18 14:05:50,883 INFO misc.py line 119 87073] Train: [41/100][1538/1557] Data 0.016 (0.233) Batch 0.762 (1.185) Remain 30:14:59 loss: 0.5235 Lr: 0.00343 [2024-02-18 14:05:51,631 INFO misc.py line 119 87073] Train: [41/100][1539/1557] Data 0.005 (0.232) Batch 0.742 (1.185) Remain 30:14:31 loss: 0.3409 Lr: 0.00343 [2024-02-18 14:05:52,723 INFO misc.py line 119 87073] Train: [41/100][1540/1557] Data 0.011 (0.232) Batch 1.098 (1.185) Remain 30:14:25 loss: 0.1771 Lr: 0.00343 [2024-02-18 14:05:53,733 INFO misc.py line 119 87073] Train: [41/100][1541/1557] Data 0.005 (0.232) Batch 1.008 (1.185) Remain 30:14:13 loss: 0.1537 Lr: 0.00343 [2024-02-18 14:05:54,624 INFO misc.py line 119 87073] Train: [41/100][1542/1557] Data 0.007 (0.232) Batch 0.894 (1.185) Remain 30:13:54 loss: 0.4747 Lr: 0.00343 [2024-02-18 14:05:55,748 INFO misc.py line 119 87073] Train: [41/100][1543/1557] Data 0.004 (0.232) Batch 1.123 (1.185) Remain 30:13:49 loss: 0.4956 Lr: 0.00343 [2024-02-18 14:05:56,765 INFO misc.py line 119 87073] Train: [41/100][1544/1557] Data 0.006 (0.232) Batch 1.018 (1.184) Remain 30:13:38 loss: 0.3937 Lr: 0.00343 [2024-02-18 14:05:57,544 INFO misc.py line 119 87073] Train: [41/100][1545/1557] Data 0.004 (0.232) Batch 0.779 (1.184) Remain 30:13:13 loss: 0.2538 Lr: 0.00343 [2024-02-18 14:05:58,320 INFO misc.py line 119 87073] Train: [41/100][1546/1557] Data 0.004 (0.231) Batch 0.769 (1.184) Remain 30:12:47 loss: 0.3643 Lr: 0.00343 [2024-02-18 14:05:59,697 INFO misc.py line 119 87073] Train: [41/100][1547/1557] Data 0.011 (0.231) Batch 1.372 (1.184) Remain 30:12:57 loss: 0.3180 Lr: 0.00343 [2024-02-18 14:06:00,596 INFO misc.py line 119 87073] Train: [41/100][1548/1557] Data 0.017 (0.231) Batch 0.910 (1.184) Remain 30:12:40 loss: 0.4657 Lr: 0.00343 [2024-02-18 14:06:01,764 INFO misc.py line 119 87073] Train: [41/100][1549/1557] Data 0.005 (0.231) Batch 1.167 (1.184) Remain 30:12:37 loss: 0.4214 Lr: 0.00343 [2024-02-18 14:06:02,822 INFO misc.py line 119 87073] Train: [41/100][1550/1557] Data 0.007 (0.231) Batch 1.054 (1.184) Remain 30:12:28 loss: 0.2318 Lr: 0.00343 [2024-02-18 14:06:03,870 INFO misc.py line 119 87073] Train: [41/100][1551/1557] Data 0.011 (0.231) Batch 1.044 (1.184) Remain 30:12:19 loss: 0.1510 Lr: 0.00343 [2024-02-18 14:06:04,729 INFO misc.py line 119 87073] Train: [41/100][1552/1557] Data 0.015 (0.231) Batch 0.869 (1.183) Remain 30:11:59 loss: 0.3106 Lr: 0.00343 [2024-02-18 14:06:05,496 INFO misc.py line 119 87073] Train: [41/100][1553/1557] Data 0.005 (0.230) Batch 0.766 (1.183) Remain 30:11:33 loss: 0.3593 Lr: 0.00343 [2024-02-18 14:06:06,788 INFO misc.py line 119 87073] Train: [41/100][1554/1557] Data 0.005 (0.230) Batch 1.259 (1.183) Remain 30:11:36 loss: 0.2505 Lr: 0.00343 [2024-02-18 14:06:07,796 INFO misc.py line 119 87073] Train: [41/100][1555/1557] Data 0.039 (0.230) Batch 1.032 (1.183) Remain 30:11:26 loss: 0.3644 Lr: 0.00343 [2024-02-18 14:06:08,621 INFO misc.py line 119 87073] Train: [41/100][1556/1557] Data 0.015 (0.230) Batch 0.835 (1.183) Remain 30:11:05 loss: 0.4851 Lr: 0.00343 [2024-02-18 14:06:09,675 INFO misc.py line 119 87073] Train: [41/100][1557/1557] Data 0.004 (0.230) Batch 1.054 (1.183) Remain 30:10:56 loss: 0.3622 Lr: 0.00343 [2024-02-18 14:06:09,675 INFO misc.py line 136 87073] Train result: loss: 0.3765 [2024-02-18 14:06:09,675 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 14:06:37,686 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7699 [2024-02-18 14:06:38,464 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5994 [2024-02-18 14:06:40,596 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4205 [2024-02-18 14:06:42,804 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2902 [2024-02-18 14:06:47,749 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2463 [2024-02-18 14:06:48,449 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1809 [2024-02-18 14:06:49,711 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2472 [2024-02-18 14:06:52,663 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2780 [2024-02-18 14:06:54,469 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2280 [2024-02-18 14:06:56,140 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7156/0.7902/0.9132. [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9235/0.9722 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9808/0.9858 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8650/0.9786 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0309/0.2056 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3419/0.3756 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6581/0.6721 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8453/0.9362 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8410/0.9144 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9153/0.9747 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7884/0.9045 [2024-02-18 14:06:56,140 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7882/0.8828 [2024-02-18 14:06:56,141 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7425/0.8147 [2024-02-18 14:06:56,141 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5824/0.6554 [2024-02-18 14:06:56,141 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 14:06:56,143 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 14:06:56,143 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 14:07:03,977 INFO misc.py line 119 87073] Train: [42/100][1/1557] Data 1.330 (1.330) Batch 2.224 (2.224) Remain 56:45:45 loss: 0.4502 Lr: 0.00343 [2024-02-18 14:07:05,028 INFO misc.py line 119 87073] Train: [42/100][2/1557] Data 0.007 (0.007) Batch 1.053 (1.053) Remain 26:52:31 loss: 0.3097 Lr: 0.00343 [2024-02-18 14:07:06,107 INFO misc.py line 119 87073] Train: [42/100][3/1557] Data 0.005 (0.005) Batch 1.077 (1.077) Remain 27:29:12 loss: 0.4716 Lr: 0.00343 [2024-02-18 14:07:07,223 INFO misc.py line 119 87073] Train: [42/100][4/1557] Data 0.006 (0.006) Batch 1.118 (1.118) Remain 28:31:30 loss: 0.1689 Lr: 0.00343 [2024-02-18 14:07:07,960 INFO misc.py line 119 87073] Train: [42/100][5/1557] Data 0.005 (0.006) Batch 0.738 (0.928) Remain 23:40:24 loss: 0.3216 Lr: 0.00343 [2024-02-18 14:07:08,744 INFO misc.py line 119 87073] Train: [42/100][6/1557] Data 0.003 (0.005) Batch 0.779 (0.878) Remain 22:24:14 loss: 0.5860 Lr: 0.00343 [2024-02-18 14:07:17,560 INFO misc.py line 119 87073] Train: [42/100][7/1557] Data 0.009 (0.006) Batch 8.821 (2.864) Remain 73:04:12 loss: 0.2389 Lr: 0.00343 [2024-02-18 14:07:18,522 INFO misc.py line 119 87073] Train: [42/100][8/1557] Data 0.004 (0.005) Batch 0.962 (2.483) Remain 63:21:52 loss: 0.7396 Lr: 0.00343 [2024-02-18 14:07:19,563 INFO misc.py line 119 87073] Train: [42/100][9/1557] Data 0.004 (0.005) Batch 1.042 (2.243) Remain 57:13:59 loss: 0.5813 Lr: 0.00343 [2024-02-18 14:07:20,638 INFO misc.py line 119 87073] Train: [42/100][10/1557] Data 0.003 (0.005) Batch 1.075 (2.076) Remain 52:58:25 loss: 0.4257 Lr: 0.00343 [2024-02-18 14:07:21,481 INFO misc.py line 119 87073] Train: [42/100][11/1557] Data 0.004 (0.005) Batch 0.843 (1.922) Remain 49:02:20 loss: 0.6914 Lr: 0.00343 [2024-02-18 14:07:22,184 INFO misc.py line 119 87073] Train: [42/100][12/1557] Data 0.003 (0.005) Batch 0.698 (1.786) Remain 45:34:03 loss: 0.3520 Lr: 0.00343 [2024-02-18 14:07:22,982 INFO misc.py line 119 87073] Train: [42/100][13/1557] Data 0.010 (0.005) Batch 0.800 (1.687) Remain 43:03:09 loss: 0.2638 Lr: 0.00343 [2024-02-18 14:07:24,144 INFO misc.py line 119 87073] Train: [42/100][14/1557] Data 0.007 (0.005) Batch 1.164 (1.640) Remain 41:50:14 loss: 0.2073 Lr: 0.00343 [2024-02-18 14:07:25,017 INFO misc.py line 119 87073] Train: [42/100][15/1557] Data 0.006 (0.005) Batch 0.873 (1.576) Remain 40:12:23 loss: 0.4420 Lr: 0.00343 [2024-02-18 14:07:25,918 INFO misc.py line 119 87073] Train: [42/100][16/1557] Data 0.005 (0.005) Batch 0.894 (1.523) Remain 38:52:06 loss: 0.6518 Lr: 0.00343 [2024-02-18 14:07:26,769 INFO misc.py line 119 87073] Train: [42/100][17/1557] Data 0.012 (0.006) Batch 0.858 (1.476) Remain 37:39:22 loss: 0.5614 Lr: 0.00343 [2024-02-18 14:07:27,883 INFO misc.py line 119 87073] Train: [42/100][18/1557] Data 0.004 (0.006) Batch 1.113 (1.452) Remain 37:02:17 loss: 0.6335 Lr: 0.00343 [2024-02-18 14:07:28,672 INFO misc.py line 119 87073] Train: [42/100][19/1557] Data 0.005 (0.006) Batch 0.790 (1.410) Remain 35:58:59 loss: 0.7058 Lr: 0.00343 [2024-02-18 14:07:29,461 INFO misc.py line 119 87073] Train: [42/100][20/1557] Data 0.004 (0.006) Batch 0.781 (1.373) Remain 35:02:16 loss: 0.6195 Lr: 0.00343 [2024-02-18 14:07:30,639 INFO misc.py line 119 87073] Train: [42/100][21/1557] Data 0.013 (0.006) Batch 1.175 (1.362) Remain 34:45:24 loss: 0.2360 Lr: 0.00343 [2024-02-18 14:07:31,527 INFO misc.py line 119 87073] Train: [42/100][22/1557] Data 0.015 (0.006) Batch 0.897 (1.338) Remain 34:07:54 loss: 0.2455 Lr: 0.00343 [2024-02-18 14:07:32,597 INFO misc.py line 119 87073] Train: [42/100][23/1557] Data 0.006 (0.006) Batch 1.072 (1.325) Remain 33:47:30 loss: 0.4002 Lr: 0.00343 [2024-02-18 14:07:33,618 INFO misc.py line 119 87073] Train: [42/100][24/1557] Data 0.004 (0.006) Batch 1.020 (1.310) Remain 33:25:17 loss: 0.6725 Lr: 0.00343 [2024-02-18 14:07:34,502 INFO misc.py line 119 87073] Train: [42/100][25/1557] Data 0.005 (0.006) Batch 0.883 (1.291) Remain 32:55:32 loss: 0.2364 Lr: 0.00343 [2024-02-18 14:07:35,260 INFO misc.py line 119 87073] Train: [42/100][26/1557] Data 0.006 (0.006) Batch 0.759 (1.268) Remain 32:20:08 loss: 0.5270 Lr: 0.00343 [2024-02-18 14:07:36,010 INFO misc.py line 119 87073] Train: [42/100][27/1557] Data 0.006 (0.006) Batch 0.749 (1.246) Remain 31:47:04 loss: 0.1749 Lr: 0.00343 [2024-02-18 14:07:37,264 INFO misc.py line 119 87073] Train: [42/100][28/1557] Data 0.006 (0.006) Batch 1.241 (1.246) Remain 31:46:43 loss: 0.1430 Lr: 0.00343 [2024-02-18 14:07:38,213 INFO misc.py line 119 87073] Train: [42/100][29/1557] Data 0.019 (0.007) Batch 0.964 (1.235) Remain 31:30:07 loss: 0.2669 Lr: 0.00343 [2024-02-18 14:07:39,284 INFO misc.py line 119 87073] Train: [42/100][30/1557] Data 0.004 (0.007) Batch 1.071 (1.229) Remain 31:20:50 loss: 0.2990 Lr: 0.00343 [2024-02-18 14:07:40,436 INFO misc.py line 119 87073] Train: [42/100][31/1557] Data 0.004 (0.007) Batch 1.151 (1.226) Remain 31:16:34 loss: 0.9045 Lr: 0.00343 [2024-02-18 14:07:41,415 INFO misc.py line 119 87073] Train: [42/100][32/1557] Data 0.004 (0.006) Batch 0.980 (1.218) Remain 31:03:33 loss: 0.2274 Lr: 0.00343 [2024-02-18 14:07:42,132 INFO misc.py line 119 87073] Train: [42/100][33/1557] Data 0.004 (0.006) Batch 0.713 (1.201) Remain 30:37:47 loss: 0.4666 Lr: 0.00343 [2024-02-18 14:07:42,882 INFO misc.py line 119 87073] Train: [42/100][34/1557] Data 0.008 (0.006) Batch 0.751 (1.186) Remain 30:15:34 loss: 0.5035 Lr: 0.00343 [2024-02-18 14:07:44,000 INFO misc.py line 119 87073] Train: [42/100][35/1557] Data 0.006 (0.006) Batch 1.107 (1.184) Remain 30:11:47 loss: 0.1530 Lr: 0.00343 [2024-02-18 14:07:44,982 INFO misc.py line 119 87073] Train: [42/100][36/1557] Data 0.018 (0.007) Batch 0.994 (1.178) Remain 30:02:59 loss: 0.5557 Lr: 0.00343 [2024-02-18 14:07:45,906 INFO misc.py line 119 87073] Train: [42/100][37/1557] Data 0.005 (0.007) Batch 0.923 (1.171) Remain 29:51:30 loss: 0.3986 Lr: 0.00343 [2024-02-18 14:07:46,785 INFO misc.py line 119 87073] Train: [42/100][38/1557] Data 0.006 (0.007) Batch 0.875 (1.162) Remain 29:38:33 loss: 0.5668 Lr: 0.00343 [2024-02-18 14:07:47,697 INFO misc.py line 119 87073] Train: [42/100][39/1557] Data 0.009 (0.007) Batch 0.917 (1.155) Remain 29:28:07 loss: 0.4067 Lr: 0.00343 [2024-02-18 14:07:48,440 INFO misc.py line 119 87073] Train: [42/100][40/1557] Data 0.004 (0.007) Batch 0.744 (1.144) Remain 29:11:05 loss: 0.2484 Lr: 0.00343 [2024-02-18 14:07:49,180 INFO misc.py line 119 87073] Train: [42/100][41/1557] Data 0.003 (0.007) Batch 0.737 (1.134) Remain 28:54:40 loss: 0.2493 Lr: 0.00343 [2024-02-18 14:07:50,284 INFO misc.py line 119 87073] Train: [42/100][42/1557] Data 0.005 (0.006) Batch 1.099 (1.133) Remain 28:53:17 loss: 0.2525 Lr: 0.00343 [2024-02-18 14:07:51,191 INFO misc.py line 119 87073] Train: [42/100][43/1557] Data 0.011 (0.007) Batch 0.912 (1.127) Remain 28:44:49 loss: 0.7331 Lr: 0.00343 [2024-02-18 14:07:52,049 INFO misc.py line 119 87073] Train: [42/100][44/1557] Data 0.006 (0.007) Batch 0.860 (1.121) Remain 28:34:51 loss: 0.8263 Lr: 0.00343 [2024-02-18 14:07:52,906 INFO misc.py line 119 87073] Train: [42/100][45/1557] Data 0.004 (0.007) Batch 0.848 (1.114) Remain 28:24:53 loss: 0.3136 Lr: 0.00343 [2024-02-18 14:07:53,824 INFO misc.py line 119 87073] Train: [42/100][46/1557] Data 0.013 (0.007) Batch 0.927 (1.110) Remain 28:18:12 loss: 0.1915 Lr: 0.00343 [2024-02-18 14:07:54,617 INFO misc.py line 119 87073] Train: [42/100][47/1557] Data 0.005 (0.007) Batch 0.792 (1.103) Remain 28:07:07 loss: 0.2377 Lr: 0.00343 [2024-02-18 14:07:55,384 INFO misc.py line 119 87073] Train: [42/100][48/1557] Data 0.007 (0.007) Batch 0.762 (1.095) Remain 27:55:32 loss: 0.3867 Lr: 0.00343 [2024-02-18 14:07:56,645 INFO misc.py line 119 87073] Train: [42/100][49/1557] Data 0.010 (0.007) Batch 1.256 (1.098) Remain 28:00:53 loss: 0.2131 Lr: 0.00343 [2024-02-18 14:07:57,733 INFO misc.py line 119 87073] Train: [42/100][50/1557] Data 0.015 (0.007) Batch 1.089 (1.098) Remain 28:00:34 loss: 0.2424 Lr: 0.00343 [2024-02-18 14:07:58,675 INFO misc.py line 119 87073] Train: [42/100][51/1557] Data 0.013 (0.007) Batch 0.952 (1.095) Remain 27:55:53 loss: 0.2694 Lr: 0.00343 [2024-02-18 14:07:59,502 INFO misc.py line 119 87073] Train: [42/100][52/1557] Data 0.004 (0.007) Batch 0.827 (1.090) Remain 27:47:28 loss: 0.3654 Lr: 0.00343 [2024-02-18 14:08:00,523 INFO misc.py line 119 87073] Train: [42/100][53/1557] Data 0.005 (0.007) Batch 1.010 (1.088) Remain 27:45:00 loss: 0.4744 Lr: 0.00343 [2024-02-18 14:08:01,271 INFO misc.py line 119 87073] Train: [42/100][54/1557] Data 0.015 (0.007) Batch 0.760 (1.082) Remain 27:35:09 loss: 0.3181 Lr: 0.00343 [2024-02-18 14:08:02,029 INFO misc.py line 119 87073] Train: [42/100][55/1557] Data 0.003 (0.007) Batch 0.757 (1.075) Remain 27:25:34 loss: 0.2910 Lr: 0.00343 [2024-02-18 14:08:03,262 INFO misc.py line 119 87073] Train: [42/100][56/1557] Data 0.004 (0.007) Batch 1.226 (1.078) Remain 27:29:54 loss: 0.2498 Lr: 0.00343 [2024-02-18 14:08:04,249 INFO misc.py line 119 87073] Train: [42/100][57/1557] Data 0.012 (0.007) Batch 0.994 (1.077) Remain 27:27:30 loss: 0.6248 Lr: 0.00343 [2024-02-18 14:08:05,146 INFO misc.py line 119 87073] Train: [42/100][58/1557] Data 0.003 (0.007) Batch 0.898 (1.073) Remain 27:22:31 loss: 0.7214 Lr: 0.00343 [2024-02-18 14:08:06,239 INFO misc.py line 119 87073] Train: [42/100][59/1557] Data 0.003 (0.007) Batch 1.092 (1.074) Remain 27:23:00 loss: 0.5980 Lr: 0.00343 [2024-02-18 14:08:07,243 INFO misc.py line 119 87073] Train: [42/100][60/1557] Data 0.004 (0.007) Batch 1.004 (1.073) Remain 27:21:07 loss: 0.3307 Lr: 0.00343 [2024-02-18 14:08:08,036 INFO misc.py line 119 87073] Train: [42/100][61/1557] Data 0.003 (0.007) Batch 0.793 (1.068) Remain 27:13:44 loss: 0.4479 Lr: 0.00343 [2024-02-18 14:08:08,783 INFO misc.py line 119 87073] Train: [42/100][62/1557] Data 0.004 (0.007) Batch 0.746 (1.062) Remain 27:05:22 loss: 0.3269 Lr: 0.00343 [2024-02-18 14:08:26,672 INFO misc.py line 119 87073] Train: [42/100][63/1557] Data 6.051 (0.107) Batch 17.888 (1.343) Remain 34:14:25 loss: 0.0952 Lr: 0.00343 [2024-02-18 14:08:27,770 INFO misc.py line 119 87073] Train: [42/100][64/1557] Data 0.005 (0.106) Batch 1.099 (1.339) Remain 34:08:18 loss: 0.3773 Lr: 0.00343 [2024-02-18 14:08:28,708 INFO misc.py line 119 87073] Train: [42/100][65/1557] Data 0.004 (0.104) Batch 0.936 (1.332) Remain 33:58:20 loss: 0.0953 Lr: 0.00343 [2024-02-18 14:08:29,668 INFO misc.py line 119 87073] Train: [42/100][66/1557] Data 0.006 (0.103) Batch 0.962 (1.326) Remain 33:49:19 loss: 0.9741 Lr: 0.00343 [2024-02-18 14:08:30,960 INFO misc.py line 119 87073] Train: [42/100][67/1557] Data 0.004 (0.101) Batch 1.290 (1.326) Remain 33:48:26 loss: 0.7995 Lr: 0.00343 [2024-02-18 14:08:31,724 INFO misc.py line 119 87073] Train: [42/100][68/1557] Data 0.006 (0.100) Batch 0.764 (1.317) Remain 33:35:11 loss: 0.4473 Lr: 0.00343 [2024-02-18 14:08:32,486 INFO misc.py line 119 87073] Train: [42/100][69/1557] Data 0.006 (0.098) Batch 0.753 (1.309) Remain 33:22:04 loss: 0.2753 Lr: 0.00343 [2024-02-18 14:08:33,718 INFO misc.py line 119 87073] Train: [42/100][70/1557] Data 0.015 (0.097) Batch 1.229 (1.307) Remain 33:20:14 loss: 0.1514 Lr: 0.00343 [2024-02-18 14:08:34,748 INFO misc.py line 119 87073] Train: [42/100][71/1557] Data 0.018 (0.096) Batch 1.031 (1.303) Remain 33:13:59 loss: 0.2703 Lr: 0.00343 [2024-02-18 14:08:35,617 INFO misc.py line 119 87073] Train: [42/100][72/1557] Data 0.017 (0.095) Batch 0.882 (1.297) Remain 33:04:37 loss: 0.1702 Lr: 0.00343 [2024-02-18 14:08:36,518 INFO misc.py line 119 87073] Train: [42/100][73/1557] Data 0.004 (0.093) Batch 0.902 (1.292) Remain 32:55:58 loss: 0.1828 Lr: 0.00343 [2024-02-18 14:08:37,421 INFO misc.py line 119 87073] Train: [42/100][74/1557] Data 0.004 (0.092) Batch 0.896 (1.286) Remain 32:47:25 loss: 0.6284 Lr: 0.00343 [2024-02-18 14:08:38,227 INFO misc.py line 119 87073] Train: [42/100][75/1557] Data 0.010 (0.091) Batch 0.813 (1.279) Remain 32:37:20 loss: 0.6408 Lr: 0.00343 [2024-02-18 14:08:39,015 INFO misc.py line 119 87073] Train: [42/100][76/1557] Data 0.004 (0.090) Batch 0.788 (1.273) Remain 32:27:00 loss: 0.4043 Lr: 0.00343 [2024-02-18 14:08:40,189 INFO misc.py line 119 87073] Train: [42/100][77/1557] Data 0.003 (0.089) Batch 1.165 (1.271) Remain 32:24:45 loss: 0.1821 Lr: 0.00343 [2024-02-18 14:08:41,170 INFO misc.py line 119 87073] Train: [42/100][78/1557] Data 0.013 (0.088) Batch 0.991 (1.268) Remain 32:19:01 loss: 0.7616 Lr: 0.00343 [2024-02-18 14:08:42,239 INFO misc.py line 119 87073] Train: [42/100][79/1557] Data 0.004 (0.086) Batch 1.057 (1.265) Remain 32:14:45 loss: 0.1597 Lr: 0.00343 [2024-02-18 14:08:43,317 INFO misc.py line 119 87073] Train: [42/100][80/1557] Data 0.016 (0.086) Batch 1.083 (1.262) Remain 32:11:07 loss: 0.4575 Lr: 0.00343 [2024-02-18 14:08:44,245 INFO misc.py line 119 87073] Train: [42/100][81/1557] Data 0.011 (0.085) Batch 0.935 (1.258) Remain 32:04:40 loss: 1.0850 Lr: 0.00343 [2024-02-18 14:08:45,009 INFO misc.py line 119 87073] Train: [42/100][82/1557] Data 0.004 (0.084) Batch 0.763 (1.252) Remain 31:55:04 loss: 0.3866 Lr: 0.00343 [2024-02-18 14:08:45,769 INFO misc.py line 119 87073] Train: [42/100][83/1557] Data 0.004 (0.083) Batch 0.759 (1.246) Remain 31:45:37 loss: 0.4157 Lr: 0.00343 [2024-02-18 14:08:47,116 INFO misc.py line 119 87073] Train: [42/100][84/1557] Data 0.005 (0.082) Batch 1.338 (1.247) Remain 31:47:20 loss: 0.3783 Lr: 0.00343 [2024-02-18 14:08:48,054 INFO misc.py line 119 87073] Train: [42/100][85/1557] Data 0.015 (0.081) Batch 0.949 (1.243) Remain 31:41:45 loss: 0.3151 Lr: 0.00343 [2024-02-18 14:08:49,305 INFO misc.py line 119 87073] Train: [42/100][86/1557] Data 0.005 (0.080) Batch 1.239 (1.243) Remain 31:41:38 loss: 0.5859 Lr: 0.00343 [2024-02-18 14:08:50,207 INFO misc.py line 119 87073] Train: [42/100][87/1557] Data 0.017 (0.079) Batch 0.915 (1.239) Remain 31:35:38 loss: 0.3658 Lr: 0.00343 [2024-02-18 14:08:51,217 INFO misc.py line 119 87073] Train: [42/100][88/1557] Data 0.004 (0.078) Batch 1.011 (1.237) Remain 31:31:30 loss: 0.5724 Lr: 0.00343 [2024-02-18 14:08:51,961 INFO misc.py line 119 87073] Train: [42/100][89/1557] Data 0.004 (0.077) Batch 0.743 (1.231) Remain 31:22:42 loss: 0.7176 Lr: 0.00342 [2024-02-18 14:08:52,718 INFO misc.py line 119 87073] Train: [42/100][90/1557] Data 0.005 (0.077) Batch 0.757 (1.225) Remain 31:14:20 loss: 0.3406 Lr: 0.00342 [2024-02-18 14:08:53,819 INFO misc.py line 119 87073] Train: [42/100][91/1557] Data 0.005 (0.076) Batch 1.101 (1.224) Remain 31:12:09 loss: 0.1480 Lr: 0.00342 [2024-02-18 14:08:54,834 INFO misc.py line 119 87073] Train: [42/100][92/1557] Data 0.005 (0.075) Batch 1.009 (1.222) Remain 31:08:27 loss: 0.6565 Lr: 0.00342 [2024-02-18 14:08:55,739 INFO misc.py line 119 87073] Train: [42/100][93/1557] Data 0.010 (0.074) Batch 0.911 (1.218) Remain 31:03:09 loss: 0.6171 Lr: 0.00342 [2024-02-18 14:08:56,810 INFO misc.py line 119 87073] Train: [42/100][94/1557] Data 0.005 (0.073) Batch 1.071 (1.217) Remain 31:00:39 loss: 0.4873 Lr: 0.00342 [2024-02-18 14:08:57,804 INFO misc.py line 119 87073] Train: [42/100][95/1557] Data 0.005 (0.073) Batch 0.993 (1.214) Remain 30:56:55 loss: 0.4716 Lr: 0.00342 [2024-02-18 14:08:58,648 INFO misc.py line 119 87073] Train: [42/100][96/1557] Data 0.006 (0.072) Batch 0.846 (1.210) Remain 30:50:51 loss: 0.5247 Lr: 0.00342 [2024-02-18 14:08:59,388 INFO misc.py line 119 87073] Train: [42/100][97/1557] Data 0.004 (0.071) Batch 0.736 (1.205) Remain 30:43:07 loss: 0.3097 Lr: 0.00342 [2024-02-18 14:09:00,518 INFO misc.py line 119 87073] Train: [42/100][98/1557] Data 0.007 (0.071) Batch 1.124 (1.204) Remain 30:41:47 loss: 0.2227 Lr: 0.00342 [2024-02-18 14:09:01,490 INFO misc.py line 119 87073] Train: [42/100][99/1557] Data 0.014 (0.070) Batch 0.979 (1.202) Remain 30:38:11 loss: 0.4185 Lr: 0.00342 [2024-02-18 14:09:02,447 INFO misc.py line 119 87073] Train: [42/100][100/1557] Data 0.008 (0.069) Batch 0.957 (1.199) Remain 30:34:18 loss: 0.6034 Lr: 0.00342 [2024-02-18 14:09:03,399 INFO misc.py line 119 87073] Train: [42/100][101/1557] Data 0.006 (0.069) Batch 0.953 (1.197) Remain 30:30:27 loss: 0.2948 Lr: 0.00342 [2024-02-18 14:09:04,322 INFO misc.py line 119 87073] Train: [42/100][102/1557] Data 0.005 (0.068) Batch 0.913 (1.194) Remain 30:26:02 loss: 0.4456 Lr: 0.00342 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line 119 87073] Train: [42/100][165/1557] Data 0.005 (0.080) Batch 1.100 (1.200) Remain 30:33:18 loss: 0.3724 Lr: 0.00342 [2024-02-18 14:10:21,191 INFO misc.py line 119 87073] Train: [42/100][166/1557] Data 0.007 (0.079) Batch 0.754 (1.197) Remain 30:29:06 loss: 0.3305 Lr: 0.00342 [2024-02-18 14:10:21,887 INFO misc.py line 119 87073] Train: [42/100][167/1557] Data 0.005 (0.079) Batch 0.695 (1.194) Remain 30:24:24 loss: 0.4135 Lr: 0.00342 [2024-02-18 14:10:23,056 INFO misc.py line 119 87073] Train: [42/100][168/1557] Data 0.005 (0.078) Batch 1.168 (1.194) Remain 30:24:09 loss: 0.1347 Lr: 0.00342 [2024-02-18 14:10:24,020 INFO misc.py line 119 87073] Train: [42/100][169/1557] Data 0.007 (0.078) Batch 0.966 (1.192) Remain 30:22:02 loss: 0.2615 Lr: 0.00342 [2024-02-18 14:10:24,988 INFO misc.py line 119 87073] Train: [42/100][170/1557] Data 0.005 (0.077) Batch 0.970 (1.191) Remain 30:19:58 loss: 0.3912 Lr: 0.00342 [2024-02-18 14:10:25,937 INFO misc.py line 119 87073] Train: 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Batch 1.125 (1.284) Remain 32:42:15 loss: 0.7857 Lr: 0.00342 [2024-02-18 14:10:50,479 INFO misc.py line 119 87073] Train: [42/100][178/1557] Data 0.003 (0.108) Batch 0.938 (1.282) Remain 32:39:12 loss: 0.2945 Lr: 0.00342 [2024-02-18 14:10:51,697 INFO misc.py line 119 87073] Train: [42/100][179/1557] Data 0.005 (0.107) Batch 1.219 (1.282) Remain 32:38:38 loss: 0.4378 Lr: 0.00342 [2024-02-18 14:10:54,429 INFO misc.py line 119 87073] Train: [42/100][180/1557] Data 1.091 (0.113) Batch 2.723 (1.290) Remain 32:51:03 loss: 0.6625 Lr: 0.00342 [2024-02-18 14:10:55,227 INFO misc.py line 119 87073] Train: [42/100][181/1557] Data 0.013 (0.112) Batch 0.807 (1.287) Remain 32:46:53 loss: 0.4492 Lr: 0.00342 [2024-02-18 14:10:56,459 INFO misc.py line 119 87073] Train: [42/100][182/1557] Data 0.003 (0.112) Batch 1.228 (1.287) Remain 32:46:21 loss: 0.1583 Lr: 0.00342 [2024-02-18 14:10:57,444 INFO misc.py line 119 87073] Train: [42/100][183/1557] Data 0.007 (0.111) Batch 0.988 (1.285) Remain 32:43:48 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line 119 87073] Train: [42/100][221/1557] Data 0.006 (0.093) Batch 0.895 (1.223) Remain 31:07:30 loss: 0.3146 Lr: 0.00342 [2024-02-18 14:11:33,436 INFO misc.py line 119 87073] Train: [42/100][222/1557] Data 0.005 (0.092) Batch 0.783 (1.221) Remain 31:04:24 loss: 0.4241 Lr: 0.00342 [2024-02-18 14:11:34,231 INFO misc.py line 119 87073] Train: [42/100][223/1557] Data 0.004 (0.092) Batch 0.795 (1.219) Remain 31:01:26 loss: 0.4059 Lr: 0.00342 [2024-02-18 14:11:35,357 INFO misc.py line 119 87073] Train: [42/100][224/1557] Data 0.004 (0.091) Batch 1.115 (1.218) Remain 31:00:42 loss: 0.1628 Lr: 0.00342 [2024-02-18 14:11:36,382 INFO misc.py line 119 87073] Train: [42/100][225/1557] Data 0.015 (0.091) Batch 1.025 (1.217) Remain 30:59:21 loss: 0.5421 Lr: 0.00342 [2024-02-18 14:11:37,453 INFO misc.py line 119 87073] Train: [42/100][226/1557] Data 0.016 (0.091) Batch 1.078 (1.217) Remain 30:58:22 loss: 0.4304 Lr: 0.00342 [2024-02-18 14:11:38,375 INFO misc.py line 119 87073] Train: 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Batch 0.836 (1.278) Remain 32:32:20 loss: 0.0997 Lr: 0.00342 [2024-02-18 14:12:01,063 INFO misc.py line 119 87073] Train: [42/100][234/1557] Data 0.005 (0.113) Batch 0.924 (1.277) Remain 32:29:58 loss: 0.6967 Lr: 0.00342 [2024-02-18 14:12:02,084 INFO misc.py line 119 87073] Train: [42/100][235/1557] Data 0.005 (0.113) Batch 1.012 (1.276) Remain 32:28:12 loss: 0.7536 Lr: 0.00342 [2024-02-18 14:12:02,863 INFO misc.py line 119 87073] Train: [42/100][236/1557] Data 0.014 (0.112) Batch 0.788 (1.274) Remain 32:24:59 loss: 0.3220 Lr: 0.00342 [2024-02-18 14:12:03,607 INFO misc.py line 119 87073] Train: [42/100][237/1557] Data 0.005 (0.112) Batch 0.743 (1.271) Remain 32:21:30 loss: 0.2828 Lr: 0.00342 [2024-02-18 14:12:04,704 INFO misc.py line 119 87073] Train: [42/100][238/1557] Data 0.005 (0.111) Batch 1.090 (1.271) Remain 32:20:18 loss: 0.2202 Lr: 0.00342 [2024-02-18 14:12:05,580 INFO misc.py line 119 87073] Train: [42/100][239/1557] Data 0.013 (0.111) Batch 0.884 (1.269) Remain 32:17:46 loss: 0.3135 Lr: 0.00342 [2024-02-18 14:12:06,636 INFO misc.py line 119 87073] Train: [42/100][240/1557] Data 0.005 (0.110) Batch 1.056 (1.268) Remain 32:16:23 loss: 1.0476 Lr: 0.00342 [2024-02-18 14:12:07,546 INFO misc.py line 119 87073] Train: [42/100][241/1557] Data 0.005 (0.110) Batch 0.910 (1.267) Remain 32:14:04 loss: 0.3932 Lr: 0.00342 [2024-02-18 14:12:08,500 INFO misc.py line 119 87073] Train: [42/100][242/1557] Data 0.004 (0.109) Batch 0.953 (1.265) Remain 32:12:02 loss: 0.5911 Lr: 0.00342 [2024-02-18 14:12:09,241 INFO misc.py line 119 87073] Train: [42/100][243/1557] Data 0.005 (0.109) Batch 0.735 (1.263) Remain 32:08:39 loss: 0.2511 Lr: 0.00342 [2024-02-18 14:12:10,004 INFO misc.py line 119 87073] Train: [42/100][244/1557] Data 0.011 (0.109) Batch 0.769 (1.261) Remain 32:05:30 loss: 0.3241 Lr: 0.00342 [2024-02-18 14:12:11,179 INFO misc.py line 119 87073] Train: [42/100][245/1557] Data 0.005 (0.108) Batch 1.175 (1.261) Remain 32:04:56 loss: 0.2545 Lr: 0.00342 [2024-02-18 14:12:12,045 INFO misc.py line 119 87073] Train: [42/100][246/1557] Data 0.005 (0.108) Batch 0.866 (1.259) Remain 32:02:26 loss: 0.3413 Lr: 0.00342 [2024-02-18 14:12:12,956 INFO misc.py line 119 87073] Train: [42/100][247/1557] Data 0.004 (0.107) Batch 0.900 (1.258) Remain 32:00:10 loss: 0.3805 Lr: 0.00342 [2024-02-18 14:12:13,843 INFO misc.py line 119 87073] Train: [42/100][248/1557] Data 0.015 (0.107) Batch 0.896 (1.256) Remain 31:57:54 loss: 0.2403 Lr: 0.00342 [2024-02-18 14:12:14,795 INFO misc.py line 119 87073] Train: [42/100][249/1557] Data 0.006 (0.107) Batch 0.953 (1.255) Remain 31:56:00 loss: 0.1744 Lr: 0.00342 [2024-02-18 14:12:15,636 INFO misc.py line 119 87073] Train: [42/100][250/1557] Data 0.004 (0.106) Batch 0.842 (1.253) Remain 31:53:25 loss: 0.3613 Lr: 0.00342 [2024-02-18 14:12:16,397 INFO misc.py line 119 87073] Train: [42/100][251/1557] Data 0.004 (0.106) Batch 0.760 (1.251) Remain 31:50:22 loss: 0.3120 Lr: 0.00342 [2024-02-18 14:12:17,691 INFO misc.py line 119 87073] Train: [42/100][252/1557] Data 0.005 (0.105) Batch 1.286 (1.251) Remain 31:50:34 loss: 0.1390 Lr: 0.00342 [2024-02-18 14:12:18,956 INFO misc.py line 119 87073] Train: [42/100][253/1557] Data 0.012 (0.105) Batch 1.272 (1.251) Remain 31:50:40 loss: 0.3368 Lr: 0.00342 [2024-02-18 14:12:20,188 INFO misc.py line 119 87073] Train: [42/100][254/1557] Data 0.005 (0.105) Batch 1.234 (1.251) Remain 31:50:32 loss: 0.3653 Lr: 0.00342 [2024-02-18 14:12:21,159 INFO misc.py line 119 87073] Train: [42/100][255/1557] Data 0.004 (0.104) Batch 0.971 (1.250) Remain 31:48:49 loss: 0.4783 Lr: 0.00342 [2024-02-18 14:12:22,060 INFO misc.py line 119 87073] Train: [42/100][256/1557] Data 0.004 (0.104) Batch 0.899 (1.249) Remain 31:46:41 loss: 0.5459 Lr: 0.00342 [2024-02-18 14:12:22,827 INFO misc.py line 119 87073] Train: [42/100][257/1557] Data 0.006 (0.103) Batch 0.767 (1.247) Remain 31:43:46 loss: 0.6414 Lr: 0.00342 [2024-02-18 14:12:23,596 INFO misc.py line 119 87073] Train: [42/100][258/1557] Data 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[2024-02-18 14:12:35,635 INFO misc.py line 119 87073] Train: [42/100][271/1557] Data 0.005 (0.098) Batch 0.820 (1.230) Remain 31:16:59 loss: 0.4164 Lr: 0.00342 [2024-02-18 14:12:36,458 INFO misc.py line 119 87073] Train: [42/100][272/1557] Data 0.006 (0.098) Batch 0.825 (1.228) Remain 31:14:40 loss: 0.4301 Lr: 0.00342 [2024-02-18 14:12:37,723 INFO misc.py line 119 87073] Train: [42/100][273/1557] Data 0.003 (0.098) Batch 1.263 (1.228) Remain 31:14:51 loss: 0.1950 Lr: 0.00342 [2024-02-18 14:12:38,769 INFO misc.py line 119 87073] Train: [42/100][274/1557] Data 0.006 (0.097) Batch 1.044 (1.228) Remain 31:13:47 loss: 0.4744 Lr: 0.00342 [2024-02-18 14:12:39,660 INFO misc.py line 119 87073] Train: [42/100][275/1557] Data 0.007 (0.097) Batch 0.895 (1.226) Remain 31:11:54 loss: 0.2805 Lr: 0.00342 [2024-02-18 14:12:41,011 INFO misc.py line 119 87073] Train: [42/100][276/1557] Data 0.003 (0.097) Batch 1.346 (1.227) Remain 31:12:33 loss: 0.7681 Lr: 0.00342 [2024-02-18 14:12:41,956 INFO misc.py line 119 87073] Train: [42/100][277/1557] Data 0.010 (0.096) Batch 0.950 (1.226) Remain 31:10:59 loss: 0.8870 Lr: 0.00342 [2024-02-18 14:12:42,741 INFO misc.py line 119 87073] Train: [42/100][278/1557] Data 0.003 (0.096) Batch 0.783 (1.224) Remain 31:08:31 loss: 0.3915 Lr: 0.00342 [2024-02-18 14:12:43,528 INFO misc.py line 119 87073] Train: [42/100][279/1557] Data 0.006 (0.096) Batch 0.787 (1.223) Remain 31:06:05 loss: 0.4396 Lr: 0.00342 [2024-02-18 14:12:44,739 INFO misc.py line 119 87073] Train: [42/100][280/1557] Data 0.005 (0.095) Batch 1.211 (1.222) Remain 31:06:00 loss: 0.1451 Lr: 0.00342 [2024-02-18 14:12:45,658 INFO misc.py line 119 87073] Train: [42/100][281/1557] Data 0.005 (0.095) Batch 0.920 (1.221) Remain 31:04:19 loss: 0.5075 Lr: 0.00342 [2024-02-18 14:12:46,631 INFO misc.py line 119 87073] Train: [42/100][282/1557] Data 0.005 (0.095) Batch 0.973 (1.221) Remain 31:02:56 loss: 0.5987 Lr: 0.00342 [2024-02-18 14:12:47,590 INFO misc.py line 119 87073] Train: 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loss: 0.2641 Lr: 0.00341 [2024-02-18 14:13:15,377 INFO misc.py line 119 87073] Train: [42/100][296/1557] Data 0.006 (0.110) Batch 0.851 (1.260) Remain 32:03:22 loss: 0.2288 Lr: 0.00341 [2024-02-18 14:13:16,266 INFO misc.py line 119 87073] Train: [42/100][297/1557] Data 0.006 (0.109) Batch 0.888 (1.259) Remain 32:01:25 loss: 0.3236 Lr: 0.00341 [2024-02-18 14:13:17,240 INFO misc.py line 119 87073] Train: [42/100][298/1557] Data 0.006 (0.109) Batch 0.974 (1.258) Remain 31:59:55 loss: 0.5070 Lr: 0.00341 [2024-02-18 14:13:18,081 INFO misc.py line 119 87073] Train: [42/100][299/1557] Data 0.005 (0.109) Batch 0.842 (1.257) Remain 31:57:46 loss: 0.3921 Lr: 0.00341 [2024-02-18 14:13:18,856 INFO misc.py line 119 87073] Train: [42/100][300/1557] Data 0.003 (0.108) Batch 0.773 (1.255) Remain 31:55:15 loss: 0.6695 Lr: 0.00341 [2024-02-18 14:13:20,053 INFO misc.py line 119 87073] Train: [42/100][301/1557] Data 0.006 (0.108) Batch 1.191 (1.255) Remain 31:54:54 loss: 0.2436 Lr: 0.00341 [2024-02-18 14:13:21,221 INFO misc.py line 119 87073] Train: [42/100][302/1557] Data 0.013 (0.108) Batch 1.157 (1.254) Remain 31:54:23 loss: 0.5076 Lr: 0.00341 [2024-02-18 14:13:22,268 INFO misc.py line 119 87073] Train: [42/100][303/1557] Data 0.024 (0.107) Batch 1.044 (1.254) Remain 31:53:17 loss: 0.6311 Lr: 0.00341 [2024-02-18 14:13:23,216 INFO misc.py line 119 87073] Train: [42/100][304/1557] Data 0.027 (0.107) Batch 0.971 (1.253) Remain 31:51:50 loss: 0.4746 Lr: 0.00341 [2024-02-18 14:13:24,194 INFO misc.py line 119 87073] Train: [42/100][305/1557] Data 0.004 (0.107) Batch 0.978 (1.252) Remain 31:50:26 loss: 0.5193 Lr: 0.00341 [2024-02-18 14:13:24,940 INFO misc.py line 119 87073] Train: [42/100][306/1557] Data 0.004 (0.106) Batch 0.746 (1.250) Remain 31:47:51 loss: 0.2552 Lr: 0.00341 [2024-02-18 14:13:25,696 INFO misc.py line 119 87073] Train: [42/100][307/1557] Data 0.004 (0.106) Batch 0.752 (1.249) Remain 31:45:20 loss: 0.2614 Lr: 0.00341 [2024-02-18 14:13:27,044 INFO misc.py line 119 87073] Train: [42/100][308/1557] Data 0.007 (0.106) Batch 1.338 (1.249) Remain 31:45:46 loss: 0.1207 Lr: 0.00341 [2024-02-18 14:13:28,060 INFO misc.py line 119 87073] Train: [42/100][309/1557] Data 0.017 (0.105) Batch 1.022 (1.248) Remain 31:44:37 loss: 0.5167 Lr: 0.00341 [2024-02-18 14:13:29,094 INFO misc.py line 119 87073] Train: [42/100][310/1557] Data 0.012 (0.105) Batch 1.033 (1.247) Remain 31:43:31 loss: 0.3596 Lr: 0.00341 [2024-02-18 14:13:30,112 INFO misc.py line 119 87073] Train: [42/100][311/1557] Data 0.012 (0.105) Batch 1.025 (1.247) Remain 31:42:24 loss: 0.4785 Lr: 0.00341 [2024-02-18 14:13:31,114 INFO misc.py line 119 87073] Train: [42/100][312/1557] Data 0.005 (0.104) Batch 1.001 (1.246) Remain 31:41:10 loss: 0.3363 Lr: 0.00341 [2024-02-18 14:13:31,801 INFO misc.py line 119 87073] Train: [42/100][313/1557] Data 0.005 (0.104) Batch 0.688 (1.244) Remain 31:38:24 loss: 0.2630 Lr: 0.00341 [2024-02-18 14:13:32,607 INFO misc.py line 119 87073] Train: [42/100][314/1557] Data 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[2024-02-18 14:13:45,521 INFO misc.py line 119 87073] Train: [42/100][327/1557] Data 0.007 (0.100) Batch 0.775 (1.233) Remain 31:20:42 loss: 0.3674 Lr: 0.00341 [2024-02-18 14:13:46,356 INFO misc.py line 119 87073] Train: [42/100][328/1557] Data 0.004 (0.100) Batch 0.835 (1.232) Remain 31:18:48 loss: 0.1501 Lr: 0.00341 [2024-02-18 14:13:47,652 INFO misc.py line 119 87073] Train: [42/100][329/1557] Data 0.004 (0.099) Batch 1.293 (1.232) Remain 31:19:05 loss: 0.0953 Lr: 0.00341 [2024-02-18 14:13:48,591 INFO misc.py line 119 87073] Train: [42/100][330/1557] Data 0.007 (0.099) Batch 0.941 (1.231) Remain 31:17:42 loss: 0.4693 Lr: 0.00341 [2024-02-18 14:13:49,663 INFO misc.py line 119 87073] Train: [42/100][331/1557] Data 0.005 (0.099) Batch 1.072 (1.230) Remain 31:16:56 loss: 0.4912 Lr: 0.00341 [2024-02-18 14:13:50,589 INFO misc.py line 119 87073] Train: [42/100][332/1557] Data 0.005 (0.099) Batch 0.927 (1.229) Remain 31:15:31 loss: 0.4979 Lr: 0.00341 [2024-02-18 14:13:51,522 INFO misc.py line 119 87073] Train: [42/100][333/1557] Data 0.004 (0.098) Batch 0.932 (1.229) Remain 31:14:07 loss: 0.7950 Lr: 0.00341 [2024-02-18 14:13:52,290 INFO misc.py line 119 87073] Train: [42/100][334/1557] Data 0.004 (0.098) Batch 0.756 (1.227) Remain 31:11:55 loss: 0.1755 Lr: 0.00341 [2024-02-18 14:13:53,023 INFO misc.py line 119 87073] Train: [42/100][335/1557] Data 0.016 (0.098) Batch 0.746 (1.226) Remain 31:09:41 loss: 0.3737 Lr: 0.00341 [2024-02-18 14:13:54,128 INFO misc.py line 119 87073] Train: [42/100][336/1557] Data 0.004 (0.098) Batch 1.105 (1.225) Remain 31:09:07 loss: 0.1707 Lr: 0.00341 [2024-02-18 14:13:55,284 INFO misc.py line 119 87073] Train: [42/100][337/1557] Data 0.004 (0.097) Batch 1.155 (1.225) Remain 31:08:46 loss: 0.3428 Lr: 0.00341 [2024-02-18 14:13:56,339 INFO misc.py line 119 87073] Train: [42/100][338/1557] Data 0.006 (0.097) Batch 1.055 (1.225) Remain 31:07:59 loss: 0.5705 Lr: 0.00341 [2024-02-18 14:13:57,304 INFO misc.py line 119 87073] Train: 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Batch 0.984 (1.269) Remain 32:16:12 loss: 0.7204 Lr: 0.00341 [2024-02-18 14:14:21,140 INFO misc.py line 119 87073] Train: [42/100][346/1557] Data 0.004 (0.113) Batch 0.890 (1.268) Remain 32:14:30 loss: 0.6146 Lr: 0.00341 [2024-02-18 14:14:22,163 INFO misc.py line 119 87073] Train: [42/100][347/1557] Data 0.014 (0.113) Batch 1.027 (1.268) Remain 32:13:24 loss: 0.3256 Lr: 0.00341 [2024-02-18 14:14:22,921 INFO misc.py line 119 87073] Train: [42/100][348/1557] Data 0.010 (0.113) Batch 0.764 (1.266) Remain 32:11:09 loss: 0.2206 Lr: 0.00341 [2024-02-18 14:14:23,680 INFO misc.py line 119 87073] Train: [42/100][349/1557] Data 0.004 (0.112) Batch 0.756 (1.265) Remain 32:08:53 loss: 0.3279 Lr: 0.00341 [2024-02-18 14:14:24,821 INFO misc.py line 119 87073] Train: [42/100][350/1557] Data 0.006 (0.112) Batch 1.138 (1.264) Remain 32:08:19 loss: 0.2126 Lr: 0.00341 [2024-02-18 14:14:25,792 INFO misc.py line 119 87073] Train: [42/100][351/1557] Data 0.010 (0.112) Batch 0.977 (1.263) Remain 32:07:02 loss: 1.0065 Lr: 0.00341 [2024-02-18 14:14:26,909 INFO misc.py line 119 87073] Train: [42/100][352/1557] Data 0.004 (0.111) Batch 1.116 (1.263) Remain 32:06:22 loss: 0.6019 Lr: 0.00341 [2024-02-18 14:14:27,776 INFO misc.py line 119 87073] Train: [42/100][353/1557] Data 0.004 (0.111) Batch 0.866 (1.262) Remain 32:04:37 loss: 0.7001 Lr: 0.00341 [2024-02-18 14:14:28,663 INFO misc.py line 119 87073] Train: [42/100][354/1557] Data 0.006 (0.111) Batch 0.883 (1.261) Remain 32:02:57 loss: 0.5738 Lr: 0.00341 [2024-02-18 14:14:31,023 INFO misc.py line 119 87073] Train: [42/100][355/1557] Data 0.839 (0.113) Batch 2.358 (1.264) Remain 32:07:41 loss: 0.2833 Lr: 0.00341 [2024-02-18 14:14:31,844 INFO misc.py line 119 87073] Train: [42/100][356/1557] Data 0.011 (0.112) Batch 0.827 (1.263) Remain 32:05:47 loss: 0.4674 Lr: 0.00341 [2024-02-18 14:14:32,998 INFO misc.py line 119 87073] Train: [42/100][357/1557] Data 0.004 (0.112) Batch 1.154 (1.262) Remain 32:05:17 loss: 0.1406 Lr: 0.00341 [2024-02-18 14:14:34,007 INFO misc.py line 119 87073] Train: [42/100][358/1557] Data 0.008 (0.112) Batch 1.010 (1.262) Remain 32:04:11 loss: 0.3528 Lr: 0.00341 [2024-02-18 14:14:35,006 INFO misc.py line 119 87073] Train: [42/100][359/1557] Data 0.004 (0.112) Batch 0.998 (1.261) Remain 32:03:02 loss: 0.3996 Lr: 0.00341 [2024-02-18 14:14:35,965 INFO misc.py line 119 87073] Train: [42/100][360/1557] Data 0.005 (0.111) Batch 0.960 (1.260) Remain 32:01:43 loss: 0.5422 Lr: 0.00341 [2024-02-18 14:14:36,871 INFO misc.py line 119 87073] Train: [42/100][361/1557] Data 0.004 (0.111) Batch 0.907 (1.259) Remain 32:00:12 loss: 0.2855 Lr: 0.00341 [2024-02-18 14:14:37,579 INFO misc.py line 119 87073] Train: [42/100][362/1557] Data 0.004 (0.111) Batch 0.701 (1.258) Remain 31:57:48 loss: 0.2855 Lr: 0.00341 [2024-02-18 14:14:38,389 INFO misc.py line 119 87073] Train: [42/100][363/1557] Data 0.011 (0.110) Batch 0.817 (1.256) Remain 31:55:55 loss: 0.3234 Lr: 0.00341 [2024-02-18 14:14:39,699 INFO misc.py line 119 87073] Train: [42/100][364/1557] Data 0.004 (0.110) Batch 1.309 (1.256) Remain 31:56:07 loss: 0.2045 Lr: 0.00341 [2024-02-18 14:14:40,556 INFO misc.py line 119 87073] Train: [42/100][365/1557] Data 0.006 (0.110) Batch 0.858 (1.255) Remain 31:54:25 loss: 0.6767 Lr: 0.00341 [2024-02-18 14:14:41,498 INFO misc.py line 119 87073] Train: [42/100][366/1557] Data 0.004 (0.110) Batch 0.942 (1.255) Remain 31:53:05 loss: 0.3212 Lr: 0.00341 [2024-02-18 14:14:42,414 INFO misc.py line 119 87073] Train: [42/100][367/1557] Data 0.004 (0.109) Batch 0.912 (1.254) Remain 31:51:37 loss: 0.3196 Lr: 0.00341 [2024-02-18 14:14:43,336 INFO misc.py line 119 87073] Train: [42/100][368/1557] Data 0.009 (0.109) Batch 0.924 (1.253) Remain 31:50:13 loss: 0.4272 Lr: 0.00341 [2024-02-18 14:14:44,139 INFO misc.py line 119 87073] Train: [42/100][369/1557] Data 0.006 (0.109) Batch 0.805 (1.251) Remain 31:48:20 loss: 1.0463 Lr: 0.00341 [2024-02-18 14:14:44,915 INFO misc.py line 119 87073] Train: [42/100][370/1557] Data 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Batch 0.976 (1.267) Remain 32:10:56 loss: 0.3399 Lr: 0.00341 [2024-02-18 14:16:42,626 INFO misc.py line 119 87073] Train: [42/100][458/1557] Data 0.005 (0.116) Batch 1.080 (1.267) Remain 32:10:17 loss: 0.4180 Lr: 0.00341 [2024-02-18 14:16:43,684 INFO misc.py line 119 87073] Train: [42/100][459/1557] Data 0.004 (0.116) Batch 1.057 (1.267) Remain 32:09:34 loss: 0.5469 Lr: 0.00341 [2024-02-18 14:16:44,445 INFO misc.py line 119 87073] Train: [42/100][460/1557] Data 0.004 (0.115) Batch 0.760 (1.266) Remain 32:07:51 loss: 0.3264 Lr: 0.00341 [2024-02-18 14:16:45,225 INFO misc.py line 119 87073] Train: [42/100][461/1557] Data 0.005 (0.115) Batch 0.779 (1.264) Remain 32:06:13 loss: 0.3263 Lr: 0.00341 [2024-02-18 14:16:46,418 INFO misc.py line 119 87073] Train: [42/100][462/1557] Data 0.006 (0.115) Batch 1.187 (1.264) Remain 32:05:56 loss: 0.2462 Lr: 0.00341 [2024-02-18 14:16:47,402 INFO misc.py line 119 87073] Train: [42/100][463/1557] Data 0.013 (0.115) Batch 0.992 (1.264) Remain 32:05:00 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Batch 1.045 (1.264) Remain 32:03:54 loss: 0.5051 Lr: 0.00340 [2024-02-18 14:17:51,521 INFO misc.py line 119 87073] Train: [42/100][514/1557] Data 0.007 (0.115) Batch 0.953 (1.263) Remain 32:02:57 loss: 0.3207 Lr: 0.00340 [2024-02-18 14:17:52,692 INFO misc.py line 119 87073] Train: [42/100][515/1557] Data 0.005 (0.115) Batch 1.172 (1.263) Remain 32:02:40 loss: 0.4319 Lr: 0.00340 [2024-02-18 14:17:53,403 INFO misc.py line 119 87073] Train: [42/100][516/1557] Data 0.004 (0.115) Batch 0.710 (1.262) Remain 32:01:00 loss: 0.3142 Lr: 0.00340 [2024-02-18 14:17:54,115 INFO misc.py line 119 87073] Train: [42/100][517/1557] Data 0.004 (0.115) Batch 0.708 (1.261) Remain 31:59:20 loss: 0.4310 Lr: 0.00340 [2024-02-18 14:17:55,250 INFO misc.py line 119 87073] Train: [42/100][518/1557] Data 0.007 (0.114) Batch 1.136 (1.260) Remain 31:58:57 loss: 0.1569 Lr: 0.00340 [2024-02-18 14:17:56,429 INFO misc.py line 119 87073] Train: [42/100][519/1557] Data 0.007 (0.114) Batch 1.182 (1.260) Remain 31:58:42 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line 119 87073] Train: [42/100][557/1557] Data 0.005 (0.109) Batch 1.132 (1.243) Remain 31:32:01 loss: 0.2524 Lr: 0.00340 [2024-02-18 14:18:35,588 INFO misc.py line 119 87073] Train: [42/100][558/1557] Data 0.007 (0.109) Batch 0.685 (1.242) Remain 31:30:28 loss: 0.2980 Lr: 0.00340 [2024-02-18 14:18:36,369 INFO misc.py line 119 87073] Train: [42/100][559/1557] Data 0.006 (0.109) Batch 0.782 (1.241) Remain 31:29:11 loss: 0.5288 Lr: 0.00340 [2024-02-18 14:18:37,558 INFO misc.py line 119 87073] Train: [42/100][560/1557] Data 0.005 (0.109) Batch 1.182 (1.241) Remain 31:29:01 loss: 0.1464 Lr: 0.00340 [2024-02-18 14:18:38,565 INFO misc.py line 119 87073] Train: [42/100][561/1557] Data 0.012 (0.108) Batch 1.008 (1.241) Remain 31:28:21 loss: 0.4741 Lr: 0.00340 [2024-02-18 14:18:39,933 INFO misc.py line 119 87073] Train: [42/100][562/1557] Data 0.011 (0.108) Batch 1.363 (1.241) Remain 31:28:40 loss: 0.3499 Lr: 0.00340 [2024-02-18 14:18:40,897 INFO misc.py line 119 87073] Train: 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Batch 0.882 (1.268) Remain 32:09:10 loss: 0.1813 Lr: 0.00340 [2024-02-18 14:19:04,634 INFO misc.py line 119 87073] Train: [42/100][570/1557] Data 0.004 (0.117) Batch 0.904 (1.267) Remain 32:08:10 loss: 0.3560 Lr: 0.00340 [2024-02-18 14:19:05,597 INFO misc.py line 119 87073] Train: [42/100][571/1557] Data 0.006 (0.117) Batch 0.965 (1.267) Remain 32:07:20 loss: 0.4137 Lr: 0.00340 [2024-02-18 14:19:06,398 INFO misc.py line 119 87073] Train: [42/100][572/1557] Data 0.004 (0.117) Batch 0.801 (1.266) Remain 32:06:04 loss: 0.2526 Lr: 0.00340 [2024-02-18 14:19:07,182 INFO misc.py line 119 87073] Train: [42/100][573/1557] Data 0.005 (0.116) Batch 0.778 (1.265) Remain 32:04:45 loss: 0.3073 Lr: 0.00340 [2024-02-18 14:19:08,358 INFO misc.py line 119 87073] Train: [42/100][574/1557] Data 0.009 (0.116) Batch 1.178 (1.265) Remain 32:04:30 loss: 0.1408 Lr: 0.00340 [2024-02-18 14:19:09,325 INFO misc.py line 119 87073] Train: [42/100][575/1557] Data 0.008 (0.116) Batch 0.970 (1.264) Remain 32:03:41 loss: 0.4037 Lr: 0.00340 [2024-02-18 14:19:10,385 INFO misc.py line 119 87073] Train: [42/100][576/1557] Data 0.003 (0.116) Batch 1.060 (1.264) Remain 32:03:07 loss: 0.4893 Lr: 0.00340 [2024-02-18 14:19:11,389 INFO misc.py line 119 87073] Train: [42/100][577/1557] Data 0.004 (0.116) Batch 1.003 (1.264) Remain 32:02:25 loss: 0.2300 Lr: 0.00340 [2024-02-18 14:19:12,300 INFO misc.py line 119 87073] Train: [42/100][578/1557] Data 0.004 (0.115) Batch 0.910 (1.263) Remain 32:01:27 loss: 0.6938 Lr: 0.00340 [2024-02-18 14:19:13,012 INFO misc.py line 119 87073] Train: [42/100][579/1557] Data 0.006 (0.115) Batch 0.703 (1.262) Remain 31:59:57 loss: 0.3674 Lr: 0.00340 [2024-02-18 14:19:13,737 INFO misc.py line 119 87073] Train: [42/100][580/1557] Data 0.014 (0.115) Batch 0.734 (1.261) Remain 31:58:33 loss: 0.1549 Lr: 0.00340 [2024-02-18 14:19:14,846 INFO misc.py line 119 87073] Train: [42/100][581/1557] Data 0.005 (0.115) Batch 1.109 (1.261) Remain 31:58:07 loss: 0.1244 Lr: 0.00340 [2024-02-18 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[2024-02-18 14:19:40,010 INFO misc.py line 119 87073] Train: [42/100][607/1557] Data 0.004 (0.110) Batch 0.774 (1.248) Remain 31:38:24 loss: 0.2091 Lr: 0.00340 [2024-02-18 14:19:40,766 INFO misc.py line 119 87073] Train: [42/100][608/1557] Data 0.004 (0.110) Batch 0.752 (1.247) Remain 31:37:08 loss: 0.3934 Lr: 0.00340 [2024-02-18 14:19:42,035 INFO misc.py line 119 87073] Train: [42/100][609/1557] Data 0.007 (0.110) Batch 1.272 (1.247) Remain 31:37:10 loss: 0.1452 Lr: 0.00340 [2024-02-18 14:19:42,990 INFO misc.py line 119 87073] Train: [42/100][610/1557] Data 0.004 (0.110) Batch 0.956 (1.247) Remain 31:36:25 loss: 0.3353 Lr: 0.00340 [2024-02-18 14:19:44,002 INFO misc.py line 119 87073] Train: [42/100][611/1557] Data 0.004 (0.109) Batch 1.012 (1.247) Remain 31:35:49 loss: 0.2498 Lr: 0.00340 [2024-02-18 14:19:45,010 INFO misc.py line 119 87073] Train: [42/100][612/1557] Data 0.005 (0.109) Batch 1.008 (1.246) Remain 31:35:12 loss: 0.4544 Lr: 0.00340 [2024-02-18 14:19:46,011 INFO misc.py line 119 87073] Train: [42/100][613/1557] Data 0.004 (0.109) Batch 1.001 (1.246) Remain 31:34:34 loss: 0.4260 Lr: 0.00340 [2024-02-18 14:19:46,717 INFO misc.py line 119 87073] Train: [42/100][614/1557] Data 0.004 (0.109) Batch 0.702 (1.245) Remain 31:33:11 loss: 0.9272 Lr: 0.00340 [2024-02-18 14:19:47,446 INFO misc.py line 119 87073] Train: [42/100][615/1557] Data 0.007 (0.109) Batch 0.732 (1.244) Remain 31:31:54 loss: 0.3479 Lr: 0.00340 [2024-02-18 14:19:48,637 INFO misc.py line 119 87073] Train: [42/100][616/1557] Data 0.005 (0.109) Batch 1.191 (1.244) Remain 31:31:45 loss: 0.1615 Lr: 0.00340 [2024-02-18 14:19:49,550 INFO misc.py line 119 87073] Train: [42/100][617/1557] Data 0.004 (0.108) Batch 0.912 (1.243) Remain 31:30:54 loss: 0.3988 Lr: 0.00340 [2024-02-18 14:19:50,427 INFO misc.py line 119 87073] Train: [42/100][618/1557] Data 0.005 (0.108) Batch 0.876 (1.243) Remain 31:29:58 loss: 0.6468 Lr: 0.00340 [2024-02-18 14:19:51,314 INFO misc.py line 119 87073] Train: 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Batch 0.986 (1.265) Remain 32:04:02 loss: 0.7665 Lr: 0.00340 [2024-02-18 14:20:14,009 INFO misc.py line 119 87073] Train: [42/100][626/1557] Data 0.004 (0.116) Batch 0.891 (1.265) Remain 32:03:06 loss: 0.1945 Lr: 0.00340 [2024-02-18 14:20:15,160 INFO misc.py line 119 87073] Train: [42/100][627/1557] Data 0.003 (0.116) Batch 1.150 (1.265) Remain 32:02:48 loss: 0.2130 Lr: 0.00340 [2024-02-18 14:20:15,935 INFO misc.py line 119 87073] Train: [42/100][628/1557] Data 0.005 (0.116) Batch 0.774 (1.264) Remain 32:01:35 loss: 0.3462 Lr: 0.00340 [2024-02-18 14:20:16,682 INFO misc.py line 119 87073] Train: [42/100][629/1557] Data 0.005 (0.115) Batch 0.748 (1.263) Remain 32:00:19 loss: 0.2576 Lr: 0.00340 [2024-02-18 14:20:17,819 INFO misc.py line 119 87073] Train: [42/100][630/1557] Data 0.004 (0.115) Batch 1.137 (1.263) Remain 31:59:59 loss: 0.1423 Lr: 0.00340 [2024-02-18 14:20:18,854 INFO misc.py line 119 87073] Train: [42/100][631/1557] Data 0.004 (0.115) Batch 1.035 (1.262) Remain 31:59:25 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Batch 1.049 (1.264) Remain 32:01:12 loss: 0.4637 Lr: 0.00340 [2024-02-18 14:21:24,194 INFO misc.py line 119 87073] Train: [42/100][682/1557] Data 0.005 (0.115) Batch 0.958 (1.264) Remain 32:00:29 loss: 0.4385 Lr: 0.00340 [2024-02-18 14:21:25,240 INFO misc.py line 119 87073] Train: [42/100][683/1557] Data 0.005 (0.115) Batch 1.048 (1.263) Remain 31:59:59 loss: 0.1275 Lr: 0.00340 [2024-02-18 14:21:26,026 INFO misc.py line 119 87073] Train: [42/100][684/1557] Data 0.004 (0.115) Batch 0.785 (1.263) Remain 31:58:54 loss: 0.1795 Lr: 0.00340 [2024-02-18 14:21:26,798 INFO misc.py line 119 87073] Train: [42/100][685/1557] Data 0.005 (0.115) Batch 0.734 (1.262) Remain 31:57:42 loss: 0.2249 Lr: 0.00340 [2024-02-18 14:21:27,954 INFO misc.py line 119 87073] Train: [42/100][686/1557] Data 0.043 (0.115) Batch 1.188 (1.262) Remain 31:57:31 loss: 0.1412 Lr: 0.00340 [2024-02-18 14:21:29,004 INFO misc.py line 119 87073] Train: [42/100][687/1557] Data 0.012 (0.115) Batch 1.058 (1.262) Remain 31:57:02 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Batch 1.022 (1.266) Remain 32:02:07 loss: 0.3859 Lr: 0.00339 [2024-02-18 14:22:36,127 INFO misc.py line 119 87073] Train: [42/100][738/1557] Data 0.005 (0.117) Batch 1.083 (1.265) Remain 32:01:43 loss: 0.6080 Lr: 0.00339 [2024-02-18 14:22:37,316 INFO misc.py line 119 87073] Train: [42/100][739/1557] Data 0.005 (0.117) Batch 1.186 (1.265) Remain 32:01:32 loss: 0.4428 Lr: 0.00339 [2024-02-18 14:22:38,083 INFO misc.py line 119 87073] Train: [42/100][740/1557] Data 0.007 (0.117) Batch 0.770 (1.265) Remain 32:00:29 loss: 0.2218 Lr: 0.00339 [2024-02-18 14:22:38,872 INFO misc.py line 119 87073] Train: [42/100][741/1557] Data 0.004 (0.117) Batch 0.789 (1.264) Remain 31:59:30 loss: 0.4705 Lr: 0.00339 [2024-02-18 14:22:40,074 INFO misc.py line 119 87073] Train: [42/100][742/1557] Data 0.004 (0.116) Batch 1.193 (1.264) Remain 31:59:19 loss: 0.2330 Lr: 0.00339 [2024-02-18 14:22:41,111 INFO misc.py line 119 87073] Train: [42/100][743/1557] Data 0.013 (0.116) Batch 1.037 (1.264) Remain 31:58:50 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Batch 0.940 (1.264) Remain 31:58:23 loss: 0.3230 Lr: 0.00339 [2024-02-18 14:23:45,624 INFO misc.py line 119 87073] Train: [42/100][794/1557] Data 0.004 (0.117) Batch 1.037 (1.264) Remain 31:57:55 loss: 0.3906 Lr: 0.00339 [2024-02-18 14:23:46,758 INFO misc.py line 119 87073] Train: [42/100][795/1557] Data 0.005 (0.117) Batch 1.134 (1.263) Remain 31:57:39 loss: 0.6851 Lr: 0.00339 [2024-02-18 14:23:47,600 INFO misc.py line 119 87073] Train: [42/100][796/1557] Data 0.006 (0.117) Batch 0.844 (1.263) Remain 31:56:50 loss: 0.4080 Lr: 0.00339 [2024-02-18 14:23:48,326 INFO misc.py line 119 87073] Train: [42/100][797/1557] Data 0.004 (0.116) Batch 0.718 (1.262) Remain 31:55:46 loss: 0.3051 Lr: 0.00339 [2024-02-18 14:23:49,515 INFO misc.py line 119 87073] Train: [42/100][798/1557] Data 0.011 (0.116) Batch 1.195 (1.262) Remain 31:55:37 loss: 0.1484 Lr: 0.00339 [2024-02-18 14:23:50,468 INFO misc.py line 119 87073] Train: [42/100][799/1557] Data 0.006 (0.116) Batch 0.955 (1.262) Remain 31:55:00 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line 119 87073] Train: [42/100][837/1557] Data 0.004 (0.111) Batch 1.017 (1.247) Remain 31:31:43 loss: 0.3679 Lr: 0.00339 [2024-02-18 14:24:26,766 INFO misc.py line 119 87073] Train: [42/100][838/1557] Data 0.011 (0.111) Batch 0.714 (1.246) Remain 31:30:44 loss: 0.3530 Lr: 0.00339 [2024-02-18 14:24:27,577 INFO misc.py line 119 87073] Train: [42/100][839/1557] Data 0.004 (0.111) Batch 0.805 (1.246) Remain 31:29:54 loss: 0.2440 Lr: 0.00339 [2024-02-18 14:24:28,765 INFO misc.py line 119 87073] Train: [42/100][840/1557] Data 0.010 (0.111) Batch 1.191 (1.246) Remain 31:29:47 loss: 0.2002 Lr: 0.00339 [2024-02-18 14:24:29,825 INFO misc.py line 119 87073] Train: [42/100][841/1557] Data 0.008 (0.111) Batch 1.054 (1.245) Remain 31:29:25 loss: 0.7283 Lr: 0.00339 [2024-02-18 14:24:30,779 INFO misc.py line 119 87073] Train: [42/100][842/1557] Data 0.014 (0.111) Batch 0.963 (1.245) Remain 31:28:53 loss: 0.4206 Lr: 0.00339 [2024-02-18 14:24:31,739 INFO misc.py line 119 87073] Train: 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Batch 0.987 (1.261) Remain 31:53:05 loss: 0.3119 Lr: 0.00339 [2024-02-18 14:24:54,038 INFO misc.py line 119 87073] Train: [42/100][850/1557] Data 0.012 (0.116) Batch 0.965 (1.261) Remain 31:52:32 loss: 0.7064 Lr: 0.00339 [2024-02-18 14:24:54,975 INFO misc.py line 119 87073] Train: [42/100][851/1557] Data 0.004 (0.116) Batch 0.938 (1.260) Remain 31:51:56 loss: 0.4142 Lr: 0.00339 [2024-02-18 14:24:55,749 INFO misc.py line 119 87073] Train: [42/100][852/1557] Data 0.003 (0.116) Batch 0.763 (1.260) Remain 31:51:02 loss: 0.1960 Lr: 0.00339 [2024-02-18 14:24:56,563 INFO misc.py line 119 87073] Train: [42/100][853/1557] Data 0.013 (0.116) Batch 0.824 (1.259) Remain 31:50:14 loss: 0.2429 Lr: 0.00339 [2024-02-18 14:24:57,740 INFO misc.py line 119 87073] Train: [42/100][854/1557] Data 0.004 (0.116) Batch 1.176 (1.259) Remain 31:50:04 loss: 0.2636 Lr: 0.00339 [2024-02-18 14:24:58,606 INFO misc.py line 119 87073] Train: [42/100][855/1557] Data 0.005 (0.116) Batch 0.867 (1.259) Remain 31:49:21 loss: 0.1807 Lr: 0.00339 [2024-02-18 14:24:59,689 INFO misc.py line 119 87073] Train: [42/100][856/1557] Data 0.004 (0.116) Batch 1.082 (1.259) Remain 31:49:00 loss: 0.2690 Lr: 0.00339 [2024-02-18 14:25:00,797 INFO misc.py line 119 87073] Train: [42/100][857/1557] Data 0.005 (0.115) Batch 1.107 (1.258) Remain 31:48:43 loss: 0.4981 Lr: 0.00339 [2024-02-18 14:25:01,652 INFO misc.py line 119 87073] Train: [42/100][858/1557] Data 0.006 (0.115) Batch 0.855 (1.258) Remain 31:47:59 loss: 0.3180 Lr: 0.00339 [2024-02-18 14:25:02,406 INFO misc.py line 119 87073] Train: [42/100][859/1557] Data 0.005 (0.115) Batch 0.746 (1.257) Remain 31:47:03 loss: 0.1350 Lr: 0.00339 [2024-02-18 14:25:03,190 INFO misc.py line 119 87073] Train: [42/100][860/1557] Data 0.014 (0.115) Batch 0.792 (1.257) Remain 31:46:13 loss: 0.2689 Lr: 0.00339 [2024-02-18 14:25:04,303 INFO misc.py line 119 87073] Train: [42/100][861/1557] Data 0.005 (0.115) Batch 1.115 (1.257) Remain 31:45:56 loss: 0.2607 Lr: 0.00339 [2024-02-18 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[2024-02-18 14:25:30,841 INFO misc.py line 119 87073] Train: [42/100][887/1557] Data 0.006 (0.113) Batch 0.747 (1.250) Remain 31:34:52 loss: 0.1997 Lr: 0.00339 [2024-02-18 14:25:31,594 INFO misc.py line 119 87073] Train: [42/100][888/1557] Data 0.004 (0.113) Batch 0.752 (1.249) Remain 31:34:00 loss: 0.2937 Lr: 0.00339 [2024-02-18 14:25:32,908 INFO misc.py line 119 87073] Train: [42/100][889/1557] Data 0.005 (0.113) Batch 1.310 (1.249) Remain 31:34:05 loss: 0.2299 Lr: 0.00339 [2024-02-18 14:25:33,924 INFO misc.py line 119 87073] Train: [42/100][890/1557] Data 0.010 (0.113) Batch 1.020 (1.249) Remain 31:33:40 loss: 0.9631 Lr: 0.00339 [2024-02-18 14:25:34,992 INFO misc.py line 119 87073] Train: [42/100][891/1557] Data 0.004 (0.113) Batch 1.067 (1.249) Remain 31:33:20 loss: 0.5596 Lr: 0.00339 [2024-02-18 14:25:35,848 INFO misc.py line 119 87073] Train: [42/100][892/1557] Data 0.006 (0.113) Batch 0.856 (1.248) Remain 31:32:39 loss: 0.3994 Lr: 0.00339 [2024-02-18 14:25:36,816 INFO misc.py line 119 87073] Train: [42/100][893/1557] Data 0.007 (0.113) Batch 0.970 (1.248) Remain 31:32:09 loss: 0.3506 Lr: 0.00339 [2024-02-18 14:25:37,621 INFO misc.py line 119 87073] Train: [42/100][894/1557] Data 0.004 (0.112) Batch 0.805 (1.247) Remain 31:31:23 loss: 0.5176 Lr: 0.00339 [2024-02-18 14:25:38,410 INFO misc.py line 119 87073] Train: [42/100][895/1557] Data 0.004 (0.112) Batch 0.785 (1.247) Remain 31:30:34 loss: 0.3126 Lr: 0.00339 [2024-02-18 14:25:39,577 INFO misc.py line 119 87073] Train: [42/100][896/1557] Data 0.008 (0.112) Batch 1.164 (1.247) Remain 31:30:24 loss: 0.1119 Lr: 0.00339 [2024-02-18 14:25:40,804 INFO misc.py line 119 87073] Train: [42/100][897/1557] Data 0.011 (0.112) Batch 1.233 (1.247) Remain 31:30:22 loss: 0.3950 Lr: 0.00339 [2024-02-18 14:25:41,892 INFO misc.py line 119 87073] Train: [42/100][898/1557] Data 0.006 (0.112) Batch 1.081 (1.247) Remain 31:30:04 loss: 0.6506 Lr: 0.00338 [2024-02-18 14:25:43,021 INFO misc.py line 119 87073] Train: 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Batch 0.942 (1.265) Remain 31:58:18 loss: 0.5138 Lr: 0.00338 [2024-02-18 14:26:08,433 INFO misc.py line 119 87073] Train: [42/100][906/1557] Data 0.005 (0.118) Batch 0.937 (1.265) Remain 31:57:43 loss: 0.5390 Lr: 0.00338 [2024-02-18 14:26:09,430 INFO misc.py line 119 87073] Train: [42/100][907/1557] Data 0.004 (0.118) Batch 0.990 (1.265) Remain 31:57:14 loss: 0.2361 Lr: 0.00338 [2024-02-18 14:26:10,150 INFO misc.py line 119 87073] Train: [42/100][908/1557] Data 0.011 (0.118) Batch 0.728 (1.264) Remain 31:56:19 loss: 0.3103 Lr: 0.00338 [2024-02-18 14:26:10,922 INFO misc.py line 119 87073] Train: [42/100][909/1557] Data 0.004 (0.118) Batch 0.765 (1.264) Remain 31:55:28 loss: 0.4302 Lr: 0.00338 [2024-02-18 14:26:12,104 INFO misc.py line 119 87073] Train: [42/100][910/1557] Data 0.011 (0.118) Batch 1.180 (1.263) Remain 31:55:18 loss: 0.2202 Lr: 0.00338 [2024-02-18 14:26:13,109 INFO misc.py line 119 87073] Train: [42/100][911/1557] Data 0.013 (0.118) Batch 1.003 (1.263) Remain 31:54:51 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87073] Train: [42/100][924/1557] Data 0.010 (0.116) Batch 1.331 (1.259) Remain 31:48:10 loss: 0.4702 Lr: 0.00338 [2024-02-18 14:26:26,763 INFO misc.py line 119 87073] Train: [42/100][925/1557] Data 0.014 (0.116) Batch 1.122 (1.259) Remain 31:47:55 loss: 0.4966 Lr: 0.00338 [2024-02-18 14:26:27,948 INFO misc.py line 119 87073] Train: [42/100][926/1557] Data 0.017 (0.116) Batch 1.187 (1.259) Remain 31:47:47 loss: 0.4168 Lr: 0.00338 [2024-02-18 14:26:28,870 INFO misc.py line 119 87073] Train: [42/100][927/1557] Data 0.014 (0.116) Batch 0.933 (1.258) Remain 31:47:13 loss: 0.2268 Lr: 0.00338 [2024-02-18 14:26:29,787 INFO misc.py line 119 87073] Train: [42/100][928/1557] Data 0.005 (0.116) Batch 0.917 (1.258) Remain 31:46:39 loss: 0.7512 Lr: 0.00338 [2024-02-18 14:26:30,541 INFO misc.py line 119 87073] Train: [42/100][929/1557] Data 0.004 (0.115) Batch 0.753 (1.257) Remain 31:45:48 loss: 0.2992 Lr: 0.00338 [2024-02-18 14:26:31,312 INFO misc.py line 119 87073] Train: [42/100][930/1557] Data 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misc.py line 119 87073] Train: [42/100][1129/1557] Data 0.004 (0.118) Batch 1.004 (1.262) Remain 31:47:43 loss: 0.4229 Lr: 0.00337 [2024-02-18 14:30:47,733 INFO misc.py line 119 87073] Train: [42/100][1130/1557] Data 0.007 (0.117) Batch 1.137 (1.261) Remain 31:47:32 loss: 0.3526 Lr: 0.00337 [2024-02-18 14:30:48,920 INFO misc.py line 119 87073] Train: [42/100][1131/1557] Data 0.014 (0.117) Batch 1.185 (1.261) Remain 31:47:24 loss: 0.4814 Lr: 0.00337 [2024-02-18 14:30:49,675 INFO misc.py line 119 87073] Train: [42/100][1132/1557] Data 0.015 (0.117) Batch 0.766 (1.261) Remain 31:46:43 loss: 0.5227 Lr: 0.00337 [2024-02-18 14:30:50,460 INFO misc.py line 119 87073] Train: [42/100][1133/1557] Data 0.004 (0.117) Batch 0.781 (1.260) Remain 31:46:03 loss: 0.6438 Lr: 0.00337 [2024-02-18 14:30:51,664 INFO misc.py line 119 87073] Train: [42/100][1134/1557] Data 0.008 (0.117) Batch 1.199 (1.260) Remain 31:45:57 loss: 0.2158 Lr: 0.00337 [2024-02-18 14:30:52,512 INFO misc.py line 119 87073] Train: 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[2024-02-18 14:31:56,847 INFO misc.py line 119 87073] Train: [42/100][1185/1557] Data 0.005 (0.117) Batch 0.880 (1.261) Remain 31:46:03 loss: 0.3849 Lr: 0.00337 [2024-02-18 14:31:57,774 INFO misc.py line 119 87073] Train: [42/100][1186/1557] Data 0.004 (0.117) Batch 0.922 (1.261) Remain 31:45:36 loss: 0.6319 Lr: 0.00337 [2024-02-18 14:31:58,609 INFO misc.py line 119 87073] Train: [42/100][1187/1557] Data 0.008 (0.117) Batch 0.839 (1.261) Remain 31:45:02 loss: 0.5735 Lr: 0.00337 [2024-02-18 14:31:59,304 INFO misc.py line 119 87073] Train: [42/100][1188/1557] Data 0.004 (0.117) Batch 0.695 (1.260) Remain 31:44:17 loss: 0.3235 Lr: 0.00337 [2024-02-18 14:32:00,098 INFO misc.py line 119 87073] Train: [42/100][1189/1557] Data 0.004 (0.117) Batch 0.790 (1.260) Remain 31:43:40 loss: 0.2486 Lr: 0.00337 [2024-02-18 14:32:01,249 INFO misc.py line 119 87073] Train: [42/100][1190/1557] Data 0.009 (0.116) Batch 1.147 (1.260) Remain 31:43:30 loss: 0.1397 Lr: 0.00337 [2024-02-18 14:32:02,211 INFO 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31:35:43 loss: 0.3074 Lr: 0.00337 [2024-02-18 14:32:20,042 INFO misc.py line 119 87073] Train: [42/100][1210/1557] Data 0.004 (0.115) Batch 0.753 (1.254) Remain 31:35:04 loss: 0.2807 Lr: 0.00337 [2024-02-18 14:32:21,168 INFO misc.py line 119 87073] Train: [42/100][1211/1557] Data 0.014 (0.114) Batch 1.129 (1.254) Remain 31:34:54 loss: 0.1773 Lr: 0.00337 [2024-02-18 14:32:22,176 INFO misc.py line 119 87073] Train: [42/100][1212/1557] Data 0.011 (0.114) Batch 1.006 (1.254) Remain 31:34:34 loss: 1.0201 Lr: 0.00337 [2024-02-18 14:32:23,151 INFO misc.py line 119 87073] Train: [42/100][1213/1557] Data 0.013 (0.114) Batch 0.985 (1.254) Remain 31:34:12 loss: 0.6017 Lr: 0.00337 [2024-02-18 14:32:24,017 INFO misc.py line 119 87073] Train: [42/100][1214/1557] Data 0.003 (0.114) Batch 0.865 (1.253) Remain 31:33:42 loss: 0.3788 Lr: 0.00337 [2024-02-18 14:32:25,072 INFO misc.py line 119 87073] Train: [42/100][1215/1557] Data 0.005 (0.114) Batch 1.052 (1.253) Remain 31:33:26 loss: 0.2451 Lr: 0.00337 [2024-02-18 14:32:25,844 INFO misc.py line 119 87073] Train: [42/100][1216/1557] Data 0.008 (0.114) Batch 0.776 (1.253) Remain 31:32:49 loss: 0.2401 Lr: 0.00337 [2024-02-18 14:32:26,572 INFO misc.py line 119 87073] Train: [42/100][1217/1557] Data 0.004 (0.114) Batch 0.721 (1.252) Remain 31:32:08 loss: 0.3714 Lr: 0.00337 [2024-02-18 14:32:27,710 INFO misc.py line 119 87073] Train: [42/100][1218/1557] Data 0.011 (0.114) Batch 1.137 (1.252) Remain 31:31:58 loss: 0.3125 Lr: 0.00337 [2024-02-18 14:32:28,657 INFO misc.py line 119 87073] Train: [42/100][1219/1557] Data 0.011 (0.114) Batch 0.952 (1.252) Remain 31:31:34 loss: 0.3052 Lr: 0.00337 [2024-02-18 14:32:29,536 INFO misc.py line 119 87073] Train: [42/100][1220/1557] Data 0.007 (0.114) Batch 0.882 (1.252) Remain 31:31:06 loss: 0.5901 Lr: 0.00337 [2024-02-18 14:32:30,525 INFO misc.py line 119 87073] Train: [42/100][1221/1557] Data 0.004 (0.114) Batch 0.989 (1.252) Remain 31:30:45 loss: 0.4135 Lr: 0.00337 [2024-02-18 14:32:31,695 INFO misc.py line 119 87073] Train: [42/100][1222/1557] Data 0.004 (0.114) Batch 1.160 (1.252) Remain 31:30:37 loss: 0.3656 Lr: 0.00337 [2024-02-18 14:32:32,388 INFO misc.py line 119 87073] Train: [42/100][1223/1557] Data 0.012 (0.113) Batch 0.702 (1.251) Remain 31:29:55 loss: 0.2506 Lr: 0.00337 [2024-02-18 14:32:33,161 INFO misc.py line 119 87073] Train: [42/100][1224/1557] Data 0.004 (0.113) Batch 0.767 (1.251) Remain 31:29:17 loss: 0.2250 Lr: 0.00337 [2024-02-18 14:32:34,366 INFO misc.py line 119 87073] Train: [42/100][1225/1557] Data 0.009 (0.113) Batch 1.207 (1.251) Remain 31:29:13 loss: 0.1416 Lr: 0.00337 [2024-02-18 14:32:35,562 INFO misc.py line 119 87073] Train: [42/100][1226/1557] Data 0.007 (0.113) Batch 1.188 (1.251) Remain 31:29:07 loss: 0.2541 Lr: 0.00337 [2024-02-18 14:32:36,691 INFO misc.py line 119 87073] Train: [42/100][1227/1557] Data 0.016 (0.113) Batch 1.132 (1.250) Remain 31:28:57 loss: 0.2504 Lr: 0.00337 [2024-02-18 14:32:37,616 INFO misc.py line 119 87073] Train: 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Remain 31:44:43 loss: 0.2289 Lr: 0.00337 [2024-02-18 14:33:07,074 INFO misc.py line 119 87073] Train: [42/100][1241/1557] Data 0.005 (0.117) Batch 1.005 (1.261) Remain 31:44:23 loss: 0.2916 Lr: 0.00337 [2024-02-18 14:33:08,107 INFO misc.py line 119 87073] Train: [42/100][1242/1557] Data 0.004 (0.117) Batch 1.032 (1.261) Remain 31:44:05 loss: 0.1469 Lr: 0.00337 [2024-02-18 14:33:09,159 INFO misc.py line 119 87073] Train: [42/100][1243/1557] Data 0.005 (0.117) Batch 1.054 (1.261) Remain 31:43:48 loss: 0.8029 Lr: 0.00337 [2024-02-18 14:33:09,940 INFO misc.py line 119 87073] Train: [42/100][1244/1557] Data 0.004 (0.117) Batch 0.776 (1.260) Remain 31:43:12 loss: 0.8503 Lr: 0.00337 [2024-02-18 14:33:10,752 INFO misc.py line 119 87073] Train: [42/100][1245/1557] Data 0.008 (0.117) Batch 0.815 (1.260) Remain 31:42:38 loss: 0.3788 Lr: 0.00337 [2024-02-18 14:33:11,917 INFO misc.py line 119 87073] Train: [42/100][1246/1557] Data 0.006 (0.117) Batch 1.166 (1.260) Remain 31:42:30 loss: 0.2442 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1253/1557] Data 0.005 (0.116) Batch 1.215 (1.258) Remain 31:39:26 loss: 0.2130 Lr: 0.00337 [2024-02-18 14:33:19,329 INFO misc.py line 119 87073] Train: [42/100][1254/1557] Data 0.011 (0.116) Batch 1.003 (1.258) Remain 31:39:07 loss: 0.3484 Lr: 0.00337 [2024-02-18 14:33:20,225 INFO misc.py line 119 87073] Train: [42/100][1255/1557] Data 0.006 (0.116) Batch 0.897 (1.257) Remain 31:38:39 loss: 0.2135 Lr: 0.00337 [2024-02-18 14:33:21,063 INFO misc.py line 119 87073] Train: [42/100][1256/1557] Data 0.004 (0.116) Batch 0.838 (1.257) Remain 31:38:08 loss: 0.5600 Lr: 0.00337 [2024-02-18 14:33:22,070 INFO misc.py line 119 87073] Train: [42/100][1257/1557] Data 0.005 (0.116) Batch 0.998 (1.257) Remain 31:37:48 loss: 0.3908 Lr: 0.00337 [2024-02-18 14:33:22,761 INFO misc.py line 119 87073] Train: [42/100][1258/1557] Data 0.013 (0.116) Batch 0.699 (1.256) Remain 31:37:06 loss: 0.2611 Lr: 0.00337 [2024-02-18 14:33:23,527 INFO misc.py line 119 87073] Train: [42/100][1259/1557] Data 0.005 (0.116) Batch 0.753 (1.256) Remain 31:36:29 loss: 0.5082 Lr: 0.00337 [2024-02-18 14:33:24,847 INFO misc.py line 119 87073] Train: [42/100][1260/1557] Data 0.018 (0.116) Batch 1.333 (1.256) Remain 31:36:33 loss: 0.1633 Lr: 0.00337 [2024-02-18 14:33:25,740 INFO misc.py line 119 87073] Train: [42/100][1261/1557] Data 0.006 (0.116) Batch 0.895 (1.256) Remain 31:36:06 loss: 0.6042 Lr: 0.00337 [2024-02-18 14:33:26,576 INFO misc.py line 119 87073] Train: [42/100][1262/1557] Data 0.004 (0.115) Batch 0.835 (1.255) Remain 31:35:34 loss: 0.2347 Lr: 0.00337 [2024-02-18 14:33:27,595 INFO misc.py line 119 87073] Train: [42/100][1263/1557] Data 0.005 (0.115) Batch 1.019 (1.255) Remain 31:35:16 loss: 0.6223 Lr: 0.00337 [2024-02-18 14:33:28,463 INFO misc.py line 119 87073] Train: [42/100][1264/1557] Data 0.005 (0.115) Batch 0.868 (1.255) Remain 31:34:47 loss: 0.3814 Lr: 0.00337 [2024-02-18 14:33:29,229 INFO misc.py line 119 87073] Train: [42/100][1265/1557] Data 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Remain 31:32:06 loss: 0.4833 Lr: 0.00337 [2024-02-18 14:33:35,900 INFO misc.py line 119 87073] Train: [42/100][1272/1557] Data 0.004 (0.115) Batch 0.786 (1.253) Remain 31:31:31 loss: 0.2742 Lr: 0.00337 [2024-02-18 14:33:36,668 INFO misc.py line 119 87073] Train: [42/100][1273/1557] Data 0.004 (0.114) Batch 0.767 (1.252) Remain 31:30:55 loss: 0.3672 Lr: 0.00337 [2024-02-18 14:33:37,775 INFO misc.py line 119 87073] Train: [42/100][1274/1557] Data 0.005 (0.114) Batch 1.104 (1.252) Remain 31:30:43 loss: 0.2032 Lr: 0.00337 [2024-02-18 14:33:38,658 INFO misc.py line 119 87073] Train: [42/100][1275/1557] Data 0.009 (0.114) Batch 0.887 (1.252) Remain 31:30:16 loss: 0.5494 Lr: 0.00337 [2024-02-18 14:33:39,646 INFO misc.py line 119 87073] Train: [42/100][1276/1557] Data 0.004 (0.114) Batch 0.988 (1.252) Remain 31:29:56 loss: 0.3477 Lr: 0.00337 [2024-02-18 14:33:40,567 INFO misc.py line 119 87073] Train: [42/100][1277/1557] Data 0.004 (0.114) Batch 0.919 (1.252) Remain 31:29:31 loss: 0.3315 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1284/1557] Data 0.004 (0.114) Batch 0.989 (1.250) Remain 31:26:51 loss: 0.2931 Lr: 0.00337 [2024-02-18 14:33:48,104 INFO misc.py line 119 87073] Train: [42/100][1285/1557] Data 0.004 (0.113) Batch 0.925 (1.250) Remain 31:26:26 loss: 0.2782 Lr: 0.00337 [2024-02-18 14:33:48,841 INFO misc.py line 119 87073] Train: [42/100][1286/1557] Data 0.004 (0.113) Batch 0.732 (1.249) Remain 31:25:49 loss: 0.2950 Lr: 0.00337 [2024-02-18 14:33:49,591 INFO misc.py line 119 87073] Train: [42/100][1287/1557] Data 0.010 (0.113) Batch 0.755 (1.249) Remain 31:25:13 loss: 0.2365 Lr: 0.00337 [2024-02-18 14:33:50,786 INFO misc.py line 119 87073] Train: [42/100][1288/1557] Data 0.004 (0.113) Batch 1.194 (1.249) Remain 31:25:07 loss: 0.1847 Lr: 0.00337 [2024-02-18 14:33:51,677 INFO misc.py line 119 87073] Train: [42/100][1289/1557] Data 0.005 (0.113) Batch 0.891 (1.248) Remain 31:24:41 loss: 0.3707 Lr: 0.00337 [2024-02-18 14:33:52,651 INFO misc.py line 119 87073] Train: [42/100][1290/1557] Data 0.005 (0.113) Batch 0.971 (1.248) Remain 31:24:20 loss: 0.8890 Lr: 0.00337 [2024-02-18 14:33:53,519 INFO misc.py line 119 87073] Train: [42/100][1291/1557] Data 0.007 (0.113) Batch 0.870 (1.248) Remain 31:23:52 loss: 0.4041 Lr: 0.00337 [2024-02-18 14:33:54,676 INFO misc.py line 119 87073] Train: [42/100][1292/1557] Data 0.006 (0.113) Batch 1.159 (1.248) Remain 31:23:45 loss: 0.2321 Lr: 0.00337 [2024-02-18 14:33:55,421 INFO misc.py line 119 87073] Train: [42/100][1293/1557] Data 0.004 (0.113) Batch 0.745 (1.248) Remain 31:23:08 loss: 0.1369 Lr: 0.00337 [2024-02-18 14:33:56,227 INFO misc.py line 119 87073] Train: [42/100][1294/1557] Data 0.004 (0.113) Batch 0.799 (1.247) Remain 31:22:36 loss: 0.4375 Lr: 0.00337 [2024-02-18 14:34:14,076 INFO misc.py line 119 87073] Train: [42/100][1295/1557] Data 5.707 (0.117) Batch 17.856 (1.260) Remain 31:41:59 loss: 0.2030 Lr: 0.00337 [2024-02-18 14:34:15,046 INFO misc.py line 119 87073] Train: [42/100][1296/1557] Data 0.004 (0.117) Batch 0.969 (1.260) Remain 31:41:37 loss: 0.4214 Lr: 0.00337 [2024-02-18 14:34:15,951 INFO misc.py line 119 87073] Train: [42/100][1297/1557] Data 0.005 (0.117) Batch 0.905 (1.260) Remain 31:41:11 loss: 0.4397 Lr: 0.00337 [2024-02-18 14:34:17,032 INFO misc.py line 119 87073] Train: [42/100][1298/1557] Data 0.004 (0.117) Batch 1.072 (1.259) Remain 31:40:56 loss: 1.1339 Lr: 0.00337 [2024-02-18 14:34:17,985 INFO misc.py line 119 87073] Train: [42/100][1299/1557] Data 0.013 (0.117) Batch 0.962 (1.259) Remain 31:40:34 loss: 0.1972 Lr: 0.00337 [2024-02-18 14:34:18,821 INFO misc.py line 119 87073] Train: [42/100][1300/1557] Data 0.005 (0.117) Batch 0.837 (1.259) Remain 31:40:04 loss: 0.5107 Lr: 0.00336 [2024-02-18 14:34:19,611 INFO misc.py line 119 87073] Train: [42/100][1301/1557] Data 0.004 (0.117) Batch 0.783 (1.258) Remain 31:39:29 loss: 0.2719 Lr: 0.00336 [2024-02-18 14:34:20,902 INFO misc.py line 119 87073] Train: [42/100][1302/1557] Data 0.012 (0.116) Batch 1.289 (1.258) Remain 31:39:30 loss: 0.1462 Lr: 0.00336 [2024-02-18 14:34:21,795 INFO misc.py line 119 87073] Train: [42/100][1303/1557] Data 0.013 (0.116) Batch 0.901 (1.258) Remain 31:39:04 loss: 0.1443 Lr: 0.00336 [2024-02-18 14:34:22,730 INFO misc.py line 119 87073] Train: [42/100][1304/1557] Data 0.006 (0.116) Batch 0.936 (1.258) Remain 31:38:40 loss: 0.7923 Lr: 0.00336 [2024-02-18 14:34:23,677 INFO misc.py line 119 87073] Train: [42/100][1305/1557] Data 0.005 (0.116) Batch 0.947 (1.258) Remain 31:38:17 loss: 0.6414 Lr: 0.00336 [2024-02-18 14:34:24,556 INFO misc.py line 119 87073] Train: [42/100][1306/1557] Data 0.005 (0.116) Batch 0.873 (1.257) Remain 31:37:49 loss: 0.8903 Lr: 0.00336 [2024-02-18 14:34:25,332 INFO misc.py line 119 87073] Train: [42/100][1307/1557] Data 0.010 (0.116) Batch 0.782 (1.257) Remain 31:37:15 loss: 0.4267 Lr: 0.00336 [2024-02-18 14:34:26,119 INFO misc.py line 119 87073] Train: [42/100][1308/1557] Data 0.005 (0.116) Batch 0.787 (1.257) Remain 31:36:41 loss: 0.5025 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1315/1557] Data 0.006 (0.115) Batch 0.718 (1.255) Remain 31:34:14 loss: 0.3452 Lr: 0.00336 [2024-02-18 14:34:34,228 INFO misc.py line 119 87073] Train: [42/100][1316/1557] Data 0.012 (0.115) Batch 1.300 (1.255) Remain 31:34:16 loss: 0.1424 Lr: 0.00336 [2024-02-18 14:34:35,233 INFO misc.py line 119 87073] Train: [42/100][1317/1557] Data 0.014 (0.115) Batch 1.010 (1.255) Remain 31:33:58 loss: 0.2112 Lr: 0.00336 [2024-02-18 14:34:36,166 INFO misc.py line 119 87073] Train: [42/100][1318/1557] Data 0.010 (0.115) Batch 0.940 (1.255) Remain 31:33:35 loss: 0.2563 Lr: 0.00336 [2024-02-18 14:34:37,226 INFO misc.py line 119 87073] Train: [42/100][1319/1557] Data 0.003 (0.115) Batch 1.060 (1.255) Remain 31:33:20 loss: 0.3970 Lr: 0.00336 [2024-02-18 14:34:38,173 INFO misc.py line 119 87073] Train: [42/100][1320/1557] Data 0.004 (0.115) Batch 0.947 (1.254) Remain 31:32:58 loss: 0.5098 Lr: 0.00336 [2024-02-18 14:34:39,077 INFO misc.py line 119 87073] Train: [42/100][1321/1557] Data 0.003 (0.115) Batch 0.904 (1.254) Remain 31:32:33 loss: 0.3549 Lr: 0.00336 [2024-02-18 14:34:39,816 INFO misc.py line 119 87073] Train: [42/100][1322/1557] Data 0.003 (0.115) Batch 0.729 (1.254) Remain 31:31:55 loss: 0.5058 Lr: 0.00336 [2024-02-18 14:34:40,881 INFO misc.py line 119 87073] Train: [42/100][1323/1557] Data 0.013 (0.115) Batch 1.066 (1.254) Remain 31:31:41 loss: 0.1201 Lr: 0.00336 [2024-02-18 14:34:41,987 INFO misc.py line 119 87073] Train: [42/100][1324/1557] Data 0.013 (0.115) Batch 1.105 (1.253) Remain 31:31:30 loss: 0.4926 Lr: 0.00336 [2024-02-18 14:34:43,102 INFO misc.py line 119 87073] Train: [42/100][1325/1557] Data 0.013 (0.115) Batch 1.122 (1.253) Remain 31:31:20 loss: 0.1914 Lr: 0.00336 [2024-02-18 14:34:44,074 INFO misc.py line 119 87073] Train: [42/100][1326/1557] Data 0.006 (0.115) Batch 0.974 (1.253) Remain 31:30:59 loss: 0.2859 Lr: 0.00336 [2024-02-18 14:34:45,268 INFO misc.py line 119 87073] Train: [42/100][1327/1557] Data 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Remain 31:29:12 loss: 0.5321 Lr: 0.00336 [2024-02-18 14:34:52,406 INFO misc.py line 119 87073] Train: [42/100][1334/1557] Data 0.015 (0.114) Batch 1.002 (1.252) Remain 31:28:54 loss: 0.1360 Lr: 0.00336 [2024-02-18 14:34:53,194 INFO misc.py line 119 87073] Train: [42/100][1335/1557] Data 0.006 (0.114) Batch 0.790 (1.252) Remain 31:28:21 loss: 0.3984 Lr: 0.00336 [2024-02-18 14:34:53,965 INFO misc.py line 119 87073] Train: [42/100][1336/1557] Data 0.004 (0.114) Batch 0.771 (1.251) Remain 31:27:47 loss: 0.2460 Lr: 0.00336 [2024-02-18 14:34:55,218 INFO misc.py line 119 87073] Train: [42/100][1337/1557] Data 0.004 (0.114) Batch 1.251 (1.251) Remain 31:27:46 loss: 0.1338 Lr: 0.00336 [2024-02-18 14:34:56,253 INFO misc.py line 119 87073] Train: [42/100][1338/1557] Data 0.006 (0.114) Batch 1.009 (1.251) Remain 31:27:28 loss: 0.7356 Lr: 0.00336 [2024-02-18 14:34:57,287 INFO misc.py line 119 87073] Train: [42/100][1339/1557] Data 0.032 (0.113) Batch 1.048 (1.251) Remain 31:27:13 loss: 0.5039 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1346/1557] Data 0.006 (0.113) Batch 0.951 (1.249) Remain 31:24:38 loss: 0.3886 Lr: 0.00336 [2024-02-18 14:35:04,813 INFO misc.py line 119 87073] Train: [42/100][1347/1557] Data 0.004 (0.113) Batch 0.961 (1.249) Remain 31:24:17 loss: 0.2307 Lr: 0.00336 [2024-02-18 14:35:05,786 INFO misc.py line 119 87073] Train: [42/100][1348/1557] Data 0.003 (0.113) Batch 0.974 (1.249) Remain 31:23:58 loss: 0.2197 Lr: 0.00336 [2024-02-18 14:35:06,530 INFO misc.py line 119 87073] Train: [42/100][1349/1557] Data 0.004 (0.113) Batch 0.742 (1.248) Remain 31:23:22 loss: 0.2730 Lr: 0.00336 [2024-02-18 14:35:07,287 INFO misc.py line 119 87073] Train: [42/100][1350/1557] Data 0.005 (0.113) Batch 0.759 (1.248) Remain 31:22:48 loss: 0.9260 Lr: 0.00336 [2024-02-18 14:35:24,563 INFO misc.py line 119 87073] Train: [42/100][1351/1557] Data 5.795 (0.117) Batch 17.275 (1.260) Remain 31:40:43 loss: 0.1359 Lr: 0.00336 [2024-02-18 14:35:25,596 INFO misc.py line 119 87073] Train: [42/100][1352/1557] Data 0.004 (0.117) Batch 1.034 (1.260) Remain 31:40:27 loss: 0.5543 Lr: 0.00336 [2024-02-18 14:35:26,609 INFO misc.py line 119 87073] Train: [42/100][1353/1557] Data 0.003 (0.117) Batch 1.012 (1.260) Remain 31:40:09 loss: 0.4135 Lr: 0.00336 [2024-02-18 14:35:27,757 INFO misc.py line 119 87073] Train: [42/100][1354/1557] Data 0.004 (0.117) Batch 1.148 (1.260) Remain 31:40:00 loss: 0.3307 Lr: 0.00336 [2024-02-18 14:35:28,662 INFO misc.py line 119 87073] Train: [42/100][1355/1557] Data 0.004 (0.116) Batch 0.905 (1.259) Remain 31:39:35 loss: 0.4779 Lr: 0.00336 [2024-02-18 14:35:29,393 INFO misc.py line 119 87073] Train: [42/100][1356/1557] Data 0.004 (0.116) Batch 0.723 (1.259) Remain 31:38:58 loss: 0.2156 Lr: 0.00336 [2024-02-18 14:35:30,163 INFO misc.py line 119 87073] Train: [42/100][1357/1557] Data 0.012 (0.116) Batch 0.777 (1.259) Remain 31:38:24 loss: 0.2622 Lr: 0.00336 [2024-02-18 14:35:31,410 INFO misc.py line 119 87073] Train: [42/100][1358/1557] Data 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Remain 31:35:50 loss: 0.2605 Lr: 0.00336 [2024-02-18 14:35:37,923 INFO misc.py line 119 87073] Train: [42/100][1365/1557] Data 0.004 (0.116) Batch 1.145 (1.257) Remain 31:35:41 loss: 0.2016 Lr: 0.00336 [2024-02-18 14:35:38,946 INFO misc.py line 119 87073] Train: [42/100][1366/1557] Data 0.004 (0.116) Batch 1.021 (1.257) Remain 31:35:24 loss: 0.3578 Lr: 0.00336 [2024-02-18 14:35:39,774 INFO misc.py line 119 87073] Train: [42/100][1367/1557] Data 0.006 (0.116) Batch 0.829 (1.256) Remain 31:34:54 loss: 0.3105 Lr: 0.00336 [2024-02-18 14:35:40,924 INFO misc.py line 119 87073] Train: [42/100][1368/1557] Data 0.005 (0.115) Batch 1.145 (1.256) Remain 31:34:46 loss: 0.3620 Lr: 0.00336 [2024-02-18 14:35:41,820 INFO misc.py line 119 87073] Train: [42/100][1369/1557] Data 0.009 (0.115) Batch 0.901 (1.256) Remain 31:34:21 loss: 0.5195 Lr: 0.00336 [2024-02-18 14:35:42,531 INFO misc.py line 119 87073] Train: [42/100][1370/1557] Data 0.004 (0.115) Batch 0.711 (1.256) Remain 31:33:44 loss: 0.3259 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1377/1557] Data 0.004 (0.115) Batch 0.778 (1.254) Remain 31:31:22 loss: 0.2658 Lr: 0.00336 [2024-02-18 14:35:50,130 INFO misc.py line 119 87073] Train: [42/100][1378/1557] Data 0.004 (0.115) Batch 0.822 (1.254) Remain 31:30:53 loss: 0.2289 Lr: 0.00336 [2024-02-18 14:35:51,162 INFO misc.py line 119 87073] Train: [42/100][1379/1557] Data 0.004 (0.115) Batch 1.031 (1.254) Remain 31:30:37 loss: 0.2195 Lr: 0.00336 [2024-02-18 14:35:52,190 INFO misc.py line 119 87073] Train: [42/100][1380/1557] Data 0.004 (0.114) Batch 1.030 (1.254) Remain 31:30:21 loss: 0.4051 Lr: 0.00336 [2024-02-18 14:35:53,385 INFO misc.py line 119 87073] Train: [42/100][1381/1557] Data 0.004 (0.114) Batch 1.190 (1.253) Remain 31:30:15 loss: 0.6352 Lr: 0.00336 [2024-02-18 14:35:54,417 INFO misc.py line 119 87073] Train: [42/100][1382/1557] Data 0.008 (0.114) Batch 1.033 (1.253) Remain 31:30:00 loss: 0.3957 Lr: 0.00336 [2024-02-18 14:35:55,405 INFO misc.py line 119 87073] Train: [42/100][1383/1557] Data 0.008 (0.114) Batch 0.991 (1.253) Remain 31:29:41 loss: 0.6976 Lr: 0.00336 [2024-02-18 14:35:56,156 INFO misc.py line 119 87073] Train: [42/100][1384/1557] Data 0.005 (0.114) Batch 0.751 (1.253) Remain 31:29:07 loss: 0.2701 Lr: 0.00336 [2024-02-18 14:35:56,883 INFO misc.py line 119 87073] Train: [42/100][1385/1557] Data 0.004 (0.114) Batch 0.717 (1.252) Remain 31:28:31 loss: 0.3889 Lr: 0.00336 [2024-02-18 14:35:58,004 INFO misc.py line 119 87073] Train: [42/100][1386/1557] Data 0.014 (0.114) Batch 1.122 (1.252) Remain 31:28:21 loss: 0.2256 Lr: 0.00336 [2024-02-18 14:35:58,907 INFO misc.py line 119 87073] Train: [42/100][1387/1557] Data 0.013 (0.114) Batch 0.913 (1.252) Remain 31:27:58 loss: 0.5048 Lr: 0.00336 [2024-02-18 14:36:00,007 INFO misc.py line 119 87073] Train: [42/100][1388/1557] Data 0.004 (0.114) Batch 1.100 (1.252) Remain 31:27:46 loss: 0.4323 Lr: 0.00336 [2024-02-18 14:36:00,877 INFO misc.py line 119 87073] Train: [42/100][1389/1557] Data 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Remain 31:25:02 loss: 0.3668 Lr: 0.00336 [2024-02-18 14:36:07,317 INFO misc.py line 119 87073] Train: [42/100][1396/1557] Data 0.004 (0.113) Batch 0.942 (1.250) Remain 31:24:40 loss: 0.5372 Lr: 0.00336 [2024-02-18 14:36:08,348 INFO misc.py line 119 87073] Train: [42/100][1397/1557] Data 0.006 (0.113) Batch 1.034 (1.250) Remain 31:24:25 loss: 0.6299 Lr: 0.00336 [2024-02-18 14:36:09,065 INFO misc.py line 119 87073] Train: [42/100][1398/1557] Data 0.003 (0.113) Batch 0.707 (1.249) Remain 31:23:49 loss: 0.2950 Lr: 0.00336 [2024-02-18 14:36:09,756 INFO misc.py line 119 87073] Train: [42/100][1399/1557] Data 0.014 (0.113) Batch 0.700 (1.249) Remain 31:23:12 loss: 0.1621 Lr: 0.00336 [2024-02-18 14:36:10,946 INFO misc.py line 119 87073] Train: [42/100][1400/1557] Data 0.004 (0.113) Batch 1.181 (1.249) Remain 31:23:06 loss: 0.1331 Lr: 0.00336 [2024-02-18 14:36:12,057 INFO misc.py line 119 87073] Train: [42/100][1401/1557] Data 0.013 (0.113) Batch 1.107 (1.249) Remain 31:22:56 loss: 0.5070 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1408/1557] Data 0.003 (0.117) Batch 0.947 (1.261) Remain 31:40:38 loss: 0.7118 Lr: 0.00336 [2024-02-18 14:36:38,377 INFO misc.py line 119 87073] Train: [42/100][1409/1557] Data 0.006 (0.117) Batch 0.951 (1.261) Remain 31:40:17 loss: 0.2760 Lr: 0.00336 [2024-02-18 14:36:39,282 INFO misc.py line 119 87073] Train: [42/100][1410/1557] Data 0.005 (0.117) Batch 0.906 (1.260) Remain 31:39:53 loss: 0.2182 Lr: 0.00336 [2024-02-18 14:36:40,152 INFO misc.py line 119 87073] Train: [42/100][1411/1557] Data 0.005 (0.117) Batch 0.867 (1.260) Remain 31:39:27 loss: 0.5135 Lr: 0.00336 [2024-02-18 14:36:40,851 INFO misc.py line 119 87073] Train: [42/100][1412/1557] Data 0.007 (0.117) Batch 0.702 (1.260) Remain 31:38:49 loss: 0.2338 Lr: 0.00336 [2024-02-18 14:36:41,642 INFO misc.py line 119 87073] Train: [42/100][1413/1557] Data 0.004 (0.117) Batch 0.783 (1.259) Remain 31:38:18 loss: 0.2548 Lr: 0.00336 [2024-02-18 14:36:42,843 INFO misc.py line 119 87073] Train: [42/100][1414/1557] Data 0.012 (0.116) Batch 1.208 (1.259) Remain 31:38:13 loss: 0.1333 Lr: 0.00336 [2024-02-18 14:36:43,863 INFO misc.py line 119 87073] Train: [42/100][1415/1557] Data 0.005 (0.116) Batch 1.015 (1.259) Remain 31:37:56 loss: 0.5599 Lr: 0.00336 [2024-02-18 14:36:44,990 INFO misc.py line 119 87073] Train: [42/100][1416/1557] Data 0.010 (0.116) Batch 1.132 (1.259) Remain 31:37:47 loss: 0.4065 Lr: 0.00336 [2024-02-18 14:36:45,884 INFO misc.py line 119 87073] Train: [42/100][1417/1557] Data 0.005 (0.116) Batch 0.895 (1.259) Remain 31:37:22 loss: 0.3907 Lr: 0.00336 [2024-02-18 14:36:46,831 INFO misc.py line 119 87073] Train: [42/100][1418/1557] Data 0.004 (0.116) Batch 0.946 (1.258) Remain 31:37:01 loss: 0.6944 Lr: 0.00336 [2024-02-18 14:36:47,638 INFO misc.py line 119 87073] Train: [42/100][1419/1557] Data 0.005 (0.116) Batch 0.798 (1.258) Remain 31:36:30 loss: 0.7221 Lr: 0.00336 [2024-02-18 14:36:48,412 INFO misc.py line 119 87073] Train: [42/100][1420/1557] Data 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Remain 31:33:50 loss: 0.3630 Lr: 0.00336 [2024-02-18 14:36:54,826 INFO misc.py line 119 87073] Train: [42/100][1427/1557] Data 0.006 (0.116) Batch 0.762 (1.256) Remain 31:33:18 loss: 0.2722 Lr: 0.00336 [2024-02-18 14:36:56,151 INFO misc.py line 119 87073] Train: [42/100][1428/1557] Data 0.010 (0.115) Batch 1.331 (1.256) Remain 31:33:21 loss: 0.1674 Lr: 0.00336 [2024-02-18 14:36:57,119 INFO misc.py line 119 87073] Train: [42/100][1429/1557] Data 0.005 (0.115) Batch 0.967 (1.256) Remain 31:33:02 loss: 0.2381 Lr: 0.00336 [2024-02-18 14:36:57,966 INFO misc.py line 119 87073] Train: [42/100][1430/1557] Data 0.006 (0.115) Batch 0.849 (1.256) Remain 31:32:35 loss: 0.2810 Lr: 0.00336 [2024-02-18 14:36:58,959 INFO misc.py line 119 87073] Train: [42/100][1431/1557] Data 0.005 (0.115) Batch 0.992 (1.255) Remain 31:32:17 loss: 0.4755 Lr: 0.00336 [2024-02-18 14:36:59,794 INFO misc.py line 119 87073] Train: [42/100][1432/1557] Data 0.005 (0.115) Batch 0.836 (1.255) Remain 31:31:49 loss: 0.4026 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1439/1557] Data 0.007 (0.115) Batch 0.876 (1.254) Remain 31:29:07 loss: 0.2911 Lr: 0.00336 [2024-02-18 14:37:06,965 INFO misc.py line 119 87073] Train: [42/100][1440/1557] Data 0.010 (0.115) Batch 0.807 (1.253) Remain 31:28:38 loss: 0.2647 Lr: 0.00336 [2024-02-18 14:37:07,703 INFO misc.py line 119 87073] Train: [42/100][1441/1557] Data 0.008 (0.114) Batch 0.741 (1.253) Remain 31:28:04 loss: 0.2719 Lr: 0.00336 [2024-02-18 14:37:08,817 INFO misc.py line 119 87073] Train: [42/100][1442/1557] Data 0.005 (0.114) Batch 1.110 (1.253) Remain 31:27:54 loss: 0.2805 Lr: 0.00336 [2024-02-18 14:37:09,763 INFO misc.py line 119 87073] Train: [42/100][1443/1557] Data 0.008 (0.114) Batch 0.950 (1.253) Remain 31:27:34 loss: 0.1924 Lr: 0.00336 [2024-02-18 14:37:10,663 INFO misc.py line 119 87073] Train: [42/100][1444/1557] Data 0.004 (0.114) Batch 0.900 (1.252) Remain 31:27:11 loss: 0.8296 Lr: 0.00336 [2024-02-18 14:37:11,653 INFO misc.py line 119 87073] Train: [42/100][1445/1557] Data 0.004 (0.114) Batch 0.990 (1.252) Remain 31:26:53 loss: 0.1906 Lr: 0.00336 [2024-02-18 14:37:12,582 INFO misc.py line 119 87073] Train: [42/100][1446/1557] Data 0.004 (0.114) Batch 0.928 (1.252) Remain 31:26:31 loss: 0.5566 Lr: 0.00336 [2024-02-18 14:37:13,388 INFO misc.py line 119 87073] Train: [42/100][1447/1557] Data 0.005 (0.114) Batch 0.803 (1.252) Remain 31:26:02 loss: 0.3488 Lr: 0.00336 [2024-02-18 14:37:14,133 INFO misc.py line 119 87073] Train: [42/100][1448/1557] Data 0.008 (0.114) Batch 0.749 (1.251) Remain 31:25:29 loss: 0.4038 Lr: 0.00336 [2024-02-18 14:37:15,406 INFO misc.py line 119 87073] Train: [42/100][1449/1557] Data 0.004 (0.114) Batch 1.266 (1.251) Remain 31:25:29 loss: 0.0926 Lr: 0.00336 [2024-02-18 14:37:16,385 INFO misc.py line 119 87073] Train: [42/100][1450/1557] Data 0.012 (0.114) Batch 0.986 (1.251) Remain 31:25:11 loss: 0.4633 Lr: 0.00336 [2024-02-18 14:37:17,274 INFO misc.py line 119 87073] Train: [42/100][1451/1557] Data 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Remain 31:22:31 loss: 0.2867 Lr: 0.00336 [2024-02-18 14:37:23,739 INFO misc.py line 119 87073] Train: [42/100][1458/1557] Data 0.004 (0.113) Batch 1.033 (1.249) Remain 31:22:16 loss: 0.4674 Lr: 0.00336 [2024-02-18 14:37:24,618 INFO misc.py line 119 87073] Train: [42/100][1459/1557] Data 0.004 (0.113) Batch 0.878 (1.249) Remain 31:21:52 loss: 0.4268 Lr: 0.00336 [2024-02-18 14:37:25,807 INFO misc.py line 119 87073] Train: [42/100][1460/1557] Data 0.005 (0.113) Batch 1.189 (1.249) Remain 31:21:47 loss: 0.6462 Lr: 0.00336 [2024-02-18 14:37:26,556 INFO misc.py line 119 87073] Train: [42/100][1461/1557] Data 0.004 (0.113) Batch 0.750 (1.249) Remain 31:21:15 loss: 0.3909 Lr: 0.00336 [2024-02-18 14:37:27,298 INFO misc.py line 119 87073] Train: [42/100][1462/1557] Data 0.004 (0.113) Batch 0.740 (1.248) Remain 31:20:42 loss: 0.1955 Lr: 0.00336 [2024-02-18 14:37:44,600 INFO misc.py line 119 87073] Train: [42/100][1463/1557] Data 5.814 (0.117) Batch 17.290 (1.259) Remain 31:37:14 loss: 0.2533 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1470/1557] Data 0.006 (0.116) Batch 1.127 (1.258) Remain 31:34:52 loss: 0.1409 Lr: 0.00336 [2024-02-18 14:37:52,097 INFO misc.py line 119 87073] Train: [42/100][1471/1557] Data 0.014 (0.116) Batch 0.855 (1.257) Remain 31:34:26 loss: 0.7732 Lr: 0.00336 [2024-02-18 14:37:53,089 INFO misc.py line 119 87073] Train: [42/100][1472/1557] Data 0.004 (0.116) Batch 0.990 (1.257) Remain 31:34:08 loss: 0.1756 Lr: 0.00336 [2024-02-18 14:37:54,078 INFO misc.py line 119 87073] Train: [42/100][1473/1557] Data 0.006 (0.116) Batch 0.990 (1.257) Remain 31:33:51 loss: 0.3397 Lr: 0.00336 [2024-02-18 14:37:55,355 INFO misc.py line 119 87073] Train: [42/100][1474/1557] Data 0.005 (0.116) Batch 1.264 (1.257) Remain 31:33:50 loss: 0.2074 Lr: 0.00336 [2024-02-18 14:37:56,108 INFO misc.py line 119 87073] Train: [42/100][1475/1557] Data 0.017 (0.116) Batch 0.766 (1.257) Remain 31:33:19 loss: 0.2760 Lr: 0.00336 [2024-02-18 14:37:56,906 INFO misc.py line 119 87073] Train: [42/100][1476/1557] Data 0.004 (0.116) Batch 0.797 (1.256) Remain 31:32:49 loss: 0.4174 Lr: 0.00336 [2024-02-18 14:37:58,045 INFO misc.py line 119 87073] Train: [42/100][1477/1557] Data 0.004 (0.116) Batch 1.131 (1.256) Remain 31:32:40 loss: 0.2296 Lr: 0.00336 [2024-02-18 14:37:59,109 INFO misc.py line 119 87073] Train: [42/100][1478/1557] Data 0.013 (0.116) Batch 1.070 (1.256) Remain 31:32:27 loss: 0.4758 Lr: 0.00336 [2024-02-18 14:38:00,003 INFO misc.py line 119 87073] Train: [42/100][1479/1557] Data 0.008 (0.116) Batch 0.897 (1.256) Remain 31:32:04 loss: 0.4279 Lr: 0.00336 [2024-02-18 14:38:00,818 INFO misc.py line 119 87073] Train: [42/100][1480/1557] Data 0.004 (0.116) Batch 0.815 (1.256) Remain 31:31:36 loss: 0.2087 Lr: 0.00336 [2024-02-18 14:38:01,806 INFO misc.py line 119 87073] Train: [42/100][1481/1557] Data 0.005 (0.115) Batch 0.988 (1.256) Remain 31:31:18 loss: 0.2662 Lr: 0.00336 [2024-02-18 14:38:02,573 INFO misc.py line 119 87073] Train: [42/100][1482/1557] Data 0.005 (0.115) Batch 0.768 (1.255) Remain 31:30:47 loss: 0.5024 Lr: 0.00336 [2024-02-18 14:38:03,341 INFO misc.py line 119 87073] Train: [42/100][1483/1557] Data 0.004 (0.115) Batch 0.762 (1.255) Remain 31:30:16 loss: 0.4674 Lr: 0.00336 [2024-02-18 14:38:04,634 INFO misc.py line 119 87073] Train: [42/100][1484/1557] Data 0.009 (0.115) Batch 1.288 (1.255) Remain 31:30:17 loss: 0.1435 Lr: 0.00336 [2024-02-18 14:38:05,519 INFO misc.py line 119 87073] Train: [42/100][1485/1557] Data 0.015 (0.115) Batch 0.896 (1.255) Remain 31:29:53 loss: 0.2866 Lr: 0.00336 [2024-02-18 14:38:06,411 INFO misc.py line 119 87073] Train: [42/100][1486/1557] Data 0.004 (0.115) Batch 0.890 (1.254) Remain 31:29:30 loss: 0.8473 Lr: 0.00336 [2024-02-18 14:38:07,370 INFO misc.py line 119 87073] Train: [42/100][1487/1557] Data 0.006 (0.115) Batch 0.960 (1.254) Remain 31:29:11 loss: 0.8689 Lr: 0.00336 [2024-02-18 14:38:08,164 INFO misc.py line 119 87073] Train: [42/100][1488/1557] Data 0.005 (0.115) Batch 0.793 (1.254) Remain 31:28:42 loss: 0.5118 Lr: 0.00336 [2024-02-18 14:38:08,863 INFO misc.py line 119 87073] Train: [42/100][1489/1557] Data 0.006 (0.115) Batch 0.701 (1.254) Remain 31:28:07 loss: 0.5956 Lr: 0.00336 [2024-02-18 14:38:09,600 INFO misc.py line 119 87073] Train: [42/100][1490/1557] Data 0.004 (0.115) Batch 0.728 (1.253) Remain 31:27:33 loss: 0.4257 Lr: 0.00336 [2024-02-18 14:38:10,763 INFO misc.py line 119 87073] Train: [42/100][1491/1557] Data 0.013 (0.115) Batch 1.166 (1.253) Remain 31:27:27 loss: 0.1353 Lr: 0.00336 [2024-02-18 14:38:11,757 INFO misc.py line 119 87073] Train: [42/100][1492/1557] Data 0.010 (0.115) Batch 0.999 (1.253) Remain 31:27:10 loss: 0.1144 Lr: 0.00336 [2024-02-18 14:38:12,574 INFO misc.py line 119 87073] Train: [42/100][1493/1557] Data 0.005 (0.115) Batch 0.816 (1.253) Remain 31:26:42 loss: 0.5755 Lr: 0.00336 [2024-02-18 14:38:13,594 INFO misc.py line 119 87073] Train: [42/100][1494/1557] Data 0.005 (0.114) Batch 1.020 (1.253) Remain 31:26:27 loss: 0.5408 Lr: 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INFO misc.py line 119 87073] Train: [42/100][1501/1557] Data 0.004 (0.114) Batch 0.895 (1.251) Remain 31:24:01 loss: 0.5553 Lr: 0.00335 [2024-02-18 14:38:20,961 INFO misc.py line 119 87073] Train: [42/100][1502/1557] Data 0.011 (0.114) Batch 0.872 (1.251) Remain 31:23:37 loss: 0.2891 Lr: 0.00335 [2024-02-18 14:38:21,747 INFO misc.py line 119 87073] Train: [42/100][1503/1557] Data 0.004 (0.114) Batch 0.785 (1.250) Remain 31:23:08 loss: 0.3522 Lr: 0.00335 [2024-02-18 14:38:22,524 INFO misc.py line 119 87073] Train: [42/100][1504/1557] Data 0.005 (0.114) Batch 0.768 (1.250) Remain 31:22:38 loss: 0.6191 Lr: 0.00335 [2024-02-18 14:38:23,868 INFO misc.py line 119 87073] Train: [42/100][1505/1557] Data 0.014 (0.114) Batch 1.348 (1.250) Remain 31:22:42 loss: 0.0613 Lr: 0.00335 [2024-02-18 14:38:24,948 INFO misc.py line 119 87073] Train: [42/100][1506/1557] Data 0.010 (0.114) Batch 1.076 (1.250) Remain 31:22:31 loss: 0.3894 Lr: 0.00335 [2024-02-18 14:38:25,859 INFO misc.py line 119 87073] Train: [42/100][1507/1557] Data 0.014 (0.114) Batch 0.921 (1.250) Remain 31:22:10 loss: 0.3115 Lr: 0.00335 [2024-02-18 14:38:26,816 INFO misc.py line 119 87073] Train: [42/100][1508/1557] Data 0.004 (0.114) Batch 0.957 (1.250) Remain 31:21:51 loss: 0.1821 Lr: 0.00335 [2024-02-18 14:38:27,732 INFO misc.py line 119 87073] Train: [42/100][1509/1557] Data 0.004 (0.113) Batch 0.915 (1.249) Remain 31:21:29 loss: 0.3435 Lr: 0.00335 [2024-02-18 14:38:28,512 INFO misc.py line 119 87073] Train: [42/100][1510/1557] Data 0.004 (0.113) Batch 0.776 (1.249) Remain 31:21:00 loss: 0.3446 Lr: 0.00335 [2024-02-18 14:38:29,294 INFO misc.py line 119 87073] Train: [42/100][1511/1557] Data 0.007 (0.113) Batch 0.786 (1.249) Remain 31:20:31 loss: 0.2266 Lr: 0.00335 [2024-02-18 14:38:30,529 INFO misc.py line 119 87073] Train: [42/100][1512/1557] Data 0.004 (0.113) Batch 1.226 (1.249) Remain 31:20:28 loss: 0.1409 Lr: 0.00335 [2024-02-18 14:38:31,474 INFO misc.py line 119 87073] Train: [42/100][1513/1557] Data 0.014 (0.113) Batch 0.955 (1.249) Remain 31:20:09 loss: 0.4112 Lr: 0.00335 [2024-02-18 14:38:32,319 INFO misc.py line 119 87073] Train: [42/100][1514/1557] Data 0.004 (0.113) Batch 0.845 (1.248) Remain 31:19:44 loss: 0.2156 Lr: 0.00335 [2024-02-18 14:38:33,214 INFO misc.py line 119 87073] Train: [42/100][1515/1557] Data 0.004 (0.113) Batch 0.888 (1.248) Remain 31:19:21 loss: 0.4055 Lr: 0.00335 [2024-02-18 14:38:34,285 INFO misc.py line 119 87073] Train: [42/100][1516/1557] Data 0.011 (0.113) Batch 1.069 (1.248) Remain 31:19:09 loss: 0.6210 Lr: 0.00335 [2024-02-18 14:38:35,044 INFO misc.py line 119 87073] Train: [42/100][1517/1557] Data 0.014 (0.113) Batch 0.767 (1.248) Remain 31:18:39 loss: 0.2970 Lr: 0.00335 [2024-02-18 14:38:35,821 INFO misc.py line 119 87073] Train: [42/100][1518/1557] Data 0.006 (0.113) Batch 0.775 (1.247) Remain 31:18:10 loss: 0.3321 Lr: 0.00335 [2024-02-18 14:38:53,335 INFO misc.py line 119 87073] Train: [42/100][1519/1557] Data 5.693 (0.116) Batch 17.518 (1.258) Remain 31:34:18 loss: 0.2097 Lr: 0.00335 [2024-02-18 14:38:54,514 INFO misc.py line 119 87073] Train: [42/100][1520/1557] Data 0.003 (0.116) Batch 1.179 (1.258) Remain 31:34:12 loss: 0.1511 Lr: 0.00335 [2024-02-18 14:38:55,472 INFO misc.py line 119 87073] Train: [42/100][1521/1557] Data 0.004 (0.116) Batch 0.958 (1.258) Remain 31:33:53 loss: 0.3169 Lr: 0.00335 [2024-02-18 14:38:56,398 INFO misc.py line 119 87073] Train: [42/100][1522/1557] Data 0.004 (0.116) Batch 0.926 (1.258) Remain 31:33:32 loss: 0.3936 Lr: 0.00335 [2024-02-18 14:38:57,383 INFO misc.py line 119 87073] Train: [42/100][1523/1557] Data 0.004 (0.116) Batch 0.985 (1.257) Remain 31:33:15 loss: 0.2053 Lr: 0.00335 [2024-02-18 14:38:58,158 INFO misc.py line 119 87073] Train: [42/100][1524/1557] Data 0.004 (0.116) Batch 0.767 (1.257) Remain 31:32:44 loss: 0.3341 Lr: 0.00335 [2024-02-18 14:38:58,939 INFO misc.py line 119 87073] Train: [42/100][1525/1557] Data 0.011 (0.116) Batch 0.789 (1.257) Remain 31:32:15 loss: 0.5095 Lr: 0.00335 [2024-02-18 14:39:00,067 INFO misc.py line 119 87073] Train: [42/100][1526/1557] Data 0.004 (0.116) Batch 1.126 (1.257) Remain 31:32:06 loss: 0.1608 Lr: 0.00335 [2024-02-18 14:39:00,915 INFO misc.py line 119 87073] Train: [42/100][1527/1557] Data 0.005 (0.116) Batch 0.849 (1.256) Remain 31:31:41 loss: 0.2271 Lr: 0.00335 [2024-02-18 14:39:01,656 INFO misc.py line 119 87073] Train: [42/100][1528/1557] Data 0.004 (0.116) Batch 0.732 (1.256) Remain 31:31:09 loss: 0.4395 Lr: 0.00335 [2024-02-18 14:39:02,581 INFO misc.py line 119 87073] Train: [42/100][1529/1557] Data 0.013 (0.116) Batch 0.933 (1.256) Remain 31:30:48 loss: 0.4405 Lr: 0.00335 [2024-02-18 14:39:03,475 INFO misc.py line 119 87073] Train: [42/100][1530/1557] Data 0.005 (0.116) Batch 0.895 (1.256) Remain 31:30:26 loss: 0.4364 Lr: 0.00335 [2024-02-18 14:39:04,200 INFO misc.py line 119 87073] Train: [42/100][1531/1557] Data 0.004 (0.116) Batch 0.714 (1.255) Remain 31:29:52 loss: 0.2118 Lr: 0.00335 [2024-02-18 14:39:04,932 INFO misc.py line 119 87073] Train: [42/100][1532/1557] Data 0.014 (0.116) Batch 0.743 (1.255) Remain 31:29:21 loss: 0.4128 Lr: 0.00335 [2024-02-18 14:39:06,135 INFO misc.py line 119 87073] Train: [42/100][1533/1557] Data 0.004 (0.115) Batch 1.203 (1.255) Remain 31:29:16 loss: 0.1530 Lr: 0.00335 [2024-02-18 14:39:07,162 INFO misc.py line 119 87073] Train: [42/100][1534/1557] Data 0.004 (0.115) Batch 1.027 (1.255) Remain 31:29:02 loss: 0.4440 Lr: 0.00335 [2024-02-18 14:39:08,079 INFO misc.py line 119 87073] Train: [42/100][1535/1557] Data 0.004 (0.115) Batch 0.918 (1.255) Remain 31:28:41 loss: 0.3255 Lr: 0.00335 [2024-02-18 14:39:09,102 INFO misc.py line 119 87073] Train: [42/100][1536/1557] Data 0.004 (0.115) Batch 1.023 (1.254) Remain 31:28:26 loss: 0.3491 Lr: 0.00335 [2024-02-18 14:39:10,204 INFO misc.py line 119 87073] Train: [42/100][1537/1557] Data 0.004 (0.115) Batch 1.102 (1.254) Remain 31:28:15 loss: 0.5672 Lr: 0.00335 [2024-02-18 14:39:10,885 INFO misc.py line 119 87073] Train: [42/100][1538/1557] Data 0.003 (0.115) Batch 0.682 (1.254) Remain 31:27:40 loss: 0.4665 Lr: 0.00335 [2024-02-18 14:39:11,661 INFO misc.py line 119 87073] Train: [42/100][1539/1557] Data 0.003 (0.115) Batch 0.774 (1.254) Remain 31:27:11 loss: 0.2239 Lr: 0.00335 [2024-02-18 14:39:13,117 INFO misc.py line 119 87073] Train: [42/100][1540/1557] Data 0.005 (0.115) Batch 1.457 (1.254) Remain 31:27:22 loss: 0.1572 Lr: 0.00335 [2024-02-18 14:39:13,965 INFO misc.py line 119 87073] Train: [42/100][1541/1557] Data 0.004 (0.115) Batch 0.849 (1.253) Remain 31:26:57 loss: 0.2559 Lr: 0.00335 [2024-02-18 14:39:14,846 INFO misc.py line 119 87073] Train: [42/100][1542/1557] Data 0.003 (0.115) Batch 0.880 (1.253) Remain 31:26:33 loss: 0.2755 Lr: 0.00335 [2024-02-18 14:39:16,021 INFO misc.py line 119 87073] Train: [42/100][1543/1557] Data 0.004 (0.115) Batch 1.175 (1.253) Remain 31:26:28 loss: 0.2897 Lr: 0.00335 [2024-02-18 14:39:16,953 INFO misc.py line 119 87073] Train: [42/100][1544/1557] Data 0.004 (0.115) Batch 0.933 (1.253) Remain 31:26:08 loss: 0.6075 Lr: 0.00335 [2024-02-18 14:39:17,704 INFO misc.py line 119 87073] Train: [42/100][1545/1557] Data 0.003 (0.115) Batch 0.750 (1.253) Remain 31:25:37 loss: 0.2778 Lr: 0.00335 [2024-02-18 14:39:18,493 INFO misc.py line 119 87073] Train: [42/100][1546/1557] Data 0.005 (0.115) Batch 0.790 (1.252) Remain 31:25:09 loss: 0.1460 Lr: 0.00335 [2024-02-18 14:39:19,539 INFO misc.py line 119 87073] Train: [42/100][1547/1557] Data 0.004 (0.114) Batch 1.045 (1.252) Remain 31:24:55 loss: 0.2034 Lr: 0.00335 [2024-02-18 14:39:20,665 INFO misc.py line 119 87073] Train: [42/100][1548/1557] Data 0.005 (0.114) Batch 1.128 (1.252) Remain 31:24:47 loss: 0.5304 Lr: 0.00335 [2024-02-18 14:39:21,563 INFO misc.py line 119 87073] Train: [42/100][1549/1557] Data 0.004 (0.114) Batch 0.897 (1.252) Remain 31:24:25 loss: 0.5673 Lr: 0.00335 [2024-02-18 14:39:22,485 INFO misc.py line 119 87073] Train: [42/100][1550/1557] Data 0.004 (0.114) Batch 0.918 (1.252) Remain 31:24:04 loss: 0.3847 Lr: 0.00335 [2024-02-18 14:39:23,407 INFO misc.py line 119 87073] Train: [42/100][1551/1557] Data 0.007 (0.114) Batch 0.926 (1.251) Remain 31:23:44 loss: 0.3718 Lr: 0.00335 [2024-02-18 14:39:24,154 INFO misc.py line 119 87073] Train: [42/100][1552/1557] Data 0.004 (0.114) Batch 0.746 (1.251) Remain 31:23:13 loss: 0.4825 Lr: 0.00335 [2024-02-18 14:39:25,007 INFO misc.py line 119 87073] Train: [42/100][1553/1557] Data 0.004 (0.114) Batch 0.853 (1.251) Remain 31:22:48 loss: 0.1628 Lr: 0.00335 [2024-02-18 14:39:26,110 INFO misc.py line 119 87073] Train: [42/100][1554/1557] Data 0.005 (0.114) Batch 1.101 (1.251) Remain 31:22:38 loss: 0.2520 Lr: 0.00335 [2024-02-18 14:39:27,081 INFO misc.py line 119 87073] Train: [42/100][1555/1557] Data 0.007 (0.114) Batch 0.973 (1.251) Remain 31:22:21 loss: 0.2186 Lr: 0.00335 [2024-02-18 14:39:28,032 INFO misc.py line 119 87073] Train: [42/100][1556/1557] Data 0.006 (0.114) Batch 0.952 (1.250) Remain 31:22:02 loss: 0.1670 Lr: 0.00335 [2024-02-18 14:39:28,889 INFO misc.py line 119 87073] Train: [42/100][1557/1557] Data 0.004 (0.114) Batch 0.856 (1.250) Remain 31:21:38 loss: 0.6208 Lr: 0.00335 [2024-02-18 14:39:28,890 INFO misc.py line 136 87073] Train result: loss: 0.3845 [2024-02-18 14:39:28,890 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 14:39:52,468 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6010 [2024-02-18 14:39:54,173 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7744 [2024-02-18 14:39:57,171 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3012 [2024-02-18 14:39:59,377 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4589 [2024-02-18 14:40:04,323 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.4071 [2024-02-18 14:40:05,021 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0858 [2024-02-18 14:40:06,280 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2599 [2024-02-18 14:40:09,235 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2761 [2024-02-18 14:40:11,043 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2406 [2024-02-18 14:40:12,713 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7142/0.7773/0.9134. [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9377/0.9674 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9824/0.9896 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8649/0.9712 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4677/0.5529 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6081/0.6186 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7507/0.8283 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8508/0.9149 [2024-02-18 14:40:12,713 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9241/0.9688 [2024-02-18 14:40:12,714 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8301/0.8502 [2024-02-18 14:40:12,714 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7865/0.8675 [2024-02-18 14:40:12,714 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6729/0.8501 [2024-02-18 14:40:12,714 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6089/0.7249 [2024-02-18 14:40:12,714 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 14:40:12,716 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 14:40:12,716 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 14:40:20,140 INFO misc.py line 119 87073] Train: [43/100][1/1557] Data 1.510 (1.510) Batch 2.265 (2.265) Remain 56:48:22 loss: 0.4037 Lr: 0.00335 [2024-02-18 14:40:21,289 INFO misc.py line 119 87073] Train: [43/100][2/1557] Data 0.006 (0.006) Batch 1.150 (1.150) Remain 28:50:54 loss: 0.5557 Lr: 0.00335 [2024-02-18 14:40:22,206 INFO misc.py line 119 87073] Train: [43/100][3/1557] Data 0.006 (0.006) Batch 0.917 (0.917) Remain 22:59:50 loss: 0.2761 Lr: 0.00335 [2024-02-18 14:40:23,279 INFO misc.py line 119 87073] Train: [43/100][4/1557] Data 0.006 (0.006) Batch 1.074 (1.074) Remain 26:55:40 loss: 0.2943 Lr: 0.00335 [2024-02-18 14:40:24,077 INFO misc.py line 119 87073] Train: [43/100][5/1557] Data 0.004 (0.005) Batch 0.796 (0.935) Remain 23:26:33 loss: 0.2931 Lr: 0.00335 [2024-02-18 14:40:24,817 INFO misc.py line 119 87073] Train: [43/100][6/1557] Data 0.007 (0.006) Batch 0.735 (0.868) Remain 21:46:31 loss: 0.4980 Lr: 0.00335 [2024-02-18 14:40:26,138 INFO misc.py line 119 87073] Train: [43/100][7/1557] Data 0.012 (0.007) Batch 1.316 (0.980) Remain 24:35:10 loss: 0.3837 Lr: 0.00335 [2024-02-18 14:40:27,202 INFO misc.py line 119 87073] Train: [43/100][8/1557] Data 0.016 (0.009) Batch 1.068 (0.998) Remain 25:01:31 loss: 0.3494 Lr: 0.00335 [2024-02-18 14:40:28,242 INFO misc.py line 119 87073] Train: [43/100][9/1557] Data 0.013 (0.010) Batch 1.038 (1.005) Remain 25:11:43 loss: 0.3308 Lr: 0.00335 [2024-02-18 14:40:29,247 INFO misc.py line 119 87073] Train: [43/100][10/1557] Data 0.014 (0.010) Batch 1.013 (1.006) Remain 25:13:31 loss: 0.2409 Lr: 0.00335 [2024-02-18 14:40:30,085 INFO misc.py line 119 87073] Train: [43/100][11/1557] Data 0.006 (0.010) Batch 0.839 (0.985) Remain 24:42:13 loss: 0.4388 Lr: 0.00335 [2024-02-18 14:40:30,931 INFO misc.py line 119 87073] Train: [43/100][12/1557] Data 0.004 (0.009) Batch 0.847 (0.970) Remain 24:19:13 loss: 0.6070 Lr: 0.00335 [2024-02-18 14:40:31,739 INFO misc.py line 119 87073] Train: [43/100][13/1557] Data 0.003 (0.009) Batch 0.802 (0.953) Remain 23:53:54 loss: 0.5050 Lr: 0.00335 [2024-02-18 14:40:32,939 INFO misc.py line 119 87073] Train: [43/100][14/1557] Data 0.009 (0.009) Batch 1.201 (0.975) Remain 24:27:51 loss: 0.1902 Lr: 0.00335 [2024-02-18 14:40:33,838 INFO misc.py line 119 87073] Train: [43/100][15/1557] Data 0.008 (0.009) Batch 0.902 (0.969) Remain 24:18:35 loss: 0.1353 Lr: 0.00335 [2024-02-18 14:40:34,804 INFO misc.py line 119 87073] Train: [43/100][16/1557] Data 0.006 (0.008) Batch 0.968 (0.969) Remain 24:18:25 loss: 0.5081 Lr: 0.00335 [2024-02-18 14:40:35,800 INFO misc.py line 119 87073] Train: [43/100][17/1557] Data 0.003 (0.008) Batch 0.996 (0.971) Remain 24:21:16 loss: 0.5892 Lr: 0.00335 [2024-02-18 14:40:36,572 INFO misc.py line 119 87073] Train: [43/100][18/1557] Data 0.004 (0.008) Batch 0.771 (0.958) Remain 24:01:09 loss: 0.4174 Lr: 0.00335 [2024-02-18 14:40:37,346 INFO misc.py line 119 87073] Train: [43/100][19/1557] Data 0.006 (0.008) Batch 0.774 (0.946) Remain 23:43:53 loss: 0.3048 Lr: 0.00335 [2024-02-18 14:40:38,110 INFO misc.py line 119 87073] Train: [43/100][20/1557] Data 0.004 (0.007) Batch 0.760 (0.935) Remain 23:27:22 loss: 0.4136 Lr: 0.00335 [2024-02-18 14:40:39,374 INFO misc.py line 119 87073] Train: [43/100][21/1557] Data 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Train: [43/100][40/1557] Data 0.004 (0.008) Batch 0.799 (0.971) Remain 24:21:00 loss: 0.1367 Lr: 0.00335 [2024-02-18 14:40:58,931 INFO misc.py line 119 87073] Train: [43/100][41/1557] Data 0.005 (0.008) Batch 0.789 (0.966) Remain 24:13:45 loss: 0.3185 Lr: 0.00335 [2024-02-18 14:41:00,202 INFO misc.py line 119 87073] Train: [43/100][42/1557] Data 0.009 (0.008) Batch 1.268 (0.974) Remain 24:25:23 loss: 0.1422 Lr: 0.00335 [2024-02-18 14:41:01,114 INFO misc.py line 119 87073] Train: [43/100][43/1557] Data 0.012 (0.008) Batch 0.920 (0.973) Remain 24:23:19 loss: 0.5392 Lr: 0.00335 [2024-02-18 14:41:02,161 INFO misc.py line 119 87073] Train: [43/100][44/1557] Data 0.004 (0.008) Batch 1.046 (0.974) Remain 24:26:00 loss: 0.2183 Lr: 0.00335 [2024-02-18 14:41:03,315 INFO misc.py line 119 87073] Train: [43/100][45/1557] Data 0.005 (0.008) Batch 1.150 (0.979) Remain 24:32:16 loss: 0.2970 Lr: 0.00335 [2024-02-18 14:41:04,232 INFO misc.py line 119 87073] Train: [43/100][46/1557] Data 0.010 (0.008) Batch 0.919 (0.977) Remain 24:30:10 loss: 0.6559 Lr: 0.00335 [2024-02-18 14:41:04,949 INFO misc.py line 119 87073] Train: [43/100][47/1557] Data 0.008 (0.008) Batch 0.719 (0.971) Remain 24:21:20 loss: 0.3044 Lr: 0.00335 [2024-02-18 14:41:05,734 INFO misc.py line 119 87073] Train: [43/100][48/1557] Data 0.005 (0.008) Batch 0.777 (0.967) Remain 24:14:49 loss: 0.5348 Lr: 0.00335 [2024-02-18 14:41:06,974 INFO misc.py line 119 87073] Train: [43/100][49/1557] Data 0.013 (0.008) Batch 1.247 (0.973) Remain 24:23:56 loss: 0.1527 Lr: 0.00335 [2024-02-18 14:41:07,954 INFO misc.py line 119 87073] Train: [43/100][50/1557] Data 0.006 (0.008) Batch 0.983 (0.973) Remain 24:24:14 loss: 0.3210 Lr: 0.00335 [2024-02-18 14:41:09,156 INFO misc.py line 119 87073] Train: [43/100][51/1557] Data 0.004 (0.008) Batch 1.198 (0.978) Remain 24:31:16 loss: 0.6742 Lr: 0.00335 [2024-02-18 14:41:10,090 INFO misc.py line 119 87073] Train: [43/100][52/1557] Data 0.007 (0.008) Batch 0.936 (0.977) Remain 24:29:58 loss: 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INFO misc.py line 119 87073] Train: [43/100][59/1557] Data 0.004 (0.007) Batch 0.953 (0.970) Remain 24:18:15 loss: 0.7982 Lr: 0.00335 [2024-02-18 14:41:17,354 INFO misc.py line 119 87073] Train: [43/100][60/1557] Data 0.005 (0.007) Batch 0.855 (0.968) Remain 24:15:13 loss: 0.9059 Lr: 0.00335 [2024-02-18 14:41:18,152 INFO misc.py line 119 87073] Train: [43/100][61/1557] Data 0.005 (0.007) Batch 0.798 (0.965) Remain 24:10:48 loss: 0.1578 Lr: 0.00335 [2024-02-18 14:41:18,964 INFO misc.py line 119 87073] Train: [43/100][62/1557] Data 0.005 (0.007) Batch 0.803 (0.962) Remain 24:06:40 loss: 0.1533 Lr: 0.00335 [2024-02-18 14:41:28,800 INFO misc.py line 119 87073] Train: [43/100][63/1557] Data 4.060 (0.075) Batch 9.846 (1.110) Remain 27:49:21 loss: 0.2690 Lr: 0.00335 [2024-02-18 14:41:29,659 INFO misc.py line 119 87073] Train: [43/100][64/1557] Data 0.005 (0.074) Batch 0.859 (1.106) Remain 27:43:08 loss: 0.3092 Lr: 0.00335 [2024-02-18 14:41:30,728 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [43/100][165/1557] Data 0.005 (0.056) Batch 0.944 (1.073) Remain 26:52:22 loss: 0.4588 Lr: 0.00334 [2024-02-18 14:43:16,854 INFO misc.py line 119 87073] Train: [43/100][166/1557] Data 0.004 (0.056) Batch 0.783 (1.071) Remain 26:49:40 loss: 0.5399 Lr: 0.00334 [2024-02-18 14:43:17,626 INFO misc.py line 119 87073] Train: [43/100][167/1557] Data 0.005 (0.056) Batch 0.765 (1.070) Remain 26:46:51 loss: 0.3411 Lr: 0.00334 [2024-02-18 14:43:18,854 INFO misc.py line 119 87073] Train: [43/100][168/1557] Data 0.013 (0.055) Batch 1.228 (1.071) Remain 26:48:16 loss: 0.2477 Lr: 0.00334 [2024-02-18 14:43:19,932 INFO misc.py line 119 87073] Train: [43/100][169/1557] Data 0.012 (0.055) Batch 1.075 (1.071) Remain 26:48:18 loss: 0.1961 Lr: 0.00334 [2024-02-18 14:43:20,831 INFO misc.py line 119 87073] Train: [43/100][170/1557] Data 0.015 (0.055) Batch 0.910 (1.070) Remain 26:46:50 loss: 0.3493 Lr: 0.00334 [2024-02-18 14:43:21,832 INFO misc.py line 119 87073] Train: 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Batch 1.115 (1.117) Remain 27:58:29 loss: 0.7688 Lr: 0.00334 [2024-02-18 14:43:37,823 INFO misc.py line 119 87073] Train: [43/100][178/1557] Data 0.011 (0.074) Batch 1.189 (1.118) Remain 27:59:05 loss: 0.3148 Lr: 0.00334 [2024-02-18 14:43:39,040 INFO misc.py line 119 87073] Train: [43/100][179/1557] Data 0.005 (0.074) Batch 1.217 (1.118) Remain 27:59:55 loss: 0.7768 Lr: 0.00334 [2024-02-18 14:43:41,667 INFO misc.py line 119 87073] Train: [43/100][180/1557] Data 0.995 (0.079) Batch 2.628 (1.127) Remain 28:12:43 loss: 0.5146 Lr: 0.00334 [2024-02-18 14:43:42,450 INFO misc.py line 119 87073] Train: [43/100][181/1557] Data 0.004 (0.079) Batch 0.774 (1.125) Remain 28:09:43 loss: 0.2279 Lr: 0.00334 [2024-02-18 14:43:43,652 INFO misc.py line 119 87073] Train: [43/100][182/1557] Data 0.012 (0.078) Batch 1.203 (1.125) Remain 28:10:21 loss: 0.3781 Lr: 0.00334 [2024-02-18 14:43:44,789 INFO misc.py line 119 87073] Train: [43/100][183/1557] Data 0.011 (0.078) Batch 1.131 (1.125) Remain 28:10:23 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87073] Train: [43/100][252/1557] Data 0.012 (0.073) Batch 1.162 (1.110) Remain 27:46:35 loss: 0.1386 Lr: 0.00334 [2024-02-18 14:44:59,766 INFO misc.py line 119 87073] Train: [43/100][253/1557] Data 0.013 (0.073) Batch 1.066 (1.110) Remain 27:46:18 loss: 0.3550 Lr: 0.00334 [2024-02-18 14:45:00,746 INFO misc.py line 119 87073] Train: [43/100][254/1557] Data 0.008 (0.073) Batch 0.983 (1.110) Remain 27:45:32 loss: 0.4229 Lr: 0.00334 [2024-02-18 14:45:01,672 INFO misc.py line 119 87073] Train: [43/100][255/1557] Data 0.005 (0.073) Batch 0.927 (1.109) Remain 27:44:25 loss: 0.1958 Lr: 0.00334 [2024-02-18 14:45:02,542 INFO misc.py line 119 87073] Train: [43/100][256/1557] Data 0.004 (0.072) Batch 0.871 (1.108) Remain 27:42:59 loss: 0.2016 Lr: 0.00334 [2024-02-18 14:45:03,321 INFO misc.py line 119 87073] Train: [43/100][257/1557] Data 0.004 (0.072) Batch 0.772 (1.107) Remain 27:40:59 loss: 0.3176 Lr: 0.00334 [2024-02-18 14:45:04,126 INFO misc.py line 119 87073] Train: [43/100][258/1557] Data 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[2024-02-18 14:45:16,154 INFO misc.py line 119 87073] Train: [43/100][271/1557] Data 0.004 (0.069) Batch 0.751 (1.097) Remain 27:25:52 loss: 0.2887 Lr: 0.00334 [2024-02-18 14:45:16,955 INFO misc.py line 119 87073] Train: [43/100][272/1557] Data 0.005 (0.068) Batch 0.796 (1.096) Remain 27:24:10 loss: 0.2071 Lr: 0.00334 [2024-02-18 14:45:18,258 INFO misc.py line 119 87073] Train: [43/100][273/1557] Data 0.010 (0.068) Batch 1.297 (1.096) Remain 27:25:16 loss: 0.2685 Lr: 0.00334 [2024-02-18 14:45:19,172 INFO misc.py line 119 87073] Train: [43/100][274/1557] Data 0.016 (0.068) Batch 0.927 (1.096) Remain 27:24:18 loss: 0.9572 Lr: 0.00334 [2024-02-18 14:45:20,278 INFO misc.py line 119 87073] Train: [43/100][275/1557] Data 0.004 (0.068) Batch 1.105 (1.096) Remain 27:24:20 loss: 0.2567 Lr: 0.00334 [2024-02-18 14:45:21,230 INFO misc.py line 119 87073] Train: [43/100][276/1557] Data 0.004 (0.068) Batch 0.952 (1.095) Remain 27:23:32 loss: 0.2940 Lr: 0.00334 [2024-02-18 14:45:22,155 INFO misc.py line 119 87073] Train: [43/100][277/1557] Data 0.004 (0.067) Batch 0.924 (1.095) Remain 27:22:34 loss: 0.3142 Lr: 0.00334 [2024-02-18 14:45:22,845 INFO misc.py line 119 87073] Train: [43/100][278/1557] Data 0.006 (0.067) Batch 0.683 (1.093) Remain 27:20:18 loss: 0.4161 Lr: 0.00334 [2024-02-18 14:45:23,616 INFO misc.py line 119 87073] Train: [43/100][279/1557] Data 0.013 (0.067) Batch 0.778 (1.092) Remain 27:18:34 loss: 0.3997 Lr: 0.00334 [2024-02-18 14:45:24,927 INFO misc.py line 119 87073] Train: [43/100][280/1557] Data 0.006 (0.067) Batch 1.300 (1.093) Remain 27:19:41 loss: 0.1664 Lr: 0.00334 [2024-02-18 14:45:25,957 INFO misc.py line 119 87073] Train: [43/100][281/1557] Data 0.016 (0.066) Batch 1.028 (1.093) Remain 27:19:19 loss: 0.4153 Lr: 0.00334 [2024-02-18 14:45:26,779 INFO misc.py line 119 87073] Train: [43/100][282/1557] Data 0.018 (0.066) Batch 0.837 (1.092) Remain 27:17:55 loss: 0.4953 Lr: 0.00334 [2024-02-18 14:45:27,892 INFO misc.py line 119 87073] Train: 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Batch 0.993 (1.120) Remain 27:59:52 loss: 0.6498 Lr: 0.00334 [2024-02-18 14:45:43,508 INFO misc.py line 119 87073] Train: [43/100][290/1557] Data 0.004 (0.079) Batch 1.067 (1.120) Remain 27:59:34 loss: 0.1774 Lr: 0.00334 [2024-02-18 14:45:44,563 INFO misc.py line 119 87073] Train: [43/100][291/1557] Data 0.006 (0.079) Batch 1.056 (1.119) Remain 27:59:13 loss: 0.4779 Lr: 0.00334 [2024-02-18 14:45:45,377 INFO misc.py line 119 87073] Train: [43/100][292/1557] Data 0.004 (0.079) Batch 0.815 (1.118) Remain 27:57:37 loss: 0.3335 Lr: 0.00334 [2024-02-18 14:45:46,149 INFO misc.py line 119 87073] Train: [43/100][293/1557] Data 0.003 (0.078) Batch 0.763 (1.117) Remain 27:55:46 loss: 0.2023 Lr: 0.00334 [2024-02-18 14:45:47,454 INFO misc.py line 119 87073] Train: [43/100][294/1557] Data 0.012 (0.078) Batch 1.311 (1.118) Remain 27:56:44 loss: 0.0844 Lr: 0.00334 [2024-02-18 14:45:48,395 INFO misc.py line 119 87073] Train: [43/100][295/1557] Data 0.007 (0.078) Batch 0.944 (1.117) Remain 27:55:50 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Batch 0.799 (1.120) Remain 27:58:56 loss: 0.2475 Lr: 0.00333 [2024-02-18 14:46:46,181 INFO misc.py line 119 87073] Train: [43/100][346/1557] Data 0.010 (0.078) Batch 1.007 (1.119) Remain 27:58:26 loss: 0.2180 Lr: 0.00333 [2024-02-18 14:46:47,161 INFO misc.py line 119 87073] Train: [43/100][347/1557] Data 0.006 (0.078) Batch 0.981 (1.119) Remain 27:57:48 loss: 0.4992 Lr: 0.00333 [2024-02-18 14:46:47,908 INFO misc.py line 119 87073] Train: [43/100][348/1557] Data 0.004 (0.078) Batch 0.747 (1.118) Remain 27:56:10 loss: 0.3933 Lr: 0.00333 [2024-02-18 14:46:48,787 INFO misc.py line 119 87073] Train: [43/100][349/1557] Data 0.005 (0.078) Batch 0.874 (1.117) Remain 27:55:06 loss: 0.4438 Lr: 0.00333 [2024-02-18 14:46:50,022 INFO misc.py line 119 87073] Train: [43/100][350/1557] Data 0.010 (0.078) Batch 1.232 (1.118) Remain 27:55:34 loss: 0.1372 Lr: 0.00333 [2024-02-18 14:46:51,047 INFO misc.py line 119 87073] Train: [43/100][351/1557] Data 0.012 (0.077) Batch 1.020 (1.117) Remain 27:55:08 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Batch 0.980 (1.124) Remain 28:04:40 loss: 0.6213 Lr: 0.00333 [2024-02-18 14:47:50,537 INFO misc.py line 119 87073] Train: [43/100][402/1557] Data 0.005 (0.082) Batch 0.858 (1.124) Remain 28:03:39 loss: 0.3970 Lr: 0.00333 [2024-02-18 14:47:51,532 INFO misc.py line 119 87073] Train: [43/100][403/1557] Data 0.004 (0.081) Batch 0.987 (1.123) Remain 28:03:07 loss: 0.1038 Lr: 0.00333 [2024-02-18 14:47:52,336 INFO misc.py line 119 87073] Train: [43/100][404/1557] Data 0.012 (0.081) Batch 0.812 (1.123) Remain 28:01:56 loss: 0.3283 Lr: 0.00333 [2024-02-18 14:47:53,099 INFO misc.py line 119 87073] Train: [43/100][405/1557] Data 0.004 (0.081) Batch 0.764 (1.122) Remain 28:00:35 loss: 0.1752 Lr: 0.00333 [2024-02-18 14:47:54,211 INFO misc.py line 119 87073] Train: [43/100][406/1557] Data 0.004 (0.081) Batch 1.112 (1.122) Remain 28:00:31 loss: 0.1889 Lr: 0.00333 [2024-02-18 14:47:55,110 INFO misc.py line 119 87073] Train: [43/100][407/1557] Data 0.004 (0.081) Batch 0.899 (1.121) Remain 27:59:41 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Batch 1.033 (1.124) Remain 28:02:29 loss: 0.2454 Lr: 0.00333 [2024-02-18 14:48:53,256 INFO misc.py line 119 87073] Train: [43/100][458/1557] Data 0.005 (0.081) Batch 0.961 (1.123) Remain 28:01:55 loss: 0.5184 Lr: 0.00333 [2024-02-18 14:48:54,155 INFO misc.py line 119 87073] Train: [43/100][459/1557] Data 0.004 (0.081) Batch 0.899 (1.123) Remain 28:01:10 loss: 0.4679 Lr: 0.00333 [2024-02-18 14:48:54,924 INFO misc.py line 119 87073] Train: [43/100][460/1557] Data 0.005 (0.081) Batch 0.770 (1.122) Remain 28:00:00 loss: 0.4092 Lr: 0.00333 [2024-02-18 14:48:55,681 INFO misc.py line 119 87073] Train: [43/100][461/1557] Data 0.004 (0.081) Batch 0.756 (1.121) Remain 27:58:47 loss: 0.3659 Lr: 0.00333 [2024-02-18 14:48:56,850 INFO misc.py line 119 87073] Train: [43/100][462/1557] Data 0.005 (0.081) Batch 1.169 (1.121) Remain 27:58:55 loss: 0.1358 Lr: 0.00333 [2024-02-18 14:48:57,775 INFO misc.py line 119 87073] Train: [43/100][463/1557] Data 0.005 (0.081) Batch 0.925 (1.121) Remain 27:58:16 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line 119 87073] Train: [43/100][557/1557] Data 0.006 (0.078) Batch 0.947 (1.114) Remain 27:45:58 loss: 0.2025 Lr: 0.00332 [2024-02-18 14:50:40,009 INFO misc.py line 119 87073] Train: [43/100][558/1557] Data 0.005 (0.077) Batch 0.780 (1.113) Remain 27:45:03 loss: 0.1459 Lr: 0.00332 [2024-02-18 14:50:40,738 INFO misc.py line 119 87073] Train: [43/100][559/1557] Data 0.005 (0.077) Batch 0.729 (1.112) Remain 27:44:00 loss: 0.2623 Lr: 0.00332 [2024-02-18 14:50:41,969 INFO misc.py line 119 87073] Train: [43/100][560/1557] Data 0.005 (0.077) Batch 1.226 (1.113) Remain 27:44:17 loss: 0.1839 Lr: 0.00332 [2024-02-18 14:50:42,764 INFO misc.py line 119 87073] Train: [43/100][561/1557] Data 0.009 (0.077) Batch 0.798 (1.112) Remain 27:43:26 loss: 0.4067 Lr: 0.00332 [2024-02-18 14:50:43,778 INFO misc.py line 119 87073] Train: [43/100][562/1557] Data 0.006 (0.077) Batch 1.016 (1.112) Remain 27:43:09 loss: 0.3450 Lr: 0.00332 [2024-02-18 14:50:44,739 INFO misc.py line 119 87073] Train: 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Batch 0.884 (1.125) Remain 28:02:44 loss: 0.2387 Lr: 0.00332 [2024-02-18 14:51:00,000 INFO misc.py line 119 87073] Train: [43/100][570/1557] Data 0.006 (0.083) Batch 0.972 (1.125) Remain 28:02:19 loss: 0.3013 Lr: 0.00332 [2024-02-18 14:51:00,906 INFO misc.py line 119 87073] Train: [43/100][571/1557] Data 0.008 (0.083) Batch 0.909 (1.124) Remain 28:01:44 loss: 0.3350 Lr: 0.00332 [2024-02-18 14:51:01,601 INFO misc.py line 119 87073] Train: [43/100][572/1557] Data 0.005 (0.082) Batch 0.695 (1.124) Remain 28:00:35 loss: 0.2678 Lr: 0.00332 [2024-02-18 14:51:02,397 INFO misc.py line 119 87073] Train: [43/100][573/1557] Data 0.004 (0.082) Batch 0.787 (1.123) Remain 27:59:41 loss: 0.4735 Lr: 0.00332 [2024-02-18 14:51:03,605 INFO misc.py line 119 87073] Train: [43/100][574/1557] Data 0.013 (0.082) Batch 1.208 (1.123) Remain 27:59:53 loss: 0.2133 Lr: 0.00332 [2024-02-18 14:51:04,732 INFO misc.py line 119 87073] Train: [43/100][575/1557] Data 0.012 (0.082) Batch 1.121 (1.123) Remain 27:59:52 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line 119 87073] Train: [43/100][725/1557] Data 0.011 (0.079) Batch 1.020 (1.116) Remain 27:46:45 loss: 0.4354 Lr: 0.00332 [2024-02-18 14:53:49,000 INFO misc.py line 119 87073] Train: [43/100][726/1557] Data 0.015 (0.079) Batch 0.774 (1.116) Remain 27:46:02 loss: 0.4979 Lr: 0.00332 [2024-02-18 14:53:49,818 INFO misc.py line 119 87073] Train: [43/100][727/1557] Data 0.004 (0.079) Batch 0.817 (1.115) Remain 27:45:23 loss: 0.2603 Lr: 0.00332 [2024-02-18 14:53:51,136 INFO misc.py line 119 87073] Train: [43/100][728/1557] Data 0.005 (0.079) Batch 1.317 (1.116) Remain 27:45:47 loss: 0.1983 Lr: 0.00332 [2024-02-18 14:53:52,213 INFO misc.py line 119 87073] Train: [43/100][729/1557] Data 0.006 (0.079) Batch 1.069 (1.116) Remain 27:45:40 loss: 0.6596 Lr: 0.00332 [2024-02-18 14:53:53,231 INFO misc.py line 119 87073] Train: [43/100][730/1557] Data 0.014 (0.079) Batch 1.016 (1.116) Remain 27:45:27 loss: 0.8177 Lr: 0.00332 [2024-02-18 14:53:54,364 INFO misc.py line 119 87073] Train: 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Batch 0.839 (1.125) Remain 27:59:48 loss: 0.5447 Lr: 0.00332 [2024-02-18 14:54:09,154 INFO misc.py line 119 87073] Train: [43/100][738/1557] Data 0.004 (0.083) Batch 0.997 (1.125) Remain 27:59:31 loss: 0.3458 Lr: 0.00332 [2024-02-18 14:54:10,095 INFO misc.py line 119 87073] Train: [43/100][739/1557] Data 0.012 (0.083) Batch 0.948 (1.125) Remain 27:59:09 loss: 0.2599 Lr: 0.00332 [2024-02-18 14:54:10,831 INFO misc.py line 119 87073] Train: [43/100][740/1557] Data 0.005 (0.083) Batch 0.736 (1.124) Remain 27:58:20 loss: 0.2274 Lr: 0.00332 [2024-02-18 14:54:11,602 INFO misc.py line 119 87073] Train: [43/100][741/1557] Data 0.005 (0.083) Batch 0.768 (1.124) Remain 27:57:36 loss: 0.2866 Lr: 0.00332 [2024-02-18 14:54:12,888 INFO misc.py line 119 87073] Train: [43/100][742/1557] Data 0.008 (0.082) Batch 1.285 (1.124) Remain 27:57:54 loss: 0.1309 Lr: 0.00332 [2024-02-18 14:54:13,840 INFO misc.py line 119 87073] Train: [43/100][743/1557] Data 0.009 (0.082) Batch 0.957 (1.124) Remain 27:57:33 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(0.081) Batch 1.030 (1.123) Remain 27:47:03 loss: 0.9095 Lr: 0.00329 [2024-02-18 15:03:25,605 INFO misc.py line 119 87073] Train: [43/100][1235/1557] Data 0.003 (0.081) Batch 1.042 (1.123) Remain 27:46:56 loss: 0.4849 Lr: 0.00329 [2024-02-18 15:03:26,722 INFO misc.py line 119 87073] Train: [43/100][1236/1557] Data 0.004 (0.081) Batch 1.115 (1.123) Remain 27:46:55 loss: 0.4487 Lr: 0.00329 [2024-02-18 15:03:27,589 INFO misc.py line 119 87073] Train: [43/100][1237/1557] Data 0.006 (0.081) Batch 0.869 (1.123) Remain 27:46:35 loss: 0.6366 Lr: 0.00329 [2024-02-18 15:03:28,361 INFO misc.py line 119 87073] Train: [43/100][1238/1557] Data 0.003 (0.081) Batch 0.771 (1.122) Remain 27:46:09 loss: 0.3477 Lr: 0.00329 [2024-02-18 15:03:38,419 INFO misc.py line 119 87073] Train: [43/100][1239/1557] Data 3.905 (0.084) Batch 10.058 (1.130) Remain 27:56:51 loss: 0.1454 Lr: 0.00329 [2024-02-18 15:03:39,527 INFO misc.py line 119 87073] Train: [43/100][1240/1557] Data 0.004 (0.084) Batch 1.109 (1.130) Remain 27:56:49 loss: 0.3548 Lr: 0.00329 [2024-02-18 15:03:40,427 INFO misc.py line 119 87073] Train: [43/100][1241/1557] Data 0.005 (0.084) Batch 0.900 (1.129) Remain 27:56:31 loss: 0.4365 Lr: 0.00329 [2024-02-18 15:03:41,370 INFO misc.py line 119 87073] Train: [43/100][1242/1557] Data 0.004 (0.084) Batch 0.943 (1.129) Remain 27:56:17 loss: 0.4677 Lr: 0.00329 [2024-02-18 15:03:42,372 INFO misc.py line 119 87073] Train: [43/100][1243/1557] Data 0.003 (0.084) Batch 1.002 (1.129) Remain 27:56:06 loss: 0.5718 Lr: 0.00329 [2024-02-18 15:03:43,177 INFO misc.py line 119 87073] Train: [43/100][1244/1557] Data 0.004 (0.084) Batch 0.806 (1.129) Remain 27:55:42 loss: 0.2879 Lr: 0.00329 [2024-02-18 15:03:43,943 INFO misc.py line 119 87073] Train: [43/100][1245/1557] Data 0.004 (0.084) Batch 0.766 (1.129) Remain 27:55:15 loss: 0.3810 Lr: 0.00329 [2024-02-18 15:03:45,238 INFO misc.py line 119 87073] Train: [43/100][1246/1557] Data 0.003 (0.084) Batch 1.285 (1.129) Remain 27:55:25 loss: 0.1407 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1253/1557] Data 0.004 (0.083) Batch 1.348 (1.128) Remain 27:54:07 loss: 0.2634 Lr: 0.00329 [2024-02-18 15:03:52,985 INFO misc.py line 119 87073] Train: [43/100][1254/1557] Data 0.016 (0.083) Batch 0.842 (1.128) Remain 27:53:45 loss: 0.6279 Lr: 0.00329 [2024-02-18 15:03:53,951 INFO misc.py line 119 87073] Train: [43/100][1255/1557] Data 0.005 (0.083) Batch 0.966 (1.128) Remain 27:53:33 loss: 0.4157 Lr: 0.00329 [2024-02-18 15:03:54,836 INFO misc.py line 119 87073] Train: [43/100][1256/1557] Data 0.004 (0.083) Batch 0.886 (1.127) Remain 27:53:14 loss: 0.4016 Lr: 0.00329 [2024-02-18 15:03:55,718 INFO misc.py line 119 87073] Train: [43/100][1257/1557] Data 0.005 (0.083) Batch 0.881 (1.127) Remain 27:52:56 loss: 0.7929 Lr: 0.00329 [2024-02-18 15:03:56,540 INFO misc.py line 119 87073] Train: [43/100][1258/1557] Data 0.006 (0.083) Batch 0.824 (1.127) Remain 27:52:33 loss: 0.1871 Lr: 0.00329 [2024-02-18 15:03:57,334 INFO misc.py line 119 87073] Train: [43/100][1259/1557] Data 0.003 (0.083) Batch 0.793 (1.127) Remain 27:52:08 loss: 0.3023 Lr: 0.00329 [2024-02-18 15:03:58,415 INFO misc.py line 119 87073] Train: [43/100][1260/1557] Data 0.004 (0.083) Batch 1.081 (1.127) Remain 27:52:04 loss: 0.1210 Lr: 0.00329 [2024-02-18 15:03:59,507 INFO misc.py line 119 87073] Train: [43/100][1261/1557] Data 0.004 (0.083) Batch 1.084 (1.127) Remain 27:52:00 loss: 0.3651 Lr: 0.00329 [2024-02-18 15:04:00,447 INFO misc.py line 119 87073] Train: [43/100][1262/1557] Data 0.013 (0.083) Batch 0.948 (1.126) Remain 27:51:46 loss: 0.3519 Lr: 0.00329 [2024-02-18 15:04:01,432 INFO misc.py line 119 87073] Train: [43/100][1263/1557] Data 0.004 (0.083) Batch 0.985 (1.126) Remain 27:51:35 loss: 0.3109 Lr: 0.00329 [2024-02-18 15:04:02,370 INFO misc.py line 119 87073] Train: [43/100][1264/1557] Data 0.004 (0.083) Batch 0.938 (1.126) Remain 27:51:20 loss: 0.4998 Lr: 0.00329 [2024-02-18 15:04:03,131 INFO misc.py line 119 87073] Train: [43/100][1265/1557] Data 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Remain 27:50:14 loss: 0.3354 Lr: 0.00329 [2024-02-18 15:04:10,122 INFO misc.py line 119 87073] Train: [43/100][1272/1557] Data 0.014 (0.082) Batch 0.701 (1.125) Remain 27:49:43 loss: 0.2729 Lr: 0.00329 [2024-02-18 15:04:10,876 INFO misc.py line 119 87073] Train: [43/100][1273/1557] Data 0.003 (0.082) Batch 0.752 (1.125) Remain 27:49:16 loss: 0.3339 Lr: 0.00329 [2024-02-18 15:04:12,198 INFO misc.py line 119 87073] Train: [43/100][1274/1557] Data 0.005 (0.082) Batch 1.312 (1.125) Remain 27:49:28 loss: 0.3073 Lr: 0.00329 [2024-02-18 15:04:13,216 INFO misc.py line 119 87073] Train: [43/100][1275/1557] Data 0.014 (0.082) Batch 1.029 (1.125) Remain 27:49:20 loss: 0.1451 Lr: 0.00329 [2024-02-18 15:04:14,173 INFO misc.py line 119 87073] Train: [43/100][1276/1557] Data 0.003 (0.082) Batch 0.956 (1.125) Remain 27:49:07 loss: 0.3494 Lr: 0.00329 [2024-02-18 15:04:15,183 INFO misc.py line 119 87073] Train: [43/100][1277/1557] Data 0.004 (0.082) Batch 1.011 (1.125) Remain 27:48:58 loss: 0.4322 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1284/1557] Data 0.004 (0.082) Batch 1.072 (1.124) Remain 27:47:42 loss: 0.3784 Lr: 0.00329 [2024-02-18 15:04:23,034 INFO misc.py line 119 87073] Train: [43/100][1285/1557] Data 0.004 (0.082) Batch 0.950 (1.124) Remain 27:47:29 loss: 0.3570 Lr: 0.00329 [2024-02-18 15:04:23,787 INFO misc.py line 119 87073] Train: [43/100][1286/1557] Data 0.004 (0.081) Batch 0.744 (1.124) Remain 27:47:02 loss: 0.4390 Lr: 0.00329 [2024-02-18 15:04:24,561 INFO misc.py line 119 87073] Train: [43/100][1287/1557] Data 0.014 (0.081) Batch 0.782 (1.123) Remain 27:46:37 loss: 0.1574 Lr: 0.00329 [2024-02-18 15:04:25,752 INFO misc.py line 119 87073] Train: [43/100][1288/1557] Data 0.004 (0.081) Batch 1.191 (1.123) Remain 27:46:41 loss: 0.1816 Lr: 0.00329 [2024-02-18 15:04:26,655 INFO misc.py line 119 87073] Train: [43/100][1289/1557] Data 0.005 (0.081) Batch 0.902 (1.123) Remain 27:46:24 loss: 0.3641 Lr: 0.00329 [2024-02-18 15:04:27,631 INFO misc.py line 119 87073] Train: [43/100][1290/1557] Data 0.006 (0.081) Batch 0.967 (1.123) Remain 27:46:12 loss: 0.5348 Lr: 0.00329 [2024-02-18 15:04:28,682 INFO misc.py line 119 87073] Train: [43/100][1291/1557] Data 0.014 (0.081) Batch 1.058 (1.123) Remain 27:46:07 loss: 0.6927 Lr: 0.00329 [2024-02-18 15:04:29,579 INFO misc.py line 119 87073] Train: [43/100][1292/1557] Data 0.008 (0.081) Batch 0.901 (1.123) Remain 27:45:50 loss: 0.6209 Lr: 0.00329 [2024-02-18 15:04:30,300 INFO misc.py line 119 87073] Train: [43/100][1293/1557] Data 0.004 (0.081) Batch 0.721 (1.123) Remain 27:45:21 loss: 0.2626 Lr: 0.00329 [2024-02-18 15:04:30,946 INFO misc.py line 119 87073] Train: [43/100][1294/1557] Data 0.004 (0.081) Batch 0.645 (1.122) Remain 27:44:47 loss: 0.2439 Lr: 0.00329 [2024-02-18 15:04:41,758 INFO misc.py line 119 87073] Train: [43/100][1295/1557] Data 3.942 (0.084) Batch 10.813 (1.130) Remain 27:55:54 loss: 0.1410 Lr: 0.00329 [2024-02-18 15:04:42,690 INFO misc.py line 119 87073] Train: [43/100][1296/1557] Data 0.004 (0.084) Batch 0.931 (1.130) Remain 27:55:39 loss: 0.6410 Lr: 0.00329 [2024-02-18 15:04:43,598 INFO misc.py line 119 87073] Train: [43/100][1297/1557] Data 0.005 (0.084) Batch 0.899 (1.129) Remain 27:55:22 loss: 0.3246 Lr: 0.00329 [2024-02-18 15:04:44,660 INFO misc.py line 119 87073] Train: [43/100][1298/1557] Data 0.014 (0.084) Batch 1.066 (1.129) Remain 27:55:16 loss: 0.4432 Lr: 0.00329 [2024-02-18 15:04:45,685 INFO misc.py line 119 87073] Train: [43/100][1299/1557] Data 0.010 (0.084) Batch 1.023 (1.129) Remain 27:55:08 loss: 0.3222 Lr: 0.00329 [2024-02-18 15:04:46,436 INFO misc.py line 119 87073] Train: [43/100][1300/1557] Data 0.012 (0.084) Batch 0.759 (1.129) Remain 27:54:41 loss: 0.3099 Lr: 0.00329 [2024-02-18 15:04:47,227 INFO misc.py line 119 87073] Train: [43/100][1301/1557] Data 0.004 (0.084) Batch 0.782 (1.129) Remain 27:54:17 loss: 0.5937 Lr: 0.00329 [2024-02-18 15:04:48,503 INFO misc.py line 119 87073] Train: [43/100][1302/1557] Data 0.013 (0.084) Batch 1.273 (1.129) Remain 27:54:25 loss: 0.1533 Lr: 0.00329 [2024-02-18 15:04:49,396 INFO misc.py line 119 87073] Train: [43/100][1303/1557] Data 0.016 (0.084) Batch 0.904 (1.129) Remain 27:54:09 loss: 0.5090 Lr: 0.00329 [2024-02-18 15:04:50,371 INFO misc.py line 119 87073] Train: [43/100][1304/1557] Data 0.006 (0.083) Batch 0.976 (1.128) Remain 27:53:57 loss: 0.5494 Lr: 0.00329 [2024-02-18 15:04:51,264 INFO misc.py line 119 87073] Train: [43/100][1305/1557] Data 0.004 (0.083) Batch 0.893 (1.128) Remain 27:53:40 loss: 0.1904 Lr: 0.00329 [2024-02-18 15:04:52,153 INFO misc.py line 119 87073] Train: [43/100][1306/1557] Data 0.004 (0.083) Batch 0.880 (1.128) Remain 27:53:22 loss: 0.7789 Lr: 0.00329 [2024-02-18 15:04:52,909 INFO misc.py line 119 87073] Train: [43/100][1307/1557] Data 0.013 (0.083) Batch 0.766 (1.128) Remain 27:52:56 loss: 0.4487 Lr: 0.00329 [2024-02-18 15:04:53,685 INFO misc.py line 119 87073] Train: [43/100][1308/1557] Data 0.004 (0.083) Batch 0.772 (1.128) Remain 27:52:31 loss: 0.3451 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1315/1557] Data 0.004 (0.083) Batch 0.787 (1.127) Remain 27:50:51 loss: 0.4222 Lr: 0.00329 [2024-02-18 15:05:01,400 INFO misc.py line 119 87073] Train: [43/100][1316/1557] Data 0.010 (0.083) Batch 1.171 (1.127) Remain 27:50:53 loss: 0.1543 Lr: 0.00329 [2024-02-18 15:05:02,319 INFO misc.py line 119 87073] Train: [43/100][1317/1557] Data 0.015 (0.083) Batch 0.931 (1.126) Remain 27:50:38 loss: 0.1179 Lr: 0.00329 [2024-02-18 15:05:03,296 INFO misc.py line 119 87073] Train: [43/100][1318/1557] Data 0.004 (0.083) Batch 0.977 (1.126) Remain 27:50:27 loss: 0.5408 Lr: 0.00329 [2024-02-18 15:05:04,202 INFO misc.py line 119 87073] Train: [43/100][1319/1557] Data 0.004 (0.083) Batch 0.905 (1.126) Remain 27:50:11 loss: 0.2941 Lr: 0.00329 [2024-02-18 15:05:05,170 INFO misc.py line 119 87073] Train: [43/100][1320/1557] Data 0.004 (0.083) Batch 0.957 (1.126) Remain 27:49:58 loss: 0.3099 Lr: 0.00329 [2024-02-18 15:05:05,915 INFO misc.py line 119 87073] Train: [43/100][1321/1557] Data 0.015 (0.082) Batch 0.754 (1.126) Remain 27:49:32 loss: 0.2685 Lr: 0.00329 [2024-02-18 15:05:06,727 INFO misc.py line 119 87073] Train: [43/100][1322/1557] Data 0.006 (0.082) Batch 0.813 (1.125) Remain 27:49:10 loss: 0.2369 Lr: 0.00329 [2024-02-18 15:05:08,046 INFO misc.py line 119 87073] Train: [43/100][1323/1557] Data 0.006 (0.082) Batch 1.313 (1.126) Remain 27:49:21 loss: 0.1258 Lr: 0.00329 [2024-02-18 15:05:09,162 INFO misc.py line 119 87073] Train: [43/100][1324/1557] Data 0.012 (0.082) Batch 1.120 (1.126) Remain 27:49:20 loss: 0.5001 Lr: 0.00329 [2024-02-18 15:05:10,240 INFO misc.py line 119 87073] Train: [43/100][1325/1557] Data 0.007 (0.082) Batch 1.073 (1.126) Remain 27:49:15 loss: 0.6919 Lr: 0.00329 [2024-02-18 15:05:11,208 INFO misc.py line 119 87073] Train: [43/100][1326/1557] Data 0.012 (0.082) Batch 0.975 (1.125) Remain 27:49:04 loss: 0.2266 Lr: 0.00329 [2024-02-18 15:05:12,157 INFO misc.py line 119 87073] Train: [43/100][1327/1557] Data 0.005 (0.082) Batch 0.950 (1.125) Remain 27:48:51 loss: 0.2342 Lr: 0.00329 [2024-02-18 15:05:12,873 INFO misc.py line 119 87073] Train: [43/100][1328/1557] Data 0.004 (0.082) Batch 0.710 (1.125) Remain 27:48:22 loss: 0.4089 Lr: 0.00329 [2024-02-18 15:05:13,642 INFO misc.py line 119 87073] Train: [43/100][1329/1557] Data 0.010 (0.082) Batch 0.775 (1.125) Remain 27:47:57 loss: 0.4709 Lr: 0.00329 [2024-02-18 15:05:14,944 INFO misc.py line 119 87073] Train: [43/100][1330/1557] Data 0.004 (0.082) Batch 1.294 (1.125) Remain 27:48:08 loss: 0.1260 Lr: 0.00329 [2024-02-18 15:05:16,093 INFO misc.py line 119 87073] Train: [43/100][1331/1557] Data 0.011 (0.082) Batch 1.145 (1.125) Remain 27:48:08 loss: 0.0956 Lr: 0.00329 [2024-02-18 15:05:17,121 INFO misc.py line 119 87073] Train: [43/100][1332/1557] Data 0.015 (0.082) Batch 1.038 (1.125) Remain 27:48:01 loss: 0.5659 Lr: 0.00329 [2024-02-18 15:05:18,139 INFO misc.py line 119 87073] Train: [43/100][1333/1557] Data 0.005 (0.082) Batch 1.019 (1.125) Remain 27:47:53 loss: 0.3044 Lr: 0.00329 [2024-02-18 15:05:19,302 INFO misc.py line 119 87073] Train: [43/100][1334/1557] Data 0.005 (0.082) Batch 1.151 (1.125) Remain 27:47:53 loss: 0.2628 Lr: 0.00329 [2024-02-18 15:05:20,089 INFO misc.py line 119 87073] Train: [43/100][1335/1557] Data 0.016 (0.082) Batch 0.799 (1.125) Remain 27:47:31 loss: 0.4901 Lr: 0.00329 [2024-02-18 15:05:20,882 INFO misc.py line 119 87073] Train: [43/100][1336/1557] Data 0.004 (0.082) Batch 0.793 (1.124) Remain 27:47:07 loss: 0.2837 Lr: 0.00329 [2024-02-18 15:05:22,195 INFO misc.py line 119 87073] Train: [43/100][1337/1557] Data 0.004 (0.082) Batch 1.302 (1.124) Remain 27:47:18 loss: 0.3700 Lr: 0.00329 [2024-02-18 15:05:23,117 INFO misc.py line 119 87073] Train: [43/100][1338/1557] Data 0.014 (0.082) Batch 0.931 (1.124) Remain 27:47:04 loss: 0.5366 Lr: 0.00329 [2024-02-18 15:05:24,017 INFO misc.py line 119 87073] Train: [43/100][1339/1557] Data 0.005 (0.081) Batch 0.901 (1.124) Remain 27:46:48 loss: 0.1658 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1346/1557] Data 0.003 (0.081) Batch 0.961 (1.123) Remain 27:45:18 loss: 0.7733 Lr: 0.00328 [2024-02-18 15:05:31,575 INFO misc.py line 119 87073] Train: [43/100][1347/1557] Data 0.004 (0.081) Batch 0.930 (1.123) Remain 27:45:04 loss: 0.6342 Lr: 0.00328 [2024-02-18 15:05:32,563 INFO misc.py line 119 87073] Train: [43/100][1348/1557] Data 0.005 (0.081) Batch 0.983 (1.123) Remain 27:44:54 loss: 0.1695 Lr: 0.00328 [2024-02-18 15:05:33,343 INFO misc.py line 119 87073] Train: [43/100][1349/1557] Data 0.010 (0.081) Batch 0.786 (1.123) Remain 27:44:30 loss: 0.4886 Lr: 0.00328 [2024-02-18 15:05:34,094 INFO misc.py line 119 87073] Train: [43/100][1350/1557] Data 0.003 (0.081) Batch 0.746 (1.122) Remain 27:44:04 loss: 0.3326 Lr: 0.00328 [2024-02-18 15:05:44,935 INFO misc.py line 119 87073] Train: [43/100][1351/1557] Data 3.967 (0.084) Batch 10.836 (1.130) Remain 27:54:44 loss: 0.1122 Lr: 0.00328 [2024-02-18 15:05:46,092 INFO misc.py line 119 87073] Train: [43/100][1352/1557] Data 0.015 (0.084) Batch 1.160 (1.130) Remain 27:54:45 loss: 0.2380 Lr: 0.00328 [2024-02-18 15:05:46,880 INFO misc.py line 119 87073] Train: [43/100][1353/1557] Data 0.011 (0.084) Batch 0.795 (1.129) Remain 27:54:22 loss: 0.3042 Lr: 0.00328 [2024-02-18 15:05:47,792 INFO misc.py line 119 87073] Train: [43/100][1354/1557] Data 0.004 (0.084) Batch 0.912 (1.129) Remain 27:54:06 loss: 0.6747 Lr: 0.00328 [2024-02-18 15:05:48,728 INFO misc.py line 119 87073] Train: [43/100][1355/1557] Data 0.004 (0.084) Batch 0.932 (1.129) Remain 27:53:52 loss: 0.3167 Lr: 0.00328 [2024-02-18 15:05:49,533 INFO misc.py line 119 87073] Train: [43/100][1356/1557] Data 0.008 (0.083) Batch 0.808 (1.129) Remain 27:53:30 loss: 0.2590 Lr: 0.00328 [2024-02-18 15:05:50,318 INFO misc.py line 119 87073] Train: [43/100][1357/1557] Data 0.005 (0.083) Batch 0.786 (1.129) Remain 27:53:06 loss: 0.5831 Lr: 0.00328 [2024-02-18 15:05:51,499 INFO misc.py line 119 87073] Train: [43/100][1358/1557] Data 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Remain 27:51:30 loss: 0.1224 Lr: 0.00328 [2024-02-18 15:05:58,088 INFO misc.py line 119 87073] Train: [43/100][1365/1557] Data 0.011 (0.083) Batch 1.221 (1.128) Remain 27:51:35 loss: 0.1725 Lr: 0.00328 [2024-02-18 15:05:58,976 INFO misc.py line 119 87073] Train: [43/100][1366/1557] Data 0.008 (0.083) Batch 0.893 (1.127) Remain 27:51:18 loss: 0.3487 Lr: 0.00328 [2024-02-18 15:05:59,997 INFO misc.py line 119 87073] Train: [43/100][1367/1557] Data 0.004 (0.083) Batch 1.021 (1.127) Remain 27:51:10 loss: 0.4366 Lr: 0.00328 [2024-02-18 15:06:00,990 INFO misc.py line 119 87073] Train: [43/100][1368/1557] Data 0.005 (0.083) Batch 0.993 (1.127) Remain 27:51:00 loss: 0.1467 Lr: 0.00328 [2024-02-18 15:06:01,877 INFO misc.py line 119 87073] Train: [43/100][1369/1557] Data 0.005 (0.083) Batch 0.888 (1.127) Remain 27:50:44 loss: 0.6421 Lr: 0.00328 [2024-02-18 15:06:02,624 INFO misc.py line 119 87073] Train: [43/100][1370/1557] Data 0.004 (0.083) Batch 0.744 (1.127) Remain 27:50:18 loss: 0.2844 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1377/1557] Data 0.005 (0.082) Batch 0.767 (1.126) Remain 27:48:40 loss: 0.4228 Lr: 0.00328 [2024-02-18 15:06:09,817 INFO misc.py line 119 87073] Train: [43/100][1378/1557] Data 0.007 (0.082) Batch 0.688 (1.126) Remain 27:48:11 loss: 0.1915 Lr: 0.00328 [2024-02-18 15:06:11,090 INFO misc.py line 119 87073] Train: [43/100][1379/1557] Data 0.005 (0.082) Batch 1.267 (1.126) Remain 27:48:19 loss: 0.1473 Lr: 0.00328 [2024-02-18 15:06:11,988 INFO misc.py line 119 87073] Train: [43/100][1380/1557] Data 0.011 (0.082) Batch 0.905 (1.125) Remain 27:48:04 loss: 0.2542 Lr: 0.00328 [2024-02-18 15:06:13,181 INFO misc.py line 119 87073] Train: [43/100][1381/1557] Data 0.004 (0.082) Batch 1.184 (1.126) Remain 27:48:06 loss: 0.3237 Lr: 0.00328 [2024-02-18 15:06:14,087 INFO misc.py line 119 87073] Train: [43/100][1382/1557] Data 0.013 (0.082) Batch 0.915 (1.125) Remain 27:47:51 loss: 0.4461 Lr: 0.00328 [2024-02-18 15:06:14,873 INFO misc.py line 119 87073] Train: [43/100][1383/1557] Data 0.004 (0.082) Batch 0.786 (1.125) Remain 27:47:28 loss: 0.3937 Lr: 0.00328 [2024-02-18 15:06:15,613 INFO misc.py line 119 87073] Train: [43/100][1384/1557] Data 0.005 (0.082) Batch 0.733 (1.125) Remain 27:47:02 loss: 0.6555 Lr: 0.00328 [2024-02-18 15:06:16,373 INFO misc.py line 119 87073] Train: [43/100][1385/1557] Data 0.012 (0.082) Batch 0.766 (1.125) Remain 27:46:38 loss: 0.1827 Lr: 0.00328 [2024-02-18 15:06:17,719 INFO misc.py line 119 87073] Train: [43/100][1386/1557] Data 0.005 (0.082) Batch 1.333 (1.125) Remain 27:46:50 loss: 0.2727 Lr: 0.00328 [2024-02-18 15:06:18,702 INFO misc.py line 119 87073] Train: [43/100][1387/1557] Data 0.018 (0.082) Batch 0.997 (1.125) Remain 27:46:41 loss: 0.7164 Lr: 0.00328 [2024-02-18 15:06:19,727 INFO misc.py line 119 87073] Train: [43/100][1388/1557] Data 0.004 (0.082) Batch 1.023 (1.125) Remain 27:46:33 loss: 0.2303 Lr: 0.00328 [2024-02-18 15:06:20,727 INFO misc.py line 119 87073] Train: [43/100][1389/1557] Data 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Remain 27:45:07 loss: 0.3975 Lr: 0.00328 [2024-02-18 15:06:27,431 INFO misc.py line 119 87073] Train: [43/100][1396/1557] Data 0.004 (0.081) Batch 1.068 (1.124) Remain 27:45:02 loss: 0.3421 Lr: 0.00328 [2024-02-18 15:06:28,346 INFO misc.py line 119 87073] Train: [43/100][1397/1557] Data 0.003 (0.081) Batch 0.914 (1.123) Remain 27:44:47 loss: 0.4730 Lr: 0.00328 [2024-02-18 15:06:29,143 INFO misc.py line 119 87073] Train: [43/100][1398/1557] Data 0.004 (0.081) Batch 0.787 (1.123) Remain 27:44:25 loss: 0.2578 Lr: 0.00328 [2024-02-18 15:06:29,997 INFO misc.py line 119 87073] Train: [43/100][1399/1557] Data 0.014 (0.081) Batch 0.864 (1.123) Remain 27:44:07 loss: 0.1884 Lr: 0.00328 [2024-02-18 15:06:31,274 INFO misc.py line 119 87073] Train: [43/100][1400/1557] Data 0.003 (0.081) Batch 1.265 (1.123) Remain 27:44:15 loss: 0.2126 Lr: 0.00328 [2024-02-18 15:06:32,206 INFO misc.py line 119 87073] Train: [43/100][1401/1557] Data 0.017 (0.081) Batch 0.944 (1.123) Remain 27:44:03 loss: 0.6572 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1408/1557] Data 0.003 (0.084) Batch 0.798 (1.130) Remain 27:53:41 loss: 0.5837 Lr: 0.00328 [2024-02-18 15:06:50,321 INFO misc.py line 119 87073] Train: [43/100][1409/1557] Data 0.004 (0.084) Batch 0.990 (1.130) Remain 27:53:31 loss: 0.3943 Lr: 0.00328 [2024-02-18 15:06:51,160 INFO misc.py line 119 87073] Train: [43/100][1410/1557] Data 0.005 (0.084) Batch 0.840 (1.129) Remain 27:53:11 loss: 0.1128 Lr: 0.00328 [2024-02-18 15:06:52,173 INFO misc.py line 119 87073] Train: [43/100][1411/1557] Data 0.004 (0.084) Batch 1.005 (1.129) Remain 27:53:02 loss: 0.9292 Lr: 0.00328 [2024-02-18 15:06:53,050 INFO misc.py line 119 87073] Train: [43/100][1412/1557] Data 0.012 (0.084) Batch 0.885 (1.129) Remain 27:52:46 loss: 0.5648 Lr: 0.00328 [2024-02-18 15:06:53,849 INFO misc.py line 119 87073] Train: [43/100][1413/1557] Data 0.004 (0.084) Batch 0.798 (1.129) Remain 27:52:24 loss: 0.4620 Lr: 0.00328 [2024-02-18 15:06:55,017 INFO misc.py line 119 87073] Train: [43/100][1414/1557] Data 0.004 (0.084) Batch 1.159 (1.129) Remain 27:52:25 loss: 0.1120 Lr: 0.00328 [2024-02-18 15:06:56,055 INFO misc.py line 119 87073] Train: [43/100][1415/1557] Data 0.014 (0.084) Batch 1.037 (1.129) Remain 27:52:18 loss: 0.4585 Lr: 0.00328 [2024-02-18 15:06:56,998 INFO misc.py line 119 87073] Train: [43/100][1416/1557] Data 0.015 (0.084) Batch 0.955 (1.129) Remain 27:52:06 loss: 0.4518 Lr: 0.00328 [2024-02-18 15:06:58,085 INFO misc.py line 119 87073] Train: [43/100][1417/1557] Data 0.004 (0.084) Batch 1.087 (1.129) Remain 27:52:02 loss: 0.1806 Lr: 0.00328 [2024-02-18 15:06:58,929 INFO misc.py line 119 87073] Train: [43/100][1418/1557] Data 0.003 (0.084) Batch 0.844 (1.128) Remain 27:51:43 loss: 0.6931 Lr: 0.00328 [2024-02-18 15:06:59,709 INFO misc.py line 119 87073] Train: [43/100][1419/1557] Data 0.004 (0.084) Batch 0.780 (1.128) Remain 27:51:20 loss: 0.3353 Lr: 0.00328 [2024-02-18 15:07:00,441 INFO misc.py line 119 87073] Train: [43/100][1420/1557] Data 0.004 (0.084) Batch 0.731 (1.128) Remain 27:50:54 loss: 0.5516 Lr: 0.00328 [2024-02-18 15:07:01,647 INFO misc.py line 119 87073] Train: [43/100][1421/1557] Data 0.005 (0.083) Batch 1.208 (1.128) Remain 27:50:58 loss: 0.2260 Lr: 0.00328 [2024-02-18 15:07:02,758 INFO misc.py line 119 87073] Train: [43/100][1422/1557] Data 0.006 (0.083) Batch 1.110 (1.128) Remain 27:50:55 loss: 0.1655 Lr: 0.00328 [2024-02-18 15:07:03,759 INFO misc.py line 119 87073] Train: [43/100][1423/1557] Data 0.004 (0.083) Batch 1.000 (1.128) Remain 27:50:46 loss: 0.4774 Lr: 0.00328 [2024-02-18 15:07:04,680 INFO misc.py line 119 87073] Train: [43/100][1424/1557] Data 0.006 (0.083) Batch 0.922 (1.128) Remain 27:50:32 loss: 0.3637 Lr: 0.00328 [2024-02-18 15:07:05,692 INFO misc.py line 119 87073] Train: [43/100][1425/1557] Data 0.004 (0.083) Batch 1.013 (1.128) Remain 27:50:24 loss: 0.6232 Lr: 0.00328 [2024-02-18 15:07:06,466 INFO misc.py line 119 87073] Train: [43/100][1426/1557] Data 0.003 (0.083) Batch 0.769 (1.127) Remain 27:50:01 loss: 0.3453 Lr: 0.00328 [2024-02-18 15:07:07,243 INFO misc.py line 119 87073] Train: [43/100][1427/1557] Data 0.009 (0.083) Batch 0.781 (1.127) Remain 27:49:38 loss: 0.2054 Lr: 0.00328 [2024-02-18 15:07:08,351 INFO misc.py line 119 87073] Train: [43/100][1428/1557] Data 0.004 (0.083) Batch 1.109 (1.127) Remain 27:49:36 loss: 0.1190 Lr: 0.00328 [2024-02-18 15:07:09,206 INFO misc.py line 119 87073] Train: [43/100][1429/1557] Data 0.004 (0.083) Batch 0.855 (1.127) Remain 27:49:17 loss: 0.4515 Lr: 0.00328 [2024-02-18 15:07:10,208 INFO misc.py line 119 87073] Train: [43/100][1430/1557] Data 0.004 (0.083) Batch 0.999 (1.127) Remain 27:49:08 loss: 0.4262 Lr: 0.00328 [2024-02-18 15:07:11,236 INFO misc.py line 119 87073] Train: [43/100][1431/1557] Data 0.007 (0.083) Batch 1.023 (1.127) Remain 27:49:01 loss: 0.2662 Lr: 0.00328 [2024-02-18 15:07:12,183 INFO misc.py line 119 87073] Train: [43/100][1432/1557] Data 0.012 (0.083) Batch 0.955 (1.127) Remain 27:48:49 loss: 0.4619 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1439/1557] Data 0.004 (0.083) Batch 1.005 (1.126) Remain 27:47:45 loss: 0.6106 Lr: 0.00328 [2024-02-18 15:07:19,884 INFO misc.py line 119 87073] Train: [43/100][1440/1557] Data 0.006 (0.082) Batch 0.717 (1.126) Remain 27:47:19 loss: 0.1599 Lr: 0.00328 [2024-02-18 15:07:20,694 INFO misc.py line 119 87073] Train: [43/100][1441/1557] Data 0.006 (0.082) Batch 0.810 (1.126) Remain 27:46:58 loss: 0.1912 Lr: 0.00328 [2024-02-18 15:07:22,021 INFO misc.py line 119 87073] Train: [43/100][1442/1557] Data 0.005 (0.082) Batch 1.325 (1.126) Remain 27:47:09 loss: 0.1384 Lr: 0.00328 [2024-02-18 15:07:22,940 INFO misc.py line 119 87073] Train: [43/100][1443/1557] Data 0.008 (0.082) Batch 0.922 (1.126) Remain 27:46:56 loss: 0.4265 Lr: 0.00328 [2024-02-18 15:07:24,124 INFO misc.py line 119 87073] Train: [43/100][1444/1557] Data 0.005 (0.082) Batch 1.184 (1.126) Remain 27:46:58 loss: 0.3344 Lr: 0.00328 [2024-02-18 15:07:25,143 INFO misc.py line 119 87073] Train: [43/100][1445/1557] Data 0.004 (0.082) Batch 1.015 (1.125) Remain 27:46:50 loss: 0.6587 Lr: 0.00328 [2024-02-18 15:07:26,098 INFO misc.py line 119 87073] Train: [43/100][1446/1557] Data 0.008 (0.082) Batch 0.960 (1.125) Remain 27:46:39 loss: 0.3920 Lr: 0.00328 [2024-02-18 15:07:26,798 INFO misc.py line 119 87073] Train: [43/100][1447/1557] Data 0.003 (0.082) Batch 0.699 (1.125) Remain 27:46:11 loss: 0.2295 Lr: 0.00328 [2024-02-18 15:07:27,577 INFO misc.py line 119 87073] Train: [43/100][1448/1557] Data 0.004 (0.082) Batch 0.779 (1.125) Remain 27:45:49 loss: 0.3305 Lr: 0.00328 [2024-02-18 15:07:28,918 INFO misc.py line 119 87073] Train: [43/100][1449/1557] Data 0.004 (0.082) Batch 1.340 (1.125) Remain 27:46:01 loss: 0.2322 Lr: 0.00328 [2024-02-18 15:07:30,021 INFO misc.py line 119 87073] Train: [43/100][1450/1557] Data 0.007 (0.082) Batch 1.098 (1.125) Remain 27:45:58 loss: 0.4284 Lr: 0.00328 [2024-02-18 15:07:31,264 INFO misc.py line 119 87073] Train: [43/100][1451/1557] Data 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Remain 27:45:02 loss: 0.5805 Lr: 0.00328 [2024-02-18 15:07:38,117 INFO misc.py line 119 87073] Train: [43/100][1458/1557] Data 0.004 (0.082) Batch 1.019 (1.124) Remain 27:44:55 loss: 0.9836 Lr: 0.00328 [2024-02-18 15:07:39,020 INFO misc.py line 119 87073] Train: [43/100][1459/1557] Data 0.004 (0.081) Batch 0.903 (1.124) Remain 27:44:40 loss: 0.5472 Lr: 0.00328 [2024-02-18 15:07:39,908 INFO misc.py line 119 87073] Train: [43/100][1460/1557] Data 0.004 (0.081) Batch 0.887 (1.124) Remain 27:44:24 loss: 0.5716 Lr: 0.00328 [2024-02-18 15:07:40,672 INFO misc.py line 119 87073] Train: [43/100][1461/1557] Data 0.005 (0.081) Batch 0.764 (1.124) Remain 27:44:01 loss: 0.3938 Lr: 0.00328 [2024-02-18 15:07:41,420 INFO misc.py line 119 87073] Train: [43/100][1462/1557] Data 0.006 (0.081) Batch 0.749 (1.124) Remain 27:43:37 loss: 0.1615 Lr: 0.00328 [2024-02-18 15:07:51,158 INFO misc.py line 119 87073] Train: [43/100][1463/1557] Data 3.680 (0.084) Batch 9.739 (1.129) Remain 27:52:20 loss: 0.2796 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1470/1557] Data 0.011 (0.083) Batch 1.211 (1.128) Remain 27:50:42 loss: 0.5239 Lr: 0.00328 [2024-02-18 15:07:58,661 INFO misc.py line 119 87073] Train: [43/100][1471/1557] Data 0.004 (0.083) Batch 1.085 (1.128) Remain 27:50:39 loss: 0.5436 Lr: 0.00328 [2024-02-18 15:07:59,692 INFO misc.py line 119 87073] Train: [43/100][1472/1557] Data 0.005 (0.083) Batch 1.029 (1.128) Remain 27:50:31 loss: 0.4887 Lr: 0.00328 [2024-02-18 15:08:00,550 INFO misc.py line 119 87073] Train: [43/100][1473/1557] Data 0.008 (0.083) Batch 0.862 (1.128) Remain 27:50:14 loss: 0.7640 Lr: 0.00328 [2024-02-18 15:08:01,577 INFO misc.py line 119 87073] Train: [43/100][1474/1557] Data 0.004 (0.083) Batch 1.027 (1.128) Remain 27:50:07 loss: 0.9528 Lr: 0.00328 [2024-02-18 15:08:02,350 INFO misc.py line 119 87073] Train: [43/100][1475/1557] Data 0.004 (0.083) Batch 0.771 (1.128) Remain 27:49:44 loss: 0.1454 Lr: 0.00328 [2024-02-18 15:08:03,120 INFO misc.py line 119 87073] Train: [43/100][1476/1557] Data 0.005 (0.083) Batch 0.761 (1.128) Remain 27:49:21 loss: 0.9177 Lr: 0.00328 [2024-02-18 15:08:04,347 INFO misc.py line 119 87073] Train: [43/100][1477/1557] Data 0.015 (0.083) Batch 1.235 (1.128) Remain 27:49:26 loss: 0.1592 Lr: 0.00328 [2024-02-18 15:08:05,406 INFO misc.py line 119 87073] Train: [43/100][1478/1557] Data 0.006 (0.083) Batch 1.053 (1.128) Remain 27:49:21 loss: 0.2679 Lr: 0.00328 [2024-02-18 15:08:06,386 INFO misc.py line 119 87073] Train: [43/100][1479/1557] Data 0.013 (0.083) Batch 0.988 (1.127) Remain 27:49:11 loss: 0.4911 Lr: 0.00328 [2024-02-18 15:08:07,284 INFO misc.py line 119 87073] Train: [43/100][1480/1557] Data 0.005 (0.083) Batch 0.898 (1.127) Remain 27:48:56 loss: 0.4127 Lr: 0.00328 [2024-02-18 15:08:08,202 INFO misc.py line 119 87073] Train: [43/100][1481/1557] Data 0.005 (0.083) Batch 0.911 (1.127) Remain 27:48:42 loss: 0.4802 Lr: 0.00328 [2024-02-18 15:08:08,997 INFO misc.py line 119 87073] Train: [43/100][1482/1557] Data 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Remain 27:46:57 loss: 0.4196 Lr: 0.00328 [2024-02-18 15:08:15,253 INFO misc.py line 119 87073] Train: [43/100][1489/1557] Data 0.004 (0.082) Batch 0.789 (1.126) Remain 27:46:36 loss: 0.3894 Lr: 0.00328 [2024-02-18 15:08:16,085 INFO misc.py line 119 87073] Train: [43/100][1490/1557] Data 0.006 (0.082) Batch 0.829 (1.126) Remain 27:46:17 loss: 0.3081 Lr: 0.00328 [2024-02-18 15:08:17,407 INFO misc.py line 119 87073] Train: [43/100][1491/1557] Data 0.010 (0.082) Batch 1.326 (1.126) Remain 27:46:28 loss: 0.1493 Lr: 0.00328 [2024-02-18 15:08:18,383 INFO misc.py line 119 87073] Train: [43/100][1492/1557] Data 0.005 (0.082) Batch 0.978 (1.126) Remain 27:46:18 loss: 0.9735 Lr: 0.00328 [2024-02-18 15:08:19,289 INFO misc.py line 119 87073] Train: [43/100][1493/1557] Data 0.004 (0.082) Batch 0.906 (1.126) Remain 27:46:04 loss: 0.5889 Lr: 0.00328 [2024-02-18 15:08:20,158 INFO misc.py line 119 87073] Train: [43/100][1494/1557] Data 0.004 (0.082) Batch 0.862 (1.125) Remain 27:45:47 loss: 0.4287 Lr: 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INFO misc.py line 119 87073] Train: [43/100][1501/1557] Data 0.012 (0.082) Batch 1.086 (1.125) Remain 27:44:45 loss: 0.1666 Lr: 0.00328 [2024-02-18 15:08:28,039 INFO misc.py line 119 87073] Train: [43/100][1502/1557] Data 0.011 (0.082) Batch 0.911 (1.125) Remain 27:44:32 loss: 0.8349 Lr: 0.00328 [2024-02-18 15:08:28,733 INFO misc.py line 119 87073] Train: [43/100][1503/1557] Data 0.009 (0.082) Batch 0.698 (1.124) Remain 27:44:05 loss: 0.2940 Lr: 0.00328 [2024-02-18 15:08:29,514 INFO misc.py line 119 87073] Train: [43/100][1504/1557] Data 0.004 (0.082) Batch 0.779 (1.124) Remain 27:43:44 loss: 0.4670 Lr: 0.00328 [2024-02-18 15:08:30,756 INFO misc.py line 119 87073] Train: [43/100][1505/1557] Data 0.005 (0.082) Batch 1.242 (1.124) Remain 27:43:49 loss: 0.2782 Lr: 0.00328 [2024-02-18 15:08:31,700 INFO misc.py line 119 87073] Train: [43/100][1506/1557] Data 0.006 (0.082) Batch 0.943 (1.124) Remain 27:43:38 loss: 0.5421 Lr: 0.00328 [2024-02-18 15:08:32,718 INFO misc.py line 119 87073] Train: [43/100][1507/1557] Data 0.007 (0.082) Batch 1.021 (1.124) Remain 27:43:30 loss: 0.3853 Lr: 0.00328 [2024-02-18 15:08:33,758 INFO misc.py line 119 87073] Train: [43/100][1508/1557] Data 0.003 (0.081) Batch 1.038 (1.124) Remain 27:43:24 loss: 0.4755 Lr: 0.00328 [2024-02-18 15:08:34,665 INFO misc.py line 119 87073] Train: [43/100][1509/1557] Data 0.006 (0.081) Batch 0.908 (1.124) Remain 27:43:10 loss: 0.3550 Lr: 0.00328 [2024-02-18 15:08:35,447 INFO misc.py line 119 87073] Train: [43/100][1510/1557] Data 0.004 (0.081) Batch 0.781 (1.124) Remain 27:42:49 loss: 0.3814 Lr: 0.00328 [2024-02-18 15:08:36,224 INFO misc.py line 119 87073] Train: [43/100][1511/1557] Data 0.005 (0.081) Batch 0.777 (1.123) Remain 27:42:28 loss: 0.2861 Lr: 0.00328 [2024-02-18 15:08:37,429 INFO misc.py line 119 87073] Train: [43/100][1512/1557] Data 0.004 (0.081) Batch 1.206 (1.123) Remain 27:42:31 loss: 0.3265 Lr: 0.00328 [2024-02-18 15:08:38,259 INFO misc.py line 119 87073] Train: [43/100][1513/1557] Data 0.004 (0.081) Batch 0.829 (1.123) Remain 27:42:13 loss: 0.3277 Lr: 0.00328 [2024-02-18 15:08:39,273 INFO misc.py line 119 87073] Train: [43/100][1514/1557] Data 0.004 (0.081) Batch 1.006 (1.123) Remain 27:42:05 loss: 0.5139 Lr: 0.00328 [2024-02-18 15:08:40,108 INFO misc.py line 119 87073] Train: [43/100][1515/1557] Data 0.014 (0.081) Batch 0.842 (1.123) Remain 27:41:47 loss: 0.2567 Lr: 0.00328 [2024-02-18 15:08:40,891 INFO misc.py line 119 87073] Train: [43/100][1516/1557] Data 0.005 (0.081) Batch 0.784 (1.123) Remain 27:41:26 loss: 0.3474 Lr: 0.00328 [2024-02-18 15:08:41,678 INFO misc.py line 119 87073] Train: [43/100][1517/1557] Data 0.005 (0.081) Batch 0.778 (1.122) Remain 27:41:05 loss: 0.3889 Lr: 0.00328 [2024-02-18 15:08:42,508 INFO misc.py line 119 87073] Train: [43/100][1518/1557] Data 0.014 (0.081) Batch 0.839 (1.122) Remain 27:40:47 loss: 0.2702 Lr: 0.00328 [2024-02-18 15:08:52,757 INFO misc.py line 119 87073] Train: [43/100][1519/1557] Data 3.479 (0.083) Batch 10.248 (1.128) Remain 27:49:40 loss: 0.1657 Lr: 0.00328 [2024-02-18 15:08:53,631 INFO misc.py line 119 87073] Train: [43/100][1520/1557] Data 0.006 (0.083) Batch 0.875 (1.128) Remain 27:49:25 loss: 0.2297 Lr: 0.00328 [2024-02-18 15:08:54,457 INFO misc.py line 119 87073] Train: [43/100][1521/1557] Data 0.005 (0.083) Batch 0.819 (1.128) Remain 27:49:05 loss: 0.4631 Lr: 0.00328 [2024-02-18 15:08:55,316 INFO misc.py line 119 87073] Train: [43/100][1522/1557] Data 0.011 (0.083) Batch 0.866 (1.128) Remain 27:48:49 loss: 0.4604 Lr: 0.00328 [2024-02-18 15:08:56,254 INFO misc.py line 119 87073] Train: [43/100][1523/1557] Data 0.006 (0.083) Batch 0.939 (1.128) Remain 27:48:37 loss: 0.2630 Lr: 0.00328 [2024-02-18 15:08:57,045 INFO misc.py line 119 87073] Train: [43/100][1524/1557] Data 0.004 (0.083) Batch 0.782 (1.127) Remain 27:48:15 loss: 0.2870 Lr: 0.00328 [2024-02-18 15:08:57,811 INFO misc.py line 119 87073] Train: [43/100][1525/1557] Data 0.013 (0.083) Batch 0.775 (1.127) Remain 27:47:54 loss: 0.3381 Lr: 0.00328 [2024-02-18 15:08:59,049 INFO misc.py line 119 87073] Train: [43/100][1526/1557] Data 0.004 (0.083) Batch 1.237 (1.127) Remain 27:47:59 loss: 0.1248 Lr: 0.00328 [2024-02-18 15:08:59,948 INFO misc.py line 119 87073] Train: [43/100][1527/1557] Data 0.004 (0.083) Batch 0.899 (1.127) Remain 27:47:45 loss: 0.4588 Lr: 0.00328 [2024-02-18 15:09:00,885 INFO misc.py line 119 87073] Train: [43/100][1528/1557] Data 0.004 (0.083) Batch 0.937 (1.127) Remain 27:47:32 loss: 0.1228 Lr: 0.00328 [2024-02-18 15:09:01,815 INFO misc.py line 119 87073] Train: [43/100][1529/1557] Data 0.004 (0.083) Batch 0.923 (1.127) Remain 27:47:19 loss: 0.0800 Lr: 0.00328 [2024-02-18 15:09:02,757 INFO misc.py line 119 87073] Train: [43/100][1530/1557] Data 0.010 (0.083) Batch 0.948 (1.127) Remain 27:47:08 loss: 0.4735 Lr: 0.00328 [2024-02-18 15:09:03,499 INFO misc.py line 119 87073] Train: [43/100][1531/1557] Data 0.005 (0.083) Batch 0.742 (1.126) Remain 27:46:44 loss: 0.2579 Lr: 0.00328 [2024-02-18 15:09:04,283 INFO misc.py line 119 87073] Train: [43/100][1532/1557] Data 0.004 (0.083) Batch 0.782 (1.126) Remain 27:46:23 loss: 0.2209 Lr: 0.00328 [2024-02-18 15:09:05,521 INFO misc.py line 119 87073] Train: [43/100][1533/1557] Data 0.007 (0.083) Batch 1.227 (1.126) Remain 27:46:28 loss: 0.2150 Lr: 0.00328 [2024-02-18 15:09:06,483 INFO misc.py line 119 87073] Train: [43/100][1534/1557] Data 0.018 (0.082) Batch 0.976 (1.126) Remain 27:46:18 loss: 0.4580 Lr: 0.00328 [2024-02-18 15:09:07,548 INFO misc.py line 119 87073] Train: [43/100][1535/1557] Data 0.003 (0.082) Batch 1.064 (1.126) Remain 27:46:13 loss: 0.4614 Lr: 0.00328 [2024-02-18 15:09:08,598 INFO misc.py line 119 87073] Train: [43/100][1536/1557] Data 0.004 (0.082) Batch 1.050 (1.126) Remain 27:46:08 loss: 0.6066 Lr: 0.00328 [2024-02-18 15:09:09,574 INFO misc.py line 119 87073] Train: [43/100][1537/1557] Data 0.003 (0.082) Batch 0.976 (1.126) Remain 27:45:58 loss: 0.6157 Lr: 0.00328 [2024-02-18 15:09:10,343 INFO misc.py line 119 87073] Train: [43/100][1538/1557] Data 0.004 (0.082) Batch 0.766 (1.126) Remain 27:45:36 loss: 0.2497 Lr: 0.00327 [2024-02-18 15:09:11,089 INFO misc.py line 119 87073] Train: [43/100][1539/1557] Data 0.007 (0.082) Batch 0.737 (1.126) Remain 27:45:13 loss: 0.1741 Lr: 0.00327 [2024-02-18 15:09:12,212 INFO misc.py line 119 87073] Train: [43/100][1540/1557] Data 0.017 (0.082) Batch 1.122 (1.126) Remain 27:45:11 loss: 0.1911 Lr: 0.00327 [2024-02-18 15:09:13,317 INFO misc.py line 119 87073] Train: [43/100][1541/1557] Data 0.018 (0.082) Batch 1.107 (1.126) Remain 27:45:09 loss: 0.2621 Lr: 0.00327 [2024-02-18 15:09:14,232 INFO misc.py line 119 87073] Train: [43/100][1542/1557] Data 0.015 (0.082) Batch 0.926 (1.125) Remain 27:44:56 loss: 0.4217 Lr: 0.00327 [2024-02-18 15:09:15,392 INFO misc.py line 119 87073] Train: [43/100][1543/1557] Data 0.004 (0.082) Batch 1.160 (1.125) Remain 27:44:57 loss: 0.2256 Lr: 0.00327 [2024-02-18 15:09:16,432 INFO misc.py line 119 87073] Train: [43/100][1544/1557] Data 0.004 (0.082) Batch 1.029 (1.125) Remain 27:44:51 loss: 0.5240 Lr: 0.00327 [2024-02-18 15:09:17,148 INFO misc.py line 119 87073] Train: [43/100][1545/1557] Data 0.015 (0.082) Batch 0.727 (1.125) Remain 27:44:27 loss: 0.4521 Lr: 0.00327 [2024-02-18 15:09:17,902 INFO misc.py line 119 87073] Train: [43/100][1546/1557] Data 0.004 (0.082) Batch 0.742 (1.125) Remain 27:44:03 loss: 0.3392 Lr: 0.00327 [2024-02-18 15:09:19,133 INFO misc.py line 119 87073] Train: [43/100][1547/1557] Data 0.016 (0.082) Batch 1.231 (1.125) Remain 27:44:08 loss: 0.1423 Lr: 0.00327 [2024-02-18 15:09:20,153 INFO misc.py line 119 87073] Train: [43/100][1548/1557] Data 0.015 (0.082) Batch 1.021 (1.125) Remain 27:44:01 loss: 0.3162 Lr: 0.00327 [2024-02-18 15:09:20,983 INFO misc.py line 119 87073] Train: [43/100][1549/1557] Data 0.015 (0.082) Batch 0.841 (1.125) Remain 27:43:44 loss: 0.5740 Lr: 0.00327 [2024-02-18 15:09:21,774 INFO misc.py line 119 87073] Train: [43/100][1550/1557] Data 0.005 (0.082) Batch 0.792 (1.124) Remain 27:43:24 loss: 0.5260 Lr: 0.00327 [2024-02-18 15:09:22,554 INFO misc.py line 119 87073] Train: [43/100][1551/1557] Data 0.004 (0.082) Batch 0.770 (1.124) Remain 27:43:02 loss: 0.3826 Lr: 0.00327 [2024-02-18 15:09:23,283 INFO misc.py line 119 87073] Train: [43/100][1552/1557] Data 0.014 (0.082) Batch 0.739 (1.124) Remain 27:42:39 loss: 0.3682 Lr: 0.00327 [2024-02-18 15:09:23,998 INFO misc.py line 119 87073] Train: [43/100][1553/1557] Data 0.004 (0.082) Batch 0.703 (1.124) Remain 27:42:14 loss: 0.2295 Lr: 0.00327 [2024-02-18 15:09:25,268 INFO misc.py line 119 87073] Train: [43/100][1554/1557] Data 0.015 (0.082) Batch 1.273 (1.124) Remain 27:42:21 loss: 0.1273 Lr: 0.00327 [2024-02-18 15:09:26,180 INFO misc.py line 119 87073] Train: [43/100][1555/1557] Data 0.013 (0.081) Batch 0.921 (1.124) Remain 27:42:08 loss: 0.5994 Lr: 0.00327 [2024-02-18 15:09:27,152 INFO misc.py line 119 87073] Train: [43/100][1556/1557] Data 0.004 (0.081) Batch 0.972 (1.124) Remain 27:41:59 loss: 0.2379 Lr: 0.00327 [2024-02-18 15:09:28,007 INFO misc.py line 119 87073] Train: [43/100][1557/1557] Data 0.005 (0.081) Batch 0.856 (1.123) Remain 27:41:42 loss: 0.3587 Lr: 0.00327 [2024-02-18 15:09:28,008 INFO misc.py line 136 87073] Train result: loss: 0.3816 [2024-02-18 15:09:28,008 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 15:09:55,083 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8122 [2024-02-18 15:09:55,860 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9140 [2024-02-18 15:09:57,983 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4328 [2024-02-18 15:10:00,190 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4895 [2024-02-18 15:10:05,133 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3128 [2024-02-18 15:10:05,832 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.3358 [2024-02-18 15:10:07,092 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2516 [2024-02-18 15:10:10,044 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4377 [2024-02-18 15:10:11,855 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3010 [2024-02-18 15:10:13,387 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7033/0.7644/0.9080. [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9166/0.9724 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9811/0.9869 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8630/0.9785 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0009/0.0078 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2818/0.3000 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6133/0.6232 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7802/0.9381 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8231/0.9108 [2024-02-18 15:10:13,388 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9147/0.9711 [2024-02-18 15:10:13,389 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8456/0.9167 [2024-02-18 15:10:13,389 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7852/0.8821 [2024-02-18 15:10:13,389 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7760/0.8195 [2024-02-18 15:10:13,389 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5617/0.6300 [2024-02-18 15:10:13,389 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 15:10:13,389 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 15:10:13,389 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 15:10:20,530 INFO misc.py line 119 87073] Train: [44/100][1/1557] Data 1.494 (1.494) Batch 2.344 (2.344) Remain 57:47:20 loss: 0.3824 Lr: 0.00327 [2024-02-18 15:10:21,555 INFO misc.py line 119 87073] Train: [44/100][2/1557] Data 0.005 (0.005) Batch 1.023 (1.023) Remain 25:13:21 loss: 0.1620 Lr: 0.00327 [2024-02-18 15:10:22,626 INFO misc.py line 119 87073] Train: [44/100][3/1557] Data 0.006 (0.006) Batch 1.063 (1.063) Remain 26:11:49 loss: 0.3159 Lr: 0.00327 [2024-02-18 15:10:23,625 INFO misc.py line 119 87073] Train: [44/100][4/1557] Data 0.015 (0.015) Batch 1.001 (1.001) Remain 24:41:10 loss: 0.3022 Lr: 0.00327 [2024-02-18 15:10:24,346 INFO misc.py line 119 87073] Train: [44/100][5/1557] Data 0.013 (0.014) Batch 0.728 (0.865) Remain 21:18:45 loss: 0.2447 Lr: 0.00327 [2024-02-18 15:10:25,130 INFO misc.py line 119 87073] Train: [44/100][6/1557] Data 0.005 (0.011) Batch 0.785 (0.838) Remain 20:39:39 loss: 0.3064 Lr: 0.00327 [2024-02-18 15:10:29,028 INFO misc.py line 119 87073] Train: [44/100][7/1557] Data 0.004 (0.009) Batch 3.887 (1.600) Remain 39:27:00 loss: 0.1486 Lr: 0.00327 [2024-02-18 15:10:30,159 INFO misc.py line 119 87073] Train: [44/100][8/1557] Data 0.015 (0.010) Batch 1.139 (1.508) Remain 37:10:30 loss: 0.4219 Lr: 0.00327 [2024-02-18 15:10:31,066 INFO misc.py line 119 87073] Train: [44/100][9/1557] Data 0.007 (0.010) Batch 0.910 (1.408) Remain 34:43:03 loss: 0.3469 Lr: 0.00327 [2024-02-18 15:10:31,957 INFO misc.py line 119 87073] Train: [44/100][10/1557] Data 0.004 (0.009) Batch 0.891 (1.335) Remain 32:53:42 loss: 0.3378 Lr: 0.00327 [2024-02-18 15:10:32,903 INFO misc.py line 119 87073] Train: [44/100][11/1557] Data 0.004 (0.008) Batch 0.946 (1.286) Remain 31:41:51 loss: 0.5744 Lr: 0.00327 [2024-02-18 15:10:33,722 INFO misc.py line 119 87073] Train: [44/100][12/1557] Data 0.004 (0.008) Batch 0.820 (1.234) Remain 30:25:11 loss: 0.4695 Lr: 0.00327 [2024-02-18 15:10:34,458 INFO misc.py line 119 87073] Train: [44/100][13/1557] Data 0.004 (0.007) Batch 0.734 (1.184) Remain 29:11:13 loss: 0.3454 Lr: 0.00327 [2024-02-18 15:10:35,755 INFO misc.py line 119 87073] Train: [44/100][14/1557] Data 0.005 (0.007) Batch 1.287 (1.193) Remain 29:24:59 loss: 0.3606 Lr: 0.00327 [2024-02-18 15:10:36,656 INFO misc.py line 119 87073] Train: [44/100][15/1557] Data 0.016 (0.008) Batch 0.913 (1.170) Remain 28:50:24 loss: 0.6962 Lr: 0.00327 [2024-02-18 15:10:37,542 INFO misc.py line 119 87073] Train: [44/100][16/1557] Data 0.003 (0.008) Batch 0.886 (1.148) Remain 28:18:04 loss: 0.4623 Lr: 0.00327 [2024-02-18 15:10:38,573 INFO misc.py line 119 87073] Train: [44/100][17/1557] Data 0.004 (0.007) Batch 1.029 (1.140) Remain 28:05:30 loss: 0.3487 Lr: 0.00327 [2024-02-18 15:10:39,570 INFO misc.py line 119 87073] Train: [44/100][18/1557] Data 0.005 (0.007) Batch 0.999 (1.130) Remain 27:51:38 loss: 0.4281 Lr: 0.00327 [2024-02-18 15:10:40,280 INFO misc.py line 119 87073] Train: [44/100][19/1557] Data 0.003 (0.007) Batch 0.708 (1.104) Remain 27:12:35 loss: 0.3224 Lr: 0.00327 [2024-02-18 15:10:41,020 INFO misc.py line 119 87073] Train: [44/100][20/1557] Data 0.005 (0.007) Batch 0.740 (1.083) Remain 26:40:55 loss: 0.2560 Lr: 0.00327 [2024-02-18 15:10:42,380 INFO misc.py line 119 87073] Train: [44/100][21/1557] Data 0.005 (0.007) Batch 1.348 (1.097) Remain 27:02:45 loss: 0.2298 Lr: 0.00327 [2024-02-18 15:10:43,223 INFO misc.py line 119 87073] Train: [44/100][22/1557] Data 0.017 (0.007) Batch 0.854 (1.085) Remain 26:43:46 loss: 0.0895 Lr: 0.00327 [2024-02-18 15:10:44,316 INFO misc.py line 119 87073] Train: [44/100][23/1557] Data 0.006 (0.007) Batch 1.095 (1.085) Remain 26:44:30 loss: 0.4713 Lr: 0.00327 [2024-02-18 15:10:45,290 INFO misc.py line 119 87073] Train: [44/100][24/1557] Data 0.004 (0.007) Batch 0.975 (1.080) Remain 26:36:42 loss: 0.3884 Lr: 0.00327 [2024-02-18 15:10:46,300 INFO misc.py line 119 87073] Train: [44/100][25/1557] Data 0.003 (0.007) Batch 1.008 (1.077) Remain 26:31:52 loss: 0.5434 Lr: 0.00327 [2024-02-18 15:10:47,106 INFO misc.py line 119 87073] Train: [44/100][26/1557] Data 0.005 (0.007) Batch 0.807 (1.065) Remain 26:14:31 loss: 0.2975 Lr: 0.00327 [2024-02-18 15:10:47,915 INFO misc.py line 119 87073] Train: [44/100][27/1557] Data 0.004 (0.007) Batch 0.808 (1.054) Remain 25:58:41 loss: 0.2986 Lr: 0.00327 [2024-02-18 15:10:49,063 INFO misc.py line 119 87073] Train: [44/100][28/1557] Data 0.005 (0.007) Batch 1.137 (1.057) Remain 26:03:34 loss: 0.1609 Lr: 0.00327 [2024-02-18 15:10:49,988 INFO misc.py line 119 87073] Train: [44/100][29/1557] Data 0.016 (0.007) Batch 0.937 (1.053) Remain 25:56:43 loss: 0.3614 Lr: 0.00327 [2024-02-18 15:10:51,098 INFO misc.py line 119 87073] Train: [44/100][30/1557] Data 0.004 (0.007) Batch 1.110 (1.055) Remain 25:59:49 loss: 0.5187 Lr: 0.00327 [2024-02-18 15:10:51,902 INFO misc.py line 119 87073] Train: [44/100][31/1557] Data 0.004 (0.007) Batch 0.803 (1.046) Remain 25:46:29 loss: 0.6314 Lr: 0.00327 [2024-02-18 15:10:52,960 INFO misc.py line 119 87073] Train: [44/100][32/1557] Data 0.006 (0.007) Batch 1.051 (1.046) Remain 25:46:45 loss: 0.7288 Lr: 0.00327 [2024-02-18 15:10:53,713 INFO misc.py line 119 87073] Train: [44/100][33/1557] Data 0.013 (0.007) Batch 0.760 (1.037) Remain 25:32:38 loss: 0.3257 Lr: 0.00327 [2024-02-18 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Train: [44/100][40/1557] Data 0.006 (0.007) Batch 0.732 (1.014) Remain 24:58:27 loss: 0.4312 Lr: 0.00327 [2024-02-18 15:11:00,890 INFO misc.py line 119 87073] Train: [44/100][41/1557] Data 0.009 (0.007) Batch 0.774 (1.007) Remain 24:49:06 loss: 0.4162 Lr: 0.00327 [2024-02-18 15:11:02,214 INFO misc.py line 119 87073] Train: [44/100][42/1557] Data 0.005 (0.007) Batch 1.319 (1.015) Remain 25:00:55 loss: 0.2246 Lr: 0.00327 [2024-02-18 15:11:03,275 INFO misc.py line 119 87073] Train: [44/100][43/1557] Data 0.010 (0.007) Batch 1.062 (1.016) Remain 25:02:38 loss: 0.4144 Lr: 0.00327 [2024-02-18 15:11:04,325 INFO misc.py line 119 87073] Train: [44/100][44/1557] Data 0.009 (0.007) Batch 1.042 (1.017) Remain 25:03:33 loss: 0.2363 Lr: 0.00327 [2024-02-18 15:11:05,283 INFO misc.py line 119 87073] Train: [44/100][45/1557] Data 0.016 (0.008) Batch 0.970 (1.016) Remain 25:01:53 loss: 0.4873 Lr: 0.00327 [2024-02-18 15:11:06,242 INFO misc.py line 119 87073] Train: [44/100][46/1557] Data 0.004 (0.008) 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loss: 0.5078 Lr: 0.00327 [2024-02-18 15:12:47,081 INFO misc.py line 119 87073] Train: [44/100][128/1557] Data 0.006 (0.104) Batch 0.996 (1.156) Remain 28:26:58 loss: 0.5198 Lr: 0.00327 [2024-02-18 15:12:48,118 INFO misc.py line 119 87073] Train: [44/100][129/1557] Data 0.009 (0.103) Batch 1.038 (1.155) Remain 28:25:34 loss: 0.2643 Lr: 0.00327 [2024-02-18 15:12:49,185 INFO misc.py line 119 87073] Train: [44/100][130/1557] Data 0.008 (0.102) Batch 1.062 (1.154) Remain 28:24:28 loss: 0.5676 Lr: 0.00327 [2024-02-18 15:12:49,925 INFO misc.py line 119 87073] Train: [44/100][131/1557] Data 0.012 (0.102) Batch 0.748 (1.151) Remain 28:19:46 loss: 0.2708 Lr: 0.00327 [2024-02-18 15:12:50,635 INFO misc.py line 119 87073] Train: [44/100][132/1557] Data 0.004 (0.101) Batch 0.708 (1.147) Remain 28:14:41 loss: 0.2615 Lr: 0.00327 [2024-02-18 15:12:51,934 INFO misc.py line 119 87073] Train: [44/100][133/1557] Data 0.005 (0.100) Batch 1.294 (1.149) Remain 28:16:19 loss: 0.1872 Lr: 0.00327 [2024-02-18 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line 119 87073] Train: [44/100][165/1557] Data 0.004 (0.082) Batch 0.946 (1.107) Remain 27:13:48 loss: 0.4596 Lr: 0.00327 [2024-02-18 15:13:22,679 INFO misc.py line 119 87073] Train: [44/100][166/1557] Data 0.004 (0.081) Batch 0.779 (1.105) Remain 27:10:49 loss: 0.2112 Lr: 0.00327 [2024-02-18 15:13:23,455 INFO misc.py line 119 87073] Train: [44/100][167/1557] Data 0.017 (0.081) Batch 0.787 (1.103) Remain 27:07:56 loss: 0.4055 Lr: 0.00327 [2024-02-18 15:13:24,706 INFO misc.py line 119 87073] Train: [44/100][168/1557] Data 0.004 (0.080) Batch 1.241 (1.104) Remain 27:09:10 loss: 0.2729 Lr: 0.00327 [2024-02-18 15:13:25,625 INFO misc.py line 119 87073] Train: [44/100][169/1557] Data 0.015 (0.080) Batch 0.929 (1.102) Remain 27:07:36 loss: 0.4550 Lr: 0.00327 [2024-02-18 15:13:26,550 INFO misc.py line 119 87073] Train: [44/100][170/1557] Data 0.005 (0.079) Batch 0.925 (1.101) Remain 27:06:01 loss: 0.2621 Lr: 0.00327 [2024-02-18 15:13:27,511 INFO misc.py line 119 87073] Train: 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Batch 0.967 (1.158) Remain 28:29:01 loss: 0.4896 Lr: 0.00327 [2024-02-18 15:13:45,016 INFO misc.py line 119 87073] Train: [44/100][178/1557] Data 0.005 (0.109) Batch 0.958 (1.157) Remain 28:27:18 loss: 0.4386 Lr: 0.00327 [2024-02-18 15:13:46,122 INFO misc.py line 119 87073] Train: [44/100][179/1557] Data 0.005 (0.108) Batch 1.106 (1.156) Remain 28:26:52 loss: 0.3048 Lr: 0.00326 [2024-02-18 15:13:48,296 INFO misc.py line 119 87073] Train: [44/100][180/1557] Data 0.922 (0.113) Batch 2.172 (1.162) Remain 28:35:19 loss: 0.1848 Lr: 0.00326 [2024-02-18 15:13:49,068 INFO misc.py line 119 87073] Train: [44/100][181/1557] Data 0.006 (0.112) Batch 0.774 (1.160) Remain 28:32:05 loss: 0.2521 Lr: 0.00326 [2024-02-18 15:13:50,317 INFO misc.py line 119 87073] Train: [44/100][182/1557] Data 0.003 (0.112) Batch 1.239 (1.160) Remain 28:32:43 loss: 0.2939 Lr: 0.00326 [2024-02-18 15:13:51,129 INFO misc.py line 119 87073] Train: [44/100][183/1557] Data 0.015 (0.111) Batch 0.822 (1.158) Remain 28:29:55 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line 119 87073] Train: [44/100][445/1557] Data 0.004 (0.111) Batch 0.848 (1.136) Remain 27:51:58 loss: 0.8624 Lr: 0.00325 [2024-02-18 15:18:45,541 INFO misc.py line 119 87073] Train: [44/100][446/1557] Data 0.005 (0.110) Batch 0.778 (1.135) Remain 27:50:45 loss: 0.3803 Lr: 0.00325 [2024-02-18 15:18:46,241 INFO misc.py line 119 87073] Train: [44/100][447/1557] Data 0.015 (0.110) Batch 0.710 (1.134) Remain 27:49:19 loss: 0.2054 Lr: 0.00325 [2024-02-18 15:18:47,505 INFO misc.py line 119 87073] Train: [44/100][448/1557] Data 0.004 (0.110) Batch 1.265 (1.135) Remain 27:49:44 loss: 0.1786 Lr: 0.00325 [2024-02-18 15:18:48,393 INFO misc.py line 119 87073] Train: [44/100][449/1557] Data 0.004 (0.110) Batch 0.889 (1.134) Remain 27:48:54 loss: 0.4008 Lr: 0.00325 [2024-02-18 15:18:49,447 INFO misc.py line 119 87073] Train: [44/100][450/1557] Data 0.003 (0.109) Batch 1.054 (1.134) Remain 27:48:37 loss: 0.5695 Lr: 0.00325 [2024-02-18 15:18:50,437 INFO misc.py line 119 87073] Train: 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Batch 1.086 (1.153) Remain 28:14:01 loss: 0.6849 Lr: 0.00324 [2024-02-18 15:22:20,996 INFO misc.py line 119 87073] Train: [44/100][626/1557] Data 0.003 (0.122) Batch 0.973 (1.153) Remain 28:13:34 loss: 0.5005 Lr: 0.00324 [2024-02-18 15:22:22,052 INFO misc.py line 119 87073] Train: [44/100][627/1557] Data 0.004 (0.122) Batch 1.056 (1.153) Remain 28:13:19 loss: 0.2398 Lr: 0.00324 [2024-02-18 15:22:22,825 INFO misc.py line 119 87073] Train: [44/100][628/1557] Data 0.004 (0.122) Batch 0.772 (1.152) Remain 28:12:24 loss: 0.1538 Lr: 0.00324 [2024-02-18 15:22:23,583 INFO misc.py line 119 87073] Train: [44/100][629/1557] Data 0.004 (0.122) Batch 0.758 (1.152) Remain 28:11:28 loss: 0.6271 Lr: 0.00324 [2024-02-18 15:22:24,904 INFO misc.py line 119 87073] Train: [44/100][630/1557] Data 0.004 (0.121) Batch 1.314 (1.152) Remain 28:11:49 loss: 0.3187 Lr: 0.00324 [2024-02-18 15:22:25,890 INFO misc.py line 119 87073] Train: [44/100][631/1557] Data 0.011 (0.121) Batch 0.993 (1.152) Remain 28:11:26 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Remain 27:56:04 loss: 0.3095 Lr: 0.00321 [2024-02-18 15:34:05,181 INFO misc.py line 119 87073] Train: [44/100][1241/1557] Data 0.004 (0.126) Batch 1.016 (1.149) Remain 27:55:53 loss: 0.4358 Lr: 0.00321 [2024-02-18 15:34:06,262 INFO misc.py line 119 87073] Train: [44/100][1242/1557] Data 0.005 (0.125) Batch 1.080 (1.149) Remain 27:55:47 loss: 0.7007 Lr: 0.00321 [2024-02-18 15:34:07,228 INFO misc.py line 119 87073] Train: [44/100][1243/1557] Data 0.005 (0.125) Batch 0.968 (1.149) Remain 27:55:33 loss: 0.0475 Lr: 0.00321 [2024-02-18 15:34:07,987 INFO misc.py line 119 87073] Train: [44/100][1244/1557] Data 0.004 (0.125) Batch 0.758 (1.149) Remain 27:55:05 loss: 0.2624 Lr: 0.00321 [2024-02-18 15:34:08,658 INFO misc.py line 119 87073] Train: [44/100][1245/1557] Data 0.004 (0.125) Batch 0.666 (1.148) Remain 27:54:30 loss: 0.3279 Lr: 0.00321 [2024-02-18 15:34:09,940 INFO misc.py line 119 87073] Train: [44/100][1246/1557] Data 0.009 (0.125) Batch 1.284 (1.148) Remain 27:54:38 loss: 0.1954 Lr: 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Train: [44/100][1259/1557] Data 0.010 (0.124) Batch 0.769 (1.146) Remain 27:51:17 loss: 0.3766 Lr: 0.00321 [2024-02-18 15:34:23,345 INFO misc.py line 119 87073] Train: [44/100][1260/1557] Data 0.003 (0.124) Batch 1.144 (1.146) Remain 27:51:16 loss: 0.2270 Lr: 0.00321 [2024-02-18 15:34:24,286 INFO misc.py line 119 87073] Train: [44/100][1261/1557] Data 0.004 (0.124) Batch 0.940 (1.146) Remain 27:51:01 loss: 0.5198 Lr: 0.00321 [2024-02-18 15:34:25,180 INFO misc.py line 119 87073] Train: [44/100][1262/1557] Data 0.004 (0.124) Batch 0.895 (1.146) Remain 27:50:42 loss: 0.4923 Lr: 0.00321 [2024-02-18 15:34:26,133 INFO misc.py line 119 87073] Train: [44/100][1263/1557] Data 0.004 (0.123) Batch 0.949 (1.146) Remain 27:50:27 loss: 0.2794 Lr: 0.00321 [2024-02-18 15:34:27,057 INFO misc.py line 119 87073] Train: [44/100][1264/1557] Data 0.008 (0.123) Batch 0.929 (1.145) Remain 27:50:11 loss: 0.4209 Lr: 0.00321 [2024-02-18 15:34:27,812 INFO misc.py line 119 87073] Train: [44/100][1265/1557] Data 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Remain 27:48:28 loss: 0.2690 Lr: 0.00321 [2024-02-18 15:34:34,382 INFO misc.py line 119 87073] Train: [44/100][1272/1557] Data 0.005 (0.123) Batch 0.689 (1.144) Remain 27:47:55 loss: 0.3044 Lr: 0.00321 [2024-02-18 15:34:35,157 INFO misc.py line 119 87073] Train: [44/100][1273/1557] Data 0.004 (0.123) Batch 0.770 (1.144) Remain 27:47:28 loss: 0.3203 Lr: 0.00321 [2024-02-18 15:34:36,491 INFO misc.py line 119 87073] Train: [44/100][1274/1557] Data 0.009 (0.122) Batch 1.332 (1.144) Remain 27:47:40 loss: 0.5715 Lr: 0.00321 [2024-02-18 15:34:37,576 INFO misc.py line 119 87073] Train: [44/100][1275/1557] Data 0.011 (0.122) Batch 1.086 (1.144) Remain 27:47:35 loss: 0.4885 Lr: 0.00321 [2024-02-18 15:34:38,512 INFO misc.py line 119 87073] Train: [44/100][1276/1557] Data 0.009 (0.122) Batch 0.942 (1.144) Remain 27:47:20 loss: 0.5979 Lr: 0.00321 [2024-02-18 15:34:39,336 INFO misc.py line 119 87073] Train: [44/100][1277/1557] Data 0.003 (0.122) Batch 0.823 (1.143) Remain 27:46:57 loss: 0.3734 Lr: 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Train: [44/100][1290/1557] Data 0.010 (0.121) Batch 0.805 (1.142) Remain 27:44:00 loss: 0.8572 Lr: 0.00321 [2024-02-18 15:34:52,661 INFO misc.py line 119 87073] Train: [44/100][1291/1557] Data 0.004 (0.121) Batch 0.837 (1.141) Remain 27:43:39 loss: 0.4690 Lr: 0.00321 [2024-02-18 15:34:53,566 INFO misc.py line 119 87073] Train: [44/100][1292/1557] Data 0.006 (0.121) Batch 0.897 (1.141) Remain 27:43:21 loss: 0.5154 Lr: 0.00321 [2024-02-18 15:34:54,362 INFO misc.py line 119 87073] Train: [44/100][1293/1557] Data 0.013 (0.121) Batch 0.804 (1.141) Remain 27:42:57 loss: 0.3849 Lr: 0.00321 [2024-02-18 15:34:55,131 INFO misc.py line 119 87073] Train: [44/100][1294/1557] Data 0.006 (0.121) Batch 0.770 (1.141) Remain 27:42:31 loss: 0.2474 Lr: 0.00321 [2024-02-18 15:35:06,598 INFO misc.py line 119 87073] Train: [44/100][1295/1557] Data 6.349 (0.126) Batch 11.468 (1.149) Remain 27:54:08 loss: 0.3099 Lr: 0.00321 [2024-02-18 15:35:07,542 INFO misc.py line 119 87073] Train: [44/100][1296/1557] Data 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Remain 27:52:32 loss: 0.2016 Lr: 0.00321 [2024-02-18 15:35:14,242 INFO misc.py line 119 87073] Train: [44/100][1303/1557] Data 0.011 (0.125) Batch 0.909 (1.147) Remain 27:52:15 loss: 0.3505 Lr: 0.00321 [2024-02-18 15:35:15,355 INFO misc.py line 119 87073] Train: [44/100][1304/1557] Data 0.004 (0.125) Batch 1.114 (1.147) Remain 27:52:12 loss: 0.2168 Lr: 0.00321 [2024-02-18 15:35:16,325 INFO misc.py line 119 87073] Train: [44/100][1305/1557] Data 0.004 (0.125) Batch 0.970 (1.147) Remain 27:51:59 loss: 0.4102 Lr: 0.00321 [2024-02-18 15:35:17,266 INFO misc.py line 119 87073] Train: [44/100][1306/1557] Data 0.004 (0.125) Batch 0.941 (1.147) Remain 27:51:44 loss: 0.4921 Lr: 0.00321 [2024-02-18 15:35:18,039 INFO misc.py line 119 87073] Train: [44/100][1307/1557] Data 0.004 (0.124) Batch 0.773 (1.147) Remain 27:51:18 loss: 0.3306 Lr: 0.00321 [2024-02-18 15:35:18,856 INFO misc.py line 119 87073] Train: [44/100][1308/1557] Data 0.003 (0.124) Batch 0.804 (1.147) Remain 27:50:54 loss: 0.6462 Lr: 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Train: [44/100][1321/1557] Data 0.004 (0.123) Batch 0.759 (1.145) Remain 27:48:21 loss: 0.2506 Lr: 0.00321 [2024-02-18 15:35:32,409 INFO misc.py line 119 87073] Train: [44/100][1322/1557] Data 0.006 (0.123) Batch 0.741 (1.145) Remain 27:47:53 loss: 0.3512 Lr: 0.00321 [2024-02-18 15:35:33,597 INFO misc.py line 119 87073] Train: [44/100][1323/1557] Data 0.005 (0.123) Batch 1.182 (1.145) Remain 27:47:54 loss: 0.1761 Lr: 0.00321 [2024-02-18 15:35:34,568 INFO misc.py line 119 87073] Train: [44/100][1324/1557] Data 0.011 (0.123) Batch 0.979 (1.145) Remain 27:47:42 loss: 0.4055 Lr: 0.00321 [2024-02-18 15:35:35,745 INFO misc.py line 119 87073] Train: [44/100][1325/1557] Data 0.004 (0.123) Batch 1.167 (1.145) Remain 27:47:42 loss: 0.8801 Lr: 0.00321 [2024-02-18 15:35:36,858 INFO misc.py line 119 87073] Train: [44/100][1326/1557] Data 0.014 (0.123) Batch 1.117 (1.145) Remain 27:47:39 loss: 0.5271 Lr: 0.00321 [2024-02-18 15:35:37,651 INFO misc.py line 119 87073] Train: [44/100][1327/1557] Data 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Remain 27:45:51 loss: 0.2941 Lr: 0.00321 [2024-02-18 15:35:44,293 INFO misc.py line 119 87073] Train: [44/100][1334/1557] Data 0.003 (0.122) Batch 0.945 (1.143) Remain 27:45:37 loss: 0.2977 Lr: 0.00321 [2024-02-18 15:35:44,966 INFO misc.py line 119 87073] Train: [44/100][1335/1557] Data 0.004 (0.122) Batch 0.668 (1.143) Remain 27:45:05 loss: 0.2229 Lr: 0.00321 [2024-02-18 15:35:45,741 INFO misc.py line 119 87073] Train: [44/100][1336/1557] Data 0.009 (0.122) Batch 0.780 (1.143) Remain 27:44:40 loss: 0.1928 Lr: 0.00321 [2024-02-18 15:35:46,938 INFO misc.py line 119 87073] Train: [44/100][1337/1557] Data 0.004 (0.122) Batch 1.196 (1.143) Remain 27:44:42 loss: 0.1612 Lr: 0.00321 [2024-02-18 15:35:47,901 INFO misc.py line 119 87073] Train: [44/100][1338/1557] Data 0.005 (0.122) Batch 0.963 (1.143) Remain 27:44:29 loss: 0.4973 Lr: 0.00321 [2024-02-18 15:35:48,816 INFO misc.py line 119 87073] Train: [44/100][1339/1557] Data 0.005 (0.122) Batch 0.916 (1.142) Remain 27:44:14 loss: 0.5744 Lr: 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INFO misc.py line 119 87073] Train: [44/100][1346/1557] Data 0.004 (0.121) Batch 1.048 (1.141) Remain 27:42:40 loss: 0.5403 Lr: 0.00321 [2024-02-18 15:35:56,484 INFO misc.py line 119 87073] Train: [44/100][1347/1557] Data 0.004 (0.121) Batch 0.984 (1.141) Remain 27:42:29 loss: 0.4396 Lr: 0.00321 [2024-02-18 15:35:57,467 INFO misc.py line 119 87073] Train: [44/100][1348/1557] Data 0.004 (0.121) Batch 0.981 (1.141) Remain 27:42:17 loss: 0.3271 Lr: 0.00321 [2024-02-18 15:35:58,317 INFO misc.py line 119 87073] Train: [44/100][1349/1557] Data 0.005 (0.121) Batch 0.851 (1.141) Remain 27:41:57 loss: 0.2339 Lr: 0.00321 [2024-02-18 15:35:59,053 INFO misc.py line 119 87073] Train: [44/100][1350/1557] Data 0.004 (0.121) Batch 0.725 (1.141) Remain 27:41:29 loss: 0.2102 Lr: 0.00321 [2024-02-18 15:36:10,125 INFO misc.py line 119 87073] Train: [44/100][1351/1557] Data 6.383 (0.125) Batch 11.082 (1.148) Remain 27:52:13 loss: 0.2611 Lr: 0.00321 [2024-02-18 15:36:11,217 INFO misc.py line 119 87073] Train: [44/100][1352/1557] Data 0.005 (0.125) Batch 1.092 (1.148) Remain 27:52:08 loss: 0.5447 Lr: 0.00321 [2024-02-18 15:36:12,234 INFO misc.py line 119 87073] Train: [44/100][1353/1557] Data 0.004 (0.125) Batch 1.016 (1.148) Remain 27:51:58 loss: 0.2320 Lr: 0.00321 [2024-02-18 15:36:13,333 INFO misc.py line 119 87073] Train: [44/100][1354/1557] Data 0.006 (0.125) Batch 1.091 (1.148) Remain 27:51:53 loss: 0.6345 Lr: 0.00321 [2024-02-18 15:36:14,283 INFO misc.py line 119 87073] Train: [44/100][1355/1557] Data 0.013 (0.125) Batch 0.959 (1.148) Remain 27:51:40 loss: 0.3869 Lr: 0.00321 [2024-02-18 15:36:15,003 INFO misc.py line 119 87073] Train: [44/100][1356/1557] Data 0.003 (0.125) Batch 0.719 (1.147) Remain 27:51:11 loss: 0.4273 Lr: 0.00321 [2024-02-18 15:36:15,708 INFO misc.py line 119 87073] Train: [44/100][1357/1557] Data 0.005 (0.125) Batch 0.700 (1.147) Remain 27:50:41 loss: 0.2773 Lr: 0.00321 [2024-02-18 15:36:16,996 INFO misc.py line 119 87073] Train: [44/100][1358/1557] Data 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Remain 27:49:21 loss: 0.4113 Lr: 0.00320 [2024-02-18 15:36:23,956 INFO misc.py line 119 87073] Train: [44/100][1365/1557] Data 0.017 (0.124) Batch 1.335 (1.146) Remain 27:49:32 loss: 0.2464 Lr: 0.00320 [2024-02-18 15:36:25,000 INFO misc.py line 119 87073] Train: [44/100][1366/1557] Data 0.016 (0.124) Batch 1.044 (1.146) Remain 27:49:24 loss: 0.2039 Lr: 0.00320 [2024-02-18 15:36:26,043 INFO misc.py line 119 87073] Train: [44/100][1367/1557] Data 0.015 (0.124) Batch 1.045 (1.146) Remain 27:49:17 loss: 0.3579 Lr: 0.00320 [2024-02-18 15:36:27,018 INFO misc.py line 119 87073] Train: [44/100][1368/1557] Data 0.014 (0.124) Batch 0.984 (1.146) Remain 27:49:05 loss: 0.2073 Lr: 0.00320 [2024-02-18 15:36:28,156 INFO misc.py line 119 87073] Train: [44/100][1369/1557] Data 0.004 (0.124) Batch 1.139 (1.146) Remain 27:49:04 loss: 0.6429 Lr: 0.00320 [2024-02-18 15:36:28,950 INFO misc.py line 119 87073] Train: [44/100][1370/1557] Data 0.003 (0.124) Batch 0.794 (1.146) Remain 27:48:40 loss: 0.3069 Lr: 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Train: [44/100][1383/1557] Data 0.012 (0.123) Batch 0.993 (1.144) Remain 27:45:50 loss: 0.6267 Lr: 0.00320 [2024-02-18 15:36:42,092 INFO misc.py line 119 87073] Train: [44/100][1384/1557] Data 0.005 (0.123) Batch 0.693 (1.144) Remain 27:45:20 loss: 0.1966 Lr: 0.00320 [2024-02-18 15:36:42,866 INFO misc.py line 119 87073] Train: [44/100][1385/1557] Data 0.006 (0.122) Batch 0.773 (1.143) Remain 27:44:56 loss: 0.4929 Lr: 0.00320 [2024-02-18 15:36:44,208 INFO misc.py line 119 87073] Train: [44/100][1386/1557] Data 0.007 (0.122) Batch 1.339 (1.144) Remain 27:45:07 loss: 0.3001 Lr: 0.00320 [2024-02-18 15:36:45,301 INFO misc.py line 119 87073] Train: [44/100][1387/1557] Data 0.010 (0.122) Batch 1.086 (1.144) Remain 27:45:02 loss: 0.3964 Lr: 0.00320 [2024-02-18 15:36:46,376 INFO misc.py line 119 87073] Train: [44/100][1388/1557] Data 0.016 (0.122) Batch 1.075 (1.143) Remain 27:44:57 loss: 0.2811 Lr: 0.00320 [2024-02-18 15:36:47,495 INFO misc.py line 119 87073] Train: [44/100][1389/1557] Data 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Remain 27:43:14 loss: 0.7803 Lr: 0.00320 [2024-02-18 15:36:53,819 INFO misc.py line 119 87073] Train: [44/100][1396/1557] Data 0.006 (0.122) Batch 0.964 (1.142) Remain 27:43:01 loss: 0.3028 Lr: 0.00320 [2024-02-18 15:36:54,786 INFO misc.py line 119 87073] Train: [44/100][1397/1557] Data 0.005 (0.121) Batch 0.961 (1.142) Remain 27:42:49 loss: 0.4848 Lr: 0.00320 [2024-02-18 15:36:55,546 INFO misc.py line 119 87073] Train: [44/100][1398/1557] Data 0.010 (0.121) Batch 0.765 (1.142) Remain 27:42:24 loss: 0.2872 Lr: 0.00320 [2024-02-18 15:36:56,307 INFO misc.py line 119 87073] Train: [44/100][1399/1557] Data 0.005 (0.121) Batch 0.752 (1.142) Remain 27:41:59 loss: 0.3515 Lr: 0.00320 [2024-02-18 15:36:57,545 INFO misc.py line 119 87073] Train: [44/100][1400/1557] Data 0.014 (0.121) Batch 1.240 (1.142) Remain 27:42:04 loss: 0.2103 Lr: 0.00320 [2024-02-18 15:36:58,443 INFO misc.py line 119 87073] Train: [44/100][1401/1557] Data 0.012 (0.121) Batch 0.906 (1.142) Remain 27:41:48 loss: 0.6177 Lr: 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INFO misc.py line 119 87073] Train: [44/100][1408/1557] Data 0.007 (0.126) Batch 0.944 (1.149) Remain 27:53:13 loss: 0.2349 Lr: 0.00320 [2024-02-18 15:37:18,477 INFO misc.py line 119 87073] Train: [44/100][1409/1557] Data 0.005 (0.126) Batch 0.891 (1.149) Remain 27:52:55 loss: 0.3647 Lr: 0.00320 [2024-02-18 15:37:19,536 INFO misc.py line 119 87073] Train: [44/100][1410/1557] Data 0.011 (0.126) Batch 1.060 (1.149) Remain 27:52:49 loss: 0.5598 Lr: 0.00320 [2024-02-18 15:37:20,511 INFO misc.py line 119 87073] Train: [44/100][1411/1557] Data 0.009 (0.126) Batch 0.980 (1.149) Remain 27:52:37 loss: 0.5238 Lr: 0.00320 [2024-02-18 15:37:21,523 INFO misc.py line 119 87073] Train: [44/100][1412/1557] Data 0.004 (0.126) Batch 1.013 (1.149) Remain 27:52:28 loss: 0.3797 Lr: 0.00320 [2024-02-18 15:37:22,258 INFO misc.py line 119 87073] Train: [44/100][1413/1557] Data 0.004 (0.126) Batch 0.735 (1.149) Remain 27:52:01 loss: 0.2034 Lr: 0.00320 [2024-02-18 15:37:23,555 INFO misc.py line 119 87073] Train: [44/100][1414/1557] Data 0.003 (0.126) Batch 1.286 (1.149) Remain 27:52:08 loss: 0.2524 Lr: 0.00320 [2024-02-18 15:37:24,596 INFO misc.py line 119 87073] Train: [44/100][1415/1557] Data 0.014 (0.126) Batch 1.043 (1.149) Remain 27:52:00 loss: 0.2578 Lr: 0.00320 [2024-02-18 15:37:25,462 INFO misc.py line 119 87073] Train: [44/100][1416/1557] Data 0.013 (0.126) Batch 0.874 (1.149) Remain 27:51:42 loss: 0.2995 Lr: 0.00320 [2024-02-18 15:37:26,416 INFO misc.py line 119 87073] Train: [44/100][1417/1557] Data 0.005 (0.126) Batch 0.955 (1.148) Remain 27:51:29 loss: 0.6638 Lr: 0.00320 [2024-02-18 15:37:27,305 INFO misc.py line 119 87073] Train: [44/100][1418/1557] Data 0.003 (0.126) Batch 0.889 (1.148) Remain 27:51:12 loss: 0.7145 Lr: 0.00320 [2024-02-18 15:37:28,026 INFO misc.py line 119 87073] Train: [44/100][1419/1557] Data 0.004 (0.125) Batch 0.715 (1.148) Remain 27:50:44 loss: 0.3235 Lr: 0.00320 [2024-02-18 15:37:28,715 INFO misc.py line 119 87073] Train: [44/100][1420/1557] Data 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Remain 27:48:55 loss: 0.3240 Lr: 0.00320 [2024-02-18 15:37:35,207 INFO misc.py line 119 87073] Train: [44/100][1427/1557] Data 0.004 (0.125) Batch 0.790 (1.146) Remain 27:48:32 loss: 0.5139 Lr: 0.00320 [2024-02-18 15:37:36,410 INFO misc.py line 119 87073] Train: [44/100][1428/1557] Data 0.005 (0.125) Batch 1.196 (1.147) Remain 27:48:34 loss: 0.1449 Lr: 0.00320 [2024-02-18 15:37:37,698 INFO misc.py line 119 87073] Train: [44/100][1429/1557] Data 0.012 (0.125) Batch 1.283 (1.147) Remain 27:48:42 loss: 0.2330 Lr: 0.00320 [2024-02-18 15:37:38,712 INFO misc.py line 119 87073] Train: [44/100][1430/1557] Data 0.017 (0.125) Batch 1.024 (1.147) Remain 27:48:33 loss: 0.5019 Lr: 0.00320 [2024-02-18 15:37:39,760 INFO misc.py line 119 87073] Train: [44/100][1431/1557] Data 0.007 (0.124) Batch 1.044 (1.146) Remain 27:48:25 loss: 0.3962 Lr: 0.00320 [2024-02-18 15:37:40,715 INFO misc.py line 119 87073] Train: [44/100][1432/1557] Data 0.011 (0.124) Batch 0.964 (1.146) Remain 27:48:13 loss: 0.2153 Lr: 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Train: [44/100][1445/1557] Data 0.003 (0.123) Batch 1.071 (1.144) Remain 27:45:16 loss: 0.4067 Lr: 0.00320 [2024-02-18 15:37:53,833 INFO misc.py line 119 87073] Train: [44/100][1446/1557] Data 0.003 (0.123) Batch 0.888 (1.144) Remain 27:45:00 loss: 0.3046 Lr: 0.00320 [2024-02-18 15:37:54,532 INFO misc.py line 119 87073] Train: [44/100][1447/1557] Data 0.005 (0.123) Batch 0.693 (1.144) Remain 27:44:31 loss: 0.3267 Lr: 0.00320 [2024-02-18 15:37:55,279 INFO misc.py line 119 87073] Train: [44/100][1448/1557] Data 0.012 (0.123) Batch 0.754 (1.144) Remain 27:44:06 loss: 0.4028 Lr: 0.00320 [2024-02-18 15:37:56,527 INFO misc.py line 119 87073] Train: [44/100][1449/1557] Data 0.004 (0.123) Batch 1.248 (1.144) Remain 27:44:12 loss: 0.3719 Lr: 0.00320 [2024-02-18 15:37:57,567 INFO misc.py line 119 87073] Train: [44/100][1450/1557] Data 0.005 (0.123) Batch 1.041 (1.144) Remain 27:44:04 loss: 0.5341 Lr: 0.00320 [2024-02-18 15:37:58,372 INFO misc.py line 119 87073] Train: [44/100][1451/1557] Data 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Remain 27:42:33 loss: 0.3062 Lr: 0.00320 [2024-02-18 15:38:05,072 INFO misc.py line 119 87073] Train: [44/100][1458/1557] Data 0.004 (0.122) Batch 0.879 (1.143) Remain 27:42:16 loss: 0.4497 Lr: 0.00320 [2024-02-18 15:38:06,239 INFO misc.py line 119 87073] Train: [44/100][1459/1557] Data 0.003 (0.122) Batch 1.167 (1.143) Remain 27:42:17 loss: 0.8303 Lr: 0.00320 [2024-02-18 15:38:07,069 INFO misc.py line 119 87073] Train: [44/100][1460/1557] Data 0.004 (0.122) Batch 0.830 (1.142) Remain 27:41:57 loss: 1.1492 Lr: 0.00320 [2024-02-18 15:38:07,855 INFO misc.py line 119 87073] Train: [44/100][1461/1557] Data 0.003 (0.122) Batch 0.777 (1.142) Remain 27:41:34 loss: 0.3675 Lr: 0.00320 [2024-02-18 15:38:08,593 INFO misc.py line 119 87073] Train: [44/100][1462/1557] Data 0.013 (0.122) Batch 0.746 (1.142) Remain 27:41:09 loss: 0.5924 Lr: 0.00320 [2024-02-18 15:38:20,128 INFO misc.py line 119 87073] Train: [44/100][1463/1557] Data 6.444 (0.126) Batch 11.535 (1.149) Remain 27:51:29 loss: 0.1274 Lr: 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Train: [44/100][1476/1557] Data 0.004 (0.125) Batch 0.764 (1.147) Remain 27:48:27 loss: 0.4443 Lr: 0.00320 [2024-02-18 15:38:33,580 INFO misc.py line 119 87073] Train: [44/100][1477/1557] Data 0.012 (0.125) Batch 1.324 (1.147) Remain 27:48:37 loss: 0.2033 Lr: 0.00320 [2024-02-18 15:38:34,687 INFO misc.py line 119 87073] Train: [44/100][1478/1557] Data 0.015 (0.125) Batch 1.108 (1.147) Remain 27:48:33 loss: 0.4911 Lr: 0.00320 [2024-02-18 15:38:35,711 INFO misc.py line 119 87073] Train: [44/100][1479/1557] Data 0.013 (0.125) Batch 1.022 (1.147) Remain 27:48:25 loss: 0.5560 Lr: 0.00320 [2024-02-18 15:38:36,778 INFO misc.py line 119 87073] Train: [44/100][1480/1557] Data 0.015 (0.125) Batch 1.067 (1.147) Remain 27:48:19 loss: 0.2721 Lr: 0.00320 [2024-02-18 15:38:37,851 INFO misc.py line 119 87073] Train: [44/100][1481/1557] Data 0.015 (0.125) Batch 1.071 (1.147) Remain 27:48:13 loss: 0.2683 Lr: 0.00320 [2024-02-18 15:38:38,612 INFO misc.py line 119 87073] Train: [44/100][1482/1557] Data 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Remain 27:46:29 loss: 0.4018 Lr: 0.00320 [2024-02-18 15:38:45,019 INFO misc.py line 119 87073] Train: [44/100][1489/1557] Data 0.013 (0.124) Batch 0.785 (1.146) Remain 27:46:07 loss: 0.2926 Lr: 0.00320 [2024-02-18 15:38:45,745 INFO misc.py line 119 87073] Train: [44/100][1490/1557] Data 0.005 (0.124) Batch 0.719 (1.145) Remain 27:45:41 loss: 0.3076 Lr: 0.00320 [2024-02-18 15:38:46,928 INFO misc.py line 119 87073] Train: [44/100][1491/1557] Data 0.012 (0.124) Batch 1.180 (1.145) Remain 27:45:42 loss: 0.2388 Lr: 0.00320 [2024-02-18 15:38:47,872 INFO misc.py line 119 87073] Train: [44/100][1492/1557] Data 0.015 (0.124) Batch 0.954 (1.145) Remain 27:45:29 loss: 0.6905 Lr: 0.00320 [2024-02-18 15:38:48,865 INFO misc.py line 119 87073] Train: [44/100][1493/1557] Data 0.005 (0.124) Batch 0.995 (1.145) Remain 27:45:19 loss: 0.7873 Lr: 0.00320 [2024-02-18 15:38:49,705 INFO misc.py line 119 87073] Train: [44/100][1494/1557] Data 0.003 (0.124) Batch 0.839 (1.145) Remain 27:45:00 loss: 0.3853 Lr: 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INFO misc.py line 119 87073] Train: [44/100][1501/1557] Data 0.005 (0.123) Batch 0.950 (1.144) Remain 27:43:28 loss: 0.3829 Lr: 0.00320 [2024-02-18 15:38:57,213 INFO misc.py line 119 87073] Train: [44/100][1502/1557] Data 0.014 (0.123) Batch 0.939 (1.144) Remain 27:43:15 loss: 0.4314 Lr: 0.00320 [2024-02-18 15:38:57,974 INFO misc.py line 119 87073] Train: [44/100][1503/1557] Data 0.005 (0.123) Batch 0.762 (1.144) Remain 27:42:51 loss: 0.1662 Lr: 0.00320 [2024-02-18 15:38:58,742 INFO misc.py line 119 87073] Train: [44/100][1504/1557] Data 0.004 (0.123) Batch 0.761 (1.143) Remain 27:42:28 loss: 0.5020 Lr: 0.00320 [2024-02-18 15:38:59,828 INFO misc.py line 119 87073] Train: [44/100][1505/1557] Data 0.011 (0.123) Batch 1.084 (1.143) Remain 27:42:23 loss: 0.2207 Lr: 0.00320 [2024-02-18 15:39:00,941 INFO misc.py line 119 87073] Train: [44/100][1506/1557] Data 0.013 (0.123) Batch 1.119 (1.143) Remain 27:42:21 loss: 0.6575 Lr: 0.00320 [2024-02-18 15:39:02,063 INFO misc.py line 119 87073] Train: [44/100][1507/1557] Data 0.006 (0.123) Batch 1.114 (1.143) Remain 27:42:18 loss: 0.3237 Lr: 0.00320 [2024-02-18 15:39:03,375 INFO misc.py line 119 87073] Train: [44/100][1508/1557] Data 0.015 (0.123) Batch 1.311 (1.143) Remain 27:42:27 loss: 0.4475 Lr: 0.00320 [2024-02-18 15:39:04,336 INFO misc.py line 119 87073] Train: [44/100][1509/1557] Data 0.016 (0.123) Batch 0.972 (1.143) Remain 27:42:16 loss: 0.2159 Lr: 0.00320 [2024-02-18 15:39:05,030 INFO misc.py line 119 87073] Train: [44/100][1510/1557] Data 0.005 (0.123) Batch 0.696 (1.143) Remain 27:41:49 loss: 0.2563 Lr: 0.00320 [2024-02-18 15:39:05,749 INFO misc.py line 119 87073] Train: [44/100][1511/1557] Data 0.004 (0.123) Batch 0.710 (1.143) Remain 27:41:22 loss: 0.2196 Lr: 0.00320 [2024-02-18 15:39:07,022 INFO misc.py line 119 87073] Train: [44/100][1512/1557] Data 0.012 (0.123) Batch 1.268 (1.143) Remain 27:41:28 loss: 0.2146 Lr: 0.00320 [2024-02-18 15:39:07,871 INFO misc.py line 119 87073] Train: [44/100][1513/1557] Data 0.017 (0.122) Batch 0.862 (1.143) Remain 27:41:11 loss: 0.4075 Lr: 0.00320 [2024-02-18 15:39:08,756 INFO misc.py line 119 87073] Train: [44/100][1514/1557] Data 0.005 (0.122) Batch 0.885 (1.142) Remain 27:40:55 loss: 0.2330 Lr: 0.00320 [2024-02-18 15:39:09,870 INFO misc.py line 119 87073] Train: [44/100][1515/1557] Data 0.004 (0.122) Batch 1.106 (1.142) Remain 27:40:52 loss: 0.1547 Lr: 0.00320 [2024-02-18 15:39:10,774 INFO misc.py line 119 87073] Train: [44/100][1516/1557] Data 0.012 (0.122) Batch 0.911 (1.142) Remain 27:40:37 loss: 0.5544 Lr: 0.00320 [2024-02-18 15:39:11,558 INFO misc.py line 119 87073] Train: [44/100][1517/1557] Data 0.005 (0.122) Batch 0.784 (1.142) Remain 27:40:16 loss: 0.3064 Lr: 0.00320 [2024-02-18 15:39:12,320 INFO misc.py line 119 87073] Train: [44/100][1518/1557] Data 0.004 (0.122) Batch 0.750 (1.142) Remain 27:39:52 loss: 0.4028 Lr: 0.00320 [2024-02-18 15:39:24,889 INFO misc.py line 119 87073] Train: [44/100][1519/1557] Data 5.649 (0.126) Batch 12.581 (1.149) Remain 27:50:49 loss: 0.1457 Lr: 0.00320 [2024-02-18 15:39:25,717 INFO misc.py line 119 87073] Train: [44/100][1520/1557] Data 0.004 (0.126) Batch 0.828 (1.149) Remain 27:50:29 loss: 0.3565 Lr: 0.00320 [2024-02-18 15:39:26,514 INFO misc.py line 119 87073] Train: [44/100][1521/1557] Data 0.004 (0.126) Batch 0.790 (1.149) Remain 27:50:08 loss: 0.2716 Lr: 0.00320 [2024-02-18 15:39:27,480 INFO misc.py line 119 87073] Train: [44/100][1522/1557] Data 0.011 (0.125) Batch 0.973 (1.149) Remain 27:49:56 loss: 0.5095 Lr: 0.00320 [2024-02-18 15:39:28,526 INFO misc.py line 119 87073] Train: [44/100][1523/1557] Data 0.004 (0.125) Batch 1.045 (1.149) Remain 27:49:49 loss: 0.1608 Lr: 0.00320 [2024-02-18 15:39:29,386 INFO misc.py line 119 87073] Train: [44/100][1524/1557] Data 0.004 (0.125) Batch 0.861 (1.148) Remain 27:49:32 loss: 0.2698 Lr: 0.00320 [2024-02-18 15:39:30,132 INFO misc.py line 119 87073] Train: [44/100][1525/1557] Data 0.004 (0.125) Batch 0.735 (1.148) Remain 27:49:07 loss: 0.3929 Lr: 0.00320 [2024-02-18 15:39:31,448 INFO misc.py line 119 87073] Train: [44/100][1526/1557] Data 0.015 (0.125) Batch 1.297 (1.148) Remain 27:49:14 loss: 0.2981 Lr: 0.00320 [2024-02-18 15:39:32,353 INFO misc.py line 119 87073] Train: [44/100][1527/1557] Data 0.033 (0.125) Batch 0.935 (1.148) Remain 27:49:01 loss: 0.4610 Lr: 0.00320 [2024-02-18 15:39:33,362 INFO misc.py line 119 87073] Train: [44/100][1528/1557] Data 0.004 (0.125) Batch 1.009 (1.148) Remain 27:48:52 loss: 0.6654 Lr: 0.00320 [2024-02-18 15:39:34,348 INFO misc.py line 119 87073] Train: [44/100][1529/1557] Data 0.003 (0.125) Batch 0.985 (1.148) Remain 27:48:41 loss: 0.3380 Lr: 0.00320 [2024-02-18 15:39:35,298 INFO misc.py line 119 87073] Train: [44/100][1530/1557] Data 0.005 (0.125) Batch 0.951 (1.148) Remain 27:48:29 loss: 0.5092 Lr: 0.00320 [2024-02-18 15:39:36,021 INFO misc.py line 119 87073] Train: [44/100][1531/1557] Data 0.004 (0.125) Batch 0.723 (1.148) Remain 27:48:03 loss: 0.2397 Lr: 0.00320 [2024-02-18 15:39:36,768 INFO misc.py line 119 87073] Train: [44/100][1532/1557] Data 0.005 (0.125) Batch 0.747 (1.147) Remain 27:47:39 loss: 0.3615 Lr: 0.00320 [2024-02-18 15:39:38,088 INFO misc.py line 119 87073] Train: [44/100][1533/1557] Data 0.005 (0.125) Batch 1.313 (1.147) Remain 27:47:48 loss: 0.2608 Lr: 0.00320 [2024-02-18 15:39:39,092 INFO misc.py line 119 87073] Train: [44/100][1534/1557] Data 0.013 (0.125) Batch 1.003 (1.147) Remain 27:47:38 loss: 0.3409 Lr: 0.00320 [2024-02-18 15:39:40,147 INFO misc.py line 119 87073] Train: [44/100][1535/1557] Data 0.013 (0.124) Batch 1.062 (1.147) Remain 27:47:32 loss: 0.7948 Lr: 0.00320 [2024-02-18 15:39:41,057 INFO misc.py line 119 87073] Train: [44/100][1536/1557] Data 0.006 (0.124) Batch 0.910 (1.147) Remain 27:47:18 loss: 0.6749 Lr: 0.00320 [2024-02-18 15:39:42,281 INFO misc.py line 119 87073] Train: [44/100][1537/1557] Data 0.006 (0.124) Batch 1.215 (1.147) Remain 27:47:20 loss: 0.2247 Lr: 0.00320 [2024-02-18 15:39:42,988 INFO misc.py line 119 87073] Train: [44/100][1538/1557] Data 0.015 (0.124) Batch 0.718 (1.147) Remain 27:46:55 loss: 0.2876 Lr: 0.00320 [2024-02-18 15:39:43,776 INFO misc.py line 119 87073] Train: [44/100][1539/1557] Data 0.004 (0.124) Batch 0.788 (1.147) Remain 27:46:33 loss: 0.3089 Lr: 0.00320 [2024-02-18 15:39:44,943 INFO misc.py line 119 87073] Train: [44/100][1540/1557] Data 0.004 (0.124) Batch 1.155 (1.147) Remain 27:46:33 loss: 0.1435 Lr: 0.00320 [2024-02-18 15:39:45,960 INFO misc.py line 119 87073] Train: [44/100][1541/1557] Data 0.016 (0.124) Batch 1.018 (1.147) Remain 27:46:24 loss: 0.4536 Lr: 0.00320 [2024-02-18 15:39:47,083 INFO misc.py line 119 87073] Train: [44/100][1542/1557] Data 0.015 (0.124) Batch 1.123 (1.146) Remain 27:46:22 loss: 0.2116 Lr: 0.00320 [2024-02-18 15:39:48,031 INFO misc.py line 119 87073] Train: [44/100][1543/1557] Data 0.015 (0.124) Batch 0.960 (1.146) Remain 27:46:10 loss: 0.1030 Lr: 0.00320 [2024-02-18 15:39:49,092 INFO misc.py line 119 87073] Train: [44/100][1544/1557] Data 0.004 (0.124) Batch 1.061 (1.146) Remain 27:46:04 loss: 0.4801 Lr: 0.00320 [2024-02-18 15:39:49,851 INFO misc.py line 119 87073] Train: [44/100][1545/1557] Data 0.004 (0.124) Batch 0.759 (1.146) Remain 27:45:41 loss: 0.4838 Lr: 0.00320 [2024-02-18 15:39:50,596 INFO misc.py line 119 87073] Train: [44/100][1546/1557] Data 0.004 (0.124) Batch 0.733 (1.146) Remain 27:45:16 loss: 0.4488 Lr: 0.00320 [2024-02-18 15:39:51,855 INFO misc.py line 119 87073] Train: [44/100][1547/1557] Data 0.015 (0.124) Batch 1.259 (1.146) Remain 27:45:22 loss: 0.2833 Lr: 0.00320 [2024-02-18 15:39:52,683 INFO misc.py line 119 87073] Train: [44/100][1548/1557] Data 0.016 (0.124) Batch 0.840 (1.146) Remain 27:45:03 loss: 0.3470 Lr: 0.00320 [2024-02-18 15:39:53,712 INFO misc.py line 119 87073] Train: [44/100][1549/1557] Data 0.004 (0.123) Batch 1.028 (1.146) Remain 27:44:56 loss: 0.4910 Lr: 0.00320 [2024-02-18 15:39:54,656 INFO misc.py line 119 87073] Train: [44/100][1550/1557] Data 0.004 (0.123) Batch 0.944 (1.145) Remain 27:44:43 loss: 0.2308 Lr: 0.00320 [2024-02-18 15:39:55,639 INFO misc.py line 119 87073] Train: [44/100][1551/1557] Data 0.004 (0.123) Batch 0.983 (1.145) Remain 27:44:33 loss: 0.3196 Lr: 0.00320 [2024-02-18 15:39:56,387 INFO misc.py line 119 87073] Train: [44/100][1552/1557] Data 0.004 (0.123) Batch 0.737 (1.145) Remain 27:44:09 loss: 0.1606 Lr: 0.00320 [2024-02-18 15:39:57,139 INFO misc.py line 119 87073] Train: [44/100][1553/1557] Data 0.016 (0.123) Batch 0.762 (1.145) Remain 27:43:46 loss: 0.5382 Lr: 0.00320 [2024-02-18 15:39:58,428 INFO misc.py line 119 87073] Train: [44/100][1554/1557] Data 0.005 (0.123) Batch 1.279 (1.145) Remain 27:43:52 loss: 0.3588 Lr: 0.00320 [2024-02-18 15:39:59,688 INFO misc.py line 119 87073] Train: [44/100][1555/1557] Data 0.016 (0.123) Batch 1.262 (1.145) Remain 27:43:58 loss: 0.3450 Lr: 0.00320 [2024-02-18 15:40:00,622 INFO misc.py line 119 87073] Train: [44/100][1556/1557] Data 0.014 (0.123) Batch 0.943 (1.145) Remain 27:43:45 loss: 0.4377 Lr: 0.00320 [2024-02-18 15:40:01,654 INFO misc.py line 119 87073] Train: [44/100][1557/1557] Data 0.004 (0.123) Batch 1.032 (1.145) Remain 27:43:38 loss: 0.3733 Lr: 0.00320 [2024-02-18 15:40:01,654 INFO misc.py line 136 87073] Train result: loss: 0.3816 [2024-02-18 15:40:01,655 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 15:40:27,542 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5045 [2024-02-18 15:40:28,323 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7309 [2024-02-18 15:40:30,449 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4124 [2024-02-18 15:40:32,657 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3051 [2024-02-18 15:40:37,605 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2403 [2024-02-18 15:40:38,305 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0789 [2024-02-18 15:40:39,567 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2996 [2024-02-18 15:40:42,524 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3519 [2024-02-18 15:40:44,333 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2235 [2024-02-18 15:40:45,660 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7136/0.7743/0.9143. [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9391/0.9700 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9831/0.9909 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8616/0.9722 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3430/0.3751 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6360/0.6554 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7788/0.9080 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8314/0.8778 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9307/0.9650 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8069/0.9034 [2024-02-18 15:40:45,660 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7911/0.8772 [2024-02-18 15:40:45,661 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7487/0.8501 [2024-02-18 15:40:45,661 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6259/0.7213 [2024-02-18 15:40:45,661 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 15:40:45,662 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 15:40:45,662 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 15:40:51,958 INFO misc.py line 119 87073] Train: [45/100][1/1557] Data 1.623 (1.623) Batch 2.427 (2.427) Remain 58:47:34 loss: 0.2441 Lr: 0.00319 [2024-02-18 15:40:53,067 INFO misc.py line 119 87073] Train: [45/100][2/1557] Data 0.006 (0.006) Batch 1.111 (1.111) Remain 26:53:50 loss: 0.2661 Lr: 0.00319 [2024-02-18 15:40:54,040 INFO misc.py line 119 87073] Train: [45/100][3/1557] Data 0.006 (0.006) Batch 0.973 (0.973) Remain 23:34:22 loss: 0.2004 Lr: 0.00319 [2024-02-18 15:40:55,014 INFO misc.py line 119 87073] Train: [45/100][4/1557] Data 0.006 (0.006) Batch 0.974 (0.974) Remain 23:34:53 loss: 0.7066 Lr: 0.00319 [2024-02-18 15:40:55,832 INFO misc.py line 119 87073] Train: [45/100][5/1557] Data 0.006 (0.006) Batch 0.819 (0.896) Remain 21:42:28 loss: 0.2012 Lr: 0.00319 [2024-02-18 15:40:56,556 INFO misc.py line 119 87073] Train: [45/100][6/1557] Data 0.004 (0.005) Batch 0.720 (0.838) Remain 20:17:12 loss: 0.3402 Lr: 0.00319 [2024-02-18 15:40:59,820 INFO misc.py line 119 87073] Train: [45/100][7/1557] Data 0.007 (0.005) Batch 3.268 (1.445) Remain 35:00:02 loss: 0.1979 Lr: 0.00319 [2024-02-18 15:41:00,778 INFO misc.py line 119 87073] Train: [45/100][8/1557] Data 0.003 (0.005) Batch 0.957 (1.348) Remain 32:38:15 loss: 0.6654 Lr: 0.00319 [2024-02-18 15:41:01,790 INFO misc.py line 119 87073] Train: [45/100][9/1557] Data 0.004 (0.005) Batch 1.011 (1.292) Remain 31:16:42 loss: 0.2394 Lr: 0.00319 [2024-02-18 15:41:02,919 INFO misc.py line 119 87073] Train: [45/100][10/1557] Data 0.005 (0.005) Batch 1.130 (1.268) Remain 30:43:03 loss: 0.5064 Lr: 0.00319 [2024-02-18 15:41:03,805 INFO misc.py line 119 87073] Train: [45/100][11/1557] Data 0.004 (0.005) Batch 0.887 (1.221) Remain 29:33:41 loss: 0.2283 Lr: 0.00319 [2024-02-18 15:41:04,491 INFO misc.py line 119 87073] Train: [45/100][12/1557] Data 0.004 (0.005) Batch 0.680 (1.161) Remain 28:06:18 loss: 0.1694 Lr: 0.00319 [2024-02-18 15:41:05,222 INFO misc.py line 119 87073] Train: [45/100][13/1557] Data 0.010 (0.005) Batch 0.737 (1.118) Remain 27:04:48 loss: 0.4769 Lr: 0.00319 [2024-02-18 15:41:06,439 INFO misc.py line 119 87073] Train: [45/100][14/1557] Data 0.004 (0.005) Batch 1.203 (1.126) Remain 27:15:55 loss: 0.2858 Lr: 0.00319 [2024-02-18 15:41:07,347 INFO misc.py line 119 87073] Train: [45/100][15/1557] Data 0.018 (0.006) Batch 0.923 (1.109) Remain 26:51:19 loss: 0.5075 Lr: 0.00319 [2024-02-18 15:41:08,303 INFO misc.py line 119 87073] Train: [45/100][16/1557] Data 0.003 (0.006) Batch 0.956 (1.097) Remain 26:34:11 loss: 0.3036 Lr: 0.00319 [2024-02-18 15:41:09,318 INFO misc.py line 119 87073] Train: [45/100][17/1557] Data 0.003 (0.006) Batch 1.005 (1.091) Remain 26:24:35 loss: 0.4871 Lr: 0.00319 [2024-02-18 15:41:10,347 INFO misc.py line 119 87073] Train: [45/100][18/1557] Data 0.013 (0.006) Batch 1.035 (1.087) Remain 26:19:13 loss: 0.3677 Lr: 0.00319 [2024-02-18 15:41:11,122 INFO misc.py line 119 87073] Train: [45/100][19/1557] Data 0.007 (0.006) Batch 0.778 (1.068) Remain 25:51:10 loss: 0.2579 Lr: 0.00319 [2024-02-18 15:41:11,930 INFO misc.py line 119 87073] Train: [45/100][20/1557] Data 0.004 (0.006) Batch 0.808 (1.052) Remain 25:28:56 loss: 0.3054 Lr: 0.00319 [2024-02-18 15:41:13,028 INFO misc.py line 119 87073] Train: [45/100][21/1557] Data 0.004 (0.006) Batch 1.089 (1.054) Remain 25:31:52 loss: 0.1947 Lr: 0.00319 [2024-02-18 15:41:13,996 INFO misc.py line 119 87073] Train: [45/100][22/1557] Data 0.013 (0.006) Batch 0.977 (1.050) Remain 25:25:58 loss: 0.6065 Lr: 0.00319 [2024-02-18 15:41:15,064 INFO misc.py line 119 87073] Train: [45/100][23/1557] Data 0.006 (0.006) Batch 1.068 (1.051) Remain 25:27:12 loss: 0.7056 Lr: 0.00319 [2024-02-18 15:41:16,079 INFO misc.py line 119 87073] Train: [45/100][24/1557] Data 0.004 (0.006) Batch 1.016 (1.050) Remain 25:24:46 loss: 0.4508 Lr: 0.00319 [2024-02-18 15:41:17,099 INFO misc.py line 119 87073] Train: [45/100][25/1557] Data 0.003 (0.006) Batch 1.018 (1.048) Remain 25:22:41 loss: 0.8502 Lr: 0.00319 [2024-02-18 15:41:17,818 INFO misc.py line 119 87073] Train: [45/100][26/1557] Data 0.005 (0.006) Batch 0.720 (1.034) Remain 25:01:55 loss: 0.4790 Lr: 0.00319 [2024-02-18 15:41:18,543 INFO misc.py line 119 87073] Train: [45/100][27/1557] Data 0.003 (0.006) Batch 0.724 (1.021) Remain 24:43:07 loss: 0.4732 Lr: 0.00319 [2024-02-18 15:41:19,804 INFO misc.py line 119 87073] Train: [45/100][28/1557] Data 0.005 (0.006) Batch 1.253 (1.030) Remain 24:56:34 loss: 0.1404 Lr: 0.00319 [2024-02-18 15:41:20,942 INFO misc.py line 119 87073] Train: [45/100][29/1557] Data 0.014 (0.006) Batch 1.141 (1.034) Remain 25:02:46 loss: 0.0943 Lr: 0.00319 [2024-02-18 15:41:21,915 INFO misc.py line 119 87073] Train: [45/100][30/1557] Data 0.010 (0.006) Batch 0.978 (1.032) Remain 24:59:44 loss: 0.3686 Lr: 0.00319 [2024-02-18 15:41:22,764 INFO misc.py line 119 87073] Train: [45/100][31/1557] Data 0.005 (0.006) Batch 0.850 (1.026) Remain 24:50:16 loss: 1.2442 Lr: 0.00319 [2024-02-18 15:41:23,800 INFO misc.py line 119 87073] Train: [45/100][32/1557] Data 0.004 (0.006) Batch 1.030 (1.026) Remain 24:50:29 loss: 0.4621 Lr: 0.00319 [2024-02-18 15:41:24,611 INFO misc.py line 119 87073] Train: [45/100][33/1557] Data 0.010 (0.006) Batch 0.816 (1.019) Remain 24:40:18 loss: 0.3054 Lr: 0.00319 [2024-02-18 15:41:25,335 INFO misc.py line 119 87073] Train: [45/100][34/1557] Data 0.004 (0.006) Batch 0.723 (1.010) Remain 24:26:26 loss: 0.4815 Lr: 0.00319 [2024-02-18 15:41:26,502 INFO misc.py line 119 87073] Train: [45/100][35/1557] Data 0.005 (0.006) Batch 1.158 (1.014) Remain 24:33:10 loss: 0.2034 Lr: 0.00319 [2024-02-18 15:41:27,572 INFO misc.py line 119 87073] Train: [45/100][36/1557] Data 0.014 (0.007) Batch 1.068 (1.016) Remain 24:35:31 loss: 0.4967 Lr: 0.00319 [2024-02-18 15:41:28,722 INFO misc.py line 119 87073] Train: [45/100][37/1557] Data 0.015 (0.007) Batch 1.149 (1.020) Remain 24:41:13 loss: 0.3362 Lr: 0.00319 [2024-02-18 15:41:29,742 INFO misc.py line 119 87073] Train: [45/100][38/1557] Data 0.016 (0.007) Batch 1.023 (1.020) Remain 24:41:20 loss: 0.5845 Lr: 0.00319 [2024-02-18 15:41:30,861 INFO misc.py line 119 87073] Train: [45/100][39/1557] Data 0.013 (0.007) Batch 1.117 (1.023) Remain 24:45:15 loss: 0.3995 Lr: 0.00319 [2024-02-18 15:41:31,605 INFO misc.py line 119 87073] Train: [45/100][40/1557] Data 0.014 (0.007) Batch 0.755 (1.015) Remain 24:34:43 loss: 0.4352 Lr: 0.00319 [2024-02-18 15:41:32,391 INFO misc.py line 119 87073] Train: [45/100][41/1557] Data 0.003 (0.007) Batch 0.764 (1.009) Remain 24:25:07 loss: 0.2520 Lr: 0.00319 [2024-02-18 15:41:33,522 INFO misc.py line 119 87073] Train: [45/100][42/1557] Data 0.025 (0.008) Batch 1.141 (1.012) Remain 24:30:00 loss: 0.2679 Lr: 0.00319 [2024-02-18 15:41:34,656 INFO misc.py line 119 87073] Train: [45/100][43/1557] Data 0.016 (0.008) Batch 1.143 (1.015) Remain 24:34:45 loss: 0.6413 Lr: 0.00319 [2024-02-18 15:41:35,620 INFO misc.py line 119 87073] Train: [45/100][44/1557] Data 0.007 (0.008) Batch 0.967 (1.014) Remain 24:33:01 loss: 0.3668 Lr: 0.00319 [2024-02-18 15:41:36,580 INFO misc.py line 119 87073] Train: [45/100][45/1557] Data 0.004 (0.008) Batch 0.961 (1.013) Remain 24:31:09 loss: 0.2650 Lr: 0.00319 [2024-02-18 15:41:37,619 INFO misc.py line 119 87073] Train: [45/100][46/1557] Data 0.003 (0.008) Batch 1.039 (1.013) Remain 24:32:00 loss: 0.2457 Lr: 0.00319 [2024-02-18 15:41:38,360 INFO misc.py line 119 87073] Train: [45/100][47/1557] Data 0.004 (0.008) Batch 0.742 (1.007) Remain 24:23:01 loss: 0.1973 Lr: 0.00319 [2024-02-18 15:41:39,140 INFO misc.py line 119 87073] Train: [45/100][48/1557] Data 0.003 (0.008) Batch 0.774 (1.002) Remain 24:15:28 loss: 0.3275 Lr: 0.00319 [2024-02-18 15:41:40,423 INFO misc.py line 119 87073] Train: [45/100][49/1557] Data 0.009 (0.008) Batch 1.278 (1.008) Remain 24:24:10 loss: 0.1821 Lr: 0.00319 [2024-02-18 15:41:41,320 INFO misc.py line 119 87073] Train: [45/100][50/1557] Data 0.013 (0.008) Batch 0.906 (1.006) Remain 24:21:01 loss: 0.2187 Lr: 0.00319 [2024-02-18 15:41:42,515 INFO misc.py line 119 87073] Train: [45/100][51/1557] Data 0.004 (0.008) Batch 1.187 (1.010) Remain 24:26:28 loss: 0.4571 Lr: 0.00319 [2024-02-18 15:41:43,417 INFO misc.py line 119 87073] Train: [45/100][52/1557] Data 0.014 (0.008) Batch 0.911 (1.008) Remain 24:23:31 loss: 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line 119 87073] Train: [45/100][109/1557] Data 0.005 (0.030) Batch 1.195 (1.063) Remain 25:43:23 loss: 0.3257 Lr: 0.00319 [2024-02-18 15:42:47,447 INFO misc.py line 119 87073] Train: [45/100][110/1557] Data 0.005 (0.030) Batch 0.687 (1.060) Remain 25:38:16 loss: 0.1688 Lr: 0.00319 [2024-02-18 15:42:48,206 INFO misc.py line 119 87073] Train: [45/100][111/1557] Data 0.004 (0.030) Batch 0.758 (1.057) Remain 25:34:12 loss: 0.6040 Lr: 0.00319 [2024-02-18 15:42:49,450 INFO misc.py line 119 87073] Train: [45/100][112/1557] Data 0.005 (0.030) Batch 1.243 (1.059) Remain 25:36:39 loss: 0.1121 Lr: 0.00319 [2024-02-18 15:42:50,334 INFO misc.py line 119 87073] Train: [45/100][113/1557] Data 0.006 (0.029) Batch 0.885 (1.057) Remain 25:34:20 loss: 0.4610 Lr: 0.00319 [2024-02-18 15:42:51,307 INFO misc.py line 119 87073] Train: [45/100][114/1557] Data 0.005 (0.029) Batch 0.974 (1.056) Remain 25:33:14 loss: 0.6497 Lr: 0.00319 [2024-02-18 15:42:52,331 INFO misc.py line 119 87073] Train: 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Batch 1.017 (1.138) Remain 27:30:59 loss: 0.1415 Lr: 0.00319 [2024-02-18 15:43:09,145 INFO misc.py line 119 87073] Train: [45/100][122/1557] Data 0.004 (0.046) Batch 0.859 (1.135) Remain 27:27:33 loss: 0.5096 Lr: 0.00319 [2024-02-18 15:43:10,277 INFO misc.py line 119 87073] Train: [45/100][123/1557] Data 0.003 (0.046) Batch 1.128 (1.135) Remain 27:27:27 loss: 0.1821 Lr: 0.00319 [2024-02-18 15:43:11,021 INFO misc.py line 119 87073] Train: [45/100][124/1557] Data 0.008 (0.046) Batch 0.748 (1.132) Remain 27:22:47 loss: 0.2599 Lr: 0.00319 [2024-02-18 15:43:11,778 INFO misc.py line 119 87073] Train: [45/100][125/1557] Data 0.003 (0.045) Batch 0.748 (1.129) Remain 27:18:12 loss: 0.3029 Lr: 0.00319 [2024-02-18 15:43:13,053 INFO misc.py line 119 87073] Train: [45/100][126/1557] Data 0.012 (0.045) Batch 1.271 (1.130) Remain 27:19:52 loss: 0.2503 Lr: 0.00319 [2024-02-18 15:43:13,847 INFO misc.py line 119 87073] Train: [45/100][127/1557] Data 0.016 (0.045) Batch 0.805 (1.127) Remain 27:16:03 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line 119 87073] Train: [45/100][165/1557] Data 0.005 (0.036) Batch 1.143 (1.088) Remain 26:18:22 loss: 0.3611 Lr: 0.00319 [2024-02-18 15:43:51,062 INFO misc.py line 119 87073] Train: [45/100][166/1557] Data 0.004 (0.036) Batch 0.734 (1.086) Remain 26:15:12 loss: 0.6346 Lr: 0.00319 [2024-02-18 15:43:51,786 INFO misc.py line 119 87073] Train: [45/100][167/1557] Data 0.003 (0.036) Batch 0.712 (1.084) Remain 26:11:53 loss: 0.1706 Lr: 0.00319 [2024-02-18 15:43:53,061 INFO misc.py line 119 87073] Train: [45/100][168/1557] Data 0.015 (0.035) Batch 1.283 (1.085) Remain 26:13:36 loss: 0.1713 Lr: 0.00319 [2024-02-18 15:43:54,074 INFO misc.py line 119 87073] Train: [45/100][169/1557] Data 0.008 (0.035) Batch 1.012 (1.085) Remain 26:12:57 loss: 0.4665 Lr: 0.00319 [2024-02-18 15:43:54,866 INFO misc.py line 119 87073] Train: [45/100][170/1557] Data 0.009 (0.035) Batch 0.796 (1.083) Remain 26:10:26 loss: 0.3470 Lr: 0.00319 [2024-02-18 15:43:55,892 INFO misc.py line 119 87073] Train: 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Batch 0.852 (1.132) Remain 27:22:00 loss: 0.5062 Lr: 0.00319 [2024-02-18 15:44:12,059 INFO misc.py line 119 87073] Train: [45/100][178/1557] Data 0.004 (0.047) Batch 1.011 (1.132) Remain 27:20:58 loss: 0.1150 Lr: 0.00319 [2024-02-18 15:44:13,014 INFO misc.py line 119 87073] Train: [45/100][179/1557] Data 0.006 (0.047) Batch 0.955 (1.131) Remain 27:19:30 loss: 0.2443 Lr: 0.00319 [2024-02-18 15:44:15,803 INFO misc.py line 119 87073] Train: [45/100][180/1557] Data 2.008 (0.058) Batch 2.791 (1.140) Remain 27:33:05 loss: 0.2136 Lr: 0.00319 [2024-02-18 15:44:16,517 INFO misc.py line 119 87073] Train: [45/100][181/1557] Data 0.003 (0.057) Batch 0.710 (1.137) Remain 27:29:34 loss: 0.3467 Lr: 0.00319 [2024-02-18 15:44:17,794 INFO misc.py line 119 87073] Train: [45/100][182/1557] Data 0.007 (0.057) Batch 1.268 (1.138) Remain 27:30:36 loss: 0.1989 Lr: 0.00319 [2024-02-18 15:44:18,895 INFO misc.py line 119 87073] Train: [45/100][183/1557] Data 0.017 (0.057) Batch 1.106 (1.138) Remain 27:30:19 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87073] Train: [45/100][196/1557] Data 0.011 (0.053) Batch 1.328 (1.124) Remain 27:09:39 loss: 0.2558 Lr: 0.00319 [2024-02-18 15:44:31,954 INFO misc.py line 119 87073] Train: [45/100][197/1557] Data 0.014 (0.053) Batch 0.992 (1.123) Remain 27:08:38 loss: 0.5080 Lr: 0.00318 [2024-02-18 15:44:32,903 INFO misc.py line 119 87073] Train: [45/100][198/1557] Data 0.003 (0.053) Batch 0.949 (1.122) Remain 27:07:19 loss: 0.4668 Lr: 0.00318 [2024-02-18 15:44:33,889 INFO misc.py line 119 87073] Train: [45/100][199/1557] Data 0.004 (0.053) Batch 0.986 (1.122) Remain 27:06:18 loss: 0.5203 Lr: 0.00318 [2024-02-18 15:44:34,676 INFO misc.py line 119 87073] Train: [45/100][200/1557] Data 0.004 (0.053) Batch 0.786 (1.120) Remain 27:03:48 loss: 0.2511 Lr: 0.00318 [2024-02-18 15:44:35,446 INFO misc.py line 119 87073] Train: [45/100][201/1557] Data 0.006 (0.052) Batch 0.772 (1.118) Remain 27:01:14 loss: 0.4449 Lr: 0.00318 [2024-02-18 15:44:36,215 INFO misc.py line 119 87073] Train: [45/100][202/1557] Data 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line 119 87073] Train: [45/100][221/1557] Data 0.004 (0.048) Batch 0.942 (1.104) Remain 26:39:36 loss: 0.7772 Lr: 0.00318 [2024-02-18 15:44:55,391 INFO misc.py line 119 87073] Train: [45/100][222/1557] Data 0.015 (0.048) Batch 0.778 (1.102) Remain 26:37:26 loss: 0.2300 Lr: 0.00318 [2024-02-18 15:44:56,158 INFO misc.py line 119 87073] Train: [45/100][223/1557] Data 0.004 (0.048) Batch 0.754 (1.100) Remain 26:35:07 loss: 0.2081 Lr: 0.00318 [2024-02-18 15:44:57,421 INFO misc.py line 119 87073] Train: [45/100][224/1557] Data 0.016 (0.048) Batch 1.235 (1.101) Remain 26:35:59 loss: 0.2518 Lr: 0.00318 [2024-02-18 15:44:58,213 INFO misc.py line 119 87073] Train: [45/100][225/1557] Data 0.045 (0.048) Batch 0.833 (1.100) Remain 26:34:13 loss: 0.4723 Lr: 0.00318 [2024-02-18 15:44:59,145 INFO misc.py line 119 87073] Train: [45/100][226/1557] Data 0.004 (0.048) Batch 0.931 (1.099) Remain 26:33:06 loss: 0.0653 Lr: 0.00318 [2024-02-18 15:45:00,231 INFO misc.py line 119 87073] Train: 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Batch 0.932 (1.141) Remain 27:33:40 loss: 0.7255 Lr: 0.00318 [2024-02-18 15:45:17,366 INFO misc.py line 119 87073] Train: [45/100][234/1557] Data 0.004 (0.057) Batch 0.896 (1.140) Remain 27:32:06 loss: 0.2735 Lr: 0.00318 [2024-02-18 15:45:18,339 INFO misc.py line 119 87073] Train: [45/100][235/1557] Data 0.004 (0.057) Batch 0.973 (1.139) Remain 27:31:03 loss: 0.5550 Lr: 0.00318 [2024-02-18 15:45:19,109 INFO misc.py line 119 87073] Train: [45/100][236/1557] Data 0.003 (0.057) Batch 0.769 (1.138) Remain 27:28:43 loss: 0.4366 Lr: 0.00318 [2024-02-18 15:45:19,792 INFO misc.py line 119 87073] Train: [45/100][237/1557] Data 0.005 (0.057) Batch 0.674 (1.136) Remain 27:25:50 loss: 0.4973 Lr: 0.00318 [2024-02-18 15:45:21,006 INFO misc.py line 119 87073] Train: [45/100][238/1557] Data 0.013 (0.057) Batch 1.215 (1.136) Remain 27:26:18 loss: 0.2400 Lr: 0.00318 [2024-02-18 15:45:21,950 INFO misc.py line 119 87073] Train: [45/100][239/1557] Data 0.013 (0.056) Batch 0.953 (1.135) Remain 27:25:10 loss: 0.3841 Lr: 0.00318 [2024-02-18 15:45:22,934 INFO misc.py line 119 87073] Train: [45/100][240/1557] Data 0.004 (0.056) Batch 0.983 (1.135) Remain 27:24:13 loss: 0.7438 Lr: 0.00318 [2024-02-18 15:45:23,850 INFO misc.py line 119 87073] Train: [45/100][241/1557] Data 0.005 (0.056) Batch 0.917 (1.134) Remain 27:22:52 loss: 0.2169 Lr: 0.00318 [2024-02-18 15:45:24,956 INFO misc.py line 119 87073] Train: [45/100][242/1557] Data 0.004 (0.056) Batch 1.095 (1.133) Remain 27:22:37 loss: 0.1431 Lr: 0.00318 [2024-02-18 15:45:25,738 INFO misc.py line 119 87073] Train: [45/100][243/1557] Data 0.014 (0.056) Batch 0.793 (1.132) Remain 27:20:33 loss: 0.2935 Lr: 0.00318 [2024-02-18 15:45:26,497 INFO misc.py line 119 87073] Train: [45/100][244/1557] Data 0.003 (0.055) Batch 0.749 (1.130) Remain 27:18:13 loss: 0.4869 Lr: 0.00318 [2024-02-18 15:45:27,665 INFO misc.py line 119 87073] Train: [45/100][245/1557] Data 0.012 (0.055) Batch 1.166 (1.131) Remain 27:18:25 loss: 0.3505 Lr: 0.00318 [2024-02-18 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87073] Train: [45/100][252/1557] Data 0.013 (0.054) Batch 1.214 (1.126) Remain 27:11:59 loss: 0.1998 Lr: 0.00318 [2024-02-18 15:45:35,438 INFO misc.py line 119 87073] Train: [45/100][253/1557] Data 0.015 (0.054) Batch 0.953 (1.126) Remain 27:10:58 loss: 0.4291 Lr: 0.00318 [2024-02-18 15:45:36,361 INFO misc.py line 119 87073] Train: [45/100][254/1557] Data 0.003 (0.053) Batch 0.921 (1.125) Remain 27:09:45 loss: 0.7109 Lr: 0.00318 [2024-02-18 15:45:37,408 INFO misc.py line 119 87073] Train: [45/100][255/1557] Data 0.005 (0.053) Batch 1.049 (1.124) Remain 27:09:18 loss: 0.6713 Lr: 0.00318 [2024-02-18 15:45:38,302 INFO misc.py line 119 87073] Train: [45/100][256/1557] Data 0.004 (0.053) Batch 0.889 (1.124) Remain 27:07:56 loss: 0.7114 Lr: 0.00318 [2024-02-18 15:45:39,036 INFO misc.py line 119 87073] Train: [45/100][257/1557] Data 0.009 (0.053) Batch 0.738 (1.122) Remain 27:05:43 loss: 0.2276 Lr: 0.00318 [2024-02-18 15:45:39,825 INFO misc.py line 119 87073] Train: [45/100][258/1557] Data 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[2024-02-18 15:45:52,483 INFO misc.py line 119 87073] Train: [45/100][271/1557] Data 0.004 (0.050) Batch 0.816 (1.114) Remain 26:53:11 loss: 0.3873 Lr: 0.00318 [2024-02-18 15:45:53,284 INFO misc.py line 119 87073] Train: [45/100][272/1557] Data 0.012 (0.050) Batch 0.809 (1.112) Remain 26:51:32 loss: 0.2258 Lr: 0.00318 [2024-02-18 15:45:54,626 INFO misc.py line 119 87073] Train: [45/100][273/1557] Data 0.004 (0.050) Batch 1.333 (1.113) Remain 26:52:42 loss: 0.1804 Lr: 0.00318 [2024-02-18 15:45:55,586 INFO misc.py line 119 87073] Train: [45/100][274/1557] Data 0.013 (0.050) Batch 0.968 (1.113) Remain 26:51:54 loss: 0.5866 Lr: 0.00318 [2024-02-18 15:45:56,435 INFO misc.py line 119 87073] Train: [45/100][275/1557] Data 0.005 (0.050) Batch 0.850 (1.112) Remain 26:50:29 loss: 0.6360 Lr: 0.00318 [2024-02-18 15:45:57,410 INFO misc.py line 119 87073] Train: [45/100][276/1557] Data 0.004 (0.050) Batch 0.957 (1.111) Remain 26:49:39 loss: 0.2988 Lr: 0.00318 [2024-02-18 15:45:58,453 INFO misc.py line 119 87073] Train: [45/100][277/1557] Data 0.021 (0.050) Batch 1.057 (1.111) Remain 26:49:21 loss: 0.1608 Lr: 0.00318 [2024-02-18 15:45:59,243 INFO misc.py line 119 87073] Train: [45/100][278/1557] Data 0.008 (0.049) Batch 0.793 (1.110) Remain 26:47:39 loss: 0.2963 Lr: 0.00318 [2024-02-18 15:46:00,028 INFO misc.py line 119 87073] Train: [45/100][279/1557] Data 0.004 (0.049) Batch 0.784 (1.109) Remain 26:45:55 loss: 0.1703 Lr: 0.00318 [2024-02-18 15:46:01,274 INFO misc.py line 119 87073] Train: [45/100][280/1557] Data 0.005 (0.049) Batch 1.243 (1.109) Remain 26:46:36 loss: 0.1790 Lr: 0.00318 [2024-02-18 15:46:02,173 INFO misc.py line 119 87073] Train: [45/100][281/1557] Data 0.008 (0.049) Batch 0.904 (1.108) Remain 26:45:31 loss: 0.1972 Lr: 0.00318 [2024-02-18 15:46:02,987 INFO misc.py line 119 87073] Train: [45/100][282/1557] Data 0.003 (0.049) Batch 0.813 (1.107) Remain 26:43:58 loss: 0.4606 Lr: 0.00318 [2024-02-18 15:46:03,901 INFO misc.py line 119 87073] Train: 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Batch 1.169 (1.138) Remain 27:27:45 loss: 0.3310 Lr: 0.00318 [2024-02-18 15:46:20,404 INFO misc.py line 119 87073] Train: [45/100][290/1557] Data 0.004 (0.056) Batch 0.994 (1.137) Remain 27:27:01 loss: 0.6594 Lr: 0.00318 [2024-02-18 15:46:21,311 INFO misc.py line 119 87073] Train: [45/100][291/1557] Data 0.004 (0.056) Batch 0.906 (1.136) Remain 27:25:50 loss: 0.1878 Lr: 0.00318 [2024-02-18 15:46:22,058 INFO misc.py line 119 87073] Train: [45/100][292/1557] Data 0.006 (0.055) Batch 0.748 (1.135) Remain 27:23:52 loss: 0.2555 Lr: 0.00318 [2024-02-18 15:46:22,826 INFO misc.py line 119 87073] Train: [45/100][293/1557] Data 0.009 (0.055) Batch 0.768 (1.134) Remain 27:22:01 loss: 0.2366 Lr: 0.00318 [2024-02-18 15:46:24,029 INFO misc.py line 119 87073] Train: [45/100][294/1557] Data 0.004 (0.055) Batch 1.203 (1.134) Remain 27:22:20 loss: 0.3391 Lr: 0.00318 [2024-02-18 15:46:24,984 INFO misc.py line 119 87073] Train: [45/100][295/1557] Data 0.004 (0.055) Batch 0.956 (1.133) Remain 27:21:26 loss: 0.2814 Lr: 0.00318 [2024-02-18 15:46:25,896 INFO misc.py line 119 87073] Train: [45/100][296/1557] Data 0.003 (0.055) Batch 0.911 (1.133) Remain 27:20:19 loss: 0.0751 Lr: 0.00318 [2024-02-18 15:46:26,891 INFO misc.py line 119 87073] Train: [45/100][297/1557] Data 0.004 (0.055) Batch 0.987 (1.132) Remain 27:19:35 loss: 0.4057 Lr: 0.00318 [2024-02-18 15:46:27,767 INFO misc.py line 119 87073] Train: [45/100][298/1557] Data 0.012 (0.054) Batch 0.885 (1.131) Remain 27:18:21 loss: 0.5075 Lr: 0.00318 [2024-02-18 15:46:28,505 INFO misc.py line 119 87073] Train: [45/100][299/1557] Data 0.004 (0.054) Batch 0.738 (1.130) Remain 27:16:24 loss: 0.5278 Lr: 0.00318 [2024-02-18 15:46:29,306 INFO misc.py line 119 87073] Train: [45/100][300/1557] Data 0.003 (0.054) Batch 0.801 (1.129) Remain 27:14:47 loss: 0.2774 Lr: 0.00318 [2024-02-18 15:46:30,418 INFO misc.py line 119 87073] Train: [45/100][301/1557] Data 0.004 (0.054) Batch 1.111 (1.129) Remain 27:14:40 loss: 0.2605 Lr: 0.00318 [2024-02-18 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87073] Train: [45/100][308/1557] Data 0.004 (0.053) Batch 1.243 (1.124) Remain 27:07:49 loss: 0.1791 Lr: 0.00318 [2024-02-18 15:46:37,867 INFO misc.py line 119 87073] Train: [45/100][309/1557] Data 0.006 (0.053) Batch 0.963 (1.124) Remain 27:07:02 loss: 0.3606 Lr: 0.00318 [2024-02-18 15:46:38,778 INFO misc.py line 119 87073] Train: [45/100][310/1557] Data 0.005 (0.052) Batch 0.912 (1.123) Remain 27:06:01 loss: 0.3359 Lr: 0.00318 [2024-02-18 15:46:39,783 INFO misc.py line 119 87073] Train: [45/100][311/1557] Data 0.005 (0.052) Batch 1.001 (1.123) Remain 27:05:25 loss: 0.2674 Lr: 0.00318 [2024-02-18 15:46:40,696 INFO misc.py line 119 87073] Train: [45/100][312/1557] Data 0.010 (0.052) Batch 0.919 (1.122) Remain 27:04:27 loss: 0.4516 Lr: 0.00318 [2024-02-18 15:46:41,442 INFO misc.py line 119 87073] Train: [45/100][313/1557] Data 0.003 (0.052) Batch 0.745 (1.121) Remain 27:02:40 loss: 0.2430 Lr: 0.00318 [2024-02-18 15:46:42,237 INFO misc.py line 119 87073] Train: [45/100][314/1557] Data 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Batch 1.036 (1.132) Remain 27:19:00 loss: 0.4091 Lr: 0.00318 [2024-02-18 15:47:22,264 INFO misc.py line 119 87073] Train: [45/100][346/1557] Data 0.004 (0.056) Batch 0.963 (1.132) Remain 27:18:16 loss: 0.6228 Lr: 0.00318 [2024-02-18 15:47:23,270 INFO misc.py line 119 87073] Train: [45/100][347/1557] Data 0.006 (0.056) Batch 1.008 (1.131) Remain 27:17:43 loss: 0.3124 Lr: 0.00318 [2024-02-18 15:47:24,026 INFO misc.py line 119 87073] Train: [45/100][348/1557] Data 0.003 (0.056) Batch 0.755 (1.130) Remain 27:16:07 loss: 0.3463 Lr: 0.00318 [2024-02-18 15:47:24,752 INFO misc.py line 119 87073] Train: [45/100][349/1557] Data 0.004 (0.055) Batch 0.718 (1.129) Remain 27:14:23 loss: 0.2248 Lr: 0.00318 [2024-02-18 15:47:26,024 INFO misc.py line 119 87073] Train: [45/100][350/1557] Data 0.012 (0.055) Batch 1.270 (1.130) Remain 27:14:57 loss: 0.2423 Lr: 0.00318 [2024-02-18 15:47:26,988 INFO misc.py line 119 87073] Train: [45/100][351/1557] Data 0.014 (0.055) Batch 0.973 (1.129) Remain 27:14:17 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Batch 0.912 (1.138) Remain 27:26:48 loss: 0.4176 Lr: 0.00317 [2024-02-18 15:48:28,254 INFO misc.py line 119 87073] Train: [45/100][402/1557] Data 0.004 (0.059) Batch 1.103 (1.138) Remain 27:26:40 loss: 0.6132 Lr: 0.00317 [2024-02-18 15:48:29,188 INFO misc.py line 119 87073] Train: [45/100][403/1557] Data 0.004 (0.059) Batch 0.934 (1.138) Remain 27:25:54 loss: 0.2566 Lr: 0.00317 [2024-02-18 15:48:29,942 INFO misc.py line 119 87073] Train: [45/100][404/1557] Data 0.004 (0.059) Batch 0.753 (1.137) Remain 27:24:30 loss: 0.6806 Lr: 0.00317 [2024-02-18 15:48:30,762 INFO misc.py line 119 87073] Train: [45/100][405/1557] Data 0.005 (0.059) Batch 0.821 (1.136) Remain 27:23:20 loss: 0.1804 Lr: 0.00317 [2024-02-18 15:48:31,950 INFO misc.py line 119 87073] Train: [45/100][406/1557] Data 0.004 (0.058) Batch 1.188 (1.136) Remain 27:23:30 loss: 0.2422 Lr: 0.00317 [2024-02-18 15:48:32,987 INFO misc.py line 119 87073] Train: [45/100][407/1557] Data 0.004 (0.058) Batch 1.037 (1.136) Remain 27:23:08 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Batch 0.946 (1.139) Remain 27:26:25 loss: 0.4469 Lr: 0.00317 [2024-02-18 15:49:31,989 INFO misc.py line 119 87073] Train: [45/100][458/1557] Data 0.004 (0.057) Batch 0.871 (1.138) Remain 27:25:33 loss: 0.7604 Lr: 0.00317 [2024-02-18 15:49:32,967 INFO misc.py line 119 87073] Train: [45/100][459/1557] Data 0.005 (0.057) Batch 0.978 (1.138) Remain 27:25:01 loss: 0.4159 Lr: 0.00317 [2024-02-18 15:49:33,690 INFO misc.py line 119 87073] Train: [45/100][460/1557] Data 0.004 (0.057) Batch 0.724 (1.137) Remain 27:23:42 loss: 0.2305 Lr: 0.00317 [2024-02-18 15:49:34,483 INFO misc.py line 119 87073] Train: [45/100][461/1557] Data 0.014 (0.057) Batch 0.790 (1.136) Remain 27:22:35 loss: 0.3817 Lr: 0.00317 [2024-02-18 15:49:35,715 INFO misc.py line 119 87073] Train: [45/100][462/1557] Data 0.006 (0.057) Batch 1.226 (1.137) Remain 27:22:51 loss: 0.2528 Lr: 0.00317 [2024-02-18 15:49:36,543 INFO misc.py line 119 87073] Train: [45/100][463/1557] Data 0.012 (0.057) Batch 0.836 (1.136) Remain 27:21:53 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Batch 1.006 (1.137) Remain 27:22:53 loss: 0.6176 Lr: 0.00317 [2024-02-18 15:50:35,018 INFO misc.py line 119 87073] Train: [45/100][514/1557] Data 0.005 (0.056) Batch 0.992 (1.137) Remain 27:22:27 loss: 0.3203 Lr: 0.00317 [2024-02-18 15:50:36,021 INFO misc.py line 119 87073] Train: [45/100][515/1557] Data 0.004 (0.056) Batch 1.003 (1.137) Remain 27:22:04 loss: 0.3383 Lr: 0.00317 [2024-02-18 15:50:36,773 INFO misc.py line 119 87073] Train: [45/100][516/1557] Data 0.004 (0.056) Batch 0.742 (1.136) Remain 27:20:56 loss: 0.4607 Lr: 0.00317 [2024-02-18 15:50:37,506 INFO misc.py line 119 87073] Train: [45/100][517/1557] Data 0.014 (0.056) Batch 0.742 (1.135) Remain 27:19:48 loss: 0.2324 Lr: 0.00317 [2024-02-18 15:50:38,761 INFO misc.py line 119 87073] Train: [45/100][518/1557] Data 0.004 (0.056) Batch 1.255 (1.135) Remain 27:20:07 loss: 0.3028 Lr: 0.00317 [2024-02-18 15:50:39,607 INFO misc.py line 119 87073] Train: [45/100][519/1557] Data 0.004 (0.056) Batch 0.846 (1.135) Remain 27:19:18 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Batch 0.942 (1.139) Remain 27:21:49 loss: 0.5150 Lr: 0.00316 [2024-02-18 15:54:51,490 INFO misc.py line 119 87073] Train: [45/100][738/1557] Data 0.004 (0.060) Batch 1.111 (1.139) Remain 27:21:44 loss: 0.3546 Lr: 0.00316 [2024-02-18 15:54:52,446 INFO misc.py line 119 87073] Train: [45/100][739/1557] Data 0.003 (0.060) Batch 0.956 (1.139) Remain 27:21:21 loss: 0.1963 Lr: 0.00316 [2024-02-18 15:54:53,185 INFO misc.py line 119 87073] Train: [45/100][740/1557] Data 0.003 (0.060) Batch 0.739 (1.139) Remain 27:20:33 loss: 0.4689 Lr: 0.00316 [2024-02-18 15:54:53,913 INFO misc.py line 119 87073] Train: [45/100][741/1557] Data 0.004 (0.060) Batch 0.725 (1.138) Remain 27:19:44 loss: 0.4286 Lr: 0.00316 [2024-02-18 15:54:55,128 INFO misc.py line 119 87073] Train: [45/100][742/1557] Data 0.006 (0.060) Batch 1.209 (1.138) Remain 27:19:51 loss: 0.2405 Lr: 0.00316 [2024-02-18 15:54:56,023 INFO misc.py line 119 87073] Train: [45/100][743/1557] Data 0.013 (0.060) Batch 0.901 (1.138) Remain 27:19:22 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loss: 0.5041 Lr: 0.00315 [2024-02-18 15:55:59,757 INFO misc.py line 119 87073] Train: [45/100][800/1557] Data 0.010 (0.059) Batch 1.013 (1.136) Remain 27:16:15 loss: 0.2461 Lr: 0.00315 [2024-02-18 15:56:00,870 INFO misc.py line 119 87073] Train: [45/100][801/1557] Data 0.013 (0.059) Batch 1.120 (1.136) Remain 27:16:12 loss: 0.7348 Lr: 0.00315 [2024-02-18 15:56:01,766 INFO misc.py line 119 87073] Train: [45/100][802/1557] Data 0.006 (0.059) Batch 0.897 (1.136) Remain 27:15:45 loss: 0.3242 Lr: 0.00315 [2024-02-18 15:56:02,558 INFO misc.py line 119 87073] Train: [45/100][803/1557] Data 0.006 (0.059) Batch 0.792 (1.136) Remain 27:15:07 loss: 0.3679 Lr: 0.00315 [2024-02-18 15:56:03,319 INFO misc.py line 119 87073] Train: [45/100][804/1557] Data 0.005 (0.059) Batch 0.760 (1.135) Remain 27:14:25 loss: 0.3135 Lr: 0.00315 [2024-02-18 15:56:04,444 INFO misc.py line 119 87073] Train: [45/100][805/1557] Data 0.005 (0.059) Batch 1.126 (1.135) Remain 27:14:23 loss: 0.2284 Lr: 0.00315 [2024-02-18 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Batch 1.130 (1.138) Remain 27:17:01 loss: 0.7589 Lr: 0.00315 [2024-02-18 15:56:57,372 INFO misc.py line 119 87073] Train: [45/100][850/1557] Data 0.005 (0.059) Batch 0.942 (1.137) Remain 27:16:40 loss: 0.2724 Lr: 0.00315 [2024-02-18 15:56:58,263 INFO misc.py line 119 87073] Train: [45/100][851/1557] Data 0.005 (0.059) Batch 0.893 (1.137) Remain 27:16:14 loss: 0.2171 Lr: 0.00315 [2024-02-18 15:56:59,028 INFO misc.py line 119 87073] Train: [45/100][852/1557] Data 0.003 (0.059) Batch 0.752 (1.137) Remain 27:15:34 loss: 0.3101 Lr: 0.00315 [2024-02-18 15:56:59,827 INFO misc.py line 119 87073] Train: [45/100][853/1557] Data 0.016 (0.059) Batch 0.811 (1.136) Remain 27:14:59 loss: 0.3897 Lr: 0.00315 [2024-02-18 15:57:01,028 INFO misc.py line 119 87073] Train: [45/100][854/1557] Data 0.004 (0.059) Batch 1.201 (1.136) Remain 27:15:05 loss: 0.3894 Lr: 0.00315 [2024-02-18 15:57:02,114 INFO misc.py line 119 87073] Train: [45/100][855/1557] Data 0.004 (0.059) Batch 1.086 (1.136) Remain 27:14:59 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Remain 27:10:34 loss: 0.4096 Lr: 0.00313 [2024-02-18 16:04:23,037 INFO misc.py line 119 87073] Train: [45/100][1241/1557] Data 0.004 (0.060) Batch 0.972 (1.138) Remain 27:10:22 loss: 0.3725 Lr: 0.00313 [2024-02-18 16:04:23,946 INFO misc.py line 119 87073] Train: [45/100][1242/1557] Data 0.010 (0.060) Batch 0.915 (1.138) Remain 27:10:05 loss: 0.2426 Lr: 0.00313 [2024-02-18 16:04:24,777 INFO misc.py line 119 87073] Train: [45/100][1243/1557] Data 0.004 (0.060) Batch 0.832 (1.138) Remain 27:09:43 loss: 0.5283 Lr: 0.00313 [2024-02-18 16:04:25,544 INFO misc.py line 119 87073] Train: [45/100][1244/1557] Data 0.004 (0.060) Batch 0.761 (1.137) Remain 27:09:16 loss: 0.1322 Lr: 0.00313 [2024-02-18 16:04:26,332 INFO misc.py line 119 87073] Train: [45/100][1245/1557] Data 0.011 (0.060) Batch 0.795 (1.137) Remain 27:08:51 loss: 0.3509 Lr: 0.00313 [2024-02-18 16:04:27,552 INFO misc.py line 119 87073] Train: [45/100][1246/1557] Data 0.004 (0.060) Batch 1.220 (1.137) Remain 27:08:55 loss: 0.2643 Lr: 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INFO misc.py line 119 87073] Train: [45/100][1253/1557] Data 0.012 (0.059) Batch 1.095 (1.136) Remain 27:07:08 loss: 0.1730 Lr: 0.00313 [2024-02-18 16:04:34,998 INFO misc.py line 119 87073] Train: [45/100][1254/1557] Data 0.009 (0.059) Batch 0.925 (1.136) Remain 27:06:53 loss: 0.6619 Lr: 0.00313 [2024-02-18 16:04:35,841 INFO misc.py line 119 87073] Train: [45/100][1255/1557] Data 0.003 (0.059) Batch 0.843 (1.136) Remain 27:06:31 loss: 0.1937 Lr: 0.00313 [2024-02-18 16:04:36,748 INFO misc.py line 119 87073] Train: [45/100][1256/1557] Data 0.004 (0.059) Batch 0.899 (1.135) Remain 27:06:14 loss: 0.2744 Lr: 0.00313 [2024-02-18 16:04:37,664 INFO misc.py line 119 87073] Train: [45/100][1257/1557] Data 0.011 (0.059) Batch 0.923 (1.135) Remain 27:05:58 loss: 0.4989 Lr: 0.00313 [2024-02-18 16:04:38,527 INFO misc.py line 119 87073] Train: [45/100][1258/1557] Data 0.004 (0.059) Batch 0.863 (1.135) Remain 27:05:39 loss: 0.1977 Lr: 0.00313 [2024-02-18 16:04:39,287 INFO misc.py line 119 87073] Train: [45/100][1259/1557] Data 0.004 (0.059) Batch 0.761 (1.135) Remain 27:05:12 loss: 0.2443 Lr: 0.00313 [2024-02-18 16:04:40,610 INFO misc.py line 119 87073] Train: [45/100][1260/1557] Data 0.004 (0.059) Batch 1.317 (1.135) Remain 27:05:23 loss: 0.2234 Lr: 0.00313 [2024-02-18 16:04:41,460 INFO misc.py line 119 87073] Train: [45/100][1261/1557] Data 0.010 (0.059) Batch 0.854 (1.135) Remain 27:05:03 loss: 0.8309 Lr: 0.00313 [2024-02-18 16:04:42,471 INFO misc.py line 119 87073] Train: [45/100][1262/1557] Data 0.007 (0.059) Batch 1.013 (1.135) Remain 27:04:54 loss: 0.6721 Lr: 0.00313 [2024-02-18 16:04:43,527 INFO misc.py line 119 87073] Train: [45/100][1263/1557] Data 0.004 (0.059) Batch 1.056 (1.135) Remain 27:04:47 loss: 0.3994 Lr: 0.00313 [2024-02-18 16:04:44,420 INFO misc.py line 119 87073] Train: [45/100][1264/1557] Data 0.003 (0.059) Batch 0.892 (1.134) Remain 27:04:29 loss: 0.6344 Lr: 0.00313 [2024-02-18 16:04:45,144 INFO misc.py line 119 87073] Train: [45/100][1265/1557] Data 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Remain 27:02:38 loss: 0.3172 Lr: 0.00313 [2024-02-18 16:04:51,615 INFO misc.py line 119 87073] Train: [45/100][1272/1557] Data 0.003 (0.059) Batch 0.776 (1.133) Remain 27:02:13 loss: 0.2455 Lr: 0.00313 [2024-02-18 16:04:52,389 INFO misc.py line 119 87073] Train: [45/100][1273/1557] Data 0.003 (0.059) Batch 0.740 (1.133) Remain 27:01:45 loss: 0.1710 Lr: 0.00313 [2024-02-18 16:04:53,550 INFO misc.py line 119 87073] Train: [45/100][1274/1557] Data 0.037 (0.059) Batch 1.189 (1.133) Remain 27:01:48 loss: 0.2129 Lr: 0.00313 [2024-02-18 16:04:54,539 INFO misc.py line 119 87073] Train: [45/100][1275/1557] Data 0.010 (0.059) Batch 0.995 (1.132) Remain 27:01:38 loss: 0.3450 Lr: 0.00313 [2024-02-18 16:04:55,456 INFO misc.py line 119 87073] Train: [45/100][1276/1557] Data 0.005 (0.059) Batch 0.917 (1.132) Remain 27:01:22 loss: 0.7174 Lr: 0.00313 [2024-02-18 16:04:56,567 INFO misc.py line 119 87073] Train: [45/100][1277/1557] Data 0.004 (0.058) Batch 1.111 (1.132) Remain 27:01:19 loss: 0.3166 Lr: 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Train: [45/100][1290/1557] Data 0.003 (0.058) Batch 0.931 (1.131) Remain 26:58:32 loss: 0.5627 Lr: 0.00313 [2024-02-18 16:05:10,010 INFO misc.py line 119 87073] Train: [45/100][1291/1557] Data 0.004 (0.058) Batch 1.014 (1.130) Remain 26:58:23 loss: 0.2773 Lr: 0.00313 [2024-02-18 16:05:11,092 INFO misc.py line 119 87073] Train: [45/100][1292/1557] Data 0.004 (0.058) Batch 1.081 (1.130) Remain 26:58:18 loss: 0.2953 Lr: 0.00313 [2024-02-18 16:05:11,866 INFO misc.py line 119 87073] Train: [45/100][1293/1557] Data 0.005 (0.058) Batch 0.773 (1.130) Remain 26:57:54 loss: 0.3966 Lr: 0.00313 [2024-02-18 16:05:12,609 INFO misc.py line 119 87073] Train: [45/100][1294/1557] Data 0.007 (0.058) Batch 0.743 (1.130) Remain 26:57:27 loss: 0.4927 Lr: 0.00313 [2024-02-18 16:05:23,098 INFO misc.py line 119 87073] Train: [45/100][1295/1557] Data 2.049 (0.059) Batch 10.489 (1.137) Remain 27:07:48 loss: 0.2141 Lr: 0.00313 [2024-02-18 16:05:24,081 INFO misc.py line 119 87073] Train: [45/100][1296/1557] Data 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Remain 27:06:03 loss: 0.2341 Lr: 0.00313 [2024-02-18 16:05:30,643 INFO misc.py line 119 87073] Train: [45/100][1303/1557] Data 0.005 (0.059) Batch 1.044 (1.136) Remain 27:05:56 loss: 0.6814 Lr: 0.00313 [2024-02-18 16:05:31,658 INFO misc.py line 119 87073] Train: [45/100][1304/1557] Data 0.004 (0.059) Batch 1.015 (1.136) Remain 27:05:47 loss: 0.3106 Lr: 0.00313 [2024-02-18 16:05:32,632 INFO misc.py line 119 87073] Train: [45/100][1305/1557] Data 0.006 (0.059) Batch 0.975 (1.136) Remain 27:05:35 loss: 0.2561 Lr: 0.00313 [2024-02-18 16:05:33,533 INFO misc.py line 119 87073] Train: [45/100][1306/1557] Data 0.006 (0.059) Batch 0.901 (1.135) Remain 27:05:19 loss: 0.3310 Lr: 0.00313 [2024-02-18 16:05:34,274 INFO misc.py line 119 87073] Train: [45/100][1307/1557] Data 0.005 (0.059) Batch 0.739 (1.135) Remain 27:04:51 loss: 0.3639 Lr: 0.00313 [2024-02-18 16:05:35,040 INFO misc.py line 119 87073] Train: [45/100][1308/1557] Data 0.007 (0.059) Batch 0.769 (1.135) Remain 27:04:26 loss: 0.5345 Lr: 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INFO misc.py line 119 87073] Train: [45/100][1315/1557] Data 0.004 (0.059) Batch 0.838 (1.134) Remain 27:03:08 loss: 0.2512 Lr: 0.00313 [2024-02-18 16:05:43,184 INFO misc.py line 119 87073] Train: [45/100][1316/1557] Data 0.005 (0.058) Batch 1.262 (1.134) Remain 27:03:15 loss: 0.1496 Lr: 0.00313 [2024-02-18 16:05:44,089 INFO misc.py line 119 87073] Train: [45/100][1317/1557] Data 0.014 (0.058) Batch 0.914 (1.134) Remain 27:03:00 loss: 0.5029 Lr: 0.00313 [2024-02-18 16:05:44,975 INFO misc.py line 119 87073] Train: [45/100][1318/1557] Data 0.005 (0.058) Batch 0.886 (1.134) Remain 27:02:43 loss: 0.2659 Lr: 0.00313 [2024-02-18 16:05:45,947 INFO misc.py line 119 87073] Train: [45/100][1319/1557] Data 0.005 (0.058) Batch 0.970 (1.134) Remain 27:02:31 loss: 0.2276 Lr: 0.00313 [2024-02-18 16:05:47,023 INFO misc.py line 119 87073] Train: [45/100][1320/1557] Data 0.006 (0.058) Batch 1.072 (1.134) Remain 27:02:26 loss: 0.8325 Lr: 0.00313 [2024-02-18 16:05:47,805 INFO misc.py line 119 87073] Train: [45/100][1321/1557] Data 0.010 (0.058) Batch 0.787 (1.133) Remain 27:02:02 loss: 0.2255 Lr: 0.00313 [2024-02-18 16:05:48,574 INFO misc.py line 119 87073] Train: [45/100][1322/1557] Data 0.005 (0.058) Batch 0.769 (1.133) Remain 27:01:37 loss: 0.1669 Lr: 0.00313 [2024-02-18 16:05:49,720 INFO misc.py line 119 87073] Train: [45/100][1323/1557] Data 0.004 (0.058) Batch 1.137 (1.133) Remain 27:01:36 loss: 0.1621 Lr: 0.00313 [2024-02-18 16:05:50,642 INFO misc.py line 119 87073] Train: [45/100][1324/1557] Data 0.013 (0.058) Batch 0.931 (1.133) Remain 27:01:22 loss: 0.6662 Lr: 0.00313 [2024-02-18 16:05:51,628 INFO misc.py line 119 87073] Train: [45/100][1325/1557] Data 0.004 (0.058) Batch 0.985 (1.133) Remain 27:01:11 loss: 0.2230 Lr: 0.00313 [2024-02-18 16:05:52,505 INFO misc.py line 119 87073] Train: [45/100][1326/1557] Data 0.005 (0.058) Batch 0.878 (1.133) Remain 27:00:53 loss: 0.6282 Lr: 0.00313 [2024-02-18 16:05:53,503 INFO misc.py line 119 87073] Train: [45/100][1327/1557] Data 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Remain 26:59:25 loss: 0.3183 Lr: 0.00313 [2024-02-18 16:06:00,046 INFO misc.py line 119 87073] Train: [45/100][1334/1557] Data 0.007 (0.058) Batch 0.853 (1.131) Remain 26:59:06 loss: 0.3853 Lr: 0.00313 [2024-02-18 16:06:00,764 INFO misc.py line 119 87073] Train: [45/100][1335/1557] Data 0.006 (0.058) Batch 0.715 (1.131) Remain 26:58:38 loss: 0.1952 Lr: 0.00313 [2024-02-18 16:06:01,568 INFO misc.py line 119 87073] Train: [45/100][1336/1557] Data 0.008 (0.058) Batch 0.808 (1.131) Remain 26:58:16 loss: 0.2174 Lr: 0.00313 [2024-02-18 16:06:02,846 INFO misc.py line 119 87073] Train: [45/100][1337/1557] Data 0.004 (0.058) Batch 1.278 (1.131) Remain 26:58:25 loss: 0.1450 Lr: 0.00313 [2024-02-18 16:06:03,856 INFO misc.py line 119 87073] Train: [45/100][1338/1557] Data 0.005 (0.058) Batch 1.005 (1.131) Remain 26:58:15 loss: 0.2162 Lr: 0.00313 [2024-02-18 16:06:04,860 INFO misc.py line 119 87073] Train: [45/100][1339/1557] Data 0.010 (0.058) Batch 1.006 (1.131) Remain 26:58:06 loss: 0.3534 Lr: 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INFO misc.py line 119 87073] Train: [45/100][1346/1557] Data 0.005 (0.057) Batch 1.102 (1.130) Remain 26:57:12 loss: 0.4401 Lr: 0.00313 [2024-02-18 16:06:12,916 INFO misc.py line 119 87073] Train: [45/100][1347/1557] Data 0.006 (0.057) Batch 0.873 (1.130) Remain 26:56:54 loss: 0.2688 Lr: 0.00313 [2024-02-18 16:06:14,021 INFO misc.py line 119 87073] Train: [45/100][1348/1557] Data 0.005 (0.057) Batch 1.106 (1.130) Remain 26:56:51 loss: 0.3654 Lr: 0.00313 [2024-02-18 16:06:14,800 INFO misc.py line 119 87073] Train: [45/100][1349/1557] Data 0.004 (0.057) Batch 0.779 (1.130) Remain 26:56:28 loss: 0.3041 Lr: 0.00313 [2024-02-18 16:06:15,603 INFO misc.py line 119 87073] Train: [45/100][1350/1557] Data 0.004 (0.057) Batch 0.793 (1.130) Remain 26:56:05 loss: 0.3704 Lr: 0.00313 [2024-02-18 16:06:26,064 INFO misc.py line 119 87073] Train: [45/100][1351/1557] Data 2.520 (0.059) Batch 10.469 (1.137) Remain 27:05:59 loss: 0.2890 Lr: 0.00313 [2024-02-18 16:06:27,029 INFO misc.py line 119 87073] Train: [45/100][1352/1557] Data 0.006 (0.059) Batch 0.966 (1.136) Remain 27:05:47 loss: 0.2649 Lr: 0.00313 [2024-02-18 16:06:28,010 INFO misc.py line 119 87073] Train: [45/100][1353/1557] Data 0.006 (0.059) Batch 0.980 (1.136) Remain 27:05:36 loss: 0.5728 Lr: 0.00313 [2024-02-18 16:06:29,019 INFO misc.py line 119 87073] Train: [45/100][1354/1557] Data 0.006 (0.059) Batch 1.011 (1.136) Remain 27:05:27 loss: 0.5081 Lr: 0.00313 [2024-02-18 16:06:30,099 INFO misc.py line 119 87073] Train: [45/100][1355/1557] Data 0.006 (0.059) Batch 1.080 (1.136) Remain 27:05:22 loss: 0.2962 Lr: 0.00313 [2024-02-18 16:06:30,865 INFO misc.py line 119 87073] Train: [45/100][1356/1557] Data 0.004 (0.059) Batch 0.766 (1.136) Remain 27:04:57 loss: 0.3058 Lr: 0.00313 [2024-02-18 16:06:31,664 INFO misc.py line 119 87073] Train: [45/100][1357/1557] Data 0.004 (0.059) Batch 0.786 (1.136) Remain 27:04:34 loss: 0.2098 Lr: 0.00313 [2024-02-18 16:06:32,907 INFO misc.py line 119 87073] Train: [45/100][1358/1557] Data 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Remain 27:03:01 loss: 0.5087 Lr: 0.00313 [2024-02-18 16:06:39,310 INFO misc.py line 119 87073] Train: [45/100][1365/1557] Data 0.004 (0.058) Batch 1.059 (1.135) Remain 27:02:55 loss: 0.1681 Lr: 0.00313 [2024-02-18 16:06:40,214 INFO misc.py line 119 87073] Train: [45/100][1366/1557] Data 0.008 (0.058) Batch 0.909 (1.134) Remain 27:02:40 loss: 0.3753 Lr: 0.00313 [2024-02-18 16:06:41,085 INFO misc.py line 119 87073] Train: [45/100][1367/1557] Data 0.004 (0.058) Batch 0.869 (1.134) Remain 27:02:22 loss: 0.3201 Lr: 0.00313 [2024-02-18 16:06:42,119 INFO misc.py line 119 87073] Train: [45/100][1368/1557] Data 0.005 (0.058) Batch 1.036 (1.134) Remain 27:02:14 loss: 0.6561 Lr: 0.00312 [2024-02-18 16:06:43,099 INFO misc.py line 119 87073] Train: [45/100][1369/1557] Data 0.004 (0.058) Batch 0.975 (1.134) Remain 27:02:03 loss: 0.5209 Lr: 0.00312 [2024-02-18 16:06:43,829 INFO misc.py line 119 87073] Train: [45/100][1370/1557] Data 0.008 (0.058) Batch 0.733 (1.134) Remain 27:01:37 loss: 0.1148 Lr: 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Train: [45/100][1383/1557] Data 0.005 (0.058) Batch 0.860 (1.132) Remain 26:58:52 loss: 0.7408 Lr: 0.00312 [2024-02-18 16:06:56,893 INFO misc.py line 119 87073] Train: [45/100][1384/1557] Data 0.005 (0.058) Batch 0.740 (1.132) Remain 26:58:27 loss: 0.2536 Lr: 0.00312 [2024-02-18 16:06:57,646 INFO misc.py line 119 87073] Train: [45/100][1385/1557] Data 0.005 (0.058) Batch 0.749 (1.131) Remain 26:58:02 loss: 0.1861 Lr: 0.00312 [2024-02-18 16:06:58,792 INFO misc.py line 119 87073] Train: [45/100][1386/1557] Data 0.012 (0.058) Batch 1.144 (1.131) Remain 26:58:01 loss: 0.2603 Lr: 0.00312 [2024-02-18 16:06:59,770 INFO misc.py line 119 87073] Train: [45/100][1387/1557] Data 0.011 (0.058) Batch 0.986 (1.131) Remain 26:57:51 loss: 0.7271 Lr: 0.00312 [2024-02-18 16:07:00,897 INFO misc.py line 119 87073] Train: [45/100][1388/1557] Data 0.004 (0.058) Batch 1.126 (1.131) Remain 26:57:50 loss: 0.6403 Lr: 0.00312 [2024-02-18 16:07:01,897 INFO misc.py line 119 87073] Train: [45/100][1389/1557] Data 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Remain 26:56:24 loss: 0.2906 Lr: 0.00312 [2024-02-18 16:07:08,437 INFO misc.py line 119 87073] Train: [45/100][1396/1557] Data 0.005 (0.057) Batch 0.886 (1.130) Remain 26:56:08 loss: 0.2907 Lr: 0.00312 [2024-02-18 16:07:09,389 INFO misc.py line 119 87073] Train: [45/100][1397/1557] Data 0.006 (0.057) Batch 0.948 (1.130) Remain 26:55:55 loss: 0.2407 Lr: 0.00312 [2024-02-18 16:07:10,152 INFO misc.py line 119 87073] Train: [45/100][1398/1557] Data 0.009 (0.057) Batch 0.768 (1.130) Remain 26:55:32 loss: 0.5835 Lr: 0.00312 [2024-02-18 16:07:10,884 INFO misc.py line 119 87073] Train: [45/100][1399/1557] Data 0.005 (0.057) Batch 0.730 (1.130) Remain 26:55:06 loss: 0.1667 Lr: 0.00312 [2024-02-18 16:07:12,063 INFO misc.py line 119 87073] Train: [45/100][1400/1557] Data 0.006 (0.057) Batch 1.177 (1.130) Remain 26:55:08 loss: 0.1710 Lr: 0.00312 [2024-02-18 16:07:13,199 INFO misc.py line 119 87073] Train: [45/100][1401/1557] Data 0.009 (0.057) Batch 1.130 (1.130) Remain 26:55:07 loss: 0.3823 Lr: 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INFO misc.py line 119 87073] Train: [45/100][1408/1557] Data 0.005 (0.061) Batch 0.931 (1.138) Remain 27:06:53 loss: 0.5603 Lr: 0.00312 [2024-02-18 16:07:33,709 INFO misc.py line 119 87073] Train: [45/100][1409/1557] Data 0.004 (0.060) Batch 0.916 (1.138) Remain 27:06:38 loss: 0.1998 Lr: 0.00312 [2024-02-18 16:07:34,648 INFO misc.py line 119 87073] Train: [45/100][1410/1557] Data 0.010 (0.060) Batch 0.945 (1.138) Remain 27:06:25 loss: 0.4102 Lr: 0.00312 [2024-02-18 16:07:35,588 INFO misc.py line 119 87073] Train: [45/100][1411/1557] Data 0.004 (0.060) Batch 0.940 (1.137) Remain 27:06:12 loss: 0.6016 Lr: 0.00312 [2024-02-18 16:07:36,286 INFO misc.py line 119 87073] Train: [45/100][1412/1557] Data 0.005 (0.060) Batch 0.697 (1.137) Remain 27:05:44 loss: 0.1907 Lr: 0.00312 [2024-02-18 16:07:36,981 INFO misc.py line 119 87073] Train: [45/100][1413/1557] Data 0.006 (0.060) Batch 0.695 (1.137) Remain 27:05:16 loss: 0.1476 Lr: 0.00312 [2024-02-18 16:07:38,217 INFO misc.py line 119 87073] Train: [45/100][1414/1557] Data 0.005 (0.060) Batch 1.231 (1.137) Remain 27:05:21 loss: 0.2485 Lr: 0.00312 [2024-02-18 16:07:39,116 INFO misc.py line 119 87073] Train: [45/100][1415/1557] Data 0.010 (0.060) Batch 0.904 (1.137) Remain 27:05:05 loss: 0.3291 Lr: 0.00312 [2024-02-18 16:07:39,995 INFO misc.py line 119 87073] Train: [45/100][1416/1557] Data 0.005 (0.060) Batch 0.880 (1.137) Remain 27:04:49 loss: 0.6104 Lr: 0.00312 [2024-02-18 16:07:41,083 INFO misc.py line 119 87073] Train: [45/100][1417/1557] Data 0.005 (0.060) Batch 1.083 (1.137) Remain 27:04:44 loss: 0.2164 Lr: 0.00312 [2024-02-18 16:07:41,978 INFO misc.py line 119 87073] Train: [45/100][1418/1557] Data 0.010 (0.060) Batch 0.900 (1.136) Remain 27:04:29 loss: 0.3365 Lr: 0.00312 [2024-02-18 16:07:42,755 INFO misc.py line 119 87073] Train: [45/100][1419/1557] Data 0.004 (0.060) Batch 0.777 (1.136) Remain 27:04:06 loss: 0.1503 Lr: 0.00312 [2024-02-18 16:07:43,538 INFO misc.py line 119 87073] Train: [45/100][1420/1557] Data 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Remain 27:02:15 loss: 0.4185 Lr: 0.00312 [2024-02-18 16:07:49,751 INFO misc.py line 119 87073] Train: [45/100][1427/1557] Data 0.005 (0.060) Batch 0.737 (1.135) Remain 27:01:50 loss: 0.7412 Lr: 0.00312 [2024-02-18 16:07:51,072 INFO misc.py line 119 87073] Train: [45/100][1428/1557] Data 0.015 (0.060) Batch 1.320 (1.135) Remain 27:02:00 loss: 0.1950 Lr: 0.00312 [2024-02-18 16:07:52,024 INFO misc.py line 119 87073] Train: [45/100][1429/1557] Data 0.016 (0.060) Batch 0.963 (1.135) Remain 27:01:49 loss: 0.4417 Lr: 0.00312 [2024-02-18 16:07:52,962 INFO misc.py line 119 87073] Train: [45/100][1430/1557] Data 0.005 (0.060) Batch 0.938 (1.134) Remain 27:01:36 loss: 0.2770 Lr: 0.00312 [2024-02-18 16:07:54,003 INFO misc.py line 119 87073] Train: [45/100][1431/1557] Data 0.005 (0.060) Batch 1.043 (1.134) Remain 27:01:29 loss: 0.8294 Lr: 0.00312 [2024-02-18 16:07:54,972 INFO misc.py line 119 87073] Train: [45/100][1432/1557] Data 0.004 (0.060) Batch 0.967 (1.134) Remain 27:01:18 loss: 0.4393 Lr: 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Train: [45/100][1445/1557] Data 0.005 (0.059) Batch 1.080 (1.133) Remain 26:58:44 loss: 0.5430 Lr: 0.00312 [2024-02-18 16:08:08,229 INFO misc.py line 119 87073] Train: [45/100][1446/1557] Data 0.012 (0.059) Batch 0.856 (1.132) Remain 26:58:26 loss: 0.4281 Lr: 0.00312 [2024-02-18 16:08:08,979 INFO misc.py line 119 87073] Train: [45/100][1447/1557] Data 0.006 (0.059) Batch 0.751 (1.132) Remain 26:58:02 loss: 0.1772 Lr: 0.00312 [2024-02-18 16:08:09,752 INFO misc.py line 119 87073] Train: [45/100][1448/1557] Data 0.005 (0.059) Batch 0.775 (1.132) Remain 26:57:40 loss: 0.1835 Lr: 0.00312 [2024-02-18 16:08:11,073 INFO misc.py line 119 87073] Train: [45/100][1449/1557] Data 0.003 (0.059) Batch 1.309 (1.132) Remain 26:57:49 loss: 0.1904 Lr: 0.00312 [2024-02-18 16:08:12,033 INFO misc.py line 119 87073] Train: [45/100][1450/1557] Data 0.016 (0.059) Batch 0.972 (1.132) Remain 26:57:39 loss: 0.4153 Lr: 0.00312 [2024-02-18 16:08:12,970 INFO misc.py line 119 87073] Train: [45/100][1451/1557] Data 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Remain 26:56:01 loss: 0.5527 Lr: 0.00312 [2024-02-18 16:08:19,807 INFO misc.py line 119 87073] Train: [45/100][1458/1557] Data 0.004 (0.059) Batch 1.363 (1.131) Remain 26:56:13 loss: 0.4178 Lr: 0.00312 [2024-02-18 16:08:20,712 INFO misc.py line 119 87073] Train: [45/100][1459/1557] Data 0.015 (0.059) Batch 0.915 (1.131) Remain 26:56:00 loss: 0.3641 Lr: 0.00312 [2024-02-18 16:08:21,639 INFO misc.py line 119 87073] Train: [45/100][1460/1557] Data 0.006 (0.059) Batch 0.928 (1.131) Remain 26:55:46 loss: 0.5618 Lr: 0.00312 [2024-02-18 16:08:22,428 INFO misc.py line 119 87073] Train: [45/100][1461/1557] Data 0.004 (0.059) Batch 0.788 (1.131) Remain 26:55:25 loss: 0.2920 Lr: 0.00312 [2024-02-18 16:08:23,121 INFO misc.py line 119 87073] Train: [45/100][1462/1557] Data 0.006 (0.059) Batch 0.695 (1.130) Remain 26:54:58 loss: 0.3198 Lr: 0.00312 [2024-02-18 16:08:34,478 INFO misc.py line 119 87073] Train: [45/100][1463/1557] Data 2.706 (0.060) Batch 11.357 (1.137) Remain 27:04:58 loss: 0.2132 Lr: 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Train: [45/100][1476/1557] Data 0.011 (0.060) Batch 0.787 (1.135) Remain 27:01:57 loss: 0.6425 Lr: 0.00312 [2024-02-18 16:08:47,537 INFO misc.py line 119 87073] Train: [45/100][1477/1557] Data 0.004 (0.060) Batch 1.123 (1.135) Remain 27:01:55 loss: 0.2850 Lr: 0.00312 [2024-02-18 16:08:48,776 INFO misc.py line 119 87073] Train: [45/100][1478/1557] Data 0.004 (0.060) Batch 1.237 (1.135) Remain 27:02:00 loss: 0.7247 Lr: 0.00312 [2024-02-18 16:08:49,786 INFO misc.py line 119 87073] Train: [45/100][1479/1557] Data 0.005 (0.060) Batch 1.001 (1.135) Remain 27:01:51 loss: 0.4301 Lr: 0.00312 [2024-02-18 16:08:50,770 INFO misc.py line 119 87073] Train: [45/100][1480/1557] Data 0.014 (0.060) Batch 0.994 (1.135) Remain 27:01:42 loss: 0.1843 Lr: 0.00312 [2024-02-18 16:08:51,803 INFO misc.py line 119 87073] Train: [45/100][1481/1557] Data 0.004 (0.060) Batch 1.031 (1.135) Remain 27:01:35 loss: 0.6757 Lr: 0.00312 [2024-02-18 16:08:52,499 INFO misc.py line 119 87073] Train: [45/100][1482/1557] Data 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Remain 27:00:14 loss: 0.8747 Lr: 0.00312 [2024-02-18 16:08:59,184 INFO misc.py line 119 87073] Train: [45/100][1489/1557] Data 0.004 (0.059) Batch 0.703 (1.134) Remain 26:59:48 loss: 0.5269 Lr: 0.00312 [2024-02-18 16:08:59,974 INFO misc.py line 119 87073] Train: [45/100][1490/1557] Data 0.008 (0.059) Batch 0.788 (1.134) Remain 26:59:27 loss: 0.3310 Lr: 0.00312 [2024-02-18 16:09:01,136 INFO misc.py line 119 87073] Train: [45/100][1491/1557] Data 0.008 (0.059) Batch 1.161 (1.134) Remain 26:59:27 loss: 0.1198 Lr: 0.00312 [2024-02-18 16:09:02,132 INFO misc.py line 119 87073] Train: [45/100][1492/1557] Data 0.008 (0.059) Batch 1.000 (1.134) Remain 26:59:18 loss: 0.3565 Lr: 0.00312 [2024-02-18 16:09:03,034 INFO misc.py line 119 87073] Train: [45/100][1493/1557] Data 0.006 (0.059) Batch 0.902 (1.134) Remain 26:59:04 loss: 0.7176 Lr: 0.00312 [2024-02-18 16:09:04,148 INFO misc.py line 119 87073] Train: [45/100][1494/1557] Data 0.005 (0.059) Batch 1.114 (1.134) Remain 26:59:01 loss: 0.7652 Lr: 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INFO misc.py line 119 87073] Train: [45/100][1501/1557] Data 0.004 (0.059) Batch 1.059 (1.133) Remain 26:57:29 loss: 0.5567 Lr: 0.00312 [2024-02-18 16:09:11,594 INFO misc.py line 119 87073] Train: [45/100][1502/1557] Data 0.008 (0.059) Batch 0.985 (1.132) Remain 26:57:20 loss: 0.5661 Lr: 0.00312 [2024-02-18 16:09:12,362 INFO misc.py line 119 87073] Train: [45/100][1503/1557] Data 0.005 (0.059) Batch 0.769 (1.132) Remain 26:56:58 loss: 0.4227 Lr: 0.00312 [2024-02-18 16:09:13,123 INFO misc.py line 119 87073] Train: [45/100][1504/1557] Data 0.008 (0.059) Batch 0.756 (1.132) Remain 26:56:35 loss: 0.3955 Lr: 0.00312 [2024-02-18 16:09:14,392 INFO misc.py line 119 87073] Train: [45/100][1505/1557] Data 0.009 (0.059) Batch 1.264 (1.132) Remain 26:56:41 loss: 0.1763 Lr: 0.00312 [2024-02-18 16:09:15,579 INFO misc.py line 119 87073] Train: [45/100][1506/1557] Data 0.016 (0.059) Batch 1.187 (1.132) Remain 26:56:43 loss: 0.2442 Lr: 0.00312 [2024-02-18 16:09:16,436 INFO misc.py line 119 87073] Train: [45/100][1507/1557] Data 0.014 (0.059) Batch 0.866 (1.132) Remain 26:56:27 loss: 0.4237 Lr: 0.00312 [2024-02-18 16:09:17,408 INFO misc.py line 119 87073] Train: [45/100][1508/1557] Data 0.005 (0.059) Batch 0.972 (1.132) Remain 26:56:17 loss: 0.5085 Lr: 0.00312 [2024-02-18 16:09:18,371 INFO misc.py line 119 87073] Train: [45/100][1509/1557] Data 0.006 (0.059) Batch 0.964 (1.132) Remain 26:56:06 loss: 0.2601 Lr: 0.00312 [2024-02-18 16:09:19,064 INFO misc.py line 119 87073] Train: [45/100][1510/1557] Data 0.005 (0.059) Batch 0.692 (1.131) Remain 26:55:40 loss: 0.1968 Lr: 0.00312 [2024-02-18 16:09:19,870 INFO misc.py line 119 87073] Train: [45/100][1511/1557] Data 0.005 (0.059) Batch 0.807 (1.131) Remain 26:55:21 loss: 0.3415 Lr: 0.00312 [2024-02-18 16:09:21,121 INFO misc.py line 119 87073] Train: [45/100][1512/1557] Data 0.004 (0.059) Batch 1.251 (1.131) Remain 26:55:26 loss: 0.1570 Lr: 0.00312 [2024-02-18 16:09:21,999 INFO misc.py line 119 87073] Train: [45/100][1513/1557] Data 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Remain 27:04:13 loss: 0.2227 Lr: 0.00312 [2024-02-18 16:09:39,410 INFO misc.py line 119 87073] Train: [45/100][1520/1557] Data 0.005 (0.060) Batch 0.908 (1.137) Remain 27:03:59 loss: 0.2605 Lr: 0.00312 [2024-02-18 16:09:40,335 INFO misc.py line 119 87073] Train: [45/100][1521/1557] Data 0.005 (0.060) Batch 0.916 (1.137) Remain 27:03:45 loss: 0.5986 Lr: 0.00312 [2024-02-18 16:09:41,266 INFO misc.py line 119 87073] Train: [45/100][1522/1557] Data 0.014 (0.060) Batch 0.940 (1.137) Remain 27:03:33 loss: 0.4240 Lr: 0.00312 [2024-02-18 16:09:42,201 INFO misc.py line 119 87073] Train: [45/100][1523/1557] Data 0.006 (0.060) Batch 0.935 (1.137) Remain 27:03:21 loss: 0.6253 Lr: 0.00312 [2024-02-18 16:09:42,956 INFO misc.py line 119 87073] Train: [45/100][1524/1557] Data 0.005 (0.060) Batch 0.755 (1.137) Remain 27:02:58 loss: 0.1063 Lr: 0.00312 [2024-02-18 16:09:43,698 INFO misc.py line 119 87073] Train: [45/100][1525/1557] Data 0.005 (0.060) Batch 0.731 (1.136) Remain 27:02:34 loss: 0.2709 Lr: 0.00312 [2024-02-18 16:09:44,904 INFO misc.py line 119 87073] Train: [45/100][1526/1557] Data 0.015 (0.060) Batch 1.216 (1.136) Remain 27:02:37 loss: 0.4002 Lr: 0.00312 [2024-02-18 16:09:46,194 INFO misc.py line 119 87073] Train: [45/100][1527/1557] Data 0.005 (0.060) Batch 1.279 (1.137) Remain 27:02:44 loss: 0.3499 Lr: 0.00312 [2024-02-18 16:09:47,167 INFO misc.py line 119 87073] Train: [45/100][1528/1557] Data 0.016 (0.060) Batch 0.985 (1.136) Remain 27:02:34 loss: 0.6086 Lr: 0.00312 [2024-02-18 16:09:48,024 INFO misc.py line 119 87073] Train: [45/100][1529/1557] Data 0.004 (0.060) Batch 0.856 (1.136) Remain 27:02:18 loss: 0.2298 Lr: 0.00312 [2024-02-18 16:09:49,013 INFO misc.py line 119 87073] Train: [45/100][1530/1557] Data 0.005 (0.059) Batch 0.985 (1.136) Remain 27:02:08 loss: 0.2827 Lr: 0.00312 [2024-02-18 16:09:49,787 INFO misc.py line 119 87073] Train: [45/100][1531/1557] Data 0.009 (0.059) Batch 0.775 (1.136) Remain 27:01:47 loss: 0.1312 Lr: 0.00312 [2024-02-18 16:09:50,564 INFO misc.py line 119 87073] Train: [45/100][1532/1557] Data 0.008 (0.059) Batch 0.768 (1.136) Remain 27:01:25 loss: 0.2868 Lr: 0.00312 [2024-02-18 16:09:51,768 INFO misc.py line 119 87073] Train: [45/100][1533/1557] Data 0.016 (0.059) Batch 1.205 (1.136) Remain 27:01:28 loss: 0.1808 Lr: 0.00312 [2024-02-18 16:09:52,708 INFO misc.py line 119 87073] Train: [45/100][1534/1557] Data 0.015 (0.059) Batch 0.951 (1.136) Remain 27:01:16 loss: 0.8868 Lr: 0.00312 [2024-02-18 16:09:53,617 INFO misc.py line 119 87073] Train: [45/100][1535/1557] Data 0.004 (0.059) Batch 0.909 (1.135) Remain 27:01:02 loss: 0.4760 Lr: 0.00312 [2024-02-18 16:09:54,512 INFO misc.py line 119 87073] Train: [45/100][1536/1557] Data 0.003 (0.059) Batch 0.885 (1.135) Remain 27:00:47 loss: 0.3742 Lr: 0.00312 [2024-02-18 16:09:55,467 INFO misc.py line 119 87073] Train: [45/100][1537/1557] Data 0.013 (0.059) Batch 0.963 (1.135) Remain 27:00:37 loss: 0.2157 Lr: 0.00312 [2024-02-18 16:09:56,197 INFO misc.py line 119 87073] Train: [45/100][1538/1557] Data 0.005 (0.059) Batch 0.731 (1.135) Remain 27:00:13 loss: 0.1848 Lr: 0.00312 [2024-02-18 16:09:56,949 INFO misc.py line 119 87073] Train: [45/100][1539/1557] Data 0.004 (0.059) Batch 0.742 (1.135) Remain 26:59:50 loss: 0.4861 Lr: 0.00312 [2024-02-18 16:09:58,242 INFO misc.py line 119 87073] Train: [45/100][1540/1557] Data 0.013 (0.059) Batch 1.297 (1.135) Remain 26:59:58 loss: 0.1762 Lr: 0.00312 [2024-02-18 16:09:59,325 INFO misc.py line 119 87073] Train: [45/100][1541/1557] Data 0.011 (0.059) Batch 1.086 (1.135) Remain 26:59:54 loss: 0.3634 Lr: 0.00312 [2024-02-18 16:10:00,310 INFO misc.py line 119 87073] Train: [45/100][1542/1557] Data 0.008 (0.059) Batch 0.987 (1.135) Remain 26:59:44 loss: 0.8245 Lr: 0.00312 [2024-02-18 16:10:01,272 INFO misc.py line 119 87073] Train: [45/100][1543/1557] Data 0.005 (0.059) Batch 0.962 (1.135) Remain 26:59:34 loss: 0.3174 Lr: 0.00312 [2024-02-18 16:10:02,214 INFO misc.py line 119 87073] Train: [45/100][1544/1557] Data 0.005 (0.059) Batch 0.943 (1.134) Remain 26:59:22 loss: 0.6454 Lr: 0.00312 [2024-02-18 16:10:02,951 INFO misc.py line 119 87073] Train: [45/100][1545/1557] Data 0.005 (0.059) Batch 0.727 (1.134) Remain 26:58:58 loss: 0.2416 Lr: 0.00312 [2024-02-18 16:10:03,674 INFO misc.py line 119 87073] Train: [45/100][1546/1557] Data 0.015 (0.059) Batch 0.732 (1.134) Remain 26:58:35 loss: 0.3597 Lr: 0.00312 [2024-02-18 16:10:04,875 INFO misc.py line 119 87073] Train: [45/100][1547/1557] Data 0.005 (0.059) Batch 1.202 (1.134) Remain 26:58:37 loss: 0.1675 Lr: 0.00312 [2024-02-18 16:10:05,780 INFO misc.py line 119 87073] Train: [45/100][1548/1557] Data 0.005 (0.059) Batch 0.905 (1.134) Remain 26:58:24 loss: 0.1178 Lr: 0.00312 [2024-02-18 16:10:06,672 INFO misc.py line 119 87073] Train: [45/100][1549/1557] Data 0.005 (0.059) Batch 0.892 (1.134) Remain 26:58:09 loss: 0.4021 Lr: 0.00312 [2024-02-18 16:10:07,481 INFO misc.py line 119 87073] Train: [45/100][1550/1557] Data 0.005 (0.059) Batch 0.799 (1.133) Remain 26:57:49 loss: 0.2130 Lr: 0.00312 [2024-02-18 16:10:08,674 INFO misc.py line 119 87073] Train: [45/100][1551/1557] Data 0.014 (0.059) Batch 1.196 (1.133) Remain 26:57:52 loss: 0.2858 Lr: 0.00312 [2024-02-18 16:10:09,426 INFO misc.py line 119 87073] Train: [45/100][1552/1557] Data 0.010 (0.059) Batch 0.758 (1.133) Remain 26:57:30 loss: 0.4707 Lr: 0.00312 [2024-02-18 16:10:10,180 INFO misc.py line 119 87073] Train: [45/100][1553/1557] Data 0.004 (0.059) Batch 0.745 (1.133) Remain 26:57:07 loss: 0.3940 Lr: 0.00312 [2024-02-18 16:10:11,301 INFO misc.py line 119 87073] Train: [45/100][1554/1557] Data 0.013 (0.059) Batch 1.121 (1.133) Remain 26:57:05 loss: 0.4406 Lr: 0.00312 [2024-02-18 16:10:12,328 INFO misc.py line 119 87073] Train: [45/100][1555/1557] Data 0.013 (0.059) Batch 1.027 (1.133) Remain 26:56:58 loss: 0.4205 Lr: 0.00312 [2024-02-18 16:10:13,314 INFO misc.py line 119 87073] Train: [45/100][1556/1557] Data 0.013 (0.059) Batch 0.994 (1.133) Remain 26:56:50 loss: 0.5973 Lr: 0.00312 [2024-02-18 16:10:14,290 INFO misc.py line 119 87073] Train: [45/100][1557/1557] Data 0.005 (0.059) Batch 0.977 (1.133) Remain 26:56:40 loss: 0.6073 Lr: 0.00312 [2024-02-18 16:10:14,291 INFO misc.py line 136 87073] Train result: loss: 0.3729 [2024-02-18 16:10:14,291 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 16:10:42,876 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5998 [2024-02-18 16:10:43,656 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4961 [2024-02-18 16:10:45,790 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5346 [2024-02-18 16:10:47,998 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4728 [2024-02-18 16:10:52,935 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2805 [2024-02-18 16:10:53,637 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1963 [2024-02-18 16:10:54,898 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2600 [2024-02-18 16:10:57,849 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3004 [2024-02-18 16:10:59,657 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2276 [2024-02-18 16:11:01,128 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6970/0.7567/0.9121. [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9398/0.9712 [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9805/0.9855 [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8619/0.9759 [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2981/0.3314 [2024-02-18 16:11:01,128 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7006/0.7280 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7419/0.8537 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8379/0.8862 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9033/0.9729 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6505/0.7346 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7724/0.8635 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7580/0.8238 [2024-02-18 16:11:01,129 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6159/0.7110 [2024-02-18 16:11:01,129 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 16:11:01,130 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 16:11:01,130 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 16:11:11,640 INFO misc.py line 119 87073] Train: [46/100][1/1557] Data 1.482 (1.482) Batch 2.389 (2.389) Remain 56:49:56 loss: 0.1609 Lr: 0.00312 [2024-02-18 16:11:12,530 INFO misc.py line 119 87073] Train: [46/100][2/1557] Data 0.006 (0.006) Batch 0.878 (0.878) Remain 20:53:01 loss: 0.8581 Lr: 0.00312 [2024-02-18 16:11:13,485 INFO misc.py line 119 87073] Train: [46/100][3/1557] Data 0.018 (0.018) Batch 0.967 (0.967) Remain 23:00:10 loss: 0.2466 Lr: 0.00312 [2024-02-18 16:11:14,437 INFO misc.py line 119 87073] Train: [46/100][4/1557] Data 0.006 (0.006) Batch 0.952 (0.952) Remain 22:38:14 loss: 0.3737 Lr: 0.00312 [2024-02-18 16:11:15,213 INFO misc.py line 119 87073] Train: [46/100][5/1557] Data 0.006 (0.006) Batch 0.777 (0.865) Remain 20:33:48 loss: 0.1819 Lr: 0.00311 [2024-02-18 16:11:15,890 INFO misc.py line 119 87073] Train: [46/100][6/1557] Data 0.005 (0.005) Batch 0.676 (0.802) Remain 19:04:17 loss: 0.3670 Lr: 0.00311 [2024-02-18 16:11:18,799 INFO misc.py line 119 87073] Train: [46/100][7/1557] Data 1.997 (0.503) Batch 2.909 (1.329) Remain 31:36:03 loss: 0.2694 Lr: 0.00311 [2024-02-18 16:11:19,682 INFO misc.py line 119 87073] Train: [46/100][8/1557] Data 0.005 (0.404) Batch 0.884 (1.240) Remain 29:29:09 loss: 0.5785 Lr: 0.00311 [2024-02-18 16:11:20,773 INFO misc.py line 119 87073] Train: [46/100][9/1557] Data 0.005 (0.337) Batch 1.091 (1.215) Remain 28:53:41 loss: 0.2379 Lr: 0.00311 [2024-02-18 16:11:21,782 INFO misc.py line 119 87073] Train: [46/100][10/1557] Data 0.004 (0.290) Batch 1.010 (1.186) Remain 28:11:51 loss: 0.2286 Lr: 0.00311 [2024-02-18 16:11:22,881 INFO misc.py line 119 87073] Train: [46/100][11/1557] Data 0.003 (0.254) Batch 1.098 (1.175) Remain 27:56:14 loss: 0.4313 Lr: 0.00311 [2024-02-18 16:11:23,635 INFO misc.py line 119 87073] Train: [46/100][12/1557] Data 0.004 (0.226) Batch 0.754 (1.128) Remain 26:49:34 loss: 0.4192 Lr: 0.00311 [2024-02-18 16:11:24,384 INFO misc.py line 119 87073] Train: [46/100][13/1557] Data 0.004 (0.204) Batch 0.747 (1.090) Remain 25:55:12 loss: 0.4277 Lr: 0.00311 [2024-02-18 16:11:25,525 INFO misc.py line 119 87073] Train: [46/100][14/1557] Data 0.006 (0.186) Batch 1.143 (1.095) Remain 26:02:04 loss: 0.1751 Lr: 0.00311 [2024-02-18 16:11:26,405 INFO misc.py line 119 87073] Train: [46/100][15/1557] Data 0.004 (0.171) Batch 0.880 (1.077) Remain 25:36:32 loss: 0.2353 Lr: 0.00311 [2024-02-18 16:11:27,604 INFO misc.py line 119 87073] Train: [46/100][16/1557] Data 0.004 (0.158) Batch 1.199 (1.086) Remain 25:49:54 loss: 0.8877 Lr: 0.00311 [2024-02-18 16:11:28,555 INFO misc.py line 119 87073] Train: [46/100][17/1557] Data 0.005 (0.147) Batch 0.952 (1.077) Remain 25:36:11 loss: 0.6021 Lr: 0.00311 [2024-02-18 16:11:29,537 INFO misc.py line 119 87073] Train: [46/100][18/1557] Data 0.004 (0.138) Batch 0.977 (1.070) Remain 25:26:41 loss: 0.3146 Lr: 0.00311 [2024-02-18 16:11:30,267 INFO misc.py line 119 87073] Train: [46/100][19/1557] Data 0.010 (0.130) Batch 0.733 (1.049) Remain 24:56:40 loss: 0.2460 Lr: 0.00311 [2024-02-18 16:11:31,050 INFO misc.py line 119 87073] Train: [46/100][20/1557] Data 0.005 (0.122) Batch 0.780 (1.033) Remain 24:34:02 loss: 0.4713 Lr: 0.00311 [2024-02-18 16:11:32,380 INFO misc.py line 119 87073] Train: [46/100][21/1557] Data 0.009 (0.116) Batch 1.324 (1.049) Remain 24:57:07 loss: 0.1212 Lr: 0.00311 [2024-02-18 16:11:33,280 INFO misc.py line 119 87073] Train: [46/100][22/1557] Data 0.015 (0.111) Batch 0.906 (1.042) Remain 24:46:20 loss: 0.2858 Lr: 0.00311 [2024-02-18 16:11:34,156 INFO misc.py line 119 87073] Train: [46/100][23/1557] Data 0.009 (0.106) Batch 0.879 (1.034) Remain 24:34:42 loss: 0.2640 Lr: 0.00311 [2024-02-18 16:11:35,382 INFO misc.py line 119 87073] Train: [46/100][24/1557] Data 0.008 (0.101) Batch 1.228 (1.043) Remain 24:47:53 loss: 0.2828 Lr: 0.00311 [2024-02-18 16:11:36,466 INFO misc.py line 119 87073] Train: [46/100][25/1557] Data 0.004 (0.097) Batch 1.083 (1.045) Remain 24:50:29 loss: 0.6199 Lr: 0.00311 [2024-02-18 16:11:37,222 INFO misc.py line 119 87073] Train: [46/100][26/1557] Data 0.005 (0.093) Batch 0.756 (1.032) Remain 24:32:34 loss: 0.2035 Lr: 0.00311 [2024-02-18 16:11:37,998 INFO misc.py line 119 87073] Train: [46/100][27/1557] Data 0.005 (0.089) Batch 0.774 (1.021) Remain 24:17:11 loss: 0.5173 Lr: 0.00311 [2024-02-18 16:11:39,109 INFO misc.py line 119 87073] Train: [46/100][28/1557] Data 0.008 (0.086) Batch 1.113 (1.025) Remain 24:22:25 loss: 0.1655 Lr: 0.00311 [2024-02-18 16:11:40,049 INFO misc.py line 119 87073] Train: [46/100][29/1557] Data 0.005 (0.083) Batch 0.940 (1.022) Remain 24:17:45 loss: 0.1980 Lr: 0.00311 [2024-02-18 16:11:40,887 INFO misc.py line 119 87073] Train: [46/100][30/1557] Data 0.005 (0.080) Batch 0.837 (1.015) Remain 24:08:00 loss: 0.4301 Lr: 0.00311 [2024-02-18 16:11:42,019 INFO misc.py line 119 87073] Train: [46/100][31/1557] Data 0.005 (0.077) Batch 1.129 (1.019) Remain 24:13:49 loss: 0.3355 Lr: 0.00311 [2024-02-18 16:11:42,923 INFO misc.py line 119 87073] Train: [46/100][32/1557] Data 0.007 (0.075) Batch 0.908 (1.015) Remain 24:08:20 loss: 0.4968 Lr: 0.00311 [2024-02-18 16:11:43,650 INFO misc.py line 119 87073] Train: [46/100][33/1557] Data 0.004 (0.072) Batch 0.727 (1.006) Remain 23:54:38 loss: 0.2146 Lr: 0.00311 [2024-02-18 16:11:44,466 INFO misc.py line 119 87073] Train: [46/100][34/1557] Data 0.003 (0.070) Batch 0.813 (0.999) Remain 23:45:45 loss: 0.3656 Lr: 0.00311 [2024-02-18 16:11:45,810 INFO misc.py line 119 87073] Train: [46/100][35/1557] Data 0.006 (0.068) Batch 1.343 (1.010) Remain 24:01:03 loss: 0.1005 Lr: 0.00311 [2024-02-18 16:11:46,857 INFO misc.py line 119 87073] Train: [46/100][36/1557] Data 0.009 (0.066) Batch 1.043 (1.011) Remain 24:02:28 loss: 0.3827 Lr: 0.00311 [2024-02-18 16:11:47,951 INFO misc.py line 119 87073] Train: [46/100][37/1557] Data 0.011 (0.065) Batch 1.097 (1.014) Remain 24:06:03 loss: 0.2752 Lr: 0.00311 [2024-02-18 16:11:49,103 INFO misc.py line 119 87073] Train: [46/100][38/1557] Data 0.008 (0.063) Batch 1.146 (1.017) Remain 24:11:26 loss: 0.5825 Lr: 0.00311 [2024-02-18 16:11:50,134 INFO misc.py line 119 87073] Train: [46/100][39/1557] Data 0.014 (0.062) Batch 1.030 (1.018) Remain 24:11:55 loss: 0.5204 Lr: 0.00311 [2024-02-18 16:11:50,912 INFO misc.py line 119 87073] Train: [46/100][40/1557] Data 0.016 (0.060) Batch 0.789 (1.012) Remain 24:03:05 loss: 0.2436 Lr: 0.00311 [2024-02-18 16:11:51,696 INFO misc.py line 119 87073] Train: [46/100][41/1557] Data 0.004 (0.059) Batch 0.785 (1.006) Remain 23:54:33 loss: 0.3889 Lr: 0.00311 [2024-02-18 16:11:52,879 INFO misc.py line 119 87073] Train: [46/100][42/1557] Data 0.003 (0.058) Batch 1.152 (1.009) Remain 23:59:53 loss: 0.1080 Lr: 0.00311 [2024-02-18 16:11:53,894 INFO misc.py line 119 87073] Train: [46/100][43/1557] Data 0.036 (0.057) Batch 1.036 (1.010) Remain 24:00:48 loss: 0.7047 Lr: 0.00311 [2024-02-18 16:11:55,016 INFO misc.py line 119 87073] Train: [46/100][44/1557] Data 0.014 (0.056) Batch 1.122 (1.013) Remain 24:04:40 loss: 0.4292 Lr: 0.00311 [2024-02-18 16:11:55,979 INFO misc.py line 119 87073] Train: [46/100][45/1557] Data 0.014 (0.055) Batch 0.974 (1.012) Remain 24:03:21 loss: 0.3297 Lr: 0.00311 [2024-02-18 16:11:56,872 INFO misc.py line 119 87073] Train: [46/100][46/1557] Data 0.003 (0.054) Batch 0.893 (1.009) Remain 23:59:22 loss: 0.2820 Lr: 0.00311 [2024-02-18 16:11:57,614 INFO misc.py line 119 87073] Train: [46/100][47/1557] Data 0.003 (0.053) Batch 0.734 (1.003) Remain 23:50:26 loss: 0.1813 Lr: 0.00311 [2024-02-18 16:11:58,354 INFO misc.py line 119 87073] Train: [46/100][48/1557] Data 0.012 (0.052) Batch 0.748 (0.997) Remain 23:42:21 loss: 0.3414 Lr: 0.00311 [2024-02-18 16:11:59,550 INFO misc.py line 119 87073] Train: [46/100][49/1557] Data 0.004 (0.051) Batch 1.195 (1.001) Remain 23:48:29 loss: 0.1555 Lr: 0.00311 [2024-02-18 16:12:00,624 INFO misc.py line 119 87073] Train: [46/100][50/1557] Data 0.004 (0.050) Batch 1.073 (1.003) Remain 23:50:37 loss: 0.1821 Lr: 0.00311 [2024-02-18 16:12:01,598 INFO misc.py line 119 87073] Train: [46/100][51/1557] Data 0.005 (0.049) Batch 0.976 (1.002) Remain 23:49:48 loss: 0.3865 Lr: 0.00311 [2024-02-18 16:12:02,691 INFO misc.py line 119 87073] Train: [46/100][52/1557] Data 0.003 (0.048) Batch 1.093 (1.004) Remain 23:52:26 loss: 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Batch 0.969 (1.125) Remain 26:42:40 loss: 0.2606 Lr: 0.00311 [2024-02-18 16:13:27,102 INFO misc.py line 119 87073] Train: [46/100][122/1557] Data 0.003 (0.185) Batch 0.927 (1.123) Remain 26:40:17 loss: 0.2744 Lr: 0.00311 [2024-02-18 16:13:27,999 INFO misc.py line 119 87073] Train: [46/100][123/1557] Data 0.003 (0.183) Batch 0.897 (1.121) Remain 26:37:35 loss: 0.4139 Lr: 0.00311 [2024-02-18 16:13:28,770 INFO misc.py line 119 87073] Train: [46/100][124/1557] Data 0.010 (0.182) Batch 0.772 (1.118) Remain 26:33:27 loss: 0.3265 Lr: 0.00311 [2024-02-18 16:13:29,532 INFO misc.py line 119 87073] Train: [46/100][125/1557] Data 0.003 (0.180) Batch 0.752 (1.115) Remain 26:29:09 loss: 0.2749 Lr: 0.00311 [2024-02-18 16:13:30,755 INFO misc.py line 119 87073] Train: [46/100][126/1557] Data 0.013 (0.179) Batch 1.232 (1.116) Remain 26:30:29 loss: 0.3102 Lr: 0.00311 [2024-02-18 16:13:31,781 INFO misc.py line 119 87073] Train: [46/100][127/1557] Data 0.005 (0.177) Batch 1.017 (1.115) Remain 26:29:20 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line 119 87073] Train: [46/100][165/1557] Data 0.004 (0.137) Batch 0.934 (1.069) Remain 25:23:30 loss: 0.1953 Lr: 0.00311 [2024-02-18 16:14:07,498 INFO misc.py line 119 87073] Train: [46/100][166/1557] Data 0.003 (0.137) Batch 0.756 (1.068) Remain 25:20:44 loss: 0.2656 Lr: 0.00311 [2024-02-18 16:14:08,256 INFO misc.py line 119 87073] Train: [46/100][167/1557] Data 0.003 (0.136) Batch 0.756 (1.066) Remain 25:18:01 loss: 0.2580 Lr: 0.00311 [2024-02-18 16:14:09,501 INFO misc.py line 119 87073] Train: [46/100][168/1557] Data 0.005 (0.135) Batch 1.246 (1.067) Remain 25:19:33 loss: 0.1575 Lr: 0.00311 [2024-02-18 16:14:10,428 INFO misc.py line 119 87073] Train: [46/100][169/1557] Data 0.004 (0.134) Batch 0.926 (1.066) Remain 25:18:19 loss: 0.4384 Lr: 0.00311 [2024-02-18 16:14:11,408 INFO misc.py line 119 87073] Train: [46/100][170/1557] Data 0.006 (0.133) Batch 0.981 (1.065) Remain 25:17:35 loss: 0.6417 Lr: 0.00311 [2024-02-18 16:14:12,400 INFO misc.py line 119 87073] Train: 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Batch 1.031 (1.123) Remain 26:39:44 loss: 0.5733 Lr: 0.00311 [2024-02-18 16:14:29,799 INFO misc.py line 119 87073] Train: [46/100][178/1557] Data 0.009 (0.187) Batch 0.882 (1.122) Remain 26:37:45 loss: 0.1918 Lr: 0.00311 [2024-02-18 16:14:30,859 INFO misc.py line 119 87073] Train: [46/100][179/1557] Data 0.003 (0.186) Batch 1.060 (1.121) Remain 26:37:14 loss: 0.9148 Lr: 0.00311 [2024-02-18 16:14:33,291 INFO misc.py line 119 87073] Train: [46/100][180/1557] Data 1.400 (0.193) Batch 2.421 (1.129) Remain 26:47:41 loss: 0.1134 Lr: 0.00311 [2024-02-18 16:14:33,991 INFO misc.py line 119 87073] Train: [46/100][181/1557] Data 0.014 (0.192) Batch 0.711 (1.126) Remain 26:44:19 loss: 0.4699 Lr: 0.00311 [2024-02-18 16:14:35,260 INFO misc.py line 119 87073] Train: [46/100][182/1557] Data 0.003 (0.191) Batch 1.269 (1.127) Remain 26:45:26 loss: 0.1344 Lr: 0.00311 [2024-02-18 16:14:36,183 INFO misc.py line 119 87073] Train: [46/100][183/1557] Data 0.004 (0.190) Batch 0.923 (1.126) Remain 26:43:47 loss: 0.4232 Lr: 0.00311 [2024-02-18 16:14:37,082 INFO misc.py line 119 87073] Train: [46/100][184/1557] Data 0.004 (0.189) Batch 0.899 (1.125) Remain 26:41:59 loss: 0.3346 Lr: 0.00311 [2024-02-18 16:14:38,121 INFO misc.py line 119 87073] Train: [46/100][185/1557] Data 0.004 (0.188) Batch 1.033 (1.124) Remain 26:41:15 loss: 0.5353 Lr: 0.00311 [2024-02-18 16:14:39,074 INFO misc.py line 119 87073] Train: [46/100][186/1557] Data 0.011 (0.187) Batch 0.960 (1.123) Remain 26:39:57 loss: 0.3467 Lr: 0.00311 [2024-02-18 16:14:39,855 INFO misc.py line 119 87073] Train: [46/100][187/1557] Data 0.004 (0.186) Batch 0.781 (1.122) Remain 26:37:17 loss: 0.5241 Lr: 0.00311 [2024-02-18 16:14:40,578 INFO misc.py line 119 87073] Train: [46/100][188/1557] Data 0.004 (0.185) Batch 0.715 (1.119) Remain 26:34:08 loss: 0.5856 Lr: 0.00311 [2024-02-18 16:14:41,869 INFO misc.py line 119 87073] Train: [46/100][189/1557] Data 0.011 (0.184) Batch 1.288 (1.120) Remain 26:35:24 loss: 0.1324 Lr: 0.00311 [2024-02-18 16:14:42,722 INFO misc.py line 119 87073] Train: [46/100][190/1557] Data 0.014 (0.183) Batch 0.862 (1.119) Remain 26:33:25 loss: 0.3056 Lr: 0.00311 [2024-02-18 16:14:43,623 INFO misc.py line 119 87073] Train: [46/100][191/1557] Data 0.004 (0.182) Batch 0.901 (1.118) Remain 26:31:45 loss: 0.2219 Lr: 0.00311 [2024-02-18 16:14:44,633 INFO misc.py line 119 87073] Train: [46/100][192/1557] Data 0.005 (0.181) Batch 1.011 (1.117) Remain 26:30:56 loss: 0.2325 Lr: 0.00311 [2024-02-18 16:14:45,483 INFO misc.py line 119 87073] Train: [46/100][193/1557] Data 0.004 (0.180) Batch 0.849 (1.116) Remain 26:28:54 loss: 0.3935 Lr: 0.00311 [2024-02-18 16:14:46,222 INFO misc.py line 119 87073] Train: [46/100][194/1557] Data 0.006 (0.179) Batch 0.741 (1.114) Remain 26:26:05 loss: 0.2576 Lr: 0.00311 [2024-02-18 16:14:47,009 INFO misc.py line 119 87073] Train: [46/100][195/1557] Data 0.003 (0.178) Batch 0.781 (1.112) Remain 26:23:36 loss: 0.3029 Lr: 0.00311 [2024-02-18 16:14:48,104 INFO misc.py line 119 87073] Train: [46/100][196/1557] Data 0.010 (0.178) Batch 1.096 (1.112) Remain 26:23:27 loss: 0.1516 Lr: 0.00311 [2024-02-18 16:14:49,021 INFO misc.py line 119 87073] Train: [46/100][197/1557] Data 0.009 (0.177) Batch 0.923 (1.111) Remain 26:22:03 loss: 0.2515 Lr: 0.00311 [2024-02-18 16:14:50,004 INFO misc.py line 119 87073] Train: [46/100][198/1557] Data 0.003 (0.176) Batch 0.984 (1.110) Remain 26:21:06 loss: 0.8982 Lr: 0.00311 [2024-02-18 16:14:51,008 INFO misc.py line 119 87073] Train: [46/100][199/1557] Data 0.003 (0.175) Batch 1.004 (1.110) Remain 26:20:18 loss: 0.5601 Lr: 0.00311 [2024-02-18 16:14:52,014 INFO misc.py line 119 87073] Train: [46/100][200/1557] Data 0.003 (0.174) Batch 1.005 (1.109) Remain 26:19:32 loss: 0.4004 Lr: 0.00310 [2024-02-18 16:14:52,749 INFO misc.py line 119 87073] Train: [46/100][201/1557] Data 0.004 (0.173) Batch 0.735 (1.107) Remain 26:16:49 loss: 0.2512 Lr: 0.00310 [2024-02-18 16:14:53,534 INFO misc.py line 119 87073] Train: [46/100][202/1557] Data 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[2024-02-18 16:15:06,353 INFO misc.py line 119 87073] Train: [46/100][215/1557] Data 0.004 (0.162) Batch 1.039 (1.098) Remain 26:03:48 loss: 0.2920 Lr: 0.00310 [2024-02-18 16:15:06,957 INFO misc.py line 119 87073] Train: [46/100][216/1557] Data 0.004 (0.162) Batch 0.603 (1.096) Remain 26:00:28 loss: 0.4469 Lr: 0.00310 [2024-02-18 16:15:08,145 INFO misc.py line 119 87073] Train: [46/100][217/1557] Data 0.004 (0.161) Batch 1.179 (1.097) Remain 26:01:00 loss: 0.1119 Lr: 0.00310 [2024-02-18 16:15:09,094 INFO misc.py line 119 87073] Train: [46/100][218/1557] Data 0.013 (0.160) Batch 0.960 (1.096) Remain 26:00:05 loss: 0.3592 Lr: 0.00310 [2024-02-18 16:15:10,127 INFO misc.py line 119 87073] Train: [46/100][219/1557] Data 0.003 (0.159) Batch 1.032 (1.096) Remain 25:59:38 loss: 0.4112 Lr: 0.00310 [2024-02-18 16:15:11,137 INFO misc.py line 119 87073] Train: [46/100][220/1557] Data 0.005 (0.159) Batch 1.010 (1.095) Remain 25:59:04 loss: 0.2759 Lr: 0.00310 [2024-02-18 16:15:12,023 INFO misc.py line 119 87073] Train: [46/100][221/1557] Data 0.004 (0.158) Batch 0.887 (1.094) Remain 25:57:41 loss: 0.4418 Lr: 0.00310 [2024-02-18 16:15:12,712 INFO misc.py line 119 87073] Train: [46/100][222/1557] Data 0.004 (0.157) Batch 0.684 (1.092) Remain 25:55:00 loss: 0.2984 Lr: 0.00310 [2024-02-18 16:15:13,507 INFO misc.py line 119 87073] Train: [46/100][223/1557] Data 0.008 (0.157) Batch 0.797 (1.091) Remain 25:53:05 loss: 0.2554 Lr: 0.00310 [2024-02-18 16:15:14,735 INFO misc.py line 119 87073] Train: [46/100][224/1557] Data 0.005 (0.156) Batch 1.227 (1.092) Remain 25:53:56 loss: 0.2163 Lr: 0.00310 [2024-02-18 16:15:15,644 INFO misc.py line 119 87073] Train: [46/100][225/1557] Data 0.007 (0.155) Batch 0.912 (1.091) Remain 25:52:46 loss: 0.2851 Lr: 0.00310 [2024-02-18 16:15:16,707 INFO misc.py line 119 87073] Train: [46/100][226/1557] Data 0.004 (0.155) Batch 1.061 (1.091) Remain 25:52:33 loss: 0.7828 Lr: 0.00310 [2024-02-18 16:15:17,795 INFO misc.py line 119 87073] Train: 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Batch 1.048 (1.128) Remain 26:45:16 loss: 0.8171 Lr: 0.00310 [2024-02-18 16:15:33,692 INFO misc.py line 119 87073] Train: [46/100][234/1557] Data 0.003 (0.190) Batch 0.815 (1.126) Remain 26:43:19 loss: 0.6283 Lr: 0.00310 [2024-02-18 16:15:34,698 INFO misc.py line 119 87073] Train: [46/100][235/1557] Data 0.003 (0.189) Batch 0.996 (1.126) Remain 26:42:30 loss: 0.2934 Lr: 0.00310 [2024-02-18 16:15:35,562 INFO misc.py line 119 87073] Train: [46/100][236/1557] Data 0.013 (0.188) Batch 0.872 (1.125) Remain 26:40:56 loss: 0.3567 Lr: 0.00310 [2024-02-18 16:15:36,304 INFO misc.py line 119 87073] Train: [46/100][237/1557] Data 0.005 (0.187) Batch 0.742 (1.123) Remain 26:38:35 loss: 0.2609 Lr: 0.00310 [2024-02-18 16:15:37,495 INFO misc.py line 119 87073] Train: [46/100][238/1557] Data 0.004 (0.186) Batch 1.183 (1.123) Remain 26:38:55 loss: 0.1494 Lr: 0.00310 [2024-02-18 16:15:38,410 INFO misc.py line 119 87073] Train: [46/100][239/1557] Data 0.014 (0.186) Batch 0.924 (1.123) Remain 26:37:42 loss: 0.4345 Lr: 0.00310 [2024-02-18 16:15:39,390 INFO misc.py line 119 87073] Train: [46/100][240/1557] Data 0.004 (0.185) Batch 0.978 (1.122) Remain 26:36:49 loss: 0.7957 Lr: 0.00310 [2024-02-18 16:15:40,437 INFO misc.py line 119 87073] Train: [46/100][241/1557] Data 0.006 (0.184) Batch 1.049 (1.122) Remain 26:36:22 loss: 0.3826 Lr: 0.00310 [2024-02-18 16:15:41,465 INFO misc.py line 119 87073] Train: [46/100][242/1557] Data 0.003 (0.183) Batch 1.025 (1.121) Remain 26:35:46 loss: 0.5866 Lr: 0.00310 [2024-02-18 16:15:42,234 INFO misc.py line 119 87073] Train: [46/100][243/1557] Data 0.006 (0.183) Batch 0.769 (1.120) Remain 26:33:40 loss: 0.2326 Lr: 0.00310 [2024-02-18 16:15:42,990 INFO misc.py line 119 87073] Train: [46/100][244/1557] Data 0.006 (0.182) Batch 0.752 (1.118) Remain 26:31:28 loss: 0.3762 Lr: 0.00310 [2024-02-18 16:15:44,267 INFO misc.py line 119 87073] Train: [46/100][245/1557] Data 0.010 (0.181) Batch 1.274 (1.119) Remain 26:32:22 loss: 0.1177 Lr: 0.00310 [2024-02-18 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Batch 0.981 (1.129) Remain 26:46:20 loss: 0.7302 Lr: 0.00310 [2024-02-18 16:16:37,410 INFO misc.py line 119 87073] Train: [46/100][290/1557] Data 0.003 (0.189) Batch 0.948 (1.129) Remain 26:45:25 loss: 0.3997 Lr: 0.00310 [2024-02-18 16:16:38,317 INFO misc.py line 119 87073] Train: [46/100][291/1557] Data 0.003 (0.189) Batch 0.907 (1.128) Remain 26:44:18 loss: 0.6801 Lr: 0.00310 [2024-02-18 16:16:39,117 INFO misc.py line 119 87073] Train: [46/100][292/1557] Data 0.003 (0.188) Batch 0.792 (1.127) Remain 26:42:38 loss: 0.3390 Lr: 0.00310 [2024-02-18 16:16:39,914 INFO misc.py line 119 87073] Train: [46/100][293/1557] Data 0.011 (0.187) Batch 0.805 (1.126) Remain 26:41:02 loss: 0.2471 Lr: 0.00310 [2024-02-18 16:16:41,101 INFO misc.py line 119 87073] Train: [46/100][294/1557] Data 0.003 (0.187) Batch 1.187 (1.126) Remain 26:41:19 loss: 0.1527 Lr: 0.00310 [2024-02-18 16:16:41,903 INFO misc.py line 119 87073] Train: [46/100][295/1557] Data 0.004 (0.186) Batch 0.802 (1.125) Remain 26:39:43 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87073] Train: [46/100][308/1557] Data 0.003 (0.179) Batch 1.100 (1.116) Remain 26:27:19 loss: 0.1153 Lr: 0.00310 [2024-02-18 16:16:54,869 INFO misc.py line 119 87073] Train: [46/100][309/1557] Data 0.012 (0.178) Batch 0.951 (1.116) Remain 26:26:32 loss: 0.5512 Lr: 0.00310 [2024-02-18 16:16:55,886 INFO misc.py line 119 87073] Train: [46/100][310/1557] Data 0.004 (0.177) Batch 1.017 (1.115) Remain 26:26:04 loss: 0.2539 Lr: 0.00310 [2024-02-18 16:16:56,786 INFO misc.py line 119 87073] Train: [46/100][311/1557] Data 0.004 (0.177) Batch 0.900 (1.115) Remain 26:25:03 loss: 0.4729 Lr: 0.00310 [2024-02-18 16:16:57,858 INFO misc.py line 119 87073] Train: [46/100][312/1557] Data 0.003 (0.176) Batch 1.063 (1.114) Remain 26:24:48 loss: 0.1946 Lr: 0.00310 [2024-02-18 16:16:58,594 INFO misc.py line 119 87073] Train: [46/100][313/1557] Data 0.012 (0.176) Batch 0.745 (1.113) Remain 26:23:05 loss: 0.4037 Lr: 0.00310 [2024-02-18 16:16:59,328 INFO misc.py line 119 87073] Train: [46/100][314/1557] Data 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line 119 87073] Train: [46/100][333/1557] Data 0.011 (0.165) Batch 0.921 (1.102) Remain 26:07:21 loss: 0.4430 Lr: 0.00310 [2024-02-18 16:17:18,031 INFO misc.py line 119 87073] Train: [46/100][334/1557] Data 0.003 (0.165) Batch 0.735 (1.101) Remain 26:05:45 loss: 0.2251 Lr: 0.00310 [2024-02-18 16:17:18,728 INFO misc.py line 119 87073] Train: [46/100][335/1557] Data 0.005 (0.165) Batch 0.690 (1.100) Remain 26:03:59 loss: 0.2929 Lr: 0.00310 [2024-02-18 16:17:19,952 INFO misc.py line 119 87073] Train: [46/100][336/1557] Data 0.012 (0.164) Batch 1.227 (1.100) Remain 26:04:30 loss: 0.1592 Lr: 0.00310 [2024-02-18 16:17:21,007 INFO misc.py line 119 87073] Train: [46/100][337/1557] Data 0.009 (0.164) Batch 1.058 (1.100) Remain 26:04:18 loss: 0.5567 Lr: 0.00310 [2024-02-18 16:17:22,028 INFO misc.py line 119 87073] Train: [46/100][338/1557] Data 0.006 (0.163) Batch 1.022 (1.100) Remain 26:03:57 loss: 0.3275 Lr: 0.00310 [2024-02-18 16:17:23,251 INFO misc.py line 119 87073] Train: 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loss: 0.4096 Lr: 0.00310 [2024-02-18 16:17:46,200 INFO misc.py line 119 87073] Train: [46/100][352/1557] Data 0.003 (0.185) Batch 1.059 (1.125) Remain 26:39:25 loss: 0.4767 Lr: 0.00310 [2024-02-18 16:17:47,419 INFO misc.py line 119 87073] Train: [46/100][353/1557] Data 0.003 (0.184) Batch 1.208 (1.125) Remain 26:39:44 loss: 0.2494 Lr: 0.00310 [2024-02-18 16:17:48,357 INFO misc.py line 119 87073] Train: [46/100][354/1557] Data 0.015 (0.184) Batch 0.950 (1.125) Remain 26:39:00 loss: 0.4163 Lr: 0.00310 [2024-02-18 16:17:50,812 INFO misc.py line 119 87073] Train: [46/100][355/1557] Data 1.329 (0.187) Batch 2.455 (1.129) Remain 26:44:21 loss: 0.1327 Lr: 0.00310 [2024-02-18 16:17:51,512 INFO misc.py line 119 87073] Train: [46/100][356/1557] Data 0.004 (0.186) Batch 0.697 (1.128) Remain 26:42:36 loss: 0.3603 Lr: 0.00310 [2024-02-18 16:17:52,804 INFO misc.py line 119 87073] Train: [46/100][357/1557] Data 0.007 (0.186) Batch 1.286 (1.128) Remain 26:43:13 loss: 0.1455 Lr: 0.00310 [2024-02-18 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line 119 87073] Train: [46/100][389/1557] Data 0.004 (0.171) Batch 0.863 (1.112) Remain 26:19:36 loss: 0.5114 Lr: 0.00310 [2024-02-18 16:18:23,412 INFO misc.py line 119 87073] Train: [46/100][390/1557] Data 0.005 (0.171) Batch 0.769 (1.111) Remain 26:18:19 loss: 0.4532 Lr: 0.00310 [2024-02-18 16:18:24,161 INFO misc.py line 119 87073] Train: [46/100][391/1557] Data 0.010 (0.170) Batch 0.755 (1.110) Remain 26:16:59 loss: 0.2379 Lr: 0.00310 [2024-02-18 16:18:25,392 INFO misc.py line 119 87073] Train: [46/100][392/1557] Data 0.004 (0.170) Batch 1.231 (1.110) Remain 26:17:25 loss: 0.2166 Lr: 0.00310 [2024-02-18 16:18:26,328 INFO misc.py line 119 87073] Train: [46/100][393/1557] Data 0.004 (0.169) Batch 0.936 (1.110) Remain 26:16:46 loss: 0.4857 Lr: 0.00310 [2024-02-18 16:18:27,147 INFO misc.py line 119 87073] Train: [46/100][394/1557] Data 0.004 (0.169) Batch 0.819 (1.109) Remain 26:15:41 loss: 0.1625 Lr: 0.00309 [2024-02-18 16:18:28,101 INFO misc.py line 119 87073] Train: 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Batch 0.878 (1.130) Remain 26:45:44 loss: 1.1269 Lr: 0.00309 [2024-02-18 16:18:44,426 INFO misc.py line 119 87073] Train: [46/100][402/1557] Data 0.004 (0.191) Batch 1.058 (1.130) Remain 26:45:27 loss: 0.3872 Lr: 0.00309 [2024-02-18 16:18:45,459 INFO misc.py line 119 87073] Train: [46/100][403/1557] Data 0.008 (0.190) Batch 1.031 (1.130) Remain 26:45:05 loss: 0.5367 Lr: 0.00309 [2024-02-18 16:18:46,201 INFO misc.py line 119 87073] Train: [46/100][404/1557] Data 0.011 (0.190) Batch 0.749 (1.129) Remain 26:43:43 loss: 0.3919 Lr: 0.00309 [2024-02-18 16:18:46,946 INFO misc.py line 119 87073] Train: [46/100][405/1557] Data 0.003 (0.189) Batch 0.739 (1.128) Remain 26:42:19 loss: 0.1403 Lr: 0.00309 [2024-02-18 16:18:48,222 INFO misc.py line 119 87073] Train: [46/100][406/1557] Data 0.009 (0.189) Batch 1.266 (1.128) Remain 26:42:47 loss: 0.3768 Lr: 0.00309 [2024-02-18 16:18:49,165 INFO misc.py line 119 87073] Train: [46/100][407/1557] Data 0.021 (0.189) Batch 0.958 (1.128) Remain 26:42:10 loss: 0.3960 Lr: 0.00309 [2024-02-18 16:18:50,024 INFO misc.py line 119 87073] Train: [46/100][408/1557] Data 0.004 (0.188) Batch 0.859 (1.127) Remain 26:41:12 loss: 0.2187 Lr: 0.00309 [2024-02-18 16:18:50,922 INFO misc.py line 119 87073] Train: [46/100][409/1557] Data 0.004 (0.188) Batch 0.891 (1.127) Remain 26:40:22 loss: 0.2370 Lr: 0.00309 [2024-02-18 16:18:52,058 INFO misc.py line 119 87073] Train: [46/100][410/1557] Data 0.010 (0.187) Batch 1.136 (1.127) Remain 26:40:23 loss: 0.7422 Lr: 0.00309 [2024-02-18 16:18:52,894 INFO misc.py line 119 87073] Train: [46/100][411/1557] Data 0.011 (0.187) Batch 0.842 (1.126) Remain 26:39:22 loss: 0.4502 Lr: 0.00309 [2024-02-18 16:18:53,622 INFO misc.py line 119 87073] Train: [46/100][412/1557] Data 0.005 (0.186) Batch 0.727 (1.125) Remain 26:37:58 loss: 0.2374 Lr: 0.00309 [2024-02-18 16:18:54,907 INFO misc.py line 119 87073] Train: [46/100][413/1557] Data 0.006 (0.186) Batch 1.276 (1.125) Remain 26:38:28 loss: 0.1575 Lr: 0.00309 [2024-02-18 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[2024-02-18 16:20:22,661 INFO misc.py line 119 87073] Train: [46/100][495/1557] Data 0.011 (0.174) Batch 0.765 (1.116) Remain 26:23:54 loss: 0.2193 Lr: 0.00309 [2024-02-18 16:20:23,385 INFO misc.py line 119 87073] Train: [46/100][496/1557] Data 0.004 (0.173) Batch 0.717 (1.115) Remain 26:22:44 loss: 0.2209 Lr: 0.00309 [2024-02-18 16:20:24,576 INFO misc.py line 119 87073] Train: [46/100][497/1557] Data 0.012 (0.173) Batch 1.193 (1.116) Remain 26:22:56 loss: 0.2170 Lr: 0.00309 [2024-02-18 16:20:25,500 INFO misc.py line 119 87073] Train: [46/100][498/1557] Data 0.010 (0.173) Batch 0.930 (1.115) Remain 26:22:23 loss: 0.3375 Lr: 0.00309 [2024-02-18 16:20:26,404 INFO misc.py line 119 87073] Train: [46/100][499/1557] Data 0.005 (0.172) Batch 0.904 (1.115) Remain 26:21:45 loss: 0.3853 Lr: 0.00309 [2024-02-18 16:20:27,332 INFO misc.py line 119 87073] Train: [46/100][500/1557] Data 0.005 (0.172) Batch 0.919 (1.114) Remain 26:21:11 loss: 0.9025 Lr: 0.00309 [2024-02-18 16:20:28,368 INFO misc.py line 119 87073] Train: [46/100][501/1557] Data 0.014 (0.172) Batch 1.036 (1.114) Remain 26:20:56 loss: 0.9284 Lr: 0.00309 [2024-02-18 16:20:29,067 INFO misc.py line 119 87073] Train: [46/100][502/1557] Data 0.013 (0.171) Batch 0.708 (1.113) Remain 26:19:46 loss: 0.3030 Lr: 0.00309 [2024-02-18 16:20:29,825 INFO misc.py line 119 87073] Train: [46/100][503/1557] Data 0.005 (0.171) Batch 0.749 (1.113) Remain 26:18:43 loss: 0.3315 Lr: 0.00309 [2024-02-18 16:20:31,063 INFO misc.py line 119 87073] Train: [46/100][504/1557] Data 0.014 (0.171) Batch 1.247 (1.113) Remain 26:19:04 loss: 0.1710 Lr: 0.00309 [2024-02-18 16:20:32,004 INFO misc.py line 119 87073] Train: [46/100][505/1557] Data 0.005 (0.170) Batch 0.941 (1.113) Remain 26:18:34 loss: 0.4540 Lr: 0.00309 [2024-02-18 16:20:33,046 INFO misc.py line 119 87073] Train: [46/100][506/1557] Data 0.006 (0.170) Batch 1.041 (1.112) Remain 26:18:21 loss: 0.4848 Lr: 0.00309 [2024-02-18 16:20:34,006 INFO misc.py line 119 87073] Train: 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Batch 1.018 (1.130) Remain 26:42:33 loss: 0.1362 Lr: 0.00309 [2024-02-18 16:20:50,589 INFO misc.py line 119 87073] Train: [46/100][514/1557] Data 0.005 (0.188) Batch 1.011 (1.129) Remain 26:42:12 loss: 0.3223 Lr: 0.00309 [2024-02-18 16:20:51,696 INFO misc.py line 119 87073] Train: [46/100][515/1557] Data 0.004 (0.187) Batch 1.107 (1.129) Remain 26:42:07 loss: 0.9584 Lr: 0.00309 [2024-02-18 16:20:52,462 INFO misc.py line 119 87073] Train: [46/100][516/1557] Data 0.005 (0.187) Batch 0.766 (1.129) Remain 26:41:06 loss: 0.2286 Lr: 0.00309 [2024-02-18 16:20:53,184 INFO misc.py line 119 87073] Train: [46/100][517/1557] Data 0.004 (0.187) Batch 0.720 (1.128) Remain 26:39:57 loss: 0.5987 Lr: 0.00309 [2024-02-18 16:20:54,511 INFO misc.py line 119 87073] Train: [46/100][518/1557] Data 0.007 (0.186) Batch 1.323 (1.128) Remain 26:40:28 loss: 0.2207 Lr: 0.00309 [2024-02-18 16:20:55,496 INFO misc.py line 119 87073] Train: [46/100][519/1557] Data 0.010 (0.186) Batch 0.991 (1.128) Remain 26:40:04 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line 119 87073] Train: [46/100][613/1557] Data 0.004 (0.175) Batch 0.980 (1.118) Remain 26:23:47 loss: 0.4031 Lr: 0.00308 [2024-02-18 16:22:36,021 INFO misc.py line 119 87073] Train: [46/100][614/1557] Data 0.004 (0.175) Batch 0.743 (1.117) Remain 26:22:54 loss: 0.5862 Lr: 0.00308 [2024-02-18 16:22:36,795 INFO misc.py line 119 87073] Train: [46/100][615/1557] Data 0.011 (0.174) Batch 0.782 (1.117) Remain 26:22:06 loss: 0.4496 Lr: 0.00308 [2024-02-18 16:22:38,093 INFO misc.py line 119 87073] Train: [46/100][616/1557] Data 0.004 (0.174) Batch 1.292 (1.117) Remain 26:22:29 loss: 0.1331 Lr: 0.00308 [2024-02-18 16:22:39,015 INFO misc.py line 119 87073] Train: [46/100][617/1557] Data 0.010 (0.174) Batch 0.928 (1.116) Remain 26:22:02 loss: 0.4436 Lr: 0.00308 [2024-02-18 16:22:39,900 INFO misc.py line 119 87073] Train: [46/100][618/1557] Data 0.004 (0.174) Batch 0.885 (1.116) Remain 26:21:29 loss: 0.4767 Lr: 0.00308 [2024-02-18 16:22:40,894 INFO misc.py line 119 87073] Train: 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Batch 1.021 (1.131) Remain 26:42:17 loss: 0.3902 Lr: 0.00308 [2024-02-18 16:22:57,593 INFO misc.py line 119 87073] Train: [46/100][626/1557] Data 0.006 (0.186) Batch 0.689 (1.130) Remain 26:41:16 loss: 0.4512 Lr: 0.00308 [2024-02-18 16:22:58,541 INFO misc.py line 119 87073] Train: [46/100][627/1557] Data 0.005 (0.185) Batch 0.949 (1.130) Remain 26:40:50 loss: 0.4471 Lr: 0.00308 [2024-02-18 16:22:59,295 INFO misc.py line 119 87073] Train: [46/100][628/1557] Data 0.005 (0.185) Batch 0.753 (1.129) Remain 26:39:57 loss: 0.2435 Lr: 0.00308 [2024-02-18 16:23:00,073 INFO misc.py line 119 87073] Train: [46/100][629/1557] Data 0.005 (0.185) Batch 0.773 (1.129) Remain 26:39:08 loss: 0.2411 Lr: 0.00308 [2024-02-18 16:23:01,342 INFO misc.py line 119 87073] Train: [46/100][630/1557] Data 0.009 (0.185) Batch 1.266 (1.129) Remain 26:39:25 loss: 0.1269 Lr: 0.00308 [2024-02-18 16:23:02,311 INFO misc.py line 119 87073] Train: [46/100][631/1557] Data 0.014 (0.184) Batch 0.977 (1.129) Remain 26:39:04 loss: 0.3088 Lr: 0.00308 [2024-02-18 16:23:03,238 INFO misc.py line 119 87073] Train: [46/100][632/1557] Data 0.005 (0.184) Batch 0.928 (1.128) Remain 26:38:35 loss: 0.6177 Lr: 0.00308 [2024-02-18 16:23:04,146 INFO misc.py line 119 87073] Train: [46/100][633/1557] Data 0.004 (0.184) Batch 0.902 (1.128) Remain 26:38:04 loss: 0.2707 Lr: 0.00308 [2024-02-18 16:23:05,153 INFO misc.py line 119 87073] Train: [46/100][634/1557] Data 0.010 (0.183) Batch 1.013 (1.128) Remain 26:37:47 loss: 0.5377 Lr: 0.00308 [2024-02-18 16:23:05,879 INFO misc.py line 119 87073] Train: [46/100][635/1557] Data 0.005 (0.183) Batch 0.726 (1.127) Remain 26:36:52 loss: 0.2998 Lr: 0.00308 [2024-02-18 16:23:06,666 INFO misc.py line 119 87073] Train: [46/100][636/1557] Data 0.004 (0.183) Batch 0.779 (1.127) Remain 26:36:04 loss: 0.2915 Lr: 0.00308 [2024-02-18 16:23:07,960 INFO misc.py line 119 87073] Train: [46/100][637/1557] Data 0.012 (0.183) Batch 1.297 (1.127) Remain 26:36:26 loss: 0.1069 Lr: 0.00308 [2024-02-18 16:23:09,060 INFO misc.py line 119 87073] Train: [46/100][638/1557] Data 0.009 (0.182) Batch 1.101 (1.127) Remain 26:36:21 loss: 0.7205 Lr: 0.00308 [2024-02-18 16:23:10,063 INFO misc.py line 119 87073] Train: [46/100][639/1557] Data 0.008 (0.182) Batch 1.000 (1.127) Remain 26:36:03 loss: 0.4003 Lr: 0.00308 [2024-02-18 16:23:10,961 INFO misc.py line 119 87073] Train: [46/100][640/1557] Data 0.011 (0.182) Batch 0.906 (1.126) Remain 26:35:32 loss: 0.5662 Lr: 0.00308 [2024-02-18 16:23:11,980 INFO misc.py line 119 87073] Train: [46/100][641/1557] Data 0.005 (0.182) Batch 1.019 (1.126) Remain 26:35:17 loss: 0.0660 Lr: 0.00308 [2024-02-18 16:23:12,776 INFO misc.py line 119 87073] Train: [46/100][642/1557] Data 0.004 (0.181) Batch 0.796 (1.126) Remain 26:34:32 loss: 0.2708 Lr: 0.00308 [2024-02-18 16:23:13,551 INFO misc.py line 119 87073] Train: [46/100][643/1557] Data 0.005 (0.181) Batch 0.776 (1.125) Remain 26:33:44 loss: 0.4697 Lr: 0.00308 [2024-02-18 16:23:14,664 INFO misc.py line 119 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line 119 87073] Train: [46/100][669/1557] Data 0.014 (0.174) Batch 0.831 (1.118) Remain 26:23:22 loss: 0.3743 Lr: 0.00308 [2024-02-18 16:23:38,888 INFO misc.py line 119 87073] Train: [46/100][670/1557] Data 0.004 (0.174) Batch 0.734 (1.118) Remain 26:22:32 loss: 0.1981 Lr: 0.00308 [2024-02-18 16:23:39,621 INFO misc.py line 119 87073] Train: [46/100][671/1557] Data 0.003 (0.174) Batch 0.724 (1.117) Remain 26:21:41 loss: 0.3168 Lr: 0.00308 [2024-02-18 16:23:40,938 INFO misc.py line 119 87073] Train: [46/100][672/1557] Data 0.013 (0.173) Batch 1.327 (1.117) Remain 26:22:06 loss: 0.1899 Lr: 0.00308 [2024-02-18 16:23:41,935 INFO misc.py line 119 87073] Train: [46/100][673/1557] Data 0.004 (0.173) Batch 0.992 (1.117) Remain 26:21:49 loss: 0.3739 Lr: 0.00308 [2024-02-18 16:23:42,805 INFO misc.py line 119 87073] Train: [46/100][674/1557] Data 0.009 (0.173) Batch 0.873 (1.117) Remain 26:21:17 loss: 0.2159 Lr: 0.00308 [2024-02-18 16:23:43,687 INFO misc.py line 119 87073] Train: 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Batch 1.015 (1.128) Remain 26:37:46 loss: 0.5449 Lr: 0.00308 [2024-02-18 16:23:59,404 INFO misc.py line 119 87073] Train: [46/100][682/1557] Data 0.006 (0.184) Batch 0.831 (1.128) Remain 26:37:07 loss: 0.4269 Lr: 0.00308 [2024-02-18 16:24:00,550 INFO misc.py line 119 87073] Train: [46/100][683/1557] Data 0.004 (0.184) Batch 1.145 (1.128) Remain 26:37:08 loss: 0.3023 Lr: 0.00308 [2024-02-18 16:24:01,300 INFO misc.py line 119 87073] Train: [46/100][684/1557] Data 0.006 (0.183) Batch 0.751 (1.127) Remain 26:36:20 loss: 0.1898 Lr: 0.00308 [2024-02-18 16:24:02,012 INFO misc.py line 119 87073] Train: [46/100][685/1557] Data 0.006 (0.183) Batch 0.708 (1.127) Remain 26:35:27 loss: 0.2940 Lr: 0.00308 [2024-02-18 16:24:03,244 INFO misc.py line 119 87073] Train: [46/100][686/1557] Data 0.009 (0.183) Batch 1.235 (1.127) Remain 26:35:39 loss: 0.2051 Lr: 0.00308 [2024-02-18 16:24:04,347 INFO misc.py line 119 87073] Train: [46/100][687/1557] Data 0.006 (0.183) Batch 1.102 (1.127) Remain 26:35:35 loss: 0.2180 Lr: 0.00308 [2024-02-18 16:24:05,272 INFO misc.py line 119 87073] Train: [46/100][688/1557] Data 0.007 (0.182) Batch 0.928 (1.127) Remain 26:35:09 loss: 0.3676 Lr: 0.00308 [2024-02-18 16:24:06,352 INFO misc.py line 119 87073] Train: [46/100][689/1557] Data 0.004 (0.182) Batch 1.080 (1.127) Remain 26:35:02 loss: 0.4295 Lr: 0.00308 [2024-02-18 16:24:07,163 INFO misc.py line 119 87073] Train: [46/100][690/1557] Data 0.004 (0.182) Batch 0.812 (1.126) Remain 26:34:22 loss: 0.2161 Lr: 0.00308 [2024-02-18 16:24:07,902 INFO misc.py line 119 87073] Train: [46/100][691/1557] Data 0.003 (0.182) Batch 0.727 (1.126) Remain 26:33:32 loss: 0.2320 Lr: 0.00308 [2024-02-18 16:24:08,650 INFO misc.py line 119 87073] Train: [46/100][692/1557] Data 0.014 (0.181) Batch 0.760 (1.125) Remain 26:32:45 loss: 0.2157 Lr: 0.00308 [2024-02-18 16:24:09,883 INFO misc.py line 119 87073] Train: [46/100][693/1557] Data 0.003 (0.181) Batch 1.231 (1.125) Remain 26:32:57 loss: 0.1307 Lr: 0.00308 [2024-02-18 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26:30:06 loss: 0.3323 Lr: 0.00308 [2024-02-18 16:24:30,745 INFO misc.py line 119 87073] Train: [46/100][713/1557] Data 0.005 (0.178) Batch 0.726 (1.123) Remain 26:29:18 loss: 0.5049 Lr: 0.00308 [2024-02-18 16:24:31,871 INFO misc.py line 119 87073] Train: [46/100][714/1557] Data 0.009 (0.178) Batch 1.124 (1.123) Remain 26:29:17 loss: 0.1825 Lr: 0.00308 [2024-02-18 16:24:32,853 INFO misc.py line 119 87073] Train: [46/100][715/1557] Data 0.012 (0.178) Batch 0.988 (1.123) Remain 26:29:00 loss: 0.5264 Lr: 0.00308 [2024-02-18 16:24:33,811 INFO misc.py line 119 87073] Train: [46/100][716/1557] Data 0.006 (0.178) Batch 0.960 (1.122) Remain 26:28:39 loss: 0.3400 Lr: 0.00308 [2024-02-18 16:24:34,735 INFO misc.py line 119 87073] Train: [46/100][717/1557] Data 0.004 (0.177) Batch 0.924 (1.122) Remain 26:28:14 loss: 0.6758 Lr: 0.00308 [2024-02-18 16:24:35,746 INFO misc.py line 119 87073] Train: [46/100][718/1557] Data 0.003 (0.177) Batch 1.004 (1.122) Remain 26:27:59 loss: 0.3070 Lr: 0.00308 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Batch 0.825 (1.131) Remain 26:40:52 loss: 0.2337 Lr: 0.00308 [2024-02-18 16:25:04,821 INFO misc.py line 119 87073] Train: [46/100][738/1557] Data 0.004 (0.185) Batch 0.884 (1.131) Remain 26:40:23 loss: 0.7418 Lr: 0.00308 [2024-02-18 16:25:05,822 INFO misc.py line 119 87073] Train: [46/100][739/1557] Data 0.013 (0.185) Batch 1.010 (1.131) Remain 26:40:08 loss: 0.4503 Lr: 0.00308 [2024-02-18 16:25:06,594 INFO misc.py line 119 87073] Train: [46/100][740/1557] Data 0.005 (0.185) Batch 0.773 (1.130) Remain 26:39:25 loss: 0.3641 Lr: 0.00308 [2024-02-18 16:25:07,428 INFO misc.py line 119 87073] Train: [46/100][741/1557] Data 0.003 (0.185) Batch 0.770 (1.130) Remain 26:38:43 loss: 0.2901 Lr: 0.00308 [2024-02-18 16:25:08,694 INFO misc.py line 119 87073] Train: [46/100][742/1557] Data 0.068 (0.184) Batch 1.323 (1.130) Remain 26:39:04 loss: 0.2509 Lr: 0.00308 [2024-02-18 16:25:09,775 INFO misc.py line 119 87073] Train: [46/100][743/1557] Data 0.011 (0.184) Batch 1.082 (1.130) Remain 26:38:57 loss: 0.5027 Lr: 0.00308 [2024-02-18 16:25:10,802 INFO misc.py line 119 87073] Train: [46/100][744/1557] Data 0.009 (0.184) Batch 1.024 (1.130) Remain 26:38:44 loss: 0.4428 Lr: 0.00308 [2024-02-18 16:25:11,838 INFO misc.py line 119 87073] Train: [46/100][745/1557] Data 0.012 (0.184) Batch 1.034 (1.130) Remain 26:38:32 loss: 0.3207 Lr: 0.00308 [2024-02-18 16:25:12,863 INFO misc.py line 119 87073] Train: [46/100][746/1557] Data 0.015 (0.183) Batch 1.032 (1.130) Remain 26:38:19 loss: 0.4139 Lr: 0.00308 [2024-02-18 16:25:13,565 INFO misc.py line 119 87073] Train: [46/100][747/1557] Data 0.008 (0.183) Batch 0.707 (1.129) Remain 26:37:30 loss: 0.3098 Lr: 0.00308 [2024-02-18 16:25:14,367 INFO misc.py line 119 87073] Train: [46/100][748/1557] Data 0.004 (0.183) Batch 0.794 (1.129) Remain 26:36:51 loss: 0.2984 Lr: 0.00308 [2024-02-18 16:25:15,562 INFO misc.py line 119 87073] Train: [46/100][749/1557] Data 0.011 (0.183) Batch 1.194 (1.129) Remain 26:36:57 loss: 0.1718 Lr: 0.00308 [2024-02-18 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87073] Train: [46/100][756/1557] Data 0.004 (0.181) Batch 1.146 (1.127) Remain 26:34:25 loss: 0.2047 Lr: 0.00308 [2024-02-18 16:25:23,089 INFO misc.py line 119 87073] Train: [46/100][757/1557] Data 0.007 (0.181) Batch 0.907 (1.127) Remain 26:33:59 loss: 0.3321 Lr: 0.00308 [2024-02-18 16:25:24,178 INFO misc.py line 119 87073] Train: [46/100][758/1557] Data 0.008 (0.181) Batch 1.092 (1.127) Remain 26:33:54 loss: 0.2929 Lr: 0.00308 [2024-02-18 16:25:25,106 INFO misc.py line 119 87073] Train: [46/100][759/1557] Data 0.004 (0.180) Batch 0.926 (1.126) Remain 26:33:31 loss: 0.6067 Lr: 0.00308 [2024-02-18 16:25:26,121 INFO misc.py line 119 87073] Train: [46/100][760/1557] Data 0.007 (0.180) Batch 1.018 (1.126) Remain 26:33:17 loss: 0.4107 Lr: 0.00308 [2024-02-18 16:25:27,026 INFO misc.py line 119 87073] Train: [46/100][761/1557] Data 0.004 (0.180) Batch 0.904 (1.126) Remain 26:32:51 loss: 0.5008 Lr: 0.00308 [2024-02-18 16:25:27,760 INFO misc.py line 119 87073] Train: [46/100][762/1557] Data 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[2024-02-18 16:25:40,096 INFO misc.py line 119 87073] Train: [46/100][775/1557] Data 0.004 (0.177) Batch 0.791 (1.123) Remain 26:27:39 loss: 0.3142 Lr: 0.00308 [2024-02-18 16:25:40,840 INFO misc.py line 119 87073] Train: [46/100][776/1557] Data 0.004 (0.177) Batch 0.740 (1.122) Remain 26:26:56 loss: 0.4236 Lr: 0.00308 [2024-02-18 16:25:41,985 INFO misc.py line 119 87073] Train: [46/100][777/1557] Data 0.008 (0.176) Batch 1.143 (1.122) Remain 26:26:57 loss: 0.2898 Lr: 0.00308 [2024-02-18 16:25:43,004 INFO misc.py line 119 87073] Train: [46/100][778/1557] Data 0.010 (0.176) Batch 1.018 (1.122) Remain 26:26:45 loss: 0.2895 Lr: 0.00308 [2024-02-18 16:25:44,023 INFO misc.py line 119 87073] Train: [46/100][779/1557] Data 0.011 (0.176) Batch 1.016 (1.122) Remain 26:26:32 loss: 0.3081 Lr: 0.00308 [2024-02-18 16:25:45,033 INFO misc.py line 119 87073] Train: [46/100][780/1557] Data 0.013 (0.176) Batch 1.014 (1.122) Remain 26:26:19 loss: 0.4646 Lr: 0.00308 [2024-02-18 16:25:45,935 INFO misc.py line 119 87073] Train: [46/100][781/1557] Data 0.011 (0.176) Batch 0.906 (1.121) Remain 26:25:55 loss: 0.3868 Lr: 0.00308 [2024-02-18 16:25:46,710 INFO misc.py line 119 87073] Train: [46/100][782/1557] Data 0.006 (0.175) Batch 0.776 (1.121) Remain 26:25:16 loss: 0.1804 Lr: 0.00307 [2024-02-18 16:25:47,494 INFO misc.py line 119 87073] Train: [46/100][783/1557] Data 0.005 (0.175) Batch 0.781 (1.121) Remain 26:24:38 loss: 0.1858 Lr: 0.00307 [2024-02-18 16:25:48,773 INFO misc.py line 119 87073] Train: [46/100][784/1557] Data 0.007 (0.175) Batch 1.275 (1.121) Remain 26:24:53 loss: 0.1509 Lr: 0.00307 [2024-02-18 16:25:49,774 INFO misc.py line 119 87073] Train: [46/100][785/1557] Data 0.012 (0.175) Batch 1.003 (1.121) Remain 26:24:40 loss: 0.3468 Lr: 0.00307 [2024-02-18 16:25:50,837 INFO misc.py line 119 87073] Train: [46/100][786/1557] Data 0.009 (0.174) Batch 1.062 (1.120) Remain 26:24:32 loss: 0.6890 Lr: 0.00307 [2024-02-18 16:25:51,785 INFO misc.py line 119 87073] Train: 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Batch 1.007 (1.131) Remain 26:39:22 loss: 0.6895 Lr: 0.00307 [2024-02-18 16:26:08,101 INFO misc.py line 119 87073] Train: [46/100][794/1557] Data 0.004 (0.184) Batch 1.065 (1.131) Remain 26:39:14 loss: 0.3633 Lr: 0.00307 [2024-02-18 16:26:09,040 INFO misc.py line 119 87073] Train: [46/100][795/1557] Data 0.005 (0.184) Batch 0.939 (1.131) Remain 26:38:52 loss: 0.5550 Lr: 0.00307 [2024-02-18 16:26:09,810 INFO misc.py line 119 87073] Train: [46/100][796/1557] Data 0.005 (0.184) Batch 0.764 (1.130) Remain 26:38:12 loss: 0.1764 Lr: 0.00307 [2024-02-18 16:26:10,524 INFO misc.py line 119 87073] Train: [46/100][797/1557] Data 0.012 (0.184) Batch 0.719 (1.130) Remain 26:37:27 loss: 0.3611 Lr: 0.00307 [2024-02-18 16:26:11,715 INFO misc.py line 119 87073] Train: [46/100][798/1557] Data 0.006 (0.183) Batch 1.191 (1.130) Remain 26:37:32 loss: 0.1121 Lr: 0.00307 [2024-02-18 16:26:12,614 INFO misc.py line 119 87073] Train: [46/100][799/1557] Data 0.006 (0.183) Batch 0.897 (1.130) Remain 26:37:06 loss: 0.4970 Lr: 0.00307 [2024-02-18 16:26:13,556 INFO misc.py line 119 87073] Train: [46/100][800/1557] Data 0.008 (0.183) Batch 0.942 (1.129) Remain 26:36:45 loss: 0.6981 Lr: 0.00307 [2024-02-18 16:26:14,599 INFO misc.py line 119 87073] Train: [46/100][801/1557] Data 0.007 (0.183) Batch 1.045 (1.129) Remain 26:36:35 loss: 0.3228 Lr: 0.00307 [2024-02-18 16:26:15,611 INFO misc.py line 119 87073] Train: [46/100][802/1557] Data 0.005 (0.183) Batch 1.012 (1.129) Remain 26:36:22 loss: 0.1125 Lr: 0.00307 [2024-02-18 16:26:16,371 INFO misc.py line 119 87073] Train: [46/100][803/1557] Data 0.004 (0.182) Batch 0.757 (1.129) Remain 26:35:41 loss: 0.6085 Lr: 0.00307 [2024-02-18 16:26:17,082 INFO misc.py line 119 87073] Train: [46/100][804/1557] Data 0.009 (0.182) Batch 0.711 (1.128) Remain 26:34:56 loss: 0.3136 Lr: 0.00307 [2024-02-18 16:26:18,363 INFO misc.py line 119 87073] Train: [46/100][805/1557] Data 0.008 (0.182) Batch 1.275 (1.128) Remain 26:35:10 loss: 0.1573 Lr: 0.00307 [2024-02-18 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Batch 0.908 (1.130) Remain 26:36:38 loss: 0.2703 Lr: 0.00307 [2024-02-18 16:27:10,273 INFO misc.py line 119 87073] Train: [46/100][850/1557] Data 0.005 (0.184) Batch 0.901 (1.130) Remain 26:36:14 loss: 0.4278 Lr: 0.00307 [2024-02-18 16:27:11,337 INFO misc.py line 119 87073] Train: [46/100][851/1557] Data 0.004 (0.184) Batch 1.059 (1.130) Remain 26:36:06 loss: 0.5151 Lr: 0.00307 [2024-02-18 16:27:12,037 INFO misc.py line 119 87073] Train: [46/100][852/1557] Data 0.010 (0.184) Batch 0.704 (1.129) Remain 26:35:22 loss: 0.2746 Lr: 0.00307 [2024-02-18 16:27:12,767 INFO misc.py line 119 87073] Train: [46/100][853/1557] Data 0.005 (0.183) Batch 0.726 (1.129) Remain 26:34:41 loss: 0.2351 Lr: 0.00307 [2024-02-18 16:27:14,052 INFO misc.py line 119 87073] Train: [46/100][854/1557] Data 0.009 (0.183) Batch 1.284 (1.129) Remain 26:34:55 loss: 0.1235 Lr: 0.00307 [2024-02-18 16:27:14,930 INFO misc.py line 119 87073] Train: [46/100][855/1557] Data 0.011 (0.183) Batch 0.885 (1.128) Remain 26:34:30 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[2024-02-18 16:27:46,882 INFO misc.py line 119 87073] Train: [46/100][887/1557] Data 0.004 (0.178) Batch 0.780 (1.124) Remain 26:27:15 loss: 0.3097 Lr: 0.00307 [2024-02-18 16:27:47,607 INFO misc.py line 119 87073] Train: [46/100][888/1557] Data 0.006 (0.178) Batch 0.716 (1.123) Remain 26:26:35 loss: 0.6528 Lr: 0.00307 [2024-02-18 16:27:48,799 INFO misc.py line 119 87073] Train: [46/100][889/1557] Data 0.014 (0.177) Batch 1.193 (1.123) Remain 26:26:41 loss: 0.2343 Lr: 0.00307 [2024-02-18 16:27:50,024 INFO misc.py line 119 87073] Train: [46/100][890/1557] Data 0.012 (0.177) Batch 1.224 (1.123) Remain 26:26:49 loss: 0.4583 Lr: 0.00307 [2024-02-18 16:27:51,162 INFO misc.py line 119 87073] Train: [46/100][891/1557] Data 0.014 (0.177) Batch 1.137 (1.123) Remain 26:26:49 loss: 0.5071 Lr: 0.00307 [2024-02-18 16:27:52,425 INFO misc.py line 119 87073] Train: [46/100][892/1557] Data 0.014 (0.177) Batch 1.225 (1.124) Remain 26:26:58 loss: 0.3971 Lr: 0.00307 [2024-02-18 16:27:53,554 INFO misc.py line 119 87073] Train: [46/100][893/1557] Data 0.053 (0.177) Batch 1.165 (1.124) Remain 26:27:01 loss: 0.1376 Lr: 0.00307 [2024-02-18 16:27:54,316 INFO misc.py line 119 87073] Train: [46/100][894/1557] Data 0.017 (0.177) Batch 0.774 (1.123) Remain 26:26:26 loss: 0.2834 Lr: 0.00307 [2024-02-18 16:27:55,096 INFO misc.py line 119 87073] Train: [46/100][895/1557] Data 0.005 (0.176) Batch 0.773 (1.123) Remain 26:25:52 loss: 0.4541 Lr: 0.00307 [2024-02-18 16:27:56,325 INFO misc.py line 119 87073] Train: [46/100][896/1557] Data 0.012 (0.176) Batch 1.226 (1.123) Remain 26:26:00 loss: 0.3426 Lr: 0.00307 [2024-02-18 16:27:57,086 INFO misc.py line 119 87073] Train: [46/100][897/1557] Data 0.015 (0.176) Batch 0.771 (1.123) Remain 26:25:26 loss: 0.6071 Lr: 0.00307 [2024-02-18 16:27:58,023 INFO misc.py line 119 87073] Train: [46/100][898/1557] Data 0.003 (0.176) Batch 0.936 (1.122) Remain 26:25:07 loss: 0.3883 Lr: 0.00307 [2024-02-18 16:27:58,937 INFO misc.py line 119 87073] Train: 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Batch 1.004 (1.132) Remain 26:38:28 loss: 0.2567 Lr: 0.00307 [2024-02-18 16:28:15,273 INFO misc.py line 119 87073] Train: [46/100][906/1557] Data 0.005 (0.184) Batch 0.785 (1.132) Remain 26:37:54 loss: 0.3948 Lr: 0.00307 [2024-02-18 16:28:16,329 INFO misc.py line 119 87073] Train: [46/100][907/1557] Data 0.004 (0.184) Batch 1.054 (1.131) Remain 26:37:46 loss: 0.3649 Lr: 0.00307 [2024-02-18 16:28:17,016 INFO misc.py line 119 87073] Train: [46/100][908/1557] Data 0.006 (0.184) Batch 0.688 (1.131) Remain 26:37:03 loss: 0.2606 Lr: 0.00307 [2024-02-18 16:28:17,809 INFO misc.py line 119 87073] Train: [46/100][909/1557] Data 0.005 (0.184) Batch 0.792 (1.131) Remain 26:36:31 loss: 0.4053 Lr: 0.00307 [2024-02-18 16:28:19,108 INFO misc.py line 119 87073] Train: [46/100][910/1557] Data 0.006 (0.183) Batch 1.300 (1.131) Remain 26:36:45 loss: 0.2136 Lr: 0.00307 [2024-02-18 16:28:20,177 INFO misc.py line 119 87073] Train: [46/100][911/1557] Data 0.005 (0.183) Batch 1.068 (1.131) Remain 26:36:38 loss: 0.5041 Lr: 0.00307 [2024-02-18 16:28:21,082 INFO misc.py line 119 87073] Train: [46/100][912/1557] Data 0.008 (0.183) Batch 0.906 (1.130) Remain 26:36:16 loss: 0.2629 Lr: 0.00307 [2024-02-18 16:28:22,053 INFO misc.py line 119 87073] Train: [46/100][913/1557] Data 0.004 (0.183) Batch 0.970 (1.130) Remain 26:36:00 loss: 0.6823 Lr: 0.00307 [2024-02-18 16:28:23,127 INFO misc.py line 119 87073] Train: [46/100][914/1557] Data 0.005 (0.183) Batch 1.074 (1.130) Remain 26:35:54 loss: 0.4736 Lr: 0.00307 [2024-02-18 16:28:23,919 INFO misc.py line 119 87073] Train: [46/100][915/1557] Data 0.005 (0.182) Batch 0.789 (1.130) Remain 26:35:21 loss: 0.2529 Lr: 0.00307 [2024-02-18 16:28:24,657 INFO misc.py line 119 87073] Train: [46/100][916/1557] Data 0.007 (0.182) Batch 0.740 (1.129) Remain 26:34:44 loss: 0.2530 Lr: 0.00307 [2024-02-18 16:28:26,021 INFO misc.py line 119 87073] Train: [46/100][917/1557] Data 0.006 (0.182) Batch 1.360 (1.130) Remain 26:35:04 loss: 0.1341 Lr: 0.00307 [2024-02-18 16:28:26,866 INFO misc.py line 119 87073] Train: [46/100][918/1557] Data 0.012 (0.182) Batch 0.851 (1.129) Remain 26:34:37 loss: 0.2632 Lr: 0.00307 [2024-02-18 16:28:27,754 INFO misc.py line 119 87073] Train: [46/100][919/1557] Data 0.004 (0.182) Batch 0.888 (1.129) Remain 26:34:13 loss: 0.3242 Lr: 0.00307 [2024-02-18 16:28:28,625 INFO misc.py line 119 87073] Train: [46/100][920/1557] Data 0.005 (0.181) Batch 0.870 (1.129) Remain 26:33:48 loss: 0.4664 Lr: 0.00307 [2024-02-18 16:28:29,712 INFO misc.py line 119 87073] Train: [46/100][921/1557] Data 0.005 (0.181) Batch 1.086 (1.129) Remain 26:33:43 loss: 0.5037 Lr: 0.00307 [2024-02-18 16:28:30,449 INFO misc.py line 119 87073] Train: [46/100][922/1557] Data 0.006 (0.181) Batch 0.740 (1.128) Remain 26:33:06 loss: 0.2732 Lr: 0.00307 [2024-02-18 16:28:31,230 INFO misc.py line 119 87073] Train: [46/100][923/1557] Data 0.004 (0.181) Batch 0.780 (1.128) Remain 26:32:33 loss: 0.2117 Lr: 0.00307 [2024-02-18 16:28:32,424 INFO misc.py line 119 87073] Train: [46/100][924/1557] Data 0.005 (0.181) Batch 1.154 (1.128) Remain 26:32:34 loss: 0.1242 Lr: 0.00307 [2024-02-18 16:28:33,416 INFO misc.py line 119 87073] Train: [46/100][925/1557] Data 0.044 (0.181) Batch 1.031 (1.128) Remain 26:32:24 loss: 0.2001 Lr: 0.00307 [2024-02-18 16:28:34,464 INFO misc.py line 119 87073] Train: [46/100][926/1557] Data 0.007 (0.180) Batch 1.039 (1.128) Remain 26:32:15 loss: 0.4033 Lr: 0.00307 [2024-02-18 16:28:35,366 INFO misc.py line 119 87073] Train: [46/100][927/1557] Data 0.014 (0.180) Batch 0.912 (1.128) Remain 26:31:54 loss: 0.6056 Lr: 0.00307 [2024-02-18 16:28:36,422 INFO misc.py line 119 87073] Train: [46/100][928/1557] Data 0.005 (0.180) Batch 1.056 (1.127) Remain 26:31:47 loss: 0.4802 Lr: 0.00307 [2024-02-18 16:28:37,115 INFO misc.py line 119 87073] Train: [46/100][929/1557] Data 0.004 (0.180) Batch 0.693 (1.127) Remain 26:31:06 loss: 0.5054 Lr: 0.00307 [2024-02-18 16:28:37,853 INFO misc.py line 119 87073] Train: [46/100][930/1557] Data 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[2024-02-18 16:28:49,882 INFO misc.py line 119 87073] Train: [46/100][943/1557] Data 0.011 (0.177) Batch 0.738 (1.124) Remain 26:26:19 loss: 0.3122 Lr: 0.00307 [2024-02-18 16:28:50,644 INFO misc.py line 119 87073] Train: [46/100][944/1557] Data 0.004 (0.177) Batch 0.752 (1.123) Remain 26:25:44 loss: 0.3625 Lr: 0.00307 [2024-02-18 16:28:51,867 INFO misc.py line 119 87073] Train: [46/100][945/1557] Data 0.014 (0.177) Batch 1.223 (1.124) Remain 26:25:52 loss: 0.1506 Lr: 0.00307 [2024-02-18 16:28:52,770 INFO misc.py line 119 87073] Train: [46/100][946/1557] Data 0.015 (0.177) Batch 0.912 (1.123) Remain 26:25:32 loss: 0.8273 Lr: 0.00307 [2024-02-18 16:28:53,663 INFO misc.py line 119 87073] Train: [46/100][947/1557] Data 0.005 (0.177) Batch 0.894 (1.123) Remain 26:25:10 loss: 1.1899 Lr: 0.00307 [2024-02-18 16:28:54,615 INFO misc.py line 119 87073] Train: [46/100][948/1557] Data 0.004 (0.176) Batch 0.951 (1.123) Remain 26:24:53 loss: 0.3672 Lr: 0.00307 [2024-02-18 16:28:55,699 INFO misc.py line 119 87073] Train: [46/100][949/1557] Data 0.005 (0.176) Batch 1.081 (1.123) Remain 26:24:49 loss: 0.5536 Lr: 0.00307 [2024-02-18 16:28:56,439 INFO misc.py line 119 87073] Train: [46/100][950/1557] Data 0.007 (0.176) Batch 0.744 (1.122) Remain 26:24:14 loss: 0.1423 Lr: 0.00307 [2024-02-18 16:28:57,175 INFO misc.py line 119 87073] Train: [46/100][951/1557] Data 0.004 (0.176) Batch 0.728 (1.122) Remain 26:23:37 loss: 0.4338 Lr: 0.00307 [2024-02-18 16:28:58,428 INFO misc.py line 119 87073] Train: [46/100][952/1557] Data 0.011 (0.176) Batch 1.256 (1.122) Remain 26:23:48 loss: 0.2016 Lr: 0.00307 [2024-02-18 16:28:59,371 INFO misc.py line 119 87073] Train: [46/100][953/1557] Data 0.009 (0.175) Batch 0.947 (1.122) Remain 26:23:31 loss: 0.1982 Lr: 0.00307 [2024-02-18 16:29:00,312 INFO misc.py line 119 87073] Train: [46/100][954/1557] Data 0.005 (0.175) Batch 0.943 (1.122) Remain 26:23:14 loss: 0.4870 Lr: 0.00307 [2024-02-18 16:29:01,295 INFO misc.py line 119 87073] Train: 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Batch 0.865 (1.130) Remain 26:35:23 loss: 0.3421 Lr: 0.00307 [2024-02-18 16:29:17,310 INFO misc.py line 119 87073] Train: [46/100][962/1557] Data 0.004 (0.182) Batch 0.807 (1.130) Remain 26:34:54 loss: 0.4468 Lr: 0.00307 [2024-02-18 16:29:18,322 INFO misc.py line 119 87073] Train: [46/100][963/1557] Data 0.004 (0.182) Batch 1.009 (1.130) Remain 26:34:42 loss: 0.7162 Lr: 0.00307 [2024-02-18 16:29:19,043 INFO misc.py line 119 87073] Train: [46/100][964/1557] Data 0.008 (0.182) Batch 0.725 (1.130) Remain 26:34:05 loss: 0.2642 Lr: 0.00307 [2024-02-18 16:29:19,795 INFO misc.py line 119 87073] Train: [46/100][965/1557] Data 0.003 (0.182) Batch 0.746 (1.129) Remain 26:33:30 loss: 0.3675 Lr: 0.00307 [2024-02-18 16:29:21,076 INFO misc.py line 119 87073] Train: [46/100][966/1557] Data 0.009 (0.182) Batch 1.282 (1.129) Remain 26:33:42 loss: 0.1712 Lr: 0.00307 [2024-02-18 16:29:22,035 INFO misc.py line 119 87073] Train: [46/100][967/1557] Data 0.008 (0.182) Batch 0.960 (1.129) Remain 26:33:26 loss: 0.3153 Lr: 0.00307 [2024-02-18 16:29:23,001 INFO misc.py line 119 87073] Train: [46/100][968/1557] Data 0.006 (0.181) Batch 0.968 (1.129) Remain 26:33:11 loss: 0.3632 Lr: 0.00307 [2024-02-18 16:29:23,967 INFO misc.py line 119 87073] Train: [46/100][969/1557] Data 0.006 (0.181) Batch 0.966 (1.129) Remain 26:32:56 loss: 0.4366 Lr: 0.00307 [2024-02-18 16:29:25,259 INFO misc.py line 119 87073] Train: [46/100][970/1557] Data 0.004 (0.181) Batch 1.290 (1.129) Remain 26:33:09 loss: 0.2266 Lr: 0.00307 [2024-02-18 16:29:25,987 INFO misc.py line 119 87073] Train: [46/100][971/1557] Data 0.007 (0.181) Batch 0.729 (1.129) Remain 26:32:33 loss: 0.2483 Lr: 0.00307 [2024-02-18 16:29:26,671 INFO misc.py line 119 87073] Train: [46/100][972/1557] Data 0.006 (0.181) Batch 0.682 (1.128) Remain 26:31:52 loss: 0.3893 Lr: 0.00307 [2024-02-18 16:29:27,890 INFO misc.py line 119 87073] Train: [46/100][973/1557] Data 0.008 (0.180) Batch 1.220 (1.128) Remain 26:31:59 loss: 0.1652 Lr: 0.00307 [2024-02-18 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[2024-02-18 16:31:03,086 INFO misc.py line 119 87073] Train: [46/100][1061/1557] Data 0.012 (0.176) Batch 1.083 (1.124) Remain 26:24:53 loss: 0.2434 Lr: 0.00306 [2024-02-18 16:31:03,863 INFO misc.py line 119 87073] Train: [46/100][1062/1557] Data 0.011 (0.176) Batch 0.782 (1.124) Remain 26:24:24 loss: 0.4006 Lr: 0.00306 [2024-02-18 16:31:04,589 INFO misc.py line 119 87073] Train: [46/100][1063/1557] Data 0.007 (0.176) Batch 0.723 (1.124) Remain 26:23:51 loss: 0.4230 Lr: 0.00306 [2024-02-18 16:31:05,883 INFO misc.py line 119 87073] Train: [46/100][1064/1557] Data 0.009 (0.175) Batch 1.294 (1.124) Remain 26:24:04 loss: 0.2149 Lr: 0.00306 [2024-02-18 16:31:06,913 INFO misc.py line 119 87073] Train: [46/100][1065/1557] Data 0.009 (0.175) Batch 1.033 (1.124) Remain 26:23:55 loss: 0.2357 Lr: 0.00306 [2024-02-18 16:31:07,858 INFO misc.py line 119 87073] Train: [46/100][1066/1557] Data 0.006 (0.175) Batch 0.945 (1.124) Remain 26:23:40 loss: 0.3433 Lr: 0.00306 [2024-02-18 16:31:08,741 INFO 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26:32:15 loss: 0.0984 Lr: 0.00306 [2024-02-18 16:31:37,152 INFO misc.py line 119 87073] Train: [46/100][1086/1557] Data 0.010 (0.181) Batch 1.076 (1.130) Remain 26:32:10 loss: 0.5735 Lr: 0.00306 [2024-02-18 16:31:38,159 INFO misc.py line 119 87073] Train: [46/100][1087/1557] Data 0.011 (0.180) Batch 1.006 (1.130) Remain 26:31:59 loss: 0.5912 Lr: 0.00306 [2024-02-18 16:31:39,289 INFO misc.py line 119 87073] Train: [46/100][1088/1557] Data 0.013 (0.180) Batch 1.130 (1.130) Remain 26:31:58 loss: 0.4428 Lr: 0.00306 [2024-02-18 16:31:40,230 INFO misc.py line 119 87073] Train: [46/100][1089/1557] Data 0.012 (0.180) Batch 0.950 (1.130) Remain 26:31:43 loss: 0.7820 Lr: 0.00306 [2024-02-18 16:31:41,030 INFO misc.py line 119 87073] Train: [46/100][1090/1557] Data 0.004 (0.180) Batch 0.800 (1.129) Remain 26:31:16 loss: 0.4003 Lr: 0.00306 [2024-02-18 16:31:41,780 INFO misc.py line 119 87073] Train: [46/100][1091/1557] Data 0.004 (0.180) Batch 0.747 (1.129) Remain 26:30:45 loss: 0.1966 Lr: 0.00306 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misc.py line 119 87073] Train: [46/100][1098/1557] Data 0.014 (0.179) Batch 0.789 (1.128) Remain 26:28:51 loss: 0.2637 Lr: 0.00306 [2024-02-18 16:31:49,610 INFO misc.py line 119 87073] Train: [46/100][1099/1557] Data 0.003 (0.178) Batch 1.292 (1.128) Remain 26:29:03 loss: 0.2748 Lr: 0.00306 [2024-02-18 16:31:50,740 INFO misc.py line 119 87073] Train: [46/100][1100/1557] Data 0.014 (0.178) Batch 1.129 (1.128) Remain 26:29:02 loss: 0.6064 Lr: 0.00306 [2024-02-18 16:31:51,565 INFO misc.py line 119 87073] Train: [46/100][1101/1557] Data 0.014 (0.178) Batch 0.836 (1.128) Remain 26:28:38 loss: 0.6820 Lr: 0.00306 [2024-02-18 16:31:52,693 INFO misc.py line 119 87073] Train: [46/100][1102/1557] Data 0.004 (0.178) Batch 1.128 (1.128) Remain 26:28:37 loss: 0.5191 Lr: 0.00306 [2024-02-18 16:31:53,767 INFO misc.py line 119 87073] Train: [46/100][1103/1557] Data 0.004 (0.178) Batch 1.075 (1.128) Remain 26:28:32 loss: 0.5798 Lr: 0.00306 [2024-02-18 16:31:54,472 INFO misc.py line 119 87073] Train: 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26:25:13 loss: 0.6729 Lr: 0.00306 [2024-02-18 16:32:07,038 INFO misc.py line 119 87073] Train: [46/100][1117/1557] Data 0.010 (0.176) Batch 1.032 (1.125) Remain 26:25:05 loss: 0.4109 Lr: 0.00306 [2024-02-18 16:32:07,785 INFO misc.py line 119 87073] Train: [46/100][1118/1557] Data 0.013 (0.176) Batch 0.755 (1.125) Remain 26:24:35 loss: 0.2509 Lr: 0.00306 [2024-02-18 16:32:08,526 INFO misc.py line 119 87073] Train: [46/100][1119/1557] Data 0.004 (0.175) Batch 0.734 (1.125) Remain 26:24:05 loss: 0.3841 Lr: 0.00306 [2024-02-18 16:32:09,865 INFO misc.py line 119 87073] Train: [46/100][1120/1557] Data 0.011 (0.175) Batch 1.336 (1.125) Remain 26:24:19 loss: 0.2569 Lr: 0.00306 [2024-02-18 16:32:10,668 INFO misc.py line 119 87073] Train: [46/100][1121/1557] Data 0.014 (0.175) Batch 0.814 (1.124) Remain 26:23:55 loss: 0.4889 Lr: 0.00306 [2024-02-18 16:32:11,576 INFO misc.py line 119 87073] Train: [46/100][1122/1557] Data 0.004 (0.175) Batch 0.908 (1.124) Remain 26:23:37 loss: 0.4704 Lr: 0.00306 [2024-02-18 16:32:12,484 INFO misc.py line 119 87073] Train: [46/100][1123/1557] Data 0.004 (0.175) Batch 0.899 (1.124) Remain 26:23:19 loss: 1.1483 Lr: 0.00306 [2024-02-18 16:32:13,410 INFO misc.py line 119 87073] Train: [46/100][1124/1557] Data 0.012 (0.175) Batch 0.935 (1.124) Remain 26:23:04 loss: 0.5487 Lr: 0.00306 [2024-02-18 16:32:14,198 INFO misc.py line 119 87073] Train: [46/100][1125/1557] Data 0.003 (0.175) Batch 0.787 (1.124) Remain 26:22:37 loss: 0.1760 Lr: 0.00306 [2024-02-18 16:32:14,896 INFO misc.py line 119 87073] Train: [46/100][1126/1557] Data 0.004 (0.174) Batch 0.696 (1.123) Remain 26:22:04 loss: 0.4272 Lr: 0.00306 [2024-02-18 16:32:25,479 INFO misc.py line 119 87073] Train: [46/100][1127/1557] Data 9.501 (0.183) Batch 10.587 (1.132) Remain 26:33:54 loss: 0.3819 Lr: 0.00306 [2024-02-18 16:32:26,338 INFO misc.py line 119 87073] Train: [46/100][1128/1557] Data 0.003 (0.183) Batch 0.858 (1.131) Remain 26:33:33 loss: 0.6203 Lr: 0.00306 [2024-02-18 16:32:27,243 INFO misc.py line 119 87073] Train: [46/100][1129/1557] Data 0.004 (0.182) Batch 0.903 (1.131) Remain 26:33:14 loss: 0.5888 Lr: 0.00306 [2024-02-18 16:32:28,290 INFO misc.py line 119 87073] Train: [46/100][1130/1557] Data 0.006 (0.182) Batch 1.047 (1.131) Remain 26:33:07 loss: 0.5468 Lr: 0.00306 [2024-02-18 16:32:29,256 INFO misc.py line 119 87073] Train: [46/100][1131/1557] Data 0.007 (0.182) Batch 0.968 (1.131) Remain 26:32:54 loss: 0.4325 Lr: 0.00306 [2024-02-18 16:32:29,992 INFO misc.py line 119 87073] Train: [46/100][1132/1557] Data 0.005 (0.182) Batch 0.737 (1.131) Remain 26:32:23 loss: 0.1343 Lr: 0.00306 [2024-02-18 16:32:30,765 INFO misc.py line 119 87073] Train: [46/100][1133/1557] Data 0.004 (0.182) Batch 0.767 (1.130) Remain 26:31:55 loss: 0.1356 Lr: 0.00306 [2024-02-18 16:32:31,975 INFO misc.py line 119 87073] Train: [46/100][1134/1557] Data 0.009 (0.182) Batch 1.211 (1.130) Remain 26:31:59 loss: 0.2313 Lr: 0.00306 [2024-02-18 16:32:32,852 INFO misc.py line 119 87073] Train: 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(0.181) Batch 1.227 (1.129) Remain 26:30:10 loss: 0.1400 Lr: 0.00306 [2024-02-18 16:32:39,591 INFO misc.py line 119 87073] Train: [46/100][1142/1557] Data 0.010 (0.180) Batch 1.064 (1.129) Remain 26:30:04 loss: 0.2564 Lr: 0.00306 [2024-02-18 16:32:40,447 INFO misc.py line 119 87073] Train: [46/100][1143/1557] Data 0.015 (0.180) Batch 0.868 (1.129) Remain 26:29:44 loss: 0.4531 Lr: 0.00306 [2024-02-18 16:32:41,369 INFO misc.py line 119 87073] Train: [46/100][1144/1557] Data 0.003 (0.180) Batch 0.922 (1.129) Remain 26:29:27 loss: 0.0995 Lr: 0.00306 [2024-02-18 16:32:42,301 INFO misc.py line 119 87073] Train: [46/100][1145/1557] Data 0.003 (0.180) Batch 0.927 (1.129) Remain 26:29:11 loss: 0.2312 Lr: 0.00306 [2024-02-18 16:32:43,032 INFO misc.py line 119 87073] Train: [46/100][1146/1557] Data 0.009 (0.180) Batch 0.736 (1.128) Remain 26:28:41 loss: 0.2415 Lr: 0.00306 [2024-02-18 16:32:43,778 INFO misc.py line 119 87073] Train: [46/100][1147/1557] Data 0.004 (0.180) Batch 0.738 (1.128) Remain 26:28:11 loss: 0.3185 Lr: 0.00306 [2024-02-18 16:32:44,976 INFO misc.py line 119 87073] Train: [46/100][1148/1557] Data 0.012 (0.179) Batch 1.194 (1.128) Remain 26:28:15 loss: 0.1194 Lr: 0.00306 [2024-02-18 16:32:46,230 INFO misc.py line 119 87073] Train: [46/100][1149/1557] Data 0.015 (0.179) Batch 1.255 (1.128) Remain 26:28:23 loss: 0.4472 Lr: 0.00306 [2024-02-18 16:32:47,124 INFO misc.py line 119 87073] Train: [46/100][1150/1557] Data 0.014 (0.179) Batch 0.904 (1.128) Remain 26:28:05 loss: 0.9819 Lr: 0.00306 [2024-02-18 16:32:48,060 INFO misc.py line 119 87073] Train: [46/100][1151/1557] Data 0.004 (0.179) Batch 0.936 (1.128) Remain 26:27:50 loss: 0.5797 Lr: 0.00306 [2024-02-18 16:32:49,184 INFO misc.py line 119 87073] Train: [46/100][1152/1557] Data 0.004 (0.179) Batch 1.123 (1.128) Remain 26:27:49 loss: 0.5231 Lr: 0.00306 [2024-02-18 16:32:50,004 INFO misc.py line 119 87073] Train: [46/100][1153/1557] Data 0.005 (0.179) Batch 0.819 (1.127) Remain 26:27:25 loss: 0.4434 Lr: 0.00306 [2024-02-18 16:32:50,743 INFO misc.py line 119 87073] Train: [46/100][1154/1557] Data 0.007 (0.179) Batch 0.738 (1.127) Remain 26:26:55 loss: 0.2255 Lr: 0.00306 [2024-02-18 16:32:51,987 INFO misc.py line 119 87073] Train: [46/100][1155/1557] Data 0.006 (0.178) Batch 1.242 (1.127) Remain 26:27:03 loss: 0.1014 Lr: 0.00306 [2024-02-18 16:32:52,947 INFO misc.py line 119 87073] Train: [46/100][1156/1557] Data 0.009 (0.178) Batch 0.965 (1.127) Remain 26:26:50 loss: 0.2846 Lr: 0.00306 [2024-02-18 16:32:53,795 INFO misc.py line 119 87073] Train: [46/100][1157/1557] Data 0.004 (0.178) Batch 0.848 (1.127) Remain 26:26:28 loss: 0.4371 Lr: 0.00306 [2024-02-18 16:32:54,757 INFO misc.py line 119 87073] Train: [46/100][1158/1557] Data 0.004 (0.178) Batch 0.962 (1.127) Remain 26:26:15 loss: 0.1854 Lr: 0.00306 [2024-02-18 16:32:55,699 INFO misc.py line 119 87073] Train: [46/100][1159/1557] Data 0.005 (0.178) Batch 0.935 (1.126) Remain 26:26:00 loss: 0.4514 Lr: 0.00306 [2024-02-18 16:32:56,434 INFO misc.py line 119 87073] Train: [46/100][1160/1557] Data 0.011 (0.178) Batch 0.741 (1.126) Remain 26:25:30 loss: 0.2498 Lr: 0.00306 [2024-02-18 16:32:57,231 INFO misc.py line 119 87073] Train: [46/100][1161/1557] Data 0.004 (0.178) Batch 0.792 (1.126) Remain 26:25:05 loss: 0.4678 Lr: 0.00306 [2024-02-18 16:32:58,352 INFO misc.py line 119 87073] Train: [46/100][1162/1557] Data 0.009 (0.177) Batch 1.117 (1.126) Remain 26:25:03 loss: 0.1015 Lr: 0.00306 [2024-02-18 16:32:59,369 INFO misc.py line 119 87073] Train: [46/100][1163/1557] Data 0.013 (0.177) Batch 1.021 (1.126) Remain 26:24:54 loss: 0.3228 Lr: 0.00306 [2024-02-18 16:33:00,348 INFO misc.py line 119 87073] Train: [46/100][1164/1557] Data 0.009 (0.177) Batch 0.984 (1.126) Remain 26:24:43 loss: 0.2137 Lr: 0.00306 [2024-02-18 16:33:01,361 INFO misc.py line 119 87073] Train: [46/100][1165/1557] Data 0.005 (0.177) Batch 1.013 (1.126) Remain 26:24:34 loss: 0.5020 Lr: 0.00306 [2024-02-18 16:33:02,384 INFO misc.py line 119 87073] Train: 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(0.176) Batch 0.909 (1.124) Remain 26:22:56 loss: 0.2967 Lr: 0.00305 [2024-02-18 16:33:08,853 INFO misc.py line 119 87073] Train: [46/100][1173/1557] Data 0.003 (0.176) Batch 0.842 (1.124) Remain 26:22:35 loss: 0.2150 Lr: 0.00305 [2024-02-18 16:33:09,592 INFO misc.py line 119 87073] Train: [46/100][1174/1557] Data 0.011 (0.176) Batch 0.745 (1.124) Remain 26:22:07 loss: 0.2261 Lr: 0.00305 [2024-02-18 16:33:10,325 INFO misc.py line 119 87073] Train: [46/100][1175/1557] Data 0.004 (0.175) Batch 0.733 (1.124) Remain 26:21:37 loss: 0.3918 Lr: 0.00305 [2024-02-18 16:33:11,637 INFO misc.py line 119 87073] Train: [46/100][1176/1557] Data 0.005 (0.175) Batch 1.311 (1.124) Remain 26:21:50 loss: 0.1884 Lr: 0.00305 [2024-02-18 16:33:12,741 INFO misc.py line 119 87073] Train: [46/100][1177/1557] Data 0.006 (0.175) Batch 1.100 (1.124) Remain 26:21:47 loss: 0.4145 Lr: 0.00305 [2024-02-18 16:33:13,639 INFO misc.py line 119 87073] Train: [46/100][1178/1557] Data 0.010 (0.175) Batch 0.901 (1.124) Remain 26:21:30 loss: 0.4449 Lr: 0.00305 [2024-02-18 16:33:14,624 INFO misc.py line 119 87073] Train: [46/100][1179/1557] Data 0.009 (0.175) Batch 0.988 (1.123) Remain 26:21:19 loss: 0.4140 Lr: 0.00305 [2024-02-18 16:33:15,611 INFO misc.py line 119 87073] Train: [46/100][1180/1557] Data 0.004 (0.175) Batch 0.982 (1.123) Remain 26:21:07 loss: 0.3708 Lr: 0.00305 [2024-02-18 16:33:16,320 INFO misc.py line 119 87073] Train: [46/100][1181/1557] Data 0.010 (0.175) Batch 0.707 (1.123) Remain 26:20:37 loss: 0.2901 Lr: 0.00305 [2024-02-18 16:33:17,041 INFO misc.py line 119 87073] Train: [46/100][1182/1557] Data 0.010 (0.175) Batch 0.728 (1.123) Remain 26:20:07 loss: 0.2633 Lr: 0.00305 [2024-02-18 16:33:29,280 INFO misc.py line 119 87073] Train: [46/100][1183/1557] Data 11.034 (0.184) Batch 12.233 (1.132) Remain 26:33:21 loss: 0.2875 Lr: 0.00305 [2024-02-18 16:33:30,220 INFO misc.py line 119 87073] Train: [46/100][1184/1557] Data 0.010 (0.184) Batch 0.945 (1.132) Remain 26:33:07 loss: 0.2281 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1191/1557] Data 0.004 (0.183) Batch 1.034 (1.131) Remain 26:31:15 loss: 0.3959 Lr: 0.00305 [2024-02-18 16:33:37,665 INFO misc.py line 119 87073] Train: [46/100][1192/1557] Data 0.011 (0.182) Batch 0.985 (1.131) Remain 26:31:03 loss: 1.3073 Lr: 0.00305 [2024-02-18 16:33:38,560 INFO misc.py line 119 87073] Train: [46/100][1193/1557] Data 0.005 (0.182) Batch 0.895 (1.130) Remain 26:30:45 loss: 0.3732 Lr: 0.00305 [2024-02-18 16:33:39,384 INFO misc.py line 119 87073] Train: [46/100][1194/1557] Data 0.005 (0.182) Batch 0.821 (1.130) Remain 26:30:22 loss: 0.1821 Lr: 0.00305 [2024-02-18 16:33:40,128 INFO misc.py line 119 87073] Train: [46/100][1195/1557] Data 0.008 (0.182) Batch 0.748 (1.130) Remain 26:29:54 loss: 0.2603 Lr: 0.00305 [2024-02-18 16:33:40,873 INFO misc.py line 119 87073] Train: [46/100][1196/1557] Data 0.006 (0.182) Batch 0.740 (1.129) Remain 26:29:25 loss: 0.2193 Lr: 0.00305 [2024-02-18 16:33:42,069 INFO misc.py line 119 87073] Train: [46/100][1197/1557] Data 0.009 (0.182) Batch 1.199 (1.129) Remain 26:29:29 loss: 0.1253 Lr: 0.00305 [2024-02-18 16:33:43,003 INFO misc.py line 119 87073] Train: [46/100][1198/1557] Data 0.005 (0.181) Batch 0.934 (1.129) Remain 26:29:14 loss: 0.7584 Lr: 0.00305 [2024-02-18 16:33:43,965 INFO misc.py line 119 87073] Train: [46/100][1199/1557] Data 0.005 (0.181) Batch 0.962 (1.129) Remain 26:29:01 loss: 0.1840 Lr: 0.00305 [2024-02-18 16:33:44,854 INFO misc.py line 119 87073] Train: [46/100][1200/1557] Data 0.006 (0.181) Batch 0.890 (1.129) Remain 26:28:43 loss: 1.0233 Lr: 0.00305 [2024-02-18 16:33:45,842 INFO misc.py line 119 87073] Train: [46/100][1201/1557] Data 0.004 (0.181) Batch 0.983 (1.129) Remain 26:28:32 loss: 0.4062 Lr: 0.00305 [2024-02-18 16:33:46,520 INFO misc.py line 119 87073] Train: [46/100][1202/1557] Data 0.010 (0.181) Batch 0.683 (1.128) Remain 26:27:59 loss: 0.2246 Lr: 0.00305 [2024-02-18 16:33:47,256 INFO misc.py line 119 87073] Train: [46/100][1203/1557] Data 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Remain 26:26:22 loss: 0.2790 Lr: 0.00305 [2024-02-18 16:33:53,841 INFO misc.py line 119 87073] Train: [46/100][1210/1557] Data 0.004 (0.180) Batch 0.704 (1.127) Remain 26:25:51 loss: 0.9559 Lr: 0.00305 [2024-02-18 16:33:55,129 INFO misc.py line 119 87073] Train: [46/100][1211/1557] Data 0.004 (0.180) Batch 1.288 (1.127) Remain 26:26:01 loss: 0.1187 Lr: 0.00305 [2024-02-18 16:33:56,270 INFO misc.py line 119 87073] Train: [46/100][1212/1557] Data 0.004 (0.179) Batch 1.132 (1.127) Remain 26:26:00 loss: 0.5769 Lr: 0.00305 [2024-02-18 16:33:57,259 INFO misc.py line 119 87073] Train: [46/100][1213/1557] Data 0.014 (0.179) Batch 0.999 (1.127) Remain 26:25:50 loss: 0.4860 Lr: 0.00305 [2024-02-18 16:33:58,261 INFO misc.py line 119 87073] Train: [46/100][1214/1557] Data 0.003 (0.179) Batch 1.000 (1.127) Remain 26:25:40 loss: 0.5123 Lr: 0.00305 [2024-02-18 16:33:59,218 INFO misc.py line 119 87073] Train: [46/100][1215/1557] Data 0.005 (0.179) Batch 0.958 (1.127) Remain 26:25:27 loss: 0.9478 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1222/1557] Data 0.005 (0.178) Batch 1.031 (1.126) Remain 26:23:52 loss: 0.2664 Lr: 0.00305 [2024-02-18 16:34:06,615 INFO misc.py line 119 87073] Train: [46/100][1223/1557] Data 0.003 (0.178) Batch 0.768 (1.126) Remain 26:23:27 loss: 0.5417 Lr: 0.00305 [2024-02-18 16:34:07,431 INFO misc.py line 119 87073] Train: [46/100][1224/1557] Data 0.004 (0.178) Batch 0.787 (1.125) Remain 26:23:02 loss: 0.2758 Lr: 0.00305 [2024-02-18 16:34:08,634 INFO misc.py line 119 87073] Train: [46/100][1225/1557] Data 0.032 (0.178) Batch 1.224 (1.125) Remain 26:23:08 loss: 0.1694 Lr: 0.00305 [2024-02-18 16:34:09,537 INFO misc.py line 119 87073] Train: [46/100][1226/1557] Data 0.012 (0.177) Batch 0.911 (1.125) Remain 26:22:52 loss: 0.7780 Lr: 0.00305 [2024-02-18 16:34:10,629 INFO misc.py line 119 87073] Train: [46/100][1227/1557] Data 0.004 (0.177) Batch 1.092 (1.125) Remain 26:22:48 loss: 0.4111 Lr: 0.00305 [2024-02-18 16:34:11,601 INFO misc.py line 119 87073] Train: [46/100][1228/1557] Data 0.004 (0.177) Batch 0.972 (1.125) Remain 26:22:37 loss: 0.2024 Lr: 0.00305 [2024-02-18 16:34:12,516 INFO misc.py line 119 87073] Train: [46/100][1229/1557] Data 0.004 (0.177) Batch 0.914 (1.125) Remain 26:22:21 loss: 0.3617 Lr: 0.00305 [2024-02-18 16:34:14,604 INFO misc.py line 119 87073] Train: [46/100][1230/1557] Data 1.169 (0.178) Batch 2.085 (1.126) Remain 26:23:26 loss: 0.2191 Lr: 0.00305 [2024-02-18 16:34:15,303 INFO misc.py line 119 87073] Train: [46/100][1231/1557] Data 0.008 (0.178) Batch 0.703 (1.125) Remain 26:22:56 loss: 0.2698 Lr: 0.00305 [2024-02-18 16:34:16,651 INFO misc.py line 119 87073] Train: [46/100][1232/1557] Data 0.003 (0.178) Batch 1.337 (1.125) Remain 26:23:09 loss: 0.2294 Lr: 0.00305 [2024-02-18 16:34:17,557 INFO misc.py line 119 87073] Train: [46/100][1233/1557] Data 0.014 (0.177) Batch 0.917 (1.125) Remain 26:22:54 loss: 0.6726 Lr: 0.00305 [2024-02-18 16:34:18,356 INFO misc.py line 119 87073] Train: [46/100][1234/1557] Data 0.003 (0.177) Batch 0.799 (1.125) Remain 26:22:30 loss: 0.3107 Lr: 0.00305 [2024-02-18 16:34:19,267 INFO misc.py line 119 87073] Train: [46/100][1235/1557] Data 0.003 (0.177) Batch 0.908 (1.125) Remain 26:22:14 loss: 0.6428 Lr: 0.00305 [2024-02-18 16:34:20,294 INFO misc.py line 119 87073] Train: [46/100][1236/1557] Data 0.006 (0.177) Batch 1.023 (1.125) Remain 26:22:06 loss: 0.4113 Lr: 0.00305 [2024-02-18 16:34:21,045 INFO misc.py line 119 87073] Train: [46/100][1237/1557] Data 0.009 (0.177) Batch 0.757 (1.124) Remain 26:21:40 loss: 0.3280 Lr: 0.00305 [2024-02-18 16:34:21,776 INFO misc.py line 119 87073] Train: [46/100][1238/1557] Data 0.004 (0.177) Batch 0.724 (1.124) Remain 26:21:11 loss: 0.3082 Lr: 0.00305 [2024-02-18 16:34:33,453 INFO misc.py line 119 87073] Train: [46/100][1239/1557] Data 9.544 (0.184) Batch 11.684 (1.133) Remain 26:33:11 loss: 0.3494 Lr: 0.00305 [2024-02-18 16:34:34,272 INFO misc.py line 119 87073] Train: [46/100][1240/1557] Data 0.003 (0.184) Batch 0.818 (1.132) Remain 26:32:49 loss: 0.2840 Lr: 0.00305 [2024-02-18 16:34:35,224 INFO misc.py line 119 87073] Train: [46/100][1241/1557] Data 0.004 (0.184) Batch 0.952 (1.132) Remain 26:32:35 loss: 0.3465 Lr: 0.00305 [2024-02-18 16:34:36,026 INFO misc.py line 119 87073] Train: [46/100][1242/1557] Data 0.004 (0.184) Batch 0.803 (1.132) Remain 26:32:12 loss: 0.5080 Lr: 0.00305 [2024-02-18 16:34:37,054 INFO misc.py line 119 87073] Train: [46/100][1243/1557] Data 0.003 (0.184) Batch 1.028 (1.132) Remain 26:32:04 loss: 0.2432 Lr: 0.00305 [2024-02-18 16:34:37,795 INFO misc.py line 119 87073] Train: [46/100][1244/1557] Data 0.003 (0.184) Batch 0.740 (1.132) Remain 26:31:36 loss: 0.4582 Lr: 0.00305 [2024-02-18 16:34:38,584 INFO misc.py line 119 87073] Train: [46/100][1245/1557] Data 0.005 (0.183) Batch 0.786 (1.131) Remain 26:31:11 loss: 0.2124 Lr: 0.00305 [2024-02-18 16:34:39,823 INFO misc.py line 119 87073] Train: [46/100][1246/1557] Data 0.006 (0.183) Batch 1.233 (1.131) Remain 26:31:17 loss: 0.1500 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1253/1557] Data 0.004 (0.182) Batch 1.250 (1.130) Remain 26:29:29 loss: 0.1124 Lr: 0.00305 [2024-02-18 16:34:47,205 INFO misc.py line 119 87073] Train: [46/100][1254/1557] Data 0.012 (0.182) Batch 0.948 (1.130) Remain 26:29:16 loss: 0.5882 Lr: 0.00305 [2024-02-18 16:34:48,017 INFO misc.py line 119 87073] Train: [46/100][1255/1557] Data 0.004 (0.182) Batch 0.812 (1.130) Remain 26:28:53 loss: 0.4230 Lr: 0.00305 [2024-02-18 16:34:49,034 INFO misc.py line 119 87073] Train: [46/100][1256/1557] Data 0.004 (0.182) Batch 0.956 (1.130) Remain 26:28:41 loss: 0.5684 Lr: 0.00305 [2024-02-18 16:34:49,990 INFO misc.py line 119 87073] Train: [46/100][1257/1557] Data 0.065 (0.182) Batch 1.017 (1.130) Remain 26:28:32 loss: 0.5035 Lr: 0.00305 [2024-02-18 16:34:50,702 INFO misc.py line 119 87073] Train: [46/100][1258/1557] Data 0.003 (0.182) Batch 0.710 (1.129) Remain 26:28:03 loss: 0.3159 Lr: 0.00305 [2024-02-18 16:34:51,515 INFO misc.py line 119 87073] Train: [46/100][1259/1557] Data 0.006 (0.182) Batch 0.810 (1.129) Remain 26:27:40 loss: 0.2280 Lr: 0.00305 [2024-02-18 16:34:52,579 INFO misc.py line 119 87073] Train: [46/100][1260/1557] Data 0.008 (0.181) Batch 1.067 (1.129) Remain 26:27:35 loss: 0.1637 Lr: 0.00305 [2024-02-18 16:34:53,501 INFO misc.py line 119 87073] Train: [46/100][1261/1557] Data 0.006 (0.181) Batch 0.921 (1.129) Remain 26:27:20 loss: 0.6471 Lr: 0.00305 [2024-02-18 16:34:54,383 INFO misc.py line 119 87073] Train: [46/100][1262/1557] Data 0.008 (0.181) Batch 0.885 (1.129) Remain 26:27:02 loss: 0.4443 Lr: 0.00305 [2024-02-18 16:34:55,282 INFO misc.py line 119 87073] Train: [46/100][1263/1557] Data 0.003 (0.181) Batch 0.893 (1.128) Remain 26:26:45 loss: 0.5760 Lr: 0.00305 [2024-02-18 16:34:56,312 INFO misc.py line 119 87073] Train: [46/100][1264/1557] Data 0.009 (0.181) Batch 1.030 (1.128) Remain 26:26:38 loss: 0.1163 Lr: 0.00305 [2024-02-18 16:34:57,077 INFO misc.py line 119 87073] Train: [46/100][1265/1557] Data 0.010 (0.181) Batch 0.771 (1.128) Remain 26:26:13 loss: 0.3096 Lr: 0.00305 [2024-02-18 16:34:57,845 INFO misc.py line 119 87073] Train: [46/100][1266/1557] Data 0.003 (0.181) Batch 0.757 (1.128) Remain 26:25:47 loss: 0.4704 Lr: 0.00305 [2024-02-18 16:34:59,127 INFO misc.py line 119 87073] Train: [46/100][1267/1557] Data 0.014 (0.180) Batch 1.286 (1.128) Remain 26:25:56 loss: 0.1721 Lr: 0.00305 [2024-02-18 16:35:00,016 INFO misc.py line 119 87073] Train: [46/100][1268/1557] Data 0.011 (0.180) Batch 0.896 (1.128) Remain 26:25:39 loss: 0.5482 Lr: 0.00305 [2024-02-18 16:35:00,940 INFO misc.py line 119 87073] Train: [46/100][1269/1557] Data 0.004 (0.180) Batch 0.921 (1.128) Remain 26:25:25 loss: 0.4325 Lr: 0.00305 [2024-02-18 16:35:01,920 INFO misc.py line 119 87073] Train: [46/100][1270/1557] Data 0.007 (0.180) Batch 0.983 (1.127) Remain 26:25:14 loss: 0.4681 Lr: 0.00305 [2024-02-18 16:35:02,880 INFO misc.py line 119 87073] Train: [46/100][1271/1557] Data 0.005 (0.180) Batch 0.960 (1.127) Remain 26:25:01 loss: 0.9455 Lr: 0.00305 [2024-02-18 16:35:03,608 INFO misc.py line 119 87073] Train: [46/100][1272/1557] Data 0.005 (0.180) Batch 0.720 (1.127) Remain 26:24:33 loss: 0.1427 Lr: 0.00305 [2024-02-18 16:35:04,397 INFO misc.py line 119 87073] Train: [46/100][1273/1557] Data 0.012 (0.180) Batch 0.797 (1.127) Remain 26:24:10 loss: 0.3268 Lr: 0.00305 [2024-02-18 16:35:05,552 INFO misc.py line 119 87073] Train: [46/100][1274/1557] Data 0.004 (0.179) Batch 1.154 (1.127) Remain 26:24:11 loss: 0.1029 Lr: 0.00305 [2024-02-18 16:35:06,554 INFO misc.py line 119 87073] Train: [46/100][1275/1557] Data 0.005 (0.179) Batch 1.001 (1.127) Remain 26:24:02 loss: 0.5719 Lr: 0.00305 [2024-02-18 16:35:07,788 INFO misc.py line 119 87073] Train: [46/100][1276/1557] Data 0.005 (0.179) Batch 1.226 (1.127) Remain 26:24:07 loss: 0.6807 Lr: 0.00305 [2024-02-18 16:35:08,938 INFO misc.py line 119 87073] Train: [46/100][1277/1557] Data 0.013 (0.179) Batch 1.150 (1.127) Remain 26:24:07 loss: 0.1153 Lr: 0.00305 [2024-02-18 16:35:09,945 INFO misc.py line 119 87073] Train: [46/100][1278/1557] Data 0.013 (0.179) Batch 1.011 (1.127) Remain 26:23:59 loss: 0.2301 Lr: 0.00305 [2024-02-18 16:35:10,627 INFO misc.py line 119 87073] Train: [46/100][1279/1557] Data 0.009 (0.179) Batch 0.686 (1.126) Remain 26:23:28 loss: 0.4068 Lr: 0.00305 [2024-02-18 16:35:11,390 INFO misc.py line 119 87073] Train: [46/100][1280/1557] Data 0.004 (0.179) Batch 0.757 (1.126) Remain 26:23:03 loss: 0.1445 Lr: 0.00305 [2024-02-18 16:35:12,582 INFO misc.py line 119 87073] Train: [46/100][1281/1557] Data 0.011 (0.179) Batch 1.192 (1.126) Remain 26:23:06 loss: 0.3237 Lr: 0.00305 [2024-02-18 16:35:13,745 INFO misc.py line 119 87073] Train: [46/100][1282/1557] Data 0.012 (0.178) Batch 1.164 (1.126) Remain 26:23:07 loss: 0.4426 Lr: 0.00305 [2024-02-18 16:35:14,683 INFO misc.py line 119 87073] Train: [46/100][1283/1557] Data 0.010 (0.178) Batch 0.945 (1.126) Remain 26:22:54 loss: 0.7070 Lr: 0.00305 [2024-02-18 16:35:15,552 INFO misc.py line 119 87073] Train: [46/100][1284/1557] Data 0.004 (0.178) Batch 0.868 (1.126) Remain 26:22:36 loss: 0.3871 Lr: 0.00305 [2024-02-18 16:35:16,750 INFO misc.py line 119 87073] Train: [46/100][1285/1557] Data 0.005 (0.178) Batch 1.197 (1.126) Remain 26:22:40 loss: 0.4727 Lr: 0.00305 [2024-02-18 16:35:17,595 INFO misc.py line 119 87073] Train: [46/100][1286/1557] Data 0.005 (0.178) Batch 0.846 (1.126) Remain 26:22:20 loss: 0.3325 Lr: 0.00305 [2024-02-18 16:35:18,347 INFO misc.py line 119 87073] Train: [46/100][1287/1557] Data 0.005 (0.178) Batch 0.750 (1.125) Remain 26:21:55 loss: 0.2558 Lr: 0.00305 [2024-02-18 16:35:19,562 INFO misc.py line 119 87073] Train: [46/100][1288/1557] Data 0.006 (0.178) Batch 1.216 (1.125) Remain 26:21:59 loss: 0.1617 Lr: 0.00305 [2024-02-18 16:35:20,518 INFO misc.py line 119 87073] Train: [46/100][1289/1557] Data 0.005 (0.177) Batch 0.957 (1.125) Remain 26:21:47 loss: 0.5633 Lr: 0.00305 [2024-02-18 16:35:21,423 INFO misc.py line 119 87073] Train: [46/100][1290/1557] Data 0.004 (0.177) Batch 0.904 (1.125) Remain 26:21:32 loss: 0.9267 Lr: 0.00305 [2024-02-18 16:35:22,437 INFO misc.py line 119 87073] Train: [46/100][1291/1557] Data 0.006 (0.177) Batch 1.016 (1.125) Remain 26:21:23 loss: 0.3043 Lr: 0.00305 [2024-02-18 16:35:23,467 INFO misc.py line 119 87073] Train: [46/100][1292/1557] Data 0.003 (0.177) Batch 1.030 (1.125) Remain 26:21:16 loss: 0.4861 Lr: 0.00305 [2024-02-18 16:35:24,188 INFO misc.py line 119 87073] Train: [46/100][1293/1557] Data 0.004 (0.177) Batch 0.718 (1.125) Remain 26:20:48 loss: 0.2913 Lr: 0.00305 [2024-02-18 16:35:24,944 INFO misc.py line 119 87073] Train: [46/100][1294/1557] Data 0.006 (0.177) Batch 0.757 (1.124) Remain 26:20:23 loss: 0.3394 Lr: 0.00305 [2024-02-18 16:35:35,742 INFO misc.py line 119 87073] Train: [46/100][1295/1557] Data 9.002 (0.184) Batch 10.799 (1.132) Remain 26:30:54 loss: 0.2427 Lr: 0.00305 [2024-02-18 16:35:36,759 INFO misc.py line 119 87073] Train: [46/100][1296/1557] Data 0.004 (0.183) Batch 1.016 (1.132) Remain 26:30:45 loss: 0.6534 Lr: 0.00305 [2024-02-18 16:35:37,692 INFO misc.py line 119 87073] Train: [46/100][1297/1557] Data 0.005 (0.183) Batch 0.934 (1.132) Remain 26:30:31 loss: 1.3249 Lr: 0.00305 [2024-02-18 16:35:38,635 INFO misc.py line 119 87073] Train: [46/100][1298/1557] Data 0.003 (0.183) Batch 0.942 (1.131) Remain 26:30:17 loss: 0.3987 Lr: 0.00305 [2024-02-18 16:35:39,410 INFO misc.py line 119 87073] Train: [46/100][1299/1557] Data 0.005 (0.183) Batch 0.775 (1.131) Remain 26:29:53 loss: 0.3876 Lr: 0.00305 [2024-02-18 16:35:40,160 INFO misc.py line 119 87073] Train: [46/100][1300/1557] Data 0.005 (0.183) Batch 0.750 (1.131) Remain 26:29:27 loss: 0.3531 Lr: 0.00305 [2024-02-18 16:35:40,870 INFO misc.py line 119 87073] Train: [46/100][1301/1557] Data 0.005 (0.183) Batch 0.705 (1.130) Remain 26:28:58 loss: 0.3638 Lr: 0.00305 [2024-02-18 16:35:42,139 INFO misc.py line 119 87073] Train: [46/100][1302/1557] Data 0.009 (0.183) Batch 1.270 (1.131) Remain 26:29:06 loss: 0.1255 Lr: 0.00305 [2024-02-18 16:35:43,011 INFO misc.py line 119 87073] Train: [46/100][1303/1557] Data 0.008 (0.183) Batch 0.876 (1.130) Remain 26:28:49 loss: 0.3077 Lr: 0.00305 [2024-02-18 16:35:43,835 INFO misc.py line 119 87073] Train: [46/100][1304/1557] Data 0.004 (0.182) Batch 0.825 (1.130) Remain 26:28:28 loss: 0.5286 Lr: 0.00305 [2024-02-18 16:35:44,883 INFO misc.py line 119 87073] Train: [46/100][1305/1557] Data 0.004 (0.182) Batch 1.044 (1.130) Remain 26:28:21 loss: 0.7932 Lr: 0.00305 [2024-02-18 16:35:45,811 INFO misc.py line 119 87073] Train: [46/100][1306/1557] Data 0.007 (0.182) Batch 0.931 (1.130) Remain 26:28:07 loss: 0.3699 Lr: 0.00305 [2024-02-18 16:35:46,512 INFO misc.py line 119 87073] Train: [46/100][1307/1557] Data 0.005 (0.182) Batch 0.694 (1.130) Remain 26:27:38 loss: 0.1485 Lr: 0.00305 [2024-02-18 16:35:47,283 INFO misc.py line 119 87073] Train: [46/100][1308/1557] Data 0.010 (0.182) Batch 0.775 (1.129) Remain 26:27:14 loss: 0.2213 Lr: 0.00305 [2024-02-18 16:35:48,509 INFO misc.py line 119 87073] Train: [46/100][1309/1557] Data 0.007 (0.182) Batch 1.225 (1.129) Remain 26:27:19 loss: 0.1417 Lr: 0.00305 [2024-02-18 16:35:49,387 INFO misc.py line 119 87073] Train: [46/100][1310/1557] Data 0.007 (0.182) Batch 0.882 (1.129) Remain 26:27:02 loss: 1.0020 Lr: 0.00305 [2024-02-18 16:35:50,310 INFO misc.py line 119 87073] Train: [46/100][1311/1557] Data 0.004 (0.181) Batch 0.922 (1.129) Remain 26:26:47 loss: 0.2394 Lr: 0.00305 [2024-02-18 16:35:51,309 INFO misc.py line 119 87073] Train: [46/100][1312/1557] Data 0.005 (0.181) Batch 0.997 (1.129) Remain 26:26:38 loss: 0.5642 Lr: 0.00305 [2024-02-18 16:35:52,381 INFO misc.py line 119 87073] Train: [46/100][1313/1557] Data 0.007 (0.181) Batch 1.072 (1.129) Remain 26:26:33 loss: 0.5918 Lr: 0.00305 [2024-02-18 16:35:53,132 INFO misc.py line 119 87073] Train: [46/100][1314/1557] Data 0.006 (0.181) Batch 0.752 (1.129) Remain 26:26:07 loss: 0.2837 Lr: 0.00305 [2024-02-18 16:35:53,902 INFO misc.py line 119 87073] Train: [46/100][1315/1557] Data 0.006 (0.181) Batch 0.770 (1.128) Remain 26:25:43 loss: 0.3031 Lr: 0.00305 [2024-02-18 16:35:55,061 INFO misc.py line 119 87073] Train: [46/100][1316/1557] Data 0.005 (0.181) Batch 1.157 (1.128) Remain 26:25:44 loss: 0.1178 Lr: 0.00305 [2024-02-18 16:35:55,930 INFO misc.py line 119 87073] Train: [46/100][1317/1557] Data 0.007 (0.181) Batch 0.872 (1.128) Remain 26:25:26 loss: 0.4740 Lr: 0.00305 [2024-02-18 16:35:56,874 INFO misc.py line 119 87073] Train: [46/100][1318/1557] Data 0.003 (0.181) Batch 0.932 (1.128) Remain 26:25:13 loss: 0.4652 Lr: 0.00305 [2024-02-18 16:35:57,908 INFO misc.py line 119 87073] Train: [46/100][1319/1557] Data 0.015 (0.180) Batch 1.042 (1.128) Remain 26:25:06 loss: 0.3210 Lr: 0.00305 [2024-02-18 16:35:58,883 INFO misc.py line 119 87073] Train: [46/100][1320/1557] Data 0.007 (0.180) Batch 0.978 (1.128) Remain 26:24:55 loss: 0.2392 Lr: 0.00305 [2024-02-18 16:35:59,655 INFO misc.py line 119 87073] Train: [46/100][1321/1557] Data 0.004 (0.180) Batch 0.772 (1.128) Remain 26:24:31 loss: 0.1306 Lr: 0.00305 [2024-02-18 16:36:00,354 INFO misc.py line 119 87073] Train: [46/100][1322/1557] Data 0.004 (0.180) Batch 0.693 (1.127) Remain 26:24:03 loss: 0.4160 Lr: 0.00305 [2024-02-18 16:36:01,657 INFO misc.py line 119 87073] Train: [46/100][1323/1557] Data 0.009 (0.180) Batch 1.303 (1.127) Remain 26:24:13 loss: 0.1299 Lr: 0.00305 [2024-02-18 16:36:02,546 INFO misc.py line 119 87073] Train: [46/100][1324/1557] Data 0.010 (0.180) Batch 0.895 (1.127) Remain 26:23:57 loss: 0.1679 Lr: 0.00305 [2024-02-18 16:36:03,530 INFO misc.py line 119 87073] Train: [46/100][1325/1557] Data 0.004 (0.180) Batch 0.983 (1.127) Remain 26:23:46 loss: 0.6487 Lr: 0.00305 [2024-02-18 16:36:04,459 INFO misc.py line 119 87073] Train: [46/100][1326/1557] Data 0.005 (0.179) Batch 0.930 (1.127) Remain 26:23:33 loss: 0.3763 Lr: 0.00305 [2024-02-18 16:36:05,456 INFO misc.py line 119 87073] Train: [46/100][1327/1557] Data 0.003 (0.179) Batch 0.995 (1.127) Remain 26:23:23 loss: 0.7511 Lr: 0.00305 [2024-02-18 16:36:06,246 INFO misc.py line 119 87073] Train: [46/100][1328/1557] Data 0.005 (0.179) Batch 0.792 (1.127) Remain 26:23:01 loss: 0.3964 Lr: 0.00305 [2024-02-18 16:36:07,015 INFO misc.py line 119 87073] Train: [46/100][1329/1557] Data 0.003 (0.179) Batch 0.768 (1.126) Remain 26:22:37 loss: 0.3278 Lr: 0.00305 [2024-02-18 16:36:08,138 INFO misc.py line 119 87073] Train: [46/100][1330/1557] Data 0.004 (0.179) Batch 1.114 (1.126) Remain 26:22:35 loss: 0.1281 Lr: 0.00305 [2024-02-18 16:36:09,055 INFO misc.py line 119 87073] Train: [46/100][1331/1557] Data 0.013 (0.179) Batch 0.927 (1.126) Remain 26:22:21 loss: 0.8562 Lr: 0.00305 [2024-02-18 16:36:09,993 INFO misc.py line 119 87073] Train: [46/100][1332/1557] Data 0.004 (0.179) Batch 0.937 (1.126) Remain 26:22:08 loss: 0.8888 Lr: 0.00305 [2024-02-18 16:36:10,989 INFO misc.py line 119 87073] Train: [46/100][1333/1557] Data 0.005 (0.179) Batch 0.996 (1.126) Remain 26:21:59 loss: 0.4399 Lr: 0.00305 [2024-02-18 16:36:11,956 INFO misc.py line 119 87073] Train: [46/100][1334/1557] Data 0.004 (0.178) Batch 0.967 (1.126) Remain 26:21:47 loss: 0.2328 Lr: 0.00305 [2024-02-18 16:36:12,712 INFO misc.py line 119 87073] Train: [46/100][1335/1557] Data 0.004 (0.178) Batch 0.751 (1.126) Remain 26:21:23 loss: 0.3054 Lr: 0.00305 [2024-02-18 16:36:13,509 INFO misc.py line 119 87073] Train: [46/100][1336/1557] Data 0.010 (0.178) Batch 0.803 (1.125) Remain 26:21:01 loss: 0.5293 Lr: 0.00305 [2024-02-18 16:36:14,777 INFO misc.py line 119 87073] Train: [46/100][1337/1557] Data 0.003 (0.178) Batch 1.260 (1.125) Remain 26:21:08 loss: 0.4711 Lr: 0.00305 [2024-02-18 16:36:15,623 INFO misc.py line 119 87073] Train: [46/100][1338/1557] Data 0.012 (0.178) Batch 0.853 (1.125) Remain 26:20:50 loss: 0.6372 Lr: 0.00305 [2024-02-18 16:36:16,503 INFO misc.py line 119 87073] Train: [46/100][1339/1557] Data 0.004 (0.178) Batch 0.880 (1.125) Remain 26:20:34 loss: 0.1427 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1346/1557] Data 0.004 (0.177) Batch 1.003 (1.124) Remain 26:19:05 loss: 0.4138 Lr: 0.00305 [2024-02-18 16:36:24,209 INFO misc.py line 119 87073] Train: [46/100][1347/1557] Data 0.004 (0.177) Batch 1.117 (1.124) Remain 26:19:03 loss: 0.6495 Lr: 0.00305 [2024-02-18 16:36:25,036 INFO misc.py line 119 87073] Train: [46/100][1348/1557] Data 0.003 (0.177) Batch 0.827 (1.124) Remain 26:18:44 loss: 0.3571 Lr: 0.00305 [2024-02-18 16:36:25,809 INFO misc.py line 119 87073] Train: [46/100][1349/1557] Data 0.004 (0.176) Batch 0.771 (1.124) Remain 26:18:20 loss: 0.5242 Lr: 0.00305 [2024-02-18 16:36:26,561 INFO misc.py line 119 87073] Train: [46/100][1350/1557] Data 0.006 (0.176) Batch 0.755 (1.123) Remain 26:17:56 loss: 0.2972 Lr: 0.00305 [2024-02-18 16:36:37,514 INFO misc.py line 119 87073] Train: [46/100][1351/1557] Data 9.071 (0.183) Batch 10.946 (1.131) Remain 26:28:09 loss: 0.3394 Lr: 0.00305 [2024-02-18 16:36:38,414 INFO misc.py line 119 87073] Train: [46/100][1352/1557] Data 0.010 (0.183) Batch 0.907 (1.130) Remain 26:27:54 loss: 0.2866 Lr: 0.00305 [2024-02-18 16:36:39,284 INFO misc.py line 119 87073] Train: [46/100][1353/1557] Data 0.003 (0.183) Batch 0.869 (1.130) Remain 26:27:37 loss: 0.4253 Lr: 0.00305 [2024-02-18 16:36:40,487 INFO misc.py line 119 87073] Train: [46/100][1354/1557] Data 0.004 (0.183) Batch 1.200 (1.130) Remain 26:27:40 loss: 0.5580 Lr: 0.00305 [2024-02-18 16:36:41,507 INFO misc.py line 119 87073] Train: [46/100][1355/1557] Data 0.007 (0.182) Batch 1.017 (1.130) Remain 26:27:32 loss: 0.7869 Lr: 0.00305 [2024-02-18 16:36:42,250 INFO misc.py line 119 87073] Train: [46/100][1356/1557] Data 0.010 (0.182) Batch 0.749 (1.130) Remain 26:27:07 loss: 0.2307 Lr: 0.00305 [2024-02-18 16:36:42,997 INFO misc.py line 119 87073] Train: [46/100][1357/1557] Data 0.004 (0.182) Batch 0.744 (1.130) Remain 26:26:42 loss: 0.1816 Lr: 0.00305 [2024-02-18 16:36:44,181 INFO misc.py line 119 87073] Train: [46/100][1358/1557] Data 0.007 (0.182) Batch 1.181 (1.130) Remain 26:26:44 loss: 0.2684 Lr: 0.00305 [2024-02-18 16:36:45,125 INFO misc.py line 119 87073] Train: [46/100][1359/1557] Data 0.011 (0.182) Batch 0.951 (1.130) Remain 26:26:32 loss: 1.2682 Lr: 0.00305 [2024-02-18 16:36:46,116 INFO misc.py line 119 87073] Train: [46/100][1360/1557] Data 0.004 (0.182) Batch 0.991 (1.129) Remain 26:26:22 loss: 0.6306 Lr: 0.00305 [2024-02-18 16:36:47,048 INFO misc.py line 119 87073] Train: [46/100][1361/1557] Data 0.004 (0.182) Batch 0.933 (1.129) Remain 26:26:08 loss: 0.3663 Lr: 0.00305 [2024-02-18 16:36:48,058 INFO misc.py line 119 87073] Train: [46/100][1362/1557] Data 0.004 (0.182) Batch 1.009 (1.129) Remain 26:26:00 loss: 0.2768 Lr: 0.00304 [2024-02-18 16:36:48,767 INFO misc.py line 119 87073] Train: [46/100][1363/1557] Data 0.003 (0.181) Batch 0.697 (1.129) Remain 26:25:32 loss: 0.2691 Lr: 0.00304 [2024-02-18 16:36:49,457 INFO misc.py line 119 87073] Train: [46/100][1364/1557] Data 0.015 (0.181) Batch 0.701 (1.129) Remain 26:25:04 loss: 0.3334 Lr: 0.00304 [2024-02-18 16:36:50,698 INFO misc.py line 119 87073] Train: [46/100][1365/1557] Data 0.003 (0.181) Batch 1.242 (1.129) Remain 26:25:10 loss: 0.1583 Lr: 0.00304 [2024-02-18 16:36:51,669 INFO misc.py line 119 87073] Train: [46/100][1366/1557] Data 0.004 (0.181) Batch 0.971 (1.129) Remain 26:24:59 loss: 0.3504 Lr: 0.00304 [2024-02-18 16:36:52,497 INFO misc.py line 119 87073] Train: [46/100][1367/1557] Data 0.003 (0.181) Batch 0.828 (1.128) Remain 26:24:40 loss: 0.3582 Lr: 0.00304 [2024-02-18 16:36:53,331 INFO misc.py line 119 87073] Train: [46/100][1368/1557] Data 0.003 (0.181) Batch 0.827 (1.128) Remain 26:24:20 loss: 0.4063 Lr: 0.00304 [2024-02-18 16:36:54,180 INFO misc.py line 119 87073] Train: [46/100][1369/1557] Data 0.011 (0.181) Batch 0.855 (1.128) Remain 26:24:02 loss: 0.4475 Lr: 0.00304 [2024-02-18 16:36:54,900 INFO misc.py line 119 87073] Train: [46/100][1370/1557] Data 0.004 (0.180) Batch 0.721 (1.128) Remain 26:23:36 loss: 0.2307 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1377/1557] Data 0.010 (0.180) Batch 0.705 (1.127) Remain 26:21:57 loss: 0.3540 Lr: 0.00304 [2024-02-18 16:37:02,042 INFO misc.py line 119 87073] Train: [46/100][1378/1557] Data 0.003 (0.179) Batch 0.723 (1.126) Remain 26:21:31 loss: 0.2607 Lr: 0.00304 [2024-02-18 16:37:03,377 INFO misc.py line 119 87073] Train: [46/100][1379/1557] Data 0.011 (0.179) Batch 1.335 (1.126) Remain 26:21:43 loss: 0.1017 Lr: 0.00304 [2024-02-18 16:37:04,412 INFO misc.py line 119 87073] Train: [46/100][1380/1557] Data 0.011 (0.179) Batch 1.035 (1.126) Remain 26:21:36 loss: 0.6968 Lr: 0.00304 [2024-02-18 16:37:05,629 INFO misc.py line 119 87073] Train: [46/100][1381/1557] Data 0.010 (0.179) Batch 1.213 (1.126) Remain 26:21:40 loss: 0.3566 Lr: 0.00304 [2024-02-18 16:37:06,741 INFO misc.py line 119 87073] Train: [46/100][1382/1557] Data 0.015 (0.179) Batch 1.117 (1.126) Remain 26:21:39 loss: 0.6566 Lr: 0.00304 [2024-02-18 16:37:07,764 INFO misc.py line 119 87073] Train: [46/100][1383/1557] Data 0.009 (0.179) Batch 1.019 (1.126) Remain 26:21:31 loss: 0.8026 Lr: 0.00304 [2024-02-18 16:37:08,529 INFO misc.py line 119 87073] Train: [46/100][1384/1557] Data 0.012 (0.179) Batch 0.775 (1.126) Remain 26:21:08 loss: 0.2277 Lr: 0.00304 [2024-02-18 16:37:09,242 INFO misc.py line 119 87073] Train: [46/100][1385/1557] Data 0.003 (0.179) Batch 0.705 (1.126) Remain 26:20:42 loss: 0.2856 Lr: 0.00304 [2024-02-18 16:37:10,326 INFO misc.py line 119 87073] Train: [46/100][1386/1557] Data 0.011 (0.179) Batch 1.085 (1.126) Remain 26:20:38 loss: 0.1100 Lr: 0.00304 [2024-02-18 16:37:11,166 INFO misc.py line 119 87073] Train: [46/100][1387/1557] Data 0.010 (0.178) Batch 0.846 (1.125) Remain 26:20:20 loss: 0.7417 Lr: 0.00304 [2024-02-18 16:37:12,081 INFO misc.py line 119 87073] Train: [46/100][1388/1557] Data 0.003 (0.178) Batch 0.915 (1.125) Remain 26:20:06 loss: 0.4294 Lr: 0.00304 [2024-02-18 16:37:13,011 INFO misc.py line 119 87073] Train: [46/100][1389/1557] Data 0.003 (0.178) Batch 0.920 (1.125) Remain 26:19:52 loss: 0.6441 Lr: 0.00304 [2024-02-18 16:37:13,955 INFO misc.py line 119 87073] Train: [46/100][1390/1557] Data 0.014 (0.178) Batch 0.954 (1.125) Remain 26:19:41 loss: 0.3970 Lr: 0.00304 [2024-02-18 16:37:14,682 INFO misc.py line 119 87073] Train: [46/100][1391/1557] Data 0.003 (0.178) Batch 0.725 (1.125) Remain 26:19:15 loss: 0.2776 Lr: 0.00304 [2024-02-18 16:37:15,425 INFO misc.py line 119 87073] Train: [46/100][1392/1557] Data 0.004 (0.178) Batch 0.738 (1.125) Remain 26:18:51 loss: 0.3011 Lr: 0.00304 [2024-02-18 16:37:16,627 INFO misc.py line 119 87073] Train: [46/100][1393/1557] Data 0.009 (0.178) Batch 1.202 (1.125) Remain 26:18:54 loss: 0.1410 Lr: 0.00304 [2024-02-18 16:37:17,764 INFO misc.py line 119 87073] Train: [46/100][1394/1557] Data 0.010 (0.178) Batch 1.134 (1.125) Remain 26:18:54 loss: 0.6687 Lr: 0.00304 [2024-02-18 16:37:18,869 INFO misc.py line 119 87073] Train: [46/100][1395/1557] Data 0.013 (0.177) Batch 1.104 (1.125) Remain 26:18:51 loss: 0.4782 Lr: 0.00304 [2024-02-18 16:37:19,811 INFO misc.py line 119 87073] Train: [46/100][1396/1557] Data 0.014 (0.177) Batch 0.952 (1.124) Remain 26:18:40 loss: 0.4849 Lr: 0.00304 [2024-02-18 16:37:20,945 INFO misc.py line 119 87073] Train: [46/100][1397/1557] Data 0.003 (0.177) Batch 1.135 (1.124) Remain 26:18:39 loss: 0.2407 Lr: 0.00304 [2024-02-18 16:37:21,711 INFO misc.py line 119 87073] Train: [46/100][1398/1557] Data 0.003 (0.177) Batch 0.766 (1.124) Remain 26:18:17 loss: 0.5620 Lr: 0.00304 [2024-02-18 16:37:22,474 INFO misc.py line 119 87073] Train: [46/100][1399/1557] Data 0.003 (0.177) Batch 0.731 (1.124) Remain 26:17:52 loss: 0.2570 Lr: 0.00304 [2024-02-18 16:37:23,818 INFO misc.py line 119 87073] Train: [46/100][1400/1557] Data 0.035 (0.177) Batch 1.371 (1.124) Remain 26:18:06 loss: 0.1467 Lr: 0.00304 [2024-02-18 16:37:24,824 INFO misc.py line 119 87073] Train: [46/100][1401/1557] Data 0.008 (0.177) Batch 1.002 (1.124) Remain 26:17:57 loss: 0.5966 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1408/1557] Data 0.003 (0.183) Batch 0.915 (1.132) Remain 26:28:29 loss: 0.4387 Lr: 0.00304 [2024-02-18 16:37:44,405 INFO misc.py line 119 87073] Train: [46/100][1409/1557] Data 0.003 (0.183) Batch 1.054 (1.132) Remain 26:28:23 loss: 0.5224 Lr: 0.00304 [2024-02-18 16:37:45,384 INFO misc.py line 119 87073] Train: [46/100][1410/1557] Data 0.003 (0.183) Batch 0.979 (1.131) Remain 26:28:13 loss: 0.1130 Lr: 0.00304 [2024-02-18 16:37:46,457 INFO misc.py line 119 87073] Train: [46/100][1411/1557] Data 0.003 (0.183) Batch 1.072 (1.131) Remain 26:28:08 loss: 1.4571 Lr: 0.00304 [2024-02-18 16:37:47,213 INFO misc.py line 119 87073] Train: [46/100][1412/1557] Data 0.004 (0.183) Batch 0.757 (1.131) Remain 26:27:45 loss: 0.3926 Lr: 0.00304 [2024-02-18 16:37:47,918 INFO misc.py line 119 87073] Train: [46/100][1413/1557] Data 0.003 (0.182) Batch 0.699 (1.131) Remain 26:27:18 loss: 0.3345 Lr: 0.00304 [2024-02-18 16:37:49,145 INFO misc.py line 119 87073] Train: [46/100][1414/1557] Data 0.008 (0.182) Batch 1.233 (1.131) Remain 26:27:23 loss: 0.1165 Lr: 0.00304 [2024-02-18 16:37:50,079 INFO misc.py line 119 87073] Train: [46/100][1415/1557] Data 0.003 (0.182) Batch 0.934 (1.131) Remain 26:27:10 loss: 0.4331 Lr: 0.00304 [2024-02-18 16:37:51,045 INFO misc.py line 119 87073] Train: [46/100][1416/1557] Data 0.003 (0.182) Batch 0.966 (1.131) Remain 26:26:59 loss: 0.5069 Lr: 0.00304 [2024-02-18 16:37:52,036 INFO misc.py line 119 87073] Train: [46/100][1417/1557] Data 0.003 (0.182) Batch 0.991 (1.131) Remain 26:26:49 loss: 0.3332 Lr: 0.00304 [2024-02-18 16:37:52,757 INFO misc.py line 119 87073] Train: [46/100][1418/1557] Data 0.003 (0.182) Batch 0.718 (1.130) Remain 26:26:24 loss: 0.2122 Lr: 0.00304 [2024-02-18 16:37:53,553 INFO misc.py line 119 87073] Train: [46/100][1419/1557] Data 0.006 (0.182) Batch 0.796 (1.130) Remain 26:26:03 loss: 0.4176 Lr: 0.00304 [2024-02-18 16:37:54,332 INFO misc.py line 119 87073] Train: [46/100][1420/1557] Data 0.006 (0.182) Batch 0.781 (1.130) Remain 26:25:41 loss: 0.2464 Lr: 0.00304 [2024-02-18 16:37:55,612 INFO misc.py line 119 87073] Train: [46/100][1421/1557] Data 0.005 (0.181) Batch 1.277 (1.130) Remain 26:25:48 loss: 0.1110 Lr: 0.00304 [2024-02-18 16:37:56,459 INFO misc.py line 119 87073] Train: [46/100][1422/1557] Data 0.007 (0.181) Batch 0.849 (1.130) Remain 26:25:31 loss: 0.4946 Lr: 0.00304 [2024-02-18 16:37:57,356 INFO misc.py line 119 87073] Train: [46/100][1423/1557] Data 0.005 (0.181) Batch 0.897 (1.129) Remain 26:25:16 loss: 0.4511 Lr: 0.00304 [2024-02-18 16:37:58,512 INFO misc.py line 119 87073] Train: [46/100][1424/1557] Data 0.004 (0.181) Batch 1.157 (1.130) Remain 26:25:16 loss: 0.3712 Lr: 0.00304 [2024-02-18 16:37:59,499 INFO misc.py line 119 87073] Train: [46/100][1425/1557] Data 0.003 (0.181) Batch 0.987 (1.129) Remain 26:25:07 loss: 0.3056 Lr: 0.00304 [2024-02-18 16:38:00,262 INFO misc.py line 119 87073] Train: [46/100][1426/1557] Data 0.003 (0.181) Batch 0.764 (1.129) Remain 26:24:44 loss: 0.2495 Lr: 0.00304 [2024-02-18 16:38:01,026 INFO misc.py line 119 87073] Train: [46/100][1427/1557] Data 0.003 (0.181) Batch 0.758 (1.129) Remain 26:24:21 loss: 0.5758 Lr: 0.00304 [2024-02-18 16:38:02,123 INFO misc.py line 119 87073] Train: [46/100][1428/1557] Data 0.009 (0.181) Batch 1.094 (1.129) Remain 26:24:18 loss: 0.1067 Lr: 0.00304 [2024-02-18 16:38:03,498 INFO misc.py line 119 87073] Train: [46/100][1429/1557] Data 0.012 (0.181) Batch 1.375 (1.129) Remain 26:24:31 loss: 0.4015 Lr: 0.00304 [2024-02-18 16:38:04,521 INFO misc.py line 119 87073] Train: [46/100][1430/1557] Data 0.013 (0.180) Batch 1.030 (1.129) Remain 26:24:24 loss: 0.2785 Lr: 0.00304 [2024-02-18 16:38:05,487 INFO misc.py line 119 87073] Train: [46/100][1431/1557] Data 0.006 (0.180) Batch 0.969 (1.129) Remain 26:24:13 loss: 0.3620 Lr: 0.00304 [2024-02-18 16:38:06,608 INFO misc.py line 119 87073] Train: [46/100][1432/1557] Data 0.003 (0.180) Batch 1.120 (1.129) Remain 26:24:12 loss: 0.3493 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1439/1557] Data 0.003 (0.179) Batch 1.211 (1.128) Remain 26:22:51 loss: 0.3076 Lr: 0.00304 [2024-02-18 16:38:14,020 INFO misc.py line 119 87073] Train: [46/100][1440/1557] Data 0.003 (0.179) Batch 0.751 (1.128) Remain 26:22:28 loss: 0.2252 Lr: 0.00304 [2024-02-18 16:38:14,783 INFO misc.py line 119 87073] Train: [46/100][1441/1557] Data 0.004 (0.179) Batch 0.762 (1.127) Remain 26:22:05 loss: 0.3648 Lr: 0.00304 [2024-02-18 16:38:15,981 INFO misc.py line 119 87073] Train: [46/100][1442/1557] Data 0.003 (0.179) Batch 1.159 (1.127) Remain 26:22:06 loss: 0.1183 Lr: 0.00304 [2024-02-18 16:38:16,928 INFO misc.py line 119 87073] Train: [46/100][1443/1557] Data 0.043 (0.179) Batch 0.986 (1.127) Remain 26:21:57 loss: 1.0348 Lr: 0.00304 [2024-02-18 16:38:17,865 INFO misc.py line 119 87073] Train: [46/100][1444/1557] Data 0.004 (0.179) Batch 0.938 (1.127) Remain 26:21:44 loss: 0.5383 Lr: 0.00304 [2024-02-18 16:38:18,895 INFO misc.py line 119 87073] Train: [46/100][1445/1557] Data 0.003 (0.179) Batch 1.030 (1.127) Remain 26:21:38 loss: 0.3696 Lr: 0.00304 [2024-02-18 16:38:20,092 INFO misc.py line 119 87073] Train: [46/100][1446/1557] Data 0.003 (0.178) Batch 1.197 (1.127) Remain 26:21:41 loss: 0.3898 Lr: 0.00304 [2024-02-18 16:38:20,755 INFO misc.py line 119 87073] Train: [46/100][1447/1557] Data 0.003 (0.178) Batch 0.663 (1.127) Remain 26:21:12 loss: 0.2692 Lr: 0.00304 [2024-02-18 16:38:21,543 INFO misc.py line 119 87073] Train: [46/100][1448/1557] Data 0.004 (0.178) Batch 0.778 (1.127) Remain 26:20:51 loss: 0.2319 Lr: 0.00304 [2024-02-18 16:38:22,772 INFO misc.py line 119 87073] Train: [46/100][1449/1557] Data 0.014 (0.178) Batch 1.236 (1.127) Remain 26:20:56 loss: 0.3013 Lr: 0.00304 [2024-02-18 16:38:23,577 INFO misc.py line 119 87073] Train: [46/100][1450/1557] Data 0.008 (0.178) Batch 0.809 (1.127) Remain 26:20:37 loss: 0.4328 Lr: 0.00304 [2024-02-18 16:38:24,491 INFO misc.py line 119 87073] Train: [46/100][1451/1557] Data 0.003 (0.178) Batch 0.913 (1.126) Remain 26:20:23 loss: 0.1556 Lr: 0.00304 [2024-02-18 16:38:25,383 INFO misc.py line 119 87073] Train: [46/100][1452/1557] Data 0.004 (0.178) Batch 0.884 (1.126) Remain 26:20:08 loss: 0.4557 Lr: 0.00304 [2024-02-18 16:38:26,219 INFO misc.py line 119 87073] Train: [46/100][1453/1557] Data 0.012 (0.178) Batch 0.844 (1.126) Remain 26:19:50 loss: 0.5314 Lr: 0.00304 [2024-02-18 16:38:26,962 INFO misc.py line 119 87073] Train: [46/100][1454/1557] Data 0.004 (0.178) Batch 0.741 (1.126) Remain 26:19:27 loss: 0.3715 Lr: 0.00304 [2024-02-18 16:38:27,759 INFO misc.py line 119 87073] Train: [46/100][1455/1557] Data 0.005 (0.177) Batch 0.790 (1.126) Remain 26:19:06 loss: 0.2170 Lr: 0.00304 [2024-02-18 16:38:29,039 INFO misc.py line 119 87073] Train: [46/100][1456/1557] Data 0.012 (0.177) Batch 1.286 (1.126) Remain 26:19:14 loss: 0.1475 Lr: 0.00304 [2024-02-18 16:38:30,015 INFO misc.py line 119 87073] Train: [46/100][1457/1557] Data 0.007 (0.177) Batch 0.979 (1.126) Remain 26:19:05 loss: 1.2262 Lr: 0.00304 [2024-02-18 16:38:30,860 INFO misc.py line 119 87073] Train: [46/100][1458/1557] Data 0.003 (0.177) Batch 0.846 (1.125) Remain 26:18:47 loss: 0.1877 Lr: 0.00304 [2024-02-18 16:38:31,910 INFO misc.py line 119 87073] Train: [46/100][1459/1557] Data 0.003 (0.177) Batch 1.045 (1.125) Remain 26:18:42 loss: 0.6875 Lr: 0.00304 [2024-02-18 16:38:32,830 INFO misc.py line 119 87073] Train: [46/100][1460/1557] Data 0.008 (0.177) Batch 0.925 (1.125) Remain 26:18:29 loss: 0.3142 Lr: 0.00304 [2024-02-18 16:38:33,707 INFO misc.py line 119 87073] Train: [46/100][1461/1557] Data 0.004 (0.177) Batch 0.877 (1.125) Remain 26:18:14 loss: 0.5560 Lr: 0.00304 [2024-02-18 16:38:34,417 INFO misc.py line 119 87073] Train: [46/100][1462/1557] Data 0.003 (0.177) Batch 0.703 (1.125) Remain 26:17:48 loss: 0.1746 Lr: 0.00304 [2024-02-18 16:38:45,462 INFO misc.py line 119 87073] Train: [46/100][1463/1557] Data 9.996 (0.183) Batch 11.041 (1.131) Remain 26:27:19 loss: 0.2599 Lr: 0.00304 [2024-02-18 16:38:46,605 INFO misc.py line 119 87073] Train: [46/100][1464/1557] Data 0.014 (0.183) Batch 1.143 (1.131) Remain 26:27:18 loss: 0.2108 Lr: 0.00304 [2024-02-18 16:38:47,520 INFO misc.py line 119 87073] Train: [46/100][1465/1557] Data 0.014 (0.183) Batch 0.925 (1.131) Remain 26:27:05 loss: 0.6065 Lr: 0.00304 [2024-02-18 16:38:48,381 INFO misc.py line 119 87073] Train: [46/100][1466/1557] Data 0.004 (0.183) Batch 0.862 (1.131) Remain 26:26:49 loss: 0.4067 Lr: 0.00304 [2024-02-18 16:38:49,323 INFO misc.py line 119 87073] Train: [46/100][1467/1557] Data 0.003 (0.183) Batch 0.935 (1.131) Remain 26:26:36 loss: 0.3060 Lr: 0.00304 [2024-02-18 16:38:50,070 INFO misc.py line 119 87073] Train: [46/100][1468/1557] Data 0.010 (0.183) Batch 0.754 (1.131) Remain 26:26:13 loss: 0.4259 Lr: 0.00304 [2024-02-18 16:38:50,823 INFO misc.py line 119 87073] Train: [46/100][1469/1557] Data 0.003 (0.183) Batch 0.741 (1.131) Remain 26:25:50 loss: 0.4238 Lr: 0.00304 [2024-02-18 16:38:52,010 INFO misc.py line 119 87073] Train: [46/100][1470/1557] Data 0.015 (0.182) Batch 1.189 (1.131) Remain 26:25:52 loss: 0.2203 Lr: 0.00304 [2024-02-18 16:38:52,982 INFO misc.py line 119 87073] Train: [46/100][1471/1557] Data 0.013 (0.182) Batch 0.982 (1.130) Remain 26:25:42 loss: 0.2890 Lr: 0.00304 [2024-02-18 16:38:54,040 INFO misc.py line 119 87073] Train: [46/100][1472/1557] Data 0.003 (0.182) Batch 1.058 (1.130) Remain 26:25:37 loss: 0.2590 Lr: 0.00304 [2024-02-18 16:38:54,922 INFO misc.py line 119 87073] Train: [46/100][1473/1557] Data 0.003 (0.182) Batch 0.882 (1.130) Remain 26:25:22 loss: 0.7944 Lr: 0.00304 [2024-02-18 16:38:55,874 INFO misc.py line 119 87073] Train: [46/100][1474/1557] Data 0.003 (0.182) Batch 0.947 (1.130) Remain 26:25:10 loss: 0.6264 Lr: 0.00304 [2024-02-18 16:38:56,620 INFO misc.py line 119 87073] Train: [46/100][1475/1557] Data 0.008 (0.182) Batch 0.751 (1.130) Remain 26:24:47 loss: 0.2643 Lr: 0.00304 [2024-02-18 16:38:57,390 INFO misc.py line 119 87073] Train: [46/100][1476/1557] Data 0.003 (0.182) Batch 0.758 (1.130) Remain 26:24:25 loss: 0.4880 Lr: 0.00304 [2024-02-18 16:38:58,699 INFO misc.py line 119 87073] Train: [46/100][1477/1557] Data 0.015 (0.182) Batch 1.312 (1.130) Remain 26:24:34 loss: 0.1679 Lr: 0.00304 [2024-02-18 16:38:59,678 INFO misc.py line 119 87073] Train: [46/100][1478/1557] Data 0.012 (0.182) Batch 0.987 (1.130) Remain 26:24:25 loss: 0.2034 Lr: 0.00304 [2024-02-18 16:39:00,647 INFO misc.py line 119 87073] Train: [46/100][1479/1557] Data 0.003 (0.181) Batch 0.969 (1.130) Remain 26:24:15 loss: 0.4311 Lr: 0.00304 [2024-02-18 16:39:01,627 INFO misc.py line 119 87073] Train: [46/100][1480/1557] Data 0.004 (0.181) Batch 0.980 (1.129) Remain 26:24:05 loss: 0.4249 Lr: 0.00304 [2024-02-18 16:39:02,528 INFO misc.py line 119 87073] Train: [46/100][1481/1557] Data 0.003 (0.181) Batch 0.901 (1.129) Remain 26:23:51 loss: 0.1767 Lr: 0.00304 [2024-02-18 16:39:03,284 INFO misc.py line 119 87073] Train: [46/100][1482/1557] Data 0.004 (0.181) Batch 0.738 (1.129) Remain 26:23:28 loss: 0.2507 Lr: 0.00304 [2024-02-18 16:39:04,068 INFO misc.py line 119 87073] Train: [46/100][1483/1557] Data 0.022 (0.181) Batch 0.802 (1.129) Remain 26:23:08 loss: 0.2234 Lr: 0.00304 [2024-02-18 16:39:05,150 INFO misc.py line 119 87073] Train: [46/100][1484/1557] Data 0.003 (0.181) Batch 1.081 (1.129) Remain 26:23:04 loss: 0.1615 Lr: 0.00304 [2024-02-18 16:39:06,425 INFO misc.py line 119 87073] Train: [46/100][1485/1557] Data 0.005 (0.181) Batch 1.277 (1.129) Remain 26:23:11 loss: 0.4181 Lr: 0.00304 [2024-02-18 16:39:07,241 INFO misc.py line 119 87073] Train: [46/100][1486/1557] Data 0.003 (0.181) Batch 0.815 (1.129) Remain 26:22:52 loss: 0.1997 Lr: 0.00304 [2024-02-18 16:39:08,172 INFO misc.py line 119 87073] Train: [46/100][1487/1557] Data 0.004 (0.180) Batch 0.933 (1.128) Remain 26:22:40 loss: 0.9792 Lr: 0.00304 [2024-02-18 16:39:09,156 INFO misc.py line 119 87073] Train: [46/100][1488/1557] Data 0.002 (0.180) Batch 0.984 (1.128) Remain 26:22:31 loss: 0.2991 Lr: 0.00304 [2024-02-18 16:39:09,951 INFO misc.py line 119 87073] Train: [46/100][1489/1557] Data 0.003 (0.180) Batch 0.789 (1.128) Remain 26:22:10 loss: 0.2083 Lr: 0.00304 [2024-02-18 16:39:10,724 INFO misc.py line 119 87073] Train: [46/100][1490/1557] Data 0.010 (0.180) Batch 0.779 (1.128) Remain 26:21:49 loss: 0.3391 Lr: 0.00304 [2024-02-18 16:39:12,065 INFO misc.py line 119 87073] Train: [46/100][1491/1557] Data 0.003 (0.180) Batch 1.331 (1.128) Remain 26:22:00 loss: 0.1194 Lr: 0.00304 [2024-02-18 16:39:13,162 INFO misc.py line 119 87073] Train: [46/100][1492/1557] Data 0.012 (0.180) Batch 1.097 (1.128) Remain 26:21:57 loss: 0.6315 Lr: 0.00304 [2024-02-18 16:39:14,138 INFO misc.py line 119 87073] Train: [46/100][1493/1557] Data 0.013 (0.180) Batch 0.986 (1.128) Remain 26:21:48 loss: 0.3187 Lr: 0.00304 [2024-02-18 16:39:15,086 INFO misc.py line 119 87073] Train: [46/100][1494/1557] Data 0.002 (0.180) Batch 0.946 (1.128) Remain 26:21:36 loss: 0.2575 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1501/1557] Data 0.003 (0.179) Batch 0.860 (1.127) Remain 26:19:48 loss: 0.2546 Lr: 0.00304 [2024-02-18 16:39:22,150 INFO misc.py line 119 87073] Train: [46/100][1502/1557] Data 0.005 (0.179) Batch 0.963 (1.127) Remain 26:19:38 loss: 0.4652 Lr: 0.00304 [2024-02-18 16:39:22,930 INFO misc.py line 119 87073] Train: [46/100][1503/1557] Data 0.002 (0.179) Batch 0.780 (1.126) Remain 26:19:17 loss: 0.2475 Lr: 0.00304 [2024-02-18 16:39:23,722 INFO misc.py line 119 87073] Train: [46/100][1504/1557] Data 0.003 (0.178) Batch 0.779 (1.126) Remain 26:18:56 loss: 0.2811 Lr: 0.00304 [2024-02-18 16:39:25,019 INFO misc.py line 119 87073] Train: [46/100][1505/1557] Data 0.015 (0.178) Batch 1.303 (1.126) Remain 26:19:05 loss: 0.3298 Lr: 0.00304 [2024-02-18 16:39:25,940 INFO misc.py line 119 87073] Train: [46/100][1506/1557] Data 0.010 (0.178) Batch 0.927 (1.126) Remain 26:18:53 loss: 0.5969 Lr: 0.00304 [2024-02-18 16:39:26,946 INFO misc.py line 119 87073] Train: [46/100][1507/1557] Data 0.003 (0.178) Batch 1.007 (1.126) Remain 26:18:45 loss: 0.3586 Lr: 0.00304 [2024-02-18 16:39:28,027 INFO misc.py line 119 87073] Train: [46/100][1508/1557] Data 0.003 (0.178) Batch 1.078 (1.126) Remain 26:18:41 loss: 0.5526 Lr: 0.00304 [2024-02-18 16:39:29,000 INFO misc.py line 119 87073] Train: [46/100][1509/1557] Data 0.007 (0.178) Batch 0.974 (1.126) Remain 26:18:32 loss: 0.7755 Lr: 0.00304 [2024-02-18 16:39:29,695 INFO misc.py line 119 87073] Train: [46/100][1510/1557] Data 0.005 (0.178) Batch 0.694 (1.126) Remain 26:18:07 loss: 0.4515 Lr: 0.00304 [2024-02-18 16:39:30,575 INFO misc.py line 119 87073] Train: [46/100][1511/1557] Data 0.007 (0.178) Batch 0.881 (1.125) Remain 26:17:52 loss: 0.2927 Lr: 0.00304 [2024-02-18 16:39:31,848 INFO misc.py line 119 87073] Train: [46/100][1512/1557] Data 0.005 (0.178) Batch 1.274 (1.125) Remain 26:17:59 loss: 0.2359 Lr: 0.00304 [2024-02-18 16:39:32,859 INFO misc.py line 119 87073] Train: [46/100][1513/1557] Data 0.005 (0.177) Batch 1.007 (1.125) Remain 26:17:51 loss: 0.4763 Lr: 0.00304 [2024-02-18 16:39:33,727 INFO misc.py line 119 87073] Train: [46/100][1514/1557] Data 0.008 (0.177) Batch 0.871 (1.125) Remain 26:17:36 loss: 0.2980 Lr: 0.00304 [2024-02-18 16:39:34,600 INFO misc.py line 119 87073] Train: [46/100][1515/1557] Data 0.004 (0.177) Batch 0.873 (1.125) Remain 26:17:21 loss: 0.3963 Lr: 0.00304 [2024-02-18 16:39:35,533 INFO misc.py line 119 87073] Train: [46/100][1516/1557] Data 0.004 (0.177) Batch 0.932 (1.125) Remain 26:17:09 loss: 0.4046 Lr: 0.00304 [2024-02-18 16:39:36,362 INFO misc.py line 119 87073] Train: [46/100][1517/1557] Data 0.005 (0.177) Batch 0.830 (1.125) Remain 26:16:51 loss: 0.2367 Lr: 0.00304 [2024-02-18 16:39:37,149 INFO misc.py line 119 87073] Train: [46/100][1518/1557] Data 0.003 (0.177) Batch 0.787 (1.125) Remain 26:16:32 loss: 0.3154 Lr: 0.00304 [2024-02-18 16:39:48,381 INFO misc.py line 119 87073] Train: [46/100][1519/1557] Data 9.158 (0.183) Batch 11.229 (1.131) Remain 26:25:51 loss: 0.4403 Lr: 0.00304 [2024-02-18 16:39:49,281 INFO misc.py line 119 87073] Train: [46/100][1520/1557] Data 0.007 (0.183) Batch 0.903 (1.131) Remain 26:25:37 loss: 0.8091 Lr: 0.00304 [2024-02-18 16:39:50,216 INFO misc.py line 119 87073] Train: [46/100][1521/1557] Data 0.004 (0.183) Batch 0.935 (1.131) Remain 26:25:25 loss: 0.2576 Lr: 0.00304 [2024-02-18 16:39:51,334 INFO misc.py line 119 87073] Train: [46/100][1522/1557] Data 0.005 (0.182) Batch 1.119 (1.131) Remain 26:25:23 loss: 0.7259 Lr: 0.00304 [2024-02-18 16:39:52,235 INFO misc.py line 119 87073] Train: [46/100][1523/1557] Data 0.003 (0.182) Batch 0.900 (1.131) Remain 26:25:10 loss: 0.3159 Lr: 0.00304 [2024-02-18 16:39:52,992 INFO misc.py line 119 87073] Train: [46/100][1524/1557] Data 0.004 (0.182) Batch 0.754 (1.131) Remain 26:24:48 loss: 0.3434 Lr: 0.00304 [2024-02-18 16:39:53,721 INFO misc.py line 119 87073] Train: [46/100][1525/1557] Data 0.008 (0.182) Batch 0.734 (1.130) Remain 26:24:25 loss: 0.3893 Lr: 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INFO misc.py line 119 87073] Train: [46/100][1532/1557] Data 0.013 (0.181) Batch 0.752 (1.129) Remain 26:22:47 loss: 0.1544 Lr: 0.00304 [2024-02-18 16:40:01,219 INFO misc.py line 119 87073] Train: [46/100][1533/1557] Data 0.004 (0.181) Batch 1.208 (1.129) Remain 26:22:51 loss: 0.1250 Lr: 0.00304 [2024-02-18 16:40:02,229 INFO misc.py line 119 87073] Train: [46/100][1534/1557] Data 0.004 (0.181) Batch 1.009 (1.129) Remain 26:22:43 loss: 0.4333 Lr: 0.00304 [2024-02-18 16:40:03,122 INFO misc.py line 119 87073] Train: [46/100][1535/1557] Data 0.004 (0.181) Batch 0.894 (1.129) Remain 26:22:29 loss: 0.5699 Lr: 0.00304 [2024-02-18 16:40:04,080 INFO misc.py line 119 87073] Train: [46/100][1536/1557] Data 0.004 (0.181) Batch 0.952 (1.129) Remain 26:22:18 loss: 0.3432 Lr: 0.00304 [2024-02-18 16:40:04,951 INFO misc.py line 119 87073] Train: [46/100][1537/1557] Data 0.009 (0.181) Batch 0.878 (1.129) Remain 26:22:03 loss: 0.1600 Lr: 0.00304 [2024-02-18 16:40:05,666 INFO misc.py line 119 87073] Train: [46/100][1538/1557] Data 0.003 (0.181) Batch 0.714 (1.128) Remain 26:21:39 loss: 0.2247 Lr: 0.00304 [2024-02-18 16:40:06,350 INFO misc.py line 119 87073] Train: [46/100][1539/1557] Data 0.004 (0.180) Batch 0.676 (1.128) Remain 26:21:13 loss: 0.5071 Lr: 0.00304 [2024-02-18 16:40:07,469 INFO misc.py line 119 87073] Train: [46/100][1540/1557] Data 0.011 (0.180) Batch 1.110 (1.128) Remain 26:21:11 loss: 0.1804 Lr: 0.00304 [2024-02-18 16:40:08,421 INFO misc.py line 119 87073] Train: [46/100][1541/1557] Data 0.021 (0.180) Batch 0.969 (1.128) Remain 26:21:01 loss: 0.5625 Lr: 0.00304 [2024-02-18 16:40:09,380 INFO misc.py line 119 87073] Train: [46/100][1542/1557] Data 0.003 (0.180) Batch 0.959 (1.128) Remain 26:20:51 loss: 0.1765 Lr: 0.00304 [2024-02-18 16:40:10,392 INFO misc.py line 119 87073] Train: [46/100][1543/1557] Data 0.003 (0.180) Batch 1.012 (1.128) Remain 26:20:44 loss: 0.4378 Lr: 0.00304 [2024-02-18 16:40:11,512 INFO misc.py line 119 87073] Train: [46/100][1544/1557] Data 0.003 (0.180) Batch 1.119 (1.128) Remain 26:20:42 loss: 0.2580 Lr: 0.00304 [2024-02-18 16:40:12,257 INFO misc.py line 119 87073] Train: [46/100][1545/1557] Data 0.003 (0.180) Batch 0.746 (1.128) Remain 26:20:20 loss: 0.2427 Lr: 0.00304 [2024-02-18 16:40:13,007 INFO misc.py line 119 87073] Train: [46/100][1546/1557] Data 0.003 (0.180) Batch 0.741 (1.127) Remain 26:19:58 loss: 0.2976 Lr: 0.00304 [2024-02-18 16:40:14,201 INFO misc.py line 119 87073] Train: [46/100][1547/1557] Data 0.013 (0.180) Batch 1.195 (1.127) Remain 26:20:00 loss: 0.1885 Lr: 0.00304 [2024-02-18 16:40:15,228 INFO misc.py line 119 87073] Train: [46/100][1548/1557] Data 0.011 (0.179) Batch 1.026 (1.127) Remain 26:19:54 loss: 0.4720 Lr: 0.00304 [2024-02-18 16:40:16,268 INFO misc.py line 119 87073] Train: [46/100][1549/1557] Data 0.012 (0.179) Batch 1.037 (1.127) Remain 26:19:48 loss: 0.6666 Lr: 0.00304 [2024-02-18 16:40:17,415 INFO misc.py line 119 87073] Train: [46/100][1550/1557] Data 0.015 (0.179) Batch 1.103 (1.127) Remain 26:19:45 loss: 0.2301 Lr: 0.00304 [2024-02-18 16:40:18,325 INFO misc.py line 119 87073] Train: [46/100][1551/1557] Data 0.059 (0.179) Batch 0.967 (1.127) Remain 26:19:35 loss: 0.4109 Lr: 0.00304 [2024-02-18 16:40:18,988 INFO misc.py line 119 87073] Train: [46/100][1552/1557] Data 0.003 (0.179) Batch 0.663 (1.127) Remain 26:19:09 loss: 0.3502 Lr: 0.00304 [2024-02-18 16:40:19,711 INFO misc.py line 119 87073] Train: [46/100][1553/1557] Data 0.003 (0.179) Batch 0.712 (1.127) Remain 26:18:45 loss: 0.3502 Lr: 0.00304 [2024-02-18 16:40:20,818 INFO misc.py line 119 87073] Train: [46/100][1554/1557] Data 0.014 (0.179) Batch 1.106 (1.127) Remain 26:18:43 loss: 0.1381 Lr: 0.00304 [2024-02-18 16:40:21,876 INFO misc.py line 119 87073] Train: [46/100][1555/1557] Data 0.015 (0.179) Batch 1.054 (1.127) Remain 26:18:38 loss: 1.1243 Lr: 0.00303 [2024-02-18 16:40:22,899 INFO misc.py line 119 87073] Train: [46/100][1556/1557] Data 0.020 (0.179) Batch 1.030 (1.126) Remain 26:18:32 loss: 0.5125 Lr: 0.00303 [2024-02-18 16:40:23,945 INFO misc.py line 119 87073] Train: [46/100][1557/1557] Data 0.012 (0.179) Batch 1.049 (1.126) Remain 26:18:26 loss: 0.5468 Lr: 0.00303 [2024-02-18 16:40:23,945 INFO misc.py line 136 87073] Train result: loss: 0.3723 [2024-02-18 16:40:23,946 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 16:40:53,761 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8894 [2024-02-18 16:40:54,537 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5548 [2024-02-18 16:40:56,659 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4521 [2024-02-18 16:40:58,867 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2780 [2024-02-18 16:41:03,815 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2622 [2024-02-18 16:41:04,517 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1500 [2024-02-18 16:41:05,778 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2203 [2024-02-18 16:41:08,730 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3187 [2024-02-18 16:41:10,537 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2416 [2024-02-18 16:41:12,017 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7217/0.7777/0.9175. [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9298/0.9683 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9822/0.9881 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8783/0.9681 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3393/0.3615 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6856/0.7165 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7828/0.9459 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8493/0.9115 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9271/0.9721 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8471/0.8781 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7768/0.8881 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7587/0.7851 [2024-02-18 16:41:12,017 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6256/0.7272 [2024-02-18 16:41:12,018 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 16:41:12,019 INFO misc.py line 165 87073] Currently Best mIoU: 0.7277 [2024-02-18 16:41:12,019 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 16:41:18,325 INFO misc.py line 119 87073] Train: [47/100][1/1557] Data 1.522 (1.522) Batch 2.327 (2.327) Remain 54:20:57 loss: 0.1571 Lr: 0.00303 [2024-02-18 16:41:19,401 INFO misc.py line 119 87073] Train: [47/100][2/1557] Data 0.006 (0.006) Batch 1.077 (1.077) Remain 25:09:44 loss: 0.4767 Lr: 0.00303 [2024-02-18 16:41:20,272 INFO misc.py line 119 87073] Train: [47/100][3/1557] Data 0.005 (0.005) Batch 0.870 (0.870) Remain 20:19:24 loss: 0.9186 Lr: 0.00303 [2024-02-18 16:41:21,354 INFO misc.py line 119 87073] Train: [47/100][4/1557] Data 0.005 (0.005) Batch 1.082 (1.082) Remain 25:16:47 loss: 0.4720 Lr: 0.00303 [2024-02-18 16:41:22,210 INFO misc.py line 119 87073] Train: [47/100][5/1557] Data 0.004 (0.004) Batch 0.857 (0.970) Remain 22:38:52 loss: 0.3560 Lr: 0.00303 [2024-02-18 16:41:22,928 INFO misc.py line 119 87073] Train: [47/100][6/1557] Data 0.003 (0.004) Batch 0.711 (0.883) Remain 20:37:52 loss: 0.2908 Lr: 0.00303 [2024-02-18 16:41:26,769 INFO misc.py line 119 87073] Train: [47/100][7/1557] Data 0.010 (0.005) Batch 3.837 (1.622) Remain 37:52:28 loss: 0.0847 Lr: 0.00303 [2024-02-18 16:41:27,664 INFO misc.py line 119 87073] Train: [47/100][8/1557] Data 0.014 (0.007) Batch 0.905 (1.478) Remain 34:31:36 loss: 0.5303 Lr: 0.00303 [2024-02-18 16:41:28,811 INFO misc.py line 119 87073] Train: [47/100][9/1557] Data 0.005 (0.007) Batch 1.147 (1.423) Remain 33:14:05 loss: 0.3042 Lr: 0.00303 [2024-02-18 16:41:29,765 INFO misc.py line 119 87073] Train: [47/100][10/1557] Data 0.005 (0.007) Batch 0.954 (1.356) Remain 31:40:15 loss: 0.5307 Lr: 0.00303 [2024-02-18 16:41:30,683 INFO misc.py line 119 87073] Train: [47/100][11/1557] Data 0.004 (0.006) Batch 0.920 (1.302) Remain 30:23:45 loss: 0.5523 Lr: 0.00303 [2024-02-18 16:41:31,411 INFO misc.py line 119 87073] Train: [47/100][12/1557] Data 0.003 (0.006) Batch 0.720 (1.237) Remain 28:53:10 loss: 0.4047 Lr: 0.00303 [2024-02-18 16:41:32,112 INFO misc.py line 119 87073] Train: [47/100][13/1557] Data 0.010 (0.006) Batch 0.708 (1.184) Remain 27:38:59 loss: 0.4782 Lr: 0.00303 [2024-02-18 16:41:33,259 INFO misc.py line 119 87073] Train: [47/100][14/1557] Data 0.003 (0.006) Batch 1.141 (1.180) Remain 27:33:27 loss: 0.2058 Lr: 0.00303 [2024-02-18 16:41:34,287 INFO misc.py line 119 87073] Train: [47/100][15/1557] Data 0.010 (0.006) Batch 1.030 (1.168) Remain 27:15:56 loss: 0.3564 Lr: 0.00303 [2024-02-18 16:41:35,391 INFO misc.py line 119 87073] Train: [47/100][16/1557] Data 0.008 (0.006) Batch 1.108 (1.163) Remain 27:09:26 loss: 0.8180 Lr: 0.00303 [2024-02-18 16:41:36,328 INFO misc.py line 119 87073] Train: [47/100][17/1557] Data 0.004 (0.006) Batch 0.938 (1.147) Remain 26:46:55 loss: 0.5593 Lr: 0.00303 [2024-02-18 16:41:37,195 INFO misc.py line 119 87073] Train: [47/100][18/1557] Data 0.003 (0.006) Batch 0.867 (1.128) Remain 26:20:43 loss: 0.2026 Lr: 0.00303 [2024-02-18 16:41:37,881 INFO misc.py line 119 87073] Train: [47/100][19/1557] Data 0.003 (0.006) Batch 0.681 (1.100) Remain 25:41:34 loss: 0.1105 Lr: 0.00303 [2024-02-18 16:41:38,631 INFO misc.py line 119 87073] Train: [47/100][20/1557] Data 0.008 (0.006) Batch 0.749 (1.080) Remain 25:12:34 loss: 0.2606 Lr: 0.00303 [2024-02-18 16:41:39,939 INFO misc.py line 119 87073] Train: [47/100][21/1557] Data 0.009 (0.006) Batch 1.307 (1.092) Remain 25:30:13 loss: 0.2525 Lr: 0.00303 [2024-02-18 16:41:40,996 INFO misc.py line 119 87073] Train: [47/100][22/1557] Data 0.011 (0.006) Batch 1.049 (1.090) Remain 25:27:02 loss: 0.1942 Lr: 0.00303 [2024-02-18 16:41:41,943 INFO misc.py line 119 87073] Train: [47/100][23/1557] Data 0.018 (0.007) Batch 0.962 (1.084) Remain 25:18:01 loss: 0.3104 Lr: 0.00303 [2024-02-18 16:41:42,785 INFO misc.py line 119 87073] Train: [47/100][24/1557] Data 0.003 (0.007) Batch 0.841 (1.072) Remain 25:01:47 loss: 0.2801 Lr: 0.00303 [2024-02-18 16:41:43,801 INFO misc.py line 119 87073] Train: [47/100][25/1557] Data 0.005 (0.007) Batch 1.011 (1.069) Remain 24:57:52 loss: 0.8640 Lr: 0.00303 [2024-02-18 16:41:44,628 INFO misc.py line 119 87073] Train: [47/100][26/1557] Data 0.011 (0.007) Batch 0.832 (1.059) Remain 24:43:24 loss: 0.2351 Lr: 0.00303 [2024-02-18 16:41:45,390 INFO misc.py line 119 87073] Train: [47/100][27/1557] Data 0.007 (0.007) Batch 0.765 (1.047) Remain 24:26:12 loss: 0.3567 Lr: 0.00303 [2024-02-18 16:41:46,650 INFO misc.py line 119 87073] Train: [47/100][28/1557] Data 0.003 (0.007) Batch 1.254 (1.055) Remain 24:37:48 loss: 0.3143 Lr: 0.00303 [2024-02-18 16:41:47,427 INFO misc.py line 119 87073] Train: [47/100][29/1557] Data 0.009 (0.007) Batch 0.781 (1.044) Remain 24:23:02 loss: 0.4486 Lr: 0.00303 [2024-02-18 16:41:48,434 INFO misc.py line 119 87073] Train: [47/100][30/1557] Data 0.005 (0.007) Batch 1.008 (1.043) Remain 24:21:07 loss: 0.4032 Lr: 0.00303 [2024-02-18 16:41:49,354 INFO misc.py line 119 87073] Train: [47/100][31/1557] Data 0.004 (0.007) Batch 0.919 (1.039) Remain 24:14:55 loss: 0.2528 Lr: 0.00303 [2024-02-18 16:41:50,425 INFO misc.py line 119 87073] Train: [47/100][32/1557] Data 0.005 (0.007) Batch 1.071 (1.040) Remain 24:16:29 loss: 0.5246 Lr: 0.00303 [2024-02-18 16:41:51,203 INFO misc.py line 119 87073] Train: [47/100][33/1557] Data 0.005 (0.006) Batch 0.777 (1.031) Remain 24:04:11 loss: 0.1228 Lr: 0.00303 [2024-02-18 16:41:52,077 INFO misc.py line 119 87073] Train: [47/100][34/1557] Data 0.006 (0.006) Batch 0.875 (1.026) Remain 23:57:08 loss: 0.2589 Lr: 0.00303 [2024-02-18 16:41:53,305 INFO misc.py line 119 87073] Train: [47/100][35/1557] Data 0.004 (0.006) Batch 1.226 (1.032) Remain 24:05:53 loss: 0.1572 Lr: 0.00303 [2024-02-18 16:41:54,221 INFO misc.py line 119 87073] Train: [47/100][36/1557] Data 0.005 (0.006) Batch 0.918 (1.029) Remain 24:01:01 loss: 0.6028 Lr: 0.00303 [2024-02-18 16:41:55,112 INFO misc.py line 119 87073] Train: [47/100][37/1557] Data 0.004 (0.006) Batch 0.890 (1.025) Remain 23:55:17 loss: 0.4602 Lr: 0.00303 [2024-02-18 16:41:56,002 INFO misc.py line 119 87073] Train: [47/100][38/1557] Data 0.005 (0.006) Batch 0.890 (1.021) Remain 23:49:52 loss: 0.4126 Lr: 0.00303 [2024-02-18 16:41:56,953 INFO misc.py line 119 87073] Train: [47/100][39/1557] Data 0.004 (0.006) Batch 0.952 (1.019) Remain 23:47:10 loss: 0.4106 Lr: 0.00303 [2024-02-18 16:41:57,714 INFO misc.py line 119 87073] Train: [47/100][40/1557] Data 0.004 (0.006) Batch 0.761 (1.012) Remain 23:37:23 loss: 0.6771 Lr: 0.00303 [2024-02-18 16:41:58,462 INFO misc.py line 119 87073] Train: [47/100][41/1557] Data 0.004 (0.006) Batch 0.745 (1.005) Remain 23:27:31 loss: 0.1793 Lr: 0.00303 [2024-02-18 16:41:59,802 INFO misc.py line 119 87073] Train: [47/100][42/1557] Data 0.007 (0.006) Batch 1.343 (1.014) Remain 23:39:38 loss: 0.1858 Lr: 0.00303 [2024-02-18 16:42:00,683 INFO misc.py line 119 87073] Train: [47/100][43/1557] Data 0.005 (0.006) Batch 0.881 (1.010) Remain 23:34:58 loss: 0.3150 Lr: 0.00303 [2024-02-18 16:42:01,635 INFO misc.py line 119 87073] Train: [47/100][44/1557] Data 0.004 (0.006) Batch 0.953 (1.009) Remain 23:32:59 loss: 0.3728 Lr: 0.00303 [2024-02-18 16:42:02,606 INFO misc.py line 119 87073] Train: [47/100][45/1557] Data 0.004 (0.006) Batch 0.971 (1.008) Remain 23:31:43 loss: 0.2786 Lr: 0.00303 [2024-02-18 16:42:03,517 INFO misc.py line 119 87073] Train: [47/100][46/1557] Data 0.004 (0.006) Batch 0.911 (1.006) Remain 23:28:32 loss: 0.4142 Lr: 0.00303 [2024-02-18 16:42:04,311 INFO misc.py line 119 87073] Train: [47/100][47/1557] Data 0.003 (0.006) Batch 0.787 (1.001) Remain 23:21:33 loss: 0.4983 Lr: 0.00303 [2024-02-18 16:42:05,136 INFO misc.py line 119 87073] Train: [47/100][48/1557] Data 0.010 (0.006) Batch 0.830 (0.997) Remain 23:16:13 loss: 0.2369 Lr: 0.00303 [2024-02-18 16:42:06,272 INFO misc.py line 119 87073] Train: [47/100][49/1557] Data 0.006 (0.006) Batch 1.137 (1.000) Remain 23:20:27 loss: 0.3635 Lr: 0.00303 [2024-02-18 16:42:07,500 INFO misc.py line 119 87073] Train: [47/100][50/1557] Data 0.006 (0.006) Batch 1.201 (1.004) Remain 23:26:25 loss: 0.3541 Lr: 0.00303 [2024-02-18 16:42:08,394 INFO misc.py line 119 87073] Train: [47/100][51/1557] Data 0.033 (0.006) Batch 0.923 (1.003) Remain 23:24:02 loss: 0.1825 Lr: 0.00303 [2024-02-18 16:42:09,363 INFO misc.py line 119 87073] Train: [47/100][52/1557] Data 0.003 (0.006) Batch 0.969 (1.002) Remain 23:23:03 loss: 0.3422 Lr: 0.00303 [2024-02-18 16:42:10,268 INFO misc.py line 119 87073] Train: [47/100][53/1557] Data 0.004 (0.006) Batch 0.905 (1.000) Remain 23:20:19 loss: 0.4584 Lr: 0.00303 [2024-02-18 16:42:11,043 INFO misc.py line 119 87073] Train: [47/100][54/1557] Data 0.004 (0.006) Batch 0.773 (0.995) Remain 23:14:04 loss: 0.2923 Lr: 0.00303 [2024-02-18 16:42:11,776 INFO misc.py line 119 87073] Train: [47/100][55/1557] Data 0.006 (0.006) Batch 0.735 (0.990) Remain 23:07:02 loss: 0.4973 Lr: 0.00303 [2024-02-18 16:42:13,045 INFO misc.py line 119 87073] Train: [47/100][56/1557] Data 0.004 (0.006) Batch 1.269 (0.996) Remain 23:14:22 loss: 0.2466 Lr: 0.00303 [2024-02-18 16:42:14,082 INFO misc.py line 119 87073] Train: [47/100][57/1557] Data 0.004 (0.006) Batch 1.030 (0.996) Remain 23:15:15 loss: 0.5232 Lr: 0.00303 [2024-02-18 16:42:15,067 INFO misc.py line 119 87073] Train: [47/100][58/1557] Data 0.012 (0.006) Batch 0.993 (0.996) Remain 23:15:08 loss: 0.4781 Lr: 0.00303 [2024-02-18 16:42:16,031 INFO misc.py line 119 87073] Train: [47/100][59/1557] Data 0.003 (0.006) Batch 0.964 (0.996) Remain 23:14:19 loss: 0.2811 Lr: 0.00303 [2024-02-18 16:42:17,059 INFO misc.py line 119 87073] Train: [47/100][60/1557] Data 0.003 (0.006) Batch 1.028 (0.996) Remain 23:15:05 loss: 0.6994 Lr: 0.00303 [2024-02-18 16:42:17,840 INFO misc.py line 119 87073] Train: [47/100][61/1557] Data 0.004 (0.006) Batch 0.779 (0.993) Remain 23:09:49 loss: 0.2751 Lr: 0.00303 [2024-02-18 16:42:18,527 INFO misc.py line 119 87073] Train: [47/100][62/1557] Data 0.006 (0.006) Batch 0.679 (0.987) Remain 23:02:22 loss: 0.3715 Lr: 0.00303 [2024-02-18 16:42:30,490 INFO misc.py line 119 87073] Train: [47/100][63/1557] Data 2.987 (0.056) Batch 11.973 (1.170) Remain 27:18:44 loss: 0.1142 Lr: 0.00303 [2024-02-18 16:42:31,523 INFO misc.py line 119 87073] Train: [47/100][64/1557] Data 0.004 (0.055) Batch 1.033 (1.168) Remain 27:15:34 loss: 0.4366 Lr: 0.00303 [2024-02-18 16:42:32,438 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [47/100][109/1557] Data 0.004 (0.035) Batch 0.991 (1.079) Remain 25:10:39 loss: 0.2740 Lr: 0.00303 [2024-02-18 16:43:15,494 INFO misc.py line 119 87073] Train: [47/100][110/1557] Data 0.003 (0.035) Batch 0.788 (1.077) Remain 25:06:49 loss: 0.2611 Lr: 0.00303 [2024-02-18 16:43:16,228 INFO misc.py line 119 87073] Train: [47/100][111/1557] Data 0.019 (0.034) Batch 0.749 (1.074) Remain 25:02:33 loss: 0.4202 Lr: 0.00303 [2024-02-18 16:43:17,413 INFO misc.py line 119 87073] Train: [47/100][112/1557] Data 0.003 (0.034) Batch 1.185 (1.075) Remain 25:03:57 loss: 0.2208 Lr: 0.00303 [2024-02-18 16:43:18,490 INFO misc.py line 119 87073] Train: [47/100][113/1557] Data 0.003 (0.034) Batch 1.076 (1.075) Remain 25:03:58 loss: 0.3730 Lr: 0.00303 [2024-02-18 16:43:19,442 INFO misc.py line 119 87073] Train: [47/100][114/1557] Data 0.004 (0.034) Batch 0.953 (1.074) Remain 25:02:25 loss: 0.1499 Lr: 0.00303 [2024-02-18 16:43:20,463 INFO misc.py line 119 87073] Train: 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Batch 0.908 (1.150) Remain 26:49:11 loss: 0.8668 Lr: 0.00303 [2024-02-18 16:43:36,813 INFO misc.py line 119 87073] Train: [47/100][122/1557] Data 0.004 (0.053) Batch 0.841 (1.147) Remain 26:45:32 loss: 0.0895 Lr: 0.00303 [2024-02-18 16:43:37,822 INFO misc.py line 119 87073] Train: [47/100][123/1557] Data 0.003 (0.053) Batch 1.004 (1.146) Remain 26:43:50 loss: 0.6871 Lr: 0.00303 [2024-02-18 16:43:38,579 INFO misc.py line 119 87073] Train: [47/100][124/1557] Data 0.008 (0.053) Batch 0.761 (1.143) Remain 26:39:22 loss: 0.3103 Lr: 0.00303 [2024-02-18 16:43:39,375 INFO misc.py line 119 87073] Train: [47/100][125/1557] Data 0.004 (0.052) Batch 0.789 (1.140) Remain 26:35:18 loss: 0.3110 Lr: 0.00303 [2024-02-18 16:43:40,621 INFO misc.py line 119 87073] Train: [47/100][126/1557] Data 0.010 (0.052) Batch 1.241 (1.141) Remain 26:36:26 loss: 0.1627 Lr: 0.00303 [2024-02-18 16:43:41,778 INFO misc.py line 119 87073] Train: [47/100][127/1557] Data 0.015 (0.052) Batch 1.166 (1.141) Remain 26:36:41 loss: 0.1235 Lr: 0.00303 [2024-02-18 16:43:42,706 INFO misc.py line 119 87073] Train: [47/100][128/1557] Data 0.007 (0.051) Batch 0.931 (1.139) Remain 26:34:19 loss: 0.8678 Lr: 0.00303 [2024-02-18 16:43:43,536 INFO misc.py line 119 87073] Train: [47/100][129/1557] Data 0.005 (0.051) Batch 0.831 (1.137) Remain 26:30:52 loss: 0.4585 Lr: 0.00303 [2024-02-18 16:43:44,655 INFO misc.py line 119 87073] Train: [47/100][130/1557] Data 0.003 (0.051) Batch 1.110 (1.137) Remain 26:30:33 loss: 0.3988 Lr: 0.00303 [2024-02-18 16:43:45,407 INFO misc.py line 119 87073] Train: [47/100][131/1557] Data 0.012 (0.050) Batch 0.761 (1.134) Remain 26:26:25 loss: 0.4401 Lr: 0.00303 [2024-02-18 16:43:46,204 INFO misc.py line 119 87073] Train: [47/100][132/1557] Data 0.003 (0.050) Batch 0.786 (1.131) Remain 26:22:38 loss: 0.3615 Lr: 0.00303 [2024-02-18 16:43:47,527 INFO misc.py line 119 87073] Train: [47/100][133/1557] Data 0.013 (0.050) Batch 1.324 (1.133) Remain 26:24:42 loss: 0.1200 Lr: 0.00303 [2024-02-18 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87073] Train: [47/100][140/1557] Data 0.019 (0.048) Batch 1.248 (1.124) Remain 26:12:46 loss: 0.1756 Lr: 0.00303 [2024-02-18 16:43:55,202 INFO misc.py line 119 87073] Train: [47/100][141/1557] Data 0.008 (0.047) Batch 0.910 (1.123) Remain 26:10:35 loss: 0.3928 Lr: 0.00303 [2024-02-18 16:43:56,153 INFO misc.py line 119 87073] Train: [47/100][142/1557] Data 0.003 (0.047) Batch 0.950 (1.121) Remain 26:08:49 loss: 0.1727 Lr: 0.00303 [2024-02-18 16:43:57,179 INFO misc.py line 119 87073] Train: [47/100][143/1557] Data 0.004 (0.047) Batch 1.027 (1.121) Remain 26:07:51 loss: 0.4684 Lr: 0.00303 [2024-02-18 16:43:58,099 INFO misc.py line 119 87073] Train: [47/100][144/1557] Data 0.003 (0.046) Batch 0.919 (1.119) Remain 26:05:50 loss: 0.3920 Lr: 0.00303 [2024-02-18 16:43:58,874 INFO misc.py line 119 87073] Train: [47/100][145/1557] Data 0.004 (0.046) Batch 0.764 (1.117) Remain 26:02:19 loss: 0.3899 Lr: 0.00303 [2024-02-18 16:43:59,655 INFO misc.py line 119 87073] Train: [47/100][146/1557] Data 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Batch 1.067 (1.150) Remain 26:48:28 loss: 0.1669 Lr: 0.00303 [2024-02-18 16:44:41,353 INFO misc.py line 119 87073] Train: [47/100][178/1557] Data 0.003 (0.056) Batch 0.936 (1.149) Remain 26:46:44 loss: 0.6144 Lr: 0.00303 [2024-02-18 16:44:42,278 INFO misc.py line 119 87073] Train: [47/100][179/1557] Data 0.003 (0.055) Batch 0.916 (1.148) Remain 26:44:52 loss: 0.2380 Lr: 0.00303 [2024-02-18 16:44:44,829 INFO misc.py line 119 87073] Train: [47/100][180/1557] Data 0.711 (0.059) Batch 2.560 (1.156) Remain 26:56:00 loss: 0.3678 Lr: 0.00303 [2024-02-18 16:44:45,587 INFO misc.py line 119 87073] Train: [47/100][181/1557] Data 0.003 (0.059) Batch 0.749 (1.153) Remain 26:52:47 loss: 0.3506 Lr: 0.00303 [2024-02-18 16:44:46,806 INFO misc.py line 119 87073] Train: [47/100][182/1557] Data 0.011 (0.058) Batch 1.220 (1.154) Remain 26:53:17 loss: 0.1468 Lr: 0.00303 [2024-02-18 16:44:47,629 INFO misc.py line 119 87073] Train: [47/100][183/1557] Data 0.011 (0.058) Batch 0.831 (1.152) Remain 26:50:46 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Batch 0.963 (1.158) Remain 26:57:52 loss: 0.3858 Lr: 0.00302 [2024-02-18 16:45:47,517 INFO misc.py line 119 87073] Train: [47/100][234/1557] Data 0.007 (0.060) Batch 0.957 (1.157) Remain 26:56:38 loss: 0.4157 Lr: 0.00302 [2024-02-18 16:45:48,479 INFO misc.py line 119 87073] Train: [47/100][235/1557] Data 0.007 (0.060) Batch 0.962 (1.156) Remain 26:55:27 loss: 0.2709 Lr: 0.00302 [2024-02-18 16:45:49,296 INFO misc.py line 119 87073] Train: [47/100][236/1557] Data 0.006 (0.060) Batch 0.813 (1.155) Remain 26:53:22 loss: 0.2149 Lr: 0.00302 [2024-02-18 16:45:49,996 INFO misc.py line 119 87073] Train: [47/100][237/1557] Data 0.010 (0.059) Batch 0.706 (1.153) Remain 26:50:40 loss: 0.3920 Lr: 0.00302 [2024-02-18 16:45:51,178 INFO misc.py line 119 87073] Train: [47/100][238/1557] Data 0.004 (0.059) Batch 1.183 (1.153) Remain 26:50:50 loss: 0.2059 Lr: 0.00302 [2024-02-18 16:45:52,233 INFO misc.py line 119 87073] Train: [47/100][239/1557] Data 0.004 (0.059) Batch 1.056 (1.152) Remain 26:50:14 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Batch 1.031 (1.152) Remain 26:48:57 loss: 0.3031 Lr: 0.00302 [2024-02-18 16:46:50,977 INFO misc.py line 119 87073] Train: [47/100][290/1557] Data 0.005 (0.058) Batch 1.190 (1.152) Remain 26:49:07 loss: 0.4817 Lr: 0.00302 [2024-02-18 16:46:52,132 INFO misc.py line 119 87073] Train: [47/100][291/1557] Data 0.003 (0.058) Batch 1.155 (1.152) Remain 26:49:07 loss: 0.2143 Lr: 0.00302 [2024-02-18 16:46:52,902 INFO misc.py line 119 87073] Train: [47/100][292/1557] Data 0.003 (0.058) Batch 0.770 (1.151) Remain 26:47:15 loss: 0.4879 Lr: 0.00302 [2024-02-18 16:46:53,563 INFO misc.py line 119 87073] Train: [47/100][293/1557] Data 0.002 (0.058) Batch 0.661 (1.149) Remain 26:44:52 loss: 0.2438 Lr: 0.00302 [2024-02-18 16:46:54,753 INFO misc.py line 119 87073] Train: [47/100][294/1557] Data 0.003 (0.058) Batch 1.188 (1.149) Remain 26:45:02 loss: 0.1941 Lr: 0.00302 [2024-02-18 16:46:55,768 INFO misc.py line 119 87073] Train: [47/100][295/1557] Data 0.005 (0.057) Batch 1.014 (1.149) Remain 26:44:22 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[2024-02-18 16:47:26,000 INFO misc.py line 119 87073] Train: [47/100][327/1557] Data 0.014 (0.052) Batch 0.788 (1.129) Remain 26:15:37 loss: 0.4958 Lr: 0.00302 [2024-02-18 16:47:26,712 INFO misc.py line 119 87073] Train: [47/100][328/1557] Data 0.003 (0.052) Batch 0.709 (1.127) Remain 26:13:48 loss: 0.2318 Lr: 0.00302 [2024-02-18 16:47:27,908 INFO misc.py line 119 87073] Train: [47/100][329/1557] Data 0.006 (0.052) Batch 1.199 (1.128) Remain 26:14:05 loss: 0.2658 Lr: 0.00302 [2024-02-18 16:47:28,797 INFO misc.py line 119 87073] Train: [47/100][330/1557] Data 0.004 (0.052) Batch 0.889 (1.127) Remain 26:13:03 loss: 0.4672 Lr: 0.00302 [2024-02-18 16:47:29,914 INFO misc.py line 119 87073] Train: [47/100][331/1557] Data 0.004 (0.052) Batch 1.116 (1.127) Remain 26:12:59 loss: 0.6941 Lr: 0.00302 [2024-02-18 16:47:30,935 INFO misc.py line 119 87073] Train: [47/100][332/1557] Data 0.004 (0.052) Batch 1.022 (1.127) Remain 26:12:31 loss: 0.6878 Lr: 0.00302 [2024-02-18 16:47:32,105 INFO misc.py 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Batch 1.010 (1.148) Remain 26:41:59 loss: 0.7597 Lr: 0.00302 [2024-02-18 16:47:53,884 INFO misc.py line 119 87073] Train: [47/100][346/1557] Data 0.006 (0.057) Batch 1.000 (1.147) Remain 26:41:21 loss: 0.9862 Lr: 0.00302 [2024-02-18 16:47:54,756 INFO misc.py line 119 87073] Train: [47/100][347/1557] Data 0.025 (0.057) Batch 0.894 (1.147) Remain 26:40:19 loss: 0.6787 Lr: 0.00302 [2024-02-18 16:47:55,508 INFO misc.py line 119 87073] Train: [47/100][348/1557] Data 0.003 (0.057) Batch 0.752 (1.146) Remain 26:38:42 loss: 0.3649 Lr: 0.00302 [2024-02-18 16:47:56,236 INFO misc.py line 119 87073] Train: [47/100][349/1557] Data 0.003 (0.057) Batch 0.721 (1.144) Remain 26:36:58 loss: 0.3818 Lr: 0.00302 [2024-02-18 16:47:57,589 INFO misc.py line 119 87073] Train: [47/100][350/1557] Data 0.011 (0.056) Batch 1.299 (1.145) Remain 26:37:34 loss: 0.1898 Lr: 0.00302 [2024-02-18 16:47:58,514 INFO misc.py line 119 87073] Train: [47/100][351/1557] Data 0.065 (0.056) Batch 0.986 (1.144) Remain 26:36:54 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line 119 87073] Train: [47/100][389/1557] Data 0.003 (0.053) Batch 1.055 (1.130) Remain 26:15:40 loss: 0.2111 Lr: 0.00301 [2024-02-18 16:48:37,057 INFO misc.py line 119 87073] Train: [47/100][390/1557] Data 0.003 (0.053) Batch 0.734 (1.129) Remain 26:14:13 loss: 0.6951 Lr: 0.00301 [2024-02-18 16:48:37,865 INFO misc.py line 119 87073] Train: [47/100][391/1557] Data 0.003 (0.053) Batch 0.798 (1.128) Remain 26:13:01 loss: 0.1656 Lr: 0.00301 [2024-02-18 16:48:39,043 INFO misc.py line 119 87073] Train: [47/100][392/1557] Data 0.013 (0.052) Batch 1.177 (1.128) Remain 26:13:10 loss: 0.1806 Lr: 0.00301 [2024-02-18 16:48:40,165 INFO misc.py line 119 87073] Train: [47/100][393/1557] Data 0.015 (0.052) Batch 1.124 (1.128) Remain 26:13:08 loss: 0.3615 Lr: 0.00301 [2024-02-18 16:48:41,013 INFO misc.py line 119 87073] Train: [47/100][394/1557] Data 0.013 (0.052) Batch 0.858 (1.127) Remain 26:12:10 loss: 0.3683 Lr: 0.00301 [2024-02-18 16:48:42,033 INFO misc.py line 119 87073] Train: 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Batch 1.083 (1.148) Remain 26:38:14 loss: 0.4675 Lr: 0.00301 [2024-02-18 16:51:06,376 INFO misc.py line 119 87073] Train: [47/100][514/1557] Data 0.004 (0.058) Batch 0.854 (1.147) Remain 26:37:25 loss: 0.1226 Lr: 0.00301 [2024-02-18 16:51:07,248 INFO misc.py line 119 87073] Train: [47/100][515/1557] Data 0.004 (0.058) Batch 0.864 (1.146) Remain 26:36:38 loss: 0.2312 Lr: 0.00301 [2024-02-18 16:51:08,010 INFO misc.py line 119 87073] Train: [47/100][516/1557] Data 0.011 (0.058) Batch 0.771 (1.146) Remain 26:35:36 loss: 0.2192 Lr: 0.00301 [2024-02-18 16:51:08,758 INFO misc.py line 119 87073] Train: [47/100][517/1557] Data 0.004 (0.058) Batch 0.743 (1.145) Remain 26:34:29 loss: 0.4563 Lr: 0.00301 [2024-02-18 16:51:09,965 INFO misc.py line 119 87073] Train: [47/100][518/1557] Data 0.009 (0.058) Batch 1.203 (1.145) Remain 26:34:37 loss: 0.2036 Lr: 0.00301 [2024-02-18 16:51:11,062 INFO misc.py line 119 87073] Train: [47/100][519/1557] Data 0.012 (0.058) Batch 1.100 (1.145) Remain 26:34:29 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Batch 0.844 (1.148) Remain 26:37:09 loss: 0.7555 Lr: 0.00300 [2024-02-18 16:53:15,454 INFO misc.py line 119 87073] Train: [47/100][626/1557] Data 0.003 (0.058) Batch 0.926 (1.148) Remain 26:36:38 loss: 0.3447 Lr: 0.00300 [2024-02-18 16:53:16,343 INFO misc.py line 119 87073] Train: [47/100][627/1557] Data 0.012 (0.058) Batch 0.898 (1.148) Remain 26:36:04 loss: 0.2498 Lr: 0.00300 [2024-02-18 16:53:17,084 INFO misc.py line 119 87073] Train: [47/100][628/1557] Data 0.003 (0.058) Batch 0.740 (1.147) Remain 26:35:08 loss: 0.1514 Lr: 0.00300 [2024-02-18 16:53:17,823 INFO misc.py line 119 87073] Train: [47/100][629/1557] Data 0.003 (0.058) Batch 0.735 (1.146) Remain 26:34:12 loss: 0.3378 Lr: 0.00300 [2024-02-18 16:53:19,028 INFO misc.py line 119 87073] Train: [47/100][630/1557] Data 0.007 (0.058) Batch 1.203 (1.146) Remain 26:34:19 loss: 0.1486 Lr: 0.00300 [2024-02-18 16:53:20,102 INFO misc.py line 119 87073] Train: [47/100][631/1557] Data 0.009 (0.058) Batch 1.070 (1.146) Remain 26:34:07 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line 119 87073] Train: [47/100][669/1557] Data 0.003 (0.055) Batch 0.931 (1.136) Remain 26:18:36 loss: 0.3984 Lr: 0.00300 [2024-02-18 16:53:57,293 INFO misc.py line 119 87073] Train: [47/100][670/1557] Data 0.011 (0.055) Batch 0.736 (1.135) Remain 26:17:45 loss: 0.4348 Lr: 0.00300 [2024-02-18 16:53:58,099 INFO misc.py line 119 87073] Train: [47/100][671/1557] Data 0.003 (0.055) Batch 0.803 (1.134) Remain 26:17:02 loss: 0.2762 Lr: 0.00300 [2024-02-18 16:53:59,372 INFO misc.py line 119 87073] Train: [47/100][672/1557] Data 0.006 (0.055) Batch 1.272 (1.135) Remain 26:17:18 loss: 0.2439 Lr: 0.00300 [2024-02-18 16:54:00,397 INFO misc.py line 119 87073] Train: [47/100][673/1557] Data 0.008 (0.055) Batch 1.017 (1.134) Remain 26:17:02 loss: 0.2945 Lr: 0.00300 [2024-02-18 16:54:01,244 INFO misc.py line 119 87073] Train: [47/100][674/1557] Data 0.016 (0.055) Batch 0.860 (1.134) Remain 26:16:27 loss: 0.5072 Lr: 0.00300 [2024-02-18 16:54:02,302 INFO misc.py line 119 87073] Train: 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Batch 0.992 (1.148) Remain 26:35:13 loss: 0.6013 Lr: 0.00300 [2024-02-18 16:54:19,271 INFO misc.py line 119 87073] Train: [47/100][682/1557] Data 0.003 (0.059) Batch 0.869 (1.147) Remain 26:34:38 loss: 0.6040 Lr: 0.00300 [2024-02-18 16:54:20,125 INFO misc.py line 119 87073] Train: [47/100][683/1557] Data 0.003 (0.059) Batch 0.845 (1.147) Remain 26:33:59 loss: 0.7455 Lr: 0.00300 [2024-02-18 16:54:20,839 INFO misc.py line 119 87073] Train: [47/100][684/1557] Data 0.011 (0.059) Batch 0.721 (1.146) Remain 26:33:06 loss: 0.2260 Lr: 0.00300 [2024-02-18 16:54:21,561 INFO misc.py line 119 87073] Train: [47/100][685/1557] Data 0.004 (0.059) Batch 0.716 (1.146) Remain 26:32:12 loss: 0.3899 Lr: 0.00300 [2024-02-18 16:54:22,833 INFO misc.py line 119 87073] Train: [47/100][686/1557] Data 0.010 (0.059) Batch 1.272 (1.146) Remain 26:32:27 loss: 0.1866 Lr: 0.00300 [2024-02-18 16:54:23,746 INFO misc.py line 119 87073] Train: [47/100][687/1557] Data 0.010 (0.059) Batch 0.919 (1.145) Remain 26:31:58 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Batch 0.921 (1.147) Remain 26:33:21 loss: 0.4819 Lr: 0.00300 [2024-02-18 16:55:23,181 INFO misc.py line 119 87073] Train: [47/100][738/1557] Data 0.004 (0.061) Batch 0.924 (1.147) Remain 26:32:55 loss: 0.2607 Lr: 0.00300 [2024-02-18 16:55:24,134 INFO misc.py line 119 87073] Train: [47/100][739/1557] Data 0.003 (0.061) Batch 0.953 (1.147) Remain 26:32:32 loss: 0.2999 Lr: 0.00300 [2024-02-18 16:55:24,902 INFO misc.py line 119 87073] Train: [47/100][740/1557] Data 0.003 (0.061) Batch 0.762 (1.146) Remain 26:31:47 loss: 0.4112 Lr: 0.00300 [2024-02-18 16:55:25,667 INFO misc.py line 119 87073] Train: [47/100][741/1557] Data 0.010 (0.061) Batch 0.770 (1.146) Remain 26:31:04 loss: 0.1681 Lr: 0.00300 [2024-02-18 16:55:26,890 INFO misc.py line 119 87073] Train: [47/100][742/1557] Data 0.004 (0.061) Batch 1.223 (1.146) Remain 26:31:11 loss: 0.1715 Lr: 0.00300 [2024-02-18 16:55:27,806 INFO misc.py line 119 87073] Train: [47/100][743/1557] Data 0.004 (0.061) Batch 0.916 (1.145) Remain 26:30:44 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Batch 1.054 (1.146) Remain 26:31:02 loss: 0.4255 Lr: 0.00299 [2024-02-18 16:56:26,703 INFO misc.py line 119 87073] Train: [47/100][794/1557] Data 0.003 (0.061) Batch 0.916 (1.146) Remain 26:30:37 loss: 0.4205 Lr: 0.00299 [2024-02-18 16:56:27,566 INFO misc.py line 119 87073] Train: [47/100][795/1557] Data 0.004 (0.061) Batch 0.858 (1.146) Remain 26:30:06 loss: 0.3394 Lr: 0.00299 [2024-02-18 16:56:28,345 INFO misc.py line 119 87073] Train: [47/100][796/1557] Data 0.009 (0.061) Batch 0.784 (1.145) Remain 26:29:26 loss: 0.2524 Lr: 0.00299 [2024-02-18 16:56:29,085 INFO misc.py line 119 87073] Train: [47/100][797/1557] Data 0.004 (0.061) Batch 0.734 (1.145) Remain 26:28:42 loss: 0.2515 Lr: 0.00299 [2024-02-18 16:56:30,359 INFO misc.py line 119 87073] Train: [47/100][798/1557] Data 0.010 (0.061) Batch 1.273 (1.145) Remain 26:28:54 loss: 0.2129 Lr: 0.00299 [2024-02-18 16:56:31,346 INFO misc.py line 119 87073] Train: [47/100][799/1557] Data 0.012 (0.061) Batch 0.996 (1.145) Remain 26:28:38 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Batch 0.935 (1.148) Remain 26:30:54 loss: 0.3511 Lr: 0.00299 [2024-02-18 16:58:36,267 INFO misc.py line 119 87073] Train: [47/100][906/1557] Data 0.004 (0.061) Batch 0.791 (1.147) Remain 26:30:20 loss: 0.4134 Lr: 0.00299 [2024-02-18 16:58:37,273 INFO misc.py line 119 87073] Train: [47/100][907/1557] Data 0.011 (0.061) Batch 1.003 (1.147) Remain 26:30:06 loss: 0.4148 Lr: 0.00299 [2024-02-18 16:58:38,054 INFO misc.py line 119 87073] Train: [47/100][908/1557] Data 0.013 (0.061) Batch 0.791 (1.147) Remain 26:29:32 loss: 0.2144 Lr: 0.00299 [2024-02-18 16:58:38,925 INFO misc.py line 119 87073] Train: [47/100][909/1557] Data 0.003 (0.061) Batch 0.871 (1.146) Remain 26:29:06 loss: 0.1434 Lr: 0.00299 [2024-02-18 16:58:40,160 INFO misc.py line 119 87073] Train: [47/100][910/1557] Data 0.003 (0.061) Batch 1.227 (1.147) Remain 26:29:12 loss: 0.1803 Lr: 0.00299 [2024-02-18 16:58:41,166 INFO misc.py line 119 87073] Train: [47/100][911/1557] Data 0.011 (0.061) Batch 1.003 (1.146) Remain 26:28:58 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Batch 0.888 (1.147) Remain 26:28:47 loss: 0.2618 Lr: 0.00298 [2024-02-18 16:59:40,005 INFO misc.py line 119 87073] Train: [47/100][962/1557] Data 0.003 (0.061) Batch 0.989 (1.147) Remain 26:28:32 loss: 0.4236 Lr: 0.00298 [2024-02-18 16:59:41,018 INFO misc.py line 119 87073] Train: [47/100][963/1557] Data 0.013 (0.061) Batch 1.014 (1.147) Remain 26:28:19 loss: 0.2317 Lr: 0.00298 [2024-02-18 16:59:41,763 INFO misc.py line 119 87073] Train: [47/100][964/1557] Data 0.012 (0.061) Batch 0.754 (1.146) Remain 26:27:44 loss: 1.1345 Lr: 0.00298 [2024-02-18 16:59:42,513 INFO misc.py line 119 87073] Train: [47/100][965/1557] Data 0.003 (0.061) Batch 0.740 (1.146) Remain 26:27:08 loss: 0.3303 Lr: 0.00298 [2024-02-18 16:59:43,728 INFO misc.py line 119 87073] Train: [47/100][966/1557] Data 0.013 (0.061) Batch 1.216 (1.146) Remain 26:27:13 loss: 0.1692 Lr: 0.00298 [2024-02-18 16:59:44,897 INFO misc.py line 119 87073] Train: [47/100][967/1557] Data 0.012 (0.061) Batch 1.170 (1.146) Remain 26:27:14 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Train: [47/100][1290/1557] Data 0.004 (0.060) Batch 1.079 (1.139) Remain 26:11:06 loss: 0.5329 Lr: 0.00297 [2024-02-18 17:05:46,753 INFO misc.py line 119 87073] Train: [47/100][1291/1557] Data 0.003 (0.060) Batch 1.032 (1.139) Remain 26:10:58 loss: 0.1934 Lr: 0.00297 [2024-02-18 17:05:47,653 INFO misc.py line 119 87073] Train: [47/100][1292/1557] Data 0.003 (0.060) Batch 0.900 (1.138) Remain 26:10:42 loss: 0.6394 Lr: 0.00297 [2024-02-18 17:05:48,414 INFO misc.py line 119 87073] Train: [47/100][1293/1557] Data 0.004 (0.060) Batch 0.754 (1.138) Remain 26:10:16 loss: 0.2987 Lr: 0.00297 [2024-02-18 17:05:49,150 INFO misc.py line 119 87073] Train: [47/100][1294/1557] Data 0.010 (0.060) Batch 0.743 (1.138) Remain 26:09:50 loss: 0.3341 Lr: 0.00297 [2024-02-18 17:06:01,061 INFO misc.py line 119 87073] Train: [47/100][1295/1557] Data 2.820 (0.062) Batch 11.911 (1.146) Remain 26:21:19 loss: 0.0794 Lr: 0.00297 [2024-02-18 17:06:02,265 INFO misc.py line 119 87073] Train: [47/100][1296/1557] Data 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Remain 26:19:47 loss: 0.1910 Lr: 0.00297 [2024-02-18 17:06:08,671 INFO misc.py line 119 87073] Train: [47/100][1303/1557] Data 0.014 (0.061) Batch 0.900 (1.145) Remain 26:19:30 loss: 0.2908 Lr: 0.00297 [2024-02-18 17:06:09,705 INFO misc.py line 119 87073] Train: [47/100][1304/1557] Data 0.004 (0.061) Batch 1.034 (1.145) Remain 26:19:22 loss: 0.4316 Lr: 0.00297 [2024-02-18 17:06:10,710 INFO misc.py line 119 87073] Train: [47/100][1305/1557] Data 0.003 (0.061) Batch 1.005 (1.145) Remain 26:19:12 loss: 0.3833 Lr: 0.00297 [2024-02-18 17:06:11,652 INFO misc.py line 119 87073] Train: [47/100][1306/1557] Data 0.003 (0.061) Batch 0.942 (1.145) Remain 26:18:58 loss: 0.4036 Lr: 0.00297 [2024-02-18 17:06:12,341 INFO misc.py line 119 87073] Train: [47/100][1307/1557] Data 0.003 (0.061) Batch 0.683 (1.144) Remain 26:18:28 loss: 0.1798 Lr: 0.00297 [2024-02-18 17:06:13,125 INFO misc.py line 119 87073] Train: [47/100][1308/1557] Data 0.010 (0.061) Batch 0.791 (1.144) Remain 26:18:04 loss: 0.1648 Lr: 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Train: [47/100][1321/1557] Data 0.003 (0.061) Batch 0.761 (1.143) Remain 26:15:52 loss: 0.2629 Lr: 0.00297 [2024-02-18 17:06:26,885 INFO misc.py line 119 87073] Train: [47/100][1322/1557] Data 0.005 (0.061) Batch 0.747 (1.142) Remain 26:15:26 loss: 0.0884 Lr: 0.00297 [2024-02-18 17:06:28,037 INFO misc.py line 119 87073] Train: [47/100][1323/1557] Data 0.005 (0.061) Batch 1.145 (1.142) Remain 26:15:25 loss: 0.2699 Lr: 0.00297 [2024-02-18 17:06:28,854 INFO misc.py line 119 87073] Train: [47/100][1324/1557] Data 0.012 (0.061) Batch 0.826 (1.142) Remain 26:15:04 loss: 0.2203 Lr: 0.00297 [2024-02-18 17:06:29,818 INFO misc.py line 119 87073] Train: [47/100][1325/1557] Data 0.005 (0.061) Batch 0.963 (1.142) Remain 26:14:52 loss: 0.4419 Lr: 0.00297 [2024-02-18 17:06:30,655 INFO misc.py line 119 87073] Train: [47/100][1326/1557] Data 0.005 (0.061) Batch 0.838 (1.142) Remain 26:14:32 loss: 0.4855 Lr: 0.00297 [2024-02-18 17:06:31,722 INFO misc.py line 119 87073] Train: [47/100][1327/1557] Data 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Remain 26:12:59 loss: 0.4651 Lr: 0.00297 [2024-02-18 17:06:38,225 INFO misc.py line 119 87073] Train: [47/100][1334/1557] Data 0.008 (0.060) Batch 0.941 (1.140) Remain 26:12:46 loss: 0.6475 Lr: 0.00297 [2024-02-18 17:06:39,000 INFO misc.py line 119 87073] Train: [47/100][1335/1557] Data 0.005 (0.060) Batch 0.777 (1.140) Remain 26:12:22 loss: 0.2405 Lr: 0.00297 [2024-02-18 17:06:39,747 INFO misc.py line 119 87073] Train: [47/100][1336/1557] Data 0.003 (0.060) Batch 0.738 (1.140) Remain 26:11:56 loss: 0.4273 Lr: 0.00297 [2024-02-18 17:06:40,974 INFO misc.py line 119 87073] Train: [47/100][1337/1557] Data 0.011 (0.060) Batch 1.232 (1.140) Remain 26:12:00 loss: 0.2964 Lr: 0.00297 [2024-02-18 17:06:41,936 INFO misc.py line 119 87073] Train: [47/100][1338/1557] Data 0.006 (0.060) Batch 0.965 (1.140) Remain 26:11:48 loss: 0.2116 Lr: 0.00297 [2024-02-18 17:06:42,925 INFO misc.py line 119 87073] Train: [47/100][1339/1557] Data 0.004 (0.060) Batch 0.990 (1.140) Remain 26:11:38 loss: 0.4544 Lr: 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INFO misc.py line 119 87073] Train: [47/100][1346/1557] Data 0.013 (0.060) Batch 0.967 (1.139) Remain 26:10:04 loss: 0.2425 Lr: 0.00296 [2024-02-18 17:06:50,597 INFO misc.py line 119 87073] Train: [47/100][1347/1557] Data 0.004 (0.060) Batch 1.093 (1.139) Remain 26:10:00 loss: 0.4261 Lr: 0.00296 [2024-02-18 17:06:51,510 INFO misc.py line 119 87073] Train: [47/100][1348/1557] Data 0.005 (0.060) Batch 0.914 (1.138) Remain 26:09:45 loss: 0.3458 Lr: 0.00296 [2024-02-18 17:06:52,296 INFO misc.py line 119 87073] Train: [47/100][1349/1557] Data 0.003 (0.060) Batch 0.780 (1.138) Remain 26:09:22 loss: 0.2845 Lr: 0.00296 [2024-02-18 17:06:53,214 INFO misc.py line 119 87073] Train: [47/100][1350/1557] Data 0.009 (0.060) Batch 0.924 (1.138) Remain 26:09:07 loss: 0.4024 Lr: 0.00296 [2024-02-18 17:07:06,428 INFO misc.py line 119 87073] Train: [47/100][1351/1557] Data 3.068 (0.062) Batch 13.214 (1.147) Remain 26:21:27 loss: 0.1022 Lr: 0.00296 [2024-02-18 17:07:07,297 INFO misc.py line 119 87073] Train: [47/100][1352/1557] Data 0.004 (0.062) Batch 0.869 (1.147) Remain 26:21:09 loss: 0.3013 Lr: 0.00296 [2024-02-18 17:07:08,181 INFO misc.py line 119 87073] Train: [47/100][1353/1557] Data 0.003 (0.062) Batch 0.876 (1.147) Remain 26:20:51 loss: 0.4696 Lr: 0.00296 [2024-02-18 17:07:09,099 INFO misc.py line 119 87073] Train: [47/100][1354/1557] Data 0.012 (0.062) Batch 0.926 (1.146) Remain 26:20:37 loss: 0.3512 Lr: 0.00296 [2024-02-18 17:07:10,121 INFO misc.py line 119 87073] Train: [47/100][1355/1557] Data 0.004 (0.062) Batch 1.022 (1.146) Remain 26:20:28 loss: 0.1218 Lr: 0.00296 [2024-02-18 17:07:10,886 INFO misc.py line 119 87073] Train: [47/100][1356/1557] Data 0.003 (0.062) Batch 0.764 (1.146) Remain 26:20:03 loss: 0.2647 Lr: 0.00296 [2024-02-18 17:07:11,640 INFO misc.py line 119 87073] Train: [47/100][1357/1557] Data 0.006 (0.062) Batch 0.747 (1.146) Remain 26:19:38 loss: 0.4219 Lr: 0.00296 [2024-02-18 17:07:12,862 INFO misc.py line 119 87073] Train: [47/100][1358/1557] Data 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Remain 26:18:10 loss: 0.2458 Lr: 0.00296 [2024-02-18 17:07:19,530 INFO misc.py line 119 87073] Train: [47/100][1365/1557] Data 0.010 (0.061) Batch 1.192 (1.145) Remain 26:18:11 loss: 0.1707 Lr: 0.00296 [2024-02-18 17:07:20,467 INFO misc.py line 119 87073] Train: [47/100][1366/1557] Data 0.010 (0.061) Batch 0.944 (1.145) Remain 26:17:58 loss: 0.4310 Lr: 0.00296 [2024-02-18 17:07:21,413 INFO misc.py line 119 87073] Train: [47/100][1367/1557] Data 0.003 (0.061) Batch 0.946 (1.145) Remain 26:17:45 loss: 0.7639 Lr: 0.00296 [2024-02-18 17:07:22,449 INFO misc.py line 119 87073] Train: [47/100][1368/1557] Data 0.003 (0.061) Batch 1.032 (1.144) Remain 26:17:37 loss: 0.6565 Lr: 0.00296 [2024-02-18 17:07:23,345 INFO misc.py line 119 87073] Train: [47/100][1369/1557] Data 0.007 (0.061) Batch 0.900 (1.144) Remain 26:17:21 loss: 0.2108 Lr: 0.00296 [2024-02-18 17:07:24,091 INFO misc.py line 119 87073] Train: [47/100][1370/1557] Data 0.003 (0.061) Batch 0.738 (1.144) Remain 26:16:55 loss: 0.3430 Lr: 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Train: [47/100][1383/1557] Data 0.003 (0.061) Batch 0.968 (1.142) Remain 26:14:19 loss: 0.4455 Lr: 0.00296 [2024-02-18 17:07:37,353 INFO misc.py line 119 87073] Train: [47/100][1384/1557] Data 0.003 (0.061) Batch 0.756 (1.142) Remain 26:13:55 loss: 0.3006 Lr: 0.00296 [2024-02-18 17:07:38,086 INFO misc.py line 119 87073] Train: [47/100][1385/1557] Data 0.003 (0.060) Batch 0.728 (1.142) Remain 26:13:29 loss: 0.2960 Lr: 0.00296 [2024-02-18 17:07:39,340 INFO misc.py line 119 87073] Train: [47/100][1386/1557] Data 0.009 (0.060) Batch 1.252 (1.142) Remain 26:13:34 loss: 0.1843 Lr: 0.00296 [2024-02-18 17:07:40,223 INFO misc.py line 119 87073] Train: [47/100][1387/1557] Data 0.011 (0.060) Batch 0.890 (1.142) Remain 26:13:18 loss: 0.3598 Lr: 0.00296 [2024-02-18 17:07:41,091 INFO misc.py line 119 87073] Train: [47/100][1388/1557] Data 0.003 (0.060) Batch 0.868 (1.141) Remain 26:13:01 loss: 0.1510 Lr: 0.00296 [2024-02-18 17:07:42,113 INFO misc.py line 119 87073] Train: [47/100][1389/1557] Data 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Remain 26:11:38 loss: 0.1628 Lr: 0.00296 [2024-02-18 17:07:48,842 INFO misc.py line 119 87073] Train: [47/100][1396/1557] Data 0.004 (0.060) Batch 1.008 (1.140) Remain 26:11:29 loss: 0.6456 Lr: 0.00296 [2024-02-18 17:07:49,710 INFO misc.py line 119 87073] Train: [47/100][1397/1557] Data 0.004 (0.060) Batch 0.868 (1.140) Remain 26:11:12 loss: 0.7255 Lr: 0.00296 [2024-02-18 17:07:50,454 INFO misc.py line 119 87073] Train: [47/100][1398/1557] Data 0.005 (0.060) Batch 0.742 (1.140) Remain 26:10:47 loss: 0.3295 Lr: 0.00296 [2024-02-18 17:07:51,183 INFO misc.py line 119 87073] Train: [47/100][1399/1557] Data 0.008 (0.060) Batch 0.732 (1.140) Remain 26:10:22 loss: 0.4003 Lr: 0.00296 [2024-02-18 17:07:52,353 INFO misc.py line 119 87073] Train: [47/100][1400/1557] Data 0.004 (0.060) Batch 1.170 (1.140) Remain 26:10:23 loss: 0.2218 Lr: 0.00296 [2024-02-18 17:07:53,185 INFO misc.py line 119 87073] Train: [47/100][1401/1557] Data 0.004 (0.060) Batch 0.829 (1.139) Remain 26:10:03 loss: 0.1723 Lr: 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INFO misc.py line 119 87073] Train: [47/100][1408/1557] Data 0.004 (0.062) Batch 0.853 (1.147) Remain 26:20:17 loss: 0.3792 Lr: 0.00296 [2024-02-18 17:08:12,686 INFO misc.py line 119 87073] Train: [47/100][1409/1557] Data 0.004 (0.062) Batch 0.952 (1.147) Remain 26:20:05 loss: 0.4129 Lr: 0.00296 [2024-02-18 17:08:13,805 INFO misc.py line 119 87073] Train: [47/100][1410/1557] Data 0.007 (0.062) Batch 1.118 (1.147) Remain 26:20:02 loss: 0.3021 Lr: 0.00296 [2024-02-18 17:08:14,712 INFO misc.py line 119 87073] Train: [47/100][1411/1557] Data 0.008 (0.062) Batch 0.911 (1.147) Remain 26:19:47 loss: 0.5717 Lr: 0.00296 [2024-02-18 17:08:15,435 INFO misc.py line 119 87073] Train: [47/100][1412/1557] Data 0.004 (0.062) Batch 0.722 (1.146) Remain 26:19:21 loss: 0.2426 Lr: 0.00296 [2024-02-18 17:08:16,160 INFO misc.py line 119 87073] Train: [47/100][1413/1557] Data 0.004 (0.062) Batch 0.722 (1.146) Remain 26:18:55 loss: 0.7732 Lr: 0.00296 [2024-02-18 17:08:17,317 INFO misc.py line 119 87073] Train: [47/100][1414/1557] Data 0.007 (0.062) Batch 1.156 (1.146) Remain 26:18:54 loss: 0.1711 Lr: 0.00296 [2024-02-18 17:08:18,132 INFO misc.py line 119 87073] Train: [47/100][1415/1557] Data 0.010 (0.062) Batch 0.818 (1.146) Remain 26:18:34 loss: 0.4395 Lr: 0.00296 [2024-02-18 17:08:18,899 INFO misc.py line 119 87073] Train: [47/100][1416/1557] Data 0.006 (0.062) Batch 0.768 (1.146) Remain 26:18:11 loss: 0.5578 Lr: 0.00296 [2024-02-18 17:08:19,862 INFO misc.py line 119 87073] Train: [47/100][1417/1557] Data 0.006 (0.062) Batch 0.959 (1.145) Remain 26:17:59 loss: 0.5544 Lr: 0.00296 [2024-02-18 17:08:20,799 INFO misc.py line 119 87073] Train: [47/100][1418/1557] Data 0.010 (0.062) Batch 0.942 (1.145) Remain 26:17:46 loss: 0.5610 Lr: 0.00296 [2024-02-18 17:08:21,527 INFO misc.py line 119 87073] Train: [47/100][1419/1557] Data 0.005 (0.062) Batch 0.728 (1.145) Remain 26:17:20 loss: 0.1268 Lr: 0.00296 [2024-02-18 17:08:22,283 INFO misc.py line 119 87073] Train: [47/100][1420/1557] Data 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Remain 26:15:50 loss: 0.6132 Lr: 0.00296 [2024-02-18 17:08:28,933 INFO misc.py line 119 87073] Train: [47/100][1427/1557] Data 0.004 (0.062) Batch 0.813 (1.144) Remain 26:15:29 loss: 0.3281 Lr: 0.00296 [2024-02-18 17:08:30,139 INFO misc.py line 119 87073] Train: [47/100][1428/1557] Data 0.004 (0.062) Batch 1.182 (1.144) Remain 26:15:30 loss: 0.2462 Lr: 0.00296 [2024-02-18 17:08:31,309 INFO misc.py line 119 87073] Train: [47/100][1429/1557] Data 0.029 (0.062) Batch 1.187 (1.144) Remain 26:15:32 loss: 0.2501 Lr: 0.00296 [2024-02-18 17:08:32,207 INFO misc.py line 119 87073] Train: [47/100][1430/1557] Data 0.011 (0.062) Batch 0.904 (1.144) Remain 26:15:17 loss: 0.4144 Lr: 0.00296 [2024-02-18 17:08:33,196 INFO misc.py line 119 87073] Train: [47/100][1431/1557] Data 0.005 (0.061) Batch 0.991 (1.144) Remain 26:15:07 loss: 0.4194 Lr: 0.00296 [2024-02-18 17:08:34,133 INFO misc.py line 119 87073] Train: [47/100][1432/1557] Data 0.003 (0.061) Batch 0.937 (1.143) Remain 26:14:54 loss: 0.4273 Lr: 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INFO misc.py line 119 87073] Train: [47/100][1439/1557] Data 0.004 (0.061) Batch 1.142 (1.143) Remain 26:13:39 loss: 0.4456 Lr: 0.00296 [2024-02-18 17:08:41,700 INFO misc.py line 119 87073] Train: [47/100][1440/1557] Data 0.004 (0.061) Batch 0.725 (1.142) Remain 26:13:13 loss: 0.4562 Lr: 0.00296 [2024-02-18 17:08:42,469 INFO misc.py line 119 87073] Train: [47/100][1441/1557] Data 0.004 (0.061) Batch 0.761 (1.142) Remain 26:12:50 loss: 0.2274 Lr: 0.00296 [2024-02-18 17:08:43,839 INFO misc.py line 119 87073] Train: [47/100][1442/1557] Data 0.012 (0.061) Batch 1.366 (1.142) Remain 26:13:02 loss: 0.1747 Lr: 0.00296 [2024-02-18 17:08:44,717 INFO misc.py line 119 87073] Train: [47/100][1443/1557] Data 0.015 (0.061) Batch 0.891 (1.142) Remain 26:12:47 loss: 0.3878 Lr: 0.00296 [2024-02-18 17:08:45,721 INFO misc.py line 119 87073] Train: [47/100][1444/1557] Data 0.003 (0.061) Batch 1.004 (1.142) Remain 26:12:37 loss: 0.4346 Lr: 0.00296 [2024-02-18 17:08:46,691 INFO misc.py line 119 87073] Train: [47/100][1445/1557] Data 0.004 (0.061) Batch 0.970 (1.142) Remain 26:12:26 loss: 0.1847 Lr: 0.00296 [2024-02-18 17:08:47,572 INFO misc.py line 119 87073] Train: [47/100][1446/1557] Data 0.003 (0.061) Batch 0.879 (1.142) Remain 26:12:10 loss: 0.2856 Lr: 0.00296 [2024-02-18 17:08:48,340 INFO misc.py line 119 87073] Train: [47/100][1447/1557] Data 0.007 (0.061) Batch 0.768 (1.141) Remain 26:11:48 loss: 0.5189 Lr: 0.00296 [2024-02-18 17:08:49,106 INFO misc.py line 119 87073] Train: [47/100][1448/1557] Data 0.007 (0.061) Batch 0.766 (1.141) Remain 26:11:25 loss: 0.3341 Lr: 0.00296 [2024-02-18 17:08:50,309 INFO misc.py line 119 87073] Train: [47/100][1449/1557] Data 0.006 (0.061) Batch 1.204 (1.141) Remain 26:11:28 loss: 0.2496 Lr: 0.00296 [2024-02-18 17:08:51,168 INFO misc.py line 119 87073] Train: [47/100][1450/1557] Data 0.005 (0.061) Batch 0.860 (1.141) Remain 26:11:10 loss: 0.8576 Lr: 0.00296 [2024-02-18 17:08:52,270 INFO misc.py line 119 87073] Train: [47/100][1451/1557] Data 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Remain 26:09:51 loss: 0.3677 Lr: 0.00296 [2024-02-18 17:08:58,826 INFO misc.py line 119 87073] Train: [47/100][1458/1557] Data 0.004 (0.060) Batch 0.932 (1.140) Remain 26:09:38 loss: 0.5419 Lr: 0.00296 [2024-02-18 17:08:59,861 INFO misc.py line 119 87073] Train: [47/100][1459/1557] Data 0.007 (0.060) Batch 1.037 (1.140) Remain 26:09:31 loss: 0.4374 Lr: 0.00296 [2024-02-18 17:09:01,030 INFO misc.py line 119 87073] Train: [47/100][1460/1557] Data 0.004 (0.060) Batch 1.170 (1.140) Remain 26:09:31 loss: 0.5877 Lr: 0.00296 [2024-02-18 17:09:01,809 INFO misc.py line 119 87073] Train: [47/100][1461/1557] Data 0.003 (0.060) Batch 0.778 (1.140) Remain 26:09:10 loss: 0.4829 Lr: 0.00296 [2024-02-18 17:09:02,539 INFO misc.py line 119 87073] Train: [47/100][1462/1557] Data 0.004 (0.060) Batch 0.686 (1.139) Remain 26:08:43 loss: 0.3763 Lr: 0.00296 [2024-02-18 17:09:13,891 INFO misc.py line 119 87073] Train: [47/100][1463/1557] Data 3.230 (0.062) Batch 11.394 (1.146) Remain 26:18:22 loss: 0.1108 Lr: 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INFO misc.py line 119 87073] Train: [47/100][1470/1557] Data 0.004 (0.062) Batch 1.261 (1.145) Remain 26:16:54 loss: 0.2244 Lr: 0.00296 [2024-02-18 17:09:21,343 INFO misc.py line 119 87073] Train: [47/100][1471/1557] Data 0.004 (0.062) Batch 0.856 (1.145) Remain 26:16:36 loss: 0.3515 Lr: 0.00296 [2024-02-18 17:09:22,312 INFO misc.py line 119 87073] Train: [47/100][1472/1557] Data 0.007 (0.062) Batch 0.969 (1.145) Remain 26:16:25 loss: 0.4038 Lr: 0.00296 [2024-02-18 17:09:23,222 INFO misc.py line 119 87073] Train: [47/100][1473/1557] Data 0.004 (0.062) Batch 0.910 (1.145) Remain 26:16:11 loss: 0.4333 Lr: 0.00296 [2024-02-18 17:09:24,182 INFO misc.py line 119 87073] Train: [47/100][1474/1557] Data 0.003 (0.062) Batch 0.960 (1.145) Remain 26:15:59 loss: 0.7663 Lr: 0.00296 [2024-02-18 17:09:24,934 INFO misc.py line 119 87073] Train: [47/100][1475/1557] Data 0.003 (0.062) Batch 0.751 (1.144) Remain 26:15:36 loss: 0.3132 Lr: 0.00296 [2024-02-18 17:09:25,692 INFO misc.py line 119 87073] Train: [47/100][1476/1557] Data 0.004 (0.062) Batch 0.758 (1.144) Remain 26:15:13 loss: 0.2990 Lr: 0.00296 [2024-02-18 17:09:26,999 INFO misc.py line 119 87073] Train: [47/100][1477/1557] Data 0.004 (0.062) Batch 1.304 (1.144) Remain 26:15:21 loss: 0.1705 Lr: 0.00296 [2024-02-18 17:09:27,982 INFO misc.py line 119 87073] Train: [47/100][1478/1557] Data 0.007 (0.062) Batch 0.986 (1.144) Remain 26:15:11 loss: 0.5273 Lr: 0.00296 [2024-02-18 17:09:29,048 INFO misc.py line 119 87073] Train: [47/100][1479/1557] Data 0.004 (0.062) Batch 1.067 (1.144) Remain 26:15:06 loss: 0.5035 Lr: 0.00296 [2024-02-18 17:09:29,997 INFO misc.py line 119 87073] Train: [47/100][1480/1557] Data 0.003 (0.062) Batch 0.949 (1.144) Remain 26:14:54 loss: 0.6110 Lr: 0.00296 [2024-02-18 17:09:31,055 INFO misc.py line 119 87073] Train: [47/100][1481/1557] Data 0.004 (0.062) Batch 1.058 (1.144) Remain 26:14:48 loss: 0.3547 Lr: 0.00296 [2024-02-18 17:09:31,771 INFO misc.py line 119 87073] Train: [47/100][1482/1557] Data 0.003 (0.062) Batch 0.716 (1.144) Remain 26:14:23 loss: 0.3466 Lr: 0.00296 [2024-02-18 17:09:32,542 INFO misc.py line 119 87073] Train: [47/100][1483/1557] Data 0.003 (0.062) Batch 0.766 (1.143) Remain 26:14:00 loss: 0.2585 Lr: 0.00296 [2024-02-18 17:09:33,717 INFO misc.py line 119 87073] Train: [47/100][1484/1557] Data 0.007 (0.062) Batch 1.178 (1.143) Remain 26:14:01 loss: 0.2355 Lr: 0.00296 [2024-02-18 17:09:34,743 INFO misc.py line 119 87073] Train: [47/100][1485/1557] Data 0.006 (0.062) Batch 1.019 (1.143) Remain 26:13:53 loss: 0.2941 Lr: 0.00296 [2024-02-18 17:09:35,768 INFO misc.py line 119 87073] Train: [47/100][1486/1557] Data 0.012 (0.062) Batch 1.030 (1.143) Remain 26:13:46 loss: 0.3243 Lr: 0.00296 [2024-02-18 17:09:36,728 INFO misc.py line 119 87073] Train: [47/100][1487/1557] Data 0.006 (0.062) Batch 0.963 (1.143) Remain 26:13:34 loss: 0.4767 Lr: 0.00296 [2024-02-18 17:09:37,562 INFO misc.py line 119 87073] Train: [47/100][1488/1557] Data 0.003 (0.061) Batch 0.834 (1.143) Remain 26:13:16 loss: 0.3555 Lr: 0.00296 [2024-02-18 17:09:38,314 INFO misc.py line 119 87073] Train: [47/100][1489/1557] Data 0.003 (0.061) Batch 0.745 (1.143) Remain 26:12:53 loss: 0.8068 Lr: 0.00296 [2024-02-18 17:09:39,015 INFO misc.py line 119 87073] Train: [47/100][1490/1557] Data 0.010 (0.061) Batch 0.709 (1.142) Remain 26:12:28 loss: 0.3671 Lr: 0.00296 [2024-02-18 17:09:40,203 INFO misc.py line 119 87073] Train: [47/100][1491/1557] Data 0.003 (0.061) Batch 1.183 (1.142) Remain 26:12:29 loss: 0.1329 Lr: 0.00296 [2024-02-18 17:09:41,129 INFO misc.py line 119 87073] Train: [47/100][1492/1557] Data 0.007 (0.061) Batch 0.929 (1.142) Remain 26:12:16 loss: 0.8134 Lr: 0.00296 [2024-02-18 17:09:42,049 INFO misc.py line 119 87073] Train: [47/100][1493/1557] Data 0.005 (0.061) Batch 0.921 (1.142) Remain 26:12:02 loss: 0.3535 Lr: 0.00296 [2024-02-18 17:09:42,985 INFO misc.py line 119 87073] Train: [47/100][1494/1557] Data 0.003 (0.061) Batch 0.934 (1.142) Remain 26:11:50 loss: 0.3296 Lr: 0.00296 [2024-02-18 17:09:43,842 INFO misc.py line 119 87073] Train: [47/100][1495/1557] Data 0.006 (0.061) Batch 0.859 (1.142) Remain 26:11:33 loss: 0.7544 Lr: 0.00296 [2024-02-18 17:09:44,609 INFO misc.py line 119 87073] Train: [47/100][1496/1557] Data 0.003 (0.061) Batch 0.767 (1.142) Remain 26:11:11 loss: 0.3036 Lr: 0.00296 [2024-02-18 17:09:45,335 INFO misc.py line 119 87073] Train: [47/100][1497/1557] Data 0.004 (0.061) Batch 0.726 (1.141) Remain 26:10:47 loss: 0.1590 Lr: 0.00296 [2024-02-18 17:09:46,742 INFO misc.py line 119 87073] Train: [47/100][1498/1557] Data 0.003 (0.061) Batch 1.403 (1.141) Remain 26:11:00 loss: 0.2624 Lr: 0.00296 [2024-02-18 17:09:47,727 INFO misc.py line 119 87073] Train: [47/100][1499/1557] Data 0.007 (0.061) Batch 0.989 (1.141) Remain 26:10:51 loss: 0.5604 Lr: 0.00296 [2024-02-18 17:09:48,823 INFO misc.py line 119 87073] Train: [47/100][1500/1557] Data 0.003 (0.061) Batch 1.094 (1.141) Remain 26:10:47 loss: 0.2900 Lr: 0.00296 [2024-02-18 17:09:49,572 INFO misc.py line 119 87073] Train: [47/100][1501/1557] Data 0.004 (0.061) Batch 0.750 (1.141) Remain 26:10:24 loss: 0.2024 Lr: 0.00296 [2024-02-18 17:09:50,329 INFO misc.py line 119 87073] Train: [47/100][1502/1557] Data 0.004 (0.061) Batch 0.754 (1.141) Remain 26:10:02 loss: 0.4819 Lr: 0.00296 [2024-02-18 17:09:51,072 INFO misc.py line 119 87073] Train: [47/100][1503/1557] Data 0.008 (0.061) Batch 0.748 (1.141) Remain 26:09:39 loss: 0.2629 Lr: 0.00296 [2024-02-18 17:09:51,870 INFO misc.py line 119 87073] Train: [47/100][1504/1557] Data 0.002 (0.061) Batch 0.791 (1.140) Remain 26:09:19 loss: 0.3259 Lr: 0.00296 [2024-02-18 17:09:52,996 INFO misc.py line 119 87073] Train: [47/100][1505/1557] Data 0.009 (0.061) Batch 1.130 (1.140) Remain 26:09:17 loss: 0.2846 Lr: 0.00296 [2024-02-18 17:09:53,799 INFO misc.py line 119 87073] Train: [47/100][1506/1557] Data 0.006 (0.061) Batch 0.805 (1.140) Remain 26:08:57 loss: 0.5680 Lr: 0.00296 [2024-02-18 17:09:54,808 INFO misc.py line 119 87073] Train: [47/100][1507/1557] Data 0.003 (0.061) Batch 1.010 (1.140) Remain 26:08:49 loss: 0.5100 Lr: 0.00296 [2024-02-18 17:09:55,794 INFO misc.py line 119 87073] Train: [47/100][1508/1557] Data 0.003 (0.061) Batch 0.985 (1.140) Remain 26:08:39 loss: 0.2326 Lr: 0.00296 [2024-02-18 17:09:56,711 INFO misc.py line 119 87073] Train: [47/100][1509/1557] Data 0.004 (0.061) Batch 0.917 (1.140) Remain 26:08:26 loss: 0.5583 Lr: 0.00296 [2024-02-18 17:09:57,481 INFO misc.py line 119 87073] Train: [47/100][1510/1557] Data 0.004 (0.061) Batch 0.768 (1.139) Remain 26:08:05 loss: 0.2897 Lr: 0.00296 [2024-02-18 17:09:58,203 INFO misc.py line 119 87073] Train: [47/100][1511/1557] Data 0.005 (0.061) Batch 0.724 (1.139) Remain 26:07:41 loss: 0.2750 Lr: 0.00296 [2024-02-18 17:09:59,370 INFO misc.py line 119 87073] Train: [47/100][1512/1557] Data 0.003 (0.061) Batch 1.167 (1.139) Remain 26:07:41 loss: 0.3085 Lr: 0.00296 [2024-02-18 17:10:00,312 INFO misc.py line 119 87073] Train: [47/100][1513/1557] Data 0.003 (0.061) Batch 0.942 (1.139) Remain 26:07:29 loss: 0.3505 Lr: 0.00296 [2024-02-18 17:10:01,268 INFO misc.py line 119 87073] Train: [47/100][1514/1557] Data 0.003 (0.060) Batch 0.955 (1.139) Remain 26:07:18 loss: 0.3017 Lr: 0.00296 [2024-02-18 17:10:02,080 INFO misc.py line 119 87073] Train: [47/100][1515/1557] Data 0.003 (0.060) Batch 0.804 (1.139) Remain 26:06:59 loss: 0.8504 Lr: 0.00296 [2024-02-18 17:10:02,943 INFO misc.py line 119 87073] Train: [47/100][1516/1557] Data 0.012 (0.060) Batch 0.871 (1.139) Remain 26:06:43 loss: 0.3271 Lr: 0.00296 [2024-02-18 17:10:03,701 INFO misc.py line 119 87073] Train: [47/100][1517/1557] Data 0.003 (0.060) Batch 0.759 (1.138) Remain 26:06:21 loss: 0.4058 Lr: 0.00296 [2024-02-18 17:10:04,372 INFO misc.py line 119 87073] Train: [47/100][1518/1557] Data 0.003 (0.060) Batch 0.662 (1.138) Remain 26:05:54 loss: 0.1338 Lr: 0.00296 [2024-02-18 17:10:16,103 INFO misc.py line 119 87073] Train: [47/100][1519/1557] Data 2.860 (0.062) Batch 11.740 (1.145) Remain 26:15:30 loss: 0.1689 Lr: 0.00296 [2024-02-18 17:10:17,019 INFO misc.py line 119 87073] Train: [47/100][1520/1557] Data 0.003 (0.062) Batch 0.915 (1.145) Remain 26:15:16 loss: 0.1701 Lr: 0.00296 [2024-02-18 17:10:17,905 INFO misc.py line 119 87073] Train: [47/100][1521/1557] Data 0.004 (0.062) Batch 0.887 (1.145) Remain 26:15:01 loss: 0.2970 Lr: 0.00296 [2024-02-18 17:10:18,769 INFO misc.py line 119 87073] Train: [47/100][1522/1557] Data 0.003 (0.062) Batch 0.854 (1.144) Remain 26:14:44 loss: 0.7445 Lr: 0.00296 [2024-02-18 17:10:19,677 INFO misc.py line 119 87073] Train: [47/100][1523/1557] Data 0.012 (0.062) Batch 0.917 (1.144) Remain 26:14:31 loss: 0.6238 Lr: 0.00296 [2024-02-18 17:10:20,450 INFO misc.py line 119 87073] Train: [47/100][1524/1557] Data 0.003 (0.062) Batch 0.772 (1.144) Remain 26:14:10 loss: 0.2594 Lr: 0.00296 [2024-02-18 17:10:21,232 INFO misc.py line 119 87073] Train: [47/100][1525/1557] Data 0.003 (0.062) Batch 0.774 (1.144) Remain 26:13:48 loss: 0.3198 Lr: 0.00296 [2024-02-18 17:10:22,463 INFO misc.py line 119 87073] Train: [47/100][1526/1557] Data 0.012 (0.062) Batch 1.229 (1.144) Remain 26:13:52 loss: 0.1729 Lr: 0.00296 [2024-02-18 17:10:23,513 INFO misc.py line 119 87073] Train: [47/100][1527/1557] Data 0.013 (0.062) Batch 1.048 (1.144) Remain 26:13:45 loss: 1.2171 Lr: 0.00296 [2024-02-18 17:10:24,429 INFO misc.py line 119 87073] Train: [47/100][1528/1557] Data 0.015 (0.062) Batch 0.928 (1.144) Remain 26:13:33 loss: 0.3160 Lr: 0.00296 [2024-02-18 17:10:25,343 INFO misc.py line 119 87073] Train: [47/100][1529/1557] Data 0.003 (0.062) Batch 0.913 (1.144) Remain 26:13:19 loss: 0.2014 Lr: 0.00296 [2024-02-18 17:10:26,288 INFO misc.py line 119 87073] Train: [47/100][1530/1557] Data 0.005 (0.062) Batch 0.942 (1.143) Remain 26:13:07 loss: 0.4828 Lr: 0.00296 [2024-02-18 17:10:27,079 INFO misc.py line 119 87073] Train: [47/100][1531/1557] Data 0.008 (0.062) Batch 0.796 (1.143) Remain 26:12:47 loss: 0.5228 Lr: 0.00296 [2024-02-18 17:10:27,840 INFO misc.py line 119 87073] Train: [47/100][1532/1557] Data 0.003 (0.062) Batch 0.761 (1.143) Remain 26:12:25 loss: 0.4090 Lr: 0.00296 [2024-02-18 17:10:29,100 INFO misc.py line 119 87073] Train: [47/100][1533/1557] Data 0.003 (0.062) Batch 1.248 (1.143) Remain 26:12:30 loss: 0.1690 Lr: 0.00296 [2024-02-18 17:10:29,920 INFO misc.py line 119 87073] Train: [47/100][1534/1557] Data 0.014 (0.062) Batch 0.831 (1.143) Remain 26:12:12 loss: 0.6989 Lr: 0.00296 [2024-02-18 17:10:31,119 INFO misc.py line 119 87073] Train: [47/100][1535/1557] Data 0.004 (0.062) Batch 1.194 (1.143) Remain 26:12:13 loss: 0.2476 Lr: 0.00296 [2024-02-18 17:10:31,959 INFO misc.py line 119 87073] Train: [47/100][1536/1557] Data 0.008 (0.062) Batch 0.846 (1.143) Remain 26:11:56 loss: 0.5881 Lr: 0.00295 [2024-02-18 17:10:32,983 INFO misc.py line 119 87073] Train: [47/100][1537/1557] Data 0.003 (0.062) Batch 1.018 (1.143) Remain 26:11:48 loss: 0.1969 Lr: 0.00295 [2024-02-18 17:10:33,750 INFO misc.py line 119 87073] Train: [47/100][1538/1557] Data 0.008 (0.062) Batch 0.773 (1.142) Remain 26:11:27 loss: 0.3732 Lr: 0.00295 [2024-02-18 17:10:34,482 INFO misc.py line 119 87073] Train: [47/100][1539/1557] Data 0.003 (0.061) Batch 0.721 (1.142) Remain 26:11:04 loss: 0.2151 Lr: 0.00295 [2024-02-18 17:10:35,621 INFO misc.py line 119 87073] Train: [47/100][1540/1557] Data 0.015 (0.061) Batch 1.142 (1.142) Remain 26:11:02 loss: 0.2095 Lr: 0.00295 [2024-02-18 17:10:36,486 INFO misc.py line 119 87073] Train: [47/100][1541/1557] Data 0.011 (0.061) Batch 0.873 (1.142) Remain 26:10:47 loss: 0.2859 Lr: 0.00295 [2024-02-18 17:10:37,392 INFO misc.py line 119 87073] Train: [47/100][1542/1557] Data 0.003 (0.061) Batch 0.906 (1.142) Remain 26:10:33 loss: 0.3998 Lr: 0.00295 [2024-02-18 17:10:38,304 INFO misc.py line 119 87073] Train: [47/100][1543/1557] Data 0.003 (0.061) Batch 0.901 (1.142) Remain 26:10:19 loss: 0.2716 Lr: 0.00295 [2024-02-18 17:10:39,364 INFO misc.py line 119 87073] Train: [47/100][1544/1557] Data 0.014 (0.061) Batch 1.060 (1.142) Remain 26:10:14 loss: 0.2830 Lr: 0.00295 [2024-02-18 17:10:40,130 INFO misc.py line 119 87073] Train: [47/100][1545/1557] Data 0.014 (0.061) Batch 0.777 (1.141) Remain 26:09:53 loss: 0.3043 Lr: 0.00295 [2024-02-18 17:10:40,899 INFO misc.py line 119 87073] Train: [47/100][1546/1557] Data 0.003 (0.061) Batch 0.757 (1.141) Remain 26:09:31 loss: 0.2570 Lr: 0.00295 [2024-02-18 17:10:42,104 INFO misc.py line 119 87073] Train: [47/100][1547/1557] Data 0.015 (0.061) Batch 1.207 (1.141) Remain 26:09:34 loss: 0.1511 Lr: 0.00295 [2024-02-18 17:10:43,171 INFO misc.py line 119 87073] Train: [47/100][1548/1557] Data 0.014 (0.061) Batch 1.067 (1.141) Remain 26:09:28 loss: 0.1170 Lr: 0.00295 [2024-02-18 17:10:43,989 INFO misc.py line 119 87073] Train: [47/100][1549/1557] Data 0.013 (0.061) Batch 0.828 (1.141) Remain 26:09:11 loss: 0.8132 Lr: 0.00295 [2024-02-18 17:10:45,025 INFO misc.py line 119 87073] Train: [47/100][1550/1557] Data 0.003 (0.061) Batch 1.035 (1.141) Remain 26:09:04 loss: 0.5594 Lr: 0.00295 [2024-02-18 17:10:45,996 INFO misc.py line 119 87073] Train: [47/100][1551/1557] Data 0.003 (0.061) Batch 0.972 (1.141) Remain 26:08:54 loss: 0.4739 Lr: 0.00295 [2024-02-18 17:10:46,743 INFO misc.py line 119 87073] Train: [47/100][1552/1557] Data 0.003 (0.061) Batch 0.746 (1.140) Remain 26:08:32 loss: 0.4662 Lr: 0.00295 [2024-02-18 17:10:47,442 INFO misc.py line 119 87073] Train: [47/100][1553/1557] Data 0.003 (0.061) Batch 0.688 (1.140) Remain 26:08:06 loss: 0.2388 Lr: 0.00295 [2024-02-18 17:10:48,762 INFO misc.py line 119 87073] Train: [47/100][1554/1557] Data 0.014 (0.061) Batch 1.320 (1.140) Remain 26:08:15 loss: 0.1874 Lr: 0.00295 [2024-02-18 17:10:49,726 INFO misc.py line 119 87073] Train: [47/100][1555/1557] Data 0.015 (0.061) Batch 0.975 (1.140) Remain 26:08:05 loss: 0.4240 Lr: 0.00295 [2024-02-18 17:10:50,655 INFO misc.py line 119 87073] Train: [47/100][1556/1557] Data 0.003 (0.061) Batch 0.929 (1.140) Remain 26:07:52 loss: 0.2427 Lr: 0.00295 [2024-02-18 17:10:51,617 INFO misc.py line 119 87073] Train: [47/100][1557/1557] Data 0.003 (0.061) Batch 0.962 (1.140) Remain 26:07:42 loss: 0.6759 Lr: 0.00295 [2024-02-18 17:10:51,618 INFO misc.py line 136 87073] Train result: loss: 0.3687 [2024-02-18 17:10:51,618 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 17:11:19,939 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.3473 [2024-02-18 17:11:20,717 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7142 [2024-02-18 17:11:22,844 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4815 [2024-02-18 17:11:25,052 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3075 [2024-02-18 17:11:30,003 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2251 [2024-02-18 17:11:30,703 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1305 [2024-02-18 17:11:31,971 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3152 [2024-02-18 17:11:34,926 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2774 [2024-02-18 17:11:36,734 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3202 [2024-02-18 17:11:38,552 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7304/0.8336/0.9177. [2024-02-18 17:11:38,552 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9304/0.9583 [2024-02-18 17:11:38,552 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9814/0.9869 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8781/0.9393 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0460/0.3680 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.5389/0.8607 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6486/0.6697 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7909/0.9458 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8311/0.9240 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9170/0.9668 [2024-02-18 17:11:38,553 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7201/0.7399 [2024-02-18 17:11:38,554 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7962/0.9239 [2024-02-18 17:11:38,554 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7885/0.8348 [2024-02-18 17:11:38,554 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6280/0.7186 [2024-02-18 17:11:38,554 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 17:11:38,557 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7304 [2024-02-18 17:11:38,557 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 17:11:38,557 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 17:11:50,427 INFO misc.py line 119 87073] Train: [48/100][1/1557] Data 1.670 (1.670) Batch 2.680 (2.680) Remain 61:26:15 loss: 0.3243 Lr: 0.00295 [2024-02-18 17:11:51,341 INFO misc.py line 119 87073] Train: [48/100][2/1557] Data 0.005 (0.005) Batch 0.906 (0.906) Remain 20:46:03 loss: 0.3793 Lr: 0.00295 [2024-02-18 17:11:52,391 INFO misc.py line 119 87073] Train: [48/100][3/1557] Data 0.012 (0.012) Batch 1.044 (1.044) Remain 23:55:35 loss: 0.4311 Lr: 0.00295 [2024-02-18 17:11:53,463 INFO misc.py line 119 87073] Train: [48/100][4/1557] Data 0.018 (0.018) Batch 1.078 (1.078) Remain 24:42:43 loss: 0.7936 Lr: 0.00295 [2024-02-18 17:11:54,238 INFO misc.py line 119 87073] Train: [48/100][5/1557] Data 0.011 (0.015) Batch 0.783 (0.931) Remain 21:19:48 loss: 0.3502 Lr: 0.00295 [2024-02-18 17:11:54,989 INFO misc.py line 119 87073] Train: [48/100][6/1557] Data 0.004 (0.011) Batch 0.746 (0.869) Remain 19:55:22 loss: 0.2534 Lr: 0.00295 [2024-02-18 17:11:56,210 INFO misc.py line 119 87073] Train: [48/100][7/1557] Data 0.008 (0.010) Batch 1.218 (0.956) Remain 21:55:10 loss: 0.2216 Lr: 0.00295 [2024-02-18 17:11:57,187 INFO misc.py line 119 87073] Train: [48/100][8/1557] Data 0.013 (0.011) Batch 0.986 (0.962) Remain 22:03:18 loss: 0.4871 Lr: 0.00295 [2024-02-18 17:11:58,026 INFO misc.py line 119 87073] Train: [48/100][9/1557] Data 0.004 (0.010) Batch 0.837 (0.941) Remain 21:34:30 loss: 0.3311 Lr: 0.00295 [2024-02-18 17:11:59,036 INFO misc.py line 119 87073] Train: [48/100][10/1557] Data 0.006 (0.009) Batch 0.982 (0.947) Remain 21:42:25 loss: 0.2973 Lr: 0.00295 [2024-02-18 17:11:59,880 INFO misc.py line 119 87073] Train: [48/100][11/1557] Data 0.033 (0.012) Batch 0.874 (0.938) Remain 21:29:52 loss: 0.2403 Lr: 0.00295 [2024-02-18 17:12:00,599 INFO misc.py line 119 87073] Train: [48/100][12/1557] Data 0.003 (0.011) Batch 0.719 (0.914) Remain 20:56:22 loss: 0.2519 Lr: 0.00295 [2024-02-18 17:12:01,349 INFO misc.py line 119 87073] Train: [48/100][13/1557] Data 0.004 (0.010) Batch 0.743 (0.897) Remain 20:32:52 loss: 0.3523 Lr: 0.00295 [2024-02-18 17:12:02,594 INFO misc.py line 119 87073] Train: [48/100][14/1557] Data 0.010 (0.010) Batch 1.250 (0.929) Remain 21:17:00 loss: 0.1849 Lr: 0.00295 [2024-02-18 17:12:03,552 INFO misc.py line 119 87073] Train: [48/100][15/1557] Data 0.006 (0.010) Batch 0.961 (0.931) Remain 21:20:44 loss: 0.6060 Lr: 0.00295 [2024-02-18 17:12:04,564 INFO misc.py line 119 87073] Train: [48/100][16/1557] Data 0.003 (0.010) Batch 1.011 (0.938) Remain 21:29:11 loss: 0.2568 Lr: 0.00295 [2024-02-18 17:12:05,401 INFO misc.py line 119 87073] Train: [48/100][17/1557] Data 0.003 (0.009) Batch 0.837 (0.930) Remain 21:19:18 loss: 0.2215 Lr: 0.00295 [2024-02-18 17:12:06,444 INFO misc.py line 119 87073] Train: [48/100][18/1557] Data 0.004 (0.009) Batch 1.033 (0.937) Remain 21:28:44 loss: 0.5986 Lr: 0.00295 [2024-02-18 17:12:07,197 INFO misc.py line 119 87073] Train: [48/100][19/1557] Data 0.013 (0.009) Batch 0.762 (0.926) Remain 21:13:38 loss: 0.4006 Lr: 0.00295 [2024-02-18 17:12:07,888 INFO misc.py line 119 87073] Train: [48/100][20/1557] Data 0.004 (0.009) Batch 0.683 (0.912) Remain 20:53:57 loss: 0.2268 Lr: 0.00295 [2024-02-18 17:12:09,038 INFO misc.py line 119 87073] Train: [48/100][21/1557] Data 0.012 (0.009) Batch 1.153 (0.925) Remain 21:12:19 loss: 0.1445 Lr: 0.00295 [2024-02-18 17:12:10,021 INFO misc.py line 119 87073] Train: [48/100][22/1557] Data 0.009 (0.009) Batch 0.989 (0.929) Remain 21:16:53 loss: 0.5178 Lr: 0.00295 [2024-02-18 17:12:11,082 INFO misc.py line 119 87073] Train: [48/100][23/1557] Data 0.003 (0.009) Batch 1.062 (0.935) Remain 21:26:00 loss: 0.1516 Lr: 0.00295 [2024-02-18 17:12:12,085 INFO misc.py line 119 87073] Train: [48/100][24/1557] Data 0.002 (0.008) Batch 1.003 (0.939) Remain 21:30:25 loss: 0.1915 Lr: 0.00295 [2024-02-18 17:12:12,990 INFO misc.py line 119 87073] Train: [48/100][25/1557] Data 0.003 (0.008) Batch 0.905 (0.937) Remain 21:28:17 loss: 0.5144 Lr: 0.00295 [2024-02-18 17:12:13,742 INFO misc.py line 119 87073] Train: [48/100][26/1557] Data 0.004 (0.008) Batch 0.751 (0.929) Remain 21:17:09 loss: 0.3177 Lr: 0.00295 [2024-02-18 17:12:14,493 INFO misc.py line 119 87073] Train: [48/100][27/1557] Data 0.004 (0.008) Batch 0.752 (0.922) Remain 21:07:01 loss: 0.2680 Lr: 0.00295 [2024-02-18 17:12:15,786 INFO misc.py line 119 87073] Train: [48/100][28/1557] Data 0.003 (0.007) Batch 1.286 (0.936) Remain 21:27:02 loss: 0.1261 Lr: 0.00295 [2024-02-18 17:12:16,672 INFO misc.py line 119 87073] Train: [48/100][29/1557] Data 0.010 (0.008) Batch 0.893 (0.934) Remain 21:24:44 loss: 0.5589 Lr: 0.00295 [2024-02-18 17:12:17,675 INFO misc.py line 119 87073] Train: [48/100][30/1557] Data 0.003 (0.007) Batch 1.003 (0.937) Remain 21:28:12 loss: 0.5210 Lr: 0.00295 [2024-02-18 17:12:18,624 INFO misc.py line 119 87073] Train: [48/100][31/1557] Data 0.004 (0.007) Batch 0.949 (0.937) Remain 21:28:47 loss: 0.3324 Lr: 0.00295 [2024-02-18 17:12:19,493 INFO misc.py line 119 87073] Train: [48/100][32/1557] Data 0.003 (0.007) Batch 0.869 (0.935) Remain 21:25:31 loss: 0.2929 Lr: 0.00295 [2024-02-18 17:12:20,232 INFO misc.py line 119 87073] Train: [48/100][33/1557] Data 0.004 (0.007) Batch 0.737 (0.928) Remain 21:16:26 loss: 0.5376 Lr: 0.00295 [2024-02-18 17:12:20,997 INFO misc.py line 119 87073] Train: [48/100][34/1557] Data 0.005 (0.007) Batch 0.766 (0.923) Remain 21:09:13 loss: 0.4118 Lr: 0.00295 [2024-02-18 17:12:22,272 INFO misc.py line 119 87073] Train: [48/100][35/1557] Data 0.004 (0.007) Batch 1.266 (0.934) Remain 21:23:55 loss: 0.3047 Lr: 0.00295 [2024-02-18 17:12:23,250 INFO misc.py line 119 87073] Train: [48/100][36/1557] Data 0.014 (0.007) Batch 0.987 (0.936) Remain 21:26:07 loss: 0.6578 Lr: 0.00295 [2024-02-18 17:12:24,099 INFO misc.py line 119 87073] Train: [48/100][37/1557] Data 0.006 (0.007) Batch 0.849 (0.933) Remain 21:22:37 loss: 0.6080 Lr: 0.00295 [2024-02-18 17:12:25,026 INFO misc.py line 119 87073] Train: [48/100][38/1557] Data 0.005 (0.007) Batch 0.923 (0.933) Remain 21:22:13 loss: 0.4307 Lr: 0.00295 [2024-02-18 17:12:25,942 INFO misc.py line 119 87073] Train: [48/100][39/1557] Data 0.008 (0.007) Batch 0.920 (0.932) Remain 21:21:43 loss: 0.8511 Lr: 0.00295 [2024-02-18 17:12:26,633 INFO misc.py line 119 87073] Train: [48/100][40/1557] Data 0.004 (0.007) Batch 0.690 (0.926) Remain 21:12:43 loss: 0.1397 Lr: 0.00295 [2024-02-18 17:12:27,390 INFO misc.py line 119 87073] Train: [48/100][41/1557] Data 0.004 (0.007) Batch 0.756 (0.921) Remain 21:06:32 loss: 0.4365 Lr: 0.00295 [2024-02-18 17:12:28,573 INFO misc.py line 119 87073] Train: [48/100][42/1557] Data 0.005 (0.007) Batch 1.185 (0.928) Remain 21:15:49 loss: 0.1330 Lr: 0.00295 [2024-02-18 17:12:29,407 INFO misc.py line 119 87073] Train: [48/100][43/1557] Data 0.005 (0.007) Batch 0.835 (0.926) Remain 21:12:35 loss: 0.1948 Lr: 0.00295 [2024-02-18 17:12:30,445 INFO misc.py line 119 87073] Train: [48/100][44/1557] Data 0.004 (0.007) Batch 1.038 (0.929) Remain 21:16:21 loss: 1.2107 Lr: 0.00295 [2024-02-18 17:12:31,467 INFO misc.py line 119 87073] Train: [48/100][45/1557] Data 0.003 (0.007) Batch 1.022 (0.931) Remain 21:19:24 loss: 0.2942 Lr: 0.00295 [2024-02-18 17:12:32,449 INFO misc.py line 119 87073] Train: [48/100][46/1557] Data 0.003 (0.007) Batch 0.981 (0.932) Remain 21:20:59 loss: 0.2846 Lr: 0.00295 [2024-02-18 17:12:33,143 INFO misc.py line 119 87073] Train: [48/100][47/1557] Data 0.004 (0.006) Batch 0.695 (0.927) Remain 21:13:34 loss: 0.2929 Lr: 0.00295 [2024-02-18 17:12:33,882 INFO misc.py line 119 87073] Train: [48/100][48/1557] Data 0.003 (0.006) Batch 0.735 (0.922) Remain 21:07:41 loss: 0.5189 Lr: 0.00295 [2024-02-18 17:12:35,188 INFO misc.py line 119 87073] Train: [48/100][49/1557] Data 0.007 (0.006) Batch 1.308 (0.931) Remain 21:19:12 loss: 0.1040 Lr: 0.00295 [2024-02-18 17:12:36,086 INFO misc.py line 119 87073] Train: [48/100][50/1557] Data 0.005 (0.006) Batch 0.900 (0.930) Remain 21:18:17 loss: 0.3380 Lr: 0.00295 [2024-02-18 17:12:37,008 INFO misc.py line 119 87073] Train: [48/100][51/1557] Data 0.004 (0.006) Batch 0.922 (0.930) Remain 21:18:02 loss: 0.2759 Lr: 0.00295 [2024-02-18 17:12:38,000 INFO misc.py line 119 87073] Train: [48/100][52/1557] Data 0.004 (0.006) Batch 0.992 (0.931) Remain 21:19:46 loss: 0.7290 Lr: 0.00295 [2024-02-18 17:12:39,023 INFO misc.py line 119 87073] Train: [48/100][53/1557] Data 0.003 (0.006) Batch 1.022 (0.933) Remain 21:22:16 loss: 0.1870 Lr: 0.00295 [2024-02-18 17:12:39,739 INFO misc.py line 119 87073] Train: [48/100][54/1557] Data 0.003 (0.006) Batch 0.715 (0.929) Remain 21:16:23 loss: 0.2893 Lr: 0.00295 [2024-02-18 17:12:40,552 INFO misc.py line 119 87073] Train: [48/100][55/1557] Data 0.004 (0.006) Batch 0.815 (0.926) Remain 21:13:22 loss: 0.1409 Lr: 0.00295 [2024-02-18 17:12:41,705 INFO misc.py line 119 87073] Train: [48/100][56/1557] Data 0.003 (0.006) Batch 1.153 (0.931) Remain 21:19:13 loss: 0.2872 Lr: 0.00295 [2024-02-18 17:12:42,697 INFO misc.py line 119 87073] Train: [48/100][57/1557] Data 0.003 (0.006) Batch 0.992 (0.932) Remain 21:20:45 loss: 0.4608 Lr: 0.00295 [2024-02-18 17:12:43,634 INFO misc.py line 119 87073] Train: [48/100][58/1557] Data 0.004 (0.006) Batch 0.937 (0.932) Remain 21:20:51 loss: 0.1866 Lr: 0.00295 [2024-02-18 17:12:44,663 INFO misc.py line 119 87073] Train: [48/100][59/1557] Data 0.004 (0.006) Batch 1.029 (0.934) Remain 21:23:14 loss: 0.3965 Lr: 0.00295 [2024-02-18 17:12:45,597 INFO misc.py line 119 87073] Train: [48/100][60/1557] Data 0.003 (0.006) Batch 0.934 (0.934) Remain 21:23:13 loss: 0.1916 Lr: 0.00295 [2024-02-18 17:12:46,334 INFO misc.py line 119 87073] Train: [48/100][61/1557] Data 0.004 (0.006) Batch 0.733 (0.930) Remain 21:18:26 loss: 0.4390 Lr: 0.00295 [2024-02-18 17:12:47,209 INFO misc.py line 119 87073] Train: [48/100][62/1557] Data 0.007 (0.006) Batch 0.880 (0.929) Remain 21:17:15 loss: 0.4044 Lr: 0.00295 [2024-02-18 17:12:55,916 INFO misc.py line 119 87073] Train: [48/100][63/1557] Data 6.524 (0.115) Batch 8.705 (1.059) Remain 24:15:21 loss: 0.1513 Lr: 0.00295 [2024-02-18 17:12:56,864 INFO misc.py line 119 87073] Train: [48/100][64/1557] Data 0.005 (0.113) Batch 0.944 (1.057) Remain 24:12:44 loss: 0.3149 Lr: 0.00295 [2024-02-18 17:12:57,830 INFO misc.py line 119 87073] Train: [48/100][65/1557] Data 0.011 (0.111) Batch 0.972 (1.056) Remain 24:10:49 loss: 0.2964 Lr: 0.00295 [2024-02-18 17:12:58,944 INFO misc.py line 119 87073] Train: [48/100][66/1557] Data 0.003 (0.109) Batch 1.114 (1.057) Remain 24:12:05 loss: 0.5673 Lr: 0.00295 [2024-02-18 17:13:00,084 INFO misc.py line 119 87073] Train: [48/100][67/1557] Data 0.003 (0.108) Batch 1.139 (1.058) Remain 24:13:50 loss: 0.6012 Lr: 0.00295 [2024-02-18 17:13:00,858 INFO misc.py line 119 87073] Train: [48/100][68/1557] Data 0.004 (0.106) Batch 0.774 (1.054) Remain 24:07:50 loss: 0.2742 Lr: 0.00295 [2024-02-18 17:13:01,655 INFO misc.py line 119 87073] Train: [48/100][69/1557] Data 0.003 (0.105) Batch 0.788 (1.050) Remain 24:02:17 loss: 0.3447 Lr: 0.00295 [2024-02-18 17:13:02,948 INFO misc.py line 119 87073] Train: [48/100][70/1557] Data 0.011 (0.103) Batch 1.290 (1.053) Remain 24:07:12 loss: 0.1921 Lr: 0.00295 [2024-02-18 17:13:04,004 INFO misc.py line 119 87073] Train: [48/100][71/1557] Data 0.015 (0.102) Batch 1.058 (1.053) Remain 24:07:17 loss: 0.1437 Lr: 0.00295 [2024-02-18 17:13:04,957 INFO misc.py line 119 87073] Train: [48/100][72/1557] Data 0.013 (0.101) Batch 0.963 (1.052) Remain 24:05:28 loss: 0.7056 Lr: 0.00295 [2024-02-18 17:13:05,896 INFO misc.py line 119 87073] Train: [48/100][73/1557] Data 0.003 (0.099) Batch 0.938 (1.050) Remain 24:03:13 loss: 0.4885 Lr: 0.00295 [2024-02-18 17:13:06,652 INFO misc.py line 119 87073] Train: [48/100][74/1557] Data 0.004 (0.098) Batch 0.756 (1.046) Remain 23:57:30 loss: 0.5062 Lr: 0.00295 [2024-02-18 17:13:07,427 INFO misc.py line 119 87073] Train: [48/100][75/1557] Data 0.003 (0.097) Batch 0.766 (1.042) Remain 23:52:09 loss: 0.5263 Lr: 0.00295 [2024-02-18 17:13:08,136 INFO misc.py line 119 87073] Train: [48/100][76/1557] Data 0.012 (0.095) Batch 0.718 (1.038) Remain 23:46:02 loss: 0.3339 Lr: 0.00295 [2024-02-18 17:13:09,343 INFO misc.py line 119 87073] Train: [48/100][77/1557] Data 0.003 (0.094) Batch 1.207 (1.040) Remain 23:49:09 loss: 0.0857 Lr: 0.00295 [2024-02-18 17:13:10,205 INFO misc.py line 119 87073] Train: [48/100][78/1557] Data 0.004 (0.093) Batch 0.861 (1.038) Remain 23:45:51 loss: 0.6749 Lr: 0.00295 [2024-02-18 17:13:11,145 INFO misc.py line 119 87073] Train: [48/100][79/1557] Data 0.003 (0.092) Batch 0.933 (1.036) Remain 23:43:56 loss: 0.4776 Lr: 0.00295 [2024-02-18 17:13:11,995 INFO misc.py line 119 87073] Train: [48/100][80/1557] Data 0.011 (0.091) Batch 0.858 (1.034) Remain 23:40:44 loss: 0.5286 Lr: 0.00295 [2024-02-18 17:13:12,987 INFO misc.py line 119 87073] Train: [48/100][81/1557] Data 0.004 (0.090) Batch 0.992 (1.033) Remain 23:39:59 loss: 0.2230 Lr: 0.00295 [2024-02-18 17:13:13,767 INFO misc.py line 119 87073] Train: [48/100][82/1557] Data 0.003 (0.088) Batch 0.775 (1.030) Remain 23:35:28 loss: 0.6215 Lr: 0.00295 [2024-02-18 17:13:14,490 INFO misc.py line 119 87073] Train: [48/100][83/1557] Data 0.008 (0.087) Batch 0.727 (1.026) Remain 23:30:14 loss: 0.2447 Lr: 0.00295 [2024-02-18 17:13:15,676 INFO misc.py line 119 87073] Train: [48/100][84/1557] Data 0.004 (0.086) Batch 1.188 (1.028) Remain 23:32:57 loss: 0.1387 Lr: 0.00295 [2024-02-18 17:13:16,736 INFO misc.py line 119 87073] Train: [48/100][85/1557] Data 0.004 (0.085) Batch 1.059 (1.029) Remain 23:33:27 loss: 0.1614 Lr: 0.00295 [2024-02-18 17:13:17,555 INFO misc.py line 119 87073] Train: [48/100][86/1557] Data 0.004 (0.084) Batch 0.820 (1.026) Remain 23:29:58 loss: 0.5301 Lr: 0.00295 [2024-02-18 17:13:18,435 INFO misc.py line 119 87073] Train: [48/100][87/1557] Data 0.003 (0.083) Batch 0.879 (1.025) Remain 23:27:33 loss: 0.2503 Lr: 0.00295 [2024-02-18 17:13:19,339 INFO misc.py line 119 87073] Train: [48/100][88/1557] Data 0.006 (0.083) Batch 0.904 (1.023) Remain 23:25:36 loss: 0.2247 Lr: 0.00295 [2024-02-18 17:13:20,132 INFO misc.py line 119 87073] Train: [48/100][89/1557] Data 0.003 (0.082) Batch 0.793 (1.020) Remain 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Train: [48/100][102/1557] Data 0.003 (0.072) Batch 1.053 (1.015) Remain 23:14:10 loss: 0.2376 Lr: 0.00295 [2024-02-18 17:13:33,650 INFO misc.py line 119 87073] Train: [48/100][103/1557] Data 0.003 (0.071) Batch 0.794 (1.013) Remain 23:11:07 loss: 0.2549 Lr: 0.00295 [2024-02-18 17:13:34,397 INFO misc.py line 119 87073] Train: [48/100][104/1557] Data 0.003 (0.071) Batch 0.737 (1.010) Remain 23:07:21 loss: 0.5290 Lr: 0.00295 [2024-02-18 17:13:35,713 INFO misc.py line 119 87073] Train: [48/100][105/1557] Data 0.013 (0.070) Batch 1.312 (1.013) Remain 23:11:24 loss: 0.1335 Lr: 0.00295 [2024-02-18 17:13:36,723 INFO misc.py line 119 87073] Train: [48/100][106/1557] Data 0.017 (0.070) Batch 1.008 (1.013) Remain 23:11:19 loss: 0.0613 Lr: 0.00295 [2024-02-18 17:13:37,758 INFO misc.py line 119 87073] Train: [48/100][107/1557] Data 0.019 (0.069) Batch 1.038 (1.013) Remain 23:11:38 loss: 0.4006 Lr: 0.00295 [2024-02-18 17:13:38,597 INFO misc.py line 119 87073] Train: [48/100][108/1557] Data 0.017 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line 119 87073] Train: [48/100][127/1557] Data 0.015 (0.110) Batch 0.928 (1.056) Remain 24:09:56 loss: 0.5121 Lr: 0.00295 [2024-02-18 17:14:04,247 INFO misc.py line 119 87073] Train: [48/100][128/1557] Data 0.003 (0.110) Batch 0.944 (1.055) Remain 24:08:41 loss: 0.2555 Lr: 0.00295 [2024-02-18 17:14:05,252 INFO misc.py line 119 87073] Train: [48/100][129/1557] Data 0.003 (0.109) Batch 1.006 (1.055) Remain 24:08:08 loss: 0.0781 Lr: 0.00295 [2024-02-18 17:14:06,240 INFO misc.py line 119 87073] Train: [48/100][130/1557] Data 0.003 (0.108) Batch 0.988 (1.054) Remain 24:07:24 loss: 0.2053 Lr: 0.00295 [2024-02-18 17:14:07,037 INFO misc.py line 119 87073] Train: [48/100][131/1557] Data 0.003 (0.107) Batch 0.787 (1.052) Remain 24:04:31 loss: 0.2448 Lr: 0.00295 [2024-02-18 17:14:07,806 INFO misc.py line 119 87073] Train: [48/100][132/1557] Data 0.012 (0.106) Batch 0.778 (1.050) Remain 24:01:35 loss: 0.3912 Lr: 0.00295 [2024-02-18 17:14:08,973 INFO misc.py line 119 87073] Train: 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Batch 0.771 (1.045) Remain 23:54:23 loss: 0.5310 Lr: 0.00295 [2024-02-18 17:14:15,617 INFO misc.py line 119 87073] Train: [48/100][140/1557] Data 0.003 (0.100) Batch 1.163 (1.046) Remain 23:55:33 loss: 0.1384 Lr: 0.00295 [2024-02-18 17:14:16,519 INFO misc.py line 119 87073] Train: [48/100][141/1557] Data 0.003 (0.100) Batch 0.902 (1.045) Remain 23:54:07 loss: 0.6332 Lr: 0.00295 [2024-02-18 17:14:17,583 INFO misc.py line 119 87073] Train: [48/100][142/1557] Data 0.003 (0.099) Batch 1.064 (1.045) Remain 23:54:17 loss: 0.3771 Lr: 0.00295 [2024-02-18 17:14:18,579 INFO misc.py line 119 87073] Train: [48/100][143/1557] Data 0.003 (0.098) Batch 0.996 (1.044) Remain 23:53:47 loss: 0.2541 Lr: 0.00295 [2024-02-18 17:14:19,625 INFO misc.py line 119 87073] Train: [48/100][144/1557] Data 0.003 (0.098) Batch 1.046 (1.044) Remain 23:53:47 loss: 0.2893 Lr: 0.00295 [2024-02-18 17:14:20,330 INFO misc.py line 119 87073] Train: [48/100][145/1557] Data 0.003 (0.097) Batch 0.705 (1.042) Remain 23:50:30 loss: 0.2065 Lr: 0.00295 [2024-02-18 17:14:21,033 INFO misc.py line 119 87073] Train: [48/100][146/1557] Data 0.003 (0.096) Batch 0.692 (1.039) Remain 23:47:07 loss: 0.1645 Lr: 0.00295 [2024-02-18 17:14:22,338 INFO misc.py line 119 87073] Train: [48/100][147/1557] Data 0.014 (0.096) Batch 1.304 (1.041) Remain 23:49:37 loss: 0.2332 Lr: 0.00295 [2024-02-18 17:14:23,277 INFO misc.py line 119 87073] Train: [48/100][148/1557] Data 0.014 (0.095) Batch 0.949 (1.041) Remain 23:48:44 loss: 0.4079 Lr: 0.00295 [2024-02-18 17:14:24,250 INFO misc.py line 119 87073] Train: [48/100][149/1557] Data 0.005 (0.095) Batch 0.974 (1.040) Remain 23:48:05 loss: 0.4170 Lr: 0.00295 [2024-02-18 17:14:25,281 INFO misc.py line 119 87073] Train: [48/100][150/1557] Data 0.004 (0.094) Batch 1.032 (1.040) Remain 23:47:59 loss: 0.3316 Lr: 0.00295 [2024-02-18 17:14:26,359 INFO misc.py line 119 87073] Train: [48/100][151/1557] Data 0.003 (0.093) Batch 1.078 (1.040) Remain 23:48:19 loss: 0.2829 Lr: 0.00295 [2024-02-18 17:14:27,111 INFO misc.py line 119 87073] Train: [48/100][152/1557] Data 0.003 (0.093) Batch 0.752 (1.038) Remain 23:45:38 loss: 0.3848 Lr: 0.00295 [2024-02-18 17:14:27,870 INFO misc.py line 119 87073] Train: [48/100][153/1557] Data 0.004 (0.092) Batch 0.749 (1.037) Remain 23:42:59 loss: 0.3133 Lr: 0.00295 [2024-02-18 17:14:29,094 INFO misc.py line 119 87073] Train: [48/100][154/1557] Data 0.013 (0.092) Batch 1.225 (1.038) Remain 23:44:40 loss: 0.1752 Lr: 0.00295 [2024-02-18 17:14:30,013 INFO misc.py line 119 87073] Train: [48/100][155/1557] Data 0.013 (0.091) Batch 0.928 (1.037) Remain 23:43:40 loss: 0.3337 Lr: 0.00295 [2024-02-18 17:14:30,988 INFO misc.py line 119 87073] Train: [48/100][156/1557] Data 0.003 (0.091) Batch 0.975 (1.037) Remain 23:43:06 loss: 0.5300 Lr: 0.00295 [2024-02-18 17:14:31,951 INFO misc.py line 119 87073] Train: [48/100][157/1557] Data 0.003 (0.090) Batch 0.962 (1.036) Remain 23:42:25 loss: 0.4753 Lr: 0.00295 [2024-02-18 17:14:33,014 INFO misc.py line 119 87073] Train: [48/100][158/1557] Data 0.003 (0.089) Batch 1.056 (1.036) Remain 23:42:34 loss: 0.5009 Lr: 0.00295 [2024-02-18 17:14:33,650 INFO misc.py line 119 87073] Train: [48/100][159/1557] Data 0.010 (0.089) Batch 0.641 (1.034) Remain 23:39:05 loss: 0.2420 Lr: 0.00295 [2024-02-18 17:14:34,393 INFO misc.py line 119 87073] Train: [48/100][160/1557] Data 0.005 (0.088) Batch 0.736 (1.032) Remain 23:36:28 loss: 0.3999 Lr: 0.00295 [2024-02-18 17:14:35,708 INFO misc.py line 119 87073] Train: [48/100][161/1557] Data 0.012 (0.088) Batch 1.314 (1.034) Remain 23:38:54 loss: 0.1357 Lr: 0.00295 [2024-02-18 17:14:36,691 INFO misc.py line 119 87073] Train: [48/100][162/1557] Data 0.014 (0.087) Batch 0.991 (1.033) Remain 23:38:31 loss: 0.3884 Lr: 0.00295 [2024-02-18 17:14:37,685 INFO misc.py line 119 87073] Train: [48/100][163/1557] Data 0.005 (0.087) Batch 0.996 (1.033) Remain 23:38:10 loss: 0.5943 Lr: 0.00295 [2024-02-18 17:14:38,828 INFO misc.py line 119 87073] Train: [48/100][164/1557] Data 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Batch 0.720 (1.064) Remain 24:19:05 loss: 0.4545 Lr: 0.00294 [2024-02-18 17:16:17,569 INFO misc.py line 119 87073] Train: [48/100][252/1557] Data 0.004 (0.115) Batch 1.269 (1.065) Remain 24:20:12 loss: 0.1208 Lr: 0.00294 [2024-02-18 17:16:18,429 INFO misc.py line 119 87073] Train: [48/100][253/1557] Data 0.024 (0.114) Batch 0.880 (1.064) Remain 24:19:10 loss: 0.1609 Lr: 0.00294 [2024-02-18 17:16:19,492 INFO misc.py line 119 87073] Train: [48/100][254/1557] Data 0.004 (0.114) Batch 1.061 (1.064) Remain 24:19:08 loss: 0.6336 Lr: 0.00294 [2024-02-18 17:16:20,346 INFO misc.py line 119 87073] Train: [48/100][255/1557] Data 0.006 (0.113) Batch 0.856 (1.063) Remain 24:17:59 loss: 0.5419 Lr: 0.00294 [2024-02-18 17:16:21,334 INFO misc.py line 119 87073] Train: [48/100][256/1557] Data 0.003 (0.113) Batch 0.985 (1.063) Remain 24:17:32 loss: 1.2591 Lr: 0.00294 [2024-02-18 17:16:22,122 INFO misc.py line 119 87073] Train: [48/100][257/1557] Data 0.007 (0.113) Batch 0.791 (1.062) Remain 24:16:03 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Batch 0.796 (1.071) Remain 24:27:06 loss: 0.3327 Lr: 0.00293 [2024-02-18 17:18:19,235 INFO misc.py line 119 87073] Train: [48/100][364/1557] Data 0.004 (0.118) Batch 1.143 (1.072) Remain 24:27:21 loss: 0.2260 Lr: 0.00293 [2024-02-18 17:18:20,205 INFO misc.py line 119 87073] Train: [48/100][365/1557] Data 0.003 (0.118) Batch 0.970 (1.071) Remain 24:26:57 loss: 0.8804 Lr: 0.00293 [2024-02-18 17:18:21,282 INFO misc.py line 119 87073] Train: [48/100][366/1557] Data 0.003 (0.118) Batch 1.077 (1.071) Remain 24:26:57 loss: 0.4305 Lr: 0.00293 [2024-02-18 17:18:22,067 INFO misc.py line 119 87073] Train: [48/100][367/1557] Data 0.003 (0.117) Batch 0.781 (1.071) Remain 24:25:51 loss: 0.3533 Lr: 0.00293 [2024-02-18 17:18:22,851 INFO misc.py line 119 87073] Train: [48/100][368/1557] Data 0.007 (0.117) Batch 0.787 (1.070) Remain 24:24:46 loss: 0.2561 Lr: 0.00293 [2024-02-18 17:18:23,650 INFO misc.py line 119 87073] Train: [48/100][369/1557] Data 0.005 (0.117) Batch 0.800 (1.069) Remain 24:23:44 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line 119 87073] Train: [48/100][407/1557] Data 0.013 (0.123) Batch 0.922 (1.076) Remain 24:31:58 loss: 0.8978 Lr: 0.00293 [2024-02-18 17:19:07,928 INFO misc.py line 119 87073] Train: [48/100][408/1557] Data 0.003 (0.122) Batch 1.024 (1.075) Remain 24:31:47 loss: 0.3953 Lr: 0.00293 [2024-02-18 17:19:08,872 INFO misc.py line 119 87073] Train: [48/100][409/1557] Data 0.003 (0.122) Batch 0.945 (1.075) Remain 24:31:19 loss: 0.5116 Lr: 0.00293 [2024-02-18 17:19:09,960 INFO misc.py line 119 87073] Train: [48/100][410/1557] Data 0.003 (0.122) Batch 1.087 (1.075) Remain 24:31:21 loss: 0.5028 Lr: 0.00293 [2024-02-18 17:19:10,746 INFO misc.py line 119 87073] Train: [48/100][411/1557] Data 0.003 (0.121) Batch 0.786 (1.074) Remain 24:30:21 loss: 0.5114 Lr: 0.00293 [2024-02-18 17:19:11,650 INFO misc.py line 119 87073] Train: [48/100][412/1557] Data 0.003 (0.121) Batch 0.886 (1.074) Remain 24:29:42 loss: 0.1951 Lr: 0.00293 [2024-02-18 17:19:12,796 INFO misc.py line 119 87073] Train: 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Batch 0.749 (1.071) Remain 24:25:59 loss: 0.4764 Lr: 0.00293 [2024-02-18 17:19:19,236 INFO misc.py line 119 87073] Train: [48/100][420/1557] Data 0.025 (0.119) Batch 1.170 (1.072) Remain 24:26:17 loss: 0.1534 Lr: 0.00293 [2024-02-18 17:19:20,083 INFO misc.py line 119 87073] Train: [48/100][421/1557] Data 0.014 (0.119) Batch 0.856 (1.071) Remain 24:25:34 loss: 0.4462 Lr: 0.00293 [2024-02-18 17:19:21,137 INFO misc.py line 119 87073] Train: [48/100][422/1557] Data 0.005 (0.119) Batch 1.055 (1.071) Remain 24:25:30 loss: 0.7679 Lr: 0.00293 [2024-02-18 17:19:22,062 INFO misc.py line 119 87073] Train: [48/100][423/1557] Data 0.004 (0.118) Batch 0.925 (1.071) Remain 24:25:00 loss: 0.4272 Lr: 0.00293 [2024-02-18 17:19:23,098 INFO misc.py line 119 87073] Train: [48/100][424/1557] Data 0.004 (0.118) Batch 1.036 (1.071) Remain 24:24:52 loss: 0.6516 Lr: 0.00293 [2024-02-18 17:19:23,866 INFO misc.py line 119 87073] Train: [48/100][425/1557] Data 0.003 (0.118) Batch 0.757 (1.070) Remain 24:23:50 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Batch 0.857 (1.074) Remain 24:27:05 loss: 0.2913 Lr: 0.00292 [2024-02-18 17:22:21,079 INFO misc.py line 119 87073] Train: [48/100][588/1557] Data 0.003 (0.123) Batch 1.282 (1.075) Remain 24:27:33 loss: 0.1610 Lr: 0.00292 [2024-02-18 17:22:22,241 INFO misc.py line 119 87073] Train: [48/100][589/1557] Data 0.005 (0.123) Batch 1.150 (1.075) Remain 24:27:42 loss: 0.4093 Lr: 0.00292 [2024-02-18 17:22:23,160 INFO misc.py line 119 87073] Train: [48/100][590/1557] Data 0.018 (0.122) Batch 0.933 (1.075) Remain 24:27:21 loss: 0.7026 Lr: 0.00292 [2024-02-18 17:22:23,976 INFO misc.py line 119 87073] Train: [48/100][591/1557] Data 0.003 (0.122) Batch 0.817 (1.074) Remain 24:26:44 loss: 0.3316 Lr: 0.00292 [2024-02-18 17:22:25,149 INFO misc.py line 119 87073] Train: [48/100][592/1557] Data 0.003 (0.122) Batch 1.159 (1.074) Remain 24:26:55 loss: 0.5590 Lr: 0.00292 [2024-02-18 17:22:25,920 INFO misc.py line 119 87073] Train: [48/100][593/1557] Data 0.017 (0.122) Batch 0.785 (1.074) Remain 24:26:14 loss: 0.3450 Lr: 0.00292 [2024-02-18 17:22:26,707 INFO misc.py line 119 87073] Train: [48/100][594/1557] Data 0.003 (0.122) Batch 0.767 (1.073) Remain 24:25:30 loss: 0.4224 Lr: 0.00292 [2024-02-18 17:22:27,936 INFO misc.py line 119 87073] Train: [48/100][595/1557] Data 0.023 (0.122) Batch 1.245 (1.074) Remain 24:25:53 loss: 0.2741 Lr: 0.00292 [2024-02-18 17:22:28,994 INFO misc.py line 119 87073] Train: [48/100][596/1557] Data 0.007 (0.121) Batch 1.056 (1.074) Remain 24:25:49 loss: 0.3016 Lr: 0.00292 [2024-02-18 17:22:30,099 INFO misc.py line 119 87073] Train: [48/100][597/1557] Data 0.009 (0.121) Batch 1.105 (1.074) Remain 24:25:53 loss: 0.2248 Lr: 0.00292 [2024-02-18 17:22:31,127 INFO misc.py line 119 87073] Train: [48/100][598/1557] Data 0.009 (0.121) Batch 1.025 (1.074) Remain 24:25:45 loss: 0.5146 Lr: 0.00292 [2024-02-18 17:22:31,911 INFO misc.py line 119 87073] Train: [48/100][599/1557] Data 0.012 (0.121) Batch 0.793 (1.073) Remain 24:25:05 loss: 0.3688 Lr: 0.00292 [2024-02-18 17:22:32,632 INFO misc.py line 119 87073] Train: [48/100][600/1557] Data 0.003 (0.121) Batch 0.721 (1.072) Remain 24:24:16 loss: 0.4248 Lr: 0.00292 [2024-02-18 17:22:33,341 INFO misc.py line 119 87073] Train: [48/100][601/1557] Data 0.003 (0.120) Batch 0.706 (1.072) Remain 24:23:25 loss: 0.2689 Lr: 0.00292 [2024-02-18 17:22:34,610 INFO misc.py line 119 87073] Train: [48/100][602/1557] Data 0.006 (0.120) Batch 1.269 (1.072) Remain 24:23:51 loss: 0.1483 Lr: 0.00292 [2024-02-18 17:22:35,653 INFO misc.py line 119 87073] Train: [48/100][603/1557] Data 0.006 (0.120) Batch 1.044 (1.072) Remain 24:23:46 loss: 0.3942 Lr: 0.00292 [2024-02-18 17:22:36,723 INFO misc.py line 119 87073] Train: [48/100][604/1557] Data 0.006 (0.120) Batch 1.070 (1.072) Remain 24:23:44 loss: 0.4692 Lr: 0.00292 [2024-02-18 17:22:37,601 INFO misc.py line 119 87073] Train: [48/100][605/1557] Data 0.005 (0.120) Batch 0.879 (1.072) Remain 24:23:17 loss: 0.3391 Lr: 0.00292 [2024-02-18 17:22:38,591 INFO misc.py line 119 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line 119 87073] Train: [48/100][631/1557] Data 0.012 (0.125) Batch 0.910 (1.079) Remain 24:32:03 loss: 0.3900 Lr: 0.00292 [2024-02-18 17:23:10,573 INFO misc.py line 119 87073] Train: [48/100][632/1557] Data 0.003 (0.125) Batch 0.855 (1.078) Remain 24:31:33 loss: 0.3923 Lr: 0.00292 [2024-02-18 17:23:11,473 INFO misc.py line 119 87073] Train: [48/100][633/1557] Data 0.004 (0.124) Batch 0.896 (1.078) Remain 24:31:08 loss: 0.3453 Lr: 0.00292 [2024-02-18 17:23:12,600 INFO misc.py line 119 87073] Train: [48/100][634/1557] Data 0.008 (0.124) Batch 1.081 (1.078) Remain 24:31:08 loss: 0.1481 Lr: 0.00292 [2024-02-18 17:23:13,377 INFO misc.py line 119 87073] Train: [48/100][635/1557] Data 0.055 (0.124) Batch 0.827 (1.078) Remain 24:30:34 loss: 0.2040 Lr: 0.00292 [2024-02-18 17:23:14,115 INFO misc.py line 119 87073] Train: [48/100][636/1557] Data 0.005 (0.124) Batch 0.739 (1.077) Remain 24:29:49 loss: 0.4112 Lr: 0.00292 [2024-02-18 17:23:15,268 INFO misc.py line 119 87073] Train: 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Batch 0.709 (1.075) Remain 24:27:37 loss: 0.3455 Lr: 0.00292 [2024-02-18 17:23:21,895 INFO misc.py line 119 87073] Train: [48/100][644/1557] Data 0.008 (0.122) Batch 1.206 (1.076) Remain 24:27:53 loss: 0.1333 Lr: 0.00292 [2024-02-18 17:23:22,865 INFO misc.py line 119 87073] Train: [48/100][645/1557] Data 0.013 (0.122) Batch 0.980 (1.076) Remain 24:27:39 loss: 0.1917 Lr: 0.00292 [2024-02-18 17:23:23,810 INFO misc.py line 119 87073] Train: [48/100][646/1557] Data 0.004 (0.122) Batch 0.946 (1.075) Remain 24:27:22 loss: 0.3882 Lr: 0.00292 [2024-02-18 17:23:24,678 INFO misc.py line 119 87073] Train: [48/100][647/1557] Data 0.003 (0.122) Batch 0.866 (1.075) Remain 24:26:54 loss: 0.3422 Lr: 0.00292 [2024-02-18 17:23:25,599 INFO misc.py line 119 87073] Train: [48/100][648/1557] Data 0.006 (0.122) Batch 0.923 (1.075) Remain 24:26:33 loss: 0.9581 Lr: 0.00292 [2024-02-18 17:23:26,295 INFO misc.py line 119 87073] Train: [48/100][649/1557] Data 0.004 (0.121) Batch 0.697 (1.074) Remain 24:25:44 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line 119 87073] Train: [48/100][687/1557] Data 0.006 (0.125) Batch 0.878 (1.078) Remain 24:30:32 loss: 0.5261 Lr: 0.00292 [2024-02-18 17:24:10,812 INFO misc.py line 119 87073] Train: [48/100][688/1557] Data 0.004 (0.125) Batch 0.950 (1.078) Remain 24:30:16 loss: 0.7155 Lr: 0.00292 [2024-02-18 17:24:11,791 INFO misc.py line 119 87073] Train: [48/100][689/1557] Data 0.004 (0.124) Batch 0.979 (1.078) Remain 24:30:03 loss: 0.4177 Lr: 0.00292 [2024-02-18 17:24:12,721 INFO misc.py line 119 87073] Train: [48/100][690/1557] Data 0.005 (0.124) Batch 0.931 (1.078) Remain 24:29:44 loss: 0.2086 Lr: 0.00292 [2024-02-18 17:24:13,461 INFO misc.py line 119 87073] Train: [48/100][691/1557] Data 0.004 (0.124) Batch 0.738 (1.077) Remain 24:29:03 loss: 0.4882 Lr: 0.00292 [2024-02-18 17:24:14,233 INFO misc.py line 119 87073] Train: [48/100][692/1557] Data 0.006 (0.124) Batch 0.774 (1.077) Remain 24:28:26 loss: 0.3731 Lr: 0.00292 [2024-02-18 17:24:15,471 INFO misc.py line 119 87073] Train: 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Batch 0.746 (1.075) Remain 24:26:07 loss: 0.3832 Lr: 0.00292 [2024-02-18 17:24:21,844 INFO misc.py line 119 87073] Train: [48/100][700/1557] Data 0.003 (0.123) Batch 1.189 (1.075) Remain 24:26:20 loss: 0.1238 Lr: 0.00292 [2024-02-18 17:24:22,777 INFO misc.py line 119 87073] Train: [48/100][701/1557] Data 0.004 (0.122) Batch 0.933 (1.075) Remain 24:26:02 loss: 0.2690 Lr: 0.00292 [2024-02-18 17:24:23,759 INFO misc.py line 119 87073] Train: [48/100][702/1557] Data 0.003 (0.122) Batch 0.982 (1.075) Remain 24:25:50 loss: 0.4995 Lr: 0.00292 [2024-02-18 17:24:24,806 INFO misc.py line 119 87073] Train: [48/100][703/1557] Data 0.003 (0.122) Batch 1.040 (1.075) Remain 24:25:45 loss: 0.3964 Lr: 0.00292 [2024-02-18 17:24:25,727 INFO misc.py line 119 87073] Train: [48/100][704/1557] Data 0.011 (0.122) Batch 0.928 (1.075) Remain 24:25:26 loss: 0.4439 Lr: 0.00292 [2024-02-18 17:24:28,581 INFO misc.py line 119 87073] Train: [48/100][705/1557] Data 1.583 (0.124) Batch 2.854 (1.077) Remain 24:28:53 loss: 0.5637 Lr: 0.00292 [2024-02-18 17:24:29,369 INFO misc.py line 119 87073] Train: [48/100][706/1557] Data 0.004 (0.124) Batch 0.777 (1.077) Remain 24:28:17 loss: 0.2036 Lr: 0.00292 [2024-02-18 17:24:30,645 INFO misc.py line 119 87073] Train: [48/100][707/1557] Data 0.014 (0.124) Batch 1.278 (1.077) Remain 24:28:39 loss: 0.4603 Lr: 0.00292 [2024-02-18 17:24:31,835 INFO misc.py line 119 87073] Train: [48/100][708/1557] Data 0.012 (0.124) Batch 1.190 (1.077) Remain 24:28:51 loss: 0.4138 Lr: 0.00292 [2024-02-18 17:24:32,837 INFO misc.py line 119 87073] Train: [48/100][709/1557] Data 0.012 (0.123) Batch 1.003 (1.077) Remain 24:28:41 loss: 0.3954 Lr: 0.00292 [2024-02-18 17:24:33,718 INFO misc.py line 119 87073] Train: [48/100][710/1557] Data 0.012 (0.123) Batch 0.889 (1.077) Remain 24:28:19 loss: 0.6175 Lr: 0.00292 [2024-02-18 17:24:34,752 INFO misc.py line 119 87073] Train: [48/100][711/1557] Data 0.004 (0.123) Batch 1.034 (1.077) Remain 24:28:12 loss: 0.4560 Lr: 0.00292 [2024-02-18 17:24:35,451 INFO misc.py line 119 87073] Train: [48/100][712/1557] Data 0.004 (0.123) Batch 0.698 (1.076) Remain 24:27:28 loss: 0.2514 Lr: 0.00292 [2024-02-18 17:24:36,200 INFO misc.py line 119 87073] Train: [48/100][713/1557] Data 0.004 (0.123) Batch 0.747 (1.076) Remain 24:26:49 loss: 0.2848 Lr: 0.00292 [2024-02-18 17:24:37,425 INFO misc.py line 119 87073] Train: [48/100][714/1557] Data 0.007 (0.123) Batch 1.228 (1.076) Remain 24:27:05 loss: 0.1779 Lr: 0.00292 [2024-02-18 17:24:38,303 INFO misc.py line 119 87073] Train: [48/100][715/1557] Data 0.005 (0.122) Batch 0.879 (1.076) Remain 24:26:41 loss: 0.4980 Lr: 0.00292 [2024-02-18 17:24:39,277 INFO misc.py line 119 87073] Train: [48/100][716/1557] Data 0.004 (0.122) Batch 0.974 (1.076) Remain 24:26:29 loss: 0.5034 Lr: 0.00292 [2024-02-18 17:24:40,134 INFO misc.py line 119 87073] Train: [48/100][717/1557] Data 0.003 (0.122) Batch 0.857 (1.075) Remain 24:26:02 loss: 0.1635 Lr: 0.00292 [2024-02-18 17:24:41,045 INFO misc.py line 119 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Batch 0.741 (1.078) Remain 24:27:54 loss: 0.2706 Lr: 0.00291 [2024-02-18 17:26:24,536 INFO misc.py line 119 87073] Train: [48/100][812/1557] Data 0.011 (0.125) Batch 1.217 (1.078) Remain 24:28:07 loss: 0.2036 Lr: 0.00291 [2024-02-18 17:26:25,508 INFO misc.py line 119 87073] Train: [48/100][813/1557] Data 0.007 (0.125) Batch 0.977 (1.078) Remain 24:27:56 loss: 0.4331 Lr: 0.00291 [2024-02-18 17:26:26,455 INFO misc.py line 119 87073] Train: [48/100][814/1557] Data 0.003 (0.125) Batch 0.947 (1.078) Remain 24:27:41 loss: 0.5302 Lr: 0.00291 [2024-02-18 17:26:27,471 INFO misc.py line 119 87073] Train: [48/100][815/1557] Data 0.003 (0.125) Batch 1.016 (1.078) Remain 24:27:34 loss: 0.1279 Lr: 0.00291 [2024-02-18 17:26:28,506 INFO misc.py line 119 87073] Train: [48/100][816/1557] Data 0.003 (0.125) Batch 1.034 (1.078) Remain 24:27:29 loss: 0.4723 Lr: 0.00291 [2024-02-18 17:26:29,192 INFO misc.py line 119 87073] Train: [48/100][817/1557] Data 0.003 (0.125) Batch 0.684 (1.077) Remain 24:26:48 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[2024-02-18 17:28:08,904 INFO misc.py line 119 87073] Train: [48/100][905/1557] Data 0.011 (0.129) Batch 0.949 (1.083) Remain 24:32:39 loss: 0.4062 Lr: 0.00291 [2024-02-18 17:28:09,904 INFO misc.py line 119 87073] Train: [48/100][906/1557] Data 0.003 (0.129) Batch 1.000 (1.083) Remain 24:32:30 loss: 0.3837 Lr: 0.00291 [2024-02-18 17:28:10,810 INFO misc.py line 119 87073] Train: [48/100][907/1557] Data 0.005 (0.129) Batch 0.905 (1.082) Remain 24:32:13 loss: 0.3972 Lr: 0.00291 [2024-02-18 17:28:11,572 INFO misc.py line 119 87073] Train: [48/100][908/1557] Data 0.004 (0.129) Batch 0.755 (1.082) Remain 24:31:43 loss: 0.6356 Lr: 0.00291 [2024-02-18 17:28:12,337 INFO misc.py line 119 87073] Train: [48/100][909/1557] Data 0.010 (0.128) Batch 0.771 (1.082) Remain 24:31:14 loss: 0.2516 Lr: 0.00291 [2024-02-18 17:28:13,580 INFO misc.py line 119 87073] Train: [48/100][910/1557] Data 0.003 (0.128) Batch 1.238 (1.082) Remain 24:31:27 loss: 0.1830 Lr: 0.00291 [2024-02-18 17:28:14,556 INFO misc.py 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Data 0.003 (0.126) Batch 0.760 (1.079) Remain 24:25:13 loss: 0.4999 Lr: 0.00290 [2024-02-18 17:30:27,002 INFO misc.py line 119 87073] Train: [48/100][1036/1557] Data 0.011 (0.126) Batch 1.210 (1.079) Remain 24:25:23 loss: 0.1217 Lr: 0.00290 [2024-02-18 17:30:28,241 INFO misc.py line 119 87073] Train: [48/100][1037/1557] Data 0.010 (0.126) Batch 1.236 (1.079) Remain 24:25:34 loss: 0.2102 Lr: 0.00290 [2024-02-18 17:30:29,121 INFO misc.py line 119 87073] Train: [48/100][1038/1557] Data 0.012 (0.126) Batch 0.890 (1.079) Remain 24:25:18 loss: 0.5497 Lr: 0.00290 [2024-02-18 17:30:30,329 INFO misc.py line 119 87073] Train: [48/100][1039/1557] Data 0.003 (0.126) Batch 1.203 (1.079) Remain 24:25:27 loss: 0.7999 Lr: 0.00290 [2024-02-18 17:30:31,185 INFO misc.py line 119 87073] Train: [48/100][1040/1557] Data 0.008 (0.126) Batch 0.860 (1.079) Remain 24:25:08 loss: 0.4676 Lr: 0.00290 [2024-02-18 17:30:31,971 INFO misc.py line 119 87073] Train: [48/100][1041/1557] Data 0.003 (0.126) Batch 0.786 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Data 0.004 (0.125) Batch 0.847 (1.077) Remain 24:22:10 loss: 0.3405 Lr: 0.00290 [2024-02-18 17:30:58,153 INFO misc.py line 119 87073] Train: [48/100][1067/1557] Data 0.004 (0.124) Batch 0.870 (1.077) Remain 24:21:53 loss: 0.3128 Lr: 0.00290 [2024-02-18 17:30:59,205 INFO misc.py line 119 87073] Train: [48/100][1068/1557] Data 0.011 (0.124) Batch 1.053 (1.077) Remain 24:21:50 loss: 0.4116 Lr: 0.00290 [2024-02-18 17:31:00,016 INFO misc.py line 119 87073] Train: [48/100][1069/1557] Data 0.010 (0.124) Batch 0.818 (1.077) Remain 24:21:29 loss: 0.5006 Lr: 0.00290 [2024-02-18 17:31:00,716 INFO misc.py line 119 87073] Train: [48/100][1070/1557] Data 0.003 (0.124) Batch 0.701 (1.076) Remain 24:21:00 loss: 0.1970 Lr: 0.00290 [2024-02-18 17:31:08,780 INFO misc.py line 119 87073] Train: [48/100][1071/1557] Data 6.816 (0.130) Batch 8.059 (1.083) Remain 24:29:51 loss: 0.1581 Lr: 0.00290 [2024-02-18 17:31:09,575 INFO misc.py line 119 87073] Train: [48/100][1072/1557] Data 0.007 (0.130) Batch 0.800 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17:31:21,534 INFO misc.py line 119 87073] Train: [48/100][1085/1557] Data 0.008 (0.129) Batch 1.107 (1.081) Remain 24:26:35 loss: 0.1041 Lr: 0.00290 [2024-02-18 17:31:22,618 INFO misc.py line 119 87073] Train: [48/100][1086/1557] Data 0.004 (0.129) Batch 1.075 (1.081) Remain 24:26:34 loss: 0.3016 Lr: 0.00290 [2024-02-18 17:31:23,546 INFO misc.py line 119 87073] Train: [48/100][1087/1557] Data 0.013 (0.129) Batch 0.936 (1.080) Remain 24:26:22 loss: 0.3927 Lr: 0.00290 [2024-02-18 17:31:24,398 INFO misc.py line 119 87073] Train: [48/100][1088/1557] Data 0.004 (0.128) Batch 0.852 (1.080) Remain 24:26:04 loss: 0.1663 Lr: 0.00290 [2024-02-18 17:31:25,330 INFO misc.py line 119 87073] Train: [48/100][1089/1557] Data 0.004 (0.128) Batch 0.932 (1.080) Remain 24:25:51 loss: 0.2520 Lr: 0.00290 [2024-02-18 17:31:26,014 INFO misc.py line 119 87073] Train: [48/100][1090/1557] Data 0.003 (0.128) Batch 0.684 (1.080) Remain 24:25:21 loss: 0.5860 Lr: 0.00290 [2024-02-18 17:31:26,795 INFO misc.py line 119 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Data 0.004 (0.127) Batch 0.688 (1.079) Remain 24:24:08 loss: 0.2187 Lr: 0.00290 [2024-02-18 17:31:33,382 INFO misc.py line 119 87073] Train: [48/100][1098/1557] Data 0.004 (0.127) Batch 0.684 (1.079) Remain 24:23:37 loss: 0.3093 Lr: 0.00290 [2024-02-18 17:31:34,685 INFO misc.py line 119 87073] Train: [48/100][1099/1557] Data 0.009 (0.127) Batch 1.303 (1.079) Remain 24:23:53 loss: 0.1706 Lr: 0.00290 [2024-02-18 17:31:35,665 INFO misc.py line 119 87073] Train: [48/100][1100/1557] Data 0.008 (0.127) Batch 0.985 (1.079) Remain 24:23:45 loss: 0.3224 Lr: 0.00290 [2024-02-18 17:31:36,803 INFO misc.py line 119 87073] Train: [48/100][1101/1557] Data 0.004 (0.127) Batch 1.138 (1.079) Remain 24:23:48 loss: 0.4602 Lr: 0.00290 [2024-02-18 17:31:37,630 INFO misc.py line 119 87073] Train: [48/100][1102/1557] Data 0.005 (0.127) Batch 0.824 (1.078) Remain 24:23:28 loss: 0.0644 Lr: 0.00290 [2024-02-18 17:31:38,846 INFO misc.py line 119 87073] Train: [48/100][1103/1557] Data 0.008 (0.127) Batch 1.211 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87073] Train: [48/100][1122/1557] Data 0.005 (0.125) Batch 0.940 (1.076) Remain 24:20:25 loss: 0.5839 Lr: 0.00290 [2024-02-18 17:31:57,830 INFO misc.py line 119 87073] Train: [48/100][1123/1557] Data 0.004 (0.125) Batch 0.861 (1.076) Remain 24:20:08 loss: 0.7399 Lr: 0.00290 [2024-02-18 17:31:58,847 INFO misc.py line 119 87073] Train: [48/100][1124/1557] Data 0.004 (0.124) Batch 1.013 (1.076) Remain 24:20:02 loss: 0.3527 Lr: 0.00290 [2024-02-18 17:31:59,618 INFO misc.py line 119 87073] Train: [48/100][1125/1557] Data 0.008 (0.124) Batch 0.774 (1.076) Remain 24:19:39 loss: 0.4074 Lr: 0.00290 [2024-02-18 17:32:00,328 INFO misc.py line 119 87073] Train: [48/100][1126/1557] Data 0.005 (0.124) Batch 0.709 (1.076) Remain 24:19:11 loss: 0.4612 Lr: 0.00289 [2024-02-18 17:32:08,744 INFO misc.py line 119 87073] Train: [48/100][1127/1557] Data 6.801 (0.130) Batch 8.419 (1.082) Remain 24:28:02 loss: 0.2082 Lr: 0.00289 [2024-02-18 17:32:09,730 INFO misc.py line 119 87073] Train: [48/100][1128/1557] Data 0.004 (0.130) Batch 0.986 (1.082) Remain 24:27:54 loss: 0.2170 Lr: 0.00289 [2024-02-18 17:32:10,566 INFO misc.py line 119 87073] Train: [48/100][1129/1557] Data 0.003 (0.130) Batch 0.835 (1.082) Remain 24:27:35 loss: 0.5038 Lr: 0.00289 [2024-02-18 17:32:11,477 INFO misc.py line 119 87073] Train: [48/100][1130/1557] Data 0.005 (0.130) Batch 0.912 (1.082) Remain 24:27:22 loss: 0.3907 Lr: 0.00289 [2024-02-18 17:32:12,720 INFO misc.py line 119 87073] Train: [48/100][1131/1557] Data 0.004 (0.130) Batch 1.236 (1.082) Remain 24:27:32 loss: 0.3294 Lr: 0.00289 [2024-02-18 17:32:13,506 INFO misc.py line 119 87073] Train: [48/100][1132/1557] Data 0.011 (0.130) Batch 0.794 (1.082) Remain 24:27:10 loss: 0.6271 Lr: 0.00289 [2024-02-18 17:32:14,217 INFO misc.py line 119 87073] Train: [48/100][1133/1557] Data 0.003 (0.130) Batch 0.706 (1.081) Remain 24:26:42 loss: 0.3635 Lr: 0.00289 [2024-02-18 17:32:15,454 INFO misc.py line 119 87073] Train: [48/100][1134/1557] Data 0.007 (0.129) Batch 1.235 (1.081) Remain 24:26:52 loss: 0.1913 Lr: 0.00289 [2024-02-18 17:32:16,410 INFO misc.py line 119 87073] Train: [48/100][1135/1557] Data 0.010 (0.129) Batch 0.963 (1.081) Remain 24:26:42 loss: 0.4375 Lr: 0.00289 [2024-02-18 17:32:17,318 INFO misc.py line 119 87073] Train: [48/100][1136/1557] Data 0.003 (0.129) Batch 0.908 (1.081) Remain 24:26:29 loss: 0.3300 Lr: 0.00289 [2024-02-18 17:32:18,298 INFO misc.py line 119 87073] Train: [48/100][1137/1557] Data 0.003 (0.129) Batch 0.979 (1.081) Remain 24:26:20 loss: 0.4226 Lr: 0.00289 [2024-02-18 17:32:19,233 INFO misc.py line 119 87073] Train: [48/100][1138/1557] Data 0.004 (0.129) Batch 0.932 (1.081) Remain 24:26:09 loss: 0.5426 Lr: 0.00289 [2024-02-18 17:32:20,016 INFO misc.py line 119 87073] Train: [48/100][1139/1557] Data 0.007 (0.129) Batch 0.784 (1.081) Remain 24:25:46 loss: 0.2645 Lr: 0.00289 [2024-02-18 17:32:20,782 INFO misc.py line 119 87073] Train: [48/100][1140/1557] Data 0.007 (0.129) Batch 0.767 (1.080) Remain 24:25:23 loss: 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Data 0.006 (0.127) Batch 0.904 (1.079) Remain 24:22:56 loss: 0.3615 Lr: 0.00289 [2024-02-18 17:32:40,308 INFO misc.py line 119 87073] Train: [48/100][1160/1557] Data 0.004 (0.127) Batch 0.785 (1.079) Remain 24:22:34 loss: 0.1998 Lr: 0.00289 [2024-02-18 17:32:41,086 INFO misc.py line 119 87073] Train: [48/100][1161/1557] Data 0.009 (0.127) Batch 0.780 (1.078) Remain 24:22:12 loss: 0.6665 Lr: 0.00289 [2024-02-18 17:32:42,321 INFO misc.py line 119 87073] Train: [48/100][1162/1557] Data 0.006 (0.126) Batch 1.232 (1.078) Remain 24:22:22 loss: 0.1766 Lr: 0.00289 [2024-02-18 17:32:43,392 INFO misc.py line 119 87073] Train: [48/100][1163/1557] Data 0.009 (0.126) Batch 1.075 (1.078) Remain 24:22:21 loss: 0.3101 Lr: 0.00289 [2024-02-18 17:32:44,361 INFO misc.py line 119 87073] Train: [48/100][1164/1557] Data 0.005 (0.126) Batch 0.969 (1.078) Remain 24:22:12 loss: 0.4294 Lr: 0.00289 [2024-02-18 17:32:45,426 INFO misc.py line 119 87073] Train: [48/100][1165/1557] Data 0.005 (0.126) Batch 1.066 (1.078) Remain 24:22:10 loss: 0.7847 Lr: 0.00289 [2024-02-18 17:32:46,502 INFO misc.py line 119 87073] Train: [48/100][1166/1557] Data 0.003 (0.126) Batch 1.075 (1.078) Remain 24:22:09 loss: 0.3792 Lr: 0.00289 [2024-02-18 17:32:47,199 INFO misc.py line 119 87073] Train: [48/100][1167/1557] Data 0.005 (0.126) Batch 0.696 (1.078) Remain 24:21:41 loss: 0.5183 Lr: 0.00289 [2024-02-18 17:32:47,959 INFO misc.py line 119 87073] Train: [48/100][1168/1557] Data 0.007 (0.126) Batch 0.759 (1.078) Remain 24:21:18 loss: 0.2142 Lr: 0.00289 [2024-02-18 17:32:49,224 INFO misc.py line 119 87073] Train: [48/100][1169/1557] Data 0.007 (0.126) Batch 1.267 (1.078) Remain 24:21:30 loss: 0.0950 Lr: 0.00289 [2024-02-18 17:32:50,072 INFO misc.py line 119 87073] Train: [48/100][1170/1557] Data 0.005 (0.126) Batch 0.848 (1.078) Remain 24:21:13 loss: 0.3971 Lr: 0.00289 [2024-02-18 17:32:50,940 INFO misc.py line 119 87073] Train: [48/100][1171/1557] Data 0.005 (0.125) Batch 0.868 (1.078) Remain 24:20:57 loss: 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17:32:57,316 INFO misc.py line 119 87073] Train: [48/100][1178/1557] Data 0.004 (0.125) Batch 0.823 (1.077) Remain 24:19:29 loss: 0.1604 Lr: 0.00289 [2024-02-18 17:32:58,438 INFO misc.py line 119 87073] Train: [48/100][1179/1557] Data 0.005 (0.125) Batch 1.118 (1.077) Remain 24:19:30 loss: 0.4505 Lr: 0.00289 [2024-02-18 17:32:59,380 INFO misc.py line 119 87073] Train: [48/100][1180/1557] Data 0.009 (0.125) Batch 0.947 (1.076) Remain 24:19:20 loss: 0.4553 Lr: 0.00289 [2024-02-18 17:33:00,177 INFO misc.py line 119 87073] Train: [48/100][1181/1557] Data 0.004 (0.124) Batch 0.796 (1.076) Remain 24:19:00 loss: 0.2920 Lr: 0.00289 [2024-02-18 17:33:00,966 INFO misc.py line 119 87073] Train: [48/100][1182/1557] Data 0.005 (0.124) Batch 0.782 (1.076) Remain 24:18:39 loss: 0.2161 Lr: 0.00289 [2024-02-18 17:33:08,864 INFO misc.py line 119 87073] Train: [48/100][1183/1557] Data 6.505 (0.130) Batch 7.902 (1.082) Remain 24:26:28 loss: 0.1184 Lr: 0.00289 [2024-02-18 17:33:09,755 INFO misc.py line 119 87073] Train: [48/100][1184/1557] Data 0.008 (0.130) Batch 0.894 (1.082) Remain 24:26:14 loss: 0.1891 Lr: 0.00289 [2024-02-18 17:33:10,889 INFO misc.py line 119 87073] Train: [48/100][1185/1557] Data 0.004 (0.130) Batch 1.133 (1.082) Remain 24:26:16 loss: 0.3651 Lr: 0.00289 [2024-02-18 17:33:11,821 INFO misc.py line 119 87073] Train: [48/100][1186/1557] Data 0.004 (0.129) Batch 0.934 (1.082) Remain 24:26:05 loss: 0.3774 Lr: 0.00289 [2024-02-18 17:33:12,818 INFO misc.py line 119 87073] Train: [48/100][1187/1557] Data 0.003 (0.129) Batch 0.989 (1.081) Remain 24:25:58 loss: 0.5309 Lr: 0.00289 [2024-02-18 17:33:13,577 INFO misc.py line 119 87073] Train: [48/100][1188/1557] Data 0.011 (0.129) Batch 0.765 (1.081) Remain 24:25:35 loss: 0.1851 Lr: 0.00289 [2024-02-18 17:33:14,331 INFO misc.py line 119 87073] Train: [48/100][1189/1557] Data 0.005 (0.129) Batch 0.746 (1.081) Remain 24:25:11 loss: 0.5559 Lr: 0.00289 [2024-02-18 17:33:15,614 INFO misc.py line 119 87073] Train: [48/100][1190/1557] Data 0.013 (0.129) Batch 1.281 (1.081) Remain 24:25:24 loss: 0.1834 Lr: 0.00289 [2024-02-18 17:33:16,621 INFO misc.py line 119 87073] Train: [48/100][1191/1557] Data 0.015 (0.129) Batch 1.006 (1.081) Remain 24:25:17 loss: 0.3966 Lr: 0.00289 [2024-02-18 17:33:17,548 INFO misc.py line 119 87073] Train: [48/100][1192/1557] Data 0.015 (0.129) Batch 0.939 (1.081) Remain 24:25:07 loss: 0.2169 Lr: 0.00289 [2024-02-18 17:33:18,386 INFO misc.py line 119 87073] Train: [48/100][1193/1557] Data 0.005 (0.129) Batch 0.838 (1.081) Remain 24:24:49 loss: 0.3387 Lr: 0.00289 [2024-02-18 17:33:19,287 INFO misc.py line 119 87073] Train: [48/100][1194/1557] Data 0.005 (0.129) Batch 0.877 (1.081) Remain 24:24:34 loss: 0.2947 Lr: 0.00289 [2024-02-18 17:33:20,001 INFO misc.py line 119 87073] Train: [48/100][1195/1557] Data 0.028 (0.129) Batch 0.739 (1.080) Remain 24:24:10 loss: 0.1986 Lr: 0.00289 [2024-02-18 17:33:20,728 INFO misc.py line 119 87073] Train: [48/100][1196/1557] Data 0.003 (0.128) Batch 0.719 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Data 0.004 (0.126) Batch 1.230 (1.077) Remain 24:19:41 loss: 0.9614 Lr: 0.00289 [2024-02-18 17:33:45,463 INFO misc.py line 119 87073] Train: [48/100][1222/1557] Data 0.004 (0.126) Batch 0.983 (1.077) Remain 24:19:33 loss: 0.5586 Lr: 0.00289 [2024-02-18 17:33:46,175 INFO misc.py line 119 87073] Train: [48/100][1223/1557] Data 0.005 (0.126) Batch 0.712 (1.077) Remain 24:19:08 loss: 0.4900 Lr: 0.00289 [2024-02-18 17:33:46,932 INFO misc.py line 119 87073] Train: [48/100][1224/1557] Data 0.004 (0.126) Batch 0.751 (1.077) Remain 24:18:45 loss: 0.2278 Lr: 0.00289 [2024-02-18 17:33:48,241 INFO misc.py line 119 87073] Train: [48/100][1225/1557] Data 0.010 (0.126) Batch 1.314 (1.077) Remain 24:19:00 loss: 0.1145 Lr: 0.00289 [2024-02-18 17:33:49,438 INFO misc.py line 119 87073] Train: [48/100][1226/1557] Data 0.006 (0.125) Batch 1.194 (1.077) Remain 24:19:06 loss: 0.1794 Lr: 0.00289 [2024-02-18 17:33:50,482 INFO misc.py line 119 87073] Train: [48/100][1227/1557] Data 0.009 (0.125) Batch 1.045 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17:34:11,136 INFO misc.py line 119 87073] Train: [48/100][1240/1557] Data 0.012 (0.131) Batch 1.000 (1.082) Remain 24:26:07 loss: 0.1768 Lr: 0.00289 [2024-02-18 17:34:12,084 INFO misc.py line 119 87073] Train: [48/100][1241/1557] Data 0.007 (0.131) Batch 0.950 (1.082) Remain 24:25:57 loss: 0.5329 Lr: 0.00289 [2024-02-18 17:34:13,138 INFO misc.py line 119 87073] Train: [48/100][1242/1557] Data 0.006 (0.131) Batch 1.054 (1.082) Remain 24:25:54 loss: 0.2873 Lr: 0.00289 [2024-02-18 17:34:14,098 INFO misc.py line 119 87073] Train: [48/100][1243/1557] Data 0.006 (0.130) Batch 0.962 (1.082) Remain 24:25:45 loss: 0.4829 Lr: 0.00289 [2024-02-18 17:34:14,866 INFO misc.py line 119 87073] Train: [48/100][1244/1557] Data 0.004 (0.130) Batch 0.767 (1.082) Remain 24:25:23 loss: 0.7013 Lr: 0.00289 [2024-02-18 17:34:15,572 INFO misc.py line 119 87073] Train: [48/100][1245/1557] Data 0.004 (0.130) Batch 0.706 (1.081) Remain 24:24:58 loss: 0.3850 Lr: 0.00289 [2024-02-18 17:34:16,776 INFO misc.py line 119 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Data 0.004 (0.130) Batch 0.732 (1.081) Remain 24:23:45 loss: 0.2077 Lr: 0.00289 [2024-02-18 17:34:23,301 INFO misc.py line 119 87073] Train: [48/100][1253/1557] Data 0.005 (0.129) Batch 1.156 (1.081) Remain 24:23:49 loss: 0.1399 Lr: 0.00289 [2024-02-18 17:34:24,263 INFO misc.py line 119 87073] Train: [48/100][1254/1557] Data 0.011 (0.129) Batch 0.966 (1.081) Remain 24:23:40 loss: 0.3336 Lr: 0.00289 [2024-02-18 17:34:25,369 INFO misc.py line 119 87073] Train: [48/100][1255/1557] Data 0.004 (0.129) Batch 1.106 (1.081) Remain 24:23:41 loss: 0.4166 Lr: 0.00289 [2024-02-18 17:34:26,204 INFO misc.py line 119 87073] Train: [48/100][1256/1557] Data 0.004 (0.129) Batch 0.835 (1.080) Remain 24:23:24 loss: 0.2715 Lr: 0.00289 [2024-02-18 17:34:27,271 INFO misc.py line 119 87073] Train: [48/100][1257/1557] Data 0.004 (0.129) Batch 1.066 (1.080) Remain 24:23:22 loss: 0.4216 Lr: 0.00289 [2024-02-18 17:34:28,026 INFO misc.py line 119 87073] Train: [48/100][1258/1557] Data 0.005 (0.129) Batch 0.755 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17:34:40,668 INFO misc.py line 119 87073] Train: [48/100][1271/1557] Data 0.005 (0.128) Batch 0.941 (1.079) Remain 24:21:16 loss: 0.3577 Lr: 0.00289 [2024-02-18 17:34:41,387 INFO misc.py line 119 87073] Train: [48/100][1272/1557] Data 0.004 (0.128) Batch 0.711 (1.079) Remain 24:20:51 loss: 0.2487 Lr: 0.00289 [2024-02-18 17:34:42,138 INFO misc.py line 119 87073] Train: [48/100][1273/1557] Data 0.012 (0.128) Batch 0.758 (1.079) Remain 24:20:30 loss: 0.4477 Lr: 0.00289 [2024-02-18 17:34:43,420 INFO misc.py line 119 87073] Train: [48/100][1274/1557] Data 0.004 (0.127) Batch 1.276 (1.079) Remain 24:20:41 loss: 0.1435 Lr: 0.00289 [2024-02-18 17:34:44,354 INFO misc.py line 119 87073] Train: [48/100][1275/1557] Data 0.010 (0.127) Batch 0.939 (1.079) Remain 24:20:31 loss: 0.4462 Lr: 0.00289 [2024-02-18 17:34:45,264 INFO misc.py line 119 87073] Train: [48/100][1276/1557] Data 0.006 (0.127) Batch 0.910 (1.078) Remain 24:20:19 loss: 0.2000 Lr: 0.00289 [2024-02-18 17:34:46,164 INFO misc.py line 119 87073] Train: [48/100][1277/1557] Data 0.006 (0.127) Batch 0.902 (1.078) Remain 24:20:07 loss: 0.2303 Lr: 0.00289 [2024-02-18 17:34:47,091 INFO misc.py line 119 87073] Train: [48/100][1278/1557] Data 0.004 (0.127) Batch 0.925 (1.078) Remain 24:19:56 loss: 0.2064 Lr: 0.00289 [2024-02-18 17:34:47,783 INFO misc.py line 119 87073] Train: [48/100][1279/1557] Data 0.006 (0.127) Batch 0.694 (1.078) Remain 24:19:31 loss: 0.2613 Lr: 0.00289 [2024-02-18 17:34:48,623 INFO misc.py line 119 87073] Train: [48/100][1280/1557] Data 0.003 (0.127) Batch 0.839 (1.078) Remain 24:19:14 loss: 0.1902 Lr: 0.00289 [2024-02-18 17:34:49,954 INFO misc.py line 119 87073] Train: [48/100][1281/1557] Data 0.005 (0.127) Batch 1.329 (1.078) Remain 24:19:29 loss: 0.0679 Lr: 0.00289 [2024-02-18 17:34:50,794 INFO misc.py line 119 87073] Train: [48/100][1282/1557] Data 0.007 (0.127) Batch 0.842 (1.078) Remain 24:19:13 loss: 0.4266 Lr: 0.00289 [2024-02-18 17:34:51,847 INFO misc.py line 119 87073] Train: [48/100][1283/1557] Data 0.006 (0.127) Batch 1.053 (1.078) Remain 24:19:10 loss: 0.3567 Lr: 0.00289 [2024-02-18 17:34:52,746 INFO misc.py line 119 87073] Train: [48/100][1284/1557] Data 0.005 (0.127) Batch 0.898 (1.078) Remain 24:18:58 loss: 0.2850 Lr: 0.00289 [2024-02-18 17:34:53,690 INFO misc.py line 119 87073] Train: [48/100][1285/1557] Data 0.008 (0.126) Batch 0.944 (1.077) Remain 24:18:48 loss: 0.2043 Lr: 0.00289 [2024-02-18 17:34:54,366 INFO misc.py line 119 87073] Train: [48/100][1286/1557] Data 0.006 (0.126) Batch 0.677 (1.077) Remain 24:18:22 loss: 0.4103 Lr: 0.00289 [2024-02-18 17:34:55,243 INFO misc.py line 119 87073] Train: [48/100][1287/1557] Data 0.004 (0.126) Batch 0.871 (1.077) Remain 24:18:08 loss: 0.2883 Lr: 0.00289 [2024-02-18 17:34:56,420 INFO misc.py line 119 87073] Train: [48/100][1288/1557] Data 0.010 (0.126) Batch 1.178 (1.077) Remain 24:18:13 loss: 0.2455 Lr: 0.00289 [2024-02-18 17:34:57,514 INFO misc.py line 119 87073] Train: [48/100][1289/1557] Data 0.010 (0.126) Batch 1.096 (1.077) Remain 24:18:13 loss: 0.4558 Lr: 0.00289 [2024-02-18 17:34:58,813 INFO misc.py line 119 87073] Train: [48/100][1290/1557] Data 0.007 (0.126) Batch 1.298 (1.077) Remain 24:18:26 loss: 0.5406 Lr: 0.00289 [2024-02-18 17:34:59,900 INFO misc.py line 119 87073] Train: [48/100][1291/1557] Data 0.008 (0.126) Batch 1.082 (1.077) Remain 24:18:25 loss: 0.4994 Lr: 0.00289 [2024-02-18 17:35:00,850 INFO misc.py line 119 87073] Train: [48/100][1292/1557] Data 0.013 (0.126) Batch 0.959 (1.077) Remain 24:18:17 loss: 0.4581 Lr: 0.00289 [2024-02-18 17:35:01,612 INFO misc.py line 119 87073] Train: [48/100][1293/1557] Data 0.004 (0.126) Batch 0.760 (1.077) Remain 24:17:56 loss: 0.2894 Lr: 0.00289 [2024-02-18 17:35:02,401 INFO misc.py line 119 87073] Train: [48/100][1294/1557] Data 0.006 (0.126) Batch 0.787 (1.077) Remain 24:17:37 loss: 0.2861 Lr: 0.00289 [2024-02-18 17:35:10,338 INFO misc.py line 119 87073] Train: [48/100][1295/1557] Data 6.454 (0.131) Batch 7.927 (1.082) Remain 24:24:46 loss: 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87073] Train: [48/100][1308/1557] Data 0.011 (0.129) Batch 0.754 (1.080) Remain 24:22:27 loss: 0.2725 Lr: 0.00289 [2024-02-18 17:35:23,464 INFO misc.py line 119 87073] Train: [48/100][1309/1557] Data 0.004 (0.129) Batch 1.091 (1.080) Remain 24:22:26 loss: 0.1210 Lr: 0.00289 [2024-02-18 17:35:24,572 INFO misc.py line 119 87073] Train: [48/100][1310/1557] Data 0.003 (0.129) Batch 1.106 (1.080) Remain 24:22:27 loss: 0.4971 Lr: 0.00289 [2024-02-18 17:35:25,489 INFO misc.py line 119 87073] Train: [48/100][1311/1557] Data 0.006 (0.129) Batch 0.917 (1.080) Remain 24:22:15 loss: 0.1215 Lr: 0.00289 [2024-02-18 17:35:26,403 INFO misc.py line 119 87073] Train: [48/100][1312/1557] Data 0.005 (0.129) Batch 0.914 (1.080) Remain 24:22:04 loss: 0.1474 Lr: 0.00289 [2024-02-18 17:35:27,382 INFO misc.py line 119 87073] Train: [48/100][1313/1557] Data 0.005 (0.129) Batch 0.980 (1.080) Remain 24:21:57 loss: 0.2858 Lr: 0.00289 [2024-02-18 17:35:28,143 INFO misc.py line 119 87073] Train: [48/100][1314/1557] Data 0.005 (0.129) Batch 0.761 (1.080) Remain 24:21:36 loss: 0.2963 Lr: 0.00289 [2024-02-18 17:35:28,922 INFO misc.py line 119 87073] Train: [48/100][1315/1557] Data 0.004 (0.129) Batch 0.771 (1.080) Remain 24:21:16 loss: 0.2701 Lr: 0.00289 [2024-02-18 17:35:30,127 INFO misc.py line 119 87073] Train: [48/100][1316/1557] Data 0.012 (0.129) Batch 1.208 (1.080) Remain 24:21:23 loss: 0.1752 Lr: 0.00288 [2024-02-18 17:35:31,022 INFO misc.py line 119 87073] Train: [48/100][1317/1557] Data 0.009 (0.128) Batch 0.898 (1.080) Remain 24:21:10 loss: 0.4912 Lr: 0.00288 [2024-02-18 17:35:31,945 INFO misc.py line 119 87073] Train: [48/100][1318/1557] Data 0.005 (0.128) Batch 0.923 (1.080) Remain 24:21:00 loss: 0.2508 Lr: 0.00288 [2024-02-18 17:35:32,747 INFO misc.py line 119 87073] Train: [48/100][1319/1557] Data 0.005 (0.128) Batch 0.801 (1.079) Remain 24:20:41 loss: 0.4354 Lr: 0.00288 [2024-02-18 17:35:33,827 INFO misc.py line 119 87073] Train: [48/100][1320/1557] Data 0.006 (0.128) Batch 1.080 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17:35:46,713 INFO misc.py line 119 87073] Train: [48/100][1333/1557] Data 0.014 (0.127) Batch 1.120 (1.078) Remain 24:19:15 loss: 0.5847 Lr: 0.00288 [2024-02-18 17:35:47,541 INFO misc.py line 119 87073] Train: [48/100][1334/1557] Data 0.024 (0.127) Batch 0.847 (1.078) Remain 24:19:00 loss: 0.2816 Lr: 0.00288 [2024-02-18 17:35:48,289 INFO misc.py line 119 87073] Train: [48/100][1335/1557] Data 0.005 (0.127) Batch 0.749 (1.078) Remain 24:18:39 loss: 0.1673 Lr: 0.00288 [2024-02-18 17:35:49,081 INFO misc.py line 119 87073] Train: [48/100][1336/1557] Data 0.005 (0.127) Batch 0.788 (1.078) Remain 24:18:20 loss: 0.3027 Lr: 0.00288 [2024-02-18 17:35:50,391 INFO misc.py line 119 87073] Train: [48/100][1337/1557] Data 0.008 (0.127) Batch 1.304 (1.078) Remain 24:18:33 loss: 0.0945 Lr: 0.00288 [2024-02-18 17:35:51,503 INFO misc.py line 119 87073] Train: [48/100][1338/1557] Data 0.015 (0.127) Batch 1.093 (1.078) Remain 24:18:33 loss: 0.4208 Lr: 0.00288 [2024-02-18 17:35:52,327 INFO misc.py line 119 87073] Train: [48/100][1339/1557] Data 0.033 (0.127) Batch 0.853 (1.078) Remain 24:18:18 loss: 0.8548 Lr: 0.00288 [2024-02-18 17:35:53,267 INFO misc.py line 119 87073] Train: [48/100][1340/1557] Data 0.005 (0.126) Batch 0.939 (1.078) Remain 24:18:08 loss: 0.4930 Lr: 0.00288 [2024-02-18 17:35:54,214 INFO misc.py line 119 87073] Train: [48/100][1341/1557] Data 0.005 (0.126) Batch 0.948 (1.078) Remain 24:17:59 loss: 0.1837 Lr: 0.00288 [2024-02-18 17:35:54,926 INFO misc.py line 119 87073] Train: [48/100][1342/1557] Data 0.005 (0.126) Batch 0.712 (1.077) Remain 24:17:36 loss: 0.2717 Lr: 0.00288 [2024-02-18 17:35:55,688 INFO misc.py line 119 87073] Train: [48/100][1343/1557] Data 0.005 (0.126) Batch 0.762 (1.077) Remain 24:17:16 loss: 0.3248 Lr: 0.00288 [2024-02-18 17:35:56,828 INFO misc.py line 119 87073] Train: [48/100][1344/1557] Data 0.004 (0.126) Batch 1.138 (1.077) Remain 24:17:19 loss: 0.2178 Lr: 0.00288 [2024-02-18 17:35:57,855 INFO misc.py line 119 87073] Train: [48/100][1345/1557] Data 0.007 (0.126) Batch 1.029 (1.077) Remain 24:17:15 loss: 0.8468 Lr: 0.00288 [2024-02-18 17:35:58,962 INFO misc.py line 119 87073] Train: [48/100][1346/1557] Data 0.005 (0.126) Batch 1.106 (1.077) Remain 24:17:15 loss: 0.4927 Lr: 0.00288 [2024-02-18 17:35:59,910 INFO misc.py line 119 87073] Train: [48/100][1347/1557] Data 0.007 (0.126) Batch 0.949 (1.077) Remain 24:17:06 loss: 0.4906 Lr: 0.00288 [2024-02-18 17:36:00,813 INFO misc.py line 119 87073] Train: [48/100][1348/1557] Data 0.006 (0.126) Batch 0.904 (1.077) Remain 24:16:55 loss: 0.3051 Lr: 0.00288 [2024-02-18 17:36:01,550 INFO misc.py line 119 87073] Train: [48/100][1349/1557] Data 0.004 (0.126) Batch 0.736 (1.077) Remain 24:16:33 loss: 0.2118 Lr: 0.00288 [2024-02-18 17:36:02,215 INFO misc.py line 119 87073] Train: [48/100][1350/1557] Data 0.006 (0.126) Batch 0.666 (1.076) Remain 24:16:07 loss: 0.1479 Lr: 0.00288 [2024-02-18 17:36:11,188 INFO misc.py line 119 87073] Train: [48/100][1351/1557] Data 6.531 (0.130) Batch 8.973 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17:36:23,798 INFO misc.py line 119 87073] Train: [48/100][1364/1557] Data 0.005 (0.129) Batch 0.727 (1.081) Remain 24:22:20 loss: 0.5695 Lr: 0.00288 [2024-02-18 17:36:24,919 INFO misc.py line 119 87073] Train: [48/100][1365/1557] Data 0.009 (0.129) Batch 1.119 (1.081) Remain 24:22:22 loss: 0.1230 Lr: 0.00288 [2024-02-18 17:36:25,907 INFO misc.py line 119 87073] Train: [48/100][1366/1557] Data 0.012 (0.129) Batch 0.994 (1.081) Remain 24:22:15 loss: 0.4063 Lr: 0.00288 [2024-02-18 17:36:26,807 INFO misc.py line 119 87073] Train: [48/100][1367/1557] Data 0.006 (0.129) Batch 0.902 (1.081) Remain 24:22:04 loss: 0.4110 Lr: 0.00288 [2024-02-18 17:36:27,817 INFO misc.py line 119 87073] Train: [48/100][1368/1557] Data 0.004 (0.129) Batch 1.010 (1.081) Remain 24:21:58 loss: 0.6110 Lr: 0.00288 [2024-02-18 17:36:28,878 INFO misc.py line 119 87073] Train: [48/100][1369/1557] Data 0.003 (0.129) Batch 1.061 (1.081) Remain 24:21:56 loss: 0.0904 Lr: 0.00288 [2024-02-18 17:36:29,615 INFO misc.py line 119 87073] Train: [48/100][1370/1557] Data 0.004 (0.129) Batch 0.737 (1.081) Remain 24:21:35 loss: 0.1439 Lr: 0.00288 [2024-02-18 17:36:30,385 INFO misc.py line 119 87073] Train: [48/100][1371/1557] Data 0.004 (0.128) Batch 0.758 (1.080) Remain 24:21:14 loss: 0.2787 Lr: 0.00288 [2024-02-18 17:36:31,610 INFO misc.py line 119 87073] Train: [48/100][1372/1557] Data 0.015 (0.128) Batch 1.226 (1.081) Remain 24:21:22 loss: 0.2164 Lr: 0.00288 [2024-02-18 17:36:32,533 INFO misc.py line 119 87073] Train: [48/100][1373/1557] Data 0.014 (0.128) Batch 0.934 (1.080) Remain 24:21:12 loss: 0.8543 Lr: 0.00288 [2024-02-18 17:36:33,574 INFO misc.py line 119 87073] Train: [48/100][1374/1557] Data 0.004 (0.128) Batch 1.041 (1.080) Remain 24:21:09 loss: 0.3303 Lr: 0.00288 [2024-02-18 17:36:34,538 INFO misc.py line 119 87073] Train: [48/100][1375/1557] Data 0.005 (0.128) Batch 0.964 (1.080) Remain 24:21:01 loss: 0.0906 Lr: 0.00288 [2024-02-18 17:36:35,538 INFO misc.py line 119 87073] Train: [48/100][1376/1557] Data 0.004 (0.128) Batch 1.001 (1.080) Remain 24:20:55 loss: 0.5618 Lr: 0.00288 [2024-02-18 17:36:36,278 INFO misc.py line 119 87073] Train: [48/100][1377/1557] Data 0.003 (0.128) Batch 0.735 (1.080) Remain 24:20:33 loss: 0.2963 Lr: 0.00288 [2024-02-18 17:36:37,055 INFO misc.py line 119 87073] Train: [48/100][1378/1557] Data 0.008 (0.128) Batch 0.781 (1.080) Remain 24:20:15 loss: 0.4022 Lr: 0.00288 [2024-02-18 17:36:38,345 INFO misc.py line 119 87073] Train: [48/100][1379/1557] Data 0.004 (0.128) Batch 1.280 (1.080) Remain 24:20:26 loss: 0.3707 Lr: 0.00288 [2024-02-18 17:36:39,257 INFO misc.py line 119 87073] Train: [48/100][1380/1557] Data 0.015 (0.128) Batch 0.922 (1.080) Remain 24:20:15 loss: 0.5028 Lr: 0.00288 [2024-02-18 17:36:40,152 INFO misc.py line 119 87073] Train: [48/100][1381/1557] Data 0.005 (0.128) Batch 0.895 (1.080) Remain 24:20:03 loss: 0.3694 Lr: 0.00288 [2024-02-18 17:36:41,085 INFO misc.py line 119 87073] Train: [48/100][1382/1557] Data 0.004 (0.128) Batch 0.931 (1.080) Remain 24:19:53 loss: 0.7825 Lr: 0.00288 [2024-02-18 17:36:42,196 INFO misc.py line 119 87073] Train: [48/100][1383/1557] Data 0.008 (0.127) Batch 1.103 (1.080) Remain 24:19:54 loss: 0.2508 Lr: 0.00288 [2024-02-18 17:36:42,882 INFO misc.py line 119 87073] Train: [48/100][1384/1557] Data 0.015 (0.127) Batch 0.696 (1.079) Remain 24:19:30 loss: 0.3032 Lr: 0.00288 [2024-02-18 17:36:43,654 INFO misc.py line 119 87073] Train: [48/100][1385/1557] Data 0.004 (0.127) Batch 0.767 (1.079) Remain 24:19:11 loss: 0.5813 Lr: 0.00288 [2024-02-18 17:36:44,903 INFO misc.py line 119 87073] Train: [48/100][1386/1557] Data 0.010 (0.127) Batch 1.245 (1.079) Remain 24:19:19 loss: 0.1272 Lr: 0.00288 [2024-02-18 17:36:45,894 INFO misc.py line 119 87073] Train: [48/100][1387/1557] Data 0.013 (0.127) Batch 0.999 (1.079) Remain 24:19:13 loss: 0.2949 Lr: 0.00288 [2024-02-18 17:36:46,929 INFO misc.py line 119 87073] Train: [48/100][1388/1557] Data 0.006 (0.127) Batch 1.034 (1.079) Remain 24:19:10 loss: 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17:36:53,342 INFO misc.py line 119 87073] Train: [48/100][1395/1557] Data 0.004 (0.126) Batch 0.945 (1.078) Remain 24:17:56 loss: 0.2908 Lr: 0.00288 [2024-02-18 17:36:54,381 INFO misc.py line 119 87073] Train: [48/100][1396/1557] Data 0.006 (0.126) Batch 1.036 (1.078) Remain 24:17:52 loss: 0.9342 Lr: 0.00288 [2024-02-18 17:36:55,182 INFO misc.py line 119 87073] Train: [48/100][1397/1557] Data 0.008 (0.126) Batch 0.803 (1.078) Remain 24:17:35 loss: 0.3341 Lr: 0.00288 [2024-02-18 17:36:55,901 INFO misc.py line 119 87073] Train: [48/100][1398/1557] Data 0.006 (0.126) Batch 0.718 (1.078) Remain 24:17:13 loss: 0.5999 Lr: 0.00288 [2024-02-18 17:36:56,652 INFO misc.py line 119 87073] Train: [48/100][1399/1557] Data 0.007 (0.126) Batch 0.748 (1.078) Remain 24:16:53 loss: 0.3302 Lr: 0.00288 [2024-02-18 17:36:57,843 INFO misc.py line 119 87073] Train: [48/100][1400/1557] Data 0.009 (0.126) Batch 1.192 (1.078) Remain 24:16:58 loss: 0.3880 Lr: 0.00288 [2024-02-18 17:36:58,771 INFO misc.py line 119 87073] Train: [48/100][1401/1557] Data 0.010 (0.126) Batch 0.933 (1.078) Remain 24:16:49 loss: 0.7264 Lr: 0.00288 [2024-02-18 17:36:59,752 INFO misc.py line 119 87073] Train: [48/100][1402/1557] Data 0.005 (0.126) Batch 0.981 (1.077) Remain 24:16:42 loss: 0.3005 Lr: 0.00288 [2024-02-18 17:37:00,575 INFO misc.py line 119 87073] Train: [48/100][1403/1557] Data 0.004 (0.126) Batch 0.821 (1.077) Remain 24:16:26 loss: 0.1691 Lr: 0.00288 [2024-02-18 17:37:01,597 INFO misc.py line 119 87073] Train: [48/100][1404/1557] Data 0.007 (0.126) Batch 1.017 (1.077) Remain 24:16:22 loss: 0.2968 Lr: 0.00288 [2024-02-18 17:37:04,012 INFO misc.py line 119 87073] Train: [48/100][1405/1557] Data 1.510 (0.127) Batch 2.418 (1.078) Remain 24:17:38 loss: 0.3319 Lr: 0.00288 [2024-02-18 17:37:04,769 INFO misc.py line 119 87073] Train: [48/100][1406/1557] Data 0.008 (0.127) Batch 0.761 (1.078) Remain 24:17:19 loss: 0.1300 Lr: 0.00288 [2024-02-18 17:37:13,092 INFO misc.py line 119 87073] Train: [48/100][1407/1557] Data 6.549 (0.131) Batch 8.298 (1.083) Remain 24:24:15 loss: 0.1701 Lr: 0.00288 [2024-02-18 17:37:14,069 INFO misc.py line 119 87073] Train: [48/100][1408/1557] Data 0.029 (0.131) Batch 1.002 (1.083) Remain 24:24:09 loss: 0.4274 Lr: 0.00288 [2024-02-18 17:37:14,948 INFO misc.py line 119 87073] Train: [48/100][1409/1557] Data 0.004 (0.131) Batch 0.877 (1.083) Remain 24:23:56 loss: 0.5000 Lr: 0.00288 [2024-02-18 17:37:15,814 INFO misc.py line 119 87073] Train: [48/100][1410/1557] Data 0.007 (0.131) Batch 0.867 (1.083) Remain 24:23:43 loss: 0.2789 Lr: 0.00288 [2024-02-18 17:37:16,695 INFO misc.py line 119 87073] Train: [48/100][1411/1557] Data 0.005 (0.131) Batch 0.882 (1.083) Remain 24:23:30 loss: 0.9912 Lr: 0.00288 [2024-02-18 17:37:17,419 INFO misc.py line 119 87073] Train: [48/100][1412/1557] Data 0.004 (0.131) Batch 0.722 (1.082) Remain 24:23:08 loss: 0.2078 Lr: 0.00288 [2024-02-18 17:37:18,201 INFO misc.py line 119 87073] Train: [48/100][1413/1557] Data 0.006 (0.131) Batch 0.782 (1.082) Remain 24:22:50 loss: 0.4031 Lr: 0.00288 [2024-02-18 17:37:19,453 INFO misc.py line 119 87073] Train: [48/100][1414/1557] Data 0.005 (0.130) Batch 1.250 (1.082) Remain 24:22:58 loss: 0.1675 Lr: 0.00288 [2024-02-18 17:37:20,422 INFO misc.py line 119 87073] Train: [48/100][1415/1557] Data 0.007 (0.130) Batch 0.972 (1.082) Remain 24:22:51 loss: 0.6218 Lr: 0.00288 [2024-02-18 17:37:21,460 INFO misc.py line 119 87073] Train: [48/100][1416/1557] Data 0.004 (0.130) Batch 1.037 (1.082) Remain 24:22:47 loss: 0.2062 Lr: 0.00288 [2024-02-18 17:37:22,385 INFO misc.py line 119 87073] Train: [48/100][1417/1557] Data 0.005 (0.130) Batch 0.926 (1.082) Remain 24:22:37 loss: 0.0496 Lr: 0.00288 [2024-02-18 17:37:23,284 INFO misc.py line 119 87073] Train: [48/100][1418/1557] Data 0.004 (0.130) Batch 0.898 (1.082) Remain 24:22:26 loss: 0.5352 Lr: 0.00288 [2024-02-18 17:37:24,037 INFO misc.py line 119 87073] Train: [48/100][1419/1557] Data 0.004 (0.130) Batch 0.747 (1.082) Remain 24:22:05 loss: 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17:37:30,632 INFO misc.py line 119 87073] Train: [48/100][1426/1557] Data 0.004 (0.129) Batch 0.728 (1.081) Remain 24:21:03 loss: 0.4059 Lr: 0.00288 [2024-02-18 17:37:31,356 INFO misc.py line 119 87073] Train: [48/100][1427/1557] Data 0.007 (0.129) Batch 0.717 (1.081) Remain 24:20:41 loss: 0.3775 Lr: 0.00288 [2024-02-18 17:37:32,549 INFO misc.py line 119 87073] Train: [48/100][1428/1557] Data 0.010 (0.129) Batch 1.193 (1.081) Remain 24:20:46 loss: 0.2298 Lr: 0.00288 [2024-02-18 17:37:33,645 INFO misc.py line 119 87073] Train: [48/100][1429/1557] Data 0.010 (0.129) Batch 1.101 (1.081) Remain 24:20:46 loss: 0.6024 Lr: 0.00288 [2024-02-18 17:37:34,802 INFO misc.py line 119 87073] Train: [48/100][1430/1557] Data 0.006 (0.129) Batch 1.147 (1.081) Remain 24:20:49 loss: 0.3822 Lr: 0.00288 [2024-02-18 17:37:35,714 INFO misc.py line 119 87073] Train: [48/100][1431/1557] Data 0.016 (0.129) Batch 0.922 (1.081) Remain 24:20:39 loss: 0.2392 Lr: 0.00288 [2024-02-18 17:37:36,596 INFO misc.py line 119 87073] Train: [48/100][1432/1557] Data 0.006 (0.129) Batch 0.883 (1.081) Remain 24:20:26 loss: 0.1869 Lr: 0.00288 [2024-02-18 17:37:37,375 INFO misc.py line 119 87073] Train: [48/100][1433/1557] Data 0.005 (0.129) Batch 0.779 (1.080) Remain 24:20:08 loss: 0.3643 Lr: 0.00288 [2024-02-18 17:37:38,116 INFO misc.py line 119 87073] Train: [48/100][1434/1557] Data 0.005 (0.129) Batch 0.741 (1.080) Remain 24:19:48 loss: 0.1899 Lr: 0.00288 [2024-02-18 17:37:39,459 INFO misc.py line 119 87073] Train: [48/100][1435/1557] Data 0.004 (0.129) Batch 1.338 (1.080) Remain 24:20:01 loss: 0.2221 Lr: 0.00288 [2024-02-18 17:37:40,426 INFO misc.py line 119 87073] Train: [48/100][1436/1557] Data 0.009 (0.129) Batch 0.972 (1.080) Remain 24:19:54 loss: 0.2900 Lr: 0.00288 [2024-02-18 17:37:41,438 INFO misc.py line 119 87073] Train: [48/100][1437/1557] Data 0.004 (0.129) Batch 1.013 (1.080) Remain 24:19:49 loss: 0.6985 Lr: 0.00288 [2024-02-18 17:37:42,474 INFO misc.py line 119 87073] Train: [48/100][1438/1557] Data 0.004 (0.128) Batch 1.035 (1.080) Remain 24:19:46 loss: 0.5387 Lr: 0.00288 [2024-02-18 17:37:43,277 INFO misc.py line 119 87073] Train: [48/100][1439/1557] Data 0.004 (0.128) Batch 0.801 (1.080) Remain 24:19:29 loss: 0.2398 Lr: 0.00288 [2024-02-18 17:37:44,010 INFO misc.py line 119 87073] Train: [48/100][1440/1557] Data 0.006 (0.128) Batch 0.735 (1.080) Remain 24:19:08 loss: 0.2158 Lr: 0.00288 [2024-02-18 17:37:44,770 INFO misc.py line 119 87073] Train: [48/100][1441/1557] Data 0.005 (0.128) Batch 0.760 (1.080) Remain 24:18:49 loss: 0.4653 Lr: 0.00288 [2024-02-18 17:37:46,042 INFO misc.py line 119 87073] Train: [48/100][1442/1557] Data 0.005 (0.128) Batch 1.267 (1.080) Remain 24:18:59 loss: 0.1483 Lr: 0.00288 [2024-02-18 17:37:47,008 INFO misc.py line 119 87073] Train: [48/100][1443/1557] Data 0.010 (0.128) Batch 0.970 (1.080) Remain 24:18:51 loss: 0.1885 Lr: 0.00288 [2024-02-18 17:37:47,955 INFO misc.py line 119 87073] Train: [48/100][1444/1557] Data 0.006 (0.128) Batch 0.948 (1.080) Remain 24:18:43 loss: 0.2937 Lr: 0.00288 [2024-02-18 17:37:48,865 INFO misc.py line 119 87073] Train: [48/100][1445/1557] Data 0.005 (0.128) Batch 0.909 (1.079) Remain 24:18:32 loss: 0.5396 Lr: 0.00288 [2024-02-18 17:37:49,976 INFO misc.py line 119 87073] Train: [48/100][1446/1557] Data 0.005 (0.128) Batch 1.110 (1.079) Remain 24:18:33 loss: 0.8573 Lr: 0.00288 [2024-02-18 17:37:50,672 INFO misc.py line 119 87073] Train: [48/100][1447/1557] Data 0.006 (0.128) Batch 0.697 (1.079) Remain 24:18:10 loss: 0.2233 Lr: 0.00288 [2024-02-18 17:37:51,411 INFO misc.py line 119 87073] Train: [48/100][1448/1557] Data 0.005 (0.128) Batch 0.739 (1.079) Remain 24:17:50 loss: 0.4107 Lr: 0.00288 [2024-02-18 17:37:52,781 INFO misc.py line 119 87073] Train: [48/100][1449/1557] Data 0.005 (0.127) Batch 1.370 (1.079) Remain 24:18:05 loss: 0.0860 Lr: 0.00288 [2024-02-18 17:37:53,772 INFO misc.py line 119 87073] Train: [48/100][1450/1557] Data 0.006 (0.127) Batch 0.991 (1.079) Remain 24:17:59 loss: 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17:37:59,733 INFO misc.py line 119 87073] Train: [48/100][1457/1557] Data 0.006 (0.127) Batch 0.842 (1.078) Remain 24:16:23 loss: 0.4550 Lr: 0.00288 [2024-02-18 17:38:00,714 INFO misc.py line 119 87073] Train: [48/100][1458/1557] Data 0.004 (0.127) Batch 0.980 (1.078) Remain 24:16:17 loss: 0.5842 Lr: 0.00288 [2024-02-18 17:38:01,943 INFO misc.py line 119 87073] Train: [48/100][1459/1557] Data 0.005 (0.127) Batch 1.224 (1.078) Remain 24:16:24 loss: 0.1283 Lr: 0.00288 [2024-02-18 17:38:02,828 INFO misc.py line 119 87073] Train: [48/100][1460/1557] Data 0.011 (0.127) Batch 0.891 (1.078) Remain 24:16:12 loss: 0.2803 Lr: 0.00288 [2024-02-18 17:38:03,529 INFO misc.py line 119 87073] Train: [48/100][1461/1557] Data 0.005 (0.126) Batch 0.701 (1.078) Remain 24:15:50 loss: 0.3142 Lr: 0.00288 [2024-02-18 17:38:04,402 INFO misc.py line 119 87073] Train: [48/100][1462/1557] Data 0.004 (0.126) Batch 0.872 (1.077) Remain 24:15:38 loss: 0.3989 Lr: 0.00288 [2024-02-18 17:38:12,954 INFO misc.py line 119 87073] Train: [48/100][1463/1557] Data 6.253 (0.131) Batch 8.554 (1.083) Remain 24:22:32 loss: 0.2421 Lr: 0.00288 [2024-02-18 17:38:13,838 INFO misc.py line 119 87073] Train: [48/100][1464/1557] Data 0.004 (0.131) Batch 0.883 (1.082) Remain 24:22:20 loss: 0.5508 Lr: 0.00288 [2024-02-18 17:38:14,833 INFO misc.py line 119 87073] Train: [48/100][1465/1557] Data 0.005 (0.130) Batch 0.992 (1.082) Remain 24:22:14 loss: 0.1667 Lr: 0.00288 [2024-02-18 17:38:15,747 INFO misc.py line 119 87073] Train: [48/100][1466/1557] Data 0.008 (0.130) Batch 0.916 (1.082) Remain 24:22:03 loss: 0.2325 Lr: 0.00288 [2024-02-18 17:38:16,888 INFO misc.py line 119 87073] Train: [48/100][1467/1557] Data 0.004 (0.130) Batch 1.142 (1.082) Remain 24:22:05 loss: 0.9323 Lr: 0.00288 [2024-02-18 17:38:17,688 INFO misc.py line 119 87073] Train: [48/100][1468/1557] Data 0.003 (0.130) Batch 0.799 (1.082) Remain 24:21:49 loss: 0.3758 Lr: 0.00288 [2024-02-18 17:38:18,488 INFO misc.py line 119 87073] Train: [48/100][1469/1557] Data 0.005 (0.130) Batch 0.787 (1.082) Remain 24:21:31 loss: 0.6238 Lr: 0.00288 [2024-02-18 17:38:19,784 INFO misc.py line 119 87073] Train: [48/100][1470/1557] Data 0.017 (0.130) Batch 1.299 (1.082) Remain 24:21:42 loss: 0.2762 Lr: 0.00288 [2024-02-18 17:38:20,708 INFO misc.py line 119 87073] Train: [48/100][1471/1557] Data 0.014 (0.130) Batch 0.934 (1.082) Remain 24:21:33 loss: 0.1078 Lr: 0.00288 [2024-02-18 17:38:21,564 INFO misc.py line 119 87073] Train: [48/100][1472/1557] Data 0.004 (0.130) Batch 0.854 (1.082) Remain 24:21:19 loss: 0.3160 Lr: 0.00288 [2024-02-18 17:38:22,486 INFO misc.py line 119 87073] Train: [48/100][1473/1557] Data 0.006 (0.130) Batch 0.915 (1.082) Remain 24:21:09 loss: 0.7625 Lr: 0.00288 [2024-02-18 17:38:23,553 INFO misc.py line 119 87073] Train: [48/100][1474/1557] Data 0.013 (0.130) Batch 1.066 (1.082) Remain 24:21:07 loss: 0.7115 Lr: 0.00288 [2024-02-18 17:38:24,326 INFO misc.py line 119 87073] Train: [48/100][1475/1557] Data 0.013 (0.130) Batch 0.781 (1.081) Remain 24:20:50 loss: 0.4739 Lr: 0.00288 [2024-02-18 17:38:25,048 INFO misc.py line 119 87073] Train: [48/100][1476/1557] Data 0.005 (0.130) Batch 0.719 (1.081) Remain 24:20:28 loss: 0.3698 Lr: 0.00288 [2024-02-18 17:38:26,208 INFO misc.py line 119 87073] Train: [48/100][1477/1557] Data 0.007 (0.129) Batch 1.156 (1.081) Remain 24:20:32 loss: 0.2866 Lr: 0.00288 [2024-02-18 17:38:27,180 INFO misc.py line 119 87073] Train: [48/100][1478/1557] Data 0.012 (0.129) Batch 0.980 (1.081) Remain 24:20:25 loss: 0.4440 Lr: 0.00288 [2024-02-18 17:38:28,033 INFO misc.py line 119 87073] Train: [48/100][1479/1557] Data 0.004 (0.129) Batch 0.845 (1.081) Remain 24:20:11 loss: 0.4108 Lr: 0.00288 [2024-02-18 17:38:28,922 INFO misc.py line 119 87073] Train: [48/100][1480/1557] Data 0.012 (0.129) Batch 0.892 (1.081) Remain 24:19:59 loss: 0.6374 Lr: 0.00288 [2024-02-18 17:38:29,746 INFO misc.py line 119 87073] Train: [48/100][1481/1557] Data 0.008 (0.129) Batch 0.828 (1.081) Remain 24:19:44 loss: 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17:38:36,385 INFO misc.py line 119 87073] Train: [48/100][1488/1557] Data 0.004 (0.129) Batch 0.963 (1.080) Remain 24:18:46 loss: 0.5539 Lr: 0.00288 [2024-02-18 17:38:37,171 INFO misc.py line 119 87073] Train: [48/100][1489/1557] Data 0.014 (0.128) Batch 0.796 (1.080) Remain 24:18:29 loss: 0.2619 Lr: 0.00288 [2024-02-18 17:38:37,944 INFO misc.py line 119 87073] Train: [48/100][1490/1557] Data 0.004 (0.128) Batch 0.772 (1.080) Remain 24:18:11 loss: 0.2030 Lr: 0.00288 [2024-02-18 17:38:39,288 INFO misc.py line 119 87073] Train: [48/100][1491/1557] Data 0.005 (0.128) Batch 1.333 (1.080) Remain 24:18:24 loss: 0.2098 Lr: 0.00288 [2024-02-18 17:38:40,399 INFO misc.py line 119 87073] Train: [48/100][1492/1557] Data 0.015 (0.128) Batch 1.118 (1.080) Remain 24:18:25 loss: 0.4856 Lr: 0.00288 [2024-02-18 17:38:41,455 INFO misc.py line 119 87073] Train: [48/100][1493/1557] Data 0.008 (0.128) Batch 1.051 (1.080) Remain 24:18:22 loss: 0.2193 Lr: 0.00288 [2024-02-18 17:38:42,390 INFO misc.py line 119 87073] Train: [48/100][1494/1557] Data 0.014 (0.128) Batch 0.945 (1.080) Remain 24:18:14 loss: 0.4657 Lr: 0.00288 [2024-02-18 17:38:43,410 INFO misc.py line 119 87073] Train: [48/100][1495/1557] Data 0.004 (0.128) Batch 1.019 (1.080) Remain 24:18:10 loss: 0.3758 Lr: 0.00288 [2024-02-18 17:38:44,227 INFO misc.py line 119 87073] Train: [48/100][1496/1557] Data 0.004 (0.128) Batch 0.816 (1.080) Remain 24:17:54 loss: 0.7638 Lr: 0.00288 [2024-02-18 17:38:45,013 INFO misc.py line 119 87073] Train: [48/100][1497/1557] Data 0.005 (0.128) Batch 0.783 (1.079) Remain 24:17:37 loss: 0.4384 Lr: 0.00288 [2024-02-18 17:38:46,226 INFO misc.py line 119 87073] Train: [48/100][1498/1557] Data 0.008 (0.128) Batch 1.207 (1.079) Remain 24:17:43 loss: 0.1502 Lr: 0.00288 [2024-02-18 17:38:47,093 INFO misc.py line 119 87073] Train: [48/100][1499/1557] Data 0.016 (0.128) Batch 0.877 (1.079) Remain 24:17:31 loss: 0.3543 Lr: 0.00288 [2024-02-18 17:38:47,998 INFO misc.py line 119 87073] Train: [48/100][1500/1557] Data 0.006 (0.128) Batch 0.905 (1.079) Remain 24:17:20 loss: 0.7283 Lr: 0.00288 [2024-02-18 17:38:49,036 INFO misc.py line 119 87073] Train: [48/100][1501/1557] Data 0.005 (0.127) Batch 1.040 (1.079) Remain 24:17:17 loss: 0.5562 Lr: 0.00288 [2024-02-18 17:38:49,969 INFO misc.py line 119 87073] Train: [48/100][1502/1557] Data 0.003 (0.127) Batch 0.932 (1.079) Remain 24:17:08 loss: 0.3246 Lr: 0.00288 [2024-02-18 17:38:50,742 INFO misc.py line 119 87073] Train: [48/100][1503/1557] Data 0.005 (0.127) Batch 0.763 (1.079) Remain 24:16:50 loss: 0.1518 Lr: 0.00288 [2024-02-18 17:38:51,520 INFO misc.py line 119 87073] Train: [48/100][1504/1557] Data 0.014 (0.127) Batch 0.788 (1.079) Remain 24:16:33 loss: 0.3564 Lr: 0.00288 [2024-02-18 17:38:52,794 INFO misc.py line 119 87073] Train: [48/100][1505/1557] Data 0.005 (0.127) Batch 1.268 (1.079) Remain 24:16:42 loss: 0.1304 Lr: 0.00288 [2024-02-18 17:38:53,814 INFO misc.py line 119 87073] Train: [48/100][1506/1557] Data 0.010 (0.127) Batch 1.024 (1.079) Remain 24:16:38 loss: 0.2241 Lr: 0.00288 [2024-02-18 17:38:54,838 INFO misc.py line 119 87073] Train: [48/100][1507/1557] Data 0.007 (0.127) Batch 1.021 (1.079) Remain 24:16:34 loss: 0.6583 Lr: 0.00287 [2024-02-18 17:38:55,721 INFO misc.py line 119 87073] Train: [48/100][1508/1557] Data 0.009 (0.127) Batch 0.887 (1.079) Remain 24:16:23 loss: 0.1870 Lr: 0.00287 [2024-02-18 17:38:56,675 INFO misc.py line 119 87073] Train: [48/100][1509/1557] Data 0.005 (0.127) Batch 0.953 (1.079) Remain 24:16:15 loss: 0.3947 Lr: 0.00287 [2024-02-18 17:38:57,420 INFO misc.py line 119 87073] Train: [48/100][1510/1557] Data 0.006 (0.127) Batch 0.745 (1.078) Remain 24:15:56 loss: 0.2245 Lr: 0.00287 [2024-02-18 17:38:58,177 INFO misc.py line 119 87073] Train: [48/100][1511/1557] Data 0.005 (0.127) Batch 0.755 (1.078) Remain 24:15:37 loss: 0.3934 Lr: 0.00287 [2024-02-18 17:38:59,404 INFO misc.py line 119 87073] Train: [48/100][1512/1557] Data 0.007 (0.127) Batch 1.230 (1.078) Remain 24:15:44 loss: 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17:39:12,757 INFO misc.py line 119 87073] Train: [48/100][1519/1557] Data 6.862 (0.131) Batch 8.137 (1.082) Remain 24:20:47 loss: 0.2112 Lr: 0.00287 [2024-02-18 17:39:13,773 INFO misc.py line 119 87073] Train: [48/100][1520/1557] Data 0.016 (0.130) Batch 1.016 (1.082) Remain 24:20:42 loss: 0.1549 Lr: 0.00287 [2024-02-18 17:39:14,819 INFO misc.py line 119 87073] Train: [48/100][1521/1557] Data 0.018 (0.130) Batch 1.056 (1.082) Remain 24:20:39 loss: 0.2982 Lr: 0.00287 [2024-02-18 17:39:15,835 INFO misc.py line 119 87073] Train: [48/100][1522/1557] Data 0.009 (0.130) Batch 1.018 (1.082) Remain 24:20:35 loss: 0.4487 Lr: 0.00287 [2024-02-18 17:39:16,769 INFO misc.py line 119 87073] Train: [48/100][1523/1557] Data 0.005 (0.130) Batch 0.935 (1.082) Remain 24:20:26 loss: 0.7259 Lr: 0.00287 [2024-02-18 17:39:17,486 INFO misc.py line 119 87073] Train: [48/100][1524/1557] Data 0.004 (0.130) Batch 0.716 (1.082) Remain 24:20:05 loss: 0.2084 Lr: 0.00287 [2024-02-18 17:39:18,183 INFO misc.py line 119 87073] Train: [48/100][1525/1557] Data 0.004 (0.130) Batch 0.696 (1.081) Remain 24:19:44 loss: 0.3531 Lr: 0.00287 [2024-02-18 17:39:19,475 INFO misc.py line 119 87073] Train: [48/100][1526/1557] Data 0.007 (0.130) Batch 1.288 (1.081) Remain 24:19:54 loss: 0.1696 Lr: 0.00287 [2024-02-18 17:39:20,426 INFO misc.py line 119 87073] Train: [48/100][1527/1557] Data 0.010 (0.130) Batch 0.955 (1.081) Remain 24:19:46 loss: 0.5363 Lr: 0.00287 [2024-02-18 17:39:21,441 INFO misc.py line 119 87073] Train: [48/100][1528/1557] Data 0.007 (0.130) Batch 1.016 (1.081) Remain 24:19:41 loss: 0.2980 Lr: 0.00287 [2024-02-18 17:39:22,615 INFO misc.py line 119 87073] Train: [48/100][1529/1557] Data 0.005 (0.130) Batch 1.170 (1.081) Remain 24:19:45 loss: 0.6566 Lr: 0.00287 [2024-02-18 17:39:23,527 INFO misc.py line 119 87073] Train: [48/100][1530/1557] Data 0.009 (0.130) Batch 0.916 (1.081) Remain 24:19:35 loss: 0.4421 Lr: 0.00287 [2024-02-18 17:39:24,278 INFO misc.py line 119 87073] Train: [48/100][1531/1557] Data 0.005 (0.130) Batch 0.752 (1.081) Remain 24:19:17 loss: 0.2860 Lr: 0.00287 [2024-02-18 17:39:25,060 INFO misc.py line 119 87073] Train: [48/100][1532/1557] Data 0.004 (0.130) Batch 0.776 (1.081) Remain 24:18:59 loss: 0.3343 Lr: 0.00287 [2024-02-18 17:39:26,282 INFO misc.py line 119 87073] Train: [48/100][1533/1557] Data 0.010 (0.129) Batch 1.217 (1.081) Remain 24:19:06 loss: 0.1086 Lr: 0.00287 [2024-02-18 17:39:27,331 INFO misc.py line 119 87073] Train: [48/100][1534/1557] Data 0.014 (0.129) Batch 1.051 (1.081) Remain 24:19:03 loss: 0.9992 Lr: 0.00287 [2024-02-18 17:39:28,560 INFO misc.py line 119 87073] Train: [48/100][1535/1557] Data 0.013 (0.129) Batch 1.233 (1.081) Remain 24:19:10 loss: 0.2635 Lr: 0.00287 [2024-02-18 17:39:29,606 INFO misc.py line 119 87073] Train: [48/100][1536/1557] Data 0.009 (0.129) Batch 1.045 (1.081) Remain 24:19:07 loss: 0.3880 Lr: 0.00287 [2024-02-18 17:39:30,648 INFO misc.py line 119 87073] Train: [48/100][1537/1557] Data 0.010 (0.129) Batch 1.042 (1.081) Remain 24:19:04 loss: 0.3869 Lr: 0.00287 [2024-02-18 17:39:31,422 INFO misc.py line 119 87073] Train: [48/100][1538/1557] Data 0.009 (0.129) Batch 0.779 (1.081) Remain 24:18:47 loss: 0.2951 Lr: 0.00287 [2024-02-18 17:39:32,208 INFO misc.py line 119 87073] Train: [48/100][1539/1557] Data 0.004 (0.129) Batch 0.779 (1.081) Remain 24:18:30 loss: 0.6149 Lr: 0.00287 [2024-02-18 17:39:33,424 INFO misc.py line 119 87073] Train: [48/100][1540/1557] Data 0.011 (0.129) Batch 1.199 (1.081) Remain 24:18:35 loss: 0.1215 Lr: 0.00287 [2024-02-18 17:39:34,444 INFO misc.py line 119 87073] Train: [48/100][1541/1557] Data 0.028 (0.129) Batch 1.036 (1.081) Remain 24:18:31 loss: 0.3514 Lr: 0.00287 [2024-02-18 17:39:35,251 INFO misc.py line 119 87073] Train: [48/100][1542/1557] Data 0.012 (0.129) Batch 0.815 (1.080) Remain 24:18:16 loss: 0.3562 Lr: 0.00287 [2024-02-18 17:39:36,238 INFO misc.py line 119 87073] Train: [48/100][1543/1557] Data 0.004 (0.129) Batch 0.986 (1.080) Remain 24:18:10 loss: 0.4954 Lr: 0.00287 [2024-02-18 17:39:37,407 INFO misc.py line 119 87073] Train: [48/100][1544/1557] Data 0.004 (0.129) Batch 1.169 (1.080) Remain 24:18:14 loss: 0.9307 Lr: 0.00287 [2024-02-18 17:39:38,125 INFO misc.py line 119 87073] Train: [48/100][1545/1557] Data 0.007 (0.129) Batch 0.718 (1.080) Remain 24:17:54 loss: 0.1704 Lr: 0.00287 [2024-02-18 17:39:38,877 INFO misc.py line 119 87073] Train: [48/100][1546/1557] Data 0.004 (0.128) Batch 0.740 (1.080) Remain 24:17:35 loss: 0.1489 Lr: 0.00287 [2024-02-18 17:39:40,149 INFO misc.py line 119 87073] Train: [48/100][1547/1557] Data 0.016 (0.128) Batch 1.273 (1.080) Remain 24:17:44 loss: 0.2795 Lr: 0.00287 [2024-02-18 17:39:41,246 INFO misc.py line 119 87073] Train: [48/100][1548/1557] Data 0.015 (0.128) Batch 1.096 (1.080) Remain 24:17:44 loss: 0.4575 Lr: 0.00287 [2024-02-18 17:39:42,195 INFO misc.py line 119 87073] Train: [48/100][1549/1557] Data 0.016 (0.128) Batch 0.961 (1.080) Remain 24:17:36 loss: 0.3772 Lr: 0.00287 [2024-02-18 17:39:43,123 INFO misc.py line 119 87073] Train: [48/100][1550/1557] Data 0.004 (0.128) Batch 0.927 (1.080) Remain 24:17:27 loss: 0.5578 Lr: 0.00287 [2024-02-18 17:39:43,965 INFO misc.py line 119 87073] Train: [48/100][1551/1557] Data 0.005 (0.128) Batch 0.830 (1.080) Remain 24:17:13 loss: 0.3758 Lr: 0.00287 [2024-02-18 17:39:44,747 INFO misc.py line 119 87073] Train: [48/100][1552/1557] Data 0.017 (0.128) Batch 0.795 (1.080) Remain 24:16:57 loss: 0.2324 Lr: 0.00287 [2024-02-18 17:39:45,500 INFO misc.py line 119 87073] Train: [48/100][1553/1557] Data 0.004 (0.128) Batch 0.741 (1.079) Remain 24:16:38 loss: 0.6879 Lr: 0.00287 [2024-02-18 17:39:46,703 INFO misc.py line 119 87073] Train: [48/100][1554/1557] Data 0.016 (0.128) Batch 1.205 (1.080) Remain 24:16:44 loss: 0.2643 Lr: 0.00287 [2024-02-18 17:39:47,846 INFO misc.py line 119 87073] Train: [48/100][1555/1557] Data 0.014 (0.128) Batch 1.140 (1.080) Remain 24:16:46 loss: 0.6145 Lr: 0.00287 [2024-02-18 17:39:48,683 INFO misc.py line 119 87073] Train: [48/100][1556/1557] Data 0.017 (0.128) Batch 0.850 (1.079) Remain 24:16:33 loss: 0.3950 Lr: 0.00287 [2024-02-18 17:39:49,807 INFO misc.py line 119 87073] Train: [48/100][1557/1557] Data 0.004 (0.128) Batch 1.123 (1.079) Remain 24:16:34 loss: 0.2184 Lr: 0.00287 [2024-02-18 17:39:49,808 INFO misc.py line 136 87073] Train result: loss: 0.3756 [2024-02-18 17:39:49,808 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 17:40:18,791 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5179 [2024-02-18 17:40:19,570 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5401 [2024-02-18 17:40:21,698 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4884 [2024-02-18 17:40:23,904 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3123 [2024-02-18 17:40:28,847 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2238 [2024-02-18 17:40:29,547 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1989 [2024-02-18 17:40:30,809 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2283 [2024-02-18 17:40:33,765 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3449 [2024-02-18 17:40:35,579 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2738 [2024-02-18 17:40:37,052 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7119/0.7842/0.9134. [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9300/0.9755 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9828/0.9912 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8698/0.9664 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0056/0.0374 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4592/0.5320 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6217/0.6338 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7462/0.8515 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8414/0.9208 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9230/0.9605 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8178/0.8523 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7951/0.9030 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6758/0.9111 [2024-02-18 17:40:37,053 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5862/0.6587 [2024-02-18 17:40:37,054 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 17:40:37,059 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 17:40:37,059 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 17:40:44,010 INFO misc.py line 119 87073] Train: [49/100][1/1557] Data 1.283 (1.283) Batch 2.029 (2.029) Remain 45:38:13 loss: 0.1329 Lr: 0.00287 [2024-02-18 17:40:44,990 INFO misc.py line 119 87073] Train: [49/100][2/1557] Data 0.008 (0.008) Batch 0.981 (0.981) Remain 22:04:20 loss: 0.5325 Lr: 0.00287 [2024-02-18 17:40:45,873 INFO misc.py line 119 87073] Train: [49/100][3/1557] Data 0.006 (0.006) Batch 0.883 (0.883) Remain 19:51:44 loss: 0.0824 Lr: 0.00287 [2024-02-18 17:40:46,947 INFO misc.py line 119 87073] Train: [49/100][4/1557] Data 0.007 (0.007) Batch 1.074 (1.074) Remain 24:09:35 loss: 0.6392 Lr: 0.00287 [2024-02-18 17:40:47,713 INFO misc.py line 119 87073] Train: [49/100][5/1557] Data 0.006 (0.006) Batch 0.767 (0.921) Remain 20:42:15 loss: 0.3802 Lr: 0.00287 [2024-02-18 17:40:48,526 INFO misc.py line 119 87073] Train: [49/100][6/1557] Data 0.004 (0.005) Batch 0.778 (0.873) Remain 19:37:59 loss: 0.2407 Lr: 0.00287 [2024-02-18 17:40:49,739 INFO misc.py line 119 87073] Train: [49/100][7/1557] Data 0.101 (0.029) Batch 1.245 (0.966) Remain 21:43:33 loss: 0.1992 Lr: 0.00287 [2024-02-18 17:40:50,642 INFO misc.py line 119 87073] Train: [49/100][8/1557] Data 0.006 (0.025) Batch 0.905 (0.954) Remain 21:26:58 loss: 0.4332 Lr: 0.00287 [2024-02-18 17:40:51,561 INFO misc.py line 119 87073] Train: [49/100][9/1557] Data 0.006 (0.022) Batch 0.920 (0.948) Remain 21:19:16 loss: 0.3048 Lr: 0.00287 [2024-02-18 17:40:52,478 INFO misc.py line 119 87073] Train: [49/100][10/1557] Data 0.005 (0.019) Batch 0.916 (0.944) Remain 21:13:02 loss: 0.2768 Lr: 0.00287 [2024-02-18 17:40:53,726 INFO misc.py line 119 87073] Train: [49/100][11/1557] Data 0.006 (0.017) Batch 1.246 (0.981) Remain 22:03:58 loss: 0.8105 Lr: 0.00287 [2024-02-18 17:40:54,493 INFO misc.py line 119 87073] Train: [49/100][12/1557] Data 0.008 (0.016) Batch 0.770 (0.958) Remain 21:32:13 loss: 0.2954 Lr: 0.00287 [2024-02-18 17:40:55,240 INFO misc.py line 119 87073] Train: [49/100][13/1557] Data 0.005 (0.015) Batch 0.731 (0.935) Remain 21:01:38 loss: 0.2059 Lr: 0.00287 [2024-02-18 17:40:59,430 INFO misc.py line 119 87073] Train: [49/100][14/1557] Data 3.076 (0.293) Batch 4.207 (1.233) Remain 27:42:52 loss: 0.2313 Lr: 0.00287 [2024-02-18 17:41:00,506 INFO misc.py line 119 87073] Train: [49/100][15/1557] Data 0.005 (0.269) Batch 1.075 (1.219) Remain 27:25:07 loss: 0.2712 Lr: 0.00287 [2024-02-18 17:41:01,360 INFO misc.py line 119 87073] Train: [49/100][16/1557] Data 0.005 (0.249) Batch 0.855 (1.191) Remain 26:47:18 loss: 0.4902 Lr: 0.00287 [2024-02-18 17:41:02,377 INFO misc.py line 119 87073] Train: [49/100][17/1557] Data 0.005 (0.232) Batch 0.991 (1.177) Remain 26:27:59 loss: 3.2231 Lr: 0.00287 [2024-02-18 17:41:03,279 INFO misc.py line 119 87073] Train: [49/100][18/1557] Data 0.030 (0.218) Batch 0.925 (1.160) Remain 26:05:17 loss: 0.4901 Lr: 0.00287 [2024-02-18 17:41:04,012 INFO misc.py line 119 87073] Train: [49/100][19/1557] Data 0.007 (0.205) Batch 0.734 (1.134) Remain 25:29:21 loss: 0.2833 Lr: 0.00287 [2024-02-18 17:41:04,785 INFO misc.py line 119 87073] Train: [49/100][20/1557] Data 0.006 (0.193) Batch 0.770 (1.112) Remain 25:00:30 loss: 0.4606 Lr: 0.00287 [2024-02-18 17:41:06,141 INFO misc.py line 119 87073] Train: [49/100][21/1557] Data 0.008 (0.183) Batch 1.354 (1.126) Remain 25:18:34 loss: 0.1855 Lr: 0.00287 [2024-02-18 17:41:07,078 INFO misc.py line 119 87073] Train: [49/100][22/1557] Data 0.012 (0.174) Batch 0.941 (1.116) Remain 25:05:28 loss: 0.5586 Lr: 0.00287 [2024-02-18 17:41:07,971 INFO misc.py line 119 87073] Train: [49/100][23/1557] Data 0.007 (0.166) Batch 0.895 (1.105) Remain 24:50:35 loss: 0.2637 Lr: 0.00287 [2024-02-18 17:41:09,047 INFO misc.py line 119 87073] Train: [49/100][24/1557] Data 0.005 (0.158) Batch 1.076 (1.104) Remain 24:48:41 loss: 0.7148 Lr: 0.00287 [2024-02-18 17:41:10,100 INFO misc.py line 119 87073] Train: [49/100][25/1557] Data 0.004 (0.151) Batch 1.053 (1.101) Remain 24:45:34 loss: 0.2975 Lr: 0.00287 [2024-02-18 17:41:10,800 INFO misc.py line 119 87073] Train: [49/100][26/1557] Data 0.004 (0.145) Batch 0.699 (1.084) Remain 24:21:56 loss: 0.1411 Lr: 0.00287 [2024-02-18 17:41:11,601 INFO misc.py line 119 87073] Train: [49/100][27/1557] Data 0.006 (0.139) Batch 0.801 (1.072) Remain 24:06:00 loss: 0.2171 Lr: 0.00287 [2024-02-18 17:41:12,817 INFO misc.py line 119 87073] Train: [49/100][28/1557] Data 0.006 (0.133) Batch 1.210 (1.077) Remain 24:13:26 loss: 0.1791 Lr: 0.00287 [2024-02-18 17:41:13,732 INFO misc.py line 119 87073] Train: [49/100][29/1557] Data 0.013 (0.129) Batch 0.922 (1.071) Remain 24:05:21 loss: 0.3932 Lr: 0.00287 [2024-02-18 17:41:14,704 INFO misc.py line 119 87073] Train: [49/100][30/1557] Data 0.007 (0.124) Batch 0.972 (1.068) Remain 24:00:22 loss: 0.3750 Lr: 0.00287 [2024-02-18 17:41:15,802 INFO misc.py line 119 87073] Train: [49/100][31/1557] Data 0.005 (0.120) Batch 1.098 (1.069) Remain 24:01:48 loss: 0.6353 Lr: 0.00287 [2024-02-18 17:41:16,803 INFO misc.py line 119 87073] Train: [49/100][32/1557] Data 0.006 (0.116) Batch 1.002 (1.067) Remain 23:58:40 loss: 0.3656 Lr: 0.00287 [2024-02-18 17:41:17,594 INFO misc.py line 119 87073] Train: [49/100][33/1557] Data 0.004 (0.112) Batch 0.791 (1.057) Remain 23:46:15 loss: 0.1594 Lr: 0.00287 [2024-02-18 17:41:18,307 INFO misc.py line 119 87073] Train: [49/100][34/1557] Data 0.004 (0.109) Batch 0.710 (1.046) Remain 23:31:07 loss: 0.2892 Lr: 0.00287 [2024-02-18 17:41:19,461 INFO misc.py line 119 87073] Train: [49/100][35/1557] Data 0.007 (0.106) Batch 1.152 (1.049) Remain 23:35:33 loss: 0.2780 Lr: 0.00287 [2024-02-18 17:41:20,501 INFO misc.py line 119 87073] Train: [49/100][36/1557] Data 0.010 (0.103) Batch 1.044 (1.049) Remain 23:35:18 loss: 0.2056 Lr: 0.00287 [2024-02-18 17:41:21,365 INFO misc.py line 119 87073] Train: [49/100][37/1557] Data 0.006 (0.100) Batch 0.865 (1.044) Remain 23:27:57 loss: 0.2863 Lr: 0.00287 [2024-02-18 17:41:22,330 INFO misc.py line 119 87073] Train: [49/100][38/1557] Data 0.005 (0.097) Batch 0.963 (1.042) Remain 23:24:49 loss: 0.6076 Lr: 0.00287 [2024-02-18 17:41:23,201 INFO misc.py line 119 87073] Train: [49/100][39/1557] Data 0.007 (0.095) Batch 0.873 (1.037) Remain 23:18:29 loss: 0.3519 Lr: 0.00287 [2024-02-18 17:41:23,924 INFO misc.py line 119 87073] Train: [49/100][40/1557] Data 0.006 (0.092) Batch 0.723 (1.028) Remain 23:07:01 loss: 0.3525 Lr: 0.00287 [2024-02-18 17:41:24,730 INFO misc.py line 119 87073] Train: [49/100][41/1557] Data 0.006 (0.090) Batch 0.798 (1.022) Remain 22:58:50 loss: 0.5903 Lr: 0.00287 [2024-02-18 17:41:25,930 INFO misc.py line 119 87073] Train: [49/100][42/1557] Data 0.013 (0.088) Batch 1.203 (1.027) Remain 23:05:03 loss: 0.3153 Lr: 0.00287 [2024-02-18 17:41:26,888 INFO misc.py line 119 87073] Train: [49/100][43/1557] Data 0.011 (0.086) Batch 0.964 (1.025) Remain 23:02:53 loss: 0.4461 Lr: 0.00287 [2024-02-18 17:41:28,001 INFO misc.py line 119 87073] Train: [49/100][44/1557] Data 0.005 (0.084) Batch 1.114 (1.028) Remain 23:05:47 loss: 0.5033 Lr: 0.00287 [2024-02-18 17:41:28,864 INFO misc.py line 119 87073] Train: [49/100][45/1557] Data 0.004 (0.082) Batch 0.862 (1.024) Remain 23:00:27 loss: 0.3635 Lr: 0.00287 [2024-02-18 17:41:29,914 INFO misc.py line 119 87073] Train: [49/100][46/1557] Data 0.005 (0.080) Batch 1.051 (1.024) Remain 23:01:17 loss: 0.3497 Lr: 0.00287 [2024-02-18 17:41:30,591 INFO misc.py line 119 87073] Train: [49/100][47/1557] Data 0.005 (0.079) Batch 0.677 (1.016) Remain 22:50:37 loss: 0.3020 Lr: 0.00287 [2024-02-18 17:41:31,414 INFO misc.py line 119 87073] Train: [49/100][48/1557] Data 0.004 (0.077) Batch 0.815 (1.012) Remain 22:44:35 loss: 0.2085 Lr: 0.00287 [2024-02-18 17:41:32,544 INFO misc.py line 119 87073] Train: [49/100][49/1557] Data 0.012 (0.076) Batch 1.133 (1.014) Remain 22:48:06 loss: 0.1275 Lr: 0.00287 [2024-02-18 17:41:33,640 INFO misc.py line 119 87073] Train: [49/100][50/1557] Data 0.010 (0.074) Batch 1.096 (1.016) Remain 22:50:26 loss: 0.3777 Lr: 0.00287 [2024-02-18 17:41:34,554 INFO misc.py line 119 87073] Train: [49/100][51/1557] Data 0.009 (0.073) Batch 0.920 (1.014) Remain 22:47:43 loss: 0.3969 Lr: 0.00287 [2024-02-18 17:41:35,431 INFO misc.py line 119 87073] Train: [49/100][52/1557] Data 0.004 (0.071) Batch 0.876 (1.011) Remain 22:43:53 loss: 0.6576 Lr: 0.00287 [2024-02-18 17:41:36,251 INFO misc.py line 119 87073] Train: [49/100][53/1557] Data 0.005 (0.070) Batch 0.817 (1.008) Remain 22:38:38 loss: 0.1931 Lr: 0.00287 [2024-02-18 17:41:37,002 INFO misc.py line 119 87073] Train: [49/100][54/1557] Data 0.008 (0.069) Batch 0.752 (1.002) Remain 22:31:52 loss: 0.2100 Lr: 0.00287 [2024-02-18 17:41:37,739 INFO misc.py line 119 87073] Train: [49/100][55/1557] Data 0.007 (0.068) Batch 0.735 (0.997) Remain 22:24:54 loss: 0.2245 Lr: 0.00287 [2024-02-18 17:41:38,999 INFO misc.py line 119 87073] Train: [49/100][56/1557] Data 0.008 (0.067) Batch 1.257 (1.002) Remain 22:31:30 loss: 0.1960 Lr: 0.00287 [2024-02-18 17:41:39,957 INFO misc.py line 119 87073] Train: [49/100][57/1557] Data 0.012 (0.066) Batch 0.966 (1.002) Remain 22:30:35 loss: 0.4236 Lr: 0.00287 [2024-02-18 17:41:40,896 INFO misc.py line 119 87073] Train: [49/100][58/1557] Data 0.003 (0.064) Batch 0.937 (1.000) Remain 22:29:00 loss: 0.3227 Lr: 0.00287 [2024-02-18 17:41:41,955 INFO misc.py line 119 87073] Train: [49/100][59/1557] Data 0.005 (0.063) Batch 1.059 (1.001) Remain 22:30:23 loss: 0.3141 Lr: 0.00287 [2024-02-18 17:41:42,894 INFO misc.py line 119 87073] Train: [49/100][60/1557] Data 0.004 (0.062) Batch 0.939 (1.000) Remain 22:28:53 loss: 0.2964 Lr: 0.00287 [2024-02-18 17:41:43,666 INFO misc.py line 119 87073] Train: [49/100][61/1557] Data 0.006 (0.061) Batch 0.765 (0.996) Remain 22:23:23 loss: 0.3959 Lr: 0.00287 [2024-02-18 17:41:44,452 INFO misc.py line 119 87073] Train: [49/100][62/1557] Data 0.012 (0.061) Batch 0.794 (0.993) Remain 22:18:45 loss: 0.5809 Lr: 0.00287 [2024-02-18 17:41:52,996 INFO misc.py line 119 87073] Train: [49/100][63/1557] Data 5.861 (0.157) Batch 8.545 (1.119) Remain 25:08:26 loss: 0.1486 Lr: 0.00287 [2024-02-18 17:41:53,863 INFO misc.py line 119 87073] Train: [49/100][64/1557] Data 0.004 (0.155) Batch 0.866 (1.115) Remain 25:02:50 loss: 0.3548 Lr: 0.00287 [2024-02-18 17:41:54,838 INFO misc.py line 119 87073] Train: [49/100][65/1557] Data 0.005 (0.152) Batch 0.967 (1.112) Remain 24:59:37 loss: 0.4053 Lr: 0.00287 [2024-02-18 17:41:55,833 INFO misc.py line 119 87073] Train: [49/100][66/1557] Data 0.012 (0.150) Batch 1.001 (1.110) Remain 24:57:14 loss: 0.4092 Lr: 0.00287 [2024-02-18 17:41:56,798 INFO misc.py line 119 87073] Train: [49/100][67/1557] Data 0.005 (0.148) Batch 0.966 (1.108) Remain 24:54:10 loss: 0.3546 Lr: 0.00287 [2024-02-18 17:41:57,529 INFO misc.py line 119 87073] Train: [49/100][68/1557] Data 0.004 (0.146) Batch 0.731 (1.102) Remain 24:46:20 loss: 0.1680 Lr: 0.00287 [2024-02-18 17:41:58,319 INFO misc.py line 119 87073] Train: [49/100][69/1557] Data 0.004 (0.143) Batch 0.789 (1.098) Remain 24:39:55 loss: 0.1888 Lr: 0.00287 [2024-02-18 17:42:02,552 INFO misc.py line 119 87073] Train: [49/100][70/1557] Data 3.072 (0.187) Batch 4.234 (1.144) Remain 25:43:00 loss: 0.1445 Lr: 0.00287 [2024-02-18 17:42:03,461 INFO misc.py line 119 87073] Train: [49/100][71/1557] Data 0.005 (0.184) Batch 0.908 (1.141) Remain 25:38:18 loss: 0.1973 Lr: 0.00287 [2024-02-18 17:42:04,401 INFO misc.py line 119 87073] Train: [49/100][72/1557] Data 0.005 (0.182) Batch 0.938 (1.138) Remain 25:34:19 loss: 0.4303 Lr: 0.00287 [2024-02-18 17:42:05,548 INFO misc.py line 119 87073] Train: [49/100][73/1557] Data 0.007 (0.179) Batch 1.146 (1.138) Remain 25:34:28 loss: 0.3221 Lr: 0.00287 [2024-02-18 17:42:06,471 INFO misc.py line 119 87073] Train: [49/100][74/1557] Data 0.008 (0.177) Batch 0.927 (1.135) Remain 25:30:25 loss: 0.5853 Lr: 0.00287 [2024-02-18 17:42:07,264 INFO misc.py line 119 87073] Train: [49/100][75/1557] Data 0.004 (0.175) Batch 0.793 (1.130) Remain 25:23:59 loss: 0.3915 Lr: 0.00287 [2024-02-18 17:42:08,061 INFO misc.py line 119 87073] Train: [49/100][76/1557] Data 0.004 (0.172) Batch 0.791 (1.126) Remain 25:17:42 loss: 0.3922 Lr: 0.00287 [2024-02-18 17:42:09,383 INFO misc.py line 119 87073] Train: [49/100][77/1557] Data 0.010 (0.170) Batch 1.320 (1.128) Remain 25:21:13 loss: 0.1534 Lr: 0.00287 [2024-02-18 17:42:10,292 INFO misc.py line 119 87073] Train: [49/100][78/1557] Data 0.012 (0.168) Batch 0.916 (1.126) Remain 25:17:24 loss: 0.1467 Lr: 0.00287 [2024-02-18 17:42:11,247 INFO misc.py line 119 87073] Train: [49/100][79/1557] Data 0.005 (0.166) Batch 0.956 (1.123) Remain 25:14:22 loss: 0.2668 Lr: 0.00287 [2024-02-18 17:42:12,223 INFO misc.py line 119 87073] Train: [49/100][80/1557] Data 0.003 (0.164) Batch 0.976 (1.121) Remain 25:11:46 loss: 0.7699 Lr: 0.00287 [2024-02-18 17:42:13,171 INFO misc.py line 119 87073] Train: [49/100][81/1557] Data 0.004 (0.162) Batch 0.947 (1.119) Remain 25:08:44 loss: 0.2999 Lr: 0.00287 [2024-02-18 17:42:13,862 INFO misc.py line 119 87073] Train: [49/100][82/1557] Data 0.005 (0.160) Batch 0.687 (1.114) Remain 25:01:20 loss: 0.2195 Lr: 0.00287 [2024-02-18 17:42:14,597 INFO misc.py line 119 87073] Train: [49/100][83/1557] Data 0.009 (0.158) Batch 0.740 (1.109) Remain 24:55:01 loss: 0.5134 Lr: 0.00287 [2024-02-18 17:42:15,906 INFO misc.py line 119 87073] Train: [49/100][84/1557] Data 0.004 (0.156) Batch 1.299 (1.111) Remain 24:58:10 loss: 0.2899 Lr: 0.00287 [2024-02-18 17:42:16,847 INFO misc.py line 119 87073] Train: [49/100][85/1557] Data 0.013 (0.154) Batch 0.950 (1.109) Remain 24:55:30 loss: 0.3455 Lr: 0.00287 [2024-02-18 17:42:17,743 INFO misc.py line 119 87073] Train: [49/100][86/1557] Data 0.006 (0.152) Batch 0.895 (1.107) Remain 24:52:00 loss: 0.2212 Lr: 0.00287 [2024-02-18 17:42:18,890 INFO misc.py line 119 87073] Train: [49/100][87/1557] Data 0.006 (0.151) Batch 1.149 (1.107) Remain 24:52:39 loss: 0.1271 Lr: 0.00287 [2024-02-18 17:42:19,863 INFO misc.py line 119 87073] Train: [49/100][88/1557] Data 0.003 (0.149) Batch 0.973 (1.106) Remain 24:50:30 loss: 0.3602 Lr: 0.00287 [2024-02-18 17:42:20,628 INFO misc.py line 119 87073] Train: [49/100][89/1557] Data 0.003 (0.147) Batch 0.764 (1.102) Remain 24:45:08 loss: 0.3592 Lr: 0.00287 [2024-02-18 17:42:21,399 INFO misc.py line 119 87073] Train: [49/100][90/1557] Data 0.005 (0.146) Batch 0.766 (1.098) Remain 24:39:54 loss: 0.2428 Lr: 0.00287 [2024-02-18 17:42:22,573 INFO misc.py line 119 87073] Train: [49/100][91/1557] Data 0.010 (0.144) Batch 1.179 (1.099) Remain 24:41:07 loss: 0.3579 Lr: 0.00287 [2024-02-18 17:42:23,554 INFO misc.py line 119 87073] Train: [49/100][92/1557] Data 0.005 (0.142) Batch 0.983 (1.098) Remain 24:39:21 loss: 0.4587 Lr: 0.00287 [2024-02-18 17:42:24,532 INFO misc.py line 119 87073] Train: [49/100][93/1557] Data 0.004 (0.141) Batch 0.978 (1.096) Remain 24:37:33 loss: 0.3036 Lr: 0.00287 [2024-02-18 17:42:25,538 INFO misc.py line 119 87073] Train: [49/100][94/1557] Data 0.004 (0.139) Batch 1.005 (1.095) Remain 24:36:10 loss: 0.5353 Lr: 0.00287 [2024-02-18 17:42:26,559 INFO misc.py line 119 87073] Train: [49/100][95/1557] Data 0.004 (0.138) Batch 1.021 (1.094) Remain 24:35:04 loss: 0.5557 Lr: 0.00287 [2024-02-18 17:42:27,305 INFO misc.py line 119 87073] Train: [49/100][96/1557] Data 0.004 (0.136) Batch 0.743 (1.091) Remain 24:29:58 loss: 0.7091 Lr: 0.00287 [2024-02-18 17:42:28,020 INFO misc.py line 119 87073] Train: [49/100][97/1557] Data 0.007 (0.135) Batch 0.718 (1.087) Remain 24:24:36 loss: 0.4985 Lr: 0.00287 [2024-02-18 17:42:29,189 INFO misc.py line 119 87073] Train: [49/100][98/1557] Data 0.004 (0.134) Batch 1.169 (1.088) Remain 24:25:45 loss: 0.2500 Lr: 0.00287 [2024-02-18 17:42:30,391 INFO misc.py line 119 87073] Train: [49/100][99/1557] Data 0.005 (0.132) Batch 1.195 (1.089) Remain 24:27:15 loss: 0.7742 Lr: 0.00287 [2024-02-18 17:42:31,412 INFO misc.py line 119 87073] Train: [49/100][100/1557] Data 0.010 (0.131) Batch 1.021 (1.088) Remain 24:26:17 loss: 0.4740 Lr: 0.00287 [2024-02-18 17:42:32,500 INFO misc.py line 119 87073] Train: [49/100][101/1557] Data 0.010 (0.130) Batch 1.085 (1.088) Remain 24:26:14 loss: 0.6687 Lr: 0.00287 [2024-02-18 17:42:33,406 INFO misc.py line 119 87073] Train: [49/100][102/1557] Data 0.013 (0.129) Batch 0.915 (1.086) Remain 24:23:51 loss: 0.4671 Lr: 0.00287 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line 119 87073] Train: [49/100][109/1557] Data 0.011 (0.121) Batch 0.902 (1.073) Remain 24:06:15 loss: 0.4843 Lr: 0.00287 [2024-02-18 17:42:40,437 INFO misc.py line 119 87073] Train: [49/100][110/1557] Data 0.003 (0.120) Batch 0.801 (1.071) Remain 24:02:48 loss: 0.2941 Lr: 0.00287 [2024-02-18 17:42:41,238 INFO misc.py line 119 87073] Train: [49/100][111/1557] Data 0.006 (0.119) Batch 0.802 (1.068) Remain 23:59:26 loss: 0.3039 Lr: 0.00287 [2024-02-18 17:42:42,483 INFO misc.py line 119 87073] Train: [49/100][112/1557] Data 0.005 (0.118) Batch 1.240 (1.070) Remain 24:01:33 loss: 0.1359 Lr: 0.00287 [2024-02-18 17:42:43,469 INFO misc.py line 119 87073] Train: [49/100][113/1557] Data 0.009 (0.117) Batch 0.991 (1.069) Remain 24:00:34 loss: 0.1693 Lr: 0.00287 [2024-02-18 17:42:44,419 INFO misc.py line 119 87073] Train: [49/100][114/1557] Data 0.004 (0.116) Batch 0.950 (1.068) Remain 23:59:06 loss: 0.1758 Lr: 0.00287 [2024-02-18 17:42:45,507 INFO misc.py line 119 87073] Train: 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Batch 1.130 (1.126) Remain 25:16:34 loss: 0.4614 Lr: 0.00287 [2024-02-18 17:42:59,634 INFO misc.py line 119 87073] Train: [49/100][122/1557] Data 0.004 (0.168) Batch 0.942 (1.124) Remain 25:14:29 loss: 0.1624 Lr: 0.00287 [2024-02-18 17:43:00,564 INFO misc.py line 119 87073] Train: [49/100][123/1557] Data 0.005 (0.167) Batch 0.929 (1.122) Remain 25:12:16 loss: 0.1712 Lr: 0.00287 [2024-02-18 17:43:01,336 INFO misc.py line 119 87073] Train: [49/100][124/1557] Data 0.006 (0.165) Batch 0.768 (1.119) Remain 25:08:19 loss: 0.2081 Lr: 0.00287 [2024-02-18 17:43:02,096 INFO misc.py line 119 87073] Train: [49/100][125/1557] Data 0.010 (0.164) Batch 0.766 (1.117) Remain 25:04:23 loss: 0.4152 Lr: 0.00287 [2024-02-18 17:43:07,053 INFO misc.py line 119 87073] Train: [49/100][126/1557] Data 3.826 (0.194) Batch 4.956 (1.148) Remain 25:46:25 loss: 0.2357 Lr: 0.00287 [2024-02-18 17:43:07,932 INFO misc.py line 119 87073] Train: [49/100][127/1557] Data 0.007 (0.192) Batch 0.881 (1.146) Remain 25:43:30 loss: 0.2506 Lr: 0.00287 [2024-02-18 17:43:09,015 INFO misc.py line 119 87073] Train: [49/100][128/1557] Data 0.004 (0.191) Batch 1.081 (1.145) Remain 25:42:47 loss: 0.4848 Lr: 0.00287 [2024-02-18 17:43:09,958 INFO misc.py line 119 87073] Train: [49/100][129/1557] Data 0.006 (0.189) Batch 0.945 (1.144) Remain 25:40:37 loss: 0.3250 Lr: 0.00287 [2024-02-18 17:43:10,992 INFO misc.py line 119 87073] Train: [49/100][130/1557] Data 0.004 (0.188) Batch 1.034 (1.143) Remain 25:39:26 loss: 0.4764 Lr: 0.00287 [2024-02-18 17:43:11,717 INFO misc.py line 119 87073] Train: [49/100][131/1557] Data 0.004 (0.186) Batch 0.720 (1.139) Remain 25:34:58 loss: 0.4050 Lr: 0.00287 [2024-02-18 17:43:12,500 INFO misc.py line 119 87073] Train: [49/100][132/1557] Data 0.009 (0.185) Batch 0.788 (1.137) Remain 25:31:17 loss: 0.2982 Lr: 0.00287 [2024-02-18 17:43:13,750 INFO misc.py line 119 87073] Train: [49/100][133/1557] Data 0.004 (0.184) Batch 1.240 (1.137) Remain 25:32:20 loss: 0.1079 Lr: 0.00287 [2024-02-18 17:43:14,682 INFO misc.py line 119 87073] Train: [49/100][134/1557] Data 0.015 (0.182) Batch 0.942 (1.136) Remain 25:30:18 loss: 1.2901 Lr: 0.00287 [2024-02-18 17:43:15,699 INFO misc.py line 119 87073] Train: [49/100][135/1557] Data 0.004 (0.181) Batch 1.018 (1.135) Remain 25:29:05 loss: 0.2020 Lr: 0.00287 [2024-02-18 17:43:16,707 INFO misc.py line 119 87073] Train: [49/100][136/1557] Data 0.004 (0.180) Batch 1.006 (1.134) Remain 25:27:45 loss: 0.0999 Lr: 0.00287 [2024-02-18 17:43:17,606 INFO misc.py line 119 87073] Train: [49/100][137/1557] Data 0.005 (0.178) Batch 0.899 (1.132) Remain 25:25:22 loss: 0.4007 Lr: 0.00287 [2024-02-18 17:43:18,380 INFO misc.py line 119 87073] Train: [49/100][138/1557] Data 0.005 (0.177) Batch 0.767 (1.130) Remain 25:21:42 loss: 0.3558 Lr: 0.00287 [2024-02-18 17:43:19,119 INFO misc.py line 119 87073] Train: [49/100][139/1557] Data 0.013 (0.176) Batch 0.748 (1.127) Remain 25:17:54 loss: 0.3618 Lr: 0.00287 [2024-02-18 17:43:20,345 INFO misc.py line 119 87073] Train: [49/100][140/1557] Data 0.004 (0.175) Batch 1.226 (1.128) Remain 25:18:51 loss: 0.1998 Lr: 0.00286 [2024-02-18 17:43:21,346 INFO misc.py line 119 87073] Train: [49/100][141/1557] Data 0.005 (0.173) Batch 1.002 (1.127) Remain 25:17:37 loss: 0.4545 Lr: 0.00286 [2024-02-18 17:43:22,283 INFO misc.py line 119 87073] Train: [49/100][142/1557] Data 0.004 (0.172) Batch 0.936 (1.125) Remain 25:15:45 loss: 0.2422 Lr: 0.00286 [2024-02-18 17:43:23,264 INFO misc.py line 119 87073] Train: [49/100][143/1557] Data 0.004 (0.171) Batch 0.980 (1.124) Remain 25:14:20 loss: 0.6815 Lr: 0.00286 [2024-02-18 17:43:24,467 INFO misc.py line 119 87073] Train: [49/100][144/1557] Data 0.004 (0.170) Batch 1.194 (1.125) Remain 25:14:59 loss: 0.4768 Lr: 0.00286 [2024-02-18 17:43:25,192 INFO misc.py line 119 87073] Train: [49/100][145/1557] Data 0.013 (0.169) Batch 0.735 (1.122) Remain 25:11:16 loss: 0.5164 Lr: 0.00286 [2024-02-18 17:43:25,884 INFO misc.py line 119 87073] Train: [49/100][146/1557] Data 0.004 (0.168) Batch 0.678 (1.119) Remain 25:07:04 loss: 0.2714 Lr: 0.00286 [2024-02-18 17:43:27,141 INFO misc.py line 119 87073] Train: [49/100][147/1557] Data 0.017 (0.167) Batch 1.259 (1.120) Remain 25:08:22 loss: 0.1775 Lr: 0.00286 [2024-02-18 17:43:28,149 INFO misc.py line 119 87073] Train: [49/100][148/1557] Data 0.018 (0.165) Batch 1.014 (1.119) Remain 25:07:22 loss: 0.6310 Lr: 0.00286 [2024-02-18 17:43:29,137 INFO misc.py line 119 87073] Train: [49/100][149/1557] Data 0.009 (0.164) Batch 0.993 (1.118) Remain 25:06:11 loss: 0.6062 Lr: 0.00286 [2024-02-18 17:43:30,025 INFO misc.py line 119 87073] Train: [49/100][150/1557] Data 0.004 (0.163) Batch 0.886 (1.117) Remain 25:04:02 loss: 0.5981 Lr: 0.00286 [2024-02-18 17:43:30,948 INFO misc.py line 119 87073] Train: [49/100][151/1557] Data 0.006 (0.162) Batch 0.921 (1.115) Remain 25:02:14 loss: 0.5072 Lr: 0.00286 [2024-02-18 17:43:31,681 INFO misc.py line 119 87073] Train: [49/100][152/1557] Data 0.007 (0.161) Batch 0.733 (1.113) Remain 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line 119 87073] Train: [49/100][165/1557] Data 0.005 (0.149) Batch 0.793 (1.099) Remain 24:39:35 loss: 0.3164 Lr: 0.00286 [2024-02-18 17:43:44,665 INFO misc.py line 119 87073] Train: [49/100][166/1557] Data 0.005 (0.148) Batch 0.799 (1.097) Remain 24:37:05 loss: 0.1926 Lr: 0.00286 [2024-02-18 17:43:45,409 INFO misc.py line 119 87073] Train: [49/100][167/1557] Data 0.005 (0.147) Batch 0.744 (1.095) Remain 24:34:10 loss: 0.3401 Lr: 0.00286 [2024-02-18 17:43:46,603 INFO misc.py line 119 87073] Train: [49/100][168/1557] Data 0.006 (0.146) Batch 1.193 (1.095) Remain 24:34:57 loss: 0.1320 Lr: 0.00286 [2024-02-18 17:43:47,611 INFO misc.py line 119 87073] Train: [49/100][169/1557] Data 0.006 (0.145) Batch 1.010 (1.095) Remain 24:34:14 loss: 0.3883 Lr: 0.00286 [2024-02-18 17:43:48,530 INFO misc.py line 119 87073] Train: [49/100][170/1557] Data 0.005 (0.145) Batch 0.917 (1.094) Remain 24:32:48 loss: 0.5334 Lr: 0.00286 [2024-02-18 17:43:49,430 INFO misc.py line 119 87073] Train: 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Batch 0.827 (1.128) Remain 25:18:50 loss: 0.5727 Lr: 0.00286 [2024-02-18 17:44:03,236 INFO misc.py line 119 87073] Train: [49/100][178/1557] Data 0.010 (0.177) Batch 1.085 (1.128) Remain 25:18:28 loss: 0.4113 Lr: 0.00286 [2024-02-18 17:44:04,358 INFO misc.py line 119 87073] Train: [49/100][179/1557] Data 0.006 (0.176) Batch 1.119 (1.128) Remain 25:18:23 loss: 0.1010 Lr: 0.00286 [2024-02-18 17:44:07,134 INFO misc.py line 119 87073] Train: [49/100][180/1557] Data 1.482 (0.183) Batch 2.779 (1.137) Remain 25:30:56 loss: 0.2533 Lr: 0.00286 [2024-02-18 17:44:07,925 INFO misc.py line 119 87073] Train: [49/100][181/1557] Data 0.006 (0.182) Batch 0.790 (1.135) Remain 25:28:17 loss: 0.2667 Lr: 0.00286 [2024-02-18 17:44:12,506 INFO misc.py line 119 87073] Train: [49/100][182/1557] Data 3.422 (0.200) Batch 4.583 (1.154) Remain 25:54:12 loss: 0.1380 Lr: 0.00286 [2024-02-18 17:44:13,494 INFO misc.py line 119 87073] Train: [49/100][183/1557] Data 0.005 (0.199) Batch 0.988 (1.153) Remain 25:52:56 loss: 0.4044 Lr: 0.00286 [2024-02-18 17:44:14,472 INFO misc.py line 119 87073] Train: [49/100][184/1557] Data 0.008 (0.198) Batch 0.978 (1.152) Remain 25:51:36 loss: 0.2760 Lr: 0.00286 [2024-02-18 17:44:15,483 INFO misc.py line 119 87073] Train: [49/100][185/1557] Data 0.006 (0.197) Batch 1.010 (1.152) Remain 25:50:32 loss: 0.3212 Lr: 0.00286 [2024-02-18 17:44:16,371 INFO misc.py line 119 87073] Train: [49/100][186/1557] Data 0.007 (0.196) Batch 0.889 (1.150) Remain 25:48:35 loss: 0.1630 Lr: 0.00286 [2024-02-18 17:44:17,137 INFO misc.py line 119 87073] Train: [49/100][187/1557] Data 0.005 (0.195) Batch 0.766 (1.148) Remain 25:45:45 loss: 0.1183 Lr: 0.00286 [2024-02-18 17:44:17,874 INFO misc.py line 119 87073] Train: [49/100][188/1557] Data 0.005 (0.194) Batch 0.738 (1.146) Remain 25:42:45 loss: 0.2771 Lr: 0.00286 [2024-02-18 17:44:19,217 INFO misc.py line 119 87073] Train: [49/100][189/1557] Data 0.004 (0.193) Batch 1.333 (1.147) Remain 25:44:05 loss: 0.1197 Lr: 0.00286 [2024-02-18 17:44:20,251 INFO misc.py line 119 87073] Train: [49/100][190/1557] Data 0.016 (0.192) Batch 1.037 (1.146) Remain 25:43:16 loss: 0.3452 Lr: 0.00286 [2024-02-18 17:44:21,181 INFO misc.py line 119 87073] Train: [49/100][191/1557] Data 0.013 (0.191) Batch 0.937 (1.145) Remain 25:41:45 loss: 0.6414 Lr: 0.00286 [2024-02-18 17:44:22,197 INFO misc.py line 119 87073] Train: [49/100][192/1557] Data 0.005 (0.190) Batch 1.017 (1.145) Remain 25:40:49 loss: 1.2493 Lr: 0.00286 [2024-02-18 17:44:23,219 INFO misc.py line 119 87073] Train: [49/100][193/1557] Data 0.003 (0.189) Batch 1.022 (1.144) Remain 25:39:56 loss: 0.3854 Lr: 0.00286 [2024-02-18 17:44:23,968 INFO misc.py line 119 87073] Train: [49/100][194/1557] Data 0.004 (0.188) Batch 0.747 (1.142) Remain 25:37:07 loss: 0.2571 Lr: 0.00286 [2024-02-18 17:44:24,728 INFO misc.py line 119 87073] Train: [49/100][195/1557] Data 0.005 (0.187) Batch 0.760 (1.140) Remain 25:34:25 loss: 0.2018 Lr: 0.00286 [2024-02-18 17:44:25,947 INFO misc.py line 119 87073] Train: [49/100][196/1557] Data 0.005 (0.186) Batch 1.210 (1.140) Remain 25:34:53 loss: 0.1834 Lr: 0.00286 [2024-02-18 17:44:27,076 INFO misc.py line 119 87073] Train: [49/100][197/1557] Data 0.014 (0.185) Batch 1.136 (1.140) Remain 25:34:50 loss: 0.2739 Lr: 0.00286 [2024-02-18 17:44:28,220 INFO misc.py line 119 87073] Train: [49/100][198/1557] Data 0.008 (0.184) Batch 1.136 (1.140) Remain 25:34:47 loss: 0.4440 Lr: 0.00286 [2024-02-18 17:44:29,047 INFO misc.py line 119 87073] Train: [49/100][199/1557] Data 0.017 (0.184) Batch 0.840 (1.139) Remain 25:32:42 loss: 0.2330 Lr: 0.00286 [2024-02-18 17:44:29,961 INFO misc.py line 119 87073] Train: [49/100][200/1557] Data 0.004 (0.183) Batch 0.913 (1.138) Remain 25:31:09 loss: 0.3794 Lr: 0.00286 [2024-02-18 17:44:30,706 INFO misc.py line 119 87073] Train: [49/100][201/1557] Data 0.005 (0.182) Batch 0.744 (1.136) Remain 25:28:28 loss: 0.2048 Lr: 0.00286 [2024-02-18 17:44:31,517 INFO misc.py line 119 87073] Train: [49/100][202/1557] Data 0.005 (0.181) Batch 0.812 (1.134) Remain 25:26:15 loss: 0.2068 Lr: 0.00286 [2024-02-18 17:44:32,670 INFO misc.py line 119 87073] Train: [49/100][203/1557] Data 0.003 (0.180) Batch 1.151 (1.134) Remain 25:26:21 loss: 0.2414 Lr: 0.00286 [2024-02-18 17:44:33,781 INFO misc.py line 119 87073] Train: [49/100][204/1557] Data 0.006 (0.179) Batch 1.112 (1.134) Remain 25:26:11 loss: 0.5451 Lr: 0.00286 [2024-02-18 17:44:34,732 INFO misc.py line 119 87073] Train: [49/100][205/1557] Data 0.005 (0.178) Batch 0.950 (1.133) Remain 25:24:56 loss: 0.5550 Lr: 0.00286 [2024-02-18 17:44:35,733 INFO misc.py line 119 87073] Train: [49/100][206/1557] Data 0.006 (0.177) Batch 1.002 (1.132) Remain 25:24:03 loss: 0.3096 Lr: 0.00286 [2024-02-18 17:44:36,724 INFO misc.py line 119 87073] Train: [49/100][207/1557] Data 0.004 (0.177) Batch 0.991 (1.132) Remain 25:23:06 loss: 0.5997 Lr: 0.00286 [2024-02-18 17:44:37,520 INFO misc.py line 119 87073] Train: [49/100][208/1557] Data 0.004 (0.176) Batch 0.795 (1.130) Remain 25:20:52 loss: 0.2044 Lr: 0.00286 [2024-02-18 17:44:38,308 INFO misc.py line 119 87073] Train: [49/100][209/1557] Data 0.005 (0.175) Batch 0.788 (1.128) Remain 25:18:37 loss: 0.3486 Lr: 0.00286 [2024-02-18 17:44:39,609 INFO misc.py line 119 87073] Train: [49/100][210/1557] Data 0.005 (0.174) Batch 1.299 (1.129) Remain 25:19:43 loss: 0.3244 Lr: 0.00286 [2024-02-18 17:44:40,671 INFO misc.py line 119 87073] Train: [49/100][211/1557] Data 0.006 (0.173) Batch 1.058 (1.129) Remain 25:19:14 loss: 0.3962 Lr: 0.00286 [2024-02-18 17:44:41,675 INFO misc.py line 119 87073] Train: [49/100][212/1557] Data 0.010 (0.173) Batch 1.001 (1.128) Remain 25:18:24 loss: 0.5517 Lr: 0.00286 [2024-02-18 17:44:42,594 INFO misc.py line 119 87073] Train: [49/100][213/1557] Data 0.013 (0.172) Batch 0.927 (1.127) Remain 25:17:05 loss: 0.1257 Lr: 0.00286 [2024-02-18 17:44:43,664 INFO misc.py line 119 87073] Train: [49/100][214/1557] Data 0.004 (0.171) Batch 1.071 (1.127) Remain 25:16:43 loss: 0.0976 Lr: 0.00286 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line 119 87073] Train: [49/100][221/1557] Data 0.004 (0.166) Batch 0.833 (1.121) Remain 25:08:34 loss: 0.1953 Lr: 0.00286 [2024-02-18 17:44:50,968 INFO misc.py line 119 87073] Train: [49/100][222/1557] Data 0.004 (0.165) Batch 0.710 (1.119) Remain 25:06:02 loss: 0.3510 Lr: 0.00286 [2024-02-18 17:44:51,750 INFO misc.py line 119 87073] Train: [49/100][223/1557] Data 0.006 (0.164) Batch 0.783 (1.118) Remain 25:03:57 loss: 0.2298 Lr: 0.00286 [2024-02-18 17:44:52,994 INFO misc.py line 119 87073] Train: [49/100][224/1557] Data 0.005 (0.164) Batch 1.232 (1.118) Remain 25:04:38 loss: 0.2020 Lr: 0.00286 [2024-02-18 17:44:54,106 INFO misc.py line 119 87073] Train: [49/100][225/1557] Data 0.017 (0.163) Batch 1.118 (1.118) Remain 25:04:37 loss: 0.3585 Lr: 0.00286 [2024-02-18 17:44:54,986 INFO misc.py line 119 87073] Train: [49/100][226/1557] Data 0.010 (0.162) Batch 0.887 (1.117) Remain 25:03:12 loss: 0.2714 Lr: 0.00286 [2024-02-18 17:44:55,943 INFO misc.py line 119 87073] Train: 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Batch 0.955 (1.142) Remain 25:36:52 loss: 0.3194 Lr: 0.00286 [2024-02-18 17:45:09,708 INFO misc.py line 119 87073] Train: [49/100][234/1557] Data 0.005 (0.185) Batch 1.124 (1.142) Remain 25:36:45 loss: 0.4549 Lr: 0.00286 [2024-02-18 17:45:10,567 INFO misc.py line 119 87073] Train: [49/100][235/1557] Data 0.003 (0.184) Batch 0.859 (1.141) Remain 25:35:05 loss: 0.3025 Lr: 0.00286 [2024-02-18 17:45:11,382 INFO misc.py line 119 87073] Train: [49/100][236/1557] Data 0.004 (0.184) Batch 0.810 (1.140) Remain 25:33:10 loss: 0.5768 Lr: 0.00286 [2024-02-18 17:45:12,194 INFO misc.py line 119 87073] Train: [49/100][237/1557] Data 0.008 (0.183) Batch 0.816 (1.138) Remain 25:31:17 loss: 0.4403 Lr: 0.00286 [2024-02-18 17:45:18,123 INFO misc.py line 119 87073] Train: [49/100][238/1557] Data 4.790 (0.202) Batch 5.930 (1.159) Remain 25:58:42 loss: 0.1898 Lr: 0.00286 [2024-02-18 17:45:19,060 INFO misc.py line 119 87073] Train: [49/100][239/1557] Data 0.004 (0.202) Batch 0.935 (1.158) Remain 25:57:24 loss: 0.2501 Lr: 0.00286 [2024-02-18 17:45:19,954 INFO misc.py line 119 87073] Train: [49/100][240/1557] Data 0.005 (0.201) Batch 0.892 (1.156) Remain 25:55:53 loss: 0.2106 Lr: 0.00286 [2024-02-18 17:45:20,985 INFO misc.py line 119 87073] Train: [49/100][241/1557] Data 0.008 (0.200) Batch 1.024 (1.156) Remain 25:55:07 loss: 0.4914 Lr: 0.00286 [2024-02-18 17:45:21,941 INFO misc.py line 119 87073] Train: [49/100][242/1557] Data 0.014 (0.199) Batch 0.966 (1.155) Remain 25:54:01 loss: 0.2037 Lr: 0.00286 [2024-02-18 17:45:22,668 INFO misc.py line 119 87073] Train: [49/100][243/1557] Data 0.004 (0.198) Batch 0.726 (1.153) Remain 25:51:36 loss: 0.1928 Lr: 0.00286 [2024-02-18 17:45:23,437 INFO misc.py line 119 87073] Train: [49/100][244/1557] Data 0.006 (0.198) Batch 0.769 (1.152) Remain 25:49:26 loss: 0.2218 Lr: 0.00286 [2024-02-18 17:45:24,763 INFO misc.py line 119 87073] Train: [49/100][245/1557] Data 0.005 (0.197) Batch 1.317 (1.152) Remain 25:50:20 loss: 0.1290 Lr: 0.00286 [2024-02-18 17:45:25,743 INFO misc.py line 119 87073] Train: [49/100][246/1557] Data 0.015 (0.196) Batch 0.991 (1.152) Remain 25:49:25 loss: 0.5660 Lr: 0.00286 [2024-02-18 17:45:26,702 INFO misc.py line 119 87073] Train: [49/100][247/1557] Data 0.004 (0.195) Batch 0.959 (1.151) Remain 25:48:20 loss: 0.3032 Lr: 0.00286 [2024-02-18 17:45:27,750 INFO misc.py line 119 87073] Train: [49/100][248/1557] Data 0.004 (0.194) Batch 1.048 (1.151) Remain 25:47:45 loss: 0.2411 Lr: 0.00286 [2024-02-18 17:45:28,851 INFO misc.py line 119 87073] Train: [49/100][249/1557] Data 0.004 (0.194) Batch 1.102 (1.150) Remain 25:47:28 loss: 0.5185 Lr: 0.00286 [2024-02-18 17:45:29,659 INFO misc.py line 119 87073] Train: [49/100][250/1557] Data 0.003 (0.193) Batch 0.808 (1.149) Remain 25:45:34 loss: 0.1826 Lr: 0.00286 [2024-02-18 17:45:30,456 INFO misc.py line 119 87073] Train: [49/100][251/1557] Data 0.004 (0.192) Batch 0.792 (1.147) Remain 25:43:37 loss: 0.5107 Lr: 0.00286 [2024-02-18 17:45:31,729 INFO misc.py line 119 87073] Train: [49/100][252/1557] Data 0.009 (0.191) Batch 1.268 (1.148) Remain 25:44:15 loss: 0.1317 Lr: 0.00286 [2024-02-18 17:45:32,787 INFO misc.py line 119 87073] Train: [49/100][253/1557] Data 0.014 (0.191) Batch 1.062 (1.148) Remain 25:43:46 loss: 0.4580 Lr: 0.00286 [2024-02-18 17:45:33,756 INFO misc.py line 119 87073] Train: [49/100][254/1557] Data 0.010 (0.190) Batch 0.975 (1.147) Remain 25:42:49 loss: 0.3864 Lr: 0.00286 [2024-02-18 17:45:34,799 INFO misc.py line 119 87073] Train: [49/100][255/1557] Data 0.005 (0.189) Batch 1.044 (1.147) Remain 25:42:15 loss: 0.2071 Lr: 0.00286 [2024-02-18 17:45:35,847 INFO misc.py line 119 87073] Train: [49/100][256/1557] Data 0.004 (0.188) Batch 1.049 (1.146) Remain 25:41:43 loss: 0.3794 Lr: 0.00286 [2024-02-18 17:45:36,561 INFO misc.py line 119 87073] Train: [49/100][257/1557] Data 0.003 (0.188) Batch 0.711 (1.144) Remain 25:39:23 loss: 0.2159 Lr: 0.00286 [2024-02-18 17:45:37,343 INFO misc.py line 119 87073] Train: [49/100][258/1557] Data 0.006 (0.187) Batch 0.774 (1.143) Remain 25:37:25 loss: 0.1924 Lr: 0.00286 [2024-02-18 17:45:38,534 INFO misc.py line 119 87073] Train: [49/100][259/1557] Data 0.013 (0.186) Batch 1.189 (1.143) Remain 25:37:38 loss: 0.1797 Lr: 0.00286 [2024-02-18 17:45:39,704 INFO misc.py line 119 87073] Train: [49/100][260/1557] Data 0.016 (0.186) Batch 1.169 (1.143) Remain 25:37:45 loss: 0.7331 Lr: 0.00286 [2024-02-18 17:45:40,493 INFO misc.py line 119 87073] Train: [49/100][261/1557] Data 0.017 (0.185) Batch 0.803 (1.142) Remain 25:35:58 loss: 0.1610 Lr: 0.00286 [2024-02-18 17:45:41,375 INFO misc.py line 119 87073] Train: [49/100][262/1557] Data 0.004 (0.184) Batch 0.881 (1.141) Remain 25:34:35 loss: 0.4863 Lr: 0.00286 [2024-02-18 17:45:42,305 INFO misc.py line 119 87073] Train: [49/100][263/1557] Data 0.004 (0.184) Batch 0.924 (1.140) Remain 25:33:27 loss: 0.3252 Lr: 0.00286 [2024-02-18 17:45:43,093 INFO misc.py line 119 87073] Train: [49/100][264/1557] Data 0.010 (0.183) Batch 0.794 (1.139) Remain 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[2024-02-18 17:45:50,271 INFO misc.py line 119 87073] Train: [49/100][271/1557] Data 0.005 (0.178) Batch 0.732 (1.136) Remain 25:27:32 loss: 0.3786 Lr: 0.00286 [2024-02-18 17:45:51,058 INFO misc.py line 119 87073] Train: [49/100][272/1557] Data 0.004 (0.178) Batch 0.781 (1.134) Remain 25:25:44 loss: 0.3126 Lr: 0.00286 [2024-02-18 17:45:52,175 INFO misc.py line 119 87073] Train: [49/100][273/1557] Data 0.009 (0.177) Batch 1.120 (1.134) Remain 25:25:39 loss: 0.1033 Lr: 0.00286 [2024-02-18 17:45:53,004 INFO misc.py line 119 87073] Train: [49/100][274/1557] Data 0.006 (0.177) Batch 0.830 (1.133) Remain 25:24:07 loss: 0.1186 Lr: 0.00286 [2024-02-18 17:45:54,009 INFO misc.py line 119 87073] Train: [49/100][275/1557] Data 0.005 (0.176) Batch 1.006 (1.133) Remain 25:23:28 loss: 0.5454 Lr: 0.00286 [2024-02-18 17:45:55,191 INFO misc.py line 119 87073] Train: [49/100][276/1557] Data 0.005 (0.175) Batch 1.183 (1.133) Remain 25:23:42 loss: 1.1272 Lr: 0.00286 [2024-02-18 17:45:56,104 INFO misc.py line 119 87073] Train: [49/100][277/1557] Data 0.004 (0.175) Batch 0.910 (1.132) Remain 25:22:35 loss: 0.6007 Lr: 0.00286 [2024-02-18 17:45:56,915 INFO misc.py line 119 87073] Train: [49/100][278/1557] Data 0.006 (0.174) Batch 0.812 (1.131) Remain 25:21:00 loss: 0.2717 Lr: 0.00286 [2024-02-18 17:45:57,679 INFO misc.py line 119 87073] Train: [49/100][279/1557] Data 0.006 (0.173) Batch 0.765 (1.130) Remain 25:19:12 loss: 0.3032 Lr: 0.00286 [2024-02-18 17:45:58,913 INFO misc.py line 119 87073] Train: [49/100][280/1557] Data 0.004 (0.173) Batch 1.233 (1.130) Remain 25:19:41 loss: 0.1855 Lr: 0.00286 [2024-02-18 17:45:59,986 INFO misc.py line 119 87073] Train: [49/100][281/1557] Data 0.005 (0.172) Batch 1.068 (1.130) Remain 25:19:22 loss: 0.4534 Lr: 0.00286 [2024-02-18 17:46:00,950 INFO misc.py line 119 87073] Train: [49/100][282/1557] Data 0.009 (0.172) Batch 0.970 (1.129) Remain 25:18:35 loss: 0.2898 Lr: 0.00286 [2024-02-18 17:46:01,907 INFO misc.py line 119 87073] Train: 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Batch 0.941 (1.152) Remain 25:48:30 loss: 0.4367 Lr: 0.00286 [2024-02-18 17:46:16,176 INFO misc.py line 119 87073] Train: [49/100][290/1557] Data 0.012 (0.193) Batch 0.927 (1.151) Remain 25:47:26 loss: 0.3166 Lr: 0.00286 [2024-02-18 17:46:17,012 INFO misc.py line 119 87073] Train: [49/100][291/1557] Data 0.004 (0.192) Batch 0.836 (1.150) Remain 25:45:56 loss: 0.1922 Lr: 0.00286 [2024-02-18 17:46:17,767 INFO misc.py line 119 87073] Train: [49/100][292/1557] Data 0.004 (0.191) Batch 0.748 (1.148) Remain 25:44:03 loss: 0.2717 Lr: 0.00286 [2024-02-18 17:46:18,444 INFO misc.py line 119 87073] Train: [49/100][293/1557] Data 0.012 (0.191) Batch 0.685 (1.147) Remain 25:41:53 loss: 0.4107 Lr: 0.00286 [2024-02-18 17:46:25,198 INFO misc.py line 119 87073] Train: [49/100][294/1557] Data 5.632 (0.210) Batch 6.753 (1.166) Remain 26:07:46 loss: 0.1723 Lr: 0.00286 [2024-02-18 17:46:26,146 INFO misc.py line 119 87073] Train: [49/100][295/1557] Data 0.004 (0.209) Batch 0.947 (1.165) Remain 26:06:44 loss: 0.5271 Lr: 0.00286 [2024-02-18 17:46:26,997 INFO misc.py line 119 87073] Train: [49/100][296/1557] Data 0.006 (0.208) Batch 0.851 (1.164) Remain 26:05:17 loss: 0.4364 Lr: 0.00286 [2024-02-18 17:46:28,179 INFO misc.py line 119 87073] Train: [49/100][297/1557] Data 0.005 (0.207) Batch 1.171 (1.164) Remain 26:05:17 loss: 0.4754 Lr: 0.00286 [2024-02-18 17:46:29,282 INFO misc.py line 119 87073] Train: [49/100][298/1557] Data 0.016 (0.207) Batch 1.103 (1.164) Remain 26:04:59 loss: 0.4510 Lr: 0.00286 [2024-02-18 17:46:30,040 INFO misc.py line 119 87073] Train: [49/100][299/1557] Data 0.016 (0.206) Batch 0.769 (1.163) Remain 26:03:11 loss: 0.1348 Lr: 0.00286 [2024-02-18 17:46:30,794 INFO misc.py line 119 87073] Train: [49/100][300/1557] Data 0.005 (0.205) Batch 0.754 (1.161) Remain 26:01:19 loss: 0.2365 Lr: 0.00286 [2024-02-18 17:46:32,035 INFO misc.py line 119 87073] Train: [49/100][301/1557] Data 0.004 (0.205) Batch 1.232 (1.162) Remain 26:01:36 loss: 0.1310 Lr: 0.00286 [2024-02-18 17:46:32,971 INFO misc.py line 119 87073] Train: [49/100][302/1557] Data 0.014 (0.204) Batch 0.946 (1.161) Remain 26:00:37 loss: 0.3605 Lr: 0.00286 [2024-02-18 17:46:33,730 INFO misc.py line 119 87073] Train: [49/100][303/1557] Data 0.004 (0.203) Batch 0.757 (1.160) Remain 25:58:47 loss: 0.3491 Lr: 0.00286 [2024-02-18 17:46:34,812 INFO misc.py line 119 87073] Train: [49/100][304/1557] Data 0.006 (0.203) Batch 1.080 (1.159) Remain 25:58:25 loss: 0.2593 Lr: 0.00286 [2024-02-18 17:46:35,667 INFO misc.py line 119 87073] Train: [49/100][305/1557] Data 0.008 (0.202) Batch 0.858 (1.158) Remain 25:57:03 loss: 0.5689 Lr: 0.00286 [2024-02-18 17:46:36,430 INFO misc.py line 119 87073] Train: [49/100][306/1557] Data 0.004 (0.202) Batch 0.763 (1.157) Remain 25:55:17 loss: 0.5021 Lr: 0.00286 [2024-02-18 17:46:37,221 INFO misc.py line 119 87073] Train: [49/100][307/1557] Data 0.004 (0.201) Batch 0.782 (1.156) Remain 25:53:36 loss: 0.6520 Lr: 0.00286 [2024-02-18 17:46:38,423 INFO misc.py line 119 87073] Train: [49/100][308/1557] Data 0.013 (0.200) Batch 1.206 (1.156) Remain 25:53:49 loss: 0.1591 Lr: 0.00286 [2024-02-18 17:46:39,375 INFO misc.py line 119 87073] Train: [49/100][309/1557] Data 0.009 (0.200) Batch 0.958 (1.155) Remain 25:52:55 loss: 0.4471 Lr: 0.00286 [2024-02-18 17:46:40,219 INFO misc.py line 119 87073] Train: [49/100][310/1557] Data 0.004 (0.199) Batch 0.844 (1.154) Remain 25:51:32 loss: 0.4129 Lr: 0.00286 [2024-02-18 17:46:41,106 INFO misc.py line 119 87073] Train: [49/100][311/1557] Data 0.003 (0.198) Batch 0.886 (1.153) Remain 25:50:21 loss: 0.2989 Lr: 0.00286 [2024-02-18 17:46:42,136 INFO misc.py line 119 87073] Train: [49/100][312/1557] Data 0.005 (0.198) Batch 1.025 (1.153) Remain 25:49:46 loss: 0.4999 Lr: 0.00286 [2024-02-18 17:46:42,876 INFO misc.py line 119 87073] Train: [49/100][313/1557] Data 0.009 (0.197) Batch 0.744 (1.152) Remain 25:47:59 loss: 0.4331 Lr: 0.00286 [2024-02-18 17:46:43,659 INFO misc.py line 119 87073] Train: [49/100][314/1557] Data 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[2024-02-18 17:46:56,428 INFO misc.py line 119 87073] Train: [49/100][327/1557] Data 0.004 (0.189) Batch 0.726 (1.144) Remain 25:37:03 loss: 0.5817 Lr: 0.00286 [2024-02-18 17:46:57,173 INFO misc.py line 119 87073] Train: [49/100][328/1557] Data 0.005 (0.188) Batch 0.743 (1.142) Remain 25:35:22 loss: 0.2275 Lr: 0.00286 [2024-02-18 17:46:58,235 INFO misc.py line 119 87073] Train: [49/100][329/1557] Data 0.006 (0.188) Batch 1.063 (1.142) Remain 25:35:02 loss: 0.1276 Lr: 0.00286 [2024-02-18 17:46:59,201 INFO misc.py line 119 87073] Train: [49/100][330/1557] Data 0.005 (0.187) Batch 0.967 (1.142) Remain 25:34:17 loss: 0.2915 Lr: 0.00286 [2024-02-18 17:47:00,111 INFO misc.py line 119 87073] Train: [49/100][331/1557] Data 0.004 (0.187) Batch 0.910 (1.141) Remain 25:33:19 loss: 0.3205 Lr: 0.00285 [2024-02-18 17:47:00,895 INFO misc.py line 119 87073] Train: [49/100][332/1557] Data 0.005 (0.186) Batch 0.783 (1.140) Remain 25:31:50 loss: 0.4045 Lr: 0.00285 [2024-02-18 17:47:01,717 INFO misc.py line 119 87073] Train: [49/100][333/1557] Data 0.005 (0.186) Batch 0.823 (1.139) Remain 25:30:32 loss: 0.5917 Lr: 0.00285 [2024-02-18 17:47:02,519 INFO misc.py line 119 87073] Train: [49/100][334/1557] Data 0.004 (0.185) Batch 0.802 (1.138) Remain 25:29:09 loss: 0.3594 Lr: 0.00285 [2024-02-18 17:47:03,276 INFO misc.py line 119 87073] Train: [49/100][335/1557] Data 0.004 (0.184) Batch 0.754 (1.137) Remain 25:27:34 loss: 0.2518 Lr: 0.00285 [2024-02-18 17:47:04,497 INFO misc.py line 119 87073] Train: [49/100][336/1557] Data 0.006 (0.184) Batch 1.222 (1.137) Remain 25:27:54 loss: 0.1795 Lr: 0.00285 [2024-02-18 17:47:05,319 INFO misc.py line 119 87073] Train: [49/100][337/1557] Data 0.006 (0.183) Batch 0.825 (1.136) Remain 25:26:37 loss: 0.4279 Lr: 0.00285 [2024-02-18 17:47:06,214 INFO misc.py line 119 87073] Train: [49/100][338/1557] Data 0.004 (0.183) Batch 0.895 (1.135) Remain 25:25:38 loss: 0.3592 Lr: 0.00285 [2024-02-18 17:47:06,926 INFO misc.py line 119 87073] Train: 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Batch 0.961 (1.151) Remain 25:46:58 loss: 0.6343 Lr: 0.00285 [2024-02-18 17:47:20,749 INFO misc.py line 119 87073] Train: [49/100][346/1557] Data 0.007 (0.198) Batch 1.112 (1.151) Remain 25:46:48 loss: 0.5837 Lr: 0.00285 [2024-02-18 17:47:21,587 INFO misc.py line 119 87073] Train: [49/100][347/1557] Data 0.014 (0.198) Batch 0.847 (1.150) Remain 25:45:35 loss: 0.4095 Lr: 0.00285 [2024-02-18 17:47:22,319 INFO misc.py line 119 87073] Train: [49/100][348/1557] Data 0.005 (0.197) Batch 0.734 (1.149) Remain 25:43:57 loss: 0.2285 Lr: 0.00285 [2024-02-18 17:47:23,087 INFO misc.py line 119 87073] Train: [49/100][349/1557] Data 0.004 (0.197) Batch 0.757 (1.148) Remain 25:42:25 loss: 0.2984 Lr: 0.00285 [2024-02-18 17:47:28,154 INFO misc.py line 119 87073] Train: [49/100][350/1557] Data 3.931 (0.207) Batch 5.078 (1.159) Remain 25:57:36 loss: 0.2275 Lr: 0.00285 [2024-02-18 17:47:28,965 INFO misc.py line 119 87073] Train: [49/100][351/1557] Data 0.004 (0.207) Batch 0.810 (1.158) Remain 25:56:14 loss: 0.4520 Lr: 0.00285 [2024-02-18 17:47:29,917 INFO misc.py line 119 87073] Train: [49/100][352/1557] Data 0.005 (0.206) Batch 0.949 (1.158) Remain 25:55:25 loss: 0.5473 Lr: 0.00285 [2024-02-18 17:47:30,944 INFO misc.py line 119 87073] Train: [49/100][353/1557] Data 0.007 (0.206) Batch 1.030 (1.157) Remain 25:54:54 loss: 0.5450 Lr: 0.00285 [2024-02-18 17:47:32,242 INFO misc.py line 119 87073] Train: [49/100][354/1557] Data 0.005 (0.205) Batch 1.291 (1.158) Remain 25:55:24 loss: 0.3309 Lr: 0.00285 [2024-02-18 17:47:35,099 INFO misc.py line 119 87073] Train: [49/100][355/1557] Data 1.617 (0.209) Batch 2.865 (1.163) Remain 26:01:53 loss: 0.2686 Lr: 0.00285 [2024-02-18 17:47:35,883 INFO misc.py line 119 87073] Train: [49/100][356/1557] Data 0.003 (0.208) Batch 0.774 (1.161) Remain 26:00:24 loss: 0.3257 Lr: 0.00285 [2024-02-18 17:47:37,177 INFO misc.py line 119 87073] Train: [49/100][357/1557] Data 0.014 (0.208) Batch 1.299 (1.162) Remain 26:00:54 loss: 0.1077 Lr: 0.00285 [2024-02-18 17:47:38,076 INFO misc.py line 119 87073] Train: [49/100][358/1557] Data 0.009 (0.207) Batch 0.904 (1.161) Remain 25:59:54 loss: 0.5745 Lr: 0.00285 [2024-02-18 17:47:39,137 INFO misc.py line 119 87073] Train: [49/100][359/1557] Data 0.003 (0.207) Batch 1.060 (1.161) Remain 25:59:30 loss: 0.4483 Lr: 0.00285 [2024-02-18 17:47:40,107 INFO misc.py line 119 87073] Train: [49/100][360/1557] Data 0.004 (0.206) Batch 0.970 (1.160) Remain 25:58:46 loss: 0.3892 Lr: 0.00285 [2024-02-18 17:47:41,179 INFO misc.py line 119 87073] Train: [49/100][361/1557] Data 0.003 (0.206) Batch 1.072 (1.160) Remain 25:58:25 loss: 0.4067 Lr: 0.00285 [2024-02-18 17:47:41,939 INFO misc.py line 119 87073] Train: [49/100][362/1557] Data 0.004 (0.205) Batch 0.759 (1.159) Remain 25:56:54 loss: 0.2238 Lr: 0.00285 [2024-02-18 17:47:42,682 INFO misc.py line 119 87073] Train: [49/100][363/1557] Data 0.005 (0.204) Batch 0.714 (1.158) Remain 25:55:13 loss: 0.2445 Lr: 0.00285 [2024-02-18 17:47:43,869 INFO misc.py line 119 87073] Train: [49/100][364/1557] Data 0.035 (0.204) Batch 1.205 (1.158) Remain 25:55:22 loss: 0.2319 Lr: 0.00285 [2024-02-18 17:47:44,825 INFO misc.py line 119 87073] Train: [49/100][365/1557] Data 0.017 (0.203) Batch 0.969 (1.157) Remain 25:54:39 loss: 0.4595 Lr: 0.00285 [2024-02-18 17:47:45,715 INFO misc.py line 119 87073] Train: [49/100][366/1557] Data 0.005 (0.203) Batch 0.889 (1.157) Remain 25:53:38 loss: 0.2190 Lr: 0.00285 [2024-02-18 17:47:46,725 INFO misc.py line 119 87073] Train: [49/100][367/1557] Data 0.005 (0.202) Batch 1.005 (1.156) Remain 25:53:03 loss: 0.6222 Lr: 0.00285 [2024-02-18 17:47:47,828 INFO misc.py line 119 87073] Train: [49/100][368/1557] Data 0.010 (0.202) Batch 1.105 (1.156) Remain 25:52:51 loss: 0.3037 Lr: 0.00285 [2024-02-18 17:47:48,602 INFO misc.py line 119 87073] Train: [49/100][369/1557] Data 0.009 (0.201) Batch 0.778 (1.155) Remain 25:51:26 loss: 0.1662 Lr: 0.00285 [2024-02-18 17:47:49,355 INFO misc.py line 119 87073] Train: [49/100][370/1557] Data 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Batch 0.834 (1.156) Remain 25:52:14 loss: 0.1953 Lr: 0.00285 [2024-02-18 17:48:27,138 INFO misc.py line 119 87073] Train: [49/100][402/1557] Data 0.004 (0.202) Batch 1.156 (1.156) Remain 25:52:13 loss: 0.4021 Lr: 0.00285 [2024-02-18 17:48:28,147 INFO misc.py line 119 87073] Train: [49/100][403/1557] Data 0.008 (0.202) Batch 1.008 (1.156) Remain 25:51:42 loss: 0.3278 Lr: 0.00285 [2024-02-18 17:48:28,884 INFO misc.py line 119 87073] Train: [49/100][404/1557] Data 0.009 (0.201) Batch 0.742 (1.155) Remain 25:50:17 loss: 0.2351 Lr: 0.00285 [2024-02-18 17:48:29,630 INFO misc.py line 119 87073] Train: [49/100][405/1557] Data 0.004 (0.201) Batch 0.739 (1.154) Remain 25:48:53 loss: 0.3037 Lr: 0.00285 [2024-02-18 17:48:34,176 INFO misc.py line 119 87073] Train: [49/100][406/1557] Data 3.423 (0.209) Batch 4.554 (1.162) Remain 26:00:11 loss: 0.2646 Lr: 0.00285 [2024-02-18 17:48:35,180 INFO misc.py line 119 87073] Train: [49/100][407/1557] Data 0.004 (0.208) Batch 1.004 (1.162) Remain 25:59:39 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Batch 0.863 (1.154) Remain 25:48:10 loss: 0.1946 Lr: 0.00285 [2024-02-18 17:49:30,555 INFO misc.py line 119 87073] Train: [49/100][458/1557] Data 0.004 (0.200) Batch 0.848 (1.153) Remain 25:47:15 loss: 0.1700 Lr: 0.00285 [2024-02-18 17:49:31,674 INFO misc.py line 119 87073] Train: [49/100][459/1557] Data 0.004 (0.199) Batch 1.113 (1.153) Remain 25:47:06 loss: 0.4664 Lr: 0.00285 [2024-02-18 17:49:32,444 INFO misc.py line 119 87073] Train: [49/100][460/1557] Data 0.010 (0.199) Batch 0.776 (1.152) Remain 25:45:59 loss: 0.3458 Lr: 0.00285 [2024-02-18 17:49:33,218 INFO misc.py line 119 87073] Train: [49/100][461/1557] Data 0.005 (0.198) Batch 0.770 (1.151) Remain 25:44:50 loss: 0.2787 Lr: 0.00285 [2024-02-18 17:49:38,480 INFO misc.py line 119 87073] Train: [49/100][462/1557] Data 4.152 (0.207) Batch 5.265 (1.160) Remain 25:56:51 loss: 0.2306 Lr: 0.00285 [2024-02-18 17:49:39,432 INFO misc.py line 119 87073] Train: [49/100][463/1557] Data 0.006 (0.207) Batch 0.953 (1.160) Remain 25:56:13 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Batch 1.041 (1.152) Remain 25:44:59 loss: 0.6633 Lr: 0.00285 [2024-02-18 17:50:34,437 INFO misc.py line 119 87073] Train: [49/100][514/1557] Data 0.005 (0.199) Batch 0.918 (1.152) Remain 25:44:21 loss: 0.4134 Lr: 0.00285 [2024-02-18 17:50:35,430 INFO misc.py line 119 87073] Train: [49/100][515/1557] Data 0.005 (0.198) Batch 0.989 (1.151) Remain 25:43:54 loss: 0.7115 Lr: 0.00285 [2024-02-18 17:50:36,227 INFO misc.py line 119 87073] Train: [49/100][516/1557] Data 0.008 (0.198) Batch 0.800 (1.151) Remain 25:42:58 loss: 0.4000 Lr: 0.00285 [2024-02-18 17:50:36,979 INFO misc.py line 119 87073] Train: [49/100][517/1557] Data 0.005 (0.198) Batch 0.754 (1.150) Remain 25:41:54 loss: 0.2651 Lr: 0.00285 [2024-02-18 17:50:42,654 INFO misc.py line 119 87073] Train: [49/100][518/1557] Data 4.535 (0.206) Batch 5.675 (1.159) Remain 25:53:40 loss: 0.1287 Lr: 0.00285 [2024-02-18 17:50:43,777 INFO misc.py line 119 87073] Train: [49/100][519/1557] Data 0.004 (0.206) Batch 1.123 (1.159) Remain 25:53:33 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line 119 87073] Train: [49/100][557/1557] Data 0.004 (0.195) Batch 1.043 (1.148) Remain 25:38:24 loss: 0.2102 Lr: 0.00284 [2024-02-18 17:51:22,646 INFO misc.py line 119 87073] Train: [49/100][558/1557] Data 0.004 (0.195) Batch 0.802 (1.147) Remain 25:37:32 loss: 0.1699 Lr: 0.00284 [2024-02-18 17:51:23,391 INFO misc.py line 119 87073] Train: [49/100][559/1557] Data 0.004 (0.195) Batch 0.737 (1.147) Remain 25:36:32 loss: 0.2609 Lr: 0.00284 [2024-02-18 17:51:24,693 INFO misc.py line 119 87073] Train: [49/100][560/1557] Data 0.012 (0.194) Batch 1.297 (1.147) Remain 25:36:52 loss: 0.1381 Lr: 0.00284 [2024-02-18 17:51:25,660 INFO misc.py line 119 87073] Train: [49/100][561/1557] Data 0.017 (0.194) Batch 0.979 (1.147) Remain 25:36:27 loss: 1.2431 Lr: 0.00284 [2024-02-18 17:51:26,670 INFO misc.py line 119 87073] Train: [49/100][562/1557] Data 0.005 (0.194) Batch 1.011 (1.146) Remain 25:36:06 loss: 0.4920 Lr: 0.00284 [2024-02-18 17:51:27,482 INFO misc.py line 119 87073] Train: 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loss: 0.5916 Lr: 0.00284 [2024-02-18 17:51:52,897 INFO misc.py line 119 87073] Train: [49/100][576/1557] Data 0.003 (0.211) Batch 1.108 (1.164) Remain 25:59:38 loss: 0.2604 Lr: 0.00284 [2024-02-18 17:51:54,033 INFO misc.py line 119 87073] Train: [49/100][577/1557] Data 0.004 (0.211) Batch 1.134 (1.164) Remain 25:59:33 loss: 0.4507 Lr: 0.00284 [2024-02-18 17:51:55,120 INFO misc.py line 119 87073] Train: [49/100][578/1557] Data 0.005 (0.211) Batch 1.088 (1.164) Remain 25:59:21 loss: 0.1776 Lr: 0.00284 [2024-02-18 17:51:55,836 INFO misc.py line 119 87073] Train: [49/100][579/1557] Data 0.004 (0.210) Batch 0.716 (1.163) Remain 25:58:18 loss: 0.2429 Lr: 0.00284 [2024-02-18 17:51:56,639 INFO misc.py line 119 87073] Train: [49/100][580/1557] Data 0.004 (0.210) Batch 0.793 (1.162) Remain 25:57:25 loss: 0.2544 Lr: 0.00284 [2024-02-18 17:51:57,987 INFO misc.py line 119 87073] Train: [49/100][581/1557] Data 0.013 (0.210) Batch 1.349 (1.163) Remain 25:57:50 loss: 0.1359 Lr: 0.00284 [2024-02-18 17:51:59,097 INFO misc.py line 119 87073] Train: [49/100][582/1557] Data 0.012 (0.209) Batch 1.107 (1.163) Remain 25:57:41 loss: 0.3999 Lr: 0.00284 [2024-02-18 17:51:59,988 INFO misc.py line 119 87073] Train: [49/100][583/1557] Data 0.014 (0.209) Batch 0.901 (1.162) Remain 25:57:04 loss: 0.1033 Lr: 0.00284 [2024-02-18 17:52:01,020 INFO misc.py line 119 87073] Train: [49/100][584/1557] Data 0.004 (0.209) Batch 1.031 (1.162) Remain 25:56:44 loss: 0.4373 Lr: 0.00284 [2024-02-18 17:52:01,992 INFO misc.py line 119 87073] Train: [49/100][585/1557] Data 0.005 (0.208) Batch 0.974 (1.162) Remain 25:56:17 loss: 0.6969 Lr: 0.00284 [2024-02-18 17:52:02,768 INFO misc.py line 119 87073] Train: [49/100][586/1557] Data 0.004 (0.208) Batch 0.776 (1.161) Remain 25:55:23 loss: 0.3206 Lr: 0.00284 [2024-02-18 17:52:03,574 INFO misc.py line 119 87073] Train: [49/100][587/1557] Data 0.004 (0.208) Batch 0.797 (1.160) Remain 25:54:32 loss: 0.6503 Lr: 0.00284 [2024-02-18 17:52:04,820 INFO misc.py line 119 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line 119 87073] Train: [49/100][613/1557] Data 0.004 (0.199) Batch 0.953 (1.153) Remain 25:43:29 loss: 0.4554 Lr: 0.00284 [2024-02-18 17:52:29,693 INFO misc.py line 119 87073] Train: [49/100][614/1557] Data 0.004 (0.199) Batch 0.752 (1.152) Remain 25:42:35 loss: 0.4662 Lr: 0.00284 [2024-02-18 17:52:30,415 INFO misc.py line 119 87073] Train: [49/100][615/1557] Data 0.010 (0.199) Batch 0.728 (1.151) Remain 25:41:38 loss: 0.1937 Lr: 0.00284 [2024-02-18 17:52:31,606 INFO misc.py line 119 87073] Train: [49/100][616/1557] Data 0.004 (0.198) Batch 1.191 (1.151) Remain 25:41:42 loss: 0.1704 Lr: 0.00284 [2024-02-18 17:52:32,673 INFO misc.py line 119 87073] Train: [49/100][617/1557] Data 0.005 (0.198) Batch 1.067 (1.151) Remain 25:41:30 loss: 0.4518 Lr: 0.00284 [2024-02-18 17:52:33,676 INFO misc.py line 119 87073] Train: [49/100][618/1557] Data 0.004 (0.198) Batch 1.003 (1.151) Remain 25:41:09 loss: 0.2284 Lr: 0.00284 [2024-02-18 17:52:34,498 INFO misc.py line 119 87073] Train: 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Batch 0.918 (1.160) Remain 25:53:51 loss: 0.3145 Lr: 0.00284 [2024-02-18 17:52:48,613 INFO misc.py line 119 87073] Train: [49/100][626/1557] Data 0.038 (0.206) Batch 0.920 (1.160) Remain 25:53:19 loss: 0.4933 Lr: 0.00284 [2024-02-18 17:52:49,693 INFO misc.py line 119 87073] Train: [49/100][627/1557] Data 0.006 (0.206) Batch 1.080 (1.160) Remain 25:53:08 loss: 0.2478 Lr: 0.00284 [2024-02-18 17:52:50,554 INFO misc.py line 119 87073] Train: [49/100][628/1557] Data 0.005 (0.205) Batch 0.862 (1.159) Remain 25:52:28 loss: 0.2253 Lr: 0.00284 [2024-02-18 17:52:51,287 INFO misc.py line 119 87073] Train: [49/100][629/1557] Data 0.004 (0.205) Batch 0.721 (1.159) Remain 25:51:31 loss: 0.1349 Lr: 0.00284 [2024-02-18 17:52:55,930 INFO misc.py line 119 87073] Train: [49/100][630/1557] Data 3.530 (0.210) Batch 4.653 (1.164) Remain 25:58:57 loss: 0.1438 Lr: 0.00284 [2024-02-18 17:52:56,894 INFO misc.py line 119 87073] Train: [49/100][631/1557] Data 0.006 (0.210) Batch 0.964 (1.164) Remain 25:58:31 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Batch 1.116 (1.158) Remain 25:49:50 loss: 0.2542 Lr: 0.00284 [2024-02-18 17:53:52,302 INFO misc.py line 119 87073] Train: [49/100][682/1557] Data 0.012 (0.205) Batch 1.097 (1.158) Remain 25:49:42 loss: 0.3201 Lr: 0.00284 [2024-02-18 17:53:53,326 INFO misc.py line 119 87073] Train: [49/100][683/1557] Data 0.017 (0.204) Batch 1.026 (1.158) Remain 25:49:25 loss: 0.2241 Lr: 0.00284 [2024-02-18 17:53:54,106 INFO misc.py line 119 87073] Train: [49/100][684/1557] Data 0.015 (0.204) Batch 0.790 (1.157) Remain 25:48:40 loss: 0.3599 Lr: 0.00284 [2024-02-18 17:53:54,861 INFO misc.py line 119 87073] Train: [49/100][685/1557] Data 0.005 (0.204) Batch 0.746 (1.157) Remain 25:47:51 loss: 0.1703 Lr: 0.00284 [2024-02-18 17:53:58,614 INFO misc.py line 119 87073] Train: [49/100][686/1557] Data 2.613 (0.207) Batch 3.763 (1.161) Remain 25:52:56 loss: 0.0812 Lr: 0.00284 [2024-02-18 17:53:59,804 INFO misc.py line 119 87073] Train: [49/100][687/1557] Data 0.005 (0.207) Batch 1.190 (1.161) Remain 25:52:58 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Batch 1.075 (1.159) Remain 25:49:41 loss: 0.3941 Lr: 0.00283 [2024-02-18 17:54:57,515 INFO misc.py line 119 87073] Train: [49/100][738/1557] Data 0.004 (0.204) Batch 0.947 (1.159) Remain 25:49:17 loss: 0.3522 Lr: 0.00283 [2024-02-18 17:54:58,668 INFO misc.py line 119 87073] Train: [49/100][739/1557] Data 0.005 (0.204) Batch 1.154 (1.159) Remain 25:49:15 loss: 0.3099 Lr: 0.00283 [2024-02-18 17:54:59,424 INFO misc.py line 119 87073] Train: [49/100][740/1557] Data 0.003 (0.203) Batch 0.756 (1.158) Remain 25:48:30 loss: 0.3457 Lr: 0.00283 [2024-02-18 17:55:00,178 INFO misc.py line 119 87073] Train: [49/100][741/1557] Data 0.003 (0.203) Batch 0.745 (1.158) Remain 25:47:44 loss: 0.4845 Lr: 0.00283 [2024-02-18 17:55:06,240 INFO misc.py line 119 87073] Train: [49/100][742/1557] Data 4.894 (0.210) Batch 6.069 (1.164) Remain 25:56:36 loss: 0.1596 Lr: 0.00283 [2024-02-18 17:55:07,142 INFO misc.py line 119 87073] Train: [49/100][743/1557] Data 0.006 (0.209) Batch 0.903 (1.164) Remain 25:56:07 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Batch 1.056 (1.159) Remain 25:49:14 loss: 0.4464 Lr: 0.00283 [2024-02-18 17:56:02,774 INFO misc.py line 119 87073] Train: [49/100][794/1557] Data 0.004 (0.205) Batch 0.933 (1.159) Remain 25:48:50 loss: 0.4962 Lr: 0.00283 [2024-02-18 17:56:03,722 INFO misc.py line 119 87073] Train: [49/100][795/1557] Data 0.005 (0.204) Batch 0.949 (1.159) Remain 25:48:27 loss: 0.6981 Lr: 0.00283 [2024-02-18 17:56:04,508 INFO misc.py line 119 87073] Train: [49/100][796/1557] Data 0.004 (0.204) Batch 0.784 (1.158) Remain 25:47:48 loss: 0.5215 Lr: 0.00283 [2024-02-18 17:56:05,260 INFO misc.py line 119 87073] Train: [49/100][797/1557] Data 0.005 (0.204) Batch 0.753 (1.158) Remain 25:47:06 loss: 0.3232 Lr: 0.00283 [2024-02-18 17:56:11,986 INFO misc.py line 119 87073] Train: [49/100][798/1557] Data 5.598 (0.211) Batch 6.725 (1.165) Remain 25:56:26 loss: 0.1655 Lr: 0.00283 [2024-02-18 17:56:12,958 INFO misc.py line 119 87073] Train: [49/100][799/1557] Data 0.005 (0.211) Batch 0.973 (1.165) Remain 25:56:06 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Batch 0.991 (1.159) Remain 25:48:07 loss: 0.3196 Lr: 0.00283 [2024-02-18 17:57:07,723 INFO misc.py line 119 87073] Train: [49/100][850/1557] Data 0.005 (0.206) Batch 0.974 (1.159) Remain 25:47:48 loss: 0.4293 Lr: 0.00283 [2024-02-18 17:57:08,709 INFO misc.py line 119 87073] Train: [49/100][851/1557] Data 0.005 (0.206) Batch 0.983 (1.159) Remain 25:47:30 loss: 0.1224 Lr: 0.00283 [2024-02-18 17:57:09,441 INFO misc.py line 119 87073] Train: [49/100][852/1557] Data 0.007 (0.206) Batch 0.736 (1.159) Remain 25:46:49 loss: 0.5521 Lr: 0.00283 [2024-02-18 17:57:10,210 INFO misc.py line 119 87073] Train: [49/100][853/1557] Data 0.004 (0.205) Batch 0.761 (1.158) Remain 25:46:11 loss: 0.2212 Lr: 0.00283 [2024-02-18 17:57:16,318 INFO misc.py line 119 87073] Train: [49/100][854/1557] Data 5.028 (0.211) Batch 6.116 (1.164) Remain 25:53:56 loss: 0.1331 Lr: 0.00283 [2024-02-18 17:57:17,334 INFO misc.py line 119 87073] Train: [49/100][855/1557] Data 0.004 (0.211) Batch 1.016 (1.164) Remain 25:53:41 loss: 0.7461 Lr: 0.00283 [2024-02-18 17:57:18,251 INFO misc.py line 119 87073] Train: [49/100][856/1557] Data 0.004 (0.211) Batch 0.917 (1.163) Remain 25:53:17 loss: 0.7184 Lr: 0.00283 [2024-02-18 17:57:19,187 INFO misc.py line 119 87073] Train: [49/100][857/1557] Data 0.005 (0.210) Batch 0.929 (1.163) Remain 25:52:54 loss: 0.3540 Lr: 0.00283 [2024-02-18 17:57:20,069 INFO misc.py line 119 87073] Train: [49/100][858/1557] Data 0.012 (0.210) Batch 0.888 (1.163) Remain 25:52:27 loss: 0.1678 Lr: 0.00283 [2024-02-18 17:57:20,861 INFO misc.py line 119 87073] Train: [49/100][859/1557] Data 0.005 (0.210) Batch 0.793 (1.162) Remain 25:51:51 loss: 0.4675 Lr: 0.00283 [2024-02-18 17:57:21,624 INFO misc.py line 119 87073] Train: [49/100][860/1557] Data 0.004 (0.210) Batch 0.754 (1.162) Remain 25:51:12 loss: 0.4608 Lr: 0.00283 [2024-02-18 17:57:22,944 INFO misc.py line 119 87073] Train: [49/100][861/1557] Data 0.013 (0.209) Batch 1.326 (1.162) Remain 25:51:26 loss: 0.0957 Lr: 0.00283 [2024-02-18 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[2024-02-18 17:57:49,417 INFO misc.py line 119 87073] Train: [49/100][887/1557] Data 0.003 (0.205) Batch 0.807 (1.158) Remain 25:45:17 loss: 0.2825 Lr: 0.00283 [2024-02-18 17:57:50,123 INFO misc.py line 119 87073] Train: [49/100][888/1557] Data 0.006 (0.205) Batch 0.708 (1.157) Remain 25:44:35 loss: 0.3287 Lr: 0.00283 [2024-02-18 17:57:51,212 INFO misc.py line 119 87073] Train: [49/100][889/1557] Data 0.003 (0.205) Batch 1.085 (1.157) Remain 25:44:27 loss: 0.1197 Lr: 0.00283 [2024-02-18 17:57:52,186 INFO misc.py line 119 87073] Train: [49/100][890/1557] Data 0.008 (0.205) Batch 0.977 (1.157) Remain 25:44:10 loss: 0.2739 Lr: 0.00283 [2024-02-18 17:57:53,134 INFO misc.py line 119 87073] Train: [49/100][891/1557] Data 0.004 (0.204) Batch 0.948 (1.157) Remain 25:43:50 loss: 0.1907 Lr: 0.00283 [2024-02-18 17:57:54,016 INFO misc.py line 119 87073] Train: [49/100][892/1557] Data 0.004 (0.204) Batch 0.881 (1.157) Remain 25:43:24 loss: 0.4717 Lr: 0.00283 [2024-02-18 17:57:55,160 INFO misc.py line 119 87073] Train: [49/100][893/1557] Data 0.005 (0.204) Batch 1.144 (1.157) Remain 25:43:22 loss: 0.3499 Lr: 0.00283 [2024-02-18 17:57:55,942 INFO misc.py line 119 87073] Train: [49/100][894/1557] Data 0.005 (0.204) Batch 0.780 (1.156) Remain 25:42:47 loss: 0.1723 Lr: 0.00283 [2024-02-18 17:57:56,730 INFO misc.py line 119 87073] Train: [49/100][895/1557] Data 0.007 (0.204) Batch 0.791 (1.156) Remain 25:42:13 loss: 0.1951 Lr: 0.00283 [2024-02-18 17:57:57,960 INFO misc.py line 119 87073] Train: [49/100][896/1557] Data 0.004 (0.203) Batch 1.219 (1.156) Remain 25:42:17 loss: 0.1749 Lr: 0.00283 [2024-02-18 17:57:58,930 INFO misc.py line 119 87073] Train: [49/100][897/1557] Data 0.015 (0.203) Batch 0.981 (1.156) Remain 25:42:00 loss: 0.6232 Lr: 0.00283 [2024-02-18 17:57:59,891 INFO misc.py line 119 87073] Train: [49/100][898/1557] Data 0.003 (0.203) Batch 0.962 (1.155) Remain 25:41:42 loss: 0.5540 Lr: 0.00283 [2024-02-18 17:58:00,953 INFO misc.py line 119 87073] Train: 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Batch 0.948 (1.162) Remain 25:50:28 loss: 0.9763 Lr: 0.00282 [2024-02-18 17:58:15,129 INFO misc.py line 119 87073] Train: [49/100][906/1557] Data 0.006 (0.209) Batch 1.136 (1.162) Remain 25:50:24 loss: 0.7428 Lr: 0.00282 [2024-02-18 17:58:16,023 INFO misc.py line 119 87073] Train: [49/100][907/1557] Data 0.004 (0.208) Batch 0.894 (1.162) Remain 25:49:59 loss: 0.3843 Lr: 0.00282 [2024-02-18 17:58:16,812 INFO misc.py line 119 87073] Train: [49/100][908/1557] Data 0.005 (0.208) Batch 0.781 (1.161) Remain 25:49:24 loss: 0.1287 Lr: 0.00282 [2024-02-18 17:58:17,539 INFO misc.py line 119 87073] Train: [49/100][909/1557] Data 0.013 (0.208) Batch 0.736 (1.161) Remain 25:48:46 loss: 0.2240 Lr: 0.00282 [2024-02-18 17:58:23,670 INFO misc.py line 119 87073] Train: [49/100][910/1557] Data 5.035 (0.213) Batch 6.130 (1.166) Remain 25:56:03 loss: 0.3256 Lr: 0.00282 [2024-02-18 17:58:24,644 INFO misc.py line 119 87073] Train: [49/100][911/1557] Data 0.004 (0.213) Batch 0.974 (1.166) Remain 25:55:45 loss: 0.4365 Lr: 0.00282 [2024-02-18 17:58:25,555 INFO misc.py line 119 87073] Train: [49/100][912/1557] Data 0.003 (0.213) Batch 0.911 (1.166) Remain 25:55:21 loss: 0.4134 Lr: 0.00282 [2024-02-18 17:58:26,577 INFO misc.py line 119 87073] Train: [49/100][913/1557] Data 0.004 (0.213) Batch 1.012 (1.166) Remain 25:55:07 loss: 0.3023 Lr: 0.00282 [2024-02-18 17:58:27,701 INFO misc.py line 119 87073] Train: [49/100][914/1557] Data 0.014 (0.212) Batch 1.096 (1.166) Remain 25:54:59 loss: 0.2897 Lr: 0.00282 [2024-02-18 17:58:28,456 INFO misc.py line 119 87073] Train: [49/100][915/1557] Data 0.043 (0.212) Batch 0.794 (1.165) Remain 25:54:26 loss: 0.3756 Lr: 0.00282 [2024-02-18 17:58:29,221 INFO misc.py line 119 87073] Train: [49/100][916/1557] Data 0.004 (0.212) Batch 0.761 (1.165) Remain 25:53:49 loss: 0.3552 Lr: 0.00282 [2024-02-18 17:58:30,570 INFO misc.py line 119 87073] Train: [49/100][917/1557] Data 0.007 (0.212) Batch 1.347 (1.165) Remain 25:54:04 loss: 0.1093 Lr: 0.00282 [2024-02-18 17:58:31,464 INFO misc.py line 119 87073] Train: [49/100][918/1557] Data 0.010 (0.212) Batch 0.901 (1.165) Remain 25:53:39 loss: 0.2035 Lr: 0.00282 [2024-02-18 17:58:32,550 INFO misc.py line 119 87073] Train: [49/100][919/1557] Data 0.004 (0.211) Batch 1.085 (1.164) Remain 25:53:31 loss: 0.2274 Lr: 0.00282 [2024-02-18 17:58:33,485 INFO misc.py line 119 87073] Train: [49/100][920/1557] Data 0.004 (0.211) Batch 0.935 (1.164) Remain 25:53:10 loss: 0.4454 Lr: 0.00282 [2024-02-18 17:58:34,502 INFO misc.py line 119 87073] Train: [49/100][921/1557] Data 0.004 (0.211) Batch 1.017 (1.164) Remain 25:52:56 loss: 0.3706 Lr: 0.00282 [2024-02-18 17:58:35,244 INFO misc.py line 119 87073] Train: [49/100][922/1557] Data 0.005 (0.211) Batch 0.724 (1.164) Remain 25:52:17 loss: 0.2160 Lr: 0.00282 [2024-02-18 17:58:36,027 INFO misc.py line 119 87073] Train: [49/100][923/1557] Data 0.022 (0.210) Batch 0.799 (1.163) Remain 25:51:44 loss: 0.2222 Lr: 0.00282 [2024-02-18 17:58:37,230 INFO misc.py line 119 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Batch 0.963 (1.162) Remain 25:49:06 loss: 0.4145 Lr: 0.00282 [2024-02-18 17:59:19,727 INFO misc.py line 119 87073] Train: [49/100][962/1557] Data 0.005 (0.209) Batch 0.858 (1.161) Remain 25:48:40 loss: 0.3288 Lr: 0.00282 [2024-02-18 17:59:20,537 INFO misc.py line 119 87073] Train: [49/100][963/1557] Data 0.004 (0.209) Batch 0.807 (1.161) Remain 25:48:09 loss: 0.6249 Lr: 0.00282 [2024-02-18 17:59:21,320 INFO misc.py line 119 87073] Train: [49/100][964/1557] Data 0.006 (0.209) Batch 0.785 (1.161) Remain 25:47:37 loss: 0.2803 Lr: 0.00282 [2024-02-18 17:59:22,054 INFO misc.py line 119 87073] Train: [49/100][965/1557] Data 0.004 (0.209) Batch 0.725 (1.160) Remain 25:46:59 loss: 0.1847 Lr: 0.00282 [2024-02-18 17:59:27,722 INFO misc.py line 119 87073] Train: [49/100][966/1557] Data 4.556 (0.213) Batch 5.677 (1.165) Remain 25:53:13 loss: 0.0841 Lr: 0.00282 [2024-02-18 17:59:28,599 INFO misc.py line 119 87073] Train: [49/100][967/1557] Data 0.005 (0.213) Batch 0.878 (1.165) Remain 25:52:48 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17:59:35,306 INFO misc.py line 119 87073] Train: [49/100][974/1557] Data 0.014 (0.212) Batch 1.027 (1.163) Remain 25:50:40 loss: 0.3506 Lr: 0.00282 [2024-02-18 17:59:36,305 INFO misc.py line 119 87073] Train: [49/100][975/1557] Data 0.013 (0.211) Batch 1.006 (1.163) Remain 25:50:26 loss: 0.4113 Lr: 0.00282 [2024-02-18 17:59:37,333 INFO misc.py line 119 87073] Train: [49/100][976/1557] Data 0.007 (0.211) Batch 1.026 (1.163) Remain 25:50:14 loss: 0.3735 Lr: 0.00282 [2024-02-18 17:59:38,194 INFO misc.py line 119 87073] Train: [49/100][977/1557] Data 0.008 (0.211) Batch 0.864 (1.163) Remain 25:49:48 loss: 0.4352 Lr: 0.00282 [2024-02-18 17:59:39,048 INFO misc.py line 119 87073] Train: [49/100][978/1557] Data 0.005 (0.211) Batch 0.855 (1.162) Remain 25:49:22 loss: 0.2692 Lr: 0.00282 [2024-02-18 17:59:39,819 INFO misc.py line 119 87073] Train: [49/100][979/1557] Data 0.004 (0.210) Batch 0.763 (1.162) Remain 25:48:48 loss: 0.1845 Lr: 0.00282 [2024-02-18 17:59:41,058 INFO misc.py line 119 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misc.py line 119 87073] Train: [49/100][1036/1557] Data 0.005 (0.209) Batch 1.173 (1.160) Remain 25:45:46 loss: 0.2656 Lr: 0.00282 [2024-02-18 18:00:45,437 INFO misc.py line 119 87073] Train: [49/100][1037/1557] Data 0.004 (0.209) Batch 0.898 (1.160) Remain 25:45:24 loss: 0.3047 Lr: 0.00282 [2024-02-18 18:00:46,418 INFO misc.py line 119 87073] Train: [49/100][1038/1557] Data 0.004 (0.209) Batch 0.980 (1.160) Remain 25:45:09 loss: 0.5325 Lr: 0.00282 [2024-02-18 18:00:47,247 INFO misc.py line 119 87073] Train: [49/100][1039/1557] Data 0.004 (0.209) Batch 0.824 (1.160) Remain 25:44:42 loss: 0.3038 Lr: 0.00282 [2024-02-18 18:00:48,080 INFO misc.py line 119 87073] Train: [49/100][1040/1557] Data 0.009 (0.208) Batch 0.839 (1.159) Remain 25:44:16 loss: 0.4992 Lr: 0.00282 [2024-02-18 18:00:48,880 INFO misc.py line 119 87073] Train: [49/100][1041/1557] Data 0.004 (0.208) Batch 0.798 (1.159) Remain 25:43:47 loss: 0.2356 Lr: 0.00282 [2024-02-18 18:00:49,730 INFO misc.py line 119 87073] Train: 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[2024-02-18 18:01:50,094 INFO misc.py line 119 87073] Train: [49/100][1092/1557] Data 0.008 (0.210) Batch 1.166 (1.161) Remain 25:45:22 loss: 0.1979 Lr: 0.00281 [2024-02-18 18:01:50,911 INFO misc.py line 119 87073] Train: [49/100][1093/1557] Data 0.015 (0.209) Batch 0.828 (1.161) Remain 25:44:56 loss: 0.2991 Lr: 0.00281 [2024-02-18 18:01:51,844 INFO misc.py line 119 87073] Train: [49/100][1094/1557] Data 0.004 (0.209) Batch 0.931 (1.160) Remain 25:44:39 loss: 0.2366 Lr: 0.00281 [2024-02-18 18:01:52,882 INFO misc.py line 119 87073] Train: [49/100][1095/1557] Data 0.006 (0.209) Batch 1.037 (1.160) Remain 25:44:28 loss: 0.3883 Lr: 0.00281 [2024-02-18 18:01:53,827 INFO misc.py line 119 87073] Train: [49/100][1096/1557] Data 0.008 (0.209) Batch 0.948 (1.160) Remain 25:44:12 loss: 0.6190 Lr: 0.00281 [2024-02-18 18:01:54,556 INFO misc.py line 119 87073] Train: [49/100][1097/1557] Data 0.003 (0.209) Batch 0.719 (1.160) Remain 25:43:38 loss: 0.3118 Lr: 0.00281 [2024-02-18 18:01:55,310 INFO 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misc.py line 119 87073] Train: [49/100][1129/1557] Data 0.004 (0.209) Batch 1.012 (1.160) Remain 25:43:14 loss: 0.2069 Lr: 0.00281 [2024-02-18 18:02:32,741 INFO misc.py line 119 87073] Train: [49/100][1130/1557] Data 0.005 (0.209) Batch 0.904 (1.160) Remain 25:42:55 loss: 0.2268 Lr: 0.00281 [2024-02-18 18:02:33,744 INFO misc.py line 119 87073] Train: [49/100][1131/1557] Data 0.003 (0.209) Batch 0.996 (1.159) Remain 25:42:42 loss: 0.2471 Lr: 0.00281 [2024-02-18 18:02:34,448 INFO misc.py line 119 87073] Train: [49/100][1132/1557] Data 0.010 (0.209) Batch 0.710 (1.159) Remain 25:42:09 loss: 0.1742 Lr: 0.00281 [2024-02-18 18:02:35,215 INFO misc.py line 119 87073] Train: [49/100][1133/1557] Data 0.004 (0.209) Batch 0.767 (1.159) Remain 25:41:40 loss: 0.1536 Lr: 0.00281 [2024-02-18 18:02:41,349 INFO misc.py line 119 87073] Train: [49/100][1134/1557] Data 4.981 (0.213) Batch 6.134 (1.163) Remain 25:47:30 loss: 0.3824 Lr: 0.00281 [2024-02-18 18:02:42,427 INFO misc.py line 119 87073] Train: 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[2024-02-18 18:03:36,930 INFO misc.py line 119 87073] Train: [49/100][1185/1557] Data 0.004 (0.210) Batch 0.919 (1.160) Remain 25:42:19 loss: 0.2957 Lr: 0.00281 [2024-02-18 18:03:37,801 INFO misc.py line 119 87073] Train: [49/100][1186/1557] Data 0.004 (0.210) Batch 0.841 (1.160) Remain 25:41:56 loss: 0.3062 Lr: 0.00281 [2024-02-18 18:03:38,829 INFO misc.py line 119 87073] Train: [49/100][1187/1557] Data 0.033 (0.209) Batch 1.049 (1.160) Remain 25:41:47 loss: 0.8902 Lr: 0.00281 [2024-02-18 18:03:39,568 INFO misc.py line 119 87073] Train: [49/100][1188/1557] Data 0.012 (0.209) Batch 0.746 (1.159) Remain 25:41:19 loss: 0.4215 Lr: 0.00281 [2024-02-18 18:03:40,336 INFO misc.py line 119 87073] Train: [49/100][1189/1557] Data 0.005 (0.209) Batch 0.761 (1.159) Remain 25:40:51 loss: 0.4002 Lr: 0.00281 [2024-02-18 18:03:46,990 INFO misc.py line 119 87073] Train: [49/100][1190/1557] Data 5.494 (0.213) Batch 6.662 (1.164) Remain 25:46:59 loss: 0.2755 Lr: 0.00281 [2024-02-18 18:03:48,055 INFO 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[2024-02-18 18:05:23,559 INFO misc.py line 119 87073] Train: [49/100][1278/1557] Data 0.005 (0.209) Batch 0.918 (1.159) Remain 25:39:12 loss: 0.4834 Lr: 0.00281 [2024-02-18 18:05:24,328 INFO misc.py line 119 87073] Train: [49/100][1279/1557] Data 0.023 (0.209) Batch 0.788 (1.159) Remain 25:38:47 loss: 0.4068 Lr: 0.00281 [2024-02-18 18:05:25,085 INFO misc.py line 119 87073] Train: [49/100][1280/1557] Data 0.005 (0.209) Batch 0.755 (1.158) Remain 25:38:21 loss: 0.3666 Lr: 0.00281 [2024-02-18 18:05:26,211 INFO misc.py line 119 87073] Train: [49/100][1281/1557] Data 0.006 (0.209) Batch 1.122 (1.158) Remain 25:38:18 loss: 0.1573 Lr: 0.00281 [2024-02-18 18:05:27,053 INFO misc.py line 119 87073] Train: [49/100][1282/1557] Data 0.011 (0.208) Batch 0.847 (1.158) Remain 25:37:57 loss: 0.4348 Lr: 0.00280 [2024-02-18 18:05:28,024 INFO misc.py line 119 87073] Train: [49/100][1283/1557] Data 0.006 (0.208) Batch 0.972 (1.158) Remain 25:37:44 loss: 0.4250 Lr: 0.00280 [2024-02-18 18:05:28,893 INFO misc.py line 119 87073] Train: [49/100][1284/1557] Data 0.004 (0.208) Batch 0.868 (1.158) Remain 25:37:25 loss: 0.7110 Lr: 0.00280 [2024-02-18 18:05:29,787 INFO misc.py line 119 87073] Train: [49/100][1285/1557] Data 0.004 (0.208) Batch 0.891 (1.157) Remain 25:37:08 loss: 0.5549 Lr: 0.00280 [2024-02-18 18:05:30,555 INFO misc.py line 119 87073] Train: [49/100][1286/1557] Data 0.008 (0.208) Batch 0.771 (1.157) Remain 25:36:42 loss: 0.2960 Lr: 0.00280 [2024-02-18 18:05:31,308 INFO misc.py line 119 87073] Train: [49/100][1287/1557] Data 0.004 (0.208) Batch 0.739 (1.157) Remain 25:36:15 loss: 0.5471 Lr: 0.00280 [2024-02-18 18:05:32,620 INFO misc.py line 119 87073] Train: [49/100][1288/1557] Data 0.018 (0.207) Batch 1.316 (1.157) Remain 25:36:24 loss: 0.2102 Lr: 0.00280 [2024-02-18 18:05:33,463 INFO misc.py line 119 87073] Train: [49/100][1289/1557] Data 0.015 (0.207) Batch 0.853 (1.157) Remain 25:36:04 loss: 0.3658 Lr: 0.00280 [2024-02-18 18:05:34,393 INFO misc.py line 119 87073] Train: 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(0.212) Batch 0.932 (1.161) Remain 25:42:03 loss: 0.7596 Lr: 0.00280 [2024-02-18 18:05:48,743 INFO misc.py line 119 87073] Train: [49/100][1297/1557] Data 0.005 (0.212) Batch 1.212 (1.161) Remain 25:42:05 loss: 0.1462 Lr: 0.00280 [2024-02-18 18:05:49,658 INFO misc.py line 119 87073] Train: [49/100][1298/1557] Data 0.015 (0.212) Batch 0.926 (1.161) Remain 25:41:49 loss: 0.2902 Lr: 0.00280 [2024-02-18 18:05:50,615 INFO misc.py line 119 87073] Train: [49/100][1299/1557] Data 0.004 (0.212) Batch 0.956 (1.161) Remain 25:41:36 loss: 0.5537 Lr: 0.00280 [2024-02-18 18:05:51,404 INFO misc.py line 119 87073] Train: [49/100][1300/1557] Data 0.004 (0.211) Batch 0.784 (1.161) Remain 25:41:11 loss: 0.3706 Lr: 0.00280 [2024-02-18 18:05:52,195 INFO misc.py line 119 87073] Train: [49/100][1301/1557] Data 0.009 (0.211) Batch 0.796 (1.160) Remain 25:40:48 loss: 0.6518 Lr: 0.00280 [2024-02-18 18:05:57,188 INFO misc.py line 119 87073] Train: [49/100][1302/1557] Data 3.793 (0.214) Batch 4.992 (1.163) Remain 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[2024-02-18 18:06:03,562 INFO misc.py line 119 87073] Train: [49/100][1309/1557] Data 0.007 (0.213) Batch 1.302 (1.162) Remain 25:42:45 loss: 0.1207 Lr: 0.00280 [2024-02-18 18:06:04,608 INFO misc.py line 119 87073] Train: [49/100][1310/1557] Data 0.014 (0.213) Batch 1.045 (1.162) Remain 25:42:37 loss: 0.5192 Lr: 0.00280 [2024-02-18 18:06:05,523 INFO misc.py line 119 87073] Train: [49/100][1311/1557] Data 0.015 (0.213) Batch 0.924 (1.162) Remain 25:42:21 loss: 0.3098 Lr: 0.00280 [2024-02-18 18:06:06,313 INFO misc.py line 119 87073] Train: [49/100][1312/1557] Data 0.005 (0.212) Batch 0.790 (1.162) Remain 25:41:57 loss: 0.3024 Lr: 0.00280 [2024-02-18 18:06:07,415 INFO misc.py line 119 87073] Train: [49/100][1313/1557] Data 0.006 (0.212) Batch 1.098 (1.161) Remain 25:41:52 loss: 0.2044 Lr: 0.00280 [2024-02-18 18:06:08,169 INFO misc.py line 119 87073] Train: [49/100][1314/1557] Data 0.010 (0.212) Batch 0.760 (1.161) Remain 25:41:27 loss: 0.4699 Lr: 0.00280 [2024-02-18 18:06:08,916 INFO misc.py line 119 87073] Train: [49/100][1315/1557] Data 0.005 (0.212) Batch 0.743 (1.161) Remain 25:41:00 loss: 0.4413 Lr: 0.00280 [2024-02-18 18:06:10,139 INFO misc.py line 119 87073] Train: [49/100][1316/1557] Data 0.008 (0.212) Batch 1.218 (1.161) Remain 25:41:02 loss: 0.2008 Lr: 0.00280 [2024-02-18 18:06:11,252 INFO misc.py line 119 87073] Train: [49/100][1317/1557] Data 0.013 (0.212) Batch 1.118 (1.161) Remain 25:40:59 loss: 0.4280 Lr: 0.00280 [2024-02-18 18:06:12,345 INFO misc.py line 119 87073] Train: [49/100][1318/1557] Data 0.009 (0.211) Batch 1.089 (1.161) Remain 25:40:53 loss: 0.3869 Lr: 0.00280 [2024-02-18 18:06:13,259 INFO misc.py line 119 87073] Train: [49/100][1319/1557] Data 0.011 (0.211) Batch 0.921 (1.161) Remain 25:40:38 loss: 0.1603 Lr: 0.00280 [2024-02-18 18:06:14,232 INFO misc.py line 119 87073] Train: [49/100][1320/1557] Data 0.004 (0.211) Batch 0.973 (1.160) Remain 25:40:25 loss: 0.5563 Lr: 0.00280 [2024-02-18 18:06:14,971 INFO misc.py line 119 87073] Train: 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(0.210) Batch 0.937 (1.159) Remain 25:38:23 loss: 0.1895 Lr: 0.00280 [2024-02-18 18:06:21,301 INFO misc.py line 119 87073] Train: [49/100][1328/1557] Data 0.005 (0.210) Batch 0.835 (1.159) Remain 25:38:03 loss: 0.2472 Lr: 0.00280 [2024-02-18 18:06:22,044 INFO misc.py line 119 87073] Train: [49/100][1329/1557] Data 0.004 (0.210) Batch 0.742 (1.158) Remain 25:37:36 loss: 0.1473 Lr: 0.00280 [2024-02-18 18:06:23,271 INFO misc.py line 119 87073] Train: [49/100][1330/1557] Data 0.006 (0.210) Batch 1.220 (1.159) Remain 25:37:39 loss: 0.2832 Lr: 0.00280 [2024-02-18 18:06:24,244 INFO misc.py line 119 87073] Train: [49/100][1331/1557] Data 0.013 (0.209) Batch 0.982 (1.158) Remain 25:37:27 loss: 0.2277 Lr: 0.00280 [2024-02-18 18:06:25,075 INFO misc.py line 119 87073] Train: [49/100][1332/1557] Data 0.004 (0.209) Batch 0.829 (1.158) Remain 25:37:06 loss: 0.1646 Lr: 0.00280 [2024-02-18 18:06:25,991 INFO misc.py line 119 87073] Train: [49/100][1333/1557] Data 0.007 (0.209) Batch 0.908 (1.158) Remain 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[2024-02-18 18:06:32,314 INFO misc.py line 119 87073] Train: [49/100][1340/1557] Data 0.006 (0.208) Batch 0.922 (1.157) Remain 25:34:56 loss: 0.3803 Lr: 0.00280 [2024-02-18 18:06:33,217 INFO misc.py line 119 87073] Train: [49/100][1341/1557] Data 0.006 (0.208) Batch 0.900 (1.156) Remain 25:34:40 loss: 0.5345 Lr: 0.00280 [2024-02-18 18:06:33,984 INFO misc.py line 119 87073] Train: [49/100][1342/1557] Data 0.009 (0.208) Batch 0.769 (1.156) Remain 25:34:16 loss: 0.3665 Lr: 0.00280 [2024-02-18 18:06:34,649 INFO misc.py line 119 87073] Train: [49/100][1343/1557] Data 0.007 (0.208) Batch 0.658 (1.156) Remain 25:33:45 loss: 0.4199 Lr: 0.00280 [2024-02-18 18:06:35,906 INFO misc.py line 119 87073] Train: [49/100][1344/1557] Data 0.013 (0.208) Batch 1.258 (1.156) Remain 25:33:50 loss: 0.1872 Lr: 0.00280 [2024-02-18 18:06:36,884 INFO misc.py line 119 87073] Train: [49/100][1345/1557] Data 0.013 (0.207) Batch 0.987 (1.156) Remain 25:33:39 loss: 0.4858 Lr: 0.00280 [2024-02-18 18:06:37,886 INFO 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(0.213) Batch 4.875 (1.162) Remain 25:41:46 loss: 0.0836 Lr: 0.00280 [2024-02-18 18:07:01,418 INFO misc.py line 119 87073] Train: [49/100][1359/1557] Data 0.007 (0.213) Batch 0.957 (1.162) Remain 25:41:33 loss: 0.2865 Lr: 0.00280 [2024-02-18 18:07:02,421 INFO misc.py line 119 87073] Train: [49/100][1360/1557] Data 0.005 (0.213) Batch 1.004 (1.162) Remain 25:41:22 loss: 0.1840 Lr: 0.00280 [2024-02-18 18:07:03,547 INFO misc.py line 119 87073] Train: [49/100][1361/1557] Data 0.003 (0.213) Batch 1.122 (1.162) Remain 25:41:19 loss: 0.3110 Lr: 0.00280 [2024-02-18 18:07:04,500 INFO misc.py line 119 87073] Train: [49/100][1362/1557] Data 0.010 (0.213) Batch 0.957 (1.162) Remain 25:41:06 loss: 0.1522 Lr: 0.00280 [2024-02-18 18:07:05,258 INFO misc.py line 119 87073] Train: [49/100][1363/1557] Data 0.003 (0.213) Batch 0.756 (1.161) Remain 25:40:41 loss: 0.2219 Lr: 0.00280 [2024-02-18 18:07:06,003 INFO misc.py line 119 87073] Train: [49/100][1364/1557] Data 0.005 (0.212) Batch 0.736 (1.161) Remain 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[2024-02-18 18:07:12,595 INFO misc.py line 119 87073] Train: [49/100][1371/1557] Data 0.004 (0.211) Batch 0.664 (1.160) Remain 25:38:37 loss: 0.1880 Lr: 0.00280 [2024-02-18 18:07:13,802 INFO misc.py line 119 87073] Train: [49/100][1372/1557] Data 0.017 (0.211) Batch 1.210 (1.160) Remain 25:38:39 loss: 0.1989 Lr: 0.00280 [2024-02-18 18:07:14,701 INFO misc.py line 119 87073] Train: [49/100][1373/1557] Data 0.014 (0.211) Batch 0.910 (1.160) Remain 25:38:23 loss: 0.5746 Lr: 0.00280 [2024-02-18 18:07:16,006 INFO misc.py line 119 87073] Train: [49/100][1374/1557] Data 0.004 (0.211) Batch 1.294 (1.160) Remain 25:38:30 loss: 0.5472 Lr: 0.00280 [2024-02-18 18:07:16,967 INFO misc.py line 119 87073] Train: [49/100][1375/1557] Data 0.015 (0.211) Batch 0.973 (1.160) Remain 25:38:18 loss: 0.5380 Lr: 0.00280 [2024-02-18 18:07:17,976 INFO misc.py line 119 87073] Train: [49/100][1376/1557] Data 0.004 (0.211) Batch 1.008 (1.160) Remain 25:38:08 loss: 0.3179 Lr: 0.00280 [2024-02-18 18:07:18,704 INFO 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(0.209) Batch 1.088 (1.158) Remain 25:35:23 loss: 0.4086 Lr: 0.00280 [2024-02-18 18:07:31,507 INFO misc.py line 119 87073] Train: [49/100][1390/1557] Data 0.003 (0.209) Batch 1.075 (1.158) Remain 25:35:17 loss: 0.3143 Lr: 0.00280 [2024-02-18 18:07:32,396 INFO misc.py line 119 87073] Train: [49/100][1391/1557] Data 0.003 (0.208) Batch 0.889 (1.157) Remain 25:35:00 loss: 0.3690 Lr: 0.00280 [2024-02-18 18:07:33,134 INFO misc.py line 119 87073] Train: [49/100][1392/1557] Data 0.004 (0.208) Batch 0.725 (1.157) Remain 25:34:34 loss: 0.1975 Lr: 0.00280 [2024-02-18 18:07:34,235 INFO misc.py line 119 87073] Train: [49/100][1393/1557] Data 0.016 (0.208) Batch 1.104 (1.157) Remain 25:34:30 loss: 0.2047 Lr: 0.00280 [2024-02-18 18:07:35,146 INFO misc.py line 119 87073] Train: [49/100][1394/1557] Data 0.013 (0.208) Batch 0.921 (1.157) Remain 25:34:15 loss: 0.2179 Lr: 0.00280 [2024-02-18 18:07:36,123 INFO misc.py line 119 87073] Train: [49/100][1395/1557] Data 0.004 (0.208) Batch 0.976 (1.157) Remain 25:34:04 loss: 0.3112 Lr: 0.00280 [2024-02-18 18:07:36,995 INFO misc.py line 119 87073] Train: [49/100][1396/1557] Data 0.004 (0.208) Batch 0.872 (1.157) Remain 25:33:46 loss: 0.4475 Lr: 0.00280 [2024-02-18 18:07:38,017 INFO misc.py line 119 87073] Train: [49/100][1397/1557] Data 0.004 (0.208) Batch 1.018 (1.156) Remain 25:33:37 loss: 0.3755 Lr: 0.00280 [2024-02-18 18:07:38,768 INFO misc.py line 119 87073] Train: [49/100][1398/1557] Data 0.009 (0.207) Batch 0.755 (1.156) Remain 25:33:13 loss: 0.2701 Lr: 0.00280 [2024-02-18 18:07:39,537 INFO misc.py line 119 87073] Train: [49/100][1399/1557] Data 0.006 (0.207) Batch 0.766 (1.156) Remain 25:32:50 loss: 0.2359 Lr: 0.00280 [2024-02-18 18:07:40,776 INFO misc.py line 119 87073] Train: [49/100][1400/1557] Data 0.007 (0.207) Batch 1.239 (1.156) Remain 25:32:53 loss: 0.1262 Lr: 0.00280 [2024-02-18 18:07:41,713 INFO misc.py line 119 87073] Train: [49/100][1401/1557] Data 0.008 (0.207) Batch 0.940 (1.156) Remain 25:32:40 loss: 0.3055 Lr: 0.00280 [2024-02-18 18:07:42,618 INFO misc.py line 119 87073] Train: [49/100][1402/1557] Data 0.005 (0.207) Batch 0.905 (1.156) Remain 25:32:25 loss: 0.3387 Lr: 0.00280 [2024-02-18 18:07:43,647 INFO misc.py line 119 87073] Train: [49/100][1403/1557] Data 0.004 (0.207) Batch 1.028 (1.156) Remain 25:32:16 loss: 0.3205 Lr: 0.00280 [2024-02-18 18:07:44,591 INFO misc.py line 119 87073] Train: [49/100][1404/1557] Data 0.005 (0.207) Batch 0.945 (1.155) Remain 25:32:03 loss: 0.5651 Lr: 0.00280 [2024-02-18 18:07:47,528 INFO misc.py line 119 87073] Train: [49/100][1405/1557] Data 1.736 (0.208) Batch 2.937 (1.157) Remain 25:33:43 loss: 0.2663 Lr: 0.00280 [2024-02-18 18:07:48,328 INFO misc.py line 119 87073] Train: [49/100][1406/1557] Data 0.004 (0.207) Batch 0.800 (1.156) Remain 25:33:22 loss: 0.2759 Lr: 0.00280 [2024-02-18 18:07:56,791 INFO misc.py line 119 87073] Train: [49/100][1407/1557] Data 7.467 (0.213) Batch 8.462 (1.162) Remain 25:40:15 loss: 0.1129 Lr: 0.00280 [2024-02-18 18:07:57,693 INFO misc.py line 119 87073] Train: [49/100][1408/1557] Data 0.004 (0.213) Batch 0.901 (1.161) Remain 25:39:59 loss: 0.3501 Lr: 0.00280 [2024-02-18 18:07:58,507 INFO misc.py line 119 87073] Train: [49/100][1409/1557] Data 0.006 (0.212) Batch 0.815 (1.161) Remain 25:39:38 loss: 0.5509 Lr: 0.00280 [2024-02-18 18:07:59,588 INFO misc.py line 119 87073] Train: [49/100][1410/1557] Data 0.005 (0.212) Batch 1.076 (1.161) Remain 25:39:32 loss: 0.5437 Lr: 0.00280 [2024-02-18 18:08:00,406 INFO misc.py line 119 87073] Train: [49/100][1411/1557] Data 0.010 (0.212) Batch 0.822 (1.161) Remain 25:39:12 loss: 0.1407 Lr: 0.00280 [2024-02-18 18:08:01,168 INFO misc.py line 119 87073] Train: [49/100][1412/1557] Data 0.006 (0.212) Batch 0.762 (1.161) Remain 25:38:48 loss: 0.2349 Lr: 0.00280 [2024-02-18 18:08:01,899 INFO misc.py line 119 87073] Train: [49/100][1413/1557] Data 0.006 (0.212) Batch 0.721 (1.160) Remain 25:38:22 loss: 0.4530 Lr: 0.00280 [2024-02-18 18:08:06,485 INFO misc.py line 119 87073] Train: 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(0.213) Batch 0.737 (1.162) Remain 25:40:08 loss: 0.1831 Lr: 0.00280 [2024-02-18 18:08:13,339 INFO misc.py line 119 87073] Train: [49/100][1421/1557] Data 0.012 (0.213) Batch 1.292 (1.162) Remain 25:40:14 loss: 0.1042 Lr: 0.00280 [2024-02-18 18:08:14,285 INFO misc.py line 119 87073] Train: [49/100][1422/1557] Data 0.015 (0.213) Batch 0.955 (1.162) Remain 25:40:01 loss: 0.3086 Lr: 0.00280 [2024-02-18 18:08:15,267 INFO misc.py line 119 87073] Train: [49/100][1423/1557] Data 0.004 (0.213) Batch 0.983 (1.162) Remain 25:39:50 loss: 0.2251 Lr: 0.00280 [2024-02-18 18:08:16,168 INFO misc.py line 119 87073] Train: [49/100][1424/1557] Data 0.004 (0.213) Batch 0.901 (1.161) Remain 25:39:34 loss: 0.4179 Lr: 0.00280 [2024-02-18 18:08:17,304 INFO misc.py line 119 87073] Train: [49/100][1425/1557] Data 0.005 (0.212) Batch 1.128 (1.161) Remain 25:39:31 loss: 0.3377 Lr: 0.00280 [2024-02-18 18:08:17,994 INFO misc.py line 119 87073] Train: [49/100][1426/1557] Data 0.013 (0.212) Batch 0.699 (1.161) Remain 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[2024-02-18 18:09:01,444 INFO misc.py line 119 87073] Train: [49/100][1464/1557] Data 0.026 (0.212) Batch 1.190 (1.161) Remain 25:37:43 loss: 0.5453 Lr: 0.00280 [2024-02-18 18:09:02,518 INFO misc.py line 119 87073] Train: [49/100][1465/1557] Data 0.006 (0.212) Batch 1.064 (1.160) Remain 25:37:37 loss: 0.6076 Lr: 0.00280 [2024-02-18 18:09:03,401 INFO misc.py line 119 87073] Train: [49/100][1466/1557] Data 0.016 (0.211) Batch 0.895 (1.160) Remain 25:37:21 loss: 0.2059 Lr: 0.00280 [2024-02-18 18:09:04,431 INFO misc.py line 119 87073] Train: [49/100][1467/1557] Data 0.004 (0.211) Batch 1.030 (1.160) Remain 25:37:13 loss: 0.6104 Lr: 0.00280 [2024-02-18 18:09:05,127 INFO misc.py line 119 87073] Train: [49/100][1468/1557] Data 0.004 (0.211) Batch 0.697 (1.160) Remain 25:36:47 loss: 0.1375 Lr: 0.00280 [2024-02-18 18:09:05,896 INFO misc.py line 119 87073] Train: [49/100][1469/1557] Data 0.004 (0.211) Batch 0.760 (1.160) Remain 25:36:24 loss: 0.2312 Lr: 0.00280 [2024-02-18 18:09:09,699 INFO misc.py line 119 87073] Train: [49/100][1470/1557] Data 2.669 (0.213) Batch 3.811 (1.161) Remain 25:38:47 loss: 0.0558 Lr: 0.00280 [2024-02-18 18:09:10,524 INFO misc.py line 119 87073] Train: [49/100][1471/1557] Data 0.004 (0.212) Batch 0.822 (1.161) Remain 25:38:27 loss: 0.3899 Lr: 0.00279 [2024-02-18 18:09:11,562 INFO misc.py line 119 87073] Train: [49/100][1472/1557] Data 0.007 (0.212) Batch 1.041 (1.161) Remain 25:38:19 loss: 0.3747 Lr: 0.00279 [2024-02-18 18:09:12,475 INFO misc.py line 119 87073] Train: [49/100][1473/1557] Data 0.004 (0.212) Batch 0.913 (1.161) Remain 25:38:05 loss: 0.3594 Lr: 0.00279 [2024-02-18 18:09:13,562 INFO misc.py line 119 87073] Train: [49/100][1474/1557] Data 0.004 (0.212) Batch 1.087 (1.161) Remain 25:38:00 loss: 0.6039 Lr: 0.00279 [2024-02-18 18:09:14,381 INFO misc.py line 119 87073] Train: [49/100][1475/1557] Data 0.004 (0.212) Batch 0.817 (1.161) Remain 25:37:40 loss: 0.2773 Lr: 0.00279 [2024-02-18 18:09:15,173 INFO misc.py line 119 87073] Train: 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(0.211) Batch 0.682 (1.160) Remain 25:36:13 loss: 0.3971 Lr: 0.00279 [2024-02-18 18:09:21,785 INFO misc.py line 119 87073] Train: [49/100][1483/1557] Data 0.012 (0.211) Batch 0.747 (1.159) Remain 25:35:50 loss: 0.1946 Lr: 0.00279 [2024-02-18 18:09:22,995 INFO misc.py line 119 87073] Train: [49/100][1484/1557] Data 0.005 (0.211) Batch 1.210 (1.159) Remain 25:35:51 loss: 0.2197 Lr: 0.00279 [2024-02-18 18:09:23,979 INFO misc.py line 119 87073] Train: [49/100][1485/1557] Data 0.004 (0.211) Batch 0.985 (1.159) Remain 25:35:41 loss: 0.2559 Lr: 0.00279 [2024-02-18 18:09:24,791 INFO misc.py line 119 87073] Train: [49/100][1486/1557] Data 0.004 (0.210) Batch 0.810 (1.159) Remain 25:35:21 loss: 0.5755 Lr: 0.00279 [2024-02-18 18:09:25,729 INFO misc.py line 119 87073] Train: [49/100][1487/1557] Data 0.006 (0.210) Batch 0.938 (1.159) Remain 25:35:08 loss: 0.2646 Lr: 0.00279 [2024-02-18 18:09:26,625 INFO misc.py line 119 87073] Train: [49/100][1488/1557] Data 0.006 (0.210) Batch 0.898 (1.159) Remain 25:34:53 loss: 0.2656 Lr: 0.00279 [2024-02-18 18:09:27,410 INFO misc.py line 119 87073] Train: [49/100][1489/1557] Data 0.004 (0.210) Batch 0.785 (1.159) Remain 25:34:31 loss: 0.4315 Lr: 0.00279 [2024-02-18 18:09:28,126 INFO misc.py line 119 87073] Train: [49/100][1490/1557] Data 0.003 (0.210) Batch 0.716 (1.158) Remain 25:34:07 loss: 0.2661 Lr: 0.00279 [2024-02-18 18:09:29,311 INFO misc.py line 119 87073] Train: [49/100][1491/1557] Data 0.004 (0.210) Batch 1.180 (1.158) Remain 25:34:07 loss: 0.1500 Lr: 0.00279 [2024-02-18 18:09:30,521 INFO misc.py line 119 87073] Train: [49/100][1492/1557] Data 0.010 (0.210) Batch 1.213 (1.158) Remain 25:34:08 loss: 0.5633 Lr: 0.00279 [2024-02-18 18:09:31,490 INFO misc.py line 119 87073] Train: [49/100][1493/1557] Data 0.007 (0.209) Batch 0.971 (1.158) Remain 25:33:57 loss: 0.1902 Lr: 0.00279 [2024-02-18 18:09:32,488 INFO misc.py line 119 87073] Train: [49/100][1494/1557] Data 0.005 (0.209) Batch 0.999 (1.158) Remain 25:33:48 loss: 0.3282 Lr: 0.00279 [2024-02-18 18:09:33,451 INFO misc.py line 119 87073] Train: [49/100][1495/1557] Data 0.004 (0.209) Batch 0.963 (1.158) Remain 25:33:36 loss: 0.1535 Lr: 0.00279 [2024-02-18 18:09:34,210 INFO misc.py line 119 87073] Train: [49/100][1496/1557] Data 0.004 (0.209) Batch 0.757 (1.158) Remain 25:33:14 loss: 0.5049 Lr: 0.00279 [2024-02-18 18:09:34,999 INFO misc.py line 119 87073] Train: [49/100][1497/1557] Data 0.006 (0.209) Batch 0.791 (1.157) Remain 25:32:53 loss: 0.2077 Lr: 0.00279 [2024-02-18 18:09:36,201 INFO misc.py line 119 87073] Train: [49/100][1498/1557] Data 0.004 (0.209) Batch 1.202 (1.157) Remain 25:32:54 loss: 0.1608 Lr: 0.00279 [2024-02-18 18:09:37,221 INFO misc.py line 119 87073] Train: [49/100][1499/1557] Data 0.005 (0.209) Batch 1.019 (1.157) Remain 25:32:46 loss: 0.1763 Lr: 0.00279 [2024-02-18 18:09:38,267 INFO misc.py line 119 87073] Train: [49/100][1500/1557] Data 0.005 (0.208) Batch 1.046 (1.157) Remain 25:32:39 loss: 0.5729 Lr: 0.00279 [2024-02-18 18:09:39,321 INFO misc.py line 119 87073] Train: [49/100][1501/1557] Data 0.005 (0.208) Batch 1.054 (1.157) Remain 25:32:32 loss: 0.2885 Lr: 0.00279 [2024-02-18 18:09:40,314 INFO misc.py line 119 87073] Train: [49/100][1502/1557] Data 0.006 (0.208) Batch 0.993 (1.157) Remain 25:32:22 loss: 0.5093 Lr: 0.00279 [2024-02-18 18:09:41,122 INFO misc.py line 119 87073] Train: [49/100][1503/1557] Data 0.005 (0.208) Batch 0.807 (1.157) Remain 25:32:02 loss: 0.4024 Lr: 0.00279 [2024-02-18 18:09:41,885 INFO misc.py line 119 87073] Train: [49/100][1504/1557] Data 0.004 (0.208) Batch 0.760 (1.157) Remain 25:31:40 loss: 0.1455 Lr: 0.00279 [2024-02-18 18:09:43,034 INFO misc.py line 119 87073] Train: [49/100][1505/1557] Data 0.008 (0.208) Batch 1.152 (1.157) Remain 25:31:39 loss: 0.2099 Lr: 0.00279 [2024-02-18 18:09:44,051 INFO misc.py line 119 87073] Train: [49/100][1506/1557] Data 0.005 (0.208) Batch 1.013 (1.156) Remain 25:31:30 loss: 0.1227 Lr: 0.00279 [2024-02-18 18:09:44,990 INFO misc.py line 119 87073] Train: [49/100][1507/1557] Data 0.008 (0.208) Batch 0.943 (1.156) Remain 25:31:18 loss: 0.4870 Lr: 0.00279 [2024-02-18 18:09:45,961 INFO misc.py line 119 87073] Train: [49/100][1508/1557] Data 0.004 (0.207) Batch 0.971 (1.156) Remain 25:31:07 loss: 0.2563 Lr: 0.00279 [2024-02-18 18:09:47,038 INFO misc.py line 119 87073] Train: [49/100][1509/1557] Data 0.004 (0.207) Batch 1.076 (1.156) Remain 25:31:01 loss: 0.2471 Lr: 0.00279 [2024-02-18 18:09:47,758 INFO misc.py line 119 87073] Train: [49/100][1510/1557] Data 0.006 (0.207) Batch 0.721 (1.156) Remain 25:30:37 loss: 0.3258 Lr: 0.00279 [2024-02-18 18:09:48,526 INFO misc.py line 119 87073] Train: [49/100][1511/1557] Data 0.004 (0.207) Batch 0.761 (1.156) Remain 25:30:15 loss: 0.2194 Lr: 0.00279 [2024-02-18 18:09:49,776 INFO misc.py line 119 87073] Train: [49/100][1512/1557] Data 0.013 (0.207) Batch 1.243 (1.156) Remain 25:30:19 loss: 0.0966 Lr: 0.00279 [2024-02-18 18:09:50,865 INFO misc.py line 119 87073] Train: [49/100][1513/1557] Data 0.018 (0.207) Batch 1.093 (1.156) Remain 25:30:14 loss: 0.1525 Lr: 0.00279 [2024-02-18 18:09:51,724 INFO misc.py line 119 87073] Train: [49/100][1514/1557] Data 0.014 (0.207) Batch 0.866 (1.155) Remain 25:29:58 loss: 0.4525 Lr: 0.00279 [2024-02-18 18:09:52,747 INFO misc.py line 119 87073] Train: [49/100][1515/1557] Data 0.006 (0.206) Batch 1.025 (1.155) Remain 25:29:50 loss: 0.4931 Lr: 0.00279 [2024-02-18 18:09:53,767 INFO misc.py line 119 87073] Train: [49/100][1516/1557] Data 0.004 (0.206) Batch 1.020 (1.155) Remain 25:29:42 loss: 0.4688 Lr: 0.00279 [2024-02-18 18:09:54,482 INFO misc.py line 119 87073] Train: [49/100][1517/1557] Data 0.004 (0.206) Batch 0.715 (1.155) Remain 25:29:17 loss: 0.3598 Lr: 0.00279 [2024-02-18 18:09:55,183 INFO misc.py line 119 87073] Train: [49/100][1518/1557] Data 0.004 (0.206) Batch 0.695 (1.155) Remain 25:28:52 loss: 0.4819 Lr: 0.00279 [2024-02-18 18:10:03,177 INFO misc.py line 119 87073] Train: [49/100][1519/1557] Data 6.979 (0.211) Batch 8.000 (1.159) Remain 25:34:50 loss: 0.1546 Lr: 0.00279 [2024-02-18 18:10:04,300 INFO misc.py line 119 87073] Train: [49/100][1520/1557] Data 0.006 (0.210) Batch 1.113 (1.159) Remain 25:34:46 loss: 0.7142 Lr: 0.00279 [2024-02-18 18:10:05,337 INFO misc.py line 119 87073] Train: [49/100][1521/1557] Data 0.016 (0.210) Batch 1.045 (1.159) Remain 25:34:39 loss: 0.7387 Lr: 0.00279 [2024-02-18 18:10:06,180 INFO misc.py line 119 87073] Train: [49/100][1522/1557] Data 0.008 (0.210) Batch 0.847 (1.159) Remain 25:34:21 loss: 0.4748 Lr: 0.00279 [2024-02-18 18:10:07,167 INFO misc.py line 119 87073] Train: [49/100][1523/1557] Data 0.003 (0.210) Batch 0.986 (1.159) Remain 25:34:11 loss: 0.3889 Lr: 0.00279 [2024-02-18 18:10:07,903 INFO misc.py line 119 87073] Train: [49/100][1524/1557] Data 0.004 (0.210) Batch 0.736 (1.158) Remain 25:33:48 loss: 0.1175 Lr: 0.00279 [2024-02-18 18:10:08,685 INFO misc.py line 119 87073] Train: [49/100][1525/1557] Data 0.003 (0.210) Batch 0.771 (1.158) Remain 25:33:27 loss: 0.2044 Lr: 0.00279 [2024-02-18 18:10:13,860 INFO misc.py line 119 87073] Train: [49/100][1526/1557] Data 4.052 (0.212) Batch 5.186 (1.161) Remain 25:36:56 loss: 0.0874 Lr: 0.00279 [2024-02-18 18:10:14,808 INFO misc.py line 119 87073] Train: [49/100][1527/1557] Data 0.004 (0.212) Batch 0.947 (1.161) Remain 25:36:43 loss: 0.7040 Lr: 0.00279 [2024-02-18 18:10:15,811 INFO misc.py line 119 87073] Train: [49/100][1528/1557] Data 0.005 (0.212) Batch 1.003 (1.161) Remain 25:36:34 loss: 0.2320 Lr: 0.00279 [2024-02-18 18:10:16,778 INFO misc.py line 119 87073] Train: [49/100][1529/1557] Data 0.004 (0.212) Batch 0.968 (1.160) Remain 25:36:23 loss: 0.2295 Lr: 0.00279 [2024-02-18 18:10:17,788 INFO misc.py line 119 87073] Train: [49/100][1530/1557] Data 0.003 (0.212) Batch 0.966 (1.160) Remain 25:36:11 loss: 0.1736 Lr: 0.00279 [2024-02-18 18:10:18,514 INFO misc.py line 119 87073] Train: [49/100][1531/1557] Data 0.047 (0.212) Batch 0.770 (1.160) Remain 25:35:50 loss: 0.3552 Lr: 0.00279 [2024-02-18 18:10:19,273 INFO misc.py line 119 87073] Train: [49/100][1532/1557] Data 0.004 (0.211) Batch 0.750 (1.160) Remain 25:35:28 loss: 0.5168 Lr: 0.00279 [2024-02-18 18:10:20,644 INFO misc.py line 119 87073] Train: [49/100][1533/1557] Data 0.012 (0.211) Batch 1.368 (1.160) Remain 25:35:37 loss: 0.1099 Lr: 0.00279 [2024-02-18 18:10:21,655 INFO misc.py line 119 87073] Train: [49/100][1534/1557] Data 0.015 (0.211) Batch 1.012 (1.160) Remain 25:35:28 loss: 0.2102 Lr: 0.00279 [2024-02-18 18:10:22,723 INFO misc.py line 119 87073] Train: [49/100][1535/1557] Data 0.015 (0.211) Batch 1.065 (1.160) Remain 25:35:22 loss: 0.4622 Lr: 0.00279 [2024-02-18 18:10:23,844 INFO misc.py line 119 87073] Train: [49/100][1536/1557] Data 0.018 (0.211) Batch 1.130 (1.160) Remain 25:35:20 loss: 0.8858 Lr: 0.00279 [2024-02-18 18:10:24,780 INFO misc.py line 119 87073] Train: [49/100][1537/1557] Data 0.008 (0.211) Batch 0.940 (1.160) Remain 25:35:07 loss: 0.3650 Lr: 0.00279 [2024-02-18 18:10:25,512 INFO misc.py line 119 87073] Train: [49/100][1538/1557] Data 0.004 (0.211) Batch 0.732 (1.159) Remain 25:34:44 loss: 0.2488 Lr: 0.00279 [2024-02-18 18:10:26,295 INFO misc.py line 119 87073] Train: [49/100][1539/1557] Data 0.004 (0.211) Batch 0.779 (1.159) Remain 25:34:23 loss: 0.3662 Lr: 0.00279 [2024-02-18 18:10:27,517 INFO misc.py line 119 87073] Train: [49/100][1540/1557] Data 0.008 (0.210) Batch 1.215 (1.159) Remain 25:34:25 loss: 0.1435 Lr: 0.00279 [2024-02-18 18:10:28,519 INFO misc.py line 119 87073] Train: [49/100][1541/1557] Data 0.015 (0.210) Batch 1.004 (1.159) Remain 25:34:15 loss: 0.3344 Lr: 0.00279 [2024-02-18 18:10:29,358 INFO misc.py line 119 87073] Train: [49/100][1542/1557] Data 0.013 (0.210) Batch 0.849 (1.159) Remain 25:33:58 loss: 0.2023 Lr: 0.00279 [2024-02-18 18:10:30,175 INFO misc.py line 119 87073] Train: [49/100][1543/1557] Data 0.004 (0.210) Batch 0.817 (1.159) Remain 25:33:39 loss: 0.7270 Lr: 0.00279 [2024-02-18 18:10:31,072 INFO misc.py line 119 87073] Train: [49/100][1544/1557] Data 0.005 (0.210) Batch 0.886 (1.158) Remain 25:33:24 loss: 0.1594 Lr: 0.00279 [2024-02-18 18:10:31,785 INFO misc.py line 119 87073] Train: [49/100][1545/1557] Data 0.015 (0.210) Batch 0.725 (1.158) Remain 25:33:01 loss: 0.5576 Lr: 0.00279 [2024-02-18 18:10:32,497 INFO misc.py line 119 87073] Train: [49/100][1546/1557] Data 0.004 (0.210) Batch 0.701 (1.158) Remain 25:32:36 loss: 0.1855 Lr: 0.00279 [2024-02-18 18:10:33,621 INFO misc.py line 119 87073] Train: [49/100][1547/1557] Data 0.015 (0.210) Batch 1.134 (1.158) Remain 25:32:34 loss: 0.2141 Lr: 0.00279 [2024-02-18 18:10:34,509 INFO misc.py line 119 87073] Train: [49/100][1548/1557] Data 0.006 (0.209) Batch 0.889 (1.158) Remain 25:32:19 loss: 0.4764 Lr: 0.00279 [2024-02-18 18:10:35,317 INFO misc.py line 119 87073] Train: [49/100][1549/1557] Data 0.004 (0.209) Batch 0.807 (1.157) Remain 25:32:00 loss: 0.2831 Lr: 0.00279 [2024-02-18 18:10:36,301 INFO misc.py line 119 87073] Train: [49/100][1550/1557] Data 0.005 (0.209) Batch 0.952 (1.157) Remain 25:31:48 loss: 0.4180 Lr: 0.00279 [2024-02-18 18:10:37,312 INFO misc.py line 119 87073] Train: [49/100][1551/1557] Data 0.037 (0.209) Batch 1.033 (1.157) Remain 25:31:40 loss: 0.2685 Lr: 0.00279 [2024-02-18 18:10:38,042 INFO misc.py line 119 87073] Train: [49/100][1552/1557] Data 0.015 (0.209) Batch 0.740 (1.157) Remain 25:31:18 loss: 0.3422 Lr: 0.00279 [2024-02-18 18:10:38,782 INFO misc.py line 119 87073] Train: [49/100][1553/1557] Data 0.005 (0.209) Batch 0.736 (1.157) Remain 25:30:55 loss: 0.2465 Lr: 0.00279 [2024-02-18 18:10:40,004 INFO misc.py line 119 87073] Train: [49/100][1554/1557] Data 0.009 (0.209) Batch 1.219 (1.157) Remain 25:30:57 loss: 0.2680 Lr: 0.00279 [2024-02-18 18:10:41,115 INFO misc.py line 119 87073] Train: [49/100][1555/1557] Data 0.012 (0.209) Batch 1.108 (1.157) Remain 25:30:53 loss: 0.7861 Lr: 0.00279 [2024-02-18 18:10:42,144 INFO misc.py line 119 87073] Train: [49/100][1556/1557] Data 0.016 (0.208) Batch 1.031 (1.157) Remain 25:30:46 loss: 0.4029 Lr: 0.00279 [2024-02-18 18:10:43,065 INFO misc.py line 119 87073] Train: [49/100][1557/1557] Data 0.013 (0.208) Batch 0.925 (1.156) Remain 25:30:33 loss: 0.1912 Lr: 0.00279 [2024-02-18 18:10:43,065 INFO misc.py line 136 87073] Train result: loss: 0.3555 [2024-02-18 18:10:43,065 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 18:11:09,595 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6749 [2024-02-18 18:11:10,371 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8680 [2024-02-18 18:11:12,499 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4181 [2024-02-18 18:11:14,707 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.6035 [2024-02-18 18:11:19,652 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3410 [2024-02-18 18:11:20,351 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1218 [2024-02-18 18:11:21,611 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2296 [2024-02-18 18:11:24,563 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4141 [2024-02-18 18:11:26,372 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2621 [2024-02-18 18:11:27,850 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6906/0.7460/0.9066. [2024-02-18 18:11:27,850 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9044/0.9679 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9795/0.9864 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8557/0.9734 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2814/0.3001 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6164/0.6295 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8120/0.9102 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8352/0.8964 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9193/0.9743 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.6481/0.6596 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7893/0.8951 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7695/0.8648 [2024-02-18 18:11:27,851 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5665/0.6403 [2024-02-18 18:11:27,852 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 18:11:27,854 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 18:11:27,854 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 18:11:35,469 INFO misc.py line 119 87073] Train: [50/100][1/1557] Data 1.517 (1.517) Batch 2.271 (2.271) Remain 50:05:45 loss: 0.4172 Lr: 0.00279 [2024-02-18 18:11:36,462 INFO misc.py line 119 87073] Train: [50/100][2/1557] Data 0.006 (0.006) Batch 0.991 (0.991) Remain 21:51:19 loss: 0.2349 Lr: 0.00279 [2024-02-18 18:11:37,636 INFO misc.py line 119 87073] Train: [50/100][3/1557] Data 0.008 (0.008) Batch 1.177 (1.177) Remain 25:57:10 loss: 0.2005 Lr: 0.00279 [2024-02-18 18:11:38,598 INFO misc.py line 119 87073] Train: [50/100][4/1557] Data 0.006 (0.006) Batch 0.963 (0.963) Remain 21:13:46 loss: 0.5111 Lr: 0.00279 [2024-02-18 18:11:39,305 INFO misc.py line 119 87073] Train: [50/100][5/1557] Data 0.006 (0.006) Batch 0.707 (0.835) Remain 18:24:59 loss: 0.1908 Lr: 0.00279 [2024-02-18 18:11:40,078 INFO misc.py line 119 87073] Train: [50/100][6/1557] Data 0.004 (0.005) Batch 0.773 (0.814) Remain 17:57:37 loss: 0.4533 Lr: 0.00279 [2024-02-18 18:11:41,187 INFO misc.py line 119 87073] Train: [50/100][7/1557] Data 0.005 (0.005) Batch 1.108 (0.888) Remain 19:34:38 loss: 0.1994 Lr: 0.00279 [2024-02-18 18:11:42,112 INFO misc.py line 119 87073] Train: [50/100][8/1557] Data 0.006 (0.005) Batch 0.926 (0.895) Remain 19:44:42 loss: 0.5602 Lr: 0.00279 [2024-02-18 18:11:43,092 INFO misc.py line 119 87073] Train: [50/100][9/1557] Data 0.005 (0.005) Batch 0.980 (0.909) Remain 20:03:22 loss: 0.5244 Lr: 0.00279 [2024-02-18 18:11:44,209 INFO misc.py line 119 87073] Train: [50/100][10/1557] Data 0.005 (0.005) Batch 1.117 (0.939) Remain 20:42:35 loss: 0.3861 Lr: 0.00279 [2024-02-18 18:11:45,098 INFO misc.py line 119 87073] Train: [50/100][11/1557] Data 0.006 (0.005) Batch 0.890 (0.933) Remain 20:34:26 loss: 0.5644 Lr: 0.00279 [2024-02-18 18:11:45,890 INFO misc.py line 119 87073] Train: [50/100][12/1557] Data 0.004 (0.005) Batch 0.792 (0.917) Remain 20:13:42 loss: 0.4887 Lr: 0.00279 [2024-02-18 18:11:46,630 INFO misc.py line 119 87073] Train: [50/100][13/1557] Data 0.005 (0.005) Batch 0.741 (0.900) Remain 19:50:19 loss: 0.1759 Lr: 0.00279 [2024-02-18 18:11:56,168 INFO misc.py line 119 87073] Train: [50/100][14/1557] Data 0.003 (0.005) Batch 9.538 (1.685) Remain 37:09:24 loss: 0.1242 Lr: 0.00279 [2024-02-18 18:11:57,124 INFO misc.py line 119 87073] Train: [50/100][15/1557] Data 0.004 (0.005) Batch 0.949 (1.623) Remain 35:48:12 loss: 0.5129 Lr: 0.00279 [2024-02-18 18:11:58,087 INFO misc.py line 119 87073] Train: [50/100][16/1557] Data 0.012 (0.005) Batch 0.970 (1.573) Remain 34:41:40 loss: 0.2992 Lr: 0.00279 [2024-02-18 18:11:59,075 INFO misc.py line 119 87073] Train: [50/100][17/1557] Data 0.004 (0.005) Batch 0.989 (1.532) Remain 33:46:26 loss: 0.4309 Lr: 0.00279 [2024-02-18 18:12:00,082 INFO misc.py line 119 87073] Train: [50/100][18/1557] Data 0.003 (0.005) Batch 1.006 (1.496) Remain 33:00:05 loss: 0.4283 Lr: 0.00279 [2024-02-18 18:12:00,872 INFO misc.py line 119 87073] Train: [50/100][19/1557] Data 0.004 (0.005) Batch 0.788 (1.452) Remain 32:01:27 loss: 0.3868 Lr: 0.00279 [2024-02-18 18:12:01,645 INFO misc.py line 119 87073] Train: [50/100][20/1557] Data 0.006 (0.005) Batch 0.769 (1.412) Remain 31:08:15 loss: 0.2745 Lr: 0.00279 [2024-02-18 18:12:02,726 INFO misc.py line 119 87073] Train: [50/100][21/1557] Data 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Train: [50/100][40/1557] Data 0.006 (0.007) Batch 0.693 (1.167) Remain 25:44:15 loss: 0.4413 Lr: 0.00279 [2024-02-18 18:12:21,581 INFO misc.py line 119 87073] Train: [50/100][41/1557] Data 0.004 (0.007) Batch 0.742 (1.156) Remain 25:29:25 loss: 0.2130 Lr: 0.00279 [2024-02-18 18:12:22,787 INFO misc.py line 119 87073] Train: [50/100][42/1557] Data 0.014 (0.007) Batch 1.192 (1.157) Remain 25:30:36 loss: 0.3172 Lr: 0.00279 [2024-02-18 18:12:23,741 INFO misc.py line 119 87073] Train: [50/100][43/1557] Data 0.028 (0.008) Batch 0.976 (1.153) Remain 25:24:36 loss: 0.5512 Lr: 0.00279 [2024-02-18 18:12:24,675 INFO misc.py line 119 87073] Train: [50/100][44/1557] Data 0.005 (0.008) Batch 0.934 (1.147) Remain 25:17:32 loss: 0.3727 Lr: 0.00279 [2024-02-18 18:12:25,807 INFO misc.py line 119 87073] Train: [50/100][45/1557] Data 0.005 (0.008) Batch 1.132 (1.147) Remain 25:17:02 loss: 0.4624 Lr: 0.00279 [2024-02-18 18:12:26,987 INFO misc.py line 119 87073] Train: [50/100][46/1557] Data 0.006 (0.008) 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Batch 0.914 (1.160) Remain 25:32:45 loss: 0.5980 Lr: 0.00278 [2024-02-18 18:13:55,414 INFO misc.py line 119 87073] Train: [50/100][122/1557] Data 0.005 (0.083) Batch 0.907 (1.158) Remain 25:29:55 loss: 0.3571 Lr: 0.00278 [2024-02-18 18:13:56,436 INFO misc.py line 119 87073] Train: [50/100][123/1557] Data 0.006 (0.083) Batch 1.017 (1.157) Remain 25:28:21 loss: 0.7101 Lr: 0.00278 [2024-02-18 18:13:57,217 INFO misc.py line 119 87073] Train: [50/100][124/1557] Data 0.010 (0.082) Batch 0.787 (1.154) Remain 25:24:18 loss: 0.3537 Lr: 0.00278 [2024-02-18 18:13:58,064 INFO misc.py line 119 87073] Train: [50/100][125/1557] Data 0.005 (0.081) Batch 0.848 (1.151) Remain 25:20:58 loss: 0.2915 Lr: 0.00278 [2024-02-18 18:14:06,128 INFO misc.py line 119 87073] Train: [50/100][126/1557] Data 0.003 (0.081) Batch 8.064 (1.207) Remain 26:35:12 loss: 0.3505 Lr: 0.00278 [2024-02-18 18:14:07,082 INFO misc.py line 119 87073] Train: [50/100][127/1557] Data 0.004 (0.080) Batch 0.947 (1.205) Remain 26:32:25 loss: 0.7986 Lr: 0.00278 [2024-02-18 18:14:08,029 INFO misc.py line 119 87073] Train: [50/100][128/1557] Data 0.011 (0.080) Batch 0.954 (1.203) Remain 26:29:44 loss: 0.2565 Lr: 0.00278 [2024-02-18 18:14:08,989 INFO misc.py line 119 87073] Train: [50/100][129/1557] Data 0.004 (0.079) Batch 0.960 (1.201) Remain 26:27:10 loss: 0.2914 Lr: 0.00278 [2024-02-18 18:14:09,803 INFO misc.py line 119 87073] Train: [50/100][130/1557] Data 0.005 (0.078) Batch 0.808 (1.198) Remain 26:23:03 loss: 0.2055 Lr: 0.00278 [2024-02-18 18:14:10,550 INFO misc.py line 119 87073] Train: [50/100][131/1557] Data 0.010 (0.078) Batch 0.754 (1.195) Remain 26:18:27 loss: 0.2610 Lr: 0.00278 [2024-02-18 18:14:11,297 INFO misc.py line 119 87073] Train: [50/100][132/1557] Data 0.003 (0.077) Batch 0.735 (1.191) Remain 26:13:43 loss: 0.4100 Lr: 0.00278 [2024-02-18 18:14:12,435 INFO misc.py line 119 87073] Train: [50/100][133/1557] Data 0.015 (0.077) Batch 1.141 (1.191) Remain 26:13:11 loss: 0.1235 Lr: 0.00278 [2024-02-18 18:14:13,306 INFO misc.py line 119 87073] Train: [50/100][134/1557] Data 0.013 (0.076) Batch 0.877 (1.188) Remain 26:10:00 loss: 0.5699 Lr: 0.00278 [2024-02-18 18:14:14,334 INFO misc.py line 119 87073] Train: [50/100][135/1557] Data 0.007 (0.076) Batch 1.030 (1.187) Remain 26:08:24 loss: 0.3096 Lr: 0.00278 [2024-02-18 18:14:15,196 INFO misc.py line 119 87073] Train: [50/100][136/1557] Data 0.003 (0.075) Batch 0.861 (1.185) Remain 26:05:09 loss: 0.3751 Lr: 0.00278 [2024-02-18 18:14:16,008 INFO misc.py line 119 87073] Train: [50/100][137/1557] Data 0.006 (0.075) Batch 0.813 (1.182) Remain 26:01:28 loss: 0.1222 Lr: 0.00278 [2024-02-18 18:14:16,744 INFO misc.py line 119 87073] Train: [50/100][138/1557] Data 0.003 (0.074) Batch 0.736 (1.179) Remain 25:57:05 loss: 0.1150 Lr: 0.00278 [2024-02-18 18:14:17,465 INFO misc.py line 119 87073] Train: [50/100][139/1557] Data 0.003 (0.074) Batch 0.713 (1.175) Remain 25:52:32 loss: 0.3575 Lr: 0.00278 [2024-02-18 18:14:18,610 INFO misc.py line 119 87073] Train: [50/100][140/1557] Data 0.011 (0.073) Batch 1.135 (1.175) Remain 25:52:08 loss: 0.1537 Lr: 0.00278 [2024-02-18 18:14:19,480 INFO misc.py line 119 87073] Train: [50/100][141/1557] Data 0.022 (0.073) Batch 0.887 (1.173) Remain 25:49:22 loss: 0.2893 Lr: 0.00278 [2024-02-18 18:14:20,442 INFO misc.py line 119 87073] Train: [50/100][142/1557] Data 0.004 (0.072) Batch 0.962 (1.171) Remain 25:47:21 loss: 0.2452 Lr: 0.00278 [2024-02-18 18:14:21,502 INFO misc.py line 119 87073] Train: [50/100][143/1557] Data 0.004 (0.072) Batch 1.060 (1.170) Remain 25:46:16 loss: 0.0780 Lr: 0.00278 [2024-02-18 18:14:22,415 INFO misc.py line 119 87073] Train: [50/100][144/1557] Data 0.004 (0.071) Batch 0.914 (1.169) Remain 25:43:51 loss: 0.1912 Lr: 0.00278 [2024-02-18 18:14:23,148 INFO misc.py line 119 87073] Train: [50/100][145/1557] Data 0.003 (0.071) Batch 0.722 (1.166) Remain 25:39:40 loss: 0.4271 Lr: 0.00278 [2024-02-18 18:14:23,879 INFO misc.py line 119 87073] Train: [50/100][146/1557] Data 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line 119 87073] Train: [50/100][165/1557] Data 0.004 (0.063) Batch 0.922 (1.137) Remain 25:01:06 loss: 0.4680 Lr: 0.00278 [2024-02-18 18:14:42,484 INFO misc.py line 119 87073] Train: [50/100][166/1557] Data 0.011 (0.063) Batch 0.719 (1.134) Remain 24:57:42 loss: 0.4280 Lr: 0.00278 [2024-02-18 18:14:43,263 INFO misc.py line 119 87073] Train: [50/100][167/1557] Data 0.005 (0.063) Batch 0.770 (1.132) Remain 24:54:45 loss: 0.1587 Lr: 0.00278 [2024-02-18 18:14:44,478 INFO misc.py line 119 87073] Train: [50/100][168/1557] Data 0.013 (0.062) Batch 1.216 (1.132) Remain 24:55:24 loss: 0.1383 Lr: 0.00278 [2024-02-18 18:14:45,333 INFO misc.py line 119 87073] Train: [50/100][169/1557] Data 0.013 (0.062) Batch 0.862 (1.131) Remain 24:53:14 loss: 0.1933 Lr: 0.00278 [2024-02-18 18:14:46,345 INFO misc.py line 119 87073] Train: [50/100][170/1557] Data 0.007 (0.062) Batch 1.014 (1.130) Remain 24:52:17 loss: 0.6356 Lr: 0.00278 [2024-02-18 18:14:47,243 INFO misc.py line 119 87073] Train: 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Batch 1.078 (1.169) Remain 25:43:02 loss: 0.3067 Lr: 0.00278 [2024-02-18 18:15:01,997 INFO misc.py line 119 87073] Train: [50/100][178/1557] Data 0.013 (0.088) Batch 1.032 (1.168) Remain 25:41:59 loss: 0.4331 Lr: 0.00278 [2024-02-18 18:15:02,831 INFO misc.py line 119 87073] Train: [50/100][179/1557] Data 0.010 (0.087) Batch 0.839 (1.166) Remain 25:39:30 loss: 0.1590 Lr: 0.00278 [2024-02-18 18:15:04,932 INFO misc.py line 119 87073] Train: [50/100][180/1557] Data 1.105 (0.093) Batch 2.099 (1.171) Remain 25:46:27 loss: 0.2921 Lr: 0.00278 [2024-02-18 18:15:05,748 INFO misc.py line 119 87073] Train: [50/100][181/1557] Data 0.006 (0.093) Batch 0.817 (1.169) Remain 25:43:48 loss: 0.3841 Lr: 0.00278 [2024-02-18 18:15:13,414 INFO misc.py line 119 87073] Train: [50/100][182/1557] Data 0.004 (0.092) Batch 7.666 (1.205) Remain 26:31:43 loss: 0.1382 Lr: 0.00278 [2024-02-18 18:15:14,333 INFO misc.py line 119 87073] Train: [50/100][183/1557] Data 0.004 (0.092) Batch 0.918 (1.204) Remain 26:29:35 loss: 0.4602 Lr: 0.00278 [2024-02-18 18:15:15,384 INFO misc.py line 119 87073] Train: [50/100][184/1557] Data 0.006 (0.091) Batch 1.042 (1.203) Remain 26:28:23 loss: 0.3533 Lr: 0.00278 [2024-02-18 18:15:16,238 INFO misc.py line 119 87073] Train: [50/100][185/1557] Data 0.014 (0.091) Batch 0.863 (1.201) Remain 26:25:54 loss: 0.4645 Lr: 0.00278 [2024-02-18 18:15:17,189 INFO misc.py line 119 87073] Train: [50/100][186/1557] Data 0.006 (0.090) Batch 0.952 (1.200) Remain 26:24:05 loss: 0.6629 Lr: 0.00278 [2024-02-18 18:15:17,984 INFO misc.py line 119 87073] Train: [50/100][187/1557] Data 0.005 (0.090) Batch 0.794 (1.198) Remain 26:21:09 loss: 0.3764 Lr: 0.00278 [2024-02-18 18:15:18,687 INFO misc.py line 119 87073] Train: [50/100][188/1557] Data 0.005 (0.089) Batch 0.697 (1.195) Remain 26:17:33 loss: 0.1677 Lr: 0.00278 [2024-02-18 18:15:19,795 INFO misc.py line 119 87073] Train: [50/100][189/1557] Data 0.012 (0.089) Batch 1.109 (1.194) Remain 26:16:55 loss: 0.2184 Lr: 0.00278 [2024-02-18 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25:46:50 loss: 0.3133 Lr: 0.00278 [2024-02-18 18:15:38,627 INFO misc.py line 119 87073] Train: [50/100][209/1557] Data 0.006 (0.081) Batch 0.757 (1.170) Remain 25:44:09 loss: 0.4456 Lr: 0.00278 [2024-02-18 18:15:39,840 INFO misc.py line 119 87073] Train: [50/100][210/1557] Data 0.006 (0.081) Batch 1.206 (1.170) Remain 25:44:22 loss: 0.2243 Lr: 0.00278 [2024-02-18 18:15:40,776 INFO misc.py line 119 87073] Train: [50/100][211/1557] Data 0.013 (0.080) Batch 0.944 (1.169) Remain 25:42:55 loss: 0.8724 Lr: 0.00278 [2024-02-18 18:15:41,695 INFO misc.py line 119 87073] Train: [50/100][212/1557] Data 0.005 (0.080) Batch 0.920 (1.168) Remain 25:41:19 loss: 0.4116 Lr: 0.00278 [2024-02-18 18:15:42,761 INFO misc.py line 119 87073] Train: [50/100][213/1557] Data 0.004 (0.080) Batch 1.065 (1.167) Remain 25:40:39 loss: 0.4296 Lr: 0.00278 [2024-02-18 18:15:43,685 INFO misc.py line 119 87073] Train: [50/100][214/1557] Data 0.005 (0.079) Batch 0.926 (1.166) Remain 25:39:08 loss: 0.0932 Lr: 0.00278 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Batch 0.955 (1.180) Remain 25:56:43 loss: 0.4219 Lr: 0.00278 [2024-02-18 18:16:09,935 INFO misc.py line 119 87073] Train: [50/100][234/1557] Data 0.005 (0.094) Batch 0.964 (1.179) Remain 25:55:28 loss: 0.1680 Lr: 0.00278 [2024-02-18 18:16:10,856 INFO misc.py line 119 87073] Train: [50/100][235/1557] Data 0.004 (0.093) Batch 0.921 (1.178) Remain 25:53:58 loss: 0.3361 Lr: 0.00278 [2024-02-18 18:16:11,581 INFO misc.py line 119 87073] Train: [50/100][236/1557] Data 0.004 (0.093) Batch 0.725 (1.176) Remain 25:51:23 loss: 0.8536 Lr: 0.00278 [2024-02-18 18:16:12,389 INFO misc.py line 119 87073] Train: [50/100][237/1557] Data 0.004 (0.092) Batch 0.798 (1.174) Remain 25:49:14 loss: 0.1415 Lr: 0.00278 [2024-02-18 18:16:21,531 INFO misc.py line 119 87073] Train: [50/100][238/1557] Data 0.014 (0.092) Batch 9.153 (1.208) Remain 26:34:01 loss: 0.1263 Lr: 0.00278 [2024-02-18 18:16:22,445 INFO misc.py line 119 87073] Train: [50/100][239/1557] Data 0.004 (0.092) Batch 0.910 (1.207) Remain 26:32:20 loss: 0.1809 Lr: 0.00278 [2024-02-18 18:16:23,542 INFO misc.py line 119 87073] Train: [50/100][240/1557] Data 0.009 (0.091) Batch 1.096 (1.206) Remain 26:31:42 loss: 0.1327 Lr: 0.00278 [2024-02-18 18:16:24,652 INFO misc.py line 119 87073] Train: [50/100][241/1557] Data 0.009 (0.091) Batch 1.103 (1.206) Remain 26:31:06 loss: 0.5154 Lr: 0.00278 [2024-02-18 18:16:25,818 INFO misc.py line 119 87073] Train: [50/100][242/1557] Data 0.015 (0.091) Batch 1.172 (1.206) Remain 26:30:54 loss: 0.3018 Lr: 0.00278 [2024-02-18 18:16:26,581 INFO misc.py line 119 87073] Train: [50/100][243/1557] Data 0.010 (0.090) Batch 0.768 (1.204) Remain 26:28:28 loss: 0.1849 Lr: 0.00278 [2024-02-18 18:16:27,311 INFO misc.py line 119 87073] Train: [50/100][244/1557] Data 0.005 (0.090) Batch 0.730 (1.202) Remain 26:25:51 loss: 0.2525 Lr: 0.00278 [2024-02-18 18:16:28,362 INFO misc.py line 119 87073] Train: [50/100][245/1557] Data 0.005 (0.090) Batch 1.050 (1.201) Remain 26:25:00 loss: 0.1535 Lr: 0.00278 [2024-02-18 18:16:29,440 INFO misc.py line 119 87073] Train: [50/100][246/1557] Data 0.006 (0.089) Batch 1.079 (1.201) Remain 26:24:19 loss: 0.2998 Lr: 0.00278 [2024-02-18 18:16:30,395 INFO misc.py line 119 87073] Train: [50/100][247/1557] Data 0.005 (0.089) Batch 0.954 (1.200) Remain 26:22:58 loss: 0.6114 Lr: 0.00278 [2024-02-18 18:16:31,527 INFO misc.py line 119 87073] Train: [50/100][248/1557] Data 0.007 (0.089) Batch 1.133 (1.200) Remain 26:22:35 loss: 0.3897 Lr: 0.00278 [2024-02-18 18:16:32,452 INFO misc.py line 119 87073] Train: [50/100][249/1557] Data 0.005 (0.088) Batch 0.926 (1.198) Remain 26:21:06 loss: 0.2854 Lr: 0.00278 [2024-02-18 18:16:33,197 INFO misc.py line 119 87073] Train: [50/100][250/1557] Data 0.004 (0.088) Batch 0.739 (1.197) Remain 26:18:37 loss: 0.3514 Lr: 0.00278 [2024-02-18 18:16:33,947 INFO misc.py line 119 87073] Train: [50/100][251/1557] Data 0.010 (0.088) Batch 0.756 (1.195) Remain 26:16:15 loss: 0.3431 Lr: 0.00278 [2024-02-18 18:16:35,087 INFO misc.py line 119 87073] Train: [50/100][252/1557] Data 0.004 (0.087) Batch 1.141 (1.195) Remain 26:15:57 loss: 0.3779 Lr: 0.00278 [2024-02-18 18:16:36,040 INFO misc.py line 119 87073] Train: [50/100][253/1557] Data 0.004 (0.087) Batch 0.953 (1.194) Remain 26:14:39 loss: 0.3719 Lr: 0.00278 [2024-02-18 18:16:37,090 INFO misc.py line 119 87073] Train: [50/100][254/1557] Data 0.004 (0.087) Batch 1.050 (1.193) Remain 26:13:53 loss: 0.8150 Lr: 0.00278 [2024-02-18 18:16:38,276 INFO misc.py line 119 87073] Train: [50/100][255/1557] Data 0.004 (0.086) Batch 1.185 (1.193) Remain 26:13:49 loss: 0.7677 Lr: 0.00278 [2024-02-18 18:16:39,243 INFO misc.py line 119 87073] Train: [50/100][256/1557] Data 0.005 (0.086) Batch 0.969 (1.192) Remain 26:12:38 loss: 0.3941 Lr: 0.00278 [2024-02-18 18:16:40,079 INFO misc.py line 119 87073] Train: [50/100][257/1557] Data 0.003 (0.086) Batch 0.835 (1.191) Remain 26:10:45 loss: 0.3105 Lr: 0.00278 [2024-02-18 18:16:40,987 INFO misc.py line 119 87073] Train: [50/100][258/1557] Data 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26:02:52 loss: 0.4867 Lr: 0.00278 [2024-02-18 18:16:47,661 INFO misc.py line 119 87073] Train: [50/100][265/1557] Data 0.009 (0.083) Batch 0.779 (1.183) Remain 26:00:48 loss: 0.2884 Lr: 0.00278 [2024-02-18 18:16:48,826 INFO misc.py line 119 87073] Train: [50/100][266/1557] Data 0.004 (0.083) Batch 1.165 (1.183) Remain 26:00:42 loss: 0.2517 Lr: 0.00278 [2024-02-18 18:16:49,940 INFO misc.py line 119 87073] Train: [50/100][267/1557] Data 0.004 (0.083) Batch 1.115 (1.183) Remain 26:00:20 loss: 0.1892 Lr: 0.00278 [2024-02-18 18:16:50,959 INFO misc.py line 119 87073] Train: [50/100][268/1557] Data 0.004 (0.082) Batch 1.018 (1.182) Remain 25:59:30 loss: 0.3069 Lr: 0.00278 [2024-02-18 18:16:51,756 INFO misc.py line 119 87073] Train: [50/100][269/1557] Data 0.005 (0.082) Batch 0.797 (1.181) Remain 25:57:34 loss: 0.5465 Lr: 0.00278 [2024-02-18 18:16:52,874 INFO misc.py line 119 87073] Train: [50/100][270/1557] Data 0.005 (0.082) Batch 1.114 (1.181) Remain 25:57:13 loss: 0.3751 Lr: 0.00278 [2024-02-18 18:16:53,648 INFO misc.py line 119 87073] Train: [50/100][271/1557] Data 0.009 (0.082) Batch 0.779 (1.179) Remain 25:55:13 loss: 0.3535 Lr: 0.00278 [2024-02-18 18:16:54,403 INFO misc.py line 119 87073] Train: [50/100][272/1557] Data 0.004 (0.081) Batch 0.754 (1.178) Remain 25:53:07 loss: 0.3649 Lr: 0.00278 [2024-02-18 18:16:55,658 INFO misc.py line 119 87073] Train: [50/100][273/1557] Data 0.004 (0.081) Batch 1.252 (1.178) Remain 25:53:27 loss: 0.2366 Lr: 0.00278 [2024-02-18 18:16:56,672 INFO misc.py line 119 87073] Train: [50/100][274/1557] Data 0.008 (0.081) Batch 1.018 (1.177) Remain 25:52:39 loss: 0.2333 Lr: 0.00278 [2024-02-18 18:16:57,490 INFO misc.py line 119 87073] Train: [50/100][275/1557] Data 0.005 (0.080) Batch 0.818 (1.176) Remain 25:50:54 loss: 0.4043 Lr: 0.00278 [2024-02-18 18:16:58,435 INFO misc.py line 119 87073] Train: [50/100][276/1557] Data 0.004 (0.080) Batch 0.945 (1.175) Remain 25:49:46 loss: 0.5352 Lr: 0.00278 [2024-02-18 18:16:59,380 INFO misc.py line 119 87073] Train: [50/100][277/1557] Data 0.004 (0.080) Batch 0.943 (1.174) Remain 25:48:38 loss: 0.3581 Lr: 0.00278 [2024-02-18 18:17:00,169 INFO misc.py line 119 87073] Train: [50/100][278/1557] Data 0.005 (0.080) Batch 0.791 (1.173) Remain 25:46:46 loss: 0.2343 Lr: 0.00278 [2024-02-18 18:17:00,952 INFO misc.py line 119 87073] Train: [50/100][279/1557] Data 0.003 (0.079) Batch 0.782 (1.171) Remain 25:44:53 loss: 0.2642 Lr: 0.00278 [2024-02-18 18:17:02,196 INFO misc.py line 119 87073] Train: [50/100][280/1557] Data 0.004 (0.079) Batch 1.241 (1.172) Remain 25:45:12 loss: 0.1772 Lr: 0.00278 [2024-02-18 18:17:03,055 INFO misc.py line 119 87073] Train: [50/100][281/1557] Data 0.008 (0.079) Batch 0.861 (1.171) Remain 25:43:42 loss: 0.2183 Lr: 0.00278 [2024-02-18 18:17:03,809 INFO misc.py line 119 87073] Train: [50/100][282/1557] Data 0.006 (0.079) Batch 0.753 (1.169) Remain 25:41:42 loss: 0.1849 Lr: 0.00278 [2024-02-18 18:17:04,639 INFO misc.py line 119 87073] Train: 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Batch 1.079 (1.185) Remain 26:02:45 loss: 0.3675 Lr: 0.00278 [2024-02-18 18:17:17,727 INFO misc.py line 119 87073] Train: [50/100][290/1557] Data 0.006 (0.092) Batch 1.142 (1.185) Remain 26:02:32 loss: 0.3155 Lr: 0.00278 [2024-02-18 18:17:18,532 INFO misc.py line 119 87073] Train: [50/100][291/1557] Data 0.004 (0.091) Batch 0.805 (1.184) Remain 26:00:47 loss: 0.4062 Lr: 0.00278 [2024-02-18 18:17:19,193 INFO misc.py line 119 87073] Train: [50/100][292/1557] Data 0.004 (0.091) Batch 0.651 (1.182) Remain 25:58:20 loss: 0.3017 Lr: 0.00278 [2024-02-18 18:17:19,932 INFO misc.py line 119 87073] Train: [50/100][293/1557] Data 0.013 (0.091) Batch 0.749 (1.180) Remain 25:56:20 loss: 0.1859 Lr: 0.00278 [2024-02-18 18:17:26,553 INFO misc.py line 119 87073] Train: [50/100][294/1557] Data 0.004 (0.091) Batch 6.620 (1.199) Remain 26:20:58 loss: 0.1001 Lr: 0.00277 [2024-02-18 18:17:27,482 INFO misc.py line 119 87073] Train: [50/100][295/1557] Data 0.005 (0.090) Batch 0.920 (1.198) Remain 26:19:41 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Batch 1.006 (1.183) Remain 25:58:57 loss: 0.7771 Lr: 0.00277 [2024-02-18 18:18:23,113 INFO misc.py line 119 87073] Train: [50/100][346/1557] Data 0.005 (0.091) Batch 0.860 (1.182) Remain 25:57:42 loss: 0.1599 Lr: 0.00277 [2024-02-18 18:18:24,120 INFO misc.py line 119 87073] Train: [50/100][347/1557] Data 0.004 (0.091) Batch 1.006 (1.182) Remain 25:57:00 loss: 0.2618 Lr: 0.00277 [2024-02-18 18:18:24,874 INFO misc.py line 119 87073] Train: [50/100][348/1557] Data 0.005 (0.091) Batch 0.755 (1.180) Remain 25:55:21 loss: 0.1546 Lr: 0.00277 [2024-02-18 18:18:25,609 INFO misc.py line 119 87073] Train: [50/100][349/1557] Data 0.004 (0.091) Batch 0.735 (1.179) Remain 25:53:38 loss: 0.2947 Lr: 0.00277 [2024-02-18 18:18:31,847 INFO misc.py line 119 87073] Train: [50/100][350/1557] Data 0.004 (0.090) Batch 6.238 (1.194) Remain 26:12:49 loss: 0.0855 Lr: 0.00277 [2024-02-18 18:18:33,095 INFO misc.py line 119 87073] Train: [50/100][351/1557] Data 0.004 (0.090) Batch 1.238 (1.194) Remain 26:12:58 loss: 0.3589 Lr: 0.00277 [2024-02-18 18:18:34,179 INFO misc.py line 119 87073] Train: [50/100][352/1557] Data 0.014 (0.090) Batch 1.084 (1.194) Remain 26:12:32 loss: 0.4258 Lr: 0.00277 [2024-02-18 18:18:35,132 INFO misc.py line 119 87073] Train: [50/100][353/1557] Data 0.014 (0.090) Batch 0.963 (1.193) Remain 26:11:39 loss: 0.0927 Lr: 0.00277 [2024-02-18 18:18:36,007 INFO misc.py line 119 87073] Train: [50/100][354/1557] Data 0.004 (0.089) Batch 0.874 (1.192) Remain 26:10:26 loss: 0.5495 Lr: 0.00277 [2024-02-18 18:18:38,174 INFO misc.py line 119 87073] Train: [50/100][355/1557] Data 0.792 (0.091) Batch 2.135 (1.195) Remain 26:13:57 loss: 0.2916 Lr: 0.00277 [2024-02-18 18:18:38,970 INFO misc.py line 119 87073] Train: [50/100][356/1557] Data 0.037 (0.091) Batch 0.827 (1.194) Remain 26:12:33 loss: 0.3975 Lr: 0.00277 [2024-02-18 18:18:40,042 INFO misc.py line 119 87073] Train: [50/100][357/1557] Data 0.004 (0.091) Batch 1.071 (1.193) Remain 26:12:05 loss: 0.3372 Lr: 0.00277 [2024-02-18 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Batch 1.010 (1.184) Remain 25:59:34 loss: 0.5439 Lr: 0.00277 [2024-02-18 18:19:29,957 INFO misc.py line 119 87073] Train: [50/100][402/1557] Data 0.004 (0.092) Batch 0.934 (1.184) Remain 25:58:43 loss: 0.5186 Lr: 0.00277 [2024-02-18 18:19:30,720 INFO misc.py line 119 87073] Train: [50/100][403/1557] Data 0.004 (0.092) Batch 0.763 (1.183) Remain 25:57:18 loss: 0.2483 Lr: 0.00277 [2024-02-18 18:19:31,491 INFO misc.py line 119 87073] Train: [50/100][404/1557] Data 0.004 (0.092) Batch 0.763 (1.182) Remain 25:55:55 loss: 0.4741 Lr: 0.00277 [2024-02-18 18:19:32,180 INFO misc.py line 119 87073] Train: [50/100][405/1557] Data 0.012 (0.092) Batch 0.697 (1.180) Remain 25:54:18 loss: 0.5347 Lr: 0.00277 [2024-02-18 18:19:38,924 INFO misc.py line 119 87073] Train: [50/100][406/1557] Data 0.004 (0.092) Batch 6.745 (1.194) Remain 26:12:28 loss: 0.0848 Lr: 0.00277 [2024-02-18 18:19:40,065 INFO misc.py line 119 87073] Train: [50/100][407/1557] Data 0.004 (0.091) Batch 1.131 (1.194) Remain 26:12:14 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Batch 0.970 (1.184) Remain 25:58:16 loss: 0.2754 Lr: 0.00277 [2024-02-18 18:20:36,314 INFO misc.py line 119 87073] Train: [50/100][458/1557] Data 0.006 (0.090) Batch 1.026 (1.184) Remain 25:57:48 loss: 0.6124 Lr: 0.00277 [2024-02-18 18:20:37,410 INFO misc.py line 119 87073] Train: [50/100][459/1557] Data 0.005 (0.090) Batch 1.095 (1.184) Remain 25:57:31 loss: 0.5683 Lr: 0.00277 [2024-02-18 18:20:38,194 INFO misc.py line 119 87073] Train: [50/100][460/1557] Data 0.005 (0.090) Batch 0.785 (1.183) Remain 25:56:21 loss: 0.3222 Lr: 0.00277 [2024-02-18 18:20:38,954 INFO misc.py line 119 87073] Train: [50/100][461/1557] Data 0.003 (0.089) Batch 0.754 (1.182) Remain 25:55:06 loss: 0.4153 Lr: 0.00277 [2024-02-18 18:20:48,423 INFO misc.py line 119 87073] Train: [50/100][462/1557] Data 0.009 (0.089) Batch 9.474 (1.200) Remain 26:18:51 loss: 0.1120 Lr: 0.00277 [2024-02-18 18:20:49,348 INFO misc.py line 119 87073] Train: [50/100][463/1557] Data 0.005 (0.089) Batch 0.918 (1.199) Remain 26:18:01 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Batch 0.939 (1.188) Remain 26:02:12 loss: 0.1449 Lr: 0.00276 [2024-02-18 18:21:44,578 INFO misc.py line 119 87073] Train: [50/100][514/1557] Data 0.005 (0.089) Batch 1.023 (1.188) Remain 26:01:45 loss: 0.4244 Lr: 0.00276 [2024-02-18 18:21:45,598 INFO misc.py line 119 87073] Train: [50/100][515/1557] Data 0.004 (0.089) Batch 1.019 (1.187) Remain 26:01:18 loss: 0.4060 Lr: 0.00276 [2024-02-18 18:21:46,325 INFO misc.py line 119 87073] Train: [50/100][516/1557] Data 0.005 (0.089) Batch 0.726 (1.187) Remain 26:00:06 loss: 0.3256 Lr: 0.00276 [2024-02-18 18:21:47,071 INFO misc.py line 119 87073] Train: [50/100][517/1557] Data 0.006 (0.088) Batch 0.740 (1.186) Remain 25:58:56 loss: 0.1709 Lr: 0.00276 [2024-02-18 18:21:52,450 INFO misc.py line 119 87073] Train: [50/100][518/1557] Data 0.012 (0.088) Batch 5.386 (1.194) Remain 26:09:38 loss: 0.1029 Lr: 0.00276 [2024-02-18 18:21:53,336 INFO misc.py line 119 87073] Train: [50/100][519/1557] Data 0.005 (0.088) Batch 0.878 (1.193) Remain 26:08:49 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18:21:59,697 INFO misc.py line 119 87073] Train: [50/100][526/1557] Data 0.005 (0.087) Batch 0.889 (1.189) Remain 26:03:41 loss: 0.3649 Lr: 0.00276 [2024-02-18 18:22:00,615 INFO misc.py line 119 87073] Train: [50/100][527/1557] Data 0.004 (0.087) Batch 0.909 (1.189) Remain 26:02:58 loss: 0.4691 Lr: 0.00276 [2024-02-18 18:22:01,550 INFO misc.py line 119 87073] Train: [50/100][528/1557] Data 0.013 (0.087) Batch 0.944 (1.188) Remain 26:02:20 loss: 0.2726 Lr: 0.00276 [2024-02-18 18:22:02,597 INFO misc.py line 119 87073] Train: [50/100][529/1557] Data 0.004 (0.087) Batch 1.047 (1.188) Remain 26:01:58 loss: 0.2398 Lr: 0.00276 [2024-02-18 18:22:04,941 INFO misc.py line 119 87073] Train: [50/100][530/1557] Data 1.078 (0.088) Batch 2.343 (1.190) Remain 26:04:49 loss: 0.1722 Lr: 0.00276 [2024-02-18 18:22:05,732 INFO misc.py line 119 87073] Train: [50/100][531/1557] Data 0.006 (0.088) Batch 0.786 (1.190) Remain 26:03:48 loss: 0.3489 Lr: 0.00276 [2024-02-18 18:22:06,904 INFO misc.py line 119 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line 119 87073] Train: [50/100][557/1557] Data 0.004 (0.084) Batch 0.971 (1.179) Remain 25:49:34 loss: 0.0830 Lr: 0.00276 [2024-02-18 18:22:31,595 INFO misc.py line 119 87073] Train: [50/100][558/1557] Data 0.010 (0.084) Batch 0.718 (1.178) Remain 25:48:27 loss: 0.3799 Lr: 0.00276 [2024-02-18 18:22:32,381 INFO misc.py line 119 87073] Train: [50/100][559/1557] Data 0.005 (0.084) Batch 0.776 (1.178) Remain 25:47:29 loss: 0.4101 Lr: 0.00276 [2024-02-18 18:22:33,685 INFO misc.py line 119 87073] Train: [50/100][560/1557] Data 0.015 (0.084) Batch 1.308 (1.178) Remain 25:47:47 loss: 0.1526 Lr: 0.00276 [2024-02-18 18:22:34,747 INFO misc.py line 119 87073] Train: [50/100][561/1557] Data 0.012 (0.084) Batch 1.059 (1.178) Remain 25:47:29 loss: 0.5994 Lr: 0.00276 [2024-02-18 18:22:35,814 INFO misc.py line 119 87073] Train: [50/100][562/1557] Data 0.015 (0.084) Batch 1.067 (1.177) Remain 25:47:12 loss: 0.1738 Lr: 0.00276 [2024-02-18 18:22:36,677 INFO misc.py line 119 87073] Train: 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Batch 0.938 (1.187) Remain 25:59:33 loss: 0.4007 Lr: 0.00276 [2024-02-18 18:22:50,328 INFO misc.py line 119 87073] Train: [50/100][570/1557] Data 0.007 (0.091) Batch 0.904 (1.186) Remain 25:58:52 loss: 0.5223 Lr: 0.00276 [2024-02-18 18:22:51,293 INFO misc.py line 119 87073] Train: [50/100][571/1557] Data 0.005 (0.091) Batch 0.965 (1.186) Remain 25:58:20 loss: 0.4010 Lr: 0.00276 [2024-02-18 18:22:52,078 INFO misc.py line 119 87073] Train: [50/100][572/1557] Data 0.004 (0.091) Batch 0.783 (1.185) Remain 25:57:23 loss: 0.5513 Lr: 0.00276 [2024-02-18 18:22:52,857 INFO misc.py line 119 87073] Train: [50/100][573/1557] Data 0.007 (0.091) Batch 0.772 (1.185) Remain 25:56:25 loss: 0.1766 Lr: 0.00276 [2024-02-18 18:22:57,770 INFO misc.py line 119 87073] Train: [50/100][574/1557] Data 0.014 (0.091) Batch 4.922 (1.191) Remain 26:05:00 loss: 0.0880 Lr: 0.00276 [2024-02-18 18:22:58,861 INFO misc.py line 119 87073] Train: [50/100][575/1557] Data 0.005 (0.091) Batch 1.089 (1.191) Remain 26:04:44 loss: 0.3890 Lr: 0.00276 [2024-02-18 18:22:59,996 INFO misc.py line 119 87073] Train: [50/100][576/1557] Data 0.006 (0.091) Batch 1.137 (1.191) Remain 26:04:36 loss: 0.3652 Lr: 0.00276 [2024-02-18 18:23:00,908 INFO misc.py line 119 87073] Train: [50/100][577/1557] Data 0.004 (0.090) Batch 0.911 (1.190) Remain 26:03:56 loss: 0.2528 Lr: 0.00276 [2024-02-18 18:23:01,741 INFO misc.py line 119 87073] Train: [50/100][578/1557] Data 0.005 (0.090) Batch 0.832 (1.190) Remain 26:03:06 loss: 0.3373 Lr: 0.00276 [2024-02-18 18:23:02,500 INFO misc.py line 119 87073] Train: [50/100][579/1557] Data 0.006 (0.090) Batch 0.759 (1.189) Remain 26:02:06 loss: 0.2788 Lr: 0.00276 [2024-02-18 18:23:03,184 INFO misc.py line 119 87073] Train: [50/100][580/1557] Data 0.006 (0.090) Batch 0.686 (1.188) Remain 26:00:56 loss: 0.2227 Lr: 0.00276 [2024-02-18 18:23:04,235 INFO misc.py line 119 87073] Train: [50/100][581/1557] Data 0.004 (0.090) Batch 1.045 (1.188) Remain 26:00:35 loss: 0.1345 Lr: 0.00276 [2024-02-18 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Batch 0.954 (1.183) Remain 25:53:54 loss: 0.1779 Lr: 0.00276 [2024-02-18 18:23:54,598 INFO misc.py line 119 87073] Train: [50/100][626/1557] Data 0.006 (0.092) Batch 0.854 (1.183) Remain 25:53:11 loss: 0.4059 Lr: 0.00276 [2024-02-18 18:23:55,708 INFO misc.py line 119 87073] Train: [50/100][627/1557] Data 0.005 (0.091) Batch 1.110 (1.183) Remain 25:53:01 loss: 0.6045 Lr: 0.00276 [2024-02-18 18:23:56,498 INFO misc.py line 119 87073] Train: [50/100][628/1557] Data 0.004 (0.091) Batch 0.789 (1.182) Remain 25:52:10 loss: 0.2332 Lr: 0.00276 [2024-02-18 18:23:57,321 INFO misc.py line 119 87073] Train: [50/100][629/1557] Data 0.005 (0.091) Batch 0.824 (1.182) Remain 25:51:24 loss: 0.2655 Lr: 0.00276 [2024-02-18 18:24:02,410 INFO misc.py line 119 87073] Train: [50/100][630/1557] Data 0.005 (0.091) Batch 5.089 (1.188) Remain 25:59:34 loss: 0.0828 Lr: 0.00276 [2024-02-18 18:24:03,381 INFO misc.py line 119 87073] Train: [50/100][631/1557] Data 0.004 (0.091) Batch 0.964 (1.187) Remain 25:59:05 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18:24:10,024 INFO misc.py line 119 87073] Train: [50/100][638/1557] Data 0.012 (0.090) Batch 0.996 (1.185) Remain 25:55:30 loss: 0.2647 Lr: 0.00276 [2024-02-18 18:24:10,959 INFO misc.py line 119 87073] Train: [50/100][639/1557] Data 0.004 (0.090) Batch 0.933 (1.184) Remain 25:54:58 loss: 0.1763 Lr: 0.00276 [2024-02-18 18:24:11,997 INFO misc.py line 119 87073] Train: [50/100][640/1557] Data 0.006 (0.090) Batch 1.039 (1.184) Remain 25:54:38 loss: 0.3880 Lr: 0.00276 [2024-02-18 18:24:12,984 INFO misc.py line 119 87073] Train: [50/100][641/1557] Data 0.006 (0.090) Batch 0.988 (1.184) Remain 25:54:13 loss: 0.2591 Lr: 0.00276 [2024-02-18 18:24:13,703 INFO misc.py line 119 87073] Train: [50/100][642/1557] Data 0.005 (0.089) Batch 0.720 (1.183) Remain 25:53:15 loss: 0.3752 Lr: 0.00276 [2024-02-18 18:24:14,471 INFO misc.py line 119 87073] Train: [50/100][643/1557] Data 0.004 (0.089) Batch 0.766 (1.183) Remain 25:52:22 loss: 0.1876 Lr: 0.00276 [2024-02-18 18:24:15,636 INFO misc.py line 119 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Batch 0.888 (1.181) Remain 25:49:40 loss: 0.4582 Lr: 0.00275 [2024-02-18 18:24:59,265 INFO misc.py line 119 87073] Train: [50/100][682/1557] Data 0.006 (0.091) Batch 0.870 (1.181) Remain 25:49:02 loss: 0.3585 Lr: 0.00275 [2024-02-18 18:25:00,095 INFO misc.py line 119 87073] Train: [50/100][683/1557] Data 0.004 (0.091) Batch 0.828 (1.180) Remain 25:48:20 loss: 0.3941 Lr: 0.00275 [2024-02-18 18:25:00,865 INFO misc.py line 119 87073] Train: [50/100][684/1557] Data 0.006 (0.091) Batch 0.770 (1.179) Remain 25:47:32 loss: 0.0895 Lr: 0.00275 [2024-02-18 18:25:01,640 INFO misc.py line 119 87073] Train: [50/100][685/1557] Data 0.005 (0.091) Batch 0.769 (1.179) Remain 25:46:43 loss: 0.1537 Lr: 0.00275 [2024-02-18 18:25:07,309 INFO misc.py line 119 87073] Train: [50/100][686/1557] Data 0.011 (0.090) Batch 5.677 (1.185) Remain 25:55:21 loss: 0.1103 Lr: 0.00275 [2024-02-18 18:25:08,157 INFO misc.py line 119 87073] Train: [50/100][687/1557] Data 0.004 (0.090) Batch 0.843 (1.185) Remain 25:54:40 loss: 0.4642 Lr: 0.00275 [2024-02-18 18:25:09,221 INFO misc.py line 119 87073] Train: [50/100][688/1557] Data 0.010 (0.090) Batch 1.068 (1.185) Remain 25:54:25 loss: 0.5812 Lr: 0.00275 [2024-02-18 18:25:10,240 INFO misc.py line 119 87073] Train: [50/100][689/1557] Data 0.005 (0.090) Batch 1.011 (1.185) Remain 25:54:04 loss: 0.5266 Lr: 0.00275 [2024-02-18 18:25:11,483 INFO misc.py line 119 87073] Train: [50/100][690/1557] Data 0.012 (0.090) Batch 1.248 (1.185) Remain 25:54:10 loss: 0.5022 Lr: 0.00275 [2024-02-18 18:25:12,240 INFO misc.py line 119 87073] Train: [50/100][691/1557] Data 0.007 (0.090) Batch 0.760 (1.184) Remain 25:53:21 loss: 0.2209 Lr: 0.00275 [2024-02-18 18:25:12,995 INFO misc.py line 119 87073] Train: [50/100][692/1557] Data 0.004 (0.090) Batch 0.748 (1.183) Remain 25:52:30 loss: 0.1944 Lr: 0.00275 [2024-02-18 18:25:14,045 INFO misc.py line 119 87073] Train: [50/100][693/1557] Data 0.011 (0.090) Batch 1.052 (1.183) Remain 25:52:13 loss: 0.2302 Lr: 0.00275 [2024-02-18 18:25:15,137 INFO misc.py line 119 87073] Train: [50/100][694/1557] Data 0.009 (0.089) Batch 1.095 (1.183) Remain 25:52:02 loss: 0.4380 Lr: 0.00275 [2024-02-18 18:25:16,154 INFO misc.py line 119 87073] Train: [50/100][695/1557] Data 0.005 (0.089) Batch 1.007 (1.183) Remain 25:51:41 loss: 0.5248 Lr: 0.00275 [2024-02-18 18:25:17,111 INFO misc.py line 119 87073] Train: [50/100][696/1557] Data 0.015 (0.089) Batch 0.968 (1.183) Remain 25:51:15 loss: 0.5750 Lr: 0.00275 [2024-02-18 18:25:18,232 INFO misc.py line 119 87073] Train: [50/100][697/1557] Data 0.005 (0.089) Batch 1.122 (1.182) Remain 25:51:07 loss: 0.4655 Lr: 0.00275 [2024-02-18 18:25:19,005 INFO misc.py line 119 87073] Train: [50/100][698/1557] Data 0.003 (0.089) Batch 0.771 (1.182) Remain 25:50:20 loss: 0.2707 Lr: 0.00275 [2024-02-18 18:25:19,783 INFO misc.py line 119 87073] Train: [50/100][699/1557] Data 0.005 (0.089) Batch 0.777 (1.181) Remain 25:49:33 loss: 0.2856 Lr: 0.00275 [2024-02-18 18:25:20,972 INFO misc.py line 119 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[2024-02-18 18:25:40,575 INFO misc.py line 119 87073] Train: [50/100][719/1557] Data 0.012 (0.088) Batch 0.785 (1.177) Remain 25:43:58 loss: 0.3169 Lr: 0.00275 [2024-02-18 18:25:41,323 INFO misc.py line 119 87073] Train: [50/100][720/1557] Data 0.006 (0.088) Batch 0.750 (1.177) Remain 25:43:10 loss: 0.3666 Lr: 0.00275 [2024-02-18 18:25:42,576 INFO misc.py line 119 87073] Train: [50/100][721/1557] Data 0.004 (0.088) Batch 1.248 (1.177) Remain 25:43:16 loss: 0.1589 Lr: 0.00275 [2024-02-18 18:25:43,464 INFO misc.py line 119 87073] Train: [50/100][722/1557] Data 0.010 (0.088) Batch 0.893 (1.176) Remain 25:42:44 loss: 0.3836 Lr: 0.00275 [2024-02-18 18:25:44,355 INFO misc.py line 119 87073] Train: [50/100][723/1557] Data 0.005 (0.087) Batch 0.887 (1.176) Remain 25:42:11 loss: 0.3968 Lr: 0.00275 [2024-02-18 18:25:45,370 INFO misc.py line 119 87073] Train: [50/100][724/1557] Data 0.008 (0.087) Batch 1.018 (1.176) Remain 25:41:53 loss: 0.6088 Lr: 0.00275 [2024-02-18 18:25:46,253 INFO misc.py line 119 87073] Train: [50/100][725/1557] Data 0.006 (0.087) Batch 0.880 (1.175) Remain 25:41:19 loss: 0.3211 Lr: 0.00275 [2024-02-18 18:25:47,043 INFO misc.py line 119 87073] Train: [50/100][726/1557] Data 0.009 (0.087) Batch 0.795 (1.175) Remain 25:40:37 loss: 0.2380 Lr: 0.00275 [2024-02-18 18:25:47,741 INFO misc.py line 119 87073] Train: [50/100][727/1557] Data 0.004 (0.087) Batch 0.696 (1.174) Remain 25:39:44 loss: 0.3774 Lr: 0.00275 [2024-02-18 18:25:48,949 INFO misc.py line 119 87073] Train: [50/100][728/1557] Data 0.005 (0.087) Batch 1.209 (1.174) Remain 25:39:46 loss: 0.1272 Lr: 0.00275 [2024-02-18 18:25:49,926 INFO misc.py line 119 87073] Train: [50/100][729/1557] Data 0.005 (0.087) Batch 0.977 (1.174) Remain 25:39:24 loss: 0.4073 Lr: 0.00275 [2024-02-18 18:25:50,875 INFO misc.py line 119 87073] Train: [50/100][730/1557] Data 0.006 (0.087) Batch 0.950 (1.174) Remain 25:38:58 loss: 0.3737 Lr: 0.00275 [2024-02-18 18:25:51,890 INFO misc.py line 119 87073] Train: 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Batch 0.986 (1.182) Remain 25:49:19 loss: 0.2091 Lr: 0.00275 [2024-02-18 18:26:05,847 INFO misc.py line 119 87073] Train: [50/100][738/1557] Data 0.006 (0.092) Batch 0.891 (1.181) Remain 25:48:46 loss: 0.5458 Lr: 0.00275 [2024-02-18 18:26:06,876 INFO misc.py line 119 87073] Train: [50/100][739/1557] Data 0.004 (0.091) Batch 1.028 (1.181) Remain 25:48:29 loss: 0.4510 Lr: 0.00275 [2024-02-18 18:26:07,636 INFO misc.py line 119 87073] Train: [50/100][740/1557] Data 0.005 (0.091) Batch 0.754 (1.180) Remain 25:47:42 loss: 0.5134 Lr: 0.00275 [2024-02-18 18:26:08,380 INFO misc.py line 119 87073] Train: [50/100][741/1557] Data 0.011 (0.091) Batch 0.750 (1.180) Remain 25:46:55 loss: 0.1977 Lr: 0.00275 [2024-02-18 18:26:15,809 INFO misc.py line 119 87073] Train: [50/100][742/1557] Data 0.004 (0.091) Batch 7.428 (1.188) Remain 25:57:59 loss: 0.0878 Lr: 0.00275 [2024-02-18 18:26:16,810 INFO misc.py line 119 87073] Train: [50/100][743/1557] Data 0.006 (0.091) Batch 1.002 (1.188) Remain 25:57:38 loss: 0.1616 Lr: 0.00275 [2024-02-18 18:26:18,124 INFO misc.py line 119 87073] Train: [50/100][744/1557] Data 0.005 (0.091) Batch 1.313 (1.188) Remain 25:57:50 loss: 0.1418 Lr: 0.00275 [2024-02-18 18:26:19,089 INFO misc.py line 119 87073] Train: [50/100][745/1557] Data 0.005 (0.091) Batch 0.965 (1.188) Remain 25:57:25 loss: 0.3093 Lr: 0.00275 [2024-02-18 18:26:20,164 INFO misc.py line 119 87073] Train: [50/100][746/1557] Data 0.005 (0.091) Batch 1.077 (1.188) Remain 25:57:12 loss: 0.6938 Lr: 0.00275 [2024-02-18 18:26:20,940 INFO misc.py line 119 87073] Train: [50/100][747/1557] Data 0.004 (0.091) Batch 0.774 (1.187) Remain 25:56:27 loss: 0.4708 Lr: 0.00275 [2024-02-18 18:26:21,707 INFO misc.py line 119 87073] Train: [50/100][748/1557] Data 0.005 (0.090) Batch 0.768 (1.187) Remain 25:55:42 loss: 0.3293 Lr: 0.00275 [2024-02-18 18:26:22,792 INFO misc.py line 119 87073] Train: [50/100][749/1557] Data 0.005 (0.090) Batch 1.079 (1.187) Remain 25:55:29 loss: 0.1100 Lr: 0.00275 [2024-02-18 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Batch 1.111 (1.183) Remain 25:49:47 loss: 0.6133 Lr: 0.00275 [2024-02-18 18:27:13,203 INFO misc.py line 119 87073] Train: [50/100][794/1557] Data 0.004 (0.092) Batch 1.129 (1.183) Remain 25:49:40 loss: 0.4847 Lr: 0.00275 [2024-02-18 18:27:14,186 INFO misc.py line 119 87073] Train: [50/100][795/1557] Data 0.005 (0.091) Batch 0.982 (1.183) Remain 25:49:19 loss: 1.5872 Lr: 0.00275 [2024-02-18 18:27:14,943 INFO misc.py line 119 87073] Train: [50/100][796/1557] Data 0.005 (0.091) Batch 0.758 (1.182) Remain 25:48:36 loss: 0.2691 Lr: 0.00275 [2024-02-18 18:27:15,699 INFO misc.py line 119 87073] Train: [50/100][797/1557] Data 0.006 (0.091) Batch 0.751 (1.181) Remain 25:47:52 loss: 0.3295 Lr: 0.00275 [2024-02-18 18:27:24,106 INFO misc.py line 119 87073] Train: [50/100][798/1557] Data 0.009 (0.091) Batch 8.412 (1.191) Remain 25:59:46 loss: 0.0930 Lr: 0.00275 [2024-02-18 18:27:25,463 INFO misc.py line 119 87073] Train: [50/100][799/1557] Data 0.005 (0.091) Batch 1.356 (1.191) Remain 26:00:01 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[2024-02-18 18:27:55,269 INFO misc.py line 119 87073] Train: [50/100][831/1557] Data 0.015 (0.088) Batch 0.778 (1.181) Remain 25:46:15 loss: 0.3791 Lr: 0.00275 [2024-02-18 18:27:55,984 INFO misc.py line 119 87073] Train: [50/100][832/1557] Data 0.004 (0.088) Batch 0.714 (1.180) Remain 25:45:30 loss: 0.3580 Lr: 0.00275 [2024-02-18 18:27:57,208 INFO misc.py line 119 87073] Train: [50/100][833/1557] Data 0.005 (0.088) Batch 1.219 (1.180) Remain 25:45:32 loss: 0.1615 Lr: 0.00275 [2024-02-18 18:27:58,183 INFO misc.py line 119 87073] Train: [50/100][834/1557] Data 0.011 (0.087) Batch 0.981 (1.180) Remain 25:45:12 loss: 0.2718 Lr: 0.00275 [2024-02-18 18:27:59,010 INFO misc.py line 119 87073] Train: [50/100][835/1557] Data 0.004 (0.087) Batch 0.828 (1.180) Remain 25:44:38 loss: 0.1643 Lr: 0.00275 [2024-02-18 18:28:00,041 INFO misc.py line 119 87073] Train: [50/100][836/1557] Data 0.004 (0.087) Batch 1.029 (1.179) Remain 25:44:23 loss: 0.1564 Lr: 0.00275 [2024-02-18 18:28:01,000 INFO misc.py 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Batch 0.929 (1.184) Remain 25:50:41 loss: 0.2707 Lr: 0.00275 [2024-02-18 18:28:20,470 INFO misc.py line 119 87073] Train: [50/100][850/1557] Data 0.005 (0.092) Batch 0.862 (1.184) Remain 25:50:10 loss: 0.4361 Lr: 0.00275 [2024-02-18 18:28:21,319 INFO misc.py line 119 87073] Train: [50/100][851/1557] Data 0.004 (0.091) Batch 0.846 (1.184) Remain 25:49:37 loss: 0.7347 Lr: 0.00275 [2024-02-18 18:28:22,079 INFO misc.py line 119 87073] Train: [50/100][852/1557] Data 0.007 (0.091) Batch 0.761 (1.183) Remain 25:48:57 loss: 0.1739 Lr: 0.00275 [2024-02-18 18:28:22,824 INFO misc.py line 119 87073] Train: [50/100][853/1557] Data 0.005 (0.091) Batch 0.738 (1.183) Remain 25:48:15 loss: 0.4978 Lr: 0.00275 [2024-02-18 18:28:28,799 INFO misc.py line 119 87073] Train: [50/100][854/1557] Data 0.012 (0.091) Batch 5.984 (1.188) Remain 25:55:37 loss: 0.0992 Lr: 0.00275 [2024-02-18 18:28:29,818 INFO misc.py line 119 87073] Train: [50/100][855/1557] Data 0.004 (0.091) Batch 1.014 (1.188) Remain 25:55:19 loss: 0.4439 Lr: 0.00275 [2024-02-18 18:28:30,641 INFO misc.py line 119 87073] Train: [50/100][856/1557] Data 0.009 (0.091) Batch 0.827 (1.188) Remain 25:54:45 loss: 0.5110 Lr: 0.00275 [2024-02-18 18:28:31,691 INFO misc.py line 119 87073] Train: [50/100][857/1557] Data 0.004 (0.091) Batch 1.051 (1.187) Remain 25:54:31 loss: 0.1013 Lr: 0.00275 [2024-02-18 18:28:32,640 INFO misc.py line 119 87073] Train: [50/100][858/1557] Data 0.004 (0.091) Batch 0.949 (1.187) Remain 25:54:08 loss: 0.2430 Lr: 0.00275 [2024-02-18 18:28:33,357 INFO misc.py line 119 87073] Train: [50/100][859/1557] Data 0.003 (0.091) Batch 0.701 (1.187) Remain 25:53:22 loss: 0.2760 Lr: 0.00275 [2024-02-18 18:28:34,157 INFO misc.py line 119 87073] Train: [50/100][860/1557] Data 0.019 (0.091) Batch 0.814 (1.186) Remain 25:52:47 loss: 0.1742 Lr: 0.00275 [2024-02-18 18:28:35,231 INFO misc.py line 119 87073] Train: [50/100][861/1557] Data 0.005 (0.090) Batch 1.074 (1.186) Remain 25:52:36 loss: 0.1855 Lr: 0.00275 [2024-02-18 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Batch 0.912 (1.183) Remain 25:47:57 loss: 0.4511 Lr: 0.00274 [2024-02-18 18:29:25,693 INFO misc.py line 119 87073] Train: [50/100][906/1557] Data 0.004 (0.093) Batch 0.877 (1.183) Remain 25:47:29 loss: 0.7355 Lr: 0.00274 [2024-02-18 18:29:26,577 INFO misc.py line 119 87073] Train: [50/100][907/1557] Data 0.006 (0.092) Batch 0.886 (1.182) Remain 25:47:02 loss: 0.3862 Lr: 0.00274 [2024-02-18 18:29:27,279 INFO misc.py line 119 87073] Train: [50/100][908/1557] Data 0.004 (0.092) Batch 0.700 (1.182) Remain 25:46:19 loss: 0.4712 Lr: 0.00274 [2024-02-18 18:29:28,054 INFO misc.py line 119 87073] Train: [50/100][909/1557] Data 0.006 (0.092) Batch 0.774 (1.181) Remain 25:45:43 loss: 0.3593 Lr: 0.00274 [2024-02-18 18:29:36,640 INFO misc.py line 119 87073] Train: [50/100][910/1557] Data 0.007 (0.092) Batch 8.588 (1.190) Remain 25:56:23 loss: 0.0818 Lr: 0.00274 [2024-02-18 18:29:37,644 INFO misc.py line 119 87073] Train: [50/100][911/1557] Data 0.005 (0.092) Batch 0.998 (1.189) Remain 25:56:05 loss: 0.3101 Lr: 0.00274 [2024-02-18 18:29:38,655 INFO misc.py line 119 87073] Train: [50/100][912/1557] Data 0.011 (0.092) Batch 1.010 (1.189) Remain 25:55:48 loss: 0.9161 Lr: 0.00274 [2024-02-18 18:29:39,711 INFO misc.py line 119 87073] Train: [50/100][913/1557] Data 0.011 (0.092) Batch 1.055 (1.189) Remain 25:55:35 loss: 0.3192 Lr: 0.00274 [2024-02-18 18:29:40,566 INFO misc.py line 119 87073] Train: [50/100][914/1557] Data 0.013 (0.092) Batch 0.863 (1.189) Remain 25:55:06 loss: 0.3084 Lr: 0.00274 [2024-02-18 18:29:41,307 INFO misc.py line 119 87073] Train: [50/100][915/1557] Data 0.004 (0.092) Batch 0.740 (1.188) Remain 25:54:26 loss: 0.4171 Lr: 0.00274 [2024-02-18 18:29:42,036 INFO misc.py line 119 87073] Train: [50/100][916/1557] Data 0.005 (0.092) Batch 0.722 (1.188) Remain 25:53:45 loss: 0.2543 Lr: 0.00274 [2024-02-18 18:29:43,173 INFO misc.py line 119 87073] Train: [50/100][917/1557] Data 0.013 (0.091) Batch 1.140 (1.188) Remain 25:53:40 loss: 0.1898 Lr: 0.00274 [2024-02-18 18:29:44,047 INFO misc.py line 119 87073] Train: [50/100][918/1557] Data 0.010 (0.091) Batch 0.880 (1.187) Remain 25:53:12 loss: 0.4495 Lr: 0.00274 [2024-02-18 18:29:44,844 INFO misc.py line 119 87073] Train: [50/100][919/1557] Data 0.004 (0.091) Batch 0.796 (1.187) Remain 25:52:37 loss: 0.8812 Lr: 0.00274 [2024-02-18 18:29:45,701 INFO misc.py line 119 87073] Train: [50/100][920/1557] Data 0.007 (0.091) Batch 0.853 (1.187) Remain 25:52:08 loss: 0.5330 Lr: 0.00274 [2024-02-18 18:29:46,709 INFO misc.py line 119 87073] Train: [50/100][921/1557] Data 0.011 (0.091) Batch 0.998 (1.186) Remain 25:51:50 loss: 0.2875 Lr: 0.00274 [2024-02-18 18:29:47,498 INFO misc.py line 119 87073] Train: [50/100][922/1557] Data 0.020 (0.091) Batch 0.802 (1.186) Remain 25:51:16 loss: 0.5913 Lr: 0.00274 [2024-02-18 18:29:48,297 INFO misc.py line 119 87073] Train: [50/100][923/1557] Data 0.006 (0.091) Batch 0.799 (1.185) Remain 25:50:42 loss: 0.4298 Lr: 0.00274 [2024-02-18 18:29:49,419 INFO misc.py line 119 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Batch 0.913 (1.184) Remain 25:48:27 loss: 0.2826 Lr: 0.00274 [2024-02-18 18:30:33,131 INFO misc.py line 119 87073] Train: [50/100][962/1557] Data 0.008 (0.092) Batch 0.883 (1.184) Remain 25:48:01 loss: 0.2137 Lr: 0.00274 [2024-02-18 18:30:34,020 INFO misc.py line 119 87073] Train: [50/100][963/1557] Data 0.005 (0.092) Batch 0.887 (1.184) Remain 25:47:36 loss: 0.3908 Lr: 0.00274 [2024-02-18 18:30:34,766 INFO misc.py line 119 87073] Train: [50/100][964/1557] Data 0.007 (0.092) Batch 0.747 (1.183) Remain 25:46:59 loss: 0.3067 Lr: 0.00274 [2024-02-18 18:30:35,624 INFO misc.py line 119 87073] Train: [50/100][965/1557] Data 0.006 (0.092) Batch 0.860 (1.183) Remain 25:46:32 loss: 0.3727 Lr: 0.00274 [2024-02-18 18:30:41,919 INFO misc.py line 119 87073] Train: [50/100][966/1557] Data 0.004 (0.092) Batch 6.295 (1.188) Remain 25:53:27 loss: 0.1491 Lr: 0.00274 [2024-02-18 18:30:42,774 INFO misc.py line 119 87073] Train: [50/100][967/1557] Data 0.005 (0.092) Batch 0.840 (1.188) Remain 25:52:57 loss: 0.3379 Lr: 0.00274 [2024-02-18 18:30:43,926 INFO misc.py line 119 87073] Train: [50/100][968/1557] Data 0.019 (0.092) Batch 1.161 (1.188) Remain 25:52:54 loss: 0.8867 Lr: 0.00274 [2024-02-18 18:30:44,916 INFO misc.py line 119 87073] Train: [50/100][969/1557] Data 0.009 (0.092) Batch 0.996 (1.188) Remain 25:52:37 loss: 0.2561 Lr: 0.00274 [2024-02-18 18:30:45,871 INFO misc.py line 119 87073] Train: [50/100][970/1557] Data 0.004 (0.092) Batch 0.955 (1.187) Remain 25:52:17 loss: 0.5151 Lr: 0.00274 [2024-02-18 18:30:46,672 INFO misc.py line 119 87073] Train: [50/100][971/1557] Data 0.003 (0.092) Batch 0.801 (1.187) Remain 25:51:45 loss: 0.4413 Lr: 0.00274 [2024-02-18 18:30:47,451 INFO misc.py line 119 87073] Train: [50/100][972/1557] Data 0.004 (0.092) Batch 0.775 (1.187) Remain 25:51:10 loss: 0.2669 Lr: 0.00274 [2024-02-18 18:30:48,533 INFO misc.py line 119 87073] Train: [50/100][973/1557] Data 0.008 (0.091) Batch 1.080 (1.186) Remain 25:51:00 loss: 0.2131 Lr: 0.00274 [2024-02-18 18:30:49,648 INFO misc.py line 119 87073] Train: [50/100][974/1557] Data 0.011 (0.091) Batch 1.112 (1.186) Remain 25:50:53 loss: 0.3761 Lr: 0.00274 [2024-02-18 18:30:50,750 INFO misc.py line 119 87073] Train: [50/100][975/1557] Data 0.014 (0.091) Batch 1.100 (1.186) Remain 25:50:45 loss: 0.2953 Lr: 0.00274 [2024-02-18 18:30:51,820 INFO misc.py line 119 87073] Train: [50/100][976/1557] Data 0.015 (0.091) Batch 1.075 (1.186) Remain 25:50:35 loss: 0.3183 Lr: 0.00274 [2024-02-18 18:30:53,023 INFO misc.py line 119 87073] Train: [50/100][977/1557] Data 0.010 (0.091) Batch 1.209 (1.186) Remain 25:50:35 loss: 0.4627 Lr: 0.00274 [2024-02-18 18:30:53,788 INFO misc.py line 119 87073] Train: [50/100][978/1557] Data 0.005 (0.091) Batch 0.765 (1.186) Remain 25:50:00 loss: 0.1924 Lr: 0.00274 [2024-02-18 18:30:54,538 INFO misc.py line 119 87073] Train: [50/100][979/1557] Data 0.004 (0.091) Batch 0.750 (1.185) Remain 25:49:24 loss: 0.2317 Lr: 0.00274 [2024-02-18 18:30:55,699 INFO misc.py line 119 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(0.093) Batch 0.916 (1.184) Remain 25:46:38 loss: 0.5475 Lr: 0.00274 [2024-02-18 18:31:39,026 INFO misc.py line 119 87073] Train: [50/100][1018/1557] Data 0.006 (0.092) Batch 1.010 (1.184) Remain 25:46:23 loss: 0.3242 Lr: 0.00274 [2024-02-18 18:31:39,961 INFO misc.py line 119 87073] Train: [50/100][1019/1557] Data 0.005 (0.092) Batch 0.936 (1.183) Remain 25:46:03 loss: 0.4331 Lr: 0.00274 [2024-02-18 18:31:40,707 INFO misc.py line 119 87073] Train: [50/100][1020/1557] Data 0.003 (0.092) Batch 0.734 (1.183) Remain 25:45:27 loss: 0.3105 Lr: 0.00274 [2024-02-18 18:31:41,453 INFO misc.py line 119 87073] Train: [50/100][1021/1557] Data 0.015 (0.092) Batch 0.757 (1.183) Remain 25:44:53 loss: 0.2803 Lr: 0.00274 [2024-02-18 18:31:50,300 INFO misc.py line 119 87073] Train: [50/100][1022/1557] Data 0.004 (0.092) Batch 8.847 (1.190) Remain 25:54:42 loss: 0.1019 Lr: 0.00274 [2024-02-18 18:31:51,276 INFO misc.py line 119 87073] Train: [50/100][1023/1557] Data 0.005 (0.092) Batch 0.973 (1.190) Remain 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[2024-02-18 18:36:43,311 INFO misc.py line 119 87073] Train: [50/100][1278/1557] Data 0.004 (0.090) Batch 1.032 (1.181) Remain 25:37:44 loss: 0.0583 Lr: 0.00272 [2024-02-18 18:36:44,096 INFO misc.py line 119 87073] Train: [50/100][1279/1557] Data 0.005 (0.090) Batch 0.786 (1.181) Remain 25:37:18 loss: 0.2723 Lr: 0.00272 [2024-02-18 18:36:44,835 INFO misc.py line 119 87073] Train: [50/100][1280/1557] Data 0.004 (0.090) Batch 0.738 (1.180) Remain 25:36:50 loss: 0.4134 Lr: 0.00272 [2024-02-18 18:36:46,066 INFO misc.py line 119 87073] Train: [50/100][1281/1557] Data 0.004 (0.090) Batch 1.223 (1.180) Remain 25:36:51 loss: 0.2401 Lr: 0.00272 [2024-02-18 18:36:46,935 INFO misc.py line 119 87073] Train: [50/100][1282/1557] Data 0.013 (0.090) Batch 0.878 (1.180) Remain 25:36:32 loss: 0.2290 Lr: 0.00272 [2024-02-18 18:36:47,949 INFO misc.py line 119 87073] Train: [50/100][1283/1557] Data 0.004 (0.090) Batch 1.014 (1.180) Remain 25:36:20 loss: 0.3959 Lr: 0.00272 [2024-02-18 18:36:49,015 INFO 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[2024-02-18 18:37:24,763 INFO misc.py line 119 87073] Train: [50/100][1309/1557] Data 0.005 (0.092) Batch 1.080 (1.185) Remain 25:41:56 loss: 0.1583 Lr: 0.00272 [2024-02-18 18:37:25,573 INFO misc.py line 119 87073] Train: [50/100][1310/1557] Data 0.012 (0.092) Batch 0.818 (1.184) Remain 25:41:33 loss: 0.4244 Lr: 0.00272 [2024-02-18 18:37:26,471 INFO misc.py line 119 87073] Train: [50/100][1311/1557] Data 0.004 (0.092) Batch 0.898 (1.184) Remain 25:41:15 loss: 0.5991 Lr: 0.00272 [2024-02-18 18:37:27,486 INFO misc.py line 119 87073] Train: [50/100][1312/1557] Data 0.004 (0.092) Batch 1.004 (1.184) Remain 25:41:03 loss: 0.2006 Lr: 0.00272 [2024-02-18 18:37:28,409 INFO misc.py line 119 87073] Train: [50/100][1313/1557] Data 0.015 (0.091) Batch 0.933 (1.184) Remain 25:40:47 loss: 0.2335 Lr: 0.00272 [2024-02-18 18:37:29,157 INFO misc.py line 119 87073] Train: [50/100][1314/1557] Data 0.005 (0.091) Batch 0.748 (1.183) Remain 25:40:20 loss: 0.3441 Lr: 0.00272 [2024-02-18 18:37:29,917 INFO 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[2024-02-18 18:38:35,487 INFO misc.py line 119 87073] Train: [50/100][1371/1557] Data 0.004 (0.091) Batch 0.741 (1.183) Remain 25:38:07 loss: 0.1777 Lr: 0.00272 [2024-02-18 18:38:36,697 INFO misc.py line 119 87073] Train: [50/100][1372/1557] Data 0.013 (0.091) Batch 1.215 (1.183) Remain 25:38:08 loss: 0.2247 Lr: 0.00272 [2024-02-18 18:38:37,725 INFO misc.py line 119 87073] Train: [50/100][1373/1557] Data 0.008 (0.091) Batch 1.022 (1.183) Remain 25:37:58 loss: 0.6984 Lr: 0.00272 [2024-02-18 18:38:38,729 INFO misc.py line 119 87073] Train: [50/100][1374/1557] Data 0.014 (0.091) Batch 1.011 (1.182) Remain 25:37:47 loss: 0.1852 Lr: 0.00272 [2024-02-18 18:38:39,790 INFO misc.py line 119 87073] Train: [50/100][1375/1557] Data 0.006 (0.091) Batch 1.055 (1.182) Remain 25:37:38 loss: 0.3828 Lr: 0.00272 [2024-02-18 18:38:40,903 INFO misc.py line 119 87073] Train: [50/100][1376/1557] Data 0.013 (0.091) Batch 1.118 (1.182) Remain 25:37:33 loss: 0.7381 Lr: 0.00272 [2024-02-18 18:38:41,655 INFO 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[2024-02-18 18:39:05,341 INFO misc.py line 119 87073] Train: [50/100][1402/1557] Data 0.004 (0.089) Batch 1.062 (1.178) Remain 25:31:12 loss: 0.6335 Lr: 0.00272 [2024-02-18 18:39:06,308 INFO misc.py line 119 87073] Train: [50/100][1403/1557] Data 0.004 (0.089) Batch 0.967 (1.178) Remain 25:30:59 loss: 0.4834 Lr: 0.00272 [2024-02-18 18:39:07,312 INFO misc.py line 119 87073] Train: [50/100][1404/1557] Data 0.004 (0.089) Batch 1.004 (1.177) Remain 25:30:48 loss: 0.3651 Lr: 0.00272 [2024-02-18 18:39:09,481 INFO misc.py line 119 87073] Train: [50/100][1405/1557] Data 0.629 (0.089) Batch 2.141 (1.178) Remain 25:31:40 loss: 0.1757 Lr: 0.00272 [2024-02-18 18:39:10,291 INFO misc.py line 119 87073] Train: [50/100][1406/1557] Data 0.034 (0.089) Batch 0.838 (1.178) Remain 25:31:20 loss: 0.2290 Lr: 0.00272 [2024-02-18 18:39:18,519 INFO misc.py line 119 87073] Train: [50/100][1407/1557] Data 4.043 (0.092) Batch 8.227 (1.183) Remain 25:37:51 loss: 0.1333 Lr: 0.00272 [2024-02-18 18:39:19,424 INFO 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[2024-02-18 18:39:46,803 INFO misc.py line 119 87073] Train: [50/100][1433/1557] Data 0.004 (0.091) Batch 0.736 (1.181) Remain 25:35:05 loss: 0.3884 Lr: 0.00271 [2024-02-18 18:39:47,575 INFO misc.py line 119 87073] Train: [50/100][1434/1557] Data 0.006 (0.091) Batch 0.763 (1.181) Remain 25:34:41 loss: 0.2680 Lr: 0.00271 [2024-02-18 18:39:48,764 INFO misc.py line 119 87073] Train: [50/100][1435/1557] Data 0.015 (0.091) Batch 1.199 (1.181) Remain 25:34:41 loss: 0.1822 Lr: 0.00271 [2024-02-18 18:39:49,856 INFO misc.py line 119 87073] Train: [50/100][1436/1557] Data 0.005 (0.090) Batch 1.090 (1.181) Remain 25:34:35 loss: 0.5519 Lr: 0.00271 [2024-02-18 18:39:51,082 INFO misc.py line 119 87073] Train: [50/100][1437/1557] Data 0.008 (0.090) Batch 1.219 (1.181) Remain 25:34:36 loss: 0.4198 Lr: 0.00271 [2024-02-18 18:39:52,255 INFO misc.py line 119 87073] Train: [50/100][1438/1557] Data 0.014 (0.090) Batch 1.182 (1.181) Remain 25:34:34 loss: 0.7403 Lr: 0.00271 [2024-02-18 18:39:53,241 INFO 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misc.py line 119 87073] Train: [50/100][1501/1557] Data 0.004 (0.090) Batch 0.869 (1.179) Remain 25:31:20 loss: 0.3971 Lr: 0.00271 [2024-02-18 18:41:05,404 INFO misc.py line 119 87073] Train: [50/100][1502/1557] Data 0.006 (0.090) Batch 1.064 (1.179) Remain 25:31:13 loss: 0.3055 Lr: 0.00271 [2024-02-18 18:41:06,194 INFO misc.py line 119 87073] Train: [50/100][1503/1557] Data 0.005 (0.090) Batch 0.789 (1.179) Remain 25:30:51 loss: 0.1905 Lr: 0.00271 [2024-02-18 18:41:06,956 INFO misc.py line 119 87073] Train: [50/100][1504/1557] Data 0.007 (0.090) Batch 0.759 (1.179) Remain 25:30:28 loss: 0.2838 Lr: 0.00271 [2024-02-18 18:41:08,254 INFO misc.py line 119 87073] Train: [50/100][1505/1557] Data 0.010 (0.089) Batch 1.294 (1.179) Remain 25:30:33 loss: 0.1812 Lr: 0.00271 [2024-02-18 18:41:09,255 INFO misc.py line 119 87073] Train: [50/100][1506/1557] Data 0.015 (0.089) Batch 1.002 (1.179) Remain 25:30:23 loss: 0.3512 Lr: 0.00271 [2024-02-18 18:41:10,050 INFO misc.py line 119 87073] Train: 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(0.089) Batch 0.973 (1.178) Remain 25:28:51 loss: 0.5462 Lr: 0.00271 [2024-02-18 18:41:16,740 INFO misc.py line 119 87073] Train: [50/100][1514/1557] Data 0.005 (0.089) Batch 0.859 (1.177) Remain 25:28:33 loss: 0.2777 Lr: 0.00271 [2024-02-18 18:41:17,718 INFO misc.py line 119 87073] Train: [50/100][1515/1557] Data 0.005 (0.089) Batch 0.974 (1.177) Remain 25:28:22 loss: 0.1241 Lr: 0.00271 [2024-02-18 18:41:18,781 INFO misc.py line 119 87073] Train: [50/100][1516/1557] Data 0.009 (0.089) Batch 1.064 (1.177) Remain 25:28:15 loss: 0.3663 Lr: 0.00271 [2024-02-18 18:41:19,495 INFO misc.py line 119 87073] Train: [50/100][1517/1557] Data 0.008 (0.089) Batch 0.713 (1.177) Remain 25:27:50 loss: 0.2750 Lr: 0.00271 [2024-02-18 18:41:20,321 INFO misc.py line 119 87073] Train: [50/100][1518/1557] Data 0.009 (0.089) Batch 0.820 (1.177) Remain 25:27:30 loss: 0.2186 Lr: 0.00271 [2024-02-18 18:41:28,243 INFO misc.py line 119 87073] Train: [50/100][1519/1557] Data 4.575 (0.092) Batch 7.934 (1.181) Remain 25:33:16 loss: 0.1484 Lr: 0.00271 [2024-02-18 18:41:29,473 INFO misc.py line 119 87073] Train: [50/100][1520/1557] Data 0.004 (0.092) Batch 1.223 (1.181) Remain 25:33:17 loss: 0.4459 Lr: 0.00271 [2024-02-18 18:41:30,300 INFO misc.py line 119 87073] Train: [50/100][1521/1557] Data 0.011 (0.092) Batch 0.833 (1.181) Remain 25:32:58 loss: 0.5255 Lr: 0.00271 [2024-02-18 18:41:31,257 INFO misc.py line 119 87073] Train: [50/100][1522/1557] Data 0.006 (0.092) Batch 0.957 (1.181) Remain 25:32:45 loss: 0.5926 Lr: 0.00271 [2024-02-18 18:41:32,092 INFO misc.py line 119 87073] Train: [50/100][1523/1557] Data 0.005 (0.091) Batch 0.835 (1.181) Remain 25:32:26 loss: 0.3136 Lr: 0.00271 [2024-02-18 18:41:32,899 INFO misc.py line 119 87073] Train: [50/100][1524/1557] Data 0.005 (0.091) Batch 0.800 (1.180) Remain 25:32:06 loss: 0.1490 Lr: 0.00271 [2024-02-18 18:41:33,772 INFO misc.py line 119 87073] Train: [50/100][1525/1557] Data 0.011 (0.091) Batch 0.879 (1.180) Remain 25:31:49 loss: 0.3077 Lr: 0.00271 [2024-02-18 18:41:40,877 INFO misc.py line 119 87073] Train: [50/100][1526/1557] Data 0.006 (0.091) Batch 7.107 (1.184) Remain 25:36:51 loss: 0.2251 Lr: 0.00271 [2024-02-18 18:41:41,822 INFO misc.py line 119 87073] Train: [50/100][1527/1557] Data 0.004 (0.091) Batch 0.937 (1.184) Remain 25:36:37 loss: 0.4946 Lr: 0.00271 [2024-02-18 18:41:42,987 INFO misc.py line 119 87073] Train: [50/100][1528/1557] Data 0.011 (0.091) Batch 1.167 (1.184) Remain 25:36:35 loss: 0.4458 Lr: 0.00271 [2024-02-18 18:41:43,871 INFO misc.py line 119 87073] Train: [50/100][1529/1557] Data 0.010 (0.091) Batch 0.887 (1.184) Remain 25:36:19 loss: 0.7206 Lr: 0.00271 [2024-02-18 18:41:44,915 INFO misc.py line 119 87073] Train: [50/100][1530/1557] Data 0.007 (0.091) Batch 1.045 (1.184) Remain 25:36:11 loss: 0.4434 Lr: 0.00271 [2024-02-18 18:41:45,684 INFO misc.py line 119 87073] Train: [50/100][1531/1557] Data 0.006 (0.091) Batch 0.771 (1.183) Remain 25:35:48 loss: 0.3258 Lr: 0.00271 [2024-02-18 18:41:46,456 INFO misc.py line 119 87073] Train: [50/100][1532/1557] Data 0.004 (0.091) Batch 0.766 (1.183) Remain 25:35:26 loss: 0.3325 Lr: 0.00271 [2024-02-18 18:41:47,604 INFO misc.py line 119 87073] Train: [50/100][1533/1557] Data 0.009 (0.091) Batch 1.150 (1.183) Remain 25:35:23 loss: 0.1176 Lr: 0.00271 [2024-02-18 18:41:48,668 INFO misc.py line 119 87073] Train: [50/100][1534/1557] Data 0.007 (0.091) Batch 1.060 (1.183) Remain 25:35:16 loss: 0.4079 Lr: 0.00271 [2024-02-18 18:41:49,524 INFO misc.py line 119 87073] Train: [50/100][1535/1557] Data 0.014 (0.091) Batch 0.862 (1.183) Remain 25:34:58 loss: 0.2096 Lr: 0.00271 [2024-02-18 18:41:50,658 INFO misc.py line 119 87073] Train: [50/100][1536/1557] Data 0.006 (0.091) Batch 1.136 (1.183) Remain 25:34:55 loss: 0.5358 Lr: 0.00271 [2024-02-18 18:41:51,464 INFO misc.py line 119 87073] Train: [50/100][1537/1557] Data 0.004 (0.091) Batch 0.804 (1.182) Remain 25:34:34 loss: 0.1379 Lr: 0.00271 [2024-02-18 18:41:52,196 INFO misc.py line 119 87073] Train: [50/100][1538/1557] Data 0.005 (0.091) Batch 0.731 (1.182) Remain 25:34:10 loss: 0.1801 Lr: 0.00271 [2024-02-18 18:41:52,921 INFO misc.py line 119 87073] Train: [50/100][1539/1557] Data 0.006 (0.091) Batch 0.727 (1.182) Remain 25:33:46 loss: 0.3153 Lr: 0.00271 [2024-02-18 18:41:54,049 INFO misc.py line 119 87073] Train: [50/100][1540/1557] Data 0.004 (0.091) Batch 1.128 (1.182) Remain 25:33:42 loss: 0.2155 Lr: 0.00271 [2024-02-18 18:41:55,098 INFO misc.py line 119 87073] Train: [50/100][1541/1557] Data 0.004 (0.090) Batch 1.049 (1.182) Remain 25:33:34 loss: 0.2718 Lr: 0.00271 [2024-02-18 18:41:55,958 INFO misc.py line 119 87073] Train: [50/100][1542/1557] Data 0.004 (0.090) Batch 0.859 (1.181) Remain 25:33:17 loss: 0.7237 Lr: 0.00271 [2024-02-18 18:41:56,935 INFO misc.py line 119 87073] Train: [50/100][1543/1557] Data 0.005 (0.090) Batch 0.973 (1.181) Remain 25:33:05 loss: 0.3032 Lr: 0.00271 [2024-02-18 18:41:57,832 INFO misc.py line 119 87073] Train: [50/100][1544/1557] Data 0.009 (0.090) Batch 0.900 (1.181) Remain 25:32:49 loss: 0.7654 Lr: 0.00271 [2024-02-18 18:41:58,571 INFO misc.py line 119 87073] Train: [50/100][1545/1557] Data 0.006 (0.090) Batch 0.739 (1.181) Remain 25:32:26 loss: 0.3781 Lr: 0.00271 [2024-02-18 18:41:59,367 INFO misc.py line 119 87073] Train: [50/100][1546/1557] Data 0.006 (0.090) Batch 0.796 (1.181) Remain 25:32:05 loss: 0.2891 Lr: 0.00271 [2024-02-18 18:42:00,553 INFO misc.py line 119 87073] Train: [50/100][1547/1557] Data 0.006 (0.090) Batch 1.188 (1.181) Remain 25:32:05 loss: 0.1692 Lr: 0.00271 [2024-02-18 18:42:01,525 INFO misc.py line 119 87073] Train: [50/100][1548/1557] Data 0.004 (0.090) Batch 0.971 (1.181) Remain 25:31:53 loss: 0.6368 Lr: 0.00271 [2024-02-18 18:42:02,470 INFO misc.py line 119 87073] Train: [50/100][1549/1557] Data 0.004 (0.090) Batch 0.943 (1.180) Remain 25:31:40 loss: 0.1839 Lr: 0.00271 [2024-02-18 18:42:03,479 INFO misc.py line 119 87073] Train: [50/100][1550/1557] Data 0.005 (0.090) Batch 1.010 (1.180) Remain 25:31:30 loss: 0.2799 Lr: 0.00271 [2024-02-18 18:42:04,403 INFO misc.py line 119 87073] Train: [50/100][1551/1557] Data 0.005 (0.090) Batch 0.924 (1.180) Remain 25:31:16 loss: 0.4175 Lr: 0.00271 [2024-02-18 18:42:05,136 INFO misc.py line 119 87073] Train: [50/100][1552/1557] Data 0.005 (0.090) Batch 0.730 (1.180) Remain 25:30:52 loss: 0.2159 Lr: 0.00271 [2024-02-18 18:42:05,847 INFO misc.py line 119 87073] Train: [50/100][1553/1557] Data 0.007 (0.090) Batch 0.715 (1.179) Remain 25:30:27 loss: 0.3504 Lr: 0.00271 [2024-02-18 18:42:07,041 INFO misc.py line 119 87073] Train: [50/100][1554/1557] Data 0.004 (0.090) Batch 1.193 (1.179) Remain 25:30:27 loss: 0.3003 Lr: 0.00271 [2024-02-18 18:42:07,931 INFO misc.py line 119 87073] Train: [50/100][1555/1557] Data 0.005 (0.090) Batch 0.891 (1.179) Remain 25:30:11 loss: 0.2656 Lr: 0.00271 [2024-02-18 18:42:08,803 INFO misc.py line 119 87073] Train: [50/100][1556/1557] Data 0.004 (0.090) Batch 0.865 (1.179) Remain 25:29:54 loss: 0.3539 Lr: 0.00271 [2024-02-18 18:42:09,675 INFO misc.py line 119 87073] Train: [50/100][1557/1557] Data 0.010 (0.090) Batch 0.878 (1.179) Remain 25:29:38 loss: 0.5756 Lr: 0.00271 [2024-02-18 18:42:09,675 INFO misc.py line 136 87073] Train result: loss: 0.3538 [2024-02-18 18:42:09,676 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 18:42:37,105 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.3899 [2024-02-18 18:42:37,894 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8444 [2024-02-18 18:42:40,025 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.5925 [2024-02-18 18:42:42,243 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3303 [2024-02-18 18:42:47,199 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2317 [2024-02-18 18:42:47,900 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0970 [2024-02-18 18:42:49,161 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2189 [2024-02-18 18:42:52,118 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2688 [2024-02-18 18:42:53,934 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3053 [2024-02-18 18:42:55,704 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7143/0.7858/0.9140. [2024-02-18 18:42:55,704 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9314/0.9663 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9819/0.9880 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8783/0.9601 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3986/0.4377 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6865/0.7105 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7359/0.8734 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8340/0.9115 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9290/0.9682 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8660/0.9275 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8040/0.9073 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6422/0.8676 [2024-02-18 18:42:55,705 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5976/0.6967 [2024-02-18 18:42:55,706 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 18:42:55,708 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 18:42:55,708 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 18:43:03,595 INFO misc.py line 119 87073] Train: [51/100][1/1557] Data 1.719 (1.719) Batch 2.636 (2.636) Remain 57:00:30 loss: 0.2048 Lr: 0.00271 [2024-02-18 18:43:04,515 INFO misc.py line 119 87073] Train: [51/100][2/1557] Data 0.004 (0.004) Batch 0.919 (0.919) Remain 19:52:11 loss: 0.4426 Lr: 0.00271 [2024-02-18 18:43:05,460 INFO misc.py line 119 87073] Train: [51/100][3/1557] Data 0.006 (0.006) Batch 0.944 (0.944) Remain 20:25:16 loss: 0.4219 Lr: 0.00271 [2024-02-18 18:43:06,449 INFO misc.py line 119 87073] Train: [51/100][4/1557] Data 0.006 (0.006) Batch 0.990 (0.990) Remain 21:24:33 loss: 0.5474 Lr: 0.00271 [2024-02-18 18:43:07,195 INFO misc.py line 119 87073] Train: [51/100][5/1557] Data 0.005 (0.005) Batch 0.747 (0.869) Remain 18:46:55 loss: 0.3772 Lr: 0.00271 [2024-02-18 18:43:07,981 INFO misc.py line 119 87073] Train: [51/100][6/1557] Data 0.003 (0.005) Batch 0.782 (0.840) Remain 18:09:34 loss: 0.2530 Lr: 0.00271 [2024-02-18 18:43:12,140 INFO misc.py line 119 87073] Train: [51/100][7/1557] Data 2.934 (0.737) Batch 4.156 (1.669) Remain 36:05:07 loss: 0.3467 Lr: 0.00271 [2024-02-18 18:43:13,138 INFO misc.py line 119 87073] Train: [51/100][8/1557] Data 0.011 (0.592) Batch 1.002 (1.536) Remain 33:12:07 loss: 0.3745 Lr: 0.00271 [2024-02-18 18:43:14,123 INFO misc.py line 119 87073] Train: [51/100][9/1557] Data 0.006 (0.494) Batch 0.987 (1.444) Remain 31:13:28 loss: 0.2902 Lr: 0.00271 [2024-02-18 18:43:15,275 INFO misc.py line 119 87073] Train: [51/100][10/1557] Data 0.005 (0.424) Batch 1.150 (1.402) Remain 30:18:59 loss: 0.2211 Lr: 0.00271 [2024-02-18 18:43:16,238 INFO misc.py line 119 87073] Train: [51/100][11/1557] Data 0.007 (0.372) Batch 0.965 (1.348) Remain 29:08:08 loss: 0.2602 Lr: 0.00271 [2024-02-18 18:43:16,951 INFO misc.py line 119 87073] Train: [51/100][12/1557] Data 0.004 (0.331) Batch 0.712 (1.277) Remain 27:36:32 loss: 0.4483 Lr: 0.00271 [2024-02-18 18:43:17,681 INFO misc.py line 119 87073] Train: [51/100][13/1557] Data 0.006 (0.299) Batch 0.719 (1.221) Remain 26:24:10 loss: 0.2894 Lr: 0.00271 [2024-02-18 18:43:18,880 INFO misc.py line 119 87073] Train: [51/100][14/1557] Data 0.016 (0.273) Batch 1.207 (1.220) Remain 26:22:29 loss: 0.2128 Lr: 0.00271 [2024-02-18 18:43:19,827 INFO misc.py line 119 87073] Train: [51/100][15/1557] Data 0.007 (0.251) Batch 0.950 (1.197) Remain 25:53:21 loss: 0.2191 Lr: 0.00271 [2024-02-18 18:43:20,982 INFO misc.py line 119 87073] Train: [51/100][16/1557] Data 0.003 (0.232) Batch 1.155 (1.194) Remain 25:49:04 loss: 0.3237 Lr: 0.00271 [2024-02-18 18:43:22,023 INFO misc.py line 119 87073] Train: [51/100][17/1557] Data 0.004 (0.216) Batch 1.041 (1.183) Remain 25:34:50 loss: 0.5212 Lr: 0.00271 [2024-02-18 18:43:23,218 INFO misc.py line 119 87073] Train: [51/100][18/1557] Data 0.004 (0.201) Batch 1.183 (1.183) Remain 25:34:50 loss: 0.0883 Lr: 0.00271 [2024-02-18 18:43:23,953 INFO misc.py line 119 87073] Train: [51/100][19/1557] Data 0.016 (0.190) Batch 0.747 (1.156) Remain 24:59:26 loss: 0.2455 Lr: 0.00271 [2024-02-18 18:43:24,657 INFO misc.py line 119 87073] Train: [51/100][20/1557] Data 0.005 (0.179) Batch 0.694 (1.129) Remain 24:24:11 loss: 0.3052 Lr: 0.00271 [2024-02-18 18:43:25,938 INFO misc.py line 119 87073] Train: [51/100][21/1557] Data 0.013 (0.170) Batch 1.288 (1.138) Remain 24:35:40 loss: 0.0715 Lr: 0.00271 [2024-02-18 18:43:26,876 INFO misc.py line 119 87073] Train: [51/100][22/1557] Data 0.007 (0.161) Batch 0.941 (1.127) Remain 24:22:12 loss: 0.5072 Lr: 0.00271 [2024-02-18 18:43:27,922 INFO misc.py line 119 87073] Train: [51/100][23/1557] Data 0.003 (0.153) Batch 1.046 (1.123) Remain 24:16:56 loss: 0.2711 Lr: 0.00271 [2024-02-18 18:43:28,892 INFO misc.py line 119 87073] Train: [51/100][24/1557] Data 0.004 (0.146) Batch 0.968 (1.116) Remain 24:07:22 loss: 0.4519 Lr: 0.00271 [2024-02-18 18:43:30,010 INFO misc.py line 119 87073] Train: [51/100][25/1557] Data 0.005 (0.140) Batch 1.118 (1.116) Remain 24:07:27 loss: 0.3345 Lr: 0.00271 [2024-02-18 18:43:30,694 INFO misc.py line 119 87073] Train: [51/100][26/1557] Data 0.006 (0.134) Batch 0.685 (1.097) Remain 23:43:08 loss: 0.3252 Lr: 0.00271 [2024-02-18 18:43:31,360 INFO misc.py line 119 87073] Train: [51/100][27/1557] Data 0.004 (0.129) Batch 0.662 (1.079) Remain 23:19:37 loss: 0.2622 Lr: 0.00271 [2024-02-18 18:43:32,581 INFO misc.py line 119 87073] Train: [51/100][28/1557] Data 0.008 (0.124) Batch 1.223 (1.085) Remain 23:27:05 loss: 0.2336 Lr: 0.00271 [2024-02-18 18:43:33,566 INFO misc.py line 119 87073] Train: [51/100][29/1557] Data 0.006 (0.119) Batch 0.987 (1.081) Remain 23:22:11 loss: 0.4809 Lr: 0.00271 [2024-02-18 18:43:34,627 INFO misc.py line 119 87073] Train: [51/100][30/1557] Data 0.004 (0.115) Batch 1.056 (1.080) Remain 23:20:59 loss: 0.5896 Lr: 0.00271 [2024-02-18 18:43:35,663 INFO misc.py line 119 87073] Train: [51/100][31/1557] Data 0.009 (0.111) Batch 1.037 (1.079) Remain 23:18:57 loss: 0.3203 Lr: 0.00271 [2024-02-18 18:43:36,792 INFO misc.py line 119 87073] Train: [51/100][32/1557] Data 0.007 (0.108) Batch 1.131 (1.080) Remain 23:21:15 loss: 0.3194 Lr: 0.00271 [2024-02-18 18:43:37,526 INFO misc.py line 119 87073] Train: [51/100][33/1557] Data 0.006 (0.104) Batch 0.733 (1.069) Remain 23:06:13 loss: 0.3989 Lr: 0.00271 [2024-02-18 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Train: [51/100][40/1557] Data 0.003 (0.086) Batch 0.771 (1.045) Remain 22:34:51 loss: 0.2941 Lr: 0.00271 [2024-02-18 18:43:44,896 INFO misc.py line 119 87073] Train: [51/100][41/1557] Data 0.003 (0.083) Batch 0.773 (1.038) Remain 22:25:34 loss: 0.2408 Lr: 0.00271 [2024-02-18 18:43:46,130 INFO misc.py line 119 87073] Train: [51/100][42/1557] Data 0.013 (0.082) Batch 1.235 (1.043) Remain 22:32:07 loss: 0.1626 Lr: 0.00271 [2024-02-18 18:43:47,093 INFO misc.py line 119 87073] Train: [51/100][43/1557] Data 0.012 (0.080) Batch 0.971 (1.041) Remain 22:29:46 loss: 0.4821 Lr: 0.00271 [2024-02-18 18:43:48,098 INFO misc.py line 119 87073] Train: [51/100][44/1557] Data 0.004 (0.078) Batch 1.005 (1.040) Remain 22:28:37 loss: 0.7263 Lr: 0.00271 [2024-02-18 18:43:48,952 INFO misc.py line 119 87073] Train: [51/100][45/1557] Data 0.003 (0.076) Batch 0.854 (1.036) Remain 22:22:52 loss: 0.6531 Lr: 0.00271 [2024-02-18 18:43:50,241 INFO misc.py line 119 87073] Train: [51/100][46/1557] Data 0.004 (0.075) 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INFO misc.py line 119 87073] Train: [51/100][59/1557] Data 0.004 (0.059) Batch 0.870 (1.018) Remain 21:59:21 loss: 0.2399 Lr: 0.00271 [2024-02-18 18:44:03,369 INFO misc.py line 119 87073] Train: [51/100][60/1557] Data 0.005 (0.058) Batch 0.917 (1.016) Remain 21:57:02 loss: 0.2237 Lr: 0.00271 [2024-02-18 18:44:04,169 INFO misc.py line 119 87073] Train: [51/100][61/1557] Data 0.013 (0.058) Batch 0.808 (1.012) Remain 21:52:22 loss: 0.2137 Lr: 0.00271 [2024-02-18 18:44:04,905 INFO misc.py line 119 87073] Train: [51/100][62/1557] Data 0.004 (0.057) Batch 0.735 (1.008) Remain 21:46:14 loss: 0.3219 Lr: 0.00270 [2024-02-18 18:44:17,757 INFO misc.py line 119 87073] Train: [51/100][63/1557] Data 10.017 (0.223) Batch 12.854 (1.205) Remain 26:02:11 loss: 0.1370 Lr: 0.00270 [2024-02-18 18:44:18,712 INFO misc.py line 119 87073] Train: [51/100][64/1557] Data 0.004 (0.219) Batch 0.955 (1.201) Remain 25:56:51 loss: 0.6129 Lr: 0.00270 [2024-02-18 18:44:19,632 INFO misc.py line 119 87073] Train: 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Batch 0.904 (1.160) Remain 25:03:21 loss: 0.8761 Lr: 0.00270 [2024-02-18 18:45:23,388 INFO misc.py line 119 87073] Train: [51/100][122/1557] Data 0.005 (0.201) Batch 0.988 (1.159) Remain 25:01:27 loss: 0.4717 Lr: 0.00270 [2024-02-18 18:45:24,404 INFO misc.py line 119 87073] Train: [51/100][123/1557] Data 0.012 (0.199) Batch 1.013 (1.158) Remain 24:59:51 loss: 0.6381 Lr: 0.00270 [2024-02-18 18:45:25,187 INFO misc.py line 119 87073] Train: [51/100][124/1557] Data 0.015 (0.198) Batch 0.793 (1.155) Remain 24:55:56 loss: 0.3310 Lr: 0.00270 [2024-02-18 18:45:25,978 INFO misc.py line 119 87073] Train: [51/100][125/1557] Data 0.004 (0.196) Batch 0.791 (1.152) Remain 24:52:03 loss: 0.2968 Lr: 0.00270 [2024-02-18 18:45:27,068 INFO misc.py line 119 87073] Train: [51/100][126/1557] Data 0.005 (0.195) Batch 1.079 (1.151) Remain 24:51:15 loss: 0.2574 Lr: 0.00270 [2024-02-18 18:45:28,007 INFO misc.py line 119 87073] Train: [51/100][127/1557] Data 0.015 (0.193) Batch 0.949 (1.150) Remain 24:49:08 loss: 0.8761 Lr: 0.00270 [2024-02-18 18:45:28,856 INFO misc.py line 119 87073] Train: [51/100][128/1557] Data 0.006 (0.192) Batch 0.850 (1.147) Remain 24:46:00 loss: 0.1638 Lr: 0.00270 [2024-02-18 18:45:29,948 INFO misc.py line 119 87073] Train: [51/100][129/1557] Data 0.005 (0.190) Batch 1.088 (1.147) Remain 24:45:23 loss: 0.3675 Lr: 0.00270 [2024-02-18 18:45:30,866 INFO misc.py line 119 87073] Train: [51/100][130/1557] Data 0.008 (0.189) Batch 0.921 (1.145) Remain 24:43:04 loss: 0.5348 Lr: 0.00270 [2024-02-18 18:45:31,635 INFO misc.py line 119 87073] Train: [51/100][131/1557] Data 0.005 (0.187) Batch 0.769 (1.142) Remain 24:39:15 loss: 0.2166 Lr: 0.00270 [2024-02-18 18:45:32,403 INFO misc.py line 119 87073] Train: [51/100][132/1557] Data 0.004 (0.186) Batch 0.758 (1.139) Remain 24:35:22 loss: 0.3779 Lr: 0.00270 [2024-02-18 18:45:33,699 INFO misc.py line 119 87073] Train: [51/100][133/1557] Data 0.014 (0.185) Batch 1.295 (1.140) Remain 24:36:54 loss: 0.1567 Lr: 0.00270 [2024-02-18 18:45:34,739 INFO misc.py line 119 87073] Train: [51/100][134/1557] Data 0.015 (0.183) Batch 1.037 (1.139) Remain 24:35:52 loss: 0.3837 Lr: 0.00270 [2024-02-18 18:45:35,924 INFO misc.py line 119 87073] Train: [51/100][135/1557] Data 0.019 (0.182) Batch 1.186 (1.140) Remain 24:36:19 loss: 0.1139 Lr: 0.00270 [2024-02-18 18:45:36,804 INFO misc.py line 119 87073] Train: [51/100][136/1557] Data 0.017 (0.181) Batch 0.893 (1.138) Remain 24:33:53 loss: 0.3542 Lr: 0.00270 [2024-02-18 18:45:37,847 INFO misc.py line 119 87073] Train: [51/100][137/1557] Data 0.004 (0.180) Batch 1.043 (1.137) Remain 24:32:57 loss: 0.6582 Lr: 0.00270 [2024-02-18 18:45:38,579 INFO misc.py line 119 87073] Train: [51/100][138/1557] Data 0.004 (0.178) Batch 0.731 (1.134) Remain 24:29:02 loss: 0.1987 Lr: 0.00270 [2024-02-18 18:45:39,280 INFO misc.py line 119 87073] Train: [51/100][139/1557] Data 0.004 (0.177) Batch 0.689 (1.131) Remain 24:24:47 loss: 0.2462 Lr: 0.00270 [2024-02-18 18:45:40,586 INFO misc.py line 119 87073] Train: [51/100][140/1557] Data 0.016 (0.176) Batch 1.307 (1.132) Remain 24:26:26 loss: 0.1019 Lr: 0.00270 [2024-02-18 18:45:41,446 INFO misc.py line 119 87073] Train: [51/100][141/1557] Data 0.015 (0.175) Batch 0.872 (1.130) Remain 24:23:58 loss: 0.1984 Lr: 0.00270 [2024-02-18 18:45:42,346 INFO misc.py line 119 87073] Train: [51/100][142/1557] Data 0.005 (0.173) Batch 0.899 (1.129) Remain 24:21:47 loss: 0.5691 Lr: 0.00270 [2024-02-18 18:45:43,282 INFO misc.py line 119 87073] Train: [51/100][143/1557] Data 0.005 (0.172) Batch 0.937 (1.127) Remain 24:19:59 loss: 0.3984 Lr: 0.00270 [2024-02-18 18:45:44,380 INFO misc.py line 119 87073] Train: [51/100][144/1557] Data 0.004 (0.171) Batch 1.097 (1.127) Remain 24:19:41 loss: 0.2942 Lr: 0.00270 [2024-02-18 18:45:45,104 INFO misc.py line 119 87073] Train: [51/100][145/1557] Data 0.005 (0.170) Batch 0.725 (1.124) Remain 24:16:00 loss: 0.2840 Lr: 0.00270 [2024-02-18 18:45:45,877 INFO misc.py line 119 87073] Train: [51/100][146/1557] Data 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24:03:55 loss: 0.2024 Lr: 0.00270 [2024-02-18 18:45:52,348 INFO misc.py line 119 87073] Train: [51/100][153/1557] Data 0.013 (0.161) Batch 0.750 (1.113) Remain 24:00:45 loss: 0.2653 Lr: 0.00270 [2024-02-18 18:45:53,588 INFO misc.py line 119 87073] Train: [51/100][154/1557] Data 0.004 (0.160) Batch 1.240 (1.113) Remain 24:01:49 loss: 0.1681 Lr: 0.00270 [2024-02-18 18:45:54,434 INFO misc.py line 119 87073] Train: [51/100][155/1557] Data 0.005 (0.159) Batch 0.845 (1.112) Remain 23:59:31 loss: 0.4415 Lr: 0.00270 [2024-02-18 18:45:55,452 INFO misc.py line 119 87073] Train: [51/100][156/1557] Data 0.005 (0.158) Batch 1.009 (1.111) Remain 23:58:38 loss: 0.3828 Lr: 0.00270 [2024-02-18 18:45:56,298 INFO misc.py line 119 87073] Train: [51/100][157/1557] Data 0.013 (0.157) Batch 0.856 (1.109) Remain 23:56:28 loss: 0.5093 Lr: 0.00270 [2024-02-18 18:45:57,117 INFO misc.py line 119 87073] Train: [51/100][158/1557] Data 0.004 (0.156) Batch 0.818 (1.107) Remain 23:54:01 loss: 0.6806 Lr: 0.00270 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line 119 87073] Train: [51/100][165/1557] Data 0.004 (0.150) Batch 0.970 (1.101) Remain 23:45:59 loss: 0.5491 Lr: 0.00270 [2024-02-18 18:46:04,661 INFO misc.py line 119 87073] Train: [51/100][166/1557] Data 0.003 (0.149) Batch 0.771 (1.099) Remain 23:43:20 loss: 0.2177 Lr: 0.00270 [2024-02-18 18:46:05,384 INFO misc.py line 119 87073] Train: [51/100][167/1557] Data 0.015 (0.148) Batch 0.734 (1.097) Remain 23:40:26 loss: 0.6686 Lr: 0.00270 [2024-02-18 18:46:06,519 INFO misc.py line 119 87073] Train: [51/100][168/1557] Data 0.004 (0.147) Batch 1.136 (1.097) Remain 23:40:43 loss: 0.1588 Lr: 0.00270 [2024-02-18 18:46:07,506 INFO misc.py line 119 87073] Train: [51/100][169/1557] Data 0.004 (0.146) Batch 0.987 (1.097) Remain 23:39:50 loss: 0.2057 Lr: 0.00270 [2024-02-18 18:46:08,540 INFO misc.py line 119 87073] Train: [51/100][170/1557] Data 0.004 (0.145) Batch 1.034 (1.096) Remain 23:39:20 loss: 0.4089 Lr: 0.00270 [2024-02-18 18:46:09,724 INFO misc.py line 119 87073] Train: 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Batch 0.928 (1.154) Remain 24:53:53 loss: 0.6355 Lr: 0.00270 [2024-02-18 18:46:27,209 INFO misc.py line 119 87073] Train: [51/100][178/1557] Data 0.004 (0.197) Batch 0.956 (1.153) Remain 24:52:24 loss: 0.5607 Lr: 0.00270 [2024-02-18 18:46:28,242 INFO misc.py line 119 87073] Train: [51/100][179/1557] Data 0.004 (0.196) Batch 1.032 (1.152) Remain 24:51:30 loss: 0.5239 Lr: 0.00270 [2024-02-18 18:46:30,971 INFO misc.py line 119 87073] Train: [51/100][180/1557] Data 1.428 (0.203) Batch 2.729 (1.161) Remain 25:03:01 loss: 0.3085 Lr: 0.00270 [2024-02-18 18:46:31,766 INFO misc.py line 119 87073] Train: [51/100][181/1557] Data 0.006 (0.202) Batch 0.794 (1.159) Remain 25:00:19 loss: 0.3349 Lr: 0.00270 [2024-02-18 18:46:32,883 INFO misc.py line 119 87073] Train: [51/100][182/1557] Data 0.006 (0.201) Batch 1.119 (1.159) Remain 25:00:01 loss: 0.2523 Lr: 0.00270 [2024-02-18 18:46:33,858 INFO misc.py line 119 87073] Train: [51/100][183/1557] Data 0.005 (0.200) Batch 0.975 (1.158) Remain 24:58:40 loss: 0.4974 Lr: 0.00270 [2024-02-18 18:46:35,091 INFO misc.py line 119 87073] Train: [51/100][184/1557] Data 0.004 (0.198) Batch 1.228 (1.158) Remain 24:59:09 loss: 0.3995 Lr: 0.00270 [2024-02-18 18:46:36,026 INFO misc.py line 119 87073] Train: [51/100][185/1557] Data 0.009 (0.197) Batch 0.938 (1.157) Remain 24:57:34 loss: 0.3554 Lr: 0.00270 [2024-02-18 18:46:37,013 INFO misc.py line 119 87073] Train: [51/100][186/1557] Data 0.007 (0.196) Batch 0.986 (1.156) Remain 24:56:21 loss: 0.3993 Lr: 0.00270 [2024-02-18 18:46:37,721 INFO misc.py line 119 87073] Train: [51/100][187/1557] Data 0.007 (0.195) Batch 0.711 (1.154) Remain 24:53:11 loss: 0.7078 Lr: 0.00270 [2024-02-18 18:46:38,446 INFO misc.py line 119 87073] Train: [51/100][188/1557] Data 0.005 (0.194) Batch 0.723 (1.151) Remain 24:50:10 loss: 0.3385 Lr: 0.00270 [2024-02-18 18:46:39,662 INFO misc.py line 119 87073] Train: [51/100][189/1557] Data 0.006 (0.193) Batch 1.218 (1.152) Remain 24:50:36 loss: 0.0905 Lr: 0.00270 [2024-02-18 18:46:40,532 INFO misc.py line 119 87073] Train: [51/100][190/1557] Data 0.004 (0.192) Batch 0.869 (1.150) Remain 24:48:38 loss: 0.2140 Lr: 0.00270 [2024-02-18 18:46:41,561 INFO misc.py line 119 87073] Train: [51/100][191/1557] Data 0.005 (0.191) Batch 1.030 (1.149) Remain 24:47:47 loss: 0.3961 Lr: 0.00270 [2024-02-18 18:46:42,588 INFO misc.py line 119 87073] Train: [51/100][192/1557] Data 0.004 (0.190) Batch 1.027 (1.149) Remain 24:46:56 loss: 0.4242 Lr: 0.00270 [2024-02-18 18:46:43,465 INFO misc.py line 119 87073] Train: [51/100][193/1557] Data 0.004 (0.189) Batch 0.877 (1.147) Remain 24:45:03 loss: 0.9209 Lr: 0.00270 [2024-02-18 18:46:44,196 INFO misc.py line 119 87073] Train: [51/100][194/1557] Data 0.005 (0.188) Batch 0.729 (1.145) Remain 24:42:12 loss: 0.3461 Lr: 0.00270 [2024-02-18 18:46:44,870 INFO misc.py line 119 87073] Train: [51/100][195/1557] Data 0.006 (0.187) Batch 0.676 (1.143) Remain 24:39:01 loss: 0.2211 Lr: 0.00270 [2024-02-18 18:46:46,218 INFO misc.py line 119 87073] Train: [51/100][196/1557] Data 0.004 (0.186) Batch 1.349 (1.144) Remain 24:40:23 loss: 0.2841 Lr: 0.00270 [2024-02-18 18:46:47,182 INFO misc.py line 119 87073] Train: [51/100][197/1557] Data 0.004 (0.186) Batch 0.964 (1.143) Remain 24:39:10 loss: 0.5295 Lr: 0.00270 [2024-02-18 18:46:48,112 INFO misc.py line 119 87073] Train: [51/100][198/1557] Data 0.004 (0.185) Batch 0.929 (1.142) Remain 24:37:43 loss: 0.3647 Lr: 0.00270 [2024-02-18 18:46:49,050 INFO misc.py line 119 87073] Train: [51/100][199/1557] Data 0.005 (0.184) Batch 0.939 (1.141) Remain 24:36:22 loss: 0.6665 Lr: 0.00270 [2024-02-18 18:46:50,010 INFO misc.py line 119 87073] Train: [51/100][200/1557] Data 0.003 (0.183) Batch 0.958 (1.140) Remain 24:35:09 loss: 0.4759 Lr: 0.00270 [2024-02-18 18:46:50,728 INFO misc.py line 119 87073] Train: [51/100][201/1557] Data 0.006 (0.182) Batch 0.719 (1.138) Remain 24:32:22 loss: 0.4603 Lr: 0.00270 [2024-02-18 18:46:51,389 INFO misc.py line 119 87073] Train: [51/100][202/1557] Data 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[2024-02-18 18:47:04,163 INFO misc.py line 119 87073] Train: [51/100][215/1557] Data 0.004 (0.170) Batch 0.718 (1.126) Remain 24:16:51 loss: 0.2051 Lr: 0.00270 [2024-02-18 18:47:04,958 INFO misc.py line 119 87073] Train: [51/100][216/1557] Data 0.013 (0.170) Batch 0.803 (1.124) Remain 24:14:52 loss: 0.2504 Lr: 0.00270 [2024-02-18 18:47:06,212 INFO misc.py line 119 87073] Train: [51/100][217/1557] Data 0.005 (0.169) Batch 1.242 (1.125) Remain 24:15:33 loss: 0.1321 Lr: 0.00270 [2024-02-18 18:47:07,305 INFO misc.py line 119 87073] Train: [51/100][218/1557] Data 0.017 (0.168) Batch 1.093 (1.125) Remain 24:15:20 loss: 0.2030 Lr: 0.00270 [2024-02-18 18:47:08,266 INFO misc.py line 119 87073] Train: [51/100][219/1557] Data 0.017 (0.167) Batch 0.974 (1.124) Remain 24:14:25 loss: 0.4808 Lr: 0.00270 [2024-02-18 18:47:09,178 INFO misc.py line 119 87073] Train: [51/100][220/1557] Data 0.004 (0.167) Batch 0.911 (1.123) Remain 24:13:08 loss: 0.3688 Lr: 0.00270 [2024-02-18 18:47:10,273 INFO misc.py line 119 87073] Train: [51/100][221/1557] Data 0.006 (0.166) Batch 1.042 (1.123) Remain 24:12:38 loss: 0.1970 Lr: 0.00270 [2024-02-18 18:47:11,059 INFO misc.py line 119 87073] Train: [51/100][222/1557] Data 0.058 (0.166) Batch 0.841 (1.121) Remain 24:10:57 loss: 0.1344 Lr: 0.00270 [2024-02-18 18:47:11,789 INFO misc.py line 119 87073] Train: [51/100][223/1557] Data 0.004 (0.165) Batch 0.729 (1.120) Remain 24:08:37 loss: 0.5979 Lr: 0.00270 [2024-02-18 18:47:13,047 INFO misc.py line 119 87073] Train: [51/100][224/1557] Data 0.004 (0.164) Batch 1.247 (1.120) Remain 24:09:21 loss: 0.1496 Lr: 0.00270 [2024-02-18 18:47:14,077 INFO misc.py line 119 87073] Train: [51/100][225/1557] Data 0.016 (0.163) Batch 1.031 (1.120) Remain 24:08:48 loss: 0.5371 Lr: 0.00270 [2024-02-18 18:47:15,018 INFO misc.py line 119 87073] Train: [51/100][226/1557] Data 0.014 (0.163) Batch 0.951 (1.119) Remain 24:07:48 loss: 0.2892 Lr: 0.00270 [2024-02-18 18:47:15,950 INFO misc.py line 119 87073] Train: 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Batch 0.908 (1.161) Remain 25:02:18 loss: 0.3752 Lr: 0.00270 [2024-02-18 18:47:33,491 INFO misc.py line 119 87073] Train: [51/100][234/1557] Data 0.005 (0.199) Batch 0.926 (1.160) Remain 25:00:58 loss: 0.3797 Lr: 0.00270 [2024-02-18 18:47:34,563 INFO misc.py line 119 87073] Train: [51/100][235/1557] Data 0.004 (0.198) Batch 1.062 (1.160) Remain 25:00:24 loss: 0.5470 Lr: 0.00270 [2024-02-18 18:47:35,336 INFO misc.py line 119 87073] Train: [51/100][236/1557] Data 0.015 (0.197) Batch 0.785 (1.158) Remain 24:58:18 loss: 0.1877 Lr: 0.00270 [2024-02-18 18:47:36,082 INFO misc.py line 119 87073] Train: [51/100][237/1557] Data 0.004 (0.196) Batch 0.738 (1.156) Remain 24:55:57 loss: 0.2818 Lr: 0.00270 [2024-02-18 18:47:37,287 INFO misc.py line 119 87073] Train: [51/100][238/1557] Data 0.010 (0.196) Batch 1.176 (1.157) Remain 24:56:03 loss: 0.2657 Lr: 0.00270 [2024-02-18 18:47:38,198 INFO misc.py line 119 87073] Train: [51/100][239/1557] Data 0.040 (0.195) Batch 0.947 (1.156) Remain 24:54:53 loss: 0.5206 Lr: 0.00270 [2024-02-18 18:47:39,197 INFO misc.py line 119 87073] Train: [51/100][240/1557] Data 0.004 (0.194) Batch 0.998 (1.155) Remain 24:54:00 loss: 0.3728 Lr: 0.00270 [2024-02-18 18:47:40,164 INFO misc.py line 119 87073] Train: [51/100][241/1557] Data 0.004 (0.193) Batch 0.968 (1.154) Remain 24:52:58 loss: 0.5054 Lr: 0.00270 [2024-02-18 18:47:41,152 INFO misc.py line 119 87073] Train: [51/100][242/1557] Data 0.004 (0.193) Batch 0.985 (1.154) Remain 24:52:02 loss: 0.5001 Lr: 0.00270 [2024-02-18 18:47:41,896 INFO misc.py line 119 87073] Train: [51/100][243/1557] Data 0.007 (0.192) Batch 0.737 (1.152) Remain 24:49:46 loss: 0.3560 Lr: 0.00270 [2024-02-18 18:47:42,656 INFO misc.py line 119 87073] Train: [51/100][244/1557] Data 0.013 (0.191) Batch 0.769 (1.150) Remain 24:47:41 loss: 0.5162 Lr: 0.00270 [2024-02-18 18:47:43,812 INFO misc.py line 119 87073] Train: [51/100][245/1557] Data 0.004 (0.190) Batch 1.157 (1.150) Remain 24:47:42 loss: 0.0816 Lr: 0.00270 [2024-02-18 18:47:44,717 INFO misc.py line 119 87073] Train: [51/100][246/1557] Data 0.005 (0.190) Batch 0.905 (1.149) Remain 24:46:23 loss: 0.2564 Lr: 0.00270 [2024-02-18 18:47:45,609 INFO misc.py line 119 87073] Train: [51/100][247/1557] Data 0.004 (0.189) Batch 0.885 (1.148) Remain 24:44:58 loss: 0.3005 Lr: 0.00270 [2024-02-18 18:47:46,385 INFO misc.py line 119 87073] Train: [51/100][248/1557] Data 0.010 (0.188) Batch 0.782 (1.147) Remain 24:43:01 loss: 0.2553 Lr: 0.00270 [2024-02-18 18:47:47,350 INFO misc.py line 119 87073] Train: [51/100][249/1557] Data 0.004 (0.187) Batch 0.966 (1.146) Remain 24:42:03 loss: 0.4183 Lr: 0.00270 [2024-02-18 18:47:48,120 INFO misc.py line 119 87073] Train: [51/100][250/1557] Data 0.003 (0.187) Batch 0.764 (1.144) Remain 24:40:02 loss: 0.3233 Lr: 0.00270 [2024-02-18 18:47:48,920 INFO misc.py line 119 87073] Train: [51/100][251/1557] Data 0.009 (0.186) Batch 0.805 (1.143) Remain 24:38:14 loss: 0.2407 Lr: 0.00269 [2024-02-18 18:47:50,264 INFO misc.py line 119 87073] Train: [51/100][252/1557] Data 0.004 (0.185) Batch 1.343 (1.144) Remain 24:39:16 loss: 0.1316 Lr: 0.00269 [2024-02-18 18:47:51,399 INFO misc.py line 119 87073] Train: [51/100][253/1557] Data 0.004 (0.184) Batch 1.130 (1.144) Remain 24:39:10 loss: 0.2199 Lr: 0.00269 [2024-02-18 18:47:52,603 INFO misc.py line 119 87073] Train: [51/100][254/1557] Data 0.009 (0.184) Batch 1.207 (1.144) Remain 24:39:29 loss: 0.4531 Lr: 0.00269 [2024-02-18 18:47:53,522 INFO misc.py line 119 87073] Train: [51/100][255/1557] Data 0.007 (0.183) Batch 0.922 (1.143) Remain 24:38:19 loss: 0.3098 Lr: 0.00269 [2024-02-18 18:47:54,414 INFO misc.py line 119 87073] Train: [51/100][256/1557] Data 0.004 (0.182) Batch 0.892 (1.142) Remain 24:37:01 loss: 0.1699 Lr: 0.00269 [2024-02-18 18:47:55,251 INFO misc.py line 119 87073] Train: [51/100][257/1557] Data 0.004 (0.182) Batch 0.800 (1.141) Remain 24:35:15 loss: 0.2287 Lr: 0.00269 [2024-02-18 18:47:56,023 INFO misc.py line 119 87073] Train: [51/100][258/1557] Data 0.041 (0.181) Batch 0.809 (1.139) Remain 24:33:33 loss: 0.3973 Lr: 0.00269 [2024-02-18 18:47:57,293 INFO misc.py line 119 87073] Train: [51/100][259/1557] Data 0.004 (0.180) Batch 1.264 (1.140) Remain 24:34:10 loss: 0.1785 Lr: 0.00269 [2024-02-18 18:47:58,271 INFO misc.py line 119 87073] Train: [51/100][260/1557] Data 0.009 (0.180) Batch 0.984 (1.139) Remain 24:33:22 loss: 0.3605 Lr: 0.00269 [2024-02-18 18:47:59,180 INFO misc.py line 119 87073] Train: [51/100][261/1557] Data 0.004 (0.179) Batch 0.908 (1.138) Remain 24:32:11 loss: 0.2196 Lr: 0.00269 [2024-02-18 18:48:00,119 INFO misc.py line 119 87073] Train: [51/100][262/1557] Data 0.006 (0.178) Batch 0.940 (1.138) Remain 24:31:10 loss: 0.1753 Lr: 0.00269 [2024-02-18 18:48:01,212 INFO misc.py line 119 87073] Train: [51/100][263/1557] Data 0.004 (0.178) Batch 1.081 (1.137) Remain 24:30:52 loss: 0.4400 Lr: 0.00269 [2024-02-18 18:48:01,988 INFO misc.py line 119 87073] Train: [51/100][264/1557] Data 0.016 (0.177) Batch 0.787 (1.136) Remain 24:29:07 loss: 0.2509 Lr: 0.00269 [2024-02-18 18:48:02,744 INFO misc.py line 119 87073] Train: [51/100][265/1557] Data 0.004 (0.176) Batch 0.757 (1.135) Remain 24:27:14 loss: 0.3107 Lr: 0.00269 [2024-02-18 18:48:04,014 INFO misc.py line 119 87073] Train: [51/100][266/1557] Data 0.004 (0.176) Batch 1.256 (1.135) Remain 24:27:48 loss: 0.1569 Lr: 0.00269 [2024-02-18 18:48:05,094 INFO misc.py line 119 87073] Train: [51/100][267/1557] Data 0.017 (0.175) Batch 1.086 (1.135) Remain 24:27:33 loss: 0.5870 Lr: 0.00269 [2024-02-18 18:48:06,009 INFO misc.py line 119 87073] Train: [51/100][268/1557] Data 0.012 (0.174) Batch 0.923 (1.134) Remain 24:26:30 loss: 0.1738 Lr: 0.00269 [2024-02-18 18:48:06,965 INFO misc.py line 119 87073] Train: [51/100][269/1557] Data 0.003 (0.174) Batch 0.955 (1.133) Remain 24:25:36 loss: 0.4102 Lr: 0.00269 [2024-02-18 18:48:07,860 INFO misc.py line 119 87073] Train: [51/100][270/1557] Data 0.006 (0.173) Batch 0.895 (1.133) Remain 24:24:26 loss: 0.1831 Lr: 0.00269 [2024-02-18 18:48:08,649 INFO misc.py line 119 87073] Train: [51/100][271/1557] Data 0.004 (0.173) Batch 0.780 (1.131) Remain 24:22:43 loss: 0.3687 Lr: 0.00269 [2024-02-18 18:48:09,403 INFO misc.py line 119 87073] Train: [51/100][272/1557] Data 0.012 (0.172) Batch 0.761 (1.130) Remain 24:20:55 loss: 0.4132 Lr: 0.00269 [2024-02-18 18:48:10,572 INFO misc.py line 119 87073] Train: [51/100][273/1557] Data 0.005 (0.171) Batch 1.171 (1.130) Remain 24:21:05 loss: 0.1448 Lr: 0.00269 [2024-02-18 18:48:11,526 INFO misc.py line 119 87073] Train: [51/100][274/1557] Data 0.004 (0.171) Batch 0.952 (1.129) Remain 24:20:13 loss: 0.3754 Lr: 0.00269 [2024-02-18 18:48:12,487 INFO misc.py line 119 87073] Train: [51/100][275/1557] Data 0.005 (0.170) Batch 0.961 (1.129) Remain 24:19:24 loss: 0.3737 Lr: 0.00269 [2024-02-18 18:48:13,415 INFO misc.py line 119 87073] Train: [51/100][276/1557] Data 0.005 (0.170) Batch 0.929 (1.128) Remain 24:18:26 loss: 0.5242 Lr: 0.00269 [2024-02-18 18:48:14,388 INFO misc.py line 119 87073] Train: [51/100][277/1557] Data 0.004 (0.169) Batch 0.973 (1.127) Remain 24:17:41 loss: 0.2651 Lr: 0.00269 [2024-02-18 18:48:15,113 INFO misc.py line 119 87073] Train: [51/100][278/1557] Data 0.005 (0.168) Batch 0.725 (1.126) Remain 24:15:47 loss: 0.2461 Lr: 0.00269 [2024-02-18 18:48:15,884 INFO misc.py line 119 87073] Train: [51/100][279/1557] Data 0.005 (0.168) Batch 0.761 (1.125) Remain 24:14:03 loss: 0.2311 Lr: 0.00269 [2024-02-18 18:48:17,063 INFO misc.py line 119 87073] Train: [51/100][280/1557] Data 0.014 (0.167) Batch 1.182 (1.125) Remain 24:14:18 loss: 0.2375 Lr: 0.00269 [2024-02-18 18:48:17,937 INFO misc.py line 119 87073] Train: [51/100][281/1557] Data 0.011 (0.167) Batch 0.880 (1.124) Remain 24:13:08 loss: 0.1756 Lr: 0.00269 [2024-02-18 18:48:18,948 INFO misc.py line 119 87073] Train: [51/100][282/1557] Data 0.006 (0.166) Batch 1.013 (1.124) Remain 24:12:36 loss: 0.3135 Lr: 0.00269 [2024-02-18 18:48:19,808 INFO misc.py line 119 87073] Train: 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Batch 1.050 (1.156) Remain 24:54:54 loss: 0.5846 Lr: 0.00269 [2024-02-18 18:48:37,222 INFO misc.py line 119 87073] Train: [51/100][290/1557] Data 0.004 (0.198) Batch 1.023 (1.156) Remain 24:54:16 loss: 0.3281 Lr: 0.00269 [2024-02-18 18:48:38,142 INFO misc.py line 119 87073] Train: [51/100][291/1557] Data 0.004 (0.197) Batch 0.920 (1.155) Remain 24:53:12 loss: 0.4168 Lr: 0.00269 [2024-02-18 18:48:38,918 INFO misc.py line 119 87073] Train: [51/100][292/1557] Data 0.004 (0.197) Batch 0.771 (1.154) Remain 24:51:28 loss: 0.1490 Lr: 0.00269 [2024-02-18 18:48:39,634 INFO misc.py line 119 87073] Train: [51/100][293/1557] Data 0.009 (0.196) Batch 0.721 (1.152) Remain 24:49:31 loss: 0.2189 Lr: 0.00269 [2024-02-18 18:48:40,840 INFO misc.py line 119 87073] Train: [51/100][294/1557] Data 0.004 (0.195) Batch 1.206 (1.153) Remain 24:49:44 loss: 0.2186 Lr: 0.00269 [2024-02-18 18:48:41,780 INFO misc.py line 119 87073] Train: [51/100][295/1557] Data 0.004 (0.195) Batch 0.941 (1.152) Remain 24:48:46 loss: 0.3575 Lr: 0.00269 [2024-02-18 18:48:42,741 INFO misc.py line 119 87073] Train: [51/100][296/1557] Data 0.004 (0.194) Batch 0.960 (1.151) Remain 24:47:55 loss: 0.4134 Lr: 0.00269 [2024-02-18 18:48:43,625 INFO misc.py line 119 87073] Train: [51/100][297/1557] Data 0.004 (0.193) Batch 0.874 (1.150) Remain 24:46:40 loss: 0.7703 Lr: 0.00269 [2024-02-18 18:48:44,691 INFO misc.py line 119 87073] Train: [51/100][298/1557] Data 0.014 (0.193) Batch 1.067 (1.150) Remain 24:46:17 loss: 0.2421 Lr: 0.00269 [2024-02-18 18:48:45,440 INFO misc.py line 119 87073] Train: [51/100][299/1557] Data 0.013 (0.192) Batch 0.758 (1.149) Remain 24:44:33 loss: 0.4423 Lr: 0.00269 [2024-02-18 18:48:46,210 INFO misc.py line 119 87073] Train: [51/100][300/1557] Data 0.004 (0.192) Batch 0.758 (1.147) Remain 24:42:50 loss: 0.2313 Lr: 0.00269 [2024-02-18 18:48:47,482 INFO misc.py line 119 87073] Train: [51/100][301/1557] Data 0.016 (0.191) Batch 1.272 (1.148) Remain 24:43:22 loss: 0.1005 Lr: 0.00269 [2024-02-18 18:48:48,364 INFO misc.py line 119 87073] Train: [51/100][302/1557] Data 0.016 (0.190) Batch 0.893 (1.147) Remain 24:42:14 loss: 0.4681 Lr: 0.00269 [2024-02-18 18:48:49,313 INFO misc.py line 119 87073] Train: [51/100][303/1557] Data 0.005 (0.190) Batch 0.949 (1.146) Remain 24:41:22 loss: 0.1808 Lr: 0.00269 [2024-02-18 18:48:50,215 INFO misc.py line 119 87073] Train: [51/100][304/1557] Data 0.005 (0.189) Batch 0.903 (1.145) Remain 24:40:18 loss: 0.3234 Lr: 0.00269 [2024-02-18 18:48:51,169 INFO misc.py line 119 87073] Train: [51/100][305/1557] Data 0.004 (0.189) Batch 0.944 (1.145) Remain 24:39:26 loss: 0.2610 Lr: 0.00269 [2024-02-18 18:48:51,945 INFO misc.py line 119 87073] Train: [51/100][306/1557] Data 0.014 (0.188) Batch 0.785 (1.144) Remain 24:37:52 loss: 0.2877 Lr: 0.00269 [2024-02-18 18:48:52,742 INFO misc.py line 119 87073] Train: [51/100][307/1557] Data 0.005 (0.187) Batch 0.796 (1.142) Remain 24:36:23 loss: 0.4409 Lr: 0.00269 [2024-02-18 18:48:54,013 INFO misc.py line 119 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Batch 1.121 (1.151) Remain 24:46:30 loss: 0.3969 Lr: 0.00269 [2024-02-18 18:49:39,994 INFO misc.py line 119 87073] Train: [51/100][346/1557] Data 0.004 (0.196) Batch 0.972 (1.150) Remain 24:45:48 loss: 0.4481 Lr: 0.00269 [2024-02-18 18:49:40,819 INFO misc.py line 119 87073] Train: [51/100][347/1557] Data 0.003 (0.195) Batch 0.823 (1.149) Remain 24:44:34 loss: 0.4809 Lr: 0.00269 [2024-02-18 18:49:41,564 INFO misc.py line 119 87073] Train: [51/100][348/1557] Data 0.005 (0.195) Batch 0.735 (1.148) Remain 24:42:59 loss: 0.2357 Lr: 0.00269 [2024-02-18 18:49:42,296 INFO misc.py line 119 87073] Train: [51/100][349/1557] Data 0.015 (0.194) Batch 0.743 (1.147) Remain 24:41:28 loss: 0.2268 Lr: 0.00269 [2024-02-18 18:49:43,416 INFO misc.py line 119 87073] Train: [51/100][350/1557] Data 0.004 (0.193) Batch 1.119 (1.147) Remain 24:41:20 loss: 0.2417 Lr: 0.00269 [2024-02-18 18:49:44,213 INFO misc.py line 119 87073] Train: [51/100][351/1557] Data 0.005 (0.193) Batch 0.798 (1.146) Remain 24:40:01 loss: 0.2595 Lr: 0.00269 [2024-02-18 18:49:45,010 INFO misc.py line 119 87073] Train: [51/100][352/1557] Data 0.004 (0.192) Batch 0.795 (1.145) Remain 24:38:42 loss: 0.2136 Lr: 0.00269 [2024-02-18 18:49:45,993 INFO misc.py line 119 87073] Train: [51/100][353/1557] Data 0.007 (0.192) Batch 0.984 (1.144) Remain 24:38:06 loss: 0.3403 Lr: 0.00269 [2024-02-18 18:49:47,030 INFO misc.py line 119 87073] Train: [51/100][354/1557] Data 0.005 (0.191) Batch 1.038 (1.144) Remain 24:37:41 loss: 0.4443 Lr: 0.00269 [2024-02-18 18:49:49,582 INFO misc.py line 119 87073] Train: [51/100][355/1557] Data 1.068 (0.194) Batch 2.543 (1.148) Remain 24:42:48 loss: 0.3054 Lr: 0.00269 [2024-02-18 18:49:50,394 INFO misc.py line 119 87073] Train: [51/100][356/1557] Data 0.014 (0.193) Batch 0.820 (1.147) Remain 24:41:35 loss: 0.1856 Lr: 0.00269 [2024-02-18 18:49:51,651 INFO misc.py line 119 87073] Train: [51/100][357/1557] Data 0.004 (0.193) Batch 1.246 (1.147) Remain 24:41:55 loss: 0.0802 Lr: 0.00269 [2024-02-18 18:49:52,732 INFO misc.py line 119 87073] Train: [51/100][358/1557] Data 0.016 (0.192) Batch 1.080 (1.147) Remain 24:41:39 loss: 0.2401 Lr: 0.00269 [2024-02-18 18:49:53,684 INFO misc.py line 119 87073] Train: [51/100][359/1557] Data 0.017 (0.192) Batch 0.965 (1.147) Remain 24:40:58 loss: 0.1094 Lr: 0.00269 [2024-02-18 18:49:54,798 INFO misc.py line 119 87073] Train: [51/100][360/1557] Data 0.004 (0.191) Batch 1.113 (1.147) Remain 24:40:50 loss: 0.5113 Lr: 0.00269 [2024-02-18 18:49:55,750 INFO misc.py line 119 87073] Train: [51/100][361/1557] Data 0.005 (0.191) Batch 0.953 (1.146) Remain 24:40:07 loss: 0.3502 Lr: 0.00269 [2024-02-18 18:49:56,470 INFO misc.py line 119 87073] Train: [51/100][362/1557] Data 0.004 (0.190) Batch 0.717 (1.145) Remain 24:38:33 loss: 0.3697 Lr: 0.00269 [2024-02-18 18:49:57,203 INFO misc.py line 119 87073] Train: [51/100][363/1557] Data 0.007 (0.190) Batch 0.735 (1.144) Remain 24:37:04 loss: 0.3677 Lr: 0.00269 [2024-02-18 18:49:58,454 INFO misc.py line 119 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[2024-02-18 18:50:15,894 INFO misc.py line 119 87073] Train: [51/100][383/1557] Data 0.011 (0.180) Batch 0.739 (1.133) Remain 24:22:28 loss: 0.3681 Lr: 0.00269 [2024-02-18 18:50:16,719 INFO misc.py line 119 87073] Train: [51/100][384/1557] Data 0.004 (0.180) Batch 0.813 (1.132) Remain 24:21:22 loss: 0.6006 Lr: 0.00269 [2024-02-18 18:50:17,941 INFO misc.py line 119 87073] Train: [51/100][385/1557] Data 0.015 (0.179) Batch 1.223 (1.132) Remain 24:21:40 loss: 0.2068 Lr: 0.00269 [2024-02-18 18:50:18,872 INFO misc.py line 119 87073] Train: [51/100][386/1557] Data 0.014 (0.179) Batch 0.941 (1.132) Remain 24:21:00 loss: 0.3282 Lr: 0.00269 [2024-02-18 18:50:19,899 INFO misc.py line 119 87073] Train: [51/100][387/1557] Data 0.004 (0.178) Batch 1.027 (1.131) Remain 24:20:38 loss: 0.2580 Lr: 0.00269 [2024-02-18 18:50:20,768 INFO misc.py line 119 87073] Train: [51/100][388/1557] Data 0.004 (0.178) Batch 0.867 (1.131) Remain 24:19:43 loss: 0.4292 Lr: 0.00269 [2024-02-18 18:50:21,665 INFO misc.py line 119 87073] Train: [51/100][389/1557] Data 0.007 (0.178) Batch 0.899 (1.130) Remain 24:18:56 loss: 0.2470 Lr: 0.00269 [2024-02-18 18:50:22,388 INFO misc.py line 119 87073] Train: [51/100][390/1557] Data 0.005 (0.177) Batch 0.722 (1.129) Remain 24:17:33 loss: 0.2092 Lr: 0.00269 [2024-02-18 18:50:23,214 INFO misc.py line 119 87073] Train: [51/100][391/1557] Data 0.006 (0.177) Batch 0.817 (1.128) Remain 24:16:29 loss: 0.2797 Lr: 0.00269 [2024-02-18 18:50:24,438 INFO misc.py line 119 87073] Train: [51/100][392/1557] Data 0.015 (0.176) Batch 1.231 (1.128) Remain 24:16:49 loss: 0.2259 Lr: 0.00269 [2024-02-18 18:50:25,452 INFO misc.py line 119 87073] Train: [51/100][393/1557] Data 0.008 (0.176) Batch 1.017 (1.128) Remain 24:16:25 loss: 0.2291 Lr: 0.00269 [2024-02-18 18:50:26,460 INFO misc.py line 119 87073] Train: [51/100][394/1557] Data 0.005 (0.175) Batch 1.006 (1.128) Remain 24:16:00 loss: 0.1061 Lr: 0.00269 [2024-02-18 18:50:27,296 INFO misc.py line 119 87073] Train: 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Batch 1.014 (1.152) Remain 24:47:13 loss: 0.2514 Lr: 0.00269 [2024-02-18 18:50:45,099 INFO misc.py line 119 87073] Train: [51/100][402/1557] Data 0.003 (0.197) Batch 1.082 (1.152) Remain 24:46:58 loss: 0.4051 Lr: 0.00269 [2024-02-18 18:50:45,923 INFO misc.py line 119 87073] Train: [51/100][403/1557] Data 0.004 (0.197) Batch 0.824 (1.151) Remain 24:45:53 loss: 0.3617 Lr: 0.00269 [2024-02-18 18:50:46,696 INFO misc.py line 119 87073] Train: [51/100][404/1557] Data 0.004 (0.196) Batch 0.765 (1.150) Remain 24:44:38 loss: 0.5423 Lr: 0.00269 [2024-02-18 18:50:47,455 INFO misc.py line 119 87073] Train: [51/100][405/1557] Data 0.013 (0.196) Batch 0.767 (1.149) Remain 24:43:23 loss: 0.5790 Lr: 0.00269 [2024-02-18 18:50:48,545 INFO misc.py line 119 87073] Train: [51/100][406/1557] Data 0.005 (0.195) Batch 1.090 (1.149) Remain 24:43:10 loss: 0.2253 Lr: 0.00269 [2024-02-18 18:50:49,601 INFO misc.py line 119 87073] Train: [51/100][407/1557] Data 0.004 (0.195) Batch 1.054 (1.149) Remain 24:42:51 loss: 0.5243 Lr: 0.00269 [2024-02-18 18:50:50,597 INFO misc.py line 119 87073] Train: [51/100][408/1557] Data 0.006 (0.194) Batch 0.998 (1.148) Remain 24:42:21 loss: 0.3405 Lr: 0.00269 [2024-02-18 18:50:51,575 INFO misc.py line 119 87073] Train: [51/100][409/1557] Data 0.004 (0.194) Batch 0.978 (1.148) Remain 24:41:47 loss: 0.6263 Lr: 0.00269 [2024-02-18 18:50:52,610 INFO misc.py line 119 87073] Train: [51/100][410/1557] Data 0.004 (0.193) Batch 1.036 (1.148) Remain 24:41:25 loss: 0.4472 Lr: 0.00269 [2024-02-18 18:50:53,350 INFO misc.py line 119 87073] Train: [51/100][411/1557] Data 0.003 (0.193) Batch 0.740 (1.147) Remain 24:40:06 loss: 0.3150 Lr: 0.00269 [2024-02-18 18:50:54,121 INFO misc.py line 119 87073] Train: [51/100][412/1557] Data 0.004 (0.192) Batch 0.763 (1.146) Remain 24:38:52 loss: 0.2385 Lr: 0.00269 [2024-02-18 18:50:55,325 INFO misc.py line 119 87073] Train: [51/100][413/1557] Data 0.012 (0.192) Batch 1.201 (1.146) Remain 24:39:01 loss: 0.1074 Lr: 0.00269 [2024-02-18 18:50:56,524 INFO misc.py line 119 87073] Train: [51/100][414/1557] Data 0.015 (0.192) Batch 1.198 (1.146) Remain 24:39:10 loss: 0.7015 Lr: 0.00269 [2024-02-18 18:50:57,390 INFO misc.py line 119 87073] Train: [51/100][415/1557] Data 0.017 (0.191) Batch 0.879 (1.145) Remain 24:38:19 loss: 0.3782 Lr: 0.00269 [2024-02-18 18:50:58,600 INFO misc.py line 119 87073] Train: [51/100][416/1557] Data 0.003 (0.191) Batch 1.198 (1.146) Remain 24:38:27 loss: 0.1873 Lr: 0.00269 [2024-02-18 18:50:59,547 INFO misc.py line 119 87073] Train: [51/100][417/1557] Data 0.016 (0.190) Batch 0.957 (1.145) Remain 24:37:51 loss: 0.2143 Lr: 0.00269 [2024-02-18 18:51:00,360 INFO misc.py line 119 87073] Train: [51/100][418/1557] Data 0.006 (0.190) Batch 0.813 (1.144) Remain 24:36:48 loss: 0.3287 Lr: 0.00269 [2024-02-18 18:51:01,143 INFO misc.py line 119 87073] Train: [51/100][419/1557] Data 0.006 (0.189) Batch 0.783 (1.143) Remain 24:35:39 loss: 0.2965 Lr: 0.00269 [2024-02-18 18:51:02,361 INFO misc.py line 119 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line 119 87073] Train: [51/100][445/1557] Data 0.006 (0.179) Batch 0.871 (1.134) Remain 24:22:35 loss: 0.5308 Lr: 0.00268 [2024-02-18 18:51:27,399 INFO misc.py line 119 87073] Train: [51/100][446/1557] Data 0.004 (0.178) Batch 0.833 (1.133) Remain 24:21:41 loss: 0.2950 Lr: 0.00268 [2024-02-18 18:51:28,157 INFO misc.py line 119 87073] Train: [51/100][447/1557] Data 0.008 (0.178) Batch 0.763 (1.132) Remain 24:20:35 loss: 0.2001 Lr: 0.00268 [2024-02-18 18:51:29,316 INFO misc.py line 119 87073] Train: [51/100][448/1557] Data 0.004 (0.177) Batch 1.157 (1.132) Remain 24:20:39 loss: 0.1565 Lr: 0.00268 [2024-02-18 18:51:30,303 INFO misc.py line 119 87073] Train: [51/100][449/1557] Data 0.005 (0.177) Batch 0.988 (1.132) Remain 24:20:13 loss: 0.3658 Lr: 0.00268 [2024-02-18 18:51:31,353 INFO misc.py line 119 87073] Train: [51/100][450/1557] Data 0.004 (0.177) Batch 1.048 (1.132) Remain 24:19:57 loss: 0.3347 Lr: 0.00268 [2024-02-18 18:51:32,497 INFO misc.py line 119 87073] Train: 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Batch 0.990 (1.153) Remain 24:47:15 loss: 0.7473 Lr: 0.00268 [2024-02-18 18:51:49,796 INFO misc.py line 119 87073] Train: [51/100][458/1557] Data 0.004 (0.197) Batch 0.868 (1.152) Remain 24:46:25 loss: 0.5348 Lr: 0.00268 [2024-02-18 18:51:50,800 INFO misc.py line 119 87073] Train: [51/100][459/1557] Data 0.004 (0.196) Batch 0.998 (1.152) Remain 24:45:58 loss: 0.2140 Lr: 0.00268 [2024-02-18 18:51:51,542 INFO misc.py line 119 87073] Train: [51/100][460/1557] Data 0.009 (0.196) Batch 0.746 (1.151) Remain 24:44:48 loss: 0.2281 Lr: 0.00268 [2024-02-18 18:51:52,301 INFO misc.py line 119 87073] Train: [51/100][461/1557] Data 0.005 (0.196) Batch 0.749 (1.150) Remain 24:43:39 loss: 0.2478 Lr: 0.00268 [2024-02-18 18:51:53,436 INFO misc.py line 119 87073] Train: [51/100][462/1557] Data 0.015 (0.195) Batch 1.136 (1.150) Remain 24:43:35 loss: 0.2477 Lr: 0.00268 [2024-02-18 18:51:54,562 INFO misc.py line 119 87073] Train: [51/100][463/1557] Data 0.014 (0.195) Batch 1.127 (1.150) Remain 24:43:30 loss: 0.3206 Lr: 0.00268 [2024-02-18 18:51:55,586 INFO misc.py line 119 87073] Train: [51/100][464/1557] Data 0.013 (0.194) Batch 1.023 (1.150) Remain 24:43:08 loss: 0.5293 Lr: 0.00268 [2024-02-18 18:51:56,782 INFO misc.py line 119 87073] Train: [51/100][465/1557] Data 0.014 (0.194) Batch 1.193 (1.150) Remain 24:43:14 loss: 0.2058 Lr: 0.00268 [2024-02-18 18:51:57,764 INFO misc.py line 119 87073] Train: [51/100][466/1557] Data 0.017 (0.194) Batch 0.995 (1.150) Remain 24:42:47 loss: 0.2650 Lr: 0.00268 [2024-02-18 18:51:58,538 INFO misc.py line 119 87073] Train: [51/100][467/1557] Data 0.004 (0.193) Batch 0.773 (1.149) Remain 24:41:43 loss: 0.1452 Lr: 0.00268 [2024-02-18 18:51:59,324 INFO misc.py line 119 87073] Train: [51/100][468/1557] Data 0.005 (0.193) Batch 0.780 (1.148) Remain 24:40:40 loss: 0.2856 Lr: 0.00268 [2024-02-18 18:52:00,555 INFO misc.py line 119 87073] Train: [51/100][469/1557] Data 0.010 (0.192) Batch 1.232 (1.148) Remain 24:40:53 loss: 0.0747 Lr: 0.00268 [2024-02-18 18:52:01,398 INFO misc.py line 119 87073] Train: [51/100][470/1557] Data 0.009 (0.192) Batch 0.849 (1.148) Remain 24:40:02 loss: 0.3627 Lr: 0.00268 [2024-02-18 18:52:02,641 INFO misc.py line 119 87073] Train: [51/100][471/1557] Data 0.004 (0.192) Batch 1.199 (1.148) Remain 24:40:10 loss: 0.2617 Lr: 0.00268 [2024-02-18 18:52:03,668 INFO misc.py line 119 87073] Train: [51/100][472/1557] Data 0.047 (0.191) Batch 1.060 (1.148) Remain 24:39:54 loss: 0.3523 Lr: 0.00268 [2024-02-18 18:52:04,573 INFO misc.py line 119 87073] Train: [51/100][473/1557] Data 0.014 (0.191) Batch 0.915 (1.147) Remain 24:39:15 loss: 0.5987 Lr: 0.00268 [2024-02-18 18:52:05,358 INFO misc.py line 119 87073] Train: [51/100][474/1557] Data 0.003 (0.190) Batch 0.784 (1.146) Remain 24:38:14 loss: 0.4831 Lr: 0.00268 [2024-02-18 18:52:06,162 INFO misc.py line 119 87073] Train: [51/100][475/1557] Data 0.006 (0.190) Batch 0.800 (1.146) Remain 24:37:16 loss: 0.4343 Lr: 0.00268 [2024-02-18 18:52:07,450 INFO misc.py line 119 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Batch 0.866 (1.152) Remain 24:44:21 loss: 0.4021 Lr: 0.00268 [2024-02-18 18:52:53,818 INFO misc.py line 119 87073] Train: [51/100][514/1557] Data 0.005 (0.195) Batch 1.039 (1.151) Remain 24:44:03 loss: 0.6581 Lr: 0.00268 [2024-02-18 18:52:54,841 INFO misc.py line 119 87073] Train: [51/100][515/1557] Data 0.005 (0.195) Batch 1.023 (1.151) Remain 24:43:43 loss: 0.3493 Lr: 0.00268 [2024-02-18 18:52:55,594 INFO misc.py line 119 87073] Train: [51/100][516/1557] Data 0.004 (0.194) Batch 0.751 (1.150) Remain 24:42:41 loss: 0.3746 Lr: 0.00268 [2024-02-18 18:52:56,364 INFO misc.py line 119 87073] Train: [51/100][517/1557] Data 0.005 (0.194) Batch 0.761 (1.150) Remain 24:41:41 loss: 0.5211 Lr: 0.00268 [2024-02-18 18:52:57,489 INFO misc.py line 119 87073] Train: [51/100][518/1557] Data 0.015 (0.194) Batch 1.131 (1.150) Remain 24:41:37 loss: 0.2170 Lr: 0.00268 [2024-02-18 18:52:58,377 INFO misc.py line 119 87073] Train: [51/100][519/1557] Data 0.009 (0.193) Batch 0.893 (1.149) Remain 24:40:58 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18:53:05,119 INFO misc.py line 119 87073] Train: [51/100][526/1557] Data 0.006 (0.191) Batch 0.892 (1.147) Remain 24:37:37 loss: 0.3563 Lr: 0.00268 [2024-02-18 18:53:06,196 INFO misc.py line 119 87073] Train: [51/100][527/1557] Data 0.005 (0.190) Batch 1.077 (1.146) Remain 24:37:26 loss: 0.5304 Lr: 0.00268 [2024-02-18 18:53:07,405 INFO misc.py line 119 87073] Train: [51/100][528/1557] Data 0.005 (0.190) Batch 1.209 (1.147) Remain 24:37:34 loss: 0.3268 Lr: 0.00268 [2024-02-18 18:53:08,374 INFO misc.py line 119 87073] Train: [51/100][529/1557] Data 0.006 (0.190) Batch 0.970 (1.146) Remain 24:37:07 loss: 0.5126 Lr: 0.00268 [2024-02-18 18:53:11,101 INFO misc.py line 119 87073] Train: [51/100][530/1557] Data 1.008 (0.191) Batch 2.727 (1.149) Remain 24:40:58 loss: 0.2251 Lr: 0.00268 [2024-02-18 18:53:11,919 INFO misc.py line 119 87073] Train: [51/100][531/1557] Data 0.004 (0.191) Batch 0.812 (1.149) Remain 24:40:07 loss: 0.2688 Lr: 0.00268 [2024-02-18 18:53:13,200 INFO misc.py line 119 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[2024-02-18 18:53:31,863 INFO misc.py line 119 87073] Train: [51/100][551/1557] Data 0.014 (0.184) Batch 0.713 (1.143) Remain 24:32:38 loss: 0.4012 Lr: 0.00268 [2024-02-18 18:53:32,526 INFO misc.py line 119 87073] Train: [51/100][552/1557] Data 0.004 (0.184) Batch 0.654 (1.142) Remain 24:31:28 loss: 0.3304 Lr: 0.00268 [2024-02-18 18:53:33,753 INFO misc.py line 119 87073] Train: [51/100][553/1557] Data 0.013 (0.183) Batch 1.235 (1.142) Remain 24:31:40 loss: 0.1137 Lr: 0.00268 [2024-02-18 18:53:34,785 INFO misc.py line 119 87073] Train: [51/100][554/1557] Data 0.006 (0.183) Batch 1.033 (1.142) Remain 24:31:23 loss: 0.1470 Lr: 0.00268 [2024-02-18 18:53:36,098 INFO misc.py line 119 87073] Train: [51/100][555/1557] Data 0.005 (0.183) Batch 1.307 (1.142) Remain 24:31:45 loss: 0.1981 Lr: 0.00268 [2024-02-18 18:53:37,306 INFO misc.py line 119 87073] Train: [51/100][556/1557] Data 0.010 (0.183) Batch 1.204 (1.143) Remain 24:31:53 loss: 0.5011 Lr: 0.00268 [2024-02-18 18:53:38,309 INFO misc.py line 119 87073] Train: [51/100][557/1557] Data 0.013 (0.182) Batch 1.009 (1.142) Remain 24:31:33 loss: 0.6690 Lr: 0.00268 [2024-02-18 18:53:39,026 INFO misc.py line 119 87073] Train: [51/100][558/1557] Data 0.008 (0.182) Batch 0.721 (1.142) Remain 24:30:33 loss: 0.2430 Lr: 0.00268 [2024-02-18 18:53:39,871 INFO misc.py line 119 87073] Train: [51/100][559/1557] Data 0.004 (0.182) Batch 0.834 (1.141) Remain 24:29:49 loss: 0.2307 Lr: 0.00268 [2024-02-18 18:53:40,978 INFO misc.py line 119 87073] Train: [51/100][560/1557] Data 0.015 (0.181) Batch 1.109 (1.141) Remain 24:29:44 loss: 0.1347 Lr: 0.00268 [2024-02-18 18:53:41,962 INFO misc.py line 119 87073] Train: [51/100][561/1557] Data 0.013 (0.181) Batch 0.993 (1.141) Remain 24:29:22 loss: 0.1565 Lr: 0.00268 [2024-02-18 18:53:42,890 INFO misc.py line 119 87073] Train: [51/100][562/1557] Data 0.004 (0.181) Batch 0.927 (1.140) Remain 24:28:51 loss: 0.3093 Lr: 0.00268 [2024-02-18 18:53:43,785 INFO misc.py line 119 87073] Train: 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Batch 1.057 (1.159) Remain 24:52:18 loss: 0.3098 Lr: 0.00268 [2024-02-18 18:54:02,211 INFO misc.py line 119 87073] Train: [51/100][570/1557] Data 0.003 (0.196) Batch 0.983 (1.158) Remain 24:51:52 loss: 0.4115 Lr: 0.00268 [2024-02-18 18:54:03,167 INFO misc.py line 119 87073] Train: [51/100][571/1557] Data 0.004 (0.195) Batch 0.956 (1.158) Remain 24:51:24 loss: 0.1386 Lr: 0.00268 [2024-02-18 18:54:03,980 INFO misc.py line 119 87073] Train: [51/100][572/1557] Data 0.004 (0.195) Batch 0.809 (1.157) Remain 24:50:35 loss: 0.1833 Lr: 0.00268 [2024-02-18 18:54:04,749 INFO misc.py line 119 87073] Train: [51/100][573/1557] Data 0.008 (0.195) Batch 0.773 (1.157) Remain 24:49:42 loss: 0.4514 Lr: 0.00268 [2024-02-18 18:54:05,936 INFO misc.py line 119 87073] Train: [51/100][574/1557] Data 0.004 (0.194) Batch 1.185 (1.157) Remain 24:49:45 loss: 0.2372 Lr: 0.00268 [2024-02-18 18:54:06,764 INFO misc.py line 119 87073] Train: [51/100][575/1557] Data 0.006 (0.194) Batch 0.829 (1.156) Remain 24:48:59 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Batch 0.889 (1.156) Remain 24:47:50 loss: 0.3183 Lr: 0.00268 [2024-02-18 18:55:05,342 INFO misc.py line 119 87073] Train: [51/100][626/1557] Data 0.004 (0.195) Batch 0.866 (1.156) Remain 24:47:12 loss: 0.4587 Lr: 0.00268 [2024-02-18 18:55:06,279 INFO misc.py line 119 87073] Train: [51/100][627/1557] Data 0.005 (0.195) Batch 0.937 (1.155) Remain 24:46:44 loss: 0.0482 Lr: 0.00268 [2024-02-18 18:55:06,959 INFO misc.py line 119 87073] Train: [51/100][628/1557] Data 0.005 (0.194) Batch 0.681 (1.154) Remain 24:45:44 loss: 0.2756 Lr: 0.00268 [2024-02-18 18:55:07,700 INFO misc.py line 119 87073] Train: [51/100][629/1557] Data 0.004 (0.194) Batch 0.733 (1.154) Remain 24:44:51 loss: 0.2260 Lr: 0.00267 [2024-02-18 18:55:08,894 INFO misc.py line 119 87073] Train: [51/100][630/1557] Data 0.012 (0.194) Batch 1.200 (1.154) Remain 24:44:56 loss: 0.2682 Lr: 0.00267 [2024-02-18 18:55:09,830 INFO misc.py line 119 87073] Train: [51/100][631/1557] Data 0.005 (0.194) Batch 0.938 (1.153) Remain 24:44:28 loss: 0.2057 Lr: 0.00267 [2024-02-18 18:55:10,953 INFO misc.py line 119 87073] Train: [51/100][632/1557] Data 0.005 (0.193) Batch 1.123 (1.153) Remain 24:44:23 loss: 0.4188 Lr: 0.00267 [2024-02-18 18:55:11,777 INFO misc.py line 119 87073] Train: [51/100][633/1557] Data 0.004 (0.193) Batch 0.823 (1.153) Remain 24:43:42 loss: 0.1056 Lr: 0.00267 [2024-02-18 18:55:12,761 INFO misc.py line 119 87073] Train: [51/100][634/1557] Data 0.005 (0.193) Batch 0.981 (1.153) Remain 24:43:20 loss: 0.9276 Lr: 0.00267 [2024-02-18 18:55:13,521 INFO misc.py line 119 87073] Train: [51/100][635/1557] Data 0.008 (0.192) Batch 0.763 (1.152) Remain 24:42:31 loss: 0.2224 Lr: 0.00267 [2024-02-18 18:55:14,301 INFO misc.py line 119 87073] Train: [51/100][636/1557] Data 0.005 (0.192) Batch 0.767 (1.151) Remain 24:41:43 loss: 0.4369 Lr: 0.00267 [2024-02-18 18:55:15,487 INFO misc.py line 119 87073] Train: [51/100][637/1557] Data 0.018 (0.192) Batch 1.198 (1.151) Remain 24:41:47 loss: 0.1478 Lr: 0.00267 [2024-02-18 18:55:16,260 INFO misc.py line 119 87073] Train: [51/100][638/1557] Data 0.006 (0.192) Batch 0.773 (1.151) Remain 24:41:00 loss: 0.4377 Lr: 0.00267 [2024-02-18 18:55:17,424 INFO misc.py line 119 87073] Train: [51/100][639/1557] Data 0.006 (0.191) Batch 1.165 (1.151) Remain 24:41:01 loss: 0.2833 Lr: 0.00267 [2024-02-18 18:55:18,417 INFO misc.py line 119 87073] Train: [51/100][640/1557] Data 0.006 (0.191) Batch 0.994 (1.151) Remain 24:40:40 loss: 0.2897 Lr: 0.00267 [2024-02-18 18:55:19,314 INFO misc.py line 119 87073] Train: [51/100][641/1557] Data 0.005 (0.191) Batch 0.897 (1.150) Remain 24:40:09 loss: 0.1485 Lr: 0.00267 [2024-02-18 18:55:20,094 INFO misc.py line 119 87073] Train: [51/100][642/1557] Data 0.005 (0.190) Batch 0.773 (1.150) Remain 24:39:22 loss: 0.1492 Lr: 0.00267 [2024-02-18 18:55:20,866 INFO misc.py line 119 87073] Train: [51/100][643/1557] Data 0.011 (0.190) Batch 0.778 (1.149) Remain 24:38:36 loss: 0.3181 Lr: 0.00267 [2024-02-18 18:55:22,229 INFO misc.py line 119 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0.006 (0.188) Batch 0.765 (1.147) Remain 24:36:23 loss: 0.4846 Lr: 0.00267 [2024-02-18 18:55:29,079 INFO misc.py line 119 87073] Train: [51/100][651/1557] Data 0.014 (0.188) Batch 1.206 (1.148) Remain 24:36:29 loss: 0.1386 Lr: 0.00267 [2024-02-18 18:55:30,090 INFO misc.py line 119 87073] Train: [51/100][652/1557] Data 0.013 (0.188) Batch 1.011 (1.147) Remain 24:36:12 loss: 0.3692 Lr: 0.00267 [2024-02-18 18:55:31,194 INFO misc.py line 119 87073] Train: [51/100][653/1557] Data 0.013 (0.187) Batch 1.099 (1.147) Remain 24:36:05 loss: 0.2939 Lr: 0.00267 [2024-02-18 18:55:32,289 INFO misc.py line 119 87073] Train: [51/100][654/1557] Data 0.018 (0.187) Batch 1.096 (1.147) Remain 24:35:58 loss: 0.3457 Lr: 0.00267 [2024-02-18 18:55:33,115 INFO misc.py line 119 87073] Train: [51/100][655/1557] Data 0.017 (0.187) Batch 0.838 (1.147) Remain 24:35:20 loss: 0.2951 Lr: 0.00267 [2024-02-18 18:55:33,887 INFO misc.py line 119 87073] Train: [51/100][656/1557] Data 0.005 (0.186) Batch 0.772 (1.146) Remain 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[2024-02-18 18:55:40,622 INFO misc.py line 119 87073] Train: [51/100][663/1557] Data 0.004 (0.185) Batch 0.755 (1.144) Remain 24:31:55 loss: 0.4599 Lr: 0.00267 [2024-02-18 18:55:41,300 INFO misc.py line 119 87073] Train: [51/100][664/1557] Data 0.014 (0.184) Batch 0.687 (1.143) Remain 24:31:00 loss: 0.1939 Lr: 0.00267 [2024-02-18 18:55:42,599 INFO misc.py line 119 87073] Train: [51/100][665/1557] Data 0.005 (0.184) Batch 1.299 (1.144) Remain 24:31:17 loss: 0.0990 Lr: 0.00267 [2024-02-18 18:55:43,560 INFO misc.py line 119 87073] Train: [51/100][666/1557] Data 0.005 (0.184) Batch 0.962 (1.143) Remain 24:30:55 loss: 0.4395 Lr: 0.00267 [2024-02-18 18:55:44,621 INFO misc.py line 119 87073] Train: [51/100][667/1557] Data 0.003 (0.184) Batch 1.061 (1.143) Remain 24:30:44 loss: 0.3794 Lr: 0.00267 [2024-02-18 18:55:45,620 INFO misc.py line 119 87073] Train: [51/100][668/1557] Data 0.004 (0.183) Batch 1.000 (1.143) Remain 24:30:26 loss: 0.4048 Lr: 0.00267 [2024-02-18 18:55:46,475 INFO misc.py line 119 87073] Train: [51/100][669/1557] Data 0.004 (0.183) Batch 0.854 (1.143) Remain 24:29:52 loss: 0.2068 Lr: 0.00267 [2024-02-18 18:55:47,230 INFO misc.py line 119 87073] Train: [51/100][670/1557] Data 0.004 (0.183) Batch 0.743 (1.142) Remain 24:29:04 loss: 0.3365 Lr: 0.00267 [2024-02-18 18:55:47,967 INFO misc.py line 119 87073] Train: [51/100][671/1557] Data 0.016 (0.182) Batch 0.749 (1.141) Remain 24:28:18 loss: 0.4096 Lr: 0.00267 [2024-02-18 18:55:49,080 INFO misc.py line 119 87073] Train: [51/100][672/1557] Data 0.003 (0.182) Batch 1.113 (1.141) Remain 24:28:13 loss: 0.6183 Lr: 0.00267 [2024-02-18 18:55:50,085 INFO misc.py line 119 87073] Train: [51/100][673/1557] Data 0.004 (0.182) Batch 1.006 (1.141) Remain 24:27:56 loss: 0.2209 Lr: 0.00267 [2024-02-18 18:55:51,028 INFO misc.py line 119 87073] Train: [51/100][674/1557] Data 0.003 (0.182) Batch 0.943 (1.141) Remain 24:27:32 loss: 0.2644 Lr: 0.00267 [2024-02-18 18:55:52,195 INFO misc.py line 119 87073] Train: 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Batch 1.029 (1.156) Remain 24:47:06 loss: 0.5367 Lr: 0.00267 [2024-02-18 18:56:10,504 INFO misc.py line 119 87073] Train: [51/100][682/1557] Data 0.003 (0.195) Batch 1.111 (1.156) Remain 24:46:59 loss: 0.2713 Lr: 0.00267 [2024-02-18 18:56:11,294 INFO misc.py line 119 87073] Train: [51/100][683/1557] Data 0.005 (0.195) Batch 0.792 (1.156) Remain 24:46:17 loss: 0.3764 Lr: 0.00267 [2024-02-18 18:56:12,055 INFO misc.py line 119 87073] Train: [51/100][684/1557] Data 0.003 (0.195) Batch 0.746 (1.155) Remain 24:45:29 loss: 0.2497 Lr: 0.00267 [2024-02-18 18:56:12,777 INFO misc.py line 119 87073] Train: [51/100][685/1557] Data 0.017 (0.194) Batch 0.735 (1.154) Remain 24:44:41 loss: 0.3580 Lr: 0.00267 [2024-02-18 18:56:13,884 INFO misc.py line 119 87073] Train: [51/100][686/1557] Data 0.005 (0.194) Batch 1.106 (1.154) Remain 24:44:34 loss: 0.2338 Lr: 0.00267 [2024-02-18 18:56:14,886 INFO misc.py line 119 87073] Train: [51/100][687/1557] Data 0.006 (0.194) Batch 1.002 (1.154) Remain 24:44:16 loss: 0.4749 Lr: 0.00267 [2024-02-18 18:56:15,997 INFO misc.py line 119 87073] Train: [51/100][688/1557] Data 0.006 (0.193) Batch 1.113 (1.154) Remain 24:44:10 loss: 0.2458 Lr: 0.00267 [2024-02-18 18:56:16,836 INFO misc.py line 119 87073] Train: [51/100][689/1557] Data 0.005 (0.193) Batch 0.840 (1.154) Remain 24:43:33 loss: 0.1949 Lr: 0.00267 [2024-02-18 18:56:17,854 INFO misc.py line 119 87073] Train: [51/100][690/1557] Data 0.003 (0.193) Batch 1.012 (1.153) Remain 24:43:16 loss: 0.1690 Lr: 0.00267 [2024-02-18 18:56:18,625 INFO misc.py line 119 87073] Train: [51/100][691/1557] Data 0.009 (0.193) Batch 0.775 (1.153) Remain 24:42:33 loss: 0.1586 Lr: 0.00267 [2024-02-18 18:56:19,394 INFO misc.py line 119 87073] Train: [51/100][692/1557] Data 0.005 (0.192) Batch 0.763 (1.152) Remain 24:41:48 loss: 0.5033 Lr: 0.00267 [2024-02-18 18:56:20,551 INFO misc.py line 119 87073] Train: [51/100][693/1557] Data 0.011 (0.192) Batch 1.155 (1.152) Remain 24:41:47 loss: 0.0859 Lr: 0.00267 [2024-02-18 18:56:21,495 INFO misc.py line 119 87073] Train: [51/100][694/1557] Data 0.013 (0.192) Batch 0.954 (1.152) Remain 24:41:24 loss: 0.2272 Lr: 0.00267 [2024-02-18 18:56:22,565 INFO misc.py line 119 87073] Train: [51/100][695/1557] Data 0.003 (0.192) Batch 1.068 (1.152) Remain 24:41:13 loss: 0.4140 Lr: 0.00267 [2024-02-18 18:56:23,567 INFO misc.py line 119 87073] Train: [51/100][696/1557] Data 0.005 (0.191) Batch 1.003 (1.152) Remain 24:40:55 loss: 0.4269 Lr: 0.00267 [2024-02-18 18:56:24,694 INFO misc.py line 119 87073] Train: [51/100][697/1557] Data 0.004 (0.191) Batch 1.127 (1.152) Remain 24:40:52 loss: 0.3141 Lr: 0.00267 [2024-02-18 18:56:25,408 INFO misc.py line 119 87073] Train: [51/100][698/1557] Data 0.004 (0.191) Batch 0.714 (1.151) Remain 24:40:02 loss: 0.1763 Lr: 0.00267 [2024-02-18 18:56:26,134 INFO misc.py line 119 87073] Train: [51/100][699/1557] Data 0.004 (0.190) Batch 0.716 (1.150) Remain 24:39:12 loss: 0.3831 Lr: 0.00267 [2024-02-18 18:56:27,442 INFO misc.py line 119 87073] Train: [51/100][700/1557] Data 0.014 (0.190) Batch 1.300 (1.151) Remain 24:39:28 loss: 0.1778 Lr: 0.00267 [2024-02-18 18:56:28,395 INFO misc.py line 119 87073] Train: [51/100][701/1557] Data 0.022 (0.190) Batch 0.971 (1.150) Remain 24:39:07 loss: 0.2441 Lr: 0.00267 [2024-02-18 18:56:29,388 INFO misc.py line 119 87073] Train: [51/100][702/1557] Data 0.004 (0.190) Batch 0.993 (1.150) Remain 24:38:48 loss: 0.2438 Lr: 0.00267 [2024-02-18 18:56:30,234 INFO misc.py line 119 87073] Train: [51/100][703/1557] Data 0.004 (0.189) Batch 0.845 (1.150) Remain 24:38:14 loss: 0.2157 Lr: 0.00267 [2024-02-18 18:56:31,173 INFO misc.py line 119 87073] Train: [51/100][704/1557] Data 0.005 (0.189) Batch 0.933 (1.149) Remain 24:37:49 loss: 0.3612 Lr: 0.00267 [2024-02-18 18:56:33,941 INFO misc.py line 119 87073] Train: [51/100][705/1557] Data 1.020 (0.190) Batch 2.775 (1.152) Remain 24:40:46 loss: 0.3286 Lr: 0.00267 [2024-02-18 18:56:34,740 INFO misc.py line 119 87073] Train: [51/100][706/1557] Data 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line 119 87073] Train: [51/100][725/1557] Data 0.006 (0.185) Batch 0.954 (1.147) Remain 24:34:11 loss: 0.2879 Lr: 0.00267 [2024-02-18 18:56:54,241 INFO misc.py line 119 87073] Train: [51/100][726/1557] Data 0.006 (0.185) Batch 0.746 (1.146) Remain 24:33:27 loss: 0.3324 Lr: 0.00267 [2024-02-18 18:56:54,894 INFO misc.py line 119 87073] Train: [51/100][727/1557] Data 0.008 (0.185) Batch 0.656 (1.146) Remain 24:32:34 loss: 0.2692 Lr: 0.00267 [2024-02-18 18:56:55,991 INFO misc.py line 119 87073] Train: [51/100][728/1557] Data 0.004 (0.185) Batch 1.095 (1.146) Remain 24:32:27 loss: 0.1458 Lr: 0.00267 [2024-02-18 18:56:56,801 INFO misc.py line 119 87073] Train: [51/100][729/1557] Data 0.007 (0.184) Batch 0.812 (1.145) Remain 24:31:51 loss: 0.2005 Lr: 0.00267 [2024-02-18 18:56:57,748 INFO misc.py line 119 87073] Train: [51/100][730/1557] Data 0.005 (0.184) Batch 0.947 (1.145) Remain 24:31:28 loss: 0.6315 Lr: 0.00267 [2024-02-18 18:56:58,713 INFO misc.py line 119 87073] Train: 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Batch 0.937 (1.158) Remain 24:48:00 loss: 0.4930 Lr: 0.00267 [2024-02-18 18:57:16,256 INFO misc.py line 119 87073] Train: [51/100][738/1557] Data 0.004 (0.196) Batch 0.976 (1.158) Remain 24:47:40 loss: 0.1614 Lr: 0.00267 [2024-02-18 18:57:17,281 INFO misc.py line 119 87073] Train: [51/100][739/1557] Data 0.006 (0.196) Batch 1.020 (1.157) Remain 24:47:24 loss: 0.2307 Lr: 0.00267 [2024-02-18 18:57:18,011 INFO misc.py line 119 87073] Train: [51/100][740/1557] Data 0.012 (0.196) Batch 0.735 (1.157) Remain 24:46:39 loss: 0.1974 Lr: 0.00267 [2024-02-18 18:57:18,804 INFO misc.py line 119 87073] Train: [51/100][741/1557] Data 0.006 (0.195) Batch 0.795 (1.156) Remain 24:46:00 loss: 0.2948 Lr: 0.00267 [2024-02-18 18:57:19,956 INFO misc.py line 119 87073] Train: [51/100][742/1557] Data 0.004 (0.195) Batch 1.151 (1.156) Remain 24:45:58 loss: 0.1752 Lr: 0.00267 [2024-02-18 18:57:21,013 INFO misc.py line 119 87073] Train: [51/100][743/1557] Data 0.005 (0.195) Batch 1.057 (1.156) Remain 24:45:47 loss: 0.5119 Lr: 0.00267 [2024-02-18 18:57:22,044 INFO misc.py line 119 87073] Train: [51/100][744/1557] Data 0.006 (0.194) Batch 1.027 (1.156) Remain 24:45:32 loss: 0.4373 Lr: 0.00267 [2024-02-18 18:57:22,945 INFO misc.py line 119 87073] Train: [51/100][745/1557] Data 0.010 (0.194) Batch 0.907 (1.156) Remain 24:45:05 loss: 0.2476 Lr: 0.00267 [2024-02-18 18:57:24,003 INFO misc.py line 119 87073] Train: [51/100][746/1557] Data 0.004 (0.194) Batch 1.058 (1.156) Remain 24:44:54 loss: 0.1616 Lr: 0.00267 [2024-02-18 18:57:24,802 INFO misc.py line 119 87073] Train: [51/100][747/1557] Data 0.004 (0.194) Batch 0.800 (1.155) Remain 24:44:16 loss: 0.2287 Lr: 0.00267 [2024-02-18 18:57:25,555 INFO misc.py line 119 87073] Train: [51/100][748/1557] Data 0.003 (0.193) Batch 0.745 (1.154) Remain 24:43:32 loss: 0.4133 Lr: 0.00267 [2024-02-18 18:57:26,743 INFO misc.py line 119 87073] Train: [51/100][749/1557] Data 0.011 (0.193) Batch 1.185 (1.155) Remain 24:43:34 loss: 0.1652 Lr: 0.00267 [2024-02-18 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Batch 1.006 (1.158) Remain 24:46:57 loss: 0.4591 Lr: 0.00267 [2024-02-18 18:58:21,159 INFO misc.py line 119 87073] Train: [51/100][794/1557] Data 0.006 (0.196) Batch 1.028 (1.158) Remain 24:46:43 loss: 0.4227 Lr: 0.00267 [2024-02-18 18:58:22,290 INFO misc.py line 119 87073] Train: [51/100][795/1557] Data 0.004 (0.196) Batch 1.131 (1.158) Remain 24:46:39 loss: 0.3175 Lr: 0.00267 [2024-02-18 18:58:23,121 INFO misc.py line 119 87073] Train: [51/100][796/1557] Data 0.004 (0.195) Batch 0.831 (1.157) Remain 24:46:07 loss: 0.3747 Lr: 0.00267 [2024-02-18 18:58:23,928 INFO misc.py line 119 87073] Train: [51/100][797/1557] Data 0.004 (0.195) Batch 0.800 (1.157) Remain 24:45:31 loss: 0.2613 Lr: 0.00267 [2024-02-18 18:58:25,043 INFO misc.py line 119 87073] Train: [51/100][798/1557] Data 0.011 (0.195) Batch 1.114 (1.157) Remain 24:45:25 loss: 0.2637 Lr: 0.00267 [2024-02-18 18:58:26,068 INFO misc.py line 119 87073] Train: [51/100][799/1557] Data 0.012 (0.195) Batch 1.028 (1.157) Remain 24:45:12 loss: 0.4700 Lr: 0.00267 [2024-02-18 18:58:27,045 INFO misc.py line 119 87073] Train: [51/100][800/1557] Data 0.009 (0.194) Batch 0.981 (1.156) Remain 24:44:54 loss: 0.2763 Lr: 0.00267 [2024-02-18 18:58:27,886 INFO misc.py line 119 87073] Train: [51/100][801/1557] Data 0.006 (0.194) Batch 0.841 (1.156) Remain 24:44:22 loss: 0.2772 Lr: 0.00267 [2024-02-18 18:58:29,006 INFO misc.py line 119 87073] Train: [51/100][802/1557] Data 0.005 (0.194) Batch 1.111 (1.156) Remain 24:44:17 loss: 0.3646 Lr: 0.00267 [2024-02-18 18:58:29,709 INFO misc.py line 119 87073] Train: [51/100][803/1557] Data 0.013 (0.194) Batch 0.711 (1.155) Remain 24:43:33 loss: 0.2239 Lr: 0.00267 [2024-02-18 18:58:30,443 INFO misc.py line 119 87073] Train: [51/100][804/1557] Data 0.005 (0.193) Batch 0.722 (1.155) Remain 24:42:50 loss: 0.1906 Lr: 0.00267 [2024-02-18 18:58:31,654 INFO misc.py line 119 87073] Train: [51/100][805/1557] Data 0.017 (0.193) Batch 1.213 (1.155) Remain 24:42:54 loss: 0.1308 Lr: 0.00267 [2024-02-18 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line 119 87073] Train: [51/100][837/1557] Data 0.004 (0.186) Batch 0.910 (1.147) Remain 24:31:50 loss: 0.6121 Lr: 0.00266 [2024-02-18 18:59:02,550 INFO misc.py line 119 87073] Train: [51/100][838/1557] Data 0.006 (0.186) Batch 0.740 (1.146) Remain 24:31:12 loss: 0.2350 Lr: 0.00266 [2024-02-18 18:59:03,242 INFO misc.py line 119 87073] Train: [51/100][839/1557] Data 0.004 (0.186) Batch 0.692 (1.146) Remain 24:30:29 loss: 0.1485 Lr: 0.00266 [2024-02-18 18:59:04,398 INFO misc.py line 119 87073] Train: [51/100][840/1557] Data 0.005 (0.185) Batch 1.147 (1.146) Remain 24:30:28 loss: 0.1487 Lr: 0.00266 [2024-02-18 18:59:05,500 INFO misc.py line 119 87073] Train: [51/100][841/1557] Data 0.014 (0.185) Batch 1.099 (1.146) Remain 24:30:22 loss: 0.2657 Lr: 0.00266 [2024-02-18 18:59:06,504 INFO misc.py line 119 87073] Train: [51/100][842/1557] Data 0.017 (0.185) Batch 1.005 (1.145) Remain 24:30:08 loss: 0.4594 Lr: 0.00266 [2024-02-18 18:59:07,643 INFO misc.py line 119 87073] Train: 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Batch 0.877 (1.156) Remain 24:43:58 loss: 0.3929 Lr: 0.00266 [2024-02-18 18:59:24,564 INFO misc.py line 119 87073] Train: [51/100][850/1557] Data 0.005 (0.196) Batch 0.843 (1.156) Remain 24:43:28 loss: 0.3920 Lr: 0.00266 [2024-02-18 18:59:25,582 INFO misc.py line 119 87073] Train: [51/100][851/1557] Data 0.009 (0.196) Batch 1.015 (1.156) Remain 24:43:14 loss: 0.5207 Lr: 0.00266 [2024-02-18 18:59:26,319 INFO misc.py line 119 87073] Train: [51/100][852/1557] Data 0.012 (0.196) Batch 0.745 (1.155) Remain 24:42:36 loss: 0.1676 Lr: 0.00266 [2024-02-18 18:59:27,086 INFO misc.py line 119 87073] Train: [51/100][853/1557] Data 0.004 (0.195) Batch 0.758 (1.155) Remain 24:41:59 loss: 0.2577 Lr: 0.00266 [2024-02-18 18:59:28,271 INFO misc.py line 119 87073] Train: [51/100][854/1557] Data 0.014 (0.195) Batch 1.192 (1.155) Remain 24:42:01 loss: 0.3315 Lr: 0.00266 [2024-02-18 18:59:29,244 INFO misc.py line 119 87073] Train: [51/100][855/1557] Data 0.006 (0.195) Batch 0.976 (1.155) Remain 24:41:44 loss: 0.5380 Lr: 0.00266 [2024-02-18 18:59:30,203 INFO misc.py line 119 87073] Train: [51/100][856/1557] Data 0.003 (0.195) Batch 0.957 (1.154) Remain 24:41:25 loss: 0.3527 Lr: 0.00266 [2024-02-18 18:59:31,258 INFO misc.py line 119 87073] Train: [51/100][857/1557] Data 0.005 (0.195) Batch 1.057 (1.154) Remain 24:41:15 loss: 0.4816 Lr: 0.00266 [2024-02-18 18:59:32,290 INFO misc.py line 119 87073] Train: [51/100][858/1557] Data 0.004 (0.194) Batch 1.032 (1.154) Remain 24:41:03 loss: 0.9158 Lr: 0.00266 [2024-02-18 18:59:33,075 INFO misc.py line 119 87073] Train: [51/100][859/1557] Data 0.004 (0.194) Batch 0.786 (1.154) Remain 24:40:28 loss: 0.1866 Lr: 0.00266 [2024-02-18 18:59:33,835 INFO misc.py line 119 87073] Train: [51/100][860/1557] Data 0.004 (0.194) Batch 0.737 (1.153) Remain 24:39:50 loss: 0.3628 Lr: 0.00266 [2024-02-18 18:59:35,150 INFO misc.py line 119 87073] Train: [51/100][861/1557] Data 0.026 (0.194) Batch 1.332 (1.153) Remain 24:40:05 loss: 0.0739 Lr: 0.00266 [2024-02-18 18:59:36,008 INFO misc.py line 119 87073] Train: [51/100][862/1557] Data 0.009 (0.193) Batch 0.864 (1.153) Remain 24:39:38 loss: 0.2917 Lr: 0.00266 [2024-02-18 18:59:36,919 INFO misc.py line 119 87073] Train: [51/100][863/1557] Data 0.003 (0.193) Batch 0.910 (1.153) Remain 24:39:15 loss: 0.2991 Lr: 0.00266 [2024-02-18 18:59:37,882 INFO misc.py line 119 87073] Train: [51/100][864/1557] Data 0.004 (0.193) Batch 0.963 (1.153) Remain 24:38:56 loss: 1.0195 Lr: 0.00266 [2024-02-18 18:59:38,923 INFO misc.py line 119 87073] Train: [51/100][865/1557] Data 0.005 (0.193) Batch 1.031 (1.152) Remain 24:38:44 loss: 0.3862 Lr: 0.00266 [2024-02-18 18:59:39,681 INFO misc.py line 119 87073] Train: [51/100][866/1557] Data 0.015 (0.193) Batch 0.768 (1.152) Remain 24:38:09 loss: 0.3327 Lr: 0.00266 [2024-02-18 18:59:40,420 INFO misc.py line 119 87073] Train: [51/100][867/1557] Data 0.004 (0.192) Batch 0.731 (1.152) Remain 24:37:30 loss: 0.5814 Lr: 0.00266 [2024-02-18 18:59:41,670 INFO misc.py line 119 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0.005 (0.191) Batch 0.717 (1.150) Remain 24:34:54 loss: 0.2756 Lr: 0.00266 [2024-02-18 18:59:48,078 INFO misc.py line 119 87073] Train: [51/100][875/1557] Data 0.011 (0.191) Batch 1.275 (1.150) Remain 24:35:04 loss: 0.1210 Lr: 0.00266 [2024-02-18 18:59:49,197 INFO misc.py line 119 87073] Train: [51/100][876/1557] Data 0.013 (0.191) Batch 1.116 (1.150) Remain 24:35:00 loss: 0.4647 Lr: 0.00266 [2024-02-18 18:59:50,344 INFO misc.py line 119 87073] Train: [51/100][877/1557] Data 0.015 (0.190) Batch 1.147 (1.150) Remain 24:34:58 loss: 0.3765 Lr: 0.00266 [2024-02-18 18:59:51,181 INFO misc.py line 119 87073] Train: [51/100][878/1557] Data 0.015 (0.190) Batch 0.847 (1.149) Remain 24:34:31 loss: 0.3177 Lr: 0.00266 [2024-02-18 18:59:52,081 INFO misc.py line 119 87073] Train: [51/100][879/1557] Data 0.004 (0.190) Batch 0.900 (1.149) Remain 24:34:08 loss: 0.4028 Lr: 0.00266 [2024-02-18 18:59:54,543 INFO misc.py line 119 87073] Train: [51/100][880/1557] Data 1.418 (0.191) Batch 2.458 (1.151) Remain 24:36:01 loss: 0.1748 Lr: 0.00266 [2024-02-18 18:59:55,370 INFO misc.py line 119 87073] Train: [51/100][881/1557] Data 0.010 (0.191) Batch 0.833 (1.150) Remain 24:35:32 loss: 0.2858 Lr: 0.00266 [2024-02-18 18:59:56,611 INFO misc.py line 119 87073] Train: [51/100][882/1557] Data 0.003 (0.191) Batch 1.236 (1.150) Remain 24:35:39 loss: 0.4684 Lr: 0.00266 [2024-02-18 18:59:57,512 INFO misc.py line 119 87073] Train: [51/100][883/1557] Data 0.008 (0.191) Batch 0.905 (1.150) Remain 24:35:16 loss: 0.2733 Lr: 0.00266 [2024-02-18 18:59:58,543 INFO misc.py line 119 87073] Train: [51/100][884/1557] Data 0.003 (0.190) Batch 1.031 (1.150) Remain 24:35:05 loss: 0.5422 Lr: 0.00266 [2024-02-18 18:59:59,445 INFO misc.py line 119 87073] Train: [51/100][885/1557] Data 0.004 (0.190) Batch 0.899 (1.150) Remain 24:34:42 loss: 0.1332 Lr: 0.00266 [2024-02-18 19:00:00,383 INFO misc.py line 119 87073] Train: [51/100][886/1557] Data 0.006 (0.190) Batch 0.938 (1.149) Remain 24:34:22 loss: 0.4256 Lr: 0.00266 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line 119 87073] Train: [51/100][893/1557] Data 0.004 (0.189) Batch 0.970 (1.148) Remain 24:32:15 loss: 0.4348 Lr: 0.00266 [2024-02-18 19:00:07,806 INFO misc.py line 119 87073] Train: [51/100][894/1557] Data 0.004 (0.188) Batch 0.748 (1.147) Remain 24:31:39 loss: 0.2834 Lr: 0.00266 [2024-02-18 19:00:08,570 INFO misc.py line 119 87073] Train: [51/100][895/1557] Data 0.013 (0.188) Batch 0.773 (1.147) Remain 24:31:06 loss: 0.2566 Lr: 0.00266 [2024-02-18 19:00:09,850 INFO misc.py line 119 87073] Train: [51/100][896/1557] Data 0.004 (0.188) Batch 1.271 (1.147) Remain 24:31:15 loss: 0.1598 Lr: 0.00266 [2024-02-18 19:00:10,784 INFO misc.py line 119 87073] Train: [51/100][897/1557] Data 0.013 (0.188) Batch 0.944 (1.147) Remain 24:30:57 loss: 0.4800 Lr: 0.00266 [2024-02-18 19:00:11,764 INFO misc.py line 119 87073] Train: [51/100][898/1557] Data 0.004 (0.188) Batch 0.979 (1.147) Remain 24:30:41 loss: 0.2107 Lr: 0.00266 [2024-02-18 19:00:12,866 INFO misc.py line 119 87073] Train: 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Batch 0.935 (1.158) Remain 24:44:41 loss: 0.4718 Lr: 0.00266 [2024-02-18 19:00:30,750 INFO misc.py line 119 87073] Train: [51/100][906/1557] Data 0.005 (0.198) Batch 1.014 (1.158) Remain 24:44:28 loss: 0.2297 Lr: 0.00266 [2024-02-18 19:00:31,808 INFO misc.py line 119 87073] Train: [51/100][907/1557] Data 0.004 (0.198) Batch 1.058 (1.157) Remain 24:44:18 loss: 0.3190 Lr: 0.00266 [2024-02-18 19:00:32,568 INFO misc.py line 119 87073] Train: [51/100][908/1557] Data 0.005 (0.198) Batch 0.760 (1.157) Remain 24:43:43 loss: 0.4193 Lr: 0.00266 [2024-02-18 19:00:33,438 INFO misc.py line 119 87073] Train: [51/100][909/1557] Data 0.003 (0.198) Batch 0.871 (1.157) Remain 24:43:18 loss: 0.5096 Lr: 0.00266 [2024-02-18 19:00:34,638 INFO misc.py line 119 87073] Train: [51/100][910/1557] Data 0.003 (0.197) Batch 1.192 (1.157) Remain 24:43:20 loss: 0.2211 Lr: 0.00266 [2024-02-18 19:00:35,494 INFO misc.py line 119 87073] Train: [51/100][911/1557] Data 0.011 (0.197) Batch 0.861 (1.156) Remain 24:42:53 loss: 0.1955 Lr: 0.00266 [2024-02-18 19:00:36,445 INFO misc.py line 119 87073] Train: [51/100][912/1557] Data 0.007 (0.197) Batch 0.954 (1.156) Remain 24:42:35 loss: 0.3470 Lr: 0.00266 [2024-02-18 19:00:37,312 INFO misc.py line 119 87073] Train: [51/100][913/1557] Data 0.004 (0.197) Batch 0.867 (1.156) Remain 24:42:10 loss: 0.1638 Lr: 0.00266 [2024-02-18 19:00:38,268 INFO misc.py line 119 87073] Train: [51/100][914/1557] Data 0.004 (0.196) Batch 0.955 (1.156) Remain 24:41:51 loss: 0.2948 Lr: 0.00266 [2024-02-18 19:00:39,000 INFO misc.py line 119 87073] Train: [51/100][915/1557] Data 0.005 (0.196) Batch 0.732 (1.155) Remain 24:41:15 loss: 0.4023 Lr: 0.00266 [2024-02-18 19:00:39,793 INFO misc.py line 119 87073] Train: [51/100][916/1557] Data 0.004 (0.196) Batch 0.792 (1.155) Remain 24:40:43 loss: 0.3102 Lr: 0.00266 [2024-02-18 19:00:41,039 INFO misc.py line 119 87073] Train: [51/100][917/1557] Data 0.005 (0.196) Batch 1.243 (1.155) Remain 24:40:49 loss: 0.1074 Lr: 0.00266 [2024-02-18 19:00:41,885 INFO misc.py line 119 87073] Train: [51/100][918/1557] Data 0.009 (0.196) Batch 0.851 (1.155) Remain 24:40:22 loss: 0.2683 Lr: 0.00266 [2024-02-18 19:00:42,777 INFO misc.py line 119 87073] Train: [51/100][919/1557] Data 0.004 (0.195) Batch 0.892 (1.154) Remain 24:39:59 loss: 0.7426 Lr: 0.00266 [2024-02-18 19:00:43,682 INFO misc.py line 119 87073] Train: [51/100][920/1557] Data 0.004 (0.195) Batch 0.903 (1.154) Remain 24:39:37 loss: 0.8340 Lr: 0.00266 [2024-02-18 19:00:44,806 INFO misc.py line 119 87073] Train: [51/100][921/1557] Data 0.007 (0.195) Batch 1.117 (1.154) Remain 24:39:33 loss: 0.7464 Lr: 0.00266 [2024-02-18 19:00:45,611 INFO misc.py line 119 87073] Train: [51/100][922/1557] Data 0.013 (0.195) Batch 0.813 (1.154) Remain 24:39:03 loss: 0.2112 Lr: 0.00266 [2024-02-18 19:00:46,392 INFO misc.py line 119 87073] Train: [51/100][923/1557] Data 0.004 (0.195) Batch 0.782 (1.153) Remain 24:38:31 loss: 0.4852 Lr: 0.00266 [2024-02-18 19:00:47,651 INFO misc.py line 119 87073] Train: [51/100][924/1557] Data 0.004 (0.194) Batch 1.253 (1.153) Remain 24:38:38 loss: 0.1305 Lr: 0.00266 [2024-02-18 19:00:48,695 INFO misc.py line 119 87073] Train: [51/100][925/1557] Data 0.010 (0.194) Batch 1.048 (1.153) Remain 24:38:28 loss: 0.2732 Lr: 0.00266 [2024-02-18 19:00:49,651 INFO misc.py line 119 87073] Train: [51/100][926/1557] Data 0.006 (0.194) Batch 0.958 (1.153) Remain 24:38:11 loss: 0.4130 Lr: 0.00266 [2024-02-18 19:00:50,478 INFO misc.py line 119 87073] Train: [51/100][927/1557] Data 0.004 (0.194) Batch 0.826 (1.153) Remain 24:37:42 loss: 0.3575 Lr: 0.00266 [2024-02-18 19:00:51,487 INFO misc.py line 119 87073] Train: [51/100][928/1557] Data 0.004 (0.194) Batch 1.004 (1.152) Remain 24:37:29 loss: 0.4959 Lr: 0.00266 [2024-02-18 19:00:52,205 INFO misc.py line 119 87073] Train: [51/100][929/1557] Data 0.010 (0.193) Batch 0.722 (1.152) Remain 24:36:52 loss: 0.3758 Lr: 0.00266 [2024-02-18 19:00:53,029 INFO misc.py line 119 87073] Train: [51/100][930/1557] Data 0.005 (0.193) Batch 0.814 (1.152) Remain 24:36:23 loss: 0.2956 Lr: 0.00266 [2024-02-18 19:00:54,275 INFO misc.py line 119 87073] Train: [51/100][931/1557] Data 0.016 (0.193) Batch 1.250 (1.152) Remain 24:36:30 loss: 0.1070 Lr: 0.00266 [2024-02-18 19:00:55,564 INFO misc.py line 119 87073] Train: [51/100][932/1557] Data 0.012 (0.193) Batch 1.286 (1.152) Remain 24:36:40 loss: 0.3806 Lr: 0.00266 [2024-02-18 19:00:56,782 INFO misc.py line 119 87073] Train: [51/100][933/1557] Data 0.014 (0.193) Batch 1.216 (1.152) Remain 24:36:44 loss: 0.4708 Lr: 0.00266 [2024-02-18 19:00:57,596 INFO misc.py line 119 87073] Train: [51/100][934/1557] Data 0.016 (0.192) Batch 0.827 (1.152) Remain 24:36:16 loss: 0.6722 Lr: 0.00266 [2024-02-18 19:00:58,764 INFO misc.py line 119 87073] Train: [51/100][935/1557] Data 0.004 (0.192) Batch 1.169 (1.152) Remain 24:36:16 loss: 0.5126 Lr: 0.00266 [2024-02-18 19:00:59,629 INFO misc.py line 119 87073] Train: [51/100][936/1557] Data 0.004 (0.192) Batch 0.864 (1.151) Remain 24:35:51 loss: 0.2535 Lr: 0.00266 [2024-02-18 19:01:00,391 INFO misc.py line 119 87073] Train: [51/100][937/1557] Data 0.004 (0.192) Batch 0.756 (1.151) Remain 24:35:17 loss: 0.3812 Lr: 0.00266 [2024-02-18 19:01:01,680 INFO misc.py line 119 87073] Train: [51/100][938/1557] Data 0.010 (0.192) Batch 1.289 (1.151) Remain 24:35:28 loss: 0.1368 Lr: 0.00266 [2024-02-18 19:01:02,842 INFO misc.py line 119 87073] Train: [51/100][939/1557] Data 0.011 (0.191) Batch 1.156 (1.151) Remain 24:35:27 loss: 0.3900 Lr: 0.00266 [2024-02-18 19:01:03,961 INFO misc.py line 119 87073] Train: [51/100][940/1557] Data 0.016 (0.191) Batch 1.117 (1.151) Remain 24:35:23 loss: 0.4949 Lr: 0.00266 [2024-02-18 19:01:04,919 INFO misc.py line 119 87073] Train: [51/100][941/1557] Data 0.017 (0.191) Batch 0.971 (1.151) Remain 24:35:07 loss: 0.4449 Lr: 0.00266 [2024-02-18 19:01:05,952 INFO misc.py line 119 87073] Train: [51/100][942/1557] Data 0.004 (0.191) Batch 1.034 (1.151) Remain 24:34:56 loss: 0.2046 Lr: 0.00266 [2024-02-18 19:01:06,720 INFO misc.py line 119 87073] Train: [51/100][943/1557] Data 0.004 (0.191) Batch 0.768 (1.150) Remain 24:34:24 loss: 0.2810 Lr: 0.00266 [2024-02-18 19:01:07,497 INFO misc.py line 119 87073] Train: [51/100][944/1557] Data 0.003 (0.190) Batch 0.766 (1.150) Remain 24:33:51 loss: 0.3388 Lr: 0.00266 [2024-02-18 19:01:08,770 INFO misc.py line 119 87073] Train: [51/100][945/1557] Data 0.014 (0.190) Batch 1.272 (1.150) Remain 24:34:00 loss: 0.2222 Lr: 0.00266 [2024-02-18 19:01:09,635 INFO misc.py line 119 87073] Train: [51/100][946/1557] Data 0.016 (0.190) Batch 0.877 (1.150) Remain 24:33:37 loss: 0.1452 Lr: 0.00266 [2024-02-18 19:01:10,482 INFO misc.py line 119 87073] Train: [51/100][947/1557] Data 0.004 (0.190) Batch 0.847 (1.149) Remain 24:33:11 loss: 0.4214 Lr: 0.00266 [2024-02-18 19:01:11,244 INFO misc.py line 119 87073] Train: [51/100][948/1557] Data 0.004 (0.190) Batch 0.759 (1.149) Remain 24:32:38 loss: 0.3519 Lr: 0.00266 [2024-02-18 19:01:12,158 INFO misc.py line 119 87073] Train: [51/100][949/1557] Data 0.007 (0.190) Batch 0.918 (1.149) Remain 24:32:18 loss: 0.2847 Lr: 0.00266 [2024-02-18 19:01:12,944 INFO misc.py line 119 87073] Train: [51/100][950/1557] Data 0.003 (0.189) Batch 0.785 (1.148) Remain 24:31:47 loss: 0.4462 Lr: 0.00266 [2024-02-18 19:01:13,746 INFO misc.py line 119 87073] Train: [51/100][951/1557] Data 0.004 (0.189) Batch 0.800 (1.148) Remain 24:31:18 loss: 0.2567 Lr: 0.00266 [2024-02-18 19:01:14,891 INFO misc.py line 119 87073] Train: [51/100][952/1557] Data 0.006 (0.189) Batch 1.140 (1.148) Remain 24:31:16 loss: 0.3063 Lr: 0.00266 [2024-02-18 19:01:15,757 INFO misc.py line 119 87073] Train: [51/100][953/1557] Data 0.013 (0.189) Batch 0.874 (1.148) Remain 24:30:53 loss: 0.2896 Lr: 0.00266 [2024-02-18 19:01:16,853 INFO misc.py line 119 87073] Train: [51/100][954/1557] Data 0.004 (0.189) Batch 1.096 (1.148) Remain 24:30:47 loss: 1.1291 Lr: 0.00266 [2024-02-18 19:01:17,839 INFO misc.py line 119 87073] Train: 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Batch 1.125 (1.158) Remain 24:43:56 loss: 0.3221 Lr: 0.00266 [2024-02-18 19:01:35,731 INFO misc.py line 119 87073] Train: [51/100][962/1557] Data 0.004 (0.198) Batch 0.918 (1.158) Remain 24:43:36 loss: 0.2305 Lr: 0.00266 [2024-02-18 19:01:36,779 INFO misc.py line 119 87073] Train: [51/100][963/1557] Data 0.005 (0.198) Batch 1.048 (1.158) Remain 24:43:26 loss: 0.3580 Lr: 0.00266 [2024-02-18 19:01:37,507 INFO misc.py line 119 87073] Train: [51/100][964/1557] Data 0.005 (0.198) Batch 0.720 (1.157) Remain 24:42:50 loss: 0.5557 Lr: 0.00266 [2024-02-18 19:01:38,217 INFO misc.py line 119 87073] Train: [51/100][965/1557] Data 0.012 (0.198) Batch 0.718 (1.157) Remain 24:42:13 loss: 0.2242 Lr: 0.00266 [2024-02-18 19:01:39,335 INFO misc.py line 119 87073] Train: [51/100][966/1557] Data 0.004 (0.198) Batch 1.109 (1.157) Remain 24:42:08 loss: 0.1983 Lr: 0.00266 [2024-02-18 19:01:40,458 INFO misc.py line 119 87073] Train: [51/100][967/1557] Data 0.013 (0.197) Batch 1.121 (1.157) Remain 24:42:04 loss: 0.3924 Lr: 0.00266 [2024-02-18 19:01:41,614 INFO misc.py line 119 87073] Train: [51/100][968/1557] Data 0.015 (0.197) Batch 1.155 (1.157) Remain 24:42:03 loss: 0.2638 Lr: 0.00266 [2024-02-18 19:01:42,724 INFO misc.py line 119 87073] Train: [51/100][969/1557] Data 0.016 (0.197) Batch 1.112 (1.157) Remain 24:41:58 loss: 0.5456 Lr: 0.00266 [2024-02-18 19:01:43,664 INFO misc.py line 119 87073] Train: [51/100][970/1557] Data 0.014 (0.197) Batch 0.950 (1.156) Remain 24:41:41 loss: 0.2709 Lr: 0.00266 [2024-02-18 19:01:44,488 INFO misc.py line 119 87073] Train: [51/100][971/1557] Data 0.004 (0.197) Batch 0.823 (1.156) Remain 24:41:13 loss: 0.4104 Lr: 0.00266 [2024-02-18 19:01:45,286 INFO misc.py line 119 87073] Train: [51/100][972/1557] Data 0.005 (0.196) Batch 0.796 (1.156) Remain 24:40:43 loss: 0.1802 Lr: 0.00266 [2024-02-18 19:01:46,574 INFO misc.py line 119 87073] Train: [51/100][973/1557] Data 0.006 (0.196) Batch 1.281 (1.156) Remain 24:40:52 loss: 0.0995 Lr: 0.00266 [2024-02-18 19:01:47,547 INFO misc.py line 119 87073] Train: [51/100][974/1557] Data 0.013 (0.196) Batch 0.980 (1.156) Remain 24:40:37 loss: 0.2317 Lr: 0.00266 [2024-02-18 19:01:48,410 INFO misc.py line 119 87073] Train: [51/100][975/1557] Data 0.006 (0.196) Batch 0.863 (1.155) Remain 24:40:13 loss: 0.0987 Lr: 0.00266 [2024-02-18 19:01:49,494 INFO misc.py line 119 87073] Train: [51/100][976/1557] Data 0.006 (0.196) Batch 1.086 (1.155) Remain 24:40:06 loss: 0.1964 Lr: 0.00266 [2024-02-18 19:01:50,421 INFO misc.py line 119 87073] Train: [51/100][977/1557] Data 0.004 (0.195) Batch 0.926 (1.155) Remain 24:39:47 loss: 0.2211 Lr: 0.00266 [2024-02-18 19:01:51,106 INFO misc.py line 119 87073] Train: [51/100][978/1557] Data 0.004 (0.195) Batch 0.684 (1.155) Remain 24:39:09 loss: 0.2675 Lr: 0.00266 [2024-02-18 19:01:51,834 INFO misc.py line 119 87073] Train: [51/100][979/1557] Data 0.007 (0.195) Batch 0.728 (1.154) Remain 24:38:34 loss: 0.1621 Lr: 0.00266 [2024-02-18 19:01:53,092 INFO misc.py line 119 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(0.192) Batch 0.726 (1.152) Remain 24:34:01 loss: 0.2018 Lr: 0.00265 [2024-02-18 19:03:09,583 INFO misc.py line 119 87073] Train: [51/100][1049/1557] Data 0.015 (0.192) Batch 0.752 (1.151) Remain 24:33:30 loss: 0.3010 Lr: 0.00265 [2024-02-18 19:03:10,788 INFO misc.py line 119 87073] Train: [51/100][1050/1557] Data 0.004 (0.192) Batch 1.204 (1.151) Remain 24:33:33 loss: 0.1435 Lr: 0.00265 [2024-02-18 19:03:11,859 INFO misc.py line 119 87073] Train: [51/100][1051/1557] Data 0.005 (0.192) Batch 1.071 (1.151) Remain 24:33:26 loss: 0.8061 Lr: 0.00265 [2024-02-18 19:03:12,677 INFO misc.py line 119 87073] Train: [51/100][1052/1557] Data 0.004 (0.192) Batch 0.818 (1.151) Remain 24:33:01 loss: 0.3008 Lr: 0.00265 [2024-02-18 19:03:13,794 INFO misc.py line 119 87073] Train: [51/100][1053/1557] Data 0.004 (0.192) Batch 1.109 (1.151) Remain 24:32:56 loss: 0.2291 Lr: 0.00265 [2024-02-18 19:03:14,819 INFO misc.py line 119 87073] Train: [51/100][1054/1557] Data 0.012 (0.191) Batch 1.032 (1.151) Remain 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misc.py line 119 87073] Train: [51/100][1067/1557] Data 0.029 (0.190) Batch 0.907 (1.150) Remain 24:31:04 loss: 0.0782 Lr: 0.00265 [2024-02-18 19:03:29,664 INFO misc.py line 119 87073] Train: [51/100][1068/1557] Data 0.004 (0.190) Batch 1.096 (1.149) Remain 24:30:59 loss: 0.5466 Lr: 0.00265 [2024-02-18 19:03:30,414 INFO misc.py line 119 87073] Train: [51/100][1069/1557] Data 0.005 (0.190) Batch 0.751 (1.149) Remain 24:30:29 loss: 0.2397 Lr: 0.00265 [2024-02-18 19:03:31,155 INFO misc.py line 119 87073] Train: [51/100][1070/1557] Data 0.004 (0.190) Batch 0.740 (1.149) Remain 24:29:59 loss: 0.2308 Lr: 0.00265 [2024-02-18 19:03:44,624 INFO misc.py line 119 87073] Train: [51/100][1071/1557] Data 9.669 (0.199) Batch 13.470 (1.160) Remain 24:44:44 loss: 0.2558 Lr: 0.00265 [2024-02-18 19:03:45,704 INFO misc.py line 119 87073] Train: [51/100][1072/1557] Data 0.004 (0.199) Batch 1.070 (1.160) Remain 24:44:36 loss: 0.1163 Lr: 0.00265 [2024-02-18 19:03:46,724 INFO misc.py line 119 87073] Train: 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24:40:58 loss: 0.0815 Lr: 0.00265 [2024-02-18 19:03:58,842 INFO misc.py line 119 87073] Train: [51/100][1086/1557] Data 0.011 (0.196) Batch 0.917 (1.157) Remain 24:40:40 loss: 0.3290 Lr: 0.00265 [2024-02-18 19:03:59,779 INFO misc.py line 119 87073] Train: [51/100][1087/1557] Data 0.005 (0.196) Batch 0.938 (1.157) Remain 24:40:24 loss: 0.2470 Lr: 0.00265 [2024-02-18 19:04:00,659 INFO misc.py line 119 87073] Train: [51/100][1088/1557] Data 0.005 (0.196) Batch 0.878 (1.157) Remain 24:40:03 loss: 0.1555 Lr: 0.00265 [2024-02-18 19:04:01,500 INFO misc.py line 119 87073] Train: [51/100][1089/1557] Data 0.005 (0.196) Batch 0.832 (1.157) Remain 24:39:39 loss: 0.3063 Lr: 0.00265 [2024-02-18 19:04:02,218 INFO misc.py line 119 87073] Train: [51/100][1090/1557] Data 0.014 (0.195) Batch 0.726 (1.156) Remain 24:39:07 loss: 0.2087 Lr: 0.00265 [2024-02-18 19:04:03,013 INFO misc.py line 119 87073] Train: [51/100][1091/1557] Data 0.007 (0.195) Batch 0.795 (1.156) Remain 24:38:40 loss: 0.3008 Lr: 0.00265 [2024-02-18 19:04:04,354 INFO misc.py line 119 87073] Train: [51/100][1092/1557] Data 0.006 (0.195) Batch 1.341 (1.156) Remain 24:38:52 loss: 0.2580 Lr: 0.00265 [2024-02-18 19:04:05,283 INFO misc.py line 119 87073] Train: [51/100][1093/1557] Data 0.006 (0.195) Batch 0.931 (1.156) Remain 24:38:35 loss: 0.4202 Lr: 0.00265 [2024-02-18 19:04:06,253 INFO misc.py line 119 87073] Train: [51/100][1094/1557] Data 0.004 (0.195) Batch 0.970 (1.156) Remain 24:38:21 loss: 0.2221 Lr: 0.00265 [2024-02-18 19:04:07,461 INFO misc.py line 119 87073] Train: [51/100][1095/1557] Data 0.004 (0.195) Batch 1.208 (1.156) Remain 24:38:24 loss: 0.0991 Lr: 0.00265 [2024-02-18 19:04:08,443 INFO misc.py line 119 87073] Train: [51/100][1096/1557] Data 0.004 (0.194) Batch 0.981 (1.156) Remain 24:38:10 loss: 0.1714 Lr: 0.00265 [2024-02-18 19:04:09,272 INFO misc.py line 119 87073] Train: [51/100][1097/1557] Data 0.004 (0.194) Batch 0.829 (1.155) Remain 24:37:46 loss: 0.3798 Lr: 0.00265 [2024-02-18 19:04:10,036 INFO misc.py line 119 87073] Train: [51/100][1098/1557] Data 0.004 (0.194) Batch 0.761 (1.155) Remain 24:37:17 loss: 0.5427 Lr: 0.00265 [2024-02-18 19:04:11,241 INFO misc.py line 119 87073] Train: [51/100][1099/1557] Data 0.007 (0.194) Batch 1.202 (1.155) Remain 24:37:20 loss: 0.1947 Lr: 0.00265 [2024-02-18 19:04:12,252 INFO misc.py line 119 87073] Train: [51/100][1100/1557] Data 0.011 (0.194) Batch 1.007 (1.155) Remain 24:37:08 loss: 0.3172 Lr: 0.00265 [2024-02-18 19:04:13,349 INFO misc.py line 119 87073] Train: [51/100][1101/1557] Data 0.014 (0.194) Batch 1.101 (1.155) Remain 24:37:03 loss: 0.4865 Lr: 0.00265 [2024-02-18 19:04:14,564 INFO misc.py line 119 87073] Train: [51/100][1102/1557] Data 0.010 (0.193) Batch 1.209 (1.155) Remain 24:37:06 loss: 0.5115 Lr: 0.00265 [2024-02-18 19:04:15,507 INFO misc.py line 119 87073] Train: [51/100][1103/1557] Data 0.016 (0.193) Batch 0.955 (1.155) Remain 24:36:51 loss: 0.1263 Lr: 0.00265 [2024-02-18 19:04:16,271 INFO misc.py line 119 87073] Train: 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(0.192) Batch 0.893 (1.153) Remain 24:34:45 loss: 0.2243 Lr: 0.00265 [2024-02-18 19:04:22,696 INFO misc.py line 119 87073] Train: [51/100][1111/1557] Data 0.005 (0.192) Batch 0.803 (1.153) Remain 24:34:19 loss: 0.3439 Lr: 0.00265 [2024-02-18 19:04:23,467 INFO misc.py line 119 87073] Train: [51/100][1112/1557] Data 0.006 (0.192) Batch 0.773 (1.152) Remain 24:33:52 loss: 0.3994 Lr: 0.00265 [2024-02-18 19:04:24,810 INFO misc.py line 119 87073] Train: [51/100][1113/1557] Data 0.004 (0.192) Batch 1.335 (1.153) Remain 24:34:04 loss: 0.2636 Lr: 0.00265 [2024-02-18 19:04:25,647 INFO misc.py line 119 87073] Train: [51/100][1114/1557] Data 0.012 (0.191) Batch 0.843 (1.152) Remain 24:33:41 loss: 0.3417 Lr: 0.00265 [2024-02-18 19:04:26,634 INFO misc.py line 119 87073] Train: [51/100][1115/1557] Data 0.006 (0.191) Batch 0.987 (1.152) Remain 24:33:28 loss: 0.5027 Lr: 0.00265 [2024-02-18 19:04:27,601 INFO misc.py line 119 87073] Train: [51/100][1116/1557] Data 0.007 (0.191) Batch 0.970 (1.152) Remain 24:33:15 loss: 0.5250 Lr: 0.00265 [2024-02-18 19:04:28,637 INFO misc.py line 119 87073] Train: [51/100][1117/1557] Data 0.004 (0.191) Batch 1.036 (1.152) Remain 24:33:06 loss: 0.3738 Lr: 0.00265 [2024-02-18 19:04:29,415 INFO misc.py line 119 87073] Train: [51/100][1118/1557] Data 0.004 (0.191) Batch 0.778 (1.152) Remain 24:32:39 loss: 0.2125 Lr: 0.00265 [2024-02-18 19:04:30,193 INFO misc.py line 119 87073] Train: [51/100][1119/1557] Data 0.004 (0.191) Batch 0.771 (1.151) Remain 24:32:11 loss: 0.3988 Lr: 0.00265 [2024-02-18 19:04:31,399 INFO misc.py line 119 87073] Train: [51/100][1120/1557] Data 0.010 (0.190) Batch 1.204 (1.151) Remain 24:32:14 loss: 0.2021 Lr: 0.00265 [2024-02-18 19:04:32,297 INFO misc.py line 119 87073] Train: [51/100][1121/1557] Data 0.012 (0.190) Batch 0.904 (1.151) Remain 24:31:56 loss: 0.2977 Lr: 0.00265 [2024-02-18 19:04:33,490 INFO misc.py line 119 87073] Train: [51/100][1122/1557] Data 0.006 (0.190) Batch 1.193 (1.151) Remain 24:31:57 loss: 0.8021 Lr: 0.00265 [2024-02-18 19:04:34,577 INFO misc.py line 119 87073] Train: [51/100][1123/1557] Data 0.006 (0.190) Batch 1.089 (1.151) Remain 24:31:52 loss: 0.1947 Lr: 0.00265 [2024-02-18 19:04:35,387 INFO misc.py line 119 87073] Train: [51/100][1124/1557] Data 0.005 (0.190) Batch 0.810 (1.151) Remain 24:31:28 loss: 0.1196 Lr: 0.00265 [2024-02-18 19:04:36,154 INFO misc.py line 119 87073] Train: [51/100][1125/1557] Data 0.005 (0.190) Batch 0.766 (1.150) Remain 24:31:00 loss: 0.2613 Lr: 0.00265 [2024-02-18 19:04:36,911 INFO misc.py line 119 87073] Train: [51/100][1126/1557] Data 0.005 (0.189) Batch 0.748 (1.150) Remain 24:30:31 loss: 0.2435 Lr: 0.00265 [2024-02-18 19:04:49,596 INFO misc.py line 119 87073] Train: [51/100][1127/1557] Data 10.112 (0.198) Batch 12.683 (1.160) Remain 24:43:38 loss: 0.1853 Lr: 0.00265 [2024-02-18 19:04:50,652 INFO misc.py line 119 87073] Train: [51/100][1128/1557] Data 0.016 (0.198) Batch 1.068 (1.160) Remain 24:43:30 loss: 0.3334 Lr: 0.00265 [2024-02-18 19:04:51,596 INFO misc.py line 119 87073] Train: [51/100][1129/1557] Data 0.004 (0.198) Batch 0.943 (1.160) Remain 24:43:14 loss: 0.6118 Lr: 0.00265 [2024-02-18 19:04:52,642 INFO misc.py line 119 87073] Train: [51/100][1130/1557] Data 0.005 (0.198) Batch 1.046 (1.160) Remain 24:43:05 loss: 0.3872 Lr: 0.00265 [2024-02-18 19:04:53,499 INFO misc.py line 119 87073] Train: [51/100][1131/1557] Data 0.006 (0.198) Batch 0.858 (1.160) Remain 24:42:43 loss: 0.7326 Lr: 0.00265 [2024-02-18 19:04:54,303 INFO misc.py line 119 87073] Train: [51/100][1132/1557] Data 0.004 (0.197) Batch 0.794 (1.159) Remain 24:42:18 loss: 0.3027 Lr: 0.00265 [2024-02-18 19:04:55,067 INFO misc.py line 119 87073] Train: [51/100][1133/1557] Data 0.014 (0.197) Batch 0.773 (1.159) Remain 24:41:50 loss: 0.4820 Lr: 0.00265 [2024-02-18 19:04:56,226 INFO misc.py line 119 87073] Train: [51/100][1134/1557] Data 0.005 (0.197) Batch 1.160 (1.159) Remain 24:41:49 loss: 0.2373 Lr: 0.00265 [2024-02-18 19:04:57,282 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [51/100][1160/1557] Data 0.004 (0.193) Batch 0.742 (1.154) Remain 24:35:13 loss: 0.3604 Lr: 0.00265 [2024-02-18 19:05:21,661 INFO misc.py line 119 87073] Train: [51/100][1161/1557] Data 0.004 (0.193) Batch 0.818 (1.154) Remain 24:34:50 loss: 0.2245 Lr: 0.00265 [2024-02-18 19:05:22,947 INFO misc.py line 119 87073] Train: [51/100][1162/1557] Data 0.004 (0.192) Batch 1.276 (1.154) Remain 24:34:57 loss: 0.2361 Lr: 0.00265 [2024-02-18 19:05:24,029 INFO misc.py line 119 87073] Train: [51/100][1163/1557] Data 0.013 (0.192) Batch 1.079 (1.154) Remain 24:34:51 loss: 0.3071 Lr: 0.00265 [2024-02-18 19:05:24,981 INFO misc.py line 119 87073] Train: [51/100][1164/1557] Data 0.016 (0.192) Batch 0.965 (1.154) Remain 24:34:37 loss: 0.4275 Lr: 0.00265 [2024-02-18 19:05:25,855 INFO misc.py line 119 87073] Train: [51/100][1165/1557] Data 0.004 (0.192) Batch 0.874 (1.154) Remain 24:34:17 loss: 0.2863 Lr: 0.00265 [2024-02-18 19:05:26,827 INFO misc.py line 119 87073] Train: 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INFO misc.py line 119 87073] Train: [51/100][1191/1557] Data 0.014 (0.197) Batch 0.922 (1.158) Remain 24:39:56 loss: 0.4161 Lr: 0.00265 [2024-02-18 19:06:02,422 INFO misc.py line 119 87073] Train: [51/100][1192/1557] Data 0.007 (0.197) Batch 0.865 (1.158) Remain 24:39:36 loss: 0.3611 Lr: 0.00265 [2024-02-18 19:06:03,469 INFO misc.py line 119 87073] Train: [51/100][1193/1557] Data 0.007 (0.197) Batch 1.047 (1.158) Remain 24:39:27 loss: 0.3445 Lr: 0.00265 [2024-02-18 19:06:04,473 INFO misc.py line 119 87073] Train: [51/100][1194/1557] Data 0.006 (0.197) Batch 0.998 (1.158) Remain 24:39:16 loss: 0.7686 Lr: 0.00265 [2024-02-18 19:06:05,213 INFO misc.py line 119 87073] Train: [51/100][1195/1557] Data 0.011 (0.197) Batch 0.746 (1.158) Remain 24:38:48 loss: 0.3432 Lr: 0.00264 [2024-02-18 19:06:05,984 INFO misc.py line 119 87073] Train: [51/100][1196/1557] Data 0.005 (0.197) Batch 0.763 (1.157) Remain 24:38:22 loss: 0.3520 Lr: 0.00264 [2024-02-18 19:06:07,253 INFO misc.py line 119 87073] Train: [51/100][1197/1557] Data 0.013 (0.196) Batch 1.269 (1.157) Remain 24:38:28 loss: 0.1199 Lr: 0.00264 [2024-02-18 19:06:08,221 INFO misc.py line 119 87073] Train: [51/100][1198/1557] Data 0.013 (0.196) Batch 0.977 (1.157) Remain 24:38:15 loss: 0.1008 Lr: 0.00264 [2024-02-18 19:06:09,198 INFO misc.py line 119 87073] Train: [51/100][1199/1557] Data 0.005 (0.196) Batch 0.978 (1.157) Remain 24:38:02 loss: 0.7848 Lr: 0.00264 [2024-02-18 19:06:10,128 INFO misc.py line 119 87073] Train: [51/100][1200/1557] Data 0.004 (0.196) Batch 0.930 (1.157) Remain 24:37:47 loss: 0.1957 Lr: 0.00264 [2024-02-18 19:06:10,964 INFO misc.py line 119 87073] Train: [51/100][1201/1557] Data 0.004 (0.196) Batch 0.834 (1.157) Remain 24:37:25 loss: 0.3239 Lr: 0.00264 [2024-02-18 19:06:11,683 INFO misc.py line 119 87073] Train: [51/100][1202/1557] Data 0.006 (0.196) Batch 0.721 (1.156) Remain 24:36:56 loss: 0.1851 Lr: 0.00264 [2024-02-18 19:06:12,433 INFO misc.py line 119 87073] Train: [51/100][1203/1557] Data 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Remain 24:35:25 loss: 0.2370 Lr: 0.00264 [2024-02-18 19:06:19,211 INFO misc.py line 119 87073] Train: [51/100][1210/1557] Data 0.008 (0.194) Batch 0.737 (1.155) Remain 24:34:57 loss: 0.3211 Lr: 0.00264 [2024-02-18 19:06:20,439 INFO misc.py line 119 87073] Train: [51/100][1211/1557] Data 0.004 (0.194) Batch 1.227 (1.155) Remain 24:35:01 loss: 0.2531 Lr: 0.00264 [2024-02-18 19:06:21,411 INFO misc.py line 119 87073] Train: [51/100][1212/1557] Data 0.007 (0.194) Batch 0.974 (1.155) Remain 24:34:48 loss: 0.5320 Lr: 0.00264 [2024-02-18 19:06:22,271 INFO misc.py line 119 87073] Train: [51/100][1213/1557] Data 0.004 (0.194) Batch 0.859 (1.154) Remain 24:34:28 loss: 0.4695 Lr: 0.00264 [2024-02-18 19:06:23,186 INFO misc.py line 119 87073] Train: [51/100][1214/1557] Data 0.005 (0.194) Batch 0.913 (1.154) Remain 24:34:12 loss: 0.5058 Lr: 0.00264 [2024-02-18 19:06:24,163 INFO misc.py line 119 87073] Train: [51/100][1215/1557] Data 0.007 (0.194) Batch 0.980 (1.154) Remain 24:34:00 loss: 0.2761 Lr: 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INFO misc.py line 119 87073] Train: [51/100][1222/1557] Data 0.005 (0.192) Batch 0.821 (1.153) Remain 24:31:59 loss: 0.3696 Lr: 0.00264 [2024-02-18 19:06:31,181 INFO misc.py line 119 87073] Train: [51/100][1223/1557] Data 0.008 (0.192) Batch 0.727 (1.152) Remain 24:31:31 loss: 0.2945 Lr: 0.00264 [2024-02-18 19:06:31,898 INFO misc.py line 119 87073] Train: [51/100][1224/1557] Data 0.004 (0.192) Batch 0.712 (1.152) Remain 24:31:03 loss: 0.2924 Lr: 0.00264 [2024-02-18 19:06:33,061 INFO misc.py line 119 87073] Train: [51/100][1225/1557] Data 0.009 (0.192) Batch 1.163 (1.152) Remain 24:31:02 loss: 0.2372 Lr: 0.00264 [2024-02-18 19:06:34,126 INFO misc.py line 119 87073] Train: [51/100][1226/1557] Data 0.011 (0.192) Batch 1.070 (1.152) Remain 24:30:56 loss: 0.3763 Lr: 0.00264 [2024-02-18 19:06:35,026 INFO misc.py line 119 87073] Train: [51/100][1227/1557] Data 0.004 (0.192) Batch 0.900 (1.152) Remain 24:30:39 loss: 0.5115 Lr: 0.00264 [2024-02-18 19:06:36,324 INFO misc.py line 119 87073] Train: [51/100][1228/1557] Data 0.006 (0.192) Batch 1.296 (1.152) Remain 24:30:47 loss: 0.2097 Lr: 0.00264 [2024-02-18 19:06:37,305 INFO misc.py line 119 87073] Train: [51/100][1229/1557] Data 0.008 (0.191) Batch 0.984 (1.152) Remain 24:30:35 loss: 0.6641 Lr: 0.00264 [2024-02-18 19:06:40,053 INFO misc.py line 119 87073] Train: [51/100][1230/1557] Data 1.142 (0.192) Batch 2.748 (1.153) Remain 24:32:14 loss: 0.1809 Lr: 0.00264 [2024-02-18 19:06:40,839 INFO misc.py line 119 87073] Train: [51/100][1231/1557] Data 0.005 (0.192) Batch 0.781 (1.153) Remain 24:31:49 loss: 0.1094 Lr: 0.00264 [2024-02-18 19:06:42,014 INFO misc.py line 119 87073] Train: [51/100][1232/1557] Data 0.009 (0.192) Batch 1.179 (1.153) Remain 24:31:50 loss: 0.1951 Lr: 0.00264 [2024-02-18 19:06:43,082 INFO misc.py line 119 87073] Train: [51/100][1233/1557] Data 0.005 (0.192) Batch 1.064 (1.153) Remain 24:31:43 loss: 0.2427 Lr: 0.00264 [2024-02-18 19:06:44,000 INFO misc.py line 119 87073] Train: [51/100][1234/1557] Data 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19:07:12,846 INFO misc.py line 119 87073] Train: [51/100][1253/1557] Data 0.005 (0.197) Batch 1.234 (1.158) Remain 24:38:12 loss: 0.0943 Lr: 0.00264 [2024-02-18 19:07:13,648 INFO misc.py line 119 87073] Train: [51/100][1254/1557] Data 0.005 (0.197) Batch 0.802 (1.158) Remain 24:37:49 loss: 0.2079 Lr: 0.00264 [2024-02-18 19:07:14,585 INFO misc.py line 119 87073] Train: [51/100][1255/1557] Data 0.005 (0.197) Batch 0.936 (1.157) Remain 24:37:34 loss: 0.2665 Lr: 0.00264 [2024-02-18 19:07:15,455 INFO misc.py line 119 87073] Train: [51/100][1256/1557] Data 0.005 (0.197) Batch 0.865 (1.157) Remain 24:37:15 loss: 0.1701 Lr: 0.00264 [2024-02-18 19:07:16,425 INFO misc.py line 119 87073] Train: [51/100][1257/1557] Data 0.011 (0.196) Batch 0.974 (1.157) Remain 24:37:03 loss: 0.2061 Lr: 0.00264 [2024-02-18 19:07:17,159 INFO misc.py line 119 87073] Train: [51/100][1258/1557] Data 0.007 (0.196) Batch 0.732 (1.157) Remain 24:36:36 loss: 0.2272 Lr: 0.00264 [2024-02-18 19:07:17,928 INFO misc.py line 119 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Data 0.015 (0.195) Batch 0.754 (1.156) Remain 24:35:06 loss: 0.3270 Lr: 0.00264 [2024-02-18 19:07:24,723 INFO misc.py line 119 87073] Train: [51/100][1266/1557] Data 0.006 (0.195) Batch 0.811 (1.155) Remain 24:34:44 loss: 0.3051 Lr: 0.00264 [2024-02-18 19:07:25,982 INFO misc.py line 119 87073] Train: [51/100][1267/1557] Data 0.005 (0.195) Batch 1.248 (1.155) Remain 24:34:49 loss: 0.1018 Lr: 0.00264 [2024-02-18 19:07:26,846 INFO misc.py line 119 87073] Train: [51/100][1268/1557] Data 0.016 (0.195) Batch 0.876 (1.155) Remain 24:34:31 loss: 0.2712 Lr: 0.00264 [2024-02-18 19:07:27,678 INFO misc.py line 119 87073] Train: [51/100][1269/1557] Data 0.004 (0.195) Batch 0.831 (1.155) Remain 24:34:10 loss: 0.2308 Lr: 0.00264 [2024-02-18 19:07:28,800 INFO misc.py line 119 87073] Train: [51/100][1270/1557] Data 0.004 (0.194) Batch 1.121 (1.155) Remain 24:34:07 loss: 0.2216 Lr: 0.00264 [2024-02-18 19:07:29,830 INFO misc.py line 119 87073] Train: [51/100][1271/1557] Data 0.005 (0.194) Batch 1.029 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19:07:41,953 INFO misc.py line 119 87073] Train: [51/100][1284/1557] Data 0.004 (0.192) Batch 0.997 (1.153) Remain 24:30:50 loss: 0.7430 Lr: 0.00264 [2024-02-18 19:07:42,916 INFO misc.py line 119 87073] Train: [51/100][1285/1557] Data 0.004 (0.192) Batch 0.958 (1.152) Remain 24:30:37 loss: 0.3756 Lr: 0.00264 [2024-02-18 19:07:43,698 INFO misc.py line 119 87073] Train: [51/100][1286/1557] Data 0.009 (0.192) Batch 0.785 (1.152) Remain 24:30:14 loss: 0.4140 Lr: 0.00264 [2024-02-18 19:07:44,479 INFO misc.py line 119 87073] Train: [51/100][1287/1557] Data 0.006 (0.192) Batch 0.781 (1.152) Remain 24:29:51 loss: 0.2556 Lr: 0.00264 [2024-02-18 19:07:45,593 INFO misc.py line 119 87073] Train: [51/100][1288/1557] Data 0.005 (0.192) Batch 1.110 (1.152) Remain 24:29:47 loss: 0.1467 Lr: 0.00264 [2024-02-18 19:07:46,448 INFO misc.py line 119 87073] Train: [51/100][1289/1557] Data 0.010 (0.192) Batch 0.860 (1.152) Remain 24:29:29 loss: 0.7496 Lr: 0.00264 [2024-02-18 19:07:47,557 INFO misc.py line 119 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Data 0.014 (0.198) Batch 0.834 (1.159) Remain 24:39:21 loss: 0.6088 Lr: 0.00264 [2024-02-18 19:08:05,642 INFO misc.py line 119 87073] Train: [51/100][1297/1557] Data 0.004 (0.198) Batch 0.990 (1.159) Remain 24:39:10 loss: 0.2293 Lr: 0.00264 [2024-02-18 19:08:06,526 INFO misc.py line 119 87073] Train: [51/100][1298/1557] Data 0.004 (0.197) Batch 0.884 (1.159) Remain 24:38:53 loss: 0.0846 Lr: 0.00264 [2024-02-18 19:08:07,556 INFO misc.py line 119 87073] Train: [51/100][1299/1557] Data 0.003 (0.197) Batch 1.026 (1.159) Remain 24:38:44 loss: 0.1306 Lr: 0.00264 [2024-02-18 19:08:08,330 INFO misc.py line 119 87073] Train: [51/100][1300/1557] Data 0.008 (0.197) Batch 0.777 (1.159) Remain 24:38:20 loss: 0.4679 Lr: 0.00264 [2024-02-18 19:08:09,126 INFO misc.py line 119 87073] Train: [51/100][1301/1557] Data 0.004 (0.197) Batch 0.796 (1.158) Remain 24:37:57 loss: 0.3572 Lr: 0.00264 [2024-02-18 19:08:10,244 INFO misc.py line 119 87073] Train: [51/100][1302/1557] Data 0.004 (0.197) Batch 1.111 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19:08:22,610 INFO misc.py line 119 87073] Train: [51/100][1315/1557] Data 0.006 (0.195) Batch 0.729 (1.156) Remain 24:35:02 loss: 0.0916 Lr: 0.00264 [2024-02-18 19:08:23,756 INFO misc.py line 119 87073] Train: [51/100][1316/1557] Data 0.010 (0.195) Batch 1.139 (1.156) Remain 24:34:59 loss: 0.2888 Lr: 0.00264 [2024-02-18 19:08:24,708 INFO misc.py line 119 87073] Train: [51/100][1317/1557] Data 0.015 (0.195) Batch 0.961 (1.156) Remain 24:34:47 loss: 0.2422 Lr: 0.00264 [2024-02-18 19:08:25,646 INFO misc.py line 119 87073] Train: [51/100][1318/1557] Data 0.005 (0.195) Batch 0.938 (1.156) Remain 24:34:33 loss: 0.5172 Lr: 0.00264 [2024-02-18 19:08:26,551 INFO misc.py line 119 87073] Train: [51/100][1319/1557] Data 0.004 (0.194) Batch 0.903 (1.156) Remain 24:34:17 loss: 0.4142 Lr: 0.00264 [2024-02-18 19:08:28,030 INFO misc.py line 119 87073] Train: [51/100][1320/1557] Data 0.006 (0.194) Batch 1.472 (1.156) Remain 24:34:34 loss: 0.6334 Lr: 0.00264 [2024-02-18 19:08:28,869 INFO misc.py line 119 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Data 0.004 (0.193) Batch 0.984 (1.155) Remain 24:33:16 loss: 0.2908 Lr: 0.00264 [2024-02-18 19:08:35,580 INFO misc.py line 119 87073] Train: [51/100][1328/1557] Data 0.004 (0.193) Batch 0.676 (1.155) Remain 24:32:48 loss: 0.2702 Lr: 0.00264 [2024-02-18 19:08:36,335 INFO misc.py line 119 87073] Train: [51/100][1329/1557] Data 0.007 (0.193) Batch 0.757 (1.155) Remain 24:32:23 loss: 0.1891 Lr: 0.00264 [2024-02-18 19:08:37,520 INFO misc.py line 119 87073] Train: [51/100][1330/1557] Data 0.004 (0.193) Batch 1.184 (1.155) Remain 24:32:24 loss: 0.1385 Lr: 0.00264 [2024-02-18 19:08:38,354 INFO misc.py line 119 87073] Train: [51/100][1331/1557] Data 0.004 (0.193) Batch 0.834 (1.154) Remain 24:32:04 loss: 0.2674 Lr: 0.00264 [2024-02-18 19:08:39,339 INFO misc.py line 119 87073] Train: [51/100][1332/1557] Data 0.006 (0.193) Batch 0.970 (1.154) Remain 24:31:53 loss: 0.5262 Lr: 0.00264 [2024-02-18 19:08:40,415 INFO misc.py line 119 87073] Train: [51/100][1333/1557] Data 0.020 (0.192) Batch 1.089 (1.154) Remain 24:31:48 loss: 0.2730 Lr: 0.00264 [2024-02-18 19:08:41,491 INFO misc.py line 119 87073] Train: [51/100][1334/1557] Data 0.006 (0.192) Batch 1.033 (1.154) Remain 24:31:40 loss: 0.2724 Lr: 0.00264 [2024-02-18 19:08:42,282 INFO misc.py line 119 87073] Train: [51/100][1335/1557] Data 0.050 (0.192) Batch 0.836 (1.154) Remain 24:31:20 loss: 0.4058 Lr: 0.00264 [2024-02-18 19:08:43,023 INFO misc.py line 119 87073] Train: [51/100][1336/1557] Data 0.005 (0.192) Batch 0.742 (1.153) Remain 24:30:55 loss: 0.2633 Lr: 0.00264 [2024-02-18 19:08:44,343 INFO misc.py line 119 87073] Train: [51/100][1337/1557] Data 0.003 (0.192) Batch 1.294 (1.154) Remain 24:31:02 loss: 0.1400 Lr: 0.00264 [2024-02-18 19:08:45,310 INFO misc.py line 119 87073] Train: [51/100][1338/1557] Data 0.029 (0.192) Batch 0.993 (1.153) Remain 24:30:52 loss: 0.5554 Lr: 0.00264 [2024-02-18 19:08:46,134 INFO misc.py line 119 87073] Train: [51/100][1339/1557] Data 0.004 (0.192) Batch 0.824 (1.153) Remain 24:30:32 loss: 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19:08:52,884 INFO misc.py line 119 87073] Train: [51/100][1346/1557] Data 0.008 (0.191) Batch 1.040 (1.152) Remain 24:29:08 loss: 0.5543 Lr: 0.00264 [2024-02-18 19:08:53,683 INFO misc.py line 119 87073] Train: [51/100][1347/1557] Data 0.012 (0.191) Batch 0.808 (1.152) Remain 24:28:47 loss: 0.6184 Lr: 0.00264 [2024-02-18 19:08:54,650 INFO misc.py line 119 87073] Train: [51/100][1348/1557] Data 0.004 (0.190) Batch 0.966 (1.152) Remain 24:28:36 loss: 0.5077 Lr: 0.00264 [2024-02-18 19:08:55,447 INFO misc.py line 119 87073] Train: [51/100][1349/1557] Data 0.005 (0.190) Batch 0.796 (1.152) Remain 24:28:14 loss: 0.4997 Lr: 0.00264 [2024-02-18 19:08:56,242 INFO misc.py line 119 87073] Train: [51/100][1350/1557] Data 0.006 (0.190) Batch 0.795 (1.151) Remain 24:27:53 loss: 0.3603 Lr: 0.00264 [2024-02-18 19:09:08,190 INFO misc.py line 119 87073] Train: [51/100][1351/1557] Data 10.063 (0.198) Batch 11.949 (1.159) Remain 24:38:04 loss: 0.2782 Lr: 0.00264 [2024-02-18 19:09:09,157 INFO misc.py line 119 87073] Train: [51/100][1352/1557] Data 0.005 (0.197) Batch 0.966 (1.159) Remain 24:37:52 loss: 0.4143 Lr: 0.00264 [2024-02-18 19:09:10,233 INFO misc.py line 119 87073] Train: [51/100][1353/1557] Data 0.007 (0.197) Batch 1.078 (1.159) Remain 24:37:46 loss: 0.6232 Lr: 0.00264 [2024-02-18 19:09:11,230 INFO misc.py line 119 87073] Train: [51/100][1354/1557] Data 0.005 (0.197) Batch 0.997 (1.159) Remain 24:37:36 loss: 0.5188 Lr: 0.00264 [2024-02-18 19:09:12,077 INFO misc.py line 119 87073] Train: [51/100][1355/1557] Data 0.004 (0.197) Batch 0.848 (1.159) Remain 24:37:17 loss: 0.3910 Lr: 0.00264 [2024-02-18 19:09:12,845 INFO misc.py line 119 87073] Train: [51/100][1356/1557] Data 0.004 (0.197) Batch 0.768 (1.158) Remain 24:36:54 loss: 0.2546 Lr: 0.00264 [2024-02-18 19:09:13,617 INFO misc.py line 119 87073] Train: [51/100][1357/1557] Data 0.005 (0.197) Batch 0.773 (1.158) Remain 24:36:31 loss: 0.1730 Lr: 0.00264 [2024-02-18 19:09:14,785 INFO misc.py line 119 87073] Train: 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(0.196) Batch 0.710 (1.157) Remain 24:34:47 loss: 0.2107 Lr: 0.00264 [2024-02-18 19:09:21,314 INFO misc.py line 119 87073] Train: [51/100][1365/1557] Data 0.010 (0.196) Batch 1.279 (1.157) Remain 24:34:53 loss: 0.2053 Lr: 0.00264 [2024-02-18 19:09:22,143 INFO misc.py line 119 87073] Train: [51/100][1366/1557] Data 0.013 (0.195) Batch 0.837 (1.157) Remain 24:34:34 loss: 1.0138 Lr: 0.00264 [2024-02-18 19:09:23,057 INFO misc.py line 119 87073] Train: [51/100][1367/1557] Data 0.005 (0.195) Batch 0.914 (1.157) Remain 24:34:19 loss: 0.3158 Lr: 0.00264 [2024-02-18 19:09:23,879 INFO misc.py line 119 87073] Train: [51/100][1368/1557] Data 0.005 (0.195) Batch 0.822 (1.156) Remain 24:33:59 loss: 0.1063 Lr: 0.00264 [2024-02-18 19:09:24,975 INFO misc.py line 119 87073] Train: [51/100][1369/1557] Data 0.006 (0.195) Batch 1.097 (1.156) Remain 24:33:55 loss: 0.9275 Lr: 0.00264 [2024-02-18 19:09:25,726 INFO misc.py line 119 87073] Train: [51/100][1370/1557] Data 0.004 (0.195) Batch 0.749 (1.156) Remain 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[2024-02-18 19:09:32,454 INFO misc.py line 119 87073] Train: [51/100][1377/1557] Data 0.012 (0.194) Batch 0.706 (1.155) Remain 24:32:07 loss: 0.3042 Lr: 0.00264 [2024-02-18 19:09:33,212 INFO misc.py line 119 87073] Train: [51/100][1378/1557] Data 0.005 (0.194) Batch 0.752 (1.155) Remain 24:31:43 loss: 0.2015 Lr: 0.00264 [2024-02-18 19:09:34,408 INFO misc.py line 119 87073] Train: [51/100][1379/1557] Data 0.011 (0.194) Batch 1.195 (1.155) Remain 24:31:45 loss: 0.1137 Lr: 0.00264 [2024-02-18 19:09:35,320 INFO misc.py line 119 87073] Train: [51/100][1380/1557] Data 0.011 (0.194) Batch 0.920 (1.155) Remain 24:31:30 loss: 0.3906 Lr: 0.00264 [2024-02-18 19:09:36,310 INFO misc.py line 119 87073] Train: [51/100][1381/1557] Data 0.004 (0.193) Batch 0.990 (1.154) Remain 24:31:20 loss: 0.3857 Lr: 0.00264 [2024-02-18 19:09:37,197 INFO misc.py line 119 87073] Train: [51/100][1382/1557] Data 0.005 (0.193) Batch 0.887 (1.154) Remain 24:31:04 loss: 0.1653 Lr: 0.00264 [2024-02-18 19:09:38,187 INFO misc.py line 119 87073] Train: [51/100][1383/1557] Data 0.004 (0.193) Batch 0.985 (1.154) Remain 24:30:54 loss: 0.7176 Lr: 0.00264 [2024-02-18 19:09:38,956 INFO misc.py line 119 87073] Train: [51/100][1384/1557] Data 0.009 (0.193) Batch 0.774 (1.154) Remain 24:30:31 loss: 0.1946 Lr: 0.00263 [2024-02-18 19:09:39,668 INFO misc.py line 119 87073] Train: [51/100][1385/1557] Data 0.005 (0.193) Batch 0.707 (1.154) Remain 24:30:05 loss: 0.2365 Lr: 0.00263 [2024-02-18 19:09:40,853 INFO misc.py line 119 87073] Train: [51/100][1386/1557] Data 0.011 (0.193) Batch 1.187 (1.154) Remain 24:30:06 loss: 0.1432 Lr: 0.00263 [2024-02-18 19:09:41,746 INFO misc.py line 119 87073] Train: [51/100][1387/1557] Data 0.008 (0.193) Batch 0.896 (1.153) Remain 24:29:51 loss: 0.4542 Lr: 0.00263 [2024-02-18 19:09:42,707 INFO misc.py line 119 87073] Train: [51/100][1388/1557] Data 0.004 (0.192) Batch 0.961 (1.153) Remain 24:29:39 loss: 0.5515 Lr: 0.00263 [2024-02-18 19:09:43,603 INFO misc.py line 119 87073] Train: 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(0.192) Batch 1.027 (1.152) Remain 24:28:16 loss: 0.3873 Lr: 0.00263 [2024-02-18 19:09:50,327 INFO misc.py line 119 87073] Train: [51/100][1396/1557] Data 0.007 (0.191) Batch 0.905 (1.152) Remain 24:28:02 loss: 0.5158 Lr: 0.00263 [2024-02-18 19:09:51,183 INFO misc.py line 119 87073] Train: [51/100][1397/1557] Data 0.005 (0.191) Batch 0.855 (1.152) Remain 24:27:44 loss: 0.2570 Lr: 0.00263 [2024-02-18 19:09:51,917 INFO misc.py line 119 87073] Train: [51/100][1398/1557] Data 0.006 (0.191) Batch 0.730 (1.152) Remain 24:27:20 loss: 0.1877 Lr: 0.00263 [2024-02-18 19:09:52,638 INFO misc.py line 119 87073] Train: [51/100][1399/1557] Data 0.010 (0.191) Batch 0.726 (1.151) Remain 24:26:55 loss: 0.3659 Lr: 0.00263 [2024-02-18 19:09:53,972 INFO misc.py line 119 87073] Train: [51/100][1400/1557] Data 0.005 (0.191) Batch 1.329 (1.151) Remain 24:27:04 loss: 0.3401 Lr: 0.00263 [2024-02-18 19:09:54,808 INFO misc.py line 119 87073] Train: [51/100][1401/1557] Data 0.010 (0.191) Batch 0.841 (1.151) Remain 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INFO misc.py line 119 87073] Train: [51/100][1414/1557] Data 0.012 (0.198) Batch 1.146 (1.158) Remain 24:35:12 loss: 0.1787 Lr: 0.00263 [2024-02-18 19:10:20,363 INFO misc.py line 119 87073] Train: [51/100][1415/1557] Data 0.013 (0.197) Batch 0.977 (1.158) Remain 24:35:01 loss: 0.2586 Lr: 0.00263 [2024-02-18 19:10:21,336 INFO misc.py line 119 87073] Train: [51/100][1416/1557] Data 0.005 (0.197) Batch 0.972 (1.158) Remain 24:34:49 loss: 0.3640 Lr: 0.00263 [2024-02-18 19:10:22,454 INFO misc.py line 119 87073] Train: [51/100][1417/1557] Data 0.006 (0.197) Batch 1.119 (1.158) Remain 24:34:46 loss: 0.2746 Lr: 0.00263 [2024-02-18 19:10:23,391 INFO misc.py line 119 87073] Train: [51/100][1418/1557] Data 0.005 (0.197) Batch 0.938 (1.158) Remain 24:34:33 loss: 0.3886 Lr: 0.00263 [2024-02-18 19:10:24,086 INFO misc.py line 119 87073] Train: [51/100][1419/1557] Data 0.004 (0.197) Batch 0.695 (1.157) Remain 24:34:07 loss: 0.2450 Lr: 0.00263 [2024-02-18 19:10:24,780 INFO misc.py line 119 87073] Train: [51/100][1420/1557] Data 0.004 (0.197) Batch 0.693 (1.157) Remain 24:33:41 loss: 0.2161 Lr: 0.00263 [2024-02-18 19:10:25,950 INFO misc.py line 119 87073] Train: [51/100][1421/1557] Data 0.005 (0.197) Batch 1.167 (1.157) Remain 24:33:40 loss: 0.0712 Lr: 0.00263 [2024-02-18 19:10:26,870 INFO misc.py line 119 87073] Train: [51/100][1422/1557] Data 0.008 (0.196) Batch 0.923 (1.157) Remain 24:33:27 loss: 0.3376 Lr: 0.00263 [2024-02-18 19:10:28,046 INFO misc.py line 119 87073] Train: [51/100][1423/1557] Data 0.005 (0.196) Batch 1.175 (1.157) Remain 24:33:26 loss: 0.3575 Lr: 0.00263 [2024-02-18 19:10:28,937 INFO misc.py line 119 87073] Train: [51/100][1424/1557] Data 0.005 (0.196) Batch 0.892 (1.157) Remain 24:33:11 loss: 0.3266 Lr: 0.00263 [2024-02-18 19:10:30,055 INFO misc.py line 119 87073] Train: [51/100][1425/1557] Data 0.004 (0.196) Batch 1.117 (1.157) Remain 24:33:08 loss: 0.4923 Lr: 0.00263 [2024-02-18 19:10:30,784 INFO misc.py line 119 87073] Train: [51/100][1426/1557] Data 0.005 (0.196) Batch 0.730 (1.156) Remain 24:32:44 loss: 0.4175 Lr: 0.00263 [2024-02-18 19:10:31,484 INFO misc.py line 119 87073] Train: [51/100][1427/1557] Data 0.004 (0.196) Batch 0.676 (1.156) Remain 24:32:17 loss: 0.2995 Lr: 0.00263 [2024-02-18 19:10:32,837 INFO misc.py line 119 87073] Train: [51/100][1428/1557] Data 0.027 (0.196) Batch 1.373 (1.156) Remain 24:32:27 loss: 0.1033 Lr: 0.00263 [2024-02-18 19:10:33,666 INFO misc.py line 119 87073] Train: [51/100][1429/1557] Data 0.008 (0.196) Batch 0.832 (1.156) Remain 24:32:09 loss: 0.7620 Lr: 0.00263 [2024-02-18 19:10:34,637 INFO misc.py line 119 87073] Train: [51/100][1430/1557] Data 0.005 (0.195) Batch 0.972 (1.156) Remain 24:31:58 loss: 0.1345 Lr: 0.00263 [2024-02-18 19:10:35,644 INFO misc.py line 119 87073] Train: [51/100][1431/1557] Data 0.003 (0.195) Batch 1.007 (1.156) Remain 24:31:49 loss: 0.2399 Lr: 0.00263 [2024-02-18 19:10:36,565 INFO misc.py line 119 87073] Train: [51/100][1432/1557] Data 0.003 (0.195) Batch 0.921 (1.155) Remain 24:31:35 loss: 0.6070 Lr: 0.00263 [2024-02-18 19:10:37,287 INFO misc.py line 119 87073] Train: [51/100][1433/1557] Data 0.004 (0.195) Batch 0.718 (1.155) Remain 24:31:10 loss: 0.5235 Lr: 0.00263 [2024-02-18 19:10:38,076 INFO misc.py line 119 87073] Train: [51/100][1434/1557] Data 0.007 (0.195) Batch 0.792 (1.155) Remain 24:30:50 loss: 0.4185 Lr: 0.00263 [2024-02-18 19:10:39,341 INFO misc.py line 119 87073] Train: [51/100][1435/1557] Data 0.005 (0.195) Batch 1.257 (1.155) Remain 24:30:54 loss: 0.1195 Lr: 0.00263 [2024-02-18 19:10:40,382 INFO misc.py line 119 87073] Train: [51/100][1436/1557] Data 0.014 (0.195) Batch 1.032 (1.155) Remain 24:30:46 loss: 0.3239 Lr: 0.00263 [2024-02-18 19:10:41,270 INFO misc.py line 119 87073] Train: [51/100][1437/1557] Data 0.022 (0.194) Batch 0.905 (1.155) Remain 24:30:32 loss: 0.6131 Lr: 0.00263 [2024-02-18 19:10:42,137 INFO misc.py line 119 87073] Train: [51/100][1438/1557] Data 0.005 (0.194) Batch 0.867 (1.154) Remain 24:30:15 loss: 0.5074 Lr: 0.00263 [2024-02-18 19:10:43,063 INFO misc.py line 119 87073] Train: [51/100][1439/1557] Data 0.005 (0.194) Batch 0.922 (1.154) Remain 24:30:02 loss: 0.1056 Lr: 0.00263 [2024-02-18 19:10:43,821 INFO misc.py line 119 87073] Train: [51/100][1440/1557] Data 0.007 (0.194) Batch 0.762 (1.154) Remain 24:29:40 loss: 0.2727 Lr: 0.00263 [2024-02-18 19:10:44,638 INFO misc.py line 119 87073] Train: [51/100][1441/1557] Data 0.004 (0.194) Batch 0.816 (1.154) Remain 24:29:21 loss: 0.3370 Lr: 0.00263 [2024-02-18 19:10:45,829 INFO misc.py line 119 87073] Train: [51/100][1442/1557] Data 0.006 (0.194) Batch 1.190 (1.154) Remain 24:29:22 loss: 0.1608 Lr: 0.00263 [2024-02-18 19:10:46,763 INFO misc.py line 119 87073] Train: [51/100][1443/1557] Data 0.007 (0.194) Batch 0.936 (1.154) Remain 24:29:09 loss: 0.2507 Lr: 0.00263 [2024-02-18 19:10:47,614 INFO misc.py line 119 87073] Train: [51/100][1444/1557] Data 0.004 (0.194) Batch 0.851 (1.153) Remain 24:28:52 loss: 0.2076 Lr: 0.00263 [2024-02-18 19:10:48,584 INFO misc.py line 119 87073] Train: [51/100][1445/1557] Data 0.005 (0.193) Batch 0.950 (1.153) Remain 24:28:40 loss: 0.3588 Lr: 0.00263 [2024-02-18 19:10:49,576 INFO misc.py line 119 87073] Train: [51/100][1446/1557] Data 0.025 (0.193) Batch 1.012 (1.153) Remain 24:28:31 loss: 0.4171 Lr: 0.00263 [2024-02-18 19:10:50,386 INFO misc.py line 119 87073] Train: [51/100][1447/1557] Data 0.006 (0.193) Batch 0.809 (1.153) Remain 24:28:12 loss: 0.1315 Lr: 0.00263 [2024-02-18 19:10:51,166 INFO misc.py line 119 87073] Train: [51/100][1448/1557] Data 0.007 (0.193) Batch 0.782 (1.153) Remain 24:27:51 loss: 0.3299 Lr: 0.00263 [2024-02-18 19:10:52,402 INFO misc.py line 119 87073] Train: [51/100][1449/1557] Data 0.004 (0.193) Batch 1.228 (1.153) Remain 24:27:54 loss: 0.2030 Lr: 0.00263 [2024-02-18 19:10:53,508 INFO misc.py line 119 87073] Train: [51/100][1450/1557] Data 0.012 (0.193) Batch 1.105 (1.153) Remain 24:27:50 loss: 0.2689 Lr: 0.00263 [2024-02-18 19:10:54,425 INFO misc.py line 119 87073] Train: [51/100][1451/1557] Data 0.013 (0.193) Batch 0.923 (1.153) Remain 24:27:37 loss: 0.3327 Lr: 0.00263 [2024-02-18 19:10:55,258 INFO misc.py line 119 87073] Train: [51/100][1452/1557] Data 0.006 (0.193) Batch 0.835 (1.152) Remain 24:27:19 loss: 0.5644 Lr: 0.00263 [2024-02-18 19:10:56,111 INFO misc.py line 119 87073] Train: [51/100][1453/1557] Data 0.005 (0.192) Batch 0.850 (1.152) Remain 24:27:02 loss: 0.2137 Lr: 0.00263 [2024-02-18 19:10:56,898 INFO misc.py line 119 87073] Train: [51/100][1454/1557] Data 0.008 (0.192) Batch 0.788 (1.152) Remain 24:26:42 loss: 0.4130 Lr: 0.00263 [2024-02-18 19:10:57,689 INFO misc.py line 119 87073] Train: [51/100][1455/1557] Data 0.005 (0.192) Batch 0.772 (1.152) Remain 24:26:20 loss: 0.3692 Lr: 0.00263 [2024-02-18 19:10:58,801 INFO misc.py line 119 87073] Train: [51/100][1456/1557] Data 0.025 (0.192) Batch 1.126 (1.152) Remain 24:26:18 loss: 0.1973 Lr: 0.00263 [2024-02-18 19:11:00,021 INFO misc.py line 119 87073] Train: [51/100][1457/1557] Data 0.012 (0.192) Batch 1.224 (1.152) Remain 24:26:21 loss: 0.6533 Lr: 0.00263 [2024-02-18 19:11:01,112 INFO misc.py line 119 87073] Train: [51/100][1458/1557] Data 0.007 (0.192) Batch 1.084 (1.152) Remain 24:26:16 loss: 0.4847 Lr: 0.00263 [2024-02-18 19:11:02,188 INFO misc.py line 119 87073] Train: [51/100][1459/1557] Data 0.014 (0.192) Batch 1.076 (1.152) Remain 24:26:11 loss: 0.4385 Lr: 0.00263 [2024-02-18 19:11:03,106 INFO misc.py line 119 87073] Train: [51/100][1460/1557] Data 0.013 (0.192) Batch 0.928 (1.151) Remain 24:25:58 loss: 0.3709 Lr: 0.00263 [2024-02-18 19:11:03,882 INFO misc.py line 119 87073] Train: [51/100][1461/1557] Data 0.004 (0.191) Batch 0.774 (1.151) Remain 24:25:37 loss: 0.3923 Lr: 0.00263 [2024-02-18 19:11:04,660 INFO misc.py line 119 87073] Train: [51/100][1462/1557] Data 0.006 (0.191) Batch 0.776 (1.151) Remain 24:25:16 loss: 0.2510 Lr: 0.00263 [2024-02-18 19:11:16,373 INFO misc.py line 119 87073] Train: [51/100][1463/1557] Data 9.873 (0.198) Batch 11.714 (1.158) Remain 24:34:28 loss: 0.2734 Lr: 0.00263 [2024-02-18 19:11:17,266 INFO misc.py line 119 87073] Train: [51/100][1464/1557] Data 0.007 (0.198) Batch 0.895 (1.158) Remain 24:34:13 loss: 0.1673 Lr: 0.00263 [2024-02-18 19:11:18,234 INFO misc.py line 119 87073] Train: [51/100][1465/1557] Data 0.005 (0.198) Batch 0.968 (1.158) Remain 24:34:02 loss: 0.3976 Lr: 0.00263 [2024-02-18 19:11:19,267 INFO misc.py line 119 87073] Train: [51/100][1466/1557] Data 0.006 (0.198) Batch 1.034 (1.158) Remain 24:33:54 loss: 0.1472 Lr: 0.00263 [2024-02-18 19:11:20,466 INFO misc.py line 119 87073] Train: [51/100][1467/1557] Data 0.004 (0.197) Batch 1.192 (1.158) Remain 24:33:55 loss: 0.2119 Lr: 0.00263 [2024-02-18 19:11:21,278 INFO misc.py line 119 87073] Train: [51/100][1468/1557] Data 0.012 (0.197) Batch 0.818 (1.158) Remain 24:33:36 loss: 0.4527 Lr: 0.00263 [2024-02-18 19:11:22,042 INFO misc.py line 119 87073] Train: [51/100][1469/1557] Data 0.007 (0.197) Batch 0.766 (1.157) Remain 24:33:14 loss: 0.3800 Lr: 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INFO misc.py line 119 87073] Train: [51/100][1476/1557] Data 0.004 (0.196) Batch 0.742 (1.156) Remain 24:31:56 loss: 0.4684 Lr: 0.00263 [2024-02-18 19:11:30,058 INFO misc.py line 119 87073] Train: [51/100][1477/1557] Data 0.010 (0.196) Batch 1.257 (1.156) Remain 24:32:00 loss: 0.0930 Lr: 0.00263 [2024-02-18 19:11:31,090 INFO misc.py line 119 87073] Train: [51/100][1478/1557] Data 0.007 (0.196) Batch 1.029 (1.156) Remain 24:31:53 loss: 0.2616 Lr: 0.00263 [2024-02-18 19:11:31,975 INFO misc.py line 119 87073] Train: [51/100][1479/1557] Data 0.011 (0.196) Batch 0.890 (1.156) Remain 24:31:38 loss: 0.6592 Lr: 0.00263 [2024-02-18 19:11:33,083 INFO misc.py line 119 87073] Train: [51/100][1480/1557] Data 0.005 (0.196) Batch 1.108 (1.156) Remain 24:31:34 loss: 0.3677 Lr: 0.00263 [2024-02-18 19:11:33,999 INFO misc.py line 119 87073] Train: [51/100][1481/1557] Data 0.005 (0.196) Batch 0.918 (1.156) Remain 24:31:21 loss: 0.1985 Lr: 0.00263 [2024-02-18 19:11:34,740 INFO misc.py line 119 87073] Train: [51/100][1482/1557] Data 0.004 (0.195) Batch 0.736 (1.156) Remain 24:30:58 loss: 0.2897 Lr: 0.00263 [2024-02-18 19:11:35,490 INFO misc.py line 119 87073] Train: [51/100][1483/1557] Data 0.008 (0.195) Batch 0.755 (1.155) Remain 24:30:36 loss: 0.2196 Lr: 0.00263 [2024-02-18 19:11:36,760 INFO misc.py line 119 87073] Train: [51/100][1484/1557] Data 0.003 (0.195) Batch 1.268 (1.156) Remain 24:30:41 loss: 0.1938 Lr: 0.00263 [2024-02-18 19:11:37,635 INFO misc.py line 119 87073] Train: [51/100][1485/1557] Data 0.006 (0.195) Batch 0.877 (1.155) Remain 24:30:25 loss: 0.4561 Lr: 0.00263 [2024-02-18 19:11:38,571 INFO misc.py line 119 87073] Train: [51/100][1486/1557] Data 0.004 (0.195) Batch 0.933 (1.155) Remain 24:30:12 loss: 0.4767 Lr: 0.00263 [2024-02-18 19:11:39,598 INFO misc.py line 119 87073] Train: [51/100][1487/1557] Data 0.007 (0.195) Batch 1.029 (1.155) Remain 24:30:05 loss: 0.2060 Lr: 0.00263 [2024-02-18 19:11:40,811 INFO misc.py line 119 87073] Train: [51/100][1488/1557] Data 0.005 (0.195) Batch 1.208 (1.155) Remain 24:30:06 loss: 0.2855 Lr: 0.00263 [2024-02-18 19:11:41,575 INFO misc.py line 119 87073] Train: [51/100][1489/1557] Data 0.010 (0.195) Batch 0.770 (1.155) Remain 24:29:45 loss: 0.2782 Lr: 0.00263 [2024-02-18 19:11:42,315 INFO misc.py line 119 87073] Train: [51/100][1490/1557] Data 0.004 (0.194) Batch 0.732 (1.155) Remain 24:29:22 loss: 0.1895 Lr: 0.00263 [2024-02-18 19:11:43,557 INFO misc.py line 119 87073] Train: [51/100][1491/1557] Data 0.011 (0.194) Batch 1.242 (1.155) Remain 24:29:26 loss: 0.1825 Lr: 0.00263 [2024-02-18 19:11:44,606 INFO misc.py line 119 87073] Train: [51/100][1492/1557] Data 0.011 (0.194) Batch 1.049 (1.155) Remain 24:29:19 loss: 0.5809 Lr: 0.00263 [2024-02-18 19:11:45,469 INFO misc.py line 119 87073] Train: [51/100][1493/1557] Data 0.012 (0.194) Batch 0.869 (1.154) Remain 24:29:03 loss: 0.0653 Lr: 0.00263 [2024-02-18 19:11:46,512 INFO misc.py line 119 87073] Train: [51/100][1494/1557] Data 0.006 (0.194) Batch 1.045 (1.154) Remain 24:28:57 loss: 0.6192 Lr: 0.00263 [2024-02-18 19:11:47,489 INFO misc.py line 119 87073] Train: [51/100][1495/1557] Data 0.005 (0.194) Batch 0.976 (1.154) Remain 24:28:46 loss: 0.7773 Lr: 0.00263 [2024-02-18 19:11:48,264 INFO misc.py line 119 87073] Train: [51/100][1496/1557] Data 0.006 (0.194) Batch 0.773 (1.154) Remain 24:28:26 loss: 0.3769 Lr: 0.00263 [2024-02-18 19:11:49,011 INFO misc.py line 119 87073] Train: [51/100][1497/1557] Data 0.008 (0.194) Batch 0.748 (1.154) Remain 24:28:04 loss: 0.1896 Lr: 0.00263 [2024-02-18 19:11:50,237 INFO misc.py line 119 87073] Train: [51/100][1498/1557] Data 0.007 (0.193) Batch 1.226 (1.154) Remain 24:28:06 loss: 0.1410 Lr: 0.00263 [2024-02-18 19:11:51,098 INFO misc.py line 119 87073] Train: [51/100][1499/1557] Data 0.008 (0.193) Batch 0.861 (1.153) Remain 24:27:50 loss: 0.4963 Lr: 0.00263 [2024-02-18 19:11:51,943 INFO misc.py line 119 87073] Train: [51/100][1500/1557] Data 0.006 (0.193) Batch 0.847 (1.153) Remain 24:27:33 loss: 0.1475 Lr: 0.00263 [2024-02-18 19:11:53,033 INFO misc.py line 119 87073] Train: [51/100][1501/1557] Data 0.005 (0.193) Batch 1.086 (1.153) Remain 24:27:29 loss: 1.0022 Lr: 0.00263 [2024-02-18 19:11:53,951 INFO misc.py line 119 87073] Train: [51/100][1502/1557] Data 0.010 (0.193) Batch 0.921 (1.153) Remain 24:27:16 loss: 0.2941 Lr: 0.00263 [2024-02-18 19:11:54,655 INFO misc.py line 119 87073] Train: [51/100][1503/1557] Data 0.006 (0.193) Batch 0.704 (1.153) Remain 24:26:52 loss: 0.1176 Lr: 0.00263 [2024-02-18 19:11:55,386 INFO misc.py line 119 87073] Train: [51/100][1504/1557] Data 0.005 (0.193) Batch 0.730 (1.153) Remain 24:26:29 loss: 0.1579 Lr: 0.00263 [2024-02-18 19:11:56,691 INFO misc.py line 119 87073] Train: [51/100][1505/1557] Data 0.007 (0.193) Batch 1.297 (1.153) Remain 24:26:35 loss: 0.1695 Lr: 0.00263 [2024-02-18 19:11:57,650 INFO misc.py line 119 87073] Train: [51/100][1506/1557] Data 0.015 (0.192) Batch 0.970 (1.152) Remain 24:26:25 loss: 0.4521 Lr: 0.00263 [2024-02-18 19:11:58,627 INFO misc.py line 119 87073] Train: [51/100][1507/1557] Data 0.003 (0.192) Batch 0.976 (1.152) Remain 24:26:15 loss: 0.5652 Lr: 0.00263 [2024-02-18 19:11:59,641 INFO misc.py line 119 87073] Train: [51/100][1508/1557] Data 0.004 (0.192) Batch 1.015 (1.152) Remain 24:26:07 loss: 0.3911 Lr: 0.00263 [2024-02-18 19:12:00,759 INFO misc.py line 119 87073] Train: [51/100][1509/1557] Data 0.004 (0.192) Batch 1.118 (1.152) Remain 24:26:04 loss: 0.3912 Lr: 0.00263 [2024-02-18 19:12:01,525 INFO misc.py line 119 87073] Train: [51/100][1510/1557] Data 0.004 (0.192) Batch 0.766 (1.152) Remain 24:25:43 loss: 0.3257 Lr: 0.00263 [2024-02-18 19:12:02,306 INFO misc.py line 119 87073] Train: [51/100][1511/1557] Data 0.004 (0.192) Batch 0.774 (1.152) Remain 24:25:23 loss: 0.2618 Lr: 0.00263 [2024-02-18 19:12:03,529 INFO misc.py line 119 87073] Train: [51/100][1512/1557] Data 0.011 (0.192) Batch 1.218 (1.152) Remain 24:25:25 loss: 0.1594 Lr: 0.00263 [2024-02-18 19:12:04,437 INFO misc.py line 119 87073] Train: [51/100][1513/1557] Data 0.016 (0.192) Batch 0.921 (1.152) Remain 24:25:12 loss: 0.2787 Lr: 0.00263 [2024-02-18 19:12:05,335 INFO misc.py line 119 87073] Train: [51/100][1514/1557] Data 0.003 (0.191) Batch 0.895 (1.151) Remain 24:24:58 loss: 0.5855 Lr: 0.00263 [2024-02-18 19:12:06,217 INFO misc.py line 119 87073] Train: [51/100][1515/1557] Data 0.007 (0.191) Batch 0.881 (1.151) Remain 24:24:43 loss: 0.5041 Lr: 0.00263 [2024-02-18 19:12:07,299 INFO misc.py line 119 87073] Train: [51/100][1516/1557] Data 0.009 (0.191) Batch 1.083 (1.151) Remain 24:24:39 loss: 0.2606 Lr: 0.00263 [2024-02-18 19:12:08,100 INFO misc.py line 119 87073] Train: [51/100][1517/1557] Data 0.008 (0.191) Batch 0.804 (1.151) Remain 24:24:20 loss: 0.2147 Lr: 0.00263 [2024-02-18 19:12:08,860 INFO misc.py line 119 87073] Train: [51/100][1518/1557] Data 0.006 (0.191) Batch 0.759 (1.151) Remain 24:23:59 loss: 0.1344 Lr: 0.00263 [2024-02-18 19:12:20,888 INFO misc.py line 119 87073] Train: [51/100][1519/1557] Data 9.928 (0.197) Batch 12.025 (1.158) Remain 24:33:05 loss: 0.2515 Lr: 0.00263 [2024-02-18 19:12:22,116 INFO misc.py line 119 87073] Train: [51/100][1520/1557] Data 0.009 (0.197) Batch 1.225 (1.158) Remain 24:33:08 loss: 0.4340 Lr: 0.00263 [2024-02-18 19:12:23,012 INFO misc.py line 119 87073] Train: [51/100][1521/1557] Data 0.011 (0.197) Batch 0.899 (1.158) Remain 24:32:53 loss: 0.2316 Lr: 0.00263 [2024-02-18 19:12:23,836 INFO misc.py line 119 87073] Train: [51/100][1522/1557] Data 0.012 (0.197) Batch 0.829 (1.158) Remain 24:32:36 loss: 0.2236 Lr: 0.00263 [2024-02-18 19:12:24,610 INFO misc.py line 119 87073] Train: [51/100][1523/1557] Data 0.004 (0.197) Batch 0.769 (1.157) Remain 24:32:15 loss: 0.2051 Lr: 0.00263 [2024-02-18 19:12:25,375 INFO misc.py line 119 87073] Train: [51/100][1524/1557] Data 0.008 (0.197) Batch 0.768 (1.157) Remain 24:31:54 loss: 0.2735 Lr: 0.00263 [2024-02-18 19:12:26,127 INFO misc.py line 119 87073] Train: [51/100][1525/1557] Data 0.006 (0.197) Batch 0.749 (1.157) Remain 24:31:33 loss: 0.2881 Lr: 0.00263 [2024-02-18 19:12:27,264 INFO misc.py line 119 87073] Train: [51/100][1526/1557] Data 0.008 (0.197) Batch 1.135 (1.157) Remain 24:31:31 loss: 0.2285 Lr: 0.00263 [2024-02-18 19:12:28,486 INFO misc.py line 119 87073] Train: [51/100][1527/1557] Data 0.010 (0.196) Batch 1.225 (1.157) Remain 24:31:33 loss: 0.3172 Lr: 0.00263 [2024-02-18 19:12:29,416 INFO misc.py line 119 87073] Train: [51/100][1528/1557] Data 0.009 (0.196) Batch 0.932 (1.157) Remain 24:31:20 loss: 0.2369 Lr: 0.00263 [2024-02-18 19:12:30,405 INFO misc.py line 119 87073] Train: [51/100][1529/1557] Data 0.006 (0.196) Batch 0.991 (1.157) Remain 24:31:11 loss: 0.3246 Lr: 0.00263 [2024-02-18 19:12:31,476 INFO misc.py line 119 87073] Train: [51/100][1530/1557] Data 0.003 (0.196) Batch 1.070 (1.157) Remain 24:31:06 loss: 0.1059 Lr: 0.00263 [2024-02-18 19:12:32,223 INFO misc.py line 119 87073] Train: [51/100][1531/1557] Data 0.005 (0.196) Batch 0.747 (1.156) Remain 24:30:44 loss: 0.1795 Lr: 0.00263 [2024-02-18 19:12:33,008 INFO misc.py line 119 87073] Train: [51/100][1532/1557] Data 0.004 (0.196) Batch 0.779 (1.156) Remain 24:30:24 loss: 0.2366 Lr: 0.00263 [2024-02-18 19:12:34,266 INFO misc.py line 119 87073] Train: [51/100][1533/1557] Data 0.009 (0.196) Batch 1.258 (1.156) Remain 24:30:28 loss: 0.0945 Lr: 0.00263 [2024-02-18 19:12:35,206 INFO misc.py line 119 87073] Train: [51/100][1534/1557] Data 0.010 (0.196) Batch 0.946 (1.156) Remain 24:30:16 loss: 0.1837 Lr: 0.00263 [2024-02-18 19:12:36,098 INFO misc.py line 119 87073] Train: [51/100][1535/1557] Data 0.004 (0.195) Batch 0.892 (1.156) Remain 24:30:02 loss: 0.2318 Lr: 0.00263 [2024-02-18 19:12:36,994 INFO misc.py line 119 87073] Train: [51/100][1536/1557] Data 0.004 (0.195) Batch 0.893 (1.156) Remain 24:29:48 loss: 0.3429 Lr: 0.00263 [2024-02-18 19:12:37,930 INFO misc.py line 119 87073] Train: [51/100][1537/1557] Data 0.007 (0.195) Batch 0.937 (1.155) Remain 24:29:36 loss: 0.5825 Lr: 0.00263 [2024-02-18 19:12:38,662 INFO misc.py line 119 87073] Train: [51/100][1538/1557] Data 0.011 (0.195) Batch 0.733 (1.155) Remain 24:29:14 loss: 0.2150 Lr: 0.00263 [2024-02-18 19:12:39,346 INFO misc.py line 119 87073] Train: [51/100][1539/1557] Data 0.005 (0.195) Batch 0.672 (1.155) Remain 24:28:48 loss: 0.1996 Lr: 0.00263 [2024-02-18 19:12:40,650 INFO misc.py line 119 87073] Train: [51/100][1540/1557] Data 0.015 (0.195) Batch 1.307 (1.155) Remain 24:28:55 loss: 0.1266 Lr: 0.00263 [2024-02-18 19:12:41,564 INFO misc.py line 119 87073] Train: [51/100][1541/1557] Data 0.012 (0.195) Batch 0.922 (1.155) Remain 24:28:42 loss: 0.2700 Lr: 0.00263 [2024-02-18 19:12:42,446 INFO misc.py line 119 87073] Train: [51/100][1542/1557] Data 0.004 (0.195) Batch 0.882 (1.155) Remain 24:28:27 loss: 1.1923 Lr: 0.00263 [2024-02-18 19:12:43,383 INFO misc.py line 119 87073] Train: [51/100][1543/1557] Data 0.004 (0.194) Batch 0.929 (1.154) Remain 24:28:15 loss: 0.4103 Lr: 0.00263 [2024-02-18 19:12:44,312 INFO misc.py line 119 87073] Train: [51/100][1544/1557] Data 0.012 (0.194) Batch 0.937 (1.154) Remain 24:28:03 loss: 0.4783 Lr: 0.00263 [2024-02-18 19:12:45,055 INFO misc.py line 119 87073] Train: [51/100][1545/1557] Data 0.005 (0.194) Batch 0.744 (1.154) Remain 24:27:42 loss: 0.4884 Lr: 0.00263 [2024-02-18 19:12:45,776 INFO misc.py line 119 87073] Train: [51/100][1546/1557] Data 0.004 (0.194) Batch 0.713 (1.154) Remain 24:27:19 loss: 0.3361 Lr: 0.00263 [2024-02-18 19:12:47,003 INFO misc.py line 119 87073] Train: [51/100][1547/1557] Data 0.011 (0.194) Batch 1.226 (1.154) Remain 24:27:21 loss: 0.1552 Lr: 0.00263 [2024-02-18 19:12:47,939 INFO misc.py line 119 87073] Train: [51/100][1548/1557] Data 0.013 (0.194) Batch 0.945 (1.154) Remain 24:27:10 loss: 0.3937 Lr: 0.00263 [2024-02-18 19:12:48,962 INFO misc.py line 119 87073] Train: [51/100][1549/1557] Data 0.004 (0.194) Batch 1.022 (1.154) Remain 24:27:02 loss: 0.2700 Lr: 0.00263 [2024-02-18 19:12:49,844 INFO misc.py line 119 87073] Train: [51/100][1550/1557] Data 0.004 (0.194) Batch 0.883 (1.153) Remain 24:26:47 loss: 0.4419 Lr: 0.00263 [2024-02-18 19:12:50,783 INFO misc.py line 119 87073] Train: [51/100][1551/1557] Data 0.005 (0.193) Batch 0.933 (1.153) Remain 24:26:35 loss: 0.3867 Lr: 0.00263 [2024-02-18 19:12:51,669 INFO misc.py line 119 87073] Train: [51/100][1552/1557] Data 0.010 (0.193) Batch 0.891 (1.153) Remain 24:26:21 loss: 0.1823 Lr: 0.00263 [2024-02-18 19:12:52,415 INFO misc.py line 119 87073] Train: [51/100][1553/1557] Data 0.004 (0.193) Batch 0.747 (1.153) Remain 24:26:00 loss: 0.2626 Lr: 0.00263 [2024-02-18 19:12:53,670 INFO misc.py line 119 87073] Train: [51/100][1554/1557] Data 0.004 (0.193) Batch 1.247 (1.153) Remain 24:26:04 loss: 0.2027 Lr: 0.00263 [2024-02-18 19:12:54,661 INFO misc.py line 119 87073] Train: [51/100][1555/1557] Data 0.012 (0.193) Batch 0.997 (1.153) Remain 24:25:55 loss: 0.1515 Lr: 0.00263 [2024-02-18 19:12:55,663 INFO misc.py line 119 87073] Train: [51/100][1556/1557] Data 0.006 (0.193) Batch 1.004 (1.153) Remain 24:25:46 loss: 0.3119 Lr: 0.00263 [2024-02-18 19:12:56,543 INFO misc.py line 119 87073] Train: [51/100][1557/1557] Data 0.005 (0.193) Batch 0.880 (1.153) Remain 24:25:32 loss: 0.2225 Lr: 0.00263 [2024-02-18 19:12:56,543 INFO misc.py line 136 87073] Train result: loss: 0.3432 [2024-02-18 19:12:56,544 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 19:13:25,181 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6992 [2024-02-18 19:13:25,958 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5930 [2024-02-18 19:13:28,084 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4187 [2024-02-18 19:13:30,291 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3853 [2024-02-18 19:13:35,238 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2545 [2024-02-18 19:13:35,937 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2457 [2024-02-18 19:13:37,197 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2513 [2024-02-18 19:13:40,150 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2936 [2024-02-18 19:13:41,964 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2553 [2024-02-18 19:13:43,510 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7105/0.7685/0.9075. [2024-02-18 19:13:43,510 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9178/0.9773 [2024-02-18 19:13:43,510 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9809/0.9939 [2024-02-18 19:13:43,510 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8671/0.9737 [2024-02-18 19:13:43,510 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 19:13:43,510 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3731/0.4250 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6455/0.6640 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7528/0.8957 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8287/0.9100 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9250/0.9542 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8470/0.8731 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7446/0.8258 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7830/0.8357 [2024-02-18 19:13:43,511 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5710/0.6618 [2024-02-18 19:13:43,511 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 19:13:43,513 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 19:13:43,513 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 19:13:51,119 INFO misc.py line 119 87073] Train: [52/100][1/1557] Data 1.438 (1.438) Batch 2.365 (2.365) Remain 50:07:23 loss: 0.2406 Lr: 0.00263 [2024-02-18 19:13:52,027 INFO misc.py line 119 87073] Train: [52/100][2/1557] Data 0.008 (0.008) Batch 0.900 (0.900) Remain 19:04:53 loss: 0.5679 Lr: 0.00263 [2024-02-18 19:13:53,060 INFO misc.py line 119 87073] Train: [52/100][3/1557] Data 0.016 (0.016) Batch 1.042 (1.042) Remain 22:04:21 loss: 0.3207 Lr: 0.00263 [2024-02-18 19:13:54,059 INFO misc.py line 119 87073] Train: [52/100][4/1557] Data 0.008 (0.008) Batch 0.997 (0.997) Remain 21:07:31 loss: 0.6864 Lr: 0.00263 [2024-02-18 19:13:54,841 INFO misc.py line 119 87073] Train: [52/100][5/1557] Data 0.009 (0.009) Batch 0.782 (0.889) Remain 18:50:46 loss: 0.2068 Lr: 0.00263 [2024-02-18 19:13:55,626 INFO misc.py line 119 87073] Train: [52/100][6/1557] Data 0.010 (0.009) Batch 0.788 (0.856) Remain 18:07:54 loss: 0.3133 Lr: 0.00263 [2024-02-18 19:13:56,802 INFO misc.py line 119 87073] Train: [52/100][7/1557] Data 0.212 (0.060) Batch 1.177 (0.936) Remain 19:49:57 loss: 0.2166 Lr: 0.00263 [2024-02-18 19:13:57,880 INFO misc.py line 119 87073] Train: [52/100][8/1557] Data 0.004 (0.049) Batch 1.079 (0.965) Remain 20:26:21 loss: 0.6123 Lr: 0.00263 [2024-02-18 19:13:59,006 INFO misc.py line 119 87073] Train: [52/100][9/1557] Data 0.004 (0.041) Batch 1.123 (0.991) Remain 20:59:59 loss: 0.2100 Lr: 0.00263 [2024-02-18 19:14:00,141 INFO misc.py line 119 87073] Train: [52/100][10/1557] Data 0.006 (0.036) Batch 1.134 (1.011) Remain 21:25:59 loss: 0.4950 Lr: 0.00263 [2024-02-18 19:14:01,066 INFO misc.py line 119 87073] Train: [52/100][11/1557] Data 0.008 (0.033) Batch 0.928 (1.001) Remain 21:12:39 loss: 0.4142 Lr: 0.00263 [2024-02-18 19:14:01,846 INFO misc.py line 119 87073] Train: [52/100][12/1557] Data 0.004 (0.029) Batch 0.778 (0.976) Remain 20:41:10 loss: 0.3465 Lr: 0.00263 [2024-02-18 19:14:02,602 INFO misc.py line 119 87073] Train: [52/100][13/1557] Data 0.006 (0.027) Batch 0.756 (0.954) Remain 20:13:07 loss: 0.3371 Lr: 0.00263 [2024-02-18 19:14:03,947 INFO misc.py line 119 87073] Train: [52/100][14/1557] Data 0.006 (0.025) Batch 1.344 (0.990) Remain 20:58:08 loss: 0.1130 Lr: 0.00263 [2024-02-18 19:14:04,751 INFO misc.py line 119 87073] Train: [52/100][15/1557] Data 0.007 (0.024) Batch 0.806 (0.974) Remain 20:38:41 loss: 0.3126 Lr: 0.00262 [2024-02-18 19:14:05,687 INFO misc.py line 119 87073] Train: [52/100][16/1557] Data 0.006 (0.022) Batch 0.935 (0.971) Remain 20:34:47 loss: 0.5378 Lr: 0.00262 [2024-02-18 19:14:06,688 INFO misc.py line 119 87073] Train: [52/100][17/1557] Data 0.007 (0.021) Batch 1.002 (0.973) Remain 20:37:33 loss: 0.5618 Lr: 0.00262 [2024-02-18 19:14:07,501 INFO misc.py line 119 87073] Train: [52/100][18/1557] Data 0.006 (0.020) Batch 0.813 (0.963) Remain 20:23:54 loss: 0.2377 Lr: 0.00262 [2024-02-18 19:14:08,278 INFO misc.py line 119 87073] Train: [52/100][19/1557] Data 0.007 (0.019) Batch 0.779 (0.951) Remain 20:09:17 loss: 0.2832 Lr: 0.00262 [2024-02-18 19:14:09,044 INFO misc.py line 119 87073] Train: [52/100][20/1557] Data 0.004 (0.018) Batch 0.765 (0.940) Remain 19:55:21 loss: 0.3375 Lr: 0.00262 [2024-02-18 19:14:10,380 INFO misc.py line 119 87073] Train: [52/100][21/1557] Data 0.004 (0.018) Batch 1.309 (0.961) Remain 20:21:24 loss: 0.2594 Lr: 0.00262 [2024-02-18 19:14:11,390 INFO misc.py line 119 87073] Train: [52/100][22/1557] Data 0.032 (0.018) Batch 1.027 (0.964) Remain 20:25:49 loss: 0.7523 Lr: 0.00262 [2024-02-18 19:14:12,371 INFO misc.py line 119 87073] Train: [52/100][23/1557] Data 0.014 (0.018) Batch 0.991 (0.966) Remain 20:27:29 loss: 0.1886 Lr: 0.00262 [2024-02-18 19:14:13,400 INFO misc.py line 119 87073] Train: [52/100][24/1557] Data 0.004 (0.018) Batch 1.028 (0.969) Remain 20:31:13 loss: 0.4980 Lr: 0.00262 [2024-02-18 19:14:14,225 INFO misc.py line 119 87073] Train: [52/100][25/1557] Data 0.006 (0.017) Batch 0.826 (0.962) Remain 20:22:56 loss: 0.1895 Lr: 0.00262 [2024-02-18 19:14:15,035 INFO misc.py line 119 87073] Train: [52/100][26/1557] Data 0.005 (0.016) Batch 0.805 (0.955) Remain 20:14:16 loss: 0.3615 Lr: 0.00262 [2024-02-18 19:14:15,768 INFO misc.py line 119 87073] Train: [52/100][27/1557] Data 0.009 (0.016) Batch 0.739 (0.946) Remain 20:02:48 loss: 0.2670 Lr: 0.00262 [2024-02-18 19:14:16,854 INFO misc.py line 119 87073] Train: [52/100][28/1557] Data 0.004 (0.016) Batch 1.085 (0.952) Remain 20:09:50 loss: 0.1175 Lr: 0.00262 [2024-02-18 19:14:17,773 INFO misc.py line 119 87073] Train: [52/100][29/1557] Data 0.005 (0.015) Batch 0.920 (0.951) Remain 20:08:15 loss: 0.1660 Lr: 0.00262 [2024-02-18 19:14:18,581 INFO misc.py line 119 87073] Train: [52/100][30/1557] Data 0.004 (0.015) Batch 0.799 (0.945) Remain 20:01:07 loss: 0.3820 Lr: 0.00262 [2024-02-18 19:14:19,522 INFO misc.py line 119 87073] Train: [52/100][31/1557] Data 0.013 (0.015) Batch 0.949 (0.945) Remain 20:01:17 loss: 0.3872 Lr: 0.00262 [2024-02-18 19:14:20,512 INFO misc.py line 119 87073] Train: [52/100][32/1557] Data 0.004 (0.014) Batch 0.990 (0.947) Remain 20:03:15 loss: 0.6263 Lr: 0.00262 [2024-02-18 19:14:21,290 INFO misc.py line 119 87073] Train: [52/100][33/1557] Data 0.004 (0.014) Batch 0.778 (0.941) Remain 19:56:06 loss: 0.1540 Lr: 0.00262 [2024-02-18 19:14:22,071 INFO misc.py line 119 87073] Train: [52/100][34/1557] Data 0.004 (0.014) Batch 0.780 (0.936) Remain 19:49:28 loss: 0.1955 Lr: 0.00262 [2024-02-18 19:14:23,367 INFO misc.py line 119 87073] Train: [52/100][35/1557] Data 0.005 (0.013) Batch 1.285 (0.947) Remain 20:03:18 loss: 0.3522 Lr: 0.00262 [2024-02-18 19:14:24,346 INFO misc.py line 119 87073] Train: [52/100][36/1557] Data 0.018 (0.014) Batch 0.991 (0.948) Remain 20:05:00 loss: 0.1804 Lr: 0.00262 [2024-02-18 19:14:25,426 INFO misc.py line 119 87073] Train: [52/100][37/1557] Data 0.006 (0.013) Batch 1.081 (0.952) Remain 20:09:56 loss: 0.3315 Lr: 0.00262 [2024-02-18 19:14:26,271 INFO misc.py line 119 87073] Train: [52/100][38/1557] Data 0.004 (0.013) Batch 0.845 (0.949) Remain 20:06:02 loss: 0.2896 Lr: 0.00262 [2024-02-18 19:14:27,339 INFO misc.py line 119 87073] Train: [52/100][39/1557] Data 0.003 (0.013) Batch 1.056 (0.952) Remain 20:09:49 loss: 0.6178 Lr: 0.00262 [2024-02-18 19:14:28,109 INFO misc.py line 119 87073] Train: [52/100][40/1557] Data 0.015 (0.013) Batch 0.781 (0.947) Remain 20:03:55 loss: 0.2162 Lr: 0.00262 [2024-02-18 19:14:28,901 INFO misc.py line 119 87073] Train: [52/100][41/1557] Data 0.005 (0.013) Batch 0.783 (0.943) Remain 19:58:24 loss: 0.3330 Lr: 0.00262 [2024-02-18 19:14:30,129 INFO misc.py line 119 87073] Train: [52/100][42/1557] Data 0.014 (0.013) Batch 1.227 (0.950) Remain 20:07:39 loss: 0.2982 Lr: 0.00262 [2024-02-18 19:14:31,041 INFO misc.py line 119 87073] Train: [52/100][43/1557] Data 0.014 (0.013) Batch 0.921 (0.950) Remain 20:06:42 loss: 0.6453 Lr: 0.00262 [2024-02-18 19:14:32,027 INFO misc.py line 119 87073] Train: [52/100][44/1557] Data 0.005 (0.013) Batch 0.988 (0.950) Remain 20:07:52 loss: 0.4194 Lr: 0.00262 [2024-02-18 19:14:33,040 INFO misc.py line 119 87073] Train: [52/100][45/1557] Data 0.004 (0.012) Batch 1.012 (0.952) Remain 20:09:43 loss: 0.5063 Lr: 0.00262 [2024-02-18 19:14:33,993 INFO misc.py line 119 87073] Train: [52/100][46/1557] Data 0.004 (0.012) Batch 0.953 (0.952) Remain 20:09:44 loss: 0.2447 Lr: 0.00262 [2024-02-18 19:14:34,739 INFO misc.py line 119 87073] Train: [52/100][47/1557] Data 0.005 (0.012) Batch 0.745 (0.947) Remain 20:03:45 loss: 0.1895 Lr: 0.00262 [2024-02-18 19:14:35,479 INFO misc.py line 119 87073] Train: [52/100][48/1557] Data 0.005 (0.012) Batch 0.740 (0.943) Remain 19:57:53 loss: 0.2256 Lr: 0.00262 [2024-02-18 19:14:36,669 INFO misc.py line 119 87073] Train: [52/100][49/1557] Data 0.006 (0.012) Batch 1.183 (0.948) Remain 20:04:30 loss: 0.1716 Lr: 0.00262 [2024-02-18 19:14:37,443 INFO misc.py line 119 87073] Train: [52/100][50/1557] Data 0.013 (0.012) Batch 0.782 (0.944) Remain 20:00:00 loss: 0.5511 Lr: 0.00262 [2024-02-18 19:14:38,389 INFO misc.py line 119 87073] Train: [52/100][51/1557] Data 0.004 (0.012) Batch 0.944 (0.944) Remain 19:59:59 loss: 0.3818 Lr: 0.00262 [2024-02-18 19:14:39,499 INFO misc.py line 119 87073] Train: [52/100][52/1557] Data 0.006 (0.011) Batch 1.102 (0.948) Remain 20:04:03 loss: 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INFO misc.py line 119 87073] Train: [52/100][59/1557] Data 0.016 (0.011) Batch 0.842 (0.946) Remain 20:02:13 loss: 0.5833 Lr: 0.00262 [2024-02-18 19:14:47,102 INFO misc.py line 119 87073] Train: [52/100][60/1557] Data 0.003 (0.011) Batch 1.056 (0.948) Remain 20:04:38 loss: 0.2477 Lr: 0.00262 [2024-02-18 19:14:47,897 INFO misc.py line 119 87073] Train: [52/100][61/1557] Data 0.005 (0.011) Batch 0.795 (0.945) Remain 20:01:17 loss: 0.6035 Lr: 0.00262 [2024-02-18 19:14:48,668 INFO misc.py line 119 87073] Train: [52/100][62/1557] Data 0.004 (0.011) Batch 0.760 (0.942) Remain 19:57:17 loss: 0.1401 Lr: 0.00262 [2024-02-18 19:14:59,590 INFO misc.py line 119 87073] Train: [52/100][63/1557] Data 7.085 (0.129) Batch 10.931 (1.109) Remain 23:28:47 loss: 0.1354 Lr: 0.00262 [2024-02-18 19:15:00,528 INFO misc.py line 119 87073] Train: [52/100][64/1557] Data 0.006 (0.127) Batch 0.936 (1.106) Remain 23:25:10 loss: 0.2750 Lr: 0.00262 [2024-02-18 19:15:01,434 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [52/100][109/1557] Data 0.005 (0.076) Batch 0.825 (1.034) Remain 21:53:24 loss: 0.4258 Lr: 0.00262 [2024-02-18 19:15:43,509 INFO misc.py line 119 87073] Train: [52/100][110/1557] Data 0.013 (0.075) Batch 0.803 (1.032) Remain 21:50:38 loss: 0.1929 Lr: 0.00262 [2024-02-18 19:15:44,278 INFO misc.py line 119 87073] Train: [52/100][111/1557] Data 0.006 (0.075) Batch 0.771 (1.030) Remain 21:47:32 loss: 0.1391 Lr: 0.00262 [2024-02-18 19:15:45,606 INFO misc.py line 119 87073] Train: [52/100][112/1557] Data 0.005 (0.074) Batch 1.319 (1.032) Remain 21:50:54 loss: 0.1011 Lr: 0.00262 [2024-02-18 19:15:46,676 INFO misc.py line 119 87073] Train: [52/100][113/1557] Data 0.013 (0.073) Batch 1.062 (1.033) Remain 21:51:13 loss: 0.3031 Lr: 0.00262 [2024-02-18 19:15:47,525 INFO misc.py line 119 87073] Train: [52/100][114/1557] Data 0.021 (0.073) Batch 0.864 (1.031) Remain 21:49:17 loss: 0.1350 Lr: 0.00262 [2024-02-18 19:15:48,571 INFO misc.py line 119 87073] Train: 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Batch 0.893 (1.104) Remain 23:21:20 loss: 0.1890 Lr: 0.00262 [2024-02-18 19:16:04,270 INFO misc.py line 119 87073] Train: [52/100][122/1557] Data 0.004 (0.123) Batch 0.961 (1.103) Remain 23:19:47 loss: 0.3274 Lr: 0.00262 [2024-02-18 19:16:05,398 INFO misc.py line 119 87073] Train: [52/100][123/1557] Data 0.004 (0.122) Batch 1.129 (1.103) Remain 23:20:02 loss: 0.3752 Lr: 0.00262 [2024-02-18 19:16:06,116 INFO misc.py line 119 87073] Train: [52/100][124/1557] Data 0.004 (0.121) Batch 0.717 (1.100) Remain 23:15:58 loss: 0.2047 Lr: 0.00262 [2024-02-18 19:16:06,811 INFO misc.py line 119 87073] Train: [52/100][125/1557] Data 0.005 (0.120) Batch 0.690 (1.096) Remain 23:11:42 loss: 0.4494 Lr: 0.00262 [2024-02-18 19:16:08,178 INFO misc.py line 119 87073] Train: [52/100][126/1557] Data 0.009 (0.119) Batch 1.366 (1.098) Remain 23:14:28 loss: 0.1330 Lr: 0.00262 [2024-02-18 19:16:09,410 INFO misc.py line 119 87073] Train: [52/100][127/1557] Data 0.012 (0.118) Batch 1.231 (1.100) Remain 23:15:48 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line 119 87073] Train: [52/100][165/1557] Data 0.006 (0.092) Batch 1.104 (1.063) Remain 22:29:16 loss: 0.3870 Lr: 0.00262 [2024-02-18 19:16:46,104 INFO misc.py line 119 87073] Train: [52/100][166/1557] Data 0.014 (0.092) Batch 0.769 (1.062) Remain 22:26:57 loss: 0.2087 Lr: 0.00262 [2024-02-18 19:16:46,850 INFO misc.py line 119 87073] Train: [52/100][167/1557] Data 0.006 (0.091) Batch 0.744 (1.060) Remain 22:24:29 loss: 0.2374 Lr: 0.00262 [2024-02-18 19:16:48,156 INFO misc.py line 119 87073] Train: [52/100][168/1557] Data 0.008 (0.091) Batch 1.309 (1.061) Remain 22:26:23 loss: 0.1605 Lr: 0.00262 [2024-02-18 19:16:49,050 INFO misc.py line 119 87073] Train: [52/100][169/1557] Data 0.007 (0.090) Batch 0.893 (1.060) Remain 22:25:05 loss: 0.3950 Lr: 0.00262 [2024-02-18 19:16:49,806 INFO misc.py line 119 87073] Train: [52/100][170/1557] Data 0.006 (0.090) Batch 0.757 (1.058) Remain 22:22:45 loss: 0.7025 Lr: 0.00262 [2024-02-18 19:16:50,752 INFO misc.py line 119 87073] Train: 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Batch 0.893 (1.105) Remain 23:21:36 loss: 0.5166 Lr: 0.00262 [2024-02-18 19:17:06,153 INFO misc.py line 119 87073] Train: [52/100][178/1557] Data 0.004 (0.126) Batch 0.850 (1.103) Remain 23:19:44 loss: 0.1441 Lr: 0.00262 [2024-02-18 19:17:07,157 INFO misc.py line 119 87073] Train: [52/100][179/1557] Data 0.005 (0.126) Batch 0.997 (1.103) Remain 23:18:58 loss: 0.5578 Lr: 0.00262 [2024-02-18 19:17:09,755 INFO misc.py line 119 87073] Train: [52/100][180/1557] Data 1.364 (0.133) Batch 2.604 (1.111) Remain 23:29:42 loss: 0.2649 Lr: 0.00262 [2024-02-18 19:17:10,611 INFO misc.py line 119 87073] Train: [52/100][181/1557] Data 0.006 (0.132) Batch 0.856 (1.110) Remain 23:27:52 loss: 0.2846 Lr: 0.00262 [2024-02-18 19:17:11,938 INFO misc.py line 119 87073] Train: [52/100][182/1557] Data 0.006 (0.131) Batch 1.328 (1.111) Remain 23:29:23 loss: 0.1112 Lr: 0.00262 [2024-02-18 19:17:13,060 INFO misc.py line 119 87073] Train: [52/100][183/1557] Data 0.005 (0.131) Batch 1.120 (1.111) Remain 23:29:26 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19:17:19,595 INFO misc.py line 119 87073] Train: [52/100][190/1557] Data 0.014 (0.126) Batch 0.981 (1.104) Remain 23:20:53 loss: 0.2304 Lr: 0.00262 [2024-02-18 19:17:20,470 INFO misc.py line 119 87073] Train: [52/100][191/1557] Data 0.004 (0.125) Batch 0.872 (1.103) Remain 23:19:18 loss: 0.3245 Lr: 0.00262 [2024-02-18 19:17:21,328 INFO misc.py line 119 87073] Train: [52/100][192/1557] Data 0.006 (0.125) Batch 0.855 (1.102) Remain 23:17:37 loss: 0.5165 Lr: 0.00262 [2024-02-18 19:17:22,388 INFO misc.py line 119 87073] Train: [52/100][193/1557] Data 0.009 (0.124) Batch 1.057 (1.102) Remain 23:17:18 loss: 0.6142 Lr: 0.00262 [2024-02-18 19:17:23,157 INFO misc.py line 119 87073] Train: [52/100][194/1557] Data 0.012 (0.124) Batch 0.776 (1.100) Remain 23:15:08 loss: 0.1823 Lr: 0.00262 [2024-02-18 19:17:23,909 INFO misc.py line 119 87073] Train: [52/100][195/1557] Data 0.004 (0.123) Batch 0.746 (1.098) Remain 23:12:46 loss: 0.1901 Lr: 0.00262 [2024-02-18 19:17:24,984 INFO misc.py line 119 87073] Train: [52/100][196/1557] Data 0.010 (0.122) Batch 1.080 (1.098) Remain 23:12:38 loss: 0.1661 Lr: 0.00262 [2024-02-18 19:17:25,913 INFO misc.py line 119 87073] Train: [52/100][197/1557] Data 0.006 (0.122) Batch 0.930 (1.097) Remain 23:11:31 loss: 0.4211 Lr: 0.00262 [2024-02-18 19:17:26,878 INFO misc.py line 119 87073] Train: [52/100][198/1557] Data 0.004 (0.121) Batch 0.964 (1.097) Remain 23:10:38 loss: 0.2362 Lr: 0.00262 [2024-02-18 19:17:27,730 INFO misc.py line 119 87073] Train: [52/100][199/1557] Data 0.005 (0.121) Batch 0.854 (1.095) Remain 23:09:03 loss: 0.5739 Lr: 0.00262 [2024-02-18 19:17:28,648 INFO misc.py line 119 87073] Train: [52/100][200/1557] Data 0.003 (0.120) Batch 0.916 (1.094) Remain 23:07:53 loss: 0.5670 Lr: 0.00262 [2024-02-18 19:17:29,473 INFO misc.py line 119 87073] Train: [52/100][201/1557] Data 0.004 (0.119) Batch 0.825 (1.093) Remain 23:06:08 loss: 0.3039 Lr: 0.00262 [2024-02-18 19:17:30,247 INFO misc.py line 119 87073] Train: [52/100][202/1557] Data 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line 119 87073] Train: [52/100][221/1557] Data 0.005 (0.109) Batch 1.123 (1.081) Remain 22:50:29 loss: 0.7721 Lr: 0.00261 [2024-02-18 19:17:49,468 INFO misc.py line 119 87073] Train: [52/100][222/1557] Data 0.005 (0.108) Batch 0.763 (1.079) Remain 22:48:38 loss: 0.3161 Lr: 0.00261 [2024-02-18 19:17:50,211 INFO misc.py line 119 87073] Train: [52/100][223/1557] Data 0.004 (0.108) Batch 0.733 (1.078) Remain 22:46:37 loss: 0.3043 Lr: 0.00261 [2024-02-18 19:17:51,485 INFO misc.py line 119 87073] Train: [52/100][224/1557] Data 0.013 (0.108) Batch 1.272 (1.079) Remain 22:47:43 loss: 0.1733 Lr: 0.00261 [2024-02-18 19:17:52,529 INFO misc.py line 119 87073] Train: [52/100][225/1557] Data 0.016 (0.107) Batch 1.046 (1.079) Remain 22:47:31 loss: 0.4481 Lr: 0.00261 [2024-02-18 19:17:53,650 INFO misc.py line 119 87073] Train: [52/100][226/1557] Data 0.013 (0.107) Batch 1.129 (1.079) Remain 22:47:47 loss: 0.2208 Lr: 0.00261 [2024-02-18 19:17:54,556 INFO misc.py line 119 87073] Train: 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loss: 0.4052 Lr: 0.00261 [2024-02-18 19:18:16,719 INFO misc.py line 119 87073] Train: [52/100][240/1557] Data 0.004 (0.131) Batch 1.056 (1.112) Remain 23:30:08 loss: 0.6208 Lr: 0.00261 [2024-02-18 19:18:17,632 INFO misc.py line 119 87073] Train: [52/100][241/1557] Data 0.005 (0.131) Batch 0.911 (1.112) Remain 23:29:02 loss: 0.7784 Lr: 0.00261 [2024-02-18 19:18:18,814 INFO misc.py line 119 87073] Train: [52/100][242/1557] Data 0.006 (0.130) Batch 1.184 (1.112) Remain 23:29:24 loss: 0.7743 Lr: 0.00261 [2024-02-18 19:18:19,540 INFO misc.py line 119 87073] Train: [52/100][243/1557] Data 0.005 (0.130) Batch 0.726 (1.110) Remain 23:27:21 loss: 0.2114 Lr: 0.00261 [2024-02-18 19:18:20,279 INFO misc.py line 119 87073] Train: [52/100][244/1557] Data 0.005 (0.129) Batch 0.739 (1.109) Remain 23:25:22 loss: 0.2524 Lr: 0.00261 [2024-02-18 19:18:21,564 INFO misc.py line 119 87073] Train: [52/100][245/1557] Data 0.005 (0.128) Batch 1.286 (1.110) Remain 23:26:17 loss: 0.0938 Lr: 0.00261 [2024-02-18 19:18:22,390 INFO misc.py line 119 87073] Train: [52/100][246/1557] Data 0.004 (0.128) Batch 0.824 (1.108) Remain 23:24:46 loss: 0.3323 Lr: 0.00261 [2024-02-18 19:18:23,416 INFO misc.py line 119 87073] Train: [52/100][247/1557] Data 0.007 (0.127) Batch 1.026 (1.108) Remain 23:24:20 loss: 0.1741 Lr: 0.00261 [2024-02-18 19:18:24,236 INFO misc.py line 119 87073] Train: [52/100][248/1557] Data 0.006 (0.127) Batch 0.820 (1.107) Remain 23:22:49 loss: 0.4051 Lr: 0.00261 [2024-02-18 19:18:25,215 INFO misc.py line 119 87073] Train: [52/100][249/1557] Data 0.005 (0.126) Batch 0.978 (1.106) Remain 23:22:08 loss: 0.4291 Lr: 0.00261 [2024-02-18 19:18:25,895 INFO misc.py line 119 87073] Train: [52/100][250/1557] Data 0.006 (0.126) Batch 0.681 (1.105) Remain 23:19:56 loss: 0.3616 Lr: 0.00261 [2024-02-18 19:18:26,594 INFO misc.py line 119 87073] Train: [52/100][251/1557] Data 0.005 (0.126) Batch 0.698 (1.103) Remain 23:17:51 loss: 0.3375 Lr: 0.00261 [2024-02-18 19:18:27,636 INFO misc.py line 119 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line 119 87073] Train: [52/100][277/1557] Data 0.004 (0.114) Batch 0.959 (1.086) Remain 22:56:18 loss: 0.1750 Lr: 0.00261 [2024-02-18 19:18:51,459 INFO misc.py line 119 87073] Train: [52/100][278/1557] Data 0.004 (0.114) Batch 0.746 (1.085) Remain 22:54:43 loss: 0.3099 Lr: 0.00261 [2024-02-18 19:18:52,229 INFO misc.py line 119 87073] Train: [52/100][279/1557] Data 0.005 (0.113) Batch 0.769 (1.084) Remain 22:53:14 loss: 0.4561 Lr: 0.00261 [2024-02-18 19:18:53,542 INFO misc.py line 119 87073] Train: [52/100][280/1557] Data 0.006 (0.113) Batch 1.293 (1.085) Remain 22:54:11 loss: 0.3552 Lr: 0.00261 [2024-02-18 19:18:54,556 INFO misc.py line 119 87073] Train: [52/100][281/1557] Data 0.026 (0.113) Batch 1.014 (1.084) Remain 22:53:50 loss: 0.2489 Lr: 0.00261 [2024-02-18 19:18:55,433 INFO misc.py line 119 87073] Train: [52/100][282/1557] Data 0.026 (0.112) Batch 0.898 (1.084) Remain 22:52:58 loss: 0.2545 Lr: 0.00261 [2024-02-18 19:18:56,336 INFO misc.py line 119 87073] Train: 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Batch 1.019 (1.113) Remain 23:29:26 loss: 0.6682 Lr: 0.00261 [2024-02-18 19:19:12,404 INFO misc.py line 119 87073] Train: [52/100][290/1557] Data 0.006 (0.134) Batch 1.125 (1.113) Remain 23:29:28 loss: 0.4268 Lr: 0.00261 [2024-02-18 19:19:13,445 INFO misc.py line 119 87073] Train: [52/100][291/1557] Data 0.006 (0.134) Batch 1.041 (1.112) Remain 23:29:08 loss: 0.5008 Lr: 0.00261 [2024-02-18 19:19:14,218 INFO misc.py line 119 87073] Train: [52/100][292/1557] Data 0.005 (0.133) Batch 0.772 (1.111) Remain 23:27:37 loss: 0.1372 Lr: 0.00261 [2024-02-18 19:19:15,011 INFO misc.py line 119 87073] Train: [52/100][293/1557] Data 0.006 (0.133) Batch 0.788 (1.110) Remain 23:26:11 loss: 0.2482 Lr: 0.00261 [2024-02-18 19:19:16,359 INFO misc.py line 119 87073] Train: [52/100][294/1557] Data 0.010 (0.133) Batch 1.351 (1.111) Remain 23:27:13 loss: 0.2297 Lr: 0.00261 [2024-02-18 19:19:17,233 INFO misc.py line 119 87073] Train: [52/100][295/1557] Data 0.007 (0.132) Batch 0.876 (1.110) Remain 23:26:11 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87073] Train: [52/100][308/1557] Data 0.006 (0.127) Batch 1.053 (1.103) Remain 23:16:23 loss: 0.1209 Lr: 0.00261 [2024-02-18 19:19:30,500 INFO misc.py line 119 87073] Train: [52/100][309/1557] Data 0.013 (0.126) Batch 1.133 (1.103) Remain 23:16:30 loss: 0.4688 Lr: 0.00261 [2024-02-18 19:19:31,425 INFO misc.py line 119 87073] Train: [52/100][310/1557] Data 0.009 (0.126) Batch 0.929 (1.102) Remain 23:15:46 loss: 0.3317 Lr: 0.00261 [2024-02-18 19:19:32,503 INFO misc.py line 119 87073] Train: [52/100][311/1557] Data 0.005 (0.126) Batch 1.078 (1.102) Remain 23:15:38 loss: 0.3297 Lr: 0.00261 [2024-02-18 19:19:33,527 INFO misc.py line 119 87073] Train: [52/100][312/1557] Data 0.005 (0.125) Batch 1.025 (1.102) Remain 23:15:18 loss: 0.3503 Lr: 0.00261 [2024-02-18 19:19:34,275 INFO misc.py line 119 87073] Train: [52/100][313/1557] Data 0.004 (0.125) Batch 0.748 (1.101) Remain 23:13:50 loss: 0.1681 Lr: 0.00261 [2024-02-18 19:19:34,969 INFO misc.py line 119 87073] Train: [52/100][314/1557] Data 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Batch 1.024 (1.115) Remain 23:29:29 loss: 0.1341 Lr: 0.00260 [2024-02-18 19:22:20,233 INFO misc.py line 119 87073] Train: [52/100][458/1557] Data 0.005 (0.138) Batch 0.889 (1.115) Remain 23:28:50 loss: 0.3183 Lr: 0.00260 [2024-02-18 19:22:21,403 INFO misc.py line 119 87073] Train: [52/100][459/1557] Data 0.007 (0.138) Batch 1.170 (1.115) Remain 23:28:58 loss: 0.3861 Lr: 0.00260 [2024-02-18 19:22:22,187 INFO misc.py line 119 87073] Train: [52/100][460/1557] Data 0.006 (0.137) Batch 0.786 (1.114) Remain 23:28:02 loss: 0.2004 Lr: 0.00260 [2024-02-18 19:22:22,955 INFO misc.py line 119 87073] Train: [52/100][461/1557] Data 0.005 (0.137) Batch 0.767 (1.113) Remain 23:27:04 loss: 0.5379 Lr: 0.00260 [2024-02-18 19:22:24,220 INFO misc.py line 119 87073] Train: [52/100][462/1557] Data 0.006 (0.137) Batch 1.261 (1.114) Remain 23:27:27 loss: 0.1668 Lr: 0.00260 [2024-02-18 19:22:25,078 INFO misc.py line 119 87073] Train: [52/100][463/1557] Data 0.010 (0.136) Batch 0.863 (1.113) Remain 23:26:45 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line 119 87073] Train: [52/100][501/1557] Data 0.005 (0.127) Batch 1.031 (1.101) Remain 23:11:22 loss: 0.4959 Lr: 0.00260 [2024-02-18 19:23:02,318 INFO misc.py line 119 87073] Train: [52/100][502/1557] Data 0.005 (0.126) Batch 0.728 (1.101) Remain 23:10:24 loss: 0.2385 Lr: 0.00260 [2024-02-18 19:23:03,117 INFO misc.py line 119 87073] Train: [52/100][503/1557] Data 0.003 (0.126) Batch 0.789 (1.100) Remain 23:09:36 loss: 0.2276 Lr: 0.00260 [2024-02-18 19:23:04,383 INFO misc.py line 119 87073] Train: [52/100][504/1557] Data 0.013 (0.126) Batch 1.267 (1.100) Remain 23:10:00 loss: 0.1150 Lr: 0.00260 [2024-02-18 19:23:05,349 INFO misc.py line 119 87073] Train: [52/100][505/1557] Data 0.013 (0.126) Batch 0.975 (1.100) Remain 23:09:40 loss: 0.4908 Lr: 0.00260 [2024-02-18 19:23:06,238 INFO misc.py line 119 87073] Train: [52/100][506/1557] Data 0.005 (0.125) Batch 0.889 (1.100) Remain 23:09:07 loss: 0.2543 Lr: 0.00260 [2024-02-18 19:23:07,147 INFO misc.py line 119 87073] Train: 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Batch 0.994 (1.117) Remain 23:31:02 loss: 0.2736 Lr: 0.00260 [2024-02-18 19:23:23,828 INFO misc.py line 119 87073] Train: [52/100][514/1557] Data 0.003 (0.137) Batch 0.989 (1.117) Remain 23:30:42 loss: 0.4552 Lr: 0.00260 [2024-02-18 19:23:24,820 INFO misc.py line 119 87073] Train: [52/100][515/1557] Data 0.004 (0.137) Batch 0.992 (1.117) Remain 23:30:22 loss: 0.2223 Lr: 0.00260 [2024-02-18 19:23:25,596 INFO misc.py line 119 87073] Train: [52/100][516/1557] Data 0.004 (0.137) Batch 0.775 (1.116) Remain 23:29:31 loss: 0.1030 Lr: 0.00260 [2024-02-18 19:23:26,335 INFO misc.py line 119 87073] Train: [52/100][517/1557] Data 0.004 (0.137) Batch 0.735 (1.115) Remain 23:28:33 loss: 0.2006 Lr: 0.00260 [2024-02-18 19:23:27,701 INFO misc.py line 119 87073] Train: [52/100][518/1557] Data 0.009 (0.136) Batch 1.330 (1.116) Remain 23:29:04 loss: 0.1168 Lr: 0.00260 [2024-02-18 19:23:28,773 INFO misc.py line 119 87073] Train: [52/100][519/1557] Data 0.044 (0.136) Batch 1.103 (1.116) Remain 23:29:01 loss: 0.2470 Lr: 0.00260 [2024-02-18 19:23:29,713 INFO misc.py line 119 87073] Train: [52/100][520/1557] Data 0.013 (0.136) Batch 0.948 (1.115) Remain 23:28:35 loss: 0.4361 Lr: 0.00260 [2024-02-18 19:23:30,509 INFO misc.py line 119 87073] Train: [52/100][521/1557] Data 0.004 (0.136) Batch 0.796 (1.115) Remain 23:27:48 loss: 0.2210 Lr: 0.00260 [2024-02-18 19:23:31,460 INFO misc.py line 119 87073] Train: [52/100][522/1557] Data 0.005 (0.135) Batch 0.927 (1.114) Remain 23:27:19 loss: 0.6915 Lr: 0.00260 [2024-02-18 19:23:32,199 INFO misc.py line 119 87073] Train: [52/100][523/1557] Data 0.028 (0.135) Batch 0.763 (1.114) Remain 23:26:27 loss: 0.4111 Lr: 0.00260 [2024-02-18 19:23:32,954 INFO misc.py line 119 87073] Train: [52/100][524/1557] Data 0.004 (0.135) Batch 0.752 (1.113) Remain 23:25:33 loss: 0.2861 Lr: 0.00260 [2024-02-18 19:23:34,301 INFO misc.py line 119 87073] Train: [52/100][525/1557] Data 0.007 (0.135) Batch 1.341 (1.113) Remain 23:26:05 loss: 0.0752 Lr: 0.00260 [2024-02-18 19:23:35,449 INFO misc.py line 119 87073] Train: [52/100][526/1557] Data 0.014 (0.134) Batch 1.155 (1.114) Remain 23:26:10 loss: 0.6252 Lr: 0.00260 [2024-02-18 19:23:36,519 INFO misc.py line 119 87073] Train: [52/100][527/1557] Data 0.007 (0.134) Batch 1.066 (1.113) Remain 23:26:02 loss: 0.2735 Lr: 0.00260 [2024-02-18 19:23:37,429 INFO misc.py line 119 87073] Train: [52/100][528/1557] Data 0.011 (0.134) Batch 0.917 (1.113) Remain 23:25:32 loss: 0.1596 Lr: 0.00260 [2024-02-18 19:23:38,394 INFO misc.py line 119 87073] Train: [52/100][529/1557] Data 0.004 (0.134) Batch 0.964 (1.113) Remain 23:25:10 loss: 0.4567 Lr: 0.00260 [2024-02-18 19:23:41,230 INFO misc.py line 119 87073] Train: [52/100][530/1557] Data 1.324 (0.136) Batch 2.837 (1.116) Remain 23:29:17 loss: 0.2959 Lr: 0.00260 [2024-02-18 19:23:42,030 INFO misc.py line 119 87073] Train: [52/100][531/1557] Data 0.004 (0.136) Batch 0.789 (1.115) Remain 23:28:29 loss: 0.4261 Lr: 0.00260 [2024-02-18 19:23:43,081 INFO misc.py line 119 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[2024-02-18 19:24:01,025 INFO misc.py line 119 87073] Train: [52/100][551/1557] Data 0.004 (0.131) Batch 0.800 (1.109) Remain 23:20:29 loss: 0.4474 Lr: 0.00260 [2024-02-18 19:24:01,749 INFO misc.py line 119 87073] Train: [52/100][552/1557] Data 0.010 (0.131) Batch 0.731 (1.109) Remain 23:19:35 loss: 0.1657 Lr: 0.00260 [2024-02-18 19:24:02,952 INFO misc.py line 119 87073] Train: [52/100][553/1557] Data 0.004 (0.131) Batch 1.201 (1.109) Remain 23:19:47 loss: 0.1667 Lr: 0.00260 [2024-02-18 19:24:03,967 INFO misc.py line 119 87073] Train: [52/100][554/1557] Data 0.005 (0.130) Batch 1.015 (1.109) Remain 23:19:33 loss: 0.3284 Lr: 0.00260 [2024-02-18 19:24:04,897 INFO misc.py line 119 87073] Train: [52/100][555/1557] Data 0.005 (0.130) Batch 0.930 (1.108) Remain 23:19:08 loss: 0.5532 Lr: 0.00260 [2024-02-18 19:24:05,859 INFO misc.py line 119 87073] Train: [52/100][556/1557] Data 0.004 (0.130) Batch 0.962 (1.108) Remain 23:18:46 loss: 0.5379 Lr: 0.00260 [2024-02-18 19:24:06,999 INFO misc.py line 119 87073] Train: [52/100][557/1557] Data 0.005 (0.130) Batch 1.139 (1.108) Remain 23:18:49 loss: 0.3386 Lr: 0.00260 [2024-02-18 19:24:07,751 INFO misc.py line 119 87073] Train: [52/100][558/1557] Data 0.006 (0.129) Batch 0.754 (1.108) Remain 23:18:00 loss: 0.4142 Lr: 0.00260 [2024-02-18 19:24:08,525 INFO misc.py line 119 87073] Train: [52/100][559/1557] Data 0.004 (0.129) Batch 0.772 (1.107) Remain 23:17:13 loss: 0.1420 Lr: 0.00260 [2024-02-18 19:24:09,833 INFO misc.py line 119 87073] Train: [52/100][560/1557] Data 0.005 (0.129) Batch 1.308 (1.107) Remain 23:17:39 loss: 0.2260 Lr: 0.00260 [2024-02-18 19:24:11,022 INFO misc.py line 119 87073] Train: [52/100][561/1557] Data 0.006 (0.129) Batch 1.186 (1.107) Remain 23:17:49 loss: 0.3491 Lr: 0.00260 [2024-02-18 19:24:12,138 INFO misc.py line 119 87073] Train: [52/100][562/1557] Data 0.009 (0.129) Batch 1.121 (1.107) Remain 23:17:50 loss: 0.5132 Lr: 0.00260 [2024-02-18 19:24:13,082 INFO misc.py line 119 87073] Train: 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Batch 0.926 (1.123) Remain 23:37:42 loss: 0.3498 Lr: 0.00260 [2024-02-18 19:24:29,819 INFO misc.py line 119 87073] Train: [52/100][570/1557] Data 0.005 (0.139) Batch 0.962 (1.123) Remain 23:37:19 loss: 0.5785 Lr: 0.00260 [2024-02-18 19:24:30,682 INFO misc.py line 119 87073] Train: [52/100][571/1557] Data 0.003 (0.138) Batch 0.863 (1.123) Remain 23:36:43 loss: 0.5754 Lr: 0.00260 [2024-02-18 19:24:31,443 INFO misc.py line 119 87073] Train: [52/100][572/1557] Data 0.004 (0.138) Batch 0.754 (1.122) Remain 23:35:53 loss: 0.1701 Lr: 0.00260 [2024-02-18 19:24:32,187 INFO misc.py line 119 87073] Train: [52/100][573/1557] Data 0.010 (0.138) Batch 0.750 (1.121) Remain 23:35:02 loss: 0.2331 Lr: 0.00260 [2024-02-18 19:24:33,394 INFO misc.py line 119 87073] Train: [52/100][574/1557] Data 0.004 (0.138) Batch 1.208 (1.121) Remain 23:35:13 loss: 0.1925 Lr: 0.00260 [2024-02-18 19:24:34,388 INFO misc.py line 119 87073] Train: [52/100][575/1557] Data 0.004 (0.138) Batch 0.994 (1.121) Remain 23:34:55 loss: 0.3552 Lr: 0.00260 [2024-02-18 19:24:35,193 INFO misc.py line 119 87073] Train: [52/100][576/1557] Data 0.003 (0.137) Batch 0.806 (1.121) Remain 23:34:12 loss: 0.2529 Lr: 0.00260 [2024-02-18 19:24:36,219 INFO misc.py line 119 87073] Train: [52/100][577/1557] Data 0.004 (0.137) Batch 1.016 (1.120) Remain 23:33:57 loss: 0.3780 Lr: 0.00260 [2024-02-18 19:24:36,961 INFO misc.py line 119 87073] Train: [52/100][578/1557] Data 0.013 (0.137) Batch 0.750 (1.120) Remain 23:33:07 loss: 0.2273 Lr: 0.00260 [2024-02-18 19:24:37,703 INFO misc.py line 119 87073] Train: [52/100][579/1557] Data 0.005 (0.137) Batch 0.703 (1.119) Remain 23:32:11 loss: 0.1533 Lr: 0.00260 [2024-02-18 19:24:38,388 INFO misc.py line 119 87073] Train: [52/100][580/1557] Data 0.044 (0.136) Batch 0.725 (1.118) Remain 23:31:18 loss: 0.3146 Lr: 0.00260 [2024-02-18 19:24:39,684 INFO misc.py line 119 87073] Train: [52/100][581/1557] Data 0.004 (0.136) Batch 1.296 (1.119) Remain 23:31:41 loss: 0.0553 Lr: 0.00259 [2024-02-18 19:24:40,745 INFO misc.py line 119 87073] Train: [52/100][582/1557] Data 0.004 (0.136) Batch 1.060 (1.119) Remain 23:31:32 loss: 0.3778 Lr: 0.00259 [2024-02-18 19:24:41,572 INFO misc.py line 119 87073] Train: [52/100][583/1557] Data 0.006 (0.136) Batch 0.828 (1.118) Remain 23:30:53 loss: 0.2342 Lr: 0.00259 [2024-02-18 19:24:42,548 INFO misc.py line 119 87073] Train: [52/100][584/1557] Data 0.004 (0.136) Batch 0.975 (1.118) Remain 23:30:33 loss: 0.4951 Lr: 0.00259 [2024-02-18 19:24:43,460 INFO misc.py line 119 87073] Train: [52/100][585/1557] Data 0.005 (0.135) Batch 0.913 (1.118) Remain 23:30:05 loss: 0.3425 Lr: 0.00259 [2024-02-18 19:24:44,213 INFO misc.py line 119 87073] Train: [52/100][586/1557] Data 0.004 (0.135) Batch 0.753 (1.117) Remain 23:29:17 loss: 0.6521 Lr: 0.00259 [2024-02-18 19:24:44,970 INFO misc.py line 119 87073] Train: [52/100][587/1557] Data 0.005 (0.135) Batch 0.747 (1.116) Remain 23:28:28 loss: 0.3768 Lr: 0.00259 [2024-02-18 19:24:46,057 INFO misc.py line 119 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Batch 0.986 (1.122) Remain 23:32:30 loss: 0.3545 Lr: 0.00259 [2024-02-18 19:27:37,342 INFO misc.py line 119 87073] Train: [52/100][738/1557] Data 0.005 (0.139) Batch 0.965 (1.121) Remain 23:32:12 loss: 0.3614 Lr: 0.00259 [2024-02-18 19:27:38,173 INFO misc.py line 119 87073] Train: [52/100][739/1557] Data 0.004 (0.139) Batch 0.830 (1.121) Remain 23:31:41 loss: 0.3064 Lr: 0.00259 [2024-02-18 19:27:38,973 INFO misc.py line 119 87073] Train: [52/100][740/1557] Data 0.005 (0.139) Batch 0.794 (1.121) Remain 23:31:07 loss: 0.2401 Lr: 0.00259 [2024-02-18 19:27:39,754 INFO misc.py line 119 87073] Train: [52/100][741/1557] Data 0.012 (0.139) Batch 0.788 (1.120) Remain 23:30:32 loss: 0.4151 Lr: 0.00259 [2024-02-18 19:27:40,981 INFO misc.py line 119 87073] Train: [52/100][742/1557] Data 0.004 (0.138) Batch 1.225 (1.120) Remain 23:30:41 loss: 0.1458 Lr: 0.00259 [2024-02-18 19:27:41,972 INFO misc.py line 119 87073] Train: [52/100][743/1557] Data 0.005 (0.138) Batch 0.993 (1.120) Remain 23:30:27 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line 119 87073] Train: [52/100][781/1557] Data 0.004 (0.132) Batch 0.990 (1.112) Remain 23:20:04 loss: 0.3191 Lr: 0.00258 [2024-02-18 19:28:19,333 INFO misc.py line 119 87073] Train: [52/100][782/1557] Data 0.003 (0.132) Batch 0.773 (1.112) Remain 23:19:30 loss: 0.3783 Lr: 0.00258 [2024-02-18 19:28:20,048 INFO misc.py line 119 87073] Train: [52/100][783/1557] Data 0.004 (0.132) Batch 0.705 (1.112) Remain 23:18:50 loss: 0.1716 Lr: 0.00258 [2024-02-18 19:28:21,342 INFO misc.py line 119 87073] Train: [52/100][784/1557] Data 0.013 (0.131) Batch 1.293 (1.112) Remain 23:19:06 loss: 0.1873 Lr: 0.00258 [2024-02-18 19:28:22,273 INFO misc.py line 119 87073] Train: [52/100][785/1557] Data 0.013 (0.131) Batch 0.941 (1.112) Remain 23:18:49 loss: 0.5982 Lr: 0.00258 [2024-02-18 19:28:23,393 INFO misc.py line 119 87073] Train: [52/100][786/1557] Data 0.004 (0.131) Batch 1.119 (1.112) Remain 23:18:48 loss: 0.7556 Lr: 0.00258 [2024-02-18 19:28:24,370 INFO misc.py line 119 87073] Train: 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Batch 0.904 (1.122) Remain 23:32:11 loss: 0.2123 Lr: 0.00258 [2024-02-18 19:28:40,716 INFO misc.py line 119 87073] Train: [52/100][794/1557] Data 0.013 (0.138) Batch 1.052 (1.122) Remain 23:32:03 loss: 0.4350 Lr: 0.00258 [2024-02-18 19:28:41,544 INFO misc.py line 119 87073] Train: [52/100][795/1557] Data 0.012 (0.138) Batch 0.835 (1.122) Remain 23:31:35 loss: 0.2434 Lr: 0.00258 [2024-02-18 19:28:42,268 INFO misc.py line 119 87073] Train: [52/100][796/1557] Data 0.004 (0.138) Batch 0.725 (1.121) Remain 23:30:56 loss: 0.2104 Lr: 0.00258 [2024-02-18 19:28:43,216 INFO misc.py line 119 87073] Train: [52/100][797/1557] Data 0.003 (0.138) Batch 0.948 (1.121) Remain 23:30:38 loss: 0.2719 Lr: 0.00258 [2024-02-18 19:28:44,454 INFO misc.py line 119 87073] Train: [52/100][798/1557] Data 0.004 (0.137) Batch 1.225 (1.121) Remain 23:30:47 loss: 0.2457 Lr: 0.00258 [2024-02-18 19:28:45,331 INFO misc.py line 119 87073] Train: [52/100][799/1557] Data 0.015 (0.137) Batch 0.889 (1.121) Remain 23:30:24 loss: 0.5743 Lr: 0.00258 [2024-02-18 19:28:46,247 INFO misc.py line 119 87073] Train: [52/100][800/1557] Data 0.004 (0.137) Batch 0.915 (1.121) Remain 23:30:03 loss: 0.7187 Lr: 0.00258 [2024-02-18 19:28:47,202 INFO misc.py line 119 87073] Train: [52/100][801/1557] Data 0.004 (0.137) Batch 0.955 (1.120) Remain 23:29:47 loss: 0.1586 Lr: 0.00258 [2024-02-18 19:28:48,148 INFO misc.py line 119 87073] Train: [52/100][802/1557] Data 0.006 (0.137) Batch 0.946 (1.120) Remain 23:29:29 loss: 0.8102 Lr: 0.00258 [2024-02-18 19:28:48,912 INFO misc.py line 119 87073] Train: [52/100][803/1557] Data 0.006 (0.137) Batch 0.765 (1.120) Remain 23:28:54 loss: 0.4639 Lr: 0.00258 [2024-02-18 19:28:49,710 INFO misc.py line 119 87073] Train: [52/100][804/1557] Data 0.004 (0.136) Batch 0.798 (1.119) Remain 23:28:23 loss: 0.4439 Lr: 0.00258 [2024-02-18 19:28:51,025 INFO misc.py line 119 87073] Train: [52/100][805/1557] Data 0.004 (0.136) Batch 1.310 (1.120) Remain 23:28:40 loss: 0.1841 Lr: 0.00258 [2024-02-18 19:28:51,929 INFO misc.py line 119 87073] Train: [52/100][806/1557] Data 0.009 (0.136) Batch 0.907 (1.119) Remain 23:28:19 loss: 0.4577 Lr: 0.00258 [2024-02-18 19:28:52,813 INFO misc.py line 119 87073] Train: [52/100][807/1557] Data 0.006 (0.136) Batch 0.885 (1.119) Remain 23:27:55 loss: 0.5121 Lr: 0.00258 [2024-02-18 19:28:53,800 INFO misc.py line 119 87073] Train: [52/100][808/1557] Data 0.006 (0.136) Batch 0.988 (1.119) Remain 23:27:42 loss: 0.2750 Lr: 0.00258 [2024-02-18 19:28:54,745 INFO misc.py line 119 87073] Train: [52/100][809/1557] Data 0.004 (0.136) Batch 0.937 (1.119) Remain 23:27:24 loss: 0.5710 Lr: 0.00258 [2024-02-18 19:28:55,501 INFO misc.py line 119 87073] Train: [52/100][810/1557] Data 0.012 (0.136) Batch 0.764 (1.118) Remain 23:26:50 loss: 0.2802 Lr: 0.00258 [2024-02-18 19:28:56,278 INFO misc.py line 119 87073] Train: [52/100][811/1557] Data 0.004 (0.135) Batch 0.765 (1.118) Remain 23:26:15 loss: 0.3734 Lr: 0.00258 [2024-02-18 19:28:57,349 INFO misc.py line 119 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line 119 87073] Train: [52/100][837/1557] Data 0.004 (0.131) Batch 0.937 (1.112) Remain 23:18:50 loss: 0.4521 Lr: 0.00258 [2024-02-18 19:29:21,478 INFO misc.py line 119 87073] Train: [52/100][838/1557] Data 0.003 (0.131) Batch 0.737 (1.112) Remain 23:18:15 loss: 0.1915 Lr: 0.00258 [2024-02-18 19:29:22,185 INFO misc.py line 119 87073] Train: [52/100][839/1557] Data 0.012 (0.131) Batch 0.717 (1.111) Remain 23:17:39 loss: 0.6048 Lr: 0.00258 [2024-02-18 19:29:23,443 INFO misc.py line 119 87073] Train: [52/100][840/1557] Data 0.003 (0.131) Batch 1.257 (1.112) Remain 23:17:51 loss: 0.2667 Lr: 0.00258 [2024-02-18 19:29:24,282 INFO misc.py line 119 87073] Train: [52/100][841/1557] Data 0.004 (0.131) Batch 0.840 (1.111) Remain 23:17:25 loss: 0.5653 Lr: 0.00258 [2024-02-18 19:29:25,394 INFO misc.py line 119 87073] Train: [52/100][842/1557] Data 0.003 (0.131) Batch 1.111 (1.111) Remain 23:17:24 loss: 0.4907 Lr: 0.00258 [2024-02-18 19:29:26,275 INFO misc.py line 119 87073] Train: 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Batch 0.945 (1.120) Remain 23:28:15 loss: 0.2301 Lr: 0.00258 [2024-02-18 19:29:41,577 INFO misc.py line 119 87073] Train: [52/100][850/1557] Data 0.009 (0.138) Batch 1.010 (1.120) Remain 23:28:04 loss: 0.6353 Lr: 0.00258 [2024-02-18 19:29:42,529 INFO misc.py line 119 87073] Train: [52/100][851/1557] Data 0.011 (0.138) Batch 0.959 (1.120) Remain 23:27:49 loss: 0.4770 Lr: 0.00258 [2024-02-18 19:29:43,231 INFO misc.py line 119 87073] Train: [52/100][852/1557] Data 0.004 (0.138) Batch 0.701 (1.119) Remain 23:27:10 loss: 0.2883 Lr: 0.00258 [2024-02-18 19:29:44,147 INFO misc.py line 119 87073] Train: [52/100][853/1557] Data 0.004 (0.138) Batch 0.908 (1.119) Remain 23:26:50 loss: 0.1320 Lr: 0.00258 [2024-02-18 19:29:45,433 INFO misc.py line 119 87073] Train: [52/100][854/1557] Data 0.013 (0.138) Batch 1.287 (1.119) Remain 23:27:04 loss: 0.0985 Lr: 0.00258 [2024-02-18 19:29:46,487 INFO misc.py line 119 87073] Train: [52/100][855/1557] Data 0.011 (0.137) Batch 1.053 (1.119) Remain 23:26:57 loss: 0.2124 Lr: 0.00258 [2024-02-18 19:29:47,587 INFO misc.py line 119 87073] Train: [52/100][856/1557] Data 0.013 (0.137) Batch 1.097 (1.119) Remain 23:26:54 loss: 0.7747 Lr: 0.00258 [2024-02-18 19:29:48,591 INFO misc.py line 119 87073] Train: [52/100][857/1557] Data 0.015 (0.137) Batch 1.011 (1.119) Remain 23:26:44 loss: 0.2831 Lr: 0.00258 [2024-02-18 19:29:49,503 INFO misc.py line 119 87073] Train: [52/100][858/1557] Data 0.008 (0.137) Batch 0.915 (1.119) Remain 23:26:25 loss: 0.4478 Lr: 0.00258 [2024-02-18 19:29:50,264 INFO misc.py line 119 87073] Train: [52/100][859/1557] Data 0.004 (0.137) Batch 0.762 (1.118) Remain 23:25:52 loss: 0.3646 Lr: 0.00258 [2024-02-18 19:29:51,005 INFO misc.py line 119 87073] Train: [52/100][860/1557] Data 0.004 (0.137) Batch 0.734 (1.118) Remain 23:25:17 loss: 0.2258 Lr: 0.00258 [2024-02-18 19:29:52,320 INFO misc.py line 119 87073] Train: [52/100][861/1557] Data 0.010 (0.137) Batch 1.311 (1.118) Remain 23:25:33 loss: 0.0829 Lr: 0.00258 [2024-02-18 19:29:53,236 INFO misc.py line 119 87073] Train: [52/100][862/1557] Data 0.015 (0.136) Batch 0.927 (1.118) Remain 23:25:15 loss: 0.2452 Lr: 0.00258 [2024-02-18 19:29:54,281 INFO misc.py line 119 87073] Train: [52/100][863/1557] Data 0.003 (0.136) Batch 1.045 (1.118) Remain 23:25:08 loss: 0.2363 Lr: 0.00258 [2024-02-18 19:29:55,348 INFO misc.py line 119 87073] Train: [52/100][864/1557] Data 0.004 (0.136) Batch 1.067 (1.118) Remain 23:25:02 loss: 0.2695 Lr: 0.00258 [2024-02-18 19:29:56,431 INFO misc.py line 119 87073] Train: [52/100][865/1557] Data 0.004 (0.136) Batch 1.084 (1.118) Remain 23:24:58 loss: 0.4018 Lr: 0.00258 [2024-02-18 19:29:57,158 INFO misc.py line 119 87073] Train: [52/100][866/1557] Data 0.003 (0.136) Batch 0.726 (1.117) Remain 23:24:23 loss: 0.1953 Lr: 0.00258 [2024-02-18 19:29:57,939 INFO misc.py line 119 87073] Train: [52/100][867/1557] Data 0.004 (0.136) Batch 0.773 (1.117) Remain 23:23:51 loss: 0.2461 Lr: 0.00258 [2024-02-18 19:29:59,016 INFO misc.py line 119 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Batch 0.998 (1.122) Remain 23:30:07 loss: 0.6630 Lr: 0.00258 [2024-02-18 19:30:46,299 INFO misc.py line 119 87073] Train: [52/100][906/1557] Data 0.004 (0.138) Batch 0.928 (1.122) Remain 23:29:50 loss: 0.5567 Lr: 0.00258 [2024-02-18 19:30:47,304 INFO misc.py line 119 87073] Train: [52/100][907/1557] Data 0.004 (0.138) Batch 1.005 (1.122) Remain 23:29:39 loss: 0.1977 Lr: 0.00258 [2024-02-18 19:30:48,045 INFO misc.py line 119 87073] Train: [52/100][908/1557] Data 0.004 (0.138) Batch 0.741 (1.122) Remain 23:29:06 loss: 0.3279 Lr: 0.00258 [2024-02-18 19:30:48,793 INFO misc.py line 119 87073] Train: [52/100][909/1557] Data 0.004 (0.138) Batch 0.739 (1.121) Remain 23:28:33 loss: 0.1403 Lr: 0.00258 [2024-02-18 19:30:50,076 INFO misc.py line 119 87073] Train: [52/100][910/1557] Data 0.013 (0.138) Batch 1.285 (1.121) Remain 23:28:46 loss: 0.1418 Lr: 0.00258 [2024-02-18 19:30:51,264 INFO misc.py line 119 87073] Train: [52/100][911/1557] Data 0.010 (0.137) Batch 1.184 (1.121) Remain 23:28:50 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Batch 1.020 (1.120) Remain 23:26:18 loss: 0.3399 Lr: 0.00257 [2024-02-18 19:31:47,061 INFO misc.py line 119 87073] Train: [52/100][962/1557] Data 0.011 (0.137) Batch 0.959 (1.120) Remain 23:26:04 loss: 0.2141 Lr: 0.00257 [2024-02-18 19:31:47,804 INFO misc.py line 119 87073] Train: [52/100][963/1557] Data 0.004 (0.137) Batch 0.743 (1.120) Remain 23:25:33 loss: 0.1224 Lr: 0.00257 [2024-02-18 19:31:48,607 INFO misc.py line 119 87073] Train: [52/100][964/1557] Data 0.005 (0.137) Batch 0.798 (1.119) Remain 23:25:07 loss: 0.2755 Lr: 0.00257 [2024-02-18 19:31:49,403 INFO misc.py line 119 87073] Train: [52/100][965/1557] Data 0.010 (0.137) Batch 0.803 (1.119) Remain 23:24:41 loss: 0.1954 Lr: 0.00257 [2024-02-18 19:31:50,637 INFO misc.py line 119 87073] Train: [52/100][966/1557] Data 0.003 (0.137) Batch 1.222 (1.119) Remain 23:24:48 loss: 0.0979 Lr: 0.00257 [2024-02-18 19:31:51,534 INFO misc.py line 119 87073] Train: [52/100][967/1557] Data 0.015 (0.137) Batch 0.908 (1.119) Remain 23:24:30 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misc.py line 119 87073] Train: [52/100][1129/1557] Data 0.007 (0.137) Batch 1.155 (1.121) Remain 23:23:49 loss: 0.2200 Lr: 0.00257 [2024-02-18 19:34:55,681 INFO misc.py line 119 87073] Train: [52/100][1130/1557] Data 0.012 (0.137) Batch 0.820 (1.120) Remain 23:23:27 loss: 0.3773 Lr: 0.00257 [2024-02-18 19:34:56,823 INFO misc.py line 119 87073] Train: [52/100][1131/1557] Data 0.004 (0.137) Batch 1.141 (1.120) Remain 23:23:28 loss: 0.8868 Lr: 0.00257 [2024-02-18 19:34:57,630 INFO misc.py line 119 87073] Train: [52/100][1132/1557] Data 0.005 (0.137) Batch 0.808 (1.120) Remain 23:23:06 loss: 0.4602 Lr: 0.00257 [2024-02-18 19:34:58,347 INFO misc.py line 119 87073] Train: [52/100][1133/1557] Data 0.004 (0.137) Batch 0.712 (1.120) Remain 23:22:38 loss: 0.3219 Lr: 0.00257 [2024-02-18 19:34:59,589 INFO misc.py line 119 87073] Train: [52/100][1134/1557] Data 0.008 (0.137) Batch 1.244 (1.120) Remain 23:22:45 loss: 0.1686 Lr: 0.00257 [2024-02-18 19:35:00,450 INFO misc.py line 119 87073] Train: 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misc.py line 119 87073] Train: [52/100][1222/1557] Data 0.005 (0.133) Batch 0.886 (1.116) Remain 23:15:51 loss: 0.6317 Lr: 0.00256 [2024-02-18 19:36:33,777 INFO misc.py line 119 87073] Train: [52/100][1223/1557] Data 0.010 (0.133) Batch 0.760 (1.115) Remain 23:15:28 loss: 0.1738 Lr: 0.00256 [2024-02-18 19:36:34,541 INFO misc.py line 119 87073] Train: [52/100][1224/1557] Data 0.004 (0.133) Batch 0.759 (1.115) Remain 23:15:05 loss: 0.3443 Lr: 0.00256 [2024-02-18 19:36:35,714 INFO misc.py line 119 87073] Train: [52/100][1225/1557] Data 0.009 (0.133) Batch 1.174 (1.115) Remain 23:15:08 loss: 0.2072 Lr: 0.00256 [2024-02-18 19:36:36,771 INFO misc.py line 119 87073] Train: [52/100][1226/1557] Data 0.008 (0.133) Batch 1.055 (1.115) Remain 23:15:03 loss: 0.6483 Lr: 0.00256 [2024-02-18 19:36:37,693 INFO misc.py line 119 87073] Train: [52/100][1227/1557] Data 0.010 (0.133) Batch 0.928 (1.115) Remain 23:14:50 loss: 0.6574 Lr: 0.00256 [2024-02-18 19:36:38,668 INFO misc.py line 119 87073] Train: 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Remain 23:23:30 loss: 0.5810 Lr: 0.00256 [2024-02-18 19:37:01,881 INFO misc.py line 119 87073] Train: [52/100][1241/1557] Data 0.005 (0.138) Batch 0.895 (1.122) Remain 23:23:15 loss: 0.5575 Lr: 0.00256 [2024-02-18 19:37:02,970 INFO misc.py line 119 87073] Train: [52/100][1242/1557] Data 0.004 (0.138) Batch 1.090 (1.122) Remain 23:23:12 loss: 0.3588 Lr: 0.00256 [2024-02-18 19:37:03,987 INFO misc.py line 119 87073] Train: [52/100][1243/1557] Data 0.003 (0.138) Batch 1.017 (1.122) Remain 23:23:04 loss: 0.3057 Lr: 0.00256 [2024-02-18 19:37:04,750 INFO misc.py line 119 87073] Train: [52/100][1244/1557] Data 0.003 (0.138) Batch 0.762 (1.121) Remain 23:22:41 loss: 0.1841 Lr: 0.00256 [2024-02-18 19:37:05,529 INFO misc.py line 119 87073] Train: [52/100][1245/1557] Data 0.004 (0.138) Batch 0.770 (1.121) Remain 23:22:19 loss: 0.2568 Lr: 0.00256 [2024-02-18 19:37:06,851 INFO misc.py line 119 87073] Train: [52/100][1246/1557] Data 0.013 (0.138) Batch 1.328 (1.121) Remain 23:22:30 loss: 0.1592 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1253/1557] Data 0.004 (0.137) Batch 1.357 (1.120) Remain 23:21:20 loss: 0.1220 Lr: 0.00256 [2024-02-18 19:37:14,447 INFO misc.py line 119 87073] Train: [52/100][1254/1557] Data 0.009 (0.137) Batch 0.787 (1.120) Remain 23:20:59 loss: 0.3353 Lr: 0.00256 [2024-02-18 19:37:15,302 INFO misc.py line 119 87073] Train: [52/100][1255/1557] Data 0.005 (0.137) Batch 0.856 (1.120) Remain 23:20:42 loss: 0.4385 Lr: 0.00256 [2024-02-18 19:37:16,207 INFO misc.py line 119 87073] Train: [52/100][1256/1557] Data 0.004 (0.137) Batch 0.904 (1.120) Remain 23:20:28 loss: 1.0378 Lr: 0.00256 [2024-02-18 19:37:17,256 INFO misc.py line 119 87073] Train: [52/100][1257/1557] Data 0.006 (0.136) Batch 1.049 (1.120) Remain 23:20:23 loss: 0.7620 Lr: 0.00256 [2024-02-18 19:37:17,977 INFO misc.py line 119 87073] Train: [52/100][1258/1557] Data 0.007 (0.136) Batch 0.722 (1.119) Remain 23:19:58 loss: 0.1630 Lr: 0.00256 [2024-02-18 19:37:18,725 INFO misc.py line 119 87073] Train: [52/100][1259/1557] Data 0.005 (0.136) Batch 0.746 (1.119) Remain 23:19:34 loss: 0.1077 Lr: 0.00256 [2024-02-18 19:37:19,745 INFO misc.py line 119 87073] Train: [52/100][1260/1557] Data 0.007 (0.136) Batch 1.016 (1.119) Remain 23:19:27 loss: 0.1463 Lr: 0.00256 [2024-02-18 19:37:20,785 INFO misc.py line 119 87073] Train: [52/100][1261/1557] Data 0.011 (0.136) Batch 1.045 (1.119) Remain 23:19:21 loss: 0.1209 Lr: 0.00256 [2024-02-18 19:37:21,622 INFO misc.py line 119 87073] Train: [52/100][1262/1557] Data 0.006 (0.136) Batch 0.836 (1.119) Remain 23:19:03 loss: 0.3503 Lr: 0.00256 [2024-02-18 19:37:22,643 INFO misc.py line 119 87073] Train: [52/100][1263/1557] Data 0.007 (0.136) Batch 1.024 (1.119) Remain 23:18:57 loss: 0.2395 Lr: 0.00256 [2024-02-18 19:37:23,636 INFO misc.py line 119 87073] Train: [52/100][1264/1557] Data 0.005 (0.136) Batch 0.994 (1.119) Remain 23:18:48 loss: 0.3407 Lr: 0.00256 [2024-02-18 19:37:24,428 INFO misc.py line 119 87073] Train: [52/100][1265/1557] Data 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Remain 23:17:13 loss: 0.5368 Lr: 0.00256 [2024-02-18 19:37:30,706 INFO misc.py line 119 87073] Train: [52/100][1272/1557] Data 0.005 (0.135) Batch 0.723 (1.117) Remain 23:16:48 loss: 0.1504 Lr: 0.00256 [2024-02-18 19:37:31,494 INFO misc.py line 119 87073] Train: [52/100][1273/1557] Data 0.005 (0.135) Batch 0.762 (1.117) Remain 23:16:26 loss: 0.2333 Lr: 0.00256 [2024-02-18 19:37:32,649 INFO misc.py line 119 87073] Train: [52/100][1274/1557] Data 0.029 (0.135) Batch 1.180 (1.117) Remain 23:16:29 loss: 0.3014 Lr: 0.00256 [2024-02-18 19:37:33,659 INFO misc.py line 119 87073] Train: [52/100][1275/1557] Data 0.006 (0.135) Batch 1.011 (1.117) Remain 23:16:21 loss: 0.4164 Lr: 0.00256 [2024-02-18 19:37:34,512 INFO misc.py line 119 87073] Train: [52/100][1276/1557] Data 0.004 (0.135) Batch 0.855 (1.117) Remain 23:16:05 loss: 0.2926 Lr: 0.00256 [2024-02-18 19:37:35,392 INFO misc.py line 119 87073] Train: [52/100][1277/1557] Data 0.003 (0.134) Batch 0.879 (1.116) Remain 23:15:50 loss: 0.3348 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1284/1557] Data 0.004 (0.134) Batch 0.891 (1.115) Remain 23:14:18 loss: 0.4185 Lr: 0.00256 [2024-02-18 19:37:42,630 INFO misc.py line 119 87073] Train: [52/100][1285/1557] Data 0.005 (0.134) Batch 0.848 (1.115) Remain 23:14:01 loss: 0.3146 Lr: 0.00256 [2024-02-18 19:37:43,393 INFO misc.py line 119 87073] Train: [52/100][1286/1557] Data 0.010 (0.134) Batch 0.767 (1.115) Remain 23:13:40 loss: 0.5816 Lr: 0.00256 [2024-02-18 19:37:44,169 INFO misc.py line 119 87073] Train: [52/100][1287/1557] Data 0.005 (0.133) Batch 0.772 (1.115) Remain 23:13:19 loss: 0.1555 Lr: 0.00256 [2024-02-18 19:37:45,419 INFO misc.py line 119 87073] Train: [52/100][1288/1557] Data 0.009 (0.133) Batch 1.245 (1.115) Remain 23:13:25 loss: 0.2366 Lr: 0.00256 [2024-02-18 19:37:46,315 INFO misc.py line 119 87073] Train: [52/100][1289/1557] Data 0.013 (0.133) Batch 0.906 (1.115) Remain 23:13:12 loss: 0.6180 Lr: 0.00256 [2024-02-18 19:37:47,214 INFO misc.py line 119 87073] Train: [52/100][1290/1557] Data 0.004 (0.133) Batch 0.898 (1.114) Remain 23:12:58 loss: 0.4770 Lr: 0.00256 [2024-02-18 19:37:48,129 INFO misc.py line 119 87073] Train: [52/100][1291/1557] Data 0.005 (0.133) Batch 0.910 (1.114) Remain 23:12:45 loss: 0.4733 Lr: 0.00256 [2024-02-18 19:37:49,137 INFO misc.py line 119 87073] Train: [52/100][1292/1557] Data 0.009 (0.133) Batch 1.006 (1.114) Remain 23:12:38 loss: 0.3649 Lr: 0.00256 [2024-02-18 19:37:49,849 INFO misc.py line 119 87073] Train: [52/100][1293/1557] Data 0.010 (0.133) Batch 0.718 (1.114) Remain 23:12:14 loss: 0.1822 Lr: 0.00256 [2024-02-18 19:37:50,616 INFO misc.py line 119 87073] Train: [52/100][1294/1557] Data 0.004 (0.133) Batch 0.759 (1.114) Remain 23:11:52 loss: 0.3583 Lr: 0.00256 [2024-02-18 19:38:00,802 INFO misc.py line 119 87073] Train: [52/100][1295/1557] Data 7.856 (0.139) Batch 10.195 (1.121) Remain 23:20:38 loss: 0.1694 Lr: 0.00256 [2024-02-18 19:38:01,677 INFO misc.py line 119 87073] Train: [52/100][1296/1557] Data 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Remain 23:19:09 loss: 0.1226 Lr: 0.00256 [2024-02-18 19:38:08,142 INFO misc.py line 119 87073] Train: [52/100][1303/1557] Data 0.012 (0.138) Batch 0.909 (1.119) Remain 23:18:55 loss: 0.4167 Lr: 0.00256 [2024-02-18 19:38:09,075 INFO misc.py line 119 87073] Train: [52/100][1304/1557] Data 0.003 (0.138) Batch 0.932 (1.119) Remain 23:18:43 loss: 0.2974 Lr: 0.00256 [2024-02-18 19:38:10,048 INFO misc.py line 119 87073] Train: [52/100][1305/1557] Data 0.006 (0.138) Batch 0.973 (1.119) Remain 23:18:34 loss: 0.2028 Lr: 0.00256 [2024-02-18 19:38:11,095 INFO misc.py line 119 87073] Train: [52/100][1306/1557] Data 0.004 (0.138) Batch 1.048 (1.119) Remain 23:18:29 loss: 0.4680 Lr: 0.00256 [2024-02-18 19:38:11,877 INFO misc.py line 119 87073] Train: [52/100][1307/1557] Data 0.003 (0.138) Batch 0.778 (1.119) Remain 23:18:08 loss: 0.4558 Lr: 0.00256 [2024-02-18 19:38:12,627 INFO misc.py line 119 87073] Train: [52/100][1308/1557] Data 0.007 (0.137) Batch 0.751 (1.118) Remain 23:17:46 loss: 0.3926 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1315/1557] Data 0.005 (0.137) Batch 0.767 (1.118) Remain 23:16:29 loss: 0.2135 Lr: 0.00256 [2024-02-18 19:38:20,352 INFO misc.py line 119 87073] Train: [52/100][1316/1557] Data 0.004 (0.137) Batch 1.104 (1.118) Remain 23:16:27 loss: 0.1116 Lr: 0.00256 [2024-02-18 19:38:21,463 INFO misc.py line 119 87073] Train: [52/100][1317/1557] Data 0.005 (0.137) Batch 1.112 (1.118) Remain 23:16:26 loss: 0.3813 Lr: 0.00256 [2024-02-18 19:38:22,502 INFO misc.py line 119 87073] Train: [52/100][1318/1557] Data 0.005 (0.136) Batch 1.038 (1.117) Remain 23:16:20 loss: 0.5909 Lr: 0.00256 [2024-02-18 19:38:23,490 INFO misc.py line 119 87073] Train: [52/100][1319/1557] Data 0.005 (0.136) Batch 0.990 (1.117) Remain 23:16:11 loss: 0.6701 Lr: 0.00256 [2024-02-18 19:38:24,492 INFO misc.py line 119 87073] Train: [52/100][1320/1557] Data 0.005 (0.136) Batch 1.002 (1.117) Remain 23:16:04 loss: 0.1861 Lr: 0.00256 [2024-02-18 19:38:25,228 INFO misc.py line 119 87073] Train: [52/100][1321/1557] Data 0.004 (0.136) Batch 0.736 (1.117) Remain 23:15:41 loss: 0.1857 Lr: 0.00256 [2024-02-18 19:38:26,055 INFO misc.py line 119 87073] Train: [52/100][1322/1557] Data 0.004 (0.136) Batch 0.821 (1.117) Remain 23:15:23 loss: 0.3298 Lr: 0.00256 [2024-02-18 19:38:27,309 INFO misc.py line 119 87073] Train: [52/100][1323/1557] Data 0.010 (0.136) Batch 1.249 (1.117) Remain 23:15:29 loss: 0.3052 Lr: 0.00256 [2024-02-18 19:38:28,339 INFO misc.py line 119 87073] Train: [52/100][1324/1557] Data 0.015 (0.136) Batch 1.038 (1.117) Remain 23:15:24 loss: 0.5431 Lr: 0.00256 [2024-02-18 19:38:29,274 INFO misc.py line 119 87073] Train: [52/100][1325/1557] Data 0.007 (0.136) Batch 0.938 (1.117) Remain 23:15:13 loss: 0.1480 Lr: 0.00256 [2024-02-18 19:38:30,241 INFO misc.py line 119 87073] Train: [52/100][1326/1557] Data 0.003 (0.136) Batch 0.966 (1.117) Remain 23:15:03 loss: 0.2891 Lr: 0.00256 [2024-02-18 19:38:31,137 INFO misc.py line 119 87073] Train: [52/100][1327/1557] Data 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Remain 23:13:28 loss: 0.2716 Lr: 0.00256 [2024-02-18 19:38:37,511 INFO misc.py line 119 87073] Train: [52/100][1334/1557] Data 0.004 (0.135) Batch 1.002 (1.115) Remain 23:13:20 loss: 0.5508 Lr: 0.00256 [2024-02-18 19:38:38,266 INFO misc.py line 119 87073] Train: [52/100][1335/1557] Data 0.004 (0.135) Batch 0.755 (1.115) Remain 23:12:59 loss: 0.2444 Lr: 0.00255 [2024-02-18 19:38:39,054 INFO misc.py line 119 87073] Train: [52/100][1336/1557] Data 0.003 (0.135) Batch 0.776 (1.115) Remain 23:12:39 loss: 0.3921 Lr: 0.00255 [2024-02-18 19:38:40,235 INFO misc.py line 119 87073] Train: [52/100][1337/1557] Data 0.015 (0.135) Batch 1.185 (1.115) Remain 23:12:42 loss: 0.2458 Lr: 0.00255 [2024-02-18 19:38:41,185 INFO misc.py line 119 87073] Train: [52/100][1338/1557] Data 0.010 (0.134) Batch 0.958 (1.115) Remain 23:12:32 loss: 0.4339 Lr: 0.00255 [2024-02-18 19:38:42,080 INFO misc.py line 119 87073] Train: [52/100][1339/1557] Data 0.003 (0.134) Batch 0.895 (1.115) Remain 23:12:18 loss: 0.2193 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1346/1557] Data 0.003 (0.134) Batch 0.961 (1.114) Remain 23:11:16 loss: 0.5682 Lr: 0.00255 [2024-02-18 19:38:50,098 INFO misc.py line 119 87073] Train: [52/100][1347/1557] Data 0.006 (0.134) Batch 1.196 (1.114) Remain 23:11:19 loss: 0.4412 Lr: 0.00255 [2024-02-18 19:38:51,149 INFO misc.py line 119 87073] Train: [52/100][1348/1557] Data 0.004 (0.134) Batch 1.051 (1.114) Remain 23:11:15 loss: 0.3439 Lr: 0.00255 [2024-02-18 19:38:51,883 INFO misc.py line 119 87073] Train: [52/100][1349/1557] Data 0.004 (0.133) Batch 0.733 (1.114) Remain 23:10:52 loss: 0.2054 Lr: 0.00255 [2024-02-18 19:38:52,609 INFO misc.py line 119 87073] Train: [52/100][1350/1557] Data 0.004 (0.133) Batch 0.720 (1.113) Remain 23:10:29 loss: 0.2837 Lr: 0.00255 [2024-02-18 19:39:02,306 INFO misc.py line 119 87073] Train: [52/100][1351/1557] Data 7.118 (0.138) Batch 9.701 (1.120) Remain 23:18:26 loss: 0.1552 Lr: 0.00255 [2024-02-18 19:39:03,400 INFO misc.py line 119 87073] Train: [52/100][1352/1557] Data 0.007 (0.138) Batch 1.088 (1.120) Remain 23:18:23 loss: 0.4095 Lr: 0.00255 [2024-02-18 19:39:04,435 INFO misc.py line 119 87073] Train: [52/100][1353/1557] Data 0.012 (0.138) Batch 1.035 (1.120) Remain 23:18:17 loss: 0.5930 Lr: 0.00255 [2024-02-18 19:39:05,492 INFO misc.py line 119 87073] Train: [52/100][1354/1557] Data 0.012 (0.138) Batch 1.059 (1.119) Remain 23:18:13 loss: 0.3468 Lr: 0.00255 [2024-02-18 19:39:06,446 INFO misc.py line 119 87073] Train: [52/100][1355/1557] Data 0.010 (0.138) Batch 0.959 (1.119) Remain 23:18:03 loss: 1.0153 Lr: 0.00255 [2024-02-18 19:39:07,244 INFO misc.py line 119 87073] Train: [52/100][1356/1557] Data 0.005 (0.138) Batch 0.799 (1.119) Remain 23:17:44 loss: 0.2938 Lr: 0.00255 [2024-02-18 19:39:08,016 INFO misc.py line 119 87073] Train: [52/100][1357/1557] Data 0.004 (0.138) Batch 0.766 (1.119) Remain 23:17:23 loss: 0.4373 Lr: 0.00255 [2024-02-18 19:39:09,280 INFO misc.py line 119 87073] Train: [52/100][1358/1557] Data 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Remain 23:16:14 loss: 0.3750 Lr: 0.00255 [2024-02-18 19:39:16,103 INFO misc.py line 119 87073] Train: [52/100][1365/1557] Data 0.013 (0.137) Batch 1.367 (1.118) Remain 23:16:26 loss: 0.0681 Lr: 0.00255 [2024-02-18 19:39:17,030 INFO misc.py line 119 87073] Train: [52/100][1366/1557] Data 0.014 (0.137) Batch 0.937 (1.118) Remain 23:16:15 loss: 0.1997 Lr: 0.00255 [2024-02-18 19:39:17,999 INFO misc.py line 119 87073] Train: [52/100][1367/1557] Data 0.004 (0.137) Batch 0.969 (1.118) Remain 23:16:06 loss: 0.2333 Lr: 0.00255 [2024-02-18 19:39:18,902 INFO misc.py line 119 87073] Train: [52/100][1368/1557] Data 0.003 (0.137) Batch 0.903 (1.118) Remain 23:15:53 loss: 0.2963 Lr: 0.00255 [2024-02-18 19:39:19,822 INFO misc.py line 119 87073] Train: [52/100][1369/1557] Data 0.005 (0.137) Batch 0.915 (1.118) Remain 23:15:41 loss: 0.3932 Lr: 0.00255 [2024-02-18 19:39:20,560 INFO misc.py line 119 87073] Train: [52/100][1370/1557] Data 0.009 (0.137) Batch 0.741 (1.117) Remain 23:15:19 loss: 0.3027 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1377/1557] Data 0.004 (0.136) Batch 0.680 (1.116) Remain 23:13:56 loss: 0.2669 Lr: 0.00255 [2024-02-18 19:39:27,766 INFO misc.py line 119 87073] Train: [52/100][1378/1557] Data 0.004 (0.136) Batch 0.760 (1.116) Remain 23:13:36 loss: 0.2620 Lr: 0.00255 [2024-02-18 19:39:28,957 INFO misc.py line 119 87073] Train: [52/100][1379/1557] Data 0.005 (0.136) Batch 1.186 (1.116) Remain 23:13:38 loss: 0.2491 Lr: 0.00255 [2024-02-18 19:39:29,799 INFO misc.py line 119 87073] Train: [52/100][1380/1557] Data 0.010 (0.136) Batch 0.848 (1.116) Remain 23:13:23 loss: 0.2509 Lr: 0.00255 [2024-02-18 19:39:30,873 INFO misc.py line 119 87073] Train: [52/100][1381/1557] Data 0.004 (0.136) Batch 1.074 (1.116) Remain 23:13:19 loss: 0.2767 Lr: 0.00255 [2024-02-18 19:39:31,791 INFO misc.py line 119 87073] Train: [52/100][1382/1557] Data 0.004 (0.136) Batch 0.918 (1.116) Remain 23:13:07 loss: 0.4239 Lr: 0.00255 [2024-02-18 19:39:32,721 INFO misc.py line 119 87073] Train: [52/100][1383/1557] Data 0.004 (0.135) Batch 0.930 (1.116) Remain 23:12:56 loss: 0.1405 Lr: 0.00255 [2024-02-18 19:39:33,475 INFO misc.py line 119 87073] Train: [52/100][1384/1557] Data 0.003 (0.135) Batch 0.746 (1.115) Remain 23:12:35 loss: 0.2267 Lr: 0.00255 [2024-02-18 19:39:34,225 INFO misc.py line 119 87073] Train: [52/100][1385/1557] Data 0.012 (0.135) Batch 0.758 (1.115) Remain 23:12:15 loss: 0.2024 Lr: 0.00255 [2024-02-18 19:39:35,387 INFO misc.py line 119 87073] Train: [52/100][1386/1557] Data 0.004 (0.135) Batch 1.162 (1.115) Remain 23:12:16 loss: 0.2712 Lr: 0.00255 [2024-02-18 19:39:36,463 INFO misc.py line 119 87073] Train: [52/100][1387/1557] Data 0.004 (0.135) Batch 1.077 (1.115) Remain 23:12:13 loss: 0.2496 Lr: 0.00255 [2024-02-18 19:39:37,528 INFO misc.py line 119 87073] Train: [52/100][1388/1557] Data 0.003 (0.135) Batch 1.065 (1.115) Remain 23:12:09 loss: 0.3587 Lr: 0.00255 [2024-02-18 19:39:38,518 INFO misc.py line 119 87073] Train: [52/100][1389/1557] Data 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Remain 23:10:50 loss: 0.4269 Lr: 0.00255 [2024-02-18 19:39:44,993 INFO misc.py line 119 87073] Train: [52/100][1396/1557] Data 0.004 (0.134) Batch 0.971 (1.114) Remain 23:10:42 loss: 0.4252 Lr: 0.00255 [2024-02-18 19:39:46,121 INFO misc.py line 119 87073] Train: [52/100][1397/1557] Data 0.006 (0.134) Batch 1.119 (1.114) Remain 23:10:41 loss: 0.2533 Lr: 0.00255 [2024-02-18 19:39:46,860 INFO misc.py line 119 87073] Train: [52/100][1398/1557] Data 0.014 (0.134) Batch 0.750 (1.114) Remain 23:10:20 loss: 0.2980 Lr: 0.00255 [2024-02-18 19:39:47,609 INFO misc.py line 119 87073] Train: [52/100][1399/1557] Data 0.004 (0.134) Batch 0.738 (1.114) Remain 23:09:59 loss: 0.4321 Lr: 0.00255 [2024-02-18 19:39:48,898 INFO misc.py line 119 87073] Train: [52/100][1400/1557] Data 0.015 (0.134) Batch 1.287 (1.114) Remain 23:10:07 loss: 0.1644 Lr: 0.00255 [2024-02-18 19:39:49,838 INFO misc.py line 119 87073] Train: [52/100][1401/1557] Data 0.017 (0.134) Batch 0.951 (1.114) Remain 23:09:57 loss: 0.1828 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1408/1557] Data 0.006 (0.139) Batch 1.094 (1.121) Remain 23:19:17 loss: 0.1950 Lr: 0.00255 [2024-02-18 19:40:09,265 INFO misc.py line 119 87073] Train: [52/100][1409/1557] Data 0.005 (0.139) Batch 0.989 (1.121) Remain 23:19:09 loss: 0.5834 Lr: 0.00255 [2024-02-18 19:40:10,468 INFO misc.py line 119 87073] Train: [52/100][1410/1557] Data 0.004 (0.139) Batch 1.197 (1.121) Remain 23:19:12 loss: 0.4911 Lr: 0.00255 [2024-02-18 19:40:11,486 INFO misc.py line 119 87073] Train: [52/100][1411/1557] Data 0.009 (0.139) Batch 1.016 (1.121) Remain 23:19:05 loss: 0.2400 Lr: 0.00255 [2024-02-18 19:40:12,259 INFO misc.py line 119 87073] Train: [52/100][1412/1557] Data 0.011 (0.139) Batch 0.779 (1.121) Remain 23:18:46 loss: 0.1773 Lr: 0.00255 [2024-02-18 19:40:12,995 INFO misc.py line 119 87073] Train: [52/100][1413/1557] Data 0.005 (0.139) Batch 0.737 (1.121) Remain 23:18:24 loss: 0.3234 Lr: 0.00255 [2024-02-18 19:40:14,260 INFO misc.py line 119 87073] Train: [52/100][1414/1557] Data 0.004 (0.138) Batch 1.257 (1.121) Remain 23:18:30 loss: 0.1576 Lr: 0.00255 [2024-02-18 19:40:15,120 INFO misc.py line 119 87073] Train: [52/100][1415/1557] Data 0.012 (0.138) Batch 0.868 (1.120) Remain 23:18:16 loss: 0.6466 Lr: 0.00255 [2024-02-18 19:40:16,238 INFO misc.py line 119 87073] Train: [52/100][1416/1557] Data 0.007 (0.138) Batch 1.119 (1.120) Remain 23:18:14 loss: 0.5425 Lr: 0.00255 [2024-02-18 19:40:17,189 INFO misc.py line 119 87073] Train: [52/100][1417/1557] Data 0.004 (0.138) Batch 0.951 (1.120) Remain 23:18:04 loss: 0.1343 Lr: 0.00255 [2024-02-18 19:40:18,015 INFO misc.py line 119 87073] Train: [52/100][1418/1557] Data 0.004 (0.138) Batch 0.826 (1.120) Remain 23:17:48 loss: 0.7366 Lr: 0.00255 [2024-02-18 19:40:18,809 INFO misc.py line 119 87073] Train: [52/100][1419/1557] Data 0.004 (0.138) Batch 0.791 (1.120) Remain 23:17:29 loss: 0.1885 Lr: 0.00255 [2024-02-18 19:40:19,560 INFO misc.py line 119 87073] Train: [52/100][1420/1557] Data 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Remain 23:16:18 loss: 0.2458 Lr: 0.00255 [2024-02-18 19:40:26,187 INFO misc.py line 119 87073] Train: [52/100][1427/1557] Data 0.012 (0.137) Batch 0.735 (1.119) Remain 23:15:57 loss: 0.1533 Lr: 0.00255 [2024-02-18 19:40:27,268 INFO misc.py line 119 87073] Train: [52/100][1428/1557] Data 0.004 (0.137) Batch 1.074 (1.119) Remain 23:15:54 loss: 0.1179 Lr: 0.00255 [2024-02-18 19:40:28,349 INFO misc.py line 119 87073] Train: [52/100][1429/1557] Data 0.011 (0.137) Batch 1.082 (1.119) Remain 23:15:51 loss: 0.3940 Lr: 0.00255 [2024-02-18 19:40:29,211 INFO misc.py line 119 87073] Train: [52/100][1430/1557] Data 0.010 (0.137) Batch 0.867 (1.119) Remain 23:15:36 loss: 0.1212 Lr: 0.00255 [2024-02-18 19:40:30,218 INFO misc.py line 119 87073] Train: [52/100][1431/1557] Data 0.004 (0.137) Batch 1.008 (1.118) Remain 23:15:29 loss: 0.4611 Lr: 0.00255 [2024-02-18 19:40:31,016 INFO misc.py line 119 87073] Train: [52/100][1432/1557] Data 0.004 (0.137) Batch 0.798 (1.118) Remain 23:15:12 loss: 0.3904 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1439/1557] Data 0.005 (0.136) Batch 0.842 (1.117) Remain 23:13:50 loss: 0.3795 Lr: 0.00255 [2024-02-18 19:40:38,235 INFO misc.py line 119 87073] Train: [52/100][1440/1557] Data 0.005 (0.136) Batch 0.802 (1.117) Remain 23:13:33 loss: 0.4219 Lr: 0.00255 [2024-02-18 19:40:38,994 INFO misc.py line 119 87073] Train: [52/100][1441/1557] Data 0.005 (0.136) Batch 0.760 (1.117) Remain 23:13:13 loss: 0.3935 Lr: 0.00255 [2024-02-18 19:40:40,156 INFO misc.py line 119 87073] Train: [52/100][1442/1557] Data 0.004 (0.136) Batch 1.160 (1.117) Remain 23:13:14 loss: 0.2314 Lr: 0.00255 [2024-02-18 19:40:41,183 INFO misc.py line 119 87073] Train: [52/100][1443/1557] Data 0.006 (0.136) Batch 1.017 (1.117) Remain 23:13:08 loss: 0.1581 Lr: 0.00255 [2024-02-18 19:40:42,173 INFO misc.py line 119 87073] Train: [52/100][1444/1557] Data 0.016 (0.136) Batch 1.002 (1.117) Remain 23:13:01 loss: 0.3785 Lr: 0.00255 [2024-02-18 19:40:43,320 INFO misc.py line 119 87073] Train: [52/100][1445/1557] Data 0.004 (0.136) Batch 1.144 (1.117) Remain 23:13:01 loss: 0.1089 Lr: 0.00255 [2024-02-18 19:40:44,404 INFO misc.py line 119 87073] Train: [52/100][1446/1557] Data 0.007 (0.136) Batch 1.084 (1.117) Remain 23:12:58 loss: 0.6219 Lr: 0.00255 [2024-02-18 19:40:45,158 INFO misc.py line 119 87073] Train: [52/100][1447/1557] Data 0.007 (0.135) Batch 0.758 (1.116) Remain 23:12:38 loss: 0.1982 Lr: 0.00255 [2024-02-18 19:40:45,897 INFO misc.py line 119 87073] Train: [52/100][1448/1557] Data 0.004 (0.135) Batch 0.728 (1.116) Remain 23:12:17 loss: 0.1805 Lr: 0.00255 [2024-02-18 19:40:47,033 INFO misc.py line 119 87073] Train: [52/100][1449/1557] Data 0.015 (0.135) Batch 1.135 (1.116) Remain 23:12:17 loss: 0.2277 Lr: 0.00255 [2024-02-18 19:40:47,873 INFO misc.py line 119 87073] Train: [52/100][1450/1557] Data 0.017 (0.135) Batch 0.851 (1.116) Remain 23:12:02 loss: 1.2969 Lr: 0.00255 [2024-02-18 19:40:48,808 INFO misc.py line 119 87073] Train: [52/100][1451/1557] Data 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Remain 23:10:39 loss: 0.2998 Lr: 0.00255 [2024-02-18 19:40:55,142 INFO misc.py line 119 87073] Train: [52/100][1458/1557] Data 0.005 (0.135) Batch 0.922 (1.115) Remain 23:10:28 loss: 0.3404 Lr: 0.00255 [2024-02-18 19:40:56,039 INFO misc.py line 119 87073] Train: [52/100][1459/1557] Data 0.004 (0.134) Batch 0.897 (1.115) Remain 23:10:16 loss: 0.3402 Lr: 0.00255 [2024-02-18 19:40:57,017 INFO misc.py line 119 87073] Train: [52/100][1460/1557] Data 0.003 (0.134) Batch 0.975 (1.115) Remain 23:10:07 loss: 0.4098 Lr: 0.00255 [2024-02-18 19:40:57,747 INFO misc.py line 119 87073] Train: [52/100][1461/1557] Data 0.007 (0.134) Batch 0.733 (1.114) Remain 23:09:47 loss: 0.3561 Lr: 0.00255 [2024-02-18 19:40:58,529 INFO misc.py line 119 87073] Train: [52/100][1462/1557] Data 0.004 (0.134) Batch 0.775 (1.114) Remain 23:09:28 loss: 0.2918 Lr: 0.00255 [2024-02-18 19:41:08,413 INFO misc.py line 119 87073] Train: [52/100][1463/1557] Data 7.036 (0.139) Batch 9.888 (1.120) Remain 23:16:57 loss: 0.1399 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1470/1557] Data 0.004 (0.138) Batch 1.228 (1.119) Remain 23:15:23 loss: 0.1004 Lr: 0.00255 [2024-02-18 19:41:15,842 INFO misc.py line 119 87073] Train: [52/100][1471/1557] Data 0.005 (0.138) Batch 1.271 (1.119) Remain 23:15:29 loss: 0.2777 Lr: 0.00255 [2024-02-18 19:41:16,809 INFO misc.py line 119 87073] Train: [52/100][1472/1557] Data 0.014 (0.138) Batch 0.977 (1.119) Remain 23:15:21 loss: 0.3066 Lr: 0.00255 [2024-02-18 19:41:17,796 INFO misc.py line 119 87073] Train: [52/100][1473/1557] Data 0.004 (0.138) Batch 0.986 (1.119) Remain 23:15:13 loss: 0.4061 Lr: 0.00255 [2024-02-18 19:41:18,595 INFO misc.py line 119 87073] Train: [52/100][1474/1557] Data 0.004 (0.138) Batch 0.800 (1.119) Remain 23:14:56 loss: 0.9464 Lr: 0.00255 [2024-02-18 19:41:19,300 INFO misc.py line 119 87073] Train: [52/100][1475/1557] Data 0.003 (0.138) Batch 0.704 (1.118) Remain 23:14:34 loss: 0.6049 Lr: 0.00255 [2024-02-18 19:41:19,996 INFO misc.py line 119 87073] Train: [52/100][1476/1557] Data 0.004 (0.138) Batch 0.697 (1.118) Remain 23:14:11 loss: 0.2371 Lr: 0.00255 [2024-02-18 19:41:21,301 INFO misc.py line 119 87073] Train: [52/100][1477/1557] Data 0.003 (0.138) Batch 1.304 (1.118) Remain 23:14:19 loss: 0.0833 Lr: 0.00255 [2024-02-18 19:41:22,375 INFO misc.py line 119 87073] Train: [52/100][1478/1557] Data 0.004 (0.138) Batch 1.073 (1.118) Remain 23:14:16 loss: 0.5148 Lr: 0.00255 [2024-02-18 19:41:23,511 INFO misc.py line 119 87073] Train: [52/100][1479/1557] Data 0.006 (0.137) Batch 1.134 (1.118) Remain 23:14:16 loss: 0.5347 Lr: 0.00255 [2024-02-18 19:41:24,480 INFO misc.py line 119 87073] Train: [52/100][1480/1557] Data 0.006 (0.137) Batch 0.972 (1.118) Remain 23:14:07 loss: 0.3946 Lr: 0.00255 [2024-02-18 19:41:25,271 INFO misc.py line 119 87073] Train: [52/100][1481/1557] Data 0.004 (0.137) Batch 0.789 (1.118) Remain 23:13:49 loss: 0.2198 Lr: 0.00255 [2024-02-18 19:41:26,042 INFO misc.py line 119 87073] Train: [52/100][1482/1557] Data 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Remain 23:12:20 loss: 0.3726 Lr: 0.00255 [2024-02-18 19:41:32,216 INFO misc.py line 119 87073] Train: [52/100][1489/1557] Data 0.004 (0.137) Batch 0.746 (1.117) Remain 23:12:00 loss: 0.2392 Lr: 0.00255 [2024-02-18 19:41:33,012 INFO misc.py line 119 87073] Train: [52/100][1490/1557] Data 0.005 (0.136) Batch 0.788 (1.116) Remain 23:11:42 loss: 0.3119 Lr: 0.00255 [2024-02-18 19:41:34,249 INFO misc.py line 119 87073] Train: [52/100][1491/1557] Data 0.013 (0.136) Batch 1.239 (1.116) Remain 23:11:47 loss: 0.1964 Lr: 0.00255 [2024-02-18 19:41:35,192 INFO misc.py line 119 87073] Train: [52/100][1492/1557] Data 0.011 (0.136) Batch 0.950 (1.116) Remain 23:11:38 loss: 0.8525 Lr: 0.00255 [2024-02-18 19:41:36,219 INFO misc.py line 119 87073] Train: [52/100][1493/1557] Data 0.005 (0.136) Batch 1.028 (1.116) Remain 23:11:32 loss: 0.5106 Lr: 0.00255 [2024-02-18 19:41:37,148 INFO misc.py line 119 87073] Train: [52/100][1494/1557] Data 0.004 (0.136) Batch 0.928 (1.116) Remain 23:11:22 loss: 0.6888 Lr: 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INFO misc.py line 119 87073] Train: [52/100][1501/1557] Data 0.005 (0.136) Batch 1.012 (1.115) Remain 23:09:59 loss: 0.5284 Lr: 0.00255 [2024-02-18 19:41:44,545 INFO misc.py line 119 87073] Train: [52/100][1502/1557] Data 0.010 (0.135) Batch 1.084 (1.115) Remain 23:09:56 loss: 0.6803 Lr: 0.00255 [2024-02-18 19:41:45,307 INFO misc.py line 119 87073] Train: [52/100][1503/1557] Data 0.013 (0.135) Batch 0.770 (1.115) Remain 23:09:38 loss: 0.5334 Lr: 0.00255 [2024-02-18 19:41:46,064 INFO misc.py line 119 87073] Train: [52/100][1504/1557] Data 0.004 (0.135) Batch 0.749 (1.115) Remain 23:09:18 loss: 0.2273 Lr: 0.00255 [2024-02-18 19:41:47,229 INFO misc.py line 119 87073] Train: [52/100][1505/1557] Data 0.012 (0.135) Batch 1.163 (1.115) Remain 23:09:20 loss: 0.1692 Lr: 0.00255 [2024-02-18 19:41:48,194 INFO misc.py line 119 87073] Train: [52/100][1506/1557] Data 0.014 (0.135) Batch 0.975 (1.115) Remain 23:09:12 loss: 0.2918 Lr: 0.00255 [2024-02-18 19:41:49,183 INFO misc.py line 119 87073] Train: [52/100][1507/1557] Data 0.004 (0.135) Batch 0.990 (1.114) Remain 23:09:04 loss: 0.4196 Lr: 0.00255 [2024-02-18 19:41:50,125 INFO misc.py line 119 87073] Train: [52/100][1508/1557] Data 0.004 (0.135) Batch 0.941 (1.114) Remain 23:08:55 loss: 0.2706 Lr: 0.00255 [2024-02-18 19:41:50,991 INFO misc.py line 119 87073] Train: [52/100][1509/1557] Data 0.005 (0.135) Batch 0.861 (1.114) Remain 23:08:41 loss: 0.8653 Lr: 0.00255 [2024-02-18 19:41:51,727 INFO misc.py line 119 87073] Train: [52/100][1510/1557] Data 0.010 (0.135) Batch 0.741 (1.114) Remain 23:08:21 loss: 0.2058 Lr: 0.00255 [2024-02-18 19:41:52,511 INFO misc.py line 119 87073] Train: [52/100][1511/1557] Data 0.004 (0.135) Batch 0.772 (1.114) Remain 23:08:03 loss: 0.1610 Lr: 0.00255 [2024-02-18 19:41:53,816 INFO misc.py line 119 87073] Train: [52/100][1512/1557] Data 0.016 (0.135) Batch 1.306 (1.114) Remain 23:08:12 loss: 0.1440 Lr: 0.00255 [2024-02-18 19:41:54,721 INFO misc.py line 119 87073] Train: [52/100][1513/1557] Data 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Remain 23:14:56 loss: 0.1808 Lr: 0.00255 [2024-02-18 19:42:10,873 INFO misc.py line 119 87073] Train: [52/100][1520/1557] Data 0.006 (0.139) Batch 0.911 (1.119) Remain 23:14:44 loss: 0.3371 Lr: 0.00255 [2024-02-18 19:42:11,744 INFO misc.py line 119 87073] Train: [52/100][1521/1557] Data 0.011 (0.139) Batch 0.878 (1.119) Remain 23:14:31 loss: 0.5008 Lr: 0.00255 [2024-02-18 19:42:12,598 INFO misc.py line 119 87073] Train: [52/100][1522/1557] Data 0.004 (0.139) Batch 0.854 (1.119) Remain 23:14:17 loss: 0.1654 Lr: 0.00255 [2024-02-18 19:42:13,522 INFO misc.py line 119 87073] Train: [52/100][1523/1557] Data 0.003 (0.138) Batch 0.917 (1.119) Remain 23:14:06 loss: 0.3948 Lr: 0.00254 [2024-02-18 19:42:14,255 INFO misc.py line 119 87073] Train: [52/100][1524/1557] Data 0.010 (0.138) Batch 0.740 (1.118) Remain 23:13:46 loss: 0.1677 Lr: 0.00254 [2024-02-18 19:42:14,969 INFO misc.py line 119 87073] Train: [52/100][1525/1557] Data 0.004 (0.138) Batch 0.706 (1.118) Remain 23:13:25 loss: 0.1482 Lr: 0.00254 [2024-02-18 19:42:16,175 INFO misc.py line 119 87073] Train: [52/100][1526/1557] Data 0.013 (0.138) Batch 1.203 (1.118) Remain 23:13:28 loss: 0.1254 Lr: 0.00254 [2024-02-18 19:42:17,076 INFO misc.py line 119 87073] Train: [52/100][1527/1557] Data 0.014 (0.138) Batch 0.912 (1.118) Remain 23:13:17 loss: 0.3257 Lr: 0.00254 [2024-02-18 19:42:18,001 INFO misc.py line 119 87073] Train: [52/100][1528/1557] Data 0.003 (0.138) Batch 0.925 (1.118) Remain 23:13:06 loss: 0.4667 Lr: 0.00254 [2024-02-18 19:42:18,884 INFO misc.py line 119 87073] Train: [52/100][1529/1557] Data 0.004 (0.138) Batch 0.873 (1.118) Remain 23:12:53 loss: 0.5352 Lr: 0.00254 [2024-02-18 19:42:19,925 INFO misc.py line 119 87073] Train: [52/100][1530/1557] Data 0.015 (0.138) Batch 1.042 (1.118) Remain 23:12:48 loss: 0.5376 Lr: 0.00254 [2024-02-18 19:42:20,688 INFO misc.py line 119 87073] Train: [52/100][1531/1557] Data 0.013 (0.138) Batch 0.773 (1.118) Remain 23:12:30 loss: 0.2075 Lr: 0.00254 [2024-02-18 19:42:21,467 INFO misc.py line 119 87073] Train: [52/100][1532/1557] Data 0.004 (0.138) Batch 0.770 (1.117) Remain 23:12:12 loss: 0.4410 Lr: 0.00254 [2024-02-18 19:42:22,742 INFO misc.py line 119 87073] Train: [52/100][1533/1557] Data 0.013 (0.138) Batch 1.269 (1.117) Remain 23:12:18 loss: 0.0976 Lr: 0.00254 [2024-02-18 19:42:23,700 INFO misc.py line 119 87073] Train: [52/100][1534/1557] Data 0.019 (0.137) Batch 0.973 (1.117) Remain 23:12:10 loss: 0.4128 Lr: 0.00254 [2024-02-18 19:42:24,849 INFO misc.py line 119 87073] Train: [52/100][1535/1557] Data 0.004 (0.137) Batch 1.149 (1.117) Remain 23:12:11 loss: 0.4129 Lr: 0.00254 [2024-02-18 19:42:25,819 INFO misc.py line 119 87073] Train: [52/100][1536/1557] Data 0.003 (0.137) Batch 0.969 (1.117) Remain 23:12:02 loss: 0.4137 Lr: 0.00254 [2024-02-18 19:42:26,831 INFO misc.py line 119 87073] Train: [52/100][1537/1557] Data 0.004 (0.137) Batch 1.012 (1.117) Remain 23:11:56 loss: 0.4583 Lr: 0.00254 [2024-02-18 19:42:27,586 INFO misc.py line 119 87073] Train: [52/100][1538/1557] Data 0.005 (0.137) Batch 0.756 (1.117) Remain 23:11:37 loss: 0.1875 Lr: 0.00254 [2024-02-18 19:42:28,296 INFO misc.py line 119 87073] Train: [52/100][1539/1557] Data 0.004 (0.137) Batch 0.699 (1.117) Remain 23:11:16 loss: 0.2109 Lr: 0.00254 [2024-02-18 19:42:29,350 INFO misc.py line 119 87073] Train: [52/100][1540/1557] Data 0.014 (0.137) Batch 1.055 (1.117) Remain 23:11:12 loss: 0.1489 Lr: 0.00254 [2024-02-18 19:42:30,429 INFO misc.py line 119 87073] Train: [52/100][1541/1557] Data 0.013 (0.137) Batch 1.079 (1.117) Remain 23:11:09 loss: 0.3330 Lr: 0.00254 [2024-02-18 19:42:31,327 INFO misc.py line 119 87073] Train: [52/100][1542/1557] Data 0.014 (0.137) Batch 0.908 (1.116) Remain 23:10:58 loss: 0.3342 Lr: 0.00254 [2024-02-18 19:42:32,346 INFO misc.py line 119 87073] Train: [52/100][1543/1557] Data 0.004 (0.137) Batch 1.018 (1.116) Remain 23:10:52 loss: 0.3981 Lr: 0.00254 [2024-02-18 19:42:33,237 INFO misc.py line 119 87073] Train: [52/100][1544/1557] Data 0.004 (0.137) Batch 0.892 (1.116) Remain 23:10:40 loss: 0.4023 Lr: 0.00254 [2024-02-18 19:42:33,991 INFO misc.py line 119 87073] Train: [52/100][1545/1557] Data 0.004 (0.137) Batch 0.744 (1.116) Remain 23:10:21 loss: 0.2892 Lr: 0.00254 [2024-02-18 19:42:34,698 INFO misc.py line 119 87073] Train: [52/100][1546/1557] Data 0.013 (0.136) Batch 0.717 (1.116) Remain 23:10:00 loss: 0.2732 Lr: 0.00254 [2024-02-18 19:42:35,885 INFO misc.py line 119 87073] Train: [52/100][1547/1557] Data 0.003 (0.136) Batch 1.186 (1.116) Remain 23:10:02 loss: 0.2919 Lr: 0.00254 [2024-02-18 19:42:36,696 INFO misc.py line 119 87073] Train: [52/100][1548/1557] Data 0.004 (0.136) Batch 0.811 (1.116) Remain 23:09:47 loss: 0.3088 Lr: 0.00254 [2024-02-18 19:42:37,643 INFO misc.py line 119 87073] Train: [52/100][1549/1557] Data 0.003 (0.136) Batch 0.940 (1.116) Remain 23:09:37 loss: 0.3729 Lr: 0.00254 [2024-02-18 19:42:38,686 INFO misc.py line 119 87073] Train: [52/100][1550/1557] Data 0.012 (0.136) Batch 1.041 (1.115) Remain 23:09:32 loss: 0.2954 Lr: 0.00254 [2024-02-18 19:42:39,749 INFO misc.py line 119 87073] Train: [52/100][1551/1557] Data 0.013 (0.136) Batch 1.067 (1.115) Remain 23:09:29 loss: 0.5214 Lr: 0.00254 [2024-02-18 19:42:40,444 INFO misc.py line 119 87073] Train: [52/100][1552/1557] Data 0.010 (0.136) Batch 0.700 (1.115) Remain 23:09:08 loss: 0.3338 Lr: 0.00254 [2024-02-18 19:42:41,194 INFO misc.py line 119 87073] Train: [52/100][1553/1557] Data 0.004 (0.136) Batch 0.740 (1.115) Remain 23:08:48 loss: 0.2144 Lr: 0.00254 [2024-02-18 19:42:42,369 INFO misc.py line 119 87073] Train: [52/100][1554/1557] Data 0.013 (0.136) Batch 1.175 (1.115) Remain 23:08:50 loss: 0.1782 Lr: 0.00254 [2024-02-18 19:42:43,263 INFO misc.py line 119 87073] Train: [52/100][1555/1557] Data 0.014 (0.136) Batch 0.903 (1.115) Remain 23:08:39 loss: 0.4298 Lr: 0.00254 [2024-02-18 19:42:44,221 INFO misc.py line 119 87073] Train: [52/100][1556/1557] Data 0.004 (0.136) Batch 0.958 (1.115) Remain 23:08:30 loss: 0.6155 Lr: 0.00254 [2024-02-18 19:42:45,055 INFO misc.py line 119 87073] Train: [52/100][1557/1557] Data 0.004 (0.136) Batch 0.835 (1.115) Remain 23:08:16 loss: 0.5172 Lr: 0.00254 [2024-02-18 19:42:45,056 INFO misc.py line 136 87073] Train result: loss: 0.3420 [2024-02-18 19:42:45,057 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 19:43:11,871 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6700 [2024-02-18 19:43:12,648 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7008 [2024-02-18 19:43:14,773 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3559 [2024-02-18 19:43:16,980 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.5155 [2024-02-18 19:43:21,930 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2822 [2024-02-18 19:43:22,630 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0658 [2024-02-18 19:43:23,891 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2982 [2024-02-18 19:43:26,848 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4372 [2024-02-18 19:43:28,658 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2345 [2024-02-18 19:43:30,326 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7157/0.7746/0.9122. [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9326/0.9630 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9813/0.9895 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8582/0.9717 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3310/0.3707 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6603/0.6848 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8093/0.9331 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8449/0.9109 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9235/0.9689 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8705/0.9013 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7797/0.8583 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7052/0.8047 [2024-02-18 19:43:30,326 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6074/0.7124 [2024-02-18 19:43:30,327 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 19:43:30,332 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 19:43:30,332 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 19:43:37,183 INFO misc.py line 119 87073] Train: [53/100][1/1557] Data 1.521 (1.521) Batch 2.320 (2.320) Remain 48:09:22 loss: 0.4171 Lr: 0.00254 [2024-02-18 19:43:38,186 INFO misc.py line 119 87073] Train: [53/100][2/1557] Data 0.007 (0.007) Batch 0.989 (0.989) Remain 20:32:04 loss: 0.3416 Lr: 0.00254 [2024-02-18 19:43:39,096 INFO misc.py line 119 87073] Train: [53/100][3/1557] Data 0.021 (0.021) Batch 0.924 (0.924) Remain 19:11:27 loss: 0.2592 Lr: 0.00254 [2024-02-18 19:43:40,174 INFO misc.py line 119 87073] Train: [53/100][4/1557] Data 0.006 (0.006) Batch 1.079 (1.079) Remain 22:23:57 loss: 0.4582 Lr: 0.00254 [2024-02-18 19:43:40,914 INFO misc.py line 119 87073] Train: [53/100][5/1557] Data 0.005 (0.006) Batch 0.740 (0.909) Remain 18:52:44 loss: 0.1231 Lr: 0.00254 [2024-02-18 19:43:41,696 INFO misc.py line 119 87073] Train: [53/100][6/1557] Data 0.005 (0.005) Batch 0.777 (0.865) Remain 17:57:44 loss: 0.3523 Lr: 0.00254 [2024-02-18 19:43:43,054 INFO misc.py line 119 87073] Train: [53/100][7/1557] Data 0.010 (0.007) Batch 1.363 (0.990) Remain 20:32:49 loss: 0.1537 Lr: 0.00254 [2024-02-18 19:43:43,933 INFO misc.py line 119 87073] Train: [53/100][8/1557] Data 0.005 (0.006) Batch 0.878 (0.968) Remain 20:05:04 loss: 0.4995 Lr: 0.00254 [2024-02-18 19:43:44,990 INFO misc.py line 119 87073] Train: [53/100][9/1557] Data 0.005 (0.006) Batch 1.057 (0.983) Remain 20:23:40 loss: 0.6526 Lr: 0.00254 [2024-02-18 19:43:45,998 INFO misc.py line 119 87073] Train: [53/100][10/1557] Data 0.005 (0.006) Batch 1.006 (0.986) Remain 20:27:51 loss: 0.1410 Lr: 0.00254 [2024-02-18 19:43:46,998 INFO misc.py line 119 87073] Train: [53/100][11/1557] Data 0.006 (0.006) Batch 1.003 (0.988) Remain 20:30:26 loss: 0.4781 Lr: 0.00254 [2024-02-18 19:43:47,709 INFO misc.py line 119 87073] Train: [53/100][12/1557] Data 0.004 (0.006) Batch 0.711 (0.957) Remain 19:52:09 loss: 0.2646 Lr: 0.00254 [2024-02-18 19:43:48,473 INFO misc.py line 119 87073] Train: [53/100][13/1557] Data 0.004 (0.005) Batch 0.757 (0.937) Remain 19:27:09 loss: 0.5584 Lr: 0.00254 [2024-02-18 19:43:49,730 INFO misc.py line 119 87073] Train: [53/100][14/1557] Data 0.010 (0.006) Batch 1.250 (0.966) Remain 20:02:35 loss: 0.1441 Lr: 0.00254 [2024-02-18 19:43:50,747 INFO misc.py line 119 87073] Train: [53/100][15/1557] Data 0.017 (0.007) Batch 1.030 (0.971) Remain 20:09:12 loss: 0.2312 Lr: 0.00254 [2024-02-18 19:43:51,707 INFO misc.py line 119 87073] Train: [53/100][16/1557] Data 0.004 (0.007) Batch 0.961 (0.970) Remain 20:08:15 loss: 0.1674 Lr: 0.00254 [2024-02-18 19:43:52,776 INFO misc.py line 119 87073] Train: [53/100][17/1557] Data 0.004 (0.006) Batch 1.069 (0.977) Remain 20:16:59 loss: 0.3758 Lr: 0.00254 [2024-02-18 19:43:53,767 INFO misc.py line 119 87073] Train: [53/100][18/1557] Data 0.004 (0.006) Batch 0.991 (0.978) Remain 20:18:09 loss: 0.3525 Lr: 0.00254 [2024-02-18 19:43:54,444 INFO misc.py line 119 87073] Train: [53/100][19/1557] Data 0.004 (0.006) Batch 0.677 (0.959) Remain 19:54:40 loss: 0.3158 Lr: 0.00254 [2024-02-18 19:43:55,239 INFO misc.py line 119 87073] Train: [53/100][20/1557] Data 0.004 (0.006) Batch 0.786 (0.949) Remain 19:41:58 loss: 0.2800 Lr: 0.00254 [2024-02-18 19:43:56,319 INFO misc.py line 119 87073] Train: [53/100][21/1557] Data 0.012 (0.006) Batch 1.076 (0.956) Remain 19:50:43 loss: 0.1659 Lr: 0.00254 [2024-02-18 19:43:57,402 INFO misc.py line 119 87073] Train: [53/100][22/1557] Data 0.016 (0.007) Batch 1.094 (0.963) Remain 19:59:45 loss: 0.1960 Lr: 0.00254 [2024-02-18 19:43:58,315 INFO misc.py line 119 87073] Train: [53/100][23/1557] Data 0.005 (0.007) Batch 0.913 (0.961) Remain 19:56:36 loss: 0.2848 Lr: 0.00254 [2024-02-18 19:43:59,108 INFO misc.py line 119 87073] Train: [53/100][24/1557] Data 0.005 (0.007) Batch 0.793 (0.953) Remain 19:46:37 loss: 0.4464 Lr: 0.00254 [2024-02-18 19:43:59,920 INFO misc.py line 119 87073] Train: [53/100][25/1557] Data 0.007 (0.007) Batch 0.812 (0.947) Remain 19:38:39 loss: 0.2665 Lr: 0.00254 [2024-02-18 19:44:00,719 INFO misc.py line 119 87073] Train: [53/100][26/1557] Data 0.006 (0.007) Batch 0.799 (0.940) Remain 19:30:40 loss: 0.2618 Lr: 0.00254 [2024-02-18 19:44:01,475 INFO misc.py line 119 87073] Train: [53/100][27/1557] Data 0.004 (0.007) Batch 0.756 (0.933) Remain 19:21:07 loss: 0.3285 Lr: 0.00254 [2024-02-18 19:44:02,683 INFO misc.py line 119 87073] Train: [53/100][28/1557] Data 0.004 (0.006) Batch 1.209 (0.944) Remain 19:34:51 loss: 0.1918 Lr: 0.00254 [2024-02-18 19:44:03,605 INFO misc.py line 119 87073] Train: [53/100][29/1557] Data 0.004 (0.006) Batch 0.920 (0.943) Remain 19:33:44 loss: 0.2335 Lr: 0.00254 [2024-02-18 19:44:04,651 INFO misc.py line 119 87073] Train: [53/100][30/1557] Data 0.005 (0.006) Batch 1.046 (0.947) Remain 19:38:30 loss: 0.5794 Lr: 0.00254 [2024-02-18 19:44:05,528 INFO misc.py line 119 87073] Train: [53/100][31/1557] Data 0.005 (0.006) Batch 0.876 (0.944) Remain 19:35:21 loss: 0.4089 Lr: 0.00254 [2024-02-18 19:44:06,637 INFO misc.py line 119 87073] Train: [53/100][32/1557] Data 0.006 (0.006) Batch 1.110 (0.950) Remain 19:42:28 loss: 0.1346 Lr: 0.00254 [2024-02-18 19:44:07,329 INFO misc.py line 119 87073] Train: [53/100][33/1557] Data 0.004 (0.006) Batch 0.693 (0.941) Remain 19:31:48 loss: 0.4205 Lr: 0.00254 [2024-02-18 19:44:08,043 INFO misc.py line 119 87073] Train: [53/100][34/1557] Data 0.004 (0.006) Batch 0.706 (0.934) Remain 19:22:19 loss: 0.4099 Lr: 0.00254 [2024-02-18 19:44:09,243 INFO misc.py line 119 87073] Train: [53/100][35/1557] Data 0.011 (0.006) Batch 1.201 (0.942) Remain 19:32:42 loss: 0.1653 Lr: 0.00254 [2024-02-18 19:44:10,194 INFO misc.py line 119 87073] Train: [53/100][36/1557] Data 0.011 (0.006) Batch 0.957 (0.942) Remain 19:33:16 loss: 0.2098 Lr: 0.00254 [2024-02-18 19:44:11,241 INFO misc.py line 119 87073] Train: [53/100][37/1557] Data 0.005 (0.006) Batch 1.048 (0.945) Remain 19:37:07 loss: 0.3401 Lr: 0.00254 [2024-02-18 19:44:12,148 INFO misc.py line 119 87073] Train: [53/100][38/1557] Data 0.004 (0.006) Batch 0.906 (0.944) Remain 19:35:42 loss: 0.3043 Lr: 0.00254 [2024-02-18 19:44:13,059 INFO misc.py line 119 87073] Train: [53/100][39/1557] Data 0.005 (0.006) Batch 0.912 (0.943) Remain 19:34:34 loss: 0.3755 Lr: 0.00254 [2024-02-18 19:44:13,836 INFO misc.py line 119 87073] Train: [53/100][40/1557] Data 0.004 (0.006) Batch 0.775 (0.939) Remain 19:28:53 loss: 0.4961 Lr: 0.00254 [2024-02-18 19:44:14,619 INFO misc.py line 119 87073] Train: [53/100][41/1557] Data 0.006 (0.006) Batch 0.785 (0.935) Remain 19:23:49 loss: 0.1502 Lr: 0.00254 [2024-02-18 19:44:15,766 INFO misc.py line 119 87073] Train: [53/100][42/1557] Data 0.004 (0.006) Batch 1.145 (0.940) Remain 19:30:31 loss: 0.0821 Lr: 0.00254 [2024-02-18 19:44:16,797 INFO misc.py line 119 87073] Train: [53/100][43/1557] Data 0.005 (0.006) Batch 1.032 (0.943) Remain 19:33:22 loss: 0.2388 Lr: 0.00254 [2024-02-18 19:44:17,833 INFO misc.py line 119 87073] Train: [53/100][44/1557] Data 0.004 (0.006) Batch 1.035 (0.945) Remain 19:36:11 loss: 0.3074 Lr: 0.00254 [2024-02-18 19:44:18,757 INFO misc.py line 119 87073] Train: [53/100][45/1557] Data 0.005 (0.006) Batch 0.925 (0.944) Remain 19:35:34 loss: 0.4089 Lr: 0.00254 [2024-02-18 19:44:19,869 INFO misc.py line 119 87073] Train: [53/100][46/1557] Data 0.003 (0.006) Batch 1.112 (0.948) Remain 19:40:24 loss: 0.4501 Lr: 0.00254 [2024-02-18 19:44:20,707 INFO misc.py line 119 87073] Train: [53/100][47/1557] Data 0.003 (0.006) Batch 0.833 (0.946) Remain 19:37:08 loss: 0.2339 Lr: 0.00254 [2024-02-18 19:44:21,460 INFO misc.py line 119 87073] Train: [53/100][48/1557] Data 0.009 (0.006) Batch 0.757 (0.941) Remain 19:31:55 loss: 0.4021 Lr: 0.00254 [2024-02-18 19:44:22,771 INFO misc.py line 119 87073] Train: [53/100][49/1557] Data 0.004 (0.006) Batch 1.301 (0.949) Remain 19:41:37 loss: 0.1369 Lr: 0.00254 [2024-02-18 19:44:23,619 INFO misc.py line 119 87073] Train: [53/100][50/1557] Data 0.014 (0.006) Batch 0.857 (0.947) Remain 19:39:10 loss: 0.6106 Lr: 0.00254 [2024-02-18 19:44:24,539 INFO misc.py line 119 87073] Train: [53/100][51/1557] Data 0.005 (0.006) Batch 0.918 (0.947) Remain 19:38:23 loss: 0.5880 Lr: 0.00254 [2024-02-18 19:44:25,810 INFO misc.py line 119 87073] Train: [53/100][52/1557] Data 0.007 (0.006) Batch 1.272 (0.953) Remain 19:46:38 loss: 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Batch 0.840 (1.067) Remain 22:06:28 loss: 0.2725 Lr: 0.00254 [2024-02-18 19:45:45,910 INFO misc.py line 119 87073] Train: [53/100][122/1557] Data 0.004 (0.116) Batch 0.949 (1.066) Remain 22:05:13 loss: 0.2211 Lr: 0.00254 [2024-02-18 19:45:46,782 INFO misc.py line 119 87073] Train: [53/100][123/1557] Data 0.005 (0.115) Batch 0.869 (1.064) Remain 22:03:10 loss: 0.4228 Lr: 0.00254 [2024-02-18 19:45:47,532 INFO misc.py line 119 87073] Train: [53/100][124/1557] Data 0.008 (0.114) Batch 0.750 (1.061) Remain 21:59:55 loss: 0.3429 Lr: 0.00254 [2024-02-18 19:45:48,347 INFO misc.py line 119 87073] Train: [53/100][125/1557] Data 0.007 (0.113) Batch 0.819 (1.059) Remain 21:57:26 loss: 0.2076 Lr: 0.00254 [2024-02-18 19:45:49,549 INFO misc.py line 119 87073] Train: [53/100][126/1557] Data 0.004 (0.112) Batch 1.194 (1.061) Remain 21:58:47 loss: 0.1415 Lr: 0.00254 [2024-02-18 19:45:50,416 INFO misc.py line 119 87073] Train: [53/100][127/1557] Data 0.011 (0.112) Batch 0.872 (1.059) Remain 21:56:52 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87073] Train: [53/100][196/1557] Data 0.005 (0.115) Batch 1.154 (1.067) Remain 22:05:45 loss: 0.2935 Lr: 0.00253 [2024-02-18 19:47:06,100 INFO misc.py line 119 87073] Train: [53/100][197/1557] Data 0.004 (0.114) Batch 1.043 (1.067) Remain 22:05:34 loss: 0.4056 Lr: 0.00253 [2024-02-18 19:47:07,035 INFO misc.py line 119 87073] Train: [53/100][198/1557] Data 0.007 (0.113) Batch 0.937 (1.066) Remain 22:04:44 loss: 0.2097 Lr: 0.00253 [2024-02-18 19:47:07,980 INFO misc.py line 119 87073] Train: [53/100][199/1557] Data 0.004 (0.113) Batch 0.945 (1.066) Remain 22:03:56 loss: 0.3305 Lr: 0.00253 [2024-02-18 19:47:08,770 INFO misc.py line 119 87073] Train: [53/100][200/1557] Data 0.005 (0.112) Batch 0.791 (1.064) Remain 22:02:12 loss: 0.2917 Lr: 0.00253 [2024-02-18 19:47:09,555 INFO misc.py line 119 87073] Train: [53/100][201/1557] Data 0.004 (0.112) Batch 0.784 (1.063) Remain 22:00:25 loss: 0.2066 Lr: 0.00253 [2024-02-18 19:47:10,289 INFO misc.py line 119 87073] Train: [53/100][202/1557] Data 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Batch 0.925 (1.074) Remain 22:13:58 loss: 0.2702 Lr: 0.00253 [2024-02-18 19:47:47,224 INFO misc.py line 119 87073] Train: [53/100][234/1557] Data 0.005 (0.124) Batch 1.042 (1.074) Remain 22:13:46 loss: 0.6896 Lr: 0.00253 [2024-02-18 19:47:48,352 INFO misc.py line 119 87073] Train: [53/100][235/1557] Data 0.003 (0.123) Batch 1.127 (1.074) Remain 22:14:02 loss: 0.2858 Lr: 0.00253 [2024-02-18 19:47:49,086 INFO misc.py line 119 87073] Train: [53/100][236/1557] Data 0.004 (0.123) Batch 0.735 (1.073) Remain 22:12:12 loss: 0.2956 Lr: 0.00253 [2024-02-18 19:47:49,859 INFO misc.py line 119 87073] Train: [53/100][237/1557] Data 0.004 (0.122) Batch 0.770 (1.072) Remain 22:10:35 loss: 0.2131 Lr: 0.00253 [2024-02-18 19:47:51,140 INFO misc.py line 119 87073] Train: [53/100][238/1557] Data 0.006 (0.122) Batch 1.278 (1.073) Remain 22:11:39 loss: 0.1309 Lr: 0.00253 [2024-02-18 19:47:52,146 INFO misc.py line 119 87073] Train: [53/100][239/1557] Data 0.010 (0.121) Batch 1.003 (1.072) Remain 22:11:16 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Batch 0.912 (1.075) Remain 22:12:41 loss: 0.1660 Lr: 0.00252 [2024-02-18 19:49:47,629 INFO misc.py line 119 87073] Train: [53/100][346/1557] Data 0.005 (0.125) Batch 0.917 (1.074) Remain 22:12:06 loss: 0.3662 Lr: 0.00252 [2024-02-18 19:49:48,600 INFO misc.py line 119 87073] Train: [53/100][347/1557] Data 0.011 (0.125) Batch 0.976 (1.074) Remain 22:11:44 loss: 0.6253 Lr: 0.00252 [2024-02-18 19:49:49,304 INFO misc.py line 119 87073] Train: [53/100][348/1557] Data 0.005 (0.124) Batch 0.702 (1.073) Remain 22:10:22 loss: 0.4207 Lr: 0.00252 [2024-02-18 19:49:50,078 INFO misc.py line 119 87073] Train: [53/100][349/1557] Data 0.007 (0.124) Batch 0.768 (1.072) Remain 22:09:16 loss: 0.1591 Lr: 0.00252 [2024-02-18 19:49:51,357 INFO misc.py line 119 87073] Train: [53/100][350/1557] Data 0.013 (0.124) Batch 1.280 (1.073) Remain 22:09:59 loss: 0.1993 Lr: 0.00252 [2024-02-18 19:49:52,196 INFO misc.py line 119 87073] Train: [53/100][351/1557] Data 0.013 (0.123) Batch 0.848 (1.072) Remain 22:09:10 loss: 0.1762 Lr: 0.00252 [2024-02-18 19:49:53,245 INFO misc.py line 119 87073] Train: [53/100][352/1557] Data 0.004 (0.123) Batch 1.049 (1.072) Remain 22:09:04 loss: 0.1277 Lr: 0.00252 [2024-02-18 19:49:54,087 INFO misc.py line 119 87073] Train: [53/100][353/1557] Data 0.003 (0.123) Batch 0.842 (1.071) Remain 22:08:14 loss: 0.3478 Lr: 0.00252 [2024-02-18 19:49:55,031 INFO misc.py line 119 87073] Train: [53/100][354/1557] Data 0.004 (0.122) Batch 0.939 (1.071) Remain 22:07:45 loss: 0.5182 Lr: 0.00252 [2024-02-18 19:49:57,442 INFO misc.py line 119 87073] Train: [53/100][355/1557] Data 0.920 (0.124) Batch 2.414 (1.075) Remain 22:12:27 loss: 0.0974 Lr: 0.00252 [2024-02-18 19:49:58,264 INFO misc.py line 119 87073] Train: [53/100][356/1557] Data 0.006 (0.124) Batch 0.823 (1.074) Remain 22:11:33 loss: 0.3388 Lr: 0.00252 [2024-02-18 19:49:59,389 INFO misc.py line 119 87073] Train: [53/100][357/1557] Data 0.005 (0.124) Batch 1.124 (1.074) Remain 22:11:43 loss: 0.1910 Lr: 0.00252 [2024-02-18 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22:03:29 loss: 0.2149 Lr: 0.00252 [2024-02-18 19:50:18,182 INFO misc.py line 119 87073] Train: [53/100][377/1557] Data 0.007 (0.118) Batch 0.758 (1.067) Remain 22:02:26 loss: 0.3035 Lr: 0.00252 [2024-02-18 19:50:19,332 INFO misc.py line 119 87073] Train: [53/100][378/1557] Data 0.005 (0.117) Batch 1.152 (1.067) Remain 22:02:42 loss: 0.1191 Lr: 0.00252 [2024-02-18 19:50:20,539 INFO misc.py line 119 87073] Train: [53/100][379/1557] Data 0.004 (0.117) Batch 1.189 (1.068) Remain 22:03:05 loss: 0.3306 Lr: 0.00252 [2024-02-18 19:50:21,484 INFO misc.py line 119 87073] Train: [53/100][380/1557] Data 0.022 (0.117) Batch 0.964 (1.067) Remain 22:02:43 loss: 0.2472 Lr: 0.00252 [2024-02-18 19:50:22,463 INFO misc.py line 119 87073] Train: [53/100][381/1557] Data 0.004 (0.116) Batch 0.978 (1.067) Remain 22:02:24 loss: 0.2415 Lr: 0.00252 [2024-02-18 19:50:23,342 INFO misc.py line 119 87073] Train: [53/100][382/1557] Data 0.006 (0.116) Batch 0.880 (1.067) Remain 22:01:47 loss: 0.6350 Lr: 0.00252 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Batch 0.855 (1.077) Remain 22:14:14 loss: 0.5203 Lr: 0.00252 [2024-02-18 19:50:48,700 INFO misc.py line 119 87073] Train: [53/100][402/1557] Data 0.005 (0.128) Batch 0.982 (1.077) Remain 22:13:55 loss: 0.5390 Lr: 0.00252 [2024-02-18 19:50:49,604 INFO misc.py line 119 87073] Train: [53/100][403/1557] Data 0.005 (0.127) Batch 0.904 (1.076) Remain 22:13:22 loss: 0.1217 Lr: 0.00252 [2024-02-18 19:50:50,388 INFO misc.py line 119 87073] Train: [53/100][404/1557] Data 0.005 (0.127) Batch 0.785 (1.076) Remain 22:12:27 loss: 0.2313 Lr: 0.00252 [2024-02-18 19:50:51,150 INFO misc.py line 119 87073] Train: [53/100][405/1557] Data 0.004 (0.127) Batch 0.756 (1.075) Remain 22:11:27 loss: 0.4201 Lr: 0.00252 [2024-02-18 19:50:52,434 INFO misc.py line 119 87073] Train: [53/100][406/1557] Data 0.010 (0.127) Batch 1.288 (1.075) Remain 22:12:05 loss: 0.1807 Lr: 0.00252 [2024-02-18 19:50:53,376 INFO misc.py line 119 87073] Train: [53/100][407/1557] Data 0.008 (0.126) Batch 0.945 (1.075) Remain 22:11:40 loss: 0.8650 Lr: 0.00252 [2024-02-18 19:50:54,399 INFO misc.py line 119 87073] Train: [53/100][408/1557] Data 0.004 (0.126) Batch 1.023 (1.075) Remain 22:11:29 loss: 0.6241 Lr: 0.00252 [2024-02-18 19:50:55,329 INFO misc.py line 119 87073] Train: [53/100][409/1557] Data 0.004 (0.126) Batch 0.930 (1.074) Remain 22:11:01 loss: 0.6376 Lr: 0.00252 [2024-02-18 19:50:56,205 INFO misc.py line 119 87073] Train: [53/100][410/1557] Data 0.005 (0.125) Batch 0.872 (1.074) Remain 22:10:23 loss: 0.3703 Lr: 0.00252 [2024-02-18 19:50:56,942 INFO misc.py line 119 87073] Train: [53/100][411/1557] Data 0.008 (0.125) Batch 0.739 (1.073) Remain 22:09:21 loss: 0.2553 Lr: 0.00252 [2024-02-18 19:50:57,760 INFO misc.py line 119 87073] Train: [53/100][412/1557] Data 0.006 (0.125) Batch 0.819 (1.073) Remain 22:08:34 loss: 0.3135 Lr: 0.00252 [2024-02-18 19:50:58,991 INFO misc.py line 119 87073] Train: [53/100][413/1557] Data 0.004 (0.124) Batch 1.226 (1.073) Remain 22:09:01 loss: 0.0847 Lr: 0.00252 [2024-02-18 19:50:59,857 INFO misc.py line 119 87073] Train: [53/100][414/1557] Data 0.010 (0.124) Batch 0.872 (1.072) Remain 22:08:23 loss: 0.3035 Lr: 0.00252 [2024-02-18 19:51:00,660 INFO misc.py line 119 87073] Train: [53/100][415/1557] Data 0.004 (0.124) Batch 0.803 (1.072) Remain 22:07:34 loss: 0.3191 Lr: 0.00252 [2024-02-18 19:51:01,580 INFO misc.py line 119 87073] Train: [53/100][416/1557] Data 0.003 (0.124) Batch 0.898 (1.071) Remain 22:07:02 loss: 0.4918 Lr: 0.00252 [2024-02-18 19:51:02,653 INFO misc.py line 119 87073] Train: [53/100][417/1557] Data 0.025 (0.123) Batch 1.089 (1.071) Remain 22:07:04 loss: 0.7981 Lr: 0.00252 [2024-02-18 19:51:03,451 INFO misc.py line 119 87073] Train: [53/100][418/1557] Data 0.009 (0.123) Batch 0.804 (1.071) Remain 22:06:15 loss: 0.1713 Lr: 0.00252 [2024-02-18 19:51:04,193 INFO misc.py line 119 87073] Train: [53/100][419/1557] Data 0.003 (0.123) Batch 0.741 (1.070) Remain 22:05:15 loss: 0.2391 Lr: 0.00252 [2024-02-18 19:51:05,448 INFO misc.py line 119 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Batch 0.960 (1.075) Remain 22:11:20 loss: 0.4989 Lr: 0.00252 [2024-02-18 19:51:48,361 INFO misc.py line 119 87073] Train: [53/100][458/1557] Data 0.006 (0.128) Batch 1.025 (1.075) Remain 22:11:11 loss: 0.4941 Lr: 0.00252 [2024-02-18 19:51:49,300 INFO misc.py line 119 87073] Train: [53/100][459/1557] Data 0.006 (0.127) Batch 0.942 (1.075) Remain 22:10:48 loss: 0.3180 Lr: 0.00252 [2024-02-18 19:51:50,126 INFO misc.py line 119 87073] Train: [53/100][460/1557] Data 0.004 (0.127) Batch 0.797 (1.074) Remain 22:10:02 loss: 0.2284 Lr: 0.00252 [2024-02-18 19:51:50,889 INFO misc.py line 119 87073] Train: [53/100][461/1557] Data 0.033 (0.127) Batch 0.790 (1.074) Remain 22:09:15 loss: 0.1854 Lr: 0.00252 [2024-02-18 19:51:52,163 INFO misc.py line 119 87073] Train: [53/100][462/1557] Data 0.006 (0.127) Batch 1.271 (1.074) Remain 22:09:45 loss: 0.2102 Lr: 0.00252 [2024-02-18 19:51:52,954 INFO misc.py line 119 87073] Train: [53/100][463/1557] Data 0.009 (0.126) Batch 0.795 (1.074) Remain 22:08:59 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19:51:59,796 INFO misc.py line 119 87073] Train: [53/100][470/1557] Data 0.010 (0.125) Batch 1.172 (1.072) Remain 22:07:05 loss: 0.3433 Lr: 0.00252 [2024-02-18 19:52:00,766 INFO misc.py line 119 87073] Train: [53/100][471/1557] Data 0.005 (0.124) Batch 0.970 (1.072) Remain 22:06:47 loss: 0.3045 Lr: 0.00252 [2024-02-18 19:52:01,766 INFO misc.py line 119 87073] Train: [53/100][472/1557] Data 0.004 (0.124) Batch 1.000 (1.072) Remain 22:06:35 loss: 0.4815 Lr: 0.00252 [2024-02-18 19:52:02,677 INFO misc.py line 119 87073] Train: [53/100][473/1557] Data 0.005 (0.124) Batch 0.912 (1.071) Remain 22:06:09 loss: 0.1923 Lr: 0.00252 [2024-02-18 19:52:03,380 INFO misc.py line 119 87073] Train: [53/100][474/1557] Data 0.004 (0.124) Batch 0.701 (1.071) Remain 22:05:09 loss: 0.5933 Lr: 0.00252 [2024-02-18 19:52:04,177 INFO misc.py line 119 87073] Train: [53/100][475/1557] Data 0.005 (0.123) Batch 0.798 (1.070) Remain 22:04:25 loss: 0.1937 Lr: 0.00252 [2024-02-18 19:52:05,416 INFO misc.py line 119 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[2024-02-18 19:52:23,020 INFO misc.py line 119 87073] Train: [53/100][495/1557] Data 0.008 (0.119) Batch 0.719 (1.065) Remain 21:57:38 loss: 0.1891 Lr: 0.00252 [2024-02-18 19:52:23,778 INFO misc.py line 119 87073] Train: [53/100][496/1557] Data 0.005 (0.118) Batch 0.754 (1.064) Remain 21:56:50 loss: 0.2412 Lr: 0.00252 [2024-02-18 19:52:25,040 INFO misc.py line 119 87073] Train: [53/100][497/1557] Data 0.013 (0.118) Batch 1.264 (1.065) Remain 21:57:19 loss: 0.2682 Lr: 0.00252 [2024-02-18 19:52:26,155 INFO misc.py line 119 87073] Train: [53/100][498/1557] Data 0.007 (0.118) Batch 1.106 (1.065) Remain 21:57:24 loss: 0.6076 Lr: 0.00252 [2024-02-18 19:52:27,039 INFO misc.py line 119 87073] Train: [53/100][499/1557] Data 0.016 (0.118) Batch 0.894 (1.064) Remain 21:56:57 loss: 0.3249 Lr: 0.00252 [2024-02-18 19:52:28,105 INFO misc.py line 119 87073] Train: [53/100][500/1557] Data 0.005 (0.118) Batch 1.067 (1.064) Remain 21:56:57 loss: 0.3110 Lr: 0.00252 [2024-02-18 19:52:29,203 INFO misc.py line 119 87073] Train: [53/100][501/1557] Data 0.004 (0.117) Batch 1.099 (1.064) Remain 21:57:01 loss: 0.5058 Lr: 0.00252 [2024-02-18 19:52:29,939 INFO misc.py line 119 87073] Train: [53/100][502/1557] Data 0.003 (0.117) Batch 0.736 (1.064) Remain 21:56:11 loss: 0.3526 Lr: 0.00252 [2024-02-18 19:52:30,645 INFO misc.py line 119 87073] Train: [53/100][503/1557] Data 0.004 (0.117) Batch 0.700 (1.063) Remain 21:55:16 loss: 0.5567 Lr: 0.00252 [2024-02-18 19:52:31,982 INFO misc.py line 119 87073] Train: [53/100][504/1557] Data 0.009 (0.117) Batch 1.336 (1.064) Remain 21:55:55 loss: 0.1811 Lr: 0.00252 [2024-02-18 19:52:32,976 INFO misc.py line 119 87073] Train: [53/100][505/1557] Data 0.010 (0.116) Batch 0.998 (1.064) Remain 21:55:44 loss: 0.3580 Lr: 0.00252 [2024-02-18 19:52:33,938 INFO misc.py line 119 87073] Train: [53/100][506/1557] Data 0.006 (0.116) Batch 0.965 (1.063) Remain 21:55:29 loss: 0.2526 Lr: 0.00252 [2024-02-18 19:52:34,926 INFO misc.py line 119 87073] Train: 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Batch 0.950 (1.075) Remain 22:09:42 loss: 0.2896 Lr: 0.00252 [2024-02-18 19:52:48,175 INFO misc.py line 119 87073] Train: [53/100][514/1557] Data 0.006 (0.128) Batch 0.875 (1.075) Remain 22:09:12 loss: 0.2979 Lr: 0.00252 [2024-02-18 19:52:49,084 INFO misc.py line 119 87073] Train: [53/100][515/1557] Data 0.006 (0.128) Batch 0.911 (1.074) Remain 22:08:48 loss: 0.4943 Lr: 0.00252 [2024-02-18 19:52:49,835 INFO misc.py line 119 87073] Train: [53/100][516/1557] Data 0.004 (0.128) Batch 0.737 (1.074) Remain 22:07:58 loss: 0.4251 Lr: 0.00252 [2024-02-18 19:52:50,615 INFO misc.py line 119 87073] Train: [53/100][517/1557] Data 0.017 (0.127) Batch 0.793 (1.073) Remain 22:07:16 loss: 0.3427 Lr: 0.00252 [2024-02-18 19:52:51,903 INFO misc.py line 119 87073] Train: [53/100][518/1557] Data 0.003 (0.127) Batch 1.283 (1.073) Remain 22:07:45 loss: 0.3584 Lr: 0.00252 [2024-02-18 19:52:52,943 INFO misc.py line 119 87073] Train: [53/100][519/1557] Data 0.008 (0.127) Batch 1.027 (1.073) Remain 22:07:38 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Batch 1.000 (1.079) Remain 22:11:17 loss: 0.6841 Lr: 0.00251 [2024-02-18 19:55:51,539 INFO misc.py line 119 87073] Train: [53/100][682/1557] Data 0.005 (0.130) Batch 1.133 (1.079) Remain 22:11:22 loss: 0.2207 Lr: 0.00251 [2024-02-18 19:55:52,476 INFO misc.py line 119 87073] Train: [53/100][683/1557] Data 0.005 (0.129) Batch 0.939 (1.079) Remain 22:11:06 loss: 0.3927 Lr: 0.00251 [2024-02-18 19:55:53,249 INFO misc.py line 119 87073] Train: [53/100][684/1557] Data 0.004 (0.129) Batch 0.764 (1.078) Remain 22:10:30 loss: 0.2889 Lr: 0.00251 [2024-02-18 19:55:54,031 INFO misc.py line 119 87073] Train: [53/100][685/1557] Data 0.012 (0.129) Batch 0.790 (1.078) Remain 22:09:58 loss: 0.4203 Lr: 0.00251 [2024-02-18 19:55:55,243 INFO misc.py line 119 87073] Train: [53/100][686/1557] Data 0.003 (0.129) Batch 1.210 (1.078) Remain 22:10:11 loss: 0.1646 Lr: 0.00251 [2024-02-18 19:55:56,150 INFO misc.py line 119 87073] Train: [53/100][687/1557] Data 0.005 (0.129) Batch 0.909 (1.078) Remain 22:09:52 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Batch 1.001 (1.082) Remain 22:14:05 loss: 0.2735 Lr: 0.00250 [2024-02-18 19:56:54,177 INFO misc.py line 119 87073] Train: [53/100][738/1557] Data 0.008 (0.132) Batch 1.101 (1.082) Remain 22:14:06 loss: 0.1384 Lr: 0.00250 [2024-02-18 19:56:55,107 INFO misc.py line 119 87073] Train: [53/100][739/1557] Data 0.005 (0.132) Batch 0.930 (1.082) Remain 22:13:50 loss: 0.2186 Lr: 0.00250 [2024-02-18 19:56:55,883 INFO misc.py line 119 87073] Train: [53/100][740/1557] Data 0.005 (0.132) Batch 0.776 (1.081) Remain 22:13:18 loss: 0.4682 Lr: 0.00250 [2024-02-18 19:56:56,652 INFO misc.py line 119 87073] Train: [53/100][741/1557] Data 0.004 (0.132) Batch 0.769 (1.081) Remain 22:12:46 loss: 0.2053 Lr: 0.00250 [2024-02-18 19:56:57,867 INFO misc.py line 119 87073] Train: [53/100][742/1557] Data 0.005 (0.131) Batch 1.213 (1.081) Remain 22:12:58 loss: 0.1918 Lr: 0.00250 [2024-02-18 19:56:58,730 INFO misc.py line 119 87073] Train: [53/100][743/1557] Data 0.006 (0.131) Batch 0.865 (1.081) Remain 22:12:35 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Batch 0.915 (1.083) Remain 22:11:02 loss: 0.2843 Lr: 0.00249 [2024-02-18 20:00:57,328 INFO misc.py line 119 87073] Train: [53/100][962/1557] Data 0.005 (0.133) Batch 1.182 (1.083) Remain 22:11:08 loss: 0.3993 Lr: 0.00249 [2024-02-18 20:00:58,233 INFO misc.py line 119 87073] Train: [53/100][963/1557] Data 0.006 (0.133) Batch 0.907 (1.082) Remain 22:10:54 loss: 0.4084 Lr: 0.00249 [2024-02-18 20:00:59,012 INFO misc.py line 119 87073] Train: [53/100][964/1557] Data 0.004 (0.132) Batch 0.778 (1.082) Remain 22:10:29 loss: 0.4014 Lr: 0.00249 [2024-02-18 20:00:59,790 INFO misc.py line 119 87073] Train: [53/100][965/1557] Data 0.005 (0.132) Batch 0.771 (1.082) Remain 22:10:04 loss: 0.4112 Lr: 0.00249 [2024-02-18 20:01:01,085 INFO misc.py line 119 87073] Train: [53/100][966/1557] Data 0.012 (0.132) Batch 1.298 (1.082) Remain 22:10:20 loss: 0.1663 Lr: 0.00249 [2024-02-18 20:01:02,143 INFO misc.py line 119 87073] Train: [53/100][967/1557] Data 0.009 (0.132) Batch 1.049 (1.082) Remain 22:10:16 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22:01:07 loss: 0.2969 Lr: 0.00248 [2024-02-18 20:04:46,271 INFO misc.py line 119 87073] Train: [53/100][1179/1557] Data 0.005 (0.127) Batch 0.981 (1.078) Remain 22:00:59 loss: 0.2403 Lr: 0.00248 [2024-02-18 20:04:47,391 INFO misc.py line 119 87073] Train: [53/100][1180/1557] Data 0.003 (0.127) Batch 1.119 (1.078) Remain 22:01:01 loss: 0.2770 Lr: 0.00248 [2024-02-18 20:04:48,177 INFO misc.py line 119 87073] Train: [53/100][1181/1557] Data 0.004 (0.127) Batch 0.785 (1.077) Remain 22:00:42 loss: 0.1801 Lr: 0.00248 [2024-02-18 20:04:48,923 INFO misc.py line 119 87073] Train: [53/100][1182/1557] Data 0.006 (0.127) Batch 0.743 (1.077) Remain 22:00:20 loss: 0.2229 Lr: 0.00248 [2024-02-18 20:04:57,034 INFO misc.py line 119 87073] Train: [53/100][1183/1557] Data 6.770 (0.133) Batch 8.111 (1.083) Remain 22:07:37 loss: 0.1381 Lr: 0.00248 [2024-02-18 20:04:58,014 INFO misc.py line 119 87073] Train: [53/100][1184/1557] Data 0.009 (0.133) Batch 0.984 (1.083) Remain 22:07:30 loss: 0.3541 Lr: 0.00248 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22:03:05 loss: 0.2565 Lr: 0.00248 [2024-02-18 20:05:22,005 INFO misc.py line 119 87073] Train: [53/100][1210/1557] Data 0.015 (0.130) Batch 0.820 (1.079) Remain 22:02:48 loss: 0.5686 Lr: 0.00248 [2024-02-18 20:05:23,246 INFO misc.py line 119 87073] Train: [53/100][1211/1557] Data 0.006 (0.130) Batch 1.241 (1.080) Remain 22:02:57 loss: 0.1111 Lr: 0.00248 [2024-02-18 20:05:24,361 INFO misc.py line 119 87073] Train: [53/100][1212/1557] Data 0.006 (0.130) Batch 1.108 (1.080) Remain 22:02:57 loss: 0.3497 Lr: 0.00248 [2024-02-18 20:05:25,251 INFO misc.py line 119 87073] Train: [53/100][1213/1557] Data 0.013 (0.129) Batch 0.896 (1.079) Remain 22:02:45 loss: 0.2934 Lr: 0.00248 [2024-02-18 20:05:26,139 INFO misc.py line 119 87073] Train: [53/100][1214/1557] Data 0.006 (0.129) Batch 0.889 (1.079) Remain 22:02:32 loss: 0.2747 Lr: 0.00248 [2024-02-18 20:05:27,097 INFO misc.py line 119 87073] Train: [53/100][1215/1557] Data 0.004 (0.129) Batch 0.952 (1.079) Remain 22:02:24 loss: 0.4709 Lr: 0.00248 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[53/100][1538/1557] Data 0.011 (0.132) Batch 0.798 (1.082) Remain 21:59:48 loss: 0.3248 Lr: 0.00246 [2024-02-18 20:11:20,495 INFO misc.py line 119 87073] Train: [53/100][1539/1557] Data 0.006 (0.132) Batch 0.770 (1.082) Remain 21:59:32 loss: 0.3838 Lr: 0.00246 [2024-02-18 20:11:21,748 INFO misc.py line 119 87073] Train: [53/100][1540/1557] Data 0.005 (0.132) Batch 1.247 (1.082) Remain 21:59:39 loss: 0.1442 Lr: 0.00246 [2024-02-18 20:11:22,657 INFO misc.py line 119 87073] Train: [53/100][1541/1557] Data 0.012 (0.132) Batch 0.915 (1.082) Remain 21:59:30 loss: 0.5693 Lr: 0.00246 [2024-02-18 20:11:23,642 INFO misc.py line 119 87073] Train: [53/100][1542/1557] Data 0.006 (0.132) Batch 0.987 (1.082) Remain 21:59:24 loss: 0.5189 Lr: 0.00246 [2024-02-18 20:11:24,641 INFO misc.py line 119 87073] Train: [53/100][1543/1557] Data 0.004 (0.132) Batch 0.999 (1.082) Remain 21:59:19 loss: 0.4163 Lr: 0.00246 [2024-02-18 20:11:25,575 INFO misc.py line 119 87073] Train: [53/100][1544/1557] Data 0.004 (0.132) Batch 0.934 (1.081) Remain 21:59:11 loss: 0.1948 Lr: 0.00246 [2024-02-18 20:11:26,249 INFO misc.py line 119 87073] Train: [53/100][1545/1557] Data 0.004 (0.132) Batch 0.671 (1.081) Remain 21:58:51 loss: 0.1815 Lr: 0.00246 [2024-02-18 20:11:26,978 INFO misc.py line 119 87073] Train: [53/100][1546/1557] Data 0.007 (0.131) Batch 0.732 (1.081) Remain 21:58:33 loss: 0.1871 Lr: 0.00246 [2024-02-18 20:11:28,228 INFO misc.py line 119 87073] Train: [53/100][1547/1557] Data 0.004 (0.131) Batch 1.251 (1.081) Remain 21:58:40 loss: 0.1688 Lr: 0.00246 [2024-02-18 20:11:29,167 INFO misc.py line 119 87073] Train: [53/100][1548/1557] Data 0.004 (0.131) Batch 0.939 (1.081) Remain 21:58:32 loss: 0.5112 Lr: 0.00246 [2024-02-18 20:11:30,054 INFO misc.py line 119 87073] Train: [53/100][1549/1557] Data 0.004 (0.131) Batch 0.875 (1.081) Remain 21:58:21 loss: 0.3864 Lr: 0.00246 [2024-02-18 20:11:31,171 INFO misc.py line 119 87073] Train: [53/100][1550/1557] Data 0.017 (0.131) Batch 1.116 (1.081) Remain 21:58:22 loss: 0.5155 Lr: 0.00246 [2024-02-18 20:11:31,995 INFO misc.py line 119 87073] Train: [53/100][1551/1557] Data 0.017 (0.131) Batch 0.838 (1.081) Remain 21:58:09 loss: 0.4939 Lr: 0.00246 [2024-02-18 20:11:32,796 INFO misc.py line 119 87073] Train: [53/100][1552/1557] Data 0.004 (0.131) Batch 0.801 (1.081) Remain 21:57:55 loss: 0.3032 Lr: 0.00246 [2024-02-18 20:11:33,523 INFO misc.py line 119 87073] Train: [53/100][1553/1557] Data 0.004 (0.131) Batch 0.716 (1.080) Remain 21:57:37 loss: 0.2400 Lr: 0.00246 [2024-02-18 20:11:34,697 INFO misc.py line 119 87073] Train: [53/100][1554/1557] Data 0.015 (0.131) Batch 1.179 (1.080) Remain 21:57:40 loss: 0.0930 Lr: 0.00246 [2024-02-18 20:11:35,767 INFO misc.py line 119 87073] Train: [53/100][1555/1557] Data 0.009 (0.131) Batch 1.063 (1.080) Remain 21:57:38 loss: 0.3785 Lr: 0.00246 [2024-02-18 20:11:36,673 INFO misc.py line 119 87073] Train: [53/100][1556/1557] Data 0.017 (0.131) Batch 0.919 (1.080) Remain 21:57:30 loss: 0.3380 Lr: 0.00246 [2024-02-18 20:11:37,660 INFO misc.py line 119 87073] Train: [53/100][1557/1557] Data 0.004 (0.131) Batch 0.987 (1.080) Remain 21:57:24 loss: 0.4539 Lr: 0.00246 [2024-02-18 20:11:37,661 INFO misc.py line 136 87073] Train result: loss: 0.3397 [2024-02-18 20:11:37,661 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 20:12:05,849 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6697 [2024-02-18 20:12:06,624 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5602 [2024-02-18 20:12:08,750 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4051 [2024-02-18 20:12:10,956 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3140 [2024-02-18 20:12:15,900 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2243 [2024-02-18 20:12:16,604 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2239 [2024-02-18 20:12:17,867 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3075 [2024-02-18 20:12:20,824 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3767 [2024-02-18 20:12:22,637 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2290 [2024-02-18 20:12:24,129 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7117/0.7870/0.9072. [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9381/0.9587 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9897 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8650/0.9610 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0052/0.1806 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3677/0.4213 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6677/0.7199 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7407/0.8699 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8350/0.9064 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9197/0.9616 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8398/0.8669 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7675/0.8589 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7380/0.8383 [2024-02-18 20:12:24,129 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5852/0.6973 [2024-02-18 20:12:24,130 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 20:12:24,131 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 20:12:24,131 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 20:12:31,193 INFO misc.py line 119 87073] Train: [54/100][1/1557] Data 1.624 (1.624) Batch 2.476 (2.476) Remain 50:19:17 loss: 0.5647 Lr: 0.00246 [2024-02-18 20:12:32,155 INFO misc.py line 119 87073] Train: [54/100][2/1557] Data 0.005 (0.005) Batch 0.958 (0.958) Remain 19:27:49 loss: 0.7004 Lr: 0.00246 [2024-02-18 20:12:33,158 INFO misc.py line 119 87073] Train: [54/100][3/1557] Data 0.012 (0.012) Batch 1.000 (1.000) Remain 20:19:56 loss: 0.1682 Lr: 0.00246 [2024-02-18 20:12:34,125 INFO misc.py line 119 87073] Train: [54/100][4/1557] Data 0.011 (0.011) Batch 0.973 (0.973) Remain 19:46:27 loss: 0.2442 Lr: 0.00246 [2024-02-18 20:12:34,923 INFO misc.py line 119 87073] Train: [54/100][5/1557] Data 0.006 (0.008) Batch 0.799 (0.886) Remain 18:00:41 loss: 0.2505 Lr: 0.00246 [2024-02-18 20:12:35,676 INFO misc.py line 119 87073] Train: [54/100][6/1557] Data 0.004 (0.007) Batch 0.752 (0.841) Remain 17:06:13 loss: 0.2417 Lr: 0.00246 [2024-02-18 20:12:41,660 INFO misc.py line 119 87073] Train: [54/100][7/1557] Data 4.716 (1.184) Batch 5.983 (2.127) Remain 43:13:51 loss: 0.2185 Lr: 0.00246 [2024-02-18 20:12:42,594 INFO misc.py line 119 87073] Train: [54/100][8/1557] Data 0.006 (0.949) Batch 0.936 (1.889) Remain 38:23:15 loss: 0.1360 Lr: 0.00246 [2024-02-18 20:12:43,537 INFO misc.py line 119 87073] Train: [54/100][9/1557] Data 0.003 (0.791) Batch 0.941 (1.731) Remain 35:10:42 loss: 0.5200 Lr: 0.00246 [2024-02-18 20:12:44,461 INFO misc.py line 119 87073] Train: [54/100][10/1557] Data 0.006 (0.679) Batch 0.923 (1.615) Remain 32:49:54 loss: 0.3672 Lr: 0.00246 [2024-02-18 20:12:45,380 INFO misc.py line 119 87073] Train: [54/100][11/1557] Data 0.007 (0.595) Batch 0.921 (1.529) Remain 31:04:04 loss: 0.3295 Lr: 0.00246 [2024-02-18 20:12:46,091 INFO misc.py line 119 87073] Train: [54/100][12/1557] Data 0.005 (0.529) Batch 0.711 (1.438) Remain 29:13:19 loss: 0.2814 Lr: 0.00246 [2024-02-18 20:12:46,917 INFO misc.py line 119 87073] Train: [54/100][13/1557] Data 0.004 (0.477) Batch 0.804 (1.374) Remain 27:56:03 loss: 0.3004 Lr: 0.00246 [2024-02-18 20:12:47,958 INFO misc.py line 119 87073] Train: [54/100][14/1557] Data 0.025 (0.436) Batch 1.050 (1.345) Remain 27:20:06 loss: 0.1660 Lr: 0.00246 [2024-02-18 20:12:48,913 INFO misc.py line 119 87073] Train: [54/100][15/1557] Data 0.016 (0.401) Batch 0.967 (1.314) Remain 26:41:43 loss: 0.4786 Lr: 0.00246 [2024-02-18 20:12:49,870 INFO misc.py line 119 87073] Train: [54/100][16/1557] Data 0.004 (0.370) Batch 0.957 (1.286) Remain 26:08:16 loss: 0.6743 Lr: 0.00246 [2024-02-18 20:12:50,871 INFO misc.py line 119 87073] Train: [54/100][17/1557] Data 0.004 (0.344) Batch 1.000 (1.266) Remain 25:43:20 loss: 0.2961 Lr: 0.00246 [2024-02-18 20:12:51,769 INFO misc.py line 119 87073] Train: [54/100][18/1557] Data 0.005 (0.321) Batch 0.899 (1.241) Remain 25:13:30 loss: 0.3639 Lr: 0.00246 [2024-02-18 20:12:52,520 INFO misc.py line 119 87073] Train: [54/100][19/1557] Data 0.004 (0.302) Batch 0.742 (1.210) Remain 24:35:27 loss: 0.5161 Lr: 0.00246 [2024-02-18 20:12:53,246 INFO misc.py line 119 87073] Train: [54/100][20/1557] Data 0.013 (0.285) Batch 0.734 (1.182) Remain 24:01:19 loss: 0.1746 Lr: 0.00246 [2024-02-18 20:12:54,497 INFO misc.py line 119 87073] Train: [54/100][21/1557] Data 0.004 (0.269) Batch 1.252 (1.186) Remain 24:06:00 loss: 0.3066 Lr: 0.00246 [2024-02-18 20:12:55,338 INFO misc.py line 119 87073] Train: [54/100][22/1557] Data 0.004 (0.255) Batch 0.840 (1.168) Remain 23:43:47 loss: 0.2517 Lr: 0.00246 [2024-02-18 20:12:56,335 INFO misc.py line 119 87073] Train: [54/100][23/1557] Data 0.005 (0.243) Batch 0.994 (1.159) Remain 23:33:10 loss: 0.5511 Lr: 0.00246 [2024-02-18 20:12:57,251 INFO misc.py line 119 87073] Train: [54/100][24/1557] Data 0.008 (0.231) Batch 0.920 (1.148) Remain 23:19:16 loss: 0.2604 Lr: 0.00246 [2024-02-18 20:12:58,175 INFO misc.py line 119 87073] Train: [54/100][25/1557] Data 0.003 (0.221) Batch 0.924 (1.137) Remain 23:06:52 loss: 0.3815 Lr: 0.00246 [2024-02-18 20:12:58,879 INFO misc.py line 119 87073] Train: [54/100][26/1557] Data 0.004 (0.212) Batch 0.697 (1.118) Remain 22:43:30 loss: 0.2653 Lr: 0.00246 [2024-02-18 20:12:59,617 INFO misc.py line 119 87073] Train: [54/100][27/1557] Data 0.010 (0.203) Batch 0.743 (1.103) Remain 22:24:26 loss: 0.1611 Lr: 0.00246 [2024-02-18 20:13:00,917 INFO misc.py line 119 87073] Train: [54/100][28/1557] Data 0.004 (0.195) Batch 1.290 (1.110) Remain 22:33:32 loss: 0.1249 Lr: 0.00246 [2024-02-18 20:13:01,800 INFO misc.py line 119 87073] Train: [54/100][29/1557] Data 0.015 (0.188) Batch 0.895 (1.102) Remain 22:23:24 loss: 0.1452 Lr: 0.00246 [2024-02-18 20:13:02,741 INFO misc.py line 119 87073] Train: [54/100][30/1557] Data 0.004 (0.181) Batch 0.941 (1.096) Remain 22:16:07 loss: 0.2737 Lr: 0.00246 [2024-02-18 20:13:03,916 INFO misc.py line 119 87073] Train: [54/100][31/1557] Data 0.004 (0.175) Batch 1.175 (1.099) Remain 22:19:31 loss: 0.2148 Lr: 0.00246 [2024-02-18 20:13:04,873 INFO misc.py line 119 87073] Train: [54/100][32/1557] Data 0.004 (0.169) Batch 0.957 (1.094) Remain 22:13:31 loss: 0.4047 Lr: 0.00246 [2024-02-18 20:13:05,648 INFO misc.py line 119 87073] Train: [54/100][33/1557] Data 0.005 (0.164) Batch 0.775 (1.083) Remain 22:00:33 loss: 0.3386 Lr: 0.00246 [2024-02-18 20:13:06,432 INFO misc.py line 119 87073] Train: [54/100][34/1557] Data 0.005 (0.159) Batch 0.783 (1.074) Remain 21:48:43 loss: 0.3259 Lr: 0.00246 [2024-02-18 20:13:07,703 INFO misc.py line 119 87073] Train: [54/100][35/1557] Data 0.005 (0.154) Batch 1.263 (1.079) Remain 21:55:55 loss: 0.1431 Lr: 0.00246 [2024-02-18 20:13:08,713 INFO misc.py line 119 87073] Train: [54/100][36/1557] Data 0.014 (0.150) Batch 1.008 (1.077) Remain 21:53:16 loss: 0.4239 Lr: 0.00246 [2024-02-18 20:13:09,555 INFO misc.py line 119 87073] Train: [54/100][37/1557] Data 0.016 (0.146) Batch 0.854 (1.071) Remain 21:45:14 loss: 0.2306 Lr: 0.00246 [2024-02-18 20:13:10,505 INFO misc.py line 119 87073] Train: [54/100][38/1557] Data 0.004 (0.142) Batch 0.950 (1.067) Remain 21:41:01 loss: 0.5048 Lr: 0.00246 [2024-02-18 20:13:11,462 INFO misc.py line 119 87073] Train: [54/100][39/1557] Data 0.003 (0.138) Batch 0.957 (1.064) Remain 21:37:15 loss: 0.6633 Lr: 0.00246 [2024-02-18 20:13:12,286 INFO misc.py line 119 87073] Train: [54/100][40/1557] Data 0.004 (0.134) Batch 0.814 (1.057) Remain 21:28:59 loss: 0.3021 Lr: 0.00246 [2024-02-18 20:13:13,064 INFO misc.py line 119 87073] Train: [54/100][41/1557] Data 0.015 (0.131) Batch 0.789 (1.050) Remain 21:20:20 loss: 0.3735 Lr: 0.00246 [2024-02-18 20:13:14,169 INFO misc.py line 119 87073] Train: [54/100][42/1557] Data 0.004 (0.128) Batch 1.103 (1.052) Remain 21:21:58 loss: 0.1910 Lr: 0.00246 [2024-02-18 20:13:15,131 INFO misc.py line 119 87073] Train: [54/100][43/1557] Data 0.006 (0.125) Batch 0.964 (1.050) Remain 21:19:17 loss: 0.2190 Lr: 0.00246 [2024-02-18 20:13:16,035 INFO misc.py line 119 87073] Train: [54/100][44/1557] Data 0.003 (0.122) Batch 0.903 (1.046) Remain 21:14:55 loss: 0.3865 Lr: 0.00246 [2024-02-18 20:13:17,017 INFO misc.py line 119 87073] Train: [54/100][45/1557] Data 0.005 (0.119) Batch 0.983 (1.044) Remain 21:13:03 loss: 0.1875 Lr: 0.00246 [2024-02-18 20:13:17,956 INFO misc.py line 119 87073] Train: [54/100][46/1557] Data 0.004 (0.116) Batch 0.939 (1.042) Remain 21:10:03 loss: 0.3046 Lr: 0.00246 [2024-02-18 20:13:18,738 INFO misc.py line 119 87073] Train: [54/100][47/1557] Data 0.003 (0.114) Batch 0.781 (1.036) Remain 21:02:48 loss: 0.2370 Lr: 0.00246 [2024-02-18 20:13:19,509 INFO misc.py line 119 87073] Train: [54/100][48/1557] Data 0.005 (0.111) Batch 0.771 (1.030) Remain 20:55:36 loss: 0.2850 Lr: 0.00246 [2024-02-18 20:13:20,794 INFO misc.py line 119 87073] Train: [54/100][49/1557] Data 0.005 (0.109) Batch 1.285 (1.036) Remain 21:02:19 loss: 0.1791 Lr: 0.00246 [2024-02-18 20:13:21,733 INFO misc.py line 119 87073] Train: [54/100][50/1557] Data 0.006 (0.107) Batch 0.941 (1.034) Remain 20:59:50 loss: 0.6173 Lr: 0.00246 [2024-02-18 20:13:22,733 INFO misc.py line 119 87073] Train: [54/100][51/1557] Data 0.004 (0.105) Batch 1.000 (1.033) Remain 20:58:59 loss: 0.3497 Lr: 0.00246 [2024-02-18 20:13:23,655 INFO misc.py line 119 87073] Train: [54/100][52/1557] Data 0.004 (0.103) Batch 0.922 (1.031) Remain 20:56:11 loss: 0.5381 Lr: 0.00246 [2024-02-18 20:13:24,562 INFO misc.py line 119 87073] Train: [54/100][53/1557] Data 0.005 (0.101) Batch 0.900 (1.028) Remain 20:53:00 loss: 0.6251 Lr: 0.00246 [2024-02-18 20:13:25,353 INFO misc.py line 119 87073] Train: [54/100][54/1557] Data 0.011 (0.099) Batch 0.798 (1.024) Remain 20:47:29 loss: 0.4335 Lr: 0.00246 [2024-02-18 20:13:26,159 INFO misc.py line 119 87073] Train: [54/100][55/1557] Data 0.003 (0.097) Batch 0.802 (1.019) Remain 20:42:16 loss: 0.3032 Lr: 0.00246 [2024-02-18 20:13:27,502 INFO misc.py line 119 87073] Train: [54/100][56/1557] Data 0.008 (0.095) Batch 1.345 (1.025) Remain 20:49:44 loss: 0.2673 Lr: 0.00246 [2024-02-18 20:13:28,414 INFO misc.py line 119 87073] Train: [54/100][57/1557] Data 0.005 (0.094) Batch 0.914 (1.023) Remain 20:47:12 loss: 0.2499 Lr: 0.00246 [2024-02-18 20:13:29,395 INFO misc.py line 119 87073] Train: [54/100][58/1557] Data 0.004 (0.092) Batch 0.981 (1.023) Remain 20:46:15 loss: 0.6782 Lr: 0.00246 [2024-02-18 20:13:30,320 INFO misc.py line 119 87073] Train: [54/100][59/1557] Data 0.004 (0.090) Batch 0.925 (1.021) Remain 20:44:07 loss: 0.4529 Lr: 0.00246 [2024-02-18 20:13:31,253 INFO misc.py line 119 87073] Train: [54/100][60/1557] Data 0.004 (0.089) Batch 0.930 (1.019) Remain 20:42:09 loss: 0.3720 Lr: 0.00246 [2024-02-18 20:13:32,072 INFO misc.py line 119 87073] Train: [54/100][61/1557] Data 0.007 (0.088) Batch 0.821 (1.016) Remain 20:37:58 loss: 0.5283 Lr: 0.00246 [2024-02-18 20:13:32,873 INFO misc.py line 119 87073] Train: [54/100][62/1557] Data 0.005 (0.086) Batch 0.800 (1.012) Remain 20:33:30 loss: 0.1841 Lr: 0.00246 [2024-02-18 20:13:45,427 INFO misc.py line 119 87073] Train: [54/100][63/1557] Data 11.276 (0.273) Batch 12.556 (1.205) Remain 24:27:56 loss: 0.1307 Lr: 0.00246 [2024-02-18 20:13:46,383 INFO misc.py line 119 87073] Train: [54/100][64/1557] Data 0.003 (0.268) Batch 0.954 (1.201) Remain 24:22:55 loss: 0.5754 Lr: 0.00246 [2024-02-18 20:13:47,317 INFO misc.py line 119 87073] Train: 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Batch 0.951 (1.168) Remain 23:40:05 loss: 0.4349 Lr: 0.00245 [2024-02-18 20:17:02,838 INFO misc.py line 119 87073] Train: [54/100][234/1557] Data 0.007 (0.220) Batch 1.018 (1.167) Remain 23:39:17 loss: 0.3256 Lr: 0.00245 [2024-02-18 20:17:03,781 INFO misc.py line 119 87073] Train: [54/100][235/1557] Data 0.018 (0.219) Batch 0.954 (1.167) Remain 23:38:09 loss: 0.4582 Lr: 0.00245 [2024-02-18 20:17:04,579 INFO misc.py line 119 87073] Train: [54/100][236/1557] Data 0.006 (0.218) Batch 0.798 (1.165) Remain 23:36:12 loss: 0.4602 Lr: 0.00245 [2024-02-18 20:17:05,356 INFO misc.py line 119 87073] Train: [54/100][237/1557] Data 0.006 (0.217) Batch 0.776 (1.163) Remain 23:34:10 loss: 0.1849 Lr: 0.00245 [2024-02-18 20:17:06,491 INFO misc.py line 119 87073] Train: [54/100][238/1557] Data 0.007 (0.216) Batch 1.134 (1.163) Remain 23:33:59 loss: 0.2054 Lr: 0.00245 [2024-02-18 20:17:07,394 INFO misc.py line 119 87073] Train: [54/100][239/1557] Data 0.008 (0.215) Batch 0.906 (1.162) Remain 23:32:39 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Batch 1.107 (1.168) Remain 23:38:48 loss: 0.1576 Lr: 0.00245 [2024-02-18 20:18:08,018 INFO misc.py line 119 87073] Train: [54/100][290/1557] Data 0.005 (0.219) Batch 0.845 (1.167) Remain 23:37:25 loss: 0.4678 Lr: 0.00245 [2024-02-18 20:18:08,895 INFO misc.py line 119 87073] Train: [54/100][291/1557] Data 0.007 (0.218) Batch 0.877 (1.166) Remain 23:36:10 loss: 0.1995 Lr: 0.00245 [2024-02-18 20:18:09,596 INFO misc.py line 119 87073] Train: [54/100][292/1557] Data 0.006 (0.218) Batch 0.702 (1.164) Remain 23:34:12 loss: 0.6155 Lr: 0.00245 [2024-02-18 20:18:10,386 INFO misc.py line 119 87073] Train: [54/100][293/1557] Data 0.004 (0.217) Batch 0.784 (1.163) Remain 23:32:35 loss: 0.2225 Lr: 0.00245 [2024-02-18 20:18:11,511 INFO misc.py line 119 87073] Train: [54/100][294/1557] Data 0.010 (0.216) Batch 1.126 (1.163) Remain 23:32:25 loss: 0.1844 Lr: 0.00245 [2024-02-18 20:18:12,542 INFO misc.py line 119 87073] Train: [54/100][295/1557] Data 0.010 (0.215) Batch 1.029 (1.162) Remain 23:31:50 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20:18:18,964 INFO misc.py line 119 87073] Train: [54/100][302/1557] Data 0.008 (0.211) Batch 1.075 (1.157) Remain 23:24:45 loss: 0.2588 Lr: 0.00244 [2024-02-18 20:18:20,183 INFO misc.py line 119 87073] Train: [54/100][303/1557] Data 0.010 (0.210) Batch 1.208 (1.157) Remain 23:24:57 loss: 0.3602 Lr: 0.00244 [2024-02-18 20:18:21,237 INFO misc.py line 119 87073] Train: [54/100][304/1557] Data 0.019 (0.209) Batch 1.065 (1.156) Remain 23:24:33 loss: 0.3498 Lr: 0.00244 [2024-02-18 20:18:22,371 INFO misc.py line 119 87073] Train: [54/100][305/1557] Data 0.009 (0.209) Batch 1.137 (1.156) Remain 23:24:27 loss: 0.3675 Lr: 0.00244 [2024-02-18 20:18:23,158 INFO misc.py line 119 87073] Train: [54/100][306/1557] Data 0.007 (0.208) Batch 0.789 (1.155) Remain 23:22:58 loss: 0.3428 Lr: 0.00244 [2024-02-18 20:18:23,929 INFO misc.py line 119 87073] Train: [54/100][307/1557] Data 0.004 (0.207) Batch 0.769 (1.154) Remain 23:21:24 loss: 0.1801 Lr: 0.00244 [2024-02-18 20:18:25,295 INFO misc.py line 119 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[2024-02-18 20:18:43,428 INFO misc.py line 119 87073] Train: [54/100][327/1557] Data 0.003 (0.195) Batch 0.690 (1.143) Remain 23:07:37 loss: 0.1604 Lr: 0.00244 [2024-02-18 20:18:44,212 INFO misc.py line 119 87073] Train: [54/100][328/1557] Data 0.004 (0.194) Batch 0.781 (1.142) Remain 23:06:15 loss: 0.4727 Lr: 0.00244 [2024-02-18 20:18:45,453 INFO misc.py line 119 87073] Train: [54/100][329/1557] Data 0.008 (0.194) Batch 1.237 (1.142) Remain 23:06:35 loss: 0.2384 Lr: 0.00244 [2024-02-18 20:18:46,472 INFO misc.py line 119 87073] Train: [54/100][330/1557] Data 0.012 (0.193) Batch 1.025 (1.142) Remain 23:06:07 loss: 1.0919 Lr: 0.00244 [2024-02-18 20:18:47,378 INFO misc.py line 119 87073] Train: [54/100][331/1557] Data 0.006 (0.193) Batch 0.907 (1.141) Remain 23:05:14 loss: 0.5803 Lr: 0.00244 [2024-02-18 20:18:48,455 INFO misc.py line 119 87073] Train: [54/100][332/1557] Data 0.005 (0.192) Batch 1.078 (1.141) Remain 23:04:59 loss: 0.3836 Lr: 0.00244 [2024-02-18 20:18:49,374 INFO misc.py line 119 87073] Train: [54/100][333/1557] Data 0.004 (0.192) Batch 0.919 (1.140) Remain 23:04:09 loss: 0.1704 Lr: 0.00244 [2024-02-18 20:18:50,120 INFO misc.py line 119 87073] Train: [54/100][334/1557] Data 0.004 (0.191) Batch 0.738 (1.139) Remain 23:02:40 loss: 0.2266 Lr: 0.00244 [2024-02-18 20:18:50,812 INFO misc.py line 119 87073] Train: [54/100][335/1557] Data 0.011 (0.190) Batch 0.697 (1.138) Remain 23:01:02 loss: 0.3543 Lr: 0.00244 [2024-02-18 20:18:52,067 INFO misc.py line 119 87073] Train: [54/100][336/1557] Data 0.005 (0.190) Batch 1.255 (1.138) Remain 23:01:26 loss: 0.1407 Lr: 0.00244 [2024-02-18 20:18:52,953 INFO misc.py line 119 87073] Train: [54/100][337/1557] Data 0.005 (0.189) Batch 0.887 (1.137) Remain 23:00:30 loss: 0.3494 Lr: 0.00244 [2024-02-18 20:18:53,835 INFO misc.py line 119 87073] Train: [54/100][338/1557] Data 0.005 (0.189) Batch 0.882 (1.136) Remain 22:59:34 loss: 0.3801 Lr: 0.00244 [2024-02-18 20:18:54,925 INFO misc.py line 119 87073] Train: 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Batch 1.016 (1.166) Remain 23:35:28 loss: 0.6685 Lr: 0.00244 [2024-02-18 20:19:12,857 INFO misc.py line 119 87073] Train: [54/100][346/1557] Data 0.004 (0.217) Batch 0.913 (1.165) Remain 23:34:33 loss: 0.3966 Lr: 0.00244 [2024-02-18 20:19:13,839 INFO misc.py line 119 87073] Train: [54/100][347/1557] Data 0.005 (0.216) Batch 0.983 (1.165) Remain 23:33:54 loss: 0.4729 Lr: 0.00244 [2024-02-18 20:19:14,524 INFO misc.py line 119 87073] Train: [54/100][348/1557] Data 0.003 (0.216) Batch 0.684 (1.163) Remain 23:32:11 loss: 0.5327 Lr: 0.00244 [2024-02-18 20:19:15,327 INFO misc.py line 119 87073] Train: [54/100][349/1557] Data 0.004 (0.215) Batch 0.793 (1.162) Remain 23:30:52 loss: 0.1552 Lr: 0.00244 [2024-02-18 20:19:16,399 INFO misc.py line 119 87073] Train: [54/100][350/1557] Data 0.014 (0.214) Batch 1.075 (1.162) Remain 23:30:32 loss: 0.3209 Lr: 0.00244 [2024-02-18 20:19:17,462 INFO misc.py line 119 87073] Train: [54/100][351/1557] Data 0.011 (0.214) Batch 1.068 (1.162) Remain 23:30:12 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line 119 87073] Train: [54/100][389/1557] Data 0.017 (0.196) Batch 1.074 (1.146) Remain 23:09:42 loss: 0.1070 Lr: 0.00244 [2024-02-18 20:19:56,144 INFO misc.py line 119 87073] Train: [54/100][390/1557] Data 0.008 (0.196) Batch 0.822 (1.145) Remain 23:08:40 loss: 0.3300 Lr: 0.00244 [2024-02-18 20:19:56,907 INFO misc.py line 119 87073] Train: [54/100][391/1557] Data 0.003 (0.195) Batch 0.763 (1.144) Remain 23:07:27 loss: 0.5318 Lr: 0.00244 [2024-02-18 20:19:58,222 INFO misc.py line 119 87073] Train: [54/100][392/1557] Data 0.004 (0.195) Batch 1.295 (1.144) Remain 23:07:54 loss: 0.2389 Lr: 0.00244 [2024-02-18 20:19:59,348 INFO misc.py line 119 87073] Train: [54/100][393/1557] Data 0.024 (0.195) Batch 1.135 (1.144) Remain 23:07:51 loss: 0.2440 Lr: 0.00244 [2024-02-18 20:20:00,289 INFO misc.py line 119 87073] Train: [54/100][394/1557] Data 0.015 (0.194) Batch 0.951 (1.144) Remain 23:07:14 loss: 0.4709 Lr: 0.00244 [2024-02-18 20:20:01,255 INFO misc.py line 119 87073] Train: 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Batch 0.906 (1.167) Remain 23:35:54 loss: 0.4511 Lr: 0.00244 [2024-02-18 20:20:18,571 INFO misc.py line 119 87073] Train: [54/100][402/1557] Data 0.004 (0.217) Batch 0.827 (1.166) Remain 23:34:51 loss: 0.2271 Lr: 0.00244 [2024-02-18 20:20:19,504 INFO misc.py line 119 87073] Train: [54/100][403/1557] Data 0.005 (0.217) Batch 0.934 (1.166) Remain 23:34:08 loss: 0.4947 Lr: 0.00244 [2024-02-18 20:20:20,289 INFO misc.py line 119 87073] Train: [54/100][404/1557] Data 0.004 (0.216) Batch 0.785 (1.165) Remain 23:32:57 loss: 0.5126 Lr: 0.00244 [2024-02-18 20:20:21,059 INFO misc.py line 119 87073] Train: [54/100][405/1557] Data 0.004 (0.216) Batch 0.768 (1.164) Remain 23:31:44 loss: 0.2788 Lr: 0.00244 [2024-02-18 20:20:22,086 INFO misc.py line 119 87073] Train: [54/100][406/1557] Data 0.005 (0.215) Batch 1.018 (1.164) Remain 23:31:17 loss: 0.1901 Lr: 0.00244 [2024-02-18 20:20:22,918 INFO misc.py line 119 87073] Train: [54/100][407/1557] Data 0.014 (0.215) Batch 0.842 (1.163) Remain 23:30:18 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Batch 1.133 (1.163) Remain 23:29:54 loss: 0.3955 Lr: 0.00244 [2024-02-18 20:21:22,099 INFO misc.py line 119 87073] Train: [54/100][458/1557] Data 0.004 (0.215) Batch 0.827 (1.163) Remain 23:28:59 loss: 0.1218 Lr: 0.00244 [2024-02-18 20:21:23,096 INFO misc.py line 119 87073] Train: [54/100][459/1557] Data 0.006 (0.214) Batch 0.997 (1.162) Remain 23:28:32 loss: 0.4141 Lr: 0.00244 [2024-02-18 20:21:23,874 INFO misc.py line 119 87073] Train: [54/100][460/1557] Data 0.004 (0.214) Batch 0.779 (1.161) Remain 23:27:29 loss: 0.3870 Lr: 0.00244 [2024-02-18 20:21:24,631 INFO misc.py line 119 87073] Train: [54/100][461/1557] Data 0.003 (0.214) Batch 0.755 (1.160) Remain 23:26:24 loss: 0.5109 Lr: 0.00244 [2024-02-18 20:21:25,737 INFO misc.py line 119 87073] Train: [54/100][462/1557] Data 0.007 (0.213) Batch 1.106 (1.160) Remain 23:26:14 loss: 0.2687 Lr: 0.00244 [2024-02-18 20:21:26,740 INFO misc.py line 119 87073] Train: [54/100][463/1557] Data 0.006 (0.213) Batch 0.995 (1.160) Remain 23:25:47 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Batch 0.863 (1.162) Remain 23:27:24 loss: 0.1403 Lr: 0.00243 [2024-02-18 20:22:26,826 INFO misc.py line 119 87073] Train: [54/100][514/1557] Data 0.003 (0.215) Batch 1.011 (1.162) Remain 23:27:01 loss: 0.2245 Lr: 0.00243 [2024-02-18 20:22:27,923 INFO misc.py line 119 87073] Train: [54/100][515/1557] Data 0.003 (0.215) Batch 1.096 (1.162) Remain 23:26:50 loss: 0.4510 Lr: 0.00243 [2024-02-18 20:22:28,680 INFO misc.py line 119 87073] Train: [54/100][516/1557] Data 0.004 (0.214) Batch 0.757 (1.161) Remain 23:25:52 loss: 0.2784 Lr: 0.00243 [2024-02-18 20:22:29,408 INFO misc.py line 119 87073] Train: [54/100][517/1557] Data 0.004 (0.214) Batch 0.717 (1.160) Remain 23:24:48 loss: 0.6833 Lr: 0.00243 [2024-02-18 20:22:30,526 INFO misc.py line 119 87073] Train: [54/100][518/1557] Data 0.015 (0.213) Batch 1.118 (1.160) Remain 23:24:41 loss: 0.1439 Lr: 0.00243 [2024-02-18 20:22:31,337 INFO misc.py line 119 87073] Train: [54/100][519/1557] Data 0.015 (0.213) Batch 0.820 (1.159) Remain 23:23:52 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Batch 0.952 (1.165) Remain 23:29:37 loss: 0.2698 Lr: 0.00243 [2024-02-18 20:23:33,471 INFO misc.py line 119 87073] Train: [54/100][570/1557] Data 0.006 (0.217) Batch 1.033 (1.165) Remain 23:29:19 loss: 0.3632 Lr: 0.00243 [2024-02-18 20:23:34,438 INFO misc.py line 119 87073] Train: [54/100][571/1557] Data 0.004 (0.217) Batch 0.967 (1.164) Remain 23:28:52 loss: 0.2340 Lr: 0.00243 [2024-02-18 20:23:35,299 INFO misc.py line 119 87073] Train: [54/100][572/1557] Data 0.004 (0.217) Batch 0.861 (1.164) Remain 23:28:12 loss: 0.1975 Lr: 0.00243 [2024-02-18 20:23:36,061 INFO misc.py line 119 87073] Train: [54/100][573/1557] Data 0.004 (0.216) Batch 0.754 (1.163) Remain 23:27:19 loss: 0.2415 Lr: 0.00243 [2024-02-18 20:23:37,162 INFO misc.py line 119 87073] Train: [54/100][574/1557] Data 0.013 (0.216) Batch 1.099 (1.163) Remain 23:27:10 loss: 0.1847 Lr: 0.00243 [2024-02-18 20:23:38,005 INFO misc.py line 119 87073] Train: [54/100][575/1557] Data 0.014 (0.216) Batch 0.852 (1.162) Remain 23:26:29 loss: 0.1976 Lr: 0.00243 [2024-02-18 20:23:38,869 INFO misc.py line 119 87073] Train: [54/100][576/1557] Data 0.005 (0.215) Batch 0.866 (1.162) Remain 23:25:50 loss: 0.2694 Lr: 0.00243 [2024-02-18 20:23:40,000 INFO misc.py line 119 87073] Train: [54/100][577/1557] Data 0.004 (0.215) Batch 1.121 (1.162) Remain 23:25:44 loss: 0.1463 Lr: 0.00243 [2024-02-18 20:23:40,912 INFO misc.py line 119 87073] Train: [54/100][578/1557] Data 0.013 (0.215) Batch 0.922 (1.161) Remain 23:25:13 loss: 0.2584 Lr: 0.00243 [2024-02-18 20:23:41,685 INFO misc.py line 119 87073] Train: [54/100][579/1557] Data 0.004 (0.214) Batch 0.772 (1.161) Remain 23:24:22 loss: 0.3228 Lr: 0.00243 [2024-02-18 20:23:42,478 INFO misc.py line 119 87073] Train: [54/100][580/1557] Data 0.005 (0.214) Batch 0.788 (1.160) Remain 23:23:34 loss: 0.3109 Lr: 0.00243 [2024-02-18 20:23:43,660 INFO misc.py line 119 87073] Train: [54/100][581/1557] Data 0.010 (0.213) Batch 1.179 (1.160) Remain 23:23:36 loss: 0.2239 Lr: 0.00243 [2024-02-18 20:23:44,590 INFO misc.py line 119 87073] Train: [54/100][582/1557] Data 0.012 (0.213) Batch 0.938 (1.160) Remain 23:23:07 loss: 0.5622 Lr: 0.00243 [2024-02-18 20:23:45,709 INFO misc.py line 119 87073] Train: [54/100][583/1557] Data 0.005 (0.213) Batch 1.120 (1.160) Remain 23:23:00 loss: 0.6272 Lr: 0.00243 [2024-02-18 20:23:46,508 INFO misc.py line 119 87073] Train: [54/100][584/1557] Data 0.004 (0.212) Batch 0.799 (1.159) Remain 23:22:14 loss: 0.3375 Lr: 0.00243 [2024-02-18 20:23:47,585 INFO misc.py line 119 87073] Train: [54/100][585/1557] Data 0.004 (0.212) Batch 1.067 (1.159) Remain 23:22:02 loss: 0.4449 Lr: 0.00243 [2024-02-18 20:23:48,391 INFO misc.py line 119 87073] Train: [54/100][586/1557] Data 0.014 (0.212) Batch 0.816 (1.158) Remain 23:21:18 loss: 0.3521 Lr: 0.00243 [2024-02-18 20:23:49,183 INFO misc.py line 119 87073] Train: [54/100][587/1557] Data 0.005 (0.211) Batch 0.792 (1.158) Remain 23:20:31 loss: 0.1879 Lr: 0.00243 [2024-02-18 20:23:50,509 INFO misc.py line 119 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23:17:03 loss: 0.3069 Lr: 0.00243 [2024-02-18 20:24:03,394 INFO misc.py line 119 87073] Train: [54/100][601/1557] Data 0.003 (0.207) Batch 0.749 (1.154) Remain 23:16:13 loss: 0.2664 Lr: 0.00243 [2024-02-18 20:24:04,551 INFO misc.py line 119 87073] Train: [54/100][602/1557] Data 0.006 (0.206) Batch 1.148 (1.154) Remain 23:16:11 loss: 0.0752 Lr: 0.00243 [2024-02-18 20:24:05,687 INFO misc.py line 119 87073] Train: [54/100][603/1557] Data 0.014 (0.206) Batch 1.136 (1.154) Remain 23:16:07 loss: 0.1467 Lr: 0.00243 [2024-02-18 20:24:06,704 INFO misc.py line 119 87073] Train: [54/100][604/1557] Data 0.014 (0.206) Batch 1.024 (1.154) Remain 23:15:50 loss: 0.3343 Lr: 0.00243 [2024-02-18 20:24:07,597 INFO misc.py line 119 87073] Train: [54/100][605/1557] Data 0.007 (0.205) Batch 0.894 (1.154) Remain 23:15:18 loss: 0.5634 Lr: 0.00243 [2024-02-18 20:24:08,440 INFO misc.py line 119 87073] Train: [54/100][606/1557] Data 0.006 (0.205) Batch 0.844 (1.153) Remain 23:14:40 loss: 0.3416 Lr: 0.00243 [2024-02-18 20:24:09,248 INFO misc.py line 119 87073] Train: [54/100][607/1557] Data 0.004 (0.205) Batch 0.804 (1.152) Remain 23:13:56 loss: 0.3476 Lr: 0.00243 [2024-02-18 20:24:10,014 INFO misc.py line 119 87073] Train: [54/100][608/1557] Data 0.009 (0.204) Batch 0.769 (1.152) Remain 23:13:09 loss: 0.2695 Lr: 0.00243 [2024-02-18 20:24:11,331 INFO misc.py line 119 87073] Train: [54/100][609/1557] Data 0.005 (0.204) Batch 1.316 (1.152) Remain 23:13:28 loss: 0.1133 Lr: 0.00243 [2024-02-18 20:24:12,300 INFO misc.py line 119 87073] Train: [54/100][610/1557] Data 0.007 (0.204) Batch 0.972 (1.152) Remain 23:13:05 loss: 0.3574 Lr: 0.00243 [2024-02-18 20:24:13,184 INFO misc.py line 119 87073] Train: [54/100][611/1557] Data 0.004 (0.203) Batch 0.883 (1.151) Remain 23:12:32 loss: 0.3709 Lr: 0.00243 [2024-02-18 20:24:14,318 INFO misc.py line 119 87073] Train: [54/100][612/1557] Data 0.005 (0.203) Batch 1.133 (1.151) Remain 23:12:29 loss: 0.1390 Lr: 0.00243 [2024-02-18 20:24:15,253 INFO misc.py line 119 87073] Train: [54/100][613/1557] Data 0.006 (0.203) Batch 0.936 (1.151) Remain 23:12:02 loss: 0.2730 Lr: 0.00243 [2024-02-18 20:24:16,003 INFO misc.py line 119 87073] Train: [54/100][614/1557] Data 0.005 (0.202) Batch 0.745 (1.150) Remain 23:11:12 loss: 0.1712 Lr: 0.00243 [2024-02-18 20:24:16,880 INFO misc.py line 119 87073] Train: [54/100][615/1557] Data 0.010 (0.202) Batch 0.883 (1.150) Remain 23:10:39 loss: 0.8571 Lr: 0.00243 [2024-02-18 20:24:18,136 INFO misc.py line 119 87073] Train: [54/100][616/1557] Data 0.005 (0.202) Batch 1.245 (1.150) Remain 23:10:50 loss: 0.2055 Lr: 0.00243 [2024-02-18 20:24:19,240 INFO misc.py line 119 87073] Train: [54/100][617/1557] Data 0.015 (0.201) Batch 1.106 (1.150) Remain 23:10:43 loss: 0.2635 Lr: 0.00243 [2024-02-18 20:24:20,194 INFO misc.py line 119 87073] Train: [54/100][618/1557] Data 0.014 (0.201) Batch 0.962 (1.150) Remain 23:10:20 loss: 0.3870 Lr: 0.00243 [2024-02-18 20:24:21,129 INFO misc.py line 119 87073] Train: 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Batch 0.864 (1.166) Remain 23:30:08 loss: 0.4761 Lr: 0.00243 [2024-02-18 20:24:39,281 INFO misc.py line 119 87073] Train: [54/100][626/1557] Data 0.004 (0.216) Batch 0.778 (1.166) Remain 23:29:22 loss: 0.4175 Lr: 0.00243 [2024-02-18 20:24:40,120 INFO misc.py line 119 87073] Train: [54/100][627/1557] Data 0.012 (0.216) Batch 0.847 (1.165) Remain 23:28:43 loss: 0.2331 Lr: 0.00243 [2024-02-18 20:24:40,857 INFO misc.py line 119 87073] Train: [54/100][628/1557] Data 0.005 (0.215) Batch 0.737 (1.164) Remain 23:27:53 loss: 0.3704 Lr: 0.00243 [2024-02-18 20:24:41,620 INFO misc.py line 119 87073] Train: [54/100][629/1557] Data 0.005 (0.215) Batch 0.758 (1.164) Remain 23:27:04 loss: 0.4071 Lr: 0.00243 [2024-02-18 20:24:42,833 INFO misc.py line 119 87073] Train: [54/100][630/1557] Data 0.009 (0.215) Batch 1.185 (1.164) Remain 23:27:06 loss: 0.1566 Lr: 0.00243 [2024-02-18 20:24:43,960 INFO misc.py line 119 87073] Train: [54/100][631/1557] Data 0.038 (0.215) Batch 1.152 (1.164) Remain 23:27:03 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line 119 87073] Train: [54/100][669/1557] Data 0.005 (0.203) Batch 0.860 (1.153) Remain 23:13:45 loss: 0.4458 Lr: 0.00243 [2024-02-18 20:25:22,040 INFO misc.py line 119 87073] Train: [54/100][670/1557] Data 0.004 (0.202) Batch 0.789 (1.153) Remain 23:13:05 loss: 0.2626 Lr: 0.00243 [2024-02-18 20:25:22,752 INFO misc.py line 119 87073] Train: [54/100][671/1557] Data 0.004 (0.202) Batch 0.698 (1.152) Remain 23:12:14 loss: 0.2315 Lr: 0.00243 [2024-02-18 20:25:24,058 INFO misc.py line 119 87073] Train: [54/100][672/1557] Data 0.017 (0.202) Batch 1.309 (1.152) Remain 23:12:30 loss: 0.2868 Lr: 0.00242 [2024-02-18 20:25:25,108 INFO misc.py line 119 87073] Train: [54/100][673/1557] Data 0.015 (0.202) Batch 1.060 (1.152) Remain 23:12:19 loss: 0.4761 Lr: 0.00242 [2024-02-18 20:25:26,083 INFO misc.py line 119 87073] Train: [54/100][674/1557] Data 0.005 (0.201) Batch 0.976 (1.152) Remain 23:11:59 loss: 0.3820 Lr: 0.00242 [2024-02-18 20:25:27,052 INFO misc.py line 119 87073] Train: 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Batch 1.041 (1.168) Remain 23:28:43 loss: 0.4317 Lr: 0.00242 [2024-02-18 20:27:56,622 INFO misc.py line 119 87073] Train: [54/100][794/1557] Data 0.003 (0.216) Batch 1.010 (1.167) Remain 23:28:27 loss: 0.4645 Lr: 0.00242 [2024-02-18 20:27:57,666 INFO misc.py line 119 87073] Train: [54/100][795/1557] Data 0.004 (0.215) Batch 1.045 (1.167) Remain 23:28:14 loss: 0.3579 Lr: 0.00242 [2024-02-18 20:27:58,402 INFO misc.py line 119 87073] Train: [54/100][796/1557] Data 0.004 (0.215) Batch 0.735 (1.167) Remain 23:27:34 loss: 0.1906 Lr: 0.00242 [2024-02-18 20:27:59,153 INFO misc.py line 119 87073] Train: [54/100][797/1557] Data 0.004 (0.215) Batch 0.749 (1.166) Remain 23:26:55 loss: 0.2855 Lr: 0.00242 [2024-02-18 20:28:00,208 INFO misc.py line 119 87073] Train: [54/100][798/1557] Data 0.006 (0.215) Batch 1.050 (1.166) Remain 23:26:43 loss: 0.2476 Lr: 0.00242 [2024-02-18 20:28:01,118 INFO misc.py line 119 87073] Train: [54/100][799/1557] Data 0.011 (0.214) Batch 0.918 (1.166) Remain 23:26:19 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Batch 1.003 (1.166) Remain 23:23:44 loss: 0.4319 Lr: 0.00241 [2024-02-18 20:31:11,459 INFO misc.py line 119 87073] Train: [54/100][962/1557] Data 0.012 (0.215) Batch 1.038 (1.166) Remain 23:23:33 loss: 0.5066 Lr: 0.00241 [2024-02-18 20:31:12,346 INFO misc.py line 119 87073] Train: [54/100][963/1557] Data 0.008 (0.215) Batch 0.891 (1.166) Remain 23:23:11 loss: 0.4839 Lr: 0.00241 [2024-02-18 20:31:13,098 INFO misc.py line 119 87073] Train: [54/100][964/1557] Data 0.004 (0.214) Batch 0.752 (1.165) Remain 23:22:39 loss: 0.2125 Lr: 0.00241 [2024-02-18 20:31:14,028 INFO misc.py line 119 87073] Train: [54/100][965/1557] Data 0.004 (0.214) Batch 0.925 (1.165) Remain 23:22:19 loss: 0.5334 Lr: 0.00241 [2024-02-18 20:31:15,134 INFO misc.py line 119 87073] Train: [54/100][966/1557] Data 0.009 (0.214) Batch 1.106 (1.165) Remain 23:22:14 loss: 0.2790 Lr: 0.00241 [2024-02-18 20:31:16,204 INFO misc.py line 119 87073] Train: [54/100][967/1557] Data 0.010 (0.214) Batch 1.064 (1.165) Remain 23:22:05 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[2024-02-18 20:33:00,145 INFO misc.py line 119 87073] Train: [54/100][1061/1557] Data 0.016 (0.207) Batch 0.905 (1.160) Remain 23:13:57 loss: 0.5905 Lr: 0.00240 [2024-02-18 20:33:00,877 INFO misc.py line 119 87073] Train: [54/100][1062/1557] Data 0.007 (0.207) Batch 0.733 (1.159) Remain 23:13:26 loss: 0.3170 Lr: 0.00240 [2024-02-18 20:33:01,662 INFO misc.py line 119 87073] Train: [54/100][1063/1557] Data 0.005 (0.207) Batch 0.778 (1.159) Remain 23:12:59 loss: 0.4264 Lr: 0.00240 [2024-02-18 20:33:02,946 INFO misc.py line 119 87073] Train: [54/100][1064/1557] Data 0.012 (0.207) Batch 1.287 (1.159) Remain 23:13:07 loss: 0.3234 Lr: 0.00240 [2024-02-18 20:33:03,844 INFO misc.py line 119 87073] Train: [54/100][1065/1557] Data 0.009 (0.206) Batch 0.902 (1.159) Remain 23:12:48 loss: 0.3064 Lr: 0.00240 [2024-02-18 20:33:04,790 INFO misc.py line 119 87073] Train: [54/100][1066/1557] Data 0.005 (0.206) Batch 0.946 (1.159) Remain 23:12:33 loss: 0.6412 Lr: 0.00240 [2024-02-18 20:33:05,796 INFO 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INFO misc.py line 119 87073] Train: [54/100][1191/1557] Data 0.015 (0.214) Batch 0.983 (1.167) Remain 23:20:07 loss: 0.1817 Lr: 0.00240 [2024-02-18 20:35:40,535 INFO misc.py line 119 87073] Train: [54/100][1192/1557] Data 0.004 (0.213) Batch 1.031 (1.167) Remain 23:19:57 loss: 0.3390 Lr: 0.00240 [2024-02-18 20:35:41,560 INFO misc.py line 119 87073] Train: [54/100][1193/1557] Data 0.004 (0.213) Batch 1.024 (1.167) Remain 23:19:48 loss: 0.3278 Lr: 0.00240 [2024-02-18 20:35:42,542 INFO misc.py line 119 87073] Train: [54/100][1194/1557] Data 0.004 (0.213) Batch 0.982 (1.167) Remain 23:19:35 loss: 0.4553 Lr: 0.00240 [2024-02-18 20:35:43,273 INFO misc.py line 119 87073] Train: [54/100][1195/1557] Data 0.003 (0.213) Batch 0.731 (1.166) Remain 23:19:08 loss: 0.2343 Lr: 0.00240 [2024-02-18 20:35:43,973 INFO misc.py line 119 87073] Train: [54/100][1196/1557] Data 0.003 (0.213) Batch 0.691 (1.166) Remain 23:18:38 loss: 0.2248 Lr: 0.00240 [2024-02-18 20:35:45,116 INFO misc.py line 119 87073] Train: [54/100][1197/1557] Data 0.012 (0.213) Batch 1.142 (1.166) Remain 23:18:35 loss: 0.1160 Lr: 0.00240 [2024-02-18 20:35:46,149 INFO misc.py line 119 87073] Train: [54/100][1198/1557] Data 0.014 (0.212) Batch 1.034 (1.166) Remain 23:18:26 loss: 0.3080 Lr: 0.00240 [2024-02-18 20:35:47,093 INFO misc.py line 119 87073] Train: [54/100][1199/1557] Data 0.012 (0.212) Batch 0.953 (1.166) Remain 23:18:12 loss: 0.4630 Lr: 0.00240 [2024-02-18 20:35:48,017 INFO misc.py line 119 87073] Train: [54/100][1200/1557] Data 0.004 (0.212) Batch 0.923 (1.165) Remain 23:17:57 loss: 0.2156 Lr: 0.00240 [2024-02-18 20:35:48,927 INFO misc.py line 119 87073] Train: [54/100][1201/1557] Data 0.006 (0.212) Batch 0.900 (1.165) Remain 23:17:39 loss: 0.2982 Lr: 0.00240 [2024-02-18 20:35:49,603 INFO misc.py line 119 87073] Train: [54/100][1202/1557] Data 0.015 (0.212) Batch 0.686 (1.165) Remain 23:17:10 loss: 0.1609 Lr: 0.00240 [2024-02-18 20:35:50,354 INFO misc.py line 119 87073] Train: [54/100][1203/1557] Data 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Remain 23:15:33 loss: 0.2626 Lr: 0.00240 [2024-02-18 20:35:56,998 INFO misc.py line 119 87073] Train: [54/100][1210/1557] Data 0.005 (0.210) Batch 0.726 (1.163) Remain 23:15:06 loss: 0.4709 Lr: 0.00240 [2024-02-18 20:35:58,224 INFO misc.py line 119 87073] Train: [54/100][1211/1557] Data 0.004 (0.210) Batch 1.226 (1.163) Remain 23:15:08 loss: 0.2828 Lr: 0.00240 [2024-02-18 20:35:59,184 INFO misc.py line 119 87073] Train: [54/100][1212/1557] Data 0.004 (0.210) Batch 0.961 (1.163) Remain 23:14:55 loss: 0.3096 Lr: 0.00240 [2024-02-18 20:36:00,195 INFO misc.py line 119 87073] Train: [54/100][1213/1557] Data 0.003 (0.210) Batch 1.011 (1.163) Remain 23:14:45 loss: 0.3324 Lr: 0.00240 [2024-02-18 20:36:01,233 INFO misc.py line 119 87073] Train: [54/100][1214/1557] Data 0.004 (0.210) Batch 1.038 (1.163) Remain 23:14:36 loss: 0.4087 Lr: 0.00240 [2024-02-18 20:36:02,247 INFO misc.py line 119 87073] Train: [54/100][1215/1557] Data 0.003 (0.209) Batch 1.013 (1.163) Remain 23:14:26 loss: 0.6226 Lr: 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INFO misc.py line 119 87073] Train: [54/100][1222/1557] Data 0.006 (0.208) Batch 0.999 (1.161) Remain 23:12:42 loss: 0.3775 Lr: 0.00240 [2024-02-18 20:36:09,450 INFO misc.py line 119 87073] Train: [54/100][1223/1557] Data 0.004 (0.208) Batch 0.696 (1.161) Remain 23:12:13 loss: 0.3469 Lr: 0.00240 [2024-02-18 20:36:10,193 INFO misc.py line 119 87073] Train: [54/100][1224/1557] Data 0.004 (0.208) Batch 0.743 (1.161) Remain 23:11:47 loss: 0.2280 Lr: 0.00240 [2024-02-18 20:36:11,498 INFO misc.py line 119 87073] Train: [54/100][1225/1557] Data 0.004 (0.208) Batch 1.305 (1.161) Remain 23:11:55 loss: 0.1955 Lr: 0.00240 [2024-02-18 20:36:12,351 INFO misc.py line 119 87073] Train: [54/100][1226/1557] Data 0.004 (0.208) Batch 0.852 (1.160) Remain 23:11:35 loss: 0.2418 Lr: 0.00240 [2024-02-18 20:36:13,385 INFO misc.py line 119 87073] Train: [54/100][1227/1557] Data 0.005 (0.207) Batch 1.034 (1.160) Remain 23:11:27 loss: 0.5157 Lr: 0.00240 [2024-02-18 20:36:14,335 INFO misc.py line 119 87073] Train: [54/100][1228/1557] Data 0.005 (0.207) Batch 0.951 (1.160) Remain 23:11:13 loss: 0.3254 Lr: 0.00240 [2024-02-18 20:36:15,249 INFO misc.py line 119 87073] Train: [54/100][1229/1557] Data 0.004 (0.207) Batch 0.914 (1.160) Remain 23:10:58 loss: 0.2620 Lr: 0.00240 [2024-02-18 20:36:17,961 INFO misc.py line 119 87073] Train: [54/100][1230/1557] Data 1.073 (0.208) Batch 2.712 (1.161) Remain 23:12:28 loss: 0.2856 Lr: 0.00240 [2024-02-18 20:36:18,681 INFO misc.py line 119 87073] Train: [54/100][1231/1557] Data 0.003 (0.208) Batch 0.719 (1.161) Remain 23:12:01 loss: 0.2191 Lr: 0.00240 [2024-02-18 20:36:19,889 INFO misc.py line 119 87073] Train: [54/100][1232/1557] Data 0.004 (0.208) Batch 1.206 (1.161) Remain 23:12:02 loss: 0.2099 Lr: 0.00240 [2024-02-18 20:36:20,739 INFO misc.py line 119 87073] Train: [54/100][1233/1557] Data 0.007 (0.207) Batch 0.851 (1.161) Remain 23:11:43 loss: 0.7595 Lr: 0.00240 [2024-02-18 20:36:21,753 INFO misc.py line 119 87073] Train: [54/100][1234/1557] Data 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(1.168) Remain 23:20:40 loss: 1.4190 Lr: 0.00239 [2024-02-18 20:36:39,252 INFO misc.py line 119 87073] Train: [54/100][1241/1557] Data 0.004 (0.215) Batch 1.006 (1.168) Remain 23:20:30 loss: 0.0926 Lr: 0.00239 [2024-02-18 20:36:40,288 INFO misc.py line 119 87073] Train: [54/100][1242/1557] Data 0.005 (0.215) Batch 1.035 (1.168) Remain 23:20:21 loss: 0.3606 Lr: 0.00239 [2024-02-18 20:36:41,363 INFO misc.py line 119 87073] Train: [54/100][1243/1557] Data 0.005 (0.215) Batch 1.072 (1.168) Remain 23:20:14 loss: 0.5860 Lr: 0.00239 [2024-02-18 20:36:42,125 INFO misc.py line 119 87073] Train: [54/100][1244/1557] Data 0.009 (0.214) Batch 0.767 (1.168) Remain 23:19:50 loss: 0.2336 Lr: 0.00239 [2024-02-18 20:36:42,883 INFO misc.py line 119 87073] Train: [54/100][1245/1557] Data 0.003 (0.214) Batch 0.709 (1.167) Remain 23:19:22 loss: 0.2382 Lr: 0.00239 [2024-02-18 20:36:43,978 INFO misc.py line 119 87073] Train: [54/100][1246/1557] Data 0.052 (0.214) Batch 1.137 (1.167) Remain 23:19:19 loss: 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87073] Train: [54/100][1259/1557] Data 0.004 (0.212) Batch 0.790 (1.165) Remain 23:16:33 loss: 0.3196 Lr: 0.00239 [2024-02-18 20:36:57,777 INFO misc.py line 119 87073] Train: [54/100][1260/1557] Data 0.004 (0.212) Batch 1.266 (1.165) Remain 23:16:37 loss: 0.1193 Lr: 0.00239 [2024-02-18 20:36:58,889 INFO misc.py line 119 87073] Train: [54/100][1261/1557] Data 0.011 (0.212) Batch 1.119 (1.165) Remain 23:16:33 loss: 0.8659 Lr: 0.00239 [2024-02-18 20:37:00,047 INFO misc.py line 119 87073] Train: [54/100][1262/1557] Data 0.005 (0.212) Batch 1.146 (1.165) Remain 23:16:31 loss: 0.2983 Lr: 0.00239 [2024-02-18 20:37:01,066 INFO misc.py line 119 87073] Train: [54/100][1263/1557] Data 0.018 (0.212) Batch 1.031 (1.165) Remain 23:16:22 loss: 0.4162 Lr: 0.00239 [2024-02-18 20:37:02,031 INFO misc.py line 119 87073] Train: [54/100][1264/1557] Data 0.005 (0.211) Batch 0.967 (1.165) Remain 23:16:10 loss: 0.5472 Lr: 0.00239 [2024-02-18 20:37:02,884 INFO misc.py line 119 87073] Train: [54/100][1265/1557] Data 0.004 (0.211) Batch 0.853 (1.165) Remain 23:15:51 loss: 0.2268 Lr: 0.00239 [2024-02-18 20:37:03,698 INFO misc.py line 119 87073] Train: [54/100][1266/1557] Data 0.003 (0.211) Batch 0.808 (1.164) Remain 23:15:29 loss: 0.3239 Lr: 0.00239 [2024-02-18 20:37:04,930 INFO misc.py line 119 87073] Train: [54/100][1267/1557] Data 0.010 (0.211) Batch 1.231 (1.164) Remain 23:15:32 loss: 0.2585 Lr: 0.00239 [2024-02-18 20:37:06,114 INFO misc.py line 119 87073] Train: [54/100][1268/1557] Data 0.014 (0.211) Batch 1.187 (1.164) Remain 23:15:32 loss: 0.1585 Lr: 0.00239 [2024-02-18 20:37:06,994 INFO misc.py line 119 87073] Train: [54/100][1269/1557] Data 0.009 (0.211) Batch 0.885 (1.164) Remain 23:15:15 loss: 0.4193 Lr: 0.00239 [2024-02-18 20:37:07,977 INFO misc.py line 119 87073] Train: [54/100][1270/1557] Data 0.004 (0.210) Batch 0.983 (1.164) Remain 23:15:04 loss: 0.4866 Lr: 0.00239 [2024-02-18 20:37:08,891 INFO misc.py line 119 87073] Train: [54/100][1271/1557] Data 0.004 (0.210) Batch 0.913 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[2024-02-18 20:38:31,043 INFO misc.py line 119 87073] Train: [54/100][1346/1557] Data 0.005 (0.207) Batch 1.086 (1.160) Remain 23:08:46 loss: 0.7010 Lr: 0.00239 [2024-02-18 20:38:31,987 INFO misc.py line 119 87073] Train: [54/100][1347/1557] Data 0.013 (0.207) Batch 0.952 (1.160) Remain 23:08:34 loss: 0.4343 Lr: 0.00239 [2024-02-18 20:38:33,004 INFO misc.py line 119 87073] Train: [54/100][1348/1557] Data 0.004 (0.207) Batch 1.016 (1.160) Remain 23:08:25 loss: 0.4022 Lr: 0.00239 [2024-02-18 20:38:33,759 INFO misc.py line 119 87073] Train: [54/100][1349/1557] Data 0.006 (0.207) Batch 0.756 (1.159) Remain 23:08:02 loss: 0.1504 Lr: 0.00239 [2024-02-18 20:38:34,556 INFO misc.py line 119 87073] Train: [54/100][1350/1557] Data 0.004 (0.206) Batch 0.795 (1.159) Remain 23:07:41 loss: 0.2774 Lr: 0.00239 [2024-02-18 20:38:47,518 INFO misc.py line 119 87073] Train: [54/100][1351/1557] Data 11.650 (0.215) Batch 12.963 (1.168) Remain 23:18:09 loss: 0.1944 Lr: 0.00239 [2024-02-18 20:38:48,636 INFO 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misc.py line 119 87073] Train: [54/100][1383/1557] Data 0.011 (0.210) Batch 1.060 (1.163) Remain 23:11:45 loss: 0.4373 Lr: 0.00239 [2024-02-18 20:39:18,973 INFO misc.py line 119 87073] Train: [54/100][1384/1557] Data 0.006 (0.210) Batch 0.742 (1.163) Remain 23:11:22 loss: 0.2162 Lr: 0.00239 [2024-02-18 20:39:19,698 INFO misc.py line 119 87073] Train: [54/100][1385/1557] Data 0.004 (0.210) Batch 0.719 (1.162) Remain 23:10:58 loss: 0.4692 Lr: 0.00239 [2024-02-18 20:39:20,788 INFO misc.py line 119 87073] Train: [54/100][1386/1557] Data 0.010 (0.210) Batch 1.089 (1.162) Remain 23:10:53 loss: 0.0856 Lr: 0.00239 [2024-02-18 20:39:21,746 INFO misc.py line 119 87073] Train: [54/100][1387/1557] Data 0.011 (0.210) Batch 0.962 (1.162) Remain 23:10:42 loss: 0.4234 Lr: 0.00239 [2024-02-18 20:39:22,707 INFO misc.py line 119 87073] Train: [54/100][1388/1557] Data 0.008 (0.209) Batch 0.964 (1.162) Remain 23:10:30 loss: 0.4166 Lr: 0.00239 [2024-02-18 20:39:23,606 INFO misc.py line 119 87073] Train: 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23:08:11 loss: 0.2856 Lr: 0.00239 [2024-02-18 20:39:36,518 INFO misc.py line 119 87073] Train: [54/100][1402/1557] Data 0.012 (0.207) Batch 1.121 (1.160) Remain 23:08:08 loss: 0.8508 Lr: 0.00239 [2024-02-18 20:39:37,475 INFO misc.py line 119 87073] Train: [54/100][1403/1557] Data 0.007 (0.207) Batch 0.960 (1.160) Remain 23:07:56 loss: 0.6559 Lr: 0.00239 [2024-02-18 20:39:38,371 INFO misc.py line 119 87073] Train: [54/100][1404/1557] Data 0.003 (0.207) Batch 0.895 (1.160) Remain 23:07:41 loss: 0.2286 Lr: 0.00239 [2024-02-18 20:39:40,786 INFO misc.py line 119 87073] Train: [54/100][1405/1557] Data 0.791 (0.208) Batch 2.416 (1.161) Remain 23:08:45 loss: 0.3318 Lr: 0.00239 [2024-02-18 20:39:41,515 INFO misc.py line 119 87073] Train: [54/100][1406/1557] Data 0.004 (0.207) Batch 0.728 (1.161) Remain 23:08:21 loss: 0.2350 Lr: 0.00239 [2024-02-18 20:39:53,792 INFO misc.py line 119 87073] Train: [54/100][1407/1557] Data 11.006 (0.215) Batch 12.278 (1.169) Remain 23:17:48 loss: 0.1438 Lr: 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Train: [54/100][1420/1557] Data 0.005 (0.213) Batch 0.691 (1.166) Remain 23:14:35 loss: 0.2234 Lr: 0.00239 [2024-02-18 20:40:06,659 INFO misc.py line 119 87073] Train: [54/100][1421/1557] Data 0.009 (0.213) Batch 1.185 (1.166) Remain 23:14:35 loss: 0.1084 Lr: 0.00239 [2024-02-18 20:40:07,593 INFO misc.py line 119 87073] Train: [54/100][1422/1557] Data 0.014 (0.213) Batch 0.945 (1.166) Remain 23:14:22 loss: 0.2498 Lr: 0.00239 [2024-02-18 20:40:08,623 INFO misc.py line 119 87073] Train: [54/100][1423/1557] Data 0.003 (0.213) Batch 1.029 (1.166) Remain 23:14:14 loss: 0.3479 Lr: 0.00239 [2024-02-18 20:40:09,579 INFO misc.py line 119 87073] Train: [54/100][1424/1557] Data 0.004 (0.213) Batch 0.956 (1.166) Remain 23:14:03 loss: 0.4479 Lr: 0.00239 [2024-02-18 20:40:10,598 INFO misc.py line 119 87073] Train: [54/100][1425/1557] Data 0.004 (0.212) Batch 1.018 (1.166) Remain 23:13:54 loss: 0.4785 Lr: 0.00239 [2024-02-18 20:40:11,312 INFO misc.py line 119 87073] Train: [54/100][1426/1557] Data 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Remain 23:12:42 loss: 0.4121 Lr: 0.00238 [2024-02-18 20:40:18,238 INFO misc.py line 119 87073] Train: [54/100][1433/1557] Data 0.004 (0.211) Batch 0.755 (1.164) Remain 23:12:20 loss: 0.2371 Lr: 0.00238 [2024-02-18 20:40:19,027 INFO misc.py line 119 87073] Train: [54/100][1434/1557] Data 0.004 (0.211) Batch 0.781 (1.164) Remain 23:12:00 loss: 0.2686 Lr: 0.00238 [2024-02-18 20:40:20,216 INFO misc.py line 119 87073] Train: [54/100][1435/1557] Data 0.012 (0.211) Batch 1.193 (1.164) Remain 23:12:00 loss: 0.2053 Lr: 0.00238 [2024-02-18 20:40:21,148 INFO misc.py line 119 87073] Train: [54/100][1436/1557] Data 0.008 (0.211) Batch 0.936 (1.164) Remain 23:11:47 loss: 0.2775 Lr: 0.00238 [2024-02-18 20:40:21,942 INFO misc.py line 119 87073] Train: [54/100][1437/1557] Data 0.004 (0.211) Batch 0.795 (1.164) Remain 23:11:28 loss: 0.3105 Lr: 0.00238 [2024-02-18 20:40:22,992 INFO misc.py line 119 87073] Train: [54/100][1438/1557] Data 0.003 (0.211) Batch 1.042 (1.164) Remain 23:11:21 loss: 0.2252 Lr: 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INFO misc.py line 119 87073] Train: [54/100][1445/1557] Data 0.005 (0.210) Batch 1.101 (1.163) Remain 23:09:58 loss: 0.3931 Lr: 0.00238 [2024-02-18 20:40:30,710 INFO misc.py line 119 87073] Train: [54/100][1446/1557] Data 0.009 (0.209) Batch 1.082 (1.163) Remain 23:09:53 loss: 0.1158 Lr: 0.00238 [2024-02-18 20:40:31,470 INFO misc.py line 119 87073] Train: [54/100][1447/1557] Data 0.004 (0.209) Batch 0.759 (1.162) Remain 23:09:31 loss: 0.2383 Lr: 0.00238 [2024-02-18 20:40:32,241 INFO misc.py line 119 87073] Train: [54/100][1448/1557] Data 0.006 (0.209) Batch 0.764 (1.162) Remain 23:09:10 loss: 0.3828 Lr: 0.00238 [2024-02-18 20:40:33,596 INFO misc.py line 119 87073] Train: [54/100][1449/1557] Data 0.012 (0.209) Batch 1.357 (1.162) Remain 23:09:19 loss: 0.0872 Lr: 0.00238 [2024-02-18 20:40:34,585 INFO misc.py line 119 87073] Train: [54/100][1450/1557] Data 0.010 (0.209) Batch 0.995 (1.162) Remain 23:09:09 loss: 0.1146 Lr: 0.00238 [2024-02-18 20:40:35,587 INFO misc.py line 119 87073] Train: [54/100][1451/1557] Data 0.004 (0.209) Batch 1.002 (1.162) Remain 23:09:00 loss: 0.2299 Lr: 0.00238 [2024-02-18 20:40:36,542 INFO misc.py line 119 87073] Train: [54/100][1452/1557] Data 0.004 (0.209) Batch 0.956 (1.162) Remain 23:08:49 loss: 0.2334 Lr: 0.00238 [2024-02-18 20:40:37,524 INFO misc.py line 119 87073] Train: [54/100][1453/1557] Data 0.004 (0.208) Batch 0.981 (1.162) Remain 23:08:39 loss: 0.3592 Lr: 0.00238 [2024-02-18 20:40:38,368 INFO misc.py line 119 87073] Train: [54/100][1454/1557] Data 0.004 (0.208) Batch 0.837 (1.161) Remain 23:08:22 loss: 0.2857 Lr: 0.00238 [2024-02-18 20:40:39,151 INFO misc.py line 119 87073] Train: [54/100][1455/1557] Data 0.012 (0.208) Batch 0.788 (1.161) Remain 23:08:02 loss: 0.4191 Lr: 0.00238 [2024-02-18 20:40:40,450 INFO misc.py line 119 87073] Train: [54/100][1456/1557] Data 0.007 (0.208) Batch 1.292 (1.161) Remain 23:08:07 loss: 0.2360 Lr: 0.00238 [2024-02-18 20:40:41,318 INFO misc.py line 119 87073] Train: [54/100][1457/1557] Data 0.014 (0.208) Batch 0.875 (1.161) Remain 23:07:52 loss: 0.9190 Lr: 0.00238 [2024-02-18 20:40:42,371 INFO misc.py line 119 87073] Train: [54/100][1458/1557] Data 0.007 (0.208) Batch 1.056 (1.161) Remain 23:07:46 loss: 0.0972 Lr: 0.00238 [2024-02-18 20:40:43,406 INFO misc.py line 119 87073] Train: [54/100][1459/1557] Data 0.004 (0.208) Batch 1.035 (1.161) Remain 23:07:38 loss: 0.2082 Lr: 0.00238 [2024-02-18 20:40:44,362 INFO misc.py line 119 87073] Train: [54/100][1460/1557] Data 0.004 (0.207) Batch 0.955 (1.161) Remain 23:07:27 loss: 0.3298 Lr: 0.00238 [2024-02-18 20:40:45,130 INFO misc.py line 119 87073] Train: [54/100][1461/1557] Data 0.005 (0.207) Batch 0.766 (1.160) Remain 23:07:07 loss: 0.3434 Lr: 0.00238 [2024-02-18 20:40:45,900 INFO misc.py line 119 87073] Train: [54/100][1462/1557] Data 0.006 (0.207) Batch 0.771 (1.160) Remain 23:06:46 loss: 0.1892 Lr: 0.00238 [2024-02-18 20:40:58,805 INFO misc.py line 119 87073] Train: [54/100][1463/1557] Data 11.597 (0.215) Batch 12.900 (1.168) Remain 23:16:22 loss: 0.1016 Lr: 0.00238 [2024-02-18 20:40:59,754 INFO misc.py line 119 87073] Train: [54/100][1464/1557] Data 0.010 (0.215) Batch 0.954 (1.168) Remain 23:16:10 loss: 0.2393 Lr: 0.00238 [2024-02-18 20:41:00,583 INFO misc.py line 119 87073] Train: [54/100][1465/1557] Data 0.004 (0.215) Batch 0.828 (1.168) Remain 23:15:52 loss: 0.2271 Lr: 0.00238 [2024-02-18 20:41:01,519 INFO misc.py line 119 87073] Train: [54/100][1466/1557] Data 0.005 (0.215) Batch 0.934 (1.168) Remain 23:15:40 loss: 0.3068 Lr: 0.00238 [2024-02-18 20:41:02,446 INFO misc.py line 119 87073] Train: [54/100][1467/1557] Data 0.006 (0.214) Batch 0.930 (1.168) Remain 23:15:27 loss: 0.4584 Lr: 0.00238 [2024-02-18 20:41:03,204 INFO misc.py line 119 87073] Train: [54/100][1468/1557] Data 0.004 (0.214) Batch 0.758 (1.167) Remain 23:15:06 loss: 0.1797 Lr: 0.00238 [2024-02-18 20:41:03,923 INFO misc.py line 119 87073] Train: [54/100][1469/1557] Data 0.003 (0.214) Batch 0.718 (1.167) Remain 23:14:42 loss: 0.4581 Lr: 0.00238 [2024-02-18 20:41:05,031 INFO misc.py line 119 87073] Train: [54/100][1470/1557] Data 0.005 (0.214) Batch 1.108 (1.167) Remain 23:14:38 loss: 0.3154 Lr: 0.00238 [2024-02-18 20:41:06,192 INFO misc.py line 119 87073] Train: [54/100][1471/1557] Data 0.004 (0.214) Batch 1.156 (1.167) Remain 23:14:37 loss: 0.4173 Lr: 0.00238 [2024-02-18 20:41:07,273 INFO misc.py line 119 87073] Train: [54/100][1472/1557] Data 0.010 (0.214) Batch 1.085 (1.167) Remain 23:14:32 loss: 0.2153 Lr: 0.00238 [2024-02-18 20:41:08,181 INFO misc.py line 119 87073] Train: [54/100][1473/1557] Data 0.005 (0.214) Batch 0.910 (1.167) Remain 23:14:18 loss: 0.1842 Lr: 0.00238 [2024-02-18 20:41:09,007 INFO misc.py line 119 87073] Train: [54/100][1474/1557] Data 0.003 (0.213) Batch 0.825 (1.166) Remain 23:14:00 loss: 0.6818 Lr: 0.00238 [2024-02-18 20:41:09,800 INFO misc.py line 119 87073] Train: [54/100][1475/1557] Data 0.005 (0.213) Batch 0.784 (1.166) Remain 23:13:40 loss: 0.2573 Lr: 0.00238 [2024-02-18 20:41:10,545 INFO misc.py line 119 87073] Train: [54/100][1476/1557] Data 0.014 (0.213) Batch 0.755 (1.166) Remain 23:13:19 loss: 0.2110 Lr: 0.00238 [2024-02-18 20:41:11,736 INFO misc.py line 119 87073] Train: [54/100][1477/1557] Data 0.004 (0.213) Batch 1.190 (1.166) Remain 23:13:19 loss: 0.0738 Lr: 0.00238 [2024-02-18 20:41:12,997 INFO misc.py line 119 87073] Train: [54/100][1478/1557] Data 0.004 (0.213) Batch 1.258 (1.166) Remain 23:13:22 loss: 0.1227 Lr: 0.00238 [2024-02-18 20:41:14,028 INFO misc.py line 119 87073] Train: [54/100][1479/1557] Data 0.007 (0.213) Batch 1.029 (1.166) Remain 23:13:15 loss: 0.5212 Lr: 0.00238 [2024-02-18 20:41:15,037 INFO misc.py line 119 87073] Train: [54/100][1480/1557] Data 0.009 (0.213) Batch 1.004 (1.166) Remain 23:13:06 loss: 0.2150 Lr: 0.00238 [2024-02-18 20:41:16,223 INFO misc.py line 119 87073] Train: [54/100][1481/1557] Data 0.014 (0.212) Batch 1.195 (1.166) Remain 23:13:06 loss: 0.3938 Lr: 0.00238 [2024-02-18 20:41:16,967 INFO misc.py line 119 87073] Train: [54/100][1482/1557] Data 0.005 (0.212) Batch 0.747 (1.166) Remain 23:12:44 loss: 0.2653 Lr: 0.00238 [2024-02-18 20:41:17,768 INFO misc.py line 119 87073] Train: [54/100][1483/1557] Data 0.003 (0.212) Batch 0.799 (1.165) Remain 23:12:25 loss: 0.4780 Lr: 0.00238 [2024-02-18 20:41:19,134 INFO misc.py line 119 87073] Train: [54/100][1484/1557] Data 0.005 (0.212) Batch 1.362 (1.165) Remain 23:12:34 loss: 0.1058 Lr: 0.00238 [2024-02-18 20:41:20,057 INFO misc.py line 119 87073] Train: [54/100][1485/1557] Data 0.009 (0.212) Batch 0.929 (1.165) Remain 23:12:21 loss: 0.5658 Lr: 0.00238 [2024-02-18 20:41:21,107 INFO misc.py line 119 87073] Train: [54/100][1486/1557] Data 0.004 (0.212) Batch 1.048 (1.165) Remain 23:12:14 loss: 0.7074 Lr: 0.00238 [2024-02-18 20:41:22,192 INFO misc.py line 119 87073] Train: [54/100][1487/1557] Data 0.006 (0.212) Batch 1.086 (1.165) Remain 23:12:09 loss: 0.2789 Lr: 0.00238 [2024-02-18 20:41:23,042 INFO misc.py line 119 87073] Train: [54/100][1488/1557] Data 0.005 (0.211) Batch 0.851 (1.165) Remain 23:11:53 loss: 0.1172 Lr: 0.00238 [2024-02-18 20:41:23,812 INFO misc.py line 119 87073] Train: [54/100][1489/1557] Data 0.003 (0.211) Batch 0.760 (1.165) Remain 23:11:32 loss: 0.2496 Lr: 0.00238 [2024-02-18 20:41:24,631 INFO misc.py line 119 87073] Train: [54/100][1490/1557] Data 0.013 (0.211) Batch 0.829 (1.164) Remain 23:11:15 loss: 0.4449 Lr: 0.00238 [2024-02-18 20:41:25,767 INFO misc.py line 119 87073] Train: [54/100][1491/1557] Data 0.004 (0.211) Batch 1.134 (1.164) Remain 23:11:12 loss: 0.1867 Lr: 0.00238 [2024-02-18 20:41:26,753 INFO misc.py line 119 87073] Train: [54/100][1492/1557] Data 0.006 (0.211) Batch 0.987 (1.164) Remain 23:11:03 loss: 0.7031 Lr: 0.00238 [2024-02-18 20:41:27,761 INFO misc.py line 119 87073] Train: [54/100][1493/1557] Data 0.003 (0.211) Batch 1.008 (1.164) Remain 23:10:54 loss: 0.2795 Lr: 0.00238 [2024-02-18 20:41:28,673 INFO misc.py line 119 87073] Train: [54/100][1494/1557] Data 0.004 (0.211) Batch 0.911 (1.164) Remain 23:10:41 loss: 0.2864 Lr: 0.00238 [2024-02-18 20:41:29,712 INFO misc.py line 119 87073] Train: [54/100][1495/1557] Data 0.005 (0.211) Batch 1.034 (1.164) Remain 23:10:33 loss: 0.2062 Lr: 0.00238 [2024-02-18 20:41:30,481 INFO misc.py line 119 87073] Train: [54/100][1496/1557] Data 0.010 (0.210) Batch 0.776 (1.164) Remain 23:10:13 loss: 0.1894 Lr: 0.00238 [2024-02-18 20:41:31,252 INFO misc.py line 119 87073] Train: [54/100][1497/1557] Data 0.004 (0.210) Batch 0.771 (1.163) Remain 23:09:53 loss: 0.3571 Lr: 0.00238 [2024-02-18 20:41:32,375 INFO misc.py line 119 87073] Train: [54/100][1498/1557] Data 0.003 (0.210) Batch 1.116 (1.163) Remain 23:09:50 loss: 0.1968 Lr: 0.00238 [2024-02-18 20:41:33,355 INFO misc.py line 119 87073] Train: [54/100][1499/1557] Data 0.010 (0.210) Batch 0.986 (1.163) Remain 23:09:40 loss: 0.2479 Lr: 0.00238 [2024-02-18 20:41:34,418 INFO misc.py line 119 87073] Train: [54/100][1500/1557] Data 0.005 (0.210) Batch 1.064 (1.163) Remain 23:09:34 loss: 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20:41:40,851 INFO misc.py line 119 87073] Train: [54/100][1507/1557] Data 0.004 (0.209) Batch 1.010 (1.162) Remain 23:08:05 loss: 0.4329 Lr: 0.00238 [2024-02-18 20:41:41,798 INFO misc.py line 119 87073] Train: [54/100][1508/1557] Data 0.004 (0.209) Batch 0.948 (1.162) Remain 23:07:53 loss: 0.5234 Lr: 0.00238 [2024-02-18 20:41:42,901 INFO misc.py line 119 87073] Train: [54/100][1509/1557] Data 0.004 (0.209) Batch 1.102 (1.162) Remain 23:07:49 loss: 0.4475 Lr: 0.00238 [2024-02-18 20:41:43,705 INFO misc.py line 119 87073] Train: [54/100][1510/1557] Data 0.005 (0.208) Batch 0.804 (1.162) Remain 23:07:31 loss: 0.3549 Lr: 0.00238 [2024-02-18 20:41:44,489 INFO misc.py line 119 87073] Train: [54/100][1511/1557] Data 0.004 (0.208) Batch 0.784 (1.161) Remain 23:07:12 loss: 0.4025 Lr: 0.00238 [2024-02-18 20:41:45,766 INFO misc.py line 119 87073] Train: [54/100][1512/1557] Data 0.004 (0.208) Batch 1.269 (1.161) Remain 23:07:16 loss: 0.2568 Lr: 0.00238 [2024-02-18 20:41:46,708 INFO misc.py line 119 87073] Train: [54/100][1513/1557] Data 0.013 (0.208) Batch 0.950 (1.161) Remain 23:07:05 loss: 0.8267 Lr: 0.00238 [2024-02-18 20:41:47,815 INFO misc.py line 119 87073] Train: [54/100][1514/1557] Data 0.005 (0.208) Batch 1.108 (1.161) Remain 23:07:01 loss: 0.1936 Lr: 0.00238 [2024-02-18 20:41:48,836 INFO misc.py line 119 87073] Train: [54/100][1515/1557] Data 0.004 (0.208) Batch 1.020 (1.161) Remain 23:06:53 loss: 0.6300 Lr: 0.00238 [2024-02-18 20:41:49,733 INFO misc.py line 119 87073] Train: [54/100][1516/1557] Data 0.005 (0.208) Batch 0.899 (1.161) Remain 23:06:40 loss: 0.4868 Lr: 0.00238 [2024-02-18 20:41:50,451 INFO misc.py line 119 87073] Train: [54/100][1517/1557] Data 0.004 (0.208) Batch 0.712 (1.161) Remain 23:06:17 loss: 0.4773 Lr: 0.00238 [2024-02-18 20:41:51,239 INFO misc.py line 119 87073] Train: [54/100][1518/1557] Data 0.010 (0.207) Batch 0.791 (1.160) Remain 23:05:59 loss: 0.2443 Lr: 0.00238 [2024-02-18 20:42:04,020 INFO misc.py line 119 87073] Train: [54/100][1519/1557] Data 11.491 (0.215) Batch 12.784 (1.168) Remain 23:15:07 loss: 0.2500 Lr: 0.00238 [2024-02-18 20:42:04,880 INFO misc.py line 119 87073] Train: [54/100][1520/1557] Data 0.004 (0.215) Batch 0.861 (1.168) Remain 23:14:51 loss: 0.1043 Lr: 0.00238 [2024-02-18 20:42:06,153 INFO misc.py line 119 87073] Train: [54/100][1521/1557] Data 0.004 (0.215) Batch 1.273 (1.168) Remain 23:14:55 loss: 0.2294 Lr: 0.00238 [2024-02-18 20:42:07,098 INFO misc.py line 119 87073] Train: [54/100][1522/1557] Data 0.004 (0.214) Batch 0.944 (1.168) Remain 23:14:43 loss: 0.3544 Lr: 0.00238 [2024-02-18 20:42:07,951 INFO misc.py line 119 87073] Train: [54/100][1523/1557] Data 0.005 (0.214) Batch 0.853 (1.168) Remain 23:14:27 loss: 0.1925 Lr: 0.00238 [2024-02-18 20:42:08,694 INFO misc.py line 119 87073] Train: [54/100][1524/1557] Data 0.004 (0.214) Batch 0.740 (1.167) Remain 23:14:06 loss: 0.2342 Lr: 0.00238 [2024-02-18 20:42:09,466 INFO misc.py line 119 87073] Train: [54/100][1525/1557] Data 0.009 (0.214) Batch 0.774 (1.167) Remain 23:13:46 loss: 0.4768 Lr: 0.00238 [2024-02-18 20:42:10,483 INFO misc.py line 119 87073] Train: [54/100][1526/1557] Data 0.005 (0.214) Batch 1.018 (1.167) Remain 23:13:38 loss: 0.2757 Lr: 0.00238 [2024-02-18 20:42:11,519 INFO misc.py line 119 87073] Train: [54/100][1527/1557] Data 0.005 (0.214) Batch 1.036 (1.167) Remain 23:13:31 loss: 0.9014 Lr: 0.00238 [2024-02-18 20:42:12,524 INFO misc.py line 119 87073] Train: [54/100][1528/1557] Data 0.005 (0.214) Batch 1.005 (1.167) Remain 23:13:22 loss: 0.2501 Lr: 0.00238 [2024-02-18 20:42:13,676 INFO misc.py line 119 87073] Train: [54/100][1529/1557] Data 0.005 (0.213) Batch 1.152 (1.167) Remain 23:13:20 loss: 0.3970 Lr: 0.00238 [2024-02-18 20:42:14,636 INFO misc.py line 119 87073] Train: [54/100][1530/1557] Data 0.004 (0.213) Batch 0.958 (1.167) Remain 23:13:09 loss: 0.2877 Lr: 0.00238 [2024-02-18 20:42:15,400 INFO misc.py line 119 87073] Train: [54/100][1531/1557] Data 0.006 (0.213) Batch 0.766 (1.166) Remain 23:12:49 loss: 0.2864 Lr: 0.00238 [2024-02-18 20:42:16,168 INFO misc.py line 119 87073] Train: [54/100][1532/1557] Data 0.004 (0.213) Batch 0.763 (1.166) Remain 23:12:29 loss: 0.3141 Lr: 0.00238 [2024-02-18 20:42:17,383 INFO misc.py line 119 87073] Train: [54/100][1533/1557] Data 0.009 (0.213) Batch 1.220 (1.166) Remain 23:12:30 loss: 0.0742 Lr: 0.00238 [2024-02-18 20:42:18,463 INFO misc.py line 119 87073] Train: [54/100][1534/1557] Data 0.005 (0.213) Batch 1.071 (1.166) Remain 23:12:25 loss: 0.2972 Lr: 0.00238 [2024-02-18 20:42:19,414 INFO misc.py line 119 87073] Train: [54/100][1535/1557] Data 0.014 (0.213) Batch 0.960 (1.166) Remain 23:12:14 loss: 0.3698 Lr: 0.00238 [2024-02-18 20:42:20,618 INFO misc.py line 119 87073] Train: [54/100][1536/1557] Data 0.004 (0.213) Batch 1.204 (1.166) Remain 23:12:15 loss: 0.2826 Lr: 0.00238 [2024-02-18 20:42:21,660 INFO misc.py line 119 87073] Train: [54/100][1537/1557] Data 0.004 (0.212) Batch 1.040 (1.166) Remain 23:12:08 loss: 0.0388 Lr: 0.00238 [2024-02-18 20:42:22,439 INFO misc.py line 119 87073] Train: [54/100][1538/1557] Data 0.006 (0.212) Batch 0.779 (1.166) Remain 23:11:48 loss: 0.2532 Lr: 0.00238 [2024-02-18 20:42:23,182 INFO misc.py line 119 87073] Train: [54/100][1539/1557] Data 0.005 (0.212) Batch 0.744 (1.165) Remain 23:11:27 loss: 0.3907 Lr: 0.00238 [2024-02-18 20:42:24,462 INFO misc.py line 119 87073] Train: [54/100][1540/1557] Data 0.004 (0.212) Batch 1.280 (1.165) Remain 23:11:32 loss: 0.1291 Lr: 0.00238 [2024-02-18 20:42:25,358 INFO misc.py line 119 87073] Train: [54/100][1541/1557] Data 0.005 (0.212) Batch 0.897 (1.165) Remain 23:11:18 loss: 0.3698 Lr: 0.00238 [2024-02-18 20:42:26,312 INFO misc.py line 119 87073] Train: [54/100][1542/1557] Data 0.004 (0.212) Batch 0.953 (1.165) Remain 23:11:07 loss: 0.2468 Lr: 0.00238 [2024-02-18 20:42:27,275 INFO misc.py line 119 87073] Train: [54/100][1543/1557] Data 0.004 (0.212) Batch 0.964 (1.165) Remain 23:10:56 loss: 0.0827 Lr: 0.00238 [2024-02-18 20:42:28,191 INFO misc.py line 119 87073] Train: [54/100][1544/1557] Data 0.004 (0.211) Batch 0.907 (1.165) Remain 23:10:43 loss: 0.4599 Lr: 0.00238 [2024-02-18 20:42:28,924 INFO misc.py line 119 87073] Train: [54/100][1545/1557] Data 0.012 (0.211) Batch 0.741 (1.165) Remain 23:10:22 loss: 0.4652 Lr: 0.00238 [2024-02-18 20:42:29,589 INFO misc.py line 119 87073] Train: [54/100][1546/1557] Data 0.004 (0.211) Batch 0.661 (1.164) Remain 23:09:58 loss: 0.3446 Lr: 0.00238 [2024-02-18 20:42:30,814 INFO misc.py line 119 87073] Train: [54/100][1547/1557] Data 0.008 (0.211) Batch 1.228 (1.164) Remain 23:10:00 loss: 0.2762 Lr: 0.00238 [2024-02-18 20:42:31,652 INFO misc.py line 119 87073] Train: [54/100][1548/1557] Data 0.005 (0.211) Batch 0.839 (1.164) Remain 23:09:43 loss: 0.5445 Lr: 0.00238 [2024-02-18 20:42:32,647 INFO misc.py line 119 87073] Train: [54/100][1549/1557] Data 0.004 (0.211) Batch 0.994 (1.164) Remain 23:09:34 loss: 0.4483 Lr: 0.00238 [2024-02-18 20:42:33,609 INFO misc.py line 119 87073] Train: [54/100][1550/1557] Data 0.005 (0.211) Batch 0.963 (1.164) Remain 23:09:24 loss: 0.3665 Lr: 0.00238 [2024-02-18 20:42:34,436 INFO misc.py line 119 87073] Train: [54/100][1551/1557] Data 0.004 (0.211) Batch 0.822 (1.164) Remain 23:09:07 loss: 0.2419 Lr: 0.00238 [2024-02-18 20:42:35,084 INFO misc.py line 119 87073] Train: [54/100][1552/1557] Data 0.009 (0.210) Batch 0.653 (1.163) Remain 23:08:42 loss: 0.1593 Lr: 0.00238 [2024-02-18 20:42:35,849 INFO misc.py line 119 87073] Train: [54/100][1553/1557] Data 0.004 (0.210) Batch 0.761 (1.163) Remain 23:08:22 loss: 0.2344 Lr: 0.00238 [2024-02-18 20:42:36,938 INFO misc.py line 119 87073] Train: [54/100][1554/1557] Data 0.008 (0.210) Batch 1.083 (1.163) Remain 23:08:18 loss: 0.1607 Lr: 0.00238 [2024-02-18 20:42:37,791 INFO misc.py line 119 87073] Train: [54/100][1555/1557] Data 0.014 (0.210) Batch 0.864 (1.163) Remain 23:08:03 loss: 0.2886 Lr: 0.00238 [2024-02-18 20:42:38,714 INFO misc.py line 119 87073] Train: [54/100][1556/1557] Data 0.004 (0.210) Batch 0.923 (1.163) Remain 23:07:50 loss: 0.3981 Lr: 0.00238 [2024-02-18 20:42:39,706 INFO misc.py line 119 87073] Train: [54/100][1557/1557] Data 0.004 (0.210) Batch 0.992 (1.163) Remain 23:07:41 loss: 1.0170 Lr: 0.00238 [2024-02-18 20:42:39,707 INFO misc.py line 136 87073] Train result: loss: 0.3389 [2024-02-18 20:42:39,707 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 20:43:08,283 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6118 [2024-02-18 20:43:09,059 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8927 [2024-02-18 20:43:11,187 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3311 [2024-02-18 20:43:13,396 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3503 [2024-02-18 20:43:18,350 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2870 [2024-02-18 20:43:19,048 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0982 [2024-02-18 20:43:20,311 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2390 [2024-02-18 20:43:23,270 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3649 [2024-02-18 20:43:25,079 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2110 [2024-02-18 20:43:26,556 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7122/0.7732/0.9141. [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9418/0.9683 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9814/0.9873 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8667/0.9750 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3538/0.3838 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6393/0.6548 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7576/0.9116 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8467/0.9156 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9171/0.9588 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8258/0.8528 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7756/0.8444 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7312/0.8571 [2024-02-18 20:43:26,557 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6221/0.7427 [2024-02-18 20:43:26,558 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 20:43:26,561 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 20:43:26,561 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 20:43:36,779 INFO misc.py line 119 87073] Train: [55/100][1/1557] Data 1.107 (1.107) Batch 1.868 (1.868) Remain 37:10:11 loss: 0.3691 Lr: 0.00238 [2024-02-18 20:43:37,780 INFO misc.py line 119 87073] Train: [55/100][2/1557] Data 0.006 (0.006) Batch 0.960 (0.960) Remain 19:06:25 loss: 0.1903 Lr: 0.00238 [2024-02-18 20:43:38,651 INFO misc.py line 119 87073] Train: [55/100][3/1557] Data 0.047 (0.047) Batch 0.911 (0.911) Remain 18:07:08 loss: 0.4506 Lr: 0.00238 [2024-02-18 20:43:39,626 INFO misc.py line 119 87073] Train: [55/100][4/1557] Data 0.007 (0.007) Batch 0.977 (0.977) Remain 19:26:13 loss: 0.1613 Lr: 0.00238 [2024-02-18 20:43:40,376 INFO misc.py line 119 87073] Train: [55/100][5/1557] Data 0.005 (0.006) Batch 0.749 (0.863) Remain 17:10:09 loss: 0.2391 Lr: 0.00238 [2024-02-18 20:43:41,298 INFO misc.py line 119 87073] Train: [55/100][6/1557] Data 0.005 (0.006) Batch 0.922 (0.883) Remain 17:33:41 loss: 0.4240 Lr: 0.00238 [2024-02-18 20:43:50,195 INFO misc.py line 119 87073] Train: [55/100][7/1557] Data 2.262 (0.570) Batch 8.898 (2.887) Remain 57:25:18 loss: 0.1589 Lr: 0.00238 [2024-02-18 20:43:51,067 INFO misc.py line 119 87073] Train: [55/100][8/1557] Data 0.004 (0.456) Batch 0.868 (2.483) Remain 49:23:20 loss: 0.1041 Lr: 0.00238 [2024-02-18 20:43:51,968 INFO misc.py line 119 87073] Train: [55/100][9/1557] Data 0.008 (0.382) Batch 0.905 (2.220) Remain 44:09:25 loss: 0.3910 Lr: 0.00238 [2024-02-18 20:43:52,944 INFO misc.py line 119 87073] Train: [55/100][10/1557] Data 0.004 (0.328) Batch 0.976 (2.042) Remain 40:37:15 loss: 0.4028 Lr: 0.00238 [2024-02-18 20:43:53,901 INFO misc.py line 119 87073] Train: [55/100][11/1557] Data 0.005 (0.287) Batch 0.958 (1.907) Remain 37:55:26 loss: 0.1637 Lr: 0.00238 [2024-02-18 20:43:54,636 INFO misc.py line 119 87073] Train: [55/100][12/1557] Data 0.004 (0.256) Batch 0.733 (1.776) Remain 35:19:44 loss: 0.3644 Lr: 0.00238 [2024-02-18 20:43:55,388 INFO misc.py line 119 87073] Train: [55/100][13/1557] Data 0.007 (0.231) Batch 0.755 (1.674) Remain 33:17:47 loss: 0.5340 Lr: 0.00238 [2024-02-18 20:43:56,603 INFO misc.py line 119 87073] Train: [55/100][14/1557] Data 0.004 (0.210) Batch 1.214 (1.632) Remain 32:27:52 loss: 0.1991 Lr: 0.00238 [2024-02-18 20:43:57,615 INFO misc.py line 119 87073] Train: [55/100][15/1557] Data 0.005 (0.193) Batch 1.013 (1.581) Remain 31:26:18 loss: 0.3727 Lr: 0.00238 [2024-02-18 20:43:58,528 INFO misc.py line 119 87073] Train: [55/100][16/1557] Data 0.004 (0.179) Batch 0.912 (1.529) Remain 30:24:55 loss: 0.1587 Lr: 0.00238 [2024-02-18 20:43:59,697 INFO misc.py line 119 87073] Train: [55/100][17/1557] Data 0.004 (0.166) Batch 1.168 (1.503) Remain 29:54:08 loss: 0.2468 Lr: 0.00238 [2024-02-18 20:44:00,651 INFO misc.py line 119 87073] Train: [55/100][18/1557] Data 0.005 (0.155) Batch 0.954 (1.467) Remain 29:10:27 loss: 0.4066 Lr: 0.00238 [2024-02-18 20:44:01,423 INFO misc.py line 119 87073] Train: [55/100][19/1557] Data 0.005 (0.146) Batch 0.765 (1.423) Remain 28:18:05 loss: 0.2622 Lr: 0.00238 [2024-02-18 20:44:02,278 INFO misc.py line 119 87073] Train: [55/100][20/1557] Data 0.012 (0.138) Batch 0.861 (1.390) Remain 27:38:38 loss: 0.2708 Lr: 0.00238 [2024-02-18 20:44:03,562 INFO misc.py line 119 87073] Train: [55/100][21/1557] Data 0.005 (0.131) Batch 1.281 (1.384) Remain 27:31:23 loss: 0.2960 Lr: 0.00238 [2024-02-18 20:44:04,664 INFO misc.py line 119 87073] Train: [55/100][22/1557] Data 0.008 (0.124) Batch 1.098 (1.369) Remain 27:13:26 loss: 0.4232 Lr: 0.00238 [2024-02-18 20:44:05,746 INFO misc.py line 119 87073] Train: [55/100][23/1557] Data 0.012 (0.119) Batch 1.080 (1.354) Remain 26:56:12 loss: 0.7017 Lr: 0.00238 [2024-02-18 20:44:06,633 INFO misc.py line 119 87073] Train: [55/100][24/1557] Data 0.013 (0.114) Batch 0.896 (1.333) Remain 26:30:09 loss: 0.2542 Lr: 0.00238 [2024-02-18 20:44:07,605 INFO misc.py line 119 87073] Train: [55/100][25/1557] Data 0.004 (0.109) Batch 0.973 (1.316) Remain 26:10:37 loss: 0.2515 Lr: 0.00238 [2024-02-18 20:44:08,383 INFO misc.py line 119 87073] Train: [55/100][26/1557] Data 0.003 (0.104) Batch 0.776 (1.293) Remain 25:42:35 loss: 0.2795 Lr: 0.00238 [2024-02-18 20:44:09,181 INFO misc.py line 119 87073] Train: [55/100][27/1557] Data 0.005 (0.100) Batch 0.799 (1.272) Remain 25:18:00 loss: 0.1610 Lr: 0.00238 [2024-02-18 20:44:10,298 INFO misc.py line 119 87073] Train: [55/100][28/1557] Data 0.004 (0.096) Batch 1.118 (1.266) Remain 25:10:36 loss: 0.1851 Lr: 0.00238 [2024-02-18 20:44:11,396 INFO misc.py line 119 87073] Train: [55/100][29/1557] Data 0.004 (0.093) Batch 1.096 (1.259) Remain 25:02:48 loss: 0.2679 Lr: 0.00238 [2024-02-18 20:44:12,350 INFO misc.py line 119 87073] Train: [55/100][30/1557] Data 0.006 (0.089) Batch 0.956 (1.248) Remain 24:49:21 loss: 0.2287 Lr: 0.00238 [2024-02-18 20:44:13,397 INFO misc.py line 119 87073] Train: [55/100][31/1557] Data 0.004 (0.086) Batch 1.046 (1.241) Remain 24:40:42 loss: 0.6937 Lr: 0.00238 [2024-02-18 20:44:14,330 INFO misc.py line 119 87073] Train: [55/100][32/1557] Data 0.005 (0.083) Batch 0.933 (1.230) Remain 24:28:00 loss: 0.2021 Lr: 0.00238 [2024-02-18 20:44:15,122 INFO misc.py line 119 87073] Train: [55/100][33/1557] Data 0.006 (0.081) Batch 0.785 (1.216) Remain 24:10:16 loss: 0.2365 Lr: 0.00238 [2024-02-18 20:44:15,879 INFO misc.py line 119 87073] Train: [55/100][34/1557] Data 0.012 (0.079) Batch 0.765 (1.201) Remain 23:52:54 loss: 0.1648 Lr: 0.00238 [2024-02-18 20:44:17,203 INFO misc.py line 119 87073] Train: [55/100][35/1557] Data 0.004 (0.076) Batch 1.321 (1.205) Remain 23:57:21 loss: 0.1557 Lr: 0.00238 [2024-02-18 20:44:18,108 INFO misc.py line 119 87073] Train: [55/100][36/1557] Data 0.008 (0.074) Batch 0.908 (1.196) Remain 23:46:37 loss: 0.2579 Lr: 0.00238 [2024-02-18 20:44:19,055 INFO misc.py line 119 87073] Train: [55/100][37/1557] Data 0.004 (0.072) Batch 0.948 (1.188) Remain 23:37:54 loss: 0.7588 Lr: 0.00238 [2024-02-18 20:44:20,000 INFO misc.py line 119 87073] Train: [55/100][38/1557] Data 0.003 (0.070) Batch 0.944 (1.181) Remain 23:29:32 loss: 0.6621 Lr: 0.00238 [2024-02-18 20:44:21,048 INFO misc.py line 119 87073] Train: [55/100][39/1557] Data 0.004 (0.068) Batch 1.048 (1.178) Remain 23:25:07 loss: 0.3246 Lr: 0.00238 [2024-02-18 20:44:21,833 INFO misc.py line 119 87073] Train: [55/100][40/1557] Data 0.004 (0.067) Batch 0.783 (1.167) Remain 23:12:21 loss: 0.1739 Lr: 0.00238 [2024-02-18 20:44:22,592 INFO misc.py line 119 87073] Train: [55/100][41/1557] Data 0.006 (0.065) Batch 0.762 (1.156) Remain 22:59:36 loss: 0.3523 Lr: 0.00238 [2024-02-18 20:44:23,747 INFO misc.py line 119 87073] Train: [55/100][42/1557] Data 0.003 (0.063) Batch 1.154 (1.156) Remain 22:59:31 loss: 0.1257 Lr: 0.00238 [2024-02-18 20:44:24,693 INFO misc.py line 119 87073] Train: [55/100][43/1557] Data 0.005 (0.062) Batch 0.947 (1.151) Remain 22:53:15 loss: 1.1175 Lr: 0.00238 [2024-02-18 20:44:25,799 INFO misc.py line 119 87073] Train: [55/100][44/1557] Data 0.003 (0.061) Batch 1.106 (1.150) Remain 22:51:55 loss: 0.2957 Lr: 0.00238 [2024-02-18 20:44:26,825 INFO misc.py line 119 87073] Train: [55/100][45/1557] Data 0.004 (0.059) Batch 1.026 (1.147) Remain 22:48:22 loss: 0.2015 Lr: 0.00238 [2024-02-18 20:44:27,765 INFO misc.py line 119 87073] Train: [55/100][46/1557] Data 0.004 (0.058) Batch 0.940 (1.142) Remain 22:42:37 loss: 0.3776 Lr: 0.00238 [2024-02-18 20:44:28,520 INFO misc.py line 119 87073] Train: [55/100][47/1557] Data 0.004 (0.057) Batch 0.754 (1.133) Remain 22:32:04 loss: 0.3455 Lr: 0.00238 [2024-02-18 20:44:29,221 INFO misc.py line 119 87073] Train: [55/100][48/1557] Data 0.005 (0.056) Batch 0.701 (1.124) Remain 22:20:35 loss: 0.2548 Lr: 0.00238 [2024-02-18 20:44:30,520 INFO misc.py line 119 87073] Train: [55/100][49/1557] Data 0.005 (0.054) Batch 1.299 (1.128) Remain 22:25:06 loss: 0.1283 Lr: 0.00238 [2024-02-18 20:44:31,733 INFO misc.py line 119 87073] Train: [55/100][50/1557] Data 0.005 (0.053) Batch 1.203 (1.129) Remain 22:27:00 loss: 0.4356 Lr: 0.00238 [2024-02-18 20:44:32,701 INFO misc.py line 119 87073] Train: [55/100][51/1557] Data 0.015 (0.053) Batch 0.979 (1.126) Remain 22:23:14 loss: 0.2556 Lr: 0.00238 [2024-02-18 20:44:33,642 INFO misc.py line 119 87073] Train: [55/100][52/1557] Data 0.003 (0.052) Batch 0.936 (1.122) Remain 22:18:36 loss: 0.3122 Lr: 0.00238 [2024-02-18 20:44:34,523 INFO misc.py line 119 87073] Train: [55/100][53/1557] Data 0.011 (0.051) Batch 0.884 (1.117) Remain 22:12:54 loss: 0.1538 Lr: 0.00238 [2024-02-18 20:44:35,323 INFO misc.py line 119 87073] Train: [55/100][54/1557] Data 0.005 (0.050) Batch 0.777 (1.111) Remain 22:04:55 loss: 0.1459 Lr: 0.00238 [2024-02-18 20:44:36,100 INFO misc.py line 119 87073] Train: [55/100][55/1557] Data 0.029 (0.049) Batch 0.802 (1.105) Remain 21:57:49 loss: 0.2431 Lr: 0.00238 [2024-02-18 20:44:37,386 INFO misc.py line 119 87073] Train: [55/100][56/1557] Data 0.004 (0.049) Batch 1.278 (1.108) Remain 22:01:41 loss: 0.1368 Lr: 0.00238 [2024-02-18 20:44:38,392 INFO misc.py line 119 87073] Train: [55/100][57/1557] Data 0.012 (0.048) Batch 1.004 (1.106) Remain 21:59:22 loss: 0.3609 Lr: 0.00237 [2024-02-18 20:44:39,534 INFO misc.py line 119 87073] Train: [55/100][58/1557] Data 0.014 (0.047) Batch 1.144 (1.107) Remain 22:00:10 loss: 0.3780 Lr: 0.00237 [2024-02-18 20:44:40,500 INFO misc.py line 119 87073] Train: [55/100][59/1557] Data 0.012 (0.047) Batch 0.974 (1.104) Remain 21:57:19 loss: 0.1686 Lr: 0.00237 [2024-02-18 20:44:41,476 INFO misc.py line 119 87073] Train: [55/100][60/1557] Data 0.004 (0.046) Batch 0.976 (1.102) Remain 21:54:37 loss: 0.6782 Lr: 0.00237 [2024-02-18 20:44:42,287 INFO misc.py line 119 87073] Train: [55/100][61/1557] Data 0.004 (0.045) Batch 0.811 (1.097) Remain 21:48:37 loss: 0.3191 Lr: 0.00237 [2024-02-18 20:44:43,066 INFO misc.py line 119 87073] Train: [55/100][62/1557] Data 0.004 (0.045) Batch 0.775 (1.092) Remain 21:42:05 loss: 0.2381 Lr: 0.00237 [2024-02-18 20:45:04,372 INFO misc.py line 119 87073] Train: [55/100][63/1557] Data 7.785 (0.174) Batch 21.310 (1.429) Remain 28:23:57 loss: 0.1196 Lr: 0.00237 [2024-02-18 20:45:05,324 INFO misc.py line 119 87073] Train: [55/100][64/1557] Data 0.004 (0.171) Batch 0.942 (1.421) Remain 28:14:25 loss: 0.7046 Lr: 0.00237 [2024-02-18 20:45:06,217 INFO misc.py line 119 87073] Train: [55/100][65/1557] Data 0.014 (0.168) Batch 0.901 (1.412) Remain 28:04:24 loss: 0.2797 Lr: 0.00237 [2024-02-18 20:45:07,171 INFO misc.py line 119 87073] Train: [55/100][66/1557] Data 0.005 (0.166) Batch 0.955 (1.405) Remain 27:55:44 loss: 0.4468 Lr: 0.00237 [2024-02-18 20:45:08,020 INFO misc.py line 119 87073] Train: [55/100][67/1557] Data 0.004 (0.163) Batch 0.849 (1.396) Remain 27:45:20 loss: 0.3659 Lr: 0.00237 [2024-02-18 20:45:08,786 INFO misc.py line 119 87073] Train: [55/100][68/1557] Data 0.004 (0.161) Batch 0.762 (1.387) Remain 27:33:40 loss: 0.2047 Lr: 0.00237 [2024-02-18 20:45:09,599 INFO misc.py line 119 87073] Train: [55/100][69/1557] Data 0.008 (0.158) Batch 0.818 (1.378) Remain 27:23:22 loss: 0.5329 Lr: 0.00237 [2024-02-18 20:45:10,919 INFO misc.py line 119 87073] Train: [55/100][70/1557] Data 0.004 (0.156) Batch 1.313 (1.377) Remain 27:22:12 loss: 0.1629 Lr: 0.00237 [2024-02-18 20:45:11,838 INFO misc.py line 119 87073] Train: [55/100][71/1557] Data 0.009 (0.154) Batch 0.925 (1.370) Remain 27:14:15 loss: 0.3351 Lr: 0.00237 [2024-02-18 20:45:12,717 INFO misc.py line 119 87073] Train: [55/100][72/1557] Data 0.005 (0.152) Batch 0.879 (1.363) Remain 27:05:44 loss: 0.9237 Lr: 0.00237 [2024-02-18 20:45:13,666 INFO misc.py line 119 87073] Train: [55/100][73/1557] Data 0.004 (0.150) Batch 0.948 (1.357) Remain 26:58:38 loss: 0.5804 Lr: 0.00237 [2024-02-18 20:45:14,605 INFO misc.py line 119 87073] Train: [55/100][74/1557] Data 0.005 (0.148) Batch 0.940 (1.352) Remain 26:51:37 loss: 0.4702 Lr: 0.00237 [2024-02-18 20:45:15,371 INFO misc.py line 119 87073] Train: [55/100][75/1557] Data 0.004 (0.146) Batch 0.766 (1.343) Remain 26:41:54 loss: 0.2731 Lr: 0.00237 [2024-02-18 20:45:16,133 INFO misc.py line 119 87073] Train: [55/100][76/1557] Data 0.003 (0.144) Batch 0.751 (1.335) Remain 26:32:12 loss: 0.1596 Lr: 0.00237 [2024-02-18 20:45:17,469 INFO misc.py line 119 87073] Train: [55/100][77/1557] Data 0.014 (0.142) Batch 1.333 (1.335) Remain 26:32:08 loss: 0.2370 Lr: 0.00237 [2024-02-18 20:45:18,347 INFO misc.py line 119 87073] Train: [55/100][78/1557] Data 0.017 (0.140) Batch 0.891 (1.329) Remain 26:25:03 loss: 0.5489 Lr: 0.00237 [2024-02-18 20:45:19,557 INFO misc.py line 119 87073] Train: [55/100][79/1557] Data 0.004 (0.138) Batch 1.197 (1.328) Remain 26:22:58 loss: 0.9644 Lr: 0.00237 [2024-02-18 20:45:20,585 INFO misc.py line 119 87073] Train: [55/100][80/1557] Data 0.017 (0.137) Batch 1.040 (1.324) Remain 26:18:29 loss: 0.3520 Lr: 0.00237 [2024-02-18 20:45:21,631 INFO misc.py line 119 87073] Train: [55/100][81/1557] Data 0.005 (0.135) Batch 1.036 (1.320) Remain 26:14:03 loss: 0.4451 Lr: 0.00237 [2024-02-18 20:45:22,323 INFO misc.py line 119 87073] Train: [55/100][82/1557] Data 0.015 (0.134) Batch 0.701 (1.312) Remain 26:04:42 loss: 0.3101 Lr: 0.00237 [2024-02-18 20:45:23,116 INFO misc.py line 119 87073] Train: [55/100][83/1557] Data 0.006 (0.132) Batch 0.784 (1.306) Remain 25:56:48 loss: 0.2255 Lr: 0.00237 [2024-02-18 20:45:24,195 INFO misc.py line 119 87073] Train: [55/100][84/1557] Data 0.014 (0.131) Batch 1.078 (1.303) Remain 25:53:26 loss: 0.1408 Lr: 0.00237 [2024-02-18 20:45:25,112 INFO misc.py line 119 87073] Train: [55/100][85/1557] Data 0.016 (0.129) Batch 0.928 (1.298) Remain 25:47:58 loss: 0.3379 Lr: 0.00237 [2024-02-18 20:45:26,075 INFO misc.py line 119 87073] Train: [55/100][86/1557] Data 0.005 (0.128) Batch 0.960 (1.294) Remain 25:43:05 loss: 0.2699 Lr: 0.00237 [2024-02-18 20:45:27,008 INFO misc.py line 119 87073] Train: [55/100][87/1557] Data 0.007 (0.126) Batch 0.937 (1.290) Remain 25:37:59 loss: 0.3378 Lr: 0.00237 [2024-02-18 20:45:27,977 INFO misc.py line 119 87073] Train: [55/100][88/1557] Data 0.004 (0.125) Batch 0.968 (1.286) Remain 25:33:27 loss: 0.1512 Lr: 0.00237 [2024-02-18 20:45:28,793 INFO misc.py line 119 87073] Train: [55/100][89/1557] Data 0.005 (0.123) Batch 0.817 (1.281) Remain 25:26:55 loss: 0.2778 Lr: 0.00237 [2024-02-18 20:45:29,595 INFO misc.py line 119 87073] Train: [55/100][90/1557] Data 0.004 (0.122) Batch 0.801 (1.275) Remain 25:20:20 loss: 0.3609 Lr: 0.00237 [2024-02-18 20:45:30,888 INFO misc.py line 119 87073] Train: [55/100][91/1557] Data 0.004 (0.121) Batch 1.279 (1.275) Remain 25:20:22 loss: 0.1945 Lr: 0.00237 [2024-02-18 20:45:31,997 INFO misc.py line 119 87073] Train: [55/100][92/1557] Data 0.018 (0.120) Batch 1.114 (1.273) Remain 25:18:11 loss: 0.4285 Lr: 0.00237 [2024-02-18 20:45:33,234 INFO misc.py line 119 87073] Train: [55/100][93/1557] Data 0.013 (0.118) Batch 1.232 (1.273) Remain 25:17:37 loss: 0.4361 Lr: 0.00237 [2024-02-18 20:45:34,458 INFO misc.py line 119 87073] Train: [55/100][94/1557] Data 0.018 (0.117) Batch 1.226 (1.272) Remain 25:16:58 loss: 0.6083 Lr: 0.00237 [2024-02-18 20:45:35,322 INFO misc.py line 119 87073] Train: [55/100][95/1557] Data 0.016 (0.116) Batch 0.876 (1.268) Remain 25:11:49 loss: 0.4336 Lr: 0.00237 [2024-02-18 20:45:36,027 INFO misc.py line 119 87073] Train: [55/100][96/1557] Data 0.004 (0.115) Batch 0.705 (1.262) Remain 25:04:34 loss: 0.2997 Lr: 0.00237 [2024-02-18 20:45:36,732 INFO misc.py line 119 87073] Train: [55/100][97/1557] Data 0.004 (0.114) Batch 0.700 (1.256) Remain 24:57:25 loss: 0.3262 Lr: 0.00237 [2024-02-18 20:45:37,906 INFO misc.py line 119 87073] Train: [55/100][98/1557] Data 0.009 (0.113) Batch 1.167 (1.255) Remain 24:56:17 loss: 0.1117 Lr: 0.00237 [2024-02-18 20:45:38,978 INFO misc.py line 119 87073] Train: [55/100][99/1557] Data 0.016 (0.112) Batch 1.083 (1.253) Remain 24:54:08 loss: 0.5088 Lr: 0.00237 [2024-02-18 20:45:39,835 INFO misc.py line 119 87073] Train: [55/100][100/1557] Data 0.005 (0.111) Batch 0.857 (1.249) Remain 24:49:14 loss: 0.2916 Lr: 0.00237 [2024-02-18 20:45:40,782 INFO misc.py line 119 87073] Train: [55/100][101/1557] Data 0.005 (0.109) Batch 0.948 (1.246) Remain 24:45:33 loss: 0.2389 Lr: 0.00237 [2024-02-18 20:45:41,754 INFO misc.py line 119 87073] Train: [55/100][102/1557] Data 0.004 (0.108) Batch 0.973 (1.243) Remain 24:42:14 loss: 0.4937 Lr: 0.00237 [2024-02-18 20:45:42,485 INFO misc.py line 119 87073] Train: [55/100][103/1557] Data 0.004 (0.107) Batch 0.729 (1.238) Remain 24:36:05 loss: 0.2201 Lr: 0.00237 [2024-02-18 20:45:43,253 INFO misc.py line 119 87073] Train: [55/100][104/1557] Data 0.005 (0.106) Batch 0.768 (1.234) Remain 24:30:31 loss: 0.2703 Lr: 0.00237 [2024-02-18 20:45:44,476 INFO misc.py line 119 87073] Train: [55/100][105/1557] Data 0.005 (0.105) Batch 1.223 (1.234) Remain 24:30:22 loss: 0.1841 Lr: 0.00237 [2024-02-18 20:45:45,596 INFO misc.py line 119 87073] Train: [55/100][106/1557] Data 0.006 (0.104) Batch 1.120 (1.232) Remain 24:29:01 loss: 0.2605 Lr: 0.00237 [2024-02-18 20:45:46,491 INFO misc.py line 119 87073] Train: [55/100][107/1557] Data 0.006 (0.103) Batch 0.897 (1.229) Remain 24:25:09 loss: 0.3101 Lr: 0.00237 [2024-02-18 20:45:47,478 INFO misc.py line 119 87073] Train: [55/100][108/1557] Data 0.004 (0.103) Batch 0.988 (1.227) Remain 24:22:23 loss: 0.2442 Lr: 0.00237 [2024-02-18 20:45:48,511 INFO misc.py line 119 87073] Train: [55/100][109/1557] Data 0.006 (0.102) Batch 1.021 (1.225) Remain 24:20:03 loss: 0.4576 Lr: 0.00237 [2024-02-18 20:45:49,241 INFO misc.py line 119 87073] Train: [55/100][110/1557] Data 0.016 (0.101) Batch 0.741 (1.220) Remain 24:14:39 loss: 0.2027 Lr: 0.00237 [2024-02-18 20:45:50,061 INFO misc.py line 119 87073] Train: [55/100][111/1557] Data 0.004 (0.100) Batch 0.793 (1.217) Remain 24:09:55 loss: 0.1584 Lr: 0.00237 [2024-02-18 20:45:51,324 INFO misc.py line 119 87073] Train: [55/100][112/1557] Data 0.032 (0.099) Batch 1.284 (1.217) Remain 24:10:37 loss: 0.1722 Lr: 0.00237 [2024-02-18 20:45:52,362 INFO misc.py line 119 87073] Train: [55/100][113/1557] Data 0.010 (0.098) Batch 1.035 (1.215) Remain 24:08:38 loss: 0.3520 Lr: 0.00237 [2024-02-18 20:45:53,405 INFO misc.py line 119 87073] Train: [55/100][114/1557] Data 0.012 (0.098) Batch 1.047 (1.214) Remain 24:06:48 loss: 0.5474 Lr: 0.00237 [2024-02-18 20:45:54,375 INFO misc.py line 119 87073] Train: 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Batch 1.003 (1.349) Remain 26:47:42 loss: 0.2453 Lr: 0.00237 [2024-02-18 20:46:18,879 INFO misc.py line 119 87073] Train: [55/100][122/1557] Data 0.009 (0.171) Batch 1.026 (1.346) Remain 26:44:26 loss: 0.4447 Lr: 0.00237 [2024-02-18 20:46:19,830 INFO misc.py line 119 87073] Train: [55/100][123/1557] Data 0.013 (0.170) Batch 0.962 (1.343) Remain 26:40:36 loss: 0.2054 Lr: 0.00237 [2024-02-18 20:46:20,518 INFO misc.py line 119 87073] Train: [55/100][124/1557] Data 0.003 (0.168) Batch 0.687 (1.338) Remain 26:34:07 loss: 0.4064 Lr: 0.00237 [2024-02-18 20:46:21,258 INFO misc.py line 119 87073] Train: [55/100][125/1557] Data 0.004 (0.167) Batch 0.732 (1.333) Remain 26:28:10 loss: 0.2996 Lr: 0.00237 [2024-02-18 20:46:22,477 INFO misc.py line 119 87073] Train: [55/100][126/1557] Data 0.012 (0.166) Batch 1.220 (1.332) Remain 26:27:03 loss: 0.1501 Lr: 0.00237 [2024-02-18 20:46:23,455 INFO misc.py line 119 87073] Train: [55/100][127/1557] Data 0.011 (0.164) Batch 0.985 (1.329) Remain 26:23:42 loss: 0.4028 Lr: 0.00237 [2024-02-18 20:46:24,485 INFO misc.py line 119 87073] Train: [55/100][128/1557] Data 0.004 (0.163) Batch 1.030 (1.327) Remain 26:20:50 loss: 0.5267 Lr: 0.00237 [2024-02-18 20:46:25,494 INFO misc.py line 119 87073] Train: [55/100][129/1557] Data 0.004 (0.162) Batch 1.008 (1.324) Remain 26:17:48 loss: 0.3450 Lr: 0.00237 [2024-02-18 20:46:26,458 INFO misc.py line 119 87073] Train: [55/100][130/1557] Data 0.004 (0.161) Batch 0.965 (1.321) Remain 26:14:24 loss: 0.0956 Lr: 0.00237 [2024-02-18 20:46:27,264 INFO misc.py line 119 87073] Train: [55/100][131/1557] Data 0.004 (0.159) Batch 0.804 (1.317) Remain 26:09:34 loss: 0.3858 Lr: 0.00237 [2024-02-18 20:46:28,024 INFO misc.py line 119 87073] Train: [55/100][132/1557] Data 0.006 (0.158) Batch 0.750 (1.313) Remain 26:04:18 loss: 0.1841 Lr: 0.00237 [2024-02-18 20:46:29,355 INFO misc.py line 119 87073] Train: [55/100][133/1557] Data 0.015 (0.157) Batch 1.337 (1.313) Remain 26:04:30 loss: 0.2052 Lr: 0.00237 [2024-02-18 20:46:30,299 INFO misc.py line 119 87073] Train: [55/100][134/1557] Data 0.009 (0.156) Batch 0.950 (1.310) Remain 26:01:11 loss: 0.4036 Lr: 0.00237 [2024-02-18 20:46:31,305 INFO misc.py line 119 87073] Train: [55/100][135/1557] Data 0.004 (0.155) Batch 1.006 (1.308) Remain 25:58:24 loss: 0.5382 Lr: 0.00237 [2024-02-18 20:46:32,439 INFO misc.py line 119 87073] Train: [55/100][136/1557] Data 0.004 (0.154) Batch 1.134 (1.307) Remain 25:56:50 loss: 0.3835 Lr: 0.00237 [2024-02-18 20:46:33,336 INFO misc.py line 119 87073] Train: [55/100][137/1557] Data 0.004 (0.153) Batch 0.896 (1.304) Remain 25:53:10 loss: 0.1547 Lr: 0.00237 [2024-02-18 20:46:34,135 INFO misc.py line 119 87073] Train: [55/100][138/1557] Data 0.004 (0.152) Batch 0.795 (1.300) Remain 25:48:39 loss: 0.5844 Lr: 0.00237 [2024-02-18 20:46:34,897 INFO misc.py line 119 87073] Train: [55/100][139/1557] Data 0.008 (0.150) Batch 0.766 (1.296) Remain 25:43:57 loss: 0.3934 Lr: 0.00237 [2024-02-18 20:46:35,997 INFO misc.py line 119 87073] Train: [55/100][140/1557] Data 0.004 (0.149) Batch 1.100 (1.295) Remain 25:42:13 loss: 0.1192 Lr: 0.00237 [2024-02-18 20:46:36,982 INFO misc.py line 119 87073] Train: [55/100][141/1557] Data 0.005 (0.148) Batch 0.986 (1.292) Remain 25:39:32 loss: 0.6052 Lr: 0.00237 [2024-02-18 20:46:37,821 INFO misc.py line 119 87073] Train: [55/100][142/1557] Data 0.004 (0.147) Batch 0.836 (1.289) Remain 25:35:36 loss: 0.3305 Lr: 0.00237 [2024-02-18 20:46:38,743 INFO misc.py line 119 87073] Train: [55/100][143/1557] Data 0.006 (0.146) Batch 0.923 (1.286) Remain 25:32:28 loss: 0.8465 Lr: 0.00237 [2024-02-18 20:46:39,631 INFO misc.py line 119 87073] Train: [55/100][144/1557] Data 0.006 (0.145) Batch 0.889 (1.284) Remain 25:29:05 loss: 0.3979 Lr: 0.00237 [2024-02-18 20:46:40,405 INFO misc.py line 119 87073] Train: [55/100][145/1557] Data 0.004 (0.144) Batch 0.774 (1.280) Remain 25:24:47 loss: 0.2902 Lr: 0.00237 [2024-02-18 20:46:41,147 INFO misc.py line 119 87073] Train: [55/100][146/1557] Data 0.005 (0.143) Batch 0.732 (1.276) Remain 25:20:12 loss: 0.3662 Lr: 0.00237 [2024-02-18 20:46:42,437 INFO misc.py line 119 87073] Train: [55/100][147/1557] Data 0.015 (0.142) Batch 1.292 (1.276) Remain 25:20:19 loss: 0.4120 Lr: 0.00237 [2024-02-18 20:46:43,388 INFO misc.py line 119 87073] Train: [55/100][148/1557] Data 0.014 (0.142) Batch 0.961 (1.274) Remain 25:17:42 loss: 0.3292 Lr: 0.00237 [2024-02-18 20:46:44,337 INFO misc.py line 119 87073] Train: [55/100][149/1557] Data 0.003 (0.141) Batch 0.947 (1.272) Remain 25:15:01 loss: 0.8584 Lr: 0.00237 [2024-02-18 20:46:45,520 INFO misc.py line 119 87073] Train: [55/100][150/1557] Data 0.004 (0.140) Batch 1.183 (1.271) Remain 25:14:16 loss: 0.3495 Lr: 0.00237 [2024-02-18 20:46:46,665 INFO misc.py line 119 87073] Train: [55/100][151/1557] Data 0.005 (0.139) Batch 1.146 (1.270) Remain 25:13:15 loss: 0.2874 Lr: 0.00237 [2024-02-18 20:46:47,430 INFO misc.py line 119 87073] Train: [55/100][152/1557] Data 0.004 (0.138) Batch 0.763 (1.267) Remain 25:09:10 loss: 0.3327 Lr: 0.00237 [2024-02-18 20:46:48,090 INFO misc.py line 119 87073] Train: [55/100][153/1557] Data 0.006 (0.137) Batch 0.639 (1.263) Remain 25:04:10 loss: 0.3041 Lr: 0.00237 [2024-02-18 20:46:49,227 INFO misc.py line 119 87073] Train: [55/100][154/1557] Data 0.027 (0.136) Batch 1.149 (1.262) Remain 25:03:14 loss: 0.1172 Lr: 0.00237 [2024-02-18 20:46:50,178 INFO misc.py line 119 87073] Train: [55/100][155/1557] Data 0.016 (0.135) Batch 0.962 (1.260) Remain 25:00:52 loss: 0.2727 Lr: 0.00237 [2024-02-18 20:46:51,205 INFO misc.py line 119 87073] Train: [55/100][156/1557] Data 0.005 (0.135) Batch 1.027 (1.259) Remain 24:59:02 loss: 0.4568 Lr: 0.00237 [2024-02-18 20:46:52,104 INFO misc.py line 119 87073] Train: [55/100][157/1557] Data 0.005 (0.134) Batch 0.901 (1.256) Remain 24:56:14 loss: 0.4582 Lr: 0.00237 [2024-02-18 20:46:52,989 INFO misc.py line 119 87073] Train: [55/100][158/1557] Data 0.003 (0.133) Batch 0.878 (1.254) Remain 24:53:19 loss: 0.5714 Lr: 0.00237 [2024-02-18 20:46:53,751 INFO misc.py line 119 87073] Train: [55/100][159/1557] Data 0.010 (0.132) Batch 0.767 (1.251) Remain 24:49:35 loss: 0.3954 Lr: 0.00237 [2024-02-18 20:46:54,492 INFO misc.py line 119 87073] Train: [55/100][160/1557] Data 0.004 (0.131) Batch 0.735 (1.247) Remain 24:45:39 loss: 0.2901 Lr: 0.00237 [2024-02-18 20:46:55,826 INFO misc.py line 119 87073] Train: [55/100][161/1557] Data 0.010 (0.131) Batch 1.330 (1.248) Remain 24:46:15 loss: 0.1515 Lr: 0.00237 [2024-02-18 20:46:56,806 INFO misc.py line 119 87073] Train: [55/100][162/1557] Data 0.014 (0.130) Batch 0.989 (1.246) Remain 24:44:17 loss: 0.4108 Lr: 0.00237 [2024-02-18 20:46:57,766 INFO misc.py line 119 87073] Train: [55/100][163/1557] Data 0.005 (0.129) Batch 0.961 (1.244) Remain 24:42:09 loss: 0.5383 Lr: 0.00237 [2024-02-18 20:46:58,785 INFO misc.py line 119 87073] Train: [55/100][164/1557] Data 0.004 (0.128) Batch 1.019 (1.243) Remain 24:40:28 loss: 0.5588 Lr: 0.00237 [2024-02-18 20:46:59,796 INFO misc.py line 119 87073] Train: [55/100][165/1557] Data 0.004 (0.128) Batch 1.011 (1.242) Remain 24:38:44 loss: 0.6901 Lr: 0.00237 [2024-02-18 20:47:00,515 INFO misc.py line 119 87073] Train: [55/100][166/1557] Data 0.003 (0.127) Batch 0.719 (1.238) Remain 24:34:53 loss: 0.4808 Lr: 0.00237 [2024-02-18 20:47:01,247 INFO misc.py line 119 87073] Train: [55/100][167/1557] Data 0.004 (0.126) Batch 0.718 (1.235) Remain 24:31:05 loss: 0.3022 Lr: 0.00237 [2024-02-18 20:47:02,534 INFO misc.py line 119 87073] Train: [55/100][168/1557] Data 0.019 (0.125) Batch 1.291 (1.236) Remain 24:31:28 loss: 0.1466 Lr: 0.00237 [2024-02-18 20:47:03,509 INFO misc.py line 119 87073] Train: [55/100][169/1557] Data 0.014 (0.125) Batch 0.986 (1.234) Remain 24:29:40 loss: 0.4597 Lr: 0.00237 [2024-02-18 20:47:04,446 INFO misc.py line 119 87073] Train: [55/100][170/1557] Data 0.003 (0.124) Batch 0.936 (1.232) Remain 24:27:31 loss: 0.2176 Lr: 0.00237 [2024-02-18 20:47:05,458 INFO misc.py line 119 87073] Train: 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Batch 1.010 (1.324) Remain 26:16:20 loss: 0.3436 Lr: 0.00237 [2024-02-18 20:47:29,894 INFO misc.py line 119 87073] Train: [55/100][178/1557] Data 0.013 (0.170) Batch 0.900 (1.321) Remain 26:13:25 loss: 0.2224 Lr: 0.00237 [2024-02-18 20:47:30,911 INFO misc.py line 119 87073] Train: [55/100][179/1557] Data 0.005 (0.169) Batch 1.017 (1.320) Remain 26:11:21 loss: 0.5476 Lr: 0.00237 [2024-02-18 20:47:33,097 INFO misc.py line 119 87073] Train: [55/100][180/1557] Data 1.180 (0.175) Batch 2.155 (1.324) Remain 26:16:56 loss: 0.2276 Lr: 0.00237 [2024-02-18 20:47:33,950 INFO misc.py line 119 87073] Train: [55/100][181/1557] Data 0.036 (0.174) Batch 0.884 (1.322) Remain 26:13:58 loss: 0.2536 Lr: 0.00237 [2024-02-18 20:47:35,228 INFO misc.py line 119 87073] Train: [55/100][182/1557] Data 0.003 (0.173) Batch 1.268 (1.322) Remain 26:13:36 loss: 0.2026 Lr: 0.00237 [2024-02-18 20:47:36,209 INFO misc.py line 119 87073] Train: [55/100][183/1557] Data 0.014 (0.173) Batch 0.990 (1.320) Remain 26:11:23 loss: 0.4808 Lr: 0.00237 [2024-02-18 20:47:37,107 INFO misc.py line 119 87073] Train: [55/100][184/1557] Data 0.006 (0.172) Batch 0.899 (1.317) Remain 26:08:35 loss: 0.1721 Lr: 0.00237 [2024-02-18 20:47:38,112 INFO misc.py line 119 87073] Train: [55/100][185/1557] Data 0.004 (0.171) Batch 1.004 (1.316) Remain 26:06:31 loss: 0.3030 Lr: 0.00237 [2024-02-18 20:47:39,157 INFO misc.py line 119 87073] Train: [55/100][186/1557] Data 0.005 (0.170) Batch 1.046 (1.314) Remain 26:04:44 loss: 0.6077 Lr: 0.00237 [2024-02-18 20:47:39,907 INFO misc.py line 119 87073] Train: [55/100][187/1557] Data 0.004 (0.169) Batch 0.750 (1.311) Remain 26:01:04 loss: 0.3076 Lr: 0.00237 [2024-02-18 20:47:40,702 INFO misc.py line 119 87073] Train: [55/100][188/1557] Data 0.004 (0.168) Batch 0.792 (1.308) Remain 25:57:42 loss: 0.5271 Lr: 0.00237 [2024-02-18 20:47:42,042 INFO misc.py line 119 87073] Train: [55/100][189/1557] Data 0.007 (0.167) Batch 1.329 (1.308) Remain 25:57:49 loss: 0.2056 Lr: 0.00237 [2024-02-18 20:47:42,966 INFO misc.py line 119 87073] Train: [55/100][190/1557] Data 0.018 (0.166) Batch 0.938 (1.307) Remain 25:55:26 loss: 0.3938 Lr: 0.00237 [2024-02-18 20:47:43,940 INFO misc.py line 119 87073] Train: [55/100][191/1557] Data 0.004 (0.165) Batch 0.974 (1.305) Remain 25:53:18 loss: 0.3323 Lr: 0.00237 [2024-02-18 20:47:44,934 INFO misc.py line 119 87073] Train: [55/100][192/1557] Data 0.004 (0.165) Batch 0.992 (1.303) Remain 25:51:19 loss: 0.2224 Lr: 0.00237 [2024-02-18 20:47:46,238 INFO misc.py line 119 87073] Train: [55/100][193/1557] Data 0.007 (0.164) Batch 1.293 (1.303) Remain 25:51:14 loss: 0.6245 Lr: 0.00237 [2024-02-18 20:47:47,043 INFO misc.py line 119 87073] Train: [55/100][194/1557] Data 0.017 (0.163) Batch 0.818 (1.300) Remain 25:48:11 loss: 0.2379 Lr: 0.00237 [2024-02-18 20:47:47,889 INFO misc.py line 119 87073] Train: [55/100][195/1557] Data 0.003 (0.162) Batch 0.845 (1.298) Remain 25:45:20 loss: 0.2006 Lr: 0.00237 [2024-02-18 20:47:48,935 INFO misc.py line 119 87073] Train: [55/100][196/1557] Data 0.004 (0.161) Batch 1.041 (1.297) Remain 25:43:44 loss: 0.1191 Lr: 0.00237 [2024-02-18 20:47:49,946 INFO misc.py line 119 87073] Train: [55/100][197/1557] Data 0.009 (0.161) Batch 1.006 (1.295) Remain 25:41:56 loss: 0.6250 Lr: 0.00237 [2024-02-18 20:47:50,837 INFO misc.py line 119 87073] Train: [55/100][198/1557] Data 0.014 (0.160) Batch 0.901 (1.293) Remain 25:39:30 loss: 1.0235 Lr: 0.00237 [2024-02-18 20:47:51,804 INFO misc.py line 119 87073] Train: [55/100][199/1557] Data 0.004 (0.159) Batch 0.966 (1.292) Remain 25:37:30 loss: 0.1153 Lr: 0.00237 [2024-02-18 20:47:52,819 INFO misc.py line 119 87073] Train: [55/100][200/1557] Data 0.005 (0.158) Batch 1.016 (1.290) Remain 25:35:48 loss: 0.4376 Lr: 0.00237 [2024-02-18 20:47:53,598 INFO misc.py line 119 87073] Train: [55/100][201/1557] Data 0.004 (0.157) Batch 0.777 (1.288) Remain 25:32:42 loss: 0.1673 Lr: 0.00237 [2024-02-18 20:47:54,400 INFO misc.py line 119 87073] Train: [55/100][202/1557] Data 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25:17:51 loss: 0.3556 Lr: 0.00237 [2024-02-18 20:48:00,843 INFO misc.py line 119 87073] Train: [55/100][209/1557] Data 0.008 (0.152) Batch 0.765 (1.273) Remain 25:14:52 loss: 0.2245 Lr: 0.00237 [2024-02-18 20:48:02,015 INFO misc.py line 119 87073] Train: [55/100][210/1557] Data 0.005 (0.151) Batch 1.172 (1.272) Remain 25:14:16 loss: 0.1201 Lr: 0.00237 [2024-02-18 20:48:03,061 INFO misc.py line 119 87073] Train: [55/100][211/1557] Data 0.006 (0.150) Batch 1.046 (1.271) Remain 25:12:57 loss: 1.0047 Lr: 0.00237 [2024-02-18 20:48:03,897 INFO misc.py line 119 87073] Train: [55/100][212/1557] Data 0.005 (0.150) Batch 0.837 (1.269) Remain 25:10:28 loss: 0.8264 Lr: 0.00237 [2024-02-18 20:48:04,944 INFO misc.py line 119 87073] Train: [55/100][213/1557] Data 0.004 (0.149) Batch 1.040 (1.268) Remain 25:09:09 loss: 0.5221 Lr: 0.00237 [2024-02-18 20:48:05,846 INFO misc.py line 119 87073] Train: [55/100][214/1557] Data 0.010 (0.148) Batch 0.910 (1.266) Remain 25:07:06 loss: 0.2334 Lr: 0.00237 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line 119 87073] Train: [55/100][221/1557] Data 0.005 (0.144) Batch 1.101 (1.258) Remain 24:57:11 loss: 0.3354 Lr: 0.00237 [2024-02-18 20:48:13,672 INFO misc.py line 119 87073] Train: [55/100][222/1557] Data 0.004 (0.143) Batch 0.750 (1.256) Remain 24:54:24 loss: 0.2845 Lr: 0.00237 [2024-02-18 20:48:14,348 INFO misc.py line 119 87073] Train: [55/100][223/1557] Data 0.005 (0.142) Batch 0.672 (1.253) Remain 24:51:13 loss: 0.2238 Lr: 0.00237 [2024-02-18 20:48:15,712 INFO misc.py line 119 87073] Train: [55/100][224/1557] Data 0.009 (0.142) Batch 1.358 (1.254) Remain 24:51:46 loss: 0.1575 Lr: 0.00237 [2024-02-18 20:48:16,508 INFO misc.py line 119 87073] Train: [55/100][225/1557] Data 0.015 (0.141) Batch 0.807 (1.252) Remain 24:49:21 loss: 0.2432 Lr: 0.00237 [2024-02-18 20:48:17,356 INFO misc.py line 119 87073] Train: [55/100][226/1557] Data 0.004 (0.141) Batch 0.847 (1.250) Remain 24:47:10 loss: 0.3228 Lr: 0.00237 [2024-02-18 20:48:18,296 INFO misc.py line 119 87073] Train: 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Batch 1.180 (1.319) Remain 26:08:55 loss: 0.1586 Lr: 0.00237 [2024-02-18 20:48:42,839 INFO misc.py line 119 87073] Train: [55/100][234/1557] Data 0.006 (0.174) Batch 0.906 (1.317) Remain 26:06:46 loss: 0.2595 Lr: 0.00237 [2024-02-18 20:48:43,768 INFO misc.py line 119 87073] Train: [55/100][235/1557] Data 0.003 (0.173) Batch 0.928 (1.315) Remain 26:04:45 loss: 0.4360 Lr: 0.00237 [2024-02-18 20:48:44,539 INFO misc.py line 119 87073] Train: [55/100][236/1557] Data 0.004 (0.172) Batch 0.764 (1.313) Remain 26:01:55 loss: 0.3501 Lr: 0.00237 [2024-02-18 20:48:45,336 INFO misc.py line 119 87073] Train: [55/100][237/1557] Data 0.012 (0.172) Batch 0.805 (1.311) Remain 25:59:19 loss: 0.1503 Lr: 0.00237 [2024-02-18 20:48:46,618 INFO misc.py line 119 87073] Train: [55/100][238/1557] Data 0.003 (0.171) Batch 1.274 (1.310) Remain 25:59:06 loss: 0.1516 Lr: 0.00237 [2024-02-18 20:48:47,526 INFO misc.py line 119 87073] Train: [55/100][239/1557] Data 0.013 (0.170) Batch 0.915 (1.309) Remain 25:57:05 loss: 0.2404 Lr: 0.00237 [2024-02-18 20:48:48,262 INFO misc.py line 119 87073] Train: [55/100][240/1557] Data 0.005 (0.170) Batch 0.734 (1.306) Remain 25:54:11 loss: 0.0494 Lr: 0.00237 [2024-02-18 20:48:49,239 INFO misc.py line 119 87073] Train: [55/100][241/1557] Data 0.008 (0.169) Batch 0.980 (1.305) Remain 25:52:31 loss: 0.4042 Lr: 0.00237 [2024-02-18 20:48:50,121 INFO misc.py line 119 87073] Train: [55/100][242/1557] Data 0.005 (0.168) Batch 0.883 (1.303) Remain 25:50:24 loss: 0.2482 Lr: 0.00237 [2024-02-18 20:48:50,909 INFO misc.py line 119 87073] Train: [55/100][243/1557] Data 0.003 (0.168) Batch 0.787 (1.301) Remain 25:47:49 loss: 0.1659 Lr: 0.00237 [2024-02-18 20:48:51,754 INFO misc.py line 119 87073] Train: [55/100][244/1557] Data 0.005 (0.167) Batch 0.844 (1.299) Remain 25:45:32 loss: 0.4620 Lr: 0.00237 [2024-02-18 20:48:53,047 INFO misc.py line 119 87073] Train: [55/100][245/1557] Data 0.006 (0.166) Batch 1.285 (1.299) Remain 25:45:27 loss: 0.1501 Lr: 0.00237 [2024-02-18 20:48:53,898 INFO misc.py line 119 87073] Train: [55/100][246/1557] Data 0.014 (0.166) Batch 0.860 (1.297) Remain 25:43:17 loss: 0.4864 Lr: 0.00237 [2024-02-18 20:48:55,004 INFO misc.py line 119 87073] Train: [55/100][247/1557] Data 0.006 (0.165) Batch 1.105 (1.297) Remain 25:42:19 loss: 0.5092 Lr: 0.00236 [2024-02-18 20:48:56,138 INFO misc.py line 119 87073] Train: [55/100][248/1557] Data 0.006 (0.164) Batch 1.136 (1.296) Remain 25:41:31 loss: 0.3077 Lr: 0.00236 [2024-02-18 20:48:57,218 INFO misc.py line 119 87073] Train: [55/100][249/1557] Data 0.004 (0.164) Batch 1.080 (1.295) Remain 25:40:27 loss: 0.4794 Lr: 0.00236 [2024-02-18 20:48:57,980 INFO misc.py line 119 87073] Train: [55/100][250/1557] Data 0.004 (0.163) Batch 0.762 (1.293) Remain 25:37:52 loss: 0.4308 Lr: 0.00236 [2024-02-18 20:48:58,765 INFO misc.py line 119 87073] Train: [55/100][251/1557] Data 0.005 (0.162) Batch 0.780 (1.291) Remain 25:35:23 loss: 0.2010 Lr: 0.00236 [2024-02-18 20:48:59,812 INFO misc.py line 119 87073] Train: [55/100][252/1557] Data 0.010 (0.162) Batch 1.042 (1.290) Remain 25:34:10 loss: 0.1705 Lr: 0.00236 [2024-02-18 20:49:00,656 INFO misc.py line 119 87073] Train: [55/100][253/1557] Data 0.014 (0.161) Batch 0.854 (1.288) Remain 25:32:04 loss: 0.3028 Lr: 0.00236 [2024-02-18 20:49:01,613 INFO misc.py line 119 87073] Train: [55/100][254/1557] Data 0.005 (0.161) Batch 0.959 (1.287) Remain 25:30:29 loss: 0.2269 Lr: 0.00236 [2024-02-18 20:49:02,591 INFO misc.py line 119 87073] Train: [55/100][255/1557] Data 0.003 (0.160) Batch 0.975 (1.285) Remain 25:29:00 loss: 0.5841 Lr: 0.00236 [2024-02-18 20:49:03,521 INFO misc.py line 119 87073] Train: [55/100][256/1557] Data 0.007 (0.159) Batch 0.932 (1.284) Remain 25:27:19 loss: 0.4253 Lr: 0.00236 [2024-02-18 20:49:04,232 INFO misc.py line 119 87073] Train: [55/100][257/1557] Data 0.005 (0.159) Batch 0.704 (1.282) Remain 25:24:35 loss: 0.3652 Lr: 0.00236 [2024-02-18 20:49:04,983 INFO misc.py line 119 87073] Train: [55/100][258/1557] Data 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Batch 1.014 (1.310) Remain 25:57:59 loss: 0.4424 Lr: 0.00236 [2024-02-18 20:49:54,390 INFO misc.py line 119 87073] Train: [55/100][290/1557] Data 0.009 (0.172) Batch 0.945 (1.309) Remain 25:56:27 loss: 0.7328 Lr: 0.00236 [2024-02-18 20:49:55,296 INFO misc.py line 119 87073] Train: [55/100][291/1557] Data 0.006 (0.171) Batch 0.907 (1.308) Remain 25:54:46 loss: 0.3719 Lr: 0.00236 [2024-02-18 20:49:56,068 INFO misc.py line 119 87073] Train: [55/100][292/1557] Data 0.004 (0.170) Batch 0.763 (1.306) Remain 25:52:30 loss: 0.2201 Lr: 0.00236 [2024-02-18 20:49:56,787 INFO misc.py line 119 87073] Train: [55/100][293/1557] Data 0.013 (0.170) Batch 0.728 (1.304) Remain 25:50:07 loss: 0.2516 Lr: 0.00236 [2024-02-18 20:49:58,092 INFO misc.py line 119 87073] Train: [55/100][294/1557] Data 0.004 (0.169) Batch 1.305 (1.304) Remain 25:50:06 loss: 0.1757 Lr: 0.00236 [2024-02-18 20:49:59,230 INFO misc.py line 119 87073] Train: [55/100][295/1557] Data 0.004 (0.169) Batch 1.137 (1.303) Remain 25:49:24 loss: 0.5831 Lr: 0.00236 [2024-02-18 20:50:00,202 INFO misc.py line 119 87073] Train: [55/100][296/1557] Data 0.006 (0.168) Batch 0.973 (1.302) Remain 25:48:02 loss: 0.1654 Lr: 0.00236 [2024-02-18 20:50:01,218 INFO misc.py line 119 87073] Train: [55/100][297/1557] Data 0.005 (0.168) Batch 1.017 (1.301) Remain 25:46:51 loss: 0.0938 Lr: 0.00236 [2024-02-18 20:50:02,121 INFO misc.py line 119 87073] Train: [55/100][298/1557] Data 0.004 (0.167) Batch 0.903 (1.300) Remain 25:45:14 loss: 0.3131 Lr: 0.00236 [2024-02-18 20:50:02,846 INFO misc.py line 119 87073] Train: [55/100][299/1557] Data 0.003 (0.167) Batch 0.702 (1.298) Remain 25:42:49 loss: 0.2617 Lr: 0.00236 [2024-02-18 20:50:03,625 INFO misc.py line 119 87073] Train: [55/100][300/1557] Data 0.028 (0.166) Batch 0.801 (1.296) Remain 25:40:48 loss: 0.3635 Lr: 0.00236 [2024-02-18 20:50:04,934 INFO misc.py line 119 87073] Train: [55/100][301/1557] Data 0.004 (0.166) Batch 1.302 (1.296) Remain 25:40:48 loss: 0.1706 Lr: 0.00236 [2024-02-18 20:50:05,985 INFO misc.py line 119 87073] Train: [55/100][302/1557] Data 0.011 (0.165) Batch 1.047 (1.295) Remain 25:39:47 loss: 0.3691 Lr: 0.00236 [2024-02-18 20:50:06,896 INFO misc.py line 119 87073] Train: [55/100][303/1557] Data 0.015 (0.164) Batch 0.922 (1.294) Remain 25:38:17 loss: 0.1097 Lr: 0.00236 [2024-02-18 20:50:07,669 INFO misc.py line 119 87073] Train: [55/100][304/1557] Data 0.004 (0.164) Batch 0.771 (1.292) Remain 25:36:12 loss: 0.3533 Lr: 0.00236 [2024-02-18 20:50:08,611 INFO misc.py line 119 87073] Train: [55/100][305/1557] Data 0.006 (0.163) Batch 0.938 (1.291) Remain 25:34:47 loss: 0.1883 Lr: 0.00236 [2024-02-18 20:50:09,409 INFO misc.py line 119 87073] Train: [55/100][306/1557] Data 0.011 (0.163) Batch 0.804 (1.290) Remain 25:32:51 loss: 0.3185 Lr: 0.00236 [2024-02-18 20:50:10,196 INFO misc.py line 119 87073] Train: [55/100][307/1557] Data 0.003 (0.162) Batch 0.786 (1.288) Remain 25:30:52 loss: 0.2735 Lr: 0.00236 [2024-02-18 20:50:11,280 INFO misc.py line 119 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line 119 87073] Train: [55/100][333/1557] Data 0.004 (0.150) Batch 0.874 (1.261) Remain 24:57:50 loss: 0.5895 Lr: 0.00236 [2024-02-18 20:50:35,410 INFO misc.py line 119 87073] Train: [55/100][334/1557] Data 0.004 (0.150) Batch 0.738 (1.259) Remain 24:55:56 loss: 0.4218 Lr: 0.00236 [2024-02-18 20:50:36,111 INFO misc.py line 119 87073] Train: [55/100][335/1557] Data 0.013 (0.149) Batch 0.710 (1.257) Remain 24:53:57 loss: 0.3475 Lr: 0.00236 [2024-02-18 20:50:37,468 INFO misc.py line 119 87073] Train: [55/100][336/1557] Data 0.004 (0.149) Batch 1.353 (1.258) Remain 24:54:16 loss: 0.1759 Lr: 0.00236 [2024-02-18 20:50:38,248 INFO misc.py line 119 87073] Train: [55/100][337/1557] Data 0.008 (0.148) Batch 0.782 (1.256) Remain 24:52:33 loss: 0.3091 Lr: 0.00236 [2024-02-18 20:50:39,218 INFO misc.py line 119 87073] Train: [55/100][338/1557] Data 0.007 (0.148) Batch 0.973 (1.255) Remain 24:51:32 loss: 0.1169 Lr: 0.00236 [2024-02-18 20:50:40,297 INFO misc.py line 119 87073] Train: 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Batch 1.091 (1.308) Remain 25:53:27 loss: 0.2353 Lr: 0.00236 [2024-02-18 20:51:06,884 INFO misc.py line 119 87073] Train: [55/100][346/1557] Data 0.005 (0.171) Batch 1.007 (1.307) Remain 25:52:23 loss: 0.3339 Lr: 0.00236 [2024-02-18 20:51:07,865 INFO misc.py line 119 87073] Train: [55/100][347/1557] Data 0.005 (0.171) Batch 0.983 (1.306) Remain 25:51:15 loss: 0.2924 Lr: 0.00236 [2024-02-18 20:51:08,633 INFO misc.py line 119 87073] Train: [55/100][348/1557] Data 0.004 (0.170) Batch 0.766 (1.304) Remain 25:49:22 loss: 0.4742 Lr: 0.00236 [2024-02-18 20:51:09,321 INFO misc.py line 119 87073] Train: [55/100][349/1557] Data 0.005 (0.170) Batch 0.681 (1.302) Remain 25:47:12 loss: 0.2234 Lr: 0.00236 [2024-02-18 20:51:10,587 INFO misc.py line 119 87073] Train: [55/100][350/1557] Data 0.012 (0.169) Batch 1.269 (1.302) Remain 25:47:04 loss: 0.1530 Lr: 0.00236 [2024-02-18 20:51:11,684 INFO misc.py line 119 87073] Train: [55/100][351/1557] Data 0.009 (0.169) Batch 1.097 (1.302) Remain 25:46:21 loss: 0.2159 Lr: 0.00236 [2024-02-18 20:51:12,840 INFO misc.py line 119 87073] Train: [55/100][352/1557] Data 0.009 (0.168) Batch 1.145 (1.301) Remain 25:45:47 loss: 0.2400 Lr: 0.00236 [2024-02-18 20:51:13,937 INFO misc.py line 119 87073] Train: [55/100][353/1557] Data 0.021 (0.168) Batch 1.101 (1.301) Remain 25:45:05 loss: 0.2781 Lr: 0.00236 [2024-02-18 20:51:15,047 INFO misc.py line 119 87073] Train: [55/100][354/1557] Data 0.016 (0.168) Batch 1.110 (1.300) Remain 25:44:25 loss: 0.3376 Lr: 0.00236 [2024-02-18 20:51:17,222 INFO misc.py line 119 87073] Train: [55/100][355/1557] Data 1.011 (0.170) Batch 2.170 (1.303) Remain 25:47:20 loss: 0.3762 Lr: 0.00236 [2024-02-18 20:51:18,068 INFO misc.py line 119 87073] Train: [55/100][356/1557] Data 0.022 (0.170) Batch 0.862 (1.301) Remain 25:45:50 loss: 0.3158 Lr: 0.00236 [2024-02-18 20:51:19,408 INFO misc.py line 119 87073] Train: [55/100][357/1557] Data 0.004 (0.169) Batch 1.338 (1.302) Remain 25:45:56 loss: 0.1279 Lr: 0.00236 [2024-02-18 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87073] Train: [55/100][364/1557] Data 0.006 (0.166) Batch 1.086 (1.295) Remain 25:37:46 loss: 0.0916 Lr: 0.00236 [2024-02-18 20:51:27,009 INFO misc.py line 119 87073] Train: [55/100][365/1557] Data 0.012 (0.166) Batch 0.929 (1.294) Remain 25:36:33 loss: 0.5870 Lr: 0.00236 [2024-02-18 20:51:27,999 INFO misc.py line 119 87073] Train: [55/100][366/1557] Data 0.004 (0.165) Batch 0.987 (1.293) Remain 25:35:31 loss: 0.3762 Lr: 0.00236 [2024-02-18 20:51:29,085 INFO misc.py line 119 87073] Train: [55/100][367/1557] Data 0.007 (0.165) Batch 1.088 (1.292) Remain 25:34:50 loss: 0.5026 Lr: 0.00236 [2024-02-18 20:51:30,069 INFO misc.py line 119 87073] Train: [55/100][368/1557] Data 0.004 (0.164) Batch 0.984 (1.292) Remain 25:33:48 loss: 0.1314 Lr: 0.00236 [2024-02-18 20:51:30,863 INFO misc.py line 119 87073] Train: [55/100][369/1557] Data 0.005 (0.164) Batch 0.793 (1.290) Remain 25:32:10 loss: 0.2219 Lr: 0.00236 [2024-02-18 20:51:31,700 INFO misc.py line 119 87073] Train: [55/100][370/1557] Data 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25:23:47 loss: 0.3789 Lr: 0.00236 [2024-02-18 20:51:38,105 INFO misc.py line 119 87073] Train: [55/100][377/1557] Data 0.005 (0.160) Batch 0.787 (1.282) Remain 25:22:11 loss: 0.3293 Lr: 0.00236 [2024-02-18 20:51:39,304 INFO misc.py line 119 87073] Train: [55/100][378/1557] Data 0.016 (0.160) Batch 1.199 (1.282) Remain 25:21:54 loss: 0.1086 Lr: 0.00236 [2024-02-18 20:51:40,220 INFO misc.py line 119 87073] Train: [55/100][379/1557] Data 0.017 (0.160) Batch 0.928 (1.281) Remain 25:20:46 loss: 0.1975 Lr: 0.00236 [2024-02-18 20:51:41,056 INFO misc.py line 119 87073] Train: [55/100][380/1557] Data 0.004 (0.159) Batch 0.834 (1.280) Remain 25:19:20 loss: 0.4287 Lr: 0.00236 [2024-02-18 20:51:42,066 INFO misc.py line 119 87073] Train: [55/100][381/1557] Data 0.006 (0.159) Batch 1.000 (1.279) Remain 25:18:26 loss: 0.2692 Lr: 0.00236 [2024-02-18 20:51:43,050 INFO misc.py line 119 87073] Train: [55/100][382/1557] Data 0.016 (0.158) Batch 0.996 (1.278) Remain 25:17:31 loss: 0.3055 Lr: 0.00236 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Batch 0.876 (1.311) Remain 25:55:53 loss: 0.3953 Lr: 0.00236 [2024-02-18 20:52:21,320 INFO misc.py line 119 87073] Train: [55/100][402/1557] Data 0.004 (0.171) Batch 0.989 (1.310) Remain 25:54:54 loss: 0.4061 Lr: 0.00236 [2024-02-18 20:52:22,319 INFO misc.py line 119 87073] Train: [55/100][403/1557] Data 0.005 (0.171) Batch 0.995 (1.309) Remain 25:53:57 loss: 0.0692 Lr: 0.00236 [2024-02-18 20:52:23,098 INFO misc.py line 119 87073] Train: [55/100][404/1557] Data 0.009 (0.171) Batch 0.783 (1.308) Remain 25:52:22 loss: 0.1537 Lr: 0.00236 [2024-02-18 20:52:23,894 INFO misc.py line 119 87073] Train: [55/100][405/1557] Data 0.005 (0.170) Batch 0.788 (1.307) Remain 25:50:48 loss: 0.2367 Lr: 0.00236 [2024-02-18 20:52:25,200 INFO misc.py line 119 87073] Train: [55/100][406/1557] Data 0.013 (0.170) Batch 1.307 (1.307) Remain 25:50:47 loss: 0.1669 Lr: 0.00236 [2024-02-18 20:52:26,230 INFO misc.py line 119 87073] Train: [55/100][407/1557] Data 0.012 (0.169) Batch 1.026 (1.306) Remain 25:49:57 loss: 0.2558 Lr: 0.00236 [2024-02-18 20:52:27,416 INFO misc.py line 119 87073] Train: [55/100][408/1557] Data 0.015 (0.169) Batch 1.184 (1.306) Remain 25:49:34 loss: 0.4171 Lr: 0.00236 [2024-02-18 20:52:28,428 INFO misc.py line 119 87073] Train: [55/100][409/1557] Data 0.017 (0.169) Batch 1.012 (1.305) Remain 25:48:41 loss: 0.5048 Lr: 0.00236 [2024-02-18 20:52:29,215 INFO misc.py line 119 87073] Train: [55/100][410/1557] Data 0.017 (0.168) Batch 0.800 (1.304) Remain 25:47:11 loss: 0.4558 Lr: 0.00236 [2024-02-18 20:52:29,996 INFO misc.py line 119 87073] Train: [55/100][411/1557] Data 0.005 (0.168) Batch 0.780 (1.302) Remain 25:45:39 loss: 0.3706 Lr: 0.00236 [2024-02-18 20:52:30,789 INFO misc.py line 119 87073] Train: [55/100][412/1557] Data 0.006 (0.167) Batch 0.787 (1.301) Remain 25:44:08 loss: 0.2012 Lr: 0.00236 [2024-02-18 20:52:32,180 INFO misc.py line 119 87073] Train: [55/100][413/1557] Data 0.011 (0.167) Batch 1.385 (1.301) Remain 25:44:21 loss: 0.1678 Lr: 0.00236 [2024-02-18 20:52:33,193 INFO misc.py line 119 87073] Train: [55/100][414/1557] Data 0.017 (0.167) Batch 1.014 (1.301) Remain 25:43:30 loss: 0.5287 Lr: 0.00236 [2024-02-18 20:52:34,257 INFO misc.py line 119 87073] Train: [55/100][415/1557] Data 0.016 (0.166) Batch 1.074 (1.300) Remain 25:42:50 loss: 0.2695 Lr: 0.00236 [2024-02-18 20:52:35,153 INFO misc.py line 119 87073] Train: [55/100][416/1557] Data 0.006 (0.166) Batch 0.897 (1.299) Remain 25:41:39 loss: 0.2737 Lr: 0.00236 [2024-02-18 20:52:36,136 INFO misc.py line 119 87073] Train: [55/100][417/1557] Data 0.005 (0.166) Batch 0.984 (1.298) Remain 25:40:43 loss: 0.2756 Lr: 0.00236 [2024-02-18 20:52:36,896 INFO misc.py line 119 87073] Train: [55/100][418/1557] Data 0.004 (0.165) Batch 0.760 (1.297) Remain 25:39:10 loss: 0.2620 Lr: 0.00236 [2024-02-18 20:52:37,748 INFO misc.py line 119 87073] Train: [55/100][419/1557] Data 0.004 (0.165) Batch 0.851 (1.296) Remain 25:37:52 loss: 0.3062 Lr: 0.00236 [2024-02-18 20:52:38,832 INFO misc.py line 119 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0.004 (0.162) Batch 0.765 (1.291) Remain 25:31:20 loss: 0.2236 Lr: 0.00236 [2024-02-18 20:52:45,873 INFO misc.py line 119 87073] Train: [55/100][427/1557] Data 0.016 (0.162) Batch 1.321 (1.291) Remain 25:31:24 loss: 0.2221 Lr: 0.00236 [2024-02-18 20:52:46,657 INFO misc.py line 119 87073] Train: [55/100][428/1557] Data 0.011 (0.161) Batch 0.790 (1.289) Remain 25:29:59 loss: 0.2384 Lr: 0.00236 [2024-02-18 20:52:47,544 INFO misc.py line 119 87073] Train: [55/100][429/1557] Data 0.005 (0.161) Batch 0.888 (1.288) Remain 25:28:51 loss: 0.2983 Lr: 0.00236 [2024-02-18 20:52:48,440 INFO misc.py line 119 87073] Train: [55/100][430/1557] Data 0.005 (0.161) Batch 0.892 (1.288) Remain 25:27:43 loss: 0.1297 Lr: 0.00236 [2024-02-18 20:52:49,383 INFO misc.py line 119 87073] Train: [55/100][431/1557] Data 0.008 (0.160) Batch 0.946 (1.287) Remain 25:26:45 loss: 0.1629 Lr: 0.00236 [2024-02-18 20:52:50,124 INFO misc.py line 119 87073] Train: [55/100][432/1557] Data 0.005 (0.160) Batch 0.743 (1.285) Remain 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[2024-02-18 20:52:56,792 INFO misc.py line 119 87073] Train: [55/100][439/1557] Data 0.004 (0.157) Batch 0.832 (1.280) Remain 25:18:44 loss: 0.2015 Lr: 0.00235 [2024-02-18 20:52:57,563 INFO misc.py line 119 87073] Train: [55/100][440/1557] Data 0.004 (0.157) Batch 0.759 (1.279) Remain 25:17:18 loss: 0.3207 Lr: 0.00235 [2024-02-18 20:52:58,836 INFO misc.py line 119 87073] Train: [55/100][441/1557] Data 0.017 (0.157) Batch 1.277 (1.279) Remain 25:17:16 loss: 0.1988 Lr: 0.00235 [2024-02-18 20:52:59,841 INFO misc.py line 119 87073] Train: [55/100][442/1557] Data 0.012 (0.156) Batch 1.003 (1.278) Remain 25:16:30 loss: 0.3919 Lr: 0.00235 [2024-02-18 20:53:00,884 INFO misc.py line 119 87073] Train: [55/100][443/1557] Data 0.015 (0.156) Batch 1.052 (1.278) Remain 25:15:52 loss: 0.5164 Lr: 0.00235 [2024-02-18 20:53:01,955 INFO misc.py line 119 87073] Train: [55/100][444/1557] Data 0.006 (0.156) Batch 1.072 (1.277) Remain 25:15:18 loss: 0.3144 Lr: 0.00235 [2024-02-18 20:53:02,906 INFO misc.py line 119 87073] Train: [55/100][445/1557] Data 0.005 (0.155) Batch 0.950 (1.277) Remain 25:14:24 loss: 0.8387 Lr: 0.00235 [2024-02-18 20:53:03,676 INFO misc.py line 119 87073] Train: [55/100][446/1557] Data 0.006 (0.155) Batch 0.770 (1.275) Remain 25:13:01 loss: 0.3673 Lr: 0.00235 [2024-02-18 20:53:04,381 INFO misc.py line 119 87073] Train: [55/100][447/1557] Data 0.006 (0.155) Batch 0.702 (1.274) Remain 25:11:28 loss: 0.3869 Lr: 0.00235 [2024-02-18 20:53:05,680 INFO misc.py line 119 87073] Train: [55/100][448/1557] Data 0.009 (0.154) Batch 1.297 (1.274) Remain 25:11:30 loss: 0.1176 Lr: 0.00235 [2024-02-18 20:53:06,657 INFO misc.py line 119 87073] Train: [55/100][449/1557] Data 0.012 (0.154) Batch 0.984 (1.274) Remain 25:10:42 loss: 0.2698 Lr: 0.00235 [2024-02-18 20:53:07,782 INFO misc.py line 119 87073] Train: [55/100][450/1557] Data 0.003 (0.154) Batch 1.125 (1.273) Remain 25:10:18 loss: 0.4821 Lr: 0.00235 [2024-02-18 20:53:08,767 INFO misc.py line 119 87073] Train: 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Batch 0.933 (1.314) Remain 25:58:06 loss: 0.4384 Lr: 0.00235 [2024-02-18 20:53:36,039 INFO misc.py line 119 87073] Train: [55/100][458/1557] Data 0.005 (0.171) Batch 0.989 (1.313) Remain 25:57:14 loss: 0.3913 Lr: 0.00235 [2024-02-18 20:53:37,071 INFO misc.py line 119 87073] Train: [55/100][459/1557] Data 0.003 (0.171) Batch 1.032 (1.312) Remain 25:56:29 loss: 0.3360 Lr: 0.00235 [2024-02-18 20:53:37,796 INFO misc.py line 119 87073] Train: [55/100][460/1557] Data 0.004 (0.170) Batch 0.724 (1.311) Remain 25:54:56 loss: 0.3184 Lr: 0.00235 [2024-02-18 20:53:38,589 INFO misc.py line 119 87073] Train: [55/100][461/1557] Data 0.004 (0.170) Batch 0.792 (1.310) Remain 25:53:34 loss: 0.2035 Lr: 0.00235 [2024-02-18 20:53:39,864 INFO misc.py line 119 87073] Train: [55/100][462/1557] Data 0.005 (0.170) Batch 1.276 (1.310) Remain 25:53:27 loss: 0.1844 Lr: 0.00235 [2024-02-18 20:53:40,896 INFO misc.py line 119 87073] Train: [55/100][463/1557] Data 0.004 (0.169) Batch 1.030 (1.309) Remain 25:52:43 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line 119 87073] Train: [55/100][501/1557] Data 0.004 (0.157) Batch 0.909 (1.283) Remain 25:21:16 loss: 0.4072 Lr: 0.00235 [2024-02-18 20:54:18,548 INFO misc.py line 119 87073] Train: [55/100][502/1557] Data 0.004 (0.157) Batch 0.758 (1.282) Remain 25:20:00 loss: 0.2467 Lr: 0.00235 [2024-02-18 20:54:19,198 INFO misc.py line 119 87073] Train: [55/100][503/1557] Data 0.014 (0.156) Batch 0.660 (1.281) Remain 25:18:30 loss: 0.3651 Lr: 0.00235 [2024-02-18 20:54:20,485 INFO misc.py line 119 87073] Train: [55/100][504/1557] Data 0.004 (0.156) Batch 1.277 (1.281) Remain 25:18:28 loss: 0.1527 Lr: 0.00235 [2024-02-18 20:54:21,675 INFO misc.py line 119 87073] Train: [55/100][505/1557] Data 0.015 (0.156) Batch 1.190 (1.281) Remain 25:18:14 loss: 0.2518 Lr: 0.00235 [2024-02-18 20:54:22,624 INFO misc.py line 119 87073] Train: [55/100][506/1557] Data 0.014 (0.155) Batch 0.960 (1.280) Remain 25:17:27 loss: 0.3782 Lr: 0.00235 [2024-02-18 20:54:23,826 INFO misc.py line 119 87073] Train: 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Batch 0.857 (1.312) Remain 25:54:57 loss: 0.1510 Lr: 0.00235 [2024-02-18 20:54:48,808 INFO misc.py line 119 87073] Train: [55/100][514/1557] Data 0.003 (0.172) Batch 1.019 (1.311) Remain 25:54:15 loss: 0.3789 Lr: 0.00235 [2024-02-18 20:54:49,566 INFO misc.py line 119 87073] Train: [55/100][515/1557] Data 0.004 (0.171) Batch 0.758 (1.310) Remain 25:52:57 loss: 0.5186 Lr: 0.00235 [2024-02-18 20:54:50,321 INFO misc.py line 119 87073] Train: [55/100][516/1557] Data 0.004 (0.171) Batch 0.747 (1.309) Remain 25:51:37 loss: 0.4560 Lr: 0.00235 [2024-02-18 20:54:51,063 INFO misc.py line 119 87073] Train: [55/100][517/1557] Data 0.012 (0.171) Batch 0.749 (1.308) Remain 25:50:19 loss: 0.3086 Lr: 0.00235 [2024-02-18 20:54:52,341 INFO misc.py line 119 87073] Train: [55/100][518/1557] Data 0.005 (0.170) Batch 1.276 (1.308) Remain 25:50:13 loss: 0.1571 Lr: 0.00235 [2024-02-18 20:54:53,539 INFO misc.py line 119 87073] Train: [55/100][519/1557] Data 0.007 (0.170) Batch 1.190 (1.308) Remain 25:49:55 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Batch 0.964 (1.309) Remain 25:50:32 loss: 0.2238 Lr: 0.00235 [2024-02-18 20:56:00,571 INFO misc.py line 119 87073] Train: [55/100][570/1557] Data 0.004 (0.174) Batch 0.837 (1.309) Remain 25:49:31 loss: 0.1644 Lr: 0.00235 [2024-02-18 20:56:01,513 INFO misc.py line 119 87073] Train: [55/100][571/1557] Data 0.003 (0.174) Batch 0.933 (1.308) Remain 25:48:43 loss: 0.2945 Lr: 0.00235 [2024-02-18 20:56:02,244 INFO misc.py line 119 87073] Train: [55/100][572/1557] Data 0.012 (0.173) Batch 0.737 (1.307) Remain 25:47:30 loss: 0.1669 Lr: 0.00235 [2024-02-18 20:56:02,927 INFO misc.py line 119 87073] Train: [55/100][573/1557] Data 0.006 (0.173) Batch 0.682 (1.306) Remain 25:46:11 loss: 0.2368 Lr: 0.00235 [2024-02-18 20:56:04,143 INFO misc.py line 119 87073] Train: [55/100][574/1557] Data 0.007 (0.173) Batch 1.206 (1.306) Remain 25:45:58 loss: 0.1570 Lr: 0.00235 [2024-02-18 20:56:05,312 INFO misc.py line 119 87073] Train: [55/100][575/1557] Data 0.016 (0.173) Batch 1.141 (1.305) Remain 25:45:36 loss: 0.2014 Lr: 0.00235 [2024-02-18 20:56:06,448 INFO misc.py line 119 87073] Train: [55/100][576/1557] Data 0.045 (0.172) Batch 1.166 (1.305) Remain 25:45:17 loss: 0.1947 Lr: 0.00235 [2024-02-18 20:56:07,493 INFO misc.py line 119 87073] Train: [55/100][577/1557] Data 0.015 (0.172) Batch 1.045 (1.305) Remain 25:44:44 loss: 0.1631 Lr: 0.00235 [2024-02-18 20:56:08,386 INFO misc.py line 119 87073] Train: [55/100][578/1557] Data 0.016 (0.172) Batch 0.903 (1.304) Remain 25:43:53 loss: 0.2935 Lr: 0.00235 [2024-02-18 20:56:09,177 INFO misc.py line 119 87073] Train: [55/100][579/1557] Data 0.005 (0.172) Batch 0.792 (1.303) Remain 25:42:48 loss: 0.1510 Lr: 0.00235 [2024-02-18 20:56:09,938 INFO misc.py line 119 87073] Train: [55/100][580/1557] Data 0.003 (0.171) Batch 0.752 (1.302) Remain 25:41:39 loss: 0.2882 Lr: 0.00235 [2024-02-18 20:56:11,216 INFO misc.py line 119 87073] Train: [55/100][581/1557] Data 0.013 (0.171) Batch 1.276 (1.302) Remain 25:41:35 loss: 0.1683 Lr: 0.00235 [2024-02-18 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line 119 87073] Train: [55/100][613/1557] Data 0.004 (0.162) Batch 1.050 (1.283) Remain 25:18:55 loss: 1.0685 Lr: 0.00235 [2024-02-18 20:56:42,386 INFO misc.py line 119 87073] Train: [55/100][614/1557] Data 0.011 (0.162) Batch 0.842 (1.283) Remain 25:18:02 loss: 0.3713 Lr: 0.00235 [2024-02-18 20:56:43,204 INFO misc.py line 119 87073] Train: [55/100][615/1557] Data 0.005 (0.162) Batch 0.818 (1.282) Remain 25:17:07 loss: 0.1949 Lr: 0.00235 [2024-02-18 20:56:44,547 INFO misc.py line 119 87073] Train: [55/100][616/1557] Data 0.004 (0.162) Batch 1.338 (1.282) Remain 25:17:12 loss: 0.1897 Lr: 0.00235 [2024-02-18 20:56:45,625 INFO misc.py line 119 87073] Train: [55/100][617/1557] Data 0.011 (0.161) Batch 1.083 (1.282) Remain 25:16:48 loss: 0.1597 Lr: 0.00235 [2024-02-18 20:56:46,630 INFO misc.py line 119 87073] Train: [55/100][618/1557] Data 0.005 (0.161) Batch 1.001 (1.281) Remain 25:16:14 loss: 0.3377 Lr: 0.00235 [2024-02-18 20:56:47,640 INFO misc.py line 119 87073] Train: 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Batch 1.126 (1.311) Remain 25:51:10 loss: 0.4011 Lr: 0.00234 [2024-02-18 20:57:14,996 INFO misc.py line 119 87073] Train: [55/100][626/1557] Data 0.012 (0.174) Batch 0.962 (1.310) Remain 25:50:29 loss: 0.3740 Lr: 0.00234 [2024-02-18 20:57:16,041 INFO misc.py line 119 87073] Train: [55/100][627/1557] Data 0.005 (0.174) Batch 1.046 (1.310) Remain 25:49:57 loss: 0.1522 Lr: 0.00234 [2024-02-18 20:57:16,808 INFO misc.py line 119 87073] Train: [55/100][628/1557] Data 0.004 (0.173) Batch 0.767 (1.309) Remain 25:48:54 loss: 0.2407 Lr: 0.00234 [2024-02-18 20:57:17,585 INFO misc.py line 119 87073] Train: [55/100][629/1557] Data 0.004 (0.173) Batch 0.776 (1.308) Remain 25:47:53 loss: 0.3394 Lr: 0.00234 [2024-02-18 20:57:18,848 INFO misc.py line 119 87073] Train: [55/100][630/1557] Data 0.004 (0.173) Batch 1.262 (1.308) Remain 25:47:46 loss: 0.1895 Lr: 0.00234 [2024-02-18 20:57:19,834 INFO misc.py line 119 87073] Train: [55/100][631/1557] Data 0.005 (0.173) Batch 0.987 (1.308) Remain 25:47:09 loss: 0.5285 Lr: 0.00234 [2024-02-18 20:57:21,011 INFO misc.py line 119 87073] Train: [55/100][632/1557] Data 0.004 (0.172) Batch 1.171 (1.307) Remain 25:46:52 loss: 0.3698 Lr: 0.00234 [2024-02-18 20:57:21,885 INFO misc.py line 119 87073] Train: [55/100][633/1557] Data 0.010 (0.172) Batch 0.878 (1.307) Remain 25:46:02 loss: 0.1838 Lr: 0.00234 [2024-02-18 20:57:22,955 INFO misc.py line 119 87073] Train: [55/100][634/1557] Data 0.006 (0.172) Batch 1.071 (1.306) Remain 25:45:34 loss: 0.4931 Lr: 0.00234 [2024-02-18 20:57:23,761 INFO misc.py line 119 87073] Train: [55/100][635/1557] Data 0.004 (0.171) Batch 0.806 (1.306) Remain 25:44:37 loss: 0.2289 Lr: 0.00234 [2024-02-18 20:57:24,566 INFO misc.py line 119 87073] Train: [55/100][636/1557] Data 0.005 (0.171) Batch 0.803 (1.305) Remain 25:43:39 loss: 0.2460 Lr: 0.00234 [2024-02-18 20:57:25,909 INFO misc.py line 119 87073] Train: [55/100][637/1557] Data 0.007 (0.171) Batch 1.340 (1.305) Remain 25:43:42 loss: 0.2005 Lr: 0.00234 [2024-02-18 20:57:26,807 INFO misc.py line 119 87073] Train: [55/100][638/1557] Data 0.009 (0.171) Batch 0.902 (1.304) Remain 25:42:56 loss: 0.4258 Lr: 0.00234 [2024-02-18 20:57:27,867 INFO misc.py line 119 87073] Train: [55/100][639/1557] Data 0.005 (0.170) Batch 1.059 (1.304) Remain 25:42:27 loss: 1.0860 Lr: 0.00234 [2024-02-18 20:57:28,795 INFO misc.py line 119 87073] Train: [55/100][640/1557] Data 0.006 (0.170) Batch 0.928 (1.303) Remain 25:41:44 loss: 0.3149 Lr: 0.00234 [2024-02-18 20:57:29,691 INFO misc.py line 119 87073] Train: [55/100][641/1557] Data 0.006 (0.170) Batch 0.898 (1.303) Remain 25:40:57 loss: 0.3335 Lr: 0.00234 [2024-02-18 20:57:30,466 INFO misc.py line 119 87073] Train: [55/100][642/1557] Data 0.004 (0.170) Batch 0.773 (1.302) Remain 25:39:57 loss: 0.2333 Lr: 0.00234 [2024-02-18 20:57:31,168 INFO misc.py line 119 87073] Train: [55/100][643/1557] Data 0.006 (0.169) Batch 0.703 (1.301) Remain 25:38:50 loss: 0.2614 Lr: 0.00234 [2024-02-18 20:57:32,253 INFO misc.py line 119 87073] Train: [55/100][644/1557] Data 0.005 (0.169) Batch 1.076 (1.300) Remain 25:38:23 loss: 0.1670 Lr: 0.00234 [2024-02-18 20:57:33,204 INFO misc.py line 119 87073] Train: [55/100][645/1557] Data 0.015 (0.169) Batch 0.961 (1.300) Remain 25:37:44 loss: 0.2202 Lr: 0.00234 [2024-02-18 20:57:34,171 INFO misc.py line 119 87073] Train: [55/100][646/1557] Data 0.004 (0.169) Batch 0.967 (1.299) Remain 25:37:06 loss: 0.3745 Lr: 0.00234 [2024-02-18 20:57:35,154 INFO misc.py line 119 87073] Train: [55/100][647/1557] Data 0.004 (0.168) Batch 0.984 (1.299) Remain 25:36:30 loss: 0.1423 Lr: 0.00234 [2024-02-18 20:57:36,258 INFO misc.py line 119 87073] Train: [55/100][648/1557] Data 0.003 (0.168) Batch 1.104 (1.299) Remain 25:36:08 loss: 0.6399 Lr: 0.00234 [2024-02-18 20:57:36,930 INFO misc.py line 119 87073] Train: [55/100][649/1557] Data 0.004 (0.168) Batch 0.672 (1.298) Remain 25:34:57 loss: 0.1267 Lr: 0.00234 [2024-02-18 20:57:37,745 INFO misc.py line 119 87073] Train: [55/100][650/1557] Data 0.004 (0.168) Batch 0.805 (1.297) Remain 25:34:02 loss: 0.4187 Lr: 0.00234 [2024-02-18 20:57:39,066 INFO misc.py line 119 87073] Train: [55/100][651/1557] Data 0.014 (0.167) Batch 1.319 (1.297) Remain 25:34:03 loss: 0.2707 Lr: 0.00234 [2024-02-18 20:57:40,087 INFO misc.py line 119 87073] Train: [55/100][652/1557] Data 0.016 (0.167) Batch 1.030 (1.297) Remain 25:33:33 loss: 0.4084 Lr: 0.00234 [2024-02-18 20:57:41,000 INFO misc.py line 119 87073] Train: [55/100][653/1557] Data 0.008 (0.167) Batch 0.917 (1.296) Remain 25:32:50 loss: 0.4198 Lr: 0.00234 [2024-02-18 20:57:42,114 INFO misc.py line 119 87073] Train: [55/100][654/1557] Data 0.004 (0.167) Batch 1.114 (1.296) Remain 25:32:29 loss: 0.7329 Lr: 0.00234 [2024-02-18 20:57:43,101 INFO misc.py line 119 87073] Train: [55/100][655/1557] Data 0.004 (0.166) Batch 0.987 (1.295) Remain 25:31:54 loss: 0.2555 Lr: 0.00234 [2024-02-18 20:57:43,857 INFO misc.py line 119 87073] Train: [55/100][656/1557] Data 0.004 (0.166) Batch 0.756 (1.294) Remain 25:30:54 loss: 0.3375 Lr: 0.00234 [2024-02-18 20:57:44,639 INFO misc.py line 119 87073] Train: [55/100][657/1557] Data 0.003 (0.166) Batch 0.776 (1.294) Remain 25:29:56 loss: 0.2433 Lr: 0.00234 [2024-02-18 20:57:45,850 INFO misc.py line 119 87073] Train: [55/100][658/1557] Data 0.011 (0.166) Batch 1.215 (1.293) Remain 25:29:46 loss: 0.1500 Lr: 0.00234 [2024-02-18 20:57:46,724 INFO misc.py line 119 87073] Train: [55/100][659/1557] Data 0.007 (0.165) Batch 0.877 (1.293) Remain 25:29:00 loss: 0.1760 Lr: 0.00234 [2024-02-18 20:57:47,573 INFO misc.py line 119 87073] Train: [55/100][660/1557] Data 0.005 (0.165) Batch 0.848 (1.292) Remain 25:28:11 loss: 0.3391 Lr: 0.00234 [2024-02-18 20:57:48,407 INFO misc.py line 119 87073] Train: [55/100][661/1557] Data 0.006 (0.165) Batch 0.832 (1.291) Remain 25:27:20 loss: 0.6648 Lr: 0.00234 [2024-02-18 20:57:49,310 INFO misc.py line 119 87073] Train: [55/100][662/1557] Data 0.007 (0.165) Batch 0.907 (1.291) Remain 25:26:37 loss: 0.3040 Lr: 0.00234 [2024-02-18 20:57:50,083 INFO misc.py line 119 87073] Train: [55/100][663/1557] Data 0.004 (0.164) Batch 0.772 (1.290) Remain 25:25:40 loss: 0.2582 Lr: 0.00234 [2024-02-18 20:57:50,785 INFO misc.py line 119 87073] Train: [55/100][664/1557] Data 0.004 (0.164) Batch 0.692 (1.289) Remain 25:24:35 loss: 0.1686 Lr: 0.00234 [2024-02-18 20:57:52,115 INFO misc.py line 119 87073] Train: [55/100][665/1557] Data 0.013 (0.164) Batch 1.336 (1.289) Remain 25:24:38 loss: 0.2661 Lr: 0.00234 [2024-02-18 20:57:53,094 INFO misc.py line 119 87073] Train: [55/100][666/1557] Data 0.009 (0.164) Batch 0.978 (1.289) Remain 25:24:04 loss: 1.0904 Lr: 0.00234 [2024-02-18 20:57:54,251 INFO misc.py line 119 87073] Train: [55/100][667/1557] Data 0.009 (0.164) Batch 1.161 (1.289) Remain 25:23:49 loss: 0.2173 Lr: 0.00234 [2024-02-18 20:57:55,366 INFO misc.py line 119 87073] Train: [55/100][668/1557] Data 0.006 (0.163) Batch 1.115 (1.288) Remain 25:23:29 loss: 0.1509 Lr: 0.00234 [2024-02-18 20:57:56,388 INFO misc.py line 119 87073] Train: [55/100][669/1557] Data 0.006 (0.163) Batch 1.024 (1.288) Remain 25:22:59 loss: 0.3180 Lr: 0.00234 [2024-02-18 20:57:57,125 INFO misc.py line 119 87073] Train: [55/100][670/1557] Data 0.004 (0.163) Batch 0.737 (1.287) Remain 25:22:00 loss: 0.1806 Lr: 0.00234 [2024-02-18 20:57:57,867 INFO misc.py line 119 87073] Train: [55/100][671/1557] Data 0.004 (0.163) Batch 0.726 (1.286) Remain 25:20:59 loss: 0.2612 Lr: 0.00234 [2024-02-18 20:57:59,184 INFO misc.py line 119 87073] Train: [55/100][672/1557] Data 0.020 (0.162) Batch 1.331 (1.286) Remain 25:21:02 loss: 0.1664 Lr: 0.00234 [2024-02-18 20:58:00,218 INFO misc.py line 119 87073] Train: [55/100][673/1557] Data 0.005 (0.162) Batch 1.034 (1.286) Remain 25:20:34 loss: 0.3488 Lr: 0.00234 [2024-02-18 20:58:01,215 INFO misc.py line 119 87073] Train: [55/100][674/1557] Data 0.006 (0.162) Batch 0.997 (1.285) Remain 25:20:02 loss: 0.4215 Lr: 0.00234 [2024-02-18 20:58:02,210 INFO misc.py line 119 87073] Train: 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Batch 1.053 (1.316) Remain 25:56:07 loss: 0.2952 Lr: 0.00234 [2024-02-18 20:58:31,841 INFO misc.py line 119 87073] Train: [55/100][682/1557] Data 0.011 (0.172) Batch 0.856 (1.315) Remain 25:55:17 loss: 0.4910 Lr: 0.00234 [2024-02-18 20:58:32,794 INFO misc.py line 119 87073] Train: [55/100][683/1557] Data 0.004 (0.172) Batch 0.953 (1.315) Remain 25:54:38 loss: 0.3337 Lr: 0.00234 [2024-02-18 20:58:33,528 INFO misc.py line 119 87073] Train: [55/100][684/1557] Data 0.005 (0.171) Batch 0.735 (1.314) Remain 25:53:37 loss: 0.3644 Lr: 0.00234 [2024-02-18 20:58:34,256 INFO misc.py line 119 87073] Train: [55/100][685/1557] Data 0.003 (0.171) Batch 0.728 (1.313) Remain 25:52:34 loss: 0.5104 Lr: 0.00234 [2024-02-18 20:58:35,467 INFO misc.py line 119 87073] Train: [55/100][686/1557] Data 0.004 (0.171) Batch 1.208 (1.313) Remain 25:52:22 loss: 0.1667 Lr: 0.00234 [2024-02-18 20:58:36,429 INFO misc.py line 119 87073] Train: [55/100][687/1557] Data 0.007 (0.171) Batch 0.963 (1.313) Remain 25:51:44 loss: 0.3319 Lr: 0.00234 [2024-02-18 20:58:37,434 INFO misc.py line 119 87073] Train: [55/100][688/1557] Data 0.006 (0.170) Batch 1.007 (1.312) Remain 25:51:12 loss: 0.4432 Lr: 0.00234 [2024-02-18 20:58:38,367 INFO misc.py line 119 87073] Train: [55/100][689/1557] Data 0.004 (0.170) Batch 0.931 (1.312) Remain 25:50:31 loss: 0.7826 Lr: 0.00234 [2024-02-18 20:58:39,371 INFO misc.py line 119 87073] Train: [55/100][690/1557] Data 0.005 (0.170) Batch 1.005 (1.311) Remain 25:49:58 loss: 0.4403 Lr: 0.00234 [2024-02-18 20:58:40,123 INFO misc.py line 119 87073] Train: [55/100][691/1557] Data 0.004 (0.170) Batch 0.743 (1.310) Remain 25:48:58 loss: 0.2760 Lr: 0.00234 [2024-02-18 20:58:40,882 INFO misc.py line 119 87073] Train: [55/100][692/1557] Data 0.013 (0.170) Batch 0.767 (1.309) Remain 25:48:01 loss: 0.2123 Lr: 0.00234 [2024-02-18 20:58:42,241 INFO misc.py line 119 87073] Train: [55/100][693/1557] Data 0.006 (0.169) Batch 1.355 (1.310) Remain 25:48:04 loss: 0.2242 Lr: 0.00234 [2024-02-18 20:58:43,193 INFO misc.py line 119 87073] Train: [55/100][694/1557] Data 0.010 (0.169) Batch 0.957 (1.309) Remain 25:47:27 loss: 0.2841 Lr: 0.00234 [2024-02-18 20:58:44,054 INFO misc.py line 119 87073] Train: [55/100][695/1557] Data 0.005 (0.169) Batch 0.860 (1.308) Remain 25:46:39 loss: 0.2233 Lr: 0.00234 [2024-02-18 20:58:45,102 INFO misc.py line 119 87073] Train: [55/100][696/1557] Data 0.006 (0.169) Batch 1.049 (1.308) Remain 25:46:11 loss: 0.6819 Lr: 0.00234 [2024-02-18 20:58:46,030 INFO misc.py line 119 87073] Train: [55/100][697/1557] Data 0.004 (0.168) Batch 0.927 (1.307) Remain 25:45:31 loss: 0.4065 Lr: 0.00234 [2024-02-18 20:58:46,751 INFO misc.py line 119 87073] Train: [55/100][698/1557] Data 0.005 (0.168) Batch 0.710 (1.307) Remain 25:44:29 loss: 0.2803 Lr: 0.00234 [2024-02-18 20:58:47,411 INFO misc.py line 119 87073] Train: [55/100][699/1557] Data 0.016 (0.168) Batch 0.672 (1.306) Remain 25:43:23 loss: 0.1842 Lr: 0.00234 [2024-02-18 20:58:48,447 INFO misc.py line 119 87073] Train: [55/100][700/1557] Data 0.004 (0.168) Batch 1.026 (1.305) Remain 25:42:53 loss: 0.1087 Lr: 0.00234 [2024-02-18 20:58:49,522 INFO misc.py line 119 87073] Train: [55/100][701/1557] Data 0.014 (0.167) Batch 1.076 (1.305) Remain 25:42:29 loss: 0.3956 Lr: 0.00234 [2024-02-18 20:58:50,502 INFO misc.py line 119 87073] Train: [55/100][702/1557] Data 0.013 (0.167) Batch 0.987 (1.305) Remain 25:41:55 loss: 0.2510 Lr: 0.00234 [2024-02-18 20:58:51,443 INFO misc.py line 119 87073] Train: [55/100][703/1557] Data 0.006 (0.167) Batch 0.940 (1.304) Remain 25:41:17 loss: 0.2118 Lr: 0.00234 [2024-02-18 20:58:52,479 INFO misc.py line 119 87073] Train: [55/100][704/1557] Data 0.008 (0.167) Batch 1.037 (1.304) Remain 25:40:49 loss: 0.2256 Lr: 0.00234 [2024-02-18 20:58:54,796 INFO misc.py line 119 87073] Train: [55/100][705/1557] Data 1.102 (0.168) Batch 2.316 (1.305) Remain 25:42:30 loss: 0.2091 Lr: 0.00234 [2024-02-18 20:58:55,686 INFO misc.py line 119 87073] Train: [55/100][706/1557] Data 0.006 (0.168) Batch 0.875 (1.304) Remain 25:41:45 loss: 0.2912 Lr: 0.00234 [2024-02-18 20:58:56,918 INFO misc.py line 119 87073] Train: [55/100][707/1557] Data 0.021 (0.168) Batch 1.239 (1.304) Remain 25:41:37 loss: 0.3016 Lr: 0.00234 [2024-02-18 20:58:58,230 INFO misc.py line 119 87073] Train: [55/100][708/1557] Data 0.014 (0.167) Batch 1.312 (1.304) Remain 25:41:37 loss: 0.3214 Lr: 0.00234 [2024-02-18 20:58:59,071 INFO misc.py line 119 87073] Train: [55/100][709/1557] Data 0.012 (0.167) Batch 0.851 (1.304) Remain 25:40:50 loss: 0.5219 Lr: 0.00234 [2024-02-18 20:59:00,066 INFO misc.py line 119 87073] Train: [55/100][710/1557] Data 0.003 (0.167) Batch 0.993 (1.303) Remain 25:40:17 loss: 0.6826 Lr: 0.00234 [2024-02-18 20:59:00,992 INFO misc.py line 119 87073] Train: [55/100][711/1557] Data 0.005 (0.167) Batch 0.928 (1.303) Remain 25:39:38 loss: 0.5853 Lr: 0.00234 [2024-02-18 20:59:01,773 INFO misc.py line 119 87073] Train: [55/100][712/1557] Data 0.004 (0.167) Batch 0.780 (1.302) Remain 25:38:45 loss: 0.2680 Lr: 0.00234 [2024-02-18 20:59:02,543 INFO misc.py line 119 87073] Train: [55/100][713/1557] Data 0.004 (0.166) Batch 0.770 (1.301) Remain 25:37:50 loss: 0.4488 Lr: 0.00234 [2024-02-18 20:59:03,713 INFO misc.py line 119 87073] Train: [55/100][714/1557] Data 0.005 (0.166) Batch 1.169 (1.301) Remain 25:37:36 loss: 0.2008 Lr: 0.00234 [2024-02-18 20:59:04,624 INFO misc.py line 119 87073] Train: [55/100][715/1557] Data 0.005 (0.166) Batch 0.911 (1.301) Remain 25:36:56 loss: 0.3998 Lr: 0.00234 [2024-02-18 20:59:05,676 INFO misc.py line 119 87073] Train: [55/100][716/1557] Data 0.005 (0.166) Batch 1.052 (1.300) Remain 25:36:30 loss: 0.5226 Lr: 0.00234 [2024-02-18 20:59:06,518 INFO misc.py line 119 87073] Train: [55/100][717/1557] Data 0.006 (0.165) Batch 0.844 (1.300) Remain 25:35:43 loss: 0.3677 Lr: 0.00234 [2024-02-18 20:59:07,406 INFO misc.py line 119 87073] Train: [55/100][718/1557] Data 0.004 (0.165) Batch 0.885 (1.299) Remain 25:35:01 loss: 0.5350 Lr: 0.00234 [2024-02-18 20:59:08,219 INFO misc.py line 119 87073] Train: [55/100][719/1557] Data 0.007 (0.165) Batch 0.816 (1.298) Remain 25:34:11 loss: 0.2760 Lr: 0.00234 [2024-02-18 20:59:09,051 INFO misc.py line 119 87073] Train: [55/100][720/1557] Data 0.004 (0.165) Batch 0.831 (1.298) Remain 25:33:24 loss: 0.1763 Lr: 0.00234 [2024-02-18 20:59:10,333 INFO misc.py line 119 87073] Train: [55/100][721/1557] Data 0.005 (0.165) Batch 1.276 (1.298) Remain 25:33:20 loss: 0.0939 Lr: 0.00234 [2024-02-18 20:59:11,251 INFO misc.py line 119 87073] Train: [55/100][722/1557] Data 0.012 (0.164) Batch 0.925 (1.297) Remain 25:32:42 loss: 0.3940 Lr: 0.00234 [2024-02-18 20:59:12,190 INFO misc.py line 119 87073] Train: [55/100][723/1557] Data 0.004 (0.164) Batch 0.940 (1.297) Remain 25:32:06 loss: 0.3154 Lr: 0.00234 [2024-02-18 20:59:13,215 INFO misc.py line 119 87073] Train: [55/100][724/1557] Data 0.003 (0.164) Batch 1.024 (1.296) Remain 25:31:38 loss: 0.2362 Lr: 0.00234 [2024-02-18 20:59:14,119 INFO misc.py line 119 87073] Train: [55/100][725/1557] Data 0.004 (0.164) Batch 0.904 (1.296) Remain 25:30:58 loss: 0.4081 Lr: 0.00234 [2024-02-18 20:59:14,898 INFO misc.py line 119 87073] Train: [55/100][726/1557] Data 0.004 (0.163) Batch 0.777 (1.295) Remain 25:30:06 loss: 0.1326 Lr: 0.00234 [2024-02-18 20:59:15,687 INFO misc.py line 119 87073] Train: [55/100][727/1557] Data 0.006 (0.163) Batch 0.790 (1.294) Remain 25:29:15 loss: 0.2898 Lr: 0.00234 [2024-02-18 20:59:16,990 INFO misc.py line 119 87073] Train: [55/100][728/1557] Data 0.005 (0.163) Batch 1.293 (1.294) Remain 25:29:14 loss: 0.1735 Lr: 0.00234 [2024-02-18 20:59:17,896 INFO misc.py line 119 87073] Train: [55/100][729/1557] Data 0.015 (0.163) Batch 0.918 (1.294) Remain 25:28:36 loss: 0.8854 Lr: 0.00234 [2024-02-18 20:59:18,996 INFO misc.py line 119 87073] Train: [55/100][730/1557] Data 0.004 (0.163) Batch 1.100 (1.293) Remain 25:28:16 loss: 0.4591 Lr: 0.00234 [2024-02-18 20:59:19,895 INFO misc.py line 119 87073] Train: 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Batch 0.935 (1.315) Remain 25:53:18 loss: 0.4026 Lr: 0.00234 [2024-02-18 20:59:44,650 INFO misc.py line 119 87073] Train: [55/100][738/1557] Data 0.004 (0.174) Batch 0.946 (1.314) Remain 25:52:41 loss: 0.4633 Lr: 0.00234 [2024-02-18 20:59:45,569 INFO misc.py line 119 87073] Train: [55/100][739/1557] Data 0.003 (0.174) Batch 0.918 (1.314) Remain 25:52:02 loss: 0.6665 Lr: 0.00234 [2024-02-18 20:59:46,340 INFO misc.py line 119 87073] Train: [55/100][740/1557] Data 0.005 (0.173) Batch 0.769 (1.313) Remain 25:51:08 loss: 0.2557 Lr: 0.00234 [2024-02-18 20:59:47,091 INFO misc.py line 119 87073] Train: [55/100][741/1557] Data 0.007 (0.173) Batch 0.743 (1.312) Remain 25:50:12 loss: 0.4532 Lr: 0.00234 [2024-02-18 20:59:48,302 INFO misc.py line 119 87073] Train: [55/100][742/1557] Data 0.014 (0.173) Batch 1.217 (1.312) Remain 25:50:02 loss: 0.2211 Lr: 0.00234 [2024-02-18 20:59:49,301 INFO misc.py line 119 87073] Train: [55/100][743/1557] Data 0.009 (0.173) Batch 0.999 (1.312) Remain 25:49:30 loss: 0.2748 Lr: 0.00234 [2024-02-18 20:59:50,202 INFO misc.py line 119 87073] Train: [55/100][744/1557] Data 0.010 (0.173) Batch 0.906 (1.311) Remain 25:48:50 loss: 0.4657 Lr: 0.00234 [2024-02-18 20:59:51,175 INFO misc.py line 119 87073] Train: [55/100][745/1557] Data 0.004 (0.172) Batch 0.974 (1.311) Remain 25:48:17 loss: 0.2732 Lr: 0.00234 [2024-02-18 20:59:52,069 INFO misc.py line 119 87073] Train: [55/100][746/1557] Data 0.004 (0.172) Batch 0.893 (1.310) Remain 25:47:35 loss: 0.3233 Lr: 0.00234 [2024-02-18 20:59:52,864 INFO misc.py line 119 87073] Train: [55/100][747/1557] Data 0.005 (0.172) Batch 0.754 (1.309) Remain 25:46:41 loss: 0.1648 Lr: 0.00234 [2024-02-18 20:59:53,575 INFO misc.py line 119 87073] Train: [55/100][748/1557] Data 0.046 (0.172) Batch 0.753 (1.309) Remain 25:45:47 loss: 0.2210 Lr: 0.00234 [2024-02-18 20:59:54,922 INFO misc.py line 119 87073] Train: [55/100][749/1557] Data 0.004 (0.171) Batch 1.336 (1.309) Remain 25:45:48 loss: 0.2208 Lr: 0.00234 [2024-02-18 20:59:55,811 INFO misc.py line 119 87073] Train: [55/100][750/1557] Data 0.016 (0.171) Batch 0.901 (1.308) Remain 25:45:08 loss: 0.2485 Lr: 0.00234 [2024-02-18 20:59:56,747 INFO misc.py line 119 87073] Train: [55/100][751/1557] Data 0.004 (0.171) Batch 0.934 (1.308) Remain 25:44:31 loss: 0.2236 Lr: 0.00234 [2024-02-18 20:59:57,792 INFO misc.py line 119 87073] Train: [55/100][752/1557] Data 0.007 (0.171) Batch 1.047 (1.307) Remain 25:44:05 loss: 0.4309 Lr: 0.00234 [2024-02-18 20:59:58,757 INFO misc.py line 119 87073] Train: [55/100][753/1557] Data 0.004 (0.171) Batch 0.964 (1.307) Remain 25:43:32 loss: 0.1832 Lr: 0.00234 [2024-02-18 20:59:59,507 INFO misc.py line 119 87073] Train: [55/100][754/1557] Data 0.005 (0.170) Batch 0.745 (1.306) Remain 25:42:37 loss: 0.1794 Lr: 0.00234 [2024-02-18 21:00:00,275 INFO misc.py line 119 87073] Train: [55/100][755/1557] Data 0.009 (0.170) Batch 0.773 (1.305) Remain 25:41:46 loss: 0.3111 Lr: 0.00234 [2024-02-18 21:00:01,318 INFO misc.py line 119 87073] Train: [55/100][756/1557] Data 0.004 (0.170) Batch 1.042 (1.305) Remain 25:41:20 loss: 0.0880 Lr: 0.00234 [2024-02-18 21:00:02,494 INFO misc.py line 119 87073] Train: [55/100][757/1557] Data 0.006 (0.170) Batch 1.177 (1.305) Remain 25:41:06 loss: 0.5479 Lr: 0.00234 [2024-02-18 21:00:03,478 INFO misc.py line 119 87073] Train: [55/100][758/1557] Data 0.005 (0.170) Batch 0.985 (1.304) Remain 25:40:35 loss: 0.6013 Lr: 0.00234 [2024-02-18 21:00:04,391 INFO misc.py line 119 87073] Train: [55/100][759/1557] Data 0.004 (0.169) Batch 0.913 (1.304) Remain 25:39:57 loss: 0.4276 Lr: 0.00234 [2024-02-18 21:00:05,479 INFO misc.py line 119 87073] Train: [55/100][760/1557] Data 0.003 (0.169) Batch 1.087 (1.304) Remain 25:39:35 loss: 0.3750 Lr: 0.00234 [2024-02-18 21:00:06,253 INFO misc.py line 119 87073] Train: [55/100][761/1557] Data 0.005 (0.169) Batch 0.775 (1.303) Remain 25:38:45 loss: 0.3013 Lr: 0.00234 [2024-02-18 21:00:07,029 INFO misc.py line 119 87073] Train: [55/100][762/1557] Data 0.004 (0.169) Batch 0.767 (1.302) Remain 25:37:53 loss: 0.1766 Lr: 0.00234 [2024-02-18 21:00:08,279 INFO misc.py line 119 87073] Train: [55/100][763/1557] Data 0.013 (0.168) Batch 1.247 (1.302) Remain 25:37:47 loss: 0.2362 Lr: 0.00234 [2024-02-18 21:00:09,370 INFO misc.py line 119 87073] Train: [55/100][764/1557] Data 0.016 (0.168) Batch 1.097 (1.302) Remain 25:37:27 loss: 0.7823 Lr: 0.00234 [2024-02-18 21:00:10,273 INFO misc.py line 119 87073] Train: [55/100][765/1557] Data 0.009 (0.168) Batch 0.909 (1.301) Remain 25:36:49 loss: 0.5522 Lr: 0.00234 [2024-02-18 21:00:11,199 INFO misc.py line 119 87073] Train: [55/100][766/1557] Data 0.004 (0.168) Batch 0.926 (1.301) Remain 25:36:13 loss: 0.4917 Lr: 0.00234 [2024-02-18 21:00:12,254 INFO misc.py line 119 87073] Train: [55/100][767/1557] Data 0.003 (0.168) Batch 1.055 (1.301) Remain 25:35:48 loss: 0.3336 Lr: 0.00234 [2024-02-18 21:00:13,052 INFO misc.py line 119 87073] Train: [55/100][768/1557] Data 0.003 (0.167) Batch 0.798 (1.300) Remain 25:35:01 loss: 0.2357 Lr: 0.00234 [2024-02-18 21:00:13,944 INFO misc.py line 119 87073] Train: [55/100][769/1557] Data 0.003 (0.167) Batch 0.888 (1.299) Remain 25:34:21 loss: 0.3832 Lr: 0.00234 [2024-02-18 21:00:15,116 INFO misc.py line 119 87073] Train: [55/100][770/1557] Data 0.009 (0.167) Batch 1.167 (1.299) Remain 25:34:08 loss: 0.1500 Lr: 0.00234 [2024-02-18 21:00:15,946 INFO misc.py line 119 87073] Train: [55/100][771/1557] Data 0.013 (0.167) Batch 0.839 (1.299) Remain 25:33:24 loss: 0.2817 Lr: 0.00234 [2024-02-18 21:00:16,888 INFO misc.py line 119 87073] Train: [55/100][772/1557] Data 0.004 (0.167) Batch 0.942 (1.298) Remain 25:32:50 loss: 0.4609 Lr: 0.00234 [2024-02-18 21:00:17,879 INFO misc.py line 119 87073] Train: [55/100][773/1557] Data 0.004 (0.166) Batch 0.991 (1.298) Remain 25:32:20 loss: 0.1609 Lr: 0.00234 [2024-02-18 21:00:18,858 INFO misc.py line 119 87073] Train: [55/100][774/1557] Data 0.004 (0.166) Batch 0.978 (1.297) Remain 25:31:50 loss: 0.2794 Lr: 0.00234 [2024-02-18 21:00:19,623 INFO misc.py line 119 87073] Train: [55/100][775/1557] Data 0.006 (0.166) Batch 0.766 (1.297) Remain 25:30:59 loss: 0.3694 Lr: 0.00234 [2024-02-18 21:00:20,387 INFO misc.py line 119 87073] Train: [55/100][776/1557] Data 0.003 (0.166) Batch 0.752 (1.296) Remain 25:30:08 loss: 0.2297 Lr: 0.00234 [2024-02-18 21:00:21,615 INFO misc.py line 119 87073] Train: [55/100][777/1557] Data 0.015 (0.166) Batch 1.231 (1.296) Remain 25:30:01 loss: 0.2569 Lr: 0.00234 [2024-02-18 21:00:22,635 INFO misc.py line 119 87073] Train: [55/100][778/1557] Data 0.013 (0.165) Batch 1.017 (1.295) Remain 25:29:34 loss: 0.9791 Lr: 0.00234 [2024-02-18 21:00:23,585 INFO misc.py line 119 87073] Train: [55/100][779/1557] Data 0.017 (0.165) Batch 0.962 (1.295) Remain 25:29:03 loss: 0.1639 Lr: 0.00234 [2024-02-18 21:00:24,556 INFO misc.py line 119 87073] Train: [55/100][780/1557] Data 0.004 (0.165) Batch 0.971 (1.295) Remain 25:28:32 loss: 0.4992 Lr: 0.00234 [2024-02-18 21:00:25,600 INFO misc.py line 119 87073] Train: [55/100][781/1557] Data 0.004 (0.165) Batch 1.043 (1.294) Remain 25:28:08 loss: 0.7368 Lr: 0.00234 [2024-02-18 21:00:26,304 INFO misc.py line 119 87073] Train: [55/100][782/1557] Data 0.005 (0.165) Batch 0.705 (1.294) Remain 25:27:13 loss: 0.2444 Lr: 0.00234 [2024-02-18 21:00:27,067 INFO misc.py line 119 87073] Train: [55/100][783/1557] Data 0.004 (0.164) Batch 0.755 (1.293) Remain 25:26:22 loss: 0.3093 Lr: 0.00234 [2024-02-18 21:00:28,375 INFO misc.py line 119 87073] Train: [55/100][784/1557] Data 0.011 (0.164) Batch 1.309 (1.293) Remain 25:26:23 loss: 0.1323 Lr: 0.00234 [2024-02-18 21:00:29,398 INFO misc.py line 119 87073] Train: [55/100][785/1557] Data 0.009 (0.164) Batch 1.017 (1.293) Remain 25:25:56 loss: 0.1979 Lr: 0.00234 [2024-02-18 21:00:30,439 INFO misc.py line 119 87073] Train: [55/100][786/1557] Data 0.015 (0.164) Batch 1.041 (1.292) Remain 25:25:32 loss: 0.0973 Lr: 0.00234 [2024-02-18 21:00:31,315 INFO misc.py line 119 87073] Train: 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Batch 0.917 (1.316) Remain 25:53:29 loss: 0.3150 Lr: 0.00234 [2024-02-18 21:00:59,483 INFO misc.py line 119 87073] Train: [55/100][794/1557] Data 0.006 (0.173) Batch 1.208 (1.316) Remain 25:53:18 loss: 0.2197 Lr: 0.00234 [2024-02-18 21:01:00,455 INFO misc.py line 119 87073] Train: [55/100][795/1557] Data 0.007 (0.173) Batch 0.975 (1.315) Remain 25:52:46 loss: 0.4226 Lr: 0.00234 [2024-02-18 21:01:01,162 INFO misc.py line 119 87073] Train: [55/100][796/1557] Data 0.005 (0.172) Batch 0.707 (1.315) Remain 25:51:50 loss: 0.2675 Lr: 0.00234 [2024-02-18 21:01:01,917 INFO misc.py line 119 87073] Train: [55/100][797/1557] Data 0.003 (0.172) Batch 0.741 (1.314) Remain 25:50:58 loss: 0.3244 Lr: 0.00234 [2024-02-18 21:01:03,106 INFO misc.py line 119 87073] Train: [55/100][798/1557] Data 0.018 (0.172) Batch 1.191 (1.314) Remain 25:50:46 loss: 0.1702 Lr: 0.00234 [2024-02-18 21:01:04,168 INFO misc.py line 119 87073] Train: [55/100][799/1557] Data 0.016 (0.172) Batch 1.050 (1.313) Remain 25:50:21 loss: 0.4440 Lr: 0.00234 [2024-02-18 21:01:04,965 INFO misc.py line 119 87073] Train: [55/100][800/1557] Data 0.028 (0.172) Batch 0.820 (1.313) Remain 25:49:36 loss: 0.4974 Lr: 0.00234 [2024-02-18 21:01:06,110 INFO misc.py line 119 87073] Train: [55/100][801/1557] Data 0.005 (0.171) Batch 1.146 (1.313) Remain 25:49:20 loss: 0.4503 Lr: 0.00234 [2024-02-18 21:01:07,111 INFO misc.py line 119 87073] Train: [55/100][802/1557] Data 0.003 (0.171) Batch 1.002 (1.312) Remain 25:48:51 loss: 0.3945 Lr: 0.00234 [2024-02-18 21:01:07,893 INFO misc.py line 119 87073] Train: [55/100][803/1557] Data 0.004 (0.171) Batch 0.782 (1.312) Remain 25:48:02 loss: 0.2062 Lr: 0.00234 [2024-02-18 21:01:08,662 INFO misc.py line 119 87073] Train: [55/100][804/1557] Data 0.004 (0.171) Batch 0.760 (1.311) Remain 25:47:12 loss: 0.3015 Lr: 0.00234 [2024-02-18 21:01:10,028 INFO misc.py line 119 87073] Train: [55/100][805/1557] Data 0.013 (0.170) Batch 1.365 (1.311) Remain 25:47:16 loss: 0.4094 Lr: 0.00234 [2024-02-18 21:01:11,035 INFO misc.py line 119 87073] Train: [55/100][806/1557] Data 0.013 (0.170) Batch 1.012 (1.311) Remain 25:46:48 loss: 0.5719 Lr: 0.00234 [2024-02-18 21:01:11,781 INFO misc.py line 119 87073] Train: [55/100][807/1557] Data 0.008 (0.170) Batch 0.748 (1.310) Remain 25:45:57 loss: 0.4412 Lr: 0.00234 [2024-02-18 21:01:12,844 INFO misc.py line 119 87073] Train: [55/100][808/1557] Data 0.007 (0.170) Batch 1.064 (1.310) Remain 25:45:34 loss: 0.3986 Lr: 0.00234 [2024-02-18 21:01:13,870 INFO misc.py line 119 87073] Train: [55/100][809/1557] Data 0.005 (0.170) Batch 1.027 (1.309) Remain 25:45:08 loss: 0.1690 Lr: 0.00234 [2024-02-18 21:01:14,629 INFO misc.py line 119 87073] Train: [55/100][810/1557] Data 0.003 (0.169) Batch 0.759 (1.309) Remain 25:44:19 loss: 0.2475 Lr: 0.00234 [2024-02-18 21:01:15,441 INFO misc.py line 119 87073] Train: [55/100][811/1557] Data 0.004 (0.169) Batch 0.802 (1.308) Remain 25:43:33 loss: 0.1833 Lr: 0.00234 [2024-02-18 21:01:16,527 INFO misc.py line 119 87073] Train: [55/100][812/1557] Data 0.014 (0.169) Batch 1.086 (1.308) Remain 25:43:12 loss: 0.1946 Lr: 0.00234 [2024-02-18 21:01:17,539 INFO misc.py line 119 87073] Train: [55/100][813/1557] Data 0.013 (0.169) Batch 1.016 (1.307) Remain 25:42:46 loss: 0.3756 Lr: 0.00233 [2024-02-18 21:01:18,424 INFO misc.py line 119 87073] Train: [55/100][814/1557] Data 0.008 (0.169) Batch 0.890 (1.307) Remain 25:42:08 loss: 0.3556 Lr: 0.00233 [2024-02-18 21:01:19,440 INFO misc.py line 119 87073] Train: [55/100][815/1557] Data 0.004 (0.168) Batch 1.016 (1.306) Remain 25:41:41 loss: 0.2057 Lr: 0.00233 [2024-02-18 21:01:20,355 INFO misc.py line 119 87073] Train: [55/100][816/1557] Data 0.004 (0.168) Batch 0.914 (1.306) Remain 25:41:06 loss: 0.5261 Lr: 0.00233 [2024-02-18 21:01:21,166 INFO misc.py line 119 87073] Train: [55/100][817/1557] Data 0.005 (0.168) Batch 0.810 (1.305) Remain 25:40:21 loss: 0.2140 Lr: 0.00233 [2024-02-18 21:01:21,896 INFO misc.py line 119 87073] Train: [55/100][818/1557] Data 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25:36:37 loss: 0.2446 Lr: 0.00233 [2024-02-18 21:01:28,581 INFO misc.py line 119 87073] Train: [55/100][825/1557] Data 0.015 (0.167) Batch 0.774 (1.302) Remain 25:35:50 loss: 0.2879 Lr: 0.00233 [2024-02-18 21:01:29,693 INFO misc.py line 119 87073] Train: [55/100][826/1557] Data 0.005 (0.166) Batch 1.113 (1.301) Remain 25:35:33 loss: 0.1663 Lr: 0.00233 [2024-02-18 21:01:30,724 INFO misc.py line 119 87073] Train: [55/100][827/1557] Data 0.005 (0.166) Batch 1.029 (1.301) Remain 25:35:08 loss: 0.4369 Lr: 0.00233 [2024-02-18 21:01:31,668 INFO misc.py line 119 87073] Train: [55/100][828/1557] Data 0.005 (0.166) Batch 0.944 (1.301) Remain 25:34:36 loss: 0.3954 Lr: 0.00233 [2024-02-18 21:01:32,550 INFO misc.py line 119 87073] Train: [55/100][829/1557] Data 0.005 (0.166) Batch 0.884 (1.300) Remain 25:33:59 loss: 0.1398 Lr: 0.00233 [2024-02-18 21:01:33,730 INFO misc.py line 119 87073] Train: [55/100][830/1557] Data 0.003 (0.166) Batch 1.180 (1.300) Remain 25:33:47 loss: 0.1926 Lr: 0.00233 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Batch 1.066 (1.318) Remain 25:54:33 loss: 0.4849 Lr: 0.00233 [2024-02-18 21:02:14,714 INFO misc.py line 119 87073] Train: [55/100][850/1557] Data 0.006 (0.172) Batch 1.092 (1.318) Remain 25:54:13 loss: 0.1769 Lr: 0.00233 [2024-02-18 21:02:15,576 INFO misc.py line 119 87073] Train: [55/100][851/1557] Data 0.008 (0.171) Batch 0.864 (1.317) Remain 25:53:34 loss: 0.1898 Lr: 0.00233 [2024-02-18 21:02:16,288 INFO misc.py line 119 87073] Train: [55/100][852/1557] Data 0.004 (0.171) Batch 0.713 (1.316) Remain 25:52:42 loss: 0.3227 Lr: 0.00233 [2024-02-18 21:02:16,974 INFO misc.py line 119 87073] Train: [55/100][853/1557] Data 0.004 (0.171) Batch 0.677 (1.316) Remain 25:51:48 loss: 0.2189 Lr: 0.00233 [2024-02-18 21:02:18,180 INFO misc.py line 119 87073] Train: [55/100][854/1557] Data 0.012 (0.171) Batch 1.205 (1.316) Remain 25:51:37 loss: 0.2385 Lr: 0.00233 [2024-02-18 21:02:19,437 INFO misc.py line 119 87073] Train: [55/100][855/1557] Data 0.014 (0.171) Batch 1.257 (1.315) Remain 25:51:31 loss: 0.1497 Lr: 0.00233 [2024-02-18 21:02:20,536 INFO misc.py line 119 87073] Train: [55/100][856/1557] Data 0.013 (0.170) Batch 1.096 (1.315) Remain 25:51:12 loss: 0.5683 Lr: 0.00233 [2024-02-18 21:02:21,552 INFO misc.py line 119 87073] Train: [55/100][857/1557] Data 0.017 (0.170) Batch 1.020 (1.315) Remain 25:50:46 loss: 0.1472 Lr: 0.00233 [2024-02-18 21:02:22,599 INFO misc.py line 119 87073] Train: [55/100][858/1557] Data 0.012 (0.170) Batch 1.054 (1.315) Remain 25:50:23 loss: 0.3255 Lr: 0.00233 [2024-02-18 21:02:23,374 INFO misc.py line 119 87073] Train: [55/100][859/1557] Data 0.005 (0.170) Batch 0.776 (1.314) Remain 25:49:37 loss: 0.3386 Lr: 0.00233 [2024-02-18 21:02:24,147 INFO misc.py line 119 87073] Train: [55/100][860/1557] Data 0.004 (0.170) Batch 0.773 (1.313) Remain 25:48:51 loss: 0.3660 Lr: 0.00233 [2024-02-18 21:02:25,422 INFO misc.py line 119 87073] Train: [55/100][861/1557] Data 0.004 (0.170) Batch 1.264 (1.313) Remain 25:48:46 loss: 0.1461 Lr: 0.00233 [2024-02-18 21:02:26,482 INFO misc.py line 119 87073] Train: [55/100][862/1557] Data 0.014 (0.169) Batch 1.059 (1.313) Remain 25:48:23 loss: 0.3740 Lr: 0.00233 [2024-02-18 21:02:27,419 INFO misc.py line 119 87073] Train: [55/100][863/1557] Data 0.016 (0.169) Batch 0.949 (1.313) Remain 25:47:52 loss: 0.2753 Lr: 0.00233 [2024-02-18 21:02:28,200 INFO misc.py line 119 87073] Train: [55/100][864/1557] Data 0.003 (0.169) Batch 0.781 (1.312) Remain 25:47:07 loss: 0.3851 Lr: 0.00233 [2024-02-18 21:02:29,287 INFO misc.py line 119 87073] Train: [55/100][865/1557] Data 0.004 (0.169) Batch 1.084 (1.312) Remain 25:46:47 loss: 0.9576 Lr: 0.00233 [2024-02-18 21:02:30,018 INFO misc.py line 119 87073] Train: [55/100][866/1557] Data 0.007 (0.169) Batch 0.734 (1.311) Remain 25:45:59 loss: 0.2559 Lr: 0.00233 [2024-02-18 21:02:30,756 INFO misc.py line 119 87073] Train: [55/100][867/1557] Data 0.004 (0.168) Batch 0.734 (1.310) Remain 25:45:10 loss: 0.2179 Lr: 0.00233 [2024-02-18 21:02:31,726 INFO misc.py line 119 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line 119 87073] Train: [55/100][893/1557] Data 0.004 (0.165) Batch 0.756 (1.301) Remain 25:33:26 loss: 0.1437 Lr: 0.00233 [2024-02-18 21:02:57,210 INFO misc.py line 119 87073] Train: [55/100][894/1557] Data 0.013 (0.165) Batch 0.818 (1.300) Remain 25:32:47 loss: 0.2581 Lr: 0.00233 [2024-02-18 21:02:58,069 INFO misc.py line 119 87073] Train: [55/100][895/1557] Data 0.003 (0.165) Batch 0.859 (1.300) Remain 25:32:10 loss: 0.2929 Lr: 0.00233 [2024-02-18 21:02:59,347 INFO misc.py line 119 87073] Train: [55/100][896/1557] Data 0.004 (0.164) Batch 1.275 (1.300) Remain 25:32:07 loss: 0.2006 Lr: 0.00233 [2024-02-18 21:03:00,305 INFO misc.py line 119 87073] Train: [55/100][897/1557] Data 0.007 (0.164) Batch 0.961 (1.299) Remain 25:31:39 loss: 0.5691 Lr: 0.00233 [2024-02-18 21:03:01,210 INFO misc.py line 119 87073] Train: [55/100][898/1557] Data 0.004 (0.164) Batch 0.905 (1.299) Remain 25:31:06 loss: 0.3301 Lr: 0.00233 [2024-02-18 21:03:02,381 INFO misc.py line 119 87073] Train: 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Batch 0.942 (1.316) Remain 25:51:15 loss: 0.3649 Lr: 0.00233 [2024-02-18 21:03:26,930 INFO misc.py line 119 87073] Train: [55/100][906/1557] Data 0.004 (0.173) Batch 1.093 (1.316) Remain 25:50:56 loss: 0.2781 Lr: 0.00233 [2024-02-18 21:03:27,733 INFO misc.py line 119 87073] Train: [55/100][907/1557] Data 0.004 (0.172) Batch 0.800 (1.315) Remain 25:50:15 loss: 0.2084 Lr: 0.00233 [2024-02-18 21:03:28,508 INFO misc.py line 119 87073] Train: [55/100][908/1557] Data 0.007 (0.172) Batch 0.770 (1.315) Remain 25:49:31 loss: 0.3468 Lr: 0.00233 [2024-02-18 21:03:29,250 INFO misc.py line 119 87073] Train: [55/100][909/1557] Data 0.012 (0.172) Batch 0.749 (1.314) Remain 25:48:45 loss: 0.2502 Lr: 0.00233 [2024-02-18 21:03:30,423 INFO misc.py line 119 87073] Train: [55/100][910/1557] Data 0.005 (0.172) Batch 1.174 (1.314) Remain 25:48:33 loss: 0.1602 Lr: 0.00233 [2024-02-18 21:03:31,457 INFO misc.py line 119 87073] Train: [55/100][911/1557] Data 0.004 (0.172) Batch 1.033 (1.314) Remain 25:48:10 loss: 0.3905 Lr: 0.00233 [2024-02-18 21:03:32,485 INFO misc.py line 119 87073] Train: [55/100][912/1557] Data 0.005 (0.172) Batch 1.029 (1.313) Remain 25:47:46 loss: 0.2398 Lr: 0.00233 [2024-02-18 21:03:33,322 INFO misc.py line 119 87073] Train: [55/100][913/1557] Data 0.005 (0.171) Batch 0.836 (1.313) Remain 25:47:08 loss: 0.2110 Lr: 0.00233 [2024-02-18 21:03:34,631 INFO misc.py line 119 87073] Train: [55/100][914/1557] Data 0.006 (0.171) Batch 1.300 (1.313) Remain 25:47:06 loss: 0.7353 Lr: 0.00233 [2024-02-18 21:03:35,383 INFO misc.py line 119 87073] Train: [55/100][915/1557] Data 0.014 (0.171) Batch 0.762 (1.312) Remain 25:46:22 loss: 0.1481 Lr: 0.00233 [2024-02-18 21:03:36,174 INFO misc.py line 119 87073] Train: [55/100][916/1557] Data 0.004 (0.171) Batch 0.788 (1.312) Remain 25:45:40 loss: 0.3152 Lr: 0.00233 [2024-02-18 21:03:37,491 INFO misc.py line 119 87073] Train: [55/100][917/1557] Data 0.006 (0.171) Batch 1.319 (1.312) Remain 25:45:39 loss: 0.1982 Lr: 0.00233 [2024-02-18 21:03:38,594 INFO misc.py line 119 87073] Train: [55/100][918/1557] Data 0.005 (0.170) Batch 1.094 (1.311) Remain 25:45:21 loss: 0.2782 Lr: 0.00233 [2024-02-18 21:03:39,729 INFO misc.py line 119 87073] Train: [55/100][919/1557] Data 0.014 (0.170) Batch 1.133 (1.311) Remain 25:45:06 loss: 0.1583 Lr: 0.00233 [2024-02-18 21:03:40,767 INFO misc.py line 119 87073] Train: [55/100][920/1557] Data 0.016 (0.170) Batch 1.037 (1.311) Remain 25:44:43 loss: 0.2397 Lr: 0.00233 [2024-02-18 21:03:41,795 INFO misc.py line 119 87073] Train: [55/100][921/1557] Data 0.017 (0.170) Batch 1.029 (1.311) Remain 25:44:20 loss: 0.9370 Lr: 0.00233 [2024-02-18 21:03:42,536 INFO misc.py line 119 87073] Train: [55/100][922/1557] Data 0.016 (0.170) Batch 0.753 (1.310) Remain 25:43:36 loss: 0.2103 Lr: 0.00233 [2024-02-18 21:03:43,218 INFO misc.py line 119 87073] Train: [55/100][923/1557] Data 0.004 (0.170) Batch 0.681 (1.309) Remain 25:42:46 loss: 0.3370 Lr: 0.00233 [2024-02-18 21:03:44,299 INFO misc.py line 119 87073] Train: [55/100][924/1557] Data 0.006 (0.169) Batch 1.079 (1.309) Remain 25:42:27 loss: 0.1410 Lr: 0.00233 [2024-02-18 21:03:45,232 INFO misc.py line 119 87073] Train: [55/100][925/1557] Data 0.008 (0.169) Batch 0.937 (1.309) Remain 25:41:58 loss: 0.6407 Lr: 0.00233 [2024-02-18 21:03:46,350 INFO misc.py line 119 87073] Train: [55/100][926/1557] Data 0.004 (0.169) Batch 1.117 (1.308) Remain 25:41:42 loss: 0.2433 Lr: 0.00233 [2024-02-18 21:03:47,382 INFO misc.py line 119 87073] Train: [55/100][927/1557] Data 0.006 (0.169) Batch 1.033 (1.308) Remain 25:41:19 loss: 0.3022 Lr: 0.00233 [2024-02-18 21:03:48,314 INFO misc.py line 119 87073] Train: [55/100][928/1557] Data 0.004 (0.169) Batch 0.931 (1.308) Remain 25:40:49 loss: 0.3829 Lr: 0.00233 [2024-02-18 21:03:49,078 INFO misc.py line 119 87073] Train: [55/100][929/1557] Data 0.005 (0.169) Batch 0.757 (1.307) Remain 25:40:06 loss: 0.1738 Lr: 0.00233 [2024-02-18 21:03:49,843 INFO misc.py line 119 87073] Train: [55/100][930/1557] Data 0.012 (0.168) Batch 0.773 (1.307) Remain 25:39:24 loss: 0.2211 Lr: 0.00233 [2024-02-18 21:03:51,087 INFO misc.py line 119 87073] Train: [55/100][931/1557] Data 0.004 (0.168) Batch 1.243 (1.307) Remain 25:39:18 loss: 0.2003 Lr: 0.00233 [2024-02-18 21:03:52,151 INFO misc.py line 119 87073] Train: [55/100][932/1557] Data 0.004 (0.168) Batch 1.064 (1.306) Remain 25:38:58 loss: 0.2784 Lr: 0.00233 [2024-02-18 21:03:53,228 INFO misc.py line 119 87073] Train: [55/100][933/1557] Data 0.005 (0.168) Batch 1.078 (1.306) Remain 25:38:39 loss: 0.2046 Lr: 0.00233 [2024-02-18 21:03:54,177 INFO misc.py line 119 87073] Train: [55/100][934/1557] Data 0.004 (0.168) Batch 0.949 (1.306) Remain 25:38:11 loss: 0.5374 Lr: 0.00233 [2024-02-18 21:03:55,234 INFO misc.py line 119 87073] Train: [55/100][935/1557] Data 0.005 (0.168) Batch 1.055 (1.305) Remain 25:37:50 loss: 0.2679 Lr: 0.00233 [2024-02-18 21:03:56,003 INFO misc.py line 119 87073] Train: [55/100][936/1557] Data 0.007 (0.167) Batch 0.773 (1.305) Remain 25:37:09 loss: 0.2835 Lr: 0.00233 [2024-02-18 21:03:56,819 INFO misc.py line 119 87073] Train: [55/100][937/1557] Data 0.003 (0.167) Batch 0.806 (1.304) Remain 25:36:30 loss: 0.2734 Lr: 0.00233 [2024-02-18 21:03:57,946 INFO misc.py line 119 87073] Train: [55/100][938/1557] Data 0.012 (0.167) Batch 1.126 (1.304) Remain 25:36:15 loss: 0.1134 Lr: 0.00233 [2024-02-18 21:03:58,804 INFO misc.py line 119 87073] Train: [55/100][939/1557] Data 0.014 (0.167) Batch 0.867 (1.304) Remain 25:35:41 loss: 0.3866 Lr: 0.00233 [2024-02-18 21:03:59,914 INFO misc.py line 119 87073] Train: [55/100][940/1557] Data 0.004 (0.167) Batch 1.111 (1.303) Remain 25:35:25 loss: 0.6187 Lr: 0.00233 [2024-02-18 21:04:00,955 INFO misc.py line 119 87073] Train: [55/100][941/1557] Data 0.004 (0.166) Batch 1.040 (1.303) Remain 25:35:04 loss: 0.7371 Lr: 0.00233 [2024-02-18 21:04:01,969 INFO misc.py line 119 87073] Train: [55/100][942/1557] Data 0.005 (0.166) Batch 1.014 (1.303) Remain 25:34:40 loss: 0.2381 Lr: 0.00233 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line 119 87073] Train: [55/100][949/1557] Data 0.004 (0.165) Batch 0.892 (1.300) Remain 25:31:39 loss: 0.4531 Lr: 0.00233 [2024-02-18 21:04:09,584 INFO misc.py line 119 87073] Train: [55/100][950/1557] Data 0.005 (0.165) Batch 0.793 (1.300) Remain 25:31:00 loss: 0.2694 Lr: 0.00233 [2024-02-18 21:04:10,316 INFO misc.py line 119 87073] Train: [55/100][951/1557] Data 0.011 (0.165) Batch 0.738 (1.299) Remain 25:30:17 loss: 0.1864 Lr: 0.00233 [2024-02-18 21:04:11,665 INFO misc.py line 119 87073] Train: [55/100][952/1557] Data 0.004 (0.165) Batch 1.340 (1.299) Remain 25:30:19 loss: 0.1389 Lr: 0.00233 [2024-02-18 21:04:12,650 INFO misc.py line 119 87073] Train: [55/100][953/1557] Data 0.014 (0.164) Batch 0.994 (1.299) Remain 25:29:55 loss: 0.4169 Lr: 0.00233 [2024-02-18 21:04:13,745 INFO misc.py line 119 87073] Train: [55/100][954/1557] Data 0.004 (0.164) Batch 1.096 (1.299) Remain 25:29:38 loss: 0.2066 Lr: 0.00233 [2024-02-18 21:04:14,616 INFO misc.py line 119 87073] Train: 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Batch 0.925 (1.314) Remain 25:47:45 loss: 0.5707 Lr: 0.00233 [2024-02-18 21:04:38,709 INFO misc.py line 119 87073] Train: [55/100][962/1557] Data 0.005 (0.172) Batch 1.019 (1.314) Remain 25:47:22 loss: 0.6075 Lr: 0.00233 [2024-02-18 21:04:39,832 INFO misc.py line 119 87073] Train: [55/100][963/1557] Data 0.004 (0.172) Batch 1.123 (1.314) Remain 25:47:06 loss: 0.2209 Lr: 0.00233 [2024-02-18 21:04:40,655 INFO misc.py line 119 87073] Train: [55/100][964/1557] Data 0.004 (0.172) Batch 0.822 (1.313) Remain 25:46:29 loss: 0.3061 Lr: 0.00233 [2024-02-18 21:04:41,440 INFO misc.py line 119 87073] Train: [55/100][965/1557] Data 0.005 (0.171) Batch 0.784 (1.313) Remain 25:45:49 loss: 0.2889 Lr: 0.00233 [2024-02-18 21:04:42,743 INFO misc.py line 119 87073] Train: [55/100][966/1557] Data 0.005 (0.171) Batch 1.292 (1.313) Remain 25:45:46 loss: 0.1850 Lr: 0.00233 [2024-02-18 21:04:43,653 INFO misc.py line 119 87073] Train: [55/100][967/1557] Data 0.016 (0.171) Batch 0.920 (1.312) Remain 25:45:16 loss: 0.4170 Lr: 0.00233 [2024-02-18 21:04:44,665 INFO misc.py line 119 87073] Train: [55/100][968/1557] Data 0.006 (0.171) Batch 1.013 (1.312) Remain 25:44:53 loss: 0.3962 Lr: 0.00233 [2024-02-18 21:04:45,666 INFO misc.py line 119 87073] Train: [55/100][969/1557] Data 0.005 (0.171) Batch 1.003 (1.312) Remain 25:44:29 loss: 0.5315 Lr: 0.00233 [2024-02-18 21:04:46,634 INFO misc.py line 119 87073] Train: [55/100][970/1557] Data 0.004 (0.171) Batch 0.968 (1.311) Remain 25:44:02 loss: 0.4707 Lr: 0.00233 [2024-02-18 21:04:47,354 INFO misc.py line 119 87073] Train: [55/100][971/1557] Data 0.004 (0.170) Batch 0.714 (1.311) Remain 25:43:17 loss: 0.1394 Lr: 0.00233 [2024-02-18 21:04:48,075 INFO misc.py line 119 87073] Train: [55/100][972/1557] Data 0.010 (0.170) Batch 0.727 (1.310) Remain 25:42:33 loss: 0.2946 Lr: 0.00233 [2024-02-18 21:04:49,338 INFO misc.py line 119 87073] Train: [55/100][973/1557] Data 0.004 (0.170) Batch 1.262 (1.310) Remain 25:42:29 loss: 0.1563 Lr: 0.00233 [2024-02-18 21:04:50,215 INFO misc.py line 119 87073] Train: [55/100][974/1557] Data 0.004 (0.170) Batch 0.876 (1.310) Remain 25:41:56 loss: 0.3301 Lr: 0.00233 [2024-02-18 21:04:51,223 INFO misc.py line 119 87073] Train: [55/100][975/1557] Data 0.005 (0.170) Batch 0.999 (1.309) Remain 25:41:32 loss: 0.3376 Lr: 0.00233 [2024-02-18 21:04:52,331 INFO misc.py line 119 87073] Train: [55/100][976/1557] Data 0.014 (0.170) Batch 1.109 (1.309) Remain 25:41:16 loss: 0.2424 Lr: 0.00233 [2024-02-18 21:04:53,280 INFO misc.py line 119 87073] Train: [55/100][977/1557] Data 0.012 (0.169) Batch 0.957 (1.309) Remain 25:40:49 loss: 0.1479 Lr: 0.00233 [2024-02-18 21:04:54,027 INFO misc.py line 119 87073] Train: [55/100][978/1557] Data 0.005 (0.169) Batch 0.747 (1.308) Remain 25:40:07 loss: 0.0968 Lr: 0.00233 [2024-02-18 21:04:54,833 INFO misc.py line 119 87073] Train: [55/100][979/1557] Data 0.004 (0.169) Batch 0.800 (1.308) Remain 25:39:29 loss: 0.2757 Lr: 0.00233 [2024-02-18 21:04:55,907 INFO misc.py line 119 87073] Train: [55/100][980/1557] Data 0.009 (0.169) Batch 1.074 (1.307) Remain 25:39:11 loss: 0.1344 Lr: 0.00233 [2024-02-18 21:04:56,905 INFO misc.py line 119 87073] Train: [55/100][981/1557] Data 0.009 (0.169) Batch 0.999 (1.307) Remain 25:38:48 loss: 0.3083 Lr: 0.00233 [2024-02-18 21:04:57,840 INFO misc.py line 119 87073] Train: [55/100][982/1557] Data 0.008 (0.169) Batch 0.940 (1.307) Remain 25:38:20 loss: 0.4020 Lr: 0.00233 [2024-02-18 21:04:58,749 INFO misc.py line 119 87073] Train: [55/100][983/1557] Data 0.003 (0.168) Batch 0.908 (1.306) Remain 25:37:50 loss: 1.1099 Lr: 0.00233 [2024-02-18 21:04:59,748 INFO misc.py line 119 87073] Train: [55/100][984/1557] Data 0.006 (0.168) Batch 1.000 (1.306) Remain 25:37:26 loss: 0.2134 Lr: 0.00233 [2024-02-18 21:05:00,516 INFO misc.py line 119 87073] Train: [55/100][985/1557] Data 0.004 (0.168) Batch 0.766 (1.305) Remain 25:36:46 loss: 0.2584 Lr: 0.00233 [2024-02-18 21:05:01,314 INFO misc.py line 119 87073] Train: [55/100][986/1557] Data 0.006 (0.168) Batch 0.800 (1.305) Remain 25:36:09 loss: 0.1583 Lr: 0.00233 [2024-02-18 21:05:02,493 INFO misc.py line 119 87073] Train: [55/100][987/1557] Data 0.004 (0.168) Batch 1.178 (1.305) Remain 25:35:58 loss: 0.2767 Lr: 0.00233 [2024-02-18 21:05:03,500 INFO misc.py line 119 87073] Train: [55/100][988/1557] Data 0.005 (0.168) Batch 1.006 (1.304) Remain 25:35:35 loss: 0.5162 Lr: 0.00233 [2024-02-18 21:05:04,437 INFO misc.py line 119 87073] Train: [55/100][989/1557] Data 0.005 (0.167) Batch 0.939 (1.304) Remain 25:35:08 loss: 0.3239 Lr: 0.00233 [2024-02-18 21:05:05,225 INFO misc.py line 119 87073] Train: [55/100][990/1557] Data 0.004 (0.167) Batch 0.788 (1.304) Remain 25:34:30 loss: 0.1618 Lr: 0.00233 [2024-02-18 21:05:06,235 INFO misc.py line 119 87073] Train: [55/100][991/1557] Data 0.004 (0.167) Batch 1.007 (1.303) Remain 25:34:07 loss: 0.3978 Lr: 0.00233 [2024-02-18 21:05:07,011 INFO misc.py line 119 87073] Train: [55/100][992/1557] Data 0.006 (0.167) Batch 0.779 (1.303) Remain 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[2024-02-18 21:05:13,838 INFO misc.py line 119 87073] Train: [55/100][999/1557] Data 0.006 (0.166) Batch 0.696 (1.300) Remain 25:30:37 loss: 0.1647 Lr: 0.00233 [2024-02-18 21:05:14,619 INFO misc.py line 119 87073] Train: [55/100][1000/1557] Data 0.004 (0.166) Batch 0.774 (1.300) Remain 25:29:58 loss: 0.2935 Lr: 0.00233 [2024-02-18 21:05:15,906 INFO misc.py line 119 87073] Train: [55/100][1001/1557] Data 0.011 (0.166) Batch 1.289 (1.300) Remain 25:29:56 loss: 0.1047 Lr: 0.00233 [2024-02-18 21:05:16,759 INFO misc.py line 119 87073] Train: [55/100][1002/1557] Data 0.011 (0.165) Batch 0.856 (1.299) Remain 25:29:24 loss: 0.2180 Lr: 0.00232 [2024-02-18 21:05:17,723 INFO misc.py line 119 87073] Train: [55/100][1003/1557] Data 0.006 (0.165) Batch 0.966 (1.299) Remain 25:28:59 loss: 0.4850 Lr: 0.00232 [2024-02-18 21:05:18,522 INFO misc.py line 119 87073] Train: [55/100][1004/1557] Data 0.004 (0.165) Batch 0.799 (1.299) Remain 25:28:22 loss: 0.3412 Lr: 0.00232 [2024-02-18 21:05:19,446 INFO misc.py line 119 87073] Train: [55/100][1005/1557] Data 0.004 (0.165) Batch 0.915 (1.298) Remain 25:27:54 loss: 0.3011 Lr: 0.00232 [2024-02-18 21:05:20,220 INFO misc.py line 119 87073] Train: [55/100][1006/1557] Data 0.013 (0.165) Batch 0.783 (1.298) Remain 25:27:16 loss: 0.3009 Lr: 0.00232 [2024-02-18 21:05:20,997 INFO misc.py line 119 87073] Train: [55/100][1007/1557] Data 0.004 (0.165) Batch 0.777 (1.297) Remain 25:26:38 loss: 0.1875 Lr: 0.00232 [2024-02-18 21:05:22,400 INFO misc.py line 119 87073] Train: [55/100][1008/1557] Data 0.003 (0.164) Batch 1.400 (1.297) Remain 25:26:44 loss: 0.1506 Lr: 0.00232 [2024-02-18 21:05:23,363 INFO misc.py line 119 87073] Train: [55/100][1009/1557] Data 0.007 (0.164) Batch 0.964 (1.297) Remain 25:26:20 loss: 0.4080 Lr: 0.00232 [2024-02-18 21:05:24,422 INFO misc.py line 119 87073] Train: [55/100][1010/1557] Data 0.007 (0.164) Batch 1.059 (1.297) Remain 25:26:02 loss: 0.3762 Lr: 0.00232 [2024-02-18 21:05:25,245 INFO misc.py line 119 87073] Train: 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(0.172) Batch 0.858 (1.313) Remain 25:45:29 loss: 0.1582 Lr: 0.00232 [2024-02-18 21:05:51,432 INFO misc.py line 119 87073] Train: [55/100][1018/1557] Data 0.007 (0.172) Batch 1.041 (1.313) Remain 25:45:09 loss: 0.1502 Lr: 0.00232 [2024-02-18 21:05:52,572 INFO misc.py line 119 87073] Train: [55/100][1019/1557] Data 0.004 (0.171) Batch 1.140 (1.313) Remain 25:44:55 loss: 0.2866 Lr: 0.00232 [2024-02-18 21:05:53,306 INFO misc.py line 119 87073] Train: [55/100][1020/1557] Data 0.005 (0.171) Batch 0.734 (1.312) Remain 25:44:14 loss: 0.2048 Lr: 0.00232 [2024-02-18 21:05:54,047 INFO misc.py line 119 87073] Train: [55/100][1021/1557] Data 0.004 (0.171) Batch 0.734 (1.312) Remain 25:43:32 loss: 0.2462 Lr: 0.00232 [2024-02-18 21:05:55,292 INFO misc.py line 119 87073] Train: [55/100][1022/1557] Data 0.011 (0.171) Batch 1.246 (1.312) Remain 25:43:26 loss: 0.1755 Lr: 0.00232 [2024-02-18 21:05:56,336 INFO misc.py line 119 87073] Train: [55/100][1023/1557] Data 0.010 (0.171) Batch 1.048 (1.311) Remain 25:43:07 loss: 0.2857 Lr: 0.00232 [2024-02-18 21:05:57,223 INFO misc.py line 119 87073] Train: [55/100][1024/1557] Data 0.005 (0.171) Batch 0.889 (1.311) Remain 25:42:36 loss: 0.2763 Lr: 0.00232 [2024-02-18 21:05:58,264 INFO misc.py line 119 87073] Train: [55/100][1025/1557] Data 0.004 (0.170) Batch 1.041 (1.311) Remain 25:42:16 loss: 0.4802 Lr: 0.00232 [2024-02-18 21:05:59,182 INFO misc.py line 119 87073] Train: [55/100][1026/1557] Data 0.004 (0.170) Batch 0.918 (1.310) Remain 25:41:48 loss: 0.2704 Lr: 0.00232 [2024-02-18 21:05:59,923 INFO misc.py line 119 87073] Train: [55/100][1027/1557] Data 0.003 (0.170) Batch 0.733 (1.310) Remain 25:41:07 loss: 0.3503 Lr: 0.00232 [2024-02-18 21:06:00,621 INFO misc.py line 119 87073] Train: [55/100][1028/1557] Data 0.011 (0.170) Batch 0.705 (1.309) Remain 25:40:24 loss: 0.2934 Lr: 0.00232 [2024-02-18 21:06:01,971 INFO misc.py line 119 87073] Train: [55/100][1029/1557] Data 0.004 (0.170) Batch 1.340 (1.309) Remain 25:40:25 loss: 0.1726 Lr: 0.00232 [2024-02-18 21:06:02,818 INFO misc.py line 119 87073] Train: [55/100][1030/1557] Data 0.014 (0.170) Batch 0.856 (1.309) Remain 25:39:52 loss: 0.2115 Lr: 0.00232 [2024-02-18 21:06:03,878 INFO misc.py line 119 87073] Train: [55/100][1031/1557] Data 0.005 (0.169) Batch 1.062 (1.309) Remain 25:39:34 loss: 0.2462 Lr: 0.00232 [2024-02-18 21:06:04,728 INFO misc.py line 119 87073] Train: [55/100][1032/1557] Data 0.003 (0.169) Batch 0.847 (1.308) Remain 25:39:01 loss: 0.1384 Lr: 0.00232 [2024-02-18 21:06:05,702 INFO misc.py line 119 87073] Train: [55/100][1033/1557] Data 0.007 (0.169) Batch 0.977 (1.308) Remain 25:38:37 loss: 0.3349 Lr: 0.00232 [2024-02-18 21:06:06,442 INFO misc.py line 119 87073] Train: [55/100][1034/1557] Data 0.004 (0.169) Batch 0.741 (1.307) Remain 25:37:57 loss: 0.4064 Lr: 0.00232 [2024-02-18 21:06:07,194 INFO misc.py line 119 87073] Train: [55/100][1035/1557] Data 0.004 (0.169) Batch 0.743 (1.307) Remain 25:37:17 loss: 0.3226 Lr: 0.00232 [2024-02-18 21:06:08,253 INFO misc.py line 119 87073] Train: [55/100][1036/1557] Data 0.012 (0.169) Batch 1.063 (1.306) Remain 25:36:59 loss: 0.1196 Lr: 0.00232 [2024-02-18 21:06:09,270 INFO misc.py line 119 87073] Train: [55/100][1037/1557] Data 0.009 (0.169) Batch 1.012 (1.306) Remain 25:36:38 loss: 0.8464 Lr: 0.00232 [2024-02-18 21:06:10,515 INFO misc.py line 119 87073] Train: [55/100][1038/1557] Data 0.013 (0.168) Batch 1.246 (1.306) Remain 25:36:32 loss: 1.3579 Lr: 0.00232 [2024-02-18 21:06:11,688 INFO misc.py line 119 87073] Train: [55/100][1039/1557] Data 0.013 (0.168) Batch 1.170 (1.306) Remain 25:36:22 loss: 0.3307 Lr: 0.00232 [2024-02-18 21:06:12,701 INFO misc.py line 119 87073] Train: [55/100][1040/1557] Data 0.015 (0.168) Batch 1.017 (1.306) Remain 25:36:01 loss: 0.3243 Lr: 0.00232 [2024-02-18 21:06:13,445 INFO misc.py line 119 87073] Train: [55/100][1041/1557] Data 0.011 (0.168) Batch 0.752 (1.305) Remain 25:35:22 loss: 0.2472 Lr: 0.00232 [2024-02-18 21:06:14,203 INFO misc.py line 119 87073] Train: 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(0.167) Batch 0.779 (1.303) Remain 25:32:32 loss: 0.2973 Lr: 0.00232 [2024-02-18 21:06:20,888 INFO misc.py line 119 87073] Train: [55/100][1049/1557] Data 0.012 (0.167) Batch 0.683 (1.302) Remain 25:31:49 loss: 0.2666 Lr: 0.00232 [2024-02-18 21:06:22,064 INFO misc.py line 119 87073] Train: [55/100][1050/1557] Data 0.005 (0.167) Batch 1.160 (1.302) Remain 25:31:38 loss: 0.1340 Lr: 0.00232 [2024-02-18 21:06:23,086 INFO misc.py line 119 87073] Train: [55/100][1051/1557] Data 0.022 (0.166) Batch 1.032 (1.302) Remain 25:31:18 loss: 0.3085 Lr: 0.00232 [2024-02-18 21:06:24,087 INFO misc.py line 119 87073] Train: [55/100][1052/1557] Data 0.013 (0.166) Batch 0.999 (1.302) Remain 25:30:57 loss: 0.2814 Lr: 0.00232 [2024-02-18 21:06:25,108 INFO misc.py line 119 87073] Train: [55/100][1053/1557] Data 0.013 (0.166) Batch 1.031 (1.301) Remain 25:30:37 loss: 1.0462 Lr: 0.00232 [2024-02-18 21:06:26,029 INFO misc.py line 119 87073] Train: [55/100][1054/1557] Data 0.004 (0.166) Batch 0.922 (1.301) Remain 25:30:10 loss: 0.1518 Lr: 0.00232 [2024-02-18 21:06:28,183 INFO misc.py line 119 87073] Train: [55/100][1055/1557] Data 0.527 (0.166) Batch 2.147 (1.302) Remain 25:31:06 loss: 0.2145 Lr: 0.00232 [2024-02-18 21:06:29,077 INFO misc.py line 119 87073] Train: [55/100][1056/1557] Data 0.010 (0.166) Batch 0.900 (1.301) Remain 25:30:38 loss: 0.2840 Lr: 0.00232 [2024-02-18 21:06:30,336 INFO misc.py line 119 87073] Train: [55/100][1057/1557] Data 0.004 (0.166) Batch 1.255 (1.301) Remain 25:30:33 loss: 0.1494 Lr: 0.00232 [2024-02-18 21:06:31,201 INFO misc.py line 119 87073] Train: [55/100][1058/1557] Data 0.008 (0.166) Batch 0.869 (1.301) Remain 25:30:03 loss: 0.2612 Lr: 0.00232 [2024-02-18 21:06:32,112 INFO misc.py line 119 87073] Train: [55/100][1059/1557] Data 0.004 (0.166) Batch 0.911 (1.301) Remain 25:29:36 loss: 0.2286 Lr: 0.00232 [2024-02-18 21:06:33,193 INFO misc.py line 119 87073] Train: [55/100][1060/1557] Data 0.004 (0.166) Batch 1.081 (1.300) Remain 25:29:20 loss: 0.3034 Lr: 0.00232 [2024-02-18 21:06:33,998 INFO misc.py line 119 87073] Train: [55/100][1061/1557] Data 0.003 (0.165) Batch 0.804 (1.300) Remain 25:28:45 loss: 0.1882 Lr: 0.00232 [2024-02-18 21:06:34,736 INFO misc.py line 119 87073] Train: [55/100][1062/1557] Data 0.006 (0.165) Batch 0.736 (1.299) Remain 25:28:06 loss: 0.3261 Lr: 0.00232 [2024-02-18 21:06:35,485 INFO misc.py line 119 87073] Train: [55/100][1063/1557] Data 0.007 (0.165) Batch 0.751 (1.299) Remain 25:27:29 loss: 0.4116 Lr: 0.00232 [2024-02-18 21:06:36,788 INFO misc.py line 119 87073] Train: [55/100][1064/1557] Data 0.005 (0.165) Batch 1.298 (1.299) Remain 25:27:27 loss: 0.1638 Lr: 0.00232 [2024-02-18 21:06:37,705 INFO misc.py line 119 87073] Train: [55/100][1065/1557] Data 0.010 (0.165) Batch 0.923 (1.299) Remain 25:27:01 loss: 0.3953 Lr: 0.00232 [2024-02-18 21:06:38,596 INFO misc.py line 119 87073] Train: [55/100][1066/1557] Data 0.004 (0.165) Batch 0.889 (1.298) Remain 25:26:32 loss: 0.5428 Lr: 0.00232 [2024-02-18 21:06:39,549 INFO misc.py line 119 87073] Train: [55/100][1067/1557] Data 0.005 (0.164) Batch 0.950 (1.298) Remain 25:26:08 loss: 0.5144 Lr: 0.00232 [2024-02-18 21:06:40,585 INFO misc.py line 119 87073] Train: [55/100][1068/1557] Data 0.008 (0.164) Batch 1.037 (1.298) Remain 25:25:49 loss: 0.2220 Lr: 0.00232 [2024-02-18 21:06:41,388 INFO misc.py line 119 87073] Train: [55/100][1069/1557] Data 0.007 (0.164) Batch 0.806 (1.297) Remain 25:25:16 loss: 0.2482 Lr: 0.00232 [2024-02-18 21:06:42,196 INFO misc.py line 119 87073] Train: [55/100][1070/1557] Data 0.003 (0.164) Batch 0.806 (1.297) Remain 25:24:42 loss: 0.3739 Lr: 0.00232 [2024-02-18 21:07:01,248 INFO misc.py line 119 87073] Train: [55/100][1071/1557] Data 9.446 (0.173) Batch 19.053 (1.313) Remain 25:44:14 loss: 0.1669 Lr: 0.00232 [2024-02-18 21:07:02,272 INFO misc.py line 119 87073] Train: [55/100][1072/1557] Data 0.006 (0.173) Batch 1.015 (1.313) Remain 25:43:53 loss: 0.3308 Lr: 0.00232 [2024-02-18 21:07:03,396 INFO misc.py line 119 87073] Train: 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(0.172) Batch 0.964 (1.311) Remain 25:41:18 loss: 0.2827 Lr: 0.00232 [2024-02-18 21:07:10,354 INFO misc.py line 119 87073] Train: [55/100][1080/1557] Data 0.004 (0.171) Batch 1.122 (1.311) Remain 25:41:04 loss: 0.3416 Lr: 0.00232 [2024-02-18 21:07:11,225 INFO misc.py line 119 87073] Train: [55/100][1081/1557] Data 0.004 (0.171) Batch 0.869 (1.310) Remain 25:40:34 loss: 0.2044 Lr: 0.00232 [2024-02-18 21:07:12,216 INFO misc.py line 119 87073] Train: [55/100][1082/1557] Data 0.006 (0.171) Batch 0.989 (1.310) Remain 25:40:12 loss: 0.1948 Lr: 0.00232 [2024-02-18 21:07:13,020 INFO misc.py line 119 87073] Train: [55/100][1083/1557] Data 0.007 (0.171) Batch 0.807 (1.310) Remain 25:39:37 loss: 0.3231 Lr: 0.00232 [2024-02-18 21:07:13,815 INFO misc.py line 119 87073] Train: [55/100][1084/1557] Data 0.006 (0.171) Batch 0.796 (1.309) Remain 25:39:03 loss: 0.1825 Lr: 0.00232 [2024-02-18 21:07:15,162 INFO misc.py line 119 87073] Train: [55/100][1085/1557] Data 0.003 (0.171) Batch 1.334 (1.309) Remain 25:39:03 loss: 0.2149 Lr: 0.00232 [2024-02-18 21:07:16,020 INFO misc.py line 119 87073] Train: [55/100][1086/1557] Data 0.017 (0.170) Batch 0.870 (1.309) Remain 25:38:33 loss: 0.6319 Lr: 0.00232 [2024-02-18 21:07:16,951 INFO misc.py line 119 87073] Train: [55/100][1087/1557] Data 0.003 (0.170) Batch 0.931 (1.308) Remain 25:38:07 loss: 0.4623 Lr: 0.00232 [2024-02-18 21:07:17,866 INFO misc.py line 119 87073] Train: [55/100][1088/1557] Data 0.005 (0.170) Batch 0.905 (1.308) Remain 25:37:40 loss: 0.1881 Lr: 0.00232 [2024-02-18 21:07:18,851 INFO misc.py line 119 87073] Train: [55/100][1089/1557] Data 0.016 (0.170) Batch 0.996 (1.308) Remain 25:37:18 loss: 0.6414 Lr: 0.00232 [2024-02-18 21:07:19,638 INFO misc.py line 119 87073] Train: [55/100][1090/1557] Data 0.004 (0.170) Batch 0.786 (1.307) Remain 25:36:43 loss: 0.2189 Lr: 0.00232 [2024-02-18 21:07:20,405 INFO misc.py line 119 87073] Train: [55/100][1091/1557] Data 0.005 (0.170) Batch 0.763 (1.307) Remain 25:36:06 loss: 0.3170 Lr: 0.00232 [2024-02-18 21:07:21,558 INFO misc.py line 119 87073] Train: [55/100][1092/1557] Data 0.009 (0.170) Batch 1.147 (1.307) Remain 25:35:55 loss: 0.1322 Lr: 0.00232 [2024-02-18 21:07:22,626 INFO misc.py line 119 87073] Train: [55/100][1093/1557] Data 0.015 (0.169) Batch 1.072 (1.306) Remain 25:35:38 loss: 0.3358 Lr: 0.00232 [2024-02-18 21:07:23,704 INFO misc.py line 119 87073] Train: [55/100][1094/1557] Data 0.012 (0.169) Batch 1.075 (1.306) Remain 25:35:22 loss: 0.2256 Lr: 0.00232 [2024-02-18 21:07:24,831 INFO misc.py line 119 87073] Train: [55/100][1095/1557] Data 0.015 (0.169) Batch 1.126 (1.306) Remain 25:35:09 loss: 0.0818 Lr: 0.00232 [2024-02-18 21:07:25,796 INFO misc.py line 119 87073] Train: [55/100][1096/1557] Data 0.015 (0.169) Batch 0.977 (1.306) Remain 25:34:46 loss: 0.1253 Lr: 0.00232 [2024-02-18 21:07:26,550 INFO misc.py line 119 87073] Train: [55/100][1097/1557] Data 0.003 (0.169) Batch 0.753 (1.305) Remain 25:34:09 loss: 0.2174 Lr: 0.00232 [2024-02-18 21:07:27,277 INFO misc.py line 119 87073] Train: [55/100][1098/1557] Data 0.003 (0.169) Batch 0.725 (1.305) Remain 25:33:31 loss: 0.2608 Lr: 0.00232 [2024-02-18 21:07:28,603 INFO misc.py line 119 87073] Train: [55/100][1099/1557] Data 0.006 (0.169) Batch 1.327 (1.305) Remain 25:33:31 loss: 0.2161 Lr: 0.00232 [2024-02-18 21:07:29,452 INFO misc.py line 119 87073] Train: [55/100][1100/1557] Data 0.006 (0.168) Batch 0.850 (1.304) Remain 25:33:00 loss: 0.6415 Lr: 0.00232 [2024-02-18 21:07:30,335 INFO misc.py line 119 87073] Train: [55/100][1101/1557] Data 0.005 (0.168) Batch 0.885 (1.304) Remain 25:32:32 loss: 0.1568 Lr: 0.00232 [2024-02-18 21:07:31,258 INFO misc.py line 119 87073] Train: [55/100][1102/1557] Data 0.003 (0.168) Batch 0.911 (1.304) Remain 25:32:05 loss: 0.4566 Lr: 0.00232 [2024-02-18 21:07:32,245 INFO misc.py line 119 87073] Train: [55/100][1103/1557] Data 0.015 (0.168) Batch 0.998 (1.303) Remain 25:31:45 loss: 0.3003 Lr: 0.00232 [2024-02-18 21:07:33,006 INFO misc.py line 119 87073] Train: 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(0.167) Batch 1.134 (1.301) Remain 25:29:28 loss: 0.6443 Lr: 0.00232 [2024-02-18 21:07:40,150 INFO misc.py line 119 87073] Train: [55/100][1111/1557] Data 0.005 (0.167) Batch 0.779 (1.301) Remain 25:28:54 loss: 0.3550 Lr: 0.00232 [2024-02-18 21:07:40,936 INFO misc.py line 119 87073] Train: [55/100][1112/1557] Data 0.004 (0.167) Batch 0.782 (1.301) Remain 25:28:19 loss: 0.2562 Lr: 0.00232 [2024-02-18 21:07:42,270 INFO misc.py line 119 87073] Train: [55/100][1113/1557] Data 0.008 (0.167) Batch 1.332 (1.301) Remain 25:28:20 loss: 0.1826 Lr: 0.00232 [2024-02-18 21:07:43,185 INFO misc.py line 119 87073] Train: [55/100][1114/1557] Data 0.010 (0.166) Batch 0.921 (1.300) Remain 25:27:55 loss: 0.2007 Lr: 0.00232 [2024-02-18 21:07:44,039 INFO misc.py line 119 87073] Train: [55/100][1115/1557] Data 0.004 (0.166) Batch 0.855 (1.300) Remain 25:27:25 loss: 0.5348 Lr: 0.00232 [2024-02-18 21:07:45,027 INFO misc.py line 119 87073] Train: [55/100][1116/1557] Data 0.003 (0.166) Batch 0.981 (1.300) Remain 25:27:04 loss: 0.2568 Lr: 0.00232 [2024-02-18 21:07:45,961 INFO misc.py line 119 87073] Train: [55/100][1117/1557] Data 0.010 (0.166) Batch 0.941 (1.299) Remain 25:26:40 loss: 0.2309 Lr: 0.00232 [2024-02-18 21:07:46,722 INFO misc.py line 119 87073] Train: [55/100][1118/1557] Data 0.004 (0.166) Batch 0.761 (1.299) Remain 25:26:04 loss: 0.2865 Lr: 0.00232 [2024-02-18 21:07:47,587 INFO misc.py line 119 87073] Train: [55/100][1119/1557] Data 0.004 (0.166) Batch 0.856 (1.298) Remain 25:25:35 loss: 0.3104 Lr: 0.00232 [2024-02-18 21:07:48,864 INFO misc.py line 119 87073] Train: [55/100][1120/1557] Data 0.013 (0.166) Batch 1.275 (1.298) Remain 25:25:32 loss: 0.1685 Lr: 0.00232 [2024-02-18 21:07:49,823 INFO misc.py line 119 87073] Train: [55/100][1121/1557] Data 0.013 (0.165) Batch 0.968 (1.298) Remain 25:25:10 loss: 0.5162 Lr: 0.00232 [2024-02-18 21:07:50,767 INFO misc.py line 119 87073] Train: [55/100][1122/1557] Data 0.004 (0.165) Batch 0.942 (1.298) Remain 25:24:47 loss: 0.1781 Lr: 0.00232 [2024-02-18 21:07:51,796 INFO misc.py line 119 87073] Train: [55/100][1123/1557] Data 0.007 (0.165) Batch 1.029 (1.297) Remain 25:24:28 loss: 0.3814 Lr: 0.00232 [2024-02-18 21:07:53,001 INFO misc.py line 119 87073] Train: [55/100][1124/1557] Data 0.006 (0.165) Batch 1.207 (1.297) Remain 25:24:21 loss: 0.2557 Lr: 0.00232 [2024-02-18 21:07:53,804 INFO misc.py line 119 87073] Train: [55/100][1125/1557] Data 0.005 (0.165) Batch 0.804 (1.297) Remain 25:23:49 loss: 0.2792 Lr: 0.00232 [2024-02-18 21:07:54,554 INFO misc.py line 119 87073] Train: [55/100][1126/1557] Data 0.004 (0.165) Batch 0.744 (1.296) Remain 25:23:13 loss: 0.1723 Lr: 0.00232 [2024-02-18 21:08:12,800 INFO misc.py line 119 87073] Train: [55/100][1127/1557] Data 8.889 (0.172) Batch 18.252 (1.312) Remain 25:40:55 loss: 0.1429 Lr: 0.00232 [2024-02-18 21:08:13,638 INFO misc.py line 119 87073] Train: [55/100][1128/1557] Data 0.005 (0.172) Batch 0.836 (1.311) Remain 25:40:24 loss: 0.2013 Lr: 0.00232 [2024-02-18 21:08:14,596 INFO misc.py line 119 87073] Train: [55/100][1129/1557] Data 0.007 (0.172) Batch 0.960 (1.311) Remain 25:40:01 loss: 0.3947 Lr: 0.00232 [2024-02-18 21:08:15,691 INFO misc.py line 119 87073] Train: [55/100][1130/1557] Data 0.005 (0.172) Batch 1.096 (1.311) Remain 25:39:46 loss: 0.4401 Lr: 0.00232 [2024-02-18 21:08:16,768 INFO misc.py line 119 87073] Train: [55/100][1131/1557] Data 0.005 (0.172) Batch 1.077 (1.310) Remain 25:39:30 loss: 0.2075 Lr: 0.00232 [2024-02-18 21:08:17,487 INFO misc.py line 119 87073] Train: [55/100][1132/1557] Data 0.004 (0.172) Batch 0.719 (1.310) Remain 25:38:52 loss: 0.2240 Lr: 0.00232 [2024-02-18 21:08:18,287 INFO misc.py line 119 87073] Train: [55/100][1133/1557] Data 0.004 (0.172) Batch 0.789 (1.309) Remain 25:38:18 loss: 0.2202 Lr: 0.00232 [2024-02-18 21:08:19,551 INFO misc.py line 119 87073] Train: [55/100][1134/1557] Data 0.013 (0.171) Batch 1.265 (1.309) Remain 25:38:14 loss: 0.1443 Lr: 0.00232 [2024-02-18 21:08:20,439 INFO misc.py line 119 87073] Train: 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25:32:52 loss: 0.2883 Lr: 0.00232 [2024-02-18 21:08:32,675 INFO misc.py line 119 87073] Train: [55/100][1148/1557] Data 0.005 (0.169) Batch 1.059 (1.305) Remain 25:32:35 loss: 0.1327 Lr: 0.00232 [2024-02-18 21:08:33,954 INFO misc.py line 119 87073] Train: [55/100][1149/1557] Data 0.011 (0.169) Batch 1.277 (1.305) Remain 25:32:32 loss: 0.3871 Lr: 0.00232 [2024-02-18 21:08:34,885 INFO misc.py line 119 87073] Train: [55/100][1150/1557] Data 0.013 (0.169) Batch 0.941 (1.304) Remain 25:32:09 loss: 0.6224 Lr: 0.00232 [2024-02-18 21:08:35,900 INFO misc.py line 119 87073] Train: [55/100][1151/1557] Data 0.003 (0.169) Batch 1.015 (1.304) Remain 25:31:49 loss: 0.3095 Lr: 0.00232 [2024-02-18 21:08:36,806 INFO misc.py line 119 87073] Train: [55/100][1152/1557] Data 0.003 (0.169) Batch 0.906 (1.304) Remain 25:31:24 loss: 0.3428 Lr: 0.00232 [2024-02-18 21:08:37,591 INFO misc.py line 119 87073] Train: [55/100][1153/1557] Data 0.004 (0.169) Batch 0.778 (1.303) Remain 25:30:50 loss: 0.2910 Lr: 0.00232 [2024-02-18 21:08:38,344 INFO misc.py line 119 87073] Train: [55/100][1154/1557] Data 0.011 (0.169) Batch 0.760 (1.303) Remain 25:30:16 loss: 0.2649 Lr: 0.00232 [2024-02-18 21:08:39,661 INFO misc.py line 119 87073] Train: [55/100][1155/1557] Data 0.004 (0.168) Batch 1.310 (1.303) Remain 25:30:15 loss: 0.3028 Lr: 0.00232 [2024-02-18 21:08:40,447 INFO misc.py line 119 87073] Train: [55/100][1156/1557] Data 0.011 (0.168) Batch 0.793 (1.303) Remain 25:29:42 loss: 0.3257 Lr: 0.00232 [2024-02-18 21:08:41,403 INFO misc.py line 119 87073] Train: [55/100][1157/1557] Data 0.004 (0.168) Batch 0.955 (1.302) Remain 25:29:20 loss: 0.4927 Lr: 0.00232 [2024-02-18 21:08:42,283 INFO misc.py line 119 87073] Train: [55/100][1158/1557] Data 0.005 (0.168) Batch 0.880 (1.302) Remain 25:28:53 loss: 0.2297 Lr: 0.00232 [2024-02-18 21:08:43,419 INFO misc.py line 119 87073] Train: [55/100][1159/1557] Data 0.005 (0.168) Batch 1.132 (1.302) Remain 25:28:41 loss: 0.4103 Lr: 0.00232 [2024-02-18 21:08:44,188 INFO misc.py line 119 87073] Train: [55/100][1160/1557] Data 0.009 (0.168) Batch 0.775 (1.301) Remain 25:28:08 loss: 0.2798 Lr: 0.00232 [2024-02-18 21:08:44,990 INFO misc.py line 119 87073] Train: [55/100][1161/1557] Data 0.004 (0.168) Batch 0.802 (1.301) Remain 25:27:36 loss: 0.2177 Lr: 0.00232 [2024-02-18 21:08:46,122 INFO misc.py line 119 87073] Train: [55/100][1162/1557] Data 0.003 (0.167) Batch 1.130 (1.301) Remain 25:27:24 loss: 0.1192 Lr: 0.00232 [2024-02-18 21:08:47,013 INFO misc.py line 119 87073] Train: [55/100][1163/1557] Data 0.005 (0.167) Batch 0.893 (1.300) Remain 25:26:58 loss: 0.2183 Lr: 0.00232 [2024-02-18 21:08:47,824 INFO misc.py line 119 87073] Train: [55/100][1164/1557] Data 0.004 (0.167) Batch 0.811 (1.300) Remain 25:26:27 loss: 0.3501 Lr: 0.00232 [2024-02-18 21:08:48,753 INFO misc.py line 119 87073] Train: [55/100][1165/1557] Data 0.004 (0.167) Batch 0.924 (1.300) Remain 25:26:03 loss: 0.3308 Lr: 0.00232 [2024-02-18 21:08:49,694 INFO misc.py line 119 87073] Train: 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(0.166) Batch 1.042 (1.298) Remain 25:23:31 loss: 0.4335 Lr: 0.00232 [2024-02-18 21:08:56,432 INFO misc.py line 119 87073] Train: [55/100][1173/1557] Data 0.004 (0.166) Batch 0.959 (1.297) Remain 25:23:09 loss: 0.3275 Lr: 0.00232 [2024-02-18 21:08:57,151 INFO misc.py line 119 87073] Train: [55/100][1174/1557] Data 0.004 (0.166) Batch 0.719 (1.297) Remain 25:22:33 loss: 0.2576 Lr: 0.00232 [2024-02-18 21:08:57,977 INFO misc.py line 119 87073] Train: [55/100][1175/1557] Data 0.004 (0.166) Batch 0.816 (1.296) Remain 25:22:03 loss: 0.2683 Lr: 0.00232 [2024-02-18 21:08:59,277 INFO misc.py line 119 87073] Train: [55/100][1176/1557] Data 0.015 (0.165) Batch 1.295 (1.296) Remain 25:22:02 loss: 0.1928 Lr: 0.00232 [2024-02-18 21:09:00,178 INFO misc.py line 119 87073] Train: [55/100][1177/1557] Data 0.019 (0.165) Batch 0.916 (1.296) Remain 25:21:38 loss: 0.1712 Lr: 0.00232 [2024-02-18 21:09:01,084 INFO misc.py line 119 87073] Train: [55/100][1178/1557] Data 0.004 (0.165) Batch 0.906 (1.296) Remain 25:21:13 loss: 0.3768 Lr: 0.00232 [2024-02-18 21:09:02,125 INFO misc.py line 119 87073] Train: [55/100][1179/1557] Data 0.004 (0.165) Batch 1.041 (1.295) Remain 25:20:56 loss: 0.5254 Lr: 0.00232 [2024-02-18 21:09:03,069 INFO misc.py line 119 87073] Train: [55/100][1180/1557] Data 0.004 (0.165) Batch 0.941 (1.295) Remain 25:20:34 loss: 0.4036 Lr: 0.00232 [2024-02-18 21:09:03,821 INFO misc.py line 119 87073] Train: [55/100][1181/1557] Data 0.007 (0.165) Batch 0.755 (1.295) Remain 25:20:00 loss: 0.4224 Lr: 0.00232 [2024-02-18 21:09:04,616 INFO misc.py line 119 87073] Train: [55/100][1182/1557] Data 0.004 (0.165) Batch 0.783 (1.294) Remain 25:19:28 loss: 0.3672 Lr: 0.00232 [2024-02-18 21:09:24,460 INFO misc.py line 119 87073] Train: [55/100][1183/1557] Data 9.326 (0.172) Batch 19.854 (1.310) Remain 25:37:55 loss: 0.2137 Lr: 0.00232 [2024-02-18 21:09:25,519 INFO misc.py line 119 87073] Train: [55/100][1184/1557] Data 0.005 (0.172) Batch 1.058 (1.310) Remain 25:37:39 loss: 0.3876 Lr: 0.00232 [2024-02-18 21:09:26,769 INFO misc.py line 119 87073] Train: [55/100][1185/1557] Data 0.006 (0.172) Batch 1.244 (1.310) Remain 25:37:34 loss: 0.2899 Lr: 0.00232 [2024-02-18 21:09:27,765 INFO misc.py line 119 87073] Train: [55/100][1186/1557] Data 0.012 (0.172) Batch 1.000 (1.309) Remain 25:37:14 loss: 0.4544 Lr: 0.00232 [2024-02-18 21:09:28,770 INFO misc.py line 119 87073] Train: [55/100][1187/1557] Data 0.009 (0.172) Batch 1.006 (1.309) Remain 25:36:54 loss: 0.1571 Lr: 0.00232 [2024-02-18 21:09:29,559 INFO misc.py line 119 87073] Train: [55/100][1188/1557] Data 0.006 (0.172) Batch 0.792 (1.309) Remain 25:36:22 loss: 0.3941 Lr: 0.00232 [2024-02-18 21:09:30,278 INFO misc.py line 119 87073] Train: [55/100][1189/1557] Data 0.004 (0.172) Batch 0.718 (1.308) Remain 25:35:46 loss: 0.3124 Lr: 0.00232 [2024-02-18 21:09:31,509 INFO misc.py line 119 87073] Train: [55/100][1190/1557] Data 0.004 (0.171) Batch 1.222 (1.308) Remain 25:35:40 loss: 0.1687 Lr: 0.00231 [2024-02-18 21:09:32,568 INFO 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(0.170) Batch 0.677 (1.304) Remain 25:30:44 loss: 0.3315 Lr: 0.00231 [2024-02-18 21:09:44,813 INFO misc.py line 119 87073] Train: [55/100][1204/1557] Data 0.005 (0.170) Batch 1.051 (1.304) Remain 25:30:28 loss: 0.1222 Lr: 0.00231 [2024-02-18 21:09:45,757 INFO misc.py line 119 87073] Train: [55/100][1205/1557] Data 0.012 (0.169) Batch 0.950 (1.304) Remain 25:30:06 loss: 0.4557 Lr: 0.00231 [2024-02-18 21:09:46,646 INFO misc.py line 119 87073] Train: [55/100][1206/1557] Data 0.005 (0.169) Batch 0.889 (1.303) Remain 25:29:40 loss: 0.2830 Lr: 0.00231 [2024-02-18 21:09:47,683 INFO misc.py line 119 87073] Train: [55/100][1207/1557] Data 0.005 (0.169) Batch 1.038 (1.303) Remain 25:29:23 loss: 0.2216 Lr: 0.00231 [2024-02-18 21:09:48,826 INFO misc.py line 119 87073] Train: [55/100][1208/1557] Data 0.004 (0.169) Batch 1.143 (1.303) Remain 25:29:12 loss: 0.3972 Lr: 0.00231 [2024-02-18 21:09:49,466 INFO misc.py line 119 87073] Train: [55/100][1209/1557] Data 0.003 (0.169) Batch 0.640 (1.302) Remain 25:28:32 loss: 0.2966 Lr: 0.00231 [2024-02-18 21:09:50,212 INFO misc.py line 119 87073] Train: [55/100][1210/1557] Data 0.004 (0.169) Batch 0.742 (1.302) Remain 25:27:58 loss: 0.4574 Lr: 0.00231 [2024-02-18 21:09:51,501 INFO misc.py line 119 87073] Train: [55/100][1211/1557] Data 0.008 (0.169) Batch 1.284 (1.302) Remain 25:27:56 loss: 0.2401 Lr: 0.00231 [2024-02-18 21:09:52,560 INFO misc.py line 119 87073] Train: [55/100][1212/1557] Data 0.012 (0.169) Batch 1.056 (1.302) Remain 25:27:40 loss: 0.2760 Lr: 0.00231 [2024-02-18 21:09:53,550 INFO misc.py line 119 87073] Train: [55/100][1213/1557] Data 0.015 (0.168) Batch 1.000 (1.302) Remain 25:27:22 loss: 0.4379 Lr: 0.00231 [2024-02-18 21:09:54,600 INFO misc.py line 119 87073] Train: [55/100][1214/1557] Data 0.005 (0.168) Batch 1.041 (1.301) Remain 25:27:05 loss: 0.5426 Lr: 0.00231 [2024-02-18 21:09:55,472 INFO misc.py line 119 87073] Train: [55/100][1215/1557] Data 0.014 (0.168) Batch 0.882 (1.301) Remain 25:26:40 loss: 0.4886 Lr: 0.00231 [2024-02-18 21:09:56,202 INFO misc.py line 119 87073] Train: [55/100][1216/1557] Data 0.004 (0.168) Batch 0.730 (1.301) Remain 25:26:05 loss: 0.4420 Lr: 0.00231 [2024-02-18 21:09:56,930 INFO misc.py line 119 87073] Train: [55/100][1217/1557] Data 0.003 (0.168) Batch 0.699 (1.300) Remain 25:25:29 loss: 0.2568 Lr: 0.00231 [2024-02-18 21:09:58,109 INFO misc.py line 119 87073] Train: [55/100][1218/1557] Data 0.032 (0.168) Batch 1.199 (1.300) Remain 25:25:22 loss: 0.1478 Lr: 0.00231 [2024-02-18 21:09:59,189 INFO misc.py line 119 87073] Train: [55/100][1219/1557] Data 0.014 (0.168) Batch 1.079 (1.300) Remain 25:25:08 loss: 0.4987 Lr: 0.00231 [2024-02-18 21:10:00,173 INFO misc.py line 119 87073] Train: [55/100][1220/1557] Data 0.014 (0.167) Batch 0.994 (1.300) Remain 25:24:49 loss: 0.3210 Lr: 0.00231 [2024-02-18 21:10:01,140 INFO misc.py line 119 87073] Train: [55/100][1221/1557] Data 0.004 (0.167) Batch 0.967 (1.299) Remain 25:24:28 loss: 0.3326 Lr: 0.00231 [2024-02-18 21:10:02,222 INFO misc.py line 119 87073] Train: [55/100][1222/1557] Data 0.005 (0.167) Batch 1.083 (1.299) Remain 25:24:14 loss: 0.2695 Lr: 0.00231 [2024-02-18 21:10:02,984 INFO misc.py line 119 87073] Train: [55/100][1223/1557] Data 0.003 (0.167) Batch 0.762 (1.299) Remain 25:23:42 loss: 0.4018 Lr: 0.00231 [2024-02-18 21:10:03,711 INFO misc.py line 119 87073] Train: [55/100][1224/1557] Data 0.003 (0.167) Batch 0.717 (1.298) Remain 25:23:07 loss: 0.2054 Lr: 0.00231 [2024-02-18 21:10:05,001 INFO misc.py line 119 87073] Train: [55/100][1225/1557] Data 0.015 (0.167) Batch 1.290 (1.298) Remain 25:23:05 loss: 0.2492 Lr: 0.00231 [2024-02-18 21:10:05,846 INFO misc.py line 119 87073] Train: [55/100][1226/1557] Data 0.015 (0.167) Batch 0.854 (1.298) Remain 25:22:39 loss: 0.3087 Lr: 0.00231 [2024-02-18 21:10:06,888 INFO misc.py line 119 87073] Train: [55/100][1227/1557] Data 0.005 (0.167) Batch 1.043 (1.298) Remain 25:22:23 loss: 0.6858 Lr: 0.00231 [2024-02-18 21:10:08,102 INFO misc.py line 119 87073] Train: 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(0.166) Batch 0.928 (1.297) Remain 25:21:55 loss: 0.2884 Lr: 0.00231 [2024-02-18 21:10:16,580 INFO misc.py line 119 87073] Train: [55/100][1235/1557] Data 0.006 (0.166) Batch 0.934 (1.297) Remain 25:21:33 loss: 0.2338 Lr: 0.00231 [2024-02-18 21:10:17,712 INFO misc.py line 119 87073] Train: [55/100][1236/1557] Data 0.005 (0.166) Batch 1.133 (1.297) Remain 25:21:22 loss: 0.4502 Lr: 0.00231 [2024-02-18 21:10:18,435 INFO misc.py line 119 87073] Train: [55/100][1237/1557] Data 0.006 (0.166) Batch 0.724 (1.296) Remain 25:20:48 loss: 0.2615 Lr: 0.00231 [2024-02-18 21:10:19,221 INFO misc.py line 119 87073] Train: [55/100][1238/1557] Data 0.004 (0.166) Batch 0.785 (1.296) Remain 25:20:18 loss: 0.4856 Lr: 0.00231 [2024-02-18 21:10:39,204 INFO misc.py line 119 87073] Train: [55/100][1239/1557] Data 8.227 (0.172) Batch 19.985 (1.311) Remain 25:38:01 loss: 0.1290 Lr: 0.00231 [2024-02-18 21:10:40,182 INFO misc.py line 119 87073] Train: [55/100][1240/1557] Data 0.004 (0.172) Batch 0.975 (1.311) Remain 25:37:40 loss: 0.4073 Lr: 0.00231 [2024-02-18 21:10:41,148 INFO misc.py line 119 87073] Train: [55/100][1241/1557] Data 0.006 (0.172) Batch 0.968 (1.311) Remain 25:37:19 loss: 0.1994 Lr: 0.00231 [2024-02-18 21:10:42,154 INFO misc.py line 119 87073] Train: [55/100][1242/1557] Data 0.003 (0.172) Batch 1.006 (1.310) Remain 25:37:01 loss: 0.5336 Lr: 0.00231 [2024-02-18 21:10:43,140 INFO misc.py line 119 87073] Train: [55/100][1243/1557] Data 0.004 (0.172) Batch 0.983 (1.310) Remain 25:36:41 loss: 0.3159 Lr: 0.00231 [2024-02-18 21:10:43,928 INFO misc.py line 119 87073] Train: [55/100][1244/1557] Data 0.006 (0.172) Batch 0.790 (1.310) Remain 25:36:10 loss: 0.3463 Lr: 0.00231 [2024-02-18 21:10:44,806 INFO misc.py line 119 87073] Train: [55/100][1245/1557] Data 0.004 (0.172) Batch 0.877 (1.309) Remain 25:35:44 loss: 0.1475 Lr: 0.00231 [2024-02-18 21:10:46,043 INFO misc.py line 119 87073] Train: [55/100][1246/1557] Data 0.005 (0.171) Batch 1.229 (1.309) Remain 25:35:38 loss: 0.1766 Lr: 0.00231 [2024-02-18 21:10:47,090 INFO misc.py line 119 87073] Train: [55/100][1247/1557] Data 0.012 (0.171) Batch 1.044 (1.309) Remain 25:35:22 loss: 0.3766 Lr: 0.00231 [2024-02-18 21:10:48,194 INFO misc.py line 119 87073] Train: [55/100][1248/1557] Data 0.016 (0.171) Batch 1.104 (1.309) Remain 25:35:09 loss: 0.4264 Lr: 0.00231 [2024-02-18 21:10:49,216 INFO misc.py line 119 87073] Train: [55/100][1249/1557] Data 0.016 (0.171) Batch 1.033 (1.309) Remain 25:34:52 loss: 0.4668 Lr: 0.00231 [2024-02-18 21:10:50,246 INFO misc.py line 119 87073] Train: [55/100][1250/1557] Data 0.004 (0.171) Batch 1.026 (1.308) Remain 25:34:35 loss: 0.0981 Lr: 0.00231 [2024-02-18 21:10:51,057 INFO misc.py line 119 87073] Train: [55/100][1251/1557] Data 0.008 (0.171) Batch 0.816 (1.308) Remain 25:34:06 loss: 0.2814 Lr: 0.00231 [2024-02-18 21:10:51,964 INFO misc.py line 119 87073] Train: [55/100][1252/1557] Data 0.003 (0.171) Batch 0.906 (1.308) Remain 25:33:42 loss: 0.3563 Lr: 0.00231 [2024-02-18 21:10:53,335 INFO misc.py line 119 87073] Train: [55/100][1253/1557] Data 0.004 (0.171) Batch 1.365 (1.308) Remain 25:33:44 loss: 0.2216 Lr: 0.00231 [2024-02-18 21:10:54,172 INFO misc.py line 119 87073] Train: [55/100][1254/1557] Data 0.010 (0.170) Batch 0.843 (1.307) Remain 25:33:17 loss: 0.4789 Lr: 0.00231 [2024-02-18 21:10:55,125 INFO misc.py line 119 87073] Train: [55/100][1255/1557] Data 0.004 (0.170) Batch 0.953 (1.307) Remain 25:32:55 loss: 0.2675 Lr: 0.00231 [2024-02-18 21:10:56,127 INFO misc.py line 119 87073] Train: [55/100][1256/1557] Data 0.003 (0.170) Batch 1.001 (1.307) Remain 25:32:37 loss: 0.3441 Lr: 0.00231 [2024-02-18 21:10:56,979 INFO misc.py line 119 87073] Train: [55/100][1257/1557] Data 0.005 (0.170) Batch 0.853 (1.306) Remain 25:32:10 loss: 0.4749 Lr: 0.00231 [2024-02-18 21:10:57,772 INFO misc.py line 119 87073] Train: [55/100][1258/1557] Data 0.004 (0.170) Batch 0.791 (1.306) Remain 25:31:40 loss: 0.2138 Lr: 0.00231 [2024-02-18 21:10:58,538 INFO misc.py line 119 87073] Train: [55/100][1259/1557] Data 0.005 (0.170) Batch 0.767 (1.306) Remain 25:31:08 loss: 0.2057 Lr: 0.00231 [2024-02-18 21:10:59,598 INFO misc.py line 119 87073] Train: [55/100][1260/1557] Data 0.005 (0.170) Batch 1.059 (1.305) Remain 25:30:53 loss: 0.1524 Lr: 0.00231 [2024-02-18 21:11:00,484 INFO misc.py line 119 87073] Train: [55/100][1261/1557] Data 0.005 (0.169) Batch 0.888 (1.305) Remain 25:30:29 loss: 0.6920 Lr: 0.00231 [2024-02-18 21:11:01,349 INFO misc.py line 119 87073] Train: [55/100][1262/1557] Data 0.004 (0.169) Batch 0.863 (1.305) Remain 25:30:03 loss: 0.3190 Lr: 0.00231 [2024-02-18 21:11:02,427 INFO misc.py line 119 87073] Train: [55/100][1263/1557] Data 0.006 (0.169) Batch 1.070 (1.305) Remain 25:29:48 loss: 0.3708 Lr: 0.00231 [2024-02-18 21:11:03,482 INFO misc.py line 119 87073] Train: [55/100][1264/1557] Data 0.013 (0.169) Batch 1.058 (1.304) Remain 25:29:33 loss: 0.2812 Lr: 0.00231 [2024-02-18 21:11:04,243 INFO misc.py line 119 87073] Train: [55/100][1265/1557] Data 0.011 (0.169) Batch 0.768 (1.304) Remain 25:29:02 loss: 0.2673 Lr: 0.00231 [2024-02-18 21:11:05,006 INFO misc.py line 119 87073] Train: [55/100][1266/1557] Data 0.004 (0.169) Batch 0.762 (1.304) Remain 25:28:30 loss: 0.4492 Lr: 0.00231 [2024-02-18 21:11:06,250 INFO misc.py line 119 87073] Train: [55/100][1267/1557] Data 0.004 (0.169) Batch 1.234 (1.303) Remain 25:28:25 loss: 0.2354 Lr: 0.00231 [2024-02-18 21:11:07,174 INFO misc.py line 119 87073] Train: [55/100][1268/1557] Data 0.014 (0.169) Batch 0.934 (1.303) Remain 25:28:03 loss: 0.3091 Lr: 0.00231 [2024-02-18 21:11:08,087 INFO misc.py line 119 87073] Train: [55/100][1269/1557] Data 0.004 (0.168) Batch 0.913 (1.303) Remain 25:27:40 loss: 0.5659 Lr: 0.00231 [2024-02-18 21:11:09,139 INFO misc.py line 119 87073] Train: [55/100][1270/1557] Data 0.004 (0.168) Batch 1.051 (1.303) Remain 25:27:25 loss: 0.6055 Lr: 0.00231 [2024-02-18 21:11:10,237 INFO misc.py line 119 87073] Train: [55/100][1271/1557] Data 0.006 (0.168) Batch 1.100 (1.303) Remain 25:27:13 loss: 0.3846 Lr: 0.00231 [2024-02-18 21:11:10,998 INFO misc.py line 119 87073] Train: [55/100][1272/1557] Data 0.003 (0.168) Batch 0.762 (1.302) Remain 25:26:41 loss: 0.2650 Lr: 0.00231 [2024-02-18 21:11:11,672 INFO misc.py line 119 87073] Train: [55/100][1273/1557] Data 0.004 (0.168) Batch 0.670 (1.302) Remain 25:26:05 loss: 0.1894 Lr: 0.00231 [2024-02-18 21:11:12,788 INFO misc.py line 119 87073] Train: [55/100][1274/1557] Data 0.011 (0.168) Batch 1.119 (1.301) Remain 25:25:53 loss: 0.0741 Lr: 0.00231 [2024-02-18 21:11:13,674 INFO misc.py line 119 87073] Train: [55/100][1275/1557] Data 0.006 (0.168) Batch 0.886 (1.301) Remain 25:25:29 loss: 0.3966 Lr: 0.00231 [2024-02-18 21:11:14,553 INFO misc.py line 119 87073] Train: [55/100][1276/1557] Data 0.005 (0.168) Batch 0.880 (1.301) Remain 25:25:05 loss: 0.4382 Lr: 0.00231 [2024-02-18 21:11:15,652 INFO misc.py line 119 87073] Train: [55/100][1277/1557] Data 0.005 (0.167) Batch 1.094 (1.301) Remain 25:24:52 loss: 0.3295 Lr: 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INFO misc.py line 119 87073] Train: [55/100][1284/1557] Data 0.007 (0.167) Batch 0.972 (1.299) Remain 25:22:21 loss: 0.4621 Lr: 0.00231 [2024-02-18 21:11:23,041 INFO misc.py line 119 87073] Train: [55/100][1285/1557] Data 0.005 (0.166) Batch 0.871 (1.298) Remain 25:21:56 loss: 0.2326 Lr: 0.00231 [2024-02-18 21:11:23,788 INFO misc.py line 119 87073] Train: [55/100][1286/1557] Data 0.005 (0.166) Batch 0.747 (1.298) Remain 25:21:25 loss: 0.3504 Lr: 0.00231 [2024-02-18 21:11:24,534 INFO misc.py line 119 87073] Train: [55/100][1287/1557] Data 0.005 (0.166) Batch 0.741 (1.297) Remain 25:20:53 loss: 0.1426 Lr: 0.00231 [2024-02-18 21:11:25,834 INFO misc.py line 119 87073] Train: [55/100][1288/1557] Data 0.010 (0.166) Batch 1.296 (1.297) Remain 25:20:52 loss: 0.1514 Lr: 0.00231 [2024-02-18 21:11:26,802 INFO misc.py line 119 87073] Train: [55/100][1289/1557] Data 0.014 (0.166) Batch 0.979 (1.297) Remain 25:20:33 loss: 0.1490 Lr: 0.00231 [2024-02-18 21:11:27,928 INFO misc.py line 119 87073] Train: [55/100][1290/1557] Data 0.004 (0.166) Batch 1.125 (1.297) Remain 25:20:22 loss: 0.1767 Lr: 0.00231 [2024-02-18 21:11:28,939 INFO misc.py line 119 87073] Train: [55/100][1291/1557] Data 0.004 (0.166) Batch 1.007 (1.297) Remain 25:20:05 loss: 0.3787 Lr: 0.00231 [2024-02-18 21:11:29,773 INFO misc.py line 119 87073] Train: [55/100][1292/1557] Data 0.009 (0.166) Batch 0.837 (1.296) Remain 25:19:39 loss: 0.6853 Lr: 0.00231 [2024-02-18 21:11:30,540 INFO misc.py line 119 87073] Train: [55/100][1293/1557] Data 0.005 (0.165) Batch 0.769 (1.296) Remain 25:19:09 loss: 0.1671 Lr: 0.00231 [2024-02-18 21:11:31,401 INFO misc.py line 119 87073] Train: [55/100][1294/1557] Data 0.004 (0.165) Batch 0.855 (1.296) Remain 25:18:43 loss: 0.2199 Lr: 0.00231 [2024-02-18 21:11:51,593 INFO misc.py line 119 87073] Train: [55/100][1295/1557] Data 9.630 (0.173) Batch 20.198 (1.310) Remain 25:35:51 loss: 0.1429 Lr: 0.00231 [2024-02-18 21:11:52,480 INFO misc.py line 119 87073] Train: [55/100][1296/1557] Data 0.004 (0.172) Batch 0.881 (1.310) Remain 25:35:26 loss: 0.3411 Lr: 0.00231 [2024-02-18 21:11:53,367 INFO misc.py line 119 87073] Train: [55/100][1297/1557] Data 0.009 (0.172) Batch 0.891 (1.310) Remain 25:35:02 loss: 0.2491 Lr: 0.00231 [2024-02-18 21:11:54,418 INFO misc.py line 119 87073] Train: [55/100][1298/1557] Data 0.005 (0.172) Batch 1.051 (1.309) Remain 25:34:47 loss: 0.9766 Lr: 0.00231 [2024-02-18 21:11:55,279 INFO misc.py line 119 87073] Train: [55/100][1299/1557] Data 0.005 (0.172) Batch 0.862 (1.309) Remain 25:34:21 loss: 0.3019 Lr: 0.00231 [2024-02-18 21:11:56,157 INFO misc.py line 119 87073] Train: [55/100][1300/1557] Data 0.005 (0.172) Batch 0.874 (1.309) Remain 25:33:56 loss: 0.2812 Lr: 0.00231 [2024-02-18 21:11:56,935 INFO misc.py line 119 87073] Train: [55/100][1301/1557] Data 0.008 (0.172) Batch 0.781 (1.308) Remain 25:33:26 loss: 0.3185 Lr: 0.00231 [2024-02-18 21:11:58,197 INFO misc.py line 119 87073] Train: [55/100][1302/1557] Data 0.005 (0.172) Batch 1.257 (1.308) Remain 25:33:22 loss: 0.2253 Lr: 0.00231 [2024-02-18 21:11:59,212 INFO misc.py line 119 87073] Train: [55/100][1303/1557] Data 0.010 (0.172) Batch 1.013 (1.308) Remain 25:33:05 loss: 0.2247 Lr: 0.00231 [2024-02-18 21:12:00,176 INFO misc.py line 119 87073] Train: [55/100][1304/1557] Data 0.011 (0.171) Batch 0.971 (1.308) Remain 25:32:46 loss: 0.2568 Lr: 0.00231 [2024-02-18 21:12:01,131 INFO misc.py line 119 87073] Train: [55/100][1305/1557] Data 0.005 (0.171) Batch 0.954 (1.308) Remain 25:32:25 loss: 0.3687 Lr: 0.00231 [2024-02-18 21:12:02,003 INFO misc.py line 119 87073] Train: [55/100][1306/1557] Data 0.005 (0.171) Batch 0.872 (1.307) Remain 25:32:00 loss: 0.2805 Lr: 0.00231 [2024-02-18 21:12:02,752 INFO misc.py line 119 87073] Train: [55/100][1307/1557] Data 0.004 (0.171) Batch 0.742 (1.307) Remain 25:31:29 loss: 0.2323 Lr: 0.00231 [2024-02-18 21:12:03,525 INFO misc.py line 119 87073] Train: [55/100][1308/1557] Data 0.012 (0.171) Batch 0.780 (1.306) Remain 25:30:59 loss: 0.3328 Lr: 0.00231 [2024-02-18 21:12:04,847 INFO misc.py line 119 87073] Train: [55/100][1309/1557] Data 0.005 (0.171) Batch 1.320 (1.306) Remain 25:30:58 loss: 0.1369 Lr: 0.00231 [2024-02-18 21:12:05,875 INFO misc.py line 119 87073] Train: [55/100][1310/1557] Data 0.007 (0.171) Batch 1.023 (1.306) Remain 25:30:42 loss: 0.6045 Lr: 0.00231 [2024-02-18 21:12:06,946 INFO misc.py line 119 87073] Train: [55/100][1311/1557] Data 0.013 (0.171) Batch 1.075 (1.306) Remain 25:30:28 loss: 0.4468 Lr: 0.00231 [2024-02-18 21:12:07,841 INFO misc.py line 119 87073] Train: [55/100][1312/1557] Data 0.008 (0.170) Batch 0.899 (1.306) Remain 25:30:05 loss: 0.4481 Lr: 0.00231 [2024-02-18 21:12:08,933 INFO misc.py line 119 87073] Train: [55/100][1313/1557] Data 0.004 (0.170) Batch 1.092 (1.306) Remain 25:29:52 loss: 1.0950 Lr: 0.00231 [2024-02-18 21:12:09,777 INFO misc.py line 119 87073] Train: [55/100][1314/1557] Data 0.004 (0.170) Batch 0.844 (1.305) Remain 25:29:26 loss: 0.2713 Lr: 0.00231 [2024-02-18 21:12:10,594 INFO misc.py line 119 87073] Train: [55/100][1315/1557] Data 0.005 (0.170) Batch 0.816 (1.305) Remain 25:28:58 loss: 0.2332 Lr: 0.00231 [2024-02-18 21:12:11,728 INFO misc.py line 119 87073] Train: [55/100][1316/1557] Data 0.005 (0.170) Batch 1.126 (1.305) Remain 25:28:48 loss: 0.2213 Lr: 0.00231 [2024-02-18 21:12:12,613 INFO misc.py line 119 87073] Train: [55/100][1317/1557] Data 0.013 (0.170) Batch 0.893 (1.304) Remain 25:28:24 loss: 0.0726 Lr: 0.00231 [2024-02-18 21:12:13,532 INFO misc.py line 119 87073] Train: [55/100][1318/1557] Data 0.006 (0.170) Batch 0.921 (1.304) Remain 25:28:02 loss: 0.3063 Lr: 0.00231 [2024-02-18 21:12:14,473 INFO misc.py line 119 87073] Train: [55/100][1319/1557] Data 0.004 (0.170) Batch 0.936 (1.304) Remain 25:27:41 loss: 0.2235 Lr: 0.00231 [2024-02-18 21:12:15,781 INFO misc.py line 119 87073] Train: [55/100][1320/1557] Data 0.009 (0.169) Batch 1.309 (1.304) Remain 25:27:40 loss: 0.1936 Lr: 0.00231 [2024-02-18 21:12:16,548 INFO misc.py line 119 87073] Train: [55/100][1321/1557] Data 0.009 (0.169) Batch 0.771 (1.303) Remain 25:27:11 loss: 0.1280 Lr: 0.00231 [2024-02-18 21:12:17,252 INFO misc.py line 119 87073] Train: [55/100][1322/1557] Data 0.003 (0.169) Batch 0.703 (1.303) Remain 25:26:37 loss: 0.5026 Lr: 0.00231 [2024-02-18 21:12:18,533 INFO misc.py line 119 87073] Train: [55/100][1323/1557] Data 0.005 (0.169) Batch 1.274 (1.303) Remain 25:26:34 loss: 0.4019 Lr: 0.00231 [2024-02-18 21:12:19,467 INFO misc.py line 119 87073] Train: [55/100][1324/1557] Data 0.011 (0.169) Batch 0.942 (1.303) Remain 25:26:14 loss: 0.3593 Lr: 0.00231 [2024-02-18 21:12:20,406 INFO misc.py line 119 87073] Train: [55/100][1325/1557] Data 0.004 (0.169) Batch 0.938 (1.302) Remain 25:25:53 loss: 0.1585 Lr: 0.00231 [2024-02-18 21:12:21,434 INFO misc.py line 119 87073] Train: [55/100][1326/1557] Data 0.005 (0.169) Batch 1.027 (1.302) Remain 25:25:37 loss: 0.4839 Lr: 0.00231 [2024-02-18 21:12:22,214 INFO misc.py line 119 87073] Train: [55/100][1327/1557] Data 0.005 (0.169) Batch 0.780 (1.302) Remain 25:25:08 loss: 0.4967 Lr: 0.00231 [2024-02-18 21:12:23,010 INFO misc.py line 119 87073] Train: [55/100][1328/1557] Data 0.008 (0.168) Batch 0.794 (1.301) Remain 25:24:40 loss: 0.2113 Lr: 0.00231 [2024-02-18 21:12:23,741 INFO misc.py line 119 87073] Train: [55/100][1329/1557] Data 0.008 (0.168) Batch 0.733 (1.301) Remain 25:24:09 loss: 0.3406 Lr: 0.00231 [2024-02-18 21:12:24,851 INFO misc.py line 119 87073] Train: [55/100][1330/1557] Data 0.004 (0.168) Batch 1.108 (1.301) Remain 25:23:57 loss: 0.0960 Lr: 0.00231 [2024-02-18 21:12:26,046 INFO misc.py line 119 87073] Train: [55/100][1331/1557] Data 0.007 (0.168) Batch 1.192 (1.301) Remain 25:23:50 loss: 0.3796 Lr: 0.00231 [2024-02-18 21:12:27,211 INFO misc.py line 119 87073] Train: [55/100][1332/1557] Data 0.010 (0.168) Batch 1.160 (1.301) Remain 25:23:41 loss: 0.1880 Lr: 0.00231 [2024-02-18 21:12:28,300 INFO misc.py line 119 87073] Train: [55/100][1333/1557] Data 0.014 (0.168) Batch 1.098 (1.300) Remain 25:23:29 loss: 0.5712 Lr: 0.00231 [2024-02-18 21:12:29,239 INFO misc.py line 119 87073] Train: [55/100][1334/1557] Data 0.006 (0.168) Batch 0.939 (1.300) Remain 25:23:09 loss: 0.4269 Lr: 0.00231 [2024-02-18 21:12:30,006 INFO misc.py line 119 87073] Train: [55/100][1335/1557] Data 0.005 (0.168) Batch 0.767 (1.300) Remain 25:22:40 loss: 0.2769 Lr: 0.00231 [2024-02-18 21:12:30,761 INFO misc.py line 119 87073] Train: [55/100][1336/1557] Data 0.005 (0.168) Batch 0.748 (1.299) Remain 25:22:09 loss: 0.2323 Lr: 0.00231 [2024-02-18 21:12:32,017 INFO misc.py line 119 87073] Train: [55/100][1337/1557] Data 0.013 (0.167) Batch 1.262 (1.299) Remain 25:22:06 loss: 0.3439 Lr: 0.00231 [2024-02-18 21:12:32,915 INFO misc.py line 119 87073] Train: [55/100][1338/1557] Data 0.006 (0.167) Batch 0.898 (1.299) Remain 25:21:44 loss: 0.6265 Lr: 0.00231 [2024-02-18 21:12:33,892 INFO misc.py line 119 87073] Train: [55/100][1339/1557] Data 0.005 (0.167) Batch 0.978 (1.299) Remain 25:21:25 loss: 0.1475 Lr: 0.00231 [2024-02-18 21:12:34,782 INFO misc.py line 119 87073] Train: [55/100][1340/1557] Data 0.005 (0.167) Batch 0.890 (1.299) Remain 25:21:03 loss: 0.2236 Lr: 0.00231 [2024-02-18 21:12:35,892 INFO misc.py line 119 87073] Train: [55/100][1341/1557] Data 0.004 (0.167) Batch 1.111 (1.298) Remain 25:20:51 loss: 0.1861 Lr: 0.00231 [2024-02-18 21:12:36,671 INFO misc.py line 119 87073] Train: [55/100][1342/1557] Data 0.004 (0.167) Batch 0.776 (1.298) Remain 25:20:23 loss: 0.1900 Lr: 0.00231 [2024-02-18 21:12:37,430 INFO misc.py line 119 87073] Train: [55/100][1343/1557] Data 0.005 (0.167) Batch 0.760 (1.298) Remain 25:19:53 loss: 0.2059 Lr: 0.00231 [2024-02-18 21:12:38,754 INFO misc.py line 119 87073] Train: [55/100][1344/1557] Data 0.004 (0.167) Batch 1.315 (1.298) Remain 25:19:53 loss: 0.1317 Lr: 0.00231 [2024-02-18 21:12:39,761 INFO misc.py line 119 87073] Train: [55/100][1345/1557] Data 0.013 (0.166) Batch 1.016 (1.297) Remain 25:19:37 loss: 0.4119 Lr: 0.00231 [2024-02-18 21:12:40,833 INFO misc.py line 119 87073] Train: [55/100][1346/1557] Data 0.005 (0.166) Batch 1.065 (1.297) Remain 25:19:23 loss: 0.3421 Lr: 0.00231 [2024-02-18 21:12:41,836 INFO misc.py line 119 87073] Train: [55/100][1347/1557] Data 0.012 (0.166) Batch 0.999 (1.297) Remain 25:19:06 loss: 0.6834 Lr: 0.00231 [2024-02-18 21:12:42,965 INFO misc.py line 119 87073] Train: [55/100][1348/1557] Data 0.016 (0.166) Batch 1.129 (1.297) Remain 25:18:56 loss: 0.3760 Lr: 0.00231 [2024-02-18 21:12:43,641 INFO misc.py line 119 87073] Train: [55/100][1349/1557] Data 0.015 (0.166) Batch 0.688 (1.296) Remain 25:18:23 loss: 0.1896 Lr: 0.00231 [2024-02-18 21:12:44,433 INFO misc.py line 119 87073] Train: [55/100][1350/1557] Data 0.004 (0.166) Batch 0.788 (1.296) Remain 25:17:55 loss: 0.3012 Lr: 0.00231 [2024-02-18 21:13:05,971 INFO misc.py line 119 87073] Train: [55/100][1351/1557] Data 9.139 (0.173) Batch 21.542 (1.311) Remain 25:35:30 loss: 0.1193 Lr: 0.00231 [2024-02-18 21:13:06,854 INFO misc.py line 119 87073] Train: [55/100][1352/1557] Data 0.004 (0.172) Batch 0.882 (1.311) Remain 25:35:06 loss: 0.1207 Lr: 0.00231 [2024-02-18 21:13:07,801 INFO misc.py line 119 87073] Train: [55/100][1353/1557] Data 0.006 (0.172) Batch 0.948 (1.310) Remain 25:34:46 loss: 0.2293 Lr: 0.00231 [2024-02-18 21:13:08,645 INFO misc.py line 119 87073] Train: [55/100][1354/1557] Data 0.004 (0.172) Batch 0.841 (1.310) Remain 25:34:20 loss: 0.7164 Lr: 0.00231 [2024-02-18 21:13:09,519 INFO misc.py line 119 87073] Train: [55/100][1355/1557] Data 0.007 (0.172) Batch 0.876 (1.310) Remain 25:33:56 loss: 0.3383 Lr: 0.00231 [2024-02-18 21:13:10,290 INFO misc.py line 119 87073] Train: [55/100][1356/1557] Data 0.005 (0.172) Batch 0.772 (1.309) Remain 25:33:27 loss: 0.5247 Lr: 0.00231 [2024-02-18 21:13:11,045 INFO misc.py line 119 87073] Train: [55/100][1357/1557] Data 0.004 (0.172) Batch 0.754 (1.309) Remain 25:32:57 loss: 0.2424 Lr: 0.00231 [2024-02-18 21:13:12,301 INFO misc.py line 119 87073] Train: [55/100][1358/1557] Data 0.004 (0.172) Batch 1.256 (1.309) Remain 25:32:53 loss: 0.1615 Lr: 0.00231 [2024-02-18 21:13:13,212 INFO misc.py line 119 87073] Train: [55/100][1359/1557] Data 0.005 (0.172) Batch 0.909 (1.309) Remain 25:32:31 loss: 0.2718 Lr: 0.00231 [2024-02-18 21:13:14,254 INFO misc.py line 119 87073] Train: [55/100][1360/1557] Data 0.007 (0.171) Batch 1.044 (1.308) Remain 25:32:16 loss: 0.3938 Lr: 0.00231 [2024-02-18 21:13:15,282 INFO misc.py line 119 87073] Train: [55/100][1361/1557] Data 0.005 (0.171) Batch 1.029 (1.308) Remain 25:32:00 loss: 0.6146 Lr: 0.00231 [2024-02-18 21:13:16,329 INFO misc.py line 119 87073] Train: [55/100][1362/1557] Data 0.004 (0.171) Batch 1.047 (1.308) Remain 25:31:45 loss: 0.2700 Lr: 0.00231 [2024-02-18 21:13:17,146 INFO misc.py line 119 87073] Train: [55/100][1363/1557] Data 0.004 (0.171) Batch 0.815 (1.308) Remain 25:31:18 loss: 0.2743 Lr: 0.00231 [2024-02-18 21:13:17,898 INFO misc.py line 119 87073] Train: [55/100][1364/1557] Data 0.006 (0.171) Batch 0.735 (1.307) Remain 25:30:47 loss: 0.1743 Lr: 0.00231 [2024-02-18 21:13:19,240 INFO misc.py line 119 87073] Train: [55/100][1365/1557] Data 0.024 (0.171) Batch 1.359 (1.307) Remain 25:30:49 loss: 0.1765 Lr: 0.00231 [2024-02-18 21:13:20,242 INFO misc.py line 119 87073] Train: [55/100][1366/1557] Data 0.006 (0.171) Batch 0.997 (1.307) Remain 25:30:31 loss: 0.3600 Lr: 0.00231 [2024-02-18 21:13:21,320 INFO misc.py line 119 87073] Train: [55/100][1367/1557] Data 0.011 (0.171) Batch 1.080 (1.307) Remain 25:30:18 loss: 0.4121 Lr: 0.00231 [2024-02-18 21:13:22,264 INFO misc.py line 119 87073] Train: [55/100][1368/1557] Data 0.010 (0.170) Batch 0.949 (1.307) Remain 25:29:59 loss: 0.4896 Lr: 0.00231 [2024-02-18 21:13:23,213 INFO misc.py line 119 87073] Train: [55/100][1369/1557] Data 0.004 (0.170) Batch 0.948 (1.306) Remain 25:29:39 loss: 0.3864 Lr: 0.00231 [2024-02-18 21:13:23,936 INFO misc.py line 119 87073] Train: [55/100][1370/1557] Data 0.005 (0.170) Batch 0.724 (1.306) Remain 25:29:08 loss: 0.1866 Lr: 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INFO misc.py line 119 87073] Train: [55/100][1377/1557] Data 0.004 (0.169) Batch 0.802 (1.304) Remain 25:26:46 loss: 0.2494 Lr: 0.00231 [2024-02-18 21:13:31,200 INFO misc.py line 119 87073] Train: [55/100][1378/1557] Data 0.004 (0.169) Batch 0.696 (1.304) Remain 25:26:14 loss: 0.2514 Lr: 0.00231 [2024-02-18 21:13:32,524 INFO misc.py line 119 87073] Train: [55/100][1379/1557] Data 0.013 (0.169) Batch 1.332 (1.304) Remain 25:26:14 loss: 0.2164 Lr: 0.00230 [2024-02-18 21:13:33,485 INFO misc.py line 119 87073] Train: [55/100][1380/1557] Data 0.005 (0.169) Batch 0.959 (1.303) Remain 25:25:55 loss: 0.2544 Lr: 0.00230 [2024-02-18 21:13:34,506 INFO misc.py line 119 87073] Train: [55/100][1381/1557] Data 0.007 (0.169) Batch 1.024 (1.303) Remain 25:25:40 loss: 0.4284 Lr: 0.00230 [2024-02-18 21:13:35,575 INFO misc.py line 119 87073] Train: [55/100][1382/1557] Data 0.004 (0.169) Batch 1.069 (1.303) Remain 25:25:27 loss: 0.6605 Lr: 0.00230 [2024-02-18 21:13:36,530 INFO misc.py line 119 87073] Train: [55/100][1383/1557] Data 0.004 (0.169) Batch 0.954 (1.303) Remain 25:25:08 loss: 0.4658 Lr: 0.00230 [2024-02-18 21:13:37,325 INFO misc.py line 119 87073] Train: [55/100][1384/1557] Data 0.005 (0.169) Batch 0.793 (1.302) Remain 25:24:40 loss: 0.4075 Lr: 0.00230 [2024-02-18 21:13:38,043 INFO misc.py line 119 87073] Train: [55/100][1385/1557] Data 0.006 (0.168) Batch 0.702 (1.302) Remain 25:24:09 loss: 0.2123 Lr: 0.00230 [2024-02-18 21:13:39,155 INFO misc.py line 119 87073] Train: [55/100][1386/1557] Data 0.023 (0.168) Batch 1.131 (1.302) Remain 25:23:59 loss: 0.0817 Lr: 0.00230 [2024-02-18 21:13:40,208 INFO misc.py line 119 87073] Train: [55/100][1387/1557] Data 0.004 (0.168) Batch 1.052 (1.302) Remain 25:23:45 loss: 0.3266 Lr: 0.00230 [2024-02-18 21:13:41,150 INFO misc.py line 119 87073] Train: [55/100][1388/1557] Data 0.005 (0.168) Batch 0.943 (1.301) Remain 25:23:25 loss: 0.0806 Lr: 0.00230 [2024-02-18 21:13:42,103 INFO misc.py line 119 87073] Train: [55/100][1389/1557] Data 0.004 (0.168) Batch 0.953 (1.301) Remain 25:23:06 loss: 0.0877 Lr: 0.00230 [2024-02-18 21:13:43,051 INFO misc.py line 119 87073] Train: [55/100][1390/1557] Data 0.004 (0.168) Batch 0.946 (1.301) Remain 25:22:47 loss: 0.3508 Lr: 0.00230 [2024-02-18 21:13:43,819 INFO misc.py line 119 87073] Train: [55/100][1391/1557] Data 0.005 (0.168) Batch 0.769 (1.301) Remain 25:22:19 loss: 0.4490 Lr: 0.00230 [2024-02-18 21:13:44,590 INFO misc.py line 119 87073] Train: [55/100][1392/1557] Data 0.004 (0.168) Batch 0.758 (1.300) Remain 25:21:50 loss: 0.2503 Lr: 0.00230 [2024-02-18 21:13:45,903 INFO misc.py line 119 87073] Train: [55/100][1393/1557] Data 0.017 (0.168) Batch 1.317 (1.300) Remain 25:21:49 loss: 0.2060 Lr: 0.00230 [2024-02-18 21:13:46,993 INFO misc.py line 119 87073] Train: [55/100][1394/1557] Data 0.014 (0.167) Batch 1.096 (1.300) Remain 25:21:38 loss: 0.1974 Lr: 0.00230 [2024-02-18 21:13:47,887 INFO misc.py line 119 87073] Train: [55/100][1395/1557] Data 0.008 (0.167) Batch 0.897 (1.300) Remain 25:21:16 loss: 0.2745 Lr: 0.00230 [2024-02-18 21:13:48,892 INFO misc.py line 119 87073] Train: [55/100][1396/1557] Data 0.004 (0.167) Batch 1.005 (1.300) Remain 25:21:00 loss: 0.6639 Lr: 0.00230 [2024-02-18 21:13:49,936 INFO misc.py line 119 87073] Train: [55/100][1397/1557] Data 0.004 (0.167) Batch 1.044 (1.299) Remain 25:20:46 loss: 0.2768 Lr: 0.00230 [2024-02-18 21:13:50,725 INFO misc.py line 119 87073] Train: [55/100][1398/1557] Data 0.005 (0.167) Batch 0.789 (1.299) Remain 25:20:19 loss: 0.5300 Lr: 0.00230 [2024-02-18 21:13:51,413 INFO misc.py line 119 87073] Train: [55/100][1399/1557] Data 0.005 (0.167) Batch 0.677 (1.299) Remain 25:19:46 loss: 0.3198 Lr: 0.00230 [2024-02-18 21:13:52,766 INFO misc.py line 119 87073] Train: [55/100][1400/1557] Data 0.016 (0.167) Batch 1.364 (1.299) Remain 25:19:48 loss: 0.3106 Lr: 0.00230 [2024-02-18 21:13:53,663 INFO misc.py line 119 87073] Train: [55/100][1401/1557] Data 0.004 (0.167) Batch 0.895 (1.298) Remain 25:19:27 loss: 0.2152 Lr: 0.00230 [2024-02-18 21:13:54,690 INFO misc.py line 119 87073] Train: [55/100][1402/1557] Data 0.006 (0.166) Batch 1.028 (1.298) Remain 25:19:12 loss: 0.2469 Lr: 0.00230 [2024-02-18 21:13:55,554 INFO misc.py line 119 87073] Train: [55/100][1403/1557] Data 0.006 (0.166) Batch 0.863 (1.298) Remain 25:18:49 loss: 0.0668 Lr: 0.00230 [2024-02-18 21:13:56,716 INFO misc.py line 119 87073] Train: [55/100][1404/1557] Data 0.007 (0.166) Batch 1.164 (1.298) Remain 25:18:41 loss: 0.1700 Lr: 0.00230 [2024-02-18 21:13:58,947 INFO misc.py line 119 87073] Train: [55/100][1405/1557] Data 1.184 (0.167) Batch 2.227 (1.298) Remain 25:19:26 loss: 0.3881 Lr: 0.00230 [2024-02-18 21:13:59,836 INFO misc.py line 119 87073] Train: [55/100][1406/1557] Data 0.009 (0.167) Batch 0.892 (1.298) Remain 25:19:04 loss: 0.3195 Lr: 0.00230 [2024-02-18 21:14:19,421 INFO misc.py line 119 87073] Train: [55/100][1407/1557] Data 9.053 (0.173) Batch 19.586 (1.311) Remain 25:34:18 loss: 0.1416 Lr: 0.00230 [2024-02-18 21:14:20,376 INFO misc.py line 119 87073] Train: [55/100][1408/1557] Data 0.004 (0.173) Batch 0.948 (1.311) Remain 25:33:58 loss: 0.3044 Lr: 0.00230 [2024-02-18 21:14:21,559 INFO misc.py line 119 87073] Train: [55/100][1409/1557] Data 0.011 (0.173) Batch 1.183 (1.311) Remain 25:33:50 loss: 0.4451 Lr: 0.00230 [2024-02-18 21:14:22,711 INFO misc.py line 119 87073] Train: [55/100][1410/1557] Data 0.011 (0.173) Batch 1.158 (1.311) Remain 25:33:42 loss: 0.3224 Lr: 0.00230 [2024-02-18 21:14:23,669 INFO misc.py line 119 87073] Train: [55/100][1411/1557] Data 0.005 (0.173) Batch 0.959 (1.310) Remain 25:33:23 loss: 0.4157 Lr: 0.00230 [2024-02-18 21:14:24,467 INFO misc.py line 119 87073] Train: [55/100][1412/1557] Data 0.004 (0.173) Batch 0.797 (1.310) Remain 25:32:56 loss: 0.2207 Lr: 0.00230 [2024-02-18 21:14:25,216 INFO misc.py line 119 87073] Train: [55/100][1413/1557] Data 0.005 (0.172) Batch 0.747 (1.310) Remain 25:32:26 loss: 0.4433 Lr: 0.00230 [2024-02-18 21:14:26,468 INFO misc.py line 119 87073] Train: [55/100][1414/1557] Data 0.007 (0.172) Batch 1.247 (1.310) Remain 25:32:22 loss: 0.2918 Lr: 0.00230 [2024-02-18 21:14:27,315 INFO misc.py line 119 87073] Train: [55/100][1415/1557] Data 0.012 (0.172) Batch 0.854 (1.309) Remain 25:31:58 loss: 0.3254 Lr: 0.00230 [2024-02-18 21:14:28,465 INFO misc.py line 119 87073] Train: [55/100][1416/1557] Data 0.005 (0.172) Batch 1.150 (1.309) Remain 25:31:49 loss: 0.4068 Lr: 0.00230 [2024-02-18 21:14:29,454 INFO misc.py line 119 87073] Train: [55/100][1417/1557] Data 0.005 (0.172) Batch 0.990 (1.309) Remain 25:31:32 loss: 0.2231 Lr: 0.00230 [2024-02-18 21:14:30,391 INFO misc.py line 119 87073] Train: [55/100][1418/1557] Data 0.003 (0.172) Batch 0.936 (1.309) Remain 25:31:12 loss: 0.1418 Lr: 0.00230 [2024-02-18 21:14:31,197 INFO misc.py line 119 87073] Train: [55/100][1419/1557] Data 0.004 (0.172) Batch 0.802 (1.308) Remain 25:30:46 loss: 0.1893 Lr: 0.00230 [2024-02-18 21:14:32,070 INFO misc.py line 119 87073] Train: [55/100][1420/1557] Data 0.008 (0.172) Batch 0.878 (1.308) Remain 25:30:23 loss: 0.2809 Lr: 0.00230 [2024-02-18 21:14:33,397 INFO misc.py line 119 87073] Train: [55/100][1421/1557] Data 0.003 (0.172) Batch 1.317 (1.308) Remain 25:30:22 loss: 0.1996 Lr: 0.00230 [2024-02-18 21:14:34,556 INFO misc.py line 119 87073] Train: [55/100][1422/1557] Data 0.014 (0.171) Batch 1.164 (1.308) Remain 25:30:14 loss: 0.2076 Lr: 0.00230 [2024-02-18 21:14:35,532 INFO misc.py line 119 87073] Train: [55/100][1423/1557] Data 0.009 (0.171) Batch 0.981 (1.308) Remain 25:29:56 loss: 0.6149 Lr: 0.00230 [2024-02-18 21:14:36,620 INFO misc.py line 119 87073] Train: [55/100][1424/1557] Data 0.004 (0.171) Batch 1.087 (1.308) Remain 25:29:44 loss: 0.3531 Lr: 0.00230 [2024-02-18 21:14:37,793 INFO misc.py line 119 87073] Train: [55/100][1425/1557] Data 0.004 (0.171) Batch 1.170 (1.307) Remain 25:29:36 loss: 0.3912 Lr: 0.00230 [2024-02-18 21:14:38,611 INFO misc.py line 119 87073] Train: [55/100][1426/1557] Data 0.006 (0.171) Batch 0.820 (1.307) Remain 25:29:10 loss: 0.3003 Lr: 0.00230 [2024-02-18 21:14:39,388 INFO misc.py line 119 87073] Train: [55/100][1427/1557] Data 0.006 (0.171) Batch 0.777 (1.307) Remain 25:28:43 loss: 0.2037 Lr: 0.00230 [2024-02-18 21:14:40,492 INFO misc.py line 119 87073] Train: [55/100][1428/1557] Data 0.005 (0.171) Batch 1.103 (1.307) Remain 25:28:32 loss: 0.1530 Lr: 0.00230 [2024-02-18 21:14:41,466 INFO misc.py line 119 87073] Train: [55/100][1429/1557] Data 0.006 (0.171) Batch 0.974 (1.306) Remain 25:28:14 loss: 0.1567 Lr: 0.00230 [2024-02-18 21:14:42,370 INFO misc.py line 119 87073] Train: [55/100][1430/1557] Data 0.006 (0.171) Batch 0.906 (1.306) Remain 25:27:53 loss: 0.4465 Lr: 0.00230 [2024-02-18 21:14:43,341 INFO misc.py line 119 87073] Train: [55/100][1431/1557] Data 0.004 (0.170) Batch 0.971 (1.306) Remain 25:27:35 loss: 0.2746 Lr: 0.00230 [2024-02-18 21:14:44,199 INFO misc.py line 119 87073] Train: [55/100][1432/1557] Data 0.004 (0.170) Batch 0.842 (1.305) Remain 25:27:11 loss: 0.2082 Lr: 0.00230 [2024-02-18 21:14:44,985 INFO misc.py line 119 87073] Train: [55/100][1433/1557] Data 0.020 (0.170) Batch 0.801 (1.305) Remain 25:26:45 loss: 0.3236 Lr: 0.00230 [2024-02-18 21:14:45,763 INFO misc.py line 119 87073] Train: [55/100][1434/1557] Data 0.005 (0.170) Batch 0.774 (1.305) Remain 25:26:18 loss: 0.2672 Lr: 0.00230 [2024-02-18 21:14:47,089 INFO misc.py line 119 87073] Train: [55/100][1435/1557] Data 0.009 (0.170) Batch 1.325 (1.305) Remain 25:26:17 loss: 0.2050 Lr: 0.00230 [2024-02-18 21:14:47,980 INFO misc.py line 119 87073] Train: [55/100][1436/1557] Data 0.010 (0.170) Batch 0.896 (1.304) Remain 25:25:56 loss: 0.3381 Lr: 0.00230 [2024-02-18 21:14:48,978 INFO misc.py line 119 87073] Train: [55/100][1437/1557] Data 0.005 (0.170) Batch 0.998 (1.304) Remain 25:25:40 loss: 0.1406 Lr: 0.00230 [2024-02-18 21:14:49,920 INFO misc.py line 119 87073] Train: [55/100][1438/1557] Data 0.005 (0.170) Batch 0.942 (1.304) Remain 25:25:21 loss: 0.5382 Lr: 0.00230 [2024-02-18 21:14:50,821 INFO misc.py line 119 87073] Train: [55/100][1439/1557] Data 0.005 (0.169) Batch 0.899 (1.304) Remain 25:25:00 loss: 0.1099 Lr: 0.00230 [2024-02-18 21:14:51,599 INFO misc.py line 119 87073] Train: [55/100][1440/1557] Data 0.007 (0.169) Batch 0.781 (1.303) Remain 25:24:33 loss: 0.9008 Lr: 0.00230 [2024-02-18 21:14:52,384 INFO misc.py line 119 87073] Train: [55/100][1441/1557] Data 0.003 (0.169) Batch 0.784 (1.303) Remain 25:24:06 loss: 0.4479 Lr: 0.00230 [2024-02-18 21:14:53,508 INFO misc.py line 119 87073] Train: [55/100][1442/1557] Data 0.004 (0.169) Batch 1.125 (1.303) Remain 25:23:56 loss: 0.1376 Lr: 0.00230 [2024-02-18 21:14:54,467 INFO misc.py line 119 87073] Train: [55/100][1443/1557] Data 0.004 (0.169) Batch 0.959 (1.303) Remain 25:23:38 loss: 0.5510 Lr: 0.00230 [2024-02-18 21:14:55,343 INFO misc.py line 119 87073] Train: [55/100][1444/1557] Data 0.005 (0.169) Batch 0.876 (1.302) Remain 25:23:16 loss: 0.5994 Lr: 0.00230 [2024-02-18 21:14:56,481 INFO misc.py line 119 87073] Train: [55/100][1445/1557] Data 0.004 (0.169) Batch 1.138 (1.302) Remain 25:23:07 loss: 0.3058 Lr: 0.00230 [2024-02-18 21:14:57,465 INFO misc.py line 119 87073] Train: [55/100][1446/1557] Data 0.004 (0.169) Batch 0.982 (1.302) Remain 25:22:50 loss: 0.2615 Lr: 0.00230 [2024-02-18 21:14:58,195 INFO misc.py line 119 87073] Train: [55/100][1447/1557] Data 0.005 (0.169) Batch 0.732 (1.302) Remain 25:22:21 loss: 0.1794 Lr: 0.00230 [2024-02-18 21:14:58,895 INFO misc.py line 119 87073] Train: [55/100][1448/1557] Data 0.003 (0.168) Batch 0.694 (1.301) Remain 25:21:50 loss: 0.2102 Lr: 0.00230 [2024-02-18 21:15:00,197 INFO misc.py line 119 87073] Train: [55/100][1449/1557] Data 0.010 (0.168) Batch 1.304 (1.301) Remain 25:21:49 loss: 0.1811 Lr: 0.00230 [2024-02-18 21:15:01,206 INFO misc.py line 119 87073] Train: [55/100][1450/1557] Data 0.008 (0.168) Batch 1.006 (1.301) Remain 25:21:33 loss: 0.3370 Lr: 0.00230 [2024-02-18 21:15:02,087 INFO misc.py line 119 87073] Train: [55/100][1451/1557] Data 0.011 (0.168) Batch 0.887 (1.301) Remain 25:21:12 loss: 0.2100 Lr: 0.00230 [2024-02-18 21:15:02,941 INFO misc.py line 119 87073] Train: [55/100][1452/1557] Data 0.004 (0.168) Batch 0.854 (1.300) Remain 25:20:49 loss: 0.3787 Lr: 0.00230 [2024-02-18 21:15:03,783 INFO misc.py line 119 87073] Train: [55/100][1453/1557] Data 0.004 (0.168) Batch 0.842 (1.300) Remain 25:20:26 loss: 0.2683 Lr: 0.00230 [2024-02-18 21:15:04,598 INFO misc.py line 119 87073] Train: [55/100][1454/1557] Data 0.005 (0.168) Batch 0.814 (1.300) Remain 25:20:01 loss: 0.2063 Lr: 0.00230 [2024-02-18 21:15:05,388 INFO misc.py line 119 87073] Train: [55/100][1455/1557] Data 0.004 (0.168) Batch 0.790 (1.299) Remain 25:19:35 loss: 0.2734 Lr: 0.00230 [2024-02-18 21:15:06,640 INFO misc.py line 119 87073] Train: [55/100][1456/1557] Data 0.005 (0.168) Batch 1.248 (1.299) Remain 25:19:31 loss: 0.1473 Lr: 0.00230 [2024-02-18 21:15:07,523 INFO misc.py line 119 87073] Train: [55/100][1457/1557] Data 0.009 (0.167) Batch 0.887 (1.299) Remain 25:19:10 loss: 0.2484 Lr: 0.00230 [2024-02-18 21:15:08,570 INFO misc.py line 119 87073] Train: [55/100][1458/1557] Data 0.006 (0.167) Batch 1.048 (1.299) Remain 25:18:56 loss: 0.4002 Lr: 0.00230 [2024-02-18 21:15:09,429 INFO misc.py line 119 87073] Train: [55/100][1459/1557] Data 0.004 (0.167) Batch 0.857 (1.299) Remain 25:18:34 loss: 0.5070 Lr: 0.00230 [2024-02-18 21:15:10,499 INFO misc.py line 119 87073] Train: [55/100][1460/1557] Data 0.006 (0.167) Batch 1.069 (1.298) Remain 25:18:21 loss: 0.4525 Lr: 0.00230 [2024-02-18 21:15:11,251 INFO misc.py line 119 87073] Train: [55/100][1461/1557] Data 0.008 (0.167) Batch 0.755 (1.298) Remain 25:17:54 loss: 0.2697 Lr: 0.00230 [2024-02-18 21:15:12,005 INFO misc.py line 119 87073] Train: [55/100][1462/1557] Data 0.005 (0.167) Batch 0.754 (1.298) Remain 25:17:27 loss: 0.1548 Lr: 0.00230 [2024-02-18 21:15:33,497 INFO misc.py line 119 87073] Train: [55/100][1463/1557] Data 8.631 (0.173) Batch 21.492 (1.312) Remain 25:33:36 loss: 0.0873 Lr: 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INFO misc.py line 119 87073] Train: [55/100][1470/1557] Data 0.004 (0.172) Batch 1.240 (1.310) Remain 25:31:48 loss: 0.1817 Lr: 0.00230 [2024-02-18 21:15:41,538 INFO misc.py line 119 87073] Train: [55/100][1471/1557] Data 0.013 (0.172) Batch 0.913 (1.310) Remain 25:31:28 loss: 0.2638 Lr: 0.00230 [2024-02-18 21:15:42,447 INFO misc.py line 119 87073] Train: [55/100][1472/1557] Data 0.004 (0.172) Batch 0.908 (1.310) Remain 25:31:08 loss: 0.3102 Lr: 0.00230 [2024-02-18 21:15:43,521 INFO misc.py line 119 87073] Train: [55/100][1473/1557] Data 0.006 (0.172) Batch 1.074 (1.309) Remain 25:30:55 loss: 0.1488 Lr: 0.00230 [2024-02-18 21:15:44,512 INFO misc.py line 119 87073] Train: [55/100][1474/1557] Data 0.005 (0.171) Batch 0.991 (1.309) Remain 25:30:39 loss: 0.1661 Lr: 0.00230 [2024-02-18 21:15:45,283 INFO misc.py line 119 87073] Train: [55/100][1475/1557] Data 0.005 (0.171) Batch 0.769 (1.309) Remain 25:30:11 loss: 0.5462 Lr: 0.00230 [2024-02-18 21:15:46,066 INFO misc.py line 119 87073] Train: [55/100][1476/1557] Data 0.006 (0.171) Batch 0.785 (1.308) Remain 25:29:45 loss: 0.1989 Lr: 0.00230 [2024-02-18 21:15:47,390 INFO misc.py line 119 87073] Train: [55/100][1477/1557] Data 0.003 (0.171) Batch 1.314 (1.308) Remain 25:29:44 loss: 0.1083 Lr: 0.00230 [2024-02-18 21:15:48,376 INFO misc.py line 119 87073] Train: [55/100][1478/1557] Data 0.014 (0.171) Batch 0.996 (1.308) Remain 25:29:28 loss: 0.3004 Lr: 0.00230 [2024-02-18 21:15:49,258 INFO misc.py line 119 87073] Train: [55/100][1479/1557] Data 0.004 (0.171) Batch 0.881 (1.308) Remain 25:29:06 loss: 0.4076 Lr: 0.00230 [2024-02-18 21:15:50,222 INFO misc.py line 119 87073] Train: [55/100][1480/1557] Data 0.005 (0.171) Batch 0.963 (1.308) Remain 25:28:49 loss: 0.3933 Lr: 0.00230 [2024-02-18 21:15:51,082 INFO misc.py line 119 87073] Train: [55/100][1481/1557] Data 0.006 (0.171) Batch 0.861 (1.307) Remain 25:28:26 loss: 0.5133 Lr: 0.00230 [2024-02-18 21:15:51,834 INFO misc.py line 119 87073] Train: [55/100][1482/1557] Data 0.005 (0.171) Batch 0.752 (1.307) Remain 25:27:59 loss: 0.2519 Lr: 0.00230 [2024-02-18 21:15:52,649 INFO misc.py line 119 87073] Train: [55/100][1483/1557] Data 0.005 (0.170) Batch 0.815 (1.307) Remain 25:27:34 loss: 0.2133 Lr: 0.00230 [2024-02-18 21:15:53,725 INFO misc.py line 119 87073] Train: [55/100][1484/1557] Data 0.006 (0.170) Batch 1.075 (1.307) Remain 25:27:22 loss: 0.1029 Lr: 0.00230 [2024-02-18 21:15:54,665 INFO misc.py line 119 87073] Train: [55/100][1485/1557] Data 0.005 (0.170) Batch 0.942 (1.306) Remain 25:27:03 loss: 0.4654 Lr: 0.00230 [2024-02-18 21:15:55,639 INFO misc.py line 119 87073] Train: [55/100][1486/1557] Data 0.003 (0.170) Batch 0.974 (1.306) Remain 25:26:46 loss: 0.5461 Lr: 0.00230 [2024-02-18 21:15:56,436 INFO misc.py line 119 87073] Train: [55/100][1487/1557] Data 0.004 (0.170) Batch 0.797 (1.306) Remain 25:26:21 loss: 0.6078 Lr: 0.00230 [2024-02-18 21:15:57,367 INFO misc.py line 119 87073] Train: [55/100][1488/1557] Data 0.004 (0.170) Batch 0.925 (1.306) Remain 25:26:01 loss: 0.5231 Lr: 0.00230 [2024-02-18 21:15:58,142 INFO misc.py line 119 87073] Train: [55/100][1489/1557] Data 0.010 (0.170) Batch 0.780 (1.305) Remain 25:25:35 loss: 0.2506 Lr: 0.00230 [2024-02-18 21:15:58,926 INFO misc.py line 119 87073] Train: [55/100][1490/1557] Data 0.004 (0.170) Batch 0.784 (1.305) Remain 25:25:09 loss: 0.2398 Lr: 0.00230 [2024-02-18 21:16:00,173 INFO misc.py line 119 87073] Train: [55/100][1491/1557] Data 0.003 (0.170) Batch 1.236 (1.305) Remain 25:25:05 loss: 0.2504 Lr: 0.00230 [2024-02-18 21:16:01,102 INFO misc.py line 119 87073] Train: [55/100][1492/1557] Data 0.015 (0.169) Batch 0.938 (1.305) Remain 25:24:46 loss: 0.2651 Lr: 0.00230 [2024-02-18 21:16:02,024 INFO misc.py line 119 87073] Train: [55/100][1493/1557] Data 0.006 (0.169) Batch 0.925 (1.304) Remain 25:24:27 loss: 0.5914 Lr: 0.00230 [2024-02-18 21:16:02,988 INFO misc.py line 119 87073] Train: [55/100][1494/1557] Data 0.004 (0.169) Batch 0.963 (1.304) Remain 25:24:10 loss: 0.7280 Lr: 0.00230 [2024-02-18 21:16:03,877 INFO misc.py line 119 87073] Train: [55/100][1495/1557] Data 0.005 (0.169) Batch 0.884 (1.304) Remain 25:23:49 loss: 0.4835 Lr: 0.00230 [2024-02-18 21:16:04,614 INFO misc.py line 119 87073] Train: [55/100][1496/1557] Data 0.010 (0.169) Batch 0.743 (1.303) Remain 25:23:21 loss: 0.3651 Lr: 0.00230 [2024-02-18 21:16:05,374 INFO misc.py line 119 87073] Train: [55/100][1497/1557] Data 0.004 (0.169) Batch 0.719 (1.303) Remain 25:22:52 loss: 0.2781 Lr: 0.00230 [2024-02-18 21:16:06,544 INFO misc.py line 119 87073] Train: [55/100][1498/1557] Data 0.045 (0.169) Batch 1.204 (1.303) Remain 25:22:46 loss: 0.0714 Lr: 0.00230 [2024-02-18 21:16:07,589 INFO misc.py line 119 87073] Train: [55/100][1499/1557] Data 0.011 (0.169) Batch 1.041 (1.303) Remain 25:22:33 loss: 0.6068 Lr: 0.00230 [2024-02-18 21:16:08,713 INFO misc.py line 119 87073] Train: [55/100][1500/1557] Data 0.014 (0.169) Batch 1.124 (1.303) Remain 25:22:23 loss: 0.4172 Lr: 0.00230 [2024-02-18 21:16:09,764 INFO misc.py line 119 87073] Train: [55/100][1501/1557] Data 0.015 (0.169) Batch 1.056 (1.302) Remain 25:22:10 loss: 0.5003 Lr: 0.00230 [2024-02-18 21:16:10,643 INFO misc.py line 119 87073] Train: [55/100][1502/1557] Data 0.010 (0.168) Batch 0.884 (1.302) Remain 25:21:49 loss: 0.2979 Lr: 0.00230 [2024-02-18 21:16:11,430 INFO misc.py line 119 87073] Train: [55/100][1503/1557] Data 0.004 (0.168) Batch 0.787 (1.302) Remain 25:21:24 loss: 0.1174 Lr: 0.00230 [2024-02-18 21:16:12,222 INFO misc.py line 119 87073] Train: [55/100][1504/1557] Data 0.005 (0.168) Batch 0.792 (1.302) Remain 25:20:59 loss: 0.1920 Lr: 0.00230 [2024-02-18 21:16:13,600 INFO misc.py line 119 87073] Train: [55/100][1505/1557] Data 0.005 (0.168) Batch 1.367 (1.302) Remain 25:21:01 loss: 0.4843 Lr: 0.00230 [2024-02-18 21:16:14,766 INFO misc.py line 119 87073] Train: [55/100][1506/1557] Data 0.015 (0.168) Batch 1.165 (1.301) Remain 25:20:53 loss: 0.1370 Lr: 0.00230 [2024-02-18 21:16:15,786 INFO misc.py line 119 87073] Train: [55/100][1507/1557] Data 0.016 (0.168) Batch 1.032 (1.301) Remain 25:20:39 loss: 0.1680 Lr: 0.00230 [2024-02-18 21:16:16,668 INFO misc.py line 119 87073] Train: [55/100][1508/1557] Data 0.004 (0.168) Batch 0.882 (1.301) Remain 25:20:18 loss: 0.3434 Lr: 0.00230 [2024-02-18 21:16:17,687 INFO misc.py line 119 87073] Train: [55/100][1509/1557] Data 0.005 (0.168) Batch 1.018 (1.301) Remain 25:20:04 loss: 0.6775 Lr: 0.00230 [2024-02-18 21:16:18,435 INFO misc.py line 119 87073] Train: [55/100][1510/1557] Data 0.005 (0.168) Batch 0.749 (1.300) Remain 25:19:37 loss: 0.2557 Lr: 0.00230 [2024-02-18 21:16:19,184 INFO misc.py line 119 87073] Train: [55/100][1511/1557] Data 0.005 (0.167) Batch 0.748 (1.300) Remain 25:19:10 loss: 0.2756 Lr: 0.00230 [2024-02-18 21:16:20,502 INFO misc.py line 119 87073] Train: [55/100][1512/1557] Data 0.004 (0.167) Batch 1.312 (1.300) Remain 25:19:09 loss: 0.1442 Lr: 0.00230 [2024-02-18 21:16:21,452 INFO misc.py line 119 87073] Train: [55/100][1513/1557] Data 0.011 (0.167) Batch 0.956 (1.300) Remain 25:18:52 loss: 0.8472 Lr: 0.00230 [2024-02-18 21:16:22,469 INFO misc.py line 119 87073] Train: [55/100][1514/1557] Data 0.005 (0.167) Batch 1.017 (1.300) Remain 25:18:37 loss: 0.2723 Lr: 0.00230 [2024-02-18 21:16:23,271 INFO misc.py line 119 87073] Train: [55/100][1515/1557] Data 0.005 (0.167) Batch 0.802 (1.299) Remain 25:18:13 loss: 0.3356 Lr: 0.00230 [2024-02-18 21:16:24,368 INFO misc.py line 119 87073] Train: [55/100][1516/1557] Data 0.004 (0.167) Batch 1.096 (1.299) Remain 25:18:02 loss: 0.3969 Lr: 0.00230 [2024-02-18 21:16:25,128 INFO misc.py line 119 87073] Train: [55/100][1517/1557] Data 0.006 (0.167) Batch 0.762 (1.299) Remain 25:17:36 loss: 0.2441 Lr: 0.00230 [2024-02-18 21:16:25,878 INFO misc.py line 119 87073] Train: [55/100][1518/1557] Data 0.003 (0.167) Batch 0.748 (1.298) Remain 25:17:09 loss: 0.1600 Lr: 0.00230 [2024-02-18 21:16:46,045 INFO misc.py line 119 87073] Train: [55/100][1519/1557] Data 8.083 (0.172) Batch 20.167 (1.311) Remain 25:31:41 loss: 0.1377 Lr: 0.00230 [2024-02-18 21:16:47,066 INFO misc.py line 119 87073] Train: [55/100][1520/1557] Data 0.005 (0.172) Batch 1.020 (1.311) Remain 25:31:26 loss: 0.4568 Lr: 0.00230 [2024-02-18 21:16:48,096 INFO misc.py line 119 87073] Train: [55/100][1521/1557] Data 0.006 (0.172) Batch 1.029 (1.311) Remain 25:31:12 loss: 0.6458 Lr: 0.00230 [2024-02-18 21:16:49,068 INFO misc.py line 119 87073] Train: [55/100][1522/1557] Data 0.008 (0.172) Batch 0.975 (1.310) Remain 25:30:55 loss: 0.0801 Lr: 0.00230 [2024-02-18 21:16:50,076 INFO misc.py line 119 87073] Train: [55/100][1523/1557] Data 0.005 (0.171) Batch 1.007 (1.310) Remain 25:30:39 loss: 0.1837 Lr: 0.00230 [2024-02-18 21:16:50,790 INFO misc.py line 119 87073] Train: [55/100][1524/1557] Data 0.005 (0.171) Batch 0.715 (1.310) Remain 25:30:11 loss: 0.2319 Lr: 0.00230 [2024-02-18 21:16:51,588 INFO misc.py line 119 87073] Train: [55/100][1525/1557] Data 0.005 (0.171) Batch 0.798 (1.309) Remain 25:29:46 loss: 0.3053 Lr: 0.00230 [2024-02-18 21:16:52,843 INFO misc.py line 119 87073] Train: [55/100][1526/1557] Data 0.005 (0.171) Batch 1.256 (1.309) Remain 25:29:42 loss: 0.1587 Lr: 0.00230 [2024-02-18 21:16:53,929 INFO misc.py line 119 87073] Train: [55/100][1527/1557] Data 0.004 (0.171) Batch 1.078 (1.309) Remain 25:29:30 loss: 0.2901 Lr: 0.00230 [2024-02-18 21:16:54,910 INFO misc.py line 119 87073] Train: [55/100][1528/1557] Data 0.012 (0.171) Batch 0.989 (1.309) Remain 25:29:14 loss: 0.4937 Lr: 0.00230 [2024-02-18 21:16:55,741 INFO misc.py line 119 87073] Train: [55/100][1529/1557] Data 0.004 (0.171) Batch 0.831 (1.309) Remain 25:28:51 loss: 0.3801 Lr: 0.00230 [2024-02-18 21:16:56,788 INFO misc.py line 119 87073] Train: [55/100][1530/1557] Data 0.004 (0.171) Batch 1.046 (1.309) Remain 25:28:37 loss: 0.2239 Lr: 0.00230 [2024-02-18 21:16:57,652 INFO misc.py line 119 87073] Train: [55/100][1531/1557] Data 0.005 (0.171) Batch 0.864 (1.308) Remain 25:28:16 loss: 0.4352 Lr: 0.00230 [2024-02-18 21:16:58,427 INFO misc.py line 119 87073] Train: [55/100][1532/1557] Data 0.005 (0.171) Batch 0.775 (1.308) Remain 25:27:50 loss: 0.2481 Lr: 0.00230 [2024-02-18 21:16:59,791 INFO misc.py line 119 87073] Train: [55/100][1533/1557] Data 0.004 (0.170) Batch 1.354 (1.308) Remain 25:27:51 loss: 0.2346 Lr: 0.00230 [2024-02-18 21:17:00,881 INFO misc.py line 119 87073] Train: [55/100][1534/1557] Data 0.014 (0.170) Batch 1.099 (1.308) Remain 25:27:40 loss: 0.3807 Lr: 0.00230 [2024-02-18 21:17:01,804 INFO misc.py line 119 87073] Train: [55/100][1535/1557] Data 0.006 (0.170) Batch 0.923 (1.308) Remain 25:27:21 loss: 0.5107 Lr: 0.00230 [2024-02-18 21:17:02,735 INFO misc.py line 119 87073] Train: [55/100][1536/1557] Data 0.005 (0.170) Batch 0.932 (1.307) Remain 25:27:03 loss: 0.3465 Lr: 0.00230 [2024-02-18 21:17:03,736 INFO misc.py line 119 87073] Train: [55/100][1537/1557] Data 0.004 (0.170) Batch 1.001 (1.307) Remain 25:26:47 loss: 0.3520 Lr: 0.00230 [2024-02-18 21:17:04,516 INFO misc.py line 119 87073] Train: [55/100][1538/1557] Data 0.004 (0.170) Batch 0.769 (1.307) Remain 25:26:21 loss: 0.2810 Lr: 0.00230 [2024-02-18 21:17:05,222 INFO misc.py line 119 87073] Train: [55/100][1539/1557] Data 0.014 (0.170) Batch 0.716 (1.306) Remain 25:25:53 loss: 0.2464 Lr: 0.00230 [2024-02-18 21:17:06,282 INFO misc.py line 119 87073] Train: [55/100][1540/1557] Data 0.005 (0.170) Batch 1.058 (1.306) Remain 25:25:41 loss: 0.1172 Lr: 0.00230 [2024-02-18 21:17:07,200 INFO misc.py line 119 87073] Train: [55/100][1541/1557] Data 0.007 (0.170) Batch 0.921 (1.306) Remain 25:25:22 loss: 0.2987 Lr: 0.00230 [2024-02-18 21:17:08,329 INFO misc.py line 119 87073] Train: [55/100][1542/1557] Data 0.004 (0.169) Batch 1.129 (1.306) Remain 25:25:12 loss: 0.4840 Lr: 0.00230 [2024-02-18 21:17:09,403 INFO misc.py line 119 87073] Train: [55/100][1543/1557] Data 0.004 (0.169) Batch 1.074 (1.306) Remain 25:25:00 loss: 0.2864 Lr: 0.00230 [2024-02-18 21:17:10,378 INFO misc.py line 119 87073] Train: [55/100][1544/1557] Data 0.004 (0.169) Batch 0.973 (1.305) Remain 25:24:44 loss: 0.3724 Lr: 0.00230 [2024-02-18 21:17:11,153 INFO misc.py line 119 87073] Train: [55/100][1545/1557] Data 0.005 (0.169) Batch 0.775 (1.305) Remain 25:24:19 loss: 0.2068 Lr: 0.00230 [2024-02-18 21:17:11,946 INFO misc.py line 119 87073] Train: [55/100][1546/1557] Data 0.005 (0.169) Batch 0.783 (1.305) Remain 25:23:54 loss: 0.2211 Lr: 0.00230 [2024-02-18 21:17:13,255 INFO misc.py line 119 87073] Train: [55/100][1547/1557] Data 0.015 (0.169) Batch 1.309 (1.305) Remain 25:23:53 loss: 0.2651 Lr: 0.00230 [2024-02-18 21:17:14,223 INFO misc.py line 119 87073] Train: [55/100][1548/1557] Data 0.014 (0.169) Batch 0.978 (1.305) Remain 25:23:36 loss: 0.6882 Lr: 0.00230 [2024-02-18 21:17:15,163 INFO misc.py line 119 87073] Train: [55/100][1549/1557] Data 0.004 (0.169) Batch 0.939 (1.304) Remain 25:23:18 loss: 0.2242 Lr: 0.00230 [2024-02-18 21:17:16,291 INFO misc.py line 119 87073] Train: [55/100][1550/1557] Data 0.006 (0.169) Batch 1.130 (1.304) Remain 25:23:09 loss: 0.2279 Lr: 0.00230 [2024-02-18 21:17:17,189 INFO misc.py line 119 87073] Train: [55/100][1551/1557] Data 0.004 (0.168) Batch 0.897 (1.304) Remain 25:22:50 loss: 0.1340 Lr: 0.00230 [2024-02-18 21:17:17,946 INFO misc.py line 119 87073] Train: [55/100][1552/1557] Data 0.005 (0.168) Batch 0.746 (1.304) Remain 25:22:23 loss: 0.4091 Lr: 0.00230 [2024-02-18 21:17:18,608 INFO misc.py line 119 87073] Train: [55/100][1553/1557] Data 0.017 (0.168) Batch 0.673 (1.303) Remain 25:21:53 loss: 0.2437 Lr: 0.00230 [2024-02-18 21:17:19,809 INFO misc.py line 119 87073] Train: [55/100][1554/1557] Data 0.004 (0.168) Batch 1.198 (1.303) Remain 25:21:47 loss: 0.1099 Lr: 0.00230 [2024-02-18 21:17:20,846 INFO misc.py line 119 87073] Train: [55/100][1555/1557] Data 0.007 (0.168) Batch 1.037 (1.303) Remain 25:21:34 loss: 0.1001 Lr: 0.00230 [2024-02-18 21:17:21,831 INFO misc.py line 119 87073] Train: [55/100][1556/1557] Data 0.007 (0.168) Batch 0.988 (1.303) Remain 25:21:18 loss: 0.2649 Lr: 0.00230 [2024-02-18 21:17:22,712 INFO misc.py line 119 87073] Train: [55/100][1557/1557] Data 0.005 (0.168) Batch 0.881 (1.302) Remain 25:20:58 loss: 0.2317 Lr: 0.00230 [2024-02-18 21:17:22,712 INFO misc.py line 136 87073] Train result: loss: 0.3255 [2024-02-18 21:17:22,713 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 21:17:51,212 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6314 [2024-02-18 21:17:51,989 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7259 [2024-02-18 21:17:54,116 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3113 [2024-02-18 21:17:56,322 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2621 [2024-02-18 21:18:01,265 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2312 [2024-02-18 21:18:01,967 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1428 [2024-02-18 21:18:03,231 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2373 [2024-02-18 21:18:06,187 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2823 [2024-02-18 21:18:07,996 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2273 [2024-02-18 21:18:09,595 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7259/0.7883/0.9143. [2024-02-18 21:18:09,595 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9341/0.9705 [2024-02-18 21:18:09,595 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9827/0.9914 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8684/0.9702 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4476/0.5413 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6493/0.6690 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8391/0.9241 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8432/0.8960 [2024-02-18 21:18:09,596 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9303/0.9603 [2024-02-18 21:18:09,597 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8522/0.8765 [2024-02-18 21:18:09,597 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7758/0.8668 [2024-02-18 21:18:09,597 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7244/0.8991 [2024-02-18 21:18:09,597 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5896/0.6830 [2024-02-18 21:18:09,598 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 21:18:09,600 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 21:18:09,600 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 21:18:16,457 INFO misc.py line 119 87073] Train: [56/100][1/1557] Data 1.481 (1.481) Batch 2.176 (2.176) Remain 42:20:57 loss: 0.3828 Lr: 0.00230 [2024-02-18 21:18:17,511 INFO misc.py line 119 87073] Train: [56/100][2/1557] Data 0.010 (0.010) Batch 1.056 (1.056) Remain 20:33:25 loss: 0.3126 Lr: 0.00230 [2024-02-18 21:18:18,430 INFO misc.py line 119 87073] Train: [56/100][3/1557] Data 0.006 (0.006) Batch 0.918 (0.918) Remain 17:51:38 loss: 0.4817 Lr: 0.00230 [2024-02-18 21:18:19,593 INFO misc.py line 119 87073] Train: [56/100][4/1557] Data 0.008 (0.008) Batch 1.166 (1.166) Remain 22:41:11 loss: 0.3674 Lr: 0.00230 [2024-02-18 21:18:20,346 INFO misc.py line 119 87073] Train: [56/100][5/1557] Data 0.004 (0.006) Batch 0.752 (0.959) Remain 18:39:21 loss: 0.1433 Lr: 0.00230 [2024-02-18 21:18:21,122 INFO misc.py line 119 87073] Train: [56/100][6/1557] Data 0.006 (0.006) Batch 0.768 (0.895) Remain 17:24:58 loss: 0.2800 Lr: 0.00230 [2024-02-18 21:18:25,508 INFO misc.py line 119 87073] Train: [56/100][7/1557] Data 2.893 (0.728) Batch 4.394 (1.770) Remain 34:26:30 loss: 0.1417 Lr: 0.00230 [2024-02-18 21:18:26,376 INFO misc.py line 119 87073] Train: [56/100][8/1557] Data 0.005 (0.583) Batch 0.870 (1.590) Remain 30:56:18 loss: 0.6685 Lr: 0.00230 [2024-02-18 21:18:27,431 INFO misc.py line 119 87073] Train: [56/100][9/1557] Data 0.004 (0.487) Batch 1.054 (1.501) Remain 29:12:03 loss: 0.4593 Lr: 0.00230 [2024-02-18 21:18:28,384 INFO misc.py line 119 87073] Train: [56/100][10/1557] Data 0.004 (0.418) Batch 0.953 (1.422) Remain 27:40:43 loss: 0.2561 Lr: 0.00230 [2024-02-18 21:18:29,344 INFO misc.py line 119 87073] Train: [56/100][11/1557] Data 0.004 (0.366) Batch 0.960 (1.365) Remain 26:33:09 loss: 0.1965 Lr: 0.00229 [2024-02-18 21:18:30,162 INFO misc.py line 119 87073] Train: [56/100][12/1557] Data 0.004 (0.326) Batch 0.810 (1.303) Remain 25:21:08 loss: 0.1882 Lr: 0.00229 [2024-02-18 21:18:30,959 INFO misc.py line 119 87073] Train: [56/100][13/1557] Data 0.014 (0.295) Batch 0.805 (1.253) Remain 24:23:00 loss: 0.2204 Lr: 0.00229 [2024-02-18 21:18:32,086 INFO misc.py line 119 87073] Train: [56/100][14/1557] Data 0.005 (0.268) Batch 1.127 (1.242) Remain 24:09:34 loss: 0.2645 Lr: 0.00229 [2024-02-18 21:18:33,181 INFO misc.py line 119 87073] Train: [56/100][15/1557] Data 0.006 (0.246) Batch 1.095 (1.229) Remain 23:55:15 loss: 0.2232 Lr: 0.00229 [2024-02-18 21:18:34,245 INFO misc.py line 119 87073] Train: [56/100][16/1557] Data 0.006 (0.228) Batch 1.065 (1.217) Remain 23:40:29 loss: 0.1936 Lr: 0.00229 [2024-02-18 21:18:35,147 INFO misc.py line 119 87073] Train: [56/100][17/1557] Data 0.004 (0.212) Batch 0.902 (1.194) Remain 23:14:14 loss: 0.2926 Lr: 0.00229 [2024-02-18 21:18:36,195 INFO misc.py line 119 87073] Train: [56/100][18/1557] Data 0.004 (0.198) Batch 1.047 (1.184) Remain 23:02:44 loss: 0.3180 Lr: 0.00229 [2024-02-18 21:18:36,872 INFO misc.py line 119 87073] Train: [56/100][19/1557] Data 0.006 (0.186) Batch 0.679 (1.153) Remain 22:25:51 loss: 0.1664 Lr: 0.00229 [2024-02-18 21:18:37,651 INFO misc.py line 119 87073] Train: [56/100][20/1557] Data 0.004 (0.175) Batch 0.778 (1.131) Remain 22:00:05 loss: 0.1652 Lr: 0.00229 [2024-02-18 21:18:38,952 INFO misc.py line 119 87073] Train: [56/100][21/1557] Data 0.004 (0.166) Batch 1.295 (1.140) Remain 22:10:42 loss: 0.2194 Lr: 0.00229 [2024-02-18 21:18:40,045 INFO misc.py line 119 87073] Train: [56/100][22/1557] Data 0.010 (0.158) Batch 1.088 (1.137) Remain 22:07:30 loss: 0.6703 Lr: 0.00229 [2024-02-18 21:18:40,920 INFO misc.py line 119 87073] Train: [56/100][23/1557] Data 0.016 (0.150) Batch 0.887 (1.125) Remain 21:52:52 loss: 0.3662 Lr: 0.00229 [2024-02-18 21:18:41,854 INFO misc.py line 119 87073] Train: [56/100][24/1557] Data 0.004 (0.143) Batch 0.932 (1.115) Remain 21:42:09 loss: 0.4496 Lr: 0.00229 [2024-02-18 21:18:42,881 INFO misc.py line 119 87073] Train: [56/100][25/1557] Data 0.005 (0.137) Batch 1.029 (1.112) Remain 21:37:31 loss: 0.3887 Lr: 0.00229 [2024-02-18 21:18:43,692 INFO misc.py line 119 87073] Train: [56/100][26/1557] Data 0.004 (0.131) Batch 0.802 (1.098) Remain 21:21:48 loss: 0.3320 Lr: 0.00229 [2024-02-18 21:18:44,454 INFO misc.py line 119 87073] Train: [56/100][27/1557] Data 0.013 (0.126) Batch 0.771 (1.084) Remain 21:05:53 loss: 0.4272 Lr: 0.00229 [2024-02-18 21:18:45,586 INFO misc.py line 119 87073] Train: [56/100][28/1557] Data 0.003 (0.122) Batch 1.131 (1.086) Remain 21:08:01 loss: 0.2694 Lr: 0.00229 [2024-02-18 21:18:46,527 INFO misc.py line 119 87073] Train: [56/100][29/1557] Data 0.004 (0.117) Batch 0.942 (1.081) Remain 21:01:32 loss: 0.1331 Lr: 0.00229 [2024-02-18 21:18:47,387 INFO misc.py line 119 87073] Train: [56/100][30/1557] Data 0.003 (0.113) Batch 0.859 (1.073) Remain 20:51:55 loss: 0.1503 Lr: 0.00229 [2024-02-18 21:18:48,337 INFO misc.py line 119 87073] Train: [56/100][31/1557] Data 0.005 (0.109) Batch 0.950 (1.068) Remain 20:46:48 loss: 0.2824 Lr: 0.00229 [2024-02-18 21:18:49,138 INFO misc.py line 119 87073] Train: [56/100][32/1557] Data 0.006 (0.105) Batch 0.802 (1.059) Remain 20:36:03 loss: 0.1889 Lr: 0.00229 [2024-02-18 21:18:49,909 INFO misc.py line 119 87073] Train: [56/100][33/1557] Data 0.006 (0.102) Batch 0.762 (1.049) Remain 20:24:28 loss: 0.2945 Lr: 0.00229 [2024-02-18 21:18:50,667 INFO misc.py line 119 87073] Train: [56/100][34/1557] Data 0.014 (0.099) Batch 0.767 (1.040) Remain 20:13:49 loss: 0.1773 Lr: 0.00229 [2024-02-18 21:18:51,998 INFO misc.py line 119 87073] Train: [56/100][35/1557] Data 0.005 (0.096) Batch 1.285 (1.048) Remain 20:22:44 loss: 0.1361 Lr: 0.00229 [2024-02-18 21:18:53,146 INFO misc.py line 119 87073] Train: [56/100][36/1557] Data 0.051 (0.095) Batch 1.189 (1.052) Remain 20:27:44 loss: 0.2818 Lr: 0.00229 [2024-02-18 21:18:54,254 INFO misc.py line 119 87073] Train: [56/100][37/1557] Data 0.010 (0.092) Batch 1.112 (1.054) Remain 20:29:45 loss: 0.5172 Lr: 0.00229 [2024-02-18 21:18:55,109 INFO misc.py line 119 87073] Train: [56/100][38/1557] Data 0.006 (0.090) Batch 0.857 (1.048) Remain 20:23:11 loss: 0.4183 Lr: 0.00229 [2024-02-18 21:18:55,993 INFO misc.py line 119 87073] Train: [56/100][39/1557] Data 0.005 (0.088) Batch 0.884 (1.043) Remain 20:17:50 loss: 0.1139 Lr: 0.00229 [2024-02-18 21:18:56,775 INFO misc.py line 119 87073] Train: [56/100][40/1557] Data 0.005 (0.085) Batch 0.772 (1.036) Remain 20:09:15 loss: 0.3021 Lr: 0.00229 [2024-02-18 21:18:57,620 INFO misc.py line 119 87073] Train: [56/100][41/1557] Data 0.015 (0.083) Batch 0.855 (1.031) Remain 20:03:40 loss: 0.1539 Lr: 0.00229 [2024-02-18 21:18:58,648 INFO misc.py line 119 87073] Train: [56/100][42/1557] Data 0.004 (0.081) Batch 1.029 (1.031) Remain 20:03:34 loss: 0.1302 Lr: 0.00229 [2024-02-18 21:18:59,666 INFO misc.py line 119 87073] Train: [56/100][43/1557] Data 0.004 (0.080) Batch 1.014 (1.031) Remain 20:03:04 loss: 0.2990 Lr: 0.00229 [2024-02-18 21:19:00,543 INFO misc.py line 119 87073] Train: [56/100][44/1557] Data 0.008 (0.078) Batch 0.878 (1.027) Remain 19:58:42 loss: 0.3491 Lr: 0.00229 [2024-02-18 21:19:01,492 INFO misc.py line 119 87073] Train: [56/100][45/1557] Data 0.006 (0.076) Batch 0.951 (1.025) Remain 19:56:33 loss: 0.6015 Lr: 0.00229 [2024-02-18 21:19:02,732 INFO misc.py line 119 87073] Train: [56/100][46/1557] Data 0.005 (0.074) Batch 1.239 (1.030) Remain 20:02:20 loss: 0.2537 Lr: 0.00229 [2024-02-18 21:19:03,496 INFO misc.py line 119 87073] Train: [56/100][47/1557] Data 0.005 (0.073) Batch 0.766 (1.024) Remain 19:55:19 loss: 0.3174 Lr: 0.00229 [2024-02-18 21:19:04,287 INFO misc.py line 119 87073] Train: [56/100][48/1557] Data 0.004 (0.071) Batch 0.790 (1.019) Remain 19:49:13 loss: 0.3699 Lr: 0.00229 [2024-02-18 21:19:05,559 INFO misc.py line 119 87073] Train: [56/100][49/1557] Data 0.004 (0.070) Batch 1.269 (1.025) Remain 19:55:33 loss: 0.1718 Lr: 0.00229 [2024-02-18 21:19:06,598 INFO misc.py line 119 87073] Train: [56/100][50/1557] Data 0.008 (0.069) Batch 1.041 (1.025) Remain 19:55:56 loss: 0.4522 Lr: 0.00229 [2024-02-18 21:19:07,505 INFO misc.py line 119 87073] Train: [56/100][51/1557] Data 0.005 (0.067) Batch 0.909 (1.022) Remain 19:53:06 loss: 0.1223 Lr: 0.00229 [2024-02-18 21:19:08,442 INFO misc.py line 119 87073] Train: [56/100][52/1557] Data 0.004 (0.066) Batch 0.936 (1.021) Remain 19:51:02 loss: 0.2745 Lr: 0.00229 [2024-02-18 21:19:09,468 INFO misc.py line 119 87073] Train: [56/100][53/1557] Data 0.005 (0.065) Batch 1.026 (1.021) Remain 19:51:09 loss: 0.4213 Lr: 0.00229 [2024-02-18 21:19:10,259 INFO misc.py line 119 87073] Train: [56/100][54/1557] Data 0.004 (0.064) Batch 0.788 (1.016) Remain 19:45:48 loss: 0.1562 Lr: 0.00229 [2024-02-18 21:19:10,930 INFO misc.py line 119 87073] Train: [56/100][55/1557] Data 0.008 (0.062) Batch 0.675 (1.010) Remain 19:38:07 loss: 0.4985 Lr: 0.00229 [2024-02-18 21:19:12,135 INFO misc.py line 119 87073] Train: [56/100][56/1557] Data 0.003 (0.061) Batch 1.198 (1.013) Remain 19:42:15 loss: 0.1079 Lr: 0.00229 [2024-02-18 21:19:13,161 INFO misc.py line 119 87073] Train: [56/100][57/1557] Data 0.010 (0.060) Batch 1.031 (1.014) Remain 19:42:37 loss: 0.2531 Lr: 0.00229 [2024-02-18 21:19:14,189 INFO misc.py line 119 87073] Train: [56/100][58/1557] Data 0.005 (0.059) Batch 1.029 (1.014) Remain 19:42:55 loss: 0.6509 Lr: 0.00229 [2024-02-18 21:19:15,531 INFO misc.py line 119 87073] Train: [56/100][59/1557] Data 0.005 (0.058) Batch 1.340 (1.020) Remain 19:49:42 loss: 0.1330 Lr: 0.00229 [2024-02-18 21:19:16,591 INFO misc.py line 119 87073] Train: [56/100][60/1557] Data 0.007 (0.058) Batch 1.056 (1.020) Remain 19:50:25 loss: 0.1990 Lr: 0.00229 [2024-02-18 21:19:17,287 INFO misc.py line 119 87073] Train: [56/100][61/1557] Data 0.011 (0.057) Batch 0.703 (1.015) Remain 19:44:01 loss: 0.1459 Lr: 0.00229 [2024-02-18 21:19:18,043 INFO misc.py line 119 87073] Train: [56/100][62/1557] Data 0.004 (0.056) Batch 0.725 (1.010) Remain 19:38:16 loss: 0.1688 Lr: 0.00229 [2024-02-18 21:19:31,748 INFO misc.py line 119 87073] Train: [56/100][63/1557] Data 8.970 (0.204) Batch 13.736 (1.222) Remain 23:45:43 loss: 0.3979 Lr: 0.00229 [2024-02-18 21:19:32,626 INFO misc.py line 119 87073] Train: [56/100][64/1557] Data 0.004 (0.201) Batch 0.876 (1.216) Remain 23:39:05 loss: 0.3993 Lr: 0.00229 [2024-02-18 21:19:33,544 INFO misc.py line 119 87073] Train: [56/100][65/1557] Data 0.006 (0.198) Batch 0.919 (1.212) Remain 23:33:27 loss: 0.1559 Lr: 0.00229 [2024-02-18 21:19:34,415 INFO misc.py line 119 87073] Train: [56/100][66/1557] Data 0.005 (0.195) Batch 0.872 (1.206) Remain 23:27:09 loss: 0.7290 Lr: 0.00229 [2024-02-18 21:19:35,398 INFO misc.py line 119 87073] Train: [56/100][67/1557] Data 0.004 (0.192) Batch 0.982 (1.203) Remain 23:23:03 loss: 0.1701 Lr: 0.00229 [2024-02-18 21:19:36,198 INFO misc.py line 119 87073] Train: [56/100][68/1557] Data 0.005 (0.189) Batch 0.800 (1.196) Remain 23:15:49 loss: 0.2826 Lr: 0.00229 [2024-02-18 21:19:36,935 INFO misc.py line 119 87073] Train: [56/100][69/1557] Data 0.004 (0.186) Batch 0.737 (1.190) Remain 23:07:40 loss: 0.1352 Lr: 0.00229 [2024-02-18 21:19:38,060 INFO misc.py line 119 87073] Train: [56/100][70/1557] Data 0.004 (0.183) Batch 1.117 (1.188) Remain 23:06:23 loss: 0.1604 Lr: 0.00229 [2024-02-18 21:19:39,120 INFO misc.py line 119 87073] Train: [56/100][71/1557] Data 0.013 (0.181) Batch 1.058 (1.187) Remain 23:04:08 loss: 0.6539 Lr: 0.00229 [2024-02-18 21:19:40,076 INFO misc.py line 119 87073] Train: [56/100][72/1557] Data 0.015 (0.179) Batch 0.967 (1.183) Remain 23:00:23 loss: 0.1764 Lr: 0.00229 [2024-02-18 21:19:40,998 INFO misc.py line 119 87073] Train: [56/100][73/1557] Data 0.004 (0.176) Batch 0.917 (1.180) Remain 22:55:56 loss: 0.3102 Lr: 0.00229 [2024-02-18 21:19:41,839 INFO misc.py line 119 87073] Train: [56/100][74/1557] Data 0.008 (0.174) Batch 0.840 (1.175) Remain 22:50:20 loss: 0.2585 Lr: 0.00229 [2024-02-18 21:19:42,584 INFO misc.py line 119 87073] Train: [56/100][75/1557] Data 0.010 (0.171) Batch 0.750 (1.169) Remain 22:43:26 loss: 0.2303 Lr: 0.00229 [2024-02-18 21:19:43,258 INFO misc.py line 119 87073] Train: [56/100][76/1557] Data 0.004 (0.169) Batch 0.673 (1.162) Remain 22:35:30 loss: 0.2115 Lr: 0.00229 [2024-02-18 21:19:44,547 INFO misc.py line 119 87073] Train: [56/100][77/1557] Data 0.005 (0.167) Batch 1.287 (1.164) Remain 22:37:28 loss: 0.3082 Lr: 0.00229 [2024-02-18 21:19:45,473 INFO misc.py line 119 87073] Train: [56/100][78/1557] Data 0.006 (0.165) Batch 0.929 (1.161) Remain 22:33:48 loss: 0.2498 Lr: 0.00229 [2024-02-18 21:19:46,359 INFO misc.py line 119 87073] Train: [56/100][79/1557] Data 0.004 (0.163) Batch 0.884 (1.157) Remain 22:29:31 loss: 0.1747 Lr: 0.00229 [2024-02-18 21:19:47,656 INFO misc.py line 119 87073] Train: [56/100][80/1557] Data 0.007 (0.161) Batch 1.298 (1.159) Remain 22:31:39 loss: 0.4141 Lr: 0.00229 [2024-02-18 21:19:48,689 INFO misc.py line 119 87073] Train: [56/100][81/1557] Data 0.004 (0.159) Batch 1.033 (1.157) Remain 22:29:44 loss: 0.1971 Lr: 0.00229 [2024-02-18 21:19:49,420 INFO misc.py line 119 87073] Train: [56/100][82/1557] Data 0.004 (0.157) Batch 0.732 (1.152) Remain 22:23:26 loss: 0.1888 Lr: 0.00229 [2024-02-18 21:19:50,208 INFO misc.py line 119 87073] Train: [56/100][83/1557] Data 0.004 (0.155) Batch 0.784 (1.147) Remain 22:18:03 loss: 0.1704 Lr: 0.00229 [2024-02-18 21:19:51,419 INFO misc.py line 119 87073] Train: [56/100][84/1557] Data 0.007 (0.153) Batch 1.203 (1.148) Remain 22:18:51 loss: 0.1373 Lr: 0.00229 [2024-02-18 21:19:52,412 INFO misc.py line 119 87073] Train: [56/100][85/1557] Data 0.016 (0.151) Batch 1.004 (1.146) Remain 22:16:47 loss: 0.4120 Lr: 0.00229 [2024-02-18 21:19:53,331 INFO misc.py line 119 87073] Train: [56/100][86/1557] Data 0.005 (0.150) Batch 0.920 (1.143) Remain 22:13:35 loss: 0.3413 Lr: 0.00229 [2024-02-18 21:19:54,362 INFO misc.py line 119 87073] Train: [56/100][87/1557] Data 0.004 (0.148) Batch 1.031 (1.142) Remain 22:12:00 loss: 0.2715 Lr: 0.00229 [2024-02-18 21:19:55,299 INFO misc.py line 119 87073] Train: [56/100][88/1557] Data 0.003 (0.146) Batch 0.936 (1.140) Remain 22:09:10 loss: 0.3869 Lr: 0.00229 [2024-02-18 21:19:56,060 INFO misc.py line 119 87073] Train: [56/100][89/1557] Data 0.004 (0.144) Batch 0.758 (1.135) Remain 22:03:58 loss: 0.2718 Lr: 0.00229 [2024-02-18 21:19:56,832 INFO misc.py line 119 87073] Train: [56/100][90/1557] Data 0.007 (0.143) Batch 0.776 (1.131) Remain 21:59:08 loss: 0.3406 Lr: 0.00229 [2024-02-18 21:19:58,160 INFO misc.py line 119 87073] Train: [56/100][91/1557] Data 0.004 (0.141) Batch 1.318 (1.133) Remain 22:01:35 loss: 0.2257 Lr: 0.00229 [2024-02-18 21:19:59,276 INFO misc.py line 119 87073] Train: [56/100][92/1557] Data 0.013 (0.140) Batch 1.114 (1.133) Remain 22:01:19 loss: 0.2238 Lr: 0.00229 [2024-02-18 21:20:00,297 INFO misc.py line 119 87073] Train: [56/100][93/1557] Data 0.015 (0.138) Batch 1.018 (1.132) Remain 21:59:49 loss: 0.2188 Lr: 0.00229 [2024-02-18 21:20:01,211 INFO misc.py line 119 87073] Train: [56/100][94/1557] Data 0.018 (0.137) Batch 0.926 (1.129) Remain 21:57:10 loss: 0.2816 Lr: 0.00229 [2024-02-18 21:20:02,323 INFO misc.py line 119 87073] Train: [56/100][95/1557] Data 0.006 (0.136) Batch 1.113 (1.129) Remain 21:56:56 loss: 0.3178 Lr: 0.00229 [2024-02-18 21:20:03,049 INFO misc.py line 119 87073] Train: [56/100][96/1557] Data 0.005 (0.134) Batch 0.726 (1.125) Remain 21:51:52 loss: 0.4224 Lr: 0.00229 [2024-02-18 21:20:03,803 INFO misc.py line 119 87073] Train: [56/100][97/1557] Data 0.004 (0.133) Batch 0.753 (1.121) Remain 21:47:14 loss: 0.3121 Lr: 0.00229 [2024-02-18 21:20:04,865 INFO misc.py line 119 87073] Train: [56/100][98/1557] Data 0.005 (0.132) Batch 1.063 (1.120) Remain 21:46:30 loss: 0.1191 Lr: 0.00229 [2024-02-18 21:20:05,799 INFO misc.py line 119 87073] Train: [56/100][99/1557] Data 0.005 (0.130) Batch 0.935 (1.118) Remain 21:44:14 loss: 0.6699 Lr: 0.00229 [2024-02-18 21:20:06,751 INFO misc.py line 119 87073] Train: [56/100][100/1557] Data 0.004 (0.129) Batch 0.952 (1.117) Remain 21:42:12 loss: 0.3439 Lr: 0.00229 [2024-02-18 21:20:07,713 INFO misc.py line 119 87073] Train: [56/100][101/1557] Data 0.003 (0.128) Batch 0.961 (1.115) Remain 21:40:20 loss: 0.5627 Lr: 0.00229 [2024-02-18 21:20:08,775 INFO misc.py line 119 87073] Train: [56/100][102/1557] Data 0.004 (0.126) Batch 1.051 (1.115) Remain 21:39:34 loss: 0.2210 Lr: 0.00229 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loss: 0.3988 Lr: 0.00229 [2024-02-18 21:20:44,792 INFO misc.py line 119 87073] Train: [56/100][128/1557] Data 0.011 (0.182) Batch 0.954 (1.171) Remain 22:44:49 loss: 0.5657 Lr: 0.00229 [2024-02-18 21:20:45,747 INFO misc.py line 119 87073] Train: [56/100][129/1557] Data 0.005 (0.181) Batch 0.956 (1.169) Remain 22:42:49 loss: 0.2938 Lr: 0.00229 [2024-02-18 21:20:46,657 INFO misc.py line 119 87073] Train: [56/100][130/1557] Data 0.004 (0.179) Batch 0.910 (1.167) Remain 22:40:25 loss: 0.2736 Lr: 0.00229 [2024-02-18 21:20:47,404 INFO misc.py line 119 87073] Train: [56/100][131/1557] Data 0.004 (0.178) Batch 0.737 (1.164) Remain 22:36:29 loss: 0.1776 Lr: 0.00229 [2024-02-18 21:20:48,160 INFO misc.py line 119 87073] Train: [56/100][132/1557] Data 0.014 (0.177) Batch 0.766 (1.161) Remain 22:32:52 loss: 0.2207 Lr: 0.00229 [2024-02-18 21:20:49,416 INFO misc.py line 119 87073] Train: [56/100][133/1557] Data 0.003 (0.175) Batch 1.246 (1.161) Remain 22:33:36 loss: 0.1853 Lr: 0.00229 [2024-02-18 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87073] Train: [56/100][140/1557] Data 0.014 (0.167) Batch 1.168 (1.151) Remain 22:21:08 loss: 0.1155 Lr: 0.00229 [2024-02-18 21:20:57,002 INFO misc.py line 119 87073] Train: [56/100][141/1557] Data 0.013 (0.166) Batch 0.917 (1.149) Remain 22:19:08 loss: 0.3110 Lr: 0.00229 [2024-02-18 21:20:57,875 INFO misc.py line 119 87073] Train: [56/100][142/1557] Data 0.006 (0.164) Batch 0.874 (1.147) Remain 22:16:49 loss: 0.5295 Lr: 0.00229 [2024-02-18 21:20:58,873 INFO misc.py line 119 87073] Train: [56/100][143/1557] Data 0.004 (0.163) Batch 0.988 (1.146) Remain 22:15:28 loss: 0.2174 Lr: 0.00229 [2024-02-18 21:20:59,791 INFO misc.py line 119 87073] Train: [56/100][144/1557] Data 0.014 (0.162) Batch 0.928 (1.144) Remain 22:13:39 loss: 0.2090 Lr: 0.00229 [2024-02-18 21:21:00,457 INFO misc.py line 119 87073] Train: [56/100][145/1557] Data 0.004 (0.161) Batch 0.666 (1.141) Remain 22:09:42 loss: 0.2324 Lr: 0.00229 [2024-02-18 21:21:01,125 INFO misc.py line 119 87073] Train: [56/100][146/1557] Data 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line 119 87073] Train: [56/100][165/1557] Data 0.008 (0.142) Batch 0.901 (1.116) Remain 21:40:42 loss: 0.4273 Lr: 0.00229 [2024-02-18 21:21:20,041 INFO misc.py line 119 87073] Train: [56/100][166/1557] Data 0.003 (0.141) Batch 0.742 (1.114) Remain 21:38:00 loss: 0.2907 Lr: 0.00229 [2024-02-18 21:21:20,779 INFO misc.py line 119 87073] Train: [56/100][167/1557] Data 0.004 (0.140) Batch 0.736 (1.112) Remain 21:35:18 loss: 0.1737 Lr: 0.00229 [2024-02-18 21:21:22,009 INFO misc.py line 119 87073] Train: [56/100][168/1557] Data 0.005 (0.140) Batch 1.227 (1.113) Remain 21:36:06 loss: 0.2005 Lr: 0.00229 [2024-02-18 21:21:22,971 INFO misc.py line 119 87073] Train: [56/100][169/1557] Data 0.009 (0.139) Batch 0.966 (1.112) Remain 21:35:03 loss: 0.3863 Lr: 0.00229 [2024-02-18 21:21:23,880 INFO misc.py line 119 87073] Train: [56/100][170/1557] Data 0.004 (0.138) Batch 0.910 (1.110) Remain 21:33:38 loss: 0.2186 Lr: 0.00229 [2024-02-18 21:21:24,708 INFO misc.py line 119 87073] Train: 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Batch 0.941 (1.171) Remain 22:44:07 loss: 0.4091 Lr: 0.00229 [2024-02-18 21:21:43,145 INFO misc.py line 119 87073] Train: [56/100][178/1557] Data 0.006 (0.184) Batch 0.942 (1.170) Remain 22:42:34 loss: 0.3005 Lr: 0.00229 [2024-02-18 21:21:44,210 INFO misc.py line 119 87073] Train: [56/100][179/1557] Data 0.004 (0.183) Batch 1.066 (1.169) Remain 22:41:52 loss: 0.6395 Lr: 0.00229 [2024-02-18 21:21:47,059 INFO misc.py line 119 87073] Train: [56/100][180/1557] Data 1.325 (0.189) Batch 2.849 (1.179) Remain 22:52:54 loss: 0.1700 Lr: 0.00229 [2024-02-18 21:21:47,843 INFO misc.py line 119 87073] Train: [56/100][181/1557] Data 0.004 (0.188) Batch 0.783 (1.176) Remain 22:50:17 loss: 0.3090 Lr: 0.00229 [2024-02-18 21:21:48,983 INFO misc.py line 119 87073] Train: [56/100][182/1557] Data 0.005 (0.187) Batch 1.139 (1.176) Remain 22:50:01 loss: 0.2674 Lr: 0.00229 [2024-02-18 21:21:50,142 INFO misc.py line 119 87073] Train: [56/100][183/1557] Data 0.007 (0.186) Batch 1.153 (1.176) Remain 22:49:51 loss: 0.2409 Lr: 0.00229 [2024-02-18 21:21:51,063 INFO misc.py line 119 87073] Train: [56/100][184/1557] Data 0.012 (0.185) Batch 0.929 (1.175) Remain 22:48:14 loss: 0.5429 Lr: 0.00229 [2024-02-18 21:21:52,076 INFO misc.py line 119 87073] Train: [56/100][185/1557] Data 0.004 (0.184) Batch 1.012 (1.174) Remain 22:47:10 loss: 0.3544 Lr: 0.00229 [2024-02-18 21:21:53,182 INFO misc.py line 119 87073] Train: [56/100][186/1557] Data 0.006 (0.183) Batch 1.108 (1.174) Remain 22:46:44 loss: 0.3417 Lr: 0.00229 [2024-02-18 21:21:53,955 INFO misc.py line 119 87073] Train: [56/100][187/1557] Data 0.004 (0.182) Batch 0.772 (1.171) Remain 22:44:10 loss: 0.2072 Lr: 0.00229 [2024-02-18 21:21:54,753 INFO misc.py line 119 87073] Train: [56/100][188/1557] Data 0.005 (0.181) Batch 0.792 (1.169) Remain 22:41:46 loss: 0.3196 Lr: 0.00229 [2024-02-18 21:21:55,972 INFO misc.py line 119 87073] Train: [56/100][189/1557] Data 0.011 (0.181) Batch 1.214 (1.170) Remain 22:42:02 loss: 0.0995 Lr: 0.00229 [2024-02-18 21:21:56,893 INFO misc.py line 119 87073] Train: [56/100][190/1557] Data 0.015 (0.180) Batch 0.931 (1.168) Remain 22:40:32 loss: 0.1145 Lr: 0.00229 [2024-02-18 21:21:57,833 INFO misc.py line 119 87073] Train: [56/100][191/1557] Data 0.005 (0.179) Batch 0.941 (1.167) Remain 22:39:06 loss: 0.4665 Lr: 0.00229 [2024-02-18 21:21:58,868 INFO misc.py line 119 87073] Train: [56/100][192/1557] Data 0.004 (0.178) Batch 1.035 (1.166) Remain 22:38:16 loss: 0.4587 Lr: 0.00229 [2024-02-18 21:21:59,852 INFO misc.py line 119 87073] Train: [56/100][193/1557] Data 0.004 (0.177) Batch 0.985 (1.165) Remain 22:37:08 loss: 0.2503 Lr: 0.00229 [2024-02-18 21:22:00,578 INFO misc.py line 119 87073] Train: [56/100][194/1557] Data 0.004 (0.176) Batch 0.725 (1.163) Remain 22:34:26 loss: 0.1812 Lr: 0.00229 [2024-02-18 21:22:01,372 INFO misc.py line 119 87073] Train: [56/100][195/1557] Data 0.005 (0.175) Batch 0.794 (1.161) Remain 22:32:10 loss: 0.1361 Lr: 0.00229 [2024-02-18 21:22:02,533 INFO misc.py line 119 87073] Train: [56/100][196/1557] Data 0.004 (0.174) Batch 1.162 (1.161) Remain 22:32:09 loss: 0.2532 Lr: 0.00229 [2024-02-18 21:22:03,562 INFO misc.py line 119 87073] Train: [56/100][197/1557] Data 0.004 (0.173) Batch 1.028 (1.160) Remain 22:31:20 loss: 0.3566 Lr: 0.00229 [2024-02-18 21:22:04,594 INFO misc.py line 119 87073] Train: [56/100][198/1557] Data 0.004 (0.172) Batch 1.030 (1.160) Remain 22:30:32 loss: 0.3442 Lr: 0.00229 [2024-02-18 21:22:05,492 INFO misc.py line 119 87073] Train: [56/100][199/1557] Data 0.006 (0.172) Batch 0.899 (1.158) Remain 22:28:58 loss: 0.2927 Lr: 0.00229 [2024-02-18 21:22:06,400 INFO misc.py line 119 87073] Train: [56/100][200/1557] Data 0.005 (0.171) Batch 0.905 (1.157) Remain 22:27:27 loss: 0.3399 Lr: 0.00228 [2024-02-18 21:22:07,177 INFO misc.py line 119 87073] Train: [56/100][201/1557] Data 0.008 (0.170) Batch 0.781 (1.155) Remain 22:25:13 loss: 0.2288 Lr: 0.00228 [2024-02-18 21:22:07,953 INFO misc.py line 119 87073] Train: [56/100][202/1557] Data 0.004 (0.169) Batch 0.776 (1.153) Remain 22:22:59 loss: 0.4330 Lr: 0.00228 [2024-02-18 21:22:09,254 INFO misc.py line 119 87073] Train: [56/100][203/1557] Data 0.004 (0.168) Batch 1.292 (1.154) Remain 22:23:47 loss: 0.2471 Lr: 0.00228 [2024-02-18 21:22:10,235 INFO misc.py line 119 87073] Train: [56/100][204/1557] Data 0.013 (0.168) Batch 0.989 (1.153) Remain 22:22:48 loss: 0.1739 Lr: 0.00228 [2024-02-18 21:22:11,548 INFO misc.py line 119 87073] Train: [56/100][205/1557] Data 0.004 (0.167) Batch 1.300 (1.154) Remain 22:23:38 loss: 0.5342 Lr: 0.00228 [2024-02-18 21:22:12,630 INFO misc.py line 119 87073] Train: [56/100][206/1557] Data 0.017 (0.166) Batch 1.084 (1.154) Remain 22:23:12 loss: 0.3208 Lr: 0.00228 [2024-02-18 21:22:13,580 INFO misc.py line 119 87073] Train: [56/100][207/1557] Data 0.015 (0.165) Batch 0.960 (1.153) Remain 22:22:05 loss: 0.2566 Lr: 0.00228 [2024-02-18 21:22:14,342 INFO misc.py line 119 87073] Train: [56/100][208/1557] Data 0.005 (0.164) Batch 0.762 (1.151) Remain 22:19:51 loss: 0.2271 Lr: 0.00228 [2024-02-18 21:22:15,092 INFO misc.py line 119 87073] Train: [56/100][209/1557] Data 0.005 (0.164) Batch 0.746 (1.149) Remain 22:17:32 loss: 0.1669 Lr: 0.00228 [2024-02-18 21:22:16,238 INFO misc.py line 119 87073] Train: [56/100][210/1557] Data 0.008 (0.163) Batch 1.145 (1.149) Remain 22:17:30 loss: 0.1250 Lr: 0.00228 [2024-02-18 21:22:17,116 INFO misc.py line 119 87073] Train: [56/100][211/1557] Data 0.011 (0.162) Batch 0.884 (1.148) Remain 22:16:00 loss: 0.3704 Lr: 0.00228 [2024-02-18 21:22:18,036 INFO misc.py line 119 87073] Train: [56/100][212/1557] Data 0.004 (0.161) Batch 0.919 (1.146) Remain 22:14:42 loss: 0.5995 Lr: 0.00228 [2024-02-18 21:22:19,002 INFO misc.py line 119 87073] Train: [56/100][213/1557] Data 0.005 (0.161) Batch 0.959 (1.146) Remain 22:13:39 loss: 0.2982 Lr: 0.00228 [2024-02-18 21:22:19,774 INFO misc.py line 119 87073] Train: [56/100][214/1557] Data 0.012 (0.160) Batch 0.780 (1.144) Remain 22:11:37 loss: 0.1535 Lr: 0.00228 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line 119 87073] Train: [56/100][221/1557] Data 0.006 (0.155) Batch 1.011 (1.138) Remain 22:04:42 loss: 0.6347 Lr: 0.00228 [2024-02-18 21:22:27,312 INFO misc.py line 119 87073] Train: [56/100][222/1557] Data 0.008 (0.154) Batch 0.798 (1.136) Remain 22:02:53 loss: 0.3159 Lr: 0.00228 [2024-02-18 21:22:28,089 INFO misc.py line 119 87073] Train: [56/100][223/1557] Data 0.005 (0.154) Batch 0.777 (1.135) Remain 22:00:58 loss: 0.2518 Lr: 0.00228 [2024-02-18 21:22:29,248 INFO misc.py line 119 87073] Train: [56/100][224/1557] Data 0.004 (0.153) Batch 1.147 (1.135) Remain 22:01:00 loss: 0.1001 Lr: 0.00228 [2024-02-18 21:22:30,152 INFO misc.py line 119 87073] Train: [56/100][225/1557] Data 0.016 (0.152) Batch 0.916 (1.134) Remain 21:59:51 loss: 0.1012 Lr: 0.00228 [2024-02-18 21:22:31,224 INFO misc.py line 119 87073] Train: [56/100][226/1557] Data 0.004 (0.152) Batch 1.072 (1.134) Remain 21:59:30 loss: 0.2014 Lr: 0.00228 [2024-02-18 21:22:32,184 INFO misc.py line 119 87073] Train: 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Batch 0.879 (1.177) Remain 22:50:09 loss: 0.5118 Lr: 0.00228 [2024-02-18 21:22:50,305 INFO misc.py line 119 87073] Train: [56/100][234/1557] Data 0.013 (0.189) Batch 1.105 (1.177) Remain 22:49:46 loss: 0.6452 Lr: 0.00228 [2024-02-18 21:22:51,264 INFO misc.py line 119 87073] Train: [56/100][235/1557] Data 0.008 (0.188) Batch 0.964 (1.176) Remain 22:48:41 loss: 0.7284 Lr: 0.00228 [2024-02-18 21:22:51,952 INFO misc.py line 119 87073] Train: [56/100][236/1557] Data 0.004 (0.188) Batch 0.687 (1.174) Remain 22:46:13 loss: 0.1191 Lr: 0.00228 [2024-02-18 21:22:52,686 INFO misc.py line 119 87073] Train: [56/100][237/1557] Data 0.004 (0.187) Batch 0.733 (1.172) Remain 22:44:01 loss: 0.4087 Lr: 0.00228 [2024-02-18 21:22:53,793 INFO misc.py line 119 87073] Train: [56/100][238/1557] Data 0.004 (0.186) Batch 1.089 (1.172) Remain 22:43:35 loss: 0.1726 Lr: 0.00228 [2024-02-18 21:22:54,655 INFO misc.py line 119 87073] Train: [56/100][239/1557] Data 0.022 (0.185) Batch 0.876 (1.170) Remain 22:42:06 loss: 0.4524 Lr: 0.00228 [2024-02-18 21:22:55,533 INFO misc.py line 119 87073] Train: [56/100][240/1557] Data 0.009 (0.185) Batch 0.882 (1.169) Remain 22:40:40 loss: 0.3870 Lr: 0.00228 [2024-02-18 21:22:56,583 INFO misc.py line 119 87073] Train: [56/100][241/1557] Data 0.005 (0.184) Batch 1.046 (1.169) Remain 22:40:03 loss: 0.2742 Lr: 0.00228 [2024-02-18 21:22:57,751 INFO misc.py line 119 87073] Train: [56/100][242/1557] Data 0.009 (0.183) Batch 1.166 (1.169) Remain 22:40:01 loss: 0.4080 Lr: 0.00228 [2024-02-18 21:22:58,524 INFO misc.py line 119 87073] Train: [56/100][243/1557] Data 0.011 (0.182) Batch 0.778 (1.167) Remain 22:38:06 loss: 0.3114 Lr: 0.00228 [2024-02-18 21:22:59,283 INFO misc.py line 119 87073] Train: [56/100][244/1557] Data 0.006 (0.182) Batch 0.760 (1.165) Remain 22:36:07 loss: 0.1483 Lr: 0.00228 [2024-02-18 21:23:00,646 INFO misc.py line 119 87073] Train: [56/100][245/1557] Data 0.005 (0.181) Batch 1.363 (1.166) Remain 22:37:03 loss: 0.2107 Lr: 0.00228 [2024-02-18 21:23:01,597 INFO misc.py line 119 87073] Train: [56/100][246/1557] Data 0.005 (0.180) Batch 0.951 (1.165) Remain 22:36:00 loss: 0.5987 Lr: 0.00228 [2024-02-18 21:23:02,548 INFO misc.py line 119 87073] Train: [56/100][247/1557] Data 0.005 (0.180) Batch 0.951 (1.164) Remain 22:34:57 loss: 0.3139 Lr: 0.00228 [2024-02-18 21:23:03,509 INFO misc.py line 119 87073] Train: [56/100][248/1557] Data 0.004 (0.179) Batch 0.962 (1.164) Remain 22:33:58 loss: 0.5077 Lr: 0.00228 [2024-02-18 21:23:04,438 INFO misc.py line 119 87073] Train: [56/100][249/1557] Data 0.004 (0.178) Batch 0.927 (1.163) Remain 22:32:50 loss: 0.1793 Lr: 0.00228 [2024-02-18 21:23:05,167 INFO misc.py line 119 87073] Train: [56/100][250/1557] Data 0.005 (0.177) Batch 0.731 (1.161) Remain 22:30:47 loss: 0.1618 Lr: 0.00228 [2024-02-18 21:23:05,828 INFO misc.py line 119 87073] Train: [56/100][251/1557] Data 0.004 (0.177) Batch 0.648 (1.159) Remain 22:28:21 loss: 0.2729 Lr: 0.00228 [2024-02-18 21:23:07,026 INFO misc.py line 119 87073] Train: [56/100][252/1557] Data 0.016 (0.176) Batch 1.157 (1.159) Remain 22:28:20 loss: 0.1480 Lr: 0.00228 [2024-02-18 21:23:07,969 INFO misc.py line 119 87073] Train: [56/100][253/1557] Data 0.057 (0.176) Batch 0.997 (1.158) Remain 22:27:33 loss: 0.3431 Lr: 0.00228 [2024-02-18 21:23:09,102 INFO misc.py line 119 87073] Train: [56/100][254/1557] Data 0.004 (0.175) Batch 1.132 (1.158) Remain 22:27:25 loss: 0.7566 Lr: 0.00228 [2024-02-18 21:23:09,953 INFO misc.py line 119 87073] Train: [56/100][255/1557] Data 0.004 (0.174) Batch 0.850 (1.157) Remain 22:25:59 loss: 0.4461 Lr: 0.00228 [2024-02-18 21:23:10,943 INFO misc.py line 119 87073] Train: [56/100][256/1557] Data 0.006 (0.174) Batch 0.984 (1.156) Remain 22:25:10 loss: 0.3778 Lr: 0.00228 [2024-02-18 21:23:11,683 INFO misc.py line 119 87073] Train: [56/100][257/1557] Data 0.011 (0.173) Batch 0.745 (1.155) Remain 22:23:16 loss: 0.2440 Lr: 0.00228 [2024-02-18 21:23:12,447 INFO misc.py line 119 87073] Train: [56/100][258/1557] Data 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[2024-02-18 21:23:24,958 INFO misc.py line 119 87073] Train: [56/100][271/1557] Data 0.004 (0.164) Batch 0.769 (1.144) Remain 22:10:26 loss: 0.3047 Lr: 0.00228 [2024-02-18 21:23:25,682 INFO misc.py line 119 87073] Train: [56/100][272/1557] Data 0.012 (0.164) Batch 0.732 (1.142) Remain 22:08:38 loss: 0.1928 Lr: 0.00228 [2024-02-18 21:23:27,025 INFO misc.py line 119 87073] Train: [56/100][273/1557] Data 0.005 (0.163) Batch 1.299 (1.143) Remain 22:09:17 loss: 0.2557 Lr: 0.00228 [2024-02-18 21:23:28,034 INFO misc.py line 119 87073] Train: [56/100][274/1557] Data 0.049 (0.163) Batch 1.044 (1.142) Remain 22:08:50 loss: 0.4195 Lr: 0.00228 [2024-02-18 21:23:28,827 INFO misc.py line 119 87073] Train: [56/100][275/1557] Data 0.013 (0.162) Batch 0.800 (1.141) Remain 22:07:21 loss: 1.1071 Lr: 0.00228 [2024-02-18 21:23:29,813 INFO misc.py line 119 87073] Train: [56/100][276/1557] Data 0.007 (0.162) Batch 0.989 (1.141) Remain 22:06:41 loss: 0.7022 Lr: 0.00228 [2024-02-18 21:23:30,830 INFO misc.py line 119 87073] Train: [56/100][277/1557] Data 0.004 (0.161) Batch 1.017 (1.140) Remain 22:06:09 loss: 0.4346 Lr: 0.00228 [2024-02-18 21:23:31,548 INFO misc.py line 119 87073] Train: [56/100][278/1557] Data 0.006 (0.160) Batch 0.708 (1.139) Remain 22:04:18 loss: 0.2314 Lr: 0.00228 [2024-02-18 21:23:32,302 INFO misc.py line 119 87073] Train: [56/100][279/1557] Data 0.014 (0.160) Batch 0.764 (1.137) Remain 22:02:42 loss: 0.3803 Lr: 0.00228 [2024-02-18 21:23:33,434 INFO misc.py line 119 87073] Train: [56/100][280/1557] Data 0.004 (0.159) Batch 1.132 (1.137) Remain 22:02:39 loss: 0.1611 Lr: 0.00228 [2024-02-18 21:23:34,383 INFO misc.py line 119 87073] Train: [56/100][281/1557] Data 0.004 (0.159) Batch 0.948 (1.137) Remain 22:01:51 loss: 0.2696 Lr: 0.00228 [2024-02-18 21:23:35,247 INFO misc.py line 119 87073] Train: [56/100][282/1557] Data 0.005 (0.158) Batch 0.866 (1.136) Remain 22:00:42 loss: 0.1472 Lr: 0.00228 [2024-02-18 21:23:36,153 INFO misc.py line 119 87073] Train: 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Batch 0.928 (1.169) Remain 22:39:48 loss: 0.7886 Lr: 0.00228 [2024-02-18 21:23:53,738 INFO misc.py line 119 87073] Train: [56/100][290/1557] Data 0.006 (0.188) Batch 0.882 (1.168) Remain 22:38:37 loss: 0.3751 Lr: 0.00228 [2024-02-18 21:23:54,613 INFO misc.py line 119 87073] Train: [56/100][291/1557] Data 0.014 (0.187) Batch 0.887 (1.167) Remain 22:37:28 loss: 0.1247 Lr: 0.00228 [2024-02-18 21:23:55,461 INFO misc.py line 119 87073] Train: [56/100][292/1557] Data 0.003 (0.186) Batch 0.847 (1.166) Remain 22:36:09 loss: 0.1500 Lr: 0.00228 [2024-02-18 21:23:56,143 INFO misc.py line 119 87073] Train: [56/100][293/1557] Data 0.004 (0.186) Batch 0.680 (1.165) Remain 22:34:11 loss: 0.2843 Lr: 0.00228 [2024-02-18 21:23:57,346 INFO misc.py line 119 87073] Train: [56/100][294/1557] Data 0.006 (0.185) Batch 1.199 (1.165) Remain 22:34:18 loss: 0.2091 Lr: 0.00228 [2024-02-18 21:23:58,314 INFO misc.py line 119 87073] Train: [56/100][295/1557] Data 0.011 (0.185) Batch 0.972 (1.164) Remain 22:33:31 loss: 0.3500 Lr: 0.00228 [2024-02-18 21:23:59,246 INFO misc.py line 119 87073] Train: [56/100][296/1557] Data 0.007 (0.184) Batch 0.933 (1.163) Remain 22:32:35 loss: 0.4466 Lr: 0.00228 [2024-02-18 21:24:00,244 INFO misc.py line 119 87073] Train: [56/100][297/1557] Data 0.006 (0.183) Batch 0.999 (1.163) Remain 22:31:54 loss: 0.1814 Lr: 0.00228 [2024-02-18 21:24:01,167 INFO misc.py line 119 87073] Train: [56/100][298/1557] Data 0.005 (0.183) Batch 0.924 (1.162) Remain 22:30:57 loss: 0.2013 Lr: 0.00228 [2024-02-18 21:24:01,968 INFO misc.py line 119 87073] Train: [56/100][299/1557] Data 0.004 (0.182) Batch 0.793 (1.161) Remain 22:29:29 loss: 0.1281 Lr: 0.00228 [2024-02-18 21:24:02,713 INFO misc.py line 119 87073] Train: [56/100][300/1557] Data 0.012 (0.182) Batch 0.753 (1.159) Remain 22:27:52 loss: 0.3455 Lr: 0.00228 [2024-02-18 21:24:03,943 INFO misc.py line 119 87073] Train: [56/100][301/1557] Data 0.004 (0.181) Batch 1.230 (1.159) Remain 22:28:07 loss: 0.1311 Lr: 0.00228 [2024-02-18 21:24:04,850 INFO misc.py line 119 87073] Train: [56/100][302/1557] Data 0.004 (0.180) Batch 0.907 (1.159) Remain 22:27:07 loss: 0.2594 Lr: 0.00228 [2024-02-18 21:24:05,677 INFO misc.py line 119 87073] Train: [56/100][303/1557] Data 0.005 (0.180) Batch 0.827 (1.157) Remain 22:25:49 loss: 0.1044 Lr: 0.00228 [2024-02-18 21:24:06,590 INFO misc.py line 119 87073] Train: [56/100][304/1557] Data 0.005 (0.179) Batch 0.914 (1.157) Remain 22:24:51 loss: 0.2734 Lr: 0.00228 [2024-02-18 21:24:07,409 INFO misc.py line 119 87073] Train: [56/100][305/1557] Data 0.004 (0.179) Batch 0.817 (1.156) Remain 22:23:31 loss: 0.2976 Lr: 0.00228 [2024-02-18 21:24:08,181 INFO misc.py line 119 87073] Train: [56/100][306/1557] Data 0.007 (0.178) Batch 0.765 (1.154) Remain 22:22:00 loss: 0.3709 Lr: 0.00228 [2024-02-18 21:24:08,961 INFO misc.py line 119 87073] Train: [56/100][307/1557] Data 0.013 (0.178) Batch 0.789 (1.153) Remain 22:20:35 loss: 0.3111 Lr: 0.00228 [2024-02-18 21:24:10,114 INFO misc.py line 119 87073] Train: [56/100][308/1557] Data 0.003 (0.177) Batch 1.152 (1.153) Remain 22:20:34 loss: 0.2070 Lr: 0.00228 [2024-02-18 21:24:11,219 INFO misc.py line 119 87073] Train: [56/100][309/1557] Data 0.004 (0.176) Batch 1.105 (1.153) Remain 22:20:22 loss: 0.5700 Lr: 0.00228 [2024-02-18 21:24:12,048 INFO misc.py line 119 87073] Train: [56/100][310/1557] Data 0.004 (0.176) Batch 0.829 (1.152) Remain 22:19:07 loss: 0.3337 Lr: 0.00228 [2024-02-18 21:24:13,085 INFO misc.py line 119 87073] Train: [56/100][311/1557] Data 0.004 (0.175) Batch 1.029 (1.151) Remain 22:18:38 loss: 0.3899 Lr: 0.00228 [2024-02-18 21:24:14,099 INFO misc.py line 119 87073] Train: [56/100][312/1557] Data 0.011 (0.175) Batch 1.021 (1.151) Remain 22:18:08 loss: 0.6066 Lr: 0.00228 [2024-02-18 21:24:14,780 INFO misc.py line 119 87073] Train: [56/100][313/1557] Data 0.005 (0.174) Batch 0.681 (1.150) Remain 22:16:21 loss: 0.2352 Lr: 0.00228 [2024-02-18 21:24:15,558 INFO misc.py line 119 87073] Train: [56/100][314/1557] Data 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[2024-02-18 21:24:27,398 INFO misc.py line 119 87073] Train: [56/100][327/1557] Data 0.006 (0.167) Batch 0.753 (1.139) Remain 22:03:36 loss: 0.4414 Lr: 0.00228 [2024-02-18 21:24:28,196 INFO misc.py line 119 87073] Train: [56/100][328/1557] Data 0.005 (0.167) Batch 0.800 (1.138) Remain 22:02:23 loss: 0.3716 Lr: 0.00228 [2024-02-18 21:24:29,469 INFO misc.py line 119 87073] Train: [56/100][329/1557] Data 0.004 (0.166) Batch 1.270 (1.138) Remain 22:02:50 loss: 0.2843 Lr: 0.00228 [2024-02-18 21:24:30,400 INFO misc.py line 119 87073] Train: [56/100][330/1557] Data 0.006 (0.166) Batch 0.933 (1.138) Remain 22:02:05 loss: 0.1629 Lr: 0.00228 [2024-02-18 21:24:31,405 INFO misc.py line 119 87073] Train: [56/100][331/1557] Data 0.005 (0.165) Batch 1.006 (1.137) Remain 22:01:36 loss: 0.5464 Lr: 0.00228 [2024-02-18 21:24:32,407 INFO misc.py line 119 87073] Train: [56/100][332/1557] Data 0.005 (0.165) Batch 1.002 (1.137) Remain 22:01:06 loss: 0.3030 Lr: 0.00228 [2024-02-18 21:24:33,510 INFO misc.py line 119 87073] Train: [56/100][333/1557] Data 0.004 (0.164) Batch 1.103 (1.137) Remain 22:00:58 loss: 0.5803 Lr: 0.00228 [2024-02-18 21:24:34,290 INFO misc.py line 119 87073] Train: [56/100][334/1557] Data 0.004 (0.164) Batch 0.779 (1.136) Remain 21:59:41 loss: 0.5067 Lr: 0.00228 [2024-02-18 21:24:34,983 INFO misc.py line 119 87073] Train: [56/100][335/1557] Data 0.004 (0.163) Batch 0.688 (1.134) Remain 21:58:06 loss: 0.2223 Lr: 0.00228 [2024-02-18 21:24:36,207 INFO misc.py line 119 87073] Train: [56/100][336/1557] Data 0.010 (0.163) Batch 1.223 (1.134) Remain 21:58:24 loss: 0.0994 Lr: 0.00228 [2024-02-18 21:24:36,988 INFO misc.py line 119 87073] Train: [56/100][337/1557] Data 0.011 (0.162) Batch 0.788 (1.133) Remain 21:57:10 loss: 0.8497 Lr: 0.00228 [2024-02-18 21:24:37,876 INFO misc.py line 119 87073] Train: [56/100][338/1557] Data 0.005 (0.162) Batch 0.889 (1.133) Remain 21:56:18 loss: 0.2692 Lr: 0.00228 [2024-02-18 21:24:39,000 INFO misc.py line 119 87073] Train: 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Batch 1.142 (1.163) Remain 22:31:11 loss: 0.6662 Lr: 0.00228 [2024-02-18 21:24:56,972 INFO misc.py line 119 87073] Train: [56/100][346/1557] Data 0.009 (0.185) Batch 0.861 (1.162) Remain 22:30:08 loss: 0.1804 Lr: 0.00228 [2024-02-18 21:24:58,150 INFO misc.py line 119 87073] Train: [56/100][347/1557] Data 0.004 (0.184) Batch 1.179 (1.162) Remain 22:30:11 loss: 0.1666 Lr: 0.00228 [2024-02-18 21:24:58,916 INFO misc.py line 119 87073] Train: [56/100][348/1557] Data 0.003 (0.184) Batch 0.765 (1.161) Remain 22:28:49 loss: 0.1335 Lr: 0.00228 [2024-02-18 21:24:59,680 INFO misc.py line 119 87073] Train: [56/100][349/1557] Data 0.005 (0.183) Batch 0.755 (1.160) Remain 22:27:26 loss: 0.1699 Lr: 0.00228 [2024-02-18 21:25:00,872 INFO misc.py line 119 87073] Train: [56/100][350/1557] Data 0.012 (0.183) Batch 1.192 (1.160) Remain 22:27:32 loss: 0.1118 Lr: 0.00228 [2024-02-18 21:25:01,732 INFO misc.py line 119 87073] Train: [56/100][351/1557] Data 0.013 (0.182) Batch 0.868 (1.159) Remain 22:26:32 loss: 0.2473 Lr: 0.00228 [2024-02-18 21:25:03,040 INFO misc.py line 119 87073] Train: [56/100][352/1557] Data 0.004 (0.182) Batch 1.308 (1.159) Remain 22:27:01 loss: 0.2772 Lr: 0.00228 [2024-02-18 21:25:03,943 INFO misc.py line 119 87073] Train: [56/100][353/1557] Data 0.005 (0.181) Batch 0.904 (1.159) Remain 22:26:09 loss: 0.4800 Lr: 0.00228 [2024-02-18 21:25:04,918 INFO misc.py line 119 87073] Train: [56/100][354/1557] Data 0.004 (0.181) Batch 0.975 (1.158) Remain 22:25:31 loss: 0.4937 Lr: 0.00228 [2024-02-18 21:25:07,270 INFO misc.py line 119 87073] Train: [56/100][355/1557] Data 1.255 (0.184) Batch 2.346 (1.161) Remain 22:29:25 loss: 0.2214 Lr: 0.00228 [2024-02-18 21:25:08,068 INFO misc.py line 119 87073] Train: [56/100][356/1557] Data 0.011 (0.183) Batch 0.801 (1.160) Remain 22:28:13 loss: 0.2259 Lr: 0.00228 [2024-02-18 21:25:09,377 INFO misc.py line 119 87073] Train: [56/100][357/1557] Data 0.007 (0.183) Batch 1.311 (1.161) Remain 22:28:41 loss: 0.1324 Lr: 0.00228 [2024-02-18 21:25:10,253 INFO misc.py line 119 87073] Train: [56/100][358/1557] Data 0.006 (0.182) Batch 0.877 (1.160) Remain 22:27:44 loss: 0.1327 Lr: 0.00228 [2024-02-18 21:25:11,279 INFO misc.py line 119 87073] Train: [56/100][359/1557] Data 0.005 (0.182) Batch 1.027 (1.160) Remain 22:27:17 loss: 0.3821 Lr: 0.00228 [2024-02-18 21:25:12,253 INFO misc.py line 119 87073] Train: [56/100][360/1557] Data 0.003 (0.181) Batch 0.974 (1.159) Remain 22:26:40 loss: 0.2250 Lr: 0.00228 [2024-02-18 21:25:13,287 INFO misc.py line 119 87073] Train: [56/100][361/1557] Data 0.005 (0.181) Batch 1.033 (1.159) Remain 22:26:14 loss: 0.4598 Lr: 0.00228 [2024-02-18 21:25:14,082 INFO misc.py line 119 87073] Train: [56/100][362/1557] Data 0.006 (0.181) Batch 0.794 (1.158) Remain 22:25:02 loss: 0.2317 Lr: 0.00228 [2024-02-18 21:25:14,887 INFO misc.py line 119 87073] Train: [56/100][363/1557] Data 0.006 (0.180) Batch 0.806 (1.157) Remain 22:23:53 loss: 0.2923 Lr: 0.00228 [2024-02-18 21:25:15,992 INFO misc.py line 119 87073] Train: [56/100][364/1557] Data 0.005 (0.180) Batch 1.105 (1.157) Remain 22:23:42 loss: 0.1299 Lr: 0.00228 [2024-02-18 21:25:16,783 INFO misc.py line 119 87073] Train: [56/100][365/1557] Data 0.005 (0.179) Batch 0.790 (1.156) Remain 22:22:30 loss: 0.1642 Lr: 0.00228 [2024-02-18 21:25:17,721 INFO misc.py line 119 87073] Train: [56/100][366/1557] Data 0.006 (0.179) Batch 0.940 (1.155) Remain 22:21:47 loss: 0.4246 Lr: 0.00228 [2024-02-18 21:25:18,674 INFO misc.py line 119 87073] Train: [56/100][367/1557] Data 0.004 (0.178) Batch 0.952 (1.155) Remain 22:21:07 loss: 0.4667 Lr: 0.00228 [2024-02-18 21:25:19,677 INFO misc.py line 119 87073] Train: [56/100][368/1557] Data 0.005 (0.178) Batch 0.999 (1.154) Remain 22:20:36 loss: 0.5555 Lr: 0.00228 [2024-02-18 21:25:20,365 INFO misc.py line 119 87073] Train: [56/100][369/1557] Data 0.009 (0.177) Batch 0.693 (1.153) Remain 22:19:07 loss: 0.3818 Lr: 0.00228 [2024-02-18 21:25:21,255 INFO misc.py line 119 87073] Train: [56/100][370/1557] Data 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Batch 0.891 (1.165) Remain 22:32:25 loss: 0.2722 Lr: 0.00227 [2024-02-18 21:26:03,025 INFO misc.py line 119 87073] Train: [56/100][402/1557] Data 0.005 (0.186) Batch 0.984 (1.164) Remain 22:31:52 loss: 0.3600 Lr: 0.00227 [2024-02-18 21:26:04,391 INFO misc.py line 119 87073] Train: [56/100][403/1557] Data 0.024 (0.186) Batch 1.378 (1.165) Remain 22:32:28 loss: 0.3194 Lr: 0.00227 [2024-02-18 21:26:05,166 INFO misc.py line 119 87073] Train: [56/100][404/1557] Data 0.012 (0.185) Batch 0.782 (1.164) Remain 22:31:20 loss: 0.2264 Lr: 0.00227 [2024-02-18 21:26:05,875 INFO misc.py line 119 87073] Train: [56/100][405/1557] Data 0.004 (0.185) Batch 0.704 (1.163) Remain 22:29:59 loss: 0.3528 Lr: 0.00227 [2024-02-18 21:26:06,937 INFO misc.py line 119 87073] Train: [56/100][406/1557] Data 0.009 (0.184) Batch 1.055 (1.163) Remain 22:29:40 loss: 0.1210 Lr: 0.00227 [2024-02-18 21:26:07,783 INFO misc.py line 119 87073] Train: [56/100][407/1557] Data 0.016 (0.184) Batch 0.859 (1.162) Remain 22:28:46 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Batch 1.082 (1.164) Remain 22:30:40 loss: 0.7136 Lr: 0.00227 [2024-02-18 21:27:08,023 INFO misc.py line 119 87073] Train: [56/100][458/1557] Data 0.004 (0.185) Batch 1.030 (1.164) Remain 22:30:18 loss: 0.1081 Lr: 0.00227 [2024-02-18 21:27:08,941 INFO misc.py line 119 87073] Train: [56/100][459/1557] Data 0.005 (0.185) Batch 0.920 (1.163) Remain 22:29:40 loss: 0.3399 Lr: 0.00227 [2024-02-18 21:27:09,617 INFO misc.py line 119 87073] Train: [56/100][460/1557] Data 0.004 (0.185) Batch 0.674 (1.162) Remain 22:28:24 loss: 0.2719 Lr: 0.00227 [2024-02-18 21:27:10,396 INFO misc.py line 119 87073] Train: [56/100][461/1557] Data 0.006 (0.184) Batch 0.781 (1.162) Remain 22:27:25 loss: 0.2822 Lr: 0.00227 [2024-02-18 21:27:11,525 INFO misc.py line 119 87073] Train: [56/100][462/1557] Data 0.004 (0.184) Batch 1.129 (1.161) Remain 22:27:19 loss: 0.1715 Lr: 0.00227 [2024-02-18 21:27:12,591 INFO misc.py line 119 87073] Train: [56/100][463/1557] Data 0.005 (0.183) Batch 1.065 (1.161) Remain 22:27:03 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22:14:25 loss: 0.1634 Lr: 0.00227 [2024-02-18 21:27:37,324 INFO misc.py line 119 87073] Train: [56/100][489/1557] Data 0.004 (0.174) Batch 0.775 (1.150) Remain 22:13:30 loss: 0.1607 Lr: 0.00227 [2024-02-18 21:27:38,424 INFO misc.py line 119 87073] Train: [56/100][490/1557] Data 0.016 (0.174) Batch 1.101 (1.150) Remain 22:13:21 loss: 0.0922 Lr: 0.00227 [2024-02-18 21:27:39,331 INFO misc.py line 119 87073] Train: [56/100][491/1557] Data 0.016 (0.173) Batch 0.918 (1.149) Remain 22:12:47 loss: 0.1836 Lr: 0.00227 [2024-02-18 21:27:40,206 INFO misc.py line 119 87073] Train: [56/100][492/1557] Data 0.004 (0.173) Batch 0.875 (1.149) Remain 22:12:07 loss: 0.3067 Lr: 0.00227 [2024-02-18 21:27:41,167 INFO misc.py line 119 87073] Train: [56/100][493/1557] Data 0.004 (0.173) Batch 0.960 (1.148) Remain 22:11:39 loss: 0.5417 Lr: 0.00227 [2024-02-18 21:27:42,070 INFO misc.py line 119 87073] Train: [56/100][494/1557] Data 0.005 (0.172) Batch 0.904 (1.148) Remain 22:11:03 loss: 0.6577 Lr: 0.00227 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line 119 87073] Train: [56/100][501/1557] Data 0.004 (0.170) Batch 1.049 (1.145) Remain 22:07:31 loss: 0.2547 Lr: 0.00227 [2024-02-18 21:27:49,370 INFO misc.py line 119 87073] Train: [56/100][502/1557] Data 0.004 (0.170) Batch 0.726 (1.144) Remain 22:06:31 loss: 0.3627 Lr: 0.00227 [2024-02-18 21:27:50,124 INFO misc.py line 119 87073] Train: [56/100][503/1557] Data 0.004 (0.169) Batch 0.739 (1.143) Remain 22:05:34 loss: 0.3408 Lr: 0.00227 [2024-02-18 21:27:51,293 INFO misc.py line 119 87073] Train: [56/100][504/1557] Data 0.019 (0.169) Batch 1.173 (1.143) Remain 22:05:37 loss: 0.1062 Lr: 0.00227 [2024-02-18 21:27:52,193 INFO misc.py line 119 87073] Train: [56/100][505/1557] Data 0.016 (0.169) Batch 0.910 (1.143) Remain 22:05:04 loss: 0.2415 Lr: 0.00227 [2024-02-18 21:27:53,052 INFO misc.py line 119 87073] Train: [56/100][506/1557] Data 0.005 (0.169) Batch 0.860 (1.142) Remain 22:04:23 loss: 0.6065 Lr: 0.00227 [2024-02-18 21:27:54,055 INFO misc.py line 119 87073] Train: 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Batch 0.984 (1.162) Remain 22:27:23 loss: 0.2782 Lr: 0.00227 [2024-02-18 21:28:12,057 INFO misc.py line 119 87073] Train: [56/100][514/1557] Data 0.003 (0.186) Batch 0.835 (1.162) Remain 22:26:37 loss: 0.1465 Lr: 0.00227 [2024-02-18 21:28:12,878 INFO misc.py line 119 87073] Train: [56/100][515/1557] Data 0.004 (0.185) Batch 0.815 (1.161) Remain 22:25:49 loss: 0.2930 Lr: 0.00227 [2024-02-18 21:28:13,596 INFO misc.py line 119 87073] Train: [56/100][516/1557] Data 0.010 (0.185) Batch 0.722 (1.160) Remain 22:24:48 loss: 0.1589 Lr: 0.00227 [2024-02-18 21:28:14,337 INFO misc.py line 119 87073] Train: [56/100][517/1557] Data 0.005 (0.185) Batch 0.735 (1.159) Remain 22:23:49 loss: 0.3938 Lr: 0.00227 [2024-02-18 21:28:15,506 INFO misc.py line 119 87073] Train: [56/100][518/1557] Data 0.011 (0.184) Batch 1.175 (1.159) Remain 22:23:50 loss: 0.1235 Lr: 0.00227 [2024-02-18 21:28:16,474 INFO misc.py line 119 87073] Train: [56/100][519/1557] Data 0.005 (0.184) Batch 0.970 (1.159) Remain 22:23:24 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21:28:22,923 INFO misc.py line 119 87073] Train: [56/100][526/1557] Data 0.048 (0.182) Batch 0.954 (1.156) Remain 22:19:34 loss: 0.2316 Lr: 0.00227 [2024-02-18 21:28:23,799 INFO misc.py line 119 87073] Train: [56/100][527/1557] Data 0.004 (0.181) Batch 0.875 (1.155) Remain 22:18:56 loss: 0.4312 Lr: 0.00227 [2024-02-18 21:28:24,780 INFO misc.py line 119 87073] Train: [56/100][528/1557] Data 0.005 (0.181) Batch 0.981 (1.155) Remain 22:18:32 loss: 0.5657 Lr: 0.00227 [2024-02-18 21:28:26,095 INFO misc.py line 119 87073] Train: [56/100][529/1557] Data 0.006 (0.181) Batch 1.271 (1.155) Remain 22:18:46 loss: 0.2509 Lr: 0.00227 [2024-02-18 21:28:28,251 INFO misc.py line 119 87073] Train: [56/100][530/1557] Data 1.257 (0.183) Batch 2.200 (1.157) Remain 22:21:02 loss: 0.2280 Lr: 0.00227 [2024-02-18 21:28:29,073 INFO misc.py line 119 87073] Train: [56/100][531/1557] Data 0.005 (0.182) Batch 0.823 (1.157) Remain 22:20:17 loss: 0.3093 Lr: 0.00227 [2024-02-18 21:28:30,235 INFO misc.py line 119 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line 119 87073] Train: [56/100][557/1557] Data 0.013 (0.174) Batch 0.820 (1.148) Remain 22:09:43 loss: 0.5552 Lr: 0.00227 [2024-02-18 21:28:55,116 INFO misc.py line 119 87073] Train: [56/100][558/1557] Data 0.004 (0.174) Batch 0.790 (1.147) Remain 22:08:57 loss: 0.1945 Lr: 0.00227 [2024-02-18 21:28:55,854 INFO misc.py line 119 87073] Train: [56/100][559/1557] Data 0.004 (0.174) Batch 0.728 (1.146) Remain 22:08:03 loss: 0.1901 Lr: 0.00227 [2024-02-18 21:28:57,057 INFO misc.py line 119 87073] Train: [56/100][560/1557] Data 0.015 (0.173) Batch 1.202 (1.147) Remain 22:08:09 loss: 0.0997 Lr: 0.00227 [2024-02-18 21:28:57,938 INFO misc.py line 119 87073] Train: [56/100][561/1557] Data 0.016 (0.173) Batch 0.892 (1.146) Remain 22:07:36 loss: 0.4926 Lr: 0.00227 [2024-02-18 21:28:58,843 INFO misc.py line 119 87073] Train: [56/100][562/1557] Data 0.005 (0.173) Batch 0.906 (1.146) Remain 22:07:05 loss: 0.4309 Lr: 0.00227 [2024-02-18 21:28:59,928 INFO misc.py line 119 87073] Train: 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21:29:29,605 INFO misc.py line 119 87073] Train: [56/100][582/1557] Data 0.014 (0.185) Batch 1.034 (1.159) Remain 22:22:23 loss: 0.3393 Lr: 0.00226 [2024-02-18 21:29:30,568 INFO misc.py line 119 87073] Train: [56/100][583/1557] Data 0.015 (0.184) Batch 0.974 (1.159) Remain 22:21:59 loss: 0.5617 Lr: 0.00226 [2024-02-18 21:29:31,553 INFO misc.py line 119 87073] Train: [56/100][584/1557] Data 0.004 (0.184) Batch 0.985 (1.159) Remain 22:21:38 loss: 0.1226 Lr: 0.00226 [2024-02-18 21:29:32,459 INFO misc.py line 119 87073] Train: [56/100][585/1557] Data 0.004 (0.184) Batch 0.906 (1.158) Remain 22:21:06 loss: 0.4874 Lr: 0.00226 [2024-02-18 21:29:33,193 INFO misc.py line 119 87073] Train: [56/100][586/1557] Data 0.004 (0.184) Batch 0.725 (1.157) Remain 22:20:14 loss: 0.2783 Lr: 0.00226 [2024-02-18 21:29:33,901 INFO misc.py line 119 87073] Train: [56/100][587/1557] Data 0.013 (0.183) Batch 0.716 (1.157) Remain 22:19:20 loss: 0.4951 Lr: 0.00226 [2024-02-18 21:29:35,041 INFO misc.py line 119 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line 119 87073] Train: [56/100][613/1557] Data 0.011 (0.176) Batch 0.975 (1.149) Remain 22:09:42 loss: 0.3357 Lr: 0.00226 [2024-02-18 21:29:59,969 INFO misc.py line 119 87073] Train: [56/100][614/1557] Data 0.003 (0.176) Batch 0.805 (1.148) Remain 22:09:02 loss: 0.0407 Lr: 0.00226 [2024-02-18 21:30:00,730 INFO misc.py line 119 87073] Train: [56/100][615/1557] Data 0.004 (0.175) Batch 0.754 (1.148) Remain 22:08:16 loss: 0.2610 Lr: 0.00226 [2024-02-18 21:30:01,940 INFO misc.py line 119 87073] Train: [56/100][616/1557] Data 0.011 (0.175) Batch 1.204 (1.148) Remain 22:08:21 loss: 0.1027 Lr: 0.00226 [2024-02-18 21:30:02,945 INFO misc.py line 119 87073] Train: [56/100][617/1557] Data 0.017 (0.175) Batch 1.010 (1.147) Remain 22:08:05 loss: 0.1889 Lr: 0.00226 [2024-02-18 21:30:03,983 INFO misc.py line 119 87073] Train: [56/100][618/1557] Data 0.012 (0.175) Batch 1.035 (1.147) Remain 22:07:51 loss: 0.3524 Lr: 0.00226 [2024-02-18 21:30:05,215 INFO misc.py line 119 87073] Train: 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Batch 1.011 (1.164) Remain 22:27:18 loss: 0.5902 Lr: 0.00226 [2024-02-18 21:30:23,487 INFO misc.py line 119 87073] Train: [56/100][626/1557] Data 0.005 (0.187) Batch 0.961 (1.164) Remain 22:26:54 loss: 0.4044 Lr: 0.00226 [2024-02-18 21:30:24,412 INFO misc.py line 119 87073] Train: [56/100][627/1557] Data 0.004 (0.187) Batch 0.924 (1.163) Remain 22:26:26 loss: 0.7539 Lr: 0.00226 [2024-02-18 21:30:25,136 INFO misc.py line 119 87073] Train: [56/100][628/1557] Data 0.004 (0.186) Batch 0.724 (1.163) Remain 22:25:36 loss: 0.1750 Lr: 0.00226 [2024-02-18 21:30:25,905 INFO misc.py line 119 87073] Train: [56/100][629/1557] Data 0.004 (0.186) Batch 0.769 (1.162) Remain 22:24:51 loss: 0.4792 Lr: 0.00226 [2024-02-18 21:30:27,112 INFO misc.py line 119 87073] Train: [56/100][630/1557] Data 0.004 (0.186) Batch 1.206 (1.162) Remain 22:24:55 loss: 0.2605 Lr: 0.00226 [2024-02-18 21:30:28,168 INFO misc.py line 119 87073] Train: [56/100][631/1557] Data 0.006 (0.185) Batch 1.058 (1.162) Remain 22:24:42 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Batch 0.931 (1.165) Remain 22:24:47 loss: 0.2209 Lr: 0.00225 [2024-02-18 21:33:39,507 INFO misc.py line 119 87073] Train: [56/100][794/1557] Data 0.003 (0.186) Batch 0.885 (1.164) Remain 22:24:21 loss: 0.1797 Lr: 0.00225 [2024-02-18 21:33:40,563 INFO misc.py line 119 87073] Train: [56/100][795/1557] Data 0.014 (0.186) Batch 1.058 (1.164) Remain 22:24:11 loss: 0.5835 Lr: 0.00225 [2024-02-18 21:33:41,315 INFO misc.py line 119 87073] Train: [56/100][796/1557] Data 0.012 (0.186) Batch 0.760 (1.164) Remain 22:23:34 loss: 0.2669 Lr: 0.00225 [2024-02-18 21:33:42,087 INFO misc.py line 119 87073] Train: [56/100][797/1557] Data 0.004 (0.185) Batch 0.749 (1.163) Remain 22:22:57 loss: 0.2081 Lr: 0.00225 [2024-02-18 21:33:43,245 INFO misc.py line 119 87073] Train: [56/100][798/1557] Data 0.026 (0.185) Batch 1.162 (1.163) Remain 22:22:56 loss: 0.1278 Lr: 0.00225 [2024-02-18 21:33:44,314 INFO misc.py line 119 87073] Train: [56/100][799/1557] Data 0.022 (0.185) Batch 1.079 (1.163) Remain 22:22:47 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line 119 87073] Train: [56/100][837/1557] Data 0.004 (0.177) Batch 1.121 (1.154) Remain 22:11:10 loss: 0.6292 Lr: 0.00225 [2024-02-18 21:34:21,305 INFO misc.py line 119 87073] Train: [56/100][838/1557] Data 0.004 (0.177) Batch 0.663 (1.153) Remain 22:10:28 loss: 0.2807 Lr: 0.00225 [2024-02-18 21:34:22,108 INFO misc.py line 119 87073] Train: [56/100][839/1557] Data 0.004 (0.176) Batch 0.774 (1.153) Remain 22:09:56 loss: 0.2181 Lr: 0.00225 [2024-02-18 21:34:23,291 INFO misc.py line 119 87073] Train: [56/100][840/1557] Data 0.032 (0.176) Batch 1.204 (1.153) Remain 22:09:59 loss: 0.1265 Lr: 0.00225 [2024-02-18 21:34:24,060 INFO misc.py line 119 87073] Train: [56/100][841/1557] Data 0.013 (0.176) Batch 0.777 (1.152) Remain 22:09:27 loss: 0.3071 Lr: 0.00225 [2024-02-18 21:34:25,182 INFO misc.py line 119 87073] Train: [56/100][842/1557] Data 0.005 (0.176) Batch 1.123 (1.152) Remain 22:09:23 loss: 0.5357 Lr: 0.00225 [2024-02-18 21:34:26,103 INFO misc.py line 119 87073] Train: 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21:34:54,742 INFO misc.py line 119 87073] Train: [56/100][862/1557] Data 0.007 (0.184) Batch 0.976 (1.160) Remain 22:17:45 loss: 0.2017 Lr: 0.00225 [2024-02-18 21:34:55,730 INFO misc.py line 119 87073] Train: [56/100][863/1557] Data 0.003 (0.184) Batch 0.987 (1.160) Remain 22:17:30 loss: 0.2811 Lr: 0.00225 [2024-02-18 21:34:56,912 INFO misc.py line 119 87073] Train: [56/100][864/1557] Data 0.005 (0.184) Batch 1.182 (1.160) Remain 22:17:30 loss: 0.1665 Lr: 0.00225 [2024-02-18 21:34:58,040 INFO misc.py line 119 87073] Train: [56/100][865/1557] Data 0.004 (0.184) Batch 1.128 (1.160) Remain 22:17:27 loss: 0.5481 Lr: 0.00225 [2024-02-18 21:34:58,736 INFO misc.py line 119 87073] Train: [56/100][866/1557] Data 0.004 (0.183) Batch 0.694 (1.159) Remain 22:16:48 loss: 0.2475 Lr: 0.00225 [2024-02-18 21:34:59,533 INFO misc.py line 119 87073] Train: [56/100][867/1557] Data 0.006 (0.183) Batch 0.789 (1.159) Remain 22:16:17 loss: 0.2343 Lr: 0.00225 [2024-02-18 21:35:00,670 INFO misc.py line 119 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line 119 87073] Train: [56/100][893/1557] Data 0.005 (0.180) Batch 0.806 (1.154) Remain 22:10:54 loss: 0.5261 Lr: 0.00225 [2024-02-18 21:35:26,582 INFO misc.py line 119 87073] Train: [56/100][894/1557] Data 0.010 (0.179) Batch 0.713 (1.154) Remain 22:10:18 loss: 0.2388 Lr: 0.00225 [2024-02-18 21:35:27,354 INFO misc.py line 119 87073] Train: [56/100][895/1557] Data 0.004 (0.179) Batch 0.762 (1.153) Remain 22:09:47 loss: 0.1102 Lr: 0.00225 [2024-02-18 21:35:28,511 INFO misc.py line 119 87073] Train: [56/100][896/1557] Data 0.013 (0.179) Batch 1.159 (1.153) Remain 22:09:46 loss: 0.0970 Lr: 0.00225 [2024-02-18 21:35:29,491 INFO misc.py line 119 87073] Train: [56/100][897/1557] Data 0.012 (0.179) Batch 0.988 (1.153) Remain 22:09:32 loss: 0.1894 Lr: 0.00225 [2024-02-18 21:35:30,444 INFO misc.py line 119 87073] Train: [56/100][898/1557] Data 0.004 (0.179) Batch 0.953 (1.153) Remain 22:09:15 loss: 0.6889 Lr: 0.00225 [2024-02-18 21:35:31,327 INFO misc.py line 119 87073] Train: 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Batch 1.069 (1.163) Remain 22:20:59 loss: 0.5891 Lr: 0.00225 [2024-02-18 21:35:48,694 INFO misc.py line 119 87073] Train: [56/100][906/1557] Data 0.004 (0.188) Batch 0.894 (1.163) Remain 22:20:37 loss: 0.2330 Lr: 0.00225 [2024-02-18 21:35:49,710 INFO misc.py line 119 87073] Train: [56/100][907/1557] Data 0.003 (0.187) Batch 1.016 (1.163) Remain 22:20:25 loss: 0.5881 Lr: 0.00225 [2024-02-18 21:35:50,464 INFO misc.py line 119 87073] Train: [56/100][908/1557] Data 0.003 (0.187) Batch 0.746 (1.162) Remain 22:19:52 loss: 0.2150 Lr: 0.00225 [2024-02-18 21:35:51,210 INFO misc.py line 119 87073] Train: [56/100][909/1557] Data 0.012 (0.187) Batch 0.754 (1.162) Remain 22:19:19 loss: 0.1686 Lr: 0.00225 [2024-02-18 21:35:52,344 INFO misc.py line 119 87073] Train: [56/100][910/1557] Data 0.004 (0.187) Batch 1.133 (1.162) Remain 22:19:16 loss: 0.2012 Lr: 0.00225 [2024-02-18 21:35:53,255 INFO misc.py line 119 87073] Train: [56/100][911/1557] Data 0.004 (0.187) Batch 0.911 (1.162) Remain 22:18:56 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misc.py line 119 87073] Train: [56/100][1222/1557] Data 0.004 (0.181) Batch 0.956 (1.154) Remain 22:04:04 loss: 0.3658 Lr: 0.00223 [2024-02-18 21:41:45,935 INFO misc.py line 119 87073] Train: [56/100][1223/1557] Data 0.004 (0.181) Batch 0.774 (1.154) Remain 22:03:41 loss: 0.2249 Lr: 0.00223 [2024-02-18 21:41:46,681 INFO misc.py line 119 87073] Train: [56/100][1224/1557] Data 0.015 (0.181) Batch 0.757 (1.153) Remain 22:03:18 loss: 0.1808 Lr: 0.00223 [2024-02-18 21:41:47,956 INFO misc.py line 119 87073] Train: [56/100][1225/1557] Data 0.004 (0.181) Batch 1.271 (1.153) Remain 22:03:23 loss: 0.2134 Lr: 0.00223 [2024-02-18 21:41:49,051 INFO misc.py line 119 87073] Train: [56/100][1226/1557] Data 0.008 (0.180) Batch 1.092 (1.153) Remain 22:03:19 loss: 0.1716 Lr: 0.00223 [2024-02-18 21:41:50,341 INFO misc.py line 119 87073] Train: [56/100][1227/1557] Data 0.011 (0.180) Batch 1.297 (1.154) Remain 22:03:26 loss: 0.5468 Lr: 0.00223 [2024-02-18 21:41:51,356 INFO misc.py line 119 87073] Train: 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Remain 22:13:36 loss: 0.7532 Lr: 0.00223 [2024-02-18 21:42:17,488 INFO misc.py line 119 87073] Train: [56/100][1241/1557] Data 0.006 (0.188) Batch 0.905 (1.162) Remain 22:13:21 loss: 0.7353 Lr: 0.00223 [2024-02-18 21:42:18,461 INFO misc.py line 119 87073] Train: [56/100][1242/1557] Data 0.004 (0.188) Batch 0.971 (1.162) Remain 22:13:09 loss: 0.3407 Lr: 0.00223 [2024-02-18 21:42:19,463 INFO misc.py line 119 87073] Train: [56/100][1243/1557] Data 0.005 (0.188) Batch 1.003 (1.162) Remain 22:12:59 loss: 0.2599 Lr: 0.00223 [2024-02-18 21:42:20,164 INFO misc.py line 119 87073] Train: [56/100][1244/1557] Data 0.004 (0.187) Batch 0.692 (1.162) Remain 22:12:32 loss: 0.2177 Lr: 0.00223 [2024-02-18 21:42:20,923 INFO misc.py line 119 87073] Train: [56/100][1245/1557] Data 0.013 (0.187) Batch 0.768 (1.161) Remain 22:12:09 loss: 0.1438 Lr: 0.00223 [2024-02-18 21:42:22,058 INFO misc.py line 119 87073] Train: [56/100][1246/1557] Data 0.004 (0.187) Batch 1.134 (1.161) Remain 22:12:06 loss: 0.1961 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1253/1557] Data 0.012 (0.186) Batch 1.266 (1.160) Remain 22:10:38 loss: 0.2905 Lr: 0.00223 [2024-02-18 21:42:29,749 INFO misc.py line 119 87073] Train: [56/100][1254/1557] Data 0.011 (0.186) Batch 1.021 (1.160) Remain 22:10:29 loss: 0.5741 Lr: 0.00223 [2024-02-18 21:42:30,866 INFO misc.py line 119 87073] Train: [56/100][1255/1557] Data 0.005 (0.186) Batch 1.108 (1.160) Remain 22:10:25 loss: 0.1005 Lr: 0.00223 [2024-02-18 21:42:31,897 INFO misc.py line 119 87073] Train: [56/100][1256/1557] Data 0.015 (0.186) Batch 1.035 (1.160) Remain 22:10:17 loss: 0.4588 Lr: 0.00223 [2024-02-18 21:42:32,862 INFO misc.py line 119 87073] Train: [56/100][1257/1557] Data 0.010 (0.186) Batch 0.972 (1.160) Remain 22:10:05 loss: 0.3203 Lr: 0.00223 [2024-02-18 21:42:33,608 INFO misc.py line 119 87073] Train: [56/100][1258/1557] Data 0.004 (0.185) Batch 0.745 (1.160) Remain 22:09:41 loss: 0.2299 Lr: 0.00223 [2024-02-18 21:42:34,344 INFO misc.py line 119 87073] Train: [56/100][1259/1557] Data 0.005 (0.185) Batch 0.732 (1.159) Remain 22:09:17 loss: 0.1634 Lr: 0.00223 [2024-02-18 21:42:35,531 INFO misc.py line 119 87073] Train: [56/100][1260/1557] Data 0.009 (0.185) Batch 1.180 (1.159) Remain 22:09:17 loss: 0.3820 Lr: 0.00223 [2024-02-18 21:42:36,490 INFO misc.py line 119 87073] Train: [56/100][1261/1557] Data 0.015 (0.185) Batch 0.970 (1.159) Remain 22:09:05 loss: 0.9147 Lr: 0.00223 [2024-02-18 21:42:37,334 INFO misc.py line 119 87073] Train: [56/100][1262/1557] Data 0.004 (0.185) Batch 0.844 (1.159) Remain 22:08:47 loss: 0.3510 Lr: 0.00223 [2024-02-18 21:42:38,399 INFO misc.py line 119 87073] Train: [56/100][1263/1557] Data 0.004 (0.185) Batch 1.062 (1.159) Remain 22:08:41 loss: 0.3161 Lr: 0.00223 [2024-02-18 21:42:39,362 INFO misc.py line 119 87073] Train: [56/100][1264/1557] Data 0.006 (0.185) Batch 0.964 (1.159) Remain 22:08:29 loss: 0.3672 Lr: 0.00223 [2024-02-18 21:42:40,098 INFO misc.py line 119 87073] Train: [56/100][1265/1557] Data 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Remain 22:06:49 loss: 0.9269 Lr: 0.00223 [2024-02-18 21:42:46,533 INFO misc.py line 119 87073] Train: [56/100][1272/1557] Data 0.005 (0.183) Batch 0.749 (1.157) Remain 22:06:26 loss: 0.4100 Lr: 0.00223 [2024-02-18 21:42:47,320 INFO misc.py line 119 87073] Train: [56/100][1273/1557] Data 0.004 (0.183) Batch 0.778 (1.157) Remain 22:06:04 loss: 0.2586 Lr: 0.00223 [2024-02-18 21:42:48,446 INFO misc.py line 119 87073] Train: [56/100][1274/1557] Data 0.012 (0.183) Batch 1.131 (1.157) Remain 22:06:02 loss: 0.1117 Lr: 0.00223 [2024-02-18 21:42:49,300 INFO misc.py line 119 87073] Train: [56/100][1275/1557] Data 0.008 (0.183) Batch 0.856 (1.156) Remain 22:05:44 loss: 0.5360 Lr: 0.00223 [2024-02-18 21:42:50,127 INFO misc.py line 119 87073] Train: [56/100][1276/1557] Data 0.005 (0.183) Batch 0.829 (1.156) Remain 22:05:25 loss: 0.4894 Lr: 0.00223 [2024-02-18 21:42:51,282 INFO misc.py line 119 87073] Train: [56/100][1277/1557] Data 0.003 (0.183) Batch 1.154 (1.156) Remain 22:05:24 loss: 0.5398 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1284/1557] Data 0.004 (0.182) Batch 0.876 (1.155) Remain 22:03:46 loss: 0.3733 Lr: 0.00223 [2024-02-18 21:42:58,706 INFO misc.py line 119 87073] Train: [56/100][1285/1557] Data 0.011 (0.182) Batch 1.011 (1.155) Remain 22:03:37 loss: 0.3492 Lr: 0.00223 [2024-02-18 21:42:59,427 INFO misc.py line 119 87073] Train: [56/100][1286/1557] Data 0.006 (0.182) Batch 0.724 (1.154) Remain 22:03:13 loss: 0.1385 Lr: 0.00223 [2024-02-18 21:43:00,103 INFO misc.py line 119 87073] Train: [56/100][1287/1557] Data 0.004 (0.181) Batch 0.671 (1.154) Remain 22:02:46 loss: 0.4849 Lr: 0.00223 [2024-02-18 21:43:01,325 INFO misc.py line 119 87073] Train: [56/100][1288/1557] Data 0.009 (0.181) Batch 1.216 (1.154) Remain 22:02:48 loss: 0.1024 Lr: 0.00223 [2024-02-18 21:43:02,178 INFO misc.py line 119 87073] Train: [56/100][1289/1557] Data 0.016 (0.181) Batch 0.864 (1.154) Remain 22:02:31 loss: 0.8529 Lr: 0.00223 [2024-02-18 21:43:03,232 INFO misc.py line 119 87073] Train: [56/100][1290/1557] Data 0.004 (0.181) Batch 1.053 (1.154) Remain 22:02:25 loss: 0.2655 Lr: 0.00223 [2024-02-18 21:43:04,084 INFO misc.py line 119 87073] Train: [56/100][1291/1557] Data 0.005 (0.181) Batch 0.853 (1.153) Remain 22:02:07 loss: 0.3018 Lr: 0.00223 [2024-02-18 21:43:04,832 INFO misc.py line 119 87073] Train: [56/100][1292/1557] Data 0.004 (0.181) Batch 0.744 (1.153) Remain 22:01:44 loss: 0.1223 Lr: 0.00223 [2024-02-18 21:43:05,612 INFO misc.py line 119 87073] Train: [56/100][1293/1557] Data 0.009 (0.181) Batch 0.784 (1.153) Remain 22:01:24 loss: 0.4017 Lr: 0.00223 [2024-02-18 21:43:06,363 INFO misc.py line 119 87073] Train: [56/100][1294/1557] Data 0.004 (0.180) Batch 0.751 (1.153) Remain 22:01:01 loss: 0.1321 Lr: 0.00223 [2024-02-18 21:43:19,300 INFO misc.py line 119 87073] Train: [56/100][1295/1557] Data 9.633 (0.188) Batch 12.928 (1.162) Remain 22:11:27 loss: 0.1229 Lr: 0.00223 [2024-02-18 21:43:20,211 INFO misc.py line 119 87073] Train: [56/100][1296/1557] Data 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Remain 22:10:03 loss: 0.2783 Lr: 0.00223 [2024-02-18 21:43:26,935 INFO misc.py line 119 87073] Train: [56/100][1303/1557] Data 0.005 (0.187) Batch 0.934 (1.160) Remain 22:09:50 loss: 0.4069 Lr: 0.00223 [2024-02-18 21:43:27,911 INFO misc.py line 119 87073] Train: [56/100][1304/1557] Data 0.003 (0.187) Batch 0.975 (1.160) Remain 22:09:39 loss: 0.3691 Lr: 0.00223 [2024-02-18 21:43:28,918 INFO misc.py line 119 87073] Train: [56/100][1305/1557] Data 0.005 (0.186) Batch 1.007 (1.160) Remain 22:09:30 loss: 0.3647 Lr: 0.00223 [2024-02-18 21:43:29,804 INFO misc.py line 119 87073] Train: [56/100][1306/1557] Data 0.005 (0.186) Batch 0.886 (1.160) Remain 22:09:14 loss: 0.1924 Lr: 0.00223 [2024-02-18 21:43:30,537 INFO misc.py line 119 87073] Train: [56/100][1307/1557] Data 0.003 (0.186) Batch 0.724 (1.160) Remain 22:08:50 loss: 0.3340 Lr: 0.00223 [2024-02-18 21:43:31,338 INFO misc.py line 119 87073] Train: [56/100][1308/1557] Data 0.012 (0.186) Batch 0.809 (1.159) Remain 22:08:31 loss: 0.1772 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1315/1557] Data 0.003 (0.185) Batch 0.781 (1.158) Remain 22:06:49 loss: 0.3059 Lr: 0.00223 [2024-02-18 21:43:38,786 INFO misc.py line 119 87073] Train: [56/100][1316/1557] Data 0.007 (0.185) Batch 1.117 (1.158) Remain 22:06:46 loss: 0.1302 Lr: 0.00223 [2024-02-18 21:43:39,884 INFO misc.py line 119 87073] Train: [56/100][1317/1557] Data 0.008 (0.185) Batch 1.100 (1.158) Remain 22:06:41 loss: 0.3087 Lr: 0.00223 [2024-02-18 21:43:40,786 INFO misc.py line 119 87073] Train: [56/100][1318/1557] Data 0.006 (0.185) Batch 0.905 (1.158) Remain 22:06:27 loss: 0.2080 Lr: 0.00223 [2024-02-18 21:43:41,772 INFO misc.py line 119 87073] Train: [56/100][1319/1557] Data 0.004 (0.184) Batch 0.986 (1.158) Remain 22:06:17 loss: 0.1212 Lr: 0.00223 [2024-02-18 21:43:42,722 INFO misc.py line 119 87073] Train: [56/100][1320/1557] Data 0.003 (0.184) Batch 0.948 (1.157) Remain 22:06:05 loss: 0.5606 Lr: 0.00223 [2024-02-18 21:43:43,480 INFO misc.py line 119 87073] Train: [56/100][1321/1557] Data 0.006 (0.184) Batch 0.759 (1.157) Remain 22:05:43 loss: 0.3591 Lr: 0.00223 [2024-02-18 21:43:44,262 INFO misc.py line 119 87073] Train: [56/100][1322/1557] Data 0.005 (0.184) Batch 0.782 (1.157) Remain 22:05:22 loss: 0.1785 Lr: 0.00223 [2024-02-18 21:43:45,635 INFO misc.py line 119 87073] Train: [56/100][1323/1557] Data 0.005 (0.184) Batch 1.371 (1.157) Remain 22:05:32 loss: 0.0670 Lr: 0.00223 [2024-02-18 21:43:46,661 INFO misc.py line 119 87073] Train: [56/100][1324/1557] Data 0.007 (0.184) Batch 1.022 (1.157) Remain 22:05:24 loss: 0.4347 Lr: 0.00223 [2024-02-18 21:43:47,615 INFO misc.py line 119 87073] Train: [56/100][1325/1557] Data 0.010 (0.184) Batch 0.960 (1.157) Remain 22:05:12 loss: 0.2217 Lr: 0.00223 [2024-02-18 21:43:48,361 INFO misc.py line 119 87073] Train: [56/100][1326/1557] Data 0.004 (0.184) Batch 0.744 (1.156) Remain 22:04:50 loss: 0.3991 Lr: 0.00223 [2024-02-18 21:43:49,303 INFO misc.py line 119 87073] Train: [56/100][1327/1557] Data 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Remain 22:03:06 loss: 0.4485 Lr: 0.00223 [2024-02-18 21:43:55,546 INFO misc.py line 119 87073] Train: [56/100][1334/1557] Data 0.004 (0.182) Batch 0.951 (1.155) Remain 22:02:54 loss: 0.4408 Lr: 0.00223 [2024-02-18 21:43:56,210 INFO misc.py line 119 87073] Train: [56/100][1335/1557] Data 0.006 (0.182) Batch 0.666 (1.154) Remain 22:02:28 loss: 0.2823 Lr: 0.00223 [2024-02-18 21:43:56,960 INFO misc.py line 119 87073] Train: [56/100][1336/1557] Data 0.004 (0.182) Batch 0.746 (1.154) Remain 22:02:05 loss: 0.2021 Lr: 0.00223 [2024-02-18 21:43:58,213 INFO misc.py line 119 87073] Train: [56/100][1337/1557] Data 0.008 (0.182) Batch 1.256 (1.154) Remain 22:02:09 loss: 0.1576 Lr: 0.00222 [2024-02-18 21:43:59,283 INFO misc.py line 119 87073] Train: [56/100][1338/1557] Data 0.005 (0.182) Batch 1.070 (1.154) Remain 22:02:04 loss: 0.4707 Lr: 0.00222 [2024-02-18 21:44:00,374 INFO misc.py line 119 87073] Train: [56/100][1339/1557] Data 0.005 (0.182) Batch 1.086 (1.154) Remain 22:01:59 loss: 0.3170 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1346/1557] Data 0.004 (0.181) Batch 0.882 (1.153) Remain 22:00:32 loss: 0.1597 Lr: 0.00222 [2024-02-18 21:44:07,886 INFO misc.py line 119 87073] Train: [56/100][1347/1557] Data 0.004 (0.181) Batch 0.984 (1.153) Remain 22:00:22 loss: 0.3634 Lr: 0.00222 [2024-02-18 21:44:08,863 INFO misc.py line 119 87073] Train: [56/100][1348/1557] Data 0.005 (0.181) Batch 0.979 (1.153) Remain 22:00:12 loss: 0.6742 Lr: 0.00222 [2024-02-18 21:44:09,618 INFO misc.py line 119 87073] Train: [56/100][1349/1557] Data 0.004 (0.180) Batch 0.755 (1.152) Remain 21:59:51 loss: 0.1491 Lr: 0.00222 [2024-02-18 21:44:10,370 INFO misc.py line 119 87073] Train: [56/100][1350/1557] Data 0.004 (0.180) Batch 0.718 (1.152) Remain 21:59:27 loss: 0.3938 Lr: 0.00222 [2024-02-18 21:44:23,144 INFO misc.py line 119 87073] Train: [56/100][1351/1557] Data 10.259 (0.188) Batch 12.807 (1.161) Remain 22:09:20 loss: 0.1331 Lr: 0.00222 [2024-02-18 21:44:24,093 INFO misc.py line 119 87073] Train: [56/100][1352/1557] Data 0.005 (0.188) Batch 0.950 (1.161) Remain 22:09:09 loss: 0.7849 Lr: 0.00222 [2024-02-18 21:44:24,991 INFO misc.py line 119 87073] Train: [56/100][1353/1557] Data 0.004 (0.188) Batch 0.898 (1.160) Remain 22:08:54 loss: 0.4856 Lr: 0.00222 [2024-02-18 21:44:25,885 INFO misc.py line 119 87073] Train: [56/100][1354/1557] Data 0.003 (0.187) Batch 0.893 (1.160) Remain 22:08:39 loss: 0.3178 Lr: 0.00222 [2024-02-18 21:44:26,937 INFO misc.py line 119 87073] Train: [56/100][1355/1557] Data 0.005 (0.187) Batch 1.052 (1.160) Remain 22:08:33 loss: 0.3133 Lr: 0.00222 [2024-02-18 21:44:27,647 INFO misc.py line 119 87073] Train: [56/100][1356/1557] Data 0.005 (0.187) Batch 0.710 (1.160) Remain 22:08:09 loss: 0.3939 Lr: 0.00222 [2024-02-18 21:44:28,401 INFO misc.py line 119 87073] Train: [56/100][1357/1557] Data 0.005 (0.187) Batch 0.753 (1.160) Remain 22:07:47 loss: 0.2562 Lr: 0.00222 [2024-02-18 21:44:29,568 INFO misc.py line 119 87073] Train: [56/100][1358/1557] Data 0.005 (0.187) Batch 1.168 (1.160) Remain 22:07:46 loss: 0.1862 Lr: 0.00222 [2024-02-18 21:44:30,697 INFO misc.py line 119 87073] Train: [56/100][1359/1557] Data 0.004 (0.187) Batch 1.128 (1.159) Remain 22:07:43 loss: 0.3653 Lr: 0.00222 [2024-02-18 21:44:31,719 INFO misc.py line 119 87073] Train: [56/100][1360/1557] Data 0.005 (0.187) Batch 1.022 (1.159) Remain 22:07:35 loss: 0.9768 Lr: 0.00222 [2024-02-18 21:44:32,800 INFO misc.py line 119 87073] Train: [56/100][1361/1557] Data 0.005 (0.186) Batch 1.081 (1.159) Remain 22:07:30 loss: 0.3308 Lr: 0.00222 [2024-02-18 21:44:33,589 INFO misc.py line 119 87073] Train: [56/100][1362/1557] Data 0.005 (0.186) Batch 0.790 (1.159) Remain 22:07:10 loss: 0.3285 Lr: 0.00222 [2024-02-18 21:44:34,275 INFO misc.py line 119 87073] Train: [56/100][1363/1557] Data 0.004 (0.186) Batch 0.685 (1.159) Remain 22:06:45 loss: 0.1689 Lr: 0.00222 [2024-02-18 21:44:35,053 INFO misc.py line 119 87073] Train: [56/100][1364/1557] Data 0.004 (0.186) Batch 0.777 (1.158) Remain 22:06:25 loss: 0.1725 Lr: 0.00222 [2024-02-18 21:44:36,313 INFO misc.py line 119 87073] Train: [56/100][1365/1557] Data 0.006 (0.186) Batch 1.253 (1.158) Remain 22:06:28 loss: 0.1816 Lr: 0.00222 [2024-02-18 21:44:37,318 INFO misc.py line 119 87073] Train: [56/100][1366/1557] Data 0.013 (0.186) Batch 1.005 (1.158) Remain 22:06:19 loss: 0.8235 Lr: 0.00222 [2024-02-18 21:44:38,176 INFO misc.py line 119 87073] Train: [56/100][1367/1557] Data 0.012 (0.186) Batch 0.867 (1.158) Remain 22:06:04 loss: 0.2957 Lr: 0.00222 [2024-02-18 21:44:39,144 INFO misc.py line 119 87073] Train: [56/100][1368/1557] Data 0.004 (0.186) Batch 0.967 (1.158) Remain 22:05:53 loss: 0.2502 Lr: 0.00222 [2024-02-18 21:44:40,022 INFO misc.py line 119 87073] Train: [56/100][1369/1557] Data 0.005 (0.185) Batch 0.878 (1.158) Remain 22:05:38 loss: 0.5936 Lr: 0.00222 [2024-02-18 21:44:40,710 INFO misc.py line 119 87073] Train: [56/100][1370/1557] Data 0.004 (0.185) Batch 0.687 (1.157) Remain 22:05:13 loss: 0.3792 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1377/1557] Data 0.004 (0.184) Batch 0.756 (1.156) Remain 22:03:41 loss: 0.2209 Lr: 0.00222 [2024-02-18 21:44:47,890 INFO misc.py line 119 87073] Train: [56/100][1378/1557] Data 0.007 (0.184) Batch 0.760 (1.156) Remain 22:03:20 loss: 0.3224 Lr: 0.00222 [2024-02-18 21:44:49,217 INFO misc.py line 119 87073] Train: [56/100][1379/1557] Data 0.005 (0.184) Batch 1.305 (1.156) Remain 22:03:26 loss: 0.0871 Lr: 0.00222 [2024-02-18 21:44:50,068 INFO misc.py line 119 87073] Train: [56/100][1380/1557] Data 0.028 (0.184) Batch 0.875 (1.156) Remain 22:03:11 loss: 0.6920 Lr: 0.00222 [2024-02-18 21:44:50,880 INFO misc.py line 119 87073] Train: [56/100][1381/1557] Data 0.004 (0.184) Batch 0.812 (1.156) Remain 22:02:52 loss: 0.4389 Lr: 0.00222 [2024-02-18 21:44:51,723 INFO misc.py line 119 87073] Train: [56/100][1382/1557] Data 0.004 (0.184) Batch 0.840 (1.155) Remain 22:02:35 loss: 0.2636 Lr: 0.00222 [2024-02-18 21:44:52,633 INFO misc.py line 119 87073] Train: [56/100][1383/1557] Data 0.008 (0.184) Batch 0.913 (1.155) Remain 22:02:22 loss: 0.2833 Lr: 0.00222 [2024-02-18 21:44:53,391 INFO misc.py line 119 87073] Train: [56/100][1384/1557] Data 0.004 (0.183) Batch 0.758 (1.155) Remain 22:02:01 loss: 0.6620 Lr: 0.00222 [2024-02-18 21:44:54,058 INFO misc.py line 119 87073] Train: [56/100][1385/1557] Data 0.004 (0.183) Batch 0.663 (1.155) Remain 22:01:36 loss: 0.1466 Lr: 0.00222 [2024-02-18 21:44:55,175 INFO misc.py line 119 87073] Train: [56/100][1386/1557] Data 0.008 (0.183) Batch 1.111 (1.155) Remain 22:01:32 loss: 0.1437 Lr: 0.00222 [2024-02-18 21:44:56,234 INFO misc.py line 119 87073] Train: [56/100][1387/1557] Data 0.014 (0.183) Batch 1.059 (1.154) Remain 22:01:27 loss: 0.2685 Lr: 0.00222 [2024-02-18 21:44:57,143 INFO misc.py line 119 87073] Train: [56/100][1388/1557] Data 0.015 (0.183) Batch 0.919 (1.154) Remain 22:01:14 loss: 0.6342 Lr: 0.00222 [2024-02-18 21:44:57,985 INFO misc.py line 119 87073] Train: [56/100][1389/1557] Data 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Remain 21:59:47 loss: 0.3289 Lr: 0.00222 [2024-02-18 21:45:04,589 INFO misc.py line 119 87073] Train: [56/100][1396/1557] Data 0.005 (0.182) Batch 0.955 (1.153) Remain 21:59:36 loss: 0.2537 Lr: 0.00222 [2024-02-18 21:45:05,433 INFO misc.py line 119 87073] Train: [56/100][1397/1557] Data 0.005 (0.182) Batch 0.845 (1.153) Remain 21:59:20 loss: 0.4298 Lr: 0.00222 [2024-02-18 21:45:06,243 INFO misc.py line 119 87073] Train: [56/100][1398/1557] Data 0.003 (0.182) Batch 0.806 (1.153) Remain 21:59:02 loss: 0.3171 Lr: 0.00222 [2024-02-18 21:45:07,017 INFO misc.py line 119 87073] Train: [56/100][1399/1557] Data 0.009 (0.182) Batch 0.777 (1.152) Remain 21:58:42 loss: 0.2005 Lr: 0.00222 [2024-02-18 21:45:08,222 INFO misc.py line 119 87073] Train: [56/100][1400/1557] Data 0.005 (0.181) Batch 1.205 (1.152) Remain 21:58:43 loss: 0.1017 Lr: 0.00222 [2024-02-18 21:45:09,044 INFO misc.py line 119 87073] Train: [56/100][1401/1557] Data 0.006 (0.181) Batch 0.823 (1.152) Remain 21:58:26 loss: 0.1775 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1408/1557] Data 0.017 (0.189) Batch 0.865 (1.162) Remain 22:09:10 loss: 0.5447 Lr: 0.00222 [2024-02-18 21:45:31,405 INFO misc.py line 119 87073] Train: [56/100][1409/1557] Data 0.003 (0.189) Batch 0.954 (1.161) Remain 22:08:59 loss: 0.2321 Lr: 0.00222 [2024-02-18 21:45:32,278 INFO misc.py line 119 87073] Train: [56/100][1410/1557] Data 0.005 (0.188) Batch 0.874 (1.161) Remain 22:08:44 loss: 0.4161 Lr: 0.00222 [2024-02-18 21:45:33,227 INFO misc.py line 119 87073] Train: [56/100][1411/1557] Data 0.005 (0.188) Batch 0.950 (1.161) Remain 22:08:32 loss: 0.3152 Lr: 0.00222 [2024-02-18 21:45:33,975 INFO misc.py line 119 87073] Train: [56/100][1412/1557] Data 0.003 (0.188) Batch 0.747 (1.161) Remain 22:08:11 loss: 0.3901 Lr: 0.00222 [2024-02-18 21:45:34,761 INFO misc.py line 119 87073] Train: [56/100][1413/1557] Data 0.005 (0.188) Batch 0.775 (1.161) Remain 22:07:51 loss: 0.1784 Lr: 0.00222 [2024-02-18 21:45:35,863 INFO misc.py line 119 87073] Train: [56/100][1414/1557] Data 0.015 (0.188) Batch 1.104 (1.160) Remain 22:07:47 loss: 0.1229 Lr: 0.00222 [2024-02-18 21:45:36,898 INFO misc.py line 119 87073] Train: [56/100][1415/1557] Data 0.013 (0.188) Batch 1.034 (1.160) Remain 22:07:40 loss: 0.2715 Lr: 0.00222 [2024-02-18 21:45:37,831 INFO misc.py line 119 87073] Train: [56/100][1416/1557] Data 0.014 (0.188) Batch 0.942 (1.160) Remain 22:07:28 loss: 0.4074 Lr: 0.00222 [2024-02-18 21:45:38,710 INFO misc.py line 119 87073] Train: [56/100][1417/1557] Data 0.005 (0.188) Batch 0.880 (1.160) Remain 22:07:13 loss: 0.4081 Lr: 0.00222 [2024-02-18 21:45:39,822 INFO misc.py line 119 87073] Train: [56/100][1418/1557] Data 0.003 (0.187) Batch 1.112 (1.160) Remain 22:07:10 loss: 0.1163 Lr: 0.00222 [2024-02-18 21:45:40,529 INFO misc.py line 119 87073] Train: [56/100][1419/1557] Data 0.004 (0.187) Batch 0.708 (1.160) Remain 22:06:47 loss: 0.1986 Lr: 0.00222 [2024-02-18 21:45:41,317 INFO misc.py line 119 87073] Train: [56/100][1420/1557] Data 0.004 (0.187) Batch 0.781 (1.159) Remain 22:06:27 loss: 0.1535 Lr: 0.00222 [2024-02-18 21:45:42,603 INFO misc.py line 119 87073] Train: [56/100][1421/1557] Data 0.011 (0.187) Batch 1.283 (1.159) Remain 22:06:32 loss: 0.1265 Lr: 0.00222 [2024-02-18 21:45:43,694 INFO misc.py line 119 87073] Train: [56/100][1422/1557] Data 0.014 (0.187) Batch 1.093 (1.159) Remain 22:06:27 loss: 0.5293 Lr: 0.00222 [2024-02-18 21:45:44,528 INFO misc.py line 119 87073] Train: [56/100][1423/1557] Data 0.011 (0.187) Batch 0.842 (1.159) Remain 22:06:11 loss: 0.1841 Lr: 0.00222 [2024-02-18 21:45:45,385 INFO misc.py line 119 87073] Train: [56/100][1424/1557] Data 0.004 (0.187) Batch 0.855 (1.159) Remain 22:05:55 loss: 0.5827 Lr: 0.00222 [2024-02-18 21:45:46,389 INFO misc.py line 119 87073] Train: [56/100][1425/1557] Data 0.005 (0.187) Batch 1.005 (1.159) Remain 22:05:47 loss: 0.1234 Lr: 0.00222 [2024-02-18 21:45:47,170 INFO misc.py line 119 87073] Train: [56/100][1426/1557] Data 0.004 (0.186) Batch 0.780 (1.159) Remain 22:05:27 loss: 0.2349 Lr: 0.00222 [2024-02-18 21:45:47,890 INFO misc.py line 119 87073] Train: [56/100][1427/1557] Data 0.005 (0.186) Batch 0.716 (1.158) Remain 22:05:05 loss: 0.5612 Lr: 0.00222 [2024-02-18 21:45:48,976 INFO misc.py line 119 87073] Train: [56/100][1428/1557] Data 0.010 (0.186) Batch 1.085 (1.158) Remain 22:05:00 loss: 0.1607 Lr: 0.00222 [2024-02-18 21:45:49,954 INFO misc.py line 119 87073] Train: [56/100][1429/1557] Data 0.010 (0.186) Batch 0.985 (1.158) Remain 22:04:50 loss: 0.3176 Lr: 0.00222 [2024-02-18 21:45:50,866 INFO misc.py line 119 87073] Train: [56/100][1430/1557] Data 0.004 (0.186) Batch 0.911 (1.158) Remain 22:04:37 loss: 0.3288 Lr: 0.00222 [2024-02-18 21:45:51,903 INFO misc.py line 119 87073] Train: [56/100][1431/1557] Data 0.003 (0.186) Batch 1.028 (1.158) Remain 22:04:30 loss: 0.3803 Lr: 0.00222 [2024-02-18 21:45:52,786 INFO misc.py line 119 87073] Train: [56/100][1432/1557] Data 0.013 (0.186) Batch 0.890 (1.158) Remain 22:04:16 loss: 0.1865 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1439/1557] Data 0.005 (0.185) Batch 0.827 (1.157) Remain 22:02:50 loss: 0.5707 Lr: 0.00222 [2024-02-18 21:46:00,093 INFO misc.py line 119 87073] Train: [56/100][1440/1557] Data 0.046 (0.185) Batch 0.823 (1.156) Remain 22:02:33 loss: 0.1906 Lr: 0.00222 [2024-02-18 21:46:00,944 INFO misc.py line 119 87073] Train: [56/100][1441/1557] Data 0.005 (0.185) Batch 0.851 (1.156) Remain 22:02:18 loss: 0.2671 Lr: 0.00222 [2024-02-18 21:46:01,975 INFO misc.py line 119 87073] Train: [56/100][1442/1557] Data 0.006 (0.184) Batch 1.028 (1.156) Remain 22:02:10 loss: 0.1337 Lr: 0.00222 [2024-02-18 21:46:02,854 INFO misc.py line 119 87073] Train: [56/100][1443/1557] Data 0.008 (0.184) Batch 0.883 (1.156) Remain 22:01:56 loss: 0.2198 Lr: 0.00222 [2024-02-18 21:46:03,764 INFO misc.py line 119 87073] Train: [56/100][1444/1557] Data 0.004 (0.184) Batch 0.910 (1.156) Remain 22:01:43 loss: 0.3509 Lr: 0.00222 [2024-02-18 21:46:04,765 INFO misc.py line 119 87073] Train: [56/100][1445/1557] Data 0.004 (0.184) Batch 0.994 (1.156) Remain 22:01:35 loss: 0.1620 Lr: 0.00222 [2024-02-18 21:46:05,704 INFO misc.py line 119 87073] Train: [56/100][1446/1557] Data 0.011 (0.184) Batch 0.945 (1.155) Remain 22:01:23 loss: 0.3213 Lr: 0.00222 [2024-02-18 21:46:06,441 INFO misc.py line 119 87073] Train: [56/100][1447/1557] Data 0.005 (0.184) Batch 0.738 (1.155) Remain 22:01:02 loss: 0.2718 Lr: 0.00222 [2024-02-18 21:46:07,208 INFO misc.py line 119 87073] Train: [56/100][1448/1557] Data 0.003 (0.184) Batch 0.755 (1.155) Remain 22:00:42 loss: 0.2751 Lr: 0.00222 [2024-02-18 21:46:08,502 INFO misc.py line 119 87073] Train: [56/100][1449/1557] Data 0.015 (0.184) Batch 1.297 (1.155) Remain 22:00:48 loss: 0.1690 Lr: 0.00222 [2024-02-18 21:46:09,459 INFO misc.py line 119 87073] Train: [56/100][1450/1557] Data 0.012 (0.183) Batch 0.965 (1.155) Remain 22:00:38 loss: 0.1833 Lr: 0.00222 [2024-02-18 21:46:10,403 INFO misc.py line 119 87073] Train: [56/100][1451/1557] Data 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Remain 21:59:17 loss: 0.4751 Lr: 0.00222 [2024-02-18 21:46:16,907 INFO misc.py line 119 87073] Train: [56/100][1458/1557] Data 0.004 (0.182) Batch 0.912 (1.154) Remain 21:59:04 loss: 0.2201 Lr: 0.00222 [2024-02-18 21:46:17,890 INFO misc.py line 119 87073] Train: [56/100][1459/1557] Data 0.004 (0.182) Batch 0.981 (1.153) Remain 21:58:55 loss: 0.5375 Lr: 0.00222 [2024-02-18 21:46:18,908 INFO misc.py line 119 87073] Train: [56/100][1460/1557] Data 0.005 (0.182) Batch 1.016 (1.153) Remain 21:58:47 loss: 0.2787 Lr: 0.00222 [2024-02-18 21:46:19,653 INFO misc.py line 119 87073] Train: [56/100][1461/1557] Data 0.007 (0.182) Batch 0.747 (1.153) Remain 21:58:27 loss: 0.2894 Lr: 0.00222 [2024-02-18 21:46:20,365 INFO misc.py line 119 87073] Train: [56/100][1462/1557] Data 0.005 (0.182) Batch 0.709 (1.153) Remain 21:58:05 loss: 0.4428 Lr: 0.00222 [2024-02-18 21:46:33,063 INFO misc.py line 119 87073] Train: [56/100][1463/1557] Data 9.583 (0.188) Batch 12.701 (1.161) Remain 22:07:06 loss: 0.3404 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1470/1557] Data 0.008 (0.188) Batch 1.202 (1.160) Remain 22:05:59 loss: 0.2040 Lr: 0.00222 [2024-02-18 21:46:40,683 INFO misc.py line 119 87073] Train: [56/100][1471/1557] Data 0.006 (0.187) Batch 0.749 (1.160) Remain 22:05:39 loss: 0.3559 Lr: 0.00222 [2024-02-18 21:46:41,620 INFO misc.py line 119 87073] Train: [56/100][1472/1557] Data 0.005 (0.187) Batch 0.939 (1.159) Remain 22:05:28 loss: 0.5509 Lr: 0.00222 [2024-02-18 21:46:42,630 INFO misc.py line 119 87073] Train: [56/100][1473/1557] Data 0.004 (0.187) Batch 1.001 (1.159) Remain 22:05:19 loss: 0.3759 Lr: 0.00222 [2024-02-18 21:46:43,627 INFO misc.py line 119 87073] Train: [56/100][1474/1557] Data 0.013 (0.187) Batch 0.998 (1.159) Remain 22:05:10 loss: 0.3091 Lr: 0.00222 [2024-02-18 21:46:44,353 INFO misc.py line 119 87073] Train: [56/100][1475/1557] Data 0.012 (0.187) Batch 0.734 (1.159) Remain 22:04:49 loss: 0.1604 Lr: 0.00222 [2024-02-18 21:46:45,018 INFO misc.py line 119 87073] Train: [56/100][1476/1557] Data 0.004 (0.187) Batch 0.664 (1.159) Remain 22:04:25 loss: 0.3498 Lr: 0.00222 [2024-02-18 21:46:46,273 INFO misc.py line 119 87073] Train: [56/100][1477/1557] Data 0.005 (0.187) Batch 1.255 (1.159) Remain 22:04:29 loss: 0.1542 Lr: 0.00222 [2024-02-18 21:46:47,259 INFO misc.py line 119 87073] Train: [56/100][1478/1557] Data 0.005 (0.187) Batch 0.987 (1.159) Remain 22:04:19 loss: 0.5926 Lr: 0.00222 [2024-02-18 21:46:48,564 INFO misc.py line 119 87073] Train: [56/100][1479/1557] Data 0.004 (0.186) Batch 1.298 (1.159) Remain 22:04:25 loss: 0.3606 Lr: 0.00222 [2024-02-18 21:46:49,644 INFO misc.py line 119 87073] Train: [56/100][1480/1557] Data 0.012 (0.186) Batch 1.081 (1.159) Remain 22:04:20 loss: 0.2386 Lr: 0.00222 [2024-02-18 21:46:50,766 INFO misc.py line 119 87073] Train: [56/100][1481/1557] Data 0.010 (0.186) Batch 1.119 (1.159) Remain 22:04:17 loss: 0.1737 Lr: 0.00222 [2024-02-18 21:46:51,501 INFO misc.py line 119 87073] Train: [56/100][1482/1557] Data 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Remain 22:03:34 loss: 0.3762 Lr: 0.00222 [2024-02-18 21:46:58,893 INFO misc.py line 119 87073] Train: [56/100][1489/1557] Data 0.013 (0.185) Batch 0.788 (1.158) Remain 22:03:15 loss: 0.1954 Lr: 0.00222 [2024-02-18 21:46:59,684 INFO misc.py line 119 87073] Train: [56/100][1490/1557] Data 0.005 (0.185) Batch 0.794 (1.158) Remain 22:02:57 loss: 0.2045 Lr: 0.00222 [2024-02-18 21:47:00,949 INFO misc.py line 119 87073] Train: [56/100][1491/1557] Data 0.003 (0.185) Batch 1.256 (1.158) Remain 22:03:01 loss: 0.3519 Lr: 0.00222 [2024-02-18 21:47:01,844 INFO misc.py line 119 87073] Train: [56/100][1492/1557] Data 0.012 (0.185) Batch 0.903 (1.157) Remain 22:02:48 loss: 0.2217 Lr: 0.00222 [2024-02-18 21:47:02,985 INFO misc.py line 119 87073] Train: [56/100][1493/1557] Data 0.004 (0.185) Batch 1.140 (1.157) Remain 22:02:46 loss: 0.3299 Lr: 0.00222 [2024-02-18 21:47:04,057 INFO misc.py line 119 87073] Train: [56/100][1494/1557] Data 0.005 (0.185) Batch 1.073 (1.157) Remain 22:02:41 loss: 0.4846 Lr: 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INFO misc.py line 119 87073] Train: [56/100][1501/1557] Data 0.004 (0.184) Batch 1.078 (1.156) Remain 22:01:12 loss: 0.4535 Lr: 0.00222 [2024-02-18 21:47:11,438 INFO misc.py line 119 87073] Train: [56/100][1502/1557] Data 0.004 (0.184) Batch 1.049 (1.156) Remain 22:01:06 loss: 0.2337 Lr: 0.00222 [2024-02-18 21:47:12,241 INFO misc.py line 119 87073] Train: [56/100][1503/1557] Data 0.005 (0.184) Batch 0.800 (1.156) Remain 22:00:48 loss: 0.3088 Lr: 0.00222 [2024-02-18 21:47:13,000 INFO misc.py line 119 87073] Train: [56/100][1504/1557] Data 0.008 (0.183) Batch 0.761 (1.156) Remain 22:00:29 loss: 0.2988 Lr: 0.00222 [2024-02-18 21:47:14,221 INFO misc.py line 119 87073] Train: [56/100][1505/1557] Data 0.006 (0.183) Batch 1.221 (1.156) Remain 22:00:31 loss: 0.1578 Lr: 0.00222 [2024-02-18 21:47:15,178 INFO misc.py line 119 87073] Train: [56/100][1506/1557] Data 0.006 (0.183) Batch 0.958 (1.156) Remain 22:00:21 loss: 0.1742 Lr: 0.00222 [2024-02-18 21:47:16,098 INFO misc.py line 119 87073] Train: [56/100][1507/1557] Data 0.006 (0.183) Batch 0.921 (1.155) Remain 22:00:09 loss: 0.3572 Lr: 0.00222 [2024-02-18 21:47:17,084 INFO misc.py line 119 87073] Train: [56/100][1508/1557] Data 0.003 (0.183) Batch 0.986 (1.155) Remain 22:00:00 loss: 0.8475 Lr: 0.00222 [2024-02-18 21:47:18,154 INFO misc.py line 119 87073] Train: [56/100][1509/1557] Data 0.004 (0.183) Batch 1.062 (1.155) Remain 21:59:55 loss: 0.4004 Lr: 0.00222 [2024-02-18 21:47:18,836 INFO misc.py line 119 87073] Train: [56/100][1510/1557] Data 0.012 (0.183) Batch 0.690 (1.155) Remain 21:59:32 loss: 0.3177 Lr: 0.00222 [2024-02-18 21:47:19,632 INFO misc.py line 119 87073] Train: [56/100][1511/1557] Data 0.004 (0.183) Batch 0.791 (1.155) Remain 21:59:15 loss: 0.3717 Lr: 0.00222 [2024-02-18 21:47:20,828 INFO misc.py line 119 87073] Train: [56/100][1512/1557] Data 0.009 (0.183) Batch 1.196 (1.155) Remain 21:59:15 loss: 0.0789 Lr: 0.00222 [2024-02-18 21:47:21,859 INFO misc.py line 119 87073] Train: [56/100][1513/1557] Data 0.008 (0.182) Batch 1.033 (1.155) Remain 21:59:09 loss: 0.2853 Lr: 0.00222 [2024-02-18 21:47:22,712 INFO misc.py line 119 87073] Train: [56/100][1514/1557] Data 0.008 (0.182) Batch 0.854 (1.154) Remain 21:58:54 loss: 0.2937 Lr: 0.00222 [2024-02-18 21:47:23,670 INFO misc.py line 119 87073] Train: [56/100][1515/1557] Data 0.006 (0.182) Batch 0.960 (1.154) Remain 21:58:44 loss: 0.2107 Lr: 0.00222 [2024-02-18 21:47:24,705 INFO misc.py line 119 87073] Train: [56/100][1516/1557] Data 0.004 (0.182) Batch 1.035 (1.154) Remain 21:58:37 loss: 0.6204 Lr: 0.00222 [2024-02-18 21:47:25,606 INFO misc.py line 119 87073] Train: [56/100][1517/1557] Data 0.004 (0.182) Batch 0.899 (1.154) Remain 21:58:25 loss: 0.2275 Lr: 0.00222 [2024-02-18 21:47:26,313 INFO misc.py line 119 87073] Train: [56/100][1518/1557] Data 0.005 (0.182) Batch 0.701 (1.154) Remain 21:58:03 loss: 0.2553 Lr: 0.00222 [2024-02-18 21:47:38,877 INFO misc.py line 119 87073] Train: [56/100][1519/1557] Data 9.631 (0.188) Batch 12.572 (1.161) Remain 22:06:38 loss: 0.2811 Lr: 0.00222 [2024-02-18 21:47:39,880 INFO misc.py line 119 87073] Train: [56/100][1520/1557] Data 0.004 (0.188) Batch 1.003 (1.161) Remain 22:06:30 loss: 0.7067 Lr: 0.00222 [2024-02-18 21:47:41,030 INFO misc.py line 119 87073] Train: [56/100][1521/1557] Data 0.004 (0.188) Batch 1.149 (1.161) Remain 22:06:28 loss: 0.1803 Lr: 0.00222 [2024-02-18 21:47:42,168 INFO misc.py line 119 87073] Train: [56/100][1522/1557] Data 0.005 (0.188) Batch 1.138 (1.161) Remain 22:06:26 loss: 0.3451 Lr: 0.00222 [2024-02-18 21:47:43,179 INFO misc.py line 119 87073] Train: [56/100][1523/1557] Data 0.005 (0.188) Batch 1.011 (1.161) Remain 22:06:18 loss: 0.5558 Lr: 0.00222 [2024-02-18 21:47:43,966 INFO misc.py line 119 87073] Train: [56/100][1524/1557] Data 0.005 (0.187) Batch 0.786 (1.161) Remain 22:06:00 loss: 0.2706 Lr: 0.00222 [2024-02-18 21:47:44,707 INFO misc.py line 119 87073] Train: [56/100][1525/1557] Data 0.006 (0.187) Batch 0.741 (1.160) Remain 22:05:40 loss: 0.3751 Lr: 0.00222 [2024-02-18 21:47:45,858 INFO misc.py line 119 87073] Train: [56/100][1526/1557] Data 0.006 (0.187) Batch 1.150 (1.160) Remain 22:05:38 loss: 0.1376 Lr: 0.00221 [2024-02-18 21:47:46,833 INFO misc.py line 119 87073] Train: [56/100][1527/1557] Data 0.006 (0.187) Batch 0.975 (1.160) Remain 22:05:29 loss: 0.4455 Lr: 0.00221 [2024-02-18 21:47:47,798 INFO misc.py line 119 87073] Train: [56/100][1528/1557] Data 0.008 (0.187) Batch 0.967 (1.160) Remain 22:05:19 loss: 0.3355 Lr: 0.00221 [2024-02-18 21:47:48,692 INFO misc.py line 119 87073] Train: [56/100][1529/1557] Data 0.004 (0.187) Batch 0.894 (1.160) Remain 22:05:06 loss: 0.4751 Lr: 0.00221 [2024-02-18 21:47:49,624 INFO misc.py line 119 87073] Train: [56/100][1530/1557] Data 0.004 (0.187) Batch 0.931 (1.160) Remain 22:04:54 loss: 0.2750 Lr: 0.00221 [2024-02-18 21:47:50,384 INFO misc.py line 119 87073] Train: [56/100][1531/1557] Data 0.005 (0.187) Batch 0.761 (1.160) Remain 22:04:35 loss: 0.2589 Lr: 0.00221 [2024-02-18 21:47:51,147 INFO misc.py line 119 87073] Train: [56/100][1532/1557] Data 0.004 (0.187) Batch 0.763 (1.159) Remain 22:04:16 loss: 0.2045 Lr: 0.00221 [2024-02-18 21:47:52,429 INFO misc.py line 119 87073] Train: [56/100][1533/1557] Data 0.004 (0.186) Batch 1.270 (1.159) Remain 22:04:20 loss: 0.1773 Lr: 0.00221 [2024-02-18 21:47:53,392 INFO misc.py line 119 87073] Train: [56/100][1534/1557] Data 0.015 (0.186) Batch 0.972 (1.159) Remain 22:04:11 loss: 0.7992 Lr: 0.00221 [2024-02-18 21:47:54,261 INFO misc.py line 119 87073] Train: [56/100][1535/1557] Data 0.007 (0.186) Batch 0.872 (1.159) Remain 22:03:57 loss: 0.4823 Lr: 0.00221 [2024-02-18 21:47:55,396 INFO misc.py line 119 87073] Train: [56/100][1536/1557] Data 0.004 (0.186) Batch 1.134 (1.159) Remain 22:03:54 loss: 0.4872 Lr: 0.00221 [2024-02-18 21:47:56,442 INFO misc.py line 119 87073] Train: [56/100][1537/1557] Data 0.004 (0.186) Batch 1.045 (1.159) Remain 22:03:48 loss: 0.6176 Lr: 0.00221 [2024-02-18 21:47:57,142 INFO misc.py line 119 87073] Train: [56/100][1538/1557] Data 0.005 (0.186) Batch 0.701 (1.159) Remain 22:03:26 loss: 0.2496 Lr: 0.00221 [2024-02-18 21:47:57,938 INFO misc.py line 119 87073] Train: [56/100][1539/1557] Data 0.004 (0.186) Batch 0.789 (1.159) Remain 22:03:09 loss: 0.2800 Lr: 0.00221 [2024-02-18 21:47:59,061 INFO misc.py line 119 87073] Train: [56/100][1540/1557] Data 0.011 (0.186) Batch 1.122 (1.159) Remain 22:03:06 loss: 0.2025 Lr: 0.00221 [2024-02-18 21:48:00,221 INFO misc.py line 119 87073] Train: [56/100][1541/1557] Data 0.012 (0.185) Batch 1.158 (1.159) Remain 22:03:05 loss: 0.3384 Lr: 0.00221 [2024-02-18 21:48:01,098 INFO misc.py line 119 87073] Train: [56/100][1542/1557] Data 0.013 (0.185) Batch 0.886 (1.158) Remain 22:02:52 loss: 0.3759 Lr: 0.00221 [2024-02-18 21:48:02,143 INFO misc.py line 119 87073] Train: [56/100][1543/1557] Data 0.005 (0.185) Batch 1.046 (1.158) Remain 22:02:45 loss: 0.3063 Lr: 0.00221 [2024-02-18 21:48:02,993 INFO misc.py line 119 87073] Train: [56/100][1544/1557] Data 0.004 (0.185) Batch 0.850 (1.158) Remain 22:02:31 loss: 0.2792 Lr: 0.00221 [2024-02-18 21:48:03,729 INFO misc.py line 119 87073] Train: [56/100][1545/1557] Data 0.004 (0.185) Batch 0.732 (1.158) Remain 22:02:10 loss: 0.3698 Lr: 0.00221 [2024-02-18 21:48:04,432 INFO misc.py line 119 87073] Train: [56/100][1546/1557] Data 0.007 (0.185) Batch 0.705 (1.157) Remain 22:01:49 loss: 0.5916 Lr: 0.00221 [2024-02-18 21:48:05,740 INFO misc.py line 119 87073] Train: [56/100][1547/1557] Data 0.005 (0.185) Batch 1.310 (1.158) Remain 22:01:55 loss: 0.2763 Lr: 0.00221 [2024-02-18 21:48:06,752 INFO misc.py line 119 87073] Train: [56/100][1548/1557] Data 0.004 (0.185) Batch 1.006 (1.157) Remain 22:01:47 loss: 0.2765 Lr: 0.00221 [2024-02-18 21:48:07,709 INFO misc.py line 119 87073] Train: [56/100][1549/1557] Data 0.010 (0.185) Batch 0.962 (1.157) Remain 22:01:37 loss: 0.2219 Lr: 0.00221 [2024-02-18 21:48:08,774 INFO misc.py line 119 87073] Train: [56/100][1550/1557] Data 0.005 (0.184) Batch 1.065 (1.157) Remain 22:01:32 loss: 0.2689 Lr: 0.00221 [2024-02-18 21:48:09,944 INFO misc.py line 119 87073] Train: [56/100][1551/1557] Data 0.004 (0.184) Batch 1.169 (1.157) Remain 22:01:31 loss: 0.2568 Lr: 0.00221 [2024-02-18 21:48:10,721 INFO misc.py line 119 87073] Train: [56/100][1552/1557] Data 0.005 (0.184) Batch 0.778 (1.157) Remain 22:01:13 loss: 0.2354 Lr: 0.00221 [2024-02-18 21:48:11,500 INFO misc.py line 119 87073] Train: [56/100][1553/1557] Data 0.004 (0.184) Batch 0.760 (1.157) Remain 22:00:55 loss: 0.3086 Lr: 0.00221 [2024-02-18 21:48:12,607 INFO misc.py line 119 87073] Train: [56/100][1554/1557] Data 0.023 (0.184) Batch 1.115 (1.157) Remain 22:00:52 loss: 0.1386 Lr: 0.00221 [2024-02-18 21:48:13,715 INFO misc.py line 119 87073] Train: [56/100][1555/1557] Data 0.015 (0.184) Batch 1.109 (1.157) Remain 22:00:48 loss: 0.5465 Lr: 0.00221 [2024-02-18 21:48:14,639 INFO misc.py line 119 87073] Train: [56/100][1556/1557] Data 0.014 (0.184) Batch 0.933 (1.157) Remain 22:00:37 loss: 0.5424 Lr: 0.00221 [2024-02-18 21:48:15,728 INFO misc.py line 119 87073] Train: [56/100][1557/1557] Data 0.004 (0.184) Batch 1.089 (1.157) Remain 22:00:33 loss: 0.4017 Lr: 0.00221 [2024-02-18 21:48:15,729 INFO misc.py line 136 87073] Train result: loss: 0.3230 [2024-02-18 21:48:15,729 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 21:48:44,053 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6208 [2024-02-18 21:48:44,831 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4797 [2024-02-18 21:48:46,960 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3425 [2024-02-18 21:48:49,166 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4345 [2024-02-18 21:48:54,110 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2987 [2024-02-18 21:48:54,809 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1131 [2024-02-18 21:48:56,071 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3789 [2024-02-18 21:48:59,024 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3990 [2024-02-18 21:49:00,833 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2066 [2024-02-18 21:49:02,435 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7045/0.7600/0.9142. [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9313/0.9676 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9833/0.9898 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8695/0.9802 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3037/0.3126 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6769/0.6982 [2024-02-18 21:49:02,435 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8181/0.9246 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8325/0.9118 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9123/0.9698 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7217/0.7386 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7807/0.8680 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7336/0.8224 [2024-02-18 21:49:02,436 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5943/0.6957 [2024-02-18 21:49:02,436 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 21:49:02,438 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 21:49:02,438 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 21:49:09,978 INFO misc.py line 119 87073] Train: [57/100][1/1557] Data 1.794 (1.794) Batch 2.596 (2.596) Remain 49:23:46 loss: 0.1330 Lr: 0.00221 [2024-02-18 21:49:11,078 INFO misc.py line 119 87073] Train: [57/100][2/1557] Data 0.011 (0.011) Batch 1.099 (1.099) Remain 20:54:40 loss: 0.4549 Lr: 0.00221 [2024-02-18 21:49:11,978 INFO misc.py line 119 87073] Train: [57/100][3/1557] Data 0.007 (0.007) Batch 0.902 (0.902) Remain 17:10:22 loss: 0.7817 Lr: 0.00221 [2024-02-18 21:49:12,973 INFO misc.py line 119 87073] Train: [57/100][4/1557] Data 0.007 (0.007) Batch 0.996 (0.996) Remain 18:57:14 loss: 0.4052 Lr: 0.00221 [2024-02-18 21:49:13,744 INFO misc.py line 119 87073] Train: [57/100][5/1557] Data 0.004 (0.005) Batch 0.771 (0.883) Remain 16:48:40 loss: 0.3842 Lr: 0.00221 [2024-02-18 21:49:14,503 INFO misc.py line 119 87073] Train: [57/100][6/1557] Data 0.004 (0.005) Batch 0.747 (0.838) Remain 15:56:51 loss: 0.2532 Lr: 0.00221 [2024-02-18 21:49:16,560 INFO misc.py line 119 87073] Train: [57/100][7/1557] Data 0.828 (0.211) Batch 2.067 (1.145) Remain 21:47:33 loss: 0.1728 Lr: 0.00221 [2024-02-18 21:49:17,510 INFO misc.py line 119 87073] Train: [57/100][8/1557] Data 0.006 (0.170) Batch 0.951 (1.106) Remain 21:03:11 loss: 0.6180 Lr: 0.00221 [2024-02-18 21:49:18,425 INFO misc.py line 119 87073] Train: [57/100][9/1557] Data 0.005 (0.142) Batch 0.915 (1.075) Remain 20:26:45 loss: 0.5592 Lr: 0.00221 [2024-02-18 21:49:19,353 INFO misc.py line 119 87073] Train: [57/100][10/1557] Data 0.004 (0.123) Batch 0.928 (1.054) Remain 20:02:50 loss: 0.2757 Lr: 0.00221 [2024-02-18 21:49:20,375 INFO misc.py line 119 87073] Train: [57/100][11/1557] Data 0.005 (0.108) Batch 1.020 (1.049) Remain 19:58:03 loss: 0.6207 Lr: 0.00221 [2024-02-18 21:49:21,133 INFO misc.py line 119 87073] Train: [57/100][12/1557] Data 0.006 (0.097) Batch 0.759 (1.017) Remain 19:21:10 loss: 0.2582 Lr: 0.00221 [2024-02-18 21:49:21,931 INFO misc.py line 119 87073] Train: [57/100][13/1557] Data 0.006 (0.087) Batch 0.790 (0.994) Remain 18:55:13 loss: 0.2607 Lr: 0.00221 [2024-02-18 21:49:23,164 INFO misc.py line 119 87073] Train: [57/100][14/1557] Data 0.013 (0.081) Batch 1.232 (1.016) Remain 19:19:50 loss: 0.2330 Lr: 0.00221 [2024-02-18 21:49:24,126 INFO misc.py line 119 87073] Train: [57/100][15/1557] Data 0.014 (0.075) Batch 0.972 (1.012) Remain 19:15:39 loss: 0.3589 Lr: 0.00221 [2024-02-18 21:49:24,938 INFO misc.py line 119 87073] Train: [57/100][16/1557] Data 0.004 (0.070) Batch 0.813 (0.997) Remain 18:58:06 loss: 0.0842 Lr: 0.00221 [2024-02-18 21:49:25,925 INFO misc.py line 119 87073] Train: [57/100][17/1557] Data 0.004 (0.065) Batch 0.986 (0.996) Remain 18:57:12 loss: 0.1813 Lr: 0.00221 [2024-02-18 21:49:26,933 INFO misc.py line 119 87073] Train: [57/100][18/1557] Data 0.004 (0.061) Batch 1.003 (0.997) Remain 18:57:41 loss: 0.2401 Lr: 0.00221 [2024-02-18 21:49:27,674 INFO misc.py line 119 87073] Train: [57/100][19/1557] Data 0.010 (0.058) Batch 0.747 (0.981) Remain 18:39:51 loss: 0.1901 Lr: 0.00221 [2024-02-18 21:49:28,397 INFO misc.py line 119 87073] Train: [57/100][20/1557] Data 0.005 (0.055) Batch 0.719 (0.966) Remain 18:22:14 loss: 0.2238 Lr: 0.00221 [2024-02-18 21:49:29,670 INFO misc.py line 119 87073] Train: [57/100][21/1557] Data 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Train: [57/100][40/1557] Data 0.005 (0.029) Batch 0.767 (0.976) Remain 18:33:56 loss: 0.2334 Lr: 0.00221 [2024-02-18 21:49:48,855 INFO misc.py line 119 87073] Train: [57/100][41/1557] Data 0.012 (0.029) Batch 0.757 (0.970) Remain 18:27:20 loss: 0.3584 Lr: 0.00221 [2024-02-18 21:49:50,170 INFO misc.py line 119 87073] Train: [57/100][42/1557] Data 0.006 (0.028) Batch 1.313 (0.979) Remain 18:37:20 loss: 0.1335 Lr: 0.00221 [2024-02-18 21:49:51,074 INFO misc.py line 119 87073] Train: [57/100][43/1557] Data 0.009 (0.028) Batch 0.909 (0.977) Remain 18:35:18 loss: 0.2312 Lr: 0.00221 [2024-02-18 21:49:52,098 INFO misc.py line 119 87073] Train: [57/100][44/1557] Data 0.005 (0.027) Batch 1.024 (0.979) Remain 18:36:34 loss: 0.2989 Lr: 0.00221 [2024-02-18 21:49:53,147 INFO misc.py line 119 87073] Train: [57/100][45/1557] Data 0.004 (0.027) Batch 1.050 (0.980) Remain 18:38:30 loss: 0.5852 Lr: 0.00221 [2024-02-18 21:49:54,356 INFO misc.py line 119 87073] Train: [57/100][46/1557] Data 0.004 (0.026) 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INFO misc.py line 119 87073] Train: [57/100][59/1557] Data 0.004 (0.022) Batch 1.078 (0.980) Remain 18:37:43 loss: 0.3802 Lr: 0.00221 [2024-02-18 21:50:07,826 INFO misc.py line 119 87073] Train: [57/100][60/1557] Data 0.006 (0.022) Batch 0.982 (0.980) Remain 18:37:45 loss: 0.2097 Lr: 0.00221 [2024-02-18 21:50:08,579 INFO misc.py line 119 87073] Train: [57/100][61/1557] Data 0.004 (0.022) Batch 0.752 (0.976) Remain 18:33:15 loss: 0.1465 Lr: 0.00221 [2024-02-18 21:50:09,381 INFO misc.py line 119 87073] Train: [57/100][62/1557] Data 0.005 (0.021) Batch 0.802 (0.973) Remain 18:29:53 loss: 0.2007 Lr: 0.00221 [2024-02-18 21:50:20,775 INFO misc.py line 119 87073] Train: [57/100][63/1557] Data 8.449 (0.162) Batch 11.395 (1.147) Remain 21:48:01 loss: 0.1734 Lr: 0.00221 [2024-02-18 21:50:21,572 INFO misc.py line 119 87073] Train: [57/100][64/1557] Data 0.004 (0.159) Batch 0.796 (1.141) Remain 21:41:27 loss: 0.2340 Lr: 0.00221 [2024-02-18 21:50:22,538 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [57/100][109/1557] Data 0.005 (0.095) Batch 0.964 (1.070) Remain 20:20:11 loss: 0.2294 Lr: 0.00221 [2024-02-18 21:51:06,228 INFO misc.py line 119 87073] Train: [57/100][110/1557] Data 0.004 (0.094) Batch 0.793 (1.068) Remain 20:17:12 loss: 0.3271 Lr: 0.00221 [2024-02-18 21:51:07,064 INFO misc.py line 119 87073] Train: [57/100][111/1557] Data 0.004 (0.093) Batch 0.790 (1.065) Remain 20:14:15 loss: 0.2647 Lr: 0.00221 [2024-02-18 21:51:08,357 INFO misc.py line 119 87073] Train: [57/100][112/1557] Data 0.050 (0.093) Batch 1.333 (1.068) Remain 20:17:02 loss: 0.1207 Lr: 0.00221 [2024-02-18 21:51:09,206 INFO misc.py line 119 87073] Train: [57/100][113/1557] Data 0.011 (0.092) Batch 0.854 (1.066) Remain 20:14:48 loss: 0.7111 Lr: 0.00221 [2024-02-18 21:51:10,232 INFO misc.py line 119 87073] Train: [57/100][114/1557] Data 0.005 (0.091) Batch 1.027 (1.065) Remain 20:14:23 loss: 0.4044 Lr: 0.00221 [2024-02-18 21:51:11,258 INFO misc.py line 119 87073] Train: 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21:51:39,937 INFO misc.py line 119 87073] Train: [57/100][134/1557] Data 0.005 (0.136) Batch 1.355 (1.129) Remain 21:27:02 loss: 0.1805 Lr: 0.00221 [2024-02-18 21:51:40,821 INFO misc.py line 119 87073] Train: [57/100][135/1557] Data 0.009 (0.135) Batch 0.890 (1.128) Remain 21:24:57 loss: 0.3483 Lr: 0.00221 [2024-02-18 21:51:41,962 INFO misc.py line 119 87073] Train: [57/100][136/1557] Data 0.004 (0.134) Batch 1.141 (1.128) Remain 21:25:03 loss: 0.2332 Lr: 0.00221 [2024-02-18 21:51:43,072 INFO misc.py line 119 87073] Train: [57/100][137/1557] Data 0.004 (0.133) Batch 1.109 (1.128) Remain 21:24:52 loss: 0.3803 Lr: 0.00221 [2024-02-18 21:51:43,778 INFO misc.py line 119 87073] Train: [57/100][138/1557] Data 0.005 (0.132) Batch 0.707 (1.124) Remain 21:21:18 loss: 0.3162 Lr: 0.00221 [2024-02-18 21:51:44,561 INFO misc.py line 119 87073] Train: [57/100][139/1557] Data 0.004 (0.131) Batch 0.783 (1.122) Remain 21:18:25 loss: 0.3377 Lr: 0.00221 [2024-02-18 21:51:45,770 INFO misc.py line 119 87073] Train: [57/100][140/1557] Data 0.005 (0.130) Batch 1.209 (1.123) Remain 21:19:07 loss: 0.3006 Lr: 0.00221 [2024-02-18 21:51:46,643 INFO misc.py line 119 87073] Train: [57/100][141/1557] Data 0.005 (0.129) Batch 0.874 (1.121) Remain 21:17:03 loss: 0.1040 Lr: 0.00221 [2024-02-18 21:51:47,564 INFO misc.py line 119 87073] Train: [57/100][142/1557] Data 0.004 (0.129) Batch 0.921 (1.119) Remain 21:15:23 loss: 0.3315 Lr: 0.00221 [2024-02-18 21:51:48,660 INFO misc.py line 119 87073] Train: [57/100][143/1557] Data 0.004 (0.128) Batch 1.096 (1.119) Remain 21:15:11 loss: 0.5214 Lr: 0.00221 [2024-02-18 21:51:49,716 INFO misc.py line 119 87073] Train: [57/100][144/1557] Data 0.004 (0.127) Batch 1.056 (1.119) Remain 21:14:39 loss: 0.3917 Lr: 0.00221 [2024-02-18 21:51:50,493 INFO misc.py line 119 87073] Train: [57/100][145/1557] Data 0.004 (0.126) Batch 0.777 (1.116) Remain 21:11:54 loss: 0.3013 Lr: 0.00221 [2024-02-18 21:51:51,278 INFO misc.py line 119 87073] Train: [57/100][146/1557] Data 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line 119 87073] Train: [57/100][165/1557] Data 0.013 (0.111) Batch 1.252 (1.099) Remain 20:51:52 loss: 0.4454 Lr: 0.00220 [2024-02-18 21:52:10,761 INFO misc.py line 119 87073] Train: [57/100][166/1557] Data 0.014 (0.111) Batch 0.738 (1.097) Remain 20:49:19 loss: 0.4956 Lr: 0.00220 [2024-02-18 21:52:11,491 INFO misc.py line 119 87073] Train: [57/100][167/1557] Data 0.003 (0.110) Batch 0.719 (1.095) Remain 20:46:41 loss: 0.1487 Lr: 0.00220 [2024-02-18 21:52:12,759 INFO misc.py line 119 87073] Train: [57/100][168/1557] Data 0.015 (0.109) Batch 1.273 (1.096) Remain 20:47:53 loss: 0.1218 Lr: 0.00220 [2024-02-18 21:52:13,850 INFO misc.py line 119 87073] Train: [57/100][169/1557] Data 0.010 (0.109) Batch 1.086 (1.096) Remain 20:47:49 loss: 1.1080 Lr: 0.00220 [2024-02-18 21:52:14,981 INFO misc.py line 119 87073] Train: [57/100][170/1557] Data 0.014 (0.108) Batch 1.141 (1.096) Remain 20:48:06 loss: 0.3119 Lr: 0.00220 [2024-02-18 21:52:15,783 INFO misc.py line 119 87073] Train: 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Batch 0.943 (1.143) Remain 21:41:09 loss: 0.6483 Lr: 0.00220 [2024-02-18 21:52:31,687 INFO misc.py line 119 87073] Train: [57/100][178/1557] Data 0.003 (0.150) Batch 0.911 (1.141) Remain 21:39:37 loss: 0.4382 Lr: 0.00220 [2024-02-18 21:52:32,823 INFO misc.py line 119 87073] Train: [57/100][179/1557] Data 0.004 (0.150) Batch 1.136 (1.141) Remain 21:39:34 loss: 0.3345 Lr: 0.00220 [2024-02-18 21:52:35,167 INFO misc.py line 119 87073] Train: [57/100][180/1557] Data 1.628 (0.158) Batch 2.343 (1.148) Remain 21:47:17 loss: 0.2409 Lr: 0.00220 [2024-02-18 21:52:35,954 INFO misc.py line 119 87073] Train: [57/100][181/1557] Data 0.006 (0.157) Batch 0.786 (1.146) Remain 21:44:57 loss: 0.3419 Lr: 0.00220 [2024-02-18 21:52:37,105 INFO misc.py line 119 87073] Train: [57/100][182/1557] Data 0.006 (0.156) Batch 1.151 (1.146) Remain 21:44:58 loss: 0.2779 Lr: 0.00220 [2024-02-18 21:52:38,039 INFO misc.py line 119 87073] Train: [57/100][183/1557] Data 0.006 (0.155) Batch 0.934 (1.145) Remain 21:43:36 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Batch 1.003 (1.133) Remain 21:20:17 loss: 0.7040 Lr: 0.00217 [2024-02-18 22:03:04,972 INFO misc.py line 119 87073] Train: [57/100][738/1557] Data 0.014 (0.160) Batch 1.011 (1.133) Remain 21:20:04 loss: 0.5508 Lr: 0.00217 [2024-02-18 22:03:05,874 INFO misc.py line 119 87073] Train: [57/100][739/1557] Data 0.010 (0.160) Batch 0.909 (1.133) Remain 21:19:43 loss: 0.2884 Lr: 0.00217 [2024-02-18 22:03:06,633 INFO misc.py line 119 87073] Train: [57/100][740/1557] Data 0.003 (0.160) Batch 0.759 (1.133) Remain 21:19:07 loss: 0.2059 Lr: 0.00217 [2024-02-18 22:03:07,431 INFO misc.py line 119 87073] Train: [57/100][741/1557] Data 0.004 (0.160) Batch 0.789 (1.132) Remain 21:18:34 loss: 0.2220 Lr: 0.00217 [2024-02-18 22:03:08,588 INFO misc.py line 119 87073] Train: [57/100][742/1557] Data 0.012 (0.160) Batch 1.155 (1.132) Remain 21:18:35 loss: 0.2232 Lr: 0.00217 [2024-02-18 22:03:09,457 INFO misc.py line 119 87073] Train: [57/100][743/1557] Data 0.014 (0.159) Batch 0.878 (1.132) Remain 21:18:11 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INFO misc.py line 119 87073] Train: [57/100][1501/1557] Data 0.004 (0.154) Batch 1.058 (1.130) Remain 21:02:18 loss: 0.2262 Lr: 0.00213 [2024-02-18 22:17:26,133 INFO misc.py line 119 87073] Train: [57/100][1502/1557] Data 0.006 (0.154) Batch 0.947 (1.130) Remain 21:02:09 loss: 0.1340 Lr: 0.00213 [2024-02-18 22:17:26,880 INFO misc.py line 119 87073] Train: [57/100][1503/1557] Data 0.005 (0.154) Batch 0.743 (1.130) Remain 21:01:50 loss: 0.1878 Lr: 0.00213 [2024-02-18 22:17:27,719 INFO misc.py line 119 87073] Train: [57/100][1504/1557] Data 0.009 (0.154) Batch 0.844 (1.130) Remain 21:01:37 loss: 0.4307 Lr: 0.00213 [2024-02-18 22:17:28,721 INFO misc.py line 119 87073] Train: [57/100][1505/1557] Data 0.005 (0.154) Batch 1.001 (1.130) Remain 21:01:30 loss: 0.1515 Lr: 0.00213 [2024-02-18 22:17:29,682 INFO misc.py line 119 87073] Train: [57/100][1506/1557] Data 0.005 (0.154) Batch 0.962 (1.130) Remain 21:01:21 loss: 0.1100 Lr: 0.00213 [2024-02-18 22:17:30,546 INFO misc.py line 119 87073] Train: [57/100][1507/1557] Data 0.003 (0.153) Batch 0.862 (1.129) Remain 21:01:08 loss: 0.6371 Lr: 0.00213 [2024-02-18 22:17:31,455 INFO misc.py line 119 87073] Train: [57/100][1508/1557] Data 0.006 (0.153) Batch 0.906 (1.129) Remain 21:00:57 loss: 0.3909 Lr: 0.00213 [2024-02-18 22:17:32,443 INFO misc.py line 119 87073] Train: [57/100][1509/1557] Data 0.009 (0.153) Batch 0.994 (1.129) Remain 21:00:50 loss: 0.4783 Lr: 0.00213 [2024-02-18 22:17:33,252 INFO misc.py line 119 87073] Train: [57/100][1510/1557] Data 0.004 (0.153) Batch 0.809 (1.129) Remain 21:00:34 loss: 0.3041 Lr: 0.00213 [2024-02-18 22:17:34,048 INFO misc.py line 119 87073] Train: [57/100][1511/1557] Data 0.004 (0.153) Batch 0.795 (1.129) Remain 21:00:18 loss: 0.1157 Lr: 0.00213 [2024-02-18 22:17:35,300 INFO misc.py line 119 87073] Train: [57/100][1512/1557] Data 0.005 (0.153) Batch 1.245 (1.129) Remain 21:00:22 loss: 0.1149 Lr: 0.00213 [2024-02-18 22:17:36,138 INFO misc.py line 119 87073] Train: [57/100][1513/1557] Data 0.012 (0.153) Batch 0.846 (1.129) Remain 21:00:09 loss: 0.1389 Lr: 0.00213 [2024-02-18 22:17:37,152 INFO misc.py line 119 87073] Train: [57/100][1514/1557] Data 0.003 (0.153) Batch 1.014 (1.129) Remain 21:00:03 loss: 0.1532 Lr: 0.00213 [2024-02-18 22:17:37,972 INFO misc.py line 119 87073] Train: [57/100][1515/1557] Data 0.004 (0.153) Batch 0.819 (1.128) Remain 20:59:48 loss: 0.3343 Lr: 0.00213 [2024-02-18 22:17:38,853 INFO misc.py line 119 87073] Train: [57/100][1516/1557] Data 0.005 (0.153) Batch 0.881 (1.128) Remain 20:59:36 loss: 0.3282 Lr: 0.00213 [2024-02-18 22:17:39,609 INFO misc.py line 119 87073] Train: [57/100][1517/1557] Data 0.006 (0.152) Batch 0.757 (1.128) Remain 20:59:18 loss: 0.2111 Lr: 0.00213 [2024-02-18 22:17:40,416 INFO misc.py line 119 87073] Train: [57/100][1518/1557] Data 0.004 (0.152) Batch 0.797 (1.128) Remain 20:59:02 loss: 0.3314 Lr: 0.00213 [2024-02-18 22:17:51,599 INFO misc.py line 119 87073] Train: [57/100][1519/1557] Data 7.719 (0.157) Batch 11.193 (1.134) Remain 21:06:26 loss: 0.2822 Lr: 0.00213 [2024-02-18 22:17:52,445 INFO misc.py line 119 87073] Train: [57/100][1520/1557] Data 0.003 (0.157) Batch 0.846 (1.134) Remain 21:06:12 loss: 0.3001 Lr: 0.00213 [2024-02-18 22:17:53,376 INFO misc.py line 119 87073] Train: [57/100][1521/1557] Data 0.003 (0.157) Batch 0.917 (1.134) Remain 21:06:01 loss: 0.2976 Lr: 0.00213 [2024-02-18 22:17:54,387 INFO misc.py line 119 87073] Train: [57/100][1522/1557] Data 0.018 (0.157) Batch 1.016 (1.134) Remain 21:05:55 loss: 0.5204 Lr: 0.00213 [2024-02-18 22:17:55,414 INFO misc.py line 119 87073] Train: [57/100][1523/1557] Data 0.013 (0.157) Batch 1.036 (1.134) Remain 21:05:50 loss: 0.1673 Lr: 0.00213 [2024-02-18 22:17:56,189 INFO misc.py line 119 87073] Train: [57/100][1524/1557] Data 0.004 (0.157) Batch 0.776 (1.134) Remain 21:05:33 loss: 0.2058 Lr: 0.00213 [2024-02-18 22:17:56,956 INFO misc.py line 119 87073] Train: [57/100][1525/1557] Data 0.004 (0.157) Batch 0.767 (1.133) Remain 21:05:15 loss: 0.2923 Lr: 0.00213 [2024-02-18 22:17:58,276 INFO misc.py line 119 87073] Train: [57/100][1526/1557] Data 0.004 (0.157) Batch 1.314 (1.133) Remain 21:05:22 loss: 0.1463 Lr: 0.00213 [2024-02-18 22:17:59,243 INFO misc.py line 119 87073] Train: [57/100][1527/1557] Data 0.010 (0.157) Batch 0.973 (1.133) Remain 21:05:14 loss: 0.2597 Lr: 0.00213 [2024-02-18 22:18:00,239 INFO misc.py line 119 87073] Train: [57/100][1528/1557] Data 0.004 (0.156) Batch 0.995 (1.133) Remain 21:05:07 loss: 0.1817 Lr: 0.00213 [2024-02-18 22:18:01,068 INFO misc.py line 119 87073] Train: [57/100][1529/1557] Data 0.005 (0.156) Batch 0.829 (1.133) Remain 21:04:52 loss: 0.3057 Lr: 0.00213 [2024-02-18 22:18:02,167 INFO misc.py line 119 87073] Train: [57/100][1530/1557] Data 0.005 (0.156) Batch 1.099 (1.133) Remain 21:04:50 loss: 0.2380 Lr: 0.00213 [2024-02-18 22:18:02,927 INFO misc.py line 119 87073] Train: [57/100][1531/1557] Data 0.005 (0.156) Batch 0.760 (1.133) Remain 21:04:32 loss: 0.1708 Lr: 0.00213 [2024-02-18 22:18:03,675 INFO misc.py line 119 87073] Train: [57/100][1532/1557] Data 0.005 (0.156) Batch 0.741 (1.133) Remain 21:04:14 loss: 0.1715 Lr: 0.00213 [2024-02-18 22:18:04,901 INFO misc.py line 119 87073] Train: [57/100][1533/1557] Data 0.011 (0.156) Batch 1.224 (1.133) Remain 21:04:17 loss: 0.1633 Lr: 0.00213 [2024-02-18 22:18:05,867 INFO misc.py line 119 87073] Train: [57/100][1534/1557] Data 0.013 (0.156) Batch 0.976 (1.133) Remain 21:04:09 loss: 0.3880 Lr: 0.00213 [2024-02-18 22:18:06,724 INFO misc.py line 119 87073] Train: [57/100][1535/1557] Data 0.004 (0.156) Batch 0.856 (1.132) Remain 21:03:56 loss: 0.1645 Lr: 0.00213 [2024-02-18 22:18:07,685 INFO misc.py line 119 87073] Train: [57/100][1536/1557] Data 0.005 (0.156) Batch 0.959 (1.132) Remain 21:03:47 loss: 0.2983 Lr: 0.00213 [2024-02-18 22:18:08,516 INFO misc.py line 119 87073] Train: [57/100][1537/1557] Data 0.006 (0.156) Batch 0.832 (1.132) Remain 21:03:33 loss: 0.5707 Lr: 0.00213 [2024-02-18 22:18:09,200 INFO misc.py line 119 87073] Train: [57/100][1538/1557] Data 0.005 (0.156) Batch 0.685 (1.132) Remain 21:03:12 loss: 0.3121 Lr: 0.00213 [2024-02-18 22:18:09,980 INFO misc.py line 119 87073] Train: [57/100][1539/1557] Data 0.004 (0.155) Batch 0.762 (1.131) Remain 21:02:55 loss: 0.5610 Lr: 0.00213 [2024-02-18 22:18:11,263 INFO misc.py line 119 87073] Train: [57/100][1540/1557] Data 0.022 (0.155) Batch 1.288 (1.132) Remain 21:03:00 loss: 0.2351 Lr: 0.00213 [2024-02-18 22:18:12,157 INFO misc.py line 119 87073] Train: [57/100][1541/1557] Data 0.018 (0.155) Batch 0.908 (1.131) Remain 21:02:50 loss: 0.2647 Lr: 0.00213 [2024-02-18 22:18:12,986 INFO misc.py line 119 87073] Train: [57/100][1542/1557] Data 0.004 (0.155) Batch 0.828 (1.131) Remain 21:02:35 loss: 0.5515 Lr: 0.00213 [2024-02-18 22:18:13,837 INFO misc.py line 119 87073] Train: [57/100][1543/1557] Data 0.004 (0.155) Batch 0.849 (1.131) Remain 21:02:22 loss: 0.5080 Lr: 0.00213 [2024-02-18 22:18:14,791 INFO misc.py line 119 87073] Train: [57/100][1544/1557] Data 0.006 (0.155) Batch 0.956 (1.131) Remain 21:02:13 loss: 0.5259 Lr: 0.00213 [2024-02-18 22:18:15,580 INFO misc.py line 119 87073] Train: [57/100][1545/1557] Data 0.004 (0.155) Batch 0.785 (1.131) Remain 21:01:57 loss: 0.2599 Lr: 0.00213 [2024-02-18 22:18:16,362 INFO misc.py line 119 87073] Train: [57/100][1546/1557] Data 0.009 (0.155) Batch 0.775 (1.131) Remain 21:01:40 loss: 0.2994 Lr: 0.00213 [2024-02-18 22:18:17,495 INFO misc.py line 119 87073] Train: [57/100][1547/1557] Data 0.016 (0.155) Batch 1.130 (1.131) Remain 21:01:39 loss: 0.1088 Lr: 0.00213 [2024-02-18 22:18:18,593 INFO misc.py line 119 87073] Train: [57/100][1548/1557] Data 0.018 (0.155) Batch 1.104 (1.130) Remain 21:01:37 loss: 0.2209 Lr: 0.00213 [2024-02-18 22:18:19,495 INFO misc.py line 119 87073] Train: [57/100][1549/1557] Data 0.013 (0.154) Batch 0.910 (1.130) Remain 21:01:26 loss: 0.3061 Lr: 0.00213 [2024-02-18 22:18:20,349 INFO misc.py line 119 87073] Train: [57/100][1550/1557] Data 0.005 (0.154) Batch 0.855 (1.130) Remain 21:01:13 loss: 0.6139 Lr: 0.00213 [2024-02-18 22:18:21,449 INFO misc.py line 119 87073] Train: [57/100][1551/1557] Data 0.004 (0.154) Batch 1.087 (1.130) Remain 21:01:10 loss: 0.4054 Lr: 0.00213 [2024-02-18 22:18:22,202 INFO misc.py line 119 87073] Train: [57/100][1552/1557] Data 0.017 (0.154) Batch 0.765 (1.130) Remain 21:00:53 loss: 0.0967 Lr: 0.00213 [2024-02-18 22:18:22,920 INFO misc.py line 119 87073] Train: [57/100][1553/1557] Data 0.004 (0.154) Batch 0.708 (1.130) Remain 21:00:34 loss: 0.2484 Lr: 0.00213 [2024-02-18 22:18:24,198 INFO misc.py line 119 87073] Train: [57/100][1554/1557] Data 0.014 (0.154) Batch 1.274 (1.130) Remain 21:00:39 loss: 0.1317 Lr: 0.00213 [2024-02-18 22:18:25,254 INFO misc.py line 119 87073] Train: [57/100][1555/1557] Data 0.018 (0.154) Batch 1.058 (1.130) Remain 21:00:35 loss: 0.1703 Lr: 0.00213 [2024-02-18 22:18:26,342 INFO misc.py line 119 87073] Train: [57/100][1556/1557] Data 0.016 (0.154) Batch 1.090 (1.130) Remain 21:00:32 loss: 0.5310 Lr: 0.00213 [2024-02-18 22:18:27,407 INFO misc.py line 119 87073] Train: [57/100][1557/1557] Data 0.014 (0.154) Batch 1.063 (1.130) Remain 21:00:28 loss: 0.5045 Lr: 0.00213 [2024-02-18 22:18:27,407 INFO misc.py line 136 87073] Train result: loss: 0.3215 [2024-02-18 22:18:27,408 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 22:18:56,170 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8721 [2024-02-18 22:18:56,948 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7327 [2024-02-18 22:18:59,071 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3550 [2024-02-18 22:19:01,279 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3735 [2024-02-18 22:19:06,226 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3073 [2024-02-18 22:19:06,926 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0680 [2024-02-18 22:19:08,188 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2431 [2024-02-18 22:19:11,140 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4204 [2024-02-18 22:19:12,949 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2299 [2024-02-18 22:19:14,557 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7120/0.7645/0.9122. [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9326/0.9482 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9768/0.9810 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8563/0.9722 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3010/0.3169 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6567/0.6802 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8096/0.9192 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8470/0.9203 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9032/0.9652 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8266/0.8476 [2024-02-18 22:19:14,557 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7837/0.8578 [2024-02-18 22:19:14,558 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7328/0.7468 [2024-02-18 22:19:14,558 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6301/0.7825 [2024-02-18 22:19:14,558 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 22:19:14,560 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 22:19:14,561 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 22:19:21,405 INFO misc.py line 119 87073] Train: [58/100][1/1557] Data 1.474 (1.474) Batch 2.413 (2.413) Remain 44:52:20 loss: 0.3791 Lr: 0.00213 [2024-02-18 22:19:22,406 INFO misc.py line 119 87073] Train: [58/100][2/1557] Data 0.006 (0.006) Batch 1.000 (1.000) Remain 18:35:42 loss: 0.4823 Lr: 0.00213 [2024-02-18 22:19:23,313 INFO misc.py line 119 87073] Train: [58/100][3/1557] Data 0.006 (0.006) Batch 0.906 (0.906) Remain 16:51:25 loss: 0.3852 Lr: 0.00213 [2024-02-18 22:19:24,476 INFO misc.py line 119 87073] Train: [58/100][4/1557] Data 0.006 (0.006) Batch 1.166 (1.166) Remain 21:40:51 loss: 0.2817 Lr: 0.00213 [2024-02-18 22:19:25,241 INFO misc.py line 119 87073] Train: [58/100][5/1557] Data 0.005 (0.005) Batch 0.762 (0.964) Remain 17:55:44 loss: 0.1894 Lr: 0.00213 [2024-02-18 22:19:26,023 INFO misc.py line 119 87073] Train: [58/100][6/1557] Data 0.006 (0.006) Batch 0.783 (0.904) Remain 16:48:21 loss: 0.1827 Lr: 0.00213 [2024-02-18 22:19:39,606 INFO misc.py line 119 87073] Train: [58/100][7/1557] Data 12.326 (3.086) Batch 13.584 (4.074) Remain 75:45:22 loss: 0.1279 Lr: 0.00213 [2024-02-18 22:19:40,648 INFO misc.py line 119 87073] Train: [58/100][8/1557] Data 0.004 (2.470) Batch 1.042 (3.467) Remain 64:28:41 loss: 0.2991 Lr: 0.00213 [2024-02-18 22:19:41,525 INFO misc.py line 119 87073] Train: [58/100][9/1557] Data 0.004 (2.059) Batch 0.877 (3.036) Remain 56:26:59 loss: 0.4937 Lr: 0.00213 [2024-02-18 22:19:42,555 INFO misc.py line 119 87073] Train: [58/100][10/1557] Data 0.004 (1.765) Batch 1.029 (2.749) Remain 51:07:10 loss: 0.6337 Lr: 0.00213 [2024-02-18 22:19:43,470 INFO misc.py line 119 87073] Train: [58/100][11/1557] Data 0.004 (1.545) Batch 0.916 (2.520) Remain 46:51:24 loss: 0.3829 Lr: 0.00213 [2024-02-18 22:19:44,218 INFO misc.py line 119 87073] Train: [58/100][12/1557] Data 0.004 (1.374) Batch 0.740 (2.322) Remain 43:10:41 loss: 0.3428 Lr: 0.00213 [2024-02-18 22:19:44,945 INFO misc.py line 119 87073] Train: [58/100][13/1557] Data 0.012 (1.238) Batch 0.735 (2.163) Remain 40:13:35 loss: 0.3971 Lr: 0.00213 [2024-02-18 22:19:46,209 INFO misc.py line 119 87073] Train: [58/100][14/1557] Data 0.005 (1.125) Batch 1.263 (2.082) Remain 38:42:15 loss: 0.2031 Lr: 0.00213 [2024-02-18 22:19:47,177 INFO misc.py line 119 87073] Train: [58/100][15/1557] Data 0.005 (1.032) Batch 0.968 (1.989) Remain 36:58:39 loss: 0.2317 Lr: 0.00213 [2024-02-18 22:19:48,400 INFO misc.py line 119 87073] Train: [58/100][16/1557] Data 0.006 (0.953) Batch 1.218 (1.929) Remain 35:52:28 loss: 0.3447 Lr: 0.00213 [2024-02-18 22:19:49,408 INFO misc.py line 119 87073] Train: [58/100][17/1557] Data 0.011 (0.886) Batch 1.006 (1.863) Remain 34:38:49 loss: 0.2478 Lr: 0.00213 [2024-02-18 22:19:50,350 INFO misc.py line 119 87073] Train: [58/100][18/1557] Data 0.014 (0.828) Batch 0.951 (1.803) Remain 33:30:56 loss: 0.4394 Lr: 0.00213 [2024-02-18 22:19:51,078 INFO misc.py line 119 87073] Train: [58/100][19/1557] Data 0.004 (0.776) Batch 0.726 (1.735) Remain 32:15:52 loss: 0.3323 Lr: 0.00213 [2024-02-18 22:19:51,880 INFO misc.py line 119 87073] Train: [58/100][20/1557] Data 0.005 (0.731) Batch 0.798 (1.680) Remain 31:14:19 loss: 0.2332 Lr: 0.00213 [2024-02-18 22:19:53,114 INFO misc.py line 119 87073] Train: 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0.758 (1.462) Remain 27:10:32 loss: 0.1601 Lr: 0.00213 [2024-02-18 22:19:59,786 INFO misc.py line 119 87073] Train: [58/100][28/1557] Data 0.016 (0.499) Batch 1.382 (1.459) Remain 27:06:56 loss: 0.2585 Lr: 0.00213 [2024-02-18 22:20:00,810 INFO misc.py line 119 87073] Train: [58/100][29/1557] Data 0.014 (0.481) Batch 1.020 (1.442) Remain 26:48:06 loss: 0.5646 Lr: 0.00213 [2024-02-18 22:20:01,818 INFO misc.py line 119 87073] Train: [58/100][30/1557] Data 0.018 (0.464) Batch 1.011 (1.426) Remain 26:30:18 loss: 0.3322 Lr: 0.00213 [2024-02-18 22:20:02,825 INFO misc.py line 119 87073] Train: [58/100][31/1557] Data 0.013 (0.447) Batch 1.006 (1.411) Remain 26:13:32 loss: 0.0991 Lr: 0.00213 [2024-02-18 22:20:03,665 INFO misc.py line 119 87073] Train: [58/100][32/1557] Data 0.015 (0.433) Batch 0.851 (1.392) Remain 25:52:00 loss: 0.2711 Lr: 0.00213 [2024-02-18 22:20:04,443 INFO misc.py line 119 87073] Train: [58/100][33/1557] Data 0.003 (0.418) Batch 0.778 (1.371) Remain 25:29:09 loss: 0.6037 Lr: 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Train: [58/100][65/1557] Data 0.004 (0.516) Batch 0.952 (1.455) Remain 27:01:40 loss: 0.3517 Lr: 0.00213 [2024-02-18 22:20:54,415 INFO misc.py line 119 87073] Train: [58/100][66/1557] Data 0.008 (0.508) Batch 0.911 (1.446) Remain 26:52:02 loss: 0.2871 Lr: 0.00213 [2024-02-18 22:20:55,506 INFO misc.py line 119 87073] Train: [58/100][67/1557] Data 0.005 (0.500) Batch 1.091 (1.441) Remain 26:45:49 loss: 0.2160 Lr: 0.00213 [2024-02-18 22:20:56,233 INFO misc.py line 119 87073] Train: [58/100][68/1557] Data 0.004 (0.492) Batch 0.727 (1.430) Remain 26:33:34 loss: 0.4870 Lr: 0.00213 [2024-02-18 22:20:56,975 INFO misc.py line 119 87073] Train: [58/100][69/1557] Data 0.004 (0.485) Batch 0.741 (1.419) Remain 26:21:55 loss: 0.5592 Lr: 0.00213 [2024-02-18 22:20:58,246 INFO misc.py line 119 87073] Train: [58/100][70/1557] Data 0.004 (0.478) Batch 1.270 (1.417) Remain 26:19:24 loss: 0.1204 Lr: 0.00213 [2024-02-18 22:20:59,209 INFO misc.py line 119 87073] Train: [58/100][71/1557] Data 0.006 (0.471) Batch 0.965 (1.410) Remain 26:11:58 loss: 0.3433 Lr: 0.00213 [2024-02-18 22:21:00,130 INFO misc.py line 119 87073] Train: [58/100][72/1557] Data 0.004 (0.464) Batch 0.921 (1.403) Remain 26:04:03 loss: 0.1621 Lr: 0.00213 [2024-02-18 22:21:01,045 INFO misc.py line 119 87073] Train: [58/100][73/1557] Data 0.003 (0.458) Batch 0.909 (1.396) Remain 25:56:10 loss: 0.1278 Lr: 0.00213 [2024-02-18 22:21:02,076 INFO misc.py line 119 87073] Train: [58/100][74/1557] Data 0.009 (0.451) Batch 1.006 (1.391) Remain 25:50:00 loss: 0.2394 Lr: 0.00213 [2024-02-18 22:21:02,823 INFO misc.py line 119 87073] Train: [58/100][75/1557] Data 0.035 (0.445) Batch 0.776 (1.382) Remain 25:40:28 loss: 0.4574 Lr: 0.00213 [2024-02-18 22:21:03,581 INFO misc.py line 119 87073] Train: [58/100][76/1557] Data 0.005 (0.439) Batch 0.747 (1.373) Remain 25:30:45 loss: 0.1377 Lr: 0.00213 [2024-02-18 22:21:04,882 INFO misc.py line 119 87073] Train: [58/100][77/1557] Data 0.017 (0.434) Batch 1.304 (1.372) Remain 25:29:40 loss: 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Batch 0.941 (1.370) Remain 25:26:28 loss: 0.3905 Lr: 0.00213 [2024-02-18 22:22:05,910 INFO misc.py line 119 87073] Train: [58/100][122/1557] Data 0.011 (0.434) Batch 0.885 (1.366) Remain 25:21:53 loss: 0.6056 Lr: 0.00213 [2024-02-18 22:22:06,708 INFO misc.py line 119 87073] Train: [58/100][123/1557] Data 0.004 (0.430) Batch 0.796 (1.362) Remain 25:16:35 loss: 0.6970 Lr: 0.00212 [2024-02-18 22:22:07,431 INFO misc.py line 119 87073] Train: [58/100][124/1557] Data 0.007 (0.427) Batch 0.716 (1.356) Remain 25:10:37 loss: 0.2319 Lr: 0.00212 [2024-02-18 22:22:08,217 INFO misc.py line 119 87073] Train: [58/100][125/1557] Data 0.012 (0.424) Batch 0.795 (1.352) Remain 25:05:28 loss: 0.2463 Lr: 0.00212 [2024-02-18 22:22:09,443 INFO misc.py line 119 87073] Train: [58/100][126/1557] Data 0.004 (0.420) Batch 1.225 (1.351) Remain 25:04:18 loss: 0.2510 Lr: 0.00212 [2024-02-18 22:22:10,452 INFO misc.py line 119 87073] Train: [58/100][127/1557] Data 0.004 (0.417) Batch 1.009 (1.348) Remain 25:01:12 loss: 0.3853 Lr: 0.00212 [2024-02-18 22:22:11,537 INFO misc.py line 119 87073] Train: [58/100][128/1557] Data 0.004 (0.414) Batch 1.085 (1.346) Remain 24:58:50 loss: 0.4090 Lr: 0.00212 [2024-02-18 22:22:12,468 INFO misc.py line 119 87073] Train: [58/100][129/1557] Data 0.005 (0.410) Batch 0.932 (1.343) Remain 24:55:09 loss: 0.2198 Lr: 0.00212 [2024-02-18 22:22:13,354 INFO misc.py line 119 87073] Train: [58/100][130/1557] Data 0.003 (0.407) Batch 0.885 (1.339) Remain 24:51:07 loss: 0.3576 Lr: 0.00212 [2024-02-18 22:22:14,081 INFO misc.py line 119 87073] Train: [58/100][131/1557] Data 0.005 (0.404) Batch 0.727 (1.334) Remain 24:45:46 loss: 0.1957 Lr: 0.00212 [2024-02-18 22:22:14,849 INFO misc.py line 119 87073] Train: [58/100][132/1557] Data 0.005 (0.401) Batch 0.768 (1.330) Remain 24:40:52 loss: 0.4364 Lr: 0.00212 [2024-02-18 22:22:16,086 INFO misc.py line 119 87073] Train: [58/100][133/1557] Data 0.008 (0.398) Batch 1.235 (1.329) Remain 24:40:02 loss: 0.0696 Lr: 0.00212 [2024-02-18 22:22:16,973 INFO misc.py line 119 87073] Train: [58/100][134/1557] Data 0.007 (0.395) Batch 0.888 (1.326) Remain 24:36:16 loss: 0.6192 Lr: 0.00212 [2024-02-18 22:22:17,865 INFO misc.py line 119 87073] Train: [58/100][135/1557] Data 0.006 (0.392) Batch 0.894 (1.322) Remain 24:32:36 loss: 0.3550 Lr: 0.00212 [2024-02-18 22:22:18,699 INFO misc.py line 119 87073] Train: [58/100][136/1557] Data 0.005 (0.389) Batch 0.835 (1.319) Remain 24:28:29 loss: 0.2524 Lr: 0.00212 [2024-02-18 22:22:19,788 INFO misc.py line 119 87073] Train: [58/100][137/1557] Data 0.004 (0.386) Batch 1.089 (1.317) Remain 24:26:33 loss: 0.5081 Lr: 0.00212 [2024-02-18 22:22:20,562 INFO misc.py line 119 87073] Train: [58/100][138/1557] Data 0.003 (0.383) Batch 0.772 (1.313) Remain 24:22:02 loss: 0.3840 Lr: 0.00212 [2024-02-18 22:22:21,316 INFO misc.py line 119 87073] Train: [58/100][139/1557] Data 0.005 (0.380) Batch 0.746 (1.309) Remain 24:17:23 loss: 0.1946 Lr: 0.00212 [2024-02-18 22:22:22,562 INFO misc.py line 119 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line 119 87073] Train: [58/100][165/1557] Data 0.005 (0.321) Batch 0.964 (1.256) Remain 23:17:35 loss: 0.3694 Lr: 0.00212 [2024-02-18 22:22:47,441 INFO misc.py line 119 87073] Train: [58/100][166/1557] Data 0.004 (0.319) Batch 0.718 (1.252) Remain 23:13:53 loss: 0.3028 Lr: 0.00212 [2024-02-18 22:22:48,309 INFO misc.py line 119 87073] Train: [58/100][167/1557] Data 0.011 (0.317) Batch 0.876 (1.250) Remain 23:11:19 loss: 0.3182 Lr: 0.00212 [2024-02-18 22:22:49,624 INFO misc.py line 119 87073] Train: [58/100][168/1557] Data 0.003 (0.315) Batch 1.307 (1.250) Remain 23:11:40 loss: 0.3267 Lr: 0.00212 [2024-02-18 22:22:50,850 INFO misc.py line 119 87073] Train: [58/100][169/1557] Data 0.012 (0.313) Batch 1.222 (1.250) Remain 23:11:28 loss: 0.4608 Lr: 0.00212 [2024-02-18 22:22:51,755 INFO misc.py line 119 87073] Train: [58/100][170/1557] Data 0.016 (0.312) Batch 0.918 (1.248) Remain 23:09:14 loss: 0.3368 Lr: 0.00212 [2024-02-18 22:22:52,933 INFO misc.py line 119 87073] Train: 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Batch 1.092 (1.348) Remain 25:00:17 loss: 0.6213 Lr: 0.00212 [2024-02-18 22:23:18,790 INFO misc.py line 119 87073] Train: [58/100][178/1557] Data 0.007 (0.406) Batch 0.910 (1.346) Remain 24:57:29 loss: 0.4729 Lr: 0.00212 [2024-02-18 22:23:19,737 INFO misc.py line 119 87073] Train: [58/100][179/1557] Data 0.004 (0.404) Batch 0.946 (1.343) Remain 24:54:56 loss: 0.3075 Lr: 0.00212 [2024-02-18 22:23:22,576 INFO misc.py line 119 87073] Train: [58/100][180/1557] Data 1.410 (0.409) Batch 2.841 (1.352) Remain 25:04:19 loss: 0.2576 Lr: 0.00212 [2024-02-18 22:23:23,307 INFO misc.py line 119 87073] Train: [58/100][181/1557] Data 0.004 (0.407) Batch 0.721 (1.348) Remain 25:00:21 loss: 0.1630 Lr: 0.00212 [2024-02-18 22:23:24,570 INFO misc.py line 119 87073] Train: [58/100][182/1557] Data 0.013 (0.405) Batch 1.266 (1.348) Remain 24:59:49 loss: 0.2078 Lr: 0.00212 [2024-02-18 22:23:25,676 INFO misc.py line 119 87073] Train: [58/100][183/1557] Data 0.011 (0.403) Batch 1.102 (1.346) Remain 24:58:17 loss: 0.3791 Lr: 0.00212 [2024-02-18 22:23:26,603 INFO misc.py line 119 87073] Train: [58/100][184/1557] Data 0.015 (0.400) Batch 0.938 (1.344) Remain 24:55:45 loss: 0.2610 Lr: 0.00212 [2024-02-18 22:23:27,559 INFO misc.py line 119 87073] Train: [58/100][185/1557] Data 0.004 (0.398) Batch 0.956 (1.342) Remain 24:53:21 loss: 0.3898 Lr: 0.00212 [2024-02-18 22:23:28,728 INFO misc.py line 119 87073] Train: [58/100][186/1557] Data 0.003 (0.396) Batch 1.169 (1.341) Remain 24:52:16 loss: 0.7011 Lr: 0.00212 [2024-02-18 22:23:29,507 INFO misc.py line 119 87073] Train: [58/100][187/1557] Data 0.004 (0.394) Batch 0.780 (1.338) Remain 24:48:51 loss: 0.2079 Lr: 0.00212 [2024-02-18 22:23:30,265 INFO misc.py line 119 87073] Train: [58/100][188/1557] Data 0.004 (0.392) Batch 0.752 (1.335) Remain 24:45:19 loss: 0.2204 Lr: 0.00212 [2024-02-18 22:23:31,521 INFO misc.py line 119 87073] Train: [58/100][189/1557] Data 0.009 (0.390) Batch 1.250 (1.334) Remain 24:44:47 loss: 0.0749 Lr: 0.00212 [2024-02-18 22:23:32,388 INFO misc.py line 119 87073] Train: [58/100][190/1557] Data 0.016 (0.388) Batch 0.877 (1.332) Remain 24:42:02 loss: 0.4450 Lr: 0.00212 [2024-02-18 22:23:33,373 INFO misc.py line 119 87073] Train: [58/100][191/1557] Data 0.005 (0.386) Batch 0.987 (1.330) Remain 24:39:58 loss: 0.4355 Lr: 0.00212 [2024-02-18 22:23:34,563 INFO misc.py line 119 87073] Train: [58/100][192/1557] Data 0.004 (0.384) Batch 1.184 (1.329) Remain 24:39:05 loss: 0.2678 Lr: 0.00212 [2024-02-18 22:23:35,514 INFO misc.py line 119 87073] Train: [58/100][193/1557] Data 0.010 (0.382) Batch 0.958 (1.327) Remain 24:36:53 loss: 0.2073 Lr: 0.00212 [2024-02-18 22:23:36,250 INFO misc.py line 119 87073] Train: [58/100][194/1557] Data 0.004 (0.380) Batch 0.735 (1.324) Remain 24:33:25 loss: 0.4145 Lr: 0.00212 [2024-02-18 22:23:37,072 INFO misc.py line 119 87073] Train: [58/100][195/1557] Data 0.004 (0.378) Batch 0.784 (1.321) Remain 24:30:16 loss: 0.3073 Lr: 0.00212 [2024-02-18 22:23:38,279 INFO misc.py line 119 87073] Train: [58/100][196/1557] Data 0.042 (0.376) Batch 1.234 (1.321) Remain 24:29:44 loss: 0.2649 Lr: 0.00212 [2024-02-18 22:23:39,252 INFO misc.py line 119 87073] Train: [58/100][197/1557] Data 0.016 (0.374) Batch 0.985 (1.319) Remain 24:27:47 loss: 0.1371 Lr: 0.00212 [2024-02-18 22:23:40,457 INFO misc.py line 119 87073] Train: [58/100][198/1557] Data 0.003 (0.372) Batch 1.190 (1.319) Remain 24:27:02 loss: 0.1439 Lr: 0.00212 [2024-02-18 22:23:41,393 INFO misc.py line 119 87073] Train: [58/100][199/1557] Data 0.017 (0.371) Batch 0.950 (1.317) Remain 24:24:55 loss: 0.0548 Lr: 0.00212 [2024-02-18 22:23:42,369 INFO misc.py line 119 87073] Train: [58/100][200/1557] Data 0.004 (0.369) Batch 0.974 (1.315) Remain 24:22:57 loss: 0.4653 Lr: 0.00212 [2024-02-18 22:23:43,091 INFO misc.py line 119 87073] Train: [58/100][201/1557] Data 0.005 (0.367) Batch 0.724 (1.312) Remain 24:19:37 loss: 0.2873 Lr: 0.00212 [2024-02-18 22:23:43,855 INFO misc.py line 119 87073] Train: [58/100][202/1557] Data 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line 119 87073] Train: [58/100][221/1557] Data 0.005 (0.334) Batch 0.981 (1.281) Remain 23:44:08 loss: 0.5524 Lr: 0.00212 [2024-02-18 22:24:03,216 INFO misc.py line 119 87073] Train: [58/100][222/1557] Data 0.003 (0.332) Batch 0.755 (1.278) Remain 23:41:26 loss: 0.1457 Lr: 0.00212 [2024-02-18 22:24:03,971 INFO misc.py line 119 87073] Train: [58/100][223/1557] Data 0.004 (0.331) Batch 0.743 (1.276) Remain 23:38:42 loss: 0.2807 Lr: 0.00212 [2024-02-18 22:24:05,279 INFO misc.py line 119 87073] Train: [58/100][224/1557] Data 0.016 (0.329) Batch 1.312 (1.276) Remain 23:38:52 loss: 0.2089 Lr: 0.00212 [2024-02-18 22:24:06,233 INFO misc.py line 119 87073] Train: [58/100][225/1557] Data 0.013 (0.328) Batch 0.962 (1.274) Remain 23:37:17 loss: 0.1320 Lr: 0.00212 [2024-02-18 22:24:07,251 INFO misc.py line 119 87073] Train: [58/100][226/1557] Data 0.004 (0.326) Batch 1.019 (1.273) Remain 23:35:59 loss: 0.1527 Lr: 0.00212 [2024-02-18 22:24:08,312 INFO misc.py line 119 87073] Train: 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Batch 0.916 (1.350) Remain 25:00:43 loss: 0.4672 Lr: 0.00212 [2024-02-18 22:24:34,628 INFO misc.py line 119 87073] Train: [58/100][234/1557] Data 0.004 (0.400) Batch 0.900 (1.348) Remain 24:58:32 loss: 0.1417 Lr: 0.00212 [2024-02-18 22:24:35,427 INFO misc.py line 119 87073] Train: [58/100][235/1557] Data 0.008 (0.398) Batch 0.803 (1.345) Remain 24:55:54 loss: 0.1131 Lr: 0.00212 [2024-02-18 22:24:36,144 INFO misc.py line 119 87073] Train: [58/100][236/1557] Data 0.004 (0.397) Batch 0.718 (1.343) Remain 24:52:53 loss: 0.1483 Lr: 0.00212 [2024-02-18 22:24:36,896 INFO misc.py line 119 87073] Train: [58/100][237/1557] Data 0.004 (0.395) Batch 0.742 (1.340) Remain 24:50:01 loss: 0.1103 Lr: 0.00212 [2024-02-18 22:24:38,204 INFO misc.py line 119 87073] Train: [58/100][238/1557] Data 0.013 (0.393) Batch 1.305 (1.340) Remain 24:49:49 loss: 0.2361 Lr: 0.00212 [2024-02-18 22:24:39,166 INFO misc.py line 119 87073] Train: [58/100][239/1557] Data 0.017 (0.392) Batch 0.975 (1.338) Remain 24:48:05 loss: 0.7438 Lr: 0.00212 [2024-02-18 22:24:40,041 INFO misc.py line 119 87073] Train: [58/100][240/1557] Data 0.004 (0.390) Batch 0.876 (1.336) Remain 24:45:53 loss: 0.2011 Lr: 0.00212 [2024-02-18 22:24:41,151 INFO misc.py line 119 87073] Train: [58/100][241/1557] Data 0.003 (0.389) Batch 1.100 (1.335) Remain 24:44:45 loss: 0.2667 Lr: 0.00212 [2024-02-18 22:24:42,131 INFO misc.py line 119 87073] Train: [58/100][242/1557] Data 0.013 (0.387) Batch 0.990 (1.334) Remain 24:43:08 loss: 0.6443 Lr: 0.00212 [2024-02-18 22:24:42,820 INFO misc.py line 119 87073] Train: [58/100][243/1557] Data 0.004 (0.385) Batch 0.689 (1.331) Remain 24:40:07 loss: 0.6149 Lr: 0.00212 [2024-02-18 22:24:43,606 INFO misc.py line 119 87073] Train: [58/100][244/1557] Data 0.004 (0.384) Batch 0.775 (1.329) Remain 24:37:32 loss: 0.6166 Lr: 0.00212 [2024-02-18 22:24:44,823 INFO misc.py line 119 87073] Train: [58/100][245/1557] Data 0.014 (0.382) Batch 1.217 (1.329) Remain 24:37:00 loss: 0.0827 Lr: 0.00212 [2024-02-18 22:24:45,872 INFO misc.py line 119 87073] Train: [58/100][246/1557] Data 0.015 (0.381) Batch 1.054 (1.327) Remain 24:35:43 loss: 0.4185 Lr: 0.00212 [2024-02-18 22:24:46,950 INFO misc.py line 119 87073] Train: [58/100][247/1557] Data 0.010 (0.379) Batch 1.072 (1.326) Remain 24:34:31 loss: 0.4541 Lr: 0.00212 [2024-02-18 22:24:47,984 INFO misc.py line 119 87073] Train: [58/100][248/1557] Data 0.016 (0.378) Batch 1.037 (1.325) Remain 24:33:11 loss: 0.4434 Lr: 0.00212 [2024-02-18 22:24:48,878 INFO misc.py line 119 87073] Train: [58/100][249/1557] Data 0.014 (0.376) Batch 0.903 (1.323) Remain 24:31:16 loss: 0.3494 Lr: 0.00212 [2024-02-18 22:24:49,663 INFO misc.py line 119 87073] Train: [58/100][250/1557] Data 0.004 (0.375) Batch 0.785 (1.321) Remain 24:28:49 loss: 0.1637 Lr: 0.00212 [2024-02-18 22:24:50,400 INFO misc.py line 119 87073] Train: [58/100][251/1557] Data 0.006 (0.373) Batch 0.737 (1.319) Remain 24:26:10 loss: 0.1882 Lr: 0.00212 [2024-02-18 22:24:51,738 INFO misc.py line 119 87073] Train: [58/100][252/1557] Data 0.005 (0.372) Batch 1.327 (1.319) Remain 24:26:11 loss: 0.1638 Lr: 0.00212 [2024-02-18 22:24:52,840 INFO misc.py line 119 87073] Train: [58/100][253/1557] Data 0.017 (0.370) Batch 1.100 (1.318) Remain 24:25:11 loss: 0.3233 Lr: 0.00212 [2024-02-18 22:24:53,907 INFO misc.py line 119 87073] Train: [58/100][254/1557] Data 0.018 (0.369) Batch 1.081 (1.317) Remain 24:24:07 loss: 0.4723 Lr: 0.00212 [2024-02-18 22:24:54,870 INFO misc.py line 119 87073] Train: [58/100][255/1557] Data 0.005 (0.367) Batch 0.964 (1.316) Remain 24:22:32 loss: 0.3831 Lr: 0.00212 [2024-02-18 22:24:55,905 INFO misc.py line 119 87073] Train: [58/100][256/1557] Data 0.004 (0.366) Batch 1.035 (1.315) Remain 24:21:17 loss: 0.4310 Lr: 0.00212 [2024-02-18 22:24:56,640 INFO misc.py line 119 87073] Train: [58/100][257/1557] Data 0.004 (0.365) Batch 0.734 (1.312) Remain 24:18:43 loss: 0.5762 Lr: 0.00212 [2024-02-18 22:24:57,418 INFO misc.py line 119 87073] Train: [58/100][258/1557] Data 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[2024-02-18 22:25:10,348 INFO misc.py line 119 87073] Train: [58/100][271/1557] Data 0.004 (0.346) Batch 0.819 (1.295) Remain 23:59:04 loss: 0.3880 Lr: 0.00212 [2024-02-18 22:25:11,128 INFO misc.py line 119 87073] Train: [58/100][272/1557] Data 0.005 (0.345) Batch 0.773 (1.293) Remain 23:56:53 loss: 0.2603 Lr: 0.00212 [2024-02-18 22:25:12,387 INFO misc.py line 119 87073] Train: [58/100][273/1557] Data 0.013 (0.344) Batch 1.255 (1.293) Remain 23:56:43 loss: 0.1373 Lr: 0.00212 [2024-02-18 22:25:13,372 INFO misc.py line 119 87073] Train: [58/100][274/1557] Data 0.016 (0.342) Batch 0.997 (1.292) Remain 23:55:28 loss: 0.4570 Lr: 0.00212 [2024-02-18 22:25:14,233 INFO misc.py line 119 87073] Train: [58/100][275/1557] Data 0.004 (0.341) Batch 0.862 (1.290) Remain 23:53:42 loss: 0.2209 Lr: 0.00212 [2024-02-18 22:25:15,295 INFO misc.py line 119 87073] Train: [58/100][276/1557] Data 0.004 (0.340) Batch 1.054 (1.289) Remain 23:52:43 loss: 0.3463 Lr: 0.00212 [2024-02-18 22:25:16,161 INFO misc.py line 119 87073] Train: [58/100][277/1557] Data 0.012 (0.339) Batch 0.874 (1.288) Remain 23:51:00 loss: 0.3487 Lr: 0.00212 [2024-02-18 22:25:16,941 INFO misc.py line 119 87073] Train: [58/100][278/1557] Data 0.004 (0.337) Batch 0.780 (1.286) Remain 23:48:56 loss: 0.2243 Lr: 0.00212 [2024-02-18 22:25:17,707 INFO misc.py line 119 87073] Train: [58/100][279/1557] Data 0.004 (0.336) Batch 0.765 (1.284) Remain 23:46:49 loss: 0.2744 Lr: 0.00212 [2024-02-18 22:25:18,888 INFO misc.py line 119 87073] Train: [58/100][280/1557] Data 0.004 (0.335) Batch 1.171 (1.284) Remain 23:46:21 loss: 0.1975 Lr: 0.00212 [2024-02-18 22:25:20,009 INFO misc.py line 119 87073] Train: [58/100][281/1557] Data 0.014 (0.334) Batch 1.125 (1.283) Remain 23:45:41 loss: 0.1937 Lr: 0.00212 [2024-02-18 22:25:20,921 INFO misc.py line 119 87073] Train: [58/100][282/1557] Data 0.011 (0.333) Batch 0.919 (1.282) Remain 23:44:13 loss: 0.3851 Lr: 0.00212 [2024-02-18 22:25:21,799 INFO misc.py line 119 87073] Train: 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Batch 1.091 (1.344) Remain 24:53:06 loss: 0.5838 Lr: 0.00212 [2024-02-18 22:25:48,565 INFO misc.py line 119 87073] Train: [58/100][290/1557] Data 0.004 (0.393) Batch 0.901 (1.342) Remain 24:51:22 loss: 0.4421 Lr: 0.00212 [2024-02-18 22:25:49,599 INFO misc.py line 119 87073] Train: [58/100][291/1557] Data 0.005 (0.391) Batch 1.035 (1.341) Remain 24:50:09 loss: 0.3742 Lr: 0.00212 [2024-02-18 22:25:50,338 INFO misc.py line 119 87073] Train: [58/100][292/1557] Data 0.003 (0.390) Batch 0.737 (1.339) Remain 24:47:48 loss: 0.4042 Lr: 0.00212 [2024-02-18 22:25:51,006 INFO misc.py line 119 87073] Train: [58/100][293/1557] Data 0.006 (0.389) Batch 0.654 (1.337) Remain 24:45:10 loss: 0.3531 Lr: 0.00212 [2024-02-18 22:25:52,301 INFO misc.py line 119 87073] Train: [58/100][294/1557] Data 0.022 (0.387) Batch 1.301 (1.337) Remain 24:45:00 loss: 0.1069 Lr: 0.00212 [2024-02-18 22:25:53,402 INFO misc.py line 119 87073] Train: [58/100][295/1557] Data 0.015 (0.386) Batch 1.109 (1.336) Remain 24:44:07 loss: 0.5943 Lr: 0.00212 [2024-02-18 22:25:54,351 INFO misc.py line 119 87073] Train: [58/100][296/1557] Data 0.005 (0.385) Batch 0.950 (1.335) Remain 24:42:38 loss: 0.4863 Lr: 0.00212 [2024-02-18 22:25:55,298 INFO misc.py line 119 87073] Train: [58/100][297/1557] Data 0.004 (0.384) Batch 0.947 (1.333) Remain 24:41:08 loss: 0.3323 Lr: 0.00212 [2024-02-18 22:25:56,247 INFO misc.py line 119 87073] Train: [58/100][298/1557] Data 0.004 (0.382) Batch 0.950 (1.332) Remain 24:39:40 loss: 0.4057 Lr: 0.00212 [2024-02-18 22:25:56,947 INFO misc.py line 119 87073] Train: [58/100][299/1557] Data 0.003 (0.381) Batch 0.693 (1.330) Remain 24:37:15 loss: 0.1684 Lr: 0.00212 [2024-02-18 22:25:57,713 INFO misc.py line 119 87073] Train: [58/100][300/1557] Data 0.010 (0.380) Batch 0.772 (1.328) Remain 24:35:09 loss: 0.2173 Lr: 0.00212 [2024-02-18 22:25:58,882 INFO misc.py line 119 87073] Train: [58/100][301/1557] Data 0.004 (0.378) Batch 1.168 (1.327) Remain 24:34:32 loss: 0.1036 Lr: 0.00212 [2024-02-18 22:25:59,779 INFO misc.py line 119 87073] Train: [58/100][302/1557] Data 0.005 (0.377) Batch 0.898 (1.326) Remain 24:32:55 loss: 0.4965 Lr: 0.00212 [2024-02-18 22:26:00,880 INFO misc.py line 119 87073] Train: [58/100][303/1557] Data 0.004 (0.376) Batch 1.101 (1.325) Remain 24:32:03 loss: 0.3908 Lr: 0.00212 [2024-02-18 22:26:02,063 INFO misc.py line 119 87073] Train: [58/100][304/1557] Data 0.004 (0.375) Batch 1.157 (1.325) Remain 24:31:25 loss: 0.7623 Lr: 0.00212 [2024-02-18 22:26:02,959 INFO misc.py line 119 87073] Train: [58/100][305/1557] Data 0.030 (0.374) Batch 0.921 (1.323) Remain 24:29:54 loss: 0.7000 Lr: 0.00212 [2024-02-18 22:26:03,773 INFO misc.py line 119 87073] Train: [58/100][306/1557] Data 0.006 (0.372) Batch 0.814 (1.322) Remain 24:28:01 loss: 0.2948 Lr: 0.00212 [2024-02-18 22:26:04,526 INFO misc.py line 119 87073] Train: [58/100][307/1557] Data 0.004 (0.371) Batch 0.749 (1.320) Remain 24:25:54 loss: 0.2673 Lr: 0.00212 [2024-02-18 22:26:05,809 INFO misc.py line 119 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0.007 (0.363) Batch 0.834 (1.312) Remain 24:16:51 loss: 0.2165 Lr: 0.00211 [2024-02-18 22:26:12,407 INFO misc.py line 119 87073] Train: [58/100][315/1557] Data 0.008 (0.362) Batch 1.135 (1.311) Remain 24:16:12 loss: 0.2176 Lr: 0.00211 [2024-02-18 22:26:13,470 INFO misc.py line 119 87073] Train: [58/100][316/1557] Data 0.008 (0.361) Batch 1.067 (1.310) Remain 24:15:19 loss: 0.1743 Lr: 0.00211 [2024-02-18 22:26:14,420 INFO misc.py line 119 87073] Train: [58/100][317/1557] Data 0.005 (0.360) Batch 0.951 (1.309) Remain 24:14:01 loss: 0.3753 Lr: 0.00211 [2024-02-18 22:26:15,353 INFO misc.py line 119 87073] Train: [58/100][318/1557] Data 0.004 (0.358) Batch 0.932 (1.308) Remain 24:12:40 loss: 0.1078 Lr: 0.00211 [2024-02-18 22:26:16,214 INFO misc.py line 119 87073] Train: [58/100][319/1557] Data 0.005 (0.357) Batch 0.861 (1.307) Remain 24:11:04 loss: 0.3453 Lr: 0.00211 [2024-02-18 22:26:17,004 INFO misc.py line 119 87073] Train: [58/100][320/1557] Data 0.005 (0.356) Batch 0.779 (1.305) Remain 24:09:12 loss: 0.3735 Lr: 0.00211 [2024-02-18 22:26:17,790 INFO misc.py line 119 87073] Train: [58/100][321/1557] Data 0.015 (0.355) Batch 0.798 (1.303) Remain 24:07:25 loss: 0.1257 Lr: 0.00211 [2024-02-18 22:26:18,958 INFO misc.py line 119 87073] Train: [58/100][322/1557] Data 0.003 (0.354) Batch 1.167 (1.303) Remain 24:06:55 loss: 0.2118 Lr: 0.00211 [2024-02-18 22:26:19,857 INFO misc.py line 119 87073] Train: [58/100][323/1557] Data 0.004 (0.353) Batch 0.899 (1.302) Remain 24:05:30 loss: 0.1050 Lr: 0.00211 [2024-02-18 22:26:20,788 INFO misc.py line 119 87073] Train: [58/100][324/1557] Data 0.004 (0.352) Batch 0.926 (1.301) Remain 24:04:10 loss: 0.5372 Lr: 0.00211 [2024-02-18 22:26:21,883 INFO misc.py line 119 87073] Train: [58/100][325/1557] Data 0.008 (0.351) Batch 1.095 (1.300) Remain 24:03:27 loss: 0.5335 Lr: 0.00211 [2024-02-18 22:26:22,748 INFO misc.py line 119 87073] Train: [58/100][326/1557] Data 0.010 (0.350) Batch 0.869 (1.299) Remain 24:01:56 loss: 0.1214 Lr: 0.00211 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line 119 87073] Train: [58/100][333/1557] Data 0.005 (0.342) Batch 1.082 (1.291) Remain 23:53:34 loss: 0.4331 Lr: 0.00211 [2024-02-18 22:26:30,172 INFO misc.py line 119 87073] Train: [58/100][334/1557] Data 0.004 (0.341) Batch 0.777 (1.290) Remain 23:51:49 loss: 0.2829 Lr: 0.00211 [2024-02-18 22:26:30,890 INFO misc.py line 119 87073] Train: [58/100][335/1557] Data 0.005 (0.340) Batch 0.716 (1.288) Remain 23:49:53 loss: 0.1524 Lr: 0.00211 [2024-02-18 22:26:32,140 INFO misc.py line 119 87073] Train: [58/100][336/1557] Data 0.006 (0.339) Batch 1.242 (1.288) Remain 23:49:42 loss: 0.1757 Lr: 0.00211 [2024-02-18 22:26:33,127 INFO misc.py line 119 87073] Train: [58/100][337/1557] Data 0.015 (0.338) Batch 0.996 (1.287) Remain 23:48:43 loss: 0.5596 Lr: 0.00211 [2024-02-18 22:26:33,982 INFO misc.py line 119 87073] Train: [58/100][338/1557] Data 0.005 (0.337) Batch 0.856 (1.286) Remain 23:47:16 loss: 0.2846 Lr: 0.00211 [2024-02-18 22:26:34,822 INFO misc.py line 119 87073] Train: 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Batch 0.879 (1.341) Remain 24:49:03 loss: 1.0741 Lr: 0.00211 [2024-02-18 22:27:03,105 INFO misc.py line 119 87073] Train: [58/100][346/1557] Data 0.005 (0.393) Batch 1.033 (1.340) Remain 24:48:02 loss: 0.6216 Lr: 0.00211 [2024-02-18 22:27:03,858 INFO misc.py line 119 87073] Train: [58/100][347/1557] Data 0.014 (0.392) Batch 0.761 (1.339) Remain 24:46:09 loss: 0.3194 Lr: 0.00211 [2024-02-18 22:27:04,597 INFO misc.py line 119 87073] Train: [58/100][348/1557] Data 0.006 (0.391) Batch 0.731 (1.337) Remain 24:44:10 loss: 0.2694 Lr: 0.00211 [2024-02-18 22:27:05,367 INFO misc.py line 119 87073] Train: [58/100][349/1557] Data 0.012 (0.389) Batch 0.778 (1.335) Remain 24:42:21 loss: 0.5686 Lr: 0.00211 [2024-02-18 22:27:06,696 INFO misc.py line 119 87073] Train: [58/100][350/1557] Data 0.004 (0.388) Batch 1.327 (1.335) Remain 24:42:18 loss: 0.1076 Lr: 0.00211 [2024-02-18 22:27:07,608 INFO misc.py line 119 87073] Train: [58/100][351/1557] Data 0.005 (0.387) Batch 0.912 (1.334) Remain 24:40:56 loss: 0.5109 Lr: 0.00211 [2024-02-18 22:27:08,473 INFO misc.py line 119 87073] Train: [58/100][352/1557] Data 0.008 (0.386) Batch 0.866 (1.333) Remain 24:39:25 loss: 0.3087 Lr: 0.00211 [2024-02-18 22:27:09,528 INFO misc.py line 119 87073] Train: [58/100][353/1557] Data 0.005 (0.385) Batch 1.052 (1.332) Remain 24:38:31 loss: 0.5817 Lr: 0.00211 [2024-02-18 22:27:10,426 INFO misc.py line 119 87073] Train: [58/100][354/1557] Data 0.007 (0.384) Batch 0.901 (1.331) Remain 24:37:08 loss: 0.4460 Lr: 0.00211 [2024-02-18 22:27:12,763 INFO misc.py line 119 87073] Train: [58/100][355/1557] Data 1.149 (0.386) Batch 2.335 (1.334) Remain 24:40:16 loss: 0.1572 Lr: 0.00211 [2024-02-18 22:27:13,539 INFO misc.py line 119 87073] Train: [58/100][356/1557] Data 0.007 (0.385) Batch 0.778 (1.332) Remain 24:38:30 loss: 0.1981 Lr: 0.00211 [2024-02-18 22:27:14,786 INFO misc.py line 119 87073] Train: [58/100][357/1557] Data 0.003 (0.384) Batch 1.245 (1.332) Remain 24:38:12 loss: 0.1060 Lr: 0.00211 [2024-02-18 22:27:15,699 INFO misc.py line 119 87073] Train: [58/100][358/1557] Data 0.006 (0.383) Batch 0.910 (1.331) Remain 24:36:52 loss: 0.4948 Lr: 0.00211 [2024-02-18 22:27:16,864 INFO misc.py line 119 87073] Train: [58/100][359/1557] Data 0.010 (0.382) Batch 1.168 (1.330) Remain 24:36:20 loss: 0.5630 Lr: 0.00211 [2024-02-18 22:27:17,798 INFO misc.py line 119 87073] Train: [58/100][360/1557] Data 0.004 (0.381) Batch 0.934 (1.329) Remain 24:35:05 loss: 0.3421 Lr: 0.00211 [2024-02-18 22:27:18,756 INFO misc.py line 119 87073] Train: [58/100][361/1557] Data 0.004 (0.380) Batch 0.958 (1.328) Remain 24:33:55 loss: 0.4748 Lr: 0.00211 [2024-02-18 22:27:19,509 INFO misc.py line 119 87073] Train: [58/100][362/1557] Data 0.005 (0.379) Batch 0.750 (1.326) Remain 24:32:06 loss: 0.2527 Lr: 0.00211 [2024-02-18 22:27:20,223 INFO misc.py line 119 87073] Train: [58/100][363/1557] Data 0.007 (0.378) Batch 0.718 (1.325) Remain 24:30:12 loss: 0.2822 Lr: 0.00211 [2024-02-18 22:27:21,441 INFO misc.py line 119 87073] Train: [58/100][364/1557] Data 0.004 (0.377) Batch 1.210 (1.324) Remain 24:29:50 loss: 0.1405 Lr: 0.00211 [2024-02-18 22:27:22,448 INFO misc.py line 119 87073] Train: [58/100][365/1557] Data 0.016 (0.376) Batch 1.014 (1.324) Remain 24:28:51 loss: 0.4324 Lr: 0.00211 [2024-02-18 22:27:23,383 INFO misc.py line 119 87073] Train: [58/100][366/1557] Data 0.005 (0.375) Batch 0.929 (1.322) Remain 24:27:38 loss: 0.1093 Lr: 0.00211 [2024-02-18 22:27:24,362 INFO misc.py line 119 87073] Train: [58/100][367/1557] Data 0.011 (0.374) Batch 0.985 (1.322) Remain 24:26:35 loss: 0.0901 Lr: 0.00211 [2024-02-18 22:27:25,401 INFO misc.py line 119 87073] Train: [58/100][368/1557] Data 0.004 (0.373) Batch 1.040 (1.321) Remain 24:25:42 loss: 0.3321 Lr: 0.00211 [2024-02-18 22:27:26,152 INFO misc.py line 119 87073] Train: [58/100][369/1557] Data 0.004 (0.372) Batch 0.743 (1.319) Remain 24:23:56 loss: 0.2545 Lr: 0.00211 [2024-02-18 22:27:26,932 INFO misc.py line 119 87073] Train: [58/100][370/1557] Data 0.011 (0.371) Batch 0.786 (1.318) Remain 24:22:18 loss: 0.2392 Lr: 0.00211 [2024-02-18 22:27:28,055 INFO misc.py line 119 87073] Train: [58/100][371/1557] Data 0.004 (0.370) Batch 1.123 (1.317) Remain 24:21:41 loss: 0.1401 Lr: 0.00211 [2024-02-18 22:27:29,185 INFO misc.py line 119 87073] Train: [58/100][372/1557] Data 0.005 (0.369) Batch 1.128 (1.317) Remain 24:21:06 loss: 0.3706 Lr: 0.00211 [2024-02-18 22:27:30,067 INFO misc.py line 119 87073] Train: [58/100][373/1557] Data 0.007 (0.368) Batch 0.884 (1.316) Remain 24:19:47 loss: 0.6725 Lr: 0.00211 [2024-02-18 22:27:30,944 INFO misc.py line 119 87073] Train: [58/100][374/1557] Data 0.004 (0.367) Batch 0.876 (1.314) Remain 24:18:26 loss: 0.2462 Lr: 0.00211 [2024-02-18 22:27:32,064 INFO misc.py line 119 87073] Train: [58/100][375/1557] Data 0.006 (0.366) Batch 1.121 (1.314) Remain 24:17:50 loss: 0.4588 Lr: 0.00211 [2024-02-18 22:27:32,801 INFO misc.py line 119 87073] Train: [58/100][376/1557] Data 0.005 (0.365) Batch 0.737 (1.312) Remain 24:16:06 loss: 0.1627 Lr: 0.00211 [2024-02-18 22:27:33,561 INFO misc.py line 119 87073] Train: [58/100][377/1557] Data 0.004 (0.364) Batch 0.759 (1.311) Remain 24:14:26 loss: 0.1713 Lr: 0.00211 [2024-02-18 22:27:34,776 INFO misc.py line 119 87073] Train: [58/100][378/1557] Data 0.004 (0.363) Batch 1.209 (1.311) Remain 24:14:07 loss: 0.2983 Lr: 0.00211 [2024-02-18 22:27:35,782 INFO misc.py line 119 87073] Train: [58/100][379/1557] Data 0.012 (0.362) Batch 1.004 (1.310) Remain 24:13:11 loss: 0.4228 Lr: 0.00211 [2024-02-18 22:27:36,816 INFO misc.py line 119 87073] Train: [58/100][380/1557] Data 0.013 (0.361) Batch 1.039 (1.309) Remain 24:12:22 loss: 0.3527 Lr: 0.00211 [2024-02-18 22:27:37,855 INFO misc.py line 119 87073] Train: [58/100][381/1557] Data 0.009 (0.360) Batch 1.037 (1.308) Remain 24:11:33 loss: 0.5297 Lr: 0.00211 [2024-02-18 22:27:38,727 INFO misc.py line 119 87073] Train: [58/100][382/1557] Data 0.010 (0.359) Batch 0.877 (1.307) Remain 24:10:16 loss: 0.2742 Lr: 0.00211 [2024-02-18 22:27:39,465 INFO misc.py line 119 87073] Train: [58/100][383/1557] Data 0.004 (0.358) Batch 0.739 (1.306) Remain 24:08:35 loss: 0.4650 Lr: 0.00211 [2024-02-18 22:27:40,200 INFO misc.py line 119 87073] Train: [58/100][384/1557] Data 0.004 (0.357) Batch 0.726 (1.304) Remain 24:06:53 loss: 0.2514 Lr: 0.00211 [2024-02-18 22:27:41,452 INFO misc.py line 119 87073] Train: [58/100][385/1557] Data 0.012 (0.356) Batch 1.250 (1.304) Remain 24:06:42 loss: 0.1423 Lr: 0.00211 [2024-02-18 22:27:42,356 INFO misc.py line 119 87073] Train: [58/100][386/1557] Data 0.016 (0.356) Batch 0.916 (1.303) Remain 24:05:33 loss: 0.2128 Lr: 0.00211 [2024-02-18 22:27:43,447 INFO misc.py line 119 87073] Train: [58/100][387/1557] Data 0.004 (0.355) Batch 1.091 (1.302) Remain 24:04:55 loss: 0.2158 Lr: 0.00211 [2024-02-18 22:27:44,283 INFO misc.py line 119 87073] Train: [58/100][388/1557] Data 0.004 (0.354) Batch 0.835 (1.301) Remain 24:03:33 loss: 0.5755 Lr: 0.00211 [2024-02-18 22:27:45,244 INFO misc.py line 119 87073] Train: [58/100][389/1557] Data 0.006 (0.353) Batch 0.959 (1.300) Remain 24:02:32 loss: 0.6836 Lr: 0.00211 [2024-02-18 22:27:45,967 INFO misc.py line 119 87073] Train: [58/100][390/1557] Data 0.007 (0.352) Batch 0.726 (1.299) Remain 24:00:52 loss: 0.2259 Lr: 0.00211 [2024-02-18 22:27:46,731 INFO misc.py line 119 87073] Train: [58/100][391/1557] Data 0.004 (0.351) Batch 0.764 (1.297) Remain 23:59:19 loss: 0.3226 Lr: 0.00211 [2024-02-18 22:27:47,862 INFO misc.py line 119 87073] Train: [58/100][392/1557] Data 0.004 (0.350) Batch 1.119 (1.297) Remain 23:58:47 loss: 0.2449 Lr: 0.00211 [2024-02-18 22:27:48,732 INFO misc.py line 119 87073] Train: [58/100][393/1557] Data 0.017 (0.349) Batch 0.881 (1.296) Remain 23:57:35 loss: 0.3567 Lr: 0.00211 [2024-02-18 22:27:49,627 INFO misc.py line 119 87073] Train: [58/100][394/1557] Data 0.005 (0.348) Batch 0.896 (1.295) Remain 23:56:26 loss: 0.1685 Lr: 0.00211 [2024-02-18 22:27:50,548 INFO misc.py line 119 87073] Train: 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Batch 0.911 (1.338) Remain 24:43:31 loss: 0.2468 Lr: 0.00211 [2024-02-18 22:28:16,489 INFO misc.py line 119 87073] Train: [58/100][402/1557] Data 0.006 (0.390) Batch 0.841 (1.336) Remain 24:42:07 loss: 0.2525 Lr: 0.00211 [2024-02-18 22:28:17,390 INFO misc.py line 119 87073] Train: [58/100][403/1557] Data 0.011 (0.389) Batch 0.908 (1.335) Remain 24:40:54 loss: 0.3168 Lr: 0.00211 [2024-02-18 22:28:18,089 INFO misc.py line 119 87073] Train: [58/100][404/1557] Data 0.004 (0.388) Batch 0.699 (1.334) Remain 24:39:07 loss: 0.3007 Lr: 0.00211 [2024-02-18 22:28:18,862 INFO misc.py line 119 87073] Train: [58/100][405/1557] Data 0.004 (0.388) Batch 0.765 (1.332) Remain 24:37:32 loss: 0.3528 Lr: 0.00211 [2024-02-18 22:28:20,134 INFO misc.py line 119 87073] Train: [58/100][406/1557] Data 0.011 (0.387) Batch 1.267 (1.332) Remain 24:37:20 loss: 0.1324 Lr: 0.00211 [2024-02-18 22:28:21,079 INFO misc.py line 119 87073] Train: [58/100][407/1557] Data 0.017 (0.386) Batch 0.957 (1.331) Remain 24:36:17 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22:28:27,673 INFO misc.py line 119 87073] Train: [58/100][414/1557] Data 0.010 (0.379) Batch 0.923 (1.324) Remain 24:28:46 loss: 0.3651 Lr: 0.00211 [2024-02-18 22:28:28,570 INFO misc.py line 119 87073] Train: [58/100][415/1557] Data 0.005 (0.378) Batch 0.898 (1.323) Remain 24:27:36 loss: 0.3314 Lr: 0.00211 [2024-02-18 22:28:29,418 INFO misc.py line 119 87073] Train: [58/100][416/1557] Data 0.004 (0.377) Batch 0.846 (1.322) Remain 24:26:18 loss: 0.2416 Lr: 0.00211 [2024-02-18 22:28:30,470 INFO misc.py line 119 87073] Train: [58/100][417/1557] Data 0.007 (0.377) Batch 1.050 (1.322) Remain 24:25:33 loss: 0.2192 Lr: 0.00211 [2024-02-18 22:28:31,192 INFO misc.py line 119 87073] Train: [58/100][418/1557] Data 0.009 (0.376) Batch 0.727 (1.320) Remain 24:23:56 loss: 0.3860 Lr: 0.00211 [2024-02-18 22:28:31,943 INFO misc.py line 119 87073] Train: [58/100][419/1557] Data 0.004 (0.375) Batch 0.742 (1.319) Remain 24:22:22 loss: 0.4568 Lr: 0.00211 [2024-02-18 22:28:33,250 INFO misc.py line 119 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line 119 87073] Train: [58/100][445/1557] Data 0.005 (0.353) Batch 0.867 (1.297) Remain 23:57:58 loss: 0.4261 Lr: 0.00211 [2024-02-18 22:28:57,503 INFO misc.py line 119 87073] Train: [58/100][446/1557] Data 0.004 (0.353) Batch 0.775 (1.296) Remain 23:56:38 loss: 0.2641 Lr: 0.00211 [2024-02-18 22:28:58,266 INFO misc.py line 119 87073] Train: [58/100][447/1557] Data 0.011 (0.352) Batch 0.769 (1.295) Remain 23:55:18 loss: 0.4570 Lr: 0.00211 [2024-02-18 22:28:59,405 INFO misc.py line 119 87073] Train: [58/100][448/1557] Data 0.005 (0.351) Batch 1.138 (1.295) Remain 23:54:54 loss: 0.1280 Lr: 0.00211 [2024-02-18 22:29:00,538 INFO misc.py line 119 87073] Train: [58/100][449/1557] Data 0.005 (0.350) Batch 1.124 (1.294) Remain 23:54:27 loss: 0.3304 Lr: 0.00211 [2024-02-18 22:29:01,406 INFO misc.py line 119 87073] Train: [58/100][450/1557] Data 0.016 (0.349) Batch 0.877 (1.293) Remain 23:53:23 loss: 0.1657 Lr: 0.00211 [2024-02-18 22:29:02,408 INFO misc.py line 119 87073] Train: 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Batch 1.008 (1.333) Remain 24:37:45 loss: 0.5799 Lr: 0.00211 [2024-02-18 22:29:29,716 INFO misc.py line 119 87073] Train: [58/100][458/1557] Data 0.005 (0.389) Batch 1.024 (1.333) Remain 24:36:59 loss: 0.1665 Lr: 0.00211 [2024-02-18 22:29:30,697 INFO misc.py line 119 87073] Train: [58/100][459/1557] Data 0.004 (0.388) Batch 0.981 (1.332) Remain 24:36:06 loss: 0.5922 Lr: 0.00211 [2024-02-18 22:29:31,460 INFO misc.py line 119 87073] Train: [58/100][460/1557] Data 0.004 (0.387) Batch 0.762 (1.331) Remain 24:34:42 loss: 0.1660 Lr: 0.00211 [2024-02-18 22:29:32,195 INFO misc.py line 119 87073] Train: [58/100][461/1557] Data 0.004 (0.386) Batch 0.727 (1.329) Remain 24:33:13 loss: 0.2194 Lr: 0.00211 [2024-02-18 22:29:33,427 INFO misc.py line 119 87073] Train: [58/100][462/1557] Data 0.012 (0.385) Batch 1.236 (1.329) Remain 24:32:58 loss: 0.1338 Lr: 0.00211 [2024-02-18 22:29:34,296 INFO misc.py line 119 87073] Train: [58/100][463/1557] Data 0.008 (0.384) Batch 0.871 (1.328) Remain 24:31:50 loss: 0.1007 Lr: 0.00211 [2024-02-18 22:29:35,222 INFO misc.py line 119 87073] Train: [58/100][464/1557] Data 0.008 (0.384) Batch 0.928 (1.327) Remain 24:30:51 loss: 0.2761 Lr: 0.00211 [2024-02-18 22:29:36,072 INFO misc.py line 119 87073] Train: [58/100][465/1557] Data 0.005 (0.383) Batch 0.850 (1.326) Remain 24:29:41 loss: 0.1448 Lr: 0.00211 [2024-02-18 22:29:37,005 INFO misc.py line 119 87073] Train: [58/100][466/1557] Data 0.004 (0.382) Batch 0.929 (1.325) Remain 24:28:43 loss: 0.2666 Lr: 0.00211 [2024-02-18 22:29:37,778 INFO misc.py line 119 87073] Train: [58/100][467/1557] Data 0.008 (0.381) Batch 0.777 (1.324) Remain 24:27:23 loss: 0.3673 Lr: 0.00211 [2024-02-18 22:29:38,457 INFO misc.py line 119 87073] Train: [58/100][468/1557] Data 0.006 (0.380) Batch 0.674 (1.323) Remain 24:25:49 loss: 0.2272 Lr: 0.00211 [2024-02-18 22:29:39,656 INFO misc.py line 119 87073] Train: [58/100][469/1557] Data 0.010 (0.380) Batch 1.197 (1.323) Remain 24:25:29 loss: 0.1222 Lr: 0.00211 [2024-02-18 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Batch 0.925 (1.331) Remain 24:33:16 loss: 0.4129 Lr: 0.00210 [2024-02-18 22:30:42,826 INFO misc.py line 119 87073] Train: [58/100][514/1557] Data 0.003 (0.387) Batch 0.953 (1.330) Remain 24:32:25 loss: 0.2208 Lr: 0.00210 [2024-02-18 22:30:43,934 INFO misc.py line 119 87073] Train: [58/100][515/1557] Data 0.008 (0.386) Batch 1.112 (1.329) Remain 24:31:56 loss: 0.3079 Lr: 0.00210 [2024-02-18 22:30:44,670 INFO misc.py line 119 87073] Train: [58/100][516/1557] Data 0.004 (0.386) Batch 0.733 (1.328) Remain 24:30:37 loss: 0.3396 Lr: 0.00210 [2024-02-18 22:30:45,516 INFO misc.py line 119 87073] Train: [58/100][517/1557] Data 0.007 (0.385) Batch 0.845 (1.327) Remain 24:29:33 loss: 0.2934 Lr: 0.00210 [2024-02-18 22:30:46,817 INFO misc.py line 119 87073] Train: [58/100][518/1557] Data 0.008 (0.384) Batch 1.297 (1.327) Remain 24:29:28 loss: 0.1961 Lr: 0.00210 [2024-02-18 22:30:47,882 INFO misc.py line 119 87073] Train: [58/100][519/1557] Data 0.012 (0.383) Batch 1.070 (1.327) Remain 24:28:54 loss: 0.2062 Lr: 0.00210 [2024-02-18 22:30:48,930 INFO misc.py line 119 87073] Train: [58/100][520/1557] Data 0.007 (0.383) Batch 1.011 (1.326) Remain 24:28:12 loss: 0.3704 Lr: 0.00210 [2024-02-18 22:30:49,945 INFO misc.py line 119 87073] Train: [58/100][521/1557] Data 0.043 (0.382) Batch 1.042 (1.326) Remain 24:27:34 loss: 0.2652 Lr: 0.00210 [2024-02-18 22:30:51,036 INFO misc.py line 119 87073] Train: [58/100][522/1557] Data 0.016 (0.381) Batch 1.097 (1.325) Remain 24:27:03 loss: 0.2544 Lr: 0.00210 [2024-02-18 22:30:51,823 INFO misc.py line 119 87073] Train: [58/100][523/1557] Data 0.010 (0.381) Batch 0.793 (1.324) Remain 24:25:54 loss: 0.2107 Lr: 0.00210 [2024-02-18 22:30:52,588 INFO misc.py line 119 87073] Train: [58/100][524/1557] Data 0.005 (0.380) Batch 0.765 (1.323) Remain 24:24:41 loss: 0.4430 Lr: 0.00210 [2024-02-18 22:30:53,707 INFO misc.py line 119 87073] Train: [58/100][525/1557] Data 0.005 (0.379) Batch 1.113 (1.323) Remain 24:24:13 loss: 0.1765 Lr: 0.00210 [2024-02-18 22:30:54,649 INFO misc.py line 119 87073] Train: [58/100][526/1557] Data 0.011 (0.379) Batch 0.948 (1.322) Remain 24:23:25 loss: 0.3762 Lr: 0.00210 [2024-02-18 22:30:55,507 INFO misc.py line 119 87073] Train: [58/100][527/1557] Data 0.004 (0.378) Batch 0.858 (1.321) Remain 24:22:24 loss: 0.2795 Lr: 0.00210 [2024-02-18 22:30:56,437 INFO misc.py line 119 87073] Train: [58/100][528/1557] Data 0.005 (0.377) Batch 0.917 (1.320) Remain 24:21:32 loss: 0.1830 Lr: 0.00210 [2024-02-18 22:30:57,428 INFO misc.py line 119 87073] Train: [58/100][529/1557] Data 0.017 (0.376) Batch 1.003 (1.320) Remain 24:20:51 loss: 0.2979 Lr: 0.00210 [2024-02-18 22:30:59,693 INFO misc.py line 119 87073] Train: [58/100][530/1557] Data 1.237 (0.378) Batch 2.266 (1.321) Remain 24:22:49 loss: 0.1796 Lr: 0.00210 [2024-02-18 22:31:00,425 INFO misc.py line 119 87073] Train: [58/100][531/1557] Data 0.005 (0.377) Batch 0.732 (1.320) Remain 24:21:33 loss: 0.1942 Lr: 0.00210 [2024-02-18 22:31:01,725 INFO misc.py line 119 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Batch 0.808 (1.331) Remain 24:32:56 loss: 0.3388 Lr: 0.00210 [2024-02-18 22:31:57,722 INFO misc.py line 119 87073] Train: [58/100][570/1557] Data 0.006 (0.388) Batch 0.876 (1.331) Remain 24:32:01 loss: 0.7561 Lr: 0.00210 [2024-02-18 22:31:58,763 INFO misc.py line 119 87073] Train: [58/100][571/1557] Data 0.012 (0.387) Batch 1.039 (1.330) Remain 24:31:26 loss: 0.4159 Lr: 0.00210 [2024-02-18 22:31:59,506 INFO misc.py line 119 87073] Train: [58/100][572/1557] Data 0.007 (0.386) Batch 0.742 (1.329) Remain 24:30:16 loss: 0.1410 Lr: 0.00210 [2024-02-18 22:32:00,266 INFO misc.py line 119 87073] Train: [58/100][573/1557] Data 0.008 (0.386) Batch 0.762 (1.328) Remain 24:29:09 loss: 0.3267 Lr: 0.00210 [2024-02-18 22:32:01,582 INFO misc.py line 119 87073] Train: [58/100][574/1557] Data 0.006 (0.385) Batch 1.314 (1.328) Remain 24:29:06 loss: 0.1379 Lr: 0.00210 [2024-02-18 22:32:02,896 INFO misc.py line 119 87073] Train: [58/100][575/1557] Data 0.007 (0.384) Batch 1.310 (1.328) Remain 24:29:02 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0.004 (0.373) Batch 0.777 (1.315) Remain 24:14:11 loss: 0.4687 Lr: 0.00210 [2024-02-18 22:32:21,510 INFO misc.py line 119 87073] Train: [58/100][595/1557] Data 0.013 (0.372) Batch 1.088 (1.315) Remain 24:13:45 loss: 0.2078 Lr: 0.00210 [2024-02-18 22:32:22,665 INFO misc.py line 119 87073] Train: [58/100][596/1557] Data 0.017 (0.371) Batch 1.161 (1.314) Remain 24:13:26 loss: 0.4260 Lr: 0.00210 [2024-02-18 22:32:23,558 INFO misc.py line 119 87073] Train: [58/100][597/1557] Data 0.011 (0.371) Batch 0.899 (1.314) Remain 24:12:38 loss: 0.1822 Lr: 0.00210 [2024-02-18 22:32:24,413 INFO misc.py line 119 87073] Train: [58/100][598/1557] Data 0.006 (0.370) Batch 0.855 (1.313) Remain 24:11:46 loss: 0.4035 Lr: 0.00210 [2024-02-18 22:32:25,574 INFO misc.py line 119 87073] Train: [58/100][599/1557] Data 0.007 (0.370) Batch 1.155 (1.313) Remain 24:11:27 loss: 0.1238 Lr: 0.00210 [2024-02-18 22:32:26,338 INFO misc.py line 119 87073] Train: [58/100][600/1557] Data 0.011 (0.369) Batch 0.771 (1.312) Remain 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Batch 1.075 (1.328) Remain 24:28:09 loss: 0.2837 Lr: 0.00210 [2024-02-18 22:33:10,238 INFO misc.py line 119 87073] Train: [58/100][626/1557] Data 0.005 (0.384) Batch 0.830 (1.327) Remain 24:27:15 loss: 0.6800 Lr: 0.00210 [2024-02-18 22:33:11,298 INFO misc.py line 119 87073] Train: [58/100][627/1557] Data 0.004 (0.384) Batch 1.056 (1.327) Remain 24:26:44 loss: 0.4155 Lr: 0.00210 [2024-02-18 22:33:12,058 INFO misc.py line 119 87073] Train: [58/100][628/1557] Data 0.009 (0.383) Batch 0.763 (1.326) Remain 24:25:43 loss: 0.2883 Lr: 0.00210 [2024-02-18 22:33:12,828 INFO misc.py line 119 87073] Train: [58/100][629/1557] Data 0.005 (0.383) Batch 0.762 (1.325) Remain 24:24:42 loss: 0.5095 Lr: 0.00210 [2024-02-18 22:33:14,071 INFO misc.py line 119 87073] Train: [58/100][630/1557] Data 0.012 (0.382) Batch 1.242 (1.325) Remain 24:24:32 loss: 0.2513 Lr: 0.00210 [2024-02-18 22:33:14,955 INFO misc.py line 119 87073] Train: [58/100][631/1557] Data 0.014 (0.381) Batch 0.894 (1.324) Remain 24:23:45 loss: 0.2692 Lr: 0.00210 [2024-02-18 22:33:15,915 INFO misc.py line 119 87073] Train: [58/100][632/1557] Data 0.005 (0.381) Batch 0.960 (1.324) Remain 24:23:05 loss: 0.2942 Lr: 0.00210 [2024-02-18 22:33:16,743 INFO misc.py line 119 87073] Train: [58/100][633/1557] Data 0.006 (0.380) Batch 0.828 (1.323) Remain 24:22:12 loss: 0.5647 Lr: 0.00210 [2024-02-18 22:33:17,823 INFO misc.py line 119 87073] Train: [58/100][634/1557] Data 0.005 (0.380) Batch 1.075 (1.323) Remain 24:21:45 loss: 0.4482 Lr: 0.00210 [2024-02-18 22:33:18,526 INFO misc.py line 119 87073] Train: [58/100][635/1557] Data 0.010 (0.379) Batch 0.708 (1.322) Remain 24:20:39 loss: 0.1290 Lr: 0.00210 [2024-02-18 22:33:19,255 INFO misc.py line 119 87073] Train: [58/100][636/1557] Data 0.004 (0.378) Batch 0.722 (1.321) Remain 24:19:35 loss: 0.2801 Lr: 0.00210 [2024-02-18 22:33:20,430 INFO misc.py line 119 87073] Train: [58/100][637/1557] Data 0.011 (0.378) Batch 1.178 (1.320) Remain 24:19:18 loss: 0.1411 Lr: 0.00210 [2024-02-18 22:33:21,364 INFO misc.py line 119 87073] Train: [58/100][638/1557] Data 0.009 (0.377) Batch 0.939 (1.320) Remain 24:18:37 loss: 0.1075 Lr: 0.00210 [2024-02-18 22:33:22,427 INFO misc.py line 119 87073] Train: [58/100][639/1557] Data 0.004 (0.377) Batch 1.062 (1.319) Remain 24:18:09 loss: 0.8110 Lr: 0.00210 [2024-02-18 22:33:23,342 INFO misc.py line 119 87073] Train: [58/100][640/1557] Data 0.005 (0.376) Batch 0.913 (1.319) Remain 24:17:25 loss: 0.4761 Lr: 0.00210 [2024-02-18 22:33:24,205 INFO misc.py line 119 87073] Train: [58/100][641/1557] Data 0.007 (0.376) Batch 0.864 (1.318) Remain 24:16:37 loss: 0.2716 Lr: 0.00210 [2024-02-18 22:33:25,074 INFO misc.py line 119 87073] Train: [58/100][642/1557] Data 0.007 (0.375) Batch 0.871 (1.317) Remain 24:15:49 loss: 0.4757 Lr: 0.00210 [2024-02-18 22:33:25,784 INFO misc.py line 119 87073] Train: [58/100][643/1557] Data 0.004 (0.374) Batch 0.708 (1.316) Remain 24:14:45 loss: 0.2188 Lr: 0.00210 [2024-02-18 22:33:27,130 INFO misc.py line 119 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0.013 (0.370) Batch 0.762 (1.313) Remain 24:10:26 loss: 0.3267 Lr: 0.00210 [2024-02-18 22:33:33,629 INFO misc.py line 119 87073] Train: [58/100][651/1557] Data 0.005 (0.370) Batch 1.066 (1.312) Remain 24:10:00 loss: 0.1421 Lr: 0.00210 [2024-02-18 22:33:34,601 INFO misc.py line 119 87073] Train: [58/100][652/1557] Data 0.004 (0.369) Batch 0.972 (1.312) Remain 24:09:23 loss: 0.1979 Lr: 0.00210 [2024-02-18 22:33:35,685 INFO misc.py line 119 87073] Train: [58/100][653/1557] Data 0.004 (0.369) Batch 1.084 (1.311) Remain 24:08:59 loss: 0.5606 Lr: 0.00210 [2024-02-18 22:33:36,722 INFO misc.py line 119 87073] Train: [58/100][654/1557] Data 0.005 (0.368) Batch 1.037 (1.311) Remain 24:08:30 loss: 0.2912 Lr: 0.00210 [2024-02-18 22:33:37,799 INFO misc.py line 119 87073] Train: [58/100][655/1557] Data 0.004 (0.368) Batch 1.077 (1.311) Remain 24:08:05 loss: 0.2085 Lr: 0.00210 [2024-02-18 22:33:38,555 INFO misc.py line 119 87073] Train: [58/100][656/1557] Data 0.003 (0.367) Batch 0.755 (1.310) Remain 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[2024-02-18 22:33:45,091 INFO misc.py line 119 87073] Train: [58/100][663/1557] Data 0.006 (0.363) Batch 0.759 (1.306) Remain 24:02:33 loss: 0.3050 Lr: 0.00210 [2024-02-18 22:33:45,846 INFO misc.py line 119 87073] Train: [58/100][664/1557] Data 0.003 (0.363) Batch 0.747 (1.305) Remain 24:01:36 loss: 0.2276 Lr: 0.00210 [2024-02-18 22:33:47,093 INFO misc.py line 119 87073] Train: [58/100][665/1557] Data 0.011 (0.362) Batch 1.222 (1.305) Remain 24:01:26 loss: 0.3293 Lr: 0.00210 [2024-02-18 22:33:48,080 INFO misc.py line 119 87073] Train: [58/100][666/1557] Data 0.037 (0.362) Batch 1.020 (1.304) Remain 24:00:57 loss: 0.2872 Lr: 0.00210 [2024-02-18 22:33:49,121 INFO misc.py line 119 87073] Train: [58/100][667/1557] Data 0.004 (0.361) Batch 1.037 (1.304) Remain 24:00:29 loss: 0.2033 Lr: 0.00210 [2024-02-18 22:33:50,199 INFO misc.py line 119 87073] Train: [58/100][668/1557] Data 0.007 (0.361) Batch 1.080 (1.304) Remain 24:00:05 loss: 0.1879 Lr: 0.00210 [2024-02-18 22:33:51,198 INFO misc.py line 119 87073] Train: [58/100][669/1557] Data 0.008 (0.360) Batch 0.999 (1.303) Remain 23:59:33 loss: 0.1365 Lr: 0.00210 [2024-02-18 22:33:51,957 INFO misc.py line 119 87073] Train: [58/100][670/1557] Data 0.007 (0.360) Batch 0.760 (1.302) Remain 23:58:38 loss: 0.2772 Lr: 0.00210 [2024-02-18 22:33:52,777 INFO misc.py line 119 87073] Train: [58/100][671/1557] Data 0.005 (0.359) Batch 0.811 (1.302) Remain 23:57:48 loss: 0.1847 Lr: 0.00210 [2024-02-18 22:33:53,912 INFO misc.py line 119 87073] Train: [58/100][672/1557] Data 0.014 (0.358) Batch 1.140 (1.301) Remain 23:57:31 loss: 0.1251 Lr: 0.00210 [2024-02-18 22:33:54,843 INFO misc.py line 119 87073] Train: [58/100][673/1557] Data 0.009 (0.358) Batch 0.935 (1.301) Remain 23:56:53 loss: 0.0301 Lr: 0.00210 [2024-02-18 22:33:56,056 INFO misc.py line 119 87073] Train: [58/100][674/1557] Data 0.005 (0.357) Batch 1.207 (1.301) Remain 23:56:43 loss: 0.8193 Lr: 0.00210 [2024-02-18 22:33:57,094 INFO misc.py line 119 87073] Train: 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Batch 0.941 (1.325) Remain 24:23:47 loss: 0.4554 Lr: 0.00210 [2024-02-18 22:34:22,827 INFO misc.py line 119 87073] Train: [58/100][682/1557] Data 0.005 (0.382) Batch 0.962 (1.325) Remain 24:23:10 loss: 0.5138 Lr: 0.00210 [2024-02-18 22:34:23,686 INFO misc.py line 119 87073] Train: [58/100][683/1557] Data 0.009 (0.381) Batch 0.861 (1.324) Remain 24:22:23 loss: 0.5284 Lr: 0.00210 [2024-02-18 22:34:24,464 INFO misc.py line 119 87073] Train: [58/100][684/1557] Data 0.007 (0.381) Batch 0.779 (1.323) Remain 24:21:29 loss: 0.3727 Lr: 0.00210 [2024-02-18 22:34:25,162 INFO misc.py line 119 87073] Train: [58/100][685/1557] Data 0.005 (0.380) Batch 0.695 (1.322) Remain 24:20:27 loss: 0.2853 Lr: 0.00210 [2024-02-18 22:34:26,463 INFO misc.py line 119 87073] Train: [58/100][686/1557] Data 0.009 (0.379) Batch 1.302 (1.322) Remain 24:20:23 loss: 0.1149 Lr: 0.00210 [2024-02-18 22:34:27,330 INFO misc.py line 119 87073] Train: [58/100][687/1557] Data 0.007 (0.379) Batch 0.869 (1.322) Remain 24:19:38 loss: 0.5098 Lr: 0.00210 [2024-02-18 22:34:28,150 INFO misc.py line 119 87073] Train: [58/100][688/1557] Data 0.005 (0.378) Batch 0.813 (1.321) Remain 24:18:48 loss: 0.3907 Lr: 0.00210 [2024-02-18 22:34:29,261 INFO misc.py line 119 87073] Train: [58/100][689/1557] Data 0.013 (0.378) Batch 1.119 (1.321) Remain 24:18:27 loss: 0.4959 Lr: 0.00210 [2024-02-18 22:34:30,154 INFO misc.py line 119 87073] Train: [58/100][690/1557] Data 0.004 (0.377) Batch 0.893 (1.320) Remain 24:17:44 loss: 0.1531 Lr: 0.00210 [2024-02-18 22:34:30,842 INFO misc.py line 119 87073] Train: [58/100][691/1557] Data 0.004 (0.377) Batch 0.688 (1.319) Remain 24:16:42 loss: 0.2424 Lr: 0.00210 [2024-02-18 22:34:31,707 INFO misc.py line 119 87073] Train: [58/100][692/1557] Data 0.005 (0.376) Batch 0.858 (1.318) Remain 24:15:56 loss: 0.3896 Lr: 0.00210 [2024-02-18 22:34:32,938 INFO misc.py line 119 87073] Train: [58/100][693/1557] Data 0.013 (0.376) Batch 1.233 (1.318) Remain 24:15:47 loss: 0.0791 Lr: 0.00210 [2024-02-18 22:34:33,854 INFO misc.py line 119 87073] Train: [58/100][694/1557] Data 0.011 (0.375) Batch 0.921 (1.318) Remain 24:15:07 loss: 0.6117 Lr: 0.00210 [2024-02-18 22:34:34,895 INFO misc.py line 119 87073] Train: [58/100][695/1557] Data 0.005 (0.375) Batch 1.041 (1.317) Remain 24:14:40 loss: 0.2252 Lr: 0.00210 [2024-02-18 22:34:35,763 INFO misc.py line 119 87073] Train: [58/100][696/1557] Data 0.004 (0.374) Batch 0.867 (1.317) Remain 24:13:55 loss: 0.5831 Lr: 0.00209 [2024-02-18 22:34:36,830 INFO misc.py line 119 87073] Train: [58/100][697/1557] Data 0.005 (0.374) Batch 1.064 (1.316) Remain 24:13:30 loss: 0.3244 Lr: 0.00209 [2024-02-18 22:34:37,622 INFO misc.py line 119 87073] Train: [58/100][698/1557] Data 0.009 (0.373) Batch 0.795 (1.316) Remain 24:12:39 loss: 0.2001 Lr: 0.00209 [2024-02-18 22:34:38,404 INFO misc.py line 119 87073] Train: [58/100][699/1557] Data 0.005 (0.373) Batch 0.781 (1.315) Remain 24:11:47 loss: 0.2213 Lr: 0.00209 [2024-02-18 22:34:39,618 INFO misc.py line 119 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24:06:47 loss: 0.2267 Lr: 0.00209 [2024-02-18 22:34:53,177 INFO misc.py line 119 87073] Train: [58/100][713/1557] Data 0.004 (0.366) Batch 0.704 (1.310) Remain 24:05:49 loss: 0.3805 Lr: 0.00209 [2024-02-18 22:34:54,343 INFO misc.py line 119 87073] Train: [58/100][714/1557] Data 0.004 (0.366) Batch 1.165 (1.309) Remain 24:05:34 loss: 0.2017 Lr: 0.00209 [2024-02-18 22:34:55,325 INFO misc.py line 119 87073] Train: [58/100][715/1557] Data 0.006 (0.365) Batch 0.983 (1.309) Remain 24:05:03 loss: 0.2616 Lr: 0.00209 [2024-02-18 22:34:56,220 INFO misc.py line 119 87073] Train: [58/100][716/1557] Data 0.005 (0.365) Batch 0.895 (1.308) Remain 24:04:23 loss: 0.2016 Lr: 0.00209 [2024-02-18 22:34:57,286 INFO misc.py line 119 87073] Train: [58/100][717/1557] Data 0.006 (0.364) Batch 1.066 (1.308) Remain 24:03:59 loss: 0.2208 Lr: 0.00209 [2024-02-18 22:34:58,247 INFO misc.py line 119 87073] Train: [58/100][718/1557] Data 0.004 (0.364) Batch 0.961 (1.308) Remain 24:03:26 loss: 0.1772 Lr: 0.00209 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line 119 87073] Train: [58/100][725/1557] Data 0.004 (0.360) Batch 0.846 (1.304) Remain 23:59:17 loss: 0.4048 Lr: 0.00209 [2024-02-18 22:35:05,573 INFO misc.py line 119 87073] Train: [58/100][726/1557] Data 0.004 (0.360) Batch 0.776 (1.303) Remain 23:58:28 loss: 0.1961 Lr: 0.00209 [2024-02-18 22:35:06,331 INFO misc.py line 119 87073] Train: [58/100][727/1557] Data 0.012 (0.359) Batch 0.765 (1.303) Remain 23:57:37 loss: 0.2155 Lr: 0.00209 [2024-02-18 22:35:07,587 INFO misc.py line 119 87073] Train: [58/100][728/1557] Data 0.004 (0.359) Batch 1.243 (1.302) Remain 23:57:30 loss: 0.1084 Lr: 0.00209 [2024-02-18 22:35:08,680 INFO misc.py line 119 87073] Train: [58/100][729/1557] Data 0.017 (0.358) Batch 1.096 (1.302) Remain 23:57:10 loss: 0.3328 Lr: 0.00209 [2024-02-18 22:35:09,688 INFO misc.py line 119 87073] Train: [58/100][730/1557] Data 0.013 (0.358) Batch 1.008 (1.302) Remain 23:56:42 loss: 0.7441 Lr: 0.00209 [2024-02-18 22:35:10,747 INFO misc.py line 119 87073] Train: 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Batch 0.769 (1.326) Remain 24:23:05 loss: 0.3756 Lr: 0.00209 [2024-02-18 22:35:37,580 INFO misc.py line 119 87073] Train: [58/100][738/1557] Data 0.019 (0.382) Batch 1.136 (1.326) Remain 24:22:47 loss: 0.5733 Lr: 0.00209 [2024-02-18 22:35:38,419 INFO misc.py line 119 87073] Train: [58/100][739/1557] Data 0.006 (0.381) Batch 0.832 (1.325) Remain 24:22:01 loss: 0.7279 Lr: 0.00209 [2024-02-18 22:35:39,206 INFO misc.py line 119 87073] Train: [58/100][740/1557] Data 0.014 (0.381) Batch 0.795 (1.324) Remain 24:21:12 loss: 0.1808 Lr: 0.00209 [2024-02-18 22:35:39,988 INFO misc.py line 119 87073] Train: [58/100][741/1557] Data 0.006 (0.380) Batch 0.781 (1.323) Remain 24:20:22 loss: 0.3253 Lr: 0.00209 [2024-02-18 22:35:41,234 INFO misc.py line 119 87073] Train: [58/100][742/1557] Data 0.007 (0.380) Batch 1.246 (1.323) Remain 24:20:14 loss: 0.1406 Lr: 0.00209 [2024-02-18 22:35:42,286 INFO misc.py line 119 87073] Train: [58/100][743/1557] Data 0.007 (0.379) Batch 1.044 (1.323) Remain 24:19:48 loss: 0.3049 Lr: 0.00209 [2024-02-18 22:35:43,221 INFO misc.py line 119 87073] Train: [58/100][744/1557] Data 0.015 (0.379) Batch 0.945 (1.322) Remain 24:19:12 loss: 0.3404 Lr: 0.00209 [2024-02-18 22:35:44,258 INFO misc.py line 119 87073] Train: [58/100][745/1557] Data 0.005 (0.378) Batch 1.037 (1.322) Remain 24:18:46 loss: 0.3658 Lr: 0.00209 [2024-02-18 22:35:45,013 INFO misc.py line 119 87073] Train: [58/100][746/1557] Data 0.005 (0.378) Batch 0.755 (1.321) Remain 24:17:54 loss: 0.3291 Lr: 0.00209 [2024-02-18 22:35:45,777 INFO misc.py line 119 87073] Train: [58/100][747/1557] Data 0.006 (0.377) Batch 0.742 (1.320) Remain 24:17:01 loss: 0.2828 Lr: 0.00209 [2024-02-18 22:35:46,502 INFO misc.py line 119 87073] Train: [58/100][748/1557] Data 0.027 (0.377) Batch 0.747 (1.320) Remain 24:16:09 loss: 0.2978 Lr: 0.00209 [2024-02-18 22:35:47,796 INFO misc.py line 119 87073] Train: [58/100][749/1557] Data 0.006 (0.376) Batch 1.294 (1.320) Remain 24:16:05 loss: 0.2136 Lr: 0.00209 [2024-02-18 22:35:48,837 INFO misc.py line 119 87073] Train: [58/100][750/1557] Data 0.005 (0.376) Batch 1.036 (1.319) Remain 24:15:39 loss: 0.1923 Lr: 0.00209 [2024-02-18 22:35:49,782 INFO misc.py line 119 87073] Train: [58/100][751/1557] Data 0.011 (0.375) Batch 0.951 (1.319) Remain 24:15:05 loss: 0.1983 Lr: 0.00209 [2024-02-18 22:35:50,787 INFO misc.py line 119 87073] Train: [58/100][752/1557] Data 0.005 (0.375) Batch 1.005 (1.318) Remain 24:14:36 loss: 0.1897 Lr: 0.00209 [2024-02-18 22:35:51,852 INFO misc.py line 119 87073] Train: [58/100][753/1557] Data 0.005 (0.374) Batch 1.066 (1.318) Remain 24:14:12 loss: 0.6108 Lr: 0.00209 [2024-02-18 22:35:52,541 INFO misc.py line 119 87073] Train: [58/100][754/1557] Data 0.004 (0.374) Batch 0.689 (1.317) Remain 24:13:15 loss: 0.2784 Lr: 0.00209 [2024-02-18 22:35:53,412 INFO misc.py line 119 87073] Train: [58/100][755/1557] Data 0.005 (0.373) Batch 0.811 (1.317) Remain 24:12:29 loss: 0.2679 Lr: 0.00209 [2024-02-18 22:35:54,671 INFO misc.py line 119 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Batch 0.914 (1.323) Remain 24:19:16 loss: 0.2336 Lr: 0.00209 [2024-02-18 22:36:49,748 INFO misc.py line 119 87073] Train: [58/100][794/1557] Data 0.005 (0.379) Batch 0.909 (1.323) Remain 24:18:40 loss: 0.1474 Lr: 0.00209 [2024-02-18 22:36:50,706 INFO misc.py line 119 87073] Train: [58/100][795/1557] Data 0.004 (0.379) Batch 0.957 (1.322) Remain 24:18:08 loss: 0.1297 Lr: 0.00209 [2024-02-18 22:36:51,480 INFO misc.py line 119 87073] Train: [58/100][796/1557] Data 0.006 (0.378) Batch 0.767 (1.322) Remain 24:17:21 loss: 0.3107 Lr: 0.00209 [2024-02-18 22:36:52,275 INFO misc.py line 119 87073] Train: [58/100][797/1557] Data 0.013 (0.378) Batch 0.803 (1.321) Remain 24:16:36 loss: 0.3774 Lr: 0.00209 [2024-02-18 22:36:53,500 INFO misc.py line 119 87073] Train: [58/100][798/1557] Data 0.005 (0.377) Batch 1.187 (1.321) Remain 24:16:24 loss: 0.1327 Lr: 0.00209 [2024-02-18 22:36:54,499 INFO misc.py line 119 87073] Train: [58/100][799/1557] Data 0.043 (0.377) Batch 1.036 (1.321) Remain 24:15:59 loss: 0.0991 Lr: 0.00209 [2024-02-18 22:36:55,501 INFO misc.py line 119 87073] Train: [58/100][800/1557] Data 0.006 (0.376) Batch 1.003 (1.320) Remain 24:15:31 loss: 0.5430 Lr: 0.00209 [2024-02-18 22:36:56,431 INFO misc.py line 119 87073] Train: [58/100][801/1557] Data 0.004 (0.376) Batch 0.928 (1.320) Remain 24:14:57 loss: 0.3236 Lr: 0.00209 [2024-02-18 22:36:57,414 INFO misc.py line 119 87073] Train: [58/100][802/1557] Data 0.008 (0.376) Batch 0.985 (1.319) Remain 24:14:28 loss: 0.3672 Lr: 0.00209 [2024-02-18 22:36:58,193 INFO misc.py line 119 87073] Train: [58/100][803/1557] Data 0.005 (0.375) Batch 0.759 (1.319) Remain 24:13:41 loss: 0.2837 Lr: 0.00209 [2024-02-18 22:36:58,980 INFO misc.py line 119 87073] Train: [58/100][804/1557] Data 0.024 (0.375) Batch 0.806 (1.318) Remain 24:12:57 loss: 0.2417 Lr: 0.00209 [2024-02-18 22:37:00,121 INFO misc.py line 119 87073] Train: [58/100][805/1557] Data 0.005 (0.374) Batch 1.140 (1.318) Remain 24:12:41 loss: 0.1231 Lr: 0.00209 [2024-02-18 22:37:01,001 INFO misc.py line 119 87073] Train: [58/100][806/1557] Data 0.005 (0.374) Batch 0.881 (1.317) Remain 24:12:04 loss: 0.2513 Lr: 0.00209 [2024-02-18 22:37:01,909 INFO misc.py line 119 87073] Train: [58/100][807/1557] Data 0.005 (0.373) Batch 0.905 (1.317) Remain 24:11:29 loss: 0.2082 Lr: 0.00209 [2024-02-18 22:37:02,892 INFO misc.py line 119 87073] Train: [58/100][808/1557] Data 0.008 (0.373) Batch 0.985 (1.316) Remain 24:11:00 loss: 0.1559 Lr: 0.00209 [2024-02-18 22:37:04,110 INFO misc.py line 119 87073] Train: [58/100][809/1557] Data 0.005 (0.372) Batch 1.214 (1.316) Remain 24:10:50 loss: 0.3508 Lr: 0.00209 [2024-02-18 22:37:04,804 INFO misc.py line 119 87073] Train: [58/100][810/1557] Data 0.011 (0.372) Batch 0.699 (1.315) Remain 24:09:58 loss: 0.1697 Lr: 0.00209 [2024-02-18 22:37:05,536 INFO misc.py line 119 87073] Train: [58/100][811/1557] Data 0.005 (0.371) Batch 0.731 (1.315) Remain 24:09:09 loss: 0.1598 Lr: 0.00209 [2024-02-18 22:37:06,883 INFO misc.py line 119 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[2024-02-18 22:37:25,056 INFO misc.py line 119 87073] Train: [58/100][831/1557] Data 0.005 (0.363) Batch 0.755 (1.306) Remain 23:59:42 loss: 0.2607 Lr: 0.00209 [2024-02-18 22:37:25,819 INFO misc.py line 119 87073] Train: [58/100][832/1557] Data 0.010 (0.362) Batch 0.769 (1.306) Remain 23:58:58 loss: 0.6364 Lr: 0.00209 [2024-02-18 22:37:27,032 INFO misc.py line 119 87073] Train: [58/100][833/1557] Data 0.004 (0.362) Batch 1.211 (1.306) Remain 23:58:49 loss: 0.2200 Lr: 0.00209 [2024-02-18 22:37:28,211 INFO misc.py line 119 87073] Train: [58/100][834/1557] Data 0.005 (0.361) Batch 1.131 (1.305) Remain 23:58:34 loss: 0.1127 Lr: 0.00209 [2024-02-18 22:37:29,252 INFO misc.py line 119 87073] Train: [58/100][835/1557] Data 0.053 (0.361) Batch 1.086 (1.305) Remain 23:58:15 loss: 0.9358 Lr: 0.00209 [2024-02-18 22:37:30,304 INFO misc.py line 119 87073] Train: [58/100][836/1557] Data 0.008 (0.361) Batch 1.048 (1.305) Remain 23:57:53 loss: 0.7745 Lr: 0.00209 [2024-02-18 22:37:31,163 INFO misc.py line 119 87073] Train: [58/100][837/1557] Data 0.012 (0.360) Batch 0.867 (1.304) Remain 23:57:17 loss: 0.3184 Lr: 0.00209 [2024-02-18 22:37:31,942 INFO misc.py line 119 87073] Train: [58/100][838/1557] Data 0.004 (0.360) Batch 0.779 (1.304) Remain 23:56:34 loss: 0.2533 Lr: 0.00209 [2024-02-18 22:37:32,687 INFO misc.py line 119 87073] Train: [58/100][839/1557] Data 0.004 (0.359) Batch 0.738 (1.303) Remain 23:55:48 loss: 0.4420 Lr: 0.00209 [2024-02-18 22:37:33,773 INFO misc.py line 119 87073] Train: [58/100][840/1557] Data 0.011 (0.359) Batch 1.088 (1.303) Remain 23:55:30 loss: 0.2399 Lr: 0.00209 [2024-02-18 22:37:34,688 INFO misc.py line 119 87073] Train: [58/100][841/1557] Data 0.010 (0.358) Batch 0.919 (1.302) Remain 23:54:58 loss: 0.1893 Lr: 0.00209 [2024-02-18 22:37:35,636 INFO misc.py line 119 87073] Train: [58/100][842/1557] Data 0.005 (0.358) Batch 0.948 (1.302) Remain 23:54:29 loss: 0.4805 Lr: 0.00209 [2024-02-18 22:37:36,667 INFO misc.py line 119 87073] Train: 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Batch 1.179 (1.322) Remain 24:16:34 loss: 0.1640 Lr: 0.00209 [2024-02-18 22:38:02,739 INFO misc.py line 119 87073] Train: [58/100][850/1557] Data 0.005 (0.377) Batch 0.916 (1.322) Remain 24:16:01 loss: 0.3640 Lr: 0.00209 [2024-02-18 22:38:03,751 INFO misc.py line 119 87073] Train: [58/100][851/1557] Data 0.004 (0.377) Batch 1.012 (1.321) Remain 24:15:36 loss: 0.2744 Lr: 0.00209 [2024-02-18 22:38:04,456 INFO misc.py line 119 87073] Train: [58/100][852/1557] Data 0.004 (0.376) Batch 0.692 (1.321) Remain 24:14:45 loss: 0.1440 Lr: 0.00209 [2024-02-18 22:38:05,325 INFO misc.py line 119 87073] Train: [58/100][853/1557] Data 0.018 (0.376) Batch 0.882 (1.320) Remain 24:14:10 loss: 0.4868 Lr: 0.00209 [2024-02-18 22:38:06,677 INFO misc.py line 119 87073] Train: [58/100][854/1557] Data 0.004 (0.376) Batch 1.350 (1.320) Remain 24:14:11 loss: 0.1987 Lr: 0.00209 [2024-02-18 22:38:07,643 INFO misc.py line 119 87073] Train: [58/100][855/1557] Data 0.008 (0.375) Batch 0.968 (1.320) Remain 24:13:42 loss: 0.3514 Lr: 0.00209 [2024-02-18 22:38:08,618 INFO misc.py line 119 87073] Train: [58/100][856/1557] Data 0.004 (0.375) Batch 0.975 (1.319) Remain 24:13:14 loss: 0.3890 Lr: 0.00209 [2024-02-18 22:38:09,615 INFO misc.py line 119 87073] Train: [58/100][857/1557] Data 0.004 (0.374) Batch 0.997 (1.319) Remain 24:12:48 loss: 0.7165 Lr: 0.00209 [2024-02-18 22:38:10,622 INFO misc.py line 119 87073] Train: [58/100][858/1557] Data 0.005 (0.374) Batch 1.007 (1.318) Remain 24:12:22 loss: 0.1329 Lr: 0.00209 [2024-02-18 22:38:11,379 INFO misc.py line 119 87073] Train: [58/100][859/1557] Data 0.004 (0.373) Batch 0.755 (1.318) Remain 24:11:38 loss: 0.3760 Lr: 0.00209 [2024-02-18 22:38:12,093 INFO misc.py line 119 87073] Train: [58/100][860/1557] Data 0.010 (0.373) Batch 0.716 (1.317) Remain 24:10:50 loss: 0.2988 Lr: 0.00209 [2024-02-18 22:38:13,272 INFO misc.py line 119 87073] Train: [58/100][861/1557] Data 0.005 (0.372) Batch 1.179 (1.317) Remain 24:10:38 loss: 0.1095 Lr: 0.00209 [2024-02-18 22:38:14,279 INFO misc.py line 119 87073] Train: [58/100][862/1557] Data 0.004 (0.372) Batch 1.006 (1.317) Remain 24:10:13 loss: 0.5116 Lr: 0.00209 [2024-02-18 22:38:15,063 INFO misc.py line 119 87073] Train: [58/100][863/1557] Data 0.005 (0.372) Batch 0.783 (1.316) Remain 24:09:30 loss: 0.3391 Lr: 0.00209 [2024-02-18 22:38:16,021 INFO misc.py line 119 87073] Train: [58/100][864/1557] Data 0.005 (0.371) Batch 0.957 (1.316) Remain 24:09:02 loss: 0.3525 Lr: 0.00209 [2024-02-18 22:38:17,036 INFO misc.py line 119 87073] Train: [58/100][865/1557] Data 0.006 (0.371) Batch 1.015 (1.315) Remain 24:08:37 loss: 0.3195 Lr: 0.00209 [2024-02-18 22:38:17,737 INFO misc.py line 119 87073] Train: [58/100][866/1557] Data 0.006 (0.370) Batch 0.703 (1.315) Remain 24:07:49 loss: 0.3907 Lr: 0.00209 [2024-02-18 22:38:18,581 INFO misc.py line 119 87073] Train: [58/100][867/1557] Data 0.004 (0.370) Batch 0.841 (1.314) Remain 24:07:12 loss: 0.2524 Lr: 0.00209 [2024-02-18 22:38:19,900 INFO misc.py line 119 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24:03:30 loss: 0.3972 Lr: 0.00209 [2024-02-18 22:38:33,677 INFO misc.py line 119 87073] Train: [58/100][881/1557] Data 0.005 (0.366) Batch 0.728 (1.310) Remain 24:02:45 loss: 0.4048 Lr: 0.00209 [2024-02-18 22:38:34,857 INFO misc.py line 119 87073] Train: [58/100][882/1557] Data 0.004 (0.365) Batch 1.180 (1.310) Remain 24:02:34 loss: 0.2423 Lr: 0.00209 [2024-02-18 22:38:35,798 INFO misc.py line 119 87073] Train: [58/100][883/1557] Data 0.004 (0.365) Batch 0.941 (1.310) Remain 24:02:05 loss: 0.3659 Lr: 0.00209 [2024-02-18 22:38:36,677 INFO misc.py line 119 87073] Train: [58/100][884/1557] Data 0.004 (0.364) Batch 0.879 (1.309) Remain 24:01:31 loss: 0.3961 Lr: 0.00209 [2024-02-18 22:38:37,827 INFO misc.py line 119 87073] Train: [58/100][885/1557] Data 0.004 (0.364) Batch 1.144 (1.309) Remain 24:01:18 loss: 0.1582 Lr: 0.00209 [2024-02-18 22:38:38,734 INFO misc.py line 119 87073] Train: [58/100][886/1557] Data 0.010 (0.364) Batch 0.913 (1.309) Remain 24:00:47 loss: 0.4191 Lr: 0.00209 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Batch 0.911 (1.322) Remain 24:15:31 loss: 0.3193 Lr: 0.00208 [2024-02-18 22:39:16,961 INFO misc.py line 119 87073] Train: [58/100][906/1557] Data 0.004 (0.377) Batch 0.948 (1.322) Remain 24:15:02 loss: 0.7175 Lr: 0.00208 [2024-02-18 22:39:17,952 INFO misc.py line 119 87073] Train: [58/100][907/1557] Data 0.011 (0.376) Batch 0.995 (1.322) Remain 24:14:37 loss: 0.8694 Lr: 0.00208 [2024-02-18 22:39:18,700 INFO misc.py line 119 87073] Train: [58/100][908/1557] Data 0.006 (0.376) Batch 0.749 (1.321) Remain 24:13:54 loss: 0.3722 Lr: 0.00208 [2024-02-18 22:39:19,487 INFO misc.py line 119 87073] Train: [58/100][909/1557] Data 0.004 (0.376) Batch 0.786 (1.320) Remain 24:13:13 loss: 0.3073 Lr: 0.00208 [2024-02-18 22:39:20,774 INFO misc.py line 119 87073] Train: [58/100][910/1557] Data 0.006 (0.375) Batch 1.218 (1.320) Remain 24:13:05 loss: 0.1791 Lr: 0.00208 [2024-02-18 22:39:21,853 INFO misc.py line 119 87073] Train: [58/100][911/1557] Data 0.076 (0.375) Batch 1.142 (1.320) Remain 24:12:50 loss: 0.6165 Lr: 0.00208 [2024-02-18 22:39:22,906 INFO misc.py line 119 87073] Train: [58/100][912/1557] Data 0.013 (0.375) Batch 1.048 (1.320) Remain 24:12:29 loss: 0.3369 Lr: 0.00208 [2024-02-18 22:39:23,840 INFO misc.py line 119 87073] Train: [58/100][913/1557] Data 0.017 (0.374) Batch 0.947 (1.319) Remain 24:12:01 loss: 0.1819 Lr: 0.00208 [2024-02-18 22:39:24,705 INFO misc.py line 119 87073] Train: [58/100][914/1557] Data 0.005 (0.374) Batch 0.866 (1.319) Remain 24:11:27 loss: 0.3198 Lr: 0.00208 [2024-02-18 22:39:25,457 INFO misc.py line 119 87073] Train: [58/100][915/1557] Data 0.004 (0.373) Batch 0.745 (1.318) Remain 24:10:44 loss: 0.4013 Lr: 0.00208 [2024-02-18 22:39:26,222 INFO misc.py line 119 87073] Train: [58/100][916/1557] Data 0.010 (0.373) Batch 0.771 (1.318) Remain 24:10:03 loss: 0.2229 Lr: 0.00208 [2024-02-18 22:39:27,337 INFO misc.py line 119 87073] Train: [58/100][917/1557] Data 0.004 (0.373) Batch 1.115 (1.317) Remain 24:09:47 loss: 0.0728 Lr: 0.00208 [2024-02-18 22:39:28,211 INFO misc.py line 119 87073] Train: [58/100][918/1557] Data 0.004 (0.372) Batch 0.874 (1.317) Remain 24:09:14 loss: 0.4727 Lr: 0.00208 [2024-02-18 22:39:29,016 INFO misc.py line 119 87073] Train: [58/100][919/1557] Data 0.004 (0.372) Batch 0.798 (1.316) Remain 24:08:35 loss: 0.5062 Lr: 0.00208 [2024-02-18 22:39:29,960 INFO misc.py line 119 87073] Train: [58/100][920/1557] Data 0.011 (0.371) Batch 0.951 (1.316) Remain 24:08:07 loss: 0.2573 Lr: 0.00208 [2024-02-18 22:39:31,032 INFO misc.py line 119 87073] Train: [58/100][921/1557] Data 0.004 (0.371) Batch 1.072 (1.316) Remain 24:07:48 loss: 0.2079 Lr: 0.00208 [2024-02-18 22:39:31,806 INFO misc.py line 119 87073] Train: [58/100][922/1557] Data 0.004 (0.371) Batch 0.773 (1.315) Remain 24:07:08 loss: 0.2140 Lr: 0.00208 [2024-02-18 22:39:32,532 INFO misc.py line 119 87073] Train: [58/100][923/1557] Data 0.005 (0.370) Batch 0.726 (1.314) Remain 24:06:25 loss: 0.3621 Lr: 0.00208 [2024-02-18 22:39:33,873 INFO misc.py line 119 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[2024-02-18 22:39:52,017 INFO misc.py line 119 87073] Train: [58/100][943/1557] Data 0.004 (0.362) Batch 0.737 (1.307) Remain 23:58:01 loss: 0.6456 Lr: 0.00208 [2024-02-18 22:39:52,798 INFO misc.py line 119 87073] Train: [58/100][944/1557] Data 0.005 (0.362) Batch 0.775 (1.307) Remain 23:57:22 loss: 0.4371 Lr: 0.00208 [2024-02-18 22:39:54,016 INFO misc.py line 119 87073] Train: [58/100][945/1557] Data 0.010 (0.362) Batch 1.221 (1.306) Remain 23:57:15 loss: 0.2023 Lr: 0.00208 [2024-02-18 22:39:55,034 INFO misc.py line 119 87073] Train: [58/100][946/1557] Data 0.008 (0.361) Batch 1.017 (1.306) Remain 23:56:53 loss: 0.5276 Lr: 0.00208 [2024-02-18 22:39:55,964 INFO misc.py line 119 87073] Train: [58/100][947/1557] Data 0.010 (0.361) Batch 0.934 (1.306) Remain 23:56:26 loss: 0.2155 Lr: 0.00208 [2024-02-18 22:39:56,790 INFO misc.py line 119 87073] Train: [58/100][948/1557] Data 0.004 (0.361) Batch 0.827 (1.305) Remain 23:55:51 loss: 0.2831 Lr: 0.00208 [2024-02-18 22:39:57,870 INFO misc.py line 119 87073] Train: [58/100][949/1557] Data 0.005 (0.360) Batch 1.070 (1.305) Remain 23:55:33 loss: 0.1751 Lr: 0.00208 [2024-02-18 22:39:58,623 INFO misc.py line 119 87073] Train: [58/100][950/1557] Data 0.014 (0.360) Batch 0.762 (1.304) Remain 23:54:54 loss: 0.2712 Lr: 0.00208 [2024-02-18 22:39:59,443 INFO misc.py line 119 87073] Train: [58/100][951/1557] Data 0.006 (0.359) Batch 0.821 (1.304) Remain 23:54:19 loss: 0.2465 Lr: 0.00208 [2024-02-18 22:40:00,545 INFO misc.py line 119 87073] Train: [58/100][952/1557] Data 0.004 (0.359) Batch 1.093 (1.304) Remain 23:54:03 loss: 0.0999 Lr: 0.00208 [2024-02-18 22:40:01,626 INFO misc.py line 119 87073] Train: [58/100][953/1557] Data 0.012 (0.359) Batch 1.083 (1.303) Remain 23:53:47 loss: 0.2963 Lr: 0.00208 [2024-02-18 22:40:02,563 INFO misc.py line 119 87073] Train: [58/100][954/1557] Data 0.011 (0.358) Batch 0.944 (1.303) Remain 23:53:20 loss: 0.2386 Lr: 0.00208 [2024-02-18 22:40:03,452 INFO misc.py line 119 87073] Train: 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Batch 0.815 (1.322) Remain 24:14:17 loss: 0.1851 Lr: 0.00208 [2024-02-18 22:40:30,905 INFO misc.py line 119 87073] Train: [58/100][962/1557] Data 0.006 (0.378) Batch 0.846 (1.322) Remain 24:13:43 loss: 0.3275 Lr: 0.00208 [2024-02-18 22:40:31,847 INFO misc.py line 119 87073] Train: [58/100][963/1557] Data 0.010 (0.377) Batch 0.942 (1.321) Remain 24:13:15 loss: 0.2585 Lr: 0.00208 [2024-02-18 22:40:32,617 INFO misc.py line 119 87073] Train: [58/100][964/1557] Data 0.004 (0.377) Batch 0.765 (1.321) Remain 24:12:36 loss: 0.3455 Lr: 0.00208 [2024-02-18 22:40:33,352 INFO misc.py line 119 87073] Train: [58/100][965/1557] Data 0.010 (0.376) Batch 0.739 (1.320) Remain 24:11:55 loss: 0.2643 Lr: 0.00208 [2024-02-18 22:40:34,594 INFO misc.py line 119 87073] Train: [58/100][966/1557] Data 0.005 (0.376) Batch 1.243 (1.320) Remain 24:11:48 loss: 0.1057 Lr: 0.00208 [2024-02-18 22:40:35,601 INFO misc.py line 119 87073] Train: [58/100][967/1557] Data 0.005 (0.376) Batch 1.006 (1.320) Remain 24:11:25 loss: 0.1936 Lr: 0.00208 [2024-02-18 22:40:36,548 INFO misc.py line 119 87073] Train: [58/100][968/1557] Data 0.005 (0.375) Batch 0.948 (1.319) Remain 24:10:58 loss: 0.3698 Lr: 0.00208 [2024-02-18 22:40:37,577 INFO misc.py line 119 87073] Train: [58/100][969/1557] Data 0.005 (0.375) Batch 1.029 (1.319) Remain 24:10:37 loss: 0.4407 Lr: 0.00208 [2024-02-18 22:40:38,340 INFO misc.py line 119 87073] Train: [58/100][970/1557] Data 0.004 (0.374) Batch 0.760 (1.319) Remain 24:09:58 loss: 0.1236 Lr: 0.00208 [2024-02-18 22:40:39,080 INFO misc.py line 119 87073] Train: [58/100][971/1557] Data 0.008 (0.374) Batch 0.744 (1.318) Remain 24:09:17 loss: 0.2805 Lr: 0.00208 [2024-02-18 22:40:39,829 INFO misc.py line 119 87073] Train: [58/100][972/1557] Data 0.004 (0.374) Batch 0.746 (1.317) Remain 24:08:37 loss: 0.2785 Lr: 0.00208 [2024-02-18 22:40:41,079 INFO misc.py line 119 87073] Train: [58/100][973/1557] Data 0.006 (0.373) Batch 1.246 (1.317) Remain 24:08:31 loss: 0.2002 Lr: 0.00208 [2024-02-18 22:40:42,039 INFO misc.py line 119 87073] Train: [58/100][974/1557] Data 0.011 (0.373) Batch 0.967 (1.317) Remain 24:08:06 loss: 0.1255 Lr: 0.00208 [2024-02-18 22:40:42,890 INFO misc.py line 119 87073] Train: [58/100][975/1557] Data 0.003 (0.373) Batch 0.851 (1.316) Remain 24:07:33 loss: 0.4934 Lr: 0.00208 [2024-02-18 22:40:43,732 INFO misc.py line 119 87073] Train: [58/100][976/1557] Data 0.003 (0.372) Batch 0.835 (1.316) Remain 24:06:59 loss: 0.3629 Lr: 0.00208 [2024-02-18 22:40:44,626 INFO misc.py line 119 87073] Train: [58/100][977/1557] Data 0.010 (0.372) Batch 0.899 (1.316) Remain 24:06:29 loss: 0.4313 Lr: 0.00208 [2024-02-18 22:40:45,410 INFO misc.py line 119 87073] Train: [58/100][978/1557] Data 0.006 (0.371) Batch 0.784 (1.315) Remain 24:05:52 loss: 0.1922 Lr: 0.00208 [2024-02-18 22:40:46,168 INFO misc.py line 119 87073] Train: [58/100][979/1557] Data 0.005 (0.371) Batch 0.750 (1.314) Remain 24:05:13 loss: 0.4568 Lr: 0.00208 [2024-02-18 22:40:47,394 INFO misc.py line 119 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23:59:11 loss: 0.3429 Lr: 0.00208 [2024-02-18 22:40:58,774 INFO misc.py line 119 87073] Train: [58/100][993/1557] Data 0.007 (0.366) Batch 0.694 (1.309) Remain 23:58:29 loss: 0.2116 Lr: 0.00208 [2024-02-18 22:40:59,959 INFO misc.py line 119 87073] Train: [58/100][994/1557] Data 0.004 (0.366) Batch 1.183 (1.308) Remain 23:58:19 loss: 0.2009 Lr: 0.00208 [2024-02-18 22:41:00,959 INFO misc.py line 119 87073] Train: [58/100][995/1557] Data 0.006 (0.365) Batch 0.999 (1.308) Remain 23:57:57 loss: 0.2147 Lr: 0.00208 [2024-02-18 22:41:01,848 INFO misc.py line 119 87073] Train: [58/100][996/1557] Data 0.008 (0.365) Batch 0.893 (1.308) Remain 23:57:28 loss: 0.2837 Lr: 0.00208 [2024-02-18 22:41:02,746 INFO misc.py line 119 87073] Train: [58/100][997/1557] Data 0.003 (0.364) Batch 0.898 (1.307) Remain 23:57:00 loss: 0.4171 Lr: 0.00208 [2024-02-18 22:41:03,789 INFO misc.py line 119 87073] Train: [58/100][998/1557] Data 0.008 (0.364) Batch 1.043 (1.307) Remain 23:56:41 loss: 0.0964 Lr: 0.00208 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(0.379) Batch 0.852 (1.321) Remain 24:11:52 loss: 0.2009 Lr: 0.00208 [2024-02-18 22:41:43,949 INFO misc.py line 119 87073] Train: [58/100][1018/1557] Data 0.005 (0.379) Batch 0.923 (1.321) Remain 24:11:25 loss: 0.2373 Lr: 0.00208 [2024-02-18 22:41:44,864 INFO misc.py line 119 87073] Train: [58/100][1019/1557] Data 0.013 (0.378) Batch 0.925 (1.320) Remain 24:10:58 loss: 0.3535 Lr: 0.00208 [2024-02-18 22:41:45,599 INFO misc.py line 119 87073] Train: [58/100][1020/1557] Data 0.004 (0.378) Batch 0.735 (1.320) Remain 24:10:18 loss: 0.3217 Lr: 0.00208 [2024-02-18 22:41:46,419 INFO misc.py line 119 87073] Train: [58/100][1021/1557] Data 0.004 (0.377) Batch 0.816 (1.319) Remain 24:09:45 loss: 0.1817 Lr: 0.00208 [2024-02-18 22:41:47,677 INFO misc.py line 119 87073] Train: [58/100][1022/1557] Data 0.008 (0.377) Batch 1.258 (1.319) Remain 24:09:39 loss: 0.1795 Lr: 0.00208 [2024-02-18 22:41:48,651 INFO misc.py line 119 87073] Train: [58/100][1023/1557] Data 0.008 (0.377) Batch 0.977 (1.319) Remain 24:09:16 loss: 0.5516 Lr: 0.00208 [2024-02-18 22:41:49,550 INFO misc.py line 119 87073] Train: [58/100][1024/1557] Data 0.005 (0.376) Batch 0.897 (1.319) Remain 24:08:47 loss: 0.4635 Lr: 0.00208 [2024-02-18 22:41:50,480 INFO misc.py line 119 87073] Train: [58/100][1025/1557] Data 0.006 (0.376) Batch 0.928 (1.318) Remain 24:08:21 loss: 0.0969 Lr: 0.00208 [2024-02-18 22:41:51,625 INFO misc.py line 119 87073] Train: [58/100][1026/1557] Data 0.008 (0.376) Batch 1.146 (1.318) Remain 24:08:08 loss: 0.2215 Lr: 0.00208 [2024-02-18 22:41:52,332 INFO misc.py line 119 87073] Train: [58/100][1027/1557] Data 0.006 (0.375) Batch 0.710 (1.317) Remain 24:07:28 loss: 0.1795 Lr: 0.00208 [2024-02-18 22:41:53,224 INFO misc.py line 119 87073] Train: [58/100][1028/1557] Data 0.004 (0.375) Batch 0.845 (1.317) Remain 24:06:56 loss: 0.3412 Lr: 0.00208 [2024-02-18 22:41:54,471 INFO misc.py line 119 87073] Train: [58/100][1029/1557] Data 0.051 (0.375) Batch 1.290 (1.317) Remain 24:06:53 loss: 0.1036 Lr: 0.00208 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[2024-02-18 22:43:44,548 INFO misc.py line 119 87073] Train: [58/100][1123/1557] Data 0.004 (0.362) Batch 0.943 (1.305) Remain 23:51:24 loss: 0.5380 Lr: 0.00207 [2024-02-18 22:43:45,422 INFO misc.py line 119 87073] Train: [58/100][1124/1557] Data 0.004 (0.361) Batch 0.868 (1.304) Remain 23:50:57 loss: 0.5508 Lr: 0.00207 [2024-02-18 22:43:46,184 INFO misc.py line 119 87073] Train: [58/100][1125/1557] Data 0.010 (0.361) Batch 0.766 (1.304) Remain 23:50:24 loss: 0.3492 Lr: 0.00207 [2024-02-18 22:43:46,914 INFO misc.py line 119 87073] Train: [58/100][1126/1557] Data 0.005 (0.361) Batch 0.722 (1.303) Remain 23:49:48 loss: 0.2948 Lr: 0.00207 [2024-02-18 22:44:05,829 INFO misc.py line 119 87073] Train: [58/100][1127/1557] Data 17.666 (0.376) Batch 18.921 (1.319) Remain 24:06:59 loss: 0.1039 Lr: 0.00207 [2024-02-18 22:44:06,828 INFO misc.py line 119 87073] Train: [58/100][1128/1557] Data 0.007 (0.376) Batch 1.001 (1.319) Remain 24:06:39 loss: 0.4427 Lr: 0.00207 [2024-02-18 22:44:07,771 INFO 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misc.py line 119 87073] Train: [58/100][1160/1557] Data 0.004 (0.365) Batch 0.737 (1.309) Remain 23:55:00 loss: 0.2462 Lr: 0.00207 [2024-02-18 22:44:38,222 INFO misc.py line 119 87073] Train: [58/100][1161/1557] Data 0.006 (0.365) Batch 0.709 (1.308) Remain 23:54:25 loss: 0.2426 Lr: 0.00207 [2024-02-18 22:44:39,455 INFO misc.py line 119 87073] Train: [58/100][1162/1557] Data 0.035 (0.365) Batch 1.263 (1.308) Remain 23:54:21 loss: 0.1901 Lr: 0.00207 [2024-02-18 22:44:40,268 INFO misc.py line 119 87073] Train: [58/100][1163/1557] Data 0.005 (0.365) Batch 0.814 (1.308) Remain 23:53:52 loss: 0.6128 Lr: 0.00207 [2024-02-18 22:44:41,144 INFO misc.py line 119 87073] Train: [58/100][1164/1557] Data 0.004 (0.364) Batch 0.877 (1.307) Remain 23:53:26 loss: 0.1701 Lr: 0.00207 [2024-02-18 22:44:42,215 INFO misc.py line 119 87073] Train: [58/100][1165/1557] Data 0.003 (0.364) Batch 1.063 (1.307) Remain 23:53:11 loss: 0.3107 Lr: 0.00207 [2024-02-18 22:44:43,155 INFO misc.py line 119 87073] Train: 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(0.362) Batch 0.957 (1.305) Remain 23:50:20 loss: 0.3568 Lr: 0.00207 [2024-02-18 22:44:49,533 INFO misc.py line 119 87073] Train: [58/100][1173/1557] Data 0.008 (0.362) Batch 1.037 (1.304) Remain 23:50:04 loss: 0.1494 Lr: 0.00207 [2024-02-18 22:44:50,201 INFO misc.py line 119 87073] Train: [58/100][1174/1557] Data 0.012 (0.361) Batch 0.677 (1.304) Remain 23:49:27 loss: 0.1585 Lr: 0.00207 [2024-02-18 22:44:50,960 INFO misc.py line 119 87073] Train: [58/100][1175/1557] Data 0.003 (0.361) Batch 0.753 (1.303) Remain 23:48:55 loss: 0.1976 Lr: 0.00207 [2024-02-18 22:44:52,074 INFO misc.py line 119 87073] Train: [58/100][1176/1557] Data 0.008 (0.361) Batch 1.116 (1.303) Remain 23:48:43 loss: 0.1986 Lr: 0.00207 [2024-02-18 22:44:52,980 INFO misc.py line 119 87073] Train: [58/100][1177/1557] Data 0.008 (0.360) Batch 0.910 (1.303) Remain 23:48:20 loss: 0.4899 Lr: 0.00207 [2024-02-18 22:44:53,887 INFO misc.py line 119 87073] Train: [58/100][1178/1557] Data 0.003 (0.360) Batch 0.906 (1.303) Remain 23:47:56 loss: 0.3086 Lr: 0.00207 [2024-02-18 22:44:54,886 INFO misc.py line 119 87073] Train: [58/100][1179/1557] Data 0.005 (0.360) Batch 1.000 (1.302) Remain 23:47:38 loss: 0.3311 Lr: 0.00207 [2024-02-18 22:44:55,918 INFO misc.py line 119 87073] Train: [58/100][1180/1557] Data 0.003 (0.359) Batch 1.032 (1.302) Remain 23:47:22 loss: 0.3207 Lr: 0.00207 [2024-02-18 22:44:56,621 INFO misc.py line 119 87073] Train: [58/100][1181/1557] Data 0.003 (0.359) Batch 0.704 (1.302) Remain 23:46:47 loss: 0.2569 Lr: 0.00207 [2024-02-18 22:44:57,390 INFO misc.py line 119 87073] Train: [58/100][1182/1557] Data 0.003 (0.359) Batch 0.759 (1.301) Remain 23:46:15 loss: 0.3116 Lr: 0.00207 [2024-02-18 22:45:19,202 INFO misc.py line 119 87073] Train: [58/100][1183/1557] Data 20.547 (0.376) Batch 21.821 (1.319) Remain 24:05:18 loss: 0.1437 Lr: 0.00207 [2024-02-18 22:45:20,087 INFO misc.py line 119 87073] Train: [58/100][1184/1557] Data 0.003 (0.376) Batch 0.885 (1.318) Remain 24:04:52 loss: 0.3931 Lr: 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INFO misc.py line 119 87073] Train: [58/100][1191/1557] Data 0.015 (0.373) Batch 0.954 (1.316) Remain 24:02:28 loss: 0.4826 Lr: 0.00207 [2024-02-18 22:45:27,796 INFO misc.py line 119 87073] Train: [58/100][1192/1557] Data 0.003 (0.373) Batch 0.930 (1.316) Remain 24:02:05 loss: 0.3283 Lr: 0.00207 [2024-02-18 22:45:28,795 INFO misc.py line 119 87073] Train: [58/100][1193/1557] Data 0.003 (0.373) Batch 0.997 (1.316) Remain 24:01:46 loss: 0.5104 Lr: 0.00207 [2024-02-18 22:45:29,757 INFO misc.py line 119 87073] Train: [58/100][1194/1557] Data 0.005 (0.372) Batch 0.964 (1.315) Remain 24:01:25 loss: 0.4930 Lr: 0.00207 [2024-02-18 22:45:30,487 INFO misc.py line 119 87073] Train: [58/100][1195/1557] Data 0.003 (0.372) Batch 0.720 (1.315) Remain 24:00:51 loss: 0.4046 Lr: 0.00207 [2024-02-18 22:45:31,298 INFO misc.py line 119 87073] Train: [58/100][1196/1557] Data 0.014 (0.372) Batch 0.819 (1.314) Remain 24:00:23 loss: 0.1606 Lr: 0.00207 [2024-02-18 22:45:32,519 INFO misc.py line 119 87073] Train: [58/100][1197/1557] Data 0.004 (0.372) Batch 1.212 (1.314) Remain 24:00:16 loss: 0.1049 Lr: 0.00207 [2024-02-18 22:45:33,460 INFO misc.py line 119 87073] Train: [58/100][1198/1557] Data 0.014 (0.371) Batch 0.951 (1.314) Remain 23:59:54 loss: 0.5484 Lr: 0.00207 [2024-02-18 22:45:34,373 INFO misc.py line 119 87073] Train: [58/100][1199/1557] Data 0.004 (0.371) Batch 0.914 (1.314) Remain 23:59:31 loss: 0.6482 Lr: 0.00207 [2024-02-18 22:45:35,322 INFO misc.py line 119 87073] Train: [58/100][1200/1557] Data 0.003 (0.371) Batch 0.945 (1.313) Remain 23:59:09 loss: 0.3086 Lr: 0.00207 [2024-02-18 22:45:36,700 INFO misc.py line 119 87073] Train: [58/100][1201/1557] Data 0.007 (0.370) Batch 1.378 (1.313) Remain 23:59:12 loss: 0.5698 Lr: 0.00207 [2024-02-18 22:45:37,404 INFO misc.py line 119 87073] Train: [58/100][1202/1557] Data 0.007 (0.370) Batch 0.705 (1.313) Remain 23:58:37 loss: 0.1184 Lr: 0.00207 [2024-02-18 22:45:38,260 INFO misc.py line 119 87073] Train: [58/100][1203/1557] Data 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Remain 23:56:05 loss: 0.3483 Lr: 0.00207 [2024-02-18 22:45:44,696 INFO misc.py line 119 87073] Train: [58/100][1210/1557] Data 0.004 (0.368) Batch 0.711 (1.310) Remain 23:55:31 loss: 0.2379 Lr: 0.00207 [2024-02-18 22:45:45,777 INFO misc.py line 119 87073] Train: [58/100][1211/1557] Data 0.009 (0.367) Batch 1.079 (1.310) Remain 23:55:18 loss: 0.2847 Lr: 0.00207 [2024-02-18 22:45:46,704 INFO misc.py line 119 87073] Train: [58/100][1212/1557] Data 0.012 (0.367) Batch 0.935 (1.310) Remain 23:54:56 loss: 0.2050 Lr: 0.00207 [2024-02-18 22:45:47,699 INFO misc.py line 119 87073] Train: [58/100][1213/1557] Data 0.003 (0.367) Batch 0.995 (1.309) Remain 23:54:38 loss: 0.3026 Lr: 0.00207 [2024-02-18 22:45:48,622 INFO misc.py line 119 87073] Train: [58/100][1214/1557] Data 0.003 (0.366) Batch 0.922 (1.309) Remain 23:54:15 loss: 0.2682 Lr: 0.00207 [2024-02-18 22:45:49,726 INFO misc.py line 119 87073] Train: [58/100][1215/1557] Data 0.005 (0.366) Batch 1.105 (1.309) Remain 23:54:03 loss: 0.2911 Lr: 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INFO misc.py line 119 87073] Train: [58/100][1222/1557] Data 0.004 (0.364) Batch 0.934 (1.307) Remain 23:51:41 loss: 0.1904 Lr: 0.00207 [2024-02-18 22:45:57,182 INFO misc.py line 119 87073] Train: [58/100][1223/1557] Data 0.003 (0.364) Batch 0.746 (1.306) Remain 23:51:09 loss: 0.1924 Lr: 0.00207 [2024-02-18 22:45:57,957 INFO misc.py line 119 87073] Train: [58/100][1224/1557] Data 0.015 (0.364) Batch 0.786 (1.306) Remain 23:50:40 loss: 0.3242 Lr: 0.00207 [2024-02-18 22:45:59,158 INFO misc.py line 119 87073] Train: [58/100][1225/1557] Data 0.003 (0.363) Batch 1.201 (1.306) Remain 23:50:33 loss: 0.1492 Lr: 0.00207 [2024-02-18 22:46:00,294 INFO misc.py line 119 87073] Train: [58/100][1226/1557] Data 0.004 (0.363) Batch 1.136 (1.306) Remain 23:50:22 loss: 0.2495 Lr: 0.00207 [2024-02-18 22:46:01,434 INFO misc.py line 119 87073] Train: [58/100][1227/1557] Data 0.005 (0.363) Batch 1.140 (1.306) Remain 23:50:12 loss: 0.9885 Lr: 0.00207 [2024-02-18 22:46:02,156 INFO misc.py line 119 87073] Train: [58/100][1228/1557] Data 0.004 (0.362) Batch 0.720 (1.305) Remain 23:49:40 loss: 0.2807 Lr: 0.00207 [2024-02-18 22:46:03,059 INFO misc.py line 119 87073] Train: [58/100][1229/1557] Data 0.006 (0.362) Batch 0.904 (1.305) Remain 23:49:17 loss: 0.4764 Lr: 0.00207 [2024-02-18 22:46:05,349 INFO misc.py line 119 87073] Train: [58/100][1230/1557] Data 1.045 (0.363) Batch 2.291 (1.306) Remain 23:50:08 loss: 0.2081 Lr: 0.00207 [2024-02-18 22:46:06,053 INFO misc.py line 119 87073] Train: [58/100][1231/1557] Data 0.004 (0.362) Batch 0.705 (1.305) Remain 23:49:35 loss: 0.2013 Lr: 0.00207 [2024-02-18 22:46:07,496 INFO misc.py line 119 87073] Train: [58/100][1232/1557] Data 0.004 (0.362) Batch 1.440 (1.305) Remain 23:49:41 loss: 0.1655 Lr: 0.00207 [2024-02-18 22:46:08,192 INFO misc.py line 119 87073] Train: [58/100][1233/1557] Data 0.007 (0.362) Batch 0.700 (1.305) Remain 23:49:07 loss: 0.3848 Lr: 0.00207 [2024-02-18 22:46:09,042 INFO misc.py line 119 87073] Train: [58/100][1234/1557] Data 0.003 (0.361) Batch 0.847 (1.304) Remain 23:48:41 loss: 0.2097 Lr: 0.00207 [2024-02-18 22:46:10,109 INFO misc.py line 119 87073] Train: [58/100][1235/1557] Data 0.007 (0.361) Batch 1.067 (1.304) Remain 23:48:27 loss: 0.1512 Lr: 0.00207 [2024-02-18 22:46:11,096 INFO misc.py line 119 87073] Train: [58/100][1236/1557] Data 0.006 (0.361) Batch 0.989 (1.304) Remain 23:48:09 loss: 0.5020 Lr: 0.00207 [2024-02-18 22:46:11,822 INFO misc.py line 119 87073] Train: [58/100][1237/1557] Data 0.004 (0.361) Batch 0.727 (1.303) Remain 23:47:37 loss: 0.2568 Lr: 0.00207 [2024-02-18 22:46:12,566 INFO misc.py line 119 87073] Train: [58/100][1238/1557] Data 0.003 (0.360) Batch 0.737 (1.303) Remain 23:47:06 loss: 0.2289 Lr: 0.00207 [2024-02-18 22:46:31,686 INFO misc.py line 119 87073] Train: [58/100][1239/1557] Data 17.873 (0.374) Batch 19.127 (1.317) Remain 24:02:52 loss: 0.1984 Lr: 0.00207 [2024-02-18 22:46:32,515 INFO misc.py line 119 87073] Train: [58/100][1240/1557] Data 0.005 (0.374) Batch 0.830 (1.317) Remain 24:02:25 loss: 0.4685 Lr: 0.00207 [2024-02-18 22:46:33,367 INFO misc.py line 119 87073] Train: [58/100][1241/1557] Data 0.003 (0.374) Batch 0.843 (1.317) Remain 24:01:58 loss: 0.1917 Lr: 0.00207 [2024-02-18 22:46:34,395 INFO misc.py line 119 87073] Train: [58/100][1242/1557] Data 0.011 (0.374) Batch 1.027 (1.316) Remain 24:01:42 loss: 0.4371 Lr: 0.00207 [2024-02-18 22:46:35,317 INFO misc.py line 119 87073] Train: [58/100][1243/1557] Data 0.012 (0.373) Batch 0.931 (1.316) Remain 24:01:20 loss: 1.1650 Lr: 0.00207 [2024-02-18 22:46:36,022 INFO misc.py line 119 87073] Train: [58/100][1244/1557] Data 0.004 (0.373) Batch 0.706 (1.316) Remain 24:00:46 loss: 0.3264 Lr: 0.00207 [2024-02-18 22:46:36,856 INFO misc.py line 119 87073] Train: [58/100][1245/1557] Data 0.003 (0.373) Batch 0.823 (1.315) Remain 24:00:19 loss: 0.1726 Lr: 0.00207 [2024-02-18 22:46:38,180 INFO misc.py line 119 87073] Train: [58/100][1246/1557] Data 0.013 (0.372) Batch 1.303 (1.315) Remain 24:00:17 loss: 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87073] Train: [58/100][1259/1557] Data 0.004 (0.369) Batch 0.779 (1.312) Remain 23:56:01 loss: 0.1218 Lr: 0.00207 [2024-02-18 22:46:51,966 INFO misc.py line 119 87073] Train: [58/100][1260/1557] Data 0.013 (0.368) Batch 1.278 (1.312) Remain 23:55:58 loss: 0.2576 Lr: 0.00207 [2024-02-18 22:46:53,080 INFO misc.py line 119 87073] Train: [58/100][1261/1557] Data 0.016 (0.368) Batch 1.115 (1.311) Remain 23:55:46 loss: 0.1470 Lr: 0.00207 [2024-02-18 22:46:53,946 INFO misc.py line 119 87073] Train: [58/100][1262/1557] Data 0.016 (0.368) Batch 0.878 (1.311) Remain 23:55:22 loss: 0.3117 Lr: 0.00207 [2024-02-18 22:46:55,000 INFO misc.py line 119 87073] Train: [58/100][1263/1557] Data 0.003 (0.367) Batch 1.053 (1.311) Remain 23:55:07 loss: 0.3532 Lr: 0.00207 [2024-02-18 22:46:56,032 INFO misc.py line 119 87073] Train: [58/100][1264/1557] Data 0.005 (0.367) Batch 1.033 (1.311) Remain 23:54:52 loss: 0.5854 Lr: 0.00207 [2024-02-18 22:46:56,778 INFO misc.py line 119 87073] Train: [58/100][1265/1557] Data 0.004 (0.367) Batch 0.745 (1.310) Remain 23:54:21 loss: 0.1919 Lr: 0.00207 [2024-02-18 22:46:57,524 INFO misc.py line 119 87073] Train: [58/100][1266/1557] Data 0.004 (0.367) Batch 0.746 (1.310) Remain 23:53:50 loss: 0.2976 Lr: 0.00207 [2024-02-18 22:46:58,659 INFO misc.py line 119 87073] Train: [58/100][1267/1557] Data 0.004 (0.366) Batch 1.132 (1.310) Remain 23:53:40 loss: 0.1399 Lr: 0.00207 [2024-02-18 22:46:59,642 INFO misc.py line 119 87073] Train: [58/100][1268/1557] Data 0.007 (0.366) Batch 0.982 (1.309) Remain 23:53:21 loss: 0.3664 Lr: 0.00207 [2024-02-18 22:47:00,573 INFO misc.py line 119 87073] Train: [58/100][1269/1557] Data 0.009 (0.366) Batch 0.935 (1.309) Remain 23:53:01 loss: 0.5439 Lr: 0.00206 [2024-02-18 22:47:01,631 INFO misc.py line 119 87073] Train: [58/100][1270/1557] Data 0.004 (0.365) Batch 1.058 (1.309) Remain 23:52:46 loss: 0.2199 Lr: 0.00206 [2024-02-18 22:47:02,692 INFO misc.py line 119 87073] Train: [58/100][1271/1557] Data 0.005 (0.365) Batch 1.057 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22:47:14,934 INFO misc.py line 119 87073] Train: [58/100][1284/1557] Data 0.005 (0.362) Batch 0.960 (1.305) Remain 23:48:11 loss: 0.4553 Lr: 0.00206 [2024-02-18 22:47:15,855 INFO misc.py line 119 87073] Train: [58/100][1285/1557] Data 0.004 (0.361) Batch 0.922 (1.305) Remain 23:47:50 loss: 0.2502 Lr: 0.00206 [2024-02-18 22:47:16,591 INFO misc.py line 119 87073] Train: [58/100][1286/1557] Data 0.004 (0.361) Batch 0.732 (1.304) Remain 23:47:19 loss: 0.3134 Lr: 0.00206 [2024-02-18 22:47:17,317 INFO misc.py line 119 87073] Train: [58/100][1287/1557] Data 0.008 (0.361) Batch 0.729 (1.304) Remain 23:46:48 loss: 0.1693 Lr: 0.00206 [2024-02-18 22:47:18,511 INFO misc.py line 119 87073] Train: [58/100][1288/1557] Data 0.004 (0.360) Batch 1.194 (1.304) Remain 23:46:41 loss: 0.1242 Lr: 0.00206 [2024-02-18 22:47:19,488 INFO misc.py line 119 87073] Train: [58/100][1289/1557] Data 0.004 (0.360) Batch 0.977 (1.303) Remain 23:46:23 loss: 0.3719 Lr: 0.00206 [2024-02-18 22:47:20,571 INFO misc.py line 119 87073] Train: [58/100][1290/1557] Data 0.005 (0.360) Batch 1.083 (1.303) Remain 23:46:11 loss: 0.4290 Lr: 0.00206 [2024-02-18 22:47:21,467 INFO misc.py line 119 87073] Train: [58/100][1291/1557] Data 0.004 (0.360) Batch 0.896 (1.303) Remain 23:45:49 loss: 0.8162 Lr: 0.00206 [2024-02-18 22:47:22,442 INFO misc.py line 119 87073] Train: [58/100][1292/1557] Data 0.004 (0.359) Batch 0.970 (1.303) Remain 23:45:31 loss: 0.2449 Lr: 0.00206 [2024-02-18 22:47:23,201 INFO misc.py line 119 87073] Train: [58/100][1293/1557] Data 0.009 (0.359) Batch 0.764 (1.302) Remain 23:45:02 loss: 0.3879 Lr: 0.00206 [2024-02-18 22:47:23,979 INFO misc.py line 119 87073] Train: [58/100][1294/1557] Data 0.004 (0.359) Batch 0.769 (1.302) Remain 23:44:33 loss: 0.3750 Lr: 0.00206 [2024-02-18 22:47:44,632 INFO misc.py line 119 87073] Train: [58/100][1295/1557] Data 19.475 (0.374) Batch 20.661 (1.317) Remain 24:00:56 loss: 0.2012 Lr: 0.00206 [2024-02-18 22:47:45,585 INFO misc.py line 119 87073] Train: 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(0.372) Batch 1.261 (1.315) Remain 23:58:28 loss: 0.1435 Lr: 0.00206 [2024-02-18 22:47:51,966 INFO misc.py line 119 87073] Train: [58/100][1303/1557] Data 0.011 (0.371) Batch 0.857 (1.314) Remain 23:58:04 loss: 0.4230 Lr: 0.00206 [2024-02-18 22:47:52,975 INFO misc.py line 119 87073] Train: [58/100][1304/1557] Data 0.004 (0.371) Batch 1.009 (1.314) Remain 23:57:47 loss: 0.1768 Lr: 0.00206 [2024-02-18 22:47:53,888 INFO misc.py line 119 87073] Train: [58/100][1305/1557] Data 0.005 (0.371) Batch 0.912 (1.314) Remain 23:57:26 loss: 0.4443 Lr: 0.00206 [2024-02-18 22:47:54,905 INFO misc.py line 119 87073] Train: [58/100][1306/1557] Data 0.006 (0.371) Batch 1.007 (1.314) Remain 23:57:09 loss: 0.3617 Lr: 0.00206 [2024-02-18 22:47:55,655 INFO misc.py line 119 87073] Train: [58/100][1307/1557] Data 0.015 (0.370) Batch 0.761 (1.313) Remain 23:56:40 loss: 0.3081 Lr: 0.00206 [2024-02-18 22:47:56,447 INFO misc.py line 119 87073] Train: [58/100][1308/1557] Data 0.004 (0.370) Batch 0.788 (1.313) Remain 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[2024-02-18 22:48:02,872 INFO misc.py line 119 87073] Train: [58/100][1315/1557] Data 0.005 (0.368) Batch 0.725 (1.311) Remain 23:53:45 loss: 0.3692 Lr: 0.00206 [2024-02-18 22:48:04,119 INFO misc.py line 119 87073] Train: [58/100][1316/1557] Data 0.004 (0.368) Batch 1.238 (1.311) Remain 23:53:40 loss: 0.1606 Lr: 0.00206 [2024-02-18 22:48:05,257 INFO misc.py line 119 87073] Train: [58/100][1317/1557] Data 0.013 (0.367) Batch 1.135 (1.310) Remain 23:53:30 loss: 0.3806 Lr: 0.00206 [2024-02-18 22:48:06,013 INFO misc.py line 119 87073] Train: [58/100][1318/1557] Data 0.016 (0.367) Batch 0.766 (1.310) Remain 23:53:01 loss: 0.2541 Lr: 0.00206 [2024-02-18 22:48:06,975 INFO misc.py line 119 87073] Train: [58/100][1319/1557] Data 0.006 (0.367) Batch 0.964 (1.310) Remain 23:52:43 loss: 0.3612 Lr: 0.00206 [2024-02-18 22:48:07,902 INFO misc.py line 119 87073] Train: [58/100][1320/1557] Data 0.003 (0.367) Batch 0.926 (1.309) Remain 23:52:22 loss: 0.2668 Lr: 0.00206 [2024-02-18 22:48:08,717 INFO 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23:46:02 loss: 0.4136 Lr: 0.00206 [2024-02-18 22:48:26,500 INFO misc.py line 119 87073] Train: [58/100][1340/1557] Data 0.004 (0.361) Batch 0.943 (1.304) Remain 23:45:43 loss: 0.1287 Lr: 0.00206 [2024-02-18 22:48:27,676 INFO misc.py line 119 87073] Train: [58/100][1341/1557] Data 0.004 (0.361) Batch 1.176 (1.304) Remain 23:45:36 loss: 0.6921 Lr: 0.00206 [2024-02-18 22:48:28,440 INFO misc.py line 119 87073] Train: [58/100][1342/1557] Data 0.004 (0.361) Batch 0.765 (1.303) Remain 23:45:08 loss: 0.3352 Lr: 0.00206 [2024-02-18 22:48:29,160 INFO misc.py line 119 87073] Train: [58/100][1343/1557] Data 0.004 (0.360) Batch 0.712 (1.303) Remain 23:44:38 loss: 0.2310 Lr: 0.00206 [2024-02-18 22:48:30,408 INFO misc.py line 119 87073] Train: [58/100][1344/1557] Data 0.011 (0.360) Batch 1.249 (1.303) Remain 23:44:34 loss: 0.1444 Lr: 0.00206 [2024-02-18 22:48:31,390 INFO misc.py line 119 87073] Train: [58/100][1345/1557] Data 0.012 (0.360) Batch 0.985 (1.303) Remain 23:44:17 loss: 0.1372 Lr: 0.00206 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23:54:19 loss: 0.2123 Lr: 0.00206 [2024-02-18 22:49:17,960 INFO misc.py line 119 87073] Train: [58/100][1371/1557] Data 0.004 (0.369) Batch 0.764 (1.312) Remain 23:53:52 loss: 0.1349 Lr: 0.00206 [2024-02-18 22:49:19,248 INFO misc.py line 119 87073] Train: [58/100][1372/1557] Data 0.014 (0.369) Batch 1.290 (1.312) Remain 23:53:49 loss: 0.2148 Lr: 0.00206 [2024-02-18 22:49:20,096 INFO misc.py line 119 87073] Train: [58/100][1373/1557] Data 0.012 (0.369) Batch 0.855 (1.312) Remain 23:53:26 loss: 0.2670 Lr: 0.00206 [2024-02-18 22:49:21,007 INFO misc.py line 119 87073] Train: [58/100][1374/1557] Data 0.004 (0.369) Batch 0.912 (1.311) Remain 23:53:06 loss: 0.2590 Lr: 0.00206 [2024-02-18 22:49:21,951 INFO misc.py line 119 87073] Train: [58/100][1375/1557] Data 0.003 (0.368) Batch 0.941 (1.311) Remain 23:52:47 loss: 0.3992 Lr: 0.00206 [2024-02-18 22:49:22,921 INFO misc.py line 119 87073] Train: [58/100][1376/1557] Data 0.007 (0.368) Batch 0.973 (1.311) Remain 23:52:29 loss: 0.2457 Lr: 0.00206 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INFO misc.py line 119 87073] Train: [58/100][1414/1557] Data 0.005 (0.372) Batch 1.346 (1.316) Remain 23:56:56 loss: 0.2112 Lr: 0.00206 [2024-02-18 22:50:20,563 INFO misc.py line 119 87073] Train: [58/100][1415/1557] Data 0.042 (0.372) Batch 1.027 (1.315) Remain 23:56:41 loss: 0.4785 Lr: 0.00206 [2024-02-18 22:50:21,663 INFO misc.py line 119 87073] Train: [58/100][1416/1557] Data 0.006 (0.372) Batch 1.098 (1.315) Remain 23:56:30 loss: 0.2965 Lr: 0.00206 [2024-02-18 22:50:22,522 INFO misc.py line 119 87073] Train: [58/100][1417/1557] Data 0.006 (0.371) Batch 0.863 (1.315) Remain 23:56:07 loss: 0.1458 Lr: 0.00206 [2024-02-18 22:50:23,444 INFO misc.py line 119 87073] Train: [58/100][1418/1557] Data 0.004 (0.371) Batch 0.921 (1.315) Remain 23:55:48 loss: 0.7934 Lr: 0.00206 [2024-02-18 22:50:24,195 INFO misc.py line 119 87073] Train: [58/100][1419/1557] Data 0.004 (0.371) Batch 0.750 (1.314) Remain 23:55:20 loss: 0.3511 Lr: 0.00206 [2024-02-18 22:50:24,978 INFO misc.py line 119 87073] Train: [58/100][1420/1557] Data 0.005 (0.371) Batch 0.778 (1.314) Remain 23:54:54 loss: 0.1757 Lr: 0.00206 [2024-02-18 22:50:26,235 INFO misc.py line 119 87073] Train: [58/100][1421/1557] Data 0.010 (0.370) Batch 1.252 (1.314) Remain 23:54:50 loss: 0.0606 Lr: 0.00206 [2024-02-18 22:50:27,255 INFO misc.py line 119 87073] Train: [58/100][1422/1557] Data 0.016 (0.370) Batch 1.021 (1.314) Remain 23:54:35 loss: 0.5905 Lr: 0.00206 [2024-02-18 22:50:28,275 INFO misc.py line 119 87073] Train: [58/100][1423/1557] Data 0.014 (0.370) Batch 1.017 (1.313) Remain 23:54:20 loss: 0.4397 Lr: 0.00206 [2024-02-18 22:50:29,157 INFO misc.py line 119 87073] Train: [58/100][1424/1557] Data 0.017 (0.370) Batch 0.895 (1.313) Remain 23:54:00 loss: 0.4428 Lr: 0.00206 [2024-02-18 22:50:30,010 INFO misc.py line 119 87073] Train: [58/100][1425/1557] Data 0.004 (0.369) Batch 0.853 (1.313) Remain 23:53:37 loss: 0.4266 Lr: 0.00206 [2024-02-18 22:50:30,783 INFO misc.py line 119 87073] Train: [58/100][1426/1557] Data 0.004 (0.369) Batch 0.760 (1.312) Remain 23:53:10 loss: 0.6340 Lr: 0.00206 [2024-02-18 22:50:31,549 INFO misc.py line 119 87073] Train: [58/100][1427/1557] Data 0.018 (0.369) Batch 0.777 (1.312) Remain 23:52:44 loss: 0.1720 Lr: 0.00206 [2024-02-18 22:50:32,830 INFO misc.py line 119 87073] Train: [58/100][1428/1557] Data 0.006 (0.369) Batch 1.277 (1.312) Remain 23:52:42 loss: 0.1578 Lr: 0.00206 [2024-02-18 22:50:33,857 INFO misc.py line 119 87073] Train: [58/100][1429/1557] Data 0.011 (0.368) Batch 1.029 (1.312) Remain 23:52:27 loss: 0.1689 Lr: 0.00206 [2024-02-18 22:50:35,075 INFO misc.py line 119 87073] Train: [58/100][1430/1557] Data 0.009 (0.368) Batch 1.211 (1.312) Remain 23:52:21 loss: 0.3464 Lr: 0.00206 [2024-02-18 22:50:36,008 INFO misc.py line 119 87073] Train: [58/100][1431/1557] Data 0.015 (0.368) Batch 0.945 (1.311) Remain 23:52:03 loss: 0.2804 Lr: 0.00206 [2024-02-18 22:50:37,100 INFO misc.py line 119 87073] Train: [58/100][1432/1557] Data 0.003 (0.368) Batch 1.090 (1.311) Remain 23:51:52 loss: 0.2036 Lr: 0.00206 [2024-02-18 22:50:37,873 INFO misc.py line 119 87073] Train: [58/100][1433/1557] Data 0.005 (0.367) Batch 0.774 (1.311) Remain 23:51:26 loss: 0.3333 Lr: 0.00206 [2024-02-18 22:50:38,643 INFO misc.py line 119 87073] Train: [58/100][1434/1557] Data 0.005 (0.367) Batch 0.763 (1.310) Remain 23:50:59 loss: 0.2539 Lr: 0.00206 [2024-02-18 22:50:39,732 INFO misc.py line 119 87073] Train: [58/100][1435/1557] Data 0.011 (0.367) Batch 1.086 (1.310) Remain 23:50:48 loss: 0.1535 Lr: 0.00206 [2024-02-18 22:50:40,688 INFO misc.py line 119 87073] Train: [58/100][1436/1557] Data 0.014 (0.367) Batch 0.965 (1.310) Remain 23:50:31 loss: 0.2428 Lr: 0.00206 [2024-02-18 22:50:41,636 INFO misc.py line 119 87073] Train: [58/100][1437/1557] Data 0.005 (0.366) Batch 0.948 (1.310) Remain 23:50:13 loss: 0.6432 Lr: 0.00206 [2024-02-18 22:50:42,586 INFO misc.py line 119 87073] Train: [58/100][1438/1557] Data 0.005 (0.366) Batch 0.950 (1.310) Remain 23:49:55 loss: 0.3813 Lr: 0.00206 [2024-02-18 22:50:43,518 INFO misc.py line 119 87073] Train: [58/100][1439/1557] Data 0.005 (0.366) Batch 0.933 (1.309) Remain 23:49:37 loss: 0.2759 Lr: 0.00206 [2024-02-18 22:50:44,280 INFO misc.py line 119 87073] Train: [58/100][1440/1557] Data 0.004 (0.366) Batch 0.762 (1.309) Remain 23:49:10 loss: 0.2639 Lr: 0.00206 [2024-02-18 22:50:45,050 INFO misc.py line 119 87073] Train: [58/100][1441/1557] Data 0.004 (0.365) Batch 0.761 (1.309) Remain 23:48:44 loss: 0.3496 Lr: 0.00206 [2024-02-18 22:50:46,246 INFO misc.py line 119 87073] Train: [58/100][1442/1557] Data 0.013 (0.365) Batch 1.195 (1.308) Remain 23:48:38 loss: 0.2259 Lr: 0.00206 [2024-02-18 22:50:47,113 INFO misc.py line 119 87073] Train: [58/100][1443/1557] Data 0.015 (0.365) Batch 0.876 (1.308) Remain 23:48:17 loss: 0.4562 Lr: 0.00206 [2024-02-18 22:50:48,216 INFO misc.py line 119 87073] Train: [58/100][1444/1557] Data 0.005 (0.365) Batch 1.104 (1.308) Remain 23:48:06 loss: 0.3060 Lr: 0.00206 [2024-02-18 22:50:49,118 INFO misc.py line 119 87073] Train: [58/100][1445/1557] Data 0.004 (0.364) Batch 0.902 (1.308) Remain 23:47:46 loss: 0.1998 Lr: 0.00206 [2024-02-18 22:50:50,156 INFO misc.py line 119 87073] Train: [58/100][1446/1557] Data 0.004 (0.364) Batch 1.038 (1.308) Remain 23:47:33 loss: 0.2415 Lr: 0.00206 [2024-02-18 22:50:50,900 INFO misc.py line 119 87073] Train: [58/100][1447/1557] Data 0.004 (0.364) Batch 0.742 (1.307) Remain 23:47:06 loss: 0.1968 Lr: 0.00206 [2024-02-18 22:50:51,657 INFO misc.py line 119 87073] Train: [58/100][1448/1557] Data 0.007 (0.364) Batch 0.759 (1.307) Remain 23:46:40 loss: 0.2031 Lr: 0.00206 [2024-02-18 22:50:52,789 INFO misc.py line 119 87073] Train: [58/100][1449/1557] Data 0.004 (0.363) Batch 1.131 (1.307) Remain 23:46:30 loss: 0.1404 Lr: 0.00206 [2024-02-18 22:50:53,932 INFO misc.py line 119 87073] Train: [58/100][1450/1557] Data 0.004 (0.363) Batch 1.141 (1.307) Remain 23:46:22 loss: 0.4137 Lr: 0.00206 [2024-02-18 22:50:54,949 INFO misc.py line 119 87073] Train: [58/100][1451/1557] Data 0.007 (0.363) Batch 1.019 (1.306) Remain 23:46:07 loss: 0.9573 Lr: 0.00206 [2024-02-18 22:50:55,903 INFO misc.py line 119 87073] Train: [58/100][1452/1557] Data 0.005 (0.363) Batch 0.955 (1.306) Remain 23:45:50 loss: 0.3263 Lr: 0.00206 [2024-02-18 22:50:57,156 INFO misc.py line 119 87073] Train: [58/100][1453/1557] Data 0.003 (0.362) Batch 1.244 (1.306) Remain 23:45:46 loss: 0.3493 Lr: 0.00206 [2024-02-18 22:50:57,850 INFO misc.py line 119 87073] Train: [58/100][1454/1557] Data 0.012 (0.362) Batch 0.702 (1.306) Remain 23:45:17 loss: 0.2343 Lr: 0.00206 [2024-02-18 22:50:58,606 INFO misc.py line 119 87073] Train: [58/100][1455/1557] Data 0.004 (0.362) Batch 0.748 (1.305) Remain 23:44:51 loss: 0.2557 Lr: 0.00206 [2024-02-18 22:50:59,838 INFO misc.py line 119 87073] Train: [58/100][1456/1557] Data 0.012 (0.362) Batch 1.238 (1.305) Remain 23:44:47 loss: 0.2146 Lr: 0.00206 [2024-02-18 22:51:00,853 INFO misc.py line 119 87073] Train: [58/100][1457/1557] Data 0.005 (0.361) Batch 1.005 (1.305) Remain 23:44:32 loss: 0.3778 Lr: 0.00206 [2024-02-18 22:51:01,728 INFO misc.py line 119 87073] Train: [58/100][1458/1557] Data 0.016 (0.361) Batch 0.887 (1.305) Remain 23:44:12 loss: 0.4127 Lr: 0.00206 [2024-02-18 22:51:02,592 INFO misc.py line 119 87073] Train: [58/100][1459/1557] Data 0.004 (0.361) Batch 0.864 (1.304) Remain 23:43:50 loss: 0.4493 Lr: 0.00206 [2024-02-18 22:51:03,520 INFO misc.py line 119 87073] Train: [58/100][1460/1557] Data 0.003 (0.361) Batch 0.924 (1.304) Remain 23:43:32 loss: 0.4902 Lr: 0.00205 [2024-02-18 22:51:04,283 INFO misc.py line 119 87073] Train: [58/100][1461/1557] Data 0.008 (0.360) Batch 0.766 (1.304) Remain 23:43:07 loss: 0.2286 Lr: 0.00205 [2024-02-18 22:51:05,024 INFO misc.py line 119 87073] Train: [58/100][1462/1557] Data 0.005 (0.360) Batch 0.708 (1.303) Remain 23:42:39 loss: 0.1089 Lr: 0.00205 [2024-02-18 22:51:26,867 INFO misc.py line 119 87073] Train: [58/100][1463/1557] Data 20.654 (0.374) Batch 21.876 (1.318) Remain 23:58:00 loss: 0.1252 Lr: 0.00205 [2024-02-18 22:51:27,832 INFO misc.py line 119 87073] Train: [58/100][1464/1557] Data 0.005 (0.374) Batch 0.966 (1.317) Remain 23:57:43 loss: 0.1638 Lr: 0.00205 [2024-02-18 22:51:28,846 INFO misc.py line 119 87073] Train: [58/100][1465/1557] Data 0.004 (0.373) Batch 1.014 (1.317) Remain 23:57:28 loss: 0.4189 Lr: 0.00205 [2024-02-18 22:51:29,842 INFO misc.py line 119 87073] Train: [58/100][1466/1557] Data 0.004 (0.373) Batch 0.994 (1.317) Remain 23:57:12 loss: 0.0945 Lr: 0.00205 [2024-02-18 22:51:30,691 INFO misc.py line 119 87073] Train: [58/100][1467/1557] Data 0.008 (0.373) Batch 0.851 (1.317) Remain 23:56:50 loss: 0.3398 Lr: 0.00205 [2024-02-18 22:51:31,477 INFO misc.py line 119 87073] Train: [58/100][1468/1557] Data 0.004 (0.373) Batch 0.784 (1.316) Remain 23:56:25 loss: 0.5137 Lr: 0.00205 [2024-02-18 22:51:32,277 INFO misc.py line 119 87073] Train: [58/100][1469/1557] Data 0.005 (0.372) Batch 0.801 (1.316) Remain 23:56:01 loss: 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22:51:38,802 INFO misc.py line 119 87073] Train: [58/100][1476/1557] Data 0.012 (0.371) Batch 0.724 (1.314) Remain 23:53:52 loss: 0.1315 Lr: 0.00205 [2024-02-18 22:51:39,981 INFO misc.py line 119 87073] Train: [58/100][1477/1557] Data 0.005 (0.370) Batch 1.180 (1.314) Remain 23:53:45 loss: 0.1739 Lr: 0.00205 [2024-02-18 22:51:40,937 INFO misc.py line 119 87073] Train: [58/100][1478/1557] Data 0.004 (0.370) Batch 0.956 (1.314) Remain 23:53:28 loss: 0.2160 Lr: 0.00205 [2024-02-18 22:51:41,832 INFO misc.py line 119 87073] Train: [58/100][1479/1557] Data 0.004 (0.370) Batch 0.895 (1.313) Remain 23:53:08 loss: 0.5086 Lr: 0.00205 [2024-02-18 22:51:42,744 INFO misc.py line 119 87073] Train: [58/100][1480/1557] Data 0.003 (0.370) Batch 0.909 (1.313) Remain 23:52:49 loss: 0.1414 Lr: 0.00205 [2024-02-18 22:51:43,623 INFO misc.py line 119 87073] Train: [58/100][1481/1557] Data 0.006 (0.370) Batch 0.880 (1.313) Remain 23:52:28 loss: 0.4127 Lr: 0.00205 [2024-02-18 22:51:44,362 INFO misc.py line 119 87073] Train: [58/100][1482/1557] Data 0.006 (0.369) Batch 0.740 (1.312) Remain 23:52:01 loss: 0.2059 Lr: 0.00205 [2024-02-18 22:51:45,118 INFO misc.py line 119 87073] Train: [58/100][1483/1557] Data 0.005 (0.369) Batch 0.757 (1.312) Remain 23:51:35 loss: 0.1334 Lr: 0.00205 [2024-02-18 22:51:46,396 INFO misc.py line 119 87073] Train: [58/100][1484/1557] Data 0.004 (0.369) Batch 1.277 (1.312) Remain 23:51:33 loss: 0.1610 Lr: 0.00205 [2024-02-18 22:51:47,572 INFO misc.py line 119 87073] Train: [58/100][1485/1557] Data 0.005 (0.369) Batch 1.175 (1.312) Remain 23:51:25 loss: 0.3015 Lr: 0.00205 [2024-02-18 22:51:48,539 INFO misc.py line 119 87073] Train: [58/100][1486/1557] Data 0.006 (0.368) Batch 0.970 (1.312) Remain 23:51:09 loss: 0.7106 Lr: 0.00205 [2024-02-18 22:51:49,437 INFO misc.py line 119 87073] Train: [58/100][1487/1557] Data 0.003 (0.368) Batch 0.897 (1.311) Remain 23:50:49 loss: 0.4267 Lr: 0.00205 [2024-02-18 22:51:50,291 INFO misc.py line 119 87073] Train: [58/100][1488/1557] Data 0.003 (0.368) Batch 0.853 (1.311) Remain 23:50:28 loss: 0.2126 Lr: 0.00205 [2024-02-18 22:51:51,061 INFO misc.py line 119 87073] Train: [58/100][1489/1557] Data 0.005 (0.368) Batch 0.772 (1.311) Remain 23:50:03 loss: 0.4552 Lr: 0.00205 [2024-02-18 22:51:51,805 INFO misc.py line 119 87073] Train: [58/100][1490/1557] Data 0.004 (0.367) Batch 0.740 (1.310) Remain 23:49:36 loss: 0.3142 Lr: 0.00205 [2024-02-18 22:51:52,912 INFO misc.py line 119 87073] Train: [58/100][1491/1557] Data 0.006 (0.367) Batch 1.108 (1.310) Remain 23:49:26 loss: 0.1542 Lr: 0.00205 [2024-02-18 22:51:53,943 INFO misc.py line 119 87073] Train: [58/100][1492/1557] Data 0.006 (0.367) Batch 1.031 (1.310) Remain 23:49:12 loss: 0.5124 Lr: 0.00205 [2024-02-18 22:51:54,741 INFO misc.py line 119 87073] Train: [58/100][1493/1557] Data 0.006 (0.367) Batch 0.801 (1.310) Remain 23:48:49 loss: 0.4469 Lr: 0.00205 [2024-02-18 22:51:55,830 INFO misc.py line 119 87073] Train: [58/100][1494/1557] Data 0.005 (0.366) Batch 1.090 (1.310) Remain 23:48:38 loss: 0.2711 Lr: 0.00205 [2024-02-18 22:51:56,903 INFO misc.py line 119 87073] Train: [58/100][1495/1557] Data 0.003 (0.366) Batch 1.073 (1.309) Remain 23:48:26 loss: 0.2549 Lr: 0.00205 [2024-02-18 22:51:57,635 INFO misc.py line 119 87073] Train: [58/100][1496/1557] Data 0.003 (0.366) Batch 0.732 (1.309) Remain 23:47:59 loss: 0.3893 Lr: 0.00205 [2024-02-18 22:51:58,407 INFO misc.py line 119 87073] Train: [58/100][1497/1557] Data 0.003 (0.366) Batch 0.768 (1.309) Remain 23:47:34 loss: 0.2328 Lr: 0.00205 [2024-02-18 22:51:59,568 INFO misc.py line 119 87073] Train: [58/100][1498/1557] Data 0.006 (0.365) Batch 1.162 (1.309) Remain 23:47:27 loss: 0.1780 Lr: 0.00205 [2024-02-18 22:52:00,507 INFO misc.py line 119 87073] Train: [58/100][1499/1557] Data 0.005 (0.365) Batch 0.941 (1.308) Remain 23:47:09 loss: 0.3350 Lr: 0.00205 [2024-02-18 22:52:01,528 INFO misc.py line 119 87073] Train: [58/100][1500/1557] Data 0.003 (0.365) Batch 1.021 (1.308) Remain 23:46:55 loss: 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22:52:08,174 INFO misc.py line 119 87073] Train: [58/100][1507/1557] Data 0.004 (0.363) Batch 1.144 (1.306) Remain 23:44:57 loss: 1.1595 Lr: 0.00205 [2024-02-18 22:52:09,144 INFO misc.py line 119 87073] Train: [58/100][1508/1557] Data 0.005 (0.363) Batch 0.970 (1.306) Remain 23:44:41 loss: 0.3211 Lr: 0.00205 [2024-02-18 22:52:10,006 INFO misc.py line 119 87073] Train: [58/100][1509/1557] Data 0.003 (0.363) Batch 0.861 (1.306) Remain 23:44:20 loss: 0.4876 Lr: 0.00205 [2024-02-18 22:52:10,689 INFO misc.py line 119 87073] Train: [58/100][1510/1557] Data 0.004 (0.362) Batch 0.683 (1.305) Remain 23:43:52 loss: 0.1298 Lr: 0.00205 [2024-02-18 22:52:11,410 INFO misc.py line 119 87073] Train: [58/100][1511/1557] Data 0.004 (0.362) Batch 0.722 (1.305) Remain 23:43:26 loss: 0.1499 Lr: 0.00205 [2024-02-18 22:52:12,616 INFO misc.py line 119 87073] Train: [58/100][1512/1557] Data 0.003 (0.362) Batch 1.206 (1.305) Remain 23:43:20 loss: 0.1736 Lr: 0.00205 [2024-02-18 22:52:13,582 INFO misc.py line 119 87073] Train: [58/100][1513/1557] Data 0.004 (0.362) Batch 0.966 (1.305) Remain 23:43:04 loss: 0.4324 Lr: 0.00205 [2024-02-18 22:52:14,580 INFO misc.py line 119 87073] Train: [58/100][1514/1557] Data 0.003 (0.362) Batch 0.999 (1.305) Remain 23:42:49 loss: 0.1732 Lr: 0.00205 [2024-02-18 22:52:15,403 INFO misc.py line 119 87073] Train: [58/100][1515/1557] Data 0.004 (0.361) Batch 0.821 (1.304) Remain 23:42:27 loss: 0.1483 Lr: 0.00205 [2024-02-18 22:52:16,544 INFO misc.py line 119 87073] Train: [58/100][1516/1557] Data 0.004 (0.361) Batch 1.136 (1.304) Remain 23:42:19 loss: 0.3012 Lr: 0.00205 [2024-02-18 22:52:17,321 INFO misc.py line 119 87073] Train: [58/100][1517/1557] Data 0.009 (0.361) Batch 0.783 (1.304) Remain 23:41:55 loss: 0.4309 Lr: 0.00205 [2024-02-18 22:52:18,081 INFO misc.py line 119 87073] Train: [58/100][1518/1557] Data 0.004 (0.361) Batch 0.759 (1.303) Remain 23:41:30 loss: 0.3882 Lr: 0.00205 [2024-02-18 22:52:38,462 INFO misc.py line 119 87073] Train: [58/100][1519/1557] Data 19.066 (0.373) Batch 20.382 (1.316) Remain 23:55:12 loss: 0.1228 Lr: 0.00205 [2024-02-18 22:52:39,290 INFO misc.py line 119 87073] Train: [58/100][1520/1557] Data 0.004 (0.373) Batch 0.827 (1.316) Remain 23:54:50 loss: 0.3352 Lr: 0.00205 [2024-02-18 22:52:40,210 INFO misc.py line 119 87073] Train: [58/100][1521/1557] Data 0.005 (0.372) Batch 0.911 (1.315) Remain 23:54:31 loss: 0.1406 Lr: 0.00205 [2024-02-18 22:52:41,065 INFO misc.py line 119 87073] Train: [58/100][1522/1557] Data 0.013 (0.372) Batch 0.866 (1.315) Remain 23:54:10 loss: 0.4755 Lr: 0.00205 [2024-02-18 22:52:42,059 INFO misc.py line 119 87073] Train: [58/100][1523/1557] Data 0.003 (0.372) Batch 0.990 (1.315) Remain 23:53:55 loss: 0.1321 Lr: 0.00205 [2024-02-18 22:52:42,779 INFO misc.py line 119 87073] Train: [58/100][1524/1557] Data 0.008 (0.372) Batch 0.721 (1.315) Remain 23:53:28 loss: 0.2010 Lr: 0.00205 [2024-02-18 22:52:43,570 INFO misc.py line 119 87073] Train: [58/100][1525/1557] Data 0.007 (0.371) Batch 0.791 (1.314) Remain 23:53:04 loss: 0.1872 Lr: 0.00205 [2024-02-18 22:52:44,821 INFO misc.py line 119 87073] Train: [58/100][1526/1557] Data 0.007 (0.371) Batch 1.253 (1.314) Remain 23:53:00 loss: 0.1745 Lr: 0.00205 [2024-02-18 22:52:46,005 INFO misc.py line 119 87073] Train: [58/100][1527/1557] Data 0.005 (0.371) Batch 1.184 (1.314) Remain 23:52:53 loss: 0.2783 Lr: 0.00205 [2024-02-18 22:52:47,103 INFO misc.py line 119 87073] Train: [58/100][1528/1557] Data 0.004 (0.371) Batch 1.090 (1.314) Remain 23:52:42 loss: 0.3421 Lr: 0.00205 [2024-02-18 22:52:48,374 INFO misc.py line 119 87073] Train: [58/100][1529/1557] Data 0.012 (0.371) Batch 1.270 (1.314) Remain 23:52:39 loss: 0.4739 Lr: 0.00205 [2024-02-18 22:52:49,489 INFO misc.py line 119 87073] Train: [58/100][1530/1557] Data 0.014 (0.370) Batch 1.119 (1.314) Remain 23:52:29 loss: 0.4185 Lr: 0.00205 [2024-02-18 22:52:50,209 INFO misc.py line 119 87073] Train: [58/100][1531/1557] Data 0.009 (0.370) Batch 0.725 (1.313) Remain 23:52:03 loss: 0.1871 Lr: 0.00205 [2024-02-18 22:52:51,007 INFO misc.py line 119 87073] Train: [58/100][1532/1557] Data 0.004 (0.370) Batch 0.796 (1.313) Remain 23:51:39 loss: 0.2919 Lr: 0.00205 [2024-02-18 22:52:52,223 INFO misc.py line 119 87073] Train: [58/100][1533/1557] Data 0.007 (0.370) Batch 1.211 (1.313) Remain 23:51:34 loss: 0.0618 Lr: 0.00205 [2024-02-18 22:52:53,034 INFO misc.py line 119 87073] Train: [58/100][1534/1557] Data 0.012 (0.369) Batch 0.817 (1.313) Remain 23:51:11 loss: 0.2889 Lr: 0.00205 [2024-02-18 22:52:54,110 INFO misc.py line 119 87073] Train: [58/100][1535/1557] Data 0.006 (0.369) Batch 1.073 (1.313) Remain 23:51:00 loss: 0.2326 Lr: 0.00205 [2024-02-18 22:52:55,091 INFO misc.py line 119 87073] Train: [58/100][1536/1557] Data 0.009 (0.369) Batch 0.985 (1.312) Remain 23:50:44 loss: 0.3723 Lr: 0.00205 [2024-02-18 22:52:56,143 INFO misc.py line 119 87073] Train: [58/100][1537/1557] Data 0.005 (0.369) Batch 1.052 (1.312) Remain 23:50:32 loss: 0.3222 Lr: 0.00205 [2024-02-18 22:52:56,910 INFO misc.py line 119 87073] Train: [58/100][1538/1557] Data 0.005 (0.368) Batch 0.766 (1.312) Remain 23:50:07 loss: 0.2982 Lr: 0.00205 [2024-02-18 22:52:57,669 INFO misc.py line 119 87073] Train: [58/100][1539/1557] Data 0.006 (0.368) Batch 0.761 (1.311) Remain 23:49:43 loss: 0.2295 Lr: 0.00205 [2024-02-18 22:52:58,845 INFO misc.py line 119 87073] Train: [58/100][1540/1557] Data 0.004 (0.368) Batch 1.174 (1.311) Remain 23:49:36 loss: 0.1629 Lr: 0.00205 [2024-02-18 22:52:59,819 INFO misc.py line 119 87073] Train: [58/100][1541/1557] Data 0.006 (0.368) Batch 0.975 (1.311) Remain 23:49:20 loss: 0.3201 Lr: 0.00205 [2024-02-18 22:53:00,724 INFO misc.py line 119 87073] Train: [58/100][1542/1557] Data 0.005 (0.367) Batch 0.904 (1.311) Remain 23:49:01 loss: 0.3533 Lr: 0.00205 [2024-02-18 22:53:01,659 INFO misc.py line 119 87073] Train: [58/100][1543/1557] Data 0.005 (0.367) Batch 0.930 (1.311) Remain 23:48:44 loss: 0.4891 Lr: 0.00205 [2024-02-18 22:53:02,652 INFO misc.py line 119 87073] Train: [58/100][1544/1557] Data 0.011 (0.367) Batch 0.996 (1.310) Remain 23:48:29 loss: 0.3628 Lr: 0.00205 [2024-02-18 22:53:03,423 INFO misc.py line 119 87073] Train: [58/100][1545/1557] Data 0.009 (0.367) Batch 0.774 (1.310) Remain 23:48:05 loss: 0.3787 Lr: 0.00205 [2024-02-18 22:53:04,241 INFO misc.py line 119 87073] Train: [58/100][1546/1557] Data 0.006 (0.367) Batch 0.818 (1.310) Remain 23:47:43 loss: 0.1876 Lr: 0.00205 [2024-02-18 22:53:05,330 INFO misc.py line 119 87073] Train: [58/100][1547/1557] Data 0.005 (0.366) Batch 1.087 (1.310) Remain 23:47:32 loss: 0.2081 Lr: 0.00205 [2024-02-18 22:53:06,225 INFO misc.py line 119 87073] Train: [58/100][1548/1557] Data 0.007 (0.366) Batch 0.896 (1.309) Remain 23:47:13 loss: 0.3164 Lr: 0.00205 [2024-02-18 22:53:07,063 INFO misc.py line 119 87073] Train: [58/100][1549/1557] Data 0.006 (0.366) Batch 0.838 (1.309) Remain 23:46:52 loss: 0.8731 Lr: 0.00205 [2024-02-18 22:53:08,108 INFO misc.py line 119 87073] Train: [58/100][1550/1557] Data 0.007 (0.366) Batch 1.044 (1.309) Remain 23:46:40 loss: 0.3497 Lr: 0.00205 [2024-02-18 22:53:09,227 INFO misc.py line 119 87073] Train: [58/100][1551/1557] Data 0.007 (0.365) Batch 1.114 (1.309) Remain 23:46:30 loss: 0.2805 Lr: 0.00205 [2024-02-18 22:53:09,935 INFO misc.py line 119 87073] Train: [58/100][1552/1557] Data 0.012 (0.365) Batch 0.715 (1.308) Remain 23:46:04 loss: 0.1187 Lr: 0.00205 [2024-02-18 22:53:10,789 INFO misc.py line 119 87073] Train: [58/100][1553/1557] Data 0.004 (0.365) Batch 0.853 (1.308) Remain 23:45:43 loss: 0.3496 Lr: 0.00205 [2024-02-18 22:53:11,983 INFO misc.py line 119 87073] Train: [58/100][1554/1557] Data 0.005 (0.365) Batch 1.182 (1.308) Remain 23:45:37 loss: 0.2373 Lr: 0.00205 [2024-02-18 22:53:13,144 INFO misc.py line 119 87073] Train: [58/100][1555/1557] Data 0.018 (0.364) Batch 1.165 (1.308) Remain 23:45:29 loss: 0.4462 Lr: 0.00205 [2024-02-18 22:53:14,098 INFO misc.py line 119 87073] Train: [58/100][1556/1557] Data 0.013 (0.364) Batch 0.963 (1.308) Remain 23:45:13 loss: 0.3025 Lr: 0.00205 [2024-02-18 22:53:15,230 INFO misc.py line 119 87073] Train: [58/100][1557/1557] Data 0.004 (0.364) Batch 1.132 (1.308) Remain 23:45:05 loss: 0.5124 Lr: 0.00205 [2024-02-18 22:53:15,231 INFO misc.py line 136 87073] Train result: loss: 0.3141 [2024-02-18 22:53:15,231 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 22:53:44,394 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6012 [2024-02-18 22:53:45,173 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8716 [2024-02-18 22:53:47,300 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4128 [2024-02-18 22:53:49,508 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3549 [2024-02-18 22:53:54,463 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.4450 [2024-02-18 22:53:55,160 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.3017 [2024-02-18 22:53:56,421 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3507 [2024-02-18 22:53:59,374 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4335 [2024-02-18 22:54:01,184 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2690 [2024-02-18 22:54:02,591 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7069/0.7656/0.9113. [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9272/0.9736 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9823/0.9901 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8717/0.9733 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3655/0.4049 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6429/0.6602 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7393/0.8523 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8245/0.8668 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9240/0.9491 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8245/0.8536 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7843/0.8574 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7179/0.8495 [2024-02-18 22:54:02,592 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5863/0.7214 [2024-02-18 22:54:02,593 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 22:54:02,594 INFO misc.py line 165 87073] Currently Best mIoU: 0.7304 [2024-02-18 22:54:02,595 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 22:54:10,715 INFO misc.py line 119 87073] Train: [59/100][1/1557] Data 1.754 (1.754) Batch 2.440 (2.440) Remain 44:19:16 loss: 0.2377 Lr: 0.00205 [2024-02-18 22:54:11,840 INFO misc.py line 119 87073] Train: [59/100][2/1557] Data 0.010 (0.010) Batch 1.121 (1.121) Remain 20:22:05 loss: 0.3081 Lr: 0.00205 [2024-02-18 22:54:12,789 INFO misc.py line 119 87073] Train: [59/100][3/1557] Data 0.010 (0.010) Batch 0.953 (0.953) Remain 17:18:22 loss: 0.2646 Lr: 0.00205 [2024-02-18 22:54:13,957 INFO misc.py line 119 87073] Train: [59/100][4/1557] Data 0.006 (0.006) Batch 1.169 (1.169) Remain 21:14:21 loss: 0.2756 Lr: 0.00205 [2024-02-18 22:54:14,737 INFO misc.py line 119 87073] Train: [59/100][5/1557] Data 0.005 (0.006) Batch 0.780 (0.974) Remain 17:41:57 loss: 0.2447 Lr: 0.00205 [2024-02-18 22:54:15,526 INFO misc.py line 119 87073] Train: [59/100][6/1557] Data 0.006 (0.006) Batch 0.788 (0.912) Remain 16:34:08 loss: 0.3346 Lr: 0.00205 [2024-02-18 22:54:16,765 INFO misc.py line 119 87073] Train: [59/100][7/1557] Data 0.006 (0.006) Batch 1.238 (0.994) Remain 18:03:00 loss: 0.1366 Lr: 0.00205 [2024-02-18 22:54:17,658 INFO misc.py line 119 87073] Train: [59/100][8/1557] Data 0.005 (0.006) Batch 0.893 (0.974) Remain 17:41:04 loss: 0.8029 Lr: 0.00205 [2024-02-18 22:54:18,581 INFO misc.py line 119 87073] Train: [59/100][9/1557] Data 0.005 (0.005) Batch 0.924 (0.965) Remain 17:32:06 loss: 0.5523 Lr: 0.00205 [2024-02-18 22:54:19,490 INFO misc.py line 119 87073] Train: [59/100][10/1557] Data 0.005 (0.005) Batch 0.901 (0.956) Remain 17:22:03 loss: 0.3974 Lr: 0.00205 [2024-02-18 22:54:20,617 INFO misc.py line 119 87073] Train: [59/100][11/1557] Data 0.013 (0.006) Batch 1.135 (0.979) Remain 17:46:22 loss: 0.5670 Lr: 0.00205 [2024-02-18 22:54:21,421 INFO misc.py line 119 87073] Train: [59/100][12/1557] Data 0.004 (0.006) Batch 0.803 (0.959) Remain 17:25:08 loss: 0.2911 Lr: 0.00205 [2024-02-18 22:54:22,200 INFO misc.py line 119 87073] Train: [59/100][13/1557] Data 0.005 (0.006) Batch 0.779 (0.941) Remain 17:05:30 loss: 0.2761 Lr: 0.00205 [2024-02-18 22:54:23,308 INFO misc.py line 119 87073] Train: [59/100][14/1557] Data 0.005 (0.006) Batch 1.103 (0.956) Remain 17:21:32 loss: 0.1160 Lr: 0.00205 [2024-02-18 22:54:24,318 INFO misc.py line 119 87073] Train: [59/100][15/1557] Data 0.009 (0.006) Batch 1.013 (0.961) Remain 17:26:44 loss: 0.6619 Lr: 0.00205 [2024-02-18 22:54:25,297 INFO misc.py line 119 87073] Train: [59/100][16/1557] Data 0.007 (0.006) Batch 0.980 (0.962) Remain 17:28:21 loss: 0.2082 Lr: 0.00205 [2024-02-18 22:54:26,178 INFO misc.py line 119 87073] Train: [59/100][17/1557] Data 0.006 (0.006) Batch 0.882 (0.956) Remain 17:22:06 loss: 0.3814 Lr: 0.00205 [2024-02-18 22:54:27,187 INFO misc.py line 119 87073] Train: [59/100][18/1557] Data 0.005 (0.006) Batch 1.002 (0.959) Remain 17:25:23 loss: 0.6847 Lr: 0.00205 [2024-02-18 22:54:27,950 INFO misc.py line 119 87073] Train: [59/100][19/1557] Data 0.012 (0.006) Batch 0.769 (0.948) Remain 17:12:26 loss: 0.2061 Lr: 0.00205 [2024-02-18 22:54:28,733 INFO misc.py line 119 87073] Train: [59/100][20/1557] Data 0.005 (0.006) Batch 0.782 (0.938) Remain 17:01:48 loss: 0.4249 Lr: 0.00205 [2024-02-18 22:54:30,038 INFO misc.py line 119 87073] Train: [59/100][21/1557] Data 0.006 (0.006) Batch 1.300 (0.958) Remain 17:23:44 loss: 0.1262 Lr: 0.00205 [2024-02-18 22:54:30,981 INFO misc.py line 119 87073] Train: [59/100][22/1557] Data 0.010 (0.006) Batch 0.949 (0.957) Remain 17:23:13 loss: 0.3539 Lr: 0.00205 [2024-02-18 22:54:31,858 INFO misc.py line 119 87073] Train: [59/100][23/1557] Data 0.004 (0.006) Batch 0.877 (0.953) Remain 17:18:50 loss: 0.3086 Lr: 0.00205 [2024-02-18 22:54:32,753 INFO misc.py line 119 87073] Train: [59/100][24/1557] Data 0.004 (0.006) Batch 0.887 (0.950) Remain 17:15:23 loss: 0.4555 Lr: 0.00205 [2024-02-18 22:54:33,754 INFO misc.py line 119 87073] Train: [59/100][25/1557] Data 0.012 (0.007) Batch 1.006 (0.953) Remain 17:18:06 loss: 0.9729 Lr: 0.00205 [2024-02-18 22:54:34,531 INFO misc.py line 119 87073] Train: [59/100][26/1557] Data 0.008 (0.007) Batch 0.779 (0.945) Remain 17:09:53 loss: 0.5891 Lr: 0.00205 [2024-02-18 22:54:35,310 INFO misc.py line 119 87073] Train: [59/100][27/1557] Data 0.005 (0.007) Batch 0.772 (0.938) Remain 17:01:59 loss: 0.2741 Lr: 0.00205 [2024-02-18 22:54:36,414 INFO misc.py line 119 87073] Train: [59/100][28/1557] Data 0.012 (0.007) Batch 1.099 (0.945) Remain 17:08:59 loss: 0.1416 Lr: 0.00205 [2024-02-18 22:54:37,470 INFO misc.py line 119 87073] Train: [59/100][29/1557] Data 0.017 (0.007) Batch 1.065 (0.949) Remain 17:14:00 loss: 0.4583 Lr: 0.00205 [2024-02-18 22:54:38,440 INFO misc.py line 119 87073] Train: [59/100][30/1557] Data 0.009 (0.007) Batch 0.974 (0.950) Remain 17:14:59 loss: 0.2329 Lr: 0.00205 [2024-02-18 22:54:39,534 INFO misc.py line 119 87073] Train: [59/100][31/1557] Data 0.004 (0.007) Batch 1.094 (0.955) Remain 17:20:33 loss: 0.3047 Lr: 0.00205 [2024-02-18 22:54:40,498 INFO misc.py line 119 87073] Train: [59/100][32/1557] Data 0.005 (0.007) Batch 0.964 (0.955) Remain 17:20:53 loss: 0.4073 Lr: 0.00205 [2024-02-18 22:54:41,242 INFO misc.py line 119 87073] Train: [59/100][33/1557] Data 0.004 (0.007) Batch 0.744 (0.948) Remain 17:13:11 loss: 0.3594 Lr: 0.00205 [2024-02-18 22:54:42,053 INFO misc.py line 119 87073] Train: [59/100][34/1557] Data 0.004 (0.007) Batch 0.795 (0.943) Remain 17:07:46 loss: 0.1520 Lr: 0.00205 [2024-02-18 22:54:43,256 INFO misc.py line 119 87073] Train: [59/100][35/1557] Data 0.020 (0.007) Batch 1.219 (0.952) Remain 17:17:08 loss: 0.1680 Lr: 0.00205 [2024-02-18 22:54:44,143 INFO misc.py line 119 87073] Train: [59/100][36/1557] Data 0.004 (0.007) Batch 0.887 (0.950) Remain 17:14:58 loss: 0.4051 Lr: 0.00205 [2024-02-18 22:54:45,094 INFO misc.py line 119 87073] Train: [59/100][37/1557] Data 0.004 (0.007) Batch 0.950 (0.950) Remain 17:14:57 loss: 0.4182 Lr: 0.00205 [2024-02-18 22:54:45,940 INFO misc.py line 119 87073] Train: [59/100][38/1557] Data 0.004 (0.007) Batch 0.847 (0.947) Remain 17:11:44 loss: 0.4804 Lr: 0.00205 [2024-02-18 22:54:46,828 INFO misc.py line 119 87073] Train: [59/100][39/1557] Data 0.005 (0.007) Batch 0.886 (0.945) Remain 17:09:51 loss: 0.2762 Lr: 0.00205 [2024-02-18 22:54:47,612 INFO misc.py line 119 87073] Train: [59/100][40/1557] Data 0.007 (0.007) Batch 0.786 (0.941) Remain 17:05:08 loss: 0.2402 Lr: 0.00205 [2024-02-18 22:54:48,399 INFO misc.py line 119 87073] Train: [59/100][41/1557] Data 0.004 (0.007) Batch 0.770 (0.937) Remain 17:00:13 loss: 0.2413 Lr: 0.00205 [2024-02-18 22:54:49,608 INFO misc.py line 119 87073] Train: [59/100][42/1557] Data 0.021 (0.007) Batch 1.217 (0.944) Remain 17:08:02 loss: 0.1603 Lr: 0.00205 [2024-02-18 22:54:50,623 INFO misc.py line 119 87073] Train: [59/100][43/1557] Data 0.013 (0.007) Batch 1.015 (0.946) Remain 17:09:58 loss: 0.4719 Lr: 0.00205 [2024-02-18 22:54:51,624 INFO misc.py line 119 87073] Train: [59/100][44/1557] Data 0.014 (0.008) Batch 1.005 (0.947) Remain 17:11:32 loss: 0.5805 Lr: 0.00205 [2024-02-18 22:54:52,449 INFO misc.py line 119 87073] Train: [59/100][45/1557] Data 0.009 (0.008) Batch 0.829 (0.944) Remain 17:08:27 loss: 0.3035 Lr: 0.00205 [2024-02-18 22:54:53,442 INFO misc.py line 119 87073] Train: [59/100][46/1557] Data 0.005 (0.008) 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Batch 0.892 (1.069) Remain 19:22:36 loss: 0.1577 Lr: 0.00204 [2024-02-18 22:56:20,027 INFO misc.py line 119 87073] Train: [59/100][122/1557] Data 0.004 (0.056) Batch 1.134 (1.069) Remain 19:23:10 loss: 0.5203 Lr: 0.00204 [2024-02-18 22:56:21,176 INFO misc.py line 119 87073] Train: [59/100][123/1557] Data 0.003 (0.055) Batch 1.147 (1.070) Remain 19:23:52 loss: 0.1673 Lr: 0.00204 [2024-02-18 22:56:21,889 INFO misc.py line 119 87073] Train: [59/100][124/1557] Data 0.006 (0.055) Batch 0.716 (1.067) Remain 19:20:40 loss: 0.2449 Lr: 0.00204 [2024-02-18 22:56:22,626 INFO misc.py line 119 87073] Train: [59/100][125/1557] Data 0.003 (0.055) Batch 0.731 (1.064) Remain 19:17:39 loss: 0.3077 Lr: 0.00204 [2024-02-18 22:56:23,751 INFO misc.py line 119 87073] Train: [59/100][126/1557] Data 0.009 (0.054) Batch 1.126 (1.065) Remain 19:18:11 loss: 0.3370 Lr: 0.00204 [2024-02-18 22:56:24,838 INFO misc.py line 119 87073] Train: [59/100][127/1557] Data 0.009 (0.054) Batch 1.082 (1.065) Remain 19:18:19 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line 119 87073] Train: [59/100][221/1557] Data 0.004 (0.056) Batch 1.037 (1.049) Remain 18:59:47 loss: 0.3368 Lr: 0.00204 [2024-02-18 22:58:02,283 INFO misc.py line 119 87073] Train: [59/100][222/1557] Data 0.005 (0.055) Batch 0.742 (1.048) Remain 18:58:15 loss: 0.3545 Lr: 0.00204 [2024-02-18 22:58:03,064 INFO misc.py line 119 87073] Train: [59/100][223/1557] Data 0.004 (0.055) Batch 0.775 (1.047) Remain 18:56:53 loss: 0.1800 Lr: 0.00204 [2024-02-18 22:58:04,217 INFO misc.py line 119 87073] Train: [59/100][224/1557] Data 0.009 (0.055) Batch 1.147 (1.047) Remain 18:57:21 loss: 0.1245 Lr: 0.00204 [2024-02-18 22:58:05,233 INFO misc.py line 119 87073] Train: [59/100][225/1557] Data 0.016 (0.055) Batch 1.015 (1.047) Remain 18:57:11 loss: 0.6431 Lr: 0.00204 [2024-02-18 22:58:06,091 INFO misc.py line 119 87073] Train: [59/100][226/1557] Data 0.017 (0.055) Batch 0.869 (1.046) Remain 18:56:18 loss: 0.2211 Lr: 0.00204 [2024-02-18 22:58:07,000 INFO misc.py line 119 87073] Train: 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Batch 1.017 (1.073) Remain 19:24:46 loss: 0.3978 Lr: 0.00204 [2024-02-18 22:58:20,447 INFO misc.py line 119 87073] Train: [59/100][234/1557] Data 0.005 (0.068) Batch 0.975 (1.072) Remain 19:24:17 loss: 0.2705 Lr: 0.00204 [2024-02-18 22:58:21,327 INFO misc.py line 119 87073] Train: [59/100][235/1557] Data 0.007 (0.068) Batch 0.880 (1.071) Remain 19:23:22 loss: 0.3851 Lr: 0.00204 [2024-02-18 22:58:22,097 INFO misc.py line 119 87073] Train: [59/100][236/1557] Data 0.008 (0.068) Batch 0.774 (1.070) Remain 19:21:58 loss: 0.3179 Lr: 0.00204 [2024-02-18 22:58:22,899 INFO misc.py line 119 87073] Train: [59/100][237/1557] Data 0.004 (0.067) Batch 0.793 (1.069) Remain 19:20:40 loss: 0.2653 Lr: 0.00204 [2024-02-18 22:58:23,982 INFO misc.py line 119 87073] Train: [59/100][238/1557] Data 0.012 (0.067) Batch 1.083 (1.069) Remain 19:20:43 loss: 0.1900 Lr: 0.00204 [2024-02-18 22:58:25,030 INFO misc.py line 119 87073] Train: [59/100][239/1557] Data 0.012 (0.067) Batch 1.050 (1.069) Remain 19:20:37 loss: 0.0927 Lr: 0.00204 [2024-02-18 22:58:26,069 INFO misc.py line 119 87073] Train: [59/100][240/1557] Data 0.011 (0.067) Batch 1.045 (1.069) Remain 19:20:29 loss: 0.5207 Lr: 0.00204 [2024-02-18 22:58:26,983 INFO misc.py line 119 87073] Train: [59/100][241/1557] Data 0.004 (0.067) Batch 0.913 (1.068) Remain 19:19:45 loss: 0.3926 Lr: 0.00204 [2024-02-18 22:58:27,827 INFO misc.py line 119 87073] Train: [59/100][242/1557] Data 0.006 (0.066) Batch 0.844 (1.067) Remain 19:18:43 loss: 0.5088 Lr: 0.00204 [2024-02-18 22:58:28,619 INFO misc.py line 119 87073] Train: [59/100][243/1557] Data 0.007 (0.066) Batch 0.784 (1.066) Remain 19:17:25 loss: 0.3262 Lr: 0.00204 [2024-02-18 22:58:29,390 INFO misc.py line 119 87073] Train: [59/100][244/1557] Data 0.013 (0.066) Batch 0.779 (1.065) Remain 19:16:07 loss: 0.2156 Lr: 0.00204 [2024-02-18 22:58:30,697 INFO misc.py line 119 87073] Train: [59/100][245/1557] Data 0.005 (0.066) Batch 1.299 (1.066) Remain 19:17:08 loss: 0.1260 Lr: 0.00204 [2024-02-18 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Batch 1.086 (1.073) Remain 19:24:14 loss: 0.5708 Lr: 0.00203 [2024-02-18 22:59:20,556 INFO misc.py line 119 87073] Train: [59/100][290/1557] Data 0.003 (0.067) Batch 0.903 (1.072) Remain 19:23:34 loss: 0.8354 Lr: 0.00203 [2024-02-18 22:59:21,564 INFO misc.py line 119 87073] Train: [59/100][291/1557] Data 0.005 (0.066) Batch 1.007 (1.072) Remain 19:23:18 loss: 0.3953 Lr: 0.00203 [2024-02-18 22:59:22,369 INFO misc.py line 119 87073] Train: [59/100][292/1557] Data 0.006 (0.066) Batch 0.765 (1.071) Remain 19:22:08 loss: 0.2250 Lr: 0.00203 [2024-02-18 22:59:23,195 INFO misc.py line 119 87073] Train: [59/100][293/1557] Data 0.046 (0.066) Batch 0.864 (1.070) Remain 19:21:21 loss: 0.1195 Lr: 0.00203 [2024-02-18 22:59:24,280 INFO misc.py line 119 87073] Train: [59/100][294/1557] Data 0.007 (0.066) Batch 1.086 (1.070) Remain 19:21:23 loss: 0.1859 Lr: 0.00203 [2024-02-18 22:59:25,153 INFO misc.py line 119 87073] Train: [59/100][295/1557] Data 0.007 (0.066) Batch 0.875 (1.070) Remain 19:20:38 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Batch 0.915 (1.073) Remain 19:20:00 loss: 0.1934 Lr: 0.00202 [2024-02-18 23:03:20,827 INFO misc.py line 119 87073] Train: [59/100][514/1557] Data 0.007 (0.067) Batch 0.939 (1.072) Remain 19:19:42 loss: 0.3670 Lr: 0.00202 [2024-02-18 23:03:21,846 INFO misc.py line 119 87073] Train: [59/100][515/1557] Data 0.006 (0.067) Batch 1.020 (1.072) Remain 19:19:34 loss: 0.1448 Lr: 0.00202 [2024-02-18 23:03:22,619 INFO misc.py line 119 87073] Train: [59/100][516/1557] Data 0.004 (0.067) Batch 0.772 (1.072) Remain 19:18:55 loss: 0.1604 Lr: 0.00202 [2024-02-18 23:03:23,392 INFO misc.py line 119 87073] Train: [59/100][517/1557] Data 0.006 (0.067) Batch 0.774 (1.071) Remain 19:18:16 loss: 0.3584 Lr: 0.00202 [2024-02-18 23:03:24,488 INFO misc.py line 119 87073] Train: [59/100][518/1557] Data 0.004 (0.067) Batch 1.093 (1.071) Remain 19:18:18 loss: 0.0933 Lr: 0.00202 [2024-02-18 23:03:25,344 INFO misc.py line 119 87073] Train: [59/100][519/1557] Data 0.007 (0.066) Batch 0.860 (1.071) Remain 19:17:50 loss: 0.1612 Lr: 0.00202 [2024-02-18 23:03:26,385 INFO misc.py line 119 87073] Train: [59/100][520/1557] Data 0.004 (0.066) Batch 1.041 (1.071) Remain 19:17:46 loss: 0.2049 Lr: 0.00202 [2024-02-18 23:03:27,391 INFO misc.py line 119 87073] Train: [59/100][521/1557] Data 0.004 (0.066) Batch 1.006 (1.071) Remain 19:17:36 loss: 0.2312 Lr: 0.00202 [2024-02-18 23:03:28,360 INFO misc.py line 119 87073] Train: [59/100][522/1557] Data 0.004 (0.066) Batch 0.970 (1.070) Remain 19:17:23 loss: 0.2634 Lr: 0.00202 [2024-02-18 23:03:29,117 INFO misc.py line 119 87073] Train: [59/100][523/1557] Data 0.003 (0.066) Batch 0.753 (1.070) Remain 19:16:42 loss: 0.2373 Lr: 0.00202 [2024-02-18 23:03:29,779 INFO misc.py line 119 87073] Train: [59/100][524/1557] Data 0.007 (0.066) Batch 0.665 (1.069) Remain 19:15:51 loss: 0.2866 Lr: 0.00202 [2024-02-18 23:03:31,106 INFO misc.py line 119 87073] Train: [59/100][525/1557] Data 0.004 (0.066) Batch 1.323 (1.070) Remain 19:16:21 loss: 0.2272 Lr: 0.00202 [2024-02-18 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line 119 87073] Train: [59/100][557/1557] Data 0.008 (0.064) Batch 0.950 (1.067) Remain 19:12:49 loss: 0.4600 Lr: 0.00202 [2024-02-18 23:04:04,835 INFO misc.py line 119 87073] Train: [59/100][558/1557] Data 0.004 (0.064) Batch 1.029 (1.067) Remain 19:12:43 loss: 0.4433 Lr: 0.00202 [2024-02-18 23:04:05,634 INFO misc.py line 119 87073] Train: [59/100][559/1557] Data 0.004 (0.064) Batch 0.795 (1.066) Remain 19:12:10 loss: 0.2042 Lr: 0.00202 [2024-02-18 23:04:06,854 INFO misc.py line 119 87073] Train: [59/100][560/1557] Data 0.007 (0.064) Batch 1.217 (1.067) Remain 19:12:27 loss: 0.1898 Lr: 0.00202 [2024-02-18 23:04:08,042 INFO misc.py line 119 87073] Train: [59/100][561/1557] Data 0.011 (0.064) Batch 1.182 (1.067) Remain 19:12:39 loss: 0.2526 Lr: 0.00202 [2024-02-18 23:04:09,003 INFO misc.py line 119 87073] Train: [59/100][562/1557] Data 0.017 (0.064) Batch 0.974 (1.067) Remain 19:12:27 loss: 0.3939 Lr: 0.00202 [2024-02-18 23:04:09,931 INFO misc.py line 119 87073] Train: 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Batch 0.795 (1.077) Remain 19:23:43 loss: 0.3295 Lr: 0.00202 [2024-02-18 23:04:23,450 INFO misc.py line 119 87073] Train: [59/100][570/1557] Data 0.004 (0.068) Batch 1.001 (1.077) Remain 19:23:33 loss: 0.8181 Lr: 0.00202 [2024-02-18 23:04:24,427 INFO misc.py line 119 87073] Train: [59/100][571/1557] Data 0.020 (0.068) Batch 0.991 (1.077) Remain 19:23:23 loss: 0.7211 Lr: 0.00202 [2024-02-18 23:04:25,174 INFO misc.py line 119 87073] Train: [59/100][572/1557] Data 0.005 (0.068) Batch 0.741 (1.076) Remain 19:22:43 loss: 0.3292 Lr: 0.00202 [2024-02-18 23:04:25,939 INFO misc.py line 119 87073] Train: [59/100][573/1557] Data 0.010 (0.067) Batch 0.772 (1.076) Remain 19:22:08 loss: 0.1448 Lr: 0.00202 [2024-02-18 23:04:26,986 INFO misc.py line 119 87073] Train: [59/100][574/1557] Data 0.003 (0.067) Batch 1.035 (1.076) Remain 19:22:02 loss: 0.2010 Lr: 0.00202 [2024-02-18 23:04:28,042 INFO misc.py line 119 87073] Train: [59/100][575/1557] Data 0.016 (0.067) Batch 1.067 (1.076) Remain 19:22:00 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Batch 0.955 (1.077) Remain 19:20:44 loss: 0.2465 Lr: 0.00201 [2024-02-18 23:07:24,380 INFO misc.py line 119 87073] Train: [59/100][738/1557] Data 0.007 (0.068) Batch 0.972 (1.077) Remain 19:20:34 loss: 0.3301 Lr: 0.00201 [2024-02-18 23:07:25,469 INFO misc.py line 119 87073] Train: [59/100][739/1557] Data 0.004 (0.068) Batch 1.088 (1.077) Remain 19:20:34 loss: 0.0927 Lr: 0.00201 [2024-02-18 23:07:26,235 INFO misc.py line 119 87073] Train: [59/100][740/1557] Data 0.005 (0.068) Batch 0.767 (1.077) Remain 19:20:05 loss: 0.3361 Lr: 0.00201 [2024-02-18 23:07:27,007 INFO misc.py line 119 87073] Train: [59/100][741/1557] Data 0.004 (0.068) Batch 0.767 (1.076) Remain 19:19:37 loss: 0.4763 Lr: 0.00201 [2024-02-18 23:07:28,203 INFO misc.py line 119 87073] Train: [59/100][742/1557] Data 0.009 (0.068) Batch 1.197 (1.076) Remain 19:19:47 loss: 0.1041 Lr: 0.00201 [2024-02-18 23:07:29,271 INFO misc.py line 119 87073] Train: [59/100][743/1557] Data 0.007 (0.067) Batch 1.068 (1.076) Remain 19:19:45 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[2024-02-18 23:21:33,269 INFO misc.py line 119 87073] Train: [59/100][1526/1557] Data 0.009 (0.066) Batch 1.403 (1.077) Remain 19:06:34 loss: 0.1150 Lr: 0.00197 [2024-02-18 23:21:34,247 INFO misc.py line 119 87073] Train: [59/100][1527/1557] Data 0.013 (0.066) Batch 0.988 (1.077) Remain 19:06:29 loss: 0.2056 Lr: 0.00197 [2024-02-18 23:21:35,134 INFO misc.py line 119 87073] Train: [59/100][1528/1557] Data 0.003 (0.066) Batch 0.887 (1.077) Remain 19:06:20 loss: 0.6485 Lr: 0.00197 [2024-02-18 23:21:36,093 INFO misc.py line 119 87073] Train: [59/100][1529/1557] Data 0.003 (0.065) Batch 0.949 (1.077) Remain 19:06:13 loss: 0.2827 Lr: 0.00197 [2024-02-18 23:21:37,051 INFO misc.py line 119 87073] Train: [59/100][1530/1557] Data 0.012 (0.065) Batch 0.968 (1.077) Remain 19:06:08 loss: 0.4973 Lr: 0.00197 [2024-02-18 23:21:37,768 INFO misc.py line 119 87073] Train: [59/100][1531/1557] Data 0.004 (0.065) Batch 0.717 (1.077) Remain 19:05:52 loss: 0.4551 Lr: 0.00197 [2024-02-18 23:21:38,631 INFO misc.py line 119 87073] Train: [59/100][1532/1557] Data 0.003 (0.065) Batch 0.854 (1.076) Remain 19:05:41 loss: 0.2156 Lr: 0.00197 [2024-02-18 23:21:39,916 INFO misc.py line 119 87073] Train: [59/100][1533/1557] Data 0.012 (0.065) Batch 1.283 (1.077) Remain 19:05:49 loss: 0.1334 Lr: 0.00197 [2024-02-18 23:21:40,925 INFO misc.py line 119 87073] Train: [59/100][1534/1557] Data 0.015 (0.065) Batch 1.008 (1.077) Remain 19:05:45 loss: 0.1524 Lr: 0.00197 [2024-02-18 23:21:41,881 INFO misc.py line 119 87073] Train: [59/100][1535/1557] Data 0.014 (0.065) Batch 0.968 (1.076) Remain 19:05:39 loss: 0.3728 Lr: 0.00197 [2024-02-18 23:21:42,750 INFO misc.py line 119 87073] Train: [59/100][1536/1557] Data 0.003 (0.065) Batch 0.868 (1.076) Remain 19:05:29 loss: 0.2736 Lr: 0.00197 [2024-02-18 23:21:43,597 INFO misc.py line 119 87073] Train: [59/100][1537/1557] Data 0.004 (0.065) Batch 0.830 (1.076) Remain 19:05:18 loss: 0.5648 Lr: 0.00197 [2024-02-18 23:21:44,343 INFO misc.py line 119 87073] Train: [59/100][1538/1557] Data 0.021 (0.065) Batch 0.764 (1.076) Remain 19:05:04 loss: 0.5388 Lr: 0.00197 [2024-02-18 23:21:45,105 INFO misc.py line 119 87073] Train: [59/100][1539/1557] Data 0.003 (0.065) Batch 0.753 (1.076) Remain 19:04:50 loss: 0.3216 Lr: 0.00197 [2024-02-18 23:21:46,266 INFO misc.py line 119 87073] Train: [59/100][1540/1557] Data 0.012 (0.065) Batch 1.160 (1.076) Remain 19:04:52 loss: 0.1734 Lr: 0.00197 [2024-02-18 23:21:47,330 INFO misc.py line 119 87073] Train: [59/100][1541/1557] Data 0.012 (0.065) Batch 1.048 (1.076) Remain 19:04:50 loss: 0.3092 Lr: 0.00197 [2024-02-18 23:21:48,219 INFO misc.py line 119 87073] Train: [59/100][1542/1557] Data 0.028 (0.065) Batch 0.913 (1.076) Remain 19:04:42 loss: 1.0837 Lr: 0.00197 [2024-02-18 23:21:49,320 INFO misc.py line 119 87073] Train: [59/100][1543/1557] Data 0.004 (0.065) Batch 1.101 (1.076) Remain 19:04:42 loss: 0.4466 Lr: 0.00197 [2024-02-18 23:21:50,285 INFO misc.py line 119 87073] Train: [59/100][1544/1557] Data 0.004 (0.065) Batch 0.965 (1.076) Remain 19:04:36 loss: 0.3207 Lr: 0.00197 [2024-02-18 23:21:50,902 INFO misc.py line 119 87073] Train: [59/100][1545/1557] Data 0.004 (0.065) Batch 0.611 (1.075) Remain 19:04:16 loss: 0.1506 Lr: 0.00197 [2024-02-18 23:21:51,606 INFO misc.py line 119 87073] Train: [59/100][1546/1557] Data 0.010 (0.065) Batch 0.709 (1.075) Remain 19:04:00 loss: 0.3921 Lr: 0.00197 [2024-02-18 23:21:52,803 INFO misc.py line 119 87073] Train: [59/100][1547/1557] Data 0.005 (0.065) Batch 1.197 (1.075) Remain 19:04:04 loss: 0.1358 Lr: 0.00197 [2024-02-18 23:21:53,717 INFO misc.py line 119 87073] Train: [59/100][1548/1557] Data 0.005 (0.065) Batch 0.915 (1.075) Remain 19:03:56 loss: 0.4433 Lr: 0.00197 [2024-02-18 23:21:54,674 INFO misc.py line 119 87073] Train: [59/100][1549/1557] Data 0.004 (0.065) Batch 0.957 (1.075) Remain 19:03:50 loss: 0.3527 Lr: 0.00197 [2024-02-18 23:21:55,491 INFO misc.py line 119 87073] Train: [59/100][1550/1557] Data 0.005 (0.065) Batch 0.811 (1.075) Remain 19:03:38 loss: 0.3261 Lr: 0.00197 [2024-02-18 23:21:56,602 INFO misc.py line 119 87073] Train: [59/100][1551/1557] Data 0.011 (0.065) Batch 1.113 (1.075) Remain 19:03:39 loss: 0.3935 Lr: 0.00197 [2024-02-18 23:21:57,369 INFO misc.py line 119 87073] Train: [59/100][1552/1557] Data 0.008 (0.065) Batch 0.771 (1.075) Remain 19:03:25 loss: 0.5346 Lr: 0.00197 [2024-02-18 23:21:58,109 INFO misc.py line 119 87073] Train: [59/100][1553/1557] Data 0.005 (0.065) Batch 0.736 (1.074) Remain 19:03:10 loss: 0.1816 Lr: 0.00197 [2024-02-18 23:21:59,308 INFO misc.py line 119 87073] Train: [59/100][1554/1557] Data 0.009 (0.065) Batch 1.197 (1.074) Remain 19:03:14 loss: 0.2192 Lr: 0.00197 [2024-02-18 23:22:00,221 INFO misc.py line 119 87073] Train: [59/100][1555/1557] Data 0.010 (0.065) Batch 0.918 (1.074) Remain 19:03:06 loss: 0.4628 Lr: 0.00197 [2024-02-18 23:22:01,000 INFO misc.py line 119 87073] Train: [59/100][1556/1557] Data 0.004 (0.064) Batch 0.779 (1.074) Remain 19:02:53 loss: 0.0459 Lr: 0.00197 [2024-02-18 23:22:02,057 INFO misc.py line 119 87073] Train: [59/100][1557/1557] Data 0.004 (0.064) Batch 1.050 (1.074) Remain 19:02:51 loss: 0.3521 Lr: 0.00197 [2024-02-18 23:22:02,058 INFO misc.py line 136 87073] Train result: loss: 0.3133 [2024-02-18 23:22:02,058 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 23:22:31,073 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7900 [2024-02-18 23:22:31,850 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4175 [2024-02-18 23:22:33,974 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4185 [2024-02-18 23:22:36,181 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3128 [2024-02-18 23:22:41,128 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2185 [2024-02-18 23:22:41,827 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0624 [2024-02-18 23:22:43,089 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3050 [2024-02-18 23:22:46,047 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3443 [2024-02-18 23:22:47,860 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2985 [2024-02-18 23:22:49,716 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7308/0.7878/0.9193. [2024-02-18 23:22:49,716 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9330/0.9542 [2024-02-18 23:22:49,717 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9891 [2024-02-18 23:22:49,717 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8685/0.9733 [2024-02-18 23:22:49,717 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 23:22:49,717 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3832/0.4187 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7146/0.7419 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8161/0.9355 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8323/0.9121 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9205/0.9614 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8403/0.8955 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8008/0.8899 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7705/0.8340 [2024-02-18 23:22:49,720 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6381/0.7360 [2024-02-18 23:22:49,721 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 23:22:49,724 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7308 [2024-02-18 23:22:49,724 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-18 23:22:49,724 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 23:23:01,133 INFO misc.py line 119 87073] Train: [60/100][1/1557] Data 1.276 (1.276) Batch 2.224 (2.224) Remain 39:26:10 loss: 0.5262 Lr: 0.00197 [2024-02-18 23:23:02,029 INFO misc.py line 119 87073] Train: [60/100][2/1557] Data 0.007 (0.007) Batch 0.896 (0.896) Remain 15:53:35 loss: 0.3741 Lr: 0.00197 [2024-02-18 23:23:03,035 INFO misc.py line 119 87073] Train: [60/100][3/1557] Data 0.006 (0.006) Batch 1.003 (1.003) Remain 17:47:08 loss: 0.5647 Lr: 0.00197 [2024-02-18 23:23:03,896 INFO misc.py line 119 87073] Train: [60/100][4/1557] Data 0.008 (0.008) Batch 0.865 (0.865) Remain 15:20:21 loss: 0.3064 Lr: 0.00197 [2024-02-18 23:23:04,633 INFO misc.py line 119 87073] Train: [60/100][5/1557] Data 0.004 (0.006) Batch 0.738 (0.801) Remain 14:12:40 loss: 0.2066 Lr: 0.00197 [2024-02-18 23:23:05,424 INFO misc.py line 119 87073] Train: [60/100][6/1557] Data 0.003 (0.005) Batch 0.789 (0.797) Remain 14:08:07 loss: 0.3122 Lr: 0.00197 [2024-02-18 23:23:06,638 INFO misc.py line 119 87073] Train: [60/100][7/1557] Data 0.006 (0.005) Batch 1.210 (0.901) Remain 15:58:01 loss: 0.2035 Lr: 0.00197 [2024-02-18 23:23:07,700 INFO misc.py line 119 87073] Train: [60/100][8/1557] Data 0.009 (0.006) Batch 1.060 (0.932) Remain 16:31:59 loss: 0.8950 Lr: 0.00197 [2024-02-18 23:23:08,720 INFO misc.py line 119 87073] Train: [60/100][9/1557] Data 0.011 (0.007) Batch 1.020 (0.947) Remain 16:47:25 loss: 0.3658 Lr: 0.00197 [2024-02-18 23:23:09,629 INFO misc.py line 119 87073] Train: [60/100][10/1557] Data 0.012 (0.008) Batch 0.916 (0.943) Remain 16:42:38 loss: 0.0867 Lr: 0.00197 [2024-02-18 23:23:10,591 INFO misc.py line 119 87073] Train: [60/100][11/1557] Data 0.004 (0.007) Batch 0.962 (0.945) Remain 16:45:13 loss: 0.4653 Lr: 0.00197 [2024-02-18 23:23:11,342 INFO misc.py line 119 87073] Train: [60/100][12/1557] Data 0.004 (0.007) Batch 0.751 (0.923) Remain 16:22:20 loss: 0.3725 Lr: 0.00197 [2024-02-18 23:23:12,144 INFO misc.py line 119 87073] Train: [60/100][13/1557] Data 0.004 (0.007) Batch 0.792 (0.910) Remain 16:08:18 loss: 0.1271 Lr: 0.00197 [2024-02-18 23:23:13,326 INFO misc.py line 119 87073] Train: [60/100][14/1557] Data 0.014 (0.007) Batch 1.185 (0.935) Remain 16:34:50 loss: 0.1295 Lr: 0.00197 [2024-02-18 23:23:14,272 INFO misc.py line 119 87073] Train: [60/100][15/1557] Data 0.012 (0.008) Batch 0.954 (0.937) Remain 16:36:28 loss: 0.3155 Lr: 0.00197 [2024-02-18 23:23:15,262 INFO misc.py line 119 87073] Train: [60/100][16/1557] Data 0.003 (0.007) Batch 0.990 (0.941) Remain 16:40:46 loss: 0.4206 Lr: 0.00197 [2024-02-18 23:23:16,346 INFO misc.py line 119 87073] Train: [60/100][17/1557] Data 0.004 (0.007) Batch 1.085 (0.951) Remain 16:51:40 loss: 0.3006 Lr: 0.00197 [2024-02-18 23:23:17,228 INFO misc.py line 119 87073] Train: [60/100][18/1557] Data 0.003 (0.007) Batch 0.881 (0.946) Remain 16:46:42 loss: 0.2688 Lr: 0.00197 [2024-02-18 23:23:17,980 INFO misc.py line 119 87073] Train: [60/100][19/1557] Data 0.005 (0.007) Batch 0.750 (0.934) Remain 16:33:38 loss: 0.2577 Lr: 0.00197 [2024-02-18 23:23:18,752 INFO misc.py line 119 87073] Train: [60/100][20/1557] Data 0.007 (0.007) Batch 0.774 (0.925) Remain 16:23:35 loss: 0.3235 Lr: 0.00197 [2024-02-18 23:23:19,978 INFO misc.py line 119 87073] Train: [60/100][21/1557] Data 0.005 (0.007) Batch 1.224 (0.941) Remain 16:41:15 loss: 0.2712 Lr: 0.00197 [2024-02-18 23:23:20,962 INFO misc.py line 119 87073] Train: [60/100][22/1557] Data 0.007 (0.007) Batch 0.985 (0.944) Remain 16:43:40 loss: 0.4666 Lr: 0.00197 [2024-02-18 23:23:21,858 INFO misc.py line 119 87073] Train: [60/100][23/1557] Data 0.006 (0.007) Batch 0.897 (0.941) Remain 16:41:11 loss: 0.1575 Lr: 0.00197 [2024-02-18 23:23:22,897 INFO misc.py line 119 87073] Train: [60/100][24/1557] Data 0.005 (0.006) Batch 1.038 (0.946) Remain 16:46:06 loss: 0.3297 Lr: 0.00197 [2024-02-18 23:23:23,811 INFO misc.py line 119 87073] Train: [60/100][25/1557] Data 0.005 (0.006) Batch 0.914 (0.945) Remain 16:44:33 loss: 0.3750 Lr: 0.00197 [2024-02-18 23:23:24,535 INFO misc.py line 119 87073] Train: [60/100][26/1557] Data 0.005 (0.006) Batch 0.724 (0.935) Remain 16:34:20 loss: 0.3101 Lr: 0.00197 [2024-02-18 23:23:25,319 INFO misc.py line 119 87073] Train: [60/100][27/1557] Data 0.004 (0.006) Batch 0.783 (0.929) Remain 16:27:35 loss: 0.3530 Lr: 0.00197 [2024-02-18 23:23:26,621 INFO misc.py line 119 87073] Train: [60/100][28/1557] Data 0.005 (0.006) Batch 1.299 (0.943) Remain 16:43:19 loss: 0.1784 Lr: 0.00197 [2024-02-18 23:23:27,578 INFO misc.py line 119 87073] Train: [60/100][29/1557] Data 0.009 (0.006) Batch 0.962 (0.944) Remain 16:44:03 loss: 0.2764 Lr: 0.00197 [2024-02-18 23:23:28,620 INFO misc.py line 119 87073] Train: [60/100][30/1557] Data 0.004 (0.006) Batch 1.042 (0.948) Remain 16:47:53 loss: 0.5324 Lr: 0.00197 [2024-02-18 23:23:29,776 INFO misc.py line 119 87073] Train: [60/100][31/1557] Data 0.004 (0.006) Batch 1.156 (0.955) Remain 16:55:47 loss: 0.2745 Lr: 0.00197 [2024-02-18 23:23:30,936 INFO misc.py line 119 87073] Train: [60/100][32/1557] Data 0.005 (0.006) Batch 1.152 (0.962) Remain 17:03:00 loss: 0.3450 Lr: 0.00197 [2024-02-18 23:23:31,636 INFO misc.py line 119 87073] Train: [60/100][33/1557] Data 0.012 (0.006) Batch 0.708 (0.954) Remain 16:53:58 loss: 0.2221 Lr: 0.00197 [2024-02-18 23:23:32,400 INFO misc.py line 119 87073] Train: [60/100][34/1557] Data 0.004 (0.006) Batch 0.762 (0.947) Remain 16:47:23 loss: 0.3848 Lr: 0.00197 [2024-02-18 23:23:33,642 INFO misc.py line 119 87073] Train: [60/100][35/1557] Data 0.005 (0.006) Batch 1.162 (0.954) Remain 16:54:31 loss: 0.2222 Lr: 0.00197 [2024-02-18 23:23:34,623 INFO misc.py line 119 87073] Train: [60/100][36/1557] Data 0.085 (0.009) Batch 1.062 (0.957) Remain 16:57:58 loss: 0.2196 Lr: 0.00197 [2024-02-18 23:23:35,623 INFO misc.py line 119 87073] Train: [60/100][37/1557] Data 0.004 (0.008) Batch 1.000 (0.959) Remain 16:59:17 loss: 0.2299 Lr: 0.00197 [2024-02-18 23:23:36,606 INFO misc.py line 119 87073] Train: [60/100][38/1557] Data 0.004 (0.008) Batch 0.983 (0.959) Remain 17:00:01 loss: 0.2960 Lr: 0.00197 [2024-02-18 23:23:37,559 INFO misc.py line 119 87073] Train: [60/100][39/1557] Data 0.005 (0.008) Batch 0.954 (0.959) Remain 16:59:50 loss: 0.1134 Lr: 0.00197 [2024-02-18 23:23:38,355 INFO misc.py line 119 87073] Train: [60/100][40/1557] Data 0.004 (0.008) Batch 0.742 (0.953) Remain 16:53:34 loss: 0.4033 Lr: 0.00197 [2024-02-18 23:23:39,137 INFO misc.py line 119 87073] Train: [60/100][41/1557] Data 0.059 (0.009) Batch 0.834 (0.950) Remain 16:50:12 loss: 0.3018 Lr: 0.00197 [2024-02-18 23:23:40,159 INFO misc.py line 119 87073] Train: [60/100][42/1557] Data 0.007 (0.009) Batch 1.023 (0.952) Remain 16:52:10 loss: 0.1379 Lr: 0.00197 [2024-02-18 23:23:41,052 INFO misc.py line 119 87073] Train: [60/100][43/1557] Data 0.006 (0.009) Batch 0.893 (0.950) Remain 16:50:34 loss: 0.3757 Lr: 0.00197 [2024-02-18 23:23:42,062 INFO misc.py line 119 87073] Train: [60/100][44/1557] Data 0.006 (0.009) Batch 1.005 (0.952) Remain 16:51:59 loss: 0.2694 Lr: 0.00197 [2024-02-18 23:23:43,195 INFO misc.py line 119 87073] Train: [60/100][45/1557] Data 0.011 (0.009) Batch 1.139 (0.956) Remain 16:56:41 loss: 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INFO misc.py line 119 87073] Train: [60/100][52/1557] Data 0.007 (0.009) Batch 1.138 (0.949) Remain 16:48:36 loss: 0.3809 Lr: 0.00197 [2024-02-18 23:23:50,431 INFO misc.py line 119 87073] Train: [60/100][53/1557] Data 0.008 (0.009) Batch 0.910 (0.948) Remain 16:47:45 loss: 0.4487 Lr: 0.00197 [2024-02-18 23:23:51,098 INFO misc.py line 119 87073] Train: [60/100][54/1557] Data 0.007 (0.009) Batch 0.669 (0.943) Remain 16:41:55 loss: 0.1884 Lr: 0.00197 [2024-02-18 23:23:51,849 INFO misc.py line 119 87073] Train: [60/100][55/1557] Data 0.004 (0.009) Batch 0.741 (0.939) Remain 16:37:48 loss: 0.2177 Lr: 0.00197 [2024-02-18 23:23:53,099 INFO misc.py line 119 87073] Train: [60/100][56/1557] Data 0.013 (0.009) Batch 1.252 (0.945) Remain 16:44:04 loss: 0.2178 Lr: 0.00197 [2024-02-18 23:23:54,058 INFO misc.py line 119 87073] Train: [60/100][57/1557] Data 0.011 (0.009) Batch 0.966 (0.945) Remain 16:44:28 loss: 0.2380 Lr: 0.00197 [2024-02-18 23:23:54,943 INFO misc.py line 119 87073] Train: 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Train: [60/100][102/1557] Data 0.013 (0.052) Batch 1.004 (1.064) Remain 18:50:44 loss: 0.4505 Lr: 0.00196 [2024-02-18 23:24:49,147 INFO misc.py line 119 87073] Train: [60/100][103/1557] Data 0.013 (0.052) Batch 0.732 (1.061) Remain 18:47:11 loss: 0.2766 Lr: 0.00196 [2024-02-18 23:24:49,862 INFO misc.py line 119 87073] Train: [60/100][104/1557] Data 0.005 (0.051) Batch 0.712 (1.058) Remain 18:43:30 loss: 0.2436 Lr: 0.00196 [2024-02-18 23:24:51,072 INFO misc.py line 119 87073] Train: [60/100][105/1557] Data 0.008 (0.051) Batch 1.209 (1.059) Remain 18:45:03 loss: 0.2648 Lr: 0.00196 [2024-02-18 23:24:51,999 INFO misc.py line 119 87073] Train: [60/100][106/1557] Data 0.009 (0.050) Batch 0.932 (1.058) Remain 18:43:43 loss: 0.1611 Lr: 0.00196 [2024-02-18 23:24:52,922 INFO misc.py line 119 87073] Train: [60/100][107/1557] Data 0.003 (0.050) Batch 0.923 (1.057) Remain 18:42:19 loss: 0.4074 Lr: 0.00196 [2024-02-18 23:24:53,975 INFO misc.py line 119 87073] Train: [60/100][108/1557] Data 0.004 (0.049) Batch 1.054 (1.057) Remain 18:42:17 loss: 0.2616 Lr: 0.00196 [2024-02-18 23:24:55,115 INFO misc.py line 119 87073] Train: [60/100][109/1557] Data 0.004 (0.049) Batch 1.139 (1.057) Remain 18:43:05 loss: 0.5172 Lr: 0.00196 [2024-02-18 23:24:55,899 INFO misc.py line 119 87073] Train: [60/100][110/1557] Data 0.004 (0.048) Batch 0.781 (1.055) Remain 18:40:20 loss: 0.3332 Lr: 0.00196 [2024-02-18 23:24:56,671 INFO misc.py line 119 87073] Train: [60/100][111/1557] Data 0.008 (0.048) Batch 0.774 (1.052) Remain 18:37:33 loss: 0.3243 Lr: 0.00196 [2024-02-18 23:24:57,917 INFO misc.py line 119 87073] Train: [60/100][112/1557] Data 0.005 (0.048) Batch 1.240 (1.054) Remain 18:39:22 loss: 0.2594 Lr: 0.00196 [2024-02-18 23:24:58,883 INFO misc.py line 119 87073] Train: [60/100][113/1557] Data 0.010 (0.047) Batch 0.972 (1.053) Remain 18:38:33 loss: 0.0842 Lr: 0.00196 [2024-02-18 23:24:59,839 INFO misc.py line 119 87073] Train: [60/100][114/1557] Data 0.006 (0.047) Batch 0.957 (1.052) Remain 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Batch 0.737 (1.107) Remain 19:34:55 loss: 0.3321 Lr: 0.00196 [2024-02-18 23:25:34,854 INFO misc.py line 119 87073] Train: [60/100][140/1557] Data 0.009 (0.077) Batch 1.307 (1.108) Remain 19:36:27 loss: 0.1575 Lr: 0.00196 [2024-02-18 23:25:35,868 INFO misc.py line 119 87073] Train: [60/100][141/1557] Data 0.007 (0.076) Batch 1.009 (1.107) Remain 19:35:40 loss: 0.2052 Lr: 0.00196 [2024-02-18 23:25:36,833 INFO misc.py line 119 87073] Train: [60/100][142/1557] Data 0.012 (0.076) Batch 0.973 (1.106) Remain 19:34:37 loss: 0.2223 Lr: 0.00196 [2024-02-18 23:25:38,089 INFO misc.py line 119 87073] Train: [60/100][143/1557] Data 0.004 (0.075) Batch 1.250 (1.108) Remain 19:35:41 loss: 0.2301 Lr: 0.00196 [2024-02-18 23:25:39,108 INFO misc.py line 119 87073] Train: [60/100][144/1557] Data 0.011 (0.075) Batch 1.015 (1.107) Remain 19:34:58 loss: 0.1553 Lr: 0.00196 [2024-02-18 23:25:39,779 INFO misc.py line 119 87073] Train: [60/100][145/1557] Data 0.014 (0.074) Batch 0.681 (1.104) Remain 19:31:46 loss: 0.1390 Lr: 0.00196 [2024-02-18 23:25:40,504 INFO misc.py line 119 87073] Train: [60/100][146/1557] Data 0.004 (0.074) Batch 0.716 (1.101) Remain 19:28:53 loss: 0.2348 Lr: 0.00196 [2024-02-18 23:25:41,706 INFO misc.py line 119 87073] Train: [60/100][147/1557] Data 0.013 (0.074) Batch 1.202 (1.102) Remain 19:29:36 loss: 0.1778 Lr: 0.00196 [2024-02-18 23:25:42,599 INFO misc.py line 119 87073] Train: [60/100][148/1557] Data 0.013 (0.073) Batch 0.903 (1.100) Remain 19:28:08 loss: 0.2172 Lr: 0.00196 [2024-02-18 23:25:43,669 INFO misc.py line 119 87073] Train: [60/100][149/1557] Data 0.003 (0.073) Batch 1.069 (1.100) Remain 19:27:53 loss: 0.5328 Lr: 0.00196 [2024-02-18 23:25:44,676 INFO misc.py line 119 87073] Train: [60/100][150/1557] Data 0.004 (0.072) Batch 1.007 (1.100) Remain 19:27:12 loss: 0.7210 Lr: 0.00196 [2024-02-18 23:25:45,507 INFO misc.py line 119 87073] Train: [60/100][151/1557] Data 0.004 (0.072) Batch 0.831 (1.098) Remain 19:25:15 loss: 0.2021 Lr: 0.00196 [2024-02-18 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Batch 0.750 (1.125) Remain 19:53:08 loss: 0.1846 Lr: 0.00196 [2024-02-18 23:26:40,208 INFO misc.py line 119 87073] Train: [60/100][196/1557] Data 0.003 (0.089) Batch 1.204 (1.125) Remain 19:53:33 loss: 0.1595 Lr: 0.00196 [2024-02-18 23:26:41,032 INFO misc.py line 119 87073] Train: [60/100][197/1557] Data 0.003 (0.088) Batch 0.825 (1.124) Remain 19:51:53 loss: 0.2253 Lr: 0.00196 [2024-02-18 23:26:42,100 INFO misc.py line 119 87073] Train: [60/100][198/1557] Data 0.003 (0.088) Batch 1.051 (1.123) Remain 19:51:28 loss: 0.1555 Lr: 0.00196 [2024-02-18 23:26:42,896 INFO misc.py line 119 87073] Train: [60/100][199/1557] Data 0.019 (0.087) Batch 0.813 (1.122) Remain 19:49:46 loss: 0.4098 Lr: 0.00196 [2024-02-18 23:26:43,735 INFO misc.py line 119 87073] Train: [60/100][200/1557] Data 0.003 (0.087) Batch 0.839 (1.120) Remain 19:48:14 loss: 0.2576 Lr: 0.00196 [2024-02-18 23:26:44,507 INFO misc.py line 119 87073] Train: [60/100][201/1557] Data 0.003 (0.087) Batch 0.771 (1.119) Remain 19:46:20 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87073] Train: [60/100][214/1557] Data 0.006 (0.082) Batch 1.122 (1.107) Remain 19:34:22 loss: 0.2824 Lr: 0.00196 [2024-02-18 23:26:57,443 INFO misc.py line 119 87073] Train: [60/100][215/1557] Data 0.007 (0.081) Batch 0.729 (1.106) Remain 19:32:27 loss: 0.2895 Lr: 0.00196 [2024-02-18 23:26:58,199 INFO misc.py line 119 87073] Train: [60/100][216/1557] Data 0.006 (0.081) Batch 0.752 (1.104) Remain 19:30:40 loss: 0.2902 Lr: 0.00196 [2024-02-18 23:26:59,461 INFO misc.py line 119 87073] Train: [60/100][217/1557] Data 0.010 (0.080) Batch 1.262 (1.105) Remain 19:31:26 loss: 0.1681 Lr: 0.00196 [2024-02-18 23:27:00,584 INFO misc.py line 119 87073] Train: [60/100][218/1557] Data 0.010 (0.080) Batch 1.119 (1.105) Remain 19:31:29 loss: 0.1809 Lr: 0.00196 [2024-02-18 23:27:01,686 INFO misc.py line 119 87073] Train: [60/100][219/1557] Data 0.014 (0.080) Batch 1.107 (1.105) Remain 19:31:28 loss: 0.3425 Lr: 0.00196 [2024-02-18 23:27:02,694 INFO misc.py line 119 87073] Train: [60/100][220/1557] Data 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line 119 87073] Train: [60/100][239/1557] Data 0.004 (0.093) Batch 0.917 (1.143) Remain 20:11:28 loss: 0.1143 Lr: 0.00196 [2024-02-18 23:27:33,550 INFO misc.py line 119 87073] Train: [60/100][240/1557] Data 0.003 (0.092) Batch 0.780 (1.141) Remain 20:09:49 loss: 0.4680 Lr: 0.00196 [2024-02-18 23:27:34,391 INFO misc.py line 119 87073] Train: [60/100][241/1557] Data 0.013 (0.092) Batch 0.848 (1.140) Remain 20:08:29 loss: 0.6964 Lr: 0.00196 [2024-02-18 23:27:35,393 INFO misc.py line 119 87073] Train: [60/100][242/1557] Data 0.005 (0.092) Batch 1.004 (1.140) Remain 20:07:52 loss: 0.4453 Lr: 0.00196 [2024-02-18 23:27:36,132 INFO misc.py line 119 87073] Train: [60/100][243/1557] Data 0.003 (0.091) Batch 0.738 (1.138) Remain 20:06:04 loss: 0.1981 Lr: 0.00196 [2024-02-18 23:27:36,952 INFO misc.py line 119 87073] Train: [60/100][244/1557] Data 0.004 (0.091) Batch 0.812 (1.137) Remain 20:04:37 loss: 0.2178 Lr: 0.00196 [2024-02-18 23:27:38,215 INFO misc.py line 119 87073] Train: 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Batch 0.862 (1.133) Remain 20:00:19 loss: 0.2108 Lr: 0.00196 [2024-02-18 23:27:45,182 INFO misc.py line 119 87073] Train: [60/100][252/1557] Data 0.003 (0.088) Batch 1.244 (1.133) Remain 20:00:47 loss: 0.1588 Lr: 0.00196 [2024-02-18 23:27:46,142 INFO misc.py line 119 87073] Train: [60/100][253/1557] Data 0.015 (0.088) Batch 0.972 (1.132) Remain 20:00:05 loss: 0.3031 Lr: 0.00196 [2024-02-18 23:27:47,111 INFO misc.py line 119 87073] Train: [60/100][254/1557] Data 0.004 (0.088) Batch 0.968 (1.132) Remain 19:59:22 loss: 0.1529 Lr: 0.00196 [2024-02-18 23:27:48,037 INFO misc.py line 119 87073] Train: [60/100][255/1557] Data 0.003 (0.087) Batch 0.926 (1.131) Remain 19:58:29 loss: 0.1888 Lr: 0.00196 [2024-02-18 23:27:48,970 INFO misc.py line 119 87073] Train: [60/100][256/1557] Data 0.004 (0.087) Batch 0.932 (1.130) Remain 19:57:38 loss: 0.4172 Lr: 0.00196 [2024-02-18 23:27:49,738 INFO misc.py line 119 87073] Train: [60/100][257/1557] Data 0.005 (0.087) Batch 0.769 (1.129) Remain 19:56:06 loss: 0.2527 Lr: 0.00196 [2024-02-18 23:27:50,513 INFO misc.py line 119 87073] Train: [60/100][258/1557] Data 0.003 (0.086) Batch 0.765 (1.127) Remain 19:54:35 loss: 0.2497 Lr: 0.00196 [2024-02-18 23:27:51,672 INFO misc.py line 119 87073] Train: [60/100][259/1557] Data 0.013 (0.086) Batch 1.159 (1.127) Remain 19:54:41 loss: 0.2715 Lr: 0.00196 [2024-02-18 23:27:52,561 INFO misc.py line 119 87073] Train: [60/100][260/1557] Data 0.015 (0.086) Batch 0.900 (1.127) Remain 19:53:44 loss: 0.2503 Lr: 0.00196 [2024-02-18 23:27:53,688 INFO misc.py line 119 87073] Train: [60/100][261/1557] Data 0.003 (0.085) Batch 1.128 (1.127) Remain 19:53:43 loss: 0.4637 Lr: 0.00196 [2024-02-18 23:27:54,902 INFO misc.py line 119 87073] Train: [60/100][262/1557] Data 0.003 (0.085) Batch 1.207 (1.127) Remain 19:54:01 loss: 0.3301 Lr: 0.00196 [2024-02-18 23:27:55,735 INFO misc.py line 119 87073] Train: [60/100][263/1557] Data 0.010 (0.085) Batch 0.840 (1.126) Remain 19:52:50 loss: 0.1798 Lr: 0.00196 [2024-02-18 23:27:56,516 INFO misc.py line 119 87073] Train: [60/100][264/1557] Data 0.004 (0.085) Batch 0.781 (1.124) Remain 19:51:25 loss: 0.1558 Lr: 0.00196 [2024-02-18 23:27:57,250 INFO misc.py line 119 87073] Train: [60/100][265/1557] Data 0.003 (0.084) Batch 0.729 (1.123) Remain 19:49:48 loss: 0.3112 Lr: 0.00196 [2024-02-18 23:27:58,321 INFO misc.py line 119 87073] Train: [60/100][266/1557] Data 0.009 (0.084) Batch 1.065 (1.123) Remain 19:49:33 loss: 0.1089 Lr: 0.00196 [2024-02-18 23:27:59,197 INFO misc.py line 119 87073] Train: [60/100][267/1557] Data 0.016 (0.084) Batch 0.887 (1.122) Remain 19:48:35 loss: 0.1633 Lr: 0.00196 [2024-02-18 23:28:00,244 INFO misc.py line 119 87073] Train: [60/100][268/1557] Data 0.003 (0.083) Batch 1.047 (1.122) Remain 19:48:16 loss: 0.4316 Lr: 0.00196 [2024-02-18 23:28:01,162 INFO misc.py line 119 87073] Train: [60/100][269/1557] Data 0.003 (0.083) Batch 0.917 (1.121) Remain 19:47:26 loss: 0.1952 Lr: 0.00195 [2024-02-18 23:28:02,048 INFO misc.py line 119 87073] Train: [60/100][270/1557] Data 0.004 (0.083) Batch 0.887 (1.120) Remain 19:46:29 loss: 0.2897 Lr: 0.00195 [2024-02-18 23:28:02,823 INFO misc.py line 119 87073] Train: [60/100][271/1557] Data 0.003 (0.082) Batch 0.770 (1.119) Remain 19:45:05 loss: 0.1406 Lr: 0.00195 [2024-02-18 23:28:03,514 INFO misc.py line 119 87073] Train: [60/100][272/1557] Data 0.008 (0.082) Batch 0.697 (1.117) Remain 19:43:24 loss: 0.1859 Lr: 0.00195 [2024-02-18 23:28:04,722 INFO misc.py line 119 87073] Train: [60/100][273/1557] Data 0.003 (0.082) Batch 1.196 (1.117) Remain 19:43:41 loss: 0.1213 Lr: 0.00195 [2024-02-18 23:28:05,567 INFO misc.py line 119 87073] Train: [60/100][274/1557] Data 0.015 (0.082) Batch 0.857 (1.116) Remain 19:42:39 loss: 0.3902 Lr: 0.00195 [2024-02-18 23:28:06,535 INFO misc.py line 119 87073] Train: [60/100][275/1557] Data 0.004 (0.081) Batch 0.967 (1.116) Remain 19:42:03 loss: 0.2458 Lr: 0.00195 [2024-02-18 23:28:07,727 INFO misc.py line 119 87073] Train: [60/100][276/1557] Data 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line 119 87073] Train: [60/100][295/1557] Data 0.004 (0.095) Batch 1.094 (1.141) Remain 20:08:45 loss: 0.5358 Lr: 0.00195 [2024-02-18 23:28:37,337 INFO misc.py line 119 87073] Train: [60/100][296/1557] Data 0.003 (0.094) Batch 1.024 (1.141) Remain 20:08:18 loss: 0.2411 Lr: 0.00195 [2024-02-18 23:28:38,196 INFO misc.py line 119 87073] Train: [60/100][297/1557] Data 0.003 (0.094) Batch 0.859 (1.140) Remain 20:07:16 loss: 0.5936 Lr: 0.00195 [2024-02-18 23:28:39,054 INFO misc.py line 119 87073] Train: [60/100][298/1557] Data 0.003 (0.094) Batch 0.851 (1.139) Remain 20:06:13 loss: 0.1350 Lr: 0.00195 [2024-02-18 23:28:39,818 INFO misc.py line 119 87073] Train: [60/100][299/1557] Data 0.011 (0.094) Batch 0.771 (1.138) Remain 20:04:53 loss: 0.2426 Lr: 0.00195 [2024-02-18 23:28:40,607 INFO misc.py line 119 87073] Train: [60/100][300/1557] Data 0.003 (0.093) Batch 0.779 (1.137) Remain 20:03:35 loss: 0.3512 Lr: 0.00195 [2024-02-18 23:28:41,879 INFO misc.py line 119 87073] Train: 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Batch 0.748 (1.133) Remain 20:00:04 loss: 0.1913 Lr: 0.00195 [2024-02-18 23:28:48,856 INFO misc.py line 119 87073] Train: [60/100][308/1557] Data 0.011 (0.091) Batch 1.256 (1.134) Remain 20:00:28 loss: 0.1954 Lr: 0.00195 [2024-02-18 23:28:49,695 INFO misc.py line 119 87073] Train: [60/100][309/1557] Data 0.021 (0.091) Batch 0.857 (1.133) Remain 19:59:30 loss: 0.1923 Lr: 0.00195 [2024-02-18 23:28:50,688 INFO misc.py line 119 87073] Train: [60/100][310/1557] Data 0.004 (0.091) Batch 0.993 (1.132) Remain 19:59:00 loss: 0.1287 Lr: 0.00195 [2024-02-18 23:28:51,544 INFO misc.py line 119 87073] Train: [60/100][311/1557] Data 0.003 (0.090) Batch 0.856 (1.132) Remain 19:58:01 loss: 0.5523 Lr: 0.00195 [2024-02-18 23:28:52,577 INFO misc.py line 119 87073] Train: [60/100][312/1557] Data 0.004 (0.090) Batch 1.029 (1.131) Remain 19:57:39 loss: 0.2332 Lr: 0.00195 [2024-02-18 23:28:53,307 INFO misc.py line 119 87073] Train: [60/100][313/1557] Data 0.008 (0.090) Batch 0.735 (1.130) Remain 19:56:17 loss: 0.3650 Lr: 0.00195 [2024-02-18 23:28:54,032 INFO misc.py line 119 87073] Train: [60/100][314/1557] Data 0.003 (0.090) Batch 0.717 (1.129) Remain 19:54:51 loss: 0.2262 Lr: 0.00195 [2024-02-18 23:28:55,316 INFO misc.py line 119 87073] Train: [60/100][315/1557] Data 0.012 (0.089) Batch 1.286 (1.129) Remain 19:55:22 loss: 0.1721 Lr: 0.00195 [2024-02-18 23:28:56,404 INFO misc.py line 119 87073] Train: [60/100][316/1557] Data 0.010 (0.089) Batch 1.083 (1.129) Remain 19:55:12 loss: 0.6012 Lr: 0.00195 [2024-02-18 23:28:57,436 INFO misc.py line 119 87073] Train: [60/100][317/1557] Data 0.015 (0.089) Batch 1.034 (1.129) Remain 19:54:51 loss: 0.3239 Lr: 0.00195 [2024-02-18 23:28:58,498 INFO misc.py line 119 87073] Train: [60/100][318/1557] Data 0.013 (0.089) Batch 1.060 (1.128) Remain 19:54:36 loss: 0.2579 Lr: 0.00195 [2024-02-18 23:28:59,513 INFO misc.py line 119 87073] Train: [60/100][319/1557] Data 0.014 (0.088) Batch 1.014 (1.128) Remain 19:54:12 loss: 0.1544 Lr: 0.00195 [2024-02-18 23:29:00,135 INFO misc.py line 119 87073] Train: [60/100][320/1557] Data 0.016 (0.088) Batch 0.634 (1.127) Remain 19:52:32 loss: 0.1537 Lr: 0.00195 [2024-02-18 23:29:00,918 INFO misc.py line 119 87073] Train: [60/100][321/1557] Data 0.003 (0.088) Batch 0.772 (1.125) Remain 19:51:20 loss: 0.3222 Lr: 0.00195 [2024-02-18 23:29:01,986 INFO misc.py line 119 87073] Train: [60/100][322/1557] Data 0.014 (0.088) Batch 1.066 (1.125) Remain 19:51:07 loss: 0.2516 Lr: 0.00195 [2024-02-18 23:29:03,263 INFO misc.py line 119 87073] Train: [60/100][323/1557] Data 0.015 (0.087) Batch 1.273 (1.126) Remain 19:51:35 loss: 0.3994 Lr: 0.00195 [2024-02-18 23:29:04,176 INFO misc.py line 119 87073] Train: [60/100][324/1557] Data 0.021 (0.087) Batch 0.930 (1.125) Remain 19:50:56 loss: 0.2928 Lr: 0.00195 [2024-02-18 23:29:05,082 INFO misc.py line 119 87073] Train: [60/100][325/1557] Data 0.003 (0.087) Batch 0.905 (1.124) Remain 19:50:11 loss: 0.4968 Lr: 0.00195 [2024-02-18 23:29:05,949 INFO misc.py line 119 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Batch 0.794 (1.141) Remain 20:06:04 loss: 0.2248 Lr: 0.00195 [2024-02-18 23:30:59,009 INFO misc.py line 119 87073] Train: [60/100][420/1557] Data 0.014 (0.094) Batch 1.281 (1.141) Remain 20:06:24 loss: 0.1599 Lr: 0.00195 [2024-02-18 23:30:59,925 INFO misc.py line 119 87073] Train: [60/100][421/1557] Data 0.012 (0.093) Batch 0.925 (1.141) Remain 20:05:51 loss: 0.7708 Lr: 0.00195 [2024-02-18 23:31:00,908 INFO misc.py line 119 87073] Train: [60/100][422/1557] Data 0.003 (0.093) Batch 0.983 (1.141) Remain 20:05:26 loss: 0.2951 Lr: 0.00195 [2024-02-18 23:31:01,793 INFO misc.py line 119 87073] Train: [60/100][423/1557] Data 0.003 (0.093) Batch 0.884 (1.140) Remain 20:04:46 loss: 0.2570 Lr: 0.00195 [2024-02-18 23:31:02,745 INFO misc.py line 119 87073] Train: [60/100][424/1557] Data 0.004 (0.093) Batch 0.951 (1.139) Remain 20:04:16 loss: 0.0689 Lr: 0.00195 [2024-02-18 23:31:03,520 INFO misc.py line 119 87073] Train: [60/100][425/1557] Data 0.005 (0.093) Batch 0.775 (1.139) Remain 20:03:20 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Batch 0.799 (1.138) Remain 20:02:04 loss: 0.4563 Lr: 0.00194 [2024-02-18 23:32:01,642 INFO misc.py line 119 87073] Train: [60/100][476/1557] Data 0.010 (0.094) Batch 1.323 (1.139) Remain 20:02:28 loss: 0.1446 Lr: 0.00194 [2024-02-18 23:32:02,642 INFO misc.py line 119 87073] Train: [60/100][477/1557] Data 0.016 (0.094) Batch 1.001 (1.138) Remain 20:02:08 loss: 0.2201 Lr: 0.00194 [2024-02-18 23:32:03,484 INFO misc.py line 119 87073] Train: [60/100][478/1557] Data 0.014 (0.094) Batch 0.850 (1.138) Remain 20:01:29 loss: 0.2828 Lr: 0.00194 [2024-02-18 23:32:04,474 INFO misc.py line 119 87073] Train: [60/100][479/1557] Data 0.006 (0.094) Batch 0.994 (1.137) Remain 20:01:08 loss: 0.7751 Lr: 0.00194 [2024-02-18 23:32:05,566 INFO misc.py line 119 87073] Train: [60/100][480/1557] Data 0.003 (0.094) Batch 1.092 (1.137) Remain 20:01:01 loss: 0.3857 Lr: 0.00194 [2024-02-18 23:32:06,311 INFO misc.py line 119 87073] Train: [60/100][481/1557] Data 0.003 (0.093) Batch 0.746 (1.137) Remain 20:00:08 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Batch 0.753 (1.141) Remain 20:03:47 loss: 0.2884 Lr: 0.00194 [2024-02-18 23:33:06,782 INFO misc.py line 119 87073] Train: [60/100][532/1557] Data 0.009 (0.097) Batch 1.340 (1.141) Remain 20:04:09 loss: 0.1081 Lr: 0.00194 [2024-02-18 23:33:07,826 INFO misc.py line 119 87073] Train: [60/100][533/1557] Data 0.009 (0.097) Batch 1.048 (1.141) Remain 20:03:57 loss: 0.5135 Lr: 0.00194 [2024-02-18 23:33:08,842 INFO misc.py line 119 87073] Train: [60/100][534/1557] Data 0.005 (0.097) Batch 1.017 (1.141) Remain 20:03:41 loss: 0.3219 Lr: 0.00194 [2024-02-18 23:33:09,997 INFO misc.py line 119 87073] Train: [60/100][535/1557] Data 0.006 (0.097) Batch 1.100 (1.141) Remain 20:03:35 loss: 0.2915 Lr: 0.00194 [2024-02-18 23:33:10,909 INFO misc.py line 119 87073] Train: [60/100][536/1557] Data 0.059 (0.097) Batch 0.965 (1.140) Remain 20:03:13 loss: 0.1890 Lr: 0.00194 [2024-02-18 23:33:11,661 INFO misc.py line 119 87073] Train: [60/100][537/1557] Data 0.006 (0.096) Batch 0.752 (1.140) Remain 20:02:26 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23:33:18,303 INFO misc.py line 119 87073] Train: [60/100][544/1557] Data 0.003 (0.095) Batch 0.744 (1.137) Remain 19:59:40 loss: 0.2459 Lr: 0.00194 [2024-02-18 23:33:19,101 INFO misc.py line 119 87073] Train: [60/100][545/1557] Data 0.020 (0.095) Batch 0.814 (1.137) Remain 19:59:01 loss: 0.4717 Lr: 0.00194 [2024-02-18 23:33:20,163 INFO misc.py line 119 87073] Train: [60/100][546/1557] Data 0.004 (0.095) Batch 1.062 (1.137) Remain 19:58:51 loss: 0.1161 Lr: 0.00194 [2024-02-18 23:33:21,380 INFO misc.py line 119 87073] Train: [60/100][547/1557] Data 0.005 (0.095) Batch 1.208 (1.137) Remain 19:58:58 loss: 0.7770 Lr: 0.00194 [2024-02-18 23:33:22,376 INFO misc.py line 119 87073] Train: [60/100][548/1557] Data 0.013 (0.095) Batch 1.005 (1.136) Remain 19:58:42 loss: 0.6080 Lr: 0.00194 [2024-02-18 23:33:23,192 INFO misc.py line 119 87073] Train: [60/100][549/1557] Data 0.004 (0.094) Batch 0.816 (1.136) Remain 19:58:04 loss: 0.1534 Lr: 0.00194 [2024-02-18 23:33:24,084 INFO misc.py line 119 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line 119 87073] Train: [60/100][575/1557] Data 0.004 (0.099) Batch 0.911 (1.145) Remain 20:07:27 loss: 0.5937 Lr: 0.00194 [2024-02-18 23:33:59,064 INFO misc.py line 119 87073] Train: [60/100][576/1557] Data 0.003 (0.099) Batch 0.977 (1.145) Remain 20:07:07 loss: 0.1930 Lr: 0.00194 [2024-02-18 23:34:00,079 INFO misc.py line 119 87073] Train: [60/100][577/1557] Data 0.009 (0.099) Batch 1.014 (1.145) Remain 20:06:51 loss: 0.4983 Lr: 0.00194 [2024-02-18 23:34:00,900 INFO misc.py line 119 87073] Train: [60/100][578/1557] Data 0.011 (0.099) Batch 0.829 (1.144) Remain 20:06:15 loss: 0.5159 Lr: 0.00194 [2024-02-18 23:34:01,609 INFO misc.py line 119 87073] Train: [60/100][579/1557] Data 0.003 (0.099) Batch 0.709 (1.143) Remain 20:05:26 loss: 0.1886 Lr: 0.00194 [2024-02-18 23:34:02,377 INFO misc.py line 119 87073] Train: [60/100][580/1557] Data 0.003 (0.099) Batch 0.765 (1.143) Remain 20:04:44 loss: 0.2651 Lr: 0.00194 [2024-02-18 23:34:03,641 INFO misc.py line 119 87073] Train: 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Batch 0.712 (1.140) Remain 20:02:00 loss: 0.2249 Lr: 0.00194 [2024-02-18 23:34:10,215 INFO misc.py line 119 87073] Train: [60/100][588/1557] Data 0.014 (0.097) Batch 1.269 (1.140) Remain 20:02:13 loss: 0.1811 Lr: 0.00194 [2024-02-18 23:34:11,252 INFO misc.py line 119 87073] Train: [60/100][589/1557] Data 0.013 (0.097) Batch 1.037 (1.140) Remain 20:02:01 loss: 0.4925 Lr: 0.00194 [2024-02-18 23:34:12,156 INFO misc.py line 119 87073] Train: [60/100][590/1557] Data 0.014 (0.097) Batch 0.914 (1.140) Remain 20:01:35 loss: 0.4496 Lr: 0.00194 [2024-02-18 23:34:13,030 INFO misc.py line 119 87073] Train: [60/100][591/1557] Data 0.004 (0.097) Batch 0.874 (1.139) Remain 20:01:05 loss: 0.5067 Lr: 0.00194 [2024-02-18 23:34:14,016 INFO misc.py line 119 87073] Train: [60/100][592/1557] Data 0.003 (0.097) Batch 0.985 (1.139) Remain 20:00:48 loss: 0.1164 Lr: 0.00194 [2024-02-18 23:34:14,761 INFO misc.py line 119 87073] Train: [60/100][593/1557] Data 0.006 (0.097) Batch 0.747 (1.139) Remain 20:00:04 loss: 0.1544 Lr: 0.00194 [2024-02-18 23:34:15,543 INFO misc.py line 119 87073] Train: [60/100][594/1557] Data 0.004 (0.096) Batch 0.777 (1.138) Remain 19:59:24 loss: 0.4525 Lr: 0.00194 [2024-02-18 23:34:16,734 INFO misc.py line 119 87073] Train: [60/100][595/1557] Data 0.009 (0.096) Batch 1.189 (1.138) Remain 19:59:29 loss: 0.1765 Lr: 0.00194 [2024-02-18 23:34:17,526 INFO misc.py line 119 87073] Train: [60/100][596/1557] Data 0.011 (0.096) Batch 0.799 (1.137) Remain 19:58:51 loss: 0.4931 Lr: 0.00194 [2024-02-18 23:34:18,363 INFO misc.py line 119 87073] Train: [60/100][597/1557] Data 0.004 (0.096) Batch 0.836 (1.137) Remain 19:58:18 loss: 0.3606 Lr: 0.00194 [2024-02-18 23:34:19,176 INFO misc.py line 119 87073] Train: [60/100][598/1557] Data 0.005 (0.096) Batch 0.812 (1.136) Remain 19:57:43 loss: 0.2375 Lr: 0.00194 [2024-02-18 23:34:20,224 INFO misc.py line 119 87073] Train: [60/100][599/1557] Data 0.007 (0.096) Batch 1.044 (1.136) Remain 19:57:32 loss: 0.3518 Lr: 0.00194 [2024-02-18 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Batch 0.724 (1.136) Remain 19:54:56 loss: 0.1527 Lr: 0.00193 [2024-02-18 23:36:14,684 INFO misc.py line 119 87073] Train: [60/100][700/1557] Data 0.014 (0.098) Batch 1.300 (1.136) Remain 19:55:10 loss: 0.1687 Lr: 0.00193 [2024-02-18 23:36:15,478 INFO misc.py line 119 87073] Train: [60/100][701/1557] Data 0.012 (0.098) Batch 0.800 (1.135) Remain 19:54:38 loss: 0.4052 Lr: 0.00193 [2024-02-18 23:36:16,471 INFO misc.py line 119 87073] Train: [60/100][702/1557] Data 0.007 (0.098) Batch 0.997 (1.135) Remain 19:54:24 loss: 0.0919 Lr: 0.00193 [2024-02-18 23:36:17,468 INFO misc.py line 119 87073] Train: [60/100][703/1557] Data 0.003 (0.097) Batch 0.997 (1.135) Remain 19:54:11 loss: 0.3466 Lr: 0.00193 [2024-02-18 23:36:18,470 INFO misc.py line 119 87073] Train: [60/100][704/1557] Data 0.003 (0.097) Batch 1.002 (1.135) Remain 19:53:58 loss: 0.3600 Lr: 0.00193 [2024-02-18 23:36:20,725 INFO misc.py line 119 87073] Train: [60/100][705/1557] Data 1.046 (0.099) Batch 2.254 (1.136) Remain 19:55:37 loss: 0.2172 Lr: 0.00193 [2024-02-18 23:36:21,481 INFO misc.py line 119 87073] Train: [60/100][706/1557] Data 0.005 (0.098) Batch 0.756 (1.136) Remain 19:55:02 loss: 0.2014 Lr: 0.00193 [2024-02-18 23:36:22,653 INFO misc.py line 119 87073] Train: [60/100][707/1557] Data 0.003 (0.098) Batch 1.172 (1.136) Remain 19:55:04 loss: 0.1627 Lr: 0.00193 [2024-02-18 23:36:23,591 INFO misc.py line 119 87073] Train: [60/100][708/1557] Data 0.004 (0.098) Batch 0.929 (1.136) Remain 19:54:44 loss: 0.2516 Lr: 0.00193 [2024-02-18 23:36:24,537 INFO misc.py line 119 87073] Train: [60/100][709/1557] Data 0.013 (0.098) Batch 0.956 (1.135) Remain 19:54:27 loss: 0.3357 Lr: 0.00193 [2024-02-18 23:36:25,480 INFO misc.py line 119 87073] Train: [60/100][710/1557] Data 0.003 (0.098) Batch 0.935 (1.135) Remain 19:54:08 loss: 0.4848 Lr: 0.00193 [2024-02-18 23:36:26,469 INFO misc.py line 119 87073] Train: [60/100][711/1557] Data 0.011 (0.098) Batch 0.996 (1.135) Remain 19:53:55 loss: 0.5324 Lr: 0.00193 [2024-02-18 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Batch 0.749 (1.139) Remain 19:57:51 loss: 0.2025 Lr: 0.00193 [2024-02-18 23:37:21,078 INFO misc.py line 119 87073] Train: [60/100][756/1557] Data 0.009 (0.100) Batch 1.259 (1.139) Remain 19:57:59 loss: 0.2335 Lr: 0.00193 [2024-02-18 23:37:22,032 INFO misc.py line 119 87073] Train: [60/100][757/1557] Data 0.018 (0.099) Batch 0.968 (1.139) Remain 19:57:44 loss: 0.3914 Lr: 0.00193 [2024-02-18 23:37:22,900 INFO misc.py line 119 87073] Train: [60/100][758/1557] Data 0.003 (0.099) Batch 0.868 (1.139) Remain 19:57:20 loss: 0.4538 Lr: 0.00193 [2024-02-18 23:37:23,902 INFO misc.py line 119 87073] Train: [60/100][759/1557] Data 0.004 (0.099) Batch 1.000 (1.139) Remain 19:57:07 loss: 0.3826 Lr: 0.00193 [2024-02-18 23:37:24,899 INFO misc.py line 119 87073] Train: [60/100][760/1557] Data 0.007 (0.099) Batch 0.999 (1.139) Remain 19:56:55 loss: 0.2796 Lr: 0.00193 [2024-02-18 23:37:25,663 INFO misc.py line 119 87073] Train: [60/100][761/1557] Data 0.005 (0.099) Batch 0.762 (1.138) Remain 19:56:22 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23:37:32,360 INFO misc.py line 119 87073] Train: [60/100][768/1557] Data 0.005 (0.098) Batch 0.808 (1.136) Remain 19:54:30 loss: 0.1722 Lr: 0.00193 [2024-02-18 23:37:33,143 INFO misc.py line 119 87073] Train: [60/100][769/1557] Data 0.006 (0.098) Batch 0.781 (1.136) Remain 19:53:59 loss: 0.2050 Lr: 0.00193 [2024-02-18 23:37:34,218 INFO misc.py line 119 87073] Train: [60/100][770/1557] Data 0.005 (0.098) Batch 1.077 (1.136) Remain 19:53:53 loss: 0.1351 Lr: 0.00193 [2024-02-18 23:37:35,252 INFO misc.py line 119 87073] Train: [60/100][771/1557] Data 0.005 (0.098) Batch 1.031 (1.136) Remain 19:53:43 loss: 0.2572 Lr: 0.00193 [2024-02-18 23:37:36,096 INFO misc.py line 119 87073] Train: [60/100][772/1557] Data 0.008 (0.098) Batch 0.847 (1.135) Remain 19:53:19 loss: 0.3249 Lr: 0.00193 [2024-02-18 23:37:37,255 INFO misc.py line 119 87073] Train: [60/100][773/1557] Data 0.005 (0.097) Batch 1.158 (1.135) Remain 19:53:19 loss: 0.3439 Lr: 0.00193 [2024-02-18 23:37:38,172 INFO misc.py line 119 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line 119 87073] Train: [60/100][799/1557] Data 0.006 (0.100) Batch 0.992 (1.144) Remain 20:01:29 loss: 0.3314 Lr: 0.00193 [2024-02-18 23:38:14,323 INFO misc.py line 119 87073] Train: [60/100][800/1557] Data 0.006 (0.100) Batch 0.995 (1.143) Remain 20:01:16 loss: 0.3035 Lr: 0.00193 [2024-02-18 23:38:15,468 INFO misc.py line 119 87073] Train: [60/100][801/1557] Data 0.004 (0.100) Batch 1.144 (1.143) Remain 20:01:15 loss: 0.2797 Lr: 0.00193 [2024-02-18 23:38:16,416 INFO misc.py line 119 87073] Train: [60/100][802/1557] Data 0.004 (0.100) Batch 0.947 (1.143) Remain 20:00:58 loss: 0.4473 Lr: 0.00193 [2024-02-18 23:38:17,227 INFO misc.py line 119 87073] Train: [60/100][803/1557] Data 0.005 (0.100) Batch 0.811 (1.143) Remain 20:00:31 loss: 0.5799 Lr: 0.00193 [2024-02-18 23:38:17,939 INFO misc.py line 119 87073] Train: [60/100][804/1557] Data 0.005 (0.100) Batch 0.712 (1.142) Remain 19:59:56 loss: 0.3387 Lr: 0.00193 [2024-02-18 23:38:19,179 INFO misc.py line 119 87073] Train: 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Batch 0.799 (1.140) Remain 19:57:58 loss: 0.2661 Lr: 0.00193 [2024-02-18 23:38:25,788 INFO misc.py line 119 87073] Train: [60/100][812/1557] Data 0.007 (0.099) Batch 1.255 (1.141) Remain 19:58:06 loss: 0.1769 Lr: 0.00193 [2024-02-18 23:38:26,758 INFO misc.py line 119 87073] Train: [60/100][813/1557] Data 0.017 (0.099) Batch 0.980 (1.140) Remain 19:57:52 loss: 0.2811 Lr: 0.00193 [2024-02-18 23:38:27,658 INFO misc.py line 119 87073] Train: [60/100][814/1557] Data 0.004 (0.098) Batch 0.899 (1.140) Remain 19:57:32 loss: 0.6746 Lr: 0.00193 [2024-02-18 23:38:28,733 INFO misc.py line 119 87073] Train: [60/100][815/1557] Data 0.005 (0.098) Batch 1.075 (1.140) Remain 19:57:26 loss: 0.2269 Lr: 0.00193 [2024-02-18 23:38:29,819 INFO misc.py line 119 87073] Train: [60/100][816/1557] Data 0.006 (0.098) Batch 1.086 (1.140) Remain 19:57:21 loss: 0.7054 Lr: 0.00193 [2024-02-18 23:38:30,587 INFO misc.py line 119 87073] Train: [60/100][817/1557] Data 0.005 (0.098) Batch 0.766 (1.139) Remain 19:56:51 loss: 0.4799 Lr: 0.00193 [2024-02-18 23:38:31,365 INFO misc.py line 119 87073] Train: [60/100][818/1557] Data 0.009 (0.098) Batch 0.774 (1.139) Remain 19:56:21 loss: 0.3165 Lr: 0.00193 [2024-02-18 23:38:32,502 INFO misc.py line 119 87073] Train: [60/100][819/1557] Data 0.012 (0.098) Batch 1.140 (1.139) Remain 19:56:20 loss: 0.2158 Lr: 0.00193 [2024-02-18 23:38:33,563 INFO misc.py line 119 87073] Train: [60/100][820/1557] Data 0.010 (0.098) Batch 1.060 (1.139) Remain 19:56:13 loss: 0.4049 Lr: 0.00193 [2024-02-18 23:38:34,587 INFO misc.py line 119 87073] Train: [60/100][821/1557] Data 0.011 (0.098) Batch 1.024 (1.139) Remain 19:56:03 loss: 0.5439 Lr: 0.00193 [2024-02-18 23:38:35,551 INFO misc.py line 119 87073] Train: [60/100][822/1557] Data 0.011 (0.098) Batch 0.971 (1.139) Remain 19:55:49 loss: 0.2818 Lr: 0.00193 [2024-02-18 23:38:36,551 INFO misc.py line 119 87073] Train: [60/100][823/1557] Data 0.003 (0.097) Batch 0.999 (1.138) Remain 19:55:37 loss: 0.2395 Lr: 0.00193 [2024-02-18 23:38:37,312 INFO misc.py line 119 87073] Train: [60/100][824/1557] Data 0.006 (0.097) Batch 0.760 (1.138) Remain 19:55:07 loss: 0.3425 Lr: 0.00193 [2024-02-18 23:38:38,113 INFO misc.py line 119 87073] Train: [60/100][825/1557] Data 0.006 (0.097) Batch 0.795 (1.138) Remain 19:54:39 loss: 0.2911 Lr: 0.00193 [2024-02-18 23:38:39,103 INFO misc.py line 119 87073] Train: [60/100][826/1557] Data 0.011 (0.097) Batch 0.997 (1.137) Remain 19:54:27 loss: 0.1304 Lr: 0.00193 [2024-02-18 23:38:40,036 INFO misc.py line 119 87073] Train: [60/100][827/1557] Data 0.004 (0.097) Batch 0.933 (1.137) Remain 19:54:11 loss: 0.1850 Lr: 0.00193 [2024-02-18 23:38:40,980 INFO misc.py line 119 87073] Train: [60/100][828/1557] Data 0.005 (0.097) Batch 0.945 (1.137) Remain 19:53:55 loss: 0.5047 Lr: 0.00193 [2024-02-18 23:38:41,946 INFO misc.py line 119 87073] Train: [60/100][829/1557] Data 0.003 (0.097) Batch 0.965 (1.137) Remain 19:53:41 loss: 0.0961 Lr: 0.00193 [2024-02-18 23:38:42,987 INFO misc.py line 119 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Batch 0.777 (1.142) Remain 19:58:18 loss: 0.3902 Lr: 0.00192 [2024-02-18 23:39:30,826 INFO misc.py line 119 87073] Train: [60/100][868/1557] Data 0.009 (0.099) Batch 1.286 (1.142) Remain 19:58:27 loss: 0.1127 Lr: 0.00192 [2024-02-18 23:39:32,025 INFO misc.py line 119 87073] Train: [60/100][869/1557] Data 0.008 (0.099) Batch 1.200 (1.142) Remain 19:58:30 loss: 0.6451 Lr: 0.00192 [2024-02-18 23:39:32,848 INFO misc.py line 119 87073] Train: [60/100][870/1557] Data 0.008 (0.099) Batch 0.825 (1.142) Remain 19:58:06 loss: 0.5020 Lr: 0.00192 [2024-02-18 23:39:33,913 INFO misc.py line 119 87073] Train: [60/100][871/1557] Data 0.005 (0.099) Batch 1.063 (1.142) Remain 19:57:59 loss: 0.4063 Lr: 0.00192 [2024-02-18 23:39:34,897 INFO misc.py line 119 87073] Train: [60/100][872/1557] Data 0.006 (0.099) Batch 0.986 (1.141) Remain 19:57:47 loss: 0.2963 Lr: 0.00192 [2024-02-18 23:39:35,642 INFO misc.py line 119 87073] Train: [60/100][873/1557] Data 0.005 (0.099) Batch 0.744 (1.141) Remain 19:57:17 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line 119 87073] Train: [60/100][967/1557] Data 0.005 (0.101) Batch 0.875 (1.146) Remain 20:01:09 loss: 0.2889 Lr: 0.00192 [2024-02-18 23:41:29,111 INFO misc.py line 119 87073] Train: [60/100][968/1557] Data 0.004 (0.101) Batch 1.007 (1.146) Remain 20:00:59 loss: 0.7010 Lr: 0.00192 [2024-02-18 23:41:30,139 INFO misc.py line 119 87073] Train: [60/100][969/1557] Data 0.011 (0.101) Batch 1.029 (1.146) Remain 20:00:50 loss: 0.2491 Lr: 0.00192 [2024-02-18 23:41:30,961 INFO misc.py line 119 87073] Train: [60/100][970/1557] Data 0.008 (0.101) Batch 0.824 (1.146) Remain 20:00:28 loss: 0.2742 Lr: 0.00192 [2024-02-18 23:41:31,765 INFO misc.py line 119 87073] Train: [60/100][971/1557] Data 0.010 (0.101) Batch 0.807 (1.145) Remain 20:00:05 loss: 0.1740 Lr: 0.00192 [2024-02-18 23:41:32,449 INFO misc.py line 119 87073] Train: [60/100][972/1557] Data 0.003 (0.101) Batch 0.681 (1.145) Remain 19:59:34 loss: 0.3305 Lr: 0.00192 [2024-02-18 23:41:33,657 INFO misc.py line 119 87073] Train: 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Batch 0.727 (1.143) Remain 19:57:54 loss: 0.2621 Lr: 0.00192 [2024-02-18 23:41:40,322 INFO misc.py line 119 87073] Train: [60/100][980/1557] Data 0.003 (0.100) Batch 1.283 (1.144) Remain 19:58:02 loss: 0.1024 Lr: 0.00192 [2024-02-18 23:41:41,088 INFO misc.py line 119 87073] Train: [60/100][981/1557] Data 0.010 (0.100) Batch 0.773 (1.143) Remain 19:57:37 loss: 0.2612 Lr: 0.00192 [2024-02-18 23:41:42,026 INFO misc.py line 119 87073] Train: [60/100][982/1557] Data 0.003 (0.100) Batch 0.938 (1.143) Remain 19:57:23 loss: 0.4175 Lr: 0.00192 [2024-02-18 23:41:43,096 INFO misc.py line 119 87073] Train: [60/100][983/1557] Data 0.003 (0.100) Batch 1.066 (1.143) Remain 19:57:16 loss: 0.5869 Lr: 0.00192 [2024-02-18 23:41:44,055 INFO misc.py line 119 87073] Train: [60/100][984/1557] Data 0.008 (0.099) Batch 0.963 (1.143) Remain 19:57:04 loss: 0.2808 Lr: 0.00192 [2024-02-18 23:41:44,792 INFO misc.py line 119 87073] Train: [60/100][985/1557] Data 0.003 (0.099) Batch 0.737 (1.142) Remain 19:56:37 loss: 0.1825 Lr: 0.00192 [2024-02-18 23:41:45,535 INFO misc.py line 119 87073] Train: [60/100][986/1557] Data 0.003 (0.099) Batch 0.741 (1.142) Remain 19:56:10 loss: 0.3187 Lr: 0.00192 [2024-02-18 23:41:46,692 INFO misc.py line 119 87073] Train: [60/100][987/1557] Data 0.005 (0.099) Batch 1.152 (1.142) Remain 19:56:09 loss: 0.3277 Lr: 0.00192 [2024-02-18 23:41:47,712 INFO misc.py line 119 87073] Train: [60/100][988/1557] Data 0.010 (0.099) Batch 1.027 (1.142) Remain 19:56:01 loss: 0.6831 Lr: 0.00192 [2024-02-18 23:41:48,663 INFO misc.py line 119 87073] Train: [60/100][989/1557] Data 0.004 (0.099) Batch 0.951 (1.142) Remain 19:55:48 loss: 0.7014 Lr: 0.00192 [2024-02-18 23:41:49,767 INFO misc.py line 119 87073] Train: [60/100][990/1557] Data 0.003 (0.099) Batch 1.104 (1.142) Remain 19:55:44 loss: 0.3032 Lr: 0.00192 [2024-02-18 23:41:50,746 INFO misc.py line 119 87073] Train: [60/100][991/1557] Data 0.003 (0.099) Batch 0.979 (1.141) Remain 19:55:33 loss: 0.3588 Lr: 0.00192 [2024-02-18 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Data 0.005 (0.100) Batch 0.716 (1.142) Remain 19:54:53 loss: 0.1869 Lr: 0.00192 [2024-02-18 23:42:42,451 INFO misc.py line 119 87073] Train: [60/100][1036/1557] Data 0.010 (0.100) Batch 1.300 (1.142) Remain 19:55:01 loss: 0.1172 Lr: 0.00192 [2024-02-18 23:42:43,382 INFO misc.py line 119 87073] Train: [60/100][1037/1557] Data 0.011 (0.100) Batch 0.938 (1.142) Remain 19:54:48 loss: 0.4214 Lr: 0.00192 [2024-02-18 23:42:44,285 INFO misc.py line 119 87073] Train: [60/100][1038/1557] Data 0.005 (0.100) Batch 0.903 (1.141) Remain 19:54:32 loss: 0.4327 Lr: 0.00192 [2024-02-18 23:42:45,322 INFO misc.py line 119 87073] Train: [60/100][1039/1557] Data 0.004 (0.100) Batch 1.038 (1.141) Remain 19:54:25 loss: 0.3957 Lr: 0.00192 [2024-02-18 23:42:46,270 INFO misc.py line 119 87073] Train: [60/100][1040/1557] Data 0.004 (0.099) Batch 0.947 (1.141) Remain 19:54:12 loss: 0.2349 Lr: 0.00192 [2024-02-18 23:42:47,016 INFO misc.py line 119 87073] Train: [60/100][1041/1557] Data 0.003 (0.099) Batch 0.740 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Data 0.003 (0.098) Batch 1.114 (1.138) Remain 19:50:09 loss: 0.2793 Lr: 0.00191 [2024-02-18 23:43:13,214 INFO misc.py line 119 87073] Train: [60/100][1067/1557] Data 0.003 (0.098) Batch 0.893 (1.137) Remain 19:49:53 loss: 0.3270 Lr: 0.00191 [2024-02-18 23:43:14,155 INFO misc.py line 119 87073] Train: [60/100][1068/1557] Data 0.003 (0.098) Batch 0.933 (1.137) Remain 19:49:40 loss: 0.2957 Lr: 0.00191 [2024-02-18 23:43:14,912 INFO misc.py line 119 87073] Train: [60/100][1069/1557] Data 0.011 (0.098) Batch 0.765 (1.137) Remain 19:49:17 loss: 0.3068 Lr: 0.00191 [2024-02-18 23:43:15,700 INFO misc.py line 119 87073] Train: [60/100][1070/1557] Data 0.003 (0.098) Batch 0.780 (1.137) Remain 19:48:55 loss: 0.1560 Lr: 0.00191 [2024-02-18 23:43:26,266 INFO misc.py line 119 87073] Train: [60/100][1071/1557] Data 5.179 (0.103) Batch 10.573 (1.145) Remain 19:58:08 loss: 0.1014 Lr: 0.00191 [2024-02-18 23:43:27,351 INFO misc.py line 119 87073] Train: [60/100][1072/1557] Data 0.004 (0.103) Batch 1.086 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Train: [60/100][1314/1557] Data 0.003 (0.100) Batch 0.768 (1.146) Remain 19:54:28 loss: 0.1887 Lr: 0.00190 [2024-02-18 23:48:06,524 INFO misc.py line 119 87073] Train: [60/100][1315/1557] Data 0.003 (0.100) Batch 0.724 (1.146) Remain 19:54:06 loss: 0.1732 Lr: 0.00190 [2024-02-18 23:48:07,832 INFO misc.py line 119 87073] Train: [60/100][1316/1557] Data 0.013 (0.100) Batch 1.305 (1.146) Remain 19:54:13 loss: 0.1738 Lr: 0.00190 [2024-02-18 23:48:08,802 INFO misc.py line 119 87073] Train: [60/100][1317/1557] Data 0.016 (0.100) Batch 0.983 (1.146) Remain 19:54:04 loss: 0.5979 Lr: 0.00190 [2024-02-18 23:48:09,597 INFO misc.py line 119 87073] Train: [60/100][1318/1557] Data 0.003 (0.100) Batch 0.795 (1.146) Remain 19:53:46 loss: 0.0816 Lr: 0.00190 [2024-02-18 23:48:10,483 INFO misc.py line 119 87073] Train: [60/100][1319/1557] Data 0.003 (0.100) Batch 0.877 (1.145) Remain 19:53:32 loss: 0.2945 Lr: 0.00190 [2024-02-18 23:48:11,413 INFO misc.py line 119 87073] Train: [60/100][1320/1557] Data 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Remain 19:52:08 loss: 0.2369 Lr: 0.00190 [2024-02-18 23:48:17,700 INFO misc.py line 119 87073] Train: [60/100][1327/1557] Data 0.003 (0.099) Batch 0.818 (1.144) Remain 19:51:51 loss: 0.3668 Lr: 0.00190 [2024-02-18 23:48:18,402 INFO misc.py line 119 87073] Train: [60/100][1328/1557] Data 0.013 (0.099) Batch 0.709 (1.144) Remain 19:51:29 loss: 0.1892 Lr: 0.00190 [2024-02-18 23:48:19,131 INFO misc.py line 119 87073] Train: [60/100][1329/1557] Data 0.005 (0.099) Batch 0.720 (1.143) Remain 19:51:08 loss: 0.3012 Lr: 0.00190 [2024-02-18 23:48:20,220 INFO misc.py line 119 87073] Train: [60/100][1330/1557] Data 0.014 (0.099) Batch 1.092 (1.143) Remain 19:51:05 loss: 0.1504 Lr: 0.00190 [2024-02-18 23:48:21,326 INFO misc.py line 119 87073] Train: [60/100][1331/1557] Data 0.013 (0.099) Batch 1.104 (1.143) Remain 19:51:02 loss: 0.1750 Lr: 0.00190 [2024-02-18 23:48:22,388 INFO misc.py line 119 87073] Train: [60/100][1332/1557] Data 0.013 (0.099) Batch 1.063 (1.143) Remain 19:50:57 loss: 0.5990 Lr: 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Remain 19:53:00 loss: 0.1466 Lr: 0.00190 [2024-02-18 23:49:00,286 INFO misc.py line 119 87073] Train: [60/100][1358/1557] Data 0.010 (0.101) Batch 6.033 (1.149) Remain 19:56:44 loss: 0.1528 Lr: 0.00190 [2024-02-18 23:49:01,234 INFO misc.py line 119 87073] Train: [60/100][1359/1557] Data 0.005 (0.101) Batch 0.949 (1.149) Remain 19:56:34 loss: 0.2906 Lr: 0.00190 [2024-02-18 23:49:02,257 INFO misc.py line 119 87073] Train: [60/100][1360/1557] Data 0.004 (0.101) Batch 1.024 (1.149) Remain 19:56:27 loss: 0.2906 Lr: 0.00190 [2024-02-18 23:49:03,292 INFO misc.py line 119 87073] Train: [60/100][1361/1557] Data 0.004 (0.101) Batch 1.035 (1.149) Remain 19:56:21 loss: 0.7012 Lr: 0.00190 [2024-02-18 23:49:04,317 INFO misc.py line 119 87073] Train: [60/100][1362/1557] Data 0.004 (0.101) Batch 1.025 (1.149) Remain 19:56:14 loss: 0.3498 Lr: 0.00190 [2024-02-18 23:49:05,074 INFO misc.py line 119 87073] Train: [60/100][1363/1557] Data 0.003 (0.101) Batch 0.757 (1.149) Remain 19:55:55 loss: 0.2447 Lr: 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Train: [60/100][1376/1557] Data 0.003 (0.100) Batch 0.902 (1.147) Remain 19:53:42 loss: 0.1507 Lr: 0.00190 [2024-02-18 23:49:18,208 INFO misc.py line 119 87073] Train: [60/100][1377/1557] Data 0.003 (0.100) Batch 0.778 (1.146) Remain 19:53:24 loss: 0.1815 Lr: 0.00190 [2024-02-18 23:49:18,955 INFO misc.py line 119 87073] Train: [60/100][1378/1557] Data 0.010 (0.100) Batch 0.750 (1.146) Remain 19:53:05 loss: 0.2445 Lr: 0.00190 [2024-02-18 23:49:20,072 INFO misc.py line 119 87073] Train: [60/100][1379/1557] Data 0.007 (0.100) Batch 1.117 (1.146) Remain 19:53:03 loss: 0.1556 Lr: 0.00190 [2024-02-18 23:49:20,973 INFO misc.py line 119 87073] Train: [60/100][1380/1557] Data 0.007 (0.100) Batch 0.903 (1.146) Remain 19:52:51 loss: 0.5098 Lr: 0.00190 [2024-02-18 23:49:21,846 INFO misc.py line 119 87073] Train: [60/100][1381/1557] Data 0.005 (0.100) Batch 0.874 (1.146) Remain 19:52:37 loss: 0.3191 Lr: 0.00190 [2024-02-18 23:49:22,856 INFO misc.py line 119 87073] Train: [60/100][1382/1557] Data 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Remain 19:51:19 loss: 0.4265 Lr: 0.00190 [2024-02-18 23:49:29,246 INFO misc.py line 119 87073] Train: [60/100][1389/1557] Data 0.003 (0.099) Batch 0.924 (1.144) Remain 19:51:08 loss: 0.1430 Lr: 0.00190 [2024-02-18 23:49:30,168 INFO misc.py line 119 87073] Train: [60/100][1390/1557] Data 0.003 (0.099) Batch 0.920 (1.144) Remain 19:50:57 loss: 0.5032 Lr: 0.00190 [2024-02-18 23:49:30,880 INFO misc.py line 119 87073] Train: [60/100][1391/1557] Data 0.005 (0.099) Batch 0.714 (1.144) Remain 19:50:37 loss: 0.1343 Lr: 0.00190 [2024-02-18 23:49:31,624 INFO misc.py line 119 87073] Train: [60/100][1392/1557] Data 0.004 (0.099) Batch 0.738 (1.144) Remain 19:50:17 loss: 0.3561 Lr: 0.00190 [2024-02-18 23:49:32,766 INFO misc.py line 119 87073] Train: [60/100][1393/1557] Data 0.009 (0.099) Batch 1.143 (1.144) Remain 19:50:16 loss: 0.1226 Lr: 0.00190 [2024-02-18 23:49:33,882 INFO misc.py line 119 87073] Train: [60/100][1394/1557] Data 0.009 (0.099) Batch 1.115 (1.144) Remain 19:50:14 loss: 0.8541 Lr: 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INFO misc.py line 119 87073] Train: [60/100][1401/1557] Data 0.011 (0.098) Batch 1.091 (1.143) Remain 19:49:13 loss: 0.6380 Lr: 0.00190 [2024-02-18 23:49:41,782 INFO misc.py line 119 87073] Train: [60/100][1402/1557] Data 0.019 (0.098) Batch 1.061 (1.143) Remain 19:49:09 loss: 0.1432 Lr: 0.00190 [2024-02-18 23:49:42,935 INFO misc.py line 119 87073] Train: [60/100][1403/1557] Data 0.011 (0.098) Batch 1.153 (1.143) Remain 19:49:08 loss: 0.3238 Lr: 0.00190 [2024-02-18 23:49:43,849 INFO misc.py line 119 87073] Train: [60/100][1404/1557] Data 0.011 (0.098) Batch 0.921 (1.143) Remain 19:48:57 loss: 0.3989 Lr: 0.00190 [2024-02-18 23:49:46,274 INFO misc.py line 119 87073] Train: [60/100][1405/1557] Data 1.242 (0.099) Batch 2.424 (1.144) Remain 19:49:53 loss: 0.2816 Lr: 0.00190 [2024-02-18 23:49:47,067 INFO misc.py line 119 87073] Train: [60/100][1406/1557] Data 0.004 (0.099) Batch 0.794 (1.143) Remain 19:49:36 loss: 0.3510 Lr: 0.00190 [2024-02-18 23:49:57,641 INFO misc.py line 119 87073] Train: [60/100][1407/1557] Data 5.118 (0.102) Batch 10.575 (1.150) Remain 19:56:34 loss: 0.0981 Lr: 0.00190 [2024-02-18 23:49:58,577 INFO misc.py line 119 87073] Train: [60/100][1408/1557] Data 0.003 (0.102) Batch 0.936 (1.150) Remain 19:56:24 loss: 0.0910 Lr: 0.00190 [2024-02-18 23:49:59,471 INFO misc.py line 119 87073] Train: [60/100][1409/1557] Data 0.003 (0.102) Batch 0.894 (1.150) Remain 19:56:11 loss: 0.2134 Lr: 0.00190 [2024-02-18 23:50:00,473 INFO misc.py line 119 87073] Train: [60/100][1410/1557] Data 0.003 (0.102) Batch 0.996 (1.150) Remain 19:56:03 loss: 0.6279 Lr: 0.00190 [2024-02-18 23:50:01,371 INFO misc.py line 119 87073] Train: [60/100][1411/1557] Data 0.009 (0.102) Batch 0.904 (1.149) Remain 19:55:51 loss: 0.2985 Lr: 0.00190 [2024-02-18 23:50:02,143 INFO misc.py line 119 87073] Train: [60/100][1412/1557] Data 0.004 (0.102) Batch 0.773 (1.149) Remain 19:55:33 loss: 0.2335 Lr: 0.00190 [2024-02-18 23:50:02,955 INFO misc.py line 119 87073] Train: [60/100][1413/1557] Data 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Remain 19:55:31 loss: 0.3092 Lr: 0.00190 [2024-02-18 23:50:11,120 INFO misc.py line 119 87073] Train: [60/100][1420/1557] Data 0.004 (0.101) Batch 0.802 (1.149) Remain 19:55:14 loss: 0.2077 Lr: 0.00190 [2024-02-18 23:50:12,321 INFO misc.py line 119 87073] Train: [60/100][1421/1557] Data 0.011 (0.101) Batch 1.197 (1.149) Remain 19:55:15 loss: 0.2018 Lr: 0.00190 [2024-02-18 23:50:13,198 INFO misc.py line 119 87073] Train: [60/100][1422/1557] Data 0.014 (0.101) Batch 0.889 (1.149) Remain 19:55:03 loss: 0.3055 Lr: 0.00190 [2024-02-18 23:50:14,232 INFO misc.py line 119 87073] Train: [60/100][1423/1557] Data 0.003 (0.101) Batch 1.034 (1.149) Remain 19:54:56 loss: 0.6363 Lr: 0.00190 [2024-02-18 23:50:15,120 INFO misc.py line 119 87073] Train: [60/100][1424/1557] Data 0.004 (0.101) Batch 0.887 (1.149) Remain 19:54:44 loss: 0.4316 Lr: 0.00190 [2024-02-18 23:50:16,011 INFO misc.py line 119 87073] Train: [60/100][1425/1557] Data 0.004 (0.101) Batch 0.882 (1.148) Remain 19:54:31 loss: 0.4600 Lr: 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Train: [60/100][1438/1557] Data 0.003 (0.100) Batch 0.897 (1.147) Remain 19:52:22 loss: 0.4648 Lr: 0.00189 [2024-02-18 23:50:29,441 INFO misc.py line 119 87073] Train: [60/100][1439/1557] Data 0.004 (0.100) Batch 1.136 (1.147) Remain 19:52:20 loss: 0.6835 Lr: 0.00189 [2024-02-18 23:50:30,191 INFO misc.py line 119 87073] Train: [60/100][1440/1557] Data 0.003 (0.100) Batch 0.750 (1.146) Remain 19:52:02 loss: 0.2413 Lr: 0.00189 [2024-02-18 23:50:30,978 INFO misc.py line 119 87073] Train: [60/100][1441/1557] Data 0.003 (0.100) Batch 0.777 (1.146) Remain 19:51:45 loss: 0.3729 Lr: 0.00189 [2024-02-18 23:50:32,038 INFO misc.py line 119 87073] Train: [60/100][1442/1557] Data 0.012 (0.100) Batch 1.060 (1.146) Remain 19:51:40 loss: 0.1997 Lr: 0.00189 [2024-02-18 23:50:32,882 INFO misc.py line 119 87073] Train: [60/100][1443/1557] Data 0.013 (0.100) Batch 0.853 (1.146) Remain 19:51:26 loss: 0.3832 Lr: 0.00189 [2024-02-18 23:50:33,917 INFO misc.py line 119 87073] Train: [60/100][1444/1557] Data 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Remain 19:50:27 loss: 0.4042 Lr: 0.00189 [2024-02-18 23:50:40,938 INFO misc.py line 119 87073] Train: [60/100][1451/1557] Data 0.014 (0.099) Batch 1.201 (1.145) Remain 19:50:29 loss: 0.2526 Lr: 0.00189 [2024-02-18 23:50:41,930 INFO misc.py line 119 87073] Train: [60/100][1452/1557] Data 0.015 (0.099) Batch 1.002 (1.145) Remain 19:50:21 loss: 0.1610 Lr: 0.00189 [2024-02-18 23:50:43,069 INFO misc.py line 119 87073] Train: [60/100][1453/1557] Data 0.005 (0.099) Batch 1.134 (1.145) Remain 19:50:20 loss: 0.4887 Lr: 0.00189 [2024-02-18 23:50:43,729 INFO misc.py line 119 87073] Train: [60/100][1454/1557] Data 0.010 (0.099) Batch 0.666 (1.145) Remain 19:49:58 loss: 0.1878 Lr: 0.00189 [2024-02-18 23:50:44,516 INFO misc.py line 119 87073] Train: [60/100][1455/1557] Data 0.003 (0.099) Batch 0.779 (1.144) Remain 19:49:41 loss: 0.1497 Lr: 0.00189 [2024-02-18 23:50:45,758 INFO misc.py line 119 87073] Train: [60/100][1456/1557] Data 0.012 (0.099) Batch 1.242 (1.144) Remain 19:49:44 loss: 0.1633 Lr: 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INFO misc.py line 119 87073] Train: [60/100][1463/1557] Data 4.799 (0.102) Batch 10.669 (1.150) Remain 19:55:26 loss: 0.1542 Lr: 0.00189 [2024-02-18 23:51:02,790 INFO misc.py line 119 87073] Train: [60/100][1464/1557] Data 0.004 (0.102) Batch 0.848 (1.150) Remain 19:55:12 loss: 0.4737 Lr: 0.00189 [2024-02-18 23:51:03,759 INFO misc.py line 119 87073] Train: [60/100][1465/1557] Data 0.003 (0.102) Batch 0.961 (1.150) Remain 19:55:02 loss: 0.3255 Lr: 0.00189 [2024-02-18 23:51:04,675 INFO misc.py line 119 87073] Train: [60/100][1466/1557] Data 0.010 (0.102) Batch 0.924 (1.149) Remain 19:54:52 loss: 0.4870 Lr: 0.00189 [2024-02-18 23:51:05,690 INFO misc.py line 119 87073] Train: [60/100][1467/1557] Data 0.003 (0.102) Batch 1.014 (1.149) Remain 19:54:45 loss: 0.3316 Lr: 0.00189 [2024-02-18 23:51:06,433 INFO misc.py line 119 87073] Train: [60/100][1468/1557] Data 0.003 (0.102) Batch 0.744 (1.149) Remain 19:54:26 loss: 0.2289 Lr: 0.00189 [2024-02-18 23:51:07,284 INFO misc.py line 119 87073] Train: [60/100][1469/1557] Data 0.003 (0.102) Batch 0.840 (1.149) Remain 19:54:12 loss: 0.1987 Lr: 0.00189 [2024-02-18 23:51:12,530 INFO misc.py line 119 87073] Train: [60/100][1470/1557] Data 0.013 (0.102) Batch 5.256 (1.152) Remain 19:57:06 loss: 0.1690 Lr: 0.00189 [2024-02-18 23:51:13,424 INFO misc.py line 119 87073] Train: [60/100][1471/1557] Data 0.004 (0.101) Batch 0.895 (1.151) Remain 19:56:53 loss: 0.3437 Lr: 0.00189 [2024-02-18 23:51:14,291 INFO misc.py line 119 87073] Train: [60/100][1472/1557] Data 0.003 (0.101) Batch 0.865 (1.151) Remain 19:56:40 loss: 0.1774 Lr: 0.00189 [2024-02-18 23:51:15,248 INFO misc.py line 119 87073] Train: [60/100][1473/1557] Data 0.005 (0.101) Batch 0.958 (1.151) Remain 19:56:31 loss: 0.8347 Lr: 0.00189 [2024-02-18 23:51:16,192 INFO misc.py line 119 87073] Train: [60/100][1474/1557] Data 0.003 (0.101) Batch 0.945 (1.151) Remain 19:56:21 loss: 0.2636 Lr: 0.00189 [2024-02-18 23:51:16,973 INFO misc.py line 119 87073] Train: [60/100][1475/1557] Data 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Remain 19:55:22 loss: 0.3919 Lr: 0.00189 [2024-02-18 23:51:23,778 INFO misc.py line 119 87073] Train: [60/100][1482/1557] Data 0.003 (0.101) Batch 0.737 (1.150) Remain 19:55:03 loss: 0.2062 Lr: 0.00189 [2024-02-18 23:51:24,511 INFO misc.py line 119 87073] Train: [60/100][1483/1557] Data 0.010 (0.101) Batch 0.739 (1.150) Remain 19:54:45 loss: 0.2564 Lr: 0.00189 [2024-02-18 23:51:25,793 INFO misc.py line 119 87073] Train: [60/100][1484/1557] Data 0.004 (0.101) Batch 1.282 (1.150) Remain 19:54:49 loss: 0.1049 Lr: 0.00189 [2024-02-18 23:51:26,834 INFO misc.py line 119 87073] Train: [60/100][1485/1557] Data 0.005 (0.101) Batch 1.041 (1.150) Remain 19:54:43 loss: 0.3034 Lr: 0.00189 [2024-02-18 23:51:27,685 INFO misc.py line 119 87073] Train: [60/100][1486/1557] Data 0.004 (0.101) Batch 0.852 (1.149) Remain 19:54:30 loss: 0.2341 Lr: 0.00189 [2024-02-18 23:51:28,563 INFO misc.py line 119 87073] Train: [60/100][1487/1557] Data 0.004 (0.100) Batch 0.871 (1.149) Remain 19:54:17 loss: 0.5688 Lr: 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Train: [60/100][1500/1557] Data 0.011 (0.100) Batch 1.055 (1.147) Remain 19:51:59 loss: 0.5284 Lr: 0.00189 [2024-02-18 23:51:41,609 INFO misc.py line 119 87073] Train: [60/100][1501/1557] Data 0.007 (0.100) Batch 1.055 (1.147) Remain 19:51:54 loss: 0.2833 Lr: 0.00189 [2024-02-18 23:51:42,484 INFO misc.py line 119 87073] Train: [60/100][1502/1557] Data 0.008 (0.100) Batch 0.879 (1.147) Remain 19:51:42 loss: 0.2088 Lr: 0.00189 [2024-02-18 23:51:43,269 INFO misc.py line 119 87073] Train: [60/100][1503/1557] Data 0.004 (0.099) Batch 0.785 (1.147) Remain 19:51:26 loss: 0.1943 Lr: 0.00189 [2024-02-18 23:51:44,015 INFO misc.py line 119 87073] Train: [60/100][1504/1557] Data 0.003 (0.099) Batch 0.741 (1.147) Remain 19:51:08 loss: 0.2708 Lr: 0.00189 [2024-02-18 23:51:45,198 INFO misc.py line 119 87073] Train: [60/100][1505/1557] Data 0.009 (0.099) Batch 1.176 (1.147) Remain 19:51:08 loss: 0.1674 Lr: 0.00189 [2024-02-18 23:51:45,967 INFO misc.py line 119 87073] Train: [60/100][1506/1557] Data 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Remain 19:49:59 loss: 0.2775 Lr: 0.00189 [2024-02-18 23:51:52,688 INFO misc.py line 119 87073] Train: [60/100][1513/1557] Data 0.011 (0.099) Batch 0.950 (1.145) Remain 19:49:50 loss: 0.4363 Lr: 0.00189 [2024-02-18 23:51:53,791 INFO misc.py line 119 87073] Train: [60/100][1514/1557] Data 0.003 (0.099) Batch 1.104 (1.145) Remain 19:49:47 loss: 0.3729 Lr: 0.00189 [2024-02-18 23:51:54,694 INFO misc.py line 119 87073] Train: [60/100][1515/1557] Data 0.004 (0.099) Batch 0.903 (1.145) Remain 19:49:36 loss: 0.2101 Lr: 0.00189 [2024-02-18 23:51:55,676 INFO misc.py line 119 87073] Train: [60/100][1516/1557] Data 0.003 (0.099) Batch 0.982 (1.145) Remain 19:49:28 loss: 0.5458 Lr: 0.00189 [2024-02-18 23:51:56,428 INFO misc.py line 119 87073] Train: [60/100][1517/1557] Data 0.003 (0.099) Batch 0.744 (1.145) Remain 19:49:10 loss: 0.3526 Lr: 0.00189 [2024-02-18 23:51:57,184 INFO misc.py line 119 87073] Train: [60/100][1518/1557] Data 0.011 (0.099) Batch 0.763 (1.145) Remain 19:48:53 loss: 0.2703 Lr: 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INFO misc.py line 119 87073] Train: [60/100][1525/1557] Data 0.005 (0.101) Batch 0.753 (1.150) Remain 19:53:50 loss: 0.3531 Lr: 0.00189 [2024-02-18 23:52:16,793 INFO misc.py line 119 87073] Train: [60/100][1526/1557] Data 0.011 (0.101) Batch 4.149 (1.152) Remain 19:55:52 loss: 0.2058 Lr: 0.00189 [2024-02-18 23:52:17,780 INFO misc.py line 119 87073] Train: [60/100][1527/1557] Data 0.003 (0.101) Batch 0.988 (1.151) Remain 19:55:44 loss: 0.1877 Lr: 0.00189 [2024-02-18 23:52:18,652 INFO misc.py line 119 87073] Train: [60/100][1528/1557] Data 0.003 (0.101) Batch 0.872 (1.151) Remain 19:55:31 loss: 0.3881 Lr: 0.00189 [2024-02-18 23:52:19,740 INFO misc.py line 119 87073] Train: [60/100][1529/1557] Data 0.003 (0.101) Batch 1.087 (1.151) Remain 19:55:27 loss: 0.2838 Lr: 0.00189 [2024-02-18 23:52:20,554 INFO misc.py line 119 87073] Train: [60/100][1530/1557] Data 0.004 (0.101) Batch 0.810 (1.151) Remain 19:55:12 loss: 0.1615 Lr: 0.00189 [2024-02-18 23:52:21,307 INFO misc.py line 119 87073] Train: [60/100][1531/1557] Data 0.007 (0.101) Batch 0.757 (1.151) Remain 19:54:55 loss: 0.2080 Lr: 0.00189 [2024-02-18 23:52:22,055 INFO misc.py line 119 87073] Train: [60/100][1532/1557] Data 0.003 (0.101) Batch 0.738 (1.150) Remain 19:54:37 loss: 0.3392 Lr: 0.00189 [2024-02-18 23:52:23,295 INFO misc.py line 119 87073] Train: [60/100][1533/1557] Data 0.013 (0.101) Batch 1.241 (1.150) Remain 19:54:40 loss: 0.1693 Lr: 0.00189 [2024-02-18 23:52:24,243 INFO misc.py line 119 87073] Train: [60/100][1534/1557] Data 0.013 (0.101) Batch 0.957 (1.150) Remain 19:54:31 loss: 0.1103 Lr: 0.00189 [2024-02-18 23:52:25,437 INFO misc.py line 119 87073] Train: [60/100][1535/1557] Data 0.003 (0.101) Batch 1.185 (1.150) Remain 19:54:31 loss: 0.1998 Lr: 0.00189 [2024-02-18 23:52:26,650 INFO misc.py line 119 87073] Train: [60/100][1536/1557] Data 0.012 (0.101) Batch 1.211 (1.150) Remain 19:54:32 loss: 0.7518 Lr: 0.00189 [2024-02-18 23:52:27,768 INFO misc.py line 119 87073] Train: [60/100][1537/1557] Data 0.014 (0.101) Batch 1.118 (1.150) Remain 19:54:30 loss: 0.3309 Lr: 0.00189 [2024-02-18 23:52:28,480 INFO misc.py line 119 87073] Train: [60/100][1538/1557] Data 0.014 (0.101) Batch 0.723 (1.150) Remain 19:54:11 loss: 0.2326 Lr: 0.00189 [2024-02-18 23:52:29,236 INFO misc.py line 119 87073] Train: [60/100][1539/1557] Data 0.003 (0.101) Batch 0.746 (1.150) Remain 19:53:54 loss: 0.4048 Lr: 0.00189 [2024-02-18 23:52:30,509 INFO misc.py line 119 87073] Train: [60/100][1540/1557] Data 0.013 (0.101) Batch 1.274 (1.150) Remain 19:53:58 loss: 0.1504 Lr: 0.00189 [2024-02-18 23:52:31,618 INFO misc.py line 119 87073] Train: [60/100][1541/1557] Data 0.011 (0.100) Batch 1.112 (1.150) Remain 19:53:55 loss: 0.4669 Lr: 0.00189 [2024-02-18 23:52:32,450 INFO misc.py line 119 87073] Train: [60/100][1542/1557] Data 0.008 (0.100) Batch 0.836 (1.150) Remain 19:53:41 loss: 0.3438 Lr: 0.00189 [2024-02-18 23:52:33,379 INFO misc.py line 119 87073] Train: [60/100][1543/1557] Data 0.005 (0.100) Batch 0.930 (1.150) Remain 19:53:31 loss: 0.3441 Lr: 0.00189 [2024-02-18 23:52:34,317 INFO misc.py line 119 87073] Train: [60/100][1544/1557] Data 0.004 (0.100) Batch 0.938 (1.149) Remain 19:53:21 loss: 0.2360 Lr: 0.00189 [2024-02-18 23:52:34,958 INFO misc.py line 119 87073] Train: [60/100][1545/1557] Data 0.004 (0.100) Batch 0.633 (1.149) Remain 19:52:59 loss: 0.1440 Lr: 0.00189 [2024-02-18 23:52:35,698 INFO misc.py line 119 87073] Train: [60/100][1546/1557] Data 0.012 (0.100) Batch 0.749 (1.149) Remain 19:52:42 loss: 0.2793 Lr: 0.00189 [2024-02-18 23:52:36,852 INFO misc.py line 119 87073] Train: [60/100][1547/1557] Data 0.004 (0.100) Batch 1.153 (1.149) Remain 19:52:41 loss: 0.1722 Lr: 0.00189 [2024-02-18 23:52:37,863 INFO misc.py line 119 87073] Train: [60/100][1548/1557] Data 0.004 (0.100) Batch 1.012 (1.149) Remain 19:52:34 loss: 0.3026 Lr: 0.00189 [2024-02-18 23:52:38,763 INFO misc.py line 119 87073] Train: [60/100][1549/1557] Data 0.004 (0.100) Batch 0.900 (1.149) Remain 19:52:23 loss: 0.3633 Lr: 0.00189 [2024-02-18 23:52:39,637 INFO misc.py line 119 87073] Train: [60/100][1550/1557] Data 0.004 (0.100) Batch 0.869 (1.148) Remain 19:52:11 loss: 0.2248 Lr: 0.00189 [2024-02-18 23:52:40,461 INFO misc.py line 119 87073] Train: [60/100][1551/1557] Data 0.008 (0.100) Batch 0.828 (1.148) Remain 19:51:57 loss: 0.3976 Lr: 0.00189 [2024-02-18 23:52:41,215 INFO misc.py line 119 87073] Train: [60/100][1552/1557] Data 0.005 (0.100) Batch 0.755 (1.148) Remain 19:51:40 loss: 0.2576 Lr: 0.00189 [2024-02-18 23:52:41,988 INFO misc.py line 119 87073] Train: [60/100][1553/1557] Data 0.004 (0.100) Batch 0.765 (1.148) Remain 19:51:23 loss: 0.1643 Lr: 0.00189 [2024-02-18 23:52:43,094 INFO misc.py line 119 87073] Train: [60/100][1554/1557] Data 0.012 (0.100) Batch 1.109 (1.148) Remain 19:51:21 loss: 0.1433 Lr: 0.00189 [2024-02-18 23:52:43,921 INFO misc.py line 119 87073] Train: [60/100][1555/1557] Data 0.009 (0.100) Batch 0.832 (1.147) Remain 19:51:07 loss: 0.1934 Lr: 0.00189 [2024-02-18 23:52:44,773 INFO misc.py line 119 87073] Train: [60/100][1556/1557] Data 0.004 (0.100) Batch 0.852 (1.147) Remain 19:50:54 loss: 0.3075 Lr: 0.00189 [2024-02-18 23:52:45,677 INFO misc.py line 119 87073] Train: [60/100][1557/1557] Data 0.004 (0.100) Batch 0.897 (1.147) Remain 19:50:43 loss: 0.3096 Lr: 0.00189 [2024-02-18 23:52:45,678 INFO misc.py line 136 87073] Train result: loss: 0.3122 [2024-02-18 23:52:45,678 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-18 23:53:14,837 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6381 [2024-02-18 23:53:15,626 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.9208 [2024-02-18 23:53:17,752 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3765 [2024-02-18 23:53:19,963 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2666 [2024-02-18 23:53:24,911 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2639 [2024-02-18 23:53:25,614 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1384 [2024-02-18 23:53:26,879 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3038 [2024-02-18 23:53:29,831 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2573 [2024-02-18 23:53:31,643 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.1976 [2024-02-18 23:53:33,084 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7153/0.7791/0.9167. [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9424/0.9755 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9812/0.9864 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8760/0.9695 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3763/0.4238 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6319/0.6460 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8035/0.9443 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8288/0.8834 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9247/0.9694 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8102/0.9096 [2024-02-18 23:53:33,084 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7962/0.8915 [2024-02-18 23:53:33,085 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7225/0.8074 [2024-02-18 23:53:33,085 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6047/0.7210 [2024-02-18 23:53:33,085 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-18 23:53:33,086 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-18 23:53:33,086 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-18 23:53:40,174 INFO misc.py line 119 87073] Train: [61/100][1/1557] Data 1.528 (1.528) Batch 2.289 (2.289) Remain 39:36:00 loss: 0.2750 Lr: 0.00189 [2024-02-18 23:53:41,122 INFO misc.py line 119 87073] Train: [61/100][2/1557] Data 0.009 (0.009) Batch 0.948 (0.948) Remain 16:23:46 loss: 0.5318 Lr: 0.00189 [2024-02-18 23:53:42,223 INFO misc.py line 119 87073] Train: [61/100][3/1557] Data 0.009 (0.009) Batch 1.103 (1.103) Remain 19:05:09 loss: 0.6022 Lr: 0.00189 [2024-02-18 23:53:43,195 INFO misc.py line 119 87073] Train: [61/100][4/1557] Data 0.008 (0.008) Batch 0.974 (0.974) Remain 16:50:49 loss: 0.4276 Lr: 0.00189 [2024-02-18 23:53:43,949 INFO misc.py line 119 87073] Train: [61/100][5/1557] Data 0.004 (0.006) Batch 0.754 (0.864) Remain 14:56:53 loss: 0.2237 Lr: 0.00189 [2024-02-18 23:53:44,722 INFO misc.py line 119 87073] Train: [61/100][6/1557] Data 0.004 (0.005) Batch 0.766 (0.832) Remain 14:23:01 loss: 0.3344 Lr: 0.00189 [2024-02-18 23:53:47,535 INFO misc.py line 119 87073] Train: [61/100][7/1557] Data 1.873 (0.472) Batch 2.819 (1.328) Remain 22:58:44 loss: 0.1178 Lr: 0.00189 [2024-02-18 23:53:48,492 INFO misc.py line 119 87073] Train: [61/100][8/1557] Data 0.003 (0.378) Batch 0.954 (1.253) Remain 21:40:54 loss: 0.2834 Lr: 0.00189 [2024-02-18 23:53:49,414 INFO misc.py line 119 87073] Train: [61/100][9/1557] Data 0.007 (0.316) Batch 0.925 (1.199) Remain 20:44:05 loss: 0.1605 Lr: 0.00189 [2024-02-18 23:53:50,260 INFO misc.py line 119 87073] Train: [61/100][10/1557] Data 0.005 (0.272) Batch 0.847 (1.148) Remain 19:51:53 loss: 0.3898 Lr: 0.00189 [2024-02-18 23:53:51,242 INFO misc.py line 119 87073] Train: [61/100][11/1557] Data 0.004 (0.238) Batch 0.979 (1.127) Remain 19:29:55 loss: 0.5799 Lr: 0.00189 [2024-02-18 23:53:52,014 INFO misc.py line 119 87073] Train: [61/100][12/1557] Data 0.006 (0.213) Batch 0.775 (1.088) Remain 18:49:15 loss: 0.2654 Lr: 0.00189 [2024-02-18 23:53:52,727 INFO misc.py line 119 87073] Train: [61/100][13/1557] Data 0.004 (0.192) Batch 0.705 (1.050) Remain 18:09:30 loss: 0.2868 Lr: 0.00189 [2024-02-18 23:53:53,989 INFO misc.py line 119 87073] Train: [61/100][14/1557] Data 0.011 (0.175) Batch 1.262 (1.069) Remain 18:29:30 loss: 0.5529 Lr: 0.00189 [2024-02-18 23:53:54,897 INFO misc.py line 119 87073] Train: [61/100][15/1557] Data 0.011 (0.162) Batch 0.916 (1.056) Remain 18:16:15 loss: 0.5366 Lr: 0.00189 [2024-02-18 23:53:55,967 INFO misc.py line 119 87073] Train: [61/100][16/1557] Data 0.003 (0.149) Batch 1.069 (1.057) Remain 18:17:16 loss: 0.2683 Lr: 0.00189 [2024-02-18 23:53:56,881 INFO misc.py line 119 87073] Train: [61/100][17/1557] Data 0.004 (0.139) Batch 0.914 (1.047) Remain 18:06:38 loss: 0.2869 Lr: 0.00189 [2024-02-18 23:53:57,859 INFO misc.py line 119 87073] Train: [61/100][18/1557] Data 0.003 (0.130) Batch 0.978 (1.043) Remain 18:01:50 loss: 0.4308 Lr: 0.00189 [2024-02-18 23:53:58,585 INFO misc.py line 119 87073] Train: [61/100][19/1557] Data 0.003 (0.122) Batch 0.723 (1.023) Remain 17:41:05 loss: 0.2478 Lr: 0.00189 [2024-02-18 23:53:59,344 INFO misc.py line 119 87073] Train: [61/100][20/1557] Data 0.006 (0.115) Batch 0.762 (1.007) Remain 17:25:10 loss: 0.1998 Lr: 0.00189 [2024-02-18 23:54:00,514 INFO misc.py line 119 87073] Train: [61/100][21/1557] Data 0.003 (0.109) Batch 1.168 (1.016) Remain 17:34:26 loss: 0.1274 Lr: 0.00189 [2024-02-18 23:54:01,789 INFO misc.py line 119 87073] Train: [61/100][22/1557] Data 0.005 (0.103) Batch 1.272 (1.030) Remain 17:48:22 loss: 0.2602 Lr: 0.00189 [2024-02-18 23:54:02,887 INFO misc.py line 119 87073] Train: [61/100][23/1557] Data 0.008 (0.099) Batch 1.091 (1.033) Remain 17:51:32 loss: 0.1725 Lr: 0.00189 [2024-02-18 23:54:03,755 INFO misc.py line 119 87073] Train: [61/100][24/1557] Data 0.015 (0.095) Batch 0.881 (1.025) Remain 17:44:00 loss: 0.3211 Lr: 0.00189 [2024-02-18 23:54:04,864 INFO misc.py line 119 87073] Train: [61/100][25/1557] Data 0.003 (0.091) Batch 1.109 (1.029) Remain 17:47:55 loss: 0.6168 Lr: 0.00189 [2024-02-18 23:54:05,626 INFO misc.py line 119 87073] Train: [61/100][26/1557] Data 0.003 (0.087) Batch 0.762 (1.018) Remain 17:35:51 loss: 0.2111 Lr: 0.00189 [2024-02-18 23:54:06,376 INFO misc.py line 119 87073] Train: [61/100][27/1557] Data 0.003 (0.083) Batch 0.741 (1.006) Remain 17:23:53 loss: 0.2896 Lr: 0.00189 [2024-02-18 23:54:07,568 INFO misc.py line 119 87073] Train: [61/100][28/1557] Data 0.011 (0.080) Batch 1.190 (1.013) Remain 17:31:29 loss: 0.2582 Lr: 0.00189 [2024-02-18 23:54:08,624 INFO misc.py line 119 87073] Train: [61/100][29/1557] Data 0.014 (0.078) Batch 1.056 (1.015) Remain 17:33:10 loss: 0.3566 Lr: 0.00189 [2024-02-18 23:54:09,763 INFO misc.py line 119 87073] Train: [61/100][30/1557] Data 0.014 (0.075) Batch 1.141 (1.020) Remain 17:37:59 loss: 0.2117 Lr: 0.00189 [2024-02-18 23:54:10,687 INFO misc.py line 119 87073] Train: [61/100][31/1557] Data 0.012 (0.073) Batch 0.931 (1.017) Remain 17:34:41 loss: 0.4240 Lr: 0.00189 [2024-02-18 23:54:11,566 INFO misc.py line 119 87073] Train: [61/100][32/1557] Data 0.006 (0.071) Batch 0.881 (1.012) Remain 17:29:49 loss: 0.1975 Lr: 0.00189 [2024-02-18 23:54:12,411 INFO misc.py line 119 87073] Train: [61/100][33/1557] Data 0.003 (0.069) Batch 0.837 (1.006) Remain 17:23:46 loss: 0.1457 Lr: 0.00189 [2024-02-18 23:54:13,168 INFO misc.py line 119 87073] Train: [61/100][34/1557] Data 0.011 (0.067) Batch 0.763 (0.998) Remain 17:15:37 loss: 0.2215 Lr: 0.00189 [2024-02-18 23:54:14,454 INFO misc.py line 119 87073] Train: [61/100][35/1557] Data 0.004 (0.065) Batch 1.279 (1.007) Remain 17:24:42 loss: 0.1451 Lr: 0.00189 [2024-02-18 23:54:15,427 INFO misc.py line 119 87073] Train: [61/100][36/1557] Data 0.012 (0.063) Batch 0.982 (1.006) Remain 17:23:53 loss: 0.1473 Lr: 0.00189 [2024-02-18 23:54:16,373 INFO misc.py line 119 87073] Train: [61/100][37/1557] Data 0.003 (0.061) Batch 0.946 (1.004) Remain 17:22:01 loss: 0.6221 Lr: 0.00189 [2024-02-18 23:54:17,423 INFO misc.py line 119 87073] Train: [61/100][38/1557] Data 0.003 (0.060) Batch 1.050 (1.006) Remain 17:23:21 loss: 0.2446 Lr: 0.00189 [2024-02-18 23:54:18,415 INFO misc.py line 119 87073] Train: [61/100][39/1557] Data 0.003 (0.058) Batch 0.991 (1.005) Remain 17:22:56 loss: 0.3347 Lr: 0.00189 [2024-02-18 23:54:19,106 INFO misc.py line 119 87073] Train: [61/100][40/1557] Data 0.003 (0.057) Batch 0.691 (0.997) Remain 17:14:06 loss: 0.2481 Lr: 0.00189 [2024-02-18 23:54:19,858 INFO misc.py line 119 87073] Train: [61/100][41/1557] Data 0.003 (0.055) Batch 0.744 (0.990) Remain 17:07:11 loss: 0.2744 Lr: 0.00189 [2024-02-18 23:54:20,985 INFO misc.py line 119 87073] Train: [61/100][42/1557] Data 0.011 (0.054) Batch 1.126 (0.994) Remain 17:10:46 loss: 0.1404 Lr: 0.00189 [2024-02-18 23:54:21,936 INFO misc.py line 119 87073] Train: [61/100][43/1557] Data 0.013 (0.053) Batch 0.960 (0.993) Remain 17:09:53 loss: 0.4344 Lr: 0.00189 [2024-02-18 23:54:22,739 INFO misc.py line 119 87073] Train: [61/100][44/1557] Data 0.004 (0.052) Batch 0.803 (0.988) Remain 17:05:03 loss: 0.2730 Lr: 0.00189 [2024-02-18 23:54:23,585 INFO misc.py line 119 87073] Train: [61/100][45/1557] Data 0.003 (0.051) Batch 0.840 (0.985) Remain 17:01:23 loss: 0.2058 Lr: 0.00189 [2024-02-18 23:54:24,543 INFO misc.py line 119 87073] Train: [61/100][46/1557] Data 0.010 (0.050) Batch 0.964 (0.984) Remain 17:00:52 loss: 0.8493 Lr: 0.00189 [2024-02-18 23:54:25,288 INFO misc.py line 119 87073] Train: [61/100][47/1557] Data 0.003 (0.049) Batch 0.744 (0.979) Remain 16:55:11 loss: 0.2610 Lr: 0.00189 [2024-02-18 23:54:26,055 INFO misc.py line 119 87073] Train: [61/100][48/1557] Data 0.004 (0.048) Batch 0.760 (0.974) Remain 16:50:07 loss: 0.1238 Lr: 0.00189 [2024-02-18 23:54:27,294 INFO misc.py line 119 87073] Train: [61/100][49/1557] Data 0.012 (0.047) Batch 1.243 (0.980) Remain 16:56:11 loss: 0.1935 Lr: 0.00189 [2024-02-18 23:54:28,362 INFO misc.py line 119 87073] Train: [61/100][50/1557] Data 0.008 (0.046) Batch 1.053 (0.981) Remain 16:57:46 loss: 0.1092 Lr: 0.00189 [2024-02-18 23:54:29,253 INFO misc.py line 119 87073] Train: [61/100][51/1557] Data 0.022 (0.046) Batch 0.910 (0.980) Remain 16:56:13 loss: 0.2708 Lr: 0.00189 [2024-02-18 23:54:30,172 INFO misc.py line 119 87073] Train: [61/100][52/1557] Data 0.004 (0.045) Batch 0.918 (0.979) Remain 16:54:54 loss: 0.3604 Lr: 0.00189 [2024-02-18 23:54:31,127 INFO misc.py line 119 87073] Train: [61/100][53/1557] Data 0.004 (0.044) Batch 0.955 (0.978) Remain 16:54:24 loss: 0.3942 Lr: 0.00189 [2024-02-18 23:54:31,903 INFO misc.py line 119 87073] Train: [61/100][54/1557] Data 0.007 (0.043) Batch 0.774 (0.974) Remain 16:50:14 loss: 0.7671 Lr: 0.00189 [2024-02-18 23:54:32,638 INFO misc.py line 119 87073] Train: [61/100][55/1557] Data 0.007 (0.043) Batch 0.738 (0.970) Remain 16:45:30 loss: 0.4000 Lr: 0.00189 [2024-02-18 23:54:33,747 INFO misc.py line 119 87073] Train: [61/100][56/1557] Data 0.003 (0.042) Batch 1.108 (0.972) Remain 16:48:11 loss: 0.1055 Lr: 0.00189 [2024-02-18 23:54:34,752 INFO misc.py line 119 87073] Train: [61/100][57/1557] Data 0.005 (0.041) Batch 1.005 (0.973) Remain 16:48:49 loss: 0.4194 Lr: 0.00189 [2024-02-18 23:54:35,668 INFO misc.py line 119 87073] Train: [61/100][58/1557] Data 0.004 (0.040) Batch 0.917 (0.972) Remain 16:47:44 loss: 0.4467 Lr: 0.00189 [2024-02-18 23:54:36,566 INFO misc.py line 119 87073] Train: [61/100][59/1557] Data 0.004 (0.040) Batch 0.897 (0.970) Remain 16:46:20 loss: 0.1363 Lr: 0.00189 [2024-02-18 23:54:37,501 INFO misc.py line 119 87073] Train: [61/100][60/1557] Data 0.004 (0.039) Batch 0.935 (0.970) Remain 16:45:40 loss: 0.3237 Lr: 0.00189 [2024-02-18 23:54:38,234 INFO misc.py line 119 87073] Train: [61/100][61/1557] Data 0.005 (0.039) Batch 0.731 (0.966) Remain 16:41:23 loss: 0.1875 Lr: 0.00189 [2024-02-18 23:54:38,962 INFO misc.py line 119 87073] Train: [61/100][62/1557] Data 0.007 (0.038) Batch 0.727 (0.962) Remain 16:37:10 loss: 0.2681 Lr: 0.00189 [2024-02-18 23:54:49,059 INFO misc.py line 119 87073] Train: [61/100][63/1557] Data 8.090 (0.172) Batch 10.093 (1.114) Remain 19:14:58 loss: 0.1358 Lr: 0.00189 [2024-02-18 23:54:50,021 INFO misc.py line 119 87073] Train: [61/100][64/1557] Data 0.013 (0.170) Batch 0.971 (1.111) Remain 19:12:31 loss: 0.4889 Lr: 0.00189 [2024-02-18 23:54:50,913 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [61/100][109/1557] Data 0.003 (0.100) Batch 0.970 (1.038) Remain 17:55:56 loss: 0.4034 Lr: 0.00188 [2024-02-18 23:55:33,031 INFO misc.py line 119 87073] Train: [61/100][110/1557] Data 0.003 (0.100) Batch 0.743 (1.036) Remain 17:53:03 loss: 0.1282 Lr: 0.00188 [2024-02-18 23:55:33,796 INFO misc.py line 119 87073] Train: [61/100][111/1557] Data 0.003 (0.099) Batch 0.753 (1.033) Remain 17:50:19 loss: 0.1603 Lr: 0.00188 [2024-02-18 23:55:34,826 INFO misc.py line 119 87073] Train: [61/100][112/1557] Data 0.015 (0.098) Batch 1.033 (1.033) Remain 17:50:18 loss: 0.2571 Lr: 0.00188 [2024-02-18 23:55:35,589 INFO misc.py line 119 87073] Train: [61/100][113/1557] Data 0.012 (0.097) Batch 0.772 (1.031) Remain 17:47:50 loss: 0.2446 Lr: 0.00188 [2024-02-18 23:55:36,417 INFO misc.py line 119 87073] Train: [61/100][114/1557] Data 0.003 (0.096) Batch 0.828 (1.029) Remain 17:45:55 loss: 0.7019 Lr: 0.00188 [2024-02-18 23:55:37,427 INFO misc.py line 119 87073] Train: 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Batch 1.036 (1.103) Remain 19:02:32 loss: 0.3613 Lr: 0.00188 [2024-02-18 23:55:53,553 INFO misc.py line 119 87073] Train: [61/100][122/1557] Data 0.011 (0.167) Batch 1.189 (1.104) Remain 19:03:15 loss: 0.1837 Lr: 0.00188 [2024-02-18 23:55:54,512 INFO misc.py line 119 87073] Train: [61/100][123/1557] Data 0.010 (0.166) Batch 0.966 (1.102) Remain 19:02:03 loss: 0.5284 Lr: 0.00188 [2024-02-18 23:55:55,250 INFO misc.py line 119 87073] Train: [61/100][124/1557] Data 0.004 (0.165) Batch 0.738 (1.099) Remain 18:58:54 loss: 0.3118 Lr: 0.00188 [2024-02-18 23:55:56,049 INFO misc.py line 119 87073] Train: [61/100][125/1557] Data 0.003 (0.163) Batch 0.793 (1.097) Remain 18:56:17 loss: 0.2009 Lr: 0.00188 [2024-02-18 23:55:57,189 INFO misc.py line 119 87073] Train: [61/100][126/1557] Data 0.009 (0.162) Batch 1.140 (1.097) Remain 18:56:38 loss: 0.2036 Lr: 0.00188 [2024-02-18 23:55:58,023 INFO misc.py line 119 87073] Train: [61/100][127/1557] Data 0.010 (0.161) Batch 0.840 (1.095) Remain 18:54:28 loss: 0.3558 Lr: 0.00188 [2024-02-18 23:55:59,100 INFO misc.py line 119 87073] Train: [61/100][128/1557] Data 0.003 (0.160) Batch 1.077 (1.095) Remain 18:54:18 loss: 0.3893 Lr: 0.00188 [2024-02-18 23:56:00,233 INFO misc.py line 119 87073] Train: [61/100][129/1557] Data 0.003 (0.158) Batch 1.133 (1.095) Remain 18:54:35 loss: 0.2350 Lr: 0.00188 [2024-02-18 23:56:01,404 INFO misc.py line 119 87073] Train: [61/100][130/1557] Data 0.003 (0.157) Batch 1.163 (1.096) Remain 18:55:07 loss: 0.7529 Lr: 0.00188 [2024-02-18 23:56:02,191 INFO misc.py line 119 87073] Train: [61/100][131/1557] Data 0.012 (0.156) Batch 0.795 (1.094) Remain 18:52:40 loss: 0.2407 Lr: 0.00188 [2024-02-18 23:56:02,883 INFO misc.py line 119 87073] Train: [61/100][132/1557] Data 0.003 (0.155) Batch 0.684 (1.090) Remain 18:49:22 loss: 0.1837 Lr: 0.00188 [2024-02-18 23:56:04,009 INFO misc.py line 119 87073] Train: [61/100][133/1557] Data 0.011 (0.154) Batch 1.122 (1.091) Remain 18:49:36 loss: 0.3817 Lr: 0.00188 [2024-02-18 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87073] Train: [61/100][140/1557] Data 0.003 (0.146) Batch 1.220 (1.083) Remain 18:42:07 loss: 0.1241 Lr: 0.00188 [2024-02-18 23:56:11,676 INFO misc.py line 119 87073] Train: [61/100][141/1557] Data 0.004 (0.145) Batch 1.019 (1.083) Remain 18:41:37 loss: 0.4574 Lr: 0.00188 [2024-02-18 23:56:12,561 INFO misc.py line 119 87073] Train: [61/100][142/1557] Data 0.003 (0.144) Batch 0.884 (1.082) Remain 18:40:07 loss: 0.5025 Lr: 0.00188 [2024-02-18 23:56:13,796 INFO misc.py line 119 87073] Train: [61/100][143/1557] Data 0.004 (0.143) Batch 1.234 (1.083) Remain 18:41:13 loss: 0.7461 Lr: 0.00188 [2024-02-18 23:56:14,772 INFO misc.py line 119 87073] Train: [61/100][144/1557] Data 0.004 (0.142) Batch 0.975 (1.082) Remain 18:40:25 loss: 0.3306 Lr: 0.00188 [2024-02-18 23:56:15,524 INFO misc.py line 119 87073] Train: [61/100][145/1557] Data 0.005 (0.141) Batch 0.750 (1.080) Remain 18:37:59 loss: 0.2452 Lr: 0.00188 [2024-02-18 23:56:16,210 INFO misc.py line 119 87073] Train: [61/100][146/1557] Data 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line 119 87073] Train: [61/100][165/1557] Data 0.003 (0.124) Batch 0.994 (1.065) Remain 18:22:13 loss: 0.1351 Lr: 0.00188 [2024-02-18 23:56:35,444 INFO misc.py line 119 87073] Train: [61/100][166/1557] Data 0.003 (0.124) Batch 0.742 (1.063) Remain 18:20:09 loss: 0.2320 Lr: 0.00188 [2024-02-18 23:56:36,249 INFO misc.py line 119 87073] Train: [61/100][167/1557] Data 0.003 (0.123) Batch 0.795 (1.061) Remain 18:18:26 loss: 0.1936 Lr: 0.00188 [2024-02-18 23:56:37,308 INFO misc.py line 119 87073] Train: [61/100][168/1557] Data 0.013 (0.122) Batch 1.059 (1.061) Remain 18:18:25 loss: 0.2641 Lr: 0.00188 [2024-02-18 23:56:38,198 INFO misc.py line 119 87073] Train: [61/100][169/1557] Data 0.014 (0.122) Batch 0.900 (1.060) Remain 18:17:23 loss: 0.3952 Lr: 0.00188 [2024-02-18 23:56:39,117 INFO misc.py line 119 87073] Train: [61/100][170/1557] Data 0.004 (0.121) Batch 0.919 (1.059) Remain 18:16:30 loss: 0.1846 Lr: 0.00188 [2024-02-18 23:56:40,101 INFO misc.py line 119 87073] Train: 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Batch 0.922 (1.107) Remain 19:05:48 loss: 0.5377 Lr: 0.00188 [2024-02-18 23:56:55,848 INFO misc.py line 119 87073] Train: [61/100][178/1557] Data 0.003 (0.166) Batch 1.008 (1.106) Remain 19:05:12 loss: 0.1516 Lr: 0.00188 [2024-02-18 23:56:56,801 INFO misc.py line 119 87073] Train: [61/100][179/1557] Data 0.003 (0.165) Batch 0.948 (1.106) Remain 19:04:14 loss: 0.3855 Lr: 0.00188 [2024-02-18 23:56:59,028 INFO misc.py line 119 87073] Train: [61/100][180/1557] Data 1.109 (0.171) Batch 2.220 (1.112) Remain 19:10:44 loss: 0.1958 Lr: 0.00188 [2024-02-18 23:56:59,849 INFO misc.py line 119 87073] Train: [61/100][181/1557] Data 0.016 (0.170) Batch 0.834 (1.110) Remain 19:09:06 loss: 0.3152 Lr: 0.00188 [2024-02-18 23:57:00,985 INFO misc.py line 119 87073] Train: [61/100][182/1557] Data 0.003 (0.169) Batch 1.136 (1.110) Remain 19:09:14 loss: 0.2171 Lr: 0.00188 [2024-02-18 23:57:01,940 INFO misc.py line 119 87073] Train: [61/100][183/1557] Data 0.003 (0.168) Batch 0.955 (1.110) Remain 19:08:19 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87073] Train: [61/100][196/1557] Data 0.008 (0.157) Batch 1.210 (1.099) Remain 18:57:28 loss: 0.2194 Lr: 0.00188 [2024-02-18 23:57:15,363 INFO misc.py line 119 87073] Train: [61/100][197/1557] Data 0.015 (0.156) Batch 0.977 (1.099) Remain 18:56:48 loss: 0.6330 Lr: 0.00188 [2024-02-18 23:57:16,383 INFO misc.py line 119 87073] Train: [61/100][198/1557] Data 0.003 (0.156) Batch 1.021 (1.098) Remain 18:56:22 loss: 0.1822 Lr: 0.00188 [2024-02-18 23:57:17,432 INFO misc.py line 119 87073] Train: [61/100][199/1557] Data 0.003 (0.155) Batch 1.049 (1.098) Remain 18:56:05 loss: 0.4655 Lr: 0.00188 [2024-02-18 23:57:18,451 INFO misc.py line 119 87073] Train: [61/100][200/1557] Data 0.003 (0.154) Batch 1.019 (1.098) Remain 18:55:39 loss: 0.5657 Lr: 0.00188 [2024-02-18 23:57:19,210 INFO misc.py line 119 87073] Train: [61/100][201/1557] Data 0.003 (0.153) Batch 0.759 (1.096) Remain 18:53:52 loss: 0.2662 Lr: 0.00188 [2024-02-18 23:57:19,966 INFO misc.py line 119 87073] Train: [61/100][202/1557] Data 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line 119 87073] Train: [61/100][221/1557] Data 0.004 (0.140) Batch 1.109 (1.084) Remain 18:41:14 loss: 0.6456 Lr: 0.00188 [2024-02-18 23:57:39,317 INFO misc.py line 119 87073] Train: [61/100][222/1557] Data 0.004 (0.139) Batch 0.777 (1.083) Remain 18:39:45 loss: 0.3879 Lr: 0.00188 [2024-02-18 23:57:40,057 INFO misc.py line 119 87073] Train: [61/100][223/1557] Data 0.003 (0.139) Batch 0.732 (1.081) Remain 18:38:05 loss: 0.1236 Lr: 0.00188 [2024-02-18 23:57:41,179 INFO misc.py line 119 87073] Train: [61/100][224/1557] Data 0.011 (0.138) Batch 1.119 (1.081) Remain 18:38:15 loss: 0.1536 Lr: 0.00188 [2024-02-18 23:57:42,039 INFO misc.py line 119 87073] Train: [61/100][225/1557] Data 0.014 (0.138) Batch 0.871 (1.080) Remain 18:37:15 loss: 0.3245 Lr: 0.00188 [2024-02-18 23:57:42,899 INFO misc.py line 119 87073] Train: [61/100][226/1557] Data 0.003 (0.137) Batch 0.860 (1.079) Remain 18:36:13 loss: 0.3299 Lr: 0.00188 [2024-02-18 23:57:43,884 INFO misc.py line 119 87073] Train: 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Batch 1.068 (1.112) Remain 19:09:49 loss: 0.3146 Lr: 0.00188 [2024-02-18 23:57:58,927 INFO misc.py line 119 87073] Train: [61/100][234/1557] Data 0.004 (0.169) Batch 0.969 (1.111) Remain 19:09:10 loss: 0.2618 Lr: 0.00188 [2024-02-18 23:57:59,890 INFO misc.py line 119 87073] Train: [61/100][235/1557] Data 0.004 (0.169) Batch 0.957 (1.111) Remain 19:08:28 loss: 0.1551 Lr: 0.00188 [2024-02-18 23:58:00,599 INFO misc.py line 119 87073] Train: [61/100][236/1557] Data 0.009 (0.168) Batch 0.715 (1.109) Remain 19:06:41 loss: 0.1894 Lr: 0.00188 [2024-02-18 23:58:01,304 INFO misc.py line 119 87073] Train: [61/100][237/1557] Data 0.003 (0.167) Batch 0.704 (1.107) Remain 19:04:53 loss: 0.3241 Lr: 0.00188 [2024-02-18 23:58:02,483 INFO misc.py line 119 87073] Train: [61/100][238/1557] Data 0.004 (0.166) Batch 1.170 (1.107) Remain 19:05:08 loss: 0.2901 Lr: 0.00188 [2024-02-18 23:58:03,466 INFO misc.py line 119 87073] Train: [61/100][239/1557] Data 0.014 (0.166) Batch 0.993 (1.107) Remain 19:04:37 loss: 0.2308 Lr: 0.00188 [2024-02-18 23:58:04,383 INFO misc.py line 119 87073] Train: [61/100][240/1557] Data 0.004 (0.165) Batch 0.917 (1.106) Remain 19:03:46 loss: 0.4202 Lr: 0.00188 [2024-02-18 23:58:05,324 INFO misc.py line 119 87073] Train: [61/100][241/1557] Data 0.003 (0.164) Batch 0.941 (1.105) Remain 19:03:02 loss: 0.3464 Lr: 0.00188 [2024-02-18 23:58:06,268 INFO misc.py line 119 87073] Train: [61/100][242/1557] Data 0.003 (0.164) Batch 0.940 (1.105) Remain 19:02:18 loss: 0.2229 Lr: 0.00188 [2024-02-18 23:58:07,098 INFO misc.py line 119 87073] Train: [61/100][243/1557] Data 0.007 (0.163) Batch 0.834 (1.104) Remain 19:01:07 loss: 0.2424 Lr: 0.00188 [2024-02-18 23:58:07,863 INFO misc.py line 119 87073] Train: [61/100][244/1557] Data 0.003 (0.162) Batch 0.765 (1.102) Remain 18:59:39 loss: 0.1654 Lr: 0.00188 [2024-02-18 23:58:09,018 INFO misc.py line 119 87073] Train: [61/100][245/1557] Data 0.003 (0.162) Batch 1.153 (1.102) Remain 18:59:51 loss: 0.1168 Lr: 0.00188 [2024-02-18 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87073] Train: [61/100][252/1557] Data 0.008 (0.157) Batch 1.282 (1.098) Remain 18:55:19 loss: 0.1924 Lr: 0.00188 [2024-02-18 23:58:16,603 INFO misc.py line 119 87073] Train: [61/100][253/1557] Data 0.013 (0.157) Batch 0.930 (1.098) Remain 18:54:36 loss: 0.2117 Lr: 0.00188 [2024-02-18 23:58:17,543 INFO misc.py line 119 87073] Train: [61/100][254/1557] Data 0.003 (0.156) Batch 0.940 (1.097) Remain 18:53:56 loss: 0.1992 Lr: 0.00188 [2024-02-18 23:58:18,532 INFO misc.py line 119 87073] Train: [61/100][255/1557] Data 0.004 (0.156) Batch 0.989 (1.096) Remain 18:53:28 loss: 0.1104 Lr: 0.00188 [2024-02-18 23:58:19,424 INFO misc.py line 119 87073] Train: [61/100][256/1557] Data 0.004 (0.155) Batch 0.883 (1.096) Remain 18:52:35 loss: 0.2238 Lr: 0.00188 [2024-02-18 23:58:20,163 INFO misc.py line 119 87073] Train: [61/100][257/1557] Data 0.012 (0.154) Batch 0.748 (1.094) Remain 18:51:09 loss: 0.2997 Lr: 0.00188 [2024-02-18 23:58:20,904 INFO misc.py line 119 87073] Train: [61/100][258/1557] Data 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Batch 1.010 (1.113) Remain 19:10:11 loss: 0.5026 Lr: 0.00187 [2024-02-18 23:59:01,559 INFO misc.py line 119 87073] Train: [61/100][290/1557] Data 0.006 (0.168) Batch 0.946 (1.113) Remain 19:09:34 loss: 0.2063 Lr: 0.00187 [2024-02-18 23:59:02,539 INFO misc.py line 119 87073] Train: [61/100][291/1557] Data 0.004 (0.168) Batch 0.980 (1.112) Remain 19:09:04 loss: 0.4311 Lr: 0.00187 [2024-02-18 23:59:03,257 INFO misc.py line 119 87073] Train: [61/100][292/1557] Data 0.003 (0.167) Batch 0.712 (1.111) Remain 19:07:38 loss: 0.1862 Lr: 0.00187 [2024-02-18 23:59:04,000 INFO misc.py line 119 87073] Train: [61/100][293/1557] Data 0.010 (0.167) Batch 0.749 (1.110) Remain 19:06:19 loss: 0.4444 Lr: 0.00187 [2024-02-18 23:59:05,249 INFO misc.py line 119 87073] Train: [61/100][294/1557] Data 0.003 (0.166) Batch 1.249 (1.110) Remain 19:06:48 loss: 0.1468 Lr: 0.00187 [2024-02-18 23:59:06,233 INFO misc.py line 119 87073] Train: [61/100][295/1557] Data 0.004 (0.166) Batch 0.984 (1.110) Remain 19:06:20 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[2024-02-18 23:59:35,955 INFO misc.py line 119 87073] Train: [61/100][327/1557] Data 0.003 (0.150) Batch 0.739 (1.092) Remain 18:47:16 loss: 0.3150 Lr: 0.00187 [2024-02-18 23:59:36,617 INFO misc.py line 119 87073] Train: [61/100][328/1557] Data 0.011 (0.149) Batch 0.670 (1.090) Remain 18:45:55 loss: 0.1023 Lr: 0.00187 [2024-02-18 23:59:37,892 INFO misc.py line 119 87073] Train: [61/100][329/1557] Data 0.004 (0.149) Batch 1.268 (1.091) Remain 18:46:27 loss: 0.2352 Lr: 0.00187 [2024-02-18 23:59:38,838 INFO misc.py line 119 87073] Train: [61/100][330/1557] Data 0.012 (0.148) Batch 0.953 (1.091) Remain 18:46:00 loss: 0.6315 Lr: 0.00187 [2024-02-18 23:59:39,820 INFO misc.py line 119 87073] Train: [61/100][331/1557] Data 0.004 (0.148) Batch 0.982 (1.090) Remain 18:45:39 loss: 0.3721 Lr: 0.00187 [2024-02-18 23:59:40,760 INFO misc.py line 119 87073] Train: [61/100][332/1557] Data 0.004 (0.148) Batch 0.941 (1.090) Remain 18:45:09 loss: 0.1266 Lr: 0.00187 [2024-02-18 23:59:41,675 INFO misc.py line 119 87073] Train: [61/100][333/1557] Data 0.004 (0.147) Batch 0.910 (1.089) Remain 18:44:34 loss: 0.3698 Lr: 0.00187 [2024-02-18 23:59:42,413 INFO misc.py line 119 87073] Train: [61/100][334/1557] Data 0.008 (0.147) Batch 0.742 (1.088) Remain 18:43:28 loss: 0.2463 Lr: 0.00187 [2024-02-18 23:59:43,179 INFO misc.py line 119 87073] Train: [61/100][335/1557] Data 0.004 (0.146) Batch 0.766 (1.087) Remain 18:42:27 loss: 0.2550 Lr: 0.00187 [2024-02-18 23:59:44,192 INFO misc.py line 119 87073] Train: [61/100][336/1557] Data 0.005 (0.146) Batch 1.006 (1.087) Remain 18:42:11 loss: 0.3385 Lr: 0.00187 [2024-02-18 23:59:45,092 INFO misc.py line 119 87073] Train: [61/100][337/1557] Data 0.011 (0.145) Batch 0.907 (1.086) Remain 18:41:37 loss: 0.3455 Lr: 0.00187 [2024-02-18 23:59:46,048 INFO misc.py line 119 87073] Train: [61/100][338/1557] Data 0.004 (0.145) Batch 0.956 (1.086) Remain 18:41:12 loss: 0.1757 Lr: 0.00187 [2024-02-18 23:59:46,996 INFO misc.py line 119 87073] Train: 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Batch 0.883 (1.107) Remain 19:02:29 loss: 0.4585 Lr: 0.00187 [2024-02-19 00:00:01,702 INFO misc.py line 119 87073] Train: [61/100][346/1557] Data 0.004 (0.165) Batch 0.946 (1.106) Remain 19:01:59 loss: 0.7363 Lr: 0.00187 [2024-02-19 00:00:02,623 INFO misc.py line 119 87073] Train: [61/100][347/1557] Data 0.010 (0.164) Batch 0.928 (1.106) Remain 19:01:26 loss: 0.0807 Lr: 0.00187 [2024-02-19 00:00:03,379 INFO misc.py line 119 87073] Train: [61/100][348/1557] Data 0.003 (0.164) Batch 0.754 (1.105) Remain 19:00:22 loss: 0.2711 Lr: 0.00187 [2024-02-19 00:00:04,136 INFO misc.py line 119 87073] Train: [61/100][349/1557] Data 0.004 (0.163) Batch 0.749 (1.104) Remain 18:59:17 loss: 0.2304 Lr: 0.00187 [2024-02-19 00:00:05,341 INFO misc.py line 119 87073] Train: [61/100][350/1557] Data 0.013 (0.163) Batch 1.206 (1.104) Remain 18:59:34 loss: 0.3445 Lr: 0.00187 [2024-02-19 00:00:06,325 INFO misc.py line 119 87073] Train: [61/100][351/1557] Data 0.012 (0.162) Batch 0.989 (1.104) Remain 18:59:13 loss: 0.3161 Lr: 0.00187 [2024-02-19 00:00:07,331 INFO misc.py line 119 87073] Train: [61/100][352/1557] Data 0.009 (0.162) Batch 1.010 (1.103) Remain 18:58:55 loss: 0.1974 Lr: 0.00187 [2024-02-19 00:00:08,363 INFO misc.py line 119 87073] Train: [61/100][353/1557] Data 0.003 (0.161) Batch 1.031 (1.103) Remain 18:58:41 loss: 0.3654 Lr: 0.00187 [2024-02-19 00:00:09,476 INFO misc.py line 119 87073] Train: [61/100][354/1557] Data 0.003 (0.161) Batch 1.113 (1.103) Remain 18:58:42 loss: 0.3216 Lr: 0.00187 [2024-02-19 00:00:11,498 INFO misc.py line 119 87073] Train: [61/100][355/1557] Data 0.906 (0.163) Batch 2.018 (1.106) Remain 19:01:22 loss: 0.2949 Lr: 0.00187 [2024-02-19 00:00:12,380 INFO misc.py line 119 87073] Train: [61/100][356/1557] Data 0.008 (0.163) Batch 0.886 (1.105) Remain 19:00:42 loss: 0.3614 Lr: 0.00187 [2024-02-19 00:00:13,597 INFO misc.py line 119 87073] Train: [61/100][357/1557] Data 0.003 (0.162) Batch 1.203 (1.106) Remain 19:00:58 loss: 0.2105 Lr: 0.00187 [2024-02-19 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line 119 87073] Train: [61/100][389/1557] Data 0.021 (0.150) Batch 1.101 (1.092) Remain 18:46:28 loss: 0.5753 Lr: 0.00187 [2024-02-19 00:00:44,458 INFO misc.py line 119 87073] Train: [61/100][390/1557] Data 0.011 (0.149) Batch 0.700 (1.091) Remain 18:45:25 loss: 0.3923 Lr: 0.00187 [2024-02-19 00:00:45,226 INFO misc.py line 119 87073] Train: [61/100][391/1557] Data 0.003 (0.149) Batch 0.764 (1.090) Remain 18:44:31 loss: 0.2198 Lr: 0.00187 [2024-02-19 00:00:46,242 INFO misc.py line 119 87073] Train: [61/100][392/1557] Data 0.007 (0.148) Batch 1.009 (1.090) Remain 18:44:17 loss: 0.1013 Lr: 0.00187 [2024-02-19 00:00:47,223 INFO misc.py line 119 87073] Train: [61/100][393/1557] Data 0.015 (0.148) Batch 0.993 (1.090) Remain 18:44:01 loss: 0.4623 Lr: 0.00187 [2024-02-19 00:00:48,112 INFO misc.py line 119 87073] Train: [61/100][394/1557] Data 0.003 (0.148) Batch 0.886 (1.089) Remain 18:43:27 loss: 0.4061 Lr: 0.00187 [2024-02-19 00:00:49,167 INFO misc.py line 119 87073] Train: 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Batch 1.136 (1.108) Remain 19:02:49 loss: 0.2383 Lr: 0.00187 [2024-02-19 00:01:04,291 INFO misc.py line 119 87073] Train: [61/100][402/1557] Data 0.005 (0.167) Batch 1.030 (1.108) Remain 19:02:36 loss: 0.5836 Lr: 0.00187 [2024-02-19 00:01:05,368 INFO misc.py line 119 87073] Train: [61/100][403/1557] Data 0.010 (0.166) Batch 1.082 (1.108) Remain 19:02:31 loss: 0.2507 Lr: 0.00187 [2024-02-19 00:01:06,181 INFO misc.py line 119 87073] Train: [61/100][404/1557] Data 0.006 (0.166) Batch 0.813 (1.107) Remain 19:01:44 loss: 1.2358 Lr: 0.00187 [2024-02-19 00:01:06,887 INFO misc.py line 119 87073] Train: [61/100][405/1557] Data 0.004 (0.165) Batch 0.708 (1.106) Remain 19:00:42 loss: 0.3367 Lr: 0.00187 [2024-02-19 00:01:08,052 INFO misc.py line 119 87073] Train: [61/100][406/1557] Data 0.003 (0.165) Batch 1.154 (1.106) Remain 19:00:48 loss: 0.3348 Lr: 0.00187 [2024-02-19 00:01:08,830 INFO misc.py line 119 87073] Train: [61/100][407/1557] Data 0.014 (0.165) Batch 0.788 (1.105) Remain 18:59:58 loss: 0.2349 Lr: 0.00187 [2024-02-19 00:01:09,824 INFO misc.py line 119 87073] Train: [61/100][408/1557] Data 0.004 (0.164) Batch 0.995 (1.105) Remain 18:59:40 loss: 0.3626 Lr: 0.00187 [2024-02-19 00:01:10,734 INFO misc.py line 119 87073] Train: [61/100][409/1557] Data 0.003 (0.164) Batch 0.910 (1.105) Remain 18:59:09 loss: 0.4888 Lr: 0.00187 [2024-02-19 00:01:11,867 INFO misc.py line 119 87073] Train: [61/100][410/1557] Data 0.003 (0.163) Batch 1.123 (1.105) Remain 18:59:11 loss: 0.5328 Lr: 0.00187 [2024-02-19 00:01:12,584 INFO misc.py line 119 87073] Train: [61/100][411/1557] Data 0.013 (0.163) Batch 0.726 (1.104) Remain 18:58:12 loss: 0.1917 Lr: 0.00187 [2024-02-19 00:01:13,344 INFO misc.py line 119 87073] Train: [61/100][412/1557] Data 0.003 (0.163) Batch 0.754 (1.103) Remain 18:57:18 loss: 0.2139 Lr: 0.00187 [2024-02-19 00:01:14,483 INFO misc.py line 119 87073] Train: [61/100][413/1557] Data 0.009 (0.162) Batch 1.136 (1.103) Remain 18:57:22 loss: 0.1678 Lr: 0.00187 [2024-02-19 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[2024-02-19 00:04:48,083 INFO misc.py line 119 87073] Train: [61/100][607/1557] Data 0.003 (0.157) Batch 0.702 (1.102) Remain 18:53:09 loss: 0.2639 Lr: 0.00186 [2024-02-19 00:04:48,853 INFO misc.py line 119 87073] Train: [61/100][608/1557] Data 0.003 (0.157) Batch 0.762 (1.102) Remain 18:52:33 loss: 0.2039 Lr: 0.00186 [2024-02-19 00:04:50,157 INFO misc.py line 119 87073] Train: [61/100][609/1557] Data 0.011 (0.157) Batch 1.285 (1.102) Remain 18:52:51 loss: 0.1681 Lr: 0.00186 [2024-02-19 00:04:51,075 INFO misc.py line 119 87073] Train: [61/100][610/1557] Data 0.030 (0.156) Batch 0.943 (1.102) Remain 18:52:33 loss: 0.4061 Lr: 0.00186 [2024-02-19 00:04:52,076 INFO misc.py line 119 87073] Train: [61/100][611/1557] Data 0.006 (0.156) Batch 1.004 (1.102) Remain 18:52:22 loss: 0.3353 Lr: 0.00186 [2024-02-19 00:04:52,905 INFO misc.py line 119 87073] Train: [61/100][612/1557] Data 0.003 (0.156) Batch 0.828 (1.101) Remain 18:51:54 loss: 0.4289 Lr: 0.00186 [2024-02-19 00:04:53,893 INFO misc.py 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line 119 87073] Train: [61/100][669/1557] Data 0.003 (0.156) Batch 1.031 (1.099) Remain 18:48:59 loss: 0.7639 Lr: 0.00185 [2024-02-19 00:05:55,234 INFO misc.py line 119 87073] Train: [61/100][670/1557] Data 0.003 (0.156) Batch 0.765 (1.099) Remain 18:48:27 loss: 0.2847 Lr: 0.00185 [2024-02-19 00:05:55,966 INFO misc.py line 119 87073] Train: [61/100][671/1557] Data 0.003 (0.156) Batch 0.721 (1.098) Remain 18:47:51 loss: 0.4667 Lr: 0.00185 [2024-02-19 00:05:57,009 INFO misc.py line 119 87073] Train: [61/100][672/1557] Data 0.014 (0.156) Batch 1.046 (1.098) Remain 18:47:45 loss: 0.1244 Lr: 0.00185 [2024-02-19 00:05:57,906 INFO misc.py line 119 87073] Train: [61/100][673/1557] Data 0.012 (0.155) Batch 0.905 (1.098) Remain 18:47:26 loss: 0.3131 Lr: 0.00185 [2024-02-19 00:05:58,788 INFO misc.py line 119 87073] Train: [61/100][674/1557] Data 0.003 (0.155) Batch 0.881 (1.098) Remain 18:47:05 loss: 0.2705 Lr: 0.00185 [2024-02-19 00:05:59,714 INFO misc.py line 119 87073] Train: 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Batch 0.910 (1.109) Remain 18:58:43 loss: 0.3060 Lr: 0.00185 [2024-02-19 00:06:15,056 INFO misc.py line 119 87073] Train: [61/100][682/1557] Data 0.004 (0.167) Batch 0.823 (1.109) Remain 18:58:16 loss: 0.0802 Lr: 0.00185 [2024-02-19 00:06:16,032 INFO misc.py line 119 87073] Train: [61/100][683/1557] Data 0.004 (0.167) Batch 0.975 (1.109) Remain 18:58:02 loss: 0.1240 Lr: 0.00185 [2024-02-19 00:06:16,729 INFO misc.py line 119 87073] Train: [61/100][684/1557] Data 0.004 (0.167) Batch 0.698 (1.108) Remain 18:57:24 loss: 0.2818 Lr: 0.00185 [2024-02-19 00:06:17,467 INFO misc.py line 119 87073] Train: [61/100][685/1557] Data 0.004 (0.166) Batch 0.739 (1.107) Remain 18:56:50 loss: 0.2987 Lr: 0.00185 [2024-02-19 00:06:18,710 INFO misc.py line 119 87073] Train: [61/100][686/1557] Data 0.003 (0.166) Batch 1.241 (1.108) Remain 18:57:01 loss: 0.2151 Lr: 0.00185 [2024-02-19 00:06:19,780 INFO misc.py line 119 87073] Train: [61/100][687/1557] Data 0.005 (0.166) Batch 1.065 (1.108) Remain 18:56:56 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Batch 1.287 (1.112) Remain 19:00:47 loss: 0.5676 Lr: 0.00185 [2024-02-19 00:07:19,673 INFO misc.py line 119 87073] Train: [61/100][738/1557] Data 0.010 (0.168) Batch 1.097 (1.112) Remain 19:00:44 loss: 0.2172 Lr: 0.00185 [2024-02-19 00:07:20,591 INFO misc.py line 119 87073] Train: [61/100][739/1557] Data 0.011 (0.167) Batch 0.927 (1.112) Remain 19:00:28 loss: 0.1614 Lr: 0.00185 [2024-02-19 00:07:21,296 INFO misc.py line 119 87073] Train: [61/100][740/1557] Data 0.003 (0.167) Batch 0.705 (1.111) Remain 18:59:53 loss: 0.3489 Lr: 0.00185 [2024-02-19 00:07:22,034 INFO misc.py line 119 87073] Train: [61/100][741/1557] Data 0.003 (0.167) Batch 0.731 (1.111) Remain 18:59:20 loss: 0.2346 Lr: 0.00185 [2024-02-19 00:07:23,270 INFO misc.py line 119 87073] Train: [61/100][742/1557] Data 0.010 (0.167) Batch 1.237 (1.111) Remain 18:59:29 loss: 0.1671 Lr: 0.00185 [2024-02-19 00:07:24,251 INFO misc.py line 119 87073] Train: [61/100][743/1557] Data 0.009 (0.167) Batch 0.987 (1.111) Remain 18:59:18 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Batch 1.212 (1.111) Remain 18:58:54 loss: 0.3986 Lr: 0.00185 [2024-02-19 00:08:21,099 INFO misc.py line 119 87073] Train: [61/100][794/1557] Data 0.003 (0.168) Batch 0.893 (1.111) Remain 18:58:36 loss: 0.4739 Lr: 0.00185 [2024-02-19 00:08:22,152 INFO misc.py line 119 87073] Train: [61/100][795/1557] Data 0.004 (0.167) Batch 1.053 (1.111) Remain 18:58:31 loss: 0.3128 Lr: 0.00185 [2024-02-19 00:08:22,944 INFO misc.py line 119 87073] Train: [61/100][796/1557] Data 0.003 (0.167) Batch 0.792 (1.111) Remain 18:58:05 loss: 0.1426 Lr: 0.00185 [2024-02-19 00:08:23,689 INFO misc.py line 119 87073] Train: [61/100][797/1557] Data 0.004 (0.167) Batch 0.737 (1.110) Remain 18:57:35 loss: 0.1445 Lr: 0.00185 [2024-02-19 00:08:24,918 INFO misc.py line 119 87073] Train: [61/100][798/1557] Data 0.011 (0.167) Batch 1.225 (1.110) Remain 18:57:43 loss: 0.3437 Lr: 0.00185 [2024-02-19 00:08:25,868 INFO misc.py line 119 87073] Train: [61/100][799/1557] Data 0.016 (0.167) Batch 0.963 (1.110) Remain 18:57:30 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Batch 1.073 (1.114) Remain 18:59:03 loss: 0.2824 Lr: 0.00184 [2024-02-19 00:10:27,739 INFO misc.py line 119 87073] Train: [61/100][906/1557] Data 0.003 (0.168) Batch 1.103 (1.114) Remain 18:59:01 loss: 0.1313 Lr: 0.00184 [2024-02-19 00:10:28,615 INFO misc.py line 119 87073] Train: [61/100][907/1557] Data 0.007 (0.168) Batch 0.878 (1.113) Remain 18:58:44 loss: 0.2940 Lr: 0.00184 [2024-02-19 00:10:29,456 INFO misc.py line 119 87073] Train: [61/100][908/1557] Data 0.006 (0.168) Batch 0.836 (1.113) Remain 18:58:24 loss: 0.1595 Lr: 0.00184 [2024-02-19 00:10:30,215 INFO misc.py line 119 87073] Train: [61/100][909/1557] Data 0.010 (0.168) Batch 0.765 (1.113) Remain 18:57:59 loss: 0.3082 Lr: 0.00184 [2024-02-19 00:10:31,350 INFO misc.py line 119 87073] Train: [61/100][910/1557] Data 0.004 (0.167) Batch 1.135 (1.113) Remain 18:58:00 loss: 0.1363 Lr: 0.00184 [2024-02-19 00:10:32,364 INFO misc.py line 119 87073] Train: [61/100][911/1557] Data 0.004 (0.167) Batch 1.014 (1.112) Remain 18:57:52 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Batch 1.066 (1.114) Remain 18:58:15 loss: 0.1759 Lr: 0.00184 [2024-02-19 00:11:30,315 INFO misc.py line 119 87073] Train: [61/100][962/1557] Data 0.011 (0.168) Batch 1.080 (1.114) Remain 18:58:12 loss: 2.5232 Lr: 0.00184 [2024-02-19 00:11:31,300 INFO misc.py line 119 87073] Train: [61/100][963/1557] Data 0.014 (0.168) Batch 0.994 (1.114) Remain 18:58:03 loss: 0.3297 Lr: 0.00184 [2024-02-19 00:11:32,038 INFO misc.py line 119 87073] Train: [61/100][964/1557] Data 0.004 (0.168) Batch 0.739 (1.113) Remain 18:57:38 loss: 0.1263 Lr: 0.00184 [2024-02-19 00:11:32,803 INFO misc.py line 119 87073] Train: [61/100][965/1557] Data 0.003 (0.168) Batch 0.760 (1.113) Remain 18:57:15 loss: 0.2589 Lr: 0.00184 [2024-02-19 00:11:33,915 INFO misc.py line 119 87073] Train: [61/100][966/1557] Data 0.007 (0.167) Batch 1.109 (1.113) Remain 18:57:13 loss: 0.4430 Lr: 0.00184 [2024-02-19 00:11:34,904 INFO misc.py line 119 87073] Train: [61/100][967/1557] Data 0.011 (0.167) Batch 0.997 (1.113) Remain 18:57:05 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(0.168) Batch 1.025 (1.115) Remain 18:58:04 loss: 0.3093 Lr: 0.00184 [2024-02-19 00:12:33,309 INFO misc.py line 119 87073] Train: [61/100][1018/1557] Data 0.006 (0.168) Batch 0.871 (1.114) Remain 18:57:48 loss: 0.2795 Lr: 0.00184 [2024-02-19 00:12:34,388 INFO misc.py line 119 87073] Train: [61/100][1019/1557] Data 0.004 (0.168) Batch 1.075 (1.114) Remain 18:57:45 loss: 0.5174 Lr: 0.00184 [2024-02-19 00:12:35,156 INFO misc.py line 119 87073] Train: [61/100][1020/1557] Data 0.008 (0.167) Batch 0.772 (1.114) Remain 18:57:23 loss: 0.1755 Lr: 0.00184 [2024-02-19 00:12:35,857 INFO misc.py line 119 87073] Train: [61/100][1021/1557] Data 0.005 (0.167) Batch 0.693 (1.114) Remain 18:56:56 loss: 0.5413 Lr: 0.00184 [2024-02-19 00:12:37,062 INFO misc.py line 119 87073] Train: [61/100][1022/1557] Data 0.012 (0.167) Batch 1.201 (1.114) Remain 18:57:01 loss: 0.3842 Lr: 0.00184 [2024-02-19 00:12:38,008 INFO misc.py line 119 87073] Train: [61/100][1023/1557] Data 0.017 (0.167) Batch 0.957 (1.114) Remain 18:56:50 loss: 0.2794 Lr: 0.00184 [2024-02-19 00:12:38,867 INFO misc.py line 119 87073] Train: [61/100][1024/1557] Data 0.005 (0.167) Batch 0.860 (1.113) Remain 18:56:34 loss: 0.1966 Lr: 0.00184 [2024-02-19 00:12:39,756 INFO misc.py line 119 87073] Train: [61/100][1025/1557] Data 0.004 (0.167) Batch 0.884 (1.113) Remain 18:56:19 loss: 0.3809 Lr: 0.00184 [2024-02-19 00:12:40,604 INFO misc.py line 119 87073] Train: [61/100][1026/1557] Data 0.009 (0.166) Batch 0.851 (1.113) Remain 18:56:02 loss: 0.1584 Lr: 0.00184 [2024-02-19 00:12:41,337 INFO misc.py line 119 87073] Train: [61/100][1027/1557] Data 0.006 (0.166) Batch 0.733 (1.112) Remain 18:55:38 loss: 0.2664 Lr: 0.00184 [2024-02-19 00:12:42,009 INFO misc.py line 119 87073] Train: [61/100][1028/1557] Data 0.006 (0.166) Batch 0.665 (1.112) Remain 18:55:10 loss: 0.1622 Lr: 0.00184 [2024-02-19 00:12:43,216 INFO misc.py line 119 87073] Train: [61/100][1029/1557] Data 0.013 (0.166) Batch 1.207 (1.112) Remain 18:55:15 loss: 0.1089 Lr: 0.00184 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[2024-02-19 00:13:14,824 INFO misc.py line 119 87073] Train: [61/100][1061/1557] Data 0.007 (0.162) Batch 0.945 (1.108) Remain 18:50:49 loss: 0.1796 Lr: 0.00183 [2024-02-19 00:13:15,592 INFO misc.py line 119 87073] Train: [61/100][1062/1557] Data 0.011 (0.162) Batch 0.770 (1.108) Remain 18:50:29 loss: 0.3374 Lr: 0.00183 [2024-02-19 00:13:16,286 INFO misc.py line 119 87073] Train: [61/100][1063/1557] Data 0.007 (0.162) Batch 0.688 (1.108) Remain 18:50:03 loss: 0.2346 Lr: 0.00183 [2024-02-19 00:13:17,324 INFO misc.py line 119 87073] Train: [61/100][1064/1557] Data 0.014 (0.162) Batch 1.015 (1.108) Remain 18:49:57 loss: 0.1145 Lr: 0.00183 [2024-02-19 00:13:18,215 INFO misc.py line 119 87073] Train: [61/100][1065/1557] Data 0.037 (0.162) Batch 0.922 (1.107) Remain 18:49:45 loss: 0.1041 Lr: 0.00183 [2024-02-19 00:13:19,230 INFO misc.py line 119 87073] Train: [61/100][1066/1557] Data 0.006 (0.161) Batch 1.015 (1.107) Remain 18:49:39 loss: 0.0675 Lr: 0.00183 [2024-02-19 00:13:20,162 INFO 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(0.168) Batch 0.826 (1.114) Remain 18:56:13 loss: 0.3433 Lr: 0.00183 [2024-02-19 00:13:41,891 INFO misc.py line 119 87073] Train: [61/100][1080/1557] Data 0.005 (0.168) Batch 1.078 (1.114) Remain 18:56:10 loss: 0.4870 Lr: 0.00183 [2024-02-19 00:13:42,727 INFO misc.py line 119 87073] Train: [61/100][1081/1557] Data 0.006 (0.168) Batch 0.836 (1.114) Remain 18:55:53 loss: 0.1860 Lr: 0.00183 [2024-02-19 00:13:43,771 INFO misc.py line 119 87073] Train: [61/100][1082/1557] Data 0.005 (0.168) Batch 1.043 (1.114) Remain 18:55:48 loss: 0.3015 Lr: 0.00183 [2024-02-19 00:13:44,615 INFO misc.py line 119 87073] Train: [61/100][1083/1557] Data 0.006 (0.168) Batch 0.845 (1.113) Remain 18:55:32 loss: 0.2651 Lr: 0.00183 [2024-02-19 00:13:45,374 INFO misc.py line 119 87073] Train: [61/100][1084/1557] Data 0.004 (0.168) Batch 0.760 (1.113) Remain 18:55:11 loss: 0.2788 Lr: 0.00183 [2024-02-19 00:13:46,617 INFO misc.py line 119 87073] Train: [61/100][1085/1557] Data 0.003 (0.168) Batch 1.232 (1.113) Remain 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[2024-02-19 00:13:53,283 INFO misc.py line 119 87073] Train: [61/100][1092/1557] Data 0.005 (0.166) Batch 1.220 (1.112) Remain 18:54:06 loss: 0.3720 Lr: 0.00183 [2024-02-19 00:13:54,454 INFO misc.py line 119 87073] Train: [61/100][1093/1557] Data 0.004 (0.166) Batch 1.161 (1.112) Remain 18:54:07 loss: 0.4254 Lr: 0.00183 [2024-02-19 00:13:55,407 INFO misc.py line 119 87073] Train: [61/100][1094/1557] Data 0.015 (0.166) Batch 0.963 (1.112) Remain 18:53:58 loss: 0.1868 Lr: 0.00183 [2024-02-19 00:13:56,347 INFO misc.py line 119 87073] Train: [61/100][1095/1557] Data 0.005 (0.166) Batch 0.941 (1.112) Remain 18:53:47 loss: 0.2340 Lr: 0.00183 [2024-02-19 00:13:57,163 INFO misc.py line 119 87073] Train: [61/100][1096/1557] Data 0.004 (0.166) Batch 0.815 (1.112) Remain 18:53:29 loss: 0.6249 Lr: 0.00183 [2024-02-19 00:13:57,887 INFO misc.py line 119 87073] Train: [61/100][1097/1557] Data 0.003 (0.166) Batch 0.721 (1.111) Remain 18:53:07 loss: 0.4488 Lr: 0.00183 [2024-02-19 00:13:58,603 INFO misc.py line 119 87073] Train: [61/100][1098/1557] Data 0.006 (0.166) Batch 0.719 (1.111) Remain 18:52:43 loss: 0.3597 Lr: 0.00183 [2024-02-19 00:13:59,885 INFO misc.py line 119 87073] Train: [61/100][1099/1557] Data 0.004 (0.165) Batch 1.282 (1.111) Remain 18:52:52 loss: 0.1311 Lr: 0.00183 [2024-02-19 00:14:00,834 INFO misc.py line 119 87073] Train: [61/100][1100/1557] Data 0.003 (0.165) Batch 0.949 (1.111) Remain 18:52:42 loss: 0.3632 Lr: 0.00183 [2024-02-19 00:14:01,953 INFO misc.py line 119 87073] Train: [61/100][1101/1557] Data 0.004 (0.165) Batch 1.119 (1.111) Remain 18:52:41 loss: 0.3836 Lr: 0.00183 [2024-02-19 00:14:03,109 INFO misc.py line 119 87073] Train: [61/100][1102/1557] Data 0.003 (0.165) Batch 1.156 (1.111) Remain 18:52:43 loss: 0.4979 Lr: 0.00183 [2024-02-19 00:14:04,189 INFO misc.py line 119 87073] Train: [61/100][1103/1557] Data 0.004 (0.165) Batch 1.080 (1.111) Remain 18:52:40 loss: 0.2188 Lr: 0.00183 [2024-02-19 00:14:04,969 INFO misc.py line 119 87073] Train: 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[2024-02-19 00:14:22,041 INFO misc.py line 119 87073] Train: [61/100][1123/1557] Data 0.005 (0.162) Batch 0.932 (1.107) Remain 18:48:19 loss: 0.6123 Lr: 0.00183 [2024-02-19 00:14:22,937 INFO misc.py line 119 87073] Train: [61/100][1124/1557] Data 0.005 (0.162) Batch 0.894 (1.107) Remain 18:48:06 loss: 0.6118 Lr: 0.00183 [2024-02-19 00:14:23,691 INFO misc.py line 119 87073] Train: [61/100][1125/1557] Data 0.007 (0.162) Batch 0.757 (1.106) Remain 18:47:46 loss: 0.1662 Lr: 0.00183 [2024-02-19 00:14:24,444 INFO misc.py line 119 87073] Train: [61/100][1126/1557] Data 0.004 (0.162) Batch 0.747 (1.106) Remain 18:47:25 loss: 0.2661 Lr: 0.00183 [2024-02-19 00:14:34,806 INFO misc.py line 119 87073] Train: [61/100][1127/1557] Data 9.437 (0.170) Batch 10.367 (1.114) Remain 18:55:48 loss: 0.2685 Lr: 0.00183 [2024-02-19 00:14:35,877 INFO misc.py line 119 87073] Train: [61/100][1128/1557] Data 0.006 (0.170) Batch 1.066 (1.114) Remain 18:55:44 loss: 0.7174 Lr: 0.00183 [2024-02-19 00:14:36,948 INFO 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(0.168) Batch 1.215 (1.112) Remain 18:53:16 loss: 0.1416 Lr: 0.00183 [2024-02-19 00:14:48,957 INFO misc.py line 119 87073] Train: [61/100][1142/1557] Data 0.014 (0.168) Batch 1.093 (1.112) Remain 18:53:13 loss: 0.5735 Lr: 0.00183 [2024-02-19 00:14:50,231 INFO misc.py line 119 87073] Train: [61/100][1143/1557] Data 0.013 (0.168) Batch 1.274 (1.112) Remain 18:53:21 loss: 0.6855 Lr: 0.00183 [2024-02-19 00:14:51,278 INFO misc.py line 119 87073] Train: [61/100][1144/1557] Data 0.012 (0.167) Batch 1.045 (1.112) Remain 18:53:16 loss: 0.4644 Lr: 0.00183 [2024-02-19 00:14:52,199 INFO misc.py line 119 87073] Train: [61/100][1145/1557] Data 0.014 (0.167) Batch 0.931 (1.112) Remain 18:53:05 loss: 0.4149 Lr: 0.00183 [2024-02-19 00:14:52,962 INFO misc.py line 119 87073] Train: [61/100][1146/1557] Data 0.003 (0.167) Batch 0.762 (1.112) Remain 18:52:46 loss: 0.1323 Lr: 0.00183 [2024-02-19 00:14:53,701 INFO misc.py line 119 87073] Train: [61/100][1147/1557] Data 0.006 (0.167) Batch 0.734 (1.111) Remain 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[2024-02-19 00:15:00,095 INFO misc.py line 119 87073] Train: [61/100][1154/1557] Data 0.010 (0.166) Batch 0.783 (1.110) Remain 18:51:03 loss: 0.2577 Lr: 0.00183 [2024-02-19 00:15:01,515 INFO misc.py line 119 87073] Train: [61/100][1155/1557] Data 0.005 (0.166) Batch 1.417 (1.110) Remain 18:51:18 loss: 0.1295 Lr: 0.00183 [2024-02-19 00:15:02,412 INFO misc.py line 119 87073] Train: [61/100][1156/1557] Data 0.009 (0.166) Batch 0.901 (1.110) Remain 18:51:06 loss: 0.3732 Lr: 0.00183 [2024-02-19 00:15:03,441 INFO misc.py line 119 87073] Train: [61/100][1157/1557] Data 0.004 (0.166) Batch 1.030 (1.110) Remain 18:51:01 loss: 0.0821 Lr: 0.00183 [2024-02-19 00:15:04,301 INFO misc.py line 119 87073] Train: [61/100][1158/1557] Data 0.004 (0.165) Batch 0.859 (1.110) Remain 18:50:46 loss: 0.5751 Lr: 0.00183 [2024-02-19 00:15:05,331 INFO misc.py line 119 87073] Train: [61/100][1159/1557] Data 0.004 (0.165) Batch 1.022 (1.110) Remain 18:50:41 loss: 0.3688 Lr: 0.00183 [2024-02-19 00:15:06,084 INFO 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18:47:12 loss: 0.4474 Lr: 0.00183 [2024-02-19 00:15:23,652 INFO misc.py line 119 87073] Train: [61/100][1179/1557] Data 0.004 (0.163) Batch 0.828 (1.107) Remain 18:46:57 loss: 0.4075 Lr: 0.00183 [2024-02-19 00:15:24,542 INFO misc.py line 119 87073] Train: [61/100][1180/1557] Data 0.015 (0.163) Batch 0.901 (1.106) Remain 18:46:45 loss: 0.0859 Lr: 0.00183 [2024-02-19 00:15:25,324 INFO misc.py line 119 87073] Train: [61/100][1181/1557] Data 0.003 (0.162) Batch 0.780 (1.106) Remain 18:46:27 loss: 0.2008 Lr: 0.00183 [2024-02-19 00:15:26,019 INFO misc.py line 119 87073] Train: [61/100][1182/1557] Data 0.006 (0.162) Batch 0.688 (1.106) Remain 18:46:04 loss: 0.1826 Lr: 0.00183 [2024-02-19 00:15:35,940 INFO misc.py line 119 87073] Train: [61/100][1183/1557] Data 8.938 (0.170) Batch 9.930 (1.113) Remain 18:53:40 loss: 0.1856 Lr: 0.00183 [2024-02-19 00:15:36,805 INFO misc.py line 119 87073] Train: [61/100][1184/1557] Data 0.004 (0.170) Batch 0.865 (1.113) Remain 18:53:26 loss: 0.2522 Lr: 0.00183 [2024-02-19 00:15:37,737 INFO misc.py line 119 87073] Train: [61/100][1185/1557] Data 0.004 (0.169) Batch 0.923 (1.113) Remain 18:53:15 loss: 0.3485 Lr: 0.00183 [2024-02-19 00:15:38,877 INFO misc.py line 119 87073] Train: [61/100][1186/1557] Data 0.012 (0.169) Batch 1.141 (1.113) Remain 18:53:15 loss: 0.4479 Lr: 0.00183 [2024-02-19 00:15:39,917 INFO misc.py line 119 87073] Train: [61/100][1187/1557] Data 0.012 (0.169) Batch 1.046 (1.113) Remain 18:53:11 loss: 0.2117 Lr: 0.00183 [2024-02-19 00:15:40,638 INFO misc.py line 119 87073] Train: [61/100][1188/1557] Data 0.007 (0.169) Batch 0.722 (1.113) Remain 18:52:50 loss: 0.4190 Lr: 0.00183 [2024-02-19 00:15:41,409 INFO misc.py line 119 87073] Train: [61/100][1189/1557] Data 0.005 (0.169) Batch 0.763 (1.112) Remain 18:52:30 loss: 0.1906 Lr: 0.00183 [2024-02-19 00:15:42,599 INFO misc.py line 119 87073] Train: [61/100][1190/1557] Data 0.012 (0.169) Batch 1.189 (1.112) Remain 18:52:33 loss: 0.2848 Lr: 0.00183 [2024-02-19 00:15:43,484 INFO misc.py line 119 87073] Train: [61/100][1191/1557] Data 0.014 (0.169) Batch 0.894 (1.112) Remain 18:52:21 loss: 0.3424 Lr: 0.00183 [2024-02-19 00:15:44,490 INFO misc.py line 119 87073] Train: [61/100][1192/1557] Data 0.004 (0.169) Batch 1.007 (1.112) Remain 18:52:14 loss: 0.2719 Lr: 0.00183 [2024-02-19 00:15:45,547 INFO misc.py line 119 87073] Train: [61/100][1193/1557] Data 0.003 (0.168) Batch 1.057 (1.112) Remain 18:52:11 loss: 0.2484 Lr: 0.00183 [2024-02-19 00:15:46,444 INFO misc.py line 119 87073] Train: [61/100][1194/1557] Data 0.003 (0.168) Batch 0.897 (1.112) Remain 18:51:58 loss: 0.2074 Lr: 0.00183 [2024-02-19 00:15:47,220 INFO misc.py line 119 87073] Train: [61/100][1195/1557] Data 0.003 (0.168) Batch 0.763 (1.112) Remain 18:51:39 loss: 0.2543 Lr: 0.00183 [2024-02-19 00:15:47,919 INFO misc.py line 119 87073] Train: [61/100][1196/1557] Data 0.016 (0.168) Batch 0.711 (1.111) Remain 18:51:18 loss: 0.4476 Lr: 0.00183 [2024-02-19 00:15:49,125 INFO misc.py line 119 87073] Train: 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(0.167) Batch 0.655 (1.110) Remain 18:50:06 loss: 0.1009 Lr: 0.00183 [2024-02-19 00:15:55,711 INFO misc.py line 119 87073] Train: [61/100][1204/1557] Data 0.008 (0.167) Batch 1.255 (1.110) Remain 18:50:13 loss: 0.1078 Lr: 0.00183 [2024-02-19 00:15:56,646 INFO misc.py line 119 87073] Train: [61/100][1205/1557] Data 0.012 (0.167) Batch 0.944 (1.110) Remain 18:50:03 loss: 0.1393 Lr: 0.00183 [2024-02-19 00:15:57,425 INFO misc.py line 119 87073] Train: [61/100][1206/1557] Data 0.003 (0.167) Batch 0.777 (1.110) Remain 18:49:45 loss: 0.1421 Lr: 0.00183 [2024-02-19 00:15:58,492 INFO misc.py line 119 87073] Train: [61/100][1207/1557] Data 0.005 (0.167) Batch 1.060 (1.110) Remain 18:49:41 loss: 0.5013 Lr: 0.00183 [2024-02-19 00:15:59,528 INFO misc.py line 119 87073] Train: [61/100][1208/1557] Data 0.011 (0.166) Batch 1.043 (1.110) Remain 18:49:37 loss: 0.1437 Lr: 0.00183 [2024-02-19 00:16:00,204 INFO misc.py line 119 87073] Train: [61/100][1209/1557] Data 0.005 (0.166) Batch 0.677 (1.109) Remain 18:49:14 loss: 0.3562 Lr: 0.00183 [2024-02-19 00:16:00,932 INFO misc.py line 119 87073] Train: [61/100][1210/1557] Data 0.003 (0.166) Batch 0.721 (1.109) Remain 18:48:53 loss: 0.2328 Lr: 0.00183 [2024-02-19 00:16:02,218 INFO misc.py line 119 87073] Train: [61/100][1211/1557] Data 0.010 (0.166) Batch 1.287 (1.109) Remain 18:49:01 loss: 0.1774 Lr: 0.00183 [2024-02-19 00:16:03,135 INFO misc.py line 119 87073] Train: [61/100][1212/1557] Data 0.009 (0.166) Batch 0.923 (1.109) Remain 18:48:51 loss: 0.1763 Lr: 0.00183 [2024-02-19 00:16:04,037 INFO misc.py line 119 87073] Train: [61/100][1213/1557] Data 0.003 (0.166) Batch 0.900 (1.109) Remain 18:48:39 loss: 0.2383 Lr: 0.00183 [2024-02-19 00:16:05,046 INFO misc.py line 119 87073] Train: [61/100][1214/1557] Data 0.006 (0.166) Batch 1.009 (1.109) Remain 18:48:33 loss: 0.2389 Lr: 0.00183 [2024-02-19 00:16:06,368 INFO misc.py line 119 87073] Train: [61/100][1215/1557] Data 0.005 (0.165) Batch 1.323 (1.109) Remain 18:48:42 loss: 0.2146 Lr: 0.00183 [2024-02-19 00:16:07,123 INFO misc.py line 119 87073] Train: [61/100][1216/1557] Data 0.003 (0.165) Batch 0.755 (1.109) Remain 18:48:23 loss: 0.3306 Lr: 0.00183 [2024-02-19 00:16:07,889 INFO misc.py line 119 87073] Train: [61/100][1217/1557] Data 0.003 (0.165) Batch 0.762 (1.108) Remain 18:48:05 loss: 0.2516 Lr: 0.00183 [2024-02-19 00:16:09,000 INFO misc.py line 119 87073] Train: [61/100][1218/1557] Data 0.007 (0.165) Batch 1.107 (1.108) Remain 18:48:04 loss: 0.1556 Lr: 0.00183 [2024-02-19 00:16:09,815 INFO misc.py line 119 87073] Train: [61/100][1219/1557] Data 0.012 (0.165) Batch 0.822 (1.108) Remain 18:47:48 loss: 0.3342 Lr: 0.00183 [2024-02-19 00:16:10,847 INFO misc.py line 119 87073] Train: [61/100][1220/1557] Data 0.004 (0.165) Batch 1.033 (1.108) Remain 18:47:43 loss: 0.7202 Lr: 0.00183 [2024-02-19 00:16:11,745 INFO misc.py line 119 87073] Train: [61/100][1221/1557] Data 0.003 (0.165) Batch 0.897 (1.108) Remain 18:47:32 loss: 0.4168 Lr: 0.00183 [2024-02-19 00:16:12,559 INFO misc.py line 119 87073] Train: [61/100][1222/1557] Data 0.004 (0.165) Batch 0.809 (1.108) Remain 18:47:16 loss: 0.2177 Lr: 0.00183 [2024-02-19 00:16:13,326 INFO misc.py line 119 87073] Train: [61/100][1223/1557] Data 0.009 (0.164) Batch 0.771 (1.107) Remain 18:46:58 loss: 0.1428 Lr: 0.00183 [2024-02-19 00:16:14,096 INFO misc.py line 119 87073] Train: [61/100][1224/1557] Data 0.004 (0.164) Batch 0.765 (1.107) Remain 18:46:39 loss: 0.2898 Lr: 0.00183 [2024-02-19 00:16:15,379 INFO misc.py line 119 87073] Train: [61/100][1225/1557] Data 0.010 (0.164) Batch 1.283 (1.107) Remain 18:46:47 loss: 0.2765 Lr: 0.00183 [2024-02-19 00:16:16,406 INFO misc.py line 119 87073] Train: [61/100][1226/1557] Data 0.009 (0.164) Batch 1.022 (1.107) Remain 18:46:42 loss: 0.3848 Lr: 0.00183 [2024-02-19 00:16:17,426 INFO misc.py line 119 87073] Train: [61/100][1227/1557] Data 0.014 (0.164) Batch 1.021 (1.107) Remain 18:46:36 loss: 0.4851 Lr: 0.00183 [2024-02-19 00:16:18,332 INFO misc.py line 119 87073] Train: 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(0.164) Batch 0.923 (1.108) Remain 18:46:49 loss: 0.4781 Lr: 0.00183 [2024-02-19 00:16:26,579 INFO misc.py line 119 87073] Train: [61/100][1235/1557] Data 0.003 (0.164) Batch 1.007 (1.107) Remain 18:46:43 loss: 0.2300 Lr: 0.00183 [2024-02-19 00:16:27,488 INFO misc.py line 119 87073] Train: [61/100][1236/1557] Data 0.003 (0.164) Batch 0.907 (1.107) Remain 18:46:32 loss: 0.2733 Lr: 0.00183 [2024-02-19 00:16:28,260 INFO misc.py line 119 87073] Train: [61/100][1237/1557] Data 0.004 (0.164) Batch 0.770 (1.107) Remain 18:46:14 loss: 0.1623 Lr: 0.00183 [2024-02-19 00:16:28,990 INFO misc.py line 119 87073] Train: [61/100][1238/1557] Data 0.007 (0.164) Batch 0.732 (1.107) Remain 18:45:54 loss: 0.2271 Lr: 0.00182 [2024-02-19 00:16:39,503 INFO misc.py line 119 87073] Train: [61/100][1239/1557] Data 8.198 (0.170) Batch 10.514 (1.114) Remain 18:53:38 loss: 0.1566 Lr: 0.00182 [2024-02-19 00:16:40,540 INFO misc.py line 119 87073] Train: [61/100][1240/1557] Data 0.003 (0.170) Batch 1.038 (1.114) Remain 18:53:33 loss: 0.2440 Lr: 0.00182 [2024-02-19 00:16:41,534 INFO misc.py line 119 87073] Train: [61/100][1241/1557] Data 0.003 (0.170) Batch 0.993 (1.114) Remain 18:53:26 loss: 0.2447 Lr: 0.00182 [2024-02-19 00:16:42,464 INFO misc.py line 119 87073] Train: [61/100][1242/1557] Data 0.003 (0.170) Batch 0.930 (1.114) Remain 18:53:16 loss: 0.3393 Lr: 0.00182 [2024-02-19 00:16:43,336 INFO misc.py line 119 87073] Train: [61/100][1243/1557] Data 0.004 (0.170) Batch 0.864 (1.114) Remain 18:53:02 loss: 0.1963 Lr: 0.00182 [2024-02-19 00:16:44,025 INFO misc.py line 119 87073] Train: [61/100][1244/1557] Data 0.012 (0.169) Batch 0.697 (1.113) Remain 18:52:41 loss: 0.2302 Lr: 0.00182 [2024-02-19 00:16:44,813 INFO misc.py line 119 87073] Train: [61/100][1245/1557] Data 0.003 (0.169) Batch 0.778 (1.113) Remain 18:52:23 loss: 0.1900 Lr: 0.00182 [2024-02-19 00:16:46,021 INFO misc.py line 119 87073] Train: [61/100][1246/1557] Data 0.013 (0.169) Batch 1.207 (1.113) Remain 18:52:26 loss: 0.3114 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1253/1557] Data 0.012 (0.168) Batch 1.176 (1.112) Remain 18:51:05 loss: 0.2997 Lr: 0.00182 [2024-02-19 00:16:53,521 INFO misc.py line 119 87073] Train: [61/100][1254/1557] Data 0.044 (0.168) Batch 1.210 (1.112) Remain 18:51:09 loss: 0.0801 Lr: 0.00182 [2024-02-19 00:16:54,468 INFO misc.py line 119 87073] Train: [61/100][1255/1557] Data 0.012 (0.168) Batch 0.956 (1.112) Remain 18:51:00 loss: 0.2910 Lr: 0.00182 [2024-02-19 00:16:55,588 INFO misc.py line 119 87073] Train: [61/100][1256/1557] Data 0.003 (0.168) Batch 1.120 (1.112) Remain 18:51:00 loss: 0.3686 Lr: 0.00182 [2024-02-19 00:16:56,440 INFO misc.py line 119 87073] Train: [61/100][1257/1557] Data 0.003 (0.168) Batch 0.852 (1.112) Remain 18:50:46 loss: 0.1850 Lr: 0.00182 [2024-02-19 00:16:57,324 INFO misc.py line 119 87073] Train: [61/100][1258/1557] Data 0.003 (0.168) Batch 0.875 (1.112) Remain 18:50:33 loss: 0.1607 Lr: 0.00182 [2024-02-19 00:16:58,082 INFO misc.py line 119 87073] Train: [61/100][1259/1557] Data 0.011 (0.167) Batch 0.767 (1.111) Remain 18:50:15 loss: 0.2025 Lr: 0.00182 [2024-02-19 00:16:59,357 INFO misc.py line 119 87073] Train: [61/100][1260/1557] Data 0.002 (0.167) Batch 1.263 (1.111) Remain 18:50:22 loss: 0.2972 Lr: 0.00182 [2024-02-19 00:17:00,411 INFO misc.py line 119 87073] Train: [61/100][1261/1557] Data 0.015 (0.167) Batch 1.056 (1.111) Remain 18:50:18 loss: 0.4687 Lr: 0.00182 [2024-02-19 00:17:01,464 INFO misc.py line 119 87073] Train: [61/100][1262/1557] Data 0.013 (0.167) Batch 1.054 (1.111) Remain 18:50:14 loss: 0.0731 Lr: 0.00182 [2024-02-19 00:17:02,572 INFO misc.py line 119 87073] Train: [61/100][1263/1557] Data 0.012 (0.167) Batch 1.102 (1.111) Remain 18:50:12 loss: 0.4591 Lr: 0.00182 [2024-02-19 00:17:03,605 INFO misc.py line 119 87073] Train: [61/100][1264/1557] Data 0.016 (0.167) Batch 1.035 (1.111) Remain 18:50:08 loss: 0.5234 Lr: 0.00182 [2024-02-19 00:17:04,298 INFO misc.py line 119 87073] Train: [61/100][1265/1557] Data 0.015 (0.167) Batch 0.705 (1.111) Remain 18:49:47 loss: 0.2366 Lr: 0.00182 [2024-02-19 00:17:05,032 INFO misc.py line 119 87073] Train: [61/100][1266/1557] Data 0.003 (0.167) Batch 0.727 (1.111) Remain 18:49:27 loss: 0.2510 Lr: 0.00182 [2024-02-19 00:17:06,335 INFO misc.py line 119 87073] Train: [61/100][1267/1557] Data 0.010 (0.167) Batch 1.299 (1.111) Remain 18:49:35 loss: 0.1124 Lr: 0.00182 [2024-02-19 00:17:07,193 INFO misc.py line 119 87073] Train: [61/100][1268/1557] Data 0.013 (0.166) Batch 0.868 (1.111) Remain 18:49:22 loss: 0.3593 Lr: 0.00182 [2024-02-19 00:17:08,294 INFO misc.py line 119 87073] Train: [61/100][1269/1557] Data 0.003 (0.166) Batch 1.101 (1.111) Remain 18:49:21 loss: 0.2227 Lr: 0.00182 [2024-02-19 00:17:09,188 INFO misc.py line 119 87073] Train: [61/100][1270/1557] Data 0.003 (0.166) Batch 0.894 (1.110) Remain 18:49:09 loss: 0.3387 Lr: 0.00182 [2024-02-19 00:17:10,136 INFO misc.py line 119 87073] Train: [61/100][1271/1557] Data 0.003 (0.166) Batch 0.938 (1.110) Remain 18:49:00 loss: 0.2190 Lr: 0.00182 [2024-02-19 00:17:10,852 INFO misc.py line 119 87073] Train: [61/100][1272/1557] Data 0.013 (0.166) Batch 0.725 (1.110) Remain 18:48:40 loss: 0.3971 Lr: 0.00182 [2024-02-19 00:17:11,610 INFO misc.py line 119 87073] Train: [61/100][1273/1557] Data 0.005 (0.166) Batch 0.750 (1.110) Remain 18:48:22 loss: 0.1327 Lr: 0.00182 [2024-02-19 00:17:12,670 INFO misc.py line 119 87073] Train: [61/100][1274/1557] Data 0.013 (0.166) Batch 1.056 (1.110) Remain 18:48:18 loss: 0.1538 Lr: 0.00182 [2024-02-19 00:17:13,601 INFO misc.py line 119 87073] Train: [61/100][1275/1557] Data 0.016 (0.166) Batch 0.944 (1.110) Remain 18:48:09 loss: 0.4394 Lr: 0.00182 [2024-02-19 00:17:14,426 INFO misc.py line 119 87073] Train: [61/100][1276/1557] Data 0.003 (0.165) Batch 0.824 (1.109) Remain 18:47:54 loss: 0.4051 Lr: 0.00182 [2024-02-19 00:17:15,366 INFO misc.py line 119 87073] Train: [61/100][1277/1557] Data 0.003 (0.165) Batch 0.930 (1.109) Remain 18:47:45 loss: 0.4475 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1284/1557] Data 0.003 (0.164) Batch 0.941 (1.108) Remain 18:46:44 loss: 0.2588 Lr: 0.00182 [2024-02-19 00:17:22,982 INFO misc.py line 119 87073] Train: [61/100][1285/1557] Data 0.004 (0.164) Batch 0.967 (1.108) Remain 18:46:36 loss: 0.3511 Lr: 0.00182 [2024-02-19 00:17:23,761 INFO misc.py line 119 87073] Train: [61/100][1286/1557] Data 0.006 (0.164) Batch 0.782 (1.108) Remain 18:46:20 loss: 0.1413 Lr: 0.00182 [2024-02-19 00:17:24,471 INFO misc.py line 119 87073] Train: [61/100][1287/1557] Data 0.003 (0.164) Batch 0.704 (1.108) Remain 18:45:59 loss: 0.3387 Lr: 0.00182 [2024-02-19 00:17:25,489 INFO misc.py line 119 87073] Train: [61/100][1288/1557] Data 0.009 (0.164) Batch 1.016 (1.108) Remain 18:45:54 loss: 0.1033 Lr: 0.00182 [2024-02-19 00:17:26,408 INFO misc.py line 119 87073] Train: [61/100][1289/1557] Data 0.011 (0.164) Batch 0.927 (1.107) Remain 18:45:44 loss: 0.1553 Lr: 0.00182 [2024-02-19 00:17:27,265 INFO misc.py line 119 87073] Train: [61/100][1290/1557] Data 0.003 (0.164) Batch 0.857 (1.107) Remain 18:45:31 loss: 0.2723 Lr: 0.00182 [2024-02-19 00:17:28,379 INFO misc.py line 119 87073] Train: [61/100][1291/1557] Data 0.003 (0.164) Batch 1.113 (1.107) Remain 18:45:30 loss: 0.1217 Lr: 0.00182 [2024-02-19 00:17:29,269 INFO misc.py line 119 87073] Train: [61/100][1292/1557] Data 0.004 (0.163) Batch 0.890 (1.107) Remain 18:45:19 loss: 0.5125 Lr: 0.00182 [2024-02-19 00:17:30,128 INFO misc.py line 119 87073] Train: [61/100][1293/1557] Data 0.004 (0.163) Batch 0.859 (1.107) Remain 18:45:06 loss: 0.5177 Lr: 0.00182 [2024-02-19 00:17:30,892 INFO misc.py line 119 87073] Train: [61/100][1294/1557] Data 0.004 (0.163) Batch 0.760 (1.107) Remain 18:44:49 loss: 0.0787 Lr: 0.00182 [2024-02-19 00:17:41,140 INFO misc.py line 119 87073] Train: [61/100][1295/1557] Data 8.378 (0.170) Batch 10.245 (1.114) Remain 18:51:59 loss: 0.1410 Lr: 0.00182 [2024-02-19 00:17:42,202 INFO misc.py line 119 87073] Train: [61/100][1296/1557] Data 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Remain 18:51:06 loss: 0.2145 Lr: 0.00182 [2024-02-19 00:17:48,905 INFO misc.py line 119 87073] Train: [61/100][1303/1557] Data 0.013 (0.169) Batch 0.941 (1.113) Remain 18:50:57 loss: 0.1947 Lr: 0.00182 [2024-02-19 00:17:49,778 INFO misc.py line 119 87073] Train: [61/100][1304/1557] Data 0.003 (0.168) Batch 0.873 (1.113) Remain 18:50:44 loss: 0.6267 Lr: 0.00182 [2024-02-19 00:17:50,792 INFO misc.py line 119 87073] Train: [61/100][1305/1557] Data 0.004 (0.168) Batch 1.004 (1.113) Remain 18:50:38 loss: 0.2468 Lr: 0.00182 [2024-02-19 00:17:51,805 INFO misc.py line 119 87073] Train: [61/100][1306/1557] Data 0.013 (0.168) Batch 1.014 (1.112) Remain 18:50:32 loss: 0.5268 Lr: 0.00182 [2024-02-19 00:17:52,551 INFO misc.py line 119 87073] Train: [61/100][1307/1557] Data 0.013 (0.168) Batch 0.756 (1.112) Remain 18:50:15 loss: 0.2217 Lr: 0.00182 [2024-02-19 00:17:53,327 INFO misc.py line 119 87073] Train: [61/100][1308/1557] Data 0.003 (0.168) Batch 0.775 (1.112) Remain 18:49:58 loss: 0.1945 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1315/1557] Data 0.003 (0.167) Batch 0.803 (1.111) Remain 18:48:42 loss: 0.3562 Lr: 0.00182 [2024-02-19 00:18:00,883 INFO misc.py line 119 87073] Train: [61/100][1316/1557] Data 0.013 (0.167) Batch 1.227 (1.111) Remain 18:48:46 loss: 0.1942 Lr: 0.00182 [2024-02-19 00:18:01,868 INFO misc.py line 119 87073] Train: [61/100][1317/1557] Data 0.007 (0.167) Batch 0.989 (1.111) Remain 18:48:40 loss: 0.3877 Lr: 0.00182 [2024-02-19 00:18:02,744 INFO misc.py line 119 87073] Train: [61/100][1318/1557] Data 0.003 (0.167) Batch 0.876 (1.111) Remain 18:48:28 loss: 0.1722 Lr: 0.00182 [2024-02-19 00:18:03,581 INFO misc.py line 119 87073] Train: [61/100][1319/1557] Data 0.003 (0.167) Batch 0.831 (1.110) Remain 18:48:14 loss: 0.5176 Lr: 0.00182 [2024-02-19 00:18:04,626 INFO misc.py line 119 87073] Train: [61/100][1320/1557] Data 0.009 (0.166) Batch 1.046 (1.110) Remain 18:48:10 loss: 0.2284 Lr: 0.00182 [2024-02-19 00:18:05,406 INFO misc.py line 119 87073] Train: [61/100][1321/1557] Data 0.009 (0.166) Batch 0.785 (1.110) Remain 18:47:53 loss: 0.1894 Lr: 0.00182 [2024-02-19 00:18:06,204 INFO misc.py line 119 87073] Train: [61/100][1322/1557] Data 0.003 (0.166) Batch 0.799 (1.110) Remain 18:47:38 loss: 0.3220 Lr: 0.00182 [2024-02-19 00:18:07,478 INFO misc.py line 119 87073] Train: [61/100][1323/1557] Data 0.003 (0.166) Batch 1.268 (1.110) Remain 18:47:44 loss: 0.1268 Lr: 0.00182 [2024-02-19 00:18:08,526 INFO misc.py line 119 87073] Train: [61/100][1324/1557] Data 0.009 (0.166) Batch 1.041 (1.110) Remain 18:47:40 loss: 0.5000 Lr: 0.00182 [2024-02-19 00:18:09,519 INFO misc.py line 119 87073] Train: [61/100][1325/1557] Data 0.015 (0.166) Batch 1.003 (1.110) Remain 18:47:34 loss: 0.5410 Lr: 0.00182 [2024-02-19 00:18:10,418 INFO misc.py line 119 87073] Train: [61/100][1326/1557] Data 0.005 (0.166) Batch 0.901 (1.110) Remain 18:47:23 loss: 0.2639 Lr: 0.00182 [2024-02-19 00:18:11,357 INFO misc.py line 119 87073] Train: [61/100][1327/1557] Data 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Remain 18:46:17 loss: 0.2000 Lr: 0.00182 [2024-02-19 00:18:17,886 INFO misc.py line 119 87073] Train: [61/100][1334/1557] Data 0.004 (0.165) Batch 0.968 (1.109) Remain 18:46:09 loss: 0.1375 Lr: 0.00182 [2024-02-19 00:18:18,630 INFO misc.py line 119 87073] Train: [61/100][1335/1557] Data 0.004 (0.165) Batch 0.745 (1.108) Remain 18:45:52 loss: 0.1109 Lr: 0.00182 [2024-02-19 00:18:19,395 INFO misc.py line 119 87073] Train: [61/100][1336/1557] Data 0.003 (0.165) Batch 0.762 (1.108) Remain 18:45:35 loss: 0.1876 Lr: 0.00182 [2024-02-19 00:18:20,635 INFO misc.py line 119 87073] Train: [61/100][1337/1557] Data 0.007 (0.164) Batch 1.242 (1.108) Remain 18:45:40 loss: 0.1830 Lr: 0.00182 [2024-02-19 00:18:21,514 INFO misc.py line 119 87073] Train: [61/100][1338/1557] Data 0.005 (0.164) Batch 0.880 (1.108) Remain 18:45:28 loss: 0.6128 Lr: 0.00182 [2024-02-19 00:18:22,882 INFO misc.py line 119 87073] Train: [61/100][1339/1557] Data 0.003 (0.164) Batch 1.359 (1.108) Remain 18:45:39 loss: 0.3167 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1346/1557] Data 0.003 (0.163) Batch 1.153 (1.107) Remain 18:44:43 loss: 0.2233 Lr: 0.00182 [2024-02-19 00:18:30,457 INFO misc.py line 119 87073] Train: [61/100][1347/1557] Data 0.003 (0.163) Batch 0.884 (1.107) Remain 18:44:32 loss: 0.6288 Lr: 0.00182 [2024-02-19 00:18:31,390 INFO misc.py line 119 87073] Train: [61/100][1348/1557] Data 0.004 (0.163) Batch 0.926 (1.107) Remain 18:44:22 loss: 0.2797 Lr: 0.00182 [2024-02-19 00:18:32,139 INFO misc.py line 119 87073] Train: [61/100][1349/1557] Data 0.011 (0.163) Batch 0.756 (1.107) Remain 18:44:05 loss: 0.3280 Lr: 0.00182 [2024-02-19 00:18:32,918 INFO misc.py line 119 87073] Train: [61/100][1350/1557] Data 0.003 (0.163) Batch 0.770 (1.107) Remain 18:43:49 loss: 0.3609 Lr: 0.00182 [2024-02-19 00:18:44,005 INFO misc.py line 119 87073] Train: [61/100][1351/1557] Data 10.126 (0.170) Batch 11.087 (1.114) Remain 18:51:19 loss: 0.1695 Lr: 0.00182 [2024-02-19 00:18:44,894 INFO misc.py line 119 87073] Train: [61/100][1352/1557] Data 0.013 (0.170) Batch 0.898 (1.114) Remain 18:51:08 loss: 0.2391 Lr: 0.00182 [2024-02-19 00:18:45,863 INFO misc.py line 119 87073] Train: [61/100][1353/1557] Data 0.003 (0.170) Batch 0.969 (1.114) Remain 18:51:00 loss: 0.3252 Lr: 0.00182 [2024-02-19 00:18:46,918 INFO misc.py line 119 87073] Train: [61/100][1354/1557] Data 0.003 (0.170) Batch 1.055 (1.114) Remain 18:50:57 loss: 0.3107 Lr: 0.00182 [2024-02-19 00:18:47,874 INFO misc.py line 119 87073] Train: [61/100][1355/1557] Data 0.003 (0.170) Batch 0.955 (1.114) Remain 18:50:48 loss: 0.2885 Lr: 0.00182 [2024-02-19 00:18:48,646 INFO misc.py line 119 87073] Train: [61/100][1356/1557] Data 0.004 (0.170) Batch 0.773 (1.113) Remain 18:50:32 loss: 0.1962 Lr: 0.00182 [2024-02-19 00:18:49,396 INFO misc.py line 119 87073] Train: [61/100][1357/1557] Data 0.003 (0.170) Batch 0.743 (1.113) Remain 18:50:14 loss: 0.1697 Lr: 0.00182 [2024-02-19 00:18:50,646 INFO misc.py line 119 87073] Train: [61/100][1358/1557] Data 0.009 (0.169) Batch 1.249 (1.113) Remain 18:50:19 loss: 0.3822 Lr: 0.00182 [2024-02-19 00:18:51,475 INFO misc.py line 119 87073] Train: [61/100][1359/1557] Data 0.011 (0.169) Batch 0.837 (1.113) Remain 18:50:06 loss: 0.2816 Lr: 0.00182 [2024-02-19 00:18:52,622 INFO misc.py line 119 87073] Train: [61/100][1360/1557] Data 0.003 (0.169) Batch 1.145 (1.113) Remain 18:50:06 loss: 0.2629 Lr: 0.00182 [2024-02-19 00:18:53,641 INFO misc.py line 119 87073] Train: [61/100][1361/1557] Data 0.006 (0.169) Batch 1.021 (1.113) Remain 18:50:01 loss: 0.2430 Lr: 0.00182 [2024-02-19 00:18:54,620 INFO misc.py line 119 87073] Train: [61/100][1362/1557] Data 0.004 (0.169) Batch 0.978 (1.113) Remain 18:49:54 loss: 0.2610 Lr: 0.00182 [2024-02-19 00:18:55,360 INFO misc.py line 119 87073] Train: [61/100][1363/1557] Data 0.004 (0.169) Batch 0.730 (1.113) Remain 18:49:35 loss: 0.3891 Lr: 0.00182 [2024-02-19 00:18:56,078 INFO misc.py line 119 87073] Train: [61/100][1364/1557] Data 0.013 (0.169) Batch 0.727 (1.112) Remain 18:49:17 loss: 0.1733 Lr: 0.00182 [2024-02-19 00:18:57,235 INFO misc.py line 119 87073] Train: [61/100][1365/1557] Data 0.004 (0.169) Batch 1.153 (1.112) Remain 18:49:18 loss: 0.1289 Lr: 0.00182 [2024-02-19 00:18:58,143 INFO misc.py line 119 87073] Train: [61/100][1366/1557] Data 0.008 (0.168) Batch 0.913 (1.112) Remain 18:49:08 loss: 0.1919 Lr: 0.00182 [2024-02-19 00:18:59,259 INFO misc.py line 119 87073] Train: [61/100][1367/1557] Data 0.003 (0.168) Batch 1.116 (1.112) Remain 18:49:07 loss: 0.1792 Lr: 0.00182 [2024-02-19 00:19:00,124 INFO misc.py line 119 87073] Train: [61/100][1368/1557] Data 0.003 (0.168) Batch 0.865 (1.112) Remain 18:48:55 loss: 0.2444 Lr: 0.00182 [2024-02-19 00:19:01,103 INFO misc.py line 119 87073] Train: [61/100][1369/1557] Data 0.003 (0.168) Batch 0.969 (1.112) Remain 18:48:47 loss: 0.3574 Lr: 0.00182 [2024-02-19 00:19:01,852 INFO misc.py line 119 87073] Train: [61/100][1370/1557] Data 0.013 (0.168) Batch 0.758 (1.112) Remain 18:48:30 loss: 0.2061 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1377/1557] Data 0.011 (0.167) Batch 0.736 (1.111) Remain 18:47:23 loss: 0.3008 Lr: 0.00182 [2024-02-19 00:19:08,969 INFO misc.py line 119 87073] Train: [61/100][1378/1557] Data 0.006 (0.167) Batch 0.678 (1.110) Remain 18:47:02 loss: 0.2721 Lr: 0.00182 [2024-02-19 00:19:10,312 INFO misc.py line 119 87073] Train: [61/100][1379/1557] Data 0.013 (0.167) Batch 1.351 (1.111) Remain 18:47:12 loss: 0.1744 Lr: 0.00182 [2024-02-19 00:19:11,158 INFO misc.py line 119 87073] Train: [61/100][1380/1557] Data 0.005 (0.167) Batch 0.848 (1.110) Remain 18:46:59 loss: 0.1889 Lr: 0.00182 [2024-02-19 00:19:12,036 INFO misc.py line 119 87073] Train: [61/100][1381/1557] Data 0.003 (0.167) Batch 0.877 (1.110) Remain 18:46:48 loss: 0.4279 Lr: 0.00182 [2024-02-19 00:19:13,074 INFO misc.py line 119 87073] Train: [61/100][1382/1557] Data 0.003 (0.167) Batch 1.031 (1.110) Remain 18:46:43 loss: 0.2769 Lr: 0.00182 [2024-02-19 00:19:13,976 INFO misc.py line 119 87073] Train: [61/100][1383/1557] Data 0.011 (0.166) Batch 0.909 (1.110) Remain 18:46:33 loss: 0.2446 Lr: 0.00182 [2024-02-19 00:19:14,690 INFO misc.py line 119 87073] Train: [61/100][1384/1557] Data 0.003 (0.166) Batch 0.710 (1.110) Remain 18:46:14 loss: 0.2469 Lr: 0.00182 [2024-02-19 00:19:15,440 INFO misc.py line 119 87073] Train: [61/100][1385/1557] Data 0.007 (0.166) Batch 0.748 (1.109) Remain 18:45:57 loss: 0.3371 Lr: 0.00182 [2024-02-19 00:19:16,548 INFO misc.py line 119 87073] Train: [61/100][1386/1557] Data 0.008 (0.166) Batch 1.112 (1.109) Remain 18:45:56 loss: 0.1804 Lr: 0.00182 [2024-02-19 00:19:17,621 INFO misc.py line 119 87073] Train: [61/100][1387/1557] Data 0.005 (0.166) Batch 1.068 (1.109) Remain 18:45:53 loss: 0.0834 Lr: 0.00182 [2024-02-19 00:19:18,505 INFO misc.py line 119 87073] Train: [61/100][1388/1557] Data 0.010 (0.166) Batch 0.891 (1.109) Remain 18:45:43 loss: 1.0226 Lr: 0.00182 [2024-02-19 00:19:19,386 INFO misc.py line 119 87073] Train: [61/100][1389/1557] Data 0.003 (0.166) Batch 0.880 (1.109) Remain 18:45:31 loss: 0.5338 Lr: 0.00182 [2024-02-19 00:19:20,307 INFO misc.py line 119 87073] Train: [61/100][1390/1557] Data 0.004 (0.166) Batch 0.922 (1.109) Remain 18:45:22 loss: 0.2566 Lr: 0.00182 [2024-02-19 00:19:21,057 INFO misc.py line 119 87073] Train: [61/100][1391/1557] Data 0.003 (0.166) Batch 0.735 (1.109) Remain 18:45:05 loss: 0.2018 Lr: 0.00182 [2024-02-19 00:19:21,835 INFO misc.py line 119 87073] Train: [61/100][1392/1557] Data 0.018 (0.165) Batch 0.792 (1.108) Remain 18:44:50 loss: 0.2032 Lr: 0.00182 [2024-02-19 00:19:23,081 INFO misc.py line 119 87073] Train: [61/100][1393/1557] Data 0.004 (0.165) Batch 1.236 (1.109) Remain 18:44:54 loss: 0.1993 Lr: 0.00182 [2024-02-19 00:19:24,230 INFO misc.py line 119 87073] Train: [61/100][1394/1557] Data 0.013 (0.165) Batch 1.151 (1.109) Remain 18:44:55 loss: 0.4842 Lr: 0.00182 [2024-02-19 00:19:25,257 INFO misc.py line 119 87073] Train: [61/100][1395/1557] Data 0.012 (0.165) Batch 1.025 (1.108) Remain 18:44:50 loss: 0.2154 Lr: 0.00182 [2024-02-19 00:19:26,225 INFO misc.py line 119 87073] Train: [61/100][1396/1557] Data 0.014 (0.165) Batch 0.979 (1.108) Remain 18:44:43 loss: 0.3282 Lr: 0.00182 [2024-02-19 00:19:27,160 INFO misc.py line 119 87073] Train: [61/100][1397/1557] Data 0.003 (0.165) Batch 0.933 (1.108) Remain 18:44:35 loss: 0.3291 Lr: 0.00182 [2024-02-19 00:19:27,941 INFO misc.py line 119 87073] Train: [61/100][1398/1557] Data 0.005 (0.165) Batch 0.782 (1.108) Remain 18:44:19 loss: 0.1893 Lr: 0.00182 [2024-02-19 00:19:28,681 INFO misc.py line 119 87073] Train: [61/100][1399/1557] Data 0.004 (0.165) Batch 0.738 (1.108) Remain 18:44:02 loss: 0.2245 Lr: 0.00182 [2024-02-19 00:19:29,743 INFO misc.py line 119 87073] Train: [61/100][1400/1557] Data 0.007 (0.165) Batch 1.060 (1.108) Remain 18:43:59 loss: 0.1391 Lr: 0.00182 [2024-02-19 00:19:30,726 INFO misc.py line 119 87073] Train: [61/100][1401/1557] Data 0.009 (0.164) Batch 0.988 (1.108) Remain 18:43:52 loss: 0.2631 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1408/1557] Data 0.003 (0.170) Batch 1.088 (1.114) Remain 18:50:21 loss: 0.2322 Lr: 0.00182 [2024-02-19 00:19:48,623 INFO misc.py line 119 87073] Train: [61/100][1409/1557] Data 0.003 (0.170) Batch 0.998 (1.114) Remain 18:50:15 loss: 0.7625 Lr: 0.00182 [2024-02-19 00:19:49,416 INFO misc.py line 119 87073] Train: [61/100][1410/1557] Data 0.003 (0.170) Batch 0.792 (1.114) Remain 18:50:00 loss: 0.3354 Lr: 0.00182 [2024-02-19 00:19:50,372 INFO misc.py line 119 87073] Train: [61/100][1411/1557] Data 0.005 (0.170) Batch 0.948 (1.114) Remain 18:49:51 loss: 0.4090 Lr: 0.00182 [2024-02-19 00:19:51,136 INFO misc.py line 119 87073] Train: [61/100][1412/1557] Data 0.012 (0.169) Batch 0.772 (1.113) Remain 18:49:36 loss: 0.1986 Lr: 0.00182 [2024-02-19 00:19:51,805 INFO misc.py line 119 87073] Train: [61/100][1413/1557] Data 0.003 (0.169) Batch 0.660 (1.113) Remain 18:49:15 loss: 0.2189 Lr: 0.00182 [2024-02-19 00:19:52,955 INFO misc.py line 119 87073] Train: [61/100][1414/1557] Data 0.012 (0.169) Batch 1.154 (1.113) Remain 18:49:16 loss: 0.3193 Lr: 0.00182 [2024-02-19 00:19:53,860 INFO misc.py line 119 87073] Train: [61/100][1415/1557] Data 0.008 (0.169) Batch 0.910 (1.113) Remain 18:49:06 loss: 0.2230 Lr: 0.00182 [2024-02-19 00:19:54,913 INFO misc.py line 119 87073] Train: [61/100][1416/1557] Data 0.004 (0.169) Batch 1.053 (1.113) Remain 18:49:02 loss: 0.8765 Lr: 0.00182 [2024-02-19 00:19:56,022 INFO misc.py line 119 87073] Train: [61/100][1417/1557] Data 0.003 (0.169) Batch 1.109 (1.113) Remain 18:49:01 loss: 0.4016 Lr: 0.00182 [2024-02-19 00:19:56,974 INFO misc.py line 119 87073] Train: [61/100][1418/1557] Data 0.003 (0.169) Batch 0.951 (1.113) Remain 18:48:53 loss: 0.1706 Lr: 0.00182 [2024-02-19 00:19:57,674 INFO misc.py line 119 87073] Train: [61/100][1419/1557] Data 0.004 (0.169) Batch 0.695 (1.113) Remain 18:48:34 loss: 0.2747 Lr: 0.00182 [2024-02-19 00:19:58,435 INFO misc.py line 119 87073] Train: [61/100][1420/1557] Data 0.008 (0.168) Batch 0.766 (1.112) Remain 18:48:18 loss: 0.1977 Lr: 0.00182 [2024-02-19 00:19:59,604 INFO misc.py line 119 87073] Train: [61/100][1421/1557] Data 0.004 (0.168) Batch 1.168 (1.112) Remain 18:48:19 loss: 0.2131 Lr: 0.00182 [2024-02-19 00:20:00,607 INFO misc.py line 119 87073] Train: [61/100][1422/1557] Data 0.006 (0.168) Batch 1.005 (1.112) Remain 18:48:13 loss: 0.2985 Lr: 0.00182 [2024-02-19 00:20:01,631 INFO misc.py line 119 87073] Train: [61/100][1423/1557] Data 0.004 (0.168) Batch 1.024 (1.112) Remain 18:48:08 loss: 0.2310 Lr: 0.00182 [2024-02-19 00:20:02,719 INFO misc.py line 119 87073] Train: [61/100][1424/1557] Data 0.004 (0.168) Batch 1.087 (1.112) Remain 18:48:06 loss: 0.4322 Lr: 0.00182 [2024-02-19 00:20:03,747 INFO misc.py line 119 87073] Train: [61/100][1425/1557] Data 0.005 (0.168) Batch 1.029 (1.112) Remain 18:48:01 loss: 0.2572 Lr: 0.00182 [2024-02-19 00:20:04,490 INFO misc.py line 119 87073] Train: [61/100][1426/1557] Data 0.004 (0.168) Batch 0.743 (1.112) Remain 18:47:44 loss: 0.5281 Lr: 0.00182 [2024-02-19 00:20:05,230 INFO misc.py line 119 87073] Train: [61/100][1427/1557] Data 0.004 (0.168) Batch 0.734 (1.112) Remain 18:47:27 loss: 0.2860 Lr: 0.00182 [2024-02-19 00:20:06,542 INFO misc.py line 119 87073] Train: [61/100][1428/1557] Data 0.010 (0.168) Batch 1.311 (1.112) Remain 18:47:35 loss: 0.2396 Lr: 0.00182 [2024-02-19 00:20:07,251 INFO misc.py line 119 87073] Train: [61/100][1429/1557] Data 0.011 (0.167) Batch 0.717 (1.112) Remain 18:47:17 loss: 0.3238 Lr: 0.00182 [2024-02-19 00:20:08,264 INFO misc.py line 119 87073] Train: [61/100][1430/1557] Data 0.003 (0.167) Batch 1.007 (1.111) Remain 18:47:11 loss: 0.4301 Lr: 0.00182 [2024-02-19 00:20:09,213 INFO misc.py line 119 87073] Train: [61/100][1431/1557] Data 0.009 (0.167) Batch 0.955 (1.111) Remain 18:47:03 loss: 0.3995 Lr: 0.00182 [2024-02-19 00:20:10,107 INFO misc.py line 119 87073] Train: [61/100][1432/1557] Data 0.004 (0.167) Batch 0.894 (1.111) Remain 18:46:53 loss: 0.3072 Lr: 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INFO misc.py line 119 87073] Train: [61/100][1439/1557] Data 0.005 (0.166) Batch 0.991 (1.110) Remain 18:45:55 loss: 0.1368 Lr: 0.00181 [2024-02-19 00:20:17,393 INFO misc.py line 119 87073] Train: [61/100][1440/1557] Data 0.010 (0.166) Batch 0.701 (1.110) Remain 18:45:36 loss: 0.2245 Lr: 0.00181 [2024-02-19 00:20:18,167 INFO misc.py line 119 87073] Train: [61/100][1441/1557] Data 0.003 (0.166) Batch 0.773 (1.110) Remain 18:45:21 loss: 0.3566 Lr: 0.00181 [2024-02-19 00:20:19,240 INFO misc.py line 119 87073] Train: [61/100][1442/1557] Data 0.004 (0.166) Batch 1.063 (1.110) Remain 18:45:18 loss: 0.1483 Lr: 0.00181 [2024-02-19 00:20:20,231 INFO misc.py line 119 87073] Train: [61/100][1443/1557] Data 0.015 (0.166) Batch 1.001 (1.110) Remain 18:45:12 loss: 0.5198 Lr: 0.00181 [2024-02-19 00:20:21,134 INFO misc.py line 119 87073] Train: [61/100][1444/1557] Data 0.005 (0.166) Batch 0.904 (1.110) Remain 18:45:02 loss: 0.1039 Lr: 0.00181 [2024-02-19 00:20:22,014 INFO misc.py line 119 87073] Train: [61/100][1445/1557] Data 0.004 (0.166) Batch 0.873 (1.109) Remain 18:44:51 loss: 0.5265 Lr: 0.00181 [2024-02-19 00:20:22,753 INFO misc.py line 119 87073] Train: [61/100][1446/1557] Data 0.010 (0.166) Batch 0.745 (1.109) Remain 18:44:35 loss: 0.3048 Lr: 0.00181 [2024-02-19 00:20:23,511 INFO misc.py line 119 87073] Train: [61/100][1447/1557] Data 0.004 (0.165) Batch 0.751 (1.109) Remain 18:44:18 loss: 0.3822 Lr: 0.00181 [2024-02-19 00:20:24,276 INFO misc.py line 119 87073] Train: [61/100][1448/1557] Data 0.011 (0.165) Batch 0.772 (1.109) Remain 18:44:03 loss: 0.1269 Lr: 0.00181 [2024-02-19 00:20:25,534 INFO misc.py line 119 87073] Train: [61/100][1449/1557] Data 0.003 (0.165) Batch 1.248 (1.109) Remain 18:44:08 loss: 0.2793 Lr: 0.00181 [2024-02-19 00:20:26,612 INFO misc.py line 119 87073] Train: [61/100][1450/1557] Data 0.014 (0.165) Batch 1.080 (1.109) Remain 18:44:06 loss: 0.5236 Lr: 0.00181 [2024-02-19 00:20:27,549 INFO misc.py line 119 87073] Train: [61/100][1451/1557] Data 0.012 (0.165) Batch 0.946 (1.109) Remain 18:43:58 loss: 0.2223 Lr: 0.00181 [2024-02-19 00:20:28,510 INFO misc.py line 119 87073] Train: [61/100][1452/1557] Data 0.003 (0.165) Batch 0.961 (1.109) Remain 18:43:50 loss: 0.3077 Lr: 0.00181 [2024-02-19 00:20:29,489 INFO misc.py line 119 87073] Train: [61/100][1453/1557] Data 0.004 (0.165) Batch 0.980 (1.108) Remain 18:43:44 loss: 0.7045 Lr: 0.00181 [2024-02-19 00:20:30,270 INFO misc.py line 119 87073] Train: [61/100][1454/1557] Data 0.003 (0.165) Batch 0.781 (1.108) Remain 18:43:29 loss: 1.0784 Lr: 0.00181 [2024-02-19 00:20:31,037 INFO misc.py line 119 87073] Train: [61/100][1455/1557] Data 0.004 (0.165) Batch 0.764 (1.108) Remain 18:43:13 loss: 0.3704 Lr: 0.00181 [2024-02-19 00:20:32,044 INFO misc.py line 119 87073] Train: [61/100][1456/1557] Data 0.005 (0.164) Batch 1.008 (1.108) Remain 18:43:08 loss: 0.1147 Lr: 0.00181 [2024-02-19 00:20:33,224 INFO misc.py line 119 87073] Train: [61/100][1457/1557] Data 0.005 (0.164) Batch 1.173 (1.108) Remain 18:43:10 loss: 0.2818 Lr: 0.00181 [2024-02-19 00:20:34,257 INFO misc.py line 119 87073] Train: [61/100][1458/1557] Data 0.011 (0.164) Batch 1.033 (1.108) Remain 18:43:05 loss: 0.3279 Lr: 0.00181 [2024-02-19 00:20:35,349 INFO misc.py line 119 87073] Train: [61/100][1459/1557] Data 0.012 (0.164) Batch 1.096 (1.108) Remain 18:43:04 loss: 0.3164 Lr: 0.00181 [2024-02-19 00:20:36,270 INFO misc.py line 119 87073] Train: [61/100][1460/1557] Data 0.008 (0.164) Batch 0.926 (1.108) Remain 18:42:55 loss: 0.3226 Lr: 0.00181 [2024-02-19 00:20:36,989 INFO misc.py line 119 87073] Train: [61/100][1461/1557] Data 0.003 (0.164) Batch 0.719 (1.108) Remain 18:42:38 loss: 0.1869 Lr: 0.00181 [2024-02-19 00:20:37,681 INFO misc.py line 119 87073] Train: [61/100][1462/1557] Data 0.004 (0.164) Batch 0.687 (1.107) Remain 18:42:19 loss: 0.1480 Lr: 0.00181 [2024-02-19 00:20:47,678 INFO misc.py line 119 87073] Train: [61/100][1463/1557] Data 9.059 (0.170) Batch 9.996 (1.113) Remain 18:48:28 loss: 0.1716 Lr: 0.00181 [2024-02-19 00:20:48,655 INFO misc.py line 119 87073] Train: [61/100][1464/1557] Data 0.008 (0.170) Batch 0.982 (1.113) Remain 18:48:22 loss: 0.2776 Lr: 0.00181 [2024-02-19 00:20:49,577 INFO misc.py line 119 87073] Train: [61/100][1465/1557] Data 0.004 (0.170) Batch 0.923 (1.113) Remain 18:48:13 loss: 0.3780 Lr: 0.00181 [2024-02-19 00:20:50,451 INFO misc.py line 119 87073] Train: [61/100][1466/1557] Data 0.003 (0.170) Batch 0.868 (1.113) Remain 18:48:01 loss: 0.4949 Lr: 0.00181 [2024-02-19 00:20:51,481 INFO misc.py line 119 87073] Train: [61/100][1467/1557] Data 0.008 (0.169) Batch 1.027 (1.113) Remain 18:47:57 loss: 0.3781 Lr: 0.00181 [2024-02-19 00:20:52,208 INFO misc.py line 119 87073] Train: [61/100][1468/1557] Data 0.011 (0.169) Batch 0.735 (1.113) Remain 18:47:40 loss: 0.4317 Lr: 0.00181 [2024-02-19 00:20:52,946 INFO misc.py line 119 87073] Train: [61/100][1469/1557] Data 0.003 (0.169) Batch 0.728 (1.112) Remain 18:47:23 loss: 0.1373 Lr: 0.00181 [2024-02-19 00:20:54,089 INFO misc.py line 119 87073] Train: [61/100][1470/1557] Data 0.013 (0.169) Batch 1.140 (1.112) Remain 18:47:23 loss: 0.2698 Lr: 0.00181 [2024-02-19 00:20:55,129 INFO misc.py line 119 87073] Train: [61/100][1471/1557] Data 0.016 (0.169) Batch 1.040 (1.112) Remain 18:47:19 loss: 0.3662 Lr: 0.00181 [2024-02-19 00:20:56,002 INFO misc.py line 119 87073] Train: [61/100][1472/1557] Data 0.016 (0.169) Batch 0.885 (1.112) Remain 18:47:08 loss: 0.3143 Lr: 0.00181 [2024-02-19 00:20:56,946 INFO misc.py line 119 87073] Train: [61/100][1473/1557] Data 0.003 (0.169) Batch 0.944 (1.112) Remain 18:47:00 loss: 0.2789 Lr: 0.00181 [2024-02-19 00:20:57,910 INFO misc.py line 119 87073] Train: [61/100][1474/1557] Data 0.003 (0.169) Batch 0.965 (1.112) Remain 18:46:53 loss: 0.2635 Lr: 0.00181 [2024-02-19 00:20:58,684 INFO misc.py line 119 87073] Train: [61/100][1475/1557] Data 0.003 (0.169) Batch 0.764 (1.112) Remain 18:46:38 loss: 0.1253 Lr: 0.00181 [2024-02-19 00:20:59,448 INFO misc.py line 119 87073] Train: [61/100][1476/1557] Data 0.012 (0.168) Batch 0.774 (1.111) Remain 18:46:23 loss: 0.2842 Lr: 0.00181 [2024-02-19 00:21:00,689 INFO misc.py line 119 87073] Train: [61/100][1477/1557] Data 0.003 (0.168) Batch 1.235 (1.112) Remain 18:46:27 loss: 0.1428 Lr: 0.00181 [2024-02-19 00:21:01,670 INFO misc.py line 119 87073] Train: [61/100][1478/1557] Data 0.008 (0.168) Batch 0.987 (1.111) Remain 18:46:20 loss: 0.1702 Lr: 0.00181 [2024-02-19 00:21:02,517 INFO misc.py line 119 87073] Train: [61/100][1479/1557] Data 0.003 (0.168) Batch 0.847 (1.111) Remain 18:46:08 loss: 0.4035 Lr: 0.00181 [2024-02-19 00:21:03,418 INFO misc.py line 119 87073] Train: [61/100][1480/1557] Data 0.004 (0.168) Batch 0.893 (1.111) Remain 18:45:58 loss: 0.9422 Lr: 0.00181 [2024-02-19 00:21:04,826 INFO misc.py line 119 87073] Train: [61/100][1481/1557] Data 0.010 (0.168) Batch 1.409 (1.111) Remain 18:46:09 loss: 0.3616 Lr: 0.00181 [2024-02-19 00:21:05,554 INFO misc.py line 119 87073] Train: [61/100][1482/1557] Data 0.010 (0.168) Batch 0.735 (1.111) Remain 18:45:53 loss: 0.3177 Lr: 0.00181 [2024-02-19 00:21:06,320 INFO misc.py line 119 87073] Train: [61/100][1483/1557] Data 0.003 (0.168) Batch 0.757 (1.111) Remain 18:45:37 loss: 0.2512 Lr: 0.00181 [2024-02-19 00:21:07,631 INFO misc.py line 119 87073] Train: [61/100][1484/1557] Data 0.011 (0.168) Batch 1.310 (1.111) Remain 18:45:44 loss: 0.1951 Lr: 0.00181 [2024-02-19 00:21:08,664 INFO misc.py line 119 87073] Train: [61/100][1485/1557] Data 0.014 (0.167) Batch 1.037 (1.111) Remain 18:45:40 loss: 0.3149 Lr: 0.00181 [2024-02-19 00:21:09,706 INFO misc.py line 119 87073] Train: [61/100][1486/1557] Data 0.009 (0.167) Batch 1.040 (1.111) Remain 18:45:36 loss: 0.1540 Lr: 0.00181 [2024-02-19 00:21:10,613 INFO misc.py line 119 87073] Train: [61/100][1487/1557] Data 0.011 (0.167) Batch 0.913 (1.111) Remain 18:45:27 loss: 0.3751 Lr: 0.00181 [2024-02-19 00:21:11,722 INFO misc.py line 119 87073] Train: [61/100][1488/1557] Data 0.004 (0.167) Batch 1.110 (1.111) Remain 18:45:26 loss: 0.3911 Lr: 0.00181 [2024-02-19 00:21:12,476 INFO misc.py line 119 87073] Train: [61/100][1489/1557] Data 0.003 (0.167) Batch 0.754 (1.111) Remain 18:45:10 loss: 0.2617 Lr: 0.00181 [2024-02-19 00:21:13,246 INFO misc.py line 119 87073] Train: [61/100][1490/1557] Data 0.003 (0.167) Batch 0.761 (1.110) Remain 18:44:55 loss: 0.2019 Lr: 0.00181 [2024-02-19 00:21:14,560 INFO misc.py line 119 87073] Train: [61/100][1491/1557] Data 0.011 (0.167) Batch 1.312 (1.110) Remain 18:45:02 loss: 0.2154 Lr: 0.00181 [2024-02-19 00:21:15,532 INFO misc.py line 119 87073] Train: [61/100][1492/1557] Data 0.013 (0.167) Batch 0.983 (1.110) Remain 18:44:55 loss: 0.8009 Lr: 0.00181 [2024-02-19 00:21:16,505 INFO misc.py line 119 87073] Train: [61/100][1493/1557] Data 0.003 (0.167) Batch 0.972 (1.110) Remain 18:44:49 loss: 0.5992 Lr: 0.00181 [2024-02-19 00:21:17,325 INFO misc.py line 119 87073] Train: [61/100][1494/1557] Data 0.004 (0.167) Batch 0.815 (1.110) Remain 18:44:35 loss: 0.4243 Lr: 0.00181 [2024-02-19 00:21:18,322 INFO misc.py line 119 87073] Train: [61/100][1495/1557] Data 0.008 (0.166) Batch 0.997 (1.110) Remain 18:44:30 loss: 0.3464 Lr: 0.00181 [2024-02-19 00:21:19,071 INFO misc.py line 119 87073] Train: [61/100][1496/1557] Data 0.009 (0.166) Batch 0.753 (1.110) Remain 18:44:14 loss: 0.1651 Lr: 0.00181 [2024-02-19 00:21:19,842 INFO misc.py line 119 87073] Train: [61/100][1497/1557] Data 0.006 (0.166) Batch 0.766 (1.110) Remain 18:43:59 loss: 0.1433 Lr: 0.00181 [2024-02-19 00:21:20,910 INFO misc.py line 119 87073] Train: [61/100][1498/1557] Data 0.010 (0.166) Batch 1.068 (1.109) Remain 18:43:56 loss: 0.1545 Lr: 0.00181 [2024-02-19 00:21:21,760 INFO misc.py line 119 87073] Train: [61/100][1499/1557] Data 0.010 (0.166) Batch 0.856 (1.109) Remain 18:43:45 loss: 0.2350 Lr: 0.00181 [2024-02-19 00:21:22,615 INFO misc.py line 119 87073] Train: [61/100][1500/1557] Data 0.005 (0.166) Batch 0.855 (1.109) Remain 18:43:33 loss: 0.0916 Lr: 0.00181 [2024-02-19 00:21:23,597 INFO misc.py line 119 87073] Train: [61/100][1501/1557] Data 0.005 (0.166) Batch 0.975 (1.109) Remain 18:43:27 loss: 0.5494 Lr: 0.00181 [2024-02-19 00:21:24,374 INFO misc.py line 119 87073] Train: [61/100][1502/1557] Data 0.010 (0.166) Batch 0.784 (1.109) Remain 18:43:13 loss: 0.2681 Lr: 0.00181 [2024-02-19 00:21:25,126 INFO misc.py line 119 87073] Train: [61/100][1503/1557] Data 0.004 (0.166) Batch 0.752 (1.109) Remain 18:42:57 loss: 0.1892 Lr: 0.00181 [2024-02-19 00:21:25,898 INFO misc.py line 119 87073] Train: [61/100][1504/1557] Data 0.004 (0.165) Batch 0.763 (1.108) Remain 18:42:42 loss: 0.3642 Lr: 0.00181 [2024-02-19 00:21:27,194 INFO misc.py line 119 87073] Train: [61/100][1505/1557] Data 0.012 (0.165) Batch 1.293 (1.108) Remain 18:42:48 loss: 0.2645 Lr: 0.00181 [2024-02-19 00:21:28,134 INFO misc.py line 119 87073] Train: [61/100][1506/1557] Data 0.015 (0.165) Batch 0.953 (1.108) Remain 18:42:41 loss: 0.5408 Lr: 0.00181 [2024-02-19 00:21:29,168 INFO misc.py line 119 87073] Train: [61/100][1507/1557] Data 0.003 (0.165) Batch 1.034 (1.108) Remain 18:42:37 loss: 0.2322 Lr: 0.00181 [2024-02-19 00:21:30,149 INFO misc.py line 119 87073] Train: [61/100][1508/1557] Data 0.002 (0.165) Batch 0.980 (1.108) Remain 18:42:30 loss: 0.1247 Lr: 0.00181 [2024-02-19 00:21:31,368 INFO misc.py line 119 87073] Train: [61/100][1509/1557] Data 0.003 (0.165) Batch 1.215 (1.108) Remain 18:42:34 loss: 0.3035 Lr: 0.00181 [2024-02-19 00:21:32,086 INFO misc.py line 119 87073] Train: [61/100][1510/1557] Data 0.008 (0.165) Batch 0.720 (1.108) Remain 18:42:17 loss: 0.1165 Lr: 0.00181 [2024-02-19 00:21:32,812 INFO misc.py line 119 87073] Train: [61/100][1511/1557] Data 0.005 (0.165) Batch 0.724 (1.108) Remain 18:42:00 loss: 0.2076 Lr: 0.00181 [2024-02-19 00:21:33,826 INFO misc.py line 119 87073] Train: [61/100][1512/1557] Data 0.008 (0.165) Batch 1.015 (1.108) Remain 18:41:55 loss: 0.1344 Lr: 0.00181 [2024-02-19 00:21:34,836 INFO misc.py line 119 87073] Train: [61/100][1513/1557] Data 0.007 (0.165) Batch 1.006 (1.108) Remain 18:41:50 loss: 0.4554 Lr: 0.00181 [2024-02-19 00:21:35,929 INFO misc.py line 119 87073] Train: [61/100][1514/1557] Data 0.011 (0.164) Batch 1.097 (1.108) Remain 18:41:49 loss: 0.3367 Lr: 0.00181 [2024-02-19 00:21:37,043 INFO misc.py line 119 87073] Train: [61/100][1515/1557] Data 0.007 (0.164) Batch 1.112 (1.108) Remain 18:41:48 loss: 0.3339 Lr: 0.00181 [2024-02-19 00:21:38,185 INFO misc.py line 119 87073] Train: [61/100][1516/1557] Data 0.009 (0.164) Batch 1.141 (1.108) Remain 18:41:48 loss: 0.6218 Lr: 0.00181 [2024-02-19 00:21:38,915 INFO misc.py line 119 87073] Train: [61/100][1517/1557] Data 0.011 (0.164) Batch 0.738 (1.107) Remain 18:41:32 loss: 0.1575 Lr: 0.00181 [2024-02-19 00:21:39,662 INFO misc.py line 119 87073] Train: [61/100][1518/1557] Data 0.003 (0.164) Batch 0.746 (1.107) Remain 18:41:16 loss: 0.3555 Lr: 0.00181 [2024-02-19 00:21:50,258 INFO misc.py line 119 87073] Train: [61/100][1519/1557] Data 8.967 (0.170) Batch 10.586 (1.113) Remain 18:47:35 loss: 0.1531 Lr: 0.00181 [2024-02-19 00:21:51,156 INFO misc.py line 119 87073] Train: [61/100][1520/1557] Data 0.015 (0.170) Batch 0.909 (1.113) Remain 18:47:26 loss: 0.5431 Lr: 0.00181 [2024-02-19 00:21:52,104 INFO misc.py line 119 87073] Train: [61/100][1521/1557] Data 0.003 (0.170) Batch 0.948 (1.113) Remain 18:47:18 loss: 0.2846 Lr: 0.00181 [2024-02-19 00:21:53,069 INFO misc.py line 119 87073] Train: [61/100][1522/1557] Data 0.003 (0.169) Batch 0.965 (1.113) Remain 18:47:11 loss: 0.2729 Lr: 0.00181 [2024-02-19 00:21:53,945 INFO misc.py line 119 87073] Train: [61/100][1523/1557] Data 0.003 (0.169) Batch 0.867 (1.113) Remain 18:47:00 loss: 0.3208 Lr: 0.00181 [2024-02-19 00:21:54,702 INFO misc.py line 119 87073] Train: [61/100][1524/1557] Data 0.012 (0.169) Batch 0.766 (1.113) Remain 18:46:45 loss: 0.2762 Lr: 0.00181 [2024-02-19 00:21:55,485 INFO misc.py line 119 87073] Train: [61/100][1525/1557] Data 0.003 (0.169) Batch 0.772 (1.113) Remain 18:46:30 loss: 0.1923 Lr: 0.00181 [2024-02-19 00:21:56,646 INFO misc.py line 119 87073] Train: [61/100][1526/1557] Data 0.015 (0.169) Batch 1.164 (1.113) Remain 18:46:31 loss: 0.2268 Lr: 0.00181 [2024-02-19 00:21:57,556 INFO misc.py line 119 87073] Train: [61/100][1527/1557] Data 0.013 (0.169) Batch 0.919 (1.112) Remain 18:46:23 loss: 0.3651 Lr: 0.00181 [2024-02-19 00:21:58,346 INFO misc.py line 119 87073] Train: [61/100][1528/1557] Data 0.003 (0.169) Batch 0.790 (1.112) Remain 18:46:09 loss: 0.3688 Lr: 0.00181 [2024-02-19 00:21:59,503 INFO misc.py line 119 87073] Train: [61/100][1529/1557] Data 0.004 (0.169) Batch 1.147 (1.112) Remain 18:46:09 loss: 0.2351 Lr: 0.00181 [2024-02-19 00:22:00,510 INFO misc.py line 119 87073] Train: [61/100][1530/1557] Data 0.013 (0.169) Batch 1.007 (1.112) Remain 18:46:04 loss: 0.3535 Lr: 0.00181 [2024-02-19 00:22:01,248 INFO misc.py line 119 87073] Train: [61/100][1531/1557] Data 0.013 (0.169) Batch 0.745 (1.112) Remain 18:45:48 loss: 0.2166 Lr: 0.00181 [2024-02-19 00:22:01,962 INFO misc.py line 119 87073] Train: [61/100][1532/1557] Data 0.006 (0.168) Batch 0.714 (1.112) Remain 18:45:31 loss: 0.2112 Lr: 0.00181 [2024-02-19 00:22:03,145 INFO misc.py line 119 87073] Train: [61/100][1533/1557] Data 0.005 (0.168) Batch 1.177 (1.112) Remain 18:45:32 loss: 0.1252 Lr: 0.00181 [2024-02-19 00:22:04,364 INFO misc.py line 119 87073] Train: [61/100][1534/1557] Data 0.012 (0.168) Batch 1.224 (1.112) Remain 18:45:36 loss: 0.4851 Lr: 0.00181 [2024-02-19 00:22:05,317 INFO misc.py line 119 87073] Train: [61/100][1535/1557] Data 0.007 (0.168) Batch 0.954 (1.112) Remain 18:45:28 loss: 0.3328 Lr: 0.00181 [2024-02-19 00:22:06,261 INFO misc.py line 119 87073] Train: [61/100][1536/1557] Data 0.006 (0.168) Batch 0.946 (1.112) Remain 18:45:21 loss: 0.2807 Lr: 0.00181 [2024-02-19 00:22:07,437 INFO misc.py line 119 87073] Train: [61/100][1537/1557] Data 0.004 (0.168) Batch 1.175 (1.112) Remain 18:45:22 loss: 0.2653 Lr: 0.00181 [2024-02-19 00:22:08,134 INFO misc.py line 119 87073] Train: [61/100][1538/1557] Data 0.005 (0.168) Batch 0.696 (1.111) Remain 18:45:05 loss: 0.3851 Lr: 0.00181 [2024-02-19 00:22:08,875 INFO misc.py line 119 87073] Train: [61/100][1539/1557] Data 0.005 (0.168) Batch 0.742 (1.111) Remain 18:44:49 loss: 0.3117 Lr: 0.00181 [2024-02-19 00:22:10,073 INFO misc.py line 119 87073] Train: [61/100][1540/1557] Data 0.005 (0.168) Batch 1.195 (1.111) Remain 18:44:51 loss: 0.1394 Lr: 0.00181 [2024-02-19 00:22:10,979 INFO misc.py line 119 87073] Train: [61/100][1541/1557] Data 0.009 (0.167) Batch 0.907 (1.111) Remain 18:44:42 loss: 0.1024 Lr: 0.00181 [2024-02-19 00:22:11,948 INFO misc.py line 119 87073] Train: [61/100][1542/1557] Data 0.007 (0.167) Batch 0.970 (1.111) Remain 18:44:35 loss: 0.4030 Lr: 0.00181 [2024-02-19 00:22:12,846 INFO misc.py line 119 87073] Train: [61/100][1543/1557] Data 0.005 (0.167) Batch 0.896 (1.111) Remain 18:44:26 loss: 0.4393 Lr: 0.00181 [2024-02-19 00:22:13,868 INFO misc.py line 119 87073] Train: [61/100][1544/1557] Data 0.007 (0.167) Batch 1.021 (1.111) Remain 18:44:21 loss: 0.2310 Lr: 0.00181 [2024-02-19 00:22:14,628 INFO misc.py line 119 87073] Train: [61/100][1545/1557] Data 0.008 (0.167) Batch 0.764 (1.111) Remain 18:44:06 loss: 0.2765 Lr: 0.00181 [2024-02-19 00:22:15,319 INFO misc.py line 119 87073] Train: [61/100][1546/1557] Data 0.004 (0.167) Batch 0.687 (1.110) Remain 18:43:48 loss: 0.3883 Lr: 0.00181 [2024-02-19 00:22:16,575 INFO misc.py line 119 87073] Train: [61/100][1547/1557] Data 0.008 (0.167) Batch 1.251 (1.110) Remain 18:43:53 loss: 0.1352 Lr: 0.00181 [2024-02-19 00:22:17,472 INFO misc.py line 119 87073] Train: [61/100][1548/1557] Data 0.012 (0.167) Batch 0.906 (1.110) Remain 18:43:44 loss: 0.8699 Lr: 0.00181 [2024-02-19 00:22:18,435 INFO misc.py line 119 87073] Train: [61/100][1549/1557] Data 0.004 (0.167) Batch 0.963 (1.110) Remain 18:43:37 loss: 0.1960 Lr: 0.00181 [2024-02-19 00:22:19,502 INFO misc.py line 119 87073] Train: [61/100][1550/1557] Data 0.004 (0.167) Batch 1.067 (1.110) Remain 18:43:34 loss: 0.1871 Lr: 0.00181 [2024-02-19 00:22:20,525 INFO misc.py line 119 87073] Train: [61/100][1551/1557] Data 0.004 (0.166) Batch 1.022 (1.110) Remain 18:43:29 loss: 0.6333 Lr: 0.00181 [2024-02-19 00:22:21,229 INFO misc.py line 119 87073] Train: [61/100][1552/1557] Data 0.004 (0.166) Batch 0.704 (1.110) Remain 18:43:12 loss: 0.2504 Lr: 0.00181 [2024-02-19 00:22:21,874 INFO misc.py line 119 87073] Train: [61/100][1553/1557] Data 0.004 (0.166) Batch 0.637 (1.109) Remain 18:42:53 loss: 0.0978 Lr: 0.00181 [2024-02-19 00:22:22,909 INFO misc.py line 119 87073] Train: [61/100][1554/1557] Data 0.012 (0.166) Batch 1.037 (1.109) Remain 18:42:49 loss: 0.1765 Lr: 0.00181 [2024-02-19 00:22:24,037 INFO misc.py line 119 87073] Train: [61/100][1555/1557] Data 0.009 (0.166) Batch 1.117 (1.109) Remain 18:42:48 loss: 0.3635 Lr: 0.00181 [2024-02-19 00:22:25,023 INFO misc.py line 119 87073] Train: [61/100][1556/1557] Data 0.020 (0.166) Batch 1.000 (1.109) Remain 18:42:43 loss: 0.7854 Lr: 0.00181 [2024-02-19 00:22:25,927 INFO misc.py line 119 87073] Train: [61/100][1557/1557] Data 0.006 (0.166) Batch 0.904 (1.109) Remain 18:42:34 loss: 0.3486 Lr: 0.00181 [2024-02-19 00:22:25,928 INFO misc.py line 136 87073] Train result: loss: 0.3081 [2024-02-19 00:22:25,928 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 00:22:55,213 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6002 [2024-02-19 00:22:55,997 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 1.0313 [2024-02-19 00:22:58,126 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4940 [2024-02-19 00:23:00,336 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2821 [2024-02-19 00:23:05,282 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3412 [2024-02-19 00:23:05,980 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2782 [2024-02-19 00:23:07,245 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2615 [2024-02-19 00:23:10,202 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2497 [2024-02-19 00:23:12,012 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2867 [2024-02-19 00:23:13,631 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7130/0.7754/0.9092. [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9242/0.9565 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9903 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8562/0.9723 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3963/0.4611 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5973/0.6074 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7849/0.9193 [2024-02-19 00:23:13,631 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8337/0.8959 [2024-02-19 00:23:13,632 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9240/0.9662 [2024-02-19 00:23:13,632 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8503/0.9036 [2024-02-19 00:23:13,632 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7807/0.8775 [2024-02-19 00:23:13,632 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7439/0.8465 [2024-02-19 00:23:13,632 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5945/0.6835 [2024-02-19 00:23:13,632 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 00:23:13,635 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 00:23:13,635 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 00:23:20,082 INFO misc.py line 119 87073] Train: [62/100][1/1557] Data 1.490 (1.490) Batch 2.337 (2.337) Remain 39:24:38 loss: 0.6188 Lr: 0.00181 [2024-02-19 00:23:21,029 INFO misc.py line 119 87073] Train: [62/100][2/1557] Data 0.006 (0.006) Batch 0.944 (0.944) Remain 15:55:32 loss: 0.3870 Lr: 0.00181 [2024-02-19 00:23:22,054 INFO misc.py line 119 87073] Train: [62/100][3/1557] Data 0.009 (0.009) Batch 1.028 (1.028) Remain 17:20:17 loss: 0.2387 Lr: 0.00181 [2024-02-19 00:23:23,099 INFO misc.py line 119 87073] Train: [62/100][4/1557] Data 0.006 (0.006) Batch 1.044 (1.044) Remain 17:36:35 loss: 0.2327 Lr: 0.00181 [2024-02-19 00:23:23,890 INFO misc.py line 119 87073] Train: [62/100][5/1557] Data 0.006 (0.006) Batch 0.793 (0.918) Remain 15:29:29 loss: 0.2696 Lr: 0.00181 [2024-02-19 00:23:24,672 INFO misc.py line 119 87073] Train: [62/100][6/1557] Data 0.004 (0.006) Batch 0.783 (0.873) Remain 14:43:39 loss: 0.3822 Lr: 0.00181 [2024-02-19 00:23:35,060 INFO misc.py line 119 87073] Train: [62/100][7/1557] Data 9.476 (2.373) Batch 10.388 (3.252) Remain 54:50:40 loss: 0.1047 Lr: 0.00181 [2024-02-19 00:23:35,867 INFO misc.py line 119 87073] Train: [62/100][8/1557] Data 0.003 (1.899) Batch 0.803 (2.762) Remain 46:34:56 loss: 0.4578 Lr: 0.00181 [2024-02-19 00:23:36,683 INFO misc.py line 119 87073] Train: [62/100][9/1557] Data 0.008 (1.584) Batch 0.819 (2.438) Remain 41:07:14 loss: 0.3745 Lr: 0.00181 [2024-02-19 00:23:37,594 INFO misc.py line 119 87073] Train: [62/100][10/1557] Data 0.004 (1.358) Batch 0.878 (2.215) Remain 37:21:41 loss: 0.1944 Lr: 0.00181 [2024-02-19 00:23:38,575 INFO misc.py line 119 87073] Train: [62/100][11/1557] Data 0.037 (1.193) Batch 1.006 (2.064) Remain 34:48:38 loss: 0.5413 Lr: 0.00181 [2024-02-19 00:23:39,344 INFO misc.py line 119 87073] Train: [62/100][12/1557] Data 0.013 (1.062) Batch 0.778 (1.921) Remain 32:23:57 loss: 0.3886 Lr: 0.00181 [2024-02-19 00:23:40,101 INFO misc.py line 119 87073] Train: [62/100][13/1557] Data 0.004 (0.956) Batch 0.752 (1.804) Remain 30:25:39 loss: 0.2553 Lr: 0.00181 [2024-02-19 00:23:41,448 INFO misc.py line 119 87073] Train: [62/100][14/1557] Data 0.009 (0.870) Batch 1.345 (1.763) Remain 29:43:23 loss: 0.1089 Lr: 0.00181 [2024-02-19 00:23:42,270 INFO misc.py line 119 87073] Train: [62/100][15/1557] Data 0.011 (0.798) Batch 0.828 (1.685) Remain 28:24:33 loss: 0.2111 Lr: 0.00181 [2024-02-19 00:23:43,209 INFO misc.py line 119 87073] Train: [62/100][16/1557] Data 0.005 (0.737) Batch 0.939 (1.627) Remain 27:26:30 loss: 0.3388 Lr: 0.00181 [2024-02-19 00:23:44,144 INFO misc.py line 119 87073] Train: [62/100][17/1557] Data 0.005 (0.685) Batch 0.935 (1.578) Remain 26:36:27 loss: 0.2812 Lr: 0.00181 [2024-02-19 00:23:45,204 INFO misc.py line 119 87073] Train: [62/100][18/1557] Data 0.005 (0.640) Batch 1.055 (1.543) Remain 26:01:10 loss: 0.3164 Lr: 0.00181 [2024-02-19 00:23:45,955 INFO misc.py line 119 87073] Train: [62/100][19/1557] Data 0.009 (0.600) Batch 0.757 (1.494) Remain 25:11:25 loss: 0.2517 Lr: 0.00181 [2024-02-19 00:23:46,723 INFO misc.py line 119 87073] Train: [62/100][20/1557] Data 0.004 (0.565) Batch 0.764 (1.451) Remain 24:27:58 loss: 0.1802 Lr: 0.00181 [2024-02-19 00:23:48,000 INFO misc.py line 119 87073] Train: [62/100][21/1557] Data 0.007 (0.534) Batch 1.275 (1.441) Remain 24:18:02 loss: 0.3995 Lr: 0.00181 [2024-02-19 00:23:49,018 INFO misc.py line 119 87073] Train: [62/100][22/1557] Data 0.010 (0.507) Batch 1.016 (1.419) Remain 23:55:21 loss: 0.4794 Lr: 0.00181 [2024-02-19 00:23:49,908 INFO misc.py line 119 87073] Train: [62/100][23/1557] Data 0.013 (0.482) Batch 0.898 (1.393) Remain 23:29:00 loss: 0.3714 Lr: 0.00181 [2024-02-19 00:23:50,978 INFO misc.py line 119 87073] Train: [62/100][24/1557] Data 0.005 (0.459) Batch 1.070 (1.377) Remain 23:13:26 loss: 0.3492 Lr: 0.00181 [2024-02-19 00:23:51,941 INFO misc.py line 119 87073] Train: [62/100][25/1557] Data 0.005 (0.439) Batch 0.962 (1.358) Remain 22:54:18 loss: 0.1151 Lr: 0.00181 [2024-02-19 00:23:52,671 INFO misc.py line 119 87073] Train: [62/100][26/1557] Data 0.006 (0.420) Batch 0.726 (1.331) Remain 22:26:26 loss: 0.2454 Lr: 0.00181 [2024-02-19 00:23:53,449 INFO misc.py line 119 87073] Train: [62/100][27/1557] Data 0.010 (0.403) Batch 0.781 (1.308) Remain 22:03:14 loss: 0.4674 Lr: 0.00181 [2024-02-19 00:23:54,535 INFO misc.py line 119 87073] Train: [62/100][28/1557] Data 0.006 (0.387) Batch 1.088 (1.299) Remain 21:54:17 loss: 0.1233 Lr: 0.00181 [2024-02-19 00:23:55,509 INFO misc.py line 119 87073] Train: [62/100][29/1557] Data 0.005 (0.372) Batch 0.975 (1.287) Remain 21:41:38 loss: 0.2490 Lr: 0.00181 [2024-02-19 00:23:56,469 INFO misc.py line 119 87073] Train: [62/100][30/1557] Data 0.004 (0.359) Batch 0.960 (1.275) Remain 21:29:22 loss: 0.3968 Lr: 0.00181 [2024-02-19 00:23:57,463 INFO misc.py line 119 87073] Train: [62/100][31/1557] Data 0.005 (0.346) Batch 0.995 (1.265) Remain 21:19:14 loss: 0.8839 Lr: 0.00181 [2024-02-19 00:23:58,457 INFO misc.py line 119 87073] Train: [62/100][32/1557] Data 0.003 (0.334) Batch 0.995 (1.255) Remain 21:09:47 loss: 0.3871 Lr: 0.00181 [2024-02-19 00:23:59,251 INFO misc.py line 119 87073] Train: [62/100][33/1557] Data 0.004 (0.323) Batch 0.787 (1.240) Remain 20:53:59 loss: 0.2084 Lr: 0.00181 [2024-02-19 00:23:59,997 INFO misc.py line 119 87073] Train: [62/100][34/1557] Data 0.010 (0.313) Batch 0.753 (1.224) Remain 20:38:05 loss: 0.3403 Lr: 0.00181 [2024-02-19 00:24:01,206 INFO misc.py line 119 87073] Train: [62/100][35/1557] Data 0.003 (0.303) Batch 1.208 (1.224) Remain 20:37:33 loss: 0.0969 Lr: 0.00181 [2024-02-19 00:24:02,128 INFO misc.py line 119 87073] Train: [62/100][36/1557] Data 0.004 (0.294) Batch 0.922 (1.214) Remain 20:28:18 loss: 0.2935 Lr: 0.00181 [2024-02-19 00:24:03,068 INFO misc.py line 119 87073] Train: [62/100][37/1557] Data 0.004 (0.286) Batch 0.941 (1.206) Remain 20:20:08 loss: 0.2267 Lr: 0.00181 [2024-02-19 00:24:04,212 INFO misc.py line 119 87073] Train: [62/100][38/1557] Data 0.003 (0.278) Batch 1.133 (1.204) Remain 20:17:59 loss: 0.4292 Lr: 0.00181 [2024-02-19 00:24:05,188 INFO misc.py line 119 87073] Train: [62/100][39/1557] Data 0.014 (0.270) Batch 0.986 (1.198) Remain 20:11:51 loss: 0.4818 Lr: 0.00181 [2024-02-19 00:24:05,972 INFO misc.py line 119 87073] Train: [62/100][40/1557] Data 0.003 (0.263) Batch 0.785 (1.187) Remain 20:00:32 loss: 0.0823 Lr: 0.00181 [2024-02-19 00:24:06,716 INFO misc.py line 119 87073] Train: [62/100][41/1557] Data 0.003 (0.256) Batch 0.736 (1.175) Remain 19:48:31 loss: 0.3395 Lr: 0.00181 [2024-02-19 00:24:07,948 INFO misc.py line 119 87073] Train: [62/100][42/1557] Data 0.010 (0.250) Batch 1.229 (1.177) Remain 19:49:53 loss: 0.1709 Lr: 0.00181 [2024-02-19 00:24:08,706 INFO misc.py line 119 87073] Train: [62/100][43/1557] Data 0.014 (0.244) Batch 0.767 (1.166) Remain 19:39:31 loss: 0.2524 Lr: 0.00181 [2024-02-19 00:24:09,585 INFO misc.py line 119 87073] Train: [62/100][44/1557] Data 0.005 (0.238) Batch 0.878 (1.159) Remain 19:32:23 loss: 0.3497 Lr: 0.00181 [2024-02-19 00:24:10,555 INFO misc.py line 119 87073] Train: [62/100][45/1557] Data 0.006 (0.233) Batch 0.971 (1.155) Remain 19:27:51 loss: 0.5119 Lr: 0.00181 [2024-02-19 00:24:11,656 INFO misc.py line 119 87073] Train: [62/100][46/1557] Data 0.004 (0.227) Batch 1.102 (1.154) Remain 19:26:35 loss: 0.2646 Lr: 0.00181 [2024-02-19 00:24:12,493 INFO misc.py line 119 87073] Train: [62/100][47/1557] Data 0.003 (0.222) Batch 0.834 (1.146) Remain 19:19:13 loss: 0.5185 Lr: 0.00181 [2024-02-19 00:24:13,282 INFO misc.py line 119 87073] Train: [62/100][48/1557] Data 0.007 (0.217) Batch 0.789 (1.138) Remain 19:11:11 loss: 0.3073 Lr: 0.00181 [2024-02-19 00:24:14,360 INFO misc.py line 119 87073] Train: [62/100][49/1557] Data 0.006 (0.213) Batch 1.074 (1.137) Remain 19:09:44 loss: 0.1183 Lr: 0.00181 [2024-02-19 00:24:15,470 INFO misc.py line 119 87073] Train: [62/100][50/1557] Data 0.010 (0.209) Batch 1.112 (1.136) Remain 19:09:11 loss: 0.5004 Lr: 0.00181 [2024-02-19 00:24:16,607 INFO misc.py line 119 87073] Train: [62/100][51/1557] Data 0.009 (0.204) Batch 1.134 (1.136) Remain 19:09:07 loss: 0.3701 Lr: 0.00181 [2024-02-19 00:24:17,631 INFO misc.py line 119 87073] Train: [62/100][52/1557] Data 0.010 (0.200) Batch 1.028 (1.134) Remain 19:06:52 loss: 0.2204 Lr: 0.00181 [2024-02-19 00:24:18,690 INFO misc.py line 119 87073] Train: [62/100][53/1557] Data 0.007 (0.197) Batch 1.059 (1.133) Remain 19:05:19 loss: 0.3710 Lr: 0.00181 [2024-02-19 00:24:19,487 INFO misc.py line 119 87073] Train: [62/100][54/1557] Data 0.008 (0.193) Batch 0.798 (1.126) Remain 18:58:40 loss: 0.2382 Lr: 0.00181 [2024-02-19 00:24:20,258 INFO misc.py line 119 87073] Train: [62/100][55/1557] Data 0.007 (0.189) Batch 0.772 (1.119) Remain 18:51:45 loss: 0.3482 Lr: 0.00181 [2024-02-19 00:24:21,562 INFO misc.py line 119 87073] Train: [62/100][56/1557] Data 0.005 (0.186) Batch 1.302 (1.123) Remain 18:55:14 loss: 0.1795 Lr: 0.00181 [2024-02-19 00:24:22,587 INFO misc.py line 119 87073] Train: [62/100][57/1557] Data 0.007 (0.182) Batch 1.020 (1.121) Remain 18:53:17 loss: 0.1431 Lr: 0.00181 [2024-02-19 00:24:23,434 INFO misc.py line 119 87073] Train: [62/100][58/1557] Data 0.012 (0.179) Batch 0.855 (1.116) Remain 18:48:22 loss: 0.3475 Lr: 0.00181 [2024-02-19 00:24:24,440 INFO misc.py line 119 87073] Train: [62/100][59/1557] Data 0.004 (0.176) Batch 1.008 (1.114) Remain 18:46:24 loss: 0.3878 Lr: 0.00181 [2024-02-19 00:24:25,469 INFO misc.py line 119 87073] Train: [62/100][60/1557] Data 0.003 (0.173) Batch 1.026 (1.113) Remain 18:44:49 loss: 0.1032 Lr: 0.00181 [2024-02-19 00:24:26,231 INFO misc.py line 119 87073] Train: [62/100][61/1557] Data 0.006 (0.170) Batch 0.762 (1.106) Remain 18:38:40 loss: 0.2450 Lr: 0.00181 [2024-02-19 00:24:26,958 INFO misc.py line 119 87073] Train: [62/100][62/1557] Data 0.007 (0.168) Batch 0.727 (1.100) Remain 18:32:09 loss: 0.2239 Lr: 0.00181 [2024-02-19 00:24:41,881 INFO misc.py line 119 87073] Train: [62/100][63/1557] Data 13.851 (0.396) Batch 14.926 (1.330) Remain 22:25:06 loss: 0.1546 Lr: 0.00181 [2024-02-19 00:24:42,764 INFO misc.py line 119 87073] Train: [62/100][64/1557] Data 0.003 (0.389) Batch 0.883 (1.323) Remain 22:17:40 loss: 0.3163 Lr: 0.00181 [2024-02-19 00:24:43,781 INFO misc.py line 119 87073] Train: [62/100][65/1557] Data 0.004 (0.383) Batch 1.012 (1.318) Remain 22:12:35 loss: 0.2854 Lr: 0.00181 [2024-02-19 00:24:44,916 INFO misc.py line 119 87073] Train: [62/100][66/1557] Data 0.008 (0.377) Batch 1.134 (1.315) Remain 22:09:36 loss: 0.3581 Lr: 0.00181 [2024-02-19 00:24:45,889 INFO misc.py line 119 87073] Train: [62/100][67/1557] Data 0.008 (0.371) Batch 0.978 (1.310) Remain 22:04:15 loss: 0.2641 Lr: 0.00181 [2024-02-19 00:24:46,650 INFO misc.py line 119 87073] Train: [62/100][68/1557] Data 0.003 (0.366) Batch 0.759 (1.301) Remain 21:55:40 loss: 0.2703 Lr: 0.00181 [2024-02-19 00:24:47,429 INFO misc.py line 119 87073] Train: [62/100][69/1557] Data 0.006 (0.360) Batch 0.772 (1.293) Remain 21:47:32 loss: 0.4089 Lr: 0.00181 [2024-02-19 00:24:48,640 INFO misc.py line 119 87073] Train: [62/100][70/1557] Data 0.012 (0.355) Batch 1.213 (1.292) Remain 21:46:18 loss: 0.1021 Lr: 0.00181 [2024-02-19 00:24:49,617 INFO misc.py line 119 87073] Train: [62/100][71/1557] Data 0.011 (0.350) Batch 0.983 (1.288) Remain 21:41:41 loss: 0.3246 Lr: 0.00181 [2024-02-19 00:24:50,575 INFO misc.py line 119 87073] Train: [62/100][72/1557] Data 0.006 (0.345) Batch 0.957 (1.283) Remain 21:36:49 loss: 0.3490 Lr: 0.00181 [2024-02-19 00:24:51,541 INFO misc.py line 119 87073] Train: [62/100][73/1557] Data 0.006 (0.340) Batch 0.967 (1.278) Remain 21:32:13 loss: 0.2734 Lr: 0.00180 [2024-02-19 00:24:52,684 INFO misc.py line 119 87073] Train: [62/100][74/1557] Data 0.005 (0.335) Batch 1.142 (1.276) Remain 21:30:16 loss: 0.3540 Lr: 0.00180 [2024-02-19 00:24:53,463 INFO misc.py line 119 87073] Train: [62/100][75/1557] Data 0.006 (0.331) Batch 0.781 (1.270) Remain 21:23:17 loss: 0.5150 Lr: 0.00180 [2024-02-19 00:24:54,302 INFO misc.py line 119 87073] Train: [62/100][76/1557] Data 0.004 (0.326) Batch 0.838 (1.264) Remain 21:17:17 loss: 0.2169 Lr: 0.00180 [2024-02-19 00:24:55,515 INFO misc.py line 119 87073] Train: [62/100][77/1557] Data 0.004 (0.322) Batch 1.213 (1.263) Remain 21:16:34 loss: 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INFO misc.py line 119 87073] Train: [62/100][84/1557] Data 0.016 (0.295) Batch 1.074 (1.233) Remain 20:45:41 loss: 0.1185 Lr: 0.00180 [2024-02-19 00:25:02,817 INFO misc.py line 119 87073] Train: [62/100][85/1557] Data 0.014 (0.291) Batch 0.926 (1.229) Remain 20:41:53 loss: 0.5728 Lr: 0.00180 [2024-02-19 00:25:03,695 INFO misc.py line 119 87073] Train: [62/100][86/1557] Data 0.005 (0.288) Batch 0.879 (1.225) Remain 20:37:36 loss: 0.5128 Lr: 0.00180 [2024-02-19 00:25:04,785 INFO misc.py line 119 87073] Train: [62/100][87/1557] Data 0.003 (0.284) Batch 1.083 (1.223) Remain 20:35:53 loss: 0.1422 Lr: 0.00180 [2024-02-19 00:25:05,905 INFO misc.py line 119 87073] Train: [62/100][88/1557] Data 0.010 (0.281) Batch 1.123 (1.222) Remain 20:34:40 loss: 0.3217 Lr: 0.00180 [2024-02-19 00:25:06,600 INFO misc.py line 119 87073] Train: [62/100][89/1557] Data 0.008 (0.278) Batch 0.696 (1.216) Remain 20:28:28 loss: 0.1708 Lr: 0.00180 [2024-02-19 00:25:07,344 INFO misc.py line 119 87073] Train: 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INFO misc.py line 119 87073] Train: [62/100][109/1557] Data 0.004 (0.227) Batch 0.861 (1.159) Remain 19:30:51 loss: 0.1205 Lr: 0.00180 [2024-02-19 00:25:25,678 INFO misc.py line 119 87073] Train: [62/100][110/1557] Data 0.007 (0.225) Batch 0.771 (1.155) Remain 19:27:10 loss: 0.2344 Lr: 0.00180 [2024-02-19 00:25:26,441 INFO misc.py line 119 87073] Train: [62/100][111/1557] Data 0.005 (0.223) Batch 0.764 (1.152) Remain 19:23:29 loss: 0.3578 Lr: 0.00180 [2024-02-19 00:25:27,728 INFO misc.py line 119 87073] Train: [62/100][112/1557] Data 0.004 (0.221) Batch 1.286 (1.153) Remain 19:24:42 loss: 0.1752 Lr: 0.00180 [2024-02-19 00:25:28,627 INFO misc.py line 119 87073] Train: [62/100][113/1557] Data 0.006 (0.219) Batch 0.900 (1.151) Remain 19:22:21 loss: 0.2393 Lr: 0.00180 [2024-02-19 00:25:29,737 INFO misc.py line 119 87073] Train: [62/100][114/1557] Data 0.006 (0.217) Batch 1.109 (1.150) Remain 19:21:58 loss: 0.2420 Lr: 0.00180 [2024-02-19 00:25:30,842 INFO misc.py line 119 87073] Train: 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Batch 0.882 (1.268) Remain 21:20:49 loss: 0.4471 Lr: 0.00180 [2024-02-19 00:25:52,655 INFO misc.py line 119 87073] Train: [62/100][122/1557] Data 0.005 (0.333) Batch 0.965 (1.266) Remain 21:18:14 loss: 0.4866 Lr: 0.00180 [2024-02-19 00:25:53,548 INFO misc.py line 119 87073] Train: [62/100][123/1557] Data 0.006 (0.331) Batch 0.892 (1.262) Remain 21:15:04 loss: 0.2573 Lr: 0.00180 [2024-02-19 00:25:54,352 INFO misc.py line 119 87073] Train: [62/100][124/1557] Data 0.006 (0.328) Batch 0.796 (1.259) Remain 21:11:09 loss: 0.4036 Lr: 0.00180 [2024-02-19 00:25:55,092 INFO misc.py line 119 87073] Train: [62/100][125/1557] Data 0.014 (0.325) Batch 0.749 (1.254) Remain 21:06:54 loss: 0.2467 Lr: 0.00180 [2024-02-19 00:25:56,376 INFO misc.py line 119 87073] Train: [62/100][126/1557] Data 0.004 (0.323) Batch 1.274 (1.255) Remain 21:07:03 loss: 0.1074 Lr: 0.00180 [2024-02-19 00:25:57,288 INFO misc.py line 119 87073] Train: [62/100][127/1557] Data 0.014 (0.320) Batch 0.922 (1.252) Remain 21:04:19 loss: 0.5302 Lr: 0.00180 [2024-02-19 00:25:58,212 INFO misc.py line 119 87073] Train: [62/100][128/1557] Data 0.004 (0.318) Batch 0.924 (1.249) Remain 21:01:39 loss: 0.7293 Lr: 0.00180 [2024-02-19 00:25:59,091 INFO misc.py line 119 87073] Train: [62/100][129/1557] Data 0.005 (0.315) Batch 0.877 (1.246) Remain 20:58:39 loss: 0.2961 Lr: 0.00180 [2024-02-19 00:26:00,212 INFO misc.py line 119 87073] Train: [62/100][130/1557] Data 0.007 (0.313) Batch 1.115 (1.245) Remain 20:57:35 loss: 0.5107 Lr: 0.00180 [2024-02-19 00:26:01,022 INFO misc.py line 119 87073] Train: [62/100][131/1557] Data 0.012 (0.310) Batch 0.818 (1.242) Remain 20:54:11 loss: 0.2524 Lr: 0.00180 [2024-02-19 00:26:01,728 INFO misc.py line 119 87073] Train: [62/100][132/1557] Data 0.004 (0.308) Batch 0.706 (1.238) Remain 20:49:58 loss: 0.4990 Lr: 0.00180 [2024-02-19 00:26:02,902 INFO misc.py line 119 87073] Train: [62/100][133/1557] Data 0.004 (0.306) Batch 1.173 (1.237) Remain 20:49:27 loss: 0.2022 Lr: 0.00180 [2024-02-19 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87073] Train: [62/100][140/1557] Data 0.006 (0.290) Batch 1.127 (1.224) Remain 20:35:32 loss: 0.1398 Lr: 0.00180 [2024-02-19 00:26:10,753 INFO misc.py line 119 87073] Train: [62/100][141/1557] Data 0.014 (0.288) Batch 1.050 (1.222) Remain 20:34:15 loss: 0.2757 Lr: 0.00180 [2024-02-19 00:26:11,755 INFO misc.py line 119 87073] Train: [62/100][142/1557] Data 0.013 (0.286) Batch 1.002 (1.221) Remain 20:32:37 loss: 0.3215 Lr: 0.00180 [2024-02-19 00:26:12,765 INFO misc.py line 119 87073] Train: [62/100][143/1557] Data 0.014 (0.284) Batch 1.010 (1.219) Remain 20:31:05 loss: 0.2692 Lr: 0.00180 [2024-02-19 00:26:13,693 INFO misc.py line 119 87073] Train: [62/100][144/1557] Data 0.014 (0.283) Batch 0.937 (1.217) Remain 20:29:02 loss: 0.3043 Lr: 0.00180 [2024-02-19 00:26:14,450 INFO misc.py line 119 87073] Train: [62/100][145/1557] Data 0.004 (0.281) Batch 0.756 (1.214) Remain 20:25:45 loss: 0.1483 Lr: 0.00180 [2024-02-19 00:26:15,224 INFO misc.py line 119 87073] Train: [62/100][146/1557] Data 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line 119 87073] Train: [62/100][165/1557] Data 0.005 (0.247) Batch 1.257 (1.182) Remain 19:52:29 loss: 0.1166 Lr: 0.00180 [2024-02-19 00:26:34,211 INFO misc.py line 119 87073] Train: [62/100][166/1557] Data 0.004 (0.245) Batch 0.753 (1.179) Remain 19:49:49 loss: 0.1408 Lr: 0.00180 [2024-02-19 00:26:34,971 INFO misc.py line 119 87073] Train: [62/100][167/1557] Data 0.005 (0.244) Batch 0.747 (1.176) Remain 19:47:09 loss: 0.1954 Lr: 0.00180 [2024-02-19 00:26:36,213 INFO misc.py line 119 87073] Train: [62/100][168/1557] Data 0.017 (0.242) Batch 1.245 (1.177) Remain 19:47:33 loss: 0.1398 Lr: 0.00180 [2024-02-19 00:26:37,272 INFO misc.py line 119 87073] Train: [62/100][169/1557] Data 0.014 (0.241) Batch 1.060 (1.176) Remain 19:46:49 loss: 0.4031 Lr: 0.00180 [2024-02-19 00:26:38,306 INFO misc.py line 119 87073] Train: [62/100][170/1557] Data 0.012 (0.240) Batch 1.042 (1.175) Remain 19:45:59 loss: 0.1054 Lr: 0.00180 [2024-02-19 00:26:39,402 INFO misc.py line 119 87073] Train: 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Batch 0.941 (1.251) Remain 21:02:04 loss: 0.1654 Lr: 0.00180 [2024-02-19 00:27:00,634 INFO misc.py line 119 87073] Train: [62/100][178/1557] Data 0.004 (0.312) Batch 0.960 (1.249) Remain 21:00:22 loss: 0.6478 Lr: 0.00180 [2024-02-19 00:27:01,542 INFO misc.py line 119 87073] Train: [62/100][179/1557] Data 0.004 (0.311) Batch 0.907 (1.247) Remain 20:58:23 loss: 0.2484 Lr: 0.00180 [2024-02-19 00:27:03,948 INFO misc.py line 119 87073] Train: [62/100][180/1557] Data 1.002 (0.314) Batch 2.405 (1.254) Remain 21:04:58 loss: 0.1892 Lr: 0.00180 [2024-02-19 00:27:04,722 INFO misc.py line 119 87073] Train: [62/100][181/1557] Data 0.008 (0.313) Batch 0.776 (1.251) Remain 21:02:14 loss: 0.4487 Lr: 0.00180 [2024-02-19 00:27:06,027 INFO misc.py line 119 87073] Train: [62/100][182/1557] Data 0.004 (0.311) Batch 1.298 (1.251) Remain 21:02:29 loss: 0.2483 Lr: 0.00180 [2024-02-19 00:27:06,948 INFO misc.py line 119 87073] Train: [62/100][183/1557] Data 0.012 (0.309) Batch 0.929 (1.249) Remain 21:00:39 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87073] Train: [62/100][196/1557] Data 0.005 (0.289) Batch 1.173 (1.229) Remain 20:39:50 loss: 0.1439 Lr: 0.00180 [2024-02-19 00:27:20,380 INFO misc.py line 119 87073] Train: [62/100][197/1557] Data 0.007 (0.288) Batch 1.119 (1.228) Remain 20:39:15 loss: 0.2801 Lr: 0.00180 [2024-02-19 00:27:21,471 INFO misc.py line 119 87073] Train: [62/100][198/1557] Data 0.005 (0.286) Batch 1.093 (1.228) Remain 20:38:31 loss: 0.3874 Lr: 0.00180 [2024-02-19 00:27:22,402 INFO misc.py line 119 87073] Train: [62/100][199/1557] Data 0.003 (0.285) Batch 0.929 (1.226) Remain 20:36:58 loss: 0.3834 Lr: 0.00180 [2024-02-19 00:27:23,264 INFO misc.py line 119 87073] Train: [62/100][200/1557] Data 0.006 (0.283) Batch 0.863 (1.224) Remain 20:35:05 loss: 0.3043 Lr: 0.00180 [2024-02-19 00:27:24,026 INFO misc.py line 119 87073] Train: [62/100][201/1557] Data 0.004 (0.282) Batch 0.760 (1.222) Remain 20:32:42 loss: 0.4350 Lr: 0.00180 [2024-02-19 00:27:24,784 INFO misc.py line 119 87073] Train: [62/100][202/1557] Data 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line 119 87073] Train: [62/100][221/1557] Data 0.004 (0.257) Batch 1.078 (1.199) Remain 20:08:36 loss: 0.4362 Lr: 0.00180 [2024-02-19 00:27:44,201 INFO misc.py line 119 87073] Train: [62/100][222/1557] Data 0.005 (0.256) Batch 0.854 (1.197) Remain 20:07:00 loss: 0.2371 Lr: 0.00180 [2024-02-19 00:27:45,007 INFO misc.py line 119 87073] Train: [62/100][223/1557] Data 0.006 (0.255) Batch 0.802 (1.195) Remain 20:05:10 loss: 0.2301 Lr: 0.00180 [2024-02-19 00:27:46,273 INFO misc.py line 119 87073] Train: [62/100][224/1557] Data 0.010 (0.253) Batch 1.264 (1.196) Remain 20:05:28 loss: 0.1591 Lr: 0.00180 [2024-02-19 00:27:47,111 INFO misc.py line 119 87073] Train: [62/100][225/1557] Data 0.013 (0.252) Batch 0.847 (1.194) Remain 20:03:51 loss: 0.4366 Lr: 0.00180 [2024-02-19 00:27:48,044 INFO misc.py line 119 87073] Train: [62/100][226/1557] Data 0.004 (0.251) Batch 0.931 (1.193) Remain 20:02:39 loss: 0.4333 Lr: 0.00180 [2024-02-19 00:27:49,072 INFO misc.py line 119 87073] Train: 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Batch 0.887 (1.244) Remain 20:54:02 loss: 0.6783 Lr: 0.00180 [2024-02-19 00:28:09,081 INFO misc.py line 119 87073] Train: [62/100][234/1557] Data 0.004 (0.303) Batch 0.934 (1.243) Remain 20:52:40 loss: 0.2955 Lr: 0.00180 [2024-02-19 00:28:09,988 INFO misc.py line 119 87073] Train: [62/100][235/1557] Data 0.004 (0.302) Batch 0.907 (1.241) Remain 20:51:11 loss: 0.5006 Lr: 0.00180 [2024-02-19 00:28:10,725 INFO misc.py line 119 87073] Train: [62/100][236/1557] Data 0.005 (0.300) Batch 0.730 (1.239) Remain 20:48:57 loss: 0.2378 Lr: 0.00180 [2024-02-19 00:28:11,491 INFO misc.py line 119 87073] Train: [62/100][237/1557] Data 0.012 (0.299) Batch 0.773 (1.237) Remain 20:46:55 loss: 0.3525 Lr: 0.00180 [2024-02-19 00:28:12,820 INFO misc.py line 119 87073] Train: [62/100][238/1557] Data 0.004 (0.298) Batch 1.319 (1.237) Remain 20:47:15 loss: 0.1477 Lr: 0.00180 [2024-02-19 00:28:13,785 INFO misc.py line 119 87073] Train: [62/100][239/1557] Data 0.014 (0.297) Batch 0.975 (1.236) Remain 20:46:07 loss: 0.2310 Lr: 0.00180 [2024-02-19 00:28:14,853 INFO misc.py line 119 87073] Train: [62/100][240/1557] Data 0.005 (0.295) Batch 1.069 (1.235) Remain 20:45:23 loss: 0.1270 Lr: 0.00180 [2024-02-19 00:28:15,831 INFO misc.py line 119 87073] Train: [62/100][241/1557] Data 0.004 (0.294) Batch 0.978 (1.234) Remain 20:44:16 loss: 0.2456 Lr: 0.00180 [2024-02-19 00:28:16,759 INFO misc.py line 119 87073] Train: [62/100][242/1557] Data 0.004 (0.293) Batch 0.926 (1.233) Remain 20:42:57 loss: 0.2275 Lr: 0.00180 [2024-02-19 00:28:17,542 INFO misc.py line 119 87073] Train: [62/100][243/1557] Data 0.006 (0.292) Batch 0.776 (1.231) Remain 20:41:00 loss: 0.1973 Lr: 0.00180 [2024-02-19 00:28:18,268 INFO misc.py line 119 87073] Train: [62/100][244/1557] Data 0.013 (0.291) Batch 0.734 (1.229) Remain 20:38:55 loss: 0.2638 Lr: 0.00180 [2024-02-19 00:28:19,466 INFO misc.py line 119 87073] Train: [62/100][245/1557] Data 0.004 (0.289) Batch 1.197 (1.229) Remain 20:38:45 loss: 0.1868 Lr: 0.00180 [2024-02-19 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87073] Train: [62/100][252/1557] Data 0.007 (0.281) Batch 1.139 (1.222) Remain 20:31:18 loss: 0.1066 Lr: 0.00180 [2024-02-19 00:28:27,222 INFO misc.py line 119 87073] Train: [62/100][253/1557] Data 0.013 (0.280) Batch 0.960 (1.221) Remain 20:30:13 loss: 0.1989 Lr: 0.00180 [2024-02-19 00:28:28,260 INFO misc.py line 119 87073] Train: [62/100][254/1557] Data 0.006 (0.279) Batch 1.039 (1.220) Remain 20:29:28 loss: 0.3888 Lr: 0.00180 [2024-02-19 00:28:29,348 INFO misc.py line 119 87073] Train: [62/100][255/1557] Data 0.004 (0.278) Batch 1.089 (1.219) Remain 20:28:56 loss: 0.8430 Lr: 0.00180 [2024-02-19 00:28:30,223 INFO misc.py line 119 87073] Train: [62/100][256/1557] Data 0.004 (0.277) Batch 0.873 (1.218) Remain 20:27:32 loss: 0.3689 Lr: 0.00180 [2024-02-19 00:28:31,066 INFO misc.py line 119 87073] Train: [62/100][257/1557] Data 0.006 (0.276) Batch 0.835 (1.217) Remain 20:25:59 loss: 0.2684 Lr: 0.00180 [2024-02-19 00:28:31,841 INFO misc.py line 119 87073] Train: [62/100][258/1557] Data 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line 119 87073] Train: [62/100][277/1557] Data 0.005 (0.256) Batch 1.114 (1.197) Remain 20:05:26 loss: 0.2651 Lr: 0.00179 [2024-02-19 00:28:50,666 INFO misc.py line 119 87073] Train: [62/100][278/1557] Data 0.004 (0.255) Batch 0.759 (1.195) Remain 20:03:49 loss: 0.4099 Lr: 0.00179 [2024-02-19 00:28:51,434 INFO misc.py line 119 87073] Train: [62/100][279/1557] Data 0.003 (0.254) Batch 0.758 (1.193) Remain 20:02:12 loss: 0.2688 Lr: 0.00179 [2024-02-19 00:28:52,683 INFO misc.py line 119 87073] Train: [62/100][280/1557] Data 0.013 (0.254) Batch 1.252 (1.194) Remain 20:02:23 loss: 0.1916 Lr: 0.00179 [2024-02-19 00:28:53,731 INFO misc.py line 119 87073] Train: [62/100][281/1557] Data 0.010 (0.253) Batch 1.046 (1.193) Remain 20:01:50 loss: 0.2135 Lr: 0.00179 [2024-02-19 00:28:54,742 INFO misc.py line 119 87073] Train: [62/100][282/1557] Data 0.012 (0.252) Batch 1.015 (1.192) Remain 20:01:10 loss: 0.2680 Lr: 0.00179 [2024-02-19 00:28:55,685 INFO misc.py line 119 87073] Train: 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Batch 0.968 (1.235) Remain 20:43:27 loss: 0.4104 Lr: 0.00179 [2024-02-19 00:29:16,196 INFO misc.py line 119 87073] Train: [62/100][290/1557] Data 0.004 (0.295) Batch 1.068 (1.234) Remain 20:42:51 loss: 0.3160 Lr: 0.00179 [2024-02-19 00:29:17,081 INFO misc.py line 119 87073] Train: [62/100][291/1557] Data 0.004 (0.294) Batch 0.884 (1.233) Remain 20:41:36 loss: 0.3001 Lr: 0.00179 [2024-02-19 00:29:17,862 INFO misc.py line 119 87073] Train: [62/100][292/1557] Data 0.005 (0.293) Batch 0.776 (1.231) Remain 20:39:59 loss: 0.3254 Lr: 0.00179 [2024-02-19 00:29:18,580 INFO misc.py line 119 87073] Train: [62/100][293/1557] Data 0.010 (0.292) Batch 0.723 (1.229) Remain 20:38:12 loss: 0.2985 Lr: 0.00179 [2024-02-19 00:29:19,795 INFO misc.py line 119 87073] Train: [62/100][294/1557] Data 0.005 (0.291) Batch 1.214 (1.229) Remain 20:38:08 loss: 0.2042 Lr: 0.00179 [2024-02-19 00:29:20,787 INFO misc.py line 119 87073] Train: [62/100][295/1557] Data 0.007 (0.290) Batch 0.993 (1.229) Remain 20:37:18 loss: 0.4476 Lr: 0.00179 [2024-02-19 00:29:21,745 INFO misc.py line 119 87073] Train: [62/100][296/1557] Data 0.003 (0.289) Batch 0.957 (1.228) Remain 20:36:21 loss: 0.1223 Lr: 0.00179 [2024-02-19 00:29:22,760 INFO misc.py line 119 87073] Train: [62/100][297/1557] Data 0.004 (0.288) Batch 1.014 (1.227) Remain 20:35:35 loss: 0.1041 Lr: 0.00179 [2024-02-19 00:29:23,600 INFO misc.py line 119 87073] Train: [62/100][298/1557] Data 0.006 (0.287) Batch 0.840 (1.226) Remain 20:34:15 loss: 0.6189 Lr: 0.00179 [2024-02-19 00:29:24,359 INFO misc.py line 119 87073] Train: [62/100][299/1557] Data 0.005 (0.286) Batch 0.751 (1.224) Remain 20:32:37 loss: 0.2974 Lr: 0.00179 [2024-02-19 00:29:25,098 INFO misc.py line 119 87073] Train: [62/100][300/1557] Data 0.012 (0.285) Batch 0.747 (1.222) Remain 20:30:59 loss: 0.3481 Lr: 0.00179 [2024-02-19 00:29:26,313 INFO misc.py line 119 87073] Train: [62/100][301/1557] Data 0.005 (0.284) Batch 1.215 (1.222) Remain 20:30:56 loss: 0.1638 Lr: 0.00179 [2024-02-19 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87073] Train: [62/100][308/1557] Data 0.005 (0.278) Batch 1.035 (1.216) Remain 20:23:58 loss: 0.1217 Lr: 0.00179 [2024-02-19 00:29:33,722 INFO misc.py line 119 87073] Train: [62/100][309/1557] Data 0.006 (0.277) Batch 0.920 (1.215) Remain 20:22:59 loss: 0.7450 Lr: 0.00179 [2024-02-19 00:29:34,532 INFO misc.py line 119 87073] Train: [62/100][310/1557] Data 0.003 (0.276) Batch 0.810 (1.213) Remain 20:21:38 loss: 0.1732 Lr: 0.00179 [2024-02-19 00:29:35,467 INFO misc.py line 119 87073] Train: [62/100][311/1557] Data 0.004 (0.275) Batch 0.933 (1.212) Remain 20:20:42 loss: 0.2495 Lr: 0.00179 [2024-02-19 00:29:36,389 INFO misc.py line 119 87073] Train: [62/100][312/1557] Data 0.005 (0.274) Batch 0.876 (1.211) Remain 20:19:35 loss: 0.3322 Lr: 0.00179 [2024-02-19 00:29:37,141 INFO misc.py line 119 87073] Train: [62/100][313/1557] Data 0.051 (0.273) Batch 0.799 (1.210) Remain 20:18:13 loss: 0.2583 Lr: 0.00179 [2024-02-19 00:29:37,881 INFO misc.py line 119 87073] Train: [62/100][314/1557] Data 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20:11:24 loss: 0.1858 Lr: 0.00179 [2024-02-19 00:29:44,186 INFO misc.py line 119 87073] Train: [62/100][321/1557] Data 0.006 (0.267) Batch 0.668 (1.202) Remain 20:09:41 loss: 0.4281 Lr: 0.00179 [2024-02-19 00:29:45,324 INFO misc.py line 119 87073] Train: [62/100][322/1557] Data 0.014 (0.266) Batch 1.147 (1.201) Remain 20:09:30 loss: 0.1509 Lr: 0.00179 [2024-02-19 00:29:46,280 INFO misc.py line 119 87073] Train: [62/100][323/1557] Data 0.005 (0.265) Batch 0.958 (1.201) Remain 20:08:42 loss: 0.5817 Lr: 0.00179 [2024-02-19 00:29:47,305 INFO misc.py line 119 87073] Train: [62/100][324/1557] Data 0.003 (0.264) Batch 1.023 (1.200) Remain 20:08:08 loss: 0.4489 Lr: 0.00179 [2024-02-19 00:29:48,334 INFO misc.py line 119 87073] Train: [62/100][325/1557] Data 0.006 (0.263) Batch 1.030 (1.200) Remain 20:07:35 loss: 0.2213 Lr: 0.00179 [2024-02-19 00:29:49,396 INFO misc.py line 119 87073] Train: [62/100][326/1557] Data 0.004 (0.263) Batch 1.063 (1.199) Remain 20:07:08 loss: 0.1047 Lr: 0.00179 [2024-02-19 00:29:50,150 INFO misc.py line 119 87073] Train: [62/100][327/1557] Data 0.003 (0.262) Batch 0.754 (1.198) Remain 20:05:44 loss: 0.2757 Lr: 0.00179 [2024-02-19 00:29:50,905 INFO misc.py line 119 87073] Train: [62/100][328/1557] Data 0.004 (0.261) Batch 0.748 (1.196) Remain 20:04:19 loss: 0.3873 Lr: 0.00179 [2024-02-19 00:29:52,064 INFO misc.py line 119 87073] Train: [62/100][329/1557] Data 0.012 (0.260) Batch 1.154 (1.196) Remain 20:04:10 loss: 0.2231 Lr: 0.00179 [2024-02-19 00:29:52,869 INFO misc.py line 119 87073] Train: [62/100][330/1557] Data 0.015 (0.260) Batch 0.816 (1.195) Remain 20:02:58 loss: 0.2068 Lr: 0.00179 [2024-02-19 00:29:53,743 INFO misc.py line 119 87073] Train: [62/100][331/1557] Data 0.004 (0.259) Batch 0.872 (1.194) Remain 20:01:58 loss: 0.2497 Lr: 0.00179 [2024-02-19 00:29:54,801 INFO misc.py line 119 87073] Train: [62/100][332/1557] Data 0.005 (0.258) Batch 1.054 (1.194) Remain 20:01:31 loss: 0.3359 Lr: 0.00179 [2024-02-19 00:29:55,676 INFO misc.py line 119 87073] Train: [62/100][333/1557] Data 0.009 (0.257) Batch 0.880 (1.193) Remain 20:00:32 loss: 0.4300 Lr: 0.00179 [2024-02-19 00:29:56,423 INFO misc.py line 119 87073] Train: [62/100][334/1557] Data 0.004 (0.256) Batch 0.747 (1.191) Remain 19:59:10 loss: 0.4873 Lr: 0.00179 [2024-02-19 00:29:57,106 INFO misc.py line 119 87073] Train: [62/100][335/1557] Data 0.004 (0.256) Batch 0.681 (1.190) Remain 19:57:36 loss: 0.1546 Lr: 0.00179 [2024-02-19 00:29:58,450 INFO misc.py line 119 87073] Train: [62/100][336/1557] Data 0.006 (0.255) Batch 1.340 (1.190) Remain 19:58:02 loss: 0.1596 Lr: 0.00179 [2024-02-19 00:29:59,693 INFO misc.py line 119 87073] Train: [62/100][337/1557] Data 0.010 (0.254) Batch 1.240 (1.191) Remain 19:58:10 loss: 0.1301 Lr: 0.00179 [2024-02-19 00:30:00,660 INFO misc.py line 119 87073] Train: [62/100][338/1557] Data 0.013 (0.254) Batch 0.976 (1.190) Remain 19:57:30 loss: 0.2026 Lr: 0.00179 [2024-02-19 00:30:01,669 INFO misc.py line 119 87073] Train: 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Batch 0.857 (1.230) Remain 20:38:02 loss: 0.0681 Lr: 0.00179 [2024-02-19 00:30:23,713 INFO misc.py line 119 87073] Train: [62/100][346/1557] Data 0.006 (0.294) Batch 0.901 (1.229) Remain 20:37:03 loss: 0.3940 Lr: 0.00179 [2024-02-19 00:30:24,622 INFO misc.py line 119 87073] Train: [62/100][347/1557] Data 0.005 (0.293) Batch 0.904 (1.228) Remain 20:36:04 loss: 0.1527 Lr: 0.00179 [2024-02-19 00:30:25,470 INFO misc.py line 119 87073] Train: [62/100][348/1557] Data 0.010 (0.292) Batch 0.852 (1.227) Remain 20:34:57 loss: 0.2990 Lr: 0.00179 [2024-02-19 00:30:26,230 INFO misc.py line 119 87073] Train: [62/100][349/1557] Data 0.006 (0.291) Batch 0.762 (1.226) Remain 20:33:35 loss: 0.1982 Lr: 0.00179 [2024-02-19 00:30:27,485 INFO misc.py line 119 87073] Train: [62/100][350/1557] Data 0.005 (0.290) Batch 1.247 (1.226) Remain 20:33:37 loss: 0.1482 Lr: 0.00179 [2024-02-19 00:30:28,397 INFO misc.py line 119 87073] Train: [62/100][351/1557] Data 0.012 (0.290) Batch 0.920 (1.225) Remain 20:32:43 loss: 0.4569 Lr: 0.00179 [2024-02-19 00:30:29,290 INFO misc.py line 119 87073] Train: [62/100][352/1557] Data 0.004 (0.289) Batch 0.894 (1.224) Remain 20:31:44 loss: 0.4163 Lr: 0.00179 [2024-02-19 00:30:30,333 INFO misc.py line 119 87073] Train: [62/100][353/1557] Data 0.004 (0.288) Batch 1.040 (1.224) Remain 20:31:11 loss: 0.5502 Lr: 0.00179 [2024-02-19 00:30:31,271 INFO misc.py line 119 87073] Train: [62/100][354/1557] Data 0.007 (0.287) Batch 0.940 (1.223) Remain 20:30:21 loss: 0.1765 Lr: 0.00179 [2024-02-19 00:30:33,691 INFO misc.py line 119 87073] Train: [62/100][355/1557] Data 0.969 (0.289) Batch 2.420 (1.226) Remain 20:33:45 loss: 0.2860 Lr: 0.00179 [2024-02-19 00:30:34,471 INFO misc.py line 119 87073] Train: [62/100][356/1557] Data 0.005 (0.288) Batch 0.781 (1.225) Remain 20:32:28 loss: 0.4907 Lr: 0.00179 [2024-02-19 00:30:35,638 INFO misc.py line 119 87073] Train: [62/100][357/1557] Data 0.004 (0.287) Batch 1.165 (1.225) Remain 20:32:16 loss: 0.1889 Lr: 0.00179 [2024-02-19 00:30:36,645 INFO misc.py line 119 87073] Train: [62/100][358/1557] Data 0.006 (0.287) Batch 1.008 (1.224) Remain 20:31:38 loss: 0.0664 Lr: 0.00179 [2024-02-19 00:30:37,537 INFO misc.py line 119 87073] Train: [62/100][359/1557] Data 0.006 (0.286) Batch 0.892 (1.223) Remain 20:30:41 loss: 0.1817 Lr: 0.00179 [2024-02-19 00:30:38,510 INFO misc.py line 119 87073] Train: [62/100][360/1557] Data 0.006 (0.285) Batch 0.968 (1.223) Remain 20:29:56 loss: 0.2745 Lr: 0.00179 [2024-02-19 00:30:39,503 INFO misc.py line 119 87073] Train: [62/100][361/1557] Data 0.010 (0.284) Batch 0.998 (1.222) Remain 20:29:17 loss: 0.2018 Lr: 0.00179 [2024-02-19 00:30:40,268 INFO misc.py line 119 87073] Train: [62/100][362/1557] Data 0.005 (0.283) Batch 0.765 (1.221) Remain 20:27:59 loss: 0.3299 Lr: 0.00179 [2024-02-19 00:30:41,031 INFO misc.py line 119 87073] Train: [62/100][363/1557] Data 0.005 (0.283) Batch 0.758 (1.219) Remain 20:26:40 loss: 0.1924 Lr: 0.00179 [2024-02-19 00:30:42,062 INFO misc.py line 119 87073] Train: [62/100][364/1557] Data 0.010 (0.282) Batch 1.036 (1.219) Remain 20:26:09 loss: 0.0996 Lr: 0.00179 [2024-02-19 00:30:43,075 INFO misc.py line 119 87073] Train: [62/100][365/1557] Data 0.005 (0.281) Batch 1.011 (1.218) Remain 20:25:33 loss: 0.4678 Lr: 0.00179 [2024-02-19 00:30:43,912 INFO misc.py line 119 87073] Train: [62/100][366/1557] Data 0.007 (0.280) Batch 0.839 (1.217) Remain 20:24:28 loss: 0.4998 Lr: 0.00179 [2024-02-19 00:30:44,932 INFO misc.py line 119 87073] Train: [62/100][367/1557] Data 0.006 (0.280) Batch 1.021 (1.217) Remain 20:23:55 loss: 0.3641 Lr: 0.00179 [2024-02-19 00:30:45,840 INFO misc.py line 119 87073] Train: [62/100][368/1557] Data 0.004 (0.279) Batch 0.905 (1.216) Remain 20:23:02 loss: 0.0842 Lr: 0.00179 [2024-02-19 00:30:46,582 INFO misc.py line 119 87073] Train: [62/100][369/1557] Data 0.007 (0.278) Batch 0.744 (1.215) Remain 20:21:43 loss: 0.2050 Lr: 0.00179 [2024-02-19 00:30:47,328 INFO misc.py line 119 87073] Train: [62/100][370/1557] Data 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line 119 87073] Train: [62/100][389/1557] Data 0.004 (0.264) Batch 1.341 (1.203) Remain 20:09:39 loss: 0.4590 Lr: 0.00179 [2024-02-19 00:31:07,141 INFO misc.py line 119 87073] Train: [62/100][390/1557] Data 0.039 (0.264) Batch 0.746 (1.202) Remain 20:08:26 loss: 0.2318 Lr: 0.00179 [2024-02-19 00:31:07,871 INFO misc.py line 119 87073] Train: [62/100][391/1557] Data 0.004 (0.263) Batch 0.723 (1.201) Remain 20:07:11 loss: 0.2319 Lr: 0.00179 [2024-02-19 00:31:09,108 INFO misc.py line 119 87073] Train: [62/100][392/1557] Data 0.011 (0.262) Batch 1.239 (1.201) Remain 20:07:15 loss: 0.2527 Lr: 0.00179 [2024-02-19 00:31:10,317 INFO misc.py line 119 87073] Train: [62/100][393/1557] Data 0.009 (0.262) Batch 1.166 (1.201) Remain 20:07:09 loss: 0.1045 Lr: 0.00179 [2024-02-19 00:31:11,302 INFO misc.py line 119 87073] Train: [62/100][394/1557] Data 0.054 (0.261) Batch 1.031 (1.200) Remain 20:06:41 loss: 0.4649 Lr: 0.00179 [2024-02-19 00:31:12,193 INFO misc.py line 119 87073] Train: 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Batch 0.943 (1.232) Remain 20:38:49 loss: 0.5356 Lr: 0.00179 [2024-02-19 00:31:33,344 INFO misc.py line 119 87073] Train: [62/100][402/1557] Data 0.009 (0.294) Batch 0.869 (1.231) Remain 20:37:53 loss: 0.6262 Lr: 0.00179 [2024-02-19 00:31:34,333 INFO misc.py line 119 87073] Train: [62/100][403/1557] Data 0.006 (0.293) Batch 0.982 (1.231) Remain 20:37:14 loss: 0.4069 Lr: 0.00179 [2024-02-19 00:31:35,121 INFO misc.py line 119 87073] Train: [62/100][404/1557] Data 0.013 (0.292) Batch 0.797 (1.230) Remain 20:36:07 loss: 0.1459 Lr: 0.00179 [2024-02-19 00:31:35,878 INFO misc.py line 119 87073] Train: [62/100][405/1557] Data 0.004 (0.291) Batch 0.757 (1.228) Remain 20:34:55 loss: 0.2183 Lr: 0.00179 [2024-02-19 00:31:37,149 INFO misc.py line 119 87073] Train: [62/100][406/1557] Data 0.004 (0.291) Batch 1.270 (1.229) Remain 20:35:00 loss: 0.1995 Lr: 0.00179 [2024-02-19 00:31:38,080 INFO misc.py line 119 87073] Train: [62/100][407/1557] Data 0.006 (0.290) Batch 0.932 (1.228) Remain 20:34:15 loss: 0.4382 Lr: 0.00179 [2024-02-19 00:31:38,991 INFO misc.py line 119 87073] Train: [62/100][408/1557] Data 0.005 (0.289) Batch 0.912 (1.227) Remain 20:33:26 loss: 0.5783 Lr: 0.00179 [2024-02-19 00:31:39,891 INFO misc.py line 119 87073] Train: [62/100][409/1557] Data 0.004 (0.289) Batch 0.900 (1.226) Remain 20:32:37 loss: 0.2359 Lr: 0.00179 [2024-02-19 00:31:40,893 INFO misc.py line 119 87073] Train: [62/100][410/1557] Data 0.004 (0.288) Batch 0.999 (1.226) Remain 20:32:02 loss: 0.4718 Lr: 0.00179 [2024-02-19 00:31:41,654 INFO misc.py line 119 87073] Train: [62/100][411/1557] Data 0.007 (0.287) Batch 0.761 (1.225) Remain 20:30:52 loss: 0.2937 Lr: 0.00179 [2024-02-19 00:31:42,401 INFO misc.py line 119 87073] Train: [62/100][412/1557] Data 0.007 (0.287) Batch 0.748 (1.223) Remain 20:29:40 loss: 0.3115 Lr: 0.00179 [2024-02-19 00:31:43,557 INFO misc.py line 119 87073] Train: [62/100][413/1557] Data 0.006 (0.286) Batch 1.157 (1.223) Remain 20:29:29 loss: 0.2480 Lr: 0.00179 [2024-02-19 00:31:44,491 INFO misc.py line 119 87073] Train: [62/100][414/1557] Data 0.005 (0.285) Batch 0.935 (1.222) Remain 20:28:46 loss: 0.7874 Lr: 0.00179 [2024-02-19 00:31:45,709 INFO misc.py line 119 87073] Train: [62/100][415/1557] Data 0.004 (0.285) Batch 1.217 (1.222) Remain 20:28:44 loss: 0.4444 Lr: 0.00179 [2024-02-19 00:31:46,641 INFO misc.py line 119 87073] Train: [62/100][416/1557] Data 0.005 (0.284) Batch 0.932 (1.222) Remain 20:28:00 loss: 0.3571 Lr: 0.00179 [2024-02-19 00:31:47,564 INFO misc.py line 119 87073] Train: [62/100][417/1557] Data 0.005 (0.283) Batch 0.925 (1.221) Remain 20:27:16 loss: 0.4179 Lr: 0.00179 [2024-02-19 00:31:48,319 INFO misc.py line 119 87073] Train: [62/100][418/1557] Data 0.005 (0.283) Batch 0.755 (1.220) Remain 20:26:07 loss: 0.1128 Lr: 0.00179 [2024-02-19 00:31:49,056 INFO misc.py line 119 87073] Train: [62/100][419/1557] Data 0.004 (0.282) Batch 0.737 (1.219) Remain 20:24:55 loss: 0.3486 Lr: 0.00179 [2024-02-19 00:31:50,122 INFO misc.py line 119 87073] Train: [62/100][420/1557] Data 0.004 (0.281) Batch 1.066 (1.218) Remain 20:24:32 loss: 0.1729 Lr: 0.00179 [2024-02-19 00:31:51,142 INFO misc.py line 119 87073] Train: [62/100][421/1557] Data 0.004 (0.281) Batch 1.021 (1.218) Remain 20:24:02 loss: 0.3703 Lr: 0.00179 [2024-02-19 00:31:52,061 INFO misc.py line 119 87073] Train: [62/100][422/1557] Data 0.004 (0.280) Batch 0.919 (1.217) Remain 20:23:18 loss: 0.3058 Lr: 0.00179 [2024-02-19 00:31:53,222 INFO misc.py line 119 87073] Train: [62/100][423/1557] Data 0.003 (0.279) Batch 1.160 (1.217) Remain 20:23:09 loss: 0.2046 Lr: 0.00179 [2024-02-19 00:31:54,067 INFO misc.py line 119 87073] Train: [62/100][424/1557] Data 0.005 (0.279) Batch 0.846 (1.216) Remain 20:22:14 loss: 0.5331 Lr: 0.00179 [2024-02-19 00:31:54,876 INFO misc.py line 119 87073] Train: [62/100][425/1557] Data 0.004 (0.278) Batch 0.809 (1.215) Remain 20:21:15 loss: 0.4265 Lr: 0.00179 [2024-02-19 00:31:55,745 INFO misc.py line 119 87073] Train: [62/100][426/1557] Data 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20:17:56 loss: 0.2013 Lr: 0.00179 [2024-02-19 00:32:02,818 INFO misc.py line 119 87073] Train: [62/100][433/1557] Data 0.005 (0.273) Batch 0.790 (1.211) Remain 20:16:55 loss: 0.2826 Lr: 0.00179 [2024-02-19 00:32:03,932 INFO misc.py line 119 87073] Train: [62/100][434/1557] Data 0.005 (0.272) Batch 1.094 (1.211) Remain 20:16:38 loss: 0.1550 Lr: 0.00179 [2024-02-19 00:32:04,942 INFO misc.py line 119 87073] Train: [62/100][435/1557] Data 0.025 (0.272) Batch 1.028 (1.210) Remain 20:16:11 loss: 0.5701 Lr: 0.00179 [2024-02-19 00:32:06,005 INFO misc.py line 119 87073] Train: [62/100][436/1557] Data 0.007 (0.271) Batch 1.045 (1.210) Remain 20:15:47 loss: 0.2707 Lr: 0.00179 [2024-02-19 00:32:07,163 INFO misc.py line 119 87073] Train: [62/100][437/1557] Data 0.024 (0.270) Batch 1.166 (1.210) Remain 20:15:40 loss: 0.3680 Lr: 0.00179 [2024-02-19 00:32:08,166 INFO misc.py line 119 87073] Train: [62/100][438/1557] Data 0.016 (0.270) Batch 1.007 (1.209) Remain 20:15:10 loss: 0.1013 Lr: 0.00179 [2024-02-19 00:32:08,884 INFO misc.py line 119 87073] Train: [62/100][439/1557] Data 0.012 (0.269) Batch 0.726 (1.208) Remain 20:14:02 loss: 0.3309 Lr: 0.00179 [2024-02-19 00:32:09,613 INFO misc.py line 119 87073] Train: [62/100][440/1557] Data 0.004 (0.269) Batch 0.716 (1.207) Remain 20:12:53 loss: 0.1512 Lr: 0.00179 [2024-02-19 00:32:10,841 INFO misc.py line 119 87073] Train: [62/100][441/1557] Data 0.017 (0.268) Batch 1.224 (1.207) Remain 20:12:54 loss: 0.2117 Lr: 0.00179 [2024-02-19 00:32:11,699 INFO misc.py line 119 87073] Train: [62/100][442/1557] Data 0.021 (0.268) Batch 0.875 (1.206) Remain 20:12:07 loss: 0.2393 Lr: 0.00179 [2024-02-19 00:32:12,722 INFO misc.py line 119 87073] Train: [62/100][443/1557] Data 0.005 (0.267) Batch 1.024 (1.206) Remain 20:11:41 loss: 0.5679 Lr: 0.00179 [2024-02-19 00:32:13,600 INFO misc.py line 119 87073] Train: [62/100][444/1557] Data 0.004 (0.266) Batch 0.878 (1.205) Remain 20:10:55 loss: 0.2899 Lr: 0.00179 [2024-02-19 00:32:14,700 INFO misc.py line 119 87073] Train: [62/100][445/1557] Data 0.005 (0.266) Batch 1.100 (1.205) Remain 20:10:39 loss: 0.3504 Lr: 0.00179 [2024-02-19 00:32:15,491 INFO misc.py line 119 87073] Train: [62/100][446/1557] Data 0.005 (0.265) Batch 0.791 (1.204) Remain 20:09:42 loss: 0.2456 Lr: 0.00179 [2024-02-19 00:32:16,244 INFO misc.py line 119 87073] Train: [62/100][447/1557] Data 0.004 (0.265) Batch 0.753 (1.203) Remain 20:08:39 loss: 0.2942 Lr: 0.00179 [2024-02-19 00:32:17,494 INFO misc.py line 119 87073] Train: [62/100][448/1557] Data 0.005 (0.264) Batch 1.249 (1.203) Remain 20:08:44 loss: 0.2268 Lr: 0.00179 [2024-02-19 00:32:18,326 INFO misc.py line 119 87073] Train: [62/100][449/1557] Data 0.006 (0.263) Batch 0.833 (1.202) Remain 20:07:53 loss: 0.3835 Lr: 0.00179 [2024-02-19 00:32:19,211 INFO misc.py line 119 87073] Train: [62/100][450/1557] Data 0.005 (0.263) Batch 0.886 (1.202) Remain 20:07:09 loss: 0.4936 Lr: 0.00179 [2024-02-19 00:32:20,167 INFO misc.py line 119 87073] Train: 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Batch 0.922 (1.234) Remain 20:39:40 loss: 0.3482 Lr: 0.00179 [2024-02-19 00:32:43,235 INFO misc.py line 119 87073] Train: [62/100][458/1557] Data 0.005 (0.294) Batch 0.855 (1.233) Remain 20:38:48 loss: 0.4124 Lr: 0.00179 [2024-02-19 00:32:44,208 INFO misc.py line 119 87073] Train: [62/100][459/1557] Data 0.005 (0.294) Batch 0.967 (1.233) Remain 20:38:12 loss: 0.5759 Lr: 0.00179 [2024-02-19 00:32:44,997 INFO misc.py line 119 87073] Train: [62/100][460/1557] Data 0.012 (0.293) Batch 0.795 (1.232) Remain 20:37:13 loss: 0.2625 Lr: 0.00179 [2024-02-19 00:32:45,775 INFO misc.py line 119 87073] Train: [62/100][461/1557] Data 0.005 (0.292) Batch 0.779 (1.231) Remain 20:36:12 loss: 0.4080 Lr: 0.00179 [2024-02-19 00:32:47,005 INFO misc.py line 119 87073] Train: [62/100][462/1557] Data 0.004 (0.292) Batch 1.226 (1.231) Remain 20:36:10 loss: 0.1491 Lr: 0.00179 [2024-02-19 00:32:48,116 INFO misc.py line 119 87073] Train: [62/100][463/1557] Data 0.008 (0.291) Batch 1.112 (1.231) Remain 20:35:53 loss: 0.1598 Lr: 0.00179 [2024-02-19 00:32:49,236 INFO misc.py line 119 87073] Train: [62/100][464/1557] Data 0.007 (0.290) Batch 1.115 (1.230) Remain 20:35:37 loss: 0.6773 Lr: 0.00179 [2024-02-19 00:32:50,217 INFO misc.py line 119 87073] Train: [62/100][465/1557] Data 0.012 (0.290) Batch 0.989 (1.230) Remain 20:35:04 loss: 0.6633 Lr: 0.00179 [2024-02-19 00:32:51,120 INFO misc.py line 119 87073] Train: [62/100][466/1557] Data 0.005 (0.289) Batch 0.903 (1.229) Remain 20:34:20 loss: 0.6811 Lr: 0.00178 [2024-02-19 00:32:51,906 INFO misc.py line 119 87073] Train: [62/100][467/1557] Data 0.005 (0.289) Batch 0.783 (1.228) Remain 20:33:21 loss: 0.3608 Lr: 0.00178 [2024-02-19 00:32:52,615 INFO misc.py line 119 87073] Train: [62/100][468/1557] Data 0.008 (0.288) Batch 0.712 (1.227) Remain 20:32:13 loss: 0.3171 Lr: 0.00178 [2024-02-19 00:32:53,832 INFO misc.py line 119 87073] Train: [62/100][469/1557] Data 0.004 (0.287) Batch 1.217 (1.227) Remain 20:32:11 loss: 0.2016 Lr: 0.00178 [2024-02-19 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[2024-02-19 00:33:18,051 INFO misc.py line 119 87073] Train: [62/100][495/1557] Data 0.004 (0.273) Batch 0.725 (1.211) Remain 20:15:58 loss: 0.2060 Lr: 0.00178 [2024-02-19 00:33:18,781 INFO misc.py line 119 87073] Train: [62/100][496/1557] Data 0.004 (0.272) Batch 0.725 (1.210) Remain 20:14:58 loss: 0.4894 Lr: 0.00178 [2024-02-19 00:33:19,954 INFO misc.py line 119 87073] Train: [62/100][497/1557] Data 0.010 (0.271) Batch 1.174 (1.210) Remain 20:14:52 loss: 0.1873 Lr: 0.00178 [2024-02-19 00:33:21,112 INFO misc.py line 119 87073] Train: [62/100][498/1557] Data 0.009 (0.271) Batch 1.110 (1.210) Remain 20:14:39 loss: 0.2806 Lr: 0.00178 [2024-02-19 00:33:22,076 INFO misc.py line 119 87073] Train: [62/100][499/1557] Data 0.057 (0.270) Batch 1.015 (1.210) Remain 20:14:14 loss: 0.1707 Lr: 0.00178 [2024-02-19 00:33:23,187 INFO misc.py line 119 87073] Train: [62/100][500/1557] Data 0.005 (0.270) Batch 1.112 (1.210) Remain 20:14:01 loss: 0.3517 Lr: 0.00178 [2024-02-19 00:33:24,090 INFO misc.py line 119 87073] Train: [62/100][501/1557] Data 0.004 (0.269) Batch 0.903 (1.209) Remain 20:13:22 loss: 0.3022 Lr: 0.00178 [2024-02-19 00:33:24,839 INFO misc.py line 119 87073] Train: [62/100][502/1557] Data 0.004 (0.269) Batch 0.748 (1.208) Remain 20:12:25 loss: 0.2649 Lr: 0.00178 [2024-02-19 00:33:25,645 INFO misc.py line 119 87073] Train: [62/100][503/1557] Data 0.006 (0.268) Batch 0.807 (1.207) Remain 20:11:36 loss: 0.3947 Lr: 0.00178 [2024-02-19 00:33:26,956 INFO misc.py line 119 87073] Train: [62/100][504/1557] Data 0.005 (0.268) Batch 1.311 (1.207) Remain 20:11:47 loss: 0.1648 Lr: 0.00178 [2024-02-19 00:33:27,815 INFO misc.py line 119 87073] Train: [62/100][505/1557] Data 0.007 (0.267) Batch 0.860 (1.207) Remain 20:11:04 loss: 0.2927 Lr: 0.00178 [2024-02-19 00:33:28,682 INFO misc.py line 119 87073] Train: [62/100][506/1557] Data 0.005 (0.267) Batch 0.864 (1.206) Remain 20:10:22 loss: 0.2056 Lr: 0.00178 [2024-02-19 00:33:29,719 INFO misc.py line 119 87073] Train: 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Batch 0.948 (1.230) Remain 20:34:43 loss: 0.3452 Lr: 0.00178 [2024-02-19 00:33:50,573 INFO misc.py line 119 87073] Train: [62/100][514/1557] Data 0.006 (0.291) Batch 1.005 (1.230) Remain 20:34:15 loss: 0.3700 Lr: 0.00178 [2024-02-19 00:33:51,437 INFO misc.py line 119 87073] Train: [62/100][515/1557] Data 0.004 (0.290) Batch 0.862 (1.229) Remain 20:33:31 loss: 0.2785 Lr: 0.00178 [2024-02-19 00:33:52,213 INFO misc.py line 119 87073] Train: [62/100][516/1557] Data 0.006 (0.290) Batch 0.778 (1.228) Remain 20:32:37 loss: 0.2697 Lr: 0.00178 [2024-02-19 00:33:52,973 INFO misc.py line 119 87073] Train: [62/100][517/1557] Data 0.004 (0.289) Batch 0.758 (1.227) Remain 20:31:40 loss: 0.2506 Lr: 0.00178 [2024-02-19 00:33:54,158 INFO misc.py line 119 87073] Train: [62/100][518/1557] Data 0.006 (0.288) Batch 1.187 (1.227) Remain 20:31:34 loss: 0.1680 Lr: 0.00178 [2024-02-19 00:33:55,164 INFO misc.py line 119 87073] Train: [62/100][519/1557] Data 0.005 (0.288) Batch 1.006 (1.227) Remain 20:31:07 loss: 1.4539 Lr: 0.00178 [2024-02-19 00:33:56,131 INFO misc.py line 119 87073] Train: [62/100][520/1557] Data 0.005 (0.287) Batch 0.969 (1.226) Remain 20:30:36 loss: 0.6616 Lr: 0.00178 [2024-02-19 00:33:56,980 INFO misc.py line 119 87073] Train: [62/100][521/1557] Data 0.004 (0.287) Batch 0.849 (1.226) Remain 20:29:51 loss: 0.4324 Lr: 0.00178 [2024-02-19 00:33:57,853 INFO misc.py line 119 87073] Train: [62/100][522/1557] Data 0.004 (0.286) Batch 0.873 (1.225) Remain 20:29:09 loss: 0.2648 Lr: 0.00178 [2024-02-19 00:33:58,678 INFO misc.py line 119 87073] Train: [62/100][523/1557] Data 0.004 (0.286) Batch 0.825 (1.224) Remain 20:28:21 loss: 0.2472 Lr: 0.00178 [2024-02-19 00:33:59,407 INFO misc.py line 119 87073] Train: [62/100][524/1557] Data 0.003 (0.285) Batch 0.729 (1.223) Remain 20:27:23 loss: 0.2713 Lr: 0.00178 [2024-02-19 00:34:00,562 INFO misc.py line 119 87073] Train: [62/100][525/1557] Data 0.003 (0.285) Batch 1.154 (1.223) Remain 20:27:13 loss: 0.2463 Lr: 0.00178 [2024-02-19 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87073] Train: [62/100][532/1557] Data 0.003 (0.283) Batch 1.123 (1.223) Remain 20:27:04 loss: 0.1151 Lr: 0.00178 [2024-02-19 00:34:10,016 INFO misc.py line 119 87073] Train: [62/100][533/1557] Data 0.003 (0.282) Batch 0.896 (1.223) Remain 20:26:26 loss: 0.5380 Lr: 0.00178 [2024-02-19 00:34:11,068 INFO misc.py line 119 87073] Train: [62/100][534/1557] Data 0.004 (0.282) Batch 1.053 (1.222) Remain 20:26:06 loss: 0.2431 Lr: 0.00178 [2024-02-19 00:34:12,011 INFO misc.py line 119 87073] Train: [62/100][535/1557] Data 0.003 (0.281) Batch 0.942 (1.222) Remain 20:25:33 loss: 0.2798 Lr: 0.00178 [2024-02-19 00:34:13,068 INFO misc.py line 119 87073] Train: [62/100][536/1557] Data 0.004 (0.281) Batch 1.049 (1.221) Remain 20:25:12 loss: 0.1582 Lr: 0.00178 [2024-02-19 00:34:13,832 INFO misc.py line 119 87073] Train: [62/100][537/1557] Data 0.011 (0.280) Batch 0.769 (1.221) Remain 20:24:20 loss: 0.2163 Lr: 0.00178 [2024-02-19 00:34:14,600 INFO misc.py line 119 87073] Train: [62/100][538/1557] Data 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[2024-02-19 00:34:27,148 INFO misc.py line 119 87073] Train: [62/100][551/1557] Data 0.010 (0.273) Batch 0.762 (1.214) Remain 20:17:09 loss: 0.7170 Lr: 0.00178 [2024-02-19 00:34:27,937 INFO misc.py line 119 87073] Train: [62/100][552/1557] Data 0.007 (0.273) Batch 0.790 (1.213) Remain 20:16:21 loss: 0.1816 Lr: 0.00178 [2024-02-19 00:34:29,095 INFO misc.py line 119 87073] Train: [62/100][553/1557] Data 0.005 (0.272) Batch 1.158 (1.213) Remain 20:16:14 loss: 0.1632 Lr: 0.00178 [2024-02-19 00:34:30,043 INFO misc.py line 119 87073] Train: [62/100][554/1557] Data 0.005 (0.272) Batch 0.949 (1.212) Remain 20:15:44 loss: 0.4826 Lr: 0.00178 [2024-02-19 00:34:31,147 INFO misc.py line 119 87073] Train: [62/100][555/1557] Data 0.003 (0.271) Batch 1.104 (1.212) Remain 20:15:31 loss: 0.4037 Lr: 0.00178 [2024-02-19 00:34:32,119 INFO misc.py line 119 87073] Train: [62/100][556/1557] Data 0.007 (0.271) Batch 0.972 (1.212) Remain 20:15:03 loss: 0.5142 Lr: 0.00178 [2024-02-19 00:34:32,930 INFO misc.py line 119 87073] Train: [62/100][557/1557] Data 0.003 (0.270) Batch 0.811 (1.211) Remain 20:14:19 loss: 0.2036 Lr: 0.00178 [2024-02-19 00:34:33,675 INFO misc.py line 119 87073] Train: [62/100][558/1557] Data 0.004 (0.270) Batch 0.738 (1.210) Remain 20:13:26 loss: 0.2954 Lr: 0.00178 [2024-02-19 00:34:34,470 INFO misc.py line 119 87073] Train: [62/100][559/1557] Data 0.012 (0.269) Batch 0.802 (1.209) Remain 20:12:41 loss: 0.2932 Lr: 0.00178 [2024-02-19 00:34:35,783 INFO misc.py line 119 87073] Train: [62/100][560/1557] Data 0.004 (0.269) Batch 1.310 (1.210) Remain 20:12:50 loss: 0.2682 Lr: 0.00178 [2024-02-19 00:34:36,767 INFO misc.py line 119 87073] Train: [62/100][561/1557] Data 0.007 (0.268) Batch 0.988 (1.209) Remain 20:12:25 loss: 0.3915 Lr: 0.00178 [2024-02-19 00:34:37,790 INFO misc.py line 119 87073] Train: [62/100][562/1557] Data 0.003 (0.268) Batch 1.022 (1.209) Remain 20:12:04 loss: 0.1439 Lr: 0.00178 [2024-02-19 00:34:38,964 INFO misc.py line 119 87073] Train: 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Batch 0.987 (1.235) Remain 20:37:42 loss: 0.3628 Lr: 0.00178 [2024-02-19 00:35:01,780 INFO misc.py line 119 87073] Train: [62/100][570/1557] Data 0.042 (0.293) Batch 0.981 (1.234) Remain 20:37:13 loss: 0.2853 Lr: 0.00178 [2024-02-19 00:35:02,605 INFO misc.py line 119 87073] Train: [62/100][571/1557] Data 0.004 (0.293) Batch 0.825 (1.233) Remain 20:36:29 loss: 0.1860 Lr: 0.00178 [2024-02-19 00:35:03,454 INFO misc.py line 119 87073] Train: [62/100][572/1557] Data 0.004 (0.292) Batch 0.838 (1.233) Remain 20:35:46 loss: 0.3263 Lr: 0.00178 [2024-02-19 00:35:04,235 INFO misc.py line 119 87073] Train: [62/100][573/1557] Data 0.016 (0.292) Batch 0.792 (1.232) Remain 20:34:58 loss: 0.4017 Lr: 0.00178 [2024-02-19 00:35:05,430 INFO misc.py line 119 87073] Train: [62/100][574/1557] Data 0.003 (0.291) Batch 1.196 (1.232) Remain 20:34:53 loss: 0.1354 Lr: 0.00178 [2024-02-19 00:35:06,478 INFO misc.py line 119 87073] Train: [62/100][575/1557] Data 0.003 (0.291) Batch 1.047 (1.232) Remain 20:34:32 loss: 0.3897 Lr: 0.00178 [2024-02-19 00:35:07,555 INFO misc.py line 119 87073] Train: [62/100][576/1557] Data 0.004 (0.290) Batch 1.077 (1.231) Remain 20:34:15 loss: 0.2843 Lr: 0.00178 [2024-02-19 00:35:08,508 INFO misc.py line 119 87073] Train: [62/100][577/1557] Data 0.003 (0.290) Batch 0.953 (1.231) Remain 20:33:45 loss: 0.4478 Lr: 0.00178 [2024-02-19 00:35:09,425 INFO misc.py line 119 87073] Train: [62/100][578/1557] Data 0.003 (0.289) Batch 0.917 (1.230) Remain 20:33:11 loss: 0.4579 Lr: 0.00178 [2024-02-19 00:35:10,173 INFO misc.py line 119 87073] Train: [62/100][579/1557] Data 0.003 (0.289) Batch 0.747 (1.229) Remain 20:32:19 loss: 0.3823 Lr: 0.00178 [2024-02-19 00:35:10,903 INFO misc.py line 119 87073] Train: [62/100][580/1557] Data 0.004 (0.288) Batch 0.731 (1.229) Remain 20:31:26 loss: 0.1880 Lr: 0.00178 [2024-02-19 00:35:12,121 INFO misc.py line 119 87073] Train: [62/100][581/1557] Data 0.004 (0.288) Batch 1.217 (1.228) Remain 20:31:23 loss: 0.2612 Lr: 0.00178 [2024-02-19 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line 119 87073] Train: [62/100][613/1557] Data 0.010 (0.273) Batch 0.800 (1.212) Remain 20:13:52 loss: 0.1284 Lr: 0.00178 [2024-02-19 00:35:41,862 INFO misc.py line 119 87073] Train: [62/100][614/1557] Data 0.004 (0.273) Batch 0.700 (1.211) Remain 20:13:00 loss: 0.2308 Lr: 0.00178 [2024-02-19 00:35:42,620 INFO misc.py line 119 87073] Train: [62/100][615/1557] Data 0.005 (0.272) Batch 0.757 (1.210) Remain 20:12:14 loss: 0.1404 Lr: 0.00178 [2024-02-19 00:35:43,896 INFO misc.py line 119 87073] Train: [62/100][616/1557] Data 0.006 (0.272) Batch 1.278 (1.210) Remain 20:12:20 loss: 0.1330 Lr: 0.00178 [2024-02-19 00:35:44,942 INFO misc.py line 119 87073] Train: [62/100][617/1557] Data 0.004 (0.271) Batch 1.046 (1.210) Remain 20:12:03 loss: 0.1525 Lr: 0.00178 [2024-02-19 00:35:46,156 INFO misc.py line 119 87073] Train: [62/100][618/1557] Data 0.003 (0.271) Batch 1.211 (1.210) Remain 20:12:02 loss: 0.6308 Lr: 0.00178 [2024-02-19 00:35:47,038 INFO misc.py line 119 87073] Train: 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Batch 1.141 (1.233) Remain 20:35:17 loss: 0.2336 Lr: 0.00178 [2024-02-19 00:36:10,191 INFO misc.py line 119 87073] Train: [62/100][626/1557] Data 0.004 (0.294) Batch 1.039 (1.233) Remain 20:34:57 loss: 0.2099 Lr: 0.00178 [2024-02-19 00:36:11,152 INFO misc.py line 119 87073] Train: [62/100][627/1557] Data 0.004 (0.293) Batch 0.960 (1.233) Remain 20:34:29 loss: 0.5725 Lr: 0.00178 [2024-02-19 00:36:11,951 INFO misc.py line 119 87073] Train: [62/100][628/1557] Data 0.004 (0.293) Batch 0.795 (1.232) Remain 20:33:46 loss: 0.2906 Lr: 0.00178 [2024-02-19 00:36:12,702 INFO misc.py line 119 87073] Train: [62/100][629/1557] Data 0.009 (0.292) Batch 0.755 (1.231) Remain 20:32:59 loss: 0.2092 Lr: 0.00178 [2024-02-19 00:36:14,000 INFO misc.py line 119 87073] Train: [62/100][630/1557] Data 0.004 (0.292) Batch 1.299 (1.231) Remain 20:33:04 loss: 0.3411 Lr: 0.00178 [2024-02-19 00:36:14,976 INFO misc.py line 119 87073] Train: [62/100][631/1557] Data 0.003 (0.291) Batch 0.976 (1.231) Remain 20:32:39 loss: 0.5693 Lr: 0.00178 [2024-02-19 00:36:15,895 INFO misc.py line 119 87073] Train: [62/100][632/1557] Data 0.003 (0.291) Batch 0.919 (1.230) Remain 20:32:08 loss: 0.1986 Lr: 0.00178 [2024-02-19 00:36:16,858 INFO misc.py line 119 87073] Train: [62/100][633/1557] Data 0.004 (0.290) Batch 0.963 (1.230) Remain 20:31:41 loss: 0.5564 Lr: 0.00178 [2024-02-19 00:36:18,133 INFO misc.py line 119 87073] Train: [62/100][634/1557] Data 0.003 (0.290) Batch 1.276 (1.230) Remain 20:31:44 loss: 0.4892 Lr: 0.00178 [2024-02-19 00:36:18,919 INFO misc.py line 119 87073] Train: [62/100][635/1557] Data 0.003 (0.289) Batch 0.786 (1.229) Remain 20:31:01 loss: 0.4353 Lr: 0.00178 [2024-02-19 00:36:19,671 INFO misc.py line 119 87073] Train: [62/100][636/1557] Data 0.003 (0.289) Batch 0.742 (1.228) Remain 20:30:13 loss: 0.4387 Lr: 0.00178 [2024-02-19 00:36:20,883 INFO misc.py line 119 87073] Train: [62/100][637/1557] Data 0.012 (0.289) Batch 1.211 (1.228) Remain 20:30:10 loss: 0.2500 Lr: 0.00178 [2024-02-19 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0.004 (0.283) Batch 0.716 (1.223) Remain 20:24:20 loss: 0.2481 Lr: 0.00178 [2024-02-19 00:36:34,472 INFO misc.py line 119 87073] Train: [62/100][651/1557] Data 0.006 (0.283) Batch 1.228 (1.223) Remain 20:24:19 loss: 0.0906 Lr: 0.00178 [2024-02-19 00:36:35,319 INFO misc.py line 119 87073] Train: [62/100][652/1557] Data 0.005 (0.282) Batch 0.848 (1.222) Remain 20:23:44 loss: 0.7330 Lr: 0.00178 [2024-02-19 00:36:36,422 INFO misc.py line 119 87073] Train: [62/100][653/1557] Data 0.005 (0.282) Batch 1.102 (1.222) Remain 20:23:31 loss: 0.3483 Lr: 0.00178 [2024-02-19 00:36:37,305 INFO misc.py line 119 87073] Train: [62/100][654/1557] Data 0.007 (0.281) Batch 0.885 (1.222) Remain 20:22:59 loss: 0.2975 Lr: 0.00178 [2024-02-19 00:36:38,147 INFO misc.py line 119 87073] Train: [62/100][655/1557] Data 0.004 (0.281) Batch 0.838 (1.221) Remain 20:22:22 loss: 0.1139 Lr: 0.00178 [2024-02-19 00:36:38,892 INFO misc.py line 119 87073] Train: [62/100][656/1557] Data 0.009 (0.280) Batch 0.749 (1.220) Remain 20:21:38 loss: 0.2758 Lr: 0.00178 [2024-02-19 00:36:39,633 INFO misc.py line 119 87073] Train: [62/100][657/1557] Data 0.004 (0.280) Batch 0.737 (1.220) Remain 20:20:52 loss: 0.5804 Lr: 0.00178 [2024-02-19 00:36:40,707 INFO misc.py line 119 87073] Train: [62/100][658/1557] Data 0.010 (0.280) Batch 1.071 (1.219) Remain 20:20:37 loss: 0.1563 Lr: 0.00178 [2024-02-19 00:36:41,747 INFO misc.py line 119 87073] Train: [62/100][659/1557] Data 0.011 (0.279) Batch 1.046 (1.219) Remain 20:20:20 loss: 0.3053 Lr: 0.00178 [2024-02-19 00:36:42,601 INFO misc.py line 119 87073] Train: [62/100][660/1557] Data 0.006 (0.279) Batch 0.856 (1.218) Remain 20:19:46 loss: 0.3347 Lr: 0.00178 [2024-02-19 00:36:43,501 INFO misc.py line 119 87073] Train: [62/100][661/1557] Data 0.004 (0.278) Batch 0.899 (1.218) Remain 20:19:15 loss: 0.1982 Lr: 0.00178 [2024-02-19 00:36:44,503 INFO misc.py line 119 87073] Train: [62/100][662/1557] Data 0.006 (0.278) Batch 1.004 (1.218) Remain 20:18:54 loss: 0.3518 Lr: 0.00177 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Batch 0.865 (1.229) Remain 20:29:51 loss: 0.4696 Lr: 0.00177 [2024-02-19 00:37:16,297 INFO misc.py line 119 87073] Train: [62/100][682/1557] Data 0.004 (0.290) Batch 0.980 (1.229) Remain 20:29:28 loss: 0.1569 Lr: 0.00177 [2024-02-19 00:37:17,469 INFO misc.py line 119 87073] Train: [62/100][683/1557] Data 0.004 (0.290) Batch 1.172 (1.229) Remain 20:29:22 loss: 0.0932 Lr: 0.00177 [2024-02-19 00:37:18,216 INFO misc.py line 119 87073] Train: [62/100][684/1557] Data 0.003 (0.289) Batch 0.746 (1.228) Remain 20:28:38 loss: 0.5152 Lr: 0.00177 [2024-02-19 00:37:18,916 INFO misc.py line 119 87073] Train: [62/100][685/1557] Data 0.003 (0.289) Batch 0.694 (1.227) Remain 20:27:50 loss: 0.1956 Lr: 0.00177 [2024-02-19 00:37:20,182 INFO misc.py line 119 87073] Train: [62/100][686/1557] Data 0.010 (0.289) Batch 1.267 (1.227) Remain 20:27:52 loss: 0.1093 Lr: 0.00177 [2024-02-19 00:37:21,113 INFO misc.py line 119 87073] Train: [62/100][687/1557] Data 0.009 (0.288) Batch 0.936 (1.227) Remain 20:27:25 loss: 0.4138 Lr: 0.00177 [2024-02-19 00:37:21,992 INFO misc.py line 119 87073] Train: [62/100][688/1557] Data 0.004 (0.288) Batch 0.880 (1.226) Remain 20:26:54 loss: 0.2401 Lr: 0.00177 [2024-02-19 00:37:22,862 INFO misc.py line 119 87073] Train: [62/100][689/1557] Data 0.004 (0.287) Batch 0.863 (1.226) Remain 20:26:21 loss: 0.2514 Lr: 0.00177 [2024-02-19 00:37:23,846 INFO misc.py line 119 87073] Train: [62/100][690/1557] Data 0.011 (0.287) Batch 0.992 (1.225) Remain 20:25:59 loss: 0.2270 Lr: 0.00177 [2024-02-19 00:37:24,596 INFO misc.py line 119 87073] Train: [62/100][691/1557] Data 0.002 (0.287) Batch 0.750 (1.225) Remain 20:25:16 loss: 0.1870 Lr: 0.00177 [2024-02-19 00:37:25,371 INFO misc.py line 119 87073] Train: [62/100][692/1557] Data 0.004 (0.286) Batch 0.765 (1.224) Remain 20:24:35 loss: 0.2498 Lr: 0.00177 [2024-02-19 00:37:26,583 INFO misc.py line 119 87073] Train: [62/100][693/1557] Data 0.012 (0.286) Batch 1.215 (1.224) Remain 20:24:33 loss: 0.2176 Lr: 0.00177 [2024-02-19 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87073] Train: [62/100][700/1557] Data 0.011 (0.283) Batch 1.147 (1.221) Remain 20:21:48 loss: 0.1009 Lr: 0.00177 [2024-02-19 00:37:34,106 INFO misc.py line 119 87073] Train: [62/100][701/1557] Data 0.014 (0.283) Batch 0.783 (1.221) Remain 20:21:09 loss: 0.4310 Lr: 0.00177 [2024-02-19 00:37:35,014 INFO misc.py line 119 87073] Train: [62/100][702/1557] Data 0.003 (0.282) Batch 0.907 (1.220) Remain 20:20:41 loss: 0.3265 Lr: 0.00177 [2024-02-19 00:37:36,067 INFO misc.py line 119 87073] Train: [62/100][703/1557] Data 0.003 (0.282) Batch 1.045 (1.220) Remain 20:20:24 loss: 0.2836 Lr: 0.00177 [2024-02-19 00:37:37,153 INFO misc.py line 119 87073] Train: [62/100][704/1557] Data 0.012 (0.281) Batch 1.087 (1.220) Remain 20:20:12 loss: 0.2490 Lr: 0.00177 [2024-02-19 00:37:39,669 INFO misc.py line 119 87073] Train: [62/100][705/1557] Data 1.365 (0.283) Batch 2.519 (1.222) Remain 20:22:02 loss: 0.3079 Lr: 0.00177 [2024-02-19 00:37:40,438 INFO misc.py line 119 87073] Train: [62/100][706/1557] Data 0.008 (0.283) Batch 0.773 (1.221) Remain 20:21:22 loss: 0.3942 Lr: 0.00177 [2024-02-19 00:37:41,690 INFO misc.py line 119 87073] Train: [62/100][707/1557] Data 0.003 (0.282) Batch 1.242 (1.221) Remain 20:21:23 loss: 0.1010 Lr: 0.00177 [2024-02-19 00:37:42,617 INFO misc.py line 119 87073] Train: [62/100][708/1557] Data 0.013 (0.282) Batch 0.938 (1.221) Remain 20:20:57 loss: 0.3153 Lr: 0.00177 [2024-02-19 00:37:43,574 INFO misc.py line 119 87073] Train: [62/100][709/1557] Data 0.004 (0.281) Batch 0.956 (1.220) Remain 20:20:34 loss: 0.5136 Lr: 0.00177 [2024-02-19 00:37:44,421 INFO misc.py line 119 87073] Train: [62/100][710/1557] Data 0.003 (0.281) Batch 0.843 (1.220) Remain 20:20:00 loss: 0.4224 Lr: 0.00177 [2024-02-19 00:37:45,394 INFO misc.py line 119 87073] Train: [62/100][711/1557] Data 0.008 (0.281) Batch 0.978 (1.219) Remain 20:19:39 loss: 0.2824 Lr: 0.00177 [2024-02-19 00:37:46,151 INFO misc.py line 119 87073] Train: [62/100][712/1557] Data 0.004 (0.280) Batch 0.756 (1.219) Remain 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[2024-02-19 00:37:52,526 INFO misc.py line 119 87073] Train: [62/100][719/1557] Data 0.012 (0.278) Batch 0.738 (1.216) Remain 20:15:49 loss: 0.1378 Lr: 0.00177 [2024-02-19 00:37:53,234 INFO misc.py line 119 87073] Train: [62/100][720/1557] Data 0.003 (0.277) Batch 0.701 (1.215) Remain 20:15:05 loss: 0.1381 Lr: 0.00177 [2024-02-19 00:37:54,481 INFO misc.py line 119 87073] Train: [62/100][721/1557] Data 0.009 (0.277) Batch 1.248 (1.215) Remain 20:15:06 loss: 0.1349 Lr: 0.00177 [2024-02-19 00:37:55,396 INFO misc.py line 119 87073] Train: [62/100][722/1557] Data 0.008 (0.276) Batch 0.920 (1.215) Remain 20:14:40 loss: 0.2190 Lr: 0.00177 [2024-02-19 00:37:56,393 INFO misc.py line 119 87073] Train: [62/100][723/1557] Data 0.003 (0.276) Batch 0.997 (1.214) Remain 20:14:21 loss: 0.2723 Lr: 0.00177 [2024-02-19 00:37:57,400 INFO misc.py line 119 87073] Train: [62/100][724/1557] Data 0.003 (0.276) Batch 1.007 (1.214) Remain 20:14:03 loss: 0.2045 Lr: 0.00177 [2024-02-19 00:37:58,480 INFO misc.py line 119 87073] Train: [62/100][725/1557] Data 0.003 (0.275) Batch 1.080 (1.214) Remain 20:13:50 loss: 0.5100 Lr: 0.00177 [2024-02-19 00:37:59,194 INFO misc.py line 119 87073] Train: [62/100][726/1557] Data 0.004 (0.275) Batch 0.714 (1.213) Remain 20:13:08 loss: 0.4476 Lr: 0.00177 [2024-02-19 00:37:59,968 INFO misc.py line 119 87073] Train: [62/100][727/1557] Data 0.003 (0.275) Batch 0.764 (1.213) Remain 20:12:29 loss: 0.4132 Lr: 0.00177 [2024-02-19 00:38:01,294 INFO misc.py line 119 87073] Train: [62/100][728/1557] Data 0.013 (0.274) Batch 1.327 (1.213) Remain 20:12:37 loss: 0.2016 Lr: 0.00177 [2024-02-19 00:38:02,375 INFO misc.py line 119 87073] Train: [62/100][729/1557] Data 0.012 (0.274) Batch 1.083 (1.213) Remain 20:12:25 loss: 0.2014 Lr: 0.00177 [2024-02-19 00:38:03,395 INFO misc.py line 119 87073] Train: [62/100][730/1557] Data 0.011 (0.273) Batch 1.016 (1.212) Remain 20:12:08 loss: 0.3900 Lr: 0.00177 [2024-02-19 00:38:04,596 INFO misc.py line 119 87073] Train: 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Batch 0.953 (1.230) Remain 20:30:01 loss: 0.4374 Lr: 0.00177 [2024-02-19 00:38:26,021 INFO misc.py line 119 87073] Train: [62/100][738/1557] Data 0.010 (0.291) Batch 0.920 (1.230) Remain 20:29:34 loss: 0.3158 Lr: 0.00177 [2024-02-19 00:38:27,105 INFO misc.py line 119 87073] Train: [62/100][739/1557] Data 0.004 (0.291) Batch 1.084 (1.230) Remain 20:29:21 loss: 0.7017 Lr: 0.00177 [2024-02-19 00:38:27,841 INFO misc.py line 119 87073] Train: [62/100][740/1557] Data 0.003 (0.290) Batch 0.735 (1.229) Remain 20:28:40 loss: 0.1500 Lr: 0.00177 [2024-02-19 00:38:28,562 INFO misc.py line 119 87073] Train: [62/100][741/1557] Data 0.004 (0.290) Batch 0.716 (1.228) Remain 20:27:57 loss: 0.2587 Lr: 0.00177 [2024-02-19 00:38:29,809 INFO misc.py line 119 87073] Train: [62/100][742/1557] Data 0.009 (0.289) Batch 1.246 (1.228) Remain 20:27:57 loss: 0.1496 Lr: 0.00177 [2024-02-19 00:38:30,747 INFO misc.py line 119 87073] Train: [62/100][743/1557] Data 0.011 (0.289) Batch 0.945 (1.228) Remain 20:27:33 loss: 0.1943 Lr: 0.00177 [2024-02-19 00:38:31,555 INFO misc.py line 119 87073] Train: [62/100][744/1557] Data 0.004 (0.289) Batch 0.808 (1.227) Remain 20:26:57 loss: 0.5719 Lr: 0.00177 [2024-02-19 00:38:32,624 INFO misc.py line 119 87073] Train: [62/100][745/1557] Data 0.004 (0.288) Batch 1.063 (1.227) Remain 20:26:43 loss: 0.4337 Lr: 0.00177 [2024-02-19 00:38:34,018 INFO misc.py line 119 87073] Train: [62/100][746/1557] Data 0.009 (0.288) Batch 1.393 (1.227) Remain 20:26:55 loss: 0.2520 Lr: 0.00177 [2024-02-19 00:38:34,832 INFO misc.py line 119 87073] Train: [62/100][747/1557] Data 0.011 (0.287) Batch 0.822 (1.227) Remain 20:26:21 loss: 0.3069 Lr: 0.00177 [2024-02-19 00:38:35,569 INFO misc.py line 119 87073] Train: [62/100][748/1557] Data 0.004 (0.287) Batch 0.736 (1.226) Remain 20:25:40 loss: 0.4041 Lr: 0.00177 [2024-02-19 00:38:36,745 INFO misc.py line 119 87073] Train: [62/100][749/1557] Data 0.004 (0.287) Batch 1.172 (1.226) Remain 20:25:35 loss: 0.1297 Lr: 0.00177 [2024-02-19 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line 119 87073] Train: [62/100][781/1557] Data 0.004 (0.275) Batch 0.982 (1.214) Remain 20:13:01 loss: 0.1976 Lr: 0.00177 [2024-02-19 00:39:07,533 INFO misc.py line 119 87073] Train: [62/100][782/1557] Data 0.004 (0.275) Batch 0.825 (1.214) Remain 20:12:30 loss: 0.2208 Lr: 0.00177 [2024-02-19 00:39:08,297 INFO misc.py line 119 87073] Train: [62/100][783/1557] Data 0.005 (0.274) Batch 0.764 (1.213) Remain 20:11:55 loss: 0.2063 Lr: 0.00177 [2024-02-19 00:39:09,578 INFO misc.py line 119 87073] Train: [62/100][784/1557] Data 0.004 (0.274) Batch 1.280 (1.213) Remain 20:11:59 loss: 0.1759 Lr: 0.00177 [2024-02-19 00:39:10,450 INFO misc.py line 119 87073] Train: [62/100][785/1557] Data 0.004 (0.274) Batch 0.873 (1.213) Remain 20:11:31 loss: 0.4959 Lr: 0.00177 [2024-02-19 00:39:11,224 INFO misc.py line 119 87073] Train: [62/100][786/1557] Data 0.003 (0.273) Batch 0.773 (1.212) Remain 20:10:56 loss: 0.3387 Lr: 0.00177 [2024-02-19 00:39:12,210 INFO misc.py line 119 87073] Train: 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Batch 0.925 (1.229) Remain 20:27:04 loss: 0.5368 Lr: 0.00177 [2024-02-19 00:39:33,644 INFO misc.py line 119 87073] Train: [62/100][794/1557] Data 0.004 (0.290) Batch 1.065 (1.228) Remain 20:26:51 loss: 0.5321 Lr: 0.00177 [2024-02-19 00:39:34,432 INFO misc.py line 119 87073] Train: [62/100][795/1557] Data 0.003 (0.289) Batch 0.788 (1.228) Remain 20:26:16 loss: 0.3703 Lr: 0.00177 [2024-02-19 00:39:35,222 INFO misc.py line 119 87073] Train: [62/100][796/1557] Data 0.003 (0.289) Batch 0.777 (1.227) Remain 20:25:41 loss: 0.2361 Lr: 0.00177 [2024-02-19 00:39:35,969 INFO misc.py line 119 87073] Train: [62/100][797/1557] Data 0.015 (0.289) Batch 0.756 (1.227) Remain 20:25:04 loss: 0.2768 Lr: 0.00177 [2024-02-19 00:39:37,187 INFO misc.py line 119 87073] Train: [62/100][798/1557] Data 0.007 (0.288) Batch 1.218 (1.227) Remain 20:25:02 loss: 0.1280 Lr: 0.00177 [2024-02-19 00:39:38,214 INFO misc.py line 119 87073] Train: [62/100][799/1557] Data 0.007 (0.288) Batch 1.029 (1.226) Remain 20:24:46 loss: 0.4080 Lr: 0.00177 [2024-02-19 00:39:39,213 INFO misc.py line 119 87073] Train: [62/100][800/1557] Data 0.005 (0.288) Batch 0.999 (1.226) Remain 20:24:28 loss: 0.5364 Lr: 0.00177 [2024-02-19 00:39:40,262 INFO misc.py line 119 87073] Train: [62/100][801/1557] Data 0.006 (0.287) Batch 1.045 (1.226) Remain 20:24:13 loss: 0.2516 Lr: 0.00177 [2024-02-19 00:39:41,255 INFO misc.py line 119 87073] Train: [62/100][802/1557] Data 0.010 (0.287) Batch 0.999 (1.226) Remain 20:23:55 loss: 0.5284 Lr: 0.00177 [2024-02-19 00:39:42,008 INFO misc.py line 119 87073] Train: [62/100][803/1557] Data 0.003 (0.287) Batch 0.753 (1.225) Remain 20:23:18 loss: 0.3058 Lr: 0.00177 [2024-02-19 00:39:42,766 INFO misc.py line 119 87073] Train: [62/100][804/1557] Data 0.003 (0.286) Batch 0.749 (1.224) Remain 20:22:41 loss: 0.1075 Lr: 0.00177 [2024-02-19 00:39:43,970 INFO misc.py line 119 87073] Train: [62/100][805/1557] Data 0.012 (0.286) Batch 1.209 (1.224) Remain 20:22:39 loss: 0.2395 Lr: 0.00177 [2024-02-19 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Batch 0.952 (1.226) Remain 20:23:43 loss: 0.3317 Lr: 0.00177 [2024-02-19 00:40:40,533 INFO misc.py line 119 87073] Train: [62/100][850/1557] Data 0.004 (0.288) Batch 1.021 (1.226) Remain 20:23:28 loss: 0.5454 Lr: 0.00177 [2024-02-19 00:40:41,459 INFO misc.py line 119 87073] Train: [62/100][851/1557] Data 0.004 (0.288) Batch 0.927 (1.226) Remain 20:23:05 loss: 0.3746 Lr: 0.00177 [2024-02-19 00:40:42,183 INFO misc.py line 119 87073] Train: [62/100][852/1557] Data 0.003 (0.288) Batch 0.716 (1.225) Remain 20:22:28 loss: 0.1850 Lr: 0.00177 [2024-02-19 00:40:43,084 INFO misc.py line 119 87073] Train: [62/100][853/1557] Data 0.011 (0.287) Batch 0.909 (1.225) Remain 20:22:05 loss: 0.3018 Lr: 0.00177 [2024-02-19 00:40:44,232 INFO misc.py line 119 87073] Train: [62/100][854/1557] Data 0.003 (0.287) Batch 1.138 (1.225) Remain 20:21:57 loss: 0.1835 Lr: 0.00177 [2024-02-19 00:40:45,205 INFO misc.py line 119 87073] Train: [62/100][855/1557] Data 0.013 (0.287) Batch 0.983 (1.224) Remain 20:21:39 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line 119 87073] Train: [62/100][893/1557] Data 0.006 (0.276) Batch 1.087 (1.215) Remain 20:11:28 loss: 0.2015 Lr: 0.00176 [2024-02-19 00:41:24,051 INFO misc.py line 119 87073] Train: [62/100][894/1557] Data 0.007 (0.276) Batch 0.719 (1.214) Remain 20:10:54 loss: 0.3716 Lr: 0.00176 [2024-02-19 00:41:24,773 INFO misc.py line 119 87073] Train: [62/100][895/1557] Data 0.004 (0.275) Batch 0.721 (1.214) Remain 20:10:19 loss: 0.1460 Lr: 0.00176 [2024-02-19 00:41:26,083 INFO misc.py line 119 87073] Train: [62/100][896/1557] Data 0.004 (0.275) Batch 1.306 (1.214) Remain 20:10:24 loss: 0.2259 Lr: 0.00176 [2024-02-19 00:41:27,014 INFO misc.py line 119 87073] Train: [62/100][897/1557] Data 0.008 (0.275) Batch 0.936 (1.214) Remain 20:10:04 loss: 0.1272 Lr: 0.00176 [2024-02-19 00:41:28,001 INFO misc.py line 119 87073] Train: [62/100][898/1557] Data 0.003 (0.274) Batch 0.988 (1.213) Remain 20:09:48 loss: 0.3484 Lr: 0.00176 [2024-02-19 00:41:28,911 INFO misc.py line 119 87073] Train: 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Batch 1.085 (1.228) Remain 20:24:08 loss: 0.2671 Lr: 0.00176 [2024-02-19 00:41:50,399 INFO misc.py line 119 87073] Train: [62/100][906/1557] Data 0.004 (0.288) Batch 0.807 (1.227) Remain 20:23:39 loss: 0.3331 Lr: 0.00176 [2024-02-19 00:41:51,491 INFO misc.py line 119 87073] Train: [62/100][907/1557] Data 0.004 (0.288) Batch 1.088 (1.227) Remain 20:23:29 loss: 0.0849 Lr: 0.00176 [2024-02-19 00:41:52,237 INFO misc.py line 119 87073] Train: [62/100][908/1557] Data 0.008 (0.288) Batch 0.750 (1.227) Remain 20:22:56 loss: 0.3242 Lr: 0.00176 [2024-02-19 00:41:53,025 INFO misc.py line 119 87073] Train: [62/100][909/1557] Data 0.003 (0.287) Batch 0.781 (1.226) Remain 20:22:25 loss: 0.3309 Lr: 0.00176 [2024-02-19 00:41:54,288 INFO misc.py line 119 87073] Train: [62/100][910/1557] Data 0.010 (0.287) Batch 1.263 (1.226) Remain 20:22:26 loss: 0.3006 Lr: 0.00176 [2024-02-19 00:41:55,385 INFO misc.py line 119 87073] Train: [62/100][911/1557] Data 0.011 (0.287) Batch 1.101 (1.226) Remain 20:22:17 loss: 0.4231 Lr: 0.00176 [2024-02-19 00:41:56,400 INFO misc.py line 119 87073] Train: [62/100][912/1557] Data 0.006 (0.287) Batch 1.016 (1.226) Remain 20:22:02 loss: 0.5845 Lr: 0.00176 [2024-02-19 00:41:57,514 INFO misc.py line 119 87073] Train: [62/100][913/1557] Data 0.005 (0.286) Batch 1.106 (1.226) Remain 20:21:53 loss: 0.3337 Lr: 0.00176 [2024-02-19 00:41:58,433 INFO misc.py line 119 87073] Train: [62/100][914/1557] Data 0.013 (0.286) Batch 0.929 (1.225) Remain 20:21:32 loss: 0.3768 Lr: 0.00176 [2024-02-19 00:41:59,168 INFO misc.py line 119 87073] Train: [62/100][915/1557] Data 0.003 (0.286) Batch 0.734 (1.225) Remain 20:20:59 loss: 0.0755 Lr: 0.00176 [2024-02-19 00:41:59,890 INFO misc.py line 119 87073] Train: [62/100][916/1557] Data 0.004 (0.285) Batch 0.722 (1.224) Remain 20:20:24 loss: 0.2410 Lr: 0.00176 [2024-02-19 00:42:01,108 INFO misc.py line 119 87073] Train: [62/100][917/1557] Data 0.004 (0.285) Batch 1.217 (1.224) Remain 20:20:23 loss: 0.2297 Lr: 0.00176 [2024-02-19 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87073] Train: [62/100][924/1557] Data 0.004 (0.283) Batch 1.099 (1.222) Remain 20:18:00 loss: 0.1059 Lr: 0.00176 [2024-02-19 00:42:08,643 INFO misc.py line 119 87073] Train: [62/100][925/1557] Data 0.004 (0.283) Batch 1.040 (1.222) Remain 20:17:47 loss: 0.1248 Lr: 0.00176 [2024-02-19 00:42:09,655 INFO misc.py line 119 87073] Train: [62/100][926/1557] Data 0.003 (0.282) Batch 1.010 (1.222) Remain 20:17:32 loss: 0.3839 Lr: 0.00176 [2024-02-19 00:42:10,736 INFO misc.py line 119 87073] Train: [62/100][927/1557] Data 0.003 (0.282) Batch 1.081 (1.222) Remain 20:17:21 loss: 0.4534 Lr: 0.00176 [2024-02-19 00:42:11,793 INFO misc.py line 119 87073] Train: [62/100][928/1557] Data 0.004 (0.282) Batch 1.058 (1.221) Remain 20:17:09 loss: 0.2080 Lr: 0.00176 [2024-02-19 00:42:12,542 INFO misc.py line 119 87073] Train: [62/100][929/1557] Data 0.003 (0.281) Batch 0.749 (1.221) Remain 20:16:38 loss: 0.2471 Lr: 0.00176 [2024-02-19 00:42:13,330 INFO misc.py line 119 87073] Train: [62/100][930/1557] Data 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20:14:22 loss: 0.3201 Lr: 0.00176 [2024-02-19 00:42:19,891 INFO misc.py line 119 87073] Train: [62/100][937/1557] Data 0.007 (0.279) Batch 0.787 (1.218) Remain 20:13:53 loss: 0.2553 Lr: 0.00176 [2024-02-19 00:42:20,997 INFO misc.py line 119 87073] Train: [62/100][938/1557] Data 0.003 (0.279) Batch 1.104 (1.218) Remain 20:13:45 loss: 0.1489 Lr: 0.00176 [2024-02-19 00:42:21,978 INFO misc.py line 119 87073] Train: [62/100][939/1557] Data 0.004 (0.278) Batch 0.982 (1.218) Remain 20:13:28 loss: 0.2422 Lr: 0.00176 [2024-02-19 00:42:22,903 INFO misc.py line 119 87073] Train: [62/100][940/1557] Data 0.003 (0.278) Batch 0.926 (1.218) Remain 20:13:09 loss: 0.3463 Lr: 0.00176 [2024-02-19 00:42:23,854 INFO misc.py line 119 87073] Train: [62/100][941/1557] Data 0.003 (0.278) Batch 0.940 (1.217) Remain 20:12:50 loss: 0.2673 Lr: 0.00176 [2024-02-19 00:42:24,813 INFO misc.py line 119 87073] Train: [62/100][942/1557] Data 0.013 (0.278) Batch 0.968 (1.217) Remain 20:12:33 loss: 0.3941 Lr: 0.00176 [2024-02-19 00:42:25,559 INFO misc.py line 119 87073] Train: [62/100][943/1557] Data 0.006 (0.277) Batch 0.747 (1.216) Remain 20:12:01 loss: 0.1434 Lr: 0.00176 [2024-02-19 00:42:26,305 INFO misc.py line 119 87073] Train: [62/100][944/1557] Data 0.004 (0.277) Batch 0.736 (1.216) Remain 20:11:30 loss: 0.3546 Lr: 0.00176 [2024-02-19 00:42:27,448 INFO misc.py line 119 87073] Train: [62/100][945/1557] Data 0.014 (0.277) Batch 1.131 (1.216) Remain 20:11:23 loss: 0.1338 Lr: 0.00176 [2024-02-19 00:42:28,762 INFO misc.py line 119 87073] Train: [62/100][946/1557] Data 0.026 (0.276) Batch 1.329 (1.216) Remain 20:11:29 loss: 0.1927 Lr: 0.00176 [2024-02-19 00:42:29,791 INFO misc.py line 119 87073] Train: [62/100][947/1557] Data 0.011 (0.276) Batch 1.027 (1.216) Remain 20:11:16 loss: 0.0798 Lr: 0.00176 [2024-02-19 00:42:30,884 INFO misc.py line 119 87073] Train: [62/100][948/1557] Data 0.013 (0.276) Batch 1.094 (1.216) Remain 20:11:07 loss: 0.7833 Lr: 0.00176 [2024-02-19 00:42:31,935 INFO misc.py line 119 87073] Train: [62/100][949/1557] Data 0.013 (0.276) Batch 1.052 (1.216) Remain 20:10:55 loss: 0.3563 Lr: 0.00176 [2024-02-19 00:42:32,754 INFO misc.py line 119 87073] Train: [62/100][950/1557] Data 0.010 (0.275) Batch 0.826 (1.215) Remain 20:10:30 loss: 0.4132 Lr: 0.00176 [2024-02-19 00:42:33,585 INFO misc.py line 119 87073] Train: [62/100][951/1557] Data 0.003 (0.275) Batch 0.831 (1.215) Remain 20:10:04 loss: 0.2437 Lr: 0.00176 [2024-02-19 00:42:34,845 INFO misc.py line 119 87073] Train: [62/100][952/1557] Data 0.003 (0.275) Batch 1.250 (1.215) Remain 20:10:05 loss: 0.1469 Lr: 0.00176 [2024-02-19 00:42:35,790 INFO misc.py line 119 87073] Train: [62/100][953/1557] Data 0.015 (0.274) Batch 0.955 (1.214) Remain 20:09:48 loss: 0.2320 Lr: 0.00176 [2024-02-19 00:42:36,972 INFO misc.py line 119 87073] Train: [62/100][954/1557] Data 0.004 (0.274) Batch 1.170 (1.214) Remain 20:09:44 loss: 0.4531 Lr: 0.00176 [2024-02-19 00:42:38,013 INFO misc.py line 119 87073] Train: 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Batch 0.911 (1.228) Remain 20:23:09 loss: 0.5358 Lr: 0.00176 [2024-02-19 00:42:59,451 INFO misc.py line 119 87073] Train: [62/100][962/1557] Data 0.004 (0.288) Batch 0.942 (1.228) Remain 20:22:50 loss: 0.4003 Lr: 0.00176 [2024-02-19 00:43:00,336 INFO misc.py line 119 87073] Train: [62/100][963/1557] Data 0.004 (0.288) Batch 0.885 (1.227) Remain 20:22:27 loss: 0.3848 Lr: 0.00176 [2024-02-19 00:43:01,040 INFO misc.py line 119 87073] Train: [62/100][964/1557] Data 0.004 (0.287) Batch 0.696 (1.227) Remain 20:21:53 loss: 0.2546 Lr: 0.00176 [2024-02-19 00:43:01,774 INFO misc.py line 119 87073] Train: [62/100][965/1557] Data 0.012 (0.287) Batch 0.741 (1.226) Remain 20:21:22 loss: 0.3572 Lr: 0.00176 [2024-02-19 00:43:03,004 INFO misc.py line 119 87073] Train: [62/100][966/1557] Data 0.005 (0.287) Batch 1.231 (1.226) Remain 20:21:21 loss: 0.1715 Lr: 0.00176 [2024-02-19 00:43:03,817 INFO misc.py line 119 87073] Train: [62/100][967/1557] Data 0.004 (0.286) Batch 0.813 (1.226) Remain 20:20:54 loss: 0.3010 Lr: 0.00176 [2024-02-19 00:43:04,755 INFO misc.py line 119 87073] Train: [62/100][968/1557] Data 0.003 (0.286) Batch 0.937 (1.226) Remain 20:20:35 loss: 0.4966 Lr: 0.00176 [2024-02-19 00:43:05,659 INFO misc.py line 119 87073] Train: [62/100][969/1557] Data 0.005 (0.286) Batch 0.906 (1.225) Remain 20:20:14 loss: 0.3783 Lr: 0.00176 [2024-02-19 00:43:06,728 INFO misc.py line 119 87073] Train: [62/100][970/1557] Data 0.004 (0.286) Batch 1.069 (1.225) Remain 20:20:03 loss: 0.3642 Lr: 0.00176 [2024-02-19 00:43:07,499 INFO misc.py line 119 87073] Train: [62/100][971/1557] Data 0.003 (0.285) Batch 0.771 (1.225) Remain 20:19:34 loss: 0.2421 Lr: 0.00176 [2024-02-19 00:43:08,237 INFO misc.py line 119 87073] Train: [62/100][972/1557] Data 0.003 (0.285) Batch 0.730 (1.224) Remain 20:19:02 loss: 0.3215 Lr: 0.00176 [2024-02-19 00:43:09,457 INFO misc.py line 119 87073] Train: [62/100][973/1557] Data 0.011 (0.285) Batch 1.215 (1.224) Remain 20:19:00 loss: 0.1982 Lr: 0.00176 [2024-02-19 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20:13:30 loss: 0.5076 Lr: 0.00176 [2024-02-19 00:43:28,353 INFO misc.py line 119 87073] Train: [62/100][993/1557] Data 0.006 (0.279) Batch 0.737 (1.218) Remain 20:12:59 loss: 0.5017 Lr: 0.00176 [2024-02-19 00:43:29,494 INFO misc.py line 119 87073] Train: [62/100][994/1557] Data 0.007 (0.279) Batch 1.142 (1.218) Remain 20:12:53 loss: 0.2422 Lr: 0.00176 [2024-02-19 00:43:30,408 INFO misc.py line 119 87073] Train: [62/100][995/1557] Data 0.007 (0.278) Batch 0.916 (1.218) Remain 20:12:34 loss: 0.2140 Lr: 0.00176 [2024-02-19 00:43:31,412 INFO misc.py line 119 87073] Train: [62/100][996/1557] Data 0.005 (0.278) Batch 1.004 (1.218) Remain 20:12:20 loss: 0.2049 Lr: 0.00176 [2024-02-19 00:43:32,490 INFO misc.py line 119 87073] Train: [62/100][997/1557] Data 0.004 (0.278) Batch 1.078 (1.218) Remain 20:12:10 loss: 0.3844 Lr: 0.00176 [2024-02-19 00:43:33,408 INFO misc.py line 119 87073] Train: [62/100][998/1557] Data 0.003 (0.278) Batch 0.918 (1.217) Remain 20:11:51 loss: 0.2891 Lr: 0.00176 [2024-02-19 00:43:34,235 INFO misc.py line 119 87073] Train: [62/100][999/1557] Data 0.004 (0.277) Batch 0.818 (1.217) Remain 20:11:26 loss: 0.3797 Lr: 0.00176 [2024-02-19 00:43:34,929 INFO misc.py line 119 87073] Train: [62/100][1000/1557] Data 0.013 (0.277) Batch 0.703 (1.217) Remain 20:10:54 loss: 0.1736 Lr: 0.00176 [2024-02-19 00:43:36,083 INFO misc.py line 119 87073] Train: [62/100][1001/1557] Data 0.004 (0.277) Batch 1.143 (1.216) Remain 20:10:48 loss: 0.2529 Lr: 0.00176 [2024-02-19 00:43:37,043 INFO misc.py line 119 87073] Train: [62/100][1002/1557] Data 0.014 (0.277) Batch 0.972 (1.216) Remain 20:10:32 loss: 0.4260 Lr: 0.00176 [2024-02-19 00:43:38,101 INFO misc.py line 119 87073] Train: [62/100][1003/1557] Data 0.003 (0.276) Batch 1.058 (1.216) Remain 20:10:22 loss: 0.4021 Lr: 0.00176 [2024-02-19 00:43:39,097 INFO misc.py line 119 87073] Train: [62/100][1004/1557] Data 0.003 (0.276) Batch 0.995 (1.216) Remain 20:10:07 loss: 0.4141 Lr: 0.00176 [2024-02-19 00:43:39,985 INFO 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(0.288) Batch 0.924 (1.227) Remain 20:21:10 loss: 0.8221 Lr: 0.00176 [2024-02-19 00:44:07,385 INFO misc.py line 119 87073] Train: [62/100][1018/1557] Data 0.003 (0.287) Batch 0.954 (1.227) Remain 20:20:53 loss: 0.2139 Lr: 0.00176 [2024-02-19 00:44:08,353 INFO misc.py line 119 87073] Train: [62/100][1019/1557] Data 0.012 (0.287) Batch 0.977 (1.227) Remain 20:20:37 loss: 0.6761 Lr: 0.00176 [2024-02-19 00:44:09,133 INFO misc.py line 119 87073] Train: [62/100][1020/1557] Data 0.003 (0.287) Batch 0.780 (1.226) Remain 20:20:09 loss: 0.1678 Lr: 0.00176 [2024-02-19 00:44:09,870 INFO misc.py line 119 87073] Train: [62/100][1021/1557] Data 0.003 (0.287) Batch 0.737 (1.226) Remain 20:19:39 loss: 0.2656 Lr: 0.00176 [2024-02-19 00:44:11,169 INFO misc.py line 119 87073] Train: [62/100][1022/1557] Data 0.004 (0.286) Batch 1.292 (1.226) Remain 20:19:42 loss: 0.1748 Lr: 0.00176 [2024-02-19 00:44:11,944 INFO misc.py line 119 87073] Train: [62/100][1023/1557] Data 0.011 (0.286) Batch 0.782 (1.225) Remain 20:19:15 loss: 0.5810 Lr: 0.00176 [2024-02-19 00:44:12,883 INFO misc.py line 119 87073] Train: [62/100][1024/1557] Data 0.005 (0.286) Batch 0.941 (1.225) Remain 20:18:57 loss: 0.6100 Lr: 0.00176 [2024-02-19 00:44:13,974 INFO misc.py line 119 87073] Train: [62/100][1025/1557] Data 0.003 (0.285) Batch 1.090 (1.225) Remain 20:18:48 loss: 0.2791 Lr: 0.00176 [2024-02-19 00:44:15,006 INFO misc.py line 119 87073] Train: [62/100][1026/1557] Data 0.003 (0.285) Batch 1.032 (1.225) Remain 20:18:35 loss: 1.0832 Lr: 0.00176 [2024-02-19 00:44:15,832 INFO misc.py line 119 87073] Train: [62/100][1027/1557] Data 0.003 (0.285) Batch 0.783 (1.224) Remain 20:18:08 loss: 0.2644 Lr: 0.00176 [2024-02-19 00:44:16,569 INFO misc.py line 119 87073] Train: [62/100][1028/1557] Data 0.046 (0.285) Batch 0.780 (1.224) Remain 20:17:41 loss: 0.2711 Lr: 0.00176 [2024-02-19 00:44:17,769 INFO misc.py line 119 87073] Train: [62/100][1029/1557] Data 0.004 (0.284) Batch 1.200 (1.224) Remain 20:17:39 loss: 0.2495 Lr: 0.00176 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misc.py line 119 87073] Train: [62/100][1036/1557] Data 0.003 (0.282) Batch 1.087 (1.222) Remain 20:15:14 loss: 0.1231 Lr: 0.00176 [2024-02-19 00:44:24,840 INFO misc.py line 119 87073] Train: [62/100][1037/1557] Data 0.014 (0.282) Batch 0.854 (1.221) Remain 20:14:52 loss: 0.5104 Lr: 0.00176 [2024-02-19 00:44:25,967 INFO misc.py line 119 87073] Train: [62/100][1038/1557] Data 0.004 (0.282) Batch 1.128 (1.221) Remain 20:14:45 loss: 0.4168 Lr: 0.00176 [2024-02-19 00:44:26,955 INFO misc.py line 119 87073] Train: [62/100][1039/1557] Data 0.003 (0.282) Batch 0.988 (1.221) Remain 20:14:30 loss: 0.2618 Lr: 0.00176 [2024-02-19 00:44:27,849 INFO misc.py line 119 87073] Train: [62/100][1040/1557] Data 0.003 (0.281) Batch 0.894 (1.221) Remain 20:14:10 loss: 0.4568 Lr: 0.00176 [2024-02-19 00:44:28,601 INFO misc.py line 119 87073] Train: [62/100][1041/1557] Data 0.003 (0.281) Batch 0.752 (1.220) Remain 20:13:42 loss: 0.3229 Lr: 0.00176 [2024-02-19 00:44:29,367 INFO misc.py line 119 87073] Train: 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(0.279) Batch 0.794 (1.219) Remain 20:12:00 loss: 0.1890 Lr: 0.00176 [2024-02-19 00:44:36,203 INFO misc.py line 119 87073] Train: [62/100][1049/1557] Data 0.009 (0.279) Batch 0.706 (1.218) Remain 20:11:29 loss: 0.2953 Lr: 0.00176 [2024-02-19 00:44:37,313 INFO misc.py line 119 87073] Train: [62/100][1050/1557] Data 0.003 (0.279) Batch 1.109 (1.218) Remain 20:11:22 loss: 0.1587 Lr: 0.00176 [2024-02-19 00:44:38,436 INFO misc.py line 119 87073] Train: [62/100][1051/1557] Data 0.005 (0.278) Batch 1.120 (1.218) Remain 20:11:15 loss: 0.1365 Lr: 0.00176 [2024-02-19 00:44:39,397 INFO misc.py line 119 87073] Train: [62/100][1052/1557] Data 0.008 (0.278) Batch 0.962 (1.218) Remain 20:10:59 loss: 0.3686 Lr: 0.00176 [2024-02-19 00:44:40,417 INFO misc.py line 119 87073] Train: [62/100][1053/1557] Data 0.007 (0.278) Batch 1.021 (1.217) Remain 20:10:47 loss: 0.4130 Lr: 0.00176 [2024-02-19 00:44:41,446 INFO misc.py line 119 87073] Train: [62/100][1054/1557] Data 0.004 (0.278) Batch 1.030 (1.217) Remain 20:10:35 loss: 0.1636 Lr: 0.00176 [2024-02-19 00:44:43,789 INFO misc.py line 119 87073] Train: [62/100][1055/1557] Data 0.733 (0.278) Batch 2.340 (1.218) Remain 20:11:38 loss: 0.2510 Lr: 0.00176 [2024-02-19 00:44:44,554 INFO misc.py line 119 87073] Train: [62/100][1056/1557] Data 0.006 (0.278) Batch 0.768 (1.218) Remain 20:11:11 loss: 0.2748 Lr: 0.00176 [2024-02-19 00:44:45,711 INFO misc.py line 119 87073] Train: [62/100][1057/1557] Data 0.003 (0.278) Batch 1.157 (1.218) Remain 20:11:06 loss: 0.1857 Lr: 0.00175 [2024-02-19 00:44:46,799 INFO misc.py line 119 87073] Train: [62/100][1058/1557] Data 0.003 (0.277) Batch 1.088 (1.218) Remain 20:10:58 loss: 0.4090 Lr: 0.00175 [2024-02-19 00:44:47,913 INFO misc.py line 119 87073] Train: [62/100][1059/1557] Data 0.003 (0.277) Batch 1.111 (1.218) Remain 20:10:50 loss: 0.2276 Lr: 0.00175 [2024-02-19 00:44:48,833 INFO misc.py line 119 87073] Train: [62/100][1060/1557] Data 0.007 (0.277) Batch 0.920 (1.217) Remain 20:10:32 loss: 0.3213 Lr: 0.00175 [2024-02-19 00:44:49,791 INFO misc.py line 119 87073] Train: [62/100][1061/1557] Data 0.006 (0.277) Batch 0.959 (1.217) Remain 20:10:17 loss: 0.3811 Lr: 0.00175 [2024-02-19 00:44:50,565 INFO misc.py line 119 87073] Train: [62/100][1062/1557] Data 0.005 (0.276) Batch 0.771 (1.217) Remain 20:09:50 loss: 0.2563 Lr: 0.00175 [2024-02-19 00:44:51,317 INFO misc.py line 119 87073] Train: [62/100][1063/1557] Data 0.009 (0.276) Batch 0.756 (1.216) Remain 20:09:23 loss: 0.2878 Lr: 0.00175 [2024-02-19 00:44:52,569 INFO misc.py line 119 87073] Train: [62/100][1064/1557] Data 0.004 (0.276) Batch 1.250 (1.216) Remain 20:09:24 loss: 0.2122 Lr: 0.00175 [2024-02-19 00:44:53,677 INFO misc.py line 119 87073] Train: [62/100][1065/1557] Data 0.007 (0.276) Batch 1.102 (1.216) Remain 20:09:16 loss: 0.5345 Lr: 0.00175 [2024-02-19 00:44:54,878 INFO misc.py line 119 87073] Train: [62/100][1066/1557] Data 0.011 (0.275) Batch 1.199 (1.216) Remain 20:09:14 loss: 0.1706 Lr: 0.00175 [2024-02-19 00:44:55,622 INFO misc.py line 119 87073] Train: [62/100][1067/1557] Data 0.014 (0.275) Batch 0.755 (1.216) Remain 20:08:47 loss: 0.1995 Lr: 0.00175 [2024-02-19 00:44:56,527 INFO misc.py line 119 87073] Train: [62/100][1068/1557] Data 0.004 (0.275) Batch 0.904 (1.215) Remain 20:08:28 loss: 0.3402 Lr: 0.00175 [2024-02-19 00:44:57,283 INFO misc.py line 119 87073] Train: [62/100][1069/1557] Data 0.006 (0.275) Batch 0.752 (1.215) Remain 20:08:01 loss: 0.2246 Lr: 0.00175 [2024-02-19 00:44:58,048 INFO misc.py line 119 87073] Train: [62/100][1070/1557] Data 0.009 (0.274) Batch 0.769 (1.215) Remain 20:07:35 loss: 0.1758 Lr: 0.00175 [2024-02-19 00:45:13,819 INFO misc.py line 119 87073] Train: [62/100][1071/1557] Data 14.768 (0.288) Batch 15.771 (1.228) Remain 20:21:07 loss: 0.1878 Lr: 0.00175 [2024-02-19 00:45:14,836 INFO misc.py line 119 87073] Train: [62/100][1072/1557] Data 0.006 (0.288) Batch 1.018 (1.228) Remain 20:20:54 loss: 0.3643 Lr: 0.00175 [2024-02-19 00:45:15,917 INFO misc.py line 119 87073] Train: 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(0.286) Batch 1.071 (1.226) Remain 20:19:01 loss: 0.2844 Lr: 0.00175 [2024-02-19 00:45:22,499 INFO misc.py line 119 87073] Train: [62/100][1080/1557] Data 0.008 (0.286) Batch 0.940 (1.226) Remain 20:18:44 loss: 0.1871 Lr: 0.00175 [2024-02-19 00:45:23,373 INFO misc.py line 119 87073] Train: [62/100][1081/1557] Data 0.003 (0.285) Batch 0.874 (1.226) Remain 20:18:23 loss: 0.2845 Lr: 0.00175 [2024-02-19 00:45:24,461 INFO misc.py line 119 87073] Train: [62/100][1082/1557] Data 0.004 (0.285) Batch 1.083 (1.226) Remain 20:18:14 loss: 0.3206 Lr: 0.00175 [2024-02-19 00:45:25,211 INFO misc.py line 119 87073] Train: [62/100][1083/1557] Data 0.008 (0.285) Batch 0.754 (1.225) Remain 20:17:47 loss: 0.1879 Lr: 0.00175 [2024-02-19 00:45:25,989 INFO misc.py line 119 87073] Train: [62/100][1084/1557] Data 0.003 (0.285) Batch 0.778 (1.225) Remain 20:17:21 loss: 0.2754 Lr: 0.00175 [2024-02-19 00:45:27,157 INFO misc.py line 119 87073] Train: [62/100][1085/1557] Data 0.003 (0.284) Batch 1.168 (1.225) Remain 20:17:17 loss: 0.2525 Lr: 0.00175 [2024-02-19 00:45:28,011 INFO misc.py line 119 87073] Train: [62/100][1086/1557] Data 0.004 (0.284) Batch 0.855 (1.224) Remain 20:16:55 loss: 0.3681 Lr: 0.00175 [2024-02-19 00:45:28,945 INFO misc.py line 119 87073] Train: [62/100][1087/1557] Data 0.003 (0.284) Batch 0.933 (1.224) Remain 20:16:38 loss: 0.2745 Lr: 0.00175 [2024-02-19 00:45:29,873 INFO misc.py line 119 87073] Train: [62/100][1088/1557] Data 0.003 (0.283) Batch 0.929 (1.224) Remain 20:16:21 loss: 0.2673 Lr: 0.00175 [2024-02-19 00:45:30,862 INFO misc.py line 119 87073] Train: [62/100][1089/1557] Data 0.004 (0.283) Batch 0.986 (1.224) Remain 20:16:06 loss: 0.5388 Lr: 0.00175 [2024-02-19 00:45:31,596 INFO misc.py line 119 87073] Train: [62/100][1090/1557] Data 0.006 (0.283) Batch 0.736 (1.223) Remain 20:15:38 loss: 0.2071 Lr: 0.00175 [2024-02-19 00:45:32,323 INFO misc.py line 119 87073] Train: [62/100][1091/1557] Data 0.004 (0.283) Batch 0.727 (1.223) Remain 20:15:10 loss: 0.2449 Lr: 0.00175 [2024-02-19 00:45:33,482 INFO misc.py line 119 87073] Train: [62/100][1092/1557] Data 0.005 (0.282) Batch 1.158 (1.223) Remain 20:15:05 loss: 0.0953 Lr: 0.00175 [2024-02-19 00:45:34,617 INFO misc.py line 119 87073] Train: [62/100][1093/1557] Data 0.005 (0.282) Batch 1.134 (1.223) Remain 20:14:59 loss: 0.2409 Lr: 0.00175 [2024-02-19 00:45:35,669 INFO misc.py line 119 87073] Train: [62/100][1094/1557] Data 0.007 (0.282) Batch 1.049 (1.222) Remain 20:14:48 loss: 0.3011 Lr: 0.00175 [2024-02-19 00:45:36,611 INFO misc.py line 119 87073] Train: [62/100][1095/1557] Data 0.009 (0.282) Batch 0.948 (1.222) Remain 20:14:32 loss: 0.5301 Lr: 0.00175 [2024-02-19 00:45:37,677 INFO misc.py line 119 87073] Train: [62/100][1096/1557] Data 0.003 (0.281) Batch 1.067 (1.222) Remain 20:14:22 loss: 0.3782 Lr: 0.00175 [2024-02-19 00:45:38,542 INFO misc.py line 119 87073] Train: [62/100][1097/1557] Data 0.003 (0.281) Batch 0.863 (1.222) Remain 20:14:02 loss: 0.2911 Lr: 0.00175 [2024-02-19 00:45:39,299 INFO misc.py line 119 87073] Train: [62/100][1098/1557] Data 0.004 (0.281) Batch 0.758 (1.221) Remain 20:13:35 loss: 0.3273 Lr: 0.00175 [2024-02-19 00:45:40,558 INFO misc.py line 119 87073] Train: [62/100][1099/1557] Data 0.003 (0.281) Batch 1.255 (1.221) Remain 20:13:36 loss: 0.1107 Lr: 0.00175 [2024-02-19 00:45:41,500 INFO misc.py line 119 87073] Train: [62/100][1100/1557] Data 0.007 (0.280) Batch 0.946 (1.221) Remain 20:13:20 loss: 0.3386 Lr: 0.00175 [2024-02-19 00:45:42,519 INFO misc.py line 119 87073] Train: [62/100][1101/1557] Data 0.003 (0.280) Batch 1.019 (1.221) Remain 20:13:07 loss: 0.4111 Lr: 0.00175 [2024-02-19 00:45:43,439 INFO misc.py line 119 87073] Train: [62/100][1102/1557] Data 0.004 (0.280) Batch 0.921 (1.221) Remain 20:12:50 loss: 0.4700 Lr: 0.00175 [2024-02-19 00:45:44,642 INFO misc.py line 119 87073] Train: [62/100][1103/1557] Data 0.003 (0.280) Batch 1.203 (1.221) Remain 20:12:48 loss: 0.2329 Lr: 0.00175 [2024-02-19 00:45:45,401 INFO misc.py line 119 87073] Train: 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(0.278) Batch 0.831 (1.219) Remain 20:10:46 loss: 0.3388 Lr: 0.00175 [2024-02-19 00:45:51,876 INFO misc.py line 119 87073] Train: [62/100][1111/1557] Data 0.004 (0.278) Batch 0.779 (1.218) Remain 20:10:22 loss: 0.2157 Lr: 0.00175 [2024-02-19 00:45:52,676 INFO misc.py line 119 87073] Train: [62/100][1112/1557] Data 0.008 (0.277) Batch 0.805 (1.218) Remain 20:09:58 loss: 0.2309 Lr: 0.00175 [2024-02-19 00:45:53,840 INFO misc.py line 119 87073] Train: [62/100][1113/1557] Data 0.004 (0.277) Batch 1.163 (1.218) Remain 20:09:54 loss: 0.1627 Lr: 0.00175 [2024-02-19 00:45:54,799 INFO misc.py line 119 87073] Train: [62/100][1114/1557] Data 0.004 (0.277) Batch 0.960 (1.218) Remain 20:09:39 loss: 0.2786 Lr: 0.00175 [2024-02-19 00:45:55,832 INFO misc.py line 119 87073] Train: [62/100][1115/1557] Data 0.004 (0.277) Batch 1.033 (1.217) Remain 20:09:28 loss: 0.3733 Lr: 0.00175 [2024-02-19 00:45:56,773 INFO misc.py line 119 87073] Train: [62/100][1116/1557] Data 0.004 (0.276) Batch 0.941 (1.217) Remain 20:09:12 loss: 0.2931 Lr: 0.00175 [2024-02-19 00:45:57,715 INFO misc.py line 119 87073] Train: [62/100][1117/1557] Data 0.004 (0.276) Batch 0.935 (1.217) Remain 20:08:55 loss: 0.6013 Lr: 0.00175 [2024-02-19 00:45:58,465 INFO misc.py line 119 87073] Train: [62/100][1118/1557] Data 0.011 (0.276) Batch 0.758 (1.217) Remain 20:08:30 loss: 0.2289 Lr: 0.00175 [2024-02-19 00:45:59,219 INFO misc.py line 119 87073] Train: [62/100][1119/1557] Data 0.003 (0.276) Batch 0.745 (1.216) Remain 20:08:03 loss: 0.3124 Lr: 0.00175 [2024-02-19 00:46:00,530 INFO misc.py line 119 87073] Train: [62/100][1120/1557] Data 0.012 (0.276) Batch 1.310 (1.216) Remain 20:08:07 loss: 0.1195 Lr: 0.00175 [2024-02-19 00:46:01,380 INFO misc.py line 119 87073] Train: [62/100][1121/1557] Data 0.013 (0.275) Batch 0.861 (1.216) Remain 20:07:47 loss: 0.1498 Lr: 0.00175 [2024-02-19 00:46:02,253 INFO misc.py line 119 87073] Train: [62/100][1122/1557] Data 0.003 (0.275) Batch 0.873 (1.216) Remain 20:07:27 loss: 0.3187 Lr: 0.00175 [2024-02-19 00:46:03,090 INFO misc.py line 119 87073] Train: [62/100][1123/1557] Data 0.003 (0.275) Batch 0.832 (1.215) Remain 20:07:06 loss: 0.3160 Lr: 0.00175 [2024-02-19 00:46:04,153 INFO misc.py line 119 87073] Train: [62/100][1124/1557] Data 0.008 (0.275) Batch 1.057 (1.215) Remain 20:06:56 loss: 0.2060 Lr: 0.00175 [2024-02-19 00:46:04,944 INFO misc.py line 119 87073] Train: [62/100][1125/1557] Data 0.014 (0.274) Batch 0.801 (1.215) Remain 20:06:33 loss: 0.5194 Lr: 0.00175 [2024-02-19 00:46:05,789 INFO misc.py line 119 87073] Train: [62/100][1126/1557] Data 0.004 (0.274) Batch 0.843 (1.214) Remain 20:06:12 loss: 0.5029 Lr: 0.00175 [2024-02-19 00:46:21,774 INFO misc.py line 119 87073] Train: [62/100][1127/1557] Data 14.953 (0.287) Batch 15.986 (1.228) Remain 20:19:14 loss: 0.1075 Lr: 0.00175 [2024-02-19 00:46:22,741 INFO misc.py line 119 87073] Train: [62/100][1128/1557] Data 0.005 (0.287) Batch 0.964 (1.227) Remain 20:18:59 loss: 0.1300 Lr: 0.00175 [2024-02-19 00:46:23,592 INFO misc.py line 119 87073] Train: [62/100][1129/1557] Data 0.009 (0.287) Batch 0.854 (1.227) Remain 20:18:38 loss: 0.3100 Lr: 0.00175 [2024-02-19 00:46:24,557 INFO misc.py line 119 87073] Train: [62/100][1130/1557] Data 0.004 (0.286) Batch 0.966 (1.227) Remain 20:18:23 loss: 0.2769 Lr: 0.00175 [2024-02-19 00:46:25,543 INFO misc.py line 119 87073] Train: [62/100][1131/1557] Data 0.003 (0.286) Batch 0.986 (1.226) Remain 20:18:09 loss: 0.5394 Lr: 0.00175 [2024-02-19 00:46:26,341 INFO misc.py line 119 87073] Train: [62/100][1132/1557] Data 0.003 (0.286) Batch 0.797 (1.226) Remain 20:17:45 loss: 0.1448 Lr: 0.00175 [2024-02-19 00:46:27,120 INFO misc.py line 119 87073] Train: [62/100][1133/1557] Data 0.004 (0.286) Batch 0.772 (1.226) Remain 20:17:20 loss: 0.2640 Lr: 0.00175 [2024-02-19 00:46:28,384 INFO misc.py line 119 87073] Train: [62/100][1134/1557] Data 0.011 (0.285) Batch 1.263 (1.226) Remain 20:17:21 loss: 0.2070 Lr: 0.00175 [2024-02-19 00:46:29,445 INFO misc.py line 119 87073] Train: 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(0.284) Batch 1.276 (1.224) Remain 20:15:41 loss: 0.2571 Lr: 0.00175 [2024-02-19 00:46:36,304 INFO misc.py line 119 87073] Train: [62/100][1142/1557] Data 0.011 (0.283) Batch 1.071 (1.224) Remain 20:15:32 loss: 0.4148 Lr: 0.00175 [2024-02-19 00:46:37,263 INFO misc.py line 119 87073] Train: [62/100][1143/1557] Data 0.016 (0.283) Batch 0.971 (1.224) Remain 20:15:17 loss: 0.3084 Lr: 0.00175 [2024-02-19 00:46:38,227 INFO misc.py line 119 87073] Train: [62/100][1144/1557] Data 0.003 (0.283) Batch 0.964 (1.224) Remain 20:15:03 loss: 0.1030 Lr: 0.00175 [2024-02-19 00:46:39,068 INFO misc.py line 119 87073] Train: [62/100][1145/1557] Data 0.004 (0.283) Batch 0.841 (1.223) Remain 20:14:42 loss: 0.3187 Lr: 0.00175 [2024-02-19 00:46:39,877 INFO misc.py line 119 87073] Train: [62/100][1146/1557] Data 0.003 (0.282) Batch 0.803 (1.223) Remain 20:14:18 loss: 0.2528 Lr: 0.00175 [2024-02-19 00:46:40,641 INFO misc.py line 119 87073] Train: [62/100][1147/1557] Data 0.009 (0.282) Batch 0.768 (1.223) Remain 20:13:53 loss: 0.3638 Lr: 0.00175 [2024-02-19 00:46:41,724 INFO misc.py line 119 87073] Train: [62/100][1148/1557] Data 0.005 (0.282) Batch 1.084 (1.222) Remain 20:13:45 loss: 0.1287 Lr: 0.00175 [2024-02-19 00:46:42,718 INFO misc.py line 119 87073] Train: [62/100][1149/1557] Data 0.005 (0.282) Batch 0.994 (1.222) Remain 20:13:32 loss: 0.2359 Lr: 0.00175 [2024-02-19 00:46:43,573 INFO misc.py line 119 87073] Train: [62/100][1150/1557] Data 0.004 (0.282) Batch 0.855 (1.222) Remain 20:13:12 loss: 0.1037 Lr: 0.00175 [2024-02-19 00:46:44,476 INFO misc.py line 119 87073] Train: [62/100][1151/1557] Data 0.003 (0.281) Batch 0.903 (1.222) Remain 20:12:54 loss: 0.1036 Lr: 0.00175 [2024-02-19 00:46:45,377 INFO misc.py line 119 87073] Train: [62/100][1152/1557] Data 0.004 (0.281) Batch 0.902 (1.221) Remain 20:12:36 loss: 0.2998 Lr: 0.00175 [2024-02-19 00:46:46,167 INFO misc.py line 119 87073] Train: [62/100][1153/1557] Data 0.003 (0.281) Batch 0.790 (1.221) Remain 20:12:13 loss: 0.4113 Lr: 0.00175 [2024-02-19 00:46:46,968 INFO misc.py line 119 87073] Train: [62/100][1154/1557] Data 0.004 (0.281) Batch 0.791 (1.221) Remain 20:11:49 loss: 0.2154 Lr: 0.00175 [2024-02-19 00:46:48,302 INFO misc.py line 119 87073] Train: [62/100][1155/1557] Data 0.012 (0.280) Batch 1.313 (1.221) Remain 20:11:53 loss: 0.0801 Lr: 0.00175 [2024-02-19 00:46:49,333 INFO misc.py line 119 87073] Train: [62/100][1156/1557] Data 0.033 (0.280) Batch 1.057 (1.221) Remain 20:11:43 loss: 0.3563 Lr: 0.00175 [2024-02-19 00:46:50,660 INFO misc.py line 119 87073] Train: [62/100][1157/1557] Data 0.008 (0.280) Batch 1.322 (1.221) Remain 20:11:47 loss: 0.3705 Lr: 0.00175 [2024-02-19 00:46:51,567 INFO misc.py line 119 87073] Train: [62/100][1158/1557] Data 0.013 (0.280) Batch 0.915 (1.220) Remain 20:11:30 loss: 0.1809 Lr: 0.00175 [2024-02-19 00:46:52,591 INFO misc.py line 119 87073] Train: [62/100][1159/1557] Data 0.005 (0.279) Batch 1.025 (1.220) Remain 20:11:19 loss: 0.1621 Lr: 0.00175 [2024-02-19 00:46:53,392 INFO misc.py line 119 87073] Train: [62/100][1160/1557] Data 0.003 (0.279) Batch 0.801 (1.220) Remain 20:10:56 loss: 0.3062 Lr: 0.00175 [2024-02-19 00:46:54,153 INFO misc.py line 119 87073] Train: [62/100][1161/1557] Data 0.003 (0.279) Batch 0.754 (1.219) Remain 20:10:31 loss: 0.2563 Lr: 0.00175 [2024-02-19 00:46:55,172 INFO misc.py line 119 87073] Train: [62/100][1162/1557] Data 0.010 (0.279) Batch 1.018 (1.219) Remain 20:10:19 loss: 0.1178 Lr: 0.00175 [2024-02-19 00:46:56,119 INFO misc.py line 119 87073] Train: [62/100][1163/1557] Data 0.011 (0.278) Batch 0.955 (1.219) Remain 20:10:04 loss: 0.2888 Lr: 0.00175 [2024-02-19 00:46:57,116 INFO misc.py line 119 87073] Train: [62/100][1164/1557] Data 0.003 (0.278) Batch 0.997 (1.219) Remain 20:09:52 loss: 0.2985 Lr: 0.00175 [2024-02-19 00:46:57,926 INFO misc.py line 119 87073] Train: [62/100][1165/1557] Data 0.003 (0.278) Batch 0.808 (1.218) Remain 20:09:30 loss: 0.4042 Lr: 0.00175 [2024-02-19 00:46:58,892 INFO misc.py line 119 87073] Train: 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(0.276) Batch 0.994 (1.217) Remain 20:07:54 loss: 0.3717 Lr: 0.00175 [2024-02-19 00:47:05,689 INFO misc.py line 119 87073] Train: [62/100][1173/1557] Data 0.007 (0.276) Batch 0.941 (1.217) Remain 20:07:39 loss: 0.3250 Lr: 0.00175 [2024-02-19 00:47:06,470 INFO misc.py line 119 87073] Train: [62/100][1174/1557] Data 0.005 (0.276) Batch 0.780 (1.216) Remain 20:07:15 loss: 0.3376 Lr: 0.00175 [2024-02-19 00:47:07,217 INFO misc.py line 119 87073] Train: [62/100][1175/1557] Data 0.006 (0.276) Batch 0.747 (1.216) Remain 20:06:50 loss: 0.2374 Lr: 0.00175 [2024-02-19 00:47:08,457 INFO misc.py line 119 87073] Train: [62/100][1176/1557] Data 0.007 (0.275) Batch 1.242 (1.216) Remain 20:06:50 loss: 0.1727 Lr: 0.00175 [2024-02-19 00:47:09,377 INFO misc.py line 119 87073] Train: [62/100][1177/1557] Data 0.004 (0.275) Batch 0.919 (1.216) Remain 20:06:34 loss: 0.2321 Lr: 0.00175 [2024-02-19 00:47:10,328 INFO misc.py line 119 87073] Train: [62/100][1178/1557] Data 0.006 (0.275) Batch 0.953 (1.216) Remain 20:06:20 loss: 0.3039 Lr: 0.00175 [2024-02-19 00:47:11,249 INFO misc.py line 119 87073] Train: [62/100][1179/1557] Data 0.004 (0.275) Batch 0.921 (1.215) Remain 20:06:03 loss: 0.4003 Lr: 0.00175 [2024-02-19 00:47:12,108 INFO misc.py line 119 87073] Train: [62/100][1180/1557] Data 0.005 (0.275) Batch 0.859 (1.215) Remain 20:05:44 loss: 0.1303 Lr: 0.00175 [2024-02-19 00:47:12,892 INFO misc.py line 119 87073] Train: [62/100][1181/1557] Data 0.005 (0.274) Batch 0.777 (1.215) Remain 20:05:21 loss: 0.2925 Lr: 0.00175 [2024-02-19 00:47:13,624 INFO misc.py line 119 87073] Train: [62/100][1182/1557] Data 0.011 (0.274) Batch 0.740 (1.214) Remain 20:04:56 loss: 0.5640 Lr: 0.00175 [2024-02-19 00:47:30,555 INFO misc.py line 119 87073] Train: [62/100][1183/1557] Data 15.983 (0.287) Batch 16.931 (1.228) Remain 20:18:07 loss: 0.1500 Lr: 0.00175 [2024-02-19 00:47:31,449 INFO misc.py line 119 87073] Train: [62/100][1184/1557] Data 0.003 (0.287) Batch 0.889 (1.227) Remain 20:17:49 loss: 0.2534 Lr: 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INFO misc.py line 119 87073] Train: [62/100][1191/1557] Data 0.015 (0.286) Batch 0.887 (1.226) Remain 20:16:01 loss: 0.2405 Lr: 0.00175 [2024-02-19 00:47:39,120 INFO misc.py line 119 87073] Train: [62/100][1192/1557] Data 0.004 (0.285) Batch 1.069 (1.225) Remain 20:15:52 loss: 0.3762 Lr: 0.00175 [2024-02-19 00:47:40,149 INFO misc.py line 119 87073] Train: [62/100][1193/1557] Data 0.003 (0.285) Batch 1.029 (1.225) Remain 20:15:41 loss: 0.3818 Lr: 0.00175 [2024-02-19 00:47:41,053 INFO misc.py line 119 87073] Train: [62/100][1194/1557] Data 0.004 (0.285) Batch 0.903 (1.225) Remain 20:15:24 loss: 0.1942 Lr: 0.00175 [2024-02-19 00:47:41,828 INFO misc.py line 119 87073] Train: [62/100][1195/1557] Data 0.005 (0.285) Batch 0.765 (1.225) Remain 20:14:59 loss: 0.1819 Lr: 0.00175 [2024-02-19 00:47:42,559 INFO misc.py line 119 87073] Train: [62/100][1196/1557] Data 0.015 (0.284) Batch 0.742 (1.224) Remain 20:14:34 loss: 0.3203 Lr: 0.00175 [2024-02-19 00:47:43,770 INFO misc.py line 119 87073] Train: [62/100][1197/1557] Data 0.004 (0.284) Batch 1.211 (1.224) Remain 20:14:32 loss: 0.2896 Lr: 0.00175 [2024-02-19 00:47:44,722 INFO misc.py line 119 87073] Train: [62/100][1198/1557] Data 0.005 (0.284) Batch 0.953 (1.224) Remain 20:14:17 loss: 0.2697 Lr: 0.00175 [2024-02-19 00:47:45,716 INFO misc.py line 119 87073] Train: [62/100][1199/1557] Data 0.003 (0.284) Batch 0.994 (1.224) Remain 20:14:05 loss: 0.4218 Lr: 0.00175 [2024-02-19 00:47:46,770 INFO misc.py line 119 87073] Train: [62/100][1200/1557] Data 0.003 (0.283) Batch 1.052 (1.224) Remain 20:13:55 loss: 0.5368 Lr: 0.00175 [2024-02-19 00:47:47,766 INFO misc.py line 119 87073] Train: [62/100][1201/1557] Data 0.005 (0.283) Batch 0.990 (1.223) Remain 20:13:42 loss: 0.2703 Lr: 0.00175 [2024-02-19 00:47:48,525 INFO misc.py line 119 87073] Train: [62/100][1202/1557] Data 0.011 (0.283) Batch 0.766 (1.223) Remain 20:13:18 loss: 0.1868 Lr: 0.00175 [2024-02-19 00:47:49,303 INFO misc.py line 119 87073] Train: [62/100][1203/1557] Data 0.004 (0.283) Batch 0.766 (1.223) Remain 20:12:54 loss: 0.4267 Lr: 0.00175 [2024-02-19 00:47:50,408 INFO misc.py line 119 87073] Train: [62/100][1204/1557] Data 0.016 (0.282) Batch 1.116 (1.223) Remain 20:12:48 loss: 0.1407 Lr: 0.00175 [2024-02-19 00:47:51,354 INFO misc.py line 119 87073] Train: [62/100][1205/1557] Data 0.005 (0.282) Batch 0.947 (1.222) Remain 20:12:33 loss: 0.2031 Lr: 0.00175 [2024-02-19 00:47:52,279 INFO misc.py line 119 87073] Train: [62/100][1206/1557] Data 0.003 (0.282) Batch 0.924 (1.222) Remain 20:12:17 loss: 0.2386 Lr: 0.00175 [2024-02-19 00:47:53,352 INFO misc.py line 119 87073] Train: [62/100][1207/1557] Data 0.003 (0.282) Batch 1.067 (1.222) Remain 20:12:08 loss: 0.2300 Lr: 0.00175 [2024-02-19 00:47:54,208 INFO misc.py line 119 87073] Train: [62/100][1208/1557] Data 0.009 (0.282) Batch 0.862 (1.222) Remain 20:11:49 loss: 0.2945 Lr: 0.00175 [2024-02-19 00:47:54,943 INFO misc.py line 119 87073] Train: [62/100][1209/1557] Data 0.004 (0.281) Batch 0.734 (1.221) Remain 20:11:24 loss: 0.3196 Lr: 0.00175 [2024-02-19 00:47:55,602 INFO misc.py line 119 87073] Train: [62/100][1210/1557] Data 0.005 (0.281) Batch 0.656 (1.221) Remain 20:10:55 loss: 0.2544 Lr: 0.00175 [2024-02-19 00:47:56,899 INFO misc.py line 119 87073] Train: [62/100][1211/1557] Data 0.007 (0.281) Batch 1.295 (1.221) Remain 20:10:57 loss: 0.0924 Lr: 0.00175 [2024-02-19 00:47:57,855 INFO misc.py line 119 87073] Train: [62/100][1212/1557] Data 0.010 (0.281) Batch 0.962 (1.221) Remain 20:10:43 loss: 0.6480 Lr: 0.00175 [2024-02-19 00:47:58,650 INFO misc.py line 119 87073] Train: [62/100][1213/1557] Data 0.003 (0.280) Batch 0.795 (1.220) Remain 20:10:21 loss: 0.6624 Lr: 0.00175 [2024-02-19 00:47:59,499 INFO misc.py line 119 87073] Train: [62/100][1214/1557] Data 0.003 (0.280) Batch 0.840 (1.220) Remain 20:10:01 loss: 0.3438 Lr: 0.00175 [2024-02-19 00:48:00,412 INFO misc.py line 119 87073] Train: [62/100][1215/1557] Data 0.012 (0.280) Batch 0.921 (1.220) Remain 20:09:45 loss: 0.5640 Lr: 0.00175 [2024-02-19 00:48:01,184 INFO misc.py line 119 87073] Train: [62/100][1216/1557] Data 0.003 (0.280) Batch 0.773 (1.219) Remain 20:09:22 loss: 0.1641 Lr: 0.00175 [2024-02-19 00:48:01,965 INFO misc.py line 119 87073] Train: [62/100][1217/1557] Data 0.004 (0.280) Batch 0.772 (1.219) Remain 20:08:59 loss: 0.2003 Lr: 0.00175 [2024-02-19 00:48:03,001 INFO misc.py line 119 87073] Train: [62/100][1218/1557] Data 0.012 (0.279) Batch 1.036 (1.219) Remain 20:08:49 loss: 0.1622 Lr: 0.00175 [2024-02-19 00:48:04,018 INFO misc.py line 119 87073] Train: [62/100][1219/1557] Data 0.012 (0.279) Batch 1.018 (1.219) Remain 20:08:38 loss: 0.4473 Lr: 0.00175 [2024-02-19 00:48:05,119 INFO misc.py line 119 87073] Train: [62/100][1220/1557] Data 0.010 (0.279) Batch 1.105 (1.219) Remain 20:08:31 loss: 0.5940 Lr: 0.00175 [2024-02-19 00:48:06,193 INFO misc.py line 119 87073] Train: [62/100][1221/1557] Data 0.006 (0.279) Batch 1.074 (1.219) Remain 20:08:23 loss: 0.3354 Lr: 0.00175 [2024-02-19 00:48:07,143 INFO misc.py line 119 87073] Train: [62/100][1222/1557] Data 0.007 (0.278) Batch 0.950 (1.218) Remain 20:08:08 loss: 0.3783 Lr: 0.00175 [2024-02-19 00:48:07,942 INFO misc.py line 119 87073] Train: [62/100][1223/1557] Data 0.008 (0.278) Batch 0.801 (1.218) Remain 20:07:47 loss: 0.1528 Lr: 0.00175 [2024-02-19 00:48:08,656 INFO misc.py line 119 87073] Train: [62/100][1224/1557] Data 0.004 (0.278) Batch 0.703 (1.218) Remain 20:07:21 loss: 0.2416 Lr: 0.00175 [2024-02-19 00:48:09,889 INFO misc.py line 119 87073] Train: [62/100][1225/1557] Data 0.016 (0.278) Batch 1.242 (1.218) Remain 20:07:21 loss: 0.1836 Lr: 0.00175 [2024-02-19 00:48:10,868 INFO misc.py line 119 87073] Train: [62/100][1226/1557] Data 0.007 (0.278) Batch 0.979 (1.217) Remain 20:07:08 loss: 0.1658 Lr: 0.00175 [2024-02-19 00:48:11,898 INFO misc.py line 119 87073] Train: [62/100][1227/1557] Data 0.007 (0.277) Batch 1.031 (1.217) Remain 20:06:57 loss: 0.3981 Lr: 0.00175 [2024-02-19 00:48:12,929 INFO misc.py line 119 87073] Train: [62/100][1228/1557] Data 0.005 (0.277) Batch 1.031 (1.217) Remain 20:06:47 loss: 0.2930 Lr: 0.00175 [2024-02-19 00:48:13,856 INFO misc.py line 119 87073] Train: [62/100][1229/1557] Data 0.006 (0.277) Batch 0.929 (1.217) Remain 20:06:32 loss: 0.1917 Lr: 0.00175 [2024-02-19 00:48:16,679 INFO misc.py line 119 87073] Train: [62/100][1230/1557] Data 1.444 (0.278) Batch 2.822 (1.218) Remain 20:07:49 loss: 0.3240 Lr: 0.00175 [2024-02-19 00:48:17,463 INFO misc.py line 119 87073] Train: [62/100][1231/1557] Data 0.004 (0.278) Batch 0.782 (1.218) Remain 20:07:26 loss: 0.2761 Lr: 0.00175 [2024-02-19 00:48:18,787 INFO misc.py line 119 87073] Train: [62/100][1232/1557] Data 0.006 (0.277) Batch 1.318 (1.218) Remain 20:07:30 loss: 0.1571 Lr: 0.00175 [2024-02-19 00:48:19,640 INFO misc.py line 119 87073] Train: [62/100][1233/1557] Data 0.013 (0.277) Batch 0.862 (1.218) Remain 20:07:11 loss: 0.3133 Lr: 0.00175 [2024-02-19 00:48:20,525 INFO misc.py line 119 87073] Train: [62/100][1234/1557] Data 0.004 (0.277) Batch 0.885 (1.217) Remain 20:06:54 loss: 0.1388 Lr: 0.00175 [2024-02-19 00:48:21,405 INFO misc.py line 119 87073] Train: [62/100][1235/1557] Data 0.004 (0.277) Batch 0.876 (1.217) Remain 20:06:36 loss: 0.2705 Lr: 0.00175 [2024-02-19 00:48:22,345 INFO misc.py line 119 87073] Train: [62/100][1236/1557] Data 0.007 (0.276) Batch 0.945 (1.217) Remain 20:06:22 loss: 0.7813 Lr: 0.00175 [2024-02-19 00:48:23,058 INFO misc.py line 119 87073] Train: [62/100][1237/1557] Data 0.003 (0.276) Batch 0.713 (1.216) Remain 20:05:57 loss: 0.1710 Lr: 0.00175 [2024-02-19 00:48:23,850 INFO misc.py line 119 87073] Train: [62/100][1238/1557] Data 0.003 (0.276) Batch 0.790 (1.216) Remain 20:05:35 loss: 0.2451 Lr: 0.00175 [2024-02-19 00:48:38,822 INFO misc.py line 119 87073] Train: [62/100][1239/1557] Data 13.978 (0.287) Batch 14.973 (1.227) Remain 20:16:36 loss: 0.1314 Lr: 0.00175 [2024-02-19 00:48:39,761 INFO misc.py line 119 87073] Train: [62/100][1240/1557] Data 0.004 (0.287) Batch 0.940 (1.227) Remain 20:16:21 loss: 0.1001 Lr: 0.00175 [2024-02-19 00:48:40,723 INFO misc.py line 119 87073] Train: [62/100][1241/1557] Data 0.003 (0.287) Batch 0.960 (1.227) Remain 20:16:07 loss: 0.1503 Lr: 0.00175 [2024-02-19 00:48:41,737 INFO misc.py line 119 87073] Train: [62/100][1242/1557] Data 0.006 (0.286) Batch 1.009 (1.227) Remain 20:15:55 loss: 0.2976 Lr: 0.00175 [2024-02-19 00:48:42,862 INFO misc.py line 119 87073] Train: [62/100][1243/1557] Data 0.011 (0.286) Batch 1.111 (1.226) Remain 20:15:48 loss: 0.2290 Lr: 0.00175 [2024-02-19 00:48:43,569 INFO misc.py line 119 87073] Train: [62/100][1244/1557] Data 0.026 (0.286) Batch 0.729 (1.226) Remain 20:15:23 loss: 0.2986 Lr: 0.00175 [2024-02-19 00:48:44,339 INFO misc.py line 119 87073] Train: [62/100][1245/1557] Data 0.004 (0.286) Batch 0.767 (1.226) Remain 20:15:00 loss: 0.1618 Lr: 0.00175 [2024-02-19 00:48:45,569 INFO misc.py line 119 87073] Train: [62/100][1246/1557] Data 0.007 (0.286) Batch 1.230 (1.226) Remain 20:14:59 loss: 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00:48:52,574 INFO misc.py line 119 87073] Train: [62/100][1253/1557] Data 0.010 (0.284) Batch 1.204 (1.224) Remain 20:13:35 loss: 0.1942 Lr: 0.00175 [2024-02-19 00:48:53,645 INFO misc.py line 119 87073] Train: [62/100][1254/1557] Data 0.008 (0.284) Batch 1.066 (1.224) Remain 20:13:26 loss: 0.2723 Lr: 0.00174 [2024-02-19 00:48:54,809 INFO misc.py line 119 87073] Train: [62/100][1255/1557] Data 0.013 (0.284) Batch 1.168 (1.224) Remain 20:13:23 loss: 0.4828 Lr: 0.00174 [2024-02-19 00:48:55,870 INFO misc.py line 119 87073] Train: [62/100][1256/1557] Data 0.010 (0.283) Batch 1.063 (1.224) Remain 20:13:14 loss: 0.3987 Lr: 0.00174 [2024-02-19 00:48:56,851 INFO misc.py line 119 87073] Train: [62/100][1257/1557] Data 0.009 (0.283) Batch 0.983 (1.224) Remain 20:13:01 loss: 0.1400 Lr: 0.00174 [2024-02-19 00:48:57,592 INFO misc.py line 119 87073] Train: [62/100][1258/1557] Data 0.007 (0.283) Batch 0.742 (1.224) Remain 20:12:37 loss: 0.3135 Lr: 0.00174 [2024-02-19 00:48:58,383 INFO misc.py line 119 87073] Train: [62/100][1259/1557] Data 0.006 (0.283) Batch 0.788 (1.223) Remain 20:12:15 loss: 0.4535 Lr: 0.00174 [2024-02-19 00:48:59,451 INFO misc.py line 119 87073] Train: [62/100][1260/1557] Data 0.008 (0.282) Batch 1.069 (1.223) Remain 20:12:07 loss: 0.1304 Lr: 0.00174 [2024-02-19 00:49:00,434 INFO misc.py line 119 87073] Train: [62/100][1261/1557] Data 0.007 (0.282) Batch 0.987 (1.223) Remain 20:11:54 loss: 0.5958 Lr: 0.00174 [2024-02-19 00:49:01,354 INFO misc.py line 119 87073] Train: [62/100][1262/1557] Data 0.003 (0.282) Batch 0.920 (1.223) Remain 20:11:39 loss: 0.2621 Lr: 0.00174 [2024-02-19 00:49:02,402 INFO misc.py line 119 87073] Train: [62/100][1263/1557] Data 0.004 (0.282) Batch 1.048 (1.222) Remain 20:11:29 loss: 0.3576 Lr: 0.00174 [2024-02-19 00:49:03,254 INFO misc.py line 119 87073] Train: [62/100][1264/1557] Data 0.003 (0.282) Batch 0.852 (1.222) Remain 20:11:11 loss: 0.2378 Lr: 0.00174 [2024-02-19 00:49:03,972 INFO misc.py line 119 87073] Train: [62/100][1265/1557] Data 0.004 (0.281) Batch 0.716 (1.222) Remain 20:10:45 loss: 0.0924 Lr: 0.00174 [2024-02-19 00:49:04,703 INFO misc.py line 119 87073] Train: [62/100][1266/1557] Data 0.005 (0.281) Batch 0.732 (1.221) Remain 20:10:21 loss: 0.3400 Lr: 0.00174 [2024-02-19 00:49:05,949 INFO misc.py line 119 87073] Train: [62/100][1267/1557] Data 0.004 (0.281) Batch 1.245 (1.221) Remain 20:10:21 loss: 0.0909 Lr: 0.00174 [2024-02-19 00:49:07,045 INFO misc.py line 119 87073] Train: [62/100][1268/1557] Data 0.005 (0.281) Batch 1.095 (1.221) Remain 20:10:14 loss: 0.2432 Lr: 0.00174 [2024-02-19 00:49:08,056 INFO misc.py line 119 87073] Train: [62/100][1269/1557] Data 0.005 (0.280) Batch 1.012 (1.221) Remain 20:10:03 loss: 0.1875 Lr: 0.00174 [2024-02-19 00:49:08,964 INFO misc.py line 119 87073] Train: [62/100][1270/1557] Data 0.006 (0.280) Batch 0.908 (1.221) Remain 20:09:47 loss: 0.4880 Lr: 0.00174 [2024-02-19 00:49:09,751 INFO misc.py line 119 87073] Train: [62/100][1271/1557] Data 0.005 (0.280) Batch 0.787 (1.221) Remain 20:09:25 loss: 0.4055 Lr: 0.00174 [2024-02-19 00:49:10,575 INFO misc.py line 119 87073] Train: [62/100][1272/1557] Data 0.005 (0.280) Batch 0.824 (1.220) Remain 20:09:06 loss: 0.2595 Lr: 0.00174 [2024-02-19 00:49:11,313 INFO misc.py line 119 87073] Train: [62/100][1273/1557] Data 0.004 (0.280) Batch 0.738 (1.220) Remain 20:08:42 loss: 0.2721 Lr: 0.00174 [2024-02-19 00:49:12,380 INFO misc.py line 119 87073] Train: [62/100][1274/1557] Data 0.004 (0.279) Batch 1.064 (1.220) Remain 20:08:33 loss: 0.1400 Lr: 0.00174 [2024-02-19 00:49:13,318 INFO misc.py line 119 87073] Train: [62/100][1275/1557] Data 0.007 (0.279) Batch 0.936 (1.220) Remain 20:08:19 loss: 0.4669 Lr: 0.00174 [2024-02-19 00:49:14,233 INFO misc.py line 119 87073] Train: [62/100][1276/1557] Data 0.009 (0.279) Batch 0.919 (1.219) Remain 20:08:04 loss: 0.4181 Lr: 0.00174 [2024-02-19 00:49:15,132 INFO misc.py line 119 87073] Train: [62/100][1277/1557] Data 0.006 (0.279) Batch 0.896 (1.219) Remain 20:07:47 loss: 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00:49:21,786 INFO misc.py line 119 87073] Train: [62/100][1284/1557] Data 0.008 (0.277) Batch 0.912 (1.218) Remain 20:06:12 loss: 0.1663 Lr: 0.00174 [2024-02-19 00:49:23,034 INFO misc.py line 119 87073] Train: [62/100][1285/1557] Data 0.006 (0.277) Batch 1.247 (1.218) Remain 20:06:12 loss: 0.2894 Lr: 0.00174 [2024-02-19 00:49:23,785 INFO misc.py line 119 87073] Train: [62/100][1286/1557] Data 0.006 (0.277) Batch 0.753 (1.217) Remain 20:05:49 loss: 0.2752 Lr: 0.00174 [2024-02-19 00:49:24,548 INFO misc.py line 119 87073] Train: [62/100][1287/1557] Data 0.005 (0.277) Batch 0.758 (1.217) Remain 20:05:27 loss: 0.4903 Lr: 0.00174 [2024-02-19 00:49:25,772 INFO misc.py line 119 87073] Train: [62/100][1288/1557] Data 0.009 (0.276) Batch 1.222 (1.217) Remain 20:05:26 loss: 0.2177 Lr: 0.00174 [2024-02-19 00:49:26,835 INFO misc.py line 119 87073] Train: [62/100][1289/1557] Data 0.012 (0.276) Batch 1.068 (1.217) Remain 20:05:17 loss: 0.4142 Lr: 0.00174 [2024-02-19 00:49:27,856 INFO misc.py line 119 87073] Train: [62/100][1290/1557] Data 0.006 (0.276) Batch 1.000 (1.217) Remain 20:05:06 loss: 0.3293 Lr: 0.00174 [2024-02-19 00:49:28,769 INFO misc.py line 119 87073] Train: [62/100][1291/1557] Data 0.027 (0.276) Batch 0.936 (1.216) Remain 20:04:52 loss: 0.2569 Lr: 0.00174 [2024-02-19 00:49:29,705 INFO misc.py line 119 87073] Train: [62/100][1292/1557] Data 0.004 (0.276) Batch 0.936 (1.216) Remain 20:04:38 loss: 0.2538 Lr: 0.00174 [2024-02-19 00:49:30,514 INFO misc.py line 119 87073] Train: [62/100][1293/1557] Data 0.005 (0.275) Batch 0.807 (1.216) Remain 20:04:18 loss: 0.1567 Lr: 0.00174 [2024-02-19 00:49:31,282 INFO misc.py line 119 87073] Train: [62/100][1294/1557] Data 0.006 (0.275) Batch 0.770 (1.216) Remain 20:03:56 loss: 0.2656 Lr: 0.00174 [2024-02-19 00:49:48,506 INFO misc.py line 119 87073] Train: [62/100][1295/1557] Data 16.252 (0.288) Batch 17.224 (1.228) Remain 20:16:11 loss: 0.1329 Lr: 0.00174 [2024-02-19 00:49:49,805 INFO misc.py line 119 87073] Train: 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(0.286) Batch 1.195 (1.227) Remain 20:15:01 loss: 0.2072 Lr: 0.00174 [2024-02-19 00:49:56,722 INFO misc.py line 119 87073] Train: [62/100][1303/1557] Data 0.030 (0.286) Batch 0.979 (1.227) Remain 20:14:48 loss: 0.4711 Lr: 0.00174 [2024-02-19 00:49:57,577 INFO misc.py line 119 87073] Train: [62/100][1304/1557] Data 0.006 (0.286) Batch 0.856 (1.226) Remain 20:14:30 loss: 0.3397 Lr: 0.00174 [2024-02-19 00:49:58,607 INFO misc.py line 119 87073] Train: [62/100][1305/1557] Data 0.005 (0.285) Batch 1.028 (1.226) Remain 20:14:20 loss: 0.3217 Lr: 0.00174 [2024-02-19 00:49:59,521 INFO misc.py line 119 87073] Train: [62/100][1306/1557] Data 0.008 (0.285) Batch 0.916 (1.226) Remain 20:14:04 loss: 0.3299 Lr: 0.00174 [2024-02-19 00:50:00,332 INFO misc.py line 119 87073] Train: [62/100][1307/1557] Data 0.004 (0.285) Batch 0.812 (1.226) Remain 20:13:44 loss: 0.1449 Lr: 0.00174 [2024-02-19 00:50:01,105 INFO misc.py line 119 87073] Train: [62/100][1308/1557] Data 0.003 (0.285) Batch 0.763 (1.225) Remain 20:13:22 loss: 0.4504 Lr: 0.00174 [2024-02-19 00:50:02,319 INFO misc.py line 119 87073] Train: [62/100][1309/1557] Data 0.012 (0.285) Batch 1.218 (1.225) Remain 20:13:20 loss: 0.2279 Lr: 0.00174 [2024-02-19 00:50:03,178 INFO misc.py line 119 87073] Train: [62/100][1310/1557] Data 0.009 (0.284) Batch 0.864 (1.225) Remain 20:13:03 loss: 0.0978 Lr: 0.00174 [2024-02-19 00:50:04,088 INFO misc.py line 119 87073] Train: [62/100][1311/1557] Data 0.004 (0.284) Batch 0.909 (1.225) Remain 20:12:47 loss: 0.1682 Lr: 0.00174 [2024-02-19 00:50:05,056 INFO misc.py line 119 87073] Train: [62/100][1312/1557] Data 0.005 (0.284) Batch 0.968 (1.225) Remain 20:12:34 loss: 0.3805 Lr: 0.00174 [2024-02-19 00:50:05,969 INFO misc.py line 119 87073] Train: [62/100][1313/1557] Data 0.006 (0.284) Batch 0.906 (1.224) Remain 20:12:18 loss: 0.3775 Lr: 0.00174 [2024-02-19 00:50:06,652 INFO misc.py line 119 87073] Train: [62/100][1314/1557] Data 0.011 (0.284) Batch 0.690 (1.224) Remain 20:11:53 loss: 0.3248 Lr: 0.00174 [2024-02-19 00:50:07,386 INFO misc.py line 119 87073] Train: [62/100][1315/1557] Data 0.004 (0.283) Batch 0.728 (1.224) Remain 20:11:29 loss: 0.1412 Lr: 0.00174 [2024-02-19 00:50:08,418 INFO misc.py line 119 87073] Train: [62/100][1316/1557] Data 0.011 (0.283) Batch 1.034 (1.223) Remain 20:11:20 loss: 0.0858 Lr: 0.00174 [2024-02-19 00:50:09,396 INFO misc.py line 119 87073] Train: [62/100][1317/1557] Data 0.008 (0.283) Batch 0.979 (1.223) Remain 20:11:07 loss: 0.2106 Lr: 0.00174 [2024-02-19 00:50:10,307 INFO misc.py line 119 87073] Train: [62/100][1318/1557] Data 0.007 (0.283) Batch 0.913 (1.223) Remain 20:10:52 loss: 0.5080 Lr: 0.00174 [2024-02-19 00:50:11,364 INFO misc.py line 119 87073] Train: [62/100][1319/1557] Data 0.004 (0.282) Batch 1.055 (1.223) Remain 20:10:43 loss: 0.5170 Lr: 0.00174 [2024-02-19 00:50:12,366 INFO misc.py line 119 87073] Train: [62/100][1320/1557] Data 0.007 (0.282) Batch 1.004 (1.223) Remain 20:10:32 loss: 0.8405 Lr: 0.00174 [2024-02-19 00:50:13,185 INFO 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20:06:29 loss: 0.3612 Lr: 0.00174 [2024-02-19 00:50:31,395 INFO misc.py line 119 87073] Train: [62/100][1340/1557] Data 0.013 (0.278) Batch 0.743 (1.219) Remain 20:06:07 loss: 0.2506 Lr: 0.00174 [2024-02-19 00:50:32,356 INFO misc.py line 119 87073] Train: [62/100][1341/1557] Data 0.006 (0.278) Batch 0.961 (1.218) Remain 20:05:54 loss: 0.0872 Lr: 0.00174 [2024-02-19 00:50:33,118 INFO misc.py line 119 87073] Train: [62/100][1342/1557] Data 0.005 (0.278) Batch 0.763 (1.218) Remain 20:05:33 loss: 0.4668 Lr: 0.00174 [2024-02-19 00:50:33,869 INFO misc.py line 119 87073] Train: [62/100][1343/1557] Data 0.004 (0.278) Batch 0.752 (1.218) Remain 20:05:11 loss: 0.1992 Lr: 0.00174 [2024-02-19 00:50:35,148 INFO misc.py line 119 87073] Train: [62/100][1344/1557] Data 0.004 (0.277) Batch 1.278 (1.218) Remain 20:05:12 loss: 0.2145 Lr: 0.00174 [2024-02-19 00:50:36,092 INFO misc.py line 119 87073] Train: [62/100][1345/1557] Data 0.006 (0.277) Batch 0.945 (1.218) Remain 20:04:59 loss: 0.4212 Lr: 0.00174 [2024-02-19 00:50:36,962 INFO misc.py line 119 87073] Train: [62/100][1346/1557] Data 0.005 (0.277) Batch 0.872 (1.217) Remain 20:04:42 loss: 0.1741 Lr: 0.00174 [2024-02-19 00:50:37,864 INFO misc.py line 119 87073] Train: [62/100][1347/1557] Data 0.004 (0.277) Batch 0.867 (1.217) Remain 20:04:26 loss: 0.2165 Lr: 0.00174 [2024-02-19 00:50:38,828 INFO misc.py line 119 87073] Train: [62/100][1348/1557] Data 0.038 (0.277) Batch 0.993 (1.217) Remain 20:04:15 loss: 0.1518 Lr: 0.00174 [2024-02-19 00:50:39,621 INFO misc.py line 119 87073] Train: [62/100][1349/1557] Data 0.009 (0.276) Batch 0.797 (1.217) Remain 20:03:55 loss: 0.4436 Lr: 0.00174 [2024-02-19 00:50:40,374 INFO misc.py line 119 87073] Train: [62/100][1350/1557] Data 0.005 (0.276) Batch 0.733 (1.216) Remain 20:03:32 loss: 0.1518 Lr: 0.00174 [2024-02-19 00:50:56,792 INFO misc.py line 119 87073] Train: [62/100][1351/1557] Data 15.411 (0.287) Batch 16.438 (1.228) Remain 20:14:42 loss: 0.1834 Lr: 0.00174 [2024-02-19 00:50:57,746 INFO misc.py line 119 87073] Train: [62/100][1352/1557] Data 0.005 (0.287) Batch 0.953 (1.227) Remain 20:14:28 loss: 0.1981 Lr: 0.00174 [2024-02-19 00:50:58,810 INFO misc.py line 119 87073] Train: [62/100][1353/1557] Data 0.005 (0.287) Batch 1.065 (1.227) Remain 20:14:20 loss: 0.2442 Lr: 0.00174 [2024-02-19 00:50:59,695 INFO misc.py line 119 87073] Train: [62/100][1354/1557] Data 0.003 (0.287) Batch 0.885 (1.227) Remain 20:14:04 loss: 0.3384 Lr: 0.00174 [2024-02-19 00:51:00,756 INFO misc.py line 119 87073] Train: [62/100][1355/1557] Data 0.004 (0.287) Batch 1.061 (1.227) Remain 20:13:55 loss: 0.2375 Lr: 0.00174 [2024-02-19 00:51:01,524 INFO misc.py line 119 87073] Train: [62/100][1356/1557] Data 0.005 (0.286) Batch 0.769 (1.227) Remain 20:13:34 loss: 0.2484 Lr: 0.00174 [2024-02-19 00:51:02,272 INFO misc.py line 119 87073] Train: [62/100][1357/1557] Data 0.004 (0.286) Batch 0.747 (1.226) Remain 20:13:11 loss: 0.5132 Lr: 0.00174 [2024-02-19 00:51:03,588 INFO misc.py line 119 87073] Train: 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(0.285) Batch 0.732 (1.225) Remain 20:11:42 loss: 0.2251 Lr: 0.00174 [2024-02-19 00:51:10,195 INFO misc.py line 119 87073] Train: [62/100][1365/1557] Data 0.012 (0.284) Batch 1.199 (1.225) Remain 20:11:39 loss: 0.2509 Lr: 0.00174 [2024-02-19 00:51:11,250 INFO misc.py line 119 87073] Train: [62/100][1366/1557] Data 0.010 (0.284) Batch 1.057 (1.225) Remain 20:11:31 loss: 0.2261 Lr: 0.00174 [2024-02-19 00:51:12,298 INFO misc.py line 119 87073] Train: [62/100][1367/1557] Data 0.008 (0.284) Batch 1.051 (1.225) Remain 20:11:22 loss: 0.3032 Lr: 0.00174 [2024-02-19 00:51:13,309 INFO misc.py line 119 87073] Train: [62/100][1368/1557] Data 0.004 (0.284) Batch 1.005 (1.224) Remain 20:11:11 loss: 0.2053 Lr: 0.00174 [2024-02-19 00:51:14,182 INFO misc.py line 119 87073] Train: [62/100][1369/1557] Data 0.011 (0.284) Batch 0.880 (1.224) Remain 20:10:55 loss: 0.3887 Lr: 0.00174 [2024-02-19 00:51:14,940 INFO misc.py line 119 87073] Train: [62/100][1370/1557] Data 0.003 (0.283) Batch 0.758 (1.224) Remain 20:10:34 loss: 0.1327 Lr: 0.00174 [2024-02-19 00:51:15,706 INFO misc.py line 119 87073] Train: [62/100][1371/1557] Data 0.004 (0.283) Batch 0.763 (1.223) Remain 20:10:12 loss: 0.2716 Lr: 0.00174 [2024-02-19 00:51:16,838 INFO misc.py line 119 87073] Train: [62/100][1372/1557] Data 0.007 (0.283) Batch 1.133 (1.223) Remain 20:10:07 loss: 0.1157 Lr: 0.00174 [2024-02-19 00:51:17,815 INFO misc.py line 119 87073] Train: [62/100][1373/1557] Data 0.008 (0.283) Batch 0.979 (1.223) Remain 20:09:55 loss: 0.5844 Lr: 0.00174 [2024-02-19 00:51:18,905 INFO misc.py line 119 87073] Train: [62/100][1374/1557] Data 0.005 (0.283) Batch 1.089 (1.223) Remain 20:09:48 loss: 0.1414 Lr: 0.00174 [2024-02-19 00:51:20,112 INFO misc.py line 119 87073] Train: [62/100][1375/1557] Data 0.006 (0.282) Batch 1.199 (1.223) Remain 20:09:46 loss: 0.3231 Lr: 0.00174 [2024-02-19 00:51:21,045 INFO misc.py line 119 87073] Train: [62/100][1376/1557] Data 0.015 (0.282) Batch 0.943 (1.223) Remain 20:09:33 loss: 0.3237 Lr: 0.00174 [2024-02-19 00:51:21,812 INFO misc.py line 119 87073] Train: [62/100][1377/1557] Data 0.004 (0.282) Batch 0.766 (1.223) Remain 20:09:12 loss: 0.1618 Lr: 0.00174 [2024-02-19 00:51:22,572 INFO misc.py line 119 87073] Train: [62/100][1378/1557] Data 0.005 (0.282) Batch 0.756 (1.222) Remain 20:08:50 loss: 0.3549 Lr: 0.00174 [2024-02-19 00:51:23,786 INFO misc.py line 119 87073] Train: [62/100][1379/1557] Data 0.009 (0.282) Batch 1.212 (1.222) Remain 20:08:49 loss: 0.0944 Lr: 0.00174 [2024-02-19 00:51:24,750 INFO misc.py line 119 87073] Train: [62/100][1380/1557] Data 0.012 (0.281) Batch 0.969 (1.222) Remain 20:08:37 loss: 0.3054 Lr: 0.00174 [2024-02-19 00:51:25,665 INFO misc.py line 119 87073] Train: [62/100][1381/1557] Data 0.006 (0.281) Batch 0.915 (1.222) Remain 20:08:22 loss: 0.2660 Lr: 0.00174 [2024-02-19 00:51:26,802 INFO misc.py line 119 87073] Train: [62/100][1382/1557] Data 0.006 (0.281) Batch 1.137 (1.222) Remain 20:08:17 loss: 0.2417 Lr: 0.00174 [2024-02-19 00:51:27,713 INFO 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20:04:06 loss: 0.5282 Lr: 0.00174 [2024-02-19 00:51:45,642 INFO misc.py line 119 87073] Train: [62/100][1402/1557] Data 0.005 (0.277) Batch 1.009 (1.218) Remain 20:03:56 loss: 0.3105 Lr: 0.00174 [2024-02-19 00:51:46,515 INFO misc.py line 119 87073] Train: [62/100][1403/1557] Data 0.005 (0.277) Batch 0.874 (1.217) Remain 20:03:40 loss: 0.2342 Lr: 0.00174 [2024-02-19 00:51:47,556 INFO misc.py line 119 87073] Train: [62/100][1404/1557] Data 0.003 (0.277) Batch 1.040 (1.217) Remain 20:03:31 loss: 0.7686 Lr: 0.00174 [2024-02-19 00:51:49,818 INFO misc.py line 119 87073] Train: [62/100][1405/1557] Data 1.210 (0.277) Batch 2.262 (1.218) Remain 20:04:14 loss: 0.3000 Lr: 0.00174 [2024-02-19 00:51:50,715 INFO misc.py line 119 87073] Train: [62/100][1406/1557] Data 0.005 (0.277) Batch 0.899 (1.218) Remain 20:03:59 loss: 0.3035 Lr: 0.00174 [2024-02-19 00:52:08,420 INFO misc.py line 119 87073] Train: [62/100][1407/1557] Data 16.776 (0.289) Batch 17.705 (1.230) Remain 20:15:35 loss: 0.1359 Lr: 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INFO misc.py line 119 87073] Train: [62/100][1414/1557] Data 0.003 (0.288) Batch 1.261 (1.228) Remain 20:13:56 loss: 0.1669 Lr: 0.00174 [2024-02-19 00:52:15,724 INFO misc.py line 119 87073] Train: [62/100][1415/1557] Data 0.011 (0.287) Batch 0.851 (1.228) Remain 20:13:38 loss: 0.1348 Lr: 0.00174 [2024-02-19 00:52:16,747 INFO misc.py line 119 87073] Train: [62/100][1416/1557] Data 0.003 (0.287) Batch 1.022 (1.228) Remain 20:13:29 loss: 0.2525 Lr: 0.00174 [2024-02-19 00:52:17,548 INFO misc.py line 119 87073] Train: [62/100][1417/1557] Data 0.004 (0.287) Batch 0.800 (1.227) Remain 20:13:09 loss: 0.3675 Lr: 0.00174 [2024-02-19 00:52:18,608 INFO misc.py line 119 87073] Train: [62/100][1418/1557] Data 0.005 (0.287) Batch 1.059 (1.227) Remain 20:13:01 loss: 0.3929 Lr: 0.00174 [2024-02-19 00:52:19,325 INFO misc.py line 119 87073] Train: [62/100][1419/1557] Data 0.006 (0.287) Batch 0.719 (1.227) Remain 20:12:39 loss: 0.1905 Lr: 0.00174 [2024-02-19 00:52:20,111 INFO misc.py line 119 87073] Train: [62/100][1420/1557] Data 0.003 (0.286) Batch 0.777 (1.227) Remain 20:12:19 loss: 0.1574 Lr: 0.00174 [2024-02-19 00:52:21,349 INFO misc.py line 119 87073] Train: [62/100][1421/1557] Data 0.012 (0.286) Batch 1.247 (1.227) Remain 20:12:18 loss: 0.1799 Lr: 0.00174 [2024-02-19 00:52:22,279 INFO misc.py line 119 87073] Train: [62/100][1422/1557] Data 0.004 (0.286) Batch 0.930 (1.226) Remain 20:12:05 loss: 0.8227 Lr: 0.00174 [2024-02-19 00:52:23,237 INFO misc.py line 119 87073] Train: [62/100][1423/1557] Data 0.003 (0.286) Batch 0.958 (1.226) Remain 20:11:52 loss: 0.2259 Lr: 0.00174 [2024-02-19 00:52:24,147 INFO misc.py line 119 87073] Train: [62/100][1424/1557] Data 0.003 (0.286) Batch 0.910 (1.226) Remain 20:11:38 loss: 0.5786 Lr: 0.00174 [2024-02-19 00:52:25,183 INFO misc.py line 119 87073] Train: [62/100][1425/1557] Data 0.004 (0.285) Batch 1.031 (1.226) Remain 20:11:28 loss: 0.3005 Lr: 0.00174 [2024-02-19 00:52:25,915 INFO misc.py line 119 87073] Train: [62/100][1426/1557] Data 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Remain 20:10:00 loss: 0.1946 Lr: 0.00174 [2024-02-19 00:52:32,588 INFO misc.py line 119 87073] Train: [62/100][1433/1557] Data 0.003 (0.284) Batch 0.748 (1.224) Remain 20:09:39 loss: 0.1864 Lr: 0.00174 [2024-02-19 00:52:33,376 INFO misc.py line 119 87073] Train: [62/100][1434/1557] Data 0.004 (0.284) Batch 0.785 (1.224) Remain 20:09:20 loss: 0.5646 Lr: 0.00174 [2024-02-19 00:52:34,578 INFO misc.py line 119 87073] Train: [62/100][1435/1557] Data 0.007 (0.283) Batch 1.200 (1.224) Remain 20:09:18 loss: 0.1795 Lr: 0.00174 [2024-02-19 00:52:35,611 INFO misc.py line 119 87073] Train: [62/100][1436/1557] Data 0.009 (0.283) Batch 1.030 (1.224) Remain 20:09:08 loss: 0.3780 Lr: 0.00174 [2024-02-19 00:52:36,451 INFO misc.py line 119 87073] Train: [62/100][1437/1557] Data 0.012 (0.283) Batch 0.849 (1.223) Remain 20:08:52 loss: 0.2957 Lr: 0.00174 [2024-02-19 00:52:37,415 INFO misc.py line 119 87073] Train: [62/100][1438/1557] Data 0.003 (0.283) Batch 0.963 (1.223) Remain 20:08:40 loss: 0.4258 Lr: 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INFO misc.py line 119 87073] Train: [62/100][1445/1557] Data 0.003 (0.281) Batch 1.010 (1.222) Remain 20:07:03 loss: 0.3141 Lr: 0.00174 [2024-02-19 00:52:44,785 INFO misc.py line 119 87073] Train: [62/100][1446/1557] Data 0.005 (0.281) Batch 0.946 (1.222) Remain 20:06:51 loss: 0.4861 Lr: 0.00174 [2024-02-19 00:52:45,608 INFO misc.py line 119 87073] Train: [62/100][1447/1557] Data 0.003 (0.281) Batch 0.823 (1.221) Remain 20:06:33 loss: 0.3262 Lr: 0.00174 [2024-02-19 00:52:46,373 INFO misc.py line 119 87073] Train: [62/100][1448/1557] Data 0.004 (0.281) Batch 0.762 (1.221) Remain 20:06:13 loss: 0.3406 Lr: 0.00174 [2024-02-19 00:52:47,549 INFO misc.py line 119 87073] Train: [62/100][1449/1557] Data 0.006 (0.281) Batch 1.170 (1.221) Remain 20:06:10 loss: 0.1165 Lr: 0.00174 [2024-02-19 00:52:48,435 INFO misc.py line 119 87073] Train: [62/100][1450/1557] Data 0.012 (0.281) Batch 0.895 (1.221) Remain 20:05:55 loss: 0.3006 Lr: 0.00174 [2024-02-19 00:52:49,395 INFO misc.py line 119 87073] Train: [62/100][1451/1557] Data 0.003 (0.280) Batch 0.960 (1.221) Remain 20:05:43 loss: 0.2833 Lr: 0.00174 [2024-02-19 00:52:50,299 INFO misc.py line 119 87073] Train: [62/100][1452/1557] Data 0.003 (0.280) Batch 0.904 (1.220) Remain 20:05:29 loss: 0.5649 Lr: 0.00173 [2024-02-19 00:52:51,246 INFO misc.py line 119 87073] Train: [62/100][1453/1557] Data 0.003 (0.280) Batch 0.944 (1.220) Remain 20:05:17 loss: 0.2128 Lr: 0.00173 [2024-02-19 00:52:52,020 INFO misc.py line 119 87073] Train: [62/100][1454/1557] Data 0.006 (0.280) Batch 0.777 (1.220) Remain 20:04:57 loss: 0.2464 Lr: 0.00173 [2024-02-19 00:52:52,814 INFO misc.py line 119 87073] Train: [62/100][1455/1557] Data 0.004 (0.280) Batch 0.784 (1.220) Remain 20:04:38 loss: 0.2723 Lr: 0.00173 [2024-02-19 00:52:54,132 INFO misc.py line 119 87073] Train: [62/100][1456/1557] Data 0.013 (0.279) Batch 1.322 (1.220) Remain 20:04:41 loss: 0.2628 Lr: 0.00173 [2024-02-19 00:52:55,163 INFO misc.py line 119 87073] Train: [62/100][1457/1557] Data 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(1.229) Remain 20:13:31 loss: 0.1197 Lr: 0.00173 [2024-02-19 00:53:16,955 INFO misc.py line 119 87073] Train: [62/100][1464/1557] Data 0.003 (0.288) Batch 1.019 (1.229) Remain 20:13:22 loss: 0.2434 Lr: 0.00173 [2024-02-19 00:53:17,829 INFO misc.py line 119 87073] Train: [62/100][1465/1557] Data 0.004 (0.288) Batch 0.874 (1.228) Remain 20:13:06 loss: 0.2673 Lr: 0.00173 [2024-02-19 00:53:18,742 INFO misc.py line 119 87073] Train: [62/100][1466/1557] Data 0.003 (0.288) Batch 0.904 (1.228) Remain 20:12:52 loss: 0.2595 Lr: 0.00173 [2024-02-19 00:53:19,577 INFO misc.py line 119 87073] Train: [62/100][1467/1557] Data 0.013 (0.287) Batch 0.844 (1.228) Remain 20:12:35 loss: 0.4810 Lr: 0.00173 [2024-02-19 00:53:20,332 INFO misc.py line 119 87073] Train: [62/100][1468/1557] Data 0.004 (0.287) Batch 0.755 (1.227) Remain 20:12:15 loss: 0.1925 Lr: 0.00173 [2024-02-19 00:53:21,089 INFO misc.py line 119 87073] Train: [62/100][1469/1557] Data 0.004 (0.287) Batch 0.748 (1.227) Remain 20:11:54 loss: 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00:53:27,703 INFO misc.py line 119 87073] Train: [62/100][1476/1557] Data 0.004 (0.286) Batch 0.855 (1.226) Remain 20:10:26 loss: 0.3753 Lr: 0.00173 [2024-02-19 00:53:28,863 INFO misc.py line 119 87073] Train: [62/100][1477/1557] Data 0.004 (0.286) Batch 1.159 (1.226) Remain 20:10:22 loss: 0.2228 Lr: 0.00173 [2024-02-19 00:53:29,886 INFO misc.py line 119 87073] Train: [62/100][1478/1557] Data 0.005 (0.285) Batch 1.022 (1.226) Remain 20:10:13 loss: 0.3022 Lr: 0.00173 [2024-02-19 00:53:30,908 INFO misc.py line 119 87073] Train: [62/100][1479/1557] Data 0.006 (0.285) Batch 1.021 (1.226) Remain 20:10:03 loss: 0.5354 Lr: 0.00173 [2024-02-19 00:53:31,676 INFO misc.py line 119 87073] Train: [62/100][1480/1557] Data 0.008 (0.285) Batch 0.772 (1.225) Remain 20:09:44 loss: 0.1995 Lr: 0.00173 [2024-02-19 00:53:32,706 INFO misc.py line 119 87073] Train: [62/100][1481/1557] Data 0.003 (0.285) Batch 1.030 (1.225) Remain 20:09:35 loss: 0.2443 Lr: 0.00173 [2024-02-19 00:53:33,481 INFO misc.py line 119 87073] Train: [62/100][1482/1557] Data 0.004 (0.285) Batch 0.770 (1.225) Remain 20:09:16 loss: 0.1518 Lr: 0.00173 [2024-02-19 00:53:34,224 INFO misc.py line 119 87073] Train: [62/100][1483/1557] Data 0.008 (0.284) Batch 0.748 (1.224) Remain 20:08:55 loss: 0.3873 Lr: 0.00173 [2024-02-19 00:53:35,232 INFO misc.py line 119 87073] Train: [62/100][1484/1557] Data 0.003 (0.284) Batch 1.008 (1.224) Remain 20:08:45 loss: 0.1229 Lr: 0.00173 [2024-02-19 00:53:36,207 INFO misc.py line 119 87073] Train: [62/100][1485/1557] Data 0.003 (0.284) Batch 0.976 (1.224) Remain 20:08:34 loss: 0.0527 Lr: 0.00173 [2024-02-19 00:53:37,255 INFO misc.py line 119 87073] Train: [62/100][1486/1557] Data 0.003 (0.284) Batch 1.044 (1.224) Remain 20:08:26 loss: 0.9766 Lr: 0.00173 [2024-02-19 00:53:38,342 INFO misc.py line 119 87073] Train: [62/100][1487/1557] Data 0.007 (0.284) Batch 1.090 (1.224) Remain 20:08:19 loss: 0.2227 Lr: 0.00173 [2024-02-19 00:53:39,326 INFO misc.py line 119 87073] Train: [62/100][1488/1557] Data 0.004 (0.283) Batch 0.984 (1.224) Remain 20:08:08 loss: 0.8662 Lr: 0.00173 [2024-02-19 00:53:40,058 INFO misc.py line 119 87073] Train: [62/100][1489/1557] Data 0.004 (0.283) Batch 0.732 (1.223) Remain 20:07:48 loss: 0.2340 Lr: 0.00173 [2024-02-19 00:53:40,780 INFO misc.py line 119 87073] Train: [62/100][1490/1557] Data 0.004 (0.283) Batch 0.713 (1.223) Remain 20:07:26 loss: 0.2356 Lr: 0.00173 [2024-02-19 00:53:41,961 INFO misc.py line 119 87073] Train: [62/100][1491/1557] Data 0.013 (0.283) Batch 1.184 (1.223) Remain 20:07:23 loss: 0.0883 Lr: 0.00173 [2024-02-19 00:53:43,009 INFO misc.py line 119 87073] Train: [62/100][1492/1557] Data 0.010 (0.283) Batch 1.050 (1.223) Remain 20:07:15 loss: 0.6173 Lr: 0.00173 [2024-02-19 00:53:43,982 INFO misc.py line 119 87073] Train: [62/100][1493/1557] Data 0.007 (0.283) Batch 0.977 (1.223) Remain 20:07:04 loss: 0.1213 Lr: 0.00173 [2024-02-19 00:53:44,875 INFO misc.py line 119 87073] Train: [62/100][1494/1557] Data 0.004 (0.282) Batch 0.893 (1.223) Remain 20:06:50 loss: 0.3429 Lr: 0.00173 [2024-02-19 00:53:46,115 INFO misc.py line 119 87073] Train: [62/100][1495/1557] Data 0.004 (0.282) Batch 1.241 (1.223) Remain 20:06:49 loss: 0.4603 Lr: 0.00173 [2024-02-19 00:53:46,891 INFO misc.py line 119 87073] Train: [62/100][1496/1557] Data 0.003 (0.282) Batch 0.775 (1.222) Remain 20:06:30 loss: 0.2461 Lr: 0.00173 [2024-02-19 00:53:47,637 INFO misc.py line 119 87073] Train: [62/100][1497/1557] Data 0.003 (0.282) Batch 0.739 (1.222) Remain 20:06:10 loss: 0.2177 Lr: 0.00173 [2024-02-19 00:53:48,801 INFO misc.py line 119 87073] Train: [62/100][1498/1557] Data 0.009 (0.282) Batch 1.160 (1.222) Remain 20:06:06 loss: 0.1377 Lr: 0.00173 [2024-02-19 00:53:49,874 INFO misc.py line 119 87073] Train: [62/100][1499/1557] Data 0.014 (0.281) Batch 1.072 (1.222) Remain 20:05:59 loss: 0.1802 Lr: 0.00173 [2024-02-19 00:53:50,739 INFO misc.py line 119 87073] Train: [62/100][1500/1557] Data 0.015 (0.281) Batch 0.876 (1.222) Remain 20:05:44 loss: 0.1005 Lr: 0.00173 [2024-02-19 00:53:51,640 INFO misc.py line 119 87073] Train: [62/100][1501/1557] Data 0.005 (0.281) Batch 0.903 (1.221) Remain 20:05:30 loss: 0.3678 Lr: 0.00173 [2024-02-19 00:53:52,595 INFO misc.py line 119 87073] Train: [62/100][1502/1557] Data 0.003 (0.281) Batch 0.937 (1.221) Remain 20:05:18 loss: 0.2754 Lr: 0.00173 [2024-02-19 00:53:53,375 INFO misc.py line 119 87073] Train: [62/100][1503/1557] Data 0.021 (0.281) Batch 0.798 (1.221) Remain 20:05:00 loss: 0.2509 Lr: 0.00173 [2024-02-19 00:53:54,136 INFO misc.py line 119 87073] Train: [62/100][1504/1557] Data 0.003 (0.281) Batch 0.751 (1.221) Remain 20:04:40 loss: 0.1416 Lr: 0.00173 [2024-02-19 00:53:55,278 INFO misc.py line 119 87073] Train: [62/100][1505/1557] Data 0.011 (0.280) Batch 1.145 (1.221) Remain 20:04:36 loss: 0.1148 Lr: 0.00173 [2024-02-19 00:53:56,251 INFO misc.py line 119 87073] Train: [62/100][1506/1557] Data 0.010 (0.280) Batch 0.979 (1.220) Remain 20:04:25 loss: 0.1716 Lr: 0.00173 [2024-02-19 00:53:57,068 INFO misc.py line 119 87073] Train: [62/100][1507/1557] Data 0.003 (0.280) Batch 0.817 (1.220) Remain 20:04:08 loss: 0.3444 Lr: 0.00173 [2024-02-19 00:53:58,131 INFO misc.py line 119 87073] Train: [62/100][1508/1557] Data 0.003 (0.280) Batch 1.058 (1.220) Remain 20:04:01 loss: 0.5391 Lr: 0.00173 [2024-02-19 00:53:58,984 INFO misc.py line 119 87073] Train: [62/100][1509/1557] Data 0.008 (0.280) Batch 0.854 (1.220) Remain 20:03:45 loss: 0.4888 Lr: 0.00173 [2024-02-19 00:53:59,726 INFO misc.py line 119 87073] Train: [62/100][1510/1557] Data 0.009 (0.279) Batch 0.745 (1.219) Remain 20:03:25 loss: 0.2146 Lr: 0.00173 [2024-02-19 00:54:00,525 INFO misc.py line 119 87073] Train: [62/100][1511/1557] Data 0.004 (0.279) Batch 0.797 (1.219) Remain 20:03:07 loss: 0.2141 Lr: 0.00173 [2024-02-19 00:54:01,746 INFO misc.py line 119 87073] Train: [62/100][1512/1557] Data 0.006 (0.279) Batch 1.221 (1.219) Remain 20:03:06 loss: 0.1835 Lr: 0.00173 [2024-02-19 00:54:02,655 INFO misc.py line 119 87073] Train: [62/100][1513/1557] Data 0.006 (0.279) Batch 0.912 (1.219) Remain 20:02:53 loss: 0.3950 Lr: 0.00173 [2024-02-19 00:54:03,585 INFO misc.py line 119 87073] Train: [62/100][1514/1557] Data 0.003 (0.279) Batch 0.930 (1.219) Remain 20:02:40 loss: 0.2851 Lr: 0.00173 [2024-02-19 00:54:04,361 INFO misc.py line 119 87073] Train: [62/100][1515/1557] Data 0.004 (0.279) Batch 0.773 (1.218) Remain 20:02:22 loss: 0.2000 Lr: 0.00173 [2024-02-19 00:54:05,218 INFO misc.py line 119 87073] Train: [62/100][1516/1557] Data 0.007 (0.278) Batch 0.858 (1.218) Remain 20:02:06 loss: 0.1533 Lr: 0.00173 [2024-02-19 00:54:05,986 INFO misc.py line 119 87073] Train: [62/100][1517/1557] Data 0.005 (0.278) Batch 0.770 (1.218) Remain 20:01:48 loss: 0.4627 Lr: 0.00173 [2024-02-19 00:54:06,723 INFO misc.py line 119 87073] Train: [62/100][1518/1557] Data 0.003 (0.278) Batch 0.731 (1.218) Remain 20:01:27 loss: 0.4472 Lr: 0.00173 [2024-02-19 00:54:23,107 INFO misc.py line 119 87073] Train: [62/100][1519/1557] Data 15.454 (0.288) Batch 16.391 (1.228) Remain 20:11:19 loss: 0.1119 Lr: 0.00173 [2024-02-19 00:54:24,065 INFO misc.py line 119 87073] Train: [62/100][1520/1557] Data 0.003 (0.288) Batch 0.954 (1.227) Remain 20:11:07 loss: 0.8746 Lr: 0.00173 [2024-02-19 00:54:25,035 INFO misc.py line 119 87073] Train: [62/100][1521/1557] Data 0.007 (0.288) Batch 0.973 (1.227) Remain 20:10:56 loss: 0.2370 Lr: 0.00173 [2024-02-19 00:54:25,983 INFO misc.py line 119 87073] Train: [62/100][1522/1557] Data 0.004 (0.287) Batch 0.949 (1.227) Remain 20:10:44 loss: 0.4242 Lr: 0.00173 [2024-02-19 00:54:26,813 INFO misc.py line 119 87073] Train: [62/100][1523/1557] Data 0.003 (0.287) Batch 0.830 (1.227) Remain 20:10:27 loss: 0.1725 Lr: 0.00173 [2024-02-19 00:54:27,564 INFO misc.py line 119 87073] Train: [62/100][1524/1557] Data 0.003 (0.287) Batch 0.746 (1.226) Remain 20:10:07 loss: 0.3559 Lr: 0.00173 [2024-02-19 00:54:28,334 INFO misc.py line 119 87073] Train: [62/100][1525/1557] Data 0.009 (0.287) Batch 0.775 (1.226) Remain 20:09:48 loss: 0.1949 Lr: 0.00173 [2024-02-19 00:54:29,577 INFO misc.py line 119 87073] Train: [62/100][1526/1557] Data 0.003 (0.287) Batch 1.243 (1.226) Remain 20:09:48 loss: 0.1188 Lr: 0.00173 [2024-02-19 00:54:30,656 INFO misc.py line 119 87073] Train: [62/100][1527/1557] Data 0.003 (0.287) Batch 1.079 (1.226) Remain 20:09:41 loss: 0.4244 Lr: 0.00173 [2024-02-19 00:54:31,677 INFO misc.py line 119 87073] Train: [62/100][1528/1557] Data 0.003 (0.286) Batch 1.022 (1.226) Remain 20:09:31 loss: 0.5658 Lr: 0.00173 [2024-02-19 00:54:32,506 INFO misc.py line 119 87073] Train: [62/100][1529/1557] Data 0.003 (0.286) Batch 0.829 (1.226) Remain 20:09:15 loss: 0.2863 Lr: 0.00173 [2024-02-19 00:54:33,469 INFO misc.py line 119 87073] Train: [62/100][1530/1557] Data 0.003 (0.286) Batch 0.953 (1.226) Remain 20:09:03 loss: 0.1933 Lr: 0.00173 [2024-02-19 00:54:34,242 INFO misc.py line 119 87073] Train: [62/100][1531/1557] Data 0.013 (0.286) Batch 0.783 (1.225) Remain 20:08:45 loss: 0.2523 Lr: 0.00173 [2024-02-19 00:54:34,977 INFO misc.py line 119 87073] Train: [62/100][1532/1557] Data 0.003 (0.286) Batch 0.727 (1.225) Remain 20:08:24 loss: 0.4018 Lr: 0.00173 [2024-02-19 00:54:36,199 INFO misc.py line 119 87073] Train: [62/100][1533/1557] Data 0.011 (0.285) Batch 1.220 (1.225) Remain 20:08:23 loss: 0.1428 Lr: 0.00173 [2024-02-19 00:54:37,102 INFO misc.py line 119 87073] Train: [62/100][1534/1557] Data 0.014 (0.285) Batch 0.913 (1.225) Remain 20:08:09 loss: 0.4681 Lr: 0.00173 [2024-02-19 00:54:38,055 INFO misc.py line 119 87073] Train: [62/100][1535/1557] Data 0.003 (0.285) Batch 0.953 (1.225) Remain 20:07:58 loss: 0.2137 Lr: 0.00173 [2024-02-19 00:54:38,932 INFO misc.py line 119 87073] Train: [62/100][1536/1557] Data 0.003 (0.285) Batch 0.877 (1.224) Remain 20:07:43 loss: 0.4529 Lr: 0.00173 [2024-02-19 00:54:39,824 INFO misc.py line 119 87073] Train: [62/100][1537/1557] Data 0.004 (0.285) Batch 0.884 (1.224) Remain 20:07:29 loss: 0.4766 Lr: 0.00173 [2024-02-19 00:54:40,558 INFO misc.py line 119 87073] Train: [62/100][1538/1557] Data 0.011 (0.285) Batch 0.742 (1.224) Remain 20:07:09 loss: 0.3498 Lr: 0.00173 [2024-02-19 00:54:41,279 INFO misc.py line 119 87073] Train: [62/100][1539/1557] Data 0.003 (0.284) Batch 0.715 (1.223) Remain 20:06:48 loss: 0.4966 Lr: 0.00173 [2024-02-19 00:54:42,481 INFO misc.py line 119 87073] Train: [62/100][1540/1557] Data 0.009 (0.284) Batch 1.196 (1.223) Remain 20:06:46 loss: 0.1326 Lr: 0.00173 [2024-02-19 00:54:43,227 INFO misc.py line 119 87073] Train: [62/100][1541/1557] Data 0.014 (0.284) Batch 0.758 (1.223) Remain 20:06:27 loss: 0.2899 Lr: 0.00173 [2024-02-19 00:54:44,333 INFO misc.py line 119 87073] Train: [62/100][1542/1557] Data 0.003 (0.284) Batch 1.105 (1.223) Remain 20:06:21 loss: 0.5843 Lr: 0.00173 [2024-02-19 00:54:45,254 INFO misc.py line 119 87073] Train: [62/100][1543/1557] Data 0.003 (0.284) Batch 0.922 (1.223) Remain 20:06:08 loss: 0.1972 Lr: 0.00173 [2024-02-19 00:54:46,119 INFO misc.py line 119 87073] Train: [62/100][1544/1557] Data 0.003 (0.283) Batch 0.852 (1.223) Remain 20:05:53 loss: 0.4525 Lr: 0.00173 [2024-02-19 00:54:46,844 INFO misc.py line 119 87073] Train: [62/100][1545/1557] Data 0.015 (0.283) Batch 0.738 (1.222) Remain 20:05:33 loss: 0.2000 Lr: 0.00173 [2024-02-19 00:54:47,544 INFO misc.py line 119 87073] Train: [62/100][1546/1557] Data 0.003 (0.283) Batch 0.691 (1.222) Remain 20:05:11 loss: 0.3644 Lr: 0.00173 [2024-02-19 00:54:48,801 INFO misc.py line 119 87073] Train: [62/100][1547/1557] Data 0.011 (0.283) Batch 1.253 (1.222) Remain 20:05:11 loss: 0.1356 Lr: 0.00173 [2024-02-19 00:54:49,613 INFO misc.py line 119 87073] Train: [62/100][1548/1557] Data 0.016 (0.283) Batch 0.825 (1.222) Remain 20:04:55 loss: 0.5162 Lr: 0.00173 [2024-02-19 00:54:50,567 INFO misc.py line 119 87073] Train: [62/100][1549/1557] Data 0.003 (0.283) Batch 0.954 (1.222) Remain 20:04:43 loss: 0.3768 Lr: 0.00173 [2024-02-19 00:54:51,434 INFO misc.py line 119 87073] Train: [62/100][1550/1557] Data 0.003 (0.282) Batch 0.859 (1.221) Remain 20:04:28 loss: 0.2080 Lr: 0.00173 [2024-02-19 00:54:52,176 INFO misc.py line 119 87073] Train: [62/100][1551/1557] Data 0.011 (0.282) Batch 0.751 (1.221) Remain 20:04:09 loss: 0.1421 Lr: 0.00173 [2024-02-19 00:54:52,952 INFO misc.py line 119 87073] Train: [62/100][1552/1557] Data 0.003 (0.282) Batch 0.765 (1.221) Remain 20:03:50 loss: 0.1521 Lr: 0.00173 [2024-02-19 00:54:53,738 INFO misc.py line 119 87073] Train: [62/100][1553/1557] Data 0.014 (0.282) Batch 0.790 (1.220) Remain 20:03:33 loss: 0.3914 Lr: 0.00173 [2024-02-19 00:54:54,811 INFO misc.py line 119 87073] Train: [62/100][1554/1557] Data 0.010 (0.282) Batch 1.081 (1.220) Remain 20:03:26 loss: 0.1597 Lr: 0.00173 [2024-02-19 00:54:55,706 INFO misc.py line 119 87073] Train: [62/100][1555/1557] Data 0.003 (0.281) Batch 0.895 (1.220) Remain 20:03:12 loss: 0.4878 Lr: 0.00173 [2024-02-19 00:54:56,908 INFO misc.py line 119 87073] Train: [62/100][1556/1557] Data 0.003 (0.281) Batch 1.202 (1.220) Remain 20:03:11 loss: 0.3118 Lr: 0.00173 [2024-02-19 00:54:57,883 INFO misc.py line 119 87073] Train: [62/100][1557/1557] Data 0.003 (0.281) Batch 0.975 (1.220) Remain 20:03:00 loss: 0.2749 Lr: 0.00173 [2024-02-19 00:54:57,883 INFO misc.py line 136 87073] Train result: loss: 0.3091 [2024-02-19 00:54:57,883 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 00:55:27,825 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.7073 [2024-02-19 00:55:28,602 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7786 [2024-02-19 00:55:30,727 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3683 [2024-02-19 00:55:32,935 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2936 [2024-02-19 00:55:37,878 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2606 [2024-02-19 00:55:38,581 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0765 [2024-02-19 00:55:39,841 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.1982 [2024-02-19 00:55:42,794 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2496 [2024-02-19 00:55:44,604 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2921 [2024-02-19 00:55:46,280 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7144/0.7850/0.9146. [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9424/0.9641 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9819/0.9870 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8692/0.9698 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4720/0.5719 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5894/0.6036 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8122/0.9196 [2024-02-19 00:55:46,280 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8142/0.9283 [2024-02-19 00:55:46,281 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9190/0.9717 [2024-02-19 00:55:46,281 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7955/0.8955 [2024-02-19 00:55:46,281 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7884/0.8902 [2024-02-19 00:55:46,281 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6941/0.8052 [2024-02-19 00:55:46,281 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6090/0.6976 [2024-02-19 00:55:46,281 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 00:55:46,283 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 00:55:46,283 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 00:55:53,418 INFO misc.py line 119 87073] Train: [63/100][1/1557] Data 1.788 (1.788) Batch 2.615 (2.615) Remain 42:59:02 loss: 0.4514 Lr: 0.00173 [2024-02-19 00:55:54,384 INFO misc.py line 119 87073] Train: [63/100][2/1557] Data 0.005 (0.005) Batch 0.967 (0.967) Remain 15:53:13 loss: 0.1725 Lr: 0.00173 [2024-02-19 00:55:55,267 INFO misc.py line 119 87073] Train: [63/100][3/1557] Data 0.004 (0.004) Batch 0.883 (0.883) Remain 14:31:01 loss: 0.3701 Lr: 0.00173 [2024-02-19 00:55:56,331 INFO misc.py line 119 87073] Train: [63/100][4/1557] Data 0.005 (0.005) Batch 1.064 (1.064) Remain 17:29:16 loss: 0.5195 Lr: 0.00173 [2024-02-19 00:55:57,054 INFO misc.py line 119 87073] Train: [63/100][5/1557] Data 0.004 (0.005) Batch 0.723 (0.894) Remain 14:41:11 loss: 0.2036 Lr: 0.00173 [2024-02-19 00:55:57,817 INFO misc.py line 119 87073] Train: [63/100][6/1557] Data 0.003 (0.004) Batch 0.763 (0.850) Remain 13:58:09 loss: 0.3365 Lr: 0.00173 [2024-02-19 00:56:01,408 INFO misc.py line 119 87073] Train: [63/100][7/1557] Data 0.004 (0.004) Batch 3.592 (1.535) Remain 25:13:54 loss: 0.1440 Lr: 0.00173 [2024-02-19 00:56:02,397 INFO misc.py line 119 87073] Train: [63/100][8/1557] Data 0.003 (0.004) Batch 0.989 (1.426) Remain 23:26:13 loss: 0.4367 Lr: 0.00173 [2024-02-19 00:56:03,419 INFO misc.py line 119 87073] Train: [63/100][9/1557] Data 0.003 (0.004) Batch 1.022 (1.359) Remain 22:19:47 loss: 0.2125 Lr: 0.00173 [2024-02-19 00:56:04,386 INFO misc.py line 119 87073] Train: [63/100][10/1557] Data 0.003 (0.004) Batch 0.964 (1.303) Remain 21:24:12 loss: 0.5279 Lr: 0.00173 [2024-02-19 00:56:05,251 INFO misc.py line 119 87073] Train: [63/100][11/1557] Data 0.007 (0.004) Batch 0.866 (1.248) Remain 20:30:24 loss: 0.5360 Lr: 0.00173 [2024-02-19 00:56:06,065 INFO misc.py line 119 87073] Train: [63/100][12/1557] Data 0.005 (0.004) Batch 0.816 (1.200) Remain 19:43:01 loss: 0.1907 Lr: 0.00173 [2024-02-19 00:56:06,792 INFO misc.py line 119 87073] Train: [63/100][13/1557] Data 0.003 (0.004) Batch 0.726 (1.153) Remain 18:56:14 loss: 0.2005 Lr: 0.00173 [2024-02-19 00:56:07,914 INFO misc.py line 119 87073] Train: [63/100][14/1557] Data 0.003 (0.004) Batch 1.110 (1.149) Remain 18:52:25 loss: 0.1746 Lr: 0.00173 [2024-02-19 00:56:08,820 INFO misc.py line 119 87073] Train: [63/100][15/1557] Data 0.016 (0.005) Batch 0.918 (1.129) Remain 18:33:26 loss: 0.7329 Lr: 0.00173 [2024-02-19 00:56:09,766 INFO misc.py line 119 87073] Train: [63/100][16/1557] Data 0.004 (0.005) Batch 0.947 (1.115) Remain 18:19:33 loss: 0.5643 Lr: 0.00173 [2024-02-19 00:56:10,690 INFO misc.py line 119 87073] Train: [63/100][17/1557] Data 0.003 (0.005) Batch 0.924 (1.102) Remain 18:06:02 loss: 0.1588 Lr: 0.00173 [2024-02-19 00:56:11,851 INFO misc.py line 119 87073] Train: [63/100][18/1557] Data 0.003 (0.005) Batch 1.161 (1.106) Remain 18:09:55 loss: 0.3342 Lr: 0.00173 [2024-02-19 00:56:12,543 INFO misc.py line 119 87073] Train: [63/100][19/1557] Data 0.003 (0.005) Batch 0.692 (1.080) Remain 17:44:26 loss: 0.2370 Lr: 0.00173 [2024-02-19 00:56:13,315 INFO misc.py line 119 87073] Train: [63/100][20/1557] Data 0.003 (0.004) Batch 0.767 (1.061) Remain 17:26:17 loss: 0.3191 Lr: 0.00173 [2024-02-19 00:56:14,578 INFO misc.py line 119 87073] Train: [63/100][21/1557] Data 0.008 (0.005) Batch 1.259 (1.072) Remain 17:37:05 loss: 0.2260 Lr: 0.00173 [2024-02-19 00:56:15,647 INFO misc.py line 119 87073] Train: [63/100][22/1557] Data 0.012 (0.005) Batch 1.072 (1.072) Remain 17:37:03 loss: 0.1597 Lr: 0.00173 [2024-02-19 00:56:16,603 INFO misc.py line 119 87073] Train: [63/100][23/1557] Data 0.009 (0.005) Batch 0.962 (1.067) Remain 17:31:35 loss: 0.3141 Lr: 0.00173 [2024-02-19 00:56:17,548 INFO misc.py line 119 87073] Train: [63/100][24/1557] Data 0.003 (0.005) Batch 0.945 (1.061) Remain 17:25:52 loss: 0.2936 Lr: 0.00173 [2024-02-19 00:56:18,441 INFO misc.py line 119 87073] Train: [63/100][25/1557] Data 0.003 (0.005) Batch 0.892 (1.053) Remain 17:18:17 loss: 0.2969 Lr: 0.00173 [2024-02-19 00:56:19,189 INFO misc.py line 119 87073] Train: [63/100][26/1557] Data 0.003 (0.005) Batch 0.740 (1.040) Remain 17:04:51 loss: 0.4650 Lr: 0.00173 [2024-02-19 00:56:19,941 INFO misc.py line 119 87073] Train: [63/100][27/1557] Data 0.011 (0.005) Batch 0.760 (1.028) Remain 16:53:21 loss: 0.2269 Lr: 0.00173 [2024-02-19 00:56:21,246 INFO misc.py line 119 87073] Train: [63/100][28/1557] Data 0.003 (0.005) Batch 1.294 (1.039) Remain 17:03:49 loss: 0.1072 Lr: 0.00173 [2024-02-19 00:56:22,458 INFO misc.py line 119 87073] Train: [63/100][29/1557] Data 0.014 (0.005) Batch 1.212 (1.045) Remain 17:10:22 loss: 0.1905 Lr: 0.00173 [2024-02-19 00:56:23,276 INFO misc.py line 119 87073] Train: [63/100][30/1557] Data 0.014 (0.006) Batch 0.829 (1.037) Remain 17:02:26 loss: 0.4992 Lr: 0.00173 [2024-02-19 00:56:24,076 INFO misc.py line 119 87073] Train: [63/100][31/1557] Data 0.003 (0.006) Batch 0.800 (1.029) Remain 16:54:04 loss: 0.2996 Lr: 0.00173 [2024-02-19 00:56:24,999 INFO misc.py line 119 87073] Train: [63/100][32/1557] Data 0.003 (0.006) Batch 0.917 (1.025) Remain 16:50:15 loss: 0.3413 Lr: 0.00173 [2024-02-19 00:56:25,781 INFO misc.py line 119 87073] Train: [63/100][33/1557] Data 0.009 (0.006) Batch 0.788 (1.017) Remain 16:42:26 loss: 0.2867 Lr: 0.00173 [2024-02-19 00:56:26,523 INFO misc.py line 119 87073] Train: [63/100][34/1557] Data 0.003 (0.006) Batch 0.742 (1.008) Remain 16:33:39 loss: 0.1713 Lr: 0.00173 [2024-02-19 00:56:27,727 INFO misc.py line 119 87073] Train: [63/100][35/1557] Data 0.004 (0.006) Batch 1.195 (1.014) Remain 16:39:24 loss: 0.1224 Lr: 0.00173 [2024-02-19 00:56:28,660 INFO misc.py line 119 87073] Train: [63/100][36/1557] Data 0.013 (0.006) Batch 0.942 (1.012) Remain 16:37:15 loss: 0.4159 Lr: 0.00173 [2024-02-19 00:56:29,597 INFO misc.py line 119 87073] Train: [63/100][37/1557] Data 0.003 (0.006) Batch 0.937 (1.010) Remain 16:35:03 loss: 0.4198 Lr: 0.00173 [2024-02-19 00:56:30,520 INFO misc.py line 119 87073] Train: [63/100][38/1557] Data 0.004 (0.006) Batch 0.923 (1.007) Remain 16:32:36 loss: 0.3567 Lr: 0.00173 [2024-02-19 00:56:31,451 INFO misc.py line 119 87073] Train: [63/100][39/1557] Data 0.003 (0.006) Batch 0.922 (1.005) Remain 16:30:16 loss: 0.2962 Lr: 0.00173 [2024-02-19 00:56:32,222 INFO misc.py line 119 87073] Train: [63/100][40/1557] Data 0.012 (0.006) Batch 0.779 (0.999) Remain 16:24:14 loss: 0.2111 Lr: 0.00173 [2024-02-19 00:56:32,990 INFO misc.py line 119 87073] Train: [63/100][41/1557] Data 0.003 (0.006) Batch 0.759 (0.992) Remain 16:18:01 loss: 0.2202 Lr: 0.00173 [2024-02-19 00:56:34,238 INFO misc.py line 119 87073] Train: [63/100][42/1557] Data 0.012 (0.006) Batch 1.244 (0.999) Remain 16:24:21 loss: 0.2034 Lr: 0.00173 [2024-02-19 00:56:35,152 INFO misc.py line 119 87073] Train: [63/100][43/1557] Data 0.016 (0.006) Batch 0.926 (0.997) Remain 16:22:33 loss: 0.3146 Lr: 0.00173 [2024-02-19 00:56:36,082 INFO misc.py line 119 87073] Train: [63/100][44/1557] Data 0.003 (0.006) Batch 0.931 (0.996) Remain 16:20:56 loss: 0.4193 Lr: 0.00173 [2024-02-19 00:56:36,962 INFO misc.py line 119 87073] Train: [63/100][45/1557] Data 0.003 (0.006) Batch 0.877 (0.993) Remain 16:18:09 loss: 0.7077 Lr: 0.00173 [2024-02-19 00:56:37,914 INFO misc.py line 119 87073] Train: [63/100][46/1557] Data 0.007 (0.006) Batch 0.948 (0.992) Remain 16:17:06 loss: 0.7034 Lr: 0.00173 [2024-02-19 00:56:38,613 INFO misc.py line 119 87073] Train: [63/100][47/1557] Data 0.010 (0.006) Batch 0.705 (0.985) Remain 16:10:41 loss: 0.1636 Lr: 0.00173 [2024-02-19 00:56:39,351 INFO misc.py line 119 87073] Train: [63/100][48/1557] Data 0.003 (0.006) Batch 0.728 (0.979) Remain 16:05:02 loss: 0.1937 Lr: 0.00173 [2024-02-19 00:56:40,417 INFO misc.py line 119 87073] Train: [63/100][49/1557] Data 0.013 (0.006) Batch 1.071 (0.981) Remain 16:06:59 loss: 0.1823 Lr: 0.00173 [2024-02-19 00:56:41,393 INFO misc.py line 119 87073] Train: [63/100][50/1557] Data 0.008 (0.006) Batch 0.980 (0.981) Remain 16:06:56 loss: 0.3847 Lr: 0.00173 [2024-02-19 00:56:42,378 INFO misc.py line 119 87073] Train: [63/100][51/1557] Data 0.003 (0.006) Batch 0.985 (0.981) Remain 16:07:00 loss: 0.4419 Lr: 0.00173 [2024-02-19 00:56:43,392 INFO misc.py line 119 87073] Train: [63/100][52/1557] Data 0.003 (0.006) Batch 1.015 (0.982) Remain 16:07:39 loss: 0.6949 Lr: 0.00173 [2024-02-19 00:56:44,356 INFO misc.py line 119 87073] Train: [63/100][53/1557] Data 0.003 (0.006) Batch 0.964 (0.982) Remain 16:07:16 loss: 0.2667 Lr: 0.00173 [2024-02-19 00:56:45,103 INFO misc.py line 119 87073] Train: [63/100][54/1557] Data 0.003 (0.006) Batch 0.743 (0.977) Remain 16:02:39 loss: 0.2458 Lr: 0.00173 [2024-02-19 00:56:45,907 INFO misc.py line 119 87073] Train: [63/100][55/1557] Data 0.007 (0.006) Batch 0.808 (0.974) Remain 15:59:25 loss: 0.5109 Lr: 0.00173 [2024-02-19 00:56:47,136 INFO misc.py line 119 87073] Train: [63/100][56/1557] Data 0.003 (0.006) Batch 1.228 (0.979) Remain 16:04:07 loss: 0.0973 Lr: 0.00173 [2024-02-19 00:56:48,169 INFO misc.py line 119 87073] Train: [63/100][57/1557] Data 0.004 (0.006) Batch 1.023 (0.979) Remain 16:04:55 loss: 0.5771 Lr: 0.00173 [2024-02-19 00:56:49,140 INFO misc.py line 119 87073] Train: [63/100][58/1557] Data 0.015 (0.006) Batch 0.982 (0.980) Remain 16:04:57 loss: 0.3116 Lr: 0.00173 [2024-02-19 00:56:50,190 INFO misc.py line 119 87073] Train: [63/100][59/1557] Data 0.003 (0.006) Batch 1.050 (0.981) Remain 16:06:10 loss: 0.3944 Lr: 0.00173 [2024-02-19 00:56:51,045 INFO misc.py line 119 87073] Train: [63/100][60/1557] Data 0.003 (0.006) Batch 0.855 (0.979) Remain 16:03:59 loss: 0.2930 Lr: 0.00173 [2024-02-19 00:56:51,848 INFO misc.py line 119 87073] Train: [63/100][61/1557] Data 0.003 (0.006) Batch 0.795 (0.975) Remain 16:00:50 loss: 0.3094 Lr: 0.00173 [2024-02-19 00:56:52,649 INFO misc.py line 119 87073] Train: [63/100][62/1557] Data 0.012 (0.006) Batch 0.808 (0.973) Remain 15:58:01 loss: 0.1500 Lr: 0.00173 [2024-02-19 00:57:03,781 INFO misc.py line 119 87073] Train: [63/100][63/1557] Data 7.182 (0.126) Batch 11.134 (1.142) Remain 18:44:50 loss: 0.2086 Lr: 0.00173 [2024-02-19 00:57:04,697 INFO misc.py line 119 87073] Train: [63/100][64/1557] Data 0.004 (0.124) Batch 0.916 (1.138) Remain 18:41:10 loss: 0.1230 Lr: 0.00173 [2024-02-19 00:57:05,587 INFO misc.py line 119 87073] Train: [63/100][65/1557] Data 0.003 (0.122) Batch 0.882 (1.134) Remain 18:37:05 loss: 0.4232 Lr: 0.00173 [2024-02-19 00:57:06,558 INFO misc.py line 119 87073] Train: [63/100][66/1557] Data 0.011 (0.120) Batch 0.978 (1.132) Remain 18:34:37 loss: 0.2705 Lr: 0.00173 [2024-02-19 00:57:07,472 INFO misc.py line 119 87073] Train: [63/100][67/1557] Data 0.004 (0.118) Batch 0.915 (1.128) Remain 18:31:16 loss: 0.3826 Lr: 0.00173 [2024-02-19 00:57:08,309 INFO misc.py line 119 87073] Train: [63/100][68/1557] Data 0.003 (0.116) Batch 0.834 (1.124) Remain 18:26:47 loss: 0.2284 Lr: 0.00173 [2024-02-19 00:57:09,021 INFO misc.py line 119 87073] Train: [63/100][69/1557] Data 0.006 (0.115) Batch 0.714 (1.117) Remain 18:20:40 loss: 0.3289 Lr: 0.00173 [2024-02-19 00:57:10,055 INFO misc.py line 119 87073] Train: [63/100][70/1557] Data 0.003 (0.113) Batch 1.028 (1.116) Remain 18:19:19 loss: 0.1867 Lr: 0.00173 [2024-02-19 00:57:10,996 INFO misc.py line 119 87073] Train: [63/100][71/1557] Data 0.010 (0.111) Batch 0.948 (1.114) Remain 18:16:52 loss: 0.4107 Lr: 0.00173 [2024-02-19 00:57:11,879 INFO misc.py line 119 87073] Train: [63/100][72/1557] Data 0.003 (0.110) Batch 0.880 (1.110) Remain 18:13:31 loss: 0.0695 Lr: 0.00173 [2024-02-19 00:57:12,806 INFO misc.py line 119 87073] Train: [63/100][73/1557] Data 0.007 (0.108) Batch 0.928 (1.108) Remain 18:10:56 loss: 0.6714 Lr: 0.00173 [2024-02-19 00:57:13,704 INFO misc.py line 119 87073] Train: [63/100][74/1557] Data 0.006 (0.107) Batch 0.899 (1.105) Remain 18:08:01 loss: 0.4568 Lr: 0.00173 [2024-02-19 00:57:14,492 INFO misc.py line 119 87073] Train: [63/100][75/1557] Data 0.004 (0.106) Batch 0.788 (1.100) Remain 18:03:40 loss: 0.2856 Lr: 0.00173 [2024-02-19 00:57:15,237 INFO misc.py line 119 87073] Train: [63/100][76/1557] Data 0.003 (0.104) Batch 0.742 (1.095) Remain 17:58:49 loss: 0.3761 Lr: 0.00173 [2024-02-19 00:57:16,523 INFO misc.py line 119 87073] Train: [63/100][77/1557] Data 0.007 (0.103) Batch 1.289 (1.098) Remain 18:01:22 loss: 0.0803 Lr: 0.00173 [2024-02-19 00:57:17,542 INFO misc.py line 119 87073] Train: [63/100][78/1557] Data 0.003 (0.101) Batch 1.019 (1.097) Remain 18:00:19 loss: 0.7343 Lr: 0.00173 [2024-02-19 00:57:18,609 INFO misc.py line 119 87073] Train: [63/100][79/1557] Data 0.004 (0.100) Batch 1.066 (1.097) Remain 17:59:54 loss: 0.3979 Lr: 0.00173 [2024-02-19 00:57:19,535 INFO misc.py line 119 87073] Train: [63/100][80/1557] Data 0.006 (0.099) Batch 0.927 (1.094) Remain 17:57:43 loss: 0.1341 Lr: 0.00173 [2024-02-19 00:57:20,466 INFO misc.py line 119 87073] Train: [63/100][81/1557] Data 0.004 (0.098) Batch 0.931 (1.092) Remain 17:55:38 loss: 0.2195 Lr: 0.00173 [2024-02-19 00:57:21,268 INFO misc.py line 119 87073] Train: [63/100][82/1557] Data 0.003 (0.097) Batch 0.802 (1.089) Remain 17:52:00 loss: 0.2875 Lr: 0.00173 [2024-02-19 00:57:22,044 INFO misc.py line 119 87073] Train: [63/100][83/1557] Data 0.004 (0.095) Batch 0.769 (1.085) Remain 17:48:03 loss: 0.2773 Lr: 0.00173 [2024-02-19 00:57:23,274 INFO misc.py line 119 87073] Train: [63/100][84/1557] Data 0.010 (0.094) Batch 1.233 (1.086) Remain 17:49:49 loss: 0.1157 Lr: 0.00173 [2024-02-19 00:57:24,152 INFO misc.py line 119 87073] Train: [63/100][85/1557] Data 0.009 (0.093) Batch 0.882 (1.084) Remain 17:47:21 loss: 0.4265 Lr: 0.00173 [2024-02-19 00:57:24,991 INFO misc.py line 119 87073] Train: [63/100][86/1557] Data 0.003 (0.092) Batch 0.839 (1.081) Remain 17:44:26 loss: 0.4026 Lr: 0.00173 [2024-02-19 00:57:26,119 INFO misc.py line 119 87073] Train: [63/100][87/1557] Data 0.004 (0.091) Batch 1.120 (1.081) Remain 17:44:52 loss: 0.3498 Lr: 0.00173 [2024-02-19 00:57:27,038 INFO misc.py line 119 87073] Train: [63/100][88/1557] Data 0.012 (0.090) Batch 0.928 (1.080) Remain 17:43:04 loss: 0.2432 Lr: 0.00173 [2024-02-19 00:57:27,786 INFO misc.py line 119 87073] Train: [63/100][89/1557] Data 0.003 (0.089) Batch 0.748 (1.076) Remain 17:39:15 loss: 0.1833 Lr: 0.00173 [2024-02-19 00:57:28,546 INFO misc.py line 119 87073] Train: [63/100][90/1557] Data 0.003 (0.088) Batch 0.751 (1.072) Remain 17:35:33 loss: 0.2997 Lr: 0.00173 [2024-02-19 00:57:29,805 INFO misc.py line 119 87073] Train: [63/100][91/1557] Data 0.012 (0.087) Batch 1.259 (1.074) Remain 17:37:38 loss: 0.1136 Lr: 0.00173 [2024-02-19 00:57:30,669 INFO misc.py line 119 87073] Train: [63/100][92/1557] Data 0.013 (0.087) Batch 0.873 (1.072) Remain 17:35:24 loss: 0.3973 Lr: 0.00173 [2024-02-19 00:57:31,587 INFO misc.py line 119 87073] Train: [63/100][93/1557] Data 0.003 (0.086) Batch 0.917 (1.070) Remain 17:33:41 loss: 0.4232 Lr: 0.00172 [2024-02-19 00:57:32,553 INFO misc.py line 119 87073] Train: [63/100][94/1557] Data 0.004 (0.085) Batch 0.966 (1.069) Remain 17:32:32 loss: 0.4091 Lr: 0.00172 [2024-02-19 00:57:33,653 INFO misc.py line 119 87073] Train: [63/100][95/1557] Data 0.003 (0.084) Batch 1.100 (1.069) Remain 17:32:51 loss: 0.4730 Lr: 0.00172 [2024-02-19 00:57:34,465 INFO misc.py line 119 87073] Train: [63/100][96/1557] Data 0.004 (0.083) Batch 0.813 (1.067) Remain 17:30:07 loss: 0.2358 Lr: 0.00172 [2024-02-19 00:57:35,224 INFO misc.py line 119 87073] Train: [63/100][97/1557] Data 0.003 (0.082) Batch 0.750 (1.063) Remain 17:26:46 loss: 0.2661 Lr: 0.00172 [2024-02-19 00:57:36,554 INFO misc.py line 119 87073] Train: [63/100][98/1557] Data 0.012 (0.081) Batch 1.328 (1.066) Remain 17:29:30 loss: 0.2133 Lr: 0.00172 [2024-02-19 00:57:37,628 INFO misc.py line 119 87073] Train: [63/100][99/1557] Data 0.014 (0.081) Batch 1.041 (1.066) Remain 17:29:14 loss: 0.1260 Lr: 0.00172 [2024-02-19 00:57:38,580 INFO misc.py line 119 87073] Train: [63/100][100/1557] Data 0.047 (0.080) Batch 0.996 (1.065) Remain 17:28:30 loss: 0.4989 Lr: 0.00172 [2024-02-19 00:57:39,502 INFO misc.py line 119 87073] Train: [63/100][101/1557] Data 0.003 (0.080) Batch 0.922 (1.064) Remain 17:27:03 loss: 0.3153 Lr: 0.00172 [2024-02-19 00:57:40,634 INFO misc.py line 119 87073] Train: [63/100][102/1557] Data 0.003 (0.079) Batch 1.132 (1.064) Remain 17:27:42 loss: 0.2765 Lr: 0.00172 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line 119 87073] Train: [63/100][221/1557] Data 0.003 (0.105) Batch 0.966 (1.097) Remain 17:58:06 loss: 0.3036 Lr: 0.00172 [2024-02-19 00:59:55,135 INFO misc.py line 119 87073] Train: [63/100][222/1557] Data 0.004 (0.105) Batch 0.627 (1.095) Remain 17:55:59 loss: 0.3771 Lr: 0.00172 [2024-02-19 00:59:55,861 INFO misc.py line 119 87073] Train: [63/100][223/1557] Data 0.010 (0.104) Batch 0.731 (1.094) Remain 17:54:20 loss: 0.2263 Lr: 0.00172 [2024-02-19 00:59:57,155 INFO misc.py line 119 87073] Train: [63/100][224/1557] Data 0.004 (0.104) Batch 1.292 (1.095) Remain 17:55:12 loss: 0.1390 Lr: 0.00172 [2024-02-19 00:59:58,076 INFO misc.py line 119 87073] Train: [63/100][225/1557] Data 0.005 (0.103) Batch 0.923 (1.094) Remain 17:54:25 loss: 0.1490 Lr: 0.00172 [2024-02-19 00:59:59,274 INFO misc.py line 119 87073] Train: [63/100][226/1557] Data 0.003 (0.103) Batch 1.197 (1.094) Remain 17:54:52 loss: 0.2207 Lr: 0.00172 [2024-02-19 01:00:00,219 INFO misc.py line 119 87073] Train: 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Batch 0.899 (1.128) Remain 18:28:13 loss: 0.4932 Lr: 0.00172 [2024-02-19 01:00:15,650 INFO misc.py line 119 87073] Train: [63/100][234/1557] Data 0.003 (0.129) Batch 0.876 (1.127) Remain 18:27:08 loss: 0.3087 Lr: 0.00172 [2024-02-19 01:00:16,690 INFO misc.py line 119 87073] Train: [63/100][235/1557] Data 0.003 (0.129) Batch 1.031 (1.127) Remain 18:26:42 loss: 0.2582 Lr: 0.00172 [2024-02-19 01:00:17,483 INFO misc.py line 119 87073] Train: [63/100][236/1557] Data 0.012 (0.128) Batch 0.802 (1.125) Remain 18:25:19 loss: 0.4167 Lr: 0.00172 [2024-02-19 01:00:18,253 INFO misc.py line 119 87073] Train: [63/100][237/1557] Data 0.003 (0.128) Batch 0.770 (1.124) Remain 18:23:48 loss: 0.1221 Lr: 0.00172 [2024-02-19 01:00:19,269 INFO misc.py line 119 87073] Train: [63/100][238/1557] Data 0.002 (0.127) Batch 1.012 (1.123) Remain 18:23:19 loss: 0.1464 Lr: 0.00172 [2024-02-19 01:00:20,257 INFO misc.py line 119 87073] Train: [63/100][239/1557] Data 0.008 (0.126) Batch 0.991 (1.123) Remain 18:22:45 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87073] Train: [63/100][252/1557] Data 0.012 (0.120) Batch 1.277 (1.115) Remain 18:14:20 loss: 0.1185 Lr: 0.00172 [2024-02-19 01:00:33,630 INFO misc.py line 119 87073] Train: [63/100][253/1557] Data 0.012 (0.120) Batch 0.848 (1.113) Remain 18:13:16 loss: 0.3911 Lr: 0.00172 [2024-02-19 01:00:34,715 INFO misc.py line 119 87073] Train: [63/100][254/1557] Data 0.004 (0.119) Batch 1.086 (1.113) Remain 18:13:09 loss: 0.1603 Lr: 0.00172 [2024-02-19 01:00:35,624 INFO misc.py line 119 87073] Train: [63/100][255/1557] Data 0.003 (0.119) Batch 0.909 (1.113) Remain 18:12:20 loss: 0.5482 Lr: 0.00172 [2024-02-19 01:00:36,598 INFO misc.py line 119 87073] Train: [63/100][256/1557] Data 0.003 (0.118) Batch 0.974 (1.112) Remain 18:11:46 loss: 0.3704 Lr: 0.00172 [2024-02-19 01:00:37,374 INFO misc.py line 119 87073] Train: [63/100][257/1557] Data 0.003 (0.118) Batch 0.768 (1.111) Remain 18:10:26 loss: 0.3232 Lr: 0.00172 [2024-02-19 01:00:38,140 INFO misc.py line 119 87073] Train: [63/100][258/1557] Data 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line 119 87073] Train: [63/100][277/1557] Data 0.003 (0.110) Batch 0.862 (1.100) Remain 17:59:28 loss: 0.3995 Lr: 0.00172 [2024-02-19 01:00:57,413 INFO misc.py line 119 87073] Train: [63/100][278/1557] Data 0.007 (0.109) Batch 0.792 (1.099) Remain 17:58:20 loss: 0.2982 Lr: 0.00172 [2024-02-19 01:00:58,182 INFO misc.py line 119 87073] Train: [63/100][279/1557] Data 0.003 (0.109) Batch 0.769 (1.098) Remain 17:57:09 loss: 0.2902 Lr: 0.00172 [2024-02-19 01:00:59,410 INFO misc.py line 119 87073] Train: [63/100][280/1557] Data 0.003 (0.109) Batch 1.223 (1.098) Remain 17:57:35 loss: 0.1611 Lr: 0.00172 [2024-02-19 01:01:00,255 INFO misc.py line 119 87073] Train: [63/100][281/1557] Data 0.009 (0.108) Batch 0.847 (1.097) Remain 17:56:40 loss: 0.2857 Lr: 0.00172 [2024-02-19 01:01:01,224 INFO misc.py line 119 87073] Train: [63/100][282/1557] Data 0.007 (0.108) Batch 0.971 (1.097) Remain 17:56:13 loss: 0.1221 Lr: 0.00172 [2024-02-19 01:01:02,365 INFO misc.py line 119 87073] Train: 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Batch 0.916 (1.127) Remain 18:26:19 loss: 0.2931 Lr: 0.00172 [2024-02-19 01:01:18,753 INFO misc.py line 119 87073] Train: [63/100][290/1557] Data 0.009 (0.130) Batch 1.035 (1.127) Remain 18:25:59 loss: 0.1843 Lr: 0.00172 [2024-02-19 01:01:19,702 INFO misc.py line 119 87073] Train: [63/100][291/1557] Data 0.008 (0.129) Batch 0.954 (1.127) Remain 18:25:23 loss: 0.4688 Lr: 0.00171 [2024-02-19 01:01:20,464 INFO misc.py line 119 87073] Train: [63/100][292/1557] Data 0.003 (0.129) Batch 0.763 (1.125) Remain 18:24:08 loss: 0.2761 Lr: 0.00171 [2024-02-19 01:01:21,252 INFO misc.py line 119 87073] Train: [63/100][293/1557] Data 0.003 (0.128) Batch 0.779 (1.124) Remain 18:22:56 loss: 0.2431 Lr: 0.00171 [2024-02-19 01:01:22,277 INFO misc.py line 119 87073] Train: [63/100][294/1557] Data 0.011 (0.128) Batch 1.024 (1.124) Remain 18:22:35 loss: 0.2081 Lr: 0.00171 [2024-02-19 01:01:23,115 INFO misc.py line 119 87073] Train: [63/100][295/1557] Data 0.012 (0.128) Batch 0.847 (1.123) Remain 18:21:38 loss: 0.0619 Lr: 0.00171 [2024-02-19 01:01:24,001 INFO misc.py line 119 87073] Train: [63/100][296/1557] Data 0.003 (0.127) Batch 0.887 (1.122) Remain 18:20:49 loss: 0.4086 Lr: 0.00171 [2024-02-19 01:01:24,886 INFO misc.py line 119 87073] Train: [63/100][297/1557] Data 0.003 (0.127) Batch 0.877 (1.121) Remain 18:19:59 loss: 0.3923 Lr: 0.00171 [2024-02-19 01:01:25,769 INFO misc.py line 119 87073] Train: [63/100][298/1557] Data 0.012 (0.126) Batch 0.891 (1.120) Remain 18:19:12 loss: 0.1672 Lr: 0.00171 [2024-02-19 01:01:26,537 INFO misc.py line 119 87073] Train: [63/100][299/1557] Data 0.003 (0.126) Batch 0.766 (1.119) Remain 18:18:00 loss: 0.2427 Lr: 0.00171 [2024-02-19 01:01:27,340 INFO misc.py line 119 87073] Train: [63/100][300/1557] Data 0.005 (0.125) Batch 0.802 (1.118) Remain 18:16:57 loss: 0.2283 Lr: 0.00171 [2024-02-19 01:01:28,620 INFO misc.py line 119 87073] Train: [63/100][301/1557] Data 0.005 (0.125) Batch 1.262 (1.119) Remain 18:17:24 loss: 0.1637 Lr: 0.00171 [2024-02-19 01:01:29,489 INFO misc.py line 119 87073] Train: [63/100][302/1557] Data 0.023 (0.125) Batch 0.890 (1.118) Remain 18:16:38 loss: 0.1516 Lr: 0.00171 [2024-02-19 01:01:30,532 INFO misc.py line 119 87073] Train: [63/100][303/1557] Data 0.003 (0.124) Batch 1.043 (1.118) Remain 18:16:22 loss: 0.7063 Lr: 0.00171 [2024-02-19 01:01:31,391 INFO misc.py line 119 87073] Train: [63/100][304/1557] Data 0.003 (0.124) Batch 0.859 (1.117) Remain 18:15:30 loss: 0.2237 Lr: 0.00171 [2024-02-19 01:01:32,293 INFO misc.py line 119 87073] Train: [63/100][305/1557] Data 0.003 (0.123) Batch 0.901 (1.116) Remain 18:14:47 loss: 0.3902 Lr: 0.00171 [2024-02-19 01:01:33,034 INFO misc.py line 119 87073] Train: [63/100][306/1557] Data 0.004 (0.123) Batch 0.742 (1.115) Remain 18:13:33 loss: 0.1661 Lr: 0.00171 [2024-02-19 01:01:33,823 INFO misc.py line 119 87073] Train: [63/100][307/1557] Data 0.003 (0.123) Batch 0.779 (1.114) Remain 18:12:27 loss: 0.3589 Lr: 0.00171 [2024-02-19 01:01:35,074 INFO misc.py line 119 87073] Train: [63/100][308/1557] Data 0.013 (0.122) Batch 1.254 (1.114) Remain 18:12:53 loss: 0.1177 Lr: 0.00171 [2024-02-19 01:01:36,064 INFO misc.py line 119 87073] Train: [63/100][309/1557] Data 0.009 (0.122) Batch 0.997 (1.114) Remain 18:12:30 loss: 0.4370 Lr: 0.00171 [2024-02-19 01:01:36,947 INFO misc.py line 119 87073] Train: [63/100][310/1557] Data 0.003 (0.122) Batch 0.882 (1.113) Remain 18:11:44 loss: 0.6970 Lr: 0.00171 [2024-02-19 01:01:37,861 INFO misc.py line 119 87073] Train: [63/100][311/1557] Data 0.003 (0.121) Batch 0.915 (1.112) Remain 18:11:05 loss: 0.1388 Lr: 0.00171 [2024-02-19 01:01:39,032 INFO misc.py line 119 87073] Train: [63/100][312/1557] Data 0.003 (0.121) Batch 1.171 (1.113) Remain 18:11:15 loss: 0.2606 Lr: 0.00171 [2024-02-19 01:01:39,711 INFO misc.py line 119 87073] Train: [63/100][313/1557] Data 0.003 (0.120) Batch 0.679 (1.111) Remain 18:09:52 loss: 0.2435 Lr: 0.00171 [2024-02-19 01:01:40,485 INFO misc.py line 119 87073] Train: [63/100][314/1557] Data 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[2024-02-19 01:01:53,032 INFO misc.py line 119 87073] Train: [63/100][327/1557] Data 0.003 (0.116) Batch 0.771 (1.104) Remain 18:02:50 loss: 0.3420 Lr: 0.00171 [2024-02-19 01:01:53,783 INFO misc.py line 119 87073] Train: [63/100][328/1557] Data 0.003 (0.115) Batch 0.740 (1.103) Remain 18:01:43 loss: 0.3830 Lr: 0.00171 [2024-02-19 01:01:54,748 INFO misc.py line 119 87073] Train: [63/100][329/1557] Data 0.013 (0.115) Batch 0.975 (1.103) Remain 18:01:19 loss: 0.0947 Lr: 0.00171 [2024-02-19 01:01:55,570 INFO misc.py line 119 87073] Train: [63/100][330/1557] Data 0.004 (0.115) Batch 0.822 (1.102) Remain 18:00:28 loss: 0.1237 Lr: 0.00171 [2024-02-19 01:01:56,493 INFO misc.py line 119 87073] Train: [63/100][331/1557] Data 0.005 (0.114) Batch 0.916 (1.101) Remain 17:59:53 loss: 0.2883 Lr: 0.00171 [2024-02-19 01:01:57,572 INFO misc.py line 119 87073] Train: [63/100][332/1557] Data 0.010 (0.114) Batch 1.081 (1.101) Remain 17:59:48 loss: 0.3962 Lr: 0.00171 [2024-02-19 01:01:58,560 INFO misc.py line 119 87073] Train: [63/100][333/1557] Data 0.008 (0.114) Batch 0.993 (1.101) Remain 17:59:28 loss: 0.3676 Lr: 0.00171 [2024-02-19 01:01:59,326 INFO misc.py line 119 87073] Train: [63/100][334/1557] Data 0.003 (0.113) Batch 0.765 (1.100) Remain 17:58:27 loss: 0.2981 Lr: 0.00171 [2024-02-19 01:02:00,067 INFO misc.py line 119 87073] Train: [63/100][335/1557] Data 0.004 (0.113) Batch 0.733 (1.099) Remain 17:57:21 loss: 0.2297 Lr: 0.00171 [2024-02-19 01:02:01,328 INFO misc.py line 119 87073] Train: [63/100][336/1557] Data 0.012 (0.113) Batch 1.261 (1.099) Remain 17:57:49 loss: 0.1200 Lr: 0.00171 [2024-02-19 01:02:02,317 INFO misc.py line 119 87073] Train: [63/100][337/1557] Data 0.012 (0.112) Batch 0.998 (1.099) Remain 17:57:30 loss: 0.5595 Lr: 0.00171 [2024-02-19 01:02:03,270 INFO misc.py line 119 87073] Train: [63/100][338/1557] Data 0.003 (0.112) Batch 0.952 (1.099) Remain 17:57:03 loss: 0.2448 Lr: 0.00171 [2024-02-19 01:02:04,251 INFO misc.py line 119 87073] Train: 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Batch 0.856 (1.124) Remain 18:22:00 loss: 0.4838 Lr: 0.00171 [2024-02-19 01:02:20,619 INFO misc.py line 119 87073] Train: [63/100][346/1557] Data 0.009 (0.129) Batch 0.908 (1.123) Remain 18:21:22 loss: 0.1344 Lr: 0.00171 [2024-02-19 01:02:21,617 INFO misc.py line 119 87073] Train: [63/100][347/1557] Data 0.003 (0.129) Batch 0.998 (1.123) Remain 18:21:00 loss: 0.6825 Lr: 0.00171 [2024-02-19 01:02:22,334 INFO misc.py line 119 87073] Train: [63/100][348/1557] Data 0.004 (0.129) Batch 0.707 (1.122) Remain 18:19:48 loss: 0.5543 Lr: 0.00171 [2024-02-19 01:02:23,073 INFO misc.py line 119 87073] Train: [63/100][349/1557] Data 0.014 (0.128) Batch 0.749 (1.121) Remain 18:18:43 loss: 0.3158 Lr: 0.00171 [2024-02-19 01:02:24,124 INFO misc.py line 119 87073] Train: [63/100][350/1557] Data 0.004 (0.128) Batch 1.041 (1.121) Remain 18:18:28 loss: 0.1875 Lr: 0.00171 [2024-02-19 01:02:25,155 INFO misc.py line 119 87073] Train: [63/100][351/1557] Data 0.014 (0.128) Batch 1.030 (1.120) Remain 18:18:12 loss: 0.3371 Lr: 0.00171 [2024-02-19 01:02:26,101 INFO misc.py line 119 87073] Train: [63/100][352/1557] Data 0.014 (0.127) Batch 0.958 (1.120) Remain 18:17:43 loss: 0.1126 Lr: 0.00171 [2024-02-19 01:02:27,064 INFO misc.py line 119 87073] Train: [63/100][353/1557] Data 0.003 (0.127) Batch 0.962 (1.119) Remain 18:17:16 loss: 0.2461 Lr: 0.00171 [2024-02-19 01:02:27,879 INFO misc.py line 119 87073] Train: [63/100][354/1557] Data 0.004 (0.127) Batch 0.816 (1.119) Remain 18:16:24 loss: 0.5780 Lr: 0.00171 [2024-02-19 01:02:30,479 INFO misc.py line 119 87073] Train: [63/100][355/1557] Data 1.067 (0.129) Batch 2.599 (1.123) Remain 18:20:30 loss: 0.2567 Lr: 0.00171 [2024-02-19 01:02:31,251 INFO misc.py line 119 87073] Train: [63/100][356/1557] Data 0.004 (0.129) Batch 0.773 (1.122) Remain 18:19:31 loss: 0.2753 Lr: 0.00171 [2024-02-19 01:02:32,529 INFO misc.py line 119 87073] Train: [63/100][357/1557] Data 0.003 (0.129) Batch 1.274 (1.122) Remain 18:19:55 loss: 0.1463 Lr: 0.00171 [2024-02-19 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87073] Train: [63/100][364/1557] Data 0.003 (0.126) Batch 1.235 (1.119) Remain 18:16:27 loss: 0.1940 Lr: 0.00171 [2024-02-19 01:02:40,193 INFO misc.py line 119 87073] Train: [63/100][365/1557] Data 0.005 (0.126) Batch 1.039 (1.119) Remain 18:16:13 loss: 0.4556 Lr: 0.00171 [2024-02-19 01:02:41,139 INFO misc.py line 119 87073] Train: [63/100][366/1557] Data 0.005 (0.125) Batch 0.948 (1.118) Remain 18:15:44 loss: 0.2851 Lr: 0.00171 [2024-02-19 01:02:42,107 INFO misc.py line 119 87073] Train: [63/100][367/1557] Data 0.003 (0.125) Batch 0.968 (1.118) Remain 18:15:19 loss: 0.5826 Lr: 0.00171 [2024-02-19 01:02:43,126 INFO misc.py line 119 87073] Train: [63/100][368/1557] Data 0.003 (0.125) Batch 1.019 (1.117) Remain 18:15:02 loss: 0.0660 Lr: 0.00171 [2024-02-19 01:02:43,851 INFO misc.py line 119 87073] Train: [63/100][369/1557] Data 0.003 (0.124) Batch 0.716 (1.116) Remain 18:13:56 loss: 0.2514 Lr: 0.00171 [2024-02-19 01:02:44,575 INFO misc.py line 119 87073] Train: [63/100][370/1557] Data 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01:03:37,037 INFO misc.py line 119 87073] Train: [63/100][414/1557] Data 0.012 (0.129) Batch 0.941 (1.124) Remain 18:20:09 loss: 0.1175 Lr: 0.00171 [2024-02-19 01:03:37,920 INFO misc.py line 119 87073] Train: [63/100][415/1557] Data 0.004 (0.128) Batch 0.883 (1.123) Remain 18:19:33 loss: 0.2930 Lr: 0.00171 [2024-02-19 01:03:38,981 INFO misc.py line 119 87073] Train: [63/100][416/1557] Data 0.005 (0.128) Batch 1.061 (1.123) Remain 18:19:24 loss: 0.2925 Lr: 0.00171 [2024-02-19 01:03:39,846 INFO misc.py line 119 87073] Train: [63/100][417/1557] Data 0.004 (0.128) Batch 0.866 (1.122) Remain 18:18:46 loss: 0.1527 Lr: 0.00171 [2024-02-19 01:03:40,719 INFO misc.py line 119 87073] Train: [63/100][418/1557] Data 0.003 (0.127) Batch 0.867 (1.122) Remain 18:18:09 loss: 0.2552 Lr: 0.00171 [2024-02-19 01:03:41,412 INFO misc.py line 119 87073] Train: [63/100][419/1557] Data 0.009 (0.127) Batch 0.698 (1.121) Remain 18:17:08 loss: 0.4422 Lr: 0.00171 [2024-02-19 01:03:42,660 INFO misc.py line 119 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Batch 0.728 (1.127) Remain 18:22:45 loss: 0.3504 Lr: 0.00171 [2024-02-19 01:04:28,034 INFO misc.py line 119 87073] Train: [63/100][458/1557] Data 0.011 (0.131) Batch 1.098 (1.127) Remain 18:22:40 loss: 0.3689 Lr: 0.00171 [2024-02-19 01:04:29,069 INFO misc.py line 119 87073] Train: [63/100][459/1557] Data 0.012 (0.131) Batch 1.038 (1.127) Remain 18:22:27 loss: 0.2044 Lr: 0.00171 [2024-02-19 01:04:29,846 INFO misc.py line 119 87073] Train: [63/100][460/1557] Data 0.009 (0.130) Batch 0.782 (1.126) Remain 18:21:42 loss: 0.2852 Lr: 0.00171 [2024-02-19 01:04:30,657 INFO misc.py line 119 87073] Train: [63/100][461/1557] Data 0.004 (0.130) Batch 0.813 (1.125) Remain 18:21:01 loss: 0.2250 Lr: 0.00171 [2024-02-19 01:04:31,665 INFO misc.py line 119 87073] Train: [63/100][462/1557] Data 0.003 (0.130) Batch 1.001 (1.125) Remain 18:20:44 loss: 0.1834 Lr: 0.00171 [2024-02-19 01:04:32,566 INFO misc.py line 119 87073] Train: [63/100][463/1557] Data 0.010 (0.130) Batch 0.907 (1.125) Remain 18:20:15 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18:10:25 loss: 0.3591 Lr: 0.00171 [2024-02-19 01:04:56,802 INFO misc.py line 119 87073] Train: [63/100][489/1557] Data 0.005 (0.123) Batch 0.758 (1.114) Remain 18:09:40 loss: 0.3216 Lr: 0.00170 [2024-02-19 01:04:58,034 INFO misc.py line 119 87073] Train: [63/100][490/1557] Data 0.012 (0.123) Batch 1.232 (1.114) Remain 18:09:53 loss: 0.2280 Lr: 0.00170 [2024-02-19 01:04:58,901 INFO misc.py line 119 87073] Train: [63/100][491/1557] Data 0.013 (0.123) Batch 0.877 (1.114) Remain 18:09:24 loss: 0.1571 Lr: 0.00170 [2024-02-19 01:05:00,115 INFO misc.py line 119 87073] Train: [63/100][492/1557] Data 0.003 (0.122) Batch 1.205 (1.114) Remain 18:09:34 loss: 0.2744 Lr: 0.00170 [2024-02-19 01:05:01,042 INFO misc.py line 119 87073] Train: [63/100][493/1557] Data 0.011 (0.122) Batch 0.934 (1.114) Remain 18:09:11 loss: 0.2628 Lr: 0.00170 [2024-02-19 01:05:02,179 INFO misc.py line 119 87073] Train: [63/100][494/1557] Data 0.003 (0.122) Batch 1.137 (1.114) Remain 18:09:13 loss: 0.3176 Lr: 0.00170 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Batch 1.085 (1.127) Remain 18:21:53 loss: 0.1128 Lr: 0.00170 [2024-02-19 01:05:31,169 INFO misc.py line 119 87073] Train: [63/100][514/1557] Data 0.003 (0.131) Batch 1.034 (1.127) Remain 18:21:41 loss: 0.1635 Lr: 0.00170 [2024-02-19 01:05:32,207 INFO misc.py line 119 87073] Train: [63/100][515/1557] Data 0.003 (0.131) Batch 1.038 (1.127) Remain 18:21:30 loss: 0.3366 Lr: 0.00170 [2024-02-19 01:05:32,933 INFO misc.py line 119 87073] Train: [63/100][516/1557] Data 0.003 (0.131) Batch 0.726 (1.126) Remain 18:20:43 loss: 0.2246 Lr: 0.00170 [2024-02-19 01:05:33,691 INFO misc.py line 119 87073] Train: [63/100][517/1557] Data 0.003 (0.131) Batch 0.747 (1.125) Remain 18:19:58 loss: 0.4075 Lr: 0.00170 [2024-02-19 01:05:34,776 INFO misc.py line 119 87073] Train: [63/100][518/1557] Data 0.015 (0.130) Batch 1.087 (1.125) Remain 18:19:53 loss: 0.2979 Lr: 0.00170 [2024-02-19 01:05:35,693 INFO misc.py line 119 87073] Train: [63/100][519/1557] Data 0.012 (0.130) Batch 0.926 (1.125) Remain 18:19:29 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line 119 87073] Train: [63/100][557/1557] Data 0.003 (0.124) Batch 0.768 (1.116) Remain 18:09:54 loss: 0.2374 Lr: 0.00170 [2024-02-19 01:06:14,167 INFO misc.py line 119 87073] Train: [63/100][558/1557] Data 0.004 (0.123) Batch 0.762 (1.115) Remain 18:09:15 loss: 0.2288 Lr: 0.00170 [2024-02-19 01:06:14,964 INFO misc.py line 119 87073] Train: [63/100][559/1557] Data 0.006 (0.123) Batch 0.797 (1.115) Remain 18:08:40 loss: 0.1291 Lr: 0.00170 [2024-02-19 01:06:16,201 INFO misc.py line 119 87073] Train: [63/100][560/1557] Data 0.004 (0.123) Batch 1.227 (1.115) Remain 18:08:51 loss: 0.1766 Lr: 0.00170 [2024-02-19 01:06:17,219 INFO misc.py line 119 87073] Train: [63/100][561/1557] Data 0.015 (0.123) Batch 1.019 (1.115) Remain 18:08:40 loss: 0.2190 Lr: 0.00170 [2024-02-19 01:06:18,374 INFO misc.py line 119 87073] Train: [63/100][562/1557] Data 0.015 (0.123) Batch 1.155 (1.115) Remain 18:08:43 loss: 0.2791 Lr: 0.00170 [2024-02-19 01:06:19,224 INFO misc.py line 119 87073] Train: 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Batch 0.962 (1.131) Remain 18:24:54 loss: 0.3249 Lr: 0.00170 [2024-02-19 01:06:36,682 INFO misc.py line 119 87073] Train: [63/100][570/1557] Data 0.008 (0.134) Batch 1.058 (1.131) Remain 18:24:45 loss: 0.4417 Lr: 0.00170 [2024-02-19 01:06:37,637 INFO misc.py line 119 87073] Train: [63/100][571/1557] Data 0.008 (0.134) Batch 0.960 (1.131) Remain 18:24:26 loss: 0.4300 Lr: 0.00170 [2024-02-19 01:06:38,350 INFO misc.py line 119 87073] Train: [63/100][572/1557] Data 0.003 (0.133) Batch 0.714 (1.130) Remain 18:23:42 loss: 0.2575 Lr: 0.00170 [2024-02-19 01:06:39,105 INFO misc.py line 119 87073] Train: [63/100][573/1557] Data 0.003 (0.133) Batch 0.747 (1.130) Remain 18:23:02 loss: 0.3184 Lr: 0.00170 [2024-02-19 01:06:40,162 INFO misc.py line 119 87073] Train: [63/100][574/1557] Data 0.010 (0.133) Batch 1.057 (1.129) Remain 18:22:53 loss: 0.2217 Lr: 0.00170 [2024-02-19 01:06:41,045 INFO misc.py line 119 87073] Train: [63/100][575/1557] Data 0.011 (0.133) Batch 0.890 (1.129) Remain 18:22:28 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01:06:47,690 INFO misc.py line 119 87073] Train: [63/100][582/1557] Data 0.013 (0.131) Batch 1.128 (1.127) Remain 18:20:11 loss: 0.8144 Lr: 0.00170 [2024-02-19 01:06:48,684 INFO misc.py line 119 87073] Train: [63/100][583/1557] Data 0.018 (0.131) Batch 1.002 (1.127) Remain 18:19:57 loss: 0.3402 Lr: 0.00170 [2024-02-19 01:06:49,632 INFO misc.py line 119 87073] Train: [63/100][584/1557] Data 0.010 (0.131) Batch 0.955 (1.126) Remain 18:19:39 loss: 0.4138 Lr: 0.00170 [2024-02-19 01:06:50,709 INFO misc.py line 119 87073] Train: [63/100][585/1557] Data 0.003 (0.131) Batch 1.076 (1.126) Remain 18:19:33 loss: 0.4252 Lr: 0.00170 [2024-02-19 01:06:51,449 INFO misc.py line 119 87073] Train: [63/100][586/1557] Data 0.004 (0.130) Batch 0.741 (1.126) Remain 18:18:53 loss: 0.2565 Lr: 0.00170 [2024-02-19 01:06:52,242 INFO misc.py line 119 87073] Train: [63/100][587/1557] Data 0.003 (0.130) Batch 0.785 (1.125) Remain 18:18:18 loss: 0.1460 Lr: 0.00170 [2024-02-19 01:06:53,466 INFO misc.py line 119 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line 119 87073] Train: [63/100][613/1557] Data 0.004 (0.125) Batch 1.029 (1.119) Remain 18:11:32 loss: 0.4915 Lr: 0.00170 [2024-02-19 01:07:18,282 INFO misc.py line 119 87073] Train: [63/100][614/1557] Data 0.004 (0.125) Batch 0.723 (1.118) Remain 18:10:53 loss: 0.1442 Lr: 0.00170 [2024-02-19 01:07:18,963 INFO misc.py line 119 87073] Train: [63/100][615/1557] Data 0.003 (0.125) Batch 0.675 (1.117) Remain 18:10:09 loss: 0.1641 Lr: 0.00170 [2024-02-19 01:07:20,227 INFO misc.py line 119 87073] Train: [63/100][616/1557] Data 0.009 (0.124) Batch 1.264 (1.117) Remain 18:10:22 loss: 0.1868 Lr: 0.00170 [2024-02-19 01:07:21,095 INFO misc.py line 119 87073] Train: [63/100][617/1557] Data 0.009 (0.124) Batch 0.875 (1.117) Remain 18:09:58 loss: 0.3571 Lr: 0.00170 [2024-02-19 01:07:21,979 INFO misc.py line 119 87073] Train: [63/100][618/1557] Data 0.003 (0.124) Batch 0.883 (1.117) Remain 18:09:34 loss: 0.5611 Lr: 0.00170 [2024-02-19 01:07:22,988 INFO misc.py line 119 87073] Train: 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Batch 1.091 (1.131) Remain 18:23:22 loss: 0.4530 Lr: 0.00170 [2024-02-19 01:07:39,790 INFO misc.py line 119 87073] Train: [63/100][626/1557] Data 0.002 (0.133) Batch 1.119 (1.131) Remain 18:23:20 loss: 0.2780 Lr: 0.00170 [2024-02-19 01:07:40,612 INFO misc.py line 119 87073] Train: [63/100][627/1557] Data 0.003 (0.133) Batch 0.821 (1.130) Remain 18:22:50 loss: 0.2093 Lr: 0.00170 [2024-02-19 01:07:41,382 INFO misc.py line 119 87073] Train: [63/100][628/1557] Data 0.004 (0.133) Batch 0.762 (1.130) Remain 18:22:14 loss: 0.2072 Lr: 0.00170 [2024-02-19 01:07:42,152 INFO misc.py line 119 87073] Train: [63/100][629/1557] Data 0.011 (0.133) Batch 0.779 (1.129) Remain 18:21:40 loss: 0.2788 Lr: 0.00170 [2024-02-19 01:07:43,195 INFO misc.py line 119 87073] Train: [63/100][630/1557] Data 0.002 (0.132) Batch 1.042 (1.129) Remain 18:21:31 loss: 0.1356 Lr: 0.00170 [2024-02-19 01:07:44,284 INFO misc.py line 119 87073] Train: [63/100][631/1557] Data 0.003 (0.132) Batch 1.081 (1.129) Remain 18:21:25 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[2024-02-19 01:09:20,134 INFO misc.py line 119 87073] Train: [63/100][719/1557] Data 0.004 (0.127) Batch 0.702 (1.124) Remain 18:15:01 loss: 0.1978 Lr: 0.00169 [2024-02-19 01:09:20,860 INFO misc.py line 119 87073] Train: [63/100][720/1557] Data 0.003 (0.127) Batch 0.717 (1.124) Remain 18:14:26 loss: 0.1392 Lr: 0.00169 [2024-02-19 01:09:21,882 INFO misc.py line 119 87073] Train: [63/100][721/1557] Data 0.012 (0.127) Batch 1.023 (1.123) Remain 18:14:17 loss: 0.1198 Lr: 0.00169 [2024-02-19 01:09:22,935 INFO misc.py line 119 87073] Train: [63/100][722/1557] Data 0.012 (0.127) Batch 1.053 (1.123) Remain 18:14:10 loss: 0.3482 Lr: 0.00169 [2024-02-19 01:09:23,690 INFO misc.py line 119 87073] Train: [63/100][723/1557] Data 0.012 (0.127) Batch 0.763 (1.123) Remain 18:13:40 loss: 0.9465 Lr: 0.00169 [2024-02-19 01:09:24,591 INFO misc.py line 119 87073] Train: [63/100][724/1557] Data 0.004 (0.127) Batch 0.893 (1.122) Remain 18:13:20 loss: 0.1731 Lr: 0.00169 [2024-02-19 01:09:25,691 INFO misc.py line 119 87073] Train: [63/100][725/1557] Data 0.011 (0.126) Batch 1.102 (1.122) Remain 18:13:17 loss: 0.2078 Lr: 0.00169 [2024-02-19 01:09:26,428 INFO misc.py line 119 87073] Train: [63/100][726/1557] Data 0.010 (0.126) Batch 0.744 (1.122) Remain 18:12:46 loss: 0.4152 Lr: 0.00169 [2024-02-19 01:09:27,235 INFO misc.py line 119 87073] Train: [63/100][727/1557] Data 0.003 (0.126) Batch 0.801 (1.121) Remain 18:12:18 loss: 0.2338 Lr: 0.00169 [2024-02-19 01:09:28,568 INFO misc.py line 119 87073] Train: [63/100][728/1557] Data 0.009 (0.126) Batch 1.330 (1.122) Remain 18:12:34 loss: 0.1315 Lr: 0.00169 [2024-02-19 01:09:29,684 INFO misc.py line 119 87073] Train: [63/100][729/1557] Data 0.012 (0.126) Batch 1.112 (1.122) Remain 18:12:32 loss: 0.2341 Lr: 0.00169 [2024-02-19 01:09:30,476 INFO misc.py line 119 87073] Train: [63/100][730/1557] Data 0.016 (0.126) Batch 0.805 (1.121) Remain 18:12:06 loss: 0.2581 Lr: 0.00169 [2024-02-19 01:09:31,324 INFO misc.py line 119 87073] Train: 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Batch 0.981 (1.132) Remain 18:22:12 loss: 0.4471 Lr: 0.00169 [2024-02-19 01:09:46,986 INFO misc.py line 119 87073] Train: [63/100][738/1557] Data 0.003 (0.134) Batch 0.941 (1.132) Remain 18:21:56 loss: 0.2311 Lr: 0.00169 [2024-02-19 01:09:47,928 INFO misc.py line 119 87073] Train: [63/100][739/1557] Data 0.003 (0.134) Batch 0.942 (1.131) Remain 18:21:40 loss: 0.3999 Lr: 0.00169 [2024-02-19 01:09:48,680 INFO misc.py line 119 87073] Train: [63/100][740/1557] Data 0.003 (0.134) Batch 0.744 (1.131) Remain 18:21:08 loss: 0.1649 Lr: 0.00169 [2024-02-19 01:09:49,479 INFO misc.py line 119 87073] Train: [63/100][741/1557] Data 0.011 (0.133) Batch 0.806 (1.130) Remain 18:20:41 loss: 0.2020 Lr: 0.00169 [2024-02-19 01:09:50,497 INFO misc.py line 119 87073] Train: [63/100][742/1557] Data 0.005 (0.133) Batch 1.019 (1.130) Remain 18:20:31 loss: 0.1546 Lr: 0.00169 [2024-02-19 01:09:51,469 INFO misc.py line 119 87073] Train: [63/100][743/1557] Data 0.004 (0.133) Batch 0.972 (1.130) Remain 18:20:18 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Batch 1.227 (1.132) Remain 18:20:53 loss: 0.4275 Lr: 0.00169 [2024-02-19 01:10:50,410 INFO misc.py line 119 87073] Train: [63/100][794/1557] Data 0.005 (0.134) Batch 1.195 (1.132) Remain 18:20:56 loss: 0.3054 Lr: 0.00169 [2024-02-19 01:10:51,484 INFO misc.py line 119 87073] Train: [63/100][795/1557] Data 0.011 (0.134) Batch 1.071 (1.132) Remain 18:20:51 loss: 0.2996 Lr: 0.00169 [2024-02-19 01:10:52,348 INFO misc.py line 119 87073] Train: [63/100][796/1557] Data 0.014 (0.134) Batch 0.874 (1.131) Remain 18:20:30 loss: 0.2279 Lr: 0.00169 [2024-02-19 01:10:53,109 INFO misc.py line 119 87073] Train: [63/100][797/1557] Data 0.004 (0.134) Batch 0.762 (1.131) Remain 18:20:02 loss: 0.2471 Lr: 0.00169 [2024-02-19 01:10:54,183 INFO misc.py line 119 87073] Train: [63/100][798/1557] Data 0.003 (0.134) Batch 1.065 (1.131) Remain 18:19:56 loss: 0.1957 Lr: 0.00169 [2024-02-19 01:10:55,101 INFO misc.py line 119 87073] Train: [63/100][799/1557] Data 0.012 (0.133) Batch 0.926 (1.130) Remain 18:19:40 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line 119 87073] Train: [63/100][837/1557] Data 0.003 (0.128) Batch 0.937 (1.123) Remain 18:11:15 loss: 0.4979 Lr: 0.00169 [2024-02-19 01:11:32,150 INFO misc.py line 119 87073] Train: [63/100][838/1557] Data 0.010 (0.127) Batch 0.697 (1.122) Remain 18:10:44 loss: 0.2512 Lr: 0.00169 [2024-02-19 01:11:32,928 INFO misc.py line 119 87073] Train: [63/100][839/1557] Data 0.004 (0.127) Batch 0.775 (1.122) Remain 18:10:19 loss: 0.1770 Lr: 0.00169 [2024-02-19 01:11:34,190 INFO misc.py line 119 87073] Train: [63/100][840/1557] Data 0.008 (0.127) Batch 1.260 (1.122) Remain 18:10:27 loss: 0.1282 Lr: 0.00169 [2024-02-19 01:11:35,415 INFO misc.py line 119 87073] Train: [63/100][841/1557] Data 0.010 (0.127) Batch 1.221 (1.122) Remain 18:10:33 loss: 0.1279 Lr: 0.00169 [2024-02-19 01:11:36,260 INFO misc.py line 119 87073] Train: [63/100][842/1557] Data 0.014 (0.127) Batch 0.855 (1.122) Remain 18:10:14 loss: 0.3848 Lr: 0.00169 [2024-02-19 01:11:37,201 INFO misc.py line 119 87073] Train: 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Batch 0.959 (1.132) Remain 18:20:13 loss: 0.5242 Lr: 0.00169 [2024-02-19 01:11:53,920 INFO misc.py line 119 87073] Train: [63/100][850/1557] Data 0.003 (0.133) Batch 1.003 (1.132) Remain 18:20:03 loss: 0.1254 Lr: 0.00169 [2024-02-19 01:11:54,871 INFO misc.py line 119 87073] Train: [63/100][851/1557] Data 0.002 (0.133) Batch 0.950 (1.132) Remain 18:19:49 loss: 0.3392 Lr: 0.00169 [2024-02-19 01:11:55,663 INFO misc.py line 119 87073] Train: [63/100][852/1557] Data 0.003 (0.133) Batch 0.783 (1.131) Remain 18:19:24 loss: 0.1751 Lr: 0.00169 [2024-02-19 01:11:56,414 INFO misc.py line 119 87073] Train: [63/100][853/1557] Data 0.012 (0.133) Batch 0.760 (1.131) Remain 18:18:58 loss: 0.2844 Lr: 0.00169 [2024-02-19 01:11:57,440 INFO misc.py line 119 87073] Train: [63/100][854/1557] Data 0.003 (0.133) Batch 1.001 (1.131) Remain 18:18:47 loss: 0.1998 Lr: 0.00169 [2024-02-19 01:11:58,482 INFO misc.py line 119 87073] Train: [63/100][855/1557] Data 0.029 (0.133) Batch 1.062 (1.131) Remain 18:18:42 loss: 0.3800 Lr: 0.00169 [2024-02-19 01:11:59,441 INFO misc.py line 119 87073] Train: [63/100][856/1557] Data 0.008 (0.132) Batch 0.965 (1.130) Remain 18:18:29 loss: 0.4813 Lr: 0.00169 [2024-02-19 01:12:00,493 INFO misc.py line 119 87073] Train: [63/100][857/1557] Data 0.003 (0.132) Batch 1.052 (1.130) Remain 18:18:23 loss: 0.6355 Lr: 0.00169 [2024-02-19 01:12:01,371 INFO misc.py line 119 87073] Train: [63/100][858/1557] Data 0.003 (0.132) Batch 0.877 (1.130) Remain 18:18:04 loss: 0.6335 Lr: 0.00169 [2024-02-19 01:12:02,137 INFO misc.py line 119 87073] Train: [63/100][859/1557] Data 0.004 (0.132) Batch 0.758 (1.130) Remain 18:17:38 loss: 0.2062 Lr: 0.00169 [2024-02-19 01:12:02,884 INFO misc.py line 119 87073] Train: [63/100][860/1557] Data 0.011 (0.132) Batch 0.756 (1.129) Remain 18:17:11 loss: 0.2595 Lr: 0.00169 [2024-02-19 01:12:04,208 INFO misc.py line 119 87073] Train: [63/100][861/1557] Data 0.003 (0.132) Batch 1.312 (1.129) Remain 18:17:23 loss: 0.2249 Lr: 0.00169 [2024-02-19 01:12:05,198 INFO misc.py line 119 87073] Train: [63/100][862/1557] Data 0.014 (0.132) Batch 1.000 (1.129) Remain 18:17:13 loss: 0.3208 Lr: 0.00169 [2024-02-19 01:12:06,154 INFO misc.py line 119 87073] Train: [63/100][863/1557] Data 0.004 (0.131) Batch 0.958 (1.129) Remain 18:17:00 loss: 0.5257 Lr: 0.00169 [2024-02-19 01:12:07,056 INFO misc.py line 119 87073] Train: [63/100][864/1557] Data 0.003 (0.131) Batch 0.901 (1.129) Remain 18:16:43 loss: 0.1750 Lr: 0.00169 [2024-02-19 01:12:08,183 INFO misc.py line 119 87073] Train: [63/100][865/1557] Data 0.003 (0.131) Batch 1.127 (1.129) Remain 18:16:42 loss: 0.6359 Lr: 0.00169 [2024-02-19 01:12:08,951 INFO misc.py line 119 87073] Train: [63/100][866/1557] Data 0.003 (0.131) Batch 0.769 (1.128) Remain 18:16:17 loss: 0.3638 Lr: 0.00169 [2024-02-19 01:12:09,721 INFO misc.py line 119 87073] Train: [63/100][867/1557] Data 0.003 (0.131) Batch 0.762 (1.128) Remain 18:15:51 loss: 0.3643 Lr: 0.00169 [2024-02-19 01:12:10,942 INFO misc.py line 119 87073] Train: [63/100][868/1557] Data 0.011 (0.131) Batch 1.220 (1.128) Remain 18:15:56 loss: 0.1242 Lr: 0.00169 [2024-02-19 01:12:12,030 INFO misc.py line 119 87073] Train: [63/100][869/1557] Data 0.011 (0.130) Batch 1.090 (1.128) Remain 18:15:52 loss: 0.2455 Lr: 0.00169 [2024-02-19 01:12:13,076 INFO misc.py line 119 87073] Train: [63/100][870/1557] Data 0.010 (0.130) Batch 1.041 (1.128) Remain 18:15:45 loss: 0.2198 Lr: 0.00169 [2024-02-19 01:12:13,911 INFO misc.py line 119 87073] Train: [63/100][871/1557] Data 0.014 (0.130) Batch 0.846 (1.127) Remain 18:15:25 loss: 0.3489 Lr: 0.00169 [2024-02-19 01:12:14,763 INFO misc.py line 119 87073] Train: [63/100][872/1557] Data 0.003 (0.130) Batch 0.852 (1.127) Remain 18:15:06 loss: 0.2745 Lr: 0.00169 [2024-02-19 01:12:15,516 INFO misc.py line 119 87073] Train: [63/100][873/1557] Data 0.003 (0.130) Batch 0.726 (1.127) Remain 18:14:38 loss: 0.1514 Lr: 0.00169 [2024-02-19 01:12:16,257 INFO misc.py line 119 87073] Train: [63/100][874/1557] Data 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[2024-02-19 01:12:30,488 INFO misc.py line 119 87073] Train: [63/100][887/1557] Data 0.003 (0.129) Batch 0.725 (1.126) Remain 18:13:31 loss: 0.2545 Lr: 0.00169 [2024-02-19 01:12:31,242 INFO misc.py line 119 87073] Train: [63/100][888/1557] Data 0.003 (0.128) Batch 0.746 (1.125) Remain 18:13:05 loss: 0.2294 Lr: 0.00168 [2024-02-19 01:12:32,261 INFO misc.py line 119 87073] Train: [63/100][889/1557] Data 0.012 (0.128) Batch 1.017 (1.125) Remain 18:12:56 loss: 0.1361 Lr: 0.00168 [2024-02-19 01:12:33,437 INFO misc.py line 119 87073] Train: [63/100][890/1557] Data 0.014 (0.128) Batch 1.180 (1.125) Remain 18:12:59 loss: 1.2567 Lr: 0.00168 [2024-02-19 01:12:34,518 INFO misc.py line 119 87073] Train: [63/100][891/1557] Data 0.010 (0.128) Batch 1.074 (1.125) Remain 18:12:54 loss: 0.3721 Lr: 0.00168 [2024-02-19 01:12:35,421 INFO misc.py line 119 87073] Train: [63/100][892/1557] Data 0.017 (0.128) Batch 0.915 (1.125) Remain 18:12:40 loss: 0.3416 Lr: 0.00168 [2024-02-19 01:12:36,411 INFO misc.py line 119 87073] Train: [63/100][893/1557] Data 0.005 (0.128) Batch 0.991 (1.125) Remain 18:12:30 loss: 0.2884 Lr: 0.00168 [2024-02-19 01:12:37,129 INFO misc.py line 119 87073] Train: [63/100][894/1557] Data 0.003 (0.128) Batch 0.718 (1.124) Remain 18:12:02 loss: 0.1860 Lr: 0.00168 [2024-02-19 01:12:37,918 INFO misc.py line 119 87073] Train: [63/100][895/1557] Data 0.003 (0.128) Batch 0.781 (1.124) Remain 18:11:38 loss: 0.3394 Lr: 0.00168 [2024-02-19 01:12:39,187 INFO misc.py line 119 87073] Train: [63/100][896/1557] Data 0.010 (0.127) Batch 1.265 (1.124) Remain 18:11:46 loss: 0.1010 Lr: 0.00168 [2024-02-19 01:12:40,111 INFO misc.py line 119 87073] Train: [63/100][897/1557] Data 0.015 (0.127) Batch 0.935 (1.124) Remain 18:11:33 loss: 0.2787 Lr: 0.00168 [2024-02-19 01:12:41,231 INFO misc.py line 119 87073] Train: [63/100][898/1557] Data 0.003 (0.127) Batch 1.120 (1.124) Remain 18:11:32 loss: 0.2979 Lr: 0.00168 [2024-02-19 01:12:42,199 INFO misc.py line 119 87073] Train: 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Batch 0.937 (1.134) Remain 18:20:40 loss: 0.4485 Lr: 0.00168 [2024-02-19 01:12:58,745 INFO misc.py line 119 87073] Train: [63/100][906/1557] Data 0.003 (0.135) Batch 1.033 (1.133) Remain 18:20:32 loss: 0.5210 Lr: 0.00168 [2024-02-19 01:12:59,631 INFO misc.py line 119 87073] Train: [63/100][907/1557] Data 0.009 (0.134) Batch 0.890 (1.133) Remain 18:20:15 loss: 0.5093 Lr: 0.00168 [2024-02-19 01:13:00,375 INFO misc.py line 119 87073] Train: [63/100][908/1557] Data 0.003 (0.134) Batch 0.744 (1.133) Remain 18:19:49 loss: 0.1590 Lr: 0.00168 [2024-02-19 01:13:01,118 INFO misc.py line 119 87073] Train: [63/100][909/1557] Data 0.003 (0.134) Batch 0.734 (1.132) Remain 18:19:22 loss: 0.1382 Lr: 0.00168 [2024-02-19 01:13:02,201 INFO misc.py line 119 87073] Train: [63/100][910/1557] Data 0.012 (0.134) Batch 1.082 (1.132) Remain 18:19:18 loss: 0.1734 Lr: 0.00168 [2024-02-19 01:13:03,093 INFO misc.py line 119 87073] Train: [63/100][911/1557] Data 0.014 (0.134) Batch 0.903 (1.132) Remain 18:19:02 loss: 0.0897 Lr: 0.00168 [2024-02-19 01:13:04,084 INFO misc.py line 119 87073] Train: [63/100][912/1557] Data 0.003 (0.134) Batch 0.990 (1.132) Remain 18:18:52 loss: 0.2493 Lr: 0.00168 [2024-02-19 01:13:05,058 INFO misc.py line 119 87073] Train: [63/100][913/1557] Data 0.003 (0.134) Batch 0.975 (1.132) Remain 18:18:41 loss: 0.3590 Lr: 0.00168 [2024-02-19 01:13:06,027 INFO misc.py line 119 87073] Train: [63/100][914/1557] Data 0.003 (0.133) Batch 0.968 (1.131) Remain 18:18:29 loss: 0.2351 Lr: 0.00168 [2024-02-19 01:13:06,829 INFO misc.py line 119 87073] Train: [63/100][915/1557] Data 0.003 (0.133) Batch 0.794 (1.131) Remain 18:18:07 loss: 0.2072 Lr: 0.00168 [2024-02-19 01:13:07,538 INFO misc.py line 119 87073] Train: [63/100][916/1557] Data 0.011 (0.133) Batch 0.715 (1.131) Remain 18:17:39 loss: 0.3380 Lr: 0.00168 [2024-02-19 01:13:08,818 INFO misc.py line 119 87073] Train: [63/100][917/1557] Data 0.005 (0.133) Batch 1.282 (1.131) Remain 18:17:47 loss: 0.1018 Lr: 0.00168 [2024-02-19 01:13:09,852 INFO misc.py line 119 87073] Train: [63/100][918/1557] Data 0.003 (0.133) Batch 1.035 (1.131) Remain 18:17:40 loss: 0.2870 Lr: 0.00168 [2024-02-19 01:13:10,803 INFO misc.py line 119 87073] Train: [63/100][919/1557] Data 0.003 (0.133) Batch 0.950 (1.130) Remain 18:17:27 loss: 0.4319 Lr: 0.00168 [2024-02-19 01:13:11,694 INFO misc.py line 119 87073] Train: [63/100][920/1557] Data 0.005 (0.133) Batch 0.890 (1.130) Remain 18:17:11 loss: 0.2452 Lr: 0.00168 [2024-02-19 01:13:12,596 INFO misc.py line 119 87073] Train: [63/100][921/1557] Data 0.006 (0.132) Batch 0.895 (1.130) Remain 18:16:55 loss: 0.6770 Lr: 0.00168 [2024-02-19 01:13:13,291 INFO misc.py line 119 87073] Train: [63/100][922/1557] Data 0.013 (0.132) Batch 0.705 (1.130) Remain 18:16:27 loss: 0.2493 Lr: 0.00168 [2024-02-19 01:13:14,049 INFO misc.py line 119 87073] Train: [63/100][923/1557] Data 0.003 (0.132) Batch 0.748 (1.129) Remain 18:16:02 loss: 0.2056 Lr: 0.00168 [2024-02-19 01:13:15,331 INFO misc.py line 119 87073] Train: [63/100][924/1557] Data 0.012 (0.132) Batch 1.282 (1.129) Remain 18:16:10 loss: 0.1000 Lr: 0.00168 [2024-02-19 01:13:16,293 INFO misc.py line 119 87073] Train: [63/100][925/1557] Data 0.012 (0.132) Batch 0.971 (1.129) Remain 18:15:59 loss: 0.2808 Lr: 0.00168 [2024-02-19 01:13:17,145 INFO misc.py line 119 87073] Train: [63/100][926/1557] Data 0.004 (0.132) Batch 0.851 (1.129) Remain 18:15:40 loss: 0.4023 Lr: 0.00168 [2024-02-19 01:13:18,099 INFO misc.py line 119 87073] Train: [63/100][927/1557] Data 0.004 (0.132) Batch 0.945 (1.129) Remain 18:15:28 loss: 0.1719 Lr: 0.00168 [2024-02-19 01:13:18,943 INFO misc.py line 119 87073] Train: [63/100][928/1557] Data 0.013 (0.132) Batch 0.854 (1.128) Remain 18:15:09 loss: 0.3435 Lr: 0.00168 [2024-02-19 01:13:19,685 INFO misc.py line 119 87073] Train: [63/100][929/1557] Data 0.003 (0.131) Batch 0.741 (1.128) Remain 18:14:44 loss: 0.2030 Lr: 0.00168 [2024-02-19 01:13:20,463 INFO misc.py line 119 87073] Train: [63/100][930/1557] Data 0.004 (0.131) Batch 0.778 (1.128) Remain 18:14:21 loss: 0.3120 Lr: 0.00168 [2024-02-19 01:13:21,619 INFO misc.py line 119 87073] Train: [63/100][931/1557] Data 0.005 (0.131) Batch 1.152 (1.128) Remain 18:14:21 loss: 0.1965 Lr: 0.00168 [2024-02-19 01:13:22,516 INFO misc.py line 119 87073] Train: [63/100][932/1557] Data 0.007 (0.131) Batch 0.901 (1.127) Remain 18:14:06 loss: 0.2951 Lr: 0.00168 [2024-02-19 01:13:23,247 INFO misc.py line 119 87073] Train: [63/100][933/1557] Data 0.004 (0.131) Batch 0.730 (1.127) Remain 18:13:40 loss: 0.0731 Lr: 0.00168 [2024-02-19 01:13:24,167 INFO misc.py line 119 87073] Train: [63/100][934/1557] Data 0.004 (0.131) Batch 0.907 (1.127) Remain 18:13:25 loss: 0.4963 Lr: 0.00168 [2024-02-19 01:13:25,110 INFO misc.py line 119 87073] Train: [63/100][935/1557] Data 0.018 (0.131) Batch 0.958 (1.126) Remain 18:13:13 loss: 0.1832 Lr: 0.00168 [2024-02-19 01:13:25,846 INFO misc.py line 119 87073] Train: [63/100][936/1557] Data 0.003 (0.130) Batch 0.735 (1.126) Remain 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Batch 0.852 (1.133) Remain 18:18:54 loss: 0.2978 Lr: 0.00168 [2024-02-19 01:14:01,446 INFO misc.py line 119 87073] Train: [63/100][962/1557] Data 0.004 (0.134) Batch 0.961 (1.133) Remain 18:18:42 loss: 0.1902 Lr: 0.00168 [2024-02-19 01:14:02,322 INFO misc.py line 119 87073] Train: [63/100][963/1557] Data 0.007 (0.134) Batch 0.881 (1.132) Remain 18:18:26 loss: 0.4283 Lr: 0.00168 [2024-02-19 01:14:03,065 INFO misc.py line 119 87073] Train: [63/100][964/1557] Data 0.003 (0.134) Batch 0.742 (1.132) Remain 18:18:01 loss: 0.3048 Lr: 0.00168 [2024-02-19 01:14:03,817 INFO misc.py line 119 87073] Train: [63/100][965/1557] Data 0.003 (0.134) Batch 0.744 (1.132) Remain 18:17:36 loss: 0.2371 Lr: 0.00168 [2024-02-19 01:14:04,878 INFO misc.py line 119 87073] Train: [63/100][966/1557] Data 0.012 (0.133) Batch 1.063 (1.131) Remain 18:17:31 loss: 0.2433 Lr: 0.00168 [2024-02-19 01:14:05,765 INFO misc.py line 119 87073] Train: [63/100][967/1557] Data 0.010 (0.133) Batch 0.893 (1.131) Remain 18:17:15 loss: 0.4872 Lr: 0.00168 [2024-02-19 01:14:06,640 INFO misc.py line 119 87073] Train: [63/100][968/1557] Data 0.003 (0.133) Batch 0.875 (1.131) Remain 18:16:59 loss: 0.2835 Lr: 0.00168 [2024-02-19 01:14:07,524 INFO misc.py line 119 87073] Train: [63/100][969/1557] Data 0.004 (0.133) Batch 0.880 (1.131) Remain 18:16:43 loss: 0.1734 Lr: 0.00168 [2024-02-19 01:14:08,428 INFO misc.py line 119 87073] Train: [63/100][970/1557] Data 0.008 (0.133) Batch 0.908 (1.130) Remain 18:16:28 loss: 0.2696 Lr: 0.00168 [2024-02-19 01:14:09,263 INFO misc.py line 119 87073] Train: [63/100][971/1557] Data 0.003 (0.133) Batch 0.823 (1.130) Remain 18:16:08 loss: 0.2688 Lr: 0.00168 [2024-02-19 01:14:10,015 INFO misc.py line 119 87073] Train: [63/100][972/1557] Data 0.016 (0.133) Batch 0.759 (1.130) Remain 18:15:45 loss: 0.1666 Lr: 0.00168 [2024-02-19 01:14:11,271 INFO misc.py line 119 87073] Train: [63/100][973/1557] Data 0.009 (0.133) Batch 1.257 (1.130) Remain 18:15:52 loss: 0.2800 Lr: 0.00168 [2024-02-19 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87073] Train: [63/100][980/1557] Data 0.007 (0.132) Batch 1.268 (1.129) Remain 18:14:36 loss: 0.1146 Lr: 0.00168 [2024-02-19 01:14:19,167 INFO misc.py line 119 87073] Train: [63/100][981/1557] Data 0.013 (0.131) Batch 1.086 (1.129) Remain 18:14:33 loss: 0.6541 Lr: 0.00168 [2024-02-19 01:14:20,045 INFO misc.py line 119 87073] Train: [63/100][982/1557] Data 0.038 (0.131) Batch 0.913 (1.128) Remain 18:14:19 loss: 0.5460 Lr: 0.00168 [2024-02-19 01:14:20,983 INFO misc.py line 119 87073] Train: [63/100][983/1557] Data 0.003 (0.131) Batch 0.937 (1.128) Remain 18:14:06 loss: 0.1408 Lr: 0.00168 [2024-02-19 01:14:22,028 INFO misc.py line 119 87073] Train: [63/100][984/1557] Data 0.003 (0.131) Batch 1.046 (1.128) Remain 18:14:00 loss: 0.1065 Lr: 0.00168 [2024-02-19 01:14:22,818 INFO misc.py line 119 87073] Train: [63/100][985/1557] Data 0.003 (0.131) Batch 0.790 (1.128) Remain 18:13:39 loss: 0.2039 Lr: 0.00168 [2024-02-19 01:14:23,603 INFO misc.py line 119 87073] Train: [63/100][986/1557] Data 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18:12:31 loss: 0.1891 Lr: 0.00168 [2024-02-19 01:14:30,441 INFO misc.py line 119 87073] Train: [63/100][993/1557] Data 0.005 (0.130) Batch 0.748 (1.126) Remain 18:12:07 loss: 0.1961 Lr: 0.00168 [2024-02-19 01:14:31,707 INFO misc.py line 119 87073] Train: [63/100][994/1557] Data 0.012 (0.130) Batch 1.262 (1.127) Remain 18:12:14 loss: 0.1848 Lr: 0.00168 [2024-02-19 01:14:32,660 INFO misc.py line 119 87073] Train: [63/100][995/1557] Data 0.014 (0.130) Batch 0.965 (1.126) Remain 18:12:04 loss: 0.2185 Lr: 0.00168 [2024-02-19 01:14:33,648 INFO misc.py line 119 87073] Train: [63/100][996/1557] Data 0.003 (0.130) Batch 0.987 (1.126) Remain 18:11:54 loss: 0.2865 Lr: 0.00168 [2024-02-19 01:14:34,605 INFO misc.py line 119 87073] Train: [63/100][997/1557] Data 0.003 (0.130) Batch 0.957 (1.126) Remain 18:11:43 loss: 0.8083 Lr: 0.00168 [2024-02-19 01:14:35,455 INFO misc.py line 119 87073] Train: [63/100][998/1557] Data 0.003 (0.129) Batch 0.845 (1.126) Remain 18:11:26 loss: 0.1727 Lr: 0.00168 [2024-02-19 01:14:36,204 INFO misc.py line 119 87073] Train: [63/100][999/1557] Data 0.010 (0.129) Batch 0.754 (1.125) Remain 18:11:03 loss: 0.1957 Lr: 0.00168 [2024-02-19 01:14:36,996 INFO misc.py line 119 87073] Train: [63/100][1000/1557] Data 0.003 (0.129) Batch 0.785 (1.125) Remain 18:10:42 loss: 0.2110 Lr: 0.00168 [2024-02-19 01:14:38,007 INFO misc.py line 119 87073] Train: [63/100][1001/1557] Data 0.010 (0.129) Batch 1.008 (1.125) Remain 18:10:34 loss: 0.0971 Lr: 0.00168 [2024-02-19 01:14:38,904 INFO misc.py line 119 87073] Train: [63/100][1002/1557] Data 0.014 (0.129) Batch 0.907 (1.125) Remain 18:10:20 loss: 0.5242 Lr: 0.00168 [2024-02-19 01:14:40,040 INFO misc.py line 119 87073] Train: [63/100][1003/1557] Data 0.004 (0.129) Batch 1.137 (1.125) Remain 18:10:20 loss: 0.3152 Lr: 0.00168 [2024-02-19 01:14:41,029 INFO misc.py line 119 87073] Train: [63/100][1004/1557] Data 0.003 (0.129) Batch 0.988 (1.125) Remain 18:10:11 loss: 0.1170 Lr: 0.00168 [2024-02-19 01:14:41,955 INFO 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(0.134) Batch 0.943 (1.133) Remain 18:17:35 loss: 0.8060 Lr: 0.00168 [2024-02-19 01:15:04,658 INFO misc.py line 119 87073] Train: [63/100][1018/1557] Data 0.003 (0.134) Batch 1.001 (1.132) Remain 18:17:27 loss: 0.7550 Lr: 0.00168 [2024-02-19 01:15:05,617 INFO misc.py line 119 87073] Train: [63/100][1019/1557] Data 0.003 (0.134) Batch 0.949 (1.132) Remain 18:17:15 loss: 0.2069 Lr: 0.00168 [2024-02-19 01:15:06,382 INFO misc.py line 119 87073] Train: [63/100][1020/1557] Data 0.013 (0.134) Batch 0.774 (1.132) Remain 18:16:53 loss: 0.2376 Lr: 0.00168 [2024-02-19 01:15:07,132 INFO misc.py line 119 87073] Train: [63/100][1021/1557] Data 0.004 (0.134) Batch 0.743 (1.131) Remain 18:16:30 loss: 0.1976 Lr: 0.00168 [2024-02-19 01:15:08,234 INFO misc.py line 119 87073] Train: [63/100][1022/1557] Data 0.011 (0.133) Batch 1.100 (1.131) Remain 18:16:27 loss: 0.2221 Lr: 0.00168 [2024-02-19 01:15:09,126 INFO misc.py line 119 87073] Train: [63/100][1023/1557] Data 0.012 (0.133) Batch 0.902 (1.131) Remain 18:16:13 loss: 0.3283 Lr: 0.00168 [2024-02-19 01:15:10,182 INFO misc.py line 119 87073] Train: [63/100][1024/1557] Data 0.003 (0.133) Batch 1.056 (1.131) Remain 18:16:07 loss: 0.1990 Lr: 0.00168 [2024-02-19 01:15:11,377 INFO misc.py line 119 87073] Train: [63/100][1025/1557] Data 0.003 (0.133) Batch 1.186 (1.131) Remain 18:16:09 loss: 0.1600 Lr: 0.00168 [2024-02-19 01:15:12,375 INFO misc.py line 119 87073] Train: [63/100][1026/1557] Data 0.012 (0.133) Batch 1.002 (1.131) Remain 18:16:01 loss: 0.2111 Lr: 0.00168 [2024-02-19 01:15:13,105 INFO misc.py line 119 87073] Train: [63/100][1027/1557] Data 0.007 (0.133) Batch 0.734 (1.131) Remain 18:15:37 loss: 0.1891 Lr: 0.00168 [2024-02-19 01:15:13,864 INFO misc.py line 119 87073] Train: [63/100][1028/1557] Data 0.003 (0.133) Batch 0.750 (1.130) Remain 18:15:14 loss: 0.3247 Lr: 0.00168 [2024-02-19 01:15:15,140 INFO misc.py line 119 87073] Train: [63/100][1029/1557] Data 0.013 (0.133) Batch 1.278 (1.130) Remain 18:15:22 loss: 0.0852 Lr: 0.00168 [2024-02-19 01:15:16,119 INFO misc.py line 119 87073] Train: [63/100][1030/1557] Data 0.011 (0.132) Batch 0.987 (1.130) Remain 18:15:12 loss: 0.1937 Lr: 0.00168 [2024-02-19 01:15:17,030 INFO misc.py line 119 87073] Train: [63/100][1031/1557] Data 0.003 (0.132) Batch 0.911 (1.130) Remain 18:14:59 loss: 0.1855 Lr: 0.00168 [2024-02-19 01:15:18,050 INFO misc.py line 119 87073] Train: [63/100][1032/1557] Data 0.003 (0.132) Batch 1.020 (1.130) Remain 18:14:52 loss: 0.2916 Lr: 0.00168 [2024-02-19 01:15:19,151 INFO misc.py line 119 87073] Train: [63/100][1033/1557] Data 0.003 (0.132) Batch 1.101 (1.130) Remain 18:14:49 loss: 0.3948 Lr: 0.00168 [2024-02-19 01:15:19,913 INFO misc.py line 119 87073] Train: [63/100][1034/1557] Data 0.002 (0.132) Batch 0.762 (1.130) Remain 18:14:27 loss: 0.2618 Lr: 0.00168 [2024-02-19 01:15:20,688 INFO misc.py line 119 87073] Train: [63/100][1035/1557] Data 0.003 (0.132) Batch 0.764 (1.129) Remain 18:14:05 loss: 0.2113 Lr: 0.00168 [2024-02-19 01:15:21,961 INFO misc.py line 119 87073] Train: [63/100][1036/1557] Data 0.014 (0.132) Batch 1.272 (1.129) Remain 18:14:12 loss: 0.1213 Lr: 0.00168 [2024-02-19 01:15:23,012 INFO misc.py line 119 87073] Train: [63/100][1037/1557] Data 0.015 (0.132) Batch 1.052 (1.129) Remain 18:14:07 loss: 0.5676 Lr: 0.00168 [2024-02-19 01:15:24,069 INFO misc.py line 119 87073] Train: [63/100][1038/1557] Data 0.015 (0.131) Batch 1.059 (1.129) Remain 18:14:02 loss: 0.2740 Lr: 0.00168 [2024-02-19 01:15:25,104 INFO misc.py line 119 87073] Train: [63/100][1039/1557] Data 0.013 (0.131) Batch 1.024 (1.129) Remain 18:13:55 loss: 0.6945 Lr: 0.00168 [2024-02-19 01:15:26,013 INFO misc.py line 119 87073] Train: [63/100][1040/1557] Data 0.023 (0.131) Batch 0.929 (1.129) Remain 18:13:42 loss: 0.2916 Lr: 0.00168 [2024-02-19 01:15:26,804 INFO misc.py line 119 87073] Train: [63/100][1041/1557] Data 0.003 (0.131) Batch 0.790 (1.129) Remain 18:13:22 loss: 0.3550 Lr: 0.00168 [2024-02-19 01:15:27,537 INFO misc.py line 119 87073] Train: 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(0.130) Batch 0.766 (1.127) Remain 18:11:57 loss: 0.3049 Lr: 0.00168 [2024-02-19 01:15:34,088 INFO misc.py line 119 87073] Train: [63/100][1049/1557] Data 0.003 (0.130) Batch 0.765 (1.127) Remain 18:11:36 loss: 0.2307 Lr: 0.00168 [2024-02-19 01:15:35,309 INFO misc.py line 119 87073] Train: [63/100][1050/1557] Data 0.011 (0.130) Batch 1.227 (1.127) Remain 18:11:40 loss: 0.2807 Lr: 0.00168 [2024-02-19 01:15:36,196 INFO misc.py line 119 87073] Train: [63/100][1051/1557] Data 0.005 (0.130) Batch 0.889 (1.127) Remain 18:11:26 loss: 0.3542 Lr: 0.00168 [2024-02-19 01:15:37,161 INFO misc.py line 119 87073] Train: [63/100][1052/1557] Data 0.003 (0.130) Batch 0.965 (1.127) Remain 18:11:16 loss: 0.2831 Lr: 0.00168 [2024-02-19 01:15:38,143 INFO misc.py line 119 87073] Train: [63/100][1053/1557] Data 0.003 (0.130) Batch 0.982 (1.127) Remain 18:11:07 loss: 0.1801 Lr: 0.00168 [2024-02-19 01:15:38,991 INFO misc.py line 119 87073] Train: [63/100][1054/1557] Data 0.004 (0.130) Batch 0.846 (1.126) Remain 18:10:50 loss: 0.3232 Lr: 0.00168 [2024-02-19 01:15:41,520 INFO misc.py line 119 87073] Train: [63/100][1055/1557] Data 0.895 (0.130) Batch 2.528 (1.128) Remain 18:12:06 loss: 0.2015 Lr: 0.00168 [2024-02-19 01:15:42,299 INFO misc.py line 119 87073] Train: [63/100][1056/1557] Data 0.007 (0.130) Batch 0.782 (1.127) Remain 18:11:46 loss: 0.3492 Lr: 0.00168 [2024-02-19 01:15:43,354 INFO misc.py line 119 87073] Train: [63/100][1057/1557] Data 0.003 (0.130) Batch 1.055 (1.127) Remain 18:11:41 loss: 0.0982 Lr: 0.00168 [2024-02-19 01:15:44,222 INFO misc.py line 119 87073] Train: [63/100][1058/1557] Data 0.003 (0.130) Batch 0.869 (1.127) Remain 18:11:26 loss: 0.1962 Lr: 0.00168 [2024-02-19 01:15:45,331 INFO misc.py line 119 87073] Train: [63/100][1059/1557] Data 0.003 (0.130) Batch 1.100 (1.127) Remain 18:11:23 loss: 0.3169 Lr: 0.00168 [2024-02-19 01:15:46,345 INFO misc.py line 119 87073] Train: [63/100][1060/1557] Data 0.012 (0.130) Batch 1.017 (1.127) Remain 18:11:16 loss: 0.4305 Lr: 0.00168 [2024-02-19 01:15:47,360 INFO misc.py line 119 87073] Train: [63/100][1061/1557] Data 0.009 (0.130) Batch 1.011 (1.127) Remain 18:11:08 loss: 0.3046 Lr: 0.00168 [2024-02-19 01:15:48,111 INFO misc.py line 119 87073] Train: [63/100][1062/1557] Data 0.014 (0.129) Batch 0.761 (1.126) Remain 18:10:47 loss: 0.2535 Lr: 0.00168 [2024-02-19 01:15:48,871 INFO misc.py line 119 87073] Train: [63/100][1063/1557] Data 0.003 (0.129) Batch 0.748 (1.126) Remain 18:10:25 loss: 0.3173 Lr: 0.00168 [2024-02-19 01:15:50,111 INFO misc.py line 119 87073] Train: [63/100][1064/1557] Data 0.014 (0.129) Batch 1.241 (1.126) Remain 18:10:30 loss: 0.1017 Lr: 0.00168 [2024-02-19 01:15:51,275 INFO misc.py line 119 87073] Train: [63/100][1065/1557] Data 0.013 (0.129) Batch 1.172 (1.126) Remain 18:10:32 loss: 0.3714 Lr: 0.00168 [2024-02-19 01:15:52,156 INFO misc.py line 119 87073] Train: [63/100][1066/1557] Data 0.006 (0.129) Batch 0.880 (1.126) Remain 18:10:17 loss: 0.4468 Lr: 0.00168 [2024-02-19 01:15:53,068 INFO misc.py line 119 87073] Train: [63/100][1067/1557] Data 0.006 (0.129) Batch 0.914 (1.126) Remain 18:10:05 loss: 0.4544 Lr: 0.00168 [2024-02-19 01:15:54,081 INFO misc.py line 119 87073] Train: [63/100][1068/1557] Data 0.004 (0.129) Batch 1.013 (1.126) Remain 18:09:57 loss: 0.3842 Lr: 0.00168 [2024-02-19 01:15:54,788 INFO misc.py line 119 87073] Train: [63/100][1069/1557] Data 0.004 (0.129) Batch 0.699 (1.125) Remain 18:09:33 loss: 0.2776 Lr: 0.00168 [2024-02-19 01:15:55,531 INFO misc.py line 119 87073] Train: [63/100][1070/1557] Data 0.011 (0.129) Batch 0.752 (1.125) Remain 18:09:11 loss: 0.1701 Lr: 0.00168 [2024-02-19 01:16:06,254 INFO misc.py line 119 87073] Train: [63/100][1071/1557] Data 6.569 (0.135) Batch 10.722 (1.134) Remain 18:17:52 loss: 0.1620 Lr: 0.00168 [2024-02-19 01:16:07,095 INFO misc.py line 119 87073] Train: [63/100][1072/1557] Data 0.004 (0.134) Batch 0.841 (1.134) Remain 18:17:35 loss: 0.3850 Lr: 0.00168 [2024-02-19 01:16:08,117 INFO misc.py line 119 87073] Train: 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18:15:21 loss: 0.1463 Lr: 0.00168 [2024-02-19 01:16:20,709 INFO misc.py line 119 87073] Train: [63/100][1086/1557] Data 0.012 (0.133) Batch 1.094 (1.132) Remain 18:15:18 loss: 0.1845 Lr: 0.00168 [2024-02-19 01:16:21,756 INFO misc.py line 119 87073] Train: [63/100][1087/1557] Data 0.013 (0.133) Batch 1.049 (1.131) Remain 18:15:12 loss: 0.1093 Lr: 0.00168 [2024-02-19 01:16:22,823 INFO misc.py line 119 87073] Train: [63/100][1088/1557] Data 0.011 (0.133) Batch 1.069 (1.131) Remain 18:15:08 loss: 0.3632 Lr: 0.00167 [2024-02-19 01:16:23,707 INFO misc.py line 119 87073] Train: [63/100][1089/1557] Data 0.009 (0.132) Batch 0.889 (1.131) Remain 18:14:54 loss: 0.3169 Lr: 0.00167 [2024-02-19 01:16:24,490 INFO misc.py line 119 87073] Train: [63/100][1090/1557] Data 0.003 (0.132) Batch 0.784 (1.131) Remain 18:14:34 loss: 0.1966 Lr: 0.00167 [2024-02-19 01:16:25,191 INFO misc.py line 119 87073] Train: [63/100][1091/1557] Data 0.003 (0.132) Batch 0.698 (1.130) Remain 18:14:10 loss: 0.2351 Lr: 0.00167 [2024-02-19 01:16:26,476 INFO misc.py line 119 87073] Train: [63/100][1092/1557] Data 0.007 (0.132) Batch 1.279 (1.131) Remain 18:14:17 loss: 0.1372 Lr: 0.00167 [2024-02-19 01:16:27,306 INFO misc.py line 119 87073] Train: [63/100][1093/1557] Data 0.013 (0.132) Batch 0.839 (1.130) Remain 18:14:00 loss: 0.3288 Lr: 0.00167 [2024-02-19 01:16:28,174 INFO misc.py line 119 87073] Train: [63/100][1094/1557] Data 0.005 (0.132) Batch 0.869 (1.130) Remain 18:13:45 loss: 0.4285 Lr: 0.00167 [2024-02-19 01:16:29,090 INFO misc.py line 119 87073] Train: [63/100][1095/1557] Data 0.003 (0.132) Batch 0.909 (1.130) Remain 18:13:32 loss: 0.1773 Lr: 0.00167 [2024-02-19 01:16:30,057 INFO misc.py line 119 87073] Train: [63/100][1096/1557] Data 0.010 (0.132) Batch 0.974 (1.130) Remain 18:13:23 loss: 0.3414 Lr: 0.00167 [2024-02-19 01:16:30,687 INFO misc.py line 119 87073] Train: [63/100][1097/1557] Data 0.003 (0.132) Batch 0.629 (1.129) Remain 18:12:55 loss: 0.1720 Lr: 0.00167 [2024-02-19 01:16:31,426 INFO misc.py line 119 87073] Train: [63/100][1098/1557] Data 0.003 (0.131) Batch 0.732 (1.129) Remain 18:12:33 loss: 0.1992 Lr: 0.00167 [2024-02-19 01:16:32,557 INFO misc.py line 119 87073] Train: [63/100][1099/1557] Data 0.010 (0.131) Batch 1.135 (1.129) Remain 18:12:32 loss: 0.1211 Lr: 0.00167 [2024-02-19 01:16:33,510 INFO misc.py line 119 87073] Train: [63/100][1100/1557] Data 0.007 (0.131) Batch 0.957 (1.129) Remain 18:12:22 loss: 0.2786 Lr: 0.00167 [2024-02-19 01:16:34,300 INFO misc.py line 119 87073] Train: [63/100][1101/1557] Data 0.003 (0.131) Batch 0.790 (1.128) Remain 18:12:03 loss: 0.2268 Lr: 0.00167 [2024-02-19 01:16:35,220 INFO misc.py line 119 87073] Train: [63/100][1102/1557] Data 0.004 (0.131) Batch 0.914 (1.128) Remain 18:11:50 loss: 0.2404 Lr: 0.00167 [2024-02-19 01:16:36,146 INFO misc.py line 119 87073] Train: [63/100][1103/1557] Data 0.009 (0.131) Batch 0.932 (1.128) Remain 18:11:39 loss: 0.2945 Lr: 0.00167 [2024-02-19 01:16:36,927 INFO misc.py line 119 87073] Train: 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(0.130) Batch 1.110 (1.127) Remain 18:10:15 loss: 0.5934 Lr: 0.00167 [2024-02-19 01:16:43,367 INFO misc.py line 119 87073] Train: [63/100][1111/1557] Data 0.013 (0.130) Batch 0.765 (1.126) Remain 18:09:55 loss: 0.1827 Lr: 0.00167 [2024-02-19 01:16:44,134 INFO misc.py line 119 87073] Train: [63/100][1112/1557] Data 0.003 (0.130) Batch 0.756 (1.126) Remain 18:09:35 loss: 0.1617 Lr: 0.00167 [2024-02-19 01:16:45,126 INFO misc.py line 119 87073] Train: [63/100][1113/1557] Data 0.013 (0.130) Batch 1.002 (1.126) Remain 18:09:27 loss: 0.1587 Lr: 0.00167 [2024-02-19 01:16:46,121 INFO misc.py line 119 87073] Train: [63/100][1114/1557] Data 0.004 (0.130) Batch 0.996 (1.126) Remain 18:09:19 loss: 0.3585 Lr: 0.00167 [2024-02-19 01:16:47,049 INFO misc.py line 119 87073] Train: [63/100][1115/1557] Data 0.003 (0.130) Batch 0.927 (1.126) Remain 18:09:08 loss: 1.1497 Lr: 0.00167 [2024-02-19 01:16:48,181 INFO misc.py line 119 87073] Train: [63/100][1116/1557] Data 0.003 (0.129) Batch 1.132 (1.126) Remain 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[2024-02-19 01:18:13,119 INFO misc.py line 119 87073] Train: [63/100][1185/1557] Data 0.003 (0.134) Batch 1.023 (1.132) Remain 18:13:45 loss: 0.3859 Lr: 0.00167 [2024-02-19 01:18:13,927 INFO misc.py line 119 87073] Train: [63/100][1186/1557] Data 0.004 (0.134) Batch 0.808 (1.132) Remain 18:13:28 loss: 0.2690 Lr: 0.00167 [2024-02-19 01:18:14,893 INFO misc.py line 119 87073] Train: [63/100][1187/1557] Data 0.004 (0.134) Batch 0.956 (1.131) Remain 18:13:19 loss: 0.2670 Lr: 0.00167 [2024-02-19 01:18:15,593 INFO misc.py line 119 87073] Train: [63/100][1188/1557] Data 0.013 (0.134) Batch 0.708 (1.131) Remain 18:12:57 loss: 0.2203 Lr: 0.00167 [2024-02-19 01:18:16,337 INFO misc.py line 119 87073] Train: [63/100][1189/1557] Data 0.005 (0.134) Batch 0.742 (1.131) Remain 18:12:37 loss: 0.2198 Lr: 0.00167 [2024-02-19 01:18:17,427 INFO misc.py line 119 87073] Train: [63/100][1190/1557] Data 0.008 (0.134) Batch 1.083 (1.131) Remain 18:12:33 loss: 0.2670 Lr: 0.00167 [2024-02-19 01:18:18,561 INFO 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18:09:38 loss: 0.2064 Lr: 0.00167 [2024-02-19 01:18:36,465 INFO misc.py line 119 87073] Train: [63/100][1210/1557] Data 0.012 (0.132) Batch 0.770 (1.128) Remain 18:09:19 loss: 0.2908 Lr: 0.00167 [2024-02-19 01:18:37,642 INFO misc.py line 119 87073] Train: [63/100][1211/1557] Data 0.003 (0.131) Batch 1.177 (1.128) Remain 18:09:21 loss: 0.1106 Lr: 0.00167 [2024-02-19 01:18:38,658 INFO misc.py line 119 87073] Train: [63/100][1212/1557] Data 0.004 (0.131) Batch 1.016 (1.128) Remain 18:09:14 loss: 0.2285 Lr: 0.00167 [2024-02-19 01:18:39,510 INFO misc.py line 119 87073] Train: [63/100][1213/1557] Data 0.003 (0.131) Batch 0.851 (1.127) Remain 18:09:00 loss: 0.2126 Lr: 0.00167 [2024-02-19 01:18:40,393 INFO misc.py line 119 87073] Train: [63/100][1214/1557] Data 0.003 (0.131) Batch 0.876 (1.127) Remain 18:08:47 loss: 0.1518 Lr: 0.00167 [2024-02-19 01:18:41,367 INFO misc.py line 119 87073] Train: [63/100][1215/1557] Data 0.012 (0.131) Batch 0.981 (1.127) Remain 18:08:39 loss: 0.7454 Lr: 0.00167 [2024-02-19 01:18:42,167 INFO misc.py line 119 87073] Train: [63/100][1216/1557] Data 0.003 (0.131) Batch 0.800 (1.127) Remain 18:08:22 loss: 0.2937 Lr: 0.00167 [2024-02-19 01:18:42,934 INFO misc.py line 119 87073] Train: [63/100][1217/1557] Data 0.004 (0.131) Batch 0.759 (1.127) Remain 18:08:03 loss: 0.4297 Lr: 0.00167 [2024-02-19 01:18:44,229 INFO misc.py line 119 87073] Train: [63/100][1218/1557] Data 0.011 (0.131) Batch 1.291 (1.127) Remain 18:08:10 loss: 0.2130 Lr: 0.00167 [2024-02-19 01:18:45,145 INFO misc.py line 119 87073] Train: [63/100][1219/1557] Data 0.015 (0.131) Batch 0.927 (1.127) Remain 18:07:59 loss: 0.3603 Lr: 0.00167 [2024-02-19 01:18:46,291 INFO misc.py line 119 87073] Train: [63/100][1220/1557] Data 0.003 (0.131) Batch 1.146 (1.127) Remain 18:07:59 loss: 0.2404 Lr: 0.00167 [2024-02-19 01:18:47,288 INFO misc.py line 119 87073] Train: [63/100][1221/1557] Data 0.004 (0.130) Batch 0.998 (1.126) Remain 18:07:52 loss: 0.3038 Lr: 0.00167 [2024-02-19 01:18:48,087 INFO misc.py line 119 87073] Train: [63/100][1222/1557] Data 0.003 (0.130) Batch 0.798 (1.126) Remain 18:07:35 loss: 0.2711 Lr: 0.00167 [2024-02-19 01:18:48,902 INFO misc.py line 119 87073] Train: [63/100][1223/1557] Data 0.003 (0.130) Batch 0.790 (1.126) Remain 18:07:18 loss: 0.2514 Lr: 0.00167 [2024-02-19 01:18:49,671 INFO misc.py line 119 87073] Train: [63/100][1224/1557] Data 0.028 (0.130) Batch 0.794 (1.126) Remain 18:07:01 loss: 0.3761 Lr: 0.00167 [2024-02-19 01:18:50,746 INFO misc.py line 119 87073] Train: [63/100][1225/1557] Data 0.003 (0.130) Batch 1.076 (1.126) Remain 18:06:58 loss: 0.1885 Lr: 0.00167 [2024-02-19 01:18:51,661 INFO misc.py line 119 87073] Train: [63/100][1226/1557] Data 0.003 (0.130) Batch 0.915 (1.125) Remain 18:06:47 loss: 0.2182 Lr: 0.00167 [2024-02-19 01:18:52,800 INFO misc.py line 119 87073] Train: [63/100][1227/1557] Data 0.003 (0.130) Batch 1.139 (1.125) Remain 18:06:46 loss: 0.1323 Lr: 0.00167 [2024-02-19 01:18:54,186 INFO misc.py line 119 87073] Train: 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(0.130) Batch 0.959 (1.126) Remain 18:07:27 loss: 0.3412 Lr: 0.00167 [2024-02-19 01:19:02,592 INFO misc.py line 119 87073] Train: [63/100][1235/1557] Data 0.003 (0.130) Batch 0.881 (1.126) Remain 18:07:14 loss: 0.1549 Lr: 0.00167 [2024-02-19 01:19:03,698 INFO misc.py line 119 87073] Train: [63/100][1236/1557] Data 0.003 (0.130) Batch 1.107 (1.126) Remain 18:07:12 loss: 0.4088 Lr: 0.00167 [2024-02-19 01:19:04,473 INFO misc.py line 119 87073] Train: [63/100][1237/1557] Data 0.003 (0.129) Batch 0.775 (1.126) Remain 18:06:54 loss: 0.3447 Lr: 0.00167 [2024-02-19 01:19:05,139 INFO misc.py line 119 87073] Train: [63/100][1238/1557] Data 0.003 (0.129) Batch 0.665 (1.125) Remain 18:06:32 loss: 0.2511 Lr: 0.00167 [2024-02-19 01:19:16,617 INFO misc.py line 119 87073] Train: [63/100][1239/1557] Data 6.740 (0.135) Batch 11.478 (1.134) Remain 18:14:36 loss: 0.1587 Lr: 0.00167 [2024-02-19 01:19:17,607 INFO misc.py line 119 87073] Train: [63/100][1240/1557] Data 0.003 (0.135) Batch 0.991 (1.134) Remain 18:14:28 loss: 0.4424 Lr: 0.00167 [2024-02-19 01:19:18,713 INFO misc.py line 119 87073] Train: [63/100][1241/1557] Data 0.003 (0.134) Batch 1.106 (1.134) Remain 18:14:26 loss: 0.2746 Lr: 0.00167 [2024-02-19 01:19:19,584 INFO misc.py line 119 87073] Train: [63/100][1242/1557] Data 0.003 (0.134) Batch 0.871 (1.133) Remain 18:14:12 loss: 0.1900 Lr: 0.00167 [2024-02-19 01:19:20,700 INFO misc.py line 119 87073] Train: [63/100][1243/1557] Data 0.003 (0.134) Batch 1.109 (1.133) Remain 18:14:10 loss: 0.3995 Lr: 0.00167 [2024-02-19 01:19:21,453 INFO misc.py line 119 87073] Train: [63/100][1244/1557] Data 0.011 (0.134) Batch 0.761 (1.133) Remain 18:13:51 loss: 0.2231 Lr: 0.00167 [2024-02-19 01:19:22,164 INFO misc.py line 119 87073] Train: [63/100][1245/1557] Data 0.003 (0.134) Batch 0.702 (1.133) Remain 18:13:30 loss: 0.2212 Lr: 0.00167 [2024-02-19 01:19:23,183 INFO misc.py line 119 87073] Train: [63/100][1246/1557] Data 0.011 (0.134) Batch 1.019 (1.133) Remain 18:13:24 loss: 0.2704 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1253/1557] Data 0.003 (0.133) Batch 1.255 (1.132) Remain 18:12:13 loss: 0.1140 Lr: 0.00167 [2024-02-19 01:19:30,558 INFO misc.py line 119 87073] Train: [63/100][1254/1557] Data 0.004 (0.133) Batch 0.814 (1.131) Remain 18:11:57 loss: 0.3739 Lr: 0.00167 [2024-02-19 01:19:31,559 INFO misc.py line 119 87073] Train: [63/100][1255/1557] Data 0.003 (0.133) Batch 1.001 (1.131) Remain 18:11:50 loss: 0.4493 Lr: 0.00167 [2024-02-19 01:19:32,293 INFO misc.py line 119 87073] Train: [63/100][1256/1557] Data 0.003 (0.133) Batch 0.729 (1.131) Remain 18:11:30 loss: 0.2750 Lr: 0.00167 [2024-02-19 01:19:33,549 INFO misc.py line 119 87073] Train: [63/100][1257/1557] Data 0.008 (0.133) Batch 1.253 (1.131) Remain 18:11:34 loss: 0.4454 Lr: 0.00167 [2024-02-19 01:19:34,290 INFO misc.py line 119 87073] Train: [63/100][1258/1557] Data 0.011 (0.133) Batch 0.749 (1.131) Remain 18:11:16 loss: 0.2394 Lr: 0.00167 [2024-02-19 01:19:35,080 INFO misc.py line 119 87073] Train: [63/100][1259/1557] Data 0.003 (0.133) Batch 0.779 (1.130) Remain 18:10:58 loss: 0.1534 Lr: 0.00167 [2024-02-19 01:19:36,327 INFO misc.py line 119 87073] Train: [63/100][1260/1557] Data 0.014 (0.133) Batch 1.249 (1.131) Remain 18:11:03 loss: 0.1129 Lr: 0.00167 [2024-02-19 01:19:37,303 INFO misc.py line 119 87073] Train: [63/100][1261/1557] Data 0.012 (0.132) Batch 0.984 (1.130) Remain 18:10:55 loss: 0.4635 Lr: 0.00167 [2024-02-19 01:19:38,346 INFO misc.py line 119 87073] Train: [63/100][1262/1557] Data 0.004 (0.132) Batch 1.045 (1.130) Remain 18:10:50 loss: 0.3485 Lr: 0.00167 [2024-02-19 01:19:39,443 INFO misc.py line 119 87073] Train: [63/100][1263/1557] Data 0.003 (0.132) Batch 1.096 (1.130) Remain 18:10:47 loss: 0.2578 Lr: 0.00167 [2024-02-19 01:19:40,369 INFO misc.py line 119 87073] Train: [63/100][1264/1557] Data 0.003 (0.132) Batch 0.926 (1.130) Remain 18:10:37 loss: 0.2640 Lr: 0.00167 [2024-02-19 01:19:41,140 INFO misc.py line 119 87073] Train: [63/100][1265/1557] Data 0.004 (0.132) Batch 0.771 (1.130) Remain 18:10:19 loss: 0.2484 Lr: 0.00167 [2024-02-19 01:19:41,881 INFO misc.py line 119 87073] Train: [63/100][1266/1557] Data 0.003 (0.132) Batch 0.738 (1.130) Remain 18:10:00 loss: 0.4860 Lr: 0.00167 [2024-02-19 01:19:43,098 INFO misc.py line 119 87073] Train: [63/100][1267/1557] Data 0.006 (0.132) Batch 1.221 (1.130) Remain 18:10:03 loss: 0.1332 Lr: 0.00167 [2024-02-19 01:19:44,188 INFO misc.py line 119 87073] Train: [63/100][1268/1557] Data 0.004 (0.132) Batch 1.080 (1.130) Remain 18:09:59 loss: 0.3203 Lr: 0.00167 [2024-02-19 01:19:45,146 INFO misc.py line 119 87073] Train: [63/100][1269/1557] Data 0.013 (0.132) Batch 0.968 (1.129) Remain 18:09:51 loss: 0.2280 Lr: 0.00167 [2024-02-19 01:19:46,043 INFO misc.py line 119 87073] Train: [63/100][1270/1557] Data 0.003 (0.132) Batch 0.897 (1.129) Remain 18:09:39 loss: 0.3044 Lr: 0.00167 [2024-02-19 01:19:47,039 INFO misc.py line 119 87073] Train: [63/100][1271/1557] Data 0.004 (0.131) Batch 0.988 (1.129) Remain 18:09:32 loss: 1.0293 Lr: 0.00167 [2024-02-19 01:19:47,678 INFO misc.py line 119 87073] Train: [63/100][1272/1557] Data 0.012 (0.131) Batch 0.647 (1.129) Remain 18:09:09 loss: 0.2101 Lr: 0.00167 [2024-02-19 01:19:48,406 INFO misc.py line 119 87073] Train: [63/100][1273/1557] Data 0.003 (0.131) Batch 0.719 (1.128) Remain 18:08:49 loss: 0.2405 Lr: 0.00167 [2024-02-19 01:19:49,732 INFO misc.py line 119 87073] Train: [63/100][1274/1557] Data 0.013 (0.131) Batch 1.328 (1.129) Remain 18:08:57 loss: 0.1863 Lr: 0.00167 [2024-02-19 01:19:50,755 INFO misc.py line 119 87073] Train: [63/100][1275/1557] Data 0.011 (0.131) Batch 1.020 (1.129) Remain 18:08:51 loss: 0.2494 Lr: 0.00167 [2024-02-19 01:19:51,704 INFO misc.py line 119 87073] Train: [63/100][1276/1557] Data 0.013 (0.131) Batch 0.959 (1.128) Remain 18:08:42 loss: 0.8203 Lr: 0.00167 [2024-02-19 01:19:52,577 INFO misc.py line 119 87073] Train: [63/100][1277/1557] Data 0.004 (0.131) Batch 0.874 (1.128) Remain 18:08:29 loss: 0.3607 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1284/1557] Data 0.003 (0.130) Batch 0.985 (1.127) Remain 18:07:13 loss: 0.5244 Lr: 0.00167 [2024-02-19 01:19:59,954 INFO misc.py line 119 87073] Train: [63/100][1285/1557] Data 0.003 (0.130) Batch 0.981 (1.127) Remain 18:07:06 loss: 0.1937 Lr: 0.00167 [2024-02-19 01:20:00,756 INFO misc.py line 119 87073] Train: [63/100][1286/1557] Data 0.004 (0.130) Batch 0.801 (1.127) Remain 18:06:50 loss: 0.1970 Lr: 0.00167 [2024-02-19 01:20:01,558 INFO misc.py line 119 87073] Train: [63/100][1287/1557] Data 0.004 (0.130) Batch 0.804 (1.126) Remain 18:06:34 loss: 0.1384 Lr: 0.00166 [2024-02-19 01:20:02,816 INFO misc.py line 119 87073] Train: [63/100][1288/1557] Data 0.003 (0.130) Batch 1.247 (1.126) Remain 18:06:38 loss: 0.1581 Lr: 0.00166 [2024-02-19 01:20:03,670 INFO misc.py line 119 87073] Train: [63/100][1289/1557] Data 0.013 (0.130) Batch 0.864 (1.126) Remain 18:06:25 loss: 0.2339 Lr: 0.00166 [2024-02-19 01:20:04,730 INFO misc.py line 119 87073] Train: [63/100][1290/1557] Data 0.003 (0.130) Batch 1.060 (1.126) Remain 18:06:21 loss: 0.1533 Lr: 0.00166 [2024-02-19 01:20:05,728 INFO misc.py line 119 87073] Train: [63/100][1291/1557] Data 0.003 (0.129) Batch 0.998 (1.126) Remain 18:06:14 loss: 0.1397 Lr: 0.00166 [2024-02-19 01:20:06,784 INFO misc.py line 119 87073] Train: [63/100][1292/1557] Data 0.003 (0.129) Batch 1.056 (1.126) Remain 18:06:10 loss: 0.3458 Lr: 0.00166 [2024-02-19 01:20:07,685 INFO misc.py line 119 87073] Train: [63/100][1293/1557] Data 0.003 (0.129) Batch 0.901 (1.126) Remain 18:05:59 loss: 0.3481 Lr: 0.00166 [2024-02-19 01:20:08,423 INFO misc.py line 119 87073] Train: [63/100][1294/1557] Data 0.003 (0.129) Batch 0.731 (1.126) Remain 18:05:40 loss: 0.3173 Lr: 0.00166 [2024-02-19 01:20:19,221 INFO misc.py line 119 87073] Train: [63/100][1295/1557] Data 7.112 (0.135) Batch 10.805 (1.133) Remain 18:12:53 loss: 0.1527 Lr: 0.00166 [2024-02-19 01:20:20,051 INFO misc.py line 119 87073] Train: [63/100][1296/1557] Data 0.004 (0.134) Batch 0.831 (1.133) Remain 18:12:38 loss: 0.0847 Lr: 0.00166 [2024-02-19 01:20:21,101 INFO misc.py line 119 87073] Train: [63/100][1297/1557] Data 0.003 (0.134) Batch 1.024 (1.133) Remain 18:12:32 loss: 0.1529 Lr: 0.00166 [2024-02-19 01:20:21,900 INFO misc.py line 119 87073] Train: [63/100][1298/1557] Data 0.030 (0.134) Batch 0.825 (1.133) Remain 18:12:17 loss: 0.3008 Lr: 0.00166 [2024-02-19 01:20:22,836 INFO misc.py line 119 87073] Train: [63/100][1299/1557] Data 0.003 (0.134) Batch 0.937 (1.132) Remain 18:12:07 loss: 0.2635 Lr: 0.00166 [2024-02-19 01:20:23,615 INFO misc.py line 119 87073] Train: [63/100][1300/1557] Data 0.003 (0.134) Batch 0.772 (1.132) Remain 18:11:50 loss: 0.1647 Lr: 0.00166 [2024-02-19 01:20:24,413 INFO misc.py line 119 87073] Train: [63/100][1301/1557] Data 0.010 (0.134) Batch 0.805 (1.132) Remain 18:11:34 loss: 0.1603 Lr: 0.00166 [2024-02-19 01:20:25,436 INFO misc.py line 119 87073] Train: [63/100][1302/1557] Data 0.003 (0.134) Batch 1.022 (1.132) Remain 18:11:28 loss: 0.1534 Lr: 0.00166 [2024-02-19 01:20:26,501 INFO misc.py line 119 87073] Train: [63/100][1303/1557] Data 0.003 (0.134) Batch 1.065 (1.132) Remain 18:11:24 loss: 0.3002 Lr: 0.00166 [2024-02-19 01:20:27,553 INFO misc.py line 119 87073] Train: [63/100][1304/1557] Data 0.003 (0.134) Batch 1.052 (1.132) Remain 18:11:19 loss: 0.6592 Lr: 0.00166 [2024-02-19 01:20:28,315 INFO misc.py line 119 87073] Train: [63/100][1305/1557] Data 0.003 (0.134) Batch 0.762 (1.131) Remain 18:11:02 loss: 0.3170 Lr: 0.00166 [2024-02-19 01:20:29,357 INFO misc.py line 119 87073] Train: [63/100][1306/1557] Data 0.004 (0.134) Batch 1.036 (1.131) Remain 18:10:56 loss: 0.5836 Lr: 0.00166 [2024-02-19 01:20:30,121 INFO misc.py line 119 87073] Train: [63/100][1307/1557] Data 0.010 (0.133) Batch 0.771 (1.131) Remain 18:10:39 loss: 0.3129 Lr: 0.00166 [2024-02-19 01:20:30,888 INFO misc.py line 119 87073] Train: [63/100][1308/1557] Data 0.003 (0.133) Batch 0.759 (1.131) Remain 18:10:22 loss: 0.1712 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1315/1557] Data 0.005 (0.133) Batch 0.729 (1.130) Remain 18:09:22 loss: 0.2162 Lr: 0.00166 [2024-02-19 01:20:38,959 INFO misc.py line 119 87073] Train: [63/100][1316/1557] Data 0.003 (0.133) Batch 1.325 (1.130) Remain 18:09:30 loss: 0.0926 Lr: 0.00166 [2024-02-19 01:20:39,944 INFO misc.py line 119 87073] Train: [63/100][1317/1557] Data 0.006 (0.132) Batch 0.988 (1.130) Remain 18:09:22 loss: 0.4796 Lr: 0.00166 [2024-02-19 01:20:40,937 INFO misc.py line 119 87073] Train: [63/100][1318/1557] Data 0.002 (0.132) Batch 0.994 (1.130) Remain 18:09:15 loss: 0.5132 Lr: 0.00166 [2024-02-19 01:20:42,023 INFO misc.py line 119 87073] Train: [63/100][1319/1557] Data 0.003 (0.132) Batch 1.086 (1.130) Remain 18:09:12 loss: 0.2275 Lr: 0.00166 [2024-02-19 01:20:42,960 INFO misc.py line 119 87073] Train: [63/100][1320/1557] Data 0.003 (0.132) Batch 0.937 (1.130) Remain 18:09:03 loss: 0.2684 Lr: 0.00166 [2024-02-19 01:20:43,769 INFO misc.py line 119 87073] Train: [63/100][1321/1557] Data 0.003 (0.132) Batch 0.805 (1.129) Remain 18:08:47 loss: 0.1644 Lr: 0.00166 [2024-02-19 01:20:44,534 INFO misc.py line 119 87073] Train: [63/100][1322/1557] Data 0.006 (0.132) Batch 0.769 (1.129) Remain 18:08:30 loss: 0.2903 Lr: 0.00166 [2024-02-19 01:20:45,691 INFO misc.py line 119 87073] Train: [63/100][1323/1557] Data 0.003 (0.132) Batch 1.157 (1.129) Remain 18:08:30 loss: 0.2096 Lr: 0.00166 [2024-02-19 01:20:46,619 INFO misc.py line 119 87073] Train: [63/100][1324/1557] Data 0.003 (0.132) Batch 0.926 (1.129) Remain 18:08:20 loss: 0.2785 Lr: 0.00166 [2024-02-19 01:20:47,577 INFO misc.py line 119 87073] Train: [63/100][1325/1557] Data 0.005 (0.132) Batch 0.960 (1.129) Remain 18:08:12 loss: 0.1307 Lr: 0.00166 [2024-02-19 01:20:48,417 INFO misc.py line 119 87073] Train: [63/100][1326/1557] Data 0.003 (0.132) Batch 0.839 (1.129) Remain 18:07:58 loss: 0.3674 Lr: 0.00166 [2024-02-19 01:20:49,386 INFO misc.py line 119 87073] Train: [63/100][1327/1557] Data 0.004 (0.131) Batch 0.968 (1.128) Remain 18:07:50 loss: 0.5950 Lr: 0.00166 [2024-02-19 01:20:50,158 INFO misc.py line 119 87073] Train: [63/100][1328/1557] Data 0.005 (0.131) Batch 0.773 (1.128) Remain 18:07:33 loss: 0.2641 Lr: 0.00166 [2024-02-19 01:20:50,955 INFO misc.py line 119 87073] Train: [63/100][1329/1557] Data 0.004 (0.131) Batch 0.798 (1.128) Remain 18:07:18 loss: 0.4111 Lr: 0.00166 [2024-02-19 01:20:52,242 INFO misc.py line 119 87073] Train: [63/100][1330/1557] Data 0.004 (0.131) Batch 1.284 (1.128) Remain 18:07:23 loss: 0.2015 Lr: 0.00166 [2024-02-19 01:20:53,213 INFO misc.py line 119 87073] Train: [63/100][1331/1557] Data 0.007 (0.131) Batch 0.975 (1.128) Remain 18:07:16 loss: 0.4422 Lr: 0.00166 [2024-02-19 01:20:54,195 INFO misc.py line 119 87073] Train: [63/100][1332/1557] Data 0.003 (0.131) Batch 0.981 (1.128) Remain 18:07:08 loss: 0.6909 Lr: 0.00166 [2024-02-19 01:20:55,132 INFO misc.py line 119 87073] Train: [63/100][1333/1557] Data 0.004 (0.131) Batch 0.937 (1.128) Remain 18:06:59 loss: 0.2503 Lr: 0.00166 [2024-02-19 01:20:56,023 INFO misc.py line 119 87073] Train: [63/100][1334/1557] Data 0.004 (0.131) Batch 0.891 (1.128) Remain 18:06:47 loss: 0.4602 Lr: 0.00166 [2024-02-19 01:20:56,849 INFO misc.py line 119 87073] Train: [63/100][1335/1557] Data 0.003 (0.131) Batch 0.826 (1.127) Remain 18:06:33 loss: 0.1949 Lr: 0.00166 [2024-02-19 01:20:57,641 INFO misc.py line 119 87073] Train: [63/100][1336/1557] Data 0.004 (0.131) Batch 0.792 (1.127) Remain 18:06:17 loss: 0.2066 Lr: 0.00166 [2024-02-19 01:20:58,627 INFO misc.py line 119 87073] Train: [63/100][1337/1557] Data 0.003 (0.131) Batch 0.981 (1.127) Remain 18:06:10 loss: 0.1343 Lr: 0.00166 [2024-02-19 01:20:59,604 INFO misc.py line 119 87073] Train: [63/100][1338/1557] Data 0.009 (0.130) Batch 0.983 (1.127) Remain 18:06:03 loss: 0.3231 Lr: 0.00166 [2024-02-19 01:21:00,545 INFO misc.py line 119 87073] Train: [63/100][1339/1557] Data 0.003 (0.130) Batch 0.941 (1.127) Remain 18:05:53 loss: 0.3246 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1346/1557] Data 0.003 (0.130) Batch 0.958 (1.126) Remain 18:04:48 loss: 0.3382 Lr: 0.00166 [2024-02-19 01:21:08,018 INFO misc.py line 119 87073] Train: [63/100][1347/1557] Data 0.004 (0.130) Batch 0.927 (1.126) Remain 18:04:38 loss: 0.1162 Lr: 0.00166 [2024-02-19 01:21:09,077 INFO misc.py line 119 87073] Train: [63/100][1348/1557] Data 0.003 (0.130) Batch 1.059 (1.126) Remain 18:04:34 loss: 0.7357 Lr: 0.00166 [2024-02-19 01:21:09,854 INFO misc.py line 119 87073] Train: [63/100][1349/1557] Data 0.004 (0.129) Batch 0.774 (1.125) Remain 18:04:18 loss: 0.2633 Lr: 0.00166 [2024-02-19 01:21:10,627 INFO misc.py line 119 87073] Train: [63/100][1350/1557] Data 0.007 (0.129) Batch 0.776 (1.125) Remain 18:04:02 loss: 0.4311 Lr: 0.00166 [2024-02-19 01:21:20,964 INFO misc.py line 119 87073] Train: [63/100][1351/1557] Data 7.363 (0.135) Batch 10.337 (1.132) Remain 18:10:36 loss: 0.3532 Lr: 0.00166 [2024-02-19 01:21:21,930 INFO misc.py line 119 87073] Train: [63/100][1352/1557] Data 0.003 (0.135) Batch 0.966 (1.132) Remain 18:10:28 loss: 0.3721 Lr: 0.00166 [2024-02-19 01:21:22,927 INFO misc.py line 119 87073] Train: [63/100][1353/1557] Data 0.003 (0.134) Batch 0.997 (1.132) Remain 18:10:21 loss: 0.3938 Lr: 0.00166 [2024-02-19 01:21:24,052 INFO misc.py line 119 87073] Train: [63/100][1354/1557] Data 0.003 (0.134) Batch 1.125 (1.132) Remain 18:10:19 loss: 0.4178 Lr: 0.00166 [2024-02-19 01:21:24,970 INFO misc.py line 119 87073] Train: [63/100][1355/1557] Data 0.003 (0.134) Batch 0.917 (1.131) Remain 18:10:09 loss: 0.1547 Lr: 0.00166 [2024-02-19 01:21:25,710 INFO misc.py line 119 87073] Train: [63/100][1356/1557] Data 0.003 (0.134) Batch 0.730 (1.131) Remain 18:09:51 loss: 0.1511 Lr: 0.00166 [2024-02-19 01:21:26,530 INFO misc.py line 119 87073] Train: [63/100][1357/1557] Data 0.013 (0.134) Batch 0.831 (1.131) Remain 18:09:37 loss: 0.2748 Lr: 0.00166 [2024-02-19 01:21:27,566 INFO misc.py line 119 87073] Train: [63/100][1358/1557] Data 0.003 (0.134) Batch 1.033 (1.131) Remain 18:09:31 loss: 0.2231 Lr: 0.00166 [2024-02-19 01:21:28,487 INFO misc.py line 119 87073] Train: [63/100][1359/1557] Data 0.005 (0.134) Batch 0.923 (1.131) Remain 18:09:21 loss: 0.3809 Lr: 0.00166 [2024-02-19 01:21:29,353 INFO misc.py line 119 87073] Train: [63/100][1360/1557] Data 0.003 (0.134) Batch 0.864 (1.130) Remain 18:09:09 loss: 0.2087 Lr: 0.00166 [2024-02-19 01:21:30,398 INFO misc.py line 119 87073] Train: [63/100][1361/1557] Data 0.005 (0.134) Batch 1.039 (1.130) Remain 18:09:04 loss: 0.2280 Lr: 0.00166 [2024-02-19 01:21:31,449 INFO misc.py line 119 87073] Train: [63/100][1362/1557] Data 0.011 (0.134) Batch 1.058 (1.130) Remain 18:09:00 loss: 0.5046 Lr: 0.00166 [2024-02-19 01:21:32,126 INFO misc.py line 119 87073] Train: [63/100][1363/1557] Data 0.004 (0.134) Batch 0.677 (1.130) Remain 18:08:39 loss: 0.1895 Lr: 0.00166 [2024-02-19 01:21:32,875 INFO misc.py line 119 87073] Train: [63/100][1364/1557] Data 0.003 (0.133) Batch 0.744 (1.130) Remain 18:08:22 loss: 0.3386 Lr: 0.00166 [2024-02-19 01:21:34,130 INFO misc.py line 119 87073] Train: [63/100][1365/1557] Data 0.008 (0.133) Batch 1.252 (1.130) Remain 18:08:26 loss: 0.0910 Lr: 0.00166 [2024-02-19 01:21:35,099 INFO misc.py line 119 87073] Train: [63/100][1366/1557] Data 0.011 (0.133) Batch 0.978 (1.130) Remain 18:08:18 loss: 0.1976 Lr: 0.00166 [2024-02-19 01:21:36,185 INFO misc.py line 119 87073] Train: [63/100][1367/1557] Data 0.003 (0.133) Batch 1.085 (1.130) Remain 18:08:15 loss: 0.2054 Lr: 0.00166 [2024-02-19 01:21:37,196 INFO misc.py line 119 87073] Train: [63/100][1368/1557] Data 0.003 (0.133) Batch 1.011 (1.130) Remain 18:08:09 loss: 0.2421 Lr: 0.00166 [2024-02-19 01:21:38,239 INFO misc.py line 119 87073] Train: [63/100][1369/1557] Data 0.003 (0.133) Batch 1.043 (1.130) Remain 18:08:04 loss: 0.3905 Lr: 0.00166 [2024-02-19 01:21:38,998 INFO misc.py line 119 87073] Train: [63/100][1370/1557] Data 0.003 (0.133) Batch 0.759 (1.129) Remain 18:07:48 loss: 0.2179 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1377/1557] Data 0.003 (0.132) Batch 0.732 (1.128) Remain 18:06:44 loss: 0.4276 Lr: 0.00166 [2024-02-19 01:21:46,375 INFO misc.py line 119 87073] Train: [63/100][1378/1557] Data 0.013 (0.132) Batch 0.782 (1.128) Remain 18:06:29 loss: 0.4521 Lr: 0.00166 [2024-02-19 01:21:47,536 INFO misc.py line 119 87073] Train: [63/100][1379/1557] Data 0.004 (0.132) Batch 1.161 (1.128) Remain 18:06:29 loss: 0.1192 Lr: 0.00166 [2024-02-19 01:21:48,526 INFO misc.py line 119 87073] Train: [63/100][1380/1557] Data 0.004 (0.132) Batch 0.990 (1.128) Remain 18:06:22 loss: 0.3745 Lr: 0.00166 [2024-02-19 01:21:49,523 INFO misc.py line 119 87073] Train: [63/100][1381/1557] Data 0.003 (0.132) Batch 0.997 (1.128) Remain 18:06:16 loss: 0.3168 Lr: 0.00166 [2024-02-19 01:21:50,590 INFO misc.py line 119 87073] Train: [63/100][1382/1557] Data 0.003 (0.132) Batch 1.067 (1.128) Remain 18:06:12 loss: 0.5226 Lr: 0.00166 [2024-02-19 01:21:51,632 INFO misc.py line 119 87073] Train: [63/100][1383/1557] Data 0.003 (0.132) Batch 1.041 (1.128) Remain 18:06:07 loss: 0.3841 Lr: 0.00166 [2024-02-19 01:21:52,382 INFO misc.py line 119 87073] Train: [63/100][1384/1557] Data 0.003 (0.132) Batch 0.751 (1.128) Remain 18:05:50 loss: 0.2569 Lr: 0.00166 [2024-02-19 01:21:53,141 INFO misc.py line 119 87073] Train: [63/100][1385/1557] Data 0.003 (0.132) Batch 0.748 (1.127) Remain 18:05:33 loss: 0.3845 Lr: 0.00166 [2024-02-19 01:21:54,417 INFO misc.py line 119 87073] Train: [63/100][1386/1557] Data 0.015 (0.131) Batch 1.278 (1.127) Remain 18:05:38 loss: 0.1445 Lr: 0.00166 [2024-02-19 01:21:55,330 INFO misc.py line 119 87073] Train: [63/100][1387/1557] Data 0.012 (0.131) Batch 0.923 (1.127) Remain 18:05:29 loss: 0.4334 Lr: 0.00166 [2024-02-19 01:21:56,254 INFO misc.py line 119 87073] Train: [63/100][1388/1557] Data 0.003 (0.131) Batch 0.923 (1.127) Remain 18:05:19 loss: 0.1446 Lr: 0.00166 [2024-02-19 01:21:57,143 INFO misc.py line 119 87073] Train: [63/100][1389/1557] Data 0.003 (0.131) Batch 0.889 (1.127) Remain 18:05:08 loss: 0.3125 Lr: 0.00166 [2024-02-19 01:21:57,983 INFO misc.py line 119 87073] Train: [63/100][1390/1557] Data 0.003 (0.131) Batch 0.835 (1.127) Remain 18:04:55 loss: 0.3624 Lr: 0.00166 [2024-02-19 01:21:58,712 INFO misc.py line 119 87073] Train: [63/100][1391/1557] Data 0.008 (0.131) Batch 0.734 (1.126) Remain 18:04:37 loss: 0.3174 Lr: 0.00166 [2024-02-19 01:21:59,483 INFO misc.py line 119 87073] Train: [63/100][1392/1557] Data 0.004 (0.131) Batch 0.768 (1.126) Remain 18:04:21 loss: 0.4333 Lr: 0.00166 [2024-02-19 01:22:00,458 INFO misc.py line 119 87073] Train: [63/100][1393/1557] Data 0.006 (0.131) Batch 0.978 (1.126) Remain 18:04:14 loss: 0.0979 Lr: 0.00166 [2024-02-19 01:22:01,443 INFO misc.py line 119 87073] Train: [63/100][1394/1557] Data 0.003 (0.131) Batch 0.985 (1.126) Remain 18:04:07 loss: 0.2893 Lr: 0.00166 [2024-02-19 01:22:02,435 INFO misc.py line 119 87073] Train: [63/100][1395/1557] Data 0.002 (0.131) Batch 0.991 (1.126) Remain 18:04:00 loss: 0.2702 Lr: 0.00166 [2024-02-19 01:22:03,219 INFO misc.py line 119 87073] Train: [63/100][1396/1557] Data 0.004 (0.131) Batch 0.784 (1.126) Remain 18:03:45 loss: 0.2623 Lr: 0.00166 [2024-02-19 01:22:04,377 INFO misc.py line 119 87073] Train: [63/100][1397/1557] Data 0.004 (0.130) Batch 1.152 (1.126) Remain 18:03:45 loss: 0.2624 Lr: 0.00166 [2024-02-19 01:22:05,131 INFO misc.py line 119 87073] Train: [63/100][1398/1557] Data 0.009 (0.130) Batch 0.758 (1.125) Remain 18:03:29 loss: 0.2174 Lr: 0.00166 [2024-02-19 01:22:05,939 INFO misc.py line 119 87073] Train: [63/100][1399/1557] Data 0.005 (0.130) Batch 0.799 (1.125) Remain 18:03:14 loss: 0.2687 Lr: 0.00166 [2024-02-19 01:22:07,239 INFO misc.py line 119 87073] Train: [63/100][1400/1557] Data 0.013 (0.130) Batch 1.301 (1.125) Remain 18:03:20 loss: 0.1329 Lr: 0.00166 [2024-02-19 01:22:08,160 INFO misc.py line 119 87073] Train: [63/100][1401/1557] Data 0.013 (0.130) Batch 0.930 (1.125) Remain 18:03:11 loss: 0.4912 Lr: 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INFO misc.py line 119 87073] Train: [63/100][1408/1557] Data 0.003 (0.135) Batch 0.962 (1.133) Remain 18:10:14 loss: 0.3261 Lr: 0.00166 [2024-02-19 01:22:27,695 INFO misc.py line 119 87073] Train: [63/100][1409/1557] Data 0.007 (0.135) Batch 1.165 (1.133) Remain 18:10:15 loss: 0.4491 Lr: 0.00166 [2024-02-19 01:22:28,607 INFO misc.py line 119 87073] Train: [63/100][1410/1557] Data 0.004 (0.134) Batch 0.912 (1.132) Remain 18:10:04 loss: 0.2944 Lr: 0.00166 [2024-02-19 01:22:29,558 INFO misc.py line 119 87073] Train: [63/100][1411/1557] Data 0.004 (0.134) Batch 0.952 (1.132) Remain 18:09:56 loss: 0.2656 Lr: 0.00166 [2024-02-19 01:22:30,340 INFO misc.py line 119 87073] Train: [63/100][1412/1557] Data 0.003 (0.134) Batch 0.782 (1.132) Remain 18:09:40 loss: 0.1773 Lr: 0.00166 [2024-02-19 01:22:31,066 INFO misc.py line 119 87073] Train: [63/100][1413/1557] Data 0.003 (0.134) Batch 0.714 (1.132) Remain 18:09:22 loss: 0.2410 Lr: 0.00166 [2024-02-19 01:22:32,134 INFO misc.py line 119 87073] Train: [63/100][1414/1557] Data 0.015 (0.134) Batch 1.069 (1.132) Remain 18:09:18 loss: 0.2886 Lr: 0.00166 [2024-02-19 01:22:33,015 INFO misc.py line 119 87073] Train: [63/100][1415/1557] Data 0.014 (0.134) Batch 0.891 (1.132) Remain 18:09:08 loss: 0.4160 Lr: 0.00166 [2024-02-19 01:22:33,929 INFO misc.py line 119 87073] Train: [63/100][1416/1557] Data 0.004 (0.134) Batch 0.915 (1.131) Remain 18:08:58 loss: 0.4193 Lr: 0.00166 [2024-02-19 01:22:34,902 INFO misc.py line 119 87073] Train: [63/100][1417/1557] Data 0.003 (0.134) Batch 0.973 (1.131) Remain 18:08:50 loss: 0.3086 Lr: 0.00166 [2024-02-19 01:22:35,813 INFO misc.py line 119 87073] Train: [63/100][1418/1557] Data 0.003 (0.134) Batch 0.906 (1.131) Remain 18:08:40 loss: 0.3458 Lr: 0.00166 [2024-02-19 01:22:36,547 INFO misc.py line 119 87073] Train: [63/100][1419/1557] Data 0.009 (0.134) Batch 0.740 (1.131) Remain 18:08:22 loss: 0.1743 Lr: 0.00166 [2024-02-19 01:22:37,269 INFO misc.py line 119 87073] Train: [63/100][1420/1557] Data 0.003 (0.134) Batch 0.713 (1.131) Remain 18:08:04 loss: 0.2934 Lr: 0.00166 [2024-02-19 01:22:38,568 INFO misc.py line 119 87073] Train: [63/100][1421/1557] Data 0.012 (0.133) Batch 1.296 (1.131) Remain 18:08:10 loss: 0.0929 Lr: 0.00166 [2024-02-19 01:22:39,525 INFO misc.py line 119 87073] Train: [63/100][1422/1557] Data 0.015 (0.133) Batch 0.969 (1.131) Remain 18:08:02 loss: 0.1881 Lr: 0.00166 [2024-02-19 01:22:40,456 INFO misc.py line 119 87073] Train: [63/100][1423/1557] Data 0.003 (0.133) Batch 0.931 (1.130) Remain 18:07:53 loss: 0.3079 Lr: 0.00166 [2024-02-19 01:22:41,359 INFO misc.py line 119 87073] Train: [63/100][1424/1557] Data 0.004 (0.133) Batch 0.902 (1.130) Remain 18:07:43 loss: 0.2905 Lr: 0.00166 [2024-02-19 01:22:42,427 INFO misc.py line 119 87073] Train: [63/100][1425/1557] Data 0.003 (0.133) Batch 1.059 (1.130) Remain 18:07:39 loss: 0.5153 Lr: 0.00166 [2024-02-19 01:22:43,136 INFO misc.py line 119 87073] Train: [63/100][1426/1557] Data 0.012 (0.133) Batch 0.718 (1.130) Remain 18:07:21 loss: 0.2144 Lr: 0.00166 [2024-02-19 01:22:43,911 INFO misc.py line 119 87073] Train: [63/100][1427/1557] Data 0.003 (0.133) Batch 0.763 (1.130) Remain 18:07:05 loss: 0.1833 Lr: 0.00166 [2024-02-19 01:22:45,210 INFO misc.py line 119 87073] Train: [63/100][1428/1557] Data 0.015 (0.133) Batch 1.300 (1.130) Remain 18:07:10 loss: 0.1619 Lr: 0.00166 [2024-02-19 01:22:45,992 INFO misc.py line 119 87073] Train: [63/100][1429/1557] Data 0.014 (0.133) Batch 0.792 (1.130) Remain 18:06:56 loss: 0.2415 Lr: 0.00166 [2024-02-19 01:22:46,965 INFO misc.py line 119 87073] Train: [63/100][1430/1557] Data 0.003 (0.133) Batch 0.973 (1.129) Remain 18:06:48 loss: 0.2030 Lr: 0.00166 [2024-02-19 01:22:47,971 INFO misc.py line 119 87073] Train: [63/100][1431/1557] Data 0.003 (0.133) Batch 1.006 (1.129) Remain 18:06:42 loss: 0.1947 Lr: 0.00166 [2024-02-19 01:22:48,936 INFO misc.py line 119 87073] Train: [63/100][1432/1557] Data 0.005 (0.132) Batch 0.961 (1.129) Remain 18:06:34 loss: 0.1878 Lr: 0.00166 [2024-02-19 01:22:49,699 INFO misc.py line 119 87073] Train: [63/100][1433/1557] Data 0.007 (0.132) Batch 0.767 (1.129) Remain 18:06:18 loss: 0.3422 Lr: 0.00166 [2024-02-19 01:22:50,498 INFO misc.py line 119 87073] Train: [63/100][1434/1557] Data 0.004 (0.132) Batch 0.786 (1.129) Remain 18:06:03 loss: 0.1574 Lr: 0.00166 [2024-02-19 01:22:51,718 INFO misc.py line 119 87073] Train: [63/100][1435/1557] Data 0.017 (0.132) Batch 1.225 (1.129) Remain 18:06:06 loss: 0.1105 Lr: 0.00166 [2024-02-19 01:22:52,680 INFO misc.py line 119 87073] Train: [63/100][1436/1557] Data 0.012 (0.132) Batch 0.971 (1.129) Remain 18:05:59 loss: 0.0965 Lr: 0.00166 [2024-02-19 01:22:53,588 INFO misc.py line 119 87073] Train: [63/100][1437/1557] Data 0.002 (0.132) Batch 0.907 (1.129) Remain 18:05:49 loss: 0.1585 Lr: 0.00166 [2024-02-19 01:22:54,595 INFO misc.py line 119 87073] Train: [63/100][1438/1557] Data 0.003 (0.132) Batch 1.006 (1.128) Remain 18:05:43 loss: 0.3737 Lr: 0.00166 [2024-02-19 01:22:55,509 INFO misc.py line 119 87073] Train: [63/100][1439/1557] Data 0.004 (0.132) Batch 0.909 (1.128) Remain 18:05:33 loss: 0.4628 Lr: 0.00166 [2024-02-19 01:22:56,302 INFO misc.py line 119 87073] Train: [63/100][1440/1557] Data 0.010 (0.132) Batch 0.799 (1.128) Remain 18:05:18 loss: 0.2602 Lr: 0.00166 [2024-02-19 01:22:57,029 INFO misc.py line 119 87073] Train: [63/100][1441/1557] Data 0.004 (0.132) Batch 0.727 (1.128) Remain 18:05:01 loss: 0.2174 Lr: 0.00166 [2024-02-19 01:22:58,267 INFO misc.py line 119 87073] Train: [63/100][1442/1557] Data 0.003 (0.132) Batch 1.229 (1.128) Remain 18:05:04 loss: 0.2580 Lr: 0.00166 [2024-02-19 01:22:59,178 INFO misc.py line 119 87073] Train: [63/100][1443/1557] Data 0.012 (0.131) Batch 0.920 (1.128) Remain 18:04:55 loss: 0.4445 Lr: 0.00166 [2024-02-19 01:23:00,061 INFO misc.py line 119 87073] Train: [63/100][1444/1557] Data 0.003 (0.131) Batch 0.884 (1.128) Remain 18:04:44 loss: 0.1400 Lr: 0.00166 [2024-02-19 01:23:00,838 INFO misc.py line 119 87073] Train: [63/100][1445/1557] Data 0.003 (0.131) Batch 0.773 (1.127) Remain 18:04:28 loss: 0.4062 Lr: 0.00166 [2024-02-19 01:23:01,845 INFO misc.py line 119 87073] Train: [63/100][1446/1557] Data 0.007 (0.131) Batch 1.005 (1.127) Remain 18:04:22 loss: 0.2049 Lr: 0.00166 [2024-02-19 01:23:02,509 INFO misc.py line 119 87073] Train: [63/100][1447/1557] Data 0.009 (0.131) Batch 0.670 (1.127) Remain 18:04:03 loss: 0.2445 Lr: 0.00166 [2024-02-19 01:23:03,251 INFO misc.py line 119 87073] Train: [63/100][1448/1557] Data 0.003 (0.131) Batch 0.732 (1.127) Remain 18:03:46 loss: 0.2588 Lr: 0.00166 [2024-02-19 01:23:04,278 INFO misc.py line 119 87073] Train: [63/100][1449/1557] Data 0.013 (0.131) Batch 1.025 (1.127) Remain 18:03:41 loss: 0.1577 Lr: 0.00166 [2024-02-19 01:23:05,376 INFO misc.py line 119 87073] Train: [63/100][1450/1557] Data 0.015 (0.131) Batch 1.102 (1.127) Remain 18:03:39 loss: 0.2080 Lr: 0.00166 [2024-02-19 01:23:06,457 INFO misc.py line 119 87073] Train: [63/100][1451/1557] Data 0.010 (0.131) Batch 1.083 (1.127) Remain 18:03:36 loss: 0.2196 Lr: 0.00166 [2024-02-19 01:23:07,612 INFO misc.py line 119 87073] Train: [63/100][1452/1557] Data 0.009 (0.131) Batch 1.152 (1.127) Remain 18:03:36 loss: 0.1451 Lr: 0.00166 [2024-02-19 01:23:08,737 INFO misc.py line 119 87073] Train: [63/100][1453/1557] Data 0.011 (0.131) Batch 1.123 (1.127) Remain 18:03:34 loss: 0.2696 Lr: 0.00166 [2024-02-19 01:23:09,470 INFO misc.py line 119 87073] Train: [63/100][1454/1557] Data 0.014 (0.131) Batch 0.744 (1.126) Remain 18:03:18 loss: 0.2596 Lr: 0.00166 [2024-02-19 01:23:10,290 INFO misc.py line 119 87073] Train: [63/100][1455/1557] Data 0.003 (0.130) Batch 0.811 (1.126) Remain 18:03:04 loss: 0.3771 Lr: 0.00166 [2024-02-19 01:23:11,580 INFO misc.py line 119 87073] Train: [63/100][1456/1557] Data 0.012 (0.130) Batch 1.288 (1.126) Remain 18:03:10 loss: 0.1152 Lr: 0.00166 [2024-02-19 01:23:12,490 INFO misc.py line 119 87073] Train: [63/100][1457/1557] Data 0.014 (0.130) Batch 0.920 (1.126) Remain 18:03:01 loss: 0.5937 Lr: 0.00166 [2024-02-19 01:23:13,419 INFO misc.py line 119 87073] Train: [63/100][1458/1557] Data 0.004 (0.130) Batch 0.929 (1.126) Remain 18:02:52 loss: 0.2418 Lr: 0.00166 [2024-02-19 01:23:14,353 INFO misc.py line 119 87073] Train: [63/100][1459/1557] Data 0.003 (0.130) Batch 0.933 (1.126) Remain 18:02:43 loss: 0.3941 Lr: 0.00166 [2024-02-19 01:23:15,382 INFO misc.py line 119 87073] Train: [63/100][1460/1557] Data 0.005 (0.130) Batch 1.024 (1.126) Remain 18:02:38 loss: 0.2033 Lr: 0.00166 [2024-02-19 01:23:16,144 INFO misc.py line 119 87073] Train: [63/100][1461/1557] Data 0.010 (0.130) Batch 0.769 (1.125) Remain 18:02:22 loss: 0.2914 Lr: 0.00166 [2024-02-19 01:23:16,934 INFO misc.py line 119 87073] Train: [63/100][1462/1557] Data 0.003 (0.130) Batch 0.783 (1.125) Remain 18:02:08 loss: 0.0974 Lr: 0.00166 [2024-02-19 01:23:27,928 INFO misc.py line 119 87073] Train: [63/100][1463/1557] Data 6.178 (0.134) Batch 11.001 (1.132) Remain 18:08:37 loss: 0.1759 Lr: 0.00166 [2024-02-19 01:23:28,867 INFO misc.py line 119 87073] Train: [63/100][1464/1557] Data 0.004 (0.134) Batch 0.932 (1.132) Remain 18:08:28 loss: 0.8220 Lr: 0.00166 [2024-02-19 01:23:29,881 INFO misc.py line 119 87073] Train: [63/100][1465/1557] Data 0.010 (0.134) Batch 1.016 (1.132) Remain 18:08:22 loss: 0.2389 Lr: 0.00166 [2024-02-19 01:23:30,931 INFO misc.py line 119 87073] Train: [63/100][1466/1557] Data 0.008 (0.134) Batch 1.051 (1.132) Remain 18:08:18 loss: 0.2296 Lr: 0.00166 [2024-02-19 01:23:31,771 INFO misc.py line 119 87073] Train: [63/100][1467/1557] Data 0.007 (0.134) Batch 0.843 (1.131) Remain 18:08:05 loss: 0.3834 Lr: 0.00166 [2024-02-19 01:23:32,625 INFO misc.py line 119 87073] Train: [63/100][1468/1557] Data 0.004 (0.134) Batch 0.854 (1.131) Remain 18:07:53 loss: 0.3244 Lr: 0.00166 [2024-02-19 01:23:33,342 INFO misc.py line 119 87073] Train: [63/100][1469/1557] Data 0.004 (0.134) Batch 0.715 (1.131) Remain 18:07:36 loss: 0.3218 Lr: 0.00166 [2024-02-19 01:23:34,469 INFO misc.py line 119 87073] Train: [63/100][1470/1557] Data 0.006 (0.133) Batch 1.126 (1.131) Remain 18:07:34 loss: 0.2032 Lr: 0.00166 [2024-02-19 01:23:35,337 INFO misc.py line 119 87073] Train: [63/100][1471/1557] Data 0.007 (0.133) Batch 0.873 (1.131) Remain 18:07:23 loss: 0.1861 Lr: 0.00166 [2024-02-19 01:23:36,307 INFO misc.py line 119 87073] Train: [63/100][1472/1557] Data 0.003 (0.133) Batch 0.969 (1.131) Remain 18:07:16 loss: 0.5102 Lr: 0.00166 [2024-02-19 01:23:37,217 INFO misc.py line 119 87073] Train: [63/100][1473/1557] Data 0.003 (0.133) Batch 0.910 (1.131) Remain 18:07:06 loss: 0.1324 Lr: 0.00166 [2024-02-19 01:23:38,279 INFO misc.py line 119 87073] Train: [63/100][1474/1557] Data 0.005 (0.133) Batch 1.062 (1.131) Remain 18:07:02 loss: 0.2332 Lr: 0.00166 [2024-02-19 01:23:39,046 INFO misc.py line 119 87073] Train: [63/100][1475/1557] Data 0.003 (0.133) Batch 0.764 (1.130) Remain 18:06:47 loss: 0.2037 Lr: 0.00166 [2024-02-19 01:23:39,746 INFO misc.py line 119 87073] Train: [63/100][1476/1557] Data 0.007 (0.133) Batch 0.699 (1.130) Remain 18:06:29 loss: 0.2005 Lr: 0.00166 [2024-02-19 01:23:41,097 INFO misc.py line 119 87073] Train: [63/100][1477/1557] Data 0.006 (0.133) Batch 1.352 (1.130) Remain 18:06:36 loss: 0.1124 Lr: 0.00166 [2024-02-19 01:23:42,084 INFO misc.py line 119 87073] Train: [63/100][1478/1557] Data 0.007 (0.133) Batch 0.986 (1.130) Remain 18:06:29 loss: 0.1416 Lr: 0.00166 [2024-02-19 01:23:42,930 INFO misc.py line 119 87073] Train: [63/100][1479/1557] Data 0.008 (0.133) Batch 0.847 (1.130) Remain 18:06:17 loss: 0.4180 Lr: 0.00166 [2024-02-19 01:23:44,113 INFO misc.py line 119 87073] Train: [63/100][1480/1557] Data 0.006 (0.133) Batch 1.184 (1.130) Remain 18:06:18 loss: 0.3731 Lr: 0.00166 [2024-02-19 01:23:45,065 INFO misc.py line 119 87073] Train: [63/100][1481/1557] Data 0.004 (0.132) Batch 0.952 (1.130) Remain 18:06:10 loss: 0.2852 Lr: 0.00166 [2024-02-19 01:23:45,818 INFO misc.py line 119 87073] Train: [63/100][1482/1557] Data 0.004 (0.132) Batch 0.749 (1.130) Remain 18:05:54 loss: 0.3639 Lr: 0.00166 [2024-02-19 01:23:46,595 INFO misc.py line 119 87073] Train: [63/100][1483/1557] Data 0.009 (0.132) Batch 0.782 (1.129) Remain 18:05:39 loss: 0.1460 Lr: 0.00166 [2024-02-19 01:23:47,813 INFO misc.py line 119 87073] Train: [63/100][1484/1557] Data 0.004 (0.132) Batch 1.218 (1.129) Remain 18:05:42 loss: 0.0969 Lr: 0.00166 [2024-02-19 01:23:48,786 INFO misc.py line 119 87073] Train: [63/100][1485/1557] Data 0.004 (0.132) Batch 0.972 (1.129) Remain 18:05:34 loss: 0.1835 Lr: 0.00166 [2024-02-19 01:23:49,734 INFO misc.py line 119 87073] Train: [63/100][1486/1557] Data 0.005 (0.132) Batch 0.950 (1.129) Remain 18:05:26 loss: 0.1101 Lr: 0.00166 [2024-02-19 01:23:50,678 INFO misc.py line 119 87073] Train: [63/100][1487/1557] Data 0.004 (0.132) Batch 0.937 (1.129) Remain 18:05:18 loss: 0.3703 Lr: 0.00165 [2024-02-19 01:23:51,490 INFO misc.py line 119 87073] Train: [63/100][1488/1557] Data 0.011 (0.132) Batch 0.818 (1.129) Remain 18:05:05 loss: 0.7576 Lr: 0.00165 [2024-02-19 01:23:52,240 INFO misc.py line 119 87073] Train: [63/100][1489/1557] Data 0.005 (0.132) Batch 0.750 (1.129) Remain 18:04:49 loss: 0.3410 Lr: 0.00165 [2024-02-19 01:23:52,954 INFO misc.py line 119 87073] Train: [63/100][1490/1557] Data 0.004 (0.132) Batch 0.709 (1.128) Remain 18:04:31 loss: 0.1993 Lr: 0.00165 [2024-02-19 01:23:54,164 INFO misc.py line 119 87073] Train: [63/100][1491/1557] Data 0.009 (0.132) Batch 1.210 (1.128) Remain 18:04:33 loss: 0.1491 Lr: 0.00165 [2024-02-19 01:23:55,162 INFO misc.py line 119 87073] Train: [63/100][1492/1557] Data 0.010 (0.132) Batch 1.004 (1.128) Remain 18:04:27 loss: 0.4693 Lr: 0.00165 [2024-02-19 01:23:56,139 INFO misc.py line 119 87073] Train: [63/100][1493/1557] Data 0.005 (0.131) Batch 0.978 (1.128) Remain 18:04:20 loss: 0.3383 Lr: 0.00165 [2024-02-19 01:23:57,075 INFO misc.py line 119 87073] Train: [63/100][1494/1557] Data 0.004 (0.131) Batch 0.935 (1.128) Remain 18:04:12 loss: 0.2688 Lr: 0.00165 [2024-02-19 01:23:58,035 INFO misc.py line 119 87073] Train: [63/100][1495/1557] Data 0.005 (0.131) Batch 0.961 (1.128) Remain 18:04:04 loss: 0.3729 Lr: 0.00165 [2024-02-19 01:23:58,940 INFO misc.py line 119 87073] Train: [63/100][1496/1557] Data 0.004 (0.131) Batch 0.904 (1.128) Remain 18:03:54 loss: 0.2459 Lr: 0.00165 [2024-02-19 01:23:59,681 INFO misc.py line 119 87073] Train: [63/100][1497/1557] Data 0.004 (0.131) Batch 0.735 (1.127) Remain 18:03:38 loss: 0.1905 Lr: 0.00165 [2024-02-19 01:24:00,905 INFO misc.py line 119 87073] Train: [63/100][1498/1557] Data 0.011 (0.131) Batch 1.226 (1.128) Remain 18:03:41 loss: 0.2759 Lr: 0.00165 [2024-02-19 01:24:01,921 INFO misc.py line 119 87073] Train: [63/100][1499/1557] Data 0.010 (0.131) Batch 1.014 (1.127) Remain 18:03:35 loss: 0.3789 Lr: 0.00165 [2024-02-19 01:24:03,023 INFO misc.py line 119 87073] Train: [63/100][1500/1557] Data 0.011 (0.131) Batch 1.099 (1.127) Remain 18:03:33 loss: 0.3016 Lr: 0.00165 [2024-02-19 01:24:03,884 INFO misc.py line 119 87073] Train: [63/100][1501/1557] Data 0.013 (0.131) Batch 0.851 (1.127) Remain 18:03:21 loss: 0.3053 Lr: 0.00165 [2024-02-19 01:24:05,009 INFO misc.py line 119 87073] Train: [63/100][1502/1557] Data 0.024 (0.131) Batch 1.145 (1.127) Remain 18:03:21 loss: 0.6257 Lr: 0.00165 [2024-02-19 01:24:05,773 INFO misc.py line 119 87073] Train: [63/100][1503/1557] Data 0.003 (0.131) Batch 0.763 (1.127) Remain 18:03:06 loss: 0.3117 Lr: 0.00165 [2024-02-19 01:24:06,562 INFO misc.py line 119 87073] Train: [63/100][1504/1557] Data 0.003 (0.131) Batch 0.788 (1.127) Remain 18:02:52 loss: 0.4343 Lr: 0.00165 [2024-02-19 01:24:07,539 INFO misc.py line 119 87073] Train: [63/100][1505/1557] Data 0.005 (0.130) Batch 0.979 (1.127) Remain 18:02:45 loss: 0.1666 Lr: 0.00165 [2024-02-19 01:24:08,393 INFO misc.py line 119 87073] Train: [63/100][1506/1557] Data 0.003 (0.130) Batch 0.854 (1.126) Remain 18:02:33 loss: 0.4590 Lr: 0.00165 [2024-02-19 01:24:09,417 INFO misc.py line 119 87073] Train: [63/100][1507/1557] Data 0.003 (0.130) Batch 1.015 (1.126) Remain 18:02:28 loss: 0.6809 Lr: 0.00165 [2024-02-19 01:24:10,451 INFO misc.py line 119 87073] Train: [63/100][1508/1557] Data 0.012 (0.130) Batch 1.036 (1.126) Remain 18:02:23 loss: 0.5184 Lr: 0.00165 [2024-02-19 01:24:11,451 INFO misc.py line 119 87073] Train: [63/100][1509/1557] Data 0.011 (0.130) Batch 1.004 (1.126) Remain 18:02:17 loss: 0.4590 Lr: 0.00165 [2024-02-19 01:24:12,258 INFO misc.py line 119 87073] Train: [63/100][1510/1557] Data 0.007 (0.130) Batch 0.810 (1.126) Remain 18:02:04 loss: 0.2428 Lr: 0.00165 [2024-02-19 01:24:13,041 INFO misc.py line 119 87073] Train: [63/100][1511/1557] Data 0.003 (0.130) Batch 0.784 (1.126) Remain 18:01:50 loss: 0.3414 Lr: 0.00165 [2024-02-19 01:24:14,253 INFO misc.py line 119 87073] Train: [63/100][1512/1557] Data 0.003 (0.130) Batch 1.204 (1.126) Remain 18:01:52 loss: 0.1571 Lr: 0.00165 [2024-02-19 01:24:15,328 INFO misc.py line 119 87073] Train: [63/100][1513/1557] Data 0.011 (0.130) Batch 1.075 (1.126) Remain 18:01:49 loss: 0.2014 Lr: 0.00165 [2024-02-19 01:24:16,280 INFO misc.py line 119 87073] Train: [63/100][1514/1557] Data 0.012 (0.130) Batch 0.960 (1.126) Remain 18:01:41 loss: 0.1979 Lr: 0.00165 [2024-02-19 01:24:17,220 INFO misc.py line 119 87073] Train: [63/100][1515/1557] Data 0.004 (0.130) Batch 0.940 (1.126) Remain 18:01:33 loss: 0.4207 Lr: 0.00165 [2024-02-19 01:24:18,248 INFO misc.py line 119 87073] Train: [63/100][1516/1557] Data 0.003 (0.130) Batch 1.028 (1.126) Remain 18:01:28 loss: 0.3671 Lr: 0.00165 [2024-02-19 01:24:18,976 INFO misc.py line 119 87073] Train: [63/100][1517/1557] Data 0.003 (0.129) Batch 0.725 (1.125) Remain 18:01:12 loss: 0.2825 Lr: 0.00165 [2024-02-19 01:24:19,755 INFO misc.py line 119 87073] Train: [63/100][1518/1557] Data 0.007 (0.129) Batch 0.781 (1.125) Remain 18:00:58 loss: 0.1838 Lr: 0.00165 [2024-02-19 01:24:31,611 INFO misc.py line 119 87073] Train: [63/100][1519/1557] Data 7.300 (0.134) Batch 11.857 (1.132) Remain 18:07:45 loss: 0.1608 Lr: 0.00165 [2024-02-19 01:24:32,620 INFO misc.py line 119 87073] Train: [63/100][1520/1557] Data 0.003 (0.134) Batch 1.009 (1.132) Remain 18:07:39 loss: 0.2696 Lr: 0.00165 [2024-02-19 01:24:33,542 INFO misc.py line 119 87073] Train: [63/100][1521/1557] Data 0.004 (0.134) Batch 0.923 (1.132) Remain 18:07:30 loss: 0.1358 Lr: 0.00165 [2024-02-19 01:24:34,628 INFO misc.py line 119 87073] Train: [63/100][1522/1557] Data 0.003 (0.134) Batch 1.085 (1.132) Remain 18:07:27 loss: 0.2852 Lr: 0.00165 [2024-02-19 01:24:35,691 INFO misc.py line 119 87073] Train: [63/100][1523/1557] Data 0.003 (0.134) Batch 1.062 (1.132) Remain 18:07:23 loss: 0.3383 Lr: 0.00165 [2024-02-19 01:24:36,477 INFO misc.py line 119 87073] Train: [63/100][1524/1557] Data 0.005 (0.134) Batch 0.788 (1.132) Remain 18:07:09 loss: 0.2912 Lr: 0.00165 [2024-02-19 01:24:37,270 INFO misc.py line 119 87073] Train: [63/100][1525/1557] Data 0.003 (0.134) Batch 0.787 (1.131) Remain 18:06:55 loss: 0.2411 Lr: 0.00165 [2024-02-19 01:24:38,359 INFO misc.py line 119 87073] Train: [63/100][1526/1557] Data 0.009 (0.134) Batch 1.091 (1.131) Remain 18:06:52 loss: 0.2168 Lr: 0.00165 [2024-02-19 01:24:39,364 INFO misc.py line 119 87073] Train: [63/100][1527/1557] Data 0.008 (0.133) Batch 1.006 (1.131) Remain 18:06:46 loss: 0.2299 Lr: 0.00165 [2024-02-19 01:24:40,175 INFO misc.py line 119 87073] Train: [63/100][1528/1557] Data 0.007 (0.133) Batch 0.812 (1.131) Remain 18:06:33 loss: 0.7362 Lr: 0.00165 [2024-02-19 01:24:41,310 INFO misc.py line 119 87073] Train: [63/100][1529/1557] Data 0.005 (0.133) Batch 1.136 (1.131) Remain 18:06:32 loss: 0.3978 Lr: 0.00165 [2024-02-19 01:24:42,252 INFO misc.py line 119 87073] Train: [63/100][1530/1557] Data 0.004 (0.133) Batch 0.943 (1.131) Remain 18:06:24 loss: 0.3462 Lr: 0.00165 [2024-02-19 01:24:42,982 INFO misc.py line 119 87073] Train: [63/100][1531/1557] Data 0.004 (0.133) Batch 0.725 (1.131) Remain 18:06:07 loss: 0.1265 Lr: 0.00165 [2024-02-19 01:24:43,755 INFO misc.py line 119 87073] Train: [63/100][1532/1557] Data 0.009 (0.133) Batch 0.777 (1.130) Remain 18:05:53 loss: 0.2483 Lr: 0.00165 [2024-02-19 01:24:44,995 INFO misc.py line 119 87073] Train: [63/100][1533/1557] Data 0.004 (0.133) Batch 1.240 (1.131) Remain 18:05:56 loss: 0.1140 Lr: 0.00165 [2024-02-19 01:24:45,810 INFO misc.py line 119 87073] Train: [63/100][1534/1557] Data 0.004 (0.133) Batch 0.816 (1.130) Remain 18:05:43 loss: 0.2819 Lr: 0.00165 [2024-02-19 01:24:46,727 INFO misc.py line 119 87073] Train: [63/100][1535/1557] Data 0.003 (0.133) Batch 0.915 (1.130) Remain 18:05:34 loss: 0.3051 Lr: 0.00165 [2024-02-19 01:24:47,814 INFO misc.py line 119 87073] Train: [63/100][1536/1557] Data 0.006 (0.133) Batch 1.081 (1.130) Remain 18:05:31 loss: 0.1055 Lr: 0.00165 [2024-02-19 01:24:48,664 INFO misc.py line 119 87073] Train: [63/100][1537/1557] Data 0.013 (0.133) Batch 0.858 (1.130) Remain 18:05:19 loss: 0.2345 Lr: 0.00165 [2024-02-19 01:24:49,320 INFO misc.py line 119 87073] Train: [63/100][1538/1557] Data 0.003 (0.133) Batch 0.657 (1.130) Remain 18:05:00 loss: 0.2619 Lr: 0.00165 [2024-02-19 01:24:50,062 INFO misc.py line 119 87073] Train: [63/100][1539/1557] Data 0.003 (0.132) Batch 0.738 (1.129) Remain 18:04:45 loss: 0.1311 Lr: 0.00165 [2024-02-19 01:24:51,256 INFO misc.py line 119 87073] Train: [63/100][1540/1557] Data 0.007 (0.132) Batch 1.190 (1.129) Remain 18:04:46 loss: 0.2544 Lr: 0.00165 [2024-02-19 01:24:52,187 INFO misc.py line 119 87073] Train: [63/100][1541/1557] Data 0.011 (0.132) Batch 0.939 (1.129) Remain 18:04:37 loss: 0.2450 Lr: 0.00165 [2024-02-19 01:24:53,165 INFO misc.py line 119 87073] Train: [63/100][1542/1557] Data 0.003 (0.132) Batch 0.977 (1.129) Remain 18:04:31 loss: 0.2218 Lr: 0.00165 [2024-02-19 01:24:54,100 INFO misc.py line 119 87073] Train: [63/100][1543/1557] Data 0.003 (0.132) Batch 0.935 (1.129) Remain 18:04:22 loss: 0.1502 Lr: 0.00165 [2024-02-19 01:24:55,135 INFO misc.py line 119 87073] Train: [63/100][1544/1557] Data 0.003 (0.132) Batch 1.035 (1.129) Remain 18:04:18 loss: 0.2738 Lr: 0.00165 [2024-02-19 01:24:55,913 INFO misc.py line 119 87073] Train: [63/100][1545/1557] Data 0.003 (0.132) Batch 0.774 (1.129) Remain 18:04:03 loss: 0.2200 Lr: 0.00165 [2024-02-19 01:24:56,630 INFO misc.py line 119 87073] Train: [63/100][1546/1557] Data 0.007 (0.132) Batch 0.721 (1.129) Remain 18:03:47 loss: 0.3489 Lr: 0.00165 [2024-02-19 01:24:57,864 INFO misc.py line 119 87073] Train: [63/100][1547/1557] Data 0.004 (0.132) Batch 1.234 (1.129) Remain 18:03:50 loss: 0.1461 Lr: 0.00165 [2024-02-19 01:24:58,774 INFO misc.py line 119 87073] Train: [63/100][1548/1557] Data 0.004 (0.132) Batch 0.911 (1.128) Remain 18:03:40 loss: 0.2551 Lr: 0.00165 [2024-02-19 01:24:59,801 INFO misc.py line 119 87073] Train: [63/100][1549/1557] Data 0.003 (0.132) Batch 1.027 (1.128) Remain 18:03:35 loss: 0.3565 Lr: 0.00165 [2024-02-19 01:25:00,780 INFO misc.py line 119 87073] Train: [63/100][1550/1557] Data 0.003 (0.132) Batch 0.979 (1.128) Remain 18:03:29 loss: 0.1373 Lr: 0.00165 [2024-02-19 01:25:01,751 INFO misc.py line 119 87073] Train: [63/100][1551/1557] Data 0.003 (0.131) Batch 0.970 (1.128) Remain 18:03:22 loss: 0.5597 Lr: 0.00165 [2024-02-19 01:25:02,500 INFO misc.py line 119 87073] Train: [63/100][1552/1557] Data 0.003 (0.131) Batch 0.738 (1.128) Remain 18:03:06 loss: 0.1378 Lr: 0.00165 [2024-02-19 01:25:03,289 INFO misc.py line 119 87073] Train: [63/100][1553/1557] Data 0.014 (0.131) Batch 0.801 (1.128) Remain 18:02:53 loss: 0.2695 Lr: 0.00165 [2024-02-19 01:25:04,573 INFO misc.py line 119 87073] Train: [63/100][1554/1557] Data 0.003 (0.131) Batch 1.272 (1.128) Remain 18:02:57 loss: 0.2480 Lr: 0.00165 [2024-02-19 01:25:05,549 INFO misc.py line 119 87073] Train: [63/100][1555/1557] Data 0.014 (0.131) Batch 0.987 (1.128) Remain 18:02:51 loss: 0.3434 Lr: 0.00165 [2024-02-19 01:25:06,468 INFO misc.py line 119 87073] Train: [63/100][1556/1557] Data 0.003 (0.131) Batch 0.920 (1.128) Remain 18:02:42 loss: 0.2030 Lr: 0.00165 [2024-02-19 01:25:07,301 INFO misc.py line 119 87073] Train: [63/100][1557/1557] Data 0.003 (0.131) Batch 0.825 (1.127) Remain 18:02:30 loss: 0.2126 Lr: 0.00165 [2024-02-19 01:25:07,302 INFO misc.py line 136 87073] Train result: loss: 0.2985 [2024-02-19 01:25:07,302 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 01:25:35,364 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6752 [2024-02-19 01:25:36,142 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5862 [2024-02-19 01:25:38,269 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.2884 [2024-02-19 01:25:40,476 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3113 [2024-02-19 01:25:45,427 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2444 [2024-02-19 01:25:46,134 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1264 [2024-02-19 01:25:47,396 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2781 [2024-02-19 01:25:50,353 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3280 [2024-02-19 01:25:52,160 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2357 [2024-02-19 01:25:53,779 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7038/0.7614/0.9120. [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9339/0.9672 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9890 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8583/0.9747 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3404/0.3606 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6524/0.6766 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7949/0.9342 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8421/0.9113 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9136/0.9724 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7876/0.8151 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7894/0.8744 [2024-02-19 01:25:53,779 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6546/0.7348 [2024-02-19 01:25:53,780 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5988/0.6875 [2024-02-19 01:25:53,780 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 01:25:53,784 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 01:25:53,784 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 01:26:01,334 INFO misc.py line 119 87073] Train: [64/100][1/1557] Data 1.345 (1.345) Batch 2.145 (2.145) Remain 34:19:12 loss: 0.2953 Lr: 0.00165 [2024-02-19 01:26:02,362 INFO misc.py line 119 87073] Train: [64/100][2/1557] Data 0.007 (0.007) Batch 1.026 (1.026) Remain 16:25:00 loss: 0.2877 Lr: 0.00165 [2024-02-19 01:26:03,238 INFO misc.py line 119 87073] Train: [64/100][3/1557] Data 0.009 (0.009) Batch 0.881 (0.881) Remain 14:06:18 loss: 0.2255 Lr: 0.00165 [2024-02-19 01:26:04,230 INFO misc.py line 119 87073] Train: [64/100][4/1557] Data 0.004 (0.004) Batch 0.992 (0.992) Remain 15:52:18 loss: 0.2184 Lr: 0.00165 [2024-02-19 01:26:05,010 INFO misc.py line 119 87073] Train: [64/100][5/1557] Data 0.004 (0.004) Batch 0.779 (0.886) Remain 14:10:14 loss: 0.1693 Lr: 0.00165 [2024-02-19 01:26:05,819 INFO misc.py line 119 87073] Train: [64/100][6/1557] Data 0.005 (0.004) Batch 0.805 (0.859) Remain 13:44:24 loss: 0.3638 Lr: 0.00165 [2024-02-19 01:26:09,861 INFO misc.py line 119 87073] Train: [64/100][7/1557] Data 0.008 (0.005) Batch 4.047 (1.656) Remain 26:29:36 loss: 0.1576 Lr: 0.00165 [2024-02-19 01:26:10,950 INFO misc.py line 119 87073] Train: [64/100][8/1557] Data 0.004 (0.005) Batch 1.090 (1.543) Remain 24:40:51 loss: 0.3542 Lr: 0.00165 [2024-02-19 01:26:11,811 INFO misc.py line 119 87073] Train: [64/100][9/1557] Data 0.004 (0.005) Batch 0.861 (1.429) Remain 22:51:46 loss: 0.2790 Lr: 0.00165 [2024-02-19 01:26:12,676 INFO misc.py line 119 87073] Train: [64/100][10/1557] Data 0.003 (0.004) Batch 0.842 (1.345) Remain 21:31:14 loss: 0.5225 Lr: 0.00165 [2024-02-19 01:26:13,729 INFO misc.py line 119 87073] Train: [64/100][11/1557] Data 0.026 (0.007) Batch 1.068 (1.310) Remain 20:58:00 loss: 0.3053 Lr: 0.00165 [2024-02-19 01:26:14,482 INFO misc.py line 119 87073] Train: [64/100][12/1557] Data 0.011 (0.007) Batch 0.760 (1.249) Remain 19:59:17 loss: 0.1815 Lr: 0.00165 [2024-02-19 01:26:15,225 INFO misc.py line 119 87073] Train: [64/100][13/1557] Data 0.003 (0.007) Batch 0.735 (1.198) Remain 19:09:53 loss: 0.3956 Lr: 0.00165 [2024-02-19 01:26:16,291 INFO misc.py line 119 87073] Train: [64/100][14/1557] Data 0.011 (0.007) Batch 1.066 (1.186) Remain 18:58:21 loss: 0.2045 Lr: 0.00165 [2024-02-19 01:26:17,071 INFO misc.py line 119 87073] Train: [64/100][15/1557] Data 0.012 (0.008) Batch 0.788 (1.153) Remain 18:26:30 loss: 0.2932 Lr: 0.00165 [2024-02-19 01:26:18,152 INFO misc.py line 119 87073] Train: [64/100][16/1557] Data 0.003 (0.007) Batch 1.082 (1.147) Remain 18:21:13 loss: 0.1562 Lr: 0.00165 [2024-02-19 01:26:18,977 INFO misc.py line 119 87073] Train: [64/100][17/1557] Data 0.003 (0.007) Batch 0.825 (1.124) Remain 17:59:06 loss: 0.3712 Lr: 0.00165 [2024-02-19 01:26:19,985 INFO misc.py line 119 87073] Train: [64/100][18/1557] Data 0.004 (0.007) Batch 1.001 (1.116) Remain 17:51:12 loss: 0.3228 Lr: 0.00165 [2024-02-19 01:26:20,752 INFO misc.py line 119 87073] Train: [64/100][19/1557] Data 0.010 (0.007) Batch 0.774 (1.095) Remain 17:30:38 loss: 0.1034 Lr: 0.00165 [2024-02-19 01:26:21,501 INFO misc.py line 119 87073] Train: [64/100][20/1557] Data 0.004 (0.007) Batch 0.739 (1.074) Remain 17:10:31 loss: 0.2480 Lr: 0.00165 [2024-02-19 01:26:22,748 INFO misc.py line 119 87073] Train: [64/100][21/1557] Data 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Remain 17:54:15 loss: 0.2986 Lr: 0.00158 [2024-02-19 01:54:29,514 INFO misc.py line 119 87073] Train: [64/100][1489/1557] Data 0.003 (0.068) Batch 0.744 (1.148) Remain 17:53:58 loss: 0.1936 Lr: 0.00158 [2024-02-19 01:54:30,254 INFO misc.py line 119 87073] Train: [64/100][1490/1557] Data 0.006 (0.068) Batch 0.736 (1.148) Remain 17:53:41 loss: 0.3968 Lr: 0.00158 [2024-02-19 01:54:31,594 INFO misc.py line 119 87073] Train: [64/100][1491/1557] Data 0.010 (0.068) Batch 1.337 (1.148) Remain 17:53:47 loss: 0.1362 Lr: 0.00158 [2024-02-19 01:54:32,433 INFO misc.py line 119 87073] Train: [64/100][1492/1557] Data 0.013 (0.068) Batch 0.849 (1.148) Remain 17:53:35 loss: 0.3023 Lr: 0.00158 [2024-02-19 01:54:33,332 INFO misc.py line 119 87073] Train: [64/100][1493/1557] Data 0.003 (0.068) Batch 0.899 (1.148) Remain 17:53:25 loss: 0.3479 Lr: 0.00158 [2024-02-19 01:54:34,132 INFO misc.py line 119 87073] Train: [64/100][1494/1557] Data 0.003 (0.068) Batch 0.790 (1.147) Remain 17:53:10 loss: 0.3804 Lr: 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INFO misc.py line 119 87073] Train: [64/100][1501/1557] Data 0.015 (0.068) Batch 0.849 (1.146) Remain 17:52:07 loss: 0.4550 Lr: 0.00158 [2024-02-19 01:54:41,575 INFO misc.py line 119 87073] Train: [64/100][1502/1557] Data 0.005 (0.068) Batch 0.893 (1.146) Remain 17:51:56 loss: 0.1033 Lr: 0.00158 [2024-02-19 01:54:42,335 INFO misc.py line 119 87073] Train: [64/100][1503/1557] Data 0.003 (0.068) Batch 0.751 (1.146) Remain 17:51:40 loss: 0.1720 Lr: 0.00158 [2024-02-19 01:54:43,020 INFO misc.py line 119 87073] Train: [64/100][1504/1557] Data 0.013 (0.068) Batch 0.694 (1.146) Remain 17:51:22 loss: 0.2891 Lr: 0.00158 [2024-02-19 01:54:44,171 INFO misc.py line 119 87073] Train: [64/100][1505/1557] Data 0.003 (0.068) Batch 1.142 (1.146) Remain 17:51:21 loss: 0.3932 Lr: 0.00158 [2024-02-19 01:54:45,312 INFO misc.py line 119 87073] Train: [64/100][1506/1557] Data 0.013 (0.068) Batch 1.139 (1.146) Remain 17:51:19 loss: 0.1545 Lr: 0.00158 [2024-02-19 01:54:46,172 INFO misc.py line 119 87073] Train: [64/100][1507/1557] Data 0.015 (0.068) Batch 0.871 (1.146) Remain 17:51:08 loss: 0.3394 Lr: 0.00158 [2024-02-19 01:54:47,159 INFO misc.py line 119 87073] Train: [64/100][1508/1557] Data 0.004 (0.068) Batch 0.988 (1.145) Remain 17:51:01 loss: 0.8202 Lr: 0.00158 [2024-02-19 01:54:48,319 INFO misc.py line 119 87073] Train: [64/100][1509/1557] Data 0.003 (0.068) Batch 1.160 (1.145) Remain 17:51:00 loss: 0.5909 Lr: 0.00158 [2024-02-19 01:54:49,106 INFO misc.py line 119 87073] Train: [64/100][1510/1557] Data 0.003 (0.068) Batch 0.788 (1.145) Remain 17:50:46 loss: 0.2729 Lr: 0.00158 [2024-02-19 01:54:49,889 INFO misc.py line 119 87073] Train: [64/100][1511/1557] Data 0.003 (0.068) Batch 0.779 (1.145) Remain 17:50:31 loss: 0.2120 Lr: 0.00158 [2024-02-19 01:54:50,979 INFO misc.py line 119 87073] Train: [64/100][1512/1557] Data 0.006 (0.068) Batch 1.091 (1.145) Remain 17:50:28 loss: 0.1366 Lr: 0.00158 [2024-02-19 01:54:51,879 INFO misc.py line 119 87073] Train: [64/100][1513/1557] Data 0.005 (0.067) Batch 0.902 (1.145) Remain 17:50:18 loss: 0.2862 Lr: 0.00158 [2024-02-19 01:54:52,709 INFO misc.py line 119 87073] Train: [64/100][1514/1557] Data 0.003 (0.067) Batch 0.829 (1.145) Remain 17:50:05 loss: 0.3201 Lr: 0.00158 [2024-02-19 01:54:53,597 INFO misc.py line 119 87073] Train: [64/100][1515/1557] Data 0.004 (0.067) Batch 0.881 (1.144) Remain 17:49:54 loss: 0.4832 Lr: 0.00158 [2024-02-19 01:54:54,536 INFO misc.py line 119 87073] Train: [64/100][1516/1557] Data 0.011 (0.067) Batch 0.947 (1.144) Remain 17:49:46 loss: 0.3238 Lr: 0.00158 [2024-02-19 01:54:55,310 INFO misc.py line 119 87073] Train: [64/100][1517/1557] Data 0.003 (0.067) Batch 0.773 (1.144) Remain 17:49:31 loss: 0.2319 Lr: 0.00158 [2024-02-19 01:54:56,164 INFO misc.py line 119 87073] Train: [64/100][1518/1557] Data 0.003 (0.067) Batch 0.854 (1.144) Remain 17:49:19 loss: 0.2349 Lr: 0.00158 [2024-02-19 01:55:07,659 INFO misc.py line 119 87073] Train: [64/100][1519/1557] Data 2.954 (0.069) Batch 11.489 (1.151) Remain 17:55:40 loss: 0.1660 Lr: 0.00158 [2024-02-19 01:55:08,550 INFO misc.py line 119 87073] Train: [64/100][1520/1557] Data 0.010 (0.069) Batch 0.898 (1.151) Remain 17:55:30 loss: 0.1670 Lr: 0.00158 [2024-02-19 01:55:09,428 INFO misc.py line 119 87073] Train: [64/100][1521/1557] Data 0.002 (0.069) Batch 0.877 (1.150) Remain 17:55:19 loss: 0.2413 Lr: 0.00158 [2024-02-19 01:55:10,323 INFO misc.py line 119 87073] Train: [64/100][1522/1557] Data 0.004 (0.069) Batch 0.890 (1.150) Remain 17:55:08 loss: 0.4342 Lr: 0.00158 [2024-02-19 01:55:11,362 INFO misc.py line 119 87073] Train: [64/100][1523/1557] Data 0.009 (0.069) Batch 1.036 (1.150) Remain 17:55:03 loss: 0.2935 Lr: 0.00158 [2024-02-19 01:55:12,268 INFO misc.py line 119 87073] Train: [64/100][1524/1557] Data 0.011 (0.069) Batch 0.913 (1.150) Remain 17:54:53 loss: 0.2036 Lr: 0.00158 [2024-02-19 01:55:13,003 INFO misc.py line 119 87073] Train: [64/100][1525/1557] Data 0.005 (0.069) Batch 0.736 (1.150) Remain 17:54:36 loss: 0.3547 Lr: 0.00158 [2024-02-19 01:55:14,105 INFO misc.py line 119 87073] Train: [64/100][1526/1557] Data 0.003 (0.069) Batch 1.089 (1.150) Remain 17:54:33 loss: 0.2663 Lr: 0.00158 [2024-02-19 01:55:14,845 INFO misc.py line 119 87073] Train: [64/100][1527/1557] Data 0.015 (0.069) Batch 0.752 (1.149) Remain 17:54:17 loss: 0.2581 Lr: 0.00158 [2024-02-19 01:55:15,831 INFO misc.py line 119 87073] Train: [64/100][1528/1557] Data 0.003 (0.069) Batch 0.986 (1.149) Remain 17:54:10 loss: 0.3429 Lr: 0.00158 [2024-02-19 01:55:16,838 INFO misc.py line 119 87073] Train: [64/100][1529/1557] Data 0.003 (0.069) Batch 1.002 (1.149) Remain 17:54:03 loss: 0.0912 Lr: 0.00158 [2024-02-19 01:55:17,848 INFO misc.py line 119 87073] Train: [64/100][1530/1557] Data 0.008 (0.069) Batch 1.008 (1.149) Remain 17:53:57 loss: 0.1489 Lr: 0.00158 [2024-02-19 01:55:18,590 INFO misc.py line 119 87073] Train: [64/100][1531/1557] Data 0.009 (0.069) Batch 0.749 (1.149) Remain 17:53:41 loss: 0.1647 Lr: 0.00158 [2024-02-19 01:55:19,340 INFO misc.py line 119 87073] Train: [64/100][1532/1557] Data 0.003 (0.069) Batch 0.749 (1.149) Remain 17:53:25 loss: 0.1015 Lr: 0.00158 [2024-02-19 01:55:20,578 INFO misc.py line 119 87073] Train: [64/100][1533/1557] Data 0.003 (0.069) Batch 1.235 (1.149) Remain 17:53:28 loss: 0.1733 Lr: 0.00158 [2024-02-19 01:55:21,621 INFO misc.py line 119 87073] Train: [64/100][1534/1557] Data 0.006 (0.069) Batch 1.037 (1.149) Remain 17:53:22 loss: 0.2755 Lr: 0.00158 [2024-02-19 01:55:22,539 INFO misc.py line 119 87073] Train: [64/100][1535/1557] Data 0.012 (0.069) Batch 0.927 (1.148) Remain 17:53:13 loss: 0.2372 Lr: 0.00158 [2024-02-19 01:55:23,469 INFO misc.py line 119 87073] Train: [64/100][1536/1557] Data 0.003 (0.068) Batch 0.930 (1.148) Remain 17:53:04 loss: 0.5325 Lr: 0.00158 [2024-02-19 01:55:24,405 INFO misc.py line 119 87073] Train: [64/100][1537/1557] Data 0.003 (0.068) Batch 0.935 (1.148) Remain 17:52:55 loss: 0.2223 Lr: 0.00158 [2024-02-19 01:55:25,178 INFO misc.py line 119 87073] Train: [64/100][1538/1557] Data 0.003 (0.068) Batch 0.759 (1.148) Remain 17:52:40 loss: 0.1252 Lr: 0.00158 [2024-02-19 01:55:25,920 INFO misc.py line 119 87073] Train: [64/100][1539/1557] Data 0.017 (0.068) Batch 0.756 (1.148) Remain 17:52:24 loss: 0.1911 Lr: 0.00158 [2024-02-19 01:55:27,087 INFO misc.py line 119 87073] Train: [64/100][1540/1557] Data 0.003 (0.068) Batch 1.167 (1.148) Remain 17:52:24 loss: 0.1156 Lr: 0.00158 [2024-02-19 01:55:28,011 INFO misc.py line 119 87073] Train: [64/100][1541/1557] Data 0.003 (0.068) Batch 0.925 (1.147) Remain 17:52:14 loss: 0.3521 Lr: 0.00158 [2024-02-19 01:55:28,943 INFO misc.py line 119 87073] Train: [64/100][1542/1557] Data 0.003 (0.068) Batch 0.923 (1.147) Remain 17:52:05 loss: 0.2398 Lr: 0.00158 [2024-02-19 01:55:29,953 INFO misc.py line 119 87073] Train: [64/100][1543/1557] Data 0.012 (0.068) Batch 1.009 (1.147) Remain 17:51:59 loss: 0.1557 Lr: 0.00157 [2024-02-19 01:55:30,935 INFO misc.py line 119 87073] Train: [64/100][1544/1557] Data 0.013 (0.068) Batch 0.992 (1.147) Remain 17:51:52 loss: 0.3135 Lr: 0.00157 [2024-02-19 01:55:31,687 INFO misc.py line 119 87073] Train: [64/100][1545/1557] Data 0.003 (0.068) Batch 0.752 (1.147) Remain 17:51:37 loss: 0.0939 Lr: 0.00157 [2024-02-19 01:55:32,440 INFO misc.py line 119 87073] Train: [64/100][1546/1557] Data 0.003 (0.068) Batch 0.743 (1.147) Remain 17:51:21 loss: 0.3269 Lr: 0.00157 [2024-02-19 01:55:33,672 INFO misc.py line 119 87073] Train: [64/100][1547/1557] Data 0.012 (0.068) Batch 1.232 (1.147) Remain 17:51:23 loss: 0.1317 Lr: 0.00157 [2024-02-19 01:55:34,653 INFO misc.py line 119 87073] Train: [64/100][1548/1557] Data 0.013 (0.068) Batch 0.990 (1.147) Remain 17:51:16 loss: 0.2621 Lr: 0.00157 [2024-02-19 01:55:35,654 INFO misc.py line 119 87073] Train: [64/100][1549/1557] Data 0.003 (0.068) Batch 1.001 (1.146) Remain 17:51:10 loss: 0.3300 Lr: 0.00157 [2024-02-19 01:55:36,644 INFO misc.py line 119 87073] Train: [64/100][1550/1557] Data 0.003 (0.068) Batch 0.990 (1.146) Remain 17:51:03 loss: 0.4674 Lr: 0.00157 [2024-02-19 01:55:37,500 INFO misc.py line 119 87073] Train: [64/100][1551/1557] Data 0.003 (0.068) Batch 0.856 (1.146) Remain 17:50:51 loss: 0.2838 Lr: 0.00157 [2024-02-19 01:55:38,225 INFO misc.py line 119 87073] Train: [64/100][1552/1557] Data 0.003 (0.068) Batch 0.715 (1.146) Remain 17:50:34 loss: 0.2533 Lr: 0.00157 [2024-02-19 01:55:38,871 INFO misc.py line 119 87073] Train: [64/100][1553/1557] Data 0.014 (0.068) Batch 0.657 (1.146) Remain 17:50:15 loss: 0.2756 Lr: 0.00157 [2024-02-19 01:55:40,070 INFO misc.py line 119 87073] Train: [64/100][1554/1557] Data 0.003 (0.068) Batch 1.187 (1.146) Remain 17:50:16 loss: 0.1398 Lr: 0.00157 [2024-02-19 01:55:40,953 INFO misc.py line 119 87073] Train: [64/100][1555/1557] Data 0.015 (0.068) Batch 0.895 (1.145) Remain 17:50:06 loss: 0.3967 Lr: 0.00157 [2024-02-19 01:55:42,084 INFO misc.py line 119 87073] Train: [64/100][1556/1557] Data 0.003 (0.068) Batch 1.131 (1.145) Remain 17:50:04 loss: 0.3235 Lr: 0.00157 [2024-02-19 01:55:43,098 INFO misc.py line 119 87073] Train: [64/100][1557/1557] Data 0.003 (0.068) Batch 1.014 (1.145) Remain 17:49:58 loss: 0.1184 Lr: 0.00157 [2024-02-19 01:55:43,099 INFO misc.py line 136 87073] Train result: loss: 0.2842 [2024-02-19 01:55:43,099 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 01:56:08,363 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6904 [2024-02-19 01:56:09,152 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8177 [2024-02-19 01:56:11,275 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3544 [2024-02-19 01:56:13,482 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3171 [2024-02-19 01:56:18,434 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2939 [2024-02-19 01:56:19,136 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1171 [2024-02-19 01:56:20,400 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2664 [2024-02-19 01:56:23,355 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4050 [2024-02-19 01:56:25,163 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2647 [2024-02-19 01:56:26,850 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7140/0.7716/0.9135. [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9222/0.9508 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9824/0.9881 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8559/0.9736 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2843/0.2934 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6584/0.6904 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8018/0.9478 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8391/0.9244 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9085/0.9609 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8169/0.8684 [2024-02-19 01:56:26,850 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7977/0.8876 [2024-02-19 01:56:26,851 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7880/0.8277 [2024-02-19 01:56:26,851 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6265/0.7175 [2024-02-19 01:56:26,851 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 01:56:26,852 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 01:56:26,852 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 01:56:38,122 INFO misc.py line 119 87073] Train: [65/100][1/1557] Data 1.162 (1.162) Batch 1.915 (1.915) Remain 29:48:50 loss: 0.2590 Lr: 0.00157 [2024-02-19 01:56:39,244 INFO misc.py line 119 87073] Train: [65/100][2/1557] Data 0.005 (0.005) Batch 1.122 (1.122) Remain 17:28:05 loss: 0.3375 Lr: 0.00157 [2024-02-19 01:56:40,174 INFO misc.py line 119 87073] Train: [65/100][3/1557] Data 0.004 (0.004) Batch 0.931 (0.931) Remain 14:29:21 loss: 0.5146 Lr: 0.00157 [2024-02-19 01:56:41,215 INFO misc.py line 119 87073] Train: [65/100][4/1557] Data 0.003 (0.003) Batch 1.041 (1.041) Remain 16:12:14 loss: 0.2310 Lr: 0.00157 [2024-02-19 01:56:41,992 INFO misc.py line 119 87073] Train: [65/100][5/1557] Data 0.003 (0.003) Batch 0.777 (0.909) Remain 14:08:59 loss: 0.2114 Lr: 0.00157 [2024-02-19 01:56:42,730 INFO misc.py line 119 87073] Train: [65/100][6/1557] Data 0.003 (0.003) Batch 0.731 (0.850) Remain 13:13:44 loss: 0.1799 Lr: 0.00157 [2024-02-19 01:56:43,987 INFO misc.py line 119 87073] Train: [65/100][7/1557] Data 0.009 (0.005) Batch 1.264 (0.953) Remain 14:50:29 loss: 0.2102 Lr: 0.00157 [2024-02-19 01:56:44,951 INFO misc.py line 119 87073] Train: [65/100][8/1557] Data 0.003 (0.004) Batch 0.964 (0.955) Remain 14:52:28 loss: 0.1827 Lr: 0.00157 [2024-02-19 01:56:46,103 INFO misc.py line 119 87073] Train: [65/100][9/1557] Data 0.003 (0.004) Batch 1.151 (0.988) Remain 15:22:54 loss: 0.1525 Lr: 0.00157 [2024-02-19 01:56:46,977 INFO misc.py line 119 87073] Train: [65/100][10/1557] Data 0.003 (0.004) Batch 0.874 (0.972) Remain 15:07:40 loss: 0.4388 Lr: 0.00157 [2024-02-19 01:56:47,968 INFO misc.py line 119 87073] Train: [65/100][11/1557] Data 0.003 (0.004) Batch 0.982 (0.973) Remain 15:08:50 loss: 0.0860 Lr: 0.00157 [2024-02-19 01:56:48,741 INFO misc.py line 119 87073] Train: [65/100][12/1557] Data 0.013 (0.005) Batch 0.782 (0.952) Remain 14:48:59 loss: 0.1793 Lr: 0.00157 [2024-02-19 01:56:49,444 INFO misc.py line 119 87073] Train: [65/100][13/1557] Data 0.004 (0.005) Batch 0.696 (0.926) Remain 14:25:08 loss: 0.3961 Lr: 0.00157 [2024-02-19 01:56:50,747 INFO misc.py line 119 87073] Train: [65/100][14/1557] Data 0.010 (0.005) Batch 1.301 (0.960) Remain 14:56:55 loss: 0.1254 Lr: 0.00157 [2024-02-19 01:56:51,868 INFO misc.py line 119 87073] Train: [65/100][15/1557] Data 0.013 (0.006) Batch 1.125 (0.974) Remain 15:09:42 loss: 0.5011 Lr: 0.00157 [2024-02-19 01:56:52,741 INFO misc.py line 119 87073] Train: [65/100][16/1557] Data 0.009 (0.006) Batch 0.879 (0.967) Remain 15:02:50 loss: 0.1882 Lr: 0.00157 [2024-02-19 01:56:53,617 INFO misc.py line 119 87073] Train: [65/100][17/1557] Data 0.003 (0.006) Batch 0.870 (0.960) Remain 14:56:22 loss: 0.3159 Lr: 0.00157 [2024-02-19 01:56:54,573 INFO misc.py line 119 87073] Train: [65/100][18/1557] Data 0.008 (0.006) Batch 0.958 (0.960) Remain 14:56:14 loss: 0.1992 Lr: 0.00157 [2024-02-19 01:56:55,332 INFO misc.py line 119 87073] Train: [65/100][19/1557] Data 0.006 (0.006) Batch 0.763 (0.947) Remain 14:44:45 loss: 0.1877 Lr: 0.00157 [2024-02-19 01:56:56,028 INFO misc.py line 119 87073] Train: [65/100][20/1557] Data 0.003 (0.006) Batch 0.689 (0.932) Remain 14:30:32 loss: 0.2734 Lr: 0.00157 [2024-02-19 01:56:57,144 INFO misc.py line 119 87073] Train: [65/100][21/1557] Data 0.010 (0.006) Batch 1.120 (0.943) Remain 14:40:16 loss: 0.0741 Lr: 0.00157 [2024-02-19 01:56:58,110 INFO misc.py line 119 87073] Train: [65/100][22/1557] Data 0.006 (0.006) Batch 0.968 (0.944) Remain 14:41:30 loss: 0.1714 Lr: 0.00157 [2024-02-19 01:56:59,017 INFO misc.py line 119 87073] Train: [65/100][23/1557] Data 0.003 (0.006) Batch 0.908 (0.942) Remain 14:39:48 loss: 0.3105 Lr: 0.00157 [2024-02-19 01:56:59,940 INFO misc.py line 119 87073] Train: [65/100][24/1557] Data 0.003 (0.006) Batch 0.915 (0.941) Remain 14:38:35 loss: 0.1176 Lr: 0.00157 [2024-02-19 01:57:00,795 INFO misc.py line 119 87073] Train: [65/100][25/1557] Data 0.010 (0.006) Batch 0.861 (0.937) Remain 14:35:11 loss: 0.4599 Lr: 0.00157 [2024-02-19 01:57:01,563 INFO misc.py line 119 87073] Train: [65/100][26/1557] Data 0.004 (0.006) Batch 0.769 (0.930) Remain 14:28:19 loss: 0.1658 Lr: 0.00157 [2024-02-19 01:57:02,407 INFO misc.py line 119 87073] Train: [65/100][27/1557] Data 0.003 (0.006) Batch 0.810 (0.925) Remain 14:23:40 loss: 0.2623 Lr: 0.00157 [2024-02-19 01:57:03,748 INFO misc.py line 119 87073] Train: [65/100][28/1557] Data 0.037 (0.007) Batch 1.374 (0.943) Remain 14:40:25 loss: 0.3596 Lr: 0.00157 [2024-02-19 01:57:04,828 INFO misc.py line 119 87073] Train: [65/100][29/1557] Data 0.004 (0.007) Batch 1.078 (0.948) Remain 14:45:15 loss: 0.6327 Lr: 0.00157 [2024-02-19 01:57:05,723 INFO misc.py line 119 87073] Train: [65/100][30/1557] Data 0.006 (0.007) Batch 0.898 (0.946) Remain 14:43:30 loss: 0.0438 Lr: 0.00157 [2024-02-19 01:57:06,725 INFO misc.py line 119 87073] Train: [65/100][31/1557] Data 0.004 (0.007) Batch 1.002 (0.948) Remain 14:45:20 loss: 0.2838 Lr: 0.00157 [2024-02-19 01:57:07,780 INFO misc.py line 119 87073] Train: [65/100][32/1557] Data 0.003 (0.007) Batch 1.055 (0.952) Remain 14:48:46 loss: 0.5743 Lr: 0.00157 [2024-02-19 01:57:08,548 INFO misc.py line 119 87073] Train: [65/100][33/1557] Data 0.003 (0.007) Batch 0.768 (0.946) Remain 14:43:01 loss: 0.3295 Lr: 0.00157 [2024-02-19 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line 119 87073] Train: [65/100][165/1557] Data 0.004 (0.057) Batch 0.965 (1.072) Remain 16:38:49 loss: 0.6882 Lr: 0.00157 [2024-02-19 01:59:34,677 INFO misc.py line 119 87073] Train: [65/100][166/1557] Data 0.003 (0.057) Batch 0.782 (1.071) Remain 16:37:09 loss: 0.2384 Lr: 0.00157 [2024-02-19 01:59:35,401 INFO misc.py line 119 87073] Train: [65/100][167/1557] Data 0.004 (0.057) Batch 0.709 (1.068) Remain 16:35:04 loss: 0.2425 Lr: 0.00157 [2024-02-19 01:59:36,571 INFO misc.py line 119 87073] Train: [65/100][168/1557] Data 0.020 (0.056) Batch 1.175 (1.069) Remain 16:35:39 loss: 0.0957 Lr: 0.00157 [2024-02-19 01:59:37,778 INFO misc.py line 119 87073] Train: [65/100][169/1557] Data 0.015 (0.056) Batch 1.209 (1.070) Remain 16:36:25 loss: 0.7063 Lr: 0.00157 [2024-02-19 01:59:38,819 INFO misc.py line 119 87073] Train: [65/100][170/1557] Data 0.013 (0.056) Batch 1.040 (1.070) Remain 16:36:14 loss: 0.3022 Lr: 0.00157 [2024-02-19 01:59:39,699 INFO misc.py line 119 87073] Train: 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Batch 1.109 (1.129) Remain 17:29:39 loss: 0.3037 Lr: 0.00156 [2024-02-19 02:02:04,154 INFO misc.py line 119 87073] Train: [65/100][290/1557] Data 0.003 (0.082) Batch 0.965 (1.129) Remain 17:29:06 loss: 0.5918 Lr: 0.00156 [2024-02-19 02:02:05,090 INFO misc.py line 119 87073] Train: [65/100][291/1557] Data 0.004 (0.082) Batch 0.936 (1.128) Remain 17:28:28 loss: 0.3342 Lr: 0.00156 [2024-02-19 02:02:05,956 INFO misc.py line 119 87073] Train: [65/100][292/1557] Data 0.004 (0.081) Batch 0.860 (1.127) Remain 17:27:35 loss: 0.1458 Lr: 0.00156 [2024-02-19 02:02:06,680 INFO misc.py line 119 87073] Train: [65/100][293/1557] Data 0.010 (0.081) Batch 0.731 (1.126) Remain 17:26:18 loss: 0.1299 Lr: 0.00156 [2024-02-19 02:02:07,924 INFO misc.py line 119 87073] Train: [65/100][294/1557] Data 0.002 (0.081) Batch 1.242 (1.126) Remain 17:26:39 loss: 0.1561 Lr: 0.00156 [2024-02-19 02:02:08,920 INFO misc.py line 119 87073] Train: [65/100][295/1557] Data 0.005 (0.081) Batch 0.998 (1.126) Remain 17:26:13 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line 119 87073] Train: [65/100][389/1557] Data 0.012 (0.075) Batch 0.959 (1.116) Remain 17:15:44 loss: 0.4773 Lr: 0.00156 [2024-02-19 02:03:51,891 INFO misc.py line 119 87073] Train: [65/100][390/1557] Data 0.003 (0.075) Batch 0.773 (1.116) Remain 17:14:53 loss: 0.2952 Lr: 0.00156 [2024-02-19 02:03:52,814 INFO misc.py line 119 87073] Train: [65/100][391/1557] Data 0.003 (0.075) Batch 0.916 (1.115) Remain 17:14:23 loss: 0.1694 Lr: 0.00156 [2024-02-19 02:03:53,987 INFO misc.py line 119 87073] Train: [65/100][392/1557] Data 0.010 (0.075) Batch 1.169 (1.115) Remain 17:14:30 loss: 0.0878 Lr: 0.00155 [2024-02-19 02:03:54,952 INFO misc.py line 119 87073] Train: [65/100][393/1557] Data 0.015 (0.075) Batch 0.976 (1.115) Remain 17:14:09 loss: 0.2062 Lr: 0.00155 [2024-02-19 02:03:56,223 INFO misc.py line 119 87073] Train: [65/100][394/1557] Data 0.003 (0.074) Batch 1.246 (1.115) Remain 17:14:26 loss: 0.2808 Lr: 0.00155 [2024-02-19 02:03:57,286 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [65/100][445/1557] Data 0.004 (0.077) Batch 0.737 (1.116) Remain 17:14:18 loss: 0.2752 Lr: 0.00155 [2024-02-19 02:04:54,252 INFO misc.py line 119 87073] Train: [65/100][446/1557] Data 0.012 (0.077) Batch 0.801 (1.115) Remain 17:13:37 loss: 0.1434 Lr: 0.00155 [2024-02-19 02:04:54,910 INFO misc.py line 119 87073] Train: [65/100][447/1557] Data 0.003 (0.077) Batch 0.658 (1.114) Remain 17:12:38 loss: 0.3614 Lr: 0.00155 [2024-02-19 02:04:56,156 INFO misc.py line 119 87073] Train: [65/100][448/1557] Data 0.002 (0.077) Batch 1.243 (1.115) Remain 17:12:53 loss: 0.1285 Lr: 0.00155 [2024-02-19 02:04:57,193 INFO misc.py line 119 87073] Train: [65/100][449/1557] Data 0.007 (0.077) Batch 1.033 (1.114) Remain 17:12:42 loss: 0.3114 Lr: 0.00155 [2024-02-19 02:04:58,045 INFO misc.py line 119 87073] Train: [65/100][450/1557] Data 0.011 (0.077) Batch 0.859 (1.114) Remain 17:12:09 loss: 0.3251 Lr: 0.00155 [2024-02-19 02:04:59,108 INFO misc.py line 119 87073] Train: 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Remain 17:20:44 loss: 0.2180 Lr: 0.00151 [2024-02-19 02:20:10,371 INFO misc.py line 119 87073] Train: [65/100][1241/1557] Data 0.003 (0.085) Batch 0.950 (1.139) Remain 17:20:34 loss: 0.2515 Lr: 0.00151 [2024-02-19 02:20:11,289 INFO misc.py line 119 87073] Train: [65/100][1242/1557] Data 0.007 (0.085) Batch 0.901 (1.139) Remain 17:20:22 loss: 0.7066 Lr: 0.00151 [2024-02-19 02:20:12,370 INFO misc.py line 119 87073] Train: [65/100][1243/1557] Data 0.022 (0.085) Batch 1.086 (1.139) Remain 17:20:19 loss: 0.2526 Lr: 0.00151 [2024-02-19 02:20:13,166 INFO misc.py line 119 87073] Train: [65/100][1244/1557] Data 0.018 (0.085) Batch 0.807 (1.139) Remain 17:20:03 loss: 0.2976 Lr: 0.00151 [2024-02-19 02:20:13,897 INFO misc.py line 119 87073] Train: [65/100][1245/1557] Data 0.008 (0.085) Batch 0.716 (1.138) Remain 17:19:43 loss: 0.2508 Lr: 0.00151 [2024-02-19 02:20:15,201 INFO misc.py line 119 87073] Train: [65/100][1246/1557] Data 0.021 (0.085) Batch 1.317 (1.138) Remain 17:19:50 loss: 0.1241 Lr: 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Remain 17:16:09 loss: 0.2968 Lr: 0.00151 [2024-02-19 02:20:39,896 INFO misc.py line 119 87073] Train: [65/100][1272/1557] Data 0.011 (0.083) Batch 0.710 (1.135) Remain 17:15:49 loss: 0.2669 Lr: 0.00151 [2024-02-19 02:20:40,717 INFO misc.py line 119 87073] Train: [65/100][1273/1557] Data 0.003 (0.083) Batch 0.814 (1.134) Remain 17:15:34 loss: 0.3417 Lr: 0.00151 [2024-02-19 02:20:41,752 INFO misc.py line 119 87073] Train: [65/100][1274/1557] Data 0.010 (0.083) Batch 1.035 (1.134) Remain 17:15:29 loss: 0.1120 Lr: 0.00151 [2024-02-19 02:20:42,721 INFO misc.py line 119 87073] Train: [65/100][1275/1557] Data 0.010 (0.083) Batch 0.976 (1.134) Remain 17:15:21 loss: 0.2048 Lr: 0.00151 [2024-02-19 02:20:43,732 INFO misc.py line 119 87073] Train: [65/100][1276/1557] Data 0.003 (0.083) Batch 1.012 (1.134) Remain 17:15:14 loss: 0.8879 Lr: 0.00151 [2024-02-19 02:20:44,723 INFO misc.py line 119 87073] Train: [65/100][1277/1557] Data 0.007 (0.083) Batch 0.990 (1.134) Remain 17:15:07 loss: 0.1230 Lr: 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Remain 17:18:30 loss: 0.2791 Lr: 0.00151 [2024-02-19 02:21:19,535 INFO misc.py line 119 87073] Train: [65/100][1303/1557] Data 0.012 (0.085) Batch 0.972 (1.138) Remain 17:18:22 loss: 0.3350 Lr: 0.00151 [2024-02-19 02:21:20,522 INFO misc.py line 119 87073] Train: [65/100][1304/1557] Data 0.003 (0.085) Batch 0.986 (1.138) Remain 17:18:15 loss: 0.2350 Lr: 0.00151 [2024-02-19 02:21:21,555 INFO misc.py line 119 87073] Train: [65/100][1305/1557] Data 0.004 (0.085) Batch 1.034 (1.138) Remain 17:18:09 loss: 0.2718 Lr: 0.00151 [2024-02-19 02:21:22,600 INFO misc.py line 119 87073] Train: [65/100][1306/1557] Data 0.003 (0.085) Batch 1.044 (1.138) Remain 17:18:04 loss: 0.3998 Lr: 0.00151 [2024-02-19 02:21:23,321 INFO misc.py line 119 87073] Train: [65/100][1307/1557] Data 0.006 (0.085) Batch 0.721 (1.137) Remain 17:17:45 loss: 0.2408 Lr: 0.00151 [2024-02-19 02:21:24,086 INFO misc.py line 119 87073] Train: [65/100][1308/1557] Data 0.005 (0.085) Batch 0.766 (1.137) Remain 17:17:29 loss: 0.1141 Lr: 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Remain 17:13:16 loss: 0.1324 Lr: 0.00151 [2024-02-19 02:21:47,940 INFO misc.py line 119 87073] Train: [65/100][1334/1557] Data 0.003 (0.083) Batch 0.883 (1.133) Remain 17:13:04 loss: 0.2624 Lr: 0.00151 [2024-02-19 02:21:48,700 INFO misc.py line 119 87073] Train: [65/100][1335/1557] Data 0.004 (0.083) Batch 0.748 (1.133) Remain 17:12:47 loss: 0.3450 Lr: 0.00151 [2024-02-19 02:21:49,468 INFO misc.py line 119 87073] Train: [65/100][1336/1557] Data 0.016 (0.083) Batch 0.780 (1.132) Remain 17:12:32 loss: 0.2261 Lr: 0.00151 [2024-02-19 02:21:50,789 INFO misc.py line 119 87073] Train: [65/100][1337/1557] Data 0.003 (0.083) Batch 1.313 (1.132) Remain 17:12:38 loss: 0.3367 Lr: 0.00151 [2024-02-19 02:21:51,795 INFO misc.py line 119 87073] Train: [65/100][1338/1557] Data 0.012 (0.083) Batch 1.008 (1.132) Remain 17:12:32 loss: 0.6749 Lr: 0.00151 [2024-02-19 02:21:52,743 INFO misc.py line 119 87073] Train: [65/100][1339/1557] Data 0.010 (0.083) Batch 0.954 (1.132) Remain 17:12:23 loss: 0.3738 Lr: 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Train: [65/100][1352/1557] Data 0.005 (0.085) Batch 0.905 (1.137) Remain 17:16:48 loss: 0.5348 Lr: 0.00151 [2024-02-19 02:22:15,563 INFO misc.py line 119 87073] Train: [65/100][1353/1557] Data 0.004 (0.085) Batch 1.218 (1.137) Remain 17:16:50 loss: 0.3162 Lr: 0.00151 [2024-02-19 02:22:16,675 INFO misc.py line 119 87073] Train: [65/100][1354/1557] Data 0.003 (0.085) Batch 1.112 (1.137) Remain 17:16:48 loss: 0.2684 Lr: 0.00151 [2024-02-19 02:22:17,609 INFO misc.py line 119 87073] Train: [65/100][1355/1557] Data 0.003 (0.085) Batch 0.933 (1.137) Remain 17:16:38 loss: 0.1281 Lr: 0.00151 [2024-02-19 02:22:18,459 INFO misc.py line 119 87073] Train: [65/100][1356/1557] Data 0.004 (0.085) Batch 0.850 (1.137) Remain 17:16:26 loss: 0.2142 Lr: 0.00151 [2024-02-19 02:22:19,257 INFO misc.py line 119 87073] Train: [65/100][1357/1557] Data 0.003 (0.085) Batch 0.790 (1.137) Remain 17:16:11 loss: 0.1901 Lr: 0.00151 [2024-02-19 02:22:20,538 INFO misc.py line 119 87073] Train: [65/100][1358/1557] Data 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Remain 17:15:03 loss: 0.1305 Lr: 0.00151 [2024-02-19 02:22:26,806 INFO misc.py line 119 87073] Train: [65/100][1365/1557] Data 0.006 (0.085) Batch 1.069 (1.136) Remain 17:15:00 loss: 0.0927 Lr: 0.00151 [2024-02-19 02:22:27,880 INFO misc.py line 119 87073] Train: [65/100][1366/1557] Data 0.006 (0.085) Batch 1.073 (1.136) Remain 17:14:56 loss: 0.1409 Lr: 0.00151 [2024-02-19 02:22:28,749 INFO misc.py line 119 87073] Train: [65/100][1367/1557] Data 0.007 (0.085) Batch 0.872 (1.135) Remain 17:14:44 loss: 0.2719 Lr: 0.00151 [2024-02-19 02:22:29,699 INFO misc.py line 119 87073] Train: [65/100][1368/1557] Data 0.004 (0.085) Batch 0.950 (1.135) Remain 17:14:36 loss: 0.2189 Lr: 0.00151 [2024-02-19 02:22:30,765 INFO misc.py line 119 87073] Train: [65/100][1369/1557] Data 0.004 (0.084) Batch 1.067 (1.135) Remain 17:14:32 loss: 0.3755 Lr: 0.00151 [2024-02-19 02:22:31,521 INFO misc.py line 119 87073] Train: [65/100][1370/1557] Data 0.003 (0.084) Batch 0.754 (1.135) Remain 17:14:15 loss: 0.2117 Lr: 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Train: [65/100][1383/1557] Data 0.006 (0.084) Batch 1.005 (1.133) Remain 17:12:31 loss: 0.5965 Lr: 0.00151 [2024-02-19 02:22:44,680 INFO misc.py line 119 87073] Train: [65/100][1384/1557] Data 0.005 (0.084) Batch 0.683 (1.133) Remain 17:12:12 loss: 0.2216 Lr: 0.00151 [2024-02-19 02:22:45,415 INFO misc.py line 119 87073] Train: [65/100][1385/1557] Data 0.003 (0.084) Batch 0.725 (1.133) Remain 17:11:54 loss: 0.4219 Lr: 0.00151 [2024-02-19 02:22:46,514 INFO misc.py line 119 87073] Train: [65/100][1386/1557] Data 0.014 (0.084) Batch 1.099 (1.133) Remain 17:11:52 loss: 0.1554 Lr: 0.00151 [2024-02-19 02:22:47,462 INFO misc.py line 119 87073] Train: [65/100][1387/1557] Data 0.015 (0.083) Batch 0.958 (1.132) Remain 17:11:44 loss: 0.4554 Lr: 0.00151 [2024-02-19 02:22:48,449 INFO misc.py line 119 87073] Train: [65/100][1388/1557] Data 0.003 (0.083) Batch 0.987 (1.132) Remain 17:11:37 loss: 0.3073 Lr: 0.00151 [2024-02-19 02:22:49,407 INFO misc.py line 119 87073] Train: [65/100][1389/1557] Data 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Remain 17:10:44 loss: 0.2548 Lr: 0.00151 [2024-02-19 02:22:56,469 INFO misc.py line 119 87073] Train: [65/100][1396/1557] Data 0.003 (0.083) Batch 1.238 (1.132) Remain 17:10:47 loss: 0.0671 Lr: 0.00151 [2024-02-19 02:22:57,404 INFO misc.py line 119 87073] Train: [65/100][1397/1557] Data 0.003 (0.083) Batch 0.935 (1.131) Remain 17:10:38 loss: 0.1994 Lr: 0.00151 [2024-02-19 02:22:58,185 INFO misc.py line 119 87073] Train: [65/100][1398/1557] Data 0.003 (0.083) Batch 0.770 (1.131) Remain 17:10:23 loss: 0.2824 Lr: 0.00151 [2024-02-19 02:22:58,869 INFO misc.py line 119 87073] Train: [65/100][1399/1557] Data 0.014 (0.083) Batch 0.695 (1.131) Remain 17:10:05 loss: 0.2175 Lr: 0.00151 [2024-02-19 02:23:00,075 INFO misc.py line 119 87073] Train: [65/100][1400/1557] Data 0.003 (0.083) Batch 1.194 (1.131) Remain 17:10:06 loss: 0.1288 Lr: 0.00151 [2024-02-19 02:23:01,090 INFO misc.py line 119 87073] Train: [65/100][1401/1557] Data 0.015 (0.083) Batch 1.017 (1.131) Remain 17:10:01 loss: 0.2766 Lr: 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INFO misc.py line 119 87073] Train: [65/100][1408/1557] Data 0.014 (0.086) Batch 0.991 (1.138) Remain 17:16:27 loss: 0.2626 Lr: 0.00151 [2024-02-19 02:23:20,069 INFO misc.py line 119 87073] Train: [65/100][1409/1557] Data 0.003 (0.086) Batch 0.939 (1.138) Remain 17:16:18 loss: 0.2522 Lr: 0.00151 [2024-02-19 02:23:20,988 INFO misc.py line 119 87073] Train: [65/100][1410/1557] Data 0.003 (0.086) Batch 0.919 (1.138) Remain 17:16:08 loss: 0.6920 Lr: 0.00151 [2024-02-19 02:23:22,109 INFO misc.py line 119 87073] Train: [65/100][1411/1557] Data 0.004 (0.086) Batch 1.114 (1.138) Remain 17:16:06 loss: 0.4562 Lr: 0.00151 [2024-02-19 02:23:22,889 INFO misc.py line 119 87073] Train: [65/100][1412/1557] Data 0.009 (0.086) Batch 0.787 (1.137) Remain 17:15:52 loss: 0.2480 Lr: 0.00151 [2024-02-19 02:23:23,595 INFO misc.py line 119 87073] Train: [65/100][1413/1557] Data 0.003 (0.086) Batch 0.706 (1.137) Remain 17:15:34 loss: 0.3803 Lr: 0.00151 [2024-02-19 02:23:24,891 INFO misc.py line 119 87073] Train: [65/100][1414/1557] Data 0.003 (0.086) Batch 1.286 (1.137) Remain 17:15:38 loss: 0.2075 Lr: 0.00150 [2024-02-19 02:23:25,961 INFO misc.py line 119 87073] Train: [65/100][1415/1557] Data 0.013 (0.086) Batch 1.069 (1.137) Remain 17:15:35 loss: 0.3202 Lr: 0.00150 [2024-02-19 02:23:26,868 INFO misc.py line 119 87073] Train: [65/100][1416/1557] Data 0.014 (0.086) Batch 0.917 (1.137) Remain 17:15:25 loss: 0.6779 Lr: 0.00150 [2024-02-19 02:23:27,690 INFO misc.py line 119 87073] Train: [65/100][1417/1557] Data 0.004 (0.085) Batch 0.823 (1.137) Remain 17:15:12 loss: 0.4285 Lr: 0.00150 [2024-02-19 02:23:28,530 INFO misc.py line 119 87073] Train: [65/100][1418/1557] Data 0.003 (0.085) Batch 0.838 (1.137) Remain 17:14:59 loss: 0.2670 Lr: 0.00150 [2024-02-19 02:23:29,323 INFO misc.py line 119 87073] Train: [65/100][1419/1557] Data 0.005 (0.085) Batch 0.795 (1.136) Remain 17:14:45 loss: 0.1654 Lr: 0.00150 [2024-02-19 02:23:30,018 INFO misc.py line 119 87073] Train: [65/100][1420/1557] Data 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Remain 17:13:37 loss: 0.1871 Lr: 0.00150 [2024-02-19 02:23:36,509 INFO misc.py line 119 87073] Train: [65/100][1427/1557] Data 0.011 (0.085) Batch 0.778 (1.135) Remain 17:13:22 loss: 0.2788 Lr: 0.00150 [2024-02-19 02:23:37,718 INFO misc.py line 119 87073] Train: [65/100][1428/1557] Data 0.003 (0.085) Batch 1.208 (1.135) Remain 17:13:24 loss: 0.2513 Lr: 0.00150 [2024-02-19 02:23:38,661 INFO misc.py line 119 87073] Train: [65/100][1429/1557] Data 0.004 (0.085) Batch 0.943 (1.135) Remain 17:13:16 loss: 0.2453 Lr: 0.00150 [2024-02-19 02:23:39,810 INFO misc.py line 119 87073] Train: [65/100][1430/1557] Data 0.003 (0.085) Batch 1.150 (1.135) Remain 17:13:15 loss: 0.2275 Lr: 0.00150 [2024-02-19 02:23:40,878 INFO misc.py line 119 87073] Train: [65/100][1431/1557] Data 0.003 (0.085) Batch 1.066 (1.135) Remain 17:13:11 loss: 0.5749 Lr: 0.00150 [2024-02-19 02:23:41,950 INFO misc.py line 119 87073] Train: [65/100][1432/1557] Data 0.005 (0.085) Batch 1.073 (1.135) Remain 17:13:08 loss: 0.3558 Lr: 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Train: [65/100][1445/1557] Data 0.003 (0.084) Batch 0.932 (1.133) Remain 17:11:23 loss: 0.4704 Lr: 0.00150 [2024-02-19 02:23:55,181 INFO misc.py line 119 87073] Train: [65/100][1446/1557] Data 0.010 (0.084) Batch 0.867 (1.133) Remain 17:11:11 loss: 0.4775 Lr: 0.00150 [2024-02-19 02:23:55,867 INFO misc.py line 119 87073] Train: [65/100][1447/1557] Data 0.003 (0.084) Batch 0.686 (1.133) Remain 17:10:53 loss: 0.1992 Lr: 0.00150 [2024-02-19 02:23:56,652 INFO misc.py line 119 87073] Train: [65/100][1448/1557] Data 0.003 (0.084) Batch 0.784 (1.133) Remain 17:10:39 loss: 0.2044 Lr: 0.00150 [2024-02-19 02:23:57,933 INFO misc.py line 119 87073] Train: [65/100][1449/1557] Data 0.003 (0.084) Batch 1.276 (1.133) Remain 17:10:43 loss: 0.2162 Lr: 0.00150 [2024-02-19 02:23:59,094 INFO misc.py line 119 87073] Train: [65/100][1450/1557] Data 0.007 (0.084) Batch 1.157 (1.133) Remain 17:10:43 loss: 0.3298 Lr: 0.00150 [2024-02-19 02:23:59,952 INFO misc.py line 119 87073] Train: [65/100][1451/1557] Data 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Remain 17:09:47 loss: 0.6422 Lr: 0.00150 [2024-02-19 02:24:06,692 INFO misc.py line 119 87073] Train: [65/100][1458/1557] Data 0.006 (0.083) Batch 0.957 (1.132) Remain 17:09:39 loss: 0.4741 Lr: 0.00150 [2024-02-19 02:24:07,633 INFO misc.py line 119 87073] Train: [65/100][1459/1557] Data 0.004 (0.083) Batch 0.942 (1.131) Remain 17:09:31 loss: 0.4067 Lr: 0.00150 [2024-02-19 02:24:08,549 INFO misc.py line 119 87073] Train: [65/100][1460/1557] Data 0.003 (0.083) Batch 0.912 (1.131) Remain 17:09:22 loss: 0.5507 Lr: 0.00150 [2024-02-19 02:24:09,334 INFO misc.py line 119 87073] Train: [65/100][1461/1557] Data 0.007 (0.083) Batch 0.788 (1.131) Remain 17:09:08 loss: 0.1488 Lr: 0.00150 [2024-02-19 02:24:10,121 INFO misc.py line 119 87073] Train: [65/100][1462/1557] Data 0.004 (0.083) Batch 0.788 (1.131) Remain 17:08:54 loss: 0.1280 Lr: 0.00150 [2024-02-19 02:24:21,449 INFO misc.py line 119 87073] Train: [65/100][1463/1557] Data 3.742 (0.085) Batch 11.328 (1.138) Remain 17:15:14 loss: 0.2482 Lr: 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Remain 17:11:57 loss: 0.1679 Lr: 0.00150 [2024-02-19 02:24:46,070 INFO misc.py line 119 87073] Train: [65/100][1489/1557] Data 0.003 (0.084) Batch 0.750 (1.135) Remain 17:11:42 loss: 0.1888 Lr: 0.00150 [2024-02-19 02:24:46,858 INFO misc.py line 119 87073] Train: [65/100][1490/1557] Data 0.010 (0.084) Batch 0.796 (1.134) Remain 17:11:28 loss: 0.1937 Lr: 0.00150 [2024-02-19 02:24:48,105 INFO misc.py line 119 87073] Train: [65/100][1491/1557] Data 0.002 (0.084) Batch 1.236 (1.134) Remain 17:11:31 loss: 0.1567 Lr: 0.00150 [2024-02-19 02:24:49,005 INFO misc.py line 119 87073] Train: [65/100][1492/1557] Data 0.013 (0.084) Batch 0.911 (1.134) Remain 17:11:22 loss: 0.3440 Lr: 0.00150 [2024-02-19 02:24:49,983 INFO misc.py line 119 87073] Train: [65/100][1493/1557] Data 0.003 (0.084) Batch 0.977 (1.134) Remain 17:11:15 loss: 0.2319 Lr: 0.00150 [2024-02-19 02:24:50,905 INFO misc.py line 119 87073] Train: [65/100][1494/1557] Data 0.003 (0.084) Batch 0.922 (1.134) Remain 17:11:06 loss: 0.3015 Lr: 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0.011 (0.083) Batch 1.069 (1.132) Remain 17:08:40 loss: 0.1113 Lr: 0.00150 [2024-02-19 02:25:09,924 INFO misc.py line 119 87073] Train: [65/100][1514/1557] Data 0.009 (0.083) Batch 0.926 (1.132) Remain 17:08:31 loss: 0.2423 Lr: 0.00150 [2024-02-19 02:25:11,038 INFO misc.py line 119 87073] Train: [65/100][1515/1557] Data 0.004 (0.083) Batch 1.113 (1.132) Remain 17:08:29 loss: 0.3134 Lr: 0.00150 [2024-02-19 02:25:12,033 INFO misc.py line 119 87073] Train: [65/100][1516/1557] Data 0.004 (0.083) Batch 0.996 (1.131) Remain 17:08:23 loss: 0.5358 Lr: 0.00150 [2024-02-19 02:25:12,889 INFO misc.py line 119 87073] Train: [65/100][1517/1557] Data 0.003 (0.083) Batch 0.856 (1.131) Remain 17:08:12 loss: 0.5567 Lr: 0.00150 [2024-02-19 02:25:13,658 INFO misc.py line 119 87073] Train: [65/100][1518/1557] Data 0.003 (0.083) Batch 0.764 (1.131) Remain 17:07:58 loss: 0.1051 Lr: 0.00150 [2024-02-19 02:25:24,686 INFO misc.py line 119 87073] Train: [65/100][1519/1557] Data 4.224 (0.085) Batch 11.032 (1.138) Remain 17:13:53 loss: 0.1277 Lr: 0.00150 [2024-02-19 02:25:25,700 INFO misc.py line 119 87073] Train: [65/100][1520/1557] Data 0.005 (0.085) Batch 1.008 (1.137) Remain 17:13:47 loss: 0.3284 Lr: 0.00150 [2024-02-19 02:25:26,726 INFO misc.py line 119 87073] Train: [65/100][1521/1557] Data 0.010 (0.085) Batch 1.018 (1.137) Remain 17:13:42 loss: 0.2484 Lr: 0.00150 [2024-02-19 02:25:27,550 INFO misc.py line 119 87073] Train: [65/100][1522/1557] Data 0.018 (0.085) Batch 0.836 (1.137) Remain 17:13:30 loss: 0.3074 Lr: 0.00150 [2024-02-19 02:25:28,582 INFO misc.py line 119 87073] Train: [65/100][1523/1557] Data 0.006 (0.085) Batch 1.036 (1.137) Remain 17:13:25 loss: 0.2682 Lr: 0.00150 [2024-02-19 02:25:29,419 INFO misc.py line 119 87073] Train: [65/100][1524/1557] Data 0.003 (0.085) Batch 0.836 (1.137) Remain 17:13:13 loss: 0.1661 Lr: 0.00150 [2024-02-19 02:25:30,218 INFO misc.py line 119 87073] Train: [65/100][1525/1557] Data 0.003 (0.085) Batch 0.792 (1.137) Remain 17:12:59 loss: 0.2004 Lr: 0.00150 [2024-02-19 02:25:31,515 INFO misc.py line 119 87073] Train: [65/100][1526/1557] Data 0.010 (0.085) Batch 1.296 (1.137) Remain 17:13:04 loss: 0.2407 Lr: 0.00150 [2024-02-19 02:25:32,597 INFO misc.py line 119 87073] Train: [65/100][1527/1557] Data 0.012 (0.085) Batch 1.081 (1.137) Remain 17:13:01 loss: 0.3313 Lr: 0.00150 [2024-02-19 02:25:33,573 INFO misc.py line 119 87073] Train: [65/100][1528/1557] Data 0.012 (0.085) Batch 0.985 (1.137) Remain 17:12:54 loss: 0.2551 Lr: 0.00150 [2024-02-19 02:25:34,395 INFO misc.py line 119 87073] Train: [65/100][1529/1557] Data 0.003 (0.085) Batch 0.821 (1.136) Remain 17:12:42 loss: 0.2576 Lr: 0.00150 [2024-02-19 02:25:35,479 INFO misc.py line 119 87073] Train: [65/100][1530/1557] Data 0.004 (0.085) Batch 1.076 (1.136) Remain 17:12:39 loss: 0.2697 Lr: 0.00150 [2024-02-19 02:25:36,255 INFO misc.py line 119 87073] Train: [65/100][1531/1557] Data 0.011 (0.085) Batch 0.784 (1.136) Remain 17:12:25 loss: 0.2892 Lr: 0.00150 [2024-02-19 02:25:37,030 INFO misc.py line 119 87073] Train: [65/100][1532/1557] Data 0.003 (0.085) Batch 0.775 (1.136) Remain 17:12:11 loss: 0.1173 Lr: 0.00150 [2024-02-19 02:25:38,184 INFO misc.py line 119 87073] Train: [65/100][1533/1557] Data 0.003 (0.085) Batch 1.149 (1.136) Remain 17:12:10 loss: 0.1922 Lr: 0.00150 [2024-02-19 02:25:39,260 INFO misc.py line 119 87073] Train: [65/100][1534/1557] Data 0.009 (0.085) Batch 1.073 (1.136) Remain 17:12:07 loss: 0.3047 Lr: 0.00150 [2024-02-19 02:25:40,270 INFO misc.py line 119 87073] Train: [65/100][1535/1557] Data 0.012 (0.085) Batch 1.011 (1.136) Remain 17:12:01 loss: 0.3079 Lr: 0.00150 [2024-02-19 02:25:41,366 INFO misc.py line 119 87073] Train: [65/100][1536/1557] Data 0.010 (0.085) Batch 1.093 (1.136) Remain 17:11:59 loss: 0.2707 Lr: 0.00150 [2024-02-19 02:25:42,296 INFO misc.py line 119 87073] Train: [65/100][1537/1557] Data 0.014 (0.084) Batch 0.941 (1.136) Remain 17:11:51 loss: 0.1813 Lr: 0.00150 [2024-02-19 02:25:42,962 INFO misc.py line 119 87073] Train: [65/100][1538/1557] Data 0.003 (0.084) Batch 0.666 (1.135) Remain 17:11:33 loss: 0.2675 Lr: 0.00150 [2024-02-19 02:25:43,756 INFO misc.py line 119 87073] Train: [65/100][1539/1557] Data 0.003 (0.084) Batch 0.788 (1.135) Remain 17:11:19 loss: 0.2040 Lr: 0.00150 [2024-02-19 02:25:45,078 INFO misc.py line 119 87073] Train: [65/100][1540/1557] Data 0.010 (0.084) Batch 1.320 (1.135) Remain 17:11:25 loss: 0.2775 Lr: 0.00150 [2024-02-19 02:25:45,987 INFO misc.py line 119 87073] Train: [65/100][1541/1557] Data 0.012 (0.084) Batch 0.917 (1.135) Remain 17:11:16 loss: 0.2672 Lr: 0.00150 [2024-02-19 02:25:46,796 INFO misc.py line 119 87073] Train: [65/100][1542/1557] Data 0.003 (0.084) Batch 0.809 (1.135) Remain 17:11:03 loss: 0.2508 Lr: 0.00150 [2024-02-19 02:25:47,735 INFO misc.py line 119 87073] Train: [65/100][1543/1557] Data 0.003 (0.084) Batch 0.928 (1.135) Remain 17:10:55 loss: 0.2707 Lr: 0.00150 [2024-02-19 02:25:48,647 INFO misc.py line 119 87073] Train: [65/100][1544/1557] Data 0.014 (0.084) Batch 0.923 (1.135) Remain 17:10:46 loss: 0.3693 Lr: 0.00150 [2024-02-19 02:25:49,371 INFO misc.py line 119 87073] Train: [65/100][1545/1557] Data 0.003 (0.084) Batch 0.725 (1.134) Remain 17:10:30 loss: 0.2449 Lr: 0.00150 [2024-02-19 02:25:50,102 INFO misc.py line 119 87073] Train: [65/100][1546/1557] Data 0.003 (0.084) Batch 0.722 (1.134) Remain 17:10:15 loss: 0.0798 Lr: 0.00150 [2024-02-19 02:25:51,358 INFO misc.py line 119 87073] Train: [65/100][1547/1557] Data 0.011 (0.084) Batch 1.254 (1.134) Remain 17:10:18 loss: 0.1465 Lr: 0.00150 [2024-02-19 02:25:52,242 INFO misc.py line 119 87073] Train: [65/100][1548/1557] Data 0.014 (0.084) Batch 0.895 (1.134) Remain 17:10:08 loss: 0.2949 Lr: 0.00150 [2024-02-19 02:25:53,245 INFO misc.py line 119 87073] Train: [65/100][1549/1557] Data 0.003 (0.084) Batch 1.002 (1.134) Remain 17:10:03 loss: 0.3714 Lr: 0.00150 [2024-02-19 02:25:54,079 INFO misc.py line 119 87073] Train: [65/100][1550/1557] Data 0.003 (0.084) Batch 0.834 (1.134) Remain 17:09:51 loss: 0.2951 Lr: 0.00150 [2024-02-19 02:25:55,156 INFO misc.py line 119 87073] Train: [65/100][1551/1557] Data 0.003 (0.084) Batch 1.066 (1.134) Remain 17:09:47 loss: 0.2140 Lr: 0.00150 [2024-02-19 02:25:55,876 INFO misc.py line 119 87073] Train: [65/100][1552/1557] Data 0.014 (0.084) Batch 0.731 (1.133) Remain 17:09:32 loss: 0.1924 Lr: 0.00150 [2024-02-19 02:25:56,635 INFO misc.py line 119 87073] Train: [65/100][1553/1557] Data 0.003 (0.084) Batch 0.747 (1.133) Remain 17:09:17 loss: 0.3060 Lr: 0.00150 [2024-02-19 02:25:57,778 INFO misc.py line 119 87073] Train: [65/100][1554/1557] Data 0.014 (0.084) Batch 1.147 (1.133) Remain 17:09:17 loss: 0.0985 Lr: 0.00150 [2024-02-19 02:25:58,687 INFO misc.py line 119 87073] Train: [65/100][1555/1557] Data 0.010 (0.084) Batch 0.916 (1.133) Remain 17:09:08 loss: 0.3358 Lr: 0.00150 [2024-02-19 02:25:59,563 INFO misc.py line 119 87073] Train: [65/100][1556/1557] Data 0.003 (0.084) Batch 0.876 (1.133) Remain 17:08:58 loss: 0.4088 Lr: 0.00150 [2024-02-19 02:26:00,566 INFO misc.py line 119 87073] Train: [65/100][1557/1557] Data 0.003 (0.083) Batch 0.991 (1.133) Remain 17:08:52 loss: 0.4027 Lr: 0.00150 [2024-02-19 02:26:00,566 INFO misc.py line 136 87073] Train result: loss: 0.2839 [2024-02-19 02:26:00,567 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 02:26:41,731 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6611 [2024-02-19 02:26:42,511 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7265 [2024-02-19 02:26:44,639 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3481 [2024-02-19 02:26:46,849 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2673 [2024-02-19 02:26:51,788 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2151 [2024-02-19 02:26:52,492 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2613 [2024-02-19 02:26:53,753 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2990 [2024-02-19 02:26:56,707 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2391 [2024-02-19 02:26:58,515 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2609 [2024-02-19 02:27:00,104 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7181/0.7780/0.9083. [2024-02-19 02:27:00,104 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9060/0.9571 [2024-02-19 02:27:00,104 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9831/0.9900 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8542/0.9755 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.5086/0.6109 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5902/0.6052 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7864/0.9003 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8459/0.9258 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9253/0.9672 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8431/0.8912 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7838/0.8702 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7350/0.7761 [2024-02-19 02:27:00,105 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5734/0.6453 [2024-02-19 02:27:00,105 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 02:27:00,109 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 02:27:00,109 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 02:27:07,540 INFO misc.py line 119 87073] Train: [66/100][1/1557] Data 1.521 (1.521) Batch 2.425 (2.425) Remain 36:42:24 loss: 0.2785 Lr: 0.00150 [2024-02-19 02:27:08,739 INFO misc.py line 119 87073] Train: [66/100][2/1557] Data 0.004 (0.004) Batch 1.195 (1.195) Remain 18:05:15 loss: 0.6801 Lr: 0.00150 [2024-02-19 02:27:09,644 INFO misc.py line 119 87073] Train: [66/100][3/1557] Data 0.009 (0.009) Batch 0.910 (0.910) Remain 13:46:16 loss: 0.1659 Lr: 0.00150 [2024-02-19 02:27:10,621 INFO misc.py line 119 87073] Train: [66/100][4/1557] Data 0.003 (0.003) Batch 0.977 (0.977) Remain 14:47:00 loss: 0.2509 Lr: 0.00150 [2024-02-19 02:27:11,367 INFO misc.py line 119 87073] Train: [66/100][5/1557] Data 0.004 (0.004) Batch 0.747 (0.862) Remain 13:02:36 loss: 0.2751 Lr: 0.00150 [2024-02-19 02:27:12,231 INFO misc.py line 119 87073] Train: [66/100][6/1557] Data 0.003 (0.003) Batch 0.857 (0.860) Remain 13:01:16 loss: 0.5024 Lr: 0.00150 [2024-02-19 02:27:26,466 INFO misc.py line 119 87073] Train: [66/100][7/1557] Data 0.443 (0.113) Batch 14.241 (4.205) Remain 63:39:05 loss: 0.2086 Lr: 0.00150 [2024-02-19 02:27:27,605 INFO misc.py line 119 87073] Train: [66/100][8/1557] Data 0.003 (0.091) Batch 1.139 (3.592) Remain 54:22:06 loss: 0.2038 Lr: 0.00150 [2024-02-19 02:27:28,508 INFO misc.py line 119 87073] Train: [66/100][9/1557] Data 0.003 (0.076) Batch 0.903 (3.144) Remain 47:35:03 loss: 0.3564 Lr: 0.00150 [2024-02-19 02:27:29,489 INFO misc.py line 119 87073] Train: [66/100][10/1557] Data 0.003 (0.066) Batch 0.973 (2.834) Remain 42:53:21 loss: 0.5857 Lr: 0.00150 [2024-02-19 02:27:30,594 INFO misc.py line 119 87073] Train: [66/100][11/1557] Data 0.011 (0.059) Batch 1.106 (2.618) Remain 39:37:09 loss: 0.2436 Lr: 0.00150 [2024-02-19 02:27:31,284 INFO misc.py line 119 87073] Train: [66/100][12/1557] Data 0.010 (0.054) Batch 0.697 (2.404) Remain 36:23:18 loss: 0.2894 Lr: 0.00150 [2024-02-19 02:27:32,110 INFO misc.py line 119 87073] Train: [66/100][13/1557] Data 0.004 (0.049) Batch 0.821 (2.246) Remain 33:59:31 loss: 0.2595 Lr: 0.00150 [2024-02-19 02:27:33,362 INFO misc.py line 119 87073] Train: [66/100][14/1557] Data 0.008 (0.045) Batch 1.247 (2.155) Remain 32:37:03 loss: 0.1868 Lr: 0.00150 [2024-02-19 02:27:34,169 INFO misc.py line 119 87073] Train: [66/100][15/1557] Data 0.012 (0.042) Batch 0.816 (2.044) Remain 30:55:42 loss: 0.0709 Lr: 0.00150 [2024-02-19 02:27:35,075 INFO misc.py line 119 87073] Train: [66/100][16/1557] Data 0.004 (0.039) Batch 0.906 (1.956) Remain 29:36:12 loss: 0.0940 Lr: 0.00150 [2024-02-19 02:27:36,057 INFO misc.py line 119 87073] Train: [66/100][17/1557] Data 0.004 (0.037) Batch 0.975 (1.886) Remain 28:32:30 loss: 0.3858 Lr: 0.00150 [2024-02-19 02:27:37,041 INFO misc.py line 119 87073] Train: [66/100][18/1557] Data 0.011 (0.035) Batch 0.991 (1.826) Remain 27:38:19 loss: 0.1496 Lr: 0.00150 [2024-02-19 02:27:37,737 INFO misc.py line 119 87073] Train: [66/100][19/1557] Data 0.003 (0.033) Batch 0.696 (1.756) Remain 26:34:09 loss: 0.4098 Lr: 0.00150 [2024-02-19 02:27:38,507 INFO misc.py line 119 87073] Train: [66/100][20/1557] Data 0.003 (0.031) Batch 0.759 (1.697) Remain 25:40:53 loss: 0.3278 Lr: 0.00150 [2024-02-19 02:27:39,779 INFO misc.py line 119 87073] Train: [66/100][21/1557] Data 0.014 (0.030) Batch 1.274 (1.674) Remain 25:19:31 loss: 0.1321 Lr: 0.00150 [2024-02-19 02:27:40,843 INFO misc.py line 119 87073] Train: [66/100][22/1557] Data 0.012 (0.029) Batch 1.062 (1.641) Remain 24:50:17 loss: 0.3855 Lr: 0.00150 [2024-02-19 02:27:41,841 INFO misc.py line 119 87073] Train: [66/100][23/1557] Data 0.013 (0.029) Batch 1.005 (1.610) Remain 24:21:22 loss: 0.4643 Lr: 0.00150 [2024-02-19 02:27:42,685 INFO misc.py line 119 87073] Train: [66/100][24/1557] Data 0.006 (0.028) Batch 0.846 (1.573) Remain 23:48:21 loss: 0.1047 Lr: 0.00150 [2024-02-19 02:27:43,634 INFO misc.py line 119 87073] Train: [66/100][25/1557] Data 0.004 (0.026) Batch 0.949 (1.545) Remain 23:22:34 loss: 0.7396 Lr: 0.00150 [2024-02-19 02:27:44,349 INFO misc.py line 119 87073] Train: [66/100][26/1557] Data 0.003 (0.025) Batch 0.712 (1.509) Remain 22:49:41 loss: 0.1723 Lr: 0.00150 [2024-02-19 02:27:45,072 INFO misc.py line 119 87073] Train: [66/100][27/1557] Data 0.006 (0.025) Batch 0.726 (1.476) Remain 22:20:03 loss: 0.2024 Lr: 0.00150 [2024-02-19 02:27:46,115 INFO misc.py line 119 87073] Train: [66/100][28/1557] Data 0.003 (0.024) Batch 1.040 (1.459) Remain 22:04:12 loss: 0.1864 Lr: 0.00150 [2024-02-19 02:27:46,984 INFO misc.py line 119 87073] Train: [66/100][29/1557] Data 0.005 (0.023) Batch 0.871 (1.436) Remain 21:43:40 loss: 0.3999 Lr: 0.00150 [2024-02-19 02:27:47,879 INFO misc.py line 119 87073] Train: [66/100][30/1557] Data 0.003 (0.022) Batch 0.896 (1.416) Remain 21:25:29 loss: 0.3509 Lr: 0.00150 [2024-02-19 02:27:48,836 INFO misc.py line 119 87073] Train: [66/100][31/1557] Data 0.003 (0.022) Batch 0.955 (1.400) Remain 21:10:31 loss: 0.2900 Lr: 0.00150 [2024-02-19 02:27:49,840 INFO misc.py line 119 87073] Train: [66/100][32/1557] Data 0.005 (0.021) Batch 1.000 (1.386) Remain 20:57:59 loss: 0.3610 Lr: 0.00150 [2024-02-19 02:27:50,552 INFO misc.py line 119 87073] Train: [66/100][33/1557] Data 0.008 (0.021) Batch 0.717 (1.364) Remain 20:37:43 loss: 0.2319 Lr: 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line 119 87073] Train: [66/100][40/1557] Data 0.012 (0.018) Batch 0.702 (1.280) Remain 19:22:03 loss: 0.3730 Lr: 0.00150 [2024-02-19 02:27:57,858 INFO misc.py line 119 87073] Train: [66/100][41/1557] Data 0.003 (0.018) Batch 0.832 (1.269) Remain 19:11:19 loss: 0.2013 Lr: 0.00150 [2024-02-19 02:27:59,023 INFO misc.py line 119 87073] Train: [66/100][42/1557] Data 0.011 (0.018) Batch 1.162 (1.266) Remain 19:08:50 loss: 0.2770 Lr: 0.00150 [2024-02-19 02:27:59,915 INFO misc.py line 119 87073] Train: [66/100][43/1557] Data 0.013 (0.018) Batch 0.902 (1.257) Remain 19:00:33 loss: 0.1399 Lr: 0.00150 [2024-02-19 02:28:00,915 INFO misc.py line 119 87073] Train: [66/100][44/1557] Data 0.003 (0.017) Batch 1.000 (1.250) Remain 18:54:50 loss: 0.1815 Lr: 0.00150 [2024-02-19 02:28:01,876 INFO misc.py line 119 87073] Train: [66/100][45/1557] Data 0.004 (0.017) Batch 0.961 (1.244) Remain 18:48:34 loss: 0.3583 Lr: 0.00150 [2024-02-19 02:28:02,795 INFO misc.py line 119 87073] Train: [66/100][46/1557] Data 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line 119 87073] Train: [66/100][221/1557] Data 0.003 (0.111) Batch 1.071 (1.314) Remain 19:48:25 loss: 0.4298 Lr: 0.00149 [2024-02-19 02:31:56,830 INFO misc.py line 119 87073] Train: [66/100][222/1557] Data 0.003 (0.110) Batch 0.773 (1.311) Remain 19:46:09 loss: 0.1827 Lr: 0.00149 [2024-02-19 02:31:57,586 INFO misc.py line 119 87073] Train: [66/100][223/1557] Data 0.008 (0.110) Batch 0.762 (1.309) Remain 19:43:52 loss: 0.3317 Lr: 0.00149 [2024-02-19 02:31:58,716 INFO misc.py line 119 87073] Train: [66/100][224/1557] Data 0.003 (0.109) Batch 1.129 (1.308) Remain 19:43:07 loss: 0.1208 Lr: 0.00149 [2024-02-19 02:31:59,549 INFO misc.py line 119 87073] Train: [66/100][225/1557] Data 0.005 (0.109) Batch 0.833 (1.306) Remain 19:41:09 loss: 0.3562 Lr: 0.00149 [2024-02-19 02:32:00,430 INFO misc.py line 119 87073] Train: [66/100][226/1557] Data 0.003 (0.108) Batch 0.879 (1.304) Remain 19:39:24 loss: 0.4269 Lr: 0.00149 [2024-02-19 02:32:01,346 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [66/100][277/1557] Data 0.010 (0.117) Batch 0.961 (1.329) Remain 20:00:41 loss: 0.3346 Lr: 0.00148 [2024-02-19 02:33:14,436 INFO misc.py line 119 87073] Train: [66/100][278/1557] Data 0.004 (0.116) Batch 0.718 (1.327) Remain 19:58:39 loss: 0.2365 Lr: 0.00148 [2024-02-19 02:33:15,191 INFO misc.py line 119 87073] Train: [66/100][279/1557] Data 0.003 (0.116) Batch 0.752 (1.324) Remain 19:56:45 loss: 0.2517 Lr: 0.00148 [2024-02-19 02:33:16,347 INFO misc.py line 119 87073] Train: [66/100][280/1557] Data 0.006 (0.116) Batch 1.149 (1.324) Remain 19:56:09 loss: 0.1674 Lr: 0.00148 [2024-02-19 02:33:17,399 INFO misc.py line 119 87073] Train: [66/100][281/1557] Data 0.014 (0.115) Batch 1.054 (1.323) Remain 19:55:15 loss: 0.2460 Lr: 0.00148 [2024-02-19 02:33:18,294 INFO misc.py line 119 87073] Train: [66/100][282/1557] Data 0.011 (0.115) Batch 0.903 (1.321) Remain 19:53:52 loss: 0.2441 Lr: 0.00148 [2024-02-19 02:33:19,193 INFO misc.py line 119 87073] Train: 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Batch 0.854 (1.398) Remain 21:02:57 loss: 0.4274 Lr: 0.00148 [2024-02-19 02:33:50,547 INFO misc.py line 119 87073] Train: [66/100][290/1557] Data 0.007 (0.135) Batch 1.081 (1.397) Remain 21:01:56 loss: 0.3888 Lr: 0.00148 [2024-02-19 02:33:51,446 INFO misc.py line 119 87073] Train: [66/100][291/1557] Data 0.008 (0.135) Batch 0.902 (1.395) Remain 21:00:21 loss: 0.4017 Lr: 0.00148 [2024-02-19 02:33:52,231 INFO misc.py line 119 87073] Train: [66/100][292/1557] Data 0.006 (0.134) Batch 0.787 (1.393) Remain 20:58:26 loss: 0.2125 Lr: 0.00148 [2024-02-19 02:33:52,993 INFO misc.py line 119 87073] Train: [66/100][293/1557] Data 0.004 (0.134) Batch 0.755 (1.391) Remain 20:56:25 loss: 0.1013 Lr: 0.00148 [2024-02-19 02:33:54,312 INFO misc.py line 119 87073] Train: [66/100][294/1557] Data 0.010 (0.134) Batch 1.320 (1.391) Remain 20:56:11 loss: 0.2131 Lr: 0.00148 [2024-02-19 02:33:55,302 INFO misc.py line 119 87073] Train: [66/100][295/1557] Data 0.009 (0.133) Batch 0.996 (1.389) Remain 20:54:56 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Batch 1.062 (1.378) Remain 20:40:13 loss: 0.2894 Lr: 0.00147 [2024-02-19 02:38:53,551 INFO misc.py line 119 87073] Train: [66/100][514/1557] Data 0.011 (0.137) Batch 0.878 (1.378) Remain 20:39:19 loss: 0.1702 Lr: 0.00147 [2024-02-19 02:38:54,481 INFO misc.py line 119 87073] Train: [66/100][515/1557] Data 0.003 (0.136) Batch 0.930 (1.377) Remain 20:38:30 loss: 0.2822 Lr: 0.00147 [2024-02-19 02:38:55,258 INFO misc.py line 119 87073] Train: [66/100][516/1557] Data 0.003 (0.136) Batch 0.774 (1.375) Remain 20:37:25 loss: 0.1760 Lr: 0.00147 [2024-02-19 02:38:55,963 INFO misc.py line 119 87073] Train: [66/100][517/1557] Data 0.006 (0.136) Batch 0.708 (1.374) Remain 20:36:14 loss: 0.4060 Lr: 0.00147 [2024-02-19 02:38:57,118 INFO misc.py line 119 87073] Train: [66/100][518/1557] Data 0.003 (0.136) Batch 1.151 (1.374) Remain 20:35:49 loss: 0.2018 Lr: 0.00147 [2024-02-19 02:38:58,355 INFO misc.py line 119 87073] Train: [66/100][519/1557] Data 0.007 (0.135) Batch 1.231 (1.373) Remain 20:35:33 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line 119 87073] Train: [66/100][557/1557] Data 0.003 (0.128) Batch 1.008 (1.348) Remain 20:11:56 loss: 0.3281 Lr: 0.00147 [2024-02-19 02:39:37,273 INFO misc.py line 119 87073] Train: [66/100][558/1557] Data 0.003 (0.128) Batch 0.751 (1.347) Remain 20:10:57 loss: 0.2845 Lr: 0.00147 [2024-02-19 02:39:37,995 INFO misc.py line 119 87073] Train: [66/100][559/1557] Data 0.003 (0.127) Batch 0.719 (1.346) Remain 20:09:55 loss: 0.2262 Lr: 0.00147 [2024-02-19 02:39:39,106 INFO misc.py line 119 87073] Train: [66/100][560/1557] Data 0.007 (0.127) Batch 1.113 (1.346) Remain 20:09:31 loss: 0.1485 Lr: 0.00147 [2024-02-19 02:39:40,183 INFO misc.py line 119 87073] Train: [66/100][561/1557] Data 0.004 (0.127) Batch 1.075 (1.345) Remain 20:09:03 loss: 0.1326 Lr: 0.00147 [2024-02-19 02:39:41,046 INFO misc.py line 119 87073] Train: [66/100][562/1557] Data 0.007 (0.127) Batch 0.866 (1.344) Remain 20:08:15 loss: 0.3301 Lr: 0.00147 [2024-02-19 02:39:41,833 INFO misc.py line 119 87073] Train: 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Batch 1.026 (1.380) Remain 20:40:13 loss: 0.3904 Lr: 0.00147 [2024-02-19 02:40:11,706 INFO misc.py line 119 87073] Train: [66/100][570/1557] Data 0.008 (0.137) Batch 1.025 (1.379) Remain 20:39:38 loss: 0.4439 Lr: 0.00147 [2024-02-19 02:40:12,685 INFO misc.py line 119 87073] Train: [66/100][571/1557] Data 0.007 (0.137) Batch 0.983 (1.379) Remain 20:38:59 loss: 0.9743 Lr: 0.00147 [2024-02-19 02:40:13,474 INFO misc.py line 119 87073] Train: [66/100][572/1557] Data 0.003 (0.137) Batch 0.789 (1.378) Remain 20:38:01 loss: 0.3178 Lr: 0.00147 [2024-02-19 02:40:14,224 INFO misc.py line 119 87073] Train: [66/100][573/1557] Data 0.003 (0.136) Batch 0.750 (1.376) Remain 20:37:01 loss: 0.1216 Lr: 0.00147 [2024-02-19 02:40:15,384 INFO misc.py line 119 87073] Train: [66/100][574/1557] Data 0.003 (0.136) Batch 1.148 (1.376) Remain 20:36:38 loss: 0.1639 Lr: 0.00147 [2024-02-19 02:40:16,290 INFO misc.py line 119 87073] Train: [66/100][575/1557] Data 0.015 (0.136) Batch 0.918 (1.375) Remain 20:35:53 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Batch 0.913 (1.373) Remain 20:30:02 loss: 0.4698 Lr: 0.00146 [2024-02-19 02:43:58,248 INFO misc.py line 119 87073] Train: [66/100][738/1557] Data 0.003 (0.138) Batch 0.920 (1.372) Remain 20:29:27 loss: 0.3730 Lr: 0.00146 [2024-02-19 02:43:59,172 INFO misc.py line 119 87073] Train: [66/100][739/1557] Data 0.006 (0.138) Batch 0.925 (1.372) Remain 20:28:53 loss: 0.0969 Lr: 0.00146 [2024-02-19 02:43:59,898 INFO misc.py line 119 87073] Train: [66/100][740/1557] Data 0.004 (0.138) Batch 0.727 (1.371) Remain 20:28:05 loss: 0.4239 Lr: 0.00146 [2024-02-19 02:44:00,763 INFO misc.py line 119 87073] Train: [66/100][741/1557] Data 0.003 (0.138) Batch 0.853 (1.370) Remain 20:27:26 loss: 0.2605 Lr: 0.00146 [2024-02-19 02:44:01,914 INFO misc.py line 119 87073] Train: [66/100][742/1557] Data 0.014 (0.137) Batch 1.150 (1.370) Remain 20:27:08 loss: 0.2915 Lr: 0.00146 [2024-02-19 02:44:02,864 INFO misc.py line 119 87073] Train: [66/100][743/1557] Data 0.016 (0.137) Batch 0.962 (1.369) Remain 20:26:37 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Batch 1.016 (1.373) Remain 20:27:13 loss: 0.3486 Lr: 0.00146 [2024-02-19 02:46:31,888 INFO misc.py line 119 87073] Train: [66/100][850/1557] Data 0.009 (0.138) Batch 1.031 (1.372) Remain 20:26:50 loss: 0.3991 Lr: 0.00146 [2024-02-19 02:46:33,065 INFO misc.py line 119 87073] Train: [66/100][851/1557] Data 0.007 (0.137) Batch 1.174 (1.372) Remain 20:26:36 loss: 0.5671 Lr: 0.00146 [2024-02-19 02:46:33,804 INFO misc.py line 119 87073] Train: [66/100][852/1557] Data 0.010 (0.137) Batch 0.745 (1.371) Remain 20:25:55 loss: 0.2568 Lr: 0.00146 [2024-02-19 02:46:34,572 INFO misc.py line 119 87073] Train: [66/100][853/1557] Data 0.004 (0.137) Batch 0.763 (1.370) Remain 20:25:16 loss: 0.1954 Lr: 0.00146 [2024-02-19 02:46:35,792 INFO misc.py line 119 87073] Train: [66/100][854/1557] Data 0.009 (0.137) Batch 1.221 (1.370) Remain 20:25:05 loss: 0.1744 Lr: 0.00146 [2024-02-19 02:46:36,715 INFO misc.py line 119 87073] Train: [66/100][855/1557] Data 0.008 (0.137) Batch 0.928 (1.370) Remain 20:24:36 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Remain 20:13:45 loss: 0.2713 Lr: 0.00144 [2024-02-19 02:55:22,180 INFO misc.py line 119 87073] Train: [66/100][1241/1557] Data 0.003 (0.140) Batch 0.961 (1.367) Remain 20:13:26 loss: 0.3306 Lr: 0.00144 [2024-02-19 02:55:23,160 INFO misc.py line 119 87073] Train: [66/100][1242/1557] Data 0.006 (0.140) Batch 0.981 (1.367) Remain 20:13:08 loss: 0.4092 Lr: 0.00144 [2024-02-19 02:55:24,125 INFO misc.py line 119 87073] Train: [66/100][1243/1557] Data 0.004 (0.140) Batch 0.965 (1.367) Remain 20:12:49 loss: 0.3436 Lr: 0.00144 [2024-02-19 02:55:24,811 INFO misc.py line 119 87073] Train: [66/100][1244/1557] Data 0.004 (0.140) Batch 0.686 (1.366) Remain 20:12:19 loss: 0.1604 Lr: 0.00144 [2024-02-19 02:55:25,520 INFO misc.py line 119 87073] Train: [66/100][1245/1557] Data 0.003 (0.139) Batch 0.706 (1.365) Remain 20:11:49 loss: 0.3992 Lr: 0.00144 [2024-02-19 02:55:26,762 INFO misc.py line 119 87073] Train: [66/100][1246/1557] Data 0.006 (0.139) Batch 1.239 (1.365) Remain 20:11:42 loss: 0.1565 Lr: 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Train: [66/100][1259/1557] Data 0.005 (0.138) Batch 0.681 (1.361) Remain 20:07:40 loss: 0.2343 Lr: 0.00144 [2024-02-19 02:55:40,322 INFO misc.py line 119 87073] Train: [66/100][1260/1557] Data 0.007 (0.138) Batch 1.104 (1.361) Remain 20:07:28 loss: 0.1411 Lr: 0.00144 [2024-02-19 02:55:41,044 INFO misc.py line 119 87073] Train: [66/100][1261/1557] Data 0.014 (0.138) Batch 0.733 (1.360) Remain 20:07:00 loss: 0.2291 Lr: 0.00144 [2024-02-19 02:55:42,085 INFO misc.py line 119 87073] Train: [66/100][1262/1557] Data 0.004 (0.138) Batch 1.041 (1.360) Remain 20:06:45 loss: 0.5483 Lr: 0.00144 [2024-02-19 02:55:43,051 INFO misc.py line 119 87073] Train: [66/100][1263/1557] Data 0.003 (0.138) Batch 0.966 (1.360) Remain 20:06:27 loss: 0.4367 Lr: 0.00144 [2024-02-19 02:55:44,004 INFO misc.py line 119 87073] Train: [66/100][1264/1557] Data 0.003 (0.138) Batch 0.945 (1.360) Remain 20:06:08 loss: 0.2585 Lr: 0.00144 [2024-02-19 02:55:44,724 INFO misc.py line 119 87073] Train: [66/100][1265/1557] Data 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Remain 20:03:54 loss: 0.1776 Lr: 0.00144 [2024-02-19 02:55:51,321 INFO misc.py line 119 87073] Train: [66/100][1272/1557] Data 0.006 (0.137) Batch 0.773 (1.357) Remain 20:03:28 loss: 0.1653 Lr: 0.00144 [2024-02-19 02:55:52,086 INFO misc.py line 119 87073] Train: [66/100][1273/1557] Data 0.003 (0.137) Batch 0.759 (1.356) Remain 20:03:02 loss: 0.1029 Lr: 0.00144 [2024-02-19 02:55:53,225 INFO misc.py line 119 87073] Train: [66/100][1274/1557] Data 0.009 (0.136) Batch 1.144 (1.356) Remain 20:02:51 loss: 0.1602 Lr: 0.00144 [2024-02-19 02:55:54,187 INFO misc.py line 119 87073] Train: [66/100][1275/1557] Data 0.003 (0.136) Batch 0.962 (1.356) Remain 20:02:34 loss: 0.4446 Lr: 0.00144 [2024-02-19 02:55:54,983 INFO misc.py line 119 87073] Train: [66/100][1276/1557] Data 0.004 (0.136) Batch 0.796 (1.355) Remain 20:02:09 loss: 0.2877 Lr: 0.00144 [2024-02-19 02:55:56,042 INFO misc.py line 119 87073] Train: [66/100][1277/1557] Data 0.004 (0.136) Batch 1.059 (1.355) Remain 20:01:55 loss: 0.2341 Lr: 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Train: [66/100][1290/1557] Data 0.013 (0.135) Batch 0.985 (1.351) Remain 19:58:18 loss: 0.3426 Lr: 0.00144 [2024-02-19 02:56:09,686 INFO misc.py line 119 87073] Train: [66/100][1291/1557] Data 0.003 (0.135) Batch 0.845 (1.351) Remain 19:57:56 loss: 0.5508 Lr: 0.00144 [2024-02-19 02:56:10,565 INFO misc.py line 119 87073] Train: [66/100][1292/1557] Data 0.003 (0.135) Batch 0.873 (1.351) Remain 19:57:35 loss: 0.4628 Lr: 0.00144 [2024-02-19 02:56:11,331 INFO misc.py line 119 87073] Train: [66/100][1293/1557] Data 0.010 (0.135) Batch 0.773 (1.350) Remain 19:57:10 loss: 0.2891 Lr: 0.00144 [2024-02-19 02:56:12,055 INFO misc.py line 119 87073] Train: [66/100][1294/1557] Data 0.003 (0.134) Batch 0.716 (1.350) Remain 19:56:42 loss: 0.2314 Lr: 0.00144 [2024-02-19 02:56:37,393 INFO misc.py line 119 87073] Train: [66/100][1295/1557] Data 7.541 (0.140) Batch 25.346 (1.368) Remain 20:13:09 loss: 0.1450 Lr: 0.00144 [2024-02-19 02:56:38,348 INFO misc.py line 119 87073] Train: [66/100][1296/1557] Data 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Remain 20:10:54 loss: 0.1802 Lr: 0.00144 [2024-02-19 02:56:44,753 INFO misc.py line 119 87073] Train: [66/100][1303/1557] Data 0.015 (0.139) Batch 0.839 (1.365) Remain 20:10:31 loss: 0.2320 Lr: 0.00144 [2024-02-19 02:56:45,735 INFO misc.py line 119 87073] Train: [66/100][1304/1557] Data 0.003 (0.139) Batch 0.982 (1.365) Remain 20:10:14 loss: 0.3729 Lr: 0.00144 [2024-02-19 02:56:46,710 INFO misc.py line 119 87073] Train: [66/100][1305/1557] Data 0.003 (0.139) Batch 0.976 (1.365) Remain 20:09:57 loss: 0.7972 Lr: 0.00143 [2024-02-19 02:56:47,499 INFO misc.py line 119 87073] Train: [66/100][1306/1557] Data 0.003 (0.139) Batch 0.788 (1.364) Remain 20:09:32 loss: 0.2657 Lr: 0.00143 [2024-02-19 02:56:48,245 INFO misc.py line 119 87073] Train: [66/100][1307/1557] Data 0.004 (0.139) Batch 0.741 (1.364) Remain 20:09:05 loss: 0.4382 Lr: 0.00143 [2024-02-19 02:56:48,997 INFO misc.py line 119 87073] Train: [66/100][1308/1557] Data 0.008 (0.139) Batch 0.757 (1.363) Remain 20:08:39 loss: 0.1694 Lr: 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INFO misc.py line 119 87073] Train: [66/100][1315/1557] Data 0.003 (0.138) Batch 0.768 (1.361) Remain 20:06:40 loss: 0.1890 Lr: 0.00143 [2024-02-19 02:56:57,055 INFO misc.py line 119 87073] Train: [66/100][1316/1557] Data 0.014 (0.138) Batch 1.182 (1.361) Remain 20:06:32 loss: 0.1745 Lr: 0.00143 [2024-02-19 02:56:58,135 INFO misc.py line 119 87073] Train: [66/100][1317/1557] Data 0.032 (0.138) Batch 1.099 (1.361) Remain 20:06:20 loss: 0.1249 Lr: 0.00143 [2024-02-19 02:56:59,216 INFO misc.py line 119 87073] Train: [66/100][1318/1557] Data 0.013 (0.138) Batch 1.080 (1.361) Remain 20:06:07 loss: 0.2006 Lr: 0.00143 [2024-02-19 02:57:00,071 INFO misc.py line 119 87073] Train: [66/100][1319/1557] Data 0.014 (0.138) Batch 0.866 (1.361) Remain 20:05:46 loss: 0.2550 Lr: 0.00143 [2024-02-19 02:57:01,189 INFO misc.py line 119 87073] Train: [66/100][1320/1557] Data 0.003 (0.138) Batch 1.118 (1.360) Remain 20:05:35 loss: 0.6866 Lr: 0.00143 [2024-02-19 02:57:01,983 INFO misc.py line 119 87073] Train: [66/100][1321/1557] Data 0.003 (0.138) Batch 0.793 (1.360) Remain 20:05:10 loss: 0.3176 Lr: 0.00143 [2024-02-19 02:57:02,762 INFO misc.py line 119 87073] Train: [66/100][1322/1557] Data 0.003 (0.137) Batch 0.762 (1.359) Remain 20:04:45 loss: 0.1696 Lr: 0.00143 [2024-02-19 02:57:03,989 INFO misc.py line 119 87073] Train: [66/100][1323/1557] Data 0.021 (0.137) Batch 1.239 (1.359) Remain 20:04:39 loss: 0.1232 Lr: 0.00143 [2024-02-19 02:57:04,840 INFO misc.py line 119 87073] Train: [66/100][1324/1557] Data 0.008 (0.137) Batch 0.856 (1.359) Remain 20:04:17 loss: 0.2154 Lr: 0.00143 [2024-02-19 02:57:05,763 INFO misc.py line 119 87073] Train: [66/100][1325/1557] Data 0.003 (0.137) Batch 0.923 (1.359) Remain 20:03:58 loss: 0.4196 Lr: 0.00143 [2024-02-19 02:57:06,757 INFO misc.py line 119 87073] Train: [66/100][1326/1557] Data 0.003 (0.137) Batch 0.993 (1.358) Remain 20:03:42 loss: 0.2752 Lr: 0.00143 [2024-02-19 02:57:07,702 INFO misc.py line 119 87073] Train: [66/100][1327/1557] Data 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Remain 20:01:32 loss: 0.3160 Lr: 0.00143 [2024-02-19 02:57:14,283 INFO misc.py line 119 87073] Train: [66/100][1334/1557] Data 0.003 (0.136) Batch 1.019 (1.356) Remain 20:01:17 loss: 0.3858 Lr: 0.00143 [2024-02-19 02:57:15,028 INFO misc.py line 119 87073] Train: [66/100][1335/1557] Data 0.012 (0.136) Batch 0.755 (1.355) Remain 20:00:52 loss: 0.2356 Lr: 0.00143 [2024-02-19 02:57:15,764 INFO misc.py line 119 87073] Train: [66/100][1336/1557] Data 0.003 (0.136) Batch 0.730 (1.355) Remain 20:00:26 loss: 0.2357 Lr: 0.00143 [2024-02-19 02:57:17,097 INFO misc.py line 119 87073] Train: [66/100][1337/1557] Data 0.009 (0.136) Batch 1.326 (1.355) Remain 20:00:23 loss: 0.1289 Lr: 0.00143 [2024-02-19 02:57:18,108 INFO misc.py line 119 87073] Train: [66/100][1338/1557] Data 0.016 (0.136) Batch 1.014 (1.355) Remain 20:00:08 loss: 0.2164 Lr: 0.00143 [2024-02-19 02:57:18,992 INFO misc.py line 119 87073] Train: [66/100][1339/1557] Data 0.013 (0.136) Batch 0.895 (1.354) Remain 19:59:49 loss: 0.2242 Lr: 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Remain 19:58:43 loss: 0.3213 Lr: 0.00143 [2024-02-19 02:58:36,163 INFO misc.py line 119 87073] Train: [66/100][1396/1557] Data 0.003 (0.136) Batch 1.066 (1.354) Remain 19:58:31 loss: 0.3718 Lr: 0.00143 [2024-02-19 02:58:37,034 INFO misc.py line 119 87073] Train: [66/100][1397/1557] Data 0.003 (0.136) Batch 0.871 (1.354) Remain 19:58:11 loss: 0.3259 Lr: 0.00143 [2024-02-19 02:58:37,754 INFO misc.py line 119 87073] Train: [66/100][1398/1557] Data 0.003 (0.136) Batch 0.713 (1.353) Remain 19:57:45 loss: 0.1552 Lr: 0.00143 [2024-02-19 02:58:38,549 INFO misc.py line 119 87073] Train: [66/100][1399/1557] Data 0.010 (0.136) Batch 0.802 (1.353) Remain 19:57:23 loss: 0.3345 Lr: 0.00143 [2024-02-19 02:58:39,672 INFO misc.py line 119 87073] Train: [66/100][1400/1557] Data 0.003 (0.136) Batch 1.122 (1.353) Remain 19:57:13 loss: 0.1466 Lr: 0.00143 [2024-02-19 02:58:40,669 INFO misc.py line 119 87073] Train: [66/100][1401/1557] Data 0.004 (0.136) Batch 0.998 (1.353) Remain 19:56:58 loss: 0.2502 Lr: 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INFO misc.py line 119 87073] Train: [66/100][1408/1557] Data 0.016 (0.141) Batch 1.106 (1.369) Remain 20:11:31 loss: 0.2867 Lr: 0.00143 [2024-02-19 02:59:14,383 INFO misc.py line 119 87073] Train: [66/100][1409/1557] Data 0.015 (0.141) Batch 0.888 (1.369) Remain 20:11:11 loss: 0.6127 Lr: 0.00143 [2024-02-19 02:59:15,443 INFO misc.py line 119 87073] Train: [66/100][1410/1557] Data 0.004 (0.141) Batch 1.061 (1.369) Remain 20:10:58 loss: 0.4287 Lr: 0.00143 [2024-02-19 02:59:16,449 INFO misc.py line 119 87073] Train: [66/100][1411/1557] Data 0.003 (0.140) Batch 1.005 (1.368) Remain 20:10:43 loss: 0.3012 Lr: 0.00143 [2024-02-19 02:59:17,130 INFO misc.py line 119 87073] Train: [66/100][1412/1557] Data 0.003 (0.140) Batch 0.680 (1.368) Remain 20:10:16 loss: 0.2463 Lr: 0.00143 [2024-02-19 02:59:18,010 INFO misc.py line 119 87073] Train: [66/100][1413/1557] Data 0.003 (0.140) Batch 0.880 (1.368) Remain 20:09:56 loss: 0.3908 Lr: 0.00143 [2024-02-19 02:59:19,235 INFO misc.py line 119 87073] Train: [66/100][1414/1557] Data 0.004 (0.140) Batch 1.218 (1.368) Remain 20:09:49 loss: 0.1534 Lr: 0.00143 [2024-02-19 02:59:20,303 INFO misc.py line 119 87073] Train: [66/100][1415/1557] Data 0.011 (0.140) Batch 1.069 (1.367) Remain 20:09:37 loss: 0.2216 Lr: 0.00143 [2024-02-19 02:59:21,285 INFO misc.py line 119 87073] Train: [66/100][1416/1557] Data 0.010 (0.140) Batch 0.989 (1.367) Remain 20:09:21 loss: 0.5267 Lr: 0.00143 [2024-02-19 02:59:22,259 INFO misc.py line 119 87073] Train: [66/100][1417/1557] Data 0.003 (0.140) Batch 0.975 (1.367) Remain 20:09:05 loss: 0.1516 Lr: 0.00143 [2024-02-19 02:59:23,393 INFO misc.py line 119 87073] Train: [66/100][1418/1557] Data 0.003 (0.140) Batch 1.133 (1.367) Remain 20:08:55 loss: 0.5208 Lr: 0.00143 [2024-02-19 02:59:24,097 INFO misc.py line 119 87073] Train: [66/100][1419/1557] Data 0.003 (0.140) Batch 0.703 (1.366) Remain 20:08:29 loss: 0.2149 Lr: 0.00143 [2024-02-19 02:59:24,855 INFO misc.py line 119 87073] Train: [66/100][1420/1557] Data 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Remain 20:06:27 loss: 0.2683 Lr: 0.00143 [2024-02-19 02:59:31,384 INFO misc.py line 119 87073] Train: [66/100][1427/1557] Data 0.005 (0.139) Batch 0.715 (1.364) Remain 20:06:02 loss: 0.4702 Lr: 0.00143 [2024-02-19 02:59:32,523 INFO misc.py line 119 87073] Train: [66/100][1428/1557] Data 0.007 (0.139) Batch 1.134 (1.363) Remain 20:05:52 loss: 0.1488 Lr: 0.00143 [2024-02-19 02:59:33,469 INFO misc.py line 119 87073] Train: [66/100][1429/1557] Data 0.012 (0.139) Batch 0.955 (1.363) Remain 20:05:35 loss: 0.1901 Lr: 0.00143 [2024-02-19 02:59:34,451 INFO misc.py line 119 87073] Train: [66/100][1430/1557] Data 0.003 (0.139) Batch 0.982 (1.363) Remain 20:05:20 loss: 0.3739 Lr: 0.00143 [2024-02-19 02:59:35,443 INFO misc.py line 119 87073] Train: [66/100][1431/1557] Data 0.003 (0.139) Batch 0.993 (1.363) Remain 20:05:05 loss: 0.2132 Lr: 0.00143 [2024-02-19 02:59:36,445 INFO misc.py line 119 87073] Train: [66/100][1432/1557] Data 0.003 (0.138) Batch 1.002 (1.362) Remain 20:04:50 loss: 0.2851 Lr: 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Train: [66/100][1445/1557] Data 0.010 (0.137) Batch 0.931 (1.359) Remain 20:01:22 loss: 0.3609 Lr: 0.00143 [2024-02-19 02:59:50,034 INFO misc.py line 119 87073] Train: [66/100][1446/1557] Data 0.006 (0.137) Batch 1.040 (1.359) Remain 20:01:09 loss: 0.1231 Lr: 0.00143 [2024-02-19 02:59:50,814 INFO misc.py line 119 87073] Train: [66/100][1447/1557] Data 0.005 (0.137) Batch 0.781 (1.358) Remain 20:00:47 loss: 0.1461 Lr: 0.00143 [2024-02-19 02:59:51,591 INFO misc.py line 119 87073] Train: [66/100][1448/1557] Data 0.004 (0.137) Batch 0.769 (1.358) Remain 20:00:24 loss: 0.3591 Lr: 0.00143 [2024-02-19 02:59:52,834 INFO misc.py line 119 87073] Train: [66/100][1449/1557] Data 0.011 (0.137) Batch 1.240 (1.358) Remain 20:00:18 loss: 0.1340 Lr: 0.00143 [2024-02-19 02:59:53,668 INFO misc.py line 119 87073] Train: [66/100][1450/1557] Data 0.015 (0.137) Batch 0.846 (1.357) Remain 19:59:58 loss: 0.3854 Lr: 0.00143 [2024-02-19 02:59:54,507 INFO misc.py line 119 87073] Train: [66/100][1451/1557] Data 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Remain 19:57:50 loss: 0.5310 Lr: 0.00143 [2024-02-19 03:00:00,844 INFO misc.py line 119 87073] Train: [66/100][1458/1557] Data 0.004 (0.136) Batch 0.910 (1.355) Remain 19:57:33 loss: 0.2022 Lr: 0.00143 [2024-02-19 03:00:01,805 INFO misc.py line 119 87073] Train: [66/100][1459/1557] Data 0.004 (0.136) Batch 0.961 (1.355) Remain 19:57:17 loss: 0.2395 Lr: 0.00143 [2024-02-19 03:00:02,813 INFO misc.py line 119 87073] Train: [66/100][1460/1557] Data 0.004 (0.136) Batch 1.002 (1.354) Remain 19:57:03 loss: 0.1494 Lr: 0.00143 [2024-02-19 03:00:03,516 INFO misc.py line 119 87073] Train: [66/100][1461/1557] Data 0.010 (0.136) Batch 0.708 (1.354) Remain 19:56:38 loss: 0.1520 Lr: 0.00143 [2024-02-19 03:00:04,254 INFO misc.py line 119 87073] Train: [66/100][1462/1557] Data 0.006 (0.136) Batch 0.734 (1.353) Remain 19:56:14 loss: 0.2980 Lr: 0.00143 [2024-02-19 03:00:28,245 INFO misc.py line 119 87073] Train: [66/100][1463/1557] Data 7.135 (0.141) Batch 23.994 (1.369) Remain 20:09:55 loss: 0.1792 Lr: 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Train: [66/100][1476/1557] Data 0.006 (0.139) Batch 0.722 (1.365) Remain 20:06:18 loss: 0.1728 Lr: 0.00143 [2024-02-19 03:00:41,832 INFO misc.py line 119 87073] Train: [66/100][1477/1557] Data 0.016 (0.139) Batch 1.339 (1.365) Remain 20:06:15 loss: 0.1051 Lr: 0.00143 [2024-02-19 03:00:42,844 INFO misc.py line 119 87073] Train: [66/100][1478/1557] Data 0.005 (0.139) Batch 1.004 (1.365) Remain 20:06:01 loss: 0.3857 Lr: 0.00143 [2024-02-19 03:00:43,918 INFO misc.py line 119 87073] Train: [66/100][1479/1557] Data 0.012 (0.139) Batch 1.066 (1.365) Remain 20:05:49 loss: 0.6734 Lr: 0.00143 [2024-02-19 03:00:44,772 INFO misc.py line 119 87073] Train: [66/100][1480/1557] Data 0.020 (0.139) Batch 0.870 (1.364) Remain 20:05:30 loss: 0.1196 Lr: 0.00143 [2024-02-19 03:00:45,673 INFO misc.py line 119 87073] Train: [66/100][1481/1557] Data 0.005 (0.139) Batch 0.901 (1.364) Remain 20:05:12 loss: 0.1049 Lr: 0.00143 [2024-02-19 03:00:46,394 INFO misc.py line 119 87073] Train: [66/100][1482/1557] Data 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Remain 20:03:10 loss: 0.4736 Lr: 0.00143 [2024-02-19 03:00:52,856 INFO misc.py line 119 87073] Train: [66/100][1489/1557] Data 0.004 (0.138) Batch 0.788 (1.362) Remain 20:02:48 loss: 0.2922 Lr: 0.00143 [2024-02-19 03:00:53,661 INFO misc.py line 119 87073] Train: [66/100][1490/1557] Data 0.007 (0.138) Batch 0.801 (1.361) Remain 20:02:26 loss: 0.2032 Lr: 0.00143 [2024-02-19 03:00:54,896 INFO misc.py line 119 87073] Train: [66/100][1491/1557] Data 0.011 (0.138) Batch 1.236 (1.361) Remain 20:02:21 loss: 0.2565 Lr: 0.00143 [2024-02-19 03:00:55,809 INFO misc.py line 119 87073] Train: [66/100][1492/1557] Data 0.010 (0.138) Batch 0.919 (1.361) Remain 20:02:04 loss: 0.3379 Lr: 0.00143 [2024-02-19 03:00:56,764 INFO misc.py line 119 87073] Train: [66/100][1493/1557] Data 0.004 (0.138) Batch 0.954 (1.360) Remain 20:01:48 loss: 0.5189 Lr: 0.00143 [2024-02-19 03:00:57,839 INFO misc.py line 119 87073] Train: [66/100][1494/1557] Data 0.004 (0.138) Batch 1.075 (1.360) Remain 20:01:36 loss: 0.2443 Lr: 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Train: [66/100][1507/1557] Data 0.005 (0.137) Batch 0.921 (1.357) Remain 19:57:58 loss: 0.2905 Lr: 0.00143 [2024-02-19 03:01:10,882 INFO misc.py line 119 87073] Train: [66/100][1508/1557] Data 0.004 (0.137) Batch 1.051 (1.356) Remain 19:57:46 loss: 0.1021 Lr: 0.00143 [2024-02-19 03:01:12,046 INFO misc.py line 119 87073] Train: [66/100][1509/1557] Data 0.007 (0.136) Batch 1.166 (1.356) Remain 19:57:38 loss: 0.5046 Lr: 0.00143 [2024-02-19 03:01:12,748 INFO misc.py line 119 87073] Train: [66/100][1510/1557] Data 0.004 (0.136) Batch 0.702 (1.356) Remain 19:57:13 loss: 0.1729 Lr: 0.00143 [2024-02-19 03:01:13,513 INFO misc.py line 119 87073] Train: [66/100][1511/1557] Data 0.004 (0.136) Batch 0.762 (1.355) Remain 19:56:51 loss: 0.2114 Lr: 0.00143 [2024-02-19 03:01:14,630 INFO misc.py line 119 87073] Train: [66/100][1512/1557] Data 0.006 (0.136) Batch 1.117 (1.355) Remain 19:56:41 loss: 0.1509 Lr: 0.00143 [2024-02-19 03:01:15,616 INFO misc.py line 119 87073] Train: [66/100][1513/1557] Data 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Remain 20:07:33 loss: 0.1705 Lr: 0.00142 [2024-02-19 03:01:43,903 INFO misc.py line 119 87073] Train: [66/100][1520/1557] Data 0.005 (0.140) Batch 0.867 (1.367) Remain 20:07:14 loss: 0.0833 Lr: 0.00142 [2024-02-19 03:01:44,937 INFO misc.py line 119 87073] Train: [66/100][1521/1557] Data 0.006 (0.140) Batch 1.036 (1.367) Remain 20:07:01 loss: 0.2478 Lr: 0.00142 [2024-02-19 03:01:45,973 INFO misc.py line 119 87073] Train: [66/100][1522/1557] Data 0.004 (0.140) Batch 1.035 (1.367) Remain 20:06:48 loss: 0.4077 Lr: 0.00142 [2024-02-19 03:01:47,013 INFO misc.py line 119 87073] Train: [66/100][1523/1557] Data 0.006 (0.140) Batch 1.041 (1.367) Remain 20:06:36 loss: 0.3118 Lr: 0.00142 [2024-02-19 03:01:47,799 INFO misc.py line 119 87073] Train: [66/100][1524/1557] Data 0.004 (0.140) Batch 0.786 (1.366) Remain 20:06:14 loss: 0.3462 Lr: 0.00142 [2024-02-19 03:01:48,598 INFO misc.py line 119 87073] Train: [66/100][1525/1557] Data 0.004 (0.140) Batch 0.795 (1.366) Remain 20:05:53 loss: 0.2559 Lr: 0.00142 [2024-02-19 03:01:49,890 INFO misc.py line 119 87073] Train: [66/100][1526/1557] Data 0.008 (0.140) Batch 1.293 (1.366) Remain 20:05:49 loss: 0.1891 Lr: 0.00142 [2024-02-19 03:01:50,789 INFO misc.py line 119 87073] Train: [66/100][1527/1557] Data 0.006 (0.140) Batch 0.901 (1.366) Remain 20:05:31 loss: 1.4507 Lr: 0.00142 [2024-02-19 03:01:51,742 INFO misc.py line 119 87073] Train: [66/100][1528/1557] Data 0.005 (0.140) Batch 0.953 (1.365) Remain 20:05:16 loss: 0.2726 Lr: 0.00142 [2024-02-19 03:01:52,841 INFO misc.py line 119 87073] Train: [66/100][1529/1557] Data 0.004 (0.140) Batch 1.099 (1.365) Remain 20:05:05 loss: 0.3188 Lr: 0.00142 [2024-02-19 03:01:53,802 INFO misc.py line 119 87073] Train: [66/100][1530/1557] Data 0.005 (0.140) Batch 0.962 (1.365) Remain 20:04:50 loss: 0.1975 Lr: 0.00142 [2024-02-19 03:01:54,592 INFO misc.py line 119 87073] Train: [66/100][1531/1557] Data 0.004 (0.139) Batch 0.787 (1.364) Remain 20:04:28 loss: 0.2129 Lr: 0.00142 [2024-02-19 03:01:55,313 INFO misc.py line 119 87073] Train: [66/100][1532/1557] Data 0.007 (0.139) Batch 0.721 (1.364) Remain 20:04:05 loss: 0.2720 Lr: 0.00142 [2024-02-19 03:01:56,577 INFO misc.py line 119 87073] Train: [66/100][1533/1557] Data 0.007 (0.139) Batch 1.260 (1.364) Remain 20:04:00 loss: 0.1007 Lr: 0.00142 [2024-02-19 03:01:57,516 INFO misc.py line 119 87073] Train: [66/100][1534/1557] Data 0.010 (0.139) Batch 0.942 (1.364) Remain 20:03:44 loss: 0.5738 Lr: 0.00142 [2024-02-19 03:01:58,363 INFO misc.py line 119 87073] Train: [66/100][1535/1557] Data 0.008 (0.139) Batch 0.850 (1.363) Remain 20:03:25 loss: 0.1346 Lr: 0.00142 [2024-02-19 03:01:59,417 INFO misc.py line 119 87073] Train: [66/100][1536/1557] Data 0.004 (0.139) Batch 1.050 (1.363) Remain 20:03:13 loss: 0.3324 Lr: 0.00142 [2024-02-19 03:02:00,387 INFO misc.py line 119 87073] Train: [66/100][1537/1557] Data 0.007 (0.139) Batch 0.973 (1.363) Remain 20:02:58 loss: 0.3911 Lr: 0.00142 [2024-02-19 03:02:01,073 INFO misc.py line 119 87073] Train: [66/100][1538/1557] Data 0.005 (0.139) Batch 0.686 (1.362) Remain 20:02:33 loss: 0.1254 Lr: 0.00142 [2024-02-19 03:02:01,829 INFO misc.py line 119 87073] Train: [66/100][1539/1557] Data 0.003 (0.139) Batch 0.749 (1.362) Remain 20:02:10 loss: 0.2789 Lr: 0.00142 [2024-02-19 03:02:02,951 INFO misc.py line 119 87073] Train: [66/100][1540/1557] Data 0.011 (0.139) Batch 1.125 (1.362) Remain 20:02:01 loss: 0.1478 Lr: 0.00142 [2024-02-19 03:02:04,051 INFO misc.py line 119 87073] Train: [66/100][1541/1557] Data 0.008 (0.139) Batch 1.101 (1.362) Remain 20:01:51 loss: 0.3098 Lr: 0.00142 [2024-02-19 03:02:05,155 INFO misc.py line 119 87073] Train: [66/100][1542/1557] Data 0.007 (0.139) Batch 1.100 (1.362) Remain 20:01:40 loss: 0.3015 Lr: 0.00142 [2024-02-19 03:02:06,146 INFO misc.py line 119 87073] Train: [66/100][1543/1557] Data 0.011 (0.138) Batch 0.994 (1.361) Remain 20:01:26 loss: 0.5120 Lr: 0.00142 [2024-02-19 03:02:07,027 INFO misc.py line 119 87073] Train: [66/100][1544/1557] Data 0.008 (0.138) Batch 0.884 (1.361) Remain 20:01:08 loss: 0.3257 Lr: 0.00142 [2024-02-19 03:02:07,814 INFO misc.py line 119 87073] Train: [66/100][1545/1557] Data 0.004 (0.138) Batch 0.772 (1.361) Remain 20:00:47 loss: 0.2091 Lr: 0.00142 [2024-02-19 03:02:08,564 INFO misc.py line 119 87073] Train: [66/100][1546/1557] Data 0.019 (0.138) Batch 0.765 (1.360) Remain 20:00:25 loss: 0.2875 Lr: 0.00142 [2024-02-19 03:02:09,736 INFO misc.py line 119 87073] Train: [66/100][1547/1557] Data 0.004 (0.138) Batch 1.172 (1.360) Remain 20:00:17 loss: 0.1267 Lr: 0.00142 [2024-02-19 03:02:10,620 INFO misc.py line 119 87073] Train: [66/100][1548/1557] Data 0.004 (0.138) Batch 0.884 (1.360) Remain 20:00:00 loss: 0.6406 Lr: 0.00142 [2024-02-19 03:02:11,650 INFO misc.py line 119 87073] Train: [66/100][1549/1557] Data 0.005 (0.138) Batch 1.030 (1.360) Remain 19:59:47 loss: 0.3974 Lr: 0.00142 [2024-02-19 03:02:12,604 INFO misc.py line 119 87073] Train: [66/100][1550/1557] Data 0.004 (0.138) Batch 0.945 (1.359) Remain 19:59:31 loss: 0.3006 Lr: 0.00142 [2024-02-19 03:02:13,517 INFO misc.py line 119 87073] Train: [66/100][1551/1557] Data 0.013 (0.138) Batch 0.922 (1.359) Remain 19:59:15 loss: 0.6302 Lr: 0.00142 [2024-02-19 03:02:14,271 INFO misc.py line 119 87073] Train: [66/100][1552/1557] Data 0.004 (0.138) Batch 0.754 (1.359) Remain 19:58:53 loss: 0.2102 Lr: 0.00142 [2024-02-19 03:02:15,029 INFO misc.py line 119 87073] Train: [66/100][1553/1557] Data 0.004 (0.138) Batch 0.752 (1.358) Remain 19:58:31 loss: 0.1797 Lr: 0.00142 [2024-02-19 03:02:16,170 INFO misc.py line 119 87073] Train: [66/100][1554/1557] Data 0.010 (0.137) Batch 1.142 (1.358) Remain 19:58:22 loss: 0.2390 Lr: 0.00142 [2024-02-19 03:02:17,071 INFO misc.py line 119 87073] Train: [66/100][1555/1557] Data 0.009 (0.137) Batch 0.905 (1.358) Remain 19:58:05 loss: 0.3643 Lr: 0.00142 [2024-02-19 03:02:18,049 INFO misc.py line 119 87073] Train: [66/100][1556/1557] Data 0.004 (0.137) Batch 0.978 (1.358) Remain 19:57:51 loss: 0.1821 Lr: 0.00142 [2024-02-19 03:02:18,922 INFO misc.py line 119 87073] Train: [66/100][1557/1557] Data 0.004 (0.137) Batch 0.874 (1.357) Remain 19:57:33 loss: 0.4020 Lr: 0.00142 [2024-02-19 03:02:18,923 INFO misc.py line 136 87073] Train result: loss: 0.2864 [2024-02-19 03:02:18,923 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 03:02:43,125 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6311 [2024-02-19 03:02:43,907 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5126 [2024-02-19 03:02:47,033 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3377 [2024-02-19 03:02:49,241 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3420 [2024-02-19 03:02:54,192 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2438 [2024-02-19 03:02:54,898 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1919 [2024-02-19 03:02:56,158 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2639 [2024-02-19 03:02:59,117 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3329 [2024-02-19 03:03:00,933 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2289 [2024-02-19 03:03:02,472 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7229/0.7795/0.9184. [2024-02-19 03:03:02,472 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9346/0.9748 [2024-02-19 03:03:02,472 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9828/0.9892 [2024-02-19 03:03:02,472 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8784/0.9663 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4558/0.5313 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6695/0.6880 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8111/0.9473 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8536/0.9167 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9210/0.9660 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7557/0.7754 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7899/0.8620 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7380/0.7777 [2024-02-19 03:03:02,473 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6076/0.7388 [2024-02-19 03:03:02,474 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 03:03:02,476 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 03:03:02,476 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 03:03:09,632 INFO misc.py line 119 87073] Train: [67/100][1/1557] Data 1.555 (1.555) Batch 2.290 (2.290) Remain 33:40:08 loss: 0.4068 Lr: 0.00142 [2024-02-19 03:03:10,630 INFO misc.py line 119 87073] Train: [67/100][2/1557] Data 0.016 (0.016) Batch 1.001 (1.001) Remain 14:42:46 loss: 0.8094 Lr: 0.00142 [2024-02-19 03:03:11,782 INFO misc.py line 119 87073] Train: [67/100][3/1557] Data 0.013 (0.013) Batch 1.160 (1.160) Remain 17:03:25 loss: 0.1347 Lr: 0.00142 [2024-02-19 03:03:12,778 INFO misc.py line 119 87073] Train: [67/100][4/1557] Data 0.004 (0.004) Batch 0.993 (0.993) Remain 14:36:01 loss: 0.5555 Lr: 0.00142 [2024-02-19 03:03:13,488 INFO misc.py line 119 87073] Train: [67/100][5/1557] Data 0.008 (0.006) Batch 0.714 (0.853) Remain 12:32:54 loss: 0.3761 Lr: 0.00142 [2024-02-19 03:03:14,249 INFO misc.py line 119 87073] Train: [67/100][6/1557] Data 0.003 (0.005) Batch 0.755 (0.821) Remain 12:03:54 loss: 0.1833 Lr: 0.00142 [2024-02-19 03:03:16,159 INFO misc.py line 119 87073] Train: [67/100][7/1557] Data 0.678 (0.173) Batch 1.913 (1.094) Remain 16:04:51 loss: 0.3635 Lr: 0.00142 [2024-02-19 03:03:17,132 INFO misc.py line 119 87073] Train: [67/100][8/1557] Data 0.006 (0.140) Batch 0.976 (1.070) Remain 15:44:03 loss: 0.5299 Lr: 0.00142 [2024-02-19 03:03:18,102 INFO misc.py line 119 87073] Train: [67/100][9/1557] Data 0.003 (0.117) Batch 0.971 (1.054) Remain 15:29:23 loss: 0.2683 Lr: 0.00142 [2024-02-19 03:03:19,004 INFO misc.py line 119 87073] Train: [67/100][10/1557] Data 0.003 (0.101) Batch 0.901 (1.032) Remain 15:10:08 loss: 0.2789 Lr: 0.00142 [2024-02-19 03:03:20,035 INFO misc.py line 119 87073] Train: [67/100][11/1557] Data 0.004 (0.089) Batch 1.030 (1.031) Remain 15:09:54 loss: 0.3886 Lr: 0.00142 [2024-02-19 03:03:20,866 INFO misc.py line 119 87073] Train: [67/100][12/1557] Data 0.006 (0.079) Batch 0.832 (1.009) Remain 14:50:17 loss: 0.2122 Lr: 0.00142 [2024-02-19 03:03:21,605 INFO misc.py line 119 87073] Train: [67/100][13/1557] Data 0.005 (0.072) Batch 0.739 (0.982) Remain 14:26:27 loss: 0.1708 Lr: 0.00142 [2024-02-19 03:03:23,589 INFO misc.py line 119 87073] Train: [67/100][14/1557] Data 0.004 (0.066) Batch 1.984 (1.073) Remain 15:46:44 loss: 0.2082 Lr: 0.00142 [2024-02-19 03:03:24,622 INFO misc.py line 119 87073] Train: [67/100][15/1557] Data 0.006 (0.061) Batch 1.030 (1.070) Remain 15:43:31 loss: 0.6318 Lr: 0.00142 [2024-02-19 03:03:25,479 INFO misc.py line 119 87073] Train: [67/100][16/1557] Data 0.008 (0.057) Batch 0.861 (1.054) Remain 15:29:20 loss: 0.3360 Lr: 0.00142 [2024-02-19 03:03:26,477 INFO misc.py line 119 87073] Train: [67/100][17/1557] Data 0.004 (0.053) Batch 0.994 (1.049) Remain 15:25:33 loss: 0.2514 Lr: 0.00142 [2024-02-19 03:03:27,432 INFO misc.py line 119 87073] Train: [67/100][18/1557] Data 0.008 (0.050) Batch 0.960 (1.043) Remain 15:20:16 loss: 0.3928 Lr: 0.00142 [2024-02-19 03:03:28,150 INFO misc.py line 119 87073] Train: [67/100][19/1557] Data 0.003 (0.047) Batch 0.717 (1.023) Remain 15:02:15 loss: 0.2843 Lr: 0.00142 [2024-02-19 03:03:28,924 INFO misc.py line 119 87073] Train: [67/100][20/1557] Data 0.004 (0.045) Batch 0.768 (1.008) Remain 14:49:01 loss: 0.3022 Lr: 0.00142 [2024-02-19 03:03:30,051 INFO misc.py line 119 87073] Train: [67/100][21/1557] Data 0.010 (0.043) Batch 1.123 (1.014) Remain 14:54:39 loss: 0.2007 Lr: 0.00142 [2024-02-19 03:03:31,044 INFO misc.py line 119 87073] Train: [67/100][22/1557] Data 0.013 (0.041) Batch 1.002 (1.014) Remain 14:54:03 loss: 0.5293 Lr: 0.00142 [2024-02-19 03:03:31,910 INFO misc.py line 119 87073] Train: [67/100][23/1557] Data 0.004 (0.039) Batch 0.867 (1.006) Remain 14:47:35 loss: 0.2047 Lr: 0.00142 [2024-02-19 03:03:32,914 INFO misc.py line 119 87073] Train: [67/100][24/1557] Data 0.003 (0.038) Batch 1.003 (1.006) Remain 14:47:26 loss: 0.2252 Lr: 0.00142 [2024-02-19 03:03:33,745 INFO misc.py line 119 87073] Train: [67/100][25/1557] Data 0.004 (0.036) Batch 0.831 (0.998) Remain 14:40:24 loss: 0.1626 Lr: 0.00142 [2024-02-19 03:03:34,526 INFO misc.py line 119 87073] Train: [67/100][26/1557] Data 0.004 (0.035) Batch 0.782 (0.989) Remain 14:32:05 loss: 0.1294 Lr: 0.00142 [2024-02-19 03:03:35,285 INFO misc.py line 119 87073] Train: [67/100][27/1557] Data 0.003 (0.033) Batch 0.756 (0.979) Remain 14:23:30 loss: 0.2136 Lr: 0.00142 [2024-02-19 03:03:36,599 INFO misc.py line 119 87073] Train: [67/100][28/1557] Data 0.006 (0.032) Batch 1.311 (0.992) Remain 14:35:12 loss: 0.1237 Lr: 0.00142 [2024-02-19 03:03:37,400 INFO misc.py line 119 87073] Train: [67/100][29/1557] Data 0.009 (0.031) Batch 0.805 (0.985) Remain 14:28:50 loss: 0.2927 Lr: 0.00142 [2024-02-19 03:03:38,451 INFO misc.py line 119 87073] Train: [67/100][30/1557] Data 0.006 (0.030) Batch 1.052 (0.988) Remain 14:31:00 loss: 0.4696 Lr: 0.00142 [2024-02-19 03:03:39,545 INFO misc.py line 119 87073] Train: [67/100][31/1557] Data 0.003 (0.029) Batch 1.092 (0.992) Remain 14:34:17 loss: 0.3379 Lr: 0.00142 [2024-02-19 03:03:40,643 INFO misc.py line 119 87073] Train: [67/100][32/1557] Data 0.005 (0.029) Batch 1.098 (0.995) Remain 14:37:31 loss: 0.3875 Lr: 0.00142 [2024-02-19 03:03:41,374 INFO misc.py line 119 87073] Train: [67/100][33/1557] Data 0.005 (0.028) Batch 0.732 (0.986) Remain 14:29:46 loss: 0.1563 Lr: 0.00142 [2024-02-19 03:03:42,169 INFO misc.py line 119 87073] Train: [67/100][34/1557] Data 0.004 (0.027) Batch 0.790 (0.980) Remain 14:24:10 loss: 0.2087 Lr: 0.00142 [2024-02-19 03:03:43,260 INFO misc.py line 119 87073] Train: [67/100][35/1557] Data 0.008 (0.026) Batch 1.086 (0.983) Remain 14:27:03 loss: 0.1960 Lr: 0.00142 [2024-02-19 03:03:44,083 INFO misc.py line 119 87073] Train: [67/100][36/1557] Data 0.014 (0.026) Batch 0.834 (0.979) Remain 14:23:02 loss: 0.3720 Lr: 0.00142 [2024-02-19 03:03:45,196 INFO misc.py line 119 87073] Train: [67/100][37/1557] Data 0.004 (0.025) Batch 1.113 (0.983) Remain 14:26:30 loss: 0.3199 Lr: 0.00142 [2024-02-19 03:03:46,192 INFO misc.py line 119 87073] Train: [67/100][38/1557] Data 0.003 (0.025) Batch 0.995 (0.983) Remain 14:26:48 loss: 0.3128 Lr: 0.00142 [2024-02-19 03:03:47,005 INFO misc.py line 119 87073] Train: [67/100][39/1557] Data 0.004 (0.024) Batch 0.813 (0.978) Remain 14:22:37 loss: 0.3072 Lr: 0.00142 [2024-02-19 03:03:47,810 INFO misc.py line 119 87073] Train: [67/100][40/1557] Data 0.004 (0.024) Batch 0.782 (0.973) Remain 14:17:55 loss: 0.2739 Lr: 0.00142 [2024-02-19 03:03:48,558 INFO misc.py line 119 87073] Train: [67/100][41/1557] Data 0.028 (0.024) Batch 0.771 (0.968) Remain 14:13:12 loss: 0.2702 Lr: 0.00142 [2024-02-19 03:03:49,689 INFO misc.py line 119 87073] Train: [67/100][42/1557] Data 0.004 (0.023) Batch 1.127 (0.972) Remain 14:16:47 loss: 0.1313 Lr: 0.00142 [2024-02-19 03:03:50,567 INFO misc.py line 119 87073] Train: [67/100][43/1557] Data 0.009 (0.023) Batch 0.882 (0.970) Remain 14:14:47 loss: 0.2821 Lr: 0.00142 [2024-02-19 03:03:51,410 INFO misc.py line 119 87073] Train: [67/100][44/1557] Data 0.005 (0.022) Batch 0.843 (0.967) Remain 14:12:03 loss: 0.1622 Lr: 0.00142 [2024-02-19 03:03:52,473 INFO misc.py line 119 87073] Train: [67/100][45/1557] Data 0.005 (0.022) Batch 1.063 (0.969) Remain 14:14:04 loss: 0.1158 Lr: 0.00142 [2024-02-19 03:03:53,489 INFO misc.py line 119 87073] Train: [67/100][46/1557] Data 0.004 (0.022) Batch 1.015 (0.970) Remain 14:15:00 loss: 0.3461 Lr: 0.00142 [2024-02-19 03:03:54,251 INFO misc.py line 119 87073] Train: [67/100][47/1557] Data 0.005 (0.021) Batch 0.764 (0.965) Remain 14:10:52 loss: 0.3534 Lr: 0.00142 [2024-02-19 03:03:55,065 INFO misc.py line 119 87073] Train: [67/100][48/1557] Data 0.003 (0.021) Batch 0.814 (0.962) Remain 14:07:53 loss: 0.2140 Lr: 0.00142 [2024-02-19 03:03:56,319 INFO misc.py line 119 87073] Train: [67/100][49/1557] Data 0.003 (0.020) Batch 1.246 (0.968) Remain 14:13:19 loss: 0.1774 Lr: 0.00142 [2024-02-19 03:03:57,309 INFO misc.py line 119 87073] Train: [67/100][50/1557] Data 0.011 (0.020) Batch 0.997 (0.969) Remain 14:13:51 loss: 0.3815 Lr: 0.00142 [2024-02-19 03:03:58,244 INFO misc.py line 119 87073] Train: [67/100][51/1557] Data 0.003 (0.020) Batch 0.935 (0.968) Remain 14:13:13 loss: 0.3912 Lr: 0.00142 [2024-02-19 03:03:59,421 INFO misc.py line 119 87073] Train: [67/100][52/1557] Data 0.003 (0.020) Batch 1.177 (0.972) Remain 14:16:57 loss: 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Batch 1.104 (1.133) Remain 16:26:35 loss: 0.1725 Lr: 0.00139 [2024-02-19 03:16:00,703 INFO misc.py line 119 87073] Train: [67/100][682/1557] Data 0.003 (0.144) Batch 0.893 (1.132) Remain 16:26:16 loss: 0.4376 Lr: 0.00139 [2024-02-19 03:16:01,654 INFO misc.py line 119 87073] Train: [67/100][683/1557] Data 0.005 (0.144) Batch 0.948 (1.132) Remain 16:26:00 loss: 0.2863 Lr: 0.00139 [2024-02-19 03:16:02,402 INFO misc.py line 119 87073] Train: [67/100][684/1557] Data 0.006 (0.144) Batch 0.751 (1.132) Remain 16:25:30 loss: 0.4398 Lr: 0.00139 [2024-02-19 03:16:03,116 INFO misc.py line 119 87073] Train: [67/100][685/1557] Data 0.004 (0.143) Batch 0.707 (1.131) Remain 16:24:56 loss: 0.2975 Lr: 0.00139 [2024-02-19 03:16:07,879 INFO misc.py line 119 87073] Train: [67/100][686/1557] Data 0.011 (0.143) Batch 4.770 (1.136) Remain 16:29:34 loss: 0.1612 Lr: 0.00139 [2024-02-19 03:16:08,846 INFO misc.py line 119 87073] Train: [67/100][687/1557] Data 0.004 (0.143) Batch 0.968 (1.136) Remain 16:29:20 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[67/100][1538/1557] Data 0.005 (0.143) Batch 0.755 (1.131) Remain 16:09:05 loss: 0.2512 Lr: 0.00135 [2024-02-19 03:32:08,959 INFO misc.py line 119 87073] Train: [67/100][1539/1557] Data 0.003 (0.142) Batch 0.723 (1.131) Remain 16:08:50 loss: 0.1825 Lr: 0.00135 [2024-02-19 03:32:10,192 INFO misc.py line 119 87073] Train: [67/100][1540/1557] Data 0.014 (0.142) Batch 1.233 (1.131) Remain 16:08:52 loss: 0.1946 Lr: 0.00135 [2024-02-19 03:32:11,104 INFO misc.py line 119 87073] Train: [67/100][1541/1557] Data 0.013 (0.142) Batch 0.922 (1.131) Remain 16:08:44 loss: 0.8684 Lr: 0.00135 [2024-02-19 03:32:12,024 INFO misc.py line 119 87073] Train: [67/100][1542/1557] Data 0.003 (0.142) Batch 0.920 (1.131) Remain 16:08:36 loss: 0.1438 Lr: 0.00135 [2024-02-19 03:32:12,931 INFO misc.py line 119 87073] Train: [67/100][1543/1557] Data 0.003 (0.142) Batch 0.907 (1.131) Remain 16:08:27 loss: 0.2557 Lr: 0.00135 [2024-02-19 03:32:13,995 INFO misc.py line 119 87073] Train: [67/100][1544/1557] Data 0.003 (0.142) Batch 1.040 (1.131) Remain 16:08:23 loss: 0.2755 Lr: 0.00135 [2024-02-19 03:32:14,691 INFO misc.py line 119 87073] Train: [67/100][1545/1557] Data 0.027 (0.142) Batch 0.719 (1.130) Remain 16:08:08 loss: 0.3101 Lr: 0.00135 [2024-02-19 03:32:15,463 INFO misc.py line 119 87073] Train: [67/100][1546/1557] Data 0.003 (0.142) Batch 0.763 (1.130) Remain 16:07:55 loss: 0.2145 Lr: 0.00135 [2024-02-19 03:32:16,520 INFO misc.py line 119 87073] Train: [67/100][1547/1557] Data 0.013 (0.142) Batch 1.061 (1.130) Remain 16:07:52 loss: 0.2256 Lr: 0.00135 [2024-02-19 03:32:17,426 INFO misc.py line 119 87073] Train: [67/100][1548/1557] Data 0.009 (0.142) Batch 0.912 (1.130) Remain 16:07:43 loss: 0.1154 Lr: 0.00135 [2024-02-19 03:32:18,373 INFO misc.py line 119 87073] Train: [67/100][1549/1557] Data 0.003 (0.142) Batch 0.948 (1.130) Remain 16:07:36 loss: 0.4268 Lr: 0.00135 [2024-02-19 03:32:19,299 INFO misc.py line 119 87073] Train: [67/100][1550/1557] Data 0.003 (0.142) Batch 0.925 (1.130) Remain 16:07:28 loss: 0.2706 Lr: 0.00135 [2024-02-19 03:32:20,293 INFO misc.py line 119 87073] Train: [67/100][1551/1557] Data 0.003 (0.141) Batch 0.986 (1.130) Remain 16:07:22 loss: 0.2505 Lr: 0.00135 [2024-02-19 03:32:21,027 INFO misc.py line 119 87073] Train: [67/100][1552/1557] Data 0.012 (0.141) Batch 0.743 (1.129) Remain 16:07:08 loss: 0.4202 Lr: 0.00135 [2024-02-19 03:32:21,769 INFO misc.py line 119 87073] Train: [67/100][1553/1557] Data 0.003 (0.141) Batch 0.732 (1.129) Remain 16:06:54 loss: 0.3458 Lr: 0.00135 [2024-02-19 03:32:22,897 INFO misc.py line 119 87073] Train: [67/100][1554/1557] Data 0.013 (0.141) Batch 1.126 (1.129) Remain 16:06:53 loss: 0.0705 Lr: 0.00135 [2024-02-19 03:32:23,739 INFO misc.py line 119 87073] Train: [67/100][1555/1557] Data 0.015 (0.141) Batch 0.854 (1.129) Remain 16:06:43 loss: 0.3326 Lr: 0.00135 [2024-02-19 03:32:24,717 INFO misc.py line 119 87073] Train: [67/100][1556/1557] Data 0.003 (0.141) Batch 0.978 (1.129) Remain 16:06:36 loss: 0.1711 Lr: 0.00135 [2024-02-19 03:32:25,525 INFO misc.py line 119 87073] Train: [67/100][1557/1557] Data 0.003 (0.141) Batch 0.807 (1.129) Remain 16:06:25 loss: 0.5060 Lr: 0.00135 [2024-02-19 03:32:25,525 INFO misc.py line 136 87073] Train result: loss: 0.2768 [2024-02-19 03:32:25,525 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 03:32:55,506 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5067 [2024-02-19 03:32:56,283 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 1.1213 [2024-02-19 03:32:58,409 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3501 [2024-02-19 03:33:00,620 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2967 [2024-02-19 03:33:05,578 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2066 [2024-02-19 03:33:06,282 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.2385 [2024-02-19 03:33:07,543 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2960 [2024-02-19 03:33:10,500 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2662 [2024-02-19 03:33:12,311 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2523 [2024-02-19 03:33:13,942 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.6985/0.7711/0.9113. [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9377/0.9704 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9835/0.9904 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8733/0.9739 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3733/0.4203 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.5555/0.5652 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7454/0.9579 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8384/0.8911 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9160/0.9565 [2024-02-19 03:33:13,942 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7855/0.8425 [2024-02-19 03:33:13,943 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7801/0.8740 [2024-02-19 03:33:13,943 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6982/0.8967 [2024-02-19 03:33:13,943 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5930/0.6857 [2024-02-19 03:33:13,943 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 03:33:13,946 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 03:33:13,946 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 03:33:23,058 INFO misc.py line 119 87073] Train: [68/100][1/1557] Data 1.649 (1.649) Batch 2.565 (2.565) Remain 36:36:36 loss: 0.1934 Lr: 0.00135 [2024-02-19 03:33:24,177 INFO misc.py line 119 87073] Train: [68/100][2/1557] Data 0.006 (0.006) Batch 1.119 (1.119) Remain 15:57:59 loss: 0.3461 Lr: 0.00135 [2024-02-19 03:33:25,054 INFO misc.py line 119 87073] Train: [68/100][3/1557] Data 0.005 (0.005) Batch 0.879 (0.879) Remain 12:32:33 loss: 0.2834 Lr: 0.00135 [2024-02-19 03:33:26,209 INFO misc.py line 119 87073] Train: [68/100][4/1557] Data 0.005 (0.005) Batch 1.155 (1.155) Remain 16:28:45 loss: 0.2065 Lr: 0.00135 [2024-02-19 03:33:27,011 INFO misc.py line 119 87073] Train: [68/100][5/1557] Data 0.004 (0.004) Batch 0.802 (0.978) Remain 13:57:46 loss: 0.2600 Lr: 0.00135 [2024-02-19 03:33:27,806 INFO misc.py line 119 87073] Train: [68/100][6/1557] Data 0.003 (0.004) Batch 0.784 (0.914) Remain 13:02:15 loss: 0.3049 Lr: 0.00135 [2024-02-19 03:33:33,677 INFO misc.py line 119 87073] Train: [68/100][7/1557] Data 0.015 (0.006) Batch 5.882 (2.156) Remain 30:45:46 loss: 0.0777 Lr: 0.00135 [2024-02-19 03:33:34,633 INFO misc.py line 119 87073] Train: [68/100][8/1557] Data 0.003 (0.006) Batch 0.949 (1.914) Remain 27:19:01 loss: 0.2682 Lr: 0.00135 [2024-02-19 03:33:35,514 INFO misc.py line 119 87073] Train: [68/100][9/1557] Data 0.010 (0.007) Batch 0.888 (1.743) Remain 24:52:33 loss: 0.4238 Lr: 0.00135 [2024-02-19 03:33:36,710 INFO misc.py line 119 87073] Train: [68/100][10/1557] Data 0.004 (0.006) Batch 1.196 (1.665) Remain 23:45:38 loss: 0.4328 Lr: 0.00135 [2024-02-19 03:33:37,858 INFO misc.py line 119 87073] Train: [68/100][11/1557] Data 0.004 (0.006) Batch 1.148 (1.600) Remain 22:50:17 loss: 0.2913 Lr: 0.00135 [2024-02-19 03:33:38,643 INFO misc.py line 119 87073] Train: [68/100][12/1557] Data 0.004 (0.006) Batch 0.785 (1.510) Remain 21:32:41 loss: 0.2073 Lr: 0.00135 [2024-02-19 03:33:39,404 INFO misc.py line 119 87073] Train: [68/100][13/1557] Data 0.004 (0.005) Batch 0.756 (1.435) Remain 20:28:07 loss: 0.2038 Lr: 0.00135 [2024-02-19 03:33:40,691 INFO misc.py line 119 87073] Train: [68/100][14/1557] Data 0.007 (0.006) Batch 1.285 (1.421) Remain 20:16:29 loss: 0.2147 Lr: 0.00135 [2024-02-19 03:33:41,732 INFO misc.py line 119 87073] Train: [68/100][15/1557] Data 0.010 (0.006) Batch 1.041 (1.389) Remain 19:49:20 loss: 0.3288 Lr: 0.00135 [2024-02-19 03:33:42,777 INFO misc.py line 119 87073] Train: [68/100][16/1557] Data 0.010 (0.006) Batch 1.045 (1.363) Remain 19:26:37 loss: 0.5751 Lr: 0.00135 [2024-02-19 03:33:43,711 INFO misc.py line 119 87073] Train: [68/100][17/1557] Data 0.010 (0.007) Batch 0.940 (1.333) Remain 19:00:45 loss: 0.4011 Lr: 0.00135 [2024-02-19 03:33:44,541 INFO misc.py line 119 87073] Train: [68/100][18/1557] Data 0.005 (0.006) Batch 0.829 (1.299) Remain 18:32:00 loss: 0.0685 Lr: 0.00135 [2024-02-19 03:33:45,352 INFO misc.py line 119 87073] Train: [68/100][19/1557] Data 0.005 (0.006) Batch 0.810 (1.268) Remain 18:05:48 loss: 0.1821 Lr: 0.00135 [2024-02-19 03:33:46,227 INFO misc.py line 119 87073] Train: [68/100][20/1557] Data 0.006 (0.006) Batch 0.878 (1.245) Remain 17:46:08 loss: 0.1449 Lr: 0.00135 [2024-02-19 03:33:47,467 INFO misc.py line 119 87073] Train: [68/100][21/1557] Data 0.003 (0.006) Batch 1.233 (1.245) Remain 17:45:31 loss: 0.1688 Lr: 0.00135 [2024-02-19 03:33:48,365 INFO misc.py line 119 87073] Train: [68/100][22/1557] Data 0.011 (0.006) Batch 0.905 (1.227) Remain 17:30:11 loss: 0.1526 Lr: 0.00135 [2024-02-19 03:33:49,286 INFO misc.py line 119 87073] Train: [68/100][23/1557] Data 0.004 (0.006) Batch 0.921 (1.212) Remain 17:17:05 loss: 0.1258 Lr: 0.00135 [2024-02-19 03:33:50,374 INFO misc.py line 119 87073] Train: [68/100][24/1557] Data 0.003 (0.006) Batch 1.088 (1.206) Remain 17:12:01 loss: 0.3597 Lr: 0.00135 [2024-02-19 03:33:51,353 INFO misc.py line 119 87073] Train: [68/100][25/1557] Data 0.003 (0.006) Batch 0.979 (1.195) Remain 17:03:11 loss: 0.3125 Lr: 0.00135 [2024-02-19 03:33:52,108 INFO misc.py line 119 87073] Train: [68/100][26/1557] Data 0.003 (0.006) Batch 0.749 (1.176) Remain 16:46:33 loss: 0.1148 Lr: 0.00135 [2024-02-19 03:33:52,825 INFO misc.py line 119 87073] Train: [68/100][27/1557] Data 0.009 (0.006) Batch 0.722 (1.157) Remain 16:30:21 loss: 0.1393 Lr: 0.00135 [2024-02-19 03:33:54,188 INFO misc.py line 119 87073] Train: [68/100][28/1557] Data 0.004 (0.006) Batch 1.355 (1.165) Remain 16:37:06 loss: 0.0877 Lr: 0.00135 [2024-02-19 03:33:55,342 INFO misc.py line 119 87073] Train: [68/100][29/1557] Data 0.012 (0.006) Batch 1.155 (1.165) Remain 16:36:44 loss: 0.2450 Lr: 0.00135 [2024-02-19 03:33:56,303 INFO misc.py line 119 87073] Train: [68/100][30/1557] Data 0.012 (0.006) Batch 0.969 (1.157) Remain 16:30:30 loss: 0.2832 Lr: 0.00135 [2024-02-19 03:33:57,128 INFO misc.py line 119 87073] Train: [68/100][31/1557] Data 0.004 (0.006) Batch 0.825 (1.145) Remain 16:20:21 loss: 0.4017 Lr: 0.00135 [2024-02-19 03:33:58,203 INFO misc.py line 119 87073] Train: [68/100][32/1557] Data 0.003 (0.006) Batch 1.073 (1.143) Remain 16:18:11 loss: 0.2093 Lr: 0.00135 [2024-02-19 03:33:59,006 INFO misc.py line 119 87073] Train: [68/100][33/1557] Data 0.006 (0.006) Batch 0.806 (1.132) Remain 16:08:33 loss: 0.1758 Lr: 0.00135 [2024-02-19 03:33:59,762 INFO misc.py line 119 87073] Train: [68/100][34/1557] Data 0.003 (0.006) Batch 0.756 (1.120) Remain 15:58:08 loss: 0.1832 Lr: 0.00135 [2024-02-19 03:34:00,717 INFO misc.py line 119 87073] Train: [68/100][35/1557] Data 0.003 (0.006) Batch 0.954 (1.114) Remain 15:53:41 loss: 0.1372 Lr: 0.00135 [2024-02-19 03:34:01,784 INFO misc.py line 119 87073] Train: [68/100][36/1557] Data 0.004 (0.006) Batch 1.064 (1.113) Remain 15:52:22 loss: 0.3647 Lr: 0.00135 [2024-02-19 03:34:02,617 INFO misc.py line 119 87073] Train: [68/100][37/1557] Data 0.006 (0.006) Batch 0.837 (1.105) Remain 15:45:24 loss: 0.2338 Lr: 0.00135 [2024-02-19 03:34:03,646 INFO misc.py line 119 87073] Train: [68/100][38/1557] Data 0.003 (0.006) Batch 1.029 (1.103) Remain 15:43:32 loss: 0.3315 Lr: 0.00135 [2024-02-19 03:34:04,597 INFO misc.py line 119 87073] Train: [68/100][39/1557] Data 0.003 (0.006) Batch 0.950 (1.098) Remain 15:39:53 loss: 0.2776 Lr: 0.00135 [2024-02-19 03:34:05,379 INFO misc.py line 119 87073] Train: [68/100][40/1557] Data 0.004 (0.006) Batch 0.783 (1.090) Remain 15:32:35 loss: 0.2333 Lr: 0.00135 [2024-02-19 03:34:06,146 INFO misc.py line 119 87073] Train: [68/100][41/1557] Data 0.003 (0.006) Batch 0.762 (1.081) Remain 15:25:11 loss: 0.1114 Lr: 0.00135 [2024-02-19 03:34:07,364 INFO misc.py line 119 87073] Train: [68/100][42/1557] Data 0.008 (0.006) Batch 1.220 (1.085) Remain 15:28:13 loss: 0.1467 Lr: 0.00135 [2024-02-19 03:34:08,303 INFO misc.py line 119 87073] Train: [68/100][43/1557] Data 0.005 (0.006) Batch 0.941 (1.081) Remain 15:25:08 loss: 0.1591 Lr: 0.00135 [2024-02-19 03:34:09,189 INFO misc.py line 119 87073] Train: [68/100][44/1557] Data 0.002 (0.006) Batch 0.885 (1.076) Remain 15:21:01 loss: 0.1699 Lr: 0.00135 [2024-02-19 03:34:09,964 INFO misc.py line 119 87073] Train: [68/100][45/1557] Data 0.004 (0.006) Batch 0.773 (1.069) Remain 15:14:49 loss: 0.3075 Lr: 0.00135 [2024-02-19 03:34:11,089 INFO misc.py line 119 87073] Train: [68/100][46/1557] Data 0.006 (0.006) Batch 1.082 (1.070) Remain 15:15:02 loss: 0.2511 Lr: 0.00135 [2024-02-19 03:34:11,864 INFO misc.py line 119 87073] Train: [68/100][47/1557] Data 0.049 (0.007) Batch 0.820 (1.064) Remain 15:10:10 loss: 0.1133 Lr: 0.00135 [2024-02-19 03:34:12,656 INFO misc.py line 119 87073] Train: [68/100][48/1557] Data 0.004 (0.006) Batch 0.793 (1.058) Remain 15:05:00 loss: 0.3072 Lr: 0.00135 [2024-02-19 03:34:13,965 INFO misc.py line 119 87073] Train: [68/100][49/1557] Data 0.004 (0.006) Batch 1.308 (1.063) Remain 15:09:39 loss: 0.1293 Lr: 0.00135 [2024-02-19 03:34:14,925 INFO misc.py line 119 87073] Train: [68/100][50/1557] Data 0.004 (0.006) Batch 0.959 (1.061) Remain 15:07:44 loss: 0.2129 Lr: 0.00135 [2024-02-19 03:34:16,140 INFO misc.py line 119 87073] Train: [68/100][51/1557] Data 0.005 (0.006) Batch 1.216 (1.064) Remain 15:10:28 loss: 0.4448 Lr: 0.00135 [2024-02-19 03:34:16,999 INFO misc.py line 119 87073] Train: [68/100][52/1557] Data 0.004 (0.006) Batch 0.859 (1.060) Remain 15:06:53 loss: 0.2587 Lr: 0.00135 [2024-02-19 03:34:17,968 INFO misc.py line 119 87073] Train: [68/100][53/1557] Data 0.004 (0.006) Batch 0.969 (1.058) Remain 15:05:18 loss: 0.5321 Lr: 0.00135 [2024-02-19 03:34:18,742 INFO misc.py line 119 87073] Train: [68/100][54/1557] Data 0.004 (0.006) Batch 0.775 (1.053) Remain 15:00:32 loss: 0.1854 Lr: 0.00135 [2024-02-19 03:34:19,574 INFO misc.py line 119 87073] Train: [68/100][55/1557] Data 0.003 (0.006) Batch 0.830 (1.048) Remain 14:56:51 loss: 0.3700 Lr: 0.00135 [2024-02-19 03:34:20,709 INFO misc.py line 119 87073] Train: [68/100][56/1557] Data 0.005 (0.006) Batch 1.135 (1.050) Remain 14:58:13 loss: 0.1457 Lr: 0.00135 [2024-02-19 03:34:21,581 INFO misc.py line 119 87073] Train: [68/100][57/1557] Data 0.005 (0.006) Batch 0.872 (1.047) Remain 14:55:23 loss: 0.3538 Lr: 0.00135 [2024-02-19 03:34:22,619 INFO misc.py line 119 87073] Train: [68/100][58/1557] Data 0.005 (0.006) Batch 1.040 (1.047) Remain 14:55:16 loss: 0.1615 Lr: 0.00135 [2024-02-19 03:34:23,540 INFO misc.py line 119 87073] Train: [68/100][59/1557] Data 0.003 (0.006) Batch 0.920 (1.044) Remain 14:53:18 loss: 0.2773 Lr: 0.00135 [2024-02-19 03:34:24,363 INFO misc.py line 119 87073] Train: [68/100][60/1557] Data 0.004 (0.006) Batch 0.821 (1.040) Remain 14:49:56 loss: 0.4850 Lr: 0.00135 [2024-02-19 03:34:25,143 INFO misc.py line 119 87073] Train: [68/100][61/1557] Data 0.007 (0.006) Batch 0.783 (1.036) Remain 14:46:07 loss: 0.1438 Lr: 0.00135 [2024-02-19 03:34:25,922 INFO misc.py line 119 87073] Train: [68/100][62/1557] Data 0.004 (0.006) Batch 0.778 (1.032) Remain 14:42:22 loss: 0.1728 Lr: 0.00135 [2024-02-19 03:34:42,285 INFO misc.py line 119 87073] Train: [68/100][63/1557] Data 7.358 (0.129) Batch 16.364 (1.287) Remain 18:20:55 loss: 0.0763 Lr: 0.00135 [2024-02-19 03:34:43,239 INFO misc.py line 119 87073] Train: [68/100][64/1557] Data 0.004 (0.126) Batch 0.949 (1.282) Remain 18:16:09 loss: 0.1311 Lr: 0.00135 [2024-02-19 03:34:44,150 INFO misc.py line 119 87073] Train: 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Batch 1.085 (1.240) Remain 17:39:10 loss: 0.0417 Lr: 0.00134 [2024-02-19 03:35:52,238 INFO misc.py line 119 87073] Train: [68/100][122/1557] Data 0.012 (0.123) Batch 0.890 (1.237) Remain 17:36:38 loss: 0.3074 Lr: 0.00134 [2024-02-19 03:35:53,217 INFO misc.py line 119 87073] Train: [68/100][123/1557] Data 0.003 (0.122) Batch 0.980 (1.235) Remain 17:34:47 loss: 0.2523 Lr: 0.00134 [2024-02-19 03:35:53,988 INFO misc.py line 119 87073] Train: [68/100][124/1557] Data 0.003 (0.121) Batch 0.770 (1.231) Remain 17:31:29 loss: 0.1800 Lr: 0.00134 [2024-02-19 03:35:54,796 INFO misc.py line 119 87073] Train: [68/100][125/1557] Data 0.004 (0.120) Batch 0.801 (1.227) Remain 17:28:27 loss: 0.1865 Lr: 0.00134 [2024-02-19 03:35:56,113 INFO misc.py line 119 87073] Train: [68/100][126/1557] Data 0.010 (0.119) Batch 1.319 (1.228) Remain 17:29:05 loss: 0.2522 Lr: 0.00134 [2024-02-19 03:35:57,089 INFO misc.py line 119 87073] Train: [68/100][127/1557] Data 0.009 (0.118) Batch 0.981 (1.226) Remain 17:27:21 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Batch 0.889 (1.237) Remain 17:34:35 loss: 0.3323 Lr: 0.00134 [2024-02-19 03:38:10,511 INFO misc.py line 119 87073] Train: [68/100][234/1557] Data 0.004 (0.128) Batch 0.920 (1.236) Remain 17:33:24 loss: 0.2281 Lr: 0.00134 [2024-02-19 03:38:11,569 INFO misc.py line 119 87073] Train: [68/100][235/1557] Data 0.005 (0.127) Batch 1.058 (1.235) Remain 17:32:43 loss: 0.1475 Lr: 0.00134 [2024-02-19 03:38:12,335 INFO misc.py line 119 87073] Train: [68/100][236/1557] Data 0.005 (0.127) Batch 0.768 (1.233) Remain 17:30:59 loss: 0.1193 Lr: 0.00134 [2024-02-19 03:38:13,122 INFO misc.py line 119 87073] Train: [68/100][237/1557] Data 0.004 (0.126) Batch 0.777 (1.231) Remain 17:29:19 loss: 0.1597 Lr: 0.00134 [2024-02-19 03:38:14,478 INFO misc.py line 119 87073] Train: [68/100][238/1557] Data 0.012 (0.126) Batch 1.353 (1.232) Remain 17:29:44 loss: 0.1618 Lr: 0.00134 [2024-02-19 03:38:15,462 INFO misc.py line 119 87073] Train: [68/100][239/1557] Data 0.015 (0.125) Batch 0.996 (1.231) Remain 17:28:52 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Batch 1.058 (1.229) Remain 17:26:39 loss: 0.1590 Lr: 0.00134 [2024-02-19 03:39:17,501 INFO misc.py line 119 87073] Train: [68/100][290/1557] Data 0.007 (0.127) Batch 0.913 (1.228) Remain 17:25:41 loss: 0.3488 Lr: 0.00134 [2024-02-19 03:39:18,394 INFO misc.py line 119 87073] Train: [68/100][291/1557] Data 0.003 (0.127) Batch 0.893 (1.227) Remain 17:24:41 loss: 0.1393 Lr: 0.00134 [2024-02-19 03:39:19,187 INFO misc.py line 119 87073] Train: [68/100][292/1557] Data 0.003 (0.126) Batch 0.784 (1.225) Remain 17:23:21 loss: 0.1466 Lr: 0.00134 [2024-02-19 03:39:19,951 INFO misc.py line 119 87073] Train: [68/100][293/1557] Data 0.012 (0.126) Batch 0.773 (1.224) Remain 17:22:00 loss: 0.3445 Lr: 0.00134 [2024-02-19 03:39:21,197 INFO misc.py line 119 87073] Train: [68/100][294/1557] Data 0.003 (0.126) Batch 1.235 (1.224) Remain 17:22:01 loss: 0.1922 Lr: 0.00134 [2024-02-19 03:39:22,078 INFO misc.py line 119 87073] Train: [68/100][295/1557] Data 0.014 (0.125) Batch 0.891 (1.223) Remain 17:21:01 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03:39:28,789 INFO misc.py line 119 87073] Train: [68/100][302/1557] Data 0.011 (0.123) Batch 0.990 (1.217) Remain 17:15:37 loss: 0.1421 Lr: 0.00133 [2024-02-19 03:39:29,733 INFO misc.py line 119 87073] Train: [68/100][303/1557] Data 0.004 (0.122) Batch 0.944 (1.216) Remain 17:14:50 loss: 0.4062 Lr: 0.00133 [2024-02-19 03:39:30,816 INFO misc.py line 119 87073] Train: [68/100][304/1557] Data 0.003 (0.122) Batch 1.083 (1.215) Remain 17:14:26 loss: 0.1996 Lr: 0.00133 [2024-02-19 03:39:31,739 INFO misc.py line 119 87073] Train: [68/100][305/1557] Data 0.003 (0.121) Batch 0.923 (1.214) Remain 17:13:35 loss: 0.3580 Lr: 0.00133 [2024-02-19 03:39:32,541 INFO misc.py line 119 87073] Train: [68/100][306/1557] Data 0.003 (0.121) Batch 0.802 (1.213) Remain 17:12:25 loss: 0.2211 Lr: 0.00133 [2024-02-19 03:39:33,419 INFO misc.py line 119 87073] Train: [68/100][307/1557] Data 0.004 (0.121) Batch 0.869 (1.212) Remain 17:11:26 loss: 0.2155 Lr: 0.00133 [2024-02-19 03:39:34,687 INFO misc.py line 119 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line 119 87073] Train: [68/100][333/1557] Data 0.016 (0.112) Batch 1.013 (1.193) Remain 16:54:43 loss: 0.2624 Lr: 0.00133 [2024-02-19 03:39:59,412 INFO misc.py line 119 87073] Train: [68/100][334/1557] Data 0.012 (0.111) Batch 0.777 (1.191) Remain 16:53:37 loss: 0.2569 Lr: 0.00133 [2024-02-19 03:40:00,218 INFO misc.py line 119 87073] Train: [68/100][335/1557] Data 0.003 (0.111) Batch 0.806 (1.190) Remain 16:52:37 loss: 0.1595 Lr: 0.00133 [2024-02-19 03:40:01,441 INFO misc.py line 119 87073] Train: [68/100][336/1557] Data 0.003 (0.111) Batch 1.216 (1.190) Remain 16:52:40 loss: 0.1366 Lr: 0.00133 [2024-02-19 03:40:02,562 INFO misc.py line 119 87073] Train: [68/100][337/1557] Data 0.011 (0.110) Batch 1.123 (1.190) Remain 16:52:28 loss: 0.3346 Lr: 0.00133 [2024-02-19 03:40:03,514 INFO misc.py line 119 87073] Train: [68/100][338/1557] Data 0.009 (0.110) Batch 0.958 (1.189) Remain 16:51:52 loss: 0.4954 Lr: 0.00133 [2024-02-19 03:40:04,456 INFO misc.py line 119 87073] Train: 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Remain 17:05:25 loss: 0.2851 Lr: 0.00129 [2024-02-19 03:58:43,855 INFO misc.py line 119 87073] Train: [68/100][1241/1557] Data 0.012 (0.136) Batch 0.931 (1.227) Remain 17:05:12 loss: 0.2999 Lr: 0.00129 [2024-02-19 03:58:44,747 INFO misc.py line 119 87073] Train: [68/100][1242/1557] Data 0.003 (0.136) Batch 0.892 (1.227) Remain 17:04:57 loss: 0.1900 Lr: 0.00129 [2024-02-19 03:58:45,733 INFO misc.py line 119 87073] Train: [68/100][1243/1557] Data 0.004 (0.136) Batch 0.985 (1.226) Remain 17:04:46 loss: 0.2329 Lr: 0.00129 [2024-02-19 03:58:46,616 INFO misc.py line 119 87073] Train: [68/100][1244/1557] Data 0.004 (0.136) Batch 0.883 (1.226) Remain 17:04:31 loss: 0.3375 Lr: 0.00129 [2024-02-19 03:58:47,389 INFO misc.py line 119 87073] Train: [68/100][1245/1557] Data 0.003 (0.136) Batch 0.764 (1.226) Remain 17:04:11 loss: 0.2120 Lr: 0.00129 [2024-02-19 03:58:48,604 INFO misc.py line 119 87073] Train: [68/100][1246/1557] Data 0.012 (0.136) Batch 1.213 (1.226) Remain 17:04:10 loss: 0.1036 Lr: 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Train: [68/100][1259/1557] Data 0.004 (0.135) Batch 0.794 (1.223) Remain 17:01:25 loss: 0.1332 Lr: 0.00129 [2024-02-19 03:59:02,131 INFO misc.py line 119 87073] Train: [68/100][1260/1557] Data 0.004 (0.134) Batch 1.322 (1.223) Remain 17:01:28 loss: 0.1814 Lr: 0.00129 [2024-02-19 03:59:03,021 INFO misc.py line 119 87073] Train: [68/100][1261/1557] Data 0.012 (0.134) Batch 0.898 (1.223) Remain 17:01:14 loss: 0.1430 Lr: 0.00129 [2024-02-19 03:59:03,950 INFO misc.py line 119 87073] Train: [68/100][1262/1557] Data 0.003 (0.134) Batch 0.929 (1.222) Remain 17:01:01 loss: 0.3237 Lr: 0.00129 [2024-02-19 03:59:04,808 INFO misc.py line 119 87073] Train: [68/100][1263/1557] Data 0.003 (0.134) Batch 0.855 (1.222) Remain 17:00:45 loss: 0.0918 Lr: 0.00129 [2024-02-19 03:59:05,702 INFO misc.py line 119 87073] Train: [68/100][1264/1557] Data 0.006 (0.134) Batch 0.896 (1.222) Remain 17:00:31 loss: 0.2772 Lr: 0.00129 [2024-02-19 03:59:06,446 INFO misc.py line 119 87073] Train: [68/100][1265/1557] Data 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Remain 16:58:42 loss: 0.2918 Lr: 0.00129 [2024-02-19 03:59:12,500 INFO misc.py line 119 87073] Train: [68/100][1272/1557] Data 0.013 (0.133) Batch 0.782 (1.219) Remain 16:58:23 loss: 0.1351 Lr: 0.00129 [2024-02-19 03:59:13,298 INFO misc.py line 119 87073] Train: [68/100][1273/1557] Data 0.005 (0.133) Batch 0.800 (1.219) Remain 16:58:06 loss: 0.1845 Lr: 0.00129 [2024-02-19 03:59:14,498 INFO misc.py line 119 87073] Train: [68/100][1274/1557] Data 0.003 (0.133) Batch 1.189 (1.219) Remain 16:58:03 loss: 0.1243 Lr: 0.00129 [2024-02-19 03:59:15,435 INFO misc.py line 119 87073] Train: [68/100][1275/1557] Data 0.014 (0.133) Batch 0.947 (1.219) Remain 16:57:51 loss: 0.2480 Lr: 0.00129 [2024-02-19 03:59:16,356 INFO misc.py line 119 87073] Train: [68/100][1276/1557] Data 0.004 (0.133) Batch 0.922 (1.219) Remain 16:57:38 loss: 0.1533 Lr: 0.00129 [2024-02-19 03:59:17,202 INFO misc.py line 119 87073] Train: [68/100][1277/1557] Data 0.003 (0.133) Batch 0.837 (1.218) Remain 16:57:22 loss: 0.4743 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1284/1557] Data 0.003 (0.132) Batch 0.927 (1.217) Remain 16:56:00 loss: 0.2635 Lr: 0.00129 [2024-02-19 03:59:24,867 INFO misc.py line 119 87073] Train: [68/100][1285/1557] Data 0.003 (0.132) Batch 1.032 (1.217) Remain 16:55:51 loss: 0.2414 Lr: 0.00129 [2024-02-19 03:59:25,660 INFO misc.py line 119 87073] Train: [68/100][1286/1557] Data 0.003 (0.132) Batch 0.785 (1.216) Remain 16:55:33 loss: 0.1635 Lr: 0.00129 [2024-02-19 03:59:26,429 INFO misc.py line 119 87073] Train: [68/100][1287/1557] Data 0.011 (0.132) Batch 0.776 (1.216) Remain 16:55:15 loss: 0.2067 Lr: 0.00129 [2024-02-19 03:59:27,550 INFO misc.py line 119 87073] Train: [68/100][1288/1557] Data 0.003 (0.132) Batch 1.121 (1.216) Remain 16:55:10 loss: 0.1399 Lr: 0.00129 [2024-02-19 03:59:28,624 INFO misc.py line 119 87073] Train: [68/100][1289/1557] Data 0.004 (0.132) Batch 1.072 (1.216) Remain 16:55:03 loss: 0.2368 Lr: 0.00129 [2024-02-19 03:59:29,428 INFO misc.py line 119 87073] Train: [68/100][1290/1557] Data 0.005 (0.131) Batch 0.805 (1.216) Remain 16:54:46 loss: 0.2662 Lr: 0.00129 [2024-02-19 03:59:30,632 INFO misc.py line 119 87073] Train: [68/100][1291/1557] Data 0.004 (0.131) Batch 1.182 (1.215) Remain 16:54:43 loss: 0.2330 Lr: 0.00129 [2024-02-19 03:59:31,517 INFO misc.py line 119 87073] Train: [68/100][1292/1557] Data 0.026 (0.131) Batch 0.908 (1.215) Remain 16:54:30 loss: 0.6085 Lr: 0.00129 [2024-02-19 03:59:32,309 INFO misc.py line 119 87073] Train: [68/100][1293/1557] Data 0.003 (0.131) Batch 0.792 (1.215) Remain 16:54:13 loss: 0.2668 Lr: 0.00129 [2024-02-19 03:59:33,082 INFO misc.py line 119 87073] Train: [68/100][1294/1557] Data 0.003 (0.131) Batch 0.765 (1.215) Remain 16:53:54 loss: 0.2580 Lr: 0.00129 [2024-02-19 03:59:48,863 INFO misc.py line 119 87073] Train: [68/100][1295/1557] Data 6.485 (0.136) Batch 15.789 (1.226) Remain 17:03:18 loss: 0.0876 Lr: 0.00129 [2024-02-19 03:59:49,795 INFO misc.py line 119 87073] Train: [68/100][1296/1557] Data 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Remain 17:01:51 loss: 0.1084 Lr: 0.00129 [2024-02-19 03:59:56,409 INFO misc.py line 119 87073] Train: [68/100][1303/1557] Data 0.014 (0.135) Batch 1.004 (1.224) Remain 17:01:41 loss: 0.2883 Lr: 0.00129 [2024-02-19 03:59:57,405 INFO misc.py line 119 87073] Train: [68/100][1304/1557] Data 0.005 (0.135) Batch 0.996 (1.224) Remain 17:01:31 loss: 0.4618 Lr: 0.00129 [2024-02-19 03:59:58,446 INFO misc.py line 119 87073] Train: [68/100][1305/1557] Data 0.004 (0.135) Batch 1.042 (1.224) Remain 17:01:23 loss: 0.1680 Lr: 0.00129 [2024-02-19 03:59:59,393 INFO misc.py line 119 87073] Train: [68/100][1306/1557] Data 0.004 (0.135) Batch 0.947 (1.224) Remain 17:01:11 loss: 0.3898 Lr: 0.00129 [2024-02-19 04:00:00,193 INFO misc.py line 119 87073] Train: [68/100][1307/1557] Data 0.003 (0.135) Batch 0.796 (1.223) Remain 17:00:53 loss: 0.1766 Lr: 0.00129 [2024-02-19 04:00:00,956 INFO misc.py line 119 87073] Train: [68/100][1308/1557] Data 0.007 (0.135) Batch 0.763 (1.223) Remain 17:00:34 loss: 0.1758 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1315/1557] Data 0.018 (0.134) Batch 0.951 (1.221) Remain 16:59:07 loss: 0.1471 Lr: 0.00129 [2024-02-19 04:00:08,785 INFO misc.py line 119 87073] Train: [68/100][1316/1557] Data 0.004 (0.134) Batch 1.315 (1.221) Remain 16:59:10 loss: 0.1149 Lr: 0.00129 [2024-02-19 04:00:09,693 INFO misc.py line 119 87073] Train: [68/100][1317/1557] Data 0.010 (0.134) Batch 0.915 (1.221) Remain 16:58:57 loss: 0.3876 Lr: 0.00129 [2024-02-19 04:00:10,822 INFO misc.py line 119 87073] Train: [68/100][1318/1557] Data 0.003 (0.134) Batch 1.129 (1.221) Remain 16:58:52 loss: 0.4208 Lr: 0.00129 [2024-02-19 04:00:11,838 INFO misc.py line 119 87073] Train: [68/100][1319/1557] Data 0.003 (0.134) Batch 1.015 (1.221) Remain 16:58:43 loss: 0.4539 Lr: 0.00129 [2024-02-19 04:00:12,649 INFO misc.py line 119 87073] Train: [68/100][1320/1557] Data 0.004 (0.134) Batch 0.812 (1.221) Remain 16:58:26 loss: 0.3384 Lr: 0.00129 [2024-02-19 04:00:13,451 INFO misc.py line 119 87073] Train: [68/100][1321/1557] Data 0.004 (0.133) Batch 0.798 (1.220) Remain 16:58:09 loss: 0.2695 Lr: 0.00129 [2024-02-19 04:00:14,303 INFO misc.py line 119 87073] Train: [68/100][1322/1557] Data 0.007 (0.133) Batch 0.855 (1.220) Remain 16:57:54 loss: 0.3260 Lr: 0.00129 [2024-02-19 04:00:15,317 INFO misc.py line 119 87073] Train: [68/100][1323/1557] Data 0.004 (0.133) Batch 1.013 (1.220) Remain 16:57:45 loss: 0.2852 Lr: 0.00129 [2024-02-19 04:00:16,541 INFO misc.py line 119 87073] Train: [68/100][1324/1557] Data 0.004 (0.133) Batch 1.219 (1.220) Remain 16:57:44 loss: 0.2525 Lr: 0.00129 [2024-02-19 04:00:17,444 INFO misc.py line 119 87073] Train: [68/100][1325/1557] Data 0.009 (0.133) Batch 0.908 (1.220) Remain 16:57:31 loss: 0.2475 Lr: 0.00129 [2024-02-19 04:00:18,405 INFO misc.py line 119 87073] Train: [68/100][1326/1557] Data 0.004 (0.133) Batch 0.962 (1.219) Remain 16:57:20 loss: 0.5242 Lr: 0.00129 [2024-02-19 04:00:19,527 INFO misc.py line 119 87073] Train: [68/100][1327/1557] Data 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Remain 16:56:15 loss: 0.3523 Lr: 0.00129 [2024-02-19 04:00:26,369 INFO misc.py line 119 87073] Train: [68/100][1334/1557] Data 0.004 (0.132) Batch 0.917 (1.218) Remain 16:56:03 loss: 0.1364 Lr: 0.00129 [2024-02-19 04:00:27,114 INFO misc.py line 119 87073] Train: [68/100][1335/1557] Data 0.004 (0.132) Batch 0.736 (1.218) Remain 16:55:43 loss: 0.2846 Lr: 0.00129 [2024-02-19 04:00:27,881 INFO misc.py line 119 87073] Train: [68/100][1336/1557] Data 0.013 (0.132) Batch 0.776 (1.217) Remain 16:55:25 loss: 0.2019 Lr: 0.00129 [2024-02-19 04:00:29,186 INFO misc.py line 119 87073] Train: [68/100][1337/1557] Data 0.004 (0.132) Batch 1.292 (1.217) Remain 16:55:27 loss: 0.1849 Lr: 0.00129 [2024-02-19 04:00:30,143 INFO misc.py line 119 87073] Train: [68/100][1338/1557] Data 0.016 (0.132) Batch 0.970 (1.217) Remain 16:55:17 loss: 0.3793 Lr: 0.00129 [2024-02-19 04:00:31,088 INFO misc.py line 119 87073] Train: [68/100][1339/1557] Data 0.003 (0.132) Batch 0.944 (1.217) Remain 16:55:05 loss: 0.5024 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1346/1557] Data 0.003 (0.131) Batch 0.815 (1.215) Remain 16:53:31 loss: 0.2554 Lr: 0.00129 [2024-02-19 04:00:38,336 INFO misc.py line 119 87073] Train: [68/100][1347/1557] Data 0.003 (0.131) Batch 1.009 (1.215) Remain 16:53:22 loss: 0.0635 Lr: 0.00129 [2024-02-19 04:00:39,227 INFO misc.py line 119 87073] Train: [68/100][1348/1557] Data 0.010 (0.131) Batch 0.896 (1.215) Remain 16:53:09 loss: 0.2355 Lr: 0.00129 [2024-02-19 04:00:39,969 INFO misc.py line 119 87073] Train: [68/100][1349/1557] Data 0.003 (0.131) Batch 0.743 (1.215) Remain 16:52:51 loss: 0.1582 Lr: 0.00129 [2024-02-19 04:00:40,733 INFO misc.py line 119 87073] Train: [68/100][1350/1557] Data 0.003 (0.131) Batch 0.759 (1.214) Remain 16:52:33 loss: 0.1369 Lr: 0.00129 [2024-02-19 04:00:56,209 INFO misc.py line 119 87073] Train: [68/100][1351/1557] Data 7.232 (0.136) Batch 15.481 (1.225) Remain 17:01:21 loss: 0.0865 Lr: 0.00129 [2024-02-19 04:00:57,051 INFO misc.py line 119 87073] Train: [68/100][1352/1557] Data 0.003 (0.136) Batch 0.834 (1.225) Remain 17:01:05 loss: 0.0998 Lr: 0.00129 [2024-02-19 04:00:57,890 INFO misc.py line 119 87073] Train: [68/100][1353/1557] Data 0.011 (0.136) Batch 0.847 (1.224) Remain 17:00:50 loss: 0.2197 Lr: 0.00129 [2024-02-19 04:00:58,905 INFO misc.py line 119 87073] Train: [68/100][1354/1557] Data 0.003 (0.136) Batch 1.015 (1.224) Remain 17:00:41 loss: 0.4489 Lr: 0.00129 [2024-02-19 04:00:59,843 INFO misc.py line 119 87073] Train: [68/100][1355/1557] Data 0.003 (0.136) Batch 0.938 (1.224) Remain 17:00:29 loss: 0.2165 Lr: 0.00129 [2024-02-19 04:01:00,595 INFO misc.py line 119 87073] Train: [68/100][1356/1557] Data 0.004 (0.136) Batch 0.740 (1.224) Remain 17:00:10 loss: 0.2212 Lr: 0.00129 [2024-02-19 04:01:01,489 INFO misc.py line 119 87073] Train: [68/100][1357/1557] Data 0.016 (0.135) Batch 0.907 (1.223) Remain 16:59:57 loss: 0.1549 Lr: 0.00129 [2024-02-19 04:01:02,828 INFO misc.py line 119 87073] Train: [68/100][1358/1557] Data 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Remain 16:58:38 loss: 0.2343 Lr: 0.00129 [2024-02-19 04:01:09,365 INFO misc.py line 119 87073] Train: [68/100][1365/1557] Data 0.003 (0.135) Batch 1.217 (1.222) Remain 16:58:37 loss: 0.2117 Lr: 0.00129 [2024-02-19 04:01:10,435 INFO misc.py line 119 87073] Train: [68/100][1366/1557] Data 0.003 (0.135) Batch 1.070 (1.222) Remain 16:58:30 loss: 1.1995 Lr: 0.00129 [2024-02-19 04:01:11,376 INFO misc.py line 119 87073] Train: [68/100][1367/1557] Data 0.003 (0.134) Batch 0.941 (1.222) Remain 16:58:19 loss: 0.5296 Lr: 0.00129 [2024-02-19 04:01:12,318 INFO misc.py line 119 87073] Train: [68/100][1368/1557] Data 0.003 (0.134) Batch 0.943 (1.221) Remain 16:58:07 loss: 0.7615 Lr: 0.00128 [2024-02-19 04:01:13,206 INFO misc.py line 119 87073] Train: [68/100][1369/1557] Data 0.003 (0.134) Batch 0.879 (1.221) Remain 16:57:53 loss: 0.4437 Lr: 0.00128 [2024-02-19 04:01:13,930 INFO misc.py line 119 87073] Train: [68/100][1370/1557] Data 0.012 (0.134) Batch 0.732 (1.221) Remain 16:57:34 loss: 0.2384 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1377/1557] Data 0.012 (0.134) Batch 0.701 (1.219) Remain 16:56:09 loss: 0.2829 Lr: 0.00128 [2024-02-19 04:01:21,079 INFO misc.py line 119 87073] Train: [68/100][1378/1557] Data 0.003 (0.133) Batch 0.697 (1.219) Remain 16:55:49 loss: 0.1845 Lr: 0.00128 [2024-02-19 04:01:22,095 INFO misc.py line 119 87073] Train: [68/100][1379/1557] Data 0.011 (0.133) Batch 1.016 (1.219) Remain 16:55:41 loss: 0.1638 Lr: 0.00128 [2024-02-19 04:01:23,181 INFO misc.py line 119 87073] Train: [68/100][1380/1557] Data 0.011 (0.133) Batch 1.081 (1.219) Remain 16:55:34 loss: 0.7433 Lr: 0.00128 [2024-02-19 04:01:24,200 INFO misc.py line 119 87073] Train: [68/100][1381/1557] Data 0.015 (0.133) Batch 1.029 (1.219) Remain 16:55:26 loss: 0.5297 Lr: 0.00128 [2024-02-19 04:01:25,304 INFO misc.py line 119 87073] Train: [68/100][1382/1557] Data 0.005 (0.133) Batch 1.102 (1.218) Remain 16:55:21 loss: 0.4063 Lr: 0.00128 [2024-02-19 04:01:26,328 INFO misc.py line 119 87073] Train: [68/100][1383/1557] Data 0.008 (0.133) Batch 1.023 (1.218) Remain 16:55:12 loss: 0.6549 Lr: 0.00128 [2024-02-19 04:01:27,068 INFO misc.py line 119 87073] Train: [68/100][1384/1557] Data 0.009 (0.133) Batch 0.746 (1.218) Remain 16:54:54 loss: 0.3188 Lr: 0.00128 [2024-02-19 04:01:27,856 INFO misc.py line 119 87073] Train: [68/100][1385/1557] Data 0.003 (0.133) Batch 0.783 (1.218) Remain 16:54:37 loss: 0.1487 Lr: 0.00128 [2024-02-19 04:01:29,155 INFO misc.py line 119 87073] Train: [68/100][1386/1557] Data 0.008 (0.133) Batch 1.298 (1.218) Remain 16:54:39 loss: 0.1489 Lr: 0.00128 [2024-02-19 04:01:30,088 INFO misc.py line 119 87073] Train: [68/100][1387/1557] Data 0.010 (0.133) Batch 0.940 (1.218) Remain 16:54:28 loss: 0.1322 Lr: 0.00128 [2024-02-19 04:01:31,144 INFO misc.py line 119 87073] Train: [68/100][1388/1557] Data 0.003 (0.133) Batch 1.056 (1.217) Remain 16:54:21 loss: 0.1263 Lr: 0.00128 [2024-02-19 04:01:32,092 INFO misc.py line 119 87073] Train: [68/100][1389/1557] Data 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Remain 16:53:02 loss: 0.2259 Lr: 0.00128 [2024-02-19 04:01:38,861 INFO misc.py line 119 87073] Train: [68/100][1396/1557] Data 0.003 (0.132) Batch 1.155 (1.216) Remain 16:52:58 loss: 0.3638 Lr: 0.00128 [2024-02-19 04:01:39,743 INFO misc.py line 119 87073] Train: [68/100][1397/1557] Data 0.003 (0.132) Batch 0.883 (1.216) Remain 16:52:45 loss: 0.1705 Lr: 0.00128 [2024-02-19 04:01:40,435 INFO misc.py line 119 87073] Train: [68/100][1398/1557] Data 0.003 (0.132) Batch 0.691 (1.215) Remain 16:52:25 loss: 0.1337 Lr: 0.00128 [2024-02-19 04:01:41,207 INFO misc.py line 119 87073] Train: [68/100][1399/1557] Data 0.003 (0.132) Batch 0.764 (1.215) Remain 16:52:08 loss: 0.1457 Lr: 0.00128 [2024-02-19 04:01:42,365 INFO misc.py line 119 87073] Train: [68/100][1400/1557] Data 0.011 (0.131) Batch 1.160 (1.215) Remain 16:52:05 loss: 0.1159 Lr: 0.00128 [2024-02-19 04:01:43,272 INFO misc.py line 119 87073] Train: [68/100][1401/1557] Data 0.010 (0.131) Batch 0.913 (1.215) Remain 16:51:53 loss: 0.1263 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1408/1557] Data 0.003 (0.136) Batch 0.965 (1.226) Remain 17:00:55 loss: 0.2710 Lr: 0.00128 [2024-02-19 04:02:08,071 INFO misc.py line 119 87073] Train: [68/100][1409/1557] Data 0.012 (0.136) Batch 0.801 (1.225) Remain 17:00:39 loss: 0.1862 Lr: 0.00128 [2024-02-19 04:02:08,945 INFO misc.py line 119 87073] Train: [68/100][1410/1557] Data 0.003 (0.136) Batch 0.867 (1.225) Remain 17:00:25 loss: 0.1622 Lr: 0.00128 [2024-02-19 04:02:09,836 INFO misc.py line 119 87073] Train: [68/100][1411/1557] Data 0.010 (0.136) Batch 0.893 (1.225) Remain 17:00:12 loss: 0.4307 Lr: 0.00128 [2024-02-19 04:02:10,607 INFO misc.py line 119 87073] Train: [68/100][1412/1557] Data 0.007 (0.136) Batch 0.776 (1.225) Remain 16:59:55 loss: 0.3123 Lr: 0.00128 [2024-02-19 04:02:11,354 INFO misc.py line 119 87073] Train: [68/100][1413/1557] Data 0.003 (0.136) Batch 0.738 (1.224) Remain 16:59:36 loss: 0.2158 Lr: 0.00128 [2024-02-19 04:02:12,556 INFO misc.py line 119 87073] Train: [68/100][1414/1557] Data 0.012 (0.136) Batch 1.187 (1.224) Remain 16:59:34 loss: 0.1713 Lr: 0.00128 [2024-02-19 04:02:13,510 INFO misc.py line 119 87073] Train: [68/100][1415/1557] Data 0.027 (0.136) Batch 0.977 (1.224) Remain 16:59:24 loss: 0.3017 Lr: 0.00128 [2024-02-19 04:02:14,725 INFO misc.py line 119 87073] Train: [68/100][1416/1557] Data 0.003 (0.136) Batch 1.202 (1.224) Remain 16:59:22 loss: 0.4302 Lr: 0.00128 [2024-02-19 04:02:15,851 INFO misc.py line 119 87073] Train: [68/100][1417/1557] Data 0.016 (0.136) Batch 1.129 (1.224) Remain 16:59:17 loss: 0.7151 Lr: 0.00128 [2024-02-19 04:02:16,717 INFO misc.py line 119 87073] Train: [68/100][1418/1557] Data 0.014 (0.136) Batch 0.876 (1.224) Remain 16:59:04 loss: 1.1753 Lr: 0.00128 [2024-02-19 04:02:17,474 INFO misc.py line 119 87073] Train: [68/100][1419/1557] Data 0.003 (0.135) Batch 0.757 (1.223) Remain 16:58:46 loss: 0.3893 Lr: 0.00128 [2024-02-19 04:02:18,240 INFO misc.py line 119 87073] Train: [68/100][1420/1557] Data 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Remain 16:57:29 loss: 0.1744 Lr: 0.00128 [2024-02-19 04:02:24,824 INFO misc.py line 119 87073] Train: [68/100][1427/1557] Data 0.003 (0.135) Batch 0.732 (1.222) Remain 16:57:10 loss: 0.1381 Lr: 0.00128 [2024-02-19 04:02:26,166 INFO misc.py line 119 87073] Train: [68/100][1428/1557] Data 0.010 (0.135) Batch 1.319 (1.222) Remain 16:57:13 loss: 0.2244 Lr: 0.00128 [2024-02-19 04:02:27,042 INFO misc.py line 119 87073] Train: [68/100][1429/1557] Data 0.033 (0.135) Batch 0.905 (1.222) Remain 16:57:00 loss: 1.1742 Lr: 0.00128 [2024-02-19 04:02:28,162 INFO misc.py line 119 87073] Train: [68/100][1430/1557] Data 0.004 (0.134) Batch 1.120 (1.222) Remain 16:56:56 loss: 0.1514 Lr: 0.00128 [2024-02-19 04:02:29,121 INFO misc.py line 119 87073] Train: [68/100][1431/1557] Data 0.003 (0.134) Batch 0.959 (1.221) Remain 16:56:45 loss: 0.1890 Lr: 0.00128 [2024-02-19 04:02:29,980 INFO misc.py line 119 87073] Train: [68/100][1432/1557] Data 0.003 (0.134) Batch 0.859 (1.221) Remain 16:56:31 loss: 0.3108 Lr: 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INFO misc.py line 119 87073] Train: [68/100][1439/1557] Data 0.003 (0.134) Batch 1.027 (1.220) Remain 16:55:25 loss: 0.2644 Lr: 0.00128 [2024-02-19 04:02:37,625 INFO misc.py line 119 87073] Train: [68/100][1440/1557] Data 0.003 (0.134) Batch 0.769 (1.220) Remain 16:55:08 loss: 0.3112 Lr: 0.00128 [2024-02-19 04:02:38,369 INFO misc.py line 119 87073] Train: [68/100][1441/1557] Data 0.003 (0.133) Batch 0.742 (1.219) Remain 16:54:50 loss: 0.1544 Lr: 0.00128 [2024-02-19 04:02:39,687 INFO misc.py line 119 87073] Train: [68/100][1442/1557] Data 0.004 (0.133) Batch 1.307 (1.219) Remain 16:54:52 loss: 0.1150 Lr: 0.00128 [2024-02-19 04:02:40,653 INFO misc.py line 119 87073] Train: [68/100][1443/1557] Data 0.015 (0.133) Batch 0.979 (1.219) Remain 16:54:42 loss: 0.4229 Lr: 0.00128 [2024-02-19 04:02:41,498 INFO misc.py line 119 87073] Train: [68/100][1444/1557] Data 0.003 (0.133) Batch 0.844 (1.219) Remain 16:54:28 loss: 0.1347 Lr: 0.00128 [2024-02-19 04:02:42,392 INFO misc.py line 119 87073] Train: [68/100][1445/1557] Data 0.003 (0.133) Batch 0.886 (1.219) Remain 16:54:15 loss: 0.1898 Lr: 0.00128 [2024-02-19 04:02:43,409 INFO misc.py line 119 87073] Train: [68/100][1446/1557] Data 0.012 (0.133) Batch 1.021 (1.219) Remain 16:54:07 loss: 0.2677 Lr: 0.00128 [2024-02-19 04:02:44,179 INFO misc.py line 119 87073] Train: [68/100][1447/1557] Data 0.008 (0.133) Batch 0.774 (1.218) Remain 16:53:51 loss: 0.1651 Lr: 0.00128 [2024-02-19 04:02:44,941 INFO misc.py line 119 87073] Train: [68/100][1448/1557] Data 0.004 (0.133) Batch 0.756 (1.218) Remain 16:53:33 loss: 0.1330 Lr: 0.00128 [2024-02-19 04:02:46,233 INFO misc.py line 119 87073] Train: [68/100][1449/1557] Data 0.010 (0.133) Batch 1.291 (1.218) Remain 16:53:35 loss: 0.1071 Lr: 0.00128 [2024-02-19 04:02:47,400 INFO misc.py line 119 87073] Train: [68/100][1450/1557] Data 0.011 (0.133) Batch 1.162 (1.218) Remain 16:53:31 loss: 0.2283 Lr: 0.00128 [2024-02-19 04:02:48,408 INFO misc.py line 119 87073] Train: [68/100][1451/1557] Data 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Remain 16:52:19 loss: 0.2997 Lr: 0.00128 [2024-02-19 04:02:54,986 INFO misc.py line 119 87073] Train: [68/100][1458/1557] Data 0.004 (0.132) Batch 0.947 (1.216) Remain 16:52:08 loss: 0.3770 Lr: 0.00128 [2024-02-19 04:02:55,985 INFO misc.py line 119 87073] Train: [68/100][1459/1557] Data 0.003 (0.132) Batch 0.999 (1.216) Remain 16:51:59 loss: 0.1508 Lr: 0.00128 [2024-02-19 04:02:57,033 INFO misc.py line 119 87073] Train: [68/100][1460/1557] Data 0.003 (0.132) Batch 1.048 (1.216) Remain 16:51:53 loss: 0.1066 Lr: 0.00128 [2024-02-19 04:02:57,779 INFO misc.py line 119 87073] Train: [68/100][1461/1557] Data 0.003 (0.132) Batch 0.745 (1.216) Remain 16:51:35 loss: 0.1620 Lr: 0.00128 [2024-02-19 04:02:58,542 INFO misc.py line 119 87073] Train: [68/100][1462/1557] Data 0.003 (0.132) Batch 0.755 (1.216) Remain 16:51:18 loss: 0.2843 Lr: 0.00128 [2024-02-19 04:03:15,357 INFO misc.py line 119 87073] Train: [68/100][1463/1557] Data 7.167 (0.136) Batch 16.823 (1.226) Remain 17:00:11 loss: 0.0635 Lr: 0.00128 [2024-02-19 04:03:16,337 INFO misc.py line 119 87073] Train: [68/100][1464/1557] Data 0.004 (0.136) Batch 0.971 (1.226) Remain 17:00:01 loss: 0.7668 Lr: 0.00128 [2024-02-19 04:03:17,281 INFO misc.py line 119 87073] Train: [68/100][1465/1557] Data 0.012 (0.136) Batch 0.953 (1.226) Remain 16:59:50 loss: 0.4045 Lr: 0.00128 [2024-02-19 04:03:18,274 INFO misc.py line 119 87073] Train: [68/100][1466/1557] Data 0.003 (0.136) Batch 0.994 (1.226) Remain 16:59:41 loss: 0.1758 Lr: 0.00128 [2024-02-19 04:03:19,238 INFO misc.py line 119 87073] Train: [68/100][1467/1557] Data 0.003 (0.136) Batch 0.963 (1.226) Remain 16:59:31 loss: 0.4277 Lr: 0.00128 [2024-02-19 04:03:19,958 INFO misc.py line 119 87073] Train: [68/100][1468/1557] Data 0.004 (0.136) Batch 0.709 (1.225) Remain 16:59:12 loss: 0.1902 Lr: 0.00128 [2024-02-19 04:03:20,743 INFO misc.py line 119 87073] Train: [68/100][1469/1557] Data 0.015 (0.136) Batch 0.797 (1.225) Remain 16:58:56 loss: 0.2700 Lr: 0.00128 [2024-02-19 04:03:22,008 INFO misc.py line 119 87073] Train: [68/100][1470/1557] Data 0.003 (0.136) Batch 1.254 (1.225) Remain 16:58:56 loss: 0.1870 Lr: 0.00128 [2024-02-19 04:03:22,864 INFO misc.py line 119 87073] Train: [68/100][1471/1557] Data 0.014 (0.136) Batch 0.867 (1.225) Remain 16:58:42 loss: 0.4457 Lr: 0.00128 [2024-02-19 04:03:23,641 INFO misc.py line 119 87073] Train: [68/100][1472/1557] Data 0.003 (0.136) Batch 0.777 (1.224) Remain 16:58:26 loss: 0.3410 Lr: 0.00128 [2024-02-19 04:03:24,701 INFO misc.py line 119 87073] Train: [68/100][1473/1557] Data 0.003 (0.136) Batch 1.050 (1.224) Remain 16:58:19 loss: 0.3402 Lr: 0.00128 [2024-02-19 04:03:25,653 INFO misc.py line 119 87073] Train: [68/100][1474/1557] Data 0.013 (0.136) Batch 0.961 (1.224) Remain 16:58:09 loss: 0.1064 Lr: 0.00128 [2024-02-19 04:03:26,467 INFO misc.py line 119 87073] Train: [68/100][1475/1557] Data 0.004 (0.135) Batch 0.814 (1.224) Remain 16:57:54 loss: 0.1981 Lr: 0.00128 [2024-02-19 04:03:27,116 INFO misc.py line 119 87073] Train: [68/100][1476/1557] Data 0.004 (0.135) Batch 0.643 (1.223) Remain 16:57:33 loss: 0.1701 Lr: 0.00128 [2024-02-19 04:03:28,344 INFO misc.py line 119 87073] Train: [68/100][1477/1557] Data 0.010 (0.135) Batch 1.225 (1.223) Remain 16:57:32 loss: 0.1552 Lr: 0.00128 [2024-02-19 04:03:29,201 INFO misc.py line 119 87073] Train: [68/100][1478/1557] Data 0.014 (0.135) Batch 0.867 (1.223) Remain 16:57:18 loss: 0.3010 Lr: 0.00128 [2024-02-19 04:03:30,197 INFO misc.py line 119 87073] Train: [68/100][1479/1557] Data 0.003 (0.135) Batch 0.995 (1.223) Remain 16:57:09 loss: 0.1222 Lr: 0.00128 [2024-02-19 04:03:31,029 INFO misc.py line 119 87073] Train: [68/100][1480/1557] Data 0.004 (0.135) Batch 0.833 (1.223) Remain 16:56:55 loss: 0.3258 Lr: 0.00128 [2024-02-19 04:03:32,038 INFO misc.py line 119 87073] Train: [68/100][1481/1557] Data 0.003 (0.135) Batch 1.005 (1.223) Remain 16:56:46 loss: 0.5422 Lr: 0.00128 [2024-02-19 04:03:32,856 INFO misc.py line 119 87073] Train: [68/100][1482/1557] Data 0.008 (0.135) Batch 0.822 (1.222) Remain 16:56:32 loss: 0.1778 Lr: 0.00128 [2024-02-19 04:03:33,614 INFO misc.py line 119 87073] Train: [68/100][1483/1557] Data 0.003 (0.135) Batch 0.758 (1.222) Remain 16:56:15 loss: 0.1896 Lr: 0.00128 [2024-02-19 04:03:34,933 INFO misc.py line 119 87073] Train: [68/100][1484/1557] Data 0.003 (0.135) Batch 1.307 (1.222) Remain 16:56:16 loss: 0.1263 Lr: 0.00128 [2024-02-19 04:03:35,843 INFO misc.py line 119 87073] Train: [68/100][1485/1557] Data 0.015 (0.135) Batch 0.922 (1.222) Remain 16:56:05 loss: 0.1926 Lr: 0.00128 [2024-02-19 04:03:36,779 INFO misc.py line 119 87073] Train: [68/100][1486/1557] Data 0.003 (0.134) Batch 0.936 (1.222) Remain 16:55:54 loss: 0.2825 Lr: 0.00128 [2024-02-19 04:03:37,612 INFO misc.py line 119 87073] Train: [68/100][1487/1557] Data 0.003 (0.134) Batch 0.832 (1.221) Remain 16:55:40 loss: 0.3373 Lr: 0.00128 [2024-02-19 04:03:38,559 INFO misc.py line 119 87073] Train: [68/100][1488/1557] Data 0.003 (0.134) Batch 0.940 (1.221) Remain 16:55:29 loss: 0.1320 Lr: 0.00128 [2024-02-19 04:03:39,276 INFO misc.py line 119 87073] Train: [68/100][1489/1557] Data 0.011 (0.134) Batch 0.725 (1.221) Remain 16:55:11 loss: 0.4094 Lr: 0.00128 [2024-02-19 04:03:40,056 INFO misc.py line 119 87073] Train: [68/100][1490/1557] Data 0.005 (0.134) Batch 0.772 (1.221) Remain 16:54:55 loss: 0.1639 Lr: 0.00128 [2024-02-19 04:03:41,017 INFO misc.py line 119 87073] Train: [68/100][1491/1557] Data 0.012 (0.134) Batch 0.970 (1.220) Remain 16:54:45 loss: 0.1303 Lr: 0.00128 [2024-02-19 04:03:41,992 INFO misc.py line 119 87073] Train: [68/100][1492/1557] Data 0.003 (0.134) Batch 0.976 (1.220) Remain 16:54:36 loss: 0.3647 Lr: 0.00128 [2024-02-19 04:03:42,950 INFO misc.py line 119 87073] Train: [68/100][1493/1557] Data 0.003 (0.134) Batch 0.956 (1.220) Remain 16:54:26 loss: 0.1739 Lr: 0.00128 [2024-02-19 04:03:44,154 INFO misc.py line 119 87073] Train: [68/100][1494/1557] Data 0.004 (0.134) Batch 1.205 (1.220) Remain 16:54:24 loss: 0.5072 Lr: 0.00128 [2024-02-19 04:03:45,354 INFO misc.py line 119 87073] Train: [68/100][1495/1557] Data 0.004 (0.134) Batch 1.192 (1.220) Remain 16:54:22 loss: 0.2930 Lr: 0.00128 [2024-02-19 04:03:46,140 INFO misc.py line 119 87073] Train: [68/100][1496/1557] Data 0.010 (0.134) Batch 0.791 (1.220) Remain 16:54:07 loss: 0.3848 Lr: 0.00128 [2024-02-19 04:03:47,002 INFO misc.py line 119 87073] Train: [68/100][1497/1557] Data 0.006 (0.134) Batch 0.863 (1.220) Remain 16:53:53 loss: 0.3040 Lr: 0.00128 [2024-02-19 04:03:48,219 INFO misc.py line 119 87073] Train: [68/100][1498/1557] Data 0.005 (0.133) Batch 1.210 (1.220) Remain 16:53:52 loss: 0.1892 Lr: 0.00128 [2024-02-19 04:03:49,299 INFO misc.py line 119 87073] Train: [68/100][1499/1557] Data 0.013 (0.133) Batch 1.080 (1.219) Remain 16:53:46 loss: 0.5415 Lr: 0.00128 [2024-02-19 04:03:50,278 INFO misc.py line 119 87073] Train: [68/100][1500/1557] Data 0.012 (0.133) Batch 0.986 (1.219) Remain 16:53:37 loss: 0.1632 Lr: 0.00128 [2024-02-19 04:03:51,271 INFO misc.py line 119 87073] Train: [68/100][1501/1557] Data 0.005 (0.133) Batch 0.993 (1.219) Remain 16:53:28 loss: 0.2945 Lr: 0.00128 [2024-02-19 04:03:52,319 INFO misc.py line 119 87073] Train: [68/100][1502/1557] Data 0.006 (0.133) Batch 1.048 (1.219) Remain 16:53:21 loss: 0.3665 Lr: 0.00128 [2024-02-19 04:03:53,092 INFO misc.py line 119 87073] Train: [68/100][1503/1557] Data 0.007 (0.133) Batch 0.774 (1.219) Remain 16:53:05 loss: 0.1258 Lr: 0.00128 [2024-02-19 04:03:53,820 INFO misc.py line 119 87073] Train: [68/100][1504/1557] Data 0.004 (0.133) Batch 0.721 (1.218) Remain 16:52:48 loss: 0.3523 Lr: 0.00128 [2024-02-19 04:03:55,133 INFO misc.py line 119 87073] Train: [68/100][1505/1557] Data 0.012 (0.133) Batch 1.311 (1.218) Remain 16:52:49 loss: 0.1172 Lr: 0.00128 [2024-02-19 04:03:56,033 INFO misc.py line 119 87073] Train: [68/100][1506/1557] Data 0.014 (0.133) Batch 0.910 (1.218) Remain 16:52:38 loss: 0.3655 Lr: 0.00128 [2024-02-19 04:03:57,012 INFO misc.py line 119 87073] Train: [68/100][1507/1557] Data 0.004 (0.133) Batch 0.980 (1.218) Remain 16:52:29 loss: 0.2834 Lr: 0.00128 [2024-02-19 04:03:58,086 INFO misc.py line 119 87073] Train: [68/100][1508/1557] Data 0.003 (0.133) Batch 1.072 (1.218) Remain 16:52:23 loss: 0.3104 Lr: 0.00128 [2024-02-19 04:03:58,860 INFO misc.py line 119 87073] Train: [68/100][1509/1557] Data 0.005 (0.133) Batch 0.774 (1.218) Remain 16:52:07 loss: 0.3704 Lr: 0.00128 [2024-02-19 04:03:59,608 INFO misc.py line 119 87073] Train: [68/100][1510/1557] Data 0.005 (0.132) Batch 0.740 (1.217) Remain 16:51:50 loss: 0.3080 Lr: 0.00128 [2024-02-19 04:04:00,387 INFO misc.py line 119 87073] Train: [68/100][1511/1557] Data 0.012 (0.132) Batch 0.787 (1.217) Remain 16:51:34 loss: 0.3426 Lr: 0.00128 [2024-02-19 04:04:01,599 INFO misc.py line 119 87073] Train: [68/100][1512/1557] Data 0.005 (0.132) Batch 1.212 (1.217) Remain 16:51:33 loss: 0.1344 Lr: 0.00128 [2024-02-19 04:04:02,533 INFO misc.py line 119 87073] Train: [68/100][1513/1557] Data 0.005 (0.132) Batch 0.935 (1.217) Remain 16:51:22 loss: 0.4408 Lr: 0.00128 [2024-02-19 04:04:03,533 INFO misc.py line 119 87073] Train: [68/100][1514/1557] Data 0.004 (0.132) Batch 1.001 (1.217) Remain 16:51:14 loss: 0.0564 Lr: 0.00128 [2024-02-19 04:04:04,559 INFO misc.py line 119 87073] Train: [68/100][1515/1557] Data 0.003 (0.132) Batch 1.026 (1.217) Remain 16:51:07 loss: 0.5379 Lr: 0.00128 [2024-02-19 04:04:05,700 INFO misc.py line 119 87073] Train: [68/100][1516/1557] Data 0.004 (0.132) Batch 1.142 (1.217) Remain 16:51:03 loss: 0.3270 Lr: 0.00128 [2024-02-19 04:04:06,458 INFO misc.py line 119 87073] Train: [68/100][1517/1557] Data 0.003 (0.132) Batch 0.758 (1.216) Remain 16:50:47 loss: 0.1877 Lr: 0.00128 [2024-02-19 04:04:07,246 INFO misc.py line 119 87073] Train: [68/100][1518/1557] Data 0.003 (0.132) Batch 0.785 (1.216) Remain 16:50:31 loss: 0.1533 Lr: 0.00128 [2024-02-19 04:04:22,905 INFO misc.py line 119 87073] Train: [68/100][1519/1557] Data 7.401 (0.137) Batch 15.662 (1.225) Remain 16:58:25 loss: 0.0584 Lr: 0.00128 [2024-02-19 04:04:23,831 INFO misc.py line 119 87073] Train: [68/100][1520/1557] Data 0.004 (0.136) Batch 0.922 (1.225) Remain 16:58:14 loss: 0.0736 Lr: 0.00128 [2024-02-19 04:04:24,724 INFO misc.py line 119 87073] Train: [68/100][1521/1557] Data 0.007 (0.136) Batch 0.897 (1.225) Remain 16:58:02 loss: 0.4763 Lr: 0.00128 [2024-02-19 04:04:25,711 INFO misc.py line 119 87073] Train: [68/100][1522/1557] Data 0.003 (0.136) Batch 0.988 (1.225) Remain 16:57:53 loss: 0.3533 Lr: 0.00128 [2024-02-19 04:04:26,587 INFO misc.py line 119 87073] Train: [68/100][1523/1557] Data 0.002 (0.136) Batch 0.875 (1.225) Remain 16:57:40 loss: 0.6196 Lr: 0.00128 [2024-02-19 04:04:27,344 INFO misc.py line 119 87073] Train: [68/100][1524/1557] Data 0.003 (0.136) Batch 0.752 (1.224) Remain 16:57:23 loss: 0.2068 Lr: 0.00128 [2024-02-19 04:04:28,152 INFO misc.py line 119 87073] Train: [68/100][1525/1557] Data 0.009 (0.136) Batch 0.812 (1.224) Remain 16:57:09 loss: 0.1448 Lr: 0.00128 [2024-02-19 04:04:29,333 INFO misc.py line 119 87073] Train: [68/100][1526/1557] Data 0.005 (0.136) Batch 1.181 (1.224) Remain 16:57:06 loss: 0.1754 Lr: 0.00128 [2024-02-19 04:04:30,233 INFO misc.py line 119 87073] Train: [68/100][1527/1557] Data 0.005 (0.136) Batch 0.899 (1.224) Remain 16:56:54 loss: 0.5610 Lr: 0.00128 [2024-02-19 04:04:31,198 INFO misc.py line 119 87073] Train: [68/100][1528/1557] Data 0.005 (0.136) Batch 0.960 (1.224) Remain 16:56:44 loss: 0.3132 Lr: 0.00128 [2024-02-19 04:04:32,124 INFO misc.py line 119 87073] Train: [68/100][1529/1557] Data 0.010 (0.136) Batch 0.931 (1.224) Remain 16:56:34 loss: 0.1774 Lr: 0.00128 [2024-02-19 04:04:33,073 INFO misc.py line 119 87073] Train: [68/100][1530/1557] Data 0.005 (0.136) Batch 0.950 (1.223) Remain 16:56:23 loss: 0.3717 Lr: 0.00128 [2024-02-19 04:04:33,845 INFO misc.py line 119 87073] Train: [68/100][1531/1557] Data 0.004 (0.136) Batch 0.773 (1.223) Remain 16:56:07 loss: 0.2398 Lr: 0.00128 [2024-02-19 04:04:34,726 INFO misc.py line 119 87073] Train: [68/100][1532/1557] Data 0.003 (0.135) Batch 0.870 (1.223) Remain 16:55:55 loss: 0.1786 Lr: 0.00128 [2024-02-19 04:04:36,032 INFO misc.py line 119 87073] Train: [68/100][1533/1557] Data 0.014 (0.135) Batch 1.308 (1.223) Remain 16:55:56 loss: 0.2561 Lr: 0.00128 [2024-02-19 04:04:37,118 INFO misc.py line 119 87073] Train: [68/100][1534/1557] Data 0.012 (0.135) Batch 1.085 (1.223) Remain 16:55:51 loss: 0.1873 Lr: 0.00128 [2024-02-19 04:04:38,006 INFO misc.py line 119 87073] Train: [68/100][1535/1557] Data 0.013 (0.135) Batch 0.897 (1.223) Remain 16:55:39 loss: 0.4022 Lr: 0.00128 [2024-02-19 04:04:38,813 INFO misc.py line 119 87073] Train: [68/100][1536/1557] Data 0.003 (0.135) Batch 0.806 (1.222) Remain 16:55:24 loss: 0.3090 Lr: 0.00128 [2024-02-19 04:04:39,618 INFO misc.py line 119 87073] Train: [68/100][1537/1557] Data 0.004 (0.135) Batch 0.795 (1.222) Remain 16:55:09 loss: 0.1887 Lr: 0.00128 [2024-02-19 04:04:40,377 INFO misc.py line 119 87073] Train: [68/100][1538/1557] Data 0.013 (0.135) Batch 0.770 (1.222) Remain 16:54:53 loss: 0.1870 Lr: 0.00128 [2024-02-19 04:04:41,144 INFO misc.py line 119 87073] Train: [68/100][1539/1557] Data 0.004 (0.135) Batch 0.759 (1.221) Remain 16:54:37 loss: 0.3243 Lr: 0.00128 [2024-02-19 04:04:42,412 INFO misc.py line 119 87073] Train: [68/100][1540/1557] Data 0.011 (0.135) Batch 1.268 (1.221) Remain 16:54:37 loss: 0.0694 Lr: 0.00128 [2024-02-19 04:04:43,425 INFO misc.py line 119 87073] Train: [68/100][1541/1557] Data 0.011 (0.135) Batch 1.013 (1.221) Remain 16:54:29 loss: 0.1813 Lr: 0.00128 [2024-02-19 04:04:44,557 INFO misc.py line 119 87073] Train: [68/100][1542/1557] Data 0.011 (0.135) Batch 1.134 (1.221) Remain 16:54:25 loss: 0.1828 Lr: 0.00128 [2024-02-19 04:04:45,305 INFO misc.py line 119 87073] Train: [68/100][1543/1557] Data 0.009 (0.135) Batch 0.753 (1.221) Remain 16:54:09 loss: 0.9424 Lr: 0.00128 [2024-02-19 04:04:46,289 INFO misc.py line 119 87073] Train: [68/100][1544/1557] Data 0.003 (0.134) Batch 0.985 (1.221) Remain 16:54:00 loss: 0.2524 Lr: 0.00128 [2024-02-19 04:04:47,045 INFO misc.py line 119 87073] Train: [68/100][1545/1557] Data 0.003 (0.134) Batch 0.746 (1.220) Remain 16:53:43 loss: 0.1389 Lr: 0.00128 [2024-02-19 04:04:47,766 INFO misc.py line 119 87073] Train: [68/100][1546/1557] Data 0.013 (0.134) Batch 0.730 (1.220) Remain 16:53:26 loss: 0.2114 Lr: 0.00128 [2024-02-19 04:04:48,740 INFO misc.py line 119 87073] Train: [68/100][1547/1557] Data 0.004 (0.134) Batch 0.969 (1.220) Remain 16:53:17 loss: 0.2098 Lr: 0.00128 [2024-02-19 04:04:49,741 INFO misc.py line 119 87073] Train: [68/100][1548/1557] Data 0.009 (0.134) Batch 0.999 (1.220) Remain 16:53:09 loss: 0.3689 Lr: 0.00128 [2024-02-19 04:04:50,680 INFO misc.py line 119 87073] Train: [68/100][1549/1557] Data 0.011 (0.134) Batch 0.947 (1.220) Remain 16:52:59 loss: 0.4608 Lr: 0.00128 [2024-02-19 04:04:51,587 INFO misc.py line 119 87073] Train: [68/100][1550/1557] Data 0.003 (0.134) Batch 0.906 (1.219) Remain 16:52:47 loss: 0.0804 Lr: 0.00128 [2024-02-19 04:04:52,503 INFO misc.py line 119 87073] Train: [68/100][1551/1557] Data 0.003 (0.134) Batch 0.916 (1.219) Remain 16:52:36 loss: 0.5291 Lr: 0.00128 [2024-02-19 04:04:53,173 INFO misc.py line 119 87073] Train: [68/100][1552/1557] Data 0.003 (0.134) Batch 0.661 (1.219) Remain 16:52:17 loss: 0.1410 Lr: 0.00128 [2024-02-19 04:04:53,899 INFO misc.py line 119 87073] Train: [68/100][1553/1557] Data 0.012 (0.134) Batch 0.735 (1.219) Remain 16:52:00 loss: 0.1632 Lr: 0.00128 [2024-02-19 04:04:55,113 INFO misc.py line 119 87073] Train: [68/100][1554/1557] Data 0.003 (0.134) Batch 1.214 (1.219) Remain 16:51:59 loss: 0.1200 Lr: 0.00128 [2024-02-19 04:04:55,992 INFO misc.py line 119 87073] Train: [68/100][1555/1557] Data 0.003 (0.134) Batch 0.879 (1.218) Remain 16:51:47 loss: 0.2830 Lr: 0.00128 [2024-02-19 04:04:56,905 INFO misc.py line 119 87073] Train: [68/100][1556/1557] Data 0.003 (0.133) Batch 0.906 (1.218) Remain 16:51:36 loss: 0.5908 Lr: 0.00128 [2024-02-19 04:04:57,778 INFO misc.py line 119 87073] Train: [68/100][1557/1557] Data 0.010 (0.133) Batch 0.880 (1.218) Remain 16:51:23 loss: 0.3796 Lr: 0.00128 [2024-02-19 04:04:57,778 INFO misc.py line 136 87073] Train result: loss: 0.2704 [2024-02-19 04:04:57,779 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 04:05:25,463 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5994 [2024-02-19 04:05:26,240 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2183 [2024-02-19 04:05:28,367 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4023 [2024-02-19 04:05:30,578 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3685 [2024-02-19 04:05:35,516 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.1985 [2024-02-19 04:05:36,219 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0664 [2024-02-19 04:05:37,480 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3796 [2024-02-19 04:05:40,441 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2585 [2024-02-19 04:05:42,251 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3026 [2024-02-19 04:05:43,852 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7275/0.7857/0.9155. [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9261/0.9494 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9821/0.9873 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8567/0.9726 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3887/0.4428 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7177/0.7509 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8533/0.9059 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8372/0.9190 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9235/0.9754 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8042/0.8378 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7913/0.8864 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7599/0.8821 [2024-02-19 04:05:43,853 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6166/0.7044 [2024-02-19 04:05:43,854 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 04:05:43,856 INFO misc.py line 165 87073] Currently Best mIoU: 0.7308 [2024-02-19 04:05:43,856 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 04:05:50,803 INFO misc.py line 119 87073] Train: [69/100][1/1557] Data 1.551 (1.551) Batch 2.361 (2.361) Remain 32:40:33 loss: 0.0399 Lr: 0.00128 [2024-02-19 04:05:51,694 INFO misc.py line 119 87073] Train: [69/100][2/1557] Data 0.006 (0.006) Batch 0.884 (0.884) Remain 12:13:50 loss: 0.1989 Lr: 0.00128 [2024-02-19 04:05:52,602 INFO misc.py line 119 87073] Train: [69/100][3/1557] Data 0.012 (0.012) Batch 0.915 (0.915) Remain 12:39:43 loss: 0.3069 Lr: 0.00128 [2024-02-19 04:05:53,690 INFO misc.py line 119 87073] Train: [69/100][4/1557] Data 0.006 (0.006) Batch 1.088 (1.088) Remain 15:03:43 loss: 0.1511 Lr: 0.00128 [2024-02-19 04:05:54,580 INFO misc.py line 119 87073] Train: [69/100][5/1557] Data 0.004 (0.005) Batch 0.891 (0.990) Remain 13:41:57 loss: 0.2701 Lr: 0.00128 [2024-02-19 04:05:55,406 INFO misc.py line 119 87073] Train: [69/100][6/1557] Data 0.004 (0.005) Batch 0.819 (0.933) Remain 12:54:44 loss: 0.1830 Lr: 0.00128 [2024-02-19 04:05:58,781 INFO misc.py line 119 87073] Train: [69/100][7/1557] Data 0.009 (0.006) Batch 3.382 (1.545) Remain 21:23:01 loss: 0.1147 Lr: 0.00128 [2024-02-19 04:05:59,730 INFO misc.py line 119 87073] Train: [69/100][8/1557] Data 0.003 (0.005) Batch 0.948 (1.426) Remain 19:43:52 loss: 0.1790 Lr: 0.00128 [2024-02-19 04:06:00,750 INFO misc.py line 119 87073] Train: [69/100][9/1557] Data 0.003 (0.005) Batch 1.020 (1.358) Remain 18:47:44 loss: 0.4269 Lr: 0.00128 [2024-02-19 04:06:01,618 INFO misc.py line 119 87073] Train: [69/100][10/1557] Data 0.003 (0.005) Batch 0.863 (1.288) Remain 17:49:01 loss: 0.2397 Lr: 0.00128 [2024-02-19 04:06:02,631 INFO misc.py line 119 87073] Train: [69/100][11/1557] Data 0.008 (0.005) Batch 1.012 (1.253) Remain 17:20:24 loss: 0.3089 Lr: 0.00128 [2024-02-19 04:06:03,326 INFO misc.py line 119 87073] Train: [69/100][12/1557] Data 0.009 (0.006) Batch 0.699 (1.192) Remain 16:29:14 loss: 0.2837 Lr: 0.00128 [2024-02-19 04:06:03,961 INFO misc.py line 119 87073] Train: [69/100][13/1557] Data 0.005 (0.006) Batch 0.635 (1.136) Remain 15:43:00 loss: 0.4664 Lr: 0.00128 [2024-02-19 04:06:05,243 INFO misc.py line 119 87073] Train: [69/100][14/1557] Data 0.005 (0.006) Batch 1.277 (1.149) Remain 15:53:36 loss: 0.2085 Lr: 0.00128 [2024-02-19 04:06:06,137 INFO misc.py line 119 87073] Train: [69/100][15/1557] Data 0.010 (0.006) Batch 0.901 (1.128) Remain 15:36:27 loss: 0.3249 Lr: 0.00128 [2024-02-19 04:06:07,121 INFO misc.py line 119 87073] Train: [69/100][16/1557] Data 0.004 (0.006) Batch 0.984 (1.117) Remain 15:27:14 loss: 0.2652 Lr: 0.00128 [2024-02-19 04:06:08,154 INFO misc.py line 119 87073] Train: [69/100][17/1557] Data 0.004 (0.006) Batch 1.033 (1.111) Remain 15:22:15 loss: 0.2944 Lr: 0.00128 [2024-02-19 04:06:09,236 INFO misc.py line 119 87073] Train: [69/100][18/1557] Data 0.003 (0.005) Batch 1.082 (1.109) Remain 15:20:38 loss: 0.5464 Lr: 0.00128 [2024-02-19 04:06:09,912 INFO misc.py line 119 87073] Train: [69/100][19/1557] Data 0.003 (0.005) Batch 0.675 (1.082) Remain 14:58:07 loss: 0.1512 Lr: 0.00128 [2024-02-19 04:06:10,612 INFO misc.py line 119 87073] Train: [69/100][20/1557] Data 0.004 (0.005) Batch 0.698 (1.059) Remain 14:39:22 loss: 0.1586 Lr: 0.00128 [2024-02-19 04:06:11,828 INFO misc.py line 119 87073] Train: [69/100][21/1557] Data 0.006 (0.005) Batch 1.215 (1.068) Remain 14:46:31 loss: 0.0827 Lr: 0.00128 [2024-02-19 04:06:12,767 INFO misc.py line 119 87073] Train: [69/100][22/1557] Data 0.006 (0.005) Batch 0.942 (1.061) Remain 14:41:00 loss: 0.1806 Lr: 0.00128 [2024-02-19 04:06:13,754 INFO misc.py line 119 87073] Train: [69/100][23/1557] Data 0.003 (0.005) Batch 0.988 (1.058) Remain 14:37:56 loss: 0.3789 Lr: 0.00128 [2024-02-19 04:06:14,700 INFO misc.py line 119 87073] Train: [69/100][24/1557] Data 0.003 (0.005) Batch 0.946 (1.052) Remain 14:33:29 loss: 0.3801 Lr: 0.00128 [2024-02-19 04:06:15,729 INFO misc.py line 119 87073] Train: [69/100][25/1557] Data 0.003 (0.005) Batch 1.029 (1.051) Remain 14:32:35 loss: 0.3915 Lr: 0.00128 [2024-02-19 04:06:16,500 INFO misc.py line 119 87073] Train: [69/100][26/1557] Data 0.003 (0.005) Batch 0.765 (1.039) Remain 14:22:14 loss: 0.2238 Lr: 0.00128 [2024-02-19 04:06:17,203 INFO misc.py line 119 87073] Train: [69/100][27/1557] Data 0.008 (0.005) Batch 0.708 (1.025) Remain 14:10:48 loss: 0.6363 Lr: 0.00127 [2024-02-19 04:06:18,564 INFO misc.py line 119 87073] Train: [69/100][28/1557] Data 0.003 (0.005) Batch 1.356 (1.038) Remain 14:21:45 loss: 0.0976 Lr: 0.00127 [2024-02-19 04:06:19,552 INFO misc.py line 119 87073] Train: [69/100][29/1557] Data 0.009 (0.005) Batch 0.993 (1.037) Remain 14:20:18 loss: 0.2439 Lr: 0.00127 [2024-02-19 04:06:20,503 INFO misc.py line 119 87073] Train: [69/100][30/1557] Data 0.003 (0.005) Batch 0.951 (1.033) Remain 14:17:39 loss: 0.1955 Lr: 0.00127 [2024-02-19 04:06:21,565 INFO misc.py line 119 87073] Train: [69/100][31/1557] Data 0.003 (0.005) Batch 1.062 (1.034) Remain 14:18:29 loss: 0.7103 Lr: 0.00127 [2024-02-19 04:06:22,750 INFO misc.py line 119 87073] Train: [69/100][32/1557] Data 0.002 (0.005) Batch 1.185 (1.040) Remain 14:22:47 loss: 0.2128 Lr: 0.00127 [2024-02-19 04:06:23,518 INFO misc.py line 119 87073] Train: [69/100][33/1557] Data 0.003 (0.005) Batch 0.768 (1.031) Remain 14:15:15 loss: 0.2360 Lr: 0.00127 [2024-02-19 04:06:24,296 INFO misc.py line 119 87073] Train: [69/100][34/1557] Data 0.003 (0.005) Batch 0.774 (1.022) Remain 14:08:22 loss: 0.2445 Lr: 0.00127 [2024-02-19 04:06:25,414 INFO misc.py line 119 87073] Train: [69/100][35/1557] Data 0.007 (0.005) Batch 1.118 (1.025) Remain 14:10:50 loss: 0.1222 Lr: 0.00127 [2024-02-19 04:06:26,390 INFO misc.py line 119 87073] Train: [69/100][36/1557] Data 0.006 (0.005) Batch 0.979 (1.024) Remain 14:09:40 loss: 0.2152 Lr: 0.00127 [2024-02-19 04:06:27,339 INFO misc.py line 119 87073] Train: [69/100][37/1557] Data 0.002 (0.005) Batch 0.949 (1.022) Remain 14:07:49 loss: 0.3523 Lr: 0.00127 [2024-02-19 04:06:28,149 INFO misc.py line 119 87073] Train: [69/100][38/1557] Data 0.003 (0.005) Batch 0.810 (1.016) Remain 14:02:47 loss: 0.1548 Lr: 0.00127 [2024-02-19 04:06:29,352 INFO misc.py line 119 87073] Train: [69/100][39/1557] Data 0.003 (0.005) Batch 1.194 (1.021) Remain 14:06:53 loss: 0.2743 Lr: 0.00127 [2024-02-19 04:06:30,064 INFO misc.py line 119 87073] Train: [69/100][40/1557] Data 0.012 (0.005) Batch 0.720 (1.013) Remain 14:00:08 loss: 0.3156 Lr: 0.00127 [2024-02-19 04:06:30,809 INFO misc.py line 119 87073] Train: [69/100][41/1557] Data 0.003 (0.005) Batch 0.735 (1.005) Remain 13:54:03 loss: 0.1747 Lr: 0.00127 [2024-02-19 04:06:32,093 INFO misc.py line 119 87073] Train: [69/100][42/1557] Data 0.013 (0.005) Batch 1.283 (1.012) Remain 13:59:57 loss: 0.2004 Lr: 0.00127 [2024-02-19 04:06:33,164 INFO misc.py line 119 87073] Train: [69/100][43/1557] Data 0.014 (0.005) Batch 1.072 (1.014) Remain 14:01:09 loss: 0.2493 Lr: 0.00127 [2024-02-19 04:06:34,054 INFO misc.py line 119 87073] Train: [69/100][44/1557] Data 0.014 (0.005) Batch 0.900 (1.011) Remain 13:58:51 loss: 0.2049 Lr: 0.00127 [2024-02-19 04:06:34,917 INFO misc.py line 119 87073] Train: [69/100][45/1557] Data 0.003 (0.005) Batch 0.862 (1.008) Remain 13:55:53 loss: 0.2906 Lr: 0.00127 [2024-02-19 04:06:35,816 INFO misc.py line 119 87073] Train: [69/100][46/1557] Data 0.004 (0.005) Batch 0.894 (1.005) Remain 13:53:40 loss: 0.0781 Lr: 0.00127 [2024-02-19 04:06:36,554 INFO misc.py line 119 87073] Train: [69/100][47/1557] Data 0.009 (0.005) Batch 0.745 (0.999) Remain 13:48:45 loss: 0.1455 Lr: 0.00127 [2024-02-19 04:06:37,288 INFO misc.py line 119 87073] Train: [69/100][48/1557] Data 0.003 (0.005) Batch 0.725 (0.993) Remain 13:43:40 loss: 0.1638 Lr: 0.00127 [2024-02-19 04:06:38,416 INFO misc.py line 119 87073] Train: [69/100][49/1557] Data 0.012 (0.006) Batch 1.128 (0.996) Remain 13:46:06 loss: 0.2872 Lr: 0.00127 [2024-02-19 04:06:39,556 INFO misc.py line 119 87073] Train: [69/100][50/1557] Data 0.012 (0.006) Batch 1.139 (0.999) Remain 13:48:36 loss: 0.3838 Lr: 0.00127 [2024-02-19 04:06:40,601 INFO misc.py line 119 87073] Train: [69/100][51/1557] Data 0.014 (0.006) Batch 1.040 (1.000) Remain 13:49:17 loss: 0.4057 Lr: 0.00127 [2024-02-19 04:06:41,544 INFO misc.py line 119 87073] Train: [69/100][52/1557] Data 0.019 (0.006) Batch 0.959 (0.999) Remain 13:48:35 loss: 0.1890 Lr: 0.00127 [2024-02-19 04:06:42,545 INFO misc.py line 119 87073] Train: [69/100][53/1557] Data 0.003 (0.006) Batch 1.001 (0.999) Remain 13:48:36 loss: 0.2042 Lr: 0.00127 [2024-02-19 04:06:43,303 INFO misc.py line 119 87073] Train: [69/100][54/1557] Data 0.003 (0.006) Batch 0.757 (0.994) Remain 13:44:40 loss: 0.1866 Lr: 0.00127 [2024-02-19 04:06:44,223 INFO misc.py line 119 87073] Train: [69/100][55/1557] Data 0.003 (0.006) Batch 0.920 (0.993) Remain 13:43:27 loss: 0.2966 Lr: 0.00127 [2024-02-19 04:06:45,474 INFO misc.py line 119 87073] Train: [69/100][56/1557] Data 0.004 (0.006) Batch 1.240 (0.997) Remain 13:47:18 loss: 0.0929 Lr: 0.00127 [2024-02-19 04:06:46,295 INFO misc.py line 119 87073] Train: [69/100][57/1557] Data 0.015 (0.006) Batch 0.833 (0.994) Remain 13:44:45 loss: 0.6995 Lr: 0.00127 [2024-02-19 04:06:47,161 INFO misc.py line 119 87073] Train: [69/100][58/1557] Data 0.004 (0.006) Batch 0.866 (0.992) Remain 13:42:48 loss: 0.7734 Lr: 0.00127 [2024-02-19 04:06:47,923 INFO misc.py line 119 87073] Train: [69/100][59/1557] Data 0.003 (0.006) Batch 0.749 (0.988) Remain 13:39:11 loss: 0.2219 Lr: 0.00127 [2024-02-19 04:06:48,952 INFO misc.py line 119 87073] Train: [69/100][60/1557] Data 0.017 (0.006) Batch 1.034 (0.988) Remain 13:39:50 loss: 0.2360 Lr: 0.00127 [2024-02-19 04:06:49,717 INFO misc.py line 119 87073] Train: [69/100][61/1557] Data 0.012 (0.006) Batch 0.773 (0.985) Remain 13:36:44 loss: 0.1950 Lr: 0.00127 [2024-02-19 04:06:50,511 INFO misc.py line 119 87073] Train: [69/100][62/1557] Data 0.004 (0.006) Batch 0.796 (0.982) Remain 13:34:04 loss: 0.1624 Lr: 0.00127 [2024-02-19 04:07:00,902 INFO misc.py line 119 87073] Train: [69/100][63/1557] Data 5.020 (0.090) Batch 10.390 (1.138) Remain 15:44:05 loss: 0.1056 Lr: 0.00127 [2024-02-19 04:07:01,919 INFO misc.py line 119 87073] Train: [69/100][64/1557] Data 0.003 (0.088) Batch 1.013 (1.136) Remain 15:42:22 loss: 0.2144 Lr: 0.00127 [2024-02-19 04:07:02,699 INFO misc.py line 119 87073] Train: [69/100][65/1557] Data 0.007 (0.087) Batch 0.784 (1.131) Remain 15:37:39 loss: 0.1059 Lr: 0.00127 [2024-02-19 04:07:03,805 INFO misc.py line 119 87073] Train: [69/100][66/1557] Data 0.003 (0.086) Batch 1.106 (1.130) Remain 15:37:18 loss: 0.3165 Lr: 0.00127 [2024-02-19 04:07:04,866 INFO misc.py line 119 87073] Train: [69/100][67/1557] Data 0.003 (0.084) Batch 1.061 (1.129) Remain 15:36:23 loss: 0.2318 Lr: 0.00127 [2024-02-19 04:07:05,650 INFO misc.py line 119 87073] Train: [69/100][68/1557] Data 0.003 (0.083) Batch 0.783 (1.124) Remain 15:31:57 loss: 0.3212 Lr: 0.00127 [2024-02-19 04:07:06,421 INFO misc.py line 119 87073] Train: [69/100][69/1557] Data 0.004 (0.082) Batch 0.769 (1.118) Remain 15:27:28 loss: 0.2341 Lr: 0.00127 [2024-02-19 04:07:07,610 INFO misc.py line 119 87073] Train: [69/100][70/1557] Data 0.006 (0.081) Batch 1.187 (1.119) Remain 15:28:18 loss: 0.0971 Lr: 0.00127 [2024-02-19 04:07:08,587 INFO misc.py line 119 87073] Train: [69/100][71/1557] Data 0.009 (0.080) Batch 0.982 (1.117) Remain 15:26:36 loss: 0.3229 Lr: 0.00127 [2024-02-19 04:07:09,570 INFO misc.py line 119 87073] Train: [69/100][72/1557] Data 0.003 (0.079) Batch 0.982 (1.115) Remain 15:24:58 loss: 0.2302 Lr: 0.00127 [2024-02-19 04:07:10,519 INFO misc.py line 119 87073] Train: [69/100][73/1557] Data 0.004 (0.078) Batch 0.950 (1.113) Remain 15:22:59 loss: 0.2183 Lr: 0.00127 [2024-02-19 04:07:11,493 INFO misc.py line 119 87073] Train: [69/100][74/1557] Data 0.004 (0.077) Batch 0.974 (1.111) Remain 15:21:20 loss: 0.2262 Lr: 0.00127 [2024-02-19 04:07:12,286 INFO misc.py line 119 87073] Train: [69/100][75/1557] Data 0.003 (0.075) Batch 0.786 (1.107) Remain 15:17:34 loss: 0.2223 Lr: 0.00127 [2024-02-19 04:07:13,050 INFO misc.py line 119 87073] Train: [69/100][76/1557] Data 0.011 (0.075) Batch 0.771 (1.102) Remain 15:13:44 loss: 0.2439 Lr: 0.00127 [2024-02-19 04:07:14,318 INFO misc.py line 119 87073] Train: [69/100][77/1557] Data 0.003 (0.074) Batch 1.257 (1.104) Remain 15:15:27 loss: 0.0852 Lr: 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line 119 87073] Train: [69/100][84/1557] Data 0.011 (0.068) Batch 1.389 (1.090) Remain 15:03:45 loss: 0.1174 Lr: 0.00127 [2024-02-19 04:07:21,985 INFO misc.py line 119 87073] Train: [69/100][85/1557] Data 0.015 (0.067) Batch 1.070 (1.090) Remain 15:03:31 loss: 0.5997 Lr: 0.00127 [2024-02-19 04:07:22,930 INFO misc.py line 119 87073] Train: [69/100][86/1557] Data 0.014 (0.067) Batch 0.956 (1.088) Remain 15:02:10 loss: 0.3958 Lr: 0.00127 [2024-02-19 04:07:23,978 INFO misc.py line 119 87073] Train: [69/100][87/1557] Data 0.003 (0.066) Batch 1.048 (1.088) Remain 15:01:45 loss: 0.3826 Lr: 0.00127 [2024-02-19 04:07:24,903 INFO misc.py line 119 87073] Train: [69/100][88/1557] Data 0.003 (0.065) Batch 0.925 (1.086) Remain 15:00:09 loss: 0.0665 Lr: 0.00127 [2024-02-19 04:07:25,617 INFO misc.py line 119 87073] Train: [69/100][89/1557] Data 0.003 (0.064) Batch 0.707 (1.082) Remain 14:56:28 loss: 0.1815 Lr: 0.00127 [2024-02-19 04:07:26,425 INFO misc.py line 119 87073] Train: [69/100][90/1557] Data 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Batch 0.840 (1.132) Remain 15:32:23 loss: 0.3344 Lr: 0.00126 [2024-02-19 04:13:24,288 INFO misc.py line 119 87073] Train: [69/100][402/1557] Data 0.011 (0.099) Batch 1.167 (1.132) Remain 15:32:27 loss: 0.2172 Lr: 0.00126 [2024-02-19 04:13:25,394 INFO misc.py line 119 87073] Train: [69/100][403/1557] Data 0.012 (0.099) Batch 1.107 (1.132) Remain 15:32:22 loss: 0.5815 Lr: 0.00126 [2024-02-19 04:13:26,187 INFO misc.py line 119 87073] Train: [69/100][404/1557] Data 0.010 (0.099) Batch 0.800 (1.131) Remain 15:31:40 loss: 0.1590 Lr: 0.00126 [2024-02-19 04:13:26,874 INFO misc.py line 119 87073] Train: [69/100][405/1557] Data 0.003 (0.099) Batch 0.686 (1.130) Remain 15:30:44 loss: 0.1914 Lr: 0.00126 [2024-02-19 04:13:28,081 INFO misc.py line 119 87073] Train: [69/100][406/1557] Data 0.005 (0.098) Batch 1.198 (1.130) Remain 15:30:52 loss: 0.1480 Lr: 0.00126 [2024-02-19 04:13:29,196 INFO misc.py line 119 87073] Train: [69/100][407/1557] Data 0.014 (0.098) Batch 1.114 (1.130) Remain 15:30:49 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Batch 1.021 (1.131) Remain 15:26:06 loss: 0.1536 Lr: 0.00124 [2024-02-19 04:18:40,210 INFO misc.py line 119 87073] Train: [69/100][682/1557] Data 0.004 (0.102) Batch 0.990 (1.130) Remain 15:25:55 loss: 0.1997 Lr: 0.00124 [2024-02-19 04:18:41,258 INFO misc.py line 119 87073] Train: [69/100][683/1557] Data 0.004 (0.102) Batch 1.048 (1.130) Remain 15:25:47 loss: 0.1908 Lr: 0.00124 [2024-02-19 04:18:42,007 INFO misc.py line 119 87073] Train: [69/100][684/1557] Data 0.003 (0.101) Batch 0.749 (1.130) Remain 15:25:19 loss: 0.2412 Lr: 0.00124 [2024-02-19 04:18:42,721 INFO misc.py line 119 87073] Train: [69/100][685/1557] Data 0.003 (0.101) Batch 0.702 (1.129) Remain 15:24:47 loss: 0.2657 Lr: 0.00124 [2024-02-19 04:18:43,936 INFO misc.py line 119 87073] Train: [69/100][686/1557] Data 0.015 (0.101) Batch 1.215 (1.129) Remain 15:24:52 loss: 0.1170 Lr: 0.00124 [2024-02-19 04:18:44,863 INFO misc.py line 119 87073] Train: [69/100][687/1557] Data 0.014 (0.101) Batch 0.938 (1.129) Remain 15:24:37 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Batch 0.877 (1.133) Remain 15:27:10 loss: 0.0545 Lr: 0.00124 [2024-02-19 04:19:45,535 INFO misc.py line 119 87073] Train: [69/100][738/1557] Data 0.009 (0.104) Batch 1.075 (1.133) Remain 15:27:05 loss: 0.3609 Lr: 0.00124 [2024-02-19 04:19:46,541 INFO misc.py line 119 87073] Train: [69/100][739/1557] Data 0.011 (0.103) Batch 1.008 (1.133) Remain 15:26:56 loss: 0.2586 Lr: 0.00124 [2024-02-19 04:19:47,335 INFO misc.py line 119 87073] Train: [69/100][740/1557] Data 0.010 (0.103) Batch 0.799 (1.133) Remain 15:26:32 loss: 0.3244 Lr: 0.00124 [2024-02-19 04:19:48,065 INFO misc.py line 119 87073] Train: [69/100][741/1557] Data 0.005 (0.103) Batch 0.732 (1.132) Remain 15:26:05 loss: 0.1950 Lr: 0.00124 [2024-02-19 04:19:49,205 INFO misc.py line 119 87073] Train: [69/100][742/1557] Data 0.003 (0.103) Batch 1.133 (1.132) Remain 15:26:04 loss: 0.1637 Lr: 0.00124 [2024-02-19 04:19:50,148 INFO misc.py line 119 87073] Train: [69/100][743/1557] Data 0.010 (0.103) Batch 0.949 (1.132) Remain 15:25:50 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Batch 0.917 (1.134) Remain 15:24:43 loss: 0.2087 Lr: 0.00123 [2024-02-19 04:22:56,693 INFO misc.py line 119 87073] Train: [69/100][906/1557] Data 0.029 (0.104) Batch 1.036 (1.134) Remain 15:24:37 loss: 0.2430 Lr: 0.00123 [2024-02-19 04:22:57,866 INFO misc.py line 119 87073] Train: [69/100][907/1557] Data 0.014 (0.104) Batch 1.177 (1.134) Remain 15:24:38 loss: 0.3886 Lr: 0.00123 [2024-02-19 04:22:58,677 INFO misc.py line 119 87073] Train: [69/100][908/1557] Data 0.009 (0.104) Batch 0.817 (1.134) Remain 15:24:20 loss: 0.2695 Lr: 0.00123 [2024-02-19 04:22:59,452 INFO misc.py line 119 87073] Train: [69/100][909/1557] Data 0.003 (0.104) Batch 0.775 (1.133) Remain 15:23:59 loss: 0.3023 Lr: 0.00123 [2024-02-19 04:23:00,694 INFO misc.py line 119 87073] Train: [69/100][910/1557] Data 0.002 (0.103) Batch 1.233 (1.133) Remain 15:24:03 loss: 0.1280 Lr: 0.00123 [2024-02-19 04:23:01,584 INFO misc.py line 119 87073] Train: [69/100][911/1557] Data 0.012 (0.103) Batch 0.898 (1.133) Remain 15:23:50 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line 119 87073] Train: [69/100][949/1557] Data 0.003 (0.100) Batch 1.000 (1.127) Remain 15:18:00 loss: 0.3197 Lr: 0.00123 [2024-02-19 04:23:39,496 INFO misc.py line 119 87073] Train: [69/100][950/1557] Data 0.012 (0.099) Batch 0.774 (1.127) Remain 15:17:41 loss: 0.2718 Lr: 0.00123 [2024-02-19 04:23:40,281 INFO misc.py line 119 87073] Train: [69/100][951/1557] Data 0.003 (0.099) Batch 0.784 (1.126) Remain 15:17:22 loss: 0.3435 Lr: 0.00123 [2024-02-19 04:23:41,559 INFO misc.py line 119 87073] Train: [69/100][952/1557] Data 0.004 (0.099) Batch 1.258 (1.126) Remain 15:17:28 loss: 0.0934 Lr: 0.00123 [2024-02-19 04:23:42,407 INFO misc.py line 119 87073] Train: [69/100][953/1557] Data 0.025 (0.099) Batch 0.870 (1.126) Remain 15:17:14 loss: 0.5609 Lr: 0.00123 [2024-02-19 04:23:43,381 INFO misc.py line 119 87073] Train: [69/100][954/1557] Data 0.002 (0.099) Batch 0.974 (1.126) Remain 15:17:05 loss: 0.4105 Lr: 0.00123 [2024-02-19 04:23:44,270 INFO misc.py line 119 87073] Train: 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Batch 0.797 (1.135) Remain 15:24:20 loss: 0.1999 Lr: 0.00123 [2024-02-19 04:24:00,861 INFO misc.py line 119 87073] Train: [69/100][962/1557] Data 0.003 (0.103) Batch 0.905 (1.135) Remain 15:24:07 loss: 0.1429 Lr: 0.00123 [2024-02-19 04:24:01,759 INFO misc.py line 119 87073] Train: [69/100][963/1557] Data 0.003 (0.103) Batch 0.894 (1.135) Remain 15:23:54 loss: 0.2834 Lr: 0.00123 [2024-02-19 04:24:02,508 INFO misc.py line 119 87073] Train: [69/100][964/1557] Data 0.007 (0.103) Batch 0.752 (1.134) Remain 15:23:33 loss: 0.2522 Lr: 0.00123 [2024-02-19 04:24:03,295 INFO misc.py line 119 87073] Train: [69/100][965/1557] Data 0.003 (0.103) Batch 0.782 (1.134) Remain 15:23:14 loss: 0.2487 Lr: 0.00123 [2024-02-19 04:24:04,563 INFO misc.py line 119 87073] Train: [69/100][966/1557] Data 0.008 (0.103) Batch 1.269 (1.134) Remain 15:23:20 loss: 0.0826 Lr: 0.00123 [2024-02-19 04:24:05,439 INFO misc.py line 119 87073] Train: [69/100][967/1557] Data 0.007 (0.102) Batch 0.877 (1.134) Remain 15:23:06 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Remain 15:19:52 loss: 0.5664 Lr: 0.00122 [2024-02-19 04:29:18,819 INFO misc.py line 119 87073] Train: [69/100][1241/1557] Data 0.003 (0.106) Batch 0.945 (1.136) Remain 15:19:44 loss: 0.4207 Lr: 0.00122 [2024-02-19 04:29:19,803 INFO misc.py line 119 87073] Train: [69/100][1242/1557] Data 0.005 (0.106) Batch 0.979 (1.136) Remain 15:19:36 loss: 0.4532 Lr: 0.00122 [2024-02-19 04:29:20,772 INFO misc.py line 119 87073] Train: [69/100][1243/1557] Data 0.010 (0.106) Batch 0.976 (1.136) Remain 15:19:29 loss: 0.7987 Lr: 0.00122 [2024-02-19 04:29:21,482 INFO misc.py line 119 87073] Train: [69/100][1244/1557] Data 0.004 (0.105) Batch 0.711 (1.135) Remain 15:19:11 loss: 0.1139 Lr: 0.00122 [2024-02-19 04:29:22,232 INFO misc.py line 119 87073] Train: [69/100][1245/1557] Data 0.003 (0.105) Batch 0.744 (1.135) Remain 15:18:55 loss: 0.1928 Lr: 0.00122 [2024-02-19 04:29:23,455 INFO misc.py line 119 87073] Train: [69/100][1246/1557] Data 0.009 (0.105) Batch 1.224 (1.135) Remain 15:18:57 loss: 0.1080 Lr: 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Train: [69/100][1259/1557] Data 0.006 (0.104) Batch 0.813 (1.133) Remain 15:17:04 loss: 0.1940 Lr: 0.00122 [2024-02-19 04:29:36,966 INFO misc.py line 119 87073] Train: [69/100][1260/1557] Data 0.003 (0.104) Batch 1.308 (1.133) Remain 15:17:09 loss: 0.1621 Lr: 0.00122 [2024-02-19 04:29:37,918 INFO misc.py line 119 87073] Train: [69/100][1261/1557] Data 0.008 (0.104) Batch 0.956 (1.133) Remain 15:17:01 loss: 0.2517 Lr: 0.00122 [2024-02-19 04:29:38,939 INFO misc.py line 119 87073] Train: [69/100][1262/1557] Data 0.003 (0.104) Batch 1.021 (1.133) Remain 15:16:56 loss: 0.1063 Lr: 0.00122 [2024-02-19 04:29:40,085 INFO misc.py line 119 87073] Train: [69/100][1263/1557] Data 0.004 (0.104) Batch 1.147 (1.133) Remain 15:16:55 loss: 0.1932 Lr: 0.00122 [2024-02-19 04:29:41,225 INFO misc.py line 119 87073] Train: [69/100][1264/1557] Data 0.003 (0.104) Batch 1.140 (1.133) Remain 15:16:55 loss: 0.2814 Lr: 0.00122 [2024-02-19 04:29:41,967 INFO misc.py line 119 87073] Train: [69/100][1265/1557] Data 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Remain 15:16:15 loss: 0.2321 Lr: 0.00122 [2024-02-19 04:29:49,084 INFO misc.py line 119 87073] Train: [69/100][1272/1557] Data 0.013 (0.103) Batch 0.753 (1.132) Remain 15:15:59 loss: 0.1081 Lr: 0.00122 [2024-02-19 04:29:49,816 INFO misc.py line 119 87073] Train: [69/100][1273/1557] Data 0.003 (0.103) Batch 0.726 (1.132) Remain 15:15:43 loss: 0.1800 Lr: 0.00122 [2024-02-19 04:29:51,099 INFO misc.py line 119 87073] Train: [69/100][1274/1557] Data 0.009 (0.103) Batch 1.283 (1.132) Remain 15:15:47 loss: 0.1222 Lr: 0.00122 [2024-02-19 04:29:52,235 INFO misc.py line 119 87073] Train: [69/100][1275/1557] Data 0.009 (0.103) Batch 1.133 (1.132) Remain 15:15:46 loss: 0.2909 Lr: 0.00122 [2024-02-19 04:29:53,101 INFO misc.py line 119 87073] Train: [69/100][1276/1557] Data 0.011 (0.103) Batch 0.875 (1.132) Remain 15:15:35 loss: 0.6743 Lr: 0.00122 [2024-02-19 04:29:54,006 INFO misc.py line 119 87073] Train: [69/100][1277/1557] Data 0.003 (0.103) Batch 0.905 (1.131) Remain 15:15:26 loss: 0.1042 Lr: 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Train: [69/100][1290/1557] Data 0.014 (0.102) Batch 0.899 (1.129) Remain 15:13:29 loss: 0.1432 Lr: 0.00122 [2024-02-19 04:30:06,925 INFO misc.py line 119 87073] Train: [69/100][1291/1557] Data 0.004 (0.102) Batch 0.920 (1.129) Remain 15:13:20 loss: 0.2210 Lr: 0.00122 [2024-02-19 04:30:08,002 INFO misc.py line 119 87073] Train: [69/100][1292/1557] Data 0.004 (0.102) Batch 1.077 (1.129) Remain 15:13:17 loss: 0.3671 Lr: 0.00122 [2024-02-19 04:30:08,771 INFO misc.py line 119 87073] Train: [69/100][1293/1557] Data 0.003 (0.102) Batch 0.768 (1.129) Remain 15:13:02 loss: 0.6644 Lr: 0.00122 [2024-02-19 04:30:09,570 INFO misc.py line 119 87073] Train: [69/100][1294/1557] Data 0.003 (0.102) Batch 0.790 (1.129) Remain 15:12:48 loss: 0.2112 Lr: 0.00122 [2024-02-19 04:30:21,874 INFO misc.py line 119 87073] Train: [69/100][1295/1557] Data 4.919 (0.105) Batch 12.314 (1.137) Remain 15:19:47 loss: 0.1164 Lr: 0.00122 [2024-02-19 04:30:22,937 INFO misc.py line 119 87073] Train: [69/100][1296/1557] Data 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Remain 15:18:54 loss: 0.1132 Lr: 0.00122 [2024-02-19 04:30:29,608 INFO misc.py line 119 87073] Train: [69/100][1303/1557] Data 0.013 (0.105) Batch 0.977 (1.136) Remain 15:18:47 loss: 0.1391 Lr: 0.00122 [2024-02-19 04:30:30,738 INFO misc.py line 119 87073] Train: [69/100][1304/1557] Data 0.003 (0.105) Batch 1.130 (1.136) Remain 15:18:46 loss: 0.4726 Lr: 0.00122 [2024-02-19 04:30:31,766 INFO misc.py line 119 87073] Train: [69/100][1305/1557] Data 0.003 (0.105) Batch 1.028 (1.136) Remain 15:18:41 loss: 0.1835 Lr: 0.00122 [2024-02-19 04:30:32,837 INFO misc.py line 119 87073] Train: [69/100][1306/1557] Data 0.003 (0.104) Batch 1.071 (1.136) Remain 15:18:37 loss: 0.4075 Lr: 0.00122 [2024-02-19 04:30:33,613 INFO misc.py line 119 87073] Train: [69/100][1307/1557] Data 0.003 (0.104) Batch 0.776 (1.136) Remain 15:18:22 loss: 0.3814 Lr: 0.00122 [2024-02-19 04:30:34,381 INFO misc.py line 119 87073] Train: [69/100][1308/1557] Data 0.003 (0.104) Batch 0.760 (1.135) Remain 15:18:07 loss: 0.1098 Lr: 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INFO misc.py line 119 87073] Train: [69/100][1315/1557] Data 0.003 (0.104) Batch 0.733 (1.134) Remain 15:17:09 loss: 0.1813 Lr: 0.00122 [2024-02-19 04:30:42,276 INFO misc.py line 119 87073] Train: [69/100][1316/1557] Data 0.011 (0.104) Batch 1.304 (1.135) Remain 15:17:14 loss: 0.1064 Lr: 0.00122 [2024-02-19 04:30:43,295 INFO misc.py line 119 87073] Train: [69/100][1317/1557] Data 0.013 (0.104) Batch 1.020 (1.134) Remain 15:17:09 loss: 0.2336 Lr: 0.00122 [2024-02-19 04:30:44,249 INFO misc.py line 119 87073] Train: [69/100][1318/1557] Data 0.012 (0.104) Batch 0.962 (1.134) Remain 15:17:01 loss: 0.2719 Lr: 0.00122 [2024-02-19 04:30:45,086 INFO misc.py line 119 87073] Train: [69/100][1319/1557] Data 0.003 (0.103) Batch 0.838 (1.134) Remain 15:16:49 loss: 0.1198 Lr: 0.00122 [2024-02-19 04:30:46,024 INFO misc.py line 119 87073] Train: [69/100][1320/1557] Data 0.003 (0.103) Batch 0.931 (1.134) Remain 15:16:41 loss: 0.7112 Lr: 0.00122 [2024-02-19 04:30:46,787 INFO misc.py line 119 87073] Train: [69/100][1321/1557] Data 0.010 (0.103) Batch 0.770 (1.134) Remain 15:16:26 loss: 0.2638 Lr: 0.00122 [2024-02-19 04:30:47,568 INFO misc.py line 119 87073] Train: [69/100][1322/1557] Data 0.003 (0.103) Batch 0.774 (1.133) Remain 15:16:12 loss: 0.1283 Lr: 0.00122 [2024-02-19 04:30:48,781 INFO misc.py line 119 87073] Train: [69/100][1323/1557] Data 0.010 (0.103) Batch 1.209 (1.133) Remain 15:16:13 loss: 0.0922 Lr: 0.00122 [2024-02-19 04:30:49,687 INFO misc.py line 119 87073] Train: [69/100][1324/1557] Data 0.014 (0.103) Batch 0.916 (1.133) Remain 15:16:04 loss: 0.1016 Lr: 0.00122 [2024-02-19 04:30:50,665 INFO misc.py line 119 87073] Train: [69/100][1325/1557] Data 0.004 (0.103) Batch 0.978 (1.133) Remain 15:15:58 loss: 0.1586 Lr: 0.00122 [2024-02-19 04:30:51,532 INFO misc.py line 119 87073] Train: [69/100][1326/1557] Data 0.003 (0.103) Batch 0.866 (1.133) Remain 15:15:47 loss: 0.2362 Lr: 0.00122 [2024-02-19 04:30:52,448 INFO misc.py line 119 87073] Train: [69/100][1327/1557] Data 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Remain 15:14:55 loss: 0.0698 Lr: 0.00122 [2024-02-19 04:30:59,468 INFO misc.py line 119 87073] Train: [69/100][1334/1557] Data 0.003 (0.102) Batch 1.203 (1.132) Remain 15:14:56 loss: 0.2638 Lr: 0.00122 [2024-02-19 04:31:00,250 INFO misc.py line 119 87073] Train: [69/100][1335/1557] Data 0.017 (0.102) Batch 0.795 (1.132) Remain 15:14:43 loss: 0.1576 Lr: 0.00121 [2024-02-19 04:31:01,000 INFO misc.py line 119 87073] Train: [69/100][1336/1557] Data 0.003 (0.102) Batch 0.751 (1.132) Remain 15:14:28 loss: 0.2296 Lr: 0.00121 [2024-02-19 04:31:02,161 INFO misc.py line 119 87073] Train: [69/100][1337/1557] Data 0.003 (0.102) Batch 1.150 (1.132) Remain 15:14:27 loss: 0.1527 Lr: 0.00121 [2024-02-19 04:31:03,089 INFO misc.py line 119 87073] Train: [69/100][1338/1557] Data 0.014 (0.102) Batch 0.938 (1.131) Remain 15:14:19 loss: 0.2128 Lr: 0.00121 [2024-02-19 04:31:04,122 INFO misc.py line 119 87073] Train: [69/100][1339/1557] Data 0.003 (0.102) Batch 1.033 (1.131) Remain 15:14:14 loss: 0.1136 Lr: 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INFO misc.py line 119 87073] Train: [69/100][1346/1557] Data 0.003 (0.102) Batch 0.938 (1.130) Remain 15:13:16 loss: 0.1390 Lr: 0.00121 [2024-02-19 04:31:11,574 INFO misc.py line 119 87073] Train: [69/100][1347/1557] Data 0.004 (0.101) Batch 0.918 (1.130) Remain 15:13:07 loss: 0.3509 Lr: 0.00121 [2024-02-19 04:31:12,533 INFO misc.py line 119 87073] Train: [69/100][1348/1557] Data 0.009 (0.101) Batch 0.964 (1.130) Remain 15:13:00 loss: 0.1668 Lr: 0.00121 [2024-02-19 04:31:13,265 INFO misc.py line 119 87073] Train: [69/100][1349/1557] Data 0.003 (0.101) Batch 0.733 (1.130) Remain 15:12:45 loss: 0.2341 Lr: 0.00121 [2024-02-19 04:31:14,000 INFO misc.py line 119 87073] Train: [69/100][1350/1557] Data 0.003 (0.101) Batch 0.729 (1.129) Remain 15:12:29 loss: 0.2390 Lr: 0.00121 [2024-02-19 04:31:26,478 INFO misc.py line 119 87073] Train: [69/100][1351/1557] Data 4.929 (0.105) Batch 12.485 (1.138) Remain 15:19:16 loss: 0.1298 Lr: 0.00121 [2024-02-19 04:31:27,399 INFO misc.py line 119 87073] Train: [69/100][1352/1557] Data 0.003 (0.105) Batch 0.922 (1.138) Remain 15:19:08 loss: 0.2969 Lr: 0.00121 [2024-02-19 04:31:28,473 INFO misc.py line 119 87073] Train: [69/100][1353/1557] Data 0.002 (0.105) Batch 1.074 (1.138) Remain 15:19:04 loss: 0.2820 Lr: 0.00121 [2024-02-19 04:31:29,429 INFO misc.py line 119 87073] Train: [69/100][1354/1557] Data 0.003 (0.105) Batch 0.956 (1.138) Remain 15:18:56 loss: 0.0891 Lr: 0.00121 [2024-02-19 04:31:30,328 INFO misc.py line 119 87073] Train: [69/100][1355/1557] Data 0.004 (0.105) Batch 0.889 (1.137) Remain 15:18:46 loss: 0.3283 Lr: 0.00121 [2024-02-19 04:31:31,053 INFO misc.py line 119 87073] Train: [69/100][1356/1557] Data 0.012 (0.104) Batch 0.734 (1.137) Remain 15:18:31 loss: 0.1721 Lr: 0.00121 [2024-02-19 04:31:31,834 INFO misc.py line 119 87073] Train: [69/100][1357/1557] Data 0.003 (0.104) Batch 0.770 (1.137) Remain 15:18:17 loss: 0.2732 Lr: 0.00121 [2024-02-19 04:31:33,012 INFO misc.py line 119 87073] Train: [69/100][1358/1557] Data 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Remain 15:17:18 loss: 0.5048 Lr: 0.00121 [2024-02-19 04:31:39,660 INFO misc.py line 119 87073] Train: [69/100][1365/1557] Data 0.003 (0.104) Batch 1.300 (1.136) Remain 15:17:22 loss: 0.1314 Lr: 0.00121 [2024-02-19 04:31:40,549 INFO misc.py line 119 87073] Train: [69/100][1366/1557] Data 0.014 (0.104) Batch 0.899 (1.136) Remain 15:17:13 loss: 0.1796 Lr: 0.00121 [2024-02-19 04:31:41,417 INFO misc.py line 119 87073] Train: [69/100][1367/1557] Data 0.004 (0.104) Batch 0.867 (1.135) Remain 15:17:02 loss: 0.3065 Lr: 0.00121 [2024-02-19 04:31:42,275 INFO misc.py line 119 87073] Train: [69/100][1368/1557] Data 0.004 (0.104) Batch 0.855 (1.135) Remain 15:16:51 loss: 0.1630 Lr: 0.00121 [2024-02-19 04:31:43,447 INFO misc.py line 119 87073] Train: [69/100][1369/1557] Data 0.008 (0.104) Batch 1.168 (1.135) Remain 15:16:51 loss: 0.4336 Lr: 0.00121 [2024-02-19 04:31:44,164 INFO misc.py line 119 87073] Train: [69/100][1370/1557] Data 0.012 (0.103) Batch 0.725 (1.135) Remain 15:16:35 loss: 0.2596 Lr: 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INFO misc.py line 119 87073] Train: [69/100][1377/1557] Data 0.004 (0.103) Batch 0.705 (1.134) Remain 15:15:41 loss: 0.1280 Lr: 0.00121 [2024-02-19 04:31:51,594 INFO misc.py line 119 87073] Train: [69/100][1378/1557] Data 0.013 (0.103) Batch 0.790 (1.134) Remain 15:15:28 loss: 0.2759 Lr: 0.00121 [2024-02-19 04:31:52,694 INFO misc.py line 119 87073] Train: [69/100][1379/1557] Data 0.003 (0.103) Batch 1.101 (1.134) Remain 15:15:26 loss: 0.1208 Lr: 0.00121 [2024-02-19 04:31:53,626 INFO misc.py line 119 87073] Train: [69/100][1380/1557] Data 0.004 (0.103) Batch 0.933 (1.134) Remain 15:15:18 loss: 0.4233 Lr: 0.00121 [2024-02-19 04:31:54,720 INFO misc.py line 119 87073] Train: [69/100][1381/1557] Data 0.003 (0.103) Batch 1.094 (1.134) Remain 15:15:15 loss: 0.5595 Lr: 0.00121 [2024-02-19 04:31:55,687 INFO misc.py line 119 87073] Train: [69/100][1382/1557] Data 0.003 (0.103) Batch 0.966 (1.133) Remain 15:15:08 loss: 0.4272 Lr: 0.00121 [2024-02-19 04:31:56,449 INFO misc.py line 119 87073] Train: [69/100][1383/1557] Data 0.003 (0.103) Batch 0.761 (1.133) Remain 15:14:54 loss: 0.2901 Lr: 0.00121 [2024-02-19 04:31:57,190 INFO misc.py line 119 87073] Train: [69/100][1384/1557] Data 0.005 (0.102) Batch 0.742 (1.133) Remain 15:14:39 loss: 0.1870 Lr: 0.00121 [2024-02-19 04:31:57,889 INFO misc.py line 119 87073] Train: [69/100][1385/1557] Data 0.004 (0.102) Batch 0.689 (1.133) Remain 15:14:22 loss: 0.2294 Lr: 0.00121 [2024-02-19 04:31:59,182 INFO misc.py line 119 87073] Train: [69/100][1386/1557] Data 0.014 (0.102) Batch 1.294 (1.133) Remain 15:14:27 loss: 0.0881 Lr: 0.00121 [2024-02-19 04:32:00,262 INFO misc.py line 119 87073] Train: [69/100][1387/1557] Data 0.012 (0.102) Batch 1.081 (1.133) Remain 15:14:24 loss: 0.3584 Lr: 0.00121 [2024-02-19 04:32:01,197 INFO misc.py line 119 87073] Train: [69/100][1388/1557] Data 0.012 (0.102) Batch 0.943 (1.133) Remain 15:14:16 loss: 0.2465 Lr: 0.00121 [2024-02-19 04:32:02,307 INFO misc.py line 119 87073] Train: [69/100][1389/1557] Data 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Remain 15:13:17 loss: 0.6806 Lr: 0.00121 [2024-02-19 04:32:08,526 INFO misc.py line 119 87073] Train: [69/100][1396/1557] Data 0.004 (0.102) Batch 0.878 (1.131) Remain 15:13:07 loss: 0.1262 Lr: 0.00121 [2024-02-19 04:32:09,459 INFO misc.py line 119 87073] Train: [69/100][1397/1557] Data 0.004 (0.102) Batch 0.923 (1.131) Remain 15:12:59 loss: 0.6618 Lr: 0.00121 [2024-02-19 04:32:10,210 INFO misc.py line 119 87073] Train: [69/100][1398/1557] Data 0.012 (0.102) Batch 0.758 (1.131) Remain 15:12:44 loss: 0.2146 Lr: 0.00121 [2024-02-19 04:32:10,933 INFO misc.py line 119 87073] Train: [69/100][1399/1557] Data 0.006 (0.101) Batch 0.716 (1.131) Remain 15:12:29 loss: 0.3563 Lr: 0.00121 [2024-02-19 04:32:12,129 INFO misc.py line 119 87073] Train: [69/100][1400/1557] Data 0.013 (0.101) Batch 1.199 (1.131) Remain 15:12:30 loss: 0.1335 Lr: 0.00121 [2024-02-19 04:32:13,115 INFO misc.py line 119 87073] Train: [69/100][1401/1557] Data 0.011 (0.101) Batch 0.994 (1.131) Remain 15:12:24 loss: 0.3167 Lr: 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INFO misc.py line 119 87073] Train: [69/100][1408/1557] Data 0.003 (0.106) Batch 0.887 (1.137) Remain 15:17:45 loss: 0.1923 Lr: 0.00121 [2024-02-19 04:32:31,479 INFO misc.py line 119 87073] Train: [69/100][1409/1557] Data 0.004 (0.105) Batch 0.900 (1.137) Remain 15:17:36 loss: 0.1892 Lr: 0.00121 [2024-02-19 04:32:32,323 INFO misc.py line 119 87073] Train: [69/100][1410/1557] Data 0.007 (0.105) Batch 0.847 (1.137) Remain 15:17:25 loss: 0.4301 Lr: 0.00121 [2024-02-19 04:32:33,203 INFO misc.py line 119 87073] Train: [69/100][1411/1557] Data 0.003 (0.105) Batch 0.879 (1.137) Remain 15:17:15 loss: 0.2813 Lr: 0.00121 [2024-02-19 04:32:33,870 INFO misc.py line 119 87073] Train: [69/100][1412/1557] Data 0.004 (0.105) Batch 0.665 (1.136) Remain 15:16:58 loss: 0.3636 Lr: 0.00121 [2024-02-19 04:32:34,520 INFO misc.py line 119 87073] Train: [69/100][1413/1557] Data 0.007 (0.105) Batch 0.652 (1.136) Remain 15:16:40 loss: 0.2368 Lr: 0.00121 [2024-02-19 04:32:35,750 INFO misc.py line 119 87073] Train: [69/100][1414/1557] Data 0.003 (0.105) Batch 1.222 (1.136) Remain 15:16:42 loss: 0.0981 Lr: 0.00121 [2024-02-19 04:32:36,608 INFO misc.py line 119 87073] Train: [69/100][1415/1557] Data 0.011 (0.105) Batch 0.865 (1.136) Remain 15:16:31 loss: 0.2747 Lr: 0.00121 [2024-02-19 04:32:37,572 INFO misc.py line 119 87073] Train: [69/100][1416/1557] Data 0.005 (0.105) Batch 0.965 (1.136) Remain 15:16:24 loss: 0.5734 Lr: 0.00121 [2024-02-19 04:32:38,427 INFO misc.py line 119 87073] Train: [69/100][1417/1557] Data 0.005 (0.105) Batch 0.855 (1.136) Remain 15:16:13 loss: 0.2192 Lr: 0.00121 [2024-02-19 04:32:39,277 INFO misc.py line 119 87073] Train: [69/100][1418/1557] Data 0.005 (0.105) Batch 0.844 (1.135) Remain 15:16:02 loss: 0.3275 Lr: 0.00121 [2024-02-19 04:32:39,948 INFO misc.py line 119 87073] Train: [69/100][1419/1557] Data 0.009 (0.105) Batch 0.677 (1.135) Remain 15:15:45 loss: 0.1617 Lr: 0.00121 [2024-02-19 04:32:40,696 INFO misc.py line 119 87073] Train: [69/100][1420/1557] Data 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Remain 15:15:10 loss: 0.1679 Lr: 0.00121 [2024-02-19 04:32:47,856 INFO misc.py line 119 87073] Train: [69/100][1427/1557] Data 0.004 (0.104) Batch 0.770 (1.134) Remain 15:14:56 loss: 0.7569 Lr: 0.00121 [2024-02-19 04:32:49,146 INFO misc.py line 119 87073] Train: [69/100][1428/1557] Data 0.007 (0.104) Batch 1.292 (1.134) Remain 15:15:01 loss: 0.1146 Lr: 0.00121 [2024-02-19 04:32:50,052 INFO misc.py line 119 87073] Train: [69/100][1429/1557] Data 0.006 (0.104) Batch 0.908 (1.134) Remain 15:14:52 loss: 0.4920 Lr: 0.00121 [2024-02-19 04:32:51,042 INFO misc.py line 119 87073] Train: [69/100][1430/1557] Data 0.005 (0.104) Batch 0.991 (1.134) Remain 15:14:46 loss: 0.1299 Lr: 0.00121 [2024-02-19 04:32:51,923 INFO misc.py line 119 87073] Train: [69/100][1431/1557] Data 0.003 (0.104) Batch 0.880 (1.134) Remain 15:14:36 loss: 0.1398 Lr: 0.00121 [2024-02-19 04:32:52,882 INFO misc.py line 119 87073] Train: [69/100][1432/1557] Data 0.004 (0.104) Batch 0.954 (1.134) Remain 15:14:29 loss: 0.2482 Lr: 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INFO misc.py line 119 87073] Train: [69/100][1439/1557] Data 0.004 (0.103) Batch 1.105 (1.133) Remain 15:13:41 loss: 0.1307 Lr: 0.00121 [2024-02-19 04:33:00,367 INFO misc.py line 119 87073] Train: [69/100][1440/1557] Data 0.007 (0.103) Batch 0.749 (1.133) Remain 15:13:27 loss: 0.3884 Lr: 0.00121 [2024-02-19 04:33:01,147 INFO misc.py line 119 87073] Train: [69/100][1441/1557] Data 0.003 (0.103) Batch 0.780 (1.133) Remain 15:13:14 loss: 0.1715 Lr: 0.00121 [2024-02-19 04:33:02,430 INFO misc.py line 119 87073] Train: [69/100][1442/1557] Data 0.003 (0.103) Batch 1.277 (1.133) Remain 15:13:17 loss: 0.1073 Lr: 0.00121 [2024-02-19 04:33:03,318 INFO misc.py line 119 87073] Train: [69/100][1443/1557] Data 0.010 (0.103) Batch 0.895 (1.132) Remain 15:13:08 loss: 0.3767 Lr: 0.00121 [2024-02-19 04:33:04,216 INFO misc.py line 119 87073] Train: [69/100][1444/1557] Data 0.003 (0.103) Batch 0.897 (1.132) Remain 15:12:59 loss: 0.1213 Lr: 0.00121 [2024-02-19 04:33:05,241 INFO misc.py line 119 87073] Train: [69/100][1445/1557] Data 0.004 (0.103) Batch 1.025 (1.132) Remain 15:12:54 loss: 0.3742 Lr: 0.00121 [2024-02-19 04:33:06,203 INFO misc.py line 119 87073] Train: [69/100][1446/1557] Data 0.003 (0.103) Batch 0.962 (1.132) Remain 15:12:48 loss: 0.2830 Lr: 0.00121 [2024-02-19 04:33:06,958 INFO misc.py line 119 87073] Train: [69/100][1447/1557] Data 0.003 (0.103) Batch 0.750 (1.132) Remain 15:12:34 loss: 0.1084 Lr: 0.00121 [2024-02-19 04:33:07,699 INFO misc.py line 119 87073] Train: [69/100][1448/1557] Data 0.009 (0.103) Batch 0.747 (1.132) Remain 15:12:20 loss: 0.1622 Lr: 0.00121 [2024-02-19 04:33:08,832 INFO misc.py line 119 87073] Train: [69/100][1449/1557] Data 0.003 (0.103) Batch 1.132 (1.132) Remain 15:12:18 loss: 0.1624 Lr: 0.00121 [2024-02-19 04:33:09,919 INFO misc.py line 119 87073] Train: [69/100][1450/1557] Data 0.004 (0.103) Batch 1.087 (1.132) Remain 15:12:16 loss: 0.1491 Lr: 0.00121 [2024-02-19 04:33:10,946 INFO misc.py line 119 87073] Train: [69/100][1451/1557] Data 0.003 (0.103) Batch 1.026 (1.131) Remain 15:12:11 loss: 0.1221 Lr: 0.00121 [2024-02-19 04:33:11,856 INFO misc.py line 119 87073] Train: [69/100][1452/1557] Data 0.005 (0.103) Batch 0.910 (1.131) Remain 15:12:03 loss: 0.2975 Lr: 0.00121 [2024-02-19 04:33:12,845 INFO misc.py line 119 87073] Train: [69/100][1453/1557] Data 0.004 (0.102) Batch 0.989 (1.131) Remain 15:11:57 loss: 0.4949 Lr: 0.00121 [2024-02-19 04:33:13,646 INFO misc.py line 119 87073] Train: [69/100][1454/1557] Data 0.004 (0.102) Batch 0.801 (1.131) Remain 15:11:45 loss: 0.1505 Lr: 0.00121 [2024-02-19 04:33:14,364 INFO misc.py line 119 87073] Train: [69/100][1455/1557] Data 0.004 (0.102) Batch 0.717 (1.131) Remain 15:11:30 loss: 0.1922 Lr: 0.00121 [2024-02-19 04:33:15,562 INFO misc.py line 119 87073] Train: [69/100][1456/1557] Data 0.005 (0.102) Batch 1.199 (1.131) Remain 15:11:31 loss: 0.0685 Lr: 0.00121 [2024-02-19 04:33:16,566 INFO misc.py line 119 87073] Train: [69/100][1457/1557] Data 0.005 (0.102) Batch 1.004 (1.131) Remain 15:11:26 loss: 0.1831 Lr: 0.00121 [2024-02-19 04:33:17,573 INFO misc.py line 119 87073] Train: [69/100][1458/1557] Data 0.004 (0.102) Batch 1.007 (1.131) Remain 15:11:20 loss: 0.4108 Lr: 0.00121 [2024-02-19 04:33:18,566 INFO misc.py line 119 87073] Train: [69/100][1459/1557] Data 0.004 (0.102) Batch 0.994 (1.130) Remain 15:11:15 loss: 0.2704 Lr: 0.00121 [2024-02-19 04:33:19,490 INFO misc.py line 119 87073] Train: [69/100][1460/1557] Data 0.004 (0.102) Batch 0.924 (1.130) Remain 15:11:07 loss: 0.4416 Lr: 0.00121 [2024-02-19 04:33:20,243 INFO misc.py line 119 87073] Train: [69/100][1461/1557] Data 0.004 (0.102) Batch 0.749 (1.130) Remain 15:10:53 loss: 0.3092 Lr: 0.00121 [2024-02-19 04:33:21,067 INFO misc.py line 119 87073] Train: [69/100][1462/1557] Data 0.008 (0.102) Batch 0.828 (1.130) Remain 15:10:42 loss: 0.1303 Lr: 0.00121 [2024-02-19 04:33:31,334 INFO misc.py line 119 87073] Train: [69/100][1463/1557] Data 4.708 (0.105) Batch 10.268 (1.136) Remain 15:15:43 loss: 0.1453 Lr: 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Train: [69/100][1476/1557] Data 0.003 (0.104) Batch 0.795 (1.134) Remain 15:13:52 loss: 0.2516 Lr: 0.00121 [2024-02-19 04:33:44,500 INFO misc.py line 119 87073] Train: [69/100][1477/1557] Data 0.008 (0.104) Batch 1.338 (1.134) Remain 15:13:57 loss: 0.1266 Lr: 0.00121 [2024-02-19 04:33:45,436 INFO misc.py line 119 87073] Train: [69/100][1478/1557] Data 0.005 (0.104) Batch 0.937 (1.134) Remain 15:13:50 loss: 0.2312 Lr: 0.00121 [2024-02-19 04:33:46,326 INFO misc.py line 119 87073] Train: [69/100][1479/1557] Data 0.003 (0.104) Batch 0.890 (1.134) Remain 15:13:41 loss: 0.5471 Lr: 0.00121 [2024-02-19 04:33:47,196 INFO misc.py line 119 87073] Train: [69/100][1480/1557] Data 0.003 (0.104) Batch 0.869 (1.134) Remain 15:13:31 loss: 0.3582 Lr: 0.00121 [2024-02-19 04:33:48,294 INFO misc.py line 119 87073] Train: [69/100][1481/1557] Data 0.005 (0.104) Batch 1.096 (1.134) Remain 15:13:29 loss: 0.2210 Lr: 0.00121 [2024-02-19 04:33:49,049 INFO misc.py line 119 87073] Train: [69/100][1482/1557] Data 0.006 (0.104) Batch 0.758 (1.133) Remain 15:13:15 loss: 0.3933 Lr: 0.00121 [2024-02-19 04:33:49,803 INFO misc.py line 119 87073] Train: [69/100][1483/1557] Data 0.003 (0.104) Batch 0.751 (1.133) Remain 15:13:02 loss: 0.1206 Lr: 0.00121 [2024-02-19 04:33:51,060 INFO misc.py line 119 87073] Train: [69/100][1484/1557] Data 0.008 (0.104) Batch 1.258 (1.133) Remain 15:13:04 loss: 0.1416 Lr: 0.00121 [2024-02-19 04:33:51,888 INFO misc.py line 119 87073] Train: [69/100][1485/1557] Data 0.006 (0.104) Batch 0.830 (1.133) Remain 15:12:53 loss: 0.0762 Lr: 0.00121 [2024-02-19 04:33:52,860 INFO misc.py line 119 87073] Train: [69/100][1486/1557] Data 0.004 (0.103) Batch 0.972 (1.133) Remain 15:12:47 loss: 0.2081 Lr: 0.00121 [2024-02-19 04:33:53,688 INFO misc.py line 119 87073] Train: [69/100][1487/1557] Data 0.004 (0.103) Batch 0.829 (1.133) Remain 15:12:36 loss: 0.2297 Lr: 0.00121 [2024-02-19 04:33:54,812 INFO misc.py line 119 87073] Train: [69/100][1488/1557] Data 0.003 (0.103) Batch 1.120 (1.133) Remain 15:12:34 loss: 0.2954 Lr: 0.00121 [2024-02-19 04:33:55,562 INFO misc.py line 119 87073] Train: [69/100][1489/1557] Data 0.007 (0.103) Batch 0.754 (1.133) Remain 15:12:21 loss: 0.2913 Lr: 0.00121 [2024-02-19 04:33:56,353 INFO misc.py line 119 87073] Train: [69/100][1490/1557] Data 0.003 (0.103) Batch 0.784 (1.132) Remain 15:12:09 loss: 0.2106 Lr: 0.00121 [2024-02-19 04:33:57,482 INFO misc.py line 119 87073] Train: [69/100][1491/1557] Data 0.010 (0.103) Batch 1.126 (1.132) Remain 15:12:07 loss: 0.0887 Lr: 0.00121 [2024-02-19 04:33:58,417 INFO misc.py line 119 87073] Train: [69/100][1492/1557] Data 0.013 (0.103) Batch 0.945 (1.132) Remain 15:12:00 loss: 0.4096 Lr: 0.00121 [2024-02-19 04:33:59,414 INFO misc.py line 119 87073] Train: [69/100][1493/1557] Data 0.004 (0.103) Batch 0.997 (1.132) Remain 15:11:54 loss: 0.3111 Lr: 0.00121 [2024-02-19 04:34:00,528 INFO misc.py line 119 87073] Train: [69/100][1494/1557] Data 0.003 (0.103) Batch 1.114 (1.132) Remain 15:11:53 loss: 0.1975 Lr: 0.00121 [2024-02-19 04:34:01,456 INFO misc.py line 119 87073] Train: [69/100][1495/1557] Data 0.004 (0.103) Batch 0.928 (1.132) Remain 15:11:45 loss: 0.1552 Lr: 0.00121 [2024-02-19 04:34:02,213 INFO misc.py line 119 87073] Train: [69/100][1496/1557] Data 0.003 (0.103) Batch 0.755 (1.132) Remain 15:11:32 loss: 0.2089 Lr: 0.00121 [2024-02-19 04:34:03,032 INFO misc.py line 119 87073] Train: [69/100][1497/1557] Data 0.006 (0.103) Batch 0.820 (1.131) Remain 15:11:20 loss: 0.3007 Lr: 0.00121 [2024-02-19 04:34:04,395 INFO misc.py line 119 87073] Train: [69/100][1498/1557] Data 0.004 (0.103) Batch 1.358 (1.132) Remain 15:11:27 loss: 0.1127 Lr: 0.00121 [2024-02-19 04:34:05,190 INFO misc.py line 119 87073] Train: [69/100][1499/1557] Data 0.009 (0.103) Batch 0.801 (1.131) Remain 15:11:15 loss: 0.1608 Lr: 0.00121 [2024-02-19 04:34:06,212 INFO misc.py line 119 87073] Train: [69/100][1500/1557] Data 0.004 (0.103) Batch 1.022 (1.131) Remain 15:11:10 loss: 0.2746 Lr: 0.00121 [2024-02-19 04:34:07,278 INFO misc.py line 119 87073] Train: [69/100][1501/1557] Data 0.003 (0.102) Batch 1.066 (1.131) Remain 15:11:07 loss: 0.2459 Lr: 0.00121 [2024-02-19 04:34:08,166 INFO misc.py line 119 87073] Train: [69/100][1502/1557] Data 0.003 (0.102) Batch 0.888 (1.131) Remain 15:10:58 loss: 0.2703 Lr: 0.00121 [2024-02-19 04:34:08,952 INFO misc.py line 119 87073] Train: [69/100][1503/1557] Data 0.003 (0.102) Batch 0.780 (1.131) Remain 15:10:46 loss: 0.2437 Lr: 0.00121 [2024-02-19 04:34:09,730 INFO misc.py line 119 87073] Train: [69/100][1504/1557] Data 0.009 (0.102) Batch 0.783 (1.131) Remain 15:10:33 loss: 0.4723 Lr: 0.00121 [2024-02-19 04:34:10,822 INFO misc.py line 119 87073] Train: [69/100][1505/1557] Data 0.004 (0.102) Batch 1.093 (1.131) Remain 15:10:31 loss: 0.1701 Lr: 0.00121 [2024-02-19 04:34:11,771 INFO misc.py line 119 87073] Train: [69/100][1506/1557] Data 0.004 (0.102) Batch 0.949 (1.131) Remain 15:10:24 loss: 0.3759 Lr: 0.00121 [2024-02-19 04:34:12,671 INFO misc.py line 119 87073] Train: [69/100][1507/1557] Data 0.003 (0.102) Batch 0.901 (1.130) Remain 15:10:15 loss: 0.2058 Lr: 0.00121 [2024-02-19 04:34:13,605 INFO misc.py line 119 87073] Train: [69/100][1508/1557] Data 0.003 (0.102) Batch 0.930 (1.130) Remain 15:10:08 loss: 0.3975 Lr: 0.00121 [2024-02-19 04:34:14,446 INFO misc.py line 119 87073] Train: [69/100][1509/1557] Data 0.007 (0.102) Batch 0.844 (1.130) Remain 15:09:57 loss: 0.4801 Lr: 0.00121 [2024-02-19 04:34:15,197 INFO misc.py line 119 87073] Train: [69/100][1510/1557] Data 0.003 (0.102) Batch 0.751 (1.130) Remain 15:09:44 loss: 0.1561 Lr: 0.00121 [2024-02-19 04:34:16,025 INFO misc.py line 119 87073] Train: [69/100][1511/1557] Data 0.003 (0.102) Batch 0.823 (1.130) Remain 15:09:33 loss: 0.3008 Lr: 0.00121 [2024-02-19 04:34:17,250 INFO misc.py line 119 87073] Train: [69/100][1512/1557] Data 0.009 (0.102) Batch 1.224 (1.130) Remain 15:09:35 loss: 0.1309 Lr: 0.00121 [2024-02-19 04:34:18,015 INFO misc.py line 119 87073] Train: [69/100][1513/1557] Data 0.010 (0.102) Batch 0.771 (1.129) Remain 15:09:23 loss: 0.8542 Lr: 0.00121 [2024-02-19 04:34:19,253 INFO misc.py line 119 87073] Train: [69/100][1514/1557] Data 0.003 (0.102) Batch 1.237 (1.129) Remain 15:09:25 loss: 0.3012 Lr: 0.00121 [2024-02-19 04:34:20,165 INFO misc.py line 119 87073] Train: [69/100][1515/1557] Data 0.003 (0.102) Batch 0.912 (1.129) Remain 15:09:17 loss: 0.1587 Lr: 0.00121 [2024-02-19 04:34:21,169 INFO misc.py line 119 87073] Train: [69/100][1516/1557] Data 0.004 (0.102) Batch 1.004 (1.129) Remain 15:09:12 loss: 0.1384 Lr: 0.00121 [2024-02-19 04:34:21,913 INFO misc.py line 119 87073] Train: [69/100][1517/1557] Data 0.003 (0.101) Batch 0.744 (1.129) Remain 15:08:58 loss: 0.3195 Lr: 0.00121 [2024-02-19 04:34:22,625 INFO misc.py line 119 87073] Train: [69/100][1518/1557] Data 0.003 (0.101) Batch 0.711 (1.129) Remain 15:08:44 loss: 0.3063 Lr: 0.00121 [2024-02-19 04:34:33,782 INFO misc.py line 119 87073] Train: [69/100][1519/1557] Data 5.413 (0.105) Batch 11.158 (1.135) Remain 15:14:02 loss: 0.1067 Lr: 0.00121 [2024-02-19 04:34:34,828 INFO misc.py line 119 87073] Train: [69/100][1520/1557] Data 0.003 (0.105) Batch 1.046 (1.135) Remain 15:13:58 loss: 0.1888 Lr: 0.00121 [2024-02-19 04:34:35,848 INFO misc.py line 119 87073] Train: [69/100][1521/1557] Data 0.003 (0.105) Batch 1.020 (1.135) Remain 15:13:53 loss: 0.4723 Lr: 0.00121 [2024-02-19 04:34:36,646 INFO misc.py line 119 87073] Train: [69/100][1522/1557] Data 0.003 (0.105) Batch 0.798 (1.135) Remain 15:13:42 loss: 0.2448 Lr: 0.00121 [2024-02-19 04:34:37,528 INFO misc.py line 119 87073] Train: [69/100][1523/1557] Data 0.003 (0.105) Batch 0.881 (1.135) Remain 15:13:32 loss: 0.2437 Lr: 0.00121 [2024-02-19 04:34:38,277 INFO misc.py line 119 87073] Train: [69/100][1524/1557] Data 0.004 (0.105) Batch 0.750 (1.135) Remain 15:13:19 loss: 0.1996 Lr: 0.00121 [2024-02-19 04:34:39,027 INFO misc.py line 119 87073] Train: [69/100][1525/1557] Data 0.003 (0.104) Batch 0.741 (1.134) Remain 15:13:05 loss: 0.1585 Lr: 0.00121 [2024-02-19 04:34:40,281 INFO misc.py line 119 87073] Train: [69/100][1526/1557] Data 0.012 (0.104) Batch 1.262 (1.134) Remain 15:13:08 loss: 0.2549 Lr: 0.00121 [2024-02-19 04:34:41,153 INFO misc.py line 119 87073] Train: [69/100][1527/1557] Data 0.004 (0.104) Batch 0.873 (1.134) Remain 15:12:59 loss: 0.3938 Lr: 0.00121 [2024-02-19 04:34:42,184 INFO misc.py line 119 87073] Train: [69/100][1528/1557] Data 0.003 (0.104) Batch 1.031 (1.134) Remain 15:12:54 loss: 0.4523 Lr: 0.00121 [2024-02-19 04:34:43,279 INFO misc.py line 119 87073] Train: [69/100][1529/1557] Data 0.003 (0.104) Batch 1.094 (1.134) Remain 15:12:52 loss: 0.3113 Lr: 0.00121 [2024-02-19 04:34:44,171 INFO misc.py line 119 87073] Train: [69/100][1530/1557] Data 0.004 (0.104) Batch 0.893 (1.134) Remain 15:12:43 loss: 0.1654 Lr: 0.00121 [2024-02-19 04:34:44,952 INFO misc.py line 119 87073] Train: [69/100][1531/1557] Data 0.003 (0.104) Batch 0.780 (1.134) Remain 15:12:31 loss: 0.2321 Lr: 0.00121 [2024-02-19 04:34:45,734 INFO misc.py line 119 87073] Train: [69/100][1532/1557] Data 0.004 (0.104) Batch 0.782 (1.134) Remain 15:12:19 loss: 0.3443 Lr: 0.00121 [2024-02-19 04:34:47,012 INFO misc.py line 119 87073] Train: [69/100][1533/1557] Data 0.004 (0.104) Batch 1.278 (1.134) Remain 15:12:22 loss: 0.0929 Lr: 0.00121 [2024-02-19 04:34:47,937 INFO misc.py line 119 87073] Train: [69/100][1534/1557] Data 0.004 (0.104) Batch 0.926 (1.133) Remain 15:12:15 loss: 0.2412 Lr: 0.00121 [2024-02-19 04:34:48,824 INFO misc.py line 119 87073] Train: [69/100][1535/1557] Data 0.003 (0.104) Batch 0.887 (1.133) Remain 15:12:06 loss: 0.2820 Lr: 0.00121 [2024-02-19 04:34:49,637 INFO misc.py line 119 87073] Train: [69/100][1536/1557] Data 0.003 (0.104) Batch 0.812 (1.133) Remain 15:11:54 loss: 0.4030 Lr: 0.00121 [2024-02-19 04:34:50,568 INFO misc.py line 119 87073] Train: [69/100][1537/1557] Data 0.005 (0.104) Batch 0.932 (1.133) Remain 15:11:47 loss: 0.2573 Lr: 0.00121 [2024-02-19 04:34:51,297 INFO misc.py line 119 87073] Train: [69/100][1538/1557] Data 0.003 (0.104) Batch 0.728 (1.133) Remain 15:11:33 loss: 0.1902 Lr: 0.00121 [2024-02-19 04:34:52,031 INFO misc.py line 119 87073] Train: [69/100][1539/1557] Data 0.003 (0.104) Batch 0.733 (1.132) Remain 15:11:19 loss: 0.6229 Lr: 0.00121 [2024-02-19 04:34:53,343 INFO misc.py line 119 87073] Train: [69/100][1540/1557] Data 0.004 (0.104) Batch 1.313 (1.133) Remain 15:11:24 loss: 0.1108 Lr: 0.00121 [2024-02-19 04:34:54,204 INFO misc.py line 119 87073] Train: [69/100][1541/1557] Data 0.003 (0.103) Batch 0.861 (1.132) Remain 15:11:14 loss: 0.2771 Lr: 0.00121 [2024-02-19 04:34:55,087 INFO misc.py line 119 87073] Train: [69/100][1542/1557] Data 0.003 (0.103) Batch 0.882 (1.132) Remain 15:11:05 loss: 0.2462 Lr: 0.00121 [2024-02-19 04:34:56,165 INFO misc.py line 119 87073] Train: [69/100][1543/1557] Data 0.003 (0.103) Batch 1.077 (1.132) Remain 15:11:02 loss: 0.4037 Lr: 0.00121 [2024-02-19 04:34:57,111 INFO misc.py line 119 87073] Train: [69/100][1544/1557] Data 0.004 (0.103) Batch 0.948 (1.132) Remain 15:10:55 loss: 0.2328 Lr: 0.00121 [2024-02-19 04:34:57,791 INFO misc.py line 119 87073] Train: [69/100][1545/1557] Data 0.003 (0.103) Batch 0.680 (1.132) Remain 15:10:40 loss: 0.1802 Lr: 0.00121 [2024-02-19 04:34:58,556 INFO misc.py line 119 87073] Train: [69/100][1546/1557] Data 0.003 (0.103) Batch 0.762 (1.132) Remain 15:10:27 loss: 0.3399 Lr: 0.00121 [2024-02-19 04:34:59,641 INFO misc.py line 119 87073] Train: [69/100][1547/1557] Data 0.006 (0.103) Batch 1.088 (1.132) Remain 15:10:25 loss: 0.2540 Lr: 0.00121 [2024-02-19 04:35:00,491 INFO misc.py line 119 87073] Train: [69/100][1548/1557] Data 0.003 (0.103) Batch 0.850 (1.131) Remain 15:10:15 loss: 0.2557 Lr: 0.00121 [2024-02-19 04:35:01,359 INFO misc.py line 119 87073] Train: [69/100][1549/1557] Data 0.003 (0.103) Batch 0.869 (1.131) Remain 15:10:06 loss: 0.2702 Lr: 0.00121 [2024-02-19 04:35:02,487 INFO misc.py line 119 87073] Train: [69/100][1550/1557] Data 0.003 (0.103) Batch 1.127 (1.131) Remain 15:10:04 loss: 0.2798 Lr: 0.00121 [2024-02-19 04:35:03,440 INFO misc.py line 119 87073] Train: [69/100][1551/1557] Data 0.004 (0.103) Batch 0.953 (1.131) Remain 15:09:58 loss: 0.5054 Lr: 0.00121 [2024-02-19 04:35:04,167 INFO misc.py line 119 87073] Train: [69/100][1552/1557] Data 0.004 (0.103) Batch 0.722 (1.131) Remain 15:09:44 loss: 0.1894 Lr: 0.00121 [2024-02-19 04:35:04,897 INFO misc.py line 119 87073] Train: [69/100][1553/1557] Data 0.008 (0.103) Batch 0.736 (1.131) Remain 15:09:30 loss: 0.2117 Lr: 0.00121 [2024-02-19 04:35:06,250 INFO misc.py line 119 87073] Train: [69/100][1554/1557] Data 0.003 (0.103) Batch 1.313 (1.131) Remain 15:09:35 loss: 0.1045 Lr: 0.00120 [2024-02-19 04:35:07,295 INFO misc.py line 119 87073] Train: [69/100][1555/1557] Data 0.043 (0.103) Batch 1.084 (1.131) Remain 15:09:32 loss: 0.2859 Lr: 0.00120 [2024-02-19 04:35:08,297 INFO misc.py line 119 87073] Train: [69/100][1556/1557] Data 0.004 (0.103) Batch 1.002 (1.131) Remain 15:09:27 loss: 0.4236 Lr: 0.00120 [2024-02-19 04:35:09,235 INFO misc.py line 119 87073] Train: [69/100][1557/1557] Data 0.004 (0.102) Batch 0.939 (1.130) Remain 15:09:20 loss: 0.2461 Lr: 0.00120 [2024-02-19 04:35:09,236 INFO misc.py line 136 87073] Train result: loss: 0.2711 [2024-02-19 04:35:09,236 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 04:35:38,488 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4469 [2024-02-19 04:35:39,265 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7216 [2024-02-19 04:35:41,391 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3795 [2024-02-19 04:35:43,598 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2791 [2024-02-19 04:35:48,546 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2336 [2024-02-19 04:35:49,248 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0680 [2024-02-19 04:35:50,508 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3474 [2024-02-19 04:35:53,463 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2424 [2024-02-19 04:35:55,272 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3177 [2024-02-19 04:35:56,886 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7361/0.7946/0.9190. [2024-02-19 04:35:56,886 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9303/0.9595 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9820/0.9880 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8752/0.9697 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4887/0.5601 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6704/0.6931 [2024-02-19 04:35:56,887 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8148/0.9455 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8361/0.9089 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9290/0.9747 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8485/0.8816 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7907/0.8854 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7746/0.8378 [2024-02-19 04:35:56,888 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6296/0.7251 [2024-02-19 04:35:56,889 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 04:35:56,891 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7361 [2024-02-19 04:35:56,891 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 04:35:56,891 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 04:36:07,573 INFO misc.py line 119 87073] Train: [70/100][1/1557] Data 1.228 (1.228) Batch 1.942 (1.942) Remain 26:02:02 loss: 0.1344 Lr: 0.00120 [2024-02-19 04:36:08,482 INFO misc.py line 119 87073] Train: [70/100][2/1557] Data 0.006 (0.006) Batch 0.903 (0.903) Remain 12:06:06 loss: 0.1788 Lr: 0.00120 [2024-02-19 04:36:09,419 INFO misc.py line 119 87073] Train: [70/100][3/1557] Data 0.012 (0.012) Batch 0.945 (0.945) Remain 12:40:03 loss: 0.2441 Lr: 0.00120 [2024-02-19 04:36:10,230 INFO misc.py line 119 87073] Train: [70/100][4/1557] Data 0.004 (0.004) Batch 0.811 (0.811) Remain 10:52:43 loss: 0.4460 Lr: 0.00120 [2024-02-19 04:36:11,038 INFO misc.py line 119 87073] Train: [70/100][5/1557] Data 0.004 (0.004) Batch 0.804 (0.808) Remain 10:49:47 loss: 0.4358 Lr: 0.00120 [2024-02-19 04:36:11,816 INFO misc.py line 119 87073] Train: [70/100][6/1557] Data 0.007 (0.005) Batch 0.782 (0.799) Remain 10:42:49 loss: 0.1542 Lr: 0.00120 [2024-02-19 04:36:12,987 INFO misc.py line 119 87073] Train: [70/100][7/1557] Data 0.003 (0.005) Batch 1.171 (0.892) Remain 11:57:29 loss: 0.1443 Lr: 0.00120 [2024-02-19 04:36:13,988 INFO misc.py line 119 87073] Train: [70/100][8/1557] Data 0.003 (0.004) Batch 1.001 (0.914) Remain 12:14:57 loss: 0.2419 Lr: 0.00120 [2024-02-19 04:36:14,852 INFO misc.py line 119 87073] Train: [70/100][9/1557] Data 0.003 (0.004) Batch 0.864 (0.906) Remain 12:08:18 loss: 0.4005 Lr: 0.00120 [2024-02-19 04:36:15,971 INFO misc.py line 119 87073] Train: [70/100][10/1557] Data 0.004 (0.004) Batch 1.117 (0.936) Remain 12:32:35 loss: 0.7180 Lr: 0.00120 [2024-02-19 04:36:16,937 INFO misc.py line 119 87073] Train: [70/100][11/1557] Data 0.006 (0.004) Batch 0.968 (0.940) Remain 12:35:52 loss: 0.3015 Lr: 0.00120 [2024-02-19 04:36:17,680 INFO misc.py line 119 87073] Train: [70/100][12/1557] Data 0.003 (0.004) Batch 0.744 (0.918) Remain 12:18:19 loss: 0.1367 Lr: 0.00120 [2024-02-19 04:36:18,458 INFO misc.py line 119 87073] Train: [70/100][13/1557] Data 0.003 (0.004) Batch 0.776 (0.904) Remain 12:06:55 loss: 0.1729 Lr: 0.00120 [2024-02-19 04:36:19,640 INFO misc.py line 119 87073] Train: [70/100][14/1557] Data 0.004 (0.004) Batch 1.182 (0.929) Remain 12:27:15 loss: 0.1588 Lr: 0.00120 [2024-02-19 04:36:20,516 INFO misc.py line 119 87073] Train: [70/100][15/1557] Data 0.004 (0.004) Batch 0.876 (0.925) Remain 12:23:40 loss: 0.5315 Lr: 0.00120 [2024-02-19 04:36:21,488 INFO misc.py line 119 87073] Train: [70/100][16/1557] Data 0.004 (0.004) Batch 0.971 (0.928) Remain 12:26:33 loss: 0.1249 Lr: 0.00120 [2024-02-19 04:36:22,311 INFO misc.py line 119 87073] Train: [70/100][17/1557] Data 0.005 (0.004) Batch 0.824 (0.921) Remain 12:20:33 loss: 0.3412 Lr: 0.00120 [2024-02-19 04:36:23,362 INFO misc.py line 119 87073] Train: [70/100][18/1557] Data 0.004 (0.004) Batch 1.043 (0.929) Remain 12:27:03 loss: 0.1390 Lr: 0.00120 [2024-02-19 04:36:24,097 INFO misc.py line 119 87073] Train: [70/100][19/1557] Data 0.012 (0.005) Batch 0.743 (0.917) Remain 12:17:42 loss: 0.1685 Lr: 0.00120 [2024-02-19 04:36:24,835 INFO misc.py line 119 87073] Train: [70/100][20/1557] Data 0.004 (0.005) Batch 0.737 (0.907) Remain 12:09:10 loss: 0.1875 Lr: 0.00120 [2024-02-19 04:36:26,077 INFO misc.py line 119 87073] Train: [70/100][21/1557] Data 0.004 (0.005) Batch 1.243 (0.925) Remain 12:24:09 loss: 0.0670 Lr: 0.00120 [2024-02-19 04:36:27,125 INFO misc.py line 119 87073] Train: [70/100][22/1557] Data 0.004 (0.004) Batch 1.039 (0.931) Remain 12:28:57 loss: 0.3089 Lr: 0.00120 [2024-02-19 04:36:28,127 INFO misc.py line 119 87073] Train: [70/100][23/1557] Data 0.013 (0.005) Batch 1.001 (0.935) Remain 12:31:43 loss: 0.4054 Lr: 0.00120 [2024-02-19 04:36:29,395 INFO misc.py line 119 87073] Train: [70/100][24/1557] Data 0.013 (0.005) Batch 1.267 (0.951) Remain 12:44:25 loss: 0.1603 Lr: 0.00120 [2024-02-19 04:36:30,245 INFO misc.py line 119 87073] Train: [70/100][25/1557] Data 0.014 (0.006) Batch 0.861 (0.947) Remain 12:41:08 loss: 0.0858 Lr: 0.00120 [2024-02-19 04:36:31,039 INFO misc.py line 119 87073] Train: [70/100][26/1557] Data 0.003 (0.006) Batch 0.794 (0.940) Remain 12:35:48 loss: 0.1735 Lr: 0.00120 [2024-02-19 04:36:31,804 INFO misc.py line 119 87073] Train: [70/100][27/1557] Data 0.003 (0.005) Batch 0.756 (0.932) Remain 12:29:36 loss: 0.2080 Lr: 0.00120 [2024-02-19 04:36:33,006 INFO misc.py line 119 87073] Train: [70/100][28/1557] Data 0.012 (0.006) Batch 1.207 (0.943) Remain 12:38:26 loss: 0.1539 Lr: 0.00120 [2024-02-19 04:36:33,941 INFO misc.py line 119 87073] Train: [70/100][29/1557] Data 0.006 (0.006) Batch 0.937 (0.943) Remain 12:38:14 loss: 0.1366 Lr: 0.00120 [2024-02-19 04:36:34,819 INFO misc.py line 119 87073] Train: [70/100][30/1557] Data 0.006 (0.006) Batch 0.880 (0.941) Remain 12:36:20 loss: 0.0985 Lr: 0.00120 [2024-02-19 04:36:35,593 INFO misc.py line 119 87073] Train: [70/100][31/1557] Data 0.003 (0.006) Batch 0.768 (0.935) Remain 12:31:22 loss: 0.4646 Lr: 0.00120 [2024-02-19 04:36:36,547 INFO misc.py line 119 87073] Train: [70/100][32/1557] Data 0.009 (0.006) Batch 0.959 (0.935) Remain 12:32:01 loss: 0.6874 Lr: 0.00120 [2024-02-19 04:36:37,307 INFO misc.py line 119 87073] Train: [70/100][33/1557] Data 0.004 (0.006) Batch 0.758 (0.930) Remain 12:27:16 loss: 0.3593 Lr: 0.00120 [2024-02-19 04:36:38,104 INFO misc.py line 119 87073] Train: [70/100][34/1557] Data 0.006 (0.006) Batch 0.790 (0.925) Remain 12:23:38 loss: 0.2335 Lr: 0.00120 [2024-02-19 04:36:39,217 INFO misc.py line 119 87073] Train: [70/100][35/1557] Data 0.012 (0.006) Batch 1.112 (0.931) Remain 12:28:19 loss: 0.1122 Lr: 0.00120 [2024-02-19 04:36:40,192 INFO misc.py line 119 87073] Train: [70/100][36/1557] Data 0.012 (0.006) Batch 0.984 (0.933) Remain 12:29:36 loss: 0.3885 Lr: 0.00120 [2024-02-19 04:36:41,211 INFO misc.py line 119 87073] Train: [70/100][37/1557] Data 0.003 (0.006) Batch 1.019 (0.935) Remain 12:31:38 loss: 0.3661 Lr: 0.00120 [2024-02-19 04:36:42,159 INFO misc.py line 119 87073] Train: [70/100][38/1557] Data 0.003 (0.006) Batch 0.947 (0.935) Remain 12:31:54 loss: 0.2548 Lr: 0.00120 [2024-02-19 04:36:43,172 INFO misc.py line 119 87073] Train: [70/100][39/1557] Data 0.004 (0.006) Batch 1.014 (0.938) Remain 12:33:38 loss: 0.3700 Lr: 0.00120 [2024-02-19 04:36:43,957 INFO misc.py line 119 87073] Train: [70/100][40/1557] Data 0.003 (0.006) Batch 0.784 (0.933) Remain 12:30:17 loss: 0.3238 Lr: 0.00120 [2024-02-19 04:36:44,726 INFO misc.py line 119 87073] Train: [70/100][41/1557] Data 0.003 (0.006) Batch 0.765 (0.929) Remain 12:26:43 loss: 0.2739 Lr: 0.00120 [2024-02-19 04:36:45,831 INFO misc.py line 119 87073] Train: [70/100][42/1557] Data 0.007 (0.006) Batch 1.099 (0.933) Remain 12:30:12 loss: 0.1440 Lr: 0.00120 [2024-02-19 04:36:46,880 INFO misc.py line 119 87073] Train: [70/100][43/1557] Data 0.014 (0.006) Batch 1.047 (0.936) Remain 12:32:27 loss: 0.2347 Lr: 0.00120 [2024-02-19 04:36:48,019 INFO misc.py line 119 87073] Train: [70/100][44/1557] Data 0.016 (0.006) Batch 1.141 (0.941) Remain 12:36:27 loss: 0.1191 Lr: 0.00120 [2024-02-19 04:36:49,058 INFO misc.py line 119 87073] Train: [70/100][45/1557] Data 0.014 (0.006) Batch 1.040 (0.944) Remain 12:38:20 loss: 0.2955 Lr: 0.00120 [2024-02-19 04:36:50,108 INFO misc.py line 119 87073] Train: [70/100][46/1557] Data 0.013 (0.007) Batch 1.053 (0.946) Remain 12:40:22 loss: 0.6308 Lr: 0.00120 [2024-02-19 04:36:50,885 INFO misc.py line 119 87073] Train: [70/100][47/1557] Data 0.010 (0.007) Batch 0.784 (0.942) Remain 12:37:23 loss: 0.2414 Lr: 0.00120 [2024-02-19 04:36:51,655 INFO misc.py line 119 87073] Train: [70/100][48/1557] Data 0.003 (0.007) Batch 0.770 (0.939) Remain 12:34:17 loss: 0.1691 Lr: 0.00120 [2024-02-19 04:36:52,931 INFO misc.py line 119 87073] Train: [70/100][49/1557] Data 0.003 (0.006) Batch 1.245 (0.945) Remain 12:39:37 loss: 0.3435 Lr: 0.00120 [2024-02-19 04:36:53,857 INFO misc.py line 119 87073] Train: [70/100][50/1557] Data 0.035 (0.007) Batch 0.957 (0.946) Remain 12:39:49 loss: 0.4398 Lr: 0.00120 [2024-02-19 04:36:54,886 INFO misc.py line 119 87073] Train: [70/100][51/1557] Data 0.003 (0.007) Batch 1.029 (0.947) Remain 12:41:12 loss: 0.2883 Lr: 0.00120 [2024-02-19 04:36:55,727 INFO misc.py line 119 87073] Train: [70/100][52/1557] Data 0.003 (0.007) Batch 0.840 (0.945) Remain 12:39:26 loss: 0.2579 Lr: 0.00120 [2024-02-19 04:36:56,658 INFO misc.py line 119 87073] Train: [70/100][53/1557] Data 0.004 (0.007) Batch 0.925 (0.945) Remain 12:39:05 loss: 0.1851 Lr: 0.00120 [2024-02-19 04:36:57,433 INFO misc.py line 119 87073] Train: [70/100][54/1557] Data 0.009 (0.007) Batch 0.782 (0.941) Remain 12:36:30 loss: 0.1116 Lr: 0.00120 [2024-02-19 04:36:58,176 INFO misc.py line 119 87073] Train: [70/100][55/1557] Data 0.003 (0.007) Batch 0.742 (0.938) Remain 12:33:25 loss: 0.2465 Lr: 0.00120 [2024-02-19 04:36:59,443 INFO misc.py line 119 87073] Train: [70/100][56/1557] Data 0.003 (0.007) Batch 1.255 (0.944) Remain 12:38:13 loss: 0.2262 Lr: 0.00120 [2024-02-19 04:37:00,411 INFO misc.py line 119 87073] Train: [70/100][57/1557] Data 0.016 (0.007) Batch 0.980 (0.944) Remain 12:38:44 loss: 0.1334 Lr: 0.00120 [2024-02-19 04:37:01,627 INFO misc.py line 119 87073] Train: [70/100][58/1557] Data 0.004 (0.007) Batch 1.194 (0.949) Remain 12:42:22 loss: 0.1072 Lr: 0.00120 [2024-02-19 04:37:02,602 INFO misc.py line 119 87073] Train: [70/100][59/1557] Data 0.025 (0.007) Batch 0.997 (0.950) Remain 12:43:03 loss: 0.0780 Lr: 0.00120 [2024-02-19 04:37:03,488 INFO misc.py line 119 87073] Train: [70/100][60/1557] Data 0.003 (0.007) Batch 0.887 (0.949) Remain 12:42:08 loss: 0.2337 Lr: 0.00120 [2024-02-19 04:37:04,200 INFO misc.py line 119 87073] Train: [70/100][61/1557] Data 0.003 (0.007) Batch 0.709 (0.944) Remain 12:38:49 loss: 0.2998 Lr: 0.00120 [2024-02-19 04:37:04,964 INFO misc.py line 119 87073] Train: [70/100][62/1557] Data 0.006 (0.007) Batch 0.763 (0.941) Remain 12:36:19 loss: 0.2721 Lr: 0.00120 [2024-02-19 04:37:16,600 INFO misc.py line 119 87073] Train: [70/100][63/1557] Data 4.443 (0.081) Batch 11.640 (1.120) Remain 14:59:33 loss: 0.1426 Lr: 0.00120 [2024-02-19 04:37:17,477 INFO misc.py line 119 87073] Train: [70/100][64/1557] Data 0.003 (0.080) Batch 0.873 (1.116) Remain 14:56:17 loss: 0.2805 Lr: 0.00120 [2024-02-19 04:37:18,292 INFO misc.py line 119 87073] Train: [70/100][65/1557] Data 0.007 (0.078) Batch 0.819 (1.111) Remain 14:52:25 loss: 0.1127 Lr: 0.00120 [2024-02-19 04:37:19,294 INFO misc.py line 119 87073] Train: [70/100][66/1557] Data 0.003 (0.077) Batch 1.002 (1.109) Remain 14:51:01 loss: 0.1485 Lr: 0.00120 [2024-02-19 04:37:20,362 INFO misc.py line 119 87073] Train: [70/100][67/1557] Data 0.003 (0.076) Batch 1.068 (1.108) Remain 14:50:29 loss: 0.1623 Lr: 0.00120 [2024-02-19 04:37:21,135 INFO misc.py line 119 87073] Train: [70/100][68/1557] Data 0.003 (0.075) Batch 0.773 (1.103) Remain 14:46:19 loss: 0.3331 Lr: 0.00120 [2024-02-19 04:37:21,976 INFO misc.py line 119 87073] Train: [70/100][69/1557] Data 0.004 (0.074) Batch 0.837 (1.099) Remain 14:43:03 loss: 0.1298 Lr: 0.00120 [2024-02-19 04:37:23,172 INFO misc.py line 119 87073] Train: [70/100][70/1557] Data 0.007 (0.073) Batch 1.192 (1.101) Remain 14:44:09 loss: 0.1425 Lr: 0.00120 [2024-02-19 04:37:23,967 INFO misc.py line 119 87073] Train: [70/100][71/1557] Data 0.011 (0.072) Batch 0.803 (1.096) Remain 14:40:37 loss: 0.2308 Lr: 0.00120 [2024-02-19 04:37:25,044 INFO misc.py line 119 87073] Train: [70/100][72/1557] Data 0.004 (0.071) Batch 1.077 (1.096) Remain 14:40:23 loss: 0.3954 Lr: 0.00120 [2024-02-19 04:37:26,030 INFO misc.py line 119 87073] Train: [70/100][73/1557] Data 0.003 (0.070) Batch 0.986 (1.094) Remain 14:39:05 loss: 0.3975 Lr: 0.00120 [2024-02-19 04:37:26,948 INFO misc.py line 119 87073] Train: [70/100][74/1557] Data 0.004 (0.069) Batch 0.918 (1.092) Remain 14:37:04 loss: 0.1872 Lr: 0.00120 [2024-02-19 04:37:27,694 INFO misc.py line 119 87073] Train: [70/100][75/1557] Data 0.004 (0.068) Batch 0.738 (1.087) Remain 14:33:06 loss: 0.3237 Lr: 0.00120 [2024-02-19 04:37:28,449 INFO misc.py line 119 87073] Train: [70/100][76/1557] Data 0.012 (0.067) Batch 0.763 (1.083) Remain 14:29:31 loss: 0.1868 Lr: 0.00120 [2024-02-19 04:37:29,707 INFO misc.py line 119 87073] Train: [70/100][77/1557] Data 0.003 (0.067) Batch 1.248 (1.085) Remain 14:31:18 loss: 0.0727 Lr: 0.00120 [2024-02-19 04:37:30,672 INFO misc.py line 119 87073] Train: [70/100][78/1557] Data 0.014 (0.066) Batch 0.975 (1.083) Remain 14:30:06 loss: 0.2679 Lr: 0.00120 [2024-02-19 04:37:31,577 INFO misc.py line 119 87073] Train: [70/100][79/1557] Data 0.003 (0.065) Batch 0.904 (1.081) Remain 14:28:12 loss: 0.3042 Lr: 0.00120 [2024-02-19 04:37:32,666 INFO misc.py line 119 87073] Train: [70/100][80/1557] Data 0.004 (0.064) Batch 1.090 (1.081) Remain 14:28:16 loss: 0.3922 Lr: 0.00120 [2024-02-19 04:37:33,724 INFO misc.py line 119 87073] Train: [70/100][81/1557] Data 0.004 (0.063) Batch 1.058 (1.081) Remain 14:28:01 loss: 0.4844 Lr: 0.00120 [2024-02-19 04:37:34,442 INFO misc.py line 119 87073] Train: [70/100][82/1557] Data 0.003 (0.063) Batch 0.717 (1.076) Remain 14:24:18 loss: 0.3237 Lr: 0.00120 [2024-02-19 04:37:35,222 INFO misc.py line 119 87073] Train: [70/100][83/1557] Data 0.003 (0.062) Batch 0.774 (1.072) Remain 14:21:15 loss: 0.1899 Lr: 0.00120 [2024-02-19 04:37:36,428 INFO misc.py line 119 87073] Train: [70/100][84/1557] Data 0.009 (0.061) Batch 1.205 (1.074) Remain 14:22:33 loss: 0.0769 Lr: 0.00120 [2024-02-19 04:37:37,413 INFO misc.py line 119 87073] Train: [70/100][85/1557] Data 0.010 (0.061) Batch 0.993 (1.073) Remain 14:21:44 loss: 0.1078 Lr: 0.00120 [2024-02-19 04:37:38,326 INFO misc.py line 119 87073] Train: [70/100][86/1557] Data 0.002 (0.060) Batch 0.912 (1.071) Remain 14:20:10 loss: 0.7306 Lr: 0.00120 [2024-02-19 04:37:39,231 INFO misc.py line 119 87073] Train: [70/100][87/1557] Data 0.003 (0.059) Batch 0.896 (1.069) Remain 14:18:28 loss: 0.6206 Lr: 0.00120 [2024-02-19 04:37:40,257 INFO misc.py line 119 87073] Train: [70/100][88/1557] Data 0.013 (0.059) Batch 1.029 (1.069) Remain 14:18:04 loss: 0.2956 Lr: 0.00120 [2024-02-19 04:37:41,054 INFO misc.py line 119 87073] Train: [70/100][89/1557] Data 0.010 (0.058) Batch 0.803 (1.066) Remain 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Train: [70/100][102/1557] Data 0.003 (0.051) Batch 1.004 (1.053) Remain 14:05:05 loss: 0.6670 Lr: 0.00120 [2024-02-19 04:37:54,396 INFO misc.py line 119 87073] Train: [70/100][103/1557] Data 0.003 (0.051) Batch 0.754 (1.050) Remain 14:02:40 loss: 0.0790 Lr: 0.00120 [2024-02-19 04:37:55,244 INFO misc.py line 119 87073] Train: [70/100][104/1557] Data 0.004 (0.050) Batch 0.839 (1.048) Remain 14:00:59 loss: 0.2072 Lr: 0.00120 [2024-02-19 04:37:56,512 INFO misc.py line 119 87073] Train: [70/100][105/1557] Data 0.013 (0.050) Batch 1.270 (1.050) Remain 14:02:43 loss: 0.2160 Lr: 0.00120 [2024-02-19 04:37:57,483 INFO misc.py line 119 87073] Train: [70/100][106/1557] Data 0.010 (0.049) Batch 0.978 (1.049) Remain 14:02:08 loss: 0.2554 Lr: 0.00120 [2024-02-19 04:37:58,464 INFO misc.py line 119 87073] Train: [70/100][107/1557] Data 0.003 (0.049) Batch 0.981 (1.049) Remain 14:01:36 loss: 0.2857 Lr: 0.00120 [2024-02-19 04:37:59,383 INFO misc.py line 119 87073] Train: [70/100][108/1557] Data 0.004 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Batch 0.724 (1.098) Remain 14:40:22 loss: 0.1896 Lr: 0.00120 [2024-02-19 04:38:39,894 INFO misc.py line 119 87073] Train: [70/100][140/1557] Data 0.009 (0.069) Batch 1.205 (1.098) Remain 14:40:58 loss: 0.1706 Lr: 0.00120 [2024-02-19 04:38:40,754 INFO misc.py line 119 87073] Train: [70/100][141/1557] Data 0.009 (0.069) Batch 0.865 (1.097) Remain 14:39:36 loss: 0.2543 Lr: 0.00120 [2024-02-19 04:38:41,652 INFO misc.py line 119 87073] Train: [70/100][142/1557] Data 0.004 (0.069) Batch 0.897 (1.095) Remain 14:38:26 loss: 0.2538 Lr: 0.00120 [2024-02-19 04:38:42,624 INFO misc.py line 119 87073] Train: [70/100][143/1557] Data 0.004 (0.068) Batch 0.973 (1.094) Remain 14:37:43 loss: 0.1639 Lr: 0.00120 [2024-02-19 04:38:43,590 INFO misc.py line 119 87073] Train: [70/100][144/1557] Data 0.004 (0.068) Batch 0.963 (1.093) Remain 14:36:57 loss: 0.1947 Lr: 0.00120 [2024-02-19 04:38:44,467 INFO misc.py line 119 87073] Train: [70/100][145/1557] Data 0.006 (0.067) Batch 0.880 (1.092) Remain 14:35:43 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line 119 87073] Train: [70/100][183/1557] Data 0.009 (0.086) Batch 1.028 (1.125) Remain 15:01:33 loss: 0.1762 Lr: 0.00120 [2024-02-19 04:39:32,826 INFO misc.py line 119 87073] Train: [70/100][184/1557] Data 0.010 (0.085) Batch 0.910 (1.124) Remain 15:00:35 loss: 0.1745 Lr: 0.00120 [2024-02-19 04:39:33,686 INFO misc.py line 119 87073] Train: [70/100][185/1557] Data 0.004 (0.085) Batch 0.860 (1.122) Remain 14:59:24 loss: 0.0349 Lr: 0.00120 [2024-02-19 04:39:34,798 INFO misc.py line 119 87073] Train: [70/100][186/1557] Data 0.005 (0.084) Batch 1.105 (1.122) Remain 14:59:18 loss: 0.6171 Lr: 0.00120 [2024-02-19 04:39:35,557 INFO misc.py line 119 87073] Train: [70/100][187/1557] Data 0.011 (0.084) Batch 0.767 (1.120) Remain 14:57:44 loss: 0.1367 Lr: 0.00120 [2024-02-19 04:39:36,358 INFO misc.py line 119 87073] Train: [70/100][188/1557] Data 0.003 (0.084) Batch 0.788 (1.119) Remain 14:56:17 loss: 0.1354 Lr: 0.00120 [2024-02-19 04:39:37,670 INFO misc.py line 119 87073] Train: 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Batch 0.683 (1.113) Remain 14:52:03 loss: 0.2019 Lr: 0.00120 [2024-02-19 04:39:44,482 INFO misc.py line 119 87073] Train: [70/100][196/1557] Data 0.011 (0.080) Batch 1.276 (1.114) Remain 14:52:43 loss: 0.0754 Lr: 0.00120 [2024-02-19 04:39:45,564 INFO misc.py line 119 87073] Train: [70/100][197/1557] Data 0.016 (0.080) Batch 1.084 (1.114) Remain 14:52:34 loss: 0.1788 Lr: 0.00120 [2024-02-19 04:39:46,748 INFO misc.py line 119 87073] Train: [70/100][198/1557] Data 0.014 (0.080) Batch 1.182 (1.114) Remain 14:52:50 loss: 0.3337 Lr: 0.00120 [2024-02-19 04:39:48,052 INFO misc.py line 119 87073] Train: [70/100][199/1557] Data 0.016 (0.079) Batch 1.307 (1.115) Remain 14:53:36 loss: 0.4082 Lr: 0.00120 [2024-02-19 04:39:49,074 INFO misc.py line 119 87073] Train: [70/100][200/1557] Data 0.013 (0.079) Batch 1.026 (1.115) Remain 14:53:13 loss: 0.3813 Lr: 0.00120 [2024-02-19 04:39:50,057 INFO misc.py line 119 87073] Train: [70/100][201/1557] Data 0.009 (0.079) Batch 0.988 (1.114) Remain 14:52:41 loss: 0.1774 Lr: 0.00120 [2024-02-19 04:39:50,814 INFO misc.py line 119 87073] Train: [70/100][202/1557] Data 0.004 (0.078) Batch 0.757 (1.113) Remain 14:51:14 loss: 0.2797 Lr: 0.00120 [2024-02-19 04:39:52,002 INFO misc.py line 119 87073] Train: [70/100][203/1557] Data 0.003 (0.078) Batch 1.179 (1.113) Remain 14:51:29 loss: 0.1087 Lr: 0.00120 [2024-02-19 04:39:52,966 INFO misc.py line 119 87073] Train: [70/100][204/1557] Data 0.012 (0.078) Batch 0.973 (1.112) Remain 14:50:54 loss: 0.2610 Lr: 0.00120 [2024-02-19 04:39:53,944 INFO misc.py line 119 87073] Train: [70/100][205/1557] Data 0.003 (0.077) Batch 0.978 (1.112) Remain 14:50:21 loss: 0.1253 Lr: 0.00120 [2024-02-19 04:39:54,835 INFO misc.py line 119 87073] Train: [70/100][206/1557] Data 0.003 (0.077) Batch 0.891 (1.110) Remain 14:49:28 loss: 0.3259 Lr: 0.00120 [2024-02-19 04:39:55,790 INFO misc.py line 119 87073] Train: [70/100][207/1557] Data 0.003 (0.077) Batch 0.950 (1.110) Remain 14:48:49 loss: 0.2346 Lr: 0.00120 [2024-02-19 04:39:56,509 INFO misc.py line 119 87073] Train: [70/100][208/1557] Data 0.007 (0.076) Batch 0.724 (1.108) Remain 14:47:17 loss: 0.2569 Lr: 0.00120 [2024-02-19 04:39:57,252 INFO misc.py line 119 87073] Train: [70/100][209/1557] Data 0.003 (0.076) Batch 0.739 (1.106) Remain 14:45:50 loss: 0.2932 Lr: 0.00120 [2024-02-19 04:39:58,345 INFO misc.py line 119 87073] Train: [70/100][210/1557] Data 0.007 (0.076) Batch 1.092 (1.106) Remain 14:45:46 loss: 0.2184 Lr: 0.00120 [2024-02-19 04:39:59,310 INFO misc.py line 119 87073] Train: [70/100][211/1557] Data 0.007 (0.075) Batch 0.970 (1.105) Remain 14:45:13 loss: 0.3822 Lr: 0.00120 [2024-02-19 04:40:00,070 INFO misc.py line 119 87073] Train: [70/100][212/1557] Data 0.003 (0.075) Batch 0.760 (1.104) Remain 14:43:53 loss: 0.1125 Lr: 0.00120 [2024-02-19 04:40:00,927 INFO misc.py line 119 87073] Train: [70/100][213/1557] Data 0.003 (0.075) Batch 0.852 (1.102) Remain 14:42:54 loss: 0.7025 Lr: 0.00120 [2024-02-19 04:40:01,792 INFO misc.py line 119 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line 119 87073] Train: [70/100][239/1557] Data 0.004 (0.084) Batch 0.803 (1.122) Remain 14:58:21 loss: 0.3754 Lr: 0.00119 [2024-02-19 04:40:35,500 INFO misc.py line 119 87073] Train: [70/100][240/1557] Data 0.004 (0.084) Batch 1.212 (1.123) Remain 14:58:38 loss: 0.1914 Lr: 0.00119 [2024-02-19 04:40:36,464 INFO misc.py line 119 87073] Train: [70/100][241/1557] Data 0.010 (0.084) Batch 0.971 (1.122) Remain 14:58:06 loss: 0.1107 Lr: 0.00119 [2024-02-19 04:40:37,512 INFO misc.py line 119 87073] Train: [70/100][242/1557] Data 0.003 (0.083) Batch 1.048 (1.122) Remain 14:57:50 loss: 0.1081 Lr: 0.00119 [2024-02-19 04:40:38,283 INFO misc.py line 119 87073] Train: [70/100][243/1557] Data 0.003 (0.083) Batch 0.771 (1.120) Remain 14:56:39 loss: 0.2971 Lr: 0.00119 [2024-02-19 04:40:39,110 INFO misc.py line 119 87073] Train: [70/100][244/1557] Data 0.003 (0.083) Batch 0.823 (1.119) Remain 14:55:39 loss: 0.3245 Lr: 0.00119 [2024-02-19 04:40:40,287 INFO misc.py line 119 87073] Train: 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Batch 0.741 (1.113) Remain 14:50:44 loss: 0.1928 Lr: 0.00119 [2024-02-19 04:40:46,724 INFO misc.py line 119 87073] Train: [70/100][252/1557] Data 0.016 (0.080) Batch 1.253 (1.114) Remain 14:51:10 loss: 0.1149 Lr: 0.00119 [2024-02-19 04:40:47,656 INFO misc.py line 119 87073] Train: [70/100][253/1557] Data 0.016 (0.080) Batch 0.945 (1.113) Remain 14:50:37 loss: 0.2716 Lr: 0.00119 [2024-02-19 04:40:48,524 INFO misc.py line 119 87073] Train: [70/100][254/1557] Data 0.003 (0.080) Batch 0.868 (1.112) Remain 14:49:49 loss: 0.3756 Lr: 0.00119 [2024-02-19 04:40:49,437 INFO misc.py line 119 87073] Train: [70/100][255/1557] Data 0.003 (0.080) Batch 0.900 (1.111) Remain 14:49:07 loss: 0.3300 Lr: 0.00119 [2024-02-19 04:40:50,366 INFO misc.py line 119 87073] Train: [70/100][256/1557] Data 0.017 (0.079) Batch 0.943 (1.110) Remain 14:48:34 loss: 0.3787 Lr: 0.00119 [2024-02-19 04:40:51,118 INFO misc.py line 119 87073] Train: [70/100][257/1557] Data 0.003 (0.079) Batch 0.749 (1.109) Remain 14:47:25 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87073] Train: [70/100][270/1557] Data 0.012 (0.075) Batch 1.193 (1.103) Remain 14:42:08 loss: 0.5054 Lr: 0.00119 [2024-02-19 04:41:04,632 INFO misc.py line 119 87073] Train: [70/100][271/1557] Data 0.013 (0.075) Batch 0.778 (1.102) Remain 14:41:09 loss: 0.2470 Lr: 0.00119 [2024-02-19 04:41:05,364 INFO misc.py line 119 87073] Train: [70/100][272/1557] Data 0.003 (0.075) Batch 0.722 (1.100) Remain 14:40:00 loss: 0.2972 Lr: 0.00119 [2024-02-19 04:41:06,593 INFO misc.py line 119 87073] Train: [70/100][273/1557] Data 0.013 (0.075) Batch 1.227 (1.101) Remain 14:40:22 loss: 0.1308 Lr: 0.00119 [2024-02-19 04:41:07,507 INFO misc.py line 119 87073] Train: [70/100][274/1557] Data 0.014 (0.075) Batch 0.925 (1.100) Remain 14:39:50 loss: 0.6634 Lr: 0.00119 [2024-02-19 04:41:08,383 INFO misc.py line 119 87073] Train: [70/100][275/1557] Data 0.005 (0.074) Batch 0.877 (1.099) Remain 14:39:09 loss: 0.2500 Lr: 0.00119 [2024-02-19 04:41:09,256 INFO misc.py line 119 87073] Train: [70/100][276/1557] Data 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Batch 0.797 (1.121) Remain 14:53:40 loss: 0.1788 Lr: 0.00119 [2024-02-19 04:43:56,797 INFO misc.py line 119 87073] Train: [70/100][420/1557] Data 0.026 (0.085) Batch 1.183 (1.121) Remain 14:53:46 loss: 0.0690 Lr: 0.00119 [2024-02-19 04:43:57,660 INFO misc.py line 119 87073] Train: [70/100][421/1557] Data 0.014 (0.084) Batch 0.873 (1.120) Remain 14:53:16 loss: 0.3105 Lr: 0.00119 [2024-02-19 04:43:58,573 INFO misc.py line 119 87073] Train: [70/100][422/1557] Data 0.004 (0.084) Batch 0.913 (1.120) Remain 14:52:51 loss: 0.4145 Lr: 0.00119 [2024-02-19 04:43:59,552 INFO misc.py line 119 87073] Train: [70/100][423/1557] Data 0.004 (0.084) Batch 0.974 (1.119) Remain 14:52:34 loss: 0.2087 Lr: 0.00119 [2024-02-19 04:44:00,485 INFO misc.py line 119 87073] Train: [70/100][424/1557] Data 0.009 (0.084) Batch 0.938 (1.119) Remain 14:52:12 loss: 0.3053 Lr: 0.00119 [2024-02-19 04:44:01,220 INFO misc.py line 119 87073] Train: [70/100][425/1557] Data 0.004 (0.084) Batch 0.735 (1.118) Remain 14:51:27 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line 119 87073] Train: [70/100][463/1557] Data 0.009 (0.087) Batch 0.990 (1.126) Remain 14:57:28 loss: 0.3573 Lr: 0.00118 [2024-02-19 04:44:48,426 INFO misc.py line 119 87073] Train: [70/100][464/1557] Data 0.003 (0.086) Batch 0.840 (1.126) Remain 14:56:57 loss: 0.4887 Lr: 0.00118 [2024-02-19 04:44:49,384 INFO misc.py line 119 87073] Train: [70/100][465/1557] Data 0.003 (0.086) Batch 0.951 (1.125) Remain 14:56:38 loss: 0.4144 Lr: 0.00118 [2024-02-19 04:44:50,340 INFO misc.py line 119 87073] Train: [70/100][466/1557] Data 0.010 (0.086) Batch 0.963 (1.125) Remain 14:56:20 loss: 0.2607 Lr: 0.00118 [2024-02-19 04:44:51,094 INFO misc.py line 119 87073] Train: [70/100][467/1557] Data 0.003 (0.086) Batch 0.754 (1.124) Remain 14:55:41 loss: 0.2879 Lr: 0.00118 [2024-02-19 04:44:51,849 INFO misc.py line 119 87073] Train: [70/100][468/1557] Data 0.003 (0.086) Batch 0.751 (1.123) Remain 14:55:01 loss: 0.2832 Lr: 0.00118 [2024-02-19 04:44:53,078 INFO misc.py line 119 87073] Train: 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Batch 0.691 (1.120) Remain 14:52:26 loss: 0.2077 Lr: 0.00118 [2024-02-19 04:44:59,450 INFO misc.py line 119 87073] Train: [70/100][476/1557] Data 0.010 (0.084) Batch 1.192 (1.121) Remain 14:52:32 loss: 0.2006 Lr: 0.00118 [2024-02-19 04:45:00,388 INFO misc.py line 119 87073] Train: [70/100][477/1557] Data 0.010 (0.084) Batch 0.945 (1.120) Remain 14:52:13 loss: 0.1570 Lr: 0.00118 [2024-02-19 04:45:01,533 INFO misc.py line 119 87073] Train: [70/100][478/1557] Data 0.003 (0.084) Batch 1.144 (1.120) Remain 14:52:15 loss: 0.0240 Lr: 0.00118 [2024-02-19 04:45:02,484 INFO misc.py line 119 87073] Train: [70/100][479/1557] Data 0.003 (0.084) Batch 0.952 (1.120) Remain 14:51:57 loss: 0.1973 Lr: 0.00118 [2024-02-19 04:45:03,496 INFO misc.py line 119 87073] Train: [70/100][480/1557] Data 0.003 (0.084) Batch 1.011 (1.120) Remain 14:51:45 loss: 0.1406 Lr: 0.00118 [2024-02-19 04:45:04,277 INFO misc.py line 119 87073] Train: [70/100][481/1557] Data 0.004 (0.083) Batch 0.782 (1.119) Remain 14:51:10 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04:45:10,568 INFO misc.py line 119 87073] Train: [70/100][488/1557] Data 0.003 (0.082) Batch 0.738 (1.116) Remain 14:48:30 loss: 0.1317 Lr: 0.00118 [2024-02-19 04:45:11,307 INFO misc.py line 119 87073] Train: [70/100][489/1557] Data 0.004 (0.082) Batch 0.730 (1.115) Remain 14:47:51 loss: 0.1816 Lr: 0.00118 [2024-02-19 04:45:12,421 INFO misc.py line 119 87073] Train: [70/100][490/1557] Data 0.012 (0.082) Batch 1.114 (1.115) Remain 14:47:50 loss: 0.1983 Lr: 0.00118 [2024-02-19 04:45:13,455 INFO misc.py line 119 87073] Train: [70/100][491/1557] Data 0.013 (0.082) Batch 1.036 (1.115) Remain 14:47:41 loss: 0.3321 Lr: 0.00118 [2024-02-19 04:45:14,400 INFO misc.py line 119 87073] Train: [70/100][492/1557] Data 0.011 (0.082) Batch 0.951 (1.114) Remain 14:47:24 loss: 0.6390 Lr: 0.00118 [2024-02-19 04:45:15,553 INFO misc.py line 119 87073] Train: [70/100][493/1557] Data 0.005 (0.082) Batch 1.154 (1.115) Remain 14:47:26 loss: 0.1693 Lr: 0.00118 [2024-02-19 04:45:16,416 INFO misc.py line 119 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[2024-02-19 04:45:44,385 INFO misc.py line 119 87073] Train: [70/100][513/1557] Data 0.004 (0.087) Batch 0.927 (1.127) Remain 14:57:17 loss: 0.1923 Lr: 0.00118 [2024-02-19 04:45:45,242 INFO misc.py line 119 87073] Train: [70/100][514/1557] Data 0.004 (0.087) Batch 0.857 (1.127) Remain 14:56:50 loss: 0.5867 Lr: 0.00118 [2024-02-19 04:45:46,072 INFO misc.py line 119 87073] Train: [70/100][515/1557] Data 0.004 (0.087) Batch 0.824 (1.126) Remain 14:56:21 loss: 0.3672 Lr: 0.00118 [2024-02-19 04:45:46,841 INFO misc.py line 119 87073] Train: [70/100][516/1557] Data 0.009 (0.087) Batch 0.775 (1.126) Remain 14:55:47 loss: 0.1452 Lr: 0.00118 [2024-02-19 04:45:47,566 INFO misc.py line 119 87073] Train: [70/100][517/1557] Data 0.003 (0.086) Batch 0.723 (1.125) Remain 14:55:08 loss: 0.2580 Lr: 0.00118 [2024-02-19 04:45:48,817 INFO misc.py line 119 87073] Train: [70/100][518/1557] Data 0.005 (0.086) Batch 1.252 (1.125) Remain 14:55:19 loss: 0.2093 Lr: 0.00118 [2024-02-19 04:45:49,778 INFO misc.py line 119 87073] Train: [70/100][519/1557] Data 0.004 (0.086) Batch 0.962 (1.125) Remain 14:55:03 loss: 0.4046 Lr: 0.00118 [2024-02-19 04:45:50,863 INFO misc.py line 119 87073] Train: [70/100][520/1557] Data 0.003 (0.086) Batch 1.086 (1.125) Remain 14:54:58 loss: 0.2731 Lr: 0.00118 [2024-02-19 04:45:51,696 INFO misc.py line 119 87073] Train: [70/100][521/1557] Data 0.003 (0.086) Batch 0.832 (1.124) Remain 14:54:30 loss: 0.2664 Lr: 0.00118 [2024-02-19 04:45:52,634 INFO misc.py line 119 87073] Train: [70/100][522/1557] Data 0.004 (0.086) Batch 0.930 (1.124) Remain 14:54:11 loss: 0.1826 Lr: 0.00118 [2024-02-19 04:45:53,373 INFO misc.py line 119 87073] Train: [70/100][523/1557] Data 0.011 (0.085) Batch 0.747 (1.123) Remain 14:53:35 loss: 0.1613 Lr: 0.00118 [2024-02-19 04:45:54,133 INFO misc.py line 119 87073] Train: [70/100][524/1557] Data 0.004 (0.085) Batch 0.753 (1.122) Remain 14:53:00 loss: 0.3846 Lr: 0.00118 [2024-02-19 04:45:55,268 INFO misc.py line 119 87073] Train: 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Batch 0.778 (1.122) Remain 14:52:54 loss: 0.1398 Lr: 0.00118 [2024-02-19 04:46:03,321 INFO misc.py line 119 87073] Train: [70/100][532/1557] Data 0.003 (0.086) Batch 1.317 (1.123) Remain 14:53:10 loss: 0.0770 Lr: 0.00118 [2024-02-19 04:46:04,236 INFO misc.py line 119 87073] Train: [70/100][533/1557] Data 0.013 (0.086) Batch 0.922 (1.122) Remain 14:52:51 loss: 0.2831 Lr: 0.00118 [2024-02-19 04:46:05,188 INFO misc.py line 119 87073] Train: [70/100][534/1557] Data 0.005 (0.085) Batch 0.954 (1.122) Remain 14:52:35 loss: 0.5917 Lr: 0.00118 [2024-02-19 04:46:06,124 INFO misc.py line 119 87073] Train: [70/100][535/1557] Data 0.004 (0.085) Batch 0.936 (1.122) Remain 14:52:17 loss: 0.3149 Lr: 0.00118 [2024-02-19 04:46:06,946 INFO misc.py line 119 87073] Train: [70/100][536/1557] Data 0.004 (0.085) Batch 0.821 (1.121) Remain 14:51:49 loss: 0.7963 Lr: 0.00118 [2024-02-19 04:46:07,717 INFO misc.py line 119 87073] Train: [70/100][537/1557] Data 0.005 (0.085) Batch 0.771 (1.120) Remain 14:51:16 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line 119 87073] Train: [70/100][743/1557] Data 0.004 (0.089) Batch 0.940 (1.126) Remain 14:51:46 loss: 0.3462 Lr: 0.00117 [2024-02-19 04:50:03,414 INFO misc.py line 119 87073] Train: [70/100][744/1557] Data 0.003 (0.089) Batch 0.838 (1.125) Remain 14:51:27 loss: 0.1251 Lr: 0.00117 [2024-02-19 04:50:04,386 INFO misc.py line 119 87073] Train: [70/100][745/1557] Data 0.003 (0.089) Batch 0.967 (1.125) Remain 14:51:15 loss: 0.3332 Lr: 0.00117 [2024-02-19 04:50:05,285 INFO misc.py line 119 87073] Train: [70/100][746/1557] Data 0.008 (0.088) Batch 0.904 (1.125) Remain 14:51:00 loss: 0.2159 Lr: 0.00117 [2024-02-19 04:50:06,018 INFO misc.py line 119 87073] Train: [70/100][747/1557] Data 0.003 (0.088) Batch 0.733 (1.124) Remain 14:50:34 loss: 0.1990 Lr: 0.00117 [2024-02-19 04:50:06,785 INFO misc.py line 119 87073] Train: [70/100][748/1557] Data 0.004 (0.088) Batch 0.760 (1.124) Remain 14:50:09 loss: 0.2926 Lr: 0.00117 [2024-02-19 04:50:08,046 INFO misc.py line 119 87073] Train: 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Batch 0.783 (1.123) Remain 14:49:08 loss: 0.3344 Lr: 0.00117 [2024-02-19 04:50:15,065 INFO misc.py line 119 87073] Train: [70/100][756/1557] Data 0.010 (0.087) Batch 1.259 (1.123) Remain 14:49:16 loss: 0.0770 Lr: 0.00117 [2024-02-19 04:50:15,952 INFO misc.py line 119 87073] Train: [70/100][757/1557] Data 0.011 (0.087) Batch 0.893 (1.123) Remain 14:49:00 loss: 0.1591 Lr: 0.00117 [2024-02-19 04:50:16,894 INFO misc.py line 119 87073] Train: [70/100][758/1557] Data 0.004 (0.087) Batch 0.944 (1.122) Remain 14:48:48 loss: 0.1078 Lr: 0.00117 [2024-02-19 04:50:17,749 INFO misc.py line 119 87073] Train: [70/100][759/1557] Data 0.003 (0.087) Batch 0.853 (1.122) Remain 14:48:29 loss: 0.2736 Lr: 0.00117 [2024-02-19 04:50:18,854 INFO misc.py line 119 87073] Train: [70/100][760/1557] Data 0.005 (0.087) Batch 1.105 (1.122) Remain 14:48:27 loss: 0.8299 Lr: 0.00117 [2024-02-19 04:50:19,557 INFO misc.py line 119 87073] Train: [70/100][761/1557] Data 0.005 (0.087) Batch 0.705 (1.122) Remain 14:48:00 loss: 0.2191 Lr: 0.00117 [2024-02-19 04:50:20,317 INFO misc.py line 119 87073] Train: [70/100][762/1557] Data 0.003 (0.087) Batch 0.750 (1.121) Remain 14:47:36 loss: 0.3279 Lr: 0.00117 [2024-02-19 04:50:21,395 INFO misc.py line 119 87073] Train: [70/100][763/1557] Data 0.014 (0.087) Batch 1.077 (1.121) Remain 14:47:32 loss: 0.1420 Lr: 0.00117 [2024-02-19 04:50:22,375 INFO misc.py line 119 87073] Train: [70/100][764/1557] Data 0.016 (0.087) Batch 0.992 (1.121) Remain 14:47:22 loss: 0.2349 Lr: 0.00117 [2024-02-19 04:50:23,238 INFO misc.py line 119 87073] Train: [70/100][765/1557] Data 0.004 (0.086) Batch 0.864 (1.120) Remain 14:47:05 loss: 0.1589 Lr: 0.00117 [2024-02-19 04:50:24,186 INFO misc.py line 119 87073] Train: [70/100][766/1557] Data 0.003 (0.086) Batch 0.938 (1.120) Remain 14:46:53 loss: 0.2618 Lr: 0.00117 [2024-02-19 04:50:25,059 INFO misc.py line 119 87073] Train: [70/100][767/1557] Data 0.012 (0.086) Batch 0.881 (1.120) Remain 14:46:37 loss: 0.2011 Lr: 0.00117 [2024-02-19 04:50:25,843 INFO misc.py line 119 87073] Train: [70/100][768/1557] Data 0.004 (0.086) Batch 0.786 (1.120) Remain 14:46:15 loss: 0.2282 Lr: 0.00117 [2024-02-19 04:50:26,643 INFO misc.py line 119 87073] Train: [70/100][769/1557] Data 0.003 (0.086) Batch 0.799 (1.119) Remain 14:45:54 loss: 0.2909 Lr: 0.00117 [2024-02-19 04:50:27,725 INFO misc.py line 119 87073] Train: [70/100][770/1557] Data 0.004 (0.086) Batch 1.078 (1.119) Remain 14:45:50 loss: 0.2021 Lr: 0.00117 [2024-02-19 04:50:28,755 INFO misc.py line 119 87073] Train: [70/100][771/1557] Data 0.008 (0.086) Batch 1.028 (1.119) Remain 14:45:44 loss: 0.3921 Lr: 0.00117 [2024-02-19 04:50:29,945 INFO misc.py line 119 87073] Train: [70/100][772/1557] Data 0.011 (0.086) Batch 1.191 (1.119) Remain 14:45:47 loss: 0.3300 Lr: 0.00117 [2024-02-19 04:50:30,915 INFO misc.py line 119 87073] Train: [70/100][773/1557] Data 0.009 (0.086) Batch 0.976 (1.119) Remain 14:45:37 loss: 0.1746 Lr: 0.00117 [2024-02-19 04:50:31,823 INFO misc.py line 119 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[2024-02-19 04:50:59,100 INFO misc.py line 119 87073] Train: [70/100][793/1557] Data 0.004 (0.090) Batch 0.885 (1.126) Remain 14:51:04 loss: 0.4991 Lr: 0.00117 [2024-02-19 04:51:00,037 INFO misc.py line 119 87073] Train: [70/100][794/1557] Data 0.004 (0.090) Batch 0.935 (1.126) Remain 14:50:51 loss: 0.1802 Lr: 0.00117 [2024-02-19 04:51:00,925 INFO misc.py line 119 87073] Train: [70/100][795/1557] Data 0.006 (0.089) Batch 0.891 (1.126) Remain 14:50:36 loss: 0.2241 Lr: 0.00117 [2024-02-19 04:51:01,714 INFO misc.py line 119 87073] Train: [70/100][796/1557] Data 0.003 (0.089) Batch 0.788 (1.125) Remain 14:50:14 loss: 0.3735 Lr: 0.00117 [2024-02-19 04:51:02,442 INFO misc.py line 119 87073] Train: [70/100][797/1557] Data 0.004 (0.089) Batch 0.721 (1.125) Remain 14:49:49 loss: 0.3535 Lr: 0.00117 [2024-02-19 04:51:03,810 INFO misc.py line 119 87073] Train: [70/100][798/1557] Data 0.010 (0.089) Batch 1.366 (1.125) Remain 14:50:02 loss: 0.1300 Lr: 0.00117 [2024-02-19 04:51:04,669 INFO misc.py line 119 87073] Train: [70/100][799/1557] Data 0.013 (0.089) Batch 0.868 (1.125) Remain 14:49:46 loss: 0.2040 Lr: 0.00117 [2024-02-19 04:51:05,750 INFO misc.py line 119 87073] Train: [70/100][800/1557] Data 0.003 (0.089) Batch 1.082 (1.125) Remain 14:49:42 loss: 0.4751 Lr: 0.00117 [2024-02-19 04:51:06,864 INFO misc.py line 119 87073] Train: [70/100][801/1557] Data 0.003 (0.089) Batch 1.113 (1.125) Remain 14:49:41 loss: 0.1826 Lr: 0.00117 [2024-02-19 04:51:07,794 INFO misc.py line 119 87073] Train: [70/100][802/1557] Data 0.004 (0.089) Batch 0.931 (1.124) Remain 14:49:28 loss: 0.7424 Lr: 0.00117 [2024-02-19 04:51:08,581 INFO misc.py line 119 87073] Train: [70/100][803/1557] Data 0.003 (0.089) Batch 0.787 (1.124) Remain 14:49:07 loss: 0.2233 Lr: 0.00117 [2024-02-19 04:51:09,475 INFO misc.py line 119 87073] Train: [70/100][804/1557] Data 0.003 (0.088) Batch 0.883 (1.124) Remain 14:48:51 loss: 0.1151 Lr: 0.00117 [2024-02-19 04:51:10,680 INFO misc.py line 119 87073] Train: 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Data 0.016 (0.089) Batch 0.771 (1.127) Remain 14:46:50 loss: 0.3203 Lr: 0.00116 [2024-02-19 04:55:33,343 INFO misc.py line 119 87073] Train: [70/100][1036/1557] Data 0.004 (0.089) Batch 1.275 (1.127) Remain 14:46:56 loss: 0.0710 Lr: 0.00116 [2024-02-19 04:55:34,271 INFO misc.py line 119 87073] Train: [70/100][1037/1557] Data 0.016 (0.089) Batch 0.940 (1.127) Remain 14:46:46 loss: 0.2300 Lr: 0.00116 [2024-02-19 04:55:35,571 INFO misc.py line 119 87073] Train: [70/100][1038/1557] Data 0.004 (0.089) Batch 1.288 (1.127) Remain 14:46:53 loss: 0.3053 Lr: 0.00116 [2024-02-19 04:55:36,470 INFO misc.py line 119 87073] Train: [70/100][1039/1557] Data 0.016 (0.089) Batch 0.912 (1.126) Remain 14:46:42 loss: 0.2473 Lr: 0.00116 [2024-02-19 04:55:37,461 INFO misc.py line 119 87073] Train: [70/100][1040/1557] Data 0.004 (0.089) Batch 0.991 (1.126) Remain 14:46:34 loss: 0.5480 Lr: 0.00116 [2024-02-19 04:55:38,205 INFO misc.py line 119 87073] Train: [70/100][1041/1557] Data 0.004 (0.089) Batch 0.744 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Data 0.020 (0.088) Batch 0.948 (1.123) Remain 14:43:26 loss: 0.3430 Lr: 0.00116 [2024-02-19 04:56:04,024 INFO misc.py line 119 87073] Train: [70/100][1067/1557] Data 0.003 (0.088) Batch 0.868 (1.123) Remain 14:43:13 loss: 0.4035 Lr: 0.00116 [2024-02-19 04:56:05,008 INFO misc.py line 119 87073] Train: [70/100][1068/1557] Data 0.003 (0.088) Batch 0.984 (1.123) Remain 14:43:06 loss: 0.5065 Lr: 0.00116 [2024-02-19 04:56:05,780 INFO misc.py line 119 87073] Train: [70/100][1069/1557] Data 0.003 (0.087) Batch 0.762 (1.122) Remain 14:42:49 loss: 0.1901 Lr: 0.00116 [2024-02-19 04:56:06,484 INFO misc.py line 119 87073] Train: [70/100][1070/1557] Data 0.013 (0.087) Batch 0.714 (1.122) Remain 14:42:30 loss: 0.1539 Lr: 0.00116 [2024-02-19 04:56:17,544 INFO misc.py line 119 87073] Train: [70/100][1071/1557] Data 4.483 (0.092) Batch 11.060 (1.131) Remain 14:49:48 loss: 0.1684 Lr: 0.00116 [2024-02-19 04:56:18,477 INFO misc.py line 119 87073] Train: [70/100][1072/1557] Data 0.003 (0.091) Batch 0.932 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Data 0.003 (0.090) Batch 0.775 (1.127) Remain 14:45:43 loss: 0.2211 Lr: 0.00116 [2024-02-19 04:56:42,701 INFO misc.py line 119 87073] Train: [70/100][1098/1557] Data 0.012 (0.089) Batch 0.746 (1.126) Remain 14:45:25 loss: 0.1457 Lr: 0.00116 [2024-02-19 04:56:43,745 INFO misc.py line 119 87073] Train: [70/100][1099/1557] Data 0.003 (0.089) Batch 1.042 (1.126) Remain 14:45:20 loss: 0.1088 Lr: 0.00116 [2024-02-19 04:56:44,702 INFO misc.py line 119 87073] Train: [70/100][1100/1557] Data 0.005 (0.089) Batch 0.959 (1.126) Remain 14:45:12 loss: 0.2459 Lr: 0.00116 [2024-02-19 04:56:45,679 INFO misc.py line 119 87073] Train: [70/100][1101/1557] Data 0.003 (0.089) Batch 0.967 (1.126) Remain 14:45:04 loss: 0.2940 Lr: 0.00116 [2024-02-19 04:56:46,604 INFO misc.py line 119 87073] Train: [70/100][1102/1557] Data 0.013 (0.089) Batch 0.935 (1.126) Remain 14:44:55 loss: 0.1641 Lr: 0.00116 [2024-02-19 04:56:47,416 INFO misc.py line 119 87073] Train: [70/100][1103/1557] Data 0.003 (0.089) Batch 0.811 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Data 0.003 (0.091) Batch 1.194 (1.129) Remain 14:47:10 loss: 0.1113 Lr: 0.00115 [2024-02-19 04:57:20,770 INFO misc.py line 119 87073] Train: [70/100][1129/1557] Data 0.003 (0.091) Batch 0.981 (1.129) Remain 14:47:02 loss: 0.3530 Lr: 0.00115 [2024-02-19 04:57:21,754 INFO misc.py line 119 87073] Train: [70/100][1130/1557] Data 0.004 (0.091) Batch 0.984 (1.129) Remain 14:46:55 loss: 0.3172 Lr: 0.00115 [2024-02-19 04:57:22,733 INFO misc.py line 119 87073] Train: [70/100][1131/1557] Data 0.003 (0.091) Batch 0.979 (1.129) Remain 14:46:48 loss: 0.3955 Lr: 0.00115 [2024-02-19 04:57:23,488 INFO misc.py line 119 87073] Train: [70/100][1132/1557] Data 0.003 (0.091) Batch 0.755 (1.128) Remain 14:46:31 loss: 0.3371 Lr: 0.00115 [2024-02-19 04:57:24,308 INFO misc.py line 119 87073] Train: [70/100][1133/1557] Data 0.003 (0.091) Batch 0.815 (1.128) Remain 14:46:17 loss: 0.2226 Lr: 0.00115 [2024-02-19 04:57:25,561 INFO misc.py line 119 87073] Train: [70/100][1134/1557] Data 0.008 (0.091) Batch 1.257 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Data 0.003 (0.089) Batch 1.055 (1.124) Remain 14:42:32 loss: 0.1842 Lr: 0.00115 [2024-02-19 04:57:49,595 INFO misc.py line 119 87073] Train: [70/100][1160/1557] Data 0.013 (0.089) Batch 0.749 (1.124) Remain 14:42:16 loss: 0.1872 Lr: 0.00115 [2024-02-19 04:57:50,360 INFO misc.py line 119 87073] Train: [70/100][1161/1557] Data 0.003 (0.089) Batch 0.754 (1.123) Remain 14:42:00 loss: 0.2241 Lr: 0.00115 [2024-02-19 04:57:51,541 INFO misc.py line 119 87073] Train: [70/100][1162/1557] Data 0.014 (0.089) Batch 1.179 (1.123) Remain 14:42:01 loss: 0.2667 Lr: 0.00115 [2024-02-19 04:57:52,601 INFO misc.py line 119 87073] Train: [70/100][1163/1557] Data 0.016 (0.089) Batch 1.067 (1.123) Remain 14:41:57 loss: 0.2347 Lr: 0.00115 [2024-02-19 04:57:53,445 INFO misc.py line 119 87073] Train: [70/100][1164/1557] Data 0.010 (0.089) Batch 0.849 (1.123) Remain 14:41:45 loss: 0.2993 Lr: 0.00115 [2024-02-19 04:57:54,487 INFO misc.py line 119 87073] Train: [70/100][1165/1557] Data 0.006 (0.089) Batch 1.044 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Remain 14:45:11 loss: 0.1355 Lr: 0.00115 [2024-02-19 05:00:31,326 INFO misc.py line 119 87073] Train: [70/100][1296/1557] Data 0.004 (0.092) Batch 1.030 (1.131) Remain 14:45:06 loss: 0.2080 Lr: 0.00115 [2024-02-19 05:00:32,235 INFO misc.py line 119 87073] Train: [70/100][1297/1557] Data 0.003 (0.092) Batch 0.909 (1.130) Remain 14:44:57 loss: 0.1936 Lr: 0.00115 [2024-02-19 05:00:33,119 INFO misc.py line 119 87073] Train: [70/100][1298/1557] Data 0.004 (0.091) Batch 0.881 (1.130) Remain 14:44:47 loss: 0.2607 Lr: 0.00115 [2024-02-19 05:00:34,076 INFO misc.py line 119 87073] Train: [70/100][1299/1557] Data 0.008 (0.091) Batch 0.960 (1.130) Remain 14:44:40 loss: 0.2895 Lr: 0.00115 [2024-02-19 05:00:34,829 INFO misc.py line 119 87073] Train: [70/100][1300/1557] Data 0.004 (0.091) Batch 0.754 (1.130) Remain 14:44:25 loss: 0.3703 Lr: 0.00115 [2024-02-19 05:00:35,567 INFO misc.py line 119 87073] Train: [70/100][1301/1557] Data 0.003 (0.091) Batch 0.735 (1.130) Remain 14:44:10 loss: 0.1791 Lr: 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Train: [70/100][1314/1557] Data 0.005 (0.090) Batch 0.785 (1.128) Remain 14:42:43 loss: 0.1401 Lr: 0.00115 [2024-02-19 05:00:49,044 INFO misc.py line 119 87073] Train: [70/100][1315/1557] Data 0.004 (0.090) Batch 0.789 (1.128) Remain 14:42:30 loss: 0.1802 Lr: 0.00115 [2024-02-19 05:00:50,264 INFO misc.py line 119 87073] Train: [70/100][1316/1557] Data 0.007 (0.090) Batch 1.217 (1.128) Remain 14:42:32 loss: 0.0765 Lr: 0.00115 [2024-02-19 05:00:51,227 INFO misc.py line 119 87073] Train: [70/100][1317/1557] Data 0.010 (0.090) Batch 0.969 (1.128) Remain 14:42:25 loss: 0.1358 Lr: 0.00115 [2024-02-19 05:00:52,124 INFO misc.py line 119 87073] Train: [70/100][1318/1557] Data 0.004 (0.090) Batch 0.897 (1.128) Remain 14:42:16 loss: 0.4914 Lr: 0.00115 [2024-02-19 05:00:53,036 INFO misc.py line 119 87073] Train: [70/100][1319/1557] Data 0.003 (0.090) Batch 0.907 (1.127) Remain 14:42:07 loss: 0.5978 Lr: 0.00115 [2024-02-19 05:00:54,049 INFO misc.py line 119 87073] Train: [70/100][1320/1557] Data 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Remain 14:41:10 loss: 0.1879 Lr: 0.00115 [2024-02-19 05:01:00,421 INFO misc.py line 119 87073] Train: [70/100][1327/1557] Data 0.005 (0.090) Batch 0.862 (1.126) Remain 14:41:00 loss: 0.2498 Lr: 0.00115 [2024-02-19 05:01:01,193 INFO misc.py line 119 87073] Train: [70/100][1328/1557] Data 0.011 (0.090) Batch 0.776 (1.126) Remain 14:40:46 loss: 0.2360 Lr: 0.00115 [2024-02-19 05:01:02,071 INFO misc.py line 119 87073] Train: [70/100][1329/1557] Data 0.006 (0.089) Batch 0.879 (1.126) Remain 14:40:37 loss: 0.1318 Lr: 0.00115 [2024-02-19 05:01:03,162 INFO misc.py line 119 87073] Train: [70/100][1330/1557] Data 0.004 (0.089) Batch 1.090 (1.126) Remain 14:40:34 loss: 0.1658 Lr: 0.00115 [2024-02-19 05:01:03,962 INFO misc.py line 119 87073] Train: [70/100][1331/1557] Data 0.005 (0.089) Batch 0.801 (1.125) Remain 14:40:22 loss: 0.3404 Lr: 0.00114 [2024-02-19 05:01:05,065 INFO misc.py line 119 87073] Train: [70/100][1332/1557] Data 0.005 (0.089) Batch 1.101 (1.125) Remain 14:40:20 loss: 0.4251 Lr: 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Remain 14:42:18 loss: 0.3239 Lr: 0.00114 [2024-02-19 05:01:38,617 INFO misc.py line 119 87073] Train: [70/100][1358/1557] Data 0.003 (0.091) Batch 1.199 (1.129) Remain 14:42:19 loss: 0.1151 Lr: 0.00114 [2024-02-19 05:01:39,512 INFO misc.py line 119 87073] Train: [70/100][1359/1557] Data 0.008 (0.091) Batch 0.898 (1.128) Remain 14:42:10 loss: 0.2276 Lr: 0.00114 [2024-02-19 05:01:40,495 INFO misc.py line 119 87073] Train: [70/100][1360/1557] Data 0.005 (0.091) Batch 0.984 (1.128) Remain 14:42:04 loss: 0.2373 Lr: 0.00114 [2024-02-19 05:01:41,441 INFO misc.py line 119 87073] Train: [70/100][1361/1557] Data 0.005 (0.091) Batch 0.943 (1.128) Remain 14:41:56 loss: 0.3718 Lr: 0.00114 [2024-02-19 05:01:42,345 INFO misc.py line 119 87073] Train: [70/100][1362/1557] Data 0.008 (0.091) Batch 0.904 (1.128) Remain 14:41:47 loss: 0.4220 Lr: 0.00114 [2024-02-19 05:01:43,152 INFO misc.py line 119 87073] Train: [70/100][1363/1557] Data 0.006 (0.091) Batch 0.807 (1.128) Remain 14:41:35 loss: 0.1454 Lr: 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Train: [70/100][1376/1557] Data 0.005 (0.090) Batch 0.923 (1.126) Remain 14:40:00 loss: 0.0508 Lr: 0.00114 [2024-02-19 05:01:56,157 INFO misc.py line 119 87073] Train: [70/100][1377/1557] Data 0.008 (0.090) Batch 0.695 (1.126) Remain 14:39:44 loss: 0.4725 Lr: 0.00114 [2024-02-19 05:01:56,932 INFO misc.py line 119 87073] Train: [70/100][1378/1557] Data 0.006 (0.090) Batch 0.770 (1.125) Remain 14:39:31 loss: 0.1504 Lr: 0.00114 [2024-02-19 05:01:58,028 INFO misc.py line 119 87073] Train: [70/100][1379/1557] Data 0.011 (0.090) Batch 1.100 (1.125) Remain 14:39:29 loss: 0.1554 Lr: 0.00114 [2024-02-19 05:01:58,969 INFO misc.py line 119 87073] Train: [70/100][1380/1557] Data 0.007 (0.090) Batch 0.943 (1.125) Remain 14:39:22 loss: 0.4602 Lr: 0.00114 [2024-02-19 05:01:59,985 INFO misc.py line 119 87073] Train: [70/100][1381/1557] Data 0.006 (0.090) Batch 1.017 (1.125) Remain 14:39:17 loss: 0.1156 Lr: 0.00114 [2024-02-19 05:02:00,966 INFO misc.py line 119 87073] Train: [70/100][1382/1557] Data 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Remain 14:38:24 loss: 0.2494 Lr: 0.00114 [2024-02-19 05:02:07,552 INFO misc.py line 119 87073] Train: [70/100][1389/1557] Data 0.004 (0.089) Batch 1.011 (1.124) Remain 14:38:19 loss: 0.3926 Lr: 0.00114 [2024-02-19 05:02:08,690 INFO misc.py line 119 87073] Train: [70/100][1390/1557] Data 0.006 (0.089) Batch 1.140 (1.124) Remain 14:38:19 loss: 0.1140 Lr: 0.00114 [2024-02-19 05:02:09,507 INFO misc.py line 119 87073] Train: [70/100][1391/1557] Data 0.004 (0.089) Batch 0.816 (1.124) Remain 14:38:07 loss: 0.1907 Lr: 0.00114 [2024-02-19 05:02:10,380 INFO misc.py line 119 87073] Train: [70/100][1392/1557] Data 0.004 (0.089) Batch 0.873 (1.124) Remain 14:37:58 loss: 0.2717 Lr: 0.00114 [2024-02-19 05:02:11,632 INFO misc.py line 119 87073] Train: [70/100][1393/1557] Data 0.004 (0.089) Batch 1.251 (1.124) Remain 14:38:01 loss: 0.1496 Lr: 0.00114 [2024-02-19 05:02:12,439 INFO misc.py line 119 87073] Train: [70/100][1394/1557] Data 0.005 (0.089) Batch 0.807 (1.124) Remain 14:37:49 loss: 0.2507 Lr: 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Train: [70/100][1407/1557] Data 4.073 (0.092) Batch 12.200 (1.131) Remain 14:43:22 loss: 0.1698 Lr: 0.00114 [2024-02-19 05:02:38,467 INFO misc.py line 119 87073] Train: [70/100][1408/1557] Data 0.003 (0.092) Batch 1.020 (1.131) Remain 14:43:17 loss: 0.2690 Lr: 0.00114 [2024-02-19 05:02:39,473 INFO misc.py line 119 87073] Train: [70/100][1409/1557] Data 0.003 (0.091) Batch 1.006 (1.131) Remain 14:43:11 loss: 0.3726 Lr: 0.00114 [2024-02-19 05:02:40,409 INFO misc.py line 119 87073] Train: [70/100][1410/1557] Data 0.005 (0.091) Batch 0.937 (1.131) Remain 14:43:04 loss: 0.3708 Lr: 0.00114 [2024-02-19 05:02:41,387 INFO misc.py line 119 87073] Train: [70/100][1411/1557] Data 0.003 (0.091) Batch 0.973 (1.131) Remain 14:42:57 loss: 0.2017 Lr: 0.00114 [2024-02-19 05:02:42,151 INFO misc.py line 119 87073] Train: [70/100][1412/1557] Data 0.008 (0.091) Batch 0.768 (1.130) Remain 14:42:44 loss: 0.1960 Lr: 0.00114 [2024-02-19 05:02:42,938 INFO misc.py line 119 87073] Train: [70/100][1413/1557] Data 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Remain 14:42:01 loss: 0.2662 Lr: 0.00114 [2024-02-19 05:02:49,694 INFO misc.py line 119 87073] Train: [70/100][1420/1557] Data 0.005 (0.091) Batch 0.692 (1.129) Remain 14:41:46 loss: 0.1752 Lr: 0.00114 [2024-02-19 05:02:50,959 INFO misc.py line 119 87073] Train: [70/100][1421/1557] Data 0.005 (0.091) Batch 1.265 (1.129) Remain 14:41:49 loss: 0.0747 Lr: 0.00114 [2024-02-19 05:02:51,870 INFO misc.py line 119 87073] Train: [70/100][1422/1557] Data 0.006 (0.091) Batch 0.913 (1.129) Remain 14:41:41 loss: 0.3475 Lr: 0.00114 [2024-02-19 05:02:52,962 INFO misc.py line 119 87073] Train: [70/100][1423/1557] Data 0.004 (0.091) Batch 1.090 (1.129) Remain 14:41:38 loss: 0.2428 Lr: 0.00114 [2024-02-19 05:02:53,670 INFO misc.py line 119 87073] Train: [70/100][1424/1557] Data 0.005 (0.091) Batch 0.709 (1.129) Remain 14:41:23 loss: 0.3177 Lr: 0.00114 [2024-02-19 05:02:54,743 INFO misc.py line 119 87073] Train: [70/100][1425/1557] Data 0.005 (0.090) Batch 1.066 (1.129) Remain 14:41:20 loss: 0.1969 Lr: 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Train: [70/100][1438/1557] Data 0.004 (0.090) Batch 0.864 (1.127) Remain 14:39:49 loss: 0.5415 Lr: 0.00114 [2024-02-19 05:03:08,019 INFO misc.py line 119 87073] Train: [70/100][1439/1557] Data 0.005 (0.090) Batch 0.950 (1.127) Remain 14:39:42 loss: 0.5011 Lr: 0.00114 [2024-02-19 05:03:08,832 INFO misc.py line 119 87073] Train: [70/100][1440/1557] Data 0.010 (0.090) Batch 0.819 (1.127) Remain 14:39:31 loss: 0.3001 Lr: 0.00114 [2024-02-19 05:03:09,570 INFO misc.py line 119 87073] Train: [70/100][1441/1557] Data 0.004 (0.090) Batch 0.738 (1.127) Remain 14:39:17 loss: 0.2956 Lr: 0.00114 [2024-02-19 05:03:10,681 INFO misc.py line 119 87073] Train: [70/100][1442/1557] Data 0.003 (0.090) Batch 1.102 (1.127) Remain 14:39:15 loss: 0.2288 Lr: 0.00114 [2024-02-19 05:03:11,597 INFO misc.py line 119 87073] Train: [70/100][1443/1557] Data 0.014 (0.089) Batch 0.925 (1.127) Remain 14:39:07 loss: 0.1835 Lr: 0.00114 [2024-02-19 05:03:12,502 INFO misc.py line 119 87073] Train: [70/100][1444/1557] Data 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Remain 14:38:15 loss: 0.4956 Lr: 0.00114 [2024-02-19 05:03:19,064 INFO misc.py line 119 87073] Train: [70/100][1451/1557] Data 0.004 (0.089) Batch 0.958 (1.125) Remain 14:38:08 loss: 0.5141 Lr: 0.00114 [2024-02-19 05:03:20,163 INFO misc.py line 119 87073] Train: [70/100][1452/1557] Data 0.003 (0.089) Batch 1.099 (1.125) Remain 14:38:06 loss: 0.4505 Lr: 0.00114 [2024-02-19 05:03:21,503 INFO misc.py line 119 87073] Train: [70/100][1453/1557] Data 0.003 (0.089) Batch 1.328 (1.126) Remain 14:38:12 loss: 0.3151 Lr: 0.00114 [2024-02-19 05:03:22,266 INFO misc.py line 119 87073] Train: [70/100][1454/1557] Data 0.015 (0.089) Batch 0.773 (1.125) Remain 14:37:59 loss: 0.3611 Lr: 0.00114 [2024-02-19 05:03:23,016 INFO misc.py line 119 87073] Train: [70/100][1455/1557] Data 0.005 (0.089) Batch 0.743 (1.125) Remain 14:37:46 loss: 0.2791 Lr: 0.00114 [2024-02-19 05:03:24,247 INFO misc.py line 119 87073] Train: [70/100][1456/1557] Data 0.011 (0.089) Batch 1.234 (1.125) Remain 14:37:48 loss: 0.1702 Lr: 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INFO misc.py line 119 87073] Train: [70/100][1463/1557] Data 4.833 (0.092) Batch 10.978 (1.132) Remain 14:42:38 loss: 0.1398 Lr: 0.00114 [2024-02-19 05:03:42,396 INFO misc.py line 119 87073] Train: [70/100][1464/1557] Data 0.004 (0.092) Batch 0.984 (1.131) Remain 14:42:32 loss: 0.4173 Lr: 0.00114 [2024-02-19 05:03:43,280 INFO misc.py line 119 87073] Train: [70/100][1465/1557] Data 0.004 (0.092) Batch 0.884 (1.131) Remain 14:42:23 loss: 0.1445 Lr: 0.00114 [2024-02-19 05:03:44,237 INFO misc.py line 119 87073] Train: [70/100][1466/1557] Data 0.004 (0.091) Batch 0.958 (1.131) Remain 14:42:17 loss: 0.6728 Lr: 0.00114 [2024-02-19 05:03:45,187 INFO misc.py line 119 87073] Train: [70/100][1467/1557] Data 0.003 (0.091) Batch 0.948 (1.131) Remain 14:42:10 loss: 0.2986 Lr: 0.00114 [2024-02-19 05:03:45,969 INFO misc.py line 119 87073] Train: [70/100][1468/1557] Data 0.005 (0.091) Batch 0.783 (1.131) Remain 14:41:57 loss: 0.3449 Lr: 0.00114 [2024-02-19 05:03:46,746 INFO misc.py line 119 87073] Train: [70/100][1469/1557] Data 0.004 (0.091) Batch 0.775 (1.131) Remain 14:41:45 loss: 0.1647 Lr: 0.00114 [2024-02-19 05:03:47,984 INFO misc.py line 119 87073] Train: [70/100][1470/1557] Data 0.006 (0.091) Batch 1.236 (1.131) Remain 14:41:47 loss: 0.1285 Lr: 0.00114 [2024-02-19 05:03:48,975 INFO misc.py line 119 87073] Train: [70/100][1471/1557] Data 0.009 (0.091) Batch 0.993 (1.130) Remain 14:41:42 loss: 0.3763 Lr: 0.00114 [2024-02-19 05:03:50,091 INFO misc.py line 119 87073] Train: [70/100][1472/1557] Data 0.008 (0.091) Batch 1.114 (1.130) Remain 14:41:40 loss: 0.3632 Lr: 0.00114 [2024-02-19 05:03:51,040 INFO misc.py line 119 87073] Train: [70/100][1473/1557] Data 0.010 (0.091) Batch 0.952 (1.130) Remain 14:41:33 loss: 0.4281 Lr: 0.00114 [2024-02-19 05:03:52,064 INFO misc.py line 119 87073] Train: [70/100][1474/1557] Data 0.008 (0.091) Batch 1.025 (1.130) Remain 14:41:29 loss: 0.3727 Lr: 0.00114 [2024-02-19 05:03:52,818 INFO misc.py line 119 87073] Train: [70/100][1475/1557] Data 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Remain 14:40:56 loss: 0.2047 Lr: 0.00114 [2024-02-19 05:03:59,967 INFO misc.py line 119 87073] Train: [70/100][1482/1557] Data 0.004 (0.091) Batch 0.759 (1.130) Remain 14:40:44 loss: 0.1938 Lr: 0.00114 [2024-02-19 05:04:00,724 INFO misc.py line 119 87073] Train: [70/100][1483/1557] Data 0.006 (0.090) Batch 0.748 (1.129) Remain 14:40:30 loss: 0.1722 Lr: 0.00114 [2024-02-19 05:04:01,959 INFO misc.py line 119 87073] Train: [70/100][1484/1557] Data 0.013 (0.090) Batch 1.243 (1.129) Remain 14:40:33 loss: 0.0863 Lr: 0.00114 [2024-02-19 05:04:02,966 INFO misc.py line 119 87073] Train: [70/100][1485/1557] Data 0.005 (0.090) Batch 1.004 (1.129) Remain 14:40:28 loss: 0.4989 Lr: 0.00114 [2024-02-19 05:04:03,965 INFO misc.py line 119 87073] Train: [70/100][1486/1557] Data 0.009 (0.090) Batch 1.001 (1.129) Remain 14:40:23 loss: 0.6051 Lr: 0.00114 [2024-02-19 05:04:04,932 INFO misc.py line 119 87073] Train: [70/100][1487/1557] Data 0.007 (0.090) Batch 0.968 (1.129) Remain 14:40:16 loss: 0.3627 Lr: 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INFO misc.py line 119 87073] Train: [70/100][1494/1557] Data 0.008 (0.090) Batch 1.109 (1.128) Remain 14:39:40 loss: 0.1667 Lr: 0.00114 [2024-02-19 05:04:13,019 INFO misc.py line 119 87073] Train: [70/100][1495/1557] Data 0.007 (0.090) Batch 1.101 (1.128) Remain 14:39:38 loss: 0.2395 Lr: 0.00114 [2024-02-19 05:04:13,774 INFO misc.py line 119 87073] Train: [70/100][1496/1557] Data 0.005 (0.090) Batch 0.755 (1.128) Remain 14:39:25 loss: 0.3843 Lr: 0.00114 [2024-02-19 05:04:14,524 INFO misc.py line 119 87073] Train: [70/100][1497/1557] Data 0.005 (0.090) Batch 0.750 (1.128) Remain 14:39:12 loss: 0.2343 Lr: 0.00114 [2024-02-19 05:04:15,634 INFO misc.py line 119 87073] Train: [70/100][1498/1557] Data 0.005 (0.090) Batch 1.109 (1.128) Remain 14:39:10 loss: 0.2081 Lr: 0.00114 [2024-02-19 05:04:16,676 INFO misc.py line 119 87073] Train: [70/100][1499/1557] Data 0.005 (0.090) Batch 1.038 (1.128) Remain 14:39:06 loss: 0.2834 Lr: 0.00114 [2024-02-19 05:04:17,536 INFO misc.py line 119 87073] Train: [70/100][1500/1557] Data 0.009 (0.090) Batch 0.863 (1.128) Remain 14:38:57 loss: 0.5802 Lr: 0.00114 [2024-02-19 05:04:18,529 INFO misc.py line 119 87073] Train: [70/100][1501/1557] Data 0.008 (0.089) Batch 0.994 (1.128) Remain 14:38:52 loss: 0.2831 Lr: 0.00114 [2024-02-19 05:04:19,501 INFO misc.py line 119 87073] Train: [70/100][1502/1557] Data 0.006 (0.089) Batch 0.973 (1.127) Remain 14:38:46 loss: 0.5795 Lr: 0.00114 [2024-02-19 05:04:20,264 INFO misc.py line 119 87073] Train: [70/100][1503/1557] Data 0.005 (0.089) Batch 0.759 (1.127) Remain 14:38:33 loss: 0.3463 Lr: 0.00114 [2024-02-19 05:04:21,164 INFO misc.py line 119 87073] Train: [70/100][1504/1557] Data 0.009 (0.089) Batch 0.905 (1.127) Remain 14:38:25 loss: 0.1135 Lr: 0.00114 [2024-02-19 05:04:22,418 INFO misc.py line 119 87073] Train: [70/100][1505/1557] Data 0.004 (0.089) Batch 1.250 (1.127) Remain 14:38:28 loss: 0.2246 Lr: 0.00114 [2024-02-19 05:04:23,425 INFO misc.py line 119 87073] Train: [70/100][1506/1557] Data 0.008 (0.089) Batch 1.007 (1.127) Remain 14:38:23 loss: 0.2052 Lr: 0.00114 [2024-02-19 05:04:24,407 INFO misc.py line 119 87073] Train: [70/100][1507/1557] Data 0.009 (0.089) Batch 0.987 (1.127) Remain 14:38:17 loss: 0.4503 Lr: 0.00114 [2024-02-19 05:04:25,356 INFO misc.py line 119 87073] Train: [70/100][1508/1557] Data 0.003 (0.089) Batch 0.948 (1.127) Remain 14:38:11 loss: 0.4396 Lr: 0.00114 [2024-02-19 05:04:26,305 INFO misc.py line 119 87073] Train: [70/100][1509/1557] Data 0.004 (0.089) Batch 0.949 (1.127) Remain 14:38:04 loss: 0.4048 Lr: 0.00114 [2024-02-19 05:04:27,047 INFO misc.py line 119 87073] Train: [70/100][1510/1557] Data 0.004 (0.089) Batch 0.735 (1.126) Remain 14:37:51 loss: 0.1732 Lr: 0.00114 [2024-02-19 05:04:27,787 INFO misc.py line 119 87073] Train: [70/100][1511/1557] Data 0.010 (0.089) Batch 0.747 (1.126) Remain 14:37:38 loss: 0.2077 Lr: 0.00114 [2024-02-19 05:04:28,987 INFO misc.py line 119 87073] Train: [70/100][1512/1557] Data 0.003 (0.089) Batch 1.200 (1.126) Remain 14:37:39 loss: 0.2702 Lr: 0.00114 [2024-02-19 05:04:29,759 INFO misc.py line 119 87073] Train: [70/100][1513/1557] Data 0.004 (0.089) Batch 0.771 (1.126) Remain 14:37:27 loss: 0.1316 Lr: 0.00114 [2024-02-19 05:04:30,760 INFO misc.py line 119 87073] Train: [70/100][1514/1557] Data 0.004 (0.089) Batch 0.996 (1.126) Remain 14:37:22 loss: 0.4057 Lr: 0.00114 [2024-02-19 05:04:31,576 INFO misc.py line 119 87073] Train: [70/100][1515/1557] Data 0.009 (0.089) Batch 0.821 (1.126) Remain 14:37:11 loss: 0.4577 Lr: 0.00114 [2024-02-19 05:04:32,560 INFO misc.py line 119 87073] Train: [70/100][1516/1557] Data 0.005 (0.089) Batch 0.985 (1.126) Remain 14:37:06 loss: 0.3178 Lr: 0.00114 [2024-02-19 05:04:33,308 INFO misc.py line 119 87073] Train: [70/100][1517/1557] Data 0.004 (0.089) Batch 0.748 (1.125) Remain 14:36:53 loss: 0.3537 Lr: 0.00114 [2024-02-19 05:04:34,115 INFO misc.py line 119 87073] Train: [70/100][1518/1557] Data 0.003 (0.089) Batch 0.803 (1.125) Remain 14:36:42 loss: 0.1319 Lr: 0.00114 [2024-02-19 05:04:45,854 INFO misc.py line 119 87073] Train: [70/100][1519/1557] Data 4.747 (0.092) Batch 11.744 (1.132) Remain 14:42:08 loss: 0.1631 Lr: 0.00114 [2024-02-19 05:04:46,790 INFO misc.py line 119 87073] Train: [70/100][1520/1557] Data 0.004 (0.092) Batch 0.935 (1.132) Remain 14:42:01 loss: 0.1805 Lr: 0.00114 [2024-02-19 05:04:47,854 INFO misc.py line 119 87073] Train: [70/100][1521/1557] Data 0.003 (0.091) Batch 1.063 (1.132) Remain 14:41:58 loss: 0.2146 Lr: 0.00114 [2024-02-19 05:04:48,944 INFO misc.py line 119 87073] Train: [70/100][1522/1557] Data 0.004 (0.091) Batch 1.091 (1.132) Remain 14:41:55 loss: 0.3660 Lr: 0.00114 [2024-02-19 05:04:49,839 INFO misc.py line 119 87073] Train: [70/100][1523/1557] Data 0.004 (0.091) Batch 0.894 (1.132) Remain 14:41:47 loss: 0.1851 Lr: 0.00114 [2024-02-19 05:04:50,608 INFO misc.py line 119 87073] Train: [70/100][1524/1557] Data 0.004 (0.091) Batch 0.766 (1.132) Remain 14:41:34 loss: 0.3866 Lr: 0.00114 [2024-02-19 05:04:51,325 INFO misc.py line 119 87073] Train: [70/100][1525/1557] Data 0.008 (0.091) Batch 0.721 (1.131) Remain 14:41:21 loss: 0.2072 Lr: 0.00114 [2024-02-19 05:04:52,554 INFO misc.py line 119 87073] Train: [70/100][1526/1557] Data 0.003 (0.091) Batch 1.229 (1.131) Remain 14:41:23 loss: 0.1155 Lr: 0.00114 [2024-02-19 05:04:53,616 INFO misc.py line 119 87073] Train: [70/100][1527/1557] Data 0.004 (0.091) Batch 1.063 (1.131) Remain 14:41:19 loss: 0.5204 Lr: 0.00114 [2024-02-19 05:04:54,651 INFO misc.py line 119 87073] Train: [70/100][1528/1557] Data 0.003 (0.091) Batch 1.036 (1.131) Remain 14:41:15 loss: 0.3646 Lr: 0.00114 [2024-02-19 05:04:55,609 INFO misc.py line 119 87073] Train: [70/100][1529/1557] Data 0.003 (0.091) Batch 0.957 (1.131) Remain 14:41:09 loss: 0.5816 Lr: 0.00114 [2024-02-19 05:04:56,492 INFO misc.py line 119 87073] Train: [70/100][1530/1557] Data 0.003 (0.091) Batch 0.880 (1.131) Remain 14:41:00 loss: 0.2894 Lr: 0.00114 [2024-02-19 05:04:57,308 INFO misc.py line 119 87073] Train: [70/100][1531/1557] Data 0.007 (0.091) Batch 0.816 (1.131) Remain 14:40:49 loss: 0.3451 Lr: 0.00114 [2024-02-19 05:04:58,135 INFO misc.py line 119 87073] Train: [70/100][1532/1557] Data 0.008 (0.091) Batch 0.828 (1.131) Remain 14:40:39 loss: 0.1291 Lr: 0.00114 [2024-02-19 05:04:59,477 INFO misc.py line 119 87073] Train: [70/100][1533/1557] Data 0.006 (0.091) Batch 1.341 (1.131) Remain 14:40:44 loss: 0.0618 Lr: 0.00114 [2024-02-19 05:05:00,592 INFO misc.py line 119 87073] Train: [70/100][1534/1557] Data 0.009 (0.091) Batch 1.109 (1.131) Remain 14:40:42 loss: 0.2660 Lr: 0.00114 [2024-02-19 05:05:01,390 INFO misc.py line 119 87073] Train: [70/100][1535/1557] Data 0.013 (0.091) Batch 0.807 (1.131) Remain 14:40:31 loss: 0.5365 Lr: 0.00114 [2024-02-19 05:05:02,433 INFO misc.py line 119 87073] Train: [70/100][1536/1557] Data 0.004 (0.091) Batch 1.044 (1.130) Remain 14:40:28 loss: 0.4115 Lr: 0.00114 [2024-02-19 05:05:03,363 INFO misc.py line 119 87073] Train: [70/100][1537/1557] Data 0.003 (0.091) Batch 0.930 (1.130) Remain 14:40:20 loss: 0.3481 Lr: 0.00114 [2024-02-19 05:05:04,142 INFO misc.py line 119 87073] Train: [70/100][1538/1557] Data 0.004 (0.091) Batch 0.773 (1.130) Remain 14:40:08 loss: 0.1847 Lr: 0.00114 [2024-02-19 05:05:04,864 INFO misc.py line 119 87073] Train: [70/100][1539/1557] Data 0.010 (0.090) Batch 0.728 (1.130) Remain 14:39:55 loss: 0.2098 Lr: 0.00114 [2024-02-19 05:05:06,064 INFO misc.py line 119 87073] Train: [70/100][1540/1557] Data 0.004 (0.090) Batch 1.200 (1.130) Remain 14:39:56 loss: 0.0752 Lr: 0.00114 [2024-02-19 05:05:06,981 INFO misc.py line 119 87073] Train: [70/100][1541/1557] Data 0.004 (0.090) Batch 0.917 (1.130) Remain 14:39:48 loss: 0.3164 Lr: 0.00114 [2024-02-19 05:05:07,972 INFO misc.py line 119 87073] Train: [70/100][1542/1557] Data 0.004 (0.090) Batch 0.991 (1.130) Remain 14:39:43 loss: 0.3117 Lr: 0.00114 [2024-02-19 05:05:08,877 INFO misc.py line 119 87073] Train: [70/100][1543/1557] Data 0.004 (0.090) Batch 0.897 (1.130) Remain 14:39:35 loss: 0.2439 Lr: 0.00114 [2024-02-19 05:05:09,739 INFO misc.py line 119 87073] Train: [70/100][1544/1557] Data 0.012 (0.090) Batch 0.870 (1.129) Remain 14:39:26 loss: 0.0975 Lr: 0.00114 [2024-02-19 05:05:10,481 INFO misc.py line 119 87073] Train: [70/100][1545/1557] Data 0.004 (0.090) Batch 0.743 (1.129) Remain 14:39:13 loss: 0.2082 Lr: 0.00114 [2024-02-19 05:05:11,196 INFO misc.py line 119 87073] Train: [70/100][1546/1557] Data 0.003 (0.090) Batch 0.708 (1.129) Remain 14:38:59 loss: 0.1785 Lr: 0.00114 [2024-02-19 05:05:12,364 INFO misc.py line 119 87073] Train: [70/100][1547/1557] Data 0.010 (0.090) Batch 1.167 (1.129) Remain 14:38:59 loss: 0.1955 Lr: 0.00114 [2024-02-19 05:05:13,282 INFO misc.py line 119 87073] Train: [70/100][1548/1557] Data 0.011 (0.090) Batch 0.926 (1.129) Remain 14:38:52 loss: 0.1474 Lr: 0.00114 [2024-02-19 05:05:14,133 INFO misc.py line 119 87073] Train: [70/100][1549/1557] Data 0.003 (0.090) Batch 0.851 (1.129) Remain 14:38:42 loss: 0.2838 Lr: 0.00114 [2024-02-19 05:05:14,963 INFO misc.py line 119 87073] Train: [70/100][1550/1557] Data 0.004 (0.090) Batch 0.824 (1.128) Remain 14:38:32 loss: 0.1559 Lr: 0.00114 [2024-02-19 05:05:16,052 INFO misc.py line 119 87073] Train: [70/100][1551/1557] Data 0.009 (0.090) Batch 1.089 (1.128) Remain 14:38:30 loss: 0.2894 Lr: 0.00114 [2024-02-19 05:05:16,748 INFO misc.py line 119 87073] Train: [70/100][1552/1557] Data 0.010 (0.090) Batch 0.702 (1.128) Remain 14:38:16 loss: 0.2238 Lr: 0.00114 [2024-02-19 05:05:17,500 INFO misc.py line 119 87073] Train: [70/100][1553/1557] Data 0.003 (0.090) Batch 0.744 (1.128) Remain 14:38:03 loss: 0.2332 Lr: 0.00114 [2024-02-19 05:05:18,623 INFO misc.py line 119 87073] Train: [70/100][1554/1557] Data 0.012 (0.090) Batch 1.124 (1.128) Remain 14:38:02 loss: 0.1343 Lr: 0.00114 [2024-02-19 05:05:19,592 INFO misc.py line 119 87073] Train: [70/100][1555/1557] Data 0.010 (0.090) Batch 0.976 (1.128) Remain 14:37:56 loss: 0.2441 Lr: 0.00114 [2024-02-19 05:05:20,454 INFO misc.py line 119 87073] Train: [70/100][1556/1557] Data 0.003 (0.090) Batch 0.862 (1.128) Remain 14:37:47 loss: 0.3692 Lr: 0.00113 [2024-02-19 05:05:21,277 INFO misc.py line 119 87073] Train: [70/100][1557/1557] Data 0.003 (0.089) Batch 0.815 (1.127) Remain 14:37:36 loss: 0.4024 Lr: 0.00113 [2024-02-19 05:05:21,277 INFO misc.py line 136 87073] Train result: loss: 0.2707 [2024-02-19 05:05:21,277 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 05:05:45,481 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.8497 [2024-02-19 05:05:46,258 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8272 [2024-02-19 05:05:48,929 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4107 [2024-02-19 05:05:51,135 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2712 [2024-02-19 05:05:56,086 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3261 [2024-02-19 05:05:56,790 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0627 [2024-02-19 05:05:58,051 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2770 [2024-02-19 05:06:01,008 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3637 [2024-02-19 05:06:02,817 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3014 [2024-02-19 05:06:04,227 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7155/0.7754/0.9156. [2024-02-19 05:06:04,227 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9414/0.9720 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9895 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8708/0.9759 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3487/0.3711 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6831/0.7037 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7871/0.9195 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8375/0.9223 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9224/0.9738 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8364/0.8629 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7736/0.8742 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7156/0.8283 [2024-02-19 05:06:04,228 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6012/0.6869 [2024-02-19 05:06:04,229 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 05:06:04,230 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 05:06:04,230 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 05:06:11,633 INFO misc.py line 119 87073] Train: [71/100][1/1557] Data 1.647 (1.647) Batch 2.548 (2.548) Remain 33:03:47 loss: 0.1555 Lr: 0.00113 [2024-02-19 05:06:12,825 INFO misc.py line 119 87073] Train: [71/100][2/1557] Data 0.006 (0.006) Batch 1.189 (1.189) Remain 15:25:21 loss: 0.2518 Lr: 0.00113 [2024-02-19 05:06:13,662 INFO misc.py line 119 87073] Train: [71/100][3/1557] Data 0.009 (0.009) Batch 0.842 (0.842) Remain 10:55:27 loss: 0.2630 Lr: 0.00113 [2024-02-19 05:06:14,540 INFO misc.py line 119 87073] Train: [71/100][4/1557] Data 0.004 (0.004) Batch 0.878 (0.878) Remain 11:23:05 loss: 0.2885 Lr: 0.00113 [2024-02-19 05:06:15,335 INFO misc.py line 119 87073] Train: [71/100][5/1557] Data 0.004 (0.004) Batch 0.788 (0.833) Remain 10:48:25 loss: 0.3268 Lr: 0.00113 [2024-02-19 05:06:16,084 INFO misc.py line 119 87073] Train: [71/100][6/1557] Data 0.010 (0.006) Batch 0.757 (0.808) Remain 10:28:38 loss: 0.2939 Lr: 0.00113 [2024-02-19 05:06:31,346 INFO misc.py line 119 87073] Train: [71/100][7/1557] Data 0.822 (0.210) Batch 15.261 (4.421) Remain 57:21:13 loss: 0.1196 Lr: 0.00113 [2024-02-19 05:06:32,224 INFO misc.py line 119 87073] Train: [71/100][8/1557] Data 0.003 (0.169) Batch 0.878 (3.712) Remain 48:09:36 loss: 0.3680 Lr: 0.00113 [2024-02-19 05:06:33,162 INFO misc.py line 119 87073] Train: [71/100][9/1557] Data 0.004 (0.141) Batch 0.932 (3.249) Remain 42:08:50 loss: 0.2494 Lr: 0.00113 [2024-02-19 05:06:34,037 INFO misc.py line 119 87073] Train: [71/100][10/1557] Data 0.010 (0.122) Batch 0.882 (2.911) Remain 37:45:38 loss: 0.2045 Lr: 0.00113 [2024-02-19 05:06:34,864 INFO misc.py line 119 87073] Train: [71/100][11/1557] Data 0.003 (0.108) Batch 0.826 (2.650) Remain 34:22:45 loss: 0.3647 Lr: 0.00113 [2024-02-19 05:06:35,626 INFO misc.py line 119 87073] Train: [71/100][12/1557] Data 0.003 (0.096) Batch 0.751 (2.439) Remain 31:38:25 loss: 0.1815 Lr: 0.00113 [2024-02-19 05:06:36,331 INFO misc.py line 119 87073] Train: [71/100][13/1557] Data 0.015 (0.088) Batch 0.717 (2.267) Remain 29:24:20 loss: 0.1165 Lr: 0.00113 [2024-02-19 05:06:37,614 INFO misc.py line 119 87073] Train: [71/100][14/1557] Data 0.003 (0.080) Batch 1.283 (2.177) Remain 28:14:39 loss: 0.1610 Lr: 0.00113 [2024-02-19 05:06:38,331 INFO misc.py line 119 87073] Train: [71/100][15/1557] Data 0.003 (0.074) Batch 0.716 (2.056) Remain 26:39:51 loss: 0.0584 Lr: 0.00113 [2024-02-19 05:06:39,265 INFO misc.py line 119 87073] Train: [71/100][16/1557] Data 0.003 (0.068) Batch 0.927 (1.969) Remain 25:32:15 loss: 0.3200 Lr: 0.00113 [2024-02-19 05:06:40,289 INFO misc.py line 119 87073] Train: [71/100][17/1557] Data 0.011 (0.064) Batch 1.022 (1.901) Remain 24:39:37 loss: 0.3003 Lr: 0.00113 [2024-02-19 05:06:41,362 INFO misc.py line 119 87073] Train: [71/100][18/1557] Data 0.013 (0.061) Batch 1.076 (1.846) Remain 23:56:46 loss: 0.2388 Lr: 0.00113 [2024-02-19 05:06:42,118 INFO misc.py line 119 87073] Train: [71/100][19/1557] Data 0.009 (0.058) Batch 0.763 (1.779) Remain 23:04:02 loss: 0.1713 Lr: 0.00113 [2024-02-19 05:06:42,900 INFO misc.py line 119 87073] Train: [71/100][20/1557] Data 0.003 (0.054) Batch 0.773 (1.719) Remain 22:18:00 loss: 0.1980 Lr: 0.00113 [2024-02-19 05:06:44,179 INFO misc.py line 119 87073] Train: 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Train: [71/100][65/1557] Data 0.003 (0.159) Batch 0.920 (1.574) Remain 20:23:38 loss: 0.3771 Lr: 0.00113 [2024-02-19 05:07:52,165 INFO misc.py line 119 87073] Train: [71/100][66/1557] Data 0.004 (0.157) Batch 0.916 (1.564) Remain 20:15:29 loss: 0.3403 Lr: 0.00113 [2024-02-19 05:07:53,155 INFO misc.py line 119 87073] Train: [71/100][67/1557] Data 0.003 (0.154) Batch 0.986 (1.555) Remain 20:08:27 loss: 0.6628 Lr: 0.00113 [2024-02-19 05:07:53,949 INFO misc.py line 119 87073] Train: [71/100][68/1557] Data 0.008 (0.152) Batch 0.795 (1.543) Remain 19:59:20 loss: 0.2053 Lr: 0.00113 [2024-02-19 05:07:54,708 INFO misc.py line 119 87073] Train: [71/100][69/1557] Data 0.007 (0.150) Batch 0.761 (1.531) Remain 19:50:06 loss: 0.5323 Lr: 0.00113 [2024-02-19 05:07:55,986 INFO misc.py line 119 87073] Train: [71/100][70/1557] Data 0.004 (0.148) Batch 1.272 (1.527) Remain 19:47:04 loss: 0.1430 Lr: 0.00113 [2024-02-19 05:07:57,053 INFO misc.py line 119 87073] Train: [71/100][71/1557] Data 0.011 (0.146) 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INFO misc.py line 119 87073] Train: [71/100][1253/1557] Data 0.006 (0.151) Batch 1.228 (1.365) Remain 17:13:56 loss: 0.0930 Lr: 0.00108 [2024-02-19 05:34:40,645 INFO misc.py line 119 87073] Train: [71/100][1254/1557] Data 0.015 (0.151) Batch 1.056 (1.364) Remain 17:13:44 loss: 0.1596 Lr: 0.00108 [2024-02-19 05:34:41,630 INFO misc.py line 119 87073] Train: [71/100][1255/1557] Data 0.008 (0.151) Batch 0.991 (1.364) Remain 17:13:29 loss: 0.2483 Lr: 0.00108 [2024-02-19 05:34:42,802 INFO misc.py line 119 87073] Train: [71/100][1256/1557] Data 0.003 (0.151) Batch 1.169 (1.364) Remain 17:13:20 loss: 0.2557 Lr: 0.00108 [2024-02-19 05:34:43,755 INFO misc.py line 119 87073] Train: [71/100][1257/1557] Data 0.006 (0.151) Batch 0.955 (1.364) Remain 17:13:04 loss: 0.3829 Lr: 0.00108 [2024-02-19 05:34:44,472 INFO misc.py line 119 87073] Train: [71/100][1258/1557] Data 0.003 (0.151) Batch 0.712 (1.363) Remain 17:12:39 loss: 0.2298 Lr: 0.00108 [2024-02-19 05:34:45,288 INFO misc.py line 119 87073] Train: [71/100][1259/1557] Data 0.009 (0.150) Batch 0.822 (1.363) Remain 17:12:18 loss: 0.3238 Lr: 0.00108 [2024-02-19 05:34:46,573 INFO misc.py line 119 87073] Train: [71/100][1260/1557] Data 0.003 (0.150) Batch 1.275 (1.363) Remain 17:12:14 loss: 0.1234 Lr: 0.00108 [2024-02-19 05:34:47,554 INFO misc.py line 119 87073] Train: [71/100][1261/1557] Data 0.013 (0.150) Batch 0.991 (1.362) Remain 17:11:59 loss: 0.2678 Lr: 0.00108 [2024-02-19 05:34:48,679 INFO misc.py line 119 87073] Train: [71/100][1262/1557] Data 0.004 (0.150) Batch 1.125 (1.362) Remain 17:11:49 loss: 0.2420 Lr: 0.00108 [2024-02-19 05:34:49,615 INFO misc.py line 119 87073] Train: [71/100][1263/1557] Data 0.003 (0.150) Batch 0.935 (1.362) Remain 17:11:32 loss: 0.1811 Lr: 0.00108 [2024-02-19 05:34:50,565 INFO misc.py line 119 87073] Train: [71/100][1264/1557] Data 0.004 (0.150) Batch 0.951 (1.362) Remain 17:11:16 loss: 0.2740 Lr: 0.00108 [2024-02-19 05:34:51,357 INFO misc.py line 119 87073] Train: [71/100][1265/1557] Data 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Train: [71/100][1290/1557] Data 0.004 (0.147) Batch 1.033 (1.353) Remain 17:04:06 loss: 0.3824 Lr: 0.00108 [2024-02-19 05:35:15,592 INFO misc.py line 119 87073] Train: [71/100][1291/1557] Data 0.005 (0.147) Batch 0.799 (1.352) Remain 17:03:45 loss: 0.6597 Lr: 0.00108 [2024-02-19 05:35:16,605 INFO misc.py line 119 87073] Train: [71/100][1292/1557] Data 0.003 (0.147) Batch 1.010 (1.352) Remain 17:03:32 loss: 0.2763 Lr: 0.00108 [2024-02-19 05:35:17,379 INFO misc.py line 119 87073] Train: [71/100][1293/1557] Data 0.006 (0.147) Batch 0.775 (1.352) Remain 17:03:10 loss: 0.3221 Lr: 0.00108 [2024-02-19 05:35:18,133 INFO misc.py line 119 87073] Train: [71/100][1294/1557] Data 0.004 (0.147) Batch 0.754 (1.351) Remain 17:02:48 loss: 0.3856 Lr: 0.00108 [2024-02-19 05:35:42,756 INFO misc.py line 119 87073] Train: [71/100][1295/1557] Data 7.338 (0.152) Batch 24.624 (1.369) Remain 17:16:25 loss: 0.1019 Lr: 0.00108 [2024-02-19 05:35:43,724 INFO misc.py line 119 87073] Train: [71/100][1296/1557] Data 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Remain 17:14:33 loss: 0.1147 Lr: 0.00108 [2024-02-19 05:35:50,505 INFO misc.py line 119 87073] Train: [71/100][1303/1557] Data 0.013 (0.151) Batch 1.068 (1.367) Remain 17:14:21 loss: 0.2689 Lr: 0.00108 [2024-02-19 05:35:51,518 INFO misc.py line 119 87073] Train: [71/100][1304/1557] Data 0.014 (0.151) Batch 1.011 (1.367) Remain 17:14:08 loss: 0.2824 Lr: 0.00108 [2024-02-19 05:35:52,417 INFO misc.py line 119 87073] Train: [71/100][1305/1557] Data 0.015 (0.151) Batch 0.912 (1.366) Remain 17:13:50 loss: 0.1377 Lr: 0.00108 [2024-02-19 05:35:53,374 INFO misc.py line 119 87073] Train: [71/100][1306/1557] Data 0.003 (0.151) Batch 0.956 (1.366) Remain 17:13:35 loss: 0.0767 Lr: 0.00108 [2024-02-19 05:35:54,142 INFO misc.py line 119 87073] Train: [71/100][1307/1557] Data 0.003 (0.151) Batch 0.763 (1.365) Remain 17:13:12 loss: 0.2484 Lr: 0.00108 [2024-02-19 05:35:54,887 INFO misc.py line 119 87073] Train: [71/100][1308/1557] Data 0.008 (0.151) Batch 0.744 (1.365) Remain 17:12:49 loss: 0.1758 Lr: 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INFO misc.py line 119 87073] Train: [71/100][1315/1557] Data 0.003 (0.150) Batch 0.789 (1.363) Remain 17:11:10 loss: 0.2773 Lr: 0.00108 [2024-02-19 05:36:03,158 INFO misc.py line 119 87073] Train: [71/100][1316/1557] Data 0.008 (0.150) Batch 1.303 (1.363) Remain 17:11:07 loss: 0.1621 Lr: 0.00108 [2024-02-19 05:36:03,979 INFO misc.py line 119 87073] Train: [71/100][1317/1557] Data 0.012 (0.150) Batch 0.830 (1.362) Remain 17:10:47 loss: 0.1713 Lr: 0.00108 [2024-02-19 05:36:04,952 INFO misc.py line 119 87073] Train: [71/100][1318/1557] Data 0.003 (0.150) Batch 0.973 (1.362) Remain 17:10:32 loss: 0.3011 Lr: 0.00108 [2024-02-19 05:36:05,760 INFO misc.py line 119 87073] Train: [71/100][1319/1557] Data 0.003 (0.149) Batch 0.808 (1.362) Remain 17:10:12 loss: 0.3654 Lr: 0.00108 [2024-02-19 05:36:06,745 INFO misc.py line 119 87073] Train: [71/100][1320/1557] Data 0.003 (0.149) Batch 0.976 (1.361) Remain 17:09:57 loss: 0.3007 Lr: 0.00108 [2024-02-19 05:36:07,460 INFO misc.py line 119 87073] Train: [71/100][1321/1557] Data 0.012 (0.149) Batch 0.724 (1.361) Remain 17:09:34 loss: 0.2720 Lr: 0.00108 [2024-02-19 05:36:08,196 INFO misc.py line 119 87073] Train: [71/100][1322/1557] Data 0.003 (0.149) Batch 0.724 (1.361) Remain 17:09:11 loss: 0.2779 Lr: 0.00108 [2024-02-19 05:36:09,267 INFO misc.py line 119 87073] Train: [71/100][1323/1557] Data 0.015 (0.149) Batch 1.073 (1.360) Remain 17:08:59 loss: 0.1412 Lr: 0.00108 [2024-02-19 05:36:10,198 INFO misc.py line 119 87073] Train: [71/100][1324/1557] Data 0.014 (0.149) Batch 0.942 (1.360) Remain 17:08:44 loss: 0.1090 Lr: 0.00108 [2024-02-19 05:36:11,016 INFO misc.py line 119 87073] Train: [71/100][1325/1557] Data 0.003 (0.149) Batch 0.817 (1.360) Remain 17:08:24 loss: 0.4373 Lr: 0.00108 [2024-02-19 05:36:11,920 INFO misc.py line 119 87073] Train: [71/100][1326/1557] Data 0.004 (0.149) Batch 0.900 (1.359) Remain 17:08:06 loss: 0.4624 Lr: 0.00108 [2024-02-19 05:36:12,962 INFO misc.py line 119 87073] Train: [71/100][1327/1557] Data 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Remain 17:06:22 loss: 0.3826 Lr: 0.00108 [2024-02-19 05:36:19,530 INFO misc.py line 119 87073] Train: [71/100][1334/1557] Data 0.003 (0.148) Batch 0.891 (1.357) Remain 17:06:04 loss: 0.3827 Lr: 0.00108 [2024-02-19 05:36:20,307 INFO misc.py line 119 87073] Train: [71/100][1335/1557] Data 0.003 (0.148) Batch 0.769 (1.356) Remain 17:05:43 loss: 0.1835 Lr: 0.00108 [2024-02-19 05:36:21,050 INFO misc.py line 119 87073] Train: [71/100][1336/1557] Data 0.011 (0.148) Batch 0.751 (1.356) Remain 17:05:21 loss: 0.2157 Lr: 0.00108 [2024-02-19 05:36:22,290 INFO misc.py line 119 87073] Train: [71/100][1337/1557] Data 0.003 (0.148) Batch 1.239 (1.356) Remain 17:05:16 loss: 0.2389 Lr: 0.00108 [2024-02-19 05:36:23,239 INFO misc.py line 119 87073] Train: [71/100][1338/1557] Data 0.005 (0.147) Batch 0.950 (1.355) Remain 17:05:01 loss: 0.3530 Lr: 0.00108 [2024-02-19 05:36:24,416 INFO misc.py line 119 87073] Train: [71/100][1339/1557] Data 0.003 (0.147) Batch 1.174 (1.355) Remain 17:04:53 loss: 0.0531 Lr: 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Train: [71/100][1352/1557] Data 0.003 (0.152) Batch 0.903 (1.369) Remain 17:14:35 loss: 0.0784 Lr: 0.00108 [2024-02-19 05:37:00,860 INFO misc.py line 119 87073] Train: [71/100][1353/1557] Data 0.003 (0.151) Batch 0.977 (1.368) Remain 17:14:21 loss: 0.3828 Lr: 0.00108 [2024-02-19 05:37:01,742 INFO misc.py line 119 87073] Train: [71/100][1354/1557] Data 0.011 (0.151) Batch 0.890 (1.368) Remain 17:14:03 loss: 0.1023 Lr: 0.00108 [2024-02-19 05:37:02,827 INFO misc.py line 119 87073] Train: [71/100][1355/1557] Data 0.003 (0.151) Batch 1.085 (1.368) Remain 17:13:53 loss: 0.1158 Lr: 0.00108 [2024-02-19 05:37:03,581 INFO misc.py line 119 87073] Train: [71/100][1356/1557] Data 0.003 (0.151) Batch 0.754 (1.367) Remain 17:13:31 loss: 0.2266 Lr: 0.00108 [2024-02-19 05:37:04,328 INFO misc.py line 119 87073] Train: [71/100][1357/1557] Data 0.003 (0.151) Batch 0.738 (1.367) Remain 17:13:08 loss: 0.2567 Lr: 0.00108 [2024-02-19 05:37:05,670 INFO misc.py line 119 87073] Train: [71/100][1358/1557] Data 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Remain 17:11:23 loss: 0.1052 Lr: 0.00107 [2024-02-19 05:37:12,408 INFO misc.py line 119 87073] Train: [71/100][1365/1557] Data 0.004 (0.150) Batch 1.374 (1.365) Remain 17:11:22 loss: 0.1308 Lr: 0.00107 [2024-02-19 05:37:13,242 INFO misc.py line 119 87073] Train: [71/100][1366/1557] Data 0.013 (0.150) Batch 0.843 (1.364) Remain 17:11:03 loss: 0.4840 Lr: 0.00107 [2024-02-19 05:37:14,180 INFO misc.py line 119 87073] Train: [71/100][1367/1557] Data 0.005 (0.150) Batch 0.939 (1.364) Remain 17:10:48 loss: 0.6304 Lr: 0.00107 [2024-02-19 05:37:15,183 INFO misc.py line 119 87073] Train: [71/100][1368/1557] Data 0.003 (0.150) Batch 1.003 (1.364) Remain 17:10:35 loss: 0.1734 Lr: 0.00107 [2024-02-19 05:37:16,124 INFO misc.py line 119 87073] Train: [71/100][1369/1557] Data 0.003 (0.150) Batch 0.940 (1.363) Remain 17:10:19 loss: 0.3099 Lr: 0.00107 [2024-02-19 05:37:16,864 INFO misc.py line 119 87073] Train: [71/100][1370/1557] Data 0.004 (0.150) Batch 0.734 (1.363) Remain 17:09:57 loss: 0.1692 Lr: 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Train: [71/100][1383/1557] Data 0.003 (0.148) Batch 0.918 (1.359) Remain 17:06:46 loss: 0.2131 Lr: 0.00107 [2024-02-19 05:37:30,212 INFO misc.py line 119 87073] Train: [71/100][1384/1557] Data 0.003 (0.148) Batch 0.890 (1.359) Remain 17:06:30 loss: 0.2050 Lr: 0.00107 [2024-02-19 05:37:30,936 INFO misc.py line 119 87073] Train: [71/100][1385/1557] Data 0.013 (0.148) Batch 0.734 (1.358) Remain 17:06:08 loss: 0.2086 Lr: 0.00107 [2024-02-19 05:37:32,127 INFO misc.py line 119 87073] Train: [71/100][1386/1557] Data 0.003 (0.148) Batch 1.191 (1.358) Remain 17:06:01 loss: 0.2067 Lr: 0.00107 [2024-02-19 05:37:33,114 INFO misc.py line 119 87073] Train: [71/100][1387/1557] Data 0.003 (0.148) Batch 0.987 (1.358) Remain 17:05:47 loss: 1.0739 Lr: 0.00107 [2024-02-19 05:37:33,948 INFO misc.py line 119 87073] Train: [71/100][1388/1557] Data 0.004 (0.148) Batch 0.833 (1.358) Remain 17:05:29 loss: 0.2749 Lr: 0.00107 [2024-02-19 05:37:34,888 INFO misc.py line 119 87073] Train: [71/100][1389/1557] Data 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Remain 17:03:46 loss: 0.5622 Lr: 0.00107 [2024-02-19 05:37:41,634 INFO misc.py line 119 87073] Train: [71/100][1396/1557] Data 0.003 (0.147) Batch 1.060 (1.355) Remain 17:03:35 loss: 0.1312 Lr: 0.00107 [2024-02-19 05:37:42,538 INFO misc.py line 119 87073] Train: [71/100][1397/1557] Data 0.003 (0.147) Batch 0.904 (1.355) Remain 17:03:19 loss: 0.8501 Lr: 0.00107 [2024-02-19 05:37:43,272 INFO misc.py line 119 87073] Train: [71/100][1398/1557] Data 0.003 (0.147) Batch 0.722 (1.355) Remain 17:02:57 loss: 0.1832 Lr: 0.00107 [2024-02-19 05:37:43,960 INFO misc.py line 119 87073] Train: [71/100][1399/1557] Data 0.016 (0.147) Batch 0.700 (1.354) Remain 17:02:34 loss: 0.2657 Lr: 0.00107 [2024-02-19 05:37:45,202 INFO misc.py line 119 87073] Train: [71/100][1400/1557] Data 0.003 (0.147) Batch 1.241 (1.354) Remain 17:02:29 loss: 0.0736 Lr: 0.00107 [2024-02-19 05:37:46,123 INFO misc.py line 119 87073] Train: [71/100][1401/1557] Data 0.004 (0.146) Batch 0.922 (1.354) Remain 17:02:14 loss: 0.2128 Lr: 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INFO misc.py line 119 87073] Train: [71/100][1408/1557] Data 0.004 (0.153) Batch 0.869 (1.369) Remain 17:14:00 loss: 0.2690 Lr: 0.00107 [2024-02-19 05:38:18,773 INFO misc.py line 119 87073] Train: [71/100][1409/1557] Data 0.003 (0.152) Batch 0.974 (1.369) Remain 17:13:46 loss: 0.3588 Lr: 0.00107 [2024-02-19 05:38:19,787 INFO misc.py line 119 87073] Train: [71/100][1410/1557] Data 0.004 (0.152) Batch 1.009 (1.369) Remain 17:13:33 loss: 0.2242 Lr: 0.00107 [2024-02-19 05:38:21,062 INFO misc.py line 119 87073] Train: [71/100][1411/1557] Data 0.009 (0.152) Batch 1.279 (1.369) Remain 17:13:29 loss: 0.5516 Lr: 0.00107 [2024-02-19 05:38:21,809 INFO misc.py line 119 87073] Train: [71/100][1412/1557] Data 0.005 (0.152) Batch 0.747 (1.368) Remain 17:13:07 loss: 0.3584 Lr: 0.00107 [2024-02-19 05:38:22,521 INFO misc.py line 119 87073] Train: [71/100][1413/1557] Data 0.006 (0.152) Batch 0.706 (1.368) Remain 17:12:45 loss: 0.1432 Lr: 0.00107 [2024-02-19 05:38:23,802 INFO misc.py line 119 87073] Train: [71/100][1414/1557] Data 0.012 (0.152) Batch 1.279 (1.368) Remain 17:12:41 loss: 0.1045 Lr: 0.00107 [2024-02-19 05:38:24,792 INFO misc.py line 119 87073] Train: [71/100][1415/1557] Data 0.013 (0.152) Batch 0.999 (1.368) Remain 17:12:27 loss: 0.5451 Lr: 0.00107 [2024-02-19 05:38:25,681 INFO misc.py line 119 87073] Train: [71/100][1416/1557] Data 0.004 (0.152) Batch 0.888 (1.367) Remain 17:12:11 loss: 0.7725 Lr: 0.00107 [2024-02-19 05:38:26,633 INFO misc.py line 119 87073] Train: [71/100][1417/1557] Data 0.006 (0.152) Batch 0.953 (1.367) Remain 17:11:56 loss: 0.1454 Lr: 0.00107 [2024-02-19 05:38:27,580 INFO misc.py line 119 87073] Train: [71/100][1418/1557] Data 0.004 (0.152) Batch 0.949 (1.367) Remain 17:11:41 loss: 0.3931 Lr: 0.00107 [2024-02-19 05:38:28,322 INFO misc.py line 119 87073] Train: [71/100][1419/1557] Data 0.003 (0.151) Batch 0.738 (1.366) Remain 17:11:20 loss: 0.2278 Lr: 0.00107 [2024-02-19 05:38:29,058 INFO misc.py line 119 87073] Train: [71/100][1420/1557] Data 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Train: [71/100][1445/1557] Data 0.003 (0.149) Batch 0.920 (1.359) Remain 17:05:15 loss: 0.4740 Lr: 0.00107 [2024-02-19 05:38:54,286 INFO misc.py line 119 87073] Train: [71/100][1446/1557] Data 0.003 (0.149) Batch 0.915 (1.359) Remain 17:05:00 loss: 0.4469 Lr: 0.00107 [2024-02-19 05:38:55,032 INFO misc.py line 119 87073] Train: [71/100][1447/1557] Data 0.008 (0.149) Batch 0.751 (1.358) Remain 17:04:40 loss: 0.1081 Lr: 0.00107 [2024-02-19 05:38:55,814 INFO misc.py line 119 87073] Train: [71/100][1448/1557] Data 0.003 (0.149) Batch 0.770 (1.358) Remain 17:04:20 loss: 0.2651 Lr: 0.00107 [2024-02-19 05:38:57,038 INFO misc.py line 119 87073] Train: [71/100][1449/1557] Data 0.015 (0.148) Batch 1.229 (1.358) Remain 17:04:14 loss: 0.1503 Lr: 0.00107 [2024-02-19 05:38:58,074 INFO misc.py line 119 87073] Train: [71/100][1450/1557] Data 0.011 (0.148) Batch 1.033 (1.358) Remain 17:04:03 loss: 0.1914 Lr: 0.00107 [2024-02-19 05:38:58,940 INFO misc.py line 119 87073] Train: [71/100][1451/1557] Data 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INFO misc.py line 119 87073] Train: [71/100][1501/1557] Data 0.004 (0.149) Batch 0.890 (1.360) Remain 17:04:39 loss: 0.2381 Lr: 0.00107 [2024-02-19 05:40:11,764 INFO misc.py line 119 87073] Train: [71/100][1502/1557] Data 0.011 (0.149) Batch 0.972 (1.360) Remain 17:04:26 loss: 0.3979 Lr: 0.00107 [2024-02-19 05:40:12,539 INFO misc.py line 119 87073] Train: [71/100][1503/1557] Data 0.003 (0.149) Batch 0.773 (1.359) Remain 17:04:07 loss: 0.1488 Lr: 0.00107 [2024-02-19 05:40:13,220 INFO misc.py line 119 87073] Train: [71/100][1504/1557] Data 0.006 (0.149) Batch 0.681 (1.359) Remain 17:03:45 loss: 0.2331 Lr: 0.00107 [2024-02-19 05:40:14,495 INFO misc.py line 119 87073] Train: [71/100][1505/1557] Data 0.006 (0.148) Batch 1.273 (1.359) Remain 17:03:41 loss: 0.1468 Lr: 0.00107 [2024-02-19 05:40:15,581 INFO misc.py line 119 87073] Train: [71/100][1506/1557] Data 0.008 (0.148) Batch 1.080 (1.359) Remain 17:03:32 loss: 0.5664 Lr: 0.00107 [2024-02-19 05:40:16,825 INFO misc.py line 119 87073] Train: [71/100][1507/1557] Data 0.014 (0.148) Batch 1.247 (1.358) Remain 17:03:27 loss: 0.3310 Lr: 0.00107 [2024-02-19 05:40:17,743 INFO misc.py line 119 87073] Train: [71/100][1508/1557] Data 0.010 (0.148) Batch 0.924 (1.358) Remain 17:03:12 loss: 0.4124 Lr: 0.00107 [2024-02-19 05:40:18,736 INFO misc.py line 119 87073] Train: [71/100][1509/1557] Data 0.005 (0.148) Batch 0.994 (1.358) Remain 17:03:00 loss: 0.4474 Lr: 0.00107 [2024-02-19 05:40:19,526 INFO misc.py line 119 87073] Train: [71/100][1510/1557] Data 0.004 (0.148) Batch 0.788 (1.358) Remain 17:02:42 loss: 0.2562 Lr: 0.00107 [2024-02-19 05:40:20,289 INFO misc.py line 119 87073] Train: [71/100][1511/1557] Data 0.006 (0.148) Batch 0.763 (1.357) Remain 17:02:23 loss: 0.1129 Lr: 0.00107 [2024-02-19 05:40:21,491 INFO misc.py line 119 87073] Train: [71/100][1512/1557] Data 0.007 (0.148) Batch 1.199 (1.357) Remain 17:02:16 loss: 0.1931 Lr: 0.00107 [2024-02-19 05:40:22,411 INFO misc.py line 119 87073] Train: [71/100][1513/1557] Data 0.009 (0.148) Batch 0.925 (1.357) Remain 17:02:02 loss: 0.3179 Lr: 0.00107 [2024-02-19 05:40:23,397 INFO misc.py line 119 87073] Train: [71/100][1514/1557] Data 0.003 (0.148) Batch 0.982 (1.357) Remain 17:01:50 loss: 0.2965 Lr: 0.00107 [2024-02-19 05:40:24,382 INFO misc.py line 119 87073] Train: [71/100][1515/1557] Data 0.008 (0.148) Batch 0.987 (1.356) Remain 17:01:37 loss: 0.3794 Lr: 0.00107 [2024-02-19 05:40:25,274 INFO misc.py line 119 87073] Train: [71/100][1516/1557] Data 0.005 (0.147) Batch 0.893 (1.356) Remain 17:01:22 loss: 0.4667 Lr: 0.00107 [2024-02-19 05:40:26,094 INFO misc.py line 119 87073] Train: [71/100][1517/1557] Data 0.005 (0.147) Batch 0.815 (1.356) Remain 17:01:04 loss: 0.3432 Lr: 0.00107 [2024-02-19 05:40:26,861 INFO misc.py line 119 87073] Train: [71/100][1518/1557] Data 0.009 (0.147) Batch 0.773 (1.355) Remain 17:00:46 loss: 0.1611 Lr: 0.00107 [2024-02-19 05:40:51,797 INFO misc.py line 119 87073] Train: [71/100][1519/1557] Data 7.748 (0.152) Batch 24.936 (1.371) Remain 17:12:27 loss: 0.1273 Lr: 0.00107 [2024-02-19 05:40:52,612 INFO misc.py line 119 87073] Train: [71/100][1520/1557] Data 0.004 (0.152) Batch 0.814 (1.370) Remain 17:12:09 loss: 0.2280 Lr: 0.00107 [2024-02-19 05:40:53,901 INFO misc.py line 119 87073] Train: [71/100][1521/1557] Data 0.005 (0.152) Batch 1.282 (1.370) Remain 17:12:05 loss: 0.2144 Lr: 0.00107 [2024-02-19 05:40:54,968 INFO misc.py line 119 87073] Train: [71/100][1522/1557] Data 0.011 (0.152) Batch 1.074 (1.370) Remain 17:11:55 loss: 0.3037 Lr: 0.00107 [2024-02-19 05:40:55,935 INFO misc.py line 119 87073] Train: [71/100][1523/1557] Data 0.004 (0.152) Batch 0.965 (1.370) Remain 17:11:42 loss: 0.3605 Lr: 0.00107 [2024-02-19 05:40:56,740 INFO misc.py line 119 87073] Train: [71/100][1524/1557] Data 0.007 (0.152) Batch 0.805 (1.370) Remain 17:11:24 loss: 0.2499 Lr: 0.00107 [2024-02-19 05:40:57,499 INFO misc.py line 119 87073] Train: [71/100][1525/1557] Data 0.007 (0.152) Batch 0.760 (1.369) Remain 17:11:04 loss: 0.7302 Lr: 0.00107 [2024-02-19 05:40:58,696 INFO misc.py line 119 87073] Train: [71/100][1526/1557] Data 0.005 (0.152) Batch 1.187 (1.369) Remain 17:10:57 loss: 0.1153 Lr: 0.00107 [2024-02-19 05:40:59,754 INFO misc.py line 119 87073] Train: [71/100][1527/1557] Data 0.017 (0.152) Batch 1.061 (1.369) Remain 17:10:47 loss: 0.2961 Lr: 0.00107 [2024-02-19 05:41:00,608 INFO misc.py line 119 87073] Train: [71/100][1528/1557] Data 0.012 (0.151) Batch 0.857 (1.368) Remain 17:10:30 loss: 0.4581 Lr: 0.00107 [2024-02-19 05:41:01,431 INFO misc.py line 119 87073] Train: [71/100][1529/1557] Data 0.009 (0.151) Batch 0.825 (1.368) Remain 17:10:13 loss: 0.2512 Lr: 0.00107 [2024-02-19 05:41:02,340 INFO misc.py line 119 87073] Train: [71/100][1530/1557] Data 0.009 (0.151) Batch 0.910 (1.368) Remain 17:09:58 loss: 0.5430 Lr: 0.00107 [2024-02-19 05:41:03,150 INFO misc.py line 119 87073] Train: [71/100][1531/1557] Data 0.006 (0.151) Batch 0.809 (1.367) Remain 17:09:40 loss: 0.2684 Lr: 0.00107 [2024-02-19 05:41:03,878 INFO misc.py line 119 87073] Train: [71/100][1532/1557] Data 0.007 (0.151) Batch 0.730 (1.367) Remain 17:09:20 loss: 0.2961 Lr: 0.00107 [2024-02-19 05:41:05,097 INFO misc.py line 119 87073] Train: [71/100][1533/1557] Data 0.004 (0.151) Batch 1.213 (1.367) Remain 17:09:14 loss: 0.1107 Lr: 0.00107 [2024-02-19 05:41:06,070 INFO misc.py line 119 87073] Train: [71/100][1534/1557] Data 0.012 (0.151) Batch 0.979 (1.367) Remain 17:09:01 loss: 0.0865 Lr: 0.00107 [2024-02-19 05:41:07,190 INFO misc.py line 119 87073] Train: [71/100][1535/1557] Data 0.006 (0.151) Batch 1.120 (1.367) Remain 17:08:52 loss: 0.3712 Lr: 0.00107 [2024-02-19 05:41:08,307 INFO misc.py line 119 87073] Train: [71/100][1536/1557] Data 0.005 (0.151) Batch 1.118 (1.366) Remain 17:08:44 loss: 0.4242 Lr: 0.00107 [2024-02-19 05:41:09,271 INFO misc.py line 119 87073] Train: [71/100][1537/1557] Data 0.004 (0.151) Batch 0.963 (1.366) Remain 17:08:31 loss: 0.2356 Lr: 0.00107 [2024-02-19 05:41:10,069 INFO misc.py line 119 87073] Train: [71/100][1538/1557] Data 0.006 (0.151) Batch 0.798 (1.366) Remain 17:08:12 loss: 0.2995 Lr: 0.00107 [2024-02-19 05:41:10,776 INFO misc.py line 119 87073] Train: [71/100][1539/1557] Data 0.005 (0.150) Batch 0.706 (1.365) Remain 17:07:52 loss: 0.1177 Lr: 0.00107 [2024-02-19 05:41:12,102 INFO misc.py line 119 87073] Train: [71/100][1540/1557] Data 0.006 (0.150) Batch 1.325 (1.365) Remain 17:07:49 loss: 0.1037 Lr: 0.00107 [2024-02-19 05:41:13,031 INFO misc.py line 119 87073] Train: [71/100][1541/1557] Data 0.006 (0.150) Batch 0.930 (1.365) Remain 17:07:35 loss: 0.5967 Lr: 0.00107 [2024-02-19 05:41:13,999 INFO misc.py line 119 87073] Train: [71/100][1542/1557] Data 0.005 (0.150) Batch 0.967 (1.365) Remain 17:07:22 loss: 0.1200 Lr: 0.00107 [2024-02-19 05:41:15,008 INFO misc.py line 119 87073] Train: [71/100][1543/1557] Data 0.006 (0.150) Batch 1.011 (1.365) Remain 17:07:10 loss: 0.0619 Lr: 0.00107 [2024-02-19 05:41:15,898 INFO misc.py line 119 87073] Train: [71/100][1544/1557] Data 0.005 (0.150) Batch 0.890 (1.364) Remain 17:06:55 loss: 0.1902 Lr: 0.00107 [2024-02-19 05:41:16,678 INFO misc.py line 119 87073] Train: [71/100][1545/1557] Data 0.004 (0.150) Batch 0.771 (1.364) Remain 17:06:36 loss: 0.1839 Lr: 0.00107 [2024-02-19 05:41:17,502 INFO misc.py line 119 87073] Train: [71/100][1546/1557] Data 0.013 (0.150) Batch 0.832 (1.363) Remain 17:06:19 loss: 0.2418 Lr: 0.00107 [2024-02-19 05:41:18,514 INFO misc.py line 119 87073] Train: [71/100][1547/1557] Data 0.004 (0.150) Batch 1.012 (1.363) Remain 17:06:08 loss: 0.1494 Lr: 0.00107 [2024-02-19 05:41:19,475 INFO misc.py line 119 87073] Train: [71/100][1548/1557] Data 0.004 (0.150) Batch 0.960 (1.363) Remain 17:05:55 loss: 0.1287 Lr: 0.00107 [2024-02-19 05:41:20,308 INFO misc.py line 119 87073] Train: [71/100][1549/1557] Data 0.005 (0.149) Batch 0.832 (1.363) Remain 17:05:38 loss: 0.2360 Lr: 0.00107 [2024-02-19 05:41:21,440 INFO misc.py line 119 87073] Train: [71/100][1550/1557] Data 0.005 (0.149) Batch 1.123 (1.362) Remain 17:05:29 loss: 0.1637 Lr: 0.00107 [2024-02-19 05:41:22,444 INFO misc.py line 119 87073] Train: [71/100][1551/1557] Data 0.014 (0.149) Batch 1.008 (1.362) Remain 17:05:18 loss: 0.2096 Lr: 0.00107 [2024-02-19 05:41:23,122 INFO misc.py line 119 87073] Train: [71/100][1552/1557] Data 0.010 (0.149) Batch 0.682 (1.362) Remain 17:04:56 loss: 0.1693 Lr: 0.00107 [2024-02-19 05:41:23,917 INFO misc.py line 119 87073] Train: [71/100][1553/1557] Data 0.006 (0.149) Batch 0.789 (1.361) Remain 17:04:38 loss: 0.1046 Lr: 0.00107 [2024-02-19 05:41:25,149 INFO misc.py line 119 87073] Train: [71/100][1554/1557] Data 0.013 (0.149) Batch 1.234 (1.361) Remain 17:04:33 loss: 0.2566 Lr: 0.00107 [2024-02-19 05:41:26,167 INFO misc.py line 119 87073] Train: [71/100][1555/1557] Data 0.011 (0.149) Batch 1.021 (1.361) Remain 17:04:22 loss: 0.3019 Lr: 0.00107 [2024-02-19 05:41:27,035 INFO misc.py line 119 87073] Train: [71/100][1556/1557] Data 0.007 (0.149) Batch 0.870 (1.361) Remain 17:04:06 loss: 0.5523 Lr: 0.00107 [2024-02-19 05:41:27,990 INFO misc.py line 119 87073] Train: [71/100][1557/1557] Data 0.006 (0.149) Batch 0.957 (1.361) Remain 17:03:53 loss: 0.1444 Lr: 0.00107 [2024-02-19 05:41:27,991 INFO misc.py line 136 87073] Train result: loss: 0.2655 [2024-02-19 05:41:27,991 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 05:41:56,541 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5043 [2024-02-19 05:41:57,319 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.7548 [2024-02-19 05:41:59,448 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3107 [2024-02-19 05:42:01,654 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3015 [2024-02-19 05:42:06,600 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2235 [2024-02-19 05:42:07,301 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1404 [2024-02-19 05:42:08,564 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2367 [2024-02-19 05:42:11,520 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2781 [2024-02-19 05:42:13,333 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2147 [2024-02-19 05:42:14,989 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7180/0.7745/0.9184. [2024-02-19 05:42:14,989 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9353/0.9737 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9832/0.9906 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8698/0.9748 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0011/0.0049 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3689/0.4052 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6355/0.6493 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7982/0.9171 [2024-02-19 05:42:14,990 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8394/0.9138 [2024-02-19 05:42:14,991 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9222/0.9645 [2024-02-19 05:42:14,991 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7941/0.8183 [2024-02-19 05:42:14,991 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8120/0.9008 [2024-02-19 05:42:14,991 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7516/0.8530 [2024-02-19 05:42:14,991 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6223/0.7031 [2024-02-19 05:42:14,991 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 05:42:14,994 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 05:42:14,994 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 05:42:23,665 INFO misc.py line 119 87073] Train: [72/100][1/1557] Data 1.571 (1.571) Batch 2.333 (2.333) Remain 29:15:35 loss: 0.3056 Lr: 0.00107 [2024-02-19 05:42:24,881 INFO misc.py line 119 87073] Train: [72/100][2/1557] Data 0.006 (0.006) Batch 1.217 (1.217) Remain 15:15:40 loss: 0.5377 Lr: 0.00107 [2024-02-19 05:42:25,749 INFO misc.py line 119 87073] Train: [72/100][3/1557] Data 0.004 (0.004) Batch 0.867 (0.867) Remain 10:52:44 loss: 0.3269 Lr: 0.00107 [2024-02-19 05:42:26,803 INFO misc.py line 119 87073] Train: [72/100][4/1557] Data 0.005 (0.005) Batch 1.054 (1.054) Remain 13:13:28 loss: 0.3315 Lr: 0.00107 [2024-02-19 05:42:27,647 INFO misc.py line 119 87073] Train: [72/100][5/1557] Data 0.005 (0.005) Batch 0.844 (0.949) Remain 11:54:11 loss: 0.3725 Lr: 0.00107 [2024-02-19 05:42:28,417 INFO misc.py line 119 87073] Train: [72/100][6/1557] Data 0.004 (0.004) Batch 0.763 (0.887) Remain 11:07:27 loss: 0.3058 Lr: 0.00107 [2024-02-19 05:42:30,920 INFO misc.py line 119 87073] Train: [72/100][7/1557] Data 1.553 (0.392) Batch 2.509 (1.293) Remain 16:12:33 loss: 0.1566 Lr: 0.00107 [2024-02-19 05:42:31,912 INFO misc.py line 119 87073] Train: [72/100][8/1557] Data 0.006 (0.315) Batch 0.988 (1.232) Remain 15:26:41 loss: 0.3288 Lr: 0.00107 [2024-02-19 05:42:32,804 INFO misc.py line 119 87073] Train: [72/100][9/1557] Data 0.010 (0.264) Batch 0.895 (1.175) Remain 14:44:25 loss: 0.2032 Lr: 0.00107 [2024-02-19 05:42:33,712 INFO misc.py line 119 87073] Train: [72/100][10/1557] Data 0.007 (0.227) Batch 0.910 (1.137) Remain 14:15:49 loss: 0.3021 Lr: 0.00107 [2024-02-19 05:42:34,717 INFO misc.py line 119 87073] Train: [72/100][11/1557] Data 0.004 (0.199) Batch 1.000 (1.120) Remain 14:02:51 loss: 0.3203 Lr: 0.00107 [2024-02-19 05:42:35,515 INFO misc.py line 119 87073] Train: [72/100][12/1557] Data 0.010 (0.178) Batch 0.804 (1.085) Remain 13:36:22 loss: 0.0997 Lr: 0.00107 [2024-02-19 05:42:36,298 INFO misc.py line 119 87073] Train: [72/100][13/1557] Data 0.004 (0.161) Batch 0.783 (1.055) Remain 13:13:37 loss: 0.1631 Lr: 0.00107 [2024-02-19 05:42:37,409 INFO misc.py line 119 87073] Train: [72/100][14/1557] Data 0.004 (0.147) Batch 1.105 (1.059) Remain 13:17:03 loss: 0.0907 Lr: 0.00107 [2024-02-19 05:42:38,241 INFO misc.py line 119 87073] Train: [72/100][15/1557] Data 0.010 (0.135) Batch 0.839 (1.041) Remain 13:03:12 loss: 0.0836 Lr: 0.00107 [2024-02-19 05:42:39,277 INFO misc.py line 119 87073] Train: [72/100][16/1557] Data 0.004 (0.125) Batch 1.037 (1.041) Remain 13:02:55 loss: 0.8095 Lr: 0.00107 [2024-02-19 05:42:40,229 INFO misc.py line 119 87073] Train: [72/100][17/1557] Data 0.003 (0.116) Batch 0.948 (1.034) Remain 12:57:56 loss: 0.6134 Lr: 0.00107 [2024-02-19 05:42:41,007 INFO misc.py line 119 87073] Train: [72/100][18/1557] Data 0.008 (0.109) Batch 0.780 (1.017) Remain 12:45:10 loss: 0.2192 Lr: 0.00107 [2024-02-19 05:42:41,853 INFO misc.py line 119 87073] Train: [72/100][19/1557] Data 0.006 (0.103) Batch 0.844 (1.006) Remain 12:37:00 loss: 0.1371 Lr: 0.00107 [2024-02-19 05:42:42,682 INFO misc.py line 119 87073] Train: [72/100][20/1557] Data 0.007 (0.097) Batch 0.831 (0.996) Remain 12:29:14 loss: 0.1600 Lr: 0.00107 [2024-02-19 05:42:43,911 INFO misc.py line 119 87073] Train: [72/100][21/1557] Data 0.004 (0.092) Batch 1.218 (1.008) Remain 12:38:31 loss: 0.1245 Lr: 0.00107 [2024-02-19 05:42:44,807 INFO misc.py line 119 87073] Train: [72/100][22/1557] Data 0.016 (0.088) Batch 0.906 (1.003) Remain 12:34:27 loss: 0.4059 Lr: 0.00107 [2024-02-19 05:42:45,690 INFO misc.py line 119 87073] Train: [72/100][23/1557] Data 0.005 (0.084) Batch 0.880 (0.997) Remain 12:29:48 loss: 0.2163 Lr: 0.00107 [2024-02-19 05:42:46,566 INFO misc.py line 119 87073] Train: [72/100][24/1557] Data 0.009 (0.080) Batch 0.876 (0.991) Remain 12:25:28 loss: 0.1225 Lr: 0.00107 [2024-02-19 05:42:47,550 INFO misc.py line 119 87073] Train: [72/100][25/1557] Data 0.007 (0.077) Batch 0.987 (0.991) Remain 12:25:19 loss: 0.1603 Lr: 0.00107 [2024-02-19 05:42:48,274 INFO misc.py line 119 87073] Train: [72/100][26/1557] Data 0.004 (0.074) Batch 0.724 (0.979) Remain 12:16:34 loss: 0.3874 Lr: 0.00107 [2024-02-19 05:42:49,032 INFO misc.py line 119 87073] Train: [72/100][27/1557] Data 0.004 (0.071) Batch 0.753 (0.970) Remain 12:09:28 loss: 0.1724 Lr: 0.00107 [2024-02-19 05:42:50,098 INFO misc.py line 119 87073] Train: [72/100][28/1557] Data 0.009 (0.068) Batch 1.067 (0.974) Remain 12:12:22 loss: 0.1002 Lr: 0.00107 [2024-02-19 05:42:51,228 INFO misc.py line 119 87073] Train: [72/100][29/1557] Data 0.009 (0.066) Batch 1.132 (0.980) Remain 12:16:55 loss: 0.5292 Lr: 0.00107 [2024-02-19 05:42:52,136 INFO misc.py line 119 87073] Train: [72/100][30/1557] Data 0.007 (0.064) Batch 0.911 (0.977) Remain 12:14:59 loss: 0.3385 Lr: 0.00107 [2024-02-19 05:42:52,962 INFO misc.py line 119 87073] Train: [72/100][31/1557] Data 0.003 (0.062) Batch 0.826 (0.972) Remain 12:10:54 loss: 0.1091 Lr: 0.00107 [2024-02-19 05:42:53,973 INFO misc.py line 119 87073] Train: [72/100][32/1557] Data 0.004 (0.060) Batch 1.006 (0.973) Remain 12:11:46 loss: 0.1987 Lr: 0.00107 [2024-02-19 05:42:54,687 INFO misc.py line 119 87073] Train: [72/100][33/1557] Data 0.009 (0.058) Batch 0.719 (0.965) Remain 12:05:23 loss: 0.2395 Lr: 0.00107 [2024-02-19 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line 119 87073] Train: [72/100][165/1557] Data 0.004 (0.135) Batch 0.939 (1.065) Remain 13:18:32 loss: 0.2127 Lr: 0.00106 [2024-02-19 05:45:19,035 INFO misc.py line 119 87073] Train: [72/100][166/1557] Data 0.006 (0.134) Batch 0.754 (1.063) Remain 13:17:05 loss: 0.1074 Lr: 0.00106 [2024-02-19 05:45:19,787 INFO misc.py line 119 87073] Train: [72/100][167/1557] Data 0.004 (0.134) Batch 0.746 (1.061) Remain 13:15:37 loss: 0.4363 Lr: 0.00106 [2024-02-19 05:45:20,943 INFO misc.py line 119 87073] Train: [72/100][168/1557] Data 0.009 (0.133) Batch 1.158 (1.062) Remain 13:16:03 loss: 0.1066 Lr: 0.00106 [2024-02-19 05:45:21,871 INFO misc.py line 119 87073] Train: [72/100][169/1557] Data 0.008 (0.132) Batch 0.932 (1.061) Remain 13:15:26 loss: 0.3519 Lr: 0.00106 [2024-02-19 05:45:22,890 INFO misc.py line 119 87073] Train: [72/100][170/1557] Data 0.004 (0.131) Batch 1.018 (1.061) Remain 13:15:14 loss: 0.2062 Lr: 0.00106 [2024-02-19 05:45:23,948 INFO misc.py line 119 87073] Train: 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Batch 1.007 (1.108) Remain 13:50:39 loss: 0.1202 Lr: 0.00106 [2024-02-19 05:45:39,449 INFO misc.py line 119 87073] Train: [72/100][178/1557] Data 0.010 (0.178) Batch 0.884 (1.107) Remain 13:49:40 loss: 0.4452 Lr: 0.00106 [2024-02-19 05:45:40,423 INFO misc.py line 119 87073] Train: [72/100][179/1557] Data 0.006 (0.177) Batch 0.976 (1.106) Remain 13:49:05 loss: 0.3203 Lr: 0.00106 [2024-02-19 05:45:43,262 INFO misc.py line 119 87073] Train: [72/100][180/1557] Data 1.527 (0.185) Batch 2.838 (1.116) Remain 13:56:24 loss: 0.3056 Lr: 0.00106 [2024-02-19 05:45:44,074 INFO misc.py line 119 87073] Train: [72/100][181/1557] Data 0.005 (0.184) Batch 0.794 (1.114) Remain 13:55:02 loss: 0.4026 Lr: 0.00106 [2024-02-19 05:45:45,265 INFO misc.py line 119 87073] Train: [72/100][182/1557] Data 0.023 (0.183) Batch 1.203 (1.115) Remain 13:55:23 loss: 0.0954 Lr: 0.00106 [2024-02-19 05:45:46,142 INFO misc.py line 119 87073] Train: [72/100][183/1557] Data 0.011 (0.182) Batch 0.884 (1.113) Remain 13:54:24 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Remain 13:37:31 loss: 0.2829 Lr: 0.00101 [2024-02-19 06:05:28,405 INFO misc.py line 119 87073] Train: [72/100][1241/1557] Data 0.003 (0.183) Batch 0.893 (1.117) Remain 13:37:22 loss: 0.1710 Lr: 0.00101 [2024-02-19 06:05:29,351 INFO misc.py line 119 87073] Train: [72/100][1242/1557] Data 0.004 (0.183) Batch 0.943 (1.117) Remain 13:37:15 loss: 0.4995 Lr: 0.00101 [2024-02-19 06:05:30,399 INFO misc.py line 119 87073] Train: [72/100][1243/1557] Data 0.008 (0.183) Batch 1.045 (1.117) Remain 13:37:11 loss: 0.1498 Lr: 0.00101 [2024-02-19 06:05:31,187 INFO misc.py line 119 87073] Train: [72/100][1244/1557] Data 0.011 (0.182) Batch 0.796 (1.116) Remain 13:36:59 loss: 0.1897 Lr: 0.00101 [2024-02-19 06:05:31,998 INFO misc.py line 119 87073] Train: [72/100][1245/1557] Data 0.003 (0.182) Batch 0.800 (1.116) Remain 13:36:47 loss: 0.1950 Lr: 0.00101 [2024-02-19 06:05:33,299 INFO misc.py line 119 87073] Train: [72/100][1246/1557] Data 0.013 (0.182) Batch 1.303 (1.116) Remain 13:36:52 loss: 0.1413 Lr: 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INFO misc.py line 119 87073] Train: [72/100][1253/1557] Data 0.010 (0.181) Batch 1.158 (1.115) Remain 13:36:01 loss: 0.1663 Lr: 0.00101 [2024-02-19 06:05:40,737 INFO misc.py line 119 87073] Train: [72/100][1254/1557] Data 0.008 (0.181) Batch 0.866 (1.115) Remain 13:35:51 loss: 0.2839 Lr: 0.00101 [2024-02-19 06:05:41,830 INFO misc.py line 119 87073] Train: [72/100][1255/1557] Data 0.004 (0.181) Batch 1.093 (1.115) Remain 13:35:49 loss: 0.1022 Lr: 0.00101 [2024-02-19 06:05:42,811 INFO misc.py line 119 87073] Train: [72/100][1256/1557] Data 0.004 (0.181) Batch 0.981 (1.115) Remain 13:35:43 loss: 0.4003 Lr: 0.00101 [2024-02-19 06:05:43,817 INFO misc.py line 119 87073] Train: [72/100][1257/1557] Data 0.004 (0.181) Batch 1.006 (1.115) Remain 13:35:38 loss: 0.4484 Lr: 0.00101 [2024-02-19 06:05:44,549 INFO misc.py line 119 87073] Train: [72/100][1258/1557] Data 0.004 (0.180) Batch 0.732 (1.115) Remain 13:35:24 loss: 0.1576 Lr: 0.00101 [2024-02-19 06:05:45,323 INFO misc.py line 119 87073] Train: [72/100][1259/1557] Data 0.003 (0.180) Batch 0.771 (1.114) Remain 13:35:11 loss: 0.2520 Lr: 0.00101 [2024-02-19 06:05:46,350 INFO misc.py line 119 87073] Train: [72/100][1260/1557] Data 0.006 (0.180) Batch 1.022 (1.114) Remain 13:35:07 loss: 0.0843 Lr: 0.00101 [2024-02-19 06:05:47,268 INFO misc.py line 119 87073] Train: [72/100][1261/1557] Data 0.012 (0.180) Batch 0.927 (1.114) Remain 13:34:59 loss: 0.2779 Lr: 0.00101 [2024-02-19 06:05:48,171 INFO misc.py line 119 87073] Train: [72/100][1262/1557] Data 0.003 (0.180) Batch 0.902 (1.114) Remain 13:34:50 loss: 0.3329 Lr: 0.00101 [2024-02-19 06:05:49,264 INFO misc.py line 119 87073] Train: [72/100][1263/1557] Data 0.003 (0.180) Batch 1.086 (1.114) Remain 13:34:48 loss: 0.3156 Lr: 0.00101 [2024-02-19 06:05:50,264 INFO misc.py line 119 87073] Train: [72/100][1264/1557] Data 0.010 (0.180) Batch 1.001 (1.114) Remain 13:34:43 loss: 0.2203 Lr: 0.00101 [2024-02-19 06:05:50,995 INFO misc.py line 119 87073] Train: [72/100][1265/1557] Data 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Remain 13:33:54 loss: 0.2334 Lr: 0.00101 [2024-02-19 06:05:57,666 INFO misc.py line 119 87073] Train: [72/100][1272/1557] Data 0.003 (0.179) Batch 0.804 (1.113) Remain 13:33:42 loss: 0.1812 Lr: 0.00101 [2024-02-19 06:05:58,480 INFO misc.py line 119 87073] Train: [72/100][1273/1557] Data 0.003 (0.178) Batch 0.807 (1.112) Remain 13:33:31 loss: 0.1633 Lr: 0.00101 [2024-02-19 06:05:59,828 INFO misc.py line 119 87073] Train: [72/100][1274/1557] Data 0.010 (0.178) Batch 1.342 (1.113) Remain 13:33:38 loss: 0.1417 Lr: 0.00101 [2024-02-19 06:06:00,760 INFO misc.py line 119 87073] Train: [72/100][1275/1557] Data 0.017 (0.178) Batch 0.945 (1.112) Remain 13:33:31 loss: 0.5787 Lr: 0.00101 [2024-02-19 06:06:01,727 INFO misc.py line 119 87073] Train: [72/100][1276/1557] Data 0.004 (0.178) Batch 0.965 (1.112) Remain 13:33:24 loss: 0.2768 Lr: 0.00101 [2024-02-19 06:06:02,684 INFO misc.py line 119 87073] Train: [72/100][1277/1557] Data 0.005 (0.178) Batch 0.958 (1.112) Remain 13:33:18 loss: 0.2603 Lr: 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INFO misc.py line 119 87073] Train: [72/100][1284/1557] Data 0.006 (0.177) Batch 1.008 (1.111) Remain 13:32:28 loss: 0.2615 Lr: 0.00101 [2024-02-19 06:06:10,113 INFO misc.py line 119 87073] Train: [72/100][1285/1557] Data 0.006 (0.177) Batch 0.877 (1.111) Remain 13:32:19 loss: 0.2038 Lr: 0.00101 [2024-02-19 06:06:10,865 INFO misc.py line 119 87073] Train: [72/100][1286/1557] Data 0.004 (0.177) Batch 0.747 (1.111) Remain 13:32:05 loss: 0.2546 Lr: 0.00101 [2024-02-19 06:06:11,615 INFO misc.py line 119 87073] Train: [72/100][1287/1557] Data 0.009 (0.177) Batch 0.754 (1.110) Remain 13:31:52 loss: 0.2185 Lr: 0.00101 [2024-02-19 06:06:12,773 INFO misc.py line 119 87073] Train: [72/100][1288/1557] Data 0.004 (0.176) Batch 1.158 (1.111) Remain 13:31:53 loss: 0.1445 Lr: 0.00101 [2024-02-19 06:06:13,672 INFO misc.py line 119 87073] Train: [72/100][1289/1557] Data 0.005 (0.176) Batch 0.900 (1.110) Remain 13:31:44 loss: 0.4258 Lr: 0.00101 [2024-02-19 06:06:14,686 INFO misc.py line 119 87073] Train: [72/100][1290/1557] Data 0.004 (0.176) Batch 1.014 (1.110) Remain 13:31:40 loss: 0.9040 Lr: 0.00101 [2024-02-19 06:06:15,635 INFO misc.py line 119 87073] Train: [72/100][1291/1557] Data 0.004 (0.176) Batch 0.949 (1.110) Remain 13:31:33 loss: 0.4893 Lr: 0.00101 [2024-02-19 06:06:16,709 INFO misc.py line 119 87073] Train: [72/100][1292/1557] Data 0.005 (0.176) Batch 1.075 (1.110) Remain 13:31:31 loss: 0.1267 Lr: 0.00101 [2024-02-19 06:06:17,473 INFO misc.py line 119 87073] Train: [72/100][1293/1557] Data 0.004 (0.176) Batch 0.764 (1.110) Remain 13:31:18 loss: 0.2601 Lr: 0.00101 [2024-02-19 06:06:18,265 INFO misc.py line 119 87073] Train: [72/100][1294/1557] Data 0.004 (0.176) Batch 0.785 (1.110) Remain 13:31:06 loss: 0.1718 Lr: 0.00101 [2024-02-19 06:06:29,106 INFO misc.py line 119 87073] Train: [72/100][1295/1557] Data 9.825 (0.183) Batch 10.848 (1.117) Remain 13:36:35 loss: 0.1274 Lr: 0.00101 [2024-02-19 06:06:30,289 INFO misc.py line 119 87073] Train: [72/100][1296/1557] Data 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Remain 13:35:52 loss: 0.0645 Lr: 0.00101 [2024-02-19 06:06:36,788 INFO misc.py line 119 87073] Train: [72/100][1303/1557] Data 0.004 (0.182) Batch 0.900 (1.116) Remain 13:35:44 loss: 0.2840 Lr: 0.00101 [2024-02-19 06:06:37,818 INFO misc.py line 119 87073] Train: [72/100][1304/1557] Data 0.004 (0.182) Batch 1.030 (1.116) Remain 13:35:40 loss: 0.2085 Lr: 0.00101 [2024-02-19 06:06:38,764 INFO misc.py line 119 87073] Train: [72/100][1305/1557] Data 0.004 (0.182) Batch 0.946 (1.116) Remain 13:35:33 loss: 0.2698 Lr: 0.00101 [2024-02-19 06:06:39,903 INFO misc.py line 119 87073] Train: [72/100][1306/1557] Data 0.004 (0.182) Batch 1.139 (1.116) Remain 13:35:33 loss: 0.3575 Lr: 0.00101 [2024-02-19 06:06:40,633 INFO misc.py line 119 87073] Train: [72/100][1307/1557] Data 0.004 (0.181) Batch 0.727 (1.116) Remain 13:35:19 loss: 0.2488 Lr: 0.00101 [2024-02-19 06:06:41,461 INFO misc.py line 119 87073] Train: [72/100][1308/1557] Data 0.008 (0.181) Batch 0.830 (1.115) Remain 13:35:08 loss: 0.1514 Lr: 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INFO misc.py line 119 87073] Train: [72/100][1315/1557] Data 0.004 (0.180) Batch 0.764 (1.114) Remain 13:34:15 loss: 0.2914 Lr: 0.00101 [2024-02-19 06:06:49,050 INFO misc.py line 119 87073] Train: [72/100][1316/1557] Data 0.008 (0.180) Batch 1.133 (1.114) Remain 13:34:14 loss: 0.0857 Lr: 0.00101 [2024-02-19 06:06:49,886 INFO misc.py line 119 87073] Train: [72/100][1317/1557] Data 0.008 (0.180) Batch 0.838 (1.114) Remain 13:34:04 loss: 0.3488 Lr: 0.00101 [2024-02-19 06:06:50,967 INFO misc.py line 119 87073] Train: [72/100][1318/1557] Data 0.006 (0.180) Batch 1.082 (1.114) Remain 13:34:02 loss: 0.2371 Lr: 0.00101 [2024-02-19 06:06:51,859 INFO misc.py line 119 87073] Train: [72/100][1319/1557] Data 0.004 (0.180) Batch 0.892 (1.114) Remain 13:33:53 loss: 0.2451 Lr: 0.00101 [2024-02-19 06:06:52,922 INFO misc.py line 119 87073] Train: [72/100][1320/1557] Data 0.004 (0.180) Batch 1.064 (1.114) Remain 13:33:51 loss: 0.1186 Lr: 0.00101 [2024-02-19 06:06:53,694 INFO misc.py line 119 87073] Train: [72/100][1321/1557] Data 0.004 (0.180) Batch 0.771 (1.114) Remain 13:33:38 loss: 0.3805 Lr: 0.00101 [2024-02-19 06:06:54,436 INFO misc.py line 119 87073] Train: [72/100][1322/1557] Data 0.004 (0.179) Batch 0.742 (1.113) Remain 13:33:25 loss: 0.2924 Lr: 0.00101 [2024-02-19 06:06:55,580 INFO misc.py line 119 87073] Train: [72/100][1323/1557] Data 0.005 (0.179) Batch 1.144 (1.114) Remain 13:33:24 loss: 0.2115 Lr: 0.00101 [2024-02-19 06:06:56,573 INFO misc.py line 119 87073] Train: [72/100][1324/1557] Data 0.005 (0.179) Batch 0.992 (1.113) Remain 13:33:19 loss: 0.1210 Lr: 0.00101 [2024-02-19 06:06:57,574 INFO misc.py line 119 87073] Train: [72/100][1325/1557] Data 0.005 (0.179) Batch 1.001 (1.113) Remain 13:33:14 loss: 0.2480 Lr: 0.00101 [2024-02-19 06:06:58,574 INFO misc.py line 119 87073] Train: [72/100][1326/1557] Data 0.006 (0.179) Batch 0.997 (1.113) Remain 13:33:10 loss: 0.3070 Lr: 0.00101 [2024-02-19 06:06:59,490 INFO misc.py line 119 87073] Train: [72/100][1327/1557] Data 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Remain 13:32:32 loss: 0.3542 Lr: 0.00101 [2024-02-19 06:07:06,446 INFO misc.py line 119 87073] Train: [72/100][1334/1557] Data 0.004 (0.178) Batch 0.982 (1.112) Remain 13:32:27 loss: 0.3112 Lr: 0.00101 [2024-02-19 06:07:07,200 INFO misc.py line 119 87073] Train: [72/100][1335/1557] Data 0.004 (0.178) Batch 0.754 (1.112) Remain 13:32:14 loss: 0.3197 Lr: 0.00101 [2024-02-19 06:07:08,002 INFO misc.py line 119 87073] Train: [72/100][1336/1557] Data 0.005 (0.178) Batch 0.800 (1.112) Remain 13:32:02 loss: 0.2340 Lr: 0.00101 [2024-02-19 06:07:09,177 INFO misc.py line 119 87073] Train: [72/100][1337/1557] Data 0.007 (0.178) Batch 1.177 (1.112) Remain 13:32:03 loss: 0.2377 Lr: 0.00101 [2024-02-19 06:07:10,104 INFO misc.py line 119 87073] Train: [72/100][1338/1557] Data 0.006 (0.177) Batch 0.928 (1.112) Remain 13:31:56 loss: 0.1930 Lr: 0.00101 [2024-02-19 06:07:10,949 INFO misc.py line 119 87073] Train: [72/100][1339/1557] Data 0.004 (0.177) Batch 0.845 (1.112) Remain 13:31:46 loss: 0.1272 Lr: 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Train: [72/100][1352/1557] Data 0.013 (0.183) Batch 1.063 (1.117) Remain 13:35:33 loss: 0.5202 Lr: 0.00101 [2024-02-19 06:07:33,859 INFO misc.py line 119 87073] Train: [72/100][1353/1557] Data 0.006 (0.183) Batch 1.034 (1.117) Remain 13:35:29 loss: 0.1091 Lr: 0.00101 [2024-02-19 06:07:34,769 INFO misc.py line 119 87073] Train: [72/100][1354/1557] Data 0.010 (0.182) Batch 0.915 (1.117) Remain 13:35:21 loss: 0.4321 Lr: 0.00101 [2024-02-19 06:07:35,794 INFO misc.py line 119 87073] Train: [72/100][1355/1557] Data 0.004 (0.182) Batch 1.025 (1.117) Remain 13:35:17 loss: 0.3068 Lr: 0.00101 [2024-02-19 06:07:36,538 INFO misc.py line 119 87073] Train: [72/100][1356/1557] Data 0.004 (0.182) Batch 0.743 (1.117) Remain 13:35:04 loss: 0.2209 Lr: 0.00101 [2024-02-19 06:07:37,294 INFO misc.py line 119 87073] Train: [72/100][1357/1557] Data 0.004 (0.182) Batch 0.755 (1.116) Remain 13:34:51 loss: 0.2692 Lr: 0.00101 [2024-02-19 06:07:38,514 INFO misc.py line 119 87073] Train: [72/100][1358/1557] Data 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Remain 13:33:56 loss: 0.1528 Lr: 0.00101 [2024-02-19 06:07:44,784 INFO misc.py line 119 87073] Train: [72/100][1365/1557] Data 0.005 (0.181) Batch 1.152 (1.115) Remain 13:33:56 loss: 0.1269 Lr: 0.00101 [2024-02-19 06:07:45,692 INFO misc.py line 119 87073] Train: [72/100][1366/1557] Data 0.006 (0.181) Batch 0.908 (1.115) Remain 13:33:48 loss: 0.1329 Lr: 0.00101 [2024-02-19 06:07:46,847 INFO misc.py line 119 87073] Train: [72/100][1367/1557] Data 0.005 (0.181) Batch 1.156 (1.115) Remain 13:33:48 loss: 0.1838 Lr: 0.00101 [2024-02-19 06:07:47,723 INFO misc.py line 119 87073] Train: [72/100][1368/1557] Data 0.005 (0.181) Batch 0.875 (1.115) Remain 13:33:40 loss: 0.1399 Lr: 0.00101 [2024-02-19 06:07:48,704 INFO misc.py line 119 87073] Train: [72/100][1369/1557] Data 0.005 (0.180) Batch 0.982 (1.115) Remain 13:33:34 loss: 0.3445 Lr: 0.00101 [2024-02-19 06:07:49,482 INFO misc.py line 119 87073] Train: [72/100][1370/1557] Data 0.005 (0.180) Batch 0.777 (1.115) Remain 13:33:22 loss: 0.2196 Lr: 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Train: [72/100][1383/1557] Data 0.005 (0.179) Batch 1.011 (1.113) Remain 13:32:01 loss: 0.6685 Lr: 0.00101 [2024-02-19 06:08:02,656 INFO misc.py line 119 87073] Train: [72/100][1384/1557] Data 0.004 (0.179) Batch 0.775 (1.113) Remain 13:31:50 loss: 0.2621 Lr: 0.00101 [2024-02-19 06:08:03,366 INFO misc.py line 119 87073] Train: [72/100][1385/1557] Data 0.006 (0.178) Batch 0.710 (1.113) Remain 13:31:36 loss: 0.1728 Lr: 0.00101 [2024-02-19 06:08:04,680 INFO misc.py line 119 87073] Train: [72/100][1386/1557] Data 0.006 (0.178) Batch 1.314 (1.113) Remain 13:31:41 loss: 0.1743 Lr: 0.00101 [2024-02-19 06:08:05,747 INFO misc.py line 119 87073] Train: [72/100][1387/1557] Data 0.006 (0.178) Batch 1.060 (1.113) Remain 13:31:38 loss: 0.2460 Lr: 0.00101 [2024-02-19 06:08:06,710 INFO misc.py line 119 87073] Train: [72/100][1388/1557] Data 0.014 (0.178) Batch 0.970 (1.113) Remain 13:31:33 loss: 0.0701 Lr: 0.00101 [2024-02-19 06:08:07,585 INFO misc.py line 119 87073] Train: [72/100][1389/1557] Data 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Remain 13:30:42 loss: 0.2744 Lr: 0.00101 [2024-02-19 06:08:14,051 INFO misc.py line 119 87073] Train: [72/100][1396/1557] Data 0.004 (0.177) Batch 0.916 (1.111) Remain 13:30:35 loss: 0.3732 Lr: 0.00101 [2024-02-19 06:08:15,196 INFO misc.py line 119 87073] Train: [72/100][1397/1557] Data 0.007 (0.177) Batch 1.146 (1.112) Remain 13:30:35 loss: 0.4026 Lr: 0.00101 [2024-02-19 06:08:15,932 INFO misc.py line 119 87073] Train: [72/100][1398/1557] Data 0.005 (0.177) Batch 0.736 (1.111) Remain 13:30:22 loss: 0.5006 Lr: 0.00101 [2024-02-19 06:08:16,683 INFO misc.py line 119 87073] Train: [72/100][1399/1557] Data 0.004 (0.177) Batch 0.742 (1.111) Remain 13:30:09 loss: 0.3653 Lr: 0.00101 [2024-02-19 06:08:17,908 INFO misc.py line 119 87073] Train: [72/100][1400/1557] Data 0.013 (0.177) Batch 1.226 (1.111) Remain 13:30:12 loss: 0.2793 Lr: 0.00101 [2024-02-19 06:08:18,876 INFO misc.py line 119 87073] Train: [72/100][1401/1557] Data 0.012 (0.176) Batch 0.976 (1.111) Remain 13:30:06 loss: 0.2078 Lr: 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INFO misc.py line 119 87073] Train: [72/100][1408/1557] Data 0.005 (0.183) Batch 0.956 (1.117) Remain 13:34:39 loss: 0.1134 Lr: 0.00101 [2024-02-19 06:08:36,393 INFO misc.py line 119 87073] Train: [72/100][1409/1557] Data 0.008 (0.183) Batch 0.728 (1.117) Remain 13:34:26 loss: 0.3792 Lr: 0.00101 [2024-02-19 06:08:37,354 INFO misc.py line 119 87073] Train: [72/100][1410/1557] Data 0.004 (0.182) Batch 0.959 (1.117) Remain 13:34:20 loss: 0.2387 Lr: 0.00101 [2024-02-19 06:08:38,212 INFO misc.py line 119 87073] Train: [72/100][1411/1557] Data 0.006 (0.182) Batch 0.860 (1.117) Remain 13:34:11 loss: 0.1359 Lr: 0.00101 [2024-02-19 06:08:39,034 INFO misc.py line 119 87073] Train: [72/100][1412/1557] Data 0.004 (0.182) Batch 0.822 (1.117) Remain 13:34:00 loss: 0.1081 Lr: 0.00101 [2024-02-19 06:08:39,797 INFO misc.py line 119 87073] Train: [72/100][1413/1557] Data 0.004 (0.182) Batch 0.757 (1.116) Remain 13:33:48 loss: 0.1938 Lr: 0.00101 [2024-02-19 06:08:40,966 INFO misc.py line 119 87073] 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Remain 13:32:20 loss: 0.2969 Lr: 0.00101 [2024-02-19 06:08:52,728 INFO misc.py line 119 87073] Train: [72/100][1427/1557] Data 0.005 (0.180) Batch 0.805 (1.114) Remain 13:32:10 loss: 0.3332 Lr: 0.00101 [2024-02-19 06:08:53,782 INFO misc.py line 119 87073] Train: [72/100][1428/1557] Data 0.008 (0.180) Batch 1.050 (1.114) Remain 13:32:07 loss: 0.1102 Lr: 0.00101 [2024-02-19 06:08:54,690 INFO misc.py line 119 87073] Train: [72/100][1429/1557] Data 0.010 (0.180) Batch 0.913 (1.114) Remain 13:31:59 loss: 0.1686 Lr: 0.00101 [2024-02-19 06:08:55,517 INFO misc.py line 119 87073] Train: [72/100][1430/1557] Data 0.004 (0.180) Batch 0.828 (1.114) Remain 13:31:50 loss: 0.4111 Lr: 0.00100 [2024-02-19 06:08:56,576 INFO misc.py line 119 87073] Train: [72/100][1431/1557] Data 0.004 (0.180) Batch 1.053 (1.114) Remain 13:31:47 loss: 0.7103 Lr: 0.00100 [2024-02-19 06:08:57,606 INFO misc.py line 119 87073] Train: [72/100][1432/1557] Data 0.010 (0.180) Batch 1.032 (1.114) Remain 13:31:43 loss: 0.4068 Lr: 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Train: [72/100][1445/1557] Data 0.004 (0.178) Batch 0.931 (1.113) Remain 13:30:28 loss: 0.2809 Lr: 0.00100 [2024-02-19 06:09:11,080 INFO misc.py line 119 87073] Train: [72/100][1446/1557] Data 0.011 (0.178) Batch 0.992 (1.112) Remain 13:30:23 loss: 0.1846 Lr: 0.00100 [2024-02-19 06:09:11,873 INFO misc.py line 119 87073] Train: [72/100][1447/1557] Data 0.003 (0.178) Batch 0.793 (1.112) Remain 13:30:12 loss: 0.1899 Lr: 0.00100 [2024-02-19 06:09:12,678 INFO misc.py line 119 87073] Train: [72/100][1448/1557] Data 0.003 (0.178) Batch 0.776 (1.112) Remain 13:30:01 loss: 0.1403 Lr: 0.00100 [2024-02-19 06:09:13,819 INFO misc.py line 119 87073] Train: [72/100][1449/1557] Data 0.032 (0.178) Batch 1.164 (1.112) Remain 13:30:02 loss: 0.1405 Lr: 0.00100 [2024-02-19 06:09:14,898 INFO misc.py line 119 87073] Train: [72/100][1450/1557] Data 0.011 (0.178) Batch 1.078 (1.112) Remain 13:29:59 loss: 0.4905 Lr: 0.00100 [2024-02-19 06:09:15,911 INFO misc.py line 119 87073] Train: [72/100][1451/1557] Data 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Remain 13:29:20 loss: 0.4111 Lr: 0.00100 [2024-02-19 06:09:22,599 INFO misc.py line 119 87073] Train: [72/100][1458/1557] Data 0.004 (0.177) Batch 0.966 (1.111) Remain 13:29:15 loss: 0.2339 Lr: 0.00100 [2024-02-19 06:09:23,752 INFO misc.py line 119 87073] Train: [72/100][1459/1557] Data 0.004 (0.177) Batch 1.154 (1.111) Remain 13:29:15 loss: 0.3522 Lr: 0.00100 [2024-02-19 06:09:24,825 INFO misc.py line 119 87073] Train: [72/100][1460/1557] Data 0.003 (0.176) Batch 1.072 (1.111) Remain 13:29:13 loss: 0.6572 Lr: 0.00100 [2024-02-19 06:09:25,583 INFO misc.py line 119 87073] Train: [72/100][1461/1557] Data 0.004 (0.176) Batch 0.759 (1.111) Remain 13:29:01 loss: 0.2434 Lr: 0.00100 [2024-02-19 06:09:26,340 INFO misc.py line 119 87073] Train: [72/100][1462/1557] Data 0.003 (0.176) Batch 0.749 (1.111) Remain 13:28:49 loss: 0.2259 Lr: 0.00100 [2024-02-19 06:09:37,145 INFO misc.py line 119 87073] Train: [72/100][1463/1557] Data 9.730 (0.183) Batch 10.803 (1.117) Remain 13:33:38 loss: 0.2156 Lr: 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Train: [72/100][1476/1557] Data 0.003 (0.181) Batch 0.740 (1.116) Remain 13:32:21 loss: 0.2848 Lr: 0.00100 [2024-02-19 06:09:50,746 INFO misc.py line 119 87073] Train: [72/100][1477/1557] Data 0.008 (0.181) Batch 1.178 (1.116) Remain 13:32:22 loss: 0.1027 Lr: 0.00100 [2024-02-19 06:09:51,774 INFO misc.py line 119 87073] Train: [72/100][1478/1557] Data 0.012 (0.181) Batch 1.032 (1.116) Remain 13:32:18 loss: 0.2339 Lr: 0.00100 [2024-02-19 06:09:52,749 INFO misc.py line 119 87073] Train: [72/100][1479/1557] Data 0.009 (0.181) Batch 0.980 (1.116) Remain 13:32:13 loss: 0.3018 Lr: 0.00100 [2024-02-19 06:09:53,728 INFO misc.py line 119 87073] Train: [72/100][1480/1557] Data 0.004 (0.181) Batch 0.979 (1.116) Remain 13:32:08 loss: 0.3268 Lr: 0.00100 [2024-02-19 06:09:54,825 INFO misc.py line 119 87073] Train: [72/100][1481/1557] Data 0.003 (0.181) Batch 1.098 (1.116) Remain 13:32:06 loss: 0.5178 Lr: 0.00100 [2024-02-19 06:09:55,543 INFO misc.py line 119 87073] Train: [72/100][1482/1557] Data 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Remain 13:31:15 loss: 0.3158 Lr: 0.00100 [2024-02-19 06:10:02,048 INFO misc.py line 119 87073] Train: [72/100][1489/1557] Data 0.012 (0.180) Batch 0.882 (1.115) Remain 13:31:07 loss: 0.2087 Lr: 0.00100 [2024-02-19 06:10:02,820 INFO misc.py line 119 87073] Train: [72/100][1490/1557] Data 0.004 (0.180) Batch 0.773 (1.114) Remain 13:30:56 loss: 0.2660 Lr: 0.00100 [2024-02-19 06:10:04,001 INFO misc.py line 119 87073] Train: [72/100][1491/1557] Data 0.003 (0.179) Batch 1.178 (1.114) Remain 13:30:57 loss: 0.1254 Lr: 0.00100 [2024-02-19 06:10:04,985 INFO misc.py line 119 87073] Train: [72/100][1492/1557] Data 0.008 (0.179) Batch 0.988 (1.114) Remain 13:30:52 loss: 0.3795 Lr: 0.00100 [2024-02-19 06:10:05,937 INFO misc.py line 119 87073] Train: [72/100][1493/1557] Data 0.003 (0.179) Batch 0.952 (1.114) Remain 13:30:46 loss: 0.5284 Lr: 0.00100 [2024-02-19 06:10:07,028 INFO misc.py line 119 87073] Train: [72/100][1494/1557] Data 0.003 (0.179) Batch 1.090 (1.114) Remain 13:30:44 loss: 0.5740 Lr: 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0.010 (0.177) Batch 0.980 (1.112) Remain 13:28:52 loss: 0.3436 Lr: 0.00100 [2024-02-19 06:10:25,924 INFO misc.py line 119 87073] Train: [72/100][1514/1557] Data 0.004 (0.177) Batch 0.899 (1.112) Remain 13:28:44 loss: 0.4976 Lr: 0.00100 [2024-02-19 06:10:26,935 INFO misc.py line 119 87073] Train: [72/100][1515/1557] Data 0.005 (0.177) Batch 1.012 (1.112) Remain 13:28:40 loss: 0.2435 Lr: 0.00100 [2024-02-19 06:10:28,063 INFO misc.py line 119 87073] Train: [72/100][1516/1557] Data 0.004 (0.177) Batch 1.128 (1.112) Remain 13:28:40 loss: 0.2822 Lr: 0.00100 [2024-02-19 06:10:28,797 INFO misc.py line 119 87073] Train: [72/100][1517/1557] Data 0.003 (0.176) Batch 0.734 (1.112) Remain 13:28:28 loss: 0.2453 Lr: 0.00100 [2024-02-19 06:10:29,560 INFO misc.py line 119 87073] Train: [72/100][1518/1557] Data 0.003 (0.176) Batch 0.752 (1.111) Remain 13:28:16 loss: 0.1993 Lr: 0.00100 [2024-02-19 06:10:40,891 INFO misc.py line 119 87073] Train: [72/100][1519/1557] Data 10.334 (0.183) Batch 11.343 (1.118) Remain 13:33:10 loss: 0.1340 Lr: 0.00100 [2024-02-19 06:10:41,887 INFO misc.py line 119 87073] Train: [72/100][1520/1557] Data 0.003 (0.183) Batch 0.996 (1.118) Remain 13:33:05 loss: 0.4751 Lr: 0.00100 [2024-02-19 06:10:43,138 INFO misc.py line 119 87073] Train: [72/100][1521/1557] Data 0.003 (0.183) Batch 1.250 (1.118) Remain 13:33:08 loss: 0.2432 Lr: 0.00100 [2024-02-19 06:10:44,140 INFO misc.py line 119 87073] Train: [72/100][1522/1557] Data 0.004 (0.183) Batch 1.002 (1.118) Remain 13:33:03 loss: 0.5631 Lr: 0.00100 [2024-02-19 06:10:45,199 INFO misc.py line 119 87073] Train: [72/100][1523/1557] Data 0.003 (0.183) Batch 1.059 (1.118) Remain 13:33:00 loss: 0.3138 Lr: 0.00100 [2024-02-19 06:10:45,968 INFO misc.py line 119 87073] Train: [72/100][1524/1557] Data 0.004 (0.182) Batch 0.769 (1.118) Remain 13:32:49 loss: 0.3533 Lr: 0.00100 [2024-02-19 06:10:46,749 INFO misc.py line 119 87073] Train: [72/100][1525/1557] Data 0.004 (0.182) Batch 0.779 (1.118) Remain 13:32:38 loss: 0.3211 Lr: 0.00100 [2024-02-19 06:10:47,884 INFO misc.py line 119 87073] Train: [72/100][1526/1557] Data 0.005 (0.182) Batch 1.136 (1.118) Remain 13:32:38 loss: 0.1320 Lr: 0.00100 [2024-02-19 06:10:49,035 INFO misc.py line 119 87073] Train: [72/100][1527/1557] Data 0.004 (0.182) Batch 1.150 (1.118) Remain 13:32:38 loss: 0.1842 Lr: 0.00100 [2024-02-19 06:10:49,857 INFO misc.py line 119 87073] Train: [72/100][1528/1557] Data 0.006 (0.182) Batch 0.824 (1.117) Remain 13:32:28 loss: 0.3510 Lr: 0.00100 [2024-02-19 06:10:50,796 INFO misc.py line 119 87073] Train: [72/100][1529/1557] Data 0.004 (0.182) Batch 0.939 (1.117) Remain 13:32:22 loss: 0.4670 Lr: 0.00100 [2024-02-19 06:10:51,868 INFO misc.py line 119 87073] Train: [72/100][1530/1557] Data 0.004 (0.182) Batch 1.072 (1.117) Remain 13:32:19 loss: 0.2283 Lr: 0.00100 [2024-02-19 06:10:52,619 INFO misc.py line 119 87073] Train: [72/100][1531/1557] Data 0.003 (0.182) Batch 0.751 (1.117) Remain 13:32:08 loss: 0.3721 Lr: 0.00100 [2024-02-19 06:10:53,390 INFO misc.py line 119 87073] Train: [72/100][1532/1557] Data 0.003 (0.181) Batch 0.764 (1.117) Remain 13:31:57 loss: 0.1850 Lr: 0.00100 [2024-02-19 06:10:54,518 INFO misc.py line 119 87073] Train: [72/100][1533/1557] Data 0.009 (0.181) Batch 1.128 (1.117) Remain 13:31:56 loss: 0.1326 Lr: 0.00100 [2024-02-19 06:10:55,489 INFO misc.py line 119 87073] Train: [72/100][1534/1557] Data 0.009 (0.181) Batch 0.977 (1.117) Remain 13:31:51 loss: 0.3199 Lr: 0.00100 [2024-02-19 06:10:56,433 INFO misc.py line 119 87073] Train: [72/100][1535/1557] Data 0.005 (0.181) Batch 0.944 (1.117) Remain 13:31:45 loss: 0.2602 Lr: 0.00100 [2024-02-19 06:10:57,640 INFO misc.py line 119 87073] Train: [72/100][1536/1557] Data 0.003 (0.181) Batch 1.207 (1.117) Remain 13:31:46 loss: 0.6706 Lr: 0.00100 [2024-02-19 06:10:58,736 INFO misc.py line 119 87073] Train: [72/100][1537/1557] Data 0.003 (0.181) Batch 1.095 (1.117) Remain 13:31:45 loss: 0.1075 Lr: 0.00100 [2024-02-19 06:10:59,442 INFO misc.py line 119 87073] Train: [72/100][1538/1557] Data 0.004 (0.181) Batch 0.707 (1.116) Remain 13:31:32 loss: 0.2000 Lr: 0.00100 [2024-02-19 06:11:00,168 INFO misc.py line 119 87073] Train: [72/100][1539/1557] Data 0.003 (0.181) Batch 0.719 (1.116) Remain 13:31:19 loss: 0.0924 Lr: 0.00100 [2024-02-19 06:11:01,276 INFO misc.py line 119 87073] Train: [72/100][1540/1557] Data 0.009 (0.181) Batch 1.104 (1.116) Remain 13:31:18 loss: 0.1424 Lr: 0.00100 [2024-02-19 06:11:02,206 INFO misc.py line 119 87073] Train: [72/100][1541/1557] Data 0.014 (0.180) Batch 0.940 (1.116) Remain 13:31:12 loss: 0.0691 Lr: 0.00100 [2024-02-19 06:11:03,471 INFO misc.py line 119 87073] Train: [72/100][1542/1557] Data 0.004 (0.180) Batch 1.257 (1.116) Remain 13:31:15 loss: 0.2844 Lr: 0.00100 [2024-02-19 06:11:04,323 INFO misc.py line 119 87073] Train: [72/100][1543/1557] Data 0.012 (0.180) Batch 0.860 (1.116) Remain 13:31:06 loss: 0.2730 Lr: 0.00100 [2024-02-19 06:11:05,317 INFO misc.py line 119 87073] Train: [72/100][1544/1557] Data 0.003 (0.180) Batch 0.994 (1.116) Remain 13:31:02 loss: 0.2333 Lr: 0.00100 [2024-02-19 06:11:06,076 INFO misc.py line 119 87073] Train: [72/100][1545/1557] Data 0.003 (0.180) Batch 0.758 (1.116) Remain 13:30:51 loss: 0.1596 Lr: 0.00100 [2024-02-19 06:11:06,834 INFO misc.py line 119 87073] Train: [72/100][1546/1557] Data 0.003 (0.180) Batch 0.748 (1.115) Remain 13:30:39 loss: 0.1546 Lr: 0.00100 [2024-02-19 06:11:08,047 INFO misc.py line 119 87073] Train: [72/100][1547/1557] Data 0.013 (0.180) Batch 1.215 (1.115) Remain 13:30:41 loss: 0.1475 Lr: 0.00100 [2024-02-19 06:11:08,872 INFO misc.py line 119 87073] Train: [72/100][1548/1557] Data 0.011 (0.180) Batch 0.833 (1.115) Remain 13:30:32 loss: 0.0937 Lr: 0.00100 [2024-02-19 06:11:09,799 INFO misc.py line 119 87073] Train: [72/100][1549/1557] Data 0.003 (0.180) Batch 0.927 (1.115) Remain 13:30:25 loss: 0.3707 Lr: 0.00100 [2024-02-19 06:11:10,776 INFO misc.py line 119 87073] Train: [72/100][1550/1557] Data 0.003 (0.179) Batch 0.977 (1.115) Remain 13:30:20 loss: 0.4661 Lr: 0.00100 [2024-02-19 06:11:11,744 INFO misc.py line 119 87073] Train: [72/100][1551/1557] Data 0.003 (0.179) Batch 0.959 (1.115) Remain 13:30:15 loss: 0.4209 Lr: 0.00100 [2024-02-19 06:11:12,520 INFO misc.py line 119 87073] Train: [72/100][1552/1557] Data 0.012 (0.179) Batch 0.785 (1.115) Remain 13:30:04 loss: 0.2922 Lr: 0.00100 [2024-02-19 06:11:13,251 INFO misc.py line 119 87073] Train: [72/100][1553/1557] Data 0.004 (0.179) Batch 0.724 (1.115) Remain 13:29:52 loss: 0.1243 Lr: 0.00100 [2024-02-19 06:11:14,598 INFO misc.py line 119 87073] Train: [72/100][1554/1557] Data 0.010 (0.179) Batch 1.342 (1.115) Remain 13:29:57 loss: 0.1167 Lr: 0.00100 [2024-02-19 06:11:15,579 INFO misc.py line 119 87073] Train: [72/100][1555/1557] Data 0.016 (0.179) Batch 0.993 (1.115) Remain 13:29:53 loss: 0.2125 Lr: 0.00100 [2024-02-19 06:11:16,509 INFO misc.py line 119 87073] Train: [72/100][1556/1557] Data 0.003 (0.179) Batch 0.930 (1.114) Remain 13:29:47 loss: 0.3385 Lr: 0.00100 [2024-02-19 06:11:17,476 INFO misc.py line 119 87073] Train: [72/100][1557/1557] Data 0.003 (0.179) Batch 0.967 (1.114) Remain 13:29:41 loss: 0.2086 Lr: 0.00100 [2024-02-19 06:11:17,476 INFO misc.py line 136 87073] Train result: loss: 0.2592 [2024-02-19 06:11:17,476 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 06:11:45,652 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4430 [2024-02-19 06:11:46,431 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6797 [2024-02-19 06:11:48,554 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3781 [2024-02-19 06:11:50,763 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2959 [2024-02-19 06:11:55,714 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2412 [2024-02-19 06:11:56,412 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0665 [2024-02-19 06:11:57,671 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2923 [2024-02-19 06:12:00,627 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2311 [2024-02-19 06:12:02,440 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2376 [2024-02-19 06:12:04,048 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7310/0.7848/0.9188. [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9336/0.9640 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9827/0.9891 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8638/0.9759 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0020/0.0084 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4064/0.4399 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6441/0.6611 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7695/0.8915 [2024-02-19 06:12:04,048 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8512/0.9169 [2024-02-19 06:12:04,049 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9303/0.9689 [2024-02-19 06:12:04,049 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8635/0.8905 [2024-02-19 06:12:04,049 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8067/0.8859 [2024-02-19 06:12:04,049 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8038/0.8741 [2024-02-19 06:12:04,049 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6457/0.7363 [2024-02-19 06:12:04,049 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 06:12:04,051 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 06:12:04,051 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 06:12:10,679 INFO misc.py line 119 87073] Train: [73/100][1/1557] Data 1.070 (1.070) Batch 1.802 (1.802) Remain 21:49:07 loss: 0.2363 Lr: 0.00100 [2024-02-19 06:12:11,736 INFO misc.py line 119 87073] Train: [73/100][2/1557] Data 0.005 (0.005) Batch 1.057 (1.057) Remain 12:47:43 loss: 0.1347 Lr: 0.00100 [2024-02-19 06:12:12,502 INFO misc.py line 119 87073] Train: [73/100][3/1557] Data 0.005 (0.005) Batch 0.767 (0.767) Remain 09:17:12 loss: 0.3436 Lr: 0.00100 [2024-02-19 06:12:13,466 INFO misc.py line 119 87073] Train: [73/100][4/1557] Data 0.004 (0.004) Batch 0.964 (0.964) Remain 11:40:08 loss: 0.1501 Lr: 0.00100 [2024-02-19 06:12:14,241 INFO misc.py line 119 87073] Train: [73/100][5/1557] Data 0.004 (0.004) Batch 0.768 (0.866) Remain 10:29:07 loss: 0.1700 Lr: 0.00100 [2024-02-19 06:12:14,966 INFO misc.py line 119 87073] Train: [73/100][6/1557] Data 0.011 (0.007) Batch 0.733 (0.822) Remain 09:56:56 loss: 0.1841 Lr: 0.00100 [2024-02-19 06:12:16,561 INFO misc.py line 119 87073] Train: [73/100][7/1557] Data 0.003 (0.006) Batch 1.587 (1.013) Remain 12:15:54 loss: 0.1442 Lr: 0.00100 [2024-02-19 06:12:17,674 INFO misc.py line 119 87073] Train: [73/100][8/1557] Data 0.012 (0.007) Batch 1.086 (1.027) Remain 12:26:26 loss: 0.2991 Lr: 0.00100 [2024-02-19 06:12:18,779 INFO misc.py line 119 87073] Train: [73/100][9/1557] Data 0.038 (0.012) Batch 1.137 (1.046) Remain 12:39:39 loss: 0.5486 Lr: 0.00100 [2024-02-19 06:12:19,825 INFO misc.py line 119 87073] Train: [73/100][10/1557] Data 0.008 (0.012) Batch 1.039 (1.045) Remain 12:38:55 loss: 0.3427 Lr: 0.00100 [2024-02-19 06:12:20,887 INFO misc.py line 119 87073] Train: [73/100][11/1557] Data 0.013 (0.012) Batch 1.060 (1.047) Remain 12:40:15 loss: 0.3845 Lr: 0.00100 [2024-02-19 06:12:21,676 INFO misc.py line 119 87073] Train: [73/100][12/1557] Data 0.016 (0.012) Batch 0.802 (1.019) Remain 12:20:29 loss: 0.2079 Lr: 0.00100 [2024-02-19 06:12:22,478 INFO misc.py line 119 87073] Train: [73/100][13/1557] Data 0.004 (0.011) Batch 0.802 (0.998) Remain 12:04:39 loss: 0.2344 Lr: 0.00100 [2024-02-19 06:12:25,090 INFO misc.py line 119 87073] Train: [73/100][14/1557] Data 0.003 (0.011) Batch 2.612 (1.144) Remain 13:51:15 loss: 0.0927 Lr: 0.00100 [2024-02-19 06:12:26,128 INFO misc.py line 119 87073] Train: [73/100][15/1557] Data 0.003 (0.010) Batch 1.038 (1.135) Remain 13:44:46 loss: 0.1341 Lr: 0.00100 [2024-02-19 06:12:27,239 INFO misc.py line 119 87073] Train: [73/100][16/1557] Data 0.003 (0.009) Batch 1.111 (1.134) Remain 13:43:23 loss: 0.3272 Lr: 0.00100 [2024-02-19 06:12:28,302 INFO misc.py line 119 87073] Train: [73/100][17/1557] Data 0.003 (0.009) Batch 1.063 (1.129) Remain 13:39:41 loss: 0.2858 Lr: 0.00100 [2024-02-19 06:12:29,285 INFO misc.py line 119 87073] Train: [73/100][18/1557] Data 0.004 (0.009) Batch 0.983 (1.119) Remain 13:32:38 loss: 0.2542 Lr: 0.00100 [2024-02-19 06:12:29,980 INFO misc.py line 119 87073] Train: [73/100][19/1557] Data 0.003 (0.008) Batch 0.696 (1.092) Remain 13:13:24 loss: 0.3603 Lr: 0.00100 [2024-02-19 06:12:30,828 INFO misc.py line 119 87073] Train: [73/100][20/1557] Data 0.003 (0.008) Batch 0.837 (1.077) Remain 13:02:29 loss: 0.1584 Lr: 0.00100 [2024-02-19 06:12:32,008 INFO misc.py line 119 87073] Train: [73/100][21/1557] Data 0.013 (0.008) Batch 1.181 (1.083) Remain 13:06:39 loss: 0.1164 Lr: 0.00100 [2024-02-19 06:12:33,054 INFO misc.py line 119 87073] Train: [73/100][22/1557] Data 0.013 (0.009) Batch 1.045 (1.081) Remain 13:05:10 loss: 0.2175 Lr: 0.00100 [2024-02-19 06:12:34,044 INFO misc.py line 119 87073] Train: [73/100][23/1557] Data 0.013 (0.009) Batch 1.000 (1.077) Remain 13:02:12 loss: 0.4645 Lr: 0.00100 [2024-02-19 06:12:34,888 INFO misc.py line 119 87073] Train: [73/100][24/1557] Data 0.003 (0.008) Batch 0.844 (1.066) Remain 12:54:07 loss: 0.2572 Lr: 0.00100 [2024-02-19 06:12:35,843 INFO misc.py line 119 87073] Train: [73/100][25/1557] Data 0.003 (0.008) Batch 0.956 (1.061) Remain 12:50:27 loss: 0.3408 Lr: 0.00100 [2024-02-19 06:12:36,569 INFO misc.py line 119 87073] Train: [73/100][26/1557] Data 0.003 (0.008) Batch 0.722 (1.046) Remain 12:39:44 loss: 0.1477 Lr: 0.00100 [2024-02-19 06:12:37,296 INFO misc.py line 119 87073] Train: [73/100][27/1557] Data 0.008 (0.008) Batch 0.731 (1.033) Remain 12:30:10 loss: 0.1738 Lr: 0.00100 [2024-02-19 06:12:38,437 INFO misc.py line 119 87073] Train: [73/100][28/1557] Data 0.003 (0.008) Batch 1.139 (1.037) Remain 12:33:12 loss: 0.1821 Lr: 0.00100 [2024-02-19 06:12:39,469 INFO misc.py line 119 87073] Train: [73/100][29/1557] Data 0.007 (0.008) Batch 1.034 (1.037) Remain 12:33:06 loss: 0.4646 Lr: 0.00100 [2024-02-19 06:12:40,413 INFO misc.py line 119 87073] Train: [73/100][30/1557] Data 0.004 (0.008) Batch 0.943 (1.034) Remain 12:30:33 loss: 0.1798 Lr: 0.00100 [2024-02-19 06:12:41,449 INFO misc.py line 119 87073] Train: [73/100][31/1557] Data 0.005 (0.008) Batch 1.038 (1.034) Remain 12:30:39 loss: 0.0681 Lr: 0.00100 [2024-02-19 06:12:42,485 INFO misc.py line 119 87073] Train: [73/100][32/1557] Data 0.003 (0.007) Batch 1.036 (1.034) Remain 12:30:40 loss: 0.1747 Lr: 0.00100 [2024-02-19 06:12:43,262 INFO misc.py line 119 87073] Train: [73/100][33/1557] Data 0.004 (0.007) Batch 0.777 (1.025) Remain 12:24:26 loss: 0.2693 Lr: 0.00100 [2024-02-19 06:12:43,950 INFO misc.py line 119 87073] Train: [73/100][34/1557] Data 0.004 (0.007) Batch 0.682 (1.014) Remain 12:16:23 loss: 0.2797 Lr: 0.00100 [2024-02-19 06:12:45,141 INFO misc.py line 119 87073] Train: [73/100][35/1557] Data 0.010 (0.007) Batch 1.196 (1.020) Remain 12:20:29 loss: 0.4037 Lr: 0.00100 [2024-02-19 06:12:46,129 INFO misc.py line 119 87073] Train: [73/100][36/1557] Data 0.005 (0.007) Batch 0.990 (1.019) Remain 12:19:48 loss: 0.3958 Lr: 0.00100 [2024-02-19 06:12:47,053 INFO misc.py line 119 87073] Train: [73/100][37/1557] Data 0.004 (0.007) Batch 0.923 (1.016) Remain 12:17:44 loss: 0.2002 Lr: 0.00100 [2024-02-19 06:12:48,103 INFO misc.py line 119 87073] Train: [73/100][38/1557] Data 0.004 (0.007) Batch 1.050 (1.017) Remain 12:18:25 loss: 0.3411 Lr: 0.00100 [2024-02-19 06:12:49,066 INFO misc.py line 119 87073] Train: [73/100][39/1557] Data 0.004 (0.007) Batch 0.962 (1.016) Remain 12:17:18 loss: 0.1178 Lr: 0.00100 [2024-02-19 06:12:49,826 INFO misc.py line 119 87073] Train: [73/100][40/1557] Data 0.004 (0.007) Batch 0.756 (1.009) Remain 12:12:11 loss: 0.1512 Lr: 0.00100 [2024-02-19 06:12:50,561 INFO misc.py line 119 87073] Train: [73/100][41/1557] Data 0.009 (0.007) Batch 0.740 (1.002) Remain 12:07:02 loss: 0.3051 Lr: 0.00100 [2024-02-19 06:12:51,826 INFO misc.py line 119 87073] Train: [73/100][42/1557] Data 0.003 (0.007) Batch 1.263 (1.008) Remain 12:11:53 loss: 0.2526 Lr: 0.00100 [2024-02-19 06:12:52,857 INFO misc.py line 119 87073] Train: [73/100][43/1557] Data 0.006 (0.007) Batch 1.032 (1.009) Remain 12:12:18 loss: 0.3544 Lr: 0.00100 [2024-02-19 06:12:53,747 INFO misc.py line 119 87073] Train: [73/100][44/1557] Data 0.005 (0.007) Batch 0.891 (1.006) Remain 12:10:12 loss: 0.1588 Lr: 0.00100 [2024-02-19 06:12:54,741 INFO misc.py line 119 87073] Train: [73/100][45/1557] Data 0.003 (0.007) Batch 0.994 (1.006) Remain 12:09:59 loss: 0.1264 Lr: 0.00100 [2024-02-19 06:12:55,786 INFO misc.py line 119 87073] Train: [73/100][46/1557] Data 0.004 (0.007) 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line 119 87073] Train: [73/100][277/1557] Data 0.007 (0.085) Batch 1.012 (1.130) Remain 13:36:00 loss: 0.0596 Lr: 0.00099 [2024-02-19 06:17:22,982 INFO misc.py line 119 87073] Train: [73/100][278/1557] Data 0.009 (0.085) Batch 0.792 (1.129) Remain 13:35:06 loss: 0.3366 Lr: 0.00099 [2024-02-19 06:17:23,725 INFO misc.py line 119 87073] Train: [73/100][279/1557] Data 0.009 (0.085) Batch 0.747 (1.128) Remain 13:34:05 loss: 0.2349 Lr: 0.00099 [2024-02-19 06:17:24,913 INFO misc.py line 119 87073] Train: [73/100][280/1557] Data 0.003 (0.085) Batch 1.180 (1.128) Remain 13:34:12 loss: 0.1384 Lr: 0.00099 [2024-02-19 06:17:25,751 INFO misc.py line 119 87073] Train: [73/100][281/1557] Data 0.012 (0.084) Batch 0.844 (1.127) Remain 13:33:26 loss: 0.2346 Lr: 0.00099 [2024-02-19 06:17:26,785 INFO misc.py line 119 87073] Train: [73/100][282/1557] Data 0.004 (0.084) Batch 1.034 (1.126) Remain 13:33:11 loss: 0.0973 Lr: 0.00099 [2024-02-19 06:17:27,728 INFO misc.py line 119 87073] Train: 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Batch 0.930 (1.153) Remain 13:51:52 loss: 0.3633 Lr: 0.00099 [2024-02-19 06:17:43,157 INFO misc.py line 119 87073] Train: [73/100][290/1557] Data 0.006 (0.102) Batch 1.027 (1.152) Remain 13:51:32 loss: 0.2182 Lr: 0.00099 [2024-02-19 06:17:44,013 INFO misc.py line 119 87073] Train: [73/100][291/1557] Data 0.007 (0.102) Batch 0.856 (1.151) Remain 13:50:47 loss: 0.1930 Lr: 0.00099 [2024-02-19 06:17:44,732 INFO misc.py line 119 87073] Train: [73/100][292/1557] Data 0.006 (0.101) Batch 0.719 (1.150) Remain 13:49:41 loss: 0.2665 Lr: 0.00099 [2024-02-19 06:17:45,492 INFO misc.py line 119 87073] Train: [73/100][293/1557] Data 0.007 (0.101) Batch 0.758 (1.148) Remain 13:48:41 loss: 0.1875 Lr: 0.00099 [2024-02-19 06:17:47,797 INFO misc.py line 119 87073] Train: [73/100][294/1557] Data 0.008 (0.101) Batch 2.309 (1.152) Remain 13:51:33 loss: 0.0869 Lr: 0.00099 [2024-02-19 06:17:48,708 INFO misc.py line 119 87073] Train: [73/100][295/1557] Data 0.005 (0.100) Batch 0.912 (1.151) Remain 13:50:56 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Batch 0.768 (1.155) Remain 13:42:54 loss: 0.0915 Lr: 0.00096 [2024-02-19 06:28:30,656 INFO misc.py line 119 87073] Train: [73/100][850/1557] Data 0.007 (0.107) Batch 0.997 (1.155) Remain 13:42:44 loss: 0.1134 Lr: 0.00096 [2024-02-19 06:28:31,554 INFO misc.py line 119 87073] Train: [73/100][851/1557] Data 0.004 (0.107) Batch 0.896 (1.155) Remain 13:42:30 loss: 0.2813 Lr: 0.00096 [2024-02-19 06:28:32,333 INFO misc.py line 119 87073] Train: [73/100][852/1557] Data 0.006 (0.107) Batch 0.779 (1.154) Remain 13:42:10 loss: 0.2106 Lr: 0.00096 [2024-02-19 06:28:33,062 INFO misc.py line 119 87073] Train: [73/100][853/1557] Data 0.005 (0.107) Batch 0.723 (1.154) Remain 13:41:47 loss: 0.2097 Lr: 0.00096 [2024-02-19 06:28:38,346 INFO misc.py line 119 87073] Train: [73/100][854/1557] Data 0.013 (0.107) Batch 5.291 (1.158) Remain 13:45:14 loss: 0.0711 Lr: 0.00096 [2024-02-19 06:28:39,353 INFO misc.py line 119 87073] Train: [73/100][855/1557] Data 0.006 (0.107) Batch 1.008 (1.158) Remain 13:45:05 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[2024-02-19 06:41:44,964 INFO misc.py line 119 87073] Train: [73/100][1526/1557] Data 0.003 (0.109) Batch 4.891 (1.164) Remain 13:36:00 loss: 0.0832 Lr: 0.00094 [2024-02-19 06:41:45,984 INFO misc.py line 119 87073] Train: [73/100][1527/1557] Data 0.004 (0.109) Batch 1.022 (1.164) Remain 13:35:55 loss: 0.3504 Lr: 0.00094 [2024-02-19 06:41:46,980 INFO misc.py line 119 87073] Train: [73/100][1528/1557] Data 0.003 (0.109) Batch 0.996 (1.164) Remain 13:35:49 loss: 0.1597 Lr: 0.00094 [2024-02-19 06:41:47,902 INFO misc.py line 119 87073] Train: [73/100][1529/1557] Data 0.003 (0.109) Batch 0.921 (1.163) Remain 13:35:42 loss: 0.1863 Lr: 0.00094 [2024-02-19 06:41:48,940 INFO misc.py line 119 87073] Train: [73/100][1530/1557] Data 0.004 (0.109) Batch 1.038 (1.163) Remain 13:35:37 loss: 0.2959 Lr: 0.00094 [2024-02-19 06:41:49,696 INFO misc.py line 119 87073] Train: [73/100][1531/1557] Data 0.004 (0.109) Batch 0.748 (1.163) Remain 13:35:24 loss: 0.2465 Lr: 0.00094 [2024-02-19 06:41:50,524 INFO misc.py line 119 87073] Train: [73/100][1532/1557] Data 0.011 (0.109) Batch 0.836 (1.163) Remain 13:35:14 loss: 0.1216 Lr: 0.00094 [2024-02-19 06:41:51,752 INFO misc.py line 119 87073] Train: [73/100][1533/1557] Data 0.003 (0.109) Batch 1.220 (1.163) Remain 13:35:15 loss: 0.2750 Lr: 0.00094 [2024-02-19 06:41:52,741 INFO misc.py line 119 87073] Train: [73/100][1534/1557] Data 0.012 (0.109) Batch 0.997 (1.163) Remain 13:35:09 loss: 0.1569 Lr: 0.00094 [2024-02-19 06:41:53,945 INFO misc.py line 119 87073] Train: [73/100][1535/1557] Data 0.005 (0.109) Batch 1.195 (1.163) Remain 13:35:09 loss: 0.5338 Lr: 0.00094 [2024-02-19 06:41:54,899 INFO misc.py line 119 87073] Train: [73/100][1536/1557] Data 0.013 (0.109) Batch 0.963 (1.163) Remain 13:35:02 loss: 0.2811 Lr: 0.00094 [2024-02-19 06:41:55,819 INFO misc.py line 119 87073] Train: [73/100][1537/1557] Data 0.004 (0.109) Batch 0.921 (1.163) Remain 13:34:54 loss: 0.2635 Lr: 0.00094 [2024-02-19 06:41:56,511 INFO misc.py line 119 87073] Train: [73/100][1538/1557] Data 0.004 (0.109) Batch 0.679 (1.162) Remain 13:34:40 loss: 0.2523 Lr: 0.00094 [2024-02-19 06:41:57,167 INFO misc.py line 119 87073] Train: [73/100][1539/1557] Data 0.015 (0.109) Batch 0.668 (1.162) Remain 13:34:25 loss: 0.1903 Lr: 0.00094 [2024-02-19 06:41:58,364 INFO misc.py line 119 87073] Train: [73/100][1540/1557] Data 0.004 (0.108) Batch 1.190 (1.162) Remain 13:34:25 loss: 0.2127 Lr: 0.00093 [2024-02-19 06:41:59,470 INFO misc.py line 119 87073] Train: [73/100][1541/1557] Data 0.011 (0.108) Batch 1.105 (1.162) Remain 13:34:22 loss: 0.2025 Lr: 0.00093 [2024-02-19 06:42:00,427 INFO misc.py line 119 87073] Train: [73/100][1542/1557] Data 0.012 (0.108) Batch 0.965 (1.162) Remain 13:34:15 loss: 0.0815 Lr: 0.00093 [2024-02-19 06:42:01,225 INFO misc.py line 119 87073] Train: [73/100][1543/1557] Data 0.004 (0.108) Batch 0.797 (1.162) Remain 13:34:04 loss: 0.1505 Lr: 0.00093 [2024-02-19 06:42:02,105 INFO misc.py line 119 87073] Train: [73/100][1544/1557] Data 0.004 (0.108) Batch 0.870 (1.161) Remain 13:33:55 loss: 0.0820 Lr: 0.00093 [2024-02-19 06:42:02,836 INFO misc.py line 119 87073] Train: [73/100][1545/1557] Data 0.014 (0.108) Batch 0.741 (1.161) Remain 13:33:43 loss: 0.2634 Lr: 0.00093 [2024-02-19 06:42:03,578 INFO misc.py line 119 87073] Train: [73/100][1546/1557] Data 0.004 (0.108) Batch 0.733 (1.161) Remain 13:33:30 loss: 0.1392 Lr: 0.00093 [2024-02-19 06:42:04,839 INFO misc.py line 119 87073] Train: [73/100][1547/1557] Data 0.013 (0.108) Batch 1.261 (1.161) Remain 13:33:31 loss: 0.1252 Lr: 0.00093 [2024-02-19 06:42:05,788 INFO misc.py line 119 87073] Train: [73/100][1548/1557] Data 0.012 (0.108) Batch 0.958 (1.161) Remain 13:33:25 loss: 0.1949 Lr: 0.00093 [2024-02-19 06:42:06,714 INFO misc.py line 119 87073] Train: [73/100][1549/1557] Data 0.004 (0.108) Batch 0.926 (1.161) Remain 13:33:17 loss: 0.2801 Lr: 0.00093 [2024-02-19 06:42:07,587 INFO misc.py line 119 87073] Train: [73/100][1550/1557] Data 0.004 (0.108) Batch 0.865 (1.160) Remain 13:33:08 loss: 0.2948 Lr: 0.00093 [2024-02-19 06:42:08,535 INFO misc.py line 119 87073] Train: [73/100][1551/1557] Data 0.012 (0.108) Batch 0.957 (1.160) Remain 13:33:01 loss: 0.2655 Lr: 0.00093 [2024-02-19 06:42:09,307 INFO misc.py line 119 87073] Train: [73/100][1552/1557] Data 0.004 (0.108) Batch 0.772 (1.160) Remain 13:32:49 loss: 0.3706 Lr: 0.00093 [2024-02-19 06:42:10,044 INFO misc.py line 119 87073] Train: [73/100][1553/1557] Data 0.004 (0.108) Batch 0.726 (1.160) Remain 13:32:37 loss: 0.1895 Lr: 0.00093 [2024-02-19 06:42:11,222 INFO misc.py line 119 87073] Train: [73/100][1554/1557] Data 0.014 (0.108) Batch 1.179 (1.160) Remain 13:32:36 loss: 0.3834 Lr: 0.00093 [2024-02-19 06:42:12,254 INFO misc.py line 119 87073] Train: [73/100][1555/1557] Data 0.012 (0.107) Batch 1.032 (1.160) Remain 13:32:31 loss: 0.1918 Lr: 0.00093 [2024-02-19 06:42:13,118 INFO misc.py line 119 87073] Train: [73/100][1556/1557] Data 0.012 (0.107) Batch 0.873 (1.159) Remain 13:32:22 loss: 0.1400 Lr: 0.00093 [2024-02-19 06:42:14,121 INFO misc.py line 119 87073] Train: [73/100][1557/1557] Data 0.004 (0.107) Batch 1.003 (1.159) Remain 13:32:17 loss: 0.1457 Lr: 0.00093 [2024-02-19 06:42:14,122 INFO misc.py line 136 87073] Train result: loss: 0.2543 [2024-02-19 06:42:14,122 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 06:42:42,904 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5275 [2024-02-19 06:42:43,683 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4122 [2024-02-19 06:42:45,809 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4255 [2024-02-19 06:42:48,015 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3386 [2024-02-19 06:42:52,958 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.3152 [2024-02-19 06:42:53,659 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0712 [2024-02-19 06:42:54,921 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2395 [2024-02-19 06:42:57,876 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3023 [2024-02-19 06:42:59,686 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2686 [2024-02-19 06:43:01,141 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7068/0.7692/0.9111. [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9251/0.9519 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9824/0.9898 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8497/0.9750 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0004/0.0014 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3506/0.3967 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6377/0.6523 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8033/0.8930 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8444/0.9161 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9227/0.9742 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7756/0.8297 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7943/0.8999 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.6965/0.8426 [2024-02-19 06:43:01,142 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6061/0.6774 [2024-02-19 06:43:01,142 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 06:43:01,144 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 06:43:01,145 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 06:43:08,671 INFO misc.py line 119 87073] Train: [74/100][1/1557] Data 1.773 (1.773) Batch 2.806 (2.806) Remain 32:46:18 loss: 0.3787 Lr: 0.00093 [2024-02-19 06:43:09,651 INFO misc.py line 119 87073] Train: [74/100][2/1557] Data 0.006 (0.006) Batch 0.981 (0.981) Remain 11:27:16 loss: 0.3352 Lr: 0.00093 [2024-02-19 06:43:10,655 INFO misc.py line 119 87073] Train: [74/100][3/1557] Data 0.004 (0.004) Batch 1.001 (1.001) Remain 11:41:24 loss: 0.2339 Lr: 0.00093 [2024-02-19 06:43:11,627 INFO misc.py line 119 87073] Train: [74/100][4/1557] Data 0.008 (0.008) Batch 0.974 (0.974) Remain 11:22:16 loss: 0.2047 Lr: 0.00093 [2024-02-19 06:43:12,365 INFO misc.py line 119 87073] Train: [74/100][5/1557] Data 0.007 (0.007) Batch 0.738 (0.856) Remain 09:59:42 loss: 0.3905 Lr: 0.00093 [2024-02-19 06:43:13,132 INFO misc.py line 119 87073] Train: [74/100][6/1557] Data 0.005 (0.007) Batch 0.768 (0.827) Remain 09:39:06 loss: 0.1299 Lr: 0.00093 [2024-02-19 06:43:14,153 INFO misc.py line 119 87073] Train: [74/100][7/1557] Data 0.004 (0.006) Batch 1.013 (0.873) Remain 10:11:46 loss: 0.2078 Lr: 0.00093 [2024-02-19 06:43:15,080 INFO misc.py line 119 87073] Train: [74/100][8/1557] Data 0.012 (0.007) Batch 0.936 (0.886) Remain 10:20:33 loss: 0.1423 Lr: 0.00093 [2024-02-19 06:43:16,084 INFO misc.py line 119 87073] Train: [74/100][9/1557] Data 0.003 (0.007) Batch 1.002 (0.905) Remain 10:34:03 loss: 0.3342 Lr: 0.00093 [2024-02-19 06:43:16,935 INFO misc.py line 119 87073] Train: [74/100][10/1557] Data 0.005 (0.006) Batch 0.852 (0.898) Remain 10:28:42 loss: 0.1950 Lr: 0.00093 [2024-02-19 06:43:17,837 INFO misc.py line 119 87073] Train: [74/100][11/1557] Data 0.004 (0.006) Batch 0.902 (0.898) Remain 10:29:05 loss: 0.0854 Lr: 0.00093 [2024-02-19 06:43:18,619 INFO misc.py line 119 87073] Train: [74/100][12/1557] Data 0.004 (0.006) Batch 0.783 (0.885) Remain 10:20:05 loss: 0.3300 Lr: 0.00093 [2024-02-19 06:43:19,389 INFO misc.py line 119 87073] Train: [74/100][13/1557] Data 0.004 (0.006) Batch 0.771 (0.874) Remain 10:12:02 loss: 0.2092 Lr: 0.00093 [2024-02-19 06:43:20,535 INFO misc.py line 119 87073] Train: [74/100][14/1557] Data 0.003 (0.005) Batch 1.137 (0.898) Remain 10:28:46 loss: 0.2030 Lr: 0.00093 [2024-02-19 06:43:21,627 INFO misc.py line 119 87073] Train: [74/100][15/1557] Data 0.012 (0.006) Batch 1.094 (0.914) Remain 10:40:14 loss: 0.4542 Lr: 0.00093 [2024-02-19 06:43:22,542 INFO misc.py line 119 87073] Train: [74/100][16/1557] Data 0.010 (0.006) Batch 0.922 (0.915) Remain 10:40:37 loss: 0.4967 Lr: 0.00093 [2024-02-19 06:43:23,688 INFO misc.py line 119 87073] Train: [74/100][17/1557] Data 0.003 (0.006) Batch 1.146 (0.931) Remain 10:52:10 loss: 0.3884 Lr: 0.00093 [2024-02-19 06:43:24,558 INFO misc.py line 119 87073] Train: [74/100][18/1557] Data 0.004 (0.006) Batch 0.870 (0.927) Remain 10:49:18 loss: 0.2049 Lr: 0.00093 [2024-02-19 06:43:25,339 INFO misc.py line 119 87073] Train: [74/100][19/1557] Data 0.003 (0.006) Batch 0.771 (0.917) Remain 10:42:28 loss: 0.2336 Lr: 0.00093 [2024-02-19 06:43:26,091 INFO misc.py line 119 87073] Train: [74/100][20/1557] Data 0.015 (0.006) Batch 0.760 (0.908) Remain 10:35:57 loss: 0.2045 Lr: 0.00093 [2024-02-19 06:43:27,433 INFO misc.py line 119 87073] Train: [74/100][21/1557] Data 0.006 (0.006) Batch 1.342 (0.932) Remain 10:52:50 loss: 0.1752 Lr: 0.00093 [2024-02-19 06:43:28,374 INFO misc.py line 119 87073] Train: [74/100][22/1557] Data 0.006 (0.006) Batch 0.941 (0.933) Remain 10:53:09 loss: 0.2125 Lr: 0.00093 [2024-02-19 06:43:29,356 INFO misc.py line 119 87073] Train: [74/100][23/1557] Data 0.004 (0.006) Batch 0.982 (0.935) Remain 10:54:52 loss: 0.3459 Lr: 0.00093 [2024-02-19 06:43:30,269 INFO misc.py line 119 87073] Train: [74/100][24/1557] Data 0.004 (0.006) Batch 0.914 (0.934) Remain 10:54:08 loss: 0.2316 Lr: 0.00093 [2024-02-19 06:43:31,365 INFO misc.py line 119 87073] Train: [74/100][25/1557] Data 0.003 (0.006) Batch 1.095 (0.941) Remain 10:59:15 loss: 0.4816 Lr: 0.00093 [2024-02-19 06:43:32,141 INFO misc.py line 119 87073] Train: [74/100][26/1557] Data 0.005 (0.006) Batch 0.774 (0.934) Remain 10:54:09 loss: 0.2661 Lr: 0.00093 [2024-02-19 06:43:32,898 INFO misc.py line 119 87073] Train: [74/100][27/1557] Data 0.007 (0.006) Batch 0.758 (0.927) Remain 10:48:59 loss: 0.1974 Lr: 0.00093 [2024-02-19 06:43:34,109 INFO misc.py line 119 87073] Train: [74/100][28/1557] Data 0.005 (0.006) Batch 1.207 (0.938) Remain 10:56:48 loss: 0.2242 Lr: 0.00093 [2024-02-19 06:43:35,024 INFO misc.py line 119 87073] Train: [74/100][29/1557] Data 0.012 (0.006) Batch 0.918 (0.937) Remain 10:56:15 loss: 0.4324 Lr: 0.00093 [2024-02-19 06:43:36,018 INFO misc.py line 119 87073] Train: [74/100][30/1557] Data 0.006 (0.006) Batch 0.998 (0.940) Remain 10:57:47 loss: 0.2570 Lr: 0.00093 [2024-02-19 06:43:37,000 INFO misc.py line 119 87073] Train: [74/100][31/1557] Data 0.003 (0.006) Batch 0.981 (0.941) Remain 10:58:49 loss: 0.1152 Lr: 0.00093 [2024-02-19 06:43:38,069 INFO misc.py line 119 87073] Train: [74/100][32/1557] Data 0.004 (0.006) Batch 1.069 (0.945) Remain 11:01:53 loss: 0.1546 Lr: 0.00093 [2024-02-19 06:43:38,829 INFO misc.py line 119 87073] Train: [74/100][33/1557] Data 0.005 (0.006) Batch 0.761 (0.939) Remain 10:57:33 loss: 0.2817 Lr: 0.00093 [2024-02-19 06:43:39,622 INFO misc.py line 119 87073] Train: [74/100][34/1557] Data 0.004 (0.006) Batch 0.784 (0.934) Remain 10:54:02 loss: 0.2412 Lr: 0.00093 [2024-02-19 06:43:40,868 INFO misc.py line 119 87073] Train: [74/100][35/1557] Data 0.012 (0.006) Batch 1.250 (0.944) Remain 11:00:56 loss: 0.1759 Lr: 0.00093 [2024-02-19 06:43:41,971 INFO misc.py line 119 87073] Train: [74/100][36/1557] Data 0.010 (0.006) Batch 1.102 (0.949) Remain 11:04:16 loss: 0.2661 Lr: 0.00093 [2024-02-19 06:43:43,134 INFO misc.py line 119 87073] Train: [74/100][37/1557] Data 0.010 (0.006) Batch 1.166 (0.955) Remain 11:08:43 loss: 0.3061 Lr: 0.00093 [2024-02-19 06:43:43,992 INFO misc.py line 119 87073] Train: [74/100][38/1557] Data 0.007 (0.006) Batch 0.858 (0.953) Remain 11:06:46 loss: 0.1674 Lr: 0.00093 [2024-02-19 06:43:45,022 INFO misc.py line 119 87073] Train: [74/100][39/1557] Data 0.006 (0.006) Batch 1.033 (0.955) Remain 11:08:18 loss: 0.2168 Lr: 0.00093 [2024-02-19 06:43:45,772 INFO misc.py line 119 87073] Train: [74/100][40/1557] Data 0.004 (0.006) Batch 0.750 (0.949) Remain 11:04:25 loss: 0.3827 Lr: 0.00093 [2024-02-19 06:43:46,519 INFO misc.py line 119 87073] Train: [74/100][41/1557] Data 0.003 (0.006) Batch 0.745 (0.944) Remain 11:00:39 loss: 0.1825 Lr: 0.00093 [2024-02-19 06:43:47,801 INFO misc.py line 119 87073] Train: [74/100][42/1557] Data 0.005 (0.006) Batch 1.268 (0.952) Remain 11:06:26 loss: 0.2495 Lr: 0.00093 [2024-02-19 06:43:48,774 INFO misc.py line 119 87073] Train: [74/100][43/1557] Data 0.022 (0.007) Batch 0.987 (0.953) Remain 11:07:02 loss: 0.0500 Lr: 0.00093 [2024-02-19 06:43:49,741 INFO misc.py line 119 87073] Train: [74/100][44/1557] Data 0.006 (0.006) Batch 0.969 (0.953) Remain 11:07:17 loss: 0.1180 Lr: 0.00093 [2024-02-19 06:43:50,548 INFO misc.py line 119 87073] Train: [74/100][45/1557] Data 0.004 (0.006) Batch 0.806 (0.950) Remain 11:04:49 loss: 0.0852 Lr: 0.00093 [2024-02-19 06:43:51,548 INFO misc.py line 119 87073] Train: [74/100][46/1557] Data 0.005 (0.006) Batch 0.995 (0.951) Remain 11:05:33 loss: 0.6565 Lr: 0.00093 [2024-02-19 06:43:52,216 INFO misc.py line 119 87073] Train: [74/100][47/1557] Data 0.010 (0.006) Batch 0.671 (0.945) Remain 11:01:05 loss: 0.4583 Lr: 0.00093 [2024-02-19 06:43:52,975 INFO misc.py line 119 87073] Train: [74/100][48/1557] Data 0.006 (0.006) Batch 0.759 (0.940) Remain 10:58:10 loss: 0.2279 Lr: 0.00093 [2024-02-19 06:43:54,087 INFO misc.py line 119 87073] Train: [74/100][49/1557] Data 0.006 (0.006) Batch 1.110 (0.944) Remain 11:00:45 loss: 0.2004 Lr: 0.00093 [2024-02-19 06:43:54,960 INFO misc.py line 119 87073] Train: [74/100][50/1557] Data 0.008 (0.006) Batch 0.877 (0.943) Remain 10:59:43 loss: 0.2197 Lr: 0.00093 [2024-02-19 06:43:55,836 INFO misc.py line 119 87073] Train: [74/100][51/1557] Data 0.004 (0.006) Batch 0.877 (0.941) Remain 10:58:45 loss: 0.3462 Lr: 0.00093 [2024-02-19 06:43:56,701 INFO misc.py line 119 87073] Train: [74/100][52/1557] Data 0.003 (0.006) Batch 0.853 (0.940) Remain 10:57:28 loss: 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(0.081) Batch 1.006 (1.097) Remain 12:20:04 loss: 0.3734 Lr: 0.00087 [2024-02-19 07:11:21,169 INFO misc.py line 119 87073] Train: [74/100][1545/1557] Data 0.009 (0.081) Batch 0.758 (1.096) Remain 12:19:54 loss: 0.2129 Lr: 0.00087 [2024-02-19 07:11:21,917 INFO misc.py line 119 87073] Train: [74/100][1546/1557] Data 0.004 (0.081) Batch 0.741 (1.096) Remain 12:19:43 loss: 0.1424 Lr: 0.00087 [2024-02-19 07:11:23,116 INFO misc.py line 119 87073] Train: [74/100][1547/1557] Data 0.011 (0.081) Batch 1.199 (1.096) Remain 12:19:45 loss: 0.1523 Lr: 0.00087 [2024-02-19 07:11:24,183 INFO misc.py line 119 87073] Train: [74/100][1548/1557] Data 0.012 (0.081) Batch 1.065 (1.096) Remain 12:19:43 loss: 0.3611 Lr: 0.00087 [2024-02-19 07:11:25,160 INFO misc.py line 119 87073] Train: [74/100][1549/1557] Data 0.014 (0.081) Batch 0.988 (1.096) Remain 12:19:39 loss: 0.3385 Lr: 0.00087 [2024-02-19 07:11:26,059 INFO misc.py line 119 87073] Train: [74/100][1550/1557] Data 0.004 (0.081) Batch 0.899 (1.096) Remain 12:19:33 loss: 0.4038 Lr: 0.00087 [2024-02-19 07:11:26,896 INFO misc.py line 119 87073] Train: [74/100][1551/1557] Data 0.004 (0.081) Batch 0.829 (1.096) Remain 12:19:25 loss: 0.5581 Lr: 0.00087 [2024-02-19 07:11:27,613 INFO misc.py line 119 87073] Train: [74/100][1552/1557] Data 0.011 (0.081) Batch 0.725 (1.096) Remain 12:19:14 loss: 0.1522 Lr: 0.00087 [2024-02-19 07:11:28,346 INFO misc.py line 119 87073] Train: [74/100][1553/1557] Data 0.004 (0.081) Batch 0.726 (1.095) Remain 12:19:03 loss: 0.2635 Lr: 0.00087 [2024-02-19 07:11:29,599 INFO misc.py line 119 87073] Train: [74/100][1554/1557] Data 0.011 (0.081) Batch 1.253 (1.095) Remain 12:19:06 loss: 0.1045 Lr: 0.00087 [2024-02-19 07:11:30,550 INFO misc.py line 119 87073] Train: [74/100][1555/1557] Data 0.011 (0.081) Batch 0.958 (1.095) Remain 12:19:01 loss: 0.5264 Lr: 0.00087 [2024-02-19 07:11:31,518 INFO misc.py line 119 87073] Train: [74/100][1556/1557] Data 0.004 (0.081) Batch 0.967 (1.095) Remain 12:18:57 loss: 0.3374 Lr: 0.00087 [2024-02-19 07:11:32,468 INFO misc.py line 119 87073] Train: [74/100][1557/1557] Data 0.004 (0.080) Batch 0.951 (1.095) Remain 12:18:52 loss: 0.3212 Lr: 0.00087 [2024-02-19 07:11:32,468 INFO misc.py line 136 87073] Train result: loss: 0.2579 [2024-02-19 07:11:32,469 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 07:11:59,417 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5876 [2024-02-19 07:12:00,211 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3764 [2024-02-19 07:12:02,342 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4262 [2024-02-19 07:12:04,548 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3287 [2024-02-19 07:12:09,484 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2487 [2024-02-19 07:12:10,185 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1784 [2024-02-19 07:12:11,444 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3481 [2024-02-19 07:12:14,399 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2789 [2024-02-19 07:12:16,206 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2240 [2024-02-19 07:12:17,938 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7281/0.7849/0.9167. [2024-02-19 07:12:17,938 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9329/0.9618 [2024-02-19 07:12:17,938 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9902 [2024-02-19 07:12:17,938 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8629/0.9703 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4276/0.5381 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7213/0.7464 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7939/0.8594 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8563/0.9325 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9301/0.9760 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8441/0.8715 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7961/0.8886 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7005/0.7733 [2024-02-19 07:12:17,939 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6168/0.6962 [2024-02-19 07:12:17,940 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 07:12:17,942 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 07:12:17,942 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 07:12:27,149 INFO misc.py line 119 87073] Train: [75/100][1/1557] Data 1.200 (1.200) Batch 2.040 (2.040) Remain 22:56:15 loss: 0.2329 Lr: 0.00087 [2024-02-19 07:12:28,181 INFO misc.py line 119 87073] Train: [75/100][2/1557] Data 0.004 (0.004) Batch 1.031 (1.031) Remain 11:35:54 loss: 0.1542 Lr: 0.00087 [2024-02-19 07:12:29,449 INFO misc.py line 119 87073] Train: [75/100][3/1557] Data 0.005 (0.005) Batch 1.268 (1.268) Remain 14:15:13 loss: 0.3859 Lr: 0.00087 [2024-02-19 07:12:30,521 INFO misc.py line 119 87073] Train: [75/100][4/1557] Data 0.006 (0.006) Batch 1.068 (1.068) Remain 12:00:23 loss: 0.2668 Lr: 0.00087 [2024-02-19 07:12:31,277 INFO misc.py line 119 87073] Train: [75/100][5/1557] Data 0.008 (0.007) Batch 0.760 (0.914) Remain 10:16:37 loss: 0.1911 Lr: 0.00087 [2024-02-19 07:12:32,052 INFO misc.py line 119 87073] Train: [75/100][6/1557] Data 0.005 (0.006) Batch 0.775 (0.868) Remain 09:45:18 loss: 0.2853 Lr: 0.00087 [2024-02-19 07:12:33,242 INFO misc.py line 119 87073] Train: [75/100][7/1557] Data 0.005 (0.006) Batch 1.186 (0.947) Remain 10:38:56 loss: 0.1692 Lr: 0.00087 [2024-02-19 07:12:34,213 INFO misc.py line 119 87073] Train: [75/100][8/1557] Data 0.009 (0.006) Batch 0.977 (0.953) Remain 10:42:59 loss: 0.3028 Lr: 0.00087 [2024-02-19 07:12:35,204 INFO misc.py line 119 87073] Train: [75/100][9/1557] Data 0.002 (0.006) Batch 0.991 (0.959) Remain 10:47:11 loss: 0.2443 Lr: 0.00087 [2024-02-19 07:12:36,204 INFO misc.py line 119 87073] Train: [75/100][10/1557] Data 0.003 (0.005) Batch 0.995 (0.965) Remain 10:50:36 loss: 0.3367 Lr: 0.00087 [2024-02-19 07:12:37,183 INFO misc.py line 119 87073] Train: [75/100][11/1557] Data 0.007 (0.006) Batch 0.983 (0.967) Remain 10:52:10 loss: 0.0936 Lr: 0.00087 [2024-02-19 07:12:37,953 INFO misc.py line 119 87073] Train: [75/100][12/1557] Data 0.003 (0.005) Batch 0.770 (0.945) Remain 10:37:25 loss: 0.1782 Lr: 0.00087 [2024-02-19 07:12:38,700 INFO misc.py line 119 87073] Train: [75/100][13/1557] Data 0.003 (0.005) Batch 0.716 (0.922) Remain 10:21:58 loss: 0.3341 Lr: 0.00087 [2024-02-19 07:12:39,916 INFO misc.py line 119 87073] Train: [75/100][14/1557] Data 0.033 (0.008) Batch 1.237 (0.951) Remain 10:41:16 loss: 0.1087 Lr: 0.00087 [2024-02-19 07:12:40,962 INFO misc.py line 119 87073] Train: [75/100][15/1557] Data 0.012 (0.008) Batch 1.043 (0.959) Remain 10:46:28 loss: 0.1894 Lr: 0.00087 [2024-02-19 07:12:42,012 INFO misc.py line 119 87073] Train: [75/100][16/1557] Data 0.016 (0.009) Batch 1.050 (0.966) Remain 10:51:13 loss: 0.2590 Lr: 0.00087 [2024-02-19 07:12:42,921 INFO misc.py line 119 87073] Train: [75/100][17/1557] Data 0.015 (0.009) Batch 0.920 (0.962) Remain 10:49:02 loss: 0.2275 Lr: 0.00087 [2024-02-19 07:12:43,961 INFO misc.py line 119 87073] Train: [75/100][18/1557] Data 0.003 (0.009) Batch 1.039 (0.967) Remain 10:52:26 loss: 0.2267 Lr: 0.00087 [2024-02-19 07:12:44,706 INFO misc.py line 119 87073] Train: [75/100][19/1557] Data 0.005 (0.008) Batch 0.747 (0.954) Remain 10:43:08 loss: 0.1780 Lr: 0.00087 [2024-02-19 07:12:45,461 INFO misc.py line 119 87073] Train: [75/100][20/1557] Data 0.003 (0.008) Batch 0.753 (0.942) Remain 10:35:09 loss: 0.1118 Lr: 0.00087 [2024-02-19 07:12:46,686 INFO misc.py line 119 87073] Train: [75/100][21/1557] Data 0.004 (0.008) Batch 1.222 (0.957) Remain 10:45:38 loss: 0.1531 Lr: 0.00087 [2024-02-19 07:12:47,624 INFO misc.py line 119 87073] Train: [75/100][22/1557] Data 0.008 (0.008) Batch 0.943 (0.957) Remain 10:45:06 loss: 0.4737 Lr: 0.00087 [2024-02-19 07:12:48,532 INFO misc.py line 119 87073] Train: [75/100][23/1557] Data 0.003 (0.008) Batch 0.907 (0.954) Remain 10:43:25 loss: 0.1638 Lr: 0.00087 [2024-02-19 07:12:49,576 INFO misc.py line 119 87073] Train: [75/100][24/1557] Data 0.004 (0.008) Batch 1.039 (0.958) Remain 10:46:07 loss: 0.2919 Lr: 0.00087 [2024-02-19 07:12:50,477 INFO misc.py line 119 87073] Train: [75/100][25/1557] Data 0.009 (0.008) Batch 0.907 (0.956) Remain 10:44:32 loss: 0.4447 Lr: 0.00087 [2024-02-19 07:12:51,210 INFO misc.py line 119 87073] Train: [75/100][26/1557] Data 0.003 (0.007) Batch 0.732 (0.946) Remain 10:37:57 loss: 0.0845 Lr: 0.00087 [2024-02-19 07:12:51,889 INFO misc.py line 119 87073] Train: [75/100][27/1557] Data 0.004 (0.007) Batch 0.678 (0.935) Remain 10:30:24 loss: 0.1275 Lr: 0.00087 [2024-02-19 07:12:53,070 INFO misc.py line 119 87073] Train: [75/100][28/1557] Data 0.004 (0.007) Batch 1.175 (0.945) Remain 10:36:52 loss: 0.1187 Lr: 0.00087 [2024-02-19 07:12:53,963 INFO misc.py line 119 87073] Train: [75/100][29/1557] Data 0.010 (0.007) Batch 0.900 (0.943) Remain 10:35:41 loss: 0.6227 Lr: 0.00087 [2024-02-19 07:12:55,024 INFO misc.py line 119 87073] Train: [75/100][30/1557] Data 0.003 (0.007) Batch 1.062 (0.947) Remain 10:38:38 loss: 0.3516 Lr: 0.00087 [2024-02-19 07:12:56,111 INFO misc.py line 119 87073] Train: [75/100][31/1557] Data 0.003 (0.007) Batch 1.087 (0.952) Remain 10:41:59 loss: 0.1668 Lr: 0.00087 [2024-02-19 07:12:57,065 INFO misc.py line 119 87073] Train: [75/100][32/1557] Data 0.003 (0.007) Batch 0.954 (0.952) Remain 10:42:00 loss: 0.3406 Lr: 0.00087 [2024-02-19 07:12:57,801 INFO misc.py line 119 87073] Train: [75/100][33/1557] Data 0.003 (0.007) Batch 0.732 (0.945) Remain 10:37:02 loss: 0.1243 Lr: 0.00087 [2024-02-19 07:12:58,580 INFO misc.py line 119 87073] Train: [75/100][34/1557] Data 0.007 (0.007) Batch 0.783 (0.940) Remain 10:33:31 loss: 0.0800 Lr: 0.00087 [2024-02-19 07:12:59,737 INFO misc.py line 119 87073] Train: [75/100][35/1557] Data 0.002 (0.007) Batch 1.157 (0.947) Remain 10:38:04 loss: 0.1038 Lr: 0.00087 [2024-02-19 07:13:00,583 INFO misc.py line 119 87073] Train: [75/100][36/1557] Data 0.004 (0.006) Batch 0.846 (0.943) Remain 10:36:00 loss: 0.6345 Lr: 0.00087 [2024-02-19 07:13:01,605 INFO misc.py line 119 87073] Train: [75/100][37/1557] Data 0.004 (0.006) Batch 1.018 (0.946) Remain 10:37:28 loss: 0.2755 Lr: 0.00087 [2024-02-19 07:13:02,455 INFO misc.py line 119 87073] Train: [75/100][38/1557] Data 0.007 (0.006) Batch 0.853 (0.943) Remain 10:35:40 loss: 0.1799 Lr: 0.00087 [2024-02-19 07:13:03,493 INFO misc.py line 119 87073] Train: [75/100][39/1557] Data 0.004 (0.006) Batch 1.039 (0.946) Remain 10:37:27 loss: 0.4201 Lr: 0.00087 [2024-02-19 07:13:04,276 INFO misc.py line 119 87073] Train: [75/100][40/1557] Data 0.003 (0.006) Batch 0.782 (0.941) Remain 10:34:27 loss: 0.2623 Lr: 0.00087 [2024-02-19 07:13:05,013 INFO misc.py line 119 87073] Train: [75/100][41/1557] Data 0.003 (0.006) Batch 0.734 (0.936) Remain 10:30:45 loss: 0.2117 Lr: 0.00087 [2024-02-19 07:13:06,297 INFO misc.py line 119 87073] Train: [75/100][42/1557] Data 0.006 (0.006) Batch 1.286 (0.945) Remain 10:36:47 loss: 0.1764 Lr: 0.00087 [2024-02-19 07:13:07,151 INFO misc.py line 119 87073] Train: [75/100][43/1557] Data 0.005 (0.006) Batch 0.856 (0.943) Remain 10:35:17 loss: 0.2447 Lr: 0.00087 [2024-02-19 07:13:08,042 INFO misc.py line 119 87073] Train: [75/100][44/1557] Data 0.003 (0.006) Batch 0.888 (0.941) Remain 10:34:22 loss: 0.0399 Lr: 0.00087 [2024-02-19 07:13:08,909 INFO misc.py line 119 87073] Train: [75/100][45/1557] Data 0.007 (0.006) Batch 0.861 (0.939) Remain 10:33:04 loss: 0.2406 Lr: 0.00087 [2024-02-19 07:13:09,918 INFO misc.py line 119 87073] Train: [75/100][46/1557] Data 0.012 (0.006) Batch 1.015 (0.941) Remain 10:34:15 loss: 0.2584 Lr: 0.00087 [2024-02-19 07:13:10,706 INFO misc.py line 119 87073] Train: [75/100][47/1557] Data 0.005 (0.006) Batch 0.790 (0.938) Remain 10:31:54 loss: 0.1868 Lr: 0.00087 [2024-02-19 07:13:11,559 INFO misc.py line 119 87073] Train: [75/100][48/1557] Data 0.005 (0.006) Batch 0.853 (0.936) Remain 10:30:37 loss: 0.1747 Lr: 0.00087 [2024-02-19 07:13:12,736 INFO misc.py line 119 87073] Train: [75/100][49/1557] Data 0.004 (0.006) Batch 1.174 (0.941) Remain 10:34:06 loss: 0.1561 Lr: 0.00087 [2024-02-19 07:13:13,680 INFO misc.py line 119 87073] Train: [75/100][50/1557] Data 0.008 (0.006) Batch 0.947 (0.941) Remain 10:34:10 loss: 0.2680 Lr: 0.00087 [2024-02-19 07:13:14,886 INFO misc.py line 119 87073] Train: [75/100][51/1557] Data 0.003 (0.006) Batch 1.197 (0.946) Remain 10:37:45 loss: 0.4317 Lr: 0.00087 [2024-02-19 07:13:15,778 INFO misc.py line 119 87073] Train: [75/100][52/1557] Data 0.013 (0.006) Batch 0.901 (0.946) Remain 10:37:06 loss: 0.2130 Lr: 0.00087 [2024-02-19 07:13:16,779 INFO misc.py line 119 87073] Train: [75/100][53/1557] Data 0.003 (0.006) Batch 1.000 (0.947) Remain 10:37:50 loss: 0.3262 Lr: 0.00087 [2024-02-19 07:13:17,476 INFO misc.py line 119 87073] Train: [75/100][54/1557] Data 0.004 (0.006) Batch 0.698 (0.942) Remain 10:34:31 loss: 0.3184 Lr: 0.00087 [2024-02-19 07:13:18,247 INFO misc.py line 119 87073] Train: [75/100][55/1557] Data 0.003 (0.006) Batch 0.769 (0.938) Remain 10:32:16 loss: 0.1902 Lr: 0.00087 [2024-02-19 07:13:19,456 INFO misc.py line 119 87073] Train: [75/100][56/1557] Data 0.006 (0.006) Batch 1.209 (0.944) Remain 10:35:42 loss: 0.1778 Lr: 0.00087 [2024-02-19 07:13:20,516 INFO misc.py line 119 87073] Train: [75/100][57/1557] Data 0.006 (0.006) Batch 1.058 (0.946) Remain 10:37:07 loss: 0.2268 Lr: 0.00087 [2024-02-19 07:13:21,450 INFO misc.py line 119 87073] Train: [75/100][58/1557] Data 0.007 (0.006) Batch 0.938 (0.945) Remain 10:37:00 loss: 0.2149 Lr: 0.00087 [2024-02-19 07:13:22,529 INFO misc.py line 119 87073] Train: [75/100][59/1557] Data 0.004 (0.006) Batch 1.079 (0.948) Remain 10:38:35 loss: 0.7249 Lr: 0.00087 [2024-02-19 07:13:23,507 INFO misc.py line 119 87073] Train: [75/100][60/1557] Data 0.004 (0.006) Batch 0.977 (0.948) Remain 10:38:55 loss: 0.2673 Lr: 0.00087 [2024-02-19 07:13:24,267 INFO misc.py line 119 87073] Train: [75/100][61/1557] Data 0.004 (0.006) Batch 0.760 (0.945) Remain 10:36:43 loss: 0.2272 Lr: 0.00087 [2024-02-19 07:13:25,025 INFO misc.py line 119 87073] Train: [75/100][62/1557] Data 0.004 (0.006) Batch 0.754 (0.942) Remain 10:34:31 loss: 0.1866 Lr: 0.00087 [2024-02-19 07:13:33,755 INFO misc.py line 119 87073] Train: [75/100][63/1557] Data 6.374 (0.112) Batch 8.735 (1.072) Remain 12:02:00 loss: 0.1215 Lr: 0.00087 [2024-02-19 07:13:34,723 INFO misc.py line 119 87073] Train: [75/100][64/1557] Data 0.004 (0.110) Batch 0.966 (1.070) Remain 12:00:49 loss: 0.3355 Lr: 0.00087 [2024-02-19 07:13:35,570 INFO misc.py line 119 87073] Train: 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11:45:05 loss: 0.1062 Lr: 0.00081 [2024-02-19 07:38:46,665 INFO misc.py line 119 87073] Train: [75/100][1458/1557] Data 0.004 (0.119) Batch 0.974 (1.084) Remain 11:45:01 loss: 0.1885 Lr: 0.00081 [2024-02-19 07:38:47,777 INFO misc.py line 119 87073] Train: [75/100][1459/1557] Data 0.004 (0.119) Batch 1.110 (1.084) Remain 11:45:01 loss: 0.4629 Lr: 0.00081 [2024-02-19 07:38:48,740 INFO misc.py line 119 87073] Train: [75/100][1460/1557] Data 0.006 (0.119) Batch 0.965 (1.084) Remain 11:44:57 loss: 0.3862 Lr: 0.00081 [2024-02-19 07:38:49,510 INFO misc.py line 119 87073] Train: [75/100][1461/1557] Data 0.004 (0.119) Batch 0.768 (1.084) Remain 11:44:47 loss: 0.1693 Lr: 0.00081 [2024-02-19 07:38:50,287 INFO misc.py line 119 87073] Train: [75/100][1462/1557] Data 0.006 (0.119) Batch 0.778 (1.084) Remain 11:44:38 loss: 0.1446 Lr: 0.00081 [2024-02-19 07:38:59,445 INFO misc.py line 119 87073] Train: [75/100][1463/1557] Data 6.209 (0.123) Batch 9.158 (1.089) Remain 11:48:13 loss: 0.1121 Lr: 0.00081 [2024-02-19 07:39:00,481 INFO misc.py line 119 87073] Train: [75/100][1464/1557] Data 0.005 (0.123) Batch 1.036 (1.089) Remain 11:48:10 loss: 0.1443 Lr: 0.00081 [2024-02-19 07:39:01,341 INFO misc.py line 119 87073] Train: [75/100][1465/1557] Data 0.005 (0.123) Batch 0.861 (1.089) Remain 11:48:03 loss: 0.2932 Lr: 0.00081 [2024-02-19 07:39:02,317 INFO misc.py line 119 87073] Train: [75/100][1466/1557] Data 0.003 (0.123) Batch 0.974 (1.089) Remain 11:47:59 loss: 0.4109 Lr: 0.00081 [2024-02-19 07:39:03,169 INFO misc.py line 119 87073] Train: [75/100][1467/1557] Data 0.005 (0.123) Batch 0.853 (1.089) Remain 11:47:51 loss: 0.1745 Lr: 0.00081 [2024-02-19 07:39:03,938 INFO misc.py line 119 87073] Train: [75/100][1468/1557] Data 0.005 (0.123) Batch 0.760 (1.088) Remain 11:47:42 loss: 0.1377 Lr: 0.00081 [2024-02-19 07:39:04,804 INFO misc.py line 119 87073] Train: [75/100][1469/1557] Data 0.013 (0.123) Batch 0.875 (1.088) Remain 11:47:35 loss: 0.3083 Lr: 0.00081 [2024-02-19 07:39:05,993 INFO misc.py line 119 87073] Train: [75/100][1470/1557] Data 0.005 (0.123) Batch 1.189 (1.088) Remain 11:47:36 loss: 0.0963 Lr: 0.00081 [2024-02-19 07:39:07,036 INFO misc.py line 119 87073] Train: [75/100][1471/1557] Data 0.004 (0.122) Batch 1.035 (1.088) Remain 11:47:34 loss: 0.1515 Lr: 0.00081 [2024-02-19 07:39:08,118 INFO misc.py line 119 87073] Train: [75/100][1472/1557] Data 0.012 (0.122) Batch 1.086 (1.088) Remain 11:47:33 loss: 0.4145 Lr: 0.00081 [2024-02-19 07:39:09,268 INFO misc.py line 119 87073] Train: [75/100][1473/1557] Data 0.009 (0.122) Batch 1.143 (1.088) Remain 11:47:33 loss: 0.5171 Lr: 0.00081 [2024-02-19 07:39:10,181 INFO misc.py line 119 87073] Train: [75/100][1474/1557] Data 0.016 (0.122) Batch 0.924 (1.088) Remain 11:47:28 loss: 0.2427 Lr: 0.00081 [2024-02-19 07:39:10,962 INFO misc.py line 119 87073] Train: [75/100][1475/1557] Data 0.005 (0.122) Batch 0.779 (1.088) Remain 11:47:18 loss: 0.3656 Lr: 0.00081 [2024-02-19 07:39:11,747 INFO misc.py line 119 87073] Train: 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(0.122) Batch 0.740 (1.087) Remain 11:46:50 loss: 0.0940 Lr: 0.00081 [2024-02-19 07:39:18,549 INFO misc.py line 119 87073] Train: [75/100][1483/1557] Data 0.006 (0.122) Batch 0.761 (1.087) Remain 11:46:40 loss: 0.2124 Lr: 0.00081 [2024-02-19 07:39:19,622 INFO misc.py line 119 87073] Train: [75/100][1484/1557] Data 0.010 (0.121) Batch 1.066 (1.087) Remain 11:46:38 loss: 0.1182 Lr: 0.00081 [2024-02-19 07:39:20,436 INFO misc.py line 119 87073] Train: [75/100][1485/1557] Data 0.017 (0.121) Batch 0.827 (1.087) Remain 11:46:31 loss: 0.2351 Lr: 0.00081 [2024-02-19 07:39:21,530 INFO misc.py line 119 87073] Train: [75/100][1486/1557] Data 0.004 (0.121) Batch 1.094 (1.087) Remain 11:46:30 loss: 0.4187 Lr: 0.00081 [2024-02-19 07:39:22,475 INFO misc.py line 119 87073] Train: [75/100][1487/1557] Data 0.004 (0.121) Batch 0.945 (1.087) Remain 11:46:25 loss: 0.1657 Lr: 0.00081 [2024-02-19 07:39:23,461 INFO misc.py line 119 87073] Train: [75/100][1488/1557] Data 0.004 (0.121) Batch 0.985 (1.087) Remain 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[2024-02-19 07:39:30,109 INFO misc.py line 119 87073] Train: [75/100][1495/1557] Data 0.004 (0.121) Batch 0.889 (1.086) Remain 11:45:48 loss: 0.1736 Lr: 0.00081 [2024-02-19 07:39:30,829 INFO misc.py line 119 87073] Train: [75/100][1496/1557] Data 0.004 (0.121) Batch 0.716 (1.086) Remain 11:45:38 loss: 0.1286 Lr: 0.00081 [2024-02-19 07:39:31,590 INFO misc.py line 119 87073] Train: [75/100][1497/1557] Data 0.008 (0.120) Batch 0.764 (1.086) Remain 11:45:28 loss: 0.1791 Lr: 0.00081 [2024-02-19 07:39:32,861 INFO misc.py line 119 87073] Train: [75/100][1498/1557] Data 0.006 (0.120) Batch 1.263 (1.086) Remain 11:45:32 loss: 0.2233 Lr: 0.00081 [2024-02-19 07:39:33,703 INFO misc.py line 119 87073] Train: [75/100][1499/1557] Data 0.014 (0.120) Batch 0.851 (1.086) Remain 11:45:25 loss: 0.3217 Lr: 0.00081 [2024-02-19 07:39:34,801 INFO misc.py line 119 87073] Train: [75/100][1500/1557] Data 0.004 (0.120) Batch 1.098 (1.086) Remain 11:45:24 loss: 0.1896 Lr: 0.00081 [2024-02-19 07:39:35,686 INFO misc.py line 119 87073] Train: [75/100][1501/1557] Data 0.004 (0.120) Batch 0.884 (1.086) Remain 11:45:17 loss: 0.3777 Lr: 0.00081 [2024-02-19 07:39:36,686 INFO misc.py line 119 87073] Train: [75/100][1502/1557] Data 0.005 (0.120) Batch 0.994 (1.086) Remain 11:45:14 loss: 0.2927 Lr: 0.00081 [2024-02-19 07:39:37,485 INFO misc.py line 119 87073] Train: [75/100][1503/1557] Data 0.011 (0.120) Batch 0.806 (1.085) Remain 11:45:06 loss: 0.1990 Lr: 0.00081 [2024-02-19 07:39:38,285 INFO misc.py line 119 87073] Train: [75/100][1504/1557] Data 0.004 (0.120) Batch 0.800 (1.085) Remain 11:44:57 loss: 0.3137 Lr: 0.00081 [2024-02-19 07:39:39,412 INFO misc.py line 119 87073] Train: [75/100][1505/1557] Data 0.004 (0.120) Batch 1.120 (1.085) Remain 11:44:57 loss: 0.0788 Lr: 0.00081 [2024-02-19 07:39:40,391 INFO misc.py line 119 87073] Train: [75/100][1506/1557] Data 0.010 (0.120) Batch 0.987 (1.085) Remain 11:44:53 loss: 0.1579 Lr: 0.00081 [2024-02-19 07:39:41,268 INFO misc.py line 119 87073] Train: 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(0.119) Batch 0.981 (1.084) Remain 11:44:21 loss: 0.2766 Lr: 0.00081 [2024-02-19 07:39:47,844 INFO misc.py line 119 87073] Train: [75/100][1514/1557] Data 0.004 (0.119) Batch 0.828 (1.084) Remain 11:44:13 loss: 0.2005 Lr: 0.00081 [2024-02-19 07:39:48,712 INFO misc.py line 119 87073] Train: [75/100][1515/1557] Data 0.004 (0.119) Batch 0.864 (1.084) Remain 11:44:06 loss: 0.2162 Lr: 0.00081 [2024-02-19 07:39:49,871 INFO misc.py line 119 87073] Train: [75/100][1516/1557] Data 0.008 (0.119) Batch 1.159 (1.084) Remain 11:44:07 loss: 0.0677 Lr: 0.00081 [2024-02-19 07:39:50,683 INFO misc.py line 119 87073] Train: [75/100][1517/1557] Data 0.007 (0.119) Batch 0.814 (1.084) Remain 11:43:59 loss: 0.2644 Lr: 0.00081 [2024-02-19 07:39:51,447 INFO misc.py line 119 87073] Train: [75/100][1518/1557] Data 0.006 (0.119) Batch 0.766 (1.084) Remain 11:43:50 loss: 0.1826 Lr: 0.00081 [2024-02-19 07:40:00,306 INFO misc.py line 119 87073] Train: [75/100][1519/1557] Data 6.400 (0.123) Batch 8.859 (1.089) Remain 11:47:08 loss: 0.1775 Lr: 0.00081 [2024-02-19 07:40:01,423 INFO misc.py line 119 87073] Train: [75/100][1520/1557] Data 0.004 (0.123) Batch 1.115 (1.089) Remain 11:47:08 loss: 0.2100 Lr: 0.00081 [2024-02-19 07:40:02,338 INFO misc.py line 119 87073] Train: [75/100][1521/1557] Data 0.006 (0.123) Batch 0.917 (1.089) Remain 11:47:02 loss: 0.2929 Lr: 0.00081 [2024-02-19 07:40:03,244 INFO misc.py line 119 87073] Train: [75/100][1522/1557] Data 0.004 (0.123) Batch 0.906 (1.089) Remain 11:46:57 loss: 0.3757 Lr: 0.00081 [2024-02-19 07:40:04,181 INFO misc.py line 119 87073] Train: [75/100][1523/1557] Data 0.004 (0.123) Batch 0.937 (1.089) Remain 11:46:52 loss: 0.1469 Lr: 0.00081 [2024-02-19 07:40:05,064 INFO misc.py line 119 87073] Train: [75/100][1524/1557] Data 0.004 (0.123) Batch 0.883 (1.089) Remain 11:46:45 loss: 0.2504 Lr: 0.00081 [2024-02-19 07:40:05,824 INFO misc.py line 119 87073] Train: [75/100][1525/1557] Data 0.003 (0.123) Batch 0.759 (1.088) Remain 11:46:36 loss: 0.1745 Lr: 0.00081 [2024-02-19 07:40:06,917 INFO misc.py line 119 87073] Train: [75/100][1526/1557] Data 0.004 (0.122) Batch 1.086 (1.088) Remain 11:46:35 loss: 0.0998 Lr: 0.00081 [2024-02-19 07:40:07,783 INFO misc.py line 119 87073] Train: [75/100][1527/1557] Data 0.011 (0.122) Batch 0.873 (1.088) Remain 11:46:28 loss: 0.2321 Lr: 0.00081 [2024-02-19 07:40:08,901 INFO misc.py line 119 87073] Train: [75/100][1528/1557] Data 0.004 (0.122) Batch 1.118 (1.088) Remain 11:46:28 loss: 0.2500 Lr: 0.00081 [2024-02-19 07:40:09,946 INFO misc.py line 119 87073] Train: [75/100][1529/1557] Data 0.004 (0.122) Batch 1.044 (1.088) Remain 11:46:26 loss: 0.6033 Lr: 0.00081 [2024-02-19 07:40:11,068 INFO misc.py line 119 87073] Train: [75/100][1530/1557] Data 0.005 (0.122) Batch 1.123 (1.088) Remain 11:46:25 loss: 0.3149 Lr: 0.00081 [2024-02-19 07:40:11,845 INFO misc.py line 119 87073] Train: [75/100][1531/1557] Data 0.004 (0.122) Batch 0.777 (1.088) Remain 11:46:16 loss: 0.2833 Lr: 0.00081 [2024-02-19 07:40:12,589 INFO misc.py line 119 87073] Train: [75/100][1532/1557] Data 0.004 (0.122) Batch 0.739 (1.088) Remain 11:46:06 loss: 0.2959 Lr: 0.00081 [2024-02-19 07:40:13,850 INFO misc.py line 119 87073] Train: [75/100][1533/1557] Data 0.009 (0.122) Batch 1.255 (1.088) Remain 11:46:10 loss: 0.1672 Lr: 0.00081 [2024-02-19 07:40:14,794 INFO misc.py line 119 87073] Train: [75/100][1534/1557] Data 0.016 (0.122) Batch 0.955 (1.088) Remain 11:46:05 loss: 0.4107 Lr: 0.00081 [2024-02-19 07:40:15,728 INFO misc.py line 119 87073] Train: [75/100][1535/1557] Data 0.004 (0.122) Batch 0.935 (1.088) Remain 11:46:00 loss: 0.2236 Lr: 0.00081 [2024-02-19 07:40:16,765 INFO misc.py line 119 87073] Train: [75/100][1536/1557] Data 0.003 (0.122) Batch 1.036 (1.088) Remain 11:45:58 loss: 0.3396 Lr: 0.00081 [2024-02-19 07:40:17,555 INFO misc.py line 119 87073] Train: [75/100][1537/1557] Data 0.004 (0.122) Batch 0.785 (1.087) Remain 11:45:49 loss: 0.2550 Lr: 0.00081 [2024-02-19 07:40:18,295 INFO misc.py line 119 87073] Train: [75/100][1538/1557] Data 0.009 (0.122) Batch 0.745 (1.087) Remain 11:45:39 loss: 0.2933 Lr: 0.00081 [2024-02-19 07:40:19,042 INFO misc.py line 119 87073] Train: [75/100][1539/1557] Data 0.004 (0.121) Batch 0.737 (1.087) Remain 11:45:29 loss: 0.1885 Lr: 0.00081 [2024-02-19 07:40:20,206 INFO misc.py line 119 87073] Train: [75/100][1540/1557] Data 0.014 (0.121) Batch 1.165 (1.087) Remain 11:45:30 loss: 0.1528 Lr: 0.00081 [2024-02-19 07:40:21,171 INFO misc.py line 119 87073] Train: [75/100][1541/1557] Data 0.014 (0.121) Batch 0.975 (1.087) Remain 11:45:26 loss: 0.3627 Lr: 0.00081 [2024-02-19 07:40:22,092 INFO misc.py line 119 87073] Train: [75/100][1542/1557] Data 0.004 (0.121) Batch 0.921 (1.087) Remain 11:45:21 loss: 0.1512 Lr: 0.00081 [2024-02-19 07:40:22,977 INFO misc.py line 119 87073] Train: [75/100][1543/1557] Data 0.004 (0.121) Batch 0.885 (1.087) Remain 11:45:15 loss: 0.2988 Lr: 0.00081 [2024-02-19 07:40:23,759 INFO misc.py line 119 87073] Train: [75/100][1544/1557] Data 0.004 (0.121) Batch 0.771 (1.087) Remain 11:45:06 loss: 0.1643 Lr: 0.00081 [2024-02-19 07:40:24,501 INFO misc.py line 119 87073] Train: [75/100][1545/1557] Data 0.014 (0.121) Batch 0.752 (1.086) Remain 11:44:56 loss: 0.1824 Lr: 0.00081 [2024-02-19 07:40:25,280 INFO misc.py line 119 87073] Train: [75/100][1546/1557] Data 0.004 (0.121) Batch 0.767 (1.086) Remain 11:44:47 loss: 0.3128 Lr: 0.00081 [2024-02-19 07:40:26,406 INFO misc.py line 119 87073] Train: [75/100][1547/1557] Data 0.015 (0.121) Batch 1.125 (1.086) Remain 11:44:47 loss: 0.1527 Lr: 0.00081 [2024-02-19 07:40:27,299 INFO misc.py line 119 87073] Train: [75/100][1548/1557] Data 0.017 (0.121) Batch 0.906 (1.086) Remain 11:44:41 loss: 0.7104 Lr: 0.00081 [2024-02-19 07:40:28,337 INFO misc.py line 119 87073] Train: [75/100][1549/1557] Data 0.004 (0.121) Batch 1.037 (1.086) Remain 11:44:39 loss: 0.2263 Lr: 0.00081 [2024-02-19 07:40:29,213 INFO misc.py line 119 87073] Train: [75/100][1550/1557] Data 0.004 (0.121) Batch 0.877 (1.086) Remain 11:44:33 loss: 0.1524 Lr: 0.00081 [2024-02-19 07:40:30,108 INFO misc.py line 119 87073] Train: [75/100][1551/1557] Data 0.004 (0.121) Batch 0.885 (1.086) Remain 11:44:26 loss: 0.3385 Lr: 0.00081 [2024-02-19 07:40:30,809 INFO misc.py line 119 87073] Train: [75/100][1552/1557] Data 0.014 (0.121) Batch 0.711 (1.085) Remain 11:44:16 loss: 0.2051 Lr: 0.00081 [2024-02-19 07:40:31,559 INFO misc.py line 119 87073] Train: [75/100][1553/1557] Data 0.004 (0.120) Batch 0.740 (1.085) Remain 11:44:06 loss: 0.2393 Lr: 0.00081 [2024-02-19 07:40:32,841 INFO misc.py line 119 87073] Train: [75/100][1554/1557] Data 0.014 (0.120) Batch 1.279 (1.085) Remain 11:44:10 loss: 0.1563 Lr: 0.00081 [2024-02-19 07:40:33,725 INFO misc.py line 119 87073] Train: [75/100][1555/1557] Data 0.016 (0.120) Batch 0.897 (1.085) Remain 11:44:04 loss: 0.5557 Lr: 0.00081 [2024-02-19 07:40:34,546 INFO misc.py line 119 87073] Train: [75/100][1556/1557] Data 0.004 (0.120) Batch 0.820 (1.085) Remain 11:43:56 loss: 0.2962 Lr: 0.00081 [2024-02-19 07:40:35,507 INFO misc.py line 119 87073] Train: [75/100][1557/1557] Data 0.004 (0.120) Batch 0.953 (1.085) Remain 11:43:52 loss: 0.3837 Lr: 0.00081 [2024-02-19 07:40:35,508 INFO misc.py line 136 87073] Train result: loss: 0.2490 [2024-02-19 07:40:35,508 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 07:41:02,927 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5167 [2024-02-19 07:41:03,706 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4823 [2024-02-19 07:41:05,831 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3424 [2024-02-19 07:41:08,039 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3629 [2024-02-19 07:41:12,979 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2910 [2024-02-19 07:41:13,679 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0709 [2024-02-19 07:41:14,944 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2369 [2024-02-19 07:41:17,899 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2879 [2024-02-19 07:41:19,708 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3754 [2024-02-19 07:41:21,395 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7144/0.7714/0.9122. [2024-02-19 07:41:21,395 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9350/0.9614 [2024-02-19 07:41:21,395 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9879 [2024-02-19 07:41:21,395 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8502/0.9676 [2024-02-19 07:41:21,395 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 07:41:21,395 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3178/0.3943 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6514/0.6716 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8027/0.8789 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8459/0.9137 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9325/0.9693 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8454/0.8675 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7997/0.8914 [2024-02-19 07:41:21,396 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7033/0.8148 [2024-02-19 07:41:21,397 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6199/0.7098 [2024-02-19 07:41:21,397 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 07:41:21,399 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 07:41:21,399 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 07:41:29,188 INFO misc.py line 119 87073] Train: [76/100][1/1557] Data 1.407 (1.407) Batch 2.104 (2.104) Remain 22:44:48 loss: 0.1485 Lr: 0.00081 [2024-02-19 07:41:30,441 INFO misc.py line 119 87073] Train: [76/100][2/1557] Data 0.005 (0.005) Batch 1.252 (1.252) Remain 13:32:08 loss: 0.3239 Lr: 0.00081 [2024-02-19 07:41:31,407 INFO misc.py line 119 87073] Train: [76/100][3/1557] Data 0.006 (0.006) Batch 0.967 (0.967) Remain 10:27:18 loss: 0.2072 Lr: 0.00081 [2024-02-19 07:41:32,263 INFO misc.py line 119 87073] Train: [76/100][4/1557] Data 0.004 (0.004) Batch 0.856 (0.856) Remain 09:15:33 loss: 0.4975 Lr: 0.00081 [2024-02-19 07:41:33,110 INFO misc.py line 119 87073] Train: [76/100][5/1557] Data 0.004 (0.004) Batch 0.842 (0.849) Remain 09:10:48 loss: 0.2056 Lr: 0.00081 [2024-02-19 07:41:33,894 INFO misc.py line 119 87073] Train: [76/100][6/1557] Data 0.009 (0.006) Batch 0.789 (0.829) Remain 08:57:44 loss: 0.2226 Lr: 0.00081 [2024-02-19 07:41:36,045 INFO misc.py line 119 87073] Train: [76/100][7/1557] Data 0.948 (0.242) Batch 2.151 (1.160) Remain 12:32:07 loss: 0.2443 Lr: 0.00081 [2024-02-19 07:41:36,962 INFO misc.py line 119 87073] Train: [76/100][8/1557] Data 0.004 (0.194) Batch 0.918 (1.111) Remain 12:00:43 loss: 0.2913 Lr: 0.00081 [2024-02-19 07:41:37,980 INFO misc.py line 119 87073] Train: [76/100][9/1557] Data 0.004 (0.162) Batch 1.017 (1.095) Remain 11:50:31 loss: 0.3707 Lr: 0.00081 [2024-02-19 07:41:38,867 INFO misc.py line 119 87073] Train: [76/100][10/1557] Data 0.005 (0.140) Batch 0.883 (1.065) Remain 11:30:49 loss: 0.2406 Lr: 0.00081 [2024-02-19 07:41:39,954 INFO misc.py line 119 87073] Train: [76/100][11/1557] Data 0.009 (0.124) Batch 1.089 (1.068) Remain 11:32:44 loss: 0.2014 Lr: 0.00081 [2024-02-19 07:41:40,701 INFO misc.py line 119 87073] Train: [76/100][12/1557] Data 0.007 (0.111) Batch 0.749 (1.033) Remain 11:09:42 loss: 0.1854 Lr: 0.00081 [2024-02-19 07:41:41,435 INFO misc.py line 119 87073] Train: [76/100][13/1557] Data 0.005 (0.100) Batch 0.726 (1.002) Remain 10:49:46 loss: 0.2400 Lr: 0.00081 [2024-02-19 07:41:42,757 INFO misc.py line 119 87073] Train: [76/100][14/1557] Data 0.013 (0.092) Batch 1.324 (1.031) Remain 11:08:45 loss: 0.1621 Lr: 0.00081 [2024-02-19 07:41:43,640 INFO misc.py line 119 87073] Train: [76/100][15/1557] Data 0.012 (0.085) Batch 0.890 (1.019) Remain 11:01:06 loss: 0.3355 Lr: 0.00081 [2024-02-19 07:41:44,567 INFO misc.py line 119 87073] Train: [76/100][16/1557] Data 0.004 (0.079) Batch 0.927 (1.012) Remain 10:56:28 loss: 0.2519 Lr: 0.00081 [2024-02-19 07:41:45,408 INFO misc.py line 119 87073] Train: [76/100][17/1557] Data 0.005 (0.074) Batch 0.839 (1.000) Remain 10:48:26 loss: 0.3194 Lr: 0.00081 [2024-02-19 07:41:46,240 INFO misc.py line 119 87073] Train: [76/100][18/1557] Data 0.006 (0.069) Batch 0.832 (0.989) Remain 10:41:10 loss: 0.2960 Lr: 0.00081 [2024-02-19 07:41:47,012 INFO misc.py line 119 87073] Train: [76/100][19/1557] Data 0.006 (0.065) Batch 0.771 (0.975) Remain 10:32:19 loss: 0.1228 Lr: 0.00081 [2024-02-19 07:41:47,777 INFO misc.py line 119 87073] Train: [76/100][20/1557] Data 0.007 (0.062) Batch 0.757 (0.962) Remain 10:23:59 loss: 0.1447 Lr: 0.00081 [2024-02-19 07:41:48,990 INFO misc.py line 119 87073] Train: [76/100][21/1557] Data 0.015 (0.059) Batch 1.221 (0.977) Remain 10:33:18 loss: 0.1781 Lr: 0.00081 [2024-02-19 07:41:50,031 INFO misc.py line 119 87073] Train: [76/100][22/1557] Data 0.007 (0.057) Batch 1.041 (0.980) Remain 10:35:28 loss: 0.4682 Lr: 0.00081 [2024-02-19 07:41:50,965 INFO misc.py line 119 87073] Train: [76/100][23/1557] Data 0.007 (0.054) Batch 0.936 (0.978) Remain 10:34:01 loss: 0.4434 Lr: 0.00081 [2024-02-19 07:41:51,838 INFO misc.py line 119 87073] Train: [76/100][24/1557] Data 0.005 (0.052) Batch 0.874 (0.973) Remain 10:30:48 loss: 0.4285 Lr: 0.00081 [2024-02-19 07:41:52,934 INFO misc.py line 119 87073] Train: [76/100][25/1557] Data 0.004 (0.050) Batch 1.088 (0.978) Remain 10:34:10 loss: 0.1896 Lr: 0.00081 [2024-02-19 07:41:53,665 INFO misc.py line 119 87073] Train: [76/100][26/1557] Data 0.012 (0.048) Batch 0.738 (0.968) Remain 10:27:22 loss: 0.1703 Lr: 0.00081 [2024-02-19 07:41:54,402 INFO misc.py line 119 87073] Train: [76/100][27/1557] Data 0.006 (0.046) Batch 0.733 (0.958) Remain 10:21:01 loss: 0.1814 Lr: 0.00081 [2024-02-19 07:41:55,535 INFO misc.py line 119 87073] Train: [76/100][28/1557] Data 0.009 (0.045) Batch 1.132 (0.965) Remain 10:25:30 loss: 0.0953 Lr: 0.00081 [2024-02-19 07:41:56,392 INFO misc.py line 119 87073] Train: [76/100][29/1557] Data 0.011 (0.043) Batch 0.864 (0.961) Remain 10:22:58 loss: 0.1178 Lr: 0.00081 [2024-02-19 07:41:57,473 INFO misc.py line 119 87073] Train: [76/100][30/1557] Data 0.004 (0.042) Batch 1.080 (0.965) Remain 10:25:49 loss: 0.3270 Lr: 0.00081 [2024-02-19 07:41:58,689 INFO misc.py line 119 87073] Train: [76/100][31/1557] Data 0.004 (0.041) Batch 1.216 (0.974) Remain 10:31:36 loss: 0.2836 Lr: 0.00081 [2024-02-19 07:41:59,742 INFO misc.py line 119 87073] Train: [76/100][32/1557] Data 0.005 (0.039) Batch 1.045 (0.977) Remain 10:33:10 loss: 0.2849 Lr: 0.00081 [2024-02-19 07:42:00,510 INFO misc.py line 119 87073] Train: [76/100][33/1557] Data 0.012 (0.038) Batch 0.776 (0.970) Remain 10:28:48 loss: 0.4157 Lr: 0.00081 [2024-02-19 07:42:01,185 INFO misc.py line 119 87073] Train: [76/100][34/1557] Data 0.005 (0.037) Batch 0.672 (0.960) Remain 10:22:34 loss: 0.2070 Lr: 0.00081 [2024-02-19 07:42:02,474 INFO misc.py line 119 87073] Train: [76/100][35/1557] Data 0.007 (0.036) Batch 1.288 (0.971) Remain 10:29:11 loss: 0.1952 Lr: 0.00081 [2024-02-19 07:42:03,442 INFO misc.py line 119 87073] Train: [76/100][36/1557] Data 0.009 (0.036) Batch 0.970 (0.971) Remain 10:29:08 loss: 0.2887 Lr: 0.00081 [2024-02-19 07:42:04,513 INFO misc.py line 119 87073] Train: [76/100][37/1557] Data 0.007 (0.035) Batch 1.073 (0.974) Remain 10:31:05 loss: 0.4413 Lr: 0.00081 [2024-02-19 07:42:05,456 INFO misc.py line 119 87073] Train: [76/100][38/1557] Data 0.006 (0.034) Batch 0.944 (0.973) Remain 10:30:31 loss: 0.3602 Lr: 0.00081 [2024-02-19 07:42:06,406 INFO misc.py line 119 87073] Train: [76/100][39/1557] Data 0.003 (0.033) Batch 0.945 (0.972) Remain 10:29:59 loss: 0.3549 Lr: 0.00081 [2024-02-19 07:42:07,167 INFO misc.py line 119 87073] Train: [76/100][40/1557] Data 0.011 (0.032) Batch 0.766 (0.966) Remain 10:26:21 loss: 0.2820 Lr: 0.00081 [2024-02-19 07:42:07,909 INFO misc.py line 119 87073] Train: [76/100][41/1557] Data 0.005 (0.032) Batch 0.740 (0.961) Remain 10:22:28 loss: 0.1697 Lr: 0.00081 [2024-02-19 07:42:09,116 INFO misc.py line 119 87073] Train: [76/100][42/1557] Data 0.007 (0.031) Batch 1.207 (0.967) Remain 10:26:33 loss: 0.1649 Lr: 0.00081 [2024-02-19 07:42:10,201 INFO misc.py line 119 87073] Train: [76/100][43/1557] Data 0.007 (0.030) Batch 1.086 (0.970) Remain 10:28:28 loss: 0.2632 Lr: 0.00081 [2024-02-19 07:42:11,276 INFO misc.py line 119 87073] Train: [76/100][44/1557] Data 0.008 (0.030) Batch 1.073 (0.972) Remain 10:30:05 loss: 0.3272 Lr: 0.00081 [2024-02-19 07:42:12,236 INFO misc.py line 119 87073] Train: [76/100][45/1557] Data 0.007 (0.029) Batch 0.964 (0.972) Remain 10:29:56 loss: 0.2894 Lr: 0.00081 [2024-02-19 07:42:13,250 INFO misc.py line 119 87073] Train: [76/100][46/1557] Data 0.005 (0.029) Batch 1.013 (0.973) Remain 10:30:32 loss: 0.0631 Lr: 0.00081 [2024-02-19 07:42:13,930 INFO misc.py line 119 87073] Train: [76/100][47/1557] Data 0.005 (0.028) Batch 0.681 (0.966) Remain 10:26:13 loss: 0.0905 Lr: 0.00081 [2024-02-19 07:42:14,634 INFO misc.py line 119 87073] Train: [76/100][48/1557] Data 0.004 (0.028) Batch 0.701 (0.961) Remain 10:22:22 loss: 0.1078 Lr: 0.00081 [2024-02-19 07:42:15,950 INFO misc.py line 119 87073] Train: [76/100][49/1557] Data 0.007 (0.027) Batch 1.314 (0.968) Remain 10:27:20 loss: 0.1128 Lr: 0.00081 [2024-02-19 07:42:17,006 INFO misc.py line 119 87073] Train: [76/100][50/1557] Data 0.010 (0.027) Batch 1.059 (0.970) Remain 10:28:34 loss: 0.2505 Lr: 0.00081 [2024-02-19 07:42:17,941 INFO misc.py line 119 87073] Train: [76/100][51/1557] Data 0.008 (0.027) Batch 0.937 (0.969) Remain 10:28:07 loss: 0.2766 Lr: 0.00081 [2024-02-19 07:42:18,894 INFO misc.py line 119 87073] Train: [76/100][52/1557] Data 0.004 (0.026) Batch 0.953 (0.969) Remain 10:27:53 loss: 0.2377 Lr: 0.00081 [2024-02-19 07:42:19,892 INFO misc.py line 119 87073] Train: [76/100][53/1557] Data 0.004 (0.026) Batch 0.997 (0.970) Remain 10:28:14 loss: 0.3210 Lr: 0.00081 [2024-02-19 07:42:20,590 INFO misc.py line 119 87073] Train: [76/100][54/1557] Data 0.004 (0.025) Batch 0.692 (0.964) Remain 10:24:41 loss: 0.1959 Lr: 0.00081 [2024-02-19 07:42:21,350 INFO misc.py line 119 87073] Train: [76/100][55/1557] Data 0.010 (0.025) Batch 0.765 (0.960) Remain 10:22:11 loss: 0.1797 Lr: 0.00081 [2024-02-19 07:42:22,582 INFO misc.py line 119 87073] Train: [76/100][56/1557] Data 0.005 (0.025) Batch 1.233 (0.966) Remain 10:25:30 loss: 0.1310 Lr: 0.00081 [2024-02-19 07:42:23,445 INFO misc.py line 119 87073] Train: [76/100][57/1557] Data 0.005 (0.024) Batch 0.863 (0.964) Remain 10:24:15 loss: 0.3784 Lr: 0.00081 [2024-02-19 07:42:24,744 INFO misc.py line 119 87073] Train: [76/100][58/1557] Data 0.005 (0.024) Batch 1.299 (0.970) Remain 10:28:11 loss: 0.2083 Lr: 0.00081 [2024-02-19 07:42:25,713 INFO misc.py line 119 87073] Train: [76/100][59/1557] Data 0.005 (0.023) Batch 0.970 (0.970) Remain 10:28:10 loss: 0.0497 Lr: 0.00081 [2024-02-19 07:42:26,781 INFO misc.py line 119 87073] Train: [76/100][60/1557] Data 0.005 (0.023) Batch 1.069 (0.971) Remain 10:29:16 loss: 0.1215 Lr: 0.00081 [2024-02-19 07:42:27,541 INFO misc.py line 119 87073] Train: [76/100][61/1557] Data 0.003 (0.023) Batch 0.757 (0.968) Remain 10:26:52 loss: 0.1904 Lr: 0.00081 [2024-02-19 07:42:28,282 INFO misc.py line 119 87073] Train: [76/100][62/1557] Data 0.006 (0.023) Batch 0.737 (0.964) Remain 10:24:19 loss: 0.1888 Lr: 0.00081 [2024-02-19 07:42:39,066 INFO misc.py line 119 87073] Train: [76/100][63/1557] Data 8.314 (0.161) Batch 10.790 (1.128) Remain 12:10:22 loss: 0.1611 Lr: 0.00081 [2024-02-19 07:42:40,109 INFO misc.py line 119 87073] Train: [76/100][64/1557] Data 0.005 (0.158) Batch 1.043 (1.126) Remain 12:09:27 loss: 0.2321 Lr: 0.00081 [2024-02-19 07:42:41,060 INFO misc.py line 119 87073] Train: 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0.935 (1.107) Remain 11:56:39 loss: 0.4574 Lr: 0.00081 [2024-02-19 07:42:47,548 INFO misc.py line 119 87073] Train: [76/100][72/1557] Data 0.006 (0.140) Batch 0.882 (1.103) Remain 11:54:32 loss: 0.2509 Lr: 0.00081 [2024-02-19 07:42:48,513 INFO misc.py line 119 87073] Train: [76/100][73/1557] Data 0.008 (0.138) Batch 0.967 (1.102) Remain 11:53:15 loss: 0.2642 Lr: 0.00081 [2024-02-19 07:42:49,597 INFO misc.py line 119 87073] Train: [76/100][74/1557] Data 0.006 (0.137) Batch 1.083 (1.101) Remain 11:53:04 loss: 0.3629 Lr: 0.00081 [2024-02-19 07:42:50,310 INFO misc.py line 119 87073] Train: [76/100][75/1557] Data 0.007 (0.135) Batch 0.714 (1.096) Remain 11:49:34 loss: 0.1375 Lr: 0.00081 [2024-02-19 07:42:51,100 INFO misc.py line 119 87073] Train: [76/100][76/1557] Data 0.005 (0.133) Batch 0.790 (1.092) Remain 11:46:50 loss: 0.1480 Lr: 0.00081 [2024-02-19 07:42:52,394 INFO misc.py line 119 87073] Train: [76/100][77/1557] Data 0.005 (0.131) Batch 1.282 (1.094) Remain 11:48:29 loss: 0.1483 Lr: 0.00081 [2024-02-19 07:42:53,460 INFO misc.py line 119 87073] Train: [76/100][78/1557] Data 0.018 (0.130) Batch 1.078 (1.094) Remain 11:48:19 loss: 0.2934 Lr: 0.00081 [2024-02-19 07:42:54,342 INFO misc.py line 119 87073] Train: [76/100][79/1557] Data 0.005 (0.128) Batch 0.881 (1.091) Remain 11:46:29 loss: 0.3161 Lr: 0.00081 [2024-02-19 07:42:55,432 INFO misc.py line 119 87073] Train: [76/100][80/1557] Data 0.008 (0.127) Batch 1.091 (1.091) Remain 11:46:28 loss: 0.3872 Lr: 0.00081 [2024-02-19 07:42:56,445 INFO misc.py line 119 87073] Train: [76/100][81/1557] Data 0.006 (0.125) Batch 1.011 (1.090) Remain 11:45:47 loss: 0.3567 Lr: 0.00081 [2024-02-19 07:42:57,268 INFO misc.py line 119 87073] Train: [76/100][82/1557] Data 0.006 (0.124) Batch 0.825 (1.087) Remain 11:43:35 loss: 0.2336 Lr: 0.00081 [2024-02-19 07:42:58,010 INFO misc.py line 119 87073] Train: [76/100][83/1557] Data 0.006 (0.122) Batch 0.743 (1.083) Remain 11:40:47 loss: 0.2360 Lr: 0.00081 [2024-02-19 07:42:59,147 INFO misc.py line 119 87073] Train: [76/100][84/1557] Data 0.005 (0.121) Batch 1.133 (1.083) Remain 11:41:10 loss: 0.0975 Lr: 0.00081 [2024-02-19 07:43:00,110 INFO misc.py line 119 87073] Train: [76/100][85/1557] Data 0.009 (0.119) Batch 0.968 (1.082) Remain 11:40:15 loss: 0.5851 Lr: 0.00081 [2024-02-19 07:43:01,031 INFO misc.py line 119 87073] Train: [76/100][86/1557] Data 0.004 (0.118) Batch 0.920 (1.080) Remain 11:38:58 loss: 0.2195 Lr: 0.00081 [2024-02-19 07:43:01,960 INFO misc.py line 119 87073] Train: [76/100][87/1557] Data 0.005 (0.116) Batch 0.929 (1.078) Remain 11:37:47 loss: 0.0841 Lr: 0.00081 [2024-02-19 07:43:02,812 INFO misc.py line 119 87073] Train: [76/100][88/1557] Data 0.005 (0.115) Batch 0.852 (1.075) Remain 11:36:03 loss: 0.1447 Lr: 0.00081 [2024-02-19 07:43:03,587 INFO misc.py line 119 87073] Train: [76/100][89/1557] Data 0.005 (0.114) Batch 0.774 (1.072) Remain 11:33:46 loss: 0.1386 Lr: 0.00081 [2024-02-19 07:43:04,376 INFO misc.py line 119 87073] Train: [76/100][90/1557] Data 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Batch 1.017 (1.123) Remain 12:06:18 loss: 0.1635 Lr: 0.00080 [2024-02-19 07:43:44,808 INFO misc.py line 119 87073] Train: [76/100][122/1557] Data 0.014 (0.146) Batch 0.882 (1.121) Remain 12:04:58 loss: 0.1169 Lr: 0.00080 [2024-02-19 07:43:46,036 INFO misc.py line 119 87073] Train: [76/100][123/1557] Data 0.004 (0.145) Batch 1.227 (1.122) Remain 12:05:32 loss: 0.1961 Lr: 0.00080 [2024-02-19 07:43:46,807 INFO misc.py line 119 87073] Train: [76/100][124/1557] Data 0.004 (0.144) Batch 0.771 (1.119) Remain 12:03:38 loss: 0.3118 Lr: 0.00080 [2024-02-19 07:43:47,550 INFO misc.py line 119 87073] Train: [76/100][125/1557] Data 0.005 (0.143) Batch 0.740 (1.116) Remain 12:01:36 loss: 0.3877 Lr: 0.00080 [2024-02-19 07:43:48,882 INFO misc.py line 119 87073] Train: [76/100][126/1557] Data 0.008 (0.142) Batch 1.335 (1.118) Remain 12:02:44 loss: 0.0978 Lr: 0.00080 [2024-02-19 07:43:49,848 INFO misc.py line 119 87073] Train: [76/100][127/1557] Data 0.005 (0.140) Batch 0.966 (1.116) Remain 12:01:56 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07:43:56,869 INFO misc.py line 119 87073] Train: [76/100][134/1557] Data 0.006 (0.133) Batch 0.925 (1.110) Remain 11:57:53 loss: 0.4383 Lr: 0.00080 [2024-02-19 07:43:57,951 INFO misc.py line 119 87073] Train: [76/100][135/1557] Data 0.006 (0.132) Batch 1.082 (1.110) Remain 11:57:43 loss: 0.2507 Lr: 0.00080 [2024-02-19 07:43:59,093 INFO misc.py line 119 87073] Train: [76/100][136/1557] Data 0.005 (0.131) Batch 1.142 (1.110) Remain 11:57:52 loss: 0.1244 Lr: 0.00080 [2024-02-19 07:44:00,165 INFO misc.py line 119 87073] Train: [76/100][137/1557] Data 0.005 (0.130) Batch 1.072 (1.110) Remain 11:57:39 loss: 0.4707 Lr: 0.00080 [2024-02-19 07:44:00,850 INFO misc.py line 119 87073] Train: [76/100][138/1557] Data 0.005 (0.129) Batch 0.685 (1.107) Remain 11:55:36 loss: 0.2377 Lr: 0.00080 [2024-02-19 07:44:01,584 INFO misc.py line 119 87073] Train: [76/100][139/1557] Data 0.005 (0.129) Batch 0.707 (1.104) Remain 11:53:41 loss: 0.1762 Lr: 0.00080 [2024-02-19 07:44:02,718 INFO misc.py line 119 87073] Train: [76/100][140/1557] Data 0.031 (0.128) Batch 1.161 (1.104) Remain 11:53:56 loss: 0.0946 Lr: 0.00080 [2024-02-19 07:44:03,642 INFO misc.py line 119 87073] Train: [76/100][141/1557] Data 0.004 (0.127) Batch 0.924 (1.103) Remain 11:53:04 loss: 0.4480 Lr: 0.00080 [2024-02-19 07:44:04,560 INFO misc.py line 119 87073] Train: [76/100][142/1557] Data 0.006 (0.126) Batch 0.917 (1.102) Remain 11:52:11 loss: 0.3149 Lr: 0.00080 [2024-02-19 07:44:05,569 INFO misc.py line 119 87073] Train: [76/100][143/1557] Data 0.006 (0.125) Batch 1.010 (1.101) Remain 11:51:44 loss: 0.2182 Lr: 0.00080 [2024-02-19 07:44:06,607 INFO misc.py line 119 87073] Train: [76/100][144/1557] Data 0.004 (0.124) Batch 1.040 (1.101) Remain 11:51:26 loss: 0.3021 Lr: 0.00080 [2024-02-19 07:44:07,362 INFO misc.py line 119 87073] Train: [76/100][145/1557] Data 0.003 (0.123) Batch 0.752 (1.098) Remain 11:49:50 loss: 0.2297 Lr: 0.00080 [2024-02-19 07:44:08,120 INFO misc.py line 119 87073] Train: [76/100][146/1557] Data 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line 119 87073] Train: [76/100][221/1557] Data 0.005 (0.122) Batch 1.127 (1.101) Remain 11:50:02 loss: 0.3096 Lr: 0.00080 [2024-02-19 07:45:32,089 INFO misc.py line 119 87073] Train: [76/100][222/1557] Data 0.007 (0.121) Batch 0.724 (1.099) Remain 11:48:54 loss: 0.0879 Lr: 0.00080 [2024-02-19 07:45:32,838 INFO misc.py line 119 87073] Train: [76/100][223/1557] Data 0.005 (0.121) Batch 0.743 (1.097) Remain 11:47:50 loss: 0.1214 Lr: 0.00080 [2024-02-19 07:45:34,088 INFO misc.py line 119 87073] Train: [76/100][224/1557] Data 0.010 (0.120) Batch 1.251 (1.098) Remain 11:48:16 loss: 0.1551 Lr: 0.00080 [2024-02-19 07:45:35,188 INFO misc.py line 119 87073] Train: [76/100][225/1557] Data 0.011 (0.120) Batch 1.105 (1.098) Remain 11:48:16 loss: 0.3397 Lr: 0.00080 [2024-02-19 07:45:36,133 INFO misc.py line 119 87073] Train: [76/100][226/1557] Data 0.006 (0.119) Batch 0.944 (1.097) Remain 11:47:48 loss: 0.1654 Lr: 0.00080 [2024-02-19 07:45:37,115 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [76/100][333/1557] Data 0.004 (0.130) Batch 1.055 (1.109) Remain 11:53:20 loss: 0.1731 Lr: 0.00080 [2024-02-19 07:47:38,129 INFO misc.py line 119 87073] Train: [76/100][334/1557] Data 0.005 (0.130) Batch 0.737 (1.108) Remain 11:52:35 loss: 0.1239 Lr: 0.00080 [2024-02-19 07:47:38,894 INFO misc.py line 119 87073] Train: [76/100][335/1557] Data 0.004 (0.130) Batch 0.758 (1.107) Remain 11:51:53 loss: 0.1498 Lr: 0.00080 [2024-02-19 07:47:40,183 INFO misc.py line 119 87073] Train: [76/100][336/1557] Data 0.011 (0.129) Batch 1.288 (1.107) Remain 11:52:13 loss: 0.0739 Lr: 0.00080 [2024-02-19 07:47:41,174 INFO misc.py line 119 87073] Train: [76/100][337/1557] Data 0.013 (0.129) Batch 0.998 (1.107) Remain 11:52:00 loss: 0.2259 Lr: 0.00080 [2024-02-19 07:47:42,279 INFO misc.py line 119 87073] Train: [76/100][338/1557] Data 0.005 (0.128) Batch 1.106 (1.107) Remain 11:51:58 loss: 0.4595 Lr: 0.00080 [2024-02-19 07:47:43,099 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [76/100][781/1557] Data 0.009 (0.142) Batch 0.972 (1.123) Remain 11:53:55 loss: 0.7072 Lr: 0.00078 [2024-02-19 07:56:05,895 INFO misc.py line 119 87073] Train: [76/100][782/1557] Data 0.005 (0.142) Batch 0.791 (1.123) Remain 11:53:38 loss: 0.1668 Lr: 0.00078 [2024-02-19 07:56:06,715 INFO misc.py line 119 87073] Train: [76/100][783/1557] Data 0.004 (0.142) Batch 0.818 (1.122) Remain 11:53:22 loss: 0.2869 Lr: 0.00078 [2024-02-19 07:56:07,869 INFO misc.py line 119 87073] Train: [76/100][784/1557] Data 0.006 (0.141) Batch 1.148 (1.122) Remain 11:53:22 loss: 0.1220 Lr: 0.00078 [2024-02-19 07:56:08,738 INFO misc.py line 119 87073] Train: [76/100][785/1557] Data 0.012 (0.141) Batch 0.876 (1.122) Remain 11:53:09 loss: 0.1483 Lr: 0.00078 [2024-02-19 07:56:09,738 INFO misc.py line 119 87073] Train: [76/100][786/1557] Data 0.006 (0.141) Batch 1.001 (1.122) Remain 11:53:02 loss: 0.2946 Lr: 0.00078 [2024-02-19 07:56:10,843 INFO misc.py line 119 87073] Train: 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Train: [76/100][1290/1557] Data 0.011 (0.145) Batch 0.914 (1.128) Remain 11:47:19 loss: 0.5165 Lr: 0.00076 [2024-02-19 08:05:43,698 INFO misc.py line 119 87073] Train: [76/100][1291/1557] Data 0.005 (0.145) Batch 1.003 (1.128) Remain 11:47:14 loss: 0.2519 Lr: 0.00076 [2024-02-19 08:05:44,635 INFO misc.py line 119 87073] Train: [76/100][1292/1557] Data 0.004 (0.144) Batch 0.937 (1.127) Remain 11:47:07 loss: 0.4363 Lr: 0.00076 [2024-02-19 08:05:45,399 INFO misc.py line 119 87073] Train: [76/100][1293/1557] Data 0.005 (0.144) Batch 0.759 (1.127) Remain 11:46:55 loss: 0.3096 Lr: 0.00076 [2024-02-19 08:05:46,186 INFO misc.py line 119 87073] Train: [76/100][1294/1557] Data 0.009 (0.144) Batch 0.793 (1.127) Remain 11:46:44 loss: 0.1779 Lr: 0.00076 [2024-02-19 08:05:57,610 INFO misc.py line 119 87073] Train: [76/100][1295/1557] Data 7.589 (0.150) Batch 11.422 (1.135) Remain 11:51:43 loss: 0.1754 Lr: 0.00076 [2024-02-19 08:05:58,491 INFO misc.py line 119 87073] Train: [76/100][1296/1557] Data 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Remain 11:50:53 loss: 0.1370 Lr: 0.00076 [2024-02-19 08:06:05,098 INFO misc.py line 119 87073] Train: [76/100][1303/1557] Data 0.016 (0.149) Batch 1.007 (1.134) Remain 11:50:48 loss: 0.0807 Lr: 0.00076 [2024-02-19 08:06:05,974 INFO misc.py line 119 87073] Train: [76/100][1304/1557] Data 0.015 (0.149) Batch 0.885 (1.133) Remain 11:50:39 loss: 0.5223 Lr: 0.00076 [2024-02-19 08:06:06,914 INFO misc.py line 119 87073] Train: [76/100][1305/1557] Data 0.007 (0.149) Batch 0.941 (1.133) Remain 11:50:33 loss: 0.4335 Lr: 0.00076 [2024-02-19 08:06:07,890 INFO misc.py line 119 87073] Train: [76/100][1306/1557] Data 0.005 (0.149) Batch 0.975 (1.133) Remain 11:50:27 loss: 0.4631 Lr: 0.00076 [2024-02-19 08:06:08,644 INFO misc.py line 119 87073] Train: [76/100][1307/1557] Data 0.006 (0.149) Batch 0.753 (1.133) Remain 11:50:15 loss: 0.1542 Lr: 0.00076 [2024-02-19 08:06:09,431 INFO misc.py line 119 87073] Train: [76/100][1308/1557] Data 0.006 (0.149) Batch 0.788 (1.133) Remain 11:50:04 loss: 0.2449 Lr: 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Train: [76/100][1321/1557] Data 0.003 (0.147) Batch 0.750 (1.130) Remain 11:48:29 loss: 0.1840 Lr: 0.00076 [2024-02-19 08:06:22,036 INFO misc.py line 119 87073] Train: [76/100][1322/1557] Data 0.005 (0.147) Batch 0.681 (1.130) Remain 11:48:15 loss: 0.4669 Lr: 0.00076 [2024-02-19 08:06:23,365 INFO misc.py line 119 87073] Train: [76/100][1323/1557] Data 0.011 (0.147) Batch 1.328 (1.130) Remain 11:48:20 loss: 0.1954 Lr: 0.00076 [2024-02-19 08:06:24,222 INFO misc.py line 119 87073] Train: [76/100][1324/1557] Data 0.012 (0.147) Batch 0.866 (1.130) Remain 11:48:11 loss: 0.2826 Lr: 0.00076 [2024-02-19 08:06:25,133 INFO misc.py line 119 87073] Train: [76/100][1325/1557] Data 0.003 (0.147) Batch 0.911 (1.130) Remain 11:48:04 loss: 0.3102 Lr: 0.00076 [2024-02-19 08:06:26,169 INFO misc.py line 119 87073] Train: [76/100][1326/1557] Data 0.004 (0.147) Batch 1.037 (1.130) Remain 11:48:00 loss: 0.3014 Lr: 0.00076 [2024-02-19 08:06:27,273 INFO misc.py line 119 87073] Train: [76/100][1327/1557] Data 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Remain 11:47:17 loss: 0.1483 Lr: 0.00076 [2024-02-19 08:06:33,611 INFO misc.py line 119 87073] Train: [76/100][1334/1557] Data 0.004 (0.146) Batch 0.761 (1.129) Remain 11:47:06 loss: 0.2792 Lr: 0.00076 [2024-02-19 08:06:34,394 INFO misc.py line 119 87073] Train: [76/100][1335/1557] Data 0.006 (0.146) Batch 0.784 (1.128) Remain 11:46:55 loss: 0.1560 Lr: 0.00076 [2024-02-19 08:06:35,117 INFO misc.py line 119 87073] Train: [76/100][1336/1557] Data 0.005 (0.146) Batch 0.715 (1.128) Remain 11:46:42 loss: 0.1436 Lr: 0.00076 [2024-02-19 08:06:36,422 INFO misc.py line 119 87073] Train: [76/100][1337/1557] Data 0.013 (0.145) Batch 1.307 (1.128) Remain 11:46:46 loss: 0.1451 Lr: 0.00076 [2024-02-19 08:06:37,214 INFO misc.py line 119 87073] Train: [76/100][1338/1557] Data 0.011 (0.145) Batch 0.798 (1.128) Remain 11:46:36 loss: 0.1767 Lr: 0.00076 [2024-02-19 08:06:37,983 INFO misc.py line 119 87073] Train: [76/100][1339/1557] Data 0.004 (0.145) Batch 0.768 (1.128) Remain 11:46:24 loss: 0.2772 Lr: 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Train: [76/100][1352/1557] Data 0.011 (0.150) Batch 1.075 (1.132) Remain 11:48:55 loss: 0.1314 Lr: 0.00076 [2024-02-19 08:06:59,428 INFO misc.py line 119 87073] Train: [76/100][1353/1557] Data 0.009 (0.150) Batch 0.857 (1.132) Remain 11:48:46 loss: 0.2552 Lr: 0.00076 [2024-02-19 08:07:00,378 INFO misc.py line 119 87073] Train: [76/100][1354/1557] Data 0.006 (0.149) Batch 0.951 (1.132) Remain 11:48:40 loss: 0.2345 Lr: 0.00076 [2024-02-19 08:07:01,265 INFO misc.py line 119 87073] Train: [76/100][1355/1557] Data 0.003 (0.149) Batch 0.887 (1.132) Remain 11:48:32 loss: 0.2066 Lr: 0.00076 [2024-02-19 08:07:02,084 INFO misc.py line 119 87073] Train: [76/100][1356/1557] Data 0.005 (0.149) Batch 0.813 (1.131) Remain 11:48:22 loss: 0.1549 Lr: 0.00076 [2024-02-19 08:07:02,849 INFO misc.py line 119 87073] Train: [76/100][1357/1557] Data 0.010 (0.149) Batch 0.770 (1.131) Remain 11:48:11 loss: 0.1552 Lr: 0.00076 [2024-02-19 08:07:04,763 INFO misc.py line 119 87073] Train: [76/100][1358/1557] Data 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Remain 11:47:46 loss: 0.1713 Lr: 0.00076 [2024-02-19 08:07:11,308 INFO misc.py line 119 87073] Train: [76/100][1365/1557] Data 0.004 (0.148) Batch 1.169 (1.131) Remain 11:47:45 loss: 0.1729 Lr: 0.00076 [2024-02-19 08:07:12,367 INFO misc.py line 119 87073] Train: [76/100][1366/1557] Data 0.004 (0.148) Batch 1.059 (1.131) Remain 11:47:42 loss: 0.2030 Lr: 0.00076 [2024-02-19 08:07:13,355 INFO misc.py line 119 87073] Train: [76/100][1367/1557] Data 0.004 (0.148) Batch 0.988 (1.130) Remain 11:47:37 loss: 0.2188 Lr: 0.00076 [2024-02-19 08:07:14,461 INFO misc.py line 119 87073] Train: [76/100][1368/1557] Data 0.004 (0.148) Batch 1.105 (1.130) Remain 11:47:35 loss: 0.6357 Lr: 0.00076 [2024-02-19 08:07:15,535 INFO misc.py line 119 87073] Train: [76/100][1369/1557] Data 0.004 (0.148) Batch 1.074 (1.130) Remain 11:47:33 loss: 0.0808 Lr: 0.00076 [2024-02-19 08:07:16,302 INFO misc.py line 119 87073] Train: [76/100][1370/1557] Data 0.004 (0.148) Batch 0.768 (1.130) Remain 11:47:22 loss: 0.1429 Lr: 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Train: [76/100][1383/1557] Data 0.005 (0.146) Batch 1.010 (1.128) Remain 11:45:54 loss: 0.0744 Lr: 0.00076 [2024-02-19 08:07:29,099 INFO misc.py line 119 87073] Train: [76/100][1384/1557] Data 0.004 (0.146) Batch 0.772 (1.128) Remain 11:45:43 loss: 0.0737 Lr: 0.00076 [2024-02-19 08:07:29,871 INFO misc.py line 119 87073] Train: [76/100][1385/1557] Data 0.013 (0.146) Batch 0.781 (1.128) Remain 11:45:33 loss: 0.1420 Lr: 0.00076 [2024-02-19 08:07:31,038 INFO misc.py line 119 87073] Train: [76/100][1386/1557] Data 0.004 (0.146) Batch 1.168 (1.128) Remain 11:45:33 loss: 0.2271 Lr: 0.00076 [2024-02-19 08:07:32,114 INFO misc.py line 119 87073] Train: [76/100][1387/1557] Data 0.005 (0.146) Batch 1.076 (1.128) Remain 11:45:30 loss: 0.2264 Lr: 0.00076 [2024-02-19 08:07:33,039 INFO misc.py line 119 87073] Train: [76/100][1388/1557] Data 0.005 (0.146) Batch 0.925 (1.128) Remain 11:45:24 loss: 0.1564 Lr: 0.00076 [2024-02-19 08:07:34,025 INFO misc.py line 119 87073] Train: [76/100][1389/1557] Data 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Remain 11:44:44 loss: 0.2958 Lr: 0.00076 [2024-02-19 08:07:40,845 INFO misc.py line 119 87073] Train: [76/100][1396/1557] Data 0.004 (0.145) Batch 1.076 (1.127) Remain 11:44:42 loss: 0.0352 Lr: 0.00076 [2024-02-19 08:07:41,883 INFO misc.py line 119 87073] Train: [76/100][1397/1557] Data 0.004 (0.145) Batch 1.037 (1.127) Remain 11:44:38 loss: 0.4893 Lr: 0.00076 [2024-02-19 08:07:42,613 INFO misc.py line 119 87073] Train: [76/100][1398/1557] Data 0.005 (0.145) Batch 0.730 (1.126) Remain 11:44:27 loss: 0.4066 Lr: 0.00076 [2024-02-19 08:07:43,325 INFO misc.py line 119 87073] Train: [76/100][1399/1557] Data 0.004 (0.145) Batch 0.704 (1.126) Remain 11:44:14 loss: 0.1755 Lr: 0.00076 [2024-02-19 08:07:44,502 INFO misc.py line 119 87073] Train: [76/100][1400/1557] Data 0.013 (0.145) Batch 1.175 (1.126) Remain 11:44:14 loss: 0.0993 Lr: 0.00076 [2024-02-19 08:07:45,445 INFO misc.py line 119 87073] Train: [76/100][1401/1557] Data 0.015 (0.145) Batch 0.954 (1.126) Remain 11:44:08 loss: 0.2927 Lr: 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Train: [76/100][1414/1557] Data 0.006 (0.150) Batch 1.280 (1.133) Remain 11:48:28 loss: 0.1244 Lr: 0.00075 [2024-02-19 08:08:11,338 INFO misc.py line 119 87073] Train: [76/100][1415/1557] Data 0.009 (0.149) Batch 0.942 (1.133) Remain 11:48:22 loss: 0.1938 Lr: 0.00075 [2024-02-19 08:08:12,410 INFO misc.py line 119 87073] Train: [76/100][1416/1557] Data 0.005 (0.149) Batch 1.072 (1.133) Remain 11:48:19 loss: 0.3352 Lr: 0.00075 [2024-02-19 08:08:13,468 INFO misc.py line 119 87073] Train: [76/100][1417/1557] Data 0.004 (0.149) Batch 1.059 (1.133) Remain 11:48:16 loss: 0.3267 Lr: 0.00075 [2024-02-19 08:08:14,617 INFO misc.py line 119 87073] Train: [76/100][1418/1557] Data 0.004 (0.149) Batch 1.149 (1.133) Remain 11:48:15 loss: 0.1599 Lr: 0.00075 [2024-02-19 08:08:15,401 INFO misc.py line 119 87073] Train: [76/100][1419/1557] Data 0.004 (0.149) Batch 0.783 (1.133) Remain 11:48:05 loss: 0.0833 Lr: 0.00075 [2024-02-19 08:08:16,114 INFO misc.py line 119 87073] Train: [76/100][1420/1557] Data 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Remain 11:47:25 loss: 0.1861 Lr: 0.00075 [2024-02-19 08:08:22,906 INFO misc.py line 119 87073] Train: [76/100][1427/1557] Data 0.004 (0.148) Batch 0.772 (1.132) Remain 11:47:15 loss: 0.1099 Lr: 0.00075 [2024-02-19 08:08:24,095 INFO misc.py line 119 87073] Train: [76/100][1428/1557] Data 0.004 (0.148) Batch 1.187 (1.132) Remain 11:47:15 loss: 0.0726 Lr: 0.00075 [2024-02-19 08:08:25,109 INFO misc.py line 119 87073] Train: [76/100][1429/1557] Data 0.006 (0.148) Batch 1.010 (1.132) Remain 11:47:11 loss: 0.1977 Lr: 0.00075 [2024-02-19 08:08:26,174 INFO misc.py line 119 87073] Train: [76/100][1430/1557] Data 0.011 (0.148) Batch 1.071 (1.132) Remain 11:47:08 loss: 0.0830 Lr: 0.00075 [2024-02-19 08:08:27,188 INFO misc.py line 119 87073] Train: [76/100][1431/1557] Data 0.005 (0.148) Batch 1.014 (1.131) Remain 11:47:04 loss: 0.2477 Lr: 0.00075 [2024-02-19 08:08:28,054 INFO misc.py line 119 87073] Train: [76/100][1432/1557] Data 0.006 (0.148) Batch 0.866 (1.131) Remain 11:46:56 loss: 0.3566 Lr: 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Train: [76/100][1445/1557] Data 0.008 (0.146) Batch 1.082 (1.130) Remain 11:45:44 loss: 0.2910 Lr: 0.00075 [2024-02-19 08:08:41,481 INFO misc.py line 119 87073] Train: [76/100][1446/1557] Data 0.005 (0.146) Batch 0.930 (1.130) Remain 11:45:37 loss: 0.2481 Lr: 0.00075 [2024-02-19 08:08:42,182 INFO misc.py line 119 87073] Train: [76/100][1447/1557] Data 0.004 (0.146) Batch 0.700 (1.129) Remain 11:45:25 loss: 0.1461 Lr: 0.00075 [2024-02-19 08:08:42,848 INFO misc.py line 119 87073] Train: [76/100][1448/1557] Data 0.004 (0.146) Batch 0.667 (1.129) Remain 11:45:12 loss: 0.1100 Lr: 0.00075 [2024-02-19 08:08:44,127 INFO misc.py line 119 87073] Train: [76/100][1449/1557] Data 0.004 (0.146) Batch 1.269 (1.129) Remain 11:45:14 loss: 0.1214 Lr: 0.00075 [2024-02-19 08:08:45,020 INFO misc.py line 119 87073] Train: [76/100][1450/1557] Data 0.015 (0.146) Batch 0.903 (1.129) Remain 11:45:07 loss: 0.4651 Lr: 0.00075 [2024-02-19 08:08:46,216 INFO misc.py line 119 87073] Train: [76/100][1451/1557] Data 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Remain 11:44:33 loss: 0.1546 Lr: 0.00075 [2024-02-19 08:08:52,955 INFO misc.py line 119 87073] Train: [76/100][1458/1557] Data 0.005 (0.145) Batch 1.036 (1.128) Remain 11:44:30 loss: 0.2431 Lr: 0.00075 [2024-02-19 08:08:53,880 INFO misc.py line 119 87073] Train: [76/100][1459/1557] Data 0.012 (0.145) Batch 0.934 (1.128) Remain 11:44:24 loss: 0.3067 Lr: 0.00075 [2024-02-19 08:08:54,913 INFO misc.py line 119 87073] Train: [76/100][1460/1557] Data 0.003 (0.145) Batch 1.032 (1.128) Remain 11:44:20 loss: 0.4570 Lr: 0.00075 [2024-02-19 08:08:55,643 INFO misc.py line 119 87073] Train: [76/100][1461/1557] Data 0.004 (0.145) Batch 0.730 (1.128) Remain 11:44:09 loss: 0.2150 Lr: 0.00075 [2024-02-19 08:08:56,394 INFO misc.py line 119 87073] Train: [76/100][1462/1557] Data 0.004 (0.145) Batch 0.741 (1.127) Remain 11:43:58 loss: 0.3597 Lr: 0.00075 [2024-02-19 08:09:08,301 INFO misc.py line 119 87073] Train: [76/100][1463/1557] Data 7.927 (0.150) Batch 11.918 (1.135) Remain 11:48:34 loss: 0.1153 Lr: 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Train: [76/100][1476/1557] Data 0.005 (0.149) Batch 0.782 (1.133) Remain 11:47:15 loss: 0.1493 Lr: 0.00075 [2024-02-19 08:09:21,748 INFO misc.py line 119 87073] Train: [76/100][1477/1557] Data 0.005 (0.149) Batch 1.183 (1.133) Remain 11:47:16 loss: 0.2454 Lr: 0.00075 [2024-02-19 08:09:22,728 INFO misc.py line 119 87073] Train: [76/100][1478/1557] Data 0.003 (0.149) Batch 0.980 (1.133) Remain 11:47:11 loss: 0.2459 Lr: 0.00075 [2024-02-19 08:09:23,821 INFO misc.py line 119 87073] Train: [76/100][1479/1557] Data 0.004 (0.149) Batch 1.091 (1.133) Remain 11:47:08 loss: 0.1975 Lr: 0.00075 [2024-02-19 08:09:25,002 INFO misc.py line 119 87073] Train: [76/100][1480/1557] Data 0.005 (0.148) Batch 1.180 (1.133) Remain 11:47:08 loss: 0.2200 Lr: 0.00075 [2024-02-19 08:09:26,032 INFO misc.py line 119 87073] Train: [76/100][1481/1557] Data 0.006 (0.148) Batch 1.031 (1.133) Remain 11:47:05 loss: 0.1126 Lr: 0.00075 [2024-02-19 08:09:26,797 INFO misc.py line 119 87073] Train: [76/100][1482/1557] Data 0.005 (0.148) Batch 0.765 (1.133) Remain 11:46:54 loss: 0.1168 Lr: 0.00075 [2024-02-19 08:09:27,555 INFO misc.py line 119 87073] Train: [76/100][1483/1557] Data 0.006 (0.148) Batch 0.758 (1.133) Remain 11:46:44 loss: 0.2795 Lr: 0.00075 [2024-02-19 08:09:28,650 INFO misc.py line 119 87073] Train: [76/100][1484/1557] Data 0.005 (0.148) Batch 1.095 (1.133) Remain 11:46:42 loss: 0.0656 Lr: 0.00075 [2024-02-19 08:09:29,750 INFO misc.py line 119 87073] Train: [76/100][1485/1557] Data 0.006 (0.148) Batch 1.092 (1.132) Remain 11:46:39 loss: 0.2392 Lr: 0.00075 [2024-02-19 08:09:30,754 INFO misc.py line 119 87073] Train: [76/100][1486/1557] Data 0.013 (0.148) Batch 1.006 (1.132) Remain 11:46:35 loss: 0.2613 Lr: 0.00075 [2024-02-19 08:09:31,968 INFO misc.py line 119 87073] Train: [76/100][1487/1557] Data 0.011 (0.148) Batch 1.209 (1.132) Remain 11:46:36 loss: 0.2875 Lr: 0.00075 [2024-02-19 08:09:32,914 INFO misc.py line 119 87073] Train: [76/100][1488/1557] Data 0.017 (0.148) Batch 0.959 (1.132) Remain 11:46:30 loss: 0.3314 Lr: 0.00075 [2024-02-19 08:09:33,706 INFO misc.py line 119 87073] Train: [76/100][1489/1557] Data 0.004 (0.148) Batch 0.792 (1.132) Remain 11:46:21 loss: 0.2662 Lr: 0.00075 [2024-02-19 08:09:34,456 INFO misc.py line 119 87073] Train: [76/100][1490/1557] Data 0.004 (0.148) Batch 0.750 (1.132) Remain 11:46:10 loss: 0.1532 Lr: 0.00075 [2024-02-19 08:09:35,708 INFO misc.py line 119 87073] Train: [76/100][1491/1557] Data 0.004 (0.147) Batch 1.248 (1.132) Remain 11:46:12 loss: 0.1779 Lr: 0.00075 [2024-02-19 08:09:36,720 INFO misc.py line 119 87073] Train: [76/100][1492/1557] Data 0.008 (0.147) Batch 1.013 (1.132) Remain 11:46:08 loss: 0.4226 Lr: 0.00075 [2024-02-19 08:09:37,786 INFO misc.py line 119 87073] Train: [76/100][1493/1557] Data 0.008 (0.147) Batch 1.059 (1.132) Remain 11:46:05 loss: 0.3018 Lr: 0.00075 [2024-02-19 08:09:38,787 INFO misc.py line 119 87073] Train: [76/100][1494/1557] Data 0.015 (0.147) Batch 1.012 (1.132) Remain 11:46:00 loss: 0.2495 Lr: 0.00075 [2024-02-19 08:09:39,680 INFO misc.py line 119 87073] Train: [76/100][1495/1557] Data 0.005 (0.147) Batch 0.892 (1.132) Remain 11:45:53 loss: 0.1243 Lr: 0.00075 [2024-02-19 08:09:40,418 INFO misc.py line 119 87073] Train: [76/100][1496/1557] Data 0.004 (0.147) Batch 0.739 (1.131) Remain 11:45:42 loss: 0.3447 Lr: 0.00075 [2024-02-19 08:09:41,140 INFO misc.py line 119 87073] Train: [76/100][1497/1557] Data 0.003 (0.147) Batch 0.717 (1.131) Remain 11:45:31 loss: 0.1506 Lr: 0.00075 [2024-02-19 08:09:42,335 INFO misc.py line 119 87073] Train: [76/100][1498/1557] Data 0.008 (0.147) Batch 1.194 (1.131) Remain 11:45:31 loss: 0.1394 Lr: 0.00075 [2024-02-19 08:09:43,288 INFO misc.py line 119 87073] Train: [76/100][1499/1557] Data 0.008 (0.147) Batch 0.958 (1.131) Remain 11:45:26 loss: 0.2626 Lr: 0.00075 [2024-02-19 08:09:44,268 INFO misc.py line 119 87073] Train: [76/100][1500/1557] Data 0.003 (0.147) Batch 0.979 (1.131) Remain 11:45:21 loss: 0.1596 Lr: 0.00075 [2024-02-19 08:09:45,245 INFO misc.py line 119 87073] Train: [76/100][1501/1557] Data 0.005 (0.147) Batch 0.979 (1.131) Remain 11:45:16 loss: 0.1863 Lr: 0.00075 [2024-02-19 08:09:46,059 INFO misc.py line 119 87073] Train: [76/100][1502/1557] Data 0.003 (0.146) Batch 0.811 (1.131) Remain 11:45:07 loss: 0.3641 Lr: 0.00075 [2024-02-19 08:09:46,797 INFO misc.py line 119 87073] Train: [76/100][1503/1557] Data 0.006 (0.146) Batch 0.741 (1.130) Remain 11:44:56 loss: 0.1779 Lr: 0.00075 [2024-02-19 08:09:47,570 INFO misc.py line 119 87073] Train: [76/100][1504/1557] Data 0.003 (0.146) Batch 0.772 (1.130) Remain 11:44:46 loss: 0.2065 Lr: 0.00075 [2024-02-19 08:09:48,833 INFO misc.py line 119 87073] Train: [76/100][1505/1557] Data 0.004 (0.146) Batch 1.261 (1.130) Remain 11:44:48 loss: 0.1383 Lr: 0.00075 [2024-02-19 08:09:49,720 INFO misc.py line 119 87073] Train: [76/100][1506/1557] Data 0.007 (0.146) Batch 0.890 (1.130) Remain 11:44:41 loss: 0.3328 Lr: 0.00075 [2024-02-19 08:09:50,777 INFO misc.py line 119 87073] Train: [76/100][1507/1557] Data 0.004 (0.146) Batch 1.056 (1.130) Remain 11:44:38 loss: 0.1936 Lr: 0.00075 [2024-02-19 08:09:51,776 INFO misc.py line 119 87073] Train: [76/100][1508/1557] Data 0.004 (0.146) Batch 0.999 (1.130) Remain 11:44:34 loss: 0.3169 Lr: 0.00075 [2024-02-19 08:09:52,770 INFO misc.py line 119 87073] Train: [76/100][1509/1557] Data 0.004 (0.146) Batch 0.993 (1.130) Remain 11:44:29 loss: 0.2841 Lr: 0.00075 [2024-02-19 08:09:53,510 INFO misc.py line 119 87073] Train: [76/100][1510/1557] Data 0.005 (0.146) Batch 0.741 (1.129) Remain 11:44:18 loss: 0.1051 Lr: 0.00075 [2024-02-19 08:09:54,214 INFO misc.py line 119 87073] Train: [76/100][1511/1557] Data 0.004 (0.146) Batch 0.697 (1.129) Remain 11:44:06 loss: 0.1047 Lr: 0.00075 [2024-02-19 08:09:55,439 INFO misc.py line 119 87073] Train: [76/100][1512/1557] Data 0.011 (0.145) Batch 1.222 (1.129) Remain 11:44:08 loss: 0.2219 Lr: 0.00075 [2024-02-19 08:09:56,373 INFO misc.py line 119 87073] Train: [76/100][1513/1557] Data 0.014 (0.145) Batch 0.944 (1.129) Remain 11:44:02 loss: 0.3973 Lr: 0.00075 [2024-02-19 08:09:57,356 INFO misc.py line 119 87073] Train: [76/100][1514/1557] Data 0.003 (0.145) Batch 0.983 (1.129) Remain 11:43:57 loss: 0.2151 Lr: 0.00075 [2024-02-19 08:09:58,377 INFO misc.py line 119 87073] Train: [76/100][1515/1557] Data 0.003 (0.145) Batch 1.020 (1.129) Remain 11:43:53 loss: 0.4263 Lr: 0.00075 [2024-02-19 08:09:59,350 INFO misc.py line 119 87073] Train: [76/100][1516/1557] Data 0.004 (0.145) Batch 0.974 (1.129) Remain 11:43:48 loss: 0.1358 Lr: 0.00075 [2024-02-19 08:10:00,118 INFO misc.py line 119 87073] Train: [76/100][1517/1557] Data 0.003 (0.145) Batch 0.759 (1.129) Remain 11:43:38 loss: 0.2439 Lr: 0.00075 [2024-02-19 08:10:00,874 INFO misc.py line 119 87073] Train: [76/100][1518/1557] Data 0.012 (0.145) Batch 0.764 (1.128) Remain 11:43:28 loss: 0.3031 Lr: 0.00075 [2024-02-19 08:10:12,326 INFO misc.py line 119 87073] Train: [76/100][1519/1557] Data 8.073 (0.150) Batch 11.452 (1.135) Remain 11:47:42 loss: 0.1584 Lr: 0.00075 [2024-02-19 08:10:13,202 INFO misc.py line 119 87073] Train: [76/100][1520/1557] Data 0.004 (0.150) Batch 0.876 (1.135) Remain 11:47:34 loss: 0.3265 Lr: 0.00075 [2024-02-19 08:10:14,135 INFO misc.py line 119 87073] Train: [76/100][1521/1557] Data 0.004 (0.150) Batch 0.928 (1.135) Remain 11:47:28 loss: 0.1503 Lr: 0.00075 [2024-02-19 08:10:14,984 INFO misc.py line 119 87073] Train: [76/100][1522/1557] Data 0.009 (0.150) Batch 0.853 (1.135) Remain 11:47:20 loss: 0.0979 Lr: 0.00075 [2024-02-19 08:10:15,902 INFO misc.py line 119 87073] Train: [76/100][1523/1557] Data 0.006 (0.150) Batch 0.919 (1.135) Remain 11:47:13 loss: 0.1363 Lr: 0.00075 [2024-02-19 08:10:16,633 INFO misc.py line 119 87073] Train: [76/100][1524/1557] Data 0.004 (0.150) Batch 0.708 (1.134) Remain 11:47:02 loss: 0.1982 Lr: 0.00075 [2024-02-19 08:10:17,354 INFO misc.py line 119 87073] Train: [76/100][1525/1557] Data 0.027 (0.150) Batch 0.744 (1.134) Remain 11:46:51 loss: 0.6338 Lr: 0.00075 [2024-02-19 08:10:18,603 INFO misc.py line 119 87073] Train: [76/100][1526/1557] Data 0.004 (0.150) Batch 1.249 (1.134) Remain 11:46:53 loss: 0.0936 Lr: 0.00075 [2024-02-19 08:10:19,560 INFO misc.py line 119 87073] Train: [76/100][1527/1557] Data 0.004 (0.149) Batch 0.958 (1.134) Remain 11:46:47 loss: 0.3735 Lr: 0.00075 [2024-02-19 08:10:20,361 INFO misc.py line 119 87073] Train: [76/100][1528/1557] Data 0.003 (0.149) Batch 0.800 (1.134) Remain 11:46:38 loss: 0.0912 Lr: 0.00075 [2024-02-19 08:10:21,293 INFO misc.py line 119 87073] Train: [76/100][1529/1557] Data 0.004 (0.149) Batch 0.932 (1.134) Remain 11:46:32 loss: 0.5172 Lr: 0.00075 [2024-02-19 08:10:22,207 INFO misc.py line 119 87073] Train: [76/100][1530/1557] Data 0.005 (0.149) Batch 0.914 (1.133) Remain 11:46:25 loss: 0.1733 Lr: 0.00075 [2024-02-19 08:10:23,032 INFO misc.py line 119 87073] Train: [76/100][1531/1557] Data 0.004 (0.149) Batch 0.825 (1.133) Remain 11:46:17 loss: 0.1661 Lr: 0.00075 [2024-02-19 08:10:23,826 INFO misc.py line 119 87073] Train: [76/100][1532/1557] Data 0.005 (0.149) Batch 0.787 (1.133) Remain 11:46:07 loss: 0.1426 Lr: 0.00075 [2024-02-19 08:10:25,162 INFO misc.py line 119 87073] Train: [76/100][1533/1557] Data 0.011 (0.149) Batch 1.332 (1.133) Remain 11:46:11 loss: 0.1857 Lr: 0.00075 [2024-02-19 08:10:26,105 INFO misc.py line 119 87073] Train: [76/100][1534/1557] Data 0.015 (0.149) Batch 0.954 (1.133) Remain 11:46:05 loss: 0.0830 Lr: 0.00075 [2024-02-19 08:10:27,117 INFO misc.py line 119 87073] Train: [76/100][1535/1557] Data 0.003 (0.149) Batch 1.013 (1.133) Remain 11:46:01 loss: 0.3960 Lr: 0.00075 [2024-02-19 08:10:28,009 INFO misc.py line 119 87073] Train: [76/100][1536/1557] Data 0.003 (0.149) Batch 0.891 (1.133) Remain 11:45:54 loss: 0.0983 Lr: 0.00075 [2024-02-19 08:10:28,986 INFO misc.py line 119 87073] Train: [76/100][1537/1557] Data 0.004 (0.148) Batch 0.967 (1.133) Remain 11:45:49 loss: 0.1976 Lr: 0.00075 [2024-02-19 08:10:29,672 INFO misc.py line 119 87073] Train: [76/100][1538/1557] Data 0.014 (0.148) Batch 0.696 (1.132) Remain 11:45:37 loss: 0.1260 Lr: 0.00075 [2024-02-19 08:10:30,440 INFO misc.py line 119 87073] Train: [76/100][1539/1557] Data 0.003 (0.148) Batch 0.757 (1.132) Remain 11:45:27 loss: 0.1886 Lr: 0.00075 [2024-02-19 08:10:31,638 INFO misc.py line 119 87073] Train: [76/100][1540/1557] Data 0.015 (0.148) Batch 1.196 (1.132) Remain 11:45:27 loss: 0.0675 Lr: 0.00075 [2024-02-19 08:10:32,550 INFO misc.py line 119 87073] Train: [76/100][1541/1557] Data 0.017 (0.148) Batch 0.924 (1.132) Remain 11:45:21 loss: 0.4407 Lr: 0.00075 [2024-02-19 08:10:33,457 INFO misc.py line 119 87073] Train: [76/100][1542/1557] Data 0.004 (0.148) Batch 0.908 (1.132) Remain 11:45:15 loss: 0.1155 Lr: 0.00075 [2024-02-19 08:10:34,229 INFO misc.py line 119 87073] Train: [76/100][1543/1557] Data 0.003 (0.148) Batch 0.760 (1.132) Remain 11:45:04 loss: 0.1673 Lr: 0.00075 [2024-02-19 08:10:35,173 INFO misc.py line 119 87073] Train: [76/100][1544/1557] Data 0.015 (0.148) Batch 0.956 (1.132) Remain 11:44:59 loss: 0.3014 Lr: 0.00075 [2024-02-19 08:10:35,925 INFO misc.py line 119 87073] Train: [76/100][1545/1557] Data 0.003 (0.148) Batch 0.752 (1.131) Remain 11:44:49 loss: 0.1986 Lr: 0.00075 [2024-02-19 08:10:36,634 INFO misc.py line 119 87073] Train: [76/100][1546/1557] Data 0.003 (0.148) Batch 0.707 (1.131) Remain 11:44:37 loss: 0.2596 Lr: 0.00075 [2024-02-19 08:10:37,835 INFO misc.py line 119 87073] Train: [76/100][1547/1557] Data 0.005 (0.148) Batch 1.202 (1.131) Remain 11:44:38 loss: 0.1492 Lr: 0.00075 [2024-02-19 08:10:39,002 INFO misc.py line 119 87073] Train: [76/100][1548/1557] Data 0.004 (0.147) Batch 1.158 (1.131) Remain 11:44:37 loss: 0.3289 Lr: 0.00075 [2024-02-19 08:10:39,940 INFO misc.py line 119 87073] Train: [76/100][1549/1557] Data 0.014 (0.147) Batch 0.948 (1.131) Remain 11:44:32 loss: 0.2262 Lr: 0.00075 [2024-02-19 08:10:40,903 INFO misc.py line 119 87073] Train: [76/100][1550/1557] Data 0.003 (0.147) Batch 0.963 (1.131) Remain 11:44:27 loss: 0.2131 Lr: 0.00075 [2024-02-19 08:10:41,891 INFO misc.py line 119 87073] Train: [76/100][1551/1557] Data 0.003 (0.147) Batch 0.988 (1.131) Remain 11:44:22 loss: 0.2710 Lr: 0.00075 [2024-02-19 08:10:42,649 INFO misc.py line 119 87073] Train: [76/100][1552/1557] Data 0.003 (0.147) Batch 0.748 (1.131) Remain 11:44:12 loss: 0.2018 Lr: 0.00075 [2024-02-19 08:10:43,399 INFO misc.py line 119 87073] Train: [76/100][1553/1557] Data 0.015 (0.147) Batch 0.759 (1.130) Remain 11:44:02 loss: 0.3300 Lr: 0.00075 [2024-02-19 08:10:44,565 INFO misc.py line 119 87073] Train: [76/100][1554/1557] Data 0.004 (0.147) Batch 1.167 (1.130) Remain 11:44:01 loss: 0.3340 Lr: 0.00075 [2024-02-19 08:10:45,478 INFO misc.py line 119 87073] Train: [76/100][1555/1557] Data 0.003 (0.147) Batch 0.911 (1.130) Remain 11:43:55 loss: 0.4479 Lr: 0.00075 [2024-02-19 08:10:46,539 INFO misc.py line 119 87073] Train: [76/100][1556/1557] Data 0.004 (0.147) Batch 1.063 (1.130) Remain 11:43:52 loss: 0.3779 Lr: 0.00075 [2024-02-19 08:10:47,412 INFO misc.py line 119 87073] Train: [76/100][1557/1557] Data 0.004 (0.147) Batch 0.873 (1.130) Remain 11:43:45 loss: 0.2151 Lr: 0.00075 [2024-02-19 08:10:47,413 INFO misc.py line 136 87073] Train result: loss: 0.2510 [2024-02-19 08:10:47,413 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 08:11:12,778 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5139 [2024-02-19 08:11:13,555 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.8067 [2024-02-19 08:11:15,682 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3448 [2024-02-19 08:11:17,891 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.4034 [2024-02-19 08:11:22,835 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2579 [2024-02-19 08:11:23,533 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0849 [2024-02-19 08:11:24,793 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2565 [2024-02-19 08:11:27,745 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3054 [2024-02-19 08:11:29,560 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2686 [2024-02-19 08:11:31,191 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7093/0.7646/0.9108. [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9228/0.9618 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9910 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8538/0.9749 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.2956/0.3283 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6448/0.6686 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.7748/0.8771 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8485/0.9280 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9189/0.9719 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8244/0.8551 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7924/0.8820 [2024-02-19 08:11:31,191 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7622/0.8186 [2024-02-19 08:11:31,192 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.5999/0.6825 [2024-02-19 08:11:31,192 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 08:11:31,193 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 08:11:31,193 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 08:11:37,274 INFO misc.py line 119 87073] Train: [77/100][1/1557] Data 1.298 (1.298) Batch 1.917 (1.917) Remain 19:54:10 loss: 0.4628 Lr: 0.00075 [2024-02-19 08:11:38,164 INFO misc.py line 119 87073] Train: [77/100][2/1557] Data 0.009 (0.009) Batch 0.893 (0.893) Remain 09:15:54 loss: 0.4681 Lr: 0.00075 [2024-02-19 08:11:39,178 INFO misc.py line 119 87073] Train: [77/100][3/1557] Data 0.006 (0.006) Batch 1.012 (1.012) Remain 10:30:24 loss: 0.0501 Lr: 0.00075 [2024-02-19 08:11:40,116 INFO misc.py line 119 87073] Train: [77/100][4/1557] Data 0.008 (0.008) Batch 0.941 (0.941) Remain 09:45:43 loss: 0.5738 Lr: 0.00075 [2024-02-19 08:11:40,874 INFO misc.py line 119 87073] Train: [77/100][5/1557] Data 0.006 (0.007) Batch 0.758 (0.849) Remain 08:48:50 loss: 0.1206 Lr: 0.00075 [2024-02-19 08:11:41,762 INFO misc.py line 119 87073] Train: [77/100][6/1557] Data 0.005 (0.006) Batch 0.889 (0.862) Remain 08:57:02 loss: 0.3074 Lr: 0.00075 [2024-02-19 08:11:53,065 INFO misc.py line 119 87073] Train: [77/100][7/1557] Data 0.663 (0.170) Batch 11.303 (3.473) Remain 36:02:19 loss: 0.1250 Lr: 0.00075 [2024-02-19 08:11:54,041 INFO misc.py line 119 87073] Train: [77/100][8/1557] Data 0.004 (0.137) Batch 0.976 (2.973) Remain 30:51:20 loss: 0.1000 Lr: 0.00075 [2024-02-19 08:11:55,285 INFO misc.py line 119 87073] Train: [77/100][9/1557] Data 0.004 (0.115) Batch 1.236 (2.684) Remain 27:51:02 loss: 0.2724 Lr: 0.00075 [2024-02-19 08:11:56,446 INFO misc.py line 119 87073] Train: [77/100][10/1557] Data 0.012 (0.100) Batch 1.159 (2.466) Remain 25:35:19 loss: 0.2679 Lr: 0.00075 [2024-02-19 08:11:57,510 INFO misc.py line 119 87073] Train: [77/100][11/1557] Data 0.014 (0.089) Batch 1.066 (2.291) Remain 23:46:19 loss: 0.5159 Lr: 0.00075 [2024-02-19 08:11:58,257 INFO misc.py line 119 87073] Train: [77/100][12/1557] Data 0.013 (0.081) Batch 0.756 (2.120) Remain 22:00:05 loss: 0.3168 Lr: 0.00075 [2024-02-19 08:11:59,025 INFO misc.py line 119 87073] Train: [77/100][13/1557] Data 0.003 (0.073) Batch 0.760 (1.984) Remain 20:35:23 loss: 0.4273 Lr: 0.00075 [2024-02-19 08:12:00,354 INFO misc.py line 119 87073] Train: [77/100][14/1557] Data 0.011 (0.067) Batch 1.330 (1.925) Remain 19:58:18 loss: 0.1247 Lr: 0.00075 [2024-02-19 08:12:01,192 INFO misc.py line 119 87073] Train: [77/100][15/1557] Data 0.011 (0.063) Batch 0.843 (1.835) Remain 19:02:07 loss: 0.1263 Lr: 0.00075 [2024-02-19 08:12:02,094 INFO misc.py line 119 87073] Train: [77/100][16/1557] Data 0.006 (0.058) Batch 0.904 (1.763) Remain 18:17:33 loss: 0.4579 Lr: 0.00075 [2024-02-19 08:12:03,059 INFO misc.py line 119 87073] Train: [77/100][17/1557] Data 0.003 (0.054) Batch 0.947 (1.705) Remain 17:41:13 loss: 0.1895 Lr: 0.00075 [2024-02-19 08:12:04,003 INFO misc.py line 119 87073] Train: [77/100][18/1557] Data 0.022 (0.052) Batch 0.961 (1.655) Remain 17:10:21 loss: 0.2775 Lr: 0.00075 [2024-02-19 08:12:04,773 INFO misc.py line 119 87073] Train: [77/100][19/1557] Data 0.005 (0.049) Batch 0.771 (1.600) Remain 16:35:55 loss: 0.1701 Lr: 0.00075 [2024-02-19 08:12:05,526 INFO misc.py line 119 87073] Train: [77/100][20/1557] Data 0.003 (0.047) Batch 0.743 (1.549) Remain 16:04:30 loss: 0.2444 Lr: 0.00075 [2024-02-19 08:12:06,720 INFO misc.py line 119 87073] Train: [77/100][21/1557] Data 0.014 (0.045) Batch 1.193 (1.530) Remain 15:52:08 loss: 0.1167 Lr: 0.00075 [2024-02-19 08:12:08,057 INFO misc.py line 119 87073] Train: [77/100][22/1557] Data 0.015 (0.043) Batch 1.332 (1.519) Remain 15:45:37 loss: 0.1699 Lr: 0.00075 [2024-02-19 08:12:08,897 INFO misc.py line 119 87073] Train: [77/100][23/1557] Data 0.021 (0.042) Batch 0.857 (1.486) Remain 15:24:59 loss: 0.1511 Lr: 0.00075 [2024-02-19 08:12:09,874 INFO misc.py line 119 87073] Train: [77/100][24/1557] Data 0.004 (0.040) Batch 0.977 (1.462) Remain 15:09:52 loss: 0.4794 Lr: 0.00075 [2024-02-19 08:12:10,931 INFO misc.py line 119 87073] Train: [77/100][25/1557] Data 0.004 (0.039) Batch 1.057 (1.443) Remain 14:58:23 loss: 0.1636 Lr: 0.00075 [2024-02-19 08:12:11,700 INFO misc.py line 119 87073] Train: [77/100][26/1557] Data 0.004 (0.037) Batch 0.769 (1.414) Remain 14:40:07 loss: 0.2188 Lr: 0.00075 [2024-02-19 08:12:12,496 INFO misc.py line 119 87073] Train: [77/100][27/1557] Data 0.004 (0.036) Batch 0.789 (1.388) Remain 14:23:54 loss: 0.2306 Lr: 0.00075 [2024-02-19 08:12:13,680 INFO misc.py line 119 87073] Train: [77/100][28/1557] Data 0.010 (0.035) Batch 1.179 (1.380) Remain 14:18:40 loss: 0.1723 Lr: 0.00075 [2024-02-19 08:12:14,714 INFO misc.py line 119 87073] Train: [77/100][29/1557] Data 0.015 (0.034) Batch 1.038 (1.367) Remain 14:10:28 loss: 0.2240 Lr: 0.00075 [2024-02-19 08:12:15,605 INFO misc.py line 119 87073] Train: [77/100][30/1557] Data 0.011 (0.033) Batch 0.898 (1.349) Remain 13:59:38 loss: 0.3954 Lr: 0.00075 [2024-02-19 08:12:16,559 INFO misc.py line 119 87073] Train: [77/100][31/1557] Data 0.004 (0.032) Batch 0.953 (1.335) Remain 13:50:48 loss: 0.1137 Lr: 0.00075 [2024-02-19 08:12:17,715 INFO misc.py line 119 87073] Train: [77/100][32/1557] Data 0.005 (0.031) Batch 1.156 (1.329) Remain 13:46:57 loss: 0.3266 Lr: 0.00075 [2024-02-19 08:12:18,479 INFO misc.py line 119 87073] Train: [77/100][33/1557] Data 0.005 (0.030) Batch 0.765 (1.310) Remain 13:35:14 loss: 0.1914 Lr: 0.00075 [2024-02-19 08:12:19,149 INFO misc.py line 119 87073] Train: [77/100][34/1557] Data 0.004 (0.029) Batch 0.665 (1.289) Remain 13:22:15 loss: 0.1851 Lr: 0.00075 [2024-02-19 08:12:20,348 INFO misc.py line 119 87073] Train: [77/100][35/1557] Data 0.009 (0.029) Batch 1.192 (1.286) Remain 13:20:21 loss: 0.1135 Lr: 0.00075 [2024-02-19 08:12:21,211 INFO misc.py line 119 87073] Train: [77/100][36/1557] Data 0.016 (0.028) Batch 0.874 (1.274) Remain 13:12:33 loss: 0.3961 Lr: 0.00075 [2024-02-19 08:12:22,183 INFO misc.py line 119 87073] Train: [77/100][37/1557] Data 0.005 (0.028) Batch 0.973 (1.265) Remain 13:07:02 loss: 0.2308 Lr: 0.00075 [2024-02-19 08:12:23,175 INFO misc.py line 119 87073] Train: [77/100][38/1557] Data 0.004 (0.027) Batch 0.991 (1.257) Remain 13:02:08 loss: 0.1120 Lr: 0.00075 [2024-02-19 08:12:24,364 INFO misc.py line 119 87073] Train: [77/100][39/1557] Data 0.004 (0.026) Batch 1.191 (1.255) Remain 13:00:58 loss: 0.3448 Lr: 0.00075 [2024-02-19 08:12:25,117 INFO misc.py line 119 87073] Train: [77/100][40/1557] Data 0.003 (0.026) Batch 0.751 (1.242) Remain 12:52:28 loss: 0.1616 Lr: 0.00075 [2024-02-19 08:12:25,867 INFO misc.py line 119 87073] Train: [77/100][41/1557] Data 0.005 (0.025) Batch 0.750 (1.229) Remain 12:44:24 loss: 0.1246 Lr: 0.00075 [2024-02-19 08:12:26,989 INFO misc.py line 119 87073] Train: [77/100][42/1557] Data 0.005 (0.025) Batch 1.120 (1.226) Remain 12:42:38 loss: 0.2523 Lr: 0.00075 [2024-02-19 08:12:28,191 INFO misc.py line 119 87073] Train: [77/100][43/1557] Data 0.007 (0.024) Batch 1.193 (1.225) Remain 12:42:06 loss: 0.1921 Lr: 0.00075 [2024-02-19 08:12:29,253 INFO misc.py line 119 87073] Train: [77/100][44/1557] Data 0.016 (0.024) Batch 1.065 (1.221) Remain 12:39:39 loss: 0.2724 Lr: 0.00075 [2024-02-19 08:12:30,360 INFO misc.py line 119 87073] Train: [77/100][45/1557] Data 0.014 (0.024) Batch 1.114 (1.219) Remain 12:38:03 loss: 0.4881 Lr: 0.00075 [2024-02-19 08:12:31,273 INFO misc.py line 119 87073] Train: [77/100][46/1557] Data 0.007 (0.023) Batch 0.915 (1.212) Remain 12:33:38 loss: 0.3027 Lr: 0.00075 [2024-02-19 08:12:32,046 INFO misc.py line 119 87073] Train: [77/100][47/1557] Data 0.004 (0.023) Batch 0.772 (1.202) Remain 12:27:24 loss: 0.1934 Lr: 0.00075 [2024-02-19 08:12:32,793 INFO misc.py line 119 87073] Train: [77/100][48/1557] Data 0.005 (0.023) Batch 0.742 (1.191) Remain 12:21:02 loss: 0.2254 Lr: 0.00075 [2024-02-19 08:12:33,977 INFO misc.py line 119 87073] Train: [77/100][49/1557] Data 0.010 (0.022) Batch 1.184 (1.191) Remain 12:20:54 loss: 0.1333 Lr: 0.00075 [2024-02-19 08:12:34,954 INFO misc.py line 119 87073] Train: [77/100][50/1557] Data 0.011 (0.022) Batch 0.983 (1.187) Remain 12:18:08 loss: 0.1462 Lr: 0.00075 [2024-02-19 08:12:36,032 INFO misc.py line 119 87073] Train: [77/100][51/1557] Data 0.005 (0.022) Batch 1.078 (1.185) Remain 12:16:42 loss: 0.2739 Lr: 0.00075 [2024-02-19 08:12:37,230 INFO misc.py line 119 87073] Train: [77/100][52/1557] Data 0.004 (0.021) Batch 1.186 (1.185) Remain 12:16:43 loss: 0.3394 Lr: 0.00075 [2024-02-19 08:12:38,297 INFO misc.py line 119 87073] Train: [77/100][53/1557] Data 0.015 (0.021) Batch 1.070 (1.182) Remain 12:15:16 loss: 0.2166 Lr: 0.00075 [2024-02-19 08:12:39,152 INFO misc.py line 119 87073] Train: [77/100][54/1557] Data 0.012 (0.021) Batch 0.863 (1.176) Remain 12:11:22 loss: 0.2853 Lr: 0.00075 [2024-02-19 08:12:39,851 INFO misc.py line 119 87073] Train: [77/100][55/1557] Data 0.003 (0.021) Batch 0.698 (1.167) Remain 12:05:38 loss: 0.0971 Lr: 0.00075 [2024-02-19 08:12:41,190 INFO misc.py line 119 87073] Train: [77/100][56/1557] Data 0.005 (0.020) Batch 1.333 (1.170) Remain 12:07:34 loss: 0.1157 Lr: 0.00075 [2024-02-19 08:12:42,234 INFO misc.py line 119 87073] Train: [77/100][57/1557] Data 0.011 (0.020) Batch 1.042 (1.168) Remain 12:06:04 loss: 0.2906 Lr: 0.00075 [2024-02-19 08:12:43,078 INFO misc.py line 119 87073] Train: [77/100][58/1557] Data 0.013 (0.020) Batch 0.853 (1.162) Remain 12:02:29 loss: 0.4594 Lr: 0.00075 [2024-02-19 08:12:44,202 INFO misc.py line 119 87073] Train: [77/100][59/1557] Data 0.004 (0.020) Batch 1.124 (1.161) Remain 12:02:03 loss: 0.1005 Lr: 0.00075 [2024-02-19 08:12:45,213 INFO misc.py line 119 87073] Train: [77/100][60/1557] Data 0.004 (0.020) Batch 1.011 (1.159) Remain 12:00:23 loss: 0.4927 Lr: 0.00075 [2024-02-19 08:12:46,005 INFO misc.py line 119 87073] Train: [77/100][61/1557] Data 0.004 (0.019) Batch 0.791 (1.152) Remain 11:56:26 loss: 0.2356 Lr: 0.00075 [2024-02-19 08:12:46,720 INFO misc.py line 119 87073] Train: [77/100][62/1557] Data 0.005 (0.019) Batch 0.707 (1.145) Remain 11:51:43 loss: 0.2523 Lr: 0.00075 [2024-02-19 08:13:06,378 INFO misc.py line 119 87073] Train: [77/100][63/1557] Data 7.545 (0.144) Batch 19.667 (1.453) Remain 15:03:38 loss: 0.1152 Lr: 0.00075 [2024-02-19 08:13:07,419 INFO misc.py line 119 87073] Train: [77/100][64/1557] Data 0.004 (0.142) Batch 1.041 (1.447) Remain 14:59:25 loss: 0.2947 Lr: 0.00075 [2024-02-19 08:13:08,300 INFO misc.py line 119 87073] Train: [77/100][65/1557] Data 0.004 (0.140) Batch 0.880 (1.437) Remain 14:53:42 loss: 0.1988 Lr: 0.00075 [2024-02-19 08:13:09,257 INFO misc.py line 119 87073] Train: [77/100][66/1557] Data 0.005 (0.138) Batch 0.955 (1.430) Remain 14:48:55 loss: 0.2746 Lr: 0.00075 [2024-02-19 08:13:10,210 INFO misc.py line 119 87073] Train: [77/100][67/1557] Data 0.008 (0.136) Batch 0.957 (1.422) Remain 14:44:18 loss: 0.3562 Lr: 0.00075 [2024-02-19 08:13:10,963 INFO misc.py line 119 87073] Train: [77/100][68/1557] Data 0.004 (0.134) Batch 0.750 (1.412) Remain 14:37:50 loss: 0.1648 Lr: 0.00075 [2024-02-19 08:13:11,663 INFO misc.py line 119 87073] Train: [77/100][69/1557] Data 0.007 (0.132) Batch 0.695 (1.401) Remain 14:31:04 loss: 0.1665 Lr: 0.00075 [2024-02-19 08:13:12,971 INFO misc.py line 119 87073] Train: [77/100][70/1557] Data 0.010 (0.130) Batch 1.306 (1.400) Remain 14:30:09 loss: 0.1339 Lr: 0.00075 [2024-02-19 08:13:13,952 INFO misc.py line 119 87073] Train: [77/100][71/1557] Data 0.014 (0.128) Batch 0.990 (1.394) Remain 14:26:23 loss: 0.6672 Lr: 0.00075 [2024-02-19 08:13:14,830 INFO misc.py line 119 87073] Train: [77/100][72/1557] Data 0.004 (0.126) Batch 0.877 (1.386) Remain 14:21:43 loss: 0.1388 Lr: 0.00075 [2024-02-19 08:13:15,617 INFO misc.py line 119 87073] Train: [77/100][73/1557] Data 0.006 (0.125) Batch 0.786 (1.378) Remain 14:16:21 loss: 0.1148 Lr: 0.00075 [2024-02-19 08:13:16,403 INFO misc.py line 119 87073] Train: [77/100][74/1557] Data 0.006 (0.123) Batch 0.786 (1.369) Remain 14:11:09 loss: 0.1884 Lr: 0.00075 [2024-02-19 08:13:17,198 INFO misc.py line 119 87073] Train: [77/100][75/1557] Data 0.006 (0.121) Batch 0.797 (1.361) Remain 14:06:11 loss: 0.1559 Lr: 0.00075 [2024-02-19 08:13:17,953 INFO misc.py line 119 87073] Train: [77/100][76/1557] Data 0.004 (0.120) Batch 0.753 (1.353) Remain 14:00:59 loss: 0.1896 Lr: 0.00075 [2024-02-19 08:13:19,077 INFO misc.py line 119 87073] Train: [77/100][77/1557] Data 0.006 (0.118) Batch 1.115 (1.350) Remain 13:58:58 loss: 0.1182 Lr: 0.00075 [2024-02-19 08:13:20,161 INFO misc.py line 119 87073] Train: [77/100][78/1557] Data 0.015 (0.117) Batch 1.094 (1.346) Remain 13:56:49 loss: 0.5136 Lr: 0.00075 [2024-02-19 08:13:21,137 INFO misc.py line 119 87073] Train: [77/100][79/1557] Data 0.005 (0.115) Batch 0.977 (1.342) Remain 13:53:47 loss: 0.3755 Lr: 0.00075 [2024-02-19 08:13:22,021 INFO misc.py line 119 87073] Train: [77/100][80/1557] Data 0.004 (0.114) Batch 0.883 (1.336) Remain 13:50:03 loss: 0.3219 Lr: 0.00075 [2024-02-19 08:13:22,978 INFO misc.py line 119 87073] Train: [77/100][81/1557] Data 0.004 (0.113) Batch 0.958 (1.331) Remain 13:47:01 loss: 0.2576 Lr: 0.00075 [2024-02-19 08:13:23,767 INFO misc.py line 119 87073] Train: [77/100][82/1557] Data 0.004 (0.111) Batch 0.788 (1.324) Remain 13:42:44 loss: 0.1608 Lr: 0.00075 [2024-02-19 08:13:24,529 INFO misc.py line 119 87073] Train: [77/100][83/1557] Data 0.006 (0.110) Batch 0.759 (1.317) Remain 13:38:19 loss: 0.1925 Lr: 0.00075 [2024-02-19 08:13:25,676 INFO misc.py line 119 87073] Train: [77/100][84/1557] Data 0.008 (0.109) Batch 1.149 (1.315) Remain 13:37:00 loss: 0.1446 Lr: 0.00075 [2024-02-19 08:13:26,615 INFO misc.py line 119 87073] Train: [77/100][85/1557] Data 0.006 (0.107) Batch 0.942 (1.310) Remain 13:34:10 loss: 0.2142 Lr: 0.00075 [2024-02-19 08:13:27,655 INFO misc.py line 119 87073] Train: [77/100][86/1557] Data 0.004 (0.106) Batch 1.039 (1.307) Remain 13:32:07 loss: 0.6789 Lr: 0.00075 [2024-02-19 08:13:28,591 INFO misc.py line 119 87073] Train: [77/100][87/1557] Data 0.005 (0.105) Batch 0.935 (1.303) Remain 13:29:20 loss: 0.3214 Lr: 0.00075 [2024-02-19 08:13:29,545 INFO misc.py line 119 87073] Train: [77/100][88/1557] Data 0.005 (0.104) Batch 0.955 (1.298) Remain 13:26:47 loss: 0.2818 Lr: 0.00075 [2024-02-19 08:13:30,335 INFO misc.py line 119 87073] Train: [77/100][89/1557] Data 0.003 (0.103) Batch 0.783 (1.292) Remain 13:23:02 loss: 0.2441 Lr: 0.00075 [2024-02-19 08:13:31,140 INFO misc.py line 119 87073] Train: 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Remain 12:24:35 loss: 0.2334 Lr: 0.00070 [2024-02-19 08:40:28,793 INFO misc.py line 119 87073] Train: [77/100][1396/1557] Data 0.004 (0.128) Batch 0.851 (1.242) Remain 12:24:24 loss: 0.1602 Lr: 0.00070 [2024-02-19 08:40:29,698 INFO misc.py line 119 87073] Train: [77/100][1397/1557] Data 0.004 (0.128) Batch 0.905 (1.241) Remain 12:24:14 loss: 0.3593 Lr: 0.00070 [2024-02-19 08:40:30,338 INFO misc.py line 119 87073] Train: [77/100][1398/1557] Data 0.003 (0.127) Batch 0.640 (1.241) Remain 12:23:57 loss: 0.1474 Lr: 0.00070 [2024-02-19 08:40:31,021 INFO misc.py line 119 87073] Train: [77/100][1399/1557] Data 0.004 (0.127) Batch 0.678 (1.241) Remain 12:23:42 loss: 0.5979 Lr: 0.00070 [2024-02-19 08:40:32,272 INFO misc.py line 119 87073] Train: [77/100][1400/1557] Data 0.009 (0.127) Batch 1.251 (1.241) Remain 12:23:41 loss: 0.1262 Lr: 0.00070 [2024-02-19 08:40:33,333 INFO misc.py line 119 87073] Train: [77/100][1401/1557] Data 0.009 (0.127) Batch 1.059 (1.240) Remain 12:23:35 loss: 0.0728 Lr: 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Remain 12:27:11 loss: 0.1614 Lr: 0.00070 [2024-02-19 08:41:14,901 INFO misc.py line 119 87073] Train: [77/100][1427/1557] Data 0.011 (0.131) Batch 0.789 (1.247) Remain 12:26:58 loss: 0.1733 Lr: 0.00070 [2024-02-19 08:41:16,097 INFO misc.py line 119 87073] Train: [77/100][1428/1557] Data 0.009 (0.131) Batch 1.201 (1.247) Remain 12:26:55 loss: 0.1223 Lr: 0.00070 [2024-02-19 08:41:16,870 INFO misc.py line 119 87073] Train: [77/100][1429/1557] Data 0.004 (0.131) Batch 0.773 (1.247) Remain 12:26:42 loss: 0.3295 Lr: 0.00070 [2024-02-19 08:41:17,823 INFO misc.py line 119 87073] Train: [77/100][1430/1557] Data 0.004 (0.131) Batch 0.947 (1.246) Remain 12:26:33 loss: 0.0788 Lr: 0.00070 [2024-02-19 08:41:18,719 INFO misc.py line 119 87073] Train: [77/100][1431/1557] Data 0.010 (0.130) Batch 0.902 (1.246) Remain 12:26:23 loss: 0.1857 Lr: 0.00070 [2024-02-19 08:41:19,788 INFO misc.py line 119 87073] Train: [77/100][1432/1557] Data 0.005 (0.130) Batch 1.069 (1.246) Remain 12:26:18 loss: 0.1220 Lr: 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Remain 12:22:39 loss: 0.5793 Lr: 0.00069 [2024-02-19 08:41:44,207 INFO misc.py line 119 87073] Train: [77/100][1458/1557] Data 0.004 (0.128) Batch 0.850 (1.241) Remain 12:22:28 loss: 0.4036 Lr: 0.00069 [2024-02-19 08:41:45,041 INFO misc.py line 119 87073] Train: [77/100][1459/1557] Data 0.004 (0.128) Batch 0.833 (1.240) Remain 12:22:17 loss: 0.0275 Lr: 0.00069 [2024-02-19 08:41:45,943 INFO misc.py line 119 87073] Train: [77/100][1460/1557] Data 0.006 (0.128) Batch 0.903 (1.240) Remain 12:22:08 loss: 0.2646 Lr: 0.00069 [2024-02-19 08:41:46,700 INFO misc.py line 119 87073] Train: [77/100][1461/1557] Data 0.004 (0.128) Batch 0.757 (1.240) Remain 12:21:54 loss: 0.2033 Lr: 0.00069 [2024-02-19 08:41:47,452 INFO misc.py line 119 87073] Train: [77/100][1462/1557] Data 0.004 (0.128) Batch 0.746 (1.239) Remain 12:21:41 loss: 0.1980 Lr: 0.00069 [2024-02-19 08:42:03,189 INFO misc.py line 119 87073] Train: [77/100][1463/1557] Data 6.865 (0.132) Batch 15.744 (1.249) Remain 12:27:36 loss: 0.0774 Lr: 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Remain 12:24:14 loss: 0.1743 Lr: 0.00069 [2024-02-19 08:42:28,113 INFO misc.py line 119 87073] Train: [77/100][1489/1557] Data 0.004 (0.130) Batch 0.780 (1.244) Remain 12:24:01 loss: 0.1919 Lr: 0.00069 [2024-02-19 08:42:28,891 INFO misc.py line 119 87073] Train: [77/100][1490/1557] Data 0.004 (0.130) Batch 0.768 (1.244) Remain 12:23:49 loss: 0.1854 Lr: 0.00069 [2024-02-19 08:42:30,085 INFO misc.py line 119 87073] Train: [77/100][1491/1557] Data 0.013 (0.130) Batch 1.196 (1.244) Remain 12:23:46 loss: 0.1959 Lr: 0.00069 [2024-02-19 08:42:31,002 INFO misc.py line 119 87073] Train: [77/100][1492/1557] Data 0.012 (0.130) Batch 0.925 (1.244) Remain 12:23:37 loss: 0.4407 Lr: 0.00069 [2024-02-19 08:42:31,754 INFO misc.py line 119 87073] Train: [77/100][1493/1557] Data 0.003 (0.130) Batch 0.751 (1.243) Remain 12:23:24 loss: 0.1421 Lr: 0.00069 [2024-02-19 08:42:32,733 INFO misc.py line 119 87073] Train: [77/100][1494/1557] Data 0.004 (0.130) Batch 0.979 (1.243) Remain 12:23:17 loss: 0.2741 Lr: 0.00069 [2024-02-19 08:42:33,607 INFO misc.py line 119 87073] Train: [77/100][1495/1557] Data 0.004 (0.130) Batch 0.874 (1.243) Remain 12:23:07 loss: 0.3786 Lr: 0.00069 [2024-02-19 08:42:34,391 INFO misc.py line 119 87073] Train: [77/100][1496/1557] Data 0.004 (0.130) Batch 0.784 (1.243) Remain 12:22:54 loss: 0.1560 Lr: 0.00069 [2024-02-19 08:42:35,119 INFO misc.py line 119 87073] Train: [77/100][1497/1557] Data 0.005 (0.130) Batch 0.723 (1.242) Remain 12:22:41 loss: 0.4037 Lr: 0.00069 [2024-02-19 08:42:36,378 INFO misc.py line 119 87073] Train: [77/100][1498/1557] Data 0.011 (0.129) Batch 1.257 (1.242) Remain 12:22:40 loss: 0.1039 Lr: 0.00069 [2024-02-19 08:42:37,229 INFO misc.py line 119 87073] Train: [77/100][1499/1557] Data 0.012 (0.129) Batch 0.859 (1.242) Remain 12:22:29 loss: 0.1237 Lr: 0.00069 [2024-02-19 08:42:38,022 INFO misc.py line 119 87073] Train: [77/100][1500/1557] Data 0.005 (0.129) Batch 0.792 (1.242) Remain 12:22:17 loss: 0.2128 Lr: 0.00069 [2024-02-19 08:42:39,139 INFO misc.py line 119 87073] Train: [77/100][1501/1557] Data 0.004 (0.129) Batch 1.108 (1.242) Remain 12:22:13 loss: 0.1420 Lr: 0.00069 [2024-02-19 08:42:40,060 INFO misc.py line 119 87073] Train: [77/100][1502/1557] Data 0.014 (0.129) Batch 0.931 (1.241) Remain 12:22:04 loss: 0.2114 Lr: 0.00069 [2024-02-19 08:42:40,856 INFO misc.py line 119 87073] Train: [77/100][1503/1557] Data 0.004 (0.129) Batch 0.794 (1.241) Remain 12:21:52 loss: 0.1160 Lr: 0.00069 [2024-02-19 08:42:41,651 INFO misc.py line 119 87073] Train: [77/100][1504/1557] Data 0.006 (0.129) Batch 0.796 (1.241) Remain 12:21:40 loss: 0.2042 Lr: 0.00069 [2024-02-19 08:42:42,796 INFO misc.py line 119 87073] Train: [77/100][1505/1557] Data 0.005 (0.129) Batch 1.139 (1.241) Remain 12:21:37 loss: 0.0925 Lr: 0.00069 [2024-02-19 08:42:43,889 INFO misc.py line 119 87073] Train: [77/100][1506/1557] Data 0.011 (0.129) Batch 1.092 (1.241) Remain 12:21:32 loss: 0.3686 Lr: 0.00069 [2024-02-19 08:42:44,732 INFO misc.py line 119 87073] Train: [77/100][1507/1557] Data 0.012 (0.129) Batch 0.851 (1.240) Remain 12:21:21 loss: 0.1684 Lr: 0.00069 [2024-02-19 08:42:45,612 INFO misc.py line 119 87073] Train: [77/100][1508/1557] Data 0.004 (0.129) Batch 0.878 (1.240) Remain 12:21:11 loss: 0.2156 Lr: 0.00069 [2024-02-19 08:42:46,611 INFO misc.py line 119 87073] Train: [77/100][1509/1557] Data 0.006 (0.129) Batch 1.000 (1.240) Remain 12:21:04 loss: 0.5091 Lr: 0.00069 [2024-02-19 08:42:47,404 INFO misc.py line 119 87073] Train: [77/100][1510/1557] Data 0.004 (0.129) Batch 0.794 (1.240) Remain 12:20:53 loss: 0.2014 Lr: 0.00069 [2024-02-19 08:42:48,164 INFO misc.py line 119 87073] Train: [77/100][1511/1557] Data 0.004 (0.128) Batch 0.760 (1.239) Remain 12:20:40 loss: 0.1037 Lr: 0.00069 [2024-02-19 08:42:49,528 INFO misc.py line 119 87073] Train: [77/100][1512/1557] Data 0.004 (0.128) Batch 1.351 (1.239) Remain 12:20:41 loss: 0.1119 Lr: 0.00069 [2024-02-19 08:42:50,471 INFO misc.py line 119 87073] Train: [77/100][1513/1557] Data 0.016 (0.128) Batch 0.955 (1.239) Remain 12:20:33 loss: 0.4582 Lr: 0.00069 [2024-02-19 08:42:51,422 INFO misc.py line 119 87073] Train: [77/100][1514/1557] Data 0.003 (0.128) Batch 0.951 (1.239) Remain 12:20:25 loss: 0.2818 Lr: 0.00069 [2024-02-19 08:42:52,546 INFO misc.py line 119 87073] Train: [77/100][1515/1557] Data 0.005 (0.128) Batch 1.124 (1.239) Remain 12:20:21 loss: 0.2082 Lr: 0.00069 [2024-02-19 08:42:53,420 INFO misc.py line 119 87073] Train: [77/100][1516/1557] Data 0.004 (0.128) Batch 0.874 (1.239) Remain 12:20:11 loss: 0.4563 Lr: 0.00069 [2024-02-19 08:42:54,157 INFO misc.py line 119 87073] Train: [77/100][1517/1557] Data 0.004 (0.128) Batch 0.728 (1.238) Remain 12:19:58 loss: 0.1888 Lr: 0.00069 [2024-02-19 08:42:55,031 INFO misc.py line 119 87073] Train: [77/100][1518/1557] Data 0.012 (0.128) Batch 0.883 (1.238) Remain 12:19:49 loss: 0.3137 Lr: 0.00069 [2024-02-19 08:43:12,143 INFO misc.py line 119 87073] Train: [77/100][1519/1557] Data 6.109 (0.132) Batch 17.112 (1.249) Remain 12:26:03 loss: 0.0973 Lr: 0.00069 [2024-02-19 08:43:13,091 INFO misc.py line 119 87073] Train: [77/100][1520/1557] Data 0.005 (0.132) Batch 0.947 (1.248) Remain 12:25:54 loss: 0.4167 Lr: 0.00069 [2024-02-19 08:43:13,868 INFO misc.py line 119 87073] Train: [77/100][1521/1557] Data 0.004 (0.132) Batch 0.769 (1.248) Remain 12:25:42 loss: 0.1888 Lr: 0.00069 [2024-02-19 08:43:14,829 INFO misc.py line 119 87073] Train: [77/100][1522/1557] Data 0.013 (0.132) Batch 0.970 (1.248) Remain 12:25:34 loss: 0.1766 Lr: 0.00069 [2024-02-19 08:43:15,725 INFO misc.py line 119 87073] Train: [77/100][1523/1557] Data 0.004 (0.131) Batch 0.896 (1.248) Remain 12:25:24 loss: 0.1503 Lr: 0.00069 [2024-02-19 08:43:16,513 INFO misc.py line 119 87073] Train: [77/100][1524/1557] Data 0.004 (0.131) Batch 0.781 (1.247) Remain 12:25:12 loss: 0.2677 Lr: 0.00069 [2024-02-19 08:43:17,217 INFO misc.py line 119 87073] Train: [77/100][1525/1557] Data 0.011 (0.131) Batch 0.711 (1.247) Remain 12:24:58 loss: 0.1633 Lr: 0.00069 [2024-02-19 08:43:18,497 INFO misc.py line 119 87073] Train: [77/100][1526/1557] Data 0.005 (0.131) Batch 1.281 (1.247) Remain 12:24:58 loss: 0.2305 Lr: 0.00069 [2024-02-19 08:43:19,413 INFO misc.py line 119 87073] Train: [77/100][1527/1557] Data 0.004 (0.131) Batch 0.915 (1.247) Remain 12:24:49 loss: 0.3543 Lr: 0.00069 [2024-02-19 08:43:20,488 INFO misc.py line 119 87073] Train: [77/100][1528/1557] Data 0.004 (0.131) Batch 1.076 (1.247) Remain 12:24:43 loss: 0.5297 Lr: 0.00069 [2024-02-19 08:43:21,481 INFO misc.py line 119 87073] Train: [77/100][1529/1557] Data 0.004 (0.131) Batch 0.993 (1.247) Remain 12:24:36 loss: 0.1619 Lr: 0.00069 [2024-02-19 08:43:22,428 INFO misc.py line 119 87073] Train: [77/100][1530/1557] Data 0.003 (0.131) Batch 0.947 (1.246) Remain 12:24:28 loss: 0.2667 Lr: 0.00069 [2024-02-19 08:43:23,250 INFO misc.py line 119 87073] Train: [77/100][1531/1557] Data 0.003 (0.131) Batch 0.813 (1.246) Remain 12:24:17 loss: 0.2402 Lr: 0.00069 [2024-02-19 08:43:24,040 INFO misc.py line 119 87073] Train: [77/100][1532/1557] Data 0.012 (0.131) Batch 0.798 (1.246) Remain 12:24:05 loss: 0.3572 Lr: 0.00069 [2024-02-19 08:43:25,279 INFO misc.py line 119 87073] Train: [77/100][1533/1557] Data 0.005 (0.131) Batch 1.228 (1.246) Remain 12:24:03 loss: 0.2351 Lr: 0.00069 [2024-02-19 08:43:26,398 INFO misc.py line 119 87073] Train: [77/100][1534/1557] Data 0.015 (0.131) Batch 1.118 (1.246) Remain 12:23:59 loss: 0.0820 Lr: 0.00069 [2024-02-19 08:43:27,290 INFO misc.py line 119 87073] Train: [77/100][1535/1557] Data 0.017 (0.131) Batch 0.905 (1.246) Remain 12:23:50 loss: 0.4752 Lr: 0.00069 [2024-02-19 08:43:28,165 INFO misc.py line 119 87073] Train: [77/100][1536/1557] Data 0.003 (0.130) Batch 0.874 (1.245) Remain 12:23:40 loss: 0.2182 Lr: 0.00069 [2024-02-19 08:43:28,961 INFO misc.py line 119 87073] Train: [77/100][1537/1557] Data 0.004 (0.130) Batch 0.782 (1.245) Remain 12:23:28 loss: 0.3853 Lr: 0.00069 [2024-02-19 08:43:29,668 INFO misc.py line 119 87073] Train: [77/100][1538/1557] Data 0.018 (0.130) Batch 0.722 (1.245) Remain 12:23:14 loss: 0.1540 Lr: 0.00069 [2024-02-19 08:43:30,418 INFO misc.py line 119 87073] Train: [77/100][1539/1557] Data 0.004 (0.130) Batch 0.737 (1.244) Remain 12:23:01 loss: 0.1846 Lr: 0.00069 [2024-02-19 08:43:31,622 INFO misc.py line 119 87073] Train: [77/100][1540/1557] Data 0.017 (0.130) Batch 1.203 (1.244) Remain 12:22:59 loss: 0.1398 Lr: 0.00069 [2024-02-19 08:43:32,383 INFO misc.py line 119 87073] Train: [77/100][1541/1557] Data 0.017 (0.130) Batch 0.774 (1.244) Remain 12:22:47 loss: 0.4933 Lr: 0.00069 [2024-02-19 08:43:33,262 INFO misc.py line 119 87073] Train: [77/100][1542/1557] Data 0.004 (0.130) Batch 0.874 (1.244) Remain 12:22:37 loss: 0.1295 Lr: 0.00069 [2024-02-19 08:43:34,197 INFO misc.py line 119 87073] Train: [77/100][1543/1557] Data 0.009 (0.130) Batch 0.933 (1.244) Remain 12:22:28 loss: 0.4341 Lr: 0.00069 [2024-02-19 08:43:35,083 INFO misc.py line 119 87073] Train: [77/100][1544/1557] Data 0.011 (0.130) Batch 0.893 (1.243) Remain 12:22:19 loss: 0.3126 Lr: 0.00069 [2024-02-19 08:43:35,789 INFO misc.py line 119 87073] Train: [77/100][1545/1557] Data 0.004 (0.130) Batch 0.704 (1.243) Remain 12:22:05 loss: 0.3236 Lr: 0.00069 [2024-02-19 08:43:36,578 INFO misc.py line 119 87073] Train: [77/100][1546/1557] Data 0.007 (0.130) Batch 0.782 (1.243) Remain 12:21:53 loss: 0.2457 Lr: 0.00069 [2024-02-19 08:43:37,755 INFO misc.py line 119 87073] Train: [77/100][1547/1557] Data 0.013 (0.130) Batch 1.178 (1.243) Remain 12:21:51 loss: 0.2068 Lr: 0.00069 [2024-02-19 08:43:38,664 INFO misc.py line 119 87073] Train: [77/100][1548/1557] Data 0.013 (0.130) Batch 0.918 (1.242) Remain 12:21:42 loss: 0.1911 Lr: 0.00069 [2024-02-19 08:43:39,588 INFO misc.py line 119 87073] Train: [77/100][1549/1557] Data 0.004 (0.129) Batch 0.924 (1.242) Remain 12:21:33 loss: 0.3193 Lr: 0.00069 [2024-02-19 08:43:40,774 INFO misc.py line 119 87073] Train: [77/100][1550/1557] Data 0.004 (0.129) Batch 1.183 (1.242) Remain 12:21:31 loss: 0.3826 Lr: 0.00069 [2024-02-19 08:43:41,725 INFO misc.py line 119 87073] Train: [77/100][1551/1557] Data 0.006 (0.129) Batch 0.954 (1.242) Remain 12:21:23 loss: 0.1697 Lr: 0.00069 [2024-02-19 08:43:42,481 INFO misc.py line 119 87073] Train: [77/100][1552/1557] Data 0.004 (0.129) Batch 0.756 (1.242) Remain 12:21:10 loss: 0.3207 Lr: 0.00069 [2024-02-19 08:43:43,215 INFO misc.py line 119 87073] Train: [77/100][1553/1557] Data 0.004 (0.129) Batch 0.713 (1.241) Remain 12:20:57 loss: 0.1735 Lr: 0.00069 [2024-02-19 08:43:44,345 INFO misc.py line 119 87073] Train: [77/100][1554/1557] Data 0.025 (0.129) Batch 1.138 (1.241) Remain 12:20:53 loss: 0.1168 Lr: 0.00069 [2024-02-19 08:43:45,373 INFO misc.py line 119 87073] Train: [77/100][1555/1557] Data 0.016 (0.129) Batch 1.029 (1.241) Remain 12:20:47 loss: 0.4028 Lr: 0.00069 [2024-02-19 08:43:46,287 INFO misc.py line 119 87073] Train: [77/100][1556/1557] Data 0.015 (0.129) Batch 0.925 (1.241) Remain 12:20:38 loss: 0.0876 Lr: 0.00069 [2024-02-19 08:43:47,281 INFO misc.py line 119 87073] Train: [77/100][1557/1557] Data 0.004 (0.129) Batch 0.994 (1.241) Remain 12:20:31 loss: 0.1346 Lr: 0.00069 [2024-02-19 08:43:47,281 INFO misc.py line 136 87073] Train result: loss: 0.2433 [2024-02-19 08:43:47,282 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 08:44:15,339 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.3719 [2024-02-19 08:44:16,118 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3996 [2024-02-19 08:44:18,244 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.4669 [2024-02-19 08:44:20,455 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3605 [2024-02-19 08:44:25,401 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2604 [2024-02-19 08:44:26,103 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1078 [2024-02-19 08:44:27,362 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3605 [2024-02-19 08:44:30,316 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2521 [2024-02-19 08:44:32,125 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2464 [2024-02-19 08:44:33,532 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7279/0.7820/0.9174. [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9358/0.9736 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9835/0.9903 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8639/0.9697 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4125/0.4688 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6760/0.7053 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8201/0.8914 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8366/0.8985 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9255/0.9722 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8246/0.8468 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7955/0.9078 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7826/0.8633 [2024-02-19 08:44:33,533 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6062/0.6786 [2024-02-19 08:44:33,534 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 08:44:33,536 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 08:44:33,536 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 08:44:40,520 INFO misc.py line 119 87073] Train: [78/100][1/1557] Data 1.855 (1.855) Batch 2.677 (2.677) Remain 26:37:28 loss: 0.1828 Lr: 0.00069 [2024-02-19 08:44:41,501 INFO misc.py line 119 87073] Train: [78/100][2/1557] Data 0.010 (0.010) Batch 0.984 (0.984) Remain 09:47:13 loss: 0.3263 Lr: 0.00069 [2024-02-19 08:44:42,731 INFO misc.py line 119 87073] Train: [78/100][3/1557] Data 0.009 (0.009) Batch 1.230 (1.230) Remain 12:14:09 loss: 0.1914 Lr: 0.00069 [2024-02-19 08:44:43,705 INFO misc.py line 119 87073] Train: [78/100][4/1557] Data 0.005 (0.005) Batch 0.975 (0.975) Remain 09:41:55 loss: 0.1778 Lr: 0.00069 [2024-02-19 08:44:44,478 INFO misc.py line 119 87073] Train: [78/100][5/1557] Data 0.004 (0.005) Batch 0.773 (0.874) Remain 08:41:41 loss: 0.2398 Lr: 0.00069 [2024-02-19 08:44:45,196 INFO misc.py line 119 87073] Train: [78/100][6/1557] Data 0.004 (0.004) Batch 0.712 (0.820) Remain 08:09:23 loss: 0.2501 Lr: 0.00069 [2024-02-19 08:44:49,203 INFO misc.py line 119 87073] Train: [78/100][7/1557] Data 2.724 (0.684) Batch 4.012 (1.618) Remain 16:05:35 loss: 0.1070 Lr: 0.00069 [2024-02-19 08:44:50,271 INFO misc.py line 119 87073] Train: [78/100][8/1557] Data 0.005 (0.549) Batch 1.068 (1.508) Remain 14:59:54 loss: 0.1612 Lr: 0.00069 [2024-02-19 08:44:51,275 INFO misc.py line 119 87073] Train: [78/100][9/1557] Data 0.004 (0.458) Batch 1.004 (1.424) Remain 14:09:44 loss: 0.1186 Lr: 0.00069 [2024-02-19 08:44:52,156 INFO misc.py line 119 87073] Train: [78/100][10/1557] Data 0.004 (0.393) Batch 0.882 (1.347) Remain 13:23:29 loss: 0.2248 Lr: 0.00069 [2024-02-19 08:44:53,187 INFO misc.py line 119 87073] Train: [78/100][11/1557] Data 0.004 (0.344) Batch 1.031 (1.307) Remain 12:59:55 loss: 0.2822 Lr: 0.00069 [2024-02-19 08:44:53,993 INFO misc.py line 119 87073] Train: [78/100][12/1557] Data 0.003 (0.307) Batch 0.804 (1.251) Remain 12:26:33 loss: 0.1961 Lr: 0.00069 [2024-02-19 08:44:54,688 INFO misc.py line 119 87073] Train: [78/100][13/1557] Data 0.006 (0.276) Batch 0.697 (1.196) Remain 11:53:27 loss: 0.1966 Lr: 0.00069 [2024-02-19 08:44:56,000 INFO misc.py line 119 87073] Train: [78/100][14/1557] Data 0.004 (0.252) Batch 1.308 (1.206) Remain 11:59:31 loss: 0.0891 Lr: 0.00069 [2024-02-19 08:44:56,894 INFO misc.py line 119 87073] Train: [78/100][15/1557] Data 0.008 (0.231) Batch 0.897 (1.180) Remain 11:44:07 loss: 0.5418 Lr: 0.00069 [2024-02-19 08:44:57,886 INFO misc.py line 119 87073] Train: [78/100][16/1557] Data 0.005 (0.214) Batch 0.993 (1.166) Remain 11:35:32 loss: 0.5446 Lr: 0.00069 [2024-02-19 08:44:58,924 INFO misc.py line 119 87073] Train: [78/100][17/1557] Data 0.004 (0.199) Batch 1.038 (1.157) Remain 11:30:04 loss: 0.5386 Lr: 0.00069 [2024-02-19 08:45:00,080 INFO misc.py line 119 87073] Train: [78/100][18/1557] Data 0.004 (0.186) Batch 1.155 (1.157) Remain 11:29:59 loss: 0.1559 Lr: 0.00069 [2024-02-19 08:45:00,876 INFO misc.py line 119 87073] Train: [78/100][19/1557] Data 0.004 (0.175) Batch 0.794 (1.134) Remain 11:16:28 loss: 0.1828 Lr: 0.00069 [2024-02-19 08:45:01,675 INFO misc.py line 119 87073] Train: [78/100][20/1557] Data 0.005 (0.165) Batch 0.801 (1.114) Remain 11:04:45 loss: 0.2483 Lr: 0.00069 [2024-02-19 08:45:02,797 INFO misc.py line 119 87073] Train: [78/100][21/1557] Data 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loss: 0.5796 Lr: 0.00069 [2024-02-19 08:47:05,065 INFO misc.py line 119 87073] Train: [78/100][128/1557] Data 0.006 (0.194) Batch 1.043 (1.139) Remain 11:17:11 loss: 0.1116 Lr: 0.00069 [2024-02-19 08:47:05,883 INFO misc.py line 119 87073] Train: [78/100][129/1557] Data 0.005 (0.192) Batch 0.819 (1.136) Remain 11:15:39 loss: 0.6309 Lr: 0.00069 [2024-02-19 08:47:06,799 INFO misc.py line 119 87073] Train: [78/100][130/1557] Data 0.004 (0.191) Batch 0.916 (1.134) Remain 11:14:36 loss: 0.1939 Lr: 0.00069 [2024-02-19 08:47:07,567 INFO misc.py line 119 87073] Train: [78/100][131/1557] Data 0.004 (0.189) Batch 0.760 (1.131) Remain 11:12:51 loss: 0.3103 Lr: 0.00069 [2024-02-19 08:47:08,339 INFO misc.py line 119 87073] Train: [78/100][132/1557] Data 0.012 (0.188) Batch 0.779 (1.129) Remain 11:11:12 loss: 0.1664 Lr: 0.00069 [2024-02-19 08:47:09,434 INFO misc.py line 119 87073] Train: [78/100][133/1557] Data 0.005 (0.186) Batch 1.095 (1.128) Remain 11:11:02 loss: 0.2234 Lr: 0.00069 [2024-02-19 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Batch 0.811 (1.135) Remain 11:14:21 loss: 0.1298 Lr: 0.00068 [2024-02-19 08:48:01,249 INFO misc.py line 119 87073] Train: [78/100][178/1557] Data 0.004 (0.193) Batch 0.940 (1.134) Remain 11:13:40 loss: 0.2625 Lr: 0.00068 [2024-02-19 08:48:02,167 INFO misc.py line 119 87073] Train: [78/100][179/1557] Data 0.011 (0.192) Batch 0.925 (1.133) Remain 11:12:56 loss: 0.3804 Lr: 0.00068 [2024-02-19 08:48:05,047 INFO misc.py line 119 87073] Train: [78/100][180/1557] Data 1.036 (0.197) Batch 2.880 (1.143) Remain 11:18:47 loss: 0.1341 Lr: 0.00068 [2024-02-19 08:48:05,755 INFO misc.py line 119 87073] Train: [78/100][181/1557] Data 0.004 (0.196) Batch 0.699 (1.141) Remain 11:17:17 loss: 0.1937 Lr: 0.00068 [2024-02-19 08:48:07,127 INFO misc.py line 119 87073] Train: [78/100][182/1557] Data 0.014 (0.195) Batch 1.368 (1.142) Remain 11:18:01 loss: 0.0970 Lr: 0.00068 [2024-02-19 08:48:08,038 INFO misc.py line 119 87073] Train: [78/100][183/1557] Data 0.017 (0.194) Batch 0.923 (1.141) Remain 11:17:17 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Train: [78/100][1197/1557] Data 0.005 (0.187) Batch 1.142 (1.140) Remain 10:57:41 loss: 0.0637 Lr: 0.00065 [2024-02-19 09:07:24,790 INFO misc.py line 119 87073] Train: [78/100][1198/1557] Data 0.008 (0.187) Batch 0.848 (1.140) Remain 10:57:31 loss: 0.3810 Lr: 0.00065 [2024-02-19 09:07:25,642 INFO misc.py line 119 87073] Train: [78/100][1199/1557] Data 0.006 (0.187) Batch 0.852 (1.140) Remain 10:57:22 loss: 0.1035 Lr: 0.00065 [2024-02-19 09:07:26,648 INFO misc.py line 119 87073] Train: [78/100][1200/1557] Data 0.005 (0.187) Batch 1.001 (1.139) Remain 10:57:17 loss: 0.1584 Lr: 0.00065 [2024-02-19 09:07:27,641 INFO misc.py line 119 87073] Train: [78/100][1201/1557] Data 0.011 (0.187) Batch 0.998 (1.139) Remain 10:57:11 loss: 0.1337 Lr: 0.00065 [2024-02-19 09:07:28,344 INFO misc.py line 119 87073] Train: [78/100][1202/1557] Data 0.006 (0.186) Batch 0.704 (1.139) Remain 10:56:58 loss: 0.1643 Lr: 0.00065 [2024-02-19 09:07:29,089 INFO misc.py line 119 87073] Train: [78/100][1203/1557] Data 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Remain 10:56:04 loss: 0.2309 Lr: 0.00065 [2024-02-19 09:07:35,444 INFO misc.py line 119 87073] Train: [78/100][1210/1557] Data 0.004 (0.185) Batch 0.723 (1.137) Remain 10:55:51 loss: 0.1870 Lr: 0.00065 [2024-02-19 09:07:36,570 INFO misc.py line 119 87073] Train: [78/100][1211/1557] Data 0.007 (0.185) Batch 1.129 (1.137) Remain 10:55:49 loss: 0.1181 Lr: 0.00065 [2024-02-19 09:07:37,569 INFO misc.py line 119 87073] Train: [78/100][1212/1557] Data 0.005 (0.185) Batch 0.996 (1.137) Remain 10:55:44 loss: 0.1209 Lr: 0.00065 [2024-02-19 09:07:38,625 INFO misc.py line 119 87073] Train: [78/100][1213/1557] Data 0.009 (0.185) Batch 1.059 (1.137) Remain 10:55:41 loss: 0.2543 Lr: 0.00065 [2024-02-19 09:07:39,693 INFO misc.py line 119 87073] Train: [78/100][1214/1557] Data 0.005 (0.185) Batch 1.068 (1.137) Remain 10:55:38 loss: 0.3458 Lr: 0.00065 [2024-02-19 09:07:40,635 INFO misc.py line 119 87073] Train: [78/100][1215/1557] Data 0.005 (0.184) Batch 0.943 (1.137) Remain 10:55:31 loss: 0.1675 Lr: 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INFO misc.py line 119 87073] Train: [78/100][1222/1557] Data 0.009 (0.183) Batch 1.078 (1.136) Remain 10:54:53 loss: 0.2918 Lr: 0.00065 [2024-02-19 09:07:48,293 INFO misc.py line 119 87073] Train: [78/100][1223/1557] Data 0.005 (0.183) Batch 0.745 (1.136) Remain 10:54:41 loss: 0.1677 Lr: 0.00065 [2024-02-19 09:07:49,051 INFO misc.py line 119 87073] Train: [78/100][1224/1557] Data 0.005 (0.183) Batch 0.752 (1.135) Remain 10:54:29 loss: 0.2259 Lr: 0.00065 [2024-02-19 09:07:50,218 INFO misc.py line 119 87073] Train: [78/100][1225/1557] Data 0.010 (0.183) Batch 1.168 (1.135) Remain 10:54:29 loss: 0.1217 Lr: 0.00065 [2024-02-19 09:07:51,337 INFO misc.py line 119 87073] Train: [78/100][1226/1557] Data 0.010 (0.183) Batch 1.119 (1.135) Remain 10:54:27 loss: 0.1772 Lr: 0.00065 [2024-02-19 09:07:52,357 INFO misc.py line 119 87073] Train: [78/100][1227/1557] Data 0.010 (0.183) Batch 1.018 (1.135) Remain 10:54:23 loss: 0.3191 Lr: 0.00065 [2024-02-19 09:07:53,419 INFO misc.py line 119 87073] Train: [78/100][1228/1557] Data 0.013 (0.183) Batch 1.069 (1.135) Remain 10:54:20 loss: 0.3333 Lr: 0.00065 [2024-02-19 09:07:54,278 INFO misc.py line 119 87073] Train: [78/100][1229/1557] Data 0.006 (0.182) Batch 0.858 (1.135) Remain 10:54:11 loss: 0.7086 Lr: 0.00065 [2024-02-19 09:07:56,737 INFO misc.py line 119 87073] Train: [78/100][1230/1557] Data 1.018 (0.183) Batch 2.442 (1.136) Remain 10:54:47 loss: 0.1642 Lr: 0.00065 [2024-02-19 09:07:57,440 INFO misc.py line 119 87073] Train: [78/100][1231/1557] Data 0.024 (0.183) Batch 0.721 (1.136) Remain 10:54:34 loss: 0.2318 Lr: 0.00065 [2024-02-19 09:07:58,711 INFO misc.py line 119 87073] Train: [78/100][1232/1557] Data 0.005 (0.183) Batch 1.273 (1.136) Remain 10:54:37 loss: 0.1018 Lr: 0.00065 [2024-02-19 09:07:59,719 INFO misc.py line 119 87073] Train: [78/100][1233/1557] Data 0.004 (0.183) Batch 1.008 (1.136) Remain 10:54:32 loss: 0.1555 Lr: 0.00065 [2024-02-19 09:08:00,660 INFO misc.py line 119 87073] Train: [78/100][1234/1557] Data 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Remain 10:54:52 loss: 0.4233 Lr: 0.00065 [2024-02-19 09:08:45,921 INFO misc.py line 119 87073] Train: [78/100][1272/1557] Data 0.003 (0.184) Batch 0.715 (1.137) Remain 10:54:39 loss: 0.1646 Lr: 0.00065 [2024-02-19 09:08:46,680 INFO misc.py line 119 87073] Train: [78/100][1273/1557] Data 0.004 (0.184) Batch 0.756 (1.137) Remain 10:54:28 loss: 0.2668 Lr: 0.00065 [2024-02-19 09:08:47,969 INFO misc.py line 119 87073] Train: [78/100][1274/1557] Data 0.008 (0.184) Batch 1.284 (1.137) Remain 10:54:31 loss: 0.1196 Lr: 0.00065 [2024-02-19 09:08:48,822 INFO misc.py line 119 87073] Train: [78/100][1275/1557] Data 0.013 (0.184) Batch 0.861 (1.137) Remain 10:54:22 loss: 0.2172 Lr: 0.00065 [2024-02-19 09:08:49,724 INFO misc.py line 119 87073] Train: [78/100][1276/1557] Data 0.004 (0.184) Batch 0.901 (1.137) Remain 10:54:15 loss: 0.1321 Lr: 0.00064 [2024-02-19 09:08:50,724 INFO misc.py line 119 87073] Train: [78/100][1277/1557] Data 0.006 (0.184) Batch 0.999 (1.137) Remain 10:54:10 loss: 0.2949 Lr: 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INFO misc.py line 119 87073] Train: [78/100][1315/1557] Data 0.015 (0.186) Batch 0.807 (1.138) Remain 10:54:21 loss: 0.2683 Lr: 0.00064 [2024-02-19 09:09:37,202 INFO misc.py line 119 87073] Train: [78/100][1316/1557] Data 0.005 (0.186) Batch 1.226 (1.138) Remain 10:54:22 loss: 0.2510 Lr: 0.00064 [2024-02-19 09:09:38,133 INFO misc.py line 119 87073] Train: [78/100][1317/1557] Data 0.011 (0.186) Batch 0.936 (1.138) Remain 10:54:15 loss: 0.2804 Lr: 0.00064 [2024-02-19 09:09:39,006 INFO misc.py line 119 87073] Train: [78/100][1318/1557] Data 0.006 (0.186) Batch 0.874 (1.138) Remain 10:54:07 loss: 0.1433 Lr: 0.00064 [2024-02-19 09:09:39,867 INFO misc.py line 119 87073] Train: [78/100][1319/1557] Data 0.004 (0.185) Batch 0.861 (1.138) Remain 10:53:59 loss: 0.3369 Lr: 0.00064 [2024-02-19 09:09:40,745 INFO misc.py line 119 87073] Train: [78/100][1320/1557] Data 0.004 (0.185) Batch 0.876 (1.137) Remain 10:53:51 loss: 0.1912 Lr: 0.00064 [2024-02-19 09:09:41,528 INFO misc.py line 119 87073] 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Remain 10:52:32 loss: 0.3702 Lr: 0.00064 [2024-02-19 09:11:43,827 INFO misc.py line 119 87073] Train: [78/100][1427/1557] Data 0.003 (0.187) Batch 0.798 (1.138) Remain 10:52:23 loss: 0.2032 Lr: 0.00064 [2024-02-19 09:11:45,052 INFO misc.py line 119 87073] Train: [78/100][1428/1557] Data 0.004 (0.186) Batch 1.215 (1.138) Remain 10:52:23 loss: 0.1600 Lr: 0.00064 [2024-02-19 09:11:46,036 INFO misc.py line 119 87073] Train: [78/100][1429/1557] Data 0.014 (0.186) Batch 0.993 (1.138) Remain 10:52:19 loss: 0.2796 Lr: 0.00064 [2024-02-19 09:11:47,025 INFO misc.py line 119 87073] Train: [78/100][1430/1557] Data 0.005 (0.186) Batch 0.990 (1.138) Remain 10:52:14 loss: 0.3512 Lr: 0.00064 [2024-02-19 09:11:48,074 INFO misc.py line 119 87073] Train: [78/100][1431/1557] Data 0.004 (0.186) Batch 1.049 (1.138) Remain 10:52:11 loss: 0.3440 Lr: 0.00064 [2024-02-19 09:11:48,932 INFO misc.py line 119 87073] Train: [78/100][1432/1557] Data 0.004 (0.186) Batch 0.857 (1.138) Remain 10:52:03 loss: 0.3667 Lr: 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INFO misc.py line 119 87073] Train: [78/100][1501/1557] Data 0.004 (0.184) Batch 0.916 (1.136) Remain 10:49:35 loss: 0.3616 Lr: 0.00064 [2024-02-19 09:13:05,291 INFO misc.py line 119 87073] Train: [78/100][1502/1557] Data 0.009 (0.184) Batch 0.858 (1.136) Remain 10:49:27 loss: 0.2084 Lr: 0.00064 [2024-02-19 09:13:06,029 INFO misc.py line 119 87073] Train: [78/100][1503/1557] Data 0.005 (0.184) Batch 0.738 (1.136) Remain 10:49:17 loss: 0.3142 Lr: 0.00064 [2024-02-19 09:13:06,824 INFO misc.py line 119 87073] Train: [78/100][1504/1557] Data 0.007 (0.184) Batch 0.796 (1.135) Remain 10:49:08 loss: 0.1602 Lr: 0.00064 [2024-02-19 09:13:08,024 INFO misc.py line 119 87073] Train: [78/100][1505/1557] Data 0.005 (0.184) Batch 1.199 (1.135) Remain 10:49:09 loss: 0.1435 Lr: 0.00064 [2024-02-19 09:13:09,242 INFO misc.py line 119 87073] Train: [78/100][1506/1557] Data 0.007 (0.184) Batch 1.206 (1.135) Remain 10:49:09 loss: 0.5304 Lr: 0.00064 [2024-02-19 09:13:10,416 INFO misc.py line 119 87073] Train: [78/100][1507/1557] Data 0.017 (0.184) Batch 1.187 (1.135) Remain 10:49:09 loss: 0.3697 Lr: 0.00064 [2024-02-19 09:13:11,399 INFO misc.py line 119 87073] Train: [78/100][1508/1557] Data 0.005 (0.183) Batch 0.982 (1.135) Remain 10:49:05 loss: 0.2739 Lr: 0.00064 [2024-02-19 09:13:12,328 INFO misc.py line 119 87073] Train: [78/100][1509/1557] Data 0.005 (0.183) Batch 0.929 (1.135) Remain 10:48:59 loss: 0.1997 Lr: 0.00064 [2024-02-19 09:13:13,101 INFO misc.py line 119 87073] Train: [78/100][1510/1557] Data 0.005 (0.183) Batch 0.774 (1.135) Remain 10:48:49 loss: 0.1424 Lr: 0.00064 [2024-02-19 09:13:13,833 INFO misc.py line 119 87073] Train: [78/100][1511/1557] Data 0.005 (0.183) Batch 0.732 (1.135) Remain 10:48:39 loss: 0.0882 Lr: 0.00064 [2024-02-19 09:13:15,015 INFO misc.py line 119 87073] Train: [78/100][1512/1557] Data 0.004 (0.183) Batch 1.182 (1.135) Remain 10:48:39 loss: 0.0675 Lr: 0.00064 [2024-02-19 09:13:15,985 INFO misc.py line 119 87073] Train: [78/100][1513/1557] Data 0.004 (0.183) Batch 0.969 (1.135) Remain 10:48:34 loss: 0.1789 Lr: 0.00064 [2024-02-19 09:13:16,892 INFO misc.py line 119 87073] Train: [78/100][1514/1557] Data 0.006 (0.183) Batch 0.906 (1.134) Remain 10:48:28 loss: 0.5826 Lr: 0.00064 [2024-02-19 09:13:17,949 INFO misc.py line 119 87073] Train: [78/100][1515/1557] Data 0.005 (0.183) Batch 1.054 (1.134) Remain 10:48:25 loss: 0.1953 Lr: 0.00064 [2024-02-19 09:13:18,845 INFO misc.py line 119 87073] Train: [78/100][1516/1557] Data 0.008 (0.182) Batch 0.901 (1.134) Remain 10:48:18 loss: 0.2709 Lr: 0.00064 [2024-02-19 09:13:19,700 INFO misc.py line 119 87073] Train: [78/100][1517/1557] Data 0.004 (0.182) Batch 0.855 (1.134) Remain 10:48:11 loss: 0.1733 Lr: 0.00064 [2024-02-19 09:13:20,543 INFO misc.py line 119 87073] Train: [78/100][1518/1557] Data 0.004 (0.182) Batch 0.834 (1.134) Remain 10:48:03 loss: 0.1625 Lr: 0.00064 [2024-02-19 09:13:31,099 INFO misc.py line 119 87073] Train: [78/100][1519/1557] Data 9.228 (0.188) Batch 10.565 (1.140) Remain 10:51:35 loss: 0.1320 Lr: 0.00064 [2024-02-19 09:13:32,054 INFO misc.py line 119 87073] Train: [78/100][1520/1557] Data 0.005 (0.188) Batch 0.954 (1.140) Remain 10:51:30 loss: 0.1315 Lr: 0.00064 [2024-02-19 09:13:32,839 INFO misc.py line 119 87073] Train: [78/100][1521/1557] Data 0.005 (0.188) Batch 0.786 (1.140) Remain 10:51:21 loss: 0.1760 Lr: 0.00064 [2024-02-19 09:13:33,866 INFO misc.py line 119 87073] Train: [78/100][1522/1557] Data 0.004 (0.188) Batch 1.025 (1.140) Remain 10:51:17 loss: 0.1093 Lr: 0.00064 [2024-02-19 09:13:34,950 INFO misc.py line 119 87073] Train: [78/100][1523/1557] Data 0.006 (0.188) Batch 1.080 (1.140) Remain 10:51:15 loss: 0.2207 Lr: 0.00064 [2024-02-19 09:13:35,702 INFO misc.py line 119 87073] Train: [78/100][1524/1557] Data 0.009 (0.188) Batch 0.758 (1.139) Remain 10:51:05 loss: 0.2172 Lr: 0.00064 [2024-02-19 09:13:36,452 INFO misc.py line 119 87073] Train: [78/100][1525/1557] Data 0.004 (0.187) Batch 0.744 (1.139) Remain 10:50:55 loss: 0.2881 Lr: 0.00064 [2024-02-19 09:13:37,726 INFO misc.py line 119 87073] Train: [78/100][1526/1557] Data 0.011 (0.187) Batch 1.271 (1.139) Remain 10:50:57 loss: 0.0728 Lr: 0.00064 [2024-02-19 09:13:38,791 INFO misc.py line 119 87073] Train: [78/100][1527/1557] Data 0.013 (0.187) Batch 1.061 (1.139) Remain 10:50:54 loss: 0.1559 Lr: 0.00064 [2024-02-19 09:13:39,772 INFO misc.py line 119 87073] Train: [78/100][1528/1557] Data 0.016 (0.187) Batch 0.994 (1.139) Remain 10:50:49 loss: 0.2965 Lr: 0.00064 [2024-02-19 09:13:40,750 INFO misc.py line 119 87073] Train: [78/100][1529/1557] Data 0.004 (0.187) Batch 0.977 (1.139) Remain 10:50:44 loss: 0.3547 Lr: 0.00064 [2024-02-19 09:13:41,782 INFO misc.py line 119 87073] Train: [78/100][1530/1557] Data 0.004 (0.187) Batch 1.033 (1.139) Remain 10:50:41 loss: 0.1472 Lr: 0.00064 [2024-02-19 09:13:42,551 INFO misc.py line 119 87073] Train: [78/100][1531/1557] Data 0.004 (0.187) Batch 0.769 (1.139) Remain 10:50:32 loss: 0.4414 Lr: 0.00064 [2024-02-19 09:13:43,295 INFO misc.py line 119 87073] Train: [78/100][1532/1557] Data 0.004 (0.187) Batch 0.743 (1.138) Remain 10:50:22 loss: 0.1977 Lr: 0.00064 [2024-02-19 09:13:44,484 INFO misc.py line 119 87073] Train: [78/100][1533/1557] Data 0.005 (0.187) Batch 1.180 (1.138) Remain 10:50:21 loss: 0.0967 Lr: 0.00064 [2024-02-19 09:13:45,400 INFO misc.py line 119 87073] Train: [78/100][1534/1557] Data 0.014 (0.186) Batch 0.925 (1.138) Remain 10:50:15 loss: 0.1177 Lr: 0.00064 [2024-02-19 09:13:46,433 INFO misc.py line 119 87073] Train: [78/100][1535/1557] Data 0.005 (0.186) Batch 1.033 (1.138) Remain 10:50:12 loss: 0.1079 Lr: 0.00064 [2024-02-19 09:13:47,343 INFO misc.py line 119 87073] Train: [78/100][1536/1557] Data 0.005 (0.186) Batch 0.909 (1.138) Remain 10:50:06 loss: 0.1116 Lr: 0.00064 [2024-02-19 09:13:48,240 INFO misc.py line 119 87073] Train: [78/100][1537/1557] Data 0.006 (0.186) Batch 0.887 (1.138) Remain 10:49:59 loss: 0.0624 Lr: 0.00064 [2024-02-19 09:13:49,017 INFO misc.py line 119 87073] Train: [78/100][1538/1557] Data 0.016 (0.186) Batch 0.788 (1.138) Remain 10:49:50 loss: 0.1952 Lr: 0.00064 [2024-02-19 09:13:49,746 INFO misc.py line 119 87073] Train: [78/100][1539/1557] Data 0.004 (0.186) Batch 0.730 (1.137) Remain 10:49:40 loss: 0.1297 Lr: 0.00064 [2024-02-19 09:13:50,977 INFO misc.py line 119 87073] Train: [78/100][1540/1557] Data 0.004 (0.186) Batch 1.230 (1.137) Remain 10:49:41 loss: 0.1881 Lr: 0.00064 [2024-02-19 09:13:51,965 INFO misc.py line 119 87073] Train: [78/100][1541/1557] Data 0.005 (0.186) Batch 0.988 (1.137) Remain 10:49:36 loss: 0.4366 Lr: 0.00064 [2024-02-19 09:13:52,797 INFO misc.py line 119 87073] Train: [78/100][1542/1557] Data 0.005 (0.185) Batch 0.832 (1.137) Remain 10:49:28 loss: 0.0629 Lr: 0.00064 [2024-02-19 09:13:53,795 INFO misc.py line 119 87073] Train: [78/100][1543/1557] Data 0.005 (0.185) Batch 0.996 (1.137) Remain 10:49:24 loss: 0.2628 Lr: 0.00064 [2024-02-19 09:13:54,725 INFO misc.py line 119 87073] Train: [78/100][1544/1557] Data 0.007 (0.185) Batch 0.931 (1.137) Remain 10:49:18 loss: 0.2225 Lr: 0.00064 [2024-02-19 09:13:55,471 INFO misc.py line 119 87073] Train: [78/100][1545/1557] Data 0.006 (0.185) Batch 0.746 (1.137) Remain 10:49:08 loss: 0.2098 Lr: 0.00064 [2024-02-19 09:13:56,189 INFO misc.py line 119 87073] Train: [78/100][1546/1557] Data 0.006 (0.185) Batch 0.718 (1.136) Remain 10:48:58 loss: 0.1132 Lr: 0.00064 [2024-02-19 09:13:57,352 INFO misc.py line 119 87073] Train: [78/100][1547/1557] Data 0.005 (0.185) Batch 1.163 (1.136) Remain 10:48:57 loss: 0.1088 Lr: 0.00064 [2024-02-19 09:13:58,136 INFO misc.py line 119 87073] Train: [78/100][1548/1557] Data 0.006 (0.185) Batch 0.785 (1.136) Remain 10:48:49 loss: 0.1956 Lr: 0.00064 [2024-02-19 09:13:59,087 INFO misc.py line 119 87073] Train: [78/100][1549/1557] Data 0.004 (0.185) Batch 0.952 (1.136) Remain 10:48:43 loss: 0.1998 Lr: 0.00064 [2024-02-19 09:14:00,139 INFO misc.py line 119 87073] Train: [78/100][1550/1557] Data 0.005 (0.185) Batch 1.050 (1.136) Remain 10:48:40 loss: 0.2377 Lr: 0.00064 [2024-02-19 09:14:00,957 INFO misc.py line 119 87073] Train: [78/100][1551/1557] Data 0.005 (0.184) Batch 0.818 (1.136) Remain 10:48:32 loss: 0.1943 Lr: 0.00064 [2024-02-19 09:14:01,688 INFO misc.py line 119 87073] Train: [78/100][1552/1557] Data 0.005 (0.184) Batch 0.729 (1.136) Remain 10:48:22 loss: 0.1493 Lr: 0.00064 [2024-02-19 09:14:02,448 INFO misc.py line 119 87073] Train: [78/100][1553/1557] Data 0.007 (0.184) Batch 0.763 (1.135) Remain 10:48:13 loss: 0.1760 Lr: 0.00064 [2024-02-19 09:14:03,753 INFO misc.py line 119 87073] Train: [78/100][1554/1557] Data 0.005 (0.184) Batch 1.301 (1.135) Remain 10:48:15 loss: 0.0882 Lr: 0.00064 [2024-02-19 09:14:04,795 INFO misc.py line 119 87073] Train: [78/100][1555/1557] Data 0.009 (0.184) Batch 1.046 (1.135) Remain 10:48:12 loss: 0.4239 Lr: 0.00064 [2024-02-19 09:14:05,851 INFO misc.py line 119 87073] Train: [78/100][1556/1557] Data 0.005 (0.184) Batch 1.055 (1.135) Remain 10:48:09 loss: 0.3895 Lr: 0.00064 [2024-02-19 09:14:06,895 INFO misc.py line 119 87073] Train: [78/100][1557/1557] Data 0.006 (0.184) Batch 1.041 (1.135) Remain 10:48:06 loss: 0.1188 Lr: 0.00064 [2024-02-19 09:14:06,896 INFO misc.py line 136 87073] Train result: loss: 0.2349 [2024-02-19 09:14:06,896 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 09:14:34,249 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5447 [2024-02-19 09:14:35,033 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3811 [2024-02-19 09:14:37,156 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3323 [2024-02-19 09:14:39,365 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3850 [2024-02-19 09:14:44,306 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2142 [2024-02-19 09:14:45,006 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1513 [2024-02-19 09:14:46,271 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3238 [2024-02-19 09:14:49,228 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2564 [2024-02-19 09:14:51,043 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2646 [2024-02-19 09:14:52,574 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7316/0.7863/0.9202. [2024-02-19 09:14:52,574 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9305/0.9708 [2024-02-19 09:14:52,574 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9831/0.9891 [2024-02-19 09:14:52,574 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8722/0.9701 [2024-02-19 09:14:52,574 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 09:14:52,574 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4127/0.4617 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6908/0.7191 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8503/0.9521 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8402/0.9288 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9232/0.9752 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7915/0.8159 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8014/0.8976 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7942/0.8457 [2024-02-19 09:14:52,575 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6203/0.6956 [2024-02-19 09:14:52,576 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 09:14:52,578 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 09:14:52,578 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 09:14:59,538 INFO misc.py line 119 87073] Train: [79/100][1/1557] Data 1.345 (1.345) Batch 1.988 (1.988) Remain 18:54:46 loss: 0.1241 Lr: 0.00063 [2024-02-19 09:15:00,420 INFO misc.py line 119 87073] Train: [79/100][2/1557] Data 0.007 (0.007) Batch 0.883 (0.883) Remain 08:23:55 loss: 0.1399 Lr: 0.00063 [2024-02-19 09:15:01,446 INFO misc.py line 119 87073] Train: [79/100][3/1557] Data 0.006 (0.006) Batch 1.015 (1.015) Remain 09:39:11 loss: 0.0938 Lr: 0.00063 [2024-02-19 09:15:02,325 INFO misc.py line 119 87073] Train: [79/100][4/1557] Data 0.019 (0.019) Batch 0.892 (0.892) Remain 08:29:12 loss: 0.3686 Lr: 0.00063 [2024-02-19 09:15:03,145 INFO misc.py line 119 87073] Train: [79/100][5/1557] Data 0.005 (0.012) Batch 0.819 (0.856) Remain 08:08:21 loss: 0.2208 Lr: 0.00063 [2024-02-19 09:15:04,024 INFO misc.py line 119 87073] Train: [79/100][6/1557] Data 0.006 (0.010) Batch 0.879 (0.863) Remain 08:12:45 loss: 0.1547 Lr: 0.00063 [2024-02-19 09:15:06,504 INFO misc.py line 119 87073] Train: [79/100][7/1557] Data 0.006 (0.009) Batch 2.482 (1.268) Remain 12:03:43 loss: 0.1442 Lr: 0.00063 [2024-02-19 09:15:07,495 INFO misc.py line 119 87073] Train: [79/100][8/1557] Data 0.004 (0.008) Batch 0.987 (1.212) Remain 11:31:36 loss: 0.3553 Lr: 0.00063 [2024-02-19 09:15:08,628 INFO misc.py line 119 87073] Train: [79/100][9/1557] Data 0.008 (0.008) Batch 1.135 (1.199) Remain 11:24:20 loss: 0.2807 Lr: 0.00063 [2024-02-19 09:15:09,716 INFO misc.py line 119 87073] Train: [79/100][10/1557] Data 0.006 (0.008) Batch 1.089 (1.183) Remain 11:15:18 loss: 0.3760 Lr: 0.00063 [2024-02-19 09:15:10,631 INFO misc.py line 119 87073] Train: [79/100][11/1557] Data 0.005 (0.007) Batch 0.915 (1.150) Remain 10:56:09 loss: 0.2195 Lr: 0.00063 [2024-02-19 09:15:11,403 INFO misc.py line 119 87073] Train: [79/100][12/1557] Data 0.005 (0.007) Batch 0.769 (1.107) Remain 10:31:59 loss: 0.1627 Lr: 0.00063 [2024-02-19 09:15:12,164 INFO misc.py line 119 87073] Train: [79/100][13/1557] Data 0.008 (0.007) Batch 0.765 (1.073) Remain 10:12:24 loss: 0.2234 Lr: 0.00063 [2024-02-19 09:15:13,447 INFO misc.py line 119 87073] Train: [79/100][14/1557] Data 0.004 (0.007) Batch 1.281 (1.092) Remain 10:23:10 loss: 0.1832 Lr: 0.00063 [2024-02-19 09:15:14,292 INFO misc.py line 119 87073] Train: [79/100][15/1557] Data 0.006 (0.007) Batch 0.846 (1.072) Remain 10:11:27 loss: 0.1305 Lr: 0.00063 [2024-02-19 09:15:15,369 INFO misc.py line 119 87073] Train: [79/100][16/1557] Data 0.005 (0.007) Batch 1.078 (1.072) Remain 10:11:44 loss: 0.0418 Lr: 0.00063 [2024-02-19 09:15:16,307 INFO misc.py line 119 87073] Train: [79/100][17/1557] Data 0.004 (0.006) Batch 0.938 (1.062) Remain 10:06:14 loss: 0.0523 Lr: 0.00063 [2024-02-19 09:15:17,352 INFO misc.py line 119 87073] Train: [79/100][18/1557] Data 0.004 (0.006) Batch 1.046 (1.061) Remain 10:05:34 loss: 0.2644 Lr: 0.00063 [2024-02-19 09:15:18,173 INFO misc.py line 119 87073] Train: [79/100][19/1557] Data 0.004 (0.006) Batch 0.820 (1.046) Remain 09:56:58 loss: 0.1457 Lr: 0.00063 [2024-02-19 09:15:18,909 INFO misc.py line 119 87073] Train: [79/100][20/1557] Data 0.005 (0.006) Batch 0.727 (1.027) Remain 09:46:13 loss: 0.1627 Lr: 0.00063 [2024-02-19 09:15:24,023 INFO misc.py line 119 87073] Train: [79/100][21/1557] Data 0.014 (0.006) Batch 5.124 (1.255) Remain 11:56:02 loss: 0.1225 Lr: 0.00063 [2024-02-19 09:15:25,010 INFO misc.py line 119 87073] Train: [79/100][22/1557] Data 0.004 (0.006) Batch 0.986 (1.241) Remain 11:47:57 loss: 0.6990 Lr: 0.00063 [2024-02-19 09:15:26,092 INFO misc.py line 119 87073] Train: [79/100][23/1557] Data 0.005 (0.006) Batch 1.083 (1.233) Remain 11:43:26 loss: 0.2642 Lr: 0.00063 [2024-02-19 09:15:26,961 INFO misc.py line 119 87073] Train: [79/100][24/1557] Data 0.004 (0.006) Batch 0.867 (1.216) Remain 11:33:29 loss: 0.1817 Lr: 0.00063 [2024-02-19 09:15:27,883 INFO misc.py line 119 87073] Train: [79/100][25/1557] Data 0.005 (0.006) Batch 0.913 (1.202) Remain 11:25:37 loss: 0.6865 Lr: 0.00063 [2024-02-19 09:15:28,690 INFO misc.py line 119 87073] Train: [79/100][26/1557] Data 0.014 (0.006) Batch 0.817 (1.185) Remain 11:16:03 loss: 0.1356 Lr: 0.00063 [2024-02-19 09:15:29,488 INFO misc.py line 119 87073] Train: [79/100][27/1557] Data 0.004 (0.006) Batch 0.797 (1.169) Remain 11:06:48 loss: 0.0956 Lr: 0.00063 [2024-02-19 09:15:30,639 INFO misc.py line 119 87073] Train: [79/100][28/1557] Data 0.005 (0.006) Batch 1.148 (1.168) Remain 11:06:18 loss: 0.0956 Lr: 0.00063 [2024-02-19 09:15:31,552 INFO misc.py line 119 87073] Train: [79/100][29/1557] Data 0.009 (0.006) Batch 0.916 (1.158) Remain 11:00:45 loss: 0.1836 Lr: 0.00063 [2024-02-19 09:15:32,607 INFO misc.py line 119 87073] Train: [79/100][30/1557] Data 0.006 (0.006) Batch 1.057 (1.155) Remain 10:58:35 loss: 0.1502 Lr: 0.00063 [2024-02-19 09:15:33,635 INFO misc.py line 119 87073] Train: [79/100][31/1557] Data 0.004 (0.006) Batch 1.028 (1.150) Remain 10:55:59 loss: 0.2963 Lr: 0.00063 [2024-02-19 09:15:34,649 INFO misc.py line 119 87073] Train: [79/100][32/1557] Data 0.004 (0.006) Batch 1.015 (1.145) Remain 10:53:18 loss: 0.3292 Lr: 0.00063 [2024-02-19 09:15:35,420 INFO misc.py line 119 87073] Train: [79/100][33/1557] Data 0.003 (0.006) Batch 0.770 (1.133) Remain 10:46:09 loss: 0.1642 Lr: 0.00063 [2024-02-19 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line 119 87073] Train: [79/100][389/1557] Data 0.003 (0.076) Batch 1.066 (1.126) Remain 10:35:15 loss: 0.2246 Lr: 0.00062 [2024-02-19 09:22:16,673 INFO misc.py line 119 87073] Train: [79/100][390/1557] Data 0.004 (0.076) Batch 0.796 (1.125) Remain 10:34:45 loss: 0.1852 Lr: 0.00062 [2024-02-19 09:22:17,461 INFO misc.py line 119 87073] Train: [79/100][391/1557] Data 0.004 (0.075) Batch 0.780 (1.124) Remain 10:34:13 loss: 0.2244 Lr: 0.00062 [2024-02-19 09:22:18,548 INFO misc.py line 119 87073] Train: [79/100][392/1557] Data 0.011 (0.075) Batch 1.091 (1.124) Remain 10:34:09 loss: 0.1197 Lr: 0.00062 [2024-02-19 09:22:19,608 INFO misc.py line 119 87073] Train: [79/100][393/1557] Data 0.008 (0.075) Batch 1.061 (1.124) Remain 10:34:03 loss: 0.0881 Lr: 0.00062 [2024-02-19 09:22:20,507 INFO misc.py line 119 87073] Train: [79/100][394/1557] Data 0.007 (0.075) Batch 0.902 (1.123) Remain 10:33:43 loss: 0.5311 Lr: 0.00062 [2024-02-19 09:22:21,553 INFO misc.py line 119 87073] Train: 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Remain 10:36:33 loss: 0.3282 Lr: 0.00059 [2024-02-19 09:38:53,497 INFO misc.py line 119 87073] Train: [79/100][1241/1557] Data 0.004 (0.083) Batch 0.979 (1.157) Remain 10:36:27 loss: 0.2128 Lr: 0.00059 [2024-02-19 09:38:54,432 INFO misc.py line 119 87073] Train: [79/100][1242/1557] Data 0.006 (0.083) Batch 0.934 (1.157) Remain 10:36:20 loss: 0.0714 Lr: 0.00059 [2024-02-19 09:38:55,327 INFO misc.py line 119 87073] Train: [79/100][1243/1557] Data 0.005 (0.083) Batch 0.888 (1.156) Remain 10:36:12 loss: 0.1185 Lr: 0.00059 [2024-02-19 09:38:56,169 INFO misc.py line 119 87073] Train: [79/100][1244/1557] Data 0.013 (0.083) Batch 0.850 (1.156) Remain 10:36:03 loss: 0.1135 Lr: 0.00059 [2024-02-19 09:38:56,921 INFO misc.py line 119 87073] Train: [79/100][1245/1557] Data 0.004 (0.083) Batch 0.753 (1.156) Remain 10:35:51 loss: 0.1510 Lr: 0.00059 [2024-02-19 09:38:58,157 INFO misc.py line 119 87073] Train: [79/100][1246/1557] Data 0.004 (0.083) Batch 1.224 (1.156) Remain 10:35:52 loss: 0.1736 Lr: 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Train: [79/100][1290/1557] Data 0.004 (0.080) Batch 1.042 (1.151) Remain 10:32:09 loss: 0.0756 Lr: 0.00059 [2024-02-19 09:39:43,222 INFO misc.py line 119 87073] Train: [79/100][1291/1557] Data 0.003 (0.080) Batch 0.925 (1.150) Remain 10:32:02 loss: 0.4511 Lr: 0.00059 [2024-02-19 09:39:44,335 INFO misc.py line 119 87073] Train: [79/100][1292/1557] Data 0.004 (0.080) Batch 1.112 (1.150) Remain 10:32:00 loss: 0.1782 Lr: 0.00059 [2024-02-19 09:39:45,046 INFO misc.py line 119 87073] Train: [79/100][1293/1557] Data 0.004 (0.080) Batch 0.710 (1.150) Remain 10:31:47 loss: 0.3528 Lr: 0.00059 [2024-02-19 09:39:45,795 INFO misc.py line 119 87073] Train: [79/100][1294/1557] Data 0.005 (0.080) Batch 0.746 (1.150) Remain 10:31:36 loss: 0.2211 Lr: 0.00059 [2024-02-19 09:39:58,641 INFO misc.py line 119 87073] Train: [79/100][1295/1557] Data 3.943 (0.083) Batch 12.841 (1.159) Remain 10:36:33 loss: 0.1194 Lr: 0.00059 [2024-02-19 09:39:59,662 INFO misc.py line 119 87073] Train: [79/100][1296/1557] Data 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Remain 10:35:56 loss: 0.1039 Lr: 0.00059 [2024-02-19 09:40:06,563 INFO misc.py line 119 87073] Train: [79/100][1303/1557] Data 0.013 (0.083) Batch 0.958 (1.158) Remain 10:35:50 loss: 0.2991 Lr: 0.00059 [2024-02-19 09:40:07,539 INFO misc.py line 119 87073] Train: [79/100][1304/1557] Data 0.003 (0.083) Batch 0.975 (1.158) Remain 10:35:44 loss: 0.1865 Lr: 0.00059 [2024-02-19 09:40:08,709 INFO misc.py line 119 87073] Train: [79/100][1305/1557] Data 0.004 (0.083) Batch 1.170 (1.158) Remain 10:35:43 loss: 0.3044 Lr: 0.00059 [2024-02-19 09:40:09,651 INFO misc.py line 119 87073] Train: [79/100][1306/1557] Data 0.005 (0.083) Batch 0.941 (1.157) Remain 10:35:37 loss: 0.5402 Lr: 0.00059 [2024-02-19 09:40:10,451 INFO misc.py line 119 87073] Train: [79/100][1307/1557] Data 0.005 (0.083) Batch 0.799 (1.157) Remain 10:35:26 loss: 0.2426 Lr: 0.00059 [2024-02-19 09:40:11,210 INFO misc.py line 119 87073] Train: [79/100][1308/1557] Data 0.006 (0.082) Batch 0.756 (1.157) Remain 10:35:15 loss: 0.1887 Lr: 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Remain 10:32:44 loss: 0.5010 Lr: 0.00059 [2024-02-19 09:40:36,049 INFO misc.py line 119 87073] Train: [79/100][1334/1557] Data 0.003 (0.081) Batch 0.854 (1.153) Remain 10:32:36 loss: 0.5044 Lr: 0.00059 [2024-02-19 09:40:36,790 INFO misc.py line 119 87073] Train: [79/100][1335/1557] Data 0.003 (0.081) Batch 0.730 (1.153) Remain 10:32:24 loss: 0.1863 Lr: 0.00059 [2024-02-19 09:40:37,541 INFO misc.py line 119 87073] Train: [79/100][1336/1557] Data 0.014 (0.081) Batch 0.760 (1.152) Remain 10:32:13 loss: 0.2054 Lr: 0.00059 [2024-02-19 09:40:38,785 INFO misc.py line 119 87073] Train: [79/100][1337/1557] Data 0.005 (0.081) Batch 1.244 (1.152) Remain 10:32:14 loss: 0.0944 Lr: 0.00059 [2024-02-19 09:40:39,729 INFO misc.py line 119 87073] Train: [79/100][1338/1557] Data 0.007 (0.081) Batch 0.943 (1.152) Remain 10:32:08 loss: 0.4106 Lr: 0.00059 [2024-02-19 09:40:40,606 INFO misc.py line 119 87073] Train: [79/100][1339/1557] Data 0.007 (0.081) Batch 0.879 (1.152) Remain 10:32:00 loss: 0.1134 Lr: 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Remain 10:33:04 loss: 0.3523 Lr: 0.00059 [2024-02-19 09:41:15,761 INFO misc.py line 119 87073] Train: [79/100][1365/1557] Data 0.004 (0.082) Batch 2.502 (1.156) Remain 10:33:35 loss: 0.1515 Lr: 0.00059 [2024-02-19 09:41:16,683 INFO misc.py line 119 87073] Train: [79/100][1366/1557] Data 0.017 (0.082) Batch 0.934 (1.156) Remain 10:33:29 loss: 0.2070 Lr: 0.00059 [2024-02-19 09:41:17,666 INFO misc.py line 119 87073] Train: [79/100][1367/1557] Data 0.004 (0.082) Batch 0.983 (1.156) Remain 10:33:24 loss: 0.3145 Lr: 0.00059 [2024-02-19 09:41:18,715 INFO misc.py line 119 87073] Train: [79/100][1368/1557] Data 0.004 (0.082) Batch 1.048 (1.156) Remain 10:33:20 loss: 0.2394 Lr: 0.00059 [2024-02-19 09:41:19,574 INFO misc.py line 119 87073] Train: [79/100][1369/1557] Data 0.005 (0.082) Batch 0.860 (1.155) Remain 10:33:12 loss: 0.1533 Lr: 0.00059 [2024-02-19 09:41:20,332 INFO misc.py line 119 87073] Train: [79/100][1370/1557] Data 0.004 (0.082) Batch 0.745 (1.155) Remain 10:33:00 loss: 0.1927 Lr: 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Train: [79/100][1383/1557] Data 0.004 (0.081) Batch 0.946 (1.153) Remain 10:31:40 loss: 0.1499 Lr: 0.00059 [2024-02-19 09:41:33,364 INFO misc.py line 119 87073] Train: [79/100][1384/1557] Data 0.013 (0.081) Batch 0.769 (1.153) Remain 10:31:30 loss: 0.2503 Lr: 0.00059 [2024-02-19 09:41:34,104 INFO misc.py line 119 87073] Train: [79/100][1385/1557] Data 0.003 (0.081) Batch 0.731 (1.152) Remain 10:31:19 loss: 0.2713 Lr: 0.00059 [2024-02-19 09:41:35,353 INFO misc.py line 119 87073] Train: [79/100][1386/1557] Data 0.013 (0.081) Batch 1.258 (1.153) Remain 10:31:20 loss: 0.1056 Lr: 0.00059 [2024-02-19 09:41:36,286 INFO misc.py line 119 87073] Train: [79/100][1387/1557] Data 0.004 (0.081) Batch 0.933 (1.152) Remain 10:31:14 loss: 0.6068 Lr: 0.00059 [2024-02-19 09:41:37,171 INFO misc.py line 119 87073] Train: [79/100][1388/1557] Data 0.004 (0.081) Batch 0.884 (1.152) Remain 10:31:06 loss: 0.0868 Lr: 0.00059 [2024-02-19 09:41:38,154 INFO misc.py line 119 87073] Train: [79/100][1389/1557] Data 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Remain 10:30:28 loss: 0.2812 Lr: 0.00059 [2024-02-19 09:41:44,840 INFO misc.py line 119 87073] Train: [79/100][1396/1557] Data 0.003 (0.080) Batch 0.862 (1.151) Remain 10:30:21 loss: 0.3788 Lr: 0.00059 [2024-02-19 09:41:45,655 INFO misc.py line 119 87073] Train: [79/100][1397/1557] Data 0.004 (0.080) Batch 0.815 (1.151) Remain 10:30:11 loss: 0.2069 Lr: 0.00059 [2024-02-19 09:41:46,425 INFO misc.py line 119 87073] Train: [79/100][1398/1557] Data 0.004 (0.080) Batch 0.770 (1.151) Remain 10:30:01 loss: 0.1872 Lr: 0.00059 [2024-02-19 09:41:47,153 INFO misc.py line 119 87073] Train: [79/100][1399/1557] Data 0.004 (0.080) Batch 0.720 (1.150) Remain 10:29:50 loss: 0.1325 Lr: 0.00059 [2024-02-19 09:41:48,321 INFO misc.py line 119 87073] Train: [79/100][1400/1557] Data 0.012 (0.080) Batch 1.168 (1.150) Remain 10:29:49 loss: 0.0959 Lr: 0.00059 [2024-02-19 09:41:49,288 INFO misc.py line 119 87073] Train: [79/100][1401/1557] Data 0.013 (0.080) Batch 0.974 (1.150) Remain 10:29:44 loss: 0.0854 Lr: 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Train: [79/100][1414/1557] Data 0.006 (0.083) Batch 1.260 (1.157) Remain 10:33:03 loss: 0.0951 Lr: 0.00059 [2024-02-19 09:42:14,420 INFO misc.py line 119 87073] Train: [79/100][1415/1557] Data 0.032 (0.083) Batch 0.971 (1.157) Remain 10:32:58 loss: 0.3370 Lr: 0.00059 [2024-02-19 09:42:15,218 INFO misc.py line 119 87073] Train: [79/100][1416/1557] Data 0.004 (0.083) Batch 0.798 (1.156) Remain 10:32:48 loss: 0.2902 Lr: 0.00059 [2024-02-19 09:42:16,204 INFO misc.py line 119 87073] Train: [79/100][1417/1557] Data 0.004 (0.083) Batch 0.979 (1.156) Remain 10:32:43 loss: 0.5910 Lr: 0.00059 [2024-02-19 09:42:17,238 INFO misc.py line 119 87073] Train: [79/100][1418/1557] Data 0.011 (0.083) Batch 1.034 (1.156) Remain 10:32:39 loss: 2.8413 Lr: 0.00059 [2024-02-19 09:42:18,055 INFO misc.py line 119 87073] Train: [79/100][1419/1557] Data 0.012 (0.083) Batch 0.824 (1.156) Remain 10:32:30 loss: 0.1467 Lr: 0.00059 [2024-02-19 09:42:18,783 INFO misc.py line 119 87073] Train: [79/100][1420/1557] Data 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Remain 10:32:01 loss: 0.1565 Lr: 0.00059 [2024-02-19 09:42:25,992 INFO misc.py line 119 87073] Train: [79/100][1427/1557] Data 0.012 (0.082) Batch 0.772 (1.155) Remain 10:31:51 loss: 0.1826 Lr: 0.00059 [2024-02-19 09:42:27,191 INFO misc.py line 119 87073] Train: [79/100][1428/1557] Data 0.004 (0.082) Batch 1.199 (1.155) Remain 10:31:51 loss: 0.1889 Lr: 0.00059 [2024-02-19 09:42:28,206 INFO misc.py line 119 87073] Train: [79/100][1429/1557] Data 0.004 (0.082) Batch 1.012 (1.155) Remain 10:31:46 loss: 0.4623 Lr: 0.00059 [2024-02-19 09:42:29,178 INFO misc.py line 119 87073] Train: [79/100][1430/1557] Data 0.007 (0.082) Batch 0.974 (1.155) Remain 10:31:41 loss: 0.4750 Lr: 0.00059 [2024-02-19 09:42:30,224 INFO misc.py line 119 87073] Train: [79/100][1431/1557] Data 0.004 (0.082) Batch 1.046 (1.155) Remain 10:31:37 loss: 0.3401 Lr: 0.00059 [2024-02-19 09:42:31,121 INFO misc.py line 119 87073] Train: [79/100][1432/1557] Data 0.004 (0.082) Batch 0.895 (1.154) Remain 10:31:30 loss: 0.1384 Lr: 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Train: [79/100][1445/1557] Data 0.003 (0.081) Batch 0.942 (1.153) Remain 10:30:14 loss: 0.1445 Lr: 0.00058 [2024-02-19 09:42:44,486 INFO misc.py line 119 87073] Train: [79/100][1446/1557] Data 0.003 (0.081) Batch 1.050 (1.152) Remain 10:30:11 loss: 0.3412 Lr: 0.00058 [2024-02-19 09:42:45,222 INFO misc.py line 119 87073] Train: [79/100][1447/1557] Data 0.006 (0.081) Batch 0.732 (1.152) Remain 10:30:00 loss: 0.1749 Lr: 0.00058 [2024-02-19 09:42:46,006 INFO misc.py line 119 87073] Train: [79/100][1448/1557] Data 0.010 (0.081) Batch 0.790 (1.152) Remain 10:29:50 loss: 0.3589 Lr: 0.00058 [2024-02-19 09:42:47,283 INFO misc.py line 119 87073] Train: [79/100][1449/1557] Data 0.004 (0.081) Batch 1.275 (1.152) Remain 10:29:52 loss: 0.1336 Lr: 0.00058 [2024-02-19 09:42:48,234 INFO misc.py line 119 87073] Train: [79/100][1450/1557] Data 0.007 (0.081) Batch 0.952 (1.152) Remain 10:29:46 loss: 0.6945 Lr: 0.00058 [2024-02-19 09:42:49,174 INFO misc.py line 119 87073] Train: [79/100][1451/1557] Data 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Remain 10:29:02 loss: 0.1992 Lr: 0.00058 [2024-02-19 09:42:55,885 INFO misc.py line 119 87073] Train: [79/100][1458/1557] Data 0.004 (0.081) Batch 1.185 (1.151) Remain 10:29:02 loss: 0.3684 Lr: 0.00058 [2024-02-19 09:42:56,959 INFO misc.py line 119 87073] Train: [79/100][1459/1557] Data 0.018 (0.081) Batch 1.088 (1.151) Remain 10:28:59 loss: 0.0992 Lr: 0.00058 [2024-02-19 09:42:57,922 INFO misc.py line 119 87073] Train: [79/100][1460/1557] Data 0.003 (0.081) Batch 0.961 (1.151) Remain 10:28:54 loss: 0.5521 Lr: 0.00058 [2024-02-19 09:42:58,659 INFO misc.py line 119 87073] Train: [79/100][1461/1557] Data 0.006 (0.081) Batch 0.738 (1.150) Remain 10:28:43 loss: 0.1941 Lr: 0.00058 [2024-02-19 09:42:59,394 INFO misc.py line 119 87073] Train: [79/100][1462/1557] Data 0.004 (0.081) Batch 0.734 (1.150) Remain 10:28:33 loss: 0.1894 Lr: 0.00058 [2024-02-19 09:43:09,750 INFO misc.py line 119 87073] Train: [79/100][1463/1557] Data 3.781 (0.083) Batch 10.356 (1.156) Remain 10:31:58 loss: 0.1929 Lr: 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Train: [79/100][1476/1557] Data 0.005 (0.082) Batch 0.700 (1.154) Remain 10:30:42 loss: 0.1538 Lr: 0.00058 [2024-02-19 09:43:26,951 INFO misc.py line 119 87073] Train: [79/100][1477/1557] Data 0.004 (0.082) Batch 4.927 (1.157) Remain 10:32:04 loss: 0.1619 Lr: 0.00058 [2024-02-19 09:43:27,837 INFO misc.py line 119 87073] Train: [79/100][1478/1557] Data 0.014 (0.082) Batch 0.897 (1.157) Remain 10:31:57 loss: 0.1055 Lr: 0.00058 [2024-02-19 09:43:29,085 INFO misc.py line 119 87073] Train: [79/100][1479/1557] Data 0.004 (0.082) Batch 1.247 (1.157) Remain 10:31:58 loss: 0.1034 Lr: 0.00058 [2024-02-19 09:43:30,196 INFO misc.py line 119 87073] Train: [79/100][1480/1557] Data 0.005 (0.082) Batch 1.111 (1.157) Remain 10:31:56 loss: 0.8413 Lr: 0.00058 [2024-02-19 09:43:31,281 INFO misc.py line 119 87073] Train: [79/100][1481/1557] Data 0.005 (0.082) Batch 1.076 (1.157) Remain 10:31:53 loss: 0.2217 Lr: 0.00058 [2024-02-19 09:43:32,046 INFO misc.py line 119 87073] Train: [79/100][1482/1557] Data 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Remain 10:31:09 loss: 0.1650 Lr: 0.00058 [2024-02-19 09:43:38,525 INFO misc.py line 119 87073] Train: [79/100][1489/1557] Data 0.006 (0.082) Batch 0.799 (1.156) Remain 10:31:00 loss: 0.2973 Lr: 0.00058 [2024-02-19 09:43:39,312 INFO misc.py line 119 87073] Train: [79/100][1490/1557] Data 0.006 (0.082) Batch 0.783 (1.155) Remain 10:30:50 loss: 0.1527 Lr: 0.00058 [2024-02-19 09:43:40,568 INFO misc.py line 119 87073] Train: [79/100][1491/1557] Data 0.010 (0.082) Batch 1.255 (1.155) Remain 10:30:51 loss: 0.0735 Lr: 0.00058 [2024-02-19 09:43:41,582 INFO misc.py line 119 87073] Train: [79/100][1492/1557] Data 0.012 (0.082) Batch 1.020 (1.155) Remain 10:30:47 loss: 0.1709 Lr: 0.00058 [2024-02-19 09:43:42,551 INFO misc.py line 119 87073] Train: [79/100][1493/1557] Data 0.005 (0.082) Batch 0.969 (1.155) Remain 10:30:42 loss: 0.2524 Lr: 0.00058 [2024-02-19 09:43:43,395 INFO misc.py line 119 87073] Train: [79/100][1494/1557] Data 0.006 (0.081) Batch 0.845 (1.155) Remain 10:30:34 loss: 0.2347 Lr: 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Train: [79/100][1507/1557] Data 0.004 (0.081) Batch 1.033 (1.153) Remain 10:29:15 loss: 0.3347 Lr: 0.00058 [2024-02-19 09:43:56,520 INFO misc.py line 119 87073] Train: [79/100][1508/1557] Data 0.011 (0.081) Batch 1.075 (1.153) Remain 10:29:12 loss: 0.3566 Lr: 0.00058 [2024-02-19 09:43:57,538 INFO misc.py line 119 87073] Train: [79/100][1509/1557] Data 0.005 (0.081) Batch 1.016 (1.153) Remain 10:29:08 loss: 0.2819 Lr: 0.00058 [2024-02-19 09:43:58,291 INFO misc.py line 119 87073] Train: [79/100][1510/1557] Data 0.006 (0.081) Batch 0.755 (1.153) Remain 10:28:58 loss: 0.3673 Lr: 0.00058 [2024-02-19 09:43:59,030 INFO misc.py line 119 87073] Train: [79/100][1511/1557] Data 0.004 (0.081) Batch 0.735 (1.152) Remain 10:28:48 loss: 0.1785 Lr: 0.00058 [2024-02-19 09:44:00,133 INFO misc.py line 119 87073] Train: [79/100][1512/1557] Data 0.008 (0.081) Batch 1.103 (1.152) Remain 10:28:45 loss: 0.1452 Lr: 0.00058 [2024-02-19 09:44:01,019 INFO misc.py line 119 87073] Train: [79/100][1513/1557] Data 0.008 (0.081) Batch 0.890 (1.152) Remain 10:28:39 loss: 0.3203 Lr: 0.00058 [2024-02-19 09:44:01,842 INFO misc.py line 119 87073] Train: [79/100][1514/1557] Data 0.004 (0.080) Batch 0.823 (1.152) Remain 10:28:30 loss: 0.0422 Lr: 0.00058 [2024-02-19 09:44:02,704 INFO misc.py line 119 87073] Train: [79/100][1515/1557] Data 0.004 (0.080) Batch 0.861 (1.152) Remain 10:28:23 loss: 0.4540 Lr: 0.00058 [2024-02-19 09:44:03,615 INFO misc.py line 119 87073] Train: [79/100][1516/1557] Data 0.006 (0.080) Batch 0.912 (1.151) Remain 10:28:16 loss: 0.2903 Lr: 0.00058 [2024-02-19 09:44:04,372 INFO misc.py line 119 87073] Train: [79/100][1517/1557] Data 0.003 (0.080) Batch 0.756 (1.151) Remain 10:28:07 loss: 0.4271 Lr: 0.00058 [2024-02-19 09:44:05,104 INFO misc.py line 119 87073] Train: [79/100][1518/1557] Data 0.005 (0.080) Batch 0.732 (1.151) Remain 10:27:57 loss: 0.1759 Lr: 0.00058 [2024-02-19 09:44:15,844 INFO misc.py line 119 87073] Train: [79/100][1519/1557] Data 4.089 (0.083) Batch 10.740 (1.157) Remain 10:31:22 loss: 0.1283 Lr: 0.00058 [2024-02-19 09:44:16,843 INFO misc.py line 119 87073] Train: [79/100][1520/1557] Data 0.004 (0.083) Batch 1.000 (1.157) Remain 10:31:18 loss: 0.0426 Lr: 0.00058 [2024-02-19 09:44:17,978 INFO misc.py line 119 87073] Train: [79/100][1521/1557] Data 0.003 (0.083) Batch 1.133 (1.157) Remain 10:31:16 loss: 0.1558 Lr: 0.00058 [2024-02-19 09:44:18,933 INFO misc.py line 119 87073] Train: [79/100][1522/1557] Data 0.005 (0.083) Batch 0.956 (1.157) Remain 10:31:11 loss: 0.0705 Lr: 0.00058 [2024-02-19 09:44:19,833 INFO misc.py line 119 87073] Train: [79/100][1523/1557] Data 0.004 (0.083) Batch 0.899 (1.157) Remain 10:31:04 loss: 0.1389 Lr: 0.00058 [2024-02-19 09:44:20,636 INFO misc.py line 119 87073] Train: [79/100][1524/1557] Data 0.006 (0.083) Batch 0.802 (1.157) Remain 10:30:55 loss: 0.1817 Lr: 0.00058 [2024-02-19 09:44:21,408 INFO misc.py line 119 87073] Train: [79/100][1525/1557] Data 0.006 (0.083) Batch 0.773 (1.156) Remain 10:30:46 loss: 0.0962 Lr: 0.00058 [2024-02-19 09:44:22,657 INFO misc.py line 119 87073] Train: [79/100][1526/1557] Data 0.004 (0.083) Batch 1.248 (1.156) Remain 10:30:47 loss: 0.1680 Lr: 0.00058 [2024-02-19 09:44:23,615 INFO misc.py line 119 87073] Train: [79/100][1527/1557] Data 0.005 (0.083) Batch 0.959 (1.156) Remain 10:30:41 loss: 0.4474 Lr: 0.00058 [2024-02-19 09:44:24,482 INFO misc.py line 119 87073] Train: [79/100][1528/1557] Data 0.003 (0.082) Batch 0.868 (1.156) Remain 10:30:34 loss: 0.1582 Lr: 0.00058 [2024-02-19 09:44:25,377 INFO misc.py line 119 87073] Train: [79/100][1529/1557] Data 0.004 (0.082) Batch 0.894 (1.156) Remain 10:30:27 loss: 0.1564 Lr: 0.00058 [2024-02-19 09:44:26,384 INFO misc.py line 119 87073] Train: [79/100][1530/1557] Data 0.005 (0.082) Batch 1.007 (1.156) Remain 10:30:23 loss: 0.4426 Lr: 0.00058 [2024-02-19 09:44:27,184 INFO misc.py line 119 87073] Train: [79/100][1531/1557] Data 0.005 (0.082) Batch 0.800 (1.156) Remain 10:30:14 loss: 0.1419 Lr: 0.00058 [2024-02-19 09:44:27,879 INFO misc.py line 119 87073] Train: [79/100][1532/1557] Data 0.005 (0.082) Batch 0.696 (1.155) Remain 10:30:03 loss: 0.2090 Lr: 0.00058 [2024-02-19 09:44:31,788 INFO misc.py line 119 87073] Train: [79/100][1533/1557] Data 0.003 (0.082) Batch 3.909 (1.157) Remain 10:31:01 loss: 0.1298 Lr: 0.00058 [2024-02-19 09:44:32,769 INFO misc.py line 119 87073] Train: [79/100][1534/1557] Data 0.003 (0.082) Batch 0.982 (1.157) Remain 10:30:56 loss: 0.2118 Lr: 0.00058 [2024-02-19 09:44:33,633 INFO misc.py line 119 87073] Train: [79/100][1535/1557] Data 0.003 (0.082) Batch 0.862 (1.157) Remain 10:30:48 loss: 0.0869 Lr: 0.00058 [2024-02-19 09:44:34,663 INFO misc.py line 119 87073] Train: [79/100][1536/1557] Data 0.005 (0.082) Batch 1.021 (1.157) Remain 10:30:44 loss: 0.3471 Lr: 0.00058 [2024-02-19 09:44:35,783 INFO misc.py line 119 87073] Train: [79/100][1537/1557] Data 0.014 (0.082) Batch 1.125 (1.157) Remain 10:30:42 loss: 0.4822 Lr: 0.00058 [2024-02-19 09:44:36,548 INFO misc.py line 119 87073] Train: [79/100][1538/1557] Data 0.009 (0.082) Batch 0.770 (1.156) Remain 10:30:33 loss: 0.1942 Lr: 0.00058 [2024-02-19 09:44:37,262 INFO misc.py line 119 87073] Train: [79/100][1539/1557] Data 0.004 (0.082) Batch 0.709 (1.156) Remain 10:30:22 loss: 0.1631 Lr: 0.00058 [2024-02-19 09:44:38,397 INFO misc.py line 119 87073] Train: [79/100][1540/1557] Data 0.009 (0.082) Batch 1.134 (1.156) Remain 10:30:21 loss: 0.1923 Lr: 0.00058 [2024-02-19 09:44:39,516 INFO misc.py line 119 87073] Train: [79/100][1541/1557] Data 0.010 (0.082) Batch 1.115 (1.156) Remain 10:30:19 loss: 0.3009 Lr: 0.00058 [2024-02-19 09:44:40,630 INFO misc.py line 119 87073] Train: [79/100][1542/1557] Data 0.014 (0.082) Batch 1.115 (1.156) Remain 10:30:17 loss: 0.2938 Lr: 0.00058 [2024-02-19 09:44:41,484 INFO misc.py line 119 87073] Train: [79/100][1543/1557] Data 0.012 (0.082) Batch 0.863 (1.156) Remain 10:30:09 loss: 0.2517 Lr: 0.00058 [2024-02-19 09:44:42,540 INFO misc.py line 119 87073] Train: [79/100][1544/1557] Data 0.003 (0.082) Batch 1.057 (1.156) Remain 10:30:06 loss: 0.1584 Lr: 0.00058 [2024-02-19 09:44:43,286 INFO misc.py line 119 87073] Train: [79/100][1545/1557] Data 0.003 (0.082) Batch 0.745 (1.156) Remain 10:29:56 loss: 0.1588 Lr: 0.00058 [2024-02-19 09:44:44,077 INFO misc.py line 119 87073] Train: [79/100][1546/1557] Data 0.004 (0.082) Batch 0.778 (1.155) Remain 10:29:47 loss: 0.1137 Lr: 0.00058 [2024-02-19 09:44:45,286 INFO misc.py line 119 87073] Train: [79/100][1547/1557] Data 0.017 (0.082) Batch 1.210 (1.155) Remain 10:29:47 loss: 0.0858 Lr: 0.00058 [2024-02-19 09:44:46,247 INFO misc.py line 119 87073] Train: [79/100][1548/1557] Data 0.016 (0.081) Batch 0.972 (1.155) Remain 10:29:42 loss: 0.0983 Lr: 0.00058 [2024-02-19 09:44:47,197 INFO misc.py line 119 87073] Train: [79/100][1549/1557] Data 0.004 (0.081) Batch 0.950 (1.155) Remain 10:29:37 loss: 0.4810 Lr: 0.00058 [2024-02-19 09:44:48,228 INFO misc.py line 119 87073] Train: [79/100][1550/1557] Data 0.004 (0.081) Batch 1.031 (1.155) Remain 10:29:33 loss: 0.8376 Lr: 0.00058 [2024-02-19 09:44:49,266 INFO misc.py line 119 87073] Train: [79/100][1551/1557] Data 0.004 (0.081) Batch 1.038 (1.155) Remain 10:29:29 loss: 0.6909 Lr: 0.00058 [2024-02-19 09:44:50,007 INFO misc.py line 119 87073] Train: [79/100][1552/1557] Data 0.004 (0.081) Batch 0.741 (1.155) Remain 10:29:19 loss: 0.1413 Lr: 0.00058 [2024-02-19 09:44:50,772 INFO misc.py line 119 87073] Train: [79/100][1553/1557] Data 0.004 (0.081) Batch 0.752 (1.154) Remain 10:29:10 loss: 0.2043 Lr: 0.00058 [2024-02-19 09:44:52,086 INFO misc.py line 119 87073] Train: [79/100][1554/1557] Data 0.017 (0.081) Batch 1.317 (1.155) Remain 10:29:12 loss: 0.1037 Lr: 0.00058 [2024-02-19 09:44:52,933 INFO misc.py line 119 87073] Train: [79/100][1555/1557] Data 0.013 (0.081) Batch 0.856 (1.154) Remain 10:29:04 loss: 0.5063 Lr: 0.00058 [2024-02-19 09:44:53,809 INFO misc.py line 119 87073] Train: [79/100][1556/1557] Data 0.004 (0.081) Batch 0.876 (1.154) Remain 10:28:57 loss: 0.3349 Lr: 0.00058 [2024-02-19 09:44:54,742 INFO misc.py line 119 87073] Train: [79/100][1557/1557] Data 0.004 (0.081) Batch 0.922 (1.154) Remain 10:28:51 loss: 0.3146 Lr: 0.00058 [2024-02-19 09:44:54,744 INFO misc.py line 136 87073] Train result: loss: 0.2353 [2024-02-19 09:44:54,744 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 09:45:23,170 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.6837 [2024-02-19 09:45:23,950 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3305 [2024-02-19 09:45:26,074 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3342 [2024-02-19 09:45:28,283 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3753 [2024-02-19 09:45:33,237 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2414 [2024-02-19 09:45:33,935 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0705 [2024-02-19 09:45:35,196 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3076 [2024-02-19 09:45:38,152 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.4285 [2024-02-19 09:45:39,960 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2558 [2024-02-19 09:45:41,546 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7255/0.7720/0.9175. [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9307/0.9608 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9828/0.9895 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8548/0.9819 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3627/0.3812 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6455/0.6607 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8696/0.9413 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8549/0.9267 [2024-02-19 09:45:41,546 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9225/0.9683 [2024-02-19 09:45:41,547 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7598/0.7805 [2024-02-19 09:45:41,547 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8005/0.8751 [2024-02-19 09:45:41,547 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8129/0.8516 [2024-02-19 09:45:41,547 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6353/0.7178 [2024-02-19 09:45:41,547 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 09:45:41,550 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 09:45:41,550 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 09:45:49,131 INFO misc.py line 119 87073] Train: [80/100][1/1557] Data 1.267 (1.267) Batch 2.160 (2.160) Remain 19:37:05 loss: 0.2381 Lr: 0.00058 [2024-02-19 09:45:50,246 INFO misc.py line 119 87073] Train: [80/100][2/1557] Data 0.005 (0.005) Batch 1.115 (1.115) Remain 10:07:33 loss: 0.1306 Lr: 0.00058 [2024-02-19 09:45:51,192 INFO misc.py line 119 87073] Train: [80/100][3/1557] Data 0.005 (0.005) Batch 0.946 (0.946) Remain 08:35:20 loss: 0.6501 Lr: 0.00058 [2024-02-19 09:45:52,176 INFO misc.py line 119 87073] Train: [80/100][4/1557] Data 0.006 (0.006) Batch 0.984 (0.984) Remain 08:56:19 loss: 0.2085 Lr: 0.00058 [2024-02-19 09:45:52,978 INFO misc.py line 119 87073] Train: [80/100][5/1557] Data 0.006 (0.006) Batch 0.802 (0.893) Remain 08:06:36 loss: 0.2278 Lr: 0.00058 [2024-02-19 09:45:53,770 INFO misc.py line 119 87073] Train: [80/100][6/1557] Data 0.006 (0.006) Batch 0.792 (0.859) Remain 07:48:11 loss: 0.0921 Lr: 0.00058 [2024-02-19 09:45:54,975 INFO misc.py line 119 87073] Train: [80/100][7/1557] Data 0.006 (0.006) Batch 1.196 (0.944) Remain 08:34:03 loss: 0.0856 Lr: 0.00058 [2024-02-19 09:45:56,028 INFO misc.py line 119 87073] Train: [80/100][8/1557] Data 0.014 (0.008) Batch 1.063 (0.967) Remain 08:47:02 loss: 0.2726 Lr: 0.00058 [2024-02-19 09:45:56,953 INFO misc.py line 119 87073] Train: [80/100][9/1557] Data 0.004 (0.007) Batch 0.926 (0.960) Remain 08:43:14 loss: 0.1619 Lr: 0.00058 [2024-02-19 09:45:57,750 INFO misc.py line 119 87073] Train: [80/100][10/1557] Data 0.004 (0.006) Batch 0.797 (0.937) Remain 08:30:30 loss: 0.1382 Lr: 0.00058 [2024-02-19 09:45:58,627 INFO misc.py line 119 87073] Train: [80/100][11/1557] Data 0.004 (0.006) Batch 0.865 (0.928) Remain 08:25:34 loss: 0.1535 Lr: 0.00058 [2024-02-19 09:45:59,345 INFO misc.py line 119 87073] Train: [80/100][12/1557] Data 0.016 (0.007) Batch 0.730 (0.906) Remain 08:13:34 loss: 0.3767 Lr: 0.00058 [2024-02-19 09:46:00,081 INFO misc.py line 119 87073] Train: [80/100][13/1557] Data 0.004 (0.007) Batch 0.733 (0.889) Remain 08:04:09 loss: 0.1417 Lr: 0.00058 [2024-02-19 09:46:01,761 INFO misc.py line 119 87073] Train: [80/100][14/1557] Data 0.005 (0.007) Batch 1.675 (0.960) Remain 08:43:05 loss: 0.2127 Lr: 0.00058 [2024-02-19 09:46:02,685 INFO misc.py line 119 87073] Train: [80/100][15/1557] Data 0.011 (0.007) Batch 0.931 (0.958) Remain 08:41:44 loss: 0.2826 Lr: 0.00058 [2024-02-19 09:46:03,911 INFO misc.py line 119 87073] Train: [80/100][16/1557] Data 0.004 (0.007) Batch 1.215 (0.978) Remain 08:52:30 loss: 0.0791 Lr: 0.00058 [2024-02-19 09:46:04,906 INFO misc.py line 119 87073] Train: [80/100][17/1557] Data 0.015 (0.007) Batch 0.999 (0.979) Remain 08:53:17 loss: 0.4256 Lr: 0.00058 [2024-02-19 09:46:05,873 INFO misc.py line 119 87073] Train: [80/100][18/1557] Data 0.012 (0.008) Batch 0.973 (0.979) Remain 08:53:03 loss: 0.0969 Lr: 0.00058 [2024-02-19 09:46:06,644 INFO misc.py line 119 87073] Train: [80/100][19/1557] Data 0.005 (0.007) Batch 0.772 (0.966) Remain 08:46:00 loss: 0.1268 Lr: 0.00058 [2024-02-19 09:46:07,452 INFO misc.py line 119 87073] Train: [80/100][20/1557] Data 0.004 (0.007) Batch 0.802 (0.956) Remain 08:40:45 loss: 0.2471 Lr: 0.00058 [2024-02-19 09:46:08,689 INFO misc.py line 119 87073] Train: [80/100][21/1557] Data 0.009 (0.007) Batch 1.231 (0.971) Remain 08:49:03 loss: 0.0947 Lr: 0.00058 [2024-02-19 09:46:09,590 INFO misc.py line 119 87073] Train: [80/100][22/1557] Data 0.015 (0.008) Batch 0.911 (0.968) Remain 08:47:19 loss: 0.2667 Lr: 0.00058 [2024-02-19 09:46:10,439 INFO misc.py line 119 87073] Train: [80/100][23/1557] Data 0.005 (0.008) Batch 0.849 (0.962) Remain 08:44:02 loss: 0.0898 Lr: 0.00058 [2024-02-19 09:46:11,428 INFO misc.py line 119 87073] Train: [80/100][24/1557] Data 0.005 (0.008) Batch 0.982 (0.963) Remain 08:44:32 loss: 0.1287 Lr: 0.00058 [2024-02-19 09:46:12,279 INFO misc.py line 119 87073] Train: [80/100][25/1557] Data 0.012 (0.008) Batch 0.860 (0.959) Remain 08:41:58 loss: 0.1630 Lr: 0.00058 [2024-02-19 09:46:12,978 INFO misc.py line 119 87073] Train: [80/100][26/1557] Data 0.004 (0.008) Batch 0.698 (0.947) Remain 08:35:47 loss: 0.1925 Lr: 0.00058 [2024-02-19 09:46:13,764 INFO misc.py line 119 87073] Train: [80/100][27/1557] Data 0.004 (0.007) Batch 0.786 (0.941) Remain 08:32:07 loss: 0.1815 Lr: 0.00058 [2024-02-19 09:46:14,968 INFO misc.py line 119 87073] Train: [80/100][28/1557] Data 0.003 (0.007) Batch 1.201 (0.951) Remain 08:37:46 loss: 0.2100 Lr: 0.00058 [2024-02-19 09:46:15,798 INFO misc.py line 119 87073] Train: [80/100][29/1557] Data 0.007 (0.007) Batch 0.832 (0.946) Remain 08:35:16 loss: 0.2381 Lr: 0.00058 [2024-02-19 09:46:16,948 INFO misc.py line 119 87073] Train: [80/100][30/1557] Data 0.005 (0.007) Batch 1.151 (0.954) Remain 08:39:22 loss: 0.2855 Lr: 0.00058 [2024-02-19 09:46:17,937 INFO misc.py line 119 87073] Train: [80/100][31/1557] Data 0.004 (0.007) Batch 0.990 (0.955) Remain 08:40:03 loss: 0.2620 Lr: 0.00058 [2024-02-19 09:46:18,924 INFO misc.py line 119 87073] Train: [80/100][32/1557] Data 0.004 (0.007) Batch 0.987 (0.956) Remain 08:40:38 loss: 0.3112 Lr: 0.00058 [2024-02-19 09:46:19,709 INFO misc.py line 119 87073] Train: [80/100][33/1557] Data 0.003 (0.007) Batch 0.783 (0.951) Remain 08:37:28 loss: 0.2458 Lr: 0.00058 [2024-02-19 09:46:20,383 INFO misc.py line 119 87073] Train: [80/100][34/1557] Data 0.006 (0.007) Batch 0.675 (0.942) Remain 08:32:36 loss: 0.2363 Lr: 0.00058 [2024-02-19 09:46:21,698 INFO misc.py line 119 87073] Train: [80/100][35/1557] Data 0.005 (0.007) Batch 1.311 (0.953) Remain 08:38:52 loss: 0.3371 Lr: 0.00058 [2024-02-19 09:46:22,661 INFO misc.py line 119 87073] Train: [80/100][36/1557] Data 0.009 (0.007) Batch 0.969 (0.954) Remain 08:39:07 loss: 0.4637 Lr: 0.00058 [2024-02-19 09:46:23,587 INFO misc.py line 119 87073] Train: [80/100][37/1557] Data 0.004 (0.007) Batch 0.926 (0.953) Remain 08:38:39 loss: 0.2706 Lr: 0.00058 [2024-02-19 09:46:24,617 INFO misc.py line 119 87073] Train: [80/100][38/1557] Data 0.004 (0.007) Batch 1.030 (0.955) Remain 08:39:49 loss: 0.1923 Lr: 0.00058 [2024-02-19 09:46:25,646 INFO misc.py line 119 87073] Train: [80/100][39/1557] Data 0.005 (0.007) Batch 1.028 (0.957) Remain 08:40:55 loss: 0.3473 Lr: 0.00058 [2024-02-19 09:46:26,401 INFO misc.py line 119 87073] Train: [80/100][40/1557] Data 0.004 (0.007) Batch 0.754 (0.952) Remain 08:37:54 loss: 0.1809 Lr: 0.00058 [2024-02-19 09:46:27,166 INFO misc.py line 119 87073] Train: [80/100][41/1557] Data 0.006 (0.007) Batch 0.767 (0.947) Remain 08:35:15 loss: 0.1707 Lr: 0.00058 [2024-02-19 09:46:28,328 INFO misc.py line 119 87073] Train: [80/100][42/1557] Data 0.004 (0.006) Batch 1.163 (0.952) Remain 08:38:15 loss: 0.1727 Lr: 0.00058 [2024-02-19 09:46:29,462 INFO misc.py line 119 87073] Train: [80/100][43/1557] Data 0.005 (0.006) Batch 1.131 (0.957) Remain 08:40:39 loss: 0.5353 Lr: 0.00058 [2024-02-19 09:46:30,417 INFO misc.py line 119 87073] Train: [80/100][44/1557] Data 0.007 (0.006) Batch 0.957 (0.957) Remain 08:40:39 loss: 0.2786 Lr: 0.00058 [2024-02-19 09:46:31,390 INFO misc.py line 119 87073] Train: [80/100][45/1557] Data 0.005 (0.006) Batch 0.974 (0.957) Remain 08:40:51 loss: 0.1882 Lr: 0.00058 [2024-02-19 09:46:32,288 INFO misc.py line 119 87073] Train: [80/100][46/1557] Data 0.004 (0.006) 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[2024-02-19 10:14:54,002 INFO misc.py line 119 87073] Train: [80/100][1526/1557] Data 0.011 (0.095) Batch 3.619 (1.144) Remain 09:54:29 loss: 0.1171 Lr: 0.00053 [2024-02-19 10:14:54,990 INFO misc.py line 119 87073] Train: [80/100][1527/1557] Data 0.003 (0.094) Batch 0.987 (1.144) Remain 09:54:25 loss: 0.2324 Lr: 0.00053 [2024-02-19 10:14:55,937 INFO misc.py line 119 87073] Train: [80/100][1528/1557] Data 0.004 (0.094) Batch 0.948 (1.144) Remain 09:54:20 loss: 0.1733 Lr: 0.00053 [2024-02-19 10:14:56,873 INFO misc.py line 119 87073] Train: [80/100][1529/1557] Data 0.003 (0.094) Batch 0.933 (1.144) Remain 09:54:14 loss: 0.1975 Lr: 0.00053 [2024-02-19 10:14:57,824 INFO misc.py line 119 87073] Train: [80/100][1530/1557] Data 0.006 (0.094) Batch 0.953 (1.144) Remain 09:54:09 loss: 0.1584 Lr: 0.00053 [2024-02-19 10:14:58,584 INFO misc.py line 119 87073] Train: [80/100][1531/1557] Data 0.004 (0.094) Batch 0.761 (1.144) Remain 09:54:00 loss: 0.2211 Lr: 0.00053 [2024-02-19 10:14:59,321 INFO misc.py line 119 87073] Train: [80/100][1532/1557] Data 0.004 (0.094) Batch 0.727 (1.143) Remain 09:53:51 loss: 0.2205 Lr: 0.00053 [2024-02-19 10:15:00,605 INFO misc.py line 119 87073] Train: [80/100][1533/1557] Data 0.013 (0.094) Batch 1.286 (1.143) Remain 09:53:52 loss: 0.0945 Lr: 0.00053 [2024-02-19 10:15:01,620 INFO misc.py line 119 87073] Train: [80/100][1534/1557] Data 0.012 (0.094) Batch 1.015 (1.143) Remain 09:53:49 loss: 0.4478 Lr: 0.00053 [2024-02-19 10:15:02,701 INFO misc.py line 119 87073] Train: [80/100][1535/1557] Data 0.011 (0.094) Batch 1.077 (1.143) Remain 09:53:46 loss: 0.1693 Lr: 0.00053 [2024-02-19 10:15:03,563 INFO misc.py line 119 87073] Train: [80/100][1536/1557] Data 0.015 (0.094) Batch 0.874 (1.143) Remain 09:53:40 loss: 0.4501 Lr: 0.00053 [2024-02-19 10:15:04,397 INFO misc.py line 119 87073] Train: [80/100][1537/1557] Data 0.004 (0.094) Batch 0.833 (1.143) Remain 09:53:32 loss: 0.2212 Lr: 0.00053 [2024-02-19 10:15:05,152 INFO misc.py line 119 87073] Train: [80/100][1538/1557] Data 0.003 (0.094) Batch 0.750 (1.143) Remain 09:53:23 loss: 0.4105 Lr: 0.00053 [2024-02-19 10:15:05,875 INFO misc.py line 119 87073] Train: [80/100][1539/1557] Data 0.009 (0.094) Batch 0.729 (1.142) Remain 09:53:13 loss: 0.1807 Lr: 0.00053 [2024-02-19 10:15:07,015 INFO misc.py line 119 87073] Train: [80/100][1540/1557] Data 0.003 (0.094) Batch 1.140 (1.142) Remain 09:53:12 loss: 0.3123 Lr: 0.00053 [2024-02-19 10:15:08,023 INFO misc.py line 119 87073] Train: [80/100][1541/1557] Data 0.003 (0.094) Batch 1.007 (1.142) Remain 09:53:08 loss: 0.0567 Lr: 0.00053 [2024-02-19 10:15:08,909 INFO misc.py line 119 87073] Train: [80/100][1542/1557] Data 0.003 (0.094) Batch 0.886 (1.142) Remain 09:53:02 loss: 0.1422 Lr: 0.00053 [2024-02-19 10:15:09,841 INFO misc.py line 119 87073] Train: [80/100][1543/1557] Data 0.003 (0.094) Batch 0.922 (1.142) Remain 09:52:57 loss: 0.3605 Lr: 0.00053 [2024-02-19 10:15:10,665 INFO misc.py line 119 87073] Train: [80/100][1544/1557] Data 0.014 (0.094) Batch 0.834 (1.142) Remain 09:52:49 loss: 0.0899 Lr: 0.00053 [2024-02-19 10:15:11,379 INFO misc.py line 119 87073] Train: [80/100][1545/1557] Data 0.004 (0.093) Batch 0.714 (1.141) Remain 09:52:39 loss: 0.1118 Lr: 0.00053 [2024-02-19 10:15:12,131 INFO misc.py line 119 87073] Train: [80/100][1546/1557] Data 0.003 (0.093) Batch 0.746 (1.141) Remain 09:52:30 loss: 0.1289 Lr: 0.00053 [2024-02-19 10:15:13,400 INFO misc.py line 119 87073] Train: [80/100][1547/1557] Data 0.009 (0.093) Batch 1.266 (1.141) Remain 09:52:32 loss: 0.1439 Lr: 0.00053 [2024-02-19 10:15:14,446 INFO misc.py line 119 87073] Train: [80/100][1548/1557] Data 0.012 (0.093) Batch 1.055 (1.141) Remain 09:52:29 loss: 0.2194 Lr: 0.00053 [2024-02-19 10:15:15,334 INFO misc.py line 119 87073] Train: [80/100][1549/1557] Data 0.004 (0.093) Batch 0.888 (1.141) Remain 09:52:22 loss: 0.1303 Lr: 0.00053 [2024-02-19 10:15:16,353 INFO misc.py line 119 87073] Train: [80/100][1550/1557] Data 0.005 (0.093) Batch 1.019 (1.141) Remain 09:52:19 loss: 0.3430 Lr: 0.00053 [2024-02-19 10:15:17,221 INFO misc.py line 119 87073] Train: [80/100][1551/1557] Data 0.004 (0.093) Batch 0.869 (1.141) Remain 09:52:12 loss: 0.1841 Lr: 0.00053 [2024-02-19 10:15:18,000 INFO misc.py line 119 87073] Train: [80/100][1552/1557] Data 0.004 (0.093) Batch 0.770 (1.141) Remain 09:52:04 loss: 0.3142 Lr: 0.00053 [2024-02-19 10:15:18,706 INFO misc.py line 119 87073] Train: [80/100][1553/1557] Data 0.012 (0.093) Batch 0.715 (1.140) Remain 09:51:54 loss: 0.1576 Lr: 0.00053 [2024-02-19 10:15:19,881 INFO misc.py line 119 87073] Train: [80/100][1554/1557] Data 0.003 (0.093) Batch 1.175 (1.140) Remain 09:51:54 loss: 0.1182 Lr: 0.00053 [2024-02-19 10:15:20,744 INFO misc.py line 119 87073] Train: [80/100][1555/1557] Data 0.003 (0.093) Batch 0.863 (1.140) Remain 09:51:47 loss: 0.1140 Lr: 0.00053 [2024-02-19 10:15:21,695 INFO misc.py line 119 87073] Train: [80/100][1556/1557] Data 0.003 (0.093) Batch 0.943 (1.140) Remain 09:51:42 loss: 0.3045 Lr: 0.00053 [2024-02-19 10:15:22,619 INFO misc.py line 119 87073] Train: [80/100][1557/1557] Data 0.011 (0.093) Batch 0.931 (1.140) Remain 09:51:36 loss: 0.3805 Lr: 0.00053 [2024-02-19 10:15:22,619 INFO misc.py line 136 87073] Train result: loss: 0.2266 [2024-02-19 10:15:22,620 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 10:15:51,466 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.3794 [2024-02-19 10:15:52,243 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3920 [2024-02-19 10:15:54,369 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3144 [2024-02-19 10:15:56,579 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2716 [2024-02-19 10:16:01,517 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2292 [2024-02-19 10:16:02,223 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0779 [2024-02-19 10:16:03,483 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2848 [2024-02-19 10:16:06,437 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2513 [2024-02-19 10:16:08,245 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2414 [2024-02-19 10:16:09,625 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7307/0.7892/0.9173. [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9265/0.9612 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9825/0.9898 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8705/0.9702 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0001/0.0005 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4492/0.5043 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6831/0.7052 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8182/0.9272 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8509/0.9058 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9329/0.9719 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8427/0.8735 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7887/0.8859 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7426/0.8545 [2024-02-19 10:16:09,625 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6111/0.7091 [2024-02-19 10:16:09,626 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 10:16:09,627 INFO misc.py line 165 87073] Currently Best mIoU: 0.7361 [2024-02-19 10:16:09,627 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 10:16:16,169 INFO misc.py line 119 87073] Train: [81/100][1/1557] Data 1.159 (1.159) Batch 1.837 (1.837) Remain 15:53:18 loss: 0.4591 Lr: 0.00053 [2024-02-19 10:16:17,048 INFO misc.py line 119 87073] Train: [81/100][2/1557] Data 0.054 (0.054) Batch 0.883 (0.883) Remain 07:38:01 loss: 0.1145 Lr: 0.00053 [2024-02-19 10:16:17,976 INFO misc.py line 119 87073] Train: [81/100][3/1557] Data 0.005 (0.005) Batch 0.927 (0.927) Remain 08:01:03 loss: 0.1657 Lr: 0.00053 [2024-02-19 10:16:18,976 INFO misc.py line 119 87073] Train: [81/100][4/1557] Data 0.007 (0.007) Batch 1.001 (1.001) Remain 08:39:29 loss: 0.2436 Lr: 0.00053 [2024-02-19 10:16:19,762 INFO misc.py line 119 87073] Train: [81/100][5/1557] Data 0.008 (0.007) Batch 0.786 (0.894) Remain 07:43:41 loss: 0.2846 Lr: 0.00053 [2024-02-19 10:16:20,486 INFO misc.py line 119 87073] Train: [81/100][6/1557] Data 0.005 (0.007) Batch 0.723 (0.837) Remain 07:14:14 loss: 0.1837 Lr: 0.00053 [2024-02-19 10:16:25,980 INFO misc.py line 119 87073] Train: [81/100][7/1557] Data 0.005 (0.006) Batch 5.496 (2.002) Remain 17:18:38 loss: 0.1178 Lr: 0.00053 [2024-02-19 10:16:26,931 INFO misc.py line 119 87073] Train: [81/100][8/1557] Data 0.003 (0.006) Batch 0.948 (1.791) Remain 15:29:17 loss: 0.3769 Lr: 0.00053 [2024-02-19 10:16:27,881 INFO misc.py line 119 87073] Train: [81/100][9/1557] Data 0.006 (0.006) Batch 0.951 (1.651) Remain 14:16:37 loss: 0.1950 Lr: 0.00053 [2024-02-19 10:16:28,785 INFO misc.py line 119 87073] Train: [81/100][10/1557] Data 0.005 (0.005) Batch 0.901 (1.544) Remain 13:21:02 loss: 0.2654 Lr: 0.00053 [2024-02-19 10:16:29,830 INFO misc.py line 119 87073] Train: [81/100][11/1557] Data 0.008 (0.006) Batch 1.038 (1.481) Remain 12:48:11 loss: 0.3924 Lr: 0.00053 [2024-02-19 10:16:30,626 INFO misc.py line 119 87073] Train: [81/100][12/1557] Data 0.015 (0.007) Batch 0.807 (1.406) Remain 12:09:19 loss: 0.3181 Lr: 0.00053 [2024-02-19 10:16:31,382 INFO misc.py line 119 87073] Train: [81/100][13/1557] Data 0.004 (0.006) Batch 0.756 (1.341) Remain 11:35:34 loss: 0.1362 Lr: 0.00053 [2024-02-19 10:16:32,592 INFO misc.py line 119 87073] Train: [81/100][14/1557] Data 0.004 (0.006) Batch 1.199 (1.328) Remain 11:28:53 loss: 0.1558 Lr: 0.00053 [2024-02-19 10:16:33,496 INFO misc.py line 119 87073] Train: [81/100][15/1557] Data 0.014 (0.007) Batch 0.914 (1.293) Remain 11:10:58 loss: 0.3675 Lr: 0.00053 [2024-02-19 10:16:34,299 INFO misc.py line 119 87073] Train: [81/100][16/1557] Data 0.004 (0.007) Batch 0.803 (1.256) Remain 10:51:22 loss: 0.5633 Lr: 0.00053 [2024-02-19 10:16:35,275 INFO misc.py line 119 87073] Train: [81/100][17/1557] Data 0.005 (0.006) Batch 0.968 (1.235) Remain 10:40:40 loss: 0.4364 Lr: 0.00053 [2024-02-19 10:16:36,405 INFO misc.py line 119 87073] Train: [81/100][18/1557] Data 0.013 (0.007) Batch 1.136 (1.229) Remain 10:37:13 loss: 0.1627 Lr: 0.00053 [2024-02-19 10:16:37,187 INFO misc.py line 119 87073] Train: [81/100][19/1557] Data 0.007 (0.007) Batch 0.781 (1.201) Remain 10:22:42 loss: 0.2289 Lr: 0.00053 [2024-02-19 10:16:37,988 INFO misc.py line 119 87073] Train: [81/100][20/1557] Data 0.008 (0.007) Batch 0.804 (1.177) Remain 10:10:34 loss: 0.1701 Lr: 0.00053 [2024-02-19 10:16:39,187 INFO misc.py line 119 87073] Train: [81/100][21/1557] Data 0.005 (0.007) Batch 1.191 (1.178) Remain 10:10:57 loss: 0.1977 Lr: 0.00053 [2024-02-19 10:16:40,210 INFO misc.py line 119 87073] Train: [81/100][22/1557] Data 0.014 (0.007) Batch 1.021 (1.170) Remain 10:06:39 loss: 0.3382 Lr: 0.00053 [2024-02-19 10:16:41,213 INFO misc.py line 119 87073] Train: [81/100][23/1557] Data 0.014 (0.008) Batch 1.012 (1.162) Remain 10:02:33 loss: 0.1188 Lr: 0.00053 [2024-02-19 10:16:42,226 INFO misc.py line 119 87073] Train: [81/100][24/1557] Data 0.005 (0.007) Batch 1.005 (1.154) Remain 09:58:39 loss: 0.3682 Lr: 0.00053 [2024-02-19 10:16:43,385 INFO misc.py line 119 87073] Train: [81/100][25/1557] Data 0.014 (0.008) Batch 1.158 (1.155) Remain 09:58:43 loss: 0.4605 Lr: 0.00053 [2024-02-19 10:16:44,105 INFO misc.py line 119 87073] Train: [81/100][26/1557] Data 0.015 (0.008) Batch 0.731 (1.136) Remain 09:49:09 loss: 0.1511 Lr: 0.00053 [2024-02-19 10:16:44,874 INFO misc.py line 119 87073] Train: [81/100][27/1557] Data 0.003 (0.008) Batch 0.761 (1.120) Remain 09:41:02 loss: 0.2821 Lr: 0.00053 [2024-02-19 10:16:46,169 INFO misc.py line 119 87073] Train: [81/100][28/1557] Data 0.011 (0.008) Batch 1.286 (1.127) Remain 09:44:26 loss: 0.0951 Lr: 0.00053 [2024-02-19 10:16:47,175 INFO misc.py line 119 87073] Train: [81/100][29/1557] Data 0.022 (0.009) Batch 1.016 (1.123) Remain 09:42:12 loss: 0.3685 Lr: 0.00053 [2024-02-19 10:16:48,037 INFO misc.py line 119 87073] Train: [81/100][30/1557] Data 0.011 (0.009) Batch 0.869 (1.113) Remain 09:37:18 loss: 0.2819 Lr: 0.00053 [2024-02-19 10:16:48,892 INFO misc.py line 119 87073] Train: [81/100][31/1557] Data 0.005 (0.008) Batch 0.856 (1.104) Remain 09:32:31 loss: 0.2439 Lr: 0.00053 [2024-02-19 10:16:49,765 INFO misc.py line 119 87073] Train: [81/100][32/1557] Data 0.003 (0.008) Batch 0.870 (1.096) Remain 09:28:19 loss: 0.3460 Lr: 0.00053 [2024-02-19 10:16:50,513 INFO misc.py line 119 87073] Train: [81/100][33/1557] Data 0.006 (0.008) Batch 0.750 (1.085) Remain 09:22:19 loss: 0.1456 Lr: 0.00053 [2024-02-19 10:16:51,278 INFO misc.py line 119 87073] Train: [81/100][34/1557] Data 0.004 (0.008) Batch 0.760 (1.074) Remain 09:16:52 loss: 0.1484 Lr: 0.00053 [2024-02-19 10:16:52,479 INFO misc.py line 119 87073] Train: [81/100][35/1557] Data 0.008 (0.008) Batch 1.192 (1.078) Remain 09:18:46 loss: 0.1202 Lr: 0.00053 [2024-02-19 10:16:53,377 INFO misc.py line 119 87073] Train: [81/100][36/1557] Data 0.018 (0.008) Batch 0.910 (1.073) Remain 09:16:06 loss: 0.1219 Lr: 0.00053 [2024-02-19 10:16:54,516 INFO misc.py line 119 87073] Train: [81/100][37/1557] Data 0.006 (0.008) Batch 1.141 (1.075) Remain 09:17:08 loss: 0.0550 Lr: 0.00053 [2024-02-19 10:16:55,570 INFO misc.py line 119 87073] Train: [81/100][38/1557] Data 0.004 (0.008) Batch 1.051 (1.074) Remain 09:16:45 loss: 0.2368 Lr: 0.00053 [2024-02-19 10:16:56,344 INFO misc.py line 119 87073] Train: [81/100][39/1557] Data 0.007 (0.008) Batch 0.777 (1.066) Remain 09:12:27 loss: 0.1378 Lr: 0.00053 [2024-02-19 10:16:57,122 INFO misc.py line 119 87073] Train: [81/100][40/1557] Data 0.005 (0.008) Batch 0.778 (1.058) Remain 09:08:24 loss: 0.1243 Lr: 0.00053 [2024-02-19 10:16:57,903 INFO misc.py line 119 87073] Train: [81/100][41/1557] Data 0.005 (0.008) Batch 0.782 (1.051) Remain 09:04:37 loss: 0.2379 Lr: 0.00053 [2024-02-19 10:16:59,232 INFO misc.py line 119 87073] Train: [81/100][42/1557] Data 0.004 (0.008) Batch 1.319 (1.058) Remain 09:08:10 loss: 0.1844 Lr: 0.00053 [2024-02-19 10:17:00,177 INFO misc.py line 119 87073] Train: [81/100][43/1557] Data 0.014 (0.008) Batch 0.955 (1.055) Remain 09:06:49 loss: 0.2036 Lr: 0.00053 [2024-02-19 10:17:01,129 INFO misc.py line 119 87073] Train: [81/100][44/1557] Data 0.004 (0.008) Batch 0.952 (1.053) Remain 09:05:30 loss: 0.2227 Lr: 0.00053 [2024-02-19 10:17:02,082 INFO misc.py line 119 87073] Train: [81/100][45/1557] Data 0.004 (0.008) Batch 0.953 (1.050) Remain 09:04:15 loss: 0.1360 Lr: 0.00053 [2024-02-19 10:17:02,997 INFO misc.py line 119 87073] Train: [81/100][46/1557] Data 0.004 (0.008) Batch 0.905 (1.047) Remain 09:02:29 loss: 0.2432 Lr: 0.00053 [2024-02-19 10:17:03,780 INFO misc.py line 119 87073] Train: [81/100][47/1557] Data 0.015 (0.008) Batch 0.792 (1.041) Remain 08:59:28 loss: 0.1615 Lr: 0.00053 [2024-02-19 10:17:04,491 INFO misc.py line 119 87073] Train: [81/100][48/1557] Data 0.005 (0.008) Batch 0.711 (1.034) Remain 08:55:39 loss: 0.1644 Lr: 0.00053 [2024-02-19 10:17:05,707 INFO misc.py line 119 87073] Train: [81/100][49/1557] Data 0.005 (0.008) Batch 1.205 (1.037) Remain 08:57:34 loss: 0.1317 Lr: 0.00053 [2024-02-19 10:17:06,648 INFO misc.py line 119 87073] Train: [81/100][50/1557] Data 0.015 (0.008) Batch 0.952 (1.036) Remain 08:56:37 loss: 0.7051 Lr: 0.00053 [2024-02-19 10:17:07,717 INFO misc.py line 119 87073] Train: [81/100][51/1557] Data 0.004 (0.008) Batch 1.067 (1.036) Remain 08:56:56 loss: 0.1359 Lr: 0.00053 [2024-02-19 10:17:08,616 INFO misc.py line 119 87073] Train: [81/100][52/1557] Data 0.006 (0.008) Batch 0.901 (1.034) Remain 08:55:29 loss: 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Remain 09:48:32 loss: 0.1909 Lr: 0.00049 [2024-02-19 10:40:39,752 INFO misc.py line 119 87073] Train: [81/100][1241/1557] Data 0.005 (0.081) Batch 0.856 (1.181) Remain 09:48:23 loss: 0.3178 Lr: 0.00049 [2024-02-19 10:40:40,774 INFO misc.py line 119 87073] Train: [81/100][1242/1557] Data 0.005 (0.081) Batch 1.022 (1.181) Remain 09:48:18 loss: 0.3902 Lr: 0.00049 [2024-02-19 10:40:41,653 INFO misc.py line 119 87073] Train: [81/100][1243/1557] Data 0.005 (0.081) Batch 0.879 (1.180) Remain 09:48:09 loss: 0.0618 Lr: 0.00049 [2024-02-19 10:40:42,433 INFO misc.py line 119 87073] Train: [81/100][1244/1557] Data 0.005 (0.081) Batch 0.780 (1.180) Remain 09:47:59 loss: 0.1923 Lr: 0.00049 [2024-02-19 10:40:43,281 INFO misc.py line 119 87073] Train: [81/100][1245/1557] Data 0.005 (0.081) Batch 0.848 (1.180) Remain 09:47:49 loss: 0.1327 Lr: 0.00049 [2024-02-19 10:40:44,565 INFO misc.py line 119 87073] Train: [81/100][1246/1557] Data 0.005 (0.081) Batch 1.282 (1.180) Remain 09:47:51 loss: 0.3202 Lr: 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Train: [81/100][1259/1557] Data 0.006 (0.080) Batch 0.848 (1.177) Remain 09:46:15 loss: 0.1811 Lr: 0.00049 [2024-02-19 10:40:57,826 INFO misc.py line 119 87073] Train: [81/100][1260/1557] Data 0.005 (0.080) Batch 1.304 (1.177) Remain 09:46:17 loss: 0.1243 Lr: 0.00049 [2024-02-19 10:40:58,838 INFO misc.py line 119 87073] Train: [81/100][1261/1557] Data 0.011 (0.080) Batch 1.009 (1.177) Remain 09:46:11 loss: 0.1553 Lr: 0.00049 [2024-02-19 10:40:59,771 INFO misc.py line 119 87073] Train: [81/100][1262/1557] Data 0.017 (0.080) Batch 0.943 (1.177) Remain 09:46:05 loss: 0.2479 Lr: 0.00049 [2024-02-19 10:41:00,786 INFO misc.py line 119 87073] Train: [81/100][1263/1557] Data 0.004 (0.080) Batch 1.014 (1.177) Remain 09:46:00 loss: 0.2689 Lr: 0.00049 [2024-02-19 10:41:01,684 INFO misc.py line 119 87073] Train: [81/100][1264/1557] Data 0.005 (0.080) Batch 0.899 (1.177) Remain 09:45:52 loss: 0.1748 Lr: 0.00049 [2024-02-19 10:41:02,451 INFO misc.py line 119 87073] Train: [81/100][1265/1557] Data 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Remain 09:45:04 loss: 0.5539 Lr: 0.00049 [2024-02-19 10:41:09,027 INFO misc.py line 119 87073] Train: [81/100][1272/1557] Data 0.005 (0.079) Batch 0.796 (1.175) Remain 09:44:54 loss: 0.1505 Lr: 0.00049 [2024-02-19 10:41:09,806 INFO misc.py line 119 87073] Train: [81/100][1273/1557] Data 0.007 (0.079) Batch 0.779 (1.175) Remain 09:44:43 loss: 0.1525 Lr: 0.00049 [2024-02-19 10:41:11,113 INFO misc.py line 119 87073] Train: [81/100][1274/1557] Data 0.006 (0.079) Batch 1.305 (1.175) Remain 09:44:45 loss: 0.2034 Lr: 0.00049 [2024-02-19 10:41:11,979 INFO misc.py line 119 87073] Train: [81/100][1275/1557] Data 0.008 (0.079) Batch 0.871 (1.175) Remain 09:44:37 loss: 0.4539 Lr: 0.00049 [2024-02-19 10:41:12,857 INFO misc.py line 119 87073] Train: [81/100][1276/1557] Data 0.003 (0.079) Batch 0.876 (1.174) Remain 09:44:29 loss: 0.1508 Lr: 0.00049 [2024-02-19 10:41:13,785 INFO misc.py line 119 87073] Train: [81/100][1277/1557] Data 0.006 (0.079) Batch 0.928 (1.174) Remain 09:44:22 loss: 0.2634 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1284/1557] Data 0.004 (0.079) Batch 0.969 (1.173) Remain 09:43:32 loss: 0.3247 Lr: 0.00049 [2024-02-19 10:41:21,203 INFO misc.py line 119 87073] Train: [81/100][1285/1557] Data 0.004 (0.079) Batch 0.978 (1.173) Remain 09:43:26 loss: 0.1862 Lr: 0.00049 [2024-02-19 10:41:22,041 INFO misc.py line 119 87073] Train: [81/100][1286/1557] Data 0.003 (0.079) Batch 0.837 (1.172) Remain 09:43:17 loss: 0.2452 Lr: 0.00049 [2024-02-19 10:41:22,784 INFO misc.py line 119 87073] Train: [81/100][1287/1557] Data 0.004 (0.078) Batch 0.743 (1.172) Remain 09:43:06 loss: 0.2536 Lr: 0.00049 [2024-02-19 10:41:24,077 INFO misc.py line 119 87073] Train: [81/100][1288/1557] Data 0.005 (0.078) Batch 1.289 (1.172) Remain 09:43:08 loss: 0.1071 Lr: 0.00049 [2024-02-19 10:41:24,987 INFO misc.py line 119 87073] Train: [81/100][1289/1557] Data 0.008 (0.078) Batch 0.914 (1.172) Remain 09:43:01 loss: 0.3276 Lr: 0.00049 [2024-02-19 10:41:25,903 INFO misc.py line 119 87073] Train: [81/100][1290/1557] Data 0.004 (0.078) Batch 0.916 (1.172) Remain 09:42:54 loss: 0.3175 Lr: 0.00049 [2024-02-19 10:41:26,808 INFO misc.py line 119 87073] Train: [81/100][1291/1557] Data 0.004 (0.078) Batch 0.901 (1.171) Remain 09:42:46 loss: 0.1064 Lr: 0.00049 [2024-02-19 10:41:27,669 INFO misc.py line 119 87073] Train: [81/100][1292/1557] Data 0.007 (0.078) Batch 0.864 (1.171) Remain 09:42:38 loss: 0.1755 Lr: 0.00049 [2024-02-19 10:41:28,455 INFO misc.py line 119 87073] Train: [81/100][1293/1557] Data 0.004 (0.078) Batch 0.783 (1.171) Remain 09:42:28 loss: 0.2584 Lr: 0.00049 [2024-02-19 10:41:29,165 INFO misc.py line 119 87073] Train: [81/100][1294/1557] Data 0.007 (0.078) Batch 0.705 (1.171) Remain 09:42:16 loss: 0.1737 Lr: 0.00049 [2024-02-19 10:41:41,444 INFO misc.py line 119 87073] Train: [81/100][1295/1557] Data 3.966 (0.081) Batch 12.287 (1.179) Remain 09:46:31 loss: 0.1039 Lr: 0.00049 [2024-02-19 10:41:42,495 INFO misc.py line 119 87073] Train: [81/100][1296/1557] Data 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Remain 09:45:50 loss: 0.1485 Lr: 0.00049 [2024-02-19 10:41:49,082 INFO misc.py line 119 87073] Train: [81/100][1303/1557] Data 0.005 (0.081) Batch 0.804 (1.178) Remain 09:45:41 loss: 0.2544 Lr: 0.00049 [2024-02-19 10:41:50,108 INFO misc.py line 119 87073] Train: [81/100][1304/1557] Data 0.006 (0.081) Batch 1.029 (1.178) Remain 09:45:36 loss: 0.3676 Lr: 0.00049 [2024-02-19 10:41:51,182 INFO misc.py line 119 87073] Train: [81/100][1305/1557] Data 0.004 (0.081) Batch 1.074 (1.178) Remain 09:45:33 loss: 0.2468 Lr: 0.00049 [2024-02-19 10:41:52,135 INFO misc.py line 119 87073] Train: [81/100][1306/1557] Data 0.003 (0.080) Batch 0.953 (1.177) Remain 09:45:26 loss: 0.4136 Lr: 0.00049 [2024-02-19 10:41:52,852 INFO misc.py line 119 87073] Train: [81/100][1307/1557] Data 0.005 (0.080) Batch 0.716 (1.177) Remain 09:45:14 loss: 0.3415 Lr: 0.00049 [2024-02-19 10:41:53,595 INFO misc.py line 119 87073] Train: [81/100][1308/1557] Data 0.005 (0.080) Batch 0.744 (1.177) Remain 09:45:03 loss: 0.1941 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1315/1557] Data 0.004 (0.080) Batch 0.777 (1.176) Remain 09:44:23 loss: 0.2311 Lr: 0.00049 [2024-02-19 10:42:01,686 INFO misc.py line 119 87073] Train: [81/100][1316/1557] Data 0.004 (0.080) Batch 1.251 (1.176) Remain 09:44:24 loss: 0.0981 Lr: 0.00049 [2024-02-19 10:42:02,629 INFO misc.py line 119 87073] Train: [81/100][1317/1557] Data 0.015 (0.080) Batch 0.954 (1.176) Remain 09:44:17 loss: 0.3177 Lr: 0.00049 [2024-02-19 10:42:03,577 INFO misc.py line 119 87073] Train: [81/100][1318/1557] Data 0.005 (0.080) Batch 0.949 (1.175) Remain 09:44:11 loss: 0.3264 Lr: 0.00049 [2024-02-19 10:42:04,571 INFO misc.py line 119 87073] Train: [81/100][1319/1557] Data 0.004 (0.080) Batch 0.993 (1.175) Remain 09:44:06 loss: 0.0409 Lr: 0.00049 [2024-02-19 10:42:05,396 INFO misc.py line 119 87073] Train: [81/100][1320/1557] Data 0.005 (0.080) Batch 0.826 (1.175) Remain 09:43:57 loss: 0.1381 Lr: 0.00049 [2024-02-19 10:42:06,160 INFO misc.py line 119 87073] Train: [81/100][1321/1557] Data 0.004 (0.080) Batch 0.755 (1.175) Remain 09:43:46 loss: 0.1307 Lr: 0.00049 [2024-02-19 10:42:06,939 INFO misc.py line 119 87073] Train: [81/100][1322/1557] Data 0.013 (0.080) Batch 0.788 (1.174) Remain 09:43:36 loss: 0.1337 Lr: 0.00049 [2024-02-19 10:42:08,084 INFO misc.py line 119 87073] Train: [81/100][1323/1557] Data 0.004 (0.079) Batch 1.145 (1.174) Remain 09:43:34 loss: 0.1579 Lr: 0.00049 [2024-02-19 10:42:09,041 INFO misc.py line 119 87073] Train: [81/100][1324/1557] Data 0.004 (0.079) Batch 0.958 (1.174) Remain 09:43:28 loss: 0.6606 Lr: 0.00049 [2024-02-19 10:42:10,124 INFO misc.py line 119 87073] Train: [81/100][1325/1557] Data 0.004 (0.079) Batch 1.082 (1.174) Remain 09:43:25 loss: 0.3412 Lr: 0.00049 [2024-02-19 10:42:11,155 INFO misc.py line 119 87073] Train: [81/100][1326/1557] Data 0.005 (0.079) Batch 1.031 (1.174) Remain 09:43:21 loss: 0.5159 Lr: 0.00049 [2024-02-19 10:42:12,211 INFO misc.py line 119 87073] Train: [81/100][1327/1557] Data 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Remain 09:42:42 loss: 0.4199 Lr: 0.00049 [2024-02-19 10:42:18,853 INFO misc.py line 119 87073] Train: [81/100][1334/1557] Data 0.004 (0.079) Batch 0.850 (1.173) Remain 09:42:33 loss: 0.2011 Lr: 0.00049 [2024-02-19 10:42:19,627 INFO misc.py line 119 87073] Train: [81/100][1335/1557] Data 0.005 (0.079) Batch 0.772 (1.172) Remain 09:42:23 loss: 0.1038 Lr: 0.00049 [2024-02-19 10:42:20,407 INFO misc.py line 119 87073] Train: [81/100][1336/1557] Data 0.007 (0.079) Batch 0.781 (1.172) Remain 09:42:13 loss: 0.1936 Lr: 0.00049 [2024-02-19 10:42:21,469 INFO misc.py line 119 87073] Train: [81/100][1337/1557] Data 0.005 (0.079) Batch 1.063 (1.172) Remain 09:42:10 loss: 0.0771 Lr: 0.00049 [2024-02-19 10:42:22,382 INFO misc.py line 119 87073] Train: [81/100][1338/1557] Data 0.004 (0.079) Batch 0.913 (1.172) Remain 09:42:03 loss: 0.2444 Lr: 0.00049 [2024-02-19 10:42:23,282 INFO misc.py line 119 87073] Train: [81/100][1339/1557] Data 0.005 (0.079) Batch 0.890 (1.172) Remain 09:41:55 loss: 0.4750 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1346/1557] Data 0.010 (0.078) Batch 1.104 (1.171) Remain 09:41:23 loss: 0.1989 Lr: 0.00049 [2024-02-19 10:42:31,277 INFO misc.py line 119 87073] Train: [81/100][1347/1557] Data 0.005 (0.078) Batch 0.901 (1.171) Remain 09:41:15 loss: 0.1179 Lr: 0.00049 [2024-02-19 10:42:32,120 INFO misc.py line 119 87073] Train: [81/100][1348/1557] Data 0.004 (0.078) Batch 0.844 (1.170) Remain 09:41:07 loss: 0.4883 Lr: 0.00049 [2024-02-19 10:42:32,883 INFO misc.py line 119 87073] Train: [81/100][1349/1557] Data 0.004 (0.078) Batch 0.753 (1.170) Remain 09:40:57 loss: 0.1855 Lr: 0.00049 [2024-02-19 10:42:33,619 INFO misc.py line 119 87073] Train: [81/100][1350/1557] Data 0.013 (0.078) Batch 0.745 (1.170) Remain 09:40:46 loss: 0.1557 Lr: 0.00049 [2024-02-19 10:42:48,209 INFO misc.py line 119 87073] Train: [81/100][1351/1557] Data 4.050 (0.081) Batch 14.591 (1.180) Remain 09:45:42 loss: 0.1350 Lr: 0.00049 [2024-02-19 10:42:49,375 INFO misc.py line 119 87073] Train: [81/100][1352/1557] Data 0.004 (0.081) Batch 1.161 (1.180) Remain 09:45:40 loss: 0.2660 Lr: 0.00049 [2024-02-19 10:42:50,367 INFO misc.py line 119 87073] Train: [81/100][1353/1557] Data 0.009 (0.081) Batch 0.997 (1.180) Remain 09:45:35 loss: 0.1183 Lr: 0.00049 [2024-02-19 10:42:51,139 INFO misc.py line 119 87073] Train: [81/100][1354/1557] Data 0.004 (0.081) Batch 0.771 (1.179) Remain 09:45:25 loss: 0.2359 Lr: 0.00049 [2024-02-19 10:42:52,297 INFO misc.py line 119 87073] Train: [81/100][1355/1557] Data 0.005 (0.081) Batch 1.158 (1.179) Remain 09:45:23 loss: 0.1605 Lr: 0.00049 [2024-02-19 10:42:53,108 INFO misc.py line 119 87073] Train: [81/100][1356/1557] Data 0.004 (0.081) Batch 0.811 (1.179) Remain 09:45:14 loss: 0.1928 Lr: 0.00049 [2024-02-19 10:42:53,888 INFO misc.py line 119 87073] Train: [81/100][1357/1557] Data 0.004 (0.081) Batch 0.780 (1.179) Remain 09:45:04 loss: 0.1472 Lr: 0.00049 [2024-02-19 10:42:55,239 INFO misc.py line 119 87073] Train: [81/100][1358/1557] Data 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Remain 09:44:18 loss: 0.2806 Lr: 0.00049 [2024-02-19 10:43:01,707 INFO misc.py line 119 87073] Train: [81/100][1365/1557] Data 0.004 (0.080) Batch 1.282 (1.177) Remain 09:44:19 loss: 0.1963 Lr: 0.00049 [2024-02-19 10:43:02,681 INFO misc.py line 119 87073] Train: [81/100][1366/1557] Data 0.004 (0.080) Batch 0.974 (1.177) Remain 09:44:13 loss: 0.5717 Lr: 0.00049 [2024-02-19 10:43:03,711 INFO misc.py line 119 87073] Train: [81/100][1367/1557] Data 0.005 (0.080) Batch 1.030 (1.177) Remain 09:44:09 loss: 0.5109 Lr: 0.00049 [2024-02-19 10:43:04,815 INFO misc.py line 119 87073] Train: [81/100][1368/1557] Data 0.004 (0.080) Batch 1.103 (1.177) Remain 09:44:06 loss: 0.2805 Lr: 0.00049 [2024-02-19 10:43:05,846 INFO misc.py line 119 87073] Train: [81/100][1369/1557] Data 0.005 (0.080) Batch 1.031 (1.177) Remain 09:44:02 loss: 0.2081 Lr: 0.00049 [2024-02-19 10:43:06,623 INFO misc.py line 119 87073] Train: [81/100][1370/1557] Data 0.004 (0.080) Batch 0.776 (1.177) Remain 09:43:52 loss: 0.1261 Lr: 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Train: [81/100][1383/1557] Data 0.006 (0.079) Batch 1.078 (1.175) Remain 09:42:31 loss: 0.0833 Lr: 0.00048 [2024-02-19 10:43:19,624 INFO misc.py line 119 87073] Train: [81/100][1384/1557] Data 0.004 (0.079) Batch 0.736 (1.174) Remain 09:42:21 loss: 0.1367 Lr: 0.00048 [2024-02-19 10:43:20,364 INFO misc.py line 119 87073] Train: [81/100][1385/1557] Data 0.004 (0.079) Batch 0.727 (1.174) Remain 09:42:10 loss: 0.1230 Lr: 0.00048 [2024-02-19 10:43:21,590 INFO misc.py line 119 87073] Train: [81/100][1386/1557] Data 0.017 (0.079) Batch 1.230 (1.174) Remain 09:42:10 loss: 0.1318 Lr: 0.00048 [2024-02-19 10:43:22,539 INFO misc.py line 119 87073] Train: [81/100][1387/1557] Data 0.014 (0.079) Batch 0.958 (1.174) Remain 09:42:04 loss: 0.2436 Lr: 0.00048 [2024-02-19 10:43:23,564 INFO misc.py line 119 87073] Train: [81/100][1388/1557] Data 0.006 (0.079) Batch 1.024 (1.174) Remain 09:42:00 loss: 0.3666 Lr: 0.00048 [2024-02-19 10:43:24,503 INFO misc.py line 119 87073] Train: [81/100][1389/1557] Data 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Remain 09:41:20 loss: 0.3212 Lr: 0.00048 [2024-02-19 10:43:31,371 INFO misc.py line 119 87073] Train: [81/100][1396/1557] Data 0.004 (0.079) Batch 1.085 (1.173) Remain 09:41:17 loss: 0.5128 Lr: 0.00048 [2024-02-19 10:43:32,251 INFO misc.py line 119 87073] Train: [81/100][1397/1557] Data 0.004 (0.079) Batch 0.880 (1.172) Remain 09:41:09 loss: 0.1875 Lr: 0.00048 [2024-02-19 10:43:32,942 INFO misc.py line 119 87073] Train: [81/100][1398/1557] Data 0.004 (0.078) Batch 0.686 (1.172) Remain 09:40:58 loss: 0.1213 Lr: 0.00048 [2024-02-19 10:43:33,801 INFO misc.py line 119 87073] Train: [81/100][1399/1557] Data 0.010 (0.078) Batch 0.863 (1.172) Remain 09:40:50 loss: 0.1389 Lr: 0.00048 [2024-02-19 10:43:35,060 INFO misc.py line 119 87073] Train: [81/100][1400/1557] Data 0.006 (0.078) Batch 1.247 (1.172) Remain 09:40:50 loss: 0.0783 Lr: 0.00048 [2024-02-19 10:43:35,984 INFO misc.py line 119 87073] Train: [81/100][1401/1557] Data 0.017 (0.078) Batch 0.936 (1.172) Remain 09:40:44 loss: 0.4982 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1408/1557] Data 0.004 (0.081) Batch 0.823 (1.181) Remain 09:45:02 loss: 0.2338 Lr: 0.00048 [2024-02-19 10:43:57,669 INFO misc.py line 119 87073] Train: [81/100][1409/1557] Data 0.006 (0.081) Batch 0.900 (1.180) Remain 09:44:55 loss: 0.1010 Lr: 0.00048 [2024-02-19 10:43:58,610 INFO misc.py line 119 87073] Train: [81/100][1410/1557] Data 0.005 (0.081) Batch 0.942 (1.180) Remain 09:44:49 loss: 0.4940 Lr: 0.00048 [2024-02-19 10:43:59,480 INFO misc.py line 119 87073] Train: [81/100][1411/1557] Data 0.004 (0.081) Batch 0.870 (1.180) Remain 09:44:41 loss: 0.2798 Lr: 0.00048 [2024-02-19 10:44:00,330 INFO misc.py line 119 87073] Train: [81/100][1412/1557] Data 0.004 (0.081) Batch 0.849 (1.180) Remain 09:44:33 loss: 0.1906 Lr: 0.00048 [2024-02-19 10:44:01,115 INFO misc.py line 119 87073] Train: [81/100][1413/1557] Data 0.005 (0.081) Batch 0.784 (1.180) Remain 09:44:23 loss: 0.1519 Lr: 0.00048 [2024-02-19 10:44:02,320 INFO misc.py line 119 87073] Train: [81/100][1414/1557] Data 0.006 (0.081) Batch 1.206 (1.180) Remain 09:44:23 loss: 0.2353 Lr: 0.00048 [2024-02-19 10:44:03,230 INFO misc.py line 119 87073] Train: [81/100][1415/1557] Data 0.004 (0.081) Batch 0.910 (1.179) Remain 09:44:16 loss: 0.4954 Lr: 0.00048 [2024-02-19 10:44:03,973 INFO misc.py line 119 87073] Train: [81/100][1416/1557] Data 0.004 (0.081) Batch 0.742 (1.179) Remain 09:44:06 loss: 0.2537 Lr: 0.00048 [2024-02-19 10:44:04,798 INFO misc.py line 119 87073] Train: [81/100][1417/1557] Data 0.006 (0.081) Batch 0.826 (1.179) Remain 09:43:57 loss: 0.1786 Lr: 0.00048 [2024-02-19 10:44:05,869 INFO misc.py line 119 87073] Train: [81/100][1418/1557] Data 0.004 (0.081) Batch 1.070 (1.179) Remain 09:43:53 loss: 0.5272 Lr: 0.00048 [2024-02-19 10:44:06,618 INFO misc.py line 119 87073] Train: [81/100][1419/1557] Data 0.006 (0.080) Batch 0.749 (1.178) Remain 09:43:43 loss: 0.2059 Lr: 0.00048 [2024-02-19 10:44:07,391 INFO misc.py line 119 87073] Train: [81/100][1420/1557] Data 0.004 (0.080) Batch 0.767 (1.178) Remain 09:43:33 loss: 0.1400 Lr: 0.00048 [2024-02-19 10:44:08,609 INFO misc.py line 119 87073] Train: [81/100][1421/1557] Data 0.010 (0.080) Batch 1.222 (1.178) Remain 09:43:33 loss: 0.1435 Lr: 0.00048 [2024-02-19 10:44:09,616 INFO misc.py line 119 87073] Train: [81/100][1422/1557] Data 0.007 (0.080) Batch 1.002 (1.178) Remain 09:43:28 loss: 0.1185 Lr: 0.00048 [2024-02-19 10:44:10,498 INFO misc.py line 119 87073] Train: [81/100][1423/1557] Data 0.011 (0.080) Batch 0.888 (1.178) Remain 09:43:21 loss: 0.1970 Lr: 0.00048 [2024-02-19 10:44:11,467 INFO misc.py line 119 87073] Train: [81/100][1424/1557] Data 0.005 (0.080) Batch 0.970 (1.178) Remain 09:43:16 loss: 0.2592 Lr: 0.00048 [2024-02-19 10:44:12,366 INFO misc.py line 119 87073] Train: [81/100][1425/1557] Data 0.005 (0.080) Batch 0.899 (1.177) Remain 09:43:09 loss: 0.3740 Lr: 0.00048 [2024-02-19 10:44:13,093 INFO misc.py line 119 87073] Train: [81/100][1426/1557] Data 0.004 (0.080) Batch 0.726 (1.177) Remain 09:42:58 loss: 0.1756 Lr: 0.00048 [2024-02-19 10:44:13,878 INFO misc.py line 119 87073] Train: [81/100][1427/1557] Data 0.006 (0.080) Batch 0.785 (1.177) Remain 09:42:49 loss: 0.1610 Lr: 0.00048 [2024-02-19 10:44:15,080 INFO misc.py line 119 87073] Train: [81/100][1428/1557] Data 0.006 (0.080) Batch 1.203 (1.177) Remain 09:42:48 loss: 0.1129 Lr: 0.00048 [2024-02-19 10:44:16,110 INFO misc.py line 119 87073] Train: [81/100][1429/1557] Data 0.005 (0.080) Batch 1.030 (1.177) Remain 09:42:44 loss: 0.1234 Lr: 0.00048 [2024-02-19 10:44:17,235 INFO misc.py line 119 87073] Train: [81/100][1430/1557] Data 0.005 (0.080) Batch 1.126 (1.177) Remain 09:42:41 loss: 0.2626 Lr: 0.00048 [2024-02-19 10:44:18,093 INFO misc.py line 119 87073] Train: [81/100][1431/1557] Data 0.004 (0.080) Batch 0.859 (1.177) Remain 09:42:34 loss: 0.1026 Lr: 0.00048 [2024-02-19 10:44:19,072 INFO misc.py line 119 87073] Train: [81/100][1432/1557] Data 0.003 (0.080) Batch 0.972 (1.176) Remain 09:42:28 loss: 0.3538 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1439/1557] Data 0.006 (0.079) Batch 1.101 (1.175) Remain 09:41:50 loss: 0.2238 Lr: 0.00048 [2024-02-19 10:44:26,613 INFO misc.py line 119 87073] Train: [81/100][1440/1557] Data 0.004 (0.079) Batch 0.774 (1.175) Remain 09:41:40 loss: 0.1819 Lr: 0.00048 [2024-02-19 10:44:27,380 INFO misc.py line 119 87073] Train: [81/100][1441/1557] Data 0.003 (0.079) Batch 0.757 (1.175) Remain 09:41:31 loss: 0.1933 Lr: 0.00048 [2024-02-19 10:44:28,750 INFO misc.py line 119 87073] Train: [81/100][1442/1557] Data 0.014 (0.079) Batch 1.365 (1.175) Remain 09:41:33 loss: 0.1795 Lr: 0.00048 [2024-02-19 10:44:29,783 INFO misc.py line 119 87073] Train: [81/100][1443/1557] Data 0.018 (0.079) Batch 1.024 (1.175) Remain 09:41:29 loss: 0.0818 Lr: 0.00048 [2024-02-19 10:44:30,728 INFO misc.py line 119 87073] Train: [81/100][1444/1557] Data 0.027 (0.079) Batch 0.967 (1.175) Remain 09:41:24 loss: 0.1907 Lr: 0.00048 [2024-02-19 10:44:31,556 INFO misc.py line 119 87073] Train: [81/100][1445/1557] Data 0.004 (0.079) Batch 0.828 (1.174) Remain 09:41:15 loss: 0.4843 Lr: 0.00048 [2024-02-19 10:44:32,554 INFO misc.py line 119 87073] Train: [81/100][1446/1557] Data 0.004 (0.079) Batch 0.993 (1.174) Remain 09:41:10 loss: 0.3244 Lr: 0.00048 [2024-02-19 10:44:33,359 INFO misc.py line 119 87073] Train: [81/100][1447/1557] Data 0.010 (0.079) Batch 0.810 (1.174) Remain 09:41:02 loss: 0.1409 Lr: 0.00048 [2024-02-19 10:44:34,124 INFO misc.py line 119 87073] Train: [81/100][1448/1557] Data 0.004 (0.079) Batch 0.765 (1.174) Remain 09:40:52 loss: 0.1555 Lr: 0.00048 [2024-02-19 10:44:35,240 INFO misc.py line 119 87073] Train: [81/100][1449/1557] Data 0.004 (0.079) Batch 1.115 (1.174) Remain 09:40:50 loss: 0.0933 Lr: 0.00048 [2024-02-19 10:44:36,223 INFO misc.py line 119 87073] Train: [81/100][1450/1557] Data 0.006 (0.079) Batch 0.984 (1.174) Remain 09:40:45 loss: 0.1220 Lr: 0.00048 [2024-02-19 10:44:37,229 INFO misc.py line 119 87073] Train: [81/100][1451/1557] Data 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Remain 09:40:19 loss: 0.3458 Lr: 0.00048 [2024-02-19 10:44:44,653 INFO misc.py line 119 87073] Train: [81/100][1458/1557] Data 0.014 (0.078) Batch 1.067 (1.173) Remain 09:40:15 loss: 0.2089 Lr: 0.00048 [2024-02-19 10:44:45,728 INFO misc.py line 119 87073] Train: [81/100][1459/1557] Data 0.014 (0.078) Batch 1.078 (1.173) Remain 09:40:12 loss: 0.2392 Lr: 0.00048 [2024-02-19 10:44:46,786 INFO misc.py line 119 87073] Train: [81/100][1460/1557] Data 0.011 (0.078) Batch 1.056 (1.173) Remain 09:40:09 loss: 0.0986 Lr: 0.00048 [2024-02-19 10:44:47,510 INFO misc.py line 119 87073] Train: [81/100][1461/1557] Data 0.013 (0.078) Batch 0.734 (1.173) Remain 09:39:59 loss: 0.1754 Lr: 0.00048 [2024-02-19 10:44:48,245 INFO misc.py line 119 87073] Train: [81/100][1462/1557] Data 0.003 (0.078) Batch 0.733 (1.172) Remain 09:39:49 loss: 0.1500 Lr: 0.00048 [2024-02-19 10:45:01,288 INFO misc.py line 119 87073] Train: [81/100][1463/1557] Data 3.902 (0.081) Batch 13.044 (1.180) Remain 09:43:49 loss: 0.1255 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1470/1557] Data 0.013 (0.081) Batch 1.271 (1.179) Remain 09:43:15 loss: 0.1187 Lr: 0.00048 [2024-02-19 10:45:09,264 INFO misc.py line 119 87073] Train: [81/100][1471/1557] Data 0.014 (0.081) Batch 0.966 (1.179) Remain 09:43:10 loss: 0.2869 Lr: 0.00048 [2024-02-19 10:45:10,081 INFO misc.py line 119 87073] Train: [81/100][1472/1557] Data 0.006 (0.080) Batch 0.818 (1.179) Remain 09:43:01 loss: 0.2280 Lr: 0.00048 [2024-02-19 10:45:11,063 INFO misc.py line 119 87073] Train: [81/100][1473/1557] Data 0.004 (0.080) Batch 0.982 (1.179) Remain 09:42:56 loss: 0.5498 Lr: 0.00048 [2024-02-19 10:45:11,875 INFO misc.py line 119 87073] Train: [81/100][1474/1557] Data 0.004 (0.080) Batch 0.811 (1.179) Remain 09:42:47 loss: 0.4785 Lr: 0.00048 [2024-02-19 10:45:12,642 INFO misc.py line 119 87073] Train: [81/100][1475/1557] Data 0.006 (0.080) Batch 0.768 (1.178) Remain 09:42:38 loss: 0.1372 Lr: 0.00048 [2024-02-19 10:45:13,457 INFO misc.py line 119 87073] Train: [81/100][1476/1557] Data 0.005 (0.080) Batch 0.762 (1.178) Remain 09:42:28 loss: 0.2505 Lr: 0.00048 [2024-02-19 10:45:14,641 INFO misc.py line 119 87073] Train: [81/100][1477/1557] Data 0.057 (0.080) Batch 1.233 (1.178) Remain 09:42:28 loss: 0.1509 Lr: 0.00048 [2024-02-19 10:45:15,592 INFO misc.py line 119 87073] Train: [81/100][1478/1557] Data 0.009 (0.080) Batch 0.955 (1.178) Remain 09:42:23 loss: 0.1908 Lr: 0.00048 [2024-02-19 10:45:16,515 INFO misc.py line 119 87073] Train: [81/100][1479/1557] Data 0.004 (0.080) Batch 0.923 (1.178) Remain 09:42:16 loss: 0.1407 Lr: 0.00048 [2024-02-19 10:45:17,527 INFO misc.py line 119 87073] Train: [81/100][1480/1557] Data 0.005 (0.080) Batch 1.006 (1.178) Remain 09:42:12 loss: 0.3989 Lr: 0.00048 [2024-02-19 10:45:18,412 INFO misc.py line 119 87073] Train: [81/100][1481/1557] Data 0.011 (0.080) Batch 0.892 (1.178) Remain 09:42:05 loss: 0.3182 Lr: 0.00048 [2024-02-19 10:45:19,158 INFO misc.py line 119 87073] Train: [81/100][1482/1557] Data 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Remain 09:41:24 loss: 0.3019 Lr: 0.00048 [2024-02-19 10:45:25,788 INFO misc.py line 119 87073] Train: [81/100][1489/1557] Data 0.006 (0.080) Batch 0.779 (1.176) Remain 09:41:15 loss: 0.2067 Lr: 0.00048 [2024-02-19 10:45:26,561 INFO misc.py line 119 87073] Train: [81/100][1490/1557] Data 0.006 (0.080) Batch 0.774 (1.176) Remain 09:41:05 loss: 0.2184 Lr: 0.00048 [2024-02-19 10:45:27,695 INFO misc.py line 119 87073] Train: [81/100][1491/1557] Data 0.004 (0.080) Batch 1.133 (1.176) Remain 09:41:03 loss: 0.1679 Lr: 0.00048 [2024-02-19 10:45:28,695 INFO misc.py line 119 87073] Train: [81/100][1492/1557] Data 0.005 (0.080) Batch 0.999 (1.176) Remain 09:40:59 loss: 0.1193 Lr: 0.00048 [2024-02-19 10:45:29,595 INFO misc.py line 119 87073] Train: [81/100][1493/1557] Data 0.005 (0.079) Batch 0.902 (1.176) Remain 09:40:52 loss: 0.1211 Lr: 0.00048 [2024-02-19 10:45:30,600 INFO misc.py line 119 87073] Train: [81/100][1494/1557] Data 0.004 (0.079) Batch 1.005 (1.175) Remain 09:40:47 loss: 0.0850 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1501/1557] Data 0.005 (0.079) Batch 1.031 (1.175) Remain 09:40:11 loss: 0.1500 Lr: 0.00048 [2024-02-19 10:45:38,273 INFO misc.py line 119 87073] Train: [81/100][1502/1557] Data 0.004 (0.079) Batch 0.888 (1.174) Remain 09:40:04 loss: 0.2154 Lr: 0.00048 [2024-02-19 10:45:39,076 INFO misc.py line 119 87073] Train: [81/100][1503/1557] Data 0.004 (0.079) Batch 0.796 (1.174) Remain 09:39:55 loss: 0.1505 Lr: 0.00048 [2024-02-19 10:45:39,777 INFO misc.py line 119 87073] Train: [81/100][1504/1557] Data 0.010 (0.079) Batch 0.706 (1.174) Remain 09:39:45 loss: 0.1871 Lr: 0.00048 [2024-02-19 10:45:40,909 INFO misc.py line 119 87073] Train: [81/100][1505/1557] Data 0.004 (0.079) Batch 1.126 (1.174) Remain 09:39:43 loss: 0.1031 Lr: 0.00048 [2024-02-19 10:45:41,945 INFO misc.py line 119 87073] Train: [81/100][1506/1557] Data 0.010 (0.079) Batch 1.041 (1.174) Remain 09:39:39 loss: 0.3798 Lr: 0.00048 [2024-02-19 10:45:42,869 INFO misc.py line 119 87073] Train: [81/100][1507/1557] Data 0.006 (0.079) Batch 0.925 (1.173) Remain 09:39:33 loss: 0.1711 Lr: 0.00048 [2024-02-19 10:45:43,862 INFO misc.py line 119 87073] Train: [81/100][1508/1557] Data 0.004 (0.079) Batch 0.994 (1.173) Remain 09:39:28 loss: 0.2787 Lr: 0.00048 [2024-02-19 10:45:44,810 INFO misc.py line 119 87073] Train: [81/100][1509/1557] Data 0.004 (0.079) Batch 0.947 (1.173) Remain 09:39:22 loss: 0.5261 Lr: 0.00048 [2024-02-19 10:45:45,587 INFO misc.py line 119 87073] Train: [81/100][1510/1557] Data 0.004 (0.079) Batch 0.769 (1.173) Remain 09:39:13 loss: 0.2582 Lr: 0.00048 [2024-02-19 10:45:46,537 INFO misc.py line 119 87073] Train: [81/100][1511/1557] Data 0.013 (0.079) Batch 0.958 (1.173) Remain 09:39:08 loss: 0.0997 Lr: 0.00048 [2024-02-19 10:45:47,975 INFO misc.py line 119 87073] Train: [81/100][1512/1557] Data 0.004 (0.079) Batch 1.430 (1.173) Remain 09:39:12 loss: 0.0840 Lr: 0.00048 [2024-02-19 10:45:49,009 INFO misc.py line 119 87073] Train: [81/100][1513/1557] Data 0.012 (0.079) Batch 1.032 (1.173) Remain 09:39:08 loss: 0.0854 Lr: 0.00048 [2024-02-19 10:45:50,069 INFO misc.py line 119 87073] Train: [81/100][1514/1557] Data 0.013 (0.078) Batch 1.067 (1.173) Remain 09:39:05 loss: 0.1589 Lr: 0.00048 [2024-02-19 10:45:51,019 INFO misc.py line 119 87073] Train: [81/100][1515/1557] Data 0.007 (0.078) Batch 0.953 (1.173) Remain 09:38:59 loss: 0.2387 Lr: 0.00048 [2024-02-19 10:45:51,945 INFO misc.py line 119 87073] Train: [81/100][1516/1557] Data 0.004 (0.078) Batch 0.925 (1.172) Remain 09:38:53 loss: 0.2647 Lr: 0.00048 [2024-02-19 10:45:52,707 INFO misc.py line 119 87073] Train: [81/100][1517/1557] Data 0.005 (0.078) Batch 0.757 (1.172) Remain 09:38:44 loss: 0.2609 Lr: 0.00048 [2024-02-19 10:45:53,540 INFO misc.py line 119 87073] Train: [81/100][1518/1557] Data 0.011 (0.078) Batch 0.840 (1.172) Remain 09:38:36 loss: 0.2032 Lr: 0.00048 [2024-02-19 10:46:06,742 INFO misc.py line 119 87073] Train: [81/100][1519/1557] Data 3.967 (0.081) Batch 13.201 (1.180) Remain 09:42:30 loss: 0.1266 Lr: 0.00048 [2024-02-19 10:46:07,714 INFO misc.py line 119 87073] Train: [81/100][1520/1557] Data 0.004 (0.081) Batch 0.973 (1.180) Remain 09:42:25 loss: 0.4884 Lr: 0.00048 [2024-02-19 10:46:08,715 INFO misc.py line 119 87073] Train: [81/100][1521/1557] Data 0.003 (0.081) Batch 0.999 (1.180) Remain 09:42:20 loss: 0.3052 Lr: 0.00048 [2024-02-19 10:46:09,514 INFO misc.py line 119 87073] Train: [81/100][1522/1557] Data 0.006 (0.081) Batch 0.800 (1.179) Remain 09:42:11 loss: 0.2858 Lr: 0.00048 [2024-02-19 10:46:10,517 INFO misc.py line 119 87073] Train: [81/100][1523/1557] Data 0.006 (0.081) Batch 0.997 (1.179) Remain 09:42:07 loss: 0.1597 Lr: 0.00048 [2024-02-19 10:46:11,286 INFO misc.py line 119 87073] Train: [81/100][1524/1557] Data 0.011 (0.081) Batch 0.776 (1.179) Remain 09:41:58 loss: 0.1973 Lr: 0.00048 [2024-02-19 10:46:12,064 INFO misc.py line 119 87073] Train: [81/100][1525/1557] Data 0.004 (0.081) Batch 0.779 (1.179) Remain 09:41:49 loss: 0.2276 Lr: 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INFO misc.py line 119 87073] Train: [81/100][1532/1557] Data 0.015 (0.080) Batch 0.739 (1.178) Remain 09:41:11 loss: 0.2049 Lr: 0.00048 [2024-02-19 10:46:19,982 INFO misc.py line 119 87073] Train: [81/100][1533/1557] Data 0.004 (0.080) Batch 1.170 (1.178) Remain 09:41:10 loss: 0.2208 Lr: 0.00048 [2024-02-19 10:46:20,927 INFO misc.py line 119 87073] Train: [81/100][1534/1557] Data 0.005 (0.080) Batch 0.945 (1.178) Remain 09:41:04 loss: 0.2347 Lr: 0.00048 [2024-02-19 10:46:21,899 INFO misc.py line 119 87073] Train: [81/100][1535/1557] Data 0.005 (0.080) Batch 0.974 (1.177) Remain 09:40:59 loss: 0.1605 Lr: 0.00048 [2024-02-19 10:46:22,778 INFO misc.py line 119 87073] Train: [81/100][1536/1557] Data 0.004 (0.080) Batch 0.879 (1.177) Remain 09:40:52 loss: 0.1934 Lr: 0.00048 [2024-02-19 10:46:23,558 INFO misc.py line 119 87073] Train: [81/100][1537/1557] Data 0.005 (0.080) Batch 0.772 (1.177) Remain 09:40:43 loss: 0.2343 Lr: 0.00048 [2024-02-19 10:46:24,341 INFO misc.py line 119 87073] Train: [81/100][1538/1557] Data 0.012 (0.080) Batch 0.791 (1.177) Remain 09:40:35 loss: 0.1664 Lr: 0.00048 [2024-02-19 10:46:25,064 INFO misc.py line 119 87073] Train: [81/100][1539/1557] Data 0.004 (0.080) Batch 0.719 (1.176) Remain 09:40:25 loss: 0.2212 Lr: 0.00048 [2024-02-19 10:46:26,306 INFO misc.py line 119 87073] Train: [81/100][1540/1557] Data 0.008 (0.080) Batch 1.245 (1.177) Remain 09:40:25 loss: 0.0972 Lr: 0.00048 [2024-02-19 10:46:27,250 INFO misc.py line 119 87073] Train: [81/100][1541/1557] Data 0.006 (0.080) Batch 0.945 (1.176) Remain 09:40:19 loss: 0.0525 Lr: 0.00048 [2024-02-19 10:46:28,169 INFO misc.py line 119 87073] Train: [81/100][1542/1557] Data 0.004 (0.080) Batch 0.919 (1.176) Remain 09:40:13 loss: 0.3291 Lr: 0.00048 [2024-02-19 10:46:29,132 INFO misc.py line 119 87073] Train: [81/100][1543/1557] Data 0.004 (0.080) Batch 0.954 (1.176) Remain 09:40:08 loss: 0.5936 Lr: 0.00048 [2024-02-19 10:46:30,139 INFO misc.py line 119 87073] Train: [81/100][1544/1557] Data 0.013 (0.080) Batch 1.015 (1.176) Remain 09:40:03 loss: 0.1707 Lr: 0.00048 [2024-02-19 10:46:30,923 INFO misc.py line 119 87073] Train: [81/100][1545/1557] Data 0.006 (0.080) Batch 0.785 (1.176) Remain 09:39:55 loss: 0.1487 Lr: 0.00048 [2024-02-19 10:46:31,643 INFO misc.py line 119 87073] Train: [81/100][1546/1557] Data 0.004 (0.080) Batch 0.720 (1.175) Remain 09:39:45 loss: 0.1203 Lr: 0.00048 [2024-02-19 10:46:32,767 INFO misc.py line 119 87073] Train: [81/100][1547/1557] Data 0.005 (0.079) Batch 1.122 (1.175) Remain 09:39:43 loss: 0.0832 Lr: 0.00048 [2024-02-19 10:46:33,802 INFO misc.py line 119 87073] Train: [81/100][1548/1557] Data 0.006 (0.079) Batch 1.030 (1.175) Remain 09:39:39 loss: 0.1606 Lr: 0.00048 [2024-02-19 10:46:34,796 INFO misc.py line 119 87073] Train: [81/100][1549/1557] Data 0.011 (0.079) Batch 0.999 (1.175) Remain 09:39:34 loss: 0.4338 Lr: 0.00048 [2024-02-19 10:46:35,853 INFO misc.py line 119 87073] Train: [81/100][1550/1557] Data 0.007 (0.079) Batch 1.057 (1.175) Remain 09:39:31 loss: 0.2138 Lr: 0.00048 [2024-02-19 10:46:36,634 INFO misc.py line 119 87073] Train: [81/100][1551/1557] Data 0.006 (0.079) Batch 0.782 (1.175) Remain 09:39:22 loss: 0.2223 Lr: 0.00048 [2024-02-19 10:46:37,409 INFO misc.py line 119 87073] Train: [81/100][1552/1557] Data 0.005 (0.079) Batch 0.768 (1.175) Remain 09:39:13 loss: 0.1443 Lr: 0.00048 [2024-02-19 10:46:38,125 INFO misc.py line 119 87073] Train: [81/100][1553/1557] Data 0.012 (0.079) Batch 0.723 (1.174) Remain 09:39:03 loss: 0.1739 Lr: 0.00048 [2024-02-19 10:46:39,396 INFO misc.py line 119 87073] Train: [81/100][1554/1557] Data 0.005 (0.079) Batch 1.271 (1.174) Remain 09:39:04 loss: 0.1584 Lr: 0.00048 [2024-02-19 10:46:40,323 INFO misc.py line 119 87073] Train: [81/100][1555/1557] Data 0.005 (0.079) Batch 0.927 (1.174) Remain 09:38:58 loss: 0.2759 Lr: 0.00048 [2024-02-19 10:46:41,249 INFO misc.py line 119 87073] Train: [81/100][1556/1557] Data 0.005 (0.079) Batch 0.927 (1.174) Remain 09:38:52 loss: 0.3824 Lr: 0.00048 [2024-02-19 10:46:42,213 INFO misc.py line 119 87073] Train: [81/100][1557/1557] Data 0.004 (0.079) Batch 0.961 (1.174) Remain 09:38:47 loss: 0.5857 Lr: 0.00048 [2024-02-19 10:46:42,214 INFO misc.py line 136 87073] Train result: loss: 0.2282 [2024-02-19 10:46:42,214 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 10:47:11,592 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4973 [2024-02-19 10:47:12,372 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2654 [2024-02-19 10:47:14,500 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3462 [2024-02-19 10:47:16,713 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3187 [2024-02-19 10:47:21,663 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2341 [2024-02-19 10:47:22,361 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0672 [2024-02-19 10:47:23,621 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3706 [2024-02-19 10:47:26,575 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3055 [2024-02-19 10:47:28,388 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2808 [2024-02-19 10:47:30,002 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7493/0.8071/0.9227. [2024-02-19 10:47:30,002 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9333/0.9597 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9833/0.9894 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8720/0.9729 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0222/0.1179 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4727/0.5275 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.7066/0.7380 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8595/0.9313 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8404/0.9116 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9322/0.9773 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8608/0.9049 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8022/0.8980 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8140/0.8331 [2024-02-19 10:47:30,003 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6422/0.7312 [2024-02-19 10:47:30,004 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 10:47:30,006 INFO misc.py line 160 87073] Best validation mIoU updated to: 0.7493 [2024-02-19 10:47:30,006 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 10:47:30,006 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 10:47:39,772 INFO misc.py line 119 87073] Train: [82/100][1/1557] Data 1.004 (1.004) Batch 1.664 (1.664) Remain 13:40:29 loss: 0.1142 Lr: 0.00048 [2024-02-19 10:47:40,817 INFO misc.py line 119 87073] Train: [82/100][2/1557] Data 0.006 (0.006) Batch 1.045 (1.045) Remain 08:35:24 loss: 0.3702 Lr: 0.00048 [2024-02-19 10:47:42,017 INFO misc.py line 119 87073] Train: [82/100][3/1557] Data 0.005 (0.005) Batch 1.200 (1.200) Remain 09:51:39 loss: 0.2463 Lr: 0.00048 [2024-02-19 10:47:43,105 INFO misc.py line 119 87073] Train: [82/100][4/1557] Data 0.005 (0.005) Batch 1.088 (1.088) Remain 08:56:31 loss: 0.1183 Lr: 0.00048 [2024-02-19 10:47:43,859 INFO misc.py line 119 87073] Train: [82/100][5/1557] Data 0.004 (0.004) Batch 0.754 (0.921) Remain 07:34:05 loss: 0.2579 Lr: 0.00048 [2024-02-19 10:47:44,632 INFO misc.py line 119 87073] Train: [82/100][6/1557] Data 0.004 (0.004) Batch 0.772 (0.872) Remain 07:09:37 loss: 0.2392 Lr: 0.00048 [2024-02-19 10:47:47,464 INFO misc.py line 119 87073] Train: [82/100][7/1557] Data 1.723 (0.434) Batch 2.831 (1.361) Remain 11:11:01 loss: 0.1775 Lr: 0.00048 [2024-02-19 10:47:48,399 INFO misc.py line 119 87073] Train: [82/100][8/1557] Data 0.006 (0.348) Batch 0.937 (1.276) Remain 10:29:08 loss: 0.2562 Lr: 0.00048 [2024-02-19 10:47:49,380 INFO misc.py line 119 87073] Train: [82/100][9/1557] Data 0.005 (0.291) Batch 0.981 (1.227) Remain 10:04:51 loss: 0.2224 Lr: 0.00048 [2024-02-19 10:47:50,265 INFO misc.py line 119 87073] Train: [82/100][10/1557] Data 0.004 (0.250) Batch 0.878 (1.177) Remain 09:40:13 loss: 0.3503 Lr: 0.00048 [2024-02-19 10:47:51,367 INFO misc.py line 119 87073] Train: [82/100][11/1557] Data 0.012 (0.220) Batch 1.102 (1.168) Remain 09:35:34 loss: 0.9633 Lr: 0.00048 [2024-02-19 10:47:52,132 INFO misc.py line 119 87073] Train: [82/100][12/1557] Data 0.011 (0.197) Batch 0.772 (1.124) Remain 09:13:53 loss: 0.1873 Lr: 0.00048 [2024-02-19 10:47:52,891 INFO misc.py line 119 87073] Train: [82/100][13/1557] Data 0.004 (0.178) Batch 0.759 (1.087) Remain 08:55:53 loss: 0.1561 Lr: 0.00048 [2024-02-19 10:47:53,999 INFO misc.py line 119 87073] Train: [82/100][14/1557] Data 0.004 (0.162) Batch 1.107 (1.089) Remain 08:56:46 loss: 0.1691 Lr: 0.00048 [2024-02-19 10:47:54,940 INFO misc.py line 119 87073] Train: [82/100][15/1557] Data 0.005 (0.149) Batch 0.941 (1.077) Remain 08:50:39 loss: 0.1784 Lr: 0.00048 [2024-02-19 10:47:55,894 INFO misc.py line 119 87073] Train: [82/100][16/1557] Data 0.005 (0.138) Batch 0.954 (1.067) Remain 08:45:59 loss: 0.4052 Lr: 0.00048 [2024-02-19 10:47:56,945 INFO misc.py line 119 87073] Train: [82/100][17/1557] Data 0.005 (0.128) Batch 1.053 (1.066) Remain 08:45:26 loss: 0.2778 Lr: 0.00048 [2024-02-19 10:47:57,834 INFO misc.py line 119 87073] Train: [82/100][18/1557] Data 0.003 (0.120) Batch 0.889 (1.054) Remain 08:39:35 loss: 0.1848 Lr: 0.00048 [2024-02-19 10:47:58,608 INFO misc.py line 119 87073] Train: [82/100][19/1557] Data 0.004 (0.113) Batch 0.772 (1.037) Remain 08:30:52 loss: 0.2315 Lr: 0.00048 [2024-02-19 10:47:59,328 INFO misc.py line 119 87073] Train: [82/100][20/1557] Data 0.006 (0.107) Batch 0.723 (1.018) Remain 08:21:45 loss: 0.1960 Lr: 0.00048 [2024-02-19 10:48:00,595 INFO misc.py line 119 87073] Train: [82/100][21/1557] Data 0.003 (0.101) Batch 1.266 (1.032) Remain 08:28:31 loss: 0.1077 Lr: 0.00048 [2024-02-19 10:48:01,489 INFO misc.py line 119 87073] Train: [82/100][22/1557] Data 0.004 (0.096) Batch 0.894 (1.025) Remain 08:24:56 loss: 0.2990 Lr: 0.00048 [2024-02-19 10:48:02,414 INFO misc.py line 119 87073] Train: [82/100][23/1557] Data 0.003 (0.091) Batch 0.923 (1.020) Remain 08:22:25 loss: 0.1641 Lr: 0.00048 [2024-02-19 10:48:03,248 INFO misc.py line 119 87073] Train: [82/100][24/1557] Data 0.005 (0.087) Batch 0.835 (1.011) Remain 08:18:04 loss: 0.2345 Lr: 0.00048 [2024-02-19 10:48:04,133 INFO misc.py line 119 87073] Train: [82/100][25/1557] Data 0.005 (0.083) Batch 0.885 (1.005) Remain 08:15:14 loss: 0.1813 Lr: 0.00048 [2024-02-19 10:48:04,913 INFO misc.py line 119 87073] Train: [82/100][26/1557] Data 0.004 (0.080) Batch 0.779 (0.995) Remain 08:10:21 loss: 0.2346 Lr: 0.00048 [2024-02-19 10:48:05,700 INFO misc.py line 119 87073] Train: [82/100][27/1557] Data 0.005 (0.077) Batch 0.788 (0.987) Remain 08:06:05 loss: 0.2087 Lr: 0.00048 [2024-02-19 10:48:06,973 INFO misc.py line 119 87073] Train: [82/100][28/1557] Data 0.004 (0.074) Batch 1.263 (0.998) Remain 08:11:30 loss: 0.1843 Lr: 0.00048 [2024-02-19 10:48:07,940 INFO misc.py line 119 87073] Train: [82/100][29/1557] Data 0.015 (0.072) Batch 0.976 (0.997) Remain 08:11:04 loss: 0.2712 Lr: 0.00048 [2024-02-19 10:48:08,934 INFO misc.py line 119 87073] Train: [82/100][30/1557] Data 0.006 (0.069) Batch 0.993 (0.997) Remain 08:10:59 loss: 0.4495 Lr: 0.00048 [2024-02-19 10:48:09,758 INFO misc.py line 119 87073] Train: [82/100][31/1557] Data 0.007 (0.067) Batch 0.827 (0.991) Remain 08:07:59 loss: 0.0853 Lr: 0.00048 [2024-02-19 10:48:10,595 INFO misc.py line 119 87073] Train: [82/100][32/1557] Data 0.004 (0.065) Batch 0.834 (0.985) Remain 08:05:18 loss: 0.2758 Lr: 0.00048 [2024-02-19 10:48:11,344 INFO misc.py line 119 87073] Train: [82/100][33/1557] Data 0.006 (0.063) Batch 0.748 (0.977) Remain 08:01:24 loss: 0.2620 Lr: 0.00048 [2024-02-19 10:48:12,099 INFO misc.py line 119 87073] Train: [82/100][34/1557] Data 0.007 (0.061) Batch 0.754 (0.970) Remain 07:57:50 loss: 0.2078 Lr: 0.00048 [2024-02-19 10:48:13,230 INFO misc.py line 119 87073] Train: [82/100][35/1557] Data 0.008 (0.059) Batch 1.135 (0.975) Remain 08:00:20 loss: 0.1538 Lr: 0.00048 [2024-02-19 10:48:14,091 INFO misc.py line 119 87073] Train: [82/100][36/1557] Data 0.005 (0.058) Batch 0.861 (0.972) Remain 07:58:37 loss: 0.0523 Lr: 0.00048 [2024-02-19 10:48:15,040 INFO misc.py line 119 87073] Train: [82/100][37/1557] Data 0.005 (0.056) Batch 0.950 (0.971) Remain 07:58:17 loss: 0.4342 Lr: 0.00048 [2024-02-19 10:48:16,101 INFO misc.py line 119 87073] Train: [82/100][38/1557] Data 0.004 (0.055) Batch 1.061 (0.974) Remain 07:59:31 loss: 0.0851 Lr: 0.00048 [2024-02-19 10:48:17,155 INFO misc.py line 119 87073] Train: [82/100][39/1557] Data 0.004 (0.053) Batch 1.054 (0.976) Remain 08:00:36 loss: 0.5536 Lr: 0.00048 [2024-02-19 10:48:17,910 INFO misc.py line 119 87073] Train: [82/100][40/1557] Data 0.005 (0.052) Batch 0.755 (0.970) Remain 07:57:38 loss: 0.1204 Lr: 0.00048 [2024-02-19 10:48:18,690 INFO misc.py line 119 87073] Train: [82/100][41/1557] Data 0.004 (0.051) Batch 0.780 (0.965) Remain 07:55:09 loss: 0.3227 Lr: 0.00048 [2024-02-19 10:48:19,960 INFO misc.py line 119 87073] Train: [82/100][42/1557] Data 0.005 (0.049) Batch 1.239 (0.972) Remain 07:58:36 loss: 0.1233 Lr: 0.00048 [2024-02-19 10:48:20,907 INFO misc.py line 119 87073] Train: [82/100][43/1557] Data 0.036 (0.049) Batch 0.979 (0.972) Remain 07:58:40 loss: 0.4044 Lr: 0.00048 [2024-02-19 10:48:21,979 INFO misc.py line 119 87073] Train: [82/100][44/1557] Data 0.003 (0.048) Batch 1.070 (0.975) Remain 07:59:49 loss: 0.5803 Lr: 0.00048 [2024-02-19 10:48:23,026 INFO misc.py line 119 87073] Train: [82/100][45/1557] Data 0.006 (0.047) Batch 1.049 (0.976) Remain 08:00:41 loss: 0.3186 Lr: 0.00048 [2024-02-19 10:48:23,898 INFO misc.py line 119 87073] Train: [82/100][46/1557] Data 0.004 (0.046) Batch 0.871 (0.974) Remain 07:59:27 loss: 0.1877 Lr: 0.00048 [2024-02-19 10:48:24,696 INFO misc.py line 119 87073] Train: [82/100][47/1557] Data 0.004 (0.045) Batch 0.790 (0.970) Remain 07:57:23 loss: 0.1872 Lr: 0.00048 [2024-02-19 10:48:25,440 INFO misc.py line 119 87073] Train: [82/100][48/1557] Data 0.013 (0.044) Batch 0.752 (0.965) Remain 07:54:59 loss: 0.2340 Lr: 0.00048 [2024-02-19 10:48:26,668 INFO misc.py line 119 87073] Train: [82/100][49/1557] Data 0.005 (0.044) Batch 1.226 (0.971) Remain 07:57:45 loss: 0.1802 Lr: 0.00048 [2024-02-19 10:48:27,653 INFO misc.py line 119 87073] Train: [82/100][50/1557] Data 0.007 (0.043) Batch 0.987 (0.971) Remain 07:57:55 loss: 0.1364 Lr: 0.00048 [2024-02-19 10:48:28,700 INFO misc.py line 119 87073] Train: [82/100][51/1557] Data 0.006 (0.042) Batch 1.048 (0.973) Remain 07:58:41 loss: 0.2270 Lr: 0.00048 [2024-02-19 10:48:29,569 INFO misc.py line 119 87073] Train: [82/100][52/1557] Data 0.004 (0.041) Batch 0.868 (0.970) Remain 07:57:37 loss: 0.3075 Lr: 0.00048 [2024-02-19 10:48:30,567 INFO misc.py line 119 87073] Train: [82/100][53/1557] Data 0.005 (0.040) Batch 0.994 (0.971) Remain 07:57:50 loss: 0.1904 Lr: 0.00048 [2024-02-19 10:48:31,272 INFO misc.py line 119 87073] Train: [82/100][54/1557] Data 0.009 (0.040) Batch 0.709 (0.966) Remain 07:55:17 loss: 0.1365 Lr: 0.00048 [2024-02-19 10:48:32,025 INFO misc.py line 119 87073] Train: [82/100][55/1557] Data 0.006 (0.039) Batch 0.747 (0.962) Remain 07:53:12 loss: 0.1583 Lr: 0.00048 [2024-02-19 10:48:33,286 INFO misc.py line 119 87073] Train: [82/100][56/1557] Data 0.012 (0.039) Batch 1.257 (0.967) Remain 07:55:56 loss: 0.1376 Lr: 0.00048 [2024-02-19 10:48:34,310 INFO misc.py line 119 87073] Train: [82/100][57/1557] Data 0.015 (0.038) Batch 1.013 (0.968) Remain 07:56:20 loss: 0.4218 Lr: 0.00048 [2024-02-19 10:48:35,236 INFO misc.py line 119 87073] Train: 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Batch 0.768 (1.147) Remain 09:21:52 loss: 0.1293 Lr: 0.00047 [2024-02-19 10:51:23,571 INFO misc.py line 119 87073] Train: [82/100][196/1557] Data 0.005 (0.184) Batch 1.292 (1.148) Remain 09:22:13 loss: 0.1109 Lr: 0.00047 [2024-02-19 10:51:24,599 INFO misc.py line 119 87073] Train: [82/100][197/1557] Data 0.015 (0.183) Batch 1.027 (1.147) Remain 09:21:53 loss: 0.2773 Lr: 0.00047 [2024-02-19 10:51:25,648 INFO misc.py line 119 87073] Train: [82/100][198/1557] Data 0.016 (0.182) Batch 1.051 (1.147) Remain 09:21:37 loss: 0.0934 Lr: 0.00047 [2024-02-19 10:51:26,536 INFO misc.py line 119 87073] Train: [82/100][199/1557] Data 0.013 (0.181) Batch 0.897 (1.146) Remain 09:20:59 loss: 0.1693 Lr: 0.00047 [2024-02-19 10:51:27,565 INFO misc.py line 119 87073] Train: [82/100][200/1557] Data 0.004 (0.180) Batch 1.030 (1.145) Remain 09:20:41 loss: 0.2303 Lr: 0.00047 [2024-02-19 10:51:28,338 INFO misc.py line 119 87073] Train: [82/100][201/1557] Data 0.003 (0.179) Batch 0.771 (1.143) Remain 09:19:44 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line 119 87073] Train: [82/100][239/1557] Data 0.012 (0.194) Batch 1.018 (1.156) Remain 09:25:12 loss: 0.2587 Lr: 0.00047 [2024-02-19 10:52:15,812 INFO misc.py line 119 87073] Train: [82/100][240/1557] Data 0.007 (0.193) Batch 1.052 (1.155) Remain 09:24:58 loss: 0.2385 Lr: 0.00047 [2024-02-19 10:52:16,742 INFO misc.py line 119 87073] Train: [82/100][241/1557] Data 0.005 (0.193) Batch 0.931 (1.154) Remain 09:24:29 loss: 0.2563 Lr: 0.00047 [2024-02-19 10:52:17,683 INFO misc.py line 119 87073] Train: [82/100][242/1557] Data 0.004 (0.192) Batch 0.939 (1.153) Remain 09:24:02 loss: 0.1512 Lr: 0.00047 [2024-02-19 10:52:18,482 INFO misc.py line 119 87073] Train: [82/100][243/1557] Data 0.006 (0.191) Batch 0.801 (1.152) Remain 09:23:17 loss: 0.1953 Lr: 0.00047 [2024-02-19 10:52:19,241 INFO misc.py line 119 87073] Train: [82/100][244/1557] Data 0.004 (0.190) Batch 0.750 (1.150) Remain 09:22:27 loss: 0.2379 Lr: 0.00047 [2024-02-19 10:52:20,575 INFO misc.py line 119 87073] Train: 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Batch 0.752 (1.145) Remain 09:19:32 loss: 0.1880 Lr: 0.00047 [2024-02-19 10:52:27,117 INFO misc.py line 119 87073] Train: [82/100][252/1557] Data 0.014 (0.184) Batch 1.244 (1.145) Remain 09:19:42 loss: 0.1631 Lr: 0.00047 [2024-02-19 10:52:27,991 INFO misc.py line 119 87073] Train: [82/100][253/1557] Data 0.008 (0.184) Batch 0.879 (1.144) Remain 09:19:10 loss: 0.0977 Lr: 0.00047 [2024-02-19 10:52:28,983 INFO misc.py line 119 87073] Train: [82/100][254/1557] Data 0.003 (0.183) Batch 0.991 (1.143) Remain 09:18:51 loss: 0.2114 Lr: 0.00047 [2024-02-19 10:52:30,136 INFO misc.py line 119 87073] Train: [82/100][255/1557] Data 0.004 (0.182) Batch 1.153 (1.143) Remain 09:18:51 loss: 0.3380 Lr: 0.00047 [2024-02-19 10:52:31,184 INFO misc.py line 119 87073] Train: [82/100][256/1557] Data 0.004 (0.181) Batch 1.049 (1.143) Remain 09:18:39 loss: 0.1798 Lr: 0.00047 [2024-02-19 10:52:31,918 INFO misc.py line 119 87073] Train: [82/100][257/1557] Data 0.003 (0.181) Batch 0.734 (1.141) Remain 09:17:51 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Batch 0.756 (1.153) Remain 09:22:47 loss: 0.1970 Lr: 0.00047 [2024-02-19 10:53:33,869 INFO misc.py line 119 87073] Train: [82/100][308/1557] Data 0.003 (0.183) Batch 1.217 (1.154) Remain 09:22:51 loss: 0.1673 Lr: 0.00047 [2024-02-19 10:53:34,818 INFO misc.py line 119 87073] Train: [82/100][309/1557] Data 0.005 (0.182) Batch 0.949 (1.153) Remain 09:22:31 loss: 0.3726 Lr: 0.00047 [2024-02-19 10:53:35,798 INFO misc.py line 119 87073] Train: [82/100][310/1557] Data 0.004 (0.182) Batch 0.980 (1.152) Remain 09:22:13 loss: 0.0980 Lr: 0.00047 [2024-02-19 10:53:36,620 INFO misc.py line 119 87073] Train: [82/100][311/1557] Data 0.004 (0.181) Batch 0.819 (1.151) Remain 09:21:40 loss: 0.1457 Lr: 0.00047 [2024-02-19 10:53:37,609 INFO misc.py line 119 87073] Train: [82/100][312/1557] Data 0.007 (0.181) Batch 0.992 (1.151) Remain 09:21:24 loss: 0.2211 Lr: 0.00047 [2024-02-19 10:53:38,377 INFO misc.py line 119 87073] Train: [82/100][313/1557] Data 0.004 (0.180) Batch 0.767 (1.150) Remain 09:20:47 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Batch 0.792 (1.166) Remain 09:26:45 loss: 0.2744 Lr: 0.00047 [2024-02-19 10:55:48,436 INFO misc.py line 119 87073] Train: [82/100][420/1557] Data 0.004 (0.186) Batch 1.340 (1.166) Remain 09:26:57 loss: 0.1072 Lr: 0.00047 [2024-02-19 10:55:49,335 INFO misc.py line 119 87073] Train: [82/100][421/1557] Data 0.016 (0.186) Batch 0.910 (1.166) Remain 09:26:37 loss: 0.1677 Lr: 0.00047 [2024-02-19 10:55:50,214 INFO misc.py line 119 87073] Train: [82/100][422/1557] Data 0.005 (0.185) Batch 0.877 (1.165) Remain 09:26:16 loss: 0.2149 Lr: 0.00047 [2024-02-19 10:55:51,322 INFO misc.py line 119 87073] Train: [82/100][423/1557] Data 0.006 (0.185) Batch 1.110 (1.165) Remain 09:26:11 loss: 0.2569 Lr: 0.00047 [2024-02-19 10:55:52,215 INFO misc.py line 119 87073] Train: [82/100][424/1557] Data 0.005 (0.185) Batch 0.894 (1.164) Remain 09:25:51 loss: 0.2360 Lr: 0.00047 [2024-02-19 10:55:53,020 INFO misc.py line 119 87073] Train: [82/100][425/1557] Data 0.004 (0.184) Batch 0.805 (1.164) Remain 09:25:25 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Batch 0.771 (1.174) Remain 09:26:08 loss: 0.3337 Lr: 0.00046 [2024-02-19 11:00:14,540 INFO misc.py line 119 87073] Train: [82/100][644/1557] Data 0.014 (0.186) Batch 1.312 (1.174) Remain 09:26:13 loss: 0.1155 Lr: 0.00046 [2024-02-19 11:00:15,478 INFO misc.py line 119 87073] Train: [82/100][645/1557] Data 0.016 (0.186) Batch 0.950 (1.174) Remain 09:26:02 loss: 0.2883 Lr: 0.00046 [2024-02-19 11:00:16,534 INFO misc.py line 119 87073] Train: [82/100][646/1557] Data 0.004 (0.186) Batch 1.057 (1.173) Remain 09:25:55 loss: 0.3601 Lr: 0.00046 [2024-02-19 11:00:17,373 INFO misc.py line 119 87073] Train: [82/100][647/1557] Data 0.004 (0.186) Batch 0.837 (1.173) Remain 09:25:39 loss: 0.6634 Lr: 0.00046 [2024-02-19 11:00:18,271 INFO misc.py line 119 87073] Train: [82/100][648/1557] Data 0.006 (0.185) Batch 0.890 (1.172) Remain 09:25:25 loss: 0.4775 Lr: 0.00046 [2024-02-19 11:00:18,952 INFO misc.py line 119 87073] Train: [82/100][649/1557] Data 0.014 (0.185) Batch 0.689 (1.172) Remain 09:25:02 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line 119 87073] Train: [82/100][687/1557] Data 0.005 (0.190) Batch 1.122 (1.176) Remain 09:26:34 loss: 0.4966 Lr: 0.00046 [2024-02-19 11:01:07,617 INFO misc.py line 119 87073] Train: [82/100][688/1557] Data 0.006 (0.190) Batch 0.910 (1.176) Remain 09:26:22 loss: 0.4090 Lr: 0.00046 [2024-02-19 11:01:08,595 INFO misc.py line 119 87073] Train: [82/100][689/1557] Data 0.004 (0.190) Batch 0.977 (1.176) Remain 09:26:12 loss: 0.1919 Lr: 0.00046 [2024-02-19 11:01:09,497 INFO misc.py line 119 87073] Train: [82/100][690/1557] Data 0.006 (0.189) Batch 0.904 (1.175) Remain 09:25:59 loss: 0.2605 Lr: 0.00046 [2024-02-19 11:01:10,234 INFO misc.py line 119 87073] Train: [82/100][691/1557] Data 0.003 (0.189) Batch 0.728 (1.175) Remain 09:25:40 loss: 0.1947 Lr: 0.00046 [2024-02-19 11:01:10,990 INFO misc.py line 119 87073] Train: [82/100][692/1557] Data 0.012 (0.189) Batch 0.764 (1.174) Remain 09:25:21 loss: 0.1920 Lr: 0.00046 [2024-02-19 11:01:12,303 INFO misc.py line 119 87073] Train: 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Batch 0.742 (1.172) Remain 09:24:20 loss: 0.2097 Lr: 0.00046 [2024-02-19 11:01:19,213 INFO misc.py line 119 87073] Train: [82/100][700/1557] Data 0.013 (0.187) Batch 1.285 (1.172) Remain 09:24:23 loss: 0.2201 Lr: 0.00046 [2024-02-19 11:01:20,286 INFO misc.py line 119 87073] Train: [82/100][701/1557] Data 0.009 (0.187) Batch 1.069 (1.172) Remain 09:24:18 loss: 0.0759 Lr: 0.00046 [2024-02-19 11:01:21,213 INFO misc.py line 119 87073] Train: [82/100][702/1557] Data 0.013 (0.186) Batch 0.935 (1.172) Remain 09:24:07 loss: 0.4149 Lr: 0.00046 [2024-02-19 11:01:22,179 INFO misc.py line 119 87073] Train: [82/100][703/1557] Data 0.005 (0.186) Batch 0.966 (1.172) Remain 09:23:57 loss: 0.1556 Lr: 0.00046 [2024-02-19 11:01:23,293 INFO misc.py line 119 87073] Train: [82/100][704/1557] Data 0.005 (0.186) Batch 1.114 (1.172) Remain 09:23:53 loss: 0.4125 Lr: 0.00046 [2024-02-19 11:01:25,777 INFO misc.py line 119 87073] Train: [82/100][705/1557] Data 0.897 (0.187) Batch 2.482 (1.173) Remain 09:24:46 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11:02:36,296 INFO misc.py line 119 87073] Train: [82/100][768/1557] Data 0.005 (0.185) Batch 0.787 (1.169) Remain 09:21:24 loss: 0.2016 Lr: 0.00046 [2024-02-19 11:02:37,019 INFO misc.py line 119 87073] Train: [82/100][769/1557] Data 0.005 (0.185) Batch 0.717 (1.168) Remain 09:21:06 loss: 0.3951 Lr: 0.00046 [2024-02-19 11:02:38,367 INFO misc.py line 119 87073] Train: [82/100][770/1557] Data 0.011 (0.185) Batch 1.348 (1.169) Remain 09:21:11 loss: 0.1497 Lr: 0.00046 [2024-02-19 11:02:39,656 INFO misc.py line 119 87073] Train: [82/100][771/1557] Data 0.011 (0.185) Batch 1.289 (1.169) Remain 09:21:15 loss: 0.3248 Lr: 0.00046 [2024-02-19 11:02:40,558 INFO misc.py line 119 87073] Train: [82/100][772/1557] Data 0.011 (0.184) Batch 0.908 (1.168) Remain 09:21:04 loss: 0.1523 Lr: 0.00046 [2024-02-19 11:02:41,388 INFO misc.py line 119 87073] Train: [82/100][773/1557] Data 0.005 (0.184) Batch 0.829 (1.168) Remain 09:20:50 loss: 0.1907 Lr: 0.00046 [2024-02-19 11:02:42,514 INFO misc.py line 119 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09:19:11 loss: 0.1092 Lr: 0.00046 [2024-02-19 11:02:55,330 INFO misc.py line 119 87073] Train: [82/100][787/1557] Data 0.005 (0.181) Batch 1.042 (1.165) Remain 09:19:05 loss: 0.2727 Lr: 0.00046 [2024-02-19 11:02:56,355 INFO misc.py line 119 87073] Train: [82/100][788/1557] Data 0.004 (0.181) Batch 1.022 (1.165) Remain 09:18:59 loss: 0.1657 Lr: 0.00046 [2024-02-19 11:02:57,048 INFO misc.py line 119 87073] Train: [82/100][789/1557] Data 0.007 (0.181) Batch 0.696 (1.164) Remain 09:18:40 loss: 0.1957 Lr: 0.00046 [2024-02-19 11:02:57,838 INFO misc.py line 119 87073] Train: [82/100][790/1557] Data 0.004 (0.180) Batch 0.779 (1.164) Remain 09:18:25 loss: 0.1304 Lr: 0.00046 [2024-02-19 11:03:10,623 INFO misc.py line 119 87073] Train: [82/100][791/1557] Data 9.817 (0.193) Batch 12.797 (1.178) Remain 09:25:29 loss: 0.1210 Lr: 0.00046 [2024-02-19 11:03:11,729 INFO misc.py line 119 87073] Train: [82/100][792/1557] Data 0.004 (0.192) Batch 1.105 (1.178) Remain 09:25:25 loss: 0.1165 Lr: 0.00045 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line 119 87073] Train: [82/100][799/1557] Data 0.005 (0.191) Batch 0.864 (1.176) Remain 09:24:16 loss: 0.2083 Lr: 0.00045 [2024-02-19 11:03:19,195 INFO misc.py line 119 87073] Train: [82/100][800/1557] Data 0.005 (0.190) Batch 0.902 (1.176) Remain 09:24:05 loss: 0.1546 Lr: 0.00045 [2024-02-19 11:03:20,252 INFO misc.py line 119 87073] Train: [82/100][801/1557] Data 0.006 (0.190) Batch 1.050 (1.176) Remain 09:23:59 loss: 0.1790 Lr: 0.00045 [2024-02-19 11:03:21,302 INFO misc.py line 119 87073] Train: [82/100][802/1557] Data 0.014 (0.190) Batch 1.050 (1.176) Remain 09:23:53 loss: 0.7194 Lr: 0.00045 [2024-02-19 11:03:22,040 INFO misc.py line 119 87073] Train: [82/100][803/1557] Data 0.013 (0.190) Batch 0.746 (1.175) Remain 09:23:37 loss: 0.4242 Lr: 0.00045 [2024-02-19 11:03:22,844 INFO misc.py line 119 87073] Train: [82/100][804/1557] Data 0.005 (0.190) Batch 0.797 (1.175) Remain 09:23:22 loss: 0.2301 Lr: 0.00045 [2024-02-19 11:03:24,132 INFO misc.py line 119 87073] Train: 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Batch 0.799 (1.173) Remain 09:22:38 loss: 0.2292 Lr: 0.00045 [2024-02-19 11:03:31,366 INFO misc.py line 119 87073] Train: [82/100][812/1557] Data 0.005 (0.188) Batch 1.310 (1.173) Remain 09:22:41 loss: 0.1394 Lr: 0.00045 [2024-02-19 11:03:32,281 INFO misc.py line 119 87073] Train: [82/100][813/1557] Data 0.015 (0.188) Batch 0.927 (1.173) Remain 09:22:31 loss: 0.2964 Lr: 0.00045 [2024-02-19 11:03:33,487 INFO misc.py line 119 87073] Train: [82/100][814/1557] Data 0.003 (0.187) Batch 1.192 (1.173) Remain 09:22:31 loss: 0.2160 Lr: 0.00045 [2024-02-19 11:03:34,474 INFO misc.py line 119 87073] Train: [82/100][815/1557] Data 0.017 (0.187) Batch 1.000 (1.173) Remain 09:22:24 loss: 0.0595 Lr: 0.00045 [2024-02-19 11:03:35,503 INFO misc.py line 119 87073] Train: [82/100][816/1557] Data 0.003 (0.187) Batch 1.024 (1.173) Remain 09:22:17 loss: 0.1851 Lr: 0.00045 [2024-02-19 11:03:36,281 INFO misc.py line 119 87073] Train: [82/100][817/1557] Data 0.009 (0.187) Batch 0.783 (1.172) Remain 09:22:02 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11:03:42,756 INFO misc.py line 119 87073] Train: [82/100][824/1557] Data 0.004 (0.185) Batch 0.716 (1.170) Remain 09:20:53 loss: 0.1326 Lr: 0.00045 [2024-02-19 11:03:43,485 INFO misc.py line 119 87073] Train: [82/100][825/1557] Data 0.013 (0.185) Batch 0.738 (1.170) Remain 09:20:37 loss: 0.1414 Lr: 0.00045 [2024-02-19 11:03:44,811 INFO misc.py line 119 87073] Train: [82/100][826/1557] Data 0.005 (0.185) Batch 1.314 (1.170) Remain 09:20:41 loss: 0.1009 Lr: 0.00045 [2024-02-19 11:03:45,900 INFO misc.py line 119 87073] Train: [82/100][827/1557] Data 0.018 (0.184) Batch 1.091 (1.170) Remain 09:20:37 loss: 0.4757 Lr: 0.00045 [2024-02-19 11:03:46,733 INFO misc.py line 119 87073] Train: [82/100][828/1557] Data 0.015 (0.184) Batch 0.843 (1.169) Remain 09:20:24 loss: 0.1512 Lr: 0.00045 [2024-02-19 11:03:47,824 INFO misc.py line 119 87073] Train: [82/100][829/1557] Data 0.005 (0.184) Batch 1.092 (1.169) Remain 09:20:20 loss: 0.4877 Lr: 0.00045 [2024-02-19 11:03:48,657 INFO misc.py line 119 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line 119 87073] Train: [82/100][855/1557] Data 0.004 (0.190) Batch 0.809 (1.178) Remain 09:23:57 loss: 0.3704 Lr: 0.00045 [2024-02-19 11:04:26,498 INFO misc.py line 119 87073] Train: [82/100][856/1557] Data 0.020 (0.190) Batch 0.941 (1.178) Remain 09:23:48 loss: 0.3664 Lr: 0.00045 [2024-02-19 11:04:27,380 INFO misc.py line 119 87073] Train: [82/100][857/1557] Data 0.006 (0.190) Batch 0.884 (1.177) Remain 09:23:37 loss: 0.3975 Lr: 0.00045 [2024-02-19 11:04:28,570 INFO misc.py line 119 87073] Train: [82/100][858/1557] Data 0.004 (0.190) Batch 1.188 (1.177) Remain 09:23:36 loss: 0.0900 Lr: 0.00045 [2024-02-19 11:04:29,319 INFO misc.py line 119 87073] Train: [82/100][859/1557] Data 0.005 (0.190) Batch 0.747 (1.177) Remain 09:23:20 loss: 0.2100 Lr: 0.00045 [2024-02-19 11:04:30,068 INFO misc.py line 119 87073] Train: [82/100][860/1557] Data 0.008 (0.189) Batch 0.750 (1.176) Remain 09:23:05 loss: 0.3379 Lr: 0.00045 [2024-02-19 11:04:31,340 INFO misc.py line 119 87073] Train: 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Batch 0.743 (1.174) Remain 09:21:57 loss: 0.2446 Lr: 0.00045 [2024-02-19 11:04:37,770 INFO misc.py line 119 87073] Train: [82/100][868/1557] Data 0.013 (0.188) Batch 1.272 (1.174) Remain 09:21:59 loss: 0.2046 Lr: 0.00045 [2024-02-19 11:04:38,706 INFO misc.py line 119 87073] Train: [82/100][869/1557] Data 0.005 (0.187) Batch 0.937 (1.174) Remain 09:21:50 loss: 0.6020 Lr: 0.00045 [2024-02-19 11:04:39,831 INFO misc.py line 119 87073] Train: [82/100][870/1557] Data 0.005 (0.187) Batch 1.124 (1.174) Remain 09:21:47 loss: 0.1907 Lr: 0.00045 [2024-02-19 11:04:40,698 INFO misc.py line 119 87073] Train: [82/100][871/1557] Data 0.005 (0.187) Batch 0.869 (1.174) Remain 09:21:36 loss: 0.1239 Lr: 0.00045 [2024-02-19 11:04:41,664 INFO misc.py line 119 87073] Train: [82/100][872/1557] Data 0.004 (0.187) Batch 0.958 (1.173) Remain 09:21:27 loss: 0.1480 Lr: 0.00045 [2024-02-19 11:04:42,406 INFO misc.py line 119 87073] Train: [82/100][873/1557] Data 0.011 (0.187) Batch 0.749 (1.173) Remain 09:21:12 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11:04:50,378 INFO misc.py line 119 87073] Train: [82/100][880/1557] Data 0.863 (0.186) Batch 2.182 (1.173) Remain 09:20:56 loss: 0.1882 Lr: 0.00045 [2024-02-19 11:04:51,187 INFO misc.py line 119 87073] Train: [82/100][881/1557] Data 0.005 (0.186) Batch 0.808 (1.172) Remain 09:20:43 loss: 0.1544 Lr: 0.00045 [2024-02-19 11:04:52,378 INFO misc.py line 119 87073] Train: [82/100][882/1557] Data 0.005 (0.186) Batch 1.191 (1.172) Remain 09:20:43 loss: 0.1392 Lr: 0.00045 [2024-02-19 11:04:53,324 INFO misc.py line 119 87073] Train: [82/100][883/1557] Data 0.005 (0.186) Batch 0.948 (1.172) Remain 09:20:34 loss: 0.2374 Lr: 0.00045 [2024-02-19 11:04:54,395 INFO misc.py line 119 87073] Train: [82/100][884/1557] Data 0.004 (0.185) Batch 1.070 (1.172) Remain 09:20:30 loss: 0.0497 Lr: 0.00045 [2024-02-19 11:04:55,286 INFO misc.py line 119 87073] Train: [82/100][885/1557] Data 0.005 (0.185) Batch 0.891 (1.172) Remain 09:20:19 loss: 0.2311 Lr: 0.00045 [2024-02-19 11:04:56,166 INFO misc.py line 119 87073] Train: [82/100][886/1557] Data 0.004 (0.185) Batch 0.879 (1.171) Remain 09:20:09 loss: 0.1631 Lr: 0.00045 [2024-02-19 11:04:56,974 INFO misc.py line 119 87073] Train: [82/100][887/1557] Data 0.005 (0.185) Batch 0.808 (1.171) Remain 09:19:56 loss: 0.1647 Lr: 0.00045 [2024-02-19 11:04:57,743 INFO misc.py line 119 87073] Train: [82/100][888/1557] Data 0.005 (0.185) Batch 0.770 (1.170) Remain 09:19:42 loss: 0.1524 Lr: 0.00045 [2024-02-19 11:04:59,021 INFO misc.py line 119 87073] Train: [82/100][889/1557] Data 0.004 (0.184) Batch 1.277 (1.170) Remain 09:19:44 loss: 0.1903 Lr: 0.00045 [2024-02-19 11:04:59,949 INFO misc.py line 119 87073] Train: [82/100][890/1557] Data 0.006 (0.184) Batch 0.929 (1.170) Remain 09:19:35 loss: 0.1847 Lr: 0.00045 [2024-02-19 11:05:01,026 INFO misc.py line 119 87073] Train: [82/100][891/1557] Data 0.004 (0.184) Batch 1.078 (1.170) Remain 09:19:31 loss: 0.2695 Lr: 0.00045 [2024-02-19 11:05:01,863 INFO misc.py line 119 87073] Train: [82/100][892/1557] Data 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Batch 0.800 (1.173) Remain 09:19:19 loss: 0.3983 Lr: 0.00045 [2024-02-19 11:06:48,393 INFO misc.py line 119 87073] Train: [82/100][980/1557] Data 0.008 (0.187) Batch 1.265 (1.173) Remain 09:19:21 loss: 0.1107 Lr: 0.00045 [2024-02-19 11:06:49,475 INFO misc.py line 119 87073] Train: [82/100][981/1557] Data 0.013 (0.187) Batch 1.090 (1.173) Remain 09:19:17 loss: 0.0814 Lr: 0.00045 [2024-02-19 11:06:50,538 INFO misc.py line 119 87073] Train: [82/100][982/1557] Data 0.005 (0.187) Batch 1.053 (1.173) Remain 09:19:13 loss: 0.1465 Lr: 0.00045 [2024-02-19 11:06:51,388 INFO misc.py line 119 87073] Train: [82/100][983/1557] Data 0.015 (0.187) Batch 0.861 (1.173) Remain 09:19:02 loss: 0.6093 Lr: 0.00045 [2024-02-19 11:06:52,584 INFO misc.py line 119 87073] Train: [82/100][984/1557] Data 0.005 (0.187) Batch 1.194 (1.173) Remain 09:19:02 loss: 0.3339 Lr: 0.00045 [2024-02-19 11:06:53,340 INFO misc.py line 119 87073] Train: [82/100][985/1557] Data 0.007 (0.187) Batch 0.759 (1.172) Remain 09:18:49 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Data 0.009 (0.187) Batch 0.669 (1.173) Remain 09:18:04 loss: 0.2265 Lr: 0.00045 [2024-02-19 11:07:53,735 INFO misc.py line 119 87073] Train: [82/100][1036/1557] Data 0.004 (0.187) Batch 1.243 (1.173) Remain 09:18:05 loss: 0.1465 Lr: 0.00045 [2024-02-19 11:07:54,760 INFO misc.py line 119 87073] Train: [82/100][1037/1557] Data 0.008 (0.187) Batch 1.019 (1.173) Remain 09:18:00 loss: 0.0939 Lr: 0.00045 [2024-02-19 11:07:55,733 INFO misc.py line 119 87073] Train: [82/100][1038/1557] Data 0.013 (0.186) Batch 0.983 (1.173) Remain 09:17:53 loss: 0.1680 Lr: 0.00045 [2024-02-19 11:07:56,781 INFO misc.py line 119 87073] Train: [82/100][1039/1557] Data 0.004 (0.186) Batch 1.047 (1.173) Remain 09:17:49 loss: 0.3790 Lr: 0.00045 [2024-02-19 11:07:57,730 INFO misc.py line 119 87073] Train: [82/100][1040/1557] Data 0.004 (0.186) Batch 0.950 (1.172) Remain 09:17:41 loss: 0.9146 Lr: 0.00045 [2024-02-19 11:07:58,460 INFO misc.py line 119 87073] Train: [82/100][1041/1557] Data 0.004 (0.186) Batch 0.723 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Data 0.016 (0.183) Batch 1.015 (1.168) Remain 09:15:06 loss: 0.3027 Lr: 0.00045 [2024-02-19 11:08:24,590 INFO misc.py line 119 87073] Train: [82/100][1067/1557] Data 0.012 (0.183) Batch 1.017 (1.168) Remain 09:15:01 loss: 0.2147 Lr: 0.00045 [2024-02-19 11:08:25,599 INFO misc.py line 119 87073] Train: [82/100][1068/1557] Data 0.010 (0.183) Batch 1.014 (1.168) Remain 09:14:56 loss: 0.2276 Lr: 0.00045 [2024-02-19 11:08:26,368 INFO misc.py line 119 87073] Train: [82/100][1069/1557] Data 0.005 (0.182) Batch 0.769 (1.167) Remain 09:14:44 loss: 0.1004 Lr: 0.00045 [2024-02-19 11:08:27,145 INFO misc.py line 119 87073] Train: [82/100][1070/1557] Data 0.005 (0.182) Batch 0.770 (1.167) Remain 09:14:32 loss: 0.1693 Lr: 0.00045 [2024-02-19 11:08:40,725 INFO misc.py line 119 87073] Train: [82/100][1071/1557] Data 9.888 (0.191) Batch 13.587 (1.179) Remain 09:20:03 loss: 0.0904 Lr: 0.00045 [2024-02-19 11:08:41,972 INFO misc.py line 119 87073] Train: [82/100][1072/1557] Data 0.005 (0.191) Batch 1.238 (1.179) Remain 09:20:03 loss: 0.3740 Lr: 0.00045 [2024-02-19 11:08:42,902 INFO misc.py line 119 87073] Train: [82/100][1073/1557] Data 0.014 (0.191) Batch 0.940 (1.178) Remain 09:19:56 loss: 0.3481 Lr: 0.00045 [2024-02-19 11:08:43,913 INFO misc.py line 119 87073] Train: [82/100][1074/1557] Data 0.003 (0.191) Batch 1.010 (1.178) Remain 09:19:50 loss: 0.1383 Lr: 0.00045 [2024-02-19 11:08:44,843 INFO misc.py line 119 87073] Train: [82/100][1075/1557] Data 0.004 (0.191) Batch 0.929 (1.178) Remain 09:19:42 loss: 0.1584 Lr: 0.00045 [2024-02-19 11:08:45,641 INFO misc.py line 119 87073] Train: [82/100][1076/1557] Data 0.005 (0.190) Batch 0.791 (1.178) Remain 09:19:31 loss: 0.3410 Lr: 0.00045 [2024-02-19 11:08:46,423 INFO misc.py line 119 87073] Train: [82/100][1077/1557] Data 0.013 (0.190) Batch 0.790 (1.177) Remain 09:19:19 loss: 0.2127 Lr: 0.00045 [2024-02-19 11:08:47,565 INFO misc.py line 119 87073] Train: [82/100][1078/1557] Data 0.004 (0.190) Batch 1.143 (1.177) Remain 09:19:17 loss: 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11:08:54,339 INFO misc.py line 119 87073] Train: [82/100][1085/1557] Data 0.003 (0.189) Batch 1.267 (1.176) Remain 09:18:29 loss: 0.1177 Lr: 0.00045 [2024-02-19 11:08:55,228 INFO misc.py line 119 87073] Train: [82/100][1086/1557] Data 0.033 (0.189) Batch 0.919 (1.176) Remain 09:18:21 loss: 0.2245 Lr: 0.00045 [2024-02-19 11:08:56,242 INFO misc.py line 119 87073] Train: [82/100][1087/1557] Data 0.004 (0.189) Batch 1.014 (1.175) Remain 09:18:16 loss: 0.3894 Lr: 0.00045 [2024-02-19 11:08:57,249 INFO misc.py line 119 87073] Train: [82/100][1088/1557] Data 0.004 (0.188) Batch 1.006 (1.175) Remain 09:18:10 loss: 0.2401 Lr: 0.00045 [2024-02-19 11:08:58,174 INFO misc.py line 119 87073] Train: [82/100][1089/1557] Data 0.005 (0.188) Batch 0.926 (1.175) Remain 09:18:03 loss: 0.1483 Lr: 0.00045 [2024-02-19 11:08:58,946 INFO misc.py line 119 87073] Train: [82/100][1090/1557] Data 0.003 (0.188) Batch 0.768 (1.175) Remain 09:17:51 loss: 0.1569 Lr: 0.00045 [2024-02-19 11:08:59,732 INFO misc.py line 119 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Data 0.007 (0.187) Batch 0.801 (1.173) Remain 09:17:07 loss: 0.2619 Lr: 0.00045 [2024-02-19 11:09:06,587 INFO misc.py line 119 87073] Train: [82/100][1098/1557] Data 0.004 (0.187) Batch 0.803 (1.173) Remain 09:16:56 loss: 0.1422 Lr: 0.00045 [2024-02-19 11:09:07,680 INFO misc.py line 119 87073] Train: [82/100][1099/1557] Data 0.004 (0.187) Batch 1.083 (1.173) Remain 09:16:52 loss: 0.1271 Lr: 0.00045 [2024-02-19 11:09:08,582 INFO misc.py line 119 87073] Train: [82/100][1100/1557] Data 0.014 (0.186) Batch 0.912 (1.173) Remain 09:16:44 loss: 0.5448 Lr: 0.00045 [2024-02-19 11:09:09,634 INFO misc.py line 119 87073] Train: [82/100][1101/1557] Data 0.004 (0.186) Batch 1.052 (1.173) Remain 09:16:40 loss: 0.4963 Lr: 0.00045 [2024-02-19 11:09:10,658 INFO misc.py line 119 87073] Train: [82/100][1102/1557] Data 0.005 (0.186) Batch 1.025 (1.173) Remain 09:16:35 loss: 0.3662 Lr: 0.00045 [2024-02-19 11:09:11,572 INFO misc.py line 119 87073] Train: [82/100][1103/1557] Data 0.004 (0.186) Batch 0.913 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(0.186) Batch 1.069 (1.172) Remain 09:15:11 loss: 0.1463 Lr: 0.00044 [2024-02-19 11:10:25,059 INFO misc.py line 119 87073] Train: [82/100][1166/1557] Data 0.007 (0.185) Batch 0.955 (1.172) Remain 09:15:04 loss: 0.0897 Lr: 0.00044 [2024-02-19 11:10:25,868 INFO misc.py line 119 87073] Train: [82/100][1167/1557] Data 0.005 (0.185) Batch 0.809 (1.172) Remain 09:14:54 loss: 0.2361 Lr: 0.00044 [2024-02-19 11:10:26,591 INFO misc.py line 119 87073] Train: [82/100][1168/1557] Data 0.005 (0.185) Batch 0.723 (1.171) Remain 09:14:42 loss: 0.1709 Lr: 0.00044 [2024-02-19 11:10:27,873 INFO misc.py line 119 87073] Train: [82/100][1169/1557] Data 0.004 (0.185) Batch 1.273 (1.171) Remain 09:14:43 loss: 0.1882 Lr: 0.00044 [2024-02-19 11:10:28,752 INFO misc.py line 119 87073] Train: [82/100][1170/1557] Data 0.014 (0.185) Batch 0.889 (1.171) Remain 09:14:35 loss: 0.2910 Lr: 0.00044 [2024-02-19 11:10:29,594 INFO misc.py line 119 87073] Train: [82/100][1171/1557] Data 0.004 (0.185) Batch 0.841 (1.171) Remain 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misc.py line 119 87073] Train: [82/100][1184/1557] Data 0.017 (0.191) Batch 0.981 (1.179) Remain 09:18:02 loss: 0.3033 Lr: 0.00044 [2024-02-19 11:10:55,455 INFO misc.py line 119 87073] Train: [82/100][1185/1557] Data 0.003 (0.191) Batch 1.014 (1.179) Remain 09:17:57 loss: 0.5314 Lr: 0.00044 [2024-02-19 11:10:56,382 INFO misc.py line 119 87073] Train: [82/100][1186/1557] Data 0.004 (0.191) Batch 0.927 (1.179) Remain 09:17:50 loss: 0.2828 Lr: 0.00044 [2024-02-19 11:10:57,523 INFO misc.py line 119 87073] Train: [82/100][1187/1557] Data 0.004 (0.191) Batch 1.142 (1.179) Remain 09:17:48 loss: 0.2832 Lr: 0.00044 [2024-02-19 11:10:58,322 INFO misc.py line 119 87073] Train: [82/100][1188/1557] Data 0.003 (0.191) Batch 0.799 (1.178) Remain 09:17:38 loss: 0.2212 Lr: 0.00044 [2024-02-19 11:10:59,083 INFO misc.py line 119 87073] Train: [82/100][1189/1557] Data 0.003 (0.191) Batch 0.748 (1.178) Remain 09:17:26 loss: 0.1388 Lr: 0.00044 [2024-02-19 11:11:00,163 INFO misc.py line 119 87073] Train: 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09:15:55 loss: 0.2047 Lr: 0.00044 [2024-02-19 11:11:11,928 INFO misc.py line 119 87073] Train: [82/100][1203/1557] Data 0.017 (0.189) Batch 0.743 (1.175) Remain 09:15:44 loss: 0.1942 Lr: 0.00044 [2024-02-19 11:11:13,130 INFO misc.py line 119 87073] Train: [82/100][1204/1557] Data 0.004 (0.188) Batch 1.202 (1.175) Remain 09:15:43 loss: 0.1990 Lr: 0.00044 [2024-02-19 11:11:14,075 INFO misc.py line 119 87073] Train: [82/100][1205/1557] Data 0.004 (0.188) Batch 0.945 (1.175) Remain 09:15:37 loss: 0.2712 Lr: 0.00044 [2024-02-19 11:11:15,232 INFO misc.py line 119 87073] Train: [82/100][1206/1557] Data 0.004 (0.188) Batch 1.157 (1.175) Remain 09:15:35 loss: 0.4515 Lr: 0.00044 [2024-02-19 11:11:16,277 INFO misc.py line 119 87073] Train: [82/100][1207/1557] Data 0.004 (0.188) Batch 1.045 (1.175) Remain 09:15:31 loss: 0.3981 Lr: 0.00044 [2024-02-19 11:11:17,280 INFO misc.py line 119 87073] Train: [82/100][1208/1557] Data 0.004 (0.188) Batch 1.002 (1.174) Remain 09:15:26 loss: 0.2627 Lr: 0.00044 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Train: [82/100][1252/1557] Data 0.004 (0.191) Batch 0.791 (1.177) Remain 09:15:51 loss: 0.2728 Lr: 0.00044 [2024-02-19 11:12:13,703 INFO misc.py line 119 87073] Train: [82/100][1253/1557] Data 0.004 (0.191) Batch 1.344 (1.177) Remain 09:15:54 loss: 0.1040 Lr: 0.00044 [2024-02-19 11:12:14,668 INFO misc.py line 119 87073] Train: [82/100][1254/1557] Data 0.004 (0.191) Batch 0.965 (1.177) Remain 09:15:48 loss: 0.1016 Lr: 0.00044 [2024-02-19 11:12:15,545 INFO misc.py line 119 87073] Train: [82/100][1255/1557] Data 0.004 (0.191) Batch 0.877 (1.177) Remain 09:15:40 loss: 0.0626 Lr: 0.00044 [2024-02-19 11:12:16,655 INFO misc.py line 119 87073] Train: [82/100][1256/1557] Data 0.004 (0.191) Batch 1.110 (1.177) Remain 09:15:37 loss: 0.5600 Lr: 0.00044 [2024-02-19 11:12:17,551 INFO misc.py line 119 87073] Train: [82/100][1257/1557] Data 0.003 (0.190) Batch 0.896 (1.177) Remain 09:15:30 loss: 0.1636 Lr: 0.00044 [2024-02-19 11:12:18,309 INFO misc.py line 119 87073] Train: [82/100][1258/1557] Data 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Remain 09:14:48 loss: 0.1813 Lr: 0.00044 [2024-02-19 11:12:25,047 INFO misc.py line 119 87073] Train: [82/100][1265/1557] Data 0.008 (0.189) Batch 0.756 (1.175) Remain 09:14:37 loss: 0.1911 Lr: 0.00044 [2024-02-19 11:12:25,818 INFO misc.py line 119 87073] Train: [82/100][1266/1557] Data 0.004 (0.189) Batch 0.771 (1.175) Remain 09:14:27 loss: 0.2721 Lr: 0.00044 [2024-02-19 11:12:26,936 INFO misc.py line 119 87073] Train: [82/100][1267/1557] Data 0.004 (0.189) Batch 1.116 (1.175) Remain 09:14:24 loss: 0.2730 Lr: 0.00044 [2024-02-19 11:12:27,860 INFO misc.py line 119 87073] Train: [82/100][1268/1557] Data 0.006 (0.189) Batch 0.927 (1.175) Remain 09:14:18 loss: 0.3420 Lr: 0.00044 [2024-02-19 11:12:28,870 INFO misc.py line 119 87073] Train: [82/100][1269/1557] Data 0.003 (0.189) Batch 1.009 (1.174) Remain 09:14:13 loss: 0.0634 Lr: 0.00044 [2024-02-19 11:12:29,754 INFO misc.py line 119 87073] Train: [82/100][1270/1557] Data 0.003 (0.189) Batch 0.884 (1.174) Remain 09:14:05 loss: 0.1840 Lr: 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Train: [82/100][1283/1557] Data 0.003 (0.187) Batch 1.027 (1.172) Remain 09:12:42 loss: 0.2809 Lr: 0.00044 [2024-02-19 11:12:42,795 INFO misc.py line 119 87073] Train: [82/100][1284/1557] Data 0.004 (0.187) Batch 0.863 (1.172) Remain 09:12:34 loss: 0.7150 Lr: 0.00044 [2024-02-19 11:12:43,709 INFO misc.py line 119 87073] Train: [82/100][1285/1557] Data 0.004 (0.186) Batch 0.912 (1.171) Remain 09:12:27 loss: 0.1293 Lr: 0.00044 [2024-02-19 11:12:44,501 INFO misc.py line 119 87073] Train: [82/100][1286/1557] Data 0.006 (0.186) Batch 0.794 (1.171) Remain 09:12:17 loss: 0.1691 Lr: 0.00044 [2024-02-19 11:12:45,217 INFO misc.py line 119 87073] Train: [82/100][1287/1557] Data 0.004 (0.186) Batch 0.715 (1.171) Remain 09:12:06 loss: 0.2014 Lr: 0.00044 [2024-02-19 11:12:46,420 INFO misc.py line 119 87073] Train: [82/100][1288/1557] Data 0.004 (0.186) Batch 1.199 (1.171) Remain 09:12:05 loss: 0.1018 Lr: 0.00044 [2024-02-19 11:12:47,274 INFO misc.py line 119 87073] Train: [82/100][1289/1557] Data 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Remain 09:15:28 loss: 0.1169 Lr: 0.00044 [2024-02-19 11:13:05,351 INFO misc.py line 119 87073] Train: [82/100][1296/1557] Data 0.004 (0.191) Batch 1.132 (1.178) Remain 09:15:25 loss: 0.4254 Lr: 0.00044 [2024-02-19 11:13:06,477 INFO misc.py line 119 87073] Train: [82/100][1297/1557] Data 0.004 (0.191) Batch 1.125 (1.178) Remain 09:15:23 loss: 0.1800 Lr: 0.00044 [2024-02-19 11:13:07,312 INFO misc.py line 119 87073] Train: [82/100][1298/1557] Data 0.004 (0.191) Batch 0.835 (1.178) Remain 09:15:14 loss: 0.1697 Lr: 0.00044 [2024-02-19 11:13:08,321 INFO misc.py line 119 87073] Train: [82/100][1299/1557] Data 0.004 (0.191) Batch 1.008 (1.178) Remain 09:15:10 loss: 0.4010 Lr: 0.00044 [2024-02-19 11:13:09,090 INFO misc.py line 119 87073] Train: [82/100][1300/1557] Data 0.006 (0.191) Batch 0.772 (1.177) Remain 09:15:00 loss: 0.1120 Lr: 0.00044 [2024-02-19 11:13:09,819 INFO misc.py line 119 87073] Train: [82/100][1301/1557] Data 0.004 (0.190) Batch 0.724 (1.177) Remain 09:14:48 loss: 0.1679 Lr: 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Train: [82/100][1314/1557] Data 0.005 (0.189) Batch 0.705 (1.178) Remain 09:14:47 loss: 0.1476 Lr: 0.00044 [2024-02-19 11:13:26,573 INFO misc.py line 119 87073] Train: [82/100][1315/1557] Data 0.006 (0.189) Batch 0.804 (1.177) Remain 09:14:38 loss: 0.1713 Lr: 0.00044 [2024-02-19 11:13:27,819 INFO misc.py line 119 87073] Train: [82/100][1316/1557] Data 0.017 (0.188) Batch 1.247 (1.177) Remain 09:14:38 loss: 0.0995 Lr: 0.00044 [2024-02-19 11:13:28,830 INFO misc.py line 119 87073] Train: [82/100][1317/1557] Data 0.017 (0.188) Batch 1.013 (1.177) Remain 09:14:33 loss: 0.1228 Lr: 0.00044 [2024-02-19 11:13:29,708 INFO misc.py line 119 87073] Train: [82/100][1318/1557] Data 0.014 (0.188) Batch 0.887 (1.177) Remain 09:14:26 loss: 0.3467 Lr: 0.00044 [2024-02-19 11:13:30,813 INFO misc.py line 119 87073] Train: [82/100][1319/1557] Data 0.005 (0.188) Batch 1.106 (1.177) Remain 09:14:23 loss: 0.1995 Lr: 0.00044 [2024-02-19 11:13:32,139 INFO misc.py line 119 87073] Train: [82/100][1320/1557] Data 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Remain 09:13:48 loss: 0.2609 Lr: 0.00044 [2024-02-19 11:13:38,591 INFO misc.py line 119 87073] Train: [82/100][1327/1557] Data 0.004 (0.187) Batch 0.812 (1.176) Remain 09:13:39 loss: 0.2976 Lr: 0.00044 [2024-02-19 11:13:39,362 INFO misc.py line 119 87073] Train: [82/100][1328/1557] Data 0.005 (0.187) Batch 0.764 (1.175) Remain 09:13:29 loss: 0.3601 Lr: 0.00044 [2024-02-19 11:13:40,150 INFO misc.py line 119 87073] Train: [82/100][1329/1557] Data 0.011 (0.187) Batch 0.796 (1.175) Remain 09:13:20 loss: 0.1517 Lr: 0.00044 [2024-02-19 11:13:41,360 INFO misc.py line 119 87073] Train: [82/100][1330/1557] Data 0.004 (0.186) Batch 1.208 (1.175) Remain 09:13:19 loss: 0.2191 Lr: 0.00044 [2024-02-19 11:13:42,566 INFO misc.py line 119 87073] Train: [82/100][1331/1557] Data 0.007 (0.186) Batch 1.196 (1.175) Remain 09:13:18 loss: 0.6847 Lr: 0.00044 [2024-02-19 11:13:43,772 INFO misc.py line 119 87073] Train: [82/100][1332/1557] Data 0.017 (0.186) Batch 1.206 (1.175) Remain 09:13:18 loss: 0.1359 Lr: 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INFO misc.py line 119 87073] Train: [82/100][1339/1557] Data 0.005 (0.185) Batch 0.891 (1.174) Remain 09:12:40 loss: 0.3336 Lr: 0.00044 [2024-02-19 11:13:51,395 INFO misc.py line 119 87073] Train: [82/100][1340/1557] Data 0.006 (0.185) Batch 0.800 (1.174) Remain 09:12:31 loss: 0.0307 Lr: 0.00044 [2024-02-19 11:13:52,369 INFO misc.py line 119 87073] Train: [82/100][1341/1557] Data 0.005 (0.185) Batch 0.975 (1.174) Remain 09:12:26 loss: 0.4487 Lr: 0.00044 [2024-02-19 11:13:53,167 INFO misc.py line 119 87073] Train: [82/100][1342/1557] Data 0.004 (0.185) Batch 0.799 (1.173) Remain 09:12:17 loss: 0.2115 Lr: 0.00044 [2024-02-19 11:13:53,920 INFO misc.py line 119 87073] Train: [82/100][1343/1557] Data 0.004 (0.185) Batch 0.752 (1.173) Remain 09:12:07 loss: 0.1110 Lr: 0.00044 [2024-02-19 11:13:55,135 INFO misc.py line 119 87073] Train: [82/100][1344/1557] Data 0.004 (0.185) Batch 1.212 (1.173) Remain 09:12:06 loss: 0.0745 Lr: 0.00044 [2024-02-19 11:13:56,175 INFO misc.py line 119 87073] Train: [82/100][1345/1557] Data 0.007 (0.184) Batch 1.043 (1.173) Remain 09:12:02 loss: 0.0841 Lr: 0.00044 [2024-02-19 11:13:57,049 INFO misc.py line 119 87073] Train: [82/100][1346/1557] Data 0.005 (0.184) Batch 0.874 (1.173) Remain 09:11:55 loss: 0.2033 Lr: 0.00044 [2024-02-19 11:13:57,904 INFO misc.py line 119 87073] Train: [82/100][1347/1557] Data 0.005 (0.184) Batch 0.855 (1.173) Remain 09:11:47 loss: 0.2499 Lr: 0.00044 [2024-02-19 11:13:58,789 INFO misc.py line 119 87073] Train: [82/100][1348/1557] Data 0.005 (0.184) Batch 0.876 (1.172) Remain 09:11:40 loss: 0.4497 Lr: 0.00044 [2024-02-19 11:13:59,526 INFO misc.py line 119 87073] Train: [82/100][1349/1557] Data 0.013 (0.184) Batch 0.745 (1.172) Remain 09:11:30 loss: 0.1158 Lr: 0.00044 [2024-02-19 11:14:00,250 INFO misc.py line 119 87073] Train: [82/100][1350/1557] Data 0.005 (0.184) Batch 0.721 (1.172) Remain 09:11:19 loss: 0.1666 Lr: 0.00044 [2024-02-19 11:14:12,827 INFO misc.py line 119 87073] Train: [82/100][1351/1557] Data 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Remain 09:14:33 loss: 0.2882 Lr: 0.00044 [2024-02-19 11:14:24,325 INFO misc.py line 119 87073] Train: [82/100][1358/1557] Data 0.003 (0.190) Batch 6.194 (1.183) Remain 09:16:16 loss: 0.0910 Lr: 0.00044 [2024-02-19 11:14:25,201 INFO misc.py line 119 87073] Train: [82/100][1359/1557] Data 0.004 (0.190) Batch 0.868 (1.182) Remain 09:16:08 loss: 0.2891 Lr: 0.00044 [2024-02-19 11:14:26,130 INFO misc.py line 119 87073] Train: [82/100][1360/1557] Data 0.011 (0.189) Batch 0.936 (1.182) Remain 09:16:02 loss: 0.2164 Lr: 0.00044 [2024-02-19 11:14:27,273 INFO misc.py line 119 87073] Train: [82/100][1361/1557] Data 0.005 (0.189) Batch 1.144 (1.182) Remain 09:16:00 loss: 0.0959 Lr: 0.00044 [2024-02-19 11:14:28,150 INFO misc.py line 119 87073] Train: [82/100][1362/1557] Data 0.004 (0.189) Batch 0.877 (1.182) Remain 09:15:52 loss: 0.5437 Lr: 0.00044 [2024-02-19 11:14:28,919 INFO misc.py line 119 87073] Train: [82/100][1363/1557] Data 0.003 (0.189) Batch 0.758 (1.182) Remain 09:15:42 loss: 0.2393 Lr: 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Train: [82/100][1376/1557] Data 0.004 (0.187) Batch 0.869 (1.180) Remain 09:14:34 loss: 0.1286 Lr: 0.00044 [2024-02-19 11:14:42,439 INFO misc.py line 119 87073] Train: [82/100][1377/1557] Data 0.004 (0.187) Batch 0.774 (1.179) Remain 09:14:24 loss: 0.2038 Lr: 0.00044 [2024-02-19 11:14:43,213 INFO misc.py line 119 87073] Train: [82/100][1378/1557] Data 0.005 (0.187) Batch 0.762 (1.179) Remain 09:14:14 loss: 0.1943 Lr: 0.00044 [2024-02-19 11:14:44,388 INFO misc.py line 119 87073] Train: [82/100][1379/1557] Data 0.016 (0.187) Batch 1.176 (1.179) Remain 09:14:13 loss: 0.1454 Lr: 0.00044 [2024-02-19 11:14:45,297 INFO misc.py line 119 87073] Train: [82/100][1380/1557] Data 0.014 (0.187) Batch 0.918 (1.179) Remain 09:14:07 loss: 0.2414 Lr: 0.00044 [2024-02-19 11:14:46,526 INFO misc.py line 119 87073] Train: [82/100][1381/1557] Data 0.007 (0.187) Batch 1.223 (1.179) Remain 09:14:06 loss: 0.1321 Lr: 0.00044 [2024-02-19 11:14:47,429 INFO misc.py line 119 87073] Train: [82/100][1382/1557] Data 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Remain 09:13:20 loss: 0.2776 Lr: 0.00044 [2024-02-19 11:14:53,797 INFO misc.py line 119 87073] Train: [82/100][1389/1557] Data 0.011 (0.186) Batch 0.884 (1.177) Remain 09:13:13 loss: 0.2286 Lr: 0.00044 [2024-02-19 11:14:54,933 INFO misc.py line 119 87073] Train: [82/100][1390/1557] Data 0.006 (0.186) Batch 1.138 (1.177) Remain 09:13:11 loss: 0.2136 Lr: 0.00044 [2024-02-19 11:14:55,659 INFO misc.py line 119 87073] Train: [82/100][1391/1557] Data 0.004 (0.185) Batch 0.725 (1.177) Remain 09:13:01 loss: 0.1855 Lr: 0.00044 [2024-02-19 11:14:56,428 INFO misc.py line 119 87073] Train: [82/100][1392/1557] Data 0.004 (0.185) Batch 0.760 (1.177) Remain 09:12:51 loss: 0.2794 Lr: 0.00044 [2024-02-19 11:14:57,741 INFO misc.py line 119 87073] Train: [82/100][1393/1557] Data 0.013 (0.185) Batch 1.307 (1.177) Remain 09:12:53 loss: 0.0867 Lr: 0.00044 [2024-02-19 11:14:58,713 INFO misc.py line 119 87073] Train: [82/100][1394/1557] Data 0.020 (0.185) Batch 0.987 (1.177) Remain 09:12:48 loss: 0.2191 Lr: 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Train: [82/100][1407/1557] Data 10.049 (0.191) Batch 12.379 (1.184) Remain 09:15:46 loss: 0.1031 Lr: 0.00044 [2024-02-19 11:15:24,528 INFO misc.py line 119 87073] Train: [82/100][1408/1557] Data 0.004 (0.191) Batch 0.869 (1.183) Remain 09:15:38 loss: 0.2460 Lr: 0.00044 [2024-02-19 11:15:25,452 INFO misc.py line 119 87073] Train: [82/100][1409/1557] Data 0.005 (0.191) Batch 0.921 (1.183) Remain 09:15:32 loss: 0.3797 Lr: 0.00044 [2024-02-19 11:15:26,295 INFO misc.py line 119 87073] Train: [82/100][1410/1557] Data 0.007 (0.191) Batch 0.845 (1.183) Remain 09:15:24 loss: 0.5074 Lr: 0.00044 [2024-02-19 11:15:27,332 INFO misc.py line 119 87073] Train: [82/100][1411/1557] Data 0.004 (0.191) Batch 1.036 (1.183) Remain 09:15:20 loss: 0.4453 Lr: 0.00044 [2024-02-19 11:15:28,122 INFO misc.py line 119 87073] Train: [82/100][1412/1557] Data 0.004 (0.190) Batch 0.789 (1.182) Remain 09:15:11 loss: 0.1906 Lr: 0.00044 [2024-02-19 11:15:28,903 INFO misc.py line 119 87073] Train: [82/100][1413/1557] Data 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Remain 09:15:35 loss: 0.1268 Lr: 0.00044 [2024-02-19 11:15:38,788 INFO misc.py line 119 87073] Train: [82/100][1420/1557] Data 0.005 (0.189) Batch 0.755 (1.183) Remain 09:15:25 loss: 0.2263 Lr: 0.00044 [2024-02-19 11:15:40,099 INFO misc.py line 119 87073] Train: [82/100][1421/1557] Data 0.010 (0.189) Batch 1.305 (1.183) Remain 09:15:27 loss: 0.1086 Lr: 0.00044 [2024-02-19 11:15:40,947 INFO misc.py line 119 87073] Train: [82/100][1422/1557] Data 0.015 (0.189) Batch 0.857 (1.183) Remain 09:15:19 loss: 0.1943 Lr: 0.00044 [2024-02-19 11:15:41,923 INFO misc.py line 119 87073] Train: [82/100][1423/1557] Data 0.006 (0.189) Batch 0.978 (1.183) Remain 09:15:14 loss: 0.4565 Lr: 0.00044 [2024-02-19 11:15:42,790 INFO misc.py line 119 87073] Train: [82/100][1424/1557] Data 0.004 (0.189) Batch 0.865 (1.183) Remain 09:15:06 loss: 0.0576 Lr: 0.00044 [2024-02-19 11:15:43,853 INFO misc.py line 119 87073] Train: [82/100][1425/1557] Data 0.005 (0.189) Batch 1.062 (1.183) Remain 09:15:03 loss: 0.3607 Lr: 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INFO misc.py line 119 87073] Train: [82/100][1432/1557] Data 0.004 (0.188) Batch 0.937 (1.181) Remain 09:14:18 loss: 0.1182 Lr: 0.00044 [2024-02-19 11:15:51,075 INFO misc.py line 119 87073] Train: [82/100][1433/1557] Data 0.005 (0.188) Batch 0.785 (1.181) Remain 09:14:09 loss: 0.2138 Lr: 0.00044 [2024-02-19 11:15:51,878 INFO misc.py line 119 87073] Train: [82/100][1434/1557] Data 0.003 (0.188) Batch 0.790 (1.181) Remain 09:14:00 loss: 0.1907 Lr: 0.00044 [2024-02-19 11:15:53,029 INFO misc.py line 119 87073] Train: [82/100][1435/1557] Data 0.017 (0.187) Batch 1.157 (1.181) Remain 09:13:59 loss: 0.1214 Lr: 0.00044 [2024-02-19 11:15:54,112 INFO misc.py line 119 87073] Train: [82/100][1436/1557] Data 0.011 (0.187) Batch 1.087 (1.181) Remain 09:13:56 loss: 0.1611 Lr: 0.00044 [2024-02-19 11:15:55,184 INFO misc.py line 119 87073] Train: [82/100][1437/1557] Data 0.008 (0.187) Batch 1.064 (1.181) Remain 09:13:52 loss: 0.1168 Lr: 0.00044 [2024-02-19 11:15:56,180 INFO misc.py line 119 87073] Train: [82/100][1438/1557] Data 0.015 (0.187) Batch 1.004 (1.181) Remain 09:13:47 loss: 0.4966 Lr: 0.00044 [2024-02-19 11:15:57,137 INFO misc.py line 119 87073] Train: [82/100][1439/1557] Data 0.007 (0.187) Batch 0.959 (1.180) Remain 09:13:42 loss: 0.2632 Lr: 0.00044 [2024-02-19 11:15:57,873 INFO misc.py line 119 87073] Train: [82/100][1440/1557] Data 0.006 (0.187) Batch 0.737 (1.180) Remain 09:13:32 loss: 0.1700 Lr: 0.00044 [2024-02-19 11:15:58,635 INFO misc.py line 119 87073] Train: [82/100][1441/1557] Data 0.004 (0.187) Batch 0.761 (1.180) Remain 09:13:23 loss: 0.2495 Lr: 0.00044 [2024-02-19 11:15:59,837 INFO misc.py line 119 87073] Train: [82/100][1442/1557] Data 0.005 (0.187) Batch 1.199 (1.180) Remain 09:13:22 loss: 0.1351 Lr: 0.00044 [2024-02-19 11:16:00,881 INFO misc.py line 119 87073] Train: [82/100][1443/1557] Data 0.008 (0.186) Batch 1.040 (1.180) Remain 09:13:18 loss: 0.4666 Lr: 0.00044 [2024-02-19 11:16:01,871 INFO misc.py line 119 87073] Train: [82/100][1444/1557] Data 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Remain 09:12:40 loss: 0.2735 Lr: 0.00044 [2024-02-19 11:16:08,662 INFO misc.py line 119 87073] Train: [82/100][1451/1557] Data 0.004 (0.185) Batch 1.075 (1.179) Remain 09:12:36 loss: 0.1171 Lr: 0.00044 [2024-02-19 11:16:09,556 INFO misc.py line 119 87073] Train: [82/100][1452/1557] Data 0.004 (0.185) Batch 0.894 (1.178) Remain 09:12:30 loss: 0.2118 Lr: 0.00044 [2024-02-19 11:16:10,499 INFO misc.py line 119 87073] Train: [82/100][1453/1557] Data 0.003 (0.185) Batch 0.934 (1.178) Remain 09:12:24 loss: 0.0386 Lr: 0.00044 [2024-02-19 11:16:11,167 INFO misc.py line 119 87073] Train: [82/100][1454/1557] Data 0.014 (0.185) Batch 0.676 (1.178) Remain 09:12:13 loss: 0.1560 Lr: 0.00043 [2024-02-19 11:16:12,000 INFO misc.py line 119 87073] Train: [82/100][1455/1557] Data 0.006 (0.185) Batch 0.832 (1.178) Remain 09:12:05 loss: 0.1394 Lr: 0.00043 [2024-02-19 11:16:13,316 INFO misc.py line 119 87073] Train: [82/100][1456/1557] Data 0.007 (0.185) Batch 1.266 (1.178) Remain 09:12:06 loss: 0.0809 Lr: 0.00043 [2024-02-19 11:16:14,220 INFO misc.py line 119 87073] Train: [82/100][1457/1557] Data 0.056 (0.185) Batch 0.956 (1.178) Remain 09:12:00 loss: 0.2304 Lr: 0.00043 [2024-02-19 11:16:15,176 INFO misc.py line 119 87073] Train: [82/100][1458/1557] Data 0.004 (0.185) Batch 0.957 (1.177) Remain 09:11:55 loss: 0.3902 Lr: 0.00043 [2024-02-19 11:16:16,337 INFO misc.py line 119 87073] Train: [82/100][1459/1557] Data 0.004 (0.185) Batch 1.160 (1.177) Remain 09:11:53 loss: 0.1484 Lr: 0.00043 [2024-02-19 11:16:17,307 INFO misc.py line 119 87073] Train: [82/100][1460/1557] Data 0.005 (0.184) Batch 0.970 (1.177) Remain 09:11:48 loss: 0.2624 Lr: 0.00043 [2024-02-19 11:16:18,102 INFO misc.py line 119 87073] Train: [82/100][1461/1557] Data 0.005 (0.184) Batch 0.794 (1.177) Remain 09:11:39 loss: 0.1819 Lr: 0.00043 [2024-02-19 11:16:18,860 INFO misc.py line 119 87073] Train: [82/100][1462/1557] Data 0.005 (0.184) Batch 0.755 (1.177) Remain 09:11:30 loss: 0.2221 Lr: 0.00043 [2024-02-19 11:16:31,913 INFO misc.py line 119 87073] Train: [82/100][1463/1557] Data 10.786 (0.191) Batch 13.059 (1.185) Remain 09:15:18 loss: 0.0956 Lr: 0.00043 [2024-02-19 11:16:32,839 INFO misc.py line 119 87073] Train: [82/100][1464/1557] Data 0.004 (0.191) Batch 0.925 (1.185) Remain 09:15:12 loss: 0.2340 Lr: 0.00043 [2024-02-19 11:16:33,798 INFO misc.py line 119 87073] Train: [82/100][1465/1557] Data 0.005 (0.191) Batch 0.960 (1.185) Remain 09:15:06 loss: 0.5197 Lr: 0.00043 [2024-02-19 11:16:34,775 INFO misc.py line 119 87073] Train: [82/100][1466/1557] Data 0.004 (0.191) Batch 0.975 (1.184) Remain 09:15:01 loss: 0.1738 Lr: 0.00043 [2024-02-19 11:16:35,727 INFO misc.py line 119 87073] Train: [82/100][1467/1557] Data 0.007 (0.191) Batch 0.953 (1.184) Remain 09:14:55 loss: 0.0941 Lr: 0.00043 [2024-02-19 11:16:36,489 INFO misc.py line 119 87073] Train: [82/100][1468/1557] Data 0.005 (0.191) Batch 0.763 (1.184) Remain 09:14:46 loss: 0.2410 Lr: 0.00043 [2024-02-19 11:16:37,225 INFO misc.py line 119 87073] Train: [82/100][1469/1557] Data 0.004 (0.191) Batch 0.735 (1.184) Remain 09:14:36 loss: 0.3533 Lr: 0.00043 [2024-02-19 11:16:40,247 INFO misc.py line 119 87073] Train: [82/100][1470/1557] Data 0.005 (0.191) Batch 3.023 (1.185) Remain 09:15:10 loss: 0.1175 Lr: 0.00043 [2024-02-19 11:16:41,112 INFO misc.py line 119 87073] Train: [82/100][1471/1557] Data 0.004 (0.190) Batch 0.856 (1.185) Remain 09:15:03 loss: 0.2657 Lr: 0.00043 [2024-02-19 11:16:41,972 INFO misc.py line 119 87073] Train: [82/100][1472/1557] Data 0.013 (0.190) Batch 0.869 (1.184) Remain 09:14:55 loss: 0.0628 Lr: 0.00043 [2024-02-19 11:16:42,952 INFO misc.py line 119 87073] Train: [82/100][1473/1557] Data 0.005 (0.190) Batch 0.980 (1.184) Remain 09:14:50 loss: 0.4064 Lr: 0.00043 [2024-02-19 11:16:43,852 INFO misc.py line 119 87073] Train: [82/100][1474/1557] Data 0.004 (0.190) Batch 0.899 (1.184) Remain 09:14:44 loss: 0.2860 Lr: 0.00043 [2024-02-19 11:16:44,589 INFO misc.py line 119 87073] Train: [82/100][1475/1557] Data 0.005 (0.190) Batch 0.737 (1.184) Remain 09:14:34 loss: 0.0913 Lr: 0.00043 [2024-02-19 11:16:45,329 INFO misc.py line 119 87073] Train: [82/100][1476/1557] Data 0.005 (0.190) Batch 0.741 (1.184) Remain 09:14:24 loss: 0.1879 Lr: 0.00043 [2024-02-19 11:16:46,608 INFO misc.py line 119 87073] Train: [82/100][1477/1557] Data 0.004 (0.190) Batch 1.268 (1.184) Remain 09:14:25 loss: 0.1060 Lr: 0.00043 [2024-02-19 11:16:47,422 INFO misc.py line 119 87073] Train: [82/100][1478/1557] Data 0.015 (0.190) Batch 0.824 (1.183) Remain 09:14:17 loss: 0.1674 Lr: 0.00043 [2024-02-19 11:16:48,198 INFO misc.py line 119 87073] Train: [82/100][1479/1557] Data 0.006 (0.189) Batch 0.777 (1.183) Remain 09:14:08 loss: 0.1936 Lr: 0.00043 [2024-02-19 11:16:49,263 INFO misc.py line 119 87073] Train: [82/100][1480/1557] Data 0.005 (0.189) Batch 1.063 (1.183) Remain 09:14:04 loss: 0.2810 Lr: 0.00043 [2024-02-19 11:16:50,302 INFO misc.py line 119 87073] Train: [82/100][1481/1557] Data 0.006 (0.189) Batch 1.038 (1.183) Remain 09:14:00 loss: 0.1682 Lr: 0.00043 [2024-02-19 11:16:51,032 INFO misc.py line 119 87073] Train: [82/100][1482/1557] Data 0.008 (0.189) Batch 0.733 (1.183) Remain 09:13:51 loss: 0.2619 Lr: 0.00043 [2024-02-19 11:16:51,803 INFO misc.py line 119 87073] Train: [82/100][1483/1557] Data 0.005 (0.189) Batch 0.764 (1.182) Remain 09:13:42 loss: 0.2638 Lr: 0.00043 [2024-02-19 11:16:53,051 INFO misc.py line 119 87073] Train: [82/100][1484/1557] Data 0.012 (0.189) Batch 1.255 (1.182) Remain 09:13:42 loss: 0.1196 Lr: 0.00043 [2024-02-19 11:16:53,985 INFO misc.py line 119 87073] Train: [82/100][1485/1557] Data 0.005 (0.189) Batch 0.934 (1.182) Remain 09:13:36 loss: 0.2495 Lr: 0.00043 [2024-02-19 11:16:54,911 INFO misc.py line 119 87073] Train: [82/100][1486/1557] Data 0.005 (0.189) Batch 0.927 (1.182) Remain 09:13:30 loss: 0.2288 Lr: 0.00043 [2024-02-19 11:16:55,780 INFO misc.py line 119 87073] Train: [82/100][1487/1557] Data 0.003 (0.188) Batch 0.862 (1.182) Remain 09:13:23 loss: 0.3183 Lr: 0.00043 [2024-02-19 11:16:56,852 INFO misc.py line 119 87073] Train: [82/100][1488/1557] Data 0.011 (0.188) Batch 1.075 (1.182) Remain 09:13:19 loss: 0.3294 Lr: 0.00043 [2024-02-19 11:16:57,528 INFO misc.py line 119 87073] Train: [82/100][1489/1557] Data 0.007 (0.188) Batch 0.679 (1.181) Remain 09:13:09 loss: 0.2057 Lr: 0.00043 [2024-02-19 11:16:58,315 INFO misc.py line 119 87073] Train: [82/100][1490/1557] Data 0.004 (0.188) Batch 0.769 (1.181) Remain 09:13:00 loss: 0.1862 Lr: 0.00043 [2024-02-19 11:16:59,507 INFO misc.py line 119 87073] Train: [82/100][1491/1557] Data 0.022 (0.188) Batch 1.207 (1.181) Remain 09:12:59 loss: 0.1658 Lr: 0.00043 [2024-02-19 11:17:00,419 INFO misc.py line 119 87073] Train: [82/100][1492/1557] Data 0.007 (0.188) Batch 0.915 (1.181) Remain 09:12:53 loss: 0.3303 Lr: 0.00043 [2024-02-19 11:17:01,329 INFO misc.py line 119 87073] Train: [82/100][1493/1557] Data 0.004 (0.188) Batch 0.910 (1.181) Remain 09:12:47 loss: 0.3353 Lr: 0.00043 [2024-02-19 11:17:02,331 INFO misc.py line 119 87073] Train: [82/100][1494/1557] Data 0.004 (0.188) Batch 1.001 (1.181) Remain 09:12:42 loss: 0.2613 Lr: 0.00043 [2024-02-19 11:17:03,312 INFO misc.py line 119 87073] Train: [82/100][1495/1557] Data 0.005 (0.187) Batch 0.954 (1.180) Remain 09:12:37 loss: 0.0914 Lr: 0.00043 [2024-02-19 11:17:04,096 INFO misc.py line 119 87073] Train: [82/100][1496/1557] Data 0.033 (0.187) Batch 0.813 (1.180) Remain 09:12:28 loss: 0.1979 Lr: 0.00043 [2024-02-19 11:17:04,904 INFO misc.py line 119 87073] Train: [82/100][1497/1557] Data 0.004 (0.187) Batch 0.808 (1.180) Remain 09:12:20 loss: 0.2056 Lr: 0.00043 [2024-02-19 11:17:06,055 INFO misc.py line 119 87073] Train: [82/100][1498/1557] Data 0.004 (0.187) Batch 1.143 (1.180) Remain 09:12:18 loss: 0.1448 Lr: 0.00043 [2024-02-19 11:17:06,962 INFO misc.py line 119 87073] Train: [82/100][1499/1557] Data 0.012 (0.187) Batch 0.913 (1.180) Remain 09:12:12 loss: 0.2328 Lr: 0.00043 [2024-02-19 11:17:07,985 INFO misc.py line 119 87073] Train: [82/100][1500/1557] Data 0.008 (0.187) Batch 1.025 (1.180) Remain 09:12:08 loss: 0.1039 Lr: 0.00043 [2024-02-19 11:17:08,871 INFO misc.py line 119 87073] Train: [82/100][1501/1557] Data 0.005 (0.187) Batch 0.887 (1.179) Remain 09:12:01 loss: 0.1463 Lr: 0.00043 [2024-02-19 11:17:09,669 INFO misc.py line 119 87073] Train: [82/100][1502/1557] Data 0.004 (0.187) Batch 0.796 (1.179) Remain 09:11:53 loss: 0.0936 Lr: 0.00043 [2024-02-19 11:17:10,414 INFO misc.py line 119 87073] Train: [82/100][1503/1557] Data 0.005 (0.187) Batch 0.747 (1.179) Remain 09:11:44 loss: 0.2569 Lr: 0.00043 [2024-02-19 11:17:11,210 INFO misc.py line 119 87073] Train: [82/100][1504/1557] Data 0.004 (0.186) Batch 0.794 (1.179) Remain 09:11:35 loss: 0.3168 Lr: 0.00043 [2024-02-19 11:17:12,514 INFO misc.py line 119 87073] Train: [82/100][1505/1557] Data 0.005 (0.186) Batch 1.304 (1.179) Remain 09:11:37 loss: 0.1451 Lr: 0.00043 [2024-02-19 11:17:13,561 INFO misc.py line 119 87073] Train: [82/100][1506/1557] Data 0.006 (0.186) Batch 1.041 (1.179) Remain 09:11:33 loss: 0.3444 Lr: 0.00043 [2024-02-19 11:17:14,616 INFO misc.py line 119 87073] Train: [82/100][1507/1557] Data 0.012 (0.186) Batch 1.061 (1.179) Remain 09:11:29 loss: 0.2177 Lr: 0.00043 [2024-02-19 11:17:15,632 INFO misc.py line 119 87073] Train: [82/100][1508/1557] Data 0.007 (0.186) Batch 1.018 (1.178) Remain 09:11:25 loss: 0.1299 Lr: 0.00043 [2024-02-19 11:17:16,452 INFO misc.py line 119 87073] Train: [82/100][1509/1557] Data 0.005 (0.186) Batch 0.818 (1.178) Remain 09:11:17 loss: 0.1305 Lr: 0.00043 [2024-02-19 11:17:17,217 INFO misc.py line 119 87073] Train: [82/100][1510/1557] Data 0.008 (0.186) Batch 0.768 (1.178) Remain 09:11:09 loss: 0.2002 Lr: 0.00043 [2024-02-19 11:17:18,051 INFO misc.py line 119 87073] Train: [82/100][1511/1557] Data 0.004 (0.186) Batch 0.831 (1.178) Remain 09:11:01 loss: 0.1395 Lr: 0.00043 [2024-02-19 11:17:19,340 INFO misc.py line 119 87073] Train: [82/100][1512/1557] Data 0.007 (0.185) Batch 1.290 (1.178) Remain 09:11:02 loss: 0.0974 Lr: 0.00043 [2024-02-19 11:17:20,413 INFO misc.py line 119 87073] Train: [82/100][1513/1557] Data 0.007 (0.185) Batch 1.073 (1.178) Remain 09:10:59 loss: 0.1272 Lr: 0.00043 [2024-02-19 11:17:21,472 INFO misc.py line 119 87073] Train: [82/100][1514/1557] Data 0.006 (0.185) Batch 1.038 (1.178) Remain 09:10:55 loss: 0.2032 Lr: 0.00043 [2024-02-19 11:17:22,438 INFO misc.py line 119 87073] Train: [82/100][1515/1557] Data 0.028 (0.185) Batch 0.989 (1.178) Remain 09:10:50 loss: 0.1401 Lr: 0.00043 [2024-02-19 11:17:23,454 INFO misc.py line 119 87073] Train: [82/100][1516/1557] Data 0.004 (0.185) Batch 1.016 (1.177) Remain 09:10:46 loss: 0.2437 Lr: 0.00043 [2024-02-19 11:17:24,249 INFO misc.py line 119 87073] Train: [82/100][1517/1557] Data 0.004 (0.185) Batch 0.796 (1.177) Remain 09:10:38 loss: 0.2181 Lr: 0.00043 [2024-02-19 11:17:24,993 INFO misc.py line 119 87073] Train: [82/100][1518/1557] Data 0.004 (0.185) Batch 0.741 (1.177) Remain 09:10:29 loss: 0.1233 Lr: 0.00043 [2024-02-19 11:17:38,231 INFO misc.py line 119 87073] Train: [82/100][1519/1557] Data 9.331 (0.191) Batch 13.240 (1.185) Remain 09:14:11 loss: 0.0964 Lr: 0.00043 [2024-02-19 11:17:39,294 INFO misc.py line 119 87073] Train: [82/100][1520/1557] Data 0.006 (0.191) Batch 1.064 (1.185) Remain 09:14:07 loss: 0.1059 Lr: 0.00043 [2024-02-19 11:17:40,202 INFO misc.py line 119 87073] Train: [82/100][1521/1557] Data 0.004 (0.191) Batch 0.909 (1.185) Remain 09:14:01 loss: 0.2284 Lr: 0.00043 [2024-02-19 11:17:41,089 INFO misc.py line 119 87073] Train: [82/100][1522/1557] Data 0.003 (0.190) Batch 0.885 (1.184) Remain 09:13:54 loss: 0.0917 Lr: 0.00043 [2024-02-19 11:17:42,041 INFO misc.py line 119 87073] Train: [82/100][1523/1557] Data 0.006 (0.190) Batch 0.953 (1.184) Remain 09:13:49 loss: 0.2760 Lr: 0.00043 [2024-02-19 11:17:42,768 INFO misc.py line 119 87073] Train: [82/100][1524/1557] Data 0.004 (0.190) Batch 0.726 (1.184) Remain 09:13:39 loss: 0.1801 Lr: 0.00043 [2024-02-19 11:17:43,491 INFO misc.py line 119 87073] Train: [82/100][1525/1557] Data 0.006 (0.190) Batch 0.721 (1.184) Remain 09:13:29 loss: 0.1529 Lr: 0.00043 [2024-02-19 11:17:44,801 INFO misc.py line 119 87073] Train: [82/100][1526/1557] Data 0.009 (0.190) Batch 1.309 (1.184) Remain 09:13:31 loss: 0.0917 Lr: 0.00043 [2024-02-19 11:17:45,730 INFO misc.py line 119 87073] Train: [82/100][1527/1557] Data 0.010 (0.190) Batch 0.933 (1.184) Remain 09:13:25 loss: 0.1210 Lr: 0.00043 [2024-02-19 11:17:46,621 INFO misc.py line 119 87073] Train: [82/100][1528/1557] Data 0.006 (0.190) Batch 0.892 (1.183) Remain 09:13:18 loss: 0.1441 Lr: 0.00043 [2024-02-19 11:17:47,681 INFO misc.py line 119 87073] Train: [82/100][1529/1557] Data 0.005 (0.190) Batch 1.060 (1.183) Remain 09:13:15 loss: 0.6247 Lr: 0.00043 [2024-02-19 11:17:48,648 INFO misc.py line 119 87073] Train: [82/100][1530/1557] Data 0.005 (0.189) Batch 0.968 (1.183) Remain 09:13:10 loss: 0.1367 Lr: 0.00043 [2024-02-19 11:17:49,430 INFO misc.py line 119 87073] Train: [82/100][1531/1557] Data 0.004 (0.189) Batch 0.778 (1.183) Remain 09:13:01 loss: 0.0969 Lr: 0.00043 [2024-02-19 11:17:50,138 INFO misc.py line 119 87073] Train: [82/100][1532/1557] Data 0.008 (0.189) Batch 0.712 (1.183) Remain 09:12:51 loss: 0.2814 Lr: 0.00043 [2024-02-19 11:17:51,447 INFO misc.py line 119 87073] Train: [82/100][1533/1557] Data 0.004 (0.189) Batch 1.309 (1.183) Remain 09:12:52 loss: 0.0867 Lr: 0.00043 [2024-02-19 11:17:52,227 INFO misc.py line 119 87073] Train: [82/100][1534/1557] Data 0.005 (0.189) Batch 0.781 (1.182) Remain 09:12:44 loss: 0.2272 Lr: 0.00043 [2024-02-19 11:17:53,140 INFO misc.py line 119 87073] Train: [82/100][1535/1557] Data 0.005 (0.189) Batch 0.910 (1.182) Remain 09:12:38 loss: 0.3284 Lr: 0.00043 [2024-02-19 11:17:54,088 INFO misc.py line 119 87073] Train: [82/100][1536/1557] Data 0.007 (0.189) Batch 0.952 (1.182) Remain 09:12:32 loss: 0.2342 Lr: 0.00043 [2024-02-19 11:17:55,047 INFO misc.py line 119 87073] Train: [82/100][1537/1557] Data 0.003 (0.189) Batch 0.958 (1.182) Remain 09:12:27 loss: 0.1990 Lr: 0.00043 [2024-02-19 11:17:55,746 INFO misc.py line 119 87073] Train: [82/100][1538/1557] Data 0.004 (0.188) Batch 0.699 (1.182) Remain 09:12:17 loss: 0.2514 Lr: 0.00043 [2024-02-19 11:17:56,497 INFO misc.py line 119 87073] Train: [82/100][1539/1557] Data 0.004 (0.188) Batch 0.741 (1.181) Remain 09:12:08 loss: 0.1149 Lr: 0.00043 [2024-02-19 11:17:57,712 INFO misc.py line 119 87073] Train: [82/100][1540/1557] Data 0.014 (0.188) Batch 1.215 (1.181) Remain 09:12:07 loss: 0.1693 Lr: 0.00043 [2024-02-19 11:17:58,646 INFO misc.py line 119 87073] Train: [82/100][1541/1557] Data 0.013 (0.188) Batch 0.944 (1.181) Remain 09:12:02 loss: 0.2384 Lr: 0.00043 [2024-02-19 11:17:59,588 INFO misc.py line 119 87073] Train: [82/100][1542/1557] Data 0.004 (0.188) Batch 0.942 (1.181) Remain 09:11:56 loss: 0.1865 Lr: 0.00043 [2024-02-19 11:18:00,519 INFO misc.py line 119 87073] Train: [82/100][1543/1557] Data 0.005 (0.188) Batch 0.931 (1.181) Remain 09:11:50 loss: 0.3591 Lr: 0.00043 [2024-02-19 11:18:01,543 INFO misc.py line 119 87073] Train: [82/100][1544/1557] Data 0.004 (0.188) Batch 1.017 (1.181) Remain 09:11:46 loss: 0.1282 Lr: 0.00043 [2024-02-19 11:18:02,254 INFO misc.py line 119 87073] Train: [82/100][1545/1557] Data 0.011 (0.188) Batch 0.719 (1.180) Remain 09:11:37 loss: 0.1389 Lr: 0.00043 [2024-02-19 11:18:03,020 INFO misc.py line 119 87073] Train: [82/100][1546/1557] Data 0.004 (0.188) Batch 0.759 (1.180) Remain 09:11:28 loss: 0.4083 Lr: 0.00043 [2024-02-19 11:18:04,189 INFO misc.py line 119 87073] Train: [82/100][1547/1557] Data 0.012 (0.187) Batch 1.168 (1.180) Remain 09:11:26 loss: 0.3042 Lr: 0.00043 [2024-02-19 11:18:05,253 INFO misc.py line 119 87073] Train: [82/100][1548/1557] Data 0.013 (0.187) Batch 1.064 (1.180) Remain 09:11:23 loss: 0.5811 Lr: 0.00043 [2024-02-19 11:18:06,207 INFO misc.py line 119 87073] Train: [82/100][1549/1557] Data 0.013 (0.187) Batch 0.961 (1.180) Remain 09:11:18 loss: 0.1010 Lr: 0.00043 [2024-02-19 11:18:07,192 INFO misc.py line 119 87073] Train: [82/100][1550/1557] Data 0.005 (0.187) Batch 0.986 (1.180) Remain 09:11:13 loss: 0.2709 Lr: 0.00043 [2024-02-19 11:18:08,069 INFO misc.py line 119 87073] Train: [82/100][1551/1557] Data 0.004 (0.187) Batch 0.877 (1.180) Remain 09:11:07 loss: 0.2087 Lr: 0.00043 [2024-02-19 11:18:08,839 INFO misc.py line 119 87073] Train: [82/100][1552/1557] Data 0.004 (0.187) Batch 0.761 (1.179) Remain 09:10:58 loss: 0.1617 Lr: 0.00043 [2024-02-19 11:18:09,567 INFO misc.py line 119 87073] Train: [82/100][1553/1557] Data 0.013 (0.187) Batch 0.736 (1.179) Remain 09:10:49 loss: 0.1706 Lr: 0.00043 [2024-02-19 11:18:10,773 INFO misc.py line 119 87073] Train: [82/100][1554/1557] Data 0.004 (0.187) Batch 1.207 (1.179) Remain 09:10:48 loss: 0.1420 Lr: 0.00043 [2024-02-19 11:18:11,668 INFO misc.py line 119 87073] Train: [82/100][1555/1557] Data 0.004 (0.186) Batch 0.895 (1.179) Remain 09:10:42 loss: 0.1531 Lr: 0.00043 [2024-02-19 11:18:12,545 INFO misc.py line 119 87073] Train: [82/100][1556/1557] Data 0.004 (0.186) Batch 0.863 (1.179) Remain 09:10:35 loss: 0.3239 Lr: 0.00043 [2024-02-19 11:18:13,453 INFO misc.py line 119 87073] Train: [82/100][1557/1557] Data 0.018 (0.186) Batch 0.922 (1.179) Remain 09:10:29 loss: 0.0296 Lr: 0.00043 [2024-02-19 11:18:13,453 INFO misc.py line 136 87073] Train result: loss: 0.2279 [2024-02-19 11:18:13,453 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 11:18:43,118 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4878 [2024-02-19 11:18:43,901 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4315 [2024-02-19 11:18:46,027 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3124 [2024-02-19 11:18:48,237 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2772 [2024-02-19 11:18:53,188 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2133 [2024-02-19 11:18:53,889 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0642 [2024-02-19 11:18:55,151 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3757 [2024-02-19 11:18:58,103 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2382 [2024-02-19 11:18:59,912 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3082 [2024-02-19 11:19:01,558 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7293/0.7841/0.9181. [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9340/0.9584 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9831/0.9890 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8628/0.9775 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4053/0.4443 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6683/0.6903 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8082/0.9081 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8509/0.9225 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9274/0.9742 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8295/0.8567 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7944/0.8824 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7831/0.8688 [2024-02-19 11:19:01,559 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6343/0.7205 [2024-02-19 11:19:01,560 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 11:19:01,562 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 11:19:01,562 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 11:19:10,477 INFO misc.py line 119 87073] Train: [83/100][1/1557] Data 1.451 (1.451) Batch 2.589 (2.589) Remain 20:09:03 loss: 0.4205 Lr: 0.00043 [2024-02-19 11:19:11,436 INFO misc.py line 119 87073] Train: [83/100][2/1557] Data 0.008 (0.008) Batch 0.961 (0.961) Remain 07:29:04 loss: 0.3010 Lr: 0.00043 [2024-02-19 11:19:12,351 INFO misc.py line 119 87073] Train: [83/100][3/1557] Data 0.004 (0.004) Batch 0.915 (0.915) Remain 07:07:21 loss: 0.5068 Lr: 0.00043 [2024-02-19 11:19:13,309 INFO misc.py line 119 87073] Train: [83/100][4/1557] Data 0.010 (0.010) Batch 0.932 (0.932) Remain 07:15:25 loss: 0.4351 Lr: 0.00043 [2024-02-19 11:19:14,024 INFO misc.py line 119 87073] Train: [83/100][5/1557] Data 0.030 (0.020) Batch 0.741 (0.837) Remain 06:30:48 loss: 0.1893 Lr: 0.00043 [2024-02-19 11:19:14,800 INFO misc.py line 119 87073] Train: [83/100][6/1557] Data 0.004 (0.015) Batch 0.774 (0.816) Remain 06:20:57 loss: 0.1999 Lr: 0.00043 [2024-02-19 11:19:24,706 INFO misc.py line 119 87073] Train: [83/100][7/1557] Data 0.006 (0.012) Batch 9.908 (3.089) Remain 24:02:23 loss: 0.0707 Lr: 0.00043 [2024-02-19 11:19:25,709 INFO misc.py line 119 87073] Train: [83/100][8/1557] Data 0.004 (0.011) Batch 1.002 (2.671) Remain 20:47:28 loss: 0.0896 Lr: 0.00043 [2024-02-19 11:19:26,575 INFO misc.py line 119 87073] Train: [83/100][9/1557] Data 0.005 (0.010) Batch 0.861 (2.370) Remain 18:26:32 loss: 0.1194 Lr: 0.00043 [2024-02-19 11:19:27,693 INFO misc.py line 119 87073] Train: [83/100][10/1557] Data 0.009 (0.010) Batch 1.118 (2.191) Remain 17:03:01 loss: 0.3728 Lr: 0.00043 [2024-02-19 11:19:28,596 INFO misc.py line 119 87073] Train: [83/100][11/1557] Data 0.010 (0.010) Batch 0.907 (2.030) Remain 15:48:01 loss: 0.1495 Lr: 0.00043 [2024-02-19 11:19:29,375 INFO misc.py line 119 87073] Train: [83/100][12/1557] Data 0.006 (0.009) Batch 0.781 (1.892) Remain 14:43:11 loss: 0.1212 Lr: 0.00043 [2024-02-19 11:19:30,139 INFO misc.py line 119 87073] Train: [83/100][13/1557] Data 0.004 (0.009) Batch 0.759 (1.778) Remain 13:50:15 loss: 0.2389 Lr: 0.00043 [2024-02-19 11:19:31,277 INFO misc.py line 119 87073] Train: [83/100][14/1557] Data 0.008 (0.009) Batch 1.138 (1.720) Remain 13:23:03 loss: 0.0995 Lr: 0.00043 [2024-02-19 11:19:32,250 INFO misc.py line 119 87073] Train: [83/100][15/1557] Data 0.009 (0.009) Batch 0.977 (1.658) Remain 12:54:06 loss: 0.4130 Lr: 0.00043 [2024-02-19 11:19:33,261 INFO misc.py line 119 87073] Train: [83/100][16/1557] Data 0.005 (0.008) Batch 1.012 (1.608) Remain 12:30:52 loss: 0.0655 Lr: 0.00043 [2024-02-19 11:19:34,225 INFO misc.py line 119 87073] Train: [83/100][17/1557] Data 0.005 (0.008) Batch 0.963 (1.562) Remain 12:09:19 loss: 0.0881 Lr: 0.00043 [2024-02-19 11:19:35,392 INFO misc.py line 119 87073] Train: [83/100][18/1557] Data 0.008 (0.008) Batch 1.169 (1.536) Remain 11:57:04 loss: 0.2326 Lr: 0.00043 [2024-02-19 11:19:36,238 INFO misc.py line 119 87073] Train: [83/100][19/1557] Data 0.003 (0.008) Batch 0.843 (1.493) Remain 11:36:48 loss: 0.2827 Lr: 0.00043 [2024-02-19 11:19:36,996 INFO misc.py line 119 87073] Train: [83/100][20/1557] Data 0.007 (0.008) Batch 0.757 (1.450) Remain 11:16:35 loss: 0.1849 Lr: 0.00043 [2024-02-19 11:19:38,196 INFO misc.py line 119 87073] Train: [83/100][21/1557] Data 0.007 (0.008) Batch 1.200 (1.436) Remain 11:10:05 loss: 0.1710 Lr: 0.00043 [2024-02-19 11:19:39,258 INFO misc.py line 119 87073] Train: [83/100][22/1557] Data 0.009 (0.008) Batch 1.064 (1.416) Remain 11:00:57 loss: 0.2240 Lr: 0.00043 [2024-02-19 11:19:40,484 INFO misc.py line 119 87073] Train: [83/100][23/1557] Data 0.005 (0.008) Batch 1.218 (1.406) Remain 10:56:18 loss: 0.4638 Lr: 0.00043 [2024-02-19 11:19:41,315 INFO misc.py line 119 87073] Train: [83/100][24/1557] Data 0.012 (0.008) Batch 0.839 (1.379) Remain 10:43:40 loss: 0.2472 Lr: 0.00043 [2024-02-19 11:19:42,225 INFO misc.py line 119 87073] Train: [83/100][25/1557] Data 0.006 (0.008) Batch 0.910 (1.358) Remain 10:33:41 loss: 0.1824 Lr: 0.00043 [2024-02-19 11:19:42,990 INFO misc.py line 119 87073] Train: [83/100][26/1557] Data 0.006 (0.008) Batch 0.763 (1.332) Remain 10:21:35 loss: 0.1085 Lr: 0.00043 [2024-02-19 11:19:43,728 INFO misc.py line 119 87073] Train: [83/100][27/1557] Data 0.009 (0.008) Batch 0.739 (1.307) Remain 10:10:01 loss: 0.1515 Lr: 0.00043 [2024-02-19 11:19:45,080 INFO misc.py line 119 87073] Train: [83/100][28/1557] Data 0.007 (0.008) Batch 1.353 (1.309) Remain 10:10:51 loss: 0.1114 Lr: 0.00043 [2024-02-19 11:19:46,024 INFO misc.py line 119 87073] Train: [83/100][29/1557] Data 0.007 (0.008) Batch 0.944 (1.295) Remain 10:04:17 loss: 0.1384 Lr: 0.00043 [2024-02-19 11:19:46,947 INFO misc.py line 119 87073] Train: [83/100][30/1557] Data 0.006 (0.008) Batch 0.923 (1.281) Remain 09:57:51 loss: 0.1403 Lr: 0.00043 [2024-02-19 11:19:48,081 INFO misc.py line 119 87073] Train: [83/100][31/1557] Data 0.006 (0.008) Batch 1.135 (1.276) Remain 09:55:24 loss: 0.4322 Lr: 0.00043 [2024-02-19 11:19:48,939 INFO misc.py line 119 87073] Train: [83/100][32/1557] Data 0.005 (0.008) Batch 0.856 (1.262) Remain 09:48:37 loss: 0.2819 Lr: 0.00043 [2024-02-19 11:19:49,622 INFO misc.py line 119 87073] Train: [83/100][33/1557] Data 0.009 (0.008) Batch 0.684 (1.242) Remain 09:39:37 loss: 0.1087 Lr: 0.00043 [2024-02-19 11:19:50,376 INFO misc.py line 119 87073] Train: [83/100][34/1557] Data 0.004 (0.007) Batch 0.754 (1.227) Remain 09:32:14 loss: 0.1820 Lr: 0.00043 [2024-02-19 11:19:51,468 INFO misc.py line 119 87073] Train: [83/100][35/1557] Data 0.004 (0.007) Batch 1.091 (1.222) Remain 09:30:15 loss: 0.1097 Lr: 0.00043 [2024-02-19 11:19:52,405 INFO misc.py line 119 87073] Train: [83/100][36/1557] Data 0.006 (0.007) Batch 0.938 (1.214) Remain 09:26:12 loss: 0.1580 Lr: 0.00043 [2024-02-19 11:19:53,441 INFO misc.py line 119 87073] Train: [83/100][37/1557] Data 0.004 (0.007) Batch 1.036 (1.209) Remain 09:23:45 loss: 0.3319 Lr: 0.00043 [2024-02-19 11:19:54,398 INFO misc.py line 119 87073] Train: [83/100][38/1557] Data 0.004 (0.007) Batch 0.953 (1.201) Remain 09:20:20 loss: 0.1519 Lr: 0.00043 [2024-02-19 11:19:55,468 INFO misc.py line 119 87073] Train: [83/100][39/1557] Data 0.010 (0.007) Batch 1.070 (1.198) Remain 09:18:36 loss: 0.2008 Lr: 0.00043 [2024-02-19 11:19:56,269 INFO misc.py line 119 87073] Train: [83/100][40/1557] Data 0.009 (0.007) Batch 0.805 (1.187) Remain 09:13:38 loss: 0.0976 Lr: 0.00043 [2024-02-19 11:19:57,041 INFO misc.py line 119 87073] Train: [83/100][41/1557] Data 0.006 (0.007) Batch 0.770 (1.176) Remain 09:08:29 loss: 0.1741 Lr: 0.00043 [2024-02-19 11:19:58,308 INFO misc.py line 119 87073] Train: [83/100][42/1557] Data 0.007 (0.007) Batch 1.250 (1.178) Remain 09:09:22 loss: 0.1467 Lr: 0.00043 [2024-02-19 11:19:59,379 INFO misc.py line 119 87073] Train: [83/100][43/1557] Data 0.024 (0.008) Batch 1.089 (1.176) Remain 09:08:18 loss: 0.1971 Lr: 0.00043 [2024-02-19 11:20:00,467 INFO misc.py line 119 87073] Train: [83/100][44/1557] Data 0.006 (0.008) Batch 1.085 (1.173) Remain 09:07:15 loss: 0.3045 Lr: 0.00043 [2024-02-19 11:20:01,369 INFO misc.py line 119 87073] Train: [83/100][45/1557] Data 0.009 (0.008) Batch 0.907 (1.167) Remain 09:04:16 loss: 0.3946 Lr: 0.00043 [2024-02-19 11:20:02,319 INFO misc.py line 119 87073] Train: [83/100][46/1557] Data 0.005 (0.008) Batch 0.949 (1.162) Remain 09:01:53 loss: 0.2650 Lr: 0.00043 [2024-02-19 11:20:03,092 INFO misc.py line 119 87073] Train: [83/100][47/1557] Data 0.005 (0.007) Batch 0.766 (1.153) Remain 08:57:40 loss: 0.1320 Lr: 0.00043 [2024-02-19 11:20:03,950 INFO misc.py line 119 87073] Train: [83/100][48/1557] Data 0.013 (0.008) Batch 0.865 (1.147) Remain 08:54:39 loss: 0.1970 Lr: 0.00043 [2024-02-19 11:20:05,148 INFO misc.py line 119 87073] Train: [83/100][49/1557] Data 0.006 (0.008) Batch 1.194 (1.148) Remain 08:55:07 loss: 0.1439 Lr: 0.00043 [2024-02-19 11:20:06,235 INFO misc.py line 119 87073] Train: [83/100][50/1557] Data 0.011 (0.008) Batch 1.085 (1.146) Remain 08:54:28 loss: 0.3189 Lr: 0.00043 [2024-02-19 11:20:07,117 INFO misc.py line 119 87073] Train: [83/100][51/1557] Data 0.013 (0.008) Batch 0.890 (1.141) Remain 08:51:58 loss: 0.5096 Lr: 0.00043 [2024-02-19 11:20:08,009 INFO misc.py line 119 87073] Train: [83/100][52/1557] Data 0.004 (0.008) Batch 0.890 (1.136) Remain 08:49:34 loss: 0.2620 Lr: 0.00043 [2024-02-19 11:20:09,091 INFO misc.py line 119 87073] Train: [83/100][53/1557] Data 0.006 (0.008) Batch 1.083 (1.135) Remain 08:49:03 loss: 0.2533 Lr: 0.00043 [2024-02-19 11:20:09,810 INFO misc.py line 119 87073] Train: [83/100][54/1557] Data 0.005 (0.008) Batch 0.719 (1.127) Remain 08:45:14 loss: 0.1498 Lr: 0.00043 [2024-02-19 11:20:10,557 INFO misc.py line 119 87073] Train: [83/100][55/1557] Data 0.005 (0.008) Batch 0.741 (1.119) Remain 08:41:45 loss: 0.1651 Lr: 0.00043 [2024-02-19 11:20:11,643 INFO misc.py line 119 87073] Train: [83/100][56/1557] Data 0.011 (0.008) Batch 1.087 (1.119) Remain 08:41:27 loss: 0.0931 Lr: 0.00043 [2024-02-19 11:20:12,775 INFO misc.py line 119 87073] Train: [83/100][57/1557] Data 0.010 (0.008) Batch 1.128 (1.119) Remain 08:41:31 loss: 0.2600 Lr: 0.00043 [2024-02-19 11:20:13,786 INFO misc.py line 119 87073] Train: [83/100][58/1557] Data 0.014 (0.008) Batch 1.012 (1.117) Remain 08:40:36 loss: 0.3577 Lr: 0.00043 [2024-02-19 11:20:14,877 INFO misc.py line 119 87073] Train: [83/100][59/1557] Data 0.012 (0.008) Batch 1.095 (1.116) Remain 08:40:24 loss: 0.2876 Lr: 0.00043 [2024-02-19 11:20:15,822 INFO misc.py line 119 87073] Train: [83/100][60/1557] Data 0.008 (0.008) Batch 0.947 (1.113) Remain 08:39:00 loss: 0.3366 Lr: 0.00043 [2024-02-19 11:20:16,671 INFO misc.py line 119 87073] Train: [83/100][61/1557] Data 0.006 (0.008) Batch 0.850 (1.109) Remain 08:36:51 loss: 0.2607 Lr: 0.00043 [2024-02-19 11:20:17,443 INFO misc.py line 119 87073] Train: [83/100][62/1557] Data 0.005 (0.008) Batch 0.772 (1.103) Remain 08:34:11 loss: 0.1998 Lr: 0.00043 [2024-02-19 11:20:36,931 INFO misc.py line 119 87073] Train: [83/100][63/1557] Data 5.944 (0.107) Batch 19.488 (1.410) Remain 10:56:58 loss: 0.0584 Lr: 0.00043 [2024-02-19 11:20:38,049 INFO misc.py line 119 87073] Train: [83/100][64/1557] Data 0.005 (0.105) Batch 1.117 (1.405) Remain 10:54:42 loss: 0.2385 Lr: 0.00043 [2024-02-19 11:20:39,042 INFO misc.py line 119 87073] Train: [83/100][65/1557] Data 0.005 (0.103) Batch 0.995 (1.398) Remain 10:51:36 loss: 0.3391 Lr: 0.00043 [2024-02-19 11:20:40,047 INFO misc.py line 119 87073] Train: [83/100][66/1557] Data 0.004 (0.102) Batch 1.004 (1.392) Remain 10:48:40 loss: 0.4773 Lr: 0.00043 [2024-02-19 11:20:41,035 INFO misc.py line 119 87073] Train: [83/100][67/1557] Data 0.005 (0.100) Batch 0.987 (1.386) Remain 10:45:42 loss: 0.2683 Lr: 0.00043 [2024-02-19 11:20:41,761 INFO misc.py line 119 87073] Train: [83/100][68/1557] Data 0.005 (0.099) Batch 0.727 (1.376) Remain 10:40:57 loss: 0.2846 Lr: 0.00043 [2024-02-19 11:20:42,577 INFO misc.py line 119 87073] Train: [83/100][69/1557] Data 0.004 (0.097) Batch 0.815 (1.367) Remain 10:36:58 loss: 0.1172 Lr: 0.00043 [2024-02-19 11:20:43,723 INFO misc.py line 119 87073] Train: [83/100][70/1557] Data 0.005 (0.096) Batch 1.146 (1.364) Remain 10:35:25 loss: 0.0750 Lr: 0.00043 [2024-02-19 11:20:44,582 INFO misc.py line 119 87073] Train: [83/100][71/1557] Data 0.005 (0.095) Batch 0.859 (1.356) Remain 10:31:56 loss: 0.2239 Lr: 0.00043 [2024-02-19 11:20:45,479 INFO misc.py line 119 87073] Train: [83/100][72/1557] Data 0.005 (0.093) Batch 0.897 (1.350) Remain 10:28:48 loss: 0.1093 Lr: 0.00043 [2024-02-19 11:20:46,631 INFO misc.py line 119 87073] Train: [83/100][73/1557] Data 0.006 (0.092) Batch 1.153 (1.347) Remain 10:27:28 loss: 0.2554 Lr: 0.00043 [2024-02-19 11:20:47,563 INFO misc.py line 119 87073] Train: [83/100][74/1557] Data 0.004 (0.091) Batch 0.932 (1.341) Remain 10:24:44 loss: 0.2232 Lr: 0.00043 [2024-02-19 11:20:48,332 INFO misc.py line 119 87073] Train: [83/100][75/1557] Data 0.004 (0.090) Batch 0.768 (1.333) Remain 10:21:00 loss: 0.3931 Lr: 0.00043 [2024-02-19 11:20:49,083 INFO misc.py line 119 87073] Train: [83/100][76/1557] Data 0.005 (0.089) Batch 0.753 (1.325) Remain 10:17:16 loss: 0.1061 Lr: 0.00043 [2024-02-19 11:20:50,303 INFO misc.py line 119 87073] Train: [83/100][77/1557] Data 0.004 (0.087) Batch 1.216 (1.324) Remain 10:16:33 loss: 0.0693 Lr: 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Batch 1.151 (1.328) Remain 10:17:45 loss: 0.4769 Lr: 0.00043 [2024-02-19 11:21:49,955 INFO misc.py line 119 87073] Train: [83/100][122/1557] Data 0.010 (0.103) Batch 0.868 (1.324) Remain 10:15:56 loss: 0.1889 Lr: 0.00043 [2024-02-19 11:21:50,985 INFO misc.py line 119 87073] Train: [83/100][123/1557] Data 0.003 (0.102) Batch 1.027 (1.322) Remain 10:14:45 loss: 0.3814 Lr: 0.00043 [2024-02-19 11:21:51,720 INFO misc.py line 119 87073] Train: [83/100][124/1557] Data 0.006 (0.102) Batch 0.737 (1.317) Remain 10:12:29 loss: 0.1741 Lr: 0.00043 [2024-02-19 11:21:52,504 INFO misc.py line 119 87073] Train: [83/100][125/1557] Data 0.004 (0.101) Batch 0.776 (1.313) Remain 10:10:24 loss: 0.1351 Lr: 0.00043 [2024-02-19 11:21:53,656 INFO misc.py line 119 87073] Train: [83/100][126/1557] Data 0.012 (0.100) Batch 1.152 (1.311) Remain 10:09:46 loss: 0.0801 Lr: 0.00043 [2024-02-19 11:21:54,587 INFO misc.py line 119 87073] Train: [83/100][127/1557] Data 0.013 (0.099) Batch 0.940 (1.308) Remain 10:08:21 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loss: 0.1250 Lr: 0.00043 [2024-02-19 11:23:09,010 INFO misc.py line 119 87073] Train: [83/100][184/1557] Data 0.004 (0.111) Batch 1.085 (1.308) Remain 10:06:43 loss: 0.2829 Lr: 0.00043 [2024-02-19 11:23:09,917 INFO misc.py line 119 87073] Train: [83/100][185/1557] Data 0.004 (0.110) Batch 0.906 (1.305) Remain 10:05:40 loss: 0.1999 Lr: 0.00043 [2024-02-19 11:23:10,927 INFO misc.py line 119 87073] Train: [83/100][186/1557] Data 0.005 (0.109) Batch 1.010 (1.304) Remain 10:04:54 loss: 0.4195 Lr: 0.00043 [2024-02-19 11:23:11,692 INFO misc.py line 119 87073] Train: [83/100][187/1557] Data 0.005 (0.109) Batch 0.766 (1.301) Remain 10:03:32 loss: 0.1392 Lr: 0.00043 [2024-02-19 11:23:12,454 INFO misc.py line 119 87073] Train: [83/100][188/1557] Data 0.004 (0.108) Batch 0.758 (1.298) Remain 10:02:09 loss: 0.1277 Lr: 0.00043 [2024-02-19 11:23:13,651 INFO misc.py line 119 87073] Train: [83/100][189/1557] Data 0.008 (0.108) Batch 1.192 (1.297) Remain 10:01:51 loss: 0.0935 Lr: 0.00043 [2024-02-19 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line 119 87073] Train: [83/100][221/1557] Data 0.005 (0.093) Batch 0.931 (1.250) Remain 09:39:11 loss: 0.0492 Lr: 0.00043 [2024-02-19 11:23:45,593 INFO misc.py line 119 87073] Train: [83/100][222/1557] Data 0.004 (0.093) Batch 0.781 (1.248) Remain 09:38:10 loss: 0.1246 Lr: 0.00043 [2024-02-19 11:23:46,451 INFO misc.py line 119 87073] Train: [83/100][223/1557] Data 0.005 (0.092) Batch 0.858 (1.246) Remain 09:37:19 loss: 0.2394 Lr: 0.00043 [2024-02-19 11:23:47,575 INFO misc.py line 119 87073] Train: [83/100][224/1557] Data 0.005 (0.092) Batch 1.124 (1.245) Remain 09:37:03 loss: 0.1041 Lr: 0.00043 [2024-02-19 11:23:48,646 INFO misc.py line 119 87073] Train: [83/100][225/1557] Data 0.004 (0.091) Batch 1.070 (1.245) Remain 09:36:40 loss: 0.1664 Lr: 0.00043 [2024-02-19 11:23:49,688 INFO misc.py line 119 87073] Train: [83/100][226/1557] Data 0.005 (0.091) Batch 1.043 (1.244) Remain 09:36:13 loss: 0.4014 Lr: 0.00043 [2024-02-19 11:23:50,711 INFO misc.py line 119 87073] Train: 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Batch 0.974 (1.307) Remain 10:05:30 loss: 0.1848 Lr: 0.00042 [2024-02-19 11:24:14,109 INFO misc.py line 119 87073] Train: [83/100][234/1557] Data 0.004 (0.114) Batch 1.103 (1.306) Remain 10:05:04 loss: 0.3994 Lr: 0.00042 [2024-02-19 11:24:15,127 INFO misc.py line 119 87073] Train: [83/100][235/1557] Data 0.004 (0.113) Batch 1.018 (1.305) Remain 10:04:29 loss: 0.1911 Lr: 0.00042 [2024-02-19 11:24:15,917 INFO misc.py line 119 87073] Train: [83/100][236/1557] Data 0.004 (0.113) Batch 0.789 (1.303) Remain 10:03:26 loss: 0.2637 Lr: 0.00042 [2024-02-19 11:24:16,685 INFO misc.py line 119 87073] Train: [83/100][237/1557] Data 0.004 (0.112) Batch 0.766 (1.301) Remain 10:02:21 loss: 0.1622 Lr: 0.00042 [2024-02-19 11:24:17,814 INFO misc.py line 119 87073] Train: [83/100][238/1557] Data 0.006 (0.112) Batch 1.131 (1.300) Remain 10:01:59 loss: 0.0766 Lr: 0.00042 [2024-02-19 11:24:18,741 INFO misc.py line 119 87073] Train: [83/100][239/1557] Data 0.005 (0.111) Batch 0.927 (1.298) Remain 10:01:14 loss: 0.3066 Lr: 0.00042 [2024-02-19 11:24:19,702 INFO misc.py line 119 87073] Train: [83/100][240/1557] Data 0.004 (0.111) Batch 0.961 (1.297) Remain 10:00:33 loss: 0.3435 Lr: 0.00042 [2024-02-19 11:24:20,638 INFO misc.py line 119 87073] Train: [83/100][241/1557] Data 0.004 (0.110) Batch 0.935 (1.295) Remain 09:59:50 loss: 0.2755 Lr: 0.00042 [2024-02-19 11:24:21,683 INFO misc.py line 119 87073] Train: [83/100][242/1557] Data 0.005 (0.110) Batch 1.047 (1.294) Remain 09:59:20 loss: 0.1810 Lr: 0.00042 [2024-02-19 11:24:22,422 INFO misc.py line 119 87073] Train: [83/100][243/1557] Data 0.004 (0.109) Batch 0.737 (1.292) Remain 09:58:14 loss: 0.3813 Lr: 0.00042 [2024-02-19 11:24:23,205 INFO misc.py line 119 87073] Train: [83/100][244/1557] Data 0.005 (0.109) Batch 0.778 (1.290) Remain 09:57:13 loss: 0.2330 Lr: 0.00042 [2024-02-19 11:24:24,416 INFO misc.py line 119 87073] Train: [83/100][245/1557] Data 0.011 (0.109) Batch 1.210 (1.289) Remain 09:57:03 loss: 0.1418 Lr: 0.00042 [2024-02-19 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87073] Train: [83/100][252/1557] Data 0.049 (0.106) Batch 1.297 (1.279) Remain 09:52:08 loss: 0.1209 Lr: 0.00042 [2024-02-19 11:24:32,038 INFO misc.py line 119 87073] Train: [83/100][253/1557] Data 0.007 (0.106) Batch 1.163 (1.279) Remain 09:51:54 loss: 0.1310 Lr: 0.00042 [2024-02-19 11:24:33,070 INFO misc.py line 119 87073] Train: [83/100][254/1557] Data 0.009 (0.105) Batch 1.023 (1.278) Remain 09:51:24 loss: 0.1755 Lr: 0.00042 [2024-02-19 11:24:34,074 INFO misc.py line 119 87073] Train: [83/100][255/1557] Data 0.018 (0.105) Batch 1.007 (1.277) Remain 09:50:53 loss: 0.2529 Lr: 0.00042 [2024-02-19 11:24:35,336 INFO misc.py line 119 87073] Train: [83/100][256/1557] Data 0.014 (0.104) Batch 1.271 (1.277) Remain 09:50:51 loss: 0.3434 Lr: 0.00042 [2024-02-19 11:24:36,126 INFO misc.py line 119 87073] Train: [83/100][257/1557] Data 0.007 (0.104) Batch 0.790 (1.275) Remain 09:49:57 loss: 0.1534 Lr: 0.00042 [2024-02-19 11:24:36,888 INFO misc.py line 119 87073] Train: [83/100][258/1557] Data 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Batch 1.334 (1.304) Remain 10:02:48 loss: 0.1855 Lr: 0.00042 [2024-02-19 11:25:26,360 INFO misc.py line 119 87073] Train: [83/100][290/1557] Data 0.017 (0.112) Batch 1.057 (1.303) Remain 10:02:23 loss: 0.2256 Lr: 0.00042 [2024-02-19 11:25:27,284 INFO misc.py line 119 87073] Train: [83/100][291/1557] Data 0.014 (0.112) Batch 0.933 (1.302) Remain 10:01:46 loss: 0.3919 Lr: 0.00042 [2024-02-19 11:25:28,091 INFO misc.py line 119 87073] Train: [83/100][292/1557] Data 0.005 (0.111) Batch 0.808 (1.300) Remain 10:00:57 loss: 0.1833 Lr: 0.00042 [2024-02-19 11:25:28,841 INFO misc.py line 119 87073] Train: [83/100][293/1557] Data 0.005 (0.111) Batch 0.742 (1.298) Remain 10:00:03 loss: 0.1465 Lr: 0.00042 [2024-02-19 11:25:29,994 INFO misc.py line 119 87073] Train: [83/100][294/1557] Data 0.012 (0.111) Batch 1.151 (1.298) Remain 09:59:48 loss: 0.1130 Lr: 0.00042 [2024-02-19 11:25:30,853 INFO misc.py line 119 87073] Train: [83/100][295/1557] Data 0.014 (0.110) Batch 0.870 (1.296) Remain 09:59:06 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Batch 1.097 (1.301) Remain 10:00:01 loss: 0.2733 Lr: 0.00042 [2024-02-19 11:26:38,142 INFO misc.py line 119 87073] Train: [83/100][346/1557] Data 0.008 (0.111) Batch 0.991 (1.300) Remain 09:59:35 loss: 0.1277 Lr: 0.00042 [2024-02-19 11:26:39,078 INFO misc.py line 119 87073] Train: [83/100][347/1557] Data 0.005 (0.110) Batch 0.935 (1.299) Remain 09:59:04 loss: 0.5413 Lr: 0.00042 [2024-02-19 11:26:39,796 INFO misc.py line 119 87073] Train: [83/100][348/1557] Data 0.006 (0.110) Batch 0.717 (1.297) Remain 09:58:16 loss: 0.1525 Lr: 0.00042 [2024-02-19 11:26:40,504 INFO misc.py line 119 87073] Train: [83/100][349/1557] Data 0.006 (0.110) Batch 0.710 (1.295) Remain 09:57:28 loss: 0.1182 Lr: 0.00042 [2024-02-19 11:26:41,678 INFO misc.py line 119 87073] Train: [83/100][350/1557] Data 0.005 (0.109) Batch 1.172 (1.295) Remain 09:57:17 loss: 0.0837 Lr: 0.00042 [2024-02-19 11:26:42,654 INFO misc.py line 119 87073] Train: [83/100][351/1557] Data 0.007 (0.109) Batch 0.979 (1.294) Remain 09:56:50 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Batch 0.963 (1.297) Remain 09:57:16 loss: 0.0849 Lr: 0.00042 [2024-02-19 11:27:49,642 INFO misc.py line 119 87073] Train: [83/100][402/1557] Data 0.003 (0.112) Batch 0.988 (1.296) Remain 09:56:53 loss: 0.3157 Lr: 0.00042 [2024-02-19 11:27:50,709 INFO misc.py line 119 87073] Train: [83/100][403/1557] Data 0.003 (0.112) Batch 1.065 (1.296) Remain 09:56:36 loss: 0.2800 Lr: 0.00042 [2024-02-19 11:27:51,462 INFO misc.py line 119 87073] Train: [83/100][404/1557] Data 0.007 (0.112) Batch 0.755 (1.295) Remain 09:55:57 loss: 0.2299 Lr: 0.00042 [2024-02-19 11:27:52,223 INFO misc.py line 119 87073] Train: [83/100][405/1557] Data 0.004 (0.112) Batch 0.750 (1.293) Remain 09:55:19 loss: 0.1145 Lr: 0.00042 [2024-02-19 11:27:53,375 INFO misc.py line 119 87073] Train: [83/100][406/1557] Data 0.015 (0.111) Batch 1.152 (1.293) Remain 09:55:08 loss: 0.1328 Lr: 0.00042 [2024-02-19 11:27:54,277 INFO misc.py line 119 87073] Train: [83/100][407/1557] Data 0.015 (0.111) Batch 0.913 (1.292) Remain 09:54:40 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87073] Train: [83/100][420/1557] Data 0.014 (0.108) Batch 1.299 (1.283) Remain 09:50:16 loss: 0.1172 Lr: 0.00042 [2024-02-19 11:28:08,460 INFO misc.py line 119 87073] Train: [83/100][421/1557] Data 0.011 (0.108) Batch 1.109 (1.283) Remain 09:50:04 loss: 0.3679 Lr: 0.00042 [2024-02-19 11:28:09,430 INFO misc.py line 119 87073] Train: [83/100][422/1557] Data 0.016 (0.108) Batch 0.982 (1.282) Remain 09:49:43 loss: 0.0442 Lr: 0.00042 [2024-02-19 11:28:10,532 INFO misc.py line 119 87073] Train: [83/100][423/1557] Data 0.004 (0.107) Batch 1.102 (1.281) Remain 09:49:30 loss: 0.1388 Lr: 0.00042 [2024-02-19 11:28:11,486 INFO misc.py line 119 87073] Train: [83/100][424/1557] Data 0.004 (0.107) Batch 0.954 (1.281) Remain 09:49:07 loss: 0.3466 Lr: 0.00042 [2024-02-19 11:28:12,250 INFO misc.py line 119 87073] Train: [83/100][425/1557] Data 0.003 (0.107) Batch 0.760 (1.279) Remain 09:48:31 loss: 0.1544 Lr: 0.00042 [2024-02-19 11:28:13,030 INFO misc.py line 119 87073] Train: [83/100][426/1557] Data 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line 119 87073] Train: [83/100][445/1557] Data 0.016 (0.102) Batch 1.144 (1.265) Remain 09:41:19 loss: 0.1836 Lr: 0.00042 [2024-02-19 11:28:32,049 INFO misc.py line 119 87073] Train: [83/100][446/1557] Data 0.015 (0.102) Batch 0.740 (1.263) Remain 09:40:45 loss: 0.1565 Lr: 0.00042 [2024-02-19 11:28:32,772 INFO misc.py line 119 87073] Train: [83/100][447/1557] Data 0.004 (0.102) Batch 0.714 (1.262) Remain 09:40:09 loss: 0.2075 Lr: 0.00042 [2024-02-19 11:28:33,937 INFO misc.py line 119 87073] Train: [83/100][448/1557] Data 0.013 (0.102) Batch 1.161 (1.262) Remain 09:40:02 loss: 0.1105 Lr: 0.00042 [2024-02-19 11:28:34,888 INFO misc.py line 119 87073] Train: [83/100][449/1557] Data 0.016 (0.101) Batch 0.964 (1.261) Remain 09:39:42 loss: 0.4269 Lr: 0.00042 [2024-02-19 11:28:35,883 INFO misc.py line 119 87073] Train: [83/100][450/1557] Data 0.005 (0.101) Batch 0.995 (1.261) Remain 09:39:24 loss: 0.2145 Lr: 0.00042 [2024-02-19 11:28:36,784 INFO misc.py line 119 87073] Train: 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Batch 1.081 (1.293) Remain 09:54:05 loss: 0.2006 Lr: 0.00042 [2024-02-19 11:29:00,299 INFO misc.py line 119 87073] Train: [83/100][458/1557] Data 0.004 (0.111) Batch 0.948 (1.292) Remain 09:53:43 loss: 0.2648 Lr: 0.00042 [2024-02-19 11:29:01,155 INFO misc.py line 119 87073] Train: [83/100][459/1557] Data 0.006 (0.111) Batch 0.857 (1.291) Remain 09:53:15 loss: 0.2101 Lr: 0.00042 [2024-02-19 11:29:01,923 INFO misc.py line 119 87073] Train: [83/100][460/1557] Data 0.004 (0.110) Batch 0.763 (1.290) Remain 09:52:42 loss: 0.2951 Lr: 0.00042 [2024-02-19 11:29:02,684 INFO misc.py line 119 87073] Train: [83/100][461/1557] Data 0.009 (0.110) Batch 0.764 (1.289) Remain 09:52:09 loss: 0.1563 Lr: 0.00042 [2024-02-19 11:29:03,818 INFO misc.py line 119 87073] Train: [83/100][462/1557] Data 0.005 (0.110) Batch 1.134 (1.289) Remain 09:51:58 loss: 0.1140 Lr: 0.00042 [2024-02-19 11:29:04,732 INFO misc.py line 119 87073] Train: [83/100][463/1557] Data 0.005 (0.110) Batch 0.914 (1.288) Remain 09:51:35 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line 119 87073] Train: [83/100][501/1557] Data 0.004 (0.102) Batch 1.022 (1.262) Remain 09:38:49 loss: 0.3635 Lr: 0.00042 [2024-02-19 11:29:41,454 INFO misc.py line 119 87073] Train: [83/100][502/1557] Data 0.008 (0.102) Batch 0.751 (1.261) Remain 09:38:20 loss: 0.2500 Lr: 0.00042 [2024-02-19 11:29:42,186 INFO misc.py line 119 87073] Train: [83/100][503/1557] Data 0.006 (0.102) Batch 0.725 (1.260) Remain 09:37:49 loss: 0.1778 Lr: 0.00042 [2024-02-19 11:29:43,316 INFO misc.py line 119 87073] Train: [83/100][504/1557] Data 0.013 (0.101) Batch 1.130 (1.259) Remain 09:37:40 loss: 0.1113 Lr: 0.00042 [2024-02-19 11:29:44,354 INFO misc.py line 119 87073] Train: [83/100][505/1557] Data 0.014 (0.101) Batch 1.042 (1.259) Remain 09:37:27 loss: 0.2433 Lr: 0.00042 [2024-02-19 11:29:45,270 INFO misc.py line 119 87073] Train: [83/100][506/1557] Data 0.010 (0.101) Batch 0.921 (1.258) Remain 09:37:07 loss: 0.1790 Lr: 0.00042 [2024-02-19 11:29:46,222 INFO misc.py line 119 87073] Train: 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Batch 1.062 (1.289) Remain 09:51:02 loss: 0.3788 Lr: 0.00042 [2024-02-19 11:30:10,657 INFO misc.py line 119 87073] Train: [83/100][514/1557] Data 0.008 (0.109) Batch 0.951 (1.288) Remain 09:50:42 loss: 0.0996 Lr: 0.00042 [2024-02-19 11:30:11,846 INFO misc.py line 119 87073] Train: [83/100][515/1557] Data 0.004 (0.109) Batch 1.181 (1.288) Remain 09:50:35 loss: 0.1218 Lr: 0.00042 [2024-02-19 11:30:12,667 INFO misc.py line 119 87073] Train: [83/100][516/1557] Data 0.012 (0.109) Batch 0.827 (1.287) Remain 09:50:09 loss: 0.1259 Lr: 0.00042 [2024-02-19 11:30:13,349 INFO misc.py line 119 87073] Train: [83/100][517/1557] Data 0.005 (0.109) Batch 0.682 (1.286) Remain 09:49:36 loss: 0.1692 Lr: 0.00042 [2024-02-19 11:30:14,498 INFO misc.py line 119 87073] Train: [83/100][518/1557] Data 0.005 (0.109) Batch 1.141 (1.286) Remain 09:49:27 loss: 0.1114 Lr: 0.00042 [2024-02-19 11:30:15,448 INFO misc.py line 119 87073] Train: [83/100][519/1557] Data 0.014 (0.108) Batch 0.959 (1.285) Remain 09:49:08 loss: 0.2469 Lr: 0.00042 [2024-02-19 11:30:16,647 INFO misc.py line 119 87073] Train: [83/100][520/1557] Data 0.005 (0.108) Batch 1.193 (1.285) Remain 09:49:02 loss: 0.1055 Lr: 0.00042 [2024-02-19 11:30:17,651 INFO misc.py line 119 87073] Train: [83/100][521/1557] Data 0.010 (0.108) Batch 0.997 (1.284) Remain 09:48:45 loss: 0.3703 Lr: 0.00042 [2024-02-19 11:30:18,799 INFO misc.py line 119 87073] Train: [83/100][522/1557] Data 0.017 (0.108) Batch 1.145 (1.284) Remain 09:48:37 loss: 0.3471 Lr: 0.00042 [2024-02-19 11:30:19,526 INFO misc.py line 119 87073] Train: [83/100][523/1557] Data 0.019 (0.108) Batch 0.743 (1.283) Remain 09:48:07 loss: 0.1731 Lr: 0.00042 [2024-02-19 11:30:20,278 INFO misc.py line 119 87073] Train: [83/100][524/1557] Data 0.004 (0.107) Batch 0.738 (1.282) Remain 09:47:37 loss: 0.1982 Lr: 0.00042 [2024-02-19 11:30:21,459 INFO misc.py line 119 87073] Train: [83/100][525/1557] Data 0.017 (0.107) Batch 1.175 (1.282) Remain 09:47:30 loss: 0.1146 Lr: 0.00042 [2024-02-19 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Batch 1.124 (1.288) Remain 09:48:08 loss: 0.3648 Lr: 0.00041 [2024-02-19 11:32:34,375 INFO misc.py line 119 87073] Train: [83/100][626/1557] Data 0.004 (0.112) Batch 0.976 (1.287) Remain 09:47:53 loss: 0.2893 Lr: 0.00041 [2024-02-19 11:32:35,286 INFO misc.py line 119 87073] Train: [83/100][627/1557] Data 0.005 (0.112) Batch 0.911 (1.287) Remain 09:47:35 loss: 0.2957 Lr: 0.00041 [2024-02-19 11:32:36,155 INFO misc.py line 119 87073] Train: [83/100][628/1557] Data 0.004 (0.111) Batch 0.868 (1.286) Remain 09:47:16 loss: 0.0641 Lr: 0.00041 [2024-02-19 11:32:37,040 INFO misc.py line 119 87073] Train: [83/100][629/1557] Data 0.006 (0.111) Batch 0.879 (1.285) Remain 09:46:56 loss: 0.1400 Lr: 0.00041 [2024-02-19 11:32:38,180 INFO misc.py line 119 87073] Train: [83/100][630/1557] Data 0.011 (0.111) Batch 1.139 (1.285) Remain 09:46:49 loss: 0.1008 Lr: 0.00041 [2024-02-19 11:32:39,184 INFO misc.py line 119 87073] Train: [83/100][631/1557] Data 0.013 (0.111) Batch 1.008 (1.285) Remain 09:46:35 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line 119 87073] Train: [83/100][669/1557] Data 0.017 (0.105) Batch 1.006 (1.267) Remain 09:37:29 loss: 0.1400 Lr: 0.00041 [2024-02-19 11:33:16,590 INFO misc.py line 119 87073] Train: [83/100][670/1557] Data 0.017 (0.105) Batch 0.702 (1.266) Remain 09:37:05 loss: 0.3215 Lr: 0.00041 [2024-02-19 11:33:17,320 INFO misc.py line 119 87073] Train: [83/100][671/1557] Data 0.004 (0.105) Batch 0.725 (1.265) Remain 09:36:41 loss: 0.1412 Lr: 0.00041 [2024-02-19 11:33:18,456 INFO misc.py line 119 87073] Train: [83/100][672/1557] Data 0.008 (0.105) Batch 1.126 (1.265) Remain 09:36:34 loss: 0.0930 Lr: 0.00041 [2024-02-19 11:33:19,407 INFO misc.py line 119 87073] Train: [83/100][673/1557] Data 0.019 (0.104) Batch 0.966 (1.264) Remain 09:36:21 loss: 0.1324 Lr: 0.00041 [2024-02-19 11:33:20,346 INFO misc.py line 119 87073] Train: [83/100][674/1557] Data 0.004 (0.104) Batch 0.939 (1.264) Remain 09:36:06 loss: 0.4744 Lr: 0.00041 [2024-02-19 11:33:21,268 INFO misc.py line 119 87073] Train: 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Batch 0.987 (1.290) Remain 09:47:47 loss: 0.1264 Lr: 0.00041 [2024-02-19 11:33:47,825 INFO misc.py line 119 87073] Train: [83/100][682/1557] Data 0.005 (0.111) Batch 1.032 (1.289) Remain 09:47:36 loss: 0.2914 Lr: 0.00041 [2024-02-19 11:33:48,846 INFO misc.py line 119 87073] Train: [83/100][683/1557] Data 0.005 (0.111) Batch 1.014 (1.289) Remain 09:47:23 loss: 0.3405 Lr: 0.00041 [2024-02-19 11:33:49,513 INFO misc.py line 119 87073] Train: [83/100][684/1557] Data 0.011 (0.110) Batch 0.672 (1.288) Remain 09:46:57 loss: 0.1373 Lr: 0.00041 [2024-02-19 11:33:50,273 INFO misc.py line 119 87073] Train: [83/100][685/1557] Data 0.007 (0.110) Batch 0.762 (1.287) Remain 09:46:35 loss: 0.2894 Lr: 0.00041 [2024-02-19 11:33:51,467 INFO misc.py line 119 87073] Train: [83/100][686/1557] Data 0.005 (0.110) Batch 1.194 (1.287) Remain 09:46:30 loss: 0.1827 Lr: 0.00041 [2024-02-19 11:33:52,435 INFO misc.py line 119 87073] Train: [83/100][687/1557] Data 0.005 (0.110) Batch 0.968 (1.287) Remain 09:46:16 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[2024-02-19 11:34:24,779 INFO misc.py line 119 87073] Train: [83/100][719/1557] Data 0.014 (0.108) Batch 0.867 (1.274) Remain 09:39:58 loss: 0.1635 Lr: 0.00041 [2024-02-19 11:34:25,552 INFO misc.py line 119 87073] Train: [83/100][720/1557] Data 0.004 (0.107) Batch 0.773 (1.274) Remain 09:39:37 loss: 0.1682 Lr: 0.00041 [2024-02-19 11:34:26,749 INFO misc.py line 119 87073] Train: [83/100][721/1557] Data 0.005 (0.107) Batch 1.186 (1.274) Remain 09:39:33 loss: 0.0974 Lr: 0.00041 [2024-02-19 11:34:27,823 INFO misc.py line 119 87073] Train: [83/100][722/1557] Data 0.016 (0.107) Batch 1.073 (1.273) Remain 09:39:24 loss: 0.1964 Lr: 0.00041 [2024-02-19 11:34:28,790 INFO misc.py line 119 87073] Train: [83/100][723/1557] Data 0.016 (0.107) Batch 0.979 (1.273) Remain 09:39:12 loss: 0.2910 Lr: 0.00041 [2024-02-19 11:34:29,612 INFO misc.py line 119 87073] Train: [83/100][724/1557] Data 0.005 (0.107) Batch 0.822 (1.272) Remain 09:38:53 loss: 0.1097 Lr: 0.00041 [2024-02-19 11:34:30,684 INFO misc.py line 119 87073] Train: [83/100][725/1557] Data 0.004 (0.107) Batch 1.065 (1.272) Remain 09:38:44 loss: 0.1049 Lr: 0.00041 [2024-02-19 11:34:31,455 INFO misc.py line 119 87073] Train: [83/100][726/1557] Data 0.011 (0.107) Batch 0.779 (1.271) Remain 09:38:24 loss: 0.1255 Lr: 0.00041 [2024-02-19 11:34:32,181 INFO misc.py line 119 87073] Train: [83/100][727/1557] Data 0.004 (0.106) Batch 0.720 (1.270) Remain 09:38:02 loss: 0.1395 Lr: 0.00041 [2024-02-19 11:34:33,323 INFO misc.py line 119 87073] Train: [83/100][728/1557] Data 0.010 (0.106) Batch 1.143 (1.270) Remain 09:37:56 loss: 0.1086 Lr: 0.00041 [2024-02-19 11:34:34,170 INFO misc.py line 119 87073] Train: [83/100][729/1557] Data 0.009 (0.106) Batch 0.852 (1.270) Remain 09:37:39 loss: 0.3091 Lr: 0.00041 [2024-02-19 11:34:35,170 INFO misc.py line 119 87073] Train: [83/100][730/1557] Data 0.004 (0.106) Batch 1.001 (1.269) Remain 09:37:28 loss: 0.0826 Lr: 0.00041 [2024-02-19 11:34:36,099 INFO misc.py line 119 87073] Train: 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Batch 1.092 (1.291) Remain 09:46:57 loss: 0.0642 Lr: 0.00041 [2024-02-19 11:35:00,539 INFO misc.py line 119 87073] Train: [83/100][738/1557] Data 0.045 (0.112) Batch 0.935 (1.290) Remain 09:46:42 loss: 0.1544 Lr: 0.00041 [2024-02-19 11:35:01,507 INFO misc.py line 119 87073] Train: [83/100][739/1557] Data 0.004 (0.112) Batch 0.968 (1.290) Remain 09:46:29 loss: 0.1601 Lr: 0.00041 [2024-02-19 11:35:02,317 INFO misc.py line 119 87073] Train: [83/100][740/1557] Data 0.004 (0.112) Batch 0.803 (1.289) Remain 09:46:10 loss: 0.1210 Lr: 0.00041 [2024-02-19 11:35:03,068 INFO misc.py line 119 87073] Train: [83/100][741/1557] Data 0.011 (0.112) Batch 0.757 (1.288) Remain 09:45:49 loss: 0.1538 Lr: 0.00041 [2024-02-19 11:35:04,217 INFO misc.py line 119 87073] Train: [83/100][742/1557] Data 0.004 (0.112) Batch 1.149 (1.288) Remain 09:45:42 loss: 0.1297 Lr: 0.00041 [2024-02-19 11:35:05,139 INFO misc.py line 119 87073] Train: [83/100][743/1557] Data 0.005 (0.112) Batch 0.923 (1.288) Remain 09:45:28 loss: 0.1266 Lr: 0.00041 [2024-02-19 11:35:06,167 INFO misc.py line 119 87073] Train: [83/100][744/1557] Data 0.003 (0.111) Batch 1.027 (1.287) Remain 09:45:17 loss: 0.3047 Lr: 0.00041 [2024-02-19 11:35:07,190 INFO misc.py line 119 87073] Train: [83/100][745/1557] Data 0.004 (0.111) Batch 1.023 (1.287) Remain 09:45:06 loss: 0.1400 Lr: 0.00041 [2024-02-19 11:35:08,061 INFO misc.py line 119 87073] Train: [83/100][746/1557] Data 0.004 (0.111) Batch 0.869 (1.286) Remain 09:44:49 loss: 0.0905 Lr: 0.00041 [2024-02-19 11:35:08,853 INFO misc.py line 119 87073] Train: [83/100][747/1557] Data 0.006 (0.111) Batch 0.792 (1.286) Remain 09:44:30 loss: 0.1549 Lr: 0.00041 [2024-02-19 11:35:09,683 INFO misc.py line 119 87073] Train: [83/100][748/1557] Data 0.006 (0.111) Batch 0.831 (1.285) Remain 09:44:12 loss: 0.2307 Lr: 0.00041 [2024-02-19 11:35:10,876 INFO misc.py line 119 87073] Train: [83/100][749/1557] Data 0.004 (0.111) Batch 1.190 (1.285) Remain 09:44:07 loss: 0.1154 Lr: 0.00041 [2024-02-19 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line 119 87073] Train: [83/100][781/1557] Data 0.004 (0.106) Batch 1.014 (1.271) Remain 09:37:16 loss: 0.1076 Lr: 0.00041 [2024-02-19 11:35:42,212 INFO misc.py line 119 87073] Train: [83/100][782/1557] Data 0.005 (0.106) Batch 0.781 (1.271) Remain 09:36:58 loss: 0.1202 Lr: 0.00041 [2024-02-19 11:35:42,967 INFO misc.py line 119 87073] Train: [83/100][783/1557] Data 0.003 (0.106) Batch 0.747 (1.270) Remain 09:36:38 loss: 0.2288 Lr: 0.00041 [2024-02-19 11:35:44,047 INFO misc.py line 119 87073] Train: [83/100][784/1557] Data 0.012 (0.106) Batch 1.078 (1.270) Remain 09:36:30 loss: 0.1086 Lr: 0.00041 [2024-02-19 11:35:45,094 INFO misc.py line 119 87073] Train: [83/100][785/1557] Data 0.013 (0.106) Batch 1.045 (1.269) Remain 09:36:21 loss: 0.1264 Lr: 0.00041 [2024-02-19 11:35:45,980 INFO misc.py line 119 87073] Train: [83/100][786/1557] Data 0.015 (0.106) Batch 0.897 (1.269) Remain 09:36:07 loss: 0.1451 Lr: 0.00041 [2024-02-19 11:35:46,966 INFO misc.py line 119 87073] Train: 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Batch 0.900 (1.288) Remain 09:44:31 loss: 0.2037 Lr: 0.00041 [2024-02-19 11:36:10,765 INFO misc.py line 119 87073] Train: [83/100][794/1557] Data 0.004 (0.112) Batch 1.017 (1.288) Remain 09:44:21 loss: 0.1063 Lr: 0.00041 [2024-02-19 11:36:11,801 INFO misc.py line 119 87073] Train: [83/100][795/1557] Data 0.004 (0.112) Batch 1.036 (1.287) Remain 09:44:11 loss: 0.1604 Lr: 0.00041 [2024-02-19 11:36:12,559 INFO misc.py line 119 87073] Train: [83/100][796/1557] Data 0.004 (0.112) Batch 0.757 (1.287) Remain 09:43:51 loss: 0.1480 Lr: 0.00041 [2024-02-19 11:36:13,246 INFO misc.py line 119 87073] Train: [83/100][797/1557] Data 0.003 (0.111) Batch 0.681 (1.286) Remain 09:43:29 loss: 0.2259 Lr: 0.00041 [2024-02-19 11:36:14,401 INFO misc.py line 119 87073] Train: [83/100][798/1557] Data 0.010 (0.111) Batch 1.154 (1.286) Remain 09:43:23 loss: 0.0886 Lr: 0.00041 [2024-02-19 11:36:15,440 INFO misc.py line 119 87073] Train: [83/100][799/1557] Data 0.011 (0.111) Batch 1.036 (1.285) Remain 09:43:14 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87073] Train: [83/100][812/1557] Data 0.004 (0.110) Batch 1.238 (1.281) Remain 09:40:48 loss: 0.0996 Lr: 0.00041 [2024-02-19 11:36:29,238 INFO misc.py line 119 87073] Train: [83/100][813/1557] Data 0.020 (0.109) Batch 0.947 (1.280) Remain 09:40:35 loss: 0.3765 Lr: 0.00041 [2024-02-19 11:36:30,130 INFO misc.py line 119 87073] Train: [83/100][814/1557] Data 0.004 (0.109) Batch 0.892 (1.280) Remain 09:40:21 loss: 0.4144 Lr: 0.00041 [2024-02-19 11:36:31,035 INFO misc.py line 119 87073] Train: [83/100][815/1557] Data 0.004 (0.109) Batch 0.894 (1.279) Remain 09:40:07 loss: 0.1310 Lr: 0.00041 [2024-02-19 11:36:31,950 INFO misc.py line 119 87073] Train: [83/100][816/1557] Data 0.015 (0.109) Batch 0.926 (1.279) Remain 09:39:53 loss: 0.2441 Lr: 0.00041 [2024-02-19 11:36:32,742 INFO misc.py line 119 87073] Train: [83/100][817/1557] Data 0.004 (0.109) Batch 0.791 (1.278) Remain 09:39:36 loss: 0.2778 Lr: 0.00041 [2024-02-19 11:36:33,533 INFO misc.py line 119 87073] Train: [83/100][818/1557] Data 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Batch 0.936 (1.290) Remain 09:42:58 loss: 0.3162 Lr: 0.00041 [2024-02-19 11:38:36,580 INFO misc.py line 119 87073] Train: [83/100][906/1557] Data 0.003 (0.112) Batch 0.894 (1.289) Remain 09:42:45 loss: 0.1237 Lr: 0.00041 [2024-02-19 11:38:37,607 INFO misc.py line 119 87073] Train: [83/100][907/1557] Data 0.004 (0.112) Batch 1.026 (1.289) Remain 09:42:36 loss: 0.4394 Lr: 0.00041 [2024-02-19 11:38:38,304 INFO misc.py line 119 87073] Train: [83/100][908/1557] Data 0.003 (0.112) Batch 0.664 (1.288) Remain 09:42:16 loss: 0.1359 Lr: 0.00041 [2024-02-19 11:38:39,095 INFO misc.py line 119 87073] Train: [83/100][909/1557] Data 0.037 (0.112) Batch 0.824 (1.288) Remain 09:42:01 loss: 0.1332 Lr: 0.00041 [2024-02-19 11:38:40,312 INFO misc.py line 119 87073] Train: [83/100][910/1557] Data 0.004 (0.112) Batch 1.217 (1.288) Remain 09:41:57 loss: 0.1291 Lr: 0.00041 [2024-02-19 11:38:41,349 INFO misc.py line 119 87073] Train: [83/100][911/1557] Data 0.004 (0.112) Batch 1.038 (1.287) Remain 09:41:48 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Batch 1.021 (1.289) Remain 09:41:33 loss: 0.1384 Lr: 0.00040 [2024-02-19 11:39:48,380 INFO misc.py line 119 87073] Train: [83/100][962/1557] Data 0.006 (0.112) Batch 0.946 (1.289) Remain 09:41:22 loss: 0.4187 Lr: 0.00040 [2024-02-19 11:39:49,184 INFO misc.py line 119 87073] Train: [83/100][963/1557] Data 0.004 (0.112) Batch 0.802 (1.288) Remain 09:41:07 loss: 0.4253 Lr: 0.00040 [2024-02-19 11:39:49,971 INFO misc.py line 119 87073] Train: [83/100][964/1557] Data 0.006 (0.112) Batch 0.779 (1.288) Remain 09:40:51 loss: 0.2237 Lr: 0.00040 [2024-02-19 11:39:50,704 INFO misc.py line 119 87073] Train: [83/100][965/1557] Data 0.014 (0.111) Batch 0.743 (1.287) Remain 09:40:34 loss: 0.1693 Lr: 0.00040 [2024-02-19 11:39:51,832 INFO misc.py line 119 87073] Train: [83/100][966/1557] Data 0.003 (0.111) Batch 1.127 (1.287) Remain 09:40:28 loss: 0.0790 Lr: 0.00040 [2024-02-19 11:39:52,884 INFO misc.py line 119 87073] Train: [83/100][967/1557] Data 0.004 (0.111) Batch 1.052 (1.287) Remain 09:40:21 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[2024-02-19 11:44:33,406 INFO misc.py line 119 87073] Train: [83/100][1185/1557] Data 0.005 (0.111) Batch 0.809 (1.287) Remain 09:35:40 loss: 0.2120 Lr: 0.00040 [2024-02-19 11:44:34,359 INFO misc.py line 119 87073] Train: [83/100][1186/1557] Data 0.004 (0.111) Batch 0.954 (1.287) Remain 09:35:31 loss: 0.0509 Lr: 0.00040 [2024-02-19 11:44:35,180 INFO misc.py line 119 87073] Train: [83/100][1187/1557] Data 0.004 (0.111) Batch 0.819 (1.286) Remain 09:35:19 loss: 0.1145 Lr: 0.00040 [2024-02-19 11:44:35,930 INFO misc.py line 119 87073] Train: [83/100][1188/1557] Data 0.005 (0.111) Batch 0.742 (1.286) Remain 09:35:05 loss: 0.3360 Lr: 0.00040 [2024-02-19 11:44:36,726 INFO misc.py line 119 87073] Train: [83/100][1189/1557] Data 0.013 (0.111) Batch 0.805 (1.285) Remain 09:34:53 loss: 0.2527 Lr: 0.00040 [2024-02-19 11:44:37,834 INFO misc.py line 119 87073] Train: [83/100][1190/1557] Data 0.004 (0.111) Batch 1.108 (1.285) Remain 09:34:48 loss: 0.0902 Lr: 0.00040 [2024-02-19 11:44:38,805 INFO 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Remain 09:35:13 loss: 0.4882 Lr: 0.00040 [2024-02-19 11:45:47,205 INFO misc.py line 119 87073] Train: [83/100][1241/1557] Data 0.008 (0.111) Batch 0.982 (1.288) Remain 09:35:05 loss: 0.2587 Lr: 0.00040 [2024-02-19 11:45:48,160 INFO misc.py line 119 87073] Train: [83/100][1242/1557] Data 0.005 (0.111) Batch 0.956 (1.288) Remain 09:34:57 loss: 0.0663 Lr: 0.00040 [2024-02-19 11:45:49,110 INFO misc.py line 119 87073] Train: [83/100][1243/1557] Data 0.003 (0.111) Batch 0.949 (1.288) Remain 09:34:48 loss: 0.1827 Lr: 0.00040 [2024-02-19 11:45:49,924 INFO misc.py line 119 87073] Train: [83/100][1244/1557] Data 0.003 (0.111) Batch 0.813 (1.287) Remain 09:34:37 loss: 0.2609 Lr: 0.00040 [2024-02-19 11:45:50,669 INFO misc.py line 119 87073] Train: [83/100][1245/1557] Data 0.005 (0.111) Batch 0.746 (1.287) Remain 09:34:24 loss: 0.1215 Lr: 0.00040 [2024-02-19 11:45:51,854 INFO misc.py line 119 87073] Train: [83/100][1246/1557] Data 0.004 (0.111) Batch 1.186 (1.287) Remain 09:34:20 loss: 0.1006 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1253/1557] Data 0.014 (0.110) Batch 1.171 (1.285) Remain 09:33:17 loss: 0.0929 Lr: 0.00040 [2024-02-19 11:45:59,424 INFO misc.py line 119 87073] Train: [83/100][1254/1557] Data 0.014 (0.110) Batch 1.068 (1.285) Remain 09:33:11 loss: 0.1500 Lr: 0.00040 [2024-02-19 11:46:00,231 INFO misc.py line 119 87073] Train: [83/100][1255/1557] Data 0.029 (0.110) Batch 0.831 (1.284) Remain 09:33:00 loss: 0.3307 Lr: 0.00040 [2024-02-19 11:46:01,195 INFO misc.py line 119 87073] Train: [83/100][1256/1557] Data 0.005 (0.110) Batch 0.966 (1.284) Remain 09:32:52 loss: 0.1925 Lr: 0.00040 [2024-02-19 11:46:02,053 INFO misc.py line 119 87073] Train: [83/100][1257/1557] Data 0.004 (0.110) Batch 0.858 (1.284) Remain 09:32:42 loss: 0.3438 Lr: 0.00040 [2024-02-19 11:46:02,746 INFO misc.py line 119 87073] Train: [83/100][1258/1557] Data 0.005 (0.110) Batch 0.685 (1.283) Remain 09:32:28 loss: 0.1348 Lr: 0.00040 [2024-02-19 11:46:03,535 INFO misc.py line 119 87073] Train: [83/100][1259/1557] Data 0.012 (0.110) Batch 0.797 (1.283) Remain 09:32:16 loss: 0.2161 Lr: 0.00040 [2024-02-19 11:46:04,937 INFO misc.py line 119 87073] Train: [83/100][1260/1557] Data 0.003 (0.110) Batch 1.394 (1.283) Remain 09:32:17 loss: 0.0900 Lr: 0.00040 [2024-02-19 11:46:05,704 INFO misc.py line 119 87073] Train: [83/100][1261/1557] Data 0.012 (0.110) Batch 0.773 (1.282) Remain 09:32:05 loss: 0.1722 Lr: 0.00040 [2024-02-19 11:46:06,746 INFO misc.py line 119 87073] Train: [83/100][1262/1557] Data 0.006 (0.109) Batch 1.044 (1.282) Remain 09:31:58 loss: 0.1646 Lr: 0.00040 [2024-02-19 11:46:07,782 INFO misc.py line 119 87073] Train: [83/100][1263/1557] Data 0.004 (0.109) Batch 1.037 (1.282) Remain 09:31:52 loss: 0.6526 Lr: 0.00039 [2024-02-19 11:46:08,676 INFO misc.py line 119 87073] Train: [83/100][1264/1557] Data 0.003 (0.109) Batch 0.892 (1.282) Remain 09:31:42 loss: 0.5374 Lr: 0.00039 [2024-02-19 11:46:09,441 INFO misc.py line 119 87073] Train: [83/100][1265/1557] Data 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Remain 09:30:42 loss: 0.2016 Lr: 0.00039 [2024-02-19 11:46:15,971 INFO misc.py line 119 87073] Train: [83/100][1272/1557] Data 0.010 (0.109) Batch 0.778 (1.279) Remain 09:30:30 loss: 0.1595 Lr: 0.00039 [2024-02-19 11:46:16,780 INFO misc.py line 119 87073] Train: [83/100][1273/1557] Data 0.006 (0.109) Batch 0.809 (1.279) Remain 09:30:19 loss: 0.3023 Lr: 0.00039 [2024-02-19 11:46:18,027 INFO misc.py line 119 87073] Train: [83/100][1274/1557] Data 0.005 (0.108) Batch 1.237 (1.279) Remain 09:30:16 loss: 0.1682 Lr: 0.00039 [2024-02-19 11:46:18,882 INFO misc.py line 119 87073] Train: [83/100][1275/1557] Data 0.015 (0.108) Batch 0.867 (1.279) Remain 09:30:06 loss: 0.1213 Lr: 0.00039 [2024-02-19 11:46:19,891 INFO misc.py line 119 87073] Train: [83/100][1276/1557] Data 0.003 (0.108) Batch 1.010 (1.279) Remain 09:30:00 loss: 0.3980 Lr: 0.00039 [2024-02-19 11:46:20,839 INFO misc.py line 119 87073] Train: [83/100][1277/1557] Data 0.003 (0.108) Batch 0.947 (1.278) Remain 09:29:51 loss: 0.1676 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1284/1557] Data 0.005 (0.108) Batch 0.864 (1.276) Remain 09:28:55 loss: 0.2167 Lr: 0.00039 [2024-02-19 11:46:28,550 INFO misc.py line 119 87073] Train: [83/100][1285/1557] Data 0.007 (0.108) Batch 1.013 (1.276) Remain 09:28:49 loss: 0.2391 Lr: 0.00039 [2024-02-19 11:46:29,363 INFO misc.py line 119 87073] Train: [83/100][1286/1557] Data 0.004 (0.107) Batch 0.813 (1.276) Remain 09:28:38 loss: 0.1927 Lr: 0.00039 [2024-02-19 11:46:30,169 INFO misc.py line 119 87073] Train: [83/100][1287/1557] Data 0.004 (0.107) Batch 0.806 (1.276) Remain 09:28:27 loss: 0.1968 Lr: 0.00039 [2024-02-19 11:46:31,393 INFO misc.py line 119 87073] Train: [83/100][1288/1557] Data 0.004 (0.107) Batch 1.211 (1.276) Remain 09:28:24 loss: 0.1601 Lr: 0.00039 [2024-02-19 11:46:32,336 INFO misc.py line 119 87073] Train: [83/100][1289/1557] Data 0.016 (0.107) Batch 0.956 (1.275) Remain 09:28:16 loss: 0.4886 Lr: 0.00039 [2024-02-19 11:46:33,414 INFO misc.py line 119 87073] Train: [83/100][1290/1557] Data 0.004 (0.107) Batch 1.077 (1.275) Remain 09:28:11 loss: 0.4183 Lr: 0.00039 [2024-02-19 11:46:34,302 INFO misc.py line 119 87073] Train: [83/100][1291/1557] Data 0.005 (0.107) Batch 0.888 (1.275) Remain 09:28:01 loss: 0.4103 Lr: 0.00039 [2024-02-19 11:46:35,438 INFO misc.py line 119 87073] Train: [83/100][1292/1557] Data 0.004 (0.107) Batch 1.136 (1.275) Remain 09:27:57 loss: 0.3316 Lr: 0.00039 [2024-02-19 11:46:36,124 INFO misc.py line 119 87073] Train: [83/100][1293/1557] Data 0.004 (0.107) Batch 0.686 (1.274) Remain 09:27:44 loss: 0.2179 Lr: 0.00039 [2024-02-19 11:46:36,867 INFO misc.py line 119 87073] Train: [83/100][1294/1557] Data 0.004 (0.107) Batch 0.735 (1.274) Remain 09:27:31 loss: 0.1189 Lr: 0.00039 [2024-02-19 11:46:57,199 INFO misc.py line 119 87073] Train: [83/100][1295/1557] Data 5.236 (0.111) Batch 20.339 (1.289) Remain 09:34:05 loss: 0.0711 Lr: 0.00039 [2024-02-19 11:46:58,041 INFO misc.py line 119 87073] Train: [83/100][1296/1557] Data 0.005 (0.111) Batch 0.842 (1.288) Remain 09:33:54 loss: 0.1799 Lr: 0.00039 [2024-02-19 11:46:58,929 INFO misc.py line 119 87073] Train: [83/100][1297/1557] Data 0.004 (0.111) Batch 0.889 (1.288) Remain 09:33:44 loss: 0.2938 Lr: 0.00039 [2024-02-19 11:47:00,009 INFO misc.py line 119 87073] Train: [83/100][1298/1557] Data 0.004 (0.111) Batch 1.079 (1.288) Remain 09:33:39 loss: 0.0574 Lr: 0.00039 [2024-02-19 11:47:00,879 INFO misc.py line 119 87073] Train: [83/100][1299/1557] Data 0.004 (0.110) Batch 0.868 (1.287) Remain 09:33:29 loss: 0.6179 Lr: 0.00039 [2024-02-19 11:47:01,640 INFO misc.py line 119 87073] Train: [83/100][1300/1557] Data 0.007 (0.110) Batch 0.762 (1.287) Remain 09:33:17 loss: 0.1764 Lr: 0.00039 [2024-02-19 11:47:02,411 INFO misc.py line 119 87073] Train: [83/100][1301/1557] Data 0.005 (0.110) Batch 0.772 (1.287) Remain 09:33:05 loss: 0.0985 Lr: 0.00039 [2024-02-19 11:47:03,627 INFO misc.py line 119 87073] Train: [83/100][1302/1557] Data 0.003 (0.110) Batch 1.212 (1.287) Remain 09:33:02 loss: 0.1453 Lr: 0.00039 [2024-02-19 11:47:04,679 INFO misc.py line 119 87073] Train: [83/100][1303/1557] Data 0.009 (0.110) Batch 1.057 (1.286) Remain 09:32:56 loss: 0.2533 Lr: 0.00039 [2024-02-19 11:47:05,581 INFO misc.py line 119 87073] Train: [83/100][1304/1557] Data 0.004 (0.110) Batch 0.900 (1.286) Remain 09:32:47 loss: 0.1731 Lr: 0.00039 [2024-02-19 11:47:06,602 INFO misc.py line 119 87073] Train: [83/100][1305/1557] Data 0.005 (0.110) Batch 1.019 (1.286) Remain 09:32:40 loss: 0.1824 Lr: 0.00039 [2024-02-19 11:47:07,673 INFO misc.py line 119 87073] Train: [83/100][1306/1557] Data 0.008 (0.110) Batch 1.067 (1.286) Remain 09:32:34 loss: 0.2301 Lr: 0.00039 [2024-02-19 11:47:08,481 INFO misc.py line 119 87073] Train: [83/100][1307/1557] Data 0.013 (0.110) Batch 0.817 (1.285) Remain 09:32:23 loss: 0.1169 Lr: 0.00039 [2024-02-19 11:47:09,217 INFO misc.py line 119 87073] Train: [83/100][1308/1557] Data 0.004 (0.110) Batch 0.736 (1.285) Remain 09:32:11 loss: 0.1702 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1315/1557] Data 0.003 (0.109) Batch 0.745 (1.283) Remain 09:31:12 loss: 0.3675 Lr: 0.00039 [2024-02-19 11:47:17,037 INFO misc.py line 119 87073] Train: [83/100][1316/1557] Data 0.005 (0.109) Batch 1.242 (1.283) Remain 09:31:10 loss: 0.1127 Lr: 0.00039 [2024-02-19 11:47:18,003 INFO misc.py line 119 87073] Train: [83/100][1317/1557] Data 0.013 (0.109) Batch 0.975 (1.283) Remain 09:31:03 loss: 0.1316 Lr: 0.00039 [2024-02-19 11:47:18,967 INFO misc.py line 119 87073] Train: [83/100][1318/1557] Data 0.005 (0.109) Batch 0.963 (1.283) Remain 09:30:55 loss: 0.1972 Lr: 0.00039 [2024-02-19 11:47:19,912 INFO misc.py line 119 87073] Train: [83/100][1319/1557] Data 0.006 (0.109) Batch 0.946 (1.282) Remain 09:30:47 loss: 0.2960 Lr: 0.00039 [2024-02-19 11:47:20,953 INFO misc.py line 119 87073] Train: [83/100][1320/1557] Data 0.004 (0.109) Batch 1.041 (1.282) Remain 09:30:41 loss: 0.2455 Lr: 0.00039 [2024-02-19 11:47:21,709 INFO misc.py line 119 87073] Train: [83/100][1321/1557] Data 0.004 (0.109) Batch 0.754 (1.282) Remain 09:30:29 loss: 0.1268 Lr: 0.00039 [2024-02-19 11:47:22,412 INFO misc.py line 119 87073] Train: [83/100][1322/1557] Data 0.006 (0.109) Batch 0.703 (1.281) Remain 09:30:16 loss: 0.1697 Lr: 0.00039 [2024-02-19 11:47:23,591 INFO misc.py line 119 87073] Train: [83/100][1323/1557] Data 0.006 (0.109) Batch 1.180 (1.281) Remain 09:30:12 loss: 0.1207 Lr: 0.00039 [2024-02-19 11:47:24,588 INFO misc.py line 119 87073] Train: [83/100][1324/1557] Data 0.006 (0.109) Batch 0.998 (1.281) Remain 09:30:05 loss: 0.3226 Lr: 0.00039 [2024-02-19 11:47:25,494 INFO misc.py line 119 87073] Train: [83/100][1325/1557] Data 0.004 (0.108) Batch 0.907 (1.281) Remain 09:29:57 loss: 0.3944 Lr: 0.00039 [2024-02-19 11:47:26,481 INFO misc.py line 119 87073] Train: [83/100][1326/1557] Data 0.004 (0.108) Batch 0.962 (1.281) Remain 09:29:49 loss: 0.1598 Lr: 0.00039 [2024-02-19 11:47:27,465 INFO misc.py line 119 87073] Train: [83/100][1327/1557] Data 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Remain 09:28:58 loss: 0.1066 Lr: 0.00039 [2024-02-19 11:47:34,184 INFO misc.py line 119 87073] Train: [83/100][1334/1557] Data 0.004 (0.108) Batch 0.865 (1.279) Remain 09:28:48 loss: 0.3247 Lr: 0.00039 [2024-02-19 11:47:34,986 INFO misc.py line 119 87073] Train: [83/100][1335/1557] Data 0.005 (0.108) Batch 0.790 (1.278) Remain 09:28:37 loss: 0.2911 Lr: 0.00039 [2024-02-19 11:47:35,770 INFO misc.py line 119 87073] Train: [83/100][1336/1557] Data 0.017 (0.108) Batch 0.795 (1.278) Remain 09:28:26 loss: 0.1845 Lr: 0.00039 [2024-02-19 11:47:37,015 INFO misc.py line 119 87073] Train: [83/100][1337/1557] Data 0.005 (0.108) Batch 1.233 (1.278) Remain 09:28:24 loss: 0.1252 Lr: 0.00039 [2024-02-19 11:47:37,947 INFO misc.py line 119 87073] Train: [83/100][1338/1557] Data 0.018 (0.107) Batch 0.946 (1.278) Remain 09:28:16 loss: 0.2148 Lr: 0.00039 [2024-02-19 11:47:38,894 INFO misc.py line 119 87073] Train: [83/100][1339/1557] Data 0.004 (0.107) Batch 0.947 (1.277) Remain 09:28:08 loss: 0.2867 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1346/1557] Data 0.004 (0.107) Batch 0.838 (1.275) Remain 09:27:07 loss: 0.1025 Lr: 0.00039 [2024-02-19 11:47:46,143 INFO misc.py line 119 87073] Train: [83/100][1347/1557] Data 0.005 (0.107) Batch 0.909 (1.275) Remain 09:26:59 loss: 0.7164 Lr: 0.00039 [2024-02-19 11:47:46,936 INFO misc.py line 119 87073] Train: [83/100][1348/1557] Data 0.012 (0.107) Batch 0.801 (1.275) Remain 09:26:48 loss: 0.0746 Lr: 0.00039 [2024-02-19 11:47:47,665 INFO misc.py line 119 87073] Train: [83/100][1349/1557] Data 0.005 (0.107) Batch 0.728 (1.274) Remain 09:26:36 loss: 0.1379 Lr: 0.00039 [2024-02-19 11:47:48,443 INFO misc.py line 119 87073] Train: [83/100][1350/1557] Data 0.005 (0.107) Batch 0.777 (1.274) Remain 09:26:25 loss: 0.1321 Lr: 0.00039 [2024-02-19 11:48:07,353 INFO misc.py line 119 87073] Train: [83/100][1351/1557] Data 5.917 (0.111) Batch 18.912 (1.287) Remain 09:32:13 loss: 0.0680 Lr: 0.00039 [2024-02-19 11:48:08,211 INFO misc.py line 119 87073] Train: [83/100][1352/1557] Data 0.004 (0.111) Batch 0.858 (1.287) Remain 09:32:03 loss: 0.1845 Lr: 0.00039 [2024-02-19 11:48:09,190 INFO misc.py line 119 87073] Train: [83/100][1353/1557] Data 0.004 (0.111) Batch 0.979 (1.287) Remain 09:31:56 loss: 0.1985 Lr: 0.00039 [2024-02-19 11:48:10,222 INFO misc.py line 119 87073] Train: [83/100][1354/1557] Data 0.004 (0.111) Batch 1.033 (1.286) Remain 09:31:49 loss: 0.5564 Lr: 0.00039 [2024-02-19 11:48:11,038 INFO misc.py line 119 87073] Train: [83/100][1355/1557] Data 0.003 (0.111) Batch 0.815 (1.286) Remain 09:31:39 loss: 0.5485 Lr: 0.00039 [2024-02-19 11:48:11,827 INFO misc.py line 119 87073] Train: [83/100][1356/1557] Data 0.004 (0.111) Batch 0.786 (1.286) Remain 09:31:28 loss: 0.1863 Lr: 0.00039 [2024-02-19 11:48:12,475 INFO misc.py line 119 87073] Train: [83/100][1357/1557] Data 0.007 (0.110) Batch 0.651 (1.285) Remain 09:31:14 loss: 0.1548 Lr: 0.00039 [2024-02-19 11:48:13,600 INFO misc.py line 119 87073] Train: [83/100][1358/1557] Data 0.004 (0.110) Batch 1.123 (1.285) Remain 09:31:09 loss: 0.0853 Lr: 0.00039 [2024-02-19 11:48:14,576 INFO misc.py line 119 87073] Train: [83/100][1359/1557] Data 0.006 (0.110) Batch 0.979 (1.285) Remain 09:31:02 loss: 0.4469 Lr: 0.00039 [2024-02-19 11:48:15,570 INFO misc.py line 119 87073] Train: [83/100][1360/1557] Data 0.004 (0.110) Batch 0.993 (1.285) Remain 09:30:55 loss: 0.3282 Lr: 0.00039 [2024-02-19 11:48:16,421 INFO misc.py line 119 87073] Train: [83/100][1361/1557] Data 0.004 (0.110) Batch 0.851 (1.284) Remain 09:30:45 loss: 0.0728 Lr: 0.00039 [2024-02-19 11:48:17,313 INFO misc.py line 119 87073] Train: [83/100][1362/1557] Data 0.005 (0.110) Batch 0.885 (1.284) Remain 09:30:36 loss: 0.1522 Lr: 0.00039 [2024-02-19 11:48:18,036 INFO misc.py line 119 87073] Train: [83/100][1363/1557] Data 0.012 (0.110) Batch 0.731 (1.284) Remain 09:30:24 loss: 0.1934 Lr: 0.00039 [2024-02-19 11:48:18,832 INFO misc.py line 119 87073] Train: [83/100][1364/1557] Data 0.004 (0.110) Batch 0.787 (1.283) Remain 09:30:13 loss: 0.1664 Lr: 0.00039 [2024-02-19 11:48:20,051 INFO misc.py line 119 87073] Train: [83/100][1365/1557] Data 0.013 (0.110) Batch 1.216 (1.283) Remain 09:30:10 loss: 0.1138 Lr: 0.00039 [2024-02-19 11:48:20,926 INFO misc.py line 119 87073] Train: [83/100][1366/1557] Data 0.016 (0.110) Batch 0.886 (1.283) Remain 09:30:01 loss: 0.1210 Lr: 0.00039 [2024-02-19 11:48:21,923 INFO misc.py line 119 87073] Train: [83/100][1367/1557] Data 0.005 (0.110) Batch 0.999 (1.283) Remain 09:29:54 loss: 0.1256 Lr: 0.00039 [2024-02-19 11:48:22,880 INFO misc.py line 119 87073] Train: [83/100][1368/1557] Data 0.004 (0.110) Batch 0.956 (1.282) Remain 09:29:47 loss: 0.0926 Lr: 0.00039 [2024-02-19 11:48:23,709 INFO misc.py line 119 87073] Train: [83/100][1369/1557] Data 0.004 (0.110) Batch 0.827 (1.282) Remain 09:29:37 loss: 0.1966 Lr: 0.00039 [2024-02-19 11:48:24,399 INFO misc.py line 119 87073] Train: [83/100][1370/1557] Data 0.006 (0.109) Batch 0.691 (1.282) Remain 09:29:24 loss: 0.1828 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1377/1557] Data 0.008 (0.109) Batch 0.746 (1.280) Remain 09:28:36 loss: 0.4286 Lr: 0.00039 [2024-02-19 11:48:32,102 INFO misc.py line 119 87073] Train: [83/100][1378/1557] Data 0.004 (0.109) Batch 0.712 (1.280) Remain 09:28:24 loss: 0.1322 Lr: 0.00039 [2024-02-19 11:48:33,283 INFO misc.py line 119 87073] Train: [83/100][1379/1557] Data 0.014 (0.109) Batch 1.137 (1.280) Remain 09:28:20 loss: 0.1651 Lr: 0.00039 [2024-02-19 11:48:34,135 INFO misc.py line 119 87073] Train: [83/100][1380/1557] Data 0.058 (0.109) Batch 0.906 (1.279) Remain 09:28:11 loss: 0.2188 Lr: 0.00039 [2024-02-19 11:48:35,175 INFO misc.py line 119 87073] Train: [83/100][1381/1557] Data 0.006 (0.109) Batch 1.039 (1.279) Remain 09:28:05 loss: 0.1014 Lr: 0.00039 [2024-02-19 11:48:36,142 INFO misc.py line 119 87073] Train: [83/100][1382/1557] Data 0.005 (0.109) Batch 0.968 (1.279) Remain 09:27:58 loss: 0.2700 Lr: 0.00039 [2024-02-19 11:48:37,162 INFO misc.py line 119 87073] Train: [83/100][1383/1557] Data 0.004 (0.109) Batch 1.019 (1.279) Remain 09:27:52 loss: 0.4272 Lr: 0.00039 [2024-02-19 11:48:37,849 INFO misc.py line 119 87073] Train: [83/100][1384/1557] Data 0.005 (0.108) Batch 0.688 (1.278) Remain 09:27:39 loss: 0.1701 Lr: 0.00039 [2024-02-19 11:48:38,620 INFO misc.py line 119 87073] Train: [83/100][1385/1557] Data 0.004 (0.108) Batch 0.761 (1.278) Remain 09:27:28 loss: 0.1825 Lr: 0.00039 [2024-02-19 11:48:39,910 INFO misc.py line 119 87073] Train: [83/100][1386/1557] Data 0.014 (0.108) Batch 1.291 (1.278) Remain 09:27:27 loss: 0.1281 Lr: 0.00039 [2024-02-19 11:48:40,773 INFO misc.py line 119 87073] Train: [83/100][1387/1557] Data 0.013 (0.108) Batch 0.873 (1.278) Remain 09:27:18 loss: 0.2051 Lr: 0.00039 [2024-02-19 11:48:41,852 INFO misc.py line 119 87073] Train: [83/100][1388/1557] Data 0.003 (0.108) Batch 1.080 (1.278) Remain 09:27:13 loss: 0.3139 Lr: 0.00039 [2024-02-19 11:48:42,758 INFO misc.py line 119 87073] Train: [83/100][1389/1557] Data 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Remain 09:26:20 loss: 0.2668 Lr: 0.00039 [2024-02-19 11:48:49,426 INFO misc.py line 119 87073] Train: [83/100][1396/1557] Data 0.004 (0.108) Batch 0.924 (1.276) Remain 09:26:12 loss: 0.1600 Lr: 0.00039 [2024-02-19 11:48:50,464 INFO misc.py line 119 87073] Train: [83/100][1397/1557] Data 0.004 (0.107) Batch 1.036 (1.276) Remain 09:26:06 loss: 0.2463 Lr: 0.00039 [2024-02-19 11:48:51,183 INFO misc.py line 119 87073] Train: [83/100][1398/1557] Data 0.006 (0.107) Batch 0.721 (1.275) Remain 09:25:54 loss: 0.1661 Lr: 0.00039 [2024-02-19 11:48:51,931 INFO misc.py line 119 87073] Train: [83/100][1399/1557] Data 0.004 (0.107) Batch 0.743 (1.275) Remain 09:25:43 loss: 0.1266 Lr: 0.00039 [2024-02-19 11:48:53,074 INFO misc.py line 119 87073] Train: [83/100][1400/1557] Data 0.009 (0.107) Batch 1.147 (1.275) Remain 09:25:39 loss: 0.0939 Lr: 0.00039 [2024-02-19 11:48:53,985 INFO misc.py line 119 87073] Train: [83/100][1401/1557] Data 0.006 (0.107) Batch 0.911 (1.274) Remain 09:25:31 loss: 0.0746 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1408/1557] Data 0.004 (0.112) Batch 1.155 (1.287) Remain 09:30:49 loss: 0.1677 Lr: 0.00039 [2024-02-19 11:49:21,040 INFO misc.py line 119 87073] Train: [83/100][1409/1557] Data 0.005 (0.112) Batch 0.890 (1.286) Remain 09:30:40 loss: 0.2591 Lr: 0.00039 [2024-02-19 11:49:21,916 INFO misc.py line 119 87073] Train: [83/100][1410/1557] Data 0.005 (0.112) Batch 0.876 (1.286) Remain 09:30:31 loss: 0.0701 Lr: 0.00039 [2024-02-19 11:49:22,814 INFO misc.py line 119 87073] Train: [83/100][1411/1557] Data 0.003 (0.112) Batch 0.894 (1.286) Remain 09:30:22 loss: 0.1595 Lr: 0.00039 [2024-02-19 11:49:23,599 INFO misc.py line 119 87073] Train: [83/100][1412/1557] Data 0.008 (0.112) Batch 0.789 (1.285) Remain 09:30:11 loss: 0.1614 Lr: 0.00039 [2024-02-19 11:49:24,390 INFO misc.py line 119 87073] Train: [83/100][1413/1557] Data 0.003 (0.111) Batch 0.791 (1.285) Remain 09:30:01 loss: 0.1919 Lr: 0.00039 [2024-02-19 11:49:25,556 INFO misc.py line 119 87073] Train: [83/100][1414/1557] Data 0.003 (0.111) Batch 1.164 (1.285) Remain 09:29:57 loss: 0.0742 Lr: 0.00039 [2024-02-19 11:49:26,498 INFO misc.py line 119 87073] Train: [83/100][1415/1557] Data 0.006 (0.111) Batch 0.944 (1.285) Remain 09:29:49 loss: 0.3466 Lr: 0.00039 [2024-02-19 11:49:27,527 INFO misc.py line 119 87073] Train: [83/100][1416/1557] Data 0.004 (0.111) Batch 1.029 (1.285) Remain 09:29:43 loss: 0.2223 Lr: 0.00039 [2024-02-19 11:49:28,559 INFO misc.py line 119 87073] Train: [83/100][1417/1557] Data 0.005 (0.111) Batch 1.033 (1.284) Remain 09:29:37 loss: 0.2011 Lr: 0.00039 [2024-02-19 11:49:29,509 INFO misc.py line 119 87073] Train: [83/100][1418/1557] Data 0.004 (0.111) Batch 0.949 (1.284) Remain 09:29:30 loss: 0.4908 Lr: 0.00039 [2024-02-19 11:49:30,341 INFO misc.py line 119 87073] Train: [83/100][1419/1557] Data 0.004 (0.111) Batch 0.831 (1.284) Remain 09:29:20 loss: 0.3200 Lr: 0.00039 [2024-02-19 11:49:31,081 INFO misc.py line 119 87073] Train: [83/100][1420/1557] Data 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Remain 09:28:28 loss: 0.0878 Lr: 0.00039 [2024-02-19 11:49:37,728 INFO misc.py line 119 87073] Train: [83/100][1427/1557] Data 0.006 (0.110) Batch 0.698 (1.282) Remain 09:28:16 loss: 0.1507 Lr: 0.00039 [2024-02-19 11:49:39,104 INFO misc.py line 119 87073] Train: [83/100][1428/1557] Data 0.005 (0.110) Batch 1.372 (1.282) Remain 09:28:16 loss: 0.1052 Lr: 0.00039 [2024-02-19 11:49:40,330 INFO misc.py line 119 87073] Train: [83/100][1429/1557] Data 0.010 (0.110) Batch 1.228 (1.282) Remain 09:28:14 loss: 0.1835 Lr: 0.00039 [2024-02-19 11:49:41,421 INFO misc.py line 119 87073] Train: [83/100][1430/1557] Data 0.008 (0.110) Batch 1.088 (1.282) Remain 09:28:09 loss: 0.1439 Lr: 0.00039 [2024-02-19 11:49:42,342 INFO misc.py line 119 87073] Train: [83/100][1431/1557] Data 0.011 (0.110) Batch 0.927 (1.282) Remain 09:28:01 loss: 0.3821 Lr: 0.00039 [2024-02-19 11:49:43,279 INFO misc.py line 119 87073] Train: [83/100][1432/1557] Data 0.004 (0.110) Batch 0.934 (1.281) Remain 09:27:53 loss: 0.2657 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1439/1557] Data 0.004 (0.110) Batch 0.883 (1.279) Remain 09:26:56 loss: 0.0512 Lr: 0.00039 [2024-02-19 11:49:50,442 INFO misc.py line 119 87073] Train: [83/100][1440/1557] Data 0.004 (0.109) Batch 0.818 (1.279) Remain 09:26:46 loss: 0.2417 Lr: 0.00039 [2024-02-19 11:49:51,199 INFO misc.py line 119 87073] Train: [83/100][1441/1557] Data 0.005 (0.109) Batch 0.749 (1.279) Remain 09:26:35 loss: 0.1950 Lr: 0.00039 [2024-02-19 11:49:52,486 INFO misc.py line 119 87073] Train: [83/100][1442/1557] Data 0.012 (0.109) Batch 1.289 (1.279) Remain 09:26:34 loss: 0.1076 Lr: 0.00039 [2024-02-19 11:49:53,607 INFO misc.py line 119 87073] Train: [83/100][1443/1557] Data 0.010 (0.109) Batch 1.119 (1.279) Remain 09:26:30 loss: 0.1803 Lr: 0.00039 [2024-02-19 11:49:54,555 INFO misc.py line 119 87073] Train: [83/100][1444/1557] Data 0.012 (0.109) Batch 0.956 (1.278) Remain 09:26:22 loss: 0.1791 Lr: 0.00039 [2024-02-19 11:49:55,420 INFO misc.py line 119 87073] Train: [83/100][1445/1557] Data 0.005 (0.109) Batch 0.866 (1.278) Remain 09:26:14 loss: 0.0930 Lr: 0.00039 [2024-02-19 11:49:56,744 INFO misc.py line 119 87073] Train: [83/100][1446/1557] Data 0.004 (0.109) Batch 1.322 (1.278) Remain 09:26:13 loss: 0.3550 Lr: 0.00039 [2024-02-19 11:49:57,501 INFO misc.py line 119 87073] Train: [83/100][1447/1557] Data 0.005 (0.109) Batch 0.758 (1.278) Remain 09:26:02 loss: 0.1395 Lr: 0.00039 [2024-02-19 11:49:58,351 INFO misc.py line 119 87073] Train: [83/100][1448/1557] Data 0.005 (0.109) Batch 0.842 (1.278) Remain 09:25:53 loss: 0.2300 Lr: 0.00039 [2024-02-19 11:49:59,561 INFO misc.py line 119 87073] Train: [83/100][1449/1557] Data 0.012 (0.109) Batch 1.208 (1.277) Remain 09:25:50 loss: 0.0683 Lr: 0.00039 [2024-02-19 11:50:00,450 INFO misc.py line 119 87073] Train: [83/100][1450/1557] Data 0.015 (0.109) Batch 0.900 (1.277) Remain 09:25:42 loss: 0.2176 Lr: 0.00039 [2024-02-19 11:50:01,528 INFO misc.py line 119 87073] Train: [83/100][1451/1557] Data 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Remain 09:24:48 loss: 0.3963 Lr: 0.00039 [2024-02-19 11:50:07,951 INFO misc.py line 119 87073] Train: [83/100][1458/1557] Data 0.004 (0.108) Batch 1.017 (1.275) Remain 09:24:42 loss: 0.3744 Lr: 0.00039 [2024-02-19 11:50:08,767 INFO misc.py line 119 87073] Train: [83/100][1459/1557] Data 0.003 (0.108) Batch 0.816 (1.275) Remain 09:24:33 loss: 0.1234 Lr: 0.00039 [2024-02-19 11:50:09,824 INFO misc.py line 119 87073] Train: [83/100][1460/1557] Data 0.004 (0.108) Batch 1.051 (1.275) Remain 09:24:27 loss: 0.0723 Lr: 0.00039 [2024-02-19 11:50:10,587 INFO misc.py line 119 87073] Train: [83/100][1461/1557] Data 0.010 (0.108) Batch 0.769 (1.275) Remain 09:24:17 loss: 0.2825 Lr: 0.00039 [2024-02-19 11:50:11,346 INFO misc.py line 119 87073] Train: [83/100][1462/1557] Data 0.004 (0.108) Batch 0.757 (1.274) Remain 09:24:06 loss: 0.2108 Lr: 0.00039 [2024-02-19 11:50:30,459 INFO misc.py line 119 87073] Train: [83/100][1463/1557] Data 6.060 (0.112) Batch 19.115 (1.286) Remain 09:29:29 loss: 0.0772 Lr: 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INFO misc.py line 119 87073] Train: [83/100][1470/1557] Data 0.013 (0.111) Batch 1.225 (1.285) Remain 09:28:37 loss: 0.1086 Lr: 0.00039 [2024-02-19 11:50:38,094 INFO misc.py line 119 87073] Train: [83/100][1471/1557] Data 0.013 (0.111) Batch 1.013 (1.285) Remain 09:28:31 loss: 0.3529 Lr: 0.00039 [2024-02-19 11:50:39,050 INFO misc.py line 119 87073] Train: [83/100][1472/1557] Data 0.011 (0.111) Batch 0.963 (1.284) Remain 09:28:24 loss: 0.2073 Lr: 0.00039 [2024-02-19 11:50:40,102 INFO misc.py line 119 87073] Train: [83/100][1473/1557] Data 0.005 (0.111) Batch 1.053 (1.284) Remain 09:28:18 loss: 0.1576 Lr: 0.00039 [2024-02-19 11:50:41,036 INFO misc.py line 119 87073] Train: [83/100][1474/1557] Data 0.004 (0.111) Batch 0.934 (1.284) Remain 09:28:11 loss: 0.3191 Lr: 0.00039 [2024-02-19 11:50:41,786 INFO misc.py line 119 87073] Train: [83/100][1475/1557] Data 0.004 (0.111) Batch 0.749 (1.284) Remain 09:28:00 loss: 0.3020 Lr: 0.00039 [2024-02-19 11:50:42,557 INFO misc.py line 119 87073] Train: [83/100][1476/1557] Data 0.005 (0.111) Batch 0.772 (1.283) Remain 09:27:49 loss: 0.0900 Lr: 0.00039 [2024-02-19 11:50:43,827 INFO misc.py line 119 87073] Train: [83/100][1477/1557] Data 0.003 (0.111) Batch 1.260 (1.283) Remain 09:27:48 loss: 0.0772 Lr: 0.00039 [2024-02-19 11:50:44,698 INFO misc.py line 119 87073] Train: [83/100][1478/1557] Data 0.013 (0.111) Batch 0.879 (1.283) Remain 09:27:39 loss: 0.2363 Lr: 0.00039 [2024-02-19 11:50:45,650 INFO misc.py line 119 87073] Train: [83/100][1479/1557] Data 0.005 (0.111) Batch 0.953 (1.283) Remain 09:27:32 loss: 0.4281 Lr: 0.00039 [2024-02-19 11:50:46,567 INFO misc.py line 119 87073] Train: [83/100][1480/1557] Data 0.005 (0.111) Batch 0.917 (1.282) Remain 09:27:24 loss: 0.3229 Lr: 0.00039 [2024-02-19 11:50:47,407 INFO misc.py line 119 87073] Train: [83/100][1481/1557] Data 0.005 (0.111) Batch 0.839 (1.282) Remain 09:27:15 loss: 0.1701 Lr: 0.00039 [2024-02-19 11:50:48,176 INFO misc.py line 119 87073] Train: [83/100][1482/1557] Data 0.006 (0.111) Batch 0.769 (1.282) Remain 09:27:04 loss: 0.3630 Lr: 0.00039 [2024-02-19 11:50:48,884 INFO misc.py line 119 87073] Train: [83/100][1483/1557] Data 0.005 (0.111) Batch 0.707 (1.281) Remain 09:26:53 loss: 0.2221 Lr: 0.00039 [2024-02-19 11:50:50,274 INFO misc.py line 119 87073] Train: [83/100][1484/1557] Data 0.006 (0.111) Batch 1.382 (1.282) Remain 09:26:53 loss: 0.1131 Lr: 0.00039 [2024-02-19 11:50:51,246 INFO misc.py line 119 87073] Train: [83/100][1485/1557] Data 0.015 (0.110) Batch 0.983 (1.281) Remain 09:26:47 loss: 0.2539 Lr: 0.00039 [2024-02-19 11:50:52,225 INFO misc.py line 119 87073] Train: [83/100][1486/1557] Data 0.003 (0.110) Batch 0.978 (1.281) Remain 09:26:40 loss: 0.1830 Lr: 0.00039 [2024-02-19 11:50:53,156 INFO misc.py line 119 87073] Train: [83/100][1487/1557] Data 0.004 (0.110) Batch 0.932 (1.281) Remain 09:26:32 loss: 0.1527 Lr: 0.00039 [2024-02-19 11:50:54,046 INFO misc.py line 119 87073] Train: [83/100][1488/1557] Data 0.004 (0.110) Batch 0.881 (1.281) Remain 09:26:24 loss: 0.3122 Lr: 0.00039 [2024-02-19 11:50:54,816 INFO misc.py line 119 87073] Train: [83/100][1489/1557] Data 0.012 (0.110) Batch 0.779 (1.280) Remain 09:26:14 loss: 0.3279 Lr: 0.00039 [2024-02-19 11:50:55,614 INFO misc.py line 119 87073] Train: [83/100][1490/1557] Data 0.004 (0.110) Batch 0.796 (1.280) Remain 09:26:04 loss: 0.2898 Lr: 0.00039 [2024-02-19 11:50:56,727 INFO misc.py line 119 87073] Train: [83/100][1491/1557] Data 0.005 (0.110) Batch 1.102 (1.280) Remain 09:25:59 loss: 0.0970 Lr: 0.00039 [2024-02-19 11:50:57,822 INFO misc.py line 119 87073] Train: [83/100][1492/1557] Data 0.016 (0.110) Batch 1.097 (1.280) Remain 09:25:55 loss: 0.1264 Lr: 0.00039 [2024-02-19 11:50:58,935 INFO misc.py line 119 87073] Train: [83/100][1493/1557] Data 0.014 (0.110) Batch 1.112 (1.280) Remain 09:25:51 loss: 0.5729 Lr: 0.00039 [2024-02-19 11:50:59,788 INFO misc.py line 119 87073] Train: [83/100][1494/1557] Data 0.015 (0.110) Batch 0.864 (1.279) Remain 09:25:42 loss: 0.1525 Lr: 0.00039 [2024-02-19 11:51:00,592 INFO misc.py line 119 87073] Train: [83/100][1495/1557] Data 0.004 (0.110) Batch 0.801 (1.279) Remain 09:25:32 loss: 0.2041 Lr: 0.00039 [2024-02-19 11:51:01,365 INFO misc.py line 119 87073] Train: [83/100][1496/1557] Data 0.007 (0.110) Batch 0.765 (1.279) Remain 09:25:22 loss: 0.1177 Lr: 0.00039 [2024-02-19 11:51:02,070 INFO misc.py line 119 87073] Train: [83/100][1497/1557] Data 0.014 (0.110) Batch 0.715 (1.278) Remain 09:25:10 loss: 0.3419 Lr: 0.00039 [2024-02-19 11:51:03,264 INFO misc.py line 119 87073] Train: [83/100][1498/1557] Data 0.004 (0.110) Batch 1.195 (1.278) Remain 09:25:08 loss: 0.1004 Lr: 0.00039 [2024-02-19 11:51:04,369 INFO misc.py line 119 87073] Train: [83/100][1499/1557] Data 0.004 (0.109) Batch 1.103 (1.278) Remain 09:25:03 loss: 0.3376 Lr: 0.00039 [2024-02-19 11:51:05,236 INFO misc.py line 119 87073] Train: [83/100][1500/1557] Data 0.005 (0.109) Batch 0.869 (1.278) Remain 09:24:55 loss: 0.1215 Lr: 0.00039 [2024-02-19 11:51:06,051 INFO misc.py line 119 87073] Train: [83/100][1501/1557] Data 0.003 (0.109) Batch 0.805 (1.277) Remain 09:24:45 loss: 0.0470 Lr: 0.00039 [2024-02-19 11:51:07,037 INFO misc.py line 119 87073] Train: [83/100][1502/1557] Data 0.013 (0.109) Batch 0.995 (1.277) Remain 09:24:39 loss: 0.6518 Lr: 0.00039 [2024-02-19 11:51:07,813 INFO misc.py line 119 87073] Train: [83/100][1503/1557] Data 0.003 (0.109) Batch 0.776 (1.277) Remain 09:24:29 loss: 0.0667 Lr: 0.00039 [2024-02-19 11:51:08,625 INFO misc.py line 119 87073] Train: [83/100][1504/1557] Data 0.004 (0.109) Batch 0.812 (1.277) Remain 09:24:19 loss: 0.0964 Lr: 0.00039 [2024-02-19 11:51:09,790 INFO misc.py line 119 87073] Train: [83/100][1505/1557] Data 0.004 (0.109) Batch 1.152 (1.277) Remain 09:24:16 loss: 0.1417 Lr: 0.00039 [2024-02-19 11:51:10,664 INFO misc.py line 119 87073] Train: [83/100][1506/1557] Data 0.017 (0.109) Batch 0.888 (1.276) Remain 09:24:08 loss: 0.3308 Lr: 0.00039 [2024-02-19 11:51:11,587 INFO misc.py line 119 87073] Train: [83/100][1507/1557] Data 0.004 (0.109) Batch 0.921 (1.276) Remain 09:24:00 loss: 0.3492 Lr: 0.00039 [2024-02-19 11:51:12,443 INFO misc.py line 119 87073] Train: [83/100][1508/1557] Data 0.005 (0.109) Batch 0.858 (1.276) Remain 09:23:51 loss: 0.2956 Lr: 0.00039 [2024-02-19 11:51:13,498 INFO misc.py line 119 87073] Train: [83/100][1509/1557] Data 0.003 (0.109) Batch 1.050 (1.276) Remain 09:23:46 loss: 0.1356 Lr: 0.00039 [2024-02-19 11:51:14,264 INFO misc.py line 119 87073] Train: [83/100][1510/1557] Data 0.009 (0.109) Batch 0.771 (1.275) Remain 09:23:36 loss: 0.2785 Lr: 0.00039 [2024-02-19 11:51:15,061 INFO misc.py line 119 87073] Train: [83/100][1511/1557] Data 0.004 (0.109) Batch 0.795 (1.275) Remain 09:23:26 loss: 0.1295 Lr: 0.00039 [2024-02-19 11:51:16,245 INFO misc.py line 119 87073] Train: [83/100][1512/1557] Data 0.005 (0.109) Batch 1.172 (1.275) Remain 09:23:23 loss: 0.1154 Lr: 0.00039 [2024-02-19 11:51:17,300 INFO misc.py line 119 87073] Train: [83/100][1513/1557] Data 0.017 (0.109) Batch 1.055 (1.275) Remain 09:23:18 loss: 0.4038 Lr: 0.00039 [2024-02-19 11:51:18,264 INFO misc.py line 119 87073] Train: [83/100][1514/1557] Data 0.016 (0.108) Batch 0.976 (1.275) Remain 09:23:11 loss: 0.0924 Lr: 0.00039 [2024-02-19 11:51:19,117 INFO misc.py line 119 87073] Train: [83/100][1515/1557] Data 0.005 (0.108) Batch 0.854 (1.274) Remain 09:23:03 loss: 0.1857 Lr: 0.00039 [2024-02-19 11:51:20,037 INFO misc.py line 119 87073] Train: [83/100][1516/1557] Data 0.003 (0.108) Batch 0.920 (1.274) Remain 09:22:55 loss: 0.2639 Lr: 0.00039 [2024-02-19 11:51:20,833 INFO misc.py line 119 87073] Train: [83/100][1517/1557] Data 0.004 (0.108) Batch 0.795 (1.274) Remain 09:22:46 loss: 0.2793 Lr: 0.00039 [2024-02-19 11:51:21,595 INFO misc.py line 119 87073] Train: [83/100][1518/1557] Data 0.004 (0.108) Batch 0.759 (1.273) Remain 09:22:35 loss: 0.1541 Lr: 0.00039 [2024-02-19 11:51:39,515 INFO misc.py line 119 87073] Train: [83/100][1519/1557] Data 5.424 (0.112) Batch 17.923 (1.284) Remain 09:27:25 loss: 0.0656 Lr: 0.00039 [2024-02-19 11:51:40,506 INFO misc.py line 119 87073] Train: [83/100][1520/1557] Data 0.004 (0.112) Batch 0.992 (1.284) Remain 09:27:19 loss: 0.4340 Lr: 0.00039 [2024-02-19 11:51:41,472 INFO misc.py line 119 87073] Train: [83/100][1521/1557] Data 0.004 (0.112) Batch 0.965 (1.284) Remain 09:27:12 loss: 0.1739 Lr: 0.00039 [2024-02-19 11:51:42,608 INFO misc.py line 119 87073] Train: [83/100][1522/1557] Data 0.003 (0.111) Batch 1.136 (1.284) Remain 09:27:08 loss: 0.5670 Lr: 0.00039 [2024-02-19 11:51:43,593 INFO misc.py line 119 87073] Train: [83/100][1523/1557] Data 0.005 (0.111) Batch 0.985 (1.284) Remain 09:27:02 loss: 0.0921 Lr: 0.00039 [2024-02-19 11:51:44,304 INFO misc.py line 119 87073] Train: [83/100][1524/1557] Data 0.004 (0.111) Batch 0.712 (1.283) Remain 09:26:50 loss: 0.2282 Lr: 0.00039 [2024-02-19 11:51:45,054 INFO misc.py line 119 87073] Train: [83/100][1525/1557] Data 0.004 (0.111) Batch 0.742 (1.283) Remain 09:26:40 loss: 0.1593 Lr: 0.00039 [2024-02-19 11:51:46,242 INFO misc.py line 119 87073] Train: [83/100][1526/1557] Data 0.012 (0.111) Batch 1.188 (1.283) Remain 09:26:37 loss: 0.1025 Lr: 0.00039 [2024-02-19 11:51:47,203 INFO misc.py line 119 87073] Train: [83/100][1527/1557] Data 0.012 (0.111) Batch 0.969 (1.283) Remain 09:26:30 loss: 0.3338 Lr: 0.00039 [2024-02-19 11:51:48,084 INFO misc.py line 119 87073] Train: [83/100][1528/1557] Data 0.004 (0.111) Batch 0.880 (1.282) Remain 09:26:22 loss: 0.1761 Lr: 0.00039 [2024-02-19 11:51:48,906 INFO misc.py line 119 87073] Train: [83/100][1529/1557] Data 0.005 (0.111) Batch 0.821 (1.282) Remain 09:26:12 loss: 0.2429 Lr: 0.00039 [2024-02-19 11:51:49,834 INFO misc.py line 119 87073] Train: [83/100][1530/1557] Data 0.006 (0.111) Batch 0.930 (1.282) Remain 09:26:05 loss: 0.0675 Lr: 0.00039 [2024-02-19 11:51:50,610 INFO misc.py line 119 87073] Train: [83/100][1531/1557] Data 0.004 (0.111) Batch 0.776 (1.282) Remain 09:25:55 loss: 0.2607 Lr: 0.00039 [2024-02-19 11:51:51,277 INFO misc.py line 119 87073] Train: [83/100][1532/1557] Data 0.003 (0.111) Batch 0.661 (1.281) Remain 09:25:43 loss: 0.1388 Lr: 0.00039 [2024-02-19 11:51:52,549 INFO misc.py line 119 87073] Train: [83/100][1533/1557] Data 0.009 (0.111) Batch 1.275 (1.281) Remain 09:25:42 loss: 0.0987 Lr: 0.00039 [2024-02-19 11:51:53,518 INFO misc.py line 119 87073] Train: [83/100][1534/1557] Data 0.006 (0.111) Batch 0.970 (1.281) Remain 09:25:35 loss: 0.3508 Lr: 0.00039 [2024-02-19 11:51:54,723 INFO misc.py line 119 87073] Train: [83/100][1535/1557] Data 0.005 (0.111) Batch 1.194 (1.281) Remain 09:25:32 loss: 0.1150 Lr: 0.00039 [2024-02-19 11:51:55,929 INFO misc.py line 119 87073] Train: [83/100][1536/1557] Data 0.016 (0.111) Batch 1.209 (1.281) Remain 09:25:30 loss: 0.1022 Lr: 0.00039 [2024-02-19 11:51:56,777 INFO misc.py line 119 87073] Train: [83/100][1537/1557] Data 0.013 (0.110) Batch 0.857 (1.281) Remain 09:25:21 loss: 0.2637 Lr: 0.00039 [2024-02-19 11:51:57,527 INFO misc.py line 119 87073] Train: [83/100][1538/1557] Data 0.003 (0.110) Batch 0.749 (1.280) Remain 09:25:11 loss: 0.1367 Lr: 0.00039 [2024-02-19 11:51:58,305 INFO misc.py line 119 87073] Train: [83/100][1539/1557] Data 0.004 (0.110) Batch 0.766 (1.280) Remain 09:25:00 loss: 0.1652 Lr: 0.00039 [2024-02-19 11:51:59,682 INFO misc.py line 119 87073] Train: [83/100][1540/1557] Data 0.016 (0.110) Batch 1.388 (1.280) Remain 09:25:01 loss: 0.1130 Lr: 0.00039 [2024-02-19 11:52:00,762 INFO misc.py line 119 87073] Train: [83/100][1541/1557] Data 0.005 (0.110) Batch 1.071 (1.280) Remain 09:24:56 loss: 0.1181 Lr: 0.00039 [2024-02-19 11:52:01,679 INFO misc.py line 119 87073] Train: [83/100][1542/1557] Data 0.013 (0.110) Batch 0.927 (1.280) Remain 09:24:49 loss: 0.3398 Lr: 0.00039 [2024-02-19 11:52:02,674 INFO misc.py line 119 87073] Train: [83/100][1543/1557] Data 0.003 (0.110) Batch 0.995 (1.279) Remain 09:24:43 loss: 0.6098 Lr: 0.00039 [2024-02-19 11:52:03,780 INFO misc.py line 119 87073] Train: [83/100][1544/1557] Data 0.004 (0.110) Batch 1.106 (1.279) Remain 09:24:38 loss: 0.6340 Lr: 0.00039 [2024-02-19 11:52:04,456 INFO misc.py line 119 87073] Train: [83/100][1545/1557] Data 0.004 (0.110) Batch 0.676 (1.279) Remain 09:24:27 loss: 0.2712 Lr: 0.00039 [2024-02-19 11:52:05,225 INFO misc.py line 119 87073] Train: [83/100][1546/1557] Data 0.003 (0.110) Batch 0.757 (1.279) Remain 09:24:16 loss: 0.1481 Lr: 0.00039 [2024-02-19 11:52:06,408 INFO misc.py line 119 87073] Train: [83/100][1547/1557] Data 0.016 (0.110) Batch 1.192 (1.279) Remain 09:24:14 loss: 0.1368 Lr: 0.00039 [2024-02-19 11:52:07,429 INFO misc.py line 119 87073] Train: [83/100][1548/1557] Data 0.008 (0.110) Batch 1.018 (1.278) Remain 09:24:08 loss: 0.1532 Lr: 0.00039 [2024-02-19 11:52:08,282 INFO misc.py line 119 87073] Train: [83/100][1549/1557] Data 0.011 (0.110) Batch 0.860 (1.278) Remain 09:24:00 loss: 0.1576 Lr: 0.00039 [2024-02-19 11:52:09,143 INFO misc.py line 119 87073] Train: [83/100][1550/1557] Data 0.004 (0.110) Batch 0.860 (1.278) Remain 09:23:51 loss: 0.0663 Lr: 0.00039 [2024-02-19 11:52:10,108 INFO misc.py line 119 87073] Train: [83/100][1551/1557] Data 0.004 (0.110) Batch 0.954 (1.278) Remain 09:23:44 loss: 0.3113 Lr: 0.00039 [2024-02-19 11:52:10,877 INFO misc.py line 119 87073] Train: [83/100][1552/1557] Data 0.016 (0.109) Batch 0.781 (1.277) Remain 09:23:34 loss: 0.2697 Lr: 0.00039 [2024-02-19 11:52:11,596 INFO misc.py line 119 87073] Train: [83/100][1553/1557] Data 0.004 (0.109) Batch 0.708 (1.277) Remain 09:23:23 loss: 0.1951 Lr: 0.00039 [2024-02-19 11:52:12,839 INFO misc.py line 119 87073] Train: [83/100][1554/1557] Data 0.014 (0.109) Batch 1.243 (1.277) Remain 09:23:22 loss: 0.1660 Lr: 0.00039 [2024-02-19 11:52:13,795 INFO misc.py line 119 87073] Train: [83/100][1555/1557] Data 0.015 (0.109) Batch 0.967 (1.277) Remain 09:23:15 loss: 0.3976 Lr: 0.00039 [2024-02-19 11:52:14,985 INFO misc.py line 119 87073] Train: [83/100][1556/1557] Data 0.004 (0.109) Batch 1.170 (1.277) Remain 09:23:12 loss: 0.2667 Lr: 0.00039 [2024-02-19 11:52:15,915 INFO misc.py line 119 87073] Train: [83/100][1557/1557] Data 0.023 (0.109) Batch 0.950 (1.276) Remain 09:23:05 loss: 0.4630 Lr: 0.00039 [2024-02-19 11:52:15,915 INFO misc.py line 136 87073] Train result: loss: 0.2205 [2024-02-19 11:52:15,916 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 11:52:43,827 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4861 [2024-02-19 11:52:44,620 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2367 [2024-02-19 11:52:46,747 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3187 [2024-02-19 11:52:48,958 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3818 [2024-02-19 11:52:53,908 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2406 [2024-02-19 11:52:54,606 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0915 [2024-02-19 11:52:55,865 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.4127 [2024-02-19 11:52:58,820 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2961 [2024-02-19 11:53:00,631 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2507 [2024-02-19 11:53:02,107 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7339/0.7892/0.9163. [2024-02-19 11:53:02,107 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9254/0.9602 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9836/0.9895 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8631/0.9728 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3725/0.4385 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6979/0.7268 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8363/0.9315 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8467/0.9195 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9329/0.9740 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8598/0.9130 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7890/0.8770 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8134/0.8582 [2024-02-19 11:53:02,108 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6203/0.6980 [2024-02-19 11:53:02,108 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 11:53:02,110 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 11:53:02,110 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 11:53:10,316 INFO misc.py line 119 87073] Train: [84/100][1/1557] Data 1.425 (1.425) Batch 2.251 (2.251) Remain 16:32:47 loss: 0.2171 Lr: 0.00039 [2024-02-19 11:53:11,155 INFO misc.py line 119 87073] Train: [84/100][2/1557] Data 0.005 (0.005) Batch 0.837 (0.837) Remain 06:09:06 loss: 0.1960 Lr: 0.00039 [2024-02-19 11:53:12,139 INFO misc.py line 119 87073] Train: [84/100][3/1557] Data 0.008 (0.008) Batch 0.986 (0.986) Remain 07:14:52 loss: 0.1245 Lr: 0.00039 [2024-02-19 11:53:13,190 INFO misc.py line 119 87073] Train: [84/100][4/1557] Data 0.006 (0.006) Batch 1.053 (1.053) Remain 07:44:20 loss: 0.2452 Lr: 0.00039 [2024-02-19 11:53:13,947 INFO misc.py line 119 87073] Train: [84/100][5/1557] Data 0.004 (0.005) Batch 0.757 (0.905) Remain 06:39:00 loss: 0.2623 Lr: 0.00039 [2024-02-19 11:53:14,656 INFO misc.py line 119 87073] Train: [84/100][6/1557] Data 0.004 (0.005) Batch 0.708 (0.839) Remain 06:10:03 loss: 0.1898 Lr: 0.00039 [2024-02-19 11:53:15,804 INFO misc.py line 119 87073] Train: [84/100][7/1557] Data 0.006 (0.005) Batch 1.141 (0.915) Remain 06:43:20 loss: 0.1563 Lr: 0.00039 [2024-02-19 11:53:16,903 INFO misc.py line 119 87073] Train: [84/100][8/1557] Data 0.012 (0.006) Batch 1.099 (0.951) Remain 06:59:35 loss: 0.2725 Lr: 0.00039 [2024-02-19 11:53:17,702 INFO misc.py line 119 87073] Train: [84/100][9/1557] Data 0.012 (0.007) Batch 0.807 (0.927) Remain 06:48:58 loss: 0.2926 Lr: 0.00039 [2024-02-19 11:53:18,644 INFO misc.py line 119 87073] Train: [84/100][10/1557] Data 0.005 (0.007) Batch 0.941 (0.929) Remain 06:49:50 loss: 0.2188 Lr: 0.00039 [2024-02-19 11:53:19,525 INFO misc.py line 119 87073] Train: [84/100][11/1557] Data 0.005 (0.007) Batch 0.877 (0.923) Remain 06:46:54 loss: 0.3799 Lr: 0.00039 [2024-02-19 11:53:20,254 INFO misc.py line 119 87073] Train: [84/100][12/1557] Data 0.009 (0.007) Batch 0.733 (0.902) Remain 06:37:34 loss: 0.1289 Lr: 0.00039 [2024-02-19 11:53:20,992 INFO misc.py line 119 87073] Train: [84/100][13/1557] Data 0.006 (0.007) Batch 0.736 (0.885) Remain 06:30:15 loss: 0.4256 Lr: 0.00039 [2024-02-19 11:53:22,304 INFO misc.py line 119 87073] Train: [84/100][14/1557] Data 0.008 (0.007) Batch 1.308 (0.924) Remain 06:47:12 loss: 0.0811 Lr: 0.00039 [2024-02-19 11:53:23,388 INFO misc.py line 119 87073] Train: [84/100][15/1557] Data 0.011 (0.007) Batch 1.089 (0.937) Remain 06:53:16 loss: 0.1751 Lr: 0.00039 [2024-02-19 11:53:24,424 INFO misc.py line 119 87073] Train: [84/100][16/1557] Data 0.006 (0.007) Batch 1.032 (0.945) Remain 06:56:29 loss: 0.2824 Lr: 0.00039 [2024-02-19 11:53:25,365 INFO misc.py line 119 87073] Train: [84/100][17/1557] Data 0.010 (0.007) Batch 0.946 (0.945) Remain 06:56:30 loss: 0.1148 Lr: 0.00039 [2024-02-19 11:53:26,258 INFO misc.py line 119 87073] Train: [84/100][18/1557] Data 0.005 (0.007) Batch 0.893 (0.941) Remain 06:54:59 loss: 0.0529 Lr: 0.00039 [2024-02-19 11:53:26,964 INFO misc.py line 119 87073] Train: [84/100][19/1557] Data 0.005 (0.007) Batch 0.705 (0.927) Remain 06:48:28 loss: 0.1266 Lr: 0.00039 [2024-02-19 11:53:27,728 INFO misc.py line 119 87073] Train: [84/100][20/1557] Data 0.005 (0.007) Batch 0.764 (0.917) Remain 06:44:14 loss: 0.1855 Lr: 0.00039 [2024-02-19 11:53:28,973 INFO misc.py line 119 87073] Train: [84/100][21/1557] Data 0.006 (0.007) Batch 1.246 (0.935) Remain 06:52:16 loss: 0.0926 Lr: 0.00039 [2024-02-19 11:53:30,005 INFO misc.py line 119 87073] Train: [84/100][22/1557] Data 0.004 (0.007) Batch 1.018 (0.940) Remain 06:54:10 loss: 0.3276 Lr: 0.00039 [2024-02-19 11:53:30,844 INFO misc.py line 119 87073] Train: [84/100][23/1557] Data 0.019 (0.007) Batch 0.853 (0.935) Remain 06:52:15 loss: 0.5826 Lr: 0.00039 [2024-02-19 11:53:32,012 INFO misc.py line 119 87073] Train: [84/100][24/1557] Data 0.004 (0.007) Batch 1.168 (0.946) Remain 06:57:07 loss: 0.1235 Lr: 0.00039 [2024-02-19 11:53:32,957 INFO misc.py line 119 87073] Train: [84/100][25/1557] Data 0.004 (0.007) Batch 0.946 (0.946) Remain 06:57:05 loss: 0.4792 Lr: 0.00039 [2024-02-19 11:53:33,734 INFO misc.py line 119 87073] Train: [84/100][26/1557] Data 0.004 (0.007) Batch 0.776 (0.939) Remain 06:53:48 loss: 0.1452 Lr: 0.00039 [2024-02-19 11:53:34,586 INFO misc.py line 119 87073] Train: [84/100][27/1557] Data 0.004 (0.007) Batch 0.852 (0.935) Remain 06:52:12 loss: 0.2091 Lr: 0.00039 [2024-02-19 11:53:35,861 INFO misc.py line 119 87073] Train: [84/100][28/1557] Data 0.004 (0.007) Batch 1.268 (0.949) Remain 06:58:03 loss: 0.1376 Lr: 0.00039 [2024-02-19 11:53:36,801 INFO misc.py line 119 87073] Train: [84/100][29/1557] Data 0.010 (0.007) Batch 0.947 (0.949) Remain 06:58:00 loss: 0.3396 Lr: 0.00039 [2024-02-19 11:53:37,764 INFO misc.py line 119 87073] Train: [84/100][30/1557] Data 0.004 (0.007) Batch 0.963 (0.949) Remain 06:58:14 loss: 0.2267 Lr: 0.00039 [2024-02-19 11:53:38,773 INFO misc.py line 119 87073] Train: [84/100][31/1557] Data 0.004 (0.007) Batch 1.009 (0.951) Remain 06:59:10 loss: 0.2058 Lr: 0.00039 [2024-02-19 11:53:39,722 INFO misc.py line 119 87073] Train: [84/100][32/1557] Data 0.004 (0.007) Batch 0.949 (0.951) Remain 06:59:06 loss: 0.4079 Lr: 0.00039 [2024-02-19 11:53:40,506 INFO misc.py line 119 87073] Train: [84/100][33/1557] Data 0.004 (0.006) Batch 0.772 (0.945) Remain 06:56:28 loss: 0.1802 Lr: 0.00039 [2024-02-19 11:53:41,285 INFO misc.py line 119 87073] Train: [84/100][34/1557] Data 0.016 (0.007) Batch 0.789 (0.940) Remain 06:54:13 loss: 0.1798 Lr: 0.00039 [2024-02-19 11:53:42,437 INFO misc.py line 119 87073] Train: [84/100][35/1557] Data 0.005 (0.007) Batch 1.151 (0.947) Remain 06:57:07 loss: 0.1773 Lr: 0.00039 [2024-02-19 11:53:43,392 INFO misc.py line 119 87073] Train: [84/100][36/1557] Data 0.007 (0.007) Batch 0.956 (0.947) Remain 06:57:13 loss: 0.1675 Lr: 0.00039 [2024-02-19 11:53:44,423 INFO misc.py line 119 87073] Train: [84/100][37/1557] Data 0.005 (0.007) Batch 1.028 (0.949) Remain 06:58:15 loss: 0.3277 Lr: 0.00039 [2024-02-19 11:53:45,354 INFO misc.py line 119 87073] Train: [84/100][38/1557] Data 0.008 (0.007) Batch 0.934 (0.949) Remain 06:58:03 loss: 0.6594 Lr: 0.00039 [2024-02-19 11:53:46,244 INFO misc.py line 119 87073] Train: [84/100][39/1557] Data 0.005 (0.007) Batch 0.887 (0.947) Remain 06:57:17 loss: 0.0623 Lr: 0.00039 [2024-02-19 11:53:47,025 INFO misc.py line 119 87073] Train: [84/100][40/1557] Data 0.008 (0.007) Batch 0.784 (0.943) Remain 06:55:19 loss: 0.1746 Lr: 0.00039 [2024-02-19 11:53:47,787 INFO misc.py line 119 87073] Train: [84/100][41/1557] Data 0.005 (0.007) Batch 0.749 (0.938) Remain 06:53:03 loss: 0.1449 Lr: 0.00039 [2024-02-19 11:53:48,894 INFO misc.py line 119 87073] Train: [84/100][42/1557] Data 0.017 (0.007) Batch 1.108 (0.942) Remain 06:54:58 loss: 0.1612 Lr: 0.00039 [2024-02-19 11:53:50,015 INFO misc.py line 119 87073] Train: [84/100][43/1557] Data 0.017 (0.007) Batch 1.121 (0.947) Remain 06:56:55 loss: 0.3731 Lr: 0.00039 [2024-02-19 11:53:50,992 INFO misc.py line 119 87073] Train: [84/100][44/1557] Data 0.017 (0.007) Batch 0.989 (0.948) Remain 06:57:21 loss: 0.0963 Lr: 0.00039 [2024-02-19 11:53:51,948 INFO misc.py line 119 87073] Train: [84/100][45/1557] Data 0.006 (0.007) Batch 0.957 (0.948) Remain 06:57:26 loss: 0.5370 Lr: 0.00039 [2024-02-19 11:53:52,843 INFO misc.py line 119 87073] Train: [84/100][46/1557] Data 0.004 (0.007) Batch 0.893 (0.947) Remain 06:56:51 loss: 0.3232 Lr: 0.00039 [2024-02-19 11:53:53,588 INFO misc.py line 119 87073] Train: [84/100][47/1557] Data 0.005 (0.007) Batch 0.739 (0.942) Remain 06:54:46 loss: 0.1430 Lr: 0.00039 [2024-02-19 11:53:54,243 INFO misc.py line 119 87073] Train: [84/100][48/1557] Data 0.012 (0.007) Batch 0.663 (0.936) Remain 06:52:01 loss: 0.2397 Lr: 0.00039 [2024-02-19 11:53:55,413 INFO misc.py line 119 87073] Train: [84/100][49/1557] Data 0.004 (0.007) Batch 1.159 (0.941) Remain 06:54:09 loss: 0.0982 Lr: 0.00039 [2024-02-19 11:53:56,425 INFO misc.py line 119 87073] Train: [84/100][50/1557] Data 0.014 (0.007) Batch 1.013 (0.942) Remain 06:54:48 loss: 0.1498 Lr: 0.00039 [2024-02-19 11:53:57,357 INFO misc.py line 119 87073] Train: [84/100][51/1557] Data 0.014 (0.008) Batch 0.942 (0.942) Remain 06:54:47 loss: 0.5926 Lr: 0.00039 [2024-02-19 11:53:58,312 INFO misc.py line 119 87073] Train: [84/100][52/1557] Data 0.004 (0.007) Batch 0.955 (0.942) Remain 06:54:53 loss: 0.1594 Lr: 0.00039 [2024-02-19 11:53:59,086 INFO misc.py line 119 87073] Train: [84/100][53/1557] Data 0.004 (0.007) Batch 0.774 (0.939) Remain 06:53:23 loss: 0.1699 Lr: 0.00039 [2024-02-19 11:53:59,975 INFO misc.py line 119 87073] Train: [84/100][54/1557] Data 0.004 (0.007) Batch 0.873 (0.938) Remain 06:52:48 loss: 0.1939 Lr: 0.00039 [2024-02-19 11:54:00,757 INFO misc.py line 119 87073] Train: [84/100][55/1557] Data 0.020 (0.008) Batch 0.799 (0.935) Remain 06:51:36 loss: 0.3100 Lr: 0.00039 [2024-02-19 11:54:02,128 INFO misc.py line 119 87073] Train: [84/100][56/1557] Data 0.004 (0.008) Batch 1.362 (0.943) Remain 06:55:08 loss: 0.0788 Lr: 0.00039 [2024-02-19 11:54:03,067 INFO misc.py line 119 87073] Train: [84/100][57/1557] Data 0.013 (0.008) Batch 0.948 (0.943) Remain 06:55:10 loss: 0.1652 Lr: 0.00038 [2024-02-19 11:54:04,332 INFO misc.py line 119 87073] Train: [84/100][58/1557] Data 0.005 (0.008) Batch 1.265 (0.949) Remain 06:57:43 loss: 0.1689 Lr: 0.00038 [2024-02-19 11:54:05,463 INFO misc.py line 119 87073] Train: [84/100][59/1557] Data 0.004 (0.008) Batch 1.128 (0.952) Remain 06:59:07 loss: 0.7180 Lr: 0.00038 [2024-02-19 11:54:06,493 INFO misc.py line 119 87073] Train: [84/100][60/1557] Data 0.007 (0.008) Batch 1.032 (0.954) Remain 06:59:43 loss: 0.1788 Lr: 0.00038 [2024-02-19 11:54:07,267 INFO misc.py line 119 87073] Train: [84/100][61/1557] Data 0.005 (0.007) Batch 0.774 (0.950) Remain 06:58:20 loss: 0.2711 Lr: 0.00038 [2024-02-19 11:54:07,995 INFO misc.py line 119 87073] Train: [84/100][62/1557] Data 0.006 (0.007) Batch 0.729 (0.947) Remain 06:56:40 loss: 0.0715 Lr: 0.00038 [2024-02-19 11:54:15,232 INFO misc.py line 119 87073] Train: [84/100][63/1557] Data 4.825 (0.088) Batch 7.234 (1.052) Remain 07:42:46 loss: 0.1229 Lr: 0.00038 [2024-02-19 11:54:16,303 INFO misc.py line 119 87073] Train: [84/100][64/1557] Data 0.008 (0.086) Batch 1.064 (1.052) Remain 07:42:50 loss: 0.0823 Lr: 0.00038 [2024-02-19 11:54:17,289 INFO misc.py line 119 87073] Train: [84/100][65/1557] Data 0.013 (0.085) Batch 0.996 (1.051) Remain 07:42:26 loss: 0.2495 Lr: 0.00038 [2024-02-19 11:54:18,229 INFO misc.py line 119 87073] Train: [84/100][66/1557] Data 0.004 (0.084) Batch 0.940 (1.049) Remain 07:41:38 loss: 0.5173 Lr: 0.00038 [2024-02-19 11:54:19,255 INFO misc.py line 119 87073] Train: [84/100][67/1557] Data 0.003 (0.083) Batch 1.025 (1.049) Remain 07:41:27 loss: 0.2327 Lr: 0.00038 [2024-02-19 11:54:20,010 INFO misc.py line 119 87073] Train: [84/100][68/1557] Data 0.004 (0.081) Batch 0.756 (1.044) Remain 07:39:27 loss: 0.2081 Lr: 0.00038 [2024-02-19 11:54:20,830 INFO misc.py line 119 87073] Train: [84/100][69/1557] Data 0.003 (0.080) Batch 0.810 (1.041) Remain 07:37:52 loss: 0.4270 Lr: 0.00038 [2024-02-19 11:54:22,102 INFO misc.py line 119 87073] Train: [84/100][70/1557] Data 0.013 (0.079) Batch 1.273 (1.044) Remain 07:39:23 loss: 0.0976 Lr: 0.00038 [2024-02-19 11:54:23,048 INFO misc.py line 119 87073] Train: [84/100][71/1557] Data 0.013 (0.078) Batch 0.954 (1.043) Remain 07:38:47 loss: 0.3572 Lr: 0.00038 [2024-02-19 11:54:23,906 INFO misc.py line 119 87073] Train: [84/100][72/1557] Data 0.004 (0.077) Batch 0.859 (1.040) Remain 07:37:36 loss: 0.7317 Lr: 0.00038 [2024-02-19 11:54:24,710 INFO misc.py line 119 87073] Train: [84/100][73/1557] Data 0.003 (0.076) Batch 0.798 (1.037) Remain 07:36:04 loss: 0.1392 Lr: 0.00038 [2024-02-19 11:54:25,699 INFO misc.py line 119 87073] Train: [84/100][74/1557] Data 0.009 (0.075) Batch 0.994 (1.036) Remain 07:35:47 loss: 0.0717 Lr: 0.00038 [2024-02-19 11:54:26,485 INFO misc.py line 119 87073] Train: [84/100][75/1557] Data 0.004 (0.074) Batch 0.785 (1.033) Remain 07:34:14 loss: 0.1656 Lr: 0.00038 [2024-02-19 11:54:27,258 INFO misc.py line 119 87073] Train: [84/100][76/1557] Data 0.005 (0.073) Batch 0.774 (1.029) Remain 07:32:39 loss: 0.1445 Lr: 0.00038 [2024-02-19 11:54:28,541 INFO misc.py line 119 87073] Train: [84/100][77/1557] Data 0.003 (0.072) Batch 1.270 (1.032) Remain 07:34:04 loss: 0.1278 Lr: 0.00038 [2024-02-19 11:54:29,455 INFO misc.py line 119 87073] Train: [84/100][78/1557] Data 0.017 (0.072) Batch 0.925 (1.031) Remain 07:33:26 loss: 0.1612 Lr: 0.00038 [2024-02-19 11:54:30,379 INFO misc.py line 119 87073] Train: [84/100][79/1557] Data 0.005 (0.071) Batch 0.926 (1.029) Remain 07:32:48 loss: 0.1564 Lr: 0.00038 [2024-02-19 11:54:31,370 INFO misc.py line 119 87073] Train: [84/100][80/1557] Data 0.003 (0.070) Batch 0.990 (1.029) Remain 07:32:34 loss: 0.2179 Lr: 0.00038 [2024-02-19 11:54:32,407 INFO misc.py line 119 87073] Train: [84/100][81/1557] Data 0.004 (0.069) Batch 1.037 (1.029) Remain 07:32:35 loss: 0.2466 Lr: 0.00038 [2024-02-19 11:54:33,163 INFO misc.py line 119 87073] Train: [84/100][82/1557] Data 0.004 (0.068) Batch 0.746 (1.026) Remain 07:31:00 loss: 0.0967 Lr: 0.00038 [2024-02-19 11:54:33,878 INFO misc.py line 119 87073] Train: [84/100][83/1557] Data 0.014 (0.068) Batch 0.724 (1.022) Remain 07:29:19 loss: 0.1528 Lr: 0.00038 [2024-02-19 11:54:35,089 INFO misc.py line 119 87073] Train: [84/100][84/1557] Data 0.005 (0.067) Batch 1.210 (1.024) Remain 07:30:20 loss: 0.1511 Lr: 0.00038 [2024-02-19 11:54:36,051 INFO misc.py line 119 87073] Train: [84/100][85/1557] Data 0.006 (0.066) Batch 0.964 (1.023) Remain 07:29:59 loss: 0.1132 Lr: 0.00038 [2024-02-19 11:54:37,026 INFO misc.py line 119 87073] Train: [84/100][86/1557] Data 0.005 (0.065) Batch 0.974 (1.023) Remain 07:29:42 loss: 0.0733 Lr: 0.00038 [2024-02-19 11:54:37,960 INFO misc.py line 119 87073] Train: [84/100][87/1557] Data 0.005 (0.065) Batch 0.934 (1.022) Remain 07:29:13 loss: 0.0971 Lr: 0.00038 [2024-02-19 11:54:39,053 INFO misc.py line 119 87073] Train: [84/100][88/1557] Data 0.005 (0.064) Batch 1.091 (1.023) Remain 07:29:34 loss: 0.1022 Lr: 0.00038 [2024-02-19 11:54:39,830 INFO misc.py line 119 87073] Train: [84/100][89/1557] Data 0.007 (0.063) Batch 0.778 (1.020) Remain 07:28:18 loss: 0.1462 Lr: 0.00038 [2024-02-19 11:54:40,572 INFO misc.py line 119 87073] Train: [84/100][90/1557] Data 0.006 (0.063) Batch 0.739 (1.016) Remain 07:26:52 loss: 0.2128 Lr: 0.00038 [2024-02-19 11:54:41,751 INFO misc.py line 119 87073] Train: [84/100][91/1557] Data 0.008 (0.062) Batch 1.181 (1.018) Remain 07:27:40 loss: 0.1340 Lr: 0.00038 [2024-02-19 11:54:42,705 INFO misc.py line 119 87073] Train: [84/100][92/1557] Data 0.007 (0.061) Batch 0.956 (1.018) Remain 07:27:21 loss: 0.1357 Lr: 0.00038 [2024-02-19 11:54:43,730 INFO misc.py line 119 87073] Train: [84/100][93/1557] Data 0.004 (0.061) Batch 1.024 (1.018) Remain 07:27:22 loss: 0.1919 Lr: 0.00038 [2024-02-19 11:54:44,851 INFO misc.py line 119 87073] Train: [84/100][94/1557] Data 0.008 (0.060) Batch 1.123 (1.019) Remain 07:27:51 loss: 0.1710 Lr: 0.00038 [2024-02-19 11:54:45,955 INFO misc.py line 119 87073] Train: [84/100][95/1557] Data 0.004 (0.059) Batch 1.103 (1.020) Remain 07:28:14 loss: 0.2037 Lr: 0.00038 [2024-02-19 11:54:46,717 INFO misc.py line 119 87073] Train: [84/100][96/1557] Data 0.004 (0.059) Batch 0.762 (1.017) Remain 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line 119 87073] Train: [84/100][165/1557] Data 0.003 (0.071) Batch 1.152 (1.027) Remain 07:30:18 loss: 0.2171 Lr: 0.00038 [2024-02-19 11:55:59,239 INFO misc.py line 119 87073] Train: [84/100][166/1557] Data 0.004 (0.071) Batch 0.699 (1.025) Remain 07:29:24 loss: 0.3116 Lr: 0.00038 [2024-02-19 11:55:59,989 INFO misc.py line 119 87073] Train: [84/100][167/1557] Data 0.004 (0.071) Batch 0.746 (1.023) Remain 07:28:38 loss: 0.2000 Lr: 0.00038 [2024-02-19 11:56:01,266 INFO misc.py line 119 87073] Train: [84/100][168/1557] Data 0.007 (0.070) Batch 1.273 (1.025) Remain 07:29:17 loss: 0.1256 Lr: 0.00038 [2024-02-19 11:56:02,070 INFO misc.py line 119 87073] Train: [84/100][169/1557] Data 0.012 (0.070) Batch 0.812 (1.024) Remain 07:28:42 loss: 0.2053 Lr: 0.00038 [2024-02-19 11:56:03,004 INFO misc.py line 119 87073] Train: [84/100][170/1557] Data 0.004 (0.069) Batch 0.934 (1.023) Remain 07:28:27 loss: 0.1627 Lr: 0.00038 [2024-02-19 11:56:04,193 INFO misc.py line 119 87073] Train: 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(0.098) Batch 0.865 (1.064) Remain 07:22:31 loss: 0.2951 Lr: 0.00034 [2024-02-19 12:19:59,803 INFO misc.py line 119 87073] Train: [84/100][1514/1557] Data 0.005 (0.098) Batch 1.117 (1.064) Remain 07:22:31 loss: 0.2636 Lr: 0.00034 [2024-02-19 12:20:01,048 INFO misc.py line 119 87073] Train: [84/100][1515/1557] Data 0.004 (0.098) Batch 1.233 (1.064) Remain 07:22:33 loss: 0.3003 Lr: 0.00034 [2024-02-19 12:20:01,934 INFO misc.py line 119 87073] Train: [84/100][1516/1557] Data 0.015 (0.098) Batch 0.898 (1.064) Remain 07:22:29 loss: 0.0862 Lr: 0.00034 [2024-02-19 12:20:02,674 INFO misc.py line 119 87073] Train: [84/100][1517/1557] Data 0.004 (0.098) Batch 0.739 (1.064) Remain 07:22:22 loss: 0.2157 Lr: 0.00034 [2024-02-19 12:20:03,488 INFO misc.py line 119 87073] Train: [84/100][1518/1557] Data 0.004 (0.098) Batch 0.805 (1.064) Remain 07:22:17 loss: 0.1211 Lr: 0.00034 [2024-02-19 12:20:10,393 INFO misc.py line 119 87073] Train: [84/100][1519/1557] Data 4.790 (0.101) Batch 6.913 (1.067) Remain 07:23:52 loss: 0.2026 Lr: 0.00034 [2024-02-19 12:20:11,453 INFO misc.py line 119 87073] Train: [84/100][1520/1557] Data 0.005 (0.101) Batch 1.060 (1.067) Remain 07:23:51 loss: 0.0649 Lr: 0.00034 [2024-02-19 12:20:12,299 INFO misc.py line 119 87073] Train: [84/100][1521/1557] Data 0.006 (0.101) Batch 0.845 (1.067) Remain 07:23:46 loss: 0.1333 Lr: 0.00034 [2024-02-19 12:20:13,442 INFO misc.py line 119 87073] Train: [84/100][1522/1557] Data 0.005 (0.101) Batch 1.143 (1.067) Remain 07:23:47 loss: 0.1376 Lr: 0.00034 [2024-02-19 12:20:14,412 INFO misc.py line 119 87073] Train: [84/100][1523/1557] Data 0.005 (0.101) Batch 0.971 (1.067) Remain 07:23:44 loss: 0.1906 Lr: 0.00034 [2024-02-19 12:20:15,194 INFO misc.py line 119 87073] Train: [84/100][1524/1557] Data 0.004 (0.101) Batch 0.782 (1.067) Remain 07:23:38 loss: 0.2250 Lr: 0.00034 [2024-02-19 12:20:16,002 INFO misc.py line 119 87073] Train: [84/100][1525/1557] Data 0.004 (0.101) Batch 0.807 (1.067) Remain 07:23:33 loss: 0.1684 Lr: 0.00034 [2024-02-19 12:20:17,346 INFO misc.py line 119 87073] Train: [84/100][1526/1557] Data 0.005 (0.101) Batch 1.342 (1.067) Remain 07:23:36 loss: 0.1035 Lr: 0.00034 [2024-02-19 12:20:18,568 INFO misc.py line 119 87073] Train: [84/100][1527/1557] Data 0.007 (0.101) Batch 1.213 (1.067) Remain 07:23:38 loss: 0.2252 Lr: 0.00034 [2024-02-19 12:20:19,626 INFO misc.py line 119 87073] Train: [84/100][1528/1557] Data 0.015 (0.101) Batch 1.065 (1.067) Remain 07:23:37 loss: 0.2499 Lr: 0.00034 [2024-02-19 12:20:20,481 INFO misc.py line 119 87073] Train: [84/100][1529/1557] Data 0.008 (0.101) Batch 0.857 (1.067) Remain 07:23:32 loss: 0.1090 Lr: 0.00034 [2024-02-19 12:20:21,391 INFO misc.py line 119 87073] Train: [84/100][1530/1557] Data 0.007 (0.101) Batch 0.911 (1.067) Remain 07:23:28 loss: 0.2763 Lr: 0.00034 [2024-02-19 12:20:22,140 INFO misc.py line 119 87073] Train: [84/100][1531/1557] Data 0.006 (0.100) Batch 0.749 (1.067) Remain 07:23:22 loss: 0.1651 Lr: 0.00034 [2024-02-19 12:20:22,944 INFO misc.py line 119 87073] Train: [84/100][1532/1557] Data 0.005 (0.100) Batch 0.805 (1.067) Remain 07:23:17 loss: 0.2616 Lr: 0.00034 [2024-02-19 12:20:24,292 INFO misc.py line 119 87073] Train: [84/100][1533/1557] Data 0.004 (0.100) Batch 1.338 (1.067) Remain 07:23:20 loss: 0.1016 Lr: 0.00034 [2024-02-19 12:20:25,416 INFO misc.py line 119 87073] Train: [84/100][1534/1557] Data 0.015 (0.100) Batch 1.134 (1.067) Remain 07:23:20 loss: 0.4331 Lr: 0.00034 [2024-02-19 12:20:26,416 INFO misc.py line 119 87073] Train: [84/100][1535/1557] Data 0.004 (0.100) Batch 1.001 (1.067) Remain 07:23:18 loss: 0.2138 Lr: 0.00034 [2024-02-19 12:20:27,305 INFO misc.py line 119 87073] Train: [84/100][1536/1557] Data 0.004 (0.100) Batch 0.885 (1.067) Remain 07:23:14 loss: 0.1069 Lr: 0.00034 [2024-02-19 12:20:28,190 INFO misc.py line 119 87073] Train: [84/100][1537/1557] Data 0.007 (0.100) Batch 0.888 (1.067) Remain 07:23:10 loss: 0.1497 Lr: 0.00034 [2024-02-19 12:20:28,920 INFO misc.py line 119 87073] Train: [84/100][1538/1557] Data 0.004 (0.100) Batch 0.719 (1.066) Remain 07:23:03 loss: 0.1746 Lr: 0.00034 [2024-02-19 12:20:29,628 INFO misc.py line 119 87073] Train: [84/100][1539/1557] Data 0.015 (0.100) Batch 0.718 (1.066) Remain 07:22:57 loss: 0.1521 Lr: 0.00034 [2024-02-19 12:20:30,720 INFO misc.py line 119 87073] Train: [84/100][1540/1557] Data 0.005 (0.100) Batch 1.088 (1.066) Remain 07:22:56 loss: 0.1370 Lr: 0.00034 [2024-02-19 12:20:31,751 INFO misc.py line 119 87073] Train: [84/100][1541/1557] Data 0.011 (0.100) Batch 1.025 (1.066) Remain 07:22:54 loss: 0.1694 Lr: 0.00034 [2024-02-19 12:20:32,663 INFO misc.py line 119 87073] Train: [84/100][1542/1557] Data 0.015 (0.100) Batch 0.922 (1.066) Remain 07:22:51 loss: 0.1585 Lr: 0.00034 [2024-02-19 12:20:33,570 INFO misc.py line 119 87073] Train: [84/100][1543/1557] Data 0.005 (0.100) Batch 0.908 (1.066) Remain 07:22:47 loss: 0.3468 Lr: 0.00034 [2024-02-19 12:20:34,525 INFO misc.py line 119 87073] Train: [84/100][1544/1557] Data 0.004 (0.100) Batch 0.944 (1.066) Remain 07:22:44 loss: 0.1322 Lr: 0.00034 [2024-02-19 12:20:35,250 INFO misc.py line 119 87073] Train: [84/100][1545/1557] Data 0.015 (0.100) Batch 0.735 (1.066) Remain 07:22:38 loss: 0.2604 Lr: 0.00034 [2024-02-19 12:20:36,026 INFO misc.py line 119 87073] Train: [84/100][1546/1557] Data 0.004 (0.100) Batch 0.767 (1.065) Remain 07:22:32 loss: 0.2007 Lr: 0.00034 [2024-02-19 12:20:37,164 INFO misc.py line 119 87073] Train: [84/100][1547/1557] Data 0.015 (0.099) Batch 1.142 (1.065) Remain 07:22:32 loss: 0.1674 Lr: 0.00034 [2024-02-19 12:20:38,235 INFO misc.py line 119 87073] Train: [84/100][1548/1557] Data 0.011 (0.099) Batch 1.068 (1.065) Remain 07:22:31 loss: 0.4528 Lr: 0.00034 [2024-02-19 12:20:39,128 INFO misc.py line 119 87073] Train: [84/100][1549/1557] Data 0.014 (0.099) Batch 0.902 (1.065) Remain 07:22:27 loss: 0.5496 Lr: 0.00034 [2024-02-19 12:20:39,963 INFO misc.py line 119 87073] Train: [84/100][1550/1557] Data 0.004 (0.099) Batch 0.835 (1.065) Remain 07:22:23 loss: 0.1530 Lr: 0.00034 [2024-02-19 12:20:40,851 INFO misc.py line 119 87073] Train: [84/100][1551/1557] Data 0.004 (0.099) Batch 0.880 (1.065) Remain 07:22:18 loss: 0.2277 Lr: 0.00034 [2024-02-19 12:20:41,619 INFO misc.py line 119 87073] Train: [84/100][1552/1557] Data 0.013 (0.099) Batch 0.777 (1.065) Remain 07:22:13 loss: 0.1938 Lr: 0.00034 [2024-02-19 12:20:42,365 INFO misc.py line 119 87073] Train: [84/100][1553/1557] Data 0.004 (0.099) Batch 0.737 (1.065) Remain 07:22:06 loss: 0.1516 Lr: 0.00034 [2024-02-19 12:20:43,563 INFO misc.py line 119 87073] Train: [84/100][1554/1557] Data 0.013 (0.099) Batch 1.197 (1.065) Remain 07:22:07 loss: 0.1503 Lr: 0.00034 [2024-02-19 12:20:44,481 INFO misc.py line 119 87073] Train: [84/100][1555/1557] Data 0.015 (0.099) Batch 0.928 (1.065) Remain 07:22:04 loss: 0.0715 Lr: 0.00034 [2024-02-19 12:20:45,349 INFO misc.py line 119 87073] Train: [84/100][1556/1557] Data 0.005 (0.099) Batch 0.868 (1.065) Remain 07:22:00 loss: 0.2656 Lr: 0.00034 [2024-02-19 12:20:46,353 INFO misc.py line 119 87073] Train: [84/100][1557/1557] Data 0.004 (0.099) Batch 0.993 (1.064) Remain 07:21:58 loss: 0.2248 Lr: 0.00034 [2024-02-19 12:20:46,353 INFO misc.py line 136 87073] Train result: loss: 0.2206 [2024-02-19 12:20:46,354 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 12:21:13,132 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.3705 [2024-02-19 12:21:13,914 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.2946 [2024-02-19 12:21:16,040 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3384 [2024-02-19 12:21:18,250 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3861 [2024-02-19 12:21:23,205 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2227 [2024-02-19 12:21:23,904 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0768 [2024-02-19 12:21:25,166 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3616 [2024-02-19 12:21:28,122 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3184 [2024-02-19 12:21:29,934 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2700 [2024-02-19 12:21:31,545 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7284/0.7792/0.9190. [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9394/0.9699 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9833/0.9897 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8672/0.9747 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3845/0.4368 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6636/0.6884 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8535/0.9310 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8391/0.8957 [2024-02-19 12:21:31,546 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9229/0.9729 [2024-02-19 12:21:31,547 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7946/0.8148 [2024-02-19 12:21:31,547 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7926/0.8841 [2024-02-19 12:21:31,547 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7987/0.8498 [2024-02-19 12:21:31,547 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6304/0.7215 [2024-02-19 12:21:31,548 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 12:21:31,549 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 12:21:31,550 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 12:21:38,912 INFO misc.py line 119 87073] Train: [85/100][1/1557] Data 1.662 (1.662) Batch 2.482 (2.482) Remain 17:10:36 loss: 0.1923 Lr: 0.00034 [2024-02-19 12:21:39,958 INFO misc.py line 119 87073] Train: [85/100][2/1557] Data 0.006 (0.006) Batch 1.047 (1.047) Remain 07:14:29 loss: 0.2717 Lr: 0.00034 [2024-02-19 12:21:41,066 INFO misc.py line 119 87073] Train: [85/100][3/1557] Data 0.005 (0.005) Batch 1.106 (1.106) Remain 07:39:15 loss: 0.1192 Lr: 0.00034 [2024-02-19 12:21:42,180 INFO misc.py line 119 87073] Train: [85/100][4/1557] Data 0.007 (0.007) Batch 1.106 (1.106) Remain 07:39:04 loss: 0.4275 Lr: 0.00034 [2024-02-19 12:21:42,943 INFO misc.py line 119 87073] Train: [85/100][5/1557] Data 0.015 (0.011) Batch 0.774 (0.940) Remain 06:30:08 loss: 0.2063 Lr: 0.00034 [2024-02-19 12:21:43,625 INFO misc.py line 119 87073] Train: [85/100][6/1557] Data 0.004 (0.009) Batch 0.674 (0.851) Remain 05:53:16 loss: 0.2141 Lr: 0.00034 [2024-02-19 12:21:44,870 INFO misc.py line 119 87073] Train: [85/100][7/1557] Data 0.013 (0.010) Batch 1.252 (0.951) Remain 06:34:53 loss: 0.1129 Lr: 0.00034 [2024-02-19 12:21:45,783 INFO misc.py line 119 87073] Train: [85/100][8/1557] Data 0.005 (0.009) Batch 0.913 (0.944) Remain 06:31:41 loss: 0.0996 Lr: 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[85/100][1507/1557] Data 0.003 (0.071) Batch 1.018 (1.125) Remain 07:18:54 loss: 0.3647 Lr: 0.00030 [2024-02-19 12:49:54,376 INFO misc.py line 119 87073] Train: [85/100][1508/1557] Data 0.003 (0.071) Batch 1.042 (1.125) Remain 07:18:52 loss: 0.1310 Lr: 0.00030 [2024-02-19 12:49:55,326 INFO misc.py line 119 87073] Train: [85/100][1509/1557] Data 0.003 (0.071) Batch 0.950 (1.125) Remain 07:18:48 loss: 0.2704 Lr: 0.00030 [2024-02-19 12:49:56,104 INFO misc.py line 119 87073] Train: [85/100][1510/1557] Data 0.003 (0.071) Batch 0.771 (1.125) Remain 07:18:41 loss: 0.1373 Lr: 0.00030 [2024-02-19 12:49:56,866 INFO misc.py line 119 87073] Train: [85/100][1511/1557] Data 0.010 (0.071) Batch 0.767 (1.125) Remain 07:18:35 loss: 0.2001 Lr: 0.00030 [2024-02-19 12:49:58,129 INFO misc.py line 119 87073] Train: [85/100][1512/1557] Data 0.005 (0.071) Batch 1.262 (1.125) Remain 07:18:36 loss: 0.2051 Lr: 0.00030 [2024-02-19 12:49:59,245 INFO misc.py line 119 87073] Train: [85/100][1513/1557] Data 0.007 (0.071) Batch 1.106 (1.125) Remain 07:18:34 loss: 0.0982 Lr: 0.00030 [2024-02-19 12:50:00,270 INFO misc.py line 119 87073] Train: [85/100][1514/1557] Data 0.016 (0.071) Batch 1.037 (1.125) Remain 07:18:32 loss: 0.1817 Lr: 0.00030 [2024-02-19 12:50:01,181 INFO misc.py line 119 87073] Train: [85/100][1515/1557] Data 0.004 (0.071) Batch 0.912 (1.124) Remain 07:18:27 loss: 0.2638 Lr: 0.00030 [2024-02-19 12:50:02,206 INFO misc.py line 119 87073] Train: [85/100][1516/1557] Data 0.003 (0.071) Batch 1.024 (1.124) Remain 07:18:25 loss: 0.0730 Lr: 0.00030 [2024-02-19 12:50:02,981 INFO misc.py line 119 87073] Train: [85/100][1517/1557] Data 0.004 (0.071) Batch 0.774 (1.124) Remain 07:18:18 loss: 0.1047 Lr: 0.00030 [2024-02-19 12:50:03,706 INFO misc.py line 119 87073] Train: [85/100][1518/1557] Data 0.006 (0.071) Batch 0.726 (1.124) Remain 07:18:11 loss: 0.2127 Lr: 0.00030 [2024-02-19 12:50:14,078 INFO misc.py line 119 87073] Train: [85/100][1519/1557] Data 3.642 (0.073) Batch 10.372 (1.130) Remain 07:20:33 loss: 0.0788 Lr: 0.00030 [2024-02-19 12:50:14,939 INFO misc.py line 119 87073] Train: [85/100][1520/1557] Data 0.005 (0.073) Batch 0.861 (1.130) Remain 07:20:27 loss: 0.3455 Lr: 0.00030 [2024-02-19 12:50:16,114 INFO misc.py line 119 87073] Train: [85/100][1521/1557] Data 0.005 (0.073) Batch 1.170 (1.130) Remain 07:20:27 loss: 0.4142 Lr: 0.00030 [2024-02-19 12:50:17,219 INFO misc.py line 119 87073] Train: [85/100][1522/1557] Data 0.010 (0.073) Batch 1.100 (1.130) Remain 07:20:25 loss: 0.0735 Lr: 0.00030 [2024-02-19 12:50:18,138 INFO misc.py line 119 87073] Train: [85/100][1523/1557] Data 0.014 (0.073) Batch 0.930 (1.130) Remain 07:20:21 loss: 0.4334 Lr: 0.00030 [2024-02-19 12:50:18,848 INFO misc.py line 119 87073] Train: [85/100][1524/1557] Data 0.004 (0.073) Batch 0.709 (1.129) Remain 07:20:13 loss: 0.2250 Lr: 0.00030 [2024-02-19 12:50:19,657 INFO misc.py line 119 87073] Train: [85/100][1525/1557] Data 0.003 (0.073) Batch 0.806 (1.129) Remain 07:20:07 loss: 0.1665 Lr: 0.00030 [2024-02-19 12:50:20,915 INFO misc.py line 119 87073] Train: [85/100][1526/1557] Data 0.006 (0.073) Batch 1.246 (1.129) Remain 07:20:08 loss: 0.2017 Lr: 0.00030 [2024-02-19 12:50:22,123 INFO misc.py line 119 87073] Train: [85/100][1527/1557] Data 0.018 (0.073) Batch 1.222 (1.129) Remain 07:20:08 loss: 0.2117 Lr: 0.00030 [2024-02-19 12:50:23,146 INFO misc.py line 119 87073] Train: [85/100][1528/1557] Data 0.005 (0.073) Batch 1.013 (1.129) Remain 07:20:05 loss: 0.6870 Lr: 0.00030 [2024-02-19 12:50:24,151 INFO misc.py line 119 87073] Train: [85/100][1529/1557] Data 0.015 (0.073) Batch 1.004 (1.129) Remain 07:20:02 loss: 0.0824 Lr: 0.00030 [2024-02-19 12:50:25,053 INFO misc.py line 119 87073] Train: [85/100][1530/1557] Data 0.015 (0.073) Batch 0.915 (1.129) Remain 07:19:58 loss: 0.3063 Lr: 0.00030 [2024-02-19 12:50:25,799 INFO misc.py line 119 87073] Train: [85/100][1531/1557] Data 0.004 (0.073) Batch 0.745 (1.129) Remain 07:19:51 loss: 0.1235 Lr: 0.00030 [2024-02-19 12:50:26,574 INFO misc.py line 119 87073] Train: [85/100][1532/1557] Data 0.004 (0.072) Batch 0.776 (1.129) Remain 07:19:44 loss: 0.1335 Lr: 0.00030 [2024-02-19 12:50:27,765 INFO misc.py line 119 87073] Train: [85/100][1533/1557] Data 0.004 (0.072) Batch 1.181 (1.129) Remain 07:19:44 loss: 0.0677 Lr: 0.00030 [2024-02-19 12:50:28,807 INFO misc.py line 119 87073] Train: [85/100][1534/1557] Data 0.013 (0.072) Batch 1.045 (1.129) Remain 07:19:42 loss: 0.0620 Lr: 0.00030 [2024-02-19 12:50:29,896 INFO misc.py line 119 87073] Train: [85/100][1535/1557] Data 0.011 (0.072) Batch 1.084 (1.128) Remain 07:19:40 loss: 0.3096 Lr: 0.00030 [2024-02-19 12:50:31,006 INFO misc.py line 119 87073] Train: [85/100][1536/1557] Data 0.017 (0.072) Batch 1.117 (1.128) Remain 07:19:38 loss: 0.1265 Lr: 0.00030 [2024-02-19 12:50:32,015 INFO misc.py line 119 87073] Train: [85/100][1537/1557] Data 0.008 (0.072) Batch 1.003 (1.128) Remain 07:19:35 loss: 0.1067 Lr: 0.00030 [2024-02-19 12:50:32,764 INFO misc.py line 119 87073] Train: [85/100][1538/1557] Data 0.015 (0.072) Batch 0.759 (1.128) Remain 07:19:29 loss: 0.3892 Lr: 0.00030 [2024-02-19 12:50:33,532 INFO misc.py line 119 87073] Train: [85/100][1539/1557] Data 0.004 (0.072) Batch 0.757 (1.128) Remain 07:19:22 loss: 0.2702 Lr: 0.00030 [2024-02-19 12:50:34,809 INFO misc.py line 119 87073] Train: [85/100][1540/1557] Data 0.016 (0.072) Batch 1.277 (1.128) Remain 07:19:23 loss: 0.1290 Lr: 0.00030 [2024-02-19 12:50:35,731 INFO misc.py line 119 87073] Train: [85/100][1541/1557] Data 0.015 (0.072) Batch 0.933 (1.128) Remain 07:19:19 loss: 0.2075 Lr: 0.00030 [2024-02-19 12:50:36,763 INFO misc.py line 119 87073] Train: [85/100][1542/1557] Data 0.004 (0.072) Batch 1.033 (1.128) Remain 07:19:16 loss: 0.1283 Lr: 0.00030 [2024-02-19 12:50:37,881 INFO misc.py line 119 87073] Train: [85/100][1543/1557] Data 0.004 (0.072) Batch 1.117 (1.128) Remain 07:19:15 loss: 0.2893 Lr: 0.00030 [2024-02-19 12:50:38,967 INFO misc.py line 119 87073] Train: [85/100][1544/1557] Data 0.004 (0.072) Batch 1.086 (1.128) Remain 07:19:13 loss: 0.2414 Lr: 0.00030 [2024-02-19 12:50:39,694 INFO misc.py line 119 87073] Train: [85/100][1545/1557] Data 0.004 (0.072) Batch 0.727 (1.128) Remain 07:19:06 loss: 0.2619 Lr: 0.00030 [2024-02-19 12:50:40,470 INFO misc.py line 119 87073] Train: [85/100][1546/1557] Data 0.004 (0.072) Batch 0.765 (1.127) Remain 07:19:00 loss: 0.1371 Lr: 0.00030 [2024-02-19 12:50:41,854 INFO misc.py line 119 87073] Train: [85/100][1547/1557] Data 0.015 (0.072) Batch 1.382 (1.127) Remain 07:19:02 loss: 0.1061 Lr: 0.00030 [2024-02-19 12:50:42,812 INFO misc.py line 119 87073] Train: [85/100][1548/1557] Data 0.017 (0.072) Batch 0.970 (1.127) Remain 07:18:59 loss: 0.1953 Lr: 0.00030 [2024-02-19 12:50:44,044 INFO misc.py line 119 87073] Train: [85/100][1549/1557] Data 0.004 (0.072) Batch 1.220 (1.127) Remain 07:18:59 loss: 0.3035 Lr: 0.00030 [2024-02-19 12:50:44,976 INFO misc.py line 119 87073] Train: [85/100][1550/1557] Data 0.016 (0.072) Batch 0.945 (1.127) Remain 07:18:55 loss: 0.1467 Lr: 0.00030 [2024-02-19 12:50:45,905 INFO misc.py line 119 87073] Train: [85/100][1551/1557] Data 0.004 (0.072) Batch 0.929 (1.127) Remain 07:18:51 loss: 0.4377 Lr: 0.00030 [2024-02-19 12:50:46,676 INFO misc.py line 119 87073] Train: [85/100][1552/1557] Data 0.004 (0.072) Batch 0.744 (1.127) Remain 07:18:44 loss: 0.3731 Lr: 0.00030 [2024-02-19 12:50:47,442 INFO misc.py line 119 87073] Train: [85/100][1553/1557] Data 0.032 (0.072) Batch 0.793 (1.127) Remain 07:18:38 loss: 0.1983 Lr: 0.00030 [2024-02-19 12:50:48,466 INFO misc.py line 119 87073] Train: [85/100][1554/1557] Data 0.004 (0.072) Batch 1.024 (1.127) Remain 07:18:35 loss: 0.1010 Lr: 0.00030 [2024-02-19 12:50:49,401 INFO misc.py line 119 87073] Train: [85/100][1555/1557] Data 0.004 (0.072) Batch 0.935 (1.127) Remain 07:18:31 loss: 0.2150 Lr: 0.00030 [2024-02-19 12:50:50,278 INFO misc.py line 119 87073] Train: [85/100][1556/1557] Data 0.004 (0.072) Batch 0.865 (1.126) Remain 07:18:26 loss: 0.2937 Lr: 0.00030 [2024-02-19 12:50:51,410 INFO misc.py line 119 87073] Train: [85/100][1557/1557] Data 0.016 (0.071) Batch 1.130 (1.126) Remain 07:18:25 loss: 0.0896 Lr: 0.00030 [2024-02-19 12:50:51,410 INFO misc.py line 136 87073] Train result: loss: 0.2170 [2024-02-19 12:50:51,411 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 12:51:20,364 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4919 [2024-02-19 12:51:21,140 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4675 [2024-02-19 12:51:23,266 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3391 [2024-02-19 12:51:25,473 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3323 [2024-02-19 12:51:30,420 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2152 [2024-02-19 12:51:31,122 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1009 [2024-02-19 12:51:32,381 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2859 [2024-02-19 12:51:35,342 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2438 [2024-02-19 12:51:37,150 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2715 [2024-02-19 12:51:38,702 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7349/0.7928/0.9191. [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9358/0.9682 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9894 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8705/0.9739 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4414/0.4937 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6559/0.6783 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8206/0.9267 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8528/0.9219 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9315/0.9663 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8768/0.9156 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7960/0.8839 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7695/0.8818 [2024-02-19 12:51:38,703 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6196/0.7071 [2024-02-19 12:51:38,704 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 12:51:38,705 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 12:51:38,705 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 12:51:46,694 INFO misc.py line 119 87073] Train: [86/100][1/1557] Data 1.469 (1.469) Batch 2.481 (2.481) Remain 16:05:44 loss: 0.1001 Lr: 0.00030 [2024-02-19 12:51:47,671 INFO misc.py line 119 87073] Train: [86/100][2/1557] Data 0.008 (0.008) Batch 0.980 (0.980) Remain 06:21:25 loss: 0.3173 Lr: 0.00030 [2024-02-19 12:51:48,618 INFO misc.py line 119 87073] Train: [86/100][3/1557] Data 0.005 (0.005) Batch 0.948 (0.948) Remain 06:08:50 loss: 0.1172 Lr: 0.00030 [2024-02-19 12:51:49,585 INFO misc.py line 119 87073] Train: [86/100][4/1557] Data 0.005 (0.005) Batch 0.967 (0.967) Remain 06:16:20 loss: 0.2366 Lr: 0.00030 [2024-02-19 12:51:50,331 INFO misc.py line 119 87073] Train: [86/100][5/1557] Data 0.004 (0.004) Batch 0.746 (0.857) Remain 05:33:21 loss: 0.2199 Lr: 0.00030 [2024-02-19 12:51:51,126 INFO misc.py line 119 87073] Train: [86/100][6/1557] Data 0.004 (0.004) Batch 0.792 (0.835) Remain 05:25:00 loss: 0.1966 Lr: 0.00030 [2024-02-19 12:51:52,356 INFO misc.py line 119 87073] Train: [86/100][7/1557] Data 0.006 (0.004) Batch 1.222 (0.932) Remain 06:02:34 loss: 0.0956 Lr: 0.00030 [2024-02-19 12:51:53,202 INFO misc.py line 119 87073] Train: [86/100][8/1557] Data 0.015 (0.007) Batch 0.856 (0.917) Remain 05:56:38 loss: 0.2629 Lr: 0.00030 [2024-02-19 12:51:54,126 INFO misc.py line 119 87073] Train: [86/100][9/1557] Data 0.006 (0.006) Batch 0.924 (0.918) Remain 05:57:07 loss: 0.2471 Lr: 0.00030 [2024-02-19 12:51:55,064 INFO misc.py line 119 87073] Train: [86/100][10/1557] Data 0.005 (0.006) Batch 0.937 (0.921) Remain 05:58:11 loss: 0.0932 Lr: 0.00030 [2024-02-19 12:51:55,971 INFO misc.py line 119 87073] Train: [86/100][11/1557] Data 0.005 (0.006) Batch 0.906 (0.919) Remain 05:57:29 loss: 0.1921 Lr: 0.00030 [2024-02-19 12:51:56,749 INFO misc.py line 119 87073] Train: [86/100][12/1557] Data 0.006 (0.006) Batch 0.780 (0.903) Remain 05:51:29 loss: 0.1092 Lr: 0.00030 [2024-02-19 12:51:57,565 INFO misc.py line 119 87073] Train: [86/100][13/1557] Data 0.004 (0.006) Batch 0.812 (0.894) Remain 05:47:56 loss: 0.2218 Lr: 0.00030 [2024-02-19 12:51:58,716 INFO misc.py line 119 87073] Train: [86/100][14/1557] Data 0.007 (0.006) Batch 1.143 (0.917) Remain 05:56:43 loss: 0.1367 Lr: 0.00030 [2024-02-19 12:51:59,621 INFO misc.py line 119 87073] Train: [86/100][15/1557] Data 0.015 (0.007) Batch 0.916 (0.917) Remain 05:56:40 loss: 0.3318 Lr: 0.00030 [2024-02-19 12:52:00,546 INFO misc.py line 119 87073] Train: [86/100][16/1557] Data 0.004 (0.007) Batch 0.925 (0.918) Remain 05:56:54 loss: 0.2985 Lr: 0.00030 [2024-02-19 12:52:01,468 INFO misc.py line 119 87073] Train: [86/100][17/1557] Data 0.003 (0.006) Batch 0.911 (0.917) Remain 05:56:42 loss: 0.1511 Lr: 0.00030 [2024-02-19 12:52:02,500 INFO misc.py line 119 87073] Train: [86/100][18/1557] Data 0.015 (0.007) Batch 1.042 (0.925) Remain 05:59:56 loss: 0.1847 Lr: 0.00030 [2024-02-19 12:52:03,238 INFO misc.py line 119 87073] Train: [86/100][19/1557] Data 0.004 (0.007) Batch 0.739 (0.914) Remain 05:55:22 loss: 0.2077 Lr: 0.00030 [2024-02-19 12:52:03,968 INFO misc.py line 119 87073] Train: [86/100][20/1557] Data 0.004 (0.007) Batch 0.728 (0.903) Remain 05:51:07 loss: 0.1175 Lr: 0.00030 [2024-02-19 12:52:05,109 INFO misc.py line 119 87073] Train: [86/100][21/1557] Data 0.005 (0.007) Batch 1.130 (0.915) Remain 05:56:00 loss: 0.1311 Lr: 0.00030 [2024-02-19 12:52:06,156 INFO misc.py line 119 87073] Train: [86/100][22/1557] Data 0.017 (0.007) Batch 1.048 (0.922) Remain 05:58:42 loss: 0.0824 Lr: 0.00030 [2024-02-19 12:52:07,249 INFO misc.py line 119 87073] Train: [86/100][23/1557] Data 0.016 (0.008) Batch 1.094 (0.931) Remain 06:02:01 loss: 0.2490 Lr: 0.00030 [2024-02-19 12:52:08,205 INFO misc.py line 119 87073] Train: [86/100][24/1557] Data 0.015 (0.008) Batch 0.967 (0.933) Remain 06:02:40 loss: 0.1795 Lr: 0.00030 [2024-02-19 12:52:09,247 INFO misc.py line 119 87073] Train: [86/100][25/1557] Data 0.004 (0.008) Batch 1.041 (0.938) Remain 06:04:34 loss: 0.2183 Lr: 0.00030 [2024-02-19 12:52:09,990 INFO misc.py line 119 87073] Train: [86/100][26/1557] Data 0.005 (0.008) Batch 0.745 (0.929) Remain 06:01:18 loss: 0.1533 Lr: 0.00030 [2024-02-19 12:52:10,783 INFO misc.py line 119 87073] Train: [86/100][27/1557] Data 0.003 (0.007) Batch 0.792 (0.924) Remain 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Train: [86/100][40/1557] Data 0.015 (0.008) Batch 0.750 (0.946) Remain 06:07:42 loss: 0.1338 Lr: 0.00030 [2024-02-19 12:52:24,405 INFO misc.py line 119 87073] Train: [86/100][41/1557] Data 0.004 (0.008) Batch 0.771 (0.942) Remain 06:05:54 loss: 0.1054 Lr: 0.00030 [2024-02-19 12:52:25,653 INFO misc.py line 119 87073] Train: [86/100][42/1557] Data 0.007 (0.008) Batch 1.247 (0.950) Remain 06:08:56 loss: 0.1001 Lr: 0.00030 [2024-02-19 12:52:26,559 INFO misc.py line 119 87073] Train: [86/100][43/1557] Data 0.007 (0.008) Batch 0.910 (0.949) Remain 06:08:31 loss: 0.3100 Lr: 0.00030 [2024-02-19 12:52:27,634 INFO misc.py line 119 87073] Train: [86/100][44/1557] Data 0.004 (0.008) Batch 1.074 (0.952) Remain 06:09:42 loss: 0.2455 Lr: 0.00030 [2024-02-19 12:52:28,614 INFO misc.py line 119 87073] Train: [86/100][45/1557] Data 0.006 (0.008) Batch 0.980 (0.952) Remain 06:09:57 loss: 0.2607 Lr: 0.00030 [2024-02-19 12:52:29,581 INFO misc.py line 119 87073] Train: [86/100][46/1557] Data 0.005 (0.007) 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line 119 87073] Train: [86/100][165/1557] Data 0.004 (0.086) Batch 0.948 (1.049) Remain 06:45:33 loss: 0.3845 Lr: 0.00030 [2024-02-19 12:54:39,389 INFO misc.py line 119 87073] Train: [86/100][166/1557] Data 0.003 (0.086) Batch 0.777 (1.048) Remain 06:44:53 loss: 0.2848 Lr: 0.00030 [2024-02-19 12:54:40,150 INFO misc.py line 119 87073] Train: [86/100][167/1557] Data 0.007 (0.085) Batch 0.764 (1.046) Remain 06:44:12 loss: 0.1628 Lr: 0.00030 [2024-02-19 12:54:41,297 INFO misc.py line 119 87073] Train: [86/100][168/1557] Data 0.004 (0.085) Batch 1.147 (1.047) Remain 06:44:25 loss: 0.1975 Lr: 0.00030 [2024-02-19 12:54:42,334 INFO misc.py line 119 87073] Train: [86/100][169/1557] Data 0.004 (0.084) Batch 1.038 (1.046) Remain 06:44:23 loss: 0.3161 Lr: 0.00030 [2024-02-19 12:54:43,273 INFO misc.py line 119 87073] Train: [86/100][170/1557] Data 0.003 (0.084) Batch 0.938 (1.046) Remain 06:44:07 loss: 0.3563 Lr: 0.00030 [2024-02-19 12:54:44,337 INFO misc.py line 119 87073] Train: 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Batch 0.908 (1.095) Remain 07:03:06 loss: 0.2419 Lr: 0.00030 [2024-02-19 12:55:00,101 INFO misc.py line 119 87073] Train: [86/100][178/1557] Data 0.013 (0.120) Batch 0.901 (1.094) Remain 07:02:39 loss: 0.2056 Lr: 0.00030 [2024-02-19 12:55:00,932 INFO misc.py line 119 87073] Train: [86/100][179/1557] Data 0.004 (0.119) Batch 0.829 (1.093) Remain 07:02:03 loss: 0.1094 Lr: 0.00030 [2024-02-19 12:55:03,273 INFO misc.py line 119 87073] Train: [86/100][180/1557] Data 1.120 (0.125) Batch 2.299 (1.099) Remain 07:04:40 loss: 0.2270 Lr: 0.00030 [2024-02-19 12:55:04,076 INFO misc.py line 119 87073] Train: [86/100][181/1557] Data 0.049 (0.124) Batch 0.847 (1.098) Remain 07:04:06 loss: 0.1522 Lr: 0.00030 [2024-02-19 12:55:05,330 INFO misc.py line 119 87073] Train: [86/100][182/1557] Data 0.004 (0.123) Batch 1.241 (1.099) Remain 07:04:24 loss: 0.1433 Lr: 0.00030 [2024-02-19 12:55:06,185 INFO misc.py line 119 87073] Train: [86/100][183/1557] Data 0.016 (0.123) Batch 0.867 (1.098) Remain 07:03:53 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87073] Train: [86/100][196/1557] Data 0.014 (0.115) Batch 1.208 (1.083) Remain 06:57:53 loss: 0.0642 Lr: 0.00030 [2024-02-19 12:55:18,783 INFO misc.py line 119 87073] Train: [86/100][197/1557] Data 0.014 (0.115) Batch 1.206 (1.083) Remain 06:58:06 loss: 0.0882 Lr: 0.00030 [2024-02-19 12:55:19,911 INFO misc.py line 119 87073] Train: [86/100][198/1557] Data 0.012 (0.114) Batch 1.124 (1.083) Remain 06:58:10 loss: 0.2110 Lr: 0.00030 [2024-02-19 12:55:20,891 INFO misc.py line 119 87073] Train: [86/100][199/1557] Data 0.016 (0.113) Batch 0.992 (1.083) Remain 06:57:58 loss: 0.1795 Lr: 0.00030 [2024-02-19 12:55:21,881 INFO misc.py line 119 87073] Train: [86/100][200/1557] Data 0.003 (0.113) Batch 0.990 (1.083) Remain 06:57:46 loss: 0.1295 Lr: 0.00030 [2024-02-19 12:55:22,681 INFO misc.py line 119 87073] Train: [86/100][201/1557] Data 0.004 (0.112) Batch 0.799 (1.081) Remain 06:57:12 loss: 0.1690 Lr: 0.00030 [2024-02-19 12:55:23,461 INFO misc.py line 119 87073] Train: [86/100][202/1557] Data 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(0.119) Batch 1.086 (1.106) Remain 06:42:29 loss: 0.3111 Lr: 0.00027 [2024-02-19 13:19:39,072 INFO misc.py line 119 87073] Train: [86/100][1514/1557] Data 0.006 (0.119) Batch 0.927 (1.106) Remain 06:42:25 loss: 0.1673 Lr: 0.00027 [2024-02-19 13:19:40,027 INFO misc.py line 119 87073] Train: [86/100][1515/1557] Data 0.009 (0.119) Batch 0.959 (1.105) Remain 06:42:22 loss: 0.2470 Lr: 0.00027 [2024-02-19 13:19:40,801 INFO misc.py line 119 87073] Train: [86/100][1516/1557] Data 0.005 (0.119) Batch 0.770 (1.105) Remain 06:42:16 loss: 0.0957 Lr: 0.00027 [2024-02-19 13:19:41,517 INFO misc.py line 119 87073] Train: [86/100][1517/1557] Data 0.009 (0.119) Batch 0.721 (1.105) Remain 06:42:09 loss: 0.1649 Lr: 0.00027 [2024-02-19 13:19:42,326 INFO misc.py line 119 87073] Train: [86/100][1518/1557] Data 0.003 (0.118) Batch 0.807 (1.105) Remain 06:42:04 loss: 0.1771 Lr: 0.00027 [2024-02-19 13:19:53,327 INFO misc.py line 119 87073] Train: [86/100][1519/1557] Data 6.580 (0.123) Batch 11.003 (1.111) Remain 06:44:25 loss: 0.1363 Lr: 0.00027 [2024-02-19 13:19:54,485 INFO misc.py line 119 87073] Train: [86/100][1520/1557] Data 0.004 (0.123) Batch 1.157 (1.111) Remain 06:44:25 loss: 0.2236 Lr: 0.00027 [2024-02-19 13:19:55,518 INFO misc.py line 119 87073] Train: [86/100][1521/1557] Data 0.004 (0.123) Batch 1.034 (1.111) Remain 06:44:23 loss: 0.2007 Lr: 0.00027 [2024-02-19 13:19:56,431 INFO misc.py line 119 87073] Train: [86/100][1522/1557] Data 0.003 (0.122) Batch 0.912 (1.111) Remain 06:44:19 loss: 0.1485 Lr: 0.00027 [2024-02-19 13:19:57,452 INFO misc.py line 119 87073] Train: [86/100][1523/1557] Data 0.004 (0.122) Batch 1.021 (1.111) Remain 06:44:16 loss: 0.1927 Lr: 0.00027 [2024-02-19 13:19:58,166 INFO misc.py line 119 87073] Train: [86/100][1524/1557] Data 0.003 (0.122) Batch 0.712 (1.111) Remain 06:44:10 loss: 0.1383 Lr: 0.00027 [2024-02-19 13:19:58,941 INFO misc.py line 119 87073] Train: [86/100][1525/1557] Data 0.007 (0.122) Batch 0.777 (1.111) Remain 06:44:04 loss: 0.1580 Lr: 0.00027 [2024-02-19 13:20:00,188 INFO misc.py line 119 87073] Train: [86/100][1526/1557] Data 0.004 (0.122) Batch 1.238 (1.111) Remain 06:44:04 loss: 0.1420 Lr: 0.00027 [2024-02-19 13:20:01,188 INFO misc.py line 119 87073] Train: [86/100][1527/1557] Data 0.013 (0.122) Batch 1.008 (1.111) Remain 06:44:02 loss: 0.7054 Lr: 0.00027 [2024-02-19 13:20:02,208 INFO misc.py line 119 87073] Train: [86/100][1528/1557] Data 0.005 (0.122) Batch 1.018 (1.111) Remain 06:43:59 loss: 0.1652 Lr: 0.00027 [2024-02-19 13:20:03,059 INFO misc.py line 119 87073] Train: [86/100][1529/1557] Data 0.007 (0.122) Batch 0.854 (1.110) Remain 06:43:55 loss: 0.0924 Lr: 0.00027 [2024-02-19 13:20:04,101 INFO misc.py line 119 87073] Train: [86/100][1530/1557] Data 0.004 (0.122) Batch 1.042 (1.110) Remain 06:43:53 loss: 0.3729 Lr: 0.00027 [2024-02-19 13:20:04,840 INFO misc.py line 119 87073] Train: [86/100][1531/1557] Data 0.004 (0.122) Batch 0.739 (1.110) Remain 06:43:46 loss: 0.1484 Lr: 0.00027 [2024-02-19 13:20:05,573 INFO misc.py line 119 87073] Train: [86/100][1532/1557] Data 0.005 (0.122) Batch 0.726 (1.110) Remain 06:43:40 loss: 0.2366 Lr: 0.00027 [2024-02-19 13:20:06,674 INFO misc.py line 119 87073] Train: [86/100][1533/1557] Data 0.011 (0.122) Batch 1.103 (1.110) Remain 06:43:38 loss: 0.1328 Lr: 0.00027 [2024-02-19 13:20:07,615 INFO misc.py line 119 87073] Train: [86/100][1534/1557] Data 0.009 (0.122) Batch 0.947 (1.110) Remain 06:43:35 loss: 0.3749 Lr: 0.00027 [2024-02-19 13:20:08,631 INFO misc.py line 119 87073] Train: [86/100][1535/1557] Data 0.003 (0.121) Batch 1.016 (1.110) Remain 06:43:32 loss: 0.2222 Lr: 0.00027 [2024-02-19 13:20:09,478 INFO misc.py line 119 87073] Train: [86/100][1536/1557] Data 0.003 (0.121) Batch 0.845 (1.109) Remain 06:43:28 loss: 0.3715 Lr: 0.00027 [2024-02-19 13:20:10,365 INFO misc.py line 119 87073] Train: [86/100][1537/1557] Data 0.006 (0.121) Batch 0.879 (1.109) Remain 06:43:23 loss: 0.5028 Lr: 0.00027 [2024-02-19 13:20:11,125 INFO misc.py line 119 87073] Train: [86/100][1538/1557] Data 0.013 (0.121) Batch 0.770 (1.109) Remain 06:43:17 loss: 0.1626 Lr: 0.00027 [2024-02-19 13:20:11,745 INFO misc.py line 119 87073] Train: [86/100][1539/1557] Data 0.004 (0.121) Batch 0.612 (1.109) Remain 06:43:09 loss: 0.1246 Lr: 0.00027 [2024-02-19 13:20:12,983 INFO misc.py line 119 87073] Train: [86/100][1540/1557] Data 0.011 (0.121) Batch 1.237 (1.109) Remain 06:43:10 loss: 0.0663 Lr: 0.00027 [2024-02-19 13:20:14,077 INFO misc.py line 119 87073] Train: [86/100][1541/1557] Data 0.012 (0.121) Batch 1.090 (1.109) Remain 06:43:08 loss: 0.3334 Lr: 0.00027 [2024-02-19 13:20:14,873 INFO misc.py line 119 87073] Train: [86/100][1542/1557] Data 0.016 (0.121) Batch 0.808 (1.109) Remain 06:43:03 loss: 0.2557 Lr: 0.00027 [2024-02-19 13:20:15,757 INFO misc.py line 119 87073] Train: [86/100][1543/1557] Data 0.004 (0.121) Batch 0.883 (1.109) Remain 06:42:59 loss: 0.8288 Lr: 0.00027 [2024-02-19 13:20:16,678 INFO misc.py line 119 87073] Train: [86/100][1544/1557] Data 0.004 (0.121) Batch 0.911 (1.108) Remain 06:42:55 loss: 0.0601 Lr: 0.00027 [2024-02-19 13:20:17,312 INFO misc.py line 119 87073] Train: [86/100][1545/1557] Data 0.015 (0.121) Batch 0.644 (1.108) Remain 06:42:47 loss: 0.1398 Lr: 0.00027 [2024-02-19 13:20:18,075 INFO misc.py line 119 87073] Train: [86/100][1546/1557] Data 0.004 (0.121) Batch 0.753 (1.108) Remain 06:42:41 loss: 0.2767 Lr: 0.00027 [2024-02-19 13:20:19,299 INFO misc.py line 119 87073] Train: [86/100][1547/1557] Data 0.014 (0.121) Batch 1.225 (1.108) Remain 06:42:42 loss: 0.1502 Lr: 0.00027 [2024-02-19 13:20:20,146 INFO misc.py line 119 87073] Train: [86/100][1548/1557] Data 0.013 (0.121) Batch 0.856 (1.108) Remain 06:42:37 loss: 0.7414 Lr: 0.00027 [2024-02-19 13:20:20,946 INFO misc.py line 119 87073] Train: [86/100][1549/1557] Data 0.004 (0.120) Batch 0.800 (1.108) Remain 06:42:31 loss: 0.0864 Lr: 0.00027 [2024-02-19 13:20:21,849 INFO misc.py line 119 87073] Train: [86/100][1550/1557] Data 0.004 (0.120) Batch 0.894 (1.107) Remain 06:42:27 loss: 0.1073 Lr: 0.00027 [2024-02-19 13:20:22,940 INFO misc.py line 119 87073] Train: [86/100][1551/1557] Data 0.013 (0.120) Batch 1.094 (1.107) Remain 06:42:26 loss: 0.6735 Lr: 0.00027 [2024-02-19 13:20:23,701 INFO misc.py line 119 87073] Train: [86/100][1552/1557] Data 0.010 (0.120) Batch 0.767 (1.107) Remain 06:42:20 loss: 0.2057 Lr: 0.00026 [2024-02-19 13:20:24,435 INFO misc.py line 119 87073] Train: [86/100][1553/1557] Data 0.004 (0.120) Batch 0.723 (1.107) Remain 06:42:14 loss: 0.1475 Lr: 0.00026 [2024-02-19 13:20:25,716 INFO misc.py line 119 87073] Train: [86/100][1554/1557] Data 0.015 (0.120) Batch 1.283 (1.107) Remain 06:42:15 loss: 0.0981 Lr: 0.00026 [2024-02-19 13:20:26,664 INFO misc.py line 119 87073] Train: [86/100][1555/1557] Data 0.013 (0.120) Batch 0.957 (1.107) Remain 06:42:12 loss: 0.3928 Lr: 0.00026 [2024-02-19 13:20:27,671 INFO misc.py line 119 87073] Train: [86/100][1556/1557] Data 0.004 (0.120) Batch 1.007 (1.107) Remain 06:42:09 loss: 0.1423 Lr: 0.00026 [2024-02-19 13:20:28,570 INFO misc.py line 119 87073] Train: [86/100][1557/1557] Data 0.004 (0.120) Batch 0.898 (1.107) Remain 06:42:05 loss: 0.2131 Lr: 0.00026 [2024-02-19 13:20:28,570 INFO misc.py line 136 87073] Train result: loss: 0.2167 [2024-02-19 13:20:28,570 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 13:20:57,752 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5382 [2024-02-19 13:20:58,530 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.3266 [2024-02-19 13:21:00,658 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3214 [2024-02-19 13:21:02,865 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.2623 [2024-02-19 13:21:07,811 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2286 [2024-02-19 13:21:08,510 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0815 [2024-02-19 13:21:09,770 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2834 [2024-02-19 13:21:12,722 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2233 [2024-02-19 13:21:14,538 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.3215 [2024-02-19 13:21:16,266 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7298/0.7878/0.9180. [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9350/0.9674 [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9889 [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8669/0.9746 [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4324/0.4957 [2024-02-19 13:21:16,266 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6570/0.6720 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8496/0.9242 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8513/0.9162 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9315/0.9775 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8383/0.9009 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7881/0.8786 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7361/0.8347 [2024-02-19 13:21:16,267 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6184/0.7103 [2024-02-19 13:21:16,267 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 13:21:16,268 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 13:21:16,268 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 13:21:23,855 INFO misc.py line 119 87073] Train: [87/100][1/1557] Data 1.388 (1.388) Batch 2.070 (2.070) Remain 12:32:06 loss: 0.2118 Lr: 0.00026 [2024-02-19 13:21:24,721 INFO misc.py line 119 87073] Train: [87/100][2/1557] Data 0.004 (0.004) Batch 0.865 (0.865) Remain 05:14:20 loss: 0.1596 Lr: 0.00026 [2024-02-19 13:21:25,506 INFO misc.py line 119 87073] Train: [87/100][3/1557] Data 0.006 (0.006) Batch 0.785 (0.785) Remain 04:45:16 loss: 0.4043 Lr: 0.00026 [2024-02-19 13:21:26,484 INFO misc.py line 119 87073] Train: [87/100][4/1557] Data 0.005 (0.005) Batch 0.976 (0.976) Remain 05:54:38 loss: 0.1106 Lr: 0.00026 [2024-02-19 13:21:27,309 INFO misc.py line 119 87073] Train: [87/100][5/1557] Data 0.006 (0.006) Batch 0.825 (0.901) Remain 05:27:12 loss: 0.2034 Lr: 0.00026 [2024-02-19 13:21:28,096 INFO misc.py line 119 87073] Train: [87/100][6/1557] Data 0.006 (0.006) Batch 0.788 (0.863) Remain 05:13:29 loss: 0.1001 Lr: 0.00026 [2024-02-19 13:21:29,287 INFO misc.py line 119 87073] Train: [87/100][7/1557] Data 0.005 (0.006) Batch 1.188 (0.944) Remain 05:42:56 loss: 0.1277 Lr: 0.00026 [2024-02-19 13:21:30,188 INFO misc.py line 119 87073] Train: [87/100][8/1557] Data 0.008 (0.006) Batch 0.905 (0.936) Remain 05:40:06 loss: 0.2984 Lr: 0.00026 [2024-02-19 13:21:31,284 INFO misc.py line 119 87073] Train: [87/100][9/1557] Data 0.005 (0.006) Batch 1.096 (0.963) Remain 05:49:45 loss: 0.0747 Lr: 0.00026 [2024-02-19 13:21:32,087 INFO misc.py line 119 87073] Train: [87/100][10/1557] Data 0.004 (0.006) Batch 0.803 (0.940) Remain 05:41:27 loss: 0.3156 Lr: 0.00026 [2024-02-19 13:21:32,932 INFO misc.py line 119 87073] Train: [87/100][11/1557] Data 0.004 (0.005) Batch 0.836 (0.927) Remain 05:36:43 loss: 0.0808 Lr: 0.00026 [2024-02-19 13:21:33,714 INFO misc.py line 119 87073] Train: [87/100][12/1557] Data 0.012 (0.006) Batch 0.790 (0.912) Remain 05:31:11 loss: 0.1780 Lr: 0.00026 [2024-02-19 13:21:34,446 INFO misc.py line 119 87073] Train: [87/100][13/1557] Data 0.004 (0.006) Batch 0.724 (0.893) Remain 05:24:21 loss: 0.1839 Lr: 0.00026 [2024-02-19 13:21:38,538 INFO misc.py line 119 87073] Train: [87/100][14/1557] Data 0.011 (0.006) Batch 4.099 (1.185) Remain 07:10:09 loss: 0.0758 Lr: 0.00026 [2024-02-19 13:21:39,715 INFO misc.py line 119 87073] Train: [87/100][15/1557] Data 0.004 (0.006) Batch 1.173 (1.184) Remain 07:09:46 loss: 0.0531 Lr: 0.00026 [2024-02-19 13:21:40,725 INFO misc.py line 119 87073] Train: [87/100][16/1557] Data 0.008 (0.006) Batch 1.008 (1.170) Remain 07:04:51 loss: 0.2585 Lr: 0.00026 [2024-02-19 13:21:41,731 INFO misc.py line 119 87073] Train: [87/100][17/1557] Data 0.009 (0.006) Batch 1.011 (1.159) Remain 07:00:41 loss: 0.2161 Lr: 0.00026 [2024-02-19 13:21:42,670 INFO misc.py line 119 87073] Train: [87/100][18/1557] Data 0.005 (0.006) Batch 0.940 (1.144) Remain 06:55:23 loss: 0.0542 Lr: 0.00026 [2024-02-19 13:21:43,461 INFO misc.py line 119 87073] Train: [87/100][19/1557] Data 0.004 (0.006) Batch 0.791 (1.122) Remain 06:47:20 loss: 0.1838 Lr: 0.00026 [2024-02-19 13:21:44,243 INFO misc.py line 119 87073] Train: [87/100][20/1557] Data 0.005 (0.006) Batch 0.781 (1.102) Remain 06:40:01 loss: 0.2434 Lr: 0.00026 [2024-02-19 13:21:45,607 INFO misc.py line 119 87073] Train: [87/100][21/1557] Data 0.005 (0.006) Batch 1.359 (1.116) Remain 06:45:11 loss: 0.1177 Lr: 0.00026 [2024-02-19 13:21:46,633 INFO misc.py line 119 87073] Train: [87/100][22/1557] Data 0.011 (0.006) Batch 1.020 (1.111) Remain 06:43:18 loss: 0.1033 Lr: 0.00026 [2024-02-19 13:21:47,750 INFO misc.py line 119 87073] Train: [87/100][23/1557] Data 0.017 (0.007) Batch 1.121 (1.112) Remain 06:43:28 loss: 0.4137 Lr: 0.00026 [2024-02-19 13:21:48,506 INFO misc.py line 119 87073] Train: [87/100][24/1557] Data 0.014 (0.007) Batch 0.764 (1.095) Remain 06:37:27 loss: 0.3461 Lr: 0.00026 [2024-02-19 13:21:49,402 INFO misc.py line 119 87073] Train: [87/100][25/1557] Data 0.005 (0.007) Batch 0.897 (1.086) Remain 06:34:10 loss: 0.3767 Lr: 0.00026 [2024-02-19 13:21:50,158 INFO misc.py line 119 87073] Train: [87/100][26/1557] Data 0.005 (0.007) Batch 0.753 (1.072) Remain 06:28:53 loss: 0.0844 Lr: 0.00026 [2024-02-19 13:21:50,889 INFO misc.py line 119 87073] Train: [87/100][27/1557] Data 0.007 (0.007) Batch 0.734 (1.058) Remain 06:23:45 loss: 0.2096 Lr: 0.00026 [2024-02-19 13:21:52,141 INFO misc.py line 119 87073] Train: [87/100][28/1557] Data 0.004 (0.007) Batch 1.252 (1.065) Remain 06:26:34 loss: 0.0986 Lr: 0.00026 [2024-02-19 13:21:53,023 INFO misc.py line 119 87073] Train: [87/100][29/1557] Data 0.004 (0.007) Batch 0.881 (1.058) Remain 06:23:58 loss: 0.2156 Lr: 0.00026 [2024-02-19 13:21:53,899 INFO misc.py line 119 87073] Train: [87/100][30/1557] Data 0.006 (0.007) Batch 0.875 (1.052) Remain 06:21:29 loss: 0.1345 Lr: 0.00026 [2024-02-19 13:21:54,796 INFO misc.py line 119 87073] Train: [87/100][31/1557] Data 0.007 (0.007) Batch 0.900 (1.046) Remain 06:19:30 loss: 0.0923 Lr: 0.00026 [2024-02-19 13:21:55,649 INFO misc.py line 119 87073] Train: [87/100][32/1557] Data 0.005 (0.007) Batch 0.853 (1.039) Remain 06:17:04 loss: 0.2725 Lr: 0.00026 [2024-02-19 13:21:56,456 INFO misc.py line 119 87073] Train: [87/100][33/1557] Data 0.004 (0.007) Batch 0.799 (1.031) Remain 06:14:08 loss: 0.2024 Lr: 0.00026 [2024-02-19 13:21:57,210 INFO misc.py line 119 87073] Train: [87/100][34/1557] Data 0.013 (0.007) Batch 0.761 (1.023) Remain 06:10:58 loss: 0.1146 Lr: 0.00026 [2024-02-19 13:21:58,300 INFO misc.py line 119 87073] Train: [87/100][35/1557] Data 0.005 (0.007) Batch 1.090 (1.025) Remain 06:11:42 loss: 0.1330 Lr: 0.00026 [2024-02-19 13:21:59,179 INFO misc.py line 119 87073] Train: [87/100][36/1557] Data 0.005 (0.007) Batch 0.879 (1.020) Remain 06:10:05 loss: 0.0778 Lr: 0.00026 [2024-02-19 13:22:00,143 INFO misc.py line 119 87073] Train: [87/100][37/1557] Data 0.004 (0.007) Batch 0.958 (1.019) Remain 06:09:24 loss: 0.3467 Lr: 0.00026 [2024-02-19 13:22:01,244 INFO misc.py line 119 87073] Train: [87/100][38/1557] Data 0.011 (0.007) Batch 1.102 (1.021) Remain 06:10:15 loss: 0.2202 Lr: 0.00026 [2024-02-19 13:22:02,162 INFO misc.py line 119 87073] Train: [87/100][39/1557] Data 0.010 (0.007) Batch 0.922 (1.018) Remain 06:09:14 loss: 0.2279 Lr: 0.00026 [2024-02-19 13:22:02,934 INFO misc.py line 119 87073] 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Batch 0.887 (1.146) Remain 06:51:55 loss: 0.1367 Lr: 0.00026 [2024-02-19 13:25:49,988 INFO misc.py line 119 87073] Train: [87/100][234/1557] Data 0.007 (0.080) Batch 0.878 (1.145) Remain 06:51:29 loss: 0.1171 Lr: 0.00026 [2024-02-19 13:25:50,883 INFO misc.py line 119 87073] Train: [87/100][235/1557] Data 0.005 (0.080) Batch 0.896 (1.144) Remain 06:51:05 loss: 0.0787 Lr: 0.00026 [2024-02-19 13:25:51,629 INFO misc.py line 119 87073] Train: [87/100][236/1557] Data 0.003 (0.080) Batch 0.744 (1.142) Remain 06:50:27 loss: 0.1939 Lr: 0.00026 [2024-02-19 13:25:52,327 INFO misc.py line 119 87073] Train: [87/100][237/1557] Data 0.005 (0.079) Batch 0.700 (1.140) Remain 06:49:45 loss: 0.1261 Lr: 0.00026 [2024-02-19 13:25:54,612 INFO misc.py line 119 87073] Train: [87/100][238/1557] Data 0.003 (0.079) Batch 2.284 (1.145) Remain 06:51:29 loss: 0.1127 Lr: 0.00026 [2024-02-19 13:25:55,832 INFO misc.py line 119 87073] Train: [87/100][239/1557] Data 0.004 (0.079) Batch 1.208 (1.145) Remain 06:51:33 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Batch 1.009 (1.143) Remain 06:49:38 loss: 0.1053 Lr: 0.00026 [2024-02-19 13:26:53,530 INFO misc.py line 119 87073] Train: [87/100][290/1557] Data 0.010 (0.080) Batch 1.205 (1.143) Remain 06:49:41 loss: 0.3408 Lr: 0.00026 [2024-02-19 13:26:54,685 INFO misc.py line 119 87073] Train: [87/100][291/1557] Data 0.014 (0.080) Batch 1.154 (1.143) Remain 06:49:41 loss: 0.2374 Lr: 0.00026 [2024-02-19 13:26:55,478 INFO misc.py line 119 87073] Train: [87/100][292/1557] Data 0.015 (0.080) Batch 0.803 (1.142) Remain 06:49:14 loss: 0.1605 Lr: 0.00026 [2024-02-19 13:26:56,262 INFO misc.py line 119 87073] Train: [87/100][293/1557] Data 0.004 (0.080) Batch 0.784 (1.141) Remain 06:48:47 loss: 0.2420 Lr: 0.00026 [2024-02-19 13:26:58,057 INFO misc.py line 119 87073] Train: [87/100][294/1557] Data 0.004 (0.079) Batch 1.795 (1.143) Remain 06:49:34 loss: 0.1340 Lr: 0.00026 [2024-02-19 13:26:58,978 INFO misc.py line 119 87073] Train: [87/100][295/1557] Data 0.004 (0.079) Batch 0.921 (1.142) Remain 06:49:16 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Remain 06:34:20 loss: 0.1527 Lr: 0.00024 [2024-02-19 13:45:10,068 INFO misc.py line 119 87073] Train: [87/100][1241/1557] Data 0.003 (0.082) Batch 0.903 (1.151) Remain 06:34:14 loss: 0.1794 Lr: 0.00024 [2024-02-19 13:45:10,859 INFO misc.py line 119 87073] Train: [87/100][1242/1557] Data 0.005 (0.082) Batch 0.791 (1.150) Remain 06:34:07 loss: 0.1978 Lr: 0.00024 [2024-02-19 13:45:11,761 INFO misc.py line 119 87073] Train: [87/100][1243/1557] Data 0.005 (0.082) Batch 0.902 (1.150) Remain 06:34:02 loss: 0.3145 Lr: 0.00024 [2024-02-19 13:45:12,565 INFO misc.py line 119 87073] Train: [87/100][1244/1557] Data 0.004 (0.082) Batch 0.805 (1.150) Remain 06:33:55 loss: 0.1314 Lr: 0.00024 [2024-02-19 13:45:13,330 INFO misc.py line 119 87073] Train: [87/100][1245/1557] Data 0.004 (0.082) Batch 0.763 (1.150) Remain 06:33:48 loss: 0.2223 Lr: 0.00024 [2024-02-19 13:45:16,842 INFO misc.py line 119 87073] Train: [87/100][1246/1557] Data 0.005 (0.082) Batch 3.513 (1.152) Remain 06:34:25 loss: 0.1242 Lr: 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Train: [87/100][1259/1557] Data 0.006 (0.081) Batch 0.746 (1.150) Remain 06:33:29 loss: 0.1585 Lr: 0.00024 [2024-02-19 13:45:30,393 INFO misc.py line 119 87073] Train: [87/100][1260/1557] Data 0.005 (0.081) Batch 1.104 (1.149) Remain 06:33:27 loss: 0.0894 Lr: 0.00024 [2024-02-19 13:45:31,229 INFO misc.py line 119 87073] Train: [87/100][1261/1557] Data 0.004 (0.081) Batch 0.836 (1.149) Remain 06:33:21 loss: 0.3446 Lr: 0.00024 [2024-02-19 13:45:32,289 INFO misc.py line 119 87073] Train: [87/100][1262/1557] Data 0.004 (0.081) Batch 1.060 (1.149) Remain 06:33:18 loss: 0.2261 Lr: 0.00024 [2024-02-19 13:45:33,485 INFO misc.py line 119 87073] Train: [87/100][1263/1557] Data 0.005 (0.081) Batch 1.188 (1.149) Remain 06:33:18 loss: 0.5862 Lr: 0.00024 [2024-02-19 13:45:34,532 INFO misc.py line 119 87073] Train: [87/100][1264/1557] Data 0.013 (0.081) Batch 1.047 (1.149) Remain 06:33:15 loss: 0.3794 Lr: 0.00024 [2024-02-19 13:45:35,299 INFO misc.py line 119 87073] Train: [87/100][1265/1557] Data 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Remain 06:32:46 loss: 0.1018 Lr: 0.00024 [2024-02-19 13:45:42,047 INFO misc.py line 119 87073] Train: [87/100][1272/1557] Data 0.004 (0.081) Batch 0.764 (1.148) Remain 06:32:39 loss: 0.1579 Lr: 0.00024 [2024-02-19 13:45:42,709 INFO misc.py line 119 87073] Train: [87/100][1273/1557] Data 0.005 (0.081) Batch 0.662 (1.147) Remain 06:32:30 loss: 0.2490 Lr: 0.00024 [2024-02-19 13:45:43,907 INFO misc.py line 119 87073] Train: [87/100][1274/1557] Data 0.005 (0.080) Batch 1.198 (1.147) Remain 06:32:30 loss: 0.1675 Lr: 0.00024 [2024-02-19 13:45:44,832 INFO misc.py line 119 87073] Train: [87/100][1275/1557] Data 0.005 (0.080) Batch 0.926 (1.147) Remain 06:32:25 loss: 0.3194 Lr: 0.00024 [2024-02-19 13:45:45,678 INFO misc.py line 119 87073] Train: [87/100][1276/1557] Data 0.004 (0.080) Batch 0.847 (1.147) Remain 06:32:19 loss: 0.2003 Lr: 0.00024 [2024-02-19 13:45:46,557 INFO misc.py line 119 87073] Train: [87/100][1277/1557] Data 0.004 (0.080) Batch 0.877 (1.147) Remain 06:32:13 loss: 0.2590 Lr: 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Train: [87/100][1290/1557] Data 0.004 (0.080) Batch 0.963 (1.145) Remain 06:31:13 loss: 0.1129 Lr: 0.00024 [2024-02-19 13:45:59,545 INFO misc.py line 119 87073] Train: [87/100][1291/1557] Data 0.004 (0.079) Batch 0.929 (1.144) Remain 06:31:09 loss: 0.3863 Lr: 0.00024 [2024-02-19 13:46:00,399 INFO misc.py line 119 87073] Train: [87/100][1292/1557] Data 0.004 (0.079) Batch 0.852 (1.144) Remain 06:31:03 loss: 0.4438 Lr: 0.00024 [2024-02-19 13:46:01,162 INFO misc.py line 119 87073] Train: [87/100][1293/1557] Data 0.007 (0.079) Batch 0.765 (1.144) Remain 06:30:56 loss: 0.1639 Lr: 0.00023 [2024-02-19 13:46:01,971 INFO misc.py line 119 87073] Train: [87/100][1294/1557] Data 0.004 (0.079) Batch 0.809 (1.144) Remain 06:30:49 loss: 0.1747 Lr: 0.00023 [2024-02-19 13:46:14,337 INFO misc.py line 119 87073] Train: [87/100][1295/1557] Data 3.771 (0.082) Batch 12.365 (1.152) Remain 06:33:46 loss: 0.1070 Lr: 0.00023 [2024-02-19 13:46:15,328 INFO misc.py line 119 87073] Train: [87/100][1296/1557] Data 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Remain 06:33:18 loss: 0.0913 Lr: 0.00023 [2024-02-19 13:46:22,048 INFO misc.py line 119 87073] Train: [87/100][1303/1557] Data 0.017 (0.082) Batch 0.892 (1.151) Remain 06:33:13 loss: 0.2565 Lr: 0.00023 [2024-02-19 13:46:23,056 INFO misc.py line 119 87073] Train: [87/100][1304/1557] Data 0.003 (0.082) Batch 1.007 (1.151) Remain 06:33:10 loss: 0.1443 Lr: 0.00023 [2024-02-19 13:46:24,059 INFO misc.py line 119 87073] Train: [87/100][1305/1557] Data 0.004 (0.082) Batch 1.003 (1.151) Remain 06:33:06 loss: 0.2487 Lr: 0.00023 [2024-02-19 13:46:24,933 INFO misc.py line 119 87073] Train: [87/100][1306/1557] Data 0.004 (0.082) Batch 0.872 (1.151) Remain 06:33:01 loss: 0.2236 Lr: 0.00023 [2024-02-19 13:46:25,703 INFO misc.py line 119 87073] Train: [87/100][1307/1557] Data 0.006 (0.081) Batch 0.762 (1.150) Remain 06:32:53 loss: 0.2248 Lr: 0.00023 [2024-02-19 13:46:26,517 INFO misc.py line 119 87073] Train: [87/100][1308/1557] Data 0.014 (0.081) Batch 0.824 (1.150) Remain 06:32:47 loss: 0.1065 Lr: 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INFO misc.py line 119 87073] Train: [87/100][1315/1557] Data 0.004 (0.081) Batch 0.765 (1.149) Remain 06:32:17 loss: 0.0976 Lr: 0.00023 [2024-02-19 13:46:34,346 INFO misc.py line 119 87073] Train: [87/100][1316/1557] Data 0.011 (0.081) Batch 1.157 (1.149) Remain 06:32:16 loss: 0.1442 Lr: 0.00023 [2024-02-19 13:46:35,270 INFO misc.py line 119 87073] Train: [87/100][1317/1557] Data 0.007 (0.081) Batch 0.928 (1.149) Remain 06:32:12 loss: 0.1931 Lr: 0.00023 [2024-02-19 13:46:36,265 INFO misc.py line 119 87073] Train: [87/100][1318/1557] Data 0.004 (0.081) Batch 0.995 (1.149) Remain 06:32:08 loss: 0.3448 Lr: 0.00023 [2024-02-19 13:46:37,207 INFO misc.py line 119 87073] Train: [87/100][1319/1557] Data 0.003 (0.081) Batch 0.940 (1.149) Remain 06:32:04 loss: 0.2485 Lr: 0.00023 [2024-02-19 13:46:38,156 INFO misc.py line 119 87073] Train: [87/100][1320/1557] Data 0.005 (0.081) Batch 0.948 (1.149) Remain 06:32:00 loss: 0.1450 Lr: 0.00023 [2024-02-19 13:46:38,937 INFO misc.py line 119 87073] Train: [87/100][1321/1557] Data 0.006 (0.081) Batch 0.775 (1.148) Remain 06:31:53 loss: 0.2256 Lr: 0.00023 [2024-02-19 13:46:39,750 INFO misc.py line 119 87073] Train: [87/100][1322/1557] Data 0.012 (0.081) Batch 0.821 (1.148) Remain 06:31:46 loss: 0.1708 Lr: 0.00023 [2024-02-19 13:46:40,832 INFO misc.py line 119 87073] Train: [87/100][1323/1557] Data 0.004 (0.081) Batch 1.081 (1.148) Remain 06:31:44 loss: 0.1232 Lr: 0.00023 [2024-02-19 13:46:41,574 INFO misc.py line 119 87073] Train: [87/100][1324/1557] Data 0.004 (0.081) Batch 0.741 (1.148) Remain 06:31:37 loss: 0.1765 Lr: 0.00023 [2024-02-19 13:46:42,461 INFO misc.py line 119 87073] Train: [87/100][1325/1557] Data 0.007 (0.080) Batch 0.887 (1.147) Remain 06:31:32 loss: 0.3019 Lr: 0.00023 [2024-02-19 13:46:43,411 INFO misc.py line 119 87073] Train: [87/100][1326/1557] Data 0.006 (0.080) Batch 0.951 (1.147) Remain 06:31:27 loss: 0.1471 Lr: 0.00023 [2024-02-19 13:46:44,291 INFO misc.py line 119 87073] Train: [87/100][1327/1557] Data 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Remain 06:30:51 loss: 0.5558 Lr: 0.00023 [2024-02-19 13:46:50,495 INFO misc.py line 119 87073] Train: [87/100][1334/1557] Data 0.005 (0.080) Batch 0.898 (1.146) Remain 06:30:46 loss: 0.2289 Lr: 0.00023 [2024-02-19 13:46:51,306 INFO misc.py line 119 87073] Train: [87/100][1335/1557] Data 0.006 (0.080) Batch 0.801 (1.145) Remain 06:30:40 loss: 0.1714 Lr: 0.00023 [2024-02-19 13:46:52,082 INFO misc.py line 119 87073] Train: [87/100][1336/1557] Data 0.015 (0.080) Batch 0.786 (1.145) Remain 06:30:33 loss: 0.2664 Lr: 0.00023 [2024-02-19 13:46:53,440 INFO misc.py line 119 87073] Train: [87/100][1337/1557] Data 0.004 (0.080) Batch 1.349 (1.145) Remain 06:30:35 loss: 0.1270 Lr: 0.00023 [2024-02-19 13:46:54,287 INFO misc.py line 119 87073] Train: [87/100][1338/1557] Data 0.014 (0.080) Batch 0.857 (1.145) Remain 06:30:29 loss: 0.5574 Lr: 0.00023 [2024-02-19 13:46:55,206 INFO misc.py line 119 87073] Train: [87/100][1339/1557] Data 0.004 (0.080) Batch 0.918 (1.145) Remain 06:30:25 loss: 0.1687 Lr: 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Remain 06:31:12 loss: 0.1100 Lr: 0.00023 [2024-02-19 13:47:30,131 INFO misc.py line 119 87073] Train: [87/100][1365/1557] Data 0.017 (0.081) Batch 1.232 (1.149) Remain 06:31:12 loss: 0.1064 Lr: 0.00023 [2024-02-19 13:47:31,151 INFO misc.py line 119 87073] Train: [87/100][1366/1557] Data 0.015 (0.081) Batch 1.022 (1.149) Remain 06:31:09 loss: 0.2988 Lr: 0.00023 [2024-02-19 13:47:32,127 INFO misc.py line 119 87073] Train: [87/100][1367/1557] Data 0.013 (0.081) Batch 0.985 (1.149) Remain 06:31:05 loss: 0.2310 Lr: 0.00023 [2024-02-19 13:47:33,044 INFO misc.py line 119 87073] Train: [87/100][1368/1557] Data 0.004 (0.081) Batch 0.917 (1.148) Remain 06:31:01 loss: 0.2951 Lr: 0.00023 [2024-02-19 13:47:33,940 INFO misc.py line 119 87073] Train: [87/100][1369/1557] Data 0.003 (0.081) Batch 0.893 (1.148) Remain 06:30:56 loss: 0.4741 Lr: 0.00023 [2024-02-19 13:47:34,688 INFO misc.py line 119 87073] Train: [87/100][1370/1557] Data 0.006 (0.081) Batch 0.749 (1.148) Remain 06:30:49 loss: 0.1127 Lr: 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Train: [87/100][1383/1557] Data 0.008 (0.080) Batch 0.851 (1.146) Remain 06:29:52 loss: 0.2944 Lr: 0.00023 [2024-02-19 13:47:47,468 INFO misc.py line 119 87073] Train: [87/100][1384/1557] Data 0.004 (0.080) Batch 0.693 (1.146) Remain 06:29:44 loss: 0.3014 Lr: 0.00023 [2024-02-19 13:47:48,190 INFO misc.py line 119 87073] Train: [87/100][1385/1557] Data 0.005 (0.080) Batch 0.713 (1.145) Remain 06:29:37 loss: 0.1470 Lr: 0.00023 [2024-02-19 13:47:49,364 INFO misc.py line 119 87073] Train: [87/100][1386/1557] Data 0.013 (0.080) Batch 1.178 (1.145) Remain 06:29:36 loss: 0.1056 Lr: 0.00023 [2024-02-19 13:47:50,504 INFO misc.py line 119 87073] Train: [87/100][1387/1557] Data 0.011 (0.080) Batch 1.091 (1.145) Remain 06:29:34 loss: 0.1272 Lr: 0.00023 [2024-02-19 13:47:51,534 INFO misc.py line 119 87073] Train: [87/100][1388/1557] Data 0.059 (0.080) Batch 1.085 (1.145) Remain 06:29:32 loss: 0.2952 Lr: 0.00023 [2024-02-19 13:47:52,430 INFO misc.py line 119 87073] Train: [87/100][1389/1557] Data 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Remain 06:28:59 loss: 0.1658 Lr: 0.00023 [2024-02-19 13:47:58,918 INFO misc.py line 119 87073] Train: [87/100][1396/1557] Data 0.006 (0.079) Batch 1.068 (1.144) Remain 06:28:57 loss: 0.3266 Lr: 0.00023 [2024-02-19 13:47:59,755 INFO misc.py line 119 87073] Train: [87/100][1397/1557] Data 0.005 (0.079) Batch 0.837 (1.144) Remain 06:28:51 loss: 0.2771 Lr: 0.00023 [2024-02-19 13:48:00,525 INFO misc.py line 119 87073] Train: [87/100][1398/1557] Data 0.004 (0.079) Batch 0.770 (1.143) Remain 06:28:44 loss: 0.1973 Lr: 0.00023 [2024-02-19 13:48:01,297 INFO misc.py line 119 87073] Train: [87/100][1399/1557] Data 0.004 (0.079) Batch 0.768 (1.143) Remain 06:28:38 loss: 0.2298 Lr: 0.00023 [2024-02-19 13:48:02,434 INFO misc.py line 119 87073] Train: [87/100][1400/1557] Data 0.007 (0.079) Batch 1.141 (1.143) Remain 06:28:37 loss: 0.0883 Lr: 0.00023 [2024-02-19 13:48:03,487 INFO misc.py line 119 87073] Train: [87/100][1401/1557] Data 0.005 (0.079) Batch 1.052 (1.143) Remain 06:28:34 loss: 0.4254 Lr: 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INFO misc.py line 119 87073] Train: [87/100][1408/1557] Data 0.005 (0.082) Batch 0.933 (1.150) Remain 06:30:51 loss: 0.0450 Lr: 0.00023 [2024-02-19 13:48:22,446 INFO misc.py line 119 87073] Train: [87/100][1409/1557] Data 0.006 (0.082) Batch 0.960 (1.150) Remain 06:30:47 loss: 0.0817 Lr: 0.00023 [2024-02-19 13:48:23,528 INFO misc.py line 119 87073] Train: [87/100][1410/1557] Data 0.005 (0.082) Batch 1.082 (1.150) Remain 06:30:45 loss: 0.2975 Lr: 0.00023 [2024-02-19 13:48:24,515 INFO misc.py line 119 87073] Train: [87/100][1411/1557] Data 0.005 (0.082) Batch 0.989 (1.150) Remain 06:30:42 loss: 0.0776 Lr: 0.00023 [2024-02-19 13:48:25,266 INFO misc.py line 119 87073] Train: [87/100][1412/1557] Data 0.004 (0.082) Batch 0.751 (1.150) Remain 06:30:35 loss: 0.1791 Lr: 0.00023 [2024-02-19 13:48:26,013 INFO misc.py line 119 87073] Train: [87/100][1413/1557] Data 0.004 (0.082) Batch 0.744 (1.149) Remain 06:30:28 loss: 0.1336 Lr: 0.00023 [2024-02-19 13:48:28,156 INFO misc.py line 119 87073] Train: [87/100][1414/1557] Data 0.006 (0.082) Batch 2.145 (1.150) Remain 06:30:41 loss: 0.1825 Lr: 0.00023 [2024-02-19 13:48:29,074 INFO misc.py line 119 87073] Train: [87/100][1415/1557] Data 0.004 (0.082) Batch 0.918 (1.150) Remain 06:30:37 loss: 0.5752 Lr: 0.00023 [2024-02-19 13:48:29,998 INFO misc.py line 119 87073] Train: [87/100][1416/1557] Data 0.004 (0.082) Batch 0.924 (1.150) Remain 06:30:32 loss: 0.1971 Lr: 0.00023 [2024-02-19 13:48:31,042 INFO misc.py line 119 87073] Train: [87/100][1417/1557] Data 0.004 (0.082) Batch 1.038 (1.150) Remain 06:30:29 loss: 0.1272 Lr: 0.00023 [2024-02-19 13:48:32,158 INFO misc.py line 119 87073] Train: [87/100][1418/1557] Data 0.010 (0.082) Batch 1.113 (1.150) Remain 06:30:28 loss: 0.1549 Lr: 0.00023 [2024-02-19 13:48:32,912 INFO misc.py line 119 87073] Train: [87/100][1419/1557] Data 0.013 (0.082) Batch 0.763 (1.149) Remain 06:30:21 loss: 0.2221 Lr: 0.00023 [2024-02-19 13:48:33,707 INFO misc.py line 119 87073] Train: [87/100][1420/1557] Data 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Remain 06:29:52 loss: 0.0706 Lr: 0.00023 [2024-02-19 13:48:40,284 INFO misc.py line 119 87073] Train: [87/100][1427/1557] Data 0.008 (0.081) Batch 0.765 (1.148) Remain 06:29:46 loss: 0.1214 Lr: 0.00023 [2024-02-19 13:48:41,497 INFO misc.py line 119 87073] Train: [87/100][1428/1557] Data 0.004 (0.081) Batch 1.213 (1.148) Remain 06:29:46 loss: 0.0850 Lr: 0.00023 [2024-02-19 13:48:42,380 INFO misc.py line 119 87073] Train: [87/100][1429/1557] Data 0.004 (0.081) Batch 0.882 (1.148) Remain 06:29:41 loss: 0.2344 Lr: 0.00023 [2024-02-19 13:48:43,302 INFO misc.py line 119 87073] Train: [87/100][1430/1557] Data 0.005 (0.081) Batch 0.923 (1.148) Remain 06:29:36 loss: 0.2923 Lr: 0.00023 [2024-02-19 13:48:44,351 INFO misc.py line 119 87073] Train: [87/100][1431/1557] Data 0.004 (0.081) Batch 1.041 (1.148) Remain 06:29:34 loss: 0.3141 Lr: 0.00023 [2024-02-19 13:48:45,332 INFO misc.py line 119 87073] Train: [87/100][1432/1557] Data 0.012 (0.081) Batch 0.988 (1.148) Remain 06:29:30 loss: 0.2530 Lr: 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Train: [87/100][1445/1557] Data 0.005 (0.080) Batch 0.845 (1.146) Remain 06:28:38 loss: 0.3786 Lr: 0.00023 [2024-02-19 13:48:58,558 INFO misc.py line 119 87073] Train: [87/100][1446/1557] Data 0.004 (0.080) Batch 0.937 (1.146) Remain 06:28:34 loss: 0.0934 Lr: 0.00023 [2024-02-19 13:48:59,282 INFO misc.py line 119 87073] Train: [87/100][1447/1557] Data 0.007 (0.080) Batch 0.727 (1.145) Remain 06:28:27 loss: 0.2055 Lr: 0.00023 [2024-02-19 13:49:00,029 INFO misc.py line 119 87073] Train: [87/100][1448/1557] Data 0.004 (0.080) Batch 0.746 (1.145) Remain 06:28:20 loss: 0.2191 Lr: 0.00023 [2024-02-19 13:49:01,374 INFO misc.py line 119 87073] Train: [87/100][1449/1557] Data 0.004 (0.080) Batch 1.334 (1.145) Remain 06:28:22 loss: 0.1854 Lr: 0.00023 [2024-02-19 13:49:02,421 INFO misc.py line 119 87073] Train: [87/100][1450/1557] Data 0.016 (0.080) Batch 1.050 (1.145) Remain 06:28:19 loss: 0.0870 Lr: 0.00023 [2024-02-19 13:49:03,445 INFO misc.py line 119 87073] Train: [87/100][1451/1557] Data 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Remain 06:27:52 loss: 0.1354 Lr: 0.00023 [2024-02-19 13:49:10,054 INFO misc.py line 119 87073] Train: [87/100][1458/1557] Data 0.004 (0.080) Batch 1.009 (1.144) Remain 06:27:49 loss: 0.2246 Lr: 0.00023 [2024-02-19 13:49:11,328 INFO misc.py line 119 87073] Train: [87/100][1459/1557] Data 0.004 (0.080) Batch 1.263 (1.144) Remain 06:27:49 loss: 0.3636 Lr: 0.00023 [2024-02-19 13:49:12,308 INFO misc.py line 119 87073] Train: [87/100][1460/1557] Data 0.015 (0.080) Batch 0.991 (1.144) Remain 06:27:46 loss: 0.3367 Lr: 0.00023 [2024-02-19 13:49:13,032 INFO misc.py line 119 87073] Train: [87/100][1461/1557] Data 0.004 (0.080) Batch 0.724 (1.144) Remain 06:27:39 loss: 0.2033 Lr: 0.00023 [2024-02-19 13:49:13,802 INFO misc.py line 119 87073] Train: [87/100][1462/1557] Data 0.004 (0.080) Batch 0.749 (1.143) Remain 06:27:32 loss: 0.1380 Lr: 0.00023 [2024-02-19 13:49:24,063 INFO misc.py line 119 87073] Train: [87/100][1463/1557] Data 4.253 (0.082) Batch 10.281 (1.150) Remain 06:29:39 loss: 0.1339 Lr: 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Train: [87/100][1476/1557] Data 0.003 (0.082) Batch 0.748 (1.149) Remain 06:29:03 loss: 0.1386 Lr: 0.00023 [2024-02-19 13:49:38,699 INFO misc.py line 119 87073] Train: [87/100][1477/1557] Data 0.003 (0.082) Batch 1.188 (1.149) Remain 06:29:02 loss: 0.1108 Lr: 0.00023 [2024-02-19 13:49:39,636 INFO misc.py line 119 87073] Train: [87/100][1478/1557] Data 0.007 (0.082) Batch 0.940 (1.149) Remain 06:28:58 loss: 0.5182 Lr: 0.00023 [2024-02-19 13:49:40,559 INFO misc.py line 119 87073] Train: [87/100][1479/1557] Data 0.004 (0.082) Batch 0.923 (1.148) Remain 06:28:54 loss: 0.3920 Lr: 0.00023 [2024-02-19 13:49:41,489 INFO misc.py line 119 87073] Train: [87/100][1480/1557] Data 0.003 (0.082) Batch 0.930 (1.148) Remain 06:28:50 loss: 0.2873 Lr: 0.00023 [2024-02-19 13:49:42,391 INFO misc.py line 119 87073] Train: [87/100][1481/1557] Data 0.004 (0.081) Batch 0.894 (1.148) Remain 06:28:45 loss: 0.2158 Lr: 0.00023 [2024-02-19 13:49:43,158 INFO misc.py line 119 87073] Train: [87/100][1482/1557] Data 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Remain 06:28:19 loss: 0.4523 Lr: 0.00023 [2024-02-19 13:49:49,788 INFO misc.py line 119 87073] Train: [87/100][1489/1557] Data 0.003 (0.081) Batch 0.730 (1.147) Remain 06:28:12 loss: 0.3783 Lr: 0.00023 [2024-02-19 13:49:50,551 INFO misc.py line 119 87073] Train: [87/100][1490/1557] Data 0.006 (0.081) Batch 0.765 (1.147) Remain 06:28:05 loss: 0.1104 Lr: 0.00023 [2024-02-19 13:49:51,640 INFO misc.py line 119 87073] Train: [87/100][1491/1557] Data 0.003 (0.081) Batch 1.088 (1.147) Remain 06:28:03 loss: 0.1376 Lr: 0.00023 [2024-02-19 13:49:52,539 INFO misc.py line 119 87073] Train: [87/100][1492/1557] Data 0.005 (0.081) Batch 0.900 (1.146) Remain 06:27:59 loss: 0.2310 Lr: 0.00023 [2024-02-19 13:49:53,385 INFO misc.py line 119 87073] Train: [87/100][1493/1557] Data 0.004 (0.081) Batch 0.845 (1.146) Remain 06:27:54 loss: 0.4529 Lr: 0.00023 [2024-02-19 13:49:54,286 INFO misc.py line 119 87073] Train: [87/100][1494/1557] Data 0.005 (0.081) Batch 0.903 (1.146) Remain 06:27:49 loss: 0.1932 Lr: 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INFO misc.py line 119 87073] Train: [87/100][1501/1557] Data 0.006 (0.080) Batch 0.975 (1.145) Remain 06:27:18 loss: 0.2838 Lr: 0.00023 [2024-02-19 13:50:01,682 INFO misc.py line 119 87073] Train: [87/100][1502/1557] Data 0.005 (0.080) Batch 1.099 (1.145) Remain 06:27:16 loss: 0.1288 Lr: 0.00023 [2024-02-19 13:50:02,453 INFO misc.py line 119 87073] Train: [87/100][1503/1557] Data 0.006 (0.080) Batch 0.772 (1.145) Remain 06:27:10 loss: 0.1784 Lr: 0.00023 [2024-02-19 13:50:03,175 INFO misc.py line 119 87073] Train: [87/100][1504/1557] Data 0.005 (0.080) Batch 0.715 (1.144) Remain 06:27:03 loss: 0.1746 Lr: 0.00023 [2024-02-19 13:50:04,484 INFO misc.py line 119 87073] Train: [87/100][1505/1557] Data 0.011 (0.080) Batch 1.301 (1.144) Remain 06:27:04 loss: 0.1382 Lr: 0.00023 [2024-02-19 13:50:05,485 INFO misc.py line 119 87073] Train: [87/100][1506/1557] Data 0.019 (0.080) Batch 1.002 (1.144) Remain 06:27:01 loss: 0.2680 Lr: 0.00023 [2024-02-19 13:50:06,619 INFO misc.py line 119 87073] Train: [87/100][1507/1557] Data 0.018 (0.080) Batch 1.135 (1.144) Remain 06:26:59 loss: 0.2585 Lr: 0.00023 [2024-02-19 13:50:07,676 INFO misc.py line 119 87073] Train: [87/100][1508/1557] Data 0.017 (0.080) Batch 1.062 (1.144) Remain 06:26:57 loss: 0.1197 Lr: 0.00023 [2024-02-19 13:50:08,568 INFO misc.py line 119 87073] Train: [87/100][1509/1557] Data 0.012 (0.080) Batch 0.900 (1.144) Remain 06:26:53 loss: 0.0713 Lr: 0.00023 [2024-02-19 13:50:09,336 INFO misc.py line 119 87073] Train: [87/100][1510/1557] Data 0.005 (0.080) Batch 0.769 (1.144) Remain 06:26:47 loss: 0.2266 Lr: 0.00023 [2024-02-19 13:50:10,143 INFO misc.py line 119 87073] Train: [87/100][1511/1557] Data 0.004 (0.080) Batch 0.797 (1.144) Remain 06:26:41 loss: 0.3262 Lr: 0.00023 [2024-02-19 13:50:11,307 INFO misc.py line 119 87073] Train: [87/100][1512/1557] Data 0.013 (0.080) Batch 1.161 (1.144) Remain 06:26:40 loss: 0.0939 Lr: 0.00023 [2024-02-19 13:50:12,168 INFO misc.py line 119 87073] Train: [87/100][1513/1557] Data 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Remain 06:28:20 loss: 0.1400 Lr: 0.00023 [2024-02-19 13:50:28,264 INFO misc.py line 119 87073] Train: [87/100][1520/1557] Data 0.004 (0.082) Batch 0.871 (1.149) Remain 06:28:15 loss: 0.2897 Lr: 0.00023 [2024-02-19 13:50:29,362 INFO misc.py line 119 87073] Train: [87/100][1521/1557] Data 0.004 (0.082) Batch 1.092 (1.149) Remain 06:28:13 loss: 0.2304 Lr: 0.00023 [2024-02-19 13:50:30,363 INFO misc.py line 119 87073] Train: [87/100][1522/1557] Data 0.011 (0.082) Batch 1.001 (1.149) Remain 06:28:10 loss: 0.3764 Lr: 0.00023 [2024-02-19 13:50:31,377 INFO misc.py line 119 87073] Train: [87/100][1523/1557] Data 0.011 (0.082) Batch 1.016 (1.149) Remain 06:28:07 loss: 0.3385 Lr: 0.00023 [2024-02-19 13:50:32,134 INFO misc.py line 119 87073] Train: [87/100][1524/1557] Data 0.009 (0.082) Batch 0.762 (1.148) Remain 06:28:01 loss: 0.1515 Lr: 0.00023 [2024-02-19 13:50:32,901 INFO misc.py line 119 87073] Train: [87/100][1525/1557] Data 0.004 (0.082) Batch 0.763 (1.148) Remain 06:27:55 loss: 0.1809 Lr: 0.00023 [2024-02-19 13:50:35,371 INFO misc.py line 119 87073] Train: [87/100][1526/1557] Data 0.008 (0.082) Batch 2.474 (1.149) Remain 06:28:11 loss: 0.1231 Lr: 0.00023 [2024-02-19 13:50:36,345 INFO misc.py line 119 87073] Train: [87/100][1527/1557] Data 0.005 (0.081) Batch 0.975 (1.149) Remain 06:28:08 loss: 0.3034 Lr: 0.00023 [2024-02-19 13:50:37,351 INFO misc.py line 119 87073] Train: [87/100][1528/1557] Data 0.003 (0.081) Batch 1.006 (1.149) Remain 06:28:05 loss: 0.2779 Lr: 0.00023 [2024-02-19 13:50:38,244 INFO misc.py line 119 87073] Train: [87/100][1529/1557] Data 0.003 (0.081) Batch 0.892 (1.149) Remain 06:28:00 loss: 0.2282 Lr: 0.00023 [2024-02-19 13:50:39,245 INFO misc.py line 119 87073] Train: [87/100][1530/1557] Data 0.005 (0.081) Batch 0.997 (1.148) Remain 06:27:57 loss: 0.1901 Lr: 0.00023 [2024-02-19 13:50:39,966 INFO misc.py line 119 87073] Train: [87/100][1531/1557] Data 0.009 (0.081) Batch 0.725 (1.148) Remain 06:27:50 loss: 0.3458 Lr: 0.00023 [2024-02-19 13:50:40,743 INFO misc.py line 119 87073] Train: [87/100][1532/1557] Data 0.004 (0.081) Batch 0.775 (1.148) Remain 06:27:44 loss: 0.1683 Lr: 0.00023 [2024-02-19 13:50:41,947 INFO misc.py line 119 87073] Train: [87/100][1533/1557] Data 0.007 (0.081) Batch 1.193 (1.148) Remain 06:27:44 loss: 0.0942 Lr: 0.00023 [2024-02-19 13:50:42,912 INFO misc.py line 119 87073] Train: [87/100][1534/1557] Data 0.017 (0.081) Batch 0.979 (1.148) Remain 06:27:40 loss: 0.0785 Lr: 0.00023 [2024-02-19 13:50:43,814 INFO misc.py line 119 87073] Train: [87/100][1535/1557] Data 0.003 (0.081) Batch 0.900 (1.148) Remain 06:27:36 loss: 0.2337 Lr: 0.00023 [2024-02-19 13:50:45,037 INFO misc.py line 119 87073] Train: [87/100][1536/1557] Data 0.005 (0.081) Batch 1.226 (1.148) Remain 06:27:36 loss: 0.2155 Lr: 0.00023 [2024-02-19 13:50:46,021 INFO misc.py line 119 87073] Train: [87/100][1537/1557] Data 0.004 (0.081) Batch 0.984 (1.148) Remain 06:27:32 loss: 0.3273 Lr: 0.00023 [2024-02-19 13:50:46,767 INFO misc.py line 119 87073] Train: [87/100][1538/1557] Data 0.004 (0.081) Batch 0.745 (1.147) Remain 06:27:26 loss: 0.2559 Lr: 0.00023 [2024-02-19 13:50:47,520 INFO misc.py line 119 87073] Train: [87/100][1539/1557] Data 0.004 (0.081) Batch 0.753 (1.147) Remain 06:27:19 loss: 0.1053 Lr: 0.00023 [2024-02-19 13:50:48,758 INFO misc.py line 119 87073] Train: [87/100][1540/1557] Data 0.005 (0.081) Batch 1.238 (1.147) Remain 06:27:20 loss: 0.0826 Lr: 0.00023 [2024-02-19 13:50:49,631 INFO misc.py line 119 87073] Train: [87/100][1541/1557] Data 0.005 (0.081) Batch 0.874 (1.147) Remain 06:27:15 loss: 0.2371 Lr: 0.00023 [2024-02-19 13:50:50,835 INFO misc.py line 119 87073] Train: [87/100][1542/1557] Data 0.004 (0.081) Batch 1.196 (1.147) Remain 06:27:14 loss: 0.1832 Lr: 0.00023 [2024-02-19 13:50:51,813 INFO misc.py line 119 87073] Train: [87/100][1543/1557] Data 0.012 (0.081) Batch 0.986 (1.147) Remain 06:27:11 loss: 0.0915 Lr: 0.00023 [2024-02-19 13:50:52,698 INFO misc.py line 119 87073] Train: [87/100][1544/1557] Data 0.004 (0.081) Batch 0.885 (1.147) Remain 06:27:06 loss: 0.2243 Lr: 0.00023 [2024-02-19 13:50:53,481 INFO misc.py line 119 87073] Train: [87/100][1545/1557] Data 0.004 (0.081) Batch 0.780 (1.147) Remain 06:27:00 loss: 0.2263 Lr: 0.00023 [2024-02-19 13:50:54,213 INFO misc.py line 119 87073] Train: [87/100][1546/1557] Data 0.007 (0.081) Batch 0.734 (1.146) Remain 06:26:54 loss: 0.1341 Lr: 0.00023 [2024-02-19 13:50:55,276 INFO misc.py line 119 87073] Train: [87/100][1547/1557] Data 0.004 (0.080) Batch 1.063 (1.146) Remain 06:26:52 loss: 0.1739 Lr: 0.00023 [2024-02-19 13:50:56,131 INFO misc.py line 119 87073] Train: [87/100][1548/1557] Data 0.004 (0.080) Batch 0.856 (1.146) Remain 06:26:47 loss: 0.1579 Lr: 0.00023 [2024-02-19 13:50:57,206 INFO misc.py line 119 87073] Train: [87/100][1549/1557] Data 0.004 (0.080) Batch 1.068 (1.146) Remain 06:26:45 loss: 0.1998 Lr: 0.00023 [2024-02-19 13:50:58,052 INFO misc.py line 119 87073] Train: [87/100][1550/1557] Data 0.010 (0.080) Batch 0.852 (1.146) Remain 06:26:40 loss: 0.2025 Lr: 0.00023 [2024-02-19 13:50:59,069 INFO misc.py line 119 87073] Train: [87/100][1551/1557] Data 0.004 (0.080) Batch 1.017 (1.146) Remain 06:26:37 loss: 0.4104 Lr: 0.00023 [2024-02-19 13:50:59,781 INFO misc.py line 119 87073] Train: [87/100][1552/1557] Data 0.004 (0.080) Batch 0.713 (1.145) Remain 06:26:30 loss: 0.2501 Lr: 0.00023 [2024-02-19 13:51:00,499 INFO misc.py line 119 87073] Train: [87/100][1553/1557] Data 0.004 (0.080) Batch 0.713 (1.145) Remain 06:26:23 loss: 0.1575 Lr: 0.00023 [2024-02-19 13:51:01,579 INFO misc.py line 119 87073] Train: [87/100][1554/1557] Data 0.009 (0.080) Batch 1.076 (1.145) Remain 06:26:21 loss: 0.1238 Lr: 0.00023 [2024-02-19 13:51:02,614 INFO misc.py line 119 87073] Train: [87/100][1555/1557] Data 0.012 (0.080) Batch 1.039 (1.145) Remain 06:26:19 loss: 0.1289 Lr: 0.00023 [2024-02-19 13:51:03,623 INFO misc.py line 119 87073] Train: [87/100][1556/1557] Data 0.009 (0.080) Batch 1.012 (1.145) Remain 06:26:16 loss: 0.2102 Lr: 0.00023 [2024-02-19 13:51:04,541 INFO misc.py line 119 87073] Train: [87/100][1557/1557] Data 0.005 (0.080) Batch 0.920 (1.145) Remain 06:26:12 loss: 0.0723 Lr: 0.00023 [2024-02-19 13:51:04,542 INFO misc.py line 136 87073] Train result: loss: 0.2079 [2024-02-19 13:51:04,542 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 13:51:32,567 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5493 [2024-02-19 13:51:33,348 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.6085 [2024-02-19 13:51:35,475 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3124 [2024-02-19 13:51:37,686 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3224 [2024-02-19 13:51:42,633 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2274 [2024-02-19 13:51:43,333 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0878 [2024-02-19 13:51:44,593 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3403 [2024-02-19 13:51:47,554 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2442 [2024-02-19 13:51:49,364 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2876 [2024-02-19 13:51:50,719 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7296/0.7852/0.9169. [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9331/0.9615 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9892 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8646/0.9746 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3983/0.4390 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6457/0.6612 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8201/0.9311 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8474/0.9126 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9316/0.9776 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8712/0.9066 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7966/0.8780 [2024-02-19 13:51:50,719 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7715/0.8531 [2024-02-19 13:51:50,720 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6218/0.7233 [2024-02-19 13:51:50,720 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 13:51:50,721 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 13:51:50,722 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 13:52:01,082 INFO misc.py line 119 87073] Train: [88/100][1/1557] Data 1.314 (1.314) Batch 2.059 (2.059) Remain 11:34:35 loss: 0.1042 Lr: 0.00023 [2024-02-19 13:52:02,280 INFO misc.py line 119 87073] Train: [88/100][2/1557] Data 0.006 (0.006) Batch 1.142 (1.142) Remain 06:25:02 loss: 0.0966 Lr: 0.00023 [2024-02-19 13:52:03,377 INFO misc.py line 119 87073] Train: [88/100][3/1557] Data 0.063 (0.063) Batch 1.150 (1.150) Remain 06:28:02 loss: 0.2059 Lr: 0.00023 [2024-02-19 13:52:04,386 INFO misc.py line 119 87073] Train: [88/100][4/1557] Data 0.008 (0.008) Batch 1.012 (1.012) Remain 05:41:15 loss: 0.3989 Lr: 0.00023 [2024-02-19 13:52:05,199 INFO misc.py line 119 87073] Train: [88/100][5/1557] Data 0.007 (0.008) Batch 0.816 (0.914) Remain 05:08:09 loss: 0.1763 Lr: 0.00023 [2024-02-19 13:52:05,994 INFO misc.py line 119 87073] Train: [88/100][6/1557] Data 0.003 (0.006) Batch 0.794 (0.874) Remain 04:54:43 loss: 0.3361 Lr: 0.00023 [2024-02-19 13:52:08,204 INFO misc.py line 119 87073] Train: [88/100][7/1557] Data 0.004 (0.006) Batch 2.208 (1.207) Remain 06:47:09 loss: 0.1018 Lr: 0.00023 [2024-02-19 13:52:09,142 INFO misc.py line 119 87073] Train: [88/100][8/1557] Data 0.007 (0.006) Batch 0.940 (1.154) Remain 06:29:07 loss: 0.2079 Lr: 0.00023 [2024-02-19 13:52:10,115 INFO misc.py line 119 87073] Train: [88/100][9/1557] Data 0.005 (0.006) Batch 0.972 (1.124) Remain 06:18:51 loss: 0.1827 Lr: 0.00023 [2024-02-19 13:52:10,923 INFO misc.py line 119 87073] Train: [88/100][10/1557] Data 0.005 (0.006) Batch 0.810 (1.079) Remain 06:03:43 loss: 0.1309 Lr: 0.00023 [2024-02-19 13:52:11,918 INFO misc.py line 119 87073] Train: [88/100][11/1557] Data 0.004 (0.006) Batch 0.987 (1.067) Remain 05:59:51 loss: 0.3188 Lr: 0.00023 [2024-02-19 13:52:12,663 INFO misc.py line 119 87073] Train: [88/100][12/1557] Data 0.012 (0.006) Batch 0.751 (1.032) Remain 05:47:58 loss: 0.1411 Lr: 0.00023 [2024-02-19 13:52:13,378 INFO misc.py line 119 87073] Train: [88/100][13/1557] Data 0.006 (0.006) Batch 0.708 (1.000) Remain 05:37:02 loss: 0.1985 Lr: 0.00023 [2024-02-19 13:52:14,649 INFO misc.py line 119 87073] Train: [88/100][14/1557] Data 0.012 (0.007) Batch 1.269 (1.024) Remain 05:45:16 loss: 0.0817 Lr: 0.00023 [2024-02-19 13:52:15,576 INFO misc.py line 119 87073] Train: [88/100][15/1557] Data 0.016 (0.007) Batch 0.937 (1.017) Remain 05:42:49 loss: 0.2124 Lr: 0.00023 [2024-02-19 13:52:16,609 INFO misc.py line 119 87073] Train: [88/100][16/1557] Data 0.004 (0.007) Batch 1.034 (1.018) Remain 05:43:14 loss: 0.1906 Lr: 0.00023 [2024-02-19 13:52:17,573 INFO misc.py line 119 87073] Train: [88/100][17/1557] Data 0.004 (0.007) Batch 0.964 (1.014) Remain 05:41:54 loss: 0.2162 Lr: 0.00023 [2024-02-19 13:52:18,417 INFO misc.py line 119 87073] Train: [88/100][18/1557] Data 0.004 (0.007) Batch 0.843 (1.003) Remain 05:38:01 loss: 0.2022 Lr: 0.00023 [2024-02-19 13:52:19,174 INFO misc.py line 119 87073] Train: [88/100][19/1557] Data 0.007 (0.007) Batch 0.757 (0.988) Remain 05:32:49 loss: 0.2994 Lr: 0.00023 [2024-02-19 13:52:19,886 INFO misc.py line 119 87073] Train: [88/100][20/1557] Data 0.005 (0.007) Batch 0.713 (0.971) Remain 05:27:22 loss: 0.2759 Lr: 0.00023 [2024-02-19 13:52:21,009 INFO misc.py line 119 87073] Train: [88/100][21/1557] Data 0.004 (0.007) Batch 1.124 (0.980) Remain 05:30:12 loss: 0.0721 Lr: 0.00023 [2024-02-19 13:52:22,172 INFO misc.py line 119 87073] Train: [88/100][22/1557] Data 0.004 (0.006) Batch 1.153 (0.989) Remain 05:33:15 loss: 0.3047 Lr: 0.00023 [2024-02-19 13:52:23,178 INFO misc.py line 119 87073] Train: [88/100][23/1557] Data 0.014 (0.007) Batch 1.014 (0.990) Remain 05:33:39 loss: 0.1895 Lr: 0.00023 [2024-02-19 13:52:24,299 INFO misc.py line 119 87073] Train: [88/100][24/1557] Data 0.006 (0.007) Batch 1.122 (0.996) Remain 05:35:45 loss: 0.0709 Lr: 0.00023 [2024-02-19 13:52:25,173 INFO misc.py line 119 87073] Train: [88/100][25/1557] Data 0.005 (0.007) Batch 0.875 (0.991) Remain 05:33:52 loss: 0.2279 Lr: 0.00023 [2024-02-19 13:52:25,853 INFO misc.py line 119 87073] Train: [88/100][26/1557] Data 0.005 (0.007) Batch 0.670 (0.977) Remain 05:29:09 loss: 0.1494 Lr: 0.00023 [2024-02-19 13:52:26,585 INFO misc.py line 119 87073] Train: [88/100][27/1557] Data 0.014 (0.007) Batch 0.742 (0.967) Remain 05:25:50 loss: 0.1395 Lr: 0.00023 [2024-02-19 13:52:27,873 INFO misc.py line 119 87073] Train: [88/100][28/1557] Data 0.003 (0.007) Batch 1.279 (0.980) Remain 05:30:02 loss: 0.1294 Lr: 0.00023 [2024-02-19 13:52:28,843 INFO misc.py line 119 87073] Train: [88/100][29/1557] Data 0.013 (0.007) Batch 0.980 (0.980) Remain 05:30:01 loss: 0.1761 Lr: 0.00023 [2024-02-19 13:52:29,797 INFO misc.py line 119 87073] Train: [88/100][30/1557] Data 0.003 (0.007) Batch 0.954 (0.979) Remain 05:29:41 loss: 0.0766 Lr: 0.00023 [2024-02-19 13:52:30,642 INFO misc.py line 119 87073] Train: [88/100][31/1557] Data 0.003 (0.007) Batch 0.845 (0.974) Remain 05:28:03 loss: 0.1885 Lr: 0.00023 [2024-02-19 13:52:31,471 INFO misc.py line 119 87073] Train: [88/100][32/1557] Data 0.004 (0.007) Batch 0.824 (0.969) Remain 05:26:17 loss: 0.2686 Lr: 0.00023 [2024-02-19 13:52:32,217 INFO misc.py line 119 87073] Train: [88/100][33/1557] Data 0.009 (0.007) Batch 0.750 (0.961) Remain 05:23:49 loss: 0.1149 Lr: 0.00023 [2024-02-19 13:52:32,975 INFO misc.py line 119 87073] Train: [88/100][34/1557] Data 0.004 (0.007) Batch 0.750 (0.955) Remain 05:21:30 loss: 0.2269 Lr: 0.00023 [2024-02-19 13:52:34,109 INFO misc.py line 119 87073] Train: [88/100][35/1557] Data 0.012 (0.007) Batch 1.139 (0.960) Remain 05:23:26 loss: 0.1506 Lr: 0.00023 [2024-02-19 13:52:34,955 INFO misc.py line 119 87073] Train: [88/100][36/1557] Data 0.007 (0.007) Batch 0.848 (0.957) Remain 05:22:16 loss: 0.0538 Lr: 0.00023 [2024-02-19 13:52:35,842 INFO misc.py line 119 87073] Train: [88/100][37/1557] Data 0.005 (0.007) Batch 0.888 (0.955) Remain 05:21:34 loss: 0.2561 Lr: 0.00023 [2024-02-19 13:52:36,811 INFO misc.py line 119 87073] Train: [88/100][38/1557] Data 0.003 (0.007) Batch 0.958 (0.955) Remain 05:21:35 loss: 0.1527 Lr: 0.00023 [2024-02-19 13:52:37,737 INFO misc.py line 119 87073] Train: [88/100][39/1557] Data 0.015 (0.007) Batch 0.937 (0.955) Remain 05:21:24 loss: 0.1922 Lr: 0.00023 [2024-02-19 13:52:38,470 INFO misc.py line 119 87073] Train: [88/100][40/1557] Data 0.005 (0.007) Batch 0.732 (0.949) Remain 05:19:21 loss: 0.0973 Lr: 0.00023 [2024-02-19 13:52:39,164 INFO misc.py line 119 87073] Train: [88/100][41/1557] Data 0.005 (0.007) Batch 0.689 (0.942) Remain 05:17:02 loss: 0.2533 Lr: 0.00023 [2024-02-19 13:52:40,328 INFO misc.py line 119 87073] Train: [88/100][42/1557] Data 0.010 (0.007) Batch 1.159 (0.947) Remain 05:18:54 loss: 0.0918 Lr: 0.00023 [2024-02-19 13:52:41,453 INFO misc.py line 119 87073] Train: [88/100][43/1557] Data 0.015 (0.007) Batch 1.124 (0.952) Remain 05:20:22 loss: 0.2596 Lr: 0.00023 [2024-02-19 13:52:42,386 INFO misc.py line 119 87073] Train: [88/100][44/1557] Data 0.016 (0.007) Batch 0.943 (0.951) Remain 05:20:17 loss: 0.2252 Lr: 0.00023 [2024-02-19 13:52:43,397 INFO misc.py line 119 87073] Train: [88/100][45/1557] Data 0.007 (0.007) Batch 1.012 (0.953) Remain 05:20:45 loss: 0.1860 Lr: 0.00023 [2024-02-19 13:52:44,263 INFO misc.py line 119 87073] Train: [88/100][46/1557] Data 0.005 (0.007) Batch 0.868 (0.951) Remain 05:20:04 loss: 0.2234 Lr: 0.00023 [2024-02-19 13:52:45,022 INFO misc.py line 119 87073] Train: [88/100][47/1557] Data 0.003 (0.007) Batch 0.746 (0.946) Remain 05:18:29 loss: 0.1513 Lr: 0.00023 [2024-02-19 13:52:45,765 INFO misc.py line 119 87073] Train: [88/100][48/1557] Data 0.018 (0.007) Batch 0.754 (0.942) Remain 05:17:02 loss: 0.1993 Lr: 0.00023 [2024-02-19 13:52:46,986 INFO misc.py line 119 87073] Train: [88/100][49/1557] Data 0.005 (0.007) Batch 1.221 (0.948) Remain 05:19:04 loss: 0.2989 Lr: 0.00023 [2024-02-19 13:52:48,086 INFO misc.py line 119 87073] Train: [88/100][50/1557] Data 0.005 (0.007) Batch 1.102 (0.951) Remain 05:20:09 loss: 0.2620 Lr: 0.00023 [2024-02-19 13:52:48,973 INFO misc.py line 119 87073] Train: [88/100][51/1557] Data 0.003 (0.007) Batch 0.887 (0.950) Remain 05:19:41 loss: 0.3462 Lr: 0.00023 [2024-02-19 13:52:49,948 INFO misc.py line 119 87073] Train: [88/100][52/1557] Data 0.004 (0.007) Batch 0.975 (0.951) Remain 05:19:50 loss: 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Remain 05:59:21 loss: 0.5062 Lr: 0.00020 [2024-02-19 14:17:20,927 INFO misc.py line 119 87073] Train: [88/100][1334/1557] Data 0.004 (0.087) Batch 0.900 (1.140) Remain 05:59:16 loss: 0.2393 Lr: 0.00020 [2024-02-19 14:17:21,654 INFO misc.py line 119 87073] Train: [88/100][1335/1557] Data 0.007 (0.087) Batch 0.728 (1.140) Remain 05:59:09 loss: 0.2145 Lr: 0.00020 [2024-02-19 14:17:22,445 INFO misc.py line 119 87073] Train: [88/100][1336/1557] Data 0.006 (0.087) Batch 0.792 (1.140) Remain 05:59:03 loss: 0.1301 Lr: 0.00020 [2024-02-19 14:17:23,833 INFO misc.py line 119 87073] Train: [88/100][1337/1557] Data 0.006 (0.086) Batch 1.380 (1.140) Remain 05:59:06 loss: 0.1517 Lr: 0.00020 [2024-02-19 14:17:24,765 INFO misc.py line 119 87073] Train: [88/100][1338/1557] Data 0.014 (0.086) Batch 0.942 (1.140) Remain 05:59:02 loss: 0.2254 Lr: 0.00020 [2024-02-19 14:17:25,706 INFO misc.py line 119 87073] Train: [88/100][1339/1557] Data 0.004 (0.086) Batch 0.941 (1.139) Remain 05:58:58 loss: 0.3045 Lr: 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INFO misc.py line 119 87073] Train: [88/100][1532/1557] Data 0.006 (0.088) Batch 0.764 (1.145) Remain 05:57:04 loss: 0.2025 Lr: 0.00020 [2024-02-19 14:21:15,533 INFO misc.py line 119 87073] Train: [88/100][1533/1557] Data 0.019 (0.088) Batch 1.206 (1.145) Remain 05:57:04 loss: 0.1163 Lr: 0.00020 [2024-02-19 14:21:16,299 INFO misc.py line 119 87073] Train: [88/100][1534/1557] Data 0.014 (0.088) Batch 0.776 (1.145) Remain 05:56:58 loss: 0.2615 Lr: 0.00020 [2024-02-19 14:21:17,236 INFO misc.py line 119 87073] Train: [88/100][1535/1557] Data 0.004 (0.088) Batch 0.938 (1.145) Remain 05:56:54 loss: 0.1901 Lr: 0.00020 [2024-02-19 14:21:18,271 INFO misc.py line 119 87073] Train: [88/100][1536/1557] Data 0.003 (0.088) Batch 0.977 (1.145) Remain 05:56:51 loss: 0.1875 Lr: 0.00020 [2024-02-19 14:21:19,380 INFO misc.py line 119 87073] Train: [88/100][1537/1557] Data 0.061 (0.088) Batch 1.159 (1.145) Remain 05:56:50 loss: 0.1390 Lr: 0.00020 [2024-02-19 14:21:20,093 INFO misc.py line 119 87073] Train: [88/100][1538/1557] Data 0.011 (0.088) Batch 0.719 (1.144) Remain 05:56:44 loss: 0.2066 Lr: 0.00020 [2024-02-19 14:21:20,825 INFO misc.py line 119 87073] Train: [88/100][1539/1557] Data 0.004 (0.088) Batch 0.719 (1.144) Remain 05:56:38 loss: 0.2464 Lr: 0.00020 [2024-02-19 14:21:22,122 INFO misc.py line 119 87073] Train: [88/100][1540/1557] Data 0.017 (0.088) Batch 1.297 (1.144) Remain 05:56:38 loss: 0.1194 Lr: 0.00020 [2024-02-19 14:21:23,272 INFO misc.py line 119 87073] Train: [88/100][1541/1557] Data 0.017 (0.088) Batch 1.121 (1.144) Remain 05:56:37 loss: 0.1802 Lr: 0.00020 [2024-02-19 14:21:24,414 INFO misc.py line 119 87073] Train: [88/100][1542/1557] Data 0.045 (0.088) Batch 1.176 (1.144) Remain 05:56:36 loss: 0.2309 Lr: 0.00020 [2024-02-19 14:21:25,336 INFO misc.py line 119 87073] Train: [88/100][1543/1557] Data 0.012 (0.088) Batch 0.931 (1.144) Remain 05:56:32 loss: 0.5603 Lr: 0.00020 [2024-02-19 14:21:26,417 INFO misc.py line 119 87073] Train: [88/100][1544/1557] Data 0.004 (0.087) Batch 1.080 (1.144) Remain 05:56:31 loss: 0.2705 Lr: 0.00020 [2024-02-19 14:21:27,146 INFO misc.py line 119 87073] Train: [88/100][1545/1557] Data 0.004 (0.087) Batch 0.729 (1.144) Remain 05:56:24 loss: 0.2332 Lr: 0.00020 [2024-02-19 14:21:27,919 INFO misc.py line 119 87073] Train: [88/100][1546/1557] Data 0.004 (0.087) Batch 0.765 (1.144) Remain 05:56:19 loss: 0.0848 Lr: 0.00020 [2024-02-19 14:21:29,074 INFO misc.py line 119 87073] Train: [88/100][1547/1557] Data 0.012 (0.087) Batch 1.154 (1.144) Remain 05:56:18 loss: 0.1054 Lr: 0.00020 [2024-02-19 14:21:30,088 INFO misc.py line 119 87073] Train: [88/100][1548/1557] Data 0.013 (0.087) Batch 1.009 (1.143) Remain 05:56:15 loss: 0.3131 Lr: 0.00020 [2024-02-19 14:21:30,924 INFO misc.py line 119 87073] Train: [88/100][1549/1557] Data 0.018 (0.087) Batch 0.850 (1.143) Remain 05:56:10 loss: 0.0883 Lr: 0.00020 [2024-02-19 14:21:31,798 INFO misc.py line 119 87073] Train: [88/100][1550/1557] Data 0.005 (0.087) Batch 0.874 (1.143) Remain 05:56:06 loss: 0.1105 Lr: 0.00020 [2024-02-19 14:21:32,758 INFO misc.py line 119 87073] Train: [88/100][1551/1557] Data 0.004 (0.087) Batch 0.949 (1.143) Remain 05:56:02 loss: 0.1806 Lr: 0.00020 [2024-02-19 14:21:33,473 INFO misc.py line 119 87073] Train: [88/100][1552/1557] Data 0.015 (0.087) Batch 0.726 (1.143) Remain 05:55:56 loss: 0.2853 Lr: 0.00020 [2024-02-19 14:21:34,151 INFO misc.py line 119 87073] Train: [88/100][1553/1557] Data 0.004 (0.087) Batch 0.666 (1.142) Remain 05:55:49 loss: 0.1738 Lr: 0.00020 [2024-02-19 14:21:35,309 INFO misc.py line 119 87073] Train: [88/100][1554/1557] Data 0.015 (0.087) Batch 1.158 (1.142) Remain 05:55:48 loss: 0.1025 Lr: 0.00020 [2024-02-19 14:21:36,080 INFO misc.py line 119 87073] Train: [88/100][1555/1557] Data 0.016 (0.087) Batch 0.782 (1.142) Remain 05:55:43 loss: 0.4847 Lr: 0.00020 [2024-02-19 14:21:36,970 INFO misc.py line 119 87073] Train: [88/100][1556/1557] Data 0.004 (0.087) Batch 0.890 (1.142) Remain 05:55:39 loss: 0.2545 Lr: 0.00020 [2024-02-19 14:21:38,002 INFO misc.py line 119 87073] Train: [88/100][1557/1557] Data 0.004 (0.087) Batch 1.025 (1.142) Remain 05:55:36 loss: 0.1193 Lr: 0.00020 [2024-02-19 14:21:38,003 INFO misc.py line 136 87073] Train result: loss: 0.2126 [2024-02-19 14:21:38,003 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 14:22:07,467 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4461 [2024-02-19 14:22:08,244 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5992 [2024-02-19 14:22:10,369 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3290 [2024-02-19 14:22:12,575 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3956 [2024-02-19 14:22:17,521 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2252 [2024-02-19 14:22:18,220 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0848 [2024-02-19 14:22:19,480 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3352 [2024-02-19 14:22:22,437 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3070 [2024-02-19 14:22:24,247 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2944 [2024-02-19 14:22:25,720 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7258/0.7815/0.9165. [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9295/0.9613 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9902 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8615/0.9739 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3765/0.4175 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6537/0.6718 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8182/0.9217 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8465/0.9176 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9263/0.9687 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8519/0.8802 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8013/0.8873 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7644/0.8568 [2024-02-19 14:22:25,720 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6232/0.7123 [2024-02-19 14:22:25,721 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 14:22:25,722 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 14:22:25,722 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 14:22:33,074 INFO misc.py line 119 87073] Train: [89/100][1/1557] Data 1.227 (1.227) Batch 1.954 (1.954) Remain 10:08:23 loss: 0.1264 Lr: 0.00020 [2024-02-19 14:22:34,146 INFO misc.py line 119 87073] Train: [89/100][2/1557] Data 0.009 (0.009) Batch 1.075 (1.075) Remain 05:34:44 loss: 0.2052 Lr: 0.00020 [2024-02-19 14:22:35,120 INFO misc.py line 119 87073] Train: [89/100][3/1557] Data 0.007 (0.007) Batch 0.974 (0.974) Remain 05:03:08 loss: 0.4016 Lr: 0.00020 [2024-02-19 14:22:36,225 INFO misc.py line 119 87073] Train: [89/100][4/1557] Data 0.006 (0.006) Batch 1.107 (1.107) Remain 05:44:47 loss: 0.1252 Lr: 0.00020 [2024-02-19 14:22:37,003 INFO misc.py line 119 87073] Train: [89/100][5/1557] Data 0.004 (0.005) Batch 0.775 (0.941) Remain 04:53:03 loss: 0.1295 Lr: 0.00020 [2024-02-19 14:22:37,768 INFO misc.py line 119 87073] Train: [89/100][6/1557] Data 0.007 (0.006) Batch 0.767 (0.883) Remain 04:34:55 loss: 0.0847 Lr: 0.00020 [2024-02-19 14:22:46,216 INFO misc.py line 119 87073] Train: [89/100][7/1557] Data 0.004 (0.005) Batch 8.448 (2.774) Remain 14:23:36 loss: 0.1454 Lr: 0.00020 [2024-02-19 14:22:47,194 INFO misc.py line 119 87073] Train: [89/100][8/1557] Data 0.004 (0.005) Batch 0.978 (2.415) Remain 12:31:43 loss: 0.1462 Lr: 0.00020 [2024-02-19 14:22:48,003 INFO misc.py line 119 87073] Train: [89/100][9/1557] Data 0.004 (0.005) Batch 0.808 (2.147) Remain 11:08:20 loss: 0.1634 Lr: 0.00020 [2024-02-19 14:22:48,991 INFO misc.py line 119 87073] Train: [89/100][10/1557] Data 0.005 (0.005) Batch 0.985 (1.981) Remain 10:16:37 loss: 0.3865 Lr: 0.00020 [2024-02-19 14:22:49,849 INFO misc.py line 119 87073] Train: [89/100][11/1557] Data 0.008 (0.005) Batch 0.863 (1.841) Remain 09:33:05 loss: 0.1976 Lr: 0.00020 [2024-02-19 14:22:50,630 INFO misc.py line 119 87073] Train: [89/100][12/1557] Data 0.004 (0.005) Batch 0.779 (1.723) Remain 08:56:19 loss: 0.1414 Lr: 0.00020 [2024-02-19 14:22:51,376 INFO misc.py line 119 87073] Train: [89/100][13/1557] Data 0.005 (0.005) Batch 0.739 (1.625) Remain 08:25:39 loss: 0.1685 Lr: 0.00020 [2024-02-19 14:22:52,328 INFO misc.py line 119 87073] Train: [89/100][14/1557] Data 0.012 (0.006) Batch 0.960 (1.565) Remain 08:06:49 loss: 0.1138 Lr: 0.00020 [2024-02-19 14:22:53,184 INFO misc.py line 119 87073] Train: [89/100][15/1557] Data 0.004 (0.006) Batch 0.855 (1.505) Remain 07:48:24 loss: 0.0563 Lr: 0.00020 [2024-02-19 14:22:54,134 INFO misc.py line 119 87073] Train: [89/100][16/1557] Data 0.005 (0.006) Batch 0.951 (1.463) Remain 07:35:06 loss: 0.0954 Lr: 0.00020 [2024-02-19 14:22:55,085 INFO misc.py line 119 87073] Train: [89/100][17/1557] Data 0.004 (0.005) Batch 0.950 (1.426) Remain 07:23:42 loss: 0.4110 Lr: 0.00020 [2024-02-19 14:22:55,922 INFO misc.py line 119 87073] Train: [89/100][18/1557] Data 0.004 (0.005) Batch 0.836 (1.387) Remain 07:11:26 loss: 0.1349 Lr: 0.00020 [2024-02-19 14:22:56,784 INFO misc.py line 119 87073] Train: [89/100][19/1557] Data 0.006 (0.005) Batch 0.862 (1.354) Remain 07:01:13 loss: 0.2473 Lr: 0.00020 [2024-02-19 14:22:57,469 INFO misc.py line 119 87073] Train: [89/100][20/1557] Data 0.005 (0.005) Batch 0.687 (1.315) Remain 06:48:59 loss: 0.1297 Lr: 0.00020 [2024-02-19 14:22:58,634 INFO misc.py line 119 87073] Train: [89/100][21/1557] Data 0.003 (0.005) Batch 1.151 (1.306) Remain 06:46:08 loss: 0.0706 Lr: 0.00020 [2024-02-19 14:22:59,569 INFO misc.py line 119 87073] Train: [89/100][22/1557] Data 0.017 (0.006) Batch 0.948 (1.287) Remain 06:40:15 loss: 0.0751 Lr: 0.00020 [2024-02-19 14:23:00,570 INFO misc.py line 119 87073] Train: [89/100][23/1557] Data 0.005 (0.006) Batch 1.001 (1.273) Remain 06:35:47 loss: 0.3117 Lr: 0.00020 [2024-02-19 14:23:01,513 INFO misc.py line 119 87073] Train: [89/100][24/1557] Data 0.005 (0.006) Batch 0.944 (1.257) Remain 06:30:54 loss: 0.5978 Lr: 0.00020 [2024-02-19 14:23:02,461 INFO misc.py line 119 87073] Train: [89/100][25/1557] Data 0.003 (0.006) Batch 0.947 (1.243) Remain 06:26:30 loss: 0.4039 Lr: 0.00020 [2024-02-19 14:23:03,250 INFO misc.py line 119 87073] Train: [89/100][26/1557] Data 0.005 (0.006) Batch 0.790 (1.223) Remain 06:20:21 loss: 0.3519 Lr: 0.00020 [2024-02-19 14:23:04,049 INFO misc.py line 119 87073] Train: [89/100][27/1557] Data 0.003 (0.006) Batch 0.799 (1.205) Remain 06:14:50 loss: 0.1622 Lr: 0.00020 [2024-02-19 14:23:05,294 INFO misc.py line 119 87073] Train: [89/100][28/1557] Data 0.004 (0.005) Batch 1.223 (1.206) Remain 06:15:02 loss: 0.1067 Lr: 0.00020 [2024-02-19 14:23:06,260 INFO misc.py line 119 87073] Train: [89/100][29/1557] Data 0.026 (0.006) Batch 0.987 (1.198) Remain 06:12:23 loss: 0.2632 Lr: 0.00020 [2024-02-19 14:23:07,137 INFO misc.py line 119 87073] Train: [89/100][30/1557] Data 0.006 (0.006) Batch 0.877 (1.186) Remain 06:08:40 loss: 0.3213 Lr: 0.00020 [2024-02-19 14:23:08,157 INFO misc.py line 119 87073] Train: [89/100][31/1557] Data 0.005 (0.006) Batch 1.020 (1.180) Remain 06:06:49 loss: 0.1899 Lr: 0.00020 [2024-02-19 14:23:09,213 INFO misc.py line 119 87073] Train: [89/100][32/1557] Data 0.005 (0.006) Batch 1.054 (1.176) Remain 06:05:26 loss: 0.3480 Lr: 0.00020 [2024-02-19 14:23:09,921 INFO misc.py line 119 87073] Train: [89/100][33/1557] Data 0.006 (0.006) Batch 0.710 (1.160) Remain 06:00:36 loss: 0.2023 Lr: 0.00020 [2024-02-19 14:23:10,706 INFO misc.py line 119 87073] Train: [89/100][34/1557] Data 0.004 (0.006) Batch 0.775 (1.148) Remain 05:56:43 loss: 0.1329 Lr: 0.00020 [2024-02-19 14:23:11,856 INFO misc.py line 119 87073] Train: [89/100][35/1557] Data 0.014 (0.006) Batch 1.150 (1.148) Remain 05:56:43 loss: 0.1169 Lr: 0.00020 [2024-02-19 14:23:12,824 INFO misc.py line 119 87073] Train: [89/100][36/1557] Data 0.015 (0.007) Batch 0.979 (1.143) Remain 05:55:07 loss: 0.2012 Lr: 0.00020 [2024-02-19 14:23:13,964 INFO misc.py line 119 87073] Train: [89/100][37/1557] Data 0.003 (0.006) Batch 1.139 (1.143) Remain 05:55:04 loss: 0.1705 Lr: 0.00020 [2024-02-19 14:23:14,879 INFO misc.py line 119 87073] Train: [89/100][38/1557] Data 0.004 (0.006) Batch 0.914 (1.136) Remain 05:53:01 loss: 0.2532 Lr: 0.00020 [2024-02-19 14:23:15,853 INFO misc.py line 119 87073] Train: [89/100][39/1557] Data 0.005 (0.006) Batch 0.975 (1.132) Remain 05:51:37 loss: 0.1606 Lr: 0.00020 [2024-02-19 14:23:16,618 INFO misc.py line 119 87073] Train: [89/100][40/1557] Data 0.004 (0.006) Batch 0.755 (1.121) Remain 05:48:26 loss: 0.3881 Lr: 0.00020 [2024-02-19 14:23:17,410 INFO misc.py line 119 87073] Train: [89/100][41/1557] Data 0.014 (0.007) Batch 0.802 (1.113) Remain 05:45:48 loss: 0.1844 Lr: 0.00019 [2024-02-19 14:23:18,665 INFO misc.py line 119 87073] Train: [89/100][42/1557] Data 0.005 (0.006) Batch 1.239 (1.116) Remain 05:46:47 loss: 0.0904 Lr: 0.00019 [2024-02-19 14:23:19,632 INFO misc.py line 119 87073] Train: [89/100][43/1557] Data 0.019 (0.007) Batch 0.983 (1.113) Remain 05:45:44 loss: 0.3177 Lr: 0.00019 [2024-02-19 14:23:20,498 INFO misc.py line 119 87073] Train: [89/100][44/1557] Data 0.004 (0.007) Batch 0.864 (1.107) Remain 05:43:50 loss: 0.4536 Lr: 0.00019 [2024-02-19 14:23:21,715 INFO misc.py line 119 87073] Train: [89/100][45/1557] Data 0.007 (0.007) Batch 1.212 (1.109) Remain 05:44:35 loss: 0.2443 Lr: 0.00019 [2024-02-19 14:23:22,611 INFO misc.py line 119 87073] Train: [89/100][46/1557] Data 0.011 (0.007) Batch 0.902 (1.104) Remain 05:43:05 loss: 0.2214 Lr: 0.00019 [2024-02-19 14:23:23,387 INFO misc.py line 119 87073] Train: [89/100][47/1557] Data 0.005 (0.007) Batch 0.777 (1.097) Remain 05:40:45 loss: 0.2431 Lr: 0.00019 [2024-02-19 14:23:24,081 INFO misc.py line 119 87073] Train: [89/100][48/1557] Data 0.004 (0.007) Batch 0.693 (1.088) Remain 05:37:56 loss: 0.1121 Lr: 0.00019 [2024-02-19 14:23:25,240 INFO misc.py line 119 87073] Train: [89/100][49/1557] Data 0.004 (0.007) Batch 1.156 (1.090) Remain 05:38:23 loss: 0.1315 Lr: 0.00019 [2024-02-19 14:23:26,255 INFO misc.py line 119 87073] Train: [89/100][50/1557] Data 0.008 (0.007) Batch 1.009 (1.088) Remain 05:37:50 loss: 0.2803 Lr: 0.00019 [2024-02-19 14:23:27,166 INFO misc.py line 119 87073] Train: [89/100][51/1557] Data 0.014 (0.007) Batch 0.921 (1.084) Remain 05:36:44 loss: 0.0521 Lr: 0.00019 [2024-02-19 14:23:27,969 INFO misc.py line 119 87073] Train: [89/100][52/1557] Data 0.004 (0.007) Batch 0.803 (1.079) Remain 05:34:56 loss: 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INFO misc.py line 119 87073] Train: [89/100][59/1557] Data 0.004 (0.007) Batch 1.077 (1.068) Remain 05:31:25 loss: 0.2331 Lr: 0.00019 [2024-02-19 14:23:35,907 INFO misc.py line 119 87073] Train: [89/100][60/1557] Data 0.005 (0.007) Batch 0.996 (1.066) Remain 05:31:01 loss: 0.2080 Lr: 0.00019 [2024-02-19 14:23:36,653 INFO misc.py line 119 87073] Train: [89/100][61/1557] Data 0.005 (0.007) Batch 0.744 (1.061) Remain 05:29:17 loss: 0.1899 Lr: 0.00019 [2024-02-19 14:23:37,421 INFO misc.py line 119 87073] Train: [89/100][62/1557] Data 0.008 (0.007) Batch 0.769 (1.056) Remain 05:27:43 loss: 0.0965 Lr: 0.00019 [2024-02-19 14:23:55,464 INFO misc.py line 119 87073] Train: [89/100][63/1557] Data 3.395 (0.063) Batch 18.044 (1.339) Remain 06:55:35 loss: 0.1278 Lr: 0.00019 [2024-02-19 14:23:56,589 INFO misc.py line 119 87073] Train: [89/100][64/1557] Data 0.005 (0.062) Batch 1.125 (1.336) Remain 06:54:28 loss: 0.2137 Lr: 0.00019 [2024-02-19 14:23:57,482 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [89/100][389/1557] Data 0.006 (0.069) Batch 0.893 (1.227) Remain 06:14:03 loss: 0.3028 Lr: 0.00019 [2024-02-19 14:30:29,463 INFO misc.py line 119 87073] Train: [89/100][390/1557] Data 0.004 (0.068) Batch 0.817 (1.226) Remain 06:13:42 loss: 0.1285 Lr: 0.00019 [2024-02-19 14:30:30,258 INFO misc.py line 119 87073] Train: [89/100][391/1557] Data 0.005 (0.068) Batch 0.790 (1.225) Remain 06:13:21 loss: 0.1711 Lr: 0.00019 [2024-02-19 14:30:31,572 INFO misc.py line 119 87073] Train: [89/100][392/1557] Data 0.010 (0.068) Batch 1.321 (1.225) Remain 06:13:24 loss: 0.0983 Lr: 0.00019 [2024-02-19 14:30:32,461 INFO misc.py line 119 87073] Train: [89/100][393/1557] Data 0.004 (0.068) Batch 0.889 (1.224) Remain 06:13:07 loss: 0.0819 Lr: 0.00019 [2024-02-19 14:30:33,445 INFO misc.py line 119 87073] Train: [89/100][394/1557] Data 0.004 (0.068) Batch 0.984 (1.223) Remain 06:12:54 loss: 0.1566 Lr: 0.00019 [2024-02-19 14:30:34,441 INFO misc.py line 119 87073] Train: 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Remain 06:03:21 loss: 0.2003 Lr: 0.00017 [2024-02-19 14:48:22,002 INFO misc.py line 119 87073] Train: [89/100][1241/1557] Data 0.015 (0.080) Batch 0.894 (1.249) Remain 06:03:15 loss: 0.2787 Lr: 0.00017 [2024-02-19 14:48:23,082 INFO misc.py line 119 87073] Train: [89/100][1242/1557] Data 0.005 (0.080) Batch 1.080 (1.249) Remain 06:03:11 loss: 0.2204 Lr: 0.00017 [2024-02-19 14:48:24,067 INFO misc.py line 119 87073] Train: [89/100][1243/1557] Data 0.006 (0.080) Batch 0.987 (1.249) Remain 06:03:06 loss: 0.2595 Lr: 0.00017 [2024-02-19 14:48:24,967 INFO misc.py line 119 87073] Train: [89/100][1244/1557] Data 0.003 (0.080) Batch 0.899 (1.249) Remain 06:03:00 loss: 0.1864 Lr: 0.00017 [2024-02-19 14:48:25,656 INFO misc.py line 119 87073] Train: [89/100][1245/1557] Data 0.005 (0.080) Batch 0.681 (1.248) Remain 06:02:51 loss: 0.1363 Lr: 0.00017 [2024-02-19 14:48:26,617 INFO misc.py line 119 87073] Train: [89/100][1246/1557] Data 0.011 (0.080) Batch 0.969 (1.248) Remain 06:02:45 loss: 0.1194 Lr: 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Train: [89/100][1259/1557] Data 0.014 (0.079) Batch 0.796 (1.245) Remain 06:01:30 loss: 0.1400 Lr: 0.00017 [2024-02-19 14:48:39,971 INFO misc.py line 119 87073] Train: [89/100][1260/1557] Data 0.003 (0.079) Batch 1.368 (1.245) Remain 06:01:31 loss: 0.1388 Lr: 0.00017 [2024-02-19 14:48:41,003 INFO misc.py line 119 87073] Train: [89/100][1261/1557] Data 0.016 (0.079) Batch 1.034 (1.245) Remain 06:01:26 loss: 0.2092 Lr: 0.00017 [2024-02-19 14:48:41,856 INFO misc.py line 119 87073] Train: [89/100][1262/1557] Data 0.013 (0.079) Batch 0.863 (1.244) Remain 06:01:20 loss: 0.1705 Lr: 0.00017 [2024-02-19 14:48:42,879 INFO misc.py line 119 87073] Train: [89/100][1263/1557] Data 0.003 (0.079) Batch 1.021 (1.244) Remain 06:01:16 loss: 0.1820 Lr: 0.00017 [2024-02-19 14:48:43,818 INFO misc.py line 119 87073] Train: [89/100][1264/1557] Data 0.006 (0.079) Batch 0.939 (1.244) Remain 06:01:10 loss: 0.2040 Lr: 0.00017 [2024-02-19 14:48:44,597 INFO misc.py line 119 87073] Train: [89/100][1265/1557] Data 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Remain 06:00:36 loss: 0.0436 Lr: 0.00017 [2024-02-19 14:48:51,480 INFO misc.py line 119 87073] Train: [89/100][1272/1557] Data 0.004 (0.079) Batch 0.786 (1.242) Remain 06:00:29 loss: 0.1816 Lr: 0.00017 [2024-02-19 14:48:52,177 INFO misc.py line 119 87073] Train: [89/100][1273/1557] Data 0.006 (0.079) Batch 0.698 (1.242) Remain 06:00:20 loss: 0.1080 Lr: 0.00017 [2024-02-19 14:48:53,454 INFO misc.py line 119 87073] Train: [89/100][1274/1557] Data 0.005 (0.079) Batch 1.273 (1.242) Remain 06:00:19 loss: 0.1556 Lr: 0.00017 [2024-02-19 14:48:54,462 INFO misc.py line 119 87073] Train: [89/100][1275/1557] Data 0.010 (0.079) Batch 1.011 (1.242) Remain 06:00:15 loss: 0.3236 Lr: 0.00017 [2024-02-19 14:48:55,506 INFO misc.py line 119 87073] Train: [89/100][1276/1557] Data 0.007 (0.079) Batch 1.035 (1.241) Remain 06:00:11 loss: 0.0977 Lr: 0.00017 [2024-02-19 14:48:56,532 INFO misc.py line 119 87073] Train: [89/100][1277/1557] Data 0.016 (0.078) Batch 1.032 (1.241) Remain 06:00:07 loss: 0.0722 Lr: 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Train: [89/100][1290/1557] Data 0.009 (0.078) Batch 0.904 (1.238) Remain 05:59:01 loss: 0.4414 Lr: 0.00017 [2024-02-19 14:49:10,073 INFO misc.py line 119 87073] Train: [89/100][1291/1557] Data 0.004 (0.078) Batch 1.054 (1.238) Remain 05:58:58 loss: 0.3217 Lr: 0.00017 [2024-02-19 14:49:10,976 INFO misc.py line 119 87073] Train: [89/100][1292/1557] Data 0.004 (0.078) Batch 0.903 (1.238) Remain 05:58:52 loss: 0.1702 Lr: 0.00017 [2024-02-19 14:49:11,716 INFO misc.py line 119 87073] Train: [89/100][1293/1557] Data 0.004 (0.078) Batch 0.739 (1.238) Remain 05:58:44 loss: 0.1836 Lr: 0.00017 [2024-02-19 14:49:12,487 INFO misc.py line 119 87073] Train: [89/100][1294/1557] Data 0.005 (0.078) Batch 0.771 (1.237) Remain 05:58:36 loss: 0.1629 Lr: 0.00017 [2024-02-19 14:49:29,347 INFO misc.py line 119 87073] Train: [89/100][1295/1557] Data 3.905 (0.081) Batch 16.860 (1.249) Remain 06:02:05 loss: 0.0903 Lr: 0.00017 [2024-02-19 14:49:30,255 INFO misc.py line 119 87073] Train: [89/100][1296/1557] Data 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Remain 06:01:21 loss: 0.1482 Lr: 0.00017 [2024-02-19 14:49:36,398 INFO misc.py line 119 87073] Train: [89/100][1303/1557] Data 0.004 (0.080) Batch 0.977 (1.247) Remain 06:01:16 loss: 0.0748 Lr: 0.00017 [2024-02-19 14:49:37,538 INFO misc.py line 119 87073] Train: [89/100][1304/1557] Data 0.004 (0.080) Batch 1.140 (1.247) Remain 06:01:13 loss: 0.4033 Lr: 0.00017 [2024-02-19 14:49:38,672 INFO misc.py line 119 87073] Train: [89/100][1305/1557] Data 0.003 (0.080) Batch 1.134 (1.247) Remain 06:01:11 loss: 0.4222 Lr: 0.00017 [2024-02-19 14:49:39,749 INFO misc.py line 119 87073] Train: [89/100][1306/1557] Data 0.003 (0.080) Batch 1.075 (1.247) Remain 06:01:07 loss: 0.1268 Lr: 0.00017 [2024-02-19 14:49:40,487 INFO misc.py line 119 87073] Train: [89/100][1307/1557] Data 0.006 (0.080) Batch 0.739 (1.246) Remain 06:00:59 loss: 0.1798 Lr: 0.00017 [2024-02-19 14:49:41,294 INFO misc.py line 119 87073] Train: [89/100][1308/1557] Data 0.006 (0.080) Batch 0.804 (1.246) Remain 06:00:52 loss: 0.1401 Lr: 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Train: [89/100][1321/1557] Data 0.004 (0.079) Batch 0.735 (1.243) Remain 05:59:47 loss: 0.1473 Lr: 0.00017 [2024-02-19 14:49:54,582 INFO misc.py line 119 87073] Train: [89/100][1322/1557] Data 0.004 (0.079) Batch 0.769 (1.243) Remain 05:59:40 loss: 0.1794 Lr: 0.00017 [2024-02-19 14:49:55,755 INFO misc.py line 119 87073] Train: [89/100][1323/1557] Data 0.018 (0.079) Batch 1.177 (1.243) Remain 05:59:37 loss: 0.0761 Lr: 0.00017 [2024-02-19 14:49:56,700 INFO misc.py line 119 87073] Train: [89/100][1324/1557] Data 0.014 (0.079) Batch 0.955 (1.243) Remain 05:59:32 loss: 0.1991 Lr: 0.00017 [2024-02-19 14:49:57,758 INFO misc.py line 119 87073] Train: [89/100][1325/1557] Data 0.004 (0.079) Batch 1.058 (1.243) Remain 05:59:29 loss: 0.2879 Lr: 0.00017 [2024-02-19 14:49:58,817 INFO misc.py line 119 87073] Train: [89/100][1326/1557] Data 0.004 (0.079) Batch 1.059 (1.242) Remain 05:59:25 loss: 0.1092 Lr: 0.00017 [2024-02-19 14:50:00,020 INFO misc.py line 119 87073] Train: [89/100][1327/1557] Data 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Remain 05:58:50 loss: 0.2864 Lr: 0.00017 [2024-02-19 14:50:06,392 INFO misc.py line 119 87073] Train: [89/100][1334/1557] Data 0.005 (0.078) Batch 0.873 (1.241) Remain 05:58:44 loss: 0.3258 Lr: 0.00017 [2024-02-19 14:50:07,135 INFO misc.py line 119 87073] Train: [89/100][1335/1557] Data 0.005 (0.078) Batch 0.744 (1.240) Remain 05:58:37 loss: 0.2198 Lr: 0.00017 [2024-02-19 14:50:07,920 INFO misc.py line 119 87073] Train: [89/100][1336/1557] Data 0.004 (0.078) Batch 0.786 (1.240) Remain 05:58:29 loss: 0.2144 Lr: 0.00017 [2024-02-19 14:50:09,113 INFO misc.py line 119 87073] Train: [89/100][1337/1557] Data 0.004 (0.078) Batch 1.190 (1.240) Remain 05:58:28 loss: 0.1739 Lr: 0.00017 [2024-02-19 14:50:10,165 INFO misc.py line 119 87073] Train: [89/100][1338/1557] Data 0.007 (0.078) Batch 1.043 (1.240) Remain 05:58:24 loss: 0.8958 Lr: 0.00017 [2024-02-19 14:50:11,216 INFO misc.py line 119 87073] Train: [89/100][1339/1557] Data 0.016 (0.078) Batch 1.050 (1.240) Remain 05:58:20 loss: 0.3899 Lr: 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Train: [89/100][1352/1557] Data 0.004 (0.080) Batch 0.800 (1.249) Remain 06:00:54 loss: 0.1851 Lr: 0.00017 [2024-02-19 14:50:41,614 INFO misc.py line 119 87073] Train: [89/100][1353/1557] Data 0.006 (0.079) Batch 1.073 (1.249) Remain 06:00:50 loss: 0.0519 Lr: 0.00017 [2024-02-19 14:50:42,550 INFO misc.py line 119 87073] Train: [89/100][1354/1557] Data 0.016 (0.079) Batch 0.948 (1.249) Remain 06:00:45 loss: 0.3468 Lr: 0.00017 [2024-02-19 14:50:43,652 INFO misc.py line 119 87073] Train: [89/100][1355/1557] Data 0.004 (0.079) Batch 1.101 (1.249) Remain 06:00:42 loss: 0.2914 Lr: 0.00017 [2024-02-19 14:50:44,416 INFO misc.py line 119 87073] Train: [89/100][1356/1557] Data 0.004 (0.079) Batch 0.764 (1.249) Remain 06:00:34 loss: 0.2931 Lr: 0.00017 [2024-02-19 14:50:45,211 INFO misc.py line 119 87073] Train: [89/100][1357/1557] Data 0.005 (0.079) Batch 0.785 (1.248) Remain 06:00:27 loss: 0.1374 Lr: 0.00017 [2024-02-19 14:50:46,199 INFO misc.py line 119 87073] Train: [89/100][1358/1557] Data 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Remain 05:59:51 loss: 0.1786 Lr: 0.00017 [2024-02-19 14:50:52,990 INFO misc.py line 119 87073] Train: [89/100][1365/1557] Data 0.015 (0.079) Batch 1.216 (1.247) Remain 05:59:49 loss: 0.0714 Lr: 0.00017 [2024-02-19 14:50:53,879 INFO misc.py line 119 87073] Train: [89/100][1366/1557] Data 0.013 (0.079) Batch 0.894 (1.246) Remain 05:59:44 loss: 0.6743 Lr: 0.00017 [2024-02-19 14:50:54,782 INFO misc.py line 119 87073] Train: [89/100][1367/1557] Data 0.007 (0.079) Batch 0.906 (1.246) Remain 05:59:38 loss: 0.0778 Lr: 0.00017 [2024-02-19 14:50:55,683 INFO misc.py line 119 87073] Train: [89/100][1368/1557] Data 0.004 (0.079) Batch 0.902 (1.246) Remain 05:59:32 loss: 0.1319 Lr: 0.00017 [2024-02-19 14:50:56,686 INFO misc.py line 119 87073] Train: [89/100][1369/1557] Data 0.004 (0.079) Batch 0.996 (1.246) Remain 05:59:28 loss: 0.1618 Lr: 0.00017 [2024-02-19 14:50:57,566 INFO misc.py line 119 87073] Train: [89/100][1370/1557] Data 0.010 (0.079) Batch 0.885 (1.245) Remain 05:59:22 loss: 0.1950 Lr: 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Train: [89/100][1383/1557] Data 0.008 (0.078) Batch 0.831 (1.243) Remain 05:58:19 loss: 0.1057 Lr: 0.00017 [2024-02-19 14:51:10,724 INFO misc.py line 119 87073] Train: [89/100][1384/1557] Data 0.004 (0.078) Batch 0.707 (1.242) Remain 05:58:11 loss: 0.3868 Lr: 0.00017 [2024-02-19 14:51:11,461 INFO misc.py line 119 87073] Train: [89/100][1385/1557] Data 0.004 (0.078) Batch 0.729 (1.242) Remain 05:58:03 loss: 0.1075 Lr: 0.00017 [2024-02-19 14:51:12,680 INFO misc.py line 119 87073] Train: [89/100][1386/1557] Data 0.012 (0.078) Batch 1.221 (1.242) Remain 05:58:02 loss: 0.2045 Lr: 0.00017 [2024-02-19 14:51:13,653 INFO misc.py line 119 87073] Train: [89/100][1387/1557] Data 0.010 (0.078) Batch 0.979 (1.242) Remain 05:57:57 loss: 0.2745 Lr: 0.00017 [2024-02-19 14:51:14,711 INFO misc.py line 119 87073] Train: [89/100][1388/1557] Data 0.004 (0.078) Batch 1.057 (1.242) Remain 05:57:54 loss: 0.3143 Lr: 0.00017 [2024-02-19 14:51:15,650 INFO misc.py line 119 87073] Train: [89/100][1389/1557] Data 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Remain 05:57:17 loss: 0.0695 Lr: 0.00017 [2024-02-19 14:51:22,096 INFO misc.py line 119 87073] Train: [89/100][1396/1557] Data 0.005 (0.077) Batch 0.949 (1.240) Remain 05:57:12 loss: 0.3821 Lr: 0.00017 [2024-02-19 14:51:23,062 INFO misc.py line 119 87073] Train: [89/100][1397/1557] Data 0.004 (0.077) Batch 0.965 (1.240) Remain 05:57:08 loss: 0.1182 Lr: 0.00017 [2024-02-19 14:51:23,878 INFO misc.py line 119 87073] Train: [89/100][1398/1557] Data 0.004 (0.077) Batch 0.814 (1.239) Remain 05:57:01 loss: 0.2030 Lr: 0.00017 [2024-02-19 14:51:24,632 INFO misc.py line 119 87073] Train: [89/100][1399/1557] Data 0.006 (0.077) Batch 0.746 (1.239) Remain 05:56:54 loss: 0.1639 Lr: 0.00017 [2024-02-19 14:51:25,831 INFO misc.py line 119 87073] Train: [89/100][1400/1557] Data 0.013 (0.077) Batch 1.208 (1.239) Remain 05:56:52 loss: 0.2609 Lr: 0.00017 [2024-02-19 14:51:26,850 INFO misc.py line 119 87073] Train: [89/100][1401/1557] Data 0.005 (0.077) Batch 1.011 (1.239) Remain 05:56:48 loss: 0.0971 Lr: 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Train: [89/100][1414/1557] Data 0.004 (0.080) Batch 0.935 (1.249) Remain 05:59:28 loss: 0.1291 Lr: 0.00017 [2024-02-19 14:51:58,205 INFO misc.py line 119 87073] Train: [89/100][1415/1557] Data 0.004 (0.080) Batch 0.899 (1.249) Remain 05:59:22 loss: 0.4049 Lr: 0.00017 [2024-02-19 14:51:58,979 INFO misc.py line 119 87073] Train: [89/100][1416/1557] Data 0.008 (0.080) Batch 0.776 (1.248) Remain 05:59:15 loss: 0.3426 Lr: 0.00017 [2024-02-19 14:52:00,173 INFO misc.py line 119 87073] Train: [89/100][1417/1557] Data 0.005 (0.080) Batch 1.190 (1.248) Remain 05:59:13 loss: 0.2199 Lr: 0.00017 [2024-02-19 14:52:01,304 INFO misc.py line 119 87073] Train: [89/100][1418/1557] Data 0.008 (0.080) Batch 1.128 (1.248) Remain 05:59:11 loss: 0.1217 Lr: 0.00017 [2024-02-19 14:52:02,211 INFO misc.py line 119 87073] Train: [89/100][1419/1557] Data 0.011 (0.080) Batch 0.914 (1.248) Remain 05:59:05 loss: 0.1867 Lr: 0.00017 [2024-02-19 14:52:02,908 INFO misc.py line 119 87073] Train: [89/100][1420/1557] Data 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Remain 05:58:31 loss: 0.2913 Lr: 0.00017 [2024-02-19 14:52:09,564 INFO misc.py line 119 87073] Train: [89/100][1427/1557] Data 0.005 (0.079) Batch 0.749 (1.246) Remain 05:58:23 loss: 0.2110 Lr: 0.00017 [2024-02-19 14:52:10,738 INFO misc.py line 119 87073] Train: [89/100][1428/1557] Data 0.004 (0.079) Batch 1.168 (1.246) Remain 05:58:21 loss: 0.1448 Lr: 0.00017 [2024-02-19 14:52:11,713 INFO misc.py line 119 87073] Train: [89/100][1429/1557] Data 0.011 (0.079) Batch 0.981 (1.246) Remain 05:58:17 loss: 0.4695 Lr: 0.00017 [2024-02-19 14:52:12,622 INFO misc.py line 119 87073] Train: [89/100][1430/1557] Data 0.004 (0.079) Batch 0.910 (1.246) Remain 05:58:11 loss: 0.1937 Lr: 0.00017 [2024-02-19 14:52:13,518 INFO misc.py line 119 87073] Train: [89/100][1431/1557] Data 0.003 (0.079) Batch 0.891 (1.245) Remain 05:58:06 loss: 0.4120 Lr: 0.00017 [2024-02-19 14:52:14,507 INFO misc.py line 119 87073] Train: [89/100][1432/1557] Data 0.009 (0.079) Batch 0.983 (1.245) Remain 05:58:02 loss: 0.3084 Lr: 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Train: [89/100][1445/1557] Data 0.004 (0.078) Batch 1.109 (1.243) Remain 05:57:07 loss: 0.0653 Lr: 0.00017 [2024-02-19 14:52:28,359 INFO misc.py line 119 87073] Train: [89/100][1446/1557] Data 0.013 (0.078) Batch 0.882 (1.243) Remain 05:57:01 loss: 0.0775 Lr: 0.00017 [2024-02-19 14:52:29,136 INFO misc.py line 119 87073] Train: [89/100][1447/1557] Data 0.004 (0.078) Batch 0.776 (1.242) Remain 05:56:55 loss: 0.1586 Lr: 0.00017 [2024-02-19 14:52:29,886 INFO misc.py line 119 87073] Train: [89/100][1448/1557] Data 0.004 (0.078) Batch 0.696 (1.242) Remain 05:56:47 loss: 0.1090 Lr: 0.00017 [2024-02-19 14:52:31,086 INFO misc.py line 119 87073] Train: [89/100][1449/1557] Data 0.059 (0.078) Batch 1.253 (1.242) Remain 05:56:46 loss: 0.1269 Lr: 0.00017 [2024-02-19 14:52:31,939 INFO misc.py line 119 87073] Train: [89/100][1450/1557] Data 0.005 (0.078) Batch 0.853 (1.242) Remain 05:56:40 loss: 0.1417 Lr: 0.00017 [2024-02-19 14:52:33,197 INFO misc.py line 119 87073] Train: [89/100][1451/1557] Data 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Remain 05:56:09 loss: 0.1838 Lr: 0.00017 [2024-02-19 14:52:39,771 INFO misc.py line 119 87073] Train: [89/100][1458/1557] Data 0.003 (0.078) Batch 1.023 (1.240) Remain 05:56:05 loss: 0.2111 Lr: 0.00017 [2024-02-19 14:52:40,723 INFO misc.py line 119 87073] Train: [89/100][1459/1557] Data 0.005 (0.078) Batch 0.952 (1.240) Remain 05:56:00 loss: 0.4258 Lr: 0.00017 [2024-02-19 14:52:41,669 INFO misc.py line 119 87073] Train: [89/100][1460/1557] Data 0.004 (0.077) Batch 0.946 (1.240) Remain 05:55:56 loss: 0.2650 Lr: 0.00017 [2024-02-19 14:52:42,392 INFO misc.py line 119 87073] Train: [89/100][1461/1557] Data 0.004 (0.077) Batch 0.721 (1.240) Remain 05:55:48 loss: 0.1714 Lr: 0.00017 [2024-02-19 14:52:43,157 INFO misc.py line 119 87073] Train: [89/100][1462/1557] Data 0.007 (0.077) Batch 0.767 (1.239) Remain 05:55:42 loss: 0.2491 Lr: 0.00017 [2024-02-19 14:52:58,819 INFO misc.py line 119 87073] Train: [89/100][1463/1557] Data 3.564 (0.080) Batch 15.663 (1.249) Remain 05:58:30 loss: 0.0840 Lr: 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Train: [89/100][1476/1557] Data 0.005 (0.079) Batch 0.735 (1.246) Remain 05:57:22 loss: 0.1456 Lr: 0.00017 [2024-02-19 14:53:11,819 INFO misc.py line 119 87073] Train: [89/100][1477/1557] Data 0.069 (0.079) Batch 1.253 (1.246) Remain 05:57:20 loss: 0.0744 Lr: 0.00017 [2024-02-19 14:53:12,851 INFO misc.py line 119 87073] Train: [89/100][1478/1557] Data 0.014 (0.079) Batch 1.031 (1.246) Remain 05:57:17 loss: 0.1962 Lr: 0.00017 [2024-02-19 14:53:13,933 INFO misc.py line 119 87073] Train: [89/100][1479/1557] Data 0.015 (0.079) Batch 1.080 (1.246) Remain 05:57:13 loss: 0.1664 Lr: 0.00017 [2024-02-19 14:53:14,729 INFO misc.py line 119 87073] Train: [89/100][1480/1557] Data 0.017 (0.079) Batch 0.807 (1.246) Remain 05:57:07 loss: 0.1091 Lr: 0.00017 [2024-02-19 14:53:15,669 INFO misc.py line 119 87073] Train: [89/100][1481/1557] Data 0.006 (0.079) Batch 0.942 (1.245) Remain 05:57:02 loss: 0.0608 Lr: 0.00017 [2024-02-19 14:53:16,413 INFO misc.py line 119 87073] Train: [89/100][1482/1557] Data 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Remain 05:56:31 loss: 0.4806 Lr: 0.00017 [2024-02-19 14:53:23,186 INFO misc.py line 119 87073] Train: [89/100][1489/1557] Data 0.004 (0.079) Batch 0.764 (1.244) Remain 05:56:24 loss: 0.2832 Lr: 0.00017 [2024-02-19 14:53:23,967 INFO misc.py line 119 87073] Train: [89/100][1490/1557] Data 0.009 (0.079) Batch 0.785 (1.243) Remain 05:56:17 loss: 0.2297 Lr: 0.00017 [2024-02-19 14:53:25,123 INFO misc.py line 119 87073] Train: [89/100][1491/1557] Data 0.004 (0.078) Batch 1.154 (1.243) Remain 05:56:15 loss: 0.1642 Lr: 0.00017 [2024-02-19 14:53:26,110 INFO misc.py line 119 87073] Train: [89/100][1492/1557] Data 0.006 (0.078) Batch 0.989 (1.243) Remain 05:56:11 loss: 0.2107 Lr: 0.00017 [2024-02-19 14:53:27,053 INFO misc.py line 119 87073] Train: [89/100][1493/1557] Data 0.004 (0.078) Batch 0.944 (1.243) Remain 05:56:06 loss: 0.2785 Lr: 0.00017 [2024-02-19 14:53:27,925 INFO misc.py line 119 87073] Train: [89/100][1494/1557] Data 0.004 (0.078) Batch 0.871 (1.243) Remain 05:56:01 loss: 0.2414 Lr: 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Train: [89/100][1507/1557] Data 0.004 (0.078) Batch 1.193 (1.240) Remain 05:55:03 loss: 0.1513 Lr: 0.00017 [2024-02-19 14:53:41,501 INFO misc.py line 119 87073] Train: [89/100][1508/1557] Data 0.014 (0.078) Batch 1.075 (1.240) Remain 05:55:00 loss: 0.4984 Lr: 0.00017 [2024-02-19 14:53:42,556 INFO misc.py line 119 87073] Train: [89/100][1509/1557] Data 0.014 (0.078) Batch 1.052 (1.240) Remain 05:54:56 loss: 0.1466 Lr: 0.00017 [2024-02-19 14:53:43,267 INFO misc.py line 119 87073] Train: [89/100][1510/1557] Data 0.016 (0.078) Batch 0.722 (1.240) Remain 05:54:49 loss: 0.1971 Lr: 0.00017 [2024-02-19 14:53:44,135 INFO misc.py line 119 87073] Train: [89/100][1511/1557] Data 0.006 (0.078) Batch 0.869 (1.239) Remain 05:54:44 loss: 0.1578 Lr: 0.00017 [2024-02-19 14:53:45,408 INFO misc.py line 119 87073] Train: [89/100][1512/1557] Data 0.005 (0.077) Batch 1.274 (1.239) Remain 05:54:43 loss: 0.0869 Lr: 0.00017 [2024-02-19 14:53:46,307 INFO misc.py line 119 87073] Train: [89/100][1513/1557] Data 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Remain 05:57:19 loss: 0.1753 Lr: 0.00017 [2024-02-19 14:54:09,530 INFO misc.py line 119 87073] Train: [89/100][1520/1557] Data 0.004 (0.080) Batch 0.917 (1.249) Remain 05:57:14 loss: 0.3181 Lr: 0.00017 [2024-02-19 14:54:10,618 INFO misc.py line 119 87073] Train: [89/100][1521/1557] Data 0.004 (0.079) Batch 1.087 (1.249) Remain 05:57:11 loss: 0.1853 Lr: 0.00017 [2024-02-19 14:54:11,543 INFO misc.py line 119 87073] Train: [89/100][1522/1557] Data 0.004 (0.079) Batch 0.925 (1.248) Remain 05:57:06 loss: 0.1138 Lr: 0.00017 [2024-02-19 14:54:12,627 INFO misc.py line 119 87073] Train: [89/100][1523/1557] Data 0.004 (0.079) Batch 1.085 (1.248) Remain 05:57:03 loss: 0.2873 Lr: 0.00017 [2024-02-19 14:54:13,404 INFO misc.py line 119 87073] Train: [89/100][1524/1557] Data 0.004 (0.079) Batch 0.776 (1.248) Remain 05:56:56 loss: 0.1004 Lr: 0.00017 [2024-02-19 14:54:14,173 INFO misc.py line 119 87073] Train: [89/100][1525/1557] Data 0.006 (0.079) Batch 0.755 (1.248) Remain 05:56:49 loss: 0.1280 Lr: 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INFO misc.py line 119 87073] Train: [89/100][1532/1557] Data 0.004 (0.079) Batch 0.750 (1.246) Remain 05:56:15 loss: 0.1792 Lr: 0.00017 [2024-02-19 14:54:21,842 INFO misc.py line 119 87073] Train: [89/100][1533/1557] Data 0.006 (0.079) Batch 1.235 (1.246) Remain 05:56:13 loss: 0.1180 Lr: 0.00017 [2024-02-19 14:54:22,877 INFO misc.py line 119 87073] Train: [89/100][1534/1557] Data 0.013 (0.079) Batch 1.035 (1.246) Remain 05:56:10 loss: 0.0871 Lr: 0.00017 [2024-02-19 14:54:23,842 INFO misc.py line 119 87073] Train: [89/100][1535/1557] Data 0.013 (0.079) Batch 0.974 (1.246) Remain 05:56:05 loss: 0.1888 Lr: 0.00017 [2024-02-19 14:54:24,725 INFO misc.py line 119 87073] Train: [89/100][1536/1557] Data 0.004 (0.079) Batch 0.883 (1.246) Remain 05:56:00 loss: 0.1343 Lr: 0.00017 [2024-02-19 14:54:25,671 INFO misc.py line 119 87073] Train: [89/100][1537/1557] Data 0.005 (0.079) Batch 0.935 (1.245) Remain 05:55:55 loss: 0.0940 Lr: 0.00017 [2024-02-19 14:54:26,387 INFO misc.py line 119 87073] Train: [89/100][1538/1557] Data 0.015 (0.079) Batch 0.727 (1.245) Remain 05:55:48 loss: 0.1579 Lr: 0.00017 [2024-02-19 14:54:27,137 INFO misc.py line 119 87073] Train: [89/100][1539/1557] Data 0.004 (0.079) Batch 0.747 (1.245) Remain 05:55:42 loss: 0.2448 Lr: 0.00017 [2024-02-19 14:54:28,396 INFO misc.py line 119 87073] Train: [89/100][1540/1557] Data 0.008 (0.079) Batch 1.255 (1.245) Remain 05:55:40 loss: 0.2058 Lr: 0.00017 [2024-02-19 14:54:29,462 INFO misc.py line 119 87073] Train: [89/100][1541/1557] Data 0.012 (0.079) Batch 1.065 (1.245) Remain 05:55:37 loss: 0.2877 Lr: 0.00017 [2024-02-19 14:54:30,581 INFO misc.py line 119 87073] Train: [89/100][1542/1557] Data 0.013 (0.078) Batch 1.119 (1.245) Remain 05:55:35 loss: 0.2461 Lr: 0.00017 [2024-02-19 14:54:31,585 INFO misc.py line 119 87073] Train: [89/100][1543/1557] Data 0.012 (0.078) Batch 1.004 (1.244) Remain 05:55:31 loss: 0.2270 Lr: 0.00017 [2024-02-19 14:54:32,652 INFO misc.py line 119 87073] Train: [89/100][1544/1557] Data 0.012 (0.078) Batch 1.066 (1.244) Remain 05:55:27 loss: 0.2341 Lr: 0.00017 [2024-02-19 14:54:33,417 INFO misc.py line 119 87073] Train: [89/100][1545/1557] Data 0.014 (0.078) Batch 0.775 (1.244) Remain 05:55:21 loss: 0.2420 Lr: 0.00017 [2024-02-19 14:54:34,177 INFO misc.py line 119 87073] Train: [89/100][1546/1557] Data 0.004 (0.078) Batch 0.741 (1.244) Remain 05:55:14 loss: 0.1214 Lr: 0.00017 [2024-02-19 14:54:35,302 INFO misc.py line 119 87073] Train: [89/100][1547/1557] Data 0.023 (0.078) Batch 1.138 (1.244) Remain 05:55:12 loss: 0.0843 Lr: 0.00017 [2024-02-19 14:54:36,386 INFO misc.py line 119 87073] Train: [89/100][1548/1557] Data 0.009 (0.078) Batch 1.010 (1.243) Remain 05:55:08 loss: 0.1672 Lr: 0.00017 [2024-02-19 14:54:37,392 INFO misc.py line 119 87073] Train: [89/100][1549/1557] Data 0.084 (0.078) Batch 1.076 (1.243) Remain 05:55:05 loss: 0.1849 Lr: 0.00017 [2024-02-19 14:54:38,365 INFO misc.py line 119 87073] Train: [89/100][1550/1557] Data 0.013 (0.078) Batch 0.982 (1.243) Remain 05:55:01 loss: 0.2647 Lr: 0.00017 [2024-02-19 14:54:39,348 INFO misc.py line 119 87073] Train: [89/100][1551/1557] Data 0.004 (0.078) Batch 0.983 (1.243) Remain 05:54:57 loss: 0.2158 Lr: 0.00017 [2024-02-19 14:54:40,056 INFO misc.py line 119 87073] Train: [89/100][1552/1557] Data 0.004 (0.078) Batch 0.700 (1.243) Remain 05:54:49 loss: 0.1538 Lr: 0.00017 [2024-02-19 14:54:40,807 INFO misc.py line 119 87073] Train: [89/100][1553/1557] Data 0.012 (0.078) Batch 0.759 (1.242) Remain 05:54:43 loss: 0.1647 Lr: 0.00017 [2024-02-19 14:54:42,013 INFO misc.py line 119 87073] Train: [89/100][1554/1557] Data 0.004 (0.078) Batch 1.206 (1.242) Remain 05:54:41 loss: 0.2235 Lr: 0.00017 [2024-02-19 14:54:42,786 INFO misc.py line 119 87073] Train: [89/100][1555/1557] Data 0.004 (0.078) Batch 0.773 (1.242) Remain 05:54:35 loss: 0.0787 Lr: 0.00017 [2024-02-19 14:54:43,834 INFO misc.py line 119 87073] Train: [89/100][1556/1557] Data 0.004 (0.078) Batch 1.038 (1.242) Remain 05:54:31 loss: 0.2267 Lr: 0.00017 [2024-02-19 14:54:45,111 INFO misc.py line 119 87073] Train: [89/100][1557/1557] Data 0.015 (0.078) Batch 1.282 (1.242) Remain 05:54:30 loss: 0.1687 Lr: 0.00017 [2024-02-19 14:54:45,111 INFO misc.py line 136 87073] Train result: loss: 0.2036 [2024-02-19 14:54:45,112 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 14:55:14,971 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4058 [2024-02-19 14:55:15,753 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5839 [2024-02-19 14:55:17,880 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3239 [2024-02-19 14:55:20,096 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3593 [2024-02-19 14:55:25,050 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2677 [2024-02-19 14:55:25,749 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0870 [2024-02-19 14:55:27,012 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2698 [2024-02-19 14:55:29,971 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3171 [2024-02-19 14:55:31,783 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2909 [2024-02-19 14:55:33,306 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7287/0.7817/0.9170. [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9310/0.9615 [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9828/0.9899 [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8628/0.9717 [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3664/0.4120 [2024-02-19 14:55:33,306 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6490/0.6690 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8182/0.9290 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8495/0.9188 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9319/0.9739 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8649/0.8892 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8029/0.8957 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7912/0.8355 [2024-02-19 14:55:33,307 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6228/0.7153 [2024-02-19 14:55:33,308 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 14:55:33,310 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 14:55:33,310 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 14:55:40,277 INFO misc.py line 119 87073] Train: [90/100][1/1557] Data 0.991 (0.991) Batch 1.651 (1.651) Remain 07:51:18 loss: 0.2089 Lr: 0.00017 [2024-02-19 14:55:41,290 INFO misc.py line 119 87073] Train: [90/100][2/1557] Data 0.007 (0.007) Batch 0.941 (0.941) Remain 04:28:32 loss: 0.2597 Lr: 0.00017 [2024-02-19 14:55:42,481 INFO misc.py line 119 87073] Train: [90/100][3/1557] Data 0.080 (0.080) Batch 1.256 (1.256) Remain 05:58:28 loss: 0.0797 Lr: 0.00017 [2024-02-19 14:55:43,379 INFO misc.py line 119 87073] Train: [90/100][4/1557] Data 0.014 (0.014) Batch 0.904 (0.904) Remain 04:17:56 loss: 0.0457 Lr: 0.00016 [2024-02-19 14:55:44,109 INFO misc.py line 119 87073] Train: [90/100][5/1557] Data 0.008 (0.011) Batch 0.732 (0.818) Remain 03:53:27 loss: 0.2181 Lr: 0.00016 [2024-02-19 14:55:44,871 INFO misc.py line 119 87073] Train: [90/100][6/1557] Data 0.005 (0.009) Batch 0.755 (0.797) Remain 03:47:27 loss: 0.1629 Lr: 0.00016 [2024-02-19 14:55:46,102 INFO misc.py line 119 87073] Train: [90/100][7/1557] Data 0.011 (0.010) Batch 1.238 (0.907) Remain 04:18:54 loss: 0.1415 Lr: 0.00016 [2024-02-19 14:55:47,168 INFO misc.py line 119 87073] Train: [90/100][8/1557] Data 0.005 (0.009) Batch 1.061 (0.938) Remain 04:27:38 loss: 0.3539 Lr: 0.00016 [2024-02-19 14:55:48,188 INFO misc.py line 119 87073] Train: [90/100][9/1557] Data 0.010 (0.009) Batch 1.026 (0.953) Remain 04:31:46 loss: 0.3730 Lr: 0.00016 [2024-02-19 14:55:49,097 INFO misc.py line 119 87073] Train: [90/100][10/1557] Data 0.005 (0.008) Batch 0.909 (0.946) Remain 04:29:59 loss: 0.1480 Lr: 0.00016 [2024-02-19 14:55:49,973 INFO misc.py line 119 87073] Train: [90/100][11/1557] Data 0.004 (0.008) Batch 0.874 (0.937) Remain 04:27:24 loss: 0.2026 Lr: 0.00016 [2024-02-19 14:55:50,779 INFO misc.py line 119 87073] Train: [90/100][12/1557] Data 0.007 (0.008) Batch 0.800 (0.922) Remain 04:23:03 loss: 0.2332 Lr: 0.00016 [2024-02-19 14:55:51,545 INFO misc.py line 119 87073] Train: [90/100][13/1557] Data 0.011 (0.008) Batch 0.773 (0.907) Remain 04:18:47 loss: 0.2286 Lr: 0.00016 [2024-02-19 14:55:52,626 INFO misc.py line 119 87073] Train: [90/100][14/1557] Data 0.004 (0.008) Batch 1.078 (0.923) Remain 04:23:12 loss: 0.2125 Lr: 0.00016 [2024-02-19 14:55:53,617 INFO misc.py line 119 87073] Train: [90/100][15/1557] Data 0.007 (0.008) Batch 0.992 (0.929) Remain 04:24:51 loss: 0.0574 Lr: 0.00016 [2024-02-19 14:55:54,586 INFO misc.py line 119 87073] Train: [90/100][16/1557] Data 0.006 (0.007) Batch 0.971 (0.932) Remain 04:25:45 loss: 0.4271 Lr: 0.00016 [2024-02-19 14:55:55,677 INFO misc.py line 119 87073] Train: [90/100][17/1557] Data 0.004 (0.007) Batch 1.091 (0.943) Remain 04:28:59 loss: 0.2319 Lr: 0.00016 [2024-02-19 14:55:56,650 INFO misc.py line 119 87073] Train: [90/100][18/1557] Data 0.003 (0.007) Batch 0.972 (0.945) Remain 04:29:31 loss: 0.1725 Lr: 0.00016 [2024-02-19 14:55:57,427 INFO misc.py line 119 87073] Train: [90/100][19/1557] Data 0.004 (0.007) Batch 0.778 (0.935) Remain 04:26:31 loss: 0.3816 Lr: 0.00016 [2024-02-19 14:55:58,069 INFO misc.py line 119 87073] Train: [90/100][20/1557] Data 0.004 (0.007) Batch 0.635 (0.917) Remain 04:21:29 loss: 0.0911 Lr: 0.00016 [2024-02-19 14:55:59,304 INFO misc.py line 119 87073] Train: [90/100][21/1557] Data 0.010 (0.007) Batch 1.234 (0.935) Remain 04:26:29 loss: 0.2244 Lr: 0.00016 [2024-02-19 14:56:00,257 INFO misc.py line 119 87073] Train: [90/100][22/1557] Data 0.011 (0.007) Batch 0.959 (0.936) Remain 04:26:49 loss: 0.4148 Lr: 0.00016 [2024-02-19 14:56:01,176 INFO misc.py line 119 87073] Train: [90/100][23/1557] Data 0.006 (0.007) Batch 0.920 (0.935) Remain 04:26:35 loss: 0.1529 Lr: 0.00016 [2024-02-19 14:56:02,033 INFO misc.py line 119 87073] Train: [90/100][24/1557] Data 0.004 (0.007) Batch 0.855 (0.931) Remain 04:25:29 loss: 0.2581 Lr: 0.00016 [2024-02-19 14:56:03,026 INFO misc.py line 119 87073] Train: [90/100][25/1557] Data 0.006 (0.007) Batch 0.994 (0.934) Remain 04:26:17 loss: 0.1632 Lr: 0.00016 [2024-02-19 14:56:03,680 INFO misc.py line 119 87073] Train: [90/100][26/1557] Data 0.005 (0.007) Batch 0.654 (0.922) Remain 04:22:48 loss: 0.1624 Lr: 0.00016 [2024-02-19 14:56:04,461 INFO misc.py line 119 87073] Train: [90/100][27/1557] Data 0.005 (0.007) Batch 0.771 (0.916) Remain 04:20:59 loss: 0.1629 Lr: 0.00016 [2024-02-19 14:56:05,546 INFO misc.py line 119 87073] Train: [90/100][28/1557] Data 0.014 (0.007) Batch 1.084 (0.923) Remain 04:22:53 loss: 0.0820 Lr: 0.00016 [2024-02-19 14:56:06,268 INFO misc.py line 119 87073] Train: [90/100][29/1557] Data 0.016 (0.007) Batch 0.734 (0.915) Remain 04:20:48 loss: 0.2054 Lr: 0.00016 [2024-02-19 14:56:07,267 INFO misc.py line 119 87073] Train: [90/100][30/1557] Data 0.004 (0.007) Batch 0.987 (0.918) Remain 04:21:33 loss: 0.4952 Lr: 0.00016 [2024-02-19 14:56:08,536 INFO misc.py line 119 87073] Train: [90/100][31/1557] Data 0.015 (0.007) Batch 1.271 (0.931) Remain 04:25:07 loss: 0.1369 Lr: 0.00016 [2024-02-19 14:56:09,611 INFO misc.py line 119 87073] Train: [90/100][32/1557] Data 0.013 (0.008) Batch 1.076 (0.936) Remain 04:26:32 loss: 0.1475 Lr: 0.00016 [2024-02-19 14:56:10,338 INFO misc.py line 119 87073] Train: [90/100][33/1557] Data 0.013 (0.008) Batch 0.735 (0.929) Remain 04:24:37 loss: 0.1253 Lr: 0.00016 [2024-02-19 14:56:11,084 INFO misc.py line 119 87073] Train: [90/100][34/1557] Data 0.005 (0.008) Batch 0.745 (0.923) Remain 04:22:54 loss: 0.1450 Lr: 0.00016 [2024-02-19 14:56:12,408 INFO misc.py line 119 87073] Train: [90/100][35/1557] Data 0.006 (0.008) Batch 1.318 (0.935) Remain 04:26:25 loss: 0.0909 Lr: 0.00016 [2024-02-19 14:56:13,249 INFO misc.py line 119 87073] Train: [90/100][36/1557] Data 0.013 (0.008) Batch 0.849 (0.933) Remain 04:25:39 loss: 0.0369 Lr: 0.00016 [2024-02-19 14:56:14,324 INFO misc.py line 119 87073] Train: [90/100][37/1557] Data 0.004 (0.008) Batch 1.075 (0.937) Remain 04:26:50 loss: 0.1940 Lr: 0.00016 [2024-02-19 14:56:15,303 INFO misc.py line 119 87073] Train: [90/100][38/1557] Data 0.004 (0.008) Batch 0.980 (0.938) Remain 04:27:10 loss: 0.3276 Lr: 0.00016 [2024-02-19 14:56:16,257 INFO misc.py line 119 87073] Train: [90/100][39/1557] Data 0.004 (0.008) Batch 0.953 (0.938) Remain 04:27:16 loss: 0.1543 Lr: 0.00016 [2024-02-19 14:56:17,042 INFO misc.py line 119 87073] Train: [90/100][40/1557] Data 0.004 (0.007) Batch 0.777 (0.934) Remain 04:26:01 loss: 0.1431 Lr: 0.00016 [2024-02-19 14:56:17,828 INFO misc.py line 119 87073] Train: [90/100][41/1557] Data 0.013 (0.008) Batch 0.793 (0.930) Remain 04:24:56 loss: 0.1745 Lr: 0.00016 [2024-02-19 14:56:19,111 INFO misc.py line 119 87073] Train: [90/100][42/1557] Data 0.005 (0.007) Batch 1.275 (0.939) Remain 04:27:26 loss: 0.1228 Lr: 0.00016 [2024-02-19 14:56:20,170 INFO misc.py line 119 87073] Train: [90/100][43/1557] Data 0.013 (0.008) Batch 1.061 (0.942) Remain 04:28:18 loss: 0.0923 Lr: 0.00016 [2024-02-19 14:56:21,060 INFO misc.py line 119 87073] Train: [90/100][44/1557] Data 0.011 (0.008) Batch 0.896 (0.941) Remain 04:27:57 loss: 0.2535 Lr: 0.00016 [2024-02-19 14:56:22,112 INFO misc.py line 119 87073] Train: [90/100][45/1557] Data 0.004 (0.008) Batch 1.053 (0.944) Remain 04:28:42 loss: 0.2926 Lr: 0.00016 [2024-02-19 14:56:23,042 INFO misc.py line 119 87073] Train: [90/100][46/1557] Data 0.004 (0.008) Batch 0.929 (0.943) Remain 04:28:35 loss: 0.3309 Lr: 0.00016 [2024-02-19 14:56:23,745 INFO misc.py line 119 87073] Train: [90/100][47/1557] Data 0.005 (0.007) Batch 0.697 (0.938) Remain 04:26:59 loss: 0.1742 Lr: 0.00016 [2024-02-19 14:56:24,517 INFO misc.py line 119 87073] Train: [90/100][48/1557] Data 0.010 (0.008) Batch 0.777 (0.934) Remain 04:25:56 loss: 0.1497 Lr: 0.00016 [2024-02-19 14:56:25,837 INFO misc.py line 119 87073] Train: [90/100][49/1557] Data 0.005 (0.007) Batch 1.319 (0.943) Remain 04:28:18 loss: 0.0837 Lr: 0.00016 [2024-02-19 14:56:26,629 INFO misc.py line 119 87073] Train: [90/100][50/1557] Data 0.006 (0.007) Batch 0.794 (0.940) Remain 04:27:23 loss: 0.2405 Lr: 0.00016 [2024-02-19 14:56:27,483 INFO misc.py line 119 87073] Train: [90/100][51/1557] Data 0.004 (0.007) Batch 0.854 (0.938) Remain 04:26:52 loss: 0.1525 Lr: 0.00016 [2024-02-19 14:56:28,404 INFO misc.py line 119 87073] Train: [90/100][52/1557] Data 0.004 (0.007) Batch 0.921 (0.937) Remain 04:26:45 loss: 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INFO misc.py line 119 87073] Train: [90/100][59/1557] Data 0.004 (0.007) Batch 1.045 (0.941) Remain 04:27:37 loss: 0.1471 Lr: 0.00016 [2024-02-19 14:56:36,027 INFO misc.py line 119 87073] Train: [90/100][60/1557] Data 0.003 (0.007) Batch 0.870 (0.940) Remain 04:27:15 loss: 0.2698 Lr: 0.00016 [2024-02-19 14:56:36,808 INFO misc.py line 119 87073] Train: [90/100][61/1557] Data 0.004 (0.007) Batch 0.772 (0.937) Remain 04:26:25 loss: 0.1383 Lr: 0.00016 [2024-02-19 14:56:37,608 INFO misc.py line 119 87073] Train: [90/100][62/1557] Data 0.013 (0.007) Batch 0.809 (0.935) Remain 04:25:47 loss: 0.1183 Lr: 0.00016 [2024-02-19 14:56:45,467 INFO misc.py line 119 87073] Train: [90/100][63/1557] Data 4.056 (0.075) Batch 7.856 (1.050) Remain 04:58:34 loss: 0.0902 Lr: 0.00016 [2024-02-19 14:56:46,383 INFO misc.py line 119 87073] Train: [90/100][64/1557] Data 0.007 (0.074) Batch 0.918 (1.048) Remain 04:57:57 loss: 0.1044 Lr: 0.00016 [2024-02-19 14:56:47,230 INFO misc.py line 119 87073] Train: 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Batch 0.998 (1.050) Remain 04:57:42 loss: 0.2913 Lr: 0.00016 [2024-02-19 14:57:47,458 INFO misc.py line 119 87073] Train: [90/100][122/1557] Data 0.010 (0.081) Batch 1.022 (1.050) Remain 04:57:37 loss: 0.1857 Lr: 0.00016 [2024-02-19 14:57:48,388 INFO misc.py line 119 87073] Train: [90/100][123/1557] Data 0.024 (0.080) Batch 0.950 (1.049) Remain 04:57:22 loss: 0.1446 Lr: 0.00016 [2024-02-19 14:57:49,134 INFO misc.py line 119 87073] Train: [90/100][124/1557] Data 0.004 (0.080) Batch 0.746 (1.047) Remain 04:56:38 loss: 0.1259 Lr: 0.00016 [2024-02-19 14:57:49,887 INFO misc.py line 119 87073] Train: [90/100][125/1557] Data 0.004 (0.079) Batch 0.752 (1.044) Remain 04:55:56 loss: 0.1866 Lr: 0.00016 [2024-02-19 14:57:51,016 INFO misc.py line 119 87073] Train: [90/100][126/1557] Data 0.005 (0.078) Batch 1.127 (1.045) Remain 04:56:06 loss: 0.1651 Lr: 0.00016 [2024-02-19 14:57:51,960 INFO misc.py line 119 87073] Train: [90/100][127/1557] Data 0.006 (0.078) Batch 0.945 (1.044) Remain 04:55:52 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[2024-02-19 15:21:57,192 INFO misc.py line 119 87073] Train: [90/100][1464/1557] Data 0.006 (0.090) Batch 0.863 (1.078) Remain 04:41:22 loss: 0.0800 Lr: 0.00014 [2024-02-19 15:21:58,243 INFO misc.py line 119 87073] Train: [90/100][1465/1557] Data 0.005 (0.090) Batch 1.034 (1.078) Remain 04:41:20 loss: 0.3371 Lr: 0.00014 [2024-02-19 15:21:59,074 INFO misc.py line 119 87073] Train: [90/100][1466/1557] Data 0.022 (0.090) Batch 0.849 (1.078) Remain 04:41:17 loss: 0.3027 Lr: 0.00014 [2024-02-19 15:22:00,229 INFO misc.py line 119 87073] Train: [90/100][1467/1557] Data 0.004 (0.090) Batch 1.154 (1.078) Remain 04:41:16 loss: 0.2042 Lr: 0.00014 [2024-02-19 15:22:01,067 INFO misc.py line 119 87073] Train: [90/100][1468/1557] Data 0.005 (0.090) Batch 0.839 (1.078) Remain 04:41:13 loss: 0.2326 Lr: 0.00014 [2024-02-19 15:22:01,818 INFO misc.py line 119 87073] Train: [90/100][1469/1557] Data 0.005 (0.090) Batch 0.743 (1.077) Remain 04:41:08 loss: 0.1890 Lr: 0.00014 [2024-02-19 15:22:03,026 INFO misc.py line 119 87073] Train: [90/100][1470/1557] Data 0.012 (0.090) Batch 1.205 (1.077) Remain 04:41:08 loss: 0.1435 Lr: 0.00014 [2024-02-19 15:22:03,939 INFO misc.py line 119 87073] Train: [90/100][1471/1557] Data 0.015 (0.090) Batch 0.923 (1.077) Remain 04:41:06 loss: 0.2003 Lr: 0.00014 [2024-02-19 15:22:04,826 INFO misc.py line 119 87073] Train: [90/100][1472/1557] Data 0.006 (0.090) Batch 0.889 (1.077) Remain 04:41:02 loss: 0.1722 Lr: 0.00014 [2024-02-19 15:22:05,741 INFO misc.py line 119 87073] Train: [90/100][1473/1557] Data 0.004 (0.090) Batch 0.906 (1.077) Remain 04:41:00 loss: 0.2527 Lr: 0.00014 [2024-02-19 15:22:06,752 INFO misc.py line 119 87073] Train: [90/100][1474/1557] Data 0.013 (0.090) Batch 1.011 (1.077) Remain 04:40:58 loss: 0.3281 Lr: 0.00014 [2024-02-19 15:22:07,483 INFO misc.py line 119 87073] Train: [90/100][1475/1557] Data 0.013 (0.090) Batch 0.740 (1.077) Remain 04:40:53 loss: 0.1686 Lr: 0.00014 [2024-02-19 15:22:08,273 INFO misc.py line 119 87073] Train: 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(0.089) Batch 0.757 (1.076) Remain 04:40:33 loss: 0.1829 Lr: 0.00014 [2024-02-19 15:22:14,553 INFO misc.py line 119 87073] Train: [90/100][1483/1557] Data 0.005 (0.089) Batch 0.725 (1.076) Remain 04:40:28 loss: 0.2446 Lr: 0.00014 [2024-02-19 15:22:15,666 INFO misc.py line 119 87073] Train: [90/100][1484/1557] Data 0.011 (0.089) Batch 1.111 (1.076) Remain 04:40:27 loss: 0.0979 Lr: 0.00014 [2024-02-19 15:22:16,546 INFO misc.py line 119 87073] Train: [90/100][1485/1557] Data 0.013 (0.089) Batch 0.888 (1.076) Remain 04:40:24 loss: 0.3733 Lr: 0.00014 [2024-02-19 15:22:17,488 INFO misc.py line 119 87073] Train: [90/100][1486/1557] Data 0.005 (0.089) Batch 0.942 (1.076) Remain 04:40:22 loss: 0.1267 Lr: 0.00014 [2024-02-19 15:22:18,329 INFO misc.py line 119 87073] Train: [90/100][1487/1557] Data 0.005 (0.089) Batch 0.843 (1.075) Remain 04:40:18 loss: 0.2183 Lr: 0.00014 [2024-02-19 15:22:19,353 INFO misc.py line 119 87073] Train: [90/100][1488/1557] Data 0.003 (0.089) Batch 1.012 (1.075) Remain 04:40:17 loss: 0.3488 Lr: 0.00014 [2024-02-19 15:22:20,096 INFO misc.py line 119 87073] Train: [90/100][1489/1557] Data 0.016 (0.089) Batch 0.754 (1.075) Remain 04:40:12 loss: 0.1531 Lr: 0.00014 [2024-02-19 15:22:20,864 INFO misc.py line 119 87073] Train: [90/100][1490/1557] Data 0.005 (0.089) Batch 0.759 (1.075) Remain 04:40:08 loss: 0.1582 Lr: 0.00014 [2024-02-19 15:22:22,146 INFO misc.py line 119 87073] Train: [90/100][1491/1557] Data 0.013 (0.089) Batch 1.276 (1.075) Remain 04:40:09 loss: 0.1211 Lr: 0.00014 [2024-02-19 15:22:23,151 INFO misc.py line 119 87073] Train: [90/100][1492/1557] Data 0.020 (0.089) Batch 1.014 (1.075) Remain 04:40:07 loss: 0.2828 Lr: 0.00014 [2024-02-19 15:22:24,131 INFO misc.py line 119 87073] Train: [90/100][1493/1557] Data 0.010 (0.089) Batch 0.987 (1.075) Remain 04:40:05 loss: 0.3098 Lr: 0.00014 [2024-02-19 15:22:25,145 INFO misc.py line 119 87073] Train: [90/100][1494/1557] Data 0.004 (0.089) Batch 1.012 (1.075) Remain 04:40:03 loss: 0.3127 Lr: 0.00014 [2024-02-19 15:22:26,026 INFO misc.py line 119 87073] Train: [90/100][1495/1557] Data 0.006 (0.089) Batch 0.879 (1.075) Remain 04:40:00 loss: 0.2160 Lr: 0.00014 [2024-02-19 15:22:26,840 INFO misc.py line 119 87073] Train: [90/100][1496/1557] Data 0.008 (0.089) Batch 0.809 (1.075) Remain 04:39:56 loss: 0.1663 Lr: 0.00014 [2024-02-19 15:22:27,602 INFO misc.py line 119 87073] Train: [90/100][1497/1557] Data 0.013 (0.089) Batch 0.770 (1.074) Remain 04:39:52 loss: 0.2514 Lr: 0.00014 [2024-02-19 15:22:28,834 INFO misc.py line 119 87073] Train: [90/100][1498/1557] Data 0.004 (0.089) Batch 1.232 (1.074) Remain 04:39:53 loss: 0.1490 Lr: 0.00014 [2024-02-19 15:22:29,788 INFO misc.py line 119 87073] Train: [90/100][1499/1557] Data 0.005 (0.089) Batch 0.954 (1.074) Remain 04:39:50 loss: 0.2114 Lr: 0.00014 [2024-02-19 15:22:30,881 INFO misc.py line 119 87073] Train: [90/100][1500/1557] Data 0.005 (0.088) Batch 1.094 (1.074) Remain 04:39:49 loss: 0.4835 Lr: 0.00014 [2024-02-19 15:22:31,847 INFO misc.py line 119 87073] Train: [90/100][1501/1557] Data 0.004 (0.088) Batch 0.966 (1.074) Remain 04:39:47 loss: 0.2697 Lr: 0.00014 [2024-02-19 15:22:32,883 INFO misc.py line 119 87073] Train: [90/100][1502/1557] Data 0.003 (0.088) Batch 1.036 (1.074) Remain 04:39:46 loss: 0.2823 Lr: 0.00014 [2024-02-19 15:22:33,610 INFO misc.py line 119 87073] Train: [90/100][1503/1557] Data 0.003 (0.088) Batch 0.727 (1.074) Remain 04:39:41 loss: 0.1939 Lr: 0.00014 [2024-02-19 15:22:34,327 INFO misc.py line 119 87073] Train: [90/100][1504/1557] Data 0.004 (0.088) Batch 0.709 (1.074) Remain 04:39:36 loss: 0.1769 Lr: 0.00014 [2024-02-19 15:22:35,684 INFO misc.py line 119 87073] Train: [90/100][1505/1557] Data 0.013 (0.088) Batch 1.355 (1.074) Remain 04:39:38 loss: 0.0874 Lr: 0.00014 [2024-02-19 15:22:36,656 INFO misc.py line 119 87073] Train: [90/100][1506/1557] Data 0.015 (0.088) Batch 0.983 (1.074) Remain 04:39:36 loss: 0.5282 Lr: 0.00014 [2024-02-19 15:22:37,543 INFO misc.py line 119 87073] Train: [90/100][1507/1557] Data 0.004 (0.088) Batch 0.885 (1.074) Remain 04:39:33 loss: 0.1910 Lr: 0.00014 [2024-02-19 15:22:38,508 INFO misc.py line 119 87073] Train: [90/100][1508/1557] Data 0.005 (0.088) Batch 0.959 (1.074) Remain 04:39:31 loss: 0.2683 Lr: 0.00014 [2024-02-19 15:22:39,308 INFO misc.py line 119 87073] Train: [90/100][1509/1557] Data 0.012 (0.088) Batch 0.807 (1.074) Remain 04:39:27 loss: 0.0448 Lr: 0.00014 [2024-02-19 15:22:40,078 INFO misc.py line 119 87073] Train: [90/100][1510/1557] Data 0.004 (0.088) Batch 0.771 (1.073) Remain 04:39:23 loss: 0.1185 Lr: 0.00014 [2024-02-19 15:22:40,828 INFO misc.py line 119 87073] Train: [90/100][1511/1557] Data 0.004 (0.088) Batch 0.743 (1.073) Remain 04:39:18 loss: 0.1184 Lr: 0.00014 [2024-02-19 15:22:42,086 INFO misc.py line 119 87073] Train: [90/100][1512/1557] Data 0.011 (0.088) Batch 1.255 (1.073) Remain 04:39:19 loss: 0.1150 Lr: 0.00014 [2024-02-19 15:22:43,104 INFO misc.py line 119 87073] Train: [90/100][1513/1557] Data 0.013 (0.088) Batch 1.016 (1.073) Remain 04:39:17 loss: 0.0950 Lr: 0.00014 [2024-02-19 15:22:44,124 INFO misc.py line 119 87073] Train: [90/100][1514/1557] Data 0.016 (0.088) Batch 1.023 (1.073) Remain 04:39:16 loss: 0.0601 Lr: 0.00014 [2024-02-19 15:22:45,092 INFO misc.py line 119 87073] Train: [90/100][1515/1557] Data 0.012 (0.088) Batch 0.976 (1.073) Remain 04:39:14 loss: 0.2144 Lr: 0.00014 [2024-02-19 15:22:46,034 INFO misc.py line 119 87073] Train: [90/100][1516/1557] Data 0.004 (0.088) Batch 0.943 (1.073) Remain 04:39:11 loss: 0.0558 Lr: 0.00014 [2024-02-19 15:22:46,801 INFO misc.py line 119 87073] Train: [90/100][1517/1557] Data 0.003 (0.088) Batch 0.766 (1.073) Remain 04:39:07 loss: 0.2905 Lr: 0.00014 [2024-02-19 15:22:47,612 INFO misc.py line 119 87073] Train: [90/100][1518/1557] Data 0.003 (0.087) Batch 0.799 (1.073) Remain 04:39:03 loss: 0.1344 Lr: 0.00014 [2024-02-19 15:22:55,135 INFO misc.py line 119 87073] Train: [90/100][1519/1557] Data 4.094 (0.090) Batch 7.529 (1.077) Remain 04:40:09 loss: 0.1376 Lr: 0.00014 [2024-02-19 15:22:56,125 INFO misc.py line 119 87073] Train: [90/100][1520/1557] Data 0.011 (0.090) Batch 0.997 (1.077) Remain 04:40:07 loss: 0.1625 Lr: 0.00014 [2024-02-19 15:22:57,024 INFO misc.py line 119 87073] Train: [90/100][1521/1557] Data 0.003 (0.090) Batch 0.898 (1.077) Remain 04:40:04 loss: 0.1471 Lr: 0.00014 [2024-02-19 15:22:58,084 INFO misc.py line 119 87073] Train: [90/100][1522/1557] Data 0.005 (0.090) Batch 1.060 (1.077) Remain 04:40:02 loss: 0.3715 Lr: 0.00014 [2024-02-19 15:22:59,089 INFO misc.py line 119 87073] Train: [90/100][1523/1557] Data 0.005 (0.090) Batch 1.006 (1.077) Remain 04:40:01 loss: 0.3439 Lr: 0.00014 [2024-02-19 15:22:59,860 INFO misc.py line 119 87073] Train: [90/100][1524/1557] Data 0.004 (0.090) Batch 0.771 (1.077) Remain 04:39:56 loss: 0.1609 Lr: 0.00014 [2024-02-19 15:23:00,571 INFO misc.py line 119 87073] Train: [90/100][1525/1557] Data 0.003 (0.090) Batch 0.708 (1.076) Remain 04:39:52 loss: 0.1892 Lr: 0.00014 [2024-02-19 15:23:01,653 INFO misc.py line 119 87073] Train: [90/100][1526/1557] Data 0.007 (0.090) Batch 1.076 (1.076) Remain 04:39:51 loss: 0.1797 Lr: 0.00014 [2024-02-19 15:23:02,631 INFO misc.py line 119 87073] Train: [90/100][1527/1557] Data 0.012 (0.090) Batch 0.986 (1.076) Remain 04:39:48 loss: 0.1924 Lr: 0.00014 [2024-02-19 15:23:03,730 INFO misc.py line 119 87073] Train: [90/100][1528/1557] Data 0.004 (0.090) Batch 1.098 (1.076) Remain 04:39:48 loss: 0.3399 Lr: 0.00014 [2024-02-19 15:23:04,779 INFO misc.py line 119 87073] Train: [90/100][1529/1557] Data 0.005 (0.090) Batch 1.050 (1.076) Remain 04:39:46 loss: 0.2360 Lr: 0.00014 [2024-02-19 15:23:05,833 INFO misc.py line 119 87073] Train: [90/100][1530/1557] Data 0.005 (0.090) Batch 1.054 (1.076) Remain 04:39:45 loss: 0.0445 Lr: 0.00014 [2024-02-19 15:23:06,591 INFO misc.py line 119 87073] Train: [90/100][1531/1557] Data 0.005 (0.089) Batch 0.758 (1.076) Remain 04:39:41 loss: 0.1597 Lr: 0.00014 [2024-02-19 15:23:07,358 INFO misc.py line 119 87073] Train: [90/100][1532/1557] Data 0.005 (0.089) Batch 0.757 (1.076) Remain 04:39:36 loss: 0.0975 Lr: 0.00014 [2024-02-19 15:23:08,605 INFO misc.py line 119 87073] Train: [90/100][1533/1557] Data 0.014 (0.089) Batch 1.245 (1.076) Remain 04:39:37 loss: 0.1618 Lr: 0.00014 [2024-02-19 15:23:09,545 INFO misc.py line 119 87073] Train: [90/100][1534/1557] Data 0.016 (0.089) Batch 0.951 (1.076) Remain 04:39:35 loss: 0.0753 Lr: 0.00014 [2024-02-19 15:23:10,528 INFO misc.py line 119 87073] Train: [90/100][1535/1557] Data 0.005 (0.089) Batch 0.984 (1.076) Remain 04:39:33 loss: 0.1376 Lr: 0.00014 [2024-02-19 15:23:11,403 INFO misc.py line 119 87073] Train: [90/100][1536/1557] Data 0.004 (0.089) Batch 0.875 (1.076) Remain 04:39:30 loss: 0.1556 Lr: 0.00014 [2024-02-19 15:23:12,470 INFO misc.py line 119 87073] Train: [90/100][1537/1557] Data 0.004 (0.089) Batch 1.055 (1.076) Remain 04:39:28 loss: 0.2575 Lr: 0.00014 [2024-02-19 15:23:13,213 INFO misc.py line 119 87073] Train: [90/100][1538/1557] Data 0.016 (0.089) Batch 0.755 (1.075) Remain 04:39:24 loss: 0.2252 Lr: 0.00014 [2024-02-19 15:23:13,971 INFO misc.py line 119 87073] Train: [90/100][1539/1557] Data 0.004 (0.089) Batch 0.752 (1.075) Remain 04:39:20 loss: 0.1325 Lr: 0.00014 [2024-02-19 15:23:15,255 INFO misc.py line 119 87073] Train: [90/100][1540/1557] Data 0.012 (0.089) Batch 1.274 (1.075) Remain 04:39:20 loss: 0.1137 Lr: 0.00014 [2024-02-19 15:23:16,150 INFO misc.py line 119 87073] Train: [90/100][1541/1557] Data 0.020 (0.089) Batch 0.911 (1.075) Remain 04:39:18 loss: 0.2000 Lr: 0.00014 [2024-02-19 15:23:17,038 INFO misc.py line 119 87073] Train: [90/100][1542/1557] Data 0.004 (0.089) Batch 0.888 (1.075) Remain 04:39:15 loss: 0.5162 Lr: 0.00014 [2024-02-19 15:23:17,934 INFO misc.py line 119 87073] Train: [90/100][1543/1557] Data 0.004 (0.089) Batch 0.883 (1.075) Remain 04:39:12 loss: 0.2735 Lr: 0.00014 [2024-02-19 15:23:18,892 INFO misc.py line 119 87073] Train: [90/100][1544/1557] Data 0.016 (0.089) Batch 0.970 (1.075) Remain 04:39:10 loss: 0.1075 Lr: 0.00014 [2024-02-19 15:23:19,579 INFO misc.py line 119 87073] Train: [90/100][1545/1557] Data 0.004 (0.089) Batch 0.688 (1.075) Remain 04:39:05 loss: 0.2007 Lr: 0.00014 [2024-02-19 15:23:20,317 INFO misc.py line 119 87073] Train: [90/100][1546/1557] Data 0.004 (0.089) Batch 0.726 (1.074) Remain 04:39:00 loss: 0.1479 Lr: 0.00014 [2024-02-19 15:23:21,662 INFO misc.py line 119 87073] Train: [90/100][1547/1557] Data 0.016 (0.089) Batch 1.343 (1.075) Remain 04:39:02 loss: 0.1000 Lr: 0.00014 [2024-02-19 15:23:22,745 INFO misc.py line 119 87073] Train: [90/100][1548/1557] Data 0.018 (0.089) Batch 1.084 (1.075) Remain 04:39:01 loss: 0.1043 Lr: 0.00014 [2024-02-19 15:23:23,594 INFO misc.py line 119 87073] Train: [90/100][1549/1557] Data 0.017 (0.089) Batch 0.862 (1.074) Remain 04:38:57 loss: 0.1383 Lr: 0.00014 [2024-02-19 15:23:24,559 INFO misc.py line 119 87073] Train: [90/100][1550/1557] Data 0.004 (0.088) Batch 0.965 (1.074) Remain 04:38:55 loss: 0.2633 Lr: 0.00014 [2024-02-19 15:23:25,475 INFO misc.py line 119 87073] Train: [90/100][1551/1557] Data 0.004 (0.088) Batch 0.916 (1.074) Remain 04:38:53 loss: 0.3968 Lr: 0.00014 [2024-02-19 15:23:26,261 INFO misc.py line 119 87073] Train: [90/100][1552/1557] Data 0.004 (0.088) Batch 0.772 (1.074) Remain 04:38:49 loss: 0.1543 Lr: 0.00014 [2024-02-19 15:23:26,968 INFO misc.py line 119 87073] Train: [90/100][1553/1557] Data 0.017 (0.088) Batch 0.720 (1.074) Remain 04:38:44 loss: 0.2003 Lr: 0.00014 [2024-02-19 15:23:28,316 INFO misc.py line 119 87073] Train: [90/100][1554/1557] Data 0.004 (0.088) Batch 1.307 (1.074) Remain 04:38:45 loss: 0.1324 Lr: 0.00014 [2024-02-19 15:23:29,384 INFO misc.py line 119 87073] Train: [90/100][1555/1557] Data 0.045 (0.088) Batch 1.096 (1.074) Remain 04:38:44 loss: 0.2904 Lr: 0.00014 [2024-02-19 15:23:30,259 INFO misc.py line 119 87073] Train: [90/100][1556/1557] Data 0.017 (0.088) Batch 0.889 (1.074) Remain 04:38:41 loss: 0.1876 Lr: 0.00014 [2024-02-19 15:23:31,165 INFO misc.py line 119 87073] Train: [90/100][1557/1557] Data 0.004 (0.088) Batch 0.906 (1.074) Remain 04:38:39 loss: 0.3882 Lr: 0.00014 [2024-02-19 15:23:31,166 INFO misc.py line 136 87073] Train result: loss: 0.2028 [2024-02-19 15:23:31,166 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 15:23:59,226 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4312 [2024-02-19 15:24:00,018 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4321 [2024-02-19 15:24:02,146 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3389 [2024-02-19 15:24:04,360 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3565 [2024-02-19 15:24:09,309 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2385 [2024-02-19 15:24:10,008 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1152 [2024-02-19 15:24:11,269 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3215 [2024-02-19 15:24:14,226 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2381 [2024-02-19 15:24:16,039 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2777 [2024-02-19 15:24:17,691 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7268/0.7788/0.9182. [2024-02-19 15:24:17,691 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9311/0.9653 [2024-02-19 15:24:17,691 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9892 [2024-02-19 15:24:17,691 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8672/0.9737 [2024-02-19 15:24:17,691 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 15:24:17,691 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4066/0.4594 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6801/0.6972 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8272/0.9203 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8498/0.9168 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9239/0.9780 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7903/0.8132 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7985/0.8914 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7717/0.8109 [2024-02-19 15:24:17,692 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6195/0.7089 [2024-02-19 15:24:17,693 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 15:24:17,694 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 15:24:17,694 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 15:24:25,554 INFO misc.py line 119 87073] Train: [91/100][1/1557] Data 1.311 (1.311) Batch 2.011 (2.011) Remain 08:41:50 loss: 0.0651 Lr: 0.00014 [2024-02-19 15:24:26,654 INFO misc.py line 119 87073] Train: [91/100][2/1557] Data 0.006 (0.006) Batch 1.099 (1.099) Remain 04:45:02 loss: 0.1353 Lr: 0.00014 [2024-02-19 15:24:27,606 INFO misc.py line 119 87073] Train: [91/100][3/1557] Data 0.009 (0.009) Batch 0.954 (0.954) Remain 04:07:32 loss: 0.0735 Lr: 0.00014 [2024-02-19 15:24:28,542 INFO misc.py line 119 87073] Train: [91/100][4/1557] Data 0.006 (0.006) Batch 0.936 (0.936) Remain 04:02:50 loss: 0.2194 Lr: 0.00014 [2024-02-19 15:24:29,282 INFO misc.py line 119 87073] Train: [91/100][5/1557] Data 0.005 (0.006) Batch 0.742 (0.839) Remain 03:37:41 loss: 0.2301 Lr: 0.00014 [2024-02-19 15:24:30,024 INFO misc.py line 119 87073] Train: [91/100][6/1557] Data 0.003 (0.005) Batch 0.741 (0.806) Remain 03:29:10 loss: 0.1447 Lr: 0.00014 [2024-02-19 15:24:31,202 INFO misc.py line 119 87073] Train: [91/100][7/1557] Data 0.005 (0.005) Batch 1.165 (0.896) Remain 03:52:23 loss: 0.1286 Lr: 0.00014 [2024-02-19 15:24:32,179 INFO misc.py line 119 87073] Train: [91/100][8/1557] Data 0.018 (0.007) Batch 0.991 (0.915) Remain 03:57:18 loss: 0.0909 Lr: 0.00014 [2024-02-19 15:24:33,104 INFO misc.py line 119 87073] Train: [91/100][9/1557] Data 0.004 (0.007) Batch 0.923 (0.916) Remain 03:57:39 loss: 0.1427 Lr: 0.00014 [2024-02-19 15:24:34,070 INFO misc.py line 119 87073] Train: [91/100][10/1557] Data 0.006 (0.007) Batch 0.968 (0.924) Remain 03:59:33 loss: 0.4496 Lr: 0.00014 [2024-02-19 15:24:35,189 INFO misc.py line 119 87073] Train: [91/100][11/1557] Data 0.003 (0.006) Batch 1.118 (0.948) Remain 04:05:50 loss: 0.2040 Lr: 0.00014 [2024-02-19 15:24:35,977 INFO misc.py line 119 87073] Train: [91/100][12/1557] Data 0.004 (0.006) Batch 0.787 (0.930) Remain 04:01:11 loss: 0.1032 Lr: 0.00014 [2024-02-19 15:24:36,784 INFO misc.py line 119 87073] Train: [91/100][13/1557] Data 0.006 (0.006) Batch 0.799 (0.917) Remain 03:57:45 loss: 0.0649 Lr: 0.00014 [2024-02-19 15:24:38,104 INFO misc.py line 119 87073] Train: [91/100][14/1557] Data 0.013 (0.007) Batch 1.325 (0.954) Remain 04:07:21 loss: 0.1656 Lr: 0.00014 [2024-02-19 15:24:39,235 INFO misc.py line 119 87073] Train: [91/100][15/1557] Data 0.009 (0.007) Batch 1.125 (0.968) Remain 04:11:01 loss: 0.3018 Lr: 0.00014 [2024-02-19 15:24:40,181 INFO misc.py line 119 87073] Train: [91/100][16/1557] Data 0.014 (0.007) Batch 0.958 (0.967) Remain 04:10:48 loss: 0.2257 Lr: 0.00014 [2024-02-19 15:24:41,069 INFO misc.py line 119 87073] Train: [91/100][17/1557] Data 0.003 (0.007) Batch 0.886 (0.962) Remain 04:09:16 loss: 0.2864 Lr: 0.00014 [2024-02-19 15:24:42,179 INFO misc.py line 119 87073] Train: [91/100][18/1557] Data 0.005 (0.007) Batch 1.110 (0.972) Remain 04:11:49 loss: 0.4651 Lr: 0.00014 [2024-02-19 15:24:42,910 INFO misc.py line 119 87073] Train: [91/100][19/1557] Data 0.005 (0.007) Batch 0.732 (0.957) Remain 04:07:55 loss: 0.1490 Lr: 0.00014 [2024-02-19 15:24:43,663 INFO misc.py line 119 87073] Train: [91/100][20/1557] Data 0.004 (0.007) Batch 0.744 (0.944) Remain 04:04:40 loss: 0.1835 Lr: 0.00014 [2024-02-19 15:24:44,952 INFO misc.py line 119 87073] Train: [91/100][21/1557] Data 0.013 (0.007) Batch 1.289 (0.963) Remain 04:09:37 loss: 0.1276 Lr: 0.00014 [2024-02-19 15:24:45,934 INFO misc.py line 119 87073] Train: [91/100][22/1557] Data 0.012 (0.007) Batch 0.989 (0.965) Remain 04:09:57 loss: 0.1930 Lr: 0.00014 [2024-02-19 15:24:46,993 INFO misc.py line 119 87073] Train: [91/100][23/1557] Data 0.006 (0.007) Batch 1.061 (0.969) Remain 04:11:11 loss: 0.3939 Lr: 0.00014 [2024-02-19 15:24:47,962 INFO misc.py line 119 87073] Train: [91/100][24/1557] Data 0.004 (0.007) Batch 0.969 (0.969) Remain 04:11:10 loss: 0.3145 Lr: 0.00014 [2024-02-19 15:24:48,996 INFO misc.py line 119 87073] Train: [91/100][25/1557] Data 0.003 (0.007) Batch 1.034 (0.972) Remain 04:11:55 loss: 0.1046 Lr: 0.00014 [2024-02-19 15:24:49,731 INFO misc.py line 119 87073] Train: [91/100][26/1557] Data 0.003 (0.007) Batch 0.735 (0.962) Remain 04:09:13 loss: 0.1960 Lr: 0.00014 [2024-02-19 15:24:50,480 INFO misc.py line 119 87073] Train: [91/100][27/1557] Data 0.004 (0.007) Batch 0.748 (0.953) Remain 04:06:54 loss: 0.2303 Lr: 0.00014 [2024-02-19 15:24:51,802 INFO misc.py line 119 87073] Train: [91/100][28/1557] Data 0.004 (0.007) Batch 1.310 (0.967) Remain 04:10:35 loss: 0.0836 Lr: 0.00014 [2024-02-19 15:24:52,911 INFO misc.py line 119 87073] Train: [91/100][29/1557] Data 0.017 (0.007) Batch 1.114 (0.973) Remain 04:12:01 loss: 0.1888 Lr: 0.00014 [2024-02-19 15:24:53,899 INFO misc.py line 119 87073] Train: [91/100][30/1557] Data 0.012 (0.007) Batch 0.996 (0.974) Remain 04:12:14 loss: 0.4217 Lr: 0.00014 [2024-02-19 15:24:54,834 INFO misc.py line 119 87073] Train: [91/100][31/1557] Data 0.003 (0.007) Batch 0.934 (0.972) Remain 04:11:51 loss: 0.0733 Lr: 0.00014 [2024-02-19 15:24:55,634 INFO misc.py line 119 87073] Train: [91/100][32/1557] Data 0.005 (0.007) Batch 0.799 (0.967) Remain 04:10:17 loss: 0.0778 Lr: 0.00014 [2024-02-19 15:24:56,323 INFO misc.py line 119 87073] Train: [91/100][33/1557] Data 0.006 (0.007) Batch 0.687 (0.957) Remain 04:07:51 loss: 0.1461 Lr: 0.00014 [2024-02-19 15:24:57,052 INFO misc.py line 119 87073] Train: [91/100][34/1557] Data 0.007 (0.007) Batch 0.732 (0.950) Remain 04:05:58 loss: 0.1686 Lr: 0.00014 [2024-02-19 15:24:58,295 INFO misc.py line 119 87073] Train: [91/100][35/1557] Data 0.003 (0.007) Batch 1.240 (0.959) Remain 04:08:18 loss: 0.1032 Lr: 0.00014 [2024-02-19 15:24:59,165 INFO misc.py line 119 87073] Train: [91/100][36/1557] Data 0.006 (0.007) Batch 0.871 (0.956) Remain 04:07:36 loss: 0.4943 Lr: 0.00014 [2024-02-19 15:25:00,112 INFO misc.py line 119 87073] Train: [91/100][37/1557] Data 0.005 (0.007) Batch 0.944 (0.956) Remain 04:07:29 loss: 0.0969 Lr: 0.00014 [2024-02-19 15:25:01,091 INFO misc.py line 119 87073] Train: [91/100][38/1557] Data 0.008 (0.007) Batch 0.983 (0.957) Remain 04:07:40 loss: 0.2307 Lr: 0.00014 [2024-02-19 15:25:02,049 INFO misc.py line 119 87073] Train: [91/100][39/1557] Data 0.004 (0.007) Batch 0.958 (0.957) Remain 04:07:40 loss: 0.2119 Lr: 0.00014 [2024-02-19 15:25:02,858 INFO misc.py line 119 87073] Train: [91/100][40/1557] Data 0.004 (0.007) Batch 0.804 (0.953) Remain 04:06:35 loss: 0.2656 Lr: 0.00014 [2024-02-19 15:25:03,634 INFO misc.py line 119 87073] Train: [91/100][41/1557] Data 0.008 (0.007) Batch 0.780 (0.948) Remain 04:05:23 loss: 0.1740 Lr: 0.00014 [2024-02-19 15:25:04,903 INFO misc.py line 119 87073] Train: [91/100][42/1557] Data 0.004 (0.007) Batch 1.258 (0.956) Remain 04:07:25 loss: 0.2083 Lr: 0.00014 [2024-02-19 15:25:05,868 INFO misc.py line 119 87073] Train: [91/100][43/1557] Data 0.016 (0.007) Batch 0.978 (0.957) Remain 04:07:33 loss: 0.0394 Lr: 0.00014 [2024-02-19 15:25:06,762 INFO misc.py line 119 87073] Train: [91/100][44/1557] Data 0.004 (0.007) Batch 0.894 (0.955) Remain 04:07:08 loss: 0.1581 Lr: 0.00014 [2024-02-19 15:25:07,739 INFO misc.py line 119 87073] Train: [91/100][45/1557] Data 0.004 (0.007) Batch 0.977 (0.956) Remain 04:07:15 loss: 0.2757 Lr: 0.00014 [2024-02-19 15:25:08,744 INFO misc.py line 119 87073] Train: [91/100][46/1557] Data 0.003 (0.007) Batch 0.994 (0.957) Remain 04:07:28 loss: 0.1609 Lr: 0.00014 [2024-02-19 15:25:09,536 INFO misc.py line 119 87073] Train: [91/100][47/1557] Data 0.014 (0.007) Batch 0.793 (0.953) Remain 04:06:29 loss: 0.3694 Lr: 0.00014 [2024-02-19 15:25:10,368 INFO misc.py line 119 87073] Train: [91/100][48/1557] Data 0.014 (0.007) Batch 0.840 (0.950) Remain 04:05:50 loss: 0.1514 Lr: 0.00014 [2024-02-19 15:25:11,684 INFO misc.py line 119 87073] Train: [91/100][49/1557] Data 0.005 (0.007) Batch 1.316 (0.958) Remain 04:07:52 loss: 0.0682 Lr: 0.00014 [2024-02-19 15:25:12,559 INFO misc.py line 119 87073] Train: [91/100][50/1557] Data 0.006 (0.007) Batch 0.877 (0.956) Remain 04:07:24 loss: 0.2636 Lr: 0.00014 [2024-02-19 15:25:13,958 INFO misc.py line 119 87073] Train: [91/100][51/1557] Data 0.003 (0.007) Batch 1.394 (0.966) Remain 04:09:44 loss: 0.1959 Lr: 0.00014 [2024-02-19 15:25:14,934 INFO misc.py line 119 87073] Train: [91/100][52/1557] Data 0.010 (0.007) Batch 0.981 (0.966) Remain 04:09:48 loss: 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INFO misc.py line 119 87073] Train: [91/100][59/1557] Data 0.016 (0.007) Batch 0.858 (0.961) Remain 04:08:27 loss: 0.0957 Lr: 0.00014 [2024-02-19 15:25:22,237 INFO misc.py line 119 87073] Train: [91/100][60/1557] Data 0.007 (0.007) Batch 0.809 (0.958) Remain 04:07:44 loss: 0.2040 Lr: 0.00014 [2024-02-19 15:25:23,031 INFO misc.py line 119 87073] Train: [91/100][61/1557] Data 0.008 (0.007) Batch 0.792 (0.956) Remain 04:06:59 loss: 0.2288 Lr: 0.00014 [2024-02-19 15:25:23,856 INFO misc.py line 119 87073] Train: [91/100][62/1557] Data 0.010 (0.007) Batch 0.830 (0.953) Remain 04:06:25 loss: 0.1034 Lr: 0.00014 [2024-02-19 15:25:31,908 INFO misc.py line 119 87073] Train: [91/100][63/1557] Data 5.553 (0.100) Batch 8.048 (1.072) Remain 04:36:58 loss: 0.0967 Lr: 0.00014 [2024-02-19 15:25:32,796 INFO misc.py line 119 87073] Train: [91/100][64/1557] Data 0.010 (0.098) Batch 0.889 (1.069) Remain 04:36:10 loss: 0.5355 Lr: 0.00014 [2024-02-19 15:25:33,671 INFO misc.py line 119 87073] Train: 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0.973 (1.054) Remain 04:32:11 loss: 0.2253 Lr: 0.00014 [2024-02-19 15:25:40,178 INFO misc.py line 119 87073] Train: [91/100][72/1557] Data 0.004 (0.087) Batch 0.922 (1.052) Remain 04:31:40 loss: 0.1828 Lr: 0.00014 [2024-02-19 15:25:41,045 INFO misc.py line 119 87073] Train: [91/100][73/1557] Data 0.005 (0.086) Batch 0.868 (1.049) Remain 04:30:58 loss: 0.2698 Lr: 0.00014 [2024-02-19 15:25:42,054 INFO misc.py line 119 87073] Train: [91/100][74/1557] Data 0.004 (0.085) Batch 1.001 (1.048) Remain 04:30:47 loss: 0.0602 Lr: 0.00014 [2024-02-19 15:25:42,855 INFO misc.py line 119 87073] Train: [91/100][75/1557] Data 0.011 (0.084) Batch 0.809 (1.045) Remain 04:29:54 loss: 0.1834 Lr: 0.00014 [2024-02-19 15:25:43,705 INFO misc.py line 119 87073] Train: [91/100][76/1557] Data 0.003 (0.083) Batch 0.846 (1.042) Remain 04:29:11 loss: 0.1264 Lr: 0.00014 [2024-02-19 15:25:44,989 INFO misc.py line 119 87073] Train: [91/100][77/1557] Data 0.007 (0.082) Batch 1.277 (1.046) Remain 04:29:59 loss: 0.1300 Lr: 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line 119 87073] Train: [91/100][84/1557] Data 0.015 (0.076) Batch 1.278 (1.033) Remain 04:26:42 loss: 0.1421 Lr: 0.00014 [2024-02-19 15:25:52,253 INFO misc.py line 119 87073] Train: [91/100][85/1557] Data 0.018 (0.075) Batch 0.947 (1.032) Remain 04:26:25 loss: 0.2730 Lr: 0.00014 [2024-02-19 15:25:53,038 INFO misc.py line 119 87073] Train: [91/100][86/1557] Data 0.004 (0.074) Batch 0.786 (1.029) Remain 04:25:38 loss: 0.4216 Lr: 0.00014 [2024-02-19 15:25:53,979 INFO misc.py line 119 87073] Train: [91/100][87/1557] Data 0.004 (0.073) Batch 0.932 (1.028) Remain 04:25:19 loss: 0.0836 Lr: 0.00014 [2024-02-19 15:25:55,040 INFO misc.py line 119 87073] Train: [91/100][88/1557] Data 0.012 (0.072) Batch 1.063 (1.029) Remain 04:25:24 loss: 0.1929 Lr: 0.00014 [2024-02-19 15:25:55,822 INFO misc.py line 119 87073] Train: [91/100][89/1557] Data 0.011 (0.072) Batch 0.789 (1.026) Remain 04:24:40 loss: 0.2986 Lr: 0.00014 [2024-02-19 15:25:56,592 INFO misc.py line 119 87073] Train: [91/100][90/1557] Data 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04:13:58 loss: 0.0651 Lr: 0.00011 [2024-02-19 15:51:15,317 INFO misc.py line 119 87073] Train: [91/100][1489/1557] Data 0.005 (0.104) Batch 0.770 (1.082) Remain 04:13:54 loss: 0.2323 Lr: 0.00011 [2024-02-19 15:51:16,077 INFO misc.py line 119 87073] Train: [91/100][1490/1557] Data 0.004 (0.104) Batch 0.756 (1.082) Remain 04:13:50 loss: 0.0899 Lr: 0.00011 [2024-02-19 15:51:17,332 INFO misc.py line 119 87073] Train: [91/100][1491/1557] Data 0.008 (0.104) Batch 1.258 (1.082) Remain 04:13:50 loss: 0.0761 Lr: 0.00011 [2024-02-19 15:51:18,302 INFO misc.py line 119 87073] Train: [91/100][1492/1557] Data 0.006 (0.104) Batch 0.972 (1.082) Remain 04:13:48 loss: 0.1920 Lr: 0.00011 [2024-02-19 15:51:19,203 INFO misc.py line 119 87073] Train: [91/100][1493/1557] Data 0.004 (0.103) Batch 0.900 (1.082) Remain 04:13:45 loss: 0.4790 Lr: 0.00011 [2024-02-19 15:51:20,158 INFO misc.py line 119 87073] Train: [91/100][1494/1557] Data 0.005 (0.103) Batch 0.948 (1.082) Remain 04:13:43 loss: 0.1398 Lr: 0.00011 [2024-02-19 15:51:21,067 INFO misc.py line 119 87073] Train: [91/100][1495/1557] Data 0.012 (0.103) Batch 0.916 (1.081) Remain 04:13:40 loss: 0.0688 Lr: 0.00011 [2024-02-19 15:51:21,856 INFO misc.py line 119 87073] Train: [91/100][1496/1557] Data 0.003 (0.103) Batch 0.789 (1.081) Remain 04:13:36 loss: 0.1359 Lr: 0.00011 [2024-02-19 15:51:22,634 INFO misc.py line 119 87073] Train: [91/100][1497/1557] Data 0.003 (0.103) Batch 0.772 (1.081) Remain 04:13:32 loss: 0.1581 Lr: 0.00011 [2024-02-19 15:51:23,891 INFO misc.py line 119 87073] Train: [91/100][1498/1557] Data 0.010 (0.103) Batch 1.254 (1.081) Remain 04:13:33 loss: 0.1204 Lr: 0.00011 [2024-02-19 15:51:24,877 INFO misc.py line 119 87073] Train: [91/100][1499/1557] Data 0.013 (0.103) Batch 0.994 (1.081) Remain 04:13:31 loss: 0.2139 Lr: 0.00011 [2024-02-19 15:51:25,831 INFO misc.py line 119 87073] Train: [91/100][1500/1557] Data 0.006 (0.103) Batch 0.955 (1.081) Remain 04:13:29 loss: 0.0610 Lr: 0.00011 [2024-02-19 15:51:26,903 INFO misc.py line 119 87073] Train: [91/100][1501/1557] Data 0.004 (0.103) Batch 1.071 (1.081) Remain 04:13:28 loss: 0.2759 Lr: 0.00011 [2024-02-19 15:51:27,827 INFO misc.py line 119 87073] Train: [91/100][1502/1557] Data 0.006 (0.103) Batch 0.925 (1.081) Remain 04:13:25 loss: 0.0602 Lr: 0.00011 [2024-02-19 15:51:28,573 INFO misc.py line 119 87073] Train: [91/100][1503/1557] Data 0.004 (0.103) Batch 0.736 (1.081) Remain 04:13:21 loss: 0.2365 Lr: 0.00011 [2024-02-19 15:51:29,276 INFO misc.py line 119 87073] Train: [91/100][1504/1557] Data 0.014 (0.103) Batch 0.713 (1.080) Remain 04:13:16 loss: 0.0865 Lr: 0.00011 [2024-02-19 15:51:30,488 INFO misc.py line 119 87073] Train: [91/100][1505/1557] Data 0.004 (0.103) Batch 1.200 (1.080) Remain 04:13:16 loss: 0.1395 Lr: 0.00011 [2024-02-19 15:51:31,462 INFO misc.py line 119 87073] Train: [91/100][1506/1557] Data 0.016 (0.103) Batch 0.986 (1.080) Remain 04:13:14 loss: 0.3875 Lr: 0.00011 [2024-02-19 15:51:32,434 INFO misc.py line 119 87073] Train: 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(0.102) Batch 0.970 (1.080) Remain 04:12:59 loss: 0.2943 Lr: 0.00011 [2024-02-19 15:51:39,282 INFO misc.py line 119 87073] Train: [91/100][1514/1557] Data 0.004 (0.102) Batch 1.136 (1.080) Remain 04:12:58 loss: 0.2617 Lr: 0.00011 [2024-02-19 15:51:40,425 INFO misc.py line 119 87073] Train: [91/100][1515/1557] Data 0.004 (0.102) Batch 1.142 (1.080) Remain 04:12:58 loss: 0.0770 Lr: 0.00011 [2024-02-19 15:51:41,278 INFO misc.py line 119 87073] Train: [91/100][1516/1557] Data 0.003 (0.102) Batch 0.853 (1.080) Remain 04:12:54 loss: 0.1895 Lr: 0.00011 [2024-02-19 15:51:41,974 INFO misc.py line 119 87073] Train: [91/100][1517/1557] Data 0.004 (0.102) Batch 0.696 (1.080) Remain 04:12:50 loss: 0.1541 Lr: 0.00011 [2024-02-19 15:51:42,700 INFO misc.py line 119 87073] Train: [91/100][1518/1557] Data 0.004 (0.102) Batch 0.726 (1.079) Remain 04:12:45 loss: 0.2267 Lr: 0.00011 [2024-02-19 15:51:51,401 INFO misc.py line 119 87073] Train: [91/100][1519/1557] Data 5.131 (0.105) Batch 8.699 (1.084) Remain 04:13:55 loss: 0.1589 Lr: 0.00011 [2024-02-19 15:51:52,351 INFO misc.py line 119 87073] Train: [91/100][1520/1557] Data 0.005 (0.105) Batch 0.950 (1.084) Remain 04:13:53 loss: 0.1043 Lr: 0.00011 [2024-02-19 15:51:53,482 INFO misc.py line 119 87073] Train: [91/100][1521/1557] Data 0.005 (0.105) Batch 1.132 (1.084) Remain 04:13:52 loss: 0.1206 Lr: 0.00011 [2024-02-19 15:51:54,522 INFO misc.py line 119 87073] Train: [91/100][1522/1557] Data 0.005 (0.105) Batch 1.035 (1.084) Remain 04:13:50 loss: 0.2351 Lr: 0.00011 [2024-02-19 15:51:55,543 INFO misc.py line 119 87073] Train: [91/100][1523/1557] Data 0.009 (0.105) Batch 1.022 (1.084) Remain 04:13:49 loss: 0.4042 Lr: 0.00011 [2024-02-19 15:51:56,343 INFO misc.py line 119 87073] Train: [91/100][1524/1557] Data 0.008 (0.105) Batch 0.803 (1.084) Remain 04:13:45 loss: 0.1609 Lr: 0.00011 [2024-02-19 15:51:57,148 INFO misc.py line 119 87073] Train: [91/100][1525/1557] Data 0.004 (0.105) Batch 0.805 (1.084) Remain 04:13:41 loss: 0.1839 Lr: 0.00011 [2024-02-19 15:51:58,402 INFO misc.py line 119 87073] Train: [91/100][1526/1557] Data 0.004 (0.105) Batch 1.248 (1.084) Remain 04:13:42 loss: 0.1975 Lr: 0.00011 [2024-02-19 15:51:59,458 INFO misc.py line 119 87073] Train: [91/100][1527/1557] Data 0.010 (0.105) Batch 1.056 (1.084) Remain 04:13:41 loss: 0.1442 Lr: 0.00011 [2024-02-19 15:52:00,500 INFO misc.py line 119 87073] Train: [91/100][1528/1557] Data 0.010 (0.105) Batch 1.048 (1.084) Remain 04:13:39 loss: 0.2583 Lr: 0.00011 [2024-02-19 15:52:01,425 INFO misc.py line 119 87073] Train: [91/100][1529/1557] Data 0.005 (0.105) Batch 0.926 (1.084) Remain 04:13:37 loss: 0.2144 Lr: 0.00011 [2024-02-19 15:52:02,481 INFO misc.py line 119 87073] Train: [91/100][1530/1557] Data 0.003 (0.104) Batch 1.056 (1.084) Remain 04:13:35 loss: 0.2229 Lr: 0.00011 [2024-02-19 15:52:03,205 INFO misc.py line 119 87073] Train: [91/100][1531/1557] Data 0.004 (0.104) Batch 0.724 (1.084) Remain 04:13:31 loss: 0.1121 Lr: 0.00011 [2024-02-19 15:52:03,992 INFO misc.py line 119 87073] Train: [91/100][1532/1557] Data 0.004 (0.104) Batch 0.782 (1.083) Remain 04:13:27 loss: 0.2390 Lr: 0.00011 [2024-02-19 15:52:05,308 INFO misc.py line 119 87073] Train: [91/100][1533/1557] Data 0.009 (0.104) Batch 1.320 (1.083) Remain 04:13:28 loss: 0.0952 Lr: 0.00011 [2024-02-19 15:52:06,319 INFO misc.py line 119 87073] Train: [91/100][1534/1557] Data 0.005 (0.104) Batch 0.999 (1.083) Remain 04:13:26 loss: 0.1551 Lr: 0.00011 [2024-02-19 15:52:07,161 INFO misc.py line 119 87073] Train: [91/100][1535/1557] Data 0.017 (0.104) Batch 0.855 (1.083) Remain 04:13:23 loss: 0.1120 Lr: 0.00011 [2024-02-19 15:52:08,018 INFO misc.py line 119 87073] Train: [91/100][1536/1557] Data 0.004 (0.104) Batch 0.857 (1.083) Remain 04:13:20 loss: 0.2248 Lr: 0.00011 [2024-02-19 15:52:08,908 INFO misc.py line 119 87073] Train: [91/100][1537/1557] Data 0.004 (0.104) Batch 0.870 (1.083) Remain 04:13:17 loss: 0.2409 Lr: 0.00011 [2024-02-19 15:52:09,673 INFO misc.py line 119 87073] Train: [91/100][1538/1557] Data 0.023 (0.104) Batch 0.784 (1.083) Remain 04:13:13 loss: 0.1470 Lr: 0.00011 [2024-02-19 15:52:10,471 INFO misc.py line 119 87073] Train: [91/100][1539/1557] Data 0.005 (0.104) Batch 0.798 (1.083) Remain 04:13:09 loss: 0.1436 Lr: 0.00011 [2024-02-19 15:52:11,716 INFO misc.py line 119 87073] Train: [91/100][1540/1557] Data 0.005 (0.104) Batch 1.241 (1.083) Remain 04:13:10 loss: 0.1128 Lr: 0.00011 [2024-02-19 15:52:12,805 INFO misc.py line 119 87073] Train: [91/100][1541/1557] Data 0.010 (0.104) Batch 1.089 (1.083) Remain 04:13:09 loss: 0.3307 Lr: 0.00011 [2024-02-19 15:52:13,651 INFO misc.py line 119 87073] Train: [91/100][1542/1557] Data 0.008 (0.104) Batch 0.851 (1.083) Remain 04:13:06 loss: 0.1851 Lr: 0.00011 [2024-02-19 15:52:14,563 INFO misc.py line 119 87073] Train: [91/100][1543/1557] Data 0.004 (0.104) Batch 0.912 (1.082) Remain 04:13:03 loss: 0.1657 Lr: 0.00011 [2024-02-19 15:52:15,341 INFO misc.py line 119 87073] Train: [91/100][1544/1557] Data 0.004 (0.104) Batch 0.765 (1.082) Remain 04:12:59 loss: 0.0808 Lr: 0.00011 [2024-02-19 15:52:16,083 INFO misc.py line 119 87073] Train: [91/100][1545/1557] Data 0.017 (0.104) Batch 0.755 (1.082) Remain 04:12:55 loss: 0.2287 Lr: 0.00011 [2024-02-19 15:52:16,813 INFO misc.py line 119 87073] Train: [91/100][1546/1557] Data 0.004 (0.103) Batch 0.730 (1.082) Remain 04:12:51 loss: 0.1702 Lr: 0.00011 [2024-02-19 15:52:18,056 INFO misc.py line 119 87073] Train: [91/100][1547/1557] Data 0.003 (0.103) Batch 1.238 (1.082) Remain 04:12:51 loss: 0.1114 Lr: 0.00011 [2024-02-19 15:52:19,031 INFO misc.py line 119 87073] Train: [91/100][1548/1557] Data 0.009 (0.103) Batch 0.980 (1.082) Remain 04:12:49 loss: 0.1580 Lr: 0.00011 [2024-02-19 15:52:20,097 INFO misc.py line 119 87073] Train: [91/100][1549/1557] Data 0.004 (0.103) Batch 1.066 (1.082) Remain 04:12:48 loss: 0.2949 Lr: 0.00011 [2024-02-19 15:52:20,950 INFO misc.py line 119 87073] Train: [91/100][1550/1557] Data 0.003 (0.103) Batch 0.852 (1.082) Remain 04:12:45 loss: 1.1635 Lr: 0.00011 [2024-02-19 15:52:21,906 INFO misc.py line 119 87073] Train: [91/100][1551/1557] Data 0.004 (0.103) Batch 0.949 (1.082) Remain 04:12:42 loss: 0.1690 Lr: 0.00011 [2024-02-19 15:52:22,635 INFO misc.py line 119 87073] Train: [91/100][1552/1557] Data 0.010 (0.103) Batch 0.735 (1.081) Remain 04:12:38 loss: 0.1836 Lr: 0.00011 [2024-02-19 15:52:23,380 INFO misc.py line 119 87073] Train: [91/100][1553/1557] Data 0.004 (0.103) Batch 0.741 (1.081) Remain 04:12:34 loss: 0.2386 Lr: 0.00011 [2024-02-19 15:52:24,685 INFO misc.py line 119 87073] Train: [91/100][1554/1557] Data 0.010 (0.103) Batch 1.306 (1.081) Remain 04:12:35 loss: 0.1673 Lr: 0.00011 [2024-02-19 15:52:25,540 INFO misc.py line 119 87073] Train: [91/100][1555/1557] Data 0.008 (0.103) Batch 0.860 (1.081) Remain 04:12:32 loss: 0.0981 Lr: 0.00011 [2024-02-19 15:52:26,566 INFO misc.py line 119 87073] Train: [91/100][1556/1557] Data 0.004 (0.103) Batch 1.025 (1.081) Remain 04:12:30 loss: 0.9326 Lr: 0.00011 [2024-02-19 15:52:27,612 INFO misc.py line 119 87073] Train: [91/100][1557/1557] Data 0.004 (0.103) Batch 1.046 (1.081) Remain 04:12:29 loss: 0.2216 Lr: 0.00011 [2024-02-19 15:52:27,613 INFO misc.py line 136 87073] Train result: loss: 0.1979 [2024-02-19 15:52:27,613 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 15:53:03,110 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4160 [2024-02-19 15:53:03,901 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5078 [2024-02-19 15:53:06,026 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3362 [2024-02-19 15:53:08,236 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3673 [2024-02-19 15:53:13,190 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2623 [2024-02-19 15:53:13,890 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1061 [2024-02-19 15:53:15,154 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2924 [2024-02-19 15:53:18,113 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.3217 [2024-02-19 15:53:19,922 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2809 [2024-02-19 15:53:21,542 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7284/0.7809/0.9200. [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9351/0.9643 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9894 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8706/0.9747 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3879/0.4358 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6711/0.6891 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8116/0.9275 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8493/0.9190 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9269/0.9708 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8076/0.8276 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8016/0.8925 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7836/0.8309 [2024-02-19 15:53:21,542 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6403/0.7301 [2024-02-19 15:53:21,543 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 15:53:21,545 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 15:53:21,545 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 15:53:28,657 INFO misc.py line 119 87073] Train: [92/100][1/1557] Data 1.563 (1.563) Batch 2.290 (2.290) Remain 08:54:43 loss: 0.4664 Lr: 0.00011 [2024-02-19 15:53:29,578 INFO misc.py line 119 87073] Train: [92/100][2/1557] Data 0.010 (0.010) Batch 0.907 (0.907) Remain 03:31:51 loss: 0.6571 Lr: 0.00011 [2024-02-19 15:53:30,597 INFO misc.py line 119 87073] Train: [92/100][3/1557] Data 0.023 (0.023) Batch 1.032 (1.032) Remain 04:00:59 loss: 0.0631 Lr: 0.00011 [2024-02-19 15:53:31,574 INFO misc.py line 119 87073] Train: [92/100][4/1557] Data 0.011 (0.011) Batch 0.982 (0.982) Remain 03:49:10 loss: 0.2498 Lr: 0.00011 [2024-02-19 15:53:32,331 INFO misc.py line 119 87073] Train: [92/100][5/1557] Data 0.006 (0.008) Batch 0.757 (0.869) Remain 03:22:56 loss: 0.1468 Lr: 0.00011 [2024-02-19 15:53:33,105 INFO misc.py line 119 87073] Train: [92/100][6/1557] Data 0.006 (0.007) Batch 0.773 (0.837) Remain 03:15:26 loss: 0.1442 Lr: 0.00011 [2024-02-19 15:53:34,235 INFO misc.py line 119 87073] Train: [92/100][7/1557] Data 0.007 (0.007) Batch 1.125 (0.909) Remain 03:32:12 loss: 0.1308 Lr: 0.00011 [2024-02-19 15:53:35,346 INFO misc.py line 119 87073] Train: [92/100][8/1557] Data 0.011 (0.008) Batch 1.108 (0.949) Remain 03:41:29 loss: 0.1333 Lr: 0.00011 [2024-02-19 15:53:36,471 INFO misc.py line 119 87073] Train: [92/100][9/1557] Data 0.014 (0.009) Batch 1.132 (0.979) Remain 03:48:36 loss: 0.1470 Lr: 0.00011 [2024-02-19 15:53:37,419 INFO misc.py line 119 87073] Train: [92/100][10/1557] Data 0.007 (0.009) Batch 0.949 (0.975) Remain 03:47:34 loss: 0.6563 Lr: 0.00011 [2024-02-19 15:53:38,445 INFO misc.py line 119 87073] Train: [92/100][11/1557] Data 0.006 (0.008) Batch 1.027 (0.982) Remain 03:49:04 loss: 0.0610 Lr: 0.00011 [2024-02-19 15:53:39,195 INFO misc.py line 119 87073] Train: [92/100][12/1557] Data 0.006 (0.008) Batch 0.750 (0.956) Remain 03:43:03 loss: 0.0956 Lr: 0.00011 [2024-02-19 15:53:40,024 INFO misc.py line 119 87073] Train: [92/100][13/1557] Data 0.005 (0.008) Batch 0.823 (0.943) Remain 03:39:56 loss: 0.1900 Lr: 0.00011 [2024-02-19 15:53:41,133 INFO misc.py line 119 87073] Train: [92/100][14/1557] Data 0.010 (0.008) Batch 1.103 (0.957) Remain 03:43:19 loss: 0.0817 Lr: 0.00011 [2024-02-19 15:53:42,097 INFO misc.py line 119 87073] Train: [92/100][15/1557] Data 0.017 (0.009) Batch 0.975 (0.959) Remain 03:43:39 loss: 0.3237 Lr: 0.00011 [2024-02-19 15:53:43,084 INFO misc.py line 119 87073] Train: [92/100][16/1557] Data 0.005 (0.008) Batch 0.989 (0.961) Remain 03:44:10 loss: 0.1142 Lr: 0.00011 [2024-02-19 15:53:44,050 INFO misc.py line 119 87073] Train: [92/100][17/1557] Data 0.004 (0.008) Batch 0.966 (0.961) Remain 03:44:14 loss: 0.2978 Lr: 0.00011 [2024-02-19 15:53:45,036 INFO misc.py line 119 87073] Train: [92/100][18/1557] Data 0.004 (0.008) Batch 0.986 (0.963) Remain 03:44:37 loss: 0.3958 Lr: 0.00011 [2024-02-19 15:53:45,844 INFO misc.py line 119 87073] Train: [92/100][19/1557] Data 0.003 (0.008) Batch 0.798 (0.953) Remain 03:42:12 loss: 0.2001 Lr: 0.00011 [2024-02-19 15:53:46,652 INFO misc.py line 119 87073] Train: [92/100][20/1557] Data 0.013 (0.008) Batch 0.817 (0.945) Remain 03:40:19 loss: 0.1779 Lr: 0.00011 [2024-02-19 15:53:47,972 INFO misc.py line 119 87073] Train: [92/100][21/1557] Data 0.005 (0.008) Batch 1.309 (0.965) Remain 03:45:01 loss: 0.1117 Lr: 0.00011 [2024-02-19 15:53:48,870 INFO misc.py line 119 87073] Train: [92/100][22/1557] Data 0.016 (0.008) Batch 0.909 (0.962) Remain 03:44:19 loss: 0.2150 Lr: 0.00011 [2024-02-19 15:53:49,956 INFO misc.py line 119 87073] Train: [92/100][23/1557] Data 0.004 (0.008) Batch 1.087 (0.968) Remain 03:45:45 loss: 0.2915 Lr: 0.00011 [2024-02-19 15:53:51,115 INFO misc.py line 119 87073] Train: [92/100][24/1557] Data 0.003 (0.008) Batch 1.158 (0.977) Remain 03:47:51 loss: 0.3876 Lr: 0.00011 [2024-02-19 15:53:51,967 INFO misc.py line 119 87073] Train: [92/100][25/1557] Data 0.003 (0.008) Batch 0.849 (0.971) Remain 03:46:29 loss: 0.1944 Lr: 0.00011 [2024-02-19 15:53:52,740 INFO misc.py line 119 87073] Train: [92/100][26/1557] Data 0.007 (0.008) Batch 0.774 (0.963) Remain 03:44:27 loss: 0.2161 Lr: 0.00011 [2024-02-19 15:53:53,532 INFO misc.py line 119 87073] Train: [92/100][27/1557] Data 0.006 (0.007) Batch 0.794 (0.956) Remain 03:42:48 loss: 0.1466 Lr: 0.00011 [2024-02-19 15:53:54,737 INFO misc.py line 119 87073] Train: [92/100][28/1557] Data 0.004 (0.007) Batch 1.204 (0.966) Remain 03:45:06 loss: 0.2087 Lr: 0.00011 [2024-02-19 15:53:55,645 INFO misc.py line 119 87073] Train: [92/100][29/1557] Data 0.005 (0.007) Batch 0.909 (0.964) Remain 03:44:34 loss: 0.1797 Lr: 0.00011 [2024-02-19 15:53:56,648 INFO misc.py line 119 87073] Train: [92/100][30/1557] Data 0.004 (0.007) Batch 1.003 (0.965) Remain 03:44:54 loss: 0.2451 Lr: 0.00011 [2024-02-19 15:53:57,645 INFO misc.py line 119 87073] Train: [92/100][31/1557] Data 0.005 (0.007) Batch 0.996 (0.966) Remain 03:45:08 loss: 0.2376 Lr: 0.00011 [2024-02-19 15:53:58,621 INFO misc.py line 119 87073] Train: [92/100][32/1557] Data 0.006 (0.007) Batch 0.977 (0.967) Remain 03:45:12 loss: 0.0683 Lr: 0.00011 [2024-02-19 15:53:59,408 INFO misc.py line 119 87073] Train: [92/100][33/1557] Data 0.005 (0.007) Batch 0.778 (0.960) Remain 03:43:44 loss: 0.1540 Lr: 0.00011 [2024-02-19 15:54:00,150 INFO misc.py line 119 87073] Train: [92/100][34/1557] Data 0.013 (0.007) Batch 0.750 (0.953) Remain 03:42:08 loss: 0.2738 Lr: 0.00011 [2024-02-19 15:54:01,395 INFO misc.py line 119 87073] Train: [92/100][35/1557] Data 0.004 (0.007) Batch 1.246 (0.963) Remain 03:44:15 loss: 0.1173 Lr: 0.00011 [2024-02-19 15:54:02,451 INFO misc.py line 119 87073] Train: [92/100][36/1557] Data 0.004 (0.007) Batch 1.055 (0.965) Remain 03:44:53 loss: 0.2041 Lr: 0.00011 [2024-02-19 15:54:03,347 INFO misc.py line 119 87073] Train: [92/100][37/1557] Data 0.005 (0.007) Batch 0.897 (0.963) Remain 03:44:24 loss: 0.0789 Lr: 0.00011 [2024-02-19 15:54:04,260 INFO misc.py line 119 87073] Train: [92/100][38/1557] Data 0.004 (0.007) Batch 0.907 (0.962) Remain 03:44:00 loss: 0.2716 Lr: 0.00011 [2024-02-19 15:54:05,438 INFO misc.py line 119 87073] Train: [92/100][39/1557] Data 0.010 (0.007) Batch 1.174 (0.968) Remain 03:45:22 loss: 0.1511 Lr: 0.00011 [2024-02-19 15:54:06,208 INFO misc.py line 119 87073] Train: [92/100][40/1557] Data 0.014 (0.007) Batch 0.781 (0.963) Remain 03:44:10 loss: 0.1566 Lr: 0.00011 [2024-02-19 15:54:06,937 INFO misc.py line 119 87073] Train: [92/100][41/1557] Data 0.003 (0.007) Batch 0.726 (0.956) Remain 03:42:42 loss: 0.2874 Lr: 0.00011 [2024-02-19 15:54:08,255 INFO misc.py line 119 87073] Train: [92/100][42/1557] Data 0.006 (0.007) Batch 1.319 (0.966) Remain 03:44:52 loss: 0.0671 Lr: 0.00011 [2024-02-19 15:54:09,166 INFO misc.py line 119 87073] Train: [92/100][43/1557] Data 0.004 (0.007) Batch 0.912 (0.964) Remain 03:44:32 loss: 0.2219 Lr: 0.00011 [2024-02-19 15:54:10,188 INFO misc.py line 119 87073] Train: [92/100][44/1557] Data 0.003 (0.007) Batch 1.022 (0.966) Remain 03:44:51 loss: 0.1887 Lr: 0.00011 [2024-02-19 15:54:11,136 INFO misc.py line 119 87073] Train: [92/100][45/1557] Data 0.003 (0.007) Batch 0.947 (0.965) Remain 03:44:43 loss: 0.1484 Lr: 0.00011 [2024-02-19 15:54:12,085 INFO misc.py line 119 87073] Train: [92/100][46/1557] Data 0.003 (0.007) Batch 0.948 (0.965) Remain 03:44:37 loss: 0.1983 Lr: 0.00011 [2024-02-19 15:54:12,838 INFO misc.py line 119 87073] Train: [92/100][47/1557] Data 0.004 (0.007) Batch 0.753 (0.960) Remain 03:43:29 loss: 0.1734 Lr: 0.00011 [2024-02-19 15:54:13,580 INFO misc.py line 119 87073] Train: [92/100][48/1557] Data 0.004 (0.006) Batch 0.743 (0.955) Remain 03:42:20 loss: 0.1541 Lr: 0.00011 [2024-02-19 15:54:14,716 INFO misc.py line 119 87073] Train: [92/100][49/1557] Data 0.004 (0.006) Batch 1.135 (0.959) Remain 03:43:14 loss: 0.1399 Lr: 0.00011 [2024-02-19 15:54:15,812 INFO misc.py line 119 87073] Train: [92/100][50/1557] Data 0.004 (0.006) Batch 1.095 (0.962) Remain 03:43:53 loss: 0.1555 Lr: 0.00011 [2024-02-19 15:54:16,678 INFO misc.py line 119 87073] Train: [92/100][51/1557] Data 0.005 (0.006) Batch 0.866 (0.960) Remain 03:43:24 loss: 0.2959 Lr: 0.00011 [2024-02-19 15:54:17,700 INFO misc.py line 119 87073] Train: [92/100][52/1557] Data 0.006 (0.006) Batch 0.994 (0.961) Remain 03:43:33 loss: 0.2003 Lr: 0.00011 [2024-02-19 15:54:18,759 INFO misc.py line 119 87073] Train: [92/100][53/1557] Data 0.033 (0.007) Batch 1.078 (0.963) Remain 03:44:05 loss: 0.2893 Lr: 0.00011 [2024-02-19 15:54:19,482 INFO misc.py line 119 87073] Train: [92/100][54/1557] Data 0.014 (0.007) Batch 0.732 (0.959) Remain 03:43:01 loss: 0.1727 Lr: 0.00011 [2024-02-19 15:54:20,218 INFO misc.py line 119 87073] Train: [92/100][55/1557] Data 0.006 (0.007) Batch 0.737 (0.954) Remain 03:42:00 loss: 0.2141 Lr: 0.00011 [2024-02-19 15:54:21,337 INFO misc.py line 119 87073] Train: [92/100][56/1557] Data 0.004 (0.007) Batch 1.107 (0.957) Remain 03:42:40 loss: 0.2209 Lr: 0.00011 [2024-02-19 15:54:22,276 INFO misc.py line 119 87073] Train: [92/100][57/1557] Data 0.017 (0.007) Batch 0.952 (0.957) Remain 03:42:37 loss: 0.1549 Lr: 0.00011 [2024-02-19 15:54:23,363 INFO misc.py line 119 87073] Train: [92/100][58/1557] Data 0.004 (0.007) Batch 1.087 (0.959) Remain 03:43:09 loss: 0.2992 Lr: 0.00011 [2024-02-19 15:54:24,208 INFO misc.py line 119 87073] Train: [92/100][59/1557] Data 0.004 (0.007) Batch 0.845 (0.957) Remain 03:42:40 loss: 0.4321 Lr: 0.00011 [2024-02-19 15:54:25,376 INFO misc.py line 119 87073] Train: [92/100][60/1557] Data 0.003 (0.007) Batch 1.168 (0.961) Remain 03:43:30 loss: 0.3955 Lr: 0.00011 [2024-02-19 15:54:26,150 INFO misc.py line 119 87073] Train: [92/100][61/1557] Data 0.004 (0.007) Batch 0.774 (0.958) Remain 03:42:44 loss: 0.1970 Lr: 0.00011 [2024-02-19 15:54:26,900 INFO misc.py line 119 87073] Train: [92/100][62/1557] Data 0.004 (0.007) Batch 0.743 (0.954) Remain 03:41:52 loss: 0.1921 Lr: 0.00011 [2024-02-19 15:54:35,502 INFO misc.py line 119 87073] Train: [92/100][63/1557] Data 4.529 (0.082) Batch 8.580 (1.081) Remain 04:11:24 loss: 0.2125 Lr: 0.00011 [2024-02-19 15:54:36,504 INFO misc.py line 119 87073] Train: [92/100][64/1557] Data 0.034 (0.081) Batch 1.027 (1.080) Remain 04:11:11 loss: 0.2615 Lr: 0.00011 [2024-02-19 15:54:37,589 INFO misc.py line 119 87073] Train: 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0.990 (1.068) Remain 04:08:13 loss: 0.0957 Lr: 0.00011 [2024-02-19 15:54:44,210 INFO misc.py line 119 87073] Train: [92/100][72/1557] Data 0.005 (0.073) Batch 0.977 (1.067) Remain 04:07:54 loss: 0.0735 Lr: 0.00011 [2024-02-19 15:54:45,144 INFO misc.py line 119 87073] Train: [92/100][73/1557] Data 0.004 (0.072) Batch 0.933 (1.065) Remain 04:07:26 loss: 0.1650 Lr: 0.00011 [2024-02-19 15:54:46,020 INFO misc.py line 119 87073] Train: [92/100][74/1557] Data 0.003 (0.071) Batch 0.874 (1.062) Remain 04:06:48 loss: 0.1033 Lr: 0.00011 [2024-02-19 15:54:46,762 INFO misc.py line 119 87073] Train: [92/100][75/1557] Data 0.005 (0.070) Batch 0.744 (1.058) Remain 04:05:45 loss: 0.2016 Lr: 0.00011 [2024-02-19 15:54:47,475 INFO misc.py line 119 87073] Train: [92/100][76/1557] Data 0.004 (0.069) Batch 0.703 (1.053) Remain 04:04:36 loss: 0.1383 Lr: 0.00011 [2024-02-19 15:54:48,793 INFO misc.py line 119 87073] Train: [92/100][77/1557] Data 0.013 (0.068) Batch 1.314 (1.057) Remain 04:05:24 loss: 0.0910 Lr: 0.00011 [2024-02-19 15:54:49,676 INFO misc.py line 119 87073] Train: [92/100][78/1557] Data 0.017 (0.068) Batch 0.896 (1.054) Remain 04:04:53 loss: 0.1379 Lr: 0.00011 [2024-02-19 15:54:50,713 INFO misc.py line 119 87073] Train: [92/100][79/1557] Data 0.003 (0.067) Batch 1.037 (1.054) Remain 04:04:49 loss: 0.1813 Lr: 0.00011 [2024-02-19 15:54:51,876 INFO misc.py line 119 87073] Train: [92/100][80/1557] Data 0.003 (0.066) Batch 1.163 (1.056) Remain 04:05:08 loss: 0.2316 Lr: 0.00011 [2024-02-19 15:54:52,779 INFO misc.py line 119 87073] Train: [92/100][81/1557] Data 0.004 (0.065) Batch 0.903 (1.054) Remain 04:04:40 loss: 0.4089 Lr: 0.00011 [2024-02-19 15:54:53,548 INFO misc.py line 119 87073] Train: [92/100][82/1557] Data 0.004 (0.064) Batch 0.758 (1.050) Remain 04:03:46 loss: 0.2071 Lr: 0.00011 [2024-02-19 15:54:54,317 INFO misc.py line 119 87073] Train: [92/100][83/1557] Data 0.014 (0.064) Batch 0.778 (1.047) Remain 04:02:58 loss: 0.2297 Lr: 0.00011 [2024-02-19 15:54:55,563 INFO misc.py line 119 87073] Train: [92/100][84/1557] Data 0.004 (0.063) Batch 1.246 (1.049) Remain 04:03:31 loss: 0.1818 Lr: 0.00011 [2024-02-19 15:54:56,585 INFO misc.py line 119 87073] Train: [92/100][85/1557] Data 0.005 (0.062) Batch 1.021 (1.049) Remain 04:03:25 loss: 0.1955 Lr: 0.00011 [2024-02-19 15:54:57,709 INFO misc.py line 119 87073] Train: [92/100][86/1557] Data 0.006 (0.062) Batch 1.125 (1.050) Remain 04:03:37 loss: 0.1516 Lr: 0.00011 [2024-02-19 15:54:58,787 INFO misc.py line 119 87073] Train: [92/100][87/1557] Data 0.004 (0.061) Batch 1.078 (1.050) Remain 04:03:41 loss: 0.0932 Lr: 0.00011 [2024-02-19 15:54:59,702 INFO misc.py line 119 87073] Train: [92/100][88/1557] Data 0.005 (0.060) Batch 0.915 (1.048) Remain 04:03:18 loss: 0.2054 Lr: 0.00011 [2024-02-19 15:55:00,444 INFO misc.py line 119 87073] Train: [92/100][89/1557] Data 0.004 (0.060) Batch 0.735 (1.045) Remain 04:02:26 loss: 0.1441 Lr: 0.00011 [2024-02-19 15:55:01,191 INFO misc.py line 119 87073] Train: [92/100][90/1557] Data 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03:48:39 loss: 0.2995 Lr: 0.00009 [2024-02-19 16:20:37,961 INFO misc.py line 119 87073] Train: [92/100][1489/1557] Data 0.003 (0.091) Batch 0.761 (1.095) Remain 03:48:35 loss: 0.1764 Lr: 0.00009 [2024-02-19 16:20:38,744 INFO misc.py line 119 87073] Train: [92/100][1490/1557] Data 0.005 (0.091) Batch 0.783 (1.095) Remain 03:48:31 loss: 0.1995 Lr: 0.00009 [2024-02-19 16:20:40,015 INFO misc.py line 119 87073] Train: [92/100][1491/1557] Data 0.004 (0.091) Batch 1.266 (1.095) Remain 03:48:32 loss: 0.2049 Lr: 0.00009 [2024-02-19 16:20:40,880 INFO misc.py line 119 87073] Train: [92/100][1492/1557] Data 0.009 (0.091) Batch 0.871 (1.095) Remain 03:48:29 loss: 0.1825 Lr: 0.00009 [2024-02-19 16:20:41,778 INFO misc.py line 119 87073] Train: [92/100][1493/1557] Data 0.003 (0.091) Batch 0.896 (1.095) Remain 03:48:26 loss: 0.0651 Lr: 0.00009 [2024-02-19 16:20:42,709 INFO misc.py line 119 87073] Train: [92/100][1494/1557] Data 0.005 (0.091) Batch 0.928 (1.095) Remain 03:48:23 loss: 0.2585 Lr: 0.00009 [2024-02-19 16:20:43,625 INFO misc.py line 119 87073] Train: [92/100][1495/1557] Data 0.008 (0.091) Batch 0.920 (1.095) Remain 03:48:21 loss: 0.3627 Lr: 0.00009 [2024-02-19 16:20:44,414 INFO misc.py line 119 87073] Train: [92/100][1496/1557] Data 0.004 (0.091) Batch 0.788 (1.094) Remain 03:48:17 loss: 0.0961 Lr: 0.00009 [2024-02-19 16:20:45,163 INFO misc.py line 119 87073] Train: [92/100][1497/1557] Data 0.005 (0.091) Batch 0.742 (1.094) Remain 03:48:13 loss: 0.1669 Lr: 0.00009 [2024-02-19 16:20:46,549 INFO misc.py line 119 87073] Train: [92/100][1498/1557] Data 0.012 (0.091) Batch 1.328 (1.094) Remain 03:48:14 loss: 0.2260 Lr: 0.00009 [2024-02-19 16:20:47,481 INFO misc.py line 119 87073] Train: [92/100][1499/1557] Data 0.071 (0.091) Batch 0.999 (1.094) Remain 03:48:12 loss: 0.0961 Lr: 0.00009 [2024-02-19 16:20:48,352 INFO misc.py line 119 87073] Train: [92/100][1500/1557] Data 0.004 (0.091) Batch 0.870 (1.094) Remain 03:48:09 loss: 0.5795 Lr: 0.00009 [2024-02-19 16:20:49,474 INFO misc.py line 119 87073] Train: [92/100][1501/1557] Data 0.005 (0.091) Batch 1.122 (1.094) Remain 03:48:08 loss: 0.1368 Lr: 0.00009 [2024-02-19 16:20:50,504 INFO misc.py line 119 87073] Train: [92/100][1502/1557] Data 0.004 (0.091) Batch 1.028 (1.094) Remain 03:48:07 loss: 0.2293 Lr: 0.00009 [2024-02-19 16:20:51,226 INFO misc.py line 119 87073] Train: [92/100][1503/1557] Data 0.007 (0.090) Batch 0.725 (1.094) Remain 03:48:02 loss: 0.1829 Lr: 0.00009 [2024-02-19 16:20:51,957 INFO misc.py line 119 87073] Train: [92/100][1504/1557] Data 0.005 (0.090) Batch 0.721 (1.094) Remain 03:47:58 loss: 0.1741 Lr: 0.00009 [2024-02-19 16:20:53,097 INFO misc.py line 119 87073] Train: [92/100][1505/1557] Data 0.014 (0.090) Batch 1.140 (1.094) Remain 03:47:57 loss: 0.1520 Lr: 0.00009 [2024-02-19 16:20:54,002 INFO misc.py line 119 87073] Train: [92/100][1506/1557] Data 0.014 (0.090) Batch 0.915 (1.093) Remain 03:47:55 loss: 0.3033 Lr: 0.00009 [2024-02-19 16:20:55,049 INFO misc.py line 119 87073] Train: [92/100][1507/1557] Data 0.004 (0.090) Batch 1.048 (1.093) Remain 03:47:53 loss: 0.4062 Lr: 0.00009 [2024-02-19 16:20:56,026 INFO misc.py line 119 87073] Train: [92/100][1508/1557] Data 0.003 (0.090) Batch 0.976 (1.093) Remain 03:47:51 loss: 0.1048 Lr: 0.00009 [2024-02-19 16:20:56,983 INFO misc.py line 119 87073] Train: [92/100][1509/1557] Data 0.004 (0.090) Batch 0.957 (1.093) Remain 03:47:49 loss: 0.0264 Lr: 0.00009 [2024-02-19 16:20:57,781 INFO misc.py line 119 87073] Train: [92/100][1510/1557] Data 0.005 (0.090) Batch 0.787 (1.093) Remain 03:47:45 loss: 0.1272 Lr: 0.00009 [2024-02-19 16:20:58,522 INFO misc.py line 119 87073] Train: [92/100][1511/1557] Data 0.015 (0.090) Batch 0.753 (1.093) Remain 03:47:42 loss: 0.1435 Lr: 0.00009 [2024-02-19 16:20:59,737 INFO misc.py line 119 87073] Train: [92/100][1512/1557] Data 0.003 (0.090) Batch 1.213 (1.093) Remain 03:47:41 loss: 0.2574 Lr: 0.00009 [2024-02-19 16:21:00,526 INFO misc.py line 119 87073] Train: [92/100][1513/1557] Data 0.005 (0.090) Batch 0.790 (1.093) Remain 03:47:38 loss: 0.3147 Lr: 0.00009 [2024-02-19 16:21:01,558 INFO misc.py line 119 87073] Train: [92/100][1514/1557] Data 0.005 (0.090) Batch 1.027 (1.093) Remain 03:47:36 loss: 0.1809 Lr: 0.00009 [2024-02-19 16:21:02,447 INFO misc.py line 119 87073] Train: [92/100][1515/1557] Data 0.009 (0.090) Batch 0.893 (1.092) Remain 03:47:33 loss: 0.2339 Lr: 0.00009 [2024-02-19 16:21:03,349 INFO misc.py line 119 87073] Train: [92/100][1516/1557] Data 0.005 (0.090) Batch 0.903 (1.092) Remain 03:47:31 loss: 0.1273 Lr: 0.00009 [2024-02-19 16:21:04,167 INFO misc.py line 119 87073] Train: [92/100][1517/1557] Data 0.004 (0.090) Batch 0.814 (1.092) Remain 03:47:27 loss: 0.2098 Lr: 0.00009 [2024-02-19 16:21:04,884 INFO misc.py line 119 87073] Train: [92/100][1518/1557] Data 0.008 (0.090) Batch 0.720 (1.092) Remain 03:47:23 loss: 0.3820 Lr: 0.00009 [2024-02-19 16:21:14,213 INFO misc.py line 119 87073] Train: [92/100][1519/1557] Data 3.554 (0.092) Batch 9.331 (1.097) Remain 03:48:30 loss: 0.1055 Lr: 0.00009 [2024-02-19 16:21:15,126 INFO misc.py line 119 87073] Train: [92/100][1520/1557] Data 0.004 (0.092) Batch 0.912 (1.097) Remain 03:48:27 loss: 0.2736 Lr: 0.00009 [2024-02-19 16:21:16,173 INFO misc.py line 119 87073] Train: [92/100][1521/1557] Data 0.004 (0.092) Batch 1.038 (1.097) Remain 03:48:26 loss: 0.3794 Lr: 0.00009 [2024-02-19 16:21:17,228 INFO misc.py line 119 87073] Train: [92/100][1522/1557] Data 0.013 (0.092) Batch 1.056 (1.097) Remain 03:48:24 loss: 0.1487 Lr: 0.00009 [2024-02-19 16:21:18,161 INFO misc.py line 119 87073] Train: [92/100][1523/1557] Data 0.012 (0.092) Batch 0.942 (1.097) Remain 03:48:22 loss: 0.0827 Lr: 0.00009 [2024-02-19 16:21:18,913 INFO misc.py line 119 87073] Train: [92/100][1524/1557] Data 0.004 (0.092) Batch 0.752 (1.097) Remain 03:48:18 loss: 0.0913 Lr: 0.00009 [2024-02-19 16:21:19,733 INFO misc.py line 119 87073] Train: [92/100][1525/1557] Data 0.004 (0.092) Batch 0.814 (1.097) Remain 03:48:15 loss: 0.1236 Lr: 0.00009 [2024-02-19 16:21:20,845 INFO misc.py line 119 87073] Train: [92/100][1526/1557] Data 0.010 (0.092) Batch 1.107 (1.097) Remain 03:48:14 loss: 0.0928 Lr: 0.00009 [2024-02-19 16:21:21,853 INFO misc.py line 119 87073] Train: [92/100][1527/1557] Data 0.015 (0.091) Batch 1.013 (1.097) Remain 03:48:12 loss: 0.2094 Lr: 0.00009 [2024-02-19 16:21:22,822 INFO misc.py line 119 87073] Train: [92/100][1528/1557] Data 0.010 (0.091) Batch 0.974 (1.097) Remain 03:48:10 loss: 0.3613 Lr: 0.00009 [2024-02-19 16:21:23,812 INFO misc.py line 119 87073] Train: [92/100][1529/1557] Data 0.004 (0.091) Batch 0.990 (1.096) Remain 03:48:08 loss: 0.1592 Lr: 0.00009 [2024-02-19 16:21:24,756 INFO misc.py line 119 87073] Train: [92/100][1530/1557] Data 0.004 (0.091) Batch 0.943 (1.096) Remain 03:48:06 loss: 0.1001 Lr: 0.00009 [2024-02-19 16:21:25,440 INFO misc.py line 119 87073] Train: [92/100][1531/1557] Data 0.005 (0.091) Batch 0.676 (1.096) Remain 03:48:01 loss: 0.2728 Lr: 0.00009 [2024-02-19 16:21:26,159 INFO misc.py line 119 87073] Train: [92/100][1532/1557] Data 0.013 (0.091) Batch 0.727 (1.096) Remain 03:47:57 loss: 0.1873 Lr: 0.00009 [2024-02-19 16:21:27,473 INFO misc.py line 119 87073] Train: [92/100][1533/1557] Data 0.004 (0.091) Batch 1.276 (1.096) Remain 03:47:57 loss: 0.0915 Lr: 0.00009 [2024-02-19 16:21:28,540 INFO misc.py line 119 87073] Train: [92/100][1534/1557] Data 0.042 (0.091) Batch 1.106 (1.096) Remain 03:47:56 loss: 0.0503 Lr: 0.00009 [2024-02-19 16:21:29,454 INFO misc.py line 119 87073] Train: [92/100][1535/1557] Data 0.004 (0.091) Batch 0.915 (1.096) Remain 03:47:54 loss: 0.1257 Lr: 0.00009 [2024-02-19 16:21:30,372 INFO misc.py line 119 87073] Train: [92/100][1536/1557] Data 0.003 (0.091) Batch 0.917 (1.096) Remain 03:47:51 loss: 0.2640 Lr: 0.00009 [2024-02-19 16:21:31,250 INFO misc.py line 119 87073] Train: [92/100][1537/1557] Data 0.004 (0.091) Batch 0.866 (1.096) Remain 03:47:48 loss: 0.2415 Lr: 0.00009 [2024-02-19 16:21:32,015 INFO misc.py line 119 87073] Train: [92/100][1538/1557] Data 0.015 (0.091) Batch 0.777 (1.095) Remain 03:47:44 loss: 0.2773 Lr: 0.00009 [2024-02-19 16:21:32,744 INFO misc.py line 119 87073] Train: [92/100][1539/1557] Data 0.004 (0.091) Batch 0.715 (1.095) Remain 03:47:40 loss: 0.1224 Lr: 0.00009 [2024-02-19 16:21:33,933 INFO misc.py line 119 87073] Train: [92/100][1540/1557] Data 0.017 (0.091) Batch 1.194 (1.095) Remain 03:47:40 loss: 0.1401 Lr: 0.00009 [2024-02-19 16:21:34,728 INFO misc.py line 119 87073] Train: [92/100][1541/1557] Data 0.012 (0.091) Batch 0.804 (1.095) Remain 03:47:37 loss: 0.2559 Lr: 0.00009 [2024-02-19 16:21:35,777 INFO misc.py line 119 87073] Train: [92/100][1542/1557] Data 0.004 (0.091) Batch 1.049 (1.095) Remain 03:47:35 loss: 0.1833 Lr: 0.00009 [2024-02-19 16:21:36,709 INFO misc.py line 119 87073] Train: [92/100][1543/1557] Data 0.004 (0.091) Batch 0.931 (1.095) Remain 03:47:33 loss: 0.2386 Lr: 0.00009 [2024-02-19 16:21:37,667 INFO misc.py line 119 87073] Train: [92/100][1544/1557] Data 0.004 (0.091) Batch 0.959 (1.095) Remain 03:47:30 loss: 0.1080 Lr: 0.00009 [2024-02-19 16:21:38,372 INFO misc.py line 119 87073] Train: [92/100][1545/1557] Data 0.004 (0.091) Batch 0.697 (1.095) Remain 03:47:26 loss: 0.1165 Lr: 0.00009 [2024-02-19 16:21:39,157 INFO misc.py line 119 87073] Train: [92/100][1546/1557] Data 0.012 (0.090) Batch 0.793 (1.094) Remain 03:47:23 loss: 0.1133 Lr: 0.00009 [2024-02-19 16:21:40,391 INFO misc.py line 119 87073] Train: [92/100][1547/1557] Data 0.004 (0.090) Batch 1.221 (1.094) Remain 03:47:23 loss: 0.1397 Lr: 0.00009 [2024-02-19 16:21:41,448 INFO misc.py line 119 87073] Train: [92/100][1548/1557] Data 0.017 (0.090) Batch 1.060 (1.094) Remain 03:47:21 loss: 0.4704 Lr: 0.00009 [2024-02-19 16:21:42,475 INFO misc.py line 119 87073] Train: [92/100][1549/1557] Data 0.015 (0.090) Batch 1.028 (1.094) Remain 03:47:20 loss: 0.2062 Lr: 0.00009 [2024-02-19 16:21:43,584 INFO misc.py line 119 87073] Train: [92/100][1550/1557] Data 0.013 (0.090) Batch 1.109 (1.094) Remain 03:47:19 loss: 0.1553 Lr: 0.00009 [2024-02-19 16:21:44,350 INFO misc.py line 119 87073] Train: [92/100][1551/1557] Data 0.013 (0.090) Batch 0.775 (1.094) Remain 03:47:15 loss: 0.2746 Lr: 0.00009 [2024-02-19 16:21:45,110 INFO misc.py line 119 87073] Train: [92/100][1552/1557] Data 0.005 (0.090) Batch 0.751 (1.094) Remain 03:47:11 loss: 0.1850 Lr: 0.00009 [2024-02-19 16:21:45,864 INFO misc.py line 119 87073] Train: [92/100][1553/1557] Data 0.013 (0.090) Batch 0.764 (1.094) Remain 03:47:07 loss: 0.1281 Lr: 0.00009 [2024-02-19 16:21:47,201 INFO misc.py line 119 87073] Train: [92/100][1554/1557] Data 0.004 (0.090) Batch 1.276 (1.094) Remain 03:47:08 loss: 0.0921 Lr: 0.00009 [2024-02-19 16:21:48,352 INFO misc.py line 119 87073] Train: [92/100][1555/1557] Data 0.065 (0.090) Batch 1.198 (1.094) Remain 03:47:07 loss: 0.1918 Lr: 0.00009 [2024-02-19 16:21:49,530 INFO misc.py line 119 87073] Train: [92/100][1556/1557] Data 0.017 (0.090) Batch 1.184 (1.094) Remain 03:47:07 loss: 0.0985 Lr: 0.00009 [2024-02-19 16:21:50,508 INFO misc.py line 119 87073] Train: [92/100][1557/1557] Data 0.012 (0.090) Batch 0.985 (1.094) Remain 03:47:05 loss: 0.1792 Lr: 0.00009 [2024-02-19 16:21:50,509 INFO misc.py line 136 87073] Train result: loss: 0.2004 [2024-02-19 16:21:50,509 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 16:22:20,020 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.5336 [2024-02-19 16:22:20,802 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5066 [2024-02-19 16:22:22,930 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.2995 [2024-02-19 16:22:25,144 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3933 [2024-02-19 16:22:30,094 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2371 [2024-02-19 16:22:30,791 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0949 [2024-02-19 16:22:32,050 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3012 [2024-02-19 16:22:35,004 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2848 [2024-02-19 16:22:36,816 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2708 [2024-02-19 16:22:39,558 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7306/0.7822/0.9196. [2024-02-19 16:22:39,558 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9359/0.9654 [2024-02-19 16:22:39,558 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9891 [2024-02-19 16:22:39,558 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8681/0.9735 [2024-02-19 16:22:39,558 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4078/0.4661 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6595/0.6781 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8189/0.9279 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8503/0.9136 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9261/0.9738 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8184/0.8427 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8000/0.8813 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7907/0.8163 [2024-02-19 16:22:39,559 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6398/0.7414 [2024-02-19 16:22:39,559 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 16:22:39,562 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 16:22:39,562 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 16:22:47,041 INFO misc.py line 119 87073] Train: [93/100][1/1557] Data 1.626 (1.626) Batch 2.481 (2.481) Remain 08:34:54 loss: 0.1740 Lr: 0.00009 [2024-02-19 16:22:48,194 INFO misc.py line 119 87073] Train: [93/100][2/1557] Data 0.006 (0.006) Batch 1.151 (1.151) Remain 03:58:55 loss: 0.1088 Lr: 0.00009 [2024-02-19 16:22:49,252 INFO misc.py line 119 87073] Train: [93/100][3/1557] Data 0.008 (0.008) Batch 1.061 (1.061) Remain 03:40:09 loss: 0.0942 Lr: 0.00009 [2024-02-19 16:22:50,199 INFO misc.py line 119 87073] Train: [93/100][4/1557] Data 0.005 (0.005) Batch 0.945 (0.945) Remain 03:16:09 loss: 0.3111 Lr: 0.00009 [2024-02-19 16:22:51,039 INFO misc.py line 119 87073] Train: [93/100][5/1557] Data 0.007 (0.006) Batch 0.842 (0.893) Remain 03:05:24 loss: 0.1756 Lr: 0.00009 [2024-02-19 16:22:51,762 INFO misc.py line 119 87073] Train: [93/100][6/1557] Data 0.005 (0.006) Batch 0.718 (0.835) Remain 02:53:17 loss: 0.1234 Lr: 0.00009 [2024-02-19 16:22:52,932 INFO misc.py line 119 87073] Train: [93/100][7/1557] Data 0.010 (0.007) Batch 1.169 (0.919) Remain 03:10:36 loss: 0.0656 Lr: 0.00009 [2024-02-19 16:22:53,916 INFO misc.py line 119 87073] Train: [93/100][8/1557] Data 0.010 (0.008) Batch 0.990 (0.933) Remain 03:13:34 loss: 0.1153 Lr: 0.00009 [2024-02-19 16:22:54,787 INFO misc.py line 119 87073] Train: [93/100][9/1557] Data 0.004 (0.007) Batch 0.871 (0.923) Remain 03:11:24 loss: 0.3008 Lr: 0.00009 [2024-02-19 16:22:55,768 INFO misc.py line 119 87073] Train: [93/100][10/1557] Data 0.004 (0.006) Batch 0.963 (0.928) Remain 03:12:35 loss: 0.1909 Lr: 0.00009 [2024-02-19 16:22:56,772 INFO misc.py line 119 87073] Train: [93/100][11/1557] Data 0.022 (0.008) Batch 1.017 (0.939) Remain 03:14:52 loss: 0.5772 Lr: 0.00009 [2024-02-19 16:22:57,566 INFO misc.py line 119 87073] Train: [93/100][12/1557] Data 0.009 (0.008) Batch 0.799 (0.924) Remain 03:11:36 loss: 0.2606 Lr: 0.00009 [2024-02-19 16:22:58,334 INFO misc.py line 119 87073] Train: [93/100][13/1557] Data 0.004 (0.008) Batch 0.768 (0.908) Remain 03:08:21 loss: 0.1492 Lr: 0.00009 [2024-02-19 16:22:59,547 INFO misc.py line 119 87073] Train: [93/100][14/1557] Data 0.004 (0.008) Batch 1.192 (0.934) Remain 03:13:42 loss: 0.0752 Lr: 0.00009 [2024-02-19 16:23:00,290 INFO misc.py line 119 87073] Train: [93/100][15/1557] Data 0.024 (0.009) Batch 0.761 (0.920) Remain 03:10:41 loss: 0.0714 Lr: 0.00009 [2024-02-19 16:23:01,153 INFO misc.py line 119 87073] Train: [93/100][16/1557] Data 0.008 (0.009) Batch 0.865 (0.915) Remain 03:09:48 loss: 0.1902 Lr: 0.00009 [2024-02-19 16:23:02,138 INFO misc.py line 119 87073] Train: [93/100][17/1557] Data 0.005 (0.009) Batch 0.966 (0.919) Remain 03:10:32 loss: 0.0631 Lr: 0.00009 [2024-02-19 16:23:03,081 INFO misc.py line 119 87073] Train: [93/100][18/1557] Data 0.024 (0.010) Batch 0.963 (0.922) Remain 03:11:07 loss: 0.2549 Lr: 0.00009 [2024-02-19 16:23:03,778 INFO misc.py line 119 87073] Train: [93/100][19/1557] Data 0.004 (0.009) Batch 0.696 (0.908) Remain 03:08:11 loss: 0.1492 Lr: 0.00009 [2024-02-19 16:23:04,550 INFO misc.py line 119 87073] Train: [93/100][20/1557] Data 0.005 (0.009) Batch 0.740 (0.898) Remain 03:06:07 loss: 0.2086 Lr: 0.00009 [2024-02-19 16:23:05,865 INFO misc.py line 119 87073] Train: [93/100][21/1557] Data 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87073] Train: [93/100][252/1557] Data 0.013 (0.082) Batch 1.322 (1.085) Remain 03:40:47 loss: 0.2752 Lr: 0.00008 [2024-02-19 16:27:20,488 INFO misc.py line 119 87073] Train: [93/100][253/1557] Data 0.017 (0.082) Batch 0.942 (1.085) Remain 03:40:38 loss: 0.0800 Lr: 0.00008 [2024-02-19 16:27:21,428 INFO misc.py line 119 87073] Train: [93/100][254/1557] Data 0.017 (0.081) Batch 0.953 (1.084) Remain 03:40:31 loss: 0.1623 Lr: 0.00008 [2024-02-19 16:27:22,358 INFO misc.py line 119 87073] Train: [93/100][255/1557] Data 0.003 (0.081) Batch 0.930 (1.084) Remain 03:40:22 loss: 0.0428 Lr: 0.00008 [2024-02-19 16:27:23,304 INFO misc.py line 119 87073] Train: [93/100][256/1557] Data 0.005 (0.081) Batch 0.935 (1.083) Remain 03:40:14 loss: 0.1611 Lr: 0.00008 [2024-02-19 16:27:24,069 INFO misc.py line 119 87073] Train: [93/100][257/1557] Data 0.016 (0.080) Batch 0.775 (1.082) Remain 03:39:58 loss: 0.1083 Lr: 0.00008 [2024-02-19 16:27:24,832 INFO misc.py line 119 87073] Train: [93/100][258/1557] Data 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line 119 87073] Train: [93/100][277/1557] Data 0.004 (0.075) Batch 1.059 (1.073) Remain 03:37:49 loss: 0.0550 Lr: 0.00008 [2024-02-19 16:27:44,044 INFO misc.py line 119 87073] Train: [93/100][278/1557] Data 0.005 (0.075) Batch 0.767 (1.072) Remain 03:37:34 loss: 0.1276 Lr: 0.00008 [2024-02-19 16:27:44,810 INFO misc.py line 119 87073] Train: [93/100][279/1557] Data 0.003 (0.075) Batch 0.756 (1.071) Remain 03:37:19 loss: 0.1644 Lr: 0.00008 [2024-02-19 16:27:46,035 INFO misc.py line 119 87073] Train: [93/100][280/1557] Data 0.013 (0.075) Batch 1.224 (1.071) Remain 03:37:25 loss: 0.2888 Lr: 0.00008 [2024-02-19 16:27:47,206 INFO misc.py line 119 87073] Train: [93/100][281/1557] Data 0.015 (0.074) Batch 1.169 (1.072) Remain 03:37:28 loss: 0.2137 Lr: 0.00008 [2024-02-19 16:27:48,194 INFO misc.py line 119 87073] Train: [93/100][282/1557] Data 0.017 (0.074) Batch 1.000 (1.071) Remain 03:37:24 loss: 0.0240 Lr: 0.00008 [2024-02-19 16:27:49,091 INFO misc.py line 119 87073] Train: 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Batch 1.229 (1.095) Remain 03:42:05 loss: 0.1383 Lr: 0.00008 [2024-02-19 16:28:03,507 INFO misc.py line 119 87073] Train: [93/100][290/1557] Data 0.005 (0.088) Batch 1.033 (1.095) Remain 03:42:01 loss: 0.2462 Lr: 0.00008 [2024-02-19 16:28:04,468 INFO misc.py line 119 87073] Train: [93/100][291/1557] Data 0.005 (0.088) Batch 0.961 (1.094) Remain 03:41:54 loss: 0.2628 Lr: 0.00008 [2024-02-19 16:28:05,267 INFO misc.py line 119 87073] Train: [93/100][292/1557] Data 0.005 (0.088) Batch 0.798 (1.093) Remain 03:41:41 loss: 0.1130 Lr: 0.00008 [2024-02-19 16:28:06,030 INFO misc.py line 119 87073] Train: [93/100][293/1557] Data 0.005 (0.088) Batch 0.761 (1.092) Remain 03:41:25 loss: 0.2306 Lr: 0.00008 [2024-02-19 16:28:07,199 INFO misc.py line 119 87073] Train: [93/100][294/1557] Data 0.007 (0.087) Batch 1.167 (1.093) Remain 03:41:28 loss: 0.0829 Lr: 0.00008 [2024-02-19 16:28:08,161 INFO misc.py line 119 87073] Train: [93/100][295/1557] Data 0.010 (0.087) Batch 0.968 (1.092) Remain 03:41:21 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Batch 1.277 (1.108) Remain 03:41:33 loss: 0.0756 Lr: 0.00008 [2024-02-19 16:31:13,243 INFO misc.py line 119 87073] Train: [93/100][458/1557] Data 0.004 (0.092) Batch 0.998 (1.108) Remain 03:41:29 loss: 0.2567 Lr: 0.00008 [2024-02-19 16:31:14,134 INFO misc.py line 119 87073] Train: [93/100][459/1557] Data 0.003 (0.092) Batch 0.890 (1.107) Remain 03:41:23 loss: 0.0721 Lr: 0.00008 [2024-02-19 16:31:14,909 INFO misc.py line 119 87073] Train: [93/100][460/1557] Data 0.004 (0.092) Batch 0.765 (1.106) Remain 03:41:12 loss: 0.1463 Lr: 0.00008 [2024-02-19 16:31:15,644 INFO misc.py line 119 87073] Train: [93/100][461/1557] Data 0.015 (0.092) Batch 0.745 (1.106) Remain 03:41:02 loss: 0.1968 Lr: 0.00008 [2024-02-19 16:31:16,899 INFO misc.py line 119 87073] Train: [93/100][462/1557] Data 0.004 (0.091) Batch 1.255 (1.106) Remain 03:41:05 loss: 0.0687 Lr: 0.00008 [2024-02-19 16:31:17,808 INFO misc.py line 119 87073] Train: [93/100][463/1557] Data 0.004 (0.091) Batch 0.909 (1.106) Remain 03:40:58 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line 119 87073] Train: [93/100][557/1557] Data 0.004 (0.087) Batch 0.886 (1.098) Remain 03:37:42 loss: 0.0756 Lr: 0.00008 [2024-02-19 16:32:58,224 INFO misc.py line 119 87073] Train: [93/100][558/1557] Data 0.004 (0.087) Batch 0.773 (1.097) Remain 03:37:34 loss: 0.1413 Lr: 0.00008 [2024-02-19 16:32:58,985 INFO misc.py line 119 87073] Train: [93/100][559/1557] Data 0.014 (0.086) Batch 0.771 (1.097) Remain 03:37:26 loss: 0.2129 Lr: 0.00008 [2024-02-19 16:33:00,247 INFO misc.py line 119 87073] Train: [93/100][560/1557] Data 0.004 (0.086) Batch 1.251 (1.097) Remain 03:37:28 loss: 0.1763 Lr: 0.00008 [2024-02-19 16:33:01,187 INFO misc.py line 119 87073] Train: [93/100][561/1557] Data 0.016 (0.086) Batch 0.952 (1.097) Remain 03:37:24 loss: 0.1297 Lr: 0.00008 [2024-02-19 16:33:02,030 INFO misc.py line 119 87073] Train: [93/100][562/1557] Data 0.003 (0.086) Batch 0.843 (1.096) Remain 03:37:18 loss: 0.0934 Lr: 0.00008 [2024-02-19 16:33:02,949 INFO misc.py line 119 87073] Train: 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Batch 1.135 (1.107) Remain 03:39:16 loss: 0.0344 Lr: 0.00008 [2024-02-19 16:33:16,852 INFO misc.py line 119 87073] Train: [93/100][570/1557] Data 0.005 (0.092) Batch 1.135 (1.107) Remain 03:39:16 loss: 0.1710 Lr: 0.00008 [2024-02-19 16:33:17,865 INFO misc.py line 119 87073] Train: [93/100][571/1557] Data 0.004 (0.092) Batch 1.011 (1.107) Remain 03:39:13 loss: 0.1066 Lr: 0.00008 [2024-02-19 16:33:18,610 INFO misc.py line 119 87073] Train: [93/100][572/1557] Data 0.006 (0.092) Batch 0.747 (1.106) Remain 03:39:04 loss: 0.1350 Lr: 0.00008 [2024-02-19 16:33:19,356 INFO misc.py line 119 87073] Train: [93/100][573/1557] Data 0.004 (0.092) Batch 0.736 (1.105) Remain 03:38:55 loss: 0.2465 Lr: 0.00008 [2024-02-19 16:33:20,539 INFO misc.py line 119 87073] Train: [93/100][574/1557] Data 0.013 (0.091) Batch 1.183 (1.106) Remain 03:38:56 loss: 0.0712 Lr: 0.00008 [2024-02-19 16:33:21,521 INFO misc.py line 119 87073] Train: [93/100][575/1557] Data 0.014 (0.091) Batch 0.993 (1.105) Remain 03:38:52 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Batch 0.843 (1.100) Remain 03:32:53 loss: 0.1535 Lr: 0.00008 [2024-02-19 16:38:21,326 INFO misc.py line 119 87073] Train: [93/100][850/1557] Data 0.004 (0.090) Batch 1.070 (1.100) Remain 03:32:51 loss: 0.3025 Lr: 0.00008 [2024-02-19 16:38:22,138 INFO misc.py line 119 87073] Train: [93/100][851/1557] Data 0.004 (0.090) Batch 0.813 (1.100) Remain 03:32:46 loss: 0.4265 Lr: 0.00008 [2024-02-19 16:38:22,878 INFO misc.py line 119 87073] Train: [93/100][852/1557] Data 0.004 (0.090) Batch 0.740 (1.100) Remain 03:32:40 loss: 0.1818 Lr: 0.00008 [2024-02-19 16:38:23,567 INFO misc.py line 119 87073] Train: [93/100][853/1557] Data 0.003 (0.090) Batch 0.686 (1.099) Remain 03:32:33 loss: 0.1530 Lr: 0.00008 [2024-02-19 16:38:24,787 INFO misc.py line 119 87073] Train: [93/100][854/1557] Data 0.006 (0.090) Batch 1.215 (1.099) Remain 03:32:34 loss: 0.0659 Lr: 0.00008 [2024-02-19 16:38:25,816 INFO misc.py line 119 87073] Train: [93/100][855/1557] Data 0.011 (0.090) Batch 1.026 (1.099) Remain 03:32:32 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Batch 0.812 (1.101) Remain 03:31:55 loss: 0.0653 Lr: 0.00008 [2024-02-19 16:39:23,129 INFO misc.py line 119 87073] Train: [93/100][906/1557] Data 0.005 (0.091) Batch 0.959 (1.101) Remain 03:31:52 loss: 0.3143 Lr: 0.00008 [2024-02-19 16:39:24,145 INFO misc.py line 119 87073] Train: [93/100][907/1557] Data 0.004 (0.091) Batch 1.015 (1.101) Remain 03:31:50 loss: 0.1937 Lr: 0.00008 [2024-02-19 16:39:24,908 INFO misc.py line 119 87073] Train: [93/100][908/1557] Data 0.005 (0.091) Batch 0.764 (1.100) Remain 03:31:44 loss: 0.2229 Lr: 0.00008 [2024-02-19 16:39:25,683 INFO misc.py line 119 87073] Train: [93/100][909/1557] Data 0.003 (0.091) Batch 0.771 (1.100) Remain 03:31:39 loss: 0.2151 Lr: 0.00008 [2024-02-19 16:39:26,937 INFO misc.py line 119 87073] Train: [93/100][910/1557] Data 0.007 (0.090) Batch 1.245 (1.100) Remain 03:31:40 loss: 0.0900 Lr: 0.00008 [2024-02-19 16:39:27,738 INFO misc.py line 119 87073] Train: [93/100][911/1557] Data 0.016 (0.090) Batch 0.814 (1.100) Remain 03:31:35 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line 119 87073] Train: [93/100][949/1557] Data 0.016 (0.087) Batch 1.063 (1.094) Remain 03:29:48 loss: 0.2606 Lr: 0.00008 [2024-02-19 16:40:04,869 INFO misc.py line 119 87073] Train: [93/100][950/1557] Data 0.007 (0.087) Batch 0.733 (1.094) Remain 03:29:42 loss: 0.1712 Lr: 0.00008 [2024-02-19 16:40:05,723 INFO misc.py line 119 87073] Train: [93/100][951/1557] Data 0.006 (0.087) Batch 0.842 (1.093) Remain 03:29:38 loss: 0.1412 Lr: 0.00008 [2024-02-19 16:40:07,039 INFO misc.py line 119 87073] Train: [93/100][952/1557] Data 0.017 (0.087) Batch 1.316 (1.094) Remain 03:29:40 loss: 0.2065 Lr: 0.00008 [2024-02-19 16:40:08,009 INFO misc.py line 119 87073] Train: [93/100][953/1557] Data 0.017 (0.087) Batch 0.981 (1.093) Remain 03:29:37 loss: 0.1970 Lr: 0.00008 [2024-02-19 16:40:08,995 INFO misc.py line 119 87073] Train: [93/100][954/1557] Data 0.006 (0.087) Batch 0.988 (1.093) Remain 03:29:35 loss: 0.4803 Lr: 0.00008 [2024-02-19 16:40:10,056 INFO misc.py line 119 87073] Train: 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Batch 0.859 (1.100) Remain 03:30:43 loss: 0.1524 Lr: 0.00008 [2024-02-19 16:40:24,181 INFO misc.py line 119 87073] Train: [93/100][962/1557] Data 0.009 (0.090) Batch 1.210 (1.100) Remain 03:30:43 loss: 0.1275 Lr: 0.00008 [2024-02-19 16:40:25,123 INFO misc.py line 119 87073] Train: [93/100][963/1557] Data 0.014 (0.089) Batch 0.953 (1.100) Remain 03:30:40 loss: 0.1386 Lr: 0.00008 [2024-02-19 16:40:25,888 INFO misc.py line 119 87073] Train: [93/100][964/1557] Data 0.005 (0.089) Batch 0.766 (1.100) Remain 03:30:35 loss: 0.2188 Lr: 0.00008 [2024-02-19 16:40:26,651 INFO misc.py line 119 87073] Train: [93/100][965/1557] Data 0.003 (0.089) Batch 0.750 (1.099) Remain 03:30:30 loss: 0.5061 Lr: 0.00008 [2024-02-19 16:40:27,886 INFO misc.py line 119 87073] Train: [93/100][966/1557] Data 0.016 (0.089) Batch 1.237 (1.099) Remain 03:30:30 loss: 0.0668 Lr: 0.00008 [2024-02-19 16:40:28,870 INFO misc.py line 119 87073] Train: [93/100][967/1557] Data 0.014 (0.089) Batch 0.995 (1.099) Remain 03:30:28 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misc.py line 119 87073] Train: [93/100][1501/1557] Data 0.004 (0.088) Batch 0.873 (1.093) Remain 03:19:36 loss: 0.3113 Lr: 0.00007 [2024-02-19 16:50:08,016 INFO misc.py line 119 87073] Train: [93/100][1502/1557] Data 0.011 (0.087) Batch 1.093 (1.093) Remain 03:19:35 loss: 0.3719 Lr: 0.00007 [2024-02-19 16:50:08,802 INFO misc.py line 119 87073] Train: [93/100][1503/1557] Data 0.010 (0.087) Batch 0.792 (1.093) Remain 03:19:31 loss: 0.1177 Lr: 0.00007 [2024-02-19 16:50:09,551 INFO misc.py line 119 87073] Train: [93/100][1504/1557] Data 0.004 (0.087) Batch 0.750 (1.093) Remain 03:19:28 loss: 0.1896 Lr: 0.00007 [2024-02-19 16:50:10,671 INFO misc.py line 119 87073] Train: [93/100][1505/1557] Data 0.004 (0.087) Batch 1.117 (1.093) Remain 03:19:27 loss: 0.1503 Lr: 0.00007 [2024-02-19 16:50:11,678 INFO misc.py line 119 87073] Train: [93/100][1506/1557] Data 0.006 (0.087) Batch 1.007 (1.093) Remain 03:19:25 loss: 0.0637 Lr: 0.00007 [2024-02-19 16:50:12,810 INFO misc.py line 119 87073] Train: 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(0.087) Batch 0.938 (1.092) Remain 03:19:12 loss: 0.4859 Lr: 0.00007 [2024-02-19 16:50:19,377 INFO misc.py line 119 87073] Train: [93/100][1514/1557] Data 0.006 (0.087) Batch 0.831 (1.092) Remain 03:19:09 loss: 0.0788 Lr: 0.00007 [2024-02-19 16:50:20,274 INFO misc.py line 119 87073] Train: [93/100][1515/1557] Data 0.007 (0.087) Batch 0.898 (1.092) Remain 03:19:06 loss: 0.2816 Lr: 0.00007 [2024-02-19 16:50:21,314 INFO misc.py line 119 87073] Train: [93/100][1516/1557] Data 0.006 (0.087) Batch 1.041 (1.092) Remain 03:19:05 loss: 0.1921 Lr: 0.00007 [2024-02-19 16:50:22,092 INFO misc.py line 119 87073] Train: [93/100][1517/1557] Data 0.004 (0.087) Batch 0.779 (1.092) Remain 03:19:02 loss: 0.1208 Lr: 0.00007 [2024-02-19 16:50:22,833 INFO misc.py line 119 87073] Train: [93/100][1518/1557] Data 0.003 (0.087) Batch 0.730 (1.091) Remain 03:18:58 loss: 0.1352 Lr: 0.00007 [2024-02-19 16:50:30,370 INFO misc.py line 119 87073] Train: [93/100][1519/1557] Data 4.076 (0.089) Batch 7.542 (1.096) Remain 03:19:43 loss: 0.0861 Lr: 0.00007 [2024-02-19 16:50:31,407 INFO misc.py line 119 87073] Train: [93/100][1520/1557] Data 0.010 (0.089) Batch 1.037 (1.096) Remain 03:19:42 loss: 0.3800 Lr: 0.00007 [2024-02-19 16:50:32,332 INFO misc.py line 119 87073] Train: [93/100][1521/1557] Data 0.009 (0.089) Batch 0.931 (1.096) Remain 03:19:40 loss: 0.1557 Lr: 0.00007 [2024-02-19 16:50:33,398 INFO misc.py line 119 87073] Train: [93/100][1522/1557] Data 0.003 (0.089) Batch 1.067 (1.096) Remain 03:19:38 loss: 0.4680 Lr: 0.00007 [2024-02-19 16:50:34,330 INFO misc.py line 119 87073] Train: [93/100][1523/1557] Data 0.003 (0.089) Batch 0.932 (1.095) Remain 03:19:36 loss: 0.1189 Lr: 0.00007 [2024-02-19 16:50:35,096 INFO misc.py line 119 87073] Train: [93/100][1524/1557] Data 0.003 (0.089) Batch 0.763 (1.095) Remain 03:19:33 loss: 0.2149 Lr: 0.00007 [2024-02-19 16:50:35,814 INFO misc.py line 119 87073] Train: [93/100][1525/1557] Data 0.006 (0.089) Batch 0.720 (1.095) Remain 03:19:29 loss: 0.1092 Lr: 0.00007 [2024-02-19 16:50:37,034 INFO misc.py line 119 87073] Train: [93/100][1526/1557] Data 0.003 (0.089) Batch 1.220 (1.095) Remain 03:19:29 loss: 0.0604 Lr: 0.00007 [2024-02-19 16:50:37,965 INFO misc.py line 119 87073] Train: [93/100][1527/1557] Data 0.005 (0.089) Batch 0.932 (1.095) Remain 03:19:26 loss: 0.4809 Lr: 0.00007 [2024-02-19 16:50:38,874 INFO misc.py line 119 87073] Train: [93/100][1528/1557] Data 0.008 (0.089) Batch 0.909 (1.095) Remain 03:19:24 loss: 0.2634 Lr: 0.00007 [2024-02-19 16:50:39,914 INFO misc.py line 119 87073] Train: [93/100][1529/1557] Data 0.003 (0.089) Batch 1.033 (1.095) Remain 03:19:22 loss: 0.0343 Lr: 0.00007 [2024-02-19 16:50:41,047 INFO misc.py line 119 87073] Train: [93/100][1530/1557] Data 0.010 (0.089) Batch 1.137 (1.095) Remain 03:19:22 loss: 0.3645 Lr: 0.00007 [2024-02-19 16:50:41,761 INFO misc.py line 119 87073] Train: [93/100][1531/1557] Data 0.006 (0.089) Batch 0.717 (1.095) Remain 03:19:18 loss: 0.1019 Lr: 0.00007 [2024-02-19 16:50:42,487 INFO misc.py line 119 87073] Train: [93/100][1532/1557] Data 0.002 (0.089) Batch 0.725 (1.094) Remain 03:19:14 loss: 0.2486 Lr: 0.00007 [2024-02-19 16:50:45,103 INFO misc.py line 119 87073] Train: [93/100][1533/1557] Data 0.004 (0.088) Batch 2.614 (1.095) Remain 03:19:24 loss: 0.1686 Lr: 0.00007 [2024-02-19 16:50:46,076 INFO misc.py line 119 87073] Train: [93/100][1534/1557] Data 0.006 (0.088) Batch 0.970 (1.095) Remain 03:19:22 loss: 0.2361 Lr: 0.00007 [2024-02-19 16:50:46,914 INFO misc.py line 119 87073] Train: [93/100][1535/1557] Data 0.009 (0.088) Batch 0.842 (1.095) Remain 03:19:19 loss: 0.3464 Lr: 0.00007 [2024-02-19 16:50:47,870 INFO misc.py line 119 87073] Train: [93/100][1536/1557] Data 0.005 (0.088) Batch 0.955 (1.095) Remain 03:19:17 loss: 0.1254 Lr: 0.00007 [2024-02-19 16:50:48,893 INFO misc.py line 119 87073] Train: [93/100][1537/1557] Data 0.007 (0.088) Batch 1.022 (1.095) Remain 03:19:15 loss: 0.3052 Lr: 0.00007 [2024-02-19 16:50:49,624 INFO misc.py line 119 87073] Train: [93/100][1538/1557] Data 0.006 (0.088) Batch 0.732 (1.095) Remain 03:19:11 loss: 0.2484 Lr: 0.00007 [2024-02-19 16:50:50,295 INFO misc.py line 119 87073] Train: [93/100][1539/1557] Data 0.005 (0.088) Batch 0.670 (1.094) Remain 03:19:07 loss: 0.1221 Lr: 0.00007 [2024-02-19 16:50:51,569 INFO misc.py line 119 87073] Train: [93/100][1540/1557] Data 0.007 (0.088) Batch 1.272 (1.095) Remain 03:19:08 loss: 0.0586 Lr: 0.00007 [2024-02-19 16:50:52,376 INFO misc.py line 119 87073] Train: [93/100][1541/1557] Data 0.009 (0.088) Batch 0.809 (1.094) Remain 03:19:04 loss: 0.0770 Lr: 0.00007 [2024-02-19 16:50:53,373 INFO misc.py line 119 87073] Train: [93/100][1542/1557] Data 0.008 (0.088) Batch 1.000 (1.094) Remain 03:19:03 loss: 0.0751 Lr: 0.00007 [2024-02-19 16:50:54,396 INFO misc.py line 119 87073] Train: [93/100][1543/1557] Data 0.004 (0.088) Batch 1.023 (1.094) Remain 03:19:01 loss: 0.3451 Lr: 0.00007 [2024-02-19 16:50:55,455 INFO misc.py line 119 87073] Train: [93/100][1544/1557] Data 0.004 (0.088) Batch 1.059 (1.094) Remain 03:19:00 loss: 0.2948 Lr: 0.00007 [2024-02-19 16:50:56,148 INFO misc.py line 119 87073] Train: [93/100][1545/1557] Data 0.004 (0.088) Batch 0.693 (1.094) Remain 03:18:56 loss: 0.2384 Lr: 0.00007 [2024-02-19 16:50:56,840 INFO misc.py line 119 87073] Train: [93/100][1546/1557] Data 0.004 (0.088) Batch 0.692 (1.094) Remain 03:18:52 loss: 0.1064 Lr: 0.00007 [2024-02-19 16:50:58,068 INFO misc.py line 119 87073] Train: [93/100][1547/1557] Data 0.004 (0.088) Batch 1.228 (1.094) Remain 03:18:52 loss: 0.1787 Lr: 0.00007 [2024-02-19 16:50:59,034 INFO misc.py line 119 87073] Train: [93/100][1548/1557] Data 0.004 (0.088) Batch 0.966 (1.094) Remain 03:18:50 loss: 0.3050 Lr: 0.00007 [2024-02-19 16:51:00,026 INFO misc.py line 119 87073] Train: [93/100][1549/1557] Data 0.004 (0.088) Batch 0.993 (1.094) Remain 03:18:48 loss: 0.1013 Lr: 0.00007 [2024-02-19 16:51:00,982 INFO misc.py line 119 87073] Train: [93/100][1550/1557] Data 0.004 (0.088) Batch 0.956 (1.094) Remain 03:18:46 loss: 0.3860 Lr: 0.00007 [2024-02-19 16:51:01,859 INFO misc.py line 119 87073] Train: [93/100][1551/1557] Data 0.004 (0.088) Batch 0.876 (1.093) Remain 03:18:43 loss: 0.2930 Lr: 0.00007 [2024-02-19 16:51:02,588 INFO misc.py line 119 87073] Train: [93/100][1552/1557] Data 0.004 (0.087) Batch 0.730 (1.093) Remain 03:18:40 loss: 0.1492 Lr: 0.00007 [2024-02-19 16:51:03,260 INFO misc.py line 119 87073] Train: [93/100][1553/1557] Data 0.004 (0.087) Batch 0.672 (1.093) Remain 03:18:35 loss: 0.1420 Lr: 0.00007 [2024-02-19 16:51:04,325 INFO misc.py line 119 87073] Train: [93/100][1554/1557] Data 0.004 (0.087) Batch 1.066 (1.093) Remain 03:18:34 loss: 0.1138 Lr: 0.00007 [2024-02-19 16:51:05,253 INFO misc.py line 119 87073] Train: [93/100][1555/1557] Data 0.004 (0.087) Batch 0.927 (1.093) Remain 03:18:32 loss: 0.2966 Lr: 0.00007 [2024-02-19 16:51:06,202 INFO misc.py line 119 87073] Train: [93/100][1556/1557] Data 0.004 (0.087) Batch 0.949 (1.093) Remain 03:18:30 loss: 0.1413 Lr: 0.00007 [2024-02-19 16:51:07,069 INFO misc.py line 119 87073] Train: [93/100][1557/1557] Data 0.004 (0.087) Batch 0.868 (1.093) Remain 03:18:27 loss: 0.3464 Lr: 0.00007 [2024-02-19 16:51:07,070 INFO misc.py line 136 87073] Train result: loss: 0.2001 [2024-02-19 16:51:07,071 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 16:51:35,042 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4563 [2024-02-19 16:51:35,821 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4858 [2024-02-19 16:51:37,948 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3252 [2024-02-19 16:51:40,157 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3251 [2024-02-19 16:51:45,108 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2367 [2024-02-19 16:51:45,810 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0780 [2024-02-19 16:51:47,071 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2637 [2024-02-19 16:51:50,023 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2505 [2024-02-19 16:51:51,837 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2732 [2024-02-19 16:51:53,447 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7306/0.7840/0.9188. [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9343/0.9641 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9827/0.9888 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8657/0.9720 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3870/0.4385 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6635/0.6814 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8203/0.9348 [2024-02-19 16:51:53,448 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8487/0.9202 [2024-02-19 16:51:53,449 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9275/0.9766 [2024-02-19 16:51:53,449 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8578/0.8886 [2024-02-19 16:51:53,449 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8029/0.8915 [2024-02-19 16:51:53,449 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7733/0.8091 [2024-02-19 16:51:53,449 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6346/0.7263 [2024-02-19 16:51:53,451 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 16:51:53,455 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 16:51:53,456 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 16:52:01,864 INFO misc.py line 119 87073] Train: [94/100][1/1557] Data 1.755 (1.755) Batch 2.701 (2.701) Remain 08:10:33 loss: 0.0607 Lr: 0.00007 [2024-02-19 16:52:02,785 INFO misc.py line 119 87073] Train: [94/100][2/1557] Data 0.005 (0.005) Batch 0.919 (0.919) Remain 02:46:50 loss: 0.0862 Lr: 0.00007 [2024-02-19 16:52:03,700 INFO misc.py line 119 87073] Train: [94/100][3/1557] Data 0.007 (0.007) Batch 0.915 (0.915) Remain 02:46:10 loss: 0.2282 Lr: 0.00007 [2024-02-19 16:52:04,787 INFO misc.py line 119 87073] Train: [94/100][4/1557] Data 0.007 (0.007) Batch 1.088 (1.088) Remain 03:17:33 loss: 0.1402 Lr: 0.00007 [2024-02-19 16:52:05,625 INFO misc.py line 119 87073] Train: [94/100][5/1557] Data 0.006 (0.007) Batch 0.837 (0.963) Remain 02:54:46 loss: 0.2815 Lr: 0.00007 [2024-02-19 16:52:06,400 INFO misc.py line 119 87073] Train: [94/100][6/1557] Data 0.009 (0.007) Batch 0.777 (0.901) Remain 02:43:31 loss: 0.1917 Lr: 0.00007 [2024-02-19 16:52:07,395 INFO misc.py line 119 87073] Train: [94/100][7/1557] Data 0.005 (0.007) Batch 0.986 (0.922) Remain 02:47:23 loss: 0.1601 Lr: 0.00007 [2024-02-19 16:52:08,283 INFO misc.py line 119 87073] Train: [94/100][8/1557] Data 0.015 (0.008) Batch 0.897 (0.917) Remain 02:46:26 loss: 0.0978 Lr: 0.00007 [2024-02-19 16:52:09,300 INFO misc.py line 119 87073] Train: [94/100][9/1557] Data 0.005 (0.008) Batch 1.017 (0.934) Remain 02:49:28 loss: 0.0957 Lr: 0.00007 [2024-02-19 16:52:10,237 INFO misc.py line 119 87073] Train: [94/100][10/1557] Data 0.004 (0.007) Batch 0.937 (0.934) Remain 02:49:32 loss: 0.1131 Lr: 0.00007 [2024-02-19 16:52:11,054 INFO misc.py line 119 87073] Train: [94/100][11/1557] Data 0.005 (0.007) Batch 0.815 (0.919) Remain 02:46:49 loss: 0.1277 Lr: 0.00007 [2024-02-19 16:52:11,814 INFO misc.py line 119 87073] Train: [94/100][12/1557] Data 0.005 (0.007) Batch 0.761 (0.902) Remain 02:43:37 loss: 0.1447 Lr: 0.00007 [2024-02-19 16:52:12,576 INFO misc.py line 119 87073] Train: [94/100][13/1557] Data 0.005 (0.007) Batch 0.762 (0.888) Remain 02:41:03 loss: 0.1264 Lr: 0.00007 [2024-02-19 16:52:13,757 INFO misc.py line 119 87073] Train: [94/100][14/1557] Data 0.005 (0.007) Batch 1.177 (0.914) Remain 02:45:49 loss: 0.0821 Lr: 0.00007 [2024-02-19 16:52:14,858 INFO misc.py line 119 87073] Train: [94/100][15/1557] Data 0.009 (0.007) Batch 1.104 (0.930) Remain 02:48:40 loss: 0.1245 Lr: 0.00007 [2024-02-19 16:52:15,785 INFO misc.py line 119 87073] Train: [94/100][16/1557] Data 0.007 (0.007) Batch 0.928 (0.930) Remain 02:48:37 loss: 0.3617 Lr: 0.00007 [2024-02-19 16:52:16,588 INFO misc.py line 119 87073] Train: [94/100][17/1557] Data 0.007 (0.007) Batch 0.801 (0.920) Remain 02:46:56 loss: 0.0930 Lr: 0.00007 [2024-02-19 16:52:17,737 INFO misc.py line 119 87073] Train: [94/100][18/1557] Data 0.008 (0.007) Batch 1.117 (0.934) Remain 02:49:18 loss: 0.2649 Lr: 0.00007 [2024-02-19 16:52:18,462 INFO misc.py line 119 87073] Train: [94/100][19/1557] Data 0.041 (0.009) Batch 0.760 (0.923) Remain 02:47:18 loss: 0.1641 Lr: 0.00007 [2024-02-19 16:52:19,230 INFO misc.py line 119 87073] Train: [94/100][20/1557] Data 0.005 (0.009) Batch 0.761 (0.913) Remain 02:45:34 loss: 0.1661 Lr: 0.00007 [2024-02-19 16:52:20,502 INFO misc.py line 119 87073] Train: [94/100][21/1557] Data 0.014 (0.009) Batch 1.274 (0.933) Remain 02:49:11 loss: 0.2326 Lr: 0.00007 [2024-02-19 16:52:21,441 INFO misc.py line 119 87073] Train: [94/100][22/1557] Data 0.011 (0.009) Batch 0.942 (0.934) Remain 02:49:15 loss: 0.3052 Lr: 0.00007 [2024-02-19 16:52:22,312 INFO misc.py line 119 87073] Train: [94/100][23/1557] Data 0.007 (0.009) Batch 0.870 (0.931) Remain 02:48:40 loss: 0.0988 Lr: 0.00007 [2024-02-19 16:52:23,215 INFO misc.py line 119 87073] Train: [94/100][24/1557] Data 0.009 (0.009) Batch 0.904 (0.929) Remain 02:48:25 loss: 0.2426 Lr: 0.00007 [2024-02-19 16:52:24,272 INFO misc.py line 119 87073] Train: [94/100][25/1557] Data 0.007 (0.009) Batch 1.058 (0.935) Remain 02:49:28 loss: 0.2015 Lr: 0.00007 [2024-02-19 16:52:24,985 INFO misc.py line 119 87073] Train: [94/100][26/1557] Data 0.006 (0.009) Batch 0.714 (0.926) Remain 02:47:43 loss: 0.1385 Lr: 0.00007 [2024-02-19 16:52:25,739 INFO misc.py line 119 87073] Train: [94/100][27/1557] Data 0.005 (0.009) Batch 0.751 (0.918) Remain 02:46:23 loss: 0.2212 Lr: 0.00007 [2024-02-19 16:52:26,959 INFO misc.py line 119 87073] Train: [94/100][28/1557] Data 0.007 (0.009) Batch 1.222 (0.930) Remain 02:48:34 loss: 0.1401 Lr: 0.00007 [2024-02-19 16:52:27,827 INFO misc.py line 119 87073] Train: [94/100][29/1557] Data 0.005 (0.008) Batch 0.869 (0.928) Remain 02:48:07 loss: 0.2671 Lr: 0.00007 [2024-02-19 16:52:28,755 INFO misc.py line 119 87073] Train: [94/100][30/1557] Data 0.005 (0.008) Batch 0.927 (0.928) Remain 02:48:06 loss: 0.2917 Lr: 0.00007 [2024-02-19 16:52:29,713 INFO misc.py line 119 87073] Train: [94/100][31/1557] Data 0.006 (0.008) Batch 0.958 (0.929) Remain 02:48:16 loss: 0.1497 Lr: 0.00007 [2024-02-19 16:52:30,575 INFO misc.py line 119 87073] Train: [94/100][32/1557] Data 0.007 (0.008) Batch 0.857 (0.927) Remain 02:47:49 loss: 0.3158 Lr: 0.00007 [2024-02-19 16:52:31,326 INFO misc.py line 119 87073] Train: [94/100][33/1557] Data 0.011 (0.008) Batch 0.757 (0.921) Remain 02:46:46 loss: 0.1684 Lr: 0.00007 [2024-02-19 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line 119 87073] Train: [94/100][165/1557] Data 0.003 (0.045) Batch 1.098 (1.061) Remain 03:09:47 loss: 0.1046 Lr: 0.00007 [2024-02-19 16:54:56,267 INFO misc.py line 119 87073] Train: [94/100][166/1557] Data 0.003 (0.045) Batch 0.703 (1.059) Remain 03:09:23 loss: 0.1377 Lr: 0.00007 [2024-02-19 16:54:57,024 INFO misc.py line 119 87073] Train: [94/100][167/1557] Data 0.004 (0.044) Batch 0.745 (1.057) Remain 03:09:01 loss: 0.1220 Lr: 0.00007 [2024-02-19 16:54:58,223 INFO misc.py line 119 87073] Train: [94/100][168/1557] Data 0.016 (0.044) Batch 1.207 (1.058) Remain 03:09:10 loss: 0.1353 Lr: 0.00007 [2024-02-19 16:54:59,256 INFO misc.py line 119 87073] Train: [94/100][169/1557] Data 0.008 (0.044) Batch 1.038 (1.058) Remain 03:09:07 loss: 0.2132 Lr: 0.00007 [2024-02-19 16:55:00,089 INFO misc.py line 119 87073] Train: [94/100][170/1557] Data 0.004 (0.044) Batch 0.832 (1.056) Remain 03:08:52 loss: 0.2288 Lr: 0.00007 [2024-02-19 16:55:01,012 INFO misc.py line 119 87073] Train: 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(0.073) Batch 0.993 (1.149) Remain 02:59:43 loss: 0.3162 Lr: 0.00005 [2024-02-19 17:20:59,590 INFO misc.py line 119 87073] Train: [94/100][1514/1557] Data 0.004 (0.073) Batch 0.985 (1.149) Remain 02:59:41 loss: 0.2004 Lr: 0.00005 [2024-02-19 17:21:00,618 INFO misc.py line 119 87073] Train: [94/100][1515/1557] Data 0.004 (0.073) Batch 1.027 (1.149) Remain 02:59:39 loss: 0.2174 Lr: 0.00005 [2024-02-19 17:21:01,485 INFO misc.py line 119 87073] Train: [94/100][1516/1557] Data 0.005 (0.073) Batch 0.866 (1.149) Remain 02:59:37 loss: 0.1785 Lr: 0.00005 [2024-02-19 17:21:02,308 INFO misc.py line 119 87073] Train: [94/100][1517/1557] Data 0.006 (0.073) Batch 0.824 (1.148) Remain 02:59:33 loss: 0.1368 Lr: 0.00005 [2024-02-19 17:21:03,187 INFO misc.py line 119 87073] Train: [94/100][1518/1557] Data 0.005 (0.073) Batch 0.880 (1.148) Remain 02:59:31 loss: 0.2699 Lr: 0.00005 [2024-02-19 17:21:10,068 INFO misc.py line 119 87073] Train: [94/100][1519/1557] Data 3.432 (0.075) Batch 6.876 (1.152) Remain 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[2024-02-19 17:21:21,748 INFO misc.py line 119 87073] Train: [94/100][1526/1557] Data 0.008 (0.075) Batch 6.110 (1.154) Remain 03:00:19 loss: 0.1290 Lr: 0.00005 [2024-02-19 17:21:22,572 INFO misc.py line 119 87073] Train: [94/100][1527/1557] Data 0.004 (0.075) Batch 0.823 (1.154) Remain 03:00:16 loss: 0.1249 Lr: 0.00005 [2024-02-19 17:21:23,524 INFO misc.py line 119 87073] Train: [94/100][1528/1557] Data 0.006 (0.075) Batch 0.953 (1.154) Remain 03:00:13 loss: 0.1774 Lr: 0.00005 [2024-02-19 17:21:24,546 INFO misc.py line 119 87073] Train: [94/100][1529/1557] Data 0.004 (0.075) Batch 1.022 (1.154) Remain 03:00:12 loss: 0.1368 Lr: 0.00005 [2024-02-19 17:21:25,512 INFO misc.py line 119 87073] Train: [94/100][1530/1557] Data 0.005 (0.075) Batch 0.966 (1.154) Remain 03:00:09 loss: 0.3442 Lr: 0.00005 [2024-02-19 17:21:26,215 INFO misc.py line 119 87073] Train: [94/100][1531/1557] Data 0.004 (0.075) Batch 0.694 (1.153) Remain 03:00:05 loss: 0.1724 Lr: 0.00005 [2024-02-19 17:21:26,916 INFO misc.py line 119 87073] Train: [94/100][1532/1557] Data 0.013 (0.075) Batch 0.710 (1.153) Remain 03:00:01 loss: 0.0965 Lr: 0.00005 [2024-02-19 17:21:28,139 INFO misc.py line 119 87073] Train: [94/100][1533/1557] Data 0.004 (0.075) Batch 1.221 (1.153) Remain 03:00:01 loss: 0.1440 Lr: 0.00005 [2024-02-19 17:21:29,164 INFO misc.py line 119 87073] Train: [94/100][1534/1557] Data 0.006 (0.075) Batch 1.025 (1.153) Remain 02:59:59 loss: 0.2377 Lr: 0.00005 [2024-02-19 17:21:30,063 INFO misc.py line 119 87073] Train: [94/100][1535/1557] Data 0.005 (0.075) Batch 0.900 (1.153) Remain 02:59:56 loss: 0.1408 Lr: 0.00005 [2024-02-19 17:21:31,040 INFO misc.py line 119 87073] Train: [94/100][1536/1557] Data 0.004 (0.075) Batch 0.975 (1.153) Remain 02:59:54 loss: 0.2231 Lr: 0.00005 [2024-02-19 17:21:32,024 INFO misc.py line 119 87073] Train: [94/100][1537/1557] Data 0.006 (0.075) Batch 0.985 (1.153) Remain 02:59:52 loss: 0.1382 Lr: 0.00005 [2024-02-19 17:21:32,797 INFO misc.py line 119 87073] Train: [94/100][1538/1557] Data 0.005 (0.075) Batch 0.774 (1.153) Remain 02:59:48 loss: 0.1658 Lr: 0.00005 [2024-02-19 17:21:33,447 INFO misc.py line 119 87073] Train: [94/100][1539/1557] Data 0.004 (0.074) Batch 0.647 (1.152) Remain 02:59:44 loss: 0.1718 Lr: 0.00005 [2024-02-19 17:21:34,783 INFO misc.py line 119 87073] Train: [94/100][1540/1557] Data 0.007 (0.074) Batch 1.336 (1.152) Remain 02:59:44 loss: 0.1988 Lr: 0.00005 [2024-02-19 17:21:35,649 INFO misc.py line 119 87073] Train: [94/100][1541/1557] Data 0.008 (0.074) Batch 0.866 (1.152) Remain 02:59:41 loss: 0.4179 Lr: 0.00005 [2024-02-19 17:21:36,579 INFO misc.py line 119 87073] Train: [94/100][1542/1557] Data 0.007 (0.074) Batch 0.933 (1.152) Remain 02:59:38 loss: 0.1690 Lr: 0.00005 [2024-02-19 17:21:37,559 INFO misc.py line 119 87073] Train: [94/100][1543/1557] Data 0.005 (0.074) Batch 0.980 (1.152) Remain 02:59:36 loss: 0.1025 Lr: 0.00005 [2024-02-19 17:21:38,459 INFO misc.py line 119 87073] Train: [94/100][1544/1557] Data 0.004 (0.074) Batch 0.898 (1.152) Remain 02:59:34 loss: 0.2030 Lr: 0.00005 [2024-02-19 17:21:39,199 INFO misc.py line 119 87073] Train: [94/100][1545/1557] Data 0.008 (0.074) Batch 0.740 (1.151) Remain 02:59:30 loss: 0.1703 Lr: 0.00005 [2024-02-19 17:21:39,953 INFO misc.py line 119 87073] Train: [94/100][1546/1557] Data 0.007 (0.074) Batch 0.758 (1.151) Remain 02:59:26 loss: 0.1389 Lr: 0.00005 [2024-02-19 17:21:40,970 INFO misc.py line 119 87073] Train: [94/100][1547/1557] Data 0.004 (0.074) Batch 1.015 (1.151) Remain 02:59:24 loss: 0.1070 Lr: 0.00005 [2024-02-19 17:21:42,042 INFO misc.py line 119 87073] Train: [94/100][1548/1557] Data 0.005 (0.074) Batch 1.073 (1.151) Remain 02:59:23 loss: 0.1966 Lr: 0.00005 [2024-02-19 17:21:42,996 INFO misc.py line 119 87073] Train: [94/100][1549/1557] Data 0.006 (0.074) Batch 0.953 (1.151) Remain 02:59:20 loss: 0.2422 Lr: 0.00005 [2024-02-19 17:21:44,178 INFO misc.py line 119 87073] Train: [94/100][1550/1557] Data 0.005 (0.074) Batch 1.183 (1.151) Remain 02:59:19 loss: 0.1232 Lr: 0.00005 [2024-02-19 17:21:45,065 INFO misc.py line 119 87073] Train: [94/100][1551/1557] Data 0.006 (0.074) Batch 0.887 (1.151) Remain 02:59:17 loss: 0.1426 Lr: 0.00005 [2024-02-19 17:21:45,890 INFO misc.py line 119 87073] Train: [94/100][1552/1557] Data 0.004 (0.074) Batch 0.824 (1.151) Remain 02:59:14 loss: 0.2125 Lr: 0.00005 [2024-02-19 17:21:46,630 INFO misc.py line 119 87073] Train: [94/100][1553/1557] Data 0.005 (0.074) Batch 0.741 (1.150) Remain 02:59:10 loss: 0.1645 Lr: 0.00005 [2024-02-19 17:21:47,892 INFO misc.py line 119 87073] Train: [94/100][1554/1557] Data 0.004 (0.074) Batch 1.261 (1.150) Remain 02:59:10 loss: 0.1030 Lr: 0.00005 [2024-02-19 17:21:48,908 INFO misc.py line 119 87073] Train: [94/100][1555/1557] Data 0.005 (0.074) Batch 1.016 (1.150) Remain 02:59:08 loss: 0.1658 Lr: 0.00005 [2024-02-19 17:21:49,796 INFO misc.py line 119 87073] Train: [94/100][1556/1557] Data 0.005 (0.074) Batch 0.888 (1.150) Remain 02:59:05 loss: 0.1192 Lr: 0.00005 [2024-02-19 17:21:50,878 INFO misc.py line 119 87073] Train: [94/100][1557/1557] Data 0.007 (0.074) Batch 1.083 (1.150) Remain 02:59:03 loss: 0.0614 Lr: 0.00005 [2024-02-19 17:21:50,878 INFO misc.py line 136 87073] Train result: loss: 0.1984 [2024-02-19 17:21:50,878 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 17:22:19,360 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4207 [2024-02-19 17:22:20,139 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4546 [2024-02-19 17:22:22,267 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3274 [2024-02-19 17:22:24,478 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3435 [2024-02-19 17:22:29,435 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2400 [2024-02-19 17:22:30,134 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0879 [2024-02-19 17:22:31,396 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2592 [2024-02-19 17:22:34,345 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2633 [2024-02-19 17:22:36,158 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2779 [2024-02-19 17:22:37,711 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7261/0.7825/0.9192. [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9340/0.9634 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9832/0.9893 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8706/0.9735 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4015/0.4622 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6652/0.6838 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8215/0.9376 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8510/0.9197 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9218/0.9756 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7818/0.8029 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.7982/0.8875 [2024-02-19 17:22:37,712 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7787/0.8562 [2024-02-19 17:22:37,713 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6312/0.7215 [2024-02-19 17:22:37,713 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 17:22:37,716 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 17:22:37,716 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 17:22:45,314 INFO misc.py line 119 87073] Train: [95/100][1/1557] Data 1.478 (1.478) Batch 2.391 (2.391) Remain 06:12:11 loss: 0.2272 Lr: 0.00005 [2024-02-19 17:22:46,113 INFO misc.py line 119 87073] Train: [95/100][2/1557] Data 0.005 (0.005) Batch 0.790 (0.790) Remain 02:02:57 loss: 0.0867 Lr: 0.00005 [2024-02-19 17:22:47,206 INFO misc.py line 119 87073] Train: [95/100][3/1557] Data 0.014 (0.014) Batch 1.102 (1.102) Remain 02:51:29 loss: 0.4335 Lr: 0.00005 [2024-02-19 17:22:48,319 INFO misc.py line 119 87073] Train: [95/100][4/1557] Data 0.006 (0.006) Batch 1.112 (1.112) Remain 02:53:02 loss: 0.3365 Lr: 0.00005 [2024-02-19 17:22:49,110 INFO misc.py line 119 87073] Train: [95/100][5/1557] Data 0.007 (0.006) Batch 0.792 (0.952) Remain 02:28:06 loss: 0.1324 Lr: 0.00005 [2024-02-19 17:22:49,835 INFO misc.py line 119 87073] Train: [95/100][6/1557] Data 0.006 (0.006) Batch 0.726 (0.877) Remain 02:16:23 loss: 0.1542 Lr: 0.00005 [2024-02-19 17:23:01,247 INFO misc.py line 119 87073] Train: [95/100][7/1557] Data 0.004 (0.006) Batch 11.411 (3.510) Remain 09:06:06 loss: 0.2199 Lr: 0.00005 [2024-02-19 17:23:02,220 INFO misc.py line 119 87073] Train: [95/100][8/1557] Data 0.007 (0.006) Batch 0.974 (3.003) Remain 07:47:08 loss: 0.0702 Lr: 0.00005 [2024-02-19 17:23:03,051 INFO misc.py line 119 87073] Train: [95/100][9/1557] Data 0.005 (0.006) Batch 0.830 (2.641) Remain 06:50:45 loss: 0.1495 Lr: 0.00005 [2024-02-19 17:23:04,117 INFO misc.py line 119 87073] Train: [95/100][10/1557] Data 0.005 (0.006) Batch 1.063 (2.415) Remain 06:15:40 loss: 0.1547 Lr: 0.00005 [2024-02-19 17:23:04,974 INFO misc.py line 119 87073] Train: [95/100][11/1557] Data 0.008 (0.006) Batch 0.860 (2.221) Remain 05:45:23 loss: 0.1475 Lr: 0.00005 [2024-02-19 17:23:05,720 INFO misc.py line 119 87073] Train: [95/100][12/1557] Data 0.006 (0.006) Batch 0.746 (2.057) Remain 05:19:52 loss: 0.3300 Lr: 0.00005 [2024-02-19 17:23:06,505 INFO misc.py line 119 87073] Train: [95/100][13/1557] Data 0.005 (0.006) Batch 0.775 (1.929) Remain 04:59:53 loss: 0.0901 Lr: 0.00005 [2024-02-19 17:23:07,668 INFO misc.py line 119 87073] Train: [95/100][14/1557] Data 0.015 (0.007) Batch 1.168 (1.860) Remain 04:49:06 loss: 0.1167 Lr: 0.00005 [2024-02-19 17:23:08,753 INFO misc.py line 119 87073] Train: [95/100][15/1557] Data 0.011 (0.007) Batch 1.078 (1.795) Remain 04:38:57 loss: 0.2021 Lr: 0.00005 [2024-02-19 17:23:09,805 INFO misc.py line 119 87073] Train: [95/100][16/1557] Data 0.018 (0.008) Batch 1.066 (1.738) Remain 04:30:13 loss: 0.3289 Lr: 0.00005 [2024-02-19 17:23:10,752 INFO misc.py line 119 87073] Train: [95/100][17/1557] Data 0.004 (0.008) Batch 0.947 (1.682) Remain 04:21:23 loss: 0.1239 Lr: 0.00005 [2024-02-19 17:23:11,670 INFO misc.py line 119 87073] Train: [95/100][18/1557] Data 0.004 (0.007) Batch 0.918 (1.631) Remain 04:13:27 loss: 0.1717 Lr: 0.00005 [2024-02-19 17:23:12,465 INFO misc.py line 119 87073] Train: [95/100][19/1557] Data 0.004 (0.007) Batch 0.794 (1.579) Remain 04:05:18 loss: 0.3108 Lr: 0.00005 [2024-02-19 17:23:13,215 INFO misc.py line 119 87073] Train: [95/100][20/1557] Data 0.004 (0.007) Batch 0.749 (1.530) Remain 03:57:41 loss: 0.3158 Lr: 0.00005 [2024-02-19 17:23:14,324 INFO misc.py line 119 87073] Train: [95/100][21/1557] Data 0.005 (0.007) Batch 1.051 (1.503) Remain 03:53:32 loss: 0.1447 Lr: 0.00005 [2024-02-19 17:23:15,653 INFO misc.py line 119 87073] Train: [95/100][22/1557] Data 0.063 (0.010) Batch 1.375 (1.497) Remain 03:52:27 loss: 0.1259 Lr: 0.00005 [2024-02-19 17:23:16,771 INFO misc.py line 119 87073] Train: [95/100][23/1557] Data 0.018 (0.010) Batch 1.120 (1.478) Remain 03:49:30 loss: 0.1051 Lr: 0.00005 [2024-02-19 17:23:17,670 INFO misc.py line 119 87073] Train: [95/100][24/1557] Data 0.016 (0.011) Batch 0.912 (1.451) Remain 03:45:17 loss: 0.0853 Lr: 0.00005 [2024-02-19 17:23:18,683 INFO misc.py line 119 87073] Train: [95/100][25/1557] Data 0.003 (0.010) Batch 1.011 (1.431) Remain 03:42:10 loss: 0.1643 Lr: 0.00005 [2024-02-19 17:23:19,446 INFO misc.py line 119 87073] Train: [95/100][26/1557] Data 0.005 (0.010) Batch 0.765 (1.402) Remain 03:37:39 loss: 0.1675 Lr: 0.00005 [2024-02-19 17:23:20,164 INFO misc.py line 119 87073] Train: [95/100][27/1557] Data 0.004 (0.010) Batch 0.706 (1.373) Remain 03:33:07 loss: 0.1658 Lr: 0.00005 [2024-02-19 17:23:21,463 INFO misc.py line 119 87073] Train: [95/100][28/1557] Data 0.015 (0.010) Batch 1.297 (1.370) Remain 03:32:38 loss: 0.0956 Lr: 0.00005 [2024-02-19 17:23:22,605 INFO misc.py line 119 87073] Train: [95/100][29/1557] Data 0.017 (0.010) Batch 1.152 (1.361) Remain 03:31:18 loss: 0.4208 Lr: 0.00005 [2024-02-19 17:23:23,449 INFO misc.py line 119 87073] Train: [95/100][30/1557] Data 0.008 (0.010) Batch 0.847 (1.342) Remain 03:28:19 loss: 0.2138 Lr: 0.00005 [2024-02-19 17:23:24,459 INFO misc.py line 119 87073] Train: [95/100][31/1557] Data 0.004 (0.010) Batch 1.009 (1.330) Remain 03:26:27 loss: 0.3169 Lr: 0.00005 [2024-02-19 17:23:25,430 INFO misc.py line 119 87073] Train: [95/100][32/1557] Data 0.006 (0.010) Batch 0.971 (1.318) Remain 03:24:30 loss: 0.2995 Lr: 0.00005 [2024-02-19 17:23:26,206 INFO misc.py line 119 87073] Train: [95/100][33/1557] Data 0.006 (0.010) Batch 0.777 (1.300) Remain 03:21:41 loss: 0.1313 Lr: 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line 119 87073] Train: [95/100][40/1557] Data 0.006 (0.009) Batch 0.787 (1.232) Remain 03:11:01 loss: 0.1787 Lr: 0.00005 [2024-02-19 17:23:33,527 INFO misc.py line 119 87073] Train: [95/100][41/1557] Data 0.009 (0.009) Batch 0.731 (1.219) Remain 03:08:57 loss: 0.1278 Lr: 0.00005 [2024-02-19 17:23:34,650 INFO misc.py line 119 87073] Train: [95/100][42/1557] Data 0.005 (0.009) Batch 1.122 (1.216) Remain 03:08:33 loss: 0.0567 Lr: 0.00005 [2024-02-19 17:23:35,652 INFO misc.py line 119 87073] Train: [95/100][43/1557] Data 0.005 (0.009) Batch 1.003 (1.211) Remain 03:07:42 loss: 0.2460 Lr: 0.00005 [2024-02-19 17:23:36,481 INFO misc.py line 119 87073] Train: [95/100][44/1557] Data 0.005 (0.009) Batch 0.828 (1.202) Remain 03:06:14 loss: 0.2887 Lr: 0.00005 [2024-02-19 17:23:37,399 INFO misc.py line 119 87073] Train: [95/100][45/1557] Data 0.006 (0.009) Batch 0.917 (1.195) Remain 03:05:10 loss: 0.1787 Lr: 0.00005 [2024-02-19 17:23:38,395 INFO misc.py line 119 87073] Train: [95/100][46/1557] Data 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line 119 87073] Train: [95/100][221/1557] Data 0.005 (0.051) Batch 0.903 (1.286) Remain 03:15:26 loss: 0.4459 Lr: 0.00005 [2024-02-19 17:27:28,207 INFO misc.py line 119 87073] Train: [95/100][222/1557] Data 0.006 (0.051) Batch 0.722 (1.283) Remain 03:15:01 loss: 0.1835 Lr: 0.00005 [2024-02-19 17:27:29,014 INFO misc.py line 119 87073] Train: [95/100][223/1557] Data 0.007 (0.051) Batch 0.808 (1.281) Remain 03:14:40 loss: 0.2270 Lr: 0.00005 [2024-02-19 17:27:30,159 INFO misc.py line 119 87073] Train: [95/100][224/1557] Data 0.004 (0.051) Batch 1.143 (1.280) Remain 03:14:33 loss: 0.0490 Lr: 0.00005 [2024-02-19 17:27:31,297 INFO misc.py line 119 87073] Train: [95/100][225/1557] Data 0.009 (0.051) Batch 1.138 (1.280) Remain 03:14:26 loss: 0.0632 Lr: 0.00005 [2024-02-19 17:27:32,369 INFO misc.py line 119 87073] Train: [95/100][226/1557] Data 0.007 (0.050) Batch 1.073 (1.279) Remain 03:14:17 loss: 0.2920 Lr: 0.00005 [2024-02-19 17:27:33,304 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [95/100][277/1557] Data 0.006 (0.052) Batch 0.996 (1.295) Remain 03:15:43 loss: 0.1224 Lr: 0.00005 [2024-02-19 17:28:42,857 INFO misc.py line 119 87073] Train: [95/100][278/1557] Data 0.011 (0.052) Batch 0.698 (1.293) Remain 03:15:22 loss: 0.1583 Lr: 0.00005 [2024-02-19 17:28:43,633 INFO misc.py line 119 87073] Train: [95/100][279/1557] Data 0.005 (0.051) Batch 0.775 (1.291) Remain 03:15:03 loss: 0.0889 Lr: 0.00005 [2024-02-19 17:28:44,762 INFO misc.py line 119 87073] Train: [95/100][280/1557] Data 0.005 (0.051) Batch 1.128 (1.291) Remain 03:14:57 loss: 0.1752 Lr: 0.00005 [2024-02-19 17:28:45,641 INFO misc.py line 119 87073] Train: [95/100][281/1557] Data 0.006 (0.051) Batch 0.880 (1.289) Remain 03:14:42 loss: 0.4422 Lr: 0.00005 [2024-02-19 17:28:46,697 INFO misc.py line 119 87073] Train: [95/100][282/1557] Data 0.005 (0.051) Batch 1.054 (1.288) Remain 03:14:33 loss: 0.2362 Lr: 0.00005 [2024-02-19 17:28:47,854 INFO misc.py line 119 87073] Train: 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Remain 02:56:23 loss: 0.0816 Lr: 0.00004 [2024-02-19 17:51:17,948 INFO misc.py line 119 87073] Train: [95/100][1303/1557] Data 0.012 (0.061) Batch 0.871 (1.316) Remain 02:56:18 loss: 0.2271 Lr: 0.00004 [2024-02-19 17:51:18,981 INFO misc.py line 119 87073] Train: [95/100][1304/1557] Data 0.004 (0.061) Batch 1.032 (1.316) Remain 02:56:15 loss: 0.3864 Lr: 0.00004 [2024-02-19 17:51:19,988 INFO misc.py line 119 87073] Train: [95/100][1305/1557] Data 0.005 (0.061) Batch 1.008 (1.315) Remain 02:56:12 loss: 0.2705 Lr: 0.00004 [2024-02-19 17:51:20,889 INFO misc.py line 119 87073] Train: [95/100][1306/1557] Data 0.004 (0.060) Batch 0.901 (1.315) Remain 02:56:08 loss: 0.2111 Lr: 0.00004 [2024-02-19 17:51:21,658 INFO misc.py line 119 87073] Train: [95/100][1307/1557] Data 0.004 (0.060) Batch 0.759 (1.315) Remain 02:56:04 loss: 0.2210 Lr: 0.00004 [2024-02-19 17:51:22,452 INFO misc.py line 119 87073] Train: [95/100][1308/1557] Data 0.013 (0.060) Batch 0.804 (1.314) Remain 02:55:59 loss: 0.2085 Lr: 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Remain 02:53:07 loss: 0.1280 Lr: 0.00004 [2024-02-19 17:53:55,157 INFO misc.py line 119 87073] Train: [95/100][1427/1557] Data 0.004 (0.061) Batch 0.709 (1.312) Remain 02:53:02 loss: 0.2229 Lr: 0.00004 [2024-02-19 17:53:56,507 INFO misc.py line 119 87073] Train: [95/100][1428/1557] Data 0.013 (0.061) Batch 1.349 (1.312) Remain 02:53:01 loss: 0.1594 Lr: 0.00004 [2024-02-19 17:53:57,594 INFO misc.py line 119 87073] Train: [95/100][1429/1557] Data 0.014 (0.061) Batch 1.088 (1.312) Remain 02:52:58 loss: 0.0806 Lr: 0.00004 [2024-02-19 17:53:58,721 INFO misc.py line 119 87073] Train: [95/100][1430/1557] Data 0.012 (0.061) Batch 1.129 (1.311) Remain 02:52:56 loss: 0.1596 Lr: 0.00004 [2024-02-19 17:53:59,838 INFO misc.py line 119 87073] Train: [95/100][1431/1557] Data 0.010 (0.061) Batch 1.118 (1.311) Remain 02:52:54 loss: 0.1597 Lr: 0.00004 [2024-02-19 17:54:00,891 INFO misc.py line 119 87073] Train: [95/100][1432/1557] Data 0.009 (0.061) Batch 1.057 (1.311) Remain 02:52:51 loss: 0.2093 Lr: 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0.00004 [2024-02-19 17:56:08,097 INFO misc.py line 119 87073] Train: [95/100][1526/1557] Data 0.014 (0.061) Batch 1.148 (1.314) Remain 02:51:08 loss: 0.0933 Lr: 0.00004 [2024-02-19 17:56:09,203 INFO misc.py line 119 87073] Train: [95/100][1527/1557] Data 0.010 (0.061) Batch 1.066 (1.314) Remain 02:51:05 loss: 0.2401 Lr: 0.00004 [2024-02-19 17:56:10,170 INFO misc.py line 119 87073] Train: [95/100][1528/1557] Data 0.051 (0.061) Batch 1.013 (1.313) Remain 02:51:03 loss: 0.3710 Lr: 0.00004 [2024-02-19 17:56:11,126 INFO misc.py line 119 87073] Train: [95/100][1529/1557] Data 0.005 (0.061) Batch 0.958 (1.313) Remain 02:50:59 loss: 0.2337 Lr: 0.00004 [2024-02-19 17:56:11,934 INFO misc.py line 119 87073] Train: [95/100][1530/1557] Data 0.003 (0.061) Batch 0.808 (1.313) Remain 02:50:56 loss: 0.1499 Lr: 0.00004 [2024-02-19 17:56:12,700 INFO misc.py line 119 87073] Train: [95/100][1531/1557] Data 0.003 (0.061) Batch 0.756 (1.312) Remain 02:50:51 loss: 0.1102 Lr: 0.00004 [2024-02-19 17:56:13,485 INFO misc.py line 119 87073] Train: [95/100][1532/1557] Data 0.013 (0.061) Batch 0.792 (1.312) Remain 02:50:47 loss: 0.1402 Lr: 0.00004 [2024-02-19 17:56:14,622 INFO misc.py line 119 87073] Train: [95/100][1533/1557] Data 0.006 (0.061) Batch 1.118 (1.312) Remain 02:50:45 loss: 0.1079 Lr: 0.00004 [2024-02-19 17:56:15,612 INFO misc.py line 119 87073] Train: [95/100][1534/1557] Data 0.024 (0.061) Batch 1.008 (1.312) Remain 02:50:42 loss: 0.3319 Lr: 0.00004 [2024-02-19 17:56:16,715 INFO misc.py line 119 87073] Train: [95/100][1535/1557] Data 0.007 (0.061) Batch 1.105 (1.312) Remain 02:50:40 loss: 0.2959 Lr: 0.00004 [2024-02-19 17:56:17,657 INFO misc.py line 119 87073] Train: [95/100][1536/1557] Data 0.005 (0.061) Batch 0.940 (1.311) Remain 02:50:37 loss: 0.1623 Lr: 0.00004 [2024-02-19 17:56:18,508 INFO misc.py line 119 87073] Train: [95/100][1537/1557] Data 0.007 (0.061) Batch 0.853 (1.311) Remain 02:50:33 loss: 0.3807 Lr: 0.00004 [2024-02-19 17:56:19,278 INFO misc.py line 119 87073] Train: [95/100][1538/1557] Data 0.005 (0.061) Batch 0.768 (1.311) Remain 02:50:29 loss: 0.1662 Lr: 0.00004 [2024-02-19 17:56:20,029 INFO misc.py line 119 87073] Train: [95/100][1539/1557] Data 0.007 (0.061) Batch 0.726 (1.310) Remain 02:50:25 loss: 0.2281 Lr: 0.00004 [2024-02-19 17:56:21,338 INFO misc.py line 119 87073] Train: [95/100][1540/1557] Data 0.032 (0.061) Batch 1.335 (1.310) Remain 02:50:23 loss: 0.0722 Lr: 0.00004 [2024-02-19 17:56:22,268 INFO misc.py line 119 87073] Train: [95/100][1541/1557] Data 0.004 (0.061) Batch 0.930 (1.310) Remain 02:50:20 loss: 0.2216 Lr: 0.00004 [2024-02-19 17:56:23,160 INFO misc.py line 119 87073] Train: [95/100][1542/1557] Data 0.005 (0.061) Batch 0.892 (1.310) Remain 02:50:17 loss: 0.4406 Lr: 0.00004 [2024-02-19 17:56:24,043 INFO misc.py line 119 87073] Train: [95/100][1543/1557] Data 0.004 (0.061) Batch 0.882 (1.310) Remain 02:50:13 loss: 0.1267 Lr: 0.00004 [2024-02-19 17:56:25,057 INFO misc.py line 119 87073] Train: [95/100][1544/1557] Data 0.007 (0.061) Batch 1.008 (1.309) Remain 02:50:10 loss: 0.1779 Lr: 0.00004 [2024-02-19 17:56:25,809 INFO misc.py line 119 87073] Train: [95/100][1545/1557] Data 0.010 (0.061) Batch 0.759 (1.309) Remain 02:50:06 loss: 0.1504 Lr: 0.00004 [2024-02-19 17:56:26,523 INFO misc.py line 119 87073] Train: [95/100][1546/1557] Data 0.004 (0.061) Batch 0.707 (1.309) Remain 02:50:02 loss: 0.1452 Lr: 0.00004 [2024-02-19 17:56:27,825 INFO misc.py line 119 87073] Train: [95/100][1547/1557] Data 0.011 (0.061) Batch 1.305 (1.309) Remain 02:50:01 loss: 0.0663 Lr: 0.00004 [2024-02-19 17:56:28,722 INFO misc.py line 119 87073] Train: [95/100][1548/1557] Data 0.007 (0.060) Batch 0.900 (1.308) Remain 02:49:57 loss: 0.1114 Lr: 0.00004 [2024-02-19 17:56:29,743 INFO misc.py line 119 87073] Train: [95/100][1549/1557] Data 0.004 (0.060) Batch 1.021 (1.308) Remain 02:49:55 loss: 0.1593 Lr: 0.00004 [2024-02-19 17:56:30,586 INFO misc.py line 119 87073] Train: [95/100][1550/1557] Data 0.004 (0.060) Batch 0.843 (1.308) Remain 02:49:51 loss: 0.1765 Lr: 0.00004 [2024-02-19 17:56:31,478 INFO misc.py line 119 87073] Train: [95/100][1551/1557] Data 0.004 (0.060) Batch 0.889 (1.308) Remain 02:49:48 loss: 0.1525 Lr: 0.00004 [2024-02-19 17:56:32,207 INFO misc.py line 119 87073] Train: [95/100][1552/1557] Data 0.007 (0.060) Batch 0.732 (1.307) Remain 02:49:43 loss: 0.1766 Lr: 0.00004 [2024-02-19 17:56:32,992 INFO misc.py line 119 87073] Train: [95/100][1553/1557] Data 0.004 (0.060) Batch 0.761 (1.307) Remain 02:49:39 loss: 0.1552 Lr: 0.00004 [2024-02-19 17:56:34,053 INFO misc.py line 119 87073] Train: [95/100][1554/1557] Data 0.027 (0.060) Batch 1.076 (1.307) Remain 02:49:37 loss: 0.0700 Lr: 0.00004 [2024-02-19 17:56:34,917 INFO misc.py line 119 87073] Train: [95/100][1555/1557] Data 0.011 (0.060) Batch 0.871 (1.307) Remain 02:49:33 loss: 0.1757 Lr: 0.00004 [2024-02-19 17:56:35,876 INFO misc.py line 119 87073] Train: [95/100][1556/1557] Data 0.004 (0.060) Batch 0.960 (1.306) Remain 02:49:30 loss: 0.1704 Lr: 0.00004 [2024-02-19 17:56:36,820 INFO misc.py line 119 87073] Train: [95/100][1557/1557] Data 0.004 (0.060) Batch 0.944 (1.306) Remain 02:49:27 loss: 0.2579 Lr: 0.00004 [2024-02-19 17:56:36,821 INFO misc.py line 136 87073] Train result: loss: 0.2002 [2024-02-19 17:56:36,821 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 17:57:06,870 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4455 [2024-02-19 17:57:07,657 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4386 [2024-02-19 17:57:09,785 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3292 [2024-02-19 17:57:11,996 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3644 [2024-02-19 17:57:16,948 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2309 [2024-02-19 17:57:17,643 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0996 [2024-02-19 17:57:18,903 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2677 [2024-02-19 17:57:21,857 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2450 [2024-02-19 17:57:23,669 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2737 [2024-02-19 17:57:25,510 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7242/0.7826/0.9182. [2024-02-19 17:57:25,510 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9349/0.9652 [2024-02-19 17:57:25,510 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9827/0.9889 [2024-02-19 17:57:25,510 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8669/0.9710 [2024-02-19 17:57:25,510 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 17:57:25,510 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.4037/0.4758 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6504/0.6672 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8183/0.9272 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8537/0.9229 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9189/0.9759 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7912/0.8170 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8012/0.8883 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7621/0.8531 [2024-02-19 17:57:25,511 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6308/0.7208 [2024-02-19 17:57:25,511 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 17:57:25,512 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 17:57:25,512 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 17:57:33,909 INFO misc.py line 119 87073] Train: [96/100][1/1557] Data 1.033 (1.033) Batch 1.731 (1.731) Remain 03:44:32 loss: 0.1006 Lr: 0.00004 [2024-02-19 17:57:34,905 INFO misc.py line 119 87073] Train: [96/100][2/1557] Data 0.008 (0.008) Batch 0.983 (0.983) Remain 02:07:30 loss: 0.2365 Lr: 0.00004 [2024-02-19 17:57:36,030 INFO misc.py line 119 87073] Train: [96/100][3/1557] Data 0.019 (0.019) Batch 1.137 (1.137) Remain 02:27:28 loss: 0.0206 Lr: 0.00004 [2024-02-19 17:57:37,046 INFO misc.py line 119 87073] Train: [96/100][4/1557] Data 0.009 (0.009) Batch 1.017 (1.017) Remain 02:11:51 loss: 0.2494 Lr: 0.00004 [2024-02-19 17:57:37,833 INFO misc.py line 119 87073] Train: [96/100][5/1557] Data 0.007 (0.008) Batch 0.786 (0.902) Remain 01:56:54 loss: 0.1242 Lr: 0.00004 [2024-02-19 17:57:38,558 INFO misc.py line 119 87073] Train: [96/100][6/1557] Data 0.008 (0.008) Batch 0.729 (0.844) Remain 01:49:25 loss: 0.2870 Lr: 0.00004 [2024-02-19 17:57:40,555 INFO misc.py line 119 87073] Train: [96/100][7/1557] Data 0.003 (0.007) Batch 1.996 (1.132) Remain 02:26:45 loss: 0.0858 Lr: 0.00004 [2024-02-19 17:57:41,588 INFO misc.py line 119 87073] Train: [96/100][8/1557] Data 0.004 (0.006) Batch 1.032 (1.112) Remain 02:24:07 loss: 0.1507 Lr: 0.00004 [2024-02-19 17:57:42,600 INFO misc.py line 119 87073] Train: [96/100][9/1557] Data 0.005 (0.006) Batch 1.013 (1.095) Remain 02:21:58 loss: 0.1434 Lr: 0.00004 [2024-02-19 17:57:43,664 INFO misc.py line 119 87073] Train: [96/100][10/1557] Data 0.004 (0.006) Batch 1.064 (1.091) Remain 02:21:22 loss: 0.9772 Lr: 0.00004 [2024-02-19 17:57:44,548 INFO misc.py line 119 87073] Train: [96/100][11/1557] Data 0.004 (0.005) Batch 0.884 (1.065) Remain 02:18:00 loss: 0.2443 Lr: 0.00003 [2024-02-19 17:57:45,333 INFO misc.py line 119 87073] Train: [96/100][12/1557] Data 0.005 (0.005) Batch 0.754 (1.031) Remain 02:13:30 loss: 0.3677 Lr: 0.00003 [2024-02-19 17:57:46,068 INFO misc.py line 119 87073] Train: [96/100][13/1557] Data 0.035 (0.008) Batch 0.766 (1.004) Remain 02:10:04 loss: 0.1360 Lr: 0.00003 [2024-02-19 17:57:47,268 INFO misc.py line 119 87073] Train: [96/100][14/1557] Data 0.004 (0.008) Batch 1.200 (1.022) Remain 02:12:21 loss: 0.0903 Lr: 0.00003 [2024-02-19 17:57:48,256 INFO misc.py line 119 87073] Train: [96/100][15/1557] Data 0.004 (0.008) Batch 0.986 (1.019) Remain 02:11:56 loss: 0.2312 Lr: 0.00003 [2024-02-19 17:57:49,239 INFO misc.py line 119 87073] Train: [96/100][16/1557] Data 0.006 (0.008) Batch 0.984 (1.016) Remain 02:11:35 loss: 0.1085 Lr: 0.00003 [2024-02-19 17:57:50,189 INFO misc.py line 119 87073] Train: [96/100][17/1557] Data 0.005 (0.007) Batch 0.951 (1.012) Remain 02:10:57 loss: 0.0626 Lr: 0.00003 [2024-02-19 17:57:51,277 INFO misc.py line 119 87073] Train: [96/100][18/1557] Data 0.004 (0.007) Batch 1.086 (1.017) Remain 02:11:35 loss: 0.3610 Lr: 0.00003 [2024-02-19 17:57:52,096 INFO misc.py line 119 87073] Train: [96/100][19/1557] Data 0.006 (0.007) Batch 0.821 (1.004) Remain 02:09:59 loss: 0.2785 Lr: 0.00003 [2024-02-19 17:57:52,800 INFO misc.py line 119 87073] Train: [96/100][20/1557] Data 0.005 (0.007) Batch 0.696 (0.986) Remain 02:07:37 loss: 0.2549 Lr: 0.00003 [2024-02-19 17:57:54,116 INFO misc.py line 119 87073] Train: [96/100][21/1557] Data 0.012 (0.007) Batch 1.317 (1.005) Remain 02:09:59 loss: 0.1919 Lr: 0.00003 [2024-02-19 17:57:55,283 INFO misc.py line 119 87073] Train: [96/100][22/1557] Data 0.011 (0.007) Batch 1.169 (1.013) Remain 02:11:05 loss: 0.1415 Lr: 0.00003 [2024-02-19 17:57:56,245 INFO misc.py line 119 87073] Train: [96/100][23/1557] Data 0.010 (0.008) Batch 0.966 (1.011) Remain 02:10:45 loss: 0.1322 Lr: 0.00003 [2024-02-19 17:57:57,083 INFO misc.py line 119 87073] Train: [96/100][24/1557] Data 0.006 (0.007) Batch 0.840 (1.003) Remain 02:09:41 loss: 0.1639 Lr: 0.00003 [2024-02-19 17:57:58,023 INFO misc.py line 119 87073] Train: [96/100][25/1557] Data 0.005 (0.007) Batch 0.940 (1.000) Remain 02:09:18 loss: 0.0658 Lr: 0.00003 [2024-02-19 17:57:58,793 INFO misc.py line 119 87073] Train: [96/100][26/1557] Data 0.005 (0.007) Batch 0.770 (0.990) Remain 02:07:59 loss: 0.1499 Lr: 0.00003 [2024-02-19 17:57:59,532 INFO misc.py line 119 87073] Train: [96/100][27/1557] Data 0.004 (0.007) Batch 0.730 (0.979) Remain 02:06:34 loss: 0.2472 Lr: 0.00003 [2024-02-19 17:58:00,784 INFO misc.py line 119 87073] Train: [96/100][28/1557] Data 0.014 (0.007) Batch 1.253 (0.990) Remain 02:07:59 loss: 0.0990 Lr: 0.00003 [2024-02-19 17:58:01,685 INFO misc.py line 119 87073] Train: [96/100][29/1557] Data 0.012 (0.008) Batch 0.909 (0.987) Remain 02:07:33 loss: 0.3028 Lr: 0.00003 [2024-02-19 17:58:02,731 INFO misc.py line 119 87073] Train: [96/100][30/1557] Data 0.004 (0.007) Batch 1.045 (0.989) Remain 02:07:49 loss: 0.1597 Lr: 0.00003 [2024-02-19 17:58:03,759 INFO misc.py line 119 87073] Train: [96/100][31/1557] Data 0.005 (0.007) Batch 1.029 (0.990) Remain 02:07:59 loss: 0.1921 Lr: 0.00003 [2024-02-19 17:58:04,623 INFO misc.py line 119 87073] Train: [96/100][32/1557] Data 0.004 (0.007) Batch 0.863 (0.986) Remain 02:07:24 loss: 0.2309 Lr: 0.00003 [2024-02-19 17:58:05,399 INFO misc.py line 119 87073] Train: [96/100][33/1557] Data 0.005 (0.007) Batch 0.775 (0.979) Remain 02:06:29 loss: 0.2925 Lr: 0.00003 [2024-02-19 17:58:06,201 INFO misc.py line 119 87073] Train: [96/100][34/1557] Data 0.006 (0.007) Batch 0.801 (0.973) Remain 02:05:43 loss: 0.2401 Lr: 0.00003 [2024-02-19 17:58:07,348 INFO misc.py line 119 87073] Train: [96/100][35/1557] Data 0.007 (0.007) Batch 1.148 (0.979) Remain 02:06:25 loss: 0.1689 Lr: 0.00003 [2024-02-19 17:58:08,567 INFO misc.py line 119 87073] Train: [96/100][36/1557] Data 0.005 (0.007) Batch 1.214 (0.986) Remain 02:07:19 loss: 0.1215 Lr: 0.00003 [2024-02-19 17:58:09,398 INFO misc.py line 119 87073] Train: [96/100][37/1557] Data 0.010 (0.007) Batch 0.837 (0.981) Remain 02:06:44 loss: 0.2764 Lr: 0.00003 [2024-02-19 17:58:10,274 INFO misc.py line 119 87073] Train: [96/100][38/1557] Data 0.005 (0.007) Batch 0.876 (0.978) Remain 02:06:20 loss: 0.1815 Lr: 0.00003 [2024-02-19 17:58:11,088 INFO misc.py line 119 87073] Train: [96/100][39/1557] Data 0.005 (0.007) Batch 0.814 (0.974) Remain 02:05:43 loss: 0.1152 Lr: 0.00003 [2024-02-19 17:58:11,842 INFO misc.py line 119 87073] Train: [96/100][40/1557] Data 0.005 (0.007) Batch 0.756 (0.968) Remain 02:04:56 loss: 0.1218 Lr: 0.00003 [2024-02-19 17:58:12,645 INFO misc.py line 119 87073] Train: [96/100][41/1557] Data 0.004 (0.007) Batch 0.800 (0.964) Remain 02:04:21 loss: 0.1728 Lr: 0.00003 [2024-02-19 17:58:13,815 INFO misc.py line 119 87073] Train: [96/100][42/1557] Data 0.007 (0.007) Batch 1.167 (0.969) Remain 02:05:01 loss: 0.1855 Lr: 0.00003 [2024-02-19 17:58:14,992 INFO misc.py line 119 87073] Train: [96/100][43/1557] Data 0.011 (0.007) Batch 1.182 (0.974) Remain 02:05:41 loss: 0.1790 Lr: 0.00003 [2024-02-19 17:58:15,844 INFO misc.py line 119 87073] Train: [96/100][44/1557] Data 0.005 (0.007) Batch 0.851 (0.971) Remain 02:05:17 loss: 0.2202 Lr: 0.00003 [2024-02-19 17:58:16,724 INFO misc.py line 119 87073] Train: [96/100][45/1557] Data 0.005 (0.007) Batch 0.880 (0.969) Remain 02:04:59 loss: 0.1949 Lr: 0.00003 [2024-02-19 17:58:17,756 INFO misc.py line 119 87073] Train: [96/100][46/1557] Data 0.007 (0.007) Batch 1.033 (0.970) Remain 02:05:10 loss: 0.2704 Lr: 0.00003 [2024-02-19 17:58:18,485 INFO misc.py line 119 87073] Train: [96/100][47/1557] Data 0.005 (0.007) Batch 0.722 (0.965) Remain 02:04:25 loss: 0.1974 Lr: 0.00003 [2024-02-19 17:58:19,289 INFO misc.py line 119 87073] Train: [96/100][48/1557] Data 0.013 (0.007) Batch 0.811 (0.961) Remain 02:03:58 loss: 0.1380 Lr: 0.00003 [2024-02-19 17:58:20,552 INFO misc.py line 119 87073] Train: [96/100][49/1557] Data 0.004 (0.007) Batch 1.261 (0.968) Remain 02:04:47 loss: 0.1157 Lr: 0.00003 [2024-02-19 17:58:21,852 INFO misc.py line 119 87073] Train: [96/100][50/1557] Data 0.006 (0.007) Batch 1.296 (0.975) Remain 02:05:40 loss: 0.1586 Lr: 0.00003 [2024-02-19 17:58:22,876 INFO misc.py line 119 87073] Train: [96/100][51/1557] Data 0.011 (0.007) Batch 1.030 (0.976) Remain 02:05:48 loss: 0.0705 Lr: 0.00003 [2024-02-19 17:58:23,722 INFO misc.py line 119 87073] Train: [96/100][52/1557] Data 0.004 (0.007) Batch 0.846 (0.973) Remain 02:05:26 loss: 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Batch 1.023 (1.147) Remain 02:24:23 loss: 0.3994 Lr: 0.00003 [2024-02-19 18:02:00,925 INFO misc.py line 119 87073] Train: [96/100][234/1557] Data 0.004 (0.062) Batch 1.045 (1.147) Remain 02:24:19 loss: 0.2212 Lr: 0.00003 [2024-02-19 18:02:01,734 INFO misc.py line 119 87073] Train: [96/100][235/1557] Data 0.004 (0.062) Batch 0.809 (1.145) Remain 02:24:06 loss: 0.4112 Lr: 0.00003 [2024-02-19 18:02:02,445 INFO misc.py line 119 87073] Train: [96/100][236/1557] Data 0.004 (0.061) Batch 0.704 (1.143) Remain 02:23:51 loss: 0.3379 Lr: 0.00003 [2024-02-19 18:02:03,231 INFO misc.py line 119 87073] Train: [96/100][237/1557] Data 0.012 (0.061) Batch 0.793 (1.142) Remain 02:23:39 loss: 0.2022 Lr: 0.00003 [2024-02-19 18:02:04,442 INFO misc.py line 119 87073] Train: [96/100][238/1557] Data 0.005 (0.061) Batch 1.211 (1.142) Remain 02:23:40 loss: 0.1099 Lr: 0.00003 [2024-02-19 18:02:05,309 INFO misc.py line 119 87073] Train: [96/100][239/1557] Data 0.004 (0.061) Batch 0.867 (1.141) Remain 02:23:30 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Train: [96/100][1259/1557] Data 0.004 (0.075) Batch 0.797 (1.152) Remain 02:05:17 loss: 0.2120 Lr: 0.00002 [2024-02-19 18:21:44,116 INFO misc.py line 119 87073] Train: [96/100][1260/1557] Data 0.008 (0.075) Batch 1.297 (1.152) Remain 02:05:16 loss: 0.1725 Lr: 0.00002 [2024-02-19 18:21:45,155 INFO misc.py line 119 87073] Train: [96/100][1261/1557] Data 0.012 (0.075) Batch 1.047 (1.152) Remain 02:05:15 loss: 0.2261 Lr: 0.00002 [2024-02-19 18:21:46,272 INFO misc.py line 119 87073] Train: [96/100][1262/1557] Data 0.005 (0.075) Batch 1.112 (1.152) Remain 02:05:13 loss: 0.3161 Lr: 0.00002 [2024-02-19 18:21:47,044 INFO misc.py line 119 87073] Train: [96/100][1263/1557] Data 0.009 (0.075) Batch 0.777 (1.152) Remain 02:05:10 loss: 0.2680 Lr: 0.00002 [2024-02-19 18:21:48,009 INFO misc.py line 119 87073] Train: [96/100][1264/1557] Data 0.006 (0.075) Batch 0.965 (1.151) Remain 02:05:08 loss: 0.1917 Lr: 0.00002 [2024-02-19 18:21:48,751 INFO misc.py line 119 87073] Train: [96/100][1265/1557] Data 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Remain 02:04:55 loss: 0.1201 Lr: 0.00002 [2024-02-19 18:21:55,860 INFO misc.py line 119 87073] Train: [96/100][1272/1557] Data 0.005 (0.075) Batch 0.721 (1.150) Remain 02:04:52 loss: 0.2451 Lr: 0.00002 [2024-02-19 18:21:56,653 INFO misc.py line 119 87073] Train: [96/100][1273/1557] Data 0.003 (0.075) Batch 0.792 (1.150) Remain 02:04:49 loss: 0.1441 Lr: 0.00002 [2024-02-19 18:21:57,815 INFO misc.py line 119 87073] Train: [96/100][1274/1557] Data 0.004 (0.074) Batch 1.161 (1.150) Remain 02:04:48 loss: 0.1993 Lr: 0.00002 [2024-02-19 18:21:59,078 INFO misc.py line 119 87073] Train: [96/100][1275/1557] Data 0.005 (0.074) Batch 1.259 (1.150) Remain 02:04:47 loss: 0.3809 Lr: 0.00002 [2024-02-19 18:21:59,922 INFO misc.py line 119 87073] Train: [96/100][1276/1557] Data 0.010 (0.074) Batch 0.849 (1.150) Remain 02:04:45 loss: 0.1264 Lr: 0.00002 [2024-02-19 18:22:00,941 INFO misc.py line 119 87073] Train: [96/100][1277/1557] Data 0.004 (0.074) Batch 1.019 (1.150) Remain 02:04:43 loss: 0.2222 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1284/1557] Data 0.005 (0.074) Batch 1.017 (1.149) Remain 02:04:29 loss: 0.3095 Lr: 0.00002 [2024-02-19 18:22:08,864 INFO misc.py line 119 87073] Train: [96/100][1285/1557] Data 0.005 (0.074) Batch 0.966 (1.149) Remain 02:04:27 loss: 0.2981 Lr: 0.00002 [2024-02-19 18:22:09,604 INFO misc.py line 119 87073] Train: [96/100][1286/1557] Data 0.005 (0.074) Batch 0.741 (1.149) Remain 02:04:24 loss: 0.2130 Lr: 0.00002 [2024-02-19 18:22:10,380 INFO misc.py line 119 87073] Train: [96/100][1287/1557] Data 0.004 (0.074) Batch 0.775 (1.148) Remain 02:04:21 loss: 0.2554 Lr: 0.00002 [2024-02-19 18:22:11,592 INFO misc.py line 119 87073] Train: [96/100][1288/1557] Data 0.004 (0.074) Batch 1.212 (1.148) Remain 02:04:20 loss: 0.1985 Lr: 0.00002 [2024-02-19 18:22:12,608 INFO misc.py line 119 87073] Train: [96/100][1289/1557] Data 0.005 (0.074) Batch 1.013 (1.148) Remain 02:04:18 loss: 0.2100 Lr: 0.00002 [2024-02-19 18:22:13,557 INFO misc.py line 119 87073] Train: [96/100][1290/1557] Data 0.008 (0.074) Batch 0.952 (1.148) Remain 02:04:16 loss: 0.1496 Lr: 0.00002 [2024-02-19 18:22:14,408 INFO misc.py line 119 87073] Train: [96/100][1291/1557] Data 0.006 (0.074) Batch 0.852 (1.148) Remain 02:04:13 loss: 0.0958 Lr: 0.00002 [2024-02-19 18:22:15,389 INFO misc.py line 119 87073] Train: [96/100][1292/1557] Data 0.005 (0.073) Batch 0.979 (1.148) Remain 02:04:11 loss: 0.3155 Lr: 0.00002 [2024-02-19 18:22:16,171 INFO misc.py line 119 87073] Train: [96/100][1293/1557] Data 0.006 (0.073) Batch 0.784 (1.147) Remain 02:04:08 loss: 0.1662 Lr: 0.00002 [2024-02-19 18:22:16,973 INFO misc.py line 119 87073] Train: [96/100][1294/1557] Data 0.005 (0.073) Batch 0.803 (1.147) Remain 02:04:06 loss: 0.1793 Lr: 0.00002 [2024-02-19 18:22:28,788 INFO misc.py line 119 87073] Train: [96/100][1295/1557] Data 3.153 (0.076) Batch 11.806 (1.155) Remain 02:04:58 loss: 0.0849 Lr: 0.00002 [2024-02-19 18:22:29,815 INFO misc.py line 119 87073] Train: [96/100][1296/1557] Data 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Remain 02:04:44 loss: 0.1889 Lr: 0.00002 [2024-02-19 18:22:36,540 INFO misc.py line 119 87073] Train: [96/100][1303/1557] Data 0.043 (0.075) Batch 0.876 (1.154) Remain 02:04:41 loss: 0.2008 Lr: 0.00002 [2024-02-19 18:22:37,553 INFO misc.py line 119 87073] Train: [96/100][1304/1557] Data 0.005 (0.075) Batch 1.013 (1.154) Remain 02:04:39 loss: 0.0880 Lr: 0.00002 [2024-02-19 18:22:38,589 INFO misc.py line 119 87073] Train: [96/100][1305/1557] Data 0.005 (0.075) Batch 1.036 (1.154) Remain 02:04:38 loss: 0.1725 Lr: 0.00002 [2024-02-19 18:22:39,572 INFO misc.py line 119 87073] Train: [96/100][1306/1557] Data 0.006 (0.075) Batch 0.985 (1.154) Remain 02:04:36 loss: 0.2456 Lr: 0.00002 [2024-02-19 18:22:40,380 INFO misc.py line 119 87073] Train: [96/100][1307/1557] Data 0.004 (0.075) Batch 0.806 (1.154) Remain 02:04:33 loss: 0.2860 Lr: 0.00002 [2024-02-19 18:22:41,122 INFO misc.py line 119 87073] Train: [96/100][1308/1557] Data 0.005 (0.075) Batch 0.742 (1.153) Remain 02:04:30 loss: 0.1739 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1315/1557] Data 0.005 (0.075) Batch 0.754 (1.152) Remain 02:04:14 loss: 0.2032 Lr: 0.00002 [2024-02-19 18:22:49,013 INFO misc.py line 119 87073] Train: [96/100][1316/1557] Data 0.004 (0.075) Batch 1.314 (1.152) Remain 02:04:14 loss: 0.1788 Lr: 0.00002 [2024-02-19 18:22:49,875 INFO misc.py line 119 87073] Train: [96/100][1317/1557] Data 0.004 (0.075) Batch 0.862 (1.152) Remain 02:04:11 loss: 0.3029 Lr: 0.00002 [2024-02-19 18:22:50,779 INFO misc.py line 119 87073] Train: [96/100][1318/1557] Data 0.005 (0.075) Batch 0.901 (1.152) Remain 02:04:09 loss: 0.0785 Lr: 0.00002 [2024-02-19 18:22:51,803 INFO misc.py line 119 87073] Train: [96/100][1319/1557] Data 0.008 (0.075) Batch 0.969 (1.152) Remain 02:04:07 loss: 0.0915 Lr: 0.00002 [2024-02-19 18:22:52,874 INFO misc.py line 119 87073] Train: [96/100][1320/1557] Data 0.063 (0.075) Batch 1.128 (1.152) Remain 02:04:06 loss: 0.0850 Lr: 0.00002 [2024-02-19 18:22:53,570 INFO misc.py line 119 87073] Train: [96/100][1321/1557] Data 0.006 (0.074) Batch 0.696 (1.151) Remain 02:04:02 loss: 0.0935 Lr: 0.00002 [2024-02-19 18:22:54,348 INFO misc.py line 119 87073] Train: [96/100][1322/1557] Data 0.006 (0.074) Batch 0.779 (1.151) Remain 02:03:59 loss: 0.2336 Lr: 0.00002 [2024-02-19 18:22:55,508 INFO misc.py line 119 87073] Train: [96/100][1323/1557] Data 0.005 (0.074) Batch 1.160 (1.151) Remain 02:03:58 loss: 0.1407 Lr: 0.00002 [2024-02-19 18:22:56,546 INFO misc.py line 119 87073] Train: [96/100][1324/1557] Data 0.010 (0.074) Batch 1.032 (1.151) Remain 02:03:56 loss: 0.2669 Lr: 0.00002 [2024-02-19 18:22:57,534 INFO misc.py line 119 87073] Train: [96/100][1325/1557] Data 0.011 (0.074) Batch 0.994 (1.151) Remain 02:03:54 loss: 0.2251 Lr: 0.00002 [2024-02-19 18:22:58,434 INFO misc.py line 119 87073] Train: [96/100][1326/1557] Data 0.005 (0.074) Batch 0.900 (1.151) Remain 02:03:52 loss: 0.1468 Lr: 0.00002 [2024-02-19 18:22:59,399 INFO misc.py line 119 87073] Train: [96/100][1327/1557] Data 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Remain 02:03:36 loss: 0.3849 Lr: 0.00002 [2024-02-19 18:23:05,698 INFO misc.py line 119 87073] Train: [96/100][1334/1557] Data 0.007 (0.074) Batch 0.909 (1.149) Remain 02:03:33 loss: 0.2250 Lr: 0.00002 [2024-02-19 18:23:06,539 INFO misc.py line 119 87073] Train: [96/100][1335/1557] Data 0.005 (0.074) Batch 0.842 (1.149) Remain 02:03:31 loss: 0.3367 Lr: 0.00002 [2024-02-19 18:23:07,285 INFO misc.py line 119 87073] Train: [96/100][1336/1557] Data 0.004 (0.074) Batch 0.743 (1.149) Remain 02:03:28 loss: 0.1325 Lr: 0.00002 [2024-02-19 18:23:08,535 INFO misc.py line 119 87073] Train: [96/100][1337/1557] Data 0.007 (0.074) Batch 1.247 (1.149) Remain 02:03:27 loss: 0.0962 Lr: 0.00002 [2024-02-19 18:23:09,622 INFO misc.py line 119 87073] Train: [96/100][1338/1557] Data 0.009 (0.074) Batch 1.083 (1.149) Remain 02:03:25 loss: 0.2040 Lr: 0.00002 [2024-02-19 18:23:10,520 INFO misc.py line 119 87073] Train: [96/100][1339/1557] Data 0.014 (0.074) Batch 0.906 (1.149) Remain 02:03:23 loss: 0.1491 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1346/1557] Data 0.006 (0.073) Batch 0.969 (1.147) Remain 02:03:08 loss: 0.1190 Lr: 0.00002 [2024-02-19 18:23:18,080 INFO misc.py line 119 87073] Train: [96/100][1347/1557] Data 0.004 (0.073) Batch 1.056 (1.147) Remain 02:03:06 loss: 0.2582 Lr: 0.00002 [2024-02-19 18:23:18,960 INFO misc.py line 119 87073] Train: [96/100][1348/1557] Data 0.004 (0.073) Batch 0.880 (1.147) Remain 02:03:04 loss: 0.2593 Lr: 0.00002 [2024-02-19 18:23:19,697 INFO misc.py line 119 87073] Train: [96/100][1349/1557] Data 0.006 (0.073) Batch 0.735 (1.147) Remain 02:03:01 loss: 0.1980 Lr: 0.00002 [2024-02-19 18:23:20,404 INFO misc.py line 119 87073] Train: [96/100][1350/1557] Data 0.007 (0.073) Batch 0.708 (1.147) Remain 02:02:57 loss: 0.0984 Lr: 0.00002 [2024-02-19 18:23:32,324 INFO misc.py line 119 87073] Train: [96/100][1351/1557] Data 3.753 (0.076) Batch 11.920 (1.155) Remain 02:03:48 loss: 0.1767 Lr: 0.00002 [2024-02-19 18:23:33,407 INFO misc.py line 119 87073] Train: [96/100][1352/1557] Data 0.005 (0.076) Batch 1.083 (1.154) Remain 02:03:46 loss: 0.0632 Lr: 0.00002 [2024-02-19 18:23:34,359 INFO misc.py line 119 87073] Train: [96/100][1353/1557] Data 0.005 (0.076) Batch 0.952 (1.154) Remain 02:03:44 loss: 0.3893 Lr: 0.00002 [2024-02-19 18:23:35,345 INFO misc.py line 119 87073] Train: [96/100][1354/1557] Data 0.006 (0.076) Batch 0.985 (1.154) Remain 02:03:42 loss: 0.1294 Lr: 0.00002 [2024-02-19 18:23:36,360 INFO misc.py line 119 87073] Train: [96/100][1355/1557] Data 0.005 (0.076) Batch 1.016 (1.154) Remain 02:03:40 loss: 0.1918 Lr: 0.00002 [2024-02-19 18:23:37,093 INFO misc.py line 119 87073] Train: [96/100][1356/1557] Data 0.005 (0.075) Batch 0.733 (1.154) Remain 02:03:37 loss: 0.0993 Lr: 0.00002 [2024-02-19 18:23:38,039 INFO misc.py line 119 87073] Train: [96/100][1357/1557] Data 0.005 (0.075) Batch 0.947 (1.154) Remain 02:03:35 loss: 0.2722 Lr: 0.00002 [2024-02-19 18:23:39,214 INFO misc.py line 119 87073] Train: [96/100][1358/1557] Data 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Remain 02:03:19 loss: 0.2250 Lr: 0.00002 [2024-02-19 18:23:45,769 INFO misc.py line 119 87073] Train: [96/100][1365/1557] Data 0.006 (0.075) Batch 1.302 (1.153) Remain 02:03:19 loss: 0.1796 Lr: 0.00002 [2024-02-19 18:23:46,790 INFO misc.py line 119 87073] Train: [96/100][1366/1557] Data 0.008 (0.075) Batch 1.016 (1.152) Remain 02:03:17 loss: 0.2557 Lr: 0.00002 [2024-02-19 18:23:47,806 INFO misc.py line 119 87073] Train: [96/100][1367/1557] Data 0.014 (0.075) Batch 1.024 (1.152) Remain 02:03:15 loss: 0.2816 Lr: 0.00002 [2024-02-19 18:23:48,958 INFO misc.py line 119 87073] Train: [96/100][1368/1557] Data 0.007 (0.075) Batch 1.082 (1.152) Remain 02:03:14 loss: 0.1770 Lr: 0.00002 [2024-02-19 18:23:49,899 INFO misc.py line 119 87073] Train: [96/100][1369/1557] Data 0.076 (0.075) Batch 1.011 (1.152) Remain 02:03:12 loss: 0.0976 Lr: 0.00002 [2024-02-19 18:23:50,650 INFO misc.py line 119 87073] Train: [96/100][1370/1557] Data 0.007 (0.075) Batch 0.753 (1.152) Remain 02:03:09 loss: 0.1036 Lr: 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Train: [96/100][1383/1557] Data 0.004 (0.074) Batch 0.894 (1.150) Remain 02:02:41 loss: 0.0337 Lr: 0.00002 [2024-02-19 18:24:03,661 INFO misc.py line 119 87073] Train: [96/100][1384/1557] Data 0.005 (0.074) Batch 0.754 (1.150) Remain 02:02:38 loss: 0.2473 Lr: 0.00002 [2024-02-19 18:24:04,405 INFO misc.py line 119 87073] Train: [96/100][1385/1557] Data 0.004 (0.074) Batch 0.734 (1.149) Remain 02:02:35 loss: 0.1041 Lr: 0.00002 [2024-02-19 18:24:05,473 INFO misc.py line 119 87073] Train: [96/100][1386/1557] Data 0.013 (0.074) Batch 1.068 (1.149) Remain 02:02:34 loss: 0.2723 Lr: 0.00002 [2024-02-19 18:24:06,592 INFO misc.py line 119 87073] Train: [96/100][1387/1557] Data 0.013 (0.074) Batch 1.117 (1.149) Remain 02:02:32 loss: 0.1350 Lr: 0.00002 [2024-02-19 18:24:07,483 INFO misc.py line 119 87073] Train: [96/100][1388/1557] Data 0.015 (0.074) Batch 0.902 (1.149) Remain 02:02:30 loss: 0.3616 Lr: 0.00002 [2024-02-19 18:24:08,389 INFO misc.py line 119 87073] Train: [96/100][1389/1557] Data 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Remain 02:02:14 loss: 0.2693 Lr: 0.00002 [2024-02-19 18:24:14,722 INFO misc.py line 119 87073] Train: [96/100][1396/1557] Data 0.005 (0.074) Batch 0.918 (1.148) Remain 02:02:12 loss: 0.1354 Lr: 0.00002 [2024-02-19 18:24:15,643 INFO misc.py line 119 87073] Train: [96/100][1397/1557] Data 0.004 (0.074) Batch 0.920 (1.147) Remain 02:02:10 loss: 0.1892 Lr: 0.00002 [2024-02-19 18:24:16,447 INFO misc.py line 119 87073] Train: [96/100][1398/1557] Data 0.005 (0.073) Batch 0.805 (1.147) Remain 02:02:07 loss: 0.1156 Lr: 0.00002 [2024-02-19 18:24:17,193 INFO misc.py line 119 87073] Train: [96/100][1399/1557] Data 0.004 (0.073) Batch 0.740 (1.147) Remain 02:02:04 loss: 0.1317 Lr: 0.00002 [2024-02-19 18:24:18,312 INFO misc.py line 119 87073] Train: [96/100][1400/1557] Data 0.010 (0.073) Batch 1.120 (1.147) Remain 02:02:03 loss: 0.1376 Lr: 0.00002 [2024-02-19 18:24:19,230 INFO misc.py line 119 87073] Train: [96/100][1401/1557] Data 0.009 (0.073) Batch 0.922 (1.147) Remain 02:02:01 loss: 0.1030 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1408/1557] Data 0.005 (0.077) Batch 0.991 (1.156) Remain 02:02:53 loss: 0.0781 Lr: 0.00002 [2024-02-19 18:24:41,590 INFO misc.py line 119 87073] Train: [96/100][1409/1557] Data 0.004 (0.077) Batch 0.925 (1.156) Remain 02:02:51 loss: 0.0409 Lr: 0.00002 [2024-02-19 18:24:42,937 INFO misc.py line 119 87073] Train: [96/100][1410/1557] Data 0.004 (0.077) Batch 1.338 (1.156) Remain 02:02:51 loss: 0.0454 Lr: 0.00002 [2024-02-19 18:24:43,911 INFO misc.py line 119 87073] Train: [96/100][1411/1557] Data 0.013 (0.077) Batch 0.980 (1.156) Remain 02:02:49 loss: 0.1524 Lr: 0.00002 [2024-02-19 18:24:44,658 INFO misc.py line 119 87073] Train: [96/100][1412/1557] Data 0.007 (0.077) Batch 0.749 (1.156) Remain 02:02:46 loss: 0.1800 Lr: 0.00002 [2024-02-19 18:24:45,418 INFO misc.py line 119 87073] Train: [96/100][1413/1557] Data 0.005 (0.077) Batch 0.760 (1.156) Remain 02:02:43 loss: 0.1527 Lr: 0.00002 [2024-02-19 18:24:46,666 INFO misc.py line 119 87073] Train: [96/100][1414/1557] Data 0.005 (0.077) Batch 1.237 (1.156) Remain 02:02:42 loss: 0.2148 Lr: 0.00002 [2024-02-19 18:24:47,830 INFO misc.py line 119 87073] Train: [96/100][1415/1557] Data 0.016 (0.077) Batch 1.164 (1.156) Remain 02:02:41 loss: 0.0631 Lr: 0.00002 [2024-02-19 18:24:48,631 INFO misc.py line 119 87073] Train: [96/100][1416/1557] Data 0.016 (0.077) Batch 0.813 (1.155) Remain 02:02:38 loss: 0.2448 Lr: 0.00002 [2024-02-19 18:24:49,822 INFO misc.py line 119 87073] Train: [96/100][1417/1557] Data 0.005 (0.077) Batch 1.189 (1.155) Remain 02:02:37 loss: 0.1808 Lr: 0.00002 [2024-02-19 18:24:50,653 INFO misc.py line 119 87073] Train: [96/100][1418/1557] Data 0.006 (0.077) Batch 0.832 (1.155) Remain 02:02:35 loss: 0.0817 Lr: 0.00002 [2024-02-19 18:24:51,450 INFO misc.py line 119 87073] Train: [96/100][1419/1557] Data 0.005 (0.077) Batch 0.795 (1.155) Remain 02:02:32 loss: 0.2019 Lr: 0.00002 [2024-02-19 18:24:52,147 INFO misc.py line 119 87073] Train: [96/100][1420/1557] Data 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Remain 02:02:18 loss: 0.1658 Lr: 0.00002 [2024-02-19 18:24:59,087 INFO misc.py line 119 87073] Train: [96/100][1427/1557] Data 0.004 (0.077) Batch 0.766 (1.154) Remain 02:02:16 loss: 0.2185 Lr: 0.00002 [2024-02-19 18:25:00,333 INFO misc.py line 119 87073] Train: [96/100][1428/1557] Data 0.011 (0.076) Batch 1.242 (1.154) Remain 02:02:15 loss: 0.1177 Lr: 0.00002 [2024-02-19 18:25:01,187 INFO misc.py line 119 87073] Train: [96/100][1429/1557] Data 0.015 (0.076) Batch 0.864 (1.154) Remain 02:02:12 loss: 0.2588 Lr: 0.00002 [2024-02-19 18:25:02,125 INFO misc.py line 119 87073] Train: [96/100][1430/1557] Data 0.005 (0.076) Batch 0.938 (1.154) Remain 02:02:10 loss: 0.3374 Lr: 0.00002 [2024-02-19 18:25:03,106 INFO misc.py line 119 87073] Train: [96/100][1431/1557] Data 0.004 (0.076) Batch 0.982 (1.153) Remain 02:02:08 loss: 0.3096 Lr: 0.00002 [2024-02-19 18:25:04,102 INFO misc.py line 119 87073] Train: [96/100][1432/1557] Data 0.004 (0.076) Batch 0.995 (1.153) Remain 02:02:06 loss: 0.2495 Lr: 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Train: [96/100][1445/1557] Data 0.004 (0.076) Batch 1.019 (1.152) Remain 02:01:42 loss: 0.1544 Lr: 0.00002 [2024-02-19 18:25:17,887 INFO misc.py line 119 87073] Train: [96/100][1446/1557] Data 0.006 (0.076) Batch 0.921 (1.152) Remain 02:01:40 loss: 0.2648 Lr: 0.00002 [2024-02-19 18:25:18,616 INFO misc.py line 119 87073] Train: [96/100][1447/1557] Data 0.004 (0.076) Batch 0.728 (1.151) Remain 02:01:37 loss: 0.2059 Lr: 0.00002 [2024-02-19 18:25:19,481 INFO misc.py line 119 87073] Train: [96/100][1448/1557] Data 0.005 (0.075) Batch 0.864 (1.151) Remain 02:01:34 loss: 0.1049 Lr: 0.00002 [2024-02-19 18:25:20,712 INFO misc.py line 119 87073] Train: [96/100][1449/1557] Data 0.006 (0.075) Batch 1.232 (1.151) Remain 02:01:34 loss: 0.2053 Lr: 0.00002 [2024-02-19 18:25:21,627 INFO misc.py line 119 87073] Train: [96/100][1450/1557] Data 0.006 (0.075) Batch 0.916 (1.151) Remain 02:01:32 loss: 0.2973 Lr: 0.00002 [2024-02-19 18:25:22,611 INFO misc.py line 119 87073] Train: [96/100][1451/1557] Data 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Remain 02:01:17 loss: 0.3841 Lr: 0.00002 [2024-02-19 18:25:29,240 INFO misc.py line 119 87073] Train: [96/100][1458/1557] Data 0.004 (0.075) Batch 1.149 (1.150) Remain 02:01:15 loss: 0.4604 Lr: 0.00002 [2024-02-19 18:25:30,103 INFO misc.py line 119 87073] Train: [96/100][1459/1557] Data 0.004 (0.075) Batch 0.863 (1.150) Remain 02:01:13 loss: 0.0796 Lr: 0.00002 [2024-02-19 18:25:31,109 INFO misc.py line 119 87073] Train: [96/100][1460/1557] Data 0.004 (0.075) Batch 1.006 (1.150) Remain 02:01:11 loss: 0.2357 Lr: 0.00002 [2024-02-19 18:25:31,828 INFO misc.py line 119 87073] Train: [96/100][1461/1557] Data 0.004 (0.075) Batch 0.720 (1.149) Remain 02:01:08 loss: 0.1777 Lr: 0.00002 [2024-02-19 18:25:32,649 INFO misc.py line 119 87073] Train: [96/100][1462/1557] Data 0.004 (0.075) Batch 0.760 (1.149) Remain 02:01:05 loss: 0.1770 Lr: 0.00002 [2024-02-19 18:25:45,133 INFO misc.py line 119 87073] Train: [96/100][1463/1557] Data 4.219 (0.078) Batch 12.543 (1.157) Remain 02:01:54 loss: 0.2211 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1470/1557] Data 0.004 (0.077) Batch 1.175 (1.156) Remain 02:01:41 loss: 0.1509 Lr: 0.00002 [2024-02-19 18:25:53,136 INFO misc.py line 119 87073] Train: [96/100][1471/1557] Data 0.016 (0.077) Batch 0.956 (1.156) Remain 02:01:39 loss: 0.1784 Lr: 0.00002 [2024-02-19 18:25:54,128 INFO misc.py line 119 87073] Train: [96/100][1472/1557] Data 0.005 (0.077) Batch 0.993 (1.156) Remain 02:01:37 loss: 0.1476 Lr: 0.00002 [2024-02-19 18:25:55,067 INFO misc.py line 119 87073] Train: [96/100][1473/1557] Data 0.005 (0.077) Batch 0.938 (1.156) Remain 02:01:35 loss: 0.1425 Lr: 0.00002 [2024-02-19 18:25:56,090 INFO misc.py line 119 87073] Train: [96/100][1474/1557] Data 0.005 (0.077) Batch 1.025 (1.156) Remain 02:01:33 loss: 0.4537 Lr: 0.00002 [2024-02-19 18:25:56,865 INFO misc.py line 119 87073] Train: [96/100][1475/1557] Data 0.003 (0.077) Batch 0.768 (1.155) Remain 02:01:30 loss: 0.2686 Lr: 0.00002 [2024-02-19 18:25:57,792 INFO misc.py line 119 87073] Train: [96/100][1476/1557] Data 0.010 (0.077) Batch 0.932 (1.155) Remain 02:01:28 loss: 0.2480 Lr: 0.00002 [2024-02-19 18:25:59,165 INFO misc.py line 119 87073] Train: [96/100][1477/1557] Data 0.006 (0.077) Batch 1.366 (1.155) Remain 02:01:28 loss: 0.2011 Lr: 0.00002 [2024-02-19 18:26:00,062 INFO misc.py line 119 87073] Train: [96/100][1478/1557] Data 0.013 (0.077) Batch 0.903 (1.155) Remain 02:01:26 loss: 0.2122 Lr: 0.00002 [2024-02-19 18:26:01,081 INFO misc.py line 119 87073] Train: [96/100][1479/1557] Data 0.005 (0.077) Batch 1.019 (1.155) Remain 02:01:24 loss: 0.3299 Lr: 0.00002 [2024-02-19 18:26:02,062 INFO misc.py line 119 87073] Train: [96/100][1480/1557] Data 0.005 (0.077) Batch 0.981 (1.155) Remain 02:01:22 loss: 0.1625 Lr: 0.00002 [2024-02-19 18:26:03,215 INFO misc.py line 119 87073] Train: [96/100][1481/1557] Data 0.005 (0.077) Batch 1.153 (1.155) Remain 02:01:21 loss: 0.1362 Lr: 0.00002 [2024-02-19 18:26:04,029 INFO misc.py line 119 87073] Train: [96/100][1482/1557] Data 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Remain 02:01:07 loss: 0.1384 Lr: 0.00002 [2024-02-19 18:26:10,646 INFO misc.py line 119 87073] Train: [96/100][1489/1557] Data 0.008 (0.076) Batch 0.783 (1.154) Remain 02:01:04 loss: 0.1502 Lr: 0.00002 [2024-02-19 18:26:11,413 INFO misc.py line 119 87073] Train: [96/100][1490/1557] Data 0.005 (0.076) Batch 0.762 (1.154) Remain 02:01:01 loss: 0.1528 Lr: 0.00002 [2024-02-19 18:26:12,610 INFO misc.py line 119 87073] Train: [96/100][1491/1557] Data 0.010 (0.076) Batch 1.200 (1.154) Remain 02:01:00 loss: 0.0908 Lr: 0.00002 [2024-02-19 18:26:13,700 INFO misc.py line 119 87073] Train: [96/100][1492/1557] Data 0.007 (0.076) Batch 1.091 (1.154) Remain 02:00:59 loss: 0.1648 Lr: 0.00002 [2024-02-19 18:26:14,804 INFO misc.py line 119 87073] Train: [96/100][1493/1557] Data 0.006 (0.076) Batch 1.106 (1.154) Remain 02:00:58 loss: 0.1186 Lr: 0.00002 [2024-02-19 18:26:15,697 INFO misc.py line 119 87073] Train: [96/100][1494/1557] Data 0.005 (0.076) Batch 0.894 (1.153) Remain 02:00:55 loss: 0.0866 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1501/1557] Data 0.005 (0.076) Batch 0.815 (1.152) Remain 02:00:41 loss: 0.2663 Lr: 0.00002 [2024-02-19 18:26:23,290 INFO misc.py line 119 87073] Train: [96/100][1502/1557] Data 0.006 (0.076) Batch 1.094 (1.152) Remain 02:00:39 loss: 0.2577 Lr: 0.00002 [2024-02-19 18:26:24,060 INFO misc.py line 119 87073] Train: [96/100][1503/1557] Data 0.010 (0.076) Batch 0.774 (1.152) Remain 02:00:36 loss: 0.2916 Lr: 0.00002 [2024-02-19 18:26:24,789 INFO misc.py line 119 87073] Train: [96/100][1504/1557] Data 0.005 (0.076) Batch 0.723 (1.152) Remain 02:00:34 loss: 0.1808 Lr: 0.00002 [2024-02-19 18:26:26,011 INFO misc.py line 119 87073] Train: [96/100][1505/1557] Data 0.010 (0.076) Batch 1.222 (1.152) Remain 02:00:33 loss: 0.2440 Lr: 0.00002 [2024-02-19 18:26:26,861 INFO misc.py line 119 87073] Train: [96/100][1506/1557] Data 0.011 (0.076) Batch 0.856 (1.152) Remain 02:00:30 loss: 0.2939 Lr: 0.00002 [2024-02-19 18:26:27,955 INFO misc.py line 119 87073] Train: [96/100][1507/1557] Data 0.004 (0.076) Batch 1.095 (1.152) Remain 02:00:29 loss: 0.2061 Lr: 0.00002 [2024-02-19 18:26:28,954 INFO misc.py line 119 87073] Train: [96/100][1508/1557] Data 0.003 (0.076) Batch 0.998 (1.151) Remain 02:00:27 loss: 0.2267 Lr: 0.00002 [2024-02-19 18:26:29,897 INFO misc.py line 119 87073] Train: [96/100][1509/1557] Data 0.005 (0.076) Batch 0.943 (1.151) Remain 02:00:25 loss: 0.1158 Lr: 0.00002 [2024-02-19 18:26:30,637 INFO misc.py line 119 87073] Train: [96/100][1510/1557] Data 0.004 (0.075) Batch 0.738 (1.151) Remain 02:00:22 loss: 0.1356 Lr: 0.00002 [2024-02-19 18:26:31,364 INFO misc.py line 119 87073] Train: [96/100][1511/1557] Data 0.006 (0.075) Batch 0.728 (1.151) Remain 02:00:19 loss: 0.1498 Lr: 0.00002 [2024-02-19 18:26:32,505 INFO misc.py line 119 87073] Train: [96/100][1512/1557] Data 0.005 (0.075) Batch 1.130 (1.151) Remain 02:00:18 loss: 0.1599 Lr: 0.00002 [2024-02-19 18:26:33,461 INFO misc.py line 119 87073] Train: [96/100][1513/1557] Data 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Remain 02:00:50 loss: 0.1086 Lr: 0.00002 [2024-02-19 18:26:51,362 INFO misc.py line 119 87073] Train: [96/100][1520/1557] Data 0.005 (0.077) Batch 1.084 (1.157) Remain 02:00:49 loss: 0.1470 Lr: 0.00002 [2024-02-19 18:26:52,433 INFO misc.py line 119 87073] Train: [96/100][1521/1557] Data 0.004 (0.077) Batch 1.071 (1.157) Remain 02:00:47 loss: 0.2642 Lr: 0.00002 [2024-02-19 18:26:53,276 INFO misc.py line 119 87073] Train: [96/100][1522/1557] Data 0.004 (0.077) Batch 0.843 (1.157) Remain 02:00:45 loss: 0.3142 Lr: 0.00002 [2024-02-19 18:26:54,221 INFO misc.py line 119 87073] Train: [96/100][1523/1557] Data 0.004 (0.077) Batch 0.941 (1.157) Remain 02:00:43 loss: 0.3794 Lr: 0.00002 [2024-02-19 18:26:54,926 INFO misc.py line 119 87073] Train: [96/100][1524/1557] Data 0.009 (0.077) Batch 0.708 (1.156) Remain 02:00:40 loss: 0.1749 Lr: 0.00002 [2024-02-19 18:26:55,652 INFO misc.py line 119 87073] Train: [96/100][1525/1557] Data 0.005 (0.077) Batch 0.717 (1.156) Remain 02:00:37 loss: 0.1295 Lr: 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INFO misc.py line 119 87073] Train: [96/100][1532/1557] Data 0.004 (0.077) Batch 0.890 (1.155) Remain 02:00:23 loss: 0.2441 Lr: 0.00002 [2024-02-19 18:27:03,714 INFO misc.py line 119 87073] Train: [96/100][1533/1557] Data 0.005 (0.077) Batch 1.301 (1.155) Remain 02:00:23 loss: 0.2105 Lr: 0.00002 [2024-02-19 18:27:04,591 INFO misc.py line 119 87073] Train: [96/100][1534/1557] Data 0.010 (0.077) Batch 0.883 (1.155) Remain 02:00:20 loss: 0.1494 Lr: 0.00002 [2024-02-19 18:27:05,529 INFO misc.py line 119 87073] Train: [96/100][1535/1557] Data 0.005 (0.077) Batch 0.937 (1.155) Remain 02:00:18 loss: 0.1341 Lr: 0.00002 [2024-02-19 18:27:06,463 INFO misc.py line 119 87073] Train: [96/100][1536/1557] Data 0.005 (0.076) Batch 0.935 (1.155) Remain 02:00:16 loss: 0.1950 Lr: 0.00002 [2024-02-19 18:27:07,225 INFO misc.py line 119 87073] Train: [96/100][1537/1557] Data 0.005 (0.076) Batch 0.749 (1.155) Remain 02:00:14 loss: 0.2325 Lr: 0.00002 [2024-02-19 18:27:07,935 INFO misc.py line 119 87073] Train: [96/100][1538/1557] Data 0.017 (0.076) Batch 0.723 (1.154) Remain 02:00:11 loss: 0.2896 Lr: 0.00002 [2024-02-19 18:27:08,701 INFO misc.py line 119 87073] Train: [96/100][1539/1557] Data 0.004 (0.076) Batch 0.753 (1.154) Remain 02:00:08 loss: 0.2365 Lr: 0.00002 [2024-02-19 18:27:10,052 INFO misc.py line 119 87073] Train: [96/100][1540/1557] Data 0.016 (0.076) Batch 1.352 (1.154) Remain 02:00:07 loss: 0.1357 Lr: 0.00002 [2024-02-19 18:27:10,956 INFO misc.py line 119 87073] Train: [96/100][1541/1557] Data 0.016 (0.076) Batch 0.916 (1.154) Remain 02:00:05 loss: 0.1806 Lr: 0.00002 [2024-02-19 18:27:11,775 INFO misc.py line 119 87073] Train: [96/100][1542/1557] Data 0.004 (0.076) Batch 0.819 (1.154) Remain 02:00:03 loss: 0.1065 Lr: 0.00002 [2024-02-19 18:27:12,661 INFO misc.py line 119 87073] Train: [96/100][1543/1557] Data 0.004 (0.076) Batch 0.862 (1.154) Remain 02:00:01 loss: 0.0523 Lr: 0.00002 [2024-02-19 18:27:13,623 INFO misc.py line 119 87073] Train: [96/100][1544/1557] Data 0.027 (0.076) Batch 0.984 (1.154) Remain 01:59:59 loss: 0.2202 Lr: 0.00002 [2024-02-19 18:27:14,328 INFO misc.py line 119 87073] Train: [96/100][1545/1557] Data 0.005 (0.076) Batch 0.706 (1.153) Remain 01:59:56 loss: 0.1290 Lr: 0.00002 [2024-02-19 18:27:14,991 INFO misc.py line 119 87073] Train: [96/100][1546/1557] Data 0.004 (0.076) Batch 0.653 (1.153) Remain 01:59:53 loss: 0.1527 Lr: 0.00002 [2024-02-19 18:27:16,130 INFO misc.py line 119 87073] Train: [96/100][1547/1557] Data 0.013 (0.076) Batch 1.125 (1.153) Remain 01:59:51 loss: 0.1209 Lr: 0.00002 [2024-02-19 18:27:17,206 INFO misc.py line 119 87073] Train: [96/100][1548/1557] Data 0.027 (0.076) Batch 1.097 (1.153) Remain 01:59:50 loss: 0.2129 Lr: 0.00002 [2024-02-19 18:27:18,000 INFO misc.py line 119 87073] Train: [96/100][1549/1557] Data 0.006 (0.076) Batch 0.796 (1.153) Remain 01:59:47 loss: 0.0838 Lr: 0.00002 [2024-02-19 18:27:19,004 INFO misc.py line 119 87073] Train: [96/100][1550/1557] Data 0.004 (0.076) Batch 1.005 (1.153) Remain 01:59:46 loss: 0.2460 Lr: 0.00002 [2024-02-19 18:27:19,895 INFO misc.py line 119 87073] Train: [96/100][1551/1557] Data 0.004 (0.076) Batch 0.890 (1.152) Remain 01:59:43 loss: 0.1872 Lr: 0.00002 [2024-02-19 18:27:20,609 INFO misc.py line 119 87073] Train: [96/100][1552/1557] Data 0.004 (0.076) Batch 0.701 (1.152) Remain 01:59:40 loss: 0.2057 Lr: 0.00002 [2024-02-19 18:27:21,355 INFO misc.py line 119 87073] Train: [96/100][1553/1557] Data 0.017 (0.076) Batch 0.758 (1.152) Remain 01:59:38 loss: 0.1476 Lr: 0.00002 [2024-02-19 18:27:22,486 INFO misc.py line 119 87073] Train: [96/100][1554/1557] Data 0.005 (0.076) Batch 1.131 (1.152) Remain 01:59:36 loss: 0.0901 Lr: 0.00002 [2024-02-19 18:27:23,380 INFO misc.py line 119 87073] Train: [96/100][1555/1557] Data 0.005 (0.076) Batch 0.896 (1.152) Remain 01:59:34 loss: 0.1658 Lr: 0.00002 [2024-02-19 18:27:24,309 INFO misc.py line 119 87073] Train: [96/100][1556/1557] Data 0.004 (0.076) Batch 0.920 (1.151) Remain 01:59:32 loss: 0.4889 Lr: 0.00002 [2024-02-19 18:27:25,204 INFO misc.py line 119 87073] Train: [96/100][1557/1557] Data 0.013 (0.076) Batch 0.903 (1.151) Remain 01:59:30 loss: 0.0933 Lr: 0.00002 [2024-02-19 18:27:25,205 INFO misc.py line 136 87073] Train result: loss: 0.1994 [2024-02-19 18:27:25,206 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 18:28:46,170 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4628 [2024-02-19 18:28:46,956 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4727 [2024-02-19 18:28:49,079 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3427 [2024-02-19 18:28:51,292 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3366 [2024-02-19 18:28:56,245 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2263 [2024-02-19 18:28:56,945 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0748 [2024-02-19 18:28:58,205 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2629 [2024-02-19 18:29:01,159 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2400 [2024-02-19 18:29:02,973 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2773 [2024-02-19 18:29:05,004 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7231/0.7791/0.9171. [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9329/0.9628 [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9891 [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8623/0.9711 [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3843/0.4451 [2024-02-19 18:29:05,005 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6485/0.6652 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8202/0.9219 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8531/0.9201 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9250/0.9771 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7936/0.8181 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8018/0.8912 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7675/0.8475 [2024-02-19 18:29:05,006 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6278/0.7190 [2024-02-19 18:29:05,007 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 18:29:05,008 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 18:29:05,009 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 18:29:13,477 INFO misc.py line 119 87073] Train: [97/100][1/1557] Data 2.570 (2.570) Batch 3.756 (3.756) Remain 06:29:47 loss: 0.2556 Lr: 0.00002 [2024-02-19 18:29:14,482 INFO misc.py line 119 87073] Train: [97/100][2/1557] Data 0.004 (0.004) Batch 1.005 (1.005) Remain 01:44:15 loss: 0.1421 Lr: 0.00002 [2024-02-19 18:29:15,311 INFO misc.py line 119 87073] Train: [97/100][3/1557] Data 0.005 (0.005) Batch 0.828 (0.828) Remain 01:25:54 loss: 0.3194 Lr: 0.00002 [2024-02-19 18:29:16,212 INFO misc.py line 119 87073] Train: [97/100][4/1557] Data 0.006 (0.006) Batch 0.897 (0.897) Remain 01:33:01 loss: 0.1980 Lr: 0.00002 [2024-02-19 18:29:17,003 INFO misc.py line 119 87073] Train: [97/100][5/1557] Data 0.009 (0.007) Batch 0.795 (0.846) Remain 01:27:45 loss: 0.1799 Lr: 0.00002 [2024-02-19 18:29:17,756 INFO misc.py line 119 87073] Train: [97/100][6/1557] Data 0.005 (0.007) Batch 0.753 (0.815) Remain 01:24:31 loss: 0.1138 Lr: 0.00002 [2024-02-19 18:29:24,221 INFO misc.py line 119 87073] Train: [97/100][7/1557] Data 0.005 (0.006) Batch 6.466 (2.228) Remain 03:50:59 loss: 0.1268 Lr: 0.00002 [2024-02-19 18:29:25,204 INFO misc.py line 119 87073] Train: [97/100][8/1557] Data 0.004 (0.006) Batch 0.982 (1.979) Remain 03:25:07 loss: 0.2261 Lr: 0.00002 [2024-02-19 18:29:26,208 INFO misc.py line 119 87073] Train: [97/100][9/1557] Data 0.005 (0.006) Batch 1.004 (1.816) Remain 03:08:15 loss: 0.1507 Lr: 0.00002 [2024-02-19 18:29:27,173 INFO misc.py line 119 87073] Train: [97/100][10/1557] Data 0.004 (0.005) Batch 0.939 (1.691) Remain 02:55:14 loss: 0.0885 Lr: 0.00002 [2024-02-19 18:29:28,184 INFO misc.py line 119 87073] Train: [97/100][11/1557] Data 0.030 (0.009) Batch 1.036 (1.609) Remain 02:46:43 loss: 0.1448 Lr: 0.00002 [2024-02-19 18:29:28,931 INFO misc.py line 119 87073] Train: [97/100][12/1557] Data 0.005 (0.008) Batch 0.748 (1.513) Remain 02:36:47 loss: 0.1460 Lr: 0.00002 [2024-02-19 18:29:29,678 INFO misc.py line 119 87073] Train: [97/100][13/1557] Data 0.004 (0.008) Batch 0.735 (1.436) Remain 02:28:42 loss: 0.1379 Lr: 0.00002 [2024-02-19 18:29:30,947 INFO misc.py line 119 87073] Train: [97/100][14/1557] Data 0.015 (0.008) Batch 1.263 (1.420) Remain 02:27:03 loss: 0.1285 Lr: 0.00002 [2024-02-19 18:29:32,047 INFO misc.py line 119 87073] Train: [97/100][15/1557] Data 0.021 (0.009) Batch 1.116 (1.395) Remain 02:24:24 loss: 0.2258 Lr: 0.00002 [2024-02-19 18:29:32,988 INFO misc.py line 119 87073] Train: [97/100][16/1557] Data 0.006 (0.009) Batch 0.940 (1.360) Remain 02:20:46 loss: 0.1322 Lr: 0.00002 [2024-02-19 18:29:33,980 INFO misc.py line 119 87073] Train: [97/100][17/1557] Data 0.006 (0.009) Batch 0.994 (1.334) Remain 02:18:02 loss: 0.1491 Lr: 0.00002 [2024-02-19 18:29:34,963 INFO misc.py line 119 87073] Train: [97/100][18/1557] Data 0.004 (0.009) Batch 0.982 (1.310) Remain 02:15:36 loss: 0.1789 Lr: 0.00002 [2024-02-19 18:29:35,708 INFO misc.py line 119 87073] Train: [97/100][19/1557] Data 0.005 (0.008) Batch 0.746 (1.275) Remain 02:11:55 loss: 0.1574 Lr: 0.00002 [2024-02-19 18:29:36,491 INFO misc.py line 119 87073] Train: [97/100][20/1557] Data 0.004 (0.008) Batch 0.777 (1.246) Remain 02:08:52 loss: 0.2508 Lr: 0.00002 [2024-02-19 18:29:37,769 INFO misc.py line 119 87073] Train: [97/100][21/1557] Data 0.010 (0.008) Batch 1.280 (1.247) Remain 02:09:03 loss: 0.1680 Lr: 0.00002 [2024-02-19 18:29:38,789 INFO misc.py line 119 87073] Train: [97/100][22/1557] Data 0.009 (0.008) Batch 1.021 (1.236) Remain 02:07:47 loss: 0.2595 Lr: 0.00002 [2024-02-19 18:29:39,889 INFO misc.py line 119 87073] Train: [97/100][23/1557] Data 0.007 (0.008) Batch 1.094 (1.228) Remain 02:07:02 loss: 0.2175 Lr: 0.00002 [2024-02-19 18:29:40,969 INFO misc.py line 119 87073] Train: [97/100][24/1557] Data 0.013 (0.008) Batch 1.085 (1.222) Remain 02:06:19 loss: 0.2782 Lr: 0.00002 [2024-02-19 18:29:41,973 INFO misc.py line 119 87073] Train: [97/100][25/1557] Data 0.009 (0.008) Batch 1.004 (1.212) Remain 02:05:16 loss: 0.1622 Lr: 0.00002 [2024-02-19 18:29:42,775 INFO misc.py line 119 87073] Train: [97/100][26/1557] Data 0.008 (0.008) Batch 0.803 (1.194) Remain 02:03:25 loss: 0.1591 Lr: 0.00002 [2024-02-19 18:29:43,531 INFO misc.py line 119 87073] Train: [97/100][27/1557] Data 0.007 (0.008) Batch 0.758 (1.176) Remain 02:01:31 loss: 0.0869 Lr: 0.00002 [2024-02-19 18:29:44,715 INFO misc.py line 119 87073] Train: [97/100][28/1557] Data 0.004 (0.008) Batch 1.178 (1.176) Remain 02:01:30 loss: 0.0891 Lr: 0.00002 [2024-02-19 18:29:45,676 INFO misc.py line 119 87073] Train: [97/100][29/1557] Data 0.011 (0.008) Batch 0.959 (1.168) Remain 02:00:38 loss: 0.1095 Lr: 0.00002 [2024-02-19 18:29:46,637 INFO misc.py line 119 87073] Train: [97/100][30/1557] Data 0.012 (0.008) Batch 0.968 (1.160) Remain 01:59:51 loss: 0.1170 Lr: 0.00002 [2024-02-19 18:29:47,573 INFO misc.py line 119 87073] Train: [97/100][31/1557] Data 0.005 (0.008) Batch 0.936 (1.152) Remain 01:59:00 loss: 0.0542 Lr: 0.00002 [2024-02-19 18:29:48,426 INFO misc.py line 119 87073] Train: [97/100][32/1557] Data 0.004 (0.008) Batch 0.846 (1.142) Remain 01:57:53 loss: 0.2645 Lr: 0.00002 [2024-02-19 18:29:49,136 INFO misc.py line 119 87073] Train: [97/100][33/1557] Data 0.012 (0.008) Batch 0.716 (1.127) Remain 01:56:24 loss: 0.2167 Lr: 0.00002 [2024-02-19 18:29:49,867 INFO misc.py line 119 87073] Train: [97/100][34/1557] Data 0.007 (0.008) Batch 0.734 (1.115) Remain 01:55:04 loss: 0.0810 Lr: 0.00002 [2024-02-19 18:29:51,220 INFO misc.py line 119 87073] Train: [97/100][35/1557] Data 0.004 (0.008) Batch 1.329 (1.121) Remain 01:55:45 loss: 0.0974 Lr: 0.00002 [2024-02-19 18:29:52,197 INFO misc.py line 119 87073] Train: [97/100][36/1557] Data 0.027 (0.009) Batch 1.000 (1.118) Remain 01:55:21 loss: 0.3324 Lr: 0.00002 [2024-02-19 18:29:53,282 INFO misc.py line 119 87073] Train: [97/100][37/1557] Data 0.004 (0.009) Batch 1.075 (1.117) Remain 01:55:12 loss: 0.0907 Lr: 0.00002 [2024-02-19 18:29:54,233 INFO misc.py line 119 87073] Train: [97/100][38/1557] Data 0.015 (0.009) Batch 0.960 (1.112) Remain 01:54:43 loss: 0.0404 Lr: 0.00002 [2024-02-19 18:29:55,145 INFO misc.py line 119 87073] Train: [97/100][39/1557] Data 0.005 (0.009) Batch 0.913 (1.107) Remain 01:54:08 loss: 0.2534 Lr: 0.00002 [2024-02-19 18:29:55,857 INFO misc.py line 119 87073] Train: [97/100][40/1557] Data 0.004 (0.008) Batch 0.700 (1.096) Remain 01:52:59 loss: 0.1634 Lr: 0.00002 [2024-02-19 18:29:56,603 INFO misc.py line 119 87073] Train: [97/100][41/1557] Data 0.017 (0.009) Batch 0.755 (1.087) Remain 01:52:02 loss: 0.3380 Lr: 0.00002 [2024-02-19 18:29:57,643 INFO misc.py line 119 87073] Train: [97/100][42/1557] Data 0.006 (0.009) Batch 1.040 (1.085) Remain 01:51:54 loss: 0.1436 Lr: 0.00002 [2024-02-19 18:29:58,736 INFO misc.py line 119 87073] Train: [97/100][43/1557] Data 0.006 (0.009) Batch 1.095 (1.086) Remain 01:51:54 loss: 0.1304 Lr: 0.00002 [2024-02-19 18:29:59,783 INFO misc.py line 119 87073] Train: [97/100][44/1557] Data 0.005 (0.009) Batch 1.030 (1.084) Remain 01:51:45 loss: 0.1190 Lr: 0.00002 [2024-02-19 18:30:00,752 INFO misc.py line 119 87073] Train: [97/100][45/1557] Data 0.022 (0.009) Batch 0.986 (1.082) Remain 01:51:29 loss: 0.2491 Lr: 0.00002 [2024-02-19 18:30:01,829 INFO misc.py line 119 87073] Train: [97/100][46/1557] Data 0.005 (0.009) Batch 1.077 (1.082) Remain 01:51:27 loss: 0.1517 Lr: 0.00002 [2024-02-19 18:30:02,598 INFO misc.py line 119 87073] Train: [97/100][47/1557] Data 0.005 (0.009) Batch 0.764 (1.075) Remain 01:50:42 loss: 0.1254 Lr: 0.00002 [2024-02-19 18:30:03,370 INFO misc.py line 119 87073] Train: [97/100][48/1557] Data 0.010 (0.009) Batch 0.758 (1.068) Remain 01:49:57 loss: 0.0937 Lr: 0.00002 [2024-02-19 18:30:04,525 INFO misc.py line 119 87073] Train: [97/100][49/1557] Data 0.024 (0.009) Batch 1.153 (1.069) Remain 01:50:07 loss: 0.0846 Lr: 0.00002 [2024-02-19 18:30:05,706 INFO misc.py line 119 87073] Train: [97/100][50/1557] Data 0.026 (0.009) Batch 1.178 (1.072) Remain 01:50:21 loss: 0.1557 Lr: 0.00002 [2024-02-19 18:30:06,649 INFO misc.py line 119 87073] Train: [97/100][51/1557] Data 0.030 (0.010) Batch 0.967 (1.070) Remain 01:50:06 loss: 0.3913 Lr: 0.00002 [2024-02-19 18:30:07,646 INFO misc.py line 119 87073] Train: [97/100][52/1557] Data 0.005 (0.010) Batch 0.998 (1.068) Remain 01:49:56 loss: 0.1325 Lr: 0.00002 [2024-02-19 18:30:08,889 INFO misc.py line 119 87073] Train: [97/100][53/1557] Data 0.004 (0.010) Batch 1.218 (1.071) Remain 01:50:13 loss: 0.1756 Lr: 0.00002 [2024-02-19 18:30:09,660 INFO misc.py line 119 87073] Train: [97/100][54/1557] Data 0.030 (0.010) Batch 0.781 (1.065) Remain 01:49:37 loss: 0.1726 Lr: 0.00002 [2024-02-19 18:30:10,393 INFO misc.py line 119 87073] Train: [97/100][55/1557] Data 0.019 (0.010) Batch 0.733 (1.059) Remain 01:48:57 loss: 0.2314 Lr: 0.00002 [2024-02-19 18:30:11,589 INFO misc.py line 119 87073] Train: [97/100][56/1557] Data 0.018 (0.010) Batch 1.187 (1.061) Remain 01:49:11 loss: 0.2038 Lr: 0.00002 [2024-02-19 18:30:12,612 INFO misc.py line 119 87073] Train: [97/100][57/1557] Data 0.027 (0.011) Batch 1.011 (1.060) Remain 01:49:04 loss: 0.2060 Lr: 0.00002 [2024-02-19 18:30:13,571 INFO misc.py line 119 87073] Train: [97/100][58/1557] Data 0.039 (0.011) Batch 0.992 (1.059) Remain 01:48:55 loss: 0.0715 Lr: 0.00002 [2024-02-19 18:30:14,613 INFO misc.py line 119 87073] Train: [97/100][59/1557] Data 0.006 (0.011) Batch 1.039 (1.059) Remain 01:48:52 loss: 0.3531 Lr: 0.00002 [2024-02-19 18:30:15,599 INFO misc.py line 119 87073] Train: [97/100][60/1557] Data 0.008 (0.011) Batch 0.979 (1.057) Remain 01:48:42 loss: 0.1740 Lr: 0.00002 [2024-02-19 18:30:16,365 INFO misc.py line 119 87073] Train: [97/100][61/1557] Data 0.016 (0.011) Batch 0.777 (1.053) Remain 01:48:11 loss: 0.2298 Lr: 0.00002 [2024-02-19 18:30:17,048 INFO misc.py line 119 87073] Train: [97/100][62/1557] Data 0.004 (0.011) Batch 0.680 (1.046) Remain 01:47:31 loss: 0.1580 Lr: 0.00002 [2024-02-19 18:30:33,779 INFO misc.py line 119 87073] Train: [97/100][63/1557] Data 4.192 (0.081) Batch 16.720 (1.308) Remain 02:14:21 loss: 0.0783 Lr: 0.00002 [2024-02-19 18:30:34,812 INFO misc.py line 119 87073] Train: [97/100][64/1557] Data 0.019 (0.080) Batch 1.048 (1.303) Remain 02:13:53 loss: 0.2131 Lr: 0.00002 [2024-02-19 18:30:35,784 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [97/100][109/1557] Data 0.013 (0.051) Batch 0.899 (1.155) Remain 01:57:48 loss: 0.5774 Lr: 0.00002 [2024-02-19 18:31:18,539 INFO misc.py line 119 87073] Train: [97/100][110/1557] Data 0.006 (0.051) Batch 0.774 (1.152) Remain 01:57:25 loss: 0.1420 Lr: 0.00002 [2024-02-19 18:31:19,334 INFO misc.py line 119 87073] Train: [97/100][111/1557] Data 0.003 (0.050) Batch 0.792 (1.148) Remain 01:57:04 loss: 0.1489 Lr: 0.00002 [2024-02-19 18:31:20,493 INFO misc.py line 119 87073] Train: [97/100][112/1557] Data 0.007 (0.050) Batch 1.160 (1.148) Remain 01:57:03 loss: 0.1274 Lr: 0.00002 [2024-02-19 18:31:21,634 INFO misc.py line 119 87073] Train: [97/100][113/1557] Data 0.006 (0.050) Batch 1.135 (1.148) Remain 01:57:01 loss: 0.3357 Lr: 0.00002 [2024-02-19 18:31:22,577 INFO misc.py line 119 87073] Train: [97/100][114/1557] Data 0.013 (0.049) Batch 0.952 (1.147) Remain 01:56:50 loss: 0.1103 Lr: 0.00002 [2024-02-19 18:31:23,620 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [97/100][221/1557] Data 0.012 (0.081) Batch 0.896 (1.190) Remain 01:59:09 loss: 0.1625 Lr: 0.00002 [2024-02-19 18:33:35,529 INFO misc.py line 119 87073] Train: [97/100][222/1557] Data 0.004 (0.081) Batch 0.769 (1.188) Remain 01:58:56 loss: 0.0678 Lr: 0.00002 [2024-02-19 18:33:36,328 INFO misc.py line 119 87073] Train: [97/100][223/1557] Data 0.005 (0.080) Batch 0.797 (1.186) Remain 01:58:44 loss: 0.0877 Lr: 0.00002 [2024-02-19 18:33:37,552 INFO misc.py line 119 87073] Train: [97/100][224/1557] Data 0.007 (0.080) Batch 1.214 (1.187) Remain 01:58:44 loss: 0.1464 Lr: 0.00002 [2024-02-19 18:33:38,510 INFO misc.py line 119 87073] Train: [97/100][225/1557] Data 0.016 (0.080) Batch 0.970 (1.186) Remain 01:58:37 loss: 0.5543 Lr: 0.00002 [2024-02-19 18:33:39,497 INFO misc.py line 119 87073] Train: [97/100][226/1557] Data 0.005 (0.080) Batch 0.986 (1.185) Remain 01:58:30 loss: 0.0934 Lr: 0.00002 [2024-02-19 18:33:40,494 INFO misc.py line 119 87073] Train: 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Batch 1.060 (1.247) Remain 02:04:32 loss: 0.5504 Lr: 0.00002 [2024-02-19 18:34:02,920 INFO misc.py line 119 87073] Train: [97/100][234/1557] Data 0.015 (0.096) Batch 0.913 (1.245) Remain 02:04:22 loss: 0.2458 Lr: 0.00002 [2024-02-19 18:34:03,875 INFO misc.py line 119 87073] Train: [97/100][235/1557] Data 0.004 (0.096) Batch 0.953 (1.244) Remain 02:04:14 loss: 0.2077 Lr: 0.00002 [2024-02-19 18:34:04,636 INFO misc.py line 119 87073] Train: [97/100][236/1557] Data 0.006 (0.095) Batch 0.762 (1.242) Remain 02:04:00 loss: 0.2567 Lr: 0.00002 [2024-02-19 18:34:05,327 INFO misc.py line 119 87073] Train: [97/100][237/1557] Data 0.004 (0.095) Batch 0.686 (1.239) Remain 02:03:45 loss: 0.1456 Lr: 0.00002 [2024-02-19 18:34:06,710 INFO misc.py line 119 87073] Train: [97/100][238/1557] Data 0.009 (0.094) Batch 1.379 (1.240) Remain 02:03:47 loss: 0.0830 Lr: 0.00002 [2024-02-19 18:34:07,570 INFO misc.py line 119 87073] Train: [97/100][239/1557] Data 0.014 (0.094) Batch 0.869 (1.238) Remain 02:03:36 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Batch 1.011 (1.241) Remain 02:02:52 loss: 0.1003 Lr: 0.00002 [2024-02-19 18:35:11,331 INFO misc.py line 119 87073] Train: [97/100][290/1557] Data 0.004 (0.097) Batch 0.997 (1.240) Remain 02:02:46 loss: 0.3444 Lr: 0.00002 [2024-02-19 18:35:12,244 INFO misc.py line 119 87073] Train: [97/100][291/1557] Data 0.004 (0.097) Batch 0.913 (1.239) Remain 02:02:38 loss: 0.2154 Lr: 0.00002 [2024-02-19 18:35:12,995 INFO misc.py line 119 87073] Train: [97/100][292/1557] Data 0.004 (0.096) Batch 0.751 (1.238) Remain 02:02:26 loss: 0.2485 Lr: 0.00002 [2024-02-19 18:35:13,670 INFO misc.py line 119 87073] Train: [97/100][293/1557] Data 0.005 (0.096) Batch 0.676 (1.236) Remain 02:02:14 loss: 0.0803 Lr: 0.00002 [2024-02-19 18:35:14,935 INFO misc.py line 119 87073] Train: [97/100][294/1557] Data 0.003 (0.096) Batch 1.265 (1.236) Remain 02:02:13 loss: 0.2171 Lr: 0.00002 [2024-02-19 18:35:15,945 INFO misc.py line 119 87073] Train: [97/100][295/1557] Data 0.004 (0.095) Batch 1.008 (1.235) Remain 02:02:07 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line 119 87073] Train: [97/100][389/1557] Data 0.005 (0.091) Batch 1.092 (1.212) Remain 01:57:57 loss: 0.5382 Lr: 0.00002 [2024-02-19 18:37:03,885 INFO misc.py line 119 87073] Train: [97/100][390/1557] Data 0.007 (0.091) Batch 0.718 (1.211) Remain 01:57:48 loss: 0.2189 Lr: 0.00002 [2024-02-19 18:37:04,631 INFO misc.py line 119 87073] Train: [97/100][391/1557] Data 0.006 (0.091) Batch 0.744 (1.210) Remain 01:57:40 loss: 0.3622 Lr: 0.00002 [2024-02-19 18:37:05,873 INFO misc.py line 119 87073] Train: [97/100][392/1557] Data 0.007 (0.091) Batch 1.243 (1.210) Remain 01:57:39 loss: 0.1039 Lr: 0.00002 [2024-02-19 18:37:06,878 INFO misc.py line 119 87073] Train: [97/100][393/1557] Data 0.006 (0.090) Batch 1.004 (1.209) Remain 01:57:35 loss: 0.0612 Lr: 0.00002 [2024-02-19 18:37:08,001 INFO misc.py line 119 87073] Train: [97/100][394/1557] Data 0.006 (0.090) Batch 1.114 (1.209) Remain 01:57:32 loss: 0.2491 Lr: 0.00002 [2024-02-19 18:37:08,909 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [97/100][669/1557] Data 0.004 (0.094) Batch 1.033 (1.213) Remain 01:52:23 loss: 0.3571 Lr: 0.00002 [2024-02-19 18:42:44,007 INFO misc.py line 119 87073] Train: [97/100][670/1557] Data 0.004 (0.093) Batch 0.769 (1.212) Remain 01:52:18 loss: 0.1928 Lr: 0.00002 [2024-02-19 18:42:44,712 INFO misc.py line 119 87073] Train: [97/100][671/1557] Data 0.004 (0.093) Batch 0.704 (1.212) Remain 01:52:13 loss: 0.1149 Lr: 0.00002 [2024-02-19 18:42:45,899 INFO misc.py line 119 87073] Train: [97/100][672/1557] Data 0.005 (0.093) Batch 1.189 (1.212) Remain 01:52:11 loss: 0.1266 Lr: 0.00002 [2024-02-19 18:42:46,847 INFO misc.py line 119 87073] Train: [97/100][673/1557] Data 0.004 (0.093) Batch 0.947 (1.211) Remain 01:52:08 loss: 0.1717 Lr: 0.00002 [2024-02-19 18:42:47,877 INFO misc.py line 119 87073] Train: [97/100][674/1557] Data 0.004 (0.093) Batch 1.031 (1.211) Remain 01:52:05 loss: 0.2631 Lr: 0.00002 [2024-02-19 18:42:49,025 INFO misc.py line 119 87073] Train: 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Batch 0.986 (1.232) Remain 01:53:53 loss: 0.1058 Lr: 0.00002 [2024-02-19 18:43:11,433 INFO misc.py line 119 87073] Train: [97/100][682/1557] Data 0.005 (0.098) Batch 0.881 (1.231) Remain 01:53:49 loss: 0.0838 Lr: 0.00002 [2024-02-19 18:43:12,314 INFO misc.py line 119 87073] Train: [97/100][683/1557] Data 0.007 (0.098) Batch 0.880 (1.231) Remain 01:53:45 loss: 0.0951 Lr: 0.00002 [2024-02-19 18:43:13,102 INFO misc.py line 119 87073] Train: [97/100][684/1557] Data 0.005 (0.098) Batch 0.788 (1.230) Remain 01:53:40 loss: 0.2719 Lr: 0.00002 [2024-02-19 18:43:13,866 INFO misc.py line 119 87073] Train: [97/100][685/1557] Data 0.005 (0.098) Batch 0.764 (1.230) Remain 01:53:35 loss: 0.1582 Lr: 0.00002 [2024-02-19 18:43:15,146 INFO misc.py line 119 87073] Train: [97/100][686/1557] Data 0.005 (0.098) Batch 1.270 (1.230) Remain 01:53:34 loss: 0.1308 Lr: 0.00002 [2024-02-19 18:43:16,156 INFO misc.py line 119 87073] Train: [97/100][687/1557] Data 0.015 (0.098) Batch 1.020 (1.229) Remain 01:53:31 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Remain 01:42:01 loss: 0.1585 Lr: 0.00001 [2024-02-19 18:54:34,251 INFO misc.py line 119 87073] Train: [97/100][1241/1557] Data 0.003 (0.100) Batch 0.902 (1.227) Remain 01:41:58 loss: 0.1966 Lr: 0.00001 [2024-02-19 18:54:35,110 INFO misc.py line 119 87073] Train: [97/100][1242/1557] Data 0.005 (0.100) Batch 0.860 (1.227) Remain 01:41:55 loss: 0.2424 Lr: 0.00001 [2024-02-19 18:54:36,156 INFO misc.py line 119 87073] Train: [97/100][1243/1557] Data 0.006 (0.100) Batch 1.046 (1.226) Remain 01:41:54 loss: 0.2438 Lr: 0.00001 [2024-02-19 18:54:36,897 INFO misc.py line 119 87073] Train: [97/100][1244/1557] Data 0.004 (0.100) Batch 0.742 (1.226) Remain 01:41:50 loss: 0.1732 Lr: 0.00001 [2024-02-19 18:54:37,632 INFO misc.py line 119 87073] Train: [97/100][1245/1557] Data 0.003 (0.100) Batch 0.727 (1.226) Remain 01:41:47 loss: 0.1819 Lr: 0.00001 [2024-02-19 18:54:38,962 INFO misc.py line 119 87073] Train: [97/100][1246/1557] Data 0.012 (0.100) Batch 1.325 (1.226) Remain 01:41:46 loss: 0.1273 Lr: 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Train: [97/100][1259/1557] Data 0.004 (0.099) Batch 0.755 (1.222) Remain 01:41:13 loss: 0.0988 Lr: 0.00001 [2024-02-19 18:54:51,670 INFO misc.py line 119 87073] Train: [97/100][1260/1557] Data 0.006 (0.099) Batch 1.145 (1.222) Remain 01:41:12 loss: 0.1377 Lr: 0.00001 [2024-02-19 18:54:52,534 INFO misc.py line 119 87073] Train: [97/100][1261/1557] Data 0.009 (0.099) Batch 0.869 (1.222) Remain 01:41:09 loss: 0.0787 Lr: 0.00001 [2024-02-19 18:54:53,469 INFO misc.py line 119 87073] Train: [97/100][1262/1557] Data 0.005 (0.098) Batch 0.935 (1.222) Remain 01:41:07 loss: 0.1463 Lr: 0.00001 [2024-02-19 18:54:54,462 INFO misc.py line 119 87073] Train: [97/100][1263/1557] Data 0.005 (0.098) Batch 0.994 (1.222) Remain 01:41:04 loss: 0.3157 Lr: 0.00001 [2024-02-19 18:54:55,493 INFO misc.py line 119 87073] Train: [97/100][1264/1557] Data 0.003 (0.098) Batch 1.031 (1.221) Remain 01:41:03 loss: 0.2134 Lr: 0.00001 [2024-02-19 18:54:56,227 INFO misc.py line 119 87073] Train: [97/100][1265/1557] Data 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Remain 01:40:47 loss: 0.1893 Lr: 0.00001 [2024-02-19 18:55:03,060 INFO misc.py line 119 87073] Train: [97/100][1272/1557] Data 0.004 (0.098) Batch 0.788 (1.220) Remain 01:40:44 loss: 0.0875 Lr: 0.00001 [2024-02-19 18:55:03,827 INFO misc.py line 119 87073] Train: [97/100][1273/1557] Data 0.005 (0.098) Batch 0.769 (1.219) Remain 01:40:41 loss: 0.0457 Lr: 0.00001 [2024-02-19 18:55:04,821 INFO misc.py line 119 87073] Train: [97/100][1274/1557] Data 0.003 (0.098) Batch 0.993 (1.219) Remain 01:40:39 loss: 0.2218 Lr: 0.00001 [2024-02-19 18:55:05,860 INFO misc.py line 119 87073] Train: [97/100][1275/1557] Data 0.004 (0.097) Batch 1.040 (1.219) Remain 01:40:37 loss: 0.1391 Lr: 0.00001 [2024-02-19 18:55:06,753 INFO misc.py line 119 87073] Train: [97/100][1276/1557] Data 0.004 (0.097) Batch 0.893 (1.219) Remain 01:40:35 loss: 0.2870 Lr: 0.00001 [2024-02-19 18:55:07,791 INFO misc.py line 119 87073] Train: [97/100][1277/1557] Data 0.004 (0.097) Batch 1.032 (1.219) Remain 01:40:33 loss: 0.3124 Lr: 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Train: [97/100][1290/1557] Data 0.011 (0.096) Batch 0.988 (1.216) Remain 01:40:03 loss: 0.2098 Lr: 0.00001 [2024-02-19 18:55:20,894 INFO misc.py line 119 87073] Train: [97/100][1291/1557] Data 0.005 (0.096) Batch 0.970 (1.216) Remain 01:40:00 loss: 0.3030 Lr: 0.00001 [2024-02-19 18:55:21,842 INFO misc.py line 119 87073] Train: [97/100][1292/1557] Data 0.004 (0.096) Batch 0.948 (1.215) Remain 01:39:58 loss: 0.1064 Lr: 0.00001 [2024-02-19 18:55:22,586 INFO misc.py line 119 87073] Train: [97/100][1293/1557] Data 0.005 (0.096) Batch 0.734 (1.215) Remain 01:39:55 loss: 0.1395 Lr: 0.00001 [2024-02-19 18:55:23,431 INFO misc.py line 119 87073] Train: [97/100][1294/1557] Data 0.014 (0.096) Batch 0.854 (1.215) Remain 01:39:53 loss: 0.1366 Lr: 0.00001 [2024-02-19 18:55:39,191 INFO misc.py line 119 87073] Train: [97/100][1295/1557] Data 3.863 (0.099) Batch 15.760 (1.226) Remain 01:40:47 loss: 0.1210 Lr: 0.00001 [2024-02-19 18:55:40,167 INFO misc.py line 119 87073] Train: [97/100][1296/1557] Data 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Remain 01:40:31 loss: 0.1552 Lr: 0.00001 [2024-02-19 18:55:46,956 INFO misc.py line 119 87073] Train: [97/100][1303/1557] Data 0.015 (0.099) Batch 1.052 (1.224) Remain 01:40:29 loss: 0.3019 Lr: 0.00001 [2024-02-19 18:55:47,805 INFO misc.py line 119 87073] Train: [97/100][1304/1557] Data 0.005 (0.098) Batch 0.850 (1.224) Remain 01:40:27 loss: 0.1844 Lr: 0.00001 [2024-02-19 18:55:48,839 INFO misc.py line 119 87073] Train: [97/100][1305/1557] Data 0.004 (0.098) Batch 1.033 (1.224) Remain 01:40:25 loss: 0.1115 Lr: 0.00001 [2024-02-19 18:55:49,969 INFO misc.py line 119 87073] Train: [97/100][1306/1557] Data 0.006 (0.098) Batch 1.131 (1.224) Remain 01:40:23 loss: 0.1476 Lr: 0.00001 [2024-02-19 18:55:50,744 INFO misc.py line 119 87073] Train: [97/100][1307/1557] Data 0.005 (0.098) Batch 0.776 (1.223) Remain 01:40:20 loss: 0.3375 Lr: 0.00001 [2024-02-19 18:55:51,431 INFO misc.py line 119 87073] Train: [97/100][1308/1557] Data 0.004 (0.098) Batch 0.686 (1.223) Remain 01:40:17 loss: 0.2780 Lr: 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Train: [97/100][1321/1557] Data 0.005 (0.097) Batch 0.762 (1.221) Remain 01:39:49 loss: 0.2310 Lr: 0.00001 [2024-02-19 18:56:04,732 INFO misc.py line 119 87073] Train: [97/100][1322/1557] Data 0.004 (0.097) Batch 0.734 (1.220) Remain 01:39:46 loss: 0.0938 Lr: 0.00001 [2024-02-19 18:56:06,063 INFO misc.py line 119 87073] Train: [97/100][1323/1557] Data 0.014 (0.097) Batch 1.328 (1.220) Remain 01:39:45 loss: 0.1124 Lr: 0.00001 [2024-02-19 18:56:07,042 INFO misc.py line 119 87073] Train: [97/100][1324/1557] Data 0.017 (0.097) Batch 0.991 (1.220) Remain 01:39:43 loss: 0.1431 Lr: 0.00001 [2024-02-19 18:56:07,957 INFO misc.py line 119 87073] Train: [97/100][1325/1557] Data 0.004 (0.097) Batch 0.914 (1.220) Remain 01:39:40 loss: 0.2981 Lr: 0.00001 [2024-02-19 18:56:08,825 INFO misc.py line 119 87073] Train: [97/100][1326/1557] Data 0.005 (0.097) Batch 0.864 (1.220) Remain 01:39:38 loss: 0.2746 Lr: 0.00001 [2024-02-19 18:56:09,896 INFO misc.py line 119 87073] Train: [97/100][1327/1557] Data 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Remain 01:39:23 loss: 0.2813 Lr: 0.00001 [2024-02-19 18:56:16,389 INFO misc.py line 119 87073] Train: [97/100][1334/1557] Data 0.006 (0.096) Batch 0.892 (1.218) Remain 01:39:20 loss: 0.1268 Lr: 0.00001 [2024-02-19 18:56:17,153 INFO misc.py line 119 87073] Train: [97/100][1335/1557] Data 0.003 (0.096) Batch 0.764 (1.218) Remain 01:39:17 loss: 0.1566 Lr: 0.00001 [2024-02-19 18:56:17,948 INFO misc.py line 119 87073] Train: [97/100][1336/1557] Data 0.004 (0.096) Batch 0.793 (1.217) Remain 01:39:14 loss: 0.1256 Lr: 0.00001 [2024-02-19 18:56:19,163 INFO misc.py line 119 87073] Train: [97/100][1337/1557] Data 0.005 (0.096) Batch 1.207 (1.217) Remain 01:39:13 loss: 0.0947 Lr: 0.00001 [2024-02-19 18:56:20,064 INFO misc.py line 119 87073] Train: [97/100][1338/1557] Data 0.014 (0.096) Batch 0.909 (1.217) Remain 01:39:11 loss: 0.1833 Lr: 0.00001 [2024-02-19 18:56:21,115 INFO misc.py line 119 87073] Train: [97/100][1339/1557] Data 0.005 (0.096) Batch 1.051 (1.217) Remain 01:39:09 loss: 0.2093 Lr: 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INFO misc.py line 119 87073] Train: [97/100][1346/1557] Data 0.004 (0.096) Batch 0.993 (1.215) Remain 01:38:53 loss: 0.3572 Lr: 0.00001 [2024-02-19 18:56:28,481 INFO misc.py line 119 87073] Train: [97/100][1347/1557] Data 0.005 (0.096) Batch 0.999 (1.215) Remain 01:38:51 loss: 0.1158 Lr: 0.00001 [2024-02-19 18:56:29,464 INFO misc.py line 119 87073] Train: [97/100][1348/1557] Data 0.004 (0.095) Batch 0.984 (1.215) Remain 01:38:49 loss: 0.0785 Lr: 0.00001 [2024-02-19 18:56:30,190 INFO misc.py line 119 87073] Train: [97/100][1349/1557] Data 0.004 (0.095) Batch 0.726 (1.215) Remain 01:38:46 loss: 0.1786 Lr: 0.00001 [2024-02-19 18:56:30,900 INFO misc.py line 119 87073] Train: [97/100][1350/1557] Data 0.004 (0.095) Batch 0.707 (1.214) Remain 01:38:43 loss: 0.2096 Lr: 0.00001 [2024-02-19 18:56:46,783 INFO misc.py line 119 87073] Train: [97/100][1351/1557] Data 4.494 (0.099) Batch 15.885 (1.225) Remain 01:39:34 loss: 0.0965 Lr: 0.00001 [2024-02-19 18:56:47,969 INFO misc.py line 119 87073] Train: [97/100][1352/1557] Data 0.006 (0.099) Batch 1.186 (1.225) Remain 01:39:33 loss: 0.1632 Lr: 0.00001 [2024-02-19 18:56:48,961 INFO misc.py line 119 87073] Train: [97/100][1353/1557] Data 0.005 (0.098) Batch 0.991 (1.225) Remain 01:39:31 loss: 0.3639 Lr: 0.00001 [2024-02-19 18:56:49,927 INFO misc.py line 119 87073] Train: [97/100][1354/1557] Data 0.005 (0.098) Batch 0.968 (1.225) Remain 01:39:29 loss: 0.3338 Lr: 0.00001 [2024-02-19 18:56:50,932 INFO misc.py line 119 87073] Train: [97/100][1355/1557] Data 0.004 (0.098) Batch 1.005 (1.225) Remain 01:39:27 loss: 0.1364 Lr: 0.00001 [2024-02-19 18:56:51,716 INFO misc.py line 119 87073] Train: [97/100][1356/1557] Data 0.004 (0.098) Batch 0.783 (1.224) Remain 01:39:24 loss: 0.1785 Lr: 0.00001 [2024-02-19 18:56:52,473 INFO misc.py line 119 87073] Train: [97/100][1357/1557] Data 0.005 (0.098) Batch 0.757 (1.224) Remain 01:39:21 loss: 0.1431 Lr: 0.00001 [2024-02-19 18:56:53,814 INFO misc.py line 119 87073] Train: [97/100][1358/1557] Data 0.004 (0.098) Batch 1.333 (1.224) Remain 01:39:20 loss: 0.1233 Lr: 0.00001 [2024-02-19 18:56:54,717 INFO misc.py line 119 87073] Train: [97/100][1359/1557] Data 0.012 (0.098) Batch 0.912 (1.224) Remain 01:39:18 loss: 0.1803 Lr: 0.00001 [2024-02-19 18:56:55,708 INFO misc.py line 119 87073] Train: [97/100][1360/1557] Data 0.004 (0.098) Batch 0.991 (1.224) Remain 01:39:16 loss: 0.2042 Lr: 0.00001 [2024-02-19 18:56:56,648 INFO misc.py line 119 87073] Train: [97/100][1361/1557] Data 0.004 (0.098) Batch 0.940 (1.223) Remain 01:39:14 loss: 0.3525 Lr: 0.00001 [2024-02-19 18:56:57,447 INFO misc.py line 119 87073] Train: [97/100][1362/1557] Data 0.004 (0.098) Batch 0.791 (1.223) Remain 01:39:11 loss: 0.1606 Lr: 0.00001 [2024-02-19 18:56:58,148 INFO misc.py line 119 87073] Train: [97/100][1363/1557] Data 0.011 (0.098) Batch 0.708 (1.223) Remain 01:39:08 loss: 0.3200 Lr: 0.00001 [2024-02-19 18:56:58,925 INFO misc.py line 119 87073] Train: [97/100][1364/1557] Data 0.005 (0.098) Batch 0.770 (1.222) Remain 01:39:05 loss: 0.1164 Lr: 0.00001 [2024-02-19 18:57:00,250 INFO misc.py line 119 87073] Train: [97/100][1365/1557] Data 0.012 (0.098) Batch 1.322 (1.222) Remain 01:39:04 loss: 0.0689 Lr: 0.00001 [2024-02-19 18:57:01,129 INFO misc.py line 119 87073] Train: [97/100][1366/1557] Data 0.015 (0.098) Batch 0.889 (1.222) Remain 01:39:02 loss: 0.5066 Lr: 0.00001 [2024-02-19 18:57:02,138 INFO misc.py line 119 87073] Train: [97/100][1367/1557] Data 0.005 (0.098) Batch 1.011 (1.222) Remain 01:39:00 loss: 0.1844 Lr: 0.00001 [2024-02-19 18:57:02,989 INFO misc.py line 119 87073] Train: [97/100][1368/1557] Data 0.004 (0.097) Batch 0.850 (1.222) Remain 01:38:57 loss: 0.3330 Lr: 0.00001 [2024-02-19 18:57:04,048 INFO misc.py line 119 87073] Train: [97/100][1369/1557] Data 0.003 (0.097) Batch 1.056 (1.222) Remain 01:38:55 loss: 0.1924 Lr: 0.00001 [2024-02-19 18:57:04,817 INFO misc.py line 119 87073] Train: [97/100][1370/1557] Data 0.008 (0.097) Batch 0.772 (1.221) Remain 01:38:53 loss: 0.1960 Lr: 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Train: [97/100][1383/1557] Data 0.015 (0.096) Batch 0.965 (1.219) Remain 01:38:24 loss: 0.5918 Lr: 0.00001 [2024-02-19 18:57:17,848 INFO misc.py line 119 87073] Train: [97/100][1384/1557] Data 0.004 (0.096) Batch 0.766 (1.218) Remain 01:38:21 loss: 0.1586 Lr: 0.00001 [2024-02-19 18:57:18,629 INFO misc.py line 119 87073] Train: [97/100][1385/1557] Data 0.004 (0.096) Batch 0.772 (1.218) Remain 01:38:18 loss: 0.1723 Lr: 0.00001 [2024-02-19 18:57:19,638 INFO misc.py line 119 87073] Train: [97/100][1386/1557] Data 0.014 (0.096) Batch 1.010 (1.218) Remain 01:38:16 loss: 0.1327 Lr: 0.00001 [2024-02-19 18:57:20,763 INFO misc.py line 119 87073] Train: [97/100][1387/1557] Data 0.013 (0.096) Batch 1.127 (1.218) Remain 01:38:15 loss: 0.2058 Lr: 0.00001 [2024-02-19 18:57:21,767 INFO misc.py line 119 87073] Train: [97/100][1388/1557] Data 0.009 (0.096) Batch 1.009 (1.218) Remain 01:38:13 loss: 0.2334 Lr: 0.00001 [2024-02-19 18:57:22,715 INFO misc.py line 119 87073] Train: [97/100][1389/1557] Data 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Remain 01:37:57 loss: 0.2145 Lr: 0.00001 [2024-02-19 18:57:29,226 INFO misc.py line 119 87073] Train: [97/100][1396/1557] Data 0.004 (0.096) Batch 0.939 (1.216) Remain 01:37:55 loss: 0.0625 Lr: 0.00001 [2024-02-19 18:57:30,293 INFO misc.py line 119 87073] Train: [97/100][1397/1557] Data 0.006 (0.096) Batch 1.066 (1.216) Remain 01:37:54 loss: 0.1995 Lr: 0.00001 [2024-02-19 18:57:31,054 INFO misc.py line 119 87073] Train: [97/100][1398/1557] Data 0.006 (0.095) Batch 0.764 (1.216) Remain 01:37:51 loss: 0.1328 Lr: 0.00001 [2024-02-19 18:57:31,815 INFO misc.py line 119 87073] Train: [97/100][1399/1557] Data 0.004 (0.095) Batch 0.749 (1.215) Remain 01:37:48 loss: 0.2013 Lr: 0.00001 [2024-02-19 18:57:32,964 INFO misc.py line 119 87073] Train: [97/100][1400/1557] Data 0.015 (0.095) Batch 1.138 (1.215) Remain 01:37:46 loss: 0.0992 Lr: 0.00001 [2024-02-19 18:57:33,855 INFO misc.py line 119 87073] Train: [97/100][1401/1557] Data 0.026 (0.095) Batch 0.912 (1.215) Remain 01:37:44 loss: 0.4719 Lr: 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Train: [97/100][1414/1557] Data 0.004 (0.099) Batch 1.361 (1.225) Remain 01:38:16 loss: 0.1003 Lr: 0.00001 [2024-02-19 18:58:04,613 INFO misc.py line 119 87073] Train: [97/100][1415/1557] Data 0.008 (0.099) Batch 1.116 (1.225) Remain 01:38:14 loss: 0.1623 Lr: 0.00001 [2024-02-19 18:58:05,542 INFO misc.py line 119 87073] Train: [97/100][1416/1557] Data 0.010 (0.099) Batch 0.934 (1.225) Remain 01:38:12 loss: 0.1601 Lr: 0.00001 [2024-02-19 18:58:06,627 INFO misc.py line 119 87073] Train: [97/100][1417/1557] Data 0.005 (0.099) Batch 1.086 (1.224) Remain 01:38:10 loss: 0.1231 Lr: 0.00001 [2024-02-19 18:58:07,502 INFO misc.py line 119 87073] Train: [97/100][1418/1557] Data 0.005 (0.099) Batch 0.876 (1.224) Remain 01:38:08 loss: 0.2783 Lr: 0.00001 [2024-02-19 18:58:08,265 INFO misc.py line 119 87073] Train: [97/100][1419/1557] Data 0.004 (0.099) Batch 0.754 (1.224) Remain 01:38:05 loss: 0.1437 Lr: 0.00001 [2024-02-19 18:58:09,016 INFO misc.py line 119 87073] Train: [97/100][1420/1557] Data 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Remain 01:37:50 loss: 0.2087 Lr: 0.00001 [2024-02-19 18:58:15,785 INFO misc.py line 119 87073] Train: [97/100][1427/1557] Data 0.003 (0.098) Batch 0.779 (1.222) Remain 01:37:47 loss: 0.1697 Lr: 0.00001 [2024-02-19 18:58:16,994 INFO misc.py line 119 87073] Train: [97/100][1428/1557] Data 0.007 (0.098) Batch 1.211 (1.222) Remain 01:37:46 loss: 0.0894 Lr: 0.00001 [2024-02-19 18:58:17,807 INFO misc.py line 119 87073] Train: [97/100][1429/1557] Data 0.006 (0.098) Batch 0.814 (1.222) Remain 01:37:44 loss: 0.1057 Lr: 0.00001 [2024-02-19 18:58:18,646 INFO misc.py line 119 87073] Train: [97/100][1430/1557] Data 0.005 (0.098) Batch 0.840 (1.222) Remain 01:37:41 loss: 0.5633 Lr: 0.00001 [2024-02-19 18:58:19,634 INFO misc.py line 119 87073] Train: [97/100][1431/1557] Data 0.004 (0.098) Batch 0.983 (1.222) Remain 01:37:39 loss: 0.2272 Lr: 0.00001 [2024-02-19 18:58:20,551 INFO misc.py line 119 87073] Train: [97/100][1432/1557] Data 0.009 (0.098) Batch 0.922 (1.221) Remain 01:37:37 loss: 0.3623 Lr: 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Train: [97/100][1445/1557] Data 0.004 (0.097) Batch 0.915 (1.218) Remain 01:37:07 loss: 0.1725 Lr: 0.00001 [2024-02-19 18:58:33,067 INFO misc.py line 119 87073] Train: [97/100][1446/1557] Data 0.005 (0.097) Batch 0.969 (1.218) Remain 01:37:05 loss: 0.2040 Lr: 0.00001 [2024-02-19 18:58:33,832 INFO misc.py line 119 87073] Train: [97/100][1447/1557] Data 0.004 (0.097) Batch 0.765 (1.218) Remain 01:37:02 loss: 0.0911 Lr: 0.00001 [2024-02-19 18:58:34,614 INFO misc.py line 119 87073] Train: [97/100][1448/1557] Data 0.004 (0.097) Batch 0.780 (1.218) Remain 01:36:59 loss: 0.0906 Lr: 0.00001 [2024-02-19 18:58:35,861 INFO misc.py line 119 87073] Train: [97/100][1449/1557] Data 0.006 (0.097) Batch 1.238 (1.218) Remain 01:36:58 loss: 0.1010 Lr: 0.00001 [2024-02-19 18:58:36,662 INFO misc.py line 119 87073] Train: [97/100][1450/1557] Data 0.016 (0.097) Batch 0.813 (1.217) Remain 01:36:55 loss: 0.2488 Lr: 0.00001 [2024-02-19 18:58:37,606 INFO misc.py line 119 87073] Train: [97/100][1451/1557] Data 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Remain 01:36:40 loss: 0.2761 Lr: 0.00001 [2024-02-19 18:58:43,966 INFO misc.py line 119 87073] Train: [97/100][1458/1557] Data 0.006 (0.096) Batch 0.899 (1.216) Remain 01:36:38 loss: 0.2812 Lr: 0.00001 [2024-02-19 18:58:44,921 INFO misc.py line 119 87073] Train: [97/100][1459/1557] Data 0.004 (0.096) Batch 0.955 (1.215) Remain 01:36:36 loss: 0.2402 Lr: 0.00001 [2024-02-19 18:58:45,972 INFO misc.py line 119 87073] Train: [97/100][1460/1557] Data 0.004 (0.096) Batch 1.045 (1.215) Remain 01:36:34 loss: 0.2838 Lr: 0.00001 [2024-02-19 18:58:46,726 INFO misc.py line 119 87073] Train: [97/100][1461/1557] Data 0.009 (0.096) Batch 0.758 (1.215) Remain 01:36:31 loss: 0.3600 Lr: 0.00001 [2024-02-19 18:58:47,417 INFO misc.py line 119 87073] Train: [97/100][1462/1557] Data 0.005 (0.096) Batch 0.684 (1.215) Remain 01:36:28 loss: 0.1214 Lr: 0.00001 [2024-02-19 18:59:03,041 INFO misc.py line 119 87073] Train: [97/100][1463/1557] Data 4.435 (0.099) Batch 15.632 (1.224) Remain 01:37:14 loss: 0.1457 Lr: 0.00001 [2024-02-19 18:59:03,959 INFO misc.py line 119 87073] Train: [97/100][1464/1557] Data 0.004 (0.099) Batch 0.917 (1.224) Remain 01:37:12 loss: 0.2348 Lr: 0.00001 [2024-02-19 18:59:05,051 INFO misc.py line 119 87073] Train: [97/100][1465/1557] Data 0.005 (0.099) Batch 1.092 (1.224) Remain 01:37:10 loss: 0.1119 Lr: 0.00001 [2024-02-19 18:59:06,022 INFO misc.py line 119 87073] Train: [97/100][1466/1557] Data 0.005 (0.099) Batch 0.971 (1.224) Remain 01:37:08 loss: 0.1668 Lr: 0.00001 [2024-02-19 18:59:06,861 INFO misc.py line 119 87073] Train: [97/100][1467/1557] Data 0.006 (0.099) Batch 0.840 (1.224) Remain 01:37:06 loss: 0.3985 Lr: 0.00001 [2024-02-19 18:59:07,655 INFO misc.py line 119 87073] Train: [97/100][1468/1557] Data 0.005 (0.099) Batch 0.785 (1.223) Remain 01:37:03 loss: 0.2097 Lr: 0.00001 [2024-02-19 18:59:08,452 INFO misc.py line 119 87073] Train: [97/100][1469/1557] Data 0.013 (0.098) Batch 0.807 (1.223) Remain 01:37:00 loss: 0.2209 Lr: 0.00001 [2024-02-19 18:59:10,726 INFO misc.py line 119 87073] Train: [97/100][1470/1557] Data 0.004 (0.098) Batch 2.273 (1.224) Remain 01:37:03 loss: 0.2260 Lr: 0.00001 [2024-02-19 18:59:11,649 INFO misc.py line 119 87073] Train: [97/100][1471/1557] Data 0.006 (0.098) Batch 0.920 (1.224) Remain 01:37:00 loss: 0.2112 Lr: 0.00001 [2024-02-19 18:59:12,678 INFO misc.py line 119 87073] Train: [97/100][1472/1557] Data 0.008 (0.098) Batch 1.012 (1.224) Remain 01:36:59 loss: 0.4394 Lr: 0.00001 [2024-02-19 18:59:13,549 INFO misc.py line 119 87073] Train: [97/100][1473/1557] Data 0.025 (0.098) Batch 0.892 (1.223) Remain 01:36:56 loss: 0.2599 Lr: 0.00001 [2024-02-19 18:59:14,450 INFO misc.py line 119 87073] Train: [97/100][1474/1557] Data 0.003 (0.098) Batch 0.900 (1.223) Remain 01:36:54 loss: 0.1130 Lr: 0.00001 [2024-02-19 18:59:15,251 INFO misc.py line 119 87073] Train: [97/100][1475/1557] Data 0.004 (0.098) Batch 0.800 (1.223) Remain 01:36:51 loss: 0.1730 Lr: 0.00001 [2024-02-19 18:59:15,982 INFO misc.py line 119 87073] Train: [97/100][1476/1557] Data 0.006 (0.098) Batch 0.732 (1.222) Remain 01:36:49 loss: 0.2170 Lr: 0.00001 [2024-02-19 18:59:17,252 INFO misc.py line 119 87073] Train: [97/100][1477/1557] Data 0.004 (0.098) Batch 1.270 (1.222) Remain 01:36:48 loss: 0.1399 Lr: 0.00001 [2024-02-19 18:59:18,519 INFO misc.py line 119 87073] Train: [97/100][1478/1557] Data 0.004 (0.098) Batch 1.259 (1.223) Remain 01:36:46 loss: 0.1681 Lr: 0.00001 [2024-02-19 18:59:19,478 INFO misc.py line 119 87073] Train: [97/100][1479/1557] Data 0.013 (0.098) Batch 0.967 (1.222) Remain 01:36:44 loss: 0.3070 Lr: 0.00001 [2024-02-19 18:59:20,757 INFO misc.py line 119 87073] Train: [97/100][1480/1557] Data 0.005 (0.098) Batch 1.273 (1.222) Remain 01:36:43 loss: 0.1222 Lr: 0.00001 [2024-02-19 18:59:21,791 INFO misc.py line 119 87073] Train: [97/100][1481/1557] Data 0.010 (0.098) Batch 1.032 (1.222) Remain 01:36:41 loss: 0.0850 Lr: 0.00001 [2024-02-19 18:59:22,485 INFO misc.py line 119 87073] Train: [97/100][1482/1557] Data 0.012 (0.098) Batch 0.703 (1.222) Remain 01:36:39 loss: 0.1898 Lr: 0.00001 [2024-02-19 18:59:23,149 INFO misc.py line 119 87073] Train: [97/100][1483/1557] Data 0.003 (0.098) Batch 0.660 (1.222) Remain 01:36:36 loss: 0.1480 Lr: 0.00001 [2024-02-19 18:59:24,560 INFO misc.py line 119 87073] Train: [97/100][1484/1557] Data 0.008 (0.098) Batch 1.414 (1.222) Remain 01:36:35 loss: 0.0717 Lr: 0.00001 [2024-02-19 18:59:25,527 INFO misc.py line 119 87073] Train: [97/100][1485/1557] Data 0.005 (0.097) Batch 0.968 (1.221) Remain 01:36:33 loss: 0.2135 Lr: 0.00001 [2024-02-19 18:59:26,492 INFO misc.py line 119 87073] Train: [97/100][1486/1557] Data 0.004 (0.097) Batch 0.964 (1.221) Remain 01:36:31 loss: 0.1068 Lr: 0.00001 [2024-02-19 18:59:27,481 INFO misc.py line 119 87073] Train: [97/100][1487/1557] Data 0.004 (0.097) Batch 0.990 (1.221) Remain 01:36:29 loss: 0.3356 Lr: 0.00001 [2024-02-19 18:59:28,329 INFO misc.py line 119 87073] Train: [97/100][1488/1557] Data 0.004 (0.097) Batch 0.848 (1.221) Remain 01:36:27 loss: 0.2733 Lr: 0.00001 [2024-02-19 18:59:29,123 INFO misc.py line 119 87073] Train: [97/100][1489/1557] Data 0.004 (0.097) Batch 0.789 (1.221) Remain 01:36:24 loss: 0.1543 Lr: 0.00001 [2024-02-19 18:59:29,891 INFO misc.py line 119 87073] Train: [97/100][1490/1557] Data 0.009 (0.097) Batch 0.773 (1.220) Remain 01:36:21 loss: 0.1975 Lr: 0.00001 [2024-02-19 18:59:31,183 INFO misc.py line 119 87073] Train: [97/100][1491/1557] Data 0.003 (0.097) Batch 1.285 (1.220) Remain 01:36:20 loss: 0.1031 Lr: 0.00001 [2024-02-19 18:59:32,148 INFO misc.py line 119 87073] Train: [97/100][1492/1557] Data 0.011 (0.097) Batch 0.969 (1.220) Remain 01:36:18 loss: 0.1891 Lr: 0.00001 [2024-02-19 18:59:33,017 INFO misc.py line 119 87073] Train: [97/100][1493/1557] Data 0.007 (0.097) Batch 0.872 (1.220) Remain 01:36:16 loss: 0.2463 Lr: 0.00001 [2024-02-19 18:59:33,992 INFO misc.py line 119 87073] Train: [97/100][1494/1557] Data 0.004 (0.097) Batch 0.970 (1.220) Remain 01:36:14 loss: 0.2850 Lr: 0.00001 [2024-02-19 18:59:34,800 INFO misc.py line 119 87073] Train: [97/100][1495/1557] Data 0.009 (0.097) Batch 0.813 (1.219) Remain 01:36:11 loss: 0.1554 Lr: 0.00001 [2024-02-19 18:59:35,605 INFO misc.py line 119 87073] Train: [97/100][1496/1557] Data 0.005 (0.097) Batch 0.805 (1.219) Remain 01:36:09 loss: 0.2467 Lr: 0.00001 [2024-02-19 18:59:36,381 INFO misc.py line 119 87073] Train: [97/100][1497/1557] Data 0.004 (0.097) Batch 0.765 (1.219) Remain 01:36:06 loss: 0.2174 Lr: 0.00001 [2024-02-19 18:59:37,470 INFO misc.py line 119 87073] Train: [97/100][1498/1557] Data 0.015 (0.097) Batch 1.092 (1.219) Remain 01:36:05 loss: 0.1223 Lr: 0.00001 [2024-02-19 18:59:38,423 INFO misc.py line 119 87073] Train: [97/100][1499/1557] Data 0.013 (0.097) Batch 0.962 (1.219) Remain 01:36:03 loss: 0.1858 Lr: 0.00001 [2024-02-19 18:59:39,312 INFO misc.py line 119 87073] Train: [97/100][1500/1557] Data 0.004 (0.097) Batch 0.889 (1.218) Remain 01:36:00 loss: 0.2647 Lr: 0.00001 [2024-02-19 18:59:40,098 INFO misc.py line 119 87073] Train: [97/100][1501/1557] Data 0.004 (0.097) Batch 0.784 (1.218) Remain 01:35:58 loss: 0.2496 Lr: 0.00001 [2024-02-19 18:59:41,010 INFO misc.py line 119 87073] Train: [97/100][1502/1557] Data 0.006 (0.096) Batch 0.913 (1.218) Remain 01:35:55 loss: 0.2125 Lr: 0.00001 [2024-02-19 18:59:41,648 INFO misc.py line 119 87073] Train: [97/100][1503/1557] Data 0.004 (0.096) Batch 0.637 (1.218) Remain 01:35:52 loss: 0.0750 Lr: 0.00001 [2024-02-19 18:59:42,396 INFO misc.py line 119 87073] Train: [97/100][1504/1557] Data 0.004 (0.096) Batch 0.744 (1.217) Remain 01:35:50 loss: 0.1885 Lr: 0.00001 [2024-02-19 18:59:43,661 INFO misc.py line 119 87073] Train: [97/100][1505/1557] Data 0.009 (0.096) Batch 1.262 (1.217) Remain 01:35:49 loss: 0.0802 Lr: 0.00001 [2024-02-19 18:59:44,726 INFO misc.py line 119 87073] Train: [97/100][1506/1557] Data 0.012 (0.096) Batch 1.060 (1.217) Remain 01:35:47 loss: 0.2882 Lr: 0.00001 [2024-02-19 18:59:45,817 INFO misc.py line 119 87073] Train: [97/100][1507/1557] Data 0.016 (0.096) Batch 1.102 (1.217) Remain 01:35:45 loss: 0.3379 Lr: 0.00001 [2024-02-19 18:59:46,786 INFO misc.py line 119 87073] Train: [97/100][1508/1557] Data 0.005 (0.096) Batch 0.970 (1.217) Remain 01:35:43 loss: 0.1598 Lr: 0.00001 [2024-02-19 18:59:47,749 INFO misc.py line 119 87073] Train: [97/100][1509/1557] Data 0.004 (0.096) Batch 0.964 (1.217) Remain 01:35:41 loss: 0.1507 Lr: 0.00001 [2024-02-19 18:59:48,533 INFO misc.py line 119 87073] Train: [97/100][1510/1557] Data 0.003 (0.096) Batch 0.784 (1.216) Remain 01:35:39 loss: 0.1714 Lr: 0.00001 [2024-02-19 18:59:49,314 INFO misc.py line 119 87073] Train: [97/100][1511/1557] Data 0.004 (0.096) Batch 0.778 (1.216) Remain 01:35:36 loss: 0.1378 Lr: 0.00001 [2024-02-19 18:59:50,560 INFO misc.py line 119 87073] Train: [97/100][1512/1557] Data 0.007 (0.096) Batch 1.243 (1.216) Remain 01:35:35 loss: 0.1747 Lr: 0.00001 [2024-02-19 18:59:51,579 INFO misc.py line 119 87073] Train: [97/100][1513/1557] Data 0.010 (0.096) Batch 1.014 (1.216) Remain 01:35:33 loss: 0.3689 Lr: 0.00001 [2024-02-19 18:59:52,367 INFO misc.py line 119 87073] Train: [97/100][1514/1557] Data 0.016 (0.096) Batch 0.800 (1.216) Remain 01:35:31 loss: 0.0546 Lr: 0.00001 [2024-02-19 18:59:53,315 INFO misc.py line 119 87073] Train: [97/100][1515/1557] Data 0.004 (0.096) Batch 0.947 (1.216) Remain 01:35:29 loss: 0.4567 Lr: 0.00001 [2024-02-19 18:59:54,263 INFO misc.py line 119 87073] Train: [97/100][1516/1557] Data 0.006 (0.096) Batch 0.948 (1.215) Remain 01:35:27 loss: 0.1925 Lr: 0.00001 [2024-02-19 18:59:55,042 INFO misc.py line 119 87073] Train: [97/100][1517/1557] Data 0.005 (0.096) Batch 0.780 (1.215) Remain 01:35:24 loss: 0.3065 Lr: 0.00001 [2024-02-19 18:59:55,714 INFO misc.py line 119 87073] Train: [97/100][1518/1557] Data 0.004 (0.096) Batch 0.664 (1.215) Remain 01:35:21 loss: 0.2080 Lr: 0.00001 [2024-02-19 19:00:11,694 INFO misc.py line 119 87073] Train: [97/100][1519/1557] Data 4.151 (0.098) Batch 15.988 (1.225) Remain 01:36:06 loss: 0.0965 Lr: 0.00001 [2024-02-19 19:00:12,526 INFO misc.py line 119 87073] Train: [97/100][1520/1557] Data 0.005 (0.098) Batch 0.833 (1.224) Remain 01:36:03 loss: 0.1318 Lr: 0.00001 [2024-02-19 19:00:13,704 INFO misc.py line 119 87073] Train: [97/100][1521/1557] Data 0.004 (0.098) Batch 1.169 (1.224) Remain 01:36:02 loss: 0.2050 Lr: 0.00001 [2024-02-19 19:00:14,690 INFO misc.py line 119 87073] Train: [97/100][1522/1557] Data 0.013 (0.098) Batch 0.995 (1.224) Remain 01:36:00 loss: 0.1142 Lr: 0.00001 [2024-02-19 19:00:15,678 INFO misc.py line 119 87073] Train: [97/100][1523/1557] Data 0.004 (0.098) Batch 0.987 (1.224) Remain 01:35:58 loss: 0.2389 Lr: 0.00001 [2024-02-19 19:00:16,451 INFO misc.py line 119 87073] Train: [97/100][1524/1557] Data 0.005 (0.098) Batch 0.773 (1.224) Remain 01:35:55 loss: 0.2161 Lr: 0.00001 [2024-02-19 19:00:17,227 INFO misc.py line 119 87073] Train: [97/100][1525/1557] Data 0.006 (0.098) Batch 0.769 (1.223) Remain 01:35:53 loss: 0.1636 Lr: 0.00001 [2024-02-19 19:00:18,579 INFO misc.py line 119 87073] Train: [97/100][1526/1557] Data 0.012 (0.098) Batch 1.360 (1.223) Remain 01:35:52 loss: 0.1686 Lr: 0.00001 [2024-02-19 19:00:19,550 INFO misc.py line 119 87073] Train: [97/100][1527/1557] Data 0.005 (0.098) Batch 0.972 (1.223) Remain 01:35:50 loss: 0.2742 Lr: 0.00001 [2024-02-19 19:00:20,607 INFO misc.py line 119 87073] Train: [97/100][1528/1557] Data 0.005 (0.098) Batch 1.058 (1.223) Remain 01:35:48 loss: 0.1055 Lr: 0.00001 [2024-02-19 19:00:21,481 INFO misc.py line 119 87073] Train: [97/100][1529/1557] Data 0.004 (0.098) Batch 0.873 (1.223) Remain 01:35:46 loss: 0.0721 Lr: 0.00001 [2024-02-19 19:00:22,436 INFO misc.py line 119 87073] Train: [97/100][1530/1557] Data 0.005 (0.098) Batch 0.955 (1.223) Remain 01:35:44 loss: 0.2759 Lr: 0.00001 [2024-02-19 19:00:23,189 INFO misc.py line 119 87073] Train: [97/100][1531/1557] Data 0.005 (0.097) Batch 0.753 (1.222) Remain 01:35:41 loss: 0.1550 Lr: 0.00001 [2024-02-19 19:00:23,979 INFO misc.py line 119 87073] Train: [97/100][1532/1557] Data 0.004 (0.097) Batch 0.783 (1.222) Remain 01:35:39 loss: 0.3780 Lr: 0.00001 [2024-02-19 19:00:25,297 INFO misc.py line 119 87073] Train: [97/100][1533/1557] Data 0.011 (0.097) Batch 1.312 (1.222) Remain 01:35:38 loss: 0.0687 Lr: 0.00001 [2024-02-19 19:00:26,101 INFO misc.py line 119 87073] Train: [97/100][1534/1557] Data 0.016 (0.097) Batch 0.817 (1.222) Remain 01:35:35 loss: 0.1464 Lr: 0.00001 [2024-02-19 19:00:27,262 INFO misc.py line 119 87073] Train: [97/100][1535/1557] Data 0.004 (0.097) Batch 1.162 (1.222) Remain 01:35:34 loss: 0.3541 Lr: 0.00001 [2024-02-19 19:00:28,225 INFO misc.py line 119 87073] Train: [97/100][1536/1557] Data 0.004 (0.097) Batch 0.962 (1.222) Remain 01:35:32 loss: 0.1652 Lr: 0.00001 [2024-02-19 19:00:29,190 INFO misc.py line 119 87073] Train: [97/100][1537/1557] Data 0.005 (0.097) Batch 0.966 (1.222) Remain 01:35:30 loss: 0.1117 Lr: 0.00001 [2024-02-19 19:00:29,860 INFO misc.py line 119 87073] Train: [97/100][1538/1557] Data 0.004 (0.097) Batch 0.661 (1.221) Remain 01:35:27 loss: 0.1862 Lr: 0.00001 [2024-02-19 19:00:30,623 INFO misc.py line 119 87073] Train: [97/100][1539/1557] Data 0.013 (0.097) Batch 0.771 (1.221) Remain 01:35:24 loss: 0.1459 Lr: 0.00001 [2024-02-19 19:00:31,823 INFO misc.py line 119 87073] Train: [97/100][1540/1557] Data 0.004 (0.097) Batch 1.200 (1.221) Remain 01:35:23 loss: 0.0735 Lr: 0.00001 [2024-02-19 19:00:32,895 INFO misc.py line 119 87073] Train: [97/100][1541/1557] Data 0.005 (0.097) Batch 1.069 (1.221) Remain 01:35:21 loss: 0.2385 Lr: 0.00001 [2024-02-19 19:00:33,958 INFO misc.py line 119 87073] Train: [97/100][1542/1557] Data 0.008 (0.097) Batch 1.066 (1.221) Remain 01:35:20 loss: 0.2440 Lr: 0.00001 [2024-02-19 19:00:35,089 INFO misc.py line 119 87073] Train: [97/100][1543/1557] Data 0.004 (0.097) Batch 1.132 (1.221) Remain 01:35:18 loss: 0.0719 Lr: 0.00001 [2024-02-19 19:00:36,174 INFO misc.py line 119 87073] Train: [97/100][1544/1557] Data 0.004 (0.097) Batch 1.084 (1.221) Remain 01:35:17 loss: 0.3045 Lr: 0.00001 [2024-02-19 19:00:36,917 INFO misc.py line 119 87073] Train: [97/100][1545/1557] Data 0.006 (0.097) Batch 0.745 (1.220) Remain 01:35:14 loss: 0.2449 Lr: 0.00001 [2024-02-19 19:00:37,650 INFO misc.py line 119 87073] Train: [97/100][1546/1557] Data 0.004 (0.097) Batch 0.720 (1.220) Remain 01:35:11 loss: 0.1284 Lr: 0.00001 [2024-02-19 19:00:38,939 INFO misc.py line 119 87073] Train: [97/100][1547/1557] Data 0.016 (0.097) Batch 1.288 (1.220) Remain 01:35:10 loss: 0.0992 Lr: 0.00001 [2024-02-19 19:00:39,859 INFO misc.py line 119 87073] Train: [97/100][1548/1557] Data 0.017 (0.096) Batch 0.933 (1.220) Remain 01:35:08 loss: 0.1509 Lr: 0.00001 [2024-02-19 19:00:40,976 INFO misc.py line 119 87073] Train: [97/100][1549/1557] Data 0.004 (0.096) Batch 1.117 (1.220) Remain 01:35:06 loss: 0.0570 Lr: 0.00001 [2024-02-19 19:00:41,983 INFO misc.py line 119 87073] Train: [97/100][1550/1557] Data 0.004 (0.096) Batch 1.008 (1.220) Remain 01:35:05 loss: 0.2481 Lr: 0.00001 [2024-02-19 19:00:42,924 INFO misc.py line 119 87073] Train: [97/100][1551/1557] Data 0.004 (0.096) Batch 0.941 (1.219) Remain 01:35:03 loss: 0.1749 Lr: 0.00001 [2024-02-19 19:00:43,684 INFO misc.py line 119 87073] Train: [97/100][1552/1557] Data 0.004 (0.096) Batch 0.746 (1.219) Remain 01:35:00 loss: 0.2979 Lr: 0.00001 [2024-02-19 19:00:44,414 INFO misc.py line 119 87073] Train: [97/100][1553/1557] Data 0.018 (0.096) Batch 0.744 (1.219) Remain 01:34:57 loss: 0.1142 Lr: 0.00001 [2024-02-19 19:00:45,401 INFO misc.py line 119 87073] Train: [97/100][1554/1557] Data 0.004 (0.096) Batch 0.976 (1.219) Remain 01:34:55 loss: 0.1970 Lr: 0.00001 [2024-02-19 19:00:46,366 INFO misc.py line 119 87073] Train: [97/100][1555/1557] Data 0.014 (0.096) Batch 0.976 (1.218) Remain 01:34:53 loss: 0.3390 Lr: 0.00001 [2024-02-19 19:00:47,427 INFO misc.py line 119 87073] Train: [97/100][1556/1557] Data 0.004 (0.096) Batch 1.060 (1.218) Remain 01:34:52 loss: 0.1115 Lr: 0.00001 [2024-02-19 19:00:48,250 INFO misc.py line 119 87073] Train: [97/100][1557/1557] Data 0.004 (0.096) Batch 0.824 (1.218) Remain 01:34:49 loss: 0.2351 Lr: 0.00001 [2024-02-19 19:00:48,251 INFO misc.py line 136 87073] Train result: loss: 0.1991 [2024-02-19 19:00:48,251 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 19:01:49,934 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4178 [2024-02-19 19:01:50,723 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5009 [2024-02-19 19:01:52,849 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3080 [2024-02-19 19:01:55,059 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3379 [2024-02-19 19:02:00,005 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2191 [2024-02-19 19:02:00,703 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.0833 [2024-02-19 19:02:01,964 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2605 [2024-02-19 19:02:04,919 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2317 [2024-02-19 19:02:06,733 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2921 [2024-02-19 19:02:08,508 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7262/0.7808/0.9191. [2024-02-19 19:02:08,508 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9358/0.9647 [2024-02-19 19:02:08,508 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9893 [2024-02-19 19:02:08,508 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8667/0.9720 [2024-02-19 19:02:08,508 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 19:02:08,508 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3950/0.4543 [2024-02-19 19:02:08,509 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6611/0.6804 [2024-02-19 19:02:08,509 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8166/0.9241 [2024-02-19 19:02:08,509 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8567/0.9265 [2024-02-19 19:02:08,509 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9227/0.9762 [2024-02-19 19:02:08,509 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7708/0.7930 [2024-02-19 19:02:08,510 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8031/0.8879 [2024-02-19 19:02:08,510 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7948/0.8553 [2024-02-19 19:02:08,510 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6339/0.7271 [2024-02-19 19:02:08,511 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 19:02:08,514 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 19:02:08,514 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 19:02:16,418 INFO misc.py line 119 87073] Train: [98/100][1/1557] Data 1.355 (1.355) Batch 2.240 (2.240) Remain 02:54:21 loss: 0.4805 Lr: 0.00001 [2024-02-19 19:02:17,419 INFO misc.py line 119 87073] Train: [98/100][2/1557] Data 0.005 (0.005) Batch 1.001 (1.001) Remain 01:17:51 loss: 0.2453 Lr: 0.00001 [2024-02-19 19:02:18,331 INFO misc.py line 119 87073] Train: [98/100][3/1557] Data 0.005 (0.005) Batch 0.913 (0.913) Remain 01:11:02 loss: 0.0873 Lr: 0.00001 [2024-02-19 19:02:19,238 INFO misc.py line 119 87073] Train: [98/100][4/1557] Data 0.004 (0.004) Batch 0.906 (0.906) Remain 01:10:29 loss: 0.1471 Lr: 0.00001 [2024-02-19 19:02:20,194 INFO misc.py line 119 87073] Train: [98/100][5/1557] Data 0.004 (0.004) Batch 0.957 (0.931) Remain 01:12:26 loss: 0.0987 Lr: 0.00001 [2024-02-19 19:02:20,974 INFO misc.py line 119 87073] Train: [98/100][6/1557] Data 0.004 (0.004) Batch 0.778 (0.880) Remain 01:08:27 loss: 0.1617 Lr: 0.00001 [2024-02-19 19:02:22,103 INFO misc.py line 119 87073] Train: [98/100][7/1557] Data 0.005 (0.004) Batch 1.121 (0.941) Remain 01:13:06 loss: 0.2100 Lr: 0.00001 [2024-02-19 19:02:22,910 INFO misc.py line 119 87073] Train: [98/100][8/1557] Data 0.013 (0.006) Batch 0.816 (0.916) Remain 01:11:09 loss: 0.3307 Lr: 0.00001 [2024-02-19 19:02:23,824 INFO misc.py line 119 87073] Train: [98/100][9/1557] Data 0.005 (0.006) Batch 0.914 (0.915) Remain 01:11:07 loss: 0.1359 Lr: 0.00001 [2024-02-19 19:02:24,800 INFO misc.py line 119 87073] Train: [98/100][10/1557] Data 0.005 (0.006) Batch 0.974 (0.924) Remain 01:11:45 loss: 0.1607 Lr: 0.00001 [2024-02-19 19:02:25,803 INFO misc.py line 119 87073] Train: [98/100][11/1557] Data 0.006 (0.006) Batch 1.003 (0.934) Remain 01:12:30 loss: 0.2835 Lr: 0.00001 [2024-02-19 19:02:26,608 INFO misc.py line 119 87073] Train: [98/100][12/1557] Data 0.005 (0.006) Batch 0.806 (0.919) Remain 01:11:23 loss: 0.2136 Lr: 0.00001 [2024-02-19 19:02:27,361 INFO misc.py line 119 87073] Train: [98/100][13/1557] Data 0.005 (0.005) Batch 0.754 (0.903) Remain 01:10:05 loss: 0.0961 Lr: 0.00001 [2024-02-19 19:02:28,594 INFO misc.py line 119 87073] Train: [98/100][14/1557] Data 0.004 (0.005) Batch 1.217 (0.932) Remain 01:12:18 loss: 0.2673 Lr: 0.00001 [2024-02-19 19:02:29,487 INFO misc.py line 119 87073] Train: [98/100][15/1557] Data 0.019 (0.006) Batch 0.908 (0.930) Remain 01:12:08 loss: 0.4083 Lr: 0.00001 [2024-02-19 19:02:30,395 INFO misc.py line 119 87073] Train: [98/100][16/1557] Data 0.004 (0.006) Batch 0.907 (0.928) Remain 01:11:59 loss: 0.5722 Lr: 0.00001 [2024-02-19 19:02:31,508 INFO misc.py line 119 87073] Train: [98/100][17/1557] Data 0.005 (0.006) Batch 1.114 (0.941) Remain 01:13:00 loss: 0.1830 Lr: 0.00001 [2024-02-19 19:02:32,606 INFO misc.py line 119 87073] Train: [98/100][18/1557] Data 0.004 (0.006) Batch 1.098 (0.952) Remain 01:13:47 loss: 0.1815 Lr: 0.00001 [2024-02-19 19:02:33,311 INFO misc.py line 119 87073] Train: [98/100][19/1557] Data 0.004 (0.006) Batch 0.705 (0.936) Remain 01:12:35 loss: 0.1863 Lr: 0.00001 [2024-02-19 19:02:34,082 INFO misc.py line 119 87073] Train: [98/100][20/1557] Data 0.004 (0.006) Batch 0.770 (0.926) Remain 01:11:48 loss: 0.1951 Lr: 0.00001 [2024-02-19 19:02:35,372 INFO misc.py line 119 87073] Train: [98/100][21/1557] Data 0.004 (0.006) Batch 1.278 (0.946) Remain 01:13:18 loss: 0.0955 Lr: 0.00001 [2024-02-19 19:02:36,193 INFO misc.py line 119 87073] Train: [98/100][22/1557] Data 0.017 (0.006) Batch 0.834 (0.940) Remain 01:12:50 loss: 0.1937 Lr: 0.00001 [2024-02-19 19:02:37,183 INFO misc.py line 119 87073] Train: [98/100][23/1557] Data 0.004 (0.006) Batch 0.989 (0.943) Remain 01:13:00 loss: 0.0646 Lr: 0.00001 [2024-02-19 19:02:38,241 INFO misc.py line 119 87073] Train: [98/100][24/1557] Data 0.004 (0.006) Batch 1.059 (0.948) Remain 01:13:25 loss: 0.2110 Lr: 0.00001 [2024-02-19 19:02:39,219 INFO misc.py line 119 87073] Train: [98/100][25/1557] Data 0.004 (0.006) Batch 0.976 (0.949) Remain 01:13:30 loss: 0.3199 Lr: 0.00001 [2024-02-19 19:02:39,898 INFO misc.py line 119 87073] Train: [98/100][26/1557] Data 0.005 (0.006) Batch 0.679 (0.938) Remain 01:12:35 loss: 0.2239 Lr: 0.00001 [2024-02-19 19:02:40,701 INFO misc.py line 119 87073] Train: [98/100][27/1557] Data 0.005 (0.006) Batch 0.793 (0.932) Remain 01:12:06 loss: 0.1314 Lr: 0.00001 [2024-02-19 19:02:41,961 INFO misc.py line 119 87073] Train: [98/100][28/1557] Data 0.016 (0.006) Batch 1.266 (0.945) Remain 01:13:07 loss: 0.0915 Lr: 0.00001 [2024-02-19 19:02:42,855 INFO misc.py line 119 87073] Train: [98/100][29/1557] Data 0.009 (0.006) Batch 0.899 (0.943) Remain 01:12:58 loss: 0.2741 Lr: 0.00001 [2024-02-19 19:02:43,883 INFO misc.py line 119 87073] Train: [98/100][30/1557] Data 0.004 (0.006) Batch 1.027 (0.946) Remain 01:13:11 loss: 0.0707 Lr: 0.00001 [2024-02-19 19:02:44,972 INFO misc.py line 119 87073] Train: [98/100][31/1557] Data 0.005 (0.006) Batch 1.087 (0.951) Remain 01:13:34 loss: 0.3303 Lr: 0.00001 [2024-02-19 19:02:46,015 INFO misc.py line 119 87073] Train: [98/100][32/1557] Data 0.006 (0.006) Batch 1.045 (0.955) Remain 01:13:48 loss: 0.1802 Lr: 0.00001 [2024-02-19 19:02:46,768 INFO misc.py line 119 87073] Train: [98/100][33/1557] Data 0.004 (0.006) Batch 0.753 (0.948) Remain 01:13:16 loss: 0.0971 Lr: 0.00001 [2024-02-19 19:02:47,465 INFO misc.py line 119 87073] Train: [98/100][34/1557] Data 0.005 (0.006) Batch 0.692 (0.940) Remain 01:12:36 loss: 0.1774 Lr: 0.00001 [2024-02-19 19:02:48,737 INFO misc.py line 119 87073] Train: [98/100][35/1557] Data 0.010 (0.006) Batch 1.269 (0.950) Remain 01:13:23 loss: 0.1509 Lr: 0.00001 [2024-02-19 19:02:49,883 INFO misc.py line 119 87073] Train: [98/100][36/1557] Data 0.014 (0.007) Batch 1.152 (0.956) Remain 01:13:51 loss: 0.4090 Lr: 0.00001 [2024-02-19 19:02:50,962 INFO misc.py line 119 87073] Train: [98/100][37/1557] Data 0.007 (0.007) Batch 1.079 (0.960) Remain 01:14:06 loss: 0.3282 Lr: 0.00001 [2024-02-19 19:02:52,131 INFO misc.py line 119 87073] Train: [98/100][38/1557] Data 0.008 (0.007) Batch 1.166 (0.965) Remain 01:14:33 loss: 0.2397 Lr: 0.00001 [2024-02-19 19:02:52,910 INFO misc.py line 119 87073] Train: [98/100][39/1557] Data 0.011 (0.007) Batch 0.784 (0.960) Remain 01:14:08 loss: 0.1831 Lr: 0.00001 [2024-02-19 19:02:53,642 INFO misc.py line 119 87073] Train: [98/100][40/1557] Data 0.006 (0.007) Batch 0.734 (0.954) Remain 01:13:39 loss: 0.1277 Lr: 0.00001 [2024-02-19 19:02:54,368 INFO misc.py line 119 87073] Train: [98/100][41/1557] Data 0.004 (0.007) Batch 0.724 (0.948) Remain 01:13:10 loss: 0.2484 Lr: 0.00001 [2024-02-19 19:02:55,685 INFO misc.py line 119 87073] Train: [98/100][42/1557] Data 0.005 (0.007) Batch 1.306 (0.957) Remain 01:13:52 loss: 0.0958 Lr: 0.00001 [2024-02-19 19:02:56,810 INFO misc.py line 119 87073] Train: [98/100][43/1557] Data 0.016 (0.007) Batch 1.127 (0.962) Remain 01:14:10 loss: 0.2865 Lr: 0.00001 [2024-02-19 19:02:57,713 INFO misc.py line 119 87073] Train: [98/100][44/1557] Data 0.014 (0.007) Batch 0.913 (0.961) Remain 01:14:04 loss: 0.1298 Lr: 0.00001 [2024-02-19 19:02:58,788 INFO misc.py line 119 87073] Train: [98/100][45/1557] Data 0.004 (0.007) Batch 1.075 (0.963) Remain 01:14:15 loss: 0.0952 Lr: 0.00001 [2024-02-19 19:02:59,997 INFO misc.py line 119 87073] Train: [98/100][46/1557] Data 0.004 (0.007) Batch 1.197 (0.969) Remain 01:14:40 loss: 0.3354 Lr: 0.00001 [2024-02-19 19:03:00,792 INFO misc.py line 119 87073] Train: [98/100][47/1557] Data 0.017 (0.007) Batch 0.807 (0.965) Remain 01:14:22 loss: 0.1524 Lr: 0.00001 [2024-02-19 19:03:01,595 INFO misc.py line 119 87073] Train: [98/100][48/1557] Data 0.004 (0.007) Batch 0.803 (0.961) Remain 01:14:04 loss: 0.0909 Lr: 0.00001 [2024-02-19 19:03:02,754 INFO misc.py line 119 87073] Train: [98/100][49/1557] Data 0.004 (0.007) Batch 1.148 (0.965) Remain 01:14:22 loss: 0.0568 Lr: 0.00001 [2024-02-19 19:03:03,881 INFO misc.py line 119 87073] Train: [98/100][50/1557] Data 0.015 (0.007) Batch 1.126 (0.969) Remain 01:14:37 loss: 0.0616 Lr: 0.00001 [2024-02-19 19:03:04,666 INFO misc.py line 119 87073] Train: [98/100][51/1557] Data 0.017 (0.007) Batch 0.795 (0.965) Remain 01:14:19 loss: 0.1570 Lr: 0.00001 [2024-02-19 19:03:05,554 INFO misc.py line 119 87073] Train: [98/100][52/1557] Data 0.007 (0.007) Batch 0.890 (0.964) Remain 01:14:11 loss: 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INFO misc.py line 119 87073] Train: [98/100][59/1557] Data 0.005 (0.008) Batch 1.114 (0.964) Remain 01:14:04 loss: 0.0862 Lr: 0.00001 [2024-02-19 19:03:13,228 INFO misc.py line 119 87073] Train: [98/100][60/1557] Data 0.004 (0.008) Batch 0.929 (0.963) Remain 01:14:00 loss: 0.3044 Lr: 0.00001 [2024-02-19 19:03:14,031 INFO misc.py line 119 87073] Train: [98/100][61/1557] Data 0.005 (0.008) Batch 0.797 (0.960) Remain 01:13:46 loss: 0.1336 Lr: 0.00001 [2024-02-19 19:03:14,785 INFO misc.py line 119 87073] Train: [98/100][62/1557] Data 0.011 (0.008) Batch 0.762 (0.957) Remain 01:13:30 loss: 0.1340 Lr: 0.00001 [2024-02-19 19:03:24,071 INFO misc.py line 119 87073] Train: [98/100][63/1557] Data 5.231 (0.095) Batch 9.285 (1.096) Remain 01:24:08 loss: 0.1714 Lr: 0.00001 [2024-02-19 19:03:24,948 INFO misc.py line 119 87073] Train: [98/100][64/1557] Data 0.004 (0.093) Batch 0.877 (1.092) Remain 01:23:51 loss: 0.2594 Lr: 0.00001 [2024-02-19 19:03:25,734 INFO misc.py line 119 87073] Train: 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line 119 87073] Train: [98/100][84/1557] Data 0.004 (0.071) Batch 1.246 (1.053) Remain 01:20:28 loss: 0.1290 Lr: 0.00001 [2024-02-19 19:03:44,547 INFO misc.py line 119 87073] Train: [98/100][85/1557] Data 0.004 (0.071) Batch 0.950 (1.051) Remain 01:20:21 loss: 0.3911 Lr: 0.00001 [2024-02-19 19:03:45,765 INFO misc.py line 119 87073] Train: [98/100][86/1557] Data 0.004 (0.070) Batch 1.215 (1.053) Remain 01:20:29 loss: 0.1979 Lr: 0.00001 [2024-02-19 19:03:46,645 INFO misc.py line 119 87073] Train: [98/100][87/1557] Data 0.007 (0.069) Batch 0.882 (1.051) Remain 01:20:19 loss: 0.0315 Lr: 0.00001 [2024-02-19 19:03:47,502 INFO misc.py line 119 87073] Train: [98/100][88/1557] Data 0.005 (0.068) Batch 0.857 (1.049) Remain 01:20:07 loss: 0.1516 Lr: 0.00001 [2024-02-19 19:03:48,320 INFO misc.py line 119 87073] Train: [98/100][89/1557] Data 0.004 (0.067) Batch 0.819 (1.046) Remain 01:19:54 loss: 0.1699 Lr: 0.00001 [2024-02-19 19:03:49,098 INFO misc.py line 119 87073] Train: [98/100][90/1557] Data 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[2024-02-19 19:27:39,537 INFO misc.py line 119 87073] Train: [98/100][1371/1557] Data 0.004 (0.108) Batch 0.691 (1.112) Remain 01:01:09 loss: 0.1596 Lr: 0.00001 [2024-02-19 19:27:40,881 INFO misc.py line 119 87073] Train: [98/100][1372/1557] Data 0.017 (0.108) Batch 1.335 (1.112) Remain 01:01:08 loss: 0.1186 Lr: 0.00001 [2024-02-19 19:27:41,717 INFO misc.py line 119 87073] Train: [98/100][1373/1557] Data 0.026 (0.108) Batch 0.855 (1.112) Remain 01:01:07 loss: 0.1829 Lr: 0.00001 [2024-02-19 19:27:42,695 INFO misc.py line 119 87073] Train: [98/100][1374/1557] Data 0.006 (0.108) Batch 0.980 (1.112) Remain 01:01:05 loss: 0.0744 Lr: 0.00001 [2024-02-19 19:27:43,817 INFO misc.py line 119 87073] Train: [98/100][1375/1557] Data 0.004 (0.107) Batch 1.121 (1.112) Remain 01:01:04 loss: 0.1350 Lr: 0.00001 [2024-02-19 19:27:44,718 INFO misc.py line 119 87073] Train: [98/100][1376/1557] Data 0.004 (0.107) Batch 0.901 (1.112) Remain 01:01:03 loss: 0.1002 Lr: 0.00001 [2024-02-19 19:27:45,446 INFO 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(0.108) Batch 0.717 (1.113) Remain 00:59:08 loss: 0.2907 Lr: 0.00001 [2024-02-19 19:29:44,898 INFO misc.py line 119 87073] Train: [98/100][1483/1557] Data 0.008 (0.108) Batch 0.731 (1.113) Remain 00:59:06 loss: 0.1544 Lr: 0.00001 [2024-02-19 19:29:46,173 INFO misc.py line 119 87073] Train: [98/100][1484/1557] Data 0.014 (0.108) Batch 1.272 (1.113) Remain 00:59:05 loss: 0.1306 Lr: 0.00001 [2024-02-19 19:29:47,047 INFO misc.py line 119 87073] Train: [98/100][1485/1557] Data 0.017 (0.108) Batch 0.887 (1.112) Remain 00:59:04 loss: 0.1021 Lr: 0.00001 [2024-02-19 19:29:48,016 INFO misc.py line 119 87073] Train: [98/100][1486/1557] Data 0.004 (0.108) Batch 0.970 (1.112) Remain 00:59:02 loss: 0.4501 Lr: 0.00001 [2024-02-19 19:29:49,043 INFO misc.py line 119 87073] Train: [98/100][1487/1557] Data 0.004 (0.108) Batch 1.026 (1.112) Remain 00:59:01 loss: 0.4150 Lr: 0.00001 [2024-02-19 19:29:49,948 INFO misc.py line 119 87073] Train: [98/100][1488/1557] Data 0.005 (0.108) Batch 0.905 (1.112) Remain 00:59:00 loss: 0.1441 Lr: 0.00001 [2024-02-19 19:29:50,742 INFO misc.py line 119 87073] Train: [98/100][1489/1557] Data 0.005 (0.108) Batch 0.783 (1.112) Remain 00:58:58 loss: 0.1947 Lr: 0.00001 [2024-02-19 19:29:51,518 INFO misc.py line 119 87073] Train: [98/100][1490/1557] Data 0.015 (0.107) Batch 0.788 (1.112) Remain 00:58:56 loss: 0.1549 Lr: 0.00001 [2024-02-19 19:29:52,734 INFO misc.py line 119 87073] Train: [98/100][1491/1557] Data 0.004 (0.107) Batch 1.215 (1.112) Remain 00:58:55 loss: 0.1159 Lr: 0.00001 [2024-02-19 19:29:53,561 INFO misc.py line 119 87073] Train: [98/100][1492/1557] Data 0.004 (0.107) Batch 0.826 (1.112) Remain 00:58:53 loss: 0.1976 Lr: 0.00001 [2024-02-19 19:29:54,486 INFO misc.py line 119 87073] Train: [98/100][1493/1557] Data 0.005 (0.107) Batch 0.925 (1.112) Remain 00:58:52 loss: 0.2757 Lr: 0.00001 [2024-02-19 19:29:55,334 INFO misc.py line 119 87073] Train: [98/100][1494/1557] Data 0.006 (0.107) Batch 0.849 (1.111) Remain 00:58:50 loss: 0.2099 Lr: 0.00001 [2024-02-19 19:29:56,260 INFO misc.py line 119 87073] Train: [98/100][1495/1557] Data 0.005 (0.107) Batch 0.926 (1.111) Remain 00:58:49 loss: 0.3264 Lr: 0.00001 [2024-02-19 19:29:56,958 INFO misc.py line 119 87073] Train: [98/100][1496/1557] Data 0.004 (0.107) Batch 0.689 (1.111) Remain 00:58:47 loss: 0.1731 Lr: 0.00001 [2024-02-19 19:29:57,594 INFO misc.py line 119 87073] Train: [98/100][1497/1557] Data 0.013 (0.107) Batch 0.645 (1.111) Remain 00:58:45 loss: 0.1045 Lr: 0.00001 [2024-02-19 19:29:58,976 INFO misc.py line 119 87073] Train: [98/100][1498/1557] Data 0.004 (0.107) Batch 1.370 (1.111) Remain 00:58:44 loss: 0.1164 Lr: 0.00001 [2024-02-19 19:29:59,871 INFO misc.py line 119 87073] Train: [98/100][1499/1557] Data 0.017 (0.107) Batch 0.908 (1.111) Remain 00:58:42 loss: 0.1231 Lr: 0.00001 [2024-02-19 19:30:00,834 INFO misc.py line 119 87073] Train: [98/100][1500/1557] Data 0.005 (0.107) Batch 0.962 (1.111) Remain 00:58:41 loss: 0.1964 Lr: 0.00001 [2024-02-19 19:30:01,764 INFO misc.py line 119 87073] Train: [98/100][1501/1557] Data 0.005 (0.107) Batch 0.929 (1.110) Remain 00:58:40 loss: 0.1066 Lr: 0.00001 [2024-02-19 19:30:02,673 INFO misc.py line 119 87073] Train: [98/100][1502/1557] Data 0.006 (0.107) Batch 0.905 (1.110) Remain 00:58:38 loss: 0.0538 Lr: 0.00001 [2024-02-19 19:30:03,458 INFO misc.py line 119 87073] Train: [98/100][1503/1557] Data 0.009 (0.107) Batch 0.789 (1.110) Remain 00:58:36 loss: 0.1371 Lr: 0.00001 [2024-02-19 19:30:04,228 INFO misc.py line 119 87073] Train: [98/100][1504/1557] Data 0.006 (0.107) Batch 0.771 (1.110) Remain 00:58:34 loss: 0.1698 Lr: 0.00001 [2024-02-19 19:30:05,343 INFO misc.py line 119 87073] Train: [98/100][1505/1557] Data 0.005 (0.106) Batch 1.107 (1.110) Remain 00:58:33 loss: 0.0825 Lr: 0.00001 [2024-02-19 19:30:06,313 INFO misc.py line 119 87073] Train: [98/100][1506/1557] Data 0.013 (0.106) Batch 0.979 (1.110) Remain 00:58:32 loss: 0.2070 Lr: 0.00001 [2024-02-19 19:30:07,362 INFO misc.py line 119 87073] Train: [98/100][1507/1557] Data 0.004 (0.106) Batch 1.048 (1.110) Remain 00:58:31 loss: 0.3047 Lr: 0.00001 [2024-02-19 19:30:08,388 INFO misc.py line 119 87073] Train: [98/100][1508/1557] Data 0.005 (0.106) Batch 1.027 (1.110) Remain 00:58:29 loss: 0.1681 Lr: 0.00001 [2024-02-19 19:30:09,335 INFO misc.py line 119 87073] Train: [98/100][1509/1557] Data 0.004 (0.106) Batch 0.945 (1.110) Remain 00:58:28 loss: 0.4029 Lr: 0.00001 [2024-02-19 19:30:10,134 INFO misc.py line 119 87073] Train: [98/100][1510/1557] Data 0.006 (0.106) Batch 0.800 (1.109) Remain 00:58:26 loss: 0.1896 Lr: 0.00001 [2024-02-19 19:30:10,928 INFO misc.py line 119 87073] Train: [98/100][1511/1557] Data 0.004 (0.106) Batch 0.792 (1.109) Remain 00:58:24 loss: 0.1560 Lr: 0.00001 [2024-02-19 19:30:12,266 INFO misc.py line 119 87073] Train: [98/100][1512/1557] Data 0.007 (0.106) Batch 1.335 (1.109) Remain 00:58:24 loss: 0.1312 Lr: 0.00001 [2024-02-19 19:30:13,216 INFO misc.py line 119 87073] Train: [98/100][1513/1557] Data 0.010 (0.106) Batch 0.955 (1.109) Remain 00:58:22 loss: 0.2172 Lr: 0.00001 [2024-02-19 19:30:14,394 INFO misc.py line 119 87073] Train: [98/100][1514/1557] Data 0.005 (0.106) Batch 1.167 (1.109) Remain 00:58:21 loss: 0.0751 Lr: 0.00001 [2024-02-19 19:30:15,448 INFO misc.py line 119 87073] Train: [98/100][1515/1557] Data 0.015 (0.106) Batch 1.065 (1.109) Remain 00:58:20 loss: 0.0709 Lr: 0.00001 [2024-02-19 19:30:16,490 INFO misc.py line 119 87073] Train: [98/100][1516/1557] Data 0.005 (0.106) Batch 1.037 (1.109) Remain 00:58:19 loss: 0.7304 Lr: 0.00001 [2024-02-19 19:30:17,329 INFO misc.py line 119 87073] Train: [98/100][1517/1557] Data 0.010 (0.106) Batch 0.844 (1.109) Remain 00:58:17 loss: 0.1906 Lr: 0.00001 [2024-02-19 19:30:18,208 INFO misc.py line 119 87073] Train: [98/100][1518/1557] Data 0.006 (0.106) Batch 0.879 (1.109) Remain 00:58:16 loss: 0.1570 Lr: 0.00001 [2024-02-19 19:30:28,031 INFO misc.py line 119 87073] Train: [98/100][1519/1557] Data 5.212 (0.109) Batch 9.823 (1.115) Remain 00:58:33 loss: 0.1332 Lr: 0.00001 [2024-02-19 19:30:28,998 INFO misc.py line 119 87073] Train: [98/100][1520/1557] Data 0.005 (0.109) Batch 0.967 (1.114) Remain 00:58:31 loss: 0.0928 Lr: 0.00001 [2024-02-19 19:30:30,002 INFO misc.py line 119 87073] Train: [98/100][1521/1557] Data 0.004 (0.109) Batch 1.003 (1.114) Remain 00:58:30 loss: 0.1021 Lr: 0.00001 [2024-02-19 19:30:30,895 INFO misc.py line 119 87073] Train: [98/100][1522/1557] Data 0.005 (0.109) Batch 0.893 (1.114) Remain 00:58:28 loss: 0.1857 Lr: 0.00001 [2024-02-19 19:30:31,873 INFO misc.py line 119 87073] Train: [98/100][1523/1557] Data 0.005 (0.109) Batch 0.978 (1.114) Remain 00:58:27 loss: 0.5198 Lr: 0.00001 [2024-02-19 19:30:32,626 INFO misc.py line 119 87073] Train: [98/100][1524/1557] Data 0.005 (0.109) Batch 0.754 (1.114) Remain 00:58:25 loss: 0.0930 Lr: 0.00001 [2024-02-19 19:30:33,298 INFO misc.py line 119 87073] Train: [98/100][1525/1557] Data 0.004 (0.109) Batch 0.668 (1.114) Remain 00:58:23 loss: 0.1361 Lr: 0.00001 [2024-02-19 19:30:34,533 INFO misc.py line 119 87073] Train: [98/100][1526/1557] Data 0.008 (0.108) Batch 1.235 (1.114) Remain 00:58:22 loss: 0.1351 Lr: 0.00001 [2024-02-19 19:30:35,537 INFO misc.py line 119 87073] Train: [98/100][1527/1557] Data 0.008 (0.108) Batch 0.997 (1.114) Remain 00:58:21 loss: 0.0601 Lr: 0.00001 [2024-02-19 19:30:36,435 INFO misc.py line 119 87073] Train: [98/100][1528/1557] Data 0.015 (0.108) Batch 0.909 (1.114) Remain 00:58:19 loss: 0.0865 Lr: 0.00001 [2024-02-19 19:30:37,252 INFO misc.py line 119 87073] Train: [98/100][1529/1557] Data 0.004 (0.108) Batch 0.817 (1.113) Remain 00:58:18 loss: 0.4402 Lr: 0.00001 [2024-02-19 19:30:38,198 INFO misc.py line 119 87073] Train: [98/100][1530/1557] Data 0.005 (0.108) Batch 0.945 (1.113) Remain 00:58:16 loss: 0.0811 Lr: 0.00001 [2024-02-19 19:30:38,993 INFO misc.py line 119 87073] Train: [98/100][1531/1557] Data 0.006 (0.108) Batch 0.796 (1.113) Remain 00:58:14 loss: 0.1734 Lr: 0.00001 [2024-02-19 19:30:39,796 INFO misc.py line 119 87073] Train: [98/100][1532/1557] Data 0.004 (0.108) Batch 0.803 (1.113) Remain 00:58:13 loss: 0.2881 Lr: 0.00001 [2024-02-19 19:30:41,056 INFO misc.py line 119 87073] Train: [98/100][1533/1557] Data 0.004 (0.108) Batch 1.256 (1.113) Remain 00:58:12 loss: 0.1769 Lr: 0.00001 [2024-02-19 19:30:42,024 INFO misc.py line 119 87073] Train: [98/100][1534/1557] Data 0.008 (0.108) Batch 0.972 (1.113) Remain 00:58:10 loss: 0.1413 Lr: 0.00001 [2024-02-19 19:30:43,007 INFO misc.py line 119 87073] Train: [98/100][1535/1557] Data 0.004 (0.108) Batch 0.981 (1.113) Remain 00:58:09 loss: 0.3731 Lr: 0.00001 [2024-02-19 19:30:44,004 INFO misc.py line 119 87073] Train: [98/100][1536/1557] Data 0.007 (0.108) Batch 0.999 (1.113) Remain 00:58:08 loss: 0.1324 Lr: 0.00001 [2024-02-19 19:30:45,028 INFO misc.py line 119 87073] Train: [98/100][1537/1557] Data 0.004 (0.108) Batch 1.022 (1.113) Remain 00:58:06 loss: 0.3026 Lr: 0.00001 [2024-02-19 19:30:45,775 INFO misc.py line 119 87073] Train: [98/100][1538/1557] Data 0.005 (0.108) Batch 0.745 (1.112) Remain 00:58:04 loss: 0.1173 Lr: 0.00001 [2024-02-19 19:30:46,473 INFO misc.py line 119 87073] Train: [98/100][1539/1557] Data 0.008 (0.108) Batch 0.701 (1.112) Remain 00:58:02 loss: 0.1703 Lr: 0.00001 [2024-02-19 19:30:47,744 INFO misc.py line 119 87073] Train: [98/100][1540/1557] Data 0.004 (0.108) Batch 1.270 (1.112) Remain 00:58:02 loss: 0.1206 Lr: 0.00001 [2024-02-19 19:30:48,752 INFO misc.py line 119 87073] Train: [98/100][1541/1557] Data 0.006 (0.107) Batch 1.009 (1.112) Remain 00:58:00 loss: 0.1946 Lr: 0.00001 [2024-02-19 19:30:49,583 INFO misc.py line 119 87073] Train: [98/100][1542/1557] Data 0.005 (0.107) Batch 0.830 (1.112) Remain 00:57:59 loss: 0.1552 Lr: 0.00001 [2024-02-19 19:30:50,527 INFO misc.py line 119 87073] Train: [98/100][1543/1557] Data 0.006 (0.107) Batch 0.940 (1.112) Remain 00:57:57 loss: 0.2295 Lr: 0.00001 [2024-02-19 19:30:51,483 INFO misc.py line 119 87073] Train: [98/100][1544/1557] Data 0.010 (0.107) Batch 0.961 (1.112) Remain 00:57:56 loss: 0.0619 Lr: 0.00001 [2024-02-19 19:30:52,211 INFO misc.py line 119 87073] Train: [98/100][1545/1557] Data 0.006 (0.107) Batch 0.728 (1.111) Remain 00:57:54 loss: 0.1930 Lr: 0.00001 [2024-02-19 19:30:52,870 INFO misc.py line 119 87073] Train: [98/100][1546/1557] Data 0.006 (0.107) Batch 0.653 (1.111) Remain 00:57:52 loss: 0.1760 Lr: 0.00001 [2024-02-19 19:30:54,060 INFO misc.py line 119 87073] Train: [98/100][1547/1557] Data 0.011 (0.107) Batch 1.184 (1.111) Remain 00:57:51 loss: 0.1386 Lr: 0.00001 [2024-02-19 19:30:55,003 INFO misc.py line 119 87073] Train: [98/100][1548/1557] Data 0.017 (0.107) Batch 0.956 (1.111) Remain 00:57:50 loss: 0.0963 Lr: 0.00001 [2024-02-19 19:30:55,904 INFO misc.py line 119 87073] Train: [98/100][1549/1557] Data 0.004 (0.107) Batch 0.901 (1.111) Remain 00:57:48 loss: 0.2165 Lr: 0.00001 [2024-02-19 19:30:56,748 INFO misc.py line 119 87073] Train: [98/100][1550/1557] Data 0.004 (0.107) Batch 0.837 (1.111) Remain 00:57:46 loss: 0.0734 Lr: 0.00001 [2024-02-19 19:30:57,816 INFO misc.py line 119 87073] Train: [98/100][1551/1557] Data 0.010 (0.107) Batch 1.073 (1.111) Remain 00:57:45 loss: 0.1349 Lr: 0.00001 [2024-02-19 19:30:58,557 INFO misc.py line 119 87073] Train: [98/100][1552/1557] Data 0.005 (0.107) Batch 0.741 (1.111) Remain 00:57:43 loss: 0.1298 Lr: 0.00001 [2024-02-19 19:30:59,301 INFO misc.py line 119 87073] Train: [98/100][1553/1557] Data 0.006 (0.107) Batch 0.738 (1.110) Remain 00:57:41 loss: 0.1653 Lr: 0.00001 [2024-02-19 19:31:00,676 INFO misc.py line 119 87073] Train: [98/100][1554/1557] Data 0.013 (0.107) Batch 1.372 (1.110) Remain 00:57:41 loss: 0.1684 Lr: 0.00001 [2024-02-19 19:31:01,780 INFO misc.py line 119 87073] Train: [98/100][1555/1557] Data 0.015 (0.107) Batch 1.102 (1.110) Remain 00:57:40 loss: 0.2607 Lr: 0.00001 [2024-02-19 19:31:02,819 INFO misc.py line 119 87073] Train: [98/100][1556/1557] Data 0.017 (0.107) Batch 1.046 (1.110) Remain 00:57:38 loss: 0.3190 Lr: 0.00001 [2024-02-19 19:31:03,810 INFO misc.py line 119 87073] Train: [98/100][1557/1557] Data 0.009 (0.106) Batch 0.996 (1.110) Remain 00:57:37 loss: 0.2239 Lr: 0.00001 [2024-02-19 19:31:03,811 INFO misc.py line 136 87073] Train result: loss: 0.1954 [2024-02-19 19:31:03,811 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 19:32:04,136 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4596 [2024-02-19 19:32:05,171 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.5421 [2024-02-19 19:32:07,296 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3225 [2024-02-19 19:32:09,506 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3870 [2024-02-19 19:32:14,441 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2669 [2024-02-19 19:32:15,139 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1088 [2024-02-19 19:32:16,400 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2487 [2024-02-19 19:32:19,355 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2536 [2024-02-19 19:32:21,166 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2639 [2024-02-19 19:32:23,036 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7255/0.7815/0.9183. [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9332/0.9627 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9892 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8659/0.9724 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3852/0.4399 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6533/0.6706 [2024-02-19 19:32:23,036 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8262/0.9322 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8572/0.9224 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9232/0.9751 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8000/0.8235 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8053/0.8890 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7690/0.8575 [2024-02-19 19:32:23,037 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6299/0.7247 [2024-02-19 19:32:23,037 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 19:32:23,040 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 19:32:23,040 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 19:32:29,636 INFO misc.py line 119 87073] Train: [99/100][1/1557] Data 1.932 (1.932) Batch 2.746 (2.746) Remain 02:22:28 loss: 0.0550 Lr: 0.00001 [2024-02-19 19:32:30,700 INFO misc.py line 119 87073] Train: [99/100][2/1557] Data 0.006 (0.006) Batch 1.060 (1.060) Remain 00:54:57 loss: 0.1570 Lr: 0.00001 [2024-02-19 19:32:31,632 INFO misc.py line 119 87073] Train: [99/100][3/1557] Data 0.011 (0.011) Batch 0.938 (0.938) Remain 00:48:37 loss: 0.2590 Lr: 0.00001 [2024-02-19 19:32:32,573 INFO misc.py line 119 87073] Train: [99/100][4/1557] Data 0.005 (0.005) Batch 0.942 (0.942) Remain 00:48:48 loss: 0.3242 Lr: 0.00001 [2024-02-19 19:32:33,408 INFO misc.py line 119 87073] Train: [99/100][5/1557] Data 0.005 (0.005) Batch 0.835 (0.888) Remain 00:46:02 loss: 0.1465 Lr: 0.00001 [2024-02-19 19:32:34,131 INFO misc.py line 119 87073] Train: [99/100][6/1557] Data 0.004 (0.005) Batch 0.720 (0.832) Remain 00:43:06 loss: 0.1148 Lr: 0.00001 [2024-02-19 19:32:37,844 INFO misc.py line 119 87073] Train: [99/100][7/1557] Data 0.008 (0.006) Batch 3.716 (1.553) Remain 01:20:25 loss: 0.1508 Lr: 0.00001 [2024-02-19 19:32:38,805 INFO misc.py line 119 87073] Train: [99/100][8/1557] Data 0.004 (0.005) Batch 0.961 (1.435) Remain 01:14:16 loss: 0.1243 Lr: 0.00001 [2024-02-19 19:32:39,727 INFO misc.py line 119 87073] Train: [99/100][9/1557] Data 0.004 (0.005) Batch 0.921 (1.349) Remain 01:09:49 loss: 0.0962 Lr: 0.00001 [2024-02-19 19:32:40,577 INFO misc.py line 119 87073] Train: [99/100][10/1557] Data 0.005 (0.005) Batch 0.845 (1.277) Remain 01:06:04 loss: 0.1915 Lr: 0.00001 [2024-02-19 19:32:41,531 INFO misc.py line 119 87073] Train: [99/100][11/1557] Data 0.011 (0.006) Batch 0.959 (1.237) Remain 01:03:59 loss: 0.2536 Lr: 0.00001 [2024-02-19 19:32:42,328 INFO misc.py line 119 87073] Train: [99/100][12/1557] Data 0.005 (0.006) Batch 0.797 (1.188) Remain 01:01:26 loss: 0.3538 Lr: 0.00001 [2024-02-19 19:32:43,048 INFO misc.py line 119 87073] Train: [99/100][13/1557] Data 0.005 (0.006) Batch 0.709 (1.141) Remain 00:58:56 loss: 0.2913 Lr: 0.00001 [2024-02-19 19:32:44,786 INFO misc.py line 119 87073] Train: [99/100][14/1557] Data 0.015 (0.007) Batch 1.749 (1.196) Remain 01:01:47 loss: 0.1501 Lr: 0.00001 [2024-02-19 19:32:45,715 INFO misc.py line 119 87073] Train: [99/100][15/1557] Data 0.004 (0.006) Batch 0.899 (1.171) Remain 01:00:29 loss: 0.1911 Lr: 0.00001 [2024-02-19 19:32:46,616 INFO misc.py line 119 87073] Train: [99/100][16/1557] Data 0.035 (0.008) Batch 0.931 (1.153) Remain 00:59:30 loss: 0.3807 Lr: 0.00001 [2024-02-19 19:32:47,647 INFO misc.py line 119 87073] Train: [99/100][17/1557] Data 0.005 (0.008) Batch 1.031 (1.144) Remain 00:59:02 loss: 0.2627 Lr: 0.00001 [2024-02-19 19:32:48,770 INFO misc.py line 119 87073] Train: [99/100][18/1557] Data 0.004 (0.008) Batch 1.124 (1.143) Remain 00:58:57 loss: 0.1860 Lr: 0.00001 [2024-02-19 19:32:49,513 INFO misc.py line 119 87073] Train: [99/100][19/1557] Data 0.003 (0.008) Batch 0.742 (1.118) Remain 00:57:38 loss: 0.1210 Lr: 0.00001 [2024-02-19 19:32:50,244 INFO misc.py line 119 87073] Train: [99/100][20/1557] Data 0.005 (0.008) Batch 0.730 (1.095) Remain 00:56:27 loss: 0.4197 Lr: 0.00001 [2024-02-19 19:32:51,574 INFO misc.py line 119 87073] Train: [99/100][21/1557] Data 0.005 (0.007) Batch 1.320 (1.107) Remain 00:57:04 loss: 0.1126 Lr: 0.00001 [2024-02-19 19:32:52,529 INFO misc.py line 119 87073] Train: [99/100][22/1557] Data 0.015 (0.008) Batch 0.965 (1.100) Remain 00:56:40 loss: 0.3021 Lr: 0.00001 [2024-02-19 19:32:53,424 INFO misc.py line 119 87073] Train: [99/100][23/1557] Data 0.005 (0.008) Batch 0.897 (1.090) Remain 00:56:08 loss: 0.3506 Lr: 0.00001 [2024-02-19 19:32:54,625 INFO misc.py line 119 87073] Train: [99/100][24/1557] Data 0.003 (0.007) Batch 1.200 (1.095) Remain 00:56:23 loss: 0.2189 Lr: 0.00001 [2024-02-19 19:32:55,651 INFO misc.py line 119 87073] Train: [99/100][25/1557] Data 0.004 (0.007) Batch 1.026 (1.092) Remain 00:56:12 loss: 0.1398 Lr: 0.00001 [2024-02-19 19:32:56,371 INFO misc.py line 119 87073] Train: [99/100][26/1557] Data 0.005 (0.007) Batch 0.721 (1.076) Remain 00:55:21 loss: 0.2425 Lr: 0.00001 [2024-02-19 19:32:57,078 INFO misc.py line 119 87073] Train: [99/100][27/1557] Data 0.004 (0.007) Batch 0.703 (1.060) Remain 00:54:32 loss: 0.1393 Lr: 0.00001 [2024-02-19 19:32:58,356 INFO misc.py line 119 87073] Train: [99/100][28/1557] Data 0.008 (0.007) Batch 1.270 (1.068) Remain 00:54:57 loss: 0.1241 Lr: 0.00001 [2024-02-19 19:32:59,216 INFO misc.py line 119 87073] Train: [99/100][29/1557] Data 0.017 (0.007) Batch 0.873 (1.061) Remain 00:54:33 loss: 0.1178 Lr: 0.00001 [2024-02-19 19:33:00,140 INFO misc.py line 119 87073] Train: [99/100][30/1557] Data 0.004 (0.007) Batch 0.922 (1.056) Remain 00:54:16 loss: 0.2344 Lr: 0.00001 [2024-02-19 19:33:01,029 INFO misc.py line 119 87073] Train: [99/100][31/1557] Data 0.006 (0.007) Batch 0.885 (1.050) Remain 00:53:56 loss: 0.2395 Lr: 0.00001 [2024-02-19 19:33:02,054 INFO misc.py line 119 87073] Train: [99/100][32/1557] Data 0.011 (0.007) Batch 1.022 (1.049) Remain 00:53:52 loss: 0.2035 Lr: 0.00001 [2024-02-19 19:33:02,765 INFO misc.py line 119 87073] Train: [99/100][33/1557] Data 0.013 (0.008) Batch 0.720 (1.038) Remain 00:53:17 loss: 0.1790 Lr: 0.00001 [2024-02-19 19:33:03,574 INFO misc.py line 119 87073] Train: [99/100][34/1557] Data 0.004 (0.007) Batch 0.799 (1.030) Remain 00:52:52 loss: 0.1385 Lr: 0.00001 [2024-02-19 19:33:04,852 INFO misc.py line 119 87073] Train: [99/100][35/1557] Data 0.015 (0.008) Batch 1.286 (1.038) Remain 00:53:16 loss: 0.1849 Lr: 0.00001 [2024-02-19 19:33:05,730 INFO misc.py line 119 87073] Train: [99/100][36/1557] Data 0.006 (0.008) Batch 0.879 (1.033) Remain 00:53:00 loss: 0.5217 Lr: 0.00001 [2024-02-19 19:33:06,668 INFO misc.py line 119 87073] Train: [99/100][37/1557] Data 0.006 (0.008) Batch 0.937 (1.030) Remain 00:52:50 loss: 0.1529 Lr: 0.00001 [2024-02-19 19:33:07,598 INFO misc.py line 119 87073] Train: [99/100][38/1557] Data 0.006 (0.008) Batch 0.932 (1.028) Remain 00:52:40 loss: 0.1953 Lr: 0.00001 [2024-02-19 19:33:08,529 INFO misc.py line 119 87073] Train: [99/100][39/1557] Data 0.005 (0.007) Batch 0.931 (1.025) Remain 00:52:31 loss: 0.0805 Lr: 0.00001 [2024-02-19 19:33:09,281 INFO misc.py line 119 87073] Train: [99/100][40/1557] Data 0.004 (0.007) Batch 0.753 (1.018) Remain 00:52:07 loss: 0.1684 Lr: 0.00001 [2024-02-19 19:33:10,109 INFO misc.py line 119 87073] Train: [99/100][41/1557] Data 0.003 (0.007) Batch 0.824 (1.012) Remain 00:51:51 loss: 0.0877 Lr: 0.00001 [2024-02-19 19:33:11,381 INFO misc.py line 119 87073] Train: [99/100][42/1557] Data 0.007 (0.007) Batch 1.264 (1.019) Remain 00:52:10 loss: 0.0779 Lr: 0.00001 [2024-02-19 19:33:12,301 INFO misc.py line 119 87073] Train: [99/100][43/1557] Data 0.017 (0.008) Batch 0.932 (1.017) Remain 00:52:02 loss: 0.2477 Lr: 0.00001 [2024-02-19 19:33:13,433 INFO misc.py line 119 87073] Train: [99/100][44/1557] Data 0.005 (0.007) Batch 1.131 (1.020) Remain 00:52:09 loss: 0.2321 Lr: 0.00001 [2024-02-19 19:33:14,417 INFO misc.py line 119 87073] Train: [99/100][45/1557] Data 0.005 (0.007) Batch 0.985 (1.019) Remain 00:52:06 loss: 0.1066 Lr: 0.00001 [2024-02-19 19:33:15,445 INFO misc.py line 119 87073] Train: [99/100][46/1557] Data 0.004 (0.007) Batch 1.028 (1.019) Remain 00:52:06 loss: 0.3365 Lr: 0.00001 [2024-02-19 19:33:16,217 INFO misc.py line 119 87073] Train: [99/100][47/1557] Data 0.004 (0.007) Batch 0.772 (1.013) Remain 00:51:47 loss: 0.1160 Lr: 0.00001 [2024-02-19 19:33:16,963 INFO misc.py line 119 87073] Train: [99/100][48/1557] Data 0.004 (0.007) Batch 0.745 (1.007) Remain 00:51:28 loss: 0.3082 Lr: 0.00001 [2024-02-19 19:33:18,078 INFO misc.py line 119 87073] Train: [99/100][49/1557] Data 0.004 (0.007) Batch 1.105 (1.009) Remain 00:51:34 loss: 0.1807 Lr: 0.00001 [2024-02-19 19:33:18,926 INFO misc.py line 119 87073] Train: [99/100][50/1557] Data 0.015 (0.007) Batch 0.859 (1.006) Remain 00:51:23 loss: 0.2735 Lr: 0.00001 [2024-02-19 19:33:19,749 INFO misc.py line 119 87073] Train: [99/100][51/1557] Data 0.005 (0.007) Batch 0.823 (1.002) Remain 00:51:10 loss: 0.2902 Lr: 0.00001 [2024-02-19 19:33:20,708 INFO misc.py line 119 87073] Train: [99/100][52/1557] Data 0.004 (0.007) Batch 0.928 (1.001) Remain 00:51:04 loss: 0.3001 Lr: 0.00001 [2024-02-19 19:33:21,732 INFO misc.py line 119 87073] Train: [99/100][53/1557] Data 0.036 (0.008) Batch 1.055 (1.002) Remain 00:51:07 loss: 0.1899 Lr: 0.00001 [2024-02-19 19:33:22,472 INFO misc.py line 119 87073] Train: [99/100][54/1557] Data 0.004 (0.008) Batch 0.741 (0.997) Remain 00:50:50 loss: 0.2855 Lr: 0.00001 [2024-02-19 19:33:23,242 INFO misc.py line 119 87073] Train: [99/100][55/1557] Data 0.004 (0.008) Batch 0.769 (0.992) Remain 00:50:36 loss: 0.1374 Lr: 0.00001 [2024-02-19 19:33:24,469 INFO misc.py line 119 87073] Train: [99/100][56/1557] Data 0.005 (0.008) Batch 1.226 (0.997) Remain 00:50:48 loss: 0.1041 Lr: 0.00001 [2024-02-19 19:33:25,481 INFO misc.py line 119 87073] Train: [99/100][57/1557] Data 0.006 (0.008) Batch 1.010 (0.997) Remain 00:50:48 loss: 0.2234 Lr: 0.00001 [2024-02-19 19:33:26,434 INFO misc.py line 119 87073] Train: [99/100][58/1557] Data 0.008 (0.008) Batch 0.956 (0.996) Remain 00:50:44 loss: 0.0793 Lr: 0.00001 [2024-02-19 19:33:27,312 INFO misc.py line 119 87073] Train: [99/100][59/1557] Data 0.005 (0.007) Batch 0.878 (0.994) Remain 00:50:37 loss: 0.2338 Lr: 0.00001 [2024-02-19 19:33:28,284 INFO misc.py line 119 87073] Train: [99/100][60/1557] Data 0.004 (0.007) Batch 0.965 (0.994) Remain 00:50:34 loss: 0.1539 Lr: 0.00001 [2024-02-19 19:33:28,995 INFO misc.py line 119 87073] Train: [99/100][61/1557] Data 0.012 (0.007) Batch 0.718 (0.989) Remain 00:50:19 loss: 0.1394 Lr: 0.00001 [2024-02-19 19:33:29,861 INFO misc.py line 119 87073] Train: [99/100][62/1557] Data 0.004 (0.007) Batch 0.866 (0.987) Remain 00:50:12 loss: 0.2962 Lr: 0.00001 [2024-02-19 19:33:41,919 INFO misc.py line 119 87073] Train: [99/100][63/1557] Data 6.242 (0.111) Batch 12.057 (1.171) Remain 00:59:34 loss: 0.0974 Lr: 0.00001 [2024-02-19 19:33:42,941 INFO misc.py line 119 87073] Train: [99/100][64/1557] Data 0.005 (0.110) Batch 1.022 (1.169) Remain 00:59:25 loss: 0.1480 Lr: 0.00001 [2024-02-19 19:33:43,811 INFO misc.py line 119 87073] Train: 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0.824 (1.144) Remain 00:58:00 loss: 0.0945 Lr: 0.00001 [2024-02-19 19:33:50,399 INFO misc.py line 119 87073] Train: [99/100][72/1557] Data 0.005 (0.098) Batch 0.994 (1.142) Remain 00:57:52 loss: 0.1024 Lr: 0.00001 [2024-02-19 19:33:51,273 INFO misc.py line 119 87073] Train: [99/100][73/1557] Data 0.004 (0.096) Batch 0.874 (1.138) Remain 00:57:39 loss: 0.3684 Lr: 0.00001 [2024-02-19 19:33:52,255 INFO misc.py line 119 87073] Train: [99/100][74/1557] Data 0.004 (0.095) Batch 0.976 (1.135) Remain 00:57:31 loss: 0.2410 Lr: 0.00001 [2024-02-19 19:33:53,040 INFO misc.py line 119 87073] Train: [99/100][75/1557] Data 0.010 (0.094) Batch 0.792 (1.131) Remain 00:57:16 loss: 0.1092 Lr: 0.00001 [2024-02-19 19:33:53,806 INFO misc.py line 119 87073] Train: [99/100][76/1557] Data 0.003 (0.093) Batch 0.763 (1.126) Remain 00:56:59 loss: 0.2060 Lr: 0.00001 [2024-02-19 19:33:55,086 INFO misc.py line 119 87073] Train: [99/100][77/1557] Data 0.018 (0.092) Batch 1.280 (1.128) Remain 00:57:04 loss: 0.0975 Lr: 0.00001 [2024-02-19 19:33:56,013 INFO misc.py line 119 87073] Train: [99/100][78/1557] Data 0.005 (0.090) Batch 0.928 (1.125) Remain 00:56:55 loss: 0.0654 Lr: 0.00001 [2024-02-19 19:33:56,914 INFO misc.py line 119 87073] Train: [99/100][79/1557] Data 0.005 (0.089) Batch 0.899 (1.122) Remain 00:56:45 loss: 0.1531 Lr: 0.00001 [2024-02-19 19:33:57,923 INFO misc.py line 119 87073] Train: [99/100][80/1557] Data 0.007 (0.088) Batch 1.010 (1.121) Remain 00:56:40 loss: 0.1931 Lr: 0.00001 [2024-02-19 19:33:58,891 INFO misc.py line 119 87073] Train: [99/100][81/1557] Data 0.006 (0.087) Batch 0.969 (1.119) Remain 00:56:33 loss: 0.2275 Lr: 0.00001 [2024-02-19 19:33:59,658 INFO misc.py line 119 87073] Train: [99/100][82/1557] Data 0.005 (0.086) Batch 0.768 (1.114) Remain 00:56:18 loss: 0.3317 Lr: 0.00001 [2024-02-19 19:34:00,518 INFO misc.py line 119 87073] Train: [99/100][83/1557] Data 0.004 (0.085) Batch 0.853 (1.111) Remain 00:56:07 loss: 0.1167 Lr: 0.00001 [2024-02-19 19:34:01,778 INFO misc.py line 119 87073] Train: [99/100][84/1557] Data 0.011 (0.084) Batch 1.258 (1.113) Remain 00:56:11 loss: 0.0981 Lr: 0.00001 [2024-02-19 19:34:02,860 INFO misc.py line 119 87073] Train: [99/100][85/1557] Data 0.013 (0.083) Batch 1.080 (1.112) Remain 00:56:09 loss: 0.2017 Lr: 0.00001 [2024-02-19 19:34:03,940 INFO misc.py line 119 87073] Train: [99/100][86/1557] Data 0.015 (0.082) Batch 1.088 (1.112) Remain 00:56:07 loss: 0.1895 Lr: 0.00001 [2024-02-19 19:34:05,173 INFO misc.py line 119 87073] Train: [99/100][87/1557] Data 0.006 (0.082) Batch 1.223 (1.113) Remain 00:56:10 loss: 0.1364 Lr: 0.00001 [2024-02-19 19:34:06,109 INFO misc.py line 119 87073] Train: [99/100][88/1557] Data 0.016 (0.081) Batch 0.948 (1.111) Remain 00:56:03 loss: 0.2467 Lr: 0.00001 [2024-02-19 19:34:06,858 INFO misc.py line 119 87073] Train: [99/100][89/1557] Data 0.005 (0.080) Batch 0.750 (1.107) Remain 00:55:49 loss: 0.1479 Lr: 0.00001 [2024-02-19 19:34:07,551 INFO misc.py line 119 87073] Train: [99/100][90/1557] Data 0.003 (0.079) Batch 0.684 (1.102) Remain 00:55:33 loss: 0.1374 Lr: 0.00001 [2024-02-19 19:34:08,887 INFO misc.py line 119 87073] Train: [99/100][91/1557] Data 0.012 (0.078) Batch 1.332 (1.105) Remain 00:55:40 loss: 0.1224 Lr: 0.00001 [2024-02-19 19:34:09,904 INFO misc.py line 119 87073] Train: [99/100][92/1557] Data 0.015 (0.078) Batch 1.020 (1.104) Remain 00:55:36 loss: 0.1088 Lr: 0.00001 [2024-02-19 19:34:10,941 INFO misc.py line 119 87073] Train: [99/100][93/1557] Data 0.013 (0.077) Batch 1.034 (1.103) Remain 00:55:33 loss: 0.1180 Lr: 0.00001 [2024-02-19 19:34:11,847 INFO misc.py line 119 87073] Train: [99/100][94/1557] Data 0.016 (0.076) Batch 0.917 (1.101) Remain 00:55:25 loss: 0.1682 Lr: 0.00001 [2024-02-19 19:34:12,823 INFO misc.py line 119 87073] Train: [99/100][95/1557] Data 0.004 (0.075) Batch 0.977 (1.100) Remain 00:55:20 loss: 0.2407 Lr: 0.00001 [2024-02-19 19:34:13,637 INFO misc.py line 119 87073] Train: [99/100][96/1557] Data 0.004 (0.075) Batch 0.814 (1.097) Remain 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Batch 0.892 (1.165) Remain 00:55:57 loss: 0.1577 Lr: 0.00001 [2024-02-19 19:37:00,516 INFO misc.py line 119 87073] Train: [99/100][234/1557] Data 0.005 (0.113) Batch 0.872 (1.164) Remain 00:55:52 loss: 0.2075 Lr: 0.00001 [2024-02-19 19:37:01,349 INFO misc.py line 119 87073] Train: [99/100][235/1557] Data 0.005 (0.113) Batch 0.833 (1.163) Remain 00:55:47 loss: 0.0959 Lr: 0.00001 [2024-02-19 19:37:02,092 INFO misc.py line 119 87073] Train: [99/100][236/1557] Data 0.005 (0.112) Batch 0.738 (1.161) Remain 00:55:40 loss: 0.1912 Lr: 0.00001 [2024-02-19 19:37:02,869 INFO misc.py line 119 87073] Train: [99/100][237/1557] Data 0.010 (0.112) Batch 0.783 (1.159) Remain 00:55:34 loss: 0.1327 Lr: 0.00001 [2024-02-19 19:37:04,094 INFO misc.py line 119 87073] Train: [99/100][238/1557] Data 0.004 (0.111) Batch 1.225 (1.159) Remain 00:55:34 loss: 0.1452 Lr: 0.00001 [2024-02-19 19:37:05,098 INFO misc.py line 119 87073] Train: [99/100][239/1557] Data 0.004 (0.111) Batch 1.004 (1.159) Remain 00:55:31 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line 119 87073] Train: [99/100][277/1557] Data 0.004 (0.097) Batch 1.029 (1.132) Remain 00:53:32 loss: 0.2535 Lr: 0.00001 [2024-02-19 19:37:42,647 INFO misc.py line 119 87073] Train: [99/100][278/1557] Data 0.004 (0.096) Batch 0.782 (1.131) Remain 00:53:27 loss: 0.1649 Lr: 0.00001 [2024-02-19 19:37:43,428 INFO misc.py line 119 87073] Train: [99/100][279/1557] Data 0.004 (0.096) Batch 0.772 (1.130) Remain 00:53:22 loss: 0.2034 Lr: 0.00001 [2024-02-19 19:37:44,653 INFO misc.py line 119 87073] Train: [99/100][280/1557] Data 0.013 (0.096) Batch 1.228 (1.130) Remain 00:53:22 loss: 0.1195 Lr: 0.00001 [2024-02-19 19:37:45,475 INFO misc.py line 119 87073] Train: [99/100][281/1557] Data 0.011 (0.095) Batch 0.828 (1.129) Remain 00:53:18 loss: 0.1563 Lr: 0.00001 [2024-02-19 19:37:46,566 INFO misc.py line 119 87073] Train: [99/100][282/1557] Data 0.006 (0.095) Batch 1.093 (1.129) Remain 00:53:16 loss: 0.1554 Lr: 0.00001 [2024-02-19 19:37:47,628 INFO misc.py line 119 87073] Train: 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Batch 1.197 (1.163) Remain 00:54:44 loss: 0.4582 Lr: 0.00001 [2024-02-19 19:38:05,167 INFO misc.py line 119 87073] Train: [99/100][290/1557] Data 0.016 (0.113) Batch 1.015 (1.162) Remain 00:54:41 loss: 0.2541 Lr: 0.00001 [2024-02-19 19:38:05,988 INFO misc.py line 119 87073] Train: [99/100][291/1557] Data 0.015 (0.113) Batch 0.833 (1.161) Remain 00:54:37 loss: 0.3817 Lr: 0.00001 [2024-02-19 19:38:06,802 INFO misc.py line 119 87073] Train: [99/100][292/1557] Data 0.004 (0.113) Batch 0.814 (1.160) Remain 00:54:32 loss: 0.1973 Lr: 0.00001 [2024-02-19 19:38:07,571 INFO misc.py line 119 87073] Train: [99/100][293/1557] Data 0.004 (0.112) Batch 0.759 (1.158) Remain 00:54:27 loss: 0.1433 Lr: 0.00001 [2024-02-19 19:38:08,911 INFO misc.py line 119 87073] Train: [99/100][294/1557] Data 0.014 (0.112) Batch 1.340 (1.159) Remain 00:54:28 loss: 0.1519 Lr: 0.00001 [2024-02-19 19:38:09,843 INFO misc.py line 119 87073] Train: [99/100][295/1557] Data 0.014 (0.112) Batch 0.936 (1.158) Remain 00:54:25 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line 119 87073] Train: [99/100][333/1557] Data 0.004 (0.100) Batch 1.000 (1.134) Remain 00:52:32 loss: 0.1966 Lr: 0.00001 [2024-02-19 19:38:46,465 INFO misc.py line 119 87073] Train: [99/100][334/1557] Data 0.005 (0.099) Batch 0.741 (1.132) Remain 00:52:28 loss: 0.1403 Lr: 0.00001 [2024-02-19 19:38:47,138 INFO misc.py line 119 87073] Train: [99/100][335/1557] Data 0.012 (0.099) Batch 0.681 (1.131) Remain 00:52:23 loss: 0.2224 Lr: 0.00001 [2024-02-19 19:38:48,296 INFO misc.py line 119 87073] Train: [99/100][336/1557] Data 0.004 (0.099) Batch 1.150 (1.131) Remain 00:52:22 loss: 0.1083 Lr: 0.00001 [2024-02-19 19:38:49,343 INFO misc.py line 119 87073] Train: [99/100][337/1557] Data 0.012 (0.098) Batch 1.045 (1.131) Remain 00:52:20 loss: 0.3483 Lr: 0.00001 [2024-02-19 19:38:50,248 INFO misc.py line 119 87073] Train: [99/100][338/1557] Data 0.014 (0.098) Batch 0.915 (1.130) Remain 00:52:17 loss: 0.2780 Lr: 0.00001 [2024-02-19 19:38:51,309 INFO misc.py line 119 87073] Train: 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Batch 0.931 (1.161) Remain 00:53:34 loss: 0.2757 Lr: 0.00001 [2024-02-19 19:39:09,521 INFO misc.py line 119 87073] Train: [99/100][346/1557] Data 0.003 (0.114) Batch 0.871 (1.160) Remain 00:53:30 loss: 0.1541 Lr: 0.00001 [2024-02-19 19:39:10,443 INFO misc.py line 119 87073] Train: [99/100][347/1557] Data 0.006 (0.114) Batch 0.922 (1.159) Remain 00:53:27 loss: 0.1949 Lr: 0.00000 [2024-02-19 19:39:11,191 INFO misc.py line 119 87073] Train: [99/100][348/1557] Data 0.006 (0.113) Batch 0.750 (1.158) Remain 00:53:23 loss: 0.2404 Lr: 0.00000 [2024-02-19 19:39:11,968 INFO misc.py line 119 87073] Train: [99/100][349/1557] Data 0.004 (0.113) Batch 0.769 (1.157) Remain 00:53:19 loss: 0.1118 Lr: 0.00000 [2024-02-19 19:39:13,294 INFO misc.py line 119 87073] Train: [99/100][350/1557] Data 0.012 (0.113) Batch 1.325 (1.158) Remain 00:53:19 loss: 0.1688 Lr: 0.00000 [2024-02-19 19:39:14,236 INFO misc.py line 119 87073] Train: [99/100][351/1557] Data 0.013 (0.112) Batch 0.951 (1.157) Remain 00:53:16 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Batch 0.983 (1.161) Remain 00:50:18 loss: 0.3783 Lr: 0.00000 [2024-02-19 19:42:24,443 INFO misc.py line 119 87073] Train: [99/100][514/1557] Data 0.005 (0.114) Batch 0.873 (1.160) Remain 00:50:16 loss: 0.0725 Lr: 0.00000 [2024-02-19 19:42:25,313 INFO misc.py line 119 87073] Train: [99/100][515/1557] Data 0.011 (0.114) Batch 0.875 (1.160) Remain 00:50:13 loss: 0.1103 Lr: 0.00000 [2024-02-19 19:42:26,015 INFO misc.py line 119 87073] Train: [99/100][516/1557] Data 0.006 (0.114) Batch 0.704 (1.159) Remain 00:50:10 loss: 0.1669 Lr: 0.00000 [2024-02-19 19:42:26,719 INFO misc.py line 119 87073] Train: [99/100][517/1557] Data 0.004 (0.114) Batch 0.695 (1.158) Remain 00:50:06 loss: 0.1776 Lr: 0.00000 [2024-02-19 19:42:27,988 INFO misc.py line 119 87073] Train: [99/100][518/1557] Data 0.013 (0.113) Batch 1.269 (1.158) Remain 00:50:06 loss: 0.1298 Lr: 0.00000 [2024-02-19 19:42:28,945 INFO misc.py line 119 87073] Train: [99/100][519/1557] Data 0.013 (0.113) Batch 0.965 (1.158) Remain 00:50:03 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line 119 87073] Train: [99/100][557/1557] Data 0.015 (0.108) Batch 0.973 (1.147) Remain 00:48:53 loss: 0.3152 Lr: 0.00000 [2024-02-19 19:43:07,847 INFO misc.py line 119 87073] Train: [99/100][558/1557] Data 0.003 (0.108) Batch 0.745 (1.146) Remain 00:48:50 loss: 0.1823 Lr: 0.00000 [2024-02-19 19:43:08,643 INFO misc.py line 119 87073] Train: [99/100][559/1557] Data 0.003 (0.108) Batch 0.787 (1.146) Remain 00:48:47 loss: 0.1444 Lr: 0.00000 [2024-02-19 19:43:09,863 INFO misc.py line 119 87073] Train: [99/100][560/1557] Data 0.012 (0.108) Batch 1.220 (1.146) Remain 00:48:46 loss: 0.2242 Lr: 0.00000 [2024-02-19 19:43:10,767 INFO misc.py line 119 87073] Train: [99/100][561/1557] Data 0.013 (0.108) Batch 0.911 (1.145) Remain 00:48:44 loss: 0.2319 Lr: 0.00000 [2024-02-19 19:43:11,536 INFO misc.py line 119 87073] Train: [99/100][562/1557] Data 0.006 (0.107) Batch 0.770 (1.145) Remain 00:48:41 loss: 0.0688 Lr: 0.00000 [2024-02-19 19:43:12,460 INFO misc.py line 119 87073] Train: 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Batch 0.978 (1.163) Remain 00:45:00 loss: 0.1691 Lr: 0.00000 [2024-02-19 19:47:51,662 INFO misc.py line 119 87073] Train: [99/100][794/1557] Data 0.007 (0.114) Batch 1.011 (1.163) Remain 00:44:58 loss: 0.1020 Lr: 0.00000 [2024-02-19 19:47:52,794 INFO misc.py line 119 87073] Train: [99/100][795/1557] Data 0.004 (0.114) Batch 1.125 (1.163) Remain 00:44:57 loss: 0.1116 Lr: 0.00000 [2024-02-19 19:47:53,590 INFO misc.py line 119 87073] Train: [99/100][796/1557] Data 0.012 (0.114) Batch 0.803 (1.163) Remain 00:44:54 loss: 0.1871 Lr: 0.00000 [2024-02-19 19:47:54,369 INFO misc.py line 119 87073] Train: [99/100][797/1557] Data 0.004 (0.114) Batch 0.779 (1.162) Remain 00:44:52 loss: 0.1405 Lr: 0.00000 [2024-02-19 19:47:55,606 INFO misc.py line 119 87073] Train: [99/100][798/1557] Data 0.004 (0.114) Batch 1.225 (1.162) Remain 00:44:51 loss: 0.1341 Lr: 0.00000 [2024-02-19 19:47:56,388 INFO misc.py line 119 87073] Train: [99/100][799/1557] Data 0.017 (0.114) Batch 0.795 (1.162) Remain 00:44:49 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line 119 87073] Train: [99/100][893/1557] Data 0.004 (0.111) Batch 0.829 (1.154) Remain 00:42:43 loss: 0.2601 Lr: 0.00000 [2024-02-19 19:49:39,481 INFO misc.py line 119 87073] Train: [99/100][894/1557] Data 0.004 (0.110) Batch 0.731 (1.154) Remain 00:42:40 loss: 0.1416 Lr: 0.00000 [2024-02-19 19:49:40,179 INFO misc.py line 119 87073] Train: [99/100][895/1557] Data 0.004 (0.110) Batch 0.692 (1.153) Remain 00:42:38 loss: 0.1709 Lr: 0.00000 [2024-02-19 19:49:41,368 INFO misc.py line 119 87073] Train: [99/100][896/1557] Data 0.009 (0.110) Batch 1.186 (1.153) Remain 00:42:37 loss: 0.0908 Lr: 0.00000 [2024-02-19 19:49:42,265 INFO misc.py line 119 87073] Train: [99/100][897/1557] Data 0.012 (0.110) Batch 0.904 (1.153) Remain 00:42:35 loss: 0.0562 Lr: 0.00000 [2024-02-19 19:49:43,191 INFO misc.py line 119 87073] Train: [99/100][898/1557] Data 0.006 (0.110) Batch 0.926 (1.153) Remain 00:42:34 loss: 0.1407 Lr: 0.00000 [2024-02-19 19:49:44,268 INFO misc.py line 119 87073] Train: 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Batch 1.051 (1.164) Remain 00:42:50 loss: 0.0530 Lr: 0.00000 [2024-02-19 19:50:02,337 INFO misc.py line 119 87073] Train: [99/100][906/1557] Data 0.004 (0.115) Batch 0.904 (1.164) Remain 00:42:49 loss: 0.4570 Lr: 0.00000 [2024-02-19 19:50:03,217 INFO misc.py line 119 87073] Train: [99/100][907/1557] Data 0.004 (0.115) Batch 0.879 (1.163) Remain 00:42:47 loss: 0.4140 Lr: 0.00000 [2024-02-19 19:50:03,953 INFO misc.py line 119 87073] Train: [99/100][908/1557] Data 0.004 (0.115) Batch 0.736 (1.163) Remain 00:42:45 loss: 0.1665 Lr: 0.00000 [2024-02-19 19:50:04,721 INFO misc.py line 119 87073] Train: [99/100][909/1557] Data 0.004 (0.115) Batch 0.745 (1.162) Remain 00:42:42 loss: 0.1352 Lr: 0.00000 [2024-02-19 19:50:06,027 INFO misc.py line 119 87073] Train: [99/100][910/1557] Data 0.030 (0.115) Batch 1.322 (1.162) Remain 00:42:42 loss: 0.0998 Lr: 0.00000 [2024-02-19 19:50:07,055 INFO misc.py line 119 87073] Train: [99/100][911/1557] Data 0.012 (0.115) Batch 1.028 (1.162) Remain 00:42:40 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Remain 00:36:17 loss: 0.2726 Lr: 0.00000 [2024-02-19 19:56:30,163 INFO misc.py line 119 87073] Train: [99/100][1241/1557] Data 0.003 (0.114) Batch 0.956 (1.162) Remain 00:36:16 loss: 0.3537 Lr: 0.00000 [2024-02-19 19:56:31,192 INFO misc.py line 119 87073] Train: [99/100][1242/1557] Data 0.003 (0.114) Batch 1.023 (1.162) Remain 00:36:15 loss: 0.2251 Lr: 0.00000 [2024-02-19 19:56:32,261 INFO misc.py line 119 87073] Train: [99/100][1243/1557] Data 0.010 (0.114) Batch 1.067 (1.162) Remain 00:36:13 loss: 0.0799 Lr: 0.00000 [2024-02-19 19:56:32,994 INFO misc.py line 119 87073] Train: [99/100][1244/1557] Data 0.012 (0.113) Batch 0.741 (1.161) Remain 00:36:11 loss: 0.1615 Lr: 0.00000 [2024-02-19 19:56:33,825 INFO misc.py line 119 87073] Train: [99/100][1245/1557] Data 0.004 (0.113) Batch 0.830 (1.161) Remain 00:36:10 loss: 0.3526 Lr: 0.00000 [2024-02-19 19:56:35,090 INFO misc.py line 119 87073] Train: [99/100][1246/1557] Data 0.004 (0.113) Batch 1.260 (1.161) Remain 00:36:09 loss: 0.1283 Lr: 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Train: [99/100][1259/1557] Data 0.004 (0.112) Batch 0.731 (1.159) Remain 00:35:49 loss: 0.1544 Lr: 0.00000 [2024-02-19 19:56:48,279 INFO misc.py line 119 87073] Train: [99/100][1260/1557] Data 0.004 (0.112) Batch 1.266 (1.159) Remain 00:35:48 loss: 0.1331 Lr: 0.00000 [2024-02-19 19:56:49,111 INFO misc.py line 119 87073] Train: [99/100][1261/1557] Data 0.005 (0.112) Batch 0.833 (1.159) Remain 00:35:46 loss: 0.2802 Lr: 0.00000 [2024-02-19 19:56:50,013 INFO misc.py line 119 87073] Train: [99/100][1262/1557] Data 0.003 (0.112) Batch 0.900 (1.158) Remain 00:35:45 loss: 0.1272 Lr: 0.00000 [2024-02-19 19:56:51,034 INFO misc.py line 119 87073] Train: [99/100][1263/1557] Data 0.005 (0.112) Batch 1.020 (1.158) Remain 00:35:43 loss: 0.1374 Lr: 0.00000 [2024-02-19 19:56:52,110 INFO misc.py line 119 87073] Train: [99/100][1264/1557] Data 0.006 (0.112) Batch 1.074 (1.158) Remain 00:35:42 loss: 0.1912 Lr: 0.00000 [2024-02-19 19:56:52,817 INFO misc.py line 119 87073] Train: [99/100][1265/1557] Data 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Remain 00:35:32 loss: 0.2886 Lr: 0.00000 [2024-02-19 19:56:59,482 INFO misc.py line 119 87073] Train: [99/100][1272/1557] Data 0.004 (0.111) Batch 0.952 (1.157) Remain 00:35:30 loss: 0.2812 Lr: 0.00000 [2024-02-19 19:57:00,261 INFO misc.py line 119 87073] Train: [99/100][1273/1557] Data 0.003 (0.111) Batch 0.778 (1.156) Remain 00:35:28 loss: 0.2259 Lr: 0.00000 [2024-02-19 19:57:01,408 INFO misc.py line 119 87073] Train: [99/100][1274/1557] Data 0.004 (0.111) Batch 1.147 (1.156) Remain 00:35:27 loss: 0.0980 Lr: 0.00000 [2024-02-19 19:57:02,422 INFO misc.py line 119 87073] Train: [99/100][1275/1557] Data 0.005 (0.111) Batch 1.009 (1.156) Remain 00:35:26 loss: 0.4111 Lr: 0.00000 [2024-02-19 19:57:03,431 INFO misc.py line 119 87073] Train: [99/100][1276/1557] Data 0.010 (0.111) Batch 1.012 (1.156) Remain 00:35:25 loss: 0.1099 Lr: 0.00000 [2024-02-19 19:57:04,520 INFO misc.py line 119 87073] Train: [99/100][1277/1557] Data 0.007 (0.111) Batch 1.090 (1.156) Remain 00:35:23 loss: 0.0601 Lr: 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Remain 00:35:02 loss: 0.0934 Lr: 0.00000 [2024-02-19 19:57:39,733 INFO misc.py line 119 87073] Train: [99/100][1303/1557] Data 0.015 (0.113) Batch 1.102 (1.160) Remain 00:35:00 loss: 0.2681 Lr: 0.00000 [2024-02-19 19:57:40,667 INFO misc.py line 119 87073] Train: [99/100][1304/1557] Data 0.016 (0.113) Batch 0.946 (1.160) Remain 00:34:59 loss: 0.4108 Lr: 0.00000 [2024-02-19 19:57:41,616 INFO misc.py line 119 87073] Train: [99/100][1305/1557] Data 0.004 (0.113) Batch 0.949 (1.160) Remain 00:34:57 loss: 0.2302 Lr: 0.00000 [2024-02-19 19:57:42,633 INFO misc.py line 119 87073] Train: [99/100][1306/1557] Data 0.003 (0.113) Batch 1.018 (1.160) Remain 00:34:56 loss: 0.1153 Lr: 0.00000 [2024-02-19 19:57:43,415 INFO misc.py line 119 87073] Train: [99/100][1307/1557] Data 0.003 (0.112) Batch 0.782 (1.159) Remain 00:34:54 loss: 0.1627 Lr: 0.00000 [2024-02-19 19:57:44,251 INFO misc.py line 119 87073] Train: [99/100][1308/1557] Data 0.003 (0.112) Batch 0.826 (1.159) Remain 00:34:53 loss: 0.1139 Lr: 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Remain 00:33:03 loss: 0.3620 Lr: 0.00000 [2024-02-19 19:59:18,708 INFO misc.py line 119 87073] Train: [99/100][1396/1557] Data 0.009 (0.110) Batch 0.881 (1.154) Remain 00:33:02 loss: 0.1790 Lr: 0.00000 [2024-02-19 19:59:19,750 INFO misc.py line 119 87073] Train: [99/100][1397/1557] Data 0.004 (0.110) Batch 1.043 (1.154) Remain 00:33:00 loss: 0.3114 Lr: 0.00000 [2024-02-19 19:59:20,444 INFO misc.py line 119 87073] Train: [99/100][1398/1557] Data 0.003 (0.109) Batch 0.694 (1.153) Remain 00:32:59 loss: 0.2930 Lr: 0.00000 [2024-02-19 19:59:21,188 INFO misc.py line 119 87073] Train: [99/100][1399/1557] Data 0.004 (0.109) Batch 0.706 (1.153) Remain 00:32:57 loss: 0.1383 Lr: 0.00000 [2024-02-19 19:59:22,434 INFO misc.py line 119 87073] Train: [99/100][1400/1557] Data 0.042 (0.109) Batch 1.271 (1.153) Remain 00:32:56 loss: 0.2232 Lr: 0.00000 [2024-02-19 19:59:23,573 INFO misc.py line 119 87073] Train: [99/100][1401/1557] Data 0.017 (0.109) Batch 1.140 (1.153) Remain 00:32:55 loss: 0.2750 Lr: 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Train: [99/100][1414/1557] Data 0.013 (0.113) Batch 1.235 (1.160) Remain 00:32:52 loss: 0.1718 Lr: 0.00000 [2024-02-19 19:59:49,364 INFO misc.py line 119 87073] Train: [99/100][1415/1557] Data 0.009 (0.113) Batch 0.815 (1.160) Remain 00:32:50 loss: 0.2897 Lr: 0.00000 [2024-02-19 19:59:50,302 INFO misc.py line 119 87073] Train: [99/100][1416/1557] Data 0.004 (0.112) Batch 0.938 (1.160) Remain 00:32:49 loss: 0.3088 Lr: 0.00000 [2024-02-19 19:59:51,313 INFO misc.py line 119 87073] Train: [99/100][1417/1557] Data 0.005 (0.112) Batch 1.005 (1.160) Remain 00:32:47 loss: 0.1258 Lr: 0.00000 [2024-02-19 19:59:52,131 INFO misc.py line 119 87073] Train: [99/100][1418/1557] Data 0.011 (0.112) Batch 0.826 (1.159) Remain 00:32:46 loss: 0.3436 Lr: 0.00000 [2024-02-19 19:59:52,915 INFO misc.py line 119 87073] Train: [99/100][1419/1557] Data 0.004 (0.112) Batch 0.782 (1.159) Remain 00:32:44 loss: 0.1297 Lr: 0.00000 [2024-02-19 19:59:53,644 INFO misc.py line 119 87073] Train: [99/100][1420/1557] Data 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Remain 00:32:34 loss: 0.1367 Lr: 0.00000 [2024-02-19 20:00:00,329 INFO misc.py line 119 87073] Train: [99/100][1427/1557] Data 0.003 (0.112) Batch 0.719 (1.158) Remain 00:32:33 loss: 0.1629 Lr: 0.00000 [2024-02-19 20:00:01,646 INFO misc.py line 119 87073] Train: [99/100][1428/1557] Data 0.013 (0.112) Batch 1.317 (1.158) Remain 00:32:32 loss: 0.1441 Lr: 0.00000 [2024-02-19 20:00:02,531 INFO misc.py line 119 87073] Train: [99/100][1429/1557] Data 0.013 (0.112) Batch 0.894 (1.158) Remain 00:32:30 loss: 0.4016 Lr: 0.00000 [2024-02-19 20:00:03,531 INFO misc.py line 119 87073] Train: [99/100][1430/1557] Data 0.005 (0.111) Batch 1.000 (1.158) Remain 00:32:29 loss: 0.3007 Lr: 0.00000 [2024-02-19 20:00:04,548 INFO misc.py line 119 87073] Train: [99/100][1431/1557] Data 0.005 (0.111) Batch 1.018 (1.158) Remain 00:32:28 loss: 0.2285 Lr: 0.00000 [2024-02-19 20:00:05,454 INFO misc.py line 119 87073] Train: [99/100][1432/1557] Data 0.003 (0.111) Batch 0.905 (1.157) Remain 00:32:26 loss: 0.2588 Lr: 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Train: [99/100][1445/1557] Data 0.004 (0.110) Batch 0.941 (1.155) Remain 00:32:08 loss: 0.2523 Lr: 0.00000 [2024-02-19 20:00:18,519 INFO misc.py line 119 87073] Train: [99/100][1446/1557] Data 0.006 (0.110) Batch 0.935 (1.155) Remain 00:32:06 loss: 0.1195 Lr: 0.00000 [2024-02-19 20:00:19,298 INFO misc.py line 119 87073] Train: [99/100][1447/1557] Data 0.004 (0.110) Batch 0.771 (1.155) Remain 00:32:05 loss: 0.1186 Lr: 0.00000 [2024-02-19 20:00:20,078 INFO misc.py line 119 87073] Train: [99/100][1448/1557] Data 0.012 (0.110) Batch 0.788 (1.155) Remain 00:32:03 loss: 0.2333 Lr: 0.00000 [2024-02-19 20:00:21,342 INFO misc.py line 119 87073] Train: [99/100][1449/1557] Data 0.004 (0.110) Batch 1.262 (1.155) Remain 00:32:02 loss: 0.0717 Lr: 0.00000 [2024-02-19 20:00:22,497 INFO misc.py line 119 87073] Train: [99/100][1450/1557] Data 0.006 (0.110) Batch 1.148 (1.155) Remain 00:32:01 loss: 0.4043 Lr: 0.00000 [2024-02-19 20:00:23,587 INFO misc.py line 119 87073] Train: [99/100][1451/1557] Data 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Remain 00:31:52 loss: 0.1611 Lr: 0.00000 [2024-02-19 20:00:30,403 INFO misc.py line 119 87073] Train: [99/100][1458/1557] Data 0.005 (0.109) Batch 0.919 (1.154) Remain 00:31:50 loss: 0.0461 Lr: 0.00000 [2024-02-19 20:00:31,455 INFO misc.py line 119 87073] Train: [99/100][1459/1557] Data 0.003 (0.109) Batch 1.051 (1.154) Remain 00:31:49 loss: 0.3408 Lr: 0.00000 [2024-02-19 20:00:32,382 INFO misc.py line 119 87073] Train: [99/100][1460/1557] Data 0.005 (0.109) Batch 0.926 (1.154) Remain 00:31:47 loss: 0.1694 Lr: 0.00000 [2024-02-19 20:00:33,120 INFO misc.py line 119 87073] Train: [99/100][1461/1557] Data 0.005 (0.109) Batch 0.739 (1.153) Remain 00:31:46 loss: 0.1325 Lr: 0.00000 [2024-02-19 20:00:33,899 INFO misc.py line 119 87073] Train: [99/100][1462/1557] Data 0.004 (0.109) Batch 0.773 (1.153) Remain 00:31:44 loss: 0.2964 Lr: 0.00000 [2024-02-19 20:00:45,809 INFO misc.py line 119 87073] Train: [99/100][1463/1557] Data 5.435 (0.113) Batch 11.916 (1.160) Remain 00:31:55 loss: 0.0860 Lr: 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Train: [99/100][1476/1557] Data 0.004 (0.112) Batch 0.761 (1.159) Remain 00:31:37 loss: 0.2379 Lr: 0.00000 [2024-02-19 20:00:59,673 INFO misc.py line 119 87073] Train: [99/100][1477/1557] Data 0.006 (0.112) Batch 1.312 (1.159) Remain 00:31:36 loss: 0.0972 Lr: 0.00000 [2024-02-19 20:01:00,644 INFO misc.py line 119 87073] Train: [99/100][1478/1557] Data 0.013 (0.112) Batch 0.980 (1.159) Remain 00:31:35 loss: 0.1866 Lr: 0.00000 [2024-02-19 20:01:01,587 INFO misc.py line 119 87073] Train: [99/100][1479/1557] Data 0.003 (0.112) Batch 0.944 (1.159) Remain 00:31:34 loss: 0.2614 Lr: 0.00000 [2024-02-19 20:01:02,761 INFO misc.py line 119 87073] Train: [99/100][1480/1557] Data 0.003 (0.112) Batch 1.174 (1.159) Remain 00:31:33 loss: 0.2679 Lr: 0.00000 [2024-02-19 20:01:03,914 INFO misc.py line 119 87073] Train: [99/100][1481/1557] Data 0.004 (0.112) Batch 1.150 (1.159) Remain 00:31:31 loss: 0.1471 Lr: 0.00000 [2024-02-19 20:01:04,678 INFO misc.py line 119 87073] Train: [99/100][1482/1557] Data 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Remain 00:31:22 loss: 0.2777 Lr: 0.00000 [2024-02-19 20:01:11,517 INFO misc.py line 119 87073] Train: [99/100][1489/1557] Data 0.016 (0.111) Batch 0.766 (1.157) Remain 00:31:20 loss: 0.2232 Lr: 0.00000 [2024-02-19 20:01:12,232 INFO misc.py line 119 87073] Train: [99/100][1490/1557] Data 0.003 (0.111) Batch 0.710 (1.157) Remain 00:31:19 loss: 0.0842 Lr: 0.00000 [2024-02-19 20:01:13,634 INFO misc.py line 119 87073] Train: [99/100][1491/1557] Data 0.009 (0.111) Batch 1.394 (1.157) Remain 00:31:18 loss: 0.1152 Lr: 0.00000 [2024-02-19 20:01:14,802 INFO misc.py line 119 87073] Train: [99/100][1492/1557] Data 0.018 (0.111) Batch 1.169 (1.157) Remain 00:31:17 loss: 0.2868 Lr: 0.00000 [2024-02-19 20:01:15,877 INFO misc.py line 119 87073] Train: [99/100][1493/1557] Data 0.016 (0.111) Batch 1.066 (1.157) Remain 00:31:15 loss: 0.4605 Lr: 0.00000 [2024-02-19 20:01:16,886 INFO misc.py line 119 87073] Train: [99/100][1494/1557] Data 0.025 (0.111) Batch 1.013 (1.157) Remain 00:31:14 loss: 0.2470 Lr: 0.00000 [2024-02-19 20:01:17,899 INFO misc.py line 119 87073] Train: [99/100][1495/1557] Data 0.020 (0.111) Batch 1.016 (1.157) Remain 00:31:13 loss: 0.2668 Lr: 0.00000 [2024-02-19 20:01:18,665 INFO misc.py line 119 87073] Train: [99/100][1496/1557] Data 0.017 (0.111) Batch 0.779 (1.157) Remain 00:31:11 loss: 0.1374 Lr: 0.00000 [2024-02-19 20:01:19,437 INFO misc.py line 119 87073] Train: [99/100][1497/1557] Data 0.004 (0.110) Batch 0.768 (1.156) Remain 00:31:10 loss: 0.1289 Lr: 0.00000 [2024-02-19 20:01:20,566 INFO misc.py line 119 87073] Train: [99/100][1498/1557] Data 0.008 (0.110) Batch 1.114 (1.156) Remain 00:31:08 loss: 0.1191 Lr: 0.00000 [2024-02-19 20:01:21,658 INFO misc.py line 119 87073] Train: [99/100][1499/1557] Data 0.023 (0.110) Batch 1.103 (1.156) Remain 00:31:07 loss: 0.3832 Lr: 0.00000 [2024-02-19 20:01:22,584 INFO misc.py line 119 87073] Train: [99/100][1500/1557] Data 0.013 (0.110) Batch 0.934 (1.156) Remain 00:31:06 loss: 0.1345 Lr: 0.00000 [2024-02-19 20:01:23,439 INFO misc.py line 119 87073] Train: [99/100][1501/1557] Data 0.004 (0.110) Batch 0.854 (1.156) Remain 00:31:04 loss: 0.2428 Lr: 0.00000 [2024-02-19 20:01:24,431 INFO misc.py line 119 87073] Train: [99/100][1502/1557] Data 0.006 (0.110) Batch 0.985 (1.156) Remain 00:31:03 loss: 0.1577 Lr: 0.00000 [2024-02-19 20:01:25,197 INFO misc.py line 119 87073] Train: [99/100][1503/1557] Data 0.013 (0.110) Batch 0.774 (1.156) Remain 00:31:01 loss: 0.2601 Lr: 0.00000 [2024-02-19 20:01:25,960 INFO misc.py line 119 87073] Train: [99/100][1504/1557] Data 0.004 (0.110) Batch 0.763 (1.155) Remain 00:31:00 loss: 0.1133 Lr: 0.00000 [2024-02-19 20:01:27,158 INFO misc.py line 119 87073] Train: [99/100][1505/1557] Data 0.004 (0.110) Batch 1.170 (1.155) Remain 00:30:59 loss: 0.0889 Lr: 0.00000 [2024-02-19 20:01:28,005 INFO misc.py line 119 87073] Train: [99/100][1506/1557] Data 0.032 (0.110) Batch 0.876 (1.155) Remain 00:30:57 loss: 0.3498 Lr: 0.00000 [2024-02-19 20:01:29,018 INFO misc.py line 119 87073] Train: [99/100][1507/1557] Data 0.004 (0.110) Batch 1.013 (1.155) Remain 00:30:56 loss: 0.1791 Lr: 0.00000 [2024-02-19 20:01:30,014 INFO misc.py line 119 87073] Train: [99/100][1508/1557] Data 0.004 (0.110) Batch 0.996 (1.155) Remain 00:30:55 loss: 0.2092 Lr: 0.00000 [2024-02-19 20:01:30,905 INFO misc.py line 119 87073] Train: [99/100][1509/1557] Data 0.003 (0.110) Batch 0.890 (1.155) Remain 00:30:53 loss: 0.1046 Lr: 0.00000 [2024-02-19 20:01:31,623 INFO misc.py line 119 87073] Train: [99/100][1510/1557] Data 0.005 (0.110) Batch 0.705 (1.155) Remain 00:30:51 loss: 0.4166 Lr: 0.00000 [2024-02-19 20:01:32,459 INFO misc.py line 119 87073] Train: [99/100][1511/1557] Data 0.018 (0.110) Batch 0.850 (1.154) Remain 00:30:50 loss: 0.1075 Lr: 0.00000 [2024-02-19 20:01:33,642 INFO misc.py line 119 87073] Train: [99/100][1512/1557] Data 0.004 (0.109) Batch 1.178 (1.154) Remain 00:30:49 loss: 0.1351 Lr: 0.00000 [2024-02-19 20:01:34,592 INFO misc.py line 119 87073] Train: [99/100][1513/1557] Data 0.010 (0.109) Batch 0.955 (1.154) Remain 00:30:47 loss: 0.2288 Lr: 0.00000 [2024-02-19 20:01:35,554 INFO misc.py line 119 87073] Train: [99/100][1514/1557] Data 0.004 (0.109) Batch 0.963 (1.154) Remain 00:30:46 loss: 0.0953 Lr: 0.00000 [2024-02-19 20:01:36,416 INFO misc.py line 119 87073] Train: [99/100][1515/1557] Data 0.004 (0.109) Batch 0.861 (1.154) Remain 00:30:45 loss: 0.0730 Lr: 0.00000 [2024-02-19 20:01:37,537 INFO misc.py line 119 87073] Train: [99/100][1516/1557] Data 0.005 (0.109) Batch 1.122 (1.154) Remain 00:30:43 loss: 0.1151 Lr: 0.00000 [2024-02-19 20:01:38,323 INFO misc.py line 119 87073] Train: [99/100][1517/1557] Data 0.005 (0.109) Batch 0.786 (1.154) Remain 00:30:42 loss: 0.1913 Lr: 0.00000 [2024-02-19 20:01:39,091 INFO misc.py line 119 87073] Train: [99/100][1518/1557] Data 0.004 (0.109) Batch 0.767 (1.153) Remain 00:30:40 loss: 0.1429 Lr: 0.00000 [2024-02-19 20:01:50,980 INFO misc.py line 119 87073] Train: [99/100][1519/1557] Data 5.642 (0.113) Batch 11.890 (1.161) Remain 00:30:51 loss: 0.0830 Lr: 0.00000 [2024-02-19 20:01:51,997 INFO misc.py line 119 87073] Train: [99/100][1520/1557] Data 0.005 (0.113) Batch 1.017 (1.160) Remain 00:30:49 loss: 0.1710 Lr: 0.00000 [2024-02-19 20:01:52,934 INFO misc.py line 119 87073] Train: [99/100][1521/1557] Data 0.004 (0.113) Batch 0.938 (1.160) Remain 00:30:48 loss: 0.3652 Lr: 0.00000 [2024-02-19 20:01:53,944 INFO misc.py line 119 87073] Train: [99/100][1522/1557] Data 0.004 (0.112) Batch 1.010 (1.160) Remain 00:30:47 loss: 0.2221 Lr: 0.00000 [2024-02-19 20:01:55,072 INFO misc.py line 119 87073] Train: [99/100][1523/1557] Data 0.003 (0.112) Batch 1.127 (1.160) Remain 00:30:45 loss: 0.3120 Lr: 0.00000 [2024-02-19 20:01:55,843 INFO misc.py line 119 87073] Train: [99/100][1524/1557] Data 0.005 (0.112) Batch 0.772 (1.160) Remain 00:30:44 loss: 0.1261 Lr: 0.00000 [2024-02-19 20:01:56,556 INFO misc.py line 119 87073] Train: [99/100][1525/1557] Data 0.003 (0.112) Batch 0.710 (1.160) Remain 00:30:42 loss: 0.1559 Lr: 0.00000 [2024-02-19 20:01:57,868 INFO misc.py line 119 87073] Train: [99/100][1526/1557] Data 0.006 (0.112) Batch 1.303 (1.160) Remain 00:30:41 loss: 0.1110 Lr: 0.00000 [2024-02-19 20:01:58,764 INFO misc.py line 119 87073] Train: [99/100][1527/1557] Data 0.015 (0.112) Batch 0.907 (1.160) Remain 00:30:40 loss: 0.1534 Lr: 0.00000 [2024-02-19 20:01:59,753 INFO misc.py line 119 87073] Train: [99/100][1528/1557] Data 0.004 (0.112) Batch 0.990 (1.159) Remain 00:30:38 loss: 0.2772 Lr: 0.00000 [2024-02-19 20:02:00,879 INFO misc.py line 119 87073] Train: [99/100][1529/1557] Data 0.003 (0.112) Batch 1.125 (1.159) Remain 00:30:37 loss: 0.2113 Lr: 0.00000 [2024-02-19 20:02:01,828 INFO misc.py line 119 87073] Train: [99/100][1530/1557] Data 0.006 (0.112) Batch 0.949 (1.159) Remain 00:30:36 loss: 0.1516 Lr: 0.00000 [2024-02-19 20:02:02,573 INFO misc.py line 119 87073] Train: [99/100][1531/1557] Data 0.004 (0.112) Batch 0.744 (1.159) Remain 00:30:34 loss: 0.1562 Lr: 0.00000 [2024-02-19 20:02:03,349 INFO misc.py line 119 87073] Train: [99/100][1532/1557] Data 0.005 (0.112) Batch 0.767 (1.159) Remain 00:30:33 loss: 0.1699 Lr: 0.00000 [2024-02-19 20:02:04,666 INFO misc.py line 119 87073] Train: [99/100][1533/1557] Data 0.014 (0.112) Batch 1.315 (1.159) Remain 00:30:32 loss: 0.0862 Lr: 0.00000 [2024-02-19 20:02:05,583 INFO misc.py line 119 87073] Train: [99/100][1534/1557] Data 0.016 (0.112) Batch 0.928 (1.159) Remain 00:30:30 loss: 0.2023 Lr: 0.00000 [2024-02-19 20:02:06,418 INFO misc.py line 119 87073] Train: [99/100][1535/1557] Data 0.004 (0.112) Batch 0.836 (1.158) Remain 00:30:29 loss: 0.1138 Lr: 0.00000 [2024-02-19 20:02:07,316 INFO misc.py line 119 87073] Train: [99/100][1536/1557] Data 0.004 (0.112) Batch 0.893 (1.158) Remain 00:30:27 loss: 0.1422 Lr: 0.00000 [2024-02-19 20:02:08,306 INFO misc.py line 119 87073] Train: [99/100][1537/1557] Data 0.009 (0.111) Batch 0.992 (1.158) Remain 00:30:26 loss: 0.1310 Lr: 0.00000 [2024-02-19 20:02:09,073 INFO misc.py line 119 87073] Train: [99/100][1538/1557] Data 0.007 (0.111) Batch 0.770 (1.158) Remain 00:30:24 loss: 0.0984 Lr: 0.00000 [2024-02-19 20:02:09,801 INFO misc.py line 119 87073] Train: [99/100][1539/1557] Data 0.004 (0.111) Batch 0.715 (1.158) Remain 00:30:23 loss: 0.2691 Lr: 0.00000 [2024-02-19 20:02:11,002 INFO misc.py line 119 87073] Train: [99/100][1540/1557] Data 0.016 (0.111) Batch 1.194 (1.158) Remain 00:30:22 loss: 0.2603 Lr: 0.00000 [2024-02-19 20:02:11,979 INFO misc.py line 119 87073] Train: [99/100][1541/1557] Data 0.023 (0.111) Batch 0.996 (1.158) Remain 00:30:20 loss: 0.1124 Lr: 0.00000 [2024-02-19 20:02:13,333 INFO misc.py line 119 87073] Train: [99/100][1542/1557] Data 0.004 (0.111) Batch 1.342 (1.158) Remain 00:30:19 loss: 0.1549 Lr: 0.00000 [2024-02-19 20:02:14,386 INFO misc.py line 119 87073] Train: [99/100][1543/1557] Data 0.017 (0.111) Batch 1.053 (1.158) Remain 00:30:18 loss: 0.1182 Lr: 0.00000 [2024-02-19 20:02:15,248 INFO misc.py line 119 87073] Train: [99/100][1544/1557] Data 0.017 (0.111) Batch 0.873 (1.157) Remain 00:30:17 loss: 0.1537 Lr: 0.00000 [2024-02-19 20:02:15,942 INFO misc.py line 119 87073] Train: [99/100][1545/1557] Data 0.005 (0.111) Batch 0.695 (1.157) Remain 00:30:15 loss: 0.1504 Lr: 0.00000 [2024-02-19 20:02:16,647 INFO misc.py line 119 87073] Train: [99/100][1546/1557] Data 0.004 (0.111) Batch 0.698 (1.157) Remain 00:30:13 loss: 0.1814 Lr: 0.00000 [2024-02-19 20:02:17,938 INFO misc.py line 119 87073] Train: [99/100][1547/1557] Data 0.011 (0.111) Batch 1.287 (1.157) Remain 00:30:12 loss: 0.2485 Lr: 0.00000 [2024-02-19 20:02:18,970 INFO misc.py line 119 87073] Train: [99/100][1548/1557] Data 0.015 (0.111) Batch 1.041 (1.157) Remain 00:30:11 loss: 0.3082 Lr: 0.00000 [2024-02-19 20:02:19,808 INFO misc.py line 119 87073] Train: [99/100][1549/1557] Data 0.006 (0.111) Batch 0.840 (1.157) Remain 00:30:10 loss: 0.0944 Lr: 0.00000 [2024-02-19 20:02:20,719 INFO misc.py line 119 87073] Train: [99/100][1550/1557] Data 0.004 (0.111) Batch 0.911 (1.156) Remain 00:30:08 loss: 0.0863 Lr: 0.00000 [2024-02-19 20:02:21,650 INFO misc.py line 119 87073] Train: [99/100][1551/1557] Data 0.004 (0.111) Batch 0.919 (1.156) Remain 00:30:07 loss: 0.1899 Lr: 0.00000 [2024-02-19 20:02:22,444 INFO misc.py line 119 87073] Train: [99/100][1552/1557] Data 0.016 (0.110) Batch 0.806 (1.156) Remain 00:30:05 loss: 0.1635 Lr: 0.00000 [2024-02-19 20:02:23,191 INFO misc.py line 119 87073] Train: [99/100][1553/1557] Data 0.004 (0.110) Batch 0.747 (1.156) Remain 00:30:04 loss: 0.1517 Lr: 0.00000 [2024-02-19 20:02:24,353 INFO misc.py line 119 87073] Train: [99/100][1554/1557] Data 0.004 (0.110) Batch 1.160 (1.156) Remain 00:30:03 loss: 0.1012 Lr: 0.00000 [2024-02-19 20:02:25,333 INFO misc.py line 119 87073] Train: [99/100][1555/1557] Data 0.005 (0.110) Batch 0.981 (1.156) Remain 00:30:01 loss: 0.1612 Lr: 0.00000 [2024-02-19 20:02:26,376 INFO misc.py line 119 87073] Train: [99/100][1556/1557] Data 0.004 (0.110) Batch 1.043 (1.156) Remain 00:30:00 loss: 0.4004 Lr: 0.00000 [2024-02-19 20:02:27,641 INFO misc.py line 119 87073] Train: [99/100][1557/1557] Data 0.004 (0.110) Batch 1.254 (1.156) Remain 00:29:59 loss: 0.0675 Lr: 0.00000 [2024-02-19 20:02:27,642 INFO misc.py line 136 87073] Train result: loss: 0.1954 [2024-02-19 20:02:27,643 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 20:02:59,690 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4234 [2024-02-19 20:03:00,482 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4877 [2024-02-19 20:03:02,610 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3322 [2024-02-19 20:03:04,832 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3458 [2024-02-19 20:03:09,791 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2329 [2024-02-19 20:03:10,489 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1025 [2024-02-19 20:03:11,753 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.3096 [2024-02-19 20:03:14,707 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2464 [2024-02-19 20:03:16,519 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2874 [2024-02-19 20:03:18,066 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7309/0.7843/0.9186. [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9324/0.9632 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9829/0.9892 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8665/0.9730 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3989/0.4586 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6604/0.6790 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8233/0.9327 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8446/0.9138 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9305/0.9763 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.8251/0.8471 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8016/0.8865 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.8043/0.8525 [2024-02-19 20:03:18,066 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6310/0.7238 [2024-02-19 20:03:18,067 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 20:03:18,068 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 20:03:18,068 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 20:03:25,190 INFO misc.py line 119 87073] Train: [100/100][1/1557] Data 1.444 (1.444) Batch 2.304 (2.304) Remain 00:59:44 loss: 0.5934 Lr: 0.00000 [2024-02-19 20:03:26,050 INFO misc.py line 119 87073] Train: [100/100][2/1557] Data 0.010 (0.010) Batch 0.863 (0.863) Remain 00:22:22 loss: 0.6768 Lr: 0.00000 [2024-02-19 20:03:27,107 INFO misc.py line 119 87073] Train: [100/100][3/1557] Data 0.005 (0.005) Batch 1.057 (1.057) Remain 00:27:22 loss: 0.6517 Lr: 0.00000 [2024-02-19 20:03:28,091 INFO misc.py line 119 87073] Train: [100/100][4/1557] Data 0.005 (0.005) Batch 0.984 (0.984) Remain 00:25:28 loss: 0.0735 Lr: 0.00000 [2024-02-19 20:03:28,850 INFO misc.py line 119 87073] Train: [100/100][5/1557] Data 0.005 (0.005) Batch 0.759 (0.871) Remain 00:22:32 loss: 0.1741 Lr: 0.00000 [2024-02-19 20:03:29,624 INFO misc.py line 119 87073] Train: [100/100][6/1557] Data 0.005 (0.005) Batch 0.770 (0.838) Remain 00:21:39 loss: 0.2767 Lr: 0.00000 [2024-02-19 20:03:30,802 INFO misc.py line 119 87073] Train: [100/100][7/1557] Data 0.009 (0.006) Batch 1.171 (0.921) Remain 00:23:47 loss: 0.0739 Lr: 0.00000 [2024-02-19 20:03:31,924 INFO misc.py line 119 87073] Train: [100/100][8/1557] Data 0.015 (0.008) Batch 1.122 (0.961) Remain 00:24:48 loss: 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20:03:38,247 INFO misc.py line 119 87073] Train: [100/100][15/1557] Data 0.004 (0.008) Batch 0.906 (0.928) Remain 00:23:51 loss: 0.4042 Lr: 0.00000 [2024-02-19 20:03:39,157 INFO misc.py line 119 87073] Train: [100/100][16/1557] Data 0.004 (0.007) Batch 0.908 (0.927) Remain 00:23:48 loss: 0.0581 Lr: 0.00000 [2024-02-19 20:03:40,158 INFO misc.py line 119 87073] Train: [100/100][17/1557] Data 0.006 (0.007) Batch 1.003 (0.932) Remain 00:23:55 loss: 0.1158 Lr: 0.00000 [2024-02-19 20:03:41,208 INFO misc.py line 119 87073] Train: [100/100][18/1557] Data 0.004 (0.007) Batch 1.007 (0.937) Remain 00:24:02 loss: 0.3410 Lr: 0.00000 [2024-02-19 20:03:41,862 INFO misc.py line 119 87073] Train: [100/100][19/1557] Data 0.047 (0.009) Batch 0.698 (0.922) Remain 00:23:38 loss: 0.1702 Lr: 0.00000 [2024-02-19 20:03:42,574 INFO misc.py line 119 87073] Train: [100/100][20/1557] Data 0.003 (0.009) Batch 0.706 (0.910) Remain 00:23:17 loss: 0.3977 Lr: 0.00000 [2024-02-19 20:03:45,953 INFO misc.py line 119 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line 119 87073] Train: [100/100][1044/1557] Data 0.014 (0.087) Batch 0.928 (1.114) Remain 00:09:31 loss: 0.1113 Lr: 0.00000 [2024-02-19 20:22:47,869 INFO misc.py line 119 87073] Train: [100/100][1045/1557] Data 0.005 (0.087) Batch 1.034 (1.114) Remain 00:09:30 loss: 0.1614 Lr: 0.00000 [2024-02-19 20:22:48,755 INFO misc.py line 119 87073] Train: [100/100][1046/1557] Data 0.005 (0.087) Batch 0.886 (1.114) Remain 00:09:29 loss: 0.0320 Lr: 0.00000 [2024-02-19 20:22:49,815 INFO misc.py line 119 87073] Train: [100/100][1047/1557] Data 0.004 (0.087) Batch 1.054 (1.114) Remain 00:09:27 loss: 0.1384 Lr: 0.00000 [2024-02-19 20:22:50,522 INFO misc.py line 119 87073] Train: [100/100][1048/1557] Data 0.010 (0.087) Batch 0.713 (1.113) Remain 00:09:26 loss: 0.1483 Lr: 0.00000 [2024-02-19 20:22:51,261 INFO misc.py line 119 87073] Train: [100/100][1049/1557] Data 0.004 (0.087) Batch 0.732 (1.113) Remain 00:09:25 loss: 0.0789 Lr: 0.00000 [2024-02-19 20:22:52,357 INFO misc.py line 119 87073] Train: 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(1.112) Remain 00:09:10 loss: 0.1304 Lr: 0.00000 [2024-02-19 20:23:05,800 INFO misc.py line 119 87073] Train: [100/100][1063/1557] Data 0.004 (0.087) Batch 0.776 (1.112) Remain 00:09:09 loss: 0.2064 Lr: 0.00000 [2024-02-19 20:23:06,944 INFO misc.py line 119 87073] Train: [100/100][1064/1557] Data 0.012 (0.087) Batch 1.144 (1.112) Remain 00:09:08 loss: 0.0971 Lr: 0.00000 [2024-02-19 20:23:07,852 INFO misc.py line 119 87073] Train: [100/100][1065/1557] Data 0.013 (0.086) Batch 0.917 (1.112) Remain 00:09:07 loss: 0.0876 Lr: 0.00000 [2024-02-19 20:23:08,740 INFO misc.py line 119 87073] Train: [100/100][1066/1557] Data 0.003 (0.086) Batch 0.888 (1.112) Remain 00:09:05 loss: 0.2107 Lr: 0.00000 [2024-02-19 20:23:09,748 INFO misc.py line 119 87073] Train: [100/100][1067/1557] Data 0.004 (0.086) Batch 1.008 (1.112) Remain 00:09:04 loss: 0.2704 Lr: 0.00000 [2024-02-19 20:23:10,746 INFO misc.py line 119 87073] Train: [100/100][1068/1557] Data 0.003 (0.086) Batch 0.989 (1.111) Remain 00:09:03 loss: 0.0923 Lr: 0.00000 [2024-02-19 20:23:11,431 INFO misc.py line 119 87073] Train: [100/100][1069/1557] Data 0.013 (0.086) Batch 0.694 (1.111) Remain 00:09:02 loss: 0.1208 Lr: 0.00000 [2024-02-19 20:23:12,142 INFO misc.py line 119 87073] Train: [100/100][1070/1557] Data 0.003 (0.086) Batch 0.706 (1.111) Remain 00:09:00 loss: 0.2504 Lr: 0.00000 [2024-02-19 20:23:20,604 INFO misc.py line 119 87073] Train: [100/100][1071/1557] Data 3.781 (0.090) Batch 8.464 (1.118) Remain 00:09:03 loss: 0.0790 Lr: 0.00000 [2024-02-19 20:23:21,516 INFO misc.py line 119 87073] Train: [100/100][1072/1557] Data 0.007 (0.089) Batch 0.914 (1.117) Remain 00:09:01 loss: 0.5016 Lr: 0.00000 [2024-02-19 20:23:22,568 INFO misc.py line 119 87073] Train: [100/100][1073/1557] Data 0.005 (0.089) Batch 1.052 (1.117) Remain 00:09:00 loss: 0.3076 Lr: 0.00000 [2024-02-19 20:23:23,556 INFO misc.py line 119 87073] Train: [100/100][1074/1557] Data 0.004 (0.089) Batch 0.988 (1.117) Remain 00:08:59 loss: 0.3217 Lr: 0.00000 [2024-02-19 20:23:24,502 INFO misc.py line 119 87073] Train: [100/100][1075/1557] Data 0.003 (0.089) Batch 0.946 (1.117) Remain 00:08:58 loss: 0.0900 Lr: 0.00000 [2024-02-19 20:23:25,273 INFO misc.py line 119 87073] Train: [100/100][1076/1557] Data 0.004 (0.089) Batch 0.771 (1.117) Remain 00:08:57 loss: 0.1226 Lr: 0.00000 [2024-02-19 20:23:26,130 INFO misc.py line 119 87073] Train: [100/100][1077/1557] Data 0.005 (0.089) Batch 0.856 (1.116) Remain 00:08:55 loss: 0.1185 Lr: 0.00000 [2024-02-19 20:23:27,230 INFO misc.py line 119 87073] Train: [100/100][1078/1557] Data 0.005 (0.089) Batch 1.100 (1.116) Remain 00:08:54 loss: 0.1073 Lr: 0.00000 [2024-02-19 20:23:28,265 INFO misc.py line 119 87073] Train: [100/100][1079/1557] Data 0.005 (0.089) Batch 1.036 (1.116) Remain 00:08:53 loss: 0.3280 Lr: 0.00000 [2024-02-19 20:23:29,236 INFO misc.py line 119 87073] Train: [100/100][1080/1557] Data 0.004 (0.089) Batch 0.970 (1.116) Remain 00:08:52 loss: 0.1368 Lr: 0.00000 [2024-02-19 20:23:30,143 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Train: [100/100][1087/1557] Data 0.004 (0.088) Batch 1.112 (1.116) Remain 00:08:44 loss: 0.3685 Lr: 0.00000 [2024-02-19 20:23:37,238 INFO misc.py line 119 87073] Train: [100/100][1088/1557] Data 0.004 (0.088) Batch 0.905 (1.115) Remain 00:08:43 loss: 0.0961 Lr: 0.00000 [2024-02-19 20:23:38,242 INFO misc.py line 119 87073] Train: [100/100][1089/1557] Data 0.003 (0.088) Batch 1.004 (1.115) Remain 00:08:41 loss: 0.1951 Lr: 0.00000 [2024-02-19 20:23:38,930 INFO misc.py line 119 87073] Train: [100/100][1090/1557] Data 0.003 (0.088) Batch 0.678 (1.115) Remain 00:08:40 loss: 0.1329 Lr: 0.00000 [2024-02-19 20:23:39,699 INFO misc.py line 119 87073] Train: [100/100][1091/1557] Data 0.013 (0.088) Batch 0.778 (1.115) Remain 00:08:39 loss: 0.3731 Lr: 0.00000 [2024-02-19 20:23:40,977 INFO misc.py line 119 87073] Train: [100/100][1092/1557] Data 0.004 (0.088) Batch 1.266 (1.115) Remain 00:08:38 loss: 0.1497 Lr: 0.00000 [2024-02-19 20:23:41,943 INFO misc.py line 119 87073] Train: [100/100][1093/1557] Data 0.017 (0.088) Batch 0.978 (1.115) Remain 00:08:37 loss: 0.3548 Lr: 0.00000 [2024-02-19 20:23:43,029 INFO misc.py line 119 87073] Train: [100/100][1094/1557] Data 0.004 (0.088) Batch 1.086 (1.115) Remain 00:08:36 loss: 0.2208 Lr: 0.00000 [2024-02-19 20:23:44,007 INFO misc.py line 119 87073] Train: [100/100][1095/1557] Data 0.005 (0.088) Batch 0.977 (1.114) Remain 00:08:34 loss: 0.1689 Lr: 0.00000 [2024-02-19 20:23:45,078 INFO misc.py line 119 87073] Train: [100/100][1096/1557] Data 0.006 (0.088) Batch 1.071 (1.114) Remain 00:08:33 loss: 0.2671 Lr: 0.00000 [2024-02-19 20:23:45,857 INFO misc.py line 119 87073] Train: [100/100][1097/1557] Data 0.004 (0.088) Batch 0.779 (1.114) Remain 00:08:32 loss: 0.2218 Lr: 0.00000 [2024-02-19 20:23:46,520 INFO misc.py line 119 87073] Train: [100/100][1098/1557] Data 0.004 (0.087) Batch 0.663 (1.114) Remain 00:08:31 loss: 0.3108 Lr: 0.00000 [2024-02-19 20:23:47,775 INFO misc.py line 119 87073] Train: [100/100][1099/1557] Data 0.004 (0.087) Batch 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line 119 87073] Train: [100/100][1124/1557] Data 0.022 (0.086) Batch 0.998 (1.111) Remain 00:08:00 loss: 0.4759 Lr: 0.00000 [2024-02-19 20:24:12,931 INFO misc.py line 119 87073] Train: [100/100][1125/1557] Data 0.004 (0.086) Batch 0.745 (1.110) Remain 00:07:59 loss: 0.1069 Lr: 0.00000 [2024-02-19 20:24:13,728 INFO misc.py line 119 87073] Train: [100/100][1126/1557] Data 0.004 (0.086) Batch 0.792 (1.110) Remain 00:07:58 loss: 0.4929 Lr: 0.00000 [2024-02-19 20:24:22,897 INFO misc.py line 119 87073] Train: [100/100][1127/1557] Data 4.202 (0.089) Batch 9.172 (1.117) Remain 00:08:00 loss: 0.0845 Lr: 0.00000 [2024-02-19 20:24:23,799 INFO misc.py line 119 87073] Train: [100/100][1128/1557] Data 0.007 (0.089) Batch 0.903 (1.117) Remain 00:07:59 loss: 0.2133 Lr: 0.00000 [2024-02-19 20:24:24,748 INFO misc.py line 119 87073] Train: [100/100][1129/1557] Data 0.005 (0.089) Batch 0.950 (1.117) Remain 00:07:58 loss: 0.3421 Lr: 0.00000 [2024-02-19 20:24:25,792 INFO misc.py line 119 87073] Train: 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(1.115) Remain 00:07:42 loss: 0.1850 Lr: 0.00000 [2024-02-19 20:24:37,892 INFO misc.py line 119 87073] Train: [100/100][1143/1557] Data 0.004 (0.088) Batch 0.893 (1.115) Remain 00:07:41 loss: 0.1365 Lr: 0.00000 [2024-02-19 20:24:38,899 INFO misc.py line 119 87073] Train: [100/100][1144/1557] Data 0.004 (0.088) Batch 1.008 (1.115) Remain 00:07:40 loss: 0.2088 Lr: 0.00000 [2024-02-19 20:24:39,784 INFO misc.py line 119 87073] Train: [100/100][1145/1557] Data 0.003 (0.088) Batch 0.884 (1.114) Remain 00:07:39 loss: 0.3633 Lr: 0.00000 [2024-02-19 20:24:40,541 INFO misc.py line 119 87073] Train: [100/100][1146/1557] Data 0.004 (0.088) Batch 0.748 (1.114) Remain 00:07:37 loss: 0.1202 Lr: 0.00000 [2024-02-19 20:24:41,269 INFO misc.py line 119 87073] Train: [100/100][1147/1557] Data 0.013 (0.088) Batch 0.736 (1.114) Remain 00:07:36 loss: 0.0783 Lr: 0.00000 [2024-02-19 20:24:42,463 INFO misc.py line 119 87073] Train: [100/100][1148/1557] Data 0.005 (0.088) Batch 1.195 (1.114) Remain 00:07:35 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INFO misc.py line 119 87073] Train: [100/100][1161/1557] Data 0.003 (0.087) Batch 0.742 (1.112) Remain 00:07:20 loss: 0.1159 Lr: 0.00000 [2024-02-19 20:24:55,583 INFO misc.py line 119 87073] Train: [100/100][1162/1557] Data 0.016 (0.087) Batch 1.157 (1.112) Remain 00:07:19 loss: 0.1479 Lr: 0.00000 [2024-02-19 20:24:56,471 INFO misc.py line 119 87073] Train: [100/100][1163/1557] Data 0.015 (0.087) Batch 0.898 (1.112) Remain 00:07:17 loss: 0.1967 Lr: 0.00000 [2024-02-19 20:24:57,425 INFO misc.py line 119 87073] Train: [100/100][1164/1557] Data 0.004 (0.087) Batch 0.954 (1.111) Remain 00:07:16 loss: 0.6915 Lr: 0.00000 [2024-02-19 20:24:58,583 INFO misc.py line 119 87073] Train: [100/100][1165/1557] Data 0.004 (0.086) Batch 1.157 (1.111) Remain 00:07:15 loss: 0.0884 Lr: 0.00000 [2024-02-19 20:24:59,853 INFO misc.py line 119 87073] Train: [100/100][1166/1557] Data 0.005 (0.086) Batch 1.264 (1.112) Remain 00:07:14 loss: 0.4908 Lr: 0.00000 [2024-02-19 20:25:00,524 INFO misc.py line 119 87073] Train: [100/100][1167/1557] Data 0.012 (0.086) Batch 0.678 (1.111) Remain 00:07:13 loss: 0.1313 Lr: 0.00000 [2024-02-19 20:25:01,342 INFO misc.py line 119 87073] Train: [100/100][1168/1557] Data 0.005 (0.086) Batch 0.748 (1.111) Remain 00:07:12 loss: 0.2009 Lr: 0.00000 [2024-02-19 20:25:02,552 INFO misc.py line 119 87073] Train: [100/100][1169/1557] Data 0.074 (0.086) Batch 1.269 (1.111) Remain 00:07:11 loss: 0.0858 Lr: 0.00000 [2024-02-19 20:25:03,410 INFO misc.py line 119 87073] Train: [100/100][1170/1557] Data 0.016 (0.086) Batch 0.869 (1.111) Remain 00:07:09 loss: 0.2888 Lr: 0.00000 [2024-02-19 20:25:04,310 INFO misc.py line 119 87073] Train: [100/100][1171/1557] Data 0.005 (0.086) Batch 0.902 (1.111) Remain 00:07:08 loss: 0.1046 Lr: 0.00000 [2024-02-19 20:25:05,226 INFO misc.py line 119 87073] Train: [100/100][1172/1557] Data 0.003 (0.086) Batch 0.909 (1.110) Remain 00:07:07 loss: 0.4472 Lr: 0.00000 [2024-02-19 20:25:06,325 INFO misc.py line 119 87073] Train: [100/100][1173/1557] Data 0.011 (0.086) Batch 1.084 (1.110) Remain 00:07:06 loss: 0.3004 Lr: 0.00000 [2024-02-19 20:25:07,067 INFO misc.py line 119 87073] Train: [100/100][1174/1557] Data 0.025 (0.086) Batch 0.763 (1.110) Remain 00:07:05 loss: 0.0974 Lr: 0.00000 [2024-02-19 20:25:07,823 INFO misc.py line 119 87073] Train: [100/100][1175/1557] Data 0.004 (0.086) Batch 0.743 (1.110) Remain 00:07:03 loss: 0.3484 Lr: 0.00000 [2024-02-19 20:25:08,981 INFO misc.py line 119 87073] Train: [100/100][1176/1557] Data 0.016 (0.086) Batch 1.157 (1.110) Remain 00:07:02 loss: 0.1118 Lr: 0.00000 [2024-02-19 20:25:09,877 INFO misc.py line 119 87073] Train: [100/100][1177/1557] Data 0.017 (0.086) Batch 0.910 (1.110) Remain 00:07:01 loss: 0.2125 Lr: 0.00000 [2024-02-19 20:25:10,756 INFO misc.py line 119 87073] Train: [100/100][1178/1557] Data 0.004 (0.086) Batch 0.878 (1.109) Remain 00:07:00 loss: 0.1006 Lr: 0.00000 [2024-02-19 20:25:11,754 INFO misc.py line 119 87073] Train: [100/100][1179/1557] Data 0.004 (0.086) Batch 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line 119 87073] Train: [100/100][1204/1557] Data 0.004 (0.087) Batch 1.223 (1.112) Remain 00:06:32 loss: 0.1309 Lr: 0.00000 [2024-02-19 20:25:44,133 INFO misc.py line 119 87073] Train: [100/100][1205/1557] Data 0.004 (0.087) Batch 1.227 (1.112) Remain 00:06:31 loss: 0.1397 Lr: 0.00000 [2024-02-19 20:25:44,959 INFO misc.py line 119 87073] Train: [100/100][1206/1557] Data 0.018 (0.087) Batch 0.840 (1.112) Remain 00:06:30 loss: 0.3271 Lr: 0.00000 [2024-02-19 20:25:45,758 INFO misc.py line 119 87073] Train: [100/100][1207/1557] Data 0.005 (0.087) Batch 0.799 (1.112) Remain 00:06:29 loss: 0.2105 Lr: 0.00000 [2024-02-19 20:25:46,781 INFO misc.py line 119 87073] Train: [100/100][1208/1557] Data 0.003 (0.087) Batch 1.012 (1.112) Remain 00:06:28 loss: 0.3189 Lr: 0.00000 [2024-02-19 20:25:47,446 INFO misc.py line 119 87073] Train: [100/100][1209/1557] Data 0.015 (0.087) Batch 0.675 (1.111) Remain 00:06:26 loss: 0.1799 Lr: 0.00000 [2024-02-19 20:25:48,261 INFO misc.py line 119 87073] Train: 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Train: [100/100][1247/1557] Data 0.005 (0.089) Batch 0.884 (1.113) Remain 00:05:45 loss: 0.3178 Lr: 0.00000 [2024-02-19 20:26:32,785 INFO misc.py line 119 87073] Train: [100/100][1248/1557] Data 0.003 (0.089) Batch 1.015 (1.113) Remain 00:05:43 loss: 0.1160 Lr: 0.00000 [2024-02-19 20:26:33,852 INFO misc.py line 119 87073] Train: [100/100][1249/1557] Data 0.004 (0.089) Batch 1.066 (1.113) Remain 00:05:42 loss: 0.2428 Lr: 0.00000 [2024-02-19 20:26:34,854 INFO misc.py line 119 87073] Train: [100/100][1250/1557] Data 0.005 (0.089) Batch 1.002 (1.113) Remain 00:05:41 loss: 0.3969 Lr: 0.00000 [2024-02-19 20:26:35,634 INFO misc.py line 119 87073] Train: [100/100][1251/1557] Data 0.005 (0.089) Batch 0.780 (1.113) Remain 00:05:40 loss: 0.2549 Lr: 0.00000 [2024-02-19 20:26:36,500 INFO misc.py line 119 87073] Train: [100/100][1252/1557] Data 0.005 (0.089) Batch 0.856 (1.112) Remain 00:05:39 loss: 0.1347 Lr: 0.00000 [2024-02-19 20:26:38,171 INFO misc.py line 119 87073] Train: [100/100][1253/1557] Data 0.014 (0.089) Batch 1.675 (1.113) Remain 00:05:38 loss: 0.1834 Lr: 0.00000 [2024-02-19 20:26:39,150 INFO misc.py line 119 87073] Train: [100/100][1254/1557] Data 0.012 (0.089) Batch 0.986 (1.113) Remain 00:05:37 loss: 0.0805 Lr: 0.00000 [2024-02-19 20:26:40,179 INFO misc.py line 119 87073] Train: [100/100][1255/1557] Data 0.004 (0.089) Batch 1.029 (1.113) Remain 00:05:36 loss: 0.0417 Lr: 0.00000 [2024-02-19 20:26:41,307 INFO misc.py line 119 87073] Train: [100/100][1256/1557] Data 0.004 (0.089) Batch 1.129 (1.113) Remain 00:05:34 loss: 0.0545 Lr: 0.00000 [2024-02-19 20:26:42,211 INFO misc.py line 119 87073] Train: [100/100][1257/1557] Data 0.004 (0.089) Batch 0.904 (1.113) Remain 00:05:33 loss: 0.4113 Lr: 0.00000 [2024-02-19 20:26:42,998 INFO misc.py line 119 87073] Train: [100/100][1258/1557] Data 0.004 (0.088) Batch 0.780 (1.112) Remain 00:05:32 loss: 0.1990 Lr: 0.00000 [2024-02-19 20:26:43,736 INFO misc.py line 119 87073] Train: [100/100][1259/1557] Data 0.011 (0.088) Batch 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Train: [100/100][1327/1557] Data 0.006 (0.087) Batch 0.976 (1.113) Remain 00:04:15 loss: 0.0982 Lr: 0.00000 [2024-02-19 20:28:01,161 INFO misc.py line 119 87073] Train: [100/100][1328/1557] Data 0.004 (0.087) Batch 0.718 (1.112) Remain 00:04:14 loss: 0.1029 Lr: 0.00000 [2024-02-19 20:28:01,979 INFO misc.py line 119 87073] Train: [100/100][1329/1557] Data 0.010 (0.087) Batch 0.823 (1.112) Remain 00:04:13 loss: 0.1774 Lr: 0.00000 [2024-02-19 20:28:03,092 INFO misc.py line 119 87073] Train: [100/100][1330/1557] Data 0.004 (0.087) Batch 1.111 (1.112) Remain 00:04:12 loss: 0.1838 Lr: 0.00000 [2024-02-19 20:28:04,048 INFO misc.py line 119 87073] Train: [100/100][1331/1557] Data 0.006 (0.087) Batch 0.959 (1.112) Remain 00:04:11 loss: 0.0714 Lr: 0.00000 [2024-02-19 20:28:05,052 INFO misc.py line 119 87073] Train: [100/100][1332/1557] Data 0.004 (0.087) Batch 1.004 (1.112) Remain 00:04:10 loss: 0.2039 Lr: 0.00000 [2024-02-19 20:28:05,942 INFO misc.py line 119 87073] Train: [100/100][1333/1557] Data 0.003 (0.087) Batch 0.890 (1.112) Remain 00:04:09 loss: 0.4576 Lr: 0.00000 [2024-02-19 20:28:06,829 INFO misc.py line 119 87073] Train: [100/100][1334/1557] Data 0.003 (0.087) Batch 0.882 (1.112) Remain 00:04:07 loss: 0.2416 Lr: 0.00000 [2024-02-19 20:28:07,523 INFO misc.py line 119 87073] Train: [100/100][1335/1557] Data 0.008 (0.087) Batch 0.697 (1.111) Remain 00:04:06 loss: 0.1439 Lr: 0.00000 [2024-02-19 20:28:08,285 INFO misc.py line 119 87073] Train: [100/100][1336/1557] Data 0.004 (0.087) Batch 0.756 (1.111) Remain 00:04:05 loss: 0.1281 Lr: 0.00000 [2024-02-19 20:28:09,514 INFO misc.py line 119 87073] Train: [100/100][1337/1557] Data 0.011 (0.087) Batch 1.221 (1.111) Remain 00:04:04 loss: 0.0733 Lr: 0.00000 [2024-02-19 20:28:10,458 INFO misc.py line 119 87073] Train: [100/100][1338/1557] Data 0.019 (0.087) Batch 0.959 (1.111) Remain 00:04:03 loss: 0.1521 Lr: 0.00000 [2024-02-19 20:28:11,414 INFO misc.py line 119 87073] Train: [100/100][1339/1557] Data 0.004 (0.087) Batch 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line 119 87073] Train: [100/100][1364/1557] Data 0.004 (0.088) Batch 0.787 (1.113) Remain 00:03:34 loss: 0.0925 Lr: 0.00000 [2024-02-19 20:28:44,837 INFO misc.py line 119 87073] Train: [100/100][1365/1557] Data 0.015 (0.088) Batch 3.018 (1.114) Remain 00:03:33 loss: 0.1273 Lr: 0.00000 [2024-02-19 20:28:46,024 INFO misc.py line 119 87073] Train: [100/100][1366/1557] Data 0.005 (0.088) Batch 1.173 (1.114) Remain 00:03:32 loss: 0.1979 Lr: 0.00000 [2024-02-19 20:28:46,990 INFO misc.py line 119 87073] Train: [100/100][1367/1557] Data 0.018 (0.088) Batch 0.981 (1.114) Remain 00:03:31 loss: 0.3321 Lr: 0.00000 [2024-02-19 20:28:48,012 INFO misc.py line 119 87073] Train: [100/100][1368/1557] Data 0.003 (0.088) Batch 1.022 (1.114) Remain 00:03:30 loss: 0.3619 Lr: 0.00000 [2024-02-19 20:28:48,870 INFO misc.py line 119 87073] Train: [100/100][1369/1557] Data 0.004 (0.088) Batch 0.858 (1.114) Remain 00:03:29 loss: 0.1672 Lr: 0.00000 [2024-02-19 20:28:49,636 INFO misc.py line 119 87073] Train: 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(1.113) Remain 00:03:14 loss: 0.1692 Lr: 0.00000 [2024-02-19 20:29:02,265 INFO misc.py line 119 87073] Train: [100/100][1383/1557] Data 0.005 (0.087) Batch 0.984 (1.112) Remain 00:03:13 loss: 0.0390 Lr: 0.00000 [2024-02-19 20:29:02,948 INFO misc.py line 119 87073] Train: [100/100][1384/1557] Data 0.012 (0.087) Batch 0.691 (1.112) Remain 00:03:12 loss: 0.1973 Lr: 0.00000 [2024-02-19 20:29:03,779 INFO misc.py line 119 87073] Train: [100/100][1385/1557] Data 0.004 (0.087) Batch 0.824 (1.112) Remain 00:03:11 loss: 0.1332 Lr: 0.00000 [2024-02-19 20:29:04,873 INFO misc.py line 119 87073] Train: [100/100][1386/1557] Data 0.011 (0.087) Batch 1.093 (1.112) Remain 00:03:10 loss: 0.1594 Lr: 0.00000 [2024-02-19 20:29:05,822 INFO misc.py line 119 87073] Train: [100/100][1387/1557] Data 0.012 (0.087) Batch 0.957 (1.112) Remain 00:03:09 loss: 0.5486 Lr: 0.00000 [2024-02-19 20:29:06,800 INFO misc.py line 119 87073] Train: [100/100][1388/1557] Data 0.003 (0.087) Batch 0.978 (1.112) Remain 00:03:07 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INFO misc.py line 119 87073] Train: [100/100][1401/1557] Data 0.016 (0.086) Batch 0.871 (1.110) Remain 00:02:53 loss: 0.2239 Lr: 0.00000 [2024-02-19 20:29:19,360 INFO misc.py line 119 87073] Train: [100/100][1402/1557] Data 0.004 (0.086) Batch 0.938 (1.110) Remain 00:02:51 loss: 0.2704 Lr: 0.00000 [2024-02-19 20:29:20,203 INFO misc.py line 119 87073] Train: [100/100][1403/1557] Data 0.004 (0.086) Batch 0.843 (1.109) Remain 00:02:50 loss: 0.2895 Lr: 0.00000 [2024-02-19 20:29:21,192 INFO misc.py line 119 87073] Train: [100/100][1404/1557] Data 0.005 (0.086) Batch 0.986 (1.109) Remain 00:02:49 loss: 0.1335 Lr: 0.00000 [2024-02-19 20:29:23,349 INFO misc.py line 119 87073] Train: [100/100][1405/1557] Data 1.418 (0.087) Batch 2.159 (1.110) Remain 00:02:48 loss: 0.1459 Lr: 0.00000 [2024-02-19 20:29:24,151 INFO misc.py line 119 87073] Train: [100/100][1406/1557] Data 0.006 (0.087) Batch 0.801 (1.110) Remain 00:02:47 loss: 0.2049 Lr: 0.00000 [2024-02-19 20:29:32,352 INFO misc.py line 119 87073] Train: [100/100][1407/1557] Data 4.271 (0.090) Batch 8.204 (1.115) Remain 00:02:47 loss: 0.0790 Lr: 0.00000 [2024-02-19 20:29:33,574 INFO misc.py line 119 87073] Train: [100/100][1408/1557] Data 0.004 (0.090) Batch 1.218 (1.115) Remain 00:02:46 loss: 0.2233 Lr: 0.00000 [2024-02-19 20:29:34,574 INFO misc.py line 119 87073] Train: [100/100][1409/1557] Data 0.007 (0.090) Batch 0.997 (1.115) Remain 00:02:44 loss: 0.2028 Lr: 0.00000 [2024-02-19 20:29:35,583 INFO misc.py line 119 87073] Train: [100/100][1410/1557] Data 0.010 (0.090) Batch 1.003 (1.115) Remain 00:02:43 loss: 0.5927 Lr: 0.00000 [2024-02-19 20:29:36,514 INFO misc.py line 119 87073] Train: [100/100][1411/1557] Data 0.016 (0.090) Batch 0.943 (1.115) Remain 00:02:42 loss: 0.2384 Lr: 0.00000 [2024-02-19 20:29:37,294 INFO misc.py line 119 87073] Train: [100/100][1412/1557] Data 0.005 (0.090) Batch 0.781 (1.114) Remain 00:02:41 loss: 0.1335 Lr: 0.00000 [2024-02-19 20:29:38,056 INFO misc.py line 119 87073] Train: [100/100][1413/1557] Data 0.004 (0.090) Batch 0.756 (1.114) Remain 00:02:40 loss: 0.1974 Lr: 0.00000 [2024-02-19 20:29:39,173 INFO misc.py line 119 87073] Train: [100/100][1414/1557] Data 0.009 (0.090) Batch 1.109 (1.114) Remain 00:02:39 loss: 0.1249 Lr: 0.00000 [2024-02-19 20:29:40,222 INFO misc.py line 119 87073] Train: [100/100][1415/1557] Data 0.017 (0.090) Batch 1.054 (1.114) Remain 00:02:38 loss: 0.2441 Lr: 0.00000 [2024-02-19 20:29:41,115 INFO misc.py line 119 87073] Train: [100/100][1416/1557] Data 0.012 (0.089) Batch 0.900 (1.114) Remain 00:02:37 loss: 0.1240 Lr: 0.00000 [2024-02-19 20:29:41,991 INFO misc.py line 119 87073] Train: [100/100][1417/1557] Data 0.005 (0.089) Batch 0.876 (1.114) Remain 00:02:35 loss: 0.2120 Lr: 0.00000 [2024-02-19 20:29:42,935 INFO misc.py line 119 87073] Train: [100/100][1418/1557] Data 0.005 (0.089) Batch 0.945 (1.114) Remain 00:02:34 loss: 0.2945 Lr: 0.00000 [2024-02-19 20:29:43,630 INFO misc.py line 119 87073] Train: [100/100][1419/1557] Data 0.005 (0.089) Batch 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Data 0.004 (0.088) Batch 0.975 (1.114) Remain 00:01:11 loss: 0.2022 Lr: 0.00000 [2024-02-19 20:31:07,490 INFO misc.py line 119 87073] Train: [100/100][1494/1557] Data 0.003 (0.088) Batch 1.143 (1.114) Remain 00:01:10 loss: 0.8763 Lr: 0.00000 [2024-02-19 20:31:08,555 INFO misc.py line 119 87073] Train: [100/100][1495/1557] Data 0.004 (0.088) Batch 1.065 (1.114) Remain 00:01:09 loss: 0.1338 Lr: 0.00000 [2024-02-19 20:31:09,334 INFO misc.py line 119 87073] Train: [100/100][1496/1557] Data 0.004 (0.088) Batch 0.778 (1.113) Remain 00:01:07 loss: 0.1133 Lr: 0.00000 [2024-02-19 20:31:10,059 INFO misc.py line 119 87073] Train: [100/100][1497/1557] Data 0.004 (0.088) Batch 0.717 (1.113) Remain 00:01:06 loss: 0.0822 Lr: 0.00000 [2024-02-19 20:31:11,209 INFO misc.py line 119 87073] Train: [100/100][1498/1557] Data 0.011 (0.088) Batch 1.146 (1.113) Remain 00:01:05 loss: 0.1736 Lr: 0.00000 [2024-02-19 20:31:12,226 INFO misc.py line 119 87073] Train: [100/100][1499/1557] Data 0.016 (0.088) Batch 1.028 (1.113) Remain 00:01:04 loss: 0.0860 Lr: 0.00000 [2024-02-19 20:31:13,290 INFO misc.py line 119 87073] Train: [100/100][1500/1557] Data 0.005 (0.088) Batch 1.061 (1.113) Remain 00:01:03 loss: 0.1509 Lr: 0.00000 [2024-02-19 20:31:14,391 INFO misc.py line 119 87073] Train: [100/100][1501/1557] Data 0.008 (0.088) Batch 1.093 (1.113) Remain 00:01:02 loss: 0.2293 Lr: 0.00000 [2024-02-19 20:31:15,243 INFO misc.py line 119 87073] Train: [100/100][1502/1557] Data 0.016 (0.088) Batch 0.863 (1.113) Remain 00:01:01 loss: 0.1153 Lr: 0.00000 [2024-02-19 20:31:15,963 INFO misc.py line 119 87073] Train: [100/100][1503/1557] Data 0.005 (0.088) Batch 0.720 (1.113) Remain 00:01:00 loss: 0.1337 Lr: 0.00000 [2024-02-19 20:31:16,728 INFO misc.py line 119 87073] Train: [100/100][1504/1557] Data 0.005 (0.087) Batch 0.764 (1.112) Remain 00:00:58 loss: 0.2167 Lr: 0.00000 [2024-02-19 20:31:17,963 INFO misc.py line 119 87073] Train: [100/100][1505/1557] Data 0.006 (0.087) Batch 1.233 (1.112) Remain 00:00:57 loss: 0.1630 Lr: 0.00000 [2024-02-19 20:31:18,865 INFO misc.py line 119 87073] Train: [100/100][1506/1557] Data 0.008 (0.087) Batch 0.906 (1.112) Remain 00:00:56 loss: 0.2011 Lr: 0.00000 [2024-02-19 20:31:19,718 INFO misc.py line 119 87073] Train: [100/100][1507/1557] Data 0.004 (0.087) Batch 0.852 (1.112) Remain 00:00:55 loss: 0.3497 Lr: 0.00000 [2024-02-19 20:31:20,541 INFO misc.py line 119 87073] Train: [100/100][1508/1557] Data 0.005 (0.087) Batch 0.820 (1.112) Remain 00:00:54 loss: 0.1593 Lr: 0.00000 [2024-02-19 20:31:21,481 INFO misc.py line 119 87073] Train: [100/100][1509/1557] Data 0.008 (0.087) Batch 0.943 (1.112) Remain 00:00:53 loss: 0.1415 Lr: 0.00000 [2024-02-19 20:31:22,190 INFO misc.py line 119 87073] Train: [100/100][1510/1557] Data 0.004 (0.087) Batch 0.707 (1.112) Remain 00:00:52 loss: 0.1868 Lr: 0.00000 [2024-02-19 20:31:22,960 INFO misc.py line 119 87073] Train: [100/100][1511/1557] Data 0.006 (0.087) Batch 0.764 (1.111) Remain 00:00:51 loss: 0.1374 Lr: 0.00000 [2024-02-19 20:31:24,052 INFO misc.py line 119 87073] Train: [100/100][1512/1557] Data 0.012 (0.087) Batch 1.093 (1.111) Remain 00:00:50 loss: 0.0776 Lr: 0.00000 [2024-02-19 20:31:24,947 INFO misc.py line 119 87073] Train: [100/100][1513/1557] Data 0.012 (0.087) Batch 0.903 (1.111) Remain 00:00:48 loss: 0.2104 Lr: 0.00000 [2024-02-19 20:31:25,802 INFO misc.py line 119 87073] Train: [100/100][1514/1557] Data 0.003 (0.087) Batch 0.854 (1.111) Remain 00:00:47 loss: 0.2177 Lr: 0.00000 [2024-02-19 20:31:26,785 INFO misc.py line 119 87073] Train: [100/100][1515/1557] Data 0.005 (0.087) Batch 0.974 (1.111) Remain 00:00:46 loss: 0.3764 Lr: 0.00000 [2024-02-19 20:31:27,765 INFO misc.py line 119 87073] Train: [100/100][1516/1557] Data 0.013 (0.087) Batch 0.989 (1.111) Remain 00:00:45 loss: 0.4392 Lr: 0.00000 [2024-02-19 20:31:28,460 INFO misc.py line 119 87073] Train: [100/100][1517/1557] Data 0.003 (0.087) Batch 0.695 (1.111) Remain 00:00:44 loss: 0.1990 Lr: 0.00000 [2024-02-19 20:31:29,213 INFO misc.py line 119 87073] Train: [100/100][1518/1557] Data 0.004 (0.087) Batch 0.753 (1.110) Remain 00:00:43 loss: 0.1791 Lr: 0.00000 [2024-02-19 20:31:37,103 INFO misc.py line 119 87073] Train: [100/100][1519/1557] Data 4.717 (0.090) Batch 7.891 (1.115) Remain 00:00:42 loss: 0.0728 Lr: 0.00000 [2024-02-19 20:31:38,041 INFO misc.py line 119 87073] Train: [100/100][1520/1557] Data 0.005 (0.090) Batch 0.937 (1.115) Remain 00:00:41 loss: 0.0788 Lr: 0.00000 [2024-02-19 20:31:38,986 INFO misc.py line 119 87073] Train: [100/100][1521/1557] Data 0.005 (0.090) Batch 0.944 (1.115) Remain 00:00:40 loss: 0.3217 Lr: 0.00000 [2024-02-19 20:31:39,998 INFO misc.py line 119 87073] Train: [100/100][1522/1557] Data 0.005 (0.090) Batch 1.012 (1.114) Remain 00:00:39 loss: 0.1099 Lr: 0.00000 [2024-02-19 20:31:40,994 INFO misc.py line 119 87073] Train: [100/100][1523/1557] Data 0.005 (0.090) Batch 0.994 (1.114) Remain 00:00:37 loss: 0.0489 Lr: 0.00000 [2024-02-19 20:31:41,769 INFO misc.py line 119 87073] Train: [100/100][1524/1557] Data 0.008 (0.089) Batch 0.778 (1.114) Remain 00:00:36 loss: 0.1006 Lr: 0.00000 [2024-02-19 20:31:42,519 INFO misc.py line 119 87073] Train: [100/100][1525/1557] Data 0.004 (0.089) Batch 0.739 (1.114) Remain 00:00:35 loss: 0.1640 Lr: 0.00000 [2024-02-19 20:31:43,616 INFO misc.py line 119 87073] Train: [100/100][1526/1557] Data 0.016 (0.089) Batch 1.098 (1.114) Remain 00:00:34 loss: 0.1054 Lr: 0.00000 [2024-02-19 20:31:44,523 INFO misc.py line 119 87073] Train: [100/100][1527/1557] Data 0.015 (0.089) Batch 0.919 (1.114) Remain 00:00:33 loss: 0.1110 Lr: 0.00000 [2024-02-19 20:31:45,462 INFO misc.py line 119 87073] Train: [100/100][1528/1557] Data 0.003 (0.089) Batch 0.938 (1.114) Remain 00:00:32 loss: 0.0930 Lr: 0.00000 [2024-02-19 20:31:46,441 INFO misc.py line 119 87073] Train: [100/100][1529/1557] Data 0.004 (0.089) Batch 0.980 (1.114) Remain 00:00:31 loss: 0.3047 Lr: 0.00000 [2024-02-19 20:31:47,328 INFO misc.py line 119 87073] Train: [100/100][1530/1557] Data 0.003 (0.089) Batch 0.885 (1.113) Remain 00:00:30 loss: 0.2425 Lr: 0.00000 [2024-02-19 20:31:48,117 INFO misc.py line 119 87073] Train: [100/100][1531/1557] Data 0.006 (0.089) Batch 0.791 (1.113) Remain 00:00:28 loss: 0.0890 Lr: 0.00000 [2024-02-19 20:31:48,922 INFO misc.py line 119 87073] Train: [100/100][1532/1557] Data 0.003 (0.089) Batch 0.793 (1.113) Remain 00:00:27 loss: 0.1310 Lr: 0.00000 [2024-02-19 20:31:52,335 INFO misc.py line 119 87073] Train: [100/100][1533/1557] Data 0.016 (0.089) Batch 3.426 (1.115) Remain 00:00:26 loss: 0.1091 Lr: 0.00000 [2024-02-19 20:31:53,264 INFO misc.py line 119 87073] Train: [100/100][1534/1557] Data 0.004 (0.089) Batch 0.929 (1.114) Remain 00:00:25 loss: 0.3287 Lr: 0.00000 [2024-02-19 20:31:54,220 INFO misc.py line 119 87073] Train: [100/100][1535/1557] Data 0.005 (0.089) Batch 0.956 (1.114) Remain 00:00:24 loss: 0.2503 Lr: 0.00000 [2024-02-19 20:31:55,366 INFO misc.py line 119 87073] Train: [100/100][1536/1557] Data 0.003 (0.089) Batch 1.147 (1.114) Remain 00:00:23 loss: 0.3695 Lr: 0.00000 [2024-02-19 20:31:56,366 INFO misc.py line 119 87073] Train: [100/100][1537/1557] Data 0.004 (0.089) Batch 0.999 (1.114) Remain 00:00:22 loss: 0.3802 Lr: 0.00000 [2024-02-19 20:31:57,102 INFO misc.py line 119 87073] Train: [100/100][1538/1557] Data 0.004 (0.089) Batch 0.736 (1.114) Remain 00:00:21 loss: 0.1814 Lr: 0.00000 [2024-02-19 20:31:57,836 INFO misc.py line 119 87073] Train: [100/100][1539/1557] Data 0.004 (0.089) Batch 0.722 (1.114) Remain 00:00:20 loss: 0.1236 Lr: 0.00000 [2024-02-19 20:31:59,062 INFO misc.py line 119 87073] Train: [100/100][1540/1557] Data 0.017 (0.089) Batch 1.227 (1.114) Remain 00:00:18 loss: 0.1054 Lr: 0.00000 [2024-02-19 20:32:00,004 INFO misc.py line 119 87073] Train: [100/100][1541/1557] Data 0.015 (0.089) Batch 0.954 (1.114) Remain 00:00:17 loss: 0.1922 Lr: 0.00000 [2024-02-19 20:32:00,938 INFO misc.py line 119 87073] Train: [100/100][1542/1557] Data 0.004 (0.089) Batch 0.934 (1.114) Remain 00:00:16 loss: 0.0757 Lr: 0.00000 [2024-02-19 20:32:01,798 INFO misc.py line 119 87073] Train: [100/100][1543/1557] Data 0.004 (0.088) Batch 0.860 (1.113) Remain 00:00:15 loss: 0.3293 Lr: 0.00000 [2024-02-19 20:32:02,772 INFO misc.py line 119 87073] Train: [100/100][1544/1557] Data 0.004 (0.088) Batch 0.962 (1.113) Remain 00:00:14 loss: 0.2524 Lr: 0.00000 [2024-02-19 20:32:03,525 INFO misc.py line 119 87073] Train: [100/100][1545/1557] Data 0.015 (0.088) Batch 0.764 (1.113) Remain 00:00:13 loss: 0.1721 Lr: 0.00000 [2024-02-19 20:32:04,265 INFO misc.py line 119 87073] Train: [100/100][1546/1557] Data 0.004 (0.088) Batch 0.729 (1.113) Remain 00:00:12 loss: 0.1732 Lr: 0.00000 [2024-02-19 20:32:05,608 INFO misc.py line 119 87073] Train: [100/100][1547/1557] Data 0.015 (0.088) Batch 1.343 (1.113) Remain 00:00:11 loss: 0.1021 Lr: 0.00000 [2024-02-19 20:32:06,556 INFO misc.py line 119 87073] Train: [100/100][1548/1557] Data 0.015 (0.088) Batch 0.959 (1.113) Remain 00:00:10 loss: 0.1262 Lr: 0.00000 [2024-02-19 20:32:07,481 INFO misc.py line 119 87073] Train: [100/100][1549/1557] Data 0.005 (0.088) Batch 0.926 (1.113) Remain 00:00:08 loss: 0.1793 Lr: 0.00000 [2024-02-19 20:32:08,347 INFO misc.py line 119 87073] Train: [100/100][1550/1557] Data 0.004 (0.088) Batch 0.866 (1.113) Remain 00:00:07 loss: 0.1261 Lr: 0.00000 [2024-02-19 20:32:09,282 INFO misc.py line 119 87073] Train: [100/100][1551/1557] Data 0.004 (0.088) Batch 0.924 (1.113) Remain 00:00:06 loss: 0.2206 Lr: 0.00000 [2024-02-19 20:32:09,952 INFO misc.py line 119 87073] Train: [100/100][1552/1557] Data 0.016 (0.088) Batch 0.681 (1.112) Remain 00:00:05 loss: 0.1674 Lr: 0.00000 [2024-02-19 20:32:10,756 INFO misc.py line 119 87073] Train: [100/100][1553/1557] Data 0.004 (0.088) Batch 0.794 (1.112) Remain 00:00:04 loss: 0.2504 Lr: 0.00000 [2024-02-19 20:32:11,836 INFO misc.py line 119 87073] Train: [100/100][1554/1557] Data 0.014 (0.088) Batch 1.079 (1.112) Remain 00:00:03 loss: 0.1333 Lr: 0.00000 [2024-02-19 20:32:12,855 INFO misc.py line 119 87073] Train: [100/100][1555/1557] Data 0.016 (0.088) Batch 1.018 (1.112) Remain 00:00:02 loss: 0.2537 Lr: 0.00000 [2024-02-19 20:32:13,893 INFO misc.py line 119 87073] Train: [100/100][1556/1557] Data 0.016 (0.088) Batch 1.042 (1.112) Remain 00:00:01 loss: 0.1256 Lr: 0.00000 [2024-02-19 20:32:14,841 INFO misc.py line 119 87073] Train: [100/100][1557/1557] Data 0.012 (0.088) Batch 0.956 (1.112) Remain 00:00:00 loss: 0.1921 Lr: 0.00000 [2024-02-19 20:32:14,841 INFO misc.py line 136 87073] Train result: loss: 0.2007 [2024-02-19 20:32:14,842 INFO evaluator.py line 112 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 20:32:41,735 INFO evaluator.py line 159 87073] Interp. Test: [1/9] Loss 0.4398 [2024-02-19 20:32:42,515 INFO evaluator.py line 159 87073] Interp. Test: [2/9] Loss 0.4797 [2024-02-19 20:32:44,638 INFO evaluator.py line 159 87073] Interp. Test: [3/9] Loss 0.3283 [2024-02-19 20:32:46,852 INFO evaluator.py line 159 87073] Interp. Test: [4/9] Loss 0.3337 [2024-02-19 20:32:51,804 INFO evaluator.py line 159 87073] Interp. Test: [5/9] Loss 0.2255 [2024-02-19 20:32:52,499 INFO evaluator.py line 159 87073] Interp. Test: [6/9] Loss 0.1091 [2024-02-19 20:32:53,758 INFO evaluator.py line 159 87073] Interp. Test: [7/9] Loss 0.2632 [2024-02-19 20:32:56,711 INFO evaluator.py line 159 87073] Interp. Test: [8/9] Loss 0.2474 [2024-02-19 20:32:58,527 INFO evaluator.py line 159 87073] Interp. Test: [9/9] Loss 0.2808 [2024-02-19 20:33:00,067 INFO evaluator.py line 174 87073] Val result: mIoU/mAcc/allAcc 0.7242/0.7790/0.9182. [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_0-ceiling Result: iou/accuracy 0.9333/0.9630 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_1-floor Result: iou/accuracy 0.9830/0.9892 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_2-wall Result: iou/accuracy 0.8655/0.9728 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_3-beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_4-column Result: iou/accuracy 0.3910/0.4462 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_5-window Result: iou/accuracy 0.6628/0.6805 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_6-door Result: iou/accuracy 0.8344/0.9294 [2024-02-19 20:33:00,067 INFO evaluator.py line 180 87073] Class_7-table Result: iou/accuracy 0.8513/0.9258 [2024-02-19 20:33:00,068 INFO evaluator.py line 180 87073] Class_8-chair Result: iou/accuracy 0.9215/0.9742 [2024-02-19 20:33:00,068 INFO evaluator.py line 180 87073] Class_9-sofa Result: iou/accuracy 0.7821/0.8051 [2024-02-19 20:33:00,068 INFO evaluator.py line 180 87073] Class_10-bookcase Result: iou/accuracy 0.8019/0.8856 [2024-02-19 20:33:00,068 INFO evaluator.py line 180 87073] Class_11-board Result: iou/accuracy 0.7577/0.8313 [2024-02-19 20:33:00,068 INFO evaluator.py line 180 87073] Class_12-clutter Result: iou/accuracy 0.6296/0.7241 [2024-02-19 20:33:00,068 INFO evaluator.py line 194 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-02-19 20:33:00,069 INFO misc.py line 165 87073] Currently Best mIoU: 0.7493 [2024-02-19 20:33:00,069 INFO misc.py line 174 87073] Saving checkpoint to: exp/s3dis/semseg-pt-v3m1-1-ppt-extreme/model/model_last.pth [2024-02-19 20:33:01,993 INFO evaluator.py line 199 87073] Best mIoU: 0.7493 [2024-02-19 20:33:01,994 INFO misc.py line 259 87073] >>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>> [2024-02-19 20:33:02,467 INFO test.py line 41 87073] => Loading config ... [2024-02-19 20:33:02,467 INFO test.py line 53 87073] => Building test dataset & dataloader ... [2024-02-19 20:33:02,470 INFO s3dis.py line 55 87073] Totally 68 x 1 samples in Area_5 set. [2024-02-19 20:33:02,470 INFO misc.py line 270 87073] => Testing on model_best ... [2024-02-19 20:33:03,975 INFO test.py line 119 87073] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-02-19 20:34:10,452 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 0/293 [2024-02-19 20:34:10,606 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 1/293 [2024-02-19 20:34:10,754 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 2/293 [2024-02-19 20:34:10,901 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 3/293 [2024-02-19 20:34:11,045 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 4/293 [2024-02-19 20:34:11,192 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 5/293 [2024-02-19 20:34:11,338 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 6/293 [2024-02-19 20:34:11,483 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 7/293 [2024-02-19 20:34:11,629 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 8/293 [2024-02-19 20:34:11,774 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 9/293 [2024-02-19 20:34:11,919 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 10/293 [2024-02-19 20:34:12,066 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 11/293 [2024-02-19 20:34:12,217 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 12/293 [2024-02-19 20:34:12,366 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 13/293 [2024-02-19 20:34:12,512 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 14/293 [2024-02-19 20:34:12,657 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 15/293 [2024-02-19 20:34:12,802 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 16/293 [2024-02-19 20:34:12,947 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 17/293 [2024-02-19 20:34:13,091 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 18/293 [2024-02-19 20:34:13,238 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 19/293 [2024-02-19 20:34:13,384 INFO test.py line 196 87073] Test: 1/9-office_16, Batch: 20/293 [2024-02-19 20:34:13,530 INFO 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20:35:30,664 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 3/325 [2024-02-19 20:35:30,892 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 4/325 [2024-02-19 20:35:31,119 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 5/325 [2024-02-19 20:35:31,347 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 6/325 [2024-02-19 20:35:31,574 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 7/325 [2024-02-19 20:35:31,801 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 8/325 [2024-02-19 20:35:32,029 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 9/325 [2024-02-19 20:35:32,256 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 10/325 [2024-02-19 20:35:32,483 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 11/325 [2024-02-19 20:35:32,710 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 12/325 [2024-02-19 20:35:32,936 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 13/325 [2024-02-19 20:35:33,164 INFO 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line 196 87073] Test: 2/9-hallway_13, Batch: 25/325 [2024-02-19 20:35:35,878 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 26/325 [2024-02-19 20:35:36,101 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 27/325 [2024-02-19 20:35:36,326 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 28/325 [2024-02-19 20:35:36,550 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 29/325 [2024-02-19 20:35:36,774 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 30/325 [2024-02-19 20:35:37,000 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 31/325 [2024-02-19 20:35:37,226 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 32/325 [2024-02-19 20:35:37,451 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 33/325 [2024-02-19 20:35:37,676 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 34/325 [2024-02-19 20:35:37,903 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 35/325 [2024-02-19 20:35:38,127 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 36/325 [2024-02-19 20:35:38,351 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 37/325 [2024-02-19 20:35:38,576 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 38/325 [2024-02-19 20:35:38,801 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 39/325 [2024-02-19 20:35:39,026 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 40/325 [2024-02-19 20:35:39,251 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 41/325 [2024-02-19 20:35:39,475 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 42/325 [2024-02-19 20:35:39,700 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 43/325 [2024-02-19 20:35:39,926 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 44/325 [2024-02-19 20:35:40,152 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 45/325 [2024-02-19 20:35:40,376 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 46/325 [2024-02-19 20:35:40,603 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 47/325 [2024-02-19 20:35:40,829 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 48/325 [2024-02-19 20:35:41,053 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 49/325 [2024-02-19 20:35:41,281 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 50/325 [2024-02-19 20:35:41,507 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 51/325 [2024-02-19 20:35:41,732 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 52/325 [2024-02-19 20:35:41,957 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 53/325 [2024-02-19 20:35:42,182 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 54/325 [2024-02-19 20:35:42,407 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 55/325 [2024-02-19 20:35:42,632 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 56/325 [2024-02-19 20:35:42,857 INFO test.py line 196 87073] Test: 2/9-hallway_13, Batch: 57/325 [2024-02-19 20:35:43,083 INFO test.py line 196 87073] Test: 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[2024-02-19 20:37:20,594 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 130/274 [2024-02-19 20:37:20,715 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 131/274 [2024-02-19 20:37:20,838 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 132/274 [2024-02-19 20:37:20,961 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 133/274 [2024-02-19 20:37:21,085 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 134/274 [2024-02-19 20:37:21,207 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 135/274 [2024-02-19 20:37:21,329 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 136/274 [2024-02-19 20:37:21,451 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 137/274 [2024-02-19 20:37:21,574 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 138/274 [2024-02-19 20:37:21,696 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 139/274 [2024-02-19 20:37:21,818 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 140/274 [2024-02-19 20:37:21,939 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 141/274 [2024-02-19 20:37:22,061 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 142/274 [2024-02-19 20:37:22,188 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 143/274 [2024-02-19 20:37:22,310 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 144/274 [2024-02-19 20:37:22,433 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 145/274 [2024-02-19 20:37:22,555 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 146/274 [2024-02-19 20:37:22,677 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 147/274 [2024-02-19 20:37:22,801 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 148/274 [2024-02-19 20:37:22,923 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 149/274 [2024-02-19 20:37:23,045 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 150/274 [2024-02-19 20:37:23,167 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 151/274 [2024-02-19 20:37:23,289 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 152/274 [2024-02-19 20:37:23,411 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 153/274 [2024-02-19 20:37:23,533 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 154/274 [2024-02-19 20:37:23,655 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 155/274 [2024-02-19 20:37:23,777 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 156/274 [2024-02-19 20:37:23,899 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 157/274 [2024-02-19 20:37:24,021 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 158/274 [2024-02-19 20:37:24,143 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 159/274 [2024-02-19 20:37:24,264 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 160/274 [2024-02-19 20:37:24,386 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 161/274 [2024-02-19 20:37:24,508 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 162/274 [2024-02-19 20:37:24,630 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 163/274 [2024-02-19 20:37:24,752 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 164/274 [2024-02-19 20:37:24,875 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 165/274 [2024-02-19 20:37:24,997 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 166/274 [2024-02-19 20:37:25,119 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 167/274 [2024-02-19 20:37:25,241 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 168/274 [2024-02-19 20:37:25,364 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 169/274 [2024-02-19 20:37:25,486 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 170/274 [2024-02-19 20:37:25,607 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 171/274 [2024-02-19 20:37:25,729 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 172/274 [2024-02-19 20:37:25,851 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 173/274 [2024-02-19 20:37:25,973 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 174/274 [2024-02-19 20:37:26,096 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 175/274 [2024-02-19 20:37:26,236 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 176/274 [2024-02-19 20:37:26,375 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 177/274 [2024-02-19 20:37:26,512 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 178/274 [2024-02-19 20:37:26,650 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 179/274 [2024-02-19 20:37:26,789 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 180/274 [2024-02-19 20:37:26,928 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 181/274 [2024-02-19 20:37:27,070 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 182/274 [2024-02-19 20:37:27,208 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 183/274 [2024-02-19 20:37:27,345 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 184/274 [2024-02-19 20:37:27,482 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 185/274 [2024-02-19 20:37:27,620 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 186/274 [2024-02-19 20:37:27,784 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 187/274 [2024-02-19 20:37:27,988 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 188/274 [2024-02-19 20:37:28,151 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 189/274 [2024-02-19 20:37:28,290 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 190/274 [2024-02-19 20:37:28,428 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 191/274 [2024-02-19 20:37:28,567 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 192/274 [2024-02-19 20:37:28,706 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 193/274 [2024-02-19 20:37:28,845 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 194/274 [2024-02-19 20:37:28,985 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 195/274 [2024-02-19 20:37:29,127 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 196/274 [2024-02-19 20:37:29,267 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 197/274 [2024-02-19 20:37:29,406 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 198/274 [2024-02-19 20:37:29,544 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 199/274 [2024-02-19 20:37:29,682 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 200/274 [2024-02-19 20:37:29,820 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 201/274 [2024-02-19 20:37:29,959 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 202/274 [2024-02-19 20:37:30,097 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 203/274 [2024-02-19 20:37:30,235 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 204/274 [2024-02-19 20:37:30,373 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 205/274 [2024-02-19 20:37:30,511 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 206/274 [2024-02-19 20:37:30,649 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 207/274 [2024-02-19 20:37:30,788 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 208/274 [2024-02-19 20:37:30,927 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 209/274 [2024-02-19 20:37:31,065 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 210/274 [2024-02-19 20:37:31,203 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 211/274 [2024-02-19 20:37:31,341 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 212/274 [2024-02-19 20:37:31,479 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 213/274 [2024-02-19 20:37:31,616 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 214/274 [2024-02-19 20:37:31,754 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 215/274 [2024-02-19 20:37:31,891 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 216/274 [2024-02-19 20:37:32,029 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 217/274 [2024-02-19 20:37:32,167 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 218/274 [2024-02-19 20:37:32,304 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 219/274 [2024-02-19 20:37:32,441 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 220/274 [2024-02-19 20:37:32,580 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 221/274 [2024-02-19 20:37:32,718 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 222/274 [2024-02-19 20:37:32,855 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 223/274 [2024-02-19 20:37:32,992 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 224/274 [2024-02-19 20:37:33,130 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 225/274 [2024-02-19 20:37:33,267 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 226/274 [2024-02-19 20:37:33,405 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 227/274 [2024-02-19 20:37:33,543 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 228/274 [2024-02-19 20:37:33,680 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 229/274 [2024-02-19 20:37:33,818 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 230/274 [2024-02-19 20:37:33,955 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 231/274 [2024-02-19 20:37:34,093 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 232/274 [2024-02-19 20:37:34,230 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 233/274 [2024-02-19 20:37:34,367 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 234/274 [2024-02-19 20:37:34,506 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 235/274 [2024-02-19 20:37:34,644 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 236/274 [2024-02-19 20:37:34,781 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 237/274 [2024-02-19 20:37:34,918 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 238/274 [2024-02-19 20:37:35,057 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 239/274 [2024-02-19 20:37:35,194 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 240/274 [2024-02-19 20:37:35,333 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 241/274 [2024-02-19 20:37:35,471 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 242/274 [2024-02-19 20:37:35,608 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 243/274 [2024-02-19 20:37:35,746 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 244/274 [2024-02-19 20:37:35,883 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 245/274 [2024-02-19 20:37:36,021 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 246/274 [2024-02-19 20:37:36,160 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 247/274 [2024-02-19 20:37:36,296 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 248/274 [2024-02-19 20:37:36,433 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 249/274 [2024-02-19 20:37:36,571 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 250/274 [2024-02-19 20:37:36,708 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 251/274 [2024-02-19 20:37:36,846 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 252/274 [2024-02-19 20:37:36,983 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 253/274 [2024-02-19 20:37:37,120 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 254/274 [2024-02-19 20:37:37,258 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 255/274 [2024-02-19 20:37:37,388 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 256/274 [2024-02-19 20:37:37,518 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 257/274 [2024-02-19 20:37:37,647 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 258/274 [2024-02-19 20:37:37,777 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 259/274 [2024-02-19 20:37:37,907 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 260/274 [2024-02-19 20:37:38,037 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 261/274 [2024-02-19 20:37:38,166 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 262/274 [2024-02-19 20:37:38,296 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 263/274 [2024-02-19 20:37:38,425 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 264/274 [2024-02-19 20:37:38,555 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 265/274 [2024-02-19 20:37:38,685 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 266/274 [2024-02-19 20:37:38,815 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 267/274 [2024-02-19 20:37:38,944 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 268/274 [2024-02-19 20:37:39,075 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 269/274 [2024-02-19 20:37:39,205 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 270/274 [2024-02-19 20:37:39,335 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 271/274 [2024-02-19 20:37:39,464 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 272/274 [2024-02-19 20:37:39,593 INFO test.py line 196 87073] Test: 3/9-office_25, Batch: 273/274 [2024-02-19 20:37:39,657 INFO test.py line 230 87073] Test: office_25 [3/9]-633286 Batch 35.739 (67.432) Accuracy 0.9426 (0.7885) mIoU 0.8236 (0.7459) [2024-02-19 20:37:39,833 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 0/256 [2024-02-19 20:37:39,998 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 1/256 [2024-02-19 20:37:40,163 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 2/256 [2024-02-19 20:37:40,326 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 3/256 [2024-02-19 20:37:40,488 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 4/256 [2024-02-19 20:37:40,651 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 5/256 [2024-02-19 20:37:40,813 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 6/256 [2024-02-19 20:37:40,975 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 7/256 [2024-02-19 20:37:41,137 INFO test.py line 196 87073] Test: 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test.py line 196 87073] Test: 4/9-office_7, Batch: 211/256 [2024-02-19 20:38:14,018 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 212/256 [2024-02-19 20:38:14,195 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 213/256 [2024-02-19 20:38:14,371 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 214/256 [2024-02-19 20:38:14,573 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 215/256 [2024-02-19 20:38:14,797 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 216/256 [2024-02-19 20:38:14,977 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 217/256 [2024-02-19 20:38:15,152 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 218/256 [2024-02-19 20:38:15,333 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 219/256 [2024-02-19 20:38:15,509 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 220/256 [2024-02-19 20:38:15,686 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 221/256 [2024-02-19 20:38:15,873 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 222/256 [2024-02-19 20:38:17,154 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 223/256 [2024-02-19 20:38:17,935 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 224/256 [2024-02-19 20:38:18,127 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 225/256 [2024-02-19 20:38:18,304 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 226/256 [2024-02-19 20:38:18,480 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 227/256 [2024-02-19 20:38:18,655 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 228/256 [2024-02-19 20:38:18,830 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 229/256 [2024-02-19 20:38:19,006 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 230/256 [2024-02-19 20:38:19,181 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 231/256 [2024-02-19 20:38:19,356 INFO test.py line 196 87073] Test: 4/9-office_7, Batch: 232/256 [2024-02-19 20:38:19,534 INFO test.py line 196 87073] Test: 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20:38:23,218 INFO test.py line 230 87073] Test: office_7 [4/9]-821442 Batch 43.560 (61.464) Accuracy 0.9605 (0.7846) mIoU 0.8893 (0.7419) [2024-02-19 20:38:23,409 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 0/259 [2024-02-19 20:38:23,583 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 1/259 [2024-02-19 20:38:23,757 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 2/259 [2024-02-19 20:38:23,933 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 3/259 [2024-02-19 20:38:24,105 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 4/259 [2024-02-19 20:38:24,278 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 5/259 [2024-02-19 20:38:24,450 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 6/259 [2024-02-19 20:38:24,623 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 7/259 [2024-02-19 20:38:24,796 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 8/259 [2024-02-19 20:38:24,968 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 9/259 [2024-02-19 20:38:25,140 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 10/259 [2024-02-19 20:38:25,312 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 11/259 [2024-02-19 20:38:25,485 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 12/259 [2024-02-19 20:38:25,658 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 13/259 [2024-02-19 20:38:25,831 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 14/259 [2024-02-19 20:38:26,003 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 15/259 [2024-02-19 20:38:26,176 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 16/259 [2024-02-19 20:38:26,349 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 17/259 [2024-02-19 20:38:26,521 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 18/259 [2024-02-19 20:38:26,694 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 19/259 [2024-02-19 20:38:26,866 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 20/259 [2024-02-19 20:38:27,039 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 21/259 [2024-02-19 20:38:27,211 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 22/259 [2024-02-19 20:38:27,384 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 23/259 [2024-02-19 20:38:27,557 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 24/259 [2024-02-19 20:38:27,730 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 25/259 [2024-02-19 20:38:27,902 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 26/259 [2024-02-19 20:38:28,075 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 27/259 [2024-02-19 20:38:28,248 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 28/259 [2024-02-19 20:38:28,420 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 29/259 [2024-02-19 20:38:28,592 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 30/259 [2024-02-19 20:38:28,765 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 31/259 [2024-02-19 20:38:28,941 INFO test.py line 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Batch: 43/259 [2024-02-19 20:38:31,022 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 44/259 [2024-02-19 20:38:31,195 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 45/259 [2024-02-19 20:38:31,367 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 46/259 [2024-02-19 20:38:31,539 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 47/259 [2024-02-19 20:38:31,713 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 48/259 [2024-02-19 20:38:31,886 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 49/259 [2024-02-19 20:38:32,058 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 50/259 [2024-02-19 20:38:32,231 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 51/259 [2024-02-19 20:38:32,403 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 52/259 [2024-02-19 20:38:32,575 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 53/259 [2024-02-19 20:38:32,747 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 54/259 [2024-02-19 20:38:32,920 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 55/259 [2024-02-19 20:38:33,091 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 56/259 [2024-02-19 20:38:33,263 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 57/259 [2024-02-19 20:38:33,436 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 58/259 [2024-02-19 20:38:33,607 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 59/259 [2024-02-19 20:38:33,779 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 60/259 [2024-02-19 20:38:33,951 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 61/259 [2024-02-19 20:38:34,123 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 62/259 [2024-02-19 20:38:34,294 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 63/259 [2024-02-19 20:38:34,466 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 64/259 [2024-02-19 20:38:34,638 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 65/259 [2024-02-19 20:38:34,810 INFO test.py line 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Batch: 77/259 [2024-02-19 20:38:36,838 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 78/259 [2024-02-19 20:38:36,997 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 79/259 [2024-02-19 20:38:37,156 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 80/259 [2024-02-19 20:38:37,315 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 81/259 [2024-02-19 20:38:37,474 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 82/259 [2024-02-19 20:38:37,632 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 83/259 [2024-02-19 20:38:37,791 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 84/259 [2024-02-19 20:38:37,949 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 85/259 [2024-02-19 20:38:38,107 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 86/259 [2024-02-19 20:38:38,266 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 87/259 [2024-02-19 20:38:38,424 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 88/259 [2024-02-19 20:38:38,583 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 89/259 [2024-02-19 20:38:38,742 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 90/259 [2024-02-19 20:38:38,901 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 91/259 [2024-02-19 20:38:39,059 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 92/259 [2024-02-19 20:38:39,219 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 93/259 [2024-02-19 20:38:39,378 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 94/259 [2024-02-19 20:38:39,537 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 95/259 [2024-02-19 20:38:39,696 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 96/259 [2024-02-19 20:38:39,855 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 97/259 [2024-02-19 20:38:40,013 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 98/259 [2024-02-19 20:38:40,172 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 99/259 [2024-02-19 20:38:40,330 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 100/259 [2024-02-19 20:38:40,489 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 101/259 [2024-02-19 20:38:40,647 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 102/259 [2024-02-19 20:38:40,807 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 103/259 [2024-02-19 20:38:40,965 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 104/259 [2024-02-19 20:38:41,124 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 105/259 [2024-02-19 20:38:41,283 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 106/259 [2024-02-19 20:38:41,441 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 107/259 [2024-02-19 20:38:41,599 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 108/259 [2024-02-19 20:38:41,757 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 109/259 [2024-02-19 20:38:41,916 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 110/259 [2024-02-19 20:38:42,074 INFO test.py line 196 87073] Test: 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20:39:05,836 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 246/259 [2024-02-19 20:39:06,011 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 247/259 [2024-02-19 20:39:06,184 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 248/259 [2024-02-19 20:39:06,356 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 249/259 [2024-02-19 20:39:06,529 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 250/259 [2024-02-19 20:39:06,701 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 251/259 [2024-02-19 20:39:06,873 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 252/259 [2024-02-19 20:39:07,046 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 253/259 [2024-02-19 20:39:07,217 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 254/259 [2024-02-19 20:39:07,389 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 255/259 [2024-02-19 20:39:07,561 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 256/259 [2024-02-19 20:39:07,733 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 257/259 [2024-02-19 20:39:07,905 INFO test.py line 196 87073] Test: 5/9-office_8, Batch: 258/259 [2024-02-19 20:39:07,970 INFO test.py line 230 87073] Test: office_8 [5/9]-884812 Batch 44.751 (58.122) Accuracy 0.9741 (0.7909) mIoU 0.9307 (0.7515) [2024-02-19 20:39:08,203 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 0/277 [2024-02-19 20:39:08,420 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 1/277 [2024-02-19 20:39:08,641 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 2/277 [2024-02-19 20:39:08,856 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 3/277 [2024-02-19 20:39:09,067 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 4/277 [2024-02-19 20:39:09,280 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 5/277 [2024-02-19 20:39:09,491 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 6/277 [2024-02-19 20:39:09,700 INFO test.py line 196 87073] Test: 6/9-hallway_10, 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[2024-02-19 20:39:12,236 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 19/277 [2024-02-19 20:39:12,445 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 20/277 [2024-02-19 20:39:12,666 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 21/277 [2024-02-19 20:39:12,875 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 22/277 [2024-02-19 20:39:13,084 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 23/277 [2024-02-19 20:39:13,297 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 24/277 [2024-02-19 20:39:13,506 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 25/277 [2024-02-19 20:39:13,718 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 26/277 [2024-02-19 20:39:13,933 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 27/277 [2024-02-19 20:39:14,148 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 28/277 [2024-02-19 20:39:14,360 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 29/277 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[2024-02-19 20:39:50,802 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 206/277 [2024-02-19 20:39:51,031 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 207/277 [2024-02-19 20:39:51,260 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 208/277 [2024-02-19 20:39:51,491 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 209/277 [2024-02-19 20:39:51,720 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 210/277 [2024-02-19 20:39:51,949 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 211/277 [2024-02-19 20:39:52,181 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 212/277 [2024-02-19 20:39:52,410 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 213/277 [2024-02-19 20:39:52,639 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 214/277 [2024-02-19 20:39:52,870 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 215/277 [2024-02-19 20:39:53,098 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 216/277 [2024-02-19 20:39:53,329 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 217/277 [2024-02-19 20:39:53,557 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 218/277 [2024-02-19 20:39:53,785 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 219/277 [2024-02-19 20:39:54,013 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 220/277 [2024-02-19 20:39:54,241 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 221/277 [2024-02-19 20:39:54,469 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 222/277 [2024-02-19 20:39:54,696 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 223/277 [2024-02-19 20:39:54,927 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 224/277 [2024-02-19 20:39:55,155 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 225/277 [2024-02-19 20:39:55,384 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 226/277 [2024-02-19 20:39:55,612 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 227/277 [2024-02-19 20:39:55,842 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 228/277 [2024-02-19 20:39:56,070 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 229/277 [2024-02-19 20:39:56,299 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 230/277 [2024-02-19 20:39:56,527 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 231/277 [2024-02-19 20:39:56,756 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 232/277 [2024-02-19 20:39:56,985 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 233/277 [2024-02-19 20:39:57,213 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 234/277 [2024-02-19 20:39:57,443 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 235/277 [2024-02-19 20:39:57,671 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 236/277 [2024-02-19 20:39:57,900 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 237/277 [2024-02-19 20:39:58,127 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 238/277 [2024-02-19 20:39:58,356 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 239/277 [2024-02-19 20:39:58,584 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 240/277 [2024-02-19 20:39:58,811 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 241/277 [2024-02-19 20:39:59,040 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 242/277 [2024-02-19 20:39:59,268 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 243/277 [2024-02-19 20:39:59,496 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 244/277 [2024-02-19 20:39:59,727 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 245/277 [2024-02-19 20:39:59,955 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 246/277 [2024-02-19 20:40:00,184 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 247/277 [2024-02-19 20:40:00,415 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 248/277 [2024-02-19 20:40:00,643 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 249/277 [2024-02-19 20:40:00,871 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 250/277 [2024-02-19 20:40:01,099 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 251/277 [2024-02-19 20:40:01,327 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 252/277 [2024-02-19 20:40:01,555 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 253/277 [2024-02-19 20:40:01,784 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 254/277 [2024-02-19 20:40:02,012 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 255/277 [2024-02-19 20:40:02,222 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 256/277 [2024-02-19 20:40:02,437 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 257/277 [2024-02-19 20:40:02,650 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 258/277 [2024-02-19 20:40:02,858 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 259/277 [2024-02-19 20:40:03,071 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 260/277 [2024-02-19 20:40:03,282 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 261/277 [2024-02-19 20:40:03,490 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 262/277 [2024-02-19 20:40:03,702 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 263/277 [2024-02-19 20:40:03,911 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 264/277 [2024-02-19 20:40:04,120 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 265/277 [2024-02-19 20:40:04,328 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 266/277 [2024-02-19 20:40:04,538 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 267/277 [2024-02-19 20:40:04,746 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 268/277 [2024-02-19 20:40:04,955 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 269/277 [2024-02-19 20:40:05,164 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 270/277 [2024-02-19 20:40:05,374 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 271/277 [2024-02-19 20:40:05,583 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 272/277 [2024-02-19 20:40:05,791 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 273/277 [2024-02-19 20:40:06,000 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 274/277 [2024-02-19 20:40:06,209 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 275/277 [2024-02-19 20:40:06,417 INFO test.py line 196 87073] Test: 6/9-hallway_10, Batch: 276/277 [2024-02-19 20:40:06,496 INFO test.py line 230 87073] Test: hallway_10 [6/9]-1210360 Batch 58.525 (58.189) Accuracy 0.9919 (0.7918) mIoU 0.9743 (0.7583) [2024-02-19 20:40:06,662 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 0/254 [2024-02-19 20:40:06,813 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 1/254 [2024-02-19 20:40:06,967 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 2/254 [2024-02-19 20:40:07,117 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 3/254 [2024-02-19 20:40:07,273 INFO test.py line 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20:40:45,054 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 252/254 [2024-02-19 20:40:45,206 INFO test.py line 196 87073] Test: 7/9-office_3, Batch: 253/254 [2024-02-19 20:40:45,309 INFO test.py line 230 87073] Test: office_3 [7/9]-760563 Batch 38.813 (55.421) Accuracy 0.9447 (0.7906) mIoU 0.8986 (0.7575) [2024-02-19 20:40:45,478 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 0/242 [2024-02-19 20:40:45,632 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 1/242 [2024-02-19 20:40:45,787 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 2/242 [2024-02-19 20:40:45,942 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 3/242 [2024-02-19 20:40:46,097 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 4/242 [2024-02-19 20:40:46,252 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 5/242 [2024-02-19 20:40:46,407 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 6/242 [2024-02-19 20:40:46,562 INFO test.py line 196 87073] Test: 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8/9-office_22, Batch: 240/242 [2024-02-19 20:41:22,521 INFO test.py line 196 87073] Test: 8/9-office_22, Batch: 241/242 [2024-02-19 20:41:22,584 INFO test.py line 230 87073] Test: office_22 [8/9]-789364 Batch 37.275 (53.153) Accuracy 0.9546 (0.7762) mIoU 0.8560 (0.7463) [2024-02-19 20:41:22,753 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 0/252 [2024-02-19 20:41:22,904 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 1/252 [2024-02-19 20:41:23,056 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 2/252 [2024-02-19 20:41:23,207 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 3/252 [2024-02-19 20:41:23,361 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 4/252 [2024-02-19 20:41:23,511 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 5/252 [2024-02-19 20:41:23,663 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 6/252 [2024-02-19 20:41:23,814 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 7/252 [2024-02-19 20:41:23,966 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 8/252 [2024-02-19 20:41:24,117 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 9/252 [2024-02-19 20:41:24,268 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 10/252 [2024-02-19 20:41:24,419 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 11/252 [2024-02-19 20:41:24,570 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 12/252 [2024-02-19 20:41:24,723 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 13/252 [2024-02-19 20:41:24,874 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 14/252 [2024-02-19 20:41:25,025 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 15/252 [2024-02-19 20:41:25,177 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 16/252 [2024-02-19 20:41:25,330 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 17/252 [2024-02-19 20:41:25,482 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 18/252 [2024-02-19 20:41:25,634 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 19/252 [2024-02-19 20:41:25,785 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 20/252 [2024-02-19 20:41:25,938 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 21/252 [2024-02-19 20:41:26,089 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 22/252 [2024-02-19 20:41:26,241 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 23/252 [2024-02-19 20:41:26,392 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 24/252 [2024-02-19 20:41:26,544 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 25/252 [2024-02-19 20:41:26,695 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 26/252 [2024-02-19 20:41:26,846 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 27/252 [2024-02-19 20:41:26,998 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 28/252 [2024-02-19 20:41:27,149 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 29/252 [2024-02-19 20:41:27,303 INFO test.py line 196 87073] Test: 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20:41:30,798 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 53/252 [2024-02-19 20:41:30,949 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 54/252 [2024-02-19 20:41:31,100 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 55/252 [2024-02-19 20:41:31,251 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 56/252 [2024-02-19 20:41:31,402 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 57/252 [2024-02-19 20:41:31,553 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 58/252 [2024-02-19 20:41:31,704 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 59/252 [2024-02-19 20:41:31,856 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 60/252 [2024-02-19 20:41:32,007 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 61/252 [2024-02-19 20:41:32,158 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 62/252 [2024-02-19 20:41:32,309 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 63/252 [2024-02-19 20:41:32,462 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 64/252 [2024-02-19 20:41:32,613 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 65/252 [2024-02-19 20:41:32,764 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 66/252 [2024-02-19 20:41:32,914 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 67/252 [2024-02-19 20:41:33,065 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 68/252 [2024-02-19 20:41:33,216 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 69/252 [2024-02-19 20:41:33,368 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 70/252 [2024-02-19 20:41:33,518 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 71/252 [2024-02-19 20:41:33,669 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 72/252 [2024-02-19 20:41:33,820 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 73/252 [2024-02-19 20:41:33,970 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 74/252 [2024-02-19 20:41:34,122 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 75/252 [2024-02-19 20:41:34,272 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 76/252 [2024-02-19 20:41:34,423 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 77/252 [2024-02-19 20:41:34,573 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 78/252 [2024-02-19 20:41:34,724 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 79/252 [2024-02-19 20:41:34,864 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 80/252 [2024-02-19 20:41:35,003 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 81/252 [2024-02-19 20:41:35,142 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 82/252 [2024-02-19 20:41:35,282 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 83/252 [2024-02-19 20:41:35,421 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 84/252 [2024-02-19 20:41:35,560 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 85/252 [2024-02-19 20:41:35,698 INFO test.py line 196 87073] Test: 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[2024-02-19 20:41:38,896 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 109/252 [2024-02-19 20:41:39,035 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 110/252 [2024-02-19 20:41:39,175 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 111/252 [2024-02-19 20:41:39,314 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 112/252 [2024-02-19 20:41:39,453 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 113/252 [2024-02-19 20:41:39,592 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 114/252 [2024-02-19 20:41:39,732 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 115/252 [2024-02-19 20:41:39,871 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 116/252 [2024-02-19 20:41:40,010 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 117/252 [2024-02-19 20:41:40,149 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 118/252 [2024-02-19 20:41:40,288 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 119/252 [2024-02-19 20:41:40,428 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 120/252 [2024-02-19 20:41:40,567 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 121/252 [2024-02-19 20:41:40,706 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 122/252 [2024-02-19 20:41:40,846 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 123/252 [2024-02-19 20:41:40,984 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 124/252 [2024-02-19 20:41:41,125 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 125/252 [2024-02-19 20:41:41,264 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 126/252 [2024-02-19 20:41:41,403 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 127/252 [2024-02-19 20:41:41,542 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 128/252 [2024-02-19 20:41:41,682 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 129/252 [2024-02-19 20:41:41,821 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 130/252 [2024-02-19 20:41:41,960 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 131/252 [2024-02-19 20:41:42,099 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 132/252 [2024-02-19 20:41:42,238 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 133/252 [2024-02-19 20:41:42,378 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 134/252 [2024-02-19 20:41:42,517 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 135/252 [2024-02-19 20:41:42,656 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 136/252 [2024-02-19 20:41:42,795 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 137/252 [2024-02-19 20:41:42,935 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 138/252 [2024-02-19 20:41:43,073 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 139/252 [2024-02-19 20:41:43,213 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 140/252 [2024-02-19 20:41:43,351 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 141/252 [2024-02-19 20:41:43,490 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 142/252 [2024-02-19 20:41:43,629 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 143/252 [2024-02-19 20:41:43,768 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 144/252 [2024-02-19 20:41:43,906 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 145/252 [2024-02-19 20:41:44,045 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 146/252 [2024-02-19 20:41:44,184 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 147/252 [2024-02-19 20:41:44,323 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 148/252 [2024-02-19 20:41:44,461 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 149/252 [2024-02-19 20:41:44,600 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 150/252 [2024-02-19 20:41:44,739 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 151/252 [2024-02-19 20:41:44,877 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 152/252 [2024-02-19 20:41:45,016 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 153/252 [2024-02-19 20:41:45,154 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 154/252 [2024-02-19 20:41:45,293 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 155/252 [2024-02-19 20:41:45,431 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 156/252 [2024-02-19 20:41:45,570 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 157/252 [2024-02-19 20:41:45,708 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 158/252 [2024-02-19 20:41:45,847 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 159/252 [2024-02-19 20:41:46,010 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 160/252 [2024-02-19 20:41:46,172 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 161/252 [2024-02-19 20:41:46,334 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 162/252 [2024-02-19 20:41:46,495 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 163/252 [2024-02-19 20:41:46,657 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 164/252 [2024-02-19 20:41:46,819 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 165/252 [2024-02-19 20:41:46,980 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 166/252 [2024-02-19 20:41:47,141 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 167/252 [2024-02-19 20:41:47,303 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 168/252 [2024-02-19 20:41:47,464 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 169/252 [2024-02-19 20:41:47,626 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 170/252 [2024-02-19 20:41:47,787 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 171/252 [2024-02-19 20:41:47,948 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 172/252 [2024-02-19 20:41:48,111 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 173/252 [2024-02-19 20:41:48,273 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 174/252 [2024-02-19 20:41:48,434 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 175/252 [2024-02-19 20:41:48,596 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 176/252 [2024-02-19 20:41:48,758 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 177/252 [2024-02-19 20:41:48,920 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 178/252 [2024-02-19 20:41:49,082 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 179/252 [2024-02-19 20:41:49,247 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 180/252 [2024-02-19 20:41:49,409 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 181/252 [2024-02-19 20:41:49,570 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 182/252 [2024-02-19 20:41:49,733 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 183/252 [2024-02-19 20:41:49,895 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 184/252 [2024-02-19 20:41:50,056 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 185/252 [2024-02-19 20:41:50,218 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 186/252 [2024-02-19 20:41:50,379 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 187/252 [2024-02-19 20:41:50,541 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 188/252 [2024-02-19 20:41:50,702 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 189/252 [2024-02-19 20:41:50,865 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 190/252 [2024-02-19 20:41:51,026 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 191/252 [2024-02-19 20:41:51,188 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 192/252 [2024-02-19 20:41:51,350 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 193/252 [2024-02-19 20:41:51,512 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 194/252 [2024-02-19 20:41:51,676 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 195/252 [2024-02-19 20:41:51,838 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 196/252 [2024-02-19 20:41:51,999 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 197/252 [2024-02-19 20:41:52,160 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 198/252 [2024-02-19 20:41:52,323 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 199/252 [2024-02-19 20:41:52,485 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 200/252 [2024-02-19 20:41:52,646 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 201/252 [2024-02-19 20:41:52,808 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 202/252 [2024-02-19 20:41:52,970 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 203/252 [2024-02-19 20:41:53,132 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 204/252 [2024-02-19 20:41:53,293 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 205/252 [2024-02-19 20:41:53,455 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 206/252 [2024-02-19 20:41:53,617 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 207/252 [2024-02-19 20:41:53,780 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 208/252 [2024-02-19 20:41:53,941 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 209/252 [2024-02-19 20:41:54,103 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 210/252 [2024-02-19 20:41:54,264 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 211/252 [2024-02-19 20:41:54,426 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 212/252 [2024-02-19 20:41:54,588 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 213/252 [2024-02-19 20:41:54,748 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 214/252 [2024-02-19 20:41:54,910 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 215/252 [2024-02-19 20:41:55,072 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 216/252 [2024-02-19 20:41:55,235 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 217/252 [2024-02-19 20:41:55,397 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 218/252 [2024-02-19 20:41:55,558 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 219/252 [2024-02-19 20:41:55,720 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 220/252 [2024-02-19 20:41:55,882 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 221/252 [2024-02-19 20:41:56,043 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 222/252 [2024-02-19 20:41:56,205 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 223/252 [2024-02-19 20:41:56,367 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 224/252 [2024-02-19 20:41:56,528 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 225/252 [2024-02-19 20:41:56,689 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 226/252 [2024-02-19 20:41:56,851 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 227/252 [2024-02-19 20:41:57,012 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 228/252 [2024-02-19 20:41:57,173 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 229/252 [2024-02-19 20:41:57,334 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 230/252 [2024-02-19 20:41:57,495 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 231/252 [2024-02-19 20:41:57,646 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 232/252 [2024-02-19 20:41:57,796 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 233/252 [2024-02-19 20:41:57,946 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 234/252 [2024-02-19 20:41:58,097 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 235/252 [2024-02-19 20:41:58,248 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 236/252 [2024-02-19 20:41:58,399 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 237/252 [2024-02-19 20:41:58,550 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 238/252 [2024-02-19 20:41:58,701 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 239/252 [2024-02-19 20:41:58,851 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 240/252 [2024-02-19 20:41:59,002 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 241/252 [2024-02-19 20:41:59,153 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 242/252 [2024-02-19 20:41:59,304 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 243/252 [2024-02-19 20:41:59,454 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 244/252 [2024-02-19 20:41:59,605 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 245/252 [2024-02-19 20:41:59,756 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 246/252 [2024-02-19 20:41:59,907 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 247/252 [2024-02-19 20:42:00,058 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 248/252 [2024-02-19 20:42:00,209 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 249/252 [2024-02-19 20:42:00,360 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 250/252 [2024-02-19 20:42:00,511 INFO test.py line 196 87073] Test: 9/9-office_28, Batch: 251/252 [2024-02-19 20:42:00,564 INFO test.py line 230 87073] Test: office_28 [9/9]-777028 Batch 37.979 (51.467) Accuracy 0.9569 (0.7792) mIoU 0.9051 (0.7483) [2024-02-19 20:42:01,787 INFO test.py line 289 87073] Syncing ... [2024-02-19 20:47:19,686 INFO test.py line 317 87073] Val result: mIoU/mAcc/allAcc 0.7539/0.8006/0.9260 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_0 - ceiling Result: iou/accuracy 0.9395/0.9660 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_1 - floor Result: iou/accuracy 0.9846/0.9904 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_2 - wall Result: iou/accuracy 0.8750/0.9763 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_3 - beam Result: iou/accuracy 0.0000/0.0000 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_4 - column Result: iou/accuracy 0.4448/0.4765 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_5 - window Result: iou/accuracy 0.7139/0.7361 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_6 - door Result: iou/accuracy 0.8647/0.9356 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_7 - table Result: iou/accuracy 0.8500/0.9183 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_8 - chair Result: iou/accuracy 0.9407/0.9803 [2024-02-19 20:47:19,686 INFO test.py line 323 87073] Class_9 - sofa Result: iou/accuracy 0.8562/0.9040 [2024-02-19 20:47:19,687 INFO test.py line 323 87073] Class_10 - bookcase Result: iou/accuracy 0.8124/0.9091 [2024-02-19 20:47:19,687 INFO test.py line 323 87073] Class_11 - board Result: iou/accuracy 0.8646/0.8772 [2024-02-19 20:47:19,687 INFO test.py line 323 87073] Class_12 - clutter Result: iou/accuracy 0.6541/0.7375 [2024-02-19 20:47:19,687 INFO test.py line 331 87073] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<