[2023-12-20 13:22:08,962 INFO train.py line 128 131400] => Loading config ... [2023-12-20 13:22:08,962 INFO train.py line 130 131400] Save path: exp/scannet/semseg-pt-v3m1-0-base [2023-12-20 13:22:09,984 INFO train.py line 131 131400] Config: weight = None resume = False evaluate = True test_only = False seed = 43244662 save_path = 'exp/scannet/semseg-pt-v3m1-0-base' num_worker = 24 batch_size = 12 batch_size_val = None batch_size_test = None epoch = 800 eval_epoch = 100 sync_bn = False enable_amp = True empty_cache = False find_unused_parameters = False mix_prob = 0.8 param_dicts = [dict(keyword='block', lr=0.0006)] 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='DefaultTrainer') test = dict(type='SemSegTester', verbose=True) model = dict( type='DefaultSegmentorV2', num_classes=20, backbone_out_channels=64, 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, 512), enc_num_head=(2, 4, 8, 16, 32), enc_patch_size=(1024, 1024, 1024, 1024, 1024), dec_depths=(2, 2, 2, 2), dec_channels=(64, 64, 128, 256), dec_num_head=(4, 4, 8, 16), dec_patch_size=(1024, 1024, 1024, 1024), 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=False, enable_flash=True, upcast_attention=False, upcast_softmax=False, cls_mode=False, pdnorm_bn=False, pdnorm_ln=False, 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) ]) optimizer = dict(type='AdamW', lr=0.006, weight_decay=0.05) scheduler = dict( type='OneCycleLR', max_lr=[0.006, 0.0006], pct_start=0.05, anneal_strategy='cos', div_factor=10.0, final_div_factor=1000.0) dataset_type = 'ScanNetDataset' data_root = 'data/scannet' data = dict( num_classes=20, ignore_index=-1, names=[ 'wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain', 'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture' ], train=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='ElasticDistortion', distortion_params=[[0.2, 0.4], [0.8, 1.6]]), 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='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment'), feat_keys=('color', 'normal')) ], test_mode=False, loop=8), val=dict( type='ScanNetDataset', split='val', data_root='data/scannet', transform=[ dict(type='CenterShift', apply_z=True), 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='Collect', keys=('coord', 'grid_coord', 'segment'), feat_keys=('color', 'normal')) ], test_mode=False), test=dict( type='ScanNetDataset', split='val', data_root='data/scannet', 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='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'index'), 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 [2023-12-20 13:22:09,984 INFO train.py line 132 131400] => Building model ... [2023-12-20 13:22:10,298 INFO train.py line 209 131400] Num params: 46167572 [2023-12-20 13:22:10,435 INFO train.py line 134 131400] => Building writer ... [2023-12-20 13:22:10,440 INFO train.py line 219 131400] Tensorboard writer logging dir: exp/scannet/semseg-pt-v3m1-0-base [2023-12-20 13:22:10,441 INFO train.py line 136 131400] => Building train dataset & dataloader ... [2023-12-20 13:22:10,515 INFO scannet.py line 72 131400] Totally 1201 x 8 samples in train set. [2023-12-20 13:22:10,517 INFO train.py line 138 131400] => Building val dataset & dataloader ... [2023-12-20 13:22:10,608 INFO scannet.py line 72 131400] Totally 312 x 1 samples in val set. [2023-12-20 13:22:10,609 INFO train.py line 140 131400] => Building optimize, scheduler, scaler(amp) ... [2023-12-20 13:22:10,625 INFO optimizer.py line 54 131400] Params Group 1 - lr: 0.006; Params: ['module.seg_head.weight', 'module.seg_head.bias', 'module.backbone.embedding.stem.conv.weight', 'module.backbone.embedding.stem.norm.weight', 'module.backbone.embedding.stem.norm.bias', 'module.backbone.enc.enc1.down.proj.weight', 'module.backbone.enc.enc1.down.proj.bias', 'module.backbone.enc.enc1.down.norm.0.weight', 'module.backbone.enc.enc1.down.norm.0.bias', 'module.backbone.enc.enc2.down.proj.weight', 'module.backbone.enc.enc2.down.proj.bias', 'module.backbone.enc.enc2.down.norm.0.weight', 'module.backbone.enc.enc2.down.norm.0.bias', 'module.backbone.enc.enc3.down.proj.weight', 'module.backbone.enc.enc3.down.proj.bias', 'module.backbone.enc.enc3.down.norm.0.weight', 'module.backbone.enc.enc3.down.norm.0.bias', 'module.backbone.enc.enc4.down.proj.weight', 'module.backbone.enc.enc4.down.proj.bias', 'module.backbone.enc.enc4.down.norm.0.weight', 'module.backbone.enc.enc4.down.norm.0.bias', 'module.backbone.dec.dec3.up.proj.0.weight', 'module.backbone.dec.dec3.up.proj.0.bias', 'module.backbone.dec.dec3.up.proj.1.weight', 'module.backbone.dec.dec3.up.proj.1.bias', 'module.backbone.dec.dec3.up.proj_skip.0.weight', 'module.backbone.dec.dec3.up.proj_skip.0.bias', 'module.backbone.dec.dec3.up.proj_skip.1.weight', 'module.backbone.dec.dec3.up.proj_skip.1.bias', 'module.backbone.dec.dec2.up.proj.0.weight', 'module.backbone.dec.dec2.up.proj.0.bias', 'module.backbone.dec.dec2.up.proj.1.weight', 'module.backbone.dec.dec2.up.proj.1.bias', 'module.backbone.dec.dec2.up.proj_skip.0.weight', 'module.backbone.dec.dec2.up.proj_skip.0.bias', 'module.backbone.dec.dec2.up.proj_skip.1.weight', 'module.backbone.dec.dec2.up.proj_skip.1.bias', 'module.backbone.dec.dec1.up.proj.0.weight', 'module.backbone.dec.dec1.up.proj.0.bias', 'module.backbone.dec.dec1.up.proj.1.weight', 'module.backbone.dec.dec1.up.proj.1.bias', 'module.backbone.dec.dec1.up.proj_skip.0.weight', 'module.backbone.dec.dec1.up.proj_skip.0.bias', 'module.backbone.dec.dec1.up.proj_skip.1.weight', 'module.backbone.dec.dec1.up.proj_skip.1.bias', 'module.backbone.dec.dec0.up.proj.0.weight', 'module.backbone.dec.dec0.up.proj.0.bias', 'module.backbone.dec.dec0.up.proj.1.weight', 'module.backbone.dec.dec0.up.proj.1.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.weight', 'module.backbone.dec.dec0.up.proj_skip.1.bias']. [2023-12-20 13:22:10,629 INFO optimizer.py line 54 131400] Params Group 2 - lr: 0.0006; 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.weight', 'module.backbone.enc.enc0.block0.cpe.2.bias', 'module.backbone.enc.enc0.block0.norm1.0.weight', 'module.backbone.enc.enc0.block0.norm1.0.bias', 'module.backbone.enc.enc0.block0.attn.qkv.weight', 'module.backbone.enc.enc0.block0.attn.qkv.bias', 'module.backbone.enc.enc0.block0.attn.proj.weight', 'module.backbone.enc.enc0.block0.attn.proj.bias', 'module.backbone.enc.enc0.block0.norm2.0.weight', 'module.backbone.enc.enc0.block0.norm2.0.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc1.weight', 'module.backbone.enc.enc0.block0.mlp.0.fc1.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc2.weight', 'module.backbone.enc.enc0.block0.mlp.0.fc2.bias', 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[2023-12-20 13:22:10,652 INFO train.py line 144 131400] => Building hooks ... [2023-12-20 13:22:10,653 INFO misc.py line 213 131400] => Loading checkpoint & weight ... [2023-12-20 13:22:10,653 INFO misc.py line 250 131400] No weight found at: None [2023-12-20 13:22:10,653 INFO train.py line 151 131400] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>> [2023-12-20 13:22:39,383 INFO misc.py line 119 131400] Train: [1/100][1/800] Data 24.403 (24.403) Batch 28.524 (28.524) Remain 633:52:09 loss: 3.9008 Lr: 0.00060 [2023-12-20 13:22:39,989 INFO misc.py line 119 131400] Train: [1/100][2/800] Data 0.005 (0.005) Batch 0.606 (0.606) Remain 13:28:13 loss: 4.2363 Lr: 0.00060 [2023-12-20 13:22:40,325 INFO misc.py line 119 131400] Train: [1/100][3/800] Data 0.003 (0.003) Batch 0.335 (0.335) Remain 07:27:06 loss: 3.6400 Lr: 0.00060 [2023-12-20 13:22:40,632 INFO misc.py line 119 131400] Train: [1/100][4/800] Data 0.003 (0.003) Batch 0.308 (0.308) Remain 06:50:20 loss: 3.6116 Lr: 0.00060 [2023-12-20 13:22:40,955 INFO misc.py line 119 131400] Train: [1/100][5/800] Data 0.003 (0.003) Batch 0.322 (0.315) Remain 07:00:02 loss: 3.1519 Lr: 0.00060 [2023-12-20 13:22:41,306 INFO misc.py line 119 131400] Train: [1/100][6/800] Data 0.003 (0.003) Batch 0.351 (0.327) Remain 07:15:49 loss: 3.1129 Lr: 0.00060 [2023-12-20 13:22:41,623 INFO misc.py line 119 131400] Train: [1/100][7/800] Data 0.004 (0.003) Batch 0.317 (0.324) Remain 07:12:25 loss: 2.7604 Lr: 0.00060 [2023-12-20 13:22:41,948 INFO misc.py line 119 131400] Train: [1/100][8/800] Data 0.004 (0.003) Batch 0.325 (0.325) Remain 07:12:38 loss: 2.6922 Lr: 0.00060 [2023-12-20 13:22:42,278 INFO misc.py line 119 131400] Train: [1/100][9/800] Data 0.004 (0.004) Batch 0.330 (0.325) Remain 07:13:46 loss: 2.9097 Lr: 0.00060 [2023-12-20 13:22:42,603 INFO misc.py line 119 131400] Train: [1/100][10/800] Data 0.004 (0.004) Batch 0.325 (0.325) Remain 07:13:39 loss: 2.9606 Lr: 0.00060 [2023-12-20 13:22:43,046 INFO misc.py line 119 131400] Train: [1/100][11/800] Data 0.004 (0.004) Batch 0.445 (0.340) Remain 07:33:34 loss: 2.1418 Lr: 0.00060 [2023-12-20 13:22:43,369 INFO misc.py line 119 131400] Train: [1/100][12/800] Data 0.003 (0.004) Batch 0.322 (0.338) Remain 07:30:50 loss: 2.5545 Lr: 0.00060 [2023-12-20 13:22:43,699 INFO misc.py line 119 131400] Train: [1/100][13/800] Data 0.004 (0.004) Batch 0.330 (0.337) Remain 07:29:45 loss: 2.3718 Lr: 0.00060 [2023-12-20 13:22:44,048 INFO misc.py line 119 131400] Train: [1/100][14/800] Data 0.004 (0.004) Batch 0.349 (0.338) Remain 07:31:07 loss: 2.7075 Lr: 0.00060 [2023-12-20 13:22:44,372 INFO misc.py line 119 131400] Train: [1/100][15/800] Data 0.003 (0.004) Batch 0.324 (0.337) Remain 07:29:34 loss: 2.3374 Lr: 0.00060 [2023-12-20 13:22:44,707 INFO misc.py line 119 131400] Train: [1/100][16/800] Data 0.004 (0.004) Batch 0.335 (0.337) Remain 07:29:18 loss: 3.3945 Lr: 0.00060 [2023-12-20 13:22:45,043 INFO misc.py line 119 131400] Train: [1/100][17/800] Data 0.003 (0.004) Batch 0.337 (0.337) Remain 07:29:15 loss: 2.3221 Lr: 0.00060 [2023-12-20 13:22:45,367 INFO misc.py line 119 131400] Train: [1/100][18/800] Data 0.005 (0.004) Batch 0.323 (0.336) Remain 07:28:01 loss: 2.5298 Lr: 0.00060 [2023-12-20 13:22:45,690 INFO misc.py line 119 131400] Train: [1/100][19/800] Data 0.004 (0.004) Batch 0.322 (0.335) Remain 07:26:52 loss: 1.8266 Lr: 0.00060 [2023-12-20 13:22:46,014 INFO misc.py line 119 131400] Train: [1/100][20/800] Data 0.005 (0.004) Batch 0.324 (0.335) Remain 07:25:58 loss: 2.5378 Lr: 0.00060 [2023-12-20 13:22:46,352 INFO misc.py line 119 131400] Train: [1/100][21/800] Data 0.004 (0.004) Batch 0.339 (0.335) Remain 07:26:17 loss: 2.6994 Lr: 0.00060 [2023-12-20 13:22:46,733 INFO misc.py line 119 131400] Train: [1/100][22/800] Data 0.003 (0.004) Batch 0.369 (0.337) Remain 07:28:41 loss: 2.2163 Lr: 0.00060 [2023-12-20 13:22:47,063 INFO misc.py line 119 131400] Train: [1/100][23/800] Data 0.017 (0.004) Batch 0.342 (0.337) Remain 07:29:04 loss: 2.8108 Lr: 0.00060 [2023-12-20 13:22:47,387 INFO misc.py line 119 131400] Train: [1/100][24/800] Data 0.003 (0.004) Batch 0.324 (0.336) Remain 07:28:15 loss: 2.5896 Lr: 0.00060 [2023-12-20 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line 119 131400] Train: [1/100][778/800] Data 0.004 (0.004) Batch 0.342 (0.324) Remain 07:08:25 loss: 1.2360 Lr: 0.00109 [2023-12-20 13:26:52,138 INFO misc.py line 119 131400] Train: [1/100][779/800] Data 0.005 (0.004) Batch 0.339 (0.324) Remain 07:08:27 loss: 1.3550 Lr: 0.00109 [2023-12-20 13:26:52,484 INFO misc.py line 119 131400] Train: [1/100][780/800] Data 0.003 (0.004) Batch 0.346 (0.325) Remain 07:08:29 loss: 1.1324 Lr: 0.00109 [2023-12-20 13:26:52,826 INFO misc.py line 119 131400] Train: [1/100][781/800] Data 0.003 (0.004) Batch 0.341 (0.325) Remain 07:08:30 loss: 1.2513 Lr: 0.00109 [2023-12-20 13:26:53,164 INFO misc.py line 119 131400] Train: [1/100][782/800] Data 0.004 (0.004) Batch 0.335 (0.325) Remain 07:08:31 loss: 1.1025 Lr: 0.00109 [2023-12-20 13:26:53,487 INFO misc.py line 119 131400] Train: [1/100][783/800] Data 0.007 (0.004) Batch 0.327 (0.325) Remain 07:08:31 loss: 1.3770 Lr: 0.00109 [2023-12-20 13:26:53,846 INFO misc.py line 119 131400] Train: [1/100][784/800] Data 0.003 (0.004) Batch 0.358 (0.325) Remain 07:08:34 loss: 1.1205 Lr: 0.00109 [2023-12-20 13:26:54,188 INFO misc.py line 119 131400] Train: [1/100][785/800] Data 0.004 (0.004) Batch 0.343 (0.325) Remain 07:08:35 loss: 1.6160 Lr: 0.00110 [2023-12-20 13:26:54,550 INFO misc.py line 119 131400] Train: [1/100][786/800] Data 0.003 (0.004) Batch 0.361 (0.325) Remain 07:08:39 loss: 1.4585 Lr: 0.00110 [2023-12-20 13:26:55,021 INFO misc.py line 119 131400] Train: [1/100][787/800] Data 0.003 (0.004) Batch 0.472 (0.325) Remain 07:08:53 loss: 1.2099 Lr: 0.00110 [2023-12-20 13:26:55,369 INFO misc.py line 119 131400] Train: [1/100][788/800] Data 0.002 (0.004) Batch 0.348 (0.325) Remain 07:08:55 loss: 0.6659 Lr: 0.00110 [2023-12-20 13:26:55,636 INFO misc.py line 119 131400] Train: [1/100][789/800] Data 0.003 (0.004) Batch 0.267 (0.325) Remain 07:08:49 loss: 1.4112 Lr: 0.00110 [2023-12-20 13:26:55,954 INFO misc.py line 119 131400] Train: [1/100][790/800] Data 0.003 (0.004) Batch 0.318 (0.325) Remain 07:08:48 loss: 0.9863 Lr: 0.00110 [2023-12-20 13:26:56,269 INFO misc.py line 119 131400] Train: [1/100][791/800] Data 0.003 (0.004) Batch 0.316 (0.325) Remain 07:08:47 loss: 1.1721 Lr: 0.00110 [2023-12-20 13:26:56,587 INFO misc.py line 119 131400] Train: [1/100][792/800] Data 0.003 (0.004) Batch 0.317 (0.325) Remain 07:08:46 loss: 1.1630 Lr: 0.00110 [2023-12-20 13:26:56,888 INFO misc.py line 119 131400] Train: [1/100][793/800] Data 0.003 (0.004) Batch 0.296 (0.325) Remain 07:08:42 loss: 1.2704 Lr: 0.00111 [2023-12-20 13:26:57,174 INFO misc.py line 119 131400] Train: [1/100][794/800] Data 0.007 (0.004) Batch 0.290 (0.325) Remain 07:08:39 loss: 0.9887 Lr: 0.00111 [2023-12-20 13:26:57,483 INFO misc.py line 119 131400] Train: [1/100][795/800] Data 0.003 (0.004) Batch 0.310 (0.325) Remain 07:08:37 loss: 1.7167 Lr: 0.00111 [2023-12-20 13:26:57,778 INFO misc.py line 119 131400] Train: [1/100][796/800] Data 0.002 (0.004) Batch 0.295 (0.325) Remain 07:08:34 loss: 1.4373 Lr: 0.00111 [2023-12-20 13:26:58,093 INFO misc.py line 119 131400] Train: [1/100][797/800] Data 0.002 (0.004) Batch 0.314 (0.325) Remain 07:08:32 loss: 1.3166 Lr: 0.00111 [2023-12-20 13:26:58,390 INFO misc.py line 119 131400] Train: [1/100][798/800] Data 0.003 (0.004) Batch 0.299 (0.325) Remain 07:08:29 loss: 1.4240 Lr: 0.00111 [2023-12-20 13:26:58,659 INFO misc.py line 119 131400] Train: [1/100][799/800] Data 0.003 (0.004) Batch 0.268 (0.325) Remain 07:08:23 loss: 1.7786 Lr: 0.00111 [2023-12-20 13:26:58,966 INFO misc.py line 119 131400] Train: [1/100][800/800] Data 0.003 (0.004) Batch 0.306 (0.325) Remain 07:08:21 loss: 1.2493 Lr: 0.00111 [2023-12-20 13:26:58,966 INFO misc.py line 136 131400] Train result: loss: 1.7374 [2023-12-20 13:26:58,966 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:27:21,343 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.5520 [2023-12-20 13:27:21,412 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.7577 [2023-12-20 13:27:21,500 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.9072 [2023-12-20 13:27:21,603 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.7015 [2023-12-20 13:27:21,720 INFO evaluator.py line 159 131400] Test: [5/78] Loss 1.5772 [2023-12-20 13:27:21,823 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.4064 [2023-12-20 13:27:21,910 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.7289 [2023-12-20 13:27:22,017 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.5533 [2023-12-20 13:27:22,096 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.7626 [2023-12-20 13:27:22,182 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.9518 [2023-12-20 13:27:22,271 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.9500 [2023-12-20 13:27:22,410 INFO evaluator.py line 159 131400] Test: [12/78] Loss 1.2379 [2023-12-20 13:27:22,529 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6902 [2023-12-20 13:27:22,685 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.9644 [2023-12-20 13:27:22,785 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.9786 [2023-12-20 13:27:22,923 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.2400 [2023-12-20 13:27:23,030 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.5798 [2023-12-20 13:27:23,140 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8249 [2023-12-20 13:27:23,252 INFO evaluator.py line 159 131400] Test: [19/78] Loss 1.3337 [2023-12-20 13:27:23,332 INFO evaluator.py line 159 131400] Test: [20/78] Loss 1.1281 [2023-12-20 13:27:23,436 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.8517 [2023-12-20 13:27:23,592 INFO evaluator.py line 159 131400] Test: [22/78] Loss 1.0082 [2023-12-20 13:27:23,717 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.1179 [2023-12-20 13:27:23,862 INFO evaluator.py line 159 131400] Test: [24/78] Loss 1.4928 [2023-12-20 13:27:24,003 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.6395 [2023-12-20 13:27:24,084 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.6227 [2023-12-20 13:27:24,239 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7185 [2023-12-20 13:27:24,362 INFO evaluator.py line 159 131400] Test: [28/78] Loss 1.2058 [2023-12-20 13:27:24,457 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.8307 [2023-12-20 13:27:24,600 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7215 [2023-12-20 13:27:24,703 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.4384 [2023-12-20 13:27:24,820 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.2881 [2023-12-20 13:27:24,905 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.6723 [2023-12-20 13:27:24,972 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.6709 [2023-12-20 13:27:25,072 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.2127 [2023-12-20 13:27:25,167 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.4071 [2023-12-20 13:27:25,302 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.6325 [2023-12-20 13:27:25,412 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.5005 [2023-12-20 13:27:25,491 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.2603 [2023-12-20 13:27:25,637 INFO evaluator.py line 159 131400] Test: [40/78] Loss 1.6280 [2023-12-20 13:27:25,786 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.4930 [2023-12-20 13:27:25,884 INFO evaluator.py line 159 131400] Test: [42/78] Loss 1.0812 [2023-12-20 13:27:26,008 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.9629 [2023-12-20 13:27:26,160 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.7500 [2023-12-20 13:27:26,279 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9860 [2023-12-20 13:27:26,381 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.6018 [2023-12-20 13:27:26,554 INFO evaluator.py line 159 131400] Test: [47/78] Loss 1.0510 [2023-12-20 13:27:26,651 INFO evaluator.py line 159 131400] Test: [48/78] Loss 1.0882 [2023-12-20 13:27:26,797 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5825 [2023-12-20 13:27:26,887 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.4215 [2023-12-20 13:27:26,969 INFO evaluator.py line 159 131400] Test: [51/78] Loss 1.2611 [2023-12-20 13:27:27,080 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.7069 [2023-12-20 13:27:27,238 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.8727 [2023-12-20 13:27:27,374 INFO evaluator.py line 159 131400] Test: [54/78] Loss 1.1876 [2023-12-20 13:27:27,480 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5477 [2023-12-20 13:27:27,571 INFO evaluator.py line 159 131400] Test: [56/78] Loss 1.0379 [2023-12-20 13:27:27,711 INFO evaluator.py line 159 131400] Test: [57/78] Loss 1.3247 [2023-12-20 13:27:27,879 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.9261 [2023-12-20 13:27:27,977 INFO evaluator.py line 159 131400] Test: [59/78] Loss 2.3257 [2023-12-20 13:27:28,074 INFO evaluator.py line 159 131400] Test: [60/78] Loss 1.4289 [2023-12-20 13:27:28,183 INFO evaluator.py line 159 131400] Test: [61/78] Loss 1.2737 [2023-12-20 13:27:28,288 INFO evaluator.py line 159 131400] Test: [62/78] Loss 1.4962 [2023-12-20 13:27:28,380 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.3783 [2023-12-20 13:27:28,491 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0783 [2023-12-20 13:27:28,619 INFO evaluator.py line 159 131400] Test: [65/78] Loss 2.1164 [2023-12-20 13:27:28,706 INFO evaluator.py line 159 131400] Test: [66/78] Loss 1.3445 [2023-12-20 13:27:28,809 INFO evaluator.py line 159 131400] Test: [67/78] Loss 1.1803 [2023-12-20 13:27:28,911 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.5466 [2023-12-20 13:27:29,001 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.6770 [2023-12-20 13:27:29,088 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.5731 [2023-12-20 13:27:29,185 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.2273 [2023-12-20 13:27:29,273 INFO evaluator.py line 159 131400] Test: [72/78] Loss 1.6887 [2023-12-20 13:27:29,410 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.7697 [2023-12-20 13:27:29,511 INFO evaluator.py line 159 131400] Test: [74/78] Loss 1.0736 [2023-12-20 13:27:29,629 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.5725 [2023-12-20 13:27:29,731 INFO evaluator.py line 159 131400] Test: [76/78] Loss 2.0094 [2023-12-20 13:27:29,819 INFO evaluator.py line 159 131400] Test: [77/78] Loss 1.0893 [2023-12-20 13:27:29,973 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.7839 [2023-12-20 13:27:31,262 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.3984/0.5227/0.7631. [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.6801/0.8940 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9463/0.9671 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.3608/0.4383 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.4886/0.7565 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.6830/0.7242 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.5678/0.7838 [2023-12-20 13:27:31,262 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.5010/0.6414 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.2116/0.3892 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.3623/0.4991 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.5734/0.8191 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.0000/0.0000 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.4286/0.7271 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.3233/0.5472 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.1738/0.1756 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.0745/0.0785 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.1764/0.1887 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.3918/0.4018 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.3835/0.4580 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.4884/0.7869 [2023-12-20 13:27:31,263 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.1522/0.1784 [2023-12-20 13:27:31,264 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:27:31,265 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.3984 [2023-12-20 13:27:31,265 INFO misc.py line 165 131400] Currently Best mIoU: 0.3984 [2023-12-20 13:27:31,265 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:27:38,295 INFO misc.py line 119 131400] Train: [2/100][1/800] Data 1.116 (1.116) Batch 1.450 (1.450) Remain 31:53:33 loss: 1.4368 Lr: 0.00112 [2023-12-20 13:27:38,624 INFO misc.py line 119 131400] Train: [2/100][2/800] Data 0.003 (0.003) Batch 0.329 (0.329) Remain 07:14:17 loss: 1.4054 Lr: 0.00112 [2023-12-20 13:27:38,950 INFO misc.py line 119 131400] Train: [2/100][3/800] Data 0.003 (0.003) Batch 0.326 (0.326) Remain 07:10:21 loss: 1.1369 Lr: 0.00112 [2023-12-20 13:27:39,226 INFO misc.py line 119 131400] Train: [2/100][4/800] Data 0.003 (0.003) Batch 0.274 (0.274) Remain 06:02:02 loss: 1.1823 Lr: 0.00112 [2023-12-20 13:27:39,550 INFO misc.py line 119 131400] Train: [2/100][5/800] Data 0.005 (0.004) Batch 0.325 (0.300) Remain 06:35:48 loss: 1.5162 Lr: 0.00112 [2023-12-20 13:27:39,855 INFO misc.py line 119 131400] Train: [2/100][6/800] Data 0.003 (0.003) Batch 0.305 (0.302) Remain 06:37:58 loss: 1.3581 Lr: 0.00112 [2023-12-20 13:27:40,160 INFO misc.py line 119 131400] Train: [2/100][7/800] Data 0.003 (0.003) Batch 0.305 (0.302) Remain 06:39:11 loss: 1.5278 Lr: 0.00112 [2023-12-20 13:27:40,480 INFO misc.py line 119 131400] Train: [2/100][8/800] Data 0.002 (0.003) Batch 0.320 (0.306) Remain 06:43:44 loss: 1.3732 Lr: 0.00112 [2023-12-20 13:27:40,805 INFO misc.py line 119 131400] Train: [2/100][9/800] Data 0.003 (0.003) Batch 0.325 (0.309) Remain 06:47:57 loss: 1.3516 Lr: 0.00113 [2023-12-20 13:27:41,120 INFO misc.py line 119 131400] Train: [2/100][10/800] Data 0.002 (0.003) Batch 0.315 (0.310) Remain 06:49:03 loss: 1.8336 Lr: 0.00113 [2023-12-20 13:27:41,441 INFO misc.py line 119 131400] Train: [2/100][11/800] Data 0.003 (0.003) Batch 0.315 (0.311) Remain 06:49:52 loss: 1.8299 Lr: 0.00113 [2023-12-20 13:27:41,756 INFO misc.py line 119 131400] Train: [2/100][12/800] Data 0.009 (0.004) Batch 0.319 (0.311) Remain 06:51:05 loss: 1.3705 Lr: 0.00113 [2023-12-20 13:27:42,066 INFO misc.py line 119 131400] Train: [2/100][13/800] Data 0.006 (0.004) Batch 0.313 (0.312) Remain 06:51:15 loss: 1.1072 Lr: 0.00113 [2023-12-20 13:27:42,369 INFO misc.py line 119 131400] Train: [2/100][14/800] Data 0.003 (0.004) Batch 0.303 (0.311) Remain 06:50:14 loss: 1.2478 Lr: 0.00113 [2023-12-20 13:27:42,682 INFO misc.py line 119 131400] Train: [2/100][15/800] Data 0.002 (0.004) Batch 0.312 (0.311) Remain 06:50:19 loss: 1.0329 Lr: 0.00113 [2023-12-20 13:27:42,970 INFO misc.py line 119 131400] Train: [2/100][16/800] Data 0.003 (0.004) Batch 0.288 (0.309) Remain 06:48:00 loss: 1.5791 Lr: 0.00113 [2023-12-20 13:27:43,327 INFO misc.py line 119 131400] Train: [2/100][17/800] Data 0.003 (0.004) Batch 0.358 (0.313) Remain 06:52:36 loss: 1.4851 Lr: 0.00114 [2023-12-20 13:27:43,647 INFO misc.py line 119 131400] Train: [2/100][18/800] Data 0.003 (0.004) Batch 0.319 (0.313) Remain 06:53:11 loss: 1.4155 Lr: 0.00114 [2023-12-20 13:27:43,952 INFO misc.py line 119 131400] Train: [2/100][19/800] Data 0.003 (0.003) Batch 0.305 (0.313) Remain 06:52:32 loss: 0.9966 Lr: 0.00114 [2023-12-20 13:27:44,271 INFO misc.py line 119 131400] Train: [2/100][20/800] Data 0.003 (0.003) Batch 0.319 (0.313) Remain 06:53:00 loss: 1.4452 Lr: 0.00114 [2023-12-20 13:27:44,604 INFO misc.py line 119 131400] Train: [2/100][21/800] Data 0.003 (0.003) Batch 0.331 (0.314) Remain 06:54:20 loss: 1.3522 Lr: 0.00114 [2023-12-20 13:27:44,936 INFO misc.py line 119 131400] Train: [2/100][22/800] Data 0.004 (0.003) Batch 0.333 (0.315) Remain 06:55:40 loss: 1.0487 Lr: 0.00114 [2023-12-20 13:27:45,216 INFO misc.py line 119 131400] Train: [2/100][23/800] Data 0.004 (0.004) Batch 0.280 (0.313) Remain 06:53:22 loss: 1.2060 Lr: 0.00114 [2023-12-20 13:27:45,550 INFO misc.py line 119 131400] Train: [2/100][24/800] Data 0.004 (0.004) Batch 0.335 (0.314) Remain 06:54:42 loss: 1.4092 Lr: 0.00114 [2023-12-20 13:27:46,020 INFO misc.py line 119 131400] Train: [2/100][25/800] Data 0.003 (0.004) Batch 0.470 (0.321) Remain 07:04:01 loss: 1.9413 Lr: 0.00115 [2023-12-20 13:27:46,293 INFO misc.py line 119 131400] Train: [2/100][26/800] Data 0.003 (0.004) Batch 0.274 (0.319) Remain 07:01:16 loss: 1.3477 Lr: 0.00115 [2023-12-20 13:27:46,621 INFO misc.py line 119 131400] Train: [2/100][27/800] Data 0.003 (0.004) Batch 0.328 (0.320) Remain 07:01:44 loss: 1.3603 Lr: 0.00115 [2023-12-20 13:27:46,951 INFO misc.py line 119 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line 119 131400] Train: [2/100][775/800] Data 0.002 (0.004) Batch 0.319 (0.322) Remain 07:01:30 loss: 1.0707 Lr: 0.00241 [2023-12-20 13:31:48,215 INFO misc.py line 119 131400] Train: [2/100][776/800] Data 0.003 (0.004) Batch 0.311 (0.322) Remain 07:01:28 loss: 0.8585 Lr: 0.00242 [2023-12-20 13:31:48,569 INFO misc.py line 119 131400] Train: [2/100][777/800] Data 0.004 (0.004) Batch 0.355 (0.323) Remain 07:01:31 loss: 1.3391 Lr: 0.00242 [2023-12-20 13:31:48,871 INFO misc.py line 119 131400] Train: [2/100][778/800] Data 0.003 (0.004) Batch 0.301 (0.322) Remain 07:01:29 loss: 1.2924 Lr: 0.00242 [2023-12-20 13:31:49,325 INFO misc.py line 119 131400] Train: [2/100][779/800] Data 0.004 (0.004) Batch 0.454 (0.323) Remain 07:01:42 loss: 0.9681 Lr: 0.00242 [2023-12-20 13:31:49,647 INFO misc.py line 119 131400] Train: [2/100][780/800] Data 0.003 (0.004) Batch 0.323 (0.323) Remain 07:01:41 loss: 1.5647 Lr: 0.00242 [2023-12-20 13:31:49,987 INFO misc.py line 119 131400] Train: [2/100][781/800] Data 0.003 (0.004) Batch 0.340 (0.323) Remain 07:01:43 loss: 1.3912 Lr: 0.00243 [2023-12-20 13:31:50,346 INFO misc.py line 119 131400] Train: [2/100][782/800] Data 0.003 (0.004) Batch 0.350 (0.323) Remain 07:01:45 loss: 1.0007 Lr: 0.00243 [2023-12-20 13:31:50,627 INFO misc.py line 119 131400] Train: [2/100][783/800] Data 0.012 (0.004) Batch 0.290 (0.323) Remain 07:01:42 loss: 1.2217 Lr: 0.00243 [2023-12-20 13:31:50,983 INFO misc.py line 119 131400] Train: [2/100][784/800] Data 0.004 (0.004) Batch 0.357 (0.323) Remain 07:01:45 loss: 1.2843 Lr: 0.00243 [2023-12-20 13:31:51,322 INFO misc.py line 119 131400] Train: [2/100][785/800] Data 0.003 (0.004) Batch 0.339 (0.323) Remain 07:01:46 loss: 1.2997 Lr: 0.00243 [2023-12-20 13:31:51,623 INFO misc.py line 119 131400] Train: [2/100][786/800] Data 0.003 (0.004) Batch 0.300 (0.323) Remain 07:01:43 loss: 0.7092 Lr: 0.00244 [2023-12-20 13:31:51,919 INFO misc.py line 119 131400] Train: [2/100][787/800] Data 0.003 (0.004) Batch 0.297 (0.323) Remain 07:01:40 loss: 1.4774 Lr: 0.00244 [2023-12-20 13:31:52,250 INFO misc.py line 119 131400] Train: [2/100][788/800] Data 0.002 (0.004) Batch 0.330 (0.323) Remain 07:01:41 loss: 1.3349 Lr: 0.00244 [2023-12-20 13:31:52,540 INFO misc.py line 119 131400] Train: [2/100][789/800] Data 0.003 (0.004) Batch 0.290 (0.323) Remain 07:01:37 loss: 1.7886 Lr: 0.00244 [2023-12-20 13:31:52,873 INFO misc.py line 119 131400] Train: [2/100][790/800] Data 0.003 (0.004) Batch 0.333 (0.323) Remain 07:01:38 loss: 0.9539 Lr: 0.00244 [2023-12-20 13:31:53,144 INFO misc.py line 119 131400] Train: [2/100][791/800] Data 0.003 (0.004) Batch 0.269 (0.323) Remain 07:01:32 loss: 1.5482 Lr: 0.00245 [2023-12-20 13:31:53,453 INFO misc.py line 119 131400] Train: [2/100][792/800] Data 0.005 (0.004) Batch 0.311 (0.323) Remain 07:01:31 loss: 0.9222 Lr: 0.00245 [2023-12-20 13:31:53,761 INFO misc.py line 119 131400] Train: [2/100][793/800] Data 0.002 (0.004) Batch 0.309 (0.323) Remain 07:01:29 loss: 1.0574 Lr: 0.00245 [2023-12-20 13:31:54,080 INFO misc.py line 119 131400] Train: [2/100][794/800] Data 0.002 (0.004) Batch 0.318 (0.323) Remain 07:01:28 loss: 0.8753 Lr: 0.00245 [2023-12-20 13:31:54,410 INFO misc.py line 119 131400] Train: [2/100][795/800] Data 0.003 (0.004) Batch 0.331 (0.323) Remain 07:01:29 loss: 1.2815 Lr: 0.00245 [2023-12-20 13:31:54,697 INFO misc.py line 119 131400] Train: [2/100][796/800] Data 0.003 (0.004) Batch 0.287 (0.323) Remain 07:01:25 loss: 1.7964 Lr: 0.00246 [2023-12-20 13:31:55,007 INFO misc.py line 119 131400] Train: [2/100][797/800] Data 0.003 (0.004) Batch 0.310 (0.322) Remain 07:01:23 loss: 0.9385 Lr: 0.00246 [2023-12-20 13:31:55,309 INFO misc.py line 119 131400] Train: [2/100][798/800] Data 0.002 (0.004) Batch 0.302 (0.322) Remain 07:01:21 loss: 1.1437 Lr: 0.00246 [2023-12-20 13:31:55,612 INFO misc.py line 119 131400] Train: [2/100][799/800] Data 0.003 (0.004) Batch 0.303 (0.322) Remain 07:01:19 loss: 1.0589 Lr: 0.00246 [2023-12-20 13:31:55,919 INFO misc.py line 119 131400] Train: [2/100][800/800] Data 0.003 (0.004) Batch 0.307 (0.322) Remain 07:01:17 loss: 1.1916 Lr: 0.00246 [2023-12-20 13:31:55,919 INFO misc.py line 136 131400] Train result: loss: 1.2615 [2023-12-20 13:31:55,920 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:32:23,766 INFO evaluator.py line 159 131400] Test: [1/78] Loss 1.2352 [2023-12-20 13:32:23,848 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.5615 [2023-12-20 13:32:23,943 INFO evaluator.py line 159 131400] Test: [3/78] Loss 1.3028 [2023-12-20 13:32:24,050 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.9045 [2023-12-20 13:32:24,163 INFO evaluator.py line 159 131400] Test: [5/78] Loss 1.0403 [2023-12-20 13:32:24,265 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.9288 [2023-12-20 13:32:24,354 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.9501 [2023-12-20 13:32:24,459 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.6326 [2023-12-20 13:32:24,544 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.8211 [2023-12-20 13:32:24,630 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.7284 [2023-12-20 13:32:24,722 INFO evaluator.py line 159 131400] Test: [11/78] Loss 1.7654 [2023-12-20 13:32:24,860 INFO evaluator.py line 159 131400] Test: [12/78] Loss 1.0061 [2023-12-20 13:32:24,976 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6722 [2023-12-20 13:32:25,134 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.8622 [2023-12-20 13:32:25,225 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.8183 [2023-12-20 13:32:25,357 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.2720 [2023-12-20 13:32:25,469 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.6001 [2023-12-20 13:32:25,581 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7027 [2023-12-20 13:32:25,692 INFO evaluator.py line 159 131400] Test: [19/78] Loss 1.1945 [2023-12-20 13:32:25,767 INFO evaluator.py line 159 131400] Test: [20/78] Loss 1.7591 [2023-12-20 13:32:25,880 INFO evaluator.py line 159 131400] Test: [21/78] Loss 2.2634 [2023-12-20 13:32:26,038 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.4746 [2023-12-20 13:32:26,162 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8308 [2023-12-20 13:32:26,304 INFO evaluator.py line 159 131400] Test: [24/78] Loss 1.1020 [2023-12-20 13:32:26,447 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.5703 [2023-12-20 13:32:26,532 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.2975 [2023-12-20 13:32:26,693 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.2115 [2023-12-20 13:32:26,815 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.9203 [2023-12-20 13:32:26,909 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.1758 [2023-12-20 13:32:27,055 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7687 [2023-12-20 13:32:27,157 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.4129 [2023-12-20 13:32:27,276 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.3371 [2023-12-20 13:32:27,359 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.6360 [2023-12-20 13:32:27,429 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.5553 [2023-12-20 13:32:27,524 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.4074 [2023-12-20 13:32:27,620 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.3845 [2023-12-20 13:32:27,749 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.7856 [2023-12-20 13:32:27,857 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.5674 [2023-12-20 13:32:27,934 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.3226 [2023-12-20 13:32:28,084 INFO evaluator.py line 159 131400] Test: [40/78] Loss 1.6541 [2023-12-20 13:32:28,236 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0974 [2023-12-20 13:32:28,332 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.9011 [2023-12-20 13:32:28,453 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.8720 [2023-12-20 13:32:28,595 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.6265 [2023-12-20 13:32:28,713 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4718 [2023-12-20 13:32:28,813 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.2977 [2023-12-20 13:32:28,978 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.8198 [2023-12-20 13:32:29,074 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.8873 [2023-12-20 13:32:29,220 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0650 [2023-12-20 13:32:29,313 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1659 [2023-12-20 13:32:29,397 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.9997 [2023-12-20 13:32:29,509 INFO evaluator.py line 159 131400] Test: [52/78] Loss 2.0452 [2023-12-20 13:32:29,662 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.6570 [2023-12-20 13:32:29,804 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.9808 [2023-12-20 13:32:29,906 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6865 [2023-12-20 13:32:30,001 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8937 [2023-12-20 13:32:30,103 INFO evaluator.py line 159 131400] Test: [57/78] Loss 1.0133 [2023-12-20 13:32:30,264 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.9423 [2023-12-20 13:32:30,362 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.9993 [2023-12-20 13:32:30,469 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.9391 [2023-12-20 13:32:30,566 INFO evaluator.py line 159 131400] Test: [61/78] Loss 1.2487 [2023-12-20 13:32:30,660 INFO evaluator.py line 159 131400] Test: [62/78] Loss 1.7805 [2023-12-20 13:32:30,749 INFO evaluator.py line 159 131400] Test: [63/78] Loss 2.0022 [2023-12-20 13:32:30,851 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0488 [2023-12-20 13:32:30,983 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.8641 [2023-12-20 13:32:31,074 INFO evaluator.py line 159 131400] Test: [66/78] Loss 1.0491 [2023-12-20 13:32:31,175 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8488 [2023-12-20 13:32:31,274 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0728 [2023-12-20 13:32:31,366 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.5003 [2023-12-20 13:32:31,453 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0839 [2023-12-20 13:32:31,554 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9742 [2023-12-20 13:32:31,652 INFO evaluator.py line 159 131400] Test: [72/78] Loss 1.2170 [2023-12-20 13:32:31,788 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3781 [2023-12-20 13:32:31,887 INFO evaluator.py line 159 131400] Test: [74/78] Loss 1.3091 [2023-12-20 13:32:32,004 INFO evaluator.py line 159 131400] Test: [75/78] Loss 2.1009 [2023-12-20 13:32:32,108 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.6039 [2023-12-20 13:32:32,194 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.9405 [2023-12-20 13:32:32,349 INFO evaluator.py line 159 131400] Test: [78/78] Loss 2.0956 [2023-12-20 13:32:33,821 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.4474/0.6414/0.7659. [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.6941/0.7966 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9471/0.9839 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.2743/0.7263 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.5431/0.5699 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.7571/0.8186 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.5670/0.8583 [2023-12-20 13:32:33,822 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.5851/0.7539 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.2526/0.3060 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.3970/0.5428 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.3045/0.3076 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.1579/0.2873 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.3391/0.8435 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.4343/0.6266 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.4898/0.7068 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.0959/0.2410 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.3330/0.5692 [2023-12-20 13:32:33,823 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.5382/0.9666 [2023-12-20 13:32:33,824 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.4514/0.6744 [2023-12-20 13:32:33,824 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.5481/0.9321 [2023-12-20 13:32:33,824 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.2382/0.3166 [2023-12-20 13:32:33,825 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:32:33,826 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.4474 [2023-12-20 13:32:33,826 INFO misc.py line 165 131400] Currently Best mIoU: 0.4474 [2023-12-20 13:32:33,826 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:32:40,492 INFO misc.py line 119 131400] Train: [3/100][1/800] Data 1.071 (1.071) Batch 1.379 (1.379) Remain 30:01:39 loss: 1.4359 Lr: 0.00247 [2023-12-20 13:32:40,795 INFO misc.py line 119 131400] Train: [3/100][2/800] Data 0.004 (0.004) Batch 0.303 (0.303) Remain 06:35:38 loss: 1.1128 Lr: 0.00247 [2023-12-20 13:32:41,105 INFO misc.py line 119 131400] Train: [3/100][3/800] Data 0.004 (0.004) Batch 0.311 (0.311) Remain 06:46:47 loss: 0.9296 Lr: 0.00247 [2023-12-20 13:32:41,440 INFO misc.py line 119 131400] Train: [3/100][4/800] Data 0.003 (0.003) Batch 0.334 (0.334) Remain 07:16:50 loss: 1.2952 Lr: 0.00247 [2023-12-20 13:32:41,762 INFO misc.py line 119 131400] Train: [3/100][5/800] Data 0.004 (0.004) Batch 0.322 (0.328) Remain 07:08:46 loss: 1.4263 Lr: 0.00247 [2023-12-20 13:32:42,106 INFO misc.py line 119 131400] Train: [3/100][6/800] Data 0.004 (0.004) Batch 0.344 (0.334) Remain 07:15:48 loss: 1.1316 Lr: 0.00248 [2023-12-20 13:32:42,424 INFO misc.py line 119 131400] Train: [3/100][7/800] Data 0.003 (0.004) Batch 0.318 (0.330) Remain 07:10:43 loss: 1.3835 Lr: 0.00248 [2023-12-20 13:32:42,790 INFO misc.py line 119 131400] Train: [3/100][8/800] Data 0.003 (0.003) Batch 0.366 (0.337) Remain 07:20:10 loss: 1.3623 Lr: 0.00248 [2023-12-20 13:32:43,105 INFO misc.py line 119 131400] Train: 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(0.325) Remain 07:04:23 loss: 1.1543 Lr: 0.00249 [2023-12-20 13:32:45,326 INFO misc.py line 119 131400] Train: [3/100][16/800] Data 0.003 (0.004) Batch 0.323 (0.325) Remain 07:04:13 loss: 1.2306 Lr: 0.00250 [2023-12-20 13:32:45,633 INFO misc.py line 119 131400] Train: [3/100][17/800] Data 0.003 (0.004) Batch 0.307 (0.323) Remain 07:02:33 loss: 1.1115 Lr: 0.00250 [2023-12-20 13:32:45,981 INFO misc.py line 119 131400] Train: [3/100][18/800] Data 0.003 (0.004) Batch 0.347 (0.325) Remain 07:04:36 loss: 0.8553 Lr: 0.00250 [2023-12-20 13:32:46,338 INFO misc.py line 119 131400] Train: [3/100][19/800] Data 0.004 (0.004) Batch 0.357 (0.327) Remain 07:07:12 loss: 1.0059 Lr: 0.00250 [2023-12-20 13:32:46,668 INFO misc.py line 119 131400] Train: [3/100][20/800] Data 0.004 (0.004) Batch 0.330 (0.327) Remain 07:07:26 loss: 1.5448 Lr: 0.00250 [2023-12-20 13:32:46,974 INFO misc.py line 119 131400] Train: [3/100][21/800] Data 0.003 (0.004) Batch 0.306 (0.326) Remain 07:05:56 loss: 1.0441 Lr: 0.00251 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line 119 131400] Train: [3/100][775/800] Data 0.003 (0.004) Batch 0.302 (0.326) Remain 07:02:01 loss: 1.8828 Lr: 0.00408 [2023-12-20 13:36:53,278 INFO misc.py line 119 131400] Train: [3/100][776/800] Data 0.002 (0.004) Batch 0.346 (0.326) Remain 07:02:02 loss: 1.0263 Lr: 0.00408 [2023-12-20 13:36:53,622 INFO misc.py line 119 131400] Train: [3/100][777/800] Data 0.004 (0.004) Batch 0.345 (0.326) Remain 07:02:04 loss: 1.6493 Lr: 0.00409 [2023-12-20 13:36:53,961 INFO misc.py line 119 131400] Train: [3/100][778/800] Data 0.003 (0.004) Batch 0.338 (0.326) Remain 07:02:05 loss: 0.9370 Lr: 0.00409 [2023-12-20 13:36:54,248 INFO misc.py line 119 131400] Train: [3/100][779/800] Data 0.003 (0.004) Batch 0.286 (0.326) Remain 07:02:00 loss: 1.1706 Lr: 0.00409 [2023-12-20 13:36:54,538 INFO misc.py line 119 131400] Train: [3/100][780/800] Data 0.003 (0.004) Batch 0.274 (0.326) Remain 07:01:55 loss: 0.9532 Lr: 0.00409 [2023-12-20 13:36:54,853 INFO misc.py line 119 131400] Train: [3/100][781/800] Data 0.020 (0.004) Batch 0.332 (0.326) Remain 07:01:55 loss: 1.3053 Lr: 0.00410 [2023-12-20 13:36:55,142 INFO misc.py line 119 131400] Train: [3/100][782/800] Data 0.003 (0.004) Batch 0.288 (0.326) Remain 07:01:51 loss: 1.0982 Lr: 0.00410 [2023-12-20 13:36:55,437 INFO misc.py line 119 131400] Train: [3/100][783/800] Data 0.003 (0.004) Batch 0.296 (0.326) Remain 07:01:48 loss: 0.7494 Lr: 0.00410 [2023-12-20 13:36:55,773 INFO misc.py line 119 131400] Train: [3/100][784/800] Data 0.003 (0.004) Batch 0.335 (0.326) Remain 07:01:48 loss: 1.2249 Lr: 0.00410 [2023-12-20 13:36:56,109 INFO misc.py line 119 131400] Train: [3/100][785/800] Data 0.003 (0.004) Batch 0.336 (0.326) Remain 07:01:49 loss: 1.0672 Lr: 0.00410 [2023-12-20 13:36:56,424 INFO misc.py line 119 131400] Train: [3/100][786/800] Data 0.003 (0.004) Batch 0.316 (0.326) Remain 07:01:48 loss: 0.9294 Lr: 0.00411 [2023-12-20 13:36:56,760 INFO misc.py line 119 131400] Train: [3/100][787/800] Data 0.003 (0.004) Batch 0.335 (0.326) Remain 07:01:48 loss: 1.2133 Lr: 0.00411 [2023-12-20 13:36:57,098 INFO misc.py line 119 131400] Train: [3/100][788/800] Data 0.004 (0.004) Batch 0.338 (0.326) Remain 07:01:49 loss: 1.0135 Lr: 0.00411 [2023-12-20 13:36:57,421 INFO misc.py line 119 131400] Train: [3/100][789/800] Data 0.003 (0.004) Batch 0.323 (0.326) Remain 07:01:48 loss: 0.8969 Lr: 0.00411 [2023-12-20 13:36:57,725 INFO misc.py line 119 131400] Train: [3/100][790/800] Data 0.003 (0.004) Batch 0.302 (0.326) Remain 07:01:46 loss: 1.2539 Lr: 0.00411 [2023-12-20 13:36:58,040 INFO misc.py line 119 131400] Train: [3/100][791/800] Data 0.004 (0.004) Batch 0.315 (0.326) Remain 07:01:44 loss: 0.6408 Lr: 0.00412 [2023-12-20 13:36:58,338 INFO misc.py line 119 131400] Train: [3/100][792/800] Data 0.004 (0.004) Batch 0.299 (0.326) Remain 07:01:41 loss: 0.7463 Lr: 0.00412 [2023-12-20 13:36:58,633 INFO misc.py line 119 131400] Train: [3/100][793/800] Data 0.002 (0.004) Batch 0.294 (0.326) Remain 07:01:38 loss: 1.3373 Lr: 0.00412 [2023-12-20 13:36:58,941 INFO misc.py line 119 131400] Train: [3/100][794/800] Data 0.002 (0.004) Batch 0.308 (0.326) Remain 07:01:36 loss: 0.8795 Lr: 0.00412 [2023-12-20 13:36:59,259 INFO misc.py line 119 131400] Train: [3/100][795/800] Data 0.002 (0.004) Batch 0.300 (0.326) Remain 07:01:33 loss: 0.8628 Lr: 0.00412 [2023-12-20 13:36:59,571 INFO misc.py line 119 131400] Train: [3/100][796/800] Data 0.021 (0.004) Batch 0.330 (0.326) Remain 07:01:33 loss: 1.2953 Lr: 0.00413 [2023-12-20 13:36:59,877 INFO misc.py line 119 131400] Train: [3/100][797/800] Data 0.003 (0.004) Batch 0.307 (0.326) Remain 07:01:31 loss: 1.1829 Lr: 0.00413 [2023-12-20 13:37:00,188 INFO misc.py line 119 131400] Train: [3/100][798/800] Data 0.002 (0.004) Batch 0.309 (0.326) Remain 07:01:29 loss: 0.8947 Lr: 0.00413 [2023-12-20 13:37:00,499 INFO misc.py line 119 131400] Train: [3/100][799/800] Data 0.004 (0.004) Batch 0.312 (0.326) Remain 07:01:27 loss: 1.1931 Lr: 0.00413 [2023-12-20 13:37:00,791 INFO misc.py line 119 131400] Train: [3/100][800/800] Data 0.003 (0.004) Batch 0.292 (0.326) Remain 07:01:24 loss: 1.2273 Lr: 0.00413 [2023-12-20 13:37:00,791 INFO misc.py line 136 131400] Train result: loss: 1.1047 [2023-12-20 13:37:00,792 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:37:22,562 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.5797 [2023-12-20 13:37:22,634 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.5072 [2023-12-20 13:37:22,733 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6940 [2023-12-20 13:37:22,843 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3443 [2023-12-20 13:37:22,962 INFO evaluator.py line 159 131400] Test: [5/78] Loss 1.0729 [2023-12-20 13:37:23,062 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6424 [2023-12-20 13:37:23,152 INFO evaluator.py line 159 131400] Test: [7/78] Loss 2.0548 [2023-12-20 13:37:23,259 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.4912 [2023-12-20 13:37:23,343 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.4936 [2023-12-20 13:37:23,427 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.6302 [2023-12-20 13:37:23,523 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6549 [2023-12-20 13:37:23,658 INFO evaluator.py line 159 131400] Test: [12/78] Loss 1.0053 [2023-12-20 13:37:23,776 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6149 [2023-12-20 13:37:23,930 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.7099 [2023-12-20 13:37:24,021 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.8781 [2023-12-20 13:37:24,153 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9974 [2023-12-20 13:37:24,264 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.5738 [2023-12-20 13:37:24,372 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5033 [2023-12-20 13:37:24,485 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.7201 [2023-12-20 13:37:24,558 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.9574 [2023-12-20 13:37:24,667 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.6294 [2023-12-20 13:37:24,826 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.3675 [2023-12-20 13:37:24,948 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.2894 [2023-12-20 13:37:25,090 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.7601 [2023-12-20 13:37:25,236 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.5348 [2023-12-20 13:37:25,318 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.1311 [2023-12-20 13:37:25,474 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8073 [2023-12-20 13:37:25,600 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.8723 [2023-12-20 13:37:25,693 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.0177 [2023-12-20 13:37:25,841 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6532 [2023-12-20 13:37:25,944 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.1853 [2023-12-20 13:37:26,064 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.5516 [2023-12-20 13:37:26,152 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5459 [2023-12-20 13:37:26,226 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3687 [2023-12-20 13:37:26,328 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7888 [2023-12-20 13:37:26,418 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.2685 [2023-12-20 13:37:26,545 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.7150 [2023-12-20 13:37:26,664 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.4175 [2023-12-20 13:37:26,754 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.3020 [2023-12-20 13:37:26,896 INFO evaluator.py line 159 131400] Test: [40/78] Loss 1.1037 [2023-12-20 13:37:27,042 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0722 [2023-12-20 13:37:27,151 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.6271 [2023-12-20 13:37:27,269 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.6248 [2023-12-20 13:37:27,409 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1209 [2023-12-20 13:37:27,530 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8551 [2023-12-20 13:37:27,640 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.0518 [2023-12-20 13:37:27,806 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6196 [2023-12-20 13:37:27,910 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.8359 [2023-12-20 13:37:28,055 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3729 [2023-12-20 13:37:28,145 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8149 [2023-12-20 13:37:28,220 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7502 [2023-12-20 13:37:28,325 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2636 [2023-12-20 13:37:28,472 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.3380 [2023-12-20 13:37:28,612 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.8639 [2023-12-20 13:37:28,713 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.8615 [2023-12-20 13:37:28,799 INFO evaluator.py line 159 131400] Test: [56/78] Loss 1.0641 [2023-12-20 13:37:28,906 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.8773 [2023-12-20 13:37:29,071 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.5800 [2023-12-20 13:37:29,172 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3815 [2023-12-20 13:37:29,267 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.8048 [2023-12-20 13:37:29,361 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.6547 [2023-12-20 13:37:29,451 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.9844 [2023-12-20 13:37:29,536 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7173 [2023-12-20 13:37:29,643 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.1668 [2023-12-20 13:37:29,770 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6849 [2023-12-20 13:37:29,852 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.9372 [2023-12-20 13:37:29,950 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8421 [2023-12-20 13:37:30,044 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.1081 [2023-12-20 13:37:30,134 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4700 [2023-12-20 13:37:30,222 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1056 [2023-12-20 13:37:30,316 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.2773 [2023-12-20 13:37:30,405 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.9341 [2023-12-20 13:37:30,548 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.4703 [2023-12-20 13:37:30,645 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7715 [2023-12-20 13:37:30,761 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.5031 [2023-12-20 13:37:30,863 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.7998 [2023-12-20 13:37:30,949 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7598 [2023-12-20 13:37:31,103 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.5001 [2023-12-20 13:37:32,335 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.5610/0.7020/0.8271. [2023-12-20 13:37:32,335 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.7641/0.8794 [2023-12-20 13:37:32,335 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9479/0.9744 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.4899/0.5915 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.5998/0.6479 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8085/0.8822 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.6610/0.8866 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.5904/0.8323 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.3902/0.4679 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4264/0.7282 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.6505/0.8999 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2826/0.4431 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.5552/0.7189 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.4744/0.6198 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.4311/0.5407 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.3141/0.3921 [2023-12-20 13:37:32,336 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.4636/0.6011 [2023-12-20 13:37:32,337 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.7516/0.9498 [2023-12-20 13:37:32,337 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.5498/0.7163 [2023-12-20 13:37:32,337 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7505/0.8379 [2023-12-20 13:37:32,337 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.3194/0.4304 [2023-12-20 13:37:32,337 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:37:32,338 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.5610 [2023-12-20 13:37:32,338 INFO misc.py line 165 131400] Currently Best mIoU: 0.5610 [2023-12-20 13:37:32,338 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:37:40,946 INFO misc.py line 119 131400] Train: [4/100][1/800] Data 1.279 (1.279) Batch 1.597 (1.597) Remain 34:24:47 loss: 0.9750 Lr: 0.00414 [2023-12-20 13:37:41,256 INFO misc.py line 119 131400] Train: [4/100][2/800] Data 0.005 (0.005) Batch 0.310 (0.310) Remain 06:41:04 loss: 0.9533 Lr: 0.00414 [2023-12-20 13:37:41,594 INFO misc.py line 119 131400] Train: [4/100][3/800] Data 0.005 (0.005) Batch 0.338 (0.338) Remain 07:17:11 loss: 0.9567 Lr: 0.00414 [2023-12-20 13:37:41,943 INFO misc.py line 119 131400] Train: [4/100][4/800] Data 0.004 (0.004) Batch 0.349 (0.349) Remain 07:31:44 loss: 0.8715 Lr: 0.00414 [2023-12-20 13:37:42,289 INFO misc.py line 119 131400] Train: [4/100][5/800] Data 0.004 (0.004) Batch 0.347 (0.348) Remain 07:29:58 loss: 0.6934 Lr: 0.00414 [2023-12-20 13:37:42,593 INFO misc.py line 119 131400] Train: [4/100][6/800] Data 0.004 (0.004) Batch 0.303 (0.333) Remain 07:10:41 loss: 0.7716 Lr: 0.00415 [2023-12-20 13:37:42,924 INFO misc.py line 119 131400] Train: [4/100][7/800] Data 0.004 (0.004) Batch 0.331 (0.333) Remain 07:10:05 loss: 0.9962 Lr: 0.00415 [2023-12-20 13:37:43,254 INFO misc.py line 119 131400] Train: [4/100][8/800] Data 0.004 (0.004) Batch 0.331 (0.332) Remain 07:09:39 loss: 1.1770 Lr: 0.00415 [2023-12-20 13:37:43,568 INFO misc.py line 119 131400] Train: 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0.00544 [2023-12-20 13:41:49,286 INFO misc.py line 119 131400] Train: [4/100][769/800] Data 0.003 (0.004) Batch 0.345 (0.323) Remain 06:54:03 loss: 1.2234 Lr: 0.00544 [2023-12-20 13:41:49,583 INFO misc.py line 119 131400] Train: [4/100][770/800] Data 0.003 (0.004) Batch 0.297 (0.323) Remain 06:54:00 loss: 0.9559 Lr: 0.00545 [2023-12-20 13:41:49,906 INFO misc.py line 119 131400] Train: [4/100][771/800] Data 0.003 (0.004) Batch 0.323 (0.323) Remain 06:54:00 loss: 0.7618 Lr: 0.00545 [2023-12-20 13:41:50,217 INFO misc.py line 119 131400] Train: [4/100][772/800] Data 0.003 (0.004) Batch 0.312 (0.323) Remain 06:53:59 loss: 1.3480 Lr: 0.00545 [2023-12-20 13:41:50,569 INFO misc.py line 119 131400] Train: [4/100][773/800] Data 0.003 (0.004) Batch 0.344 (0.323) Remain 06:54:00 loss: 1.1712 Lr: 0.00545 [2023-12-20 13:41:50,884 INFO misc.py line 119 131400] Train: [4/100][774/800] Data 0.012 (0.004) Batch 0.322 (0.323) Remain 06:54:00 loss: 0.7310 Lr: 0.00545 [2023-12-20 13:41:51,208 INFO misc.py line 119 131400] Train: [4/100][775/800] Data 0.003 (0.004) Batch 0.324 (0.323) Remain 06:54:00 loss: 0.7598 Lr: 0.00545 [2023-12-20 13:41:51,592 INFO misc.py line 119 131400] Train: [4/100][776/800] Data 0.003 (0.004) Batch 0.384 (0.323) Remain 06:54:05 loss: 0.8471 Lr: 0.00545 [2023-12-20 13:41:51,933 INFO misc.py line 119 131400] Train: [4/100][777/800] Data 0.003 (0.004) Batch 0.341 (0.323) Remain 06:54:07 loss: 0.4654 Lr: 0.00546 [2023-12-20 13:41:52,236 INFO misc.py line 119 131400] Train: [4/100][778/800] Data 0.003 (0.004) Batch 0.304 (0.323) Remain 06:54:04 loss: 1.3059 Lr: 0.00546 [2023-12-20 13:41:52,544 INFO misc.py line 119 131400] Train: [4/100][779/800] Data 0.003 (0.004) Batch 0.308 (0.323) Remain 06:54:03 loss: 1.0876 Lr: 0.00546 [2023-12-20 13:41:52,873 INFO misc.py line 119 131400] Train: [4/100][780/800] Data 0.004 (0.004) Batch 0.329 (0.323) Remain 06:54:03 loss: 1.0913 Lr: 0.00546 [2023-12-20 13:41:53,162 INFO misc.py line 119 131400] Train: [4/100][781/800] Data 0.003 (0.004) Batch 0.289 (0.323) Remain 06:53:59 loss: 0.7648 Lr: 0.00546 [2023-12-20 13:41:53,454 INFO misc.py line 119 131400] Train: [4/100][782/800] Data 0.003 (0.004) Batch 0.292 (0.323) Remain 06:53:56 loss: 1.4581 Lr: 0.00546 [2023-12-20 13:41:53,768 INFO misc.py line 119 131400] Train: [4/100][783/800] Data 0.004 (0.004) Batch 0.314 (0.323) Remain 06:53:54 loss: 0.9888 Lr: 0.00546 [2023-12-20 13:41:54,113 INFO misc.py line 119 131400] Train: [4/100][784/800] Data 0.005 (0.004) Batch 0.345 (0.323) Remain 06:53:56 loss: 1.5631 Lr: 0.00546 [2023-12-20 13:41:54,441 INFO misc.py line 119 131400] Train: [4/100][785/800] Data 0.004 (0.004) Batch 0.327 (0.323) Remain 06:53:56 loss: 1.2221 Lr: 0.00547 [2023-12-20 13:41:54,742 INFO misc.py line 119 131400] Train: [4/100][786/800] Data 0.004 (0.004) Batch 0.302 (0.323) Remain 06:53:54 loss: 0.9646 Lr: 0.00547 [2023-12-20 13:41:55,056 INFO misc.py line 119 131400] Train: [4/100][787/800] Data 0.003 (0.004) Batch 0.313 (0.323) Remain 06:53:53 loss: 0.6743 Lr: 0.00547 [2023-12-20 13:41:55,391 INFO misc.py line 119 131400] Train: [4/100][788/800] Data 0.003 (0.004) Batch 0.334 (0.323) Remain 06:53:53 loss: 0.9053 Lr: 0.00547 [2023-12-20 13:41:55,709 INFO misc.py line 119 131400] Train: [4/100][789/800] Data 0.004 (0.004) Batch 0.319 (0.323) Remain 06:53:53 loss: 0.9808 Lr: 0.00547 [2023-12-20 13:41:55,988 INFO misc.py line 119 131400] Train: [4/100][790/800] Data 0.003 (0.004) Batch 0.278 (0.323) Remain 06:53:48 loss: 0.9249 Lr: 0.00547 [2023-12-20 13:41:56,269 INFO misc.py line 119 131400] Train: [4/100][791/800] Data 0.003 (0.004) Batch 0.281 (0.323) Remain 06:53:43 loss: 0.7980 Lr: 0.00547 [2023-12-20 13:41:56,575 INFO misc.py line 119 131400] Train: [4/100][792/800] Data 0.004 (0.004) Batch 0.308 (0.323) Remain 06:53:42 loss: 1.1552 Lr: 0.00547 [2023-12-20 13:41:56,879 INFO misc.py line 119 131400] Train: [4/100][793/800] Data 0.002 (0.004) Batch 0.300 (0.323) Remain 06:53:39 loss: 0.5747 Lr: 0.00548 [2023-12-20 13:41:57,186 INFO misc.py line 119 131400] Train: [4/100][794/800] Data 0.006 (0.004) Batch 0.309 (0.323) Remain 06:53:37 loss: 0.9507 Lr: 0.00548 [2023-12-20 13:41:57,498 INFO misc.py line 119 131400] Train: [4/100][795/800] Data 0.004 (0.004) Batch 0.313 (0.323) Remain 06:53:36 loss: 0.7433 Lr: 0.00548 [2023-12-20 13:41:57,771 INFO misc.py line 119 131400] Train: [4/100][796/800] Data 0.003 (0.004) Batch 0.272 (0.323) Remain 06:53:31 loss: 0.7639 Lr: 0.00548 [2023-12-20 13:41:58,103 INFO misc.py line 119 131400] Train: [4/100][797/800] Data 0.003 (0.004) Batch 0.333 (0.323) Remain 06:53:31 loss: 1.0264 Lr: 0.00548 [2023-12-20 13:41:58,405 INFO misc.py line 119 131400] Train: [4/100][798/800] Data 0.003 (0.004) Batch 0.302 (0.323) Remain 06:53:29 loss: 0.7517 Lr: 0.00548 [2023-12-20 13:41:58,706 INFO misc.py line 119 131400] Train: [4/100][799/800] Data 0.002 (0.004) Batch 0.301 (0.323) Remain 06:53:27 loss: 0.7903 Lr: 0.00548 [2023-12-20 13:41:59,005 INFO misc.py line 119 131400] Train: [4/100][800/800] Data 0.002 (0.004) Batch 0.299 (0.323) Remain 06:53:24 loss: 0.6985 Lr: 0.00548 [2023-12-20 13:41:59,006 INFO misc.py line 136 131400] Train result: loss: 1.0130 [2023-12-20 13:41:59,006 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:42:20,965 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1443 [2023-12-20 13:42:21,040 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.4832 [2023-12-20 13:42:21,138 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.7909 [2023-12-20 13:42:21,244 INFO evaluator.py line 159 131400] Test: [4/78] Loss 2.0341 [2023-12-20 13:42:21,358 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5478 [2023-12-20 13:42:21,457 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.4401 [2023-12-20 13:42:21,549 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.5376 [2023-12-20 13:42:21,655 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1658 [2023-12-20 13:42:21,735 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.4327 [2023-12-20 13:42:21,820 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.6389 [2023-12-20 13:42:21,911 INFO evaluator.py line 159 131400] Test: [11/78] Loss 1.0663 [2023-12-20 13:42:22,046 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.8543 [2023-12-20 13:42:22,164 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4149 [2023-12-20 13:42:22,318 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.6200 [2023-12-20 13:42:22,415 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.8682 [2023-12-20 13:42:22,548 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9511 [2023-12-20 13:42:22,658 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4864 [2023-12-20 13:42:22,766 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6352 [2023-12-20 13:42:22,876 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.8468 [2023-12-20 13:42:22,954 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.9310 [2023-12-20 13:42:23,061 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.9435 [2023-12-20 13:42:23,217 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.3605 [2023-12-20 13:42:23,335 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.2473 [2023-12-20 13:42:23,476 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.7695 [2023-12-20 13:42:23,619 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.5035 [2023-12-20 13:42:23,699 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.0934 [2023-12-20 13:42:23,854 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8022 [2023-12-20 13:42:23,977 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.9115 [2023-12-20 13:42:24,085 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7649 [2023-12-20 13:42:24,230 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4017 [2023-12-20 13:42:24,332 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.1670 [2023-12-20 13:42:24,452 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.1399 [2023-12-20 13:42:24,536 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.6153 [2023-12-20 13:42:24,605 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3431 [2023-12-20 13:42:24,700 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8164 [2023-12-20 13:42:24,804 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.0356 [2023-12-20 13:42:24,933 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.2223 [2023-12-20 13:42:25,045 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.4442 [2023-12-20 13:42:25,128 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.0363 [2023-12-20 13:42:25,270 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.9803 [2023-12-20 13:42:25,414 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1093 [2023-12-20 13:42:25,513 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.4603 [2023-12-20 13:42:25,637 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.8446 [2023-12-20 13:42:25,789 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.4265 [2023-12-20 13:42:25,915 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7792 [2023-12-20 13:42:26,030 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.0621 [2023-12-20 13:42:26,199 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.8445 [2023-12-20 13:42:26,303 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.7796 [2023-12-20 13:42:26,448 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1173 [2023-12-20 13:42:26,546 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7354 [2023-12-20 13:42:26,623 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8477 [2023-12-20 13:42:26,731 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5013 [2023-12-20 13:42:26,878 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.5323 [2023-12-20 13:42:27,046 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.7397 [2023-12-20 13:42:27,153 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0430 [2023-12-20 13:42:27,275 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8065 [2023-12-20 13:42:27,382 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.6268 [2023-12-20 13:42:27,565 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.4296 [2023-12-20 13:42:27,661 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3740 [2023-12-20 13:42:27,753 INFO evaluator.py line 159 131400] Test: [60/78] Loss 1.4177 [2023-12-20 13:42:27,846 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.6746 [2023-12-20 13:42:27,939 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.9094 [2023-12-20 13:42:28,026 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.3404 [2023-12-20 13:42:28,130 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9355 [2023-12-20 13:42:28,254 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6660 [2023-12-20 13:42:28,345 INFO evaluator.py line 159 131400] Test: [66/78] Loss 1.0176 [2023-12-20 13:42:28,448 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.9880 [2023-12-20 13:42:28,546 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.1111 [2023-12-20 13:42:28,630 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.6828 [2023-12-20 13:42:28,718 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1402 [2023-12-20 13:42:28,813 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9414 [2023-12-20 13:42:28,902 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8521 [2023-12-20 13:42:29,039 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2147 [2023-12-20 13:42:29,139 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7208 [2023-12-20 13:42:29,254 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.3617 [2023-12-20 13:42:29,355 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.2338 [2023-12-20 13:42:29,442 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5994 [2023-12-20 13:42:29,595 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.6086 [2023-12-20 13:42:30,681 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.5744/0.7354/0.8303. [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.7760/0.8900 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9462/0.9761 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.4534/0.6779 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.6344/0.7728 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.7942/0.8519 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.6042/0.9576 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.5590/0.6331 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.4663/0.6513 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4138/0.4949 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.6978/0.8868 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2472/0.3651 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.4861/0.8442 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.4609/0.8529 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6336/0.7670 [2023-12-20 13:42:30,681 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.3518/0.5569 [2023-12-20 13:42:30,682 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.5294/0.6656 [2023-12-20 13:42:30,682 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8919/0.9429 [2023-12-20 13:42:30,682 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6073/0.7354 [2023-12-20 13:42:30,682 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.6984/0.9337 [2023-12-20 13:42:30,682 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.2354/0.2518 [2023-12-20 13:42:30,682 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:42:30,683 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.5744 [2023-12-20 13:42:30,683 INFO misc.py line 165 131400] Currently Best mIoU: 0.5744 [2023-12-20 13:42:30,683 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:42:38,092 INFO misc.py line 119 131400] Train: [5/100][1/800] Data 0.793 (0.793) Batch 1.099 (1.099) Remain 23:27:16 loss: 1.0492 Lr: 0.00549 [2023-12-20 13:42:38,399 INFO misc.py line 119 131400] Train: [5/100][2/800] Data 0.002 (0.002) Batch 0.306 (0.306) Remain 06:32:17 loss: 0.9776 Lr: 0.00549 [2023-12-20 13:42:38,835 INFO misc.py line 119 131400] Train: [5/100][3/800] Data 0.122 (0.122) Batch 0.436 (0.436) Remain 09:18:01 loss: 1.3684 Lr: 0.00549 [2023-12-20 13:42:39,142 INFO misc.py line 119 131400] Train: [5/100][4/800] Data 0.003 (0.003) Batch 0.307 (0.307) Remain 06:33:11 loss: 1.1968 Lr: 0.00549 [2023-12-20 13:42:39,432 INFO misc.py line 119 131400] Train: [5/100][5/800] Data 0.003 (0.003) Batch 0.290 (0.299) Remain 06:22:23 loss: 0.7794 Lr: 0.00549 [2023-12-20 13:42:39,740 INFO misc.py line 119 131400] Train: [5/100][6/800] Data 0.003 (0.003) Batch 0.307 (0.302) Remain 06:25:58 loss: 1.1904 Lr: 0.00549 [2023-12-20 13:42:40,071 INFO misc.py line 119 131400] Train: [5/100][7/800] Data 0.004 (0.003) Batch 0.331 (0.309) Remain 06:35:32 loss: 0.9855 Lr: 0.00549 [2023-12-20 13:42:40,405 INFO misc.py line 119 131400] Train: [5/100][8/800] Data 0.003 (0.003) Batch 0.334 (0.314) Remain 06:42:01 loss: 1.1563 Lr: 0.00549 [2023-12-20 13:42:40,710 INFO misc.py line 119 131400] Train: 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(0.314) Remain 06:42:03 loss: 1.2277 Lr: 0.00550 [2023-12-20 13:42:42,899 INFO misc.py line 119 131400] Train: [5/100][16/800] Data 0.002 (0.003) Batch 0.292 (0.312) Remain 06:39:50 loss: 0.5394 Lr: 0.00550 [2023-12-20 13:42:43,243 INFO misc.py line 119 131400] Train: [5/100][17/800] Data 0.005 (0.003) Batch 0.347 (0.315) Remain 06:42:59 loss: 1.4283 Lr: 0.00551 [2023-12-20 13:42:43,574 INFO misc.py line 119 131400] Train: [5/100][18/800] Data 0.003 (0.003) Batch 0.330 (0.316) Remain 06:44:18 loss: 0.8113 Lr: 0.00551 [2023-12-20 13:42:43,888 INFO misc.py line 119 131400] Train: [5/100][19/800] Data 0.003 (0.003) Batch 0.314 (0.316) Remain 06:44:08 loss: 1.1669 Lr: 0.00551 [2023-12-20 13:42:44,220 INFO misc.py line 119 131400] Train: [5/100][20/800] Data 0.003 (0.003) Batch 0.332 (0.317) Remain 06:45:22 loss: 0.8553 Lr: 0.00551 [2023-12-20 13:42:44,533 INFO misc.py line 119 131400] Train: [5/100][21/800] Data 0.003 (0.003) Batch 0.312 (0.316) Remain 06:45:00 loss: 1.2377 Lr: 0.00551 [2023-12-20 13:42:44,829 INFO misc.py line 119 131400] Train: [5/100][22/800] Data 0.003 (0.003) Batch 0.297 (0.315) Remain 06:43:42 loss: 0.6169 Lr: 0.00551 [2023-12-20 13:42:45,166 INFO misc.py line 119 131400] Train: [5/100][23/800] Data 0.003 (0.003) Batch 0.335 (0.316) Remain 06:44:58 loss: 1.1380 Lr: 0.00551 [2023-12-20 13:42:45,494 INFO misc.py line 119 131400] Train: [5/100][24/800] Data 0.005 (0.003) Batch 0.329 (0.317) Remain 06:45:44 loss: 0.7549 Lr: 0.00551 [2023-12-20 13:42:45,827 INFO misc.py line 119 131400] Train: [5/100][25/800] Data 0.004 (0.003) Batch 0.333 (0.318) Remain 06:46:39 loss: 0.8224 Lr: 0.00551 [2023-12-20 13:42:46,156 INFO misc.py line 119 131400] Train: [5/100][26/800] Data 0.003 (0.003) Batch 0.329 (0.318) Remain 06:47:17 loss: 0.7399 Lr: 0.00552 [2023-12-20 13:42:46,479 INFO misc.py line 119 131400] Train: [5/100][27/800] Data 0.003 (0.003) Batch 0.324 (0.319) Remain 06:47:34 loss: 0.7496 Lr: 0.00552 [2023-12-20 13:42:46,765 INFO misc.py line 119 131400] Train: [5/100][28/800] Data 0.002 (0.003) Batch 0.286 (0.317) Remain 06:45:53 loss: 1.1714 Lr: 0.00552 [2023-12-20 13:42:47,057 INFO misc.py line 119 131400] Train: [5/100][29/800] Data 0.002 (0.003) Batch 0.292 (0.316) Remain 06:44:37 loss: 1.1597 Lr: 0.00552 [2023-12-20 13:42:47,361 INFO misc.py line 119 131400] Train: [5/100][30/800] Data 0.003 (0.003) Batch 0.304 (0.316) Remain 06:44:01 loss: 1.0404 Lr: 0.00552 [2023-12-20 13:42:47,675 INFO misc.py line 119 131400] Train: [5/100][31/800] Data 0.004 (0.003) Batch 0.314 (0.316) Remain 06:43:55 loss: 1.1756 Lr: 0.00552 [2023-12-20 13:42:47,968 INFO misc.py line 119 131400] Train: [5/100][32/800] Data 0.003 (0.003) Batch 0.294 (0.315) Remain 06:42:58 loss: 0.7606 Lr: 0.00552 [2023-12-20 13:42:48,278 INFO misc.py line 119 131400] Train: [5/100][33/800] Data 0.003 (0.003) Batch 0.309 (0.315) Remain 06:42:42 loss: 0.8037 Lr: 0.00552 [2023-12-20 13:42:48,533 INFO misc.py line 119 131400] Train: [5/100][34/800] Data 0.003 (0.003) Batch 0.256 (0.313) Remain 06:40:15 loss: 0.7364 Lr: 0.00553 [2023-12-20 13:42:48,843 INFO misc.py line 119 131400] Train: [5/100][35/800] Data 0.002 (0.003) Batch 0.310 (0.313) Remain 06:40:08 loss: 0.9253 Lr: 0.00553 [2023-12-20 13:42:49,171 INFO misc.py line 119 131400] Train: [5/100][36/800] Data 0.003 (0.003) Batch 0.328 (0.313) Remain 06:40:43 loss: 1.3312 Lr: 0.00553 [2023-12-20 13:42:49,484 INFO misc.py line 119 131400] Train: [5/100][37/800] Data 0.003 (0.003) Batch 0.314 (0.313) Remain 06:40:44 loss: 0.9993 Lr: 0.00553 [2023-12-20 13:42:49,808 INFO misc.py line 119 131400] Train: [5/100][38/800] Data 0.003 (0.003) Batch 0.324 (0.314) Remain 06:41:07 loss: 1.0216 Lr: 0.00553 [2023-12-20 13:42:50,115 INFO misc.py line 119 131400] Train: [5/100][39/800] Data 0.003 (0.003) Batch 0.306 (0.313) Remain 06:40:51 loss: 0.7575 Lr: 0.00553 [2023-12-20 13:42:50,419 INFO misc.py line 119 131400] Train: [5/100][40/800] Data 0.003 (0.003) Batch 0.302 (0.313) Remain 06:40:28 loss: 1.0058 Lr: 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INFO misc.py line 119 131400] Train: [5/100][85/800] Data 0.004 (0.003) Batch 0.304 (0.311) Remain 06:37:28 loss: 0.6632 Lr: 0.00559 [2023-12-20 13:43:04,603 INFO misc.py line 119 131400] Train: [5/100][86/800] Data 0.003 (0.003) Batch 0.277 (0.310) Remain 06:36:56 loss: 0.6322 Lr: 0.00559 [2023-12-20 13:43:04,937 INFO misc.py line 119 131400] Train: [5/100][87/800] Data 0.003 (0.003) Batch 0.334 (0.311) Remain 06:37:18 loss: 1.2067 Lr: 0.00559 [2023-12-20 13:43:05,214 INFO misc.py line 119 131400] Train: [5/100][88/800] Data 0.003 (0.003) Batch 0.277 (0.310) Remain 06:36:46 loss: 0.8310 Lr: 0.00559 [2023-12-20 13:43:05,527 INFO misc.py line 119 131400] Train: [5/100][89/800] Data 0.002 (0.003) Batch 0.311 (0.310) Remain 06:36:47 loss: 0.6314 Lr: 0.00559 [2023-12-20 13:43:05,843 INFO misc.py line 119 131400] Train: [5/100][90/800] Data 0.004 (0.003) Batch 0.318 (0.310) Remain 06:36:53 loss: 0.7304 Lr: 0.00559 [2023-12-20 13:43:06,151 INFO misc.py line 119 131400] Train: [5/100][91/800] 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INFO misc.py line 119 131400] Train: [5/100][750/800] Data 0.004 (0.004) Batch 0.330 (0.321) Remain 06:47:25 loss: 1.1823 Lr: 0.00600 [2023-12-20 13:46:39,303 INFO misc.py line 119 131400] Train: [5/100][751/800] Data 0.004 (0.004) Batch 0.349 (0.321) Remain 06:47:28 loss: 0.7243 Lr: 0.00600 [2023-12-20 13:46:39,660 INFO misc.py line 119 131400] Train: [5/100][752/800] Data 0.003 (0.004) Batch 0.356 (0.322) Remain 06:47:31 loss: 0.5661 Lr: 0.00600 [2023-12-20 13:46:40,001 INFO misc.py line 119 131400] Train: [5/100][753/800] Data 0.004 (0.004) Batch 0.341 (0.322) Remain 06:47:33 loss: 1.0879 Lr: 0.00600 [2023-12-20 13:46:40,314 INFO misc.py line 119 131400] Train: [5/100][754/800] Data 0.004 (0.004) Batch 0.304 (0.322) Remain 06:47:30 loss: 1.4297 Lr: 0.00600 [2023-12-20 13:46:40,647 INFO misc.py line 119 131400] Train: [5/100][755/800] Data 0.012 (0.004) Batch 0.342 (0.322) Remain 06:47:32 loss: 0.9886 Lr: 0.00600 [2023-12-20 13:46:40,915 INFO misc.py line 119 131400] Train: 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0.299 (0.322) Remain 06:47:26 loss: 0.7873 Lr: 0.00600 [2023-12-20 13:46:43,183 INFO misc.py line 119 131400] Train: [5/100][763/800] Data 0.002 (0.004) Batch 0.325 (0.322) Remain 06:47:26 loss: 0.8100 Lr: 0.00600 [2023-12-20 13:46:43,457 INFO misc.py line 119 131400] Train: [5/100][764/800] Data 0.002 (0.004) Batch 0.274 (0.321) Remain 06:47:21 loss: 1.3489 Lr: 0.00600 [2023-12-20 13:46:43,773 INFO misc.py line 119 131400] Train: [5/100][765/800] Data 0.003 (0.004) Batch 0.315 (0.321) Remain 06:47:20 loss: 1.0903 Lr: 0.00600 [2023-12-20 13:46:44,045 INFO misc.py line 119 131400] Train: [5/100][766/800] Data 0.004 (0.004) Batch 0.272 (0.321) Remain 06:47:15 loss: 0.8254 Lr: 0.00600 [2023-12-20 13:46:44,357 INFO misc.py line 119 131400] Train: [5/100][767/800] Data 0.003 (0.004) Batch 0.311 (0.321) Remain 06:47:14 loss: 0.8180 Lr: 0.00600 [2023-12-20 13:46:44,659 INFO misc.py line 119 131400] Train: [5/100][768/800] Data 0.005 (0.004) Batch 0.299 (0.321) Remain 06:47:11 loss: 0.7125 Lr: 0.00600 [2023-12-20 13:46:44,949 INFO misc.py line 119 131400] Train: [5/100][769/800] Data 0.007 (0.004) Batch 0.293 (0.321) Remain 06:47:08 loss: 0.7371 Lr: 0.00600 [2023-12-20 13:46:45,263 INFO misc.py line 119 131400] Train: [5/100][770/800] Data 0.004 (0.004) Batch 0.316 (0.321) Remain 06:47:07 loss: 0.8993 Lr: 0.00600 [2023-12-20 13:46:45,592 INFO misc.py line 119 131400] Train: [5/100][771/800] Data 0.003 (0.004) Batch 0.313 (0.321) Remain 06:47:06 loss: 1.0055 Lr: 0.00600 [2023-12-20 13:46:45,880 INFO misc.py line 119 131400] Train: [5/100][772/800] Data 0.018 (0.004) Batch 0.303 (0.321) Remain 06:47:04 loss: 0.6139 Lr: 0.00600 [2023-12-20 13:46:46,204 INFO misc.py line 119 131400] Train: [5/100][773/800] Data 0.003 (0.004) Batch 0.324 (0.321) Remain 06:47:04 loss: 1.2099 Lr: 0.00600 [2023-12-20 13:46:46,473 INFO misc.py line 119 131400] Train: [5/100][774/800] Data 0.003 (0.004) Batch 0.270 (0.321) Remain 06:46:58 loss: 0.6916 Lr: 0.00600 [2023-12-20 13:46:46,801 INFO misc.py line 119 131400] Train: [5/100][775/800] Data 0.002 (0.004) Batch 0.323 (0.321) Remain 06:46:58 loss: 0.6024 Lr: 0.00600 [2023-12-20 13:46:47,097 INFO misc.py line 119 131400] Train: [5/100][776/800] Data 0.007 (0.004) Batch 0.300 (0.321) Remain 06:46:56 loss: 0.3644 Lr: 0.00600 [2023-12-20 13:46:47,393 INFO misc.py line 119 131400] Train: [5/100][777/800] Data 0.003 (0.004) Batch 0.295 (0.321) Remain 06:46:53 loss: 0.6755 Lr: 0.00600 [2023-12-20 13:46:47,696 INFO misc.py line 119 131400] Train: [5/100][778/800] Data 0.003 (0.004) Batch 0.303 (0.321) Remain 06:46:51 loss: 1.0754 Lr: 0.00600 [2023-12-20 13:46:48,003 INFO misc.py line 119 131400] Train: [5/100][779/800] Data 0.003 (0.004) Batch 0.308 (0.321) Remain 06:46:49 loss: 1.1563 Lr: 0.00600 [2023-12-20 13:46:48,306 INFO misc.py line 119 131400] Train: [5/100][780/800] Data 0.003 (0.004) Batch 0.302 (0.321) Remain 06:46:47 loss: 0.8881 Lr: 0.00600 [2023-12-20 13:46:48,623 INFO misc.py line 119 131400] Train: [5/100][781/800] Data 0.004 (0.004) Batch 0.318 (0.321) Remain 06:46:46 loss: 0.7205 Lr: 0.00600 [2023-12-20 13:46:48,957 INFO misc.py line 119 131400] Train: [5/100][782/800] Data 0.003 (0.004) Batch 0.333 (0.321) Remain 06:46:47 loss: 0.8438 Lr: 0.00600 [2023-12-20 13:46:49,295 INFO misc.py line 119 131400] Train: [5/100][783/800] Data 0.004 (0.004) Batch 0.339 (0.321) Remain 06:46:49 loss: 0.9675 Lr: 0.00600 [2023-12-20 13:46:49,594 INFO misc.py line 119 131400] Train: [5/100][784/800] Data 0.003 (0.004) Batch 0.299 (0.321) Remain 06:46:46 loss: 0.9815 Lr: 0.00600 [2023-12-20 13:46:49,881 INFO misc.py line 119 131400] Train: [5/100][785/800] Data 0.004 (0.004) Batch 0.287 (0.321) Remain 06:46:42 loss: 0.7445 Lr: 0.00600 [2023-12-20 13:46:50,219 INFO misc.py line 119 131400] Train: [5/100][786/800] Data 0.003 (0.004) Batch 0.338 (0.321) Remain 06:46:44 loss: 0.8186 Lr: 0.00600 [2023-12-20 13:46:50,505 INFO misc.py line 119 131400] Train: [5/100][787/800] Data 0.003 (0.004) Batch 0.286 (0.321) Remain 06:46:40 loss: 0.9609 Lr: 0.00600 [2023-12-20 13:46:50,801 INFO misc.py line 119 131400] Train: [5/100][788/800] Data 0.003 (0.004) Batch 0.296 (0.321) Remain 06:46:37 loss: 1.3995 Lr: 0.00600 [2023-12-20 13:46:51,126 INFO misc.py line 119 131400] Train: [5/100][789/800] Data 0.004 (0.004) Batch 0.326 (0.321) Remain 06:46:37 loss: 0.6789 Lr: 0.00600 [2023-12-20 13:46:51,451 INFO misc.py line 119 131400] Train: [5/100][790/800] Data 0.003 (0.004) Batch 0.324 (0.321) Remain 06:46:37 loss: 1.0971 Lr: 0.00600 [2023-12-20 13:46:51,807 INFO misc.py line 119 131400] Train: [5/100][791/800] Data 0.005 (0.004) Batch 0.357 (0.321) Remain 06:46:41 loss: 1.0198 Lr: 0.00600 [2023-12-20 13:46:52,126 INFO misc.py line 119 131400] Train: [5/100][792/800] Data 0.003 (0.004) Batch 0.319 (0.321) Remain 06:46:40 loss: 0.7841 Lr: 0.00600 [2023-12-20 13:46:52,433 INFO misc.py line 119 131400] Train: [5/100][793/800] Data 0.003 (0.004) Batch 0.307 (0.321) Remain 06:46:38 loss: 1.2108 Lr: 0.00600 [2023-12-20 13:46:52,741 INFO misc.py line 119 131400] Train: [5/100][794/800] Data 0.004 (0.004) Batch 0.308 (0.321) Remain 06:46:37 loss: 0.6186 Lr: 0.00600 [2023-12-20 13:46:53,038 INFO misc.py line 119 131400] Train: [5/100][795/800] Data 0.002 (0.004) Batch 0.296 (0.321) Remain 06:46:34 loss: 0.9221 Lr: 0.00600 [2023-12-20 13:46:53,360 INFO misc.py line 119 131400] Train: [5/100][796/800] Data 0.003 (0.004) Batch 0.322 (0.321) Remain 06:46:34 loss: 0.8809 Lr: 0.00600 [2023-12-20 13:46:53,687 INFO misc.py line 119 131400] Train: [5/100][797/800] Data 0.004 (0.004) Batch 0.326 (0.321) Remain 06:46:34 loss: 1.1621 Lr: 0.00600 [2023-12-20 13:46:53,976 INFO misc.py line 119 131400] Train: [5/100][798/800] Data 0.004 (0.004) Batch 0.291 (0.321) Remain 06:46:31 loss: 0.8317 Lr: 0.00600 [2023-12-20 13:46:54,279 INFO misc.py line 119 131400] Train: [5/100][799/800] Data 0.003 (0.004) Batch 0.297 (0.321) Remain 06:46:28 loss: 1.2594 Lr: 0.00600 [2023-12-20 13:46:54,580 INFO misc.py line 119 131400] Train: [5/100][800/800] Data 0.008 (0.004) Batch 0.307 (0.321) Remain 06:46:27 loss: 0.6918 Lr: 0.00600 [2023-12-20 13:46:54,580 INFO misc.py line 136 131400] Train result: loss: 0.9394 [2023-12-20 13:46:54,580 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:47:15,968 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.4768 [2023-12-20 13:47:16,050 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.3898 [2023-12-20 13:47:16,292 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6206 [2023-12-20 13:47:16,407 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.9450 [2023-12-20 13:47:16,526 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5424 [2023-12-20 13:47:16,943 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2229 [2023-12-20 13:47:17,036 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.5122 [2023-12-20 13:47:17,146 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9998 [2023-12-20 13:47:17,227 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.4247 [2023-12-20 13:47:17,317 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5913 [2023-12-20 13:47:17,412 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.8448 [2023-12-20 13:47:17,548 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.8356 [2023-12-20 13:47:17,665 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.7470 [2023-12-20 13:47:17,819 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.7201 [2023-12-20 13:47:17,911 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5692 [2023-12-20 13:47:18,048 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0546 [2023-12-20 13:47:18,157 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.7169 [2023-12-20 13:47:18,266 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8097 [2023-12-20 13:47:18,377 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.7752 [2023-12-20 13:47:18,450 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5493 [2023-12-20 13:47:18,556 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5911 [2023-12-20 13:47:18,711 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2704 [2023-12-20 13:47:18,834 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8958 [2023-12-20 13:47:18,975 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.5198 [2023-12-20 13:47:19,118 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.5532 [2023-12-20 13:47:19,201 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.9744 [2023-12-20 13:47:19,359 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9663 [2023-12-20 13:47:19,481 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.7989 [2023-12-20 13:47:19,578 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8409 [2023-12-20 13:47:19,721 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7659 [2023-12-20 13:47:19,823 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.0302 [2023-12-20 13:47:19,943 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.9153 [2023-12-20 13:47:20,028 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.7982 [2023-12-20 13:47:20,102 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.4565 [2023-12-20 13:47:20,195 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.3003 [2023-12-20 13:47:20,290 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.1455 [2023-12-20 13:47:20,419 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.2985 [2023-12-20 13:47:20,527 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3916 [2023-12-20 13:47:20,604 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.3418 [2023-12-20 13:47:20,746 INFO evaluator.py line 159 131400] Test: [40/78] Loss 1.0083 [2023-12-20 13:47:20,892 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0372 [2023-12-20 13:47:20,989 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.5770 [2023-12-20 13:47:21,108 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.6115 [2023-12-20 13:47:21,249 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.2195 [2023-12-20 13:47:21,366 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.6229 [2023-12-20 13:47:21,468 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7362 [2023-12-20 13:47:21,637 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6161 [2023-12-20 13:47:21,731 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5173 [2023-12-20 13:47:21,877 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2770 [2023-12-20 13:47:21,968 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9852 [2023-12-20 13:47:22,043 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6819 [2023-12-20 13:47:22,147 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3754 [2023-12-20 13:47:22,294 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.0913 [2023-12-20 13:47:22,428 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3998 [2023-12-20 13:47:22,532 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5385 [2023-12-20 13:47:22,618 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8314 [2023-12-20 13:47:22,725 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.7556 [2023-12-20 13:47:22,886 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.4929 [2023-12-20 13:47:22,984 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.7666 [2023-12-20 13:47:23,078 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4541 [2023-12-20 13:47:23,172 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5766 [2023-12-20 13:47:23,262 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.6885 [2023-12-20 13:47:23,348 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.0927 [2023-12-20 13:47:23,453 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8596 [2023-12-20 13:47:23,582 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5019 [2023-12-20 13:47:23,664 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.8786 [2023-12-20 13:47:23,763 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5947 [2023-12-20 13:47:23,856 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0273 [2023-12-20 13:47:23,939 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2773 [2023-12-20 13:47:24,027 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0291 [2023-12-20 13:47:24,122 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9712 [2023-12-20 13:47:24,211 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8017 [2023-12-20 13:47:24,348 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.4289 [2023-12-20 13:47:24,447 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6205 [2023-12-20 13:47:24,562 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.2345 [2023-12-20 13:47:24,663 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.3739 [2023-12-20 13:47:24,750 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6207 [2023-12-20 13:47:24,904 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3820 [2023-12-20 13:47:26,112 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6050/0.7746/0.8317. [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.7388/0.7967 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9559/0.9757 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5115/0.5883 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.6580/0.8774 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8414/0.9002 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7286/0.8954 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6336/0.7049 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.4449/0.7468 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4616/0.7861 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.6363/0.9237 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2142/0.5589 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.4499/0.8944 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5212/0.8169 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6233/0.8460 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4412/0.5442 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.5576/0.6737 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8979/0.9540 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.5660/0.6551 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8479/0.9054 [2023-12-20 13:47:26,113 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.3710/0.4477 [2023-12-20 13:47:26,114 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:47:26,115 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.6050 [2023-12-20 13:47:26,115 INFO misc.py line 165 131400] Currently Best mIoU: 0.6050 [2023-12-20 13:47:26,115 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:47:33,238 INFO misc.py line 119 131400] Train: [6/100][1/800] Data 1.057 (1.057) Batch 1.363 (1.363) Remain 28:46:28 loss: 1.2848 Lr: 0.00600 [2023-12-20 13:47:33,831 INFO misc.py line 119 131400] Train: [6/100][2/800] Data 0.293 (0.293) Batch 0.593 (0.593) Remain 12:30:40 loss: 1.0099 Lr: 0.00600 [2023-12-20 13:47:34,146 INFO misc.py line 119 131400] Train: [6/100][3/800] Data 0.004 (0.004) Batch 0.315 (0.315) Remain 06:39:24 loss: 0.9145 Lr: 0.00600 [2023-12-20 13:47:34,461 INFO misc.py line 119 131400] Train: [6/100][4/800] Data 0.002 (0.002) Batch 0.316 (0.316) Remain 06:40:02 loss: 1.0355 Lr: 0.00600 [2023-12-20 13:47:34,767 INFO misc.py line 119 131400] Train: [6/100][5/800] Data 0.003 (0.003) Batch 0.305 (0.311) Remain 06:33:20 loss: 1.1350 Lr: 0.00600 [2023-12-20 13:47:35,066 INFO misc.py line 119 131400] Train: [6/100][6/800] Data 0.003 (0.003) Batch 0.293 (0.305) Remain 06:26:00 loss: 0.9096 Lr: 0.00600 [2023-12-20 13:47:35,443 INFO misc.py line 119 131400] Train: [6/100][7/800] Data 0.008 (0.004) Batch 0.383 (0.324) Remain 06:50:52 loss: 0.8219 Lr: 0.00600 [2023-12-20 13:47:35,711 INFO misc.py line 119 131400] Train: [6/100][8/800] Data 0.003 (0.004) Batch 0.267 (0.313) Remain 06:36:18 loss: 0.5905 Lr: 0.00600 [2023-12-20 13:47:36,040 INFO misc.py line 119 131400] Train: 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131400] Train: [6/100][28/800] Data 0.004 (0.004) Batch 0.344 (0.316) Remain 06:40:14 loss: 0.7921 Lr: 0.00600 [2023-12-20 13:47:42,394 INFO misc.py line 119 131400] Train: [6/100][29/800] Data 0.003 (0.004) Batch 0.343 (0.317) Remain 06:41:31 loss: 0.8346 Lr: 0.00600 [2023-12-20 13:47:42,713 INFO misc.py line 119 131400] Train: [6/100][30/800] Data 0.007 (0.004) Batch 0.322 (0.317) Remain 06:41:45 loss: 1.0546 Lr: 0.00600 [2023-12-20 13:47:43,046 INFO misc.py line 119 131400] Train: [6/100][31/800] Data 0.003 (0.004) Batch 0.331 (0.318) Remain 06:42:23 loss: 0.7120 Lr: 0.00600 [2023-12-20 13:47:43,359 INFO misc.py line 119 131400] Train: [6/100][32/800] Data 0.004 (0.004) Batch 0.314 (0.318) Remain 06:42:12 loss: 1.1435 Lr: 0.00600 [2023-12-20 13:47:43,681 INFO misc.py line 119 131400] Train: [6/100][33/800] Data 0.003 (0.004) Batch 0.308 (0.317) Remain 06:41:49 loss: 0.7673 Lr: 0.00600 [2023-12-20 13:47:43,995 INFO misc.py line 119 131400] Train: [6/100][34/800] Data 0.017 (0.004) 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0.362 (0.325) Remain 06:47:13 loss: 0.7042 Lr: 0.00600 [2023-12-20 13:51:40,943 INFO misc.py line 119 131400] Train: [6/100][763/800] Data 0.003 (0.004) Batch 0.314 (0.325) Remain 06:47:11 loss: 0.7142 Lr: 0.00600 [2023-12-20 13:51:41,288 INFO misc.py line 119 131400] Train: [6/100][764/800] Data 0.004 (0.004) Batch 0.346 (0.325) Remain 06:47:13 loss: 0.7170 Lr: 0.00600 [2023-12-20 13:51:41,617 INFO misc.py line 119 131400] Train: [6/100][765/800] Data 0.003 (0.004) Batch 0.329 (0.325) Remain 06:47:13 loss: 0.6539 Lr: 0.00600 [2023-12-20 13:51:41,968 INFO misc.py line 119 131400] Train: [6/100][766/800] Data 0.003 (0.004) Batch 0.350 (0.325) Remain 06:47:15 loss: 0.8902 Lr: 0.00600 [2023-12-20 13:51:42,296 INFO misc.py line 119 131400] Train: [6/100][767/800] Data 0.004 (0.004) Batch 0.329 (0.325) Remain 06:47:15 loss: 0.6606 Lr: 0.00600 [2023-12-20 13:51:42,637 INFO misc.py line 119 131400] Train: [6/100][768/800] Data 0.003 (0.004) Batch 0.342 (0.325) Remain 06:47:17 loss: 1.0818 Lr: 0.00600 [2023-12-20 13:51:42,982 INFO misc.py line 119 131400] Train: [6/100][769/800] Data 0.002 (0.004) Batch 0.344 (0.325) Remain 06:47:18 loss: 1.3387 Lr: 0.00600 [2023-12-20 13:51:43,290 INFO misc.py line 119 131400] Train: [6/100][770/800] Data 0.004 (0.004) Batch 0.308 (0.325) Remain 06:47:16 loss: 0.4393 Lr: 0.00600 [2023-12-20 13:51:43,621 INFO misc.py line 119 131400] Train: [6/100][771/800] Data 0.004 (0.004) Batch 0.331 (0.325) Remain 06:47:17 loss: 1.0708 Lr: 0.00600 [2023-12-20 13:51:43,945 INFO misc.py line 119 131400] Train: [6/100][772/800] Data 0.003 (0.004) Batch 0.325 (0.325) Remain 06:47:16 loss: 0.4678 Lr: 0.00600 [2023-12-20 13:51:44,253 INFO misc.py line 119 131400] Train: [6/100][773/800] Data 0.002 (0.004) Batch 0.307 (0.325) Remain 06:47:14 loss: 0.8357 Lr: 0.00600 [2023-12-20 13:51:44,519 INFO misc.py line 119 131400] Train: [6/100][774/800] Data 0.002 (0.004) Batch 0.267 (0.325) Remain 06:47:08 loss: 0.7725 Lr: 0.00600 [2023-12-20 13:51:44,840 INFO misc.py line 119 131400] Train: [6/100][775/800] Data 0.003 (0.004) Batch 0.320 (0.325) Remain 06:47:07 loss: 1.3086 Lr: 0.00600 [2023-12-20 13:51:45,181 INFO misc.py line 119 131400] Train: [6/100][776/800] Data 0.004 (0.004) Batch 0.341 (0.325) Remain 06:47:09 loss: 0.8793 Lr: 0.00600 [2023-12-20 13:51:45,495 INFO misc.py line 119 131400] Train: [6/100][777/800] Data 0.003 (0.004) Batch 0.315 (0.325) Remain 06:47:07 loss: 0.8180 Lr: 0.00600 [2023-12-20 13:51:45,787 INFO misc.py line 119 131400] Train: [6/100][778/800] Data 0.003 (0.004) Batch 0.292 (0.325) Remain 06:47:04 loss: 1.1743 Lr: 0.00600 [2023-12-20 13:51:46,107 INFO misc.py line 119 131400] Train: [6/100][779/800] Data 0.003 (0.004) Batch 0.321 (0.325) Remain 06:47:03 loss: 1.1763 Lr: 0.00600 [2023-12-20 13:51:46,423 INFO misc.py line 119 131400] Train: [6/100][780/800] Data 0.003 (0.004) Batch 0.315 (0.325) Remain 06:47:02 loss: 0.7302 Lr: 0.00600 [2023-12-20 13:51:46,748 INFO misc.py line 119 131400] Train: [6/100][781/800] Data 0.004 (0.004) Batch 0.326 (0.325) Remain 06:47:02 loss: 1.1838 Lr: 0.00600 [2023-12-20 13:51:47,086 INFO misc.py line 119 131400] Train: [6/100][782/800] Data 0.003 (0.004) Batch 0.337 (0.325) Remain 06:47:03 loss: 1.3030 Lr: 0.00600 [2023-12-20 13:51:47,380 INFO misc.py line 119 131400] Train: [6/100][783/800] Data 0.003 (0.004) Batch 0.293 (0.325) Remain 06:46:59 loss: 1.0919 Lr: 0.00600 [2023-12-20 13:51:47,709 INFO misc.py line 119 131400] Train: [6/100][784/800] Data 0.003 (0.004) Batch 0.330 (0.325) Remain 06:46:59 loss: 0.8722 Lr: 0.00600 [2023-12-20 13:51:48,040 INFO misc.py line 119 131400] Train: [6/100][785/800] Data 0.003 (0.004) Batch 0.330 (0.325) Remain 06:47:00 loss: 0.8116 Lr: 0.00600 [2023-12-20 13:51:48,364 INFO misc.py line 119 131400] Train: [6/100][786/800] Data 0.004 (0.004) Batch 0.325 (0.325) Remain 06:46:59 loss: 0.6461 Lr: 0.00600 [2023-12-20 13:51:48,649 INFO misc.py line 119 131400] Train: [6/100][787/800] Data 0.003 (0.004) Batch 0.285 (0.325) Remain 06:46:55 loss: 0.6936 Lr: 0.00600 [2023-12-20 13:51:48,989 INFO misc.py line 119 131400] Train: [6/100][788/800] Data 0.004 (0.004) Batch 0.339 (0.325) Remain 06:46:56 loss: 1.0602 Lr: 0.00600 [2023-12-20 13:51:49,296 INFO misc.py line 119 131400] Train: [6/100][789/800] Data 0.004 (0.004) Batch 0.307 (0.325) Remain 06:46:54 loss: 0.6074 Lr: 0.00600 [2023-12-20 13:51:49,591 INFO misc.py line 119 131400] Train: [6/100][790/800] Data 0.003 (0.004) Batch 0.296 (0.325) Remain 06:46:51 loss: 0.7103 Lr: 0.00600 [2023-12-20 13:51:49,864 INFO misc.py line 119 131400] Train: [6/100][791/800] Data 0.002 (0.004) Batch 0.272 (0.325) Remain 06:46:46 loss: 1.0844 Lr: 0.00600 [2023-12-20 13:51:50,153 INFO misc.py line 119 131400] Train: [6/100][792/800] Data 0.002 (0.004) Batch 0.289 (0.324) Remain 06:46:42 loss: 0.6400 Lr: 0.00600 [2023-12-20 13:51:50,454 INFO misc.py line 119 131400] Train: [6/100][793/800] Data 0.002 (0.004) Batch 0.301 (0.324) Remain 06:46:40 loss: 0.9289 Lr: 0.00600 [2023-12-20 13:51:50,752 INFO misc.py line 119 131400] Train: [6/100][794/800] Data 0.002 (0.004) Batch 0.298 (0.324) Remain 06:46:37 loss: 0.6353 Lr: 0.00600 [2023-12-20 13:51:51,065 INFO misc.py line 119 131400] Train: [6/100][795/800] Data 0.002 (0.004) Batch 0.313 (0.324) Remain 06:46:35 loss: 0.4618 Lr: 0.00600 [2023-12-20 13:51:51,345 INFO misc.py line 119 131400] Train: [6/100][796/800] Data 0.002 (0.004) Batch 0.279 (0.324) Remain 06:46:31 loss: 0.9451 Lr: 0.00600 [2023-12-20 13:51:51,651 INFO misc.py line 119 131400] Train: [6/100][797/800] Data 0.003 (0.004) Batch 0.306 (0.324) Remain 06:46:29 loss: 0.6004 Lr: 0.00600 [2023-12-20 13:51:51,944 INFO misc.py line 119 131400] Train: [6/100][798/800] Data 0.003 (0.004) Batch 0.293 (0.324) Remain 06:46:25 loss: 0.7244 Lr: 0.00600 [2023-12-20 13:51:52,270 INFO misc.py line 119 131400] Train: [6/100][799/800] Data 0.002 (0.004) Batch 0.323 (0.324) Remain 06:46:25 loss: 0.8925 Lr: 0.00600 [2023-12-20 13:51:52,557 INFO misc.py line 119 131400] Train: [6/100][800/800] Data 0.006 (0.004) Batch 0.289 (0.324) Remain 06:46:21 loss: 0.8085 Lr: 0.00600 [2023-12-20 13:51:52,557 INFO misc.py line 136 131400] Train result: loss: 0.8739 [2023-12-20 13:51:52,565 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:52:14,553 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1090 [2023-12-20 13:52:14,999 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.4600 [2023-12-20 13:52:15,125 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6232 [2023-12-20 13:52:15,238 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5213 [2023-12-20 13:52:15,351 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4292 [2023-12-20 13:52:15,450 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.9491 [2023-12-20 13:52:15,543 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8433 [2023-12-20 13:52:15,648 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2307 [2023-12-20 13:52:15,728 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3834 [2023-12-20 13:52:15,815 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4878 [2023-12-20 13:52:15,907 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6413 [2023-12-20 13:52:16,045 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.9047 [2023-12-20 13:52:16,165 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3384 [2023-12-20 13:52:16,318 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4544 [2023-12-20 13:52:16,412 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.8031 [2023-12-20 13:52:16,546 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.1163 [2023-12-20 13:52:16,658 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4883 [2023-12-20 13:52:16,768 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4883 [2023-12-20 13:52:16,879 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.7609 [2023-12-20 13:52:16,969 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5584 [2023-12-20 13:52:17,077 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5005 [2023-12-20 13:52:17,234 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2296 [2023-12-20 13:52:17,359 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9257 [2023-12-20 13:52:17,501 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4568 [2023-12-20 13:52:17,651 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3592 [2023-12-20 13:52:17,743 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7717 [2023-12-20 13:52:17,909 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8163 [2023-12-20 13:52:18,045 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.7598 [2023-12-20 13:52:18,151 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.9400 [2023-12-20 13:52:18,298 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5043 [2023-12-20 13:52:18,400 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.9543 [2023-12-20 13:52:18,521 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6840 [2023-12-20 13:52:18,605 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.3908 [2023-12-20 13:52:18,675 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3098 [2023-12-20 13:52:18,770 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.1844 [2023-12-20 13:52:18,866 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.2297 [2023-12-20 13:52:19,007 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.3692 [2023-12-20 13:52:19,117 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2168 [2023-12-20 13:52:19,197 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.1602 [2023-12-20 13:52:19,346 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.9708 [2023-12-20 13:52:19,499 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0230 [2023-12-20 13:52:19,598 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.5922 [2023-12-20 13:52:19,731 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4676 [2023-12-20 13:52:19,874 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1650 [2023-12-20 13:52:19,990 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9711 [2023-12-20 13:52:20,094 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5536 [2023-12-20 13:52:20,261 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.8429 [2023-12-20 13:52:20,355 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4385 [2023-12-20 13:52:20,506 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2056 [2023-12-20 13:52:20,603 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.6762 [2023-12-20 13:52:20,691 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8541 [2023-12-20 13:52:20,799 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2096 [2023-12-20 13:52:20,945 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1747 [2023-12-20 13:52:21,078 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4361 [2023-12-20 13:52:21,179 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.0671 [2023-12-20 13:52:21,274 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9824 [2023-12-20 13:52:21,376 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5001 [2023-12-20 13:52:21,535 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3208 [2023-12-20 13:52:21,628 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5522 [2023-12-20 13:52:21,720 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5484 [2023-12-20 13:52:21,815 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5016 [2023-12-20 13:52:21,907 INFO evaluator.py line 159 131400] Test: [62/78] Loss 1.0652 [2023-12-20 13:52:21,996 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8593 [2023-12-20 13:52:22,095 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9224 [2023-12-20 13:52:22,220 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5926 [2023-12-20 13:52:22,303 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.9065 [2023-12-20 13:52:22,403 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8432 [2023-12-20 13:52:22,497 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0200 [2023-12-20 13:52:22,584 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3769 [2023-12-20 13:52:22,666 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0219 [2023-12-20 13:52:22,760 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.0668 [2023-12-20 13:52:22,858 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8668 [2023-12-20 13:52:22,991 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1517 [2023-12-20 13:52:23,085 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.9134 [2023-12-20 13:52:23,201 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.2244 [2023-12-20 13:52:23,303 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9814 [2023-12-20 13:52:23,388 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4488 [2023-12-20 13:52:23,545 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2249 [2023-12-20 13:52:24,827 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6475/0.7856/0.8720. [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8070/0.9036 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9586/0.9832 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5649/0.6832 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7859/0.8371 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8591/0.9219 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7766/0.8911 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6978/0.8088 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5407/0.7747 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4919/0.5409 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7751/0.8954 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3065/0.5355 [2023-12-20 13:52:24,827 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6136/0.7465 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.4920/0.5484 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7023/0.8616 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4227/0.6772 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.5230/0.7908 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8929/0.9097 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.5698/0.7955 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.6662/0.9462 [2023-12-20 13:52:24,828 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5043/0.6600 [2023-12-20 13:52:24,828 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:52:24,830 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.6475 [2023-12-20 13:52:24,830 INFO misc.py line 165 131400] Currently Best mIoU: 0.6475 [2023-12-20 13:52:24,830 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:52:30,359 INFO misc.py line 119 131400] Train: [7/100][1/800] Data 0.873 (0.873) Batch 1.182 (1.182) Remain 24:41:20 loss: 0.8977 Lr: 0.00600 [2023-12-20 13:52:30,693 INFO misc.py line 119 131400] Train: [7/100][2/800] Data 0.008 (0.008) Batch 0.334 (0.334) Remain 06:58:13 loss: 0.7576 Lr: 0.00600 [2023-12-20 13:52:30,964 INFO misc.py line 119 131400] Train: [7/100][3/800] Data 0.003 (0.003) Batch 0.271 (0.271) Remain 05:39:08 loss: 1.0138 Lr: 0.00600 [2023-12-20 13:52:31,289 INFO misc.py line 119 131400] Train: [7/100][4/800] Data 0.004 (0.004) Batch 0.318 (0.318) Remain 06:38:15 loss: 1.2160 Lr: 0.00600 [2023-12-20 13:52:31,609 INFO misc.py line 119 131400] Train: [7/100][5/800] Data 0.011 (0.007) Batch 0.327 (0.322) Remain 06:43:53 loss: 1.3151 Lr: 0.00600 [2023-12-20 13:52:31,878 INFO misc.py line 119 131400] Train: [7/100][6/800] Data 0.004 (0.006) Batch 0.270 (0.305) Remain 06:22:07 loss: 0.8228 Lr: 0.00600 [2023-12-20 13:52:32,197 INFO misc.py line 119 131400] Train: [7/100][7/800] Data 0.003 (0.005) Batch 0.319 (0.308) Remain 06:26:24 loss: 0.7322 Lr: 0.00600 [2023-12-20 13:52:32,526 INFO misc.py line 119 131400] Train: [7/100][8/800] Data 0.003 (0.005) Batch 0.329 (0.313) Remain 06:31:37 loss: 0.7843 Lr: 0.00600 [2023-12-20 13:52:32,837 INFO misc.py line 119 131400] Train: 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(0.314) Remain 06:33:53 loss: 0.9393 Lr: 0.00600 [2023-12-20 13:52:35,030 INFO misc.py line 119 131400] Train: [7/100][16/800] Data 0.003 (0.004) Batch 0.295 (0.313) Remain 06:32:00 loss: 1.0061 Lr: 0.00600 [2023-12-20 13:52:35,346 INFO misc.py line 119 131400] Train: [7/100][17/800] Data 0.003 (0.004) Batch 0.316 (0.313) Remain 06:32:15 loss: 0.9493 Lr: 0.00600 [2023-12-20 13:52:35,624 INFO misc.py line 119 131400] Train: [7/100][18/800] Data 0.003 (0.004) Batch 0.278 (0.311) Remain 06:29:19 loss: 0.8528 Lr: 0.00600 [2023-12-20 13:52:35,938 INFO misc.py line 119 131400] Train: [7/100][19/800] Data 0.003 (0.004) Batch 0.314 (0.311) Remain 06:29:35 loss: 0.7901 Lr: 0.00600 [2023-12-20 13:52:36,256 INFO misc.py line 119 131400] Train: [7/100][20/800] Data 0.003 (0.004) Batch 0.318 (0.311) Remain 06:30:08 loss: 0.9688 Lr: 0.00600 [2023-12-20 13:52:36,581 INFO misc.py line 119 131400] Train: [7/100][21/800] Data 0.003 (0.004) Batch 0.324 (0.312) Remain 06:31:02 loss: 0.8177 Lr: 0.00600 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131400] Train: [7/100][28/800] Data 0.003 (0.004) Batch 0.308 (0.318) Remain 06:37:55 loss: 0.8547 Lr: 0.00600 [2023-12-20 13:52:39,204 INFO misc.py line 119 131400] Train: [7/100][29/800] Data 0.005 (0.004) Batch 0.301 (0.317) Remain 06:37:06 loss: 0.7986 Lr: 0.00600 [2023-12-20 13:52:39,513 INFO misc.py line 119 131400] Train: [7/100][30/800] Data 0.003 (0.004) Batch 0.309 (0.317) Remain 06:36:43 loss: 0.5033 Lr: 0.00600 [2023-12-20 13:52:39,887 INFO misc.py line 119 131400] Train: [7/100][31/800] Data 0.004 (0.004) Batch 0.368 (0.318) Remain 06:39:01 loss: 0.4551 Lr: 0.00600 [2023-12-20 13:52:40,219 INFO misc.py line 119 131400] Train: [7/100][32/800] Data 0.010 (0.004) Batch 0.338 (0.319) Remain 06:39:50 loss: 1.1599 Lr: 0.00600 [2023-12-20 13:52:40,507 INFO misc.py line 119 131400] Train: [7/100][33/800] Data 0.003 (0.004) Batch 0.288 (0.318) Remain 06:38:33 loss: 1.0550 Lr: 0.00600 [2023-12-20 13:52:40,824 INFO misc.py line 119 131400] Train: [7/100][34/800] Data 0.002 (0.004) 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Batch 0.322 (0.324) Remain 06:42:38 loss: 0.8795 Lr: 0.00599 [2023-12-20 13:56:29,406 INFO misc.py line 119 131400] Train: [7/100][738/800] Data 0.005 (0.004) Batch 0.309 (0.324) Remain 06:42:36 loss: 0.7516 Lr: 0.00599 [2023-12-20 13:56:29,714 INFO misc.py line 119 131400] Train: [7/100][739/800] Data 0.003 (0.004) Batch 0.308 (0.324) Remain 06:42:34 loss: 1.0880 Lr: 0.00599 [2023-12-20 13:56:30,021 INFO misc.py line 119 131400] Train: [7/100][740/800] Data 0.003 (0.004) Batch 0.306 (0.324) Remain 06:42:31 loss: 0.7603 Lr: 0.00599 [2023-12-20 13:56:30,371 INFO misc.py line 119 131400] Train: [7/100][741/800] Data 0.005 (0.004) Batch 0.351 (0.324) Remain 06:42:34 loss: 0.7372 Lr: 0.00599 [2023-12-20 13:56:30,672 INFO misc.py line 119 131400] Train: [7/100][742/800] Data 0.003 (0.004) Batch 0.301 (0.324) Remain 06:42:31 loss: 0.8068 Lr: 0.00599 [2023-12-20 13:56:30,978 INFO misc.py line 119 131400] Train: [7/100][743/800] Data 0.003 (0.004) Batch 0.306 (0.324) Remain 06:42:29 loss: 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INFO misc.py line 119 131400] Train: [7/100][750/800] Data 0.005 (0.004) Batch 0.351 (0.324) Remain 06:42:31 loss: 1.0464 Lr: 0.00599 [2023-12-20 13:56:33,612 INFO misc.py line 119 131400] Train: [7/100][751/800] Data 0.003 (0.004) Batch 0.316 (0.324) Remain 06:42:30 loss: 0.7103 Lr: 0.00599 [2023-12-20 13:56:33,938 INFO misc.py line 119 131400] Train: [7/100][752/800] Data 0.003 (0.004) Batch 0.326 (0.324) Remain 06:42:30 loss: 0.9243 Lr: 0.00599 [2023-12-20 13:56:34,267 INFO misc.py line 119 131400] Train: [7/100][753/800] Data 0.003 (0.004) Batch 0.330 (0.324) Remain 06:42:30 loss: 0.8290 Lr: 0.00599 [2023-12-20 13:56:34,592 INFO misc.py line 119 131400] Train: [7/100][754/800] Data 0.004 (0.004) Batch 0.325 (0.324) Remain 06:42:30 loss: 0.5443 Lr: 0.00599 [2023-12-20 13:56:34,901 INFO misc.py line 119 131400] Train: [7/100][755/800] Data 0.003 (0.004) Batch 0.309 (0.324) Remain 06:42:28 loss: 0.5883 Lr: 0.00599 [2023-12-20 13:56:35,204 INFO misc.py line 119 131400] Train: 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0.325 (0.324) Remain 06:42:17 loss: 0.8158 Lr: 0.00599 [2023-12-20 13:56:37,546 INFO misc.py line 119 131400] Train: [7/100][763/800] Data 0.003 (0.004) Batch 0.466 (0.324) Remain 06:42:30 loss: 0.8118 Lr: 0.00599 [2023-12-20 13:56:37,813 INFO misc.py line 119 131400] Train: [7/100][764/800] Data 0.003 (0.004) Batch 0.267 (0.324) Remain 06:42:25 loss: 0.6545 Lr: 0.00599 [2023-12-20 13:56:38,087 INFO misc.py line 119 131400] Train: [7/100][765/800] Data 0.003 (0.004) Batch 0.274 (0.324) Remain 06:42:19 loss: 0.5706 Lr: 0.00599 [2023-12-20 13:56:38,393 INFO misc.py line 119 131400] Train: [7/100][766/800] Data 0.003 (0.004) Batch 0.306 (0.324) Remain 06:42:17 loss: 0.5996 Lr: 0.00599 [2023-12-20 13:56:38,714 INFO misc.py line 119 131400] Train: [7/100][767/800] Data 0.003 (0.004) Batch 0.321 (0.324) Remain 06:42:17 loss: 0.5809 Lr: 0.00599 [2023-12-20 13:56:39,033 INFO misc.py line 119 131400] Train: [7/100][768/800] Data 0.003 (0.004) Batch 0.320 (0.324) Remain 06:42:16 loss: 0.5966 Lr: 0.00599 [2023-12-20 13:56:39,354 INFO misc.py line 119 131400] Train: [7/100][769/800] Data 0.003 (0.004) Batch 0.320 (0.324) Remain 06:42:15 loss: 0.4929 Lr: 0.00599 [2023-12-20 13:56:39,658 INFO misc.py line 119 131400] Train: [7/100][770/800] Data 0.003 (0.004) Batch 0.304 (0.324) Remain 06:42:13 loss: 0.7452 Lr: 0.00599 [2023-12-20 13:56:40,048 INFO misc.py line 119 131400] Train: [7/100][771/800] Data 0.003 (0.004) Batch 0.390 (0.324) Remain 06:42:19 loss: 1.0353 Lr: 0.00599 [2023-12-20 13:56:40,355 INFO misc.py line 119 131400] Train: [7/100][772/800] Data 0.003 (0.004) Batch 0.308 (0.324) Remain 06:42:17 loss: 0.9635 Lr: 0.00599 [2023-12-20 13:56:40,690 INFO misc.py line 119 131400] Train: [7/100][773/800] Data 0.002 (0.004) Batch 0.334 (0.324) Remain 06:42:18 loss: 0.8088 Lr: 0.00599 [2023-12-20 13:56:40,995 INFO misc.py line 119 131400] Train: [7/100][774/800] Data 0.003 (0.004) Batch 0.306 (0.324) Remain 06:42:15 loss: 0.8603 Lr: 0.00599 [2023-12-20 13:56:41,299 INFO misc.py line 119 131400] Train: [7/100][775/800] Data 0.003 (0.004) Batch 0.304 (0.324) Remain 06:42:13 loss: 0.7885 Lr: 0.00599 [2023-12-20 13:56:41,627 INFO misc.py line 119 131400] Train: [7/100][776/800] Data 0.003 (0.004) Batch 0.327 (0.324) Remain 06:42:13 loss: 1.3687 Lr: 0.00599 [2023-12-20 13:56:41,973 INFO misc.py line 119 131400] Train: [7/100][777/800] Data 0.003 (0.004) Batch 0.345 (0.324) Remain 06:42:15 loss: 1.0535 Lr: 0.00599 [2023-12-20 13:56:42,283 INFO misc.py line 119 131400] Train: [7/100][778/800] Data 0.004 (0.004) Batch 0.311 (0.324) Remain 06:42:13 loss: 0.6881 Lr: 0.00599 [2023-12-20 13:56:42,616 INFO misc.py line 119 131400] Train: [7/100][779/800] Data 0.003 (0.004) Batch 0.333 (0.324) Remain 06:42:14 loss: 0.5798 Lr: 0.00599 [2023-12-20 13:56:42,963 INFO misc.py line 119 131400] Train: [7/100][780/800] Data 0.003 (0.004) Batch 0.347 (0.324) Remain 06:42:16 loss: 1.2548 Lr: 0.00599 [2023-12-20 13:56:43,343 INFO misc.py line 119 131400] Train: [7/100][781/800] Data 0.002 (0.004) Batch 0.379 (0.324) Remain 06:42:20 loss: 1.0279 Lr: 0.00599 [2023-12-20 13:56:43,666 INFO misc.py line 119 131400] Train: [7/100][782/800] Data 0.003 (0.004) Batch 0.323 (0.324) Remain 06:42:20 loss: 0.8566 Lr: 0.00599 [2023-12-20 13:56:43,985 INFO misc.py line 119 131400] Train: [7/100][783/800] Data 0.004 (0.004) Batch 0.317 (0.324) Remain 06:42:19 loss: 0.7631 Lr: 0.00599 [2023-12-20 13:56:44,300 INFO misc.py line 119 131400] Train: [7/100][784/800] Data 0.004 (0.004) Batch 0.317 (0.324) Remain 06:42:18 loss: 0.9628 Lr: 0.00599 [2023-12-20 13:56:44,637 INFO misc.py line 119 131400] Train: [7/100][785/800] Data 0.003 (0.004) Batch 0.337 (0.324) Remain 06:42:19 loss: 0.7310 Lr: 0.00599 [2023-12-20 13:56:44,927 INFO misc.py line 119 131400] Train: [7/100][786/800] Data 0.002 (0.004) Batch 0.289 (0.324) Remain 06:42:15 loss: 0.8676 Lr: 0.00599 [2023-12-20 13:56:45,274 INFO misc.py line 119 131400] Train: [7/100][787/800] Data 0.003 (0.004) Batch 0.345 (0.324) Remain 06:42:17 loss: 0.8248 Lr: 0.00599 [2023-12-20 13:56:45,592 INFO misc.py line 119 131400] Train: [7/100][788/800] Data 0.005 (0.004) Batch 0.320 (0.324) Remain 06:42:16 loss: 0.6508 Lr: 0.00599 [2023-12-20 13:56:45,866 INFO misc.py line 119 131400] Train: [7/100][789/800] Data 0.002 (0.004) Batch 0.274 (0.324) Remain 06:42:11 loss: 0.5376 Lr: 0.00599 [2023-12-20 13:56:46,157 INFO misc.py line 119 131400] Train: [7/100][790/800] Data 0.002 (0.004) Batch 0.291 (0.324) Remain 06:42:08 loss: 0.8209 Lr: 0.00599 [2023-12-20 13:56:46,452 INFO misc.py line 119 131400] Train: [7/100][791/800] Data 0.002 (0.004) Batch 0.295 (0.324) Remain 06:42:05 loss: 0.4293 Lr: 0.00599 [2023-12-20 13:56:46,754 INFO misc.py line 119 131400] Train: [7/100][792/800] Data 0.002 (0.004) Batch 0.301 (0.324) Remain 06:42:02 loss: 1.2050 Lr: 0.00599 [2023-12-20 13:56:47,056 INFO misc.py line 119 131400] Train: [7/100][793/800] Data 0.002 (0.004) Batch 0.303 (0.324) Remain 06:42:00 loss: 0.7301 Lr: 0.00599 [2023-12-20 13:56:47,334 INFO misc.py line 119 131400] Train: [7/100][794/800] Data 0.002 (0.004) Batch 0.277 (0.324) Remain 06:41:55 loss: 0.6840 Lr: 0.00599 [2023-12-20 13:56:47,642 INFO misc.py line 119 131400] Train: [7/100][795/800] Data 0.003 (0.004) Batch 0.309 (0.324) Remain 06:41:53 loss: 0.6587 Lr: 0.00599 [2023-12-20 13:56:47,957 INFO misc.py line 119 131400] Train: [7/100][796/800] Data 0.002 (0.004) Batch 0.315 (0.324) Remain 06:41:52 loss: 0.8479 Lr: 0.00599 [2023-12-20 13:56:48,263 INFO misc.py line 119 131400] Train: [7/100][797/800] Data 0.002 (0.004) Batch 0.305 (0.324) Remain 06:41:50 loss: 0.7364 Lr: 0.00599 [2023-12-20 13:56:48,560 INFO misc.py line 119 131400] Train: [7/100][798/800] Data 0.003 (0.004) Batch 0.298 (0.324) Remain 06:41:47 loss: 0.5764 Lr: 0.00599 [2023-12-20 13:56:48,865 INFO misc.py line 119 131400] Train: [7/100][799/800] Data 0.002 (0.004) Batch 0.301 (0.324) Remain 06:41:45 loss: 0.5633 Lr: 0.00599 [2023-12-20 13:56:49,150 INFO misc.py line 119 131400] Train: [7/100][800/800] Data 0.006 (0.004) Batch 0.289 (0.324) Remain 06:41:41 loss: 0.7898 Lr: 0.00599 [2023-12-20 13:56:49,151 INFO misc.py line 136 131400] Train result: loss: 0.8166 [2023-12-20 13:56:49,151 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 13:57:12,942 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.3216 [2023-12-20 13:57:13,020 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.4238 [2023-12-20 13:57:13,116 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6319 [2023-12-20 13:57:13,223 INFO evaluator.py line 159 131400] Test: [4/78] Loss 2.3512 [2023-12-20 13:57:13,348 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6859 [2023-12-20 13:57:13,467 INFO evaluator.py line 159 131400] Test: [6/78] Loss 3.1165 [2023-12-20 13:57:13,557 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.7880 [2023-12-20 13:57:13,665 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.4240 [2023-12-20 13:57:13,745 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3249 [2023-12-20 13:57:13,832 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5587 [2023-12-20 13:57:13,925 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.8921 [2023-12-20 13:57:14,062 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6953 [2023-12-20 13:57:14,187 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6742 [2023-12-20 13:57:14,348 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4141 [2023-12-20 13:57:14,439 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.4779 [2023-12-20 13:57:14,576 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.2394 [2023-12-20 13:57:14,684 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.7025 [2023-12-20 13:57:14,798 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.9011 [2023-12-20 13:57:14,918 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.5679 [2023-12-20 13:57:14,995 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.7713 [2023-12-20 13:57:15,104 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.7618 [2023-12-20 13:57:15,261 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.3588 [2023-12-20 13:57:15,382 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9902 [2023-12-20 13:57:15,524 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.6211 [2023-12-20 13:57:15,668 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.6767 [2023-12-20 13:57:15,756 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7760 [2023-12-20 13:57:15,918 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.2961 [2023-12-20 13:57:16,042 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.9076 [2023-12-20 13:57:16,155 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.1411 [2023-12-20 13:57:16,305 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7378 [2023-12-20 13:57:16,410 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.0653 [2023-12-20 13:57:16,532 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.0842 [2023-12-20 13:57:16,618 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.8366 [2023-12-20 13:57:16,695 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3105 [2023-12-20 13:57:16,795 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.1153 [2023-12-20 13:57:16,898 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.4765 [2023-12-20 13:57:17,029 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.3195 [2023-12-20 13:57:17,141 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.5648 [2023-12-20 13:57:17,222 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.2434 [2023-12-20 13:57:17,373 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.8456 [2023-12-20 13:57:17,525 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.3408 [2023-12-20 13:57:17,629 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.5622 [2023-12-20 13:57:17,757 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.9502 [2023-12-20 13:57:17,901 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1312 [2023-12-20 13:57:18,021 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.5837 [2023-12-20 13:57:18,127 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5086 [2023-12-20 13:57:18,302 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.7672 [2023-12-20 13:57:18,400 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.9030 [2023-12-20 13:57:18,549 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1890 [2023-12-20 13:57:18,642 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9592 [2023-12-20 13:57:18,720 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7075 [2023-12-20 13:57:18,826 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1058 [2023-12-20 13:57:18,974 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.5060 [2023-12-20 13:57:19,113 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.7252 [2023-12-20 13:57:19,218 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1879 [2023-12-20 13:57:19,306 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9658 [2023-12-20 13:57:19,409 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.7071 [2023-12-20 13:57:19,574 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3975 [2023-12-20 13:57:19,672 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2905 [2023-12-20 13:57:19,769 INFO evaluator.py line 159 131400] Test: [60/78] Loss 1.5724 [2023-12-20 13:57:19,871 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5611 [2023-12-20 13:57:19,971 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5920 [2023-12-20 13:57:20,063 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.9260 [2023-12-20 13:57:20,170 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9991 [2023-12-20 13:57:20,300 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3930 [2023-12-20 13:57:20,396 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.8398 [2023-12-20 13:57:20,500 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8302 [2023-12-20 13:57:20,596 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.9857 [2023-12-20 13:57:20,689 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.6855 [2023-12-20 13:57:20,776 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.7628 [2023-12-20 13:57:20,870 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.0568 [2023-12-20 13:57:20,962 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7796 [2023-12-20 13:57:21,097 INFO evaluator.py line 159 131400] Test: [73/78] Loss 1.0297 [2023-12-20 13:57:21,191 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.9086 [2023-12-20 13:57:21,312 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.1868 [2023-12-20 13:57:21,421 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.4320 [2023-12-20 13:57:21,510 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6767 [2023-12-20 13:57:21,667 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.5532 [2023-12-20 13:57:22,786 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6135/0.7637/0.8399. [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.7777/0.8740 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9587/0.9784 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5468/0.7332 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.6852/0.7988 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.7459/0.7691 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.5443/0.8603 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.5713/0.6922 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5088/0.5900 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4422/0.8326 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7469/0.8878 [2023-12-20 13:57:22,786 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3171/0.4884 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6092/0.7272 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.4431/0.8011 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6743/0.8437 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4505/0.6108 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6377/0.8264 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8678/0.9598 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.5928/0.6483 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7615/0.9103 [2023-12-20 13:57:22,787 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.3874/0.4412 [2023-12-20 13:57:22,787 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 13:57:22,789 INFO misc.py line 165 131400] Currently Best mIoU: 0.6475 [2023-12-20 13:57:22,789 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 13:57:25,842 INFO misc.py line 119 131400] Train: [8/100][1/800] Data 0.818 (0.818) Batch 1.120 (1.120) Remain 23:08:52 loss: 0.5724 Lr: 0.00599 [2023-12-20 13:57:26,304 INFO misc.py line 119 131400] Train: [8/100][2/800] Data 0.144 (0.144) Batch 0.462 (0.462) Remain 09:33:08 loss: 0.6484 Lr: 0.00599 [2023-12-20 13:57:26,636 INFO misc.py line 119 131400] Train: [8/100][3/800] Data 0.005 (0.005) Batch 0.332 (0.332) Remain 06:51:36 loss: 0.7016 Lr: 0.00599 [2023-12-20 13:57:26,976 INFO misc.py line 119 131400] Train: [8/100][4/800] Data 0.004 (0.004) Batch 0.340 (0.340) Remain 07:01:29 loss: 0.9350 Lr: 0.00599 [2023-12-20 13:57:27,284 INFO misc.py line 119 131400] Train: [8/100][5/800] Data 0.004 (0.004) Batch 0.309 (0.324) Remain 06:42:15 loss: 0.8023 Lr: 0.00599 [2023-12-20 13:57:27,591 INFO misc.py line 119 131400] Train: [8/100][6/800] Data 0.003 (0.004) Batch 0.307 (0.319) Remain 06:35:12 loss: 0.8773 Lr: 0.00599 [2023-12-20 13:57:27,913 INFO misc.py line 119 131400] Train: [8/100][7/800] Data 0.002 (0.003) Batch 0.322 (0.320) Remain 06:36:09 loss: 0.8790 Lr: 0.00599 [2023-12-20 13:57:28,239 INFO misc.py line 119 131400] Train: [8/100][8/800] Data 0.002 (0.003) Batch 0.326 (0.321) Remain 06:37:50 loss: 0.2655 Lr: 0.00599 [2023-12-20 13:57:28,583 INFO misc.py line 119 131400] Train: [8/100][9/800] Data 0.003 (0.003) Batch 0.343 (0.325) Remain 06:42:24 loss: 0.5453 Lr: 0.00599 [2023-12-20 13:57:28,896 INFO misc.py line 119 131400] Train: [8/100][10/800] Data 0.003 (0.003) Batch 0.309 (0.322) Remain 06:39:39 loss: 1.1724 Lr: 0.00599 [2023-12-20 13:57:29,267 INFO misc.py line 119 131400] Train: [8/100][11/800] Data 0.007 (0.004) Batch 0.376 (0.329) Remain 06:47:54 loss: 0.6604 Lr: 0.00599 [2023-12-20 13:57:29,598 INFO misc.py line 119 131400] Train: [8/100][12/800] Data 0.003 (0.003) Batch 0.331 (0.329) Remain 06:48:08 loss: 0.6775 Lr: 0.00599 [2023-12-20 13:57:29,922 INFO misc.py line 119 131400] Train: [8/100][13/800] Data 0.003 (0.003) Batch 0.321 (0.328) Remain 06:47:04 loss: 0.7169 Lr: 0.00599 [2023-12-20 13:57:30,241 INFO misc.py line 119 131400] Train: [8/100][14/800] Data 0.007 (0.004) Batch 0.323 (0.328) Remain 06:46:25 loss: 1.2615 Lr: 0.00599 [2023-12-20 13:57:30,540 INFO misc.py line 119 131400] Train: [8/100][15/800] Data 0.003 (0.004) Batch 0.299 (0.325) Remain 06:43:28 loss: 0.7725 Lr: 0.00599 [2023-12-20 13:57:30,867 INFO misc.py line 119 131400] Train: 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(0.327) Remain 06:45:45 loss: 0.9979 Lr: 0.00599 [2023-12-20 13:57:33,185 INFO misc.py line 119 131400] Train: [8/100][23/800] Data 0.004 (0.004) Batch 0.330 (0.327) Remain 06:45:53 loss: 0.5095 Lr: 0.00599 [2023-12-20 13:57:33,526 INFO misc.py line 119 131400] Train: [8/100][24/800] Data 0.004 (0.004) Batch 0.342 (0.328) Remain 06:46:44 loss: 0.5565 Lr: 0.00599 [2023-12-20 13:57:33,858 INFO misc.py line 119 131400] Train: [8/100][25/800] Data 0.003 (0.004) Batch 0.332 (0.328) Remain 06:46:58 loss: 0.4678 Lr: 0.00599 [2023-12-20 13:57:34,194 INFO misc.py line 119 131400] Train: [8/100][26/800] Data 0.003 (0.004) Batch 0.335 (0.329) Remain 06:47:19 loss: 0.6397 Lr: 0.00599 [2023-12-20 13:57:34,533 INFO misc.py line 119 131400] Train: [8/100][27/800] Data 0.004 (0.004) Batch 0.339 (0.329) Remain 06:47:51 loss: 0.7351 Lr: 0.00599 [2023-12-20 13:57:34,900 INFO misc.py line 119 131400] Train: [8/100][28/800] Data 0.004 (0.004) Batch 0.367 (0.331) Remain 06:49:44 loss: 0.4856 Lr: 0.00599 [2023-12-20 13:57:35,241 INFO misc.py line 119 131400] Train: [8/100][29/800] Data 0.003 (0.004) Batch 0.341 (0.331) Remain 06:50:15 loss: 0.7551 Lr: 0.00599 [2023-12-20 13:57:35,554 INFO misc.py line 119 131400] Train: [8/100][30/800] Data 0.003 (0.004) Batch 0.312 (0.330) Remain 06:49:23 loss: 0.8245 Lr: 0.00599 [2023-12-20 13:57:35,863 INFO misc.py line 119 131400] Train: [8/100][31/800] Data 0.004 (0.004) Batch 0.309 (0.330) Remain 06:48:26 loss: 0.8262 Lr: 0.00599 [2023-12-20 13:57:36,204 INFO misc.py line 119 131400] Train: [8/100][32/800] Data 0.004 (0.004) Batch 0.341 (0.330) Remain 06:48:56 loss: 0.7381 Lr: 0.00599 [2023-12-20 13:57:36,554 INFO misc.py line 119 131400] Train: [8/100][33/800] Data 0.003 (0.004) Batch 0.351 (0.331) Remain 06:49:47 loss: 0.7756 Lr: 0.00599 [2023-12-20 13:57:36,877 INFO misc.py line 119 131400] Train: [8/100][34/800] Data 0.003 (0.004) Batch 0.323 (0.330) Remain 06:49:30 loss: 0.9380 Lr: 0.00599 [2023-12-20 13:57:37,218 INFO misc.py line 119 131400] Train: [8/100][35/800] Data 0.003 (0.004) Batch 0.340 (0.331) Remain 06:49:51 loss: 0.6061 Lr: 0.00599 [2023-12-20 13:57:37,576 INFO misc.py line 119 131400] Train: [8/100][36/800] Data 0.004 (0.004) Batch 0.358 (0.332) Remain 06:50:53 loss: 0.6613 Lr: 0.00599 [2023-12-20 13:57:37,891 INFO misc.py line 119 131400] Train: [8/100][37/800] Data 0.003 (0.004) Batch 0.315 (0.331) Remain 06:50:16 loss: 0.6948 Lr: 0.00599 [2023-12-20 13:57:38,246 INFO misc.py line 119 131400] Train: [8/100][38/800] Data 0.003 (0.004) Batch 0.355 (0.332) Remain 06:51:07 loss: 0.9802 Lr: 0.00599 [2023-12-20 13:57:38,588 INFO misc.py line 119 131400] Train: [8/100][39/800] Data 0.004 (0.004) Batch 0.342 (0.332) Remain 06:51:27 loss: 0.7274 Lr: 0.00599 [2023-12-20 13:57:38,905 INFO misc.py line 119 131400] Train: [8/100][40/800] Data 0.004 (0.004) Batch 0.318 (0.332) Remain 06:50:58 loss: 1.2088 Lr: 0.00599 [2023-12-20 13:57:39,249 INFO misc.py line 119 131400] Train: [8/100][41/800] Data 0.004 (0.004) Batch 0.344 (0.332) Remain 06:51:21 loss: 0.7123 Lr: 0.00599 [2023-12-20 13:57:39,582 INFO misc.py line 119 131400] Train: [8/100][42/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 06:51:24 loss: 0.6519 Lr: 0.00599 [2023-12-20 13:57:39,923 INFO misc.py line 119 131400] Train: [8/100][43/800] Data 0.003 (0.004) Batch 0.341 (0.332) Remain 06:51:40 loss: 1.0288 Lr: 0.00599 [2023-12-20 13:57:40,273 INFO misc.py line 119 131400] Train: [8/100][44/800] Data 0.003 (0.004) Batch 0.345 (0.333) Remain 06:52:03 loss: 0.6860 Lr: 0.00599 [2023-12-20 13:57:40,580 INFO misc.py line 119 131400] Train: [8/100][45/800] Data 0.008 (0.004) Batch 0.312 (0.332) Remain 06:51:27 loss: 0.7300 Lr: 0.00599 [2023-12-20 13:57:40,918 INFO misc.py line 119 131400] Train: [8/100][46/800] Data 0.003 (0.004) Batch 0.337 (0.332) Remain 06:51:35 loss: 0.5932 Lr: 0.00599 [2023-12-20 13:57:41,244 INFO misc.py line 119 131400] Train: [8/100][47/800] Data 0.004 (0.004) Batch 0.327 (0.332) Remain 06:51:26 loss: 0.6086 Lr: 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119 131400] Train: [8/100][54/800] Data 0.003 (0.004) Batch 0.338 (0.333) Remain 06:52:09 loss: 0.9009 Lr: 0.00599 [2023-12-20 13:57:43,893 INFO misc.py line 119 131400] Train: [8/100][55/800] Data 0.003 (0.004) Batch 0.295 (0.332) Remain 06:51:14 loss: 0.5300 Lr: 0.00599 [2023-12-20 13:57:44,211 INFO misc.py line 119 131400] Train: [8/100][56/800] Data 0.002 (0.004) Batch 0.317 (0.332) Remain 06:50:54 loss: 1.2518 Lr: 0.00599 [2023-12-20 13:57:44,514 INFO misc.py line 119 131400] Train: [8/100][57/800] Data 0.003 (0.004) Batch 0.303 (0.331) Remain 06:50:13 loss: 0.8817 Lr: 0.00599 [2023-12-20 13:57:44,833 INFO misc.py line 119 131400] Train: [8/100][58/800] Data 0.003 (0.004) Batch 0.319 (0.331) Remain 06:49:57 loss: 0.5486 Lr: 0.00599 [2023-12-20 13:57:45,145 INFO misc.py line 119 131400] Train: [8/100][59/800] Data 0.003 (0.004) Batch 0.312 (0.331) Remain 06:49:32 loss: 0.9340 Lr: 0.00599 [2023-12-20 13:57:45,493 INFO misc.py line 119 131400] Train: [8/100][60/800] Data 0.003 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Train: [8/100][738/800] Data 0.004 (0.004) Batch 0.335 (0.326) Remain 06:40:08 loss: 1.1871 Lr: 0.00599 [2023-12-20 14:01:26,517 INFO misc.py line 119 131400] Train: [8/100][739/800] Data 0.003 (0.004) Batch 0.327 (0.326) Remain 06:40:07 loss: 0.8406 Lr: 0.00599 [2023-12-20 14:01:26,872 INFO misc.py line 119 131400] Train: [8/100][740/800] Data 0.002 (0.004) Batch 0.355 (0.326) Remain 06:40:10 loss: 0.4876 Lr: 0.00599 [2023-12-20 14:01:27,192 INFO misc.py line 119 131400] Train: [8/100][741/800] Data 0.003 (0.004) Batch 0.319 (0.326) Remain 06:40:09 loss: 0.5602 Lr: 0.00599 [2023-12-20 14:01:27,496 INFO misc.py line 119 131400] Train: [8/100][742/800] Data 0.004 (0.004) Batch 0.305 (0.326) Remain 06:40:07 loss: 0.9367 Lr: 0.00599 [2023-12-20 14:01:27,830 INFO misc.py line 119 131400] Train: [8/100][743/800] Data 0.003 (0.004) Batch 0.333 (0.326) Remain 06:40:07 loss: 1.1328 Lr: 0.00599 [2023-12-20 14:01:28,138 INFO misc.py line 119 131400] Train: [8/100][744/800] Data 0.003 (0.004) Batch 0.306 (0.326) Remain 06:40:05 loss: 0.6849 Lr: 0.00599 [2023-12-20 14:01:28,478 INFO misc.py line 119 131400] Train: [8/100][745/800] Data 0.006 (0.004) Batch 0.342 (0.326) Remain 06:40:06 loss: 0.6489 Lr: 0.00599 [2023-12-20 14:01:28,777 INFO misc.py line 119 131400] Train: [8/100][746/800] Data 0.003 (0.004) Batch 0.299 (0.326) Remain 06:40:03 loss: 0.8284 Lr: 0.00599 [2023-12-20 14:01:29,094 INFO misc.py line 119 131400] Train: [8/100][747/800] Data 0.003 (0.004) Batch 0.317 (0.326) Remain 06:40:02 loss: 0.5771 Lr: 0.00599 [2023-12-20 14:01:29,419 INFO misc.py line 119 131400] Train: [8/100][748/800] Data 0.004 (0.004) Batch 0.325 (0.326) Remain 06:40:01 loss: 1.1572 Lr: 0.00599 [2023-12-20 14:01:29,743 INFO misc.py line 119 131400] Train: [8/100][749/800] Data 0.003 (0.004) Batch 0.324 (0.326) Remain 06:40:01 loss: 0.7180 Lr: 0.00599 [2023-12-20 14:01:30,059 INFO misc.py line 119 131400] Train: [8/100][750/800] Data 0.003 (0.004) Batch 0.316 (0.326) Remain 06:40:00 loss: 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INFO misc.py line 119 131400] Train: [8/100][757/800] Data 0.005 (0.004) Batch 0.267 (0.326) Remain 06:39:47 loss: 0.8961 Lr: 0.00599 [2023-12-20 14:01:32,582 INFO misc.py line 119 131400] Train: [8/100][758/800] Data 0.003 (0.004) Batch 0.343 (0.326) Remain 06:39:49 loss: 0.9344 Lr: 0.00599 [2023-12-20 14:01:32,927 INFO misc.py line 119 131400] Train: [8/100][759/800] Data 0.003 (0.004) Batch 0.344 (0.326) Remain 06:39:50 loss: 1.3495 Lr: 0.00599 [2023-12-20 14:01:33,248 INFO misc.py line 119 131400] Train: [8/100][760/800] Data 0.005 (0.004) Batch 0.322 (0.326) Remain 06:39:49 loss: 0.7355 Lr: 0.00599 [2023-12-20 14:01:33,617 INFO misc.py line 119 131400] Train: [8/100][761/800] Data 0.004 (0.004) Batch 0.370 (0.326) Remain 06:39:53 loss: 0.6098 Lr: 0.00599 [2023-12-20 14:01:33,954 INFO misc.py line 119 131400] Train: [8/100][762/800] Data 0.003 (0.004) Batch 0.337 (0.326) Remain 06:39:54 loss: 0.6165 Lr: 0.00599 [2023-12-20 14:01:34,273 INFO misc.py line 119 131400] Train: 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0.308 (0.326) Remain 06:39:57 loss: 0.7026 Lr: 0.00599 [2023-12-20 14:01:36,585 INFO misc.py line 119 131400] Train: [8/100][770/800] Data 0.002 (0.004) Batch 0.293 (0.326) Remain 06:39:53 loss: 1.0105 Lr: 0.00599 [2023-12-20 14:01:36,919 INFO misc.py line 119 131400] Train: [8/100][771/800] Data 0.009 (0.004) Batch 0.340 (0.326) Remain 06:39:54 loss: 0.4996 Lr: 0.00599 [2023-12-20 14:01:37,237 INFO misc.py line 119 131400] Train: [8/100][772/800] Data 0.003 (0.004) Batch 0.317 (0.326) Remain 06:39:53 loss: 0.5728 Lr: 0.00599 [2023-12-20 14:01:37,599 INFO misc.py line 119 131400] Train: [8/100][773/800] Data 0.004 (0.004) Batch 0.362 (0.326) Remain 06:39:56 loss: 0.8749 Lr: 0.00599 [2023-12-20 14:01:37,911 INFO misc.py line 119 131400] Train: [8/100][774/800] Data 0.003 (0.004) Batch 0.312 (0.326) Remain 06:39:55 loss: 0.7555 Lr: 0.00599 [2023-12-20 14:01:38,249 INFO misc.py line 119 131400] Train: [8/100][775/800] Data 0.003 (0.004) Batch 0.338 (0.326) Remain 06:39:56 loss: 0.7717 Lr: 0.00599 [2023-12-20 14:01:38,575 INFO misc.py line 119 131400] Train: [8/100][776/800] Data 0.003 (0.004) Batch 0.327 (0.326) Remain 06:39:55 loss: 0.8071 Lr: 0.00599 [2023-12-20 14:01:38,905 INFO misc.py line 119 131400] Train: [8/100][777/800] Data 0.003 (0.004) Batch 0.329 (0.326) Remain 06:39:55 loss: 0.5428 Lr: 0.00599 [2023-12-20 14:01:39,236 INFO misc.py line 119 131400] Train: [8/100][778/800] Data 0.006 (0.004) Batch 0.331 (0.326) Remain 06:39:55 loss: 0.7841 Lr: 0.00599 [2023-12-20 14:01:39,568 INFO misc.py line 119 131400] Train: [8/100][779/800] Data 0.004 (0.004) Batch 0.332 (0.326) Remain 06:39:56 loss: 1.2650 Lr: 0.00599 [2023-12-20 14:01:39,909 INFO misc.py line 119 131400] Train: [8/100][780/800] Data 0.004 (0.004) Batch 0.342 (0.326) Remain 06:39:57 loss: 0.7682 Lr: 0.00599 [2023-12-20 14:01:40,228 INFO misc.py line 119 131400] Train: [8/100][781/800] Data 0.004 (0.004) Batch 0.319 (0.326) Remain 06:39:56 loss: 1.0010 Lr: 0.00599 [2023-12-20 14:01:40,562 INFO misc.py line 119 131400] Train: [8/100][782/800] Data 0.003 (0.004) Batch 0.332 (0.326) Remain 06:39:56 loss: 1.1262 Lr: 0.00599 [2023-12-20 14:01:40,883 INFO misc.py line 119 131400] Train: [8/100][783/800] Data 0.006 (0.004) Batch 0.323 (0.326) Remain 06:39:55 loss: 0.7323 Lr: 0.00599 [2023-12-20 14:01:41,177 INFO misc.py line 119 131400] Train: [8/100][784/800] Data 0.003 (0.004) Batch 0.294 (0.326) Remain 06:39:52 loss: 1.3737 Lr: 0.00599 [2023-12-20 14:01:41,507 INFO misc.py line 119 131400] Train: [8/100][785/800] Data 0.003 (0.004) Batch 0.330 (0.326) Remain 06:39:52 loss: 0.7053 Lr: 0.00599 [2023-12-20 14:01:41,851 INFO misc.py line 119 131400] Train: [8/100][786/800] Data 0.003 (0.004) Batch 0.343 (0.326) Remain 06:39:53 loss: 0.8408 Lr: 0.00599 [2023-12-20 14:01:42,169 INFO misc.py line 119 131400] Train: [8/100][787/800] Data 0.005 (0.004) Batch 0.319 (0.326) Remain 06:39:52 loss: 0.7892 Lr: 0.00599 [2023-12-20 14:01:42,499 INFO misc.py line 119 131400] Train: [8/100][788/800] Data 0.003 (0.004) Batch 0.331 (0.326) Remain 06:39:53 loss: 0.5932 Lr: 0.00599 [2023-12-20 14:01:42,780 INFO misc.py line 119 131400] Train: [8/100][789/800] Data 0.003 (0.004) Batch 0.282 (0.326) Remain 06:39:48 loss: 0.6740 Lr: 0.00599 [2023-12-20 14:01:43,104 INFO misc.py line 119 131400] Train: [8/100][790/800] Data 0.003 (0.004) Batch 0.323 (0.326) Remain 06:39:47 loss: 0.7627 Lr: 0.00599 [2023-12-20 14:01:43,423 INFO misc.py line 119 131400] Train: [8/100][791/800] Data 0.004 (0.004) Batch 0.319 (0.326) Remain 06:39:47 loss: 1.4294 Lr: 0.00599 [2023-12-20 14:01:43,754 INFO misc.py line 119 131400] Train: [8/100][792/800] Data 0.003 (0.004) Batch 0.331 (0.326) Remain 06:39:47 loss: 0.9547 Lr: 0.00599 [2023-12-20 14:01:44,050 INFO misc.py line 119 131400] Train: [8/100][793/800] Data 0.002 (0.004) Batch 0.295 (0.326) Remain 06:39:43 loss: 0.5300 Lr: 0.00599 [2023-12-20 14:01:44,347 INFO misc.py line 119 131400] Train: [8/100][794/800] Data 0.004 (0.004) Batch 0.297 (0.326) Remain 06:39:41 loss: 0.7792 Lr: 0.00599 [2023-12-20 14:01:44,621 INFO misc.py line 119 131400] Train: [8/100][795/800] Data 0.003 (0.004) Batch 0.275 (0.326) Remain 06:39:35 loss: 1.0287 Lr: 0.00599 [2023-12-20 14:01:44,906 INFO misc.py line 119 131400] Train: [8/100][796/800] Data 0.003 (0.004) Batch 0.282 (0.326) Remain 06:39:31 loss: 0.7921 Lr: 0.00599 [2023-12-20 14:01:45,211 INFO misc.py line 119 131400] Train: [8/100][797/800] Data 0.006 (0.004) Batch 0.308 (0.326) Remain 06:39:29 loss: 0.5968 Lr: 0.00599 [2023-12-20 14:01:45,537 INFO misc.py line 119 131400] Train: [8/100][798/800] Data 0.002 (0.004) Batch 0.326 (0.326) Remain 06:39:29 loss: 0.6187 Lr: 0.00599 [2023-12-20 14:01:45,836 INFO misc.py line 119 131400] Train: [8/100][799/800] Data 0.003 (0.004) Batch 0.299 (0.326) Remain 06:39:26 loss: 0.8127 Lr: 0.00599 [2023-12-20 14:01:46,102 INFO misc.py line 119 131400] Train: [8/100][800/800] Data 0.003 (0.004) Batch 0.266 (0.326) Remain 06:39:20 loss: 0.4646 Lr: 0.00599 [2023-12-20 14:01:46,102 INFO misc.py line 136 131400] Train result: loss: 0.7952 [2023-12-20 14:01:46,103 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:02:06,939 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1869 [2023-12-20 14:02:07,939 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2369 [2023-12-20 14:02:08,118 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5387 [2023-12-20 14:02:08,227 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4003 [2023-12-20 14:02:08,344 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6630 [2023-12-20 14:02:08,450 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0604 [2023-12-20 14:02:08,541 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7183 [2023-12-20 14:02:08,653 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0700 [2023-12-20 14:02:08,736 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2809 [2023-12-20 14:02:08,823 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4577 [2023-12-20 14:02:08,913 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5735 [2023-12-20 14:02:09,050 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.7092 [2023-12-20 14:02:09,176 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1636 [2023-12-20 14:02:09,332 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3424 [2023-12-20 14:02:09,424 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5013 [2023-12-20 14:02:09,556 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7232 [2023-12-20 14:02:09,664 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3952 [2023-12-20 14:02:09,771 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4930 [2023-12-20 14:02:09,892 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.5134 [2023-12-20 14:02:09,966 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4815 [2023-12-20 14:02:10,079 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4333 [2023-12-20 14:02:10,242 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1734 [2023-12-20 14:02:10,361 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9650 [2023-12-20 14:02:10,511 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2880 [2023-12-20 14:02:10,652 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2691 [2023-12-20 14:02:10,733 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7233 [2023-12-20 14:02:10,888 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6757 [2023-12-20 14:02:11,016 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5300 [2023-12-20 14:02:11,118 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8686 [2023-12-20 14:02:11,262 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2397 [2023-12-20 14:02:11,368 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7665 [2023-12-20 14:02:11,492 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6225 [2023-12-20 14:02:11,579 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.3056 [2023-12-20 14:02:11,653 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2325 [2023-12-20 14:02:11,751 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0501 [2023-12-20 14:02:11,844 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6782 [2023-12-20 14:02:11,971 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0116 [2023-12-20 14:02:12,085 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2279 [2023-12-20 14:02:12,166 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7403 [2023-12-20 14:02:12,310 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.7223 [2023-12-20 14:02:12,455 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0697 [2023-12-20 14:02:12,558 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2516 [2023-12-20 14:02:12,676 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.6090 [2023-12-20 14:02:12,820 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0146 [2023-12-20 14:02:12,942 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.2031 [2023-12-20 14:02:13,048 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4417 [2023-12-20 14:02:13,215 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5338 [2023-12-20 14:02:13,313 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5872 [2023-12-20 14:02:13,455 INFO evaluator.py line 159 131400] Test: [49/78] Loss 0.9961 [2023-12-20 14:02:13,545 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7753 [2023-12-20 14:02:13,629 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5948 [2023-12-20 14:02:13,735 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2365 [2023-12-20 14:02:13,886 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.4203 [2023-12-20 14:02:14,022 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.5403 [2023-12-20 14:02:14,129 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.8348 [2023-12-20 14:02:14,226 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6971 [2023-12-20 14:02:14,336 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4657 [2023-12-20 14:02:14,505 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3761 [2023-12-20 14:02:14,607 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2962 [2023-12-20 14:02:14,698 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.6068 [2023-12-20 14:02:14,793 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3416 [2023-12-20 14:02:14,885 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.6045 [2023-12-20 14:02:14,979 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5350 [2023-12-20 14:02:15,088 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7822 [2023-12-20 14:02:15,215 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6442 [2023-12-20 14:02:15,305 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4544 [2023-12-20 14:02:15,406 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6967 [2023-12-20 14:02:15,502 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0771 [2023-12-20 14:02:15,587 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.5832 [2023-12-20 14:02:15,681 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0375 [2023-12-20 14:02:15,774 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9423 [2023-12-20 14:02:15,867 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6965 [2023-12-20 14:02:16,006 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3449 [2023-12-20 14:02:16,111 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6704 [2023-12-20 14:02:16,232 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.0789 [2023-12-20 14:02:16,338 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.3325 [2023-12-20 14:02:16,427 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5442 [2023-12-20 14:02:16,583 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0482 [2023-12-20 14:02:17,898 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6862/0.7912/0.8852. [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8228/0.9417 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9603/0.9805 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5848/0.6594 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7833/0.8691 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8852/0.9403 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8024/0.8934 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6812/0.7256 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5857/0.7575 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5769/0.6715 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7734/0.8514 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3423/0.4583 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6134/0.7403 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6456/0.8205 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7190/0.8212 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4269/0.6271 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7170/0.8154 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8810/0.9348 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6268/0.7665 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7993/0.9207 [2023-12-20 14:02:17,899 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.4971/0.6281 [2023-12-20 14:02:17,900 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:02:17,901 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.6862 [2023-12-20 14:02:17,901 INFO misc.py line 165 131400] Currently Best mIoU: 0.6862 [2023-12-20 14:02:17,901 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:02:25,479 INFO misc.py line 119 131400] Train: [9/100][1/800] Data 1.499 (1.499) Batch 1.850 (1.850) Remain 37:49:07 loss: 0.8211 Lr: 0.00599 [2023-12-20 14:02:25,805 INFO misc.py line 119 131400] Train: [9/100][2/800] Data 0.011 (0.011) Batch 0.327 (0.327) Remain 06:40:43 loss: 0.6798 Lr: 0.00599 [2023-12-20 14:02:26,117 INFO misc.py line 119 131400] Train: [9/100][3/800] Data 0.003 (0.003) Batch 0.312 (0.312) Remain 06:22:53 loss: 0.9578 Lr: 0.00599 [2023-12-20 14:02:26,430 INFO misc.py line 119 131400] Train: [9/100][4/800] Data 0.003 (0.003) Batch 0.313 (0.313) Remain 06:24:18 loss: 0.5218 Lr: 0.00599 [2023-12-20 14:02:26,735 INFO misc.py line 119 131400] Train: [9/100][5/800] Data 0.002 (0.003) Batch 0.305 (0.309) Remain 06:19:15 loss: 0.5748 Lr: 0.00599 [2023-12-20 14:02:27,052 INFO misc.py line 119 131400] Train: [9/100][6/800] Data 0.002 (0.003) Batch 0.318 (0.312) Remain 06:22:39 loss: 0.6696 Lr: 0.00599 [2023-12-20 14:02:27,357 INFO misc.py line 119 131400] Train: [9/100][7/800] Data 0.003 (0.003) Batch 0.304 (0.310) Remain 06:20:17 loss: 0.7522 Lr: 0.00599 [2023-12-20 14:02:27,678 INFO misc.py line 119 131400] Train: [9/100][8/800] Data 0.004 (0.003) Batch 0.321 (0.312) Remain 06:22:55 loss: 0.7048 Lr: 0.00599 [2023-12-20 14:02:28,012 INFO misc.py line 119 131400] Train: [9/100][9/800] Data 0.003 (0.003) Batch 0.330 (0.315) Remain 06:26:33 loss: 0.7660 Lr: 0.00599 [2023-12-20 14:02:28,286 INFO misc.py line 119 131400] Train: [9/100][10/800] Data 0.007 (0.003) Batch 0.279 (0.310) Remain 06:20:14 loss: 0.8467 Lr: 0.00599 [2023-12-20 14:02:28,573 INFO misc.py line 119 131400] Train: [9/100][11/800] Data 0.002 (0.003) Batch 0.287 (0.307) Remain 06:16:40 loss: 0.5400 Lr: 0.00599 [2023-12-20 14:02:28,845 INFO misc.py line 119 131400] Train: [9/100][12/800] Data 0.003 (0.003) Batch 0.271 (0.303) Remain 06:11:48 loss: 0.7143 Lr: 0.00599 [2023-12-20 14:02:29,176 INFO misc.py line 119 131400] Train: [9/100][13/800] Data 0.003 (0.003) Batch 0.329 (0.306) Remain 06:14:57 loss: 1.0286 Lr: 0.00599 [2023-12-20 14:02:29,507 INFO misc.py line 119 131400] Train: [9/100][14/800] Data 0.005 (0.003) Batch 0.333 (0.308) Remain 06:17:58 loss: 0.4769 Lr: 0.00599 [2023-12-20 14:02:29,827 INFO misc.py line 119 131400] Train: [9/100][15/800] Data 0.003 (0.003) Batch 0.321 (0.309) Remain 06:19:15 loss: 0.8293 Lr: 0.00599 [2023-12-20 14:02:30,133 INFO misc.py line 119 131400] Train: [9/100][16/800] Data 0.003 (0.003) Batch 0.306 (0.309) Remain 06:18:56 loss: 0.7171 Lr: 0.00599 [2023-12-20 14:02:30,451 INFO misc.py line 119 131400] Train: [9/100][17/800] Data 0.003 (0.003) Batch 0.318 (0.310) Remain 06:19:41 loss: 0.8433 Lr: 0.00599 [2023-12-20 14:02:30,765 INFO misc.py line 119 131400] Train: [9/100][18/800] Data 0.003 (0.003) Batch 0.315 (0.310) Remain 06:20:05 loss: 0.9908 Lr: 0.00599 [2023-12-20 14:02:31,074 INFO misc.py line 119 131400] Train: [9/100][19/800] Data 0.003 (0.003) Batch 0.309 (0.310) Remain 06:20:00 loss: 1.0369 Lr: 0.00599 [2023-12-20 14:02:31,374 INFO misc.py line 119 131400] Train: [9/100][20/800] Data 0.002 (0.003) Batch 0.296 (0.309) Remain 06:19:01 loss: 0.7866 Lr: 0.00599 [2023-12-20 14:02:31,677 INFO misc.py line 119 131400] Train: [9/100][21/800] Data 0.006 (0.003) Batch 0.306 (0.309) Remain 06:18:48 loss: 0.8668 Lr: 0.00599 [2023-12-20 14:02:31,998 INFO misc.py line 119 131400] Train: [9/100][22/800] Data 0.003 (0.003) Batch 0.321 (0.310) Remain 06:19:35 loss: 0.6424 Lr: 0.00599 [2023-12-20 14:02:32,300 INFO misc.py line 119 131400] Train: [9/100][23/800] Data 0.003 (0.003) Batch 0.302 (0.309) Remain 06:19:08 loss: 0.8656 Lr: 0.00598 [2023-12-20 14:02:32,618 INFO misc.py line 119 131400] Train: [9/100][24/800] Data 0.003 (0.003) Batch 0.318 (0.310) Remain 06:19:39 loss: 0.7749 Lr: 0.00598 [2023-12-20 14:02:32,910 INFO misc.py line 119 131400] Train: [9/100][25/800] Data 0.003 (0.003) Batch 0.292 (0.309) Remain 06:18:38 loss: 0.8357 Lr: 0.00598 [2023-12-20 14:02:33,204 INFO misc.py line 119 131400] Train: [9/100][26/800] Data 0.003 (0.003) Batch 0.294 (0.308) Remain 06:17:52 loss: 0.8440 Lr: 0.00598 [2023-12-20 14:02:33,525 INFO misc.py line 119 131400] Train: [9/100][27/800] Data 0.003 (0.003) Batch 0.317 (0.309) Remain 06:18:19 loss: 1.0155 Lr: 0.00598 [2023-12-20 14:02:33,852 INFO misc.py line 119 131400] Train: [9/100][28/800] Data 0.007 (0.003) Batch 0.331 (0.309) Remain 06:19:25 loss: 0.8654 Lr: 0.00598 [2023-12-20 14:02:34,161 INFO misc.py line 119 131400] Train: [9/100][29/800] Data 0.002 (0.003) Batch 0.308 (0.309) Remain 06:19:20 loss: 0.5748 Lr: 0.00598 [2023-12-20 14:02:34,502 INFO misc.py line 119 131400] Train: [9/100][30/800] Data 0.003 (0.003) Batch 0.342 (0.311) Remain 06:20:48 loss: 1.0431 Lr: 0.00598 [2023-12-20 14:02:34,821 INFO misc.py line 119 131400] Train: [9/100][31/800] Data 0.003 (0.003) Batch 0.319 (0.311) Remain 06:21:10 loss: 0.5608 Lr: 0.00598 [2023-12-20 14:02:35,127 INFO misc.py line 119 131400] Train: [9/100][32/800] Data 0.003 (0.003) Batch 0.306 (0.311) Remain 06:20:57 loss: 0.7563 Lr: 0.00598 [2023-12-20 14:02:35,450 INFO misc.py line 119 131400] Train: [9/100][33/800] Data 0.002 (0.003) Batch 0.323 (0.311) Remain 06:21:26 loss: 0.8110 Lr: 0.00598 [2023-12-20 14:02:35,768 INFO misc.py line 119 131400] Train: [9/100][34/800] Data 0.004 (0.003) Batch 0.313 (0.311) Remain 06:21:30 loss: 0.8192 Lr: 0.00598 [2023-12-20 14:02:36,061 INFO misc.py line 119 131400] Train: [9/100][35/800] Data 0.009 (0.003) Batch 0.299 (0.311) Remain 06:21:02 loss: 0.9801 Lr: 0.00598 [2023-12-20 14:02:36,369 INFO misc.py line 119 131400] Train: [9/100][36/800] Data 0.003 (0.003) Batch 0.308 (0.311) Remain 06:20:55 loss: 1.0553 Lr: 0.00598 [2023-12-20 14:02:36,658 INFO misc.py line 119 131400] Train: [9/100][37/800] Data 0.002 (0.003) Batch 0.289 (0.310) Remain 06:20:08 loss: 0.6006 Lr: 0.00598 [2023-12-20 14:02:39,999 INFO misc.py line 119 131400] Train: [9/100][38/800] Data 0.002 (0.003) Batch 0.290 (0.309) Remain 06:19:25 loss: 0.6513 Lr: 0.00598 [2023-12-20 14:02:40,335 INFO misc.py line 119 131400] Train: [9/100][39/800] Data 3.054 (0.088) Batch 3.386 (0.395) Remain 08:04:12 loss: 0.9193 Lr: 0.00598 [2023-12-20 14:02:40,683 INFO misc.py line 119 131400] Train: [9/100][40/800] Data 0.004 (0.086) Batch 0.348 (0.394) Remain 08:02:39 loss: 0.7717 Lr: 0.00598 [2023-12-20 14:02:41,010 INFO misc.py line 119 131400] Train: [9/100][41/800] Data 0.003 (0.084) Batch 0.327 (0.392) Remain 08:00:29 loss: 0.6272 Lr: 0.00598 [2023-12-20 14:02:41,351 INFO misc.py line 119 131400] Train: [9/100][42/800] Data 0.003 (0.082) Batch 0.341 (0.391) Remain 07:58:53 loss: 0.7194 Lr: 0.00598 [2023-12-20 14:02:41,716 INFO misc.py line 119 131400] Train: [9/100][43/800] Data 0.003 (0.080) Batch 0.365 (0.390) Remain 07:58:06 loss: 0.8494 Lr: 0.00598 [2023-12-20 14:02:42,045 INFO misc.py line 119 131400] Train: [9/100][44/800] Data 0.004 (0.078) Batch 0.329 (0.389) Remain 07:56:17 loss: 0.6437 Lr: 0.00598 [2023-12-20 14:02:42,352 INFO misc.py line 119 131400] Train: [9/100][45/800] Data 0.003 (0.076) Batch 0.307 (0.387) Remain 07:53:53 loss: 0.7003 Lr: 0.00598 [2023-12-20 14:02:42,707 INFO misc.py line 119 131400] Train: [9/100][46/800] Data 0.003 (0.074) Batch 0.351 (0.386) Remain 07:52:52 loss: 0.7597 Lr: 0.00598 [2023-12-20 14:02:43,048 INFO misc.py line 119 131400] Train: [9/100][47/800] Data 0.007 (0.073) Batch 0.343 (0.385) Remain 07:51:41 loss: 0.4595 Lr: 0.00598 [2023-12-20 14:02:43,383 INFO misc.py line 119 131400] Train: [9/100][48/800] Data 0.004 (0.071) Batch 0.337 (0.384) Remain 07:50:22 loss: 0.6784 Lr: 0.00598 [2023-12-20 14:02:43,698 INFO misc.py line 119 131400] Train: [9/100][49/800] Data 0.003 (0.070) Batch 0.314 (0.382) Remain 07:48:30 loss: 0.8554 Lr: 0.00598 [2023-12-20 14:02:44,022 INFO misc.py line 119 131400] Train: [9/100][50/800] Data 0.003 (0.068) Batch 0.324 (0.381) Remain 07:46:58 loss: 0.7593 Lr: 0.00598 [2023-12-20 14:02:44,356 INFO misc.py line 119 131400] Train: [9/100][51/800] Data 0.005 (0.067) Batch 0.334 (0.380) Remain 07:45:46 loss: 0.8720 Lr: 0.00598 [2023-12-20 14:02:44,652 INFO misc.py line 119 131400] Train: [9/100][52/800] Data 0.003 (0.066) Batch 0.297 (0.378) Remain 07:43:41 loss: 0.6779 Lr: 0.00598 [2023-12-20 14:02:44,979 INFO misc.py line 119 131400] Train: [9/100][53/800] Data 0.003 (0.064) Batch 0.327 (0.377) Remain 07:42:25 loss: 1.0605 Lr: 0.00598 [2023-12-20 14:02:45,285 INFO misc.py line 119 131400] Train: [9/100][54/800] Data 0.003 (0.063) Batch 0.305 (0.376) Remain 07:40:41 loss: 0.5712 Lr: 0.00598 [2023-12-20 14:02:45,629 INFO misc.py line 119 131400] Train: [9/100][55/800] Data 0.004 (0.062) Batch 0.344 (0.375) Remain 07:39:56 loss: 0.7621 Lr: 0.00598 [2023-12-20 14:02:45,960 INFO misc.py line 119 131400] Train: [9/100][56/800] Data 0.003 (0.061) Batch 0.332 (0.374) Remain 07:38:55 loss: 1.0964 Lr: 0.00598 [2023-12-20 14:02:46,284 INFO misc.py line 119 131400] Train: [9/100][57/800] Data 0.003 (0.060) Batch 0.323 (0.373) Remain 07:37:44 loss: 1.0000 Lr: 0.00598 [2023-12-20 14:02:46,575 INFO misc.py line 119 131400] Train: [9/100][58/800] Data 0.004 (0.059) Batch 0.290 (0.372) Remain 07:35:52 loss: 0.6997 Lr: 0.00598 [2023-12-20 14:02:46,920 INFO misc.py line 119 131400] Train: [9/100][59/800] Data 0.005 (0.058) Batch 0.347 (0.371) Remain 07:35:18 loss: 1.0276 Lr: 0.00598 [2023-12-20 14:02:47,231 INFO misc.py line 119 131400] Train: [9/100][60/800] Data 0.003 (0.057) Batch 0.311 (0.370) Remain 07:34:00 loss: 0.6109 Lr: 0.00598 [2023-12-20 14:02:47,554 INFO misc.py line 119 131400] Train: [9/100][61/800] Data 0.003 (0.056) Batch 0.323 (0.370) Remain 07:33:00 loss: 0.9042 Lr: 0.00598 [2023-12-20 14:02:47,894 INFO misc.py line 119 131400] Train: [9/100][62/800] Data 0.004 (0.055) Batch 0.340 (0.369) Remain 07:32:23 loss: 0.9656 Lr: 0.00598 [2023-12-20 14:02:48,217 INFO misc.py line 119 131400] Train: [9/100][63/800] Data 0.004 (0.054) Batch 0.322 (0.368) Remain 07:31:25 loss: 0.8690 Lr: 0.00598 [2023-12-20 14:02:48,515 INFO misc.py line 119 131400] Train: [9/100][64/800] Data 0.004 (0.053) Batch 0.298 (0.367) Remain 07:30:00 loss: 1.0085 Lr: 0.00598 [2023-12-20 14:02:48,790 INFO misc.py line 119 131400] Train: [9/100][65/800] Data 0.004 (0.053) Batch 0.275 (0.366) Remain 07:28:09 loss: 0.5930 Lr: 0.00598 [2023-12-20 14:02:49,089 INFO 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line 119 131400] Train: [9/100][775/800] Data 0.003 (0.008) Batch 0.324 (0.327) Remain 06:36:43 loss: 0.8333 Lr: 0.00597 [2023-12-20 14:06:38,788 INFO misc.py line 119 131400] Train: [9/100][776/800] Data 0.004 (0.008) Batch 0.331 (0.327) Remain 06:36:43 loss: 0.6042 Lr: 0.00597 [2023-12-20 14:06:39,142 INFO misc.py line 119 131400] Train: [9/100][777/800] Data 0.003 (0.008) Batch 0.354 (0.327) Remain 06:36:46 loss: 0.6893 Lr: 0.00597 [2023-12-20 14:06:39,433 INFO misc.py line 119 131400] Train: [9/100][778/800] Data 0.003 (0.008) Batch 0.291 (0.327) Remain 06:36:42 loss: 0.7160 Lr: 0.00597 [2023-12-20 14:06:39,759 INFO misc.py line 119 131400] Train: [9/100][779/800] Data 0.005 (0.008) Batch 0.326 (0.327) Remain 06:36:42 loss: 0.5155 Lr: 0.00597 [2023-12-20 14:06:40,106 INFO misc.py line 119 131400] Train: [9/100][780/800] Data 0.004 (0.008) Batch 0.346 (0.327) Remain 06:36:43 loss: 0.9493 Lr: 0.00597 [2023-12-20 14:06:40,419 INFO misc.py line 119 131400] Train: [9/100][781/800] Data 0.005 (0.008) Batch 0.314 (0.327) Remain 06:36:41 loss: 0.6134 Lr: 0.00597 [2023-12-20 14:06:40,741 INFO misc.py line 119 131400] Train: [9/100][782/800] Data 0.003 (0.008) Batch 0.321 (0.327) Remain 06:36:41 loss: 0.6468 Lr: 0.00597 [2023-12-20 14:06:41,075 INFO misc.py line 119 131400] Train: [9/100][783/800] Data 0.004 (0.008) Batch 0.335 (0.327) Remain 06:36:41 loss: 0.7898 Lr: 0.00597 [2023-12-20 14:06:41,427 INFO misc.py line 119 131400] Train: [9/100][784/800] Data 0.003 (0.008) Batch 0.352 (0.327) Remain 06:36:43 loss: 0.7824 Lr: 0.00597 [2023-12-20 14:06:41,753 INFO misc.py line 119 131400] Train: [9/100][785/800] Data 0.004 (0.008) Batch 0.326 (0.327) Remain 06:36:43 loss: 1.3922 Lr: 0.00597 [2023-12-20 14:06:42,101 INFO misc.py line 119 131400] Train: [9/100][786/800] Data 0.003 (0.008) Batch 0.349 (0.327) Remain 06:36:44 loss: 0.5877 Lr: 0.00597 [2023-12-20 14:06:42,424 INFO misc.py line 119 131400] Train: [9/100][787/800] Data 0.003 (0.008) Batch 0.322 (0.327) Remain 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14:06:44,646 INFO misc.py line 119 131400] Train: [9/100][794/800] Data 0.003 (0.008) Batch 0.288 (0.327) Remain 06:36:35 loss: 0.5747 Lr: 0.00597 [2023-12-20 14:06:44,982 INFO misc.py line 119 131400] Train: [9/100][795/800] Data 0.003 (0.008) Batch 0.336 (0.327) Remain 06:36:36 loss: 0.8053 Lr: 0.00597 [2023-12-20 14:06:45,308 INFO misc.py line 119 131400] Train: [9/100][796/800] Data 0.003 (0.008) Batch 0.326 (0.327) Remain 06:36:35 loss: 1.0728 Lr: 0.00597 [2023-12-20 14:06:45,614 INFO misc.py line 119 131400] Train: [9/100][797/800] Data 0.003 (0.008) Batch 0.306 (0.327) Remain 06:36:33 loss: 0.5020 Lr: 0.00597 [2023-12-20 14:06:45,922 INFO misc.py line 119 131400] Train: [9/100][798/800] Data 0.002 (0.008) Batch 0.309 (0.327) Remain 06:36:31 loss: 0.6333 Lr: 0.00597 [2023-12-20 14:06:46,206 INFO misc.py line 119 131400] Train: [9/100][799/800] Data 0.002 (0.008) Batch 0.283 (0.327) Remain 06:36:27 loss: 0.6535 Lr: 0.00597 [2023-12-20 14:06:46,501 INFO misc.py line 119 131400] Train: [9/100][800/800] Data 0.002 (0.008) Batch 0.296 (0.327) Remain 06:36:24 loss: 0.8124 Lr: 0.00597 [2023-12-20 14:06:46,502 INFO misc.py line 136 131400] Train result: loss: 0.7537 [2023-12-20 14:06:46,502 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:07:08,893 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2954 [2023-12-20 14:07:09,483 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.4023 [2023-12-20 14:07:09,592 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6367 [2023-12-20 14:07:09,712 INFO evaluator.py line 159 131400] Test: [4/78] Loss 2.1725 [2023-12-20 14:07:09,824 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6081 [2023-12-20 14:07:09,924 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.6920 [2023-12-20 14:07:10,014 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.8558 [2023-12-20 14:07:10,123 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9976 [2023-12-20 14:07:10,205 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2747 [2023-12-20 14:07:10,288 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5777 [2023-12-20 14:07:10,383 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6399 [2023-12-20 14:07:10,522 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6677 [2023-12-20 14:07:10,640 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5677 [2023-12-20 14:07:10,794 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3914 [2023-12-20 14:07:10,886 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5329 [2023-12-20 14:07:11,019 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9248 [2023-12-20 14:07:11,130 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4394 [2023-12-20 14:07:11,242 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4602 [2023-12-20 14:07:11,353 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.6574 [2023-12-20 14:07:11,428 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [32/78] Loss 0.7246 [2023-12-20 14:07:13,013 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.4163 [2023-12-20 14:07:13,085 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2975 [2023-12-20 14:07:13,179 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.2333 [2023-12-20 14:07:13,273 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.8994 [2023-12-20 14:07:13,401 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.3007 [2023-12-20 14:07:13,510 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.5038 [2023-12-20 14:07:13,588 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.4157 [2023-12-20 14:07:13,734 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.7508 [2023-12-20 14:07:13,879 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1278 [2023-12-20 14:07:13,976 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2218 [2023-12-20 14:07:14,098 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.7516 [2023-12-20 14:07:14,240 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1195 [2023-12-20 14:07:14,358 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0536 [2023-12-20 14:07:14,458 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6853 [2023-12-20 14:07:14,624 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6219 [2023-12-20 14:07:14,716 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5086 [2023-12-20 14:07:14,860 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1798 [2023-12-20 14:07:14,949 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8342 [2023-12-20 14:07:15,023 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8150 [2023-12-20 14:07:15,127 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3143 [2023-12-20 14:07:15,274 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.5137 [2023-12-20 14:07:15,406 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4482 [2023-12-20 14:07:15,509 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5310 [2023-12-20 14:07:15,597 INFO evaluator.py line 159 131400] Test: [56/78] Loss 1.0636 [2023-12-20 14:07:15,699 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.7879 [2023-12-20 14:07:15,859 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3868 [2023-12-20 14:07:15,953 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2147 [2023-12-20 14:07:16,045 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3479 [2023-12-20 14:07:16,138 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4819 [2023-12-20 14:07:16,228 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5032 [2023-12-20 14:07:16,315 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5232 [2023-12-20 14:07:16,414 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.2616 [2023-12-20 14:07:16,539 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3645 [2023-12-20 14:07:16,621 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.7922 [2023-12-20 14:07:16,720 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.7915 [2023-12-20 14:07:16,812 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0799 [2023-12-20 14:07:16,894 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.6111 [2023-12-20 14:07:16,976 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.3431 [2023-12-20 14:07:17,070 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.1839 [2023-12-20 14:07:17,159 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7739 [2023-12-20 14:07:17,292 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.6719 [2023-12-20 14:07:17,385 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.8083 [2023-12-20 14:07:17,500 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.1226 [2023-12-20 14:07:17,601 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.2827 [2023-12-20 14:07:17,686 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3939 [2023-12-20 14:07:17,839 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3531 [2023-12-20 14:07:18,805 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6443/0.7933/0.8539. [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.7688/0.8428 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9599/0.9789 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5959/0.7345 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7042/0.7880 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8340/0.8537 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.6523/0.8076 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6531/0.8085 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5551/0.6777 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.4616/0.8460 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7650/0.8745 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2399/0.5741 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6021/0.8027 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5976/0.8504 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6226/0.8853 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4841/0.6481 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6633/0.8096 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8260/0.9686 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6128/0.6632 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8330/0.8632 [2023-12-20 14:07:18,806 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.4544/0.5885 [2023-12-20 14:07:18,807 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:07:18,808 INFO misc.py line 165 131400] Currently Best mIoU: 0.6862 [2023-12-20 14:07:18,808 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:07:22,782 INFO misc.py line 119 131400] Train: [10/100][1/800] Data 1.785 (1.785) Batch 2.134 (2.134) Remain 43:09:27 loss: 0.5150 Lr: 0.00597 [2023-12-20 14:07:23,126 INFO misc.py line 119 131400] Train: [10/100][2/800] Data 0.004 (0.004) Batch 0.340 (0.340) Remain 06:53:02 loss: 1.1651 Lr: 0.00597 [2023-12-20 14:07:23,438 INFO misc.py line 119 131400] Train: [10/100][3/800] Data 0.006 (0.006) Batch 0.316 (0.316) Remain 06:22:50 loss: 0.5754 Lr: 0.00597 [2023-12-20 14:07:23,776 INFO misc.py line 119 131400] Train: [10/100][4/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 06:49:43 loss: 0.5934 Lr: 0.00597 [2023-12-20 14:07:24,070 INFO misc.py line 119 131400] Train: [10/100][5/800] Data 0.004 (0.003) Batch 0.293 (0.315) Remain 06:22:42 loss: 0.8826 Lr: 0.00597 [2023-12-20 14:07:24,377 INFO misc.py line 119 131400] Train: [10/100][6/800] Data 0.004 (0.003) Batch 0.308 (0.313) Remain 06:19:38 loss: 0.5116 Lr: 0.00597 [2023-12-20 14:07:24,701 INFO misc.py line 119 131400] Train: [10/100][7/800] Data 0.004 (0.004) Batch 0.324 (0.316) Remain 06:22:56 loss: 0.8630 Lr: 0.00597 [2023-12-20 14:07:25,008 INFO misc.py line 119 131400] Train: [10/100][8/800] Data 0.004 (0.004) Batch 0.307 (0.314) Remain 06:20:56 loss: 0.9031 Lr: 0.00597 [2023-12-20 14:07:25,352 INFO misc.py line 119 131400] Train: [10/100][9/800] Data 0.003 (0.004) Batch 0.343 (0.319) Remain 06:26:51 loss: 0.8496 Lr: 0.00597 [2023-12-20 14:07:25,667 INFO misc.py line 119 131400] Train: [10/100][10/800] Data 0.004 (0.004) Batch 0.315 (0.318) Remain 06:26:08 loss: 0.5468 Lr: 0.00597 [2023-12-20 14:07:25,986 INFO misc.py line 119 131400] Train: [10/100][11/800] Data 0.005 (0.004) Batch 0.318 (0.318) Remain 06:26:08 loss: 0.8229 Lr: 0.00597 [2023-12-20 14:07:26,339 INFO misc.py line 119 131400] Train: [10/100][12/800] Data 0.005 (0.004) Batch 0.354 (0.322) Remain 06:30:53 loss: 0.6011 Lr: 0.00597 [2023-12-20 14:07:26,665 INFO misc.py line 119 131400] Train: [10/100][13/800] Data 0.006 (0.004) Batch 0.326 (0.323) Remain 06:31:21 loss: 1.0068 Lr: 0.00597 [2023-12-20 14:07:26,982 INFO misc.py line 119 131400] Train: [10/100][14/800] Data 0.004 (0.004) Batch 0.318 (0.322) Remain 06:30:48 loss: 0.7775 Lr: 0.00597 [2023-12-20 14:07:27,282 INFO misc.py line 119 131400] Train: [10/100][15/800] Data 0.004 (0.004) Batch 0.299 (0.320) Remain 06:28:29 loss: 1.1092 Lr: 0.00597 [2023-12-20 14:07:27,618 INFO misc.py line 119 131400] Train: [10/100][16/800] Data 0.005 (0.004) Batch 0.336 (0.321) Remain 06:29:58 loss: 0.5885 Lr: 0.00597 [2023-12-20 14:07:27,978 INFO misc.py line 119 131400] Train: [10/100][17/800] Data 0.003 (0.004) Batch 0.360 (0.324) Remain 06:33:18 loss: 0.9029 Lr: 0.00597 [2023-12-20 14:07:28,333 INFO misc.py line 119 131400] Train: [10/100][18/800] Data 0.004 (0.004) Batch 0.355 (0.326) Remain 06:35:48 loss: 0.6035 Lr: 0.00597 [2023-12-20 14:07:28,656 INFO misc.py line 119 131400] Train: [10/100][19/800] Data 0.003 (0.004) Batch 0.324 (0.326) Remain 06:35:35 loss: 0.8116 Lr: 0.00597 [2023-12-20 14:07:28,999 INFO misc.py line 119 131400] Train: [10/100][20/800] Data 0.003 (0.004) Batch 0.343 (0.327) Remain 06:36:46 loss: 0.5412 Lr: 0.00597 [2023-12-20 14:07:29,318 INFO misc.py line 119 131400] Train: [10/100][21/800] Data 0.004 (0.004) Batch 0.319 (0.327) Remain 06:36:11 loss: 0.8298 Lr: 0.00597 [2023-12-20 14:07:29,628 INFO misc.py line 119 131400] Train: [10/100][22/800] Data 0.004 (0.004) Batch 0.312 (0.326) Remain 06:35:13 loss: 0.5356 Lr: 0.00597 [2023-12-20 14:07:29,955 INFO misc.py line 119 131400] Train: [10/100][23/800] Data 0.003 (0.004) Batch 0.326 (0.326) Remain 06:35:13 loss: 0.9013 Lr: 0.00597 [2023-12-20 14:07:30,303 INFO misc.py line 119 131400] Train: [10/100][24/800] Data 0.003 (0.004) Batch 0.348 (0.327) Remain 06:36:31 loss: 0.6493 Lr: 0.00597 [2023-12-20 14:07:30,602 INFO misc.py line 119 131400] Train: [10/100][25/800] Data 0.003 (0.004) Batch 0.299 (0.326) Remain 06:34:59 loss: 0.7962 Lr: 0.00597 [2023-12-20 14:07:30,911 INFO misc.py line 119 131400] Train: [10/100][26/800] Data 0.003 (0.004) Batch 0.309 (0.325) Remain 06:34:06 loss: 0.7515 Lr: 0.00597 [2023-12-20 14:07:31,226 INFO misc.py line 119 131400] Train: [10/100][27/800] Data 0.003 (0.004) Batch 0.315 (0.324) Remain 06:33:34 loss: 0.5586 Lr: 0.00597 [2023-12-20 14:07:31,550 INFO misc.py line 119 131400] Train: [10/100][28/800] Data 0.003 (0.004) Batch 0.325 (0.325) Remain 06:33:34 loss: 0.8366 Lr: 0.00597 [2023-12-20 14:07:31,884 INFO misc.py line 119 131400] Train: [10/100][29/800] Data 0.004 (0.004) Batch 0.333 (0.325) Remain 06:33:57 loss: 0.7125 Lr: 0.00597 [2023-12-20 14:07:32,171 INFO misc.py line 119 131400] Train: [10/100][30/800] Data 0.004 (0.004) Batch 0.287 (0.323) Remain 06:32:14 loss: 0.5337 Lr: 0.00597 [2023-12-20 14:07:32,493 INFO misc.py line 119 131400] Train: [10/100][31/800] Data 0.004 (0.004) Batch 0.323 (0.323) Remain 06:32:13 loss: 0.7355 Lr: 0.00597 [2023-12-20 14:07:32,836 INFO misc.py line 119 131400] Train: [10/100][32/800] Data 0.004 (0.004) Batch 0.343 (0.324) Remain 06:33:02 loss: 0.8776 Lr: 0.00597 [2023-12-20 14:07:33,175 INFO misc.py line 119 131400] Train: [10/100][33/800] Data 0.003 (0.004) Batch 0.338 (0.325) Remain 06:33:36 loss: 0.9016 Lr: 0.00597 [2023-12-20 14:07:33,490 INFO misc.py line 119 131400] Train: [10/100][34/800] Data 0.004 (0.004) Batch 0.315 (0.324) Remain 06:33:13 loss: 0.8267 Lr: 0.00597 [2023-12-20 14:07:33,829 INFO misc.py line 119 131400] Train: [10/100][35/800] Data 0.004 (0.004) Batch 0.338 (0.325) Remain 06:33:45 loss: 0.6767 Lr: 0.00597 [2023-12-20 14:07:34,148 INFO misc.py line 119 131400] Train: [10/100][36/800] Data 0.005 (0.004) Batch 0.318 (0.324) Remain 06:33:30 loss: 0.9104 Lr: 0.00597 [2023-12-20 14:07:34,454 INFO misc.py line 119 131400] Train: [10/100][37/800] Data 0.005 (0.004) Batch 0.308 (0.324) Remain 06:32:54 loss: 0.8836 Lr: 0.00597 [2023-12-20 14:07:34,773 INFO misc.py line 119 131400] Train: [10/100][38/800] Data 0.003 (0.004) Batch 0.320 (0.324) Remain 06:32:45 loss: 0.9509 Lr: 0.00597 [2023-12-20 14:07:35,120 INFO misc.py line 119 131400] Train: [10/100][39/800] Data 0.003 (0.004) Batch 0.346 (0.324) Remain 06:33:29 loss: 0.3582 Lr: 0.00597 [2023-12-20 14:07:35,434 INFO misc.py line 119 131400] Train: [10/100][40/800] Data 0.004 (0.004) Batch 0.315 (0.324) Remain 06:33:11 loss: 1.3295 Lr: 0.00597 [2023-12-20 14:07:35,695 INFO misc.py line 119 131400] Train: [10/100][41/800] Data 0.003 (0.004) Batch 0.260 (0.323) Remain 06:31:08 loss: 0.2780 Lr: 0.00597 [2023-12-20 14:07:36,026 INFO misc.py line 119 131400] Train: [10/100][42/800] Data 0.002 (0.004) Batch 0.329 (0.323) Remain 06:31:20 loss: 0.8404 Lr: 0.00597 [2023-12-20 14:07:36,337 INFO misc.py line 119 131400] Train: [10/100][43/800] Data 0.005 (0.004) Batch 0.313 (0.322) Remain 06:31:02 loss: 0.5548 Lr: 0.00597 [2023-12-20 14:07:36,664 INFO misc.py line 119 131400] Train: [10/100][44/800] Data 0.002 (0.004) Batch 0.328 (0.323) Remain 06:31:11 loss: 0.6279 Lr: 0.00597 [2023-12-20 14:07:36,972 INFO misc.py line 119 131400] Train: [10/100][45/800] Data 0.003 (0.004) Batch 0.308 (0.322) Remain 06:30:45 loss: 0.7867 Lr: 0.00597 [2023-12-20 14:07:37,312 INFO misc.py line 119 131400] Train: [10/100][46/800] Data 0.002 (0.004) Batch 0.339 (0.323) Remain 06:31:13 loss: 0.9786 Lr: 0.00597 [2023-12-20 14:07:37,593 INFO misc.py line 119 131400] Train: [10/100][47/800] Data 0.003 (0.004) Batch 0.281 (0.322) Remain 06:30:05 loss: 0.7729 Lr: 0.00597 [2023-12-20 14:07:37,913 INFO misc.py line 119 131400] Train: [10/100][48/800] Data 0.002 (0.004) Batch 0.320 (0.322) Remain 06:30:02 loss: 0.7658 Lr: 0.00597 [2023-12-20 14:07:38,199 INFO misc.py line 119 131400] Train: [10/100][49/800] Data 0.003 (0.004) Batch 0.282 (0.321) Remain 06:28:58 loss: 0.5679 Lr: 0.00597 [2023-12-20 14:07:38,480 INFO misc.py line 119 131400] Train: [10/100][50/800] Data 0.006 (0.004) Batch 0.286 (0.320) Remain 06:28:04 loss: 0.5092 Lr: 0.00597 [2023-12-20 14:07:38,799 INFO misc.py line 119 131400] Train: [10/100][51/800] Data 0.002 (0.004) Batch 0.319 (0.320) Remain 06:28:01 loss: 0.5991 Lr: 0.00597 [2023-12-20 14:07:39,128 INFO misc.py line 119 131400] Train: [10/100][52/800] Data 0.003 (0.004) Batch 0.329 (0.320) Remain 06:28:14 loss: 0.7862 Lr: 0.00597 [2023-12-20 14:07:39,440 INFO misc.py line 119 131400] Train: [10/100][53/800] Data 0.003 (0.004) Batch 0.311 (0.320) Remain 06:28:01 loss: 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Batch 0.315 (0.323) Remain 06:27:18 loss: 0.6374 Lr: 0.00596 [2023-12-20 14:11:38,997 INFO misc.py line 119 131400] Train: [10/100][795/800] Data 0.002 (0.004) Batch 0.276 (0.323) Remain 06:27:14 loss: 0.8855 Lr: 0.00596 [2023-12-20 14:11:39,302 INFO misc.py line 119 131400] Train: [10/100][796/800] Data 0.002 (0.004) Batch 0.305 (0.323) Remain 06:27:12 loss: 0.5187 Lr: 0.00596 [2023-12-20 14:11:39,598 INFO misc.py line 119 131400] Train: [10/100][797/800] Data 0.003 (0.004) Batch 0.294 (0.323) Remain 06:27:09 loss: 0.4698 Lr: 0.00596 [2023-12-20 14:11:39,903 INFO misc.py line 119 131400] Train: [10/100][798/800] Data 0.004 (0.004) Batch 0.306 (0.323) Remain 06:27:07 loss: 0.5301 Lr: 0.00596 [2023-12-20 14:11:40,210 INFO misc.py line 119 131400] Train: [10/100][799/800] Data 0.003 (0.004) Batch 0.307 (0.323) Remain 06:27:05 loss: 0.4111 Lr: 0.00596 [2023-12-20 14:11:40,516 INFO misc.py line 119 131400] Train: [10/100][800/800] Data 0.003 (0.004) Batch 0.306 (0.323) Remain 06:27:04 loss: 0.7258 Lr: 0.00596 [2023-12-20 14:11:40,516 INFO misc.py line 136 131400] Train result: loss: 0.7204 [2023-12-20 14:11:40,517 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:12:03,429 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2000 [2023-12-20 14:12:03,504 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.3285 [2023-12-20 14:12:03,716 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4892 [2023-12-20 14:12:04,294 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.7084 [2023-12-20 14:12:04,410 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6523 [2023-12-20 14:12:04,515 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2708 [2023-12-20 14:12:04,602 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.5296 [2023-12-20 14:12:04,709 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3454 [2023-12-20 14:12:04,789 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3784 [2023-12-20 14:12:04,876 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5499 [2023-12-20 14:12:04,970 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6369 [2023-12-20 14:12:05,105 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.7834 [2023-12-20 14:12:05,225 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1232 [2023-12-20 14:12:05,380 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4273 [2023-12-20 14:12:05,477 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.4938 [2023-12-20 14:12:05,610 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6003 [2023-12-20 14:12:05,722 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.5213 [2023-12-20 14:12:05,830 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4011 [2023-12-20 14:12:05,945 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4138 [2023-12-20 14:12:06,023 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4763 [2023-12-20 14:12:06,133 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4888 [2023-12-20 14:12:06,291 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2322 [2023-12-20 14:12:06,416 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9425 [2023-12-20 14:12:06,559 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4724 [2023-12-20 14:12:06,705 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.4682 [2023-12-20 14:12:06,792 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5852 [2023-12-20 14:12:06,954 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5806 [2023-12-20 14:12:07,077 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.7150 [2023-12-20 14:12:07,174 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6903 [2023-12-20 14:12:07,319 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3766 [2023-12-20 14:12:07,437 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7825 [2023-12-20 14:12:07,560 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6947 [2023-12-20 14:12:07,656 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.6140 [2023-12-20 14:12:07,754 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2311 [2023-12-20 14:12:07,850 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.2192 [2023-12-20 14:12:07,946 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6020 [2023-12-20 14:12:08,075 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0492 [2023-12-20 14:12:08,187 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2012 [2023-12-20 14:12:08,268 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.3055 [2023-12-20 14:12:08,409 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6561 [2023-12-20 14:12:08,563 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0537 [2023-12-20 14:12:08,662 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.4588 [2023-12-20 14:12:08,786 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5211 [2023-12-20 14:12:08,928 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.5132 [2023-12-20 14:12:09,049 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0190 [2023-12-20 14:12:09,154 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4983 [2023-12-20 14:12:09,322 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.7064 [2023-12-20 14:12:09,418 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.6102 [2023-12-20 14:12:09,562 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1343 [2023-12-20 14:12:09,652 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8478 [2023-12-20 14:12:09,728 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8816 [2023-12-20 14:12:09,835 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0540 [2023-12-20 14:12:09,980 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2121 [2023-12-20 14:12:10,112 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3980 [2023-12-20 14:12:10,213 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.1438 [2023-12-20 14:12:10,299 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8080 [2023-12-20 14:12:10,400 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.6193 [2023-12-20 14:12:10,561 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3494 [2023-12-20 14:12:10,656 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2457 [2023-12-20 14:12:10,748 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5789 [2023-12-20 14:12:10,842 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4700 [2023-12-20 14:12:10,934 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4968 [2023-12-20 14:12:11,020 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5961 [2023-12-20 14:12:11,119 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8640 [2023-12-20 14:12:11,250 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3870 [2023-12-20 14:12:11,334 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5062 [2023-12-20 14:12:11,440 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8364 [2023-12-20 14:12:11,535 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0493 [2023-12-20 14:12:11,624 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2871 [2023-12-20 14:12:11,711 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0571 [2023-12-20 14:12:11,804 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8234 [2023-12-20 14:12:11,900 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6299 [2023-12-20 14:12:12,033 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3500 [2023-12-20 14:12:12,127 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6776 [2023-12-20 14:12:12,241 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.0007 [2023-12-20 14:12:12,343 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.2205 [2023-12-20 14:12:12,436 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5289 [2023-12-20 14:12:12,590 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3429 [2023-12-20 14:12:13,831 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6814/0.7987/0.8811. [2023-12-20 14:12:13,831 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8323/0.9067 [2023-12-20 14:12:13,831 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9611/0.9821 [2023-12-20 14:12:13,831 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5836/0.6718 [2023-12-20 14:12:13,831 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7787/0.8220 [2023-12-20 14:12:13,831 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8563/0.8859 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7460/0.9126 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6545/0.6833 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5712/0.8435 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5781/0.6644 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7151/0.9257 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3559/0.4227 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6283/0.7920 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5796/0.8554 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7497/0.8492 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4441/0.6599 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6881/0.8429 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9013/0.9485 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6042/0.6470 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8559/0.8950 [2023-12-20 14:12:13,832 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5442/0.7631 [2023-12-20 14:12:13,833 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:12:13,834 INFO misc.py line 165 131400] Currently Best mIoU: 0.6862 [2023-12-20 14:12:13,834 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:12:17,524 INFO misc.py line 119 131400] Train: [11/100][1/800] Data 1.548 (1.548) Batch 1.900 (1.900) Remain 37:59:45 loss: 0.4965 Lr: 0.00596 [2023-12-20 14:12:17,837 INFO misc.py line 119 131400] Train: [11/100][2/800] Data 0.005 (0.005) Batch 0.313 (0.313) Remain 06:15:42 loss: 0.6487 Lr: 0.00596 [2023-12-20 14:12:18,153 INFO misc.py line 119 131400] Train: [11/100][3/800] Data 0.003 (0.003) Batch 0.316 (0.316) Remain 06:19:30 loss: 0.4703 Lr: 0.00596 [2023-12-20 14:12:18,496 INFO misc.py line 119 131400] Train: [11/100][4/800] Data 0.004 (0.004) Batch 0.342 (0.342) Remain 06:50:49 loss: 0.6683 Lr: 0.00596 [2023-12-20 14:12:18,828 INFO misc.py line 119 131400] Train: [11/100][5/800] Data 0.004 (0.004) Batch 0.333 (0.337) Remain 06:44:54 loss: 0.7705 Lr: 0.00596 [2023-12-20 14:12:19,141 INFO misc.py line 119 131400] Train: [11/100][6/800] Data 0.003 (0.003) Batch 0.313 (0.329) Remain 06:34:56 loss: 0.9886 Lr: 0.00596 [2023-12-20 14:12:19,454 INFO misc.py line 119 131400] Train: [11/100][7/800] Data 0.003 (0.003) Batch 0.314 (0.325) Remain 06:30:15 loss: 0.7376 Lr: 0.00596 [2023-12-20 14:12:19,758 INFO misc.py line 119 131400] Train: [11/100][8/800] Data 0.003 (0.003) Batch 0.304 (0.321) Remain 06:25:15 loss: 0.6656 Lr: 0.00596 [2023-12-20 14:12:20,090 INFO misc.py line 119 131400] Train: [11/100][9/800] Data 0.002 (0.003) Batch 0.332 (0.323) Remain 06:27:24 loss: 0.5913 Lr: 0.00596 [2023-12-20 14:12:20,375 INFO misc.py line 119 131400] Train: [11/100][10/800] Data 0.003 (0.003) Batch 0.286 (0.318) Remain 06:21:00 loss: 0.7432 Lr: 0.00596 [2023-12-20 14:12:20,698 INFO misc.py line 119 131400] Train: [11/100][11/800] Data 0.003 (0.003) Batch 0.322 (0.318) Remain 06:21:41 loss: 0.9965 Lr: 0.00596 [2023-12-20 14:12:20,986 INFO misc.py line 119 131400] Train: [11/100][12/800] Data 0.004 (0.003) Batch 0.288 (0.315) Remain 06:17:41 loss: 0.7570 Lr: 0.00596 [2023-12-20 14:12:21,313 INFO misc.py line 119 131400] Train: [11/100][13/800] Data 0.004 (0.003) Batch 0.320 (0.315) Remain 06:18:17 loss: 0.3175 Lr: 0.00596 [2023-12-20 14:12:21,673 INFO misc.py line 119 131400] Train: [11/100][14/800] Data 0.011 (0.004) Batch 0.366 (0.320) Remain 06:23:45 loss: 0.6901 Lr: 0.00596 [2023-12-20 14:12:21,997 INFO misc.py line 119 131400] Train: [11/100][15/800] Data 0.005 (0.004) Batch 0.325 (0.320) Remain 06:24:14 loss: 0.7806 Lr: 0.00596 [2023-12-20 14:12:22,335 INFO misc.py line 119 131400] Train: [11/100][16/800] Data 0.004 (0.004) Batch 0.338 (0.322) Remain 06:25:54 loss: 0.7433 Lr: 0.00596 [2023-12-20 14:12:22,642 INFO misc.py line 119 131400] Train: [11/100][17/800] Data 0.004 (0.004) Batch 0.308 (0.321) Remain 06:24:43 loss: 0.7190 Lr: 0.00596 [2023-12-20 14:12:22,993 INFO misc.py line 119 131400] Train: [11/100][18/800] Data 0.003 (0.004) Batch 0.351 (0.323) Remain 06:27:07 loss: 0.9675 Lr: 0.00596 [2023-12-20 14:12:23,336 INFO misc.py line 119 131400] Train: [11/100][19/800] Data 0.003 (0.004) Batch 0.339 (0.324) Remain 06:28:22 loss: 0.6825 Lr: 0.00596 [2023-12-20 14:12:23,671 INFO misc.py line 119 131400] Train: [11/100][20/800] Data 0.007 (0.004) Batch 0.338 (0.325) Remain 06:29:23 loss: 0.3950 Lr: 0.00596 [2023-12-20 14:12:24,010 INFO misc.py line 119 131400] Train: [11/100][21/800] Data 0.003 (0.004) Batch 0.338 (0.325) Remain 06:30:17 loss: 0.7392 Lr: 0.00596 [2023-12-20 14:12:24,313 INFO misc.py line 119 131400] Train: [11/100][22/800] Data 0.004 (0.004) Batch 0.304 (0.324) Remain 06:28:56 loss: 0.5048 Lr: 0.00596 [2023-12-20 14:12:24,659 INFO misc.py line 119 131400] Train: [11/100][23/800] Data 0.003 (0.004) Batch 0.346 (0.325) Remain 06:30:15 loss: 0.6446 Lr: 0.00596 [2023-12-20 14:12:24,968 INFO misc.py line 119 131400] Train: [11/100][24/800] Data 0.004 (0.004) Batch 0.308 (0.325) Remain 06:29:17 loss: 0.3409 Lr: 0.00596 [2023-12-20 14:12:25,279 INFO misc.py line 119 131400] Train: [11/100][25/800] Data 0.003 (0.004) Batch 0.311 (0.324) Remain 06:28:32 loss: 0.7852 Lr: 0.00596 [2023-12-20 14:12:25,596 INFO misc.py line 119 131400] Train: [11/100][26/800] Data 0.003 (0.004) Batch 0.318 (0.324) Remain 06:28:12 loss: 0.6561 Lr: 0.00596 [2023-12-20 14:12:25,943 INFO misc.py line 119 131400] Train: [11/100][27/800] Data 0.003 (0.004) Batch 0.346 (0.325) Remain 06:29:19 loss: 0.3411 Lr: 0.00596 [2023-12-20 14:12:26,288 INFO misc.py line 119 131400] Train: [11/100][28/800] Data 0.004 (0.004) Batch 0.346 (0.325) Remain 06:30:20 loss: 0.6508 Lr: 0.00596 [2023-12-20 14:12:26,581 INFO misc.py line 119 131400] Train: [11/100][29/800] Data 0.003 (0.004) Batch 0.291 (0.324) Remain 06:28:45 loss: 0.7373 Lr: 0.00596 [2023-12-20 14:12:26,937 INFO misc.py line 119 131400] Train: [11/100][30/800] Data 0.005 (0.004) Batch 0.358 (0.325) Remain 06:30:15 loss: 1.1481 Lr: 0.00596 [2023-12-20 14:12:27,273 INFO misc.py line 119 131400] Train: [11/100][31/800] Data 0.003 (0.004) Batch 0.335 (0.326) Remain 06:30:40 loss: 0.6319 Lr: 0.00596 [2023-12-20 14:12:27,617 INFO misc.py line 119 131400] Train: [11/100][32/800] Data 0.004 (0.004) Batch 0.344 (0.326) Remain 06:31:26 loss: 0.9657 Lr: 0.00596 [2023-12-20 14:12:27,987 INFO misc.py line 119 131400] Train: [11/100][33/800] Data 0.003 (0.004) Batch 0.368 (0.328) Remain 06:33:06 loss: 1.1557 Lr: 0.00596 [2023-12-20 14:12:28,333 INFO misc.py line 119 131400] Train: [11/100][34/800] Data 0.007 (0.004) Batch 0.348 (0.328) Remain 06:33:53 loss: 0.7166 Lr: 0.00596 [2023-12-20 14:12:28,699 INFO misc.py line 119 131400] Train: [11/100][35/800] Data 0.003 (0.004) Batch 0.366 (0.330) Remain 06:35:17 loss: 0.9044 Lr: 0.00596 [2023-12-20 14:12:29,013 INFO misc.py line 119 131400] Train: [11/100][36/800] Data 0.003 (0.004) Batch 0.313 (0.329) Remain 06:34:40 loss: 0.5058 Lr: 0.00596 [2023-12-20 14:12:29,328 INFO misc.py line 119 131400] Train: [11/100][37/800] Data 0.003 (0.004) Batch 0.316 (0.329) Remain 06:34:13 loss: 0.4739 Lr: 0.00596 [2023-12-20 14:12:29,654 INFO misc.py line 119 131400] Train: [11/100][38/800] Data 0.003 (0.004) Batch 0.323 (0.329) Remain 06:34:01 loss: 0.5152 Lr: 0.00596 [2023-12-20 14:12:29,968 INFO misc.py line 119 131400] Train: [11/100][39/800] Data 0.006 (0.004) Batch 0.317 (0.328) Remain 06:33:36 loss: 0.7400 Lr: 0.00596 [2023-12-20 14:12:30,278 INFO misc.py line 119 131400] Train: [11/100][40/800] Data 0.003 (0.004) Batch 0.310 (0.328) Remain 06:33:01 loss: 0.7655 Lr: 0.00596 [2023-12-20 14:12:30,568 INFO misc.py line 119 131400] Train: [11/100][41/800] Data 0.004 (0.004) Batch 0.290 (0.327) Remain 06:31:48 loss: 0.3195 Lr: 0.00596 [2023-12-20 14:12:30,884 INFO misc.py line 119 131400] Train: [11/100][42/800] Data 0.004 (0.004) Batch 0.317 (0.326) Remain 06:31:31 loss: 0.6271 Lr: 0.00596 [2023-12-20 14:12:31,195 INFO misc.py line 119 131400] Train: [11/100][43/800] Data 0.002 (0.004) Batch 0.311 (0.326) Remain 06:31:01 loss: 0.8809 Lr: 0.00596 [2023-12-20 14:12:31,543 INFO misc.py line 119 131400] Train: [11/100][44/800] Data 0.003 (0.004) Batch 0.348 (0.327) Remain 06:31:40 loss: 0.7692 Lr: 0.00596 [2023-12-20 14:12:31,831 INFO misc.py line 119 131400] Train: [11/100][45/800] Data 0.003 (0.004) Batch 0.281 (0.326) Remain 06:30:22 loss: 0.4688 Lr: 0.00596 [2023-12-20 14:12:32,158 INFO misc.py line 119 131400] Train: [11/100][46/800] Data 0.010 (0.004) Batch 0.334 (0.326) Remain 06:30:36 loss: 0.6180 Lr: 0.00596 [2023-12-20 14:12:32,461 INFO misc.py line 119 131400] Train: [11/100][47/800] Data 0.002 (0.004) Batch 0.303 (0.325) Remain 06:29:58 loss: 0.8112 Lr: 0.00596 [2023-12-20 14:12:32,752 INFO misc.py line 119 131400] Train: [11/100][48/800] Data 0.002 (0.004) Batch 0.291 (0.324) Remain 06:29:02 loss: 0.4332 Lr: 0.00596 [2023-12-20 14:12:33,045 INFO misc.py line 119 131400] Train: [11/100][49/800] Data 0.003 (0.004) Batch 0.293 (0.324) Remain 06:28:13 loss: 0.6209 Lr: 0.00596 [2023-12-20 14:12:33,359 INFO misc.py line 119 131400] Train: [11/100][50/800] Data 0.004 (0.004) Batch 0.314 (0.324) Remain 06:27:58 loss: 0.8797 Lr: 0.00596 [2023-12-20 14:12:33,672 INFO misc.py line 119 131400] Train: [11/100][51/800] Data 0.003 (0.004) Batch 0.312 (0.323) Remain 06:27:40 loss: 0.6984 Lr: 0.00596 [2023-12-20 14:12:33,994 INFO misc.py line 119 131400] Train: [11/100][52/800] Data 0.004 (0.004) Batch 0.322 (0.323) Remain 06:27:38 loss: 0.4987 Lr: 0.00596 [2023-12-20 14:12:34,329 INFO misc.py line 119 131400] Train: [11/100][53/800] Data 0.004 (0.004) Batch 0.337 (0.324) Remain 06:27:57 loss: 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Batch 0.326 (0.329) Remain 06:30:28 loss: 1.0800 Lr: 0.00594 [2023-12-20 14:16:38,714 INFO misc.py line 119 131400] Train: [11/100][795/800] Data 0.004 (0.004) Batch 0.302 (0.329) Remain 06:30:25 loss: 0.5444 Lr: 0.00594 [2023-12-20 14:16:39,006 INFO misc.py line 119 131400] Train: [11/100][796/800] Data 0.003 (0.004) Batch 0.293 (0.329) Remain 06:30:22 loss: 0.5458 Lr: 0.00594 [2023-12-20 14:16:39,294 INFO misc.py line 119 131400] Train: [11/100][797/800] Data 0.002 (0.004) Batch 0.288 (0.329) Remain 06:30:18 loss: 0.8260 Lr: 0.00594 [2023-12-20 14:16:39,590 INFO misc.py line 119 131400] Train: [11/100][798/800] Data 0.002 (0.004) Batch 0.295 (0.329) Remain 06:30:14 loss: 0.5965 Lr: 0.00594 [2023-12-20 14:16:39,891 INFO misc.py line 119 131400] Train: [11/100][799/800] Data 0.004 (0.004) Batch 0.301 (0.329) Remain 06:30:11 loss: 0.5876 Lr: 0.00594 [2023-12-20 14:16:40,195 INFO misc.py line 119 131400] Train: [11/100][800/800] Data 0.003 (0.004) Batch 0.305 (0.329) Remain 06:30:09 loss: 0.7361 Lr: 0.00594 [2023-12-20 14:16:40,196 INFO misc.py line 136 131400] Train result: loss: 0.6820 [2023-12-20 14:16:40,196 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:17:03,757 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0923 [2023-12-20 14:17:03,856 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.5910 [2023-12-20 14:17:03,962 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5169 [2023-12-20 14:17:04,076 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1865 [2023-12-20 14:17:04,190 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3129 [2023-12-20 14:17:04,291 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1783 [2023-12-20 14:17:04,385 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1099 [2023-12-20 14:17:04,494 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3352 [2023-12-20 14:17:04,578 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3058 [2023-12-20 14:17:04,664 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5121 [2023-12-20 14:17:04,754 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7507 [2023-12-20 14:17:04,893 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.7241 [2023-12-20 14:17:05,010 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6241 [2023-12-20 14:17:05,167 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2810 [2023-12-20 14:17:05,260 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2673 [2023-12-20 14:17:05,393 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6140 [2023-12-20 14:17:05,505 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4026 [2023-12-20 14:17:05,615 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4009 [2023-12-20 14:17:05,730 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4686 [2023-12-20 14:17:05,806 INFO evaluator.py line 159 131400] Test: [20/78] Loss 1.0594 [2023-12-20 14:17:05,915 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4924 [2023-12-20 14:17:06,073 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1684 [2023-12-20 14:17:06,192 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9104 [2023-12-20 14:17:06,334 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.6010 [2023-12-20 14:17:06,476 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3517 [2023-12-20 14:17:06,567 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6032 [2023-12-20 14:17:06,726 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7603 [2023-12-20 14:17:06,853 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5764 [2023-12-20 14:17:06,946 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.1419 [2023-12-20 14:17:07,092 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4699 [2023-12-20 14:17:07,197 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.2392 [2023-12-20 14:17:07,316 INFO evaluator.py line 159 131400] Test: [32/78] Loss 1.1267 [2023-12-20 14:17:07,403 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2832 [2023-12-20 14:17:07,471 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2625 [2023-12-20 14:17:07,566 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6177 [2023-12-20 14:17:07,664 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6167 [2023-12-20 14:17:07,801 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.2407 [2023-12-20 14:17:07,911 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3646 [2023-12-20 14:17:07,992 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.8264 [2023-12-20 14:17:08,145 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6639 [2023-12-20 14:17:08,292 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0210 [2023-12-20 14:17:08,392 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2693 [2023-12-20 14:17:08,512 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5383 [2023-12-20 14:17:08,653 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1197 [2023-12-20 14:17:08,770 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9245 [2023-12-20 14:17:08,873 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5113 [2023-12-20 14:17:09,039 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4637 [2023-12-20 14:17:09,132 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4632 [2023-12-20 14:17:09,283 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1213 [2023-12-20 14:17:09,380 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.6469 [2023-12-20 14:17:09,455 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5710 [2023-12-20 14:17:09,567 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3864 [2023-12-20 14:17:09,718 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2395 [2023-12-20 14:17:09,851 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4455 [2023-12-20 14:17:09,952 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4310 [2023-12-20 14:17:10,039 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6503 [2023-12-20 14:17:10,142 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5006 [2023-12-20 14:17:10,306 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2576 [2023-12-20 14:17:10,403 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2816 [2023-12-20 14:17:10,499 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3564 [2023-12-20 14:17:10,594 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4869 [2023-12-20 14:17:10,692 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5357 [2023-12-20 14:17:10,778 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7227 [2023-12-20 14:17:10,885 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8394 [2023-12-20 14:17:11,014 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7660 [2023-12-20 14:17:11,096 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4858 [2023-12-20 14:17:11,195 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4827 [2023-12-20 14:17:11,288 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0172 [2023-12-20 14:17:11,374 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4235 [2023-12-20 14:17:11,461 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0171 [2023-12-20 14:17:11,553 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7419 [2023-12-20 14:17:11,647 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7748 [2023-12-20 14:17:11,785 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1460 [2023-12-20 14:17:11,886 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5184 [2023-12-20 14:17:12,006 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.1499 [2023-12-20 14:17:12,109 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9670 [2023-12-20 14:17:12,200 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6191 [2023-12-20 14:17:12,359 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4326 [2023-12-20 14:17:13,583 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6674/0.7723/0.8805. [2023-12-20 14:17:13,583 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8295/0.9358 [2023-12-20 14:17:13,583 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9618/0.9832 [2023-12-20 14:17:13,583 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5737/0.6927 [2023-12-20 14:17:13,583 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.5533/0.5579 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8726/0.9523 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.6549/0.8938 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6934/0.7762 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5781/0.6692 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6234/0.7885 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7666/0.8992 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3195/0.4540 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6422/0.7660 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6007/0.8537 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7041/0.8608 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4759/0.5079 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6426/0.7026 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8988/0.9385 [2023-12-20 14:17:13,584 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6180/0.7009 [2023-12-20 14:17:13,585 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8451/0.9196 [2023-12-20 14:17:13,585 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.4935/0.5932 [2023-12-20 14:17:13,585 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:17:13,587 INFO misc.py line 165 131400] Currently Best mIoU: 0.6862 [2023-12-20 14:17:13,587 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:17:16,435 INFO misc.py line 119 131400] Train: [12/100][1/800] Data 0.652 (0.652) Batch 0.915 (0.915) Remain 18:05:28 loss: 0.4100 Lr: 0.00594 [2023-12-20 14:17:16,930 INFO misc.py line 119 131400] Train: [12/100][2/800] Data 0.202 (0.202) Batch 0.496 (0.496) Remain 09:48:21 loss: 0.8001 Lr: 0.00594 [2023-12-20 14:17:17,497 INFO misc.py line 119 131400] Train: [12/100][3/800] Data 0.242 (0.242) Batch 0.566 (0.566) Remain 11:11:11 loss: 0.7490 Lr: 0.00594 [2023-12-20 14:17:17,819 INFO misc.py line 119 131400] Train: [12/100][4/800] Data 0.005 (0.005) Batch 0.324 (0.324) Remain 06:23:55 loss: 0.5251 Lr: 0.00594 [2023-12-20 14:17:18,166 INFO misc.py line 119 131400] Train: [12/100][5/800] Data 0.002 (0.003) Batch 0.345 (0.334) Remain 06:36:45 loss: 0.6381 Lr: 0.00594 [2023-12-20 14:17:18,507 INFO misc.py line 119 131400] Train: [12/100][6/800] Data 0.005 (0.004) Batch 0.342 (0.337) Remain 06:39:47 loss: 0.5917 Lr: 0.00594 [2023-12-20 14:17:18,809 INFO misc.py line 119 131400] Train: [12/100][7/800] Data 0.004 (0.004) Batch 0.302 (0.328) Remain 06:29:29 loss: 0.3905 Lr: 0.00594 [2023-12-20 14:17:19,142 INFO misc.py line 119 131400] Train: [12/100][8/800] Data 0.004 (0.004) Batch 0.333 (0.329) Remain 06:30:29 loss: 0.6926 Lr: 0.00594 [2023-12-20 14:17:19,468 INFO misc.py line 119 131400] Train: [12/100][9/800] Data 0.004 (0.004) Batch 0.327 (0.329) Remain 06:30:00 loss: 0.6002 Lr: 0.00594 [2023-12-20 14:17:19,815 INFO misc.py line 119 131400] Train: [12/100][10/800] Data 0.003 (0.004) Batch 0.347 (0.331) Remain 06:33:07 loss: 0.6140 Lr: 0.00594 [2023-12-20 14:17:20,163 INFO misc.py line 119 131400] Train: [12/100][11/800] Data 0.002 (0.004) Batch 0.348 (0.333) Remain 06:35:35 loss: 0.5495 Lr: 0.00594 [2023-12-20 14:17:20,476 INFO misc.py line 119 131400] Train: [12/100][12/800] Data 0.003 (0.003) Batch 0.313 (0.331) Remain 06:32:54 loss: 0.7092 Lr: 0.00594 [2023-12-20 14:17:20,822 INFO misc.py line 119 131400] Train: [12/100][13/800] Data 0.003 (0.003) Batch 0.346 (0.333) Remain 06:34:42 loss: 0.7730 Lr: 0.00594 [2023-12-20 14:17:21,154 INFO misc.py line 119 131400] Train: [12/100][14/800] Data 0.002 (0.003) Batch 0.332 (0.333) Remain 06:34:36 loss: 0.6885 Lr: 0.00594 [2023-12-20 14:17:21,496 INFO misc.py line 119 131400] Train: [12/100][15/800] Data 0.003 (0.003) Batch 0.342 (0.333) Remain 06:35:29 loss: 0.6065 Lr: 0.00594 [2023-12-20 14:17:21,834 INFO misc.py line 119 131400] Train: [12/100][16/800] Data 0.002 (0.003) Batch 0.338 (0.334) Remain 06:35:54 loss: 0.5426 Lr: 0.00594 [2023-12-20 14:17:22,149 INFO misc.py line 119 131400] Train: [12/100][17/800] Data 0.002 (0.003) Batch 0.316 (0.332) Remain 06:34:23 loss: 0.6384 Lr: 0.00594 [2023-12-20 14:17:22,469 INFO misc.py line 119 131400] Train: [12/100][18/800] Data 0.003 (0.003) Batch 0.320 (0.332) Remain 06:33:23 loss: 0.7461 Lr: 0.00594 [2023-12-20 14:17:22,776 INFO misc.py line 119 131400] Train: [12/100][19/800] Data 0.002 (0.003) Batch 0.306 (0.330) Remain 06:31:29 loss: 0.8173 Lr: 0.00594 [2023-12-20 14:17:23,091 INFO misc.py line 119 131400] Train: [12/100][20/800] Data 0.003 (0.003) Batch 0.315 (0.329) Remain 06:30:25 loss: 0.6760 Lr: 0.00594 [2023-12-20 14:17:23,432 INFO misc.py line 119 131400] Train: [12/100][21/800] Data 0.003 (0.003) Batch 0.342 (0.330) Remain 06:31:14 loss: 0.7188 Lr: 0.00594 [2023-12-20 14:17:23,715 INFO misc.py line 119 131400] Train: [12/100][22/800] Data 0.003 (0.003) Batch 0.283 (0.327) Remain 06:28:18 loss: 0.6382 Lr: 0.00594 [2023-12-20 14:17:24,029 INFO misc.py line 119 131400] Train: [12/100][23/800] Data 0.003 (0.003) Batch 0.314 (0.327) Remain 06:27:30 loss: 0.6371 Lr: 0.00594 [2023-12-20 14:17:24,345 INFO misc.py line 119 131400] Train: [12/100][24/800] Data 0.003 (0.003) Batch 0.316 (0.326) Remain 06:26:55 loss: 0.5466 Lr: 0.00594 [2023-12-20 14:17:24,677 INFO misc.py line 119 131400] Train: [12/100][25/800] Data 0.003 (0.003) Batch 0.332 (0.326) Remain 06:27:15 loss: 0.3415 Lr: 0.00594 [2023-12-20 14:17:24,966 INFO misc.py line 119 131400] Train: [12/100][26/800] Data 0.003 (0.003) Batch 0.289 (0.325) Remain 06:25:18 loss: 0.7012 Lr: 0.00594 [2023-12-20 14:17:25,285 INFO misc.py line 119 131400] Train: [12/100][27/800] Data 0.002 (0.003) Batch 0.318 (0.325) Remain 06:24:58 loss: 0.7788 Lr: 0.00594 [2023-12-20 14:17:25,591 INFO misc.py line 119 131400] Train: [12/100][28/800] Data 0.003 (0.003) Batch 0.306 (0.324) Remain 06:24:06 loss: 0.7312 Lr: 0.00594 [2023-12-20 14:17:25,911 INFO misc.py line 119 131400] Train: [12/100][29/800] Data 0.003 (0.003) Batch 0.320 (0.324) Remain 06:23:56 loss: 0.6978 Lr: 0.00594 [2023-12-20 14:17:26,246 INFO misc.py line 119 131400] Train: [12/100][30/800] Data 0.002 (0.003) Batch 0.334 (0.324) Remain 06:24:23 loss: 0.6548 Lr: 0.00594 [2023-12-20 14:17:26,555 INFO misc.py line 119 131400] Train: [12/100][31/800] Data 0.003 (0.003) Batch 0.309 (0.324) Remain 06:23:46 loss: 0.7321 Lr: 0.00594 [2023-12-20 14:17:26,858 INFO misc.py line 119 131400] Train: [12/100][32/800] Data 0.003 (0.003) Batch 0.303 (0.323) Remain 06:22:55 loss: 0.8351 Lr: 0.00594 [2023-12-20 14:17:27,161 INFO misc.py line 119 131400] Train: [12/100][33/800] Data 0.003 (0.003) Batch 0.303 (0.322) Remain 06:22:09 loss: 0.8202 Lr: 0.00594 [2023-12-20 14:17:27,469 INFO misc.py line 119 131400] Train: [12/100][34/800] Data 0.002 (0.003) Batch 0.308 (0.322) Remain 06:21:36 loss: 1.1813 Lr: 0.00594 [2023-12-20 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[2023-12-20 14:21:27,388 INFO misc.py line 119 131400] Train: [12/100][776/800] Data 0.004 (0.004) Batch 0.364 (0.323) Remain 06:19:26 loss: 0.5406 Lr: 0.00592 [2023-12-20 14:21:27,687 INFO misc.py line 119 131400] Train: [12/100][777/800] Data 0.003 (0.004) Batch 0.299 (0.323) Remain 06:19:23 loss: 0.5222 Lr: 0.00592 [2023-12-20 14:21:27,971 INFO misc.py line 119 131400] Train: [12/100][778/800] Data 0.003 (0.004) Batch 0.283 (0.323) Remain 06:19:19 loss: 0.6171 Lr: 0.00592 [2023-12-20 14:21:28,297 INFO misc.py line 119 131400] Train: [12/100][779/800] Data 0.005 (0.004) Batch 0.327 (0.323) Remain 06:19:19 loss: 0.4109 Lr: 0.00592 [2023-12-20 14:21:28,595 INFO misc.py line 119 131400] Train: [12/100][780/800] Data 0.004 (0.004) Batch 0.298 (0.323) Remain 06:19:17 loss: 0.4786 Lr: 0.00592 [2023-12-20 14:21:28,927 INFO misc.py line 119 131400] Train: [12/100][781/800] Data 0.003 (0.004) Batch 0.331 (0.323) Remain 06:19:17 loss: 0.3097 Lr: 0.00592 [2023-12-20 14:21:29,253 INFO misc.py line 119 131400] Train: [12/100][782/800] Data 0.005 (0.004) Batch 0.327 (0.323) Remain 06:19:17 loss: 0.7605 Lr: 0.00592 [2023-12-20 14:21:29,567 INFO misc.py line 119 131400] Train: [12/100][783/800] Data 0.004 (0.004) Batch 0.314 (0.323) Remain 06:19:16 loss: 0.7005 Lr: 0.00592 [2023-12-20 14:21:29,920 INFO misc.py line 119 131400] Train: [12/100][784/800] Data 0.004 (0.004) Batch 0.353 (0.323) Remain 06:19:18 loss: 0.6584 Lr: 0.00592 [2023-12-20 14:21:30,222 INFO misc.py line 119 131400] Train: [12/100][785/800] Data 0.004 (0.004) Batch 0.302 (0.323) Remain 06:19:16 loss: 0.2929 Lr: 0.00592 [2023-12-20 14:21:30,559 INFO misc.py line 119 131400] Train: [12/100][786/800] Data 0.004 (0.004) Batch 0.336 (0.323) Remain 06:19:17 loss: 0.5653 Lr: 0.00592 [2023-12-20 14:21:30,904 INFO misc.py line 119 131400] Train: [12/100][787/800] Data 0.006 (0.004) Batch 0.345 (0.323) Remain 06:19:19 loss: 0.6065 Lr: 0.00592 [2023-12-20 14:21:31,243 INFO misc.py line 119 131400] Train: [12/100][788/800] Data 0.003 (0.004) Batch 0.339 (0.323) Remain 06:19:20 loss: 0.5891 Lr: 0.00592 [2023-12-20 14:21:31,544 INFO misc.py line 119 131400] Train: [12/100][789/800] Data 0.003 (0.004) Batch 0.302 (0.323) Remain 06:19:17 loss: 0.6586 Lr: 0.00592 [2023-12-20 14:21:31,902 INFO misc.py line 119 131400] Train: [12/100][790/800] Data 0.003 (0.004) Batch 0.357 (0.323) Remain 06:19:20 loss: 0.6233 Lr: 0.00592 [2023-12-20 14:21:32,213 INFO misc.py line 119 131400] Train: [12/100][791/800] Data 0.004 (0.004) Batch 0.312 (0.323) Remain 06:19:19 loss: 0.6085 Lr: 0.00592 [2023-12-20 14:21:32,478 INFO misc.py line 119 131400] Train: [12/100][792/800] Data 0.003 (0.004) Batch 0.264 (0.323) Remain 06:19:13 loss: 0.4685 Lr: 0.00592 [2023-12-20 14:21:32,780 INFO misc.py line 119 131400] Train: [12/100][793/800] Data 0.004 (0.004) Batch 0.302 (0.323) Remain 06:19:11 loss: 0.6051 Lr: 0.00592 [2023-12-20 14:21:33,064 INFO misc.py line 119 131400] Train: [12/100][794/800] Data 0.003 (0.004) Batch 0.283 (0.323) Remain 06:19:07 loss: 0.5514 Lr: 0.00592 [2023-12-20 14:21:33,376 INFO misc.py line 119 131400] Train: [12/100][795/800] Data 0.005 (0.004) Batch 0.313 (0.323) Remain 06:19:06 loss: 0.8280 Lr: 0.00592 [2023-12-20 14:21:33,671 INFO misc.py line 119 131400] Train: [12/100][796/800] Data 0.003 (0.004) Batch 0.295 (0.323) Remain 06:19:03 loss: 0.4944 Lr: 0.00592 [2023-12-20 14:21:33,977 INFO misc.py line 119 131400] Train: [12/100][797/800] Data 0.002 (0.004) Batch 0.306 (0.323) Remain 06:19:01 loss: 0.6855 Lr: 0.00592 [2023-12-20 14:21:34,276 INFO misc.py line 119 131400] Train: [12/100][798/800] Data 0.002 (0.004) Batch 0.300 (0.323) Remain 06:18:59 loss: 0.9275 Lr: 0.00592 [2023-12-20 14:21:34,539 INFO misc.py line 119 131400] Train: [12/100][799/800] Data 0.003 (0.004) Batch 0.263 (0.323) Remain 06:18:53 loss: 0.5504 Lr: 0.00592 [2023-12-20 14:21:34,831 INFO misc.py line 119 131400] Train: [12/100][800/800] Data 0.002 (0.004) Batch 0.292 (0.323) Remain 06:18:50 loss: 0.5438 Lr: 0.00592 [2023-12-20 14:21:34,831 INFO misc.py line 136 131400] Train result: loss: 0.6602 [2023-12-20 14:21:34,831 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:21:56,564 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.4630 [2023-12-20 14:21:56,927 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.3561 [2023-12-20 14:21:57,032 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4440 [2023-12-20 14:21:57,165 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5065 [2023-12-20 14:21:57,275 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4400 [2023-12-20 14:21:57,380 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3594 [2023-12-20 14:21:57,475 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.5173 [2023-12-20 14:21:57,582 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6566 [2023-12-20 14:21:57,661 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2587 [2023-12-20 14:21:57,747 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4181 [2023-12-20 14:21:57,839 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.9686 [2023-12-20 14:21:57,980 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5571 [2023-12-20 14:21:58,103 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3189 [2023-12-20 14:21:58,258 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3905 [2023-12-20 14:21:58,352 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5196 [2023-12-20 14:21:58,484 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9280 [2023-12-20 14:21:58,592 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4684 [2023-12-20 14:21:58,709 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6625 [2023-12-20 14:21:58,819 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4529 [2023-12-20 14:21:58,895 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5975 [2023-12-20 14:21:59,003 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4408 [2023-12-20 14:21:59,164 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2700 [2023-12-20 14:21:59,291 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6186 [2023-12-20 14:21:59,433 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3764 [2023-12-20 14:21:59,577 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3312 [2023-12-20 14:21:59,659 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8523 [2023-12-20 14:21:59,816 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7724 [2023-12-20 14:21:59,939 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.7445 [2023-12-20 14:22:00,034 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7847 [2023-12-20 14:22:00,182 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5293 [2023-12-20 14:22:00,285 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8578 [2023-12-20 14:22:00,402 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.7110 [2023-12-20 14:22:00,486 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.4891 [2023-12-20 14:22:00,558 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2530 [2023-12-20 14:22:00,657 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6958 [2023-12-20 14:22:00,752 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.9871 [2023-12-20 14:22:00,887 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.1017 [2023-12-20 14:22:00,994 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1923 [2023-12-20 14:22:01,077 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.0910 [2023-12-20 14:22:01,219 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.7405 [2023-12-20 14:22:01,365 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0360 [2023-12-20 14:22:01,465 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3703 [2023-12-20 14:22:01,585 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5213 [2023-12-20 14:22:01,735 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8843 [2023-12-20 14:22:01,850 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7309 [2023-12-20 14:22:01,950 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6571 [2023-12-20 14:22:02,115 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3850 [2023-12-20 14:22:02,207 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4145 [2023-12-20 14:22:02,351 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2946 [2023-12-20 14:22:02,440 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7819 [2023-12-20 14:22:02,518 INFO evaluator.py line 159 131400] Test: [51/78] Loss 1.1969 [2023-12-20 14:22:02,625 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1881 [2023-12-20 14:22:02,780 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.2476 [2023-12-20 14:22:02,913 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3431 [2023-12-20 14:22:03,015 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4330 [2023-12-20 14:22:03,104 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9039 [2023-12-20 14:22:03,204 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5538 [2023-12-20 14:22:03,363 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3190 [2023-12-20 14:22:03,461 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4518 [2023-12-20 14:22:03,561 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2430 [2023-12-20 14:22:03,662 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4376 [2023-12-20 14:22:03,756 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4983 [2023-12-20 14:22:03,851 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.9610 [2023-12-20 14:22:03,952 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0265 [2023-12-20 14:22:04,082 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.8367 [2023-12-20 14:22:04,170 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.6722 [2023-12-20 14:22:04,270 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5241 [2023-12-20 14:22:04,363 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0282 [2023-12-20 14:22:04,447 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.6806 [2023-12-20 14:22:04,531 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0475 [2023-12-20 14:22:04,625 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8046 [2023-12-20 14:22:04,723 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7743 [2023-12-20 14:22:04,859 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3670 [2023-12-20 14:22:04,953 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6785 [2023-12-20 14:22:05,078 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.2246 [2023-12-20 14:22:05,185 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.0475 [2023-12-20 14:22:05,272 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6701 [2023-12-20 14:22:05,436 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0681 [2023-12-20 14:22:06,695 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6792/0.8020/0.8758. [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8256/0.8909 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9619/0.9825 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5284/0.8175 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7017/0.8909 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8753/0.9196 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7270/0.8998 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6695/0.7228 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5798/0.8294 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6001/0.8025 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7683/0.9257 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3481/0.4717 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6397/0.7734 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5878/0.7918 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7325/0.8014 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5328/0.7533 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6841/0.7602 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9250/0.9686 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6423/0.7062 [2023-12-20 14:22:06,696 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8652/0.9167 [2023-12-20 14:22:06,697 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.3882/0.4150 [2023-12-20 14:22:06,697 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:22:06,698 INFO misc.py line 165 131400] Currently Best mIoU: 0.6862 [2023-12-20 14:22:06,698 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:22:10,600 INFO misc.py line 119 131400] Train: [13/100][1/800] Data 1.183 (1.183) Batch 1.533 (1.533) Remain 29:58:07 loss: 0.7086 Lr: 0.00592 [2023-12-20 14:22:10,914 INFO misc.py line 119 131400] Train: [13/100][2/800] Data 0.004 (0.004) Batch 0.314 (0.314) Remain 06:07:59 loss: 0.7937 Lr: 0.00592 [2023-12-20 14:22:11,257 INFO misc.py line 119 131400] Train: [13/100][3/800] Data 0.004 (0.004) Batch 0.343 (0.343) Remain 06:42:41 loss: 0.3428 Lr: 0.00592 [2023-12-20 14:22:11,574 INFO misc.py line 119 131400] Train: [13/100][4/800] Data 0.004 (0.004) Batch 0.318 (0.318) Remain 06:12:56 loss: 0.5497 Lr: 0.00592 [2023-12-20 14:22:11,904 INFO misc.py line 119 131400] Train: [13/100][5/800] Data 0.003 (0.003) Batch 0.328 (0.323) Remain 06:18:40 loss: 0.5136 Lr: 0.00592 [2023-12-20 14:22:12,175 INFO misc.py line 119 131400] Train: [13/100][6/800] Data 0.005 (0.004) Batch 0.272 (0.306) Remain 05:58:56 loss: 0.4995 Lr: 0.00592 [2023-12-20 14:22:12,529 INFO misc.py line 119 131400] Train: [13/100][7/800] Data 0.005 (0.004) Batch 0.354 (0.318) Remain 06:12:55 loss: 0.6189 Lr: 0.00592 [2023-12-20 14:22:12,810 INFO misc.py line 119 131400] Train: [13/100][8/800] Data 0.005 (0.004) Batch 0.282 (0.311) Remain 06:04:31 loss: 0.5779 Lr: 0.00592 [2023-12-20 14:22:13,157 INFO misc.py line 119 131400] Train: [13/100][9/800] Data 0.004 (0.004) Batch 0.346 (0.317) Remain 06:11:19 loss: 0.5528 Lr: 0.00592 [2023-12-20 14:22:13,501 INFO misc.py line 119 131400] Train: [13/100][10/800] Data 0.005 (0.004) Batch 0.346 (0.321) Remain 06:16:13 loss: 0.7564 Lr: 0.00592 [2023-12-20 14:22:13,799 INFO misc.py line 119 131400] Train: [13/100][11/800] Data 0.003 (0.004) Batch 0.298 (0.318) Remain 06:12:49 loss: 0.5488 Lr: 0.00592 [2023-12-20 14:22:14,152 INFO misc.py line 119 131400] Train: [13/100][12/800] Data 0.005 (0.004) Batch 0.352 (0.322) Remain 06:17:17 loss: 0.5620 Lr: 0.00592 [2023-12-20 14:22:14,445 INFO misc.py line 119 131400] Train: [13/100][13/800] Data 0.004 (0.004) Batch 0.293 (0.319) Remain 06:13:58 loss: 0.7646 Lr: 0.00592 [2023-12-20 14:22:14,795 INFO misc.py line 119 131400] Train: [13/100][14/800] Data 0.004 (0.004) Batch 0.351 (0.322) Remain 06:17:26 loss: 1.0325 Lr: 0.00592 [2023-12-20 14:22:15,125 INFO misc.py line 119 131400] Train: [13/100][15/800] Data 0.003 (0.004) Batch 0.328 (0.322) Remain 06:18:03 loss: 0.5179 Lr: 0.00592 [2023-12-20 14:22:15,451 INFO misc.py line 119 131400] Train: [13/100][16/800] Data 0.005 (0.004) Batch 0.326 (0.323) Remain 06:18:22 loss: 1.2730 Lr: 0.00592 [2023-12-20 14:22:15,770 INFO misc.py line 119 131400] Train: [13/100][17/800] Data 0.004 (0.004) Batch 0.319 (0.322) Remain 06:18:05 loss: 0.6775 Lr: 0.00592 [2023-12-20 14:22:16,100 INFO misc.py line 119 131400] Train: [13/100][18/800] Data 0.004 (0.004) Batch 0.331 (0.323) Remain 06:18:44 loss: 0.6537 Lr: 0.00592 [2023-12-20 14:22:16,466 INFO misc.py line 119 131400] Train: [13/100][19/800] Data 0.004 (0.004) Batch 0.367 (0.326) Remain 06:21:57 loss: 0.8426 Lr: 0.00592 [2023-12-20 14:22:16,786 INFO misc.py line 119 131400] Train: [13/100][20/800] Data 0.003 (0.004) Batch 0.319 (0.325) Remain 06:21:29 loss: 0.7700 Lr: 0.00592 [2023-12-20 14:22:17,138 INFO misc.py line 119 131400] Train: [13/100][21/800] Data 0.004 (0.004) Batch 0.352 (0.327) Remain 06:23:14 loss: 1.0018 Lr: 0.00592 [2023-12-20 14:22:17,443 INFO misc.py line 119 131400] Train: [13/100][22/800] Data 0.004 (0.004) Batch 0.304 (0.326) Remain 06:21:50 loss: 0.5583 Lr: 0.00592 [2023-12-20 14:22:17,788 INFO misc.py line 119 131400] Train: [13/100][23/800] Data 0.004 (0.004) Batch 0.331 (0.326) Remain 06:22:10 loss: 0.6842 Lr: 0.00592 [2023-12-20 14:22:18,092 INFO misc.py line 119 131400] Train: [13/100][24/800] Data 0.019 (0.005) Batch 0.318 (0.325) Remain 06:21:45 loss: 0.5272 Lr: 0.00592 [2023-12-20 14:22:18,392 INFO misc.py line 119 131400] Train: [13/100][25/800] Data 0.004 (0.005) Batch 0.300 (0.324) Remain 06:20:24 loss: 0.7794 Lr: 0.00592 [2023-12-20 14:22:18,723 INFO misc.py line 119 131400] Train: [13/100][26/800] Data 0.003 (0.005) Batch 0.330 (0.325) Remain 06:20:41 loss: 0.4788 Lr: 0.00592 [2023-12-20 14:22:19,047 INFO misc.py line 119 131400] Train: [13/100][27/800] Data 0.005 (0.005) Batch 0.325 (0.325) Remain 06:20:42 loss: 0.5476 Lr: 0.00592 [2023-12-20 14:22:19,368 INFO misc.py line 119 131400] Train: [13/100][28/800] Data 0.004 (0.005) Batch 0.320 (0.324) Remain 06:20:29 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0.003 (0.004) Batch 0.314 (0.325) Remain 06:16:53 loss: 0.4649 Lr: 0.00590 [2023-12-20 14:26:18,354 INFO misc.py line 119 131400] Train: [13/100][764/800] Data 0.004 (0.004) Batch 0.294 (0.325) Remain 06:16:50 loss: 0.6901 Lr: 0.00590 [2023-12-20 14:26:18,704 INFO misc.py line 119 131400] Train: [13/100][765/800] Data 0.003 (0.004) Batch 0.350 (0.325) Remain 06:16:52 loss: 0.3800 Lr: 0.00590 [2023-12-20 14:26:19,021 INFO misc.py line 119 131400] Train: [13/100][766/800] Data 0.003 (0.004) Batch 0.317 (0.325) Remain 06:16:51 loss: 0.8055 Lr: 0.00590 [2023-12-20 14:26:19,336 INFO misc.py line 119 131400] Train: [13/100][767/800] Data 0.003 (0.004) Batch 0.314 (0.325) Remain 06:16:50 loss: 0.6690 Lr: 0.00590 [2023-12-20 14:26:19,664 INFO misc.py line 119 131400] Train: [13/100][768/800] Data 0.003 (0.004) Batch 0.328 (0.325) Remain 06:16:50 loss: 0.9506 Lr: 0.00590 [2023-12-20 14:26:19,997 INFO misc.py line 119 131400] Train: [13/100][769/800] Data 0.003 (0.004) Batch 0.333 (0.325) Remain 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[2023-12-20 14:26:22,249 INFO misc.py line 119 131400] Train: [13/100][776/800] Data 0.003 (0.004) Batch 0.322 (0.325) Remain 06:16:46 loss: 0.5522 Lr: 0.00590 [2023-12-20 14:26:22,550 INFO misc.py line 119 131400] Train: [13/100][777/800] Data 0.003 (0.004) Batch 0.300 (0.325) Remain 06:16:44 loss: 0.8372 Lr: 0.00590 [2023-12-20 14:26:22,868 INFO misc.py line 119 131400] Train: [13/100][778/800] Data 0.003 (0.004) Batch 0.318 (0.325) Remain 06:16:43 loss: 0.5871 Lr: 0.00590 [2023-12-20 14:26:23,198 INFO misc.py line 119 131400] Train: [13/100][779/800] Data 0.003 (0.004) Batch 0.329 (0.325) Remain 06:16:43 loss: 0.3315 Lr: 0.00590 [2023-12-20 14:26:23,521 INFO misc.py line 119 131400] Train: [13/100][780/800] Data 0.005 (0.004) Batch 0.324 (0.325) Remain 06:16:43 loss: 0.4109 Lr: 0.00590 [2023-12-20 14:26:23,819 INFO misc.py line 119 131400] Train: [13/100][781/800] Data 0.003 (0.004) Batch 0.292 (0.325) Remain 06:16:39 loss: 0.3205 Lr: 0.00590 [2023-12-20 14:26:24,197 INFO misc.py line 119 131400] Train: [13/100][782/800] Data 0.010 (0.004) Batch 0.384 (0.325) Remain 06:16:44 loss: 0.6022 Lr: 0.00590 [2023-12-20 14:26:24,534 INFO misc.py line 119 131400] Train: [13/100][783/800] Data 0.004 (0.004) Batch 0.337 (0.325) Remain 06:16:45 loss: 0.5217 Lr: 0.00590 [2023-12-20 14:26:24,871 INFO misc.py line 119 131400] Train: [13/100][784/800] Data 0.004 (0.004) Batch 0.334 (0.325) Remain 06:16:46 loss: 0.5722 Lr: 0.00590 [2023-12-20 14:26:25,181 INFO misc.py line 119 131400] Train: [13/100][785/800] Data 0.006 (0.004) Batch 0.312 (0.325) Remain 06:16:44 loss: 0.5221 Lr: 0.00590 [2023-12-20 14:26:25,505 INFO misc.py line 119 131400] Train: [13/100][786/800] Data 0.004 (0.004) Batch 0.324 (0.325) Remain 06:16:44 loss: 0.6290 Lr: 0.00590 [2023-12-20 14:26:25,843 INFO misc.py line 119 131400] Train: [13/100][787/800] Data 0.004 (0.004) Batch 0.337 (0.325) Remain 06:16:45 loss: 0.9488 Lr: 0.00590 [2023-12-20 14:26:26,172 INFO misc.py line 119 131400] Train: [13/100][788/800] Data 0.004 (0.004) Batch 0.329 (0.325) Remain 06:16:45 loss: 0.8528 Lr: 0.00590 [2023-12-20 14:26:26,503 INFO misc.py line 119 131400] Train: [13/100][789/800] Data 0.004 (0.004) Batch 0.332 (0.325) Remain 06:16:45 loss: 0.6125 Lr: 0.00590 [2023-12-20 14:26:26,813 INFO misc.py line 119 131400] Train: [13/100][790/800] Data 0.002 (0.004) Batch 0.310 (0.325) Remain 06:16:43 loss: 0.3045 Lr: 0.00590 [2023-12-20 14:26:27,102 INFO misc.py line 119 131400] Train: [13/100][791/800] Data 0.002 (0.004) Batch 0.288 (0.325) Remain 06:16:40 loss: 0.4162 Lr: 0.00590 [2023-12-20 14:26:27,404 INFO misc.py line 119 131400] Train: [13/100][792/800] Data 0.003 (0.004) Batch 0.303 (0.325) Remain 06:16:38 loss: 0.4974 Lr: 0.00590 [2023-12-20 14:26:27,721 INFO misc.py line 119 131400] Train: [13/100][793/800] Data 0.003 (0.004) Batch 0.317 (0.325) Remain 06:16:36 loss: 0.8663 Lr: 0.00590 [2023-12-20 14:26:28,022 INFO misc.py line 119 131400] Train: [13/100][794/800] Data 0.002 (0.004) Batch 0.298 (0.325) Remain 06:16:34 loss: 0.4856 Lr: 0.00590 [2023-12-20 14:26:28,333 INFO misc.py line 119 131400] Train: [13/100][795/800] Data 0.006 (0.004) Batch 0.315 (0.325) Remain 06:16:33 loss: 0.6934 Lr: 0.00590 [2023-12-20 14:26:28,633 INFO misc.py line 119 131400] Train: [13/100][796/800] Data 0.002 (0.004) Batch 0.299 (0.325) Remain 06:16:30 loss: 1.0028 Lr: 0.00590 [2023-12-20 14:26:28,957 INFO misc.py line 119 131400] Train: [13/100][797/800] Data 0.002 (0.004) Batch 0.325 (0.325) Remain 06:16:30 loss: 0.9811 Lr: 0.00590 [2023-12-20 14:26:29,267 INFO misc.py line 119 131400] Train: [13/100][798/800] Data 0.002 (0.004) Batch 0.309 (0.325) Remain 06:16:28 loss: 0.4361 Lr: 0.00590 [2023-12-20 14:26:29,565 INFO misc.py line 119 131400] Train: [13/100][799/800] Data 0.003 (0.004) Batch 0.299 (0.325) Remain 06:16:26 loss: 0.7519 Lr: 0.00590 [2023-12-20 14:26:29,876 INFO misc.py line 119 131400] Train: [13/100][800/800] Data 0.003 (0.004) Batch 0.311 (0.324) Remain 06:16:24 loss: 0.5054 Lr: 0.00590 [2023-12-20 14:26:29,876 INFO misc.py line 136 131400] Train result: loss: 0.6371 [2023-12-20 14:26:29,876 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:26:51,640 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2543 [2023-12-20 14:26:52,614 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2163 [2023-12-20 14:26:52,708 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4949 [2023-12-20 14:26:52,836 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5503 [2023-12-20 14:26:52,948 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5202 [2023-12-20 14:26:53,050 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3093 [2023-12-20 14:26:53,141 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8434 [2023-12-20 14:26:53,246 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3293 [2023-12-20 14:26:53,328 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3073 [2023-12-20 14:26:53,418 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3742 [2023-12-20 14:26:53,509 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5225 [2023-12-20 14:26:53,646 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5817 [2023-12-20 14:26:53,771 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2668 [2023-12-20 14:26:53,928 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2867 [2023-12-20 14:26:54,019 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3465 [2023-12-20 14:26:54,156 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9931 [2023-12-20 14:26:54,273 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3492 [2023-12-20 14:26:54,382 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4441 [2023-12-20 14:26:54,492 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4274 [2023-12-20 14:26:54,573 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5760 [2023-12-20 14:26:54,682 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4930 [2023-12-20 14:26:54,838 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2201 [2023-12-20 14:26:54,962 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6741 [2023-12-20 14:26:55,105 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4402 [2023-12-20 14:26:55,247 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3899 [2023-12-20 14:26:55,329 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6346 [2023-12-20 14:26:55,486 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.2619 [2023-12-20 14:26:55,607 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5716 [2023-12-20 14:26:55,712 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7180 [2023-12-20 14:26:55,857 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3021 [2023-12-20 14:26:55,967 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8417 [2023-12-20 14:26:56,087 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6184 [2023-12-20 14:26:56,184 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.4159 [2023-12-20 14:26:56,267 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2451 [2023-12-20 14:26:56,362 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0618 [2023-12-20 14:26:56,457 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5165 [2023-12-20 14:26:56,590 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0224 [2023-12-20 14:26:56,703 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1841 [2023-12-20 14:26:56,789 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.9661 [2023-12-20 14:26:56,936 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.8997 [2023-12-20 14:26:57,087 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1755 [2023-12-20 14:26:57,184 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2877 [2023-12-20 14:26:57,303 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.7070 [2023-12-20 14:26:57,447 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9758 [2023-12-20 14:26:57,564 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9455 [2023-12-20 14:26:57,668 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8365 [2023-12-20 14:26:57,835 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4269 [2023-12-20 14:26:57,930 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4240 [2023-12-20 14:26:58,073 INFO evaluator.py line 159 131400] Test: [49/78] Loss 0.8817 [2023-12-20 14:26:58,163 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7726 [2023-12-20 14:26:58,243 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6271 [2023-12-20 14:26:58,353 INFO evaluator.py line 159 131400] Test: [52/78] Loss 0.9225 [2023-12-20 14:26:58,502 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0023 [2023-12-20 14:26:58,635 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3816 [2023-12-20 14:26:58,737 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3062 [2023-12-20 14:26:58,824 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8792 [2023-12-20 14:26:58,925 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5569 [2023-12-20 14:26:59,092 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3382 [2023-12-20 14:26:59,190 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3639 [2023-12-20 14:26:59,284 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2736 [2023-12-20 14:26:59,377 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5213 [2023-12-20 14:26:59,467 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4140 [2023-12-20 14:26:59,552 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6005 [2023-12-20 14:26:59,655 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9307 [2023-12-20 14:26:59,786 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5044 [2023-12-20 14:26:59,869 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4575 [2023-12-20 14:26:59,967 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5085 [2023-12-20 14:27:00,060 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.1404 [2023-12-20 14:27:00,145 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4334 [2023-12-20 14:27:00,227 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.3113 [2023-12-20 14:27:00,320 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8968 [2023-12-20 14:27:00,409 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7639 [2023-12-20 14:27:00,542 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2736 [2023-12-20 14:27:00,635 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6579 [2023-12-20 14:27:00,751 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.0408 [2023-12-20 14:27:00,855 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9823 [2023-12-20 14:27:00,942 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3083 [2023-12-20 14:27:01,095 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2358 [2023-12-20 14:27:02,360 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6925/0.8115/0.8872. [2023-12-20 14:27:02,360 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8283/0.9007 [2023-12-20 14:27:02,360 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9617/0.9870 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5515/0.7916 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7727/0.8339 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8960/0.9253 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7988/0.8845 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7121/0.7838 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6109/0.8024 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6263/0.7499 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7662/0.8987 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3004/0.5612 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.5906/0.8970 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6072/0.8438 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6964/0.8120 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5535/0.6326 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6660/0.7463 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9098/0.9537 [2023-12-20 14:27:02,361 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6014/0.6826 [2023-12-20 14:27:02,362 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8443/0.9159 [2023-12-20 14:27:02,362 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5553/0.6280 [2023-12-20 14:27:02,362 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:27:02,364 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.6925 [2023-12-20 14:27:02,364 INFO misc.py line 165 131400] Currently Best mIoU: 0.6925 [2023-12-20 14:27:02,364 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:27:08,957 INFO misc.py line 119 131400] Train: [14/100][1/800] Data 0.988 (0.988) Batch 1.310 (1.310) Remain 25:19:56 loss: 1.0483 Lr: 0.00590 [2023-12-20 14:27:09,264 INFO misc.py line 119 131400] Train: [14/100][2/800] Data 0.003 (0.003) Batch 0.307 (0.307) Remain 05:56:04 loss: 0.3020 Lr: 0.00590 [2023-12-20 14:27:09,593 INFO misc.py line 119 131400] Train: [14/100][3/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 06:22:10 loss: 0.6633 Lr: 0.00590 [2023-12-20 14:27:09,897 INFO misc.py line 119 131400] Train: [14/100][4/800] Data 0.004 (0.004) Batch 0.304 (0.304) Remain 05:52:21 loss: 0.4758 Lr: 0.00590 [2023-12-20 14:27:10,225 INFO misc.py line 119 131400] Train: [14/100][5/800] Data 0.003 (0.004) Batch 0.328 (0.316) Remain 06:06:22 loss: 0.5280 Lr: 0.00590 [2023-12-20 14:27:10,592 INFO misc.py line 119 131400] Train: [14/100][6/800] Data 0.004 (0.004) Batch 0.367 (0.333) Remain 06:26:07 loss: 1.0534 Lr: 0.00590 [2023-12-20 14:27:10,947 INFO misc.py line 119 131400] Train: [14/100][7/800] Data 0.004 (0.004) Batch 0.351 (0.337) Remain 06:31:27 loss: 0.8307 Lr: 0.00590 [2023-12-20 14:27:11,295 INFO misc.py line 119 131400] Train: [14/100][8/800] Data 0.008 (0.005) Batch 0.351 (0.340) Remain 06:34:35 loss: 0.7622 Lr: 0.00590 [2023-12-20 14:27:11,604 INFO misc.py line 119 131400] Train: 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Batch 0.283 (0.331) Remain 06:19:33 loss: 1.0656 Lr: 0.00587 [2023-12-20 14:31:17,028 INFO misc.py line 119 131400] Train: [14/100][751/800] Data 0.004 (0.004) Batch 0.338 (0.331) Remain 06:19:34 loss: 1.0528 Lr: 0.00587 [2023-12-20 14:31:17,369 INFO misc.py line 119 131400] Train: [14/100][752/800] Data 0.010 (0.004) Batch 0.348 (0.331) Remain 06:19:35 loss: 0.4982 Lr: 0.00587 [2023-12-20 14:31:17,699 INFO misc.py line 119 131400] Train: [14/100][753/800] Data 0.003 (0.004) Batch 0.330 (0.331) Remain 06:19:35 loss: 0.5568 Lr: 0.00587 [2023-12-20 14:31:18,025 INFO misc.py line 119 131400] Train: [14/100][754/800] Data 0.003 (0.004) Batch 0.325 (0.331) Remain 06:19:34 loss: 0.4487 Lr: 0.00587 [2023-12-20 14:31:18,370 INFO misc.py line 119 131400] Train: [14/100][755/800] Data 0.004 (0.004) Batch 0.346 (0.331) Remain 06:19:35 loss: 0.4942 Lr: 0.00587 [2023-12-20 14:31:18,720 INFO misc.py line 119 131400] Train: [14/100][756/800] Data 0.003 (0.004) Batch 0.350 (0.331) Remain 06:19:36 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14:31:20,980 INFO misc.py line 119 131400] Train: [14/100][763/800] Data 0.008 (0.004) Batch 0.336 (0.331) Remain 06:19:29 loss: 0.8201 Lr: 0.00587 [2023-12-20 14:31:21,315 INFO misc.py line 119 131400] Train: [14/100][764/800] Data 0.002 (0.004) Batch 0.334 (0.331) Remain 06:19:29 loss: 0.8888 Lr: 0.00587 [2023-12-20 14:31:21,658 INFO misc.py line 119 131400] Train: [14/100][765/800] Data 0.004 (0.004) Batch 0.342 (0.331) Remain 06:19:29 loss: 0.7432 Lr: 0.00587 [2023-12-20 14:31:22,000 INFO misc.py line 119 131400] Train: [14/100][766/800] Data 0.005 (0.004) Batch 0.345 (0.331) Remain 06:19:30 loss: 1.1827 Lr: 0.00587 [2023-12-20 14:31:22,323 INFO misc.py line 119 131400] Train: [14/100][767/800] Data 0.003 (0.004) Batch 0.323 (0.331) Remain 06:19:29 loss: 0.5165 Lr: 0.00587 [2023-12-20 14:31:22,639 INFO misc.py line 119 131400] Train: [14/100][768/800] Data 0.003 (0.004) Batch 0.315 (0.331) Remain 06:19:28 loss: 0.5507 Lr: 0.00587 [2023-12-20 14:31:22,963 INFO misc.py line 119 131400] Train: [14/100][769/800] Data 0.002 (0.004) Batch 0.324 (0.331) Remain 06:19:27 loss: 0.5487 Lr: 0.00587 [2023-12-20 14:31:23,305 INFO misc.py line 119 131400] Train: [14/100][770/800] Data 0.002 (0.004) Batch 0.341 (0.331) Remain 06:19:27 loss: 0.5397 Lr: 0.00587 [2023-12-20 14:31:23,602 INFO misc.py line 119 131400] Train: [14/100][771/800] Data 0.004 (0.004) Batch 0.298 (0.331) Remain 06:19:24 loss: 0.5647 Lr: 0.00587 [2023-12-20 14:31:23,946 INFO misc.py line 119 131400] Train: [14/100][772/800] Data 0.003 (0.004) Batch 0.344 (0.331) Remain 06:19:25 loss: 0.7028 Lr: 0.00587 [2023-12-20 14:31:24,285 INFO misc.py line 119 131400] Train: [14/100][773/800] Data 0.004 (0.004) Batch 0.338 (0.331) Remain 06:19:25 loss: 0.3231 Lr: 0.00587 [2023-12-20 14:31:24,621 INFO misc.py line 119 131400] Train: [14/100][774/800] Data 0.003 (0.004) Batch 0.337 (0.331) Remain 06:19:25 loss: 0.4750 Lr: 0.00587 [2023-12-20 14:31:24,954 INFO misc.py line 119 131400] Train: [14/100][775/800] Data 0.003 (0.004) Batch 0.333 (0.331) Remain 06:19:25 loss: 0.8225 Lr: 0.00587 [2023-12-20 14:31:25,284 INFO misc.py line 119 131400] Train: [14/100][776/800] Data 0.003 (0.004) Batch 0.330 (0.331) Remain 06:19:25 loss: 0.7259 Lr: 0.00587 [2023-12-20 14:31:25,614 INFO misc.py line 119 131400] Train: [14/100][777/800] Data 0.003 (0.004) Batch 0.329 (0.331) Remain 06:19:24 loss: 0.7504 Lr: 0.00587 [2023-12-20 14:31:25,919 INFO misc.py line 119 131400] Train: [14/100][778/800] Data 0.003 (0.004) Batch 0.304 (0.331) Remain 06:19:22 loss: 0.6078 Lr: 0.00587 [2023-12-20 14:31:26,244 INFO misc.py line 119 131400] Train: [14/100][779/800] Data 0.004 (0.004) Batch 0.324 (0.331) Remain 06:19:21 loss: 0.5641 Lr: 0.00587 [2023-12-20 14:31:26,536 INFO misc.py line 119 131400] Train: [14/100][780/800] Data 0.005 (0.004) Batch 0.294 (0.331) Remain 06:19:17 loss: 0.4098 Lr: 0.00587 [2023-12-20 14:31:26,841 INFO misc.py line 119 131400] Train: [14/100][781/800] Data 0.003 (0.004) Batch 0.304 (0.331) Remain 06:19:14 loss: 0.6917 Lr: 0.00587 [2023-12-20 14:31:27,164 INFO misc.py line 119 131400] Train: [14/100][782/800] Data 0.004 (0.004) Batch 0.325 (0.331) Remain 06:19:14 loss: 0.5092 Lr: 0.00587 [2023-12-20 14:31:27,500 INFO misc.py line 119 131400] Train: [14/100][783/800] Data 0.003 (0.004) Batch 0.335 (0.331) Remain 06:19:14 loss: 0.9646 Lr: 0.00587 [2023-12-20 14:31:27,817 INFO misc.py line 119 131400] Train: [14/100][784/800] Data 0.004 (0.004) Batch 0.318 (0.331) Remain 06:19:12 loss: 0.7997 Lr: 0.00587 [2023-12-20 14:31:28,123 INFO misc.py line 119 131400] Train: [14/100][785/800] Data 0.003 (0.004) Batch 0.305 (0.331) Remain 06:19:10 loss: 0.8802 Lr: 0.00587 [2023-12-20 14:31:28,421 INFO misc.py line 119 131400] Train: [14/100][786/800] Data 0.003 (0.004) Batch 0.297 (0.331) Remain 06:19:06 loss: 0.7930 Lr: 0.00587 [2023-12-20 14:31:28,723 INFO misc.py line 119 131400] Train: [14/100][787/800] Data 0.004 (0.004) Batch 0.304 (0.331) Remain 06:19:04 loss: 0.4071 Lr: 0.00587 [2023-12-20 14:31:29,035 INFO misc.py line 119 131400] Train: [14/100][788/800] Data 0.003 (0.004) Batch 0.312 (0.330) Remain 06:19:02 loss: 0.5349 Lr: 0.00587 [2023-12-20 14:31:29,325 INFO misc.py line 119 131400] Train: [14/100][789/800] Data 0.002 (0.004) Batch 0.290 (0.330) Remain 06:18:58 loss: 0.9549 Lr: 0.00587 [2023-12-20 14:31:29,581 INFO misc.py line 119 131400] Train: [14/100][790/800] Data 0.002 (0.004) Batch 0.256 (0.330) Remain 06:18:51 loss: 0.4898 Lr: 0.00587 [2023-12-20 14:31:29,891 INFO misc.py line 119 131400] Train: [14/100][791/800] Data 0.002 (0.004) Batch 0.310 (0.330) Remain 06:18:49 loss: 0.6560 Lr: 0.00587 [2023-12-20 14:31:30,199 INFO misc.py line 119 131400] Train: [14/100][792/800] Data 0.002 (0.004) Batch 0.305 (0.330) Remain 06:18:46 loss: 0.8377 Lr: 0.00587 [2023-12-20 14:31:30,448 INFO misc.py line 119 131400] Train: [14/100][793/800] Data 0.005 (0.004) Batch 0.252 (0.330) Remain 06:18:39 loss: 0.3113 Lr: 0.00587 [2023-12-20 14:31:30,807 INFO misc.py line 119 131400] Train: [14/100][794/800] Data 0.002 (0.004) Batch 0.358 (0.330) Remain 06:18:41 loss: 0.7528 Lr: 0.00587 [2023-12-20 14:31:31,137 INFO misc.py line 119 131400] Train: [14/100][795/800] Data 0.004 (0.004) Batch 0.330 (0.330) Remain 06:18:41 loss: 0.6052 Lr: 0.00587 [2023-12-20 14:31:31,449 INFO misc.py line 119 131400] Train: [14/100][796/800] Data 0.003 (0.004) Batch 0.313 (0.330) Remain 06:18:39 loss: 0.5761 Lr: 0.00587 [2023-12-20 14:31:31,764 INFO misc.py line 119 131400] Train: [14/100][797/800] Data 0.003 (0.004) Batch 0.314 (0.330) Remain 06:18:37 loss: 0.1855 Lr: 0.00587 [2023-12-20 14:31:32,049 INFO misc.py line 119 131400] Train: [14/100][798/800] Data 0.003 (0.004) Batch 0.286 (0.330) Remain 06:18:33 loss: 0.5326 Lr: 0.00587 [2023-12-20 14:31:32,379 INFO misc.py line 119 131400] Train: [14/100][799/800] Data 0.003 (0.004) Batch 0.329 (0.330) Remain 06:18:33 loss: 0.9631 Lr: 0.00587 [2023-12-20 14:31:32,711 INFO misc.py line 119 131400] Train: [14/100][800/800] Data 0.003 (0.004) Batch 0.332 (0.330) Remain 06:18:33 loss: 0.5763 Lr: 0.00587 [2023-12-20 14:31:32,712 INFO misc.py line 136 131400] Train result: loss: 0.6359 [2023-12-20 14:31:32,713 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:31:56,996 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1518 [2023-12-20 14:31:57,068 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2374 [2023-12-20 14:31:57,158 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4848 [2023-12-20 14:31:57,272 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4180 [2023-12-20 14:31:57,389 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6623 [2023-12-20 14:31:57,494 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5153 [2023-12-20 14:31:57,586 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7358 [2023-12-20 14:31:57,694 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1827 [2023-12-20 14:31:57,781 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2942 [2023-12-20 14:31:57,865 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5291 [2023-12-20 14:31:57,965 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5575 [2023-12-20 14:31:58,108 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6249 [2023-12-20 14:31:58,233 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4720 [2023-12-20 14:31:58,399 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.5569 [2023-12-20 14:31:58,506 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5461 [2023-12-20 14:31:58,647 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.1611 [2023-12-20 14:31:58,766 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4642 [2023-12-20 14:31:58,882 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4287 [2023-12-20 14:31:58,996 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4752 [2023-12-20 14:31:59,075 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6595 [2023-12-20 14:31:59,187 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3879 [2023-12-20 14:31:59,350 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2238 [2023-12-20 14:31:59,480 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6286 [2023-12-20 14:31:59,624 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4443 [2023-12-20 14:31:59,776 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2469 [2023-12-20 14:31:59,864 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4817 [2023-12-20 14:32:00,027 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6672 [2023-12-20 14:32:00,153 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6463 [2023-12-20 14:32:00,276 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8671 [2023-12-20 14:32:00,422 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3620 [2023-12-20 14:32:00,528 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8419 [2023-12-20 14:32:00,657 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.7330 [2023-12-20 14:32:00,747 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5148 [2023-12-20 14:32:00,843 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2194 [2023-12-20 14:32:00,944 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9499 [2023-12-20 14:32:01,047 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4546 [2023-12-20 14:32:01,185 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0746 [2023-12-20 14:32:01,302 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3520 [2023-12-20 14:32:01,382 INFO evaluator.py line 159 131400] Test: [39/78] Loss 1.1046 [2023-12-20 14:32:01,526 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.7035 [2023-12-20 14:32:01,682 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0351 [2023-12-20 14:32:01,784 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.5472 [2023-12-20 14:32:01,909 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3369 [2023-12-20 14:32:02,054 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7227 [2023-12-20 14:32:02,175 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9048 [2023-12-20 14:32:02,282 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5038 [2023-12-20 14:32:02,451 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4201 [2023-12-20 14:32:02,551 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4039 [2023-12-20 14:32:02,703 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0750 [2023-12-20 14:32:02,800 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7762 [2023-12-20 14:32:02,886 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8512 [2023-12-20 14:32:02,994 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2354 [2023-12-20 14:32:03,144 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2115 [2023-12-20 14:32:03,283 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2971 [2023-12-20 14:32:03,387 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3381 [2023-12-20 14:32:03,481 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8717 [2023-12-20 14:32:03,589 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.6839 [2023-12-20 14:32:03,755 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2893 [2023-12-20 14:32:03,858 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4023 [2023-12-20 14:32:03,960 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2766 [2023-12-20 14:32:04,071 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4912 [2023-12-20 14:32:04,165 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4932 [2023-12-20 14:32:04,256 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8647 [2023-12-20 14:32:04,357 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9658 [2023-12-20 14:32:04,490 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5537 [2023-12-20 14:32:04,595 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4890 [2023-12-20 14:32:04,705 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5864 [2023-12-20 14:32:04,806 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0208 [2023-12-20 14:32:04,894 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3571 [2023-12-20 14:32:04,976 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0327 [2023-12-20 14:32:05,077 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9101 [2023-12-20 14:32:05,172 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8537 [2023-12-20 14:32:05,305 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3329 [2023-12-20 14:32:05,401 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7903 [2023-12-20 14:32:05,519 INFO evaluator.py line 159 131400] Test: [75/78] Loss 1.0465 [2023-12-20 14:32:05,625 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8552 [2023-12-20 14:32:05,712 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3453 [2023-12-20 14:32:05,865 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2443 [2023-12-20 14:32:07,092 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7012/0.8186/0.8833. [2023-12-20 14:32:07,092 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8045/0.8750 [2023-12-20 14:32:07,092 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9631/0.9835 [2023-12-20 14:32:07,092 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6193/0.7667 [2023-12-20 14:32:07,092 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7777/0.8104 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8945/0.9383 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8273/0.8975 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7302/0.8157 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5886/0.7942 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5116/0.8430 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8049/0.9204 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3551/0.5230 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6569/0.8200 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6560/0.8635 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7270/0.8214 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4900/0.6195 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6619/0.8522 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9148/0.9803 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6519/0.7050 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8512/0.9127 [2023-12-20 14:32:07,093 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5373/0.6294 [2023-12-20 14:32:07,094 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:32:07,094 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7012 [2023-12-20 14:32:07,094 INFO misc.py line 165 131400] Currently Best mIoU: 0.7012 [2023-12-20 14:32:07,094 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:32:13,415 INFO misc.py line 119 131400] Train: [15/100][1/800] Data 0.612 (0.612) Batch 0.885 (0.885) Remain 16:54:51 loss: 0.5450 Lr: 0.00587 [2023-12-20 14:32:13,699 INFO misc.py line 119 131400] Train: [15/100][2/800] Data 0.003 (0.003) Batch 0.283 (0.283) Remain 05:24:47 loss: 0.5125 Lr: 0.00587 [2023-12-20 14:32:14,028 INFO misc.py line 119 131400] Train: [15/100][3/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 06:17:17 loss: 0.5211 Lr: 0.00587 [2023-12-20 14:32:14,346 INFO misc.py line 119 131400] Train: [15/100][4/800] Data 0.003 (0.003) Batch 0.315 (0.315) Remain 06:00:54 loss: 0.6777 Lr: 0.00587 [2023-12-20 14:32:14,694 INFO misc.py line 119 131400] Train: [15/100][5/800] Data 0.008 (0.005) Batch 0.351 (0.333) Remain 06:21:26 loss: 0.8464 Lr: 0.00587 [2023-12-20 14:32:15,019 INFO misc.py line 119 131400] Train: [15/100][6/800] Data 0.005 (0.005) Batch 0.326 (0.330) Remain 06:18:52 loss: 0.4695 Lr: 0.00587 [2023-12-20 14:32:15,294 INFO misc.py line 119 131400] Train: [15/100][7/800] Data 0.003 (0.005) Batch 0.274 (0.316) Remain 06:02:41 loss: 0.5068 Lr: 0.00587 [2023-12-20 14:32:15,648 INFO misc.py line 119 131400] Train: [15/100][8/800] Data 0.004 (0.005) Batch 0.355 (0.324) Remain 06:11:27 loss: 0.4305 Lr: 0.00587 [2023-12-20 14:32:15,966 INFO misc.py line 119 131400] Train: 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06:11:57 loss: 0.9183 Lr: 0.00587 [2023-12-20 14:32:26,385 INFO misc.py line 119 131400] Train: [15/100][41/800] Data 0.003 (0.004) Batch 0.348 (0.325) Remain 06:12:39 loss: 0.3788 Lr: 0.00587 [2023-12-20 14:32:26,715 INFO misc.py line 119 131400] Train: [15/100][42/800] Data 0.004 (0.004) Batch 0.329 (0.325) Remain 06:12:46 loss: 1.1196 Lr: 0.00587 [2023-12-20 14:32:27,030 INFO misc.py line 119 131400] Train: [15/100][43/800] Data 0.004 (0.004) Batch 0.316 (0.325) Remain 06:12:30 loss: 0.5055 Lr: 0.00587 [2023-12-20 14:32:27,356 INFO misc.py line 119 131400] Train: [15/100][44/800] Data 0.003 (0.004) Batch 0.325 (0.325) Remain 06:12:31 loss: 0.9522 Lr: 0.00587 [2023-12-20 14:32:27,668 INFO misc.py line 119 131400] Train: [15/100][45/800] Data 0.003 (0.004) Batch 0.312 (0.325) Remain 06:12:10 loss: 0.3776 Lr: 0.00587 [2023-12-20 14:32:27,994 INFO misc.py line 119 131400] Train: [15/100][46/800] Data 0.003 (0.004) Batch 0.325 (0.325) Remain 06:12:09 loss: 0.9115 Lr: 0.00587 [2023-12-20 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Train: [15/100][53/800] Data 0.003 (0.004) Batch 0.335 (0.325) Remain 06:12:09 loss: 0.9133 Lr: 0.00587 [2023-12-20 14:32:30,569 INFO misc.py line 119 131400] Train: [15/100][54/800] Data 0.004 (0.004) Batch 0.301 (0.324) Remain 06:11:36 loss: 0.7421 Lr: 0.00587 [2023-12-20 14:32:30,904 INFO misc.py line 119 131400] Train: [15/100][55/800] Data 0.004 (0.004) Batch 0.335 (0.325) Remain 06:11:50 loss: 0.6608 Lr: 0.00587 [2023-12-20 14:32:31,226 INFO misc.py line 119 131400] Train: [15/100][56/800] Data 0.003 (0.004) Batch 0.322 (0.324) Remain 06:11:46 loss: 0.4792 Lr: 0.00587 [2023-12-20 14:32:31,533 INFO misc.py line 119 131400] Train: [15/100][57/800] Data 0.004 (0.004) Batch 0.308 (0.324) Remain 06:11:25 loss: 0.6460 Lr: 0.00587 [2023-12-20 14:32:31,843 INFO misc.py line 119 131400] Train: [15/100][58/800] Data 0.003 (0.004) Batch 0.310 (0.324) Remain 06:11:06 loss: 0.7129 Lr: 0.00587 [2023-12-20 14:32:32,140 INFO misc.py line 119 131400] Train: [15/100][59/800] Data 0.003 (0.004) Batch 0.291 (0.323) Remain 06:10:26 loss: 0.7415 Lr: 0.00587 [2023-12-20 14:32:32,458 INFO misc.py line 119 131400] Train: [15/100][60/800] Data 0.009 (0.004) Batch 0.323 (0.323) Remain 06:10:26 loss: 0.8722 Lr: 0.00587 [2023-12-20 14:32:32,750 INFO misc.py line 119 131400] Train: [15/100][61/800] Data 0.003 (0.004) Batch 0.292 (0.323) Remain 06:09:48 loss: 0.5618 Lr: 0.00587 [2023-12-20 14:32:33,040 INFO misc.py line 119 131400] Train: [15/100][62/800] Data 0.003 (0.004) Batch 0.290 (0.322) Remain 06:09:10 loss: 0.4620 Lr: 0.00587 [2023-12-20 14:32:33,378 INFO misc.py line 119 131400] Train: [15/100][63/800] Data 0.003 (0.004) Batch 0.338 (0.323) Remain 06:09:27 loss: 0.8166 Lr: 0.00587 [2023-12-20 14:32:33,672 INFO misc.py line 119 131400] Train: [15/100][64/800] Data 0.003 (0.004) Batch 0.295 (0.322) Remain 06:08:55 loss: 0.4393 Lr: 0.00587 [2023-12-20 14:32:33,972 INFO misc.py line 119 131400] Train: [15/100][65/800] Data 0.003 (0.004) Batch 0.299 (0.322) Remain 06:08:30 loss: 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Batch 0.301 (0.323) Remain 06:05:49 loss: 0.6031 Lr: 0.00584 [2023-12-20 14:36:15,281 INFO misc.py line 119 131400] Train: [15/100][751/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 06:05:47 loss: 0.5549 Lr: 0.00584 [2023-12-20 14:36:15,598 INFO misc.py line 119 131400] Train: [15/100][752/800] Data 0.003 (0.004) Batch 0.316 (0.323) Remain 06:05:46 loss: 0.3011 Lr: 0.00584 [2023-12-20 14:36:15,918 INFO misc.py line 119 131400] Train: [15/100][753/800] Data 0.004 (0.004) Batch 0.321 (0.323) Remain 06:05:46 loss: 1.1163 Lr: 0.00584 [2023-12-20 14:36:16,208 INFO misc.py line 119 131400] Train: [15/100][754/800] Data 0.004 (0.004) Batch 0.287 (0.322) Remain 06:05:42 loss: 0.6051 Lr: 0.00584 [2023-12-20 14:36:16,508 INFO misc.py line 119 131400] Train: [15/100][755/800] Data 0.007 (0.004) Batch 0.303 (0.322) Remain 06:05:40 loss: 0.2889 Lr: 0.00584 [2023-12-20 14:36:16,848 INFO misc.py line 119 131400] Train: [15/100][756/800] Data 0.004 (0.004) Batch 0.340 (0.322) Remain 06:05:41 loss: 0.5255 Lr: 0.00584 [2023-12-20 14:36:17,156 INFO misc.py line 119 131400] Train: [15/100][757/800] Data 0.005 (0.004) Batch 0.309 (0.322) Remain 06:05:40 loss: 0.4786 Lr: 0.00584 [2023-12-20 14:36:17,479 INFO misc.py line 119 131400] Train: [15/100][758/800] Data 0.004 (0.004) Batch 0.324 (0.322) Remain 06:05:40 loss: 0.5047 Lr: 0.00584 [2023-12-20 14:36:17,826 INFO misc.py line 119 131400] Train: [15/100][759/800] Data 0.003 (0.004) Batch 0.346 (0.322) Remain 06:05:41 loss: 0.6340 Lr: 0.00584 [2023-12-20 14:36:18,249 INFO misc.py line 119 131400] Train: [15/100][760/800] Data 0.006 (0.004) Batch 0.417 (0.323) Remain 06:05:50 loss: 0.6767 Lr: 0.00584 [2023-12-20 14:36:19,116 INFO misc.py line 119 131400] Train: [15/100][761/800] Data 0.010 (0.004) Batch 0.873 (0.323) Remain 06:06:39 loss: 0.4095 Lr: 0.00584 [2023-12-20 14:36:19,548 INFO misc.py line 119 131400] Train: [15/100][762/800] Data 0.004 (0.004) Batch 0.433 (0.323) Remain 06:06:48 loss: 0.3334 Lr: 0.00584 [2023-12-20 14:36:19,858 INFO misc.py line 119 131400] Train: [15/100][763/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 06:06:47 loss: 0.6097 Lr: 0.00584 [2023-12-20 14:36:20,170 INFO misc.py line 119 131400] Train: [15/100][764/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 06:06:45 loss: 0.3095 Lr: 0.00584 [2023-12-20 14:36:20,492 INFO misc.py line 119 131400] Train: [15/100][765/800] Data 0.003 (0.004) Batch 0.322 (0.323) Remain 06:06:45 loss: 0.4488 Lr: 0.00584 [2023-12-20 14:36:20,813 INFO misc.py line 119 131400] Train: [15/100][766/800] Data 0.003 (0.004) Batch 0.320 (0.323) Remain 06:06:44 loss: 0.6995 Lr: 0.00584 [2023-12-20 14:36:21,137 INFO misc.py line 119 131400] Train: [15/100][767/800] Data 0.005 (0.004) Batch 0.325 (0.323) Remain 06:06:44 loss: 0.6499 Lr: 0.00584 [2023-12-20 14:36:21,484 INFO misc.py line 119 131400] Train: [15/100][768/800] Data 0.003 (0.004) Batch 0.346 (0.323) Remain 06:06:46 loss: 0.7759 Lr: 0.00584 [2023-12-20 14:36:21,815 INFO misc.py line 119 131400] Train: [15/100][769/800] Data 0.004 (0.004) Batch 0.331 (0.323) Remain 06:06:46 loss: 0.9021 Lr: 0.00584 [2023-12-20 14:36:22,155 INFO misc.py line 119 131400] Train: [15/100][770/800] Data 0.004 (0.004) Batch 0.340 (0.324) Remain 06:06:47 loss: 0.4477 Lr: 0.00584 [2023-12-20 14:36:22,491 INFO misc.py line 119 131400] Train: [15/100][771/800] Data 0.004 (0.004) Batch 0.337 (0.324) Remain 06:06:48 loss: 0.5177 Lr: 0.00584 [2023-12-20 14:36:22,791 INFO misc.py line 119 131400] Train: [15/100][772/800] Data 0.003 (0.004) Batch 0.299 (0.323) Remain 06:06:46 loss: 0.7011 Lr: 0.00584 [2023-12-20 14:36:23,111 INFO misc.py line 119 131400] Train: [15/100][773/800] Data 0.004 (0.004) Batch 0.320 (0.323) Remain 06:06:45 loss: 0.4720 Lr: 0.00584 [2023-12-20 14:36:23,418 INFO misc.py line 119 131400] Train: [15/100][774/800] Data 0.004 (0.004) Batch 0.306 (0.323) Remain 06:06:43 loss: 0.4056 Lr: 0.00584 [2023-12-20 14:36:23,741 INFO misc.py line 119 131400] Train: [15/100][775/800] Data 0.003 (0.004) Batch 0.324 (0.323) Remain 06:06:43 loss: 1.0738 Lr: 0.00584 [2023-12-20 14:36:24,056 INFO misc.py line 119 131400] Train: [15/100][776/800] Data 0.003 (0.004) Batch 0.315 (0.323) Remain 06:06:42 loss: 0.4949 Lr: 0.00584 [2023-12-20 14:36:24,360 INFO misc.py line 119 131400] Train: [15/100][777/800] Data 0.003 (0.004) Batch 0.305 (0.323) Remain 06:06:40 loss: 0.4955 Lr: 0.00584 [2023-12-20 14:36:24,690 INFO misc.py line 119 131400] Train: [15/100][778/800] Data 0.003 (0.004) Batch 0.330 (0.323) Remain 06:06:40 loss: 0.9312 Lr: 0.00584 [2023-12-20 14:36:24,976 INFO misc.py line 119 131400] Train: [15/100][779/800] Data 0.003 (0.004) Batch 0.286 (0.323) Remain 06:06:36 loss: 0.7834 Lr: 0.00584 [2023-12-20 14:36:25,298 INFO misc.py line 119 131400] Train: [15/100][780/800] Data 0.004 (0.004) Batch 0.322 (0.323) Remain 06:06:36 loss: 0.3477 Lr: 0.00584 [2023-12-20 14:36:25,606 INFO misc.py line 119 131400] Train: [15/100][781/800] Data 0.003 (0.004) Batch 0.307 (0.323) Remain 06:06:34 loss: 0.6354 Lr: 0.00584 [2023-12-20 14:36:25,893 INFO misc.py line 119 131400] Train: [15/100][782/800] Data 0.004 (0.004) Batch 0.287 (0.323) Remain 06:06:31 loss: 0.4612 Lr: 0.00584 [2023-12-20 14:36:26,192 INFO misc.py line 119 131400] Train: [15/100][783/800] Data 0.004 (0.004) Batch 0.299 (0.323) Remain 06:06:28 loss: 0.4505 Lr: 0.00584 [2023-12-20 14:36:26,504 INFO misc.py line 119 131400] Train: [15/100][784/800] Data 0.013 (0.004) Batch 0.312 (0.323) Remain 06:06:27 loss: 0.3019 Lr: 0.00584 [2023-12-20 14:36:26,827 INFO misc.py line 119 131400] Train: [15/100][785/800] Data 0.002 (0.004) Batch 0.323 (0.323) Remain 06:06:27 loss: 0.6233 Lr: 0.00584 [2023-12-20 14:36:27,126 INFO misc.py line 119 131400] Train: [15/100][786/800] Data 0.002 (0.004) Batch 0.299 (0.323) Remain 06:06:24 loss: 0.6047 Lr: 0.00584 [2023-12-20 14:36:27,434 INFO misc.py line 119 131400] Train: [15/100][787/800] Data 0.003 (0.004) Batch 0.309 (0.323) Remain 06:06:23 loss: 0.4898 Lr: 0.00584 [2023-12-20 14:36:27,762 INFO misc.py line 119 131400] Train: [15/100][788/800] Data 0.003 (0.004) Batch 0.327 (0.323) Remain 06:06:23 loss: 0.7873 Lr: 0.00584 [2023-12-20 14:36:28,071 INFO misc.py line 119 131400] Train: [15/100][789/800] Data 0.004 (0.004) Batch 0.310 (0.323) Remain 06:06:21 loss: 0.3150 Lr: 0.00584 [2023-12-20 14:36:28,394 INFO misc.py line 119 131400] Train: [15/100][790/800] Data 0.004 (0.004) Batch 0.323 (0.323) Remain 06:06:21 loss: 0.6980 Lr: 0.00584 [2023-12-20 14:36:28,694 INFO misc.py line 119 131400] Train: [15/100][791/800] Data 0.003 (0.004) Batch 0.300 (0.323) Remain 06:06:19 loss: 0.3722 Lr: 0.00584 [2023-12-20 14:36:29,010 INFO misc.py line 119 131400] Train: [15/100][792/800] Data 0.003 (0.004) Batch 0.316 (0.323) Remain 06:06:18 loss: 0.4962 Lr: 0.00584 [2023-12-20 14:36:29,345 INFO misc.py line 119 131400] Train: [15/100][793/800] Data 0.003 (0.004) Batch 0.334 (0.323) Remain 06:06:18 loss: 0.4812 Lr: 0.00584 [2023-12-20 14:36:29,653 INFO misc.py line 119 131400] Train: [15/100][794/800] Data 0.003 (0.004) Batch 0.308 (0.323) Remain 06:06:17 loss: 0.6013 Lr: 0.00584 [2023-12-20 14:36:29,953 INFO misc.py line 119 131400] Train: [15/100][795/800] Data 0.003 (0.004) Batch 0.300 (0.323) Remain 06:06:14 loss: 0.5642 Lr: 0.00584 [2023-12-20 14:36:30,226 INFO misc.py line 119 131400] Train: [15/100][796/800] Data 0.003 (0.004) Batch 0.273 (0.323) Remain 06:06:10 loss: 0.5020 Lr: 0.00584 [2023-12-20 14:36:30,518 INFO misc.py line 119 131400] Train: [15/100][797/800] Data 0.002 (0.004) Batch 0.290 (0.323) Remain 06:06:07 loss: 0.6581 Lr: 0.00584 [2023-12-20 14:36:30,808 INFO misc.py line 119 131400] Train: [15/100][798/800] Data 0.005 (0.004) Batch 0.293 (0.323) Remain 06:06:04 loss: 0.4983 Lr: 0.00584 [2023-12-20 14:36:31,113 INFO misc.py line 119 131400] Train: [15/100][799/800] Data 0.003 (0.004) Batch 0.305 (0.323) Remain 06:06:02 loss: 0.7362 Lr: 0.00584 [2023-12-20 14:36:31,381 INFO misc.py line 119 131400] Train: [15/100][800/800] Data 0.003 (0.004) Batch 0.267 (0.323) Remain 06:05:57 loss: 0.7014 Lr: 0.00584 [2023-12-20 14:36:31,382 INFO misc.py line 136 131400] Train result: loss: 0.6052 [2023-12-20 14:36:31,382 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:36:52,470 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1105 [2023-12-20 14:36:52,540 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2409 [2023-12-20 14:36:52,634 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4820 [2023-12-20 14:36:52,738 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.9126 [2023-12-20 14:36:52,850 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5188 [2023-12-20 14:36:52,948 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0054 [2023-12-20 14:36:53,037 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2442 [2023-12-20 14:36:53,145 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0185 [2023-12-20 14:36:53,224 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2811 [2023-12-20 14:36:53,307 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4247 [2023-12-20 14:36:53,400 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5853 [2023-12-20 14:36:53,535 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5743 [2023-12-20 14:36:53,653 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3116 [2023-12-20 14:36:53,809 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3743 [2023-12-20 14:36:53,902 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.7124 [2023-12-20 14:36:54,034 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8608 [2023-12-20 14:36:54,145 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3578 [2023-12-20 14:36:54,253 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3887 [2023-12-20 14:36:54,364 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2536 [2023-12-20 14:36:54,437 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3954 [2023-12-20 14:36:54,541 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3733 [2023-12-20 14:36:54,697 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2796 [2023-12-20 14:36:54,817 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7553 [2023-12-20 14:36:54,957 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2315 [2023-12-20 14:36:55,100 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3439 [2023-12-20 14:36:55,180 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4238 [2023-12-20 14:36:55,338 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5040 [2023-12-20 14:36:55,461 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6055 [2023-12-20 14:36:55,556 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6616 [2023-12-20 14:36:55,700 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3465 [2023-12-20 14:36:55,801 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7128 [2023-12-20 14:36:55,920 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5253 [2023-12-20 14:36:56,003 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2724 [2023-12-20 14:36:56,071 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2032 [2023-12-20 14:36:56,165 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.1010 [2023-12-20 14:36:56,256 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.7207 [2023-12-20 14:36:56,384 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8826 [2023-12-20 14:36:56,497 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1490 [2023-12-20 14:36:56,577 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7107 [2023-12-20 14:36:56,719 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5157 [2023-12-20 14:36:56,865 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0328 [2023-12-20 14:36:56,961 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1984 [2023-12-20 14:36:57,079 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3780 [2023-12-20 14:36:57,219 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9226 [2023-12-20 14:36:57,336 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1893 [2023-12-20 14:36:57,438 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4064 [2023-12-20 14:36:57,603 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5102 [2023-12-20 14:36:57,695 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.6908 [2023-12-20 14:36:57,838 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4526 [2023-12-20 14:36:57,928 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8616 [2023-12-20 14:36:58,003 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8118 [2023-12-20 14:36:58,107 INFO evaluator.py line 159 131400] Test: [52/78] Loss 0.9916 [2023-12-20 14:36:58,253 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.1669 [2023-12-20 14:36:58,386 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3682 [2023-12-20 14:36:58,488 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3507 [2023-12-20 14:36:58,572 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6152 [2023-12-20 14:36:58,673 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5638 [2023-12-20 14:36:58,832 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2975 [2023-12-20 14:36:58,926 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5076 [2023-12-20 14:36:59,017 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3903 [2023-12-20 14:36:59,110 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2932 [2023-12-20 14:36:59,200 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3983 [2023-12-20 14:36:59,285 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5322 [2023-12-20 14:36:59,384 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6230 [2023-12-20 14:36:59,508 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3599 [2023-12-20 14:36:59,591 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.6523 [2023-12-20 14:36:59,689 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8340 [2023-12-20 14:36:59,781 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0316 [2023-12-20 14:36:59,866 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.8849 [2023-12-20 14:36:59,948 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.3499 [2023-12-20 14:37:00,041 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5663 [2023-12-20 14:37:00,129 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5652 [2023-12-20 14:37:00,261 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.6531 [2023-12-20 14:37:00,353 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5340 [2023-12-20 14:37:00,467 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8647 [2023-12-20 14:37:00,568 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8776 [2023-12-20 14:37:00,652 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6297 [2023-12-20 14:37:00,806 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0989 [2023-12-20 14:37:01,995 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6959/0.7981/0.8888. [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8295/0.9419 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9625/0.9863 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6179/0.6761 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8391/0.8695 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8637/0.8990 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7424/0.9281 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6871/0.7989 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6094/0.7253 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5775/0.6823 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7955/0.9343 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3616/0.5091 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6549/0.7784 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6529/0.8316 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6611/0.8971 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5532/0.7138 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.5402/0.5842 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9361/0.9696 [2023-12-20 14:37:01,995 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6640/0.7571 [2023-12-20 14:37:01,996 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8696/0.9009 [2023-12-20 14:37:01,996 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.4988/0.5795 [2023-12-20 14:37:01,996 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:37:01,997 INFO misc.py line 165 131400] Currently Best mIoU: 0.7012 [2023-12-20 14:37:01,997 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:37:04,670 INFO misc.py line 119 131400] Train: [16/100][1/800] Data 0.481 (0.481) Batch 0.727 (0.727) Remain 13:43:50 loss: 0.4897 Lr: 0.00584 [2023-12-20 14:37:05,152 INFO misc.py line 119 131400] Train: [16/100][2/800] Data 0.193 (0.193) Batch 0.483 (0.483) Remain 09:07:12 loss: 0.5369 Lr: 0.00584 [2023-12-20 14:37:05,473 INFO misc.py line 119 131400] Train: [16/100][3/800] Data 0.003 (0.003) Batch 0.321 (0.321) Remain 06:03:48 loss: 0.5992 Lr: 0.00584 [2023-12-20 14:37:05,805 INFO misc.py line 119 131400] Train: [16/100][4/800] Data 0.003 (0.003) Batch 0.332 (0.332) Remain 06:16:16 loss: 0.5637 Lr: 0.00584 [2023-12-20 14:37:06,080 INFO misc.py line 119 131400] Train: [16/100][5/800] Data 0.003 (0.003) Batch 0.275 (0.304) Remain 05:44:05 loss: 0.5558 Lr: 0.00584 [2023-12-20 14:37:06,413 INFO misc.py line 119 131400] Train: [16/100][6/800] Data 0.003 (0.003) Batch 0.332 (0.313) Remain 05:54:48 loss: 0.8105 Lr: 0.00584 [2023-12-20 14:37:06,725 INFO misc.py line 119 131400] Train: [16/100][7/800] Data 0.004 (0.003) Batch 0.311 (0.312) Remain 05:54:07 loss: 0.5519 Lr: 0.00584 [2023-12-20 14:37:07,058 INFO misc.py line 119 131400] Train: [16/100][8/800] Data 0.005 (0.003) Batch 0.335 (0.317) Remain 05:59:12 loss: 0.5115 Lr: 0.00584 [2023-12-20 14:37:07,356 INFO misc.py line 119 131400] Train: [16/100][9/800] Data 0.004 (0.004) Batch 0.297 (0.314) Remain 05:55:30 loss: 0.6646 Lr: 0.00584 [2023-12-20 14:37:07,686 INFO misc.py line 119 131400] Train: [16/100][10/800] Data 0.004 (0.004) Batch 0.330 (0.316) Remain 05:58:09 loss: 0.5280 Lr: 0.00584 [2023-12-20 14:37:08,021 INFO misc.py line 119 131400] Train: [16/100][11/800] Data 0.003 (0.004) Batch 0.334 (0.318) Remain 06:00:43 loss: 0.7936 Lr: 0.00584 [2023-12-20 14:37:08,357 INFO misc.py line 119 131400] Train: [16/100][12/800] Data 0.004 (0.004) Batch 0.337 (0.320) Remain 06:03:00 loss: 0.4792 Lr: 0.00584 [2023-12-20 14:37:08,709 INFO misc.py line 119 131400] Train: [16/100][13/800] Data 0.004 (0.004) Batch 0.353 (0.324) Remain 06:06:41 loss: 0.6389 Lr: 0.00584 [2023-12-20 14:37:09,014 INFO misc.py line 119 131400] Train: [16/100][14/800] Data 0.004 (0.004) Batch 0.304 (0.322) Remain 06:04:39 loss: 0.5615 Lr: 0.00584 [2023-12-20 14:37:09,361 INFO misc.py line 119 131400] Train: [16/100][15/800] Data 0.003 (0.004) Batch 0.348 (0.324) Remain 06:07:08 loss: 0.3670 Lr: 0.00584 [2023-12-20 14:37:09,674 INFO misc.py line 119 131400] Train: [16/100][16/800] Data 0.003 (0.004) Batch 0.312 (0.323) Remain 06:06:06 loss: 0.5919 Lr: 0.00584 [2023-12-20 14:37:09,970 INFO misc.py line 119 131400] Train: [16/100][17/800] Data 0.003 (0.004) Batch 0.296 (0.321) Remain 06:03:54 loss: 0.5175 Lr: 0.00584 [2023-12-20 14:37:10,336 INFO misc.py line 119 131400] Train: [16/100][18/800] Data 0.003 (0.004) Batch 0.366 (0.324) Remain 06:07:16 loss: 1.0843 Lr: 0.00584 [2023-12-20 14:37:10,689 INFO misc.py line 119 131400] Train: [16/100][19/800] Data 0.003 (0.004) Batch 0.354 (0.326) Remain 06:09:22 loss: 0.4903 Lr: 0.00584 [2023-12-20 14:37:11,026 INFO misc.py line 119 131400] Train: [16/100][20/800] Data 0.003 (0.003) Batch 0.311 (0.325) Remain 06:08:22 loss: 0.5517 Lr: 0.00584 [2023-12-20 14:37:11,356 INFO misc.py line 119 131400] Train: [16/100][21/800] Data 0.029 (0.005) Batch 0.355 (0.327) Remain 06:10:16 loss: 0.5150 Lr: 0.00584 [2023-12-20 14:37:11,653 INFO misc.py line 119 131400] Train: [16/100][22/800] Data 0.003 (0.005) Batch 0.297 (0.325) Remain 06:08:28 loss: 0.3335 Lr: 0.00584 [2023-12-20 14:37:11,928 INFO misc.py line 119 131400] Train: [16/100][23/800] Data 0.003 (0.005) Batch 0.275 (0.323) Remain 06:05:38 loss: 0.6091 Lr: 0.00584 [2023-12-20 14:37:12,236 INFO misc.py line 119 131400] Train: [16/100][24/800] Data 0.003 (0.005) Batch 0.306 (0.322) Remain 06:04:45 loss: 0.6752 Lr: 0.00584 [2023-12-20 14:37:12,568 INFO misc.py line 119 131400] Train: [16/100][25/800] Data 0.004 (0.005) Batch 0.330 (0.322) Remain 06:05:09 loss: 0.3676 Lr: 0.00584 [2023-12-20 14:37:12,875 INFO misc.py line 119 131400] Train: [16/100][26/800] Data 0.007 (0.005) Batch 0.311 (0.322) Remain 06:04:34 loss: 0.6722 Lr: 0.00584 [2023-12-20 14:37:13,212 INFO misc.py line 119 131400] Train: [16/100][27/800] Data 0.002 (0.005) Batch 0.337 (0.322) Remain 06:05:16 loss: 0.4077 Lr: 0.00584 [2023-12-20 14:37:14,221 INFO misc.py line 119 131400] Train: [16/100][28/800] Data 0.005 (0.005) Batch 1.006 (0.350) Remain 06:36:14 loss: 0.6429 Lr: 0.00584 [2023-12-20 14:37:14,555 INFO misc.py line 119 131400] Train: [16/100][29/800] Data 0.006 (0.005) Batch 0.338 (0.349) Remain 06:35:43 loss: 0.6527 Lr: 0.00584 [2023-12-20 14:37:14,883 INFO misc.py line 119 131400] Train: [16/100][30/800] Data 0.003 (0.005) Batch 0.327 (0.348) Remain 06:34:47 loss: 0.5123 Lr: 0.00584 [2023-12-20 14:37:15,247 INFO misc.py line 119 131400] Train: [16/100][31/800] Data 0.004 (0.005) Batch 0.364 (0.349) Remain 06:35:24 loss: 0.6136 Lr: 0.00584 [2023-12-20 14:37:15,587 INFO misc.py line 119 131400] Train: [16/100][32/800] Data 0.004 (0.005) Batch 0.340 (0.349) Remain 06:35:01 loss: 0.5013 Lr: 0.00584 [2023-12-20 14:37:15,921 INFO misc.py line 119 131400] Train: [16/100][33/800] Data 0.003 (0.005) Batch 0.336 (0.348) Remain 06:34:31 loss: 0.3180 Lr: 0.00584 [2023-12-20 14:37:16,223 INFO misc.py line 119 131400] Train: [16/100][34/800] Data 0.002 (0.005) Batch 0.301 (0.347) Remain 06:32:48 loss: 0.4698 Lr: 0.00584 [2023-12-20 14:37:16,533 INFO misc.py line 119 131400] Train: [16/100][35/800] Data 0.003 (0.004) Batch 0.310 (0.346) Remain 06:31:30 loss: 0.7895 Lr: 0.00584 [2023-12-20 14:37:16,869 INFO misc.py line 119 131400] Train: [16/100][36/800] Data 0.003 (0.004) Batch 0.335 (0.345) Remain 06:31:09 loss: 0.5141 Lr: 0.00584 [2023-12-20 14:37:17,174 INFO misc.py line 119 131400] Train: [16/100][37/800] Data 0.003 (0.004) Batch 0.304 (0.344) Remain 06:29:47 loss: 0.3069 Lr: 0.00584 [2023-12-20 14:37:17,528 INFO misc.py line 119 131400] Train: [16/100][38/800] Data 0.004 (0.004) Batch 0.342 (0.344) Remain 06:29:42 loss: 0.2670 Lr: 0.00584 [2023-12-20 14:37:17,863 INFO misc.py line 119 131400] Train: [16/100][39/800] Data 0.016 (0.005) Batch 0.347 (0.344) Remain 06:29:48 loss: 0.4814 Lr: 0.00584 [2023-12-20 14:37:18,241 INFO misc.py line 119 131400] Train: [16/100][40/800] Data 0.005 (0.005) Batch 0.379 (0.345) Remain 06:30:51 loss: 0.8271 Lr: 0.00584 [2023-12-20 14:37:18,512 INFO misc.py line 119 131400] Train: [16/100][41/800] Data 0.003 (0.005) Batch 0.271 (0.343) Remain 06:28:38 loss: 0.4866 Lr: 0.00584 [2023-12-20 14:37:18,878 INFO misc.py line 119 131400] Train: [16/100][42/800] Data 0.003 (0.005) Batch 0.367 (0.344) Remain 06:29:18 loss: 0.4441 Lr: 0.00584 [2023-12-20 14:37:19,183 INFO misc.py line 119 131400] Train: [16/100][43/800] Data 0.003 (0.005) Batch 0.305 (0.343) Remain 06:28:12 loss: 0.2568 Lr: 0.00584 [2023-12-20 14:37:19,481 INFO misc.py line 119 131400] Train: [16/100][44/800] Data 0.003 (0.005) Batch 0.298 (0.342) Remain 06:26:58 loss: 0.4148 Lr: 0.00584 [2023-12-20 14:37:19,785 INFO misc.py line 119 131400] Train: [16/100][45/800] Data 0.002 (0.004) Batch 0.304 (0.341) Remain 06:25:56 loss: 0.9566 Lr: 0.00584 [2023-12-20 14:37:20,113 INFO misc.py line 119 131400] Train: [16/100][46/800] Data 0.003 (0.004) Batch 0.329 (0.340) Remain 06:25:36 loss: 0.4451 Lr: 0.00584 [2023-12-20 14:37:20,423 INFO misc.py line 119 131400] Train: [16/100][47/800] Data 0.003 (0.004) Batch 0.309 (0.340) Remain 06:24:48 loss: 0.7294 Lr: 0.00584 [2023-12-20 14:37:20,761 INFO misc.py line 119 131400] Train: [16/100][48/800] Data 0.003 (0.004) Batch 0.338 (0.340) Remain 06:24:45 loss: 0.4143 Lr: 0.00584 [2023-12-20 14:37:21,077 INFO misc.py line 119 131400] Train: [16/100][49/800] Data 0.004 (0.004) Batch 0.316 (0.339) Remain 06:24:10 loss: 0.9174 Lr: 0.00584 [2023-12-20 14:37:21,336 INFO misc.py line 119 131400] Train: [16/100][50/800] Data 0.003 (0.004) Batch 0.258 (0.337) Remain 06:22:12 loss: 0.5650 Lr: 0.00584 [2023-12-20 14:37:21,679 INFO misc.py line 119 131400] Train: [16/100][51/800] Data 0.003 (0.004) Batch 0.342 (0.338) Remain 06:22:19 loss: 0.7864 Lr: 0.00584 [2023-12-20 14:37:22,003 INFO misc.py line 119 131400] Train: [16/100][52/800] Data 0.005 (0.004) Batch 0.325 (0.337) Remain 06:22:01 loss: 0.7189 Lr: 0.00584 [2023-12-20 14:37:22,314 INFO misc.py line 119 131400] Train: [16/100][53/800] Data 0.003 (0.004) Batch 0.310 (0.337) Remain 06:21:23 loss: 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INFO misc.py line 119 131400] Train: [16/100][60/800] Data 0.004 (0.004) Batch 0.346 (0.335) Remain 06:19:29 loss: 0.4571 Lr: 0.00584 [2023-12-20 14:37:24,901 INFO misc.py line 119 131400] Train: [16/100][61/800] Data 0.004 (0.004) Batch 0.324 (0.335) Remain 06:19:15 loss: 0.3679 Lr: 0.00584 [2023-12-20 14:37:25,273 INFO misc.py line 119 131400] Train: [16/100][62/800] Data 0.005 (0.004) Batch 0.373 (0.336) Remain 06:19:58 loss: 0.4816 Lr: 0.00584 [2023-12-20 14:37:25,587 INFO misc.py line 119 131400] Train: [16/100][63/800] Data 0.004 (0.004) Batch 0.315 (0.335) Remain 06:19:34 loss: 0.4815 Lr: 0.00584 [2023-12-20 14:37:25,899 INFO misc.py line 119 131400] Train: [16/100][64/800] Data 0.003 (0.004) Batch 0.312 (0.335) Remain 06:19:08 loss: 0.7673 Lr: 0.00584 [2023-12-20 14:37:26,265 INFO misc.py line 119 131400] Train: [16/100][65/800] Data 0.003 (0.004) Batch 0.366 (0.335) Remain 06:19:42 loss: 0.4949 Lr: 0.00584 [2023-12-20 14:37:26,595 INFO misc.py line 119 131400] Train: 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0.433 (0.336) Remain 06:20:25 loss: 0.8598 Lr: 0.00583 [2023-12-20 14:37:28,957 INFO misc.py line 119 131400] Train: [16/100][73/800] Data 0.003 (0.004) Batch 0.298 (0.335) Remain 06:19:48 loss: 0.6280 Lr: 0.00583 [2023-12-20 14:37:29,271 INFO misc.py line 119 131400] Train: [16/100][74/800] Data 0.003 (0.004) Batch 0.314 (0.335) Remain 06:19:27 loss: 1.0311 Lr: 0.00583 [2023-12-20 14:37:29,559 INFO misc.py line 119 131400] Train: [16/100][75/800] Data 0.002 (0.004) Batch 0.287 (0.335) Remain 06:18:42 loss: 1.0703 Lr: 0.00583 [2023-12-20 14:37:29,918 INFO misc.py line 119 131400] Train: [16/100][76/800] Data 0.003 (0.004) Batch 0.358 (0.335) Remain 06:19:03 loss: 0.6979 Lr: 0.00583 [2023-12-20 14:37:30,237 INFO misc.py line 119 131400] Train: [16/100][77/800] Data 0.004 (0.004) Batch 0.321 (0.335) Remain 06:18:50 loss: 0.6446 Lr: 0.00583 [2023-12-20 14:37:30,555 INFO misc.py line 119 131400] Train: [16/100][78/800] Data 0.003 (0.004) Batch 0.317 (0.334) Remain 06:18:33 loss: 0.6068 Lr: 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Batch 0.328 (0.324) Remain 06:03:08 loss: 0.6993 Lr: 0.00581 [2023-12-20 14:41:03,860 INFO misc.py line 119 131400] Train: [16/100][739/800] Data 0.004 (0.004) Batch 0.295 (0.324) Remain 06:03:05 loss: 0.7276 Lr: 0.00581 [2023-12-20 14:41:04,192 INFO misc.py line 119 131400] Train: [16/100][740/800] Data 0.004 (0.004) Batch 0.332 (0.324) Remain 06:03:05 loss: 0.5592 Lr: 0.00581 [2023-12-20 14:41:04,542 INFO misc.py line 119 131400] Train: [16/100][741/800] Data 0.003 (0.004) Batch 0.350 (0.324) Remain 06:03:07 loss: 0.5962 Lr: 0.00581 [2023-12-20 14:41:04,850 INFO misc.py line 119 131400] Train: [16/100][742/800] Data 0.003 (0.004) Batch 0.302 (0.324) Remain 06:03:05 loss: 0.3629 Lr: 0.00581 [2023-12-20 14:41:05,174 INFO misc.py line 119 131400] Train: [16/100][743/800] Data 0.010 (0.004) Batch 0.330 (0.324) Remain 06:03:05 loss: 0.5574 Lr: 0.00581 [2023-12-20 14:41:05,476 INFO misc.py line 119 131400] Train: [16/100][744/800] Data 0.003 (0.004) Batch 0.301 (0.324) Remain 06:03:03 loss: 0.4682 Lr: 0.00581 [2023-12-20 14:41:05,792 INFO misc.py line 119 131400] Train: [16/100][745/800] Data 0.004 (0.004) Batch 0.316 (0.324) Remain 06:03:02 loss: 0.5618 Lr: 0.00581 [2023-12-20 14:41:06,090 INFO misc.py line 119 131400] Train: [16/100][746/800] Data 0.004 (0.004) Batch 0.298 (0.324) Remain 06:02:59 loss: 0.5593 Lr: 0.00581 [2023-12-20 14:41:06,403 INFO misc.py line 119 131400] Train: [16/100][747/800] Data 0.004 (0.004) Batch 0.313 (0.324) Remain 06:02:58 loss: 0.4249 Lr: 0.00581 [2023-12-20 14:41:06,705 INFO misc.py line 119 131400] Train: [16/100][748/800] Data 0.004 (0.004) Batch 0.303 (0.324) Remain 06:02:56 loss: 0.6259 Lr: 0.00581 [2023-12-20 14:41:07,053 INFO misc.py line 119 131400] Train: [16/100][749/800] Data 0.002 (0.004) Batch 0.336 (0.324) Remain 06:02:56 loss: 0.5693 Lr: 0.00581 [2023-12-20 14:41:07,359 INFO misc.py line 119 131400] Train: [16/100][750/800] Data 0.015 (0.004) Batch 0.318 (0.324) Remain 06:02:56 loss: 0.7028 Lr: 0.00581 [2023-12-20 14:41:07,692 INFO misc.py line 119 131400] Train: [16/100][751/800] Data 0.003 (0.004) Batch 0.333 (0.324) Remain 06:02:56 loss: 0.6107 Lr: 0.00581 [2023-12-20 14:41:08,008 INFO misc.py line 119 131400] Train: [16/100][752/800] Data 0.003 (0.004) Batch 0.316 (0.324) Remain 06:02:55 loss: 0.5377 Lr: 0.00581 [2023-12-20 14:41:08,345 INFO misc.py line 119 131400] Train: [16/100][753/800] Data 0.004 (0.004) Batch 0.336 (0.324) Remain 06:02:56 loss: 0.7529 Lr: 0.00581 [2023-12-20 14:41:08,690 INFO misc.py line 119 131400] Train: [16/100][754/800] Data 0.004 (0.004) Batch 0.344 (0.324) Remain 06:02:57 loss: 0.4705 Lr: 0.00581 [2023-12-20 14:41:09,020 INFO misc.py line 119 131400] Train: [16/100][755/800] Data 0.004 (0.004) Batch 0.330 (0.324) Remain 06:02:58 loss: 0.5969 Lr: 0.00581 [2023-12-20 14:41:09,329 INFO misc.py line 119 131400] Train: [16/100][756/800] Data 0.004 (0.004) Batch 0.309 (0.324) Remain 06:02:56 loss: 0.7629 Lr: 0.00581 [2023-12-20 14:41:09,655 INFO misc.py line 119 131400] Train: [16/100][757/800] Data 0.004 (0.004) Batch 0.326 (0.324) Remain 06:02:56 loss: 0.4391 Lr: 0.00581 [2023-12-20 14:41:09,979 INFO misc.py line 119 131400] Train: [16/100][758/800] Data 0.004 (0.004) Batch 0.323 (0.324) Remain 06:02:55 loss: 0.7672 Lr: 0.00581 [2023-12-20 14:41:10,316 INFO misc.py line 119 131400] Train: [16/100][759/800] Data 0.005 (0.004) Batch 0.338 (0.324) Remain 06:02:56 loss: 0.4795 Lr: 0.00581 [2023-12-20 14:41:10,658 INFO misc.py line 119 131400] Train: [16/100][760/800] Data 0.004 (0.004) Batch 0.342 (0.324) Remain 06:02:58 loss: 1.1336 Lr: 0.00581 [2023-12-20 14:41:10,987 INFO misc.py line 119 131400] Train: [16/100][761/800] Data 0.004 (0.004) Batch 0.330 (0.324) Remain 06:02:58 loss: 0.4813 Lr: 0.00581 [2023-12-20 14:41:11,292 INFO misc.py line 119 131400] Train: [16/100][762/800] Data 0.004 (0.004) Batch 0.306 (0.324) Remain 06:02:56 loss: 0.5349 Lr: 0.00581 [2023-12-20 14:41:11,601 INFO misc.py line 119 131400] Train: [16/100][763/800] Data 0.003 (0.004) Batch 0.308 (0.324) Remain 06:02:54 loss: 0.7333 Lr: 0.00581 [2023-12-20 14:41:11,907 INFO misc.py line 119 131400] Train: [16/100][764/800] Data 0.004 (0.004) Batch 0.306 (0.324) Remain 06:02:52 loss: 0.5794 Lr: 0.00581 [2023-12-20 14:41:12,231 INFO misc.py line 119 131400] Train: [16/100][765/800] Data 0.003 (0.004) Batch 0.325 (0.324) Remain 06:02:52 loss: 0.5763 Lr: 0.00581 [2023-12-20 14:41:12,545 INFO misc.py line 119 131400] Train: [16/100][766/800] Data 0.003 (0.004) Batch 0.311 (0.324) Remain 06:02:51 loss: 0.5801 Lr: 0.00581 [2023-12-20 14:41:12,831 INFO misc.py line 119 131400] Train: [16/100][767/800] Data 0.005 (0.004) Batch 0.289 (0.324) Remain 06:02:47 loss: 0.6722 Lr: 0.00581 [2023-12-20 14:41:13,137 INFO misc.py line 119 131400] Train: [16/100][768/800] Data 0.003 (0.004) Batch 0.305 (0.324) Remain 06:02:45 loss: 0.4094 Lr: 0.00581 [2023-12-20 14:41:13,467 INFO misc.py line 119 131400] Train: [16/100][769/800] Data 0.005 (0.004) Batch 0.330 (0.324) Remain 06:02:45 loss: 0.6222 Lr: 0.00581 [2023-12-20 14:41:13,831 INFO misc.py line 119 131400] Train: [16/100][770/800] Data 0.003 (0.004) Batch 0.365 (0.324) Remain 06:02:49 loss: 0.5744 Lr: 0.00581 [2023-12-20 14:41:14,179 INFO misc.py line 119 131400] Train: [16/100][771/800] Data 0.003 (0.004) Batch 0.348 (0.324) Remain 06:02:51 loss: 0.7409 Lr: 0.00581 [2023-12-20 14:41:14,518 INFO misc.py line 119 131400] Train: [16/100][772/800] Data 0.003 (0.004) Batch 0.339 (0.324) Remain 06:02:51 loss: 0.4867 Lr: 0.00581 [2023-12-20 14:41:14,848 INFO misc.py line 119 131400] Train: [16/100][773/800] Data 0.004 (0.004) Batch 0.329 (0.324) Remain 06:02:52 loss: 0.5309 Lr: 0.00581 [2023-12-20 14:41:15,185 INFO misc.py line 119 131400] Train: [16/100][774/800] Data 0.005 (0.004) Batch 0.339 (0.324) Remain 06:02:53 loss: 0.7210 Lr: 0.00581 [2023-12-20 14:41:15,538 INFO misc.py line 119 131400] Train: [16/100][775/800] Data 0.003 (0.004) Batch 0.352 (0.324) Remain 06:02:55 loss: 0.4940 Lr: 0.00580 [2023-12-20 14:41:15,871 INFO misc.py line 119 131400] Train: [16/100][776/800] Data 0.004 (0.004) Batch 0.333 (0.324) Remain 06:02:55 loss: 0.3190 Lr: 0.00580 [2023-12-20 14:41:16,197 INFO misc.py line 119 131400] Train: [16/100][777/800] Data 0.003 (0.004) Batch 0.326 (0.324) Remain 06:02:55 loss: 0.4436 Lr: 0.00580 [2023-12-20 14:41:16,550 INFO misc.py line 119 131400] Train: [16/100][778/800] Data 0.004 (0.004) Batch 0.353 (0.324) Remain 06:02:57 loss: 0.5945 Lr: 0.00580 [2023-12-20 14:41:16,895 INFO misc.py line 119 131400] Train: [16/100][779/800] Data 0.004 (0.004) Batch 0.345 (0.324) Remain 06:02:59 loss: 0.6523 Lr: 0.00580 [2023-12-20 14:41:17,236 INFO misc.py line 119 131400] Train: [16/100][780/800] Data 0.005 (0.004) Batch 0.342 (0.324) Remain 06:03:00 loss: 0.5802 Lr: 0.00580 [2023-12-20 14:41:17,577 INFO misc.py line 119 131400] Train: [16/100][781/800] Data 0.003 (0.004) Batch 0.341 (0.324) Remain 06:03:01 loss: 0.4539 Lr: 0.00580 [2023-12-20 14:41:17,916 INFO misc.py line 119 131400] Train: [16/100][782/800] Data 0.004 (0.004) Batch 0.339 (0.324) Remain 06:03:02 loss: 0.8029 Lr: 0.00580 [2023-12-20 14:41:18,248 INFO misc.py line 119 131400] Train: [16/100][783/800] Data 0.005 (0.004) Batch 0.333 (0.324) Remain 06:03:02 loss: 0.2087 Lr: 0.00580 [2023-12-20 14:41:18,570 INFO misc.py line 119 131400] Train: [16/100][784/800] Data 0.003 (0.004) Batch 0.322 (0.324) Remain 06:03:02 loss: 0.5121 Lr: 0.00580 [2023-12-20 14:41:18,912 INFO misc.py line 119 131400] Train: [16/100][785/800] Data 0.003 (0.004) Batch 0.343 (0.324) Remain 06:03:03 loss: 0.7346 Lr: 0.00580 [2023-12-20 14:41:19,222 INFO misc.py line 119 131400] Train: [16/100][786/800] Data 0.002 (0.004) Batch 0.308 (0.324) Remain 06:03:02 loss: 0.3339 Lr: 0.00580 [2023-12-20 14:41:19,567 INFO misc.py line 119 131400] Train: [16/100][787/800] Data 0.004 (0.004) Batch 0.347 (0.324) Remain 06:03:03 loss: 0.6191 Lr: 0.00580 [2023-12-20 14:41:23,684 INFO misc.py line 119 131400] Train: [16/100][788/800] Data 0.002 (0.004) Batch 0.325 (0.324) Remain 06:03:03 loss: 0.5392 Lr: 0.00580 [2023-12-20 14:41:24,021 INFO misc.py line 119 131400] Train: [16/100][789/800] Data 3.794 (0.008) Batch 4.128 (0.329) Remain 06:08:28 loss: 0.4741 Lr: 0.00580 [2023-12-20 14:41:24,296 INFO misc.py line 119 131400] Train: [16/100][790/800] Data 0.003 (0.008) Batch 0.276 (0.329) Remain 06:08:23 loss: 0.9610 Lr: 0.00580 [2023-12-20 14:41:24,599 INFO misc.py line 119 131400] Train: [16/100][791/800] Data 0.002 (0.008) Batch 0.303 (0.329) Remain 06:08:20 loss: 0.4317 Lr: 0.00580 [2023-12-20 14:41:24,904 INFO misc.py line 119 131400] Train: [16/100][792/800] Data 0.002 (0.008) Batch 0.302 (0.329) Remain 06:08:18 loss: 0.5926 Lr: 0.00580 [2023-12-20 14:41:25,216 INFO misc.py line 119 131400] Train: [16/100][793/800] Data 0.005 (0.008) Batch 0.315 (0.329) Remain 06:08:16 loss: 0.9704 Lr: 0.00580 [2023-12-20 14:41:25,507 INFO misc.py line 119 131400] Train: [16/100][794/800] Data 0.002 (0.008) Batch 0.291 (0.329) Remain 06:08:13 loss: 0.7977 Lr: 0.00580 [2023-12-20 14:41:25,805 INFO misc.py line 119 131400] Train: [16/100][795/800] Data 0.002 (0.008) Batch 0.295 (0.329) Remain 06:08:10 loss: 0.3830 Lr: 0.00580 [2023-12-20 14:41:26,104 INFO misc.py line 119 131400] Train: [16/100][796/800] Data 0.005 (0.008) Batch 0.302 (0.329) Remain 06:08:07 loss: 0.1855 Lr: 0.00580 [2023-12-20 14:41:26,401 INFO misc.py line 119 131400] Train: [16/100][797/800] Data 0.002 (0.008) Batch 0.296 (0.329) Remain 06:08:04 loss: 0.6776 Lr: 0.00580 [2023-12-20 14:41:26,714 INFO misc.py line 119 131400] Train: [16/100][798/800] Data 0.002 (0.008) Batch 0.313 (0.329) Remain 06:08:02 loss: 0.9208 Lr: 0.00580 [2023-12-20 14:41:27,025 INFO misc.py line 119 131400] Train: [16/100][799/800] Data 0.002 (0.008) Batch 0.310 (0.329) Remain 06:08:00 loss: 0.4683 Lr: 0.00580 [2023-12-20 14:41:27,330 INFO misc.py line 119 131400] Train: [16/100][800/800] Data 0.004 (0.008) Batch 0.306 (0.329) Remain 06:07:58 loss: 0.6320 Lr: 0.00580 [2023-12-20 14:41:27,330 INFO misc.py line 136 131400] Train result: loss: 0.6048 [2023-12-20 14:41:27,331 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:41:49,744 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0625 [2023-12-20 14:41:49,816 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.3388 [2023-12-20 14:41:49,918 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2983 [2023-12-20 14:41:50,033 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1706 [2023-12-20 14:41:50,151 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4823 [2023-12-20 14:41:50,251 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.7927 [2023-12-20 14:41:50,339 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0630 [2023-12-20 14:41:50,446 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3638 [2023-12-20 14:41:50,532 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.4555 [2023-12-20 14:41:50,616 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5015 [2023-12-20 14:41:50,708 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4170 [2023-12-20 14:41:50,843 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6410 [2023-12-20 14:41:50,962 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2788 [2023-12-20 14:41:51,122 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3009 [2023-12-20 14:41:51,214 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2937 [2023-12-20 14:41:51,345 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9805 [2023-12-20 14:41:51,457 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3012 [2023-12-20 14:41:51,566 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4476 [2023-12-20 14:41:51,676 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4223 [2023-12-20 14:41:51,751 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4495 [2023-12-20 14:41:51,858 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2159 [2023-12-20 14:41:52,015 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.3332 [2023-12-20 14:41:52,135 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7228 [2023-12-20 14:41:52,275 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3114 [2023-12-20 14:41:52,417 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1895 [2023-12-20 14:41:52,498 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7643 [2023-12-20 14:41:52,653 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5929 [2023-12-20 14:41:52,780 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5891 [2023-12-20 14:41:52,873 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5249 [2023-12-20 14:41:53,019 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6616 [2023-12-20 14:41:53,120 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.9081 [2023-12-20 14:41:53,239 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.7655 [2023-12-20 14:41:53,324 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2236 [2023-12-20 14:41:53,392 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3450 [2023-12-20 14:41:53,492 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9343 [2023-12-20 14:41:53,585 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6423 [2023-12-20 14:41:53,714 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0442 [2023-12-20 14:41:53,822 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2147 [2023-12-20 14:41:53,906 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7667 [2023-12-20 14:41:54,046 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6362 [2023-12-20 14:41:54,191 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0238 [2023-12-20 14:41:54,287 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2423 [2023-12-20 14:41:54,408 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3873 [2023-12-20 14:41:54,548 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.3670 [2023-12-20 14:41:54,667 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1862 [2023-12-20 14:41:54,773 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8300 [2023-12-20 14:41:54,938 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4153 [2023-12-20 14:41:55,031 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4433 [2023-12-20 14:41:55,176 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3375 [2023-12-20 14:41:55,264 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9738 [2023-12-20 14:41:55,338 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4567 [2023-12-20 14:41:55,442 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5143 [2023-12-20 14:41:55,587 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9268 [2023-12-20 14:41:55,720 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4041 [2023-12-20 14:41:55,821 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1563 [2023-12-20 14:41:55,907 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8529 [2023-12-20 14:41:56,008 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5101 [2023-12-20 14:41:56,169 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2624 [2023-12-20 14:41:56,264 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4696 [2023-12-20 14:41:56,356 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2870 [2023-12-20 14:41:56,452 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2941 [2023-12-20 14:41:56,542 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4193 [2023-12-20 14:41:56,628 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6898 [2023-12-20 14:41:56,733 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8619 [2023-12-20 14:41:56,858 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4959 [2023-12-20 14:41:56,944 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.7124 [2023-12-20 14:41:57,046 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3795 [2023-12-20 14:41:57,138 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0188 [2023-12-20 14:41:57,223 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3077 [2023-12-20 14:41:57,305 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0194 [2023-12-20 14:41:57,398 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9422 [2023-12-20 14:41:57,492 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4906 [2023-12-20 14:41:57,625 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2207 [2023-12-20 14:41:57,719 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5880 [2023-12-20 14:41:57,835 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7105 [2023-12-20 14:41:57,939 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.1266 [2023-12-20 14:41:58,024 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.8573 [2023-12-20 14:41:58,181 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4809 [2023-12-20 14:41:59,320 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6963/0.8033/0.8916. [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8453/0.9341 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9610/0.9847 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5745/0.7131 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7968/0.8673 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8937/0.9308 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8130/0.9127 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7360/0.8688 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.5833/0.7120 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6196/0.8189 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7652/0.9356 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3649/0.4792 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6470/0.8062 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6286/0.8069 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6707/0.7132 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4502/0.7110 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6627/0.7260 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9297/0.9734 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6226/0.7158 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8747/0.9240 [2023-12-20 14:41:59,321 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.4873/0.5333 [2023-12-20 14:41:59,323 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:41:59,324 INFO misc.py line 165 131400] Currently Best mIoU: 0.7012 [2023-12-20 14:41:59,324 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:42:03,922 INFO misc.py line 119 131400] Train: [17/100][1/800] Data 0.904 (0.904) Batch 1.236 (1.236) Remain 23:04:43 loss: 0.9423 Lr: 0.00580 [2023-12-20 14:42:04,465 INFO misc.py line 119 131400] Train: [17/100][2/800] Data 0.207 (0.207) Batch 0.543 (0.543) Remain 10:08:17 loss: 0.6615 Lr: 0.00580 [2023-12-20 14:42:04,784 INFO misc.py line 119 131400] Train: [17/100][3/800] Data 0.003 (0.003) Batch 0.317 (0.317) Remain 05:55:02 loss: 0.4769 Lr: 0.00580 [2023-12-20 14:42:05,091 INFO misc.py line 119 131400] Train: [17/100][4/800] Data 0.005 (0.005) Batch 0.308 (0.308) Remain 05:45:29 loss: 0.7107 Lr: 0.00580 [2023-12-20 14:42:05,408 INFO misc.py line 119 131400] Train: [17/100][5/800] Data 0.004 (0.004) Batch 0.316 (0.312) Remain 05:49:55 loss: 0.6836 Lr: 0.00580 [2023-12-20 14:42:05,745 INFO misc.py line 119 131400] Train: [17/100][6/800] Data 0.004 (0.004) Batch 0.330 (0.318) Remain 05:56:29 loss: 0.5237 Lr: 0.00580 [2023-12-20 14:42:06,094 INFO misc.py line 119 131400] Train: [17/100][7/800] Data 0.011 (0.006) Batch 0.357 (0.328) Remain 06:07:19 loss: 1.1355 Lr: 0.00580 [2023-12-20 14:42:06,385 INFO misc.py line 119 131400] Train: [17/100][8/800] Data 0.003 (0.005) Batch 0.291 (0.321) Remain 05:59:03 loss: 0.7821 Lr: 0.00580 [2023-12-20 14:42:06,730 INFO misc.py line 119 131400] Train: [17/100][9/800] Data 0.002 (0.005) Batch 0.345 (0.325) Remain 06:03:32 loss: 0.4280 Lr: 0.00580 [2023-12-20 14:42:07,084 INFO misc.py line 119 131400] Train: [17/100][10/800] Data 0.003 (0.005) Batch 0.354 (0.329) Remain 06:08:13 loss: 0.6367 Lr: 0.00580 [2023-12-20 14:42:07,398 INFO misc.py line 119 131400] Train: [17/100][11/800] Data 0.004 (0.005) Batch 0.314 (0.327) Remain 06:06:07 loss: 0.9253 Lr: 0.00580 [2023-12-20 14:42:07,714 INFO misc.py line 119 131400] Train: [17/100][12/800] Data 0.004 (0.004) Batch 0.316 (0.326) Remain 06:04:48 loss: 0.5585 Lr: 0.00580 [2023-12-20 14:42:08,039 INFO misc.py line 119 131400] Train: [17/100][13/800] Data 0.003 (0.004) Batch 0.325 (0.326) Remain 06:04:44 loss: 1.0261 Lr: 0.00580 [2023-12-20 14:42:08,379 INFO misc.py line 119 131400] Train: [17/100][14/800] Data 0.004 (0.004) Batch 0.340 (0.327) Remain 06:06:10 loss: 0.5532 Lr: 0.00580 [2023-12-20 14:42:08,678 INFO misc.py line 119 131400] Train: [17/100][15/800] Data 0.003 (0.004) Batch 0.299 (0.325) Remain 06:03:34 loss: 0.5032 Lr: 0.00580 [2023-12-20 14:42:08,988 INFO misc.py line 119 131400] Train: [17/100][16/800] Data 0.003 (0.004) Batch 0.305 (0.323) Remain 06:01:51 loss: 1.0623 Lr: 0.00580 [2023-12-20 14:42:09,274 INFO misc.py line 119 131400] Train: [17/100][17/800] Data 0.008 (0.004) Batch 0.290 (0.321) Remain 05:59:13 loss: 0.6088 Lr: 0.00580 [2023-12-20 14:42:09,750 INFO misc.py line 119 131400] Train: [17/100][18/800] Data 0.003 (0.004) Batch 0.476 (0.331) Remain 06:10:49 loss: 1.0038 Lr: 0.00580 [2023-12-20 14:42:10,091 INFO misc.py line 119 131400] Train: [17/100][19/800] Data 0.003 (0.004) Batch 0.340 (0.332) Remain 06:11:23 loss: 0.5750 Lr: 0.00580 [2023-12-20 14:42:10,404 INFO misc.py line 119 131400] Train: [17/100][20/800] Data 0.005 (0.004) Batch 0.315 (0.331) Remain 06:10:16 loss: 0.8437 Lr: 0.00580 [2023-12-20 14:42:10,696 INFO misc.py line 119 131400] Train: [17/100][21/800] Data 0.003 (0.004) Batch 0.293 (0.329) Remain 06:07:53 loss: 0.8263 Lr: 0.00580 [2023-12-20 14:42:11,001 INFO misc.py line 119 131400] Train: [17/100][22/800] Data 0.004 (0.004) Batch 0.305 (0.327) Remain 06:06:29 loss: 0.5912 Lr: 0.00580 [2023-12-20 14:42:11,310 INFO misc.py line 119 131400] Train: [17/100][23/800] Data 0.002 (0.004) Batch 0.309 (0.326) Remain 06:05:26 loss: 0.6965 Lr: 0.00580 [2023-12-20 14:42:11,592 INFO misc.py line 119 131400] Train: [17/100][24/800] Data 0.003 (0.004) Batch 0.282 (0.324) Remain 06:03:04 loss: 0.7383 Lr: 0.00580 [2023-12-20 14:42:11,885 INFO misc.py line 119 131400] Train: [17/100][25/800] Data 0.002 (0.004) Batch 0.293 (0.323) Remain 06:01:29 loss: 0.6637 Lr: 0.00580 [2023-12-20 14:42:12,229 INFO misc.py line 119 131400] Train: [17/100][26/800] Data 0.003 (0.004) Batch 0.342 (0.324) Remain 06:02:25 loss: 0.7685 Lr: 0.00580 [2023-12-20 14:42:12,533 INFO misc.py line 119 131400] Train: [17/100][27/800] Data 0.005 (0.004) Batch 0.304 (0.323) Remain 06:01:30 loss: 0.2995 Lr: 0.00580 [2023-12-20 14:42:12,832 INFO misc.py line 119 131400] Train: [17/100][28/800] Data 0.005 (0.004) Batch 0.300 (0.322) Remain 06:00:28 loss: 0.6110 Lr: 0.00580 [2023-12-20 14:42:13,156 INFO misc.py line 119 131400] Train: [17/100][29/800] Data 0.003 (0.004) Batch 0.324 (0.322) Remain 06:00:32 loss: 0.2644 Lr: 0.00580 [2023-12-20 14:42:13,484 INFO misc.py line 119 131400] Train: [17/100][30/800] Data 0.004 (0.004) Batch 0.328 (0.322) Remain 06:00:46 loss: 0.6399 Lr: 0.00580 [2023-12-20 14:42:13,783 INFO misc.py line 119 131400] Train: [17/100][31/800] Data 0.004 (0.004) Batch 0.299 (0.321) Remain 05:59:49 loss: 0.5532 Lr: 0.00580 [2023-12-20 14:42:14,076 INFO misc.py line 119 131400] Train: [17/100][32/800] Data 0.003 (0.004) Batch 0.294 (0.320) Remain 05:58:47 loss: 0.6393 Lr: 0.00580 [2023-12-20 14:42:14,386 INFO misc.py line 119 131400] Train: [17/100][33/800] Data 0.003 (0.004) Batch 0.310 (0.320) Remain 05:58:22 loss: 0.4398 Lr: 0.00580 [2023-12-20 14:42:14,682 INFO misc.py line 119 131400] Train: [17/100][34/800] Data 0.003 (0.004) Batch 0.295 (0.319) Remain 05:57:27 loss: 0.3070 Lr: 0.00580 [2023-12-20 14:42:14,977 INFO misc.py line 119 131400] Train: [17/100][35/800] Data 0.004 (0.004) Batch 0.295 (0.319) Remain 05:56:35 loss: 0.4769 Lr: 0.00580 [2023-12-20 14:42:15,242 INFO misc.py line 119 131400] Train: [17/100][36/800] Data 0.005 (0.004) Batch 0.266 (0.317) Remain 05:54:48 loss: 0.5177 Lr: 0.00580 [2023-12-20 14:42:15,562 INFO misc.py line 119 131400] Train: [17/100][37/800] Data 0.003 (0.004) Batch 0.320 (0.317) Remain 05:54:53 loss: 0.8710 Lr: 0.00580 [2023-12-20 14:42:15,904 INFO misc.py line 119 131400] Train: [17/100][38/800] Data 0.004 (0.004) Batch 0.342 (0.318) Remain 05:55:42 loss: 0.5701 Lr: 0.00580 [2023-12-20 14:42:16,258 INFO misc.py line 119 131400] Train: [17/100][39/800] Data 0.003 (0.004) Batch 0.350 (0.319) Remain 05:56:40 loss: 0.7840 Lr: 0.00580 [2023-12-20 14:42:16,564 INFO misc.py line 119 131400] Train: [17/100][40/800] Data 0.008 (0.004) Batch 0.309 (0.318) Remain 05:56:23 loss: 0.2902 Lr: 0.00580 [2023-12-20 14:42:16,965 INFO misc.py line 119 131400] Train: [17/100][41/800] Data 0.004 (0.004) Batch 0.401 (0.321) Remain 05:58:48 loss: 0.3955 Lr: 0.00580 [2023-12-20 14:42:17,288 INFO misc.py line 119 131400] Train: [17/100][42/800] Data 0.005 (0.004) Batch 0.323 (0.321) Remain 05:58:51 loss: 0.5672 Lr: 0.00580 [2023-12-20 14:42:17,610 INFO misc.py line 119 131400] Train: [17/100][43/800] Data 0.005 (0.004) Batch 0.325 (0.321) Remain 05:58:58 loss: 0.4776 Lr: 0.00580 [2023-12-20 14:42:17,918 INFO misc.py line 119 131400] Train: [17/100][44/800] Data 0.003 (0.004) Batch 0.306 (0.320) Remain 05:58:34 loss: 0.4945 Lr: 0.00580 [2023-12-20 14:42:18,258 INFO misc.py line 119 131400] Train: [17/100][45/800] Data 0.005 (0.004) Batch 0.341 (0.321) Remain 05:59:06 loss: 0.6093 Lr: 0.00580 [2023-12-20 14:42:18,578 INFO misc.py line 119 131400] Train: [17/100][46/800] Data 0.003 (0.004) Batch 0.320 (0.321) Remain 05:59:04 loss: 0.5337 Lr: 0.00580 [2023-12-20 14:42:18,908 INFO misc.py line 119 131400] Train: [17/100][47/800] Data 0.004 (0.004) Batch 0.330 (0.321) Remain 05:59:17 loss: 0.6435 Lr: 0.00580 [2023-12-20 14:42:19,217 INFO misc.py line 119 131400] Train: [17/100][48/800] Data 0.003 (0.004) Batch 0.309 (0.321) Remain 05:58:59 loss: 0.6514 Lr: 0.00580 [2023-12-20 14:42:19,549 INFO misc.py line 119 131400] Train: [17/100][49/800] Data 0.004 (0.004) Batch 0.329 (0.321) Remain 05:59:11 loss: 0.6543 Lr: 0.00580 [2023-12-20 14:42:19,907 INFO misc.py line 119 131400] Train: [17/100][50/800] Data 0.007 (0.004) Batch 0.362 (0.322) Remain 06:00:09 loss: 0.2961 Lr: 0.00580 [2023-12-20 14:42:20,244 INFO misc.py line 119 131400] Train: [17/100][51/800] Data 0.003 (0.004) Batch 0.336 (0.322) Remain 06:00:29 loss: 0.5879 Lr: 0.00580 [2023-12-20 14:42:20,595 INFO misc.py line 119 131400] Train: [17/100][52/800] Data 0.004 (0.004) Batch 0.349 (0.323) Remain 06:01:06 loss: 0.5724 Lr: 0.00580 [2023-12-20 14:42:20,957 INFO misc.py line 119 131400] Train: [17/100][53/800] Data 0.006 (0.004) Batch 0.364 (0.323) Remain 06:02:01 loss: 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INFO misc.py line 119 131400] Train: [17/100][60/800] Data 0.003 (0.004) Batch 0.307 (0.324) Remain 06:02:53 loss: 0.8872 Lr: 0.00580 [2023-12-20 14:42:23,720 INFO misc.py line 119 131400] Train: [17/100][61/800] Data 0.003 (0.004) Batch 0.452 (0.327) Remain 06:05:21 loss: 0.7001 Lr: 0.00580 [2023-12-20 14:42:24,064 INFO misc.py line 119 131400] Train: [17/100][62/800] Data 0.003 (0.004) Batch 0.344 (0.327) Remain 06:05:40 loss: 0.4193 Lr: 0.00580 [2023-12-20 14:42:24,376 INFO misc.py line 119 131400] Train: [17/100][63/800] Data 0.004 (0.004) Batch 0.313 (0.327) Remain 06:05:24 loss: 0.5204 Lr: 0.00580 [2023-12-20 14:42:24,699 INFO misc.py line 119 131400] Train: [17/100][64/800] Data 0.003 (0.004) Batch 0.323 (0.327) Remain 06:05:20 loss: 0.5266 Lr: 0.00580 [2023-12-20 14:42:25,019 INFO misc.py line 119 131400] Train: [17/100][65/800] Data 0.003 (0.004) Batch 0.319 (0.326) Remain 06:05:11 loss: 0.4670 Lr: 0.00580 [2023-12-20 14:42:25,365 INFO misc.py line 119 131400] Train: [17/100][66/800] Data 0.005 (0.004) Batch 0.343 (0.327) Remain 06:05:29 loss: 0.6052 Lr: 0.00580 [2023-12-20 14:42:25,683 INFO misc.py line 119 131400] Train: [17/100][67/800] Data 0.006 (0.004) Batch 0.321 (0.327) Remain 06:05:23 loss: 0.7888 Lr: 0.00580 [2023-12-20 14:42:25,994 INFO misc.py line 119 131400] Train: [17/100][68/800] Data 0.004 (0.004) Batch 0.311 (0.326) Remain 06:05:06 loss: 1.4113 Lr: 0.00580 [2023-12-20 14:42:26,308 INFO misc.py line 119 131400] Train: [17/100][69/800] Data 0.003 (0.004) Batch 0.314 (0.326) Remain 06:04:54 loss: 0.7986 Lr: 0.00580 [2023-12-20 14:42:26,673 INFO misc.py line 119 131400] Train: [17/100][70/800] Data 0.004 (0.004) Batch 0.364 (0.327) Remain 06:05:32 loss: 0.2659 Lr: 0.00580 [2023-12-20 14:42:27,015 INFO misc.py line 119 131400] Train: [17/100][71/800] Data 0.004 (0.004) Batch 0.343 (0.327) Remain 06:05:47 loss: 0.2105 Lr: 0.00580 [2023-12-20 14:42:27,347 INFO misc.py line 119 131400] Train: [17/100][72/800] Data 0.003 (0.004) Batch 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[2023-12-20 14:46:14,823 INFO misc.py line 119 131400] Train: [17/100][776/800] Data 0.003 (0.004) Batch 0.326 (0.323) Remain 05:58:05 loss: 0.8145 Lr: 0.00577 [2023-12-20 14:46:15,132 INFO misc.py line 119 131400] Train: [17/100][777/800] Data 0.003 (0.004) Batch 0.309 (0.323) Remain 05:58:04 loss: 0.3407 Lr: 0.00577 [2023-12-20 14:46:15,489 INFO misc.py line 119 131400] Train: [17/100][778/800] Data 0.003 (0.004) Batch 0.357 (0.323) Remain 05:58:06 loss: 0.9150 Lr: 0.00577 [2023-12-20 14:46:15,797 INFO misc.py line 119 131400] Train: [17/100][779/800] Data 0.004 (0.004) Batch 0.308 (0.323) Remain 05:58:05 loss: 0.4816 Lr: 0.00577 [2023-12-20 14:46:16,110 INFO misc.py line 119 131400] Train: [17/100][780/800] Data 0.003 (0.004) Batch 0.313 (0.323) Remain 05:58:03 loss: 0.3857 Lr: 0.00577 [2023-12-20 14:46:16,428 INFO misc.py line 119 131400] Train: [17/100][781/800] Data 0.004 (0.004) Batch 0.318 (0.323) Remain 05:58:03 loss: 0.6759 Lr: 0.00577 [2023-12-20 14:46:16,739 INFO misc.py line 119 131400] Train: [17/100][782/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 05:58:01 loss: 0.6483 Lr: 0.00577 [2023-12-20 14:46:17,067 INFO misc.py line 119 131400] Train: [17/100][783/800] Data 0.004 (0.004) Batch 0.328 (0.323) Remain 05:58:01 loss: 0.4361 Lr: 0.00577 [2023-12-20 14:46:17,387 INFO misc.py line 119 131400] Train: [17/100][784/800] Data 0.004 (0.004) Batch 0.320 (0.323) Remain 05:58:01 loss: 0.6684 Lr: 0.00577 [2023-12-20 14:46:17,728 INFO misc.py line 119 131400] Train: [17/100][785/800] Data 0.003 (0.004) Batch 0.342 (0.323) Remain 05:58:02 loss: 0.4750 Lr: 0.00577 [2023-12-20 14:46:18,060 INFO misc.py line 119 131400] Train: [17/100][786/800] Data 0.003 (0.004) Batch 0.331 (0.323) Remain 05:58:02 loss: 0.3943 Lr: 0.00577 [2023-12-20 14:46:18,395 INFO misc.py line 119 131400] Train: [17/100][787/800] Data 0.003 (0.004) Batch 0.322 (0.323) Remain 05:58:02 loss: 0.5081 Lr: 0.00577 [2023-12-20 14:46:18,718 INFO misc.py line 119 131400] Train: 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Batch 0.296 (0.323) Remain 05:57:51 loss: 0.7180 Lr: 0.00577 [2023-12-20 14:46:20,827 INFO misc.py line 119 131400] Train: [17/100][795/800] Data 0.007 (0.004) Batch 0.286 (0.323) Remain 05:57:47 loss: 0.7460 Lr: 0.00577 [2023-12-20 14:46:21,142 INFO misc.py line 119 131400] Train: [17/100][796/800] Data 0.002 (0.004) Batch 0.315 (0.323) Remain 05:57:46 loss: 0.3203 Lr: 0.00577 [2023-12-20 14:46:21,454 INFO misc.py line 119 131400] Train: [17/100][797/800] Data 0.002 (0.004) Batch 0.312 (0.323) Remain 05:57:45 loss: 0.6248 Lr: 0.00577 [2023-12-20 14:46:21,733 INFO misc.py line 119 131400] Train: [17/100][798/800] Data 0.003 (0.004) Batch 0.279 (0.323) Remain 05:57:41 loss: 0.4425 Lr: 0.00577 [2023-12-20 14:46:22,004 INFO misc.py line 119 131400] Train: [17/100][799/800] Data 0.003 (0.004) Batch 0.271 (0.323) Remain 05:57:36 loss: 0.3394 Lr: 0.00577 [2023-12-20 14:46:22,304 INFO misc.py line 119 131400] Train: [17/100][800/800] Data 0.003 (0.004) Batch 0.299 (0.323) Remain 05:57:34 loss: 0.4492 Lr: 0.00577 [2023-12-20 14:46:22,304 INFO misc.py line 136 131400] Train result: loss: 0.5938 [2023-12-20 14:46:22,304 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:46:45,094 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0868 [2023-12-20 14:46:45,185 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2281 [2023-12-20 14:46:45,293 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4240 [2023-12-20 14:46:45,406 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1751 [2023-12-20 14:46:45,522 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4543 [2023-12-20 14:46:45,623 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9428 [2023-12-20 14:46:45,715 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7024 [2023-12-20 14:46:45,821 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.7282 [2023-12-20 14:46:45,914 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2847 [2023-12-20 14:46:46,011 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5534 [2023-12-20 14:46:46,118 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6920 [2023-12-20 14:46:46,261 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6556 [2023-12-20 14:46:46,381 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2390 [2023-12-20 14:46:46,540 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2631 [2023-12-20 14:46:46,638 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5887 [2023-12-20 14:46:46,770 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9647 [2023-12-20 14:46:46,883 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3814 [2023-12-20 14:46:46,990 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4408 [2023-12-20 14:46:47,102 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1934 [2023-12-20 14:46:47,179 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4117 [2023-12-20 14:46:47,292 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.5184 [2023-12-20 14:46:48,843 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3401 [2023-12-20 14:46:48,936 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5600 [2023-12-20 14:46:49,032 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5144 [2023-12-20 14:46:49,163 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7720 [2023-12-20 14:46:49,273 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1541 [2023-12-20 14:46:49,355 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6361 [2023-12-20 14:46:49,500 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5318 [2023-12-20 14:46:49,647 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0444 [2023-12-20 14:46:49,743 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3850 [2023-12-20 14:46:49,865 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5902 [2023-12-20 14:46:50,013 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9350 [2023-12-20 14:46:50,137 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.5444 [2023-12-20 14:46:50,250 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3467 [2023-12-20 14:46:50,423 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4377 [2023-12-20 14:46:50,516 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4369 [2023-12-20 14:46:50,661 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2725 [2023-12-20 14:46:50,759 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9561 [2023-12-20 14:46:50,839 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5587 [2023-12-20 14:46:50,949 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2591 [2023-12-20 14:46:51,103 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.4772 [2023-12-20 14:46:51,236 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3303 [2023-12-20 14:46:51,344 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6515 [2023-12-20 14:46:51,444 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7123 [2023-12-20 14:46:51,545 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4621 [2023-12-20 14:46:51,712 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3162 [2023-12-20 14:46:51,811 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5712 [2023-12-20 14:46:51,907 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1754 [2023-12-20 14:46:52,007 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3486 [2023-12-20 14:46:52,107 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4277 [2023-12-20 14:46:52,202 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5314 [2023-12-20 14:46:52,301 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9675 [2023-12-20 14:46:52,427 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7134 [2023-12-20 14:46:52,528 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5100 [2023-12-20 14:46:52,640 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6237 [2023-12-20 14:46:52,739 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0492 [2023-12-20 14:46:52,829 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2486 [2023-12-20 14:46:52,918 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0438 [2023-12-20 14:46:53,017 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7876 [2023-12-20 14:46:53,124 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7858 [2023-12-20 14:46:53,260 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1780 [2023-12-20 14:46:53,356 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7488 [2023-12-20 14:46:53,476 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8632 [2023-12-20 14:46:53,584 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.0991 [2023-12-20 14:46:53,669 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5676 [2023-12-20 14:46:53,832 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3978 [2023-12-20 14:46:55,220 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7072/0.8144/0.8956. [2023-12-20 14:46:55,220 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8441/0.9214 [2023-12-20 14:46:55,220 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9615/0.9839 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6229/0.7693 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8066/0.8661 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8970/0.9605 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8461/0.8819 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7211/0.7905 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6087/0.7409 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6368/0.8049 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7525/0.9488 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2796/0.5524 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6459/0.7408 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5990/0.8692 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7390/0.7779 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5529/0.5870 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6842/0.8069 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9258/0.9728 [2023-12-20 14:46:55,221 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6488/0.7506 [2023-12-20 14:46:55,222 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8070/0.9303 [2023-12-20 14:46:55,222 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5641/0.6324 [2023-12-20 14:46:55,222 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:46:55,223 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7072 [2023-12-20 14:46:55,224 INFO misc.py line 165 131400] Currently Best mIoU: 0.7072 [2023-12-20 14:46:55,224 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:47:01,907 INFO misc.py line 119 131400] Train: [18/100][1/800] Data 0.790 (0.790) Batch 1.121 (1.121) Remain 20:40:02 loss: 0.6453 Lr: 0.00577 [2023-12-20 14:47:02,396 INFO misc.py line 119 131400] Train: [18/100][2/800] Data 0.181 (0.181) Batch 0.489 (0.489) Remain 09:01:11 loss: 0.3054 Lr: 0.00577 [2023-12-20 14:47:02,750 INFO misc.py line 119 131400] Train: [18/100][3/800] Data 0.013 (0.013) Batch 0.354 (0.354) Remain 06:31:36 loss: 0.5453 Lr: 0.00577 [2023-12-20 14:47:03,134 INFO misc.py line 119 131400] Train: [18/100][4/800] Data 0.041 (0.041) Batch 0.385 (0.385) Remain 07:05:43 loss: 0.8513 Lr: 0.00577 [2023-12-20 14:47:03,471 INFO misc.py line 119 131400] Train: [18/100][5/800] Data 0.003 (0.022) Batch 0.336 (0.360) Remain 06:38:43 loss: 0.5244 Lr: 0.00577 [2023-12-20 14:47:03,840 INFO misc.py line 119 131400] Train: [18/100][6/800] Data 0.004 (0.016) Batch 0.370 (0.364) Remain 06:42:25 loss: 0.6003 Lr: 0.00577 [2023-12-20 14:47:04,168 INFO misc.py line 119 131400] Train: [18/100][7/800] Data 0.003 (0.013) Batch 0.327 (0.354) Remain 06:32:08 loss: 0.8389 Lr: 0.00577 [2023-12-20 14:47:04,509 INFO misc.py line 119 131400] Train: [18/100][8/800] Data 0.004 (0.011) Batch 0.342 (0.352) Remain 06:29:19 loss: 0.3074 Lr: 0.00577 [2023-12-20 14:47:04,843 INFO misc.py line 119 131400] Train: [18/100][9/800] Data 0.003 (0.009) Batch 0.327 (0.348) Remain 06:24:42 loss: 0.7771 Lr: 0.00577 [2023-12-20 14:47:05,410 INFO misc.py line 119 131400] Train: [18/100][10/800] Data 0.248 (0.044) Batch 0.574 (0.380) Remain 07:00:33 loss: 0.4540 Lr: 0.00577 [2023-12-20 14:47:05,715 INFO misc.py line 119 131400] Train: [18/100][11/800] Data 0.003 (0.038) Batch 0.305 (0.371) Remain 06:50:09 loss: 0.2812 Lr: 0.00577 [2023-12-20 14:47:06,046 INFO misc.py line 119 131400] Train: [18/100][12/800] Data 0.003 (0.035) Batch 0.330 (0.366) Remain 06:45:07 loss: 0.8322 Lr: 0.00577 [2023-12-20 14:47:06,449 INFO misc.py line 119 131400] Train: [18/100][13/800] Data 0.004 (0.031) Batch 0.403 (0.370) Remain 06:49:13 loss: 0.6802 Lr: 0.00577 [2023-12-20 14:47:06,724 INFO misc.py line 119 131400] Train: [18/100][14/800] Data 0.004 (0.029) Batch 0.276 (0.361) Remain 06:39:45 loss: 0.4710 Lr: 0.00577 [2023-12-20 14:47:07,047 INFO misc.py line 119 131400] Train: [18/100][15/800] Data 0.003 (0.027) Batch 0.322 (0.358) Remain 06:36:07 loss: 0.7491 Lr: 0.00577 [2023-12-20 14:47:07,346 INFO misc.py line 119 131400] Train: [18/100][16/800] Data 0.004 (0.025) Batch 0.298 (0.353) Remain 06:31:03 loss: 0.5168 Lr: 0.00577 [2023-12-20 14:47:07,667 INFO misc.py line 119 131400] Train: [18/100][17/800] Data 0.011 (0.024) Batch 0.323 (0.351) Remain 06:28:36 loss: 0.6697 Lr: 0.00577 [2023-12-20 14:47:07,986 INFO misc.py line 119 131400] Train: [18/100][18/800] Data 0.003 (0.023) Batch 0.318 (0.349) Remain 06:26:10 loss: 0.5704 Lr: 0.00577 [2023-12-20 14:47:08,274 INFO misc.py line 119 131400] Train: [18/100][19/800] Data 0.003 (0.021) Batch 0.289 (0.345) Remain 06:22:01 loss: 0.8017 Lr: 0.00577 [2023-12-20 14:47:08,573 INFO misc.py line 119 131400] Train: [18/100][20/800] Data 0.003 (0.020) Batch 0.299 (0.343) Remain 06:18:58 loss: 0.5174 Lr: 0.00577 [2023-12-20 14:47:08,903 INFO misc.py line 119 131400] Train: [18/100][21/800] Data 0.004 (0.019) Batch 0.330 (0.342) Remain 06:18:12 loss: 0.7774 Lr: 0.00577 [2023-12-20 14:47:09,207 INFO misc.py line 119 131400] Train: [18/100][22/800] Data 0.003 (0.018) Batch 0.304 (0.340) Remain 06:15:57 loss: 0.7190 Lr: 0.00577 [2023-12-20 14:47:09,680 INFO misc.py line 119 131400] Train: [18/100][23/800] Data 0.003 (0.018) Batch 0.473 (0.346) Remain 06:23:18 loss: 0.6846 Lr: 0.00577 [2023-12-20 14:47:09,992 INFO misc.py line 119 131400] Train: [18/100][24/800] Data 0.004 (0.017) Batch 0.311 (0.345) Remain 06:21:25 loss: 0.3651 Lr: 0.00577 [2023-12-20 14:47:10,329 INFO misc.py line 119 131400] Train: [18/100][25/800] Data 0.005 (0.017) Batch 0.338 (0.344) Remain 06:21:04 loss: 0.7514 Lr: 0.00577 [2023-12-20 14:47:10,655 INFO misc.py line 119 131400] Train: [18/100][26/800] Data 0.004 (0.016) Batch 0.326 (0.344) Remain 06:20:12 loss: 0.4518 Lr: 0.00577 [2023-12-20 14:47:10,994 INFO misc.py line 119 131400] Train: [18/100][27/800] Data 0.003 (0.015) Batch 0.337 (0.343) Remain 06:19:54 loss: 0.6179 Lr: 0.00577 [2023-12-20 14:47:11,296 INFO misc.py line 119 131400] Train: [18/100][28/800] Data 0.005 (0.015) Batch 0.303 (0.342) Remain 06:18:07 loss: 0.6380 Lr: 0.00577 [2023-12-20 14:47:11,646 INFO misc.py line 119 131400] Train: [18/100][29/800] Data 0.004 (0.015) Batch 0.350 (0.342) Remain 06:18:28 loss: 0.5284 Lr: 0.00577 [2023-12-20 14:47:11,982 INFO misc.py line 119 131400] Train: [18/100][30/800] Data 0.004 (0.014) Batch 0.336 (0.342) Remain 06:18:13 loss: 0.5472 Lr: 0.00577 [2023-12-20 14:47:12,294 INFO misc.py line 119 131400] Train: [18/100][31/800] Data 0.003 (0.014) Batch 0.313 (0.341) Remain 06:17:03 loss: 0.8104 Lr: 0.00577 [2023-12-20 14:47:12,596 INFO misc.py line 119 131400] Train: [18/100][32/800] Data 0.003 (0.013) Batch 0.302 (0.340) Remain 06:15:33 loss: 0.3912 Lr: 0.00577 [2023-12-20 14:47:12,904 INFO misc.py line 119 131400] Train: [18/100][33/800] Data 0.002 (0.013) Batch 0.309 (0.338) Remain 06:14:25 loss: 0.4983 Lr: 0.00577 [2023-12-20 14:47:13,216 INFO misc.py line 119 131400] Train: [18/100][34/800] Data 0.003 (0.013) Batch 0.308 (0.338) Remain 06:13:19 loss: 0.3980 Lr: 0.00577 [2023-12-20 14:47:13,511 INFO misc.py line 119 131400] Train: [18/100][35/800] Data 0.006 (0.013) Batch 0.299 (0.336) Remain 06:11:58 loss: 0.3355 Lr: 0.00577 [2023-12-20 14:47:13,842 INFO misc.py line 119 131400] Train: [18/100][36/800] Data 0.003 (0.012) Batch 0.330 (0.336) Remain 06:11:45 loss: 0.5698 Lr: 0.00577 [2023-12-20 14:47:14,148 INFO misc.py line 119 131400] Train: [18/100][37/800] Data 0.003 (0.012) Batch 0.307 (0.335) Remain 06:10:48 loss: 0.6094 Lr: 0.00577 [2023-12-20 14:47:14,459 INFO misc.py line 119 131400] Train: [18/100][38/800] Data 0.002 (0.012) Batch 0.310 (0.335) Remain 06:09:59 loss: 0.5379 Lr: 0.00577 [2023-12-20 14:47:14,745 INFO misc.py line 119 131400] Train: [18/100][39/800] Data 0.003 (0.011) Batch 0.288 (0.333) Remain 06:08:32 loss: 0.4259 Lr: 0.00577 [2023-12-20 14:47:15,068 INFO misc.py line 119 131400] Train: [18/100][40/800] Data 0.003 (0.011) Batch 0.323 (0.333) Remain 06:08:14 loss: 0.8401 Lr: 0.00577 [2023-12-20 14:47:15,381 INFO misc.py line 119 131400] Train: [18/100][41/800] Data 0.002 (0.011) Batch 0.312 (0.332) Remain 06:07:37 loss: 0.4801 Lr: 0.00577 [2023-12-20 14:47:15,689 INFO misc.py line 119 131400] Train: [18/100][42/800] Data 0.003 (0.011) Batch 0.309 (0.332) Remain 06:06:57 loss: 0.5290 Lr: 0.00577 [2023-12-20 14:47:16,012 INFO misc.py line 119 131400] Train: [18/100][43/800] Data 0.002 (0.011) Batch 0.322 (0.332) Remain 06:06:41 loss: 0.7453 Lr: 0.00577 [2023-12-20 14:47:16,318 INFO misc.py line 119 131400] Train: [18/100][44/800] Data 0.003 (0.010) Batch 0.306 (0.331) Remain 06:05:59 loss: 0.7505 Lr: 0.00577 [2023-12-20 14:47:16,649 INFO misc.py line 119 131400] Train: [18/100][45/800] Data 0.003 (0.010) Batch 0.327 (0.331) Remain 06:05:52 loss: 1.0116 Lr: 0.00577 [2023-12-20 14:47:16,959 INFO misc.py line 119 131400] Train: [18/100][46/800] Data 0.007 (0.010) Batch 0.315 (0.330) Remain 06:05:27 loss: 0.3010 Lr: 0.00576 [2023-12-20 14:47:17,235 INFO misc.py line 119 131400] Train: [18/100][47/800] Data 0.002 (0.010) Batch 0.275 (0.329) Remain 06:04:04 loss: 0.9525 Lr: 0.00576 [2023-12-20 14:47:17,552 INFO misc.py line 119 131400] Train: [18/100][48/800] Data 0.003 (0.010) Batch 0.317 (0.329) Remain 06:03:45 loss: 0.2487 Lr: 0.00576 [2023-12-20 14:47:17,872 INFO misc.py line 119 131400] Train: [18/100][49/800] Data 0.003 (0.010) Batch 0.319 (0.329) Remain 06:03:31 loss: 0.3362 Lr: 0.00576 [2023-12-20 14:47:18,220 INFO misc.py line 119 131400] Train: [18/100][50/800] Data 0.004 (0.010) Batch 0.349 (0.329) Remain 06:03:59 loss: 1.2244 Lr: 0.00576 [2023-12-20 14:47:18,535 INFO misc.py line 119 131400] Train: [18/100][51/800] Data 0.004 (0.009) Batch 0.314 (0.329) Remain 06:03:37 loss: 0.5154 Lr: 0.00576 [2023-12-20 14:47:18,885 INFO misc.py line 119 131400] Train: [18/100][52/800] Data 0.005 (0.009) Batch 0.349 (0.329) Remain 06:04:04 loss: 0.6650 Lr: 0.00576 [2023-12-20 14:47:19,341 INFO misc.py line 119 131400] Train: [18/100][53/800] Data 0.007 (0.009) Batch 0.458 (0.332) Remain 06:06:55 loss: 0.5748 Lr: 0.00576 [2023-12-20 14:47:19,649 INFO misc.py line 119 131400] Train: [18/100][54/800] Data 0.003 (0.009) Batch 0.305 (0.331) Remain 06:06:19 loss: 1.1774 Lr: 0.00576 [2023-12-20 14:47:20,026 INFO misc.py line 119 131400] Train: [18/100][55/800] Data 0.007 (0.009) Batch 0.380 (0.332) Remain 06:07:21 loss: 0.5465 Lr: 0.00576 [2023-12-20 14:47:20,343 INFO misc.py line 119 131400] Train: [18/100][56/800] Data 0.004 (0.009) Batch 0.318 (0.332) Remain 06:07:02 loss: 0.5462 Lr: 0.00576 [2023-12-20 14:47:20,696 INFO misc.py line 119 131400] Train: [18/100][57/800] Data 0.003 (0.009) Batch 0.353 (0.332) Remain 06:07:28 loss: 0.5072 Lr: 0.00576 [2023-12-20 14:47:21,047 INFO misc.py line 119 131400] Train: [18/100][58/800] Data 0.003 (0.009) Batch 0.350 (0.333) Remain 06:07:49 loss: 0.4585 Lr: 0.00576 [2023-12-20 14:47:21,371 INFO misc.py line 119 131400] Train: [18/100][59/800] Data 0.003 (0.009) Batch 0.324 (0.333) Remain 06:07:39 loss: 0.6043 Lr: 0.00576 [2023-12-20 14:47:21,703 INFO misc.py line 119 131400] Train: [18/100][60/800] Data 0.003 (0.009) Batch 0.331 (0.332) Remain 06:07:37 loss: 0.3636 Lr: 0.00576 [2023-12-20 14:47:22,040 INFO misc.py line 119 131400] Train: [18/100][61/800] Data 0.004 (0.008) Batch 0.337 (0.333) Remain 06:07:42 loss: 0.5618 Lr: 0.00576 [2023-12-20 14:47:22,352 INFO misc.py line 119 131400] Train: [18/100][62/800] Data 0.006 (0.008) Batch 0.313 (0.332) Remain 06:07:20 loss: 0.1666 Lr: 0.00576 [2023-12-20 14:47:22,640 INFO misc.py line 119 131400] Train: [18/100][63/800] Data 0.004 (0.008) Batch 0.288 (0.332) Remain 06:06:31 loss: 1.0857 Lr: 0.00576 [2023-12-20 14:47:22,983 INFO misc.py line 119 131400] Train: [18/100][64/800] Data 0.003 (0.008) Batch 0.342 (0.332) Remain 06:06:42 loss: 0.5058 Lr: 0.00576 [2023-12-20 14:47:23,279 INFO misc.py line 119 131400] Train: [18/100][65/800] Data 0.004 (0.008) Batch 0.296 (0.331) Remain 06:06:04 loss: 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Batch 0.344 (0.323) Remain 05:53:45 loss: 0.3424 Lr: 0.00573 [2023-12-20 14:51:04,583 INFO misc.py line 119 131400] Train: [18/100][751/800] Data 0.003 (0.004) Batch 0.317 (0.323) Remain 05:53:44 loss: 0.7518 Lr: 0.00573 [2023-12-20 14:51:04,871 INFO misc.py line 119 131400] Train: [18/100][752/800] Data 0.003 (0.004) Batch 0.288 (0.323) Remain 05:53:41 loss: 0.7093 Lr: 0.00573 [2023-12-20 14:51:05,177 INFO misc.py line 119 131400] Train: [18/100][753/800] Data 0.002 (0.004) Batch 0.303 (0.323) Remain 05:53:39 loss: 0.4953 Lr: 0.00573 [2023-12-20 14:51:05,473 INFO misc.py line 119 131400] Train: [18/100][754/800] Data 0.006 (0.004) Batch 0.299 (0.323) Remain 05:53:36 loss: 0.7143 Lr: 0.00573 [2023-12-20 14:51:05,807 INFO misc.py line 119 131400] Train: [18/100][755/800] Data 0.003 (0.004) Batch 0.334 (0.323) Remain 05:53:37 loss: 0.4219 Lr: 0.00573 [2023-12-20 14:51:06,136 INFO misc.py line 119 131400] Train: [18/100][756/800] Data 0.003 (0.004) Batch 0.329 (0.323) Remain 05:53:37 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131400] Train: [18/100][769/800] Data 0.003 (0.004) Batch 0.333 (0.323) Remain 05:53:22 loss: 0.4797 Lr: 0.00573 [2023-12-20 14:51:10,684 INFO misc.py line 119 131400] Train: [18/100][770/800] Data 0.002 (0.004) Batch 0.467 (0.323) Remain 05:53:34 loss: 0.3754 Lr: 0.00573 [2023-12-20 14:51:10,994 INFO misc.py line 119 131400] Train: [18/100][771/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 05:53:33 loss: 1.0208 Lr: 0.00573 [2023-12-20 14:51:11,312 INFO misc.py line 119 131400] Train: [18/100][772/800] Data 0.003 (0.004) Batch 0.318 (0.323) Remain 05:53:32 loss: 0.3407 Lr: 0.00573 [2023-12-20 14:51:11,625 INFO misc.py line 119 131400] Train: [18/100][773/800] Data 0.002 (0.004) Batch 0.312 (0.323) Remain 05:53:31 loss: 0.5855 Lr: 0.00573 [2023-12-20 14:51:11,964 INFO misc.py line 119 131400] Train: [18/100][774/800] Data 0.003 (0.004) Batch 0.337 (0.323) Remain 05:53:32 loss: 0.4280 Lr: 0.00573 [2023-12-20 14:51:12,287 INFO misc.py line 119 131400] Train: [18/100][775/800] Data 0.005 (0.004) Batch 0.325 (0.323) Remain 05:53:32 loss: 0.5973 Lr: 0.00573 [2023-12-20 14:51:12,611 INFO misc.py line 119 131400] Train: [18/100][776/800] Data 0.003 (0.004) Batch 0.324 (0.323) Remain 05:53:31 loss: 1.0084 Lr: 0.00573 [2023-12-20 14:51:12,912 INFO misc.py line 119 131400] Train: [18/100][777/800] Data 0.003 (0.004) Batch 0.301 (0.323) Remain 05:53:29 loss: 0.4581 Lr: 0.00573 [2023-12-20 14:51:13,197 INFO misc.py line 119 131400] Train: [18/100][778/800] Data 0.004 (0.004) Batch 0.285 (0.323) Remain 05:53:26 loss: 0.4514 Lr: 0.00573 [2023-12-20 14:51:13,483 INFO misc.py line 119 131400] Train: [18/100][779/800] Data 0.003 (0.004) Batch 0.286 (0.323) Remain 05:53:22 loss: 0.3568 Lr: 0.00573 [2023-12-20 14:51:13,832 INFO misc.py line 119 131400] Train: [18/100][780/800] Data 0.003 (0.004) Batch 0.349 (0.323) Remain 05:53:24 loss: 0.8079 Lr: 0.00573 [2023-12-20 14:51:14,153 INFO misc.py line 119 131400] Train: [18/100][781/800] Data 0.003 (0.004) Batch 0.321 (0.323) Remain 05:53:24 loss: 0.5530 Lr: 0.00573 [2023-12-20 14:51:14,460 INFO misc.py line 119 131400] Train: [18/100][782/800] Data 0.003 (0.004) Batch 0.307 (0.323) Remain 05:53:22 loss: 0.4464 Lr: 0.00573 [2023-12-20 14:51:14,735 INFO misc.py line 119 131400] Train: [18/100][783/800] Data 0.003 (0.004) Batch 0.273 (0.323) Remain 05:53:17 loss: 1.0338 Lr: 0.00573 [2023-12-20 14:51:15,162 INFO misc.py line 119 131400] Train: [18/100][784/800] Data 0.004 (0.004) Batch 0.429 (0.323) Remain 05:53:26 loss: 0.3878 Lr: 0.00573 [2023-12-20 14:51:15,519 INFO misc.py line 119 131400] Train: [18/100][785/800] Data 0.004 (0.004) Batch 0.357 (0.323) Remain 05:53:28 loss: 0.4846 Lr: 0.00573 [2023-12-20 14:51:15,843 INFO misc.py line 119 131400] Train: [18/100][786/800] Data 0.002 (0.004) Batch 0.317 (0.323) Remain 05:53:28 loss: 0.5048 Lr: 0.00573 [2023-12-20 14:51:16,175 INFO misc.py line 119 131400] Train: [18/100][787/800] Data 0.010 (0.004) Batch 0.338 (0.323) Remain 05:53:29 loss: 0.4950 Lr: 0.00573 [2023-12-20 14:51:16,497 INFO misc.py line 119 131400] Train: [18/100][788/800] Data 0.004 (0.004) Batch 0.321 (0.323) Remain 05:53:28 loss: 0.2808 Lr: 0.00573 [2023-12-20 14:51:16,815 INFO misc.py line 119 131400] Train: [18/100][789/800] Data 0.004 (0.004) Batch 0.317 (0.323) Remain 05:53:27 loss: 0.5283 Lr: 0.00573 [2023-12-20 14:51:17,099 INFO misc.py line 119 131400] Train: [18/100][790/800] Data 0.005 (0.004) Batch 0.286 (0.323) Remain 05:53:24 loss: 0.4525 Lr: 0.00573 [2023-12-20 14:51:17,433 INFO misc.py line 119 131400] Train: [18/100][791/800] Data 0.004 (0.004) Batch 0.334 (0.323) Remain 05:53:24 loss: 0.6542 Lr: 0.00573 [2023-12-20 14:51:17,788 INFO misc.py line 119 131400] Train: [18/100][792/800] Data 0.003 (0.004) Batch 0.356 (0.323) Remain 05:53:27 loss: 0.4774 Lr: 0.00573 [2023-12-20 14:51:18,162 INFO misc.py line 119 131400] Train: [18/100][793/800] Data 0.003 (0.004) Batch 0.372 (0.323) Remain 05:53:30 loss: 0.6331 Lr: 0.00573 [2023-12-20 14:51:18,468 INFO misc.py line 119 131400] Train: [18/100][794/800] Data 0.005 (0.004) Batch 0.308 (0.323) Remain 05:53:29 loss: 0.3775 Lr: 0.00573 [2023-12-20 14:51:18,784 INFO misc.py line 119 131400] Train: [18/100][795/800] Data 0.003 (0.004) Batch 0.316 (0.323) Remain 05:53:28 loss: 1.0820 Lr: 0.00573 [2023-12-20 14:51:19,080 INFO misc.py line 119 131400] Train: [18/100][796/800] Data 0.003 (0.004) Batch 0.295 (0.323) Remain 05:53:25 loss: 0.4749 Lr: 0.00573 [2023-12-20 14:51:19,395 INFO misc.py line 119 131400] Train: [18/100][797/800] Data 0.004 (0.004) Batch 0.316 (0.323) Remain 05:53:24 loss: 0.5512 Lr: 0.00573 [2023-12-20 14:51:19,685 INFO misc.py line 119 131400] Train: [18/100][798/800] Data 0.002 (0.004) Batch 0.290 (0.323) Remain 05:53:21 loss: 0.3857 Lr: 0.00573 [2023-12-20 14:51:19,951 INFO misc.py line 119 131400] Train: [18/100][799/800] Data 0.003 (0.004) Batch 0.267 (0.323) Remain 05:53:16 loss: 0.6122 Lr: 0.00573 [2023-12-20 14:51:20,253 INFO misc.py line 119 131400] Train: [18/100][800/800] Data 0.002 (0.004) Batch 0.297 (0.323) Remain 05:53:14 loss: 0.4786 Lr: 0.00573 [2023-12-20 14:51:20,253 INFO misc.py line 136 131400] Train result: loss: 0.5618 [2023-12-20 14:51:20,254 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:51:42,501 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0698 [2023-12-20 14:51:43,119 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.3091 [2023-12-20 14:51:43,221 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4239 [2023-12-20 14:51:43,535 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1225 [2023-12-20 14:51:43,665 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3017 [2023-12-20 14:51:43,775 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2670 [2023-12-20 14:51:43,873 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9619 [2023-12-20 14:51:43,984 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.5262 [2023-12-20 14:51:44,064 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3925 [2023-12-20 14:51:44,150 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4715 [2023-12-20 14:51:44,242 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6645 [2023-12-20 14:51:44,380 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.6972 [2023-12-20 14:51:44,510 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2328 [2023-12-20 14:51:44,674 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2449 [2023-12-20 14:51:44,776 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2371 [2023-12-20 14:51:44,921 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7684 [2023-12-20 14:51:45,032 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3053 [2023-12-20 14:51:45,143 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4681 [2023-12-20 14:51:45,260 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2848 [2023-12-20 14:51:45,344 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4156 [2023-12-20 14:51:45,448 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4131 [2023-12-20 14:51:45,621 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2180 [2023-12-20 14:51:45,745 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8178 [2023-12-20 14:51:45,901 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4740 [2023-12-20 14:51:46,049 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1980 [2023-12-20 14:51:46,135 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4878 [2023-12-20 14:51:46,293 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5266 [2023-12-20 14:51:46,418 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5002 [2023-12-20 14:51:46,512 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7557 [2023-12-20 14:51:46,656 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2522 [2023-12-20 14:51:46,761 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8776 [2023-12-20 14:51:46,881 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6541 [2023-12-20 14:51:46,964 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2295 [2023-12-20 14:51:47,034 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2757 [2023-12-20 14:51:47,131 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.1706 [2023-12-20 14:51:47,222 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6911 [2023-12-20 14:51:47,350 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9792 [2023-12-20 14:51:47,459 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2653 [2023-12-20 14:51:47,547 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6382 [2023-12-20 14:51:47,689 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6454 [2023-12-20 14:51:47,841 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0618 [2023-12-20 14:51:47,944 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1204 [2023-12-20 14:51:48,064 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2324 [2023-12-20 14:51:48,205 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7902 [2023-12-20 14:51:48,325 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8779 [2023-12-20 14:51:48,431 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5047 [2023-12-20 14:51:48,598 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4693 [2023-12-20 14:51:48,696 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.7833 [2023-12-20 14:51:48,844 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1574 [2023-12-20 14:51:48,935 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7380 [2023-12-20 14:51:49,012 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8406 [2023-12-20 14:51:49,115 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5205 [2023-12-20 14:51:49,261 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9776 [2023-12-20 14:51:49,400 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3714 [2023-12-20 14:51:49,501 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5684 [2023-12-20 14:51:49,589 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8236 [2023-12-20 14:51:49,697 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4247 [2023-12-20 14:51:49,856 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2807 [2023-12-20 14:51:49,953 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3306 [2023-12-20 14:51:50,047 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5151 [2023-12-20 14:51:50,143 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4630 [2023-12-20 14:51:50,237 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5206 [2023-12-20 14:51:50,326 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7242 [2023-12-20 14:51:50,431 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7930 [2023-12-20 14:51:50,557 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.2420 [2023-12-20 14:51:50,643 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3720 [2023-12-20 14:51:50,746 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.7799 [2023-12-20 14:51:50,838 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0638 [2023-12-20 14:51:50,920 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.5398 [2023-12-20 14:51:51,016 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1014 [2023-12-20 14:51:51,112 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7687 [2023-12-20 14:51:51,206 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8728 [2023-12-20 14:51:51,355 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1744 [2023-12-20 14:51:51,461 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.8046 [2023-12-20 14:51:51,575 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8003 [2023-12-20 14:51:51,677 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9245 [2023-12-20 14:51:51,771 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.8350 [2023-12-20 14:51:51,927 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3500 [2023-12-20 14:51:53,182 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7077/0.8060/0.8991. [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8557/0.9397 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9617/0.9782 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6188/0.7300 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8074/0.8745 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8907/0.9640 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8107/0.8826 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7350/0.8522 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6203/0.7433 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6358/0.7773 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7238/0.9264 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3400/0.4731 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6061/0.6942 [2023-12-20 14:51:53,182 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6345/0.7734 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7440/0.8386 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4817/0.6910 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6155/0.6834 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9412/0.9707 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6669/0.7582 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8831/0.9164 [2023-12-20 14:51:53,183 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5809/0.6531 [2023-12-20 14:51:53,183 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:51:53,184 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7077 [2023-12-20 14:51:53,184 INFO misc.py line 165 131400] Currently Best mIoU: 0.7077 [2023-12-20 14:51:53,184 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:52:00,188 INFO misc.py line 119 131400] Train: [19/100][1/800] Data 1.232 (1.232) Batch 1.562 (1.562) Remain 28:27:15 loss: 0.4562 Lr: 0.00573 [2023-12-20 14:52:00,514 INFO misc.py line 119 131400] Train: [19/100][2/800] Data 0.004 (0.004) Batch 0.327 (0.327) Remain 05:57:54 loss: 0.7028 Lr: 0.00573 [2023-12-20 14:52:00,834 INFO misc.py line 119 131400] Train: [19/100][3/800] Data 0.003 (0.003) Batch 0.320 (0.320) Remain 05:49:40 loss: 0.6985 Lr: 0.00573 [2023-12-20 14:52:01,151 INFO misc.py line 119 131400] Train: [19/100][4/800] Data 0.003 (0.003) Batch 0.315 (0.315) Remain 05:44:43 loss: 0.5600 Lr: 0.00573 [2023-12-20 14:52:01,485 INFO misc.py line 119 131400] Train: [19/100][5/800] Data 0.005 (0.004) Batch 0.335 (0.325) Remain 05:55:32 loss: 0.5690 Lr: 0.00573 [2023-12-20 14:52:01,802 INFO misc.py line 119 131400] Train: [19/100][6/800] Data 0.003 (0.004) Batch 0.317 (0.323) Remain 05:52:41 loss: 1.0958 Lr: 0.00573 [2023-12-20 14:52:02,114 INFO misc.py line 119 131400] Train: [19/100][7/800] Data 0.003 (0.004) Batch 0.312 (0.320) Remain 05:49:48 loss: 0.6077 Lr: 0.00573 [2023-12-20 14:52:02,415 INFO misc.py line 119 131400] Train: [19/100][8/800] Data 0.003 (0.003) Batch 0.300 (0.316) Remain 05:45:31 loss: 0.7811 Lr: 0.00573 [2023-12-20 14:52:02,697 INFO misc.py line 119 131400] Train: [19/100][9/800] Data 0.003 (0.003) Batch 0.282 (0.310) Remain 05:39:18 loss: 0.2567 Lr: 0.00573 [2023-12-20 14:52:03,038 INFO misc.py line 119 131400] Train: [19/100][10/800] Data 0.003 (0.003) Batch 0.340 (0.315) Remain 05:43:59 loss: 0.6162 Lr: 0.00573 [2023-12-20 14:52:03,338 INFO misc.py line 119 131400] Train: [19/100][11/800] Data 0.004 (0.003) Batch 0.297 (0.312) Remain 05:41:32 loss: 0.4177 Lr: 0.00573 [2023-12-20 14:52:03,640 INFO misc.py line 119 131400] Train: [19/100][12/800] Data 0.008 (0.004) Batch 0.306 (0.312) Remain 05:40:43 loss: 0.4869 Lr: 0.00573 [2023-12-20 14:52:03,963 INFO misc.py line 119 131400] Train: [19/100][13/800] Data 0.003 (0.004) Batch 0.323 (0.313) Remain 05:41:55 loss: 0.3907 Lr: 0.00573 [2023-12-20 14:52:04,285 INFO misc.py line 119 131400] Train: [19/100][14/800] Data 0.004 (0.004) Batch 0.323 (0.314) Remain 05:42:55 loss: 0.6818 Lr: 0.00573 [2023-12-20 14:52:04,618 INFO misc.py line 119 131400] Train: [19/100][15/800] Data 0.003 (0.004) Batch 0.333 (0.315) Remain 05:44:38 loss: 0.5626 Lr: 0.00573 [2023-12-20 14:52:04,967 INFO misc.py line 119 131400] Train: [19/100][16/800] Data 0.004 (0.004) Batch 0.348 (0.318) Remain 05:47:21 loss: 0.7227 Lr: 0.00573 [2023-12-20 14:52:05,292 INFO misc.py line 119 131400] Train: [19/100][17/800] Data 0.005 (0.004) Batch 0.327 (0.318) Remain 05:48:03 loss: 0.6081 Lr: 0.00573 [2023-12-20 14:52:05,600 INFO misc.py line 119 131400] Train: [19/100][18/800] Data 0.003 (0.004) Batch 0.308 (0.318) Remain 05:47:18 loss: 1.0051 Lr: 0.00573 [2023-12-20 14:52:05,902 INFO misc.py line 119 131400] Train: [19/100][19/800] Data 0.003 (0.004) Batch 0.301 (0.317) Remain 05:46:10 loss: 0.8758 Lr: 0.00573 [2023-12-20 14:52:06,216 INFO misc.py line 119 131400] Train: [19/100][20/800] Data 0.003 (0.004) Batch 0.314 (0.317) Remain 05:45:59 loss: 0.3580 Lr: 0.00573 [2023-12-20 14:52:06,528 INFO misc.py line 119 131400] Train: [19/100][21/800] Data 0.003 (0.004) Batch 0.307 (0.316) Remain 05:45:22 loss: 0.6905 Lr: 0.00573 [2023-12-20 14:52:06,827 INFO misc.py line 119 131400] Train: [19/100][22/800] Data 0.009 (0.004) Batch 0.305 (0.315) Remain 05:44:44 loss: 0.3842 Lr: 0.00573 [2023-12-20 14:52:07,138 INFO misc.py line 119 131400] Train: [19/100][23/800] Data 0.003 (0.004) Batch 0.311 (0.315) Remain 05:44:29 loss: 0.4008 Lr: 0.00573 [2023-12-20 14:52:07,419 INFO misc.py line 119 131400] Train: [19/100][24/800] Data 0.002 (0.004) Batch 0.281 (0.314) Remain 05:42:41 loss: 0.5459 Lr: 0.00573 [2023-12-20 14:52:07,757 INFO misc.py line 119 131400] Train: [19/100][25/800] Data 0.003 (0.004) Batch 0.339 (0.315) Remain 05:43:56 loss: 0.4740 Lr: 0.00573 [2023-12-20 14:52:08,081 INFO misc.py line 119 131400] Train: [19/100][26/800] Data 0.003 (0.004) Batch 0.323 (0.315) Remain 05:44:20 loss: 0.4803 Lr: 0.00573 [2023-12-20 14:52:08,415 INFO misc.py line 119 131400] Train: [19/100][27/800] Data 0.002 (0.004) Batch 0.335 (0.316) Remain 05:45:13 loss: 0.3520 Lr: 0.00573 [2023-12-20 14:52:08,734 INFO misc.py line 119 131400] Train: [19/100][28/800] Data 0.002 (0.004) Batch 0.319 (0.316) Remain 05:45:21 loss: 0.5142 Lr: 0.00573 [2023-12-20 14:52:09,039 INFO misc.py line 119 131400] Train: [19/100][29/800] Data 0.002 (0.004) Batch 0.305 (0.316) Remain 05:44:52 loss: 0.3502 Lr: 0.00573 [2023-12-20 14:52:09,382 INFO misc.py line 119 131400] Train: [19/100][30/800] Data 0.002 (0.003) Batch 0.342 (0.317) Remain 05:45:57 loss: 0.4198 Lr: 0.00573 [2023-12-20 14:52:09,676 INFO misc.py line 119 131400] Train: [19/100][31/800] Data 0.003 (0.003) Batch 0.293 (0.316) Remain 05:45:02 loss: 0.6143 Lr: 0.00573 [2023-12-20 14:52:10,012 INFO misc.py line 119 131400] Train: [19/100][32/800] Data 0.004 (0.003) Batch 0.336 (0.316) Remain 05:45:49 loss: 0.3693 Lr: 0.00573 [2023-12-20 14:52:10,332 INFO misc.py line 119 131400] Train: [19/100][33/800] Data 0.003 (0.003) Batch 0.320 (0.317) Remain 05:45:55 loss: 0.3403 Lr: 0.00573 [2023-12-20 14:52:10,629 INFO misc.py line 119 131400] Train: [19/100][34/800] Data 0.003 (0.003) Batch 0.298 (0.316) Remain 05:45:17 loss: 0.6866 Lr: 0.00573 [2023-12-20 14:52:10,943 INFO misc.py line 119 131400] Train: [19/100][35/800] Data 0.002 (0.003) Batch 0.314 (0.316) Remain 05:45:13 loss: 0.4606 Lr: 0.00573 [2023-12-20 14:52:11,260 INFO misc.py line 119 131400] Train: [19/100][36/800] Data 0.003 (0.003) Batch 0.316 (0.316) Remain 05:45:12 loss: 0.5510 Lr: 0.00573 [2023-12-20 14:52:11,579 INFO misc.py line 119 131400] Train: [19/100][37/800] Data 0.004 (0.003) Batch 0.319 (0.316) Remain 05:45:19 loss: 0.5568 Lr: 0.00573 [2023-12-20 14:52:11,886 INFO misc.py line 119 131400] Train: [19/100][38/800] Data 0.003 (0.003) Batch 0.305 (0.316) Remain 05:44:58 loss: 0.3923 Lr: 0.00573 [2023-12-20 14:52:12,195 INFO misc.py line 119 131400] Train: [19/100][39/800] Data 0.005 (0.003) Batch 0.306 (0.315) Remain 05:44:41 loss: 0.5256 Lr: 0.00573 [2023-12-20 14:52:12,508 INFO misc.py line 119 131400] Train: [19/100][40/800] Data 0.007 (0.004) Batch 0.317 (0.315) Remain 05:44:43 loss: 0.4779 Lr: 0.00573 [2023-12-20 14:52:12,843 INFO misc.py line 119 131400] Train: [19/100][41/800] Data 0.004 (0.004) Batch 0.335 (0.316) Remain 05:45:17 loss: 0.3365 Lr: 0.00573 [2023-12-20 14:52:13,168 INFO misc.py line 119 131400] Train: [19/100][42/800] Data 0.003 (0.004) Batch 0.325 (0.316) Remain 05:45:32 loss: 0.6353 Lr: 0.00573 [2023-12-20 14:52:13,575 INFO misc.py line 119 131400] Train: [19/100][43/800] Data 0.004 (0.004) Batch 0.407 (0.319) Remain 05:48:01 loss: 0.6448 Lr: 0.00573 [2023-12-20 14:52:13,902 INFO misc.py line 119 131400] Train: [19/100][44/800] Data 0.002 (0.004) Batch 0.327 (0.319) Remain 05:48:14 loss: 0.7056 Lr: 0.00573 [2023-12-20 14:52:14,215 INFO misc.py line 119 131400] Train: [19/100][45/800] Data 0.003 (0.004) Batch 0.313 (0.319) Remain 05:48:05 loss: 0.5743 Lr: 0.00573 [2023-12-20 14:52:14,666 INFO misc.py line 119 131400] Train: [19/100][46/800] Data 0.003 (0.003) Batch 0.451 (0.322) Remain 05:51:26 loss: 0.2886 Lr: 0.00572 [2023-12-20 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Train: [19/100][53/800] Data 0.002 (0.003) Batch 0.292 (0.319) Remain 05:48:35 loss: 0.3906 Lr: 0.00572 [2023-12-20 14:52:17,054 INFO misc.py line 119 131400] Train: [19/100][54/800] Data 0.003 (0.003) Batch 0.265 (0.318) Remain 05:47:25 loss: 0.6222 Lr: 0.00572 [2023-12-20 14:52:17,351 INFO misc.py line 119 131400] Train: [19/100][55/800] Data 0.002 (0.003) Batch 0.296 (0.318) Remain 05:46:57 loss: 0.6750 Lr: 0.00572 [2023-12-20 14:52:17,663 INFO misc.py line 119 131400] Train: [19/100][56/800] Data 0.004 (0.003) Batch 0.312 (0.318) Remain 05:46:50 loss: 0.4840 Lr: 0.00572 [2023-12-20 14:52:17,970 INFO misc.py line 119 131400] Train: [19/100][57/800] Data 0.003 (0.003) Batch 0.308 (0.317) Remain 05:46:38 loss: 0.4220 Lr: 0.00572 [2023-12-20 14:52:18,283 INFO misc.py line 119 131400] Train: [19/100][58/800] Data 0.003 (0.003) Batch 0.312 (0.317) Remain 05:46:31 loss: 0.5317 Lr: 0.00572 [2023-12-20 14:52:18,561 INFO misc.py line 119 131400] Train: [19/100][59/800] Data 0.004 (0.003) 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0.009 (0.004) Batch 0.301 (0.327) Remain 05:53:25 loss: 0.5083 Lr: 0.00569 [2023-12-20 14:56:13,666 INFO misc.py line 119 131400] Train: [19/100][776/800] Data 0.002 (0.004) Batch 0.296 (0.327) Remain 05:53:22 loss: 0.5697 Lr: 0.00569 [2023-12-20 14:56:14,009 INFO misc.py line 119 131400] Train: [19/100][777/800] Data 0.003 (0.004) Batch 0.342 (0.327) Remain 05:53:23 loss: 0.7168 Lr: 0.00569 [2023-12-20 14:56:14,322 INFO misc.py line 119 131400] Train: [19/100][778/800] Data 0.004 (0.004) Batch 0.314 (0.327) Remain 05:53:21 loss: 0.5797 Lr: 0.00569 [2023-12-20 14:56:14,633 INFO misc.py line 119 131400] Train: [19/100][779/800] Data 0.003 (0.004) Batch 0.311 (0.327) Remain 05:53:20 loss: 1.1597 Lr: 0.00569 [2023-12-20 14:56:14,938 INFO misc.py line 119 131400] Train: [19/100][780/800] Data 0.003 (0.004) Batch 0.306 (0.327) Remain 05:53:18 loss: 0.5699 Lr: 0.00569 [2023-12-20 14:56:15,252 INFO misc.py line 119 131400] Train: [19/100][781/800] Data 0.003 (0.004) Batch 0.314 (0.327) Remain 05:53:16 loss: 0.7043 Lr: 0.00569 [2023-12-20 14:56:15,576 INFO misc.py line 119 131400] Train: [19/100][782/800] Data 0.003 (0.004) Batch 0.323 (0.327) Remain 05:53:16 loss: 0.9375 Lr: 0.00569 [2023-12-20 14:56:15,898 INFO misc.py line 119 131400] Train: [19/100][783/800] Data 0.004 (0.004) Batch 0.322 (0.327) Remain 05:53:15 loss: 0.4115 Lr: 0.00569 [2023-12-20 14:56:16,210 INFO misc.py line 119 131400] Train: [19/100][784/800] Data 0.004 (0.004) Batch 0.313 (0.327) Remain 05:53:13 loss: 0.3661 Lr: 0.00569 [2023-12-20 14:56:16,545 INFO misc.py line 119 131400] Train: [19/100][785/800] Data 0.003 (0.004) Batch 0.334 (0.327) Remain 05:53:14 loss: 0.3790 Lr: 0.00569 [2023-12-20 14:56:16,843 INFO misc.py line 119 131400] Train: [19/100][786/800] Data 0.003 (0.004) Batch 0.298 (0.327) Remain 05:53:11 loss: 0.4245 Lr: 0.00569 [2023-12-20 14:56:17,166 INFO misc.py line 119 131400] Train: [19/100][787/800] Data 0.003 (0.004) Batch 0.324 (0.327) Remain 05:53:10 loss: 0.7116 Lr: 0.00569 [2023-12-20 14:56:17,474 INFO misc.py line 119 131400] Train: [19/100][788/800] Data 0.003 (0.004) Batch 0.308 (0.327) Remain 05:53:08 loss: 0.6744 Lr: 0.00569 [2023-12-20 14:56:17,770 INFO misc.py line 119 131400] Train: [19/100][789/800] Data 0.002 (0.004) Batch 0.295 (0.327) Remain 05:53:05 loss: 0.9188 Lr: 0.00569 [2023-12-20 14:56:18,061 INFO misc.py line 119 131400] Train: [19/100][790/800] Data 0.003 (0.004) Batch 0.290 (0.327) Remain 05:53:02 loss: 0.3765 Lr: 0.00569 [2023-12-20 14:56:18,330 INFO misc.py line 119 131400] Train: [19/100][791/800] Data 0.003 (0.004) Batch 0.270 (0.327) Remain 05:52:57 loss: 0.5820 Lr: 0.00569 [2023-12-20 14:56:18,599 INFO misc.py line 119 131400] Train: [19/100][792/800] Data 0.003 (0.004) Batch 0.269 (0.327) Remain 05:52:52 loss: 0.5540 Lr: 0.00568 [2023-12-20 14:56:18,925 INFO misc.py line 119 131400] Train: [19/100][793/800] Data 0.003 (0.004) Batch 0.327 (0.327) Remain 05:52:52 loss: 0.4002 Lr: 0.00568 [2023-12-20 14:56:19,221 INFO misc.py line 119 131400] Train: [19/100][794/800] Data 0.003 (0.004) Batch 0.296 (0.327) Remain 05:52:49 loss: 0.9923 Lr: 0.00568 [2023-12-20 14:56:19,523 INFO misc.py line 119 131400] Train: [19/100][795/800] Data 0.003 (0.004) Batch 0.302 (0.327) Remain 05:52:46 loss: 0.5401 Lr: 0.00568 [2023-12-20 14:56:19,800 INFO misc.py line 119 131400] Train: [19/100][796/800] Data 0.003 (0.004) Batch 0.277 (0.327) Remain 05:52:42 loss: 0.4390 Lr: 0.00568 [2023-12-20 14:56:20,100 INFO misc.py line 119 131400] Train: [19/100][797/800] Data 0.003 (0.004) Batch 0.299 (0.327) Remain 05:52:40 loss: 0.4386 Lr: 0.00568 [2023-12-20 14:56:20,401 INFO misc.py line 119 131400] Train: [19/100][798/800] Data 0.003 (0.004) Batch 0.301 (0.326) Remain 05:52:37 loss: 0.5036 Lr: 0.00568 [2023-12-20 14:56:20,712 INFO misc.py line 119 131400] Train: [19/100][799/800] Data 0.003 (0.004) Batch 0.312 (0.326) Remain 05:52:36 loss: 0.7938 Lr: 0.00568 [2023-12-20 14:56:21,019 INFO misc.py line 119 131400] Train: [19/100][800/800] Data 0.002 (0.004) Batch 0.307 (0.326) Remain 05:52:34 loss: 0.5908 Lr: 0.00568 [2023-12-20 14:56:21,020 INFO misc.py line 136 131400] Train result: loss: 0.5727 [2023-12-20 14:56:21,020 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 14:56:43,675 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1999 [2023-12-20 14:56:43,875 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2290 [2023-12-20 14:56:44,991 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4423 [2023-12-20 14:56:45,110 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1319 [2023-12-20 14:56:45,224 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3790 [2023-12-20 14:56:45,326 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1483 [2023-12-20 14:56:45,427 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.5378 [2023-12-20 14:56:45,538 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3927 [2023-12-20 14:56:45,623 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2475 [2023-12-20 14:56:45,715 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3332 [2023-12-20 14:56:45,807 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5918 [2023-12-20 14:56:45,944 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5516 [2023-12-20 14:56:46,065 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2898 [2023-12-20 14:56:46,219 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2337 [2023-12-20 14:56:46,315 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.4366 [2023-12-20 14:56:46,451 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7177 [2023-12-20 14:56:46,568 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3238 [2023-12-20 14:56:46,677 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.1049 [2023-12-20 14:56:46,788 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3636 [2023-12-20 14:56:46,862 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4922 [2023-12-20 14:56:46,970 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3516 [2023-12-20 14:56:47,127 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2334 [2023-12-20 14:56:47,249 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3737 [2023-12-20 14:56:47,391 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3908 [2023-12-20 14:56:47,535 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1632 [2023-12-20 14:56:47,618 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5420 [2023-12-20 14:56:47,775 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6265 [2023-12-20 14:56:47,902 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4122 [2023-12-20 14:56:48,003 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7211 [2023-12-20 14:56:48,148 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3558 [2023-12-20 14:56:48,254 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7474 [2023-12-20 14:56:48,375 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4616 [2023-12-20 14:56:48,460 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2600 [2023-12-20 14:56:48,553 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2121 [2023-12-20 14:56:48,648 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5365 [2023-12-20 14:56:48,740 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4529 [2023-12-20 14:56:48,868 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8974 [2023-12-20 14:56:48,979 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1463 [2023-12-20 14:56:49,083 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5197 [2023-12-20 14:56:49,225 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4893 [2023-12-20 14:56:49,378 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0756 [2023-12-20 14:56:49,478 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2426 [2023-12-20 14:56:49,605 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4395 [2023-12-20 14:56:49,748 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0918 [2023-12-20 14:56:49,867 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7745 [2023-12-20 14:56:49,968 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5059 [2023-12-20 14:56:50,136 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4149 [2023-12-20 14:56:50,232 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3889 [2023-12-20 14:56:50,386 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4065 [2023-12-20 14:56:50,487 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.5492 [2023-12-20 14:56:50,565 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8815 [2023-12-20 14:56:50,681 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1890 [2023-12-20 14:56:50,835 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.5413 [2023-12-20 14:56:50,968 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3006 [2023-12-20 14:56:51,078 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4227 [2023-12-20 14:56:51,173 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7524 [2023-12-20 14:56:51,278 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4456 [2023-12-20 14:56:51,442 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2799 [2023-12-20 14:56:51,539 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4747 [2023-12-20 14:56:51,631 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1644 [2023-12-20 14:56:51,745 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4328 [2023-12-20 14:56:51,845 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4071 [2023-12-20 14:56:51,936 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6520 [2023-12-20 14:56:52,042 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8598 [2023-12-20 14:56:52,170 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4836 [2023-12-20 14:56:52,263 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2460 [2023-12-20 14:56:52,363 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4959 [2023-12-20 14:56:52,459 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0492 [2023-12-20 14:56:52,544 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3768 [2023-12-20 14:56:52,628 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0626 [2023-12-20 14:56:52,721 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6492 [2023-12-20 14:56:52,815 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7040 [2023-12-20 14:56:52,948 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2093 [2023-12-20 14:56:53,045 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7579 [2023-12-20 14:56:53,161 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7216 [2023-12-20 14:56:53,263 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9441 [2023-12-20 14:56:53,356 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3917 [2023-12-20 14:56:53,514 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1582 [2023-12-20 14:56:54,981 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7287/0.8159/0.9014. [2023-12-20 14:56:54,981 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8481/0.9299 [2023-12-20 14:56:54,981 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9630/0.9850 [2023-12-20 14:56:54,981 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6123/0.8490 [2023-12-20 14:56:54,981 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8021/0.8266 [2023-12-20 14:56:54,981 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8989/0.9577 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8266/0.8744 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7393/0.7949 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6768/0.8455 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6294/0.7669 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7626/0.8352 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3617/0.4842 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6778/0.8127 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6924/0.8328 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7748/0.8537 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6305/0.7356 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6957/0.7872 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9350/0.9446 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6304/0.6898 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8638/0.8772 [2023-12-20 14:56:54,982 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5523/0.6352 [2023-12-20 14:56:54,983 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 14:56:54,984 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7287 [2023-12-20 14:56:54,984 INFO misc.py line 165 131400] Currently Best mIoU: 0.7287 [2023-12-20 14:56:54,984 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 14:57:01,381 INFO misc.py line 119 131400] Train: [20/100][1/800] Data 0.618 (0.618) Batch 0.876 (0.876) Remain 15:45:59 loss: 0.4271 Lr: 0.00568 [2023-12-20 14:57:01,795 INFO misc.py line 119 131400] Train: [20/100][2/800] Data 0.137 (0.137) Batch 0.416 (0.416) Remain 07:29:16 loss: 0.3999 Lr: 0.00568 [2023-12-20 14:57:02,092 INFO misc.py line 119 131400] Train: [20/100][3/800] Data 0.004 (0.004) Batch 0.297 (0.297) Remain 05:21:05 loss: 0.4075 Lr: 0.00568 [2023-12-20 14:57:02,412 INFO misc.py line 119 131400] Train: [20/100][4/800] Data 0.003 (0.003) Batch 0.314 (0.314) Remain 05:38:39 loss: 0.5836 Lr: 0.00568 [2023-12-20 14:57:02,733 INFO misc.py line 119 131400] Train: [20/100][5/800] Data 0.009 (0.006) Batch 0.327 (0.320) Remain 05:45:39 loss: 0.4628 Lr: 0.00568 [2023-12-20 14:57:03,059 INFO misc.py line 119 131400] Train: [20/100][6/800] Data 0.004 (0.005) Batch 0.326 (0.322) Remain 05:47:53 loss: 0.3269 Lr: 0.00568 [2023-12-20 14:57:03,385 INFO misc.py line 119 131400] Train: [20/100][7/800] Data 0.003 (0.005) Batch 0.326 (0.323) Remain 05:48:58 loss: 0.2058 Lr: 0.00568 [2023-12-20 14:57:03,703 INFO misc.py line 119 131400] Train: [20/100][8/800] Data 0.003 (0.004) Batch 0.318 (0.322) Remain 05:47:48 loss: 0.4310 Lr: 0.00568 [2023-12-20 14:57:04,024 INFO misc.py line 119 131400] Train: [20/100][9/800] Data 0.003 (0.004) Batch 0.321 (0.322) Remain 05:47:36 loss: 0.5349 Lr: 0.00568 [2023-12-20 14:57:04,356 INFO misc.py line 119 131400] Train: [20/100][10/800] Data 0.004 (0.004) Batch 0.332 (0.323) Remain 05:49:05 loss: 0.6500 Lr: 0.00568 [2023-12-20 14:57:04,696 INFO misc.py line 119 131400] Train: [20/100][11/800] Data 0.004 (0.004) Batch 0.339 (0.325) Remain 05:51:14 loss: 0.8314 Lr: 0.00568 [2023-12-20 14:57:05,000 INFO misc.py line 119 131400] Train: [20/100][12/800] Data 0.004 (0.004) Batch 0.305 (0.323) Remain 05:48:49 loss: 0.4915 Lr: 0.00568 [2023-12-20 14:57:05,322 INFO misc.py line 119 131400] Train: [20/100][13/800] Data 0.003 (0.004) Batch 0.322 (0.323) Remain 05:48:44 loss: 0.7654 Lr: 0.00568 [2023-12-20 14:57:05,659 INFO misc.py line 119 131400] Train: [20/100][14/800] Data 0.004 (0.004) Batch 0.334 (0.324) Remain 05:49:50 loss: 0.5042 Lr: 0.00568 [2023-12-20 14:57:06,004 INFO misc.py line 119 131400] Train: [20/100][15/800] Data 0.006 (0.004) Batch 0.348 (0.326) Remain 05:51:57 loss: 0.7093 Lr: 0.00568 [2023-12-20 14:57:06,412 INFO misc.py line 119 131400] Train: [20/100][16/800] Data 0.003 (0.004) Batch 0.408 (0.332) Remain 05:58:46 loss: 0.3008 Lr: 0.00568 [2023-12-20 14:57:06,740 INFO misc.py line 119 131400] Train: [20/100][17/800] Data 0.003 (0.004) Batch 0.324 (0.332) Remain 05:58:05 loss: 0.5629 Lr: 0.00568 [2023-12-20 14:57:07,060 INFO misc.py line 119 131400] Train: [20/100][18/800] Data 0.009 (0.004) Batch 0.324 (0.331) Remain 05:57:33 loss: 0.4783 Lr: 0.00568 [2023-12-20 14:57:07,347 INFO misc.py line 119 131400] Train: [20/100][19/800] Data 0.003 (0.004) Batch 0.287 (0.328) Remain 05:54:35 loss: 0.6926 Lr: 0.00568 [2023-12-20 14:57:07,663 INFO misc.py line 119 131400] Train: [20/100][20/800] Data 0.004 (0.004) Batch 0.315 (0.328) Remain 05:53:45 loss: 0.9031 Lr: 0.00568 [2023-12-20 14:57:07,954 INFO misc.py line 119 131400] Train: [20/100][21/800] Data 0.004 (0.004) Batch 0.291 (0.326) Remain 05:51:32 loss: 0.4301 Lr: 0.00568 [2023-12-20 14:57:08,438 INFO misc.py line 119 131400] Train: [20/100][22/800] Data 0.004 (0.004) Batch 0.484 (0.334) Remain 06:00:33 loss: 0.2927 Lr: 0.00568 [2023-12-20 14:57:08,794 INFO misc.py line 119 131400] Train: [20/100][23/800] Data 0.004 (0.004) Batch 0.356 (0.335) Remain 06:01:43 loss: 0.5452 Lr: 0.00568 [2023-12-20 14:57:09,122 INFO misc.py line 119 131400] Train: [20/100][24/800] Data 0.005 (0.004) Batch 0.329 (0.335) Remain 06:01:25 loss: 0.6468 Lr: 0.00568 [2023-12-20 14:57:09,435 INFO misc.py line 119 131400] Train: [20/100][25/800] Data 0.004 (0.004) Batch 0.311 (0.334) Remain 06:00:15 loss: 0.6222 Lr: 0.00568 [2023-12-20 14:57:09,789 INFO misc.py line 119 131400] Train: [20/100][26/800] Data 0.005 (0.004) Batch 0.351 (0.334) Remain 06:01:04 loss: 0.5718 Lr: 0.00568 [2023-12-20 14:57:10,126 INFO misc.py line 119 131400] Train: [20/100][27/800] Data 0.007 (0.004) Batch 0.340 (0.335) Remain 06:01:20 loss: 0.4667 Lr: 0.00568 [2023-12-20 14:57:10,460 INFO misc.py line 119 131400] Train: [20/100][28/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 06:01:20 loss: 0.2621 Lr: 0.00568 [2023-12-20 14:57:10,818 INFO misc.py line 119 131400] Train: [20/100][29/800] Data 0.003 (0.004) Batch 0.356 (0.336) Remain 06:02:13 loss: 0.3421 Lr: 0.00568 [2023-12-20 14:57:11,150 INFO misc.py line 119 131400] Train: [20/100][30/800] Data 0.005 (0.004) Batch 0.333 (0.335) Remain 06:02:07 loss: 0.3943 Lr: 0.00568 [2023-12-20 14:57:11,498 INFO misc.py line 119 131400] Train: [20/100][31/800] Data 0.004 (0.004) Batch 0.348 (0.336) Remain 06:02:37 loss: 0.5157 Lr: 0.00568 [2023-12-20 14:57:11,833 INFO misc.py line 119 131400] Train: [20/100][32/800] Data 0.004 (0.004) Batch 0.334 (0.336) Remain 06:02:32 loss: 0.5703 Lr: 0.00568 [2023-12-20 14:57:12,146 INFO misc.py line 119 131400] Train: [20/100][33/800] Data 0.004 (0.004) Batch 0.314 (0.335) Remain 06:01:46 loss: 0.3217 Lr: 0.00568 [2023-12-20 14:57:12,454 INFO misc.py line 119 131400] Train: [20/100][34/800] Data 0.003 (0.004) Batch 0.307 (0.334) Remain 06:00:46 loss: 0.3275 Lr: 0.00568 [2023-12-20 14:57:12,752 INFO misc.py line 119 131400] Train: [20/100][35/800] Data 0.004 (0.004) Batch 0.299 (0.333) Remain 05:59:34 loss: 0.7023 Lr: 0.00568 [2023-12-20 14:57:13,075 INFO misc.py line 119 131400] Train: [20/100][36/800] Data 0.004 (0.004) Batch 0.323 (0.333) Remain 05:59:13 loss: 0.7189 Lr: 0.00568 [2023-12-20 14:57:13,405 INFO misc.py line 119 131400] Train: [20/100][37/800] Data 0.003 (0.004) Batch 0.330 (0.333) Remain 05:59:09 loss: 0.8183 Lr: 0.00568 [2023-12-20 14:57:13,729 INFO misc.py line 119 131400] Train: [20/100][38/800] Data 0.003 (0.004) Batch 0.324 (0.332) Remain 05:58:52 loss: 0.6308 Lr: 0.00568 [2023-12-20 14:57:14,068 INFO misc.py line 119 131400] Train: [20/100][39/800] Data 0.003 (0.004) Batch 0.338 (0.333) Remain 05:59:02 loss: 1.3084 Lr: 0.00568 [2023-12-20 14:57:14,392 INFO misc.py line 119 131400] Train: [20/100][40/800] Data 0.004 (0.004) Batch 0.323 (0.332) Remain 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05:48:10 loss: 0.8053 Lr: 0.00564 [2023-12-20 15:01:16,282 INFO misc.py line 119 131400] Train: [20/100][782/800] Data 0.003 (0.004) Batch 0.308 (0.326) Remain 05:48:09 loss: 0.5656 Lr: 0.00564 [2023-12-20 15:01:16,603 INFO misc.py line 119 131400] Train: [20/100][783/800] Data 0.002 (0.004) Batch 0.320 (0.326) Remain 05:48:08 loss: 0.7228 Lr: 0.00564 [2023-12-20 15:01:16,934 INFO misc.py line 119 131400] Train: [20/100][784/800] Data 0.004 (0.004) Batch 0.332 (0.326) Remain 05:48:08 loss: 0.7642 Lr: 0.00564 [2023-12-20 15:01:17,271 INFO misc.py line 119 131400] Train: [20/100][785/800] Data 0.002 (0.004) Batch 0.335 (0.326) Remain 05:48:08 loss: 0.6304 Lr: 0.00564 [2023-12-20 15:01:17,552 INFO misc.py line 119 131400] Train: [20/100][786/800] Data 0.004 (0.004) Batch 0.282 (0.326) Remain 05:48:04 loss: 0.4367 Lr: 0.00564 [2023-12-20 15:01:17,876 INFO misc.py line 119 131400] Train: [20/100][787/800] Data 0.004 (0.004) Batch 0.324 (0.326) Remain 05:48:04 loss: 0.7766 Lr: 0.00564 [2023-12-20 15:01:18,223 INFO misc.py line 119 131400] Train: [20/100][788/800] Data 0.004 (0.004) Batch 0.346 (0.326) Remain 05:48:05 loss: 0.3597 Lr: 0.00564 [2023-12-20 15:01:18,539 INFO misc.py line 119 131400] Train: [20/100][789/800] Data 0.006 (0.004) Batch 0.317 (0.326) Remain 05:48:04 loss: 0.2718 Lr: 0.00564 [2023-12-20 15:01:18,878 INFO misc.py line 119 131400] Train: [20/100][790/800] Data 0.004 (0.004) Batch 0.330 (0.326) Remain 05:48:04 loss: 0.5576 Lr: 0.00564 [2023-12-20 15:01:19,184 INFO misc.py line 119 131400] Train: [20/100][791/800] Data 0.012 (0.004) Batch 0.315 (0.326) Remain 05:48:03 loss: 0.4017 Lr: 0.00564 [2023-12-20 15:01:19,452 INFO misc.py line 119 131400] Train: [20/100][792/800] Data 0.004 (0.004) Batch 0.268 (0.326) Remain 05:47:58 loss: 0.4695 Lr: 0.00564 [2023-12-20 15:01:19,790 INFO misc.py line 119 131400] Train: [20/100][793/800] Data 0.004 (0.004) Batch 0.338 (0.326) Remain 05:47:58 loss: 0.8694 Lr: 0.00564 [2023-12-20 15:01:20,203 INFO misc.py line 119 131400] Train: [20/100][794/800] Data 0.003 (0.004) Batch 0.414 (0.326) Remain 05:48:05 loss: 0.5507 Lr: 0.00564 [2023-12-20 15:01:20,529 INFO misc.py line 119 131400] Train: [20/100][795/800] Data 0.003 (0.004) Batch 0.326 (0.326) Remain 05:48:05 loss: 0.3761 Lr: 0.00564 [2023-12-20 15:01:20,875 INFO misc.py line 119 131400] Train: [20/100][796/800] Data 0.004 (0.004) Batch 0.347 (0.326) Remain 05:48:06 loss: 0.7366 Lr: 0.00564 [2023-12-20 15:01:21,171 INFO misc.py line 119 131400] Train: [20/100][797/800] Data 0.002 (0.004) Batch 0.296 (0.326) Remain 05:48:03 loss: 0.6511 Lr: 0.00564 [2023-12-20 15:01:21,430 INFO misc.py line 119 131400] Train: [20/100][798/800] Data 0.003 (0.004) Batch 0.259 (0.326) Remain 05:47:58 loss: 0.6909 Lr: 0.00564 [2023-12-20 15:01:21,724 INFO misc.py line 119 131400] Train: [20/100][799/800] Data 0.004 (0.004) Batch 0.294 (0.326) Remain 05:47:55 loss: 0.3826 Lr: 0.00564 [2023-12-20 15:01:22,040 INFO misc.py line 119 131400] Train: [20/100][800/800] Data 0.003 (0.004) Batch 0.316 (0.326) Remain 05:47:53 loss: 0.4488 Lr: 0.00564 [2023-12-20 15:01:22,040 INFO misc.py line 136 131400] Train result: loss: 0.5493 [2023-12-20 15:01:22,040 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:01:45,734 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0885 [2023-12-20 15:01:45,814 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2280 [2023-12-20 15:01:45,905 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4403 [2023-12-20 15:01:46,024 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1975 [2023-12-20 15:01:46,138 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4233 [2023-12-20 15:01:46,242 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3059 [2023-12-20 15:01:46,339 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7830 [2023-12-20 15:01:46,447 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.5792 [2023-12-20 15:01:46,534 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2725 [2023-12-20 15:01:46,629 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4054 [2023-12-20 15:01:46,727 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7230 [2023-12-20 15:01:46,870 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4503 [2023-12-20 15:01:46,986 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1556 [2023-12-20 15:01:47,142 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4715 [2023-12-20 15:01:47,246 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5719 [2023-12-20 15:01:47,390 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8604 [2023-12-20 15:01:47,507 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3646 [2023-12-20 15:01:47,618 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.0495 [2023-12-20 15:01:47,731 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4189 [2023-12-20 15:01:47,820 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6237 [2023-12-20 15:01:47,931 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4580 [2023-12-20 15:01:48,089 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1537 [2023-12-20 15:01:48,210 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.1382 [2023-12-20 15:01:48,354 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2298 [2023-12-20 15:01:48,496 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2349 [2023-12-20 15:01:48,578 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5236 [2023-12-20 15:01:48,734 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8078 [2023-12-20 15:01:48,857 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5102 [2023-12-20 15:01:48,958 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6124 [2023-12-20 15:01:49,104 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5444 [2023-12-20 15:01:49,205 INFO evaluator.py line 159 131400] Test: [31/78] Loss 1.0248 [2023-12-20 15:01:49,323 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.7569 [2023-12-20 15:01:49,413 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.3655 [2023-12-20 15:01:49,489 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2670 [2023-12-20 15:01:49,585 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.3679 [2023-12-20 15:01:49,686 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4980 [2023-12-20 15:01:49,817 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8796 [2023-12-20 15:01:49,929 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1117 [2023-12-20 15:01:50,012 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6483 [2023-12-20 15:01:50,155 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4586 [2023-12-20 15:01:50,301 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0310 [2023-12-20 15:01:50,399 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1757 [2023-12-20 15:01:50,527 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4387 [2023-12-20 15:01:50,677 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7600 [2023-12-20 15:01:50,796 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.6125 [2023-12-20 15:01:50,898 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5523 [2023-12-20 15:01:51,066 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4052 [2023-12-20 15:01:51,161 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3969 [2023-12-20 15:01:51,305 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0910 [2023-12-20 15:01:51,397 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7907 [2023-12-20 15:01:51,472 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6831 [2023-12-20 15:01:51,578 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3040 [2023-12-20 15:01:51,726 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8836 [2023-12-20 15:01:51,858 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3282 [2023-12-20 15:01:51,962 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2696 [2023-12-20 15:01:52,047 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7588 [2023-12-20 15:01:52,148 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4642 [2023-12-20 15:01:52,317 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2225 [2023-12-20 15:01:52,413 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5461 [2023-12-20 15:01:52,505 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3666 [2023-12-20 15:01:52,604 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2876 [2023-12-20 15:01:52,698 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3316 [2023-12-20 15:01:52,784 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.0424 [2023-12-20 15:01:52,883 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9316 [2023-12-20 15:01:53,009 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.9512 [2023-12-20 15:01:53,093 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2196 [2023-12-20 15:01:53,203 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4220 [2023-12-20 15:01:53,297 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0221 [2023-12-20 15:01:53,382 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4657 [2023-12-20 15:01:53,464 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0253 [2023-12-20 15:01:53,559 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8803 [2023-12-20 15:01:53,648 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5213 [2023-12-20 15:01:53,781 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2463 [2023-12-20 15:01:53,874 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6195 [2023-12-20 15:01:53,989 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8774 [2023-12-20 15:01:54,089 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8042 [2023-12-20 15:01:54,174 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2971 [2023-12-20 15:01:54,329 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1754 [2023-12-20 15:01:55,542 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7247/0.8171/0.9033. [2023-12-20 15:01:55,542 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8543/0.9386 [2023-12-20 15:01:55,542 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9633/0.9799 [2023-12-20 15:01:55,542 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6608/0.8352 [2023-12-20 15:01:55,542 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8141/0.8835 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9063/0.9450 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8512/0.9212 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7136/0.8252 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6224/0.7400 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6313/0.7955 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7831/0.9108 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3481/0.4105 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6264/0.7556 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6456/0.8704 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7485/0.8167 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6131/0.6753 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6838/0.7434 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9350/0.9550 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6549/0.7509 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8484/0.9369 [2023-12-20 15:01:55,543 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5896/0.6520 [2023-12-20 15:01:55,544 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:01:55,545 INFO misc.py line 165 131400] Currently Best mIoU: 0.7287 [2023-12-20 15:01:55,546 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:01:59,205 INFO misc.py line 119 131400] Train: [21/100][1/800] Data 1.216 (1.216) Batch 1.590 (1.590) Remain 28:16:18 loss: 0.4866 Lr: 0.00564 [2023-12-20 15:01:59,517 INFO misc.py line 119 131400] Train: [21/100][2/800] Data 0.003 (0.003) Batch 0.311 (0.311) Remain 05:31:40 loss: 0.7636 Lr: 0.00564 [2023-12-20 15:01:59,827 INFO misc.py line 119 131400] Train: [21/100][3/800] Data 0.005 (0.005) Batch 0.311 (0.311) Remain 05:31:13 loss: 0.4383 Lr: 0.00564 [2023-12-20 15:02:00,154 INFO misc.py line 119 131400] Train: [21/100][4/800] Data 0.005 (0.005) Batch 0.327 (0.327) Remain 05:49:08 loss: 0.6783 Lr: 0.00564 [2023-12-20 15:02:00,472 INFO misc.py line 119 131400] Train: [21/100][5/800] Data 0.004 (0.004) Batch 0.318 (0.322) Remain 05:43:57 loss: 0.4063 Lr: 0.00564 [2023-12-20 15:02:00,968 INFO misc.py line 119 131400] Train: [21/100][6/800] Data 0.004 (0.004) Batch 0.496 (0.380) Remain 06:45:42 loss: 0.6977 Lr: 0.00564 [2023-12-20 15:02:01,284 INFO misc.py line 119 131400] Train: [21/100][7/800] Data 0.003 (0.004) Batch 0.317 (0.365) Remain 06:28:45 loss: 0.3116 Lr: 0.00564 [2023-12-20 15:02:01,619 INFO misc.py line 119 131400] Train: [21/100][8/800] Data 0.003 (0.004) Batch 0.334 (0.358) Remain 06:22:10 loss: 0.5973 Lr: 0.00564 [2023-12-20 15:02:01,962 INFO misc.py line 119 131400] Train: [21/100][9/800] Data 0.004 (0.004) Batch 0.343 (0.356) Remain 06:19:25 loss: 1.0604 Lr: 0.00564 [2023-12-20 15:02:02,283 INFO misc.py line 119 131400] Train: [21/100][10/800] Data 0.004 (0.004) Batch 0.321 (0.351) Remain 06:14:09 loss: 0.9388 Lr: 0.00564 [2023-12-20 15:02:02,609 INFO misc.py line 119 131400] Train: [21/100][11/800] Data 0.004 (0.004) Batch 0.326 (0.348) Remain 06:10:51 loss: 0.6594 Lr: 0.00564 [2023-12-20 15:02:02,952 INFO misc.py line 119 131400] Train: [21/100][12/800] Data 0.003 (0.004) Batch 0.344 (0.347) Remain 06:10:21 loss: 0.6173 Lr: 0.00564 [2023-12-20 15:02:03,248 INFO misc.py line 119 131400] Train: [21/100][13/800] Data 0.002 (0.004) Batch 0.288 (0.341) Remain 06:04:04 loss: 0.3597 Lr: 0.00564 [2023-12-20 15:02:03,578 INFO misc.py line 119 131400] Train: [21/100][14/800] Data 0.010 (0.004) Batch 0.338 (0.341) Remain 06:03:43 loss: 0.6622 Lr: 0.00564 [2023-12-20 15:02:03,924 INFO misc.py line 119 131400] Train: [21/100][15/800] Data 0.003 (0.004) Batch 0.345 (0.341) Remain 06:04:06 loss: 0.7184 Lr: 0.00564 [2023-12-20 15:02:04,263 INFO misc.py line 119 131400] Train: [21/100][16/800] Data 0.004 (0.004) Batch 0.332 (0.341) Remain 06:03:22 loss: 0.5364 Lr: 0.00564 [2023-12-20 15:02:04,569 INFO misc.py line 119 131400] Train: [21/100][17/800] Data 0.010 (0.005) Batch 0.313 (0.339) Remain 06:01:15 loss: 0.2972 Lr: 0.00564 [2023-12-20 15:02:04,847 INFO misc.py line 119 131400] Train: [21/100][18/800] Data 0.003 (0.004) Batch 0.278 (0.335) Remain 05:56:54 loss: 0.5347 Lr: 0.00564 [2023-12-20 15:02:05,177 INFO misc.py line 119 131400] Train: [21/100][19/800] Data 0.002 (0.004) Batch 0.329 (0.334) Remain 05:56:32 loss: 0.4627 Lr: 0.00564 [2023-12-20 15:02:05,494 INFO misc.py line 119 131400] Train: [21/100][20/800] Data 0.003 (0.004) Batch 0.317 (0.333) Remain 05:55:28 loss: 0.7906 Lr: 0.00564 [2023-12-20 15:02:05,790 INFO misc.py line 119 131400] Train: [21/100][21/800] Data 0.004 (0.004) Batch 0.296 (0.331) Remain 05:53:16 loss: 0.4217 Lr: 0.00564 [2023-12-20 15:02:06,103 INFO misc.py line 119 131400] Train: [21/100][22/800] Data 0.002 (0.004) Batch 0.313 (0.330) Remain 05:52:14 loss: 0.6444 Lr: 0.00564 [2023-12-20 15:02:06,387 INFO misc.py line 119 131400] Train: [21/100][23/800] Data 0.003 (0.004) Batch 0.280 (0.328) Remain 05:49:31 loss: 0.8981 Lr: 0.00564 [2023-12-20 15:02:06,706 INFO misc.py line 119 131400] Train: [21/100][24/800] Data 0.007 (0.004) Batch 0.323 (0.328) Remain 05:49:16 loss: 0.2159 Lr: 0.00564 [2023-12-20 15:02:07,035 INFO misc.py line 119 131400] Train: [21/100][25/800] Data 0.004 (0.004) Batch 0.329 (0.328) Remain 05:49:21 loss: 0.6317 Lr: 0.00564 [2023-12-20 15:02:07,366 INFO misc.py line 119 131400] Train: [21/100][26/800] Data 0.003 (0.004) Batch 0.331 (0.328) Remain 05:49:30 loss: 0.5929 Lr: 0.00564 [2023-12-20 15:02:07,664 INFO misc.py line 119 131400] Train: [21/100][27/800] Data 0.003 (0.004) Batch 0.298 (0.327) Remain 05:48:10 loss: 0.4363 Lr: 0.00564 [2023-12-20 15:02:07,970 INFO misc.py line 119 131400] Train: [21/100][28/800] Data 0.003 (0.004) Batch 0.305 (0.326) Remain 05:47:14 loss: 0.5177 Lr: 0.00564 [2023-12-20 15:02:08,290 INFO misc.py line 119 131400] Train: [21/100][29/800] Data 0.005 (0.004) Batch 0.321 (0.326) Remain 05:47:03 loss: 0.5593 Lr: 0.00564 [2023-12-20 15:02:08,606 INFO misc.py line 119 131400] Train: [21/100][30/800] Data 0.004 (0.004) Batch 0.315 (0.325) Remain 05:46:37 loss: 0.3135 Lr: 0.00564 [2023-12-20 15:02:08,928 INFO misc.py line 119 131400] Train: [21/100][31/800] Data 0.005 (0.004) Batch 0.322 (0.325) Remain 05:46:30 loss: 0.9504 Lr: 0.00564 [2023-12-20 15:02:09,245 INFO misc.py line 119 131400] Train: [21/100][32/800] Data 0.004 (0.004) Batch 0.316 (0.325) Remain 05:46:10 loss: 0.3951 Lr: 0.00564 [2023-12-20 15:02:09,588 INFO misc.py line 119 131400] Train: [21/100][33/800] Data 0.005 (0.004) Batch 0.344 (0.325) Remain 05:46:51 loss: 0.6480 Lr: 0.00564 [2023-12-20 15:02:09,885 INFO misc.py line 119 131400] Train: [21/100][34/800] Data 0.004 (0.004) Batch 0.298 (0.324) Remain 05:45:54 loss: 0.2771 Lr: 0.00564 [2023-12-20 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05:45:19 loss: 0.5246 Lr: 0.00559 [2023-12-20 15:06:11,168 INFO misc.py line 119 131400] Train: [21/100][770/800] Data 0.003 (0.005) Batch 0.338 (0.328) Remain 05:45:19 loss: 0.3937 Lr: 0.00559 [2023-12-20 15:06:11,497 INFO misc.py line 119 131400] Train: [21/100][771/800] Data 0.003 (0.005) Batch 0.329 (0.328) Remain 05:45:19 loss: 0.5255 Lr: 0.00559 [2023-12-20 15:06:11,819 INFO misc.py line 119 131400] Train: [21/100][772/800] Data 0.003 (0.005) Batch 0.322 (0.328) Remain 05:45:18 loss: 0.2644 Lr: 0.00559 [2023-12-20 15:06:12,147 INFO misc.py line 119 131400] Train: [21/100][773/800] Data 0.002 (0.005) Batch 0.329 (0.328) Remain 05:45:18 loss: 0.4141 Lr: 0.00559 [2023-12-20 15:06:12,462 INFO misc.py line 119 131400] Train: [21/100][774/800] Data 0.003 (0.005) Batch 0.315 (0.328) Remain 05:45:17 loss: 0.3784 Lr: 0.00559 [2023-12-20 15:06:12,789 INFO misc.py line 119 131400] Train: [21/100][775/800] Data 0.002 (0.005) Batch 0.326 (0.328) Remain 05:45:16 loss: 0.6704 Lr: 0.00559 [2023-12-20 15:06:13,134 INFO misc.py line 119 131400] Train: [21/100][776/800] Data 0.003 (0.005) Batch 0.346 (0.328) Remain 05:45:18 loss: 0.3973 Lr: 0.00559 [2023-12-20 15:06:13,467 INFO misc.py line 119 131400] Train: [21/100][777/800] Data 0.003 (0.005) Batch 0.332 (0.328) Remain 05:45:18 loss: 1.0124 Lr: 0.00559 [2023-12-20 15:06:13,814 INFO misc.py line 119 131400] Train: [21/100][778/800] Data 0.005 (0.005) Batch 0.346 (0.328) Remain 05:45:19 loss: 0.3494 Lr: 0.00559 [2023-12-20 15:06:14,144 INFO misc.py line 119 131400] Train: [21/100][779/800] Data 0.005 (0.005) Batch 0.330 (0.328) Remain 05:45:19 loss: 0.6122 Lr: 0.00559 [2023-12-20 15:06:14,494 INFO misc.py line 119 131400] Train: [21/100][780/800] Data 0.005 (0.005) Batch 0.352 (0.328) Remain 05:45:20 loss: 0.7780 Lr: 0.00559 [2023-12-20 15:06:14,804 INFO misc.py line 119 131400] Train: [21/100][781/800] Data 0.004 (0.005) Batch 0.309 (0.328) Remain 05:45:18 loss: 0.6222 Lr: 0.00559 [2023-12-20 15:06:15,129 INFO misc.py line 119 131400] Train: [21/100][782/800] Data 0.006 (0.005) Batch 0.326 (0.328) Remain 05:45:18 loss: 0.4132 Lr: 0.00559 [2023-12-20 15:06:15,443 INFO misc.py line 119 131400] Train: [21/100][783/800] Data 0.004 (0.005) Batch 0.314 (0.328) Remain 05:45:16 loss: 0.2278 Lr: 0.00559 [2023-12-20 15:06:15,791 INFO misc.py line 119 131400] Train: [21/100][784/800] Data 0.003 (0.005) Batch 0.347 (0.328) Remain 05:45:18 loss: 0.3889 Lr: 0.00559 [2023-12-20 15:06:16,101 INFO misc.py line 119 131400] Train: [21/100][785/800] Data 0.004 (0.005) Batch 0.311 (0.328) Remain 05:45:16 loss: 0.4691 Lr: 0.00559 [2023-12-20 15:06:16,466 INFO misc.py line 119 131400] Train: [21/100][786/800] Data 0.003 (0.005) Batch 0.363 (0.328) Remain 05:45:19 loss: 0.4588 Lr: 0.00559 [2023-12-20 15:06:16,883 INFO misc.py line 119 131400] Train: [21/100][787/800] Data 0.004 (0.005) Batch 0.418 (0.328) Remain 05:45:26 loss: 0.4063 Lr: 0.00559 [2023-12-20 15:06:17,239 INFO misc.py line 119 131400] Train: [21/100][788/800] Data 0.004 (0.005) Batch 0.356 (0.328) Remain 05:45:27 loss: 0.4991 Lr: 0.00559 [2023-12-20 15:06:17,588 INFO misc.py line 119 131400] Train: [21/100][789/800] Data 0.003 (0.005) Batch 0.348 (0.328) Remain 05:45:29 loss: 0.3427 Lr: 0.00559 [2023-12-20 15:06:17,892 INFO misc.py line 119 131400] Train: [21/100][790/800] Data 0.004 (0.005) Batch 0.305 (0.328) Remain 05:45:27 loss: 0.2501 Lr: 0.00559 [2023-12-20 15:06:18,471 INFO misc.py line 119 131400] Train: [21/100][791/800] Data 0.003 (0.005) Batch 0.579 (0.328) Remain 05:45:46 loss: 0.6250 Lr: 0.00559 [2023-12-20 15:06:18,777 INFO misc.py line 119 131400] Train: [21/100][792/800] Data 0.002 (0.005) Batch 0.305 (0.328) Remain 05:45:44 loss: 0.6849 Lr: 0.00559 [2023-12-20 15:06:19,094 INFO misc.py line 119 131400] Train: [21/100][793/800] Data 0.004 (0.005) Batch 0.319 (0.328) Remain 05:45:43 loss: 0.6177 Lr: 0.00559 [2023-12-20 15:06:19,415 INFO misc.py line 119 131400] Train: [21/100][794/800] Data 0.003 (0.005) Batch 0.321 (0.328) Remain 05:45:42 loss: 0.4767 Lr: 0.00559 [2023-12-20 15:06:19,734 INFO misc.py line 119 131400] Train: [21/100][795/800] Data 0.003 (0.005) Batch 0.318 (0.328) Remain 05:45:41 loss: 0.4206 Lr: 0.00559 [2023-12-20 15:06:20,069 INFO misc.py line 119 131400] Train: [21/100][796/800] Data 0.003 (0.005) Batch 0.335 (0.328) Remain 05:45:41 loss: 0.5272 Lr: 0.00559 [2023-12-20 15:06:20,368 INFO misc.py line 119 131400] Train: [21/100][797/800] Data 0.003 (0.005) Batch 0.300 (0.328) Remain 05:45:39 loss: 0.3713 Lr: 0.00559 [2023-12-20 15:06:20,681 INFO misc.py line 119 131400] Train: [21/100][798/800] Data 0.003 (0.005) Batch 0.313 (0.328) Remain 05:45:37 loss: 0.7372 Lr: 0.00559 [2023-12-20 15:06:21,015 INFO misc.py line 119 131400] Train: [21/100][799/800] Data 0.004 (0.005) Batch 0.334 (0.328) Remain 05:45:37 loss: 0.4163 Lr: 0.00559 [2023-12-20 15:06:21,320 INFO misc.py line 119 131400] Train: [21/100][800/800] Data 0.002 (0.005) Batch 0.305 (0.328) Remain 05:45:35 loss: 0.2284 Lr: 0.00559 [2023-12-20 15:06:21,321 INFO misc.py line 136 131400] Train result: loss: 0.5302 [2023-12-20 15:06:21,321 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:06:45,145 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.3028 [2023-12-20 15:06:45,239 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1354 [2023-12-20 15:06:45,358 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4596 [2023-12-20 15:06:45,472 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5880 [2023-12-20 15:06:45,596 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2150 [2023-12-20 15:06:45,712 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.8543 [2023-12-20 15:06:45,805 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8276 [2023-12-20 15:06:45,921 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0740 [2023-12-20 15:06:46,010 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2141 [2023-12-20 15:06:46,104 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2958 [2023-12-20 15:06:46,209 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6302 [2023-12-20 15:06:46,353 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5392 [2023-12-20 15:06:46,477 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1679 [2023-12-20 15:06:46,637 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2838 [2023-12-20 15:06:46,753 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.6283 [2023-12-20 15:06:46,893 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8891 [2023-12-20 15:06:47,006 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2443 [2023-12-20 15:06:47,130 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.1145 [2023-12-20 15:06:47,254 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.5076 [2023-12-20 15:06:47,336 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.7717 [2023-12-20 15:06:47,449 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3890 [2023-12-20 15:06:47,618 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1769 [2023-12-20 15:06:47,740 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5113 [2023-12-20 15:06:47,892 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2580 [2023-12-20 15:06:48,037 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1715 [2023-12-20 15:06:48,118 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3577 [2023-12-20 15:06:48,275 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4306 [2023-12-20 15:06:48,409 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5896 [2023-12-20 15:06:48,520 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8030 [2023-12-20 15:06:48,666 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.9983 [2023-12-20 15:06:48,777 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7128 [2023-12-20 15:06:48,914 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4526 [2023-12-20 15:06:49,000 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2666 [2023-12-20 15:06:49,079 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2701 [2023-12-20 15:06:49,180 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.4437 [2023-12-20 15:06:49,277 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6476 [2023-12-20 15:06:49,413 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9609 [2023-12-20 15:06:49,527 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2174 [2023-12-20 15:06:49,621 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7767 [2023-12-20 15:06:49,767 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6139 [2023-12-20 15:06:49,914 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0248 [2023-12-20 15:06:50,021 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3158 [2023-12-20 15:06:50,148 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4882 [2023-12-20 15:06:50,296 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8619 [2023-12-20 15:06:50,413 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.6373 [2023-12-20 15:06:50,518 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2955 [2023-12-20 15:06:50,686 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4978 [2023-12-20 15:06:50,784 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.7885 [2023-12-20 15:06:50,928 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1052 [2023-12-20 15:06:51,023 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8307 [2023-12-20 15:06:51,108 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5467 [2023-12-20 15:06:51,217 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2220 [2023-12-20 15:06:51,364 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2064 [2023-12-20 15:06:51,502 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3371 [2023-12-20 15:06:51,609 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.9676 [2023-12-20 15:06:51,705 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7339 [2023-12-20 15:06:51,807 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4790 [2023-12-20 15:06:51,973 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2681 [2023-12-20 15:06:52,070 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2390 [2023-12-20 15:06:52,163 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2969 [2023-12-20 15:06:52,261 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3153 [2023-12-20 15:06:52,352 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2932 [2023-12-20 15:06:52,446 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.2889 [2023-12-20 15:06:52,545 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7318 [2023-12-20 15:06:52,674 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1603 [2023-12-20 15:06:52,760 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5388 [2023-12-20 15:06:52,866 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3643 [2023-12-20 15:06:52,964 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0110 [2023-12-20 15:06:53,050 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3449 [2023-12-20 15:06:53,137 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0136 [2023-12-20 15:06:53,235 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7389 [2023-12-20 15:06:53,325 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7075 [2023-12-20 15:06:53,458 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.8223 [2023-12-20 15:06:53,555 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7146 [2023-12-20 15:06:53,671 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7788 [2023-12-20 15:06:53,772 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.2212 [2023-12-20 15:06:53,861 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4777 [2023-12-20 15:06:54,014 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.7266 [2023-12-20 15:06:55,292 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.6993/0.8070/0.8967. [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8506/0.9370 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9635/0.9827 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5895/0.8574 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7968/0.8366 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8840/0.9303 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7763/0.9251 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7184/0.8343 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6752/0.8114 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6085/0.6584 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7759/0.8647 [2023-12-20 15:06:55,292 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3456/0.5003 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.5878/0.6691 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6475/0.8737 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7586/0.8673 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.4754/0.5391 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6623/0.8124 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9379/0.9642 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6317/0.7891 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7657/0.9172 [2023-12-20 15:06:55,293 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5350/0.5702 [2023-12-20 15:06:55,294 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:06:55,295 INFO misc.py line 165 131400] Currently Best mIoU: 0.7287 [2023-12-20 15:06:55,295 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:06:59,911 INFO misc.py line 119 131400] Train: [22/100][1/800] Data 1.395 (1.395) Batch 1.726 (1.726) Remain 30:17:50 loss: 0.6156 Lr: 0.00559 [2023-12-20 15:07:00,245 INFO misc.py line 119 131400] Train: [22/100][2/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 05:52:23 loss: 0.4359 Lr: 0.00559 [2023-12-20 15:07:00,609 INFO misc.py line 119 131400] Train: [22/100][3/800] Data 0.003 (0.003) Batch 0.363 (0.363) Remain 06:22:35 loss: 0.2931 Lr: 0.00559 [2023-12-20 15:07:00,943 INFO misc.py line 119 131400] Train: [22/100][4/800] Data 0.005 (0.005) Batch 0.335 (0.335) Remain 05:52:39 loss: 0.4404 Lr: 0.00559 [2023-12-20 15:07:01,271 INFO misc.py line 119 131400] Train: [22/100][5/800] Data 0.004 (0.004) Batch 0.327 (0.331) Remain 05:48:44 loss: 0.8896 Lr: 0.00559 [2023-12-20 15:07:01,596 INFO misc.py line 119 131400] Train: [22/100][6/800] Data 0.004 (0.004) Batch 0.326 (0.329) Remain 05:46:50 loss: 0.2950 Lr: 0.00559 [2023-12-20 15:07:01,940 INFO misc.py line 119 131400] Train: [22/100][7/800] Data 0.003 (0.004) Batch 0.344 (0.333) Remain 05:50:40 loss: 0.3774 Lr: 0.00559 [2023-12-20 15:07:02,260 INFO misc.py line 119 131400] Train: [22/100][8/800] Data 0.003 (0.004) Batch 0.320 (0.330) Remain 05:47:53 loss: 0.6651 Lr: 0.00559 [2023-12-20 15:07:02,573 INFO misc.py line 119 131400] Train: [22/100][9/800] Data 0.003 (0.004) Batch 0.313 (0.327) Remain 05:44:48 loss: 0.2721 Lr: 0.00559 [2023-12-20 15:07:02,949 INFO misc.py line 119 131400] Train: [22/100][10/800] Data 0.003 (0.003) Batch 0.376 (0.334) Remain 05:52:07 loss: 0.7207 Lr: 0.00559 [2023-12-20 15:07:03,289 INFO misc.py line 119 131400] Train: [22/100][11/800] Data 0.003 (0.003) Batch 0.339 (0.335) Remain 05:52:44 loss: 0.4222 Lr: 0.00559 [2023-12-20 15:07:03,611 INFO misc.py line 119 131400] Train: [22/100][12/800] Data 0.004 (0.003) Batch 0.323 (0.334) Remain 05:51:18 loss: 0.5575 Lr: 0.00559 [2023-12-20 15:07:03,954 INFO misc.py line 119 131400] Train: [22/100][13/800] Data 0.004 (0.003) Batch 0.343 (0.335) Remain 05:52:16 loss: 0.5414 Lr: 0.00559 [2023-12-20 15:07:04,255 INFO misc.py line 119 131400] Train: [22/100][14/800] Data 0.004 (0.003) Batch 0.301 (0.331) Remain 05:49:05 loss: 0.2703 Lr: 0.00559 [2023-12-20 15:07:04,599 INFO misc.py line 119 131400] Train: [22/100][15/800] Data 0.003 (0.003) Batch 0.344 (0.333) Remain 05:50:10 loss: 0.5503 Lr: 0.00559 [2023-12-20 15:07:04,912 INFO misc.py line 119 131400] Train: [22/100][16/800] Data 0.003 (0.003) Batch 0.313 (0.331) Remain 05:48:33 loss: 0.3880 Lr: 0.00559 [2023-12-20 15:07:05,219 INFO misc.py line 119 131400] Train: [22/100][17/800] Data 0.004 (0.003) Batch 0.302 (0.329) Remain 05:46:22 loss: 0.3664 Lr: 0.00559 [2023-12-20 15:07:05,554 INFO misc.py line 119 131400] Train: [22/100][18/800] Data 0.010 (0.004) Batch 0.341 (0.330) Remain 05:47:11 loss: 0.4409 Lr: 0.00559 [2023-12-20 15:07:05,854 INFO misc.py line 119 131400] Train: [22/100][19/800] Data 0.003 (0.004) Batch 0.300 (0.328) Remain 05:45:15 loss: 0.8161 Lr: 0.00559 [2023-12-20 15:07:06,171 INFO misc.py line 119 131400] Train: [22/100][20/800] Data 0.003 (0.004) Batch 0.316 (0.327) Remain 05:44:32 loss: 0.4876 Lr: 0.00559 [2023-12-20 15:07:06,492 INFO misc.py line 119 131400] Train: [22/100][21/800] Data 0.003 (0.004) Batch 0.321 (0.327) Remain 05:44:10 loss: 0.3385 Lr: 0.00559 [2023-12-20 15:07:06,806 INFO misc.py line 119 131400] Train: [22/100][22/800] Data 0.003 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131400] Train: [22/100][757/800] Data 0.003 (0.004) Batch 0.343 (0.330) Remain 05:43:20 loss: 0.4864 Lr: 0.00554 [2023-12-20 15:11:09,705 INFO misc.py line 119 131400] Train: [22/100][758/800] Data 0.004 (0.004) Batch 0.336 (0.330) Remain 05:43:21 loss: 0.7720 Lr: 0.00554 [2023-12-20 15:11:10,051 INFO misc.py line 119 131400] Train: [22/100][759/800] Data 0.006 (0.004) Batch 0.349 (0.330) Remain 05:43:22 loss: 0.3207 Lr: 0.00554 [2023-12-20 15:11:10,365 INFO misc.py line 119 131400] Train: [22/100][760/800] Data 0.003 (0.004) Batch 0.313 (0.330) Remain 05:43:20 loss: 0.6061 Lr: 0.00554 [2023-12-20 15:11:10,693 INFO misc.py line 119 131400] Train: [22/100][761/800] Data 0.005 (0.004) Batch 0.329 (0.330) Remain 05:43:20 loss: 0.3375 Lr: 0.00554 [2023-12-20 15:11:11,031 INFO misc.py line 119 131400] Train: [22/100][762/800] Data 0.004 (0.004) Batch 0.338 (0.330) Remain 05:43:20 loss: 0.3851 Lr: 0.00554 [2023-12-20 15:11:11,356 INFO misc.py line 119 131400] Train: [22/100][763/800] Data 0.003 (0.004) Batch 0.325 (0.330) Remain 05:43:19 loss: 0.6810 Lr: 0.00554 [2023-12-20 15:11:11,678 INFO misc.py line 119 131400] Train: [22/100][764/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 05:43:18 loss: 0.4716 Lr: 0.00554 [2023-12-20 15:11:12,008 INFO misc.py line 119 131400] Train: [22/100][765/800] Data 0.004 (0.004) Batch 0.331 (0.330) Remain 05:43:18 loss: 0.6288 Lr: 0.00554 [2023-12-20 15:11:12,331 INFO misc.py line 119 131400] Train: [22/100][766/800] Data 0.003 (0.004) Batch 0.322 (0.330) Remain 05:43:17 loss: 0.2427 Lr: 0.00554 [2023-12-20 15:11:12,651 INFO misc.py line 119 131400] Train: [22/100][767/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 05:43:16 loss: 0.5867 Lr: 0.00554 [2023-12-20 15:11:12,995 INFO misc.py line 119 131400] Train: [22/100][768/800] Data 0.003 (0.004) Batch 0.343 (0.330) Remain 05:43:17 loss: 0.6816 Lr: 0.00554 [2023-12-20 15:11:13,306 INFO misc.py line 119 131400] Train: [22/100][769/800] Data 0.004 (0.004) Batch 0.312 (0.330) Remain 05:43:15 loss: 0.5475 Lr: 0.00554 [2023-12-20 15:11:13,796 INFO misc.py line 119 131400] Train: [22/100][770/800] Data 0.004 (0.004) Batch 0.489 (0.330) Remain 05:43:27 loss: 0.7202 Lr: 0.00554 [2023-12-20 15:11:14,080 INFO misc.py line 119 131400] Train: [22/100][771/800] Data 0.005 (0.004) Batch 0.285 (0.330) Remain 05:43:23 loss: 0.4405 Lr: 0.00554 [2023-12-20 15:11:14,386 INFO misc.py line 119 131400] Train: [22/100][772/800] Data 0.005 (0.004) Batch 0.306 (0.330) Remain 05:43:21 loss: 0.7843 Lr: 0.00554 [2023-12-20 15:11:14,716 INFO misc.py line 119 131400] Train: [22/100][773/800] Data 0.003 (0.004) Batch 0.330 (0.330) Remain 05:43:21 loss: 0.3788 Lr: 0.00554 [2023-12-20 15:11:15,055 INFO misc.py line 119 131400] Train: [22/100][774/800] Data 0.004 (0.004) Batch 0.339 (0.330) Remain 05:43:21 loss: 0.8268 Lr: 0.00554 [2023-12-20 15:11:15,391 INFO misc.py line 119 131400] Train: [22/100][775/800] Data 0.005 (0.004) Batch 0.329 (0.330) Remain 05:43:21 loss: 0.5725 Lr: 0.00554 [2023-12-20 15:11:15,692 INFO misc.py line 119 131400] Train: [22/100][776/800] Data 0.011 (0.004) Batch 0.308 (0.330) Remain 05:43:19 loss: 0.6559 Lr: 0.00554 [2023-12-20 15:11:16,001 INFO misc.py line 119 131400] Train: [22/100][777/800] Data 0.003 (0.004) Batch 0.308 (0.330) Remain 05:43:17 loss: 0.8565 Lr: 0.00554 [2023-12-20 15:11:16,297 INFO misc.py line 119 131400] Train: [22/100][778/800] Data 0.005 (0.004) Batch 0.297 (0.330) Remain 05:43:14 loss: 0.5748 Lr: 0.00554 [2023-12-20 15:11:16,621 INFO misc.py line 119 131400] Train: [22/100][779/800] Data 0.003 (0.004) Batch 0.324 (0.330) Remain 05:43:13 loss: 0.3731 Lr: 0.00554 [2023-12-20 15:11:16,938 INFO misc.py line 119 131400] Train: [22/100][780/800] Data 0.004 (0.004) Batch 0.317 (0.330) Remain 05:43:11 loss: 0.3350 Lr: 0.00554 [2023-12-20 15:11:17,248 INFO misc.py line 119 131400] Train: [22/100][781/800] Data 0.005 (0.004) Batch 0.311 (0.330) Remain 05:43:10 loss: 0.6621 Lr: 0.00554 [2023-12-20 15:11:17,590 INFO misc.py line 119 131400] Train: [22/100][782/800] Data 0.003 (0.004) Batch 0.341 (0.330) Remain 05:43:10 loss: 0.3218 Lr: 0.00554 [2023-12-20 15:11:17,920 INFO misc.py line 119 131400] Train: [22/100][783/800] Data 0.004 (0.004) Batch 0.328 (0.330) Remain 05:43:10 loss: 0.3959 Lr: 0.00554 [2023-12-20 15:11:18,281 INFO misc.py line 119 131400] Train: [22/100][784/800] Data 0.006 (0.004) Batch 0.363 (0.330) Remain 05:43:12 loss: 0.3524 Lr: 0.00554 [2023-12-20 15:11:18,611 INFO misc.py line 119 131400] Train: [22/100][785/800] Data 0.003 (0.004) Batch 0.330 (0.330) Remain 05:43:12 loss: 0.3717 Lr: 0.00554 [2023-12-20 15:11:19,011 INFO misc.py line 119 131400] Train: [22/100][786/800] Data 0.003 (0.004) Batch 0.397 (0.330) Remain 05:43:17 loss: 0.4569 Lr: 0.00554 [2023-12-20 15:11:19,396 INFO misc.py line 119 131400] Train: [22/100][787/800] Data 0.006 (0.004) Batch 0.387 (0.330) Remain 05:43:21 loss: 0.5619 Lr: 0.00554 [2023-12-20 15:11:19,764 INFO misc.py line 119 131400] Train: [22/100][788/800] Data 0.005 (0.004) Batch 0.356 (0.330) Remain 05:43:23 loss: 0.5090 Lr: 0.00554 [2023-12-20 15:11:20,065 INFO misc.py line 119 131400] Train: [22/100][789/800] Data 0.020 (0.004) Batch 0.314 (0.330) Remain 05:43:21 loss: 0.3655 Lr: 0.00554 [2023-12-20 15:11:20,371 INFO misc.py line 119 131400] Train: [22/100][790/800] Data 0.004 (0.004) Batch 0.306 (0.330) Remain 05:43:19 loss: 0.5730 Lr: 0.00554 [2023-12-20 15:11:20,667 INFO misc.py line 119 131400] Train: [22/100][791/800] Data 0.004 (0.004) Batch 0.296 (0.330) Remain 05:43:16 loss: 0.2128 Lr: 0.00554 [2023-12-20 15:11:20,973 INFO misc.py line 119 131400] Train: [22/100][792/800] Data 0.003 (0.004) Batch 0.306 (0.330) Remain 05:43:14 loss: 0.6144 Lr: 0.00554 [2023-12-20 15:11:21,284 INFO misc.py line 119 131400] Train: [22/100][793/800] Data 0.003 (0.004) Batch 0.311 (0.330) Remain 05:43:12 loss: 0.4794 Lr: 0.00554 [2023-12-20 15:11:21,622 INFO misc.py line 119 131400] Train: [22/100][794/800] Data 0.003 (0.004) Batch 0.339 (0.330) Remain 05:43:12 loss: 0.5416 Lr: 0.00554 [2023-12-20 15:11:21,923 INFO misc.py line 119 131400] Train: [22/100][795/800] Data 0.003 (0.004) Batch 0.301 (0.330) Remain 05:43:09 loss: 0.7845 Lr: 0.00554 [2023-12-20 15:11:22,211 INFO misc.py line 119 131400] Train: [22/100][796/800] Data 0.003 (0.004) Batch 0.289 (0.330) Remain 05:43:06 loss: 0.4720 Lr: 0.00554 [2023-12-20 15:11:22,514 INFO misc.py line 119 131400] Train: [22/100][797/800] Data 0.003 (0.004) Batch 0.303 (0.330) Remain 05:43:03 loss: 0.5147 Lr: 0.00554 [2023-12-20 15:11:22,851 INFO misc.py line 119 131400] Train: [22/100][798/800] Data 0.003 (0.004) Batch 0.337 (0.330) Remain 05:43:04 loss: 0.4406 Lr: 0.00554 [2023-12-20 15:11:23,160 INFO misc.py line 119 131400] Train: [22/100][799/800] Data 0.004 (0.004) Batch 0.310 (0.330) Remain 05:43:02 loss: 0.5611 Lr: 0.00554 [2023-12-20 15:11:23,492 INFO misc.py line 119 131400] Train: [22/100][800/800] Data 0.003 (0.004) Batch 0.331 (0.330) Remain 05:43:01 loss: 0.3774 Lr: 0.00554 [2023-12-20 15:11:23,493 INFO misc.py line 136 131400] Train result: loss: 0.5393 [2023-12-20 15:11:23,494 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:11:44,462 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1213 [2023-12-20 15:11:46,285 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2796 [2023-12-20 15:11:46,587 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3972 [2023-12-20 15:11:46,696 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2361 [2023-12-20 15:11:46,811 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3004 [2023-12-20 15:11:46,927 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.0954 [2023-12-20 15:11:47,016 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2398 [2023-12-20 15:11:47,125 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1536 [2023-12-20 15:11:47,208 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3091 [2023-12-20 15:11:47,297 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5550 [2023-12-20 15:11:47,397 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5664 [2023-12-20 15:11:47,534 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4047 [2023-12-20 15:11:47,656 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2426 [2023-12-20 15:11:47,812 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2233 [2023-12-20 15:11:47,904 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1697 [2023-12-20 15:11:48,041 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6927 [2023-12-20 15:11:48,159 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3603 [2023-12-20 15:11:48,274 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4910 [2023-12-20 15:11:48,384 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2348 [2023-12-20 15:11:48,458 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4663 [2023-12-20 15:11:48,570 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3761 [2023-12-20 15:11:48,726 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1307 [2023-12-20 15:11:48,852 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6892 [2023-12-20 15:11:48,998 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2812 [2023-12-20 15:11:49,141 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2250 [2023-12-20 15:11:49,231 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4027 [2023-12-20 15:11:49,388 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.2856 [2023-12-20 15:11:49,519 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5592 [2023-12-20 15:11:49,613 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6329 [2023-12-20 15:11:49,758 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7591 [2023-12-20 15:11:49,874 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7117 [2023-12-20 15:11:49,998 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5745 [2023-12-20 15:11:50,089 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1678 [2023-12-20 15:11:50,161 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2036 [2023-12-20 15:11:50,263 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7561 [2023-12-20 15:11:50,353 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3909 [2023-12-20 15:11:50,481 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.1194 [2023-12-20 15:11:50,595 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1802 [2023-12-20 15:11:50,703 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5682 [2023-12-20 15:11:50,845 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4730 [2023-12-20 15:11:50,997 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0288 [2023-12-20 15:11:51,098 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0994 [2023-12-20 15:11:51,220 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2172 [2023-12-20 15:11:51,361 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0461 [2023-12-20 15:11:51,485 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8921 [2023-12-20 15:11:51,589 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3685 [2023-12-20 15:11:51,768 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3914 [2023-12-20 15:11:51,876 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5080 [2023-12-20 15:11:52,019 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2490 [2023-12-20 15:11:52,110 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8902 [2023-12-20 15:11:52,184 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4786 [2023-12-20 15:11:52,290 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2038 [2023-12-20 15:11:52,441 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8726 [2023-12-20 15:11:52,578 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3132 [2023-12-20 15:11:52,679 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2569 [2023-12-20 15:11:52,772 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8658 [2023-12-20 15:11:52,875 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4462 [2023-12-20 15:11:53,036 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3280 [2023-12-20 15:11:53,137 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1499 [2023-12-20 15:11:53,237 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2698 [2023-12-20 15:11:53,338 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3650 [2023-12-20 15:11:53,428 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3211 [2023-12-20 15:11:53,529 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7900 [2023-12-20 15:11:53,629 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9767 [2023-12-20 15:11:53,760 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.2713 [2023-12-20 15:11:53,854 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3228 [2023-12-20 15:11:53,959 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4545 [2023-12-20 15:11:54,053 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0108 [2023-12-20 15:11:54,139 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4321 [2023-12-20 15:11:54,225 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0375 [2023-12-20 15:11:54,319 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.0836 [2023-12-20 15:11:54,417 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5817 [2023-12-20 15:11:54,551 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.8582 [2023-12-20 15:11:54,648 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7283 [2023-12-20 15:11:54,771 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8844 [2023-12-20 15:11:54,875 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8637 [2023-12-20 15:11:54,973 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6041 [2023-12-20 15:11:55,127 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2778 [2023-12-20 15:11:56,647 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7345/0.8344/0.9057. [2023-12-20 15:11:56,647 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8579/0.9223 [2023-12-20 15:11:56,647 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9641/0.9844 [2023-12-20 15:11:56,647 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6523/0.8057 [2023-12-20 15:11:56,647 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7675/0.8146 [2023-12-20 15:11:56,647 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9047/0.9494 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8069/0.9081 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7364/0.8833 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6656/0.8070 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6584/0.7952 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7995/0.9429 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3506/0.5018 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6568/0.7595 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6564/0.8380 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7440/0.8926 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6428/0.7655 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7365/0.8418 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9534/0.9748 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6507/0.7209 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8826/0.9109 [2023-12-20 15:11:56,648 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6029/0.6693 [2023-12-20 15:11:56,649 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:11:56,650 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7345 [2023-12-20 15:11:56,650 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:11:56,650 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:12:03,275 INFO misc.py line 119 131400] Train: [23/100][1/800] Data 1.521 (1.521) Batch 1.866 (1.866) Remain 32:20:35 loss: 0.4920 Lr: 0.00554 [2023-12-20 15:12:03,618 INFO misc.py line 119 131400] Train: [23/100][2/800] Data 0.005 (0.005) Batch 0.343 (0.343) Remain 05:56:41 loss: 0.3052 Lr: 0.00554 [2023-12-20 15:12:03,966 INFO misc.py line 119 131400] Train: [23/100][3/800] Data 0.005 (0.005) Batch 0.344 (0.344) Remain 05:58:12 loss: 0.3886 Lr: 0.00554 [2023-12-20 15:12:04,296 INFO misc.py line 119 131400] Train: [23/100][4/800] Data 0.008 (0.008) Batch 0.335 (0.335) Remain 05:47:53 loss: 0.5262 Lr: 0.00554 [2023-12-20 15:12:04,609 INFO misc.py line 119 131400] Train: [23/100][5/800] Data 0.003 (0.005) Batch 0.313 (0.324) Remain 05:36:26 loss: 0.5192 Lr: 0.00554 [2023-12-20 15:12:04,936 INFO misc.py line 119 131400] Train: [23/100][6/800] Data 0.003 (0.005) Batch 0.327 (0.325) Remain 05:37:39 loss: 0.5475 Lr: 0.00554 [2023-12-20 15:12:05,757 INFO misc.py line 119 131400] Train: [23/100][7/800] Data 0.507 (0.130) Batch 0.820 (0.449) Remain 07:46:27 loss: 0.7955 Lr: 0.00554 [2023-12-20 15:12:06,076 INFO misc.py line 119 131400] Train: [23/100][8/800] Data 0.004 (0.105) Batch 0.320 (0.423) Remain 07:19:38 loss: 0.6705 Lr: 0.00554 [2023-12-20 15:12:06,394 INFO misc.py line 119 131400] Train: [23/100][9/800] Data 0.003 (0.088) Batch 0.318 (0.405) Remain 07:01:28 loss: 0.5446 Lr: 0.00554 [2023-12-20 15:12:06,689 INFO misc.py line 119 131400] Train: [23/100][10/800] Data 0.003 (0.076) Batch 0.295 (0.390) Remain 06:45:05 loss: 0.4025 Lr: 0.00554 [2023-12-20 15:12:07,063 INFO misc.py line 119 131400] Train: [23/100][11/800] Data 0.003 (0.067) Batch 0.374 (0.388) Remain 06:43:01 loss: 0.3620 Lr: 0.00554 [2023-12-20 15:12:07,396 INFO misc.py line 119 131400] Train: [23/100][12/800] Data 0.003 (0.060) Batch 0.333 (0.382) Remain 06:36:43 loss: 0.6763 Lr: 0.00554 [2023-12-20 15:12:07,757 INFO misc.py line 119 131400] Train: [23/100][13/800] Data 0.003 (0.054) Batch 0.361 (0.380) Remain 06:34:36 loss: 0.6781 Lr: 0.00554 [2023-12-20 15:12:08,063 INFO misc.py line 119 131400] Train: [23/100][14/800] Data 0.003 (0.049) Batch 0.306 (0.373) Remain 06:27:40 loss: 0.7523 Lr: 0.00554 [2023-12-20 15:12:08,390 INFO misc.py line 119 131400] Train: [23/100][15/800] Data 0.004 (0.046) Batch 0.327 (0.369) Remain 06:23:44 loss: 0.7603 Lr: 0.00554 [2023-12-20 15:12:08,730 INFO misc.py line 119 131400] Train: [23/100][16/800] Data 0.003 (0.042) Batch 0.339 (0.367) Remain 06:21:18 loss: 0.3479 Lr: 0.00554 [2023-12-20 15:12:09,059 INFO misc.py line 119 131400] Train: [23/100][17/800] Data 0.004 (0.040) Batch 0.330 (0.364) Remain 06:18:34 loss: 0.6034 Lr: 0.00554 [2023-12-20 15:12:09,365 INFO misc.py line 119 131400] Train: [23/100][18/800] Data 0.002 (0.037) Batch 0.302 (0.360) Remain 06:14:17 loss: 0.4032 Lr: 0.00554 [2023-12-20 15:12:09,684 INFO misc.py line 119 131400] Train: [23/100][19/800] Data 0.006 (0.035) Batch 0.323 (0.358) Remain 06:11:51 loss: 0.6406 Lr: 0.00554 [2023-12-20 15:12:10,024 INFO misc.py line 119 131400] Train: [23/100][20/800] Data 0.003 (0.033) Batch 0.340 (0.357) Remain 06:10:46 loss: 0.6015 Lr: 0.00554 [2023-12-20 15:12:10,334 INFO misc.py line 119 131400] Train: [23/100][21/800] Data 0.003 (0.032) Batch 0.309 (0.354) Remain 06:08:01 loss: 0.6879 Lr: 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line 119 131400] Train: [23/100][28/800] Data 0.004 (0.024) Batch 0.287 (0.343) Remain 05:56:48 loss: 0.8470 Lr: 0.00554 [2023-12-20 15:12:12,837 INFO misc.py line 119 131400] Train: [23/100][29/800] Data 0.003 (0.023) Batch 0.295 (0.341) Remain 05:54:52 loss: 0.5114 Lr: 0.00554 [2023-12-20 15:12:13,166 INFO misc.py line 119 131400] Train: [23/100][30/800] Data 0.002 (0.022) Batch 0.328 (0.341) Remain 05:54:19 loss: 0.4172 Lr: 0.00554 [2023-12-20 15:12:13,479 INFO misc.py line 119 131400] Train: [23/100][31/800] Data 0.004 (0.021) Batch 0.315 (0.340) Remain 05:53:20 loss: 0.3595 Lr: 0.00554 [2023-12-20 15:12:13,778 INFO misc.py line 119 131400] Train: [23/100][32/800] Data 0.003 (0.021) Batch 0.296 (0.338) Remain 05:51:45 loss: 0.2899 Lr: 0.00554 [2023-12-20 15:12:14,090 INFO misc.py line 119 131400] Train: [23/100][33/800] Data 0.006 (0.020) Batch 0.315 (0.338) Remain 05:50:56 loss: 0.6237 Lr: 0.00554 [2023-12-20 15:12:14,397 INFO misc.py line 119 131400] Train: [23/100][34/800] Data 0.003 (0.020) Batch 0.307 (0.337) Remain 05:49:53 loss: 0.5359 Lr: 0.00554 [2023-12-20 15:12:14,709 INFO misc.py line 119 131400] Train: [23/100][35/800] Data 0.003 (0.019) Batch 0.311 (0.336) Remain 05:49:03 loss: 0.4740 Lr: 0.00554 [2023-12-20 15:12:15,032 INFO misc.py line 119 131400] Train: [23/100][36/800] Data 0.005 (0.019) Batch 0.324 (0.335) Remain 05:48:40 loss: 0.2406 Lr: 0.00554 [2023-12-20 15:12:15,350 INFO misc.py line 119 131400] Train: [23/100][37/800] Data 0.003 (0.018) Batch 0.318 (0.335) Remain 05:48:08 loss: 0.4576 Lr: 0.00554 [2023-12-20 15:12:15,668 INFO misc.py line 119 131400] Train: [23/100][38/800] Data 0.002 (0.018) Batch 0.318 (0.334) Remain 05:47:37 loss: 0.3524 Lr: 0.00554 [2023-12-20 15:12:15,980 INFO misc.py line 119 131400] Train: [23/100][39/800] Data 0.002 (0.017) Batch 0.309 (0.334) Remain 05:46:53 loss: 0.7695 Lr: 0.00554 [2023-12-20 15:12:16,302 INFO misc.py line 119 131400] Train: [23/100][40/800] Data 0.006 (0.017) Batch 0.325 (0.334) Remain 05:46:38 loss: 0.3134 Lr: 0.00554 [2023-12-20 15:12:16,619 INFO misc.py line 119 131400] Train: [23/100][41/800] Data 0.002 (0.017) Batch 0.316 (0.333) Remain 05:46:09 loss: 0.3975 Lr: 0.00554 [2023-12-20 15:12:16,947 INFO misc.py line 119 131400] Train: [23/100][42/800] Data 0.005 (0.016) Batch 0.329 (0.333) Remain 05:46:02 loss: 0.7417 Lr: 0.00554 [2023-12-20 15:12:17,251 INFO misc.py line 119 131400] Train: [23/100][43/800] Data 0.003 (0.016) Batch 0.304 (0.332) Remain 05:45:16 loss: 0.4028 Lr: 0.00554 [2023-12-20 15:12:17,564 INFO misc.py line 119 131400] Train: [23/100][44/800] Data 0.002 (0.016) Batch 0.314 (0.332) Remain 05:44:48 loss: 0.5606 Lr: 0.00554 [2023-12-20 15:12:17,892 INFO misc.py line 119 131400] Train: [23/100][45/800] Data 0.003 (0.015) Batch 0.327 (0.332) Remain 05:44:41 loss: 0.6263 Lr: 0.00554 [2023-12-20 15:12:18,218 INFO misc.py line 119 131400] Train: [23/100][46/800] Data 0.003 (0.015) Batch 0.326 (0.332) Remain 05:44:33 loss: 0.4768 Lr: 0.00554 [2023-12-20 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Train: [23/100][53/800] Data 0.004 (0.013) Batch 0.347 (0.331) Remain 05:43:45 loss: 0.4411 Lr: 0.00554 [2023-12-20 15:12:20,800 INFO misc.py line 119 131400] Train: [23/100][54/800] Data 0.003 (0.013) Batch 0.298 (0.330) Remain 05:43:04 loss: 0.2326 Lr: 0.00554 [2023-12-20 15:12:21,097 INFO misc.py line 119 131400] Train: [23/100][55/800] Data 0.002 (0.013) Batch 0.297 (0.330) Remain 05:42:24 loss: 0.4785 Lr: 0.00554 [2023-12-20 15:12:21,447 INFO misc.py line 119 131400] Train: [23/100][56/800] Data 0.003 (0.013) Batch 0.350 (0.330) Remain 05:42:48 loss: 0.6615 Lr: 0.00554 [2023-12-20 15:12:21,770 INFO misc.py line 119 131400] Train: [23/100][57/800] Data 0.003 (0.013) Batch 0.323 (0.330) Remain 05:42:40 loss: 0.3749 Lr: 0.00554 [2023-12-20 15:12:22,101 INFO misc.py line 119 131400] Train: [23/100][58/800] Data 0.003 (0.012) Batch 0.330 (0.330) Remain 05:42:40 loss: 0.5775 Lr: 0.00553 [2023-12-20 15:12:22,446 INFO misc.py line 119 131400] Train: [23/100][59/800] Data 0.004 (0.012) Batch 0.345 (0.330) Remain 05:42:57 loss: 0.5353 Lr: 0.00553 [2023-12-20 15:12:22,797 INFO misc.py line 119 131400] Train: [23/100][60/800] Data 0.004 (0.012) Batch 0.350 (0.330) Remain 05:43:18 loss: 0.8129 Lr: 0.00553 [2023-12-20 15:12:23,069 INFO misc.py line 119 131400] Train: [23/100][61/800] Data 0.008 (0.012) Batch 0.272 (0.329) Remain 05:42:15 loss: 0.5484 Lr: 0.00553 [2023-12-20 15:12:23,370 INFO misc.py line 119 131400] Train: [23/100][62/800] Data 0.004 (0.012) Batch 0.301 (0.329) Remain 05:41:45 loss: 0.3408 Lr: 0.00553 [2023-12-20 15:12:23,669 INFO misc.py line 119 131400] Train: [23/100][63/800] Data 0.004 (0.012) Batch 0.297 (0.328) Remain 05:41:11 loss: 0.8087 Lr: 0.00553 [2023-12-20 15:12:23,984 INFO misc.py line 119 131400] Train: [23/100][64/800] Data 0.006 (0.012) Batch 0.318 (0.328) Remain 05:41:00 loss: 0.8661 Lr: 0.00553 [2023-12-20 15:12:24,316 INFO misc.py line 119 131400] Train: [23/100][65/800] Data 0.003 (0.012) Batch 0.332 (0.328) Remain 05:41:03 loss: 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INFO misc.py line 119 131400] Train: [23/100][72/800] Data 0.003 (0.011) Batch 0.307 (0.329) Remain 05:41:36 loss: 0.8174 Lr: 0.00553 [2023-12-20 15:12:26,988 INFO misc.py line 119 131400] Train: [23/100][73/800] Data 0.004 (0.011) Batch 0.335 (0.329) Remain 05:41:41 loss: 0.3403 Lr: 0.00553 [2023-12-20 15:12:27,306 INFO misc.py line 119 131400] Train: [23/100][74/800] Data 0.004 (0.011) Batch 0.319 (0.329) Remain 05:41:32 loss: 0.4032 Lr: 0.00553 [2023-12-20 15:12:27,591 INFO misc.py line 119 131400] Train: [23/100][75/800] Data 0.003 (0.010) Batch 0.285 (0.328) Remain 05:40:54 loss: 1.1267 Lr: 0.00553 [2023-12-20 15:12:27,897 INFO misc.py line 119 131400] Train: [23/100][76/800] Data 0.003 (0.010) Batch 0.306 (0.328) Remain 05:40:34 loss: 0.3146 Lr: 0.00553 [2023-12-20 15:12:28,199 INFO misc.py line 119 131400] Train: [23/100][77/800] Data 0.004 (0.010) Batch 0.302 (0.328) Remain 05:40:12 loss: 0.4228 Lr: 0.00553 [2023-12-20 15:12:28,575 INFO misc.py line 119 131400] Train: 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Batch 0.346 (0.335) Remain 05:43:49 loss: 0.5117 Lr: 0.00549 [2023-12-20 15:16:14,255 INFO misc.py line 119 131400] Train: [23/100][751/800] Data 0.003 (0.005) Batch 0.329 (0.335) Remain 05:43:48 loss: 0.2710 Lr: 0.00549 [2023-12-20 15:16:14,598 INFO misc.py line 119 131400] Train: [23/100][752/800] Data 0.004 (0.005) Batch 0.342 (0.335) Remain 05:43:48 loss: 0.6547 Lr: 0.00549 [2023-12-20 15:16:14,986 INFO misc.py line 119 131400] Train: [23/100][753/800] Data 0.005 (0.005) Batch 0.388 (0.335) Remain 05:43:53 loss: 0.6074 Lr: 0.00549 [2023-12-20 15:16:15,312 INFO misc.py line 119 131400] Train: [23/100][754/800] Data 0.004 (0.005) Batch 0.327 (0.335) Remain 05:43:52 loss: 0.5941 Lr: 0.00549 [2023-12-20 15:16:15,702 INFO misc.py line 119 131400] Train: [23/100][755/800] Data 0.003 (0.005) Batch 0.388 (0.335) Remain 05:43:56 loss: 0.5396 Lr: 0.00549 [2023-12-20 15:16:16,039 INFO misc.py line 119 131400] Train: [23/100][756/800] Data 0.006 (0.005) Batch 0.338 (0.335) Remain 05:43:56 loss: 0.4713 Lr: 0.00549 [2023-12-20 15:16:16,382 INFO misc.py line 119 131400] Train: [23/100][757/800] Data 0.004 (0.005) Batch 0.343 (0.335) Remain 05:43:56 loss: 0.4845 Lr: 0.00549 [2023-12-20 15:16:16,669 INFO misc.py line 119 131400] Train: [23/100][758/800] Data 0.004 (0.005) Batch 0.287 (0.335) Remain 05:43:52 loss: 0.2722 Lr: 0.00549 [2023-12-20 15:16:16,997 INFO misc.py line 119 131400] Train: [23/100][759/800] Data 0.003 (0.005) Batch 0.326 (0.335) Remain 05:43:51 loss: 0.3939 Lr: 0.00549 [2023-12-20 15:16:17,309 INFO misc.py line 119 131400] Train: [23/100][760/800] Data 0.006 (0.005) Batch 0.315 (0.335) Remain 05:43:49 loss: 0.5011 Lr: 0.00549 [2023-12-20 15:16:17,659 INFO misc.py line 119 131400] Train: [23/100][761/800] Data 0.003 (0.005) Batch 0.340 (0.335) Remain 05:43:49 loss: 0.5000 Lr: 0.00549 [2023-12-20 15:16:17,991 INFO misc.py line 119 131400] Train: [23/100][762/800] Data 0.012 (0.005) Batch 0.340 (0.335) Remain 05:43:49 loss: 0.2575 Lr: 0.00549 [2023-12-20 15:16:18,337 INFO misc.py line 119 131400] Train: [23/100][763/800] Data 0.005 (0.005) Batch 0.347 (0.335) Remain 05:43:50 loss: 0.8051 Lr: 0.00549 [2023-12-20 15:16:18,802 INFO misc.py line 119 131400] Train: [23/100][764/800] Data 0.004 (0.005) Batch 0.461 (0.335) Remain 05:43:59 loss: 0.3512 Lr: 0.00549 [2023-12-20 15:16:19,144 INFO misc.py line 119 131400] Train: [23/100][765/800] Data 0.008 (0.005) Batch 0.347 (0.335) Remain 05:44:00 loss: 0.4905 Lr: 0.00549 [2023-12-20 15:16:19,469 INFO misc.py line 119 131400] Train: [23/100][766/800] Data 0.004 (0.005) Batch 0.324 (0.335) Remain 05:43:59 loss: 0.4705 Lr: 0.00549 [2023-12-20 15:16:19,800 INFO misc.py line 119 131400] Train: [23/100][767/800] Data 0.004 (0.005) Batch 0.328 (0.335) Remain 05:43:58 loss: 0.5898 Lr: 0.00549 [2023-12-20 15:16:20,152 INFO misc.py line 119 131400] Train: [23/100][768/800] Data 0.008 (0.005) Batch 0.356 (0.335) Remain 05:43:59 loss: 0.1311 Lr: 0.00549 [2023-12-20 15:16:20,502 INFO misc.py line 119 131400] Train: [23/100][769/800] Data 0.003 (0.005) Batch 0.349 (0.335) Remain 05:44:00 loss: 0.4660 Lr: 0.00549 [2023-12-20 15:16:20,843 INFO misc.py line 119 131400] Train: [23/100][770/800] Data 0.004 (0.005) Batch 0.336 (0.335) Remain 05:44:00 loss: 0.4726 Lr: 0.00549 [2023-12-20 15:16:21,168 INFO misc.py line 119 131400] Train: [23/100][771/800] Data 0.010 (0.005) Batch 0.331 (0.335) Remain 05:43:59 loss: 0.5285 Lr: 0.00549 [2023-12-20 15:16:21,456 INFO misc.py line 119 131400] Train: [23/100][772/800] Data 0.002 (0.005) Batch 0.287 (0.335) Remain 05:43:55 loss: 0.5003 Lr: 0.00549 [2023-12-20 15:16:21,787 INFO misc.py line 119 131400] Train: [23/100][773/800] Data 0.003 (0.005) Batch 0.332 (0.335) Remain 05:43:54 loss: 0.4824 Lr: 0.00549 [2023-12-20 15:16:22,105 INFO misc.py line 119 131400] Train: [23/100][774/800] Data 0.003 (0.005) Batch 0.318 (0.335) Remain 05:43:53 loss: 0.6768 Lr: 0.00549 [2023-12-20 15:16:22,425 INFO misc.py line 119 131400] Train: [23/100][775/800] Data 0.003 (0.005) Batch 0.319 (0.335) Remain 05:43:51 loss: 0.8661 Lr: 0.00549 [2023-12-20 15:16:22,798 INFO misc.py line 119 131400] Train: [23/100][776/800] Data 0.004 (0.005) Batch 0.374 (0.335) Remain 05:43:54 loss: 0.5088 Lr: 0.00549 [2023-12-20 15:16:23,109 INFO misc.py line 119 131400] Train: [23/100][777/800] Data 0.003 (0.005) Batch 0.312 (0.335) Remain 05:43:52 loss: 0.5652 Lr: 0.00549 [2023-12-20 15:16:23,446 INFO misc.py line 119 131400] Train: [23/100][778/800] Data 0.003 (0.005) Batch 0.336 (0.335) Remain 05:43:52 loss: 0.4150 Lr: 0.00549 [2023-12-20 15:16:23,765 INFO misc.py line 119 131400] Train: [23/100][779/800] Data 0.004 (0.005) Batch 0.320 (0.335) Remain 05:43:50 loss: 1.0490 Lr: 0.00549 [2023-12-20 15:16:24,082 INFO misc.py line 119 131400] Train: [23/100][780/800] Data 0.003 (0.005) Batch 0.317 (0.335) Remain 05:43:48 loss: 0.4963 Lr: 0.00549 [2023-12-20 15:16:24,419 INFO misc.py line 119 131400] Train: [23/100][781/800] Data 0.003 (0.005) Batch 0.336 (0.335) Remain 05:43:48 loss: 0.4992 Lr: 0.00549 [2023-12-20 15:16:24,735 INFO misc.py line 119 131400] Train: [23/100][782/800] Data 0.003 (0.005) Batch 0.317 (0.335) Remain 05:43:46 loss: 0.9380 Lr: 0.00549 [2023-12-20 15:16:25,037 INFO misc.py line 119 131400] Train: [23/100][783/800] Data 0.003 (0.005) Batch 0.301 (0.335) Remain 05:43:43 loss: 0.3562 Lr: 0.00549 [2023-12-20 15:16:25,370 INFO misc.py line 119 131400] Train: [23/100][784/800] Data 0.004 (0.005) Batch 0.331 (0.335) Remain 05:43:43 loss: 0.3333 Lr: 0.00549 [2023-12-20 15:16:25,706 INFO misc.py line 119 131400] Train: [23/100][785/800] Data 0.005 (0.005) Batch 0.338 (0.335) Remain 05:43:43 loss: 0.6464 Lr: 0.00549 [2023-12-20 15:16:25,999 INFO misc.py line 119 131400] Train: [23/100][786/800] Data 0.004 (0.005) Batch 0.294 (0.335) Remain 05:43:39 loss: 0.1912 Lr: 0.00549 [2023-12-20 15:16:26,307 INFO misc.py line 119 131400] Train: [23/100][787/800] Data 0.003 (0.005) Batch 0.307 (0.335) Remain 05:43:37 loss: 0.2912 Lr: 0.00549 [2023-12-20 15:16:26,620 INFO misc.py line 119 131400] Train: [23/100][788/800] Data 0.003 (0.005) Batch 0.312 (0.335) Remain 05:43:34 loss: 0.5233 Lr: 0.00549 [2023-12-20 15:16:26,929 INFO misc.py line 119 131400] Train: [23/100][789/800] Data 0.004 (0.005) Batch 0.308 (0.335) Remain 05:43:32 loss: 0.4599 Lr: 0.00549 [2023-12-20 15:16:27,236 INFO misc.py line 119 131400] Train: [23/100][790/800] Data 0.005 (0.005) Batch 0.308 (0.335) Remain 05:43:30 loss: 0.3411 Lr: 0.00549 [2023-12-20 15:16:27,529 INFO misc.py line 119 131400] Train: [23/100][791/800] Data 0.004 (0.005) Batch 0.292 (0.334) Remain 05:43:26 loss: 0.6251 Lr: 0.00549 [2023-12-20 15:16:27,821 INFO misc.py line 119 131400] Train: [23/100][792/800] Data 0.005 (0.005) Batch 0.292 (0.334) Remain 05:43:22 loss: 0.5139 Lr: 0.00549 [2023-12-20 15:16:28,111 INFO misc.py line 119 131400] Train: [23/100][793/800] Data 0.004 (0.005) Batch 0.291 (0.334) Remain 05:43:19 loss: 0.7223 Lr: 0.00549 [2023-12-20 15:16:28,417 INFO misc.py line 119 131400] Train: [23/100][794/800] Data 0.003 (0.005) Batch 0.303 (0.334) Remain 05:43:16 loss: 0.4725 Lr: 0.00548 [2023-12-20 15:16:28,733 INFO misc.py line 119 131400] Train: [23/100][795/800] Data 0.006 (0.005) Batch 0.315 (0.334) Remain 05:43:14 loss: 0.6030 Lr: 0.00548 [2023-12-20 15:16:29,051 INFO misc.py line 119 131400] Train: [23/100][796/800] Data 0.007 (0.005) Batch 0.322 (0.334) Remain 05:43:13 loss: 0.8398 Lr: 0.00548 [2023-12-20 15:16:29,352 INFO misc.py line 119 131400] Train: [23/100][797/800] Data 0.004 (0.005) Batch 0.300 (0.334) Remain 05:43:10 loss: 0.3896 Lr: 0.00548 [2023-12-20 15:16:29,666 INFO misc.py line 119 131400] Train: [23/100][798/800] Data 0.005 (0.005) Batch 0.315 (0.334) Remain 05:43:08 loss: 0.4382 Lr: 0.00548 [2023-12-20 15:16:29,965 INFO misc.py line 119 131400] Train: [23/100][799/800] Data 0.004 (0.005) Batch 0.299 (0.334) Remain 05:43:05 loss: 0.4774 Lr: 0.00548 [2023-12-20 15:16:30,239 INFO misc.py line 119 131400] Train: [23/100][800/800] Data 0.004 (0.005) Batch 0.274 (0.334) Remain 05:43:00 loss: 0.5947 Lr: 0.00548 [2023-12-20 15:16:30,239 INFO misc.py line 136 131400] Train result: loss: 0.5269 [2023-12-20 15:16:30,240 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:16:52,696 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0738 [2023-12-20 15:16:52,779 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1811 [2023-12-20 15:16:52,868 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3004 [2023-12-20 15:16:53,063 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.8634 [2023-12-20 15:16:53,179 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3961 [2023-12-20 15:16:53,280 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.3034 [2023-12-20 15:16:53,375 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1317 [2023-12-20 15:16:53,483 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1358 [2023-12-20 15:16:53,564 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2667 [2023-12-20 15:16:53,654 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5416 [2023-12-20 15:16:53,744 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4828 [2023-12-20 15:16:53,886 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5552 [2023-12-20 15:16:54,012 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1929 [2023-12-20 15:16:54,167 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3575 [2023-12-20 15:16:54,261 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3321 [2023-12-20 15:16:54,407 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5864 [2023-12-20 15:16:54,515 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3118 [2023-12-20 15:16:54,625 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.1868 [2023-12-20 15:16:54,747 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4966 [2023-12-20 15:16:54,849 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3482 [2023-12-20 15:16:54,957 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3187 [2023-12-20 15:16:55,121 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1641 [2023-12-20 15:16:55,252 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.0572 [2023-12-20 15:16:55,400 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2750 [2023-12-20 15:16:55,550 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1948 [2023-12-20 15:16:55,637 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5815 [2023-12-20 15:16:55,806 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9347 [2023-12-20 15:16:55,930 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6303 [2023-12-20 15:16:56,038 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7441 [2023-12-20 15:16:56,188 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3913 [2023-12-20 15:16:56,300 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7094 [2023-12-20 15:16:56,420 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4981 [2023-12-20 15:16:56,508 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1587 [2023-12-20 15:16:56,586 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2083 [2023-12-20 15:16:56,694 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8406 [2023-12-20 15:16:56,794 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5298 [2023-12-20 15:16:56,930 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0109 [2023-12-20 15:16:57,046 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1230 [2023-12-20 15:16:57,136 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6150 [2023-12-20 15:16:57,286 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5939 [2023-12-20 15:16:57,433 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0222 [2023-12-20 15:16:57,531 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2335 [2023-12-20 15:16:57,663 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3069 [2023-12-20 15:16:57,811 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8841 [2023-12-20 15:16:57,928 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3503 [2023-12-20 15:16:58,034 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3928 [2023-12-20 15:16:58,205 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6866 [2023-12-20 15:16:58,301 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4819 [2023-12-20 15:16:58,450 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3256 [2023-12-20 15:16:58,541 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9480 [2023-12-20 15:16:58,615 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5280 [2023-12-20 15:16:58,721 INFO evaluator.py line 159 131400] Test: [52/78] Loss 0.8043 [2023-12-20 15:16:58,868 INFO evaluator.py line 159 131400] Test: [53/78] Loss 2.5721 [2023-12-20 15:16:59,002 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4253 [2023-12-20 15:16:59,104 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.8971 [2023-12-20 15:16:59,192 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8487 [2023-12-20 15:16:59,294 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5494 [2023-12-20 15:16:59,455 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3054 [2023-12-20 15:16:59,553 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4354 [2023-12-20 15:16:59,646 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1870 [2023-12-20 15:16:59,741 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4540 [2023-12-20 15:16:59,831 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5105 [2023-12-20 15:16:59,917 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4689 [2023-12-20 15:17:00,017 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9097 [2023-12-20 15:17:00,146 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1215 [2023-12-20 15:17:00,229 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3534 [2023-12-20 15:17:00,328 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5495 [2023-12-20 15:17:00,420 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0127 [2023-12-20 15:17:00,503 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4553 [2023-12-20 15:17:00,586 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0175 [2023-12-20 15:17:00,679 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.0658 [2023-12-20 15:17:00,769 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6067 [2023-12-20 15:17:00,902 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2205 [2023-12-20 15:17:00,997 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6619 [2023-12-20 15:17:01,111 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7257 [2023-12-20 15:17:01,212 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.0788 [2023-12-20 15:17:01,298 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3063 [2023-12-20 15:17:01,451 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1146 [2023-12-20 15:17:02,509 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7115/0.8013/0.8979. [2023-12-20 15:17:02,509 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8358/0.9407 [2023-12-20 15:17:02,509 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9627/0.9824 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6451/0.7325 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7989/0.8524 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8948/0.9371 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7939/0.9383 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7575/0.8717 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6253/0.7933 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6141/0.7327 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7805/0.9423 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3218/0.3604 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6639/0.7902 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6547/0.7831 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7401/0.8393 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5322/0.5560 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6575/0.7110 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8681/0.9826 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6681/0.7579 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8793/0.9317 [2023-12-20 15:17:02,510 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5361/0.5903 [2023-12-20 15:17:02,511 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:17:02,511 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:17:02,512 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:17:05,722 INFO misc.py line 119 131400] Train: [24/100][1/800] Data 0.896 (0.896) Batch 1.193 (1.193) Remain 20:24:24 loss: 0.7014 Lr: 0.00548 [2023-12-20 15:17:06,017 INFO misc.py line 119 131400] Train: [24/100][2/800] Data 0.003 (0.003) Batch 0.294 (0.294) Remain 05:01:36 loss: 0.3798 Lr: 0.00548 [2023-12-20 15:17:06,339 INFO misc.py line 119 131400] Train: [24/100][3/800] Data 0.005 (0.005) Batch 0.323 (0.323) Remain 05:31:20 loss: 0.4775 Lr: 0.00548 [2023-12-20 15:17:06,684 INFO misc.py line 119 131400] Train: [24/100][4/800] Data 0.004 (0.004) Batch 0.345 (0.345) Remain 05:54:34 loss: 0.4598 Lr: 0.00548 [2023-12-20 15:17:07,009 INFO misc.py line 119 131400] Train: [24/100][5/800] Data 0.004 (0.004) Batch 0.326 (0.336) Remain 05:44:32 loss: 0.4220 Lr: 0.00548 [2023-12-20 15:17:07,325 INFO misc.py line 119 131400] Train: [24/100][6/800] Data 0.003 (0.004) Batch 0.315 (0.329) Remain 05:37:28 loss: 0.4767 Lr: 0.00548 [2023-12-20 15:17:07,636 INFO misc.py line 119 131400] Train: [24/100][7/800] Data 0.005 (0.004) Batch 0.311 (0.324) Remain 05:32:55 loss: 0.2954 Lr: 0.00548 [2023-12-20 15:17:07,945 INFO misc.py line 119 131400] Train: [24/100][8/800] Data 0.003 (0.004) Batch 0.308 (0.321) Remain 05:29:37 loss: 0.8277 Lr: 0.00548 [2023-12-20 15:17:08,509 INFO misc.py line 119 131400] Train: [24/100][9/800] Data 0.004 (0.004) Batch 0.565 (0.362) Remain 06:11:17 loss: 0.7003 Lr: 0.00548 [2023-12-20 15:17:08,795 INFO misc.py line 119 131400] Train: [24/100][10/800] Data 0.004 (0.004) Batch 0.286 (0.351) Remain 06:00:09 loss: 0.3005 Lr: 0.00548 [2023-12-20 15:17:09,116 INFO misc.py line 119 131400] Train: [24/100][11/800] Data 0.004 (0.004) Batch 0.320 (0.347) Remain 05:56:11 loss: 0.7930 Lr: 0.00548 [2023-12-20 15:17:09,389 INFO misc.py line 119 131400] Train: [24/100][12/800] Data 0.005 (0.004) Batch 0.274 (0.339) Remain 05:47:51 loss: 0.3087 Lr: 0.00548 [2023-12-20 15:17:09,668 INFO misc.py line 119 131400] Train: [24/100][13/800] Data 0.003 (0.004) Batch 0.279 (0.333) Remain 05:41:43 loss: 0.6070 Lr: 0.00548 [2023-12-20 15:17:09,944 INFO misc.py line 119 131400] Train: [24/100][14/800] Data 0.004 (0.004) Batch 0.277 (0.328) Remain 05:36:27 loss: 0.3033 Lr: 0.00548 [2023-12-20 15:17:10,228 INFO misc.py line 119 131400] Train: [24/100][15/800] Data 0.003 (0.004) Batch 0.284 (0.324) Remain 05:32:42 loss: 0.3292 Lr: 0.00548 [2023-12-20 15:17:10,556 INFO misc.py line 119 131400] Train: [24/100][16/800] Data 0.003 (0.004) Batch 0.327 (0.324) Remain 05:32:57 loss: 0.3988 Lr: 0.00548 [2023-12-20 15:17:10,891 INFO misc.py line 119 131400] Train: [24/100][17/800] Data 0.004 (0.004) Batch 0.335 (0.325) Remain 05:33:43 loss: 0.5747 Lr: 0.00548 [2023-12-20 15:17:11,243 INFO misc.py line 119 131400] Train: [24/100][18/800] Data 0.006 (0.004) Batch 0.352 (0.327) Remain 05:35:35 loss: 0.3434 Lr: 0.00548 [2023-12-20 15:17:11,575 INFO misc.py line 119 131400] Train: [24/100][19/800] Data 0.003 (0.004) Batch 0.332 (0.327) Remain 05:35:53 loss: 0.8075 Lr: 0.00548 [2023-12-20 15:17:11,926 INFO misc.py line 119 131400] Train: [24/100][20/800] Data 0.004 (0.004) Batch 0.350 (0.329) Remain 05:37:16 loss: 0.6186 Lr: 0.00548 [2023-12-20 15:17:12,274 INFO misc.py line 119 131400] Train: [24/100][21/800] Data 0.005 (0.004) Batch 0.347 (0.330) Remain 05:38:19 loss: 0.4324 Lr: 0.00548 [2023-12-20 15:17:12,679 INFO misc.py line 119 131400] Train: [24/100][22/800] Data 0.006 (0.004) Batch 0.407 (0.334) Remain 05:42:30 loss: 0.5514 Lr: 0.00548 [2023-12-20 15:17:13,021 INFO misc.py line 119 131400] Train: [24/100][23/800] Data 0.003 (0.004) Batch 0.342 (0.334) Remain 05:42:54 loss: 0.4320 Lr: 0.00548 [2023-12-20 15:17:13,405 INFO misc.py line 119 131400] Train: [24/100][24/800] Data 0.004 (0.004) Batch 0.383 (0.336) Remain 05:45:17 loss: 0.8292 Lr: 0.00548 [2023-12-20 15:17:13,758 INFO misc.py line 119 131400] Train: [24/100][25/800] Data 0.004 (0.004) Batch 0.354 (0.337) Remain 05:46:07 loss: 0.5765 Lr: 0.00548 [2023-12-20 15:17:14,119 INFO misc.py line 119 131400] Train: [24/100][26/800] Data 0.003 (0.004) Batch 0.360 (0.338) Remain 05:47:06 loss: 0.3530 Lr: 0.00548 [2023-12-20 15:17:14,450 INFO misc.py line 119 131400] Train: [24/100][27/800] Data 0.004 (0.004) Batch 0.331 (0.338) Remain 05:46:48 loss: 0.5215 Lr: 0.00548 [2023-12-20 15:17:14,797 INFO misc.py line 119 131400] Train: [24/100][28/800] Data 0.005 (0.004) Batch 0.348 (0.338) Remain 05:47:12 loss: 0.6029 Lr: 0.00548 [2023-12-20 15:17:15,127 INFO misc.py line 119 131400] Train: [24/100][29/800] Data 0.004 (0.004) Batch 0.330 (0.338) Remain 05:46:52 loss: 0.3280 Lr: 0.00548 [2023-12-20 15:17:15,454 INFO misc.py line 119 131400] Train: [24/100][30/800] Data 0.004 (0.004) Batch 0.321 (0.337) Remain 05:46:13 loss: 0.4402 Lr: 0.00548 [2023-12-20 15:17:15,803 INFO misc.py line 119 131400] Train: [24/100][31/800] Data 0.009 (0.004) Batch 0.354 (0.338) Remain 05:46:48 loss: 0.2542 Lr: 0.00548 [2023-12-20 15:17:16,144 INFO misc.py line 119 131400] Train: [24/100][32/800] Data 0.005 (0.004) Batch 0.341 (0.338) Remain 05:46:55 loss: 0.3410 Lr: 0.00548 [2023-12-20 15:17:16,495 INFO misc.py line 119 131400] Train: [24/100][33/800] Data 0.004 (0.004) Batch 0.348 (0.338) Remain 05:47:15 loss: 0.2219 Lr: 0.00548 [2023-12-20 15:17:16,844 INFO misc.py line 119 131400] Train: [24/100][34/800] Data 0.007 (0.004) Batch 0.352 (0.339) Remain 05:47:42 loss: 0.7750 Lr: 0.00548 [2023-12-20 15:17:17,196 INFO misc.py line 119 131400] Train: [24/100][35/800] Data 0.005 (0.004) Batch 0.352 (0.339) Remain 05:48:07 loss: 0.6163 Lr: 0.00548 [2023-12-20 15:17:17,527 INFO misc.py line 119 131400] Train: [24/100][36/800] Data 0.004 (0.004) Batch 0.331 (0.339) Remain 05:47:51 loss: 0.3117 Lr: 0.00548 [2023-12-20 15:17:17,847 INFO misc.py line 119 131400] Train: [24/100][37/800] Data 0.005 (0.004) Batch 0.321 (0.339) Remain 05:47:19 loss: 0.2860 Lr: 0.00548 [2023-12-20 15:17:18,204 INFO misc.py line 119 131400] Train: [24/100][38/800] Data 0.003 (0.004) Batch 0.357 (0.339) Remain 05:47:50 loss: 0.5113 Lr: 0.00548 [2023-12-20 15:17:18,571 INFO misc.py line 119 131400] Train: [24/100][39/800] Data 0.003 (0.004) Batch 0.366 (0.340) Remain 05:48:36 loss: 0.2445 Lr: 0.00548 [2023-12-20 15:17:18,918 INFO misc.py line 119 131400] Train: [24/100][40/800] Data 0.005 (0.004) Batch 0.348 (0.340) Remain 05:48:50 loss: 0.3742 Lr: 0.00548 [2023-12-20 15:17:19,269 INFO misc.py line 119 131400] Train: [24/100][41/800] Data 0.003 (0.004) Batch 0.350 (0.340) Remain 05:49:05 loss: 0.5698 Lr: 0.00548 [2023-12-20 15:17:19,617 INFO misc.py line 119 131400] Train: [24/100][42/800] Data 0.004 (0.004) Batch 0.346 (0.340) Remain 05:49:14 loss: 0.5911 Lr: 0.00548 [2023-12-20 15:17:19,970 INFO misc.py line 119 131400] Train: [24/100][43/800] Data 0.007 (0.004) Batch 0.355 (0.341) Remain 05:49:36 loss: 0.5222 Lr: 0.00548 [2023-12-20 15:17:20,293 INFO misc.py line 119 131400] Train: [24/100][44/800] Data 0.004 (0.004) Batch 0.323 (0.340) Remain 05:49:09 loss: 0.3775 Lr: 0.00548 [2023-12-20 15:17:20,657 INFO misc.py line 119 131400] Train: [24/100][45/800] Data 0.006 (0.004) Batch 0.361 (0.341) Remain 05:49:39 loss: 0.7172 Lr: 0.00548 [2023-12-20 15:17:20,985 INFO misc.py line 119 131400] Train: [24/100][46/800] Data 0.008 (0.004) Batch 0.332 (0.341) Remain 05:49:25 loss: 0.4956 Lr: 0.00548 [2023-12-20 15:17:21,361 INFO misc.py line 119 131400] Train: [24/100][47/800] Data 0.003 (0.004) Batch 0.377 (0.341) Remain 05:50:16 loss: 0.2291 Lr: 0.00548 [2023-12-20 15:17:21,703 INFO misc.py line 119 131400] Train: [24/100][48/800] Data 0.003 (0.004) Batch 0.337 (0.341) Remain 05:50:09 loss: 0.4861 Lr: 0.00548 [2023-12-20 15:17:22,032 INFO misc.py line 119 131400] Train: [24/100][49/800] Data 0.009 (0.004) Batch 0.333 (0.341) Remain 05:49:58 loss: 0.5579 Lr: 0.00548 [2023-12-20 15:17:22,381 INFO misc.py line 119 131400] Train: [24/100][50/800] Data 0.004 (0.004) Batch 0.349 (0.341) Remain 05:50:08 loss: 0.6471 Lr: 0.00548 [2023-12-20 15:17:22,736 INFO misc.py line 119 131400] Train: [24/100][51/800] Data 0.003 (0.004) Batch 0.349 (0.341) Remain 05:50:18 loss: 0.5513 Lr: 0.00548 [2023-12-20 15:17:23,064 INFO misc.py line 119 131400] Train: [24/100][52/800] Data 0.010 (0.005) Batch 0.334 (0.341) Remain 05:50:07 loss: 0.2650 Lr: 0.00548 [2023-12-20 15:17:23,380 INFO misc.py line 119 131400] Train: [24/100][53/800] Data 0.004 (0.005) Batch 0.315 (0.341) Remain 05:49:35 loss: 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INFO misc.py line 119 131400] Train: [24/100][60/800] Data 0.004 (0.005) Batch 0.339 (0.339) Remain 05:47:31 loss: 0.4694 Lr: 0.00548 [2023-12-20 15:17:26,014 INFO misc.py line 119 131400] Train: [24/100][61/800] Data 0.005 (0.005) Batch 0.362 (0.339) Remain 05:47:55 loss: 0.6129 Lr: 0.00548 [2023-12-20 15:17:26,373 INFO misc.py line 119 131400] Train: [24/100][62/800] Data 0.005 (0.005) Batch 0.359 (0.340) Remain 05:48:15 loss: 0.5300 Lr: 0.00548 [2023-12-20 15:17:26,690 INFO misc.py line 119 131400] Train: [24/100][63/800] Data 0.005 (0.005) Batch 0.318 (0.339) Remain 05:47:52 loss: 0.3587 Lr: 0.00548 [2023-12-20 15:17:27,011 INFO misc.py line 119 131400] Train: [24/100][64/800] Data 0.004 (0.005) Batch 0.322 (0.339) Remain 05:47:34 loss: 0.3620 Lr: 0.00548 [2023-12-20 15:17:27,346 INFO misc.py line 119 131400] Train: [24/100][65/800] Data 0.004 (0.005) Batch 0.334 (0.339) Remain 05:47:28 loss: 0.6800 Lr: 0.00548 [2023-12-20 15:17:27,682 INFO misc.py line 119 131400] Train: 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Batch 0.361 (0.333) Remain 05:38:01 loss: 0.6834 Lr: 0.00543 [2023-12-20 15:21:11,597 INFO misc.py line 119 131400] Train: [24/100][739/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 05:38:00 loss: 0.3447 Lr: 0.00543 [2023-12-20 15:21:11,909 INFO misc.py line 119 131400] Train: [24/100][740/800] Data 0.008 (0.004) Batch 0.316 (0.333) Remain 05:37:58 loss: 0.6479 Lr: 0.00543 [2023-12-20 15:21:12,275 INFO misc.py line 119 131400] Train: [24/100][741/800] Data 0.005 (0.004) Batch 0.367 (0.333) Remain 05:38:00 loss: 0.5961 Lr: 0.00543 [2023-12-20 15:21:12,624 INFO misc.py line 119 131400] Train: [24/100][742/800] Data 0.004 (0.004) Batch 0.346 (0.333) Remain 05:38:01 loss: 0.5769 Lr: 0.00543 [2023-12-20 15:21:12,981 INFO misc.py line 119 131400] Train: [24/100][743/800] Data 0.007 (0.004) Batch 0.360 (0.333) Remain 05:38:03 loss: 0.4731 Lr: 0.00543 [2023-12-20 15:21:13,263 INFO misc.py line 119 131400] Train: [24/100][744/800] Data 0.003 (0.004) Batch 0.282 (0.333) Remain 05:37:58 loss: 0.2881 Lr: 0.00543 [2023-12-20 15:21:13,585 INFO misc.py line 119 131400] Train: [24/100][745/800] Data 0.004 (0.004) Batch 0.323 (0.333) Remain 05:37:57 loss: 0.7868 Lr: 0.00543 [2023-12-20 15:21:13,932 INFO misc.py line 119 131400] Train: [24/100][746/800] Data 0.003 (0.004) Batch 0.348 (0.333) Remain 05:37:58 loss: 0.3956 Lr: 0.00543 [2023-12-20 15:21:14,287 INFO misc.py line 119 131400] Train: [24/100][747/800] Data 0.003 (0.004) Batch 0.354 (0.333) Remain 05:37:59 loss: 0.6780 Lr: 0.00543 [2023-12-20 15:21:14,616 INFO misc.py line 119 131400] Train: [24/100][748/800] Data 0.004 (0.004) Batch 0.329 (0.333) Remain 05:37:59 loss: 0.8479 Lr: 0.00543 [2023-12-20 15:21:14,950 INFO misc.py line 119 131400] Train: [24/100][749/800] Data 0.004 (0.004) Batch 0.334 (0.333) Remain 05:37:59 loss: 0.3728 Lr: 0.00543 [2023-12-20 15:21:15,285 INFO misc.py line 119 131400] Train: [24/100][750/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 05:37:58 loss: 0.6218 Lr: 0.00543 [2023-12-20 15:21:15,607 INFO misc.py line 119 131400] Train: [24/100][751/800] Data 0.005 (0.004) Batch 0.324 (0.333) Remain 05:37:57 loss: 0.3820 Lr: 0.00543 [2023-12-20 15:21:15,934 INFO misc.py line 119 131400] Train: [24/100][752/800] Data 0.004 (0.004) Batch 0.327 (0.333) Remain 05:37:56 loss: 0.4514 Lr: 0.00543 [2023-12-20 15:21:16,267 INFO misc.py line 119 131400] Train: [24/100][753/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 05:37:56 loss: 0.7642 Lr: 0.00543 [2023-12-20 15:21:16,571 INFO misc.py line 119 131400] Train: [24/100][754/800] Data 0.003 (0.004) Batch 0.304 (0.333) Remain 05:37:53 loss: 0.3555 Lr: 0.00543 [2023-12-20 15:21:16,887 INFO misc.py line 119 131400] Train: [24/100][755/800] Data 0.003 (0.004) Batch 0.315 (0.333) Remain 05:37:51 loss: 0.5527 Lr: 0.00543 [2023-12-20 15:21:17,225 INFO misc.py line 119 131400] Train: [24/100][756/800] Data 0.005 (0.004) Batch 0.338 (0.333) Remain 05:37:51 loss: 0.2924 Lr: 0.00543 [2023-12-20 15:21:17,567 INFO misc.py line 119 131400] Train: [24/100][757/800] Data 0.004 (0.004) Batch 0.342 (0.333) Remain 05:37:52 loss: 0.4061 Lr: 0.00543 [2023-12-20 15:21:17,936 INFO misc.py line 119 131400] Train: [24/100][758/800] Data 0.004 (0.004) Batch 0.369 (0.333) Remain 05:37:54 loss: 0.4094 Lr: 0.00543 [2023-12-20 15:21:18,286 INFO misc.py line 119 131400] Train: [24/100][759/800] Data 0.005 (0.004) Batch 0.351 (0.333) Remain 05:37:55 loss: 0.6623 Lr: 0.00543 [2023-12-20 15:21:18,611 INFO misc.py line 119 131400] Train: [24/100][760/800] Data 0.004 (0.004) Batch 0.324 (0.333) Remain 05:37:54 loss: 0.2433 Lr: 0.00543 [2023-12-20 15:21:18,949 INFO misc.py line 119 131400] Train: [24/100][761/800] Data 0.004 (0.004) Batch 0.339 (0.333) Remain 05:37:55 loss: 0.4380 Lr: 0.00543 [2023-12-20 15:21:19,296 INFO misc.py line 119 131400] Train: [24/100][762/800] Data 0.004 (0.004) Batch 0.346 (0.333) Remain 05:37:55 loss: 0.4181 Lr: 0.00543 [2023-12-20 15:21:19,644 INFO misc.py line 119 131400] Train: [24/100][763/800] Data 0.004 (0.004) Batch 0.348 (0.333) Remain 05:37:56 loss: 0.2221 Lr: 0.00543 [2023-12-20 15:21:19,946 INFO misc.py line 119 131400] Train: [24/100][764/800] Data 0.004 (0.004) Batch 0.303 (0.333) Remain 05:37:53 loss: 0.2405 Lr: 0.00543 [2023-12-20 15:21:20,331 INFO misc.py line 119 131400] Train: [24/100][765/800] Data 0.003 (0.004) Batch 0.377 (0.333) Remain 05:37:57 loss: 0.5374 Lr: 0.00543 [2023-12-20 15:21:20,683 INFO misc.py line 119 131400] Train: [24/100][766/800] Data 0.011 (0.004) Batch 0.358 (0.333) Remain 05:37:58 loss: 0.4282 Lr: 0.00543 [2023-12-20 15:21:21,026 INFO misc.py line 119 131400] Train: [24/100][767/800] Data 0.004 (0.004) Batch 0.344 (0.333) Remain 05:37:59 loss: 0.3062 Lr: 0.00543 [2023-12-20 15:21:21,379 INFO misc.py line 119 131400] Train: [24/100][768/800] Data 0.004 (0.004) Batch 0.353 (0.333) Remain 05:38:00 loss: 0.7470 Lr: 0.00543 [2023-12-20 15:21:21,709 INFO misc.py line 119 131400] Train: [24/100][769/800] Data 0.004 (0.004) Batch 0.329 (0.333) Remain 05:37:59 loss: 0.6192 Lr: 0.00543 [2023-12-20 15:21:22,059 INFO misc.py line 119 131400] Train: [24/100][770/800] Data 0.005 (0.004) Batch 0.351 (0.333) Remain 05:38:00 loss: 0.6789 Lr: 0.00543 [2023-12-20 15:21:22,439 INFO misc.py line 119 131400] Train: [24/100][771/800] Data 0.004 (0.004) Batch 0.379 (0.333) Remain 05:38:04 loss: 0.5119 Lr: 0.00543 [2023-12-20 15:21:22,782 INFO misc.py line 119 131400] Train: [24/100][772/800] Data 0.004 (0.004) Batch 0.343 (0.333) Remain 05:38:04 loss: 0.4508 Lr: 0.00543 [2023-12-20 15:21:23,112 INFO misc.py line 119 131400] Train: [24/100][773/800] Data 0.005 (0.004) Batch 0.329 (0.333) Remain 05:38:03 loss: 0.8092 Lr: 0.00543 [2023-12-20 15:21:23,442 INFO misc.py line 119 131400] Train: [24/100][774/800] Data 0.005 (0.004) Batch 0.332 (0.333) Remain 05:38:03 loss: 0.4478 Lr: 0.00543 [2023-12-20 15:21:23,812 INFO misc.py line 119 131400] Train: [24/100][775/800] Data 0.003 (0.004) Batch 0.370 (0.334) Remain 05:38:05 loss: 0.4520 Lr: 0.00543 [2023-12-20 15:21:24,132 INFO misc.py line 119 131400] Train: [24/100][776/800] Data 0.003 (0.004) Batch 0.320 (0.333) Remain 05:38:04 loss: 0.4943 Lr: 0.00543 [2023-12-20 15:21:24,455 INFO misc.py line 119 131400] Train: [24/100][777/800] Data 0.004 (0.004) Batch 0.323 (0.333) Remain 05:38:03 loss: 0.4351 Lr: 0.00543 [2023-12-20 15:21:24,775 INFO misc.py line 119 131400] Train: [24/100][778/800] Data 0.002 (0.004) Batch 0.319 (0.333) Remain 05:38:01 loss: 0.5188 Lr: 0.00543 [2023-12-20 15:21:25,145 INFO misc.py line 119 131400] Train: [24/100][779/800] Data 0.004 (0.004) Batch 0.370 (0.334) Remain 05:38:04 loss: 0.3930 Lr: 0.00543 [2023-12-20 15:21:25,489 INFO misc.py line 119 131400] Train: [24/100][780/800] Data 0.004 (0.004) Batch 0.344 (0.334) Remain 05:38:04 loss: 0.7233 Lr: 0.00543 [2023-12-20 15:21:25,863 INFO misc.py line 119 131400] Train: [24/100][781/800] Data 0.003 (0.004) Batch 0.374 (0.334) Remain 05:38:07 loss: 0.4485 Lr: 0.00543 [2023-12-20 15:21:26,220 INFO misc.py line 119 131400] Train: [24/100][782/800] Data 0.004 (0.004) Batch 0.358 (0.334) Remain 05:38:09 loss: 0.8461 Lr: 0.00543 [2023-12-20 15:21:26,610 INFO misc.py line 119 131400] Train: [24/100][783/800] Data 0.003 (0.004) Batch 0.389 (0.334) Remain 05:38:13 loss: 0.9964 Lr: 0.00543 [2023-12-20 15:21:26,947 INFO misc.py line 119 131400] Train: [24/100][784/800] Data 0.005 (0.004) Batch 0.338 (0.334) Remain 05:38:13 loss: 0.4236 Lr: 0.00543 [2023-12-20 15:21:27,239 INFO misc.py line 119 131400] Train: [24/100][785/800] Data 0.002 (0.004) Batch 0.291 (0.334) Remain 05:38:09 loss: 0.4051 Lr: 0.00543 [2023-12-20 15:21:27,564 INFO misc.py line 119 131400] Train: [24/100][786/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 05:38:08 loss: 0.8054 Lr: 0.00543 [2023-12-20 15:21:27,906 INFO misc.py line 119 131400] Train: [24/100][787/800] Data 0.003 (0.004) Batch 0.339 (0.334) Remain 05:38:08 loss: 0.6122 Lr: 0.00543 [2023-12-20 15:21:28,257 INFO misc.py line 119 131400] Train: [24/100][788/800] Data 0.007 (0.004) Batch 0.354 (0.334) Remain 05:38:10 loss: 0.5742 Lr: 0.00543 [2023-12-20 15:21:28,555 INFO misc.py line 119 131400] Train: [24/100][789/800] Data 0.004 (0.004) Batch 0.299 (0.334) Remain 05:38:07 loss: 0.5422 Lr: 0.00543 [2023-12-20 15:21:28,877 INFO misc.py line 119 131400] Train: [24/100][790/800] Data 0.003 (0.004) Batch 0.322 (0.334) Remain 05:38:05 loss: 0.2923 Lr: 0.00543 [2023-12-20 15:21:29,172 INFO misc.py line 119 131400] Train: [24/100][791/800] Data 0.003 (0.004) Batch 0.295 (0.334) Remain 05:38:02 loss: 0.4658 Lr: 0.00543 [2023-12-20 15:21:29,461 INFO misc.py line 119 131400] Train: [24/100][792/800] Data 0.003 (0.004) Batch 0.286 (0.333) Remain 05:37:58 loss: 0.8091 Lr: 0.00543 [2023-12-20 15:21:29,773 INFO misc.py line 119 131400] Train: [24/100][793/800] Data 0.006 (0.004) Batch 0.315 (0.333) Remain 05:37:56 loss: 0.2601 Lr: 0.00543 [2023-12-20 15:21:30,089 INFO misc.py line 119 131400] Train: [24/100][794/800] Data 0.004 (0.004) Batch 0.316 (0.333) Remain 05:37:54 loss: 0.4162 Lr: 0.00543 [2023-12-20 15:21:30,403 INFO misc.py line 119 131400] Train: [24/100][795/800] Data 0.004 (0.004) Batch 0.314 (0.333) Remain 05:37:53 loss: 0.3237 Lr: 0.00543 [2023-12-20 15:21:30,709 INFO misc.py line 119 131400] Train: [24/100][796/800] Data 0.004 (0.004) Batch 0.305 (0.333) Remain 05:37:50 loss: 0.4197 Lr: 0.00543 [2023-12-20 15:21:31,029 INFO misc.py line 119 131400] Train: [24/100][797/800] Data 0.004 (0.004) Batch 0.322 (0.333) Remain 05:37:49 loss: 0.6531 Lr: 0.00543 [2023-12-20 15:21:31,337 INFO misc.py line 119 131400] Train: [24/100][798/800] Data 0.003 (0.004) Batch 0.308 (0.333) Remain 05:37:47 loss: 0.2889 Lr: 0.00543 [2023-12-20 15:21:31,653 INFO misc.py line 119 131400] Train: [24/100][799/800] Data 0.003 (0.004) Batch 0.313 (0.333) Remain 05:37:45 loss: 0.2499 Lr: 0.00543 [2023-12-20 15:21:31,961 INFO misc.py line 119 131400] Train: [24/100][800/800] Data 0.006 (0.004) Batch 0.312 (0.333) Remain 05:37:43 loss: 0.3196 Lr: 0.00543 [2023-12-20 15:21:31,962 INFO misc.py line 136 131400] Train result: loss: 0.5224 [2023-12-20 15:21:31,963 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:21:54,507 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1503 [2023-12-20 15:21:54,580 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1734 [2023-12-20 15:21:54,684 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4506 [2023-12-20 15:21:54,800 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.0899 [2023-12-20 15:21:54,914 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2479 [2023-12-20 15:21:55,017 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.8363 [2023-12-20 15:21:55,110 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7720 [2023-12-20 15:21:55,219 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8155 [2023-12-20 15:21:55,301 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2700 [2023-12-20 15:21:55,392 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5234 [2023-12-20 15:21:55,483 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7532 [2023-12-20 15:21:55,618 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4715 [2023-12-20 15:21:55,736 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3055 [2023-12-20 15:21:55,892 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2580 [2023-12-20 15:21:55,988 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.6629 [2023-12-20 15:21:56,123 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5745 [2023-12-20 15:21:56,230 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2892 [2023-12-20 15:21:56,341 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4275 [2023-12-20 15:21:56,453 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4535 [2023-12-20 15:21:56,528 INFO evaluator.py line 159 131400] Test: [20/78] Loss 1.6405 [2023-12-20 15:21:56,634 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4444 [2023-12-20 15:21:56,790 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2086 [2023-12-20 15:21:56,910 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3790 [2023-12-20 15:21:57,052 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2619 [2023-12-20 15:21:57,197 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2117 [2023-12-20 15:21:57,280 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4469 [2023-12-20 15:21:57,438 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5641 [2023-12-20 15:21:57,561 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5255 [2023-12-20 15:21:57,656 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5120 [2023-12-20 15:21:57,798 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4475 [2023-12-20 15:21:57,902 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6554 [2023-12-20 15:21:58,024 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4998 [2023-12-20 15:21:58,111 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1756 [2023-12-20 15:21:58,180 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1887 [2023-12-20 15:21:58,278 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9351 [2023-12-20 15:21:58,367 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4202 [2023-12-20 15:21:58,494 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8717 [2023-12-20 15:21:58,604 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1431 [2023-12-20 15:21:58,683 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6231 [2023-12-20 15:21:58,824 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5594 [2023-12-20 15:21:58,970 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0208 [2023-12-20 15:21:59,071 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1903 [2023-12-20 15:21:59,190 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4899 [2023-12-20 15:21:59,332 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7176 [2023-12-20 15:21:59,450 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9832 [2023-12-20 15:21:59,553 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4264 [2023-12-20 15:21:59,718 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4339 [2023-12-20 15:21:59,813 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.2949 [2023-12-20 15:21:59,958 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0785 [2023-12-20 15:22:00,048 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8381 [2023-12-20 15:22:00,123 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4456 [2023-12-20 15:22:00,229 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1611 [2023-12-20 15:22:00,375 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3195 [2023-12-20 15:22:00,509 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2665 [2023-12-20 15:22:00,610 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9763 [2023-12-20 15:22:00,696 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7453 [2023-12-20 15:22:00,798 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4392 [2023-12-20 15:22:00,957 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2443 [2023-12-20 15:22:01,053 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3932 [2023-12-20 15:22:01,145 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.6245 [2023-12-20 15:22:01,242 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3437 [2023-12-20 15:22:01,332 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3699 [2023-12-20 15:22:01,417 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4835 [2023-12-20 15:22:01,517 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6570 [2023-12-20 15:22:01,642 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.2358 [2023-12-20 15:22:01,724 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2993 [2023-12-20 15:22:01,823 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4270 [2023-12-20 15:22:01,915 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0146 [2023-12-20 15:22:01,997 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3464 [2023-12-20 15:22:02,081 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0180 [2023-12-20 15:22:02,174 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8102 [2023-12-20 15:22:02,263 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5984 [2023-12-20 15:22:02,397 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1820 [2023-12-20 15:22:02,492 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6017 [2023-12-20 15:22:02,606 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6381 [2023-12-20 15:22:02,706 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7490 [2023-12-20 15:22:02,793 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2697 [2023-12-20 15:22:02,945 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2830 [2023-12-20 15:22:04,085 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7282/0.8194/0.9040. [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8544/0.9491 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9623/0.9841 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6609/0.8082 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7496/0.8176 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9050/0.9515 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8245/0.9181 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7468/0.8192 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6471/0.7354 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6355/0.7505 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8072/0.9220 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3709/0.4916 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6789/0.7940 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6368/0.8812 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7360/0.8318 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5890/0.6829 [2023-12-20 15:22:04,085 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7330/0.8037 [2023-12-20 15:22:04,086 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9428/0.9603 [2023-12-20 15:22:04,086 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6520/0.7407 [2023-12-20 15:22:04,086 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8620/0.9177 [2023-12-20 15:22:04,086 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5681/0.6274 [2023-12-20 15:22:04,086 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:22:04,087 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:22:04,087 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:22:07,922 INFO misc.py line 119 131400] Train: [25/100][1/800] Data 0.802 (0.802) Batch 1.118 (1.118) Remain 18:52:45 loss: 0.3445 Lr: 0.00543 [2023-12-20 15:22:08,740 INFO misc.py line 119 131400] Train: [25/100][2/800] Data 0.504 (0.504) Batch 0.818 (0.818) Remain 13:49:09 loss: 0.6195 Lr: 0.00543 [2023-12-20 15:22:09,060 INFO misc.py line 119 131400] Train: [25/100][3/800] Data 0.003 (0.003) Batch 0.320 (0.320) Remain 05:24:24 loss: 0.4870 Lr: 0.00543 [2023-12-20 15:22:09,389 INFO misc.py line 119 131400] Train: [25/100][4/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 05:33:38 loss: 0.4692 Lr: 0.00543 [2023-12-20 15:22:09,738 INFO misc.py line 119 131400] Train: [25/100][5/800] Data 0.004 (0.004) Batch 0.345 (0.337) Remain 05:41:32 loss: 0.2808 Lr: 0.00543 [2023-12-20 15:22:10,061 INFO misc.py line 119 131400] Train: [25/100][6/800] Data 0.007 (0.005) Batch 0.327 (0.334) Remain 05:38:13 loss: 0.2013 Lr: 0.00543 [2023-12-20 15:22:10,372 INFO misc.py line 119 131400] Train: [25/100][7/800] Data 0.003 (0.005) Batch 0.311 (0.328) Remain 05:32:27 loss: 0.2910 Lr: 0.00543 [2023-12-20 15:22:10,707 INFO misc.py line 119 131400] Train: [25/100][8/800] Data 0.003 (0.004) Batch 0.334 (0.329) Remain 05:33:42 loss: 0.3563 Lr: 0.00543 [2023-12-20 15:22:11,049 INFO misc.py line 119 131400] Train: [25/100][9/800] Data 0.004 (0.004) Batch 0.342 (0.331) Remain 05:35:50 loss: 0.4643 Lr: 0.00543 [2023-12-20 15:22:11,367 INFO misc.py line 119 131400] Train: [25/100][10/800] Data 0.004 (0.004) Batch 0.318 (0.329) Remain 05:33:49 loss: 0.5633 Lr: 0.00543 [2023-12-20 15:22:11,694 INFO misc.py line 119 131400] Train: [25/100][11/800] Data 0.004 (0.004) Batch 0.327 (0.329) Remain 05:33:33 loss: 0.5994 Lr: 0.00543 [2023-12-20 15:22:12,014 INFO misc.py line 119 131400] Train: [25/100][12/800] Data 0.003 (0.004) Batch 0.320 (0.328) Remain 05:32:33 loss: 0.3247 Lr: 0.00543 [2023-12-20 15:22:12,316 INFO misc.py line 119 131400] Train: [25/100][13/800] Data 0.003 (0.004) Batch 0.302 (0.326) Remain 05:29:53 loss: 0.4517 Lr: 0.00543 [2023-12-20 15:22:12,634 INFO misc.py line 119 131400] Train: [25/100][14/800] Data 0.003 (0.004) Batch 0.316 (0.325) Remain 05:28:59 loss: 0.5537 Lr: 0.00543 [2023-12-20 15:22:12,999 INFO misc.py line 119 131400] Train: [25/100][15/800] Data 0.006 (0.004) Batch 0.363 (0.328) Remain 05:32:14 loss: 0.4099 Lr: 0.00543 [2023-12-20 15:22:13,313 INFO misc.py line 119 131400] Train: [25/100][16/800] Data 0.007 (0.004) Batch 0.318 (0.327) Remain 05:31:25 loss: 0.9601 Lr: 0.00543 [2023-12-20 15:22:13,622 INFO misc.py line 119 131400] Train: [25/100][17/800] Data 0.003 (0.004) Batch 0.310 (0.326) Remain 05:30:08 loss: 0.7212 Lr: 0.00543 [2023-12-20 15:22:13,938 INFO misc.py line 119 131400] Train: [25/100][18/800] Data 0.003 (0.004) Batch 0.316 (0.325) Remain 05:29:26 loss: 0.6177 Lr: 0.00543 [2023-12-20 15:22:14,283 INFO misc.py line 119 131400] Train: [25/100][19/800] Data 0.003 (0.004) Batch 0.346 (0.326) Remain 05:30:43 loss: 0.3507 Lr: 0.00543 [2023-12-20 15:22:14,648 INFO misc.py line 119 131400] Train: [25/100][20/800] Data 0.003 (0.004) Batch 0.364 (0.329) Remain 05:32:57 loss: 0.3989 Lr: 0.00543 [2023-12-20 15:22:14,965 INFO misc.py line 119 131400] Train: [25/100][21/800] Data 0.004 (0.004) Batch 0.317 (0.328) Remain 05:32:18 loss: 0.4552 Lr: 0.00543 [2023-12-20 15:22:15,307 INFO misc.py line 119 131400] Train: [25/100][22/800] Data 0.003 (0.004) Batch 0.343 (0.329) Remain 05:33:05 loss: 0.3099 Lr: 0.00543 [2023-12-20 15:22:15,640 INFO misc.py line 119 131400] Train: [25/100][23/800] Data 0.003 (0.004) Batch 0.332 (0.329) Remain 05:33:15 loss: 0.3976 Lr: 0.00543 [2023-12-20 15:22:15,959 INFO misc.py line 119 131400] Train: [25/100][24/800] Data 0.003 (0.004) Batch 0.319 (0.329) Remain 05:32:47 loss: 0.7581 Lr: 0.00543 [2023-12-20 15:22:16,250 INFO misc.py line 119 131400] Train: [25/100][25/800] Data 0.003 (0.004) Batch 0.286 (0.327) Remain 05:30:48 loss: 0.3185 Lr: 0.00543 [2023-12-20 15:22:16,580 INFO misc.py line 119 131400] Train: [25/100][26/800] Data 0.008 (0.004) Batch 0.336 (0.327) Remain 05:31:12 loss: 0.5249 Lr: 0.00543 [2023-12-20 15:22:16,927 INFO misc.py line 119 131400] Train: [25/100][27/800] Data 0.003 (0.004) Batch 0.346 (0.328) Remain 05:31:59 loss: 0.6251 Lr: 0.00543 [2023-12-20 15:22:17,260 INFO misc.py line 119 131400] Train: [25/100][28/800] Data 0.004 (0.004) Batch 0.327 (0.328) Remain 05:31:57 loss: 0.3369 Lr: 0.00543 [2023-12-20 15:22:17,591 INFO misc.py line 119 131400] Train: [25/100][29/800] Data 0.009 (0.004) Batch 0.338 (0.328) Remain 05:32:19 loss: 0.6739 Lr: 0.00543 [2023-12-20 15:22:17,956 INFO misc.py line 119 131400] Train: [25/100][30/800] Data 0.004 (0.004) Batch 0.365 (0.329) Remain 05:33:41 loss: 0.4949 Lr: 0.00543 [2023-12-20 15:22:18,316 INFO misc.py line 119 131400] Train: [25/100][31/800] Data 0.004 (0.004) Batch 0.349 (0.330) Remain 05:34:22 loss: 0.6738 Lr: 0.00543 [2023-12-20 15:22:18,646 INFO misc.py line 119 131400] Train: [25/100][32/800] Data 0.016 (0.005) Batch 0.341 (0.331) Remain 05:34:45 loss: 0.5799 Lr: 0.00543 [2023-12-20 15:22:19,036 INFO misc.py line 119 131400] Train: [25/100][33/800] Data 0.005 (0.005) Batch 0.390 (0.333) Remain 05:36:46 loss: 0.5356 Lr: 0.00543 [2023-12-20 15:22:19,357 INFO misc.py line 119 131400] Train: [25/100][34/800] Data 0.003 (0.005) Batch 0.310 (0.332) Remain 05:36:02 loss: 0.4438 Lr: 0.00543 [2023-12-20 15:22:19,737 INFO misc.py line 119 131400] Train: [25/100][35/800] Data 0.015 (0.005) Batch 0.391 (0.334) Remain 05:37:54 loss: 0.3509 Lr: 0.00543 [2023-12-20 15:22:20,103 INFO misc.py line 119 131400] Train: [25/100][36/800] Data 0.004 (0.005) Batch 0.365 (0.335) Remain 05:38:51 loss: 0.5160 Lr: 0.00542 [2023-12-20 15:22:20,452 INFO misc.py line 119 131400] Train: [25/100][37/800] Data 0.004 (0.005) Batch 0.344 (0.335) Remain 05:39:08 loss: 0.4092 Lr: 0.00542 [2023-12-20 15:22:20,768 INFO misc.py line 119 131400] Train: [25/100][38/800] Data 0.009 (0.005) Batch 0.322 (0.335) Remain 05:38:46 loss: 0.5327 Lr: 0.00542 [2023-12-20 15:22:21,141 INFO misc.py line 119 131400] Train: [25/100][39/800] Data 0.003 (0.005) Batch 0.373 (0.336) Remain 05:39:51 loss: 0.5120 Lr: 0.00542 [2023-12-20 15:22:21,459 INFO misc.py line 119 131400] Train: [25/100][40/800] Data 0.003 (0.005) Batch 0.315 (0.335) Remain 05:39:17 loss: 0.2808 Lr: 0.00542 [2023-12-20 15:22:21,806 INFO misc.py line 119 131400] Train: [25/100][41/800] Data 0.006 (0.005) Batch 0.349 (0.335) Remain 05:39:40 loss: 0.9830 Lr: 0.00542 [2023-12-20 15:22:22,185 INFO misc.py line 119 131400] Train: [25/100][42/800] Data 0.004 (0.005) Batch 0.378 (0.337) Remain 05:40:47 loss: 0.6329 Lr: 0.00542 [2023-12-20 15:22:22,530 INFO misc.py line 119 131400] Train: [25/100][43/800] Data 0.004 (0.005) Batch 0.330 (0.336) Remain 05:40:37 loss: 0.2801 Lr: 0.00542 [2023-12-20 15:22:22,889 INFO misc.py line 119 131400] Train: [25/100][44/800] Data 0.019 (0.005) Batch 0.374 (0.337) Remain 05:41:32 loss: 0.5790 Lr: 0.00542 [2023-12-20 15:22:23,270 INFO misc.py line 119 131400] Train: [25/100][45/800] Data 0.005 (0.005) Batch 0.381 (0.338) Remain 05:42:34 loss: 0.7234 Lr: 0.00542 [2023-12-20 15:22:23,592 INFO misc.py line 119 131400] Train: [25/100][46/800] Data 0.003 (0.005) Batch 0.318 (0.338) Remain 05:42:05 loss: 0.3855 Lr: 0.00542 [2023-12-20 15:22:23,900 INFO misc.py line 119 131400] Train: [25/100][47/800] Data 0.009 (0.005) Batch 0.312 (0.337) Remain 05:41:29 loss: 0.4232 Lr: 0.00542 [2023-12-20 15:22:24,274 INFO misc.py line 119 131400] Train: [25/100][48/800] Data 0.005 (0.005) Batch 0.374 (0.338) Remain 05:42:18 loss: 0.5513 Lr: 0.00542 [2023-12-20 15:22:24,582 INFO misc.py line 119 131400] Train: [25/100][49/800] Data 0.004 (0.005) Batch 0.309 (0.337) Remain 05:41:39 loss: 0.3084 Lr: 0.00542 [2023-12-20 15:22:24,874 INFO misc.py line 119 131400] Train: [25/100][50/800] Data 0.004 (0.005) Batch 0.291 (0.336) Remain 05:40:39 loss: 0.5105 Lr: 0.00542 [2023-12-20 15:22:25,201 INFO misc.py line 119 131400] Train: [25/100][51/800] Data 0.004 (0.005) Batch 0.328 (0.336) Remain 05:40:28 loss: 0.5696 Lr: 0.00542 [2023-12-20 15:22:25,511 INFO misc.py line 119 131400] Train: [25/100][52/800] Data 0.003 (0.005) Batch 0.310 (0.336) Remain 05:39:55 loss: 0.4740 Lr: 0.00542 [2023-12-20 15:22:25,846 INFO misc.py line 119 131400] Train: [25/100][53/800] Data 0.003 (0.005) Batch 0.335 (0.336) Remain 05:39:54 loss: 0.6797 Lr: 0.00542 [2023-12-20 15:22:26,202 INFO misc.py line 119 131400] Train: [25/100][54/800] Data 0.003 (0.005) Batch 0.355 (0.336) Remain 05:40:17 loss: 1.3032 Lr: 0.00542 [2023-12-20 15:22:26,524 INFO misc.py line 119 131400] Train: [25/100][55/800] Data 0.006 (0.005) Batch 0.323 (0.336) Remain 05:40:01 loss: 0.3795 Lr: 0.00542 [2023-12-20 15:22:26,872 INFO misc.py line 119 131400] Train: [25/100][56/800] Data 0.004 (0.005) Batch 0.344 (0.336) Remain 05:40:10 loss: 0.5996 Lr: 0.00542 [2023-12-20 15:22:27,235 INFO misc.py line 119 131400] Train: [25/100][57/800] Data 0.008 (0.005) Batch 0.366 (0.337) Remain 05:40:44 loss: 0.4199 Lr: 0.00542 [2023-12-20 15:22:27,558 INFO misc.py line 119 131400] Train: [25/100][58/800] Data 0.004 (0.005) Batch 0.323 (0.336) Remain 05:40:28 loss: 0.4534 Lr: 0.00542 [2023-12-20 15:22:27,935 INFO misc.py line 119 131400] Train: [25/100][59/800] Data 0.006 (0.005) Batch 0.378 (0.337) Remain 05:41:13 loss: 0.5443 Lr: 0.00542 [2023-12-20 15:22:28,248 INFO misc.py line 119 131400] Train: [25/100][60/800] Data 0.004 (0.005) Batch 0.313 (0.337) Remain 05:40:47 loss: 0.3132 Lr: 0.00542 [2023-12-20 15:22:28,605 INFO misc.py line 119 131400] Train: [25/100][61/800] Data 0.003 (0.005) Batch 0.357 (0.337) Remain 05:41:08 loss: 0.2555 Lr: 0.00542 [2023-12-20 15:22:28,929 INFO misc.py line 119 131400] Train: [25/100][62/800] Data 0.004 (0.005) Batch 0.324 (0.337) Remain 05:40:54 loss: 0.3811 Lr: 0.00542 [2023-12-20 15:22:29,250 INFO misc.py line 119 131400] Train: [25/100][63/800] Data 0.003 (0.005) Batch 0.321 (0.337) Remain 05:40:38 loss: 0.6604 Lr: 0.00542 [2023-12-20 15:22:29,618 INFO misc.py line 119 131400] Train: [25/100][64/800] Data 0.003 (0.005) Batch 0.368 (0.337) Remain 05:41:09 loss: 0.4971 Lr: 0.00542 [2023-12-20 15:22:29,988 INFO misc.py line 119 131400] Train: [25/100][65/800] Data 0.004 (0.005) Batch 0.370 (0.338) Remain 05:41:41 loss: 0.2516 Lr: 0.00542 [2023-12-20 15:22:30,324 INFO misc.py line 119 131400] Train: [25/100][66/800] Data 0.004 (0.005) Batch 0.335 (0.338) Remain 05:41:38 loss: 0.4126 Lr: 0.00542 [2023-12-20 15:22:30,668 INFO misc.py line 119 131400] Train: [25/100][67/800] Data 0.004 (0.005) Batch 0.344 (0.338) Remain 05:41:44 loss: 0.7914 Lr: 0.00542 [2023-12-20 15:22:31,019 INFO misc.py line 119 131400] Train: [25/100][68/800] Data 0.004 (0.005) Batch 0.352 (0.338) Remain 05:41:57 loss: 0.7121 Lr: 0.00542 [2023-12-20 15:22:31,368 INFO misc.py line 119 131400] Train: [25/100][69/800] Data 0.003 (0.005) Batch 0.348 (0.338) Remain 05:42:06 loss: 0.2580 Lr: 0.00542 [2023-12-20 15:22:31,713 INFO misc.py line 119 131400] Train: [25/100][70/800] Data 0.006 (0.005) Batch 0.346 (0.338) Remain 05:42:13 loss: 0.8076 Lr: 0.00542 [2023-12-20 15:22:32,022 INFO misc.py line 119 131400] Train: [25/100][71/800] Data 0.003 (0.005) Batch 0.305 (0.338) Remain 05:41:43 loss: 0.5648 Lr: 0.00542 [2023-12-20 15:22:32,370 INFO misc.py line 119 131400] Train: [25/100][72/800] Data 0.007 (0.005) Batch 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[2023-12-20 15:26:28,167 INFO misc.py line 119 131400] Train: [25/100][776/800] Data 0.016 (0.005) Batch 0.345 (0.335) Remain 05:35:19 loss: 0.5096 Lr: 0.00537 [2023-12-20 15:26:28,551 INFO misc.py line 119 131400] Train: [25/100][777/800] Data 0.004 (0.005) Batch 0.381 (0.335) Remain 05:35:22 loss: 0.4562 Lr: 0.00537 [2023-12-20 15:26:28,871 INFO misc.py line 119 131400] Train: [25/100][778/800] Data 0.007 (0.005) Batch 0.323 (0.335) Remain 05:35:21 loss: 0.5273 Lr: 0.00537 [2023-12-20 15:26:29,176 INFO misc.py line 119 131400] Train: [25/100][779/800] Data 0.004 (0.005) Batch 0.305 (0.335) Remain 05:35:18 loss: 0.4201 Lr: 0.00537 [2023-12-20 15:26:29,513 INFO misc.py line 119 131400] Train: [25/100][780/800] Data 0.005 (0.005) Batch 0.338 (0.335) Remain 05:35:18 loss: 0.9755 Lr: 0.00537 [2023-12-20 15:26:29,824 INFO misc.py line 119 131400] Train: [25/100][781/800] Data 0.004 (0.005) Batch 0.311 (0.335) Remain 05:35:16 loss: 0.6124 Lr: 0.00537 [2023-12-20 15:26:30,175 INFO misc.py line 119 131400] Train: [25/100][782/800] Data 0.003 (0.005) Batch 0.349 (0.335) Remain 05:35:17 loss: 0.4645 Lr: 0.00537 [2023-12-20 15:26:30,499 INFO misc.py line 119 131400] Train: [25/100][783/800] Data 0.006 (0.005) Batch 0.326 (0.335) Remain 05:35:16 loss: 0.6491 Lr: 0.00537 [2023-12-20 15:26:30,821 INFO misc.py line 119 131400] Train: [25/100][784/800] Data 0.003 (0.005) Batch 0.321 (0.335) Remain 05:35:14 loss: 0.3092 Lr: 0.00537 [2023-12-20 15:26:31,140 INFO misc.py line 119 131400] Train: [25/100][785/800] Data 0.004 (0.005) Batch 0.317 (0.335) Remain 05:35:13 loss: 0.4579 Lr: 0.00537 [2023-12-20 15:26:31,461 INFO misc.py line 119 131400] Train: [25/100][786/800] Data 0.006 (0.005) Batch 0.323 (0.335) Remain 05:35:11 loss: 0.4604 Lr: 0.00537 [2023-12-20 15:26:31,804 INFO misc.py line 119 131400] Train: [25/100][787/800] Data 0.004 (0.005) Batch 0.342 (0.335) Remain 05:35:12 loss: 0.6724 Lr: 0.00537 [2023-12-20 15:26:32,148 INFO misc.py line 119 131400] Train: 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Batch 0.285 (0.335) Remain 05:34:59 loss: 0.3940 Lr: 0.00537 [2023-12-20 15:26:34,334 INFO misc.py line 119 131400] Train: [25/100][795/800] Data 0.005 (0.005) Batch 0.320 (0.335) Remain 05:34:58 loss: 0.3651 Lr: 0.00537 [2023-12-20 15:26:34,649 INFO misc.py line 119 131400] Train: [25/100][796/800] Data 0.004 (0.005) Batch 0.315 (0.335) Remain 05:34:56 loss: 0.9290 Lr: 0.00537 [2023-12-20 15:26:34,958 INFO misc.py line 119 131400] Train: [25/100][797/800] Data 0.004 (0.005) Batch 0.309 (0.335) Remain 05:34:53 loss: 1.0361 Lr: 0.00537 [2023-12-20 15:26:35,260 INFO misc.py line 119 131400] Train: [25/100][798/800] Data 0.005 (0.005) Batch 0.301 (0.335) Remain 05:34:50 loss: 0.4928 Lr: 0.00537 [2023-12-20 15:26:35,572 INFO misc.py line 119 131400] Train: [25/100][799/800] Data 0.006 (0.005) Batch 0.314 (0.335) Remain 05:34:49 loss: 0.4006 Lr: 0.00537 [2023-12-20 15:26:35,870 INFO misc.py line 119 131400] Train: [25/100][800/800] Data 0.003 (0.005) Batch 0.298 (0.335) Remain 05:34:45 loss: 0.8033 Lr: 0.00537 [2023-12-20 15:26:35,870 INFO misc.py line 136 131400] Train result: loss: 0.5053 [2023-12-20 15:26:35,871 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:26:57,480 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1284 [2023-12-20 15:26:57,631 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2817 [2023-12-20 15:26:57,723 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3710 [2023-12-20 15:26:58,393 INFO evaluator.py line 159 131400] Test: [4/78] Loss 0.9737 [2023-12-20 15:26:58,506 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3642 [2023-12-20 15:26:58,610 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.7040 [2023-12-20 15:26:58,711 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0343 [2023-12-20 15:26:58,824 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0352 [2023-12-20 15:26:58,913 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2069 [2023-12-20 15:26:59,017 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5131 [2023-12-20 15:26:59,117 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3789 [2023-12-20 15:26:59,260 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5416 [2023-12-20 15:26:59,382 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2812 [2023-12-20 15:26:59,538 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3738 [2023-12-20 15:26:59,633 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3050 [2023-12-20 15:26:59,767 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8074 [2023-12-20 15:26:59,885 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4481 [2023-12-20 15:27:00,012 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3356 [2023-12-20 15:27:00,125 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2389 [2023-12-20 15:27:00,211 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4026 [2023-12-20 15:27:00,329 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.1907 [2023-12-20 15:27:01,951 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2168 [2023-12-20 15:27:02,050 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.4314 [2023-12-20 15:27:02,145 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6012 [2023-12-20 15:27:02,273 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0066 [2023-12-20 15:27:02,385 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1706 [2023-12-20 15:27:02,464 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6424 [2023-12-20 15:27:02,606 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4781 [2023-12-20 15:27:02,760 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0196 [2023-12-20 15:27:02,858 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1182 [2023-12-20 15:27:02,980 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4481 [2023-12-20 15:27:03,121 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7844 [2023-12-20 15:27:03,237 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8204 [2023-12-20 15:27:03,343 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4781 [2023-12-20 15:27:03,512 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4824 [2023-12-20 15:27:03,606 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4151 [2023-12-20 15:27:03,761 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4215 [2023-12-20 15:27:03,865 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8186 [2023-12-20 15:27:03,940 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6533 [2023-12-20 15:27:04,048 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0776 [2023-12-20 15:27:04,200 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3970 [2023-12-20 15:27:04,333 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4218 [2023-12-20 15:27:04,437 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6582 [2023-12-20 15:27:04,526 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8838 [2023-12-20 15:27:04,627 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3690 [2023-12-20 15:27:04,792 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2399 [2023-12-20 15:27:04,887 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1034 [2023-12-20 15:27:04,982 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3522 [2023-12-20 15:27:05,079 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5001 [2023-12-20 15:27:05,173 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4166 [2023-12-20 15:27:05,260 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.9574 [2023-12-20 15:27:05,361 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7473 [2023-12-20 15:27:05,494 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1990 [2023-12-20 15:27:05,578 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5804 [2023-12-20 15:27:05,682 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6929 [2023-12-20 15:27:05,784 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0164 [2023-12-20 15:27:05,869 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3357 [2023-12-20 15:27:05,954 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0375 [2023-12-20 15:27:06,050 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7357 [2023-12-20 15:27:06,139 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7866 [2023-12-20 15:27:06,272 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2185 [2023-12-20 15:27:06,370 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6312 [2023-12-20 15:27:06,485 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7758 [2023-12-20 15:27:06,586 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9833 [2023-12-20 15:27:06,676 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.9138 [2023-12-20 15:27:06,834 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4460 [2023-12-20 15:27:08,222 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7158/0.8138/0.8987. [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8455/0.9412 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9625/0.9885 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6348/0.7310 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7886/0.8373 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8903/0.9549 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8432/0.9463 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7451/0.8143 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6213/0.6887 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6303/0.7759 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.6861/0.9564 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3568/0.5180 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6811/0.7876 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6490/0.7783 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7118/0.7850 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5487/0.5647 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7379/0.8371 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8997/0.9800 [2023-12-20 15:27:08,223 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6381/0.8282 [2023-12-20 15:27:08,224 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8725/0.9206 [2023-12-20 15:27:08,224 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5729/0.6423 [2023-12-20 15:27:08,224 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:27:08,225 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:27:08,226 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:27:11,622 INFO misc.py line 119 131400] Train: [26/100][1/800] Data 1.404 (1.404) Batch 1.724 (1.724) Remain 28:43:30 loss: 0.8819 Lr: 0.00537 [2023-12-20 15:27:11,935 INFO misc.py line 119 131400] Train: [26/100][2/800] Data 0.004 (0.004) Batch 0.313 (0.313) Remain 05:13:03 loss: 0.5952 Lr: 0.00537 [2023-12-20 15:27:12,254 INFO misc.py line 119 131400] Train: [26/100][3/800] Data 0.004 (0.004) Batch 0.319 (0.319) Remain 05:18:56 loss: 0.4249 Lr: 0.00537 [2023-12-20 15:27:12,541 INFO misc.py line 119 131400] Train: [26/100][4/800] Data 0.003 (0.003) Batch 0.286 (0.286) Remain 04:46:23 loss: 0.5204 Lr: 0.00537 [2023-12-20 15:27:12,848 INFO misc.py line 119 131400] Train: [26/100][5/800] Data 0.004 (0.003) Batch 0.309 (0.297) Remain 04:57:26 loss: 0.5496 Lr: 0.00537 [2023-12-20 15:27:13,231 INFO misc.py line 119 131400] Train: [26/100][6/800] Data 0.003 (0.003) Batch 0.382 (0.326) Remain 05:25:36 loss: 0.3587 Lr: 0.00537 [2023-12-20 15:27:13,581 INFO misc.py line 119 131400] Train: [26/100][7/800] Data 0.004 (0.003) Batch 0.351 (0.332) Remain 05:31:51 loss: 0.5596 Lr: 0.00537 [2023-12-20 15:27:13,963 INFO misc.py line 119 131400] Train: [26/100][8/800] Data 0.003 (0.003) Batch 0.381 (0.342) Remain 05:41:42 loss: 0.5752 Lr: 0.00537 [2023-12-20 15:27:14,298 INFO misc.py line 119 131400] Train: [26/100][9/800] Data 0.004 (0.003) Batch 0.336 (0.341) Remain 05:40:42 loss: 0.6400 Lr: 0.00537 [2023-12-20 15:27:14,636 INFO misc.py line 119 131400] Train: [26/100][10/800] Data 0.004 (0.004) Batch 0.333 (0.340) Remain 05:39:34 loss: 0.4565 Lr: 0.00537 [2023-12-20 15:27:14,956 INFO misc.py line 119 131400] Train: [26/100][11/800] Data 0.008 (0.004) Batch 0.325 (0.338) Remain 05:37:45 loss: 0.2715 Lr: 0.00537 [2023-12-20 15:27:15,326 INFO misc.py line 119 131400] Train: [26/100][12/800] Data 0.003 (0.004) Batch 0.358 (0.340) Remain 05:39:59 loss: 0.4877 Lr: 0.00537 [2023-12-20 15:27:15,683 INFO misc.py line 119 131400] Train: [26/100][13/800] Data 0.016 (0.005) Batch 0.369 (0.343) Remain 05:42:51 loss: 0.4259 Lr: 0.00537 [2023-12-20 15:27:16,025 INFO misc.py line 119 131400] Train: [26/100][14/800] Data 0.004 (0.005) Batch 0.342 (0.343) Remain 05:42:44 loss: 0.5306 Lr: 0.00537 [2023-12-20 15:27:16,368 INFO misc.py line 119 131400] Train: [26/100][15/800] Data 0.003 (0.005) Batch 0.343 (0.343) Remain 05:42:42 loss: 0.7588 Lr: 0.00537 [2023-12-20 15:27:16,712 INFO misc.py line 119 131400] Train: [26/100][16/800] Data 0.004 (0.005) Batch 0.339 (0.343) Remain 05:42:24 loss: 0.5842 Lr: 0.00537 [2023-12-20 15:27:17,063 INFO misc.py line 119 131400] Train: [26/100][17/800] Data 0.009 (0.005) Batch 0.356 (0.343) Remain 05:43:21 loss: 0.4141 Lr: 0.00537 [2023-12-20 15:27:17,391 INFO misc.py line 119 131400] Train: [26/100][18/800] Data 0.005 (0.005) Batch 0.329 (0.342) Remain 05:42:23 loss: 0.4009 Lr: 0.00537 [2023-12-20 15:27:17,778 INFO misc.py line 119 131400] Train: [26/100][19/800] Data 0.004 (0.005) Batch 0.383 (0.345) Remain 05:44:53 loss: 0.1909 Lr: 0.00537 [2023-12-20 15:27:18,094 INFO misc.py line 119 131400] Train: [26/100][20/800] Data 0.008 (0.005) Batch 0.320 (0.344) Remain 05:43:24 loss: 0.3729 Lr: 0.00537 [2023-12-20 15:27:18,483 INFO misc.py line 119 131400] Train: [26/100][21/800] Data 0.004 (0.005) Batch 0.390 (0.346) Remain 05:45:57 loss: 0.6100 Lr: 0.00537 [2023-12-20 15:27:18,799 INFO misc.py line 119 131400] Train: [26/100][22/800] Data 0.003 (0.005) Batch 0.314 (0.344) Remain 05:44:14 loss: 0.2553 Lr: 0.00537 [2023-12-20 15:27:19,152 INFO misc.py line 119 131400] Train: [26/100][23/800] Data 0.005 (0.005) Batch 0.355 (0.345) Remain 05:44:44 loss: 0.5046 Lr: 0.00537 [2023-12-20 15:27:19,515 INFO misc.py line 119 131400] Train: [26/100][24/800] Data 0.005 (0.005) Batch 0.363 (0.346) Remain 05:45:36 loss: 0.5221 Lr: 0.00537 [2023-12-20 15:27:19,857 INFO misc.py line 119 131400] Train: [26/100][25/800] Data 0.004 (0.005) Batch 0.341 (0.346) Remain 05:45:22 loss: 0.3854 Lr: 0.00537 [2023-12-20 15:27:20,152 INFO misc.py line 119 131400] Train: [26/100][26/800] Data 0.006 (0.005) Batch 0.297 (0.343) Remain 05:43:15 loss: 0.5063 Lr: 0.00537 [2023-12-20 15:27:20,490 INFO misc.py line 119 131400] Train: [26/100][27/800] Data 0.004 (0.005) Batch 0.337 (0.343) Remain 05:42:59 loss: 0.3740 Lr: 0.00537 [2023-12-20 15:27:20,806 INFO misc.py line 119 131400] Train: [26/100][28/800] Data 0.004 (0.005) Batch 0.316 (0.342) Remain 05:41:53 loss: 0.4026 Lr: 0.00537 [2023-12-20 15:27:21,146 INFO misc.py line 119 131400] Train: [26/100][29/800] Data 0.004 (0.005) Batch 0.341 (0.342) Remain 05:41:52 loss: 0.2740 Lr: 0.00537 [2023-12-20 15:27:21,502 INFO misc.py line 119 131400] Train: [26/100][30/800] Data 0.003 (0.005) Batch 0.355 (0.343) Remain 05:42:21 loss: 0.4960 Lr: 0.00537 [2023-12-20 15:27:21,834 INFO misc.py line 119 131400] Train: [26/100][31/800] Data 0.004 (0.005) Batch 0.332 (0.342) Remain 05:41:58 loss: 0.3722 Lr: 0.00537 [2023-12-20 15:27:22,155 INFO misc.py line 119 131400] Train: [26/100][32/800] Data 0.004 (0.005) Batch 0.320 (0.341) Remain 05:41:13 loss: 0.2481 Lr: 0.00537 [2023-12-20 15:27:22,482 INFO misc.py line 119 131400] Train: [26/100][33/800] Data 0.005 (0.005) Batch 0.327 (0.341) Remain 05:40:43 loss: 0.7762 Lr: 0.00537 [2023-12-20 15:27:22,823 INFO misc.py line 119 131400] Train: [26/100][34/800] Data 0.004 (0.005) Batch 0.342 (0.341) Remain 05:40:45 loss: 0.4502 Lr: 0.00537 [2023-12-20 15:27:23,166 INFO misc.py line 119 131400] Train: [26/100][35/800] Data 0.004 (0.005) Batch 0.341 (0.341) Remain 05:40:45 loss: 0.2216 Lr: 0.00537 [2023-12-20 15:27:23,520 INFO misc.py line 119 131400] Train: [26/100][36/800] Data 0.004 (0.005) Batch 0.354 (0.341) Remain 05:41:09 loss: 0.6326 Lr: 0.00537 [2023-12-20 15:27:23,851 INFO misc.py line 119 131400] Train: [26/100][37/800] Data 0.005 (0.005) Batch 0.332 (0.341) Remain 05:40:52 loss: 0.4437 Lr: 0.00537 [2023-12-20 15:27:24,209 INFO misc.py line 119 131400] Train: [26/100][38/800] Data 0.004 (0.005) Batch 0.358 (0.342) Remain 05:41:20 loss: 0.5980 Lr: 0.00537 [2023-12-20 15:27:24,552 INFO misc.py line 119 131400] Train: [26/100][39/800] Data 0.005 (0.005) Batch 0.343 (0.342) Remain 05:41:23 loss: 0.3693 Lr: 0.00537 [2023-12-20 15:27:24,882 INFO misc.py line 119 131400] Train: [26/100][40/800] Data 0.003 (0.005) Batch 0.330 (0.341) Remain 05:41:04 loss: 0.4941 Lr: 0.00536 [2023-12-20 15:27:25,224 INFO misc.py line 119 131400] Train: [26/100][41/800] Data 0.003 (0.005) Batch 0.338 (0.341) Remain 05:40:59 loss: 0.4624 Lr: 0.00536 [2023-12-20 15:27:25,566 INFO misc.py line 119 131400] Train: [26/100][42/800] Data 0.007 (0.005) Batch 0.345 (0.341) Remain 05:41:05 loss: 0.6552 Lr: 0.00536 [2023-12-20 15:27:25,906 INFO misc.py line 119 131400] Train: [26/100][43/800] Data 0.004 (0.005) Batch 0.340 (0.341) Remain 05:41:03 loss: 0.9263 Lr: 0.00536 [2023-12-20 15:27:26,257 INFO misc.py line 119 131400] Train: [26/100][44/800] Data 0.004 (0.005) Batch 0.350 (0.342) Remain 05:41:16 loss: 0.2721 Lr: 0.00536 [2023-12-20 15:27:26,599 INFO misc.py line 119 131400] Train: [26/100][45/800] Data 0.004 (0.005) Batch 0.343 (0.342) Remain 05:41:17 loss: 0.6995 Lr: 0.00536 [2023-12-20 15:27:26,917 INFO misc.py line 119 131400] Train: [26/100][46/800] Data 0.004 (0.005) Batch 0.319 (0.341) Remain 05:40:45 loss: 0.7481 Lr: 0.00536 [2023-12-20 15:27:27,214 INFO misc.py line 119 131400] Train: [26/100][47/800] Data 0.003 (0.005) Batch 0.296 (0.340) Remain 05:39:44 loss: 0.5126 Lr: 0.00536 [2023-12-20 15:27:27,526 INFO misc.py line 119 131400] Train: [26/100][48/800] Data 0.003 (0.005) Batch 0.312 (0.339) Remain 05:39:06 loss: 0.3092 Lr: 0.00536 [2023-12-20 15:27:27,830 INFO misc.py line 119 131400] Train: [26/100][49/800] Data 0.003 (0.004) Batch 0.303 (0.339) Remain 05:38:18 loss: 0.4251 Lr: 0.00536 [2023-12-20 15:27:28,166 INFO misc.py line 119 131400] Train: [26/100][50/800] Data 0.005 (0.004) Batch 0.333 (0.338) Remain 05:38:11 loss: 0.5634 Lr: 0.00536 [2023-12-20 15:27:28,504 INFO misc.py line 119 131400] Train: [26/100][51/800] Data 0.007 (0.005) Batch 0.342 (0.339) Remain 05:38:15 loss: 0.3684 Lr: 0.00536 [2023-12-20 15:27:28,809 INFO misc.py line 119 131400] Train: [26/100][52/800] Data 0.004 (0.005) Batch 0.305 (0.338) Remain 05:37:34 loss: 0.3586 Lr: 0.00536 [2023-12-20 15:27:29,118 INFO misc.py line 119 131400] Train: [26/100][53/800] Data 0.003 (0.004) Batch 0.308 (0.337) Remain 05:36:58 loss: 0.3501 Lr: 0.00536 [2023-12-20 15:27:29,450 INFO misc.py line 119 131400] Train: [26/100][54/800] Data 0.004 (0.004) Batch 0.333 (0.337) Remain 05:36:53 loss: 0.3666 Lr: 0.00536 [2023-12-20 15:27:29,745 INFO misc.py line 119 131400] Train: [26/100][55/800] Data 0.003 (0.004) Batch 0.294 (0.336) Remain 05:36:03 loss: 0.4831 Lr: 0.00536 [2023-12-20 15:27:30,090 INFO misc.py line 119 131400] Train: [26/100][56/800] Data 0.003 (0.004) Batch 0.345 (0.337) Remain 05:36:13 loss: 0.5261 Lr: 0.00536 [2023-12-20 15:27:30,429 INFO misc.py line 119 131400] Train: [26/100][57/800] Data 0.003 (0.004) Batch 0.339 (0.337) Remain 05:36:15 loss: 0.4579 Lr: 0.00536 [2023-12-20 15:27:30,721 INFO misc.py line 119 131400] Train: [26/100][58/800] Data 0.003 (0.004) Batch 0.292 (0.336) Remain 05:35:27 loss: 0.5128 Lr: 0.00536 [2023-12-20 15:27:31,048 INFO misc.py line 119 131400] Train: [26/100][59/800] Data 0.003 (0.004) Batch 0.327 (0.336) Remain 05:35:17 loss: 0.2536 Lr: 0.00536 [2023-12-20 15:27:31,337 INFO misc.py line 119 131400] Train: [26/100][60/800] Data 0.003 (0.004) Batch 0.282 (0.335) Remain 05:34:19 loss: 0.9443 Lr: 0.00536 [2023-12-20 15:27:31,661 INFO misc.py line 119 131400] Train: [26/100][61/800] Data 0.011 (0.004) Batch 0.331 (0.335) Remain 05:34:15 loss: 0.6257 Lr: 0.00536 [2023-12-20 15:27:31,924 INFO misc.py line 119 131400] Train: [26/100][62/800] Data 0.003 (0.004) Batch 0.263 (0.333) Remain 05:33:03 loss: 0.7218 Lr: 0.00536 [2023-12-20 15:27:32,232 INFO misc.py line 119 131400] Train: [26/100][63/800] Data 0.003 (0.004) Batch 0.307 (0.333) Remain 05:32:36 loss: 0.3583 Lr: 0.00536 [2023-12-20 15:27:32,514 INFO misc.py line 119 131400] Train: [26/100][64/800] Data 0.004 (0.004) Batch 0.283 (0.332) Remain 05:31:46 loss: 0.5125 Lr: 0.00536 [2023-12-20 15:27:32,841 INFO misc.py line 119 131400] Train: [26/100][65/800] Data 0.003 (0.004) Batch 0.327 (0.332) Remain 05:31:41 loss: 0.4688 Lr: 0.00536 [2023-12-20 15:27:33,147 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [26/100][782/800] Data 0.004 (0.005) Batch 0.322 (0.336) Remain 05:31:31 loss: 0.1560 Lr: 0.00531 [2023-12-20 15:31:34,272 INFO misc.py line 119 131400] Train: [26/100][783/800] Data 0.004 (0.005) Batch 0.343 (0.336) Remain 05:31:32 loss: 0.3609 Lr: 0.00531 [2023-12-20 15:31:34,586 INFO misc.py line 119 131400] Train: [26/100][784/800] Data 0.004 (0.005) Batch 0.315 (0.336) Remain 05:31:30 loss: 0.2719 Lr: 0.00531 [2023-12-20 15:31:34,919 INFO misc.py line 119 131400] Train: [26/100][785/800] Data 0.003 (0.005) Batch 0.332 (0.336) Remain 05:31:29 loss: 0.4380 Lr: 0.00531 [2023-12-20 15:31:35,260 INFO misc.py line 119 131400] Train: [26/100][786/800] Data 0.004 (0.005) Batch 0.341 (0.336) Remain 05:31:29 loss: 0.2792 Lr: 0.00531 [2023-12-20 15:31:35,588 INFO misc.py line 119 131400] Train: [26/100][787/800] Data 0.004 (0.005) Batch 0.329 (0.336) Remain 05:31:28 loss: 0.4372 Lr: 0.00531 [2023-12-20 15:31:35,952 INFO misc.py line 119 131400] Train: [26/100][788/800] Data 0.003 (0.005) Batch 0.364 (0.336) Remain 05:31:30 loss: 0.7034 Lr: 0.00531 [2023-12-20 15:31:36,274 INFO misc.py line 119 131400] Train: [26/100][789/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 05:31:29 loss: 0.2400 Lr: 0.00531 [2023-12-20 15:31:36,583 INFO misc.py line 119 131400] Train: [26/100][790/800] Data 0.003 (0.005) Batch 0.310 (0.336) Remain 05:31:26 loss: 0.4132 Lr: 0.00531 [2023-12-20 15:31:36,912 INFO misc.py line 119 131400] Train: [26/100][791/800] Data 0.002 (0.005) Batch 0.328 (0.336) Remain 05:31:25 loss: 0.7702 Lr: 0.00531 [2023-12-20 15:31:37,216 INFO misc.py line 119 131400] Train: [26/100][792/800] Data 0.003 (0.005) Batch 0.304 (0.336) Remain 05:31:23 loss: 0.4754 Lr: 0.00531 [2023-12-20 15:31:37,512 INFO misc.py line 119 131400] Train: [26/100][793/800] Data 0.004 (0.005) Batch 0.295 (0.336) Remain 05:31:19 loss: 0.4049 Lr: 0.00531 [2023-12-20 15:31:37,833 INFO misc.py line 119 131400] Train: [26/100][794/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 05:31:18 loss: 0.3124 Lr: 0.00531 [2023-12-20 15:31:38,182 INFO misc.py line 119 131400] Train: [26/100][795/800] Data 0.003 (0.005) Batch 0.349 (0.336) Remain 05:31:18 loss: 0.4897 Lr: 0.00531 [2023-12-20 15:31:38,508 INFO misc.py line 119 131400] Train: [26/100][796/800] Data 0.003 (0.005) Batch 0.326 (0.336) Remain 05:31:17 loss: 0.2055 Lr: 0.00531 [2023-12-20 15:31:38,837 INFO misc.py line 119 131400] Train: [26/100][797/800] Data 0.003 (0.005) Batch 0.329 (0.336) Remain 05:31:17 loss: 0.6891 Lr: 0.00531 [2023-12-20 15:31:39,147 INFO misc.py line 119 131400] Train: [26/100][798/800] Data 0.002 (0.005) Batch 0.309 (0.336) Remain 05:31:14 loss: 0.7679 Lr: 0.00531 [2023-12-20 15:31:39,400 INFO misc.py line 119 131400] Train: [26/100][799/800] Data 0.003 (0.005) Batch 0.253 (0.336) Remain 05:31:08 loss: 0.5885 Lr: 0.00531 [2023-12-20 15:31:39,740 INFO misc.py line 119 131400] Train: [26/100][800/800] Data 0.003 (0.005) Batch 0.341 (0.336) Remain 05:31:08 loss: 0.4447 Lr: 0.00531 [2023-12-20 15:31:39,740 INFO misc.py line 136 131400] Train result: loss: 0.4865 [2023-12-20 15:31:39,741 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:32:00,486 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0956 [2023-12-20 15:32:00,573 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1594 [2023-12-20 15:32:00,669 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3705 [2023-12-20 15:32:00,779 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1241 [2023-12-20 15:32:00,892 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4732 [2023-12-20 15:32:00,994 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9161 [2023-12-20 15:32:01,086 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6179 [2023-12-20 15:32:01,193 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.4755 [2023-12-20 15:32:01,273 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2421 [2023-12-20 15:32:01,361 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4232 [2023-12-20 15:32:01,452 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3972 [2023-12-20 15:32:01,589 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3783 [2023-12-20 15:32:01,710 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1937 [2023-12-20 15:32:01,868 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2192 [2023-12-20 15:32:01,961 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1808 [2023-12-20 15:32:02,098 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.4106 [2023-12-20 15:32:02,209 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4108 [2023-12-20 15:32:02,319 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3677 [2023-12-20 15:32:02,432 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2168 [2023-12-20 15:32:02,511 INFO evaluator.py line 159 131400] Test: [20/78] Loss 1.6820 [2023-12-20 15:32:02,622 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2439 [2023-12-20 15:32:02,778 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1720 [2023-12-20 15:32:02,900 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5276 [2023-12-20 15:32:03,042 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4799 [2023-12-20 15:32:03,187 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.4614 [2023-12-20 15:32:03,269 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4052 [2023-12-20 15:32:03,427 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.3216 [2023-12-20 15:32:03,550 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5182 [2023-12-20 15:32:03,645 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4629 [2023-12-20 15:32:03,789 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2631 [2023-12-20 15:32:03,891 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6288 [2023-12-20 15:32:04,010 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5970 [2023-12-20 15:32:04,096 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5151 [2023-12-20 15:32:04,166 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1847 [2023-12-20 15:32:04,260 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6789 [2023-12-20 15:32:04,351 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.7032 [2023-12-20 15:32:04,480 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9664 [2023-12-20 15:32:04,589 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1003 [2023-12-20 15:32:04,669 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6845 [2023-12-20 15:32:04,813 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3660 [2023-12-20 15:32:04,959 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0348 [2023-12-20 15:32:05,058 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3148 [2023-12-20 15:32:05,177 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3413 [2023-12-20 15:32:05,324 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8490 [2023-12-20 15:32:05,441 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2134 [2023-12-20 15:32:05,544 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4236 [2023-12-20 15:32:05,714 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3333 [2023-12-20 15:32:05,807 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.2984 [2023-12-20 15:32:05,951 INFO evaluator.py line 159 131400] Test: [49/78] Loss 0.9150 [2023-12-20 15:32:06,042 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9866 [2023-12-20 15:32:06,121 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7833 [2023-12-20 15:32:06,224 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3433 [2023-12-20 15:32:06,370 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1140 [2023-12-20 15:32:06,503 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3235 [2023-12-20 15:32:06,605 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3683 [2023-12-20 15:32:06,695 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7523 [2023-12-20 15:32:06,796 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4350 [2023-12-20 15:32:06,958 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2399 [2023-12-20 15:32:07,053 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3729 [2023-12-20 15:32:07,152 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2292 [2023-12-20 15:32:07,249 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5278 [2023-12-20 15:32:07,339 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4483 [2023-12-20 15:32:07,427 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8222 [2023-12-20 15:32:07,525 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9312 [2023-12-20 15:32:07,649 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3315 [2023-12-20 15:32:07,735 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1755 [2023-12-20 15:32:07,834 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3215 [2023-12-20 15:32:07,926 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0275 [2023-12-20 15:32:08,009 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4414 [2023-12-20 15:32:08,092 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0343 [2023-12-20 15:32:08,185 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9727 [2023-12-20 15:32:08,274 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5496 [2023-12-20 15:32:08,406 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2073 [2023-12-20 15:32:08,500 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6429 [2023-12-20 15:32:08,615 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8025 [2023-12-20 15:32:08,715 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9574 [2023-12-20 15:32:08,800 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2779 [2023-12-20 15:32:08,955 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4608 [2023-12-20 15:32:10,152 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7226/0.8137/0.9022. [2023-12-20 15:32:10,152 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8545/0.9347 [2023-12-20 15:32:10,152 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9624/0.9842 [2023-12-20 15:32:10,152 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6374/0.8070 [2023-12-20 15:32:10,152 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7722/0.7956 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9119/0.9462 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8289/0.8882 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7146/0.7941 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6855/0.8116 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6436/0.8252 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8019/0.9045 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3716/0.4831 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6775/0.8082 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6042/0.9017 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7480/0.8404 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5035/0.5334 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7100/0.7706 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9382/0.9593 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6535/0.7181 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8811/0.9276 [2023-12-20 15:32:10,153 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5522/0.6392 [2023-12-20 15:32:10,154 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:32:10,154 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:32:10,155 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:32:13,497 INFO misc.py line 119 131400] Train: [27/100][1/800] Data 1.095 (1.095) Batch 1.435 (1.435) Remain 23:35:32 loss: 0.9212 Lr: 0.00531 [2023-12-20 15:32:13,850 INFO misc.py line 119 131400] Train: [27/100][2/800] Data 0.066 (0.066) Batch 0.354 (0.354) Remain 05:48:50 loss: 0.5600 Lr: 0.00531 [2023-12-20 15:32:14,132 INFO misc.py line 119 131400] Train: [27/100][3/800] Data 0.003 (0.003) Batch 0.281 (0.281) Remain 04:37:27 loss: 0.2779 Lr: 0.00531 [2023-12-20 15:32:14,463 INFO misc.py line 119 131400] Train: [27/100][4/800] Data 0.003 (0.003) Batch 0.326 (0.326) Remain 05:22:00 loss: 0.3745 Lr: 0.00531 [2023-12-20 15:32:14,777 INFO misc.py line 119 131400] Train: [27/100][5/800] Data 0.008 (0.006) Batch 0.319 (0.323) Remain 05:18:23 loss: 0.4747 Lr: 0.00531 [2023-12-20 15:32:15,099 INFO misc.py line 119 131400] Train: [27/100][6/800] Data 0.002 (0.004) Batch 0.322 (0.322) Remain 05:18:01 loss: 0.6979 Lr: 0.00531 [2023-12-20 15:32:15,421 INFO misc.py line 119 131400] Train: [27/100][7/800] Data 0.004 (0.004) Batch 0.322 (0.322) Remain 05:18:00 loss: 0.5208 Lr: 0.00531 [2023-12-20 15:32:15,739 INFO misc.py line 119 131400] Train: [27/100][8/800] Data 0.004 (0.004) Batch 0.316 (0.321) Remain 05:16:43 loss: 0.3925 Lr: 0.00531 [2023-12-20 15:32:16,097 INFO misc.py line 119 131400] Train: [27/100][9/800] Data 0.005 (0.004) Batch 0.359 (0.327) Remain 05:22:58 loss: 0.3709 Lr: 0.00531 [2023-12-20 15:32:16,462 INFO misc.py line 119 131400] Train: [27/100][10/800] Data 0.004 (0.004) Batch 0.366 (0.333) Remain 05:28:25 loss: 0.3727 Lr: 0.00531 [2023-12-20 15:32:16,810 INFO misc.py line 119 131400] Train: [27/100][11/800] Data 0.003 (0.004) Batch 0.348 (0.335) Remain 05:30:18 loss: 0.5721 Lr: 0.00530 [2023-12-20 15:32:17,168 INFO misc.py line 119 131400] Train: [27/100][12/800] Data 0.003 (0.004) Batch 0.357 (0.337) Remain 05:32:46 loss: 0.5099 Lr: 0.00530 [2023-12-20 15:32:17,506 INFO misc.py line 119 131400] Train: [27/100][13/800] Data 0.004 (0.004) Batch 0.337 (0.337) Remain 05:32:46 loss: 0.6782 Lr: 0.00530 [2023-12-20 15:32:17,862 INFO misc.py line 119 131400] Train: [27/100][14/800] Data 0.004 (0.004) Batch 0.357 (0.339) Remain 05:34:31 loss: 0.4194 Lr: 0.00530 [2023-12-20 15:32:18,207 INFO misc.py line 119 131400] Train: [27/100][15/800] Data 0.003 (0.004) Batch 0.345 (0.340) Remain 05:35:00 loss: 0.4747 Lr: 0.00530 [2023-12-20 15:32:18,563 INFO misc.py line 119 131400] Train: [27/100][16/800] Data 0.004 (0.004) Batch 0.356 (0.341) Remain 05:36:13 loss: 0.2061 Lr: 0.00530 [2023-12-20 15:32:18,934 INFO misc.py line 119 131400] Train: [27/100][17/800] Data 0.003 (0.004) Batch 0.371 (0.343) Remain 05:38:18 loss: 0.4808 Lr: 0.00530 [2023-12-20 15:32:19,308 INFO misc.py line 119 131400] Train: [27/100][18/800] Data 0.005 (0.004) Batch 0.372 (0.345) Remain 05:40:12 loss: 0.3103 Lr: 0.00530 [2023-12-20 15:32:19,671 INFO misc.py line 119 131400] Train: [27/100][19/800] Data 0.007 (0.004) Batch 0.365 (0.346) Remain 05:41:27 loss: 0.3875 Lr: 0.00530 [2023-12-20 15:32:20,007 INFO misc.py line 119 131400] Train: [27/100][20/800] Data 0.004 (0.004) Batch 0.335 (0.346) Remain 05:40:48 loss: 0.6601 Lr: 0.00530 [2023-12-20 15:32:20,370 INFO misc.py line 119 131400] Train: [27/100][21/800] Data 0.005 (0.004) Batch 0.364 (0.347) Remain 05:41:50 loss: 0.3061 Lr: 0.00530 [2023-12-20 15:32:20,740 INFO misc.py line 119 131400] Train: [27/100][22/800] Data 0.004 (0.004) Batch 0.369 (0.348) Remain 05:43:01 loss: 0.2288 Lr: 0.00530 [2023-12-20 15:32:21,094 INFO misc.py line 119 131400] Train: [27/100][23/800] Data 0.004 (0.004) Batch 0.353 (0.348) Remain 05:43:16 loss: 0.7899 Lr: 0.00530 [2023-12-20 15:32:21,432 INFO misc.py line 119 131400] Train: [27/100][24/800] Data 0.005 (0.004) Batch 0.339 (0.348) Remain 05:42:49 loss: 0.7129 Lr: 0.00530 [2023-12-20 15:32:21,775 INFO misc.py line 119 131400] Train: [27/100][25/800] Data 0.004 (0.004) Batch 0.343 (0.347) Remain 05:42:37 loss: 0.5633 Lr: 0.00530 [2023-12-20 15:32:22,102 INFO misc.py line 119 131400] Train: [27/100][26/800] Data 0.003 (0.004) Batch 0.327 (0.347) Remain 05:41:44 loss: 0.5124 Lr: 0.00530 [2023-12-20 15:32:22,439 INFO misc.py line 119 131400] Train: [27/100][27/800] Data 0.004 (0.004) Batch 0.337 (0.346) Remain 05:41:20 loss: 0.5562 Lr: 0.00530 [2023-12-20 15:32:22,788 INFO misc.py line 119 131400] Train: [27/100][28/800] Data 0.004 (0.004) Batch 0.350 (0.346) Remain 05:41:29 loss: 0.3317 Lr: 0.00530 [2023-12-20 15:32:23,103 INFO misc.py line 119 131400] Train: [27/100][29/800] Data 0.003 (0.004) Batch 0.313 (0.345) Remain 05:40:13 loss: 0.8088 Lr: 0.00530 [2023-12-20 15:32:23,446 INFO misc.py line 119 131400] Train: [27/100][30/800] Data 0.004 (0.004) Batch 0.344 (0.345) Remain 05:40:10 loss: 0.5109 Lr: 0.00530 [2023-12-20 15:32:23,783 INFO misc.py line 119 131400] Train: [27/100][31/800] Data 0.005 (0.004) Batch 0.336 (0.345) Remain 05:39:52 loss: 0.3878 Lr: 0.00530 [2023-12-20 15:32:24,121 INFO misc.py line 119 131400] Train: [27/100][32/800] Data 0.005 (0.004) Batch 0.340 (0.344) Remain 05:39:41 loss: 0.2691 Lr: 0.00530 [2023-12-20 15:32:24,502 INFO misc.py line 119 131400] Train: [27/100][33/800] Data 0.004 (0.004) Batch 0.381 (0.346) Remain 05:40:53 loss: 0.5396 Lr: 0.00530 [2023-12-20 15:32:24,834 INFO misc.py line 119 131400] Train: [27/100][34/800] Data 0.003 (0.004) Batch 0.332 (0.345) Remain 05:40:25 loss: 0.4299 Lr: 0.00530 [2023-12-20 15:32:25,166 INFO misc.py line 119 131400] Train: [27/100][35/800] Data 0.003 (0.004) Batch 0.332 (0.345) Remain 05:40:01 loss: 0.2921 Lr: 0.00530 [2023-12-20 15:32:25,502 INFO misc.py line 119 131400] Train: [27/100][36/800] Data 0.003 (0.004) Batch 0.336 (0.345) Remain 05:39:44 loss: 0.6687 Lr: 0.00530 [2023-12-20 15:32:25,803 INFO misc.py line 119 131400] Train: [27/100][37/800] Data 0.004 (0.004) Batch 0.302 (0.343) Remain 05:38:29 loss: 0.6076 Lr: 0.00530 [2023-12-20 15:32:26,155 INFO misc.py line 119 131400] Train: [27/100][38/800] Data 0.003 (0.004) Batch 0.343 (0.343) Remain 05:38:29 loss: 0.4051 Lr: 0.00530 [2023-12-20 15:32:26,490 INFO misc.py line 119 131400] Train: [27/100][39/800] Data 0.012 (0.004) Batch 0.343 (0.343) Remain 05:38:27 loss: 0.4404 Lr: 0.00530 [2023-12-20 15:32:26,838 INFO misc.py line 119 131400] Train: [27/100][40/800] Data 0.004 (0.004) Batch 0.349 (0.343) Remain 05:38:36 loss: 0.5938 Lr: 0.00530 [2023-12-20 15:32:27,173 INFO misc.py line 119 131400] Train: [27/100][41/800] Data 0.003 (0.004) Batch 0.329 (0.343) Remain 05:38:13 loss: 0.5511 Lr: 0.00530 [2023-12-20 15:32:27,514 INFO misc.py line 119 131400] Train: [27/100][42/800] Data 0.010 (0.004) Batch 0.344 (0.343) Remain 05:38:14 loss: 0.6321 Lr: 0.00530 [2023-12-20 15:32:27,843 INFO misc.py line 119 131400] Train: [27/100][43/800] Data 0.006 (0.004) Batch 0.332 (0.343) Remain 05:37:57 loss: 0.4737 Lr: 0.00530 [2023-12-20 15:32:28,180 INFO misc.py line 119 131400] Train: [27/100][44/800] Data 0.003 (0.004) Batch 0.334 (0.343) Remain 05:37:44 loss: 0.7821 Lr: 0.00530 [2023-12-20 15:32:28,484 INFO misc.py line 119 131400] Train: [27/100][45/800] Data 0.007 (0.005) Batch 0.308 (0.342) Remain 05:36:55 loss: 0.5488 Lr: 0.00530 [2023-12-20 15:32:28,796 INFO misc.py line 119 131400] Train: [27/100][46/800] Data 0.003 (0.004) Batch 0.311 (0.341) Remain 05:36:12 loss: 0.4971 Lr: 0.00530 [2023-12-20 15:32:29,137 INFO misc.py line 119 131400] Train: [27/100][47/800] Data 0.004 (0.004) Batch 0.341 (0.341) Remain 05:36:12 loss: 0.5099 Lr: 0.00530 [2023-12-20 15:32:29,466 INFO misc.py line 119 131400] Train: [27/100][48/800] Data 0.003 (0.004) Batch 0.328 (0.341) Remain 05:35:54 loss: 0.5020 Lr: 0.00530 [2023-12-20 15:32:29,765 INFO misc.py line 119 131400] Train: [27/100][49/800] Data 0.004 (0.004) Batch 0.298 (0.340) Remain 05:34:59 loss: 0.9220 Lr: 0.00530 [2023-12-20 15:32:30,078 INFO misc.py line 119 131400] Train: [27/100][50/800] Data 0.005 (0.004) Batch 0.315 (0.339) Remain 05:34:27 loss: 0.2940 Lr: 0.00530 [2023-12-20 15:32:30,400 INFO misc.py line 119 131400] Train: [27/100][51/800] Data 0.008 (0.005) Batch 0.314 (0.339) Remain 05:33:56 loss: 0.4780 Lr: 0.00530 [2023-12-20 15:32:30,730 INFO misc.py line 119 131400] Train: [27/100][52/800] Data 0.012 (0.005) Batch 0.337 (0.339) Remain 05:33:54 loss: 0.4321 Lr: 0.00530 [2023-12-20 15:32:31,084 INFO misc.py line 119 131400] Train: [27/100][53/800] Data 0.004 (0.005) Batch 0.354 (0.339) Remain 05:34:12 loss: 0.7137 Lr: 0.00530 [2023-12-20 15:32:31,410 INFO misc.py line 119 131400] Train: [27/100][54/800] Data 0.004 (0.005) Batch 0.326 (0.339) Remain 05:33:57 loss: 0.5624 Lr: 0.00530 [2023-12-20 15:32:31,753 INFO misc.py line 119 131400] Train: [27/100][55/800] Data 0.005 (0.005) Batch 0.342 (0.339) Remain 05:34:00 loss: 0.7305 Lr: 0.00530 [2023-12-20 15:32:32,077 INFO misc.py line 119 131400] Train: [27/100][56/800] Data 0.005 (0.005) Batch 0.326 (0.339) Remain 05:33:46 loss: 0.5650 Lr: 0.00530 [2023-12-20 15:32:32,364 INFO misc.py line 119 131400] Train: [27/100][57/800] Data 0.003 (0.005) Batch 0.286 (0.338) Remain 05:32:48 loss: 0.6551 Lr: 0.00530 [2023-12-20 15:32:32,681 INFO misc.py line 119 131400] Train: [27/100][58/800] Data 0.003 (0.005) Batch 0.317 (0.337) Remain 05:32:25 loss: 0.3333 Lr: 0.00530 [2023-12-20 15:32:33,011 INFO misc.py line 119 131400] Train: [27/100][59/800] Data 0.003 (0.005) Batch 0.330 (0.337) Remain 05:32:18 loss: 0.5443 Lr: 0.00530 [2023-12-20 15:32:33,338 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[2023-12-20 15:36:32,504 INFO misc.py line 119 131400] Train: [27/100][776/800] Data 0.009 (0.004) Batch 0.363 (0.334) Remain 05:25:27 loss: 0.6860 Lr: 0.00524 [2023-12-20 15:36:32,823 INFO misc.py line 119 131400] Train: [27/100][777/800] Data 0.004 (0.004) Batch 0.319 (0.334) Remain 05:25:26 loss: 0.4245 Lr: 0.00524 [2023-12-20 15:36:33,159 INFO misc.py line 119 131400] Train: [27/100][778/800] Data 0.003 (0.004) Batch 0.329 (0.334) Remain 05:25:25 loss: 0.3076 Lr: 0.00524 [2023-12-20 15:36:33,511 INFO misc.py line 119 131400] Train: [27/100][779/800] Data 0.010 (0.004) Batch 0.359 (0.334) Remain 05:25:27 loss: 0.6336 Lr: 0.00524 [2023-12-20 15:36:33,867 INFO misc.py line 119 131400] Train: [27/100][780/800] Data 0.004 (0.004) Batch 0.355 (0.334) Remain 05:25:28 loss: 0.6119 Lr: 0.00524 [2023-12-20 15:36:34,209 INFO misc.py line 119 131400] Train: [27/100][781/800] Data 0.004 (0.004) Batch 0.339 (0.334) Remain 05:25:28 loss: 0.2543 Lr: 0.00524 [2023-12-20 15:36:34,549 INFO misc.py line 119 131400] Train: [27/100][782/800] Data 0.008 (0.004) Batch 0.344 (0.334) Remain 05:25:28 loss: 0.3081 Lr: 0.00524 [2023-12-20 15:36:34,889 INFO misc.py line 119 131400] Train: [27/100][783/800] Data 0.004 (0.004) Batch 0.339 (0.334) Remain 05:25:28 loss: 0.4749 Lr: 0.00524 [2023-12-20 15:36:35,210 INFO misc.py line 119 131400] Train: [27/100][784/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 05:25:27 loss: 0.2475 Lr: 0.00524 [2023-12-20 15:36:35,512 INFO misc.py line 119 131400] Train: [27/100][785/800] Data 0.006 (0.004) Batch 0.303 (0.334) Remain 05:25:24 loss: 0.4639 Lr: 0.00524 [2023-12-20 15:36:35,877 INFO misc.py line 119 131400] Train: [27/100][786/800] Data 0.004 (0.004) Batch 0.365 (0.334) Remain 05:25:26 loss: 0.7945 Lr: 0.00524 [2023-12-20 15:36:36,236 INFO misc.py line 119 131400] Train: [27/100][787/800] Data 0.004 (0.004) Batch 0.352 (0.334) Remain 05:25:27 loss: 0.3649 Lr: 0.00524 [2023-12-20 15:36:36,546 INFO misc.py line 119 131400] Train: 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Batch 0.318 (0.334) Remain 05:25:17 loss: 0.4854 Lr: 0.00524 [2023-12-20 15:36:38,774 INFO misc.py line 119 131400] Train: [27/100][795/800] Data 0.007 (0.004) Batch 0.320 (0.334) Remain 05:25:15 loss: 0.4775 Lr: 0.00524 [2023-12-20 15:36:39,066 INFO misc.py line 119 131400] Train: [27/100][796/800] Data 0.003 (0.004) Batch 0.291 (0.334) Remain 05:25:12 loss: 0.3809 Lr: 0.00524 [2023-12-20 15:36:39,352 INFO misc.py line 119 131400] Train: [27/100][797/800] Data 0.003 (0.004) Batch 0.287 (0.334) Remain 05:25:08 loss: 0.2810 Lr: 0.00524 [2023-12-20 15:36:39,643 INFO misc.py line 119 131400] Train: [27/100][798/800] Data 0.002 (0.004) Batch 0.287 (0.334) Remain 05:25:04 loss: 0.3118 Lr: 0.00524 [2023-12-20 15:36:39,964 INFO misc.py line 119 131400] Train: [27/100][799/800] Data 0.006 (0.004) Batch 0.324 (0.334) Remain 05:25:03 loss: 0.3968 Lr: 0.00524 [2023-12-20 15:36:40,278 INFO misc.py line 119 131400] Train: [27/100][800/800] Data 0.003 (0.004) Batch 0.314 (0.334) Remain 05:25:01 loss: 0.4703 Lr: 0.00524 [2023-12-20 15:36:40,279 INFO misc.py line 136 131400] Train result: loss: 0.5022 [2023-12-20 15:36:40,279 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:37:01,888 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1671 [2023-12-20 15:37:01,966 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2075 [2023-12-20 15:37:02,061 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3054 [2023-12-20 15:37:02,177 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3244 [2023-12-20 15:37:02,359 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2386 [2023-12-20 15:37:02,461 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6960 [2023-12-20 15:37:02,550 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0142 [2023-12-20 15:37:02,656 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6333 [2023-12-20 15:37:02,739 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2745 [2023-12-20 15:37:02,826 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3658 [2023-12-20 15:37:02,921 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5875 [2023-12-20 15:37:03,057 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3795 [2023-12-20 15:37:03,179 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2202 [2023-12-20 15:37:03,333 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3201 [2023-12-20 15:37:03,427 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2474 [2023-12-20 15:37:03,562 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7109 [2023-12-20 15:37:03,672 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4366 [2023-12-20 15:37:03,783 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5266 [2023-12-20 15:37:03,900 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2089 [2023-12-20 15:37:03,975 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3662 [2023-12-20 15:37:04,086 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2333 [2023-12-20 15:37:04,243 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1955 [2023-12-20 15:37:04,363 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.4466 [2023-12-20 15:37:04,505 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2692 [2023-12-20 15:37:04,648 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1951 [2023-12-20 15:37:04,732 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4016 [2023-12-20 15:37:04,889 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.3375 [2023-12-20 15:37:05,013 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5527 [2023-12-20 15:37:05,108 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6968 [2023-12-20 15:37:05,257 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7928 [2023-12-20 15:37:05,361 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7635 [2023-12-20 15:37:05,479 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4501 [2023-12-20 15:37:05,566 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2202 [2023-12-20 15:37:05,638 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2311 [2023-12-20 15:37:05,732 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.4086 [2023-12-20 15:37:05,824 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4733 [2023-12-20 15:37:05,954 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8554 [2023-12-20 15:37:06,064 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1688 [2023-12-20 15:37:06,143 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7356 [2023-12-20 15:37:06,284 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5676 [2023-12-20 15:37:06,429 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0244 [2023-12-20 15:37:06,528 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2130 [2023-12-20 15:37:06,649 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3260 [2023-12-20 15:37:06,791 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8416 [2023-12-20 15:37:06,908 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7817 [2023-12-20 15:37:07,017 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4197 [2023-12-20 15:37:07,185 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3500 [2023-12-20 15:37:07,285 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3588 [2023-12-20 15:37:07,431 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1324 [2023-12-20 15:37:07,521 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7392 [2023-12-20 15:37:07,594 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7257 [2023-12-20 15:37:07,698 INFO evaluator.py line 159 131400] Test: [52/78] Loss 0.8906 [2023-12-20 15:37:07,844 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9603 [2023-12-20 15:37:07,977 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3457 [2023-12-20 15:37:08,083 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.9636 [2023-12-20 15:37:08,168 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7975 [2023-12-20 15:37:08,268 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3923 [2023-12-20 15:37:08,434 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2573 [2023-12-20 15:37:08,529 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.0645 [2023-12-20 15:37:08,622 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4355 [2023-12-20 15:37:08,721 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3153 [2023-12-20 15:37:08,810 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4836 [2023-12-20 15:37:08,897 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.9374 [2023-12-20 15:37:08,997 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0519 [2023-12-20 15:37:09,123 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.0214 [2023-12-20 15:37:09,206 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2758 [2023-12-20 15:37:09,305 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3749 [2023-12-20 15:37:09,401 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0229 [2023-12-20 15:37:09,484 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3394 [2023-12-20 15:37:09,566 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0148 [2023-12-20 15:37:09,664 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6926 [2023-12-20 15:37:09,752 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7162 [2023-12-20 15:37:09,887 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0901 [2023-12-20 15:37:09,983 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6374 [2023-12-20 15:37:10,098 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8151 [2023-12-20 15:37:10,199 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8625 [2023-12-20 15:37:10,286 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2650 [2023-12-20 15:37:10,446 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4434 [2023-12-20 15:37:11,636 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7283/0.8324/0.9021. [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8497/0.9226 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9629/0.9840 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6435/0.8381 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7847/0.8687 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9063/0.9463 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8160/0.8992 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7211/0.7992 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6435/0.7631 [2023-12-20 15:37:11,637 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6375/0.8488 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8103/0.8981 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3286/0.5807 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6515/0.7320 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6472/0.8184 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7717/0.8848 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6235/0.6686 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7180/0.8982 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9518/0.9663 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6661/0.7467 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8356/0.9266 [2023-12-20 15:37:11,638 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5976/0.6584 [2023-12-20 15:37:11,639 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:37:11,640 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:37:11,640 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:37:15,285 INFO misc.py line 119 131400] Train: [28/100][1/800] Data 1.205 (1.205) Batch 1.550 (1.550) Remain 25:08:50 loss: 0.3476 Lr: 0.00524 [2023-12-20 15:37:15,628 INFO misc.py line 119 131400] Train: [28/100][2/800] Data 0.004 (0.004) Batch 0.342 (0.342) Remain 05:32:35 loss: 0.2438 Lr: 0.00524 [2023-12-20 15:37:15,933 INFO misc.py line 119 131400] Train: [28/100][3/800] Data 0.004 (0.004) Batch 0.301 (0.301) Remain 04:52:59 loss: 0.1442 Lr: 0.00524 [2023-12-20 15:37:16,251 INFO misc.py line 119 131400] Train: [28/100][4/800] Data 0.009 (0.009) Batch 0.323 (0.323) Remain 05:14:14 loss: 0.3148 Lr: 0.00524 [2023-12-20 15:37:16,573 INFO misc.py line 119 131400] Train: [28/100][5/800] Data 0.004 (0.007) Batch 0.322 (0.323) Remain 05:14:02 loss: 0.5725 Lr: 0.00524 [2023-12-20 15:37:16,881 INFO misc.py line 119 131400] Train: [28/100][6/800] Data 0.006 (0.007) Batch 0.307 (0.318) Remain 05:09:05 loss: 0.6543 Lr: 0.00524 [2023-12-20 15:37:17,222 INFO misc.py line 119 131400] Train: [28/100][7/800] Data 0.004 (0.006) Batch 0.342 (0.324) Remain 05:14:58 loss: 0.3594 Lr: 0.00524 [2023-12-20 15:37:17,504 INFO misc.py line 119 131400] Train: [28/100][8/800] Data 0.004 (0.006) Batch 0.282 (0.315) Remain 05:06:53 loss: 0.3455 Lr: 0.00524 [2023-12-20 15:37:17,849 INFO misc.py line 119 131400] Train: [28/100][9/800] Data 0.004 (0.005) Batch 0.344 (0.320) Remain 05:11:32 loss: 0.4597 Lr: 0.00524 [2023-12-20 15:37:18,179 INFO misc.py line 119 131400] Train: [28/100][10/800] Data 0.004 (0.005) Batch 0.323 (0.321) Remain 05:12:00 loss: 0.2099 Lr: 0.00524 [2023-12-20 15:37:18,533 INFO misc.py line 119 131400] Train: [28/100][11/800] Data 0.012 (0.006) Batch 0.360 (0.326) Remain 05:16:47 loss: 0.4514 Lr: 0.00524 [2023-12-20 15:37:18,884 INFO misc.py line 119 131400] Train: [28/100][12/800] Data 0.004 (0.006) Batch 0.352 (0.329) Remain 05:19:41 loss: 0.6797 Lr: 0.00524 [2023-12-20 15:37:19,209 INFO misc.py line 119 131400] Train: [28/100][13/800] Data 0.003 (0.006) Batch 0.311 (0.327) Remain 05:17:58 loss: 0.5132 Lr: 0.00524 [2023-12-20 15:37:19,551 INFO misc.py line 119 131400] Train: [28/100][14/800] Data 0.017 (0.007) Batch 0.355 (0.329) Remain 05:20:27 loss: 0.6668 Lr: 0.00524 [2023-12-20 15:37:19,909 INFO misc.py line 119 131400] Train: [28/100][15/800] Data 0.004 (0.006) Batch 0.358 (0.332) Remain 05:22:46 loss: 0.3488 Lr: 0.00524 [2023-12-20 15:37:20,247 INFO misc.py line 119 131400] Train: [28/100][16/800] Data 0.004 (0.006) Batch 0.338 (0.332) Remain 05:23:14 loss: 0.8110 Lr: 0.00524 [2023-12-20 15:37:20,592 INFO misc.py line 119 131400] Train: [28/100][17/800] Data 0.004 (0.006) Batch 0.345 (0.333) Remain 05:24:07 loss: 0.5951 Lr: 0.00524 [2023-12-20 15:37:20,937 INFO misc.py line 119 131400] Train: [28/100][18/800] Data 0.004 (0.006) Batch 0.346 (0.334) Remain 05:24:57 loss: 0.6139 Lr: 0.00524 [2023-12-20 15:37:21,273 INFO misc.py line 119 131400] Train: [28/100][19/800] Data 0.003 (0.006) Batch 0.331 (0.334) Remain 05:24:47 loss: 0.4564 Lr: 0.00524 [2023-12-20 15:37:21,612 INFO misc.py line 119 131400] Train: [28/100][20/800] Data 0.008 (0.006) Batch 0.343 (0.334) Remain 05:25:18 loss: 0.4333 Lr: 0.00524 [2023-12-20 15:37:21,950 INFO misc.py line 119 131400] Train: [28/100][21/800] Data 0.004 (0.006) Batch 0.339 (0.335) Remain 05:25:32 loss: 0.3002 Lr: 0.00524 [2023-12-20 15:37:22,264 INFO misc.py line 119 131400] Train: [28/100][22/800] Data 0.004 (0.006) Batch 0.314 (0.333) Remain 05:24:27 loss: 1.2049 Lr: 0.00524 [2023-12-20 15:37:22,598 INFO misc.py line 119 131400] Train: [28/100][23/800] Data 0.004 (0.006) Batch 0.334 (0.334) Remain 05:24:28 loss: 0.3588 Lr: 0.00524 [2023-12-20 15:37:22,925 INFO misc.py line 119 131400] Train: [28/100][24/800] Data 0.004 (0.005) Batch 0.328 (0.333) Remain 05:24:12 loss: 0.4449 Lr: 0.00524 [2023-12-20 15:37:23,256 INFO misc.py line 119 131400] Train: [28/100][25/800] Data 0.003 (0.005) Batch 0.330 (0.333) Remain 05:24:04 loss: 0.6787 Lr: 0.00524 [2023-12-20 15:37:23,533 INFO misc.py line 119 131400] Train: [28/100][26/800] Data 0.008 (0.005) Batch 0.277 (0.331) Remain 05:21:41 loss: 0.4052 Lr: 0.00524 [2023-12-20 15:37:23,861 INFO misc.py line 119 131400] Train: [28/100][27/800] Data 0.003 (0.005) Batch 0.328 (0.331) Remain 05:21:35 loss: 0.4907 Lr: 0.00524 [2023-12-20 15:37:24,192 INFO misc.py line 119 131400] Train: [28/100][28/800] Data 0.003 (0.005) Batch 0.328 (0.330) Remain 05:21:30 loss: 0.6098 Lr: 0.00524 [2023-12-20 15:37:24,502 INFO misc.py line 119 131400] Train: [28/100][29/800] Data 0.006 (0.005) Batch 0.310 (0.330) Remain 05:20:43 loss: 0.3481 Lr: 0.00524 [2023-12-20 15:37:24,848 INFO misc.py line 119 131400] Train: [28/100][30/800] Data 0.006 (0.005) Batch 0.348 (0.330) Remain 05:21:22 loss: 0.4146 Lr: 0.00524 [2023-12-20 15:37:25,182 INFO misc.py line 119 131400] Train: [28/100][31/800] Data 0.004 (0.005) Batch 0.335 (0.331) Remain 05:21:32 loss: 0.2714 Lr: 0.00524 [2023-12-20 15:37:25,489 INFO misc.py line 119 131400] Train: [28/100][32/800] Data 0.002 (0.005) Batch 0.307 (0.330) Remain 05:20:44 loss: 0.4938 Lr: 0.00524 [2023-12-20 15:37:25,812 INFO misc.py line 119 131400] Train: [28/100][33/800] Data 0.003 (0.005) Batch 0.321 (0.329) Remain 05:20:26 loss: 0.4389 Lr: 0.00524 [2023-12-20 15:37:26,130 INFO misc.py line 119 131400] Train: [28/100][34/800] Data 0.005 (0.005) Batch 0.321 (0.329) Remain 05:20:10 loss: 0.5505 Lr: 0.00524 [2023-12-20 15:37:26,425 INFO misc.py line 119 131400] Train: [28/100][35/800] Data 0.002 (0.005) Batch 0.293 (0.328) Remain 05:19:03 loss: 0.4820 Lr: 0.00524 [2023-12-20 15:37:26,742 INFO misc.py line 119 131400] Train: [28/100][36/800] Data 0.005 (0.005) Batch 0.318 (0.328) Remain 05:18:44 loss: 0.5832 Lr: 0.00524 [2023-12-20 15:37:27,107 INFO misc.py line 119 131400] Train: [28/100][37/800] Data 0.004 (0.005) Batch 0.365 (0.329) Remain 05:19:48 loss: 0.6283 Lr: 0.00524 [2023-12-20 15:37:27,482 INFO misc.py line 119 131400] Train: [28/100][38/800] Data 0.005 (0.005) Batch 0.375 (0.330) Remain 05:21:05 loss: 0.4388 Lr: 0.00524 [2023-12-20 15:37:27,823 INFO misc.py line 119 131400] Train: [28/100][39/800] Data 0.005 (0.005) Batch 0.341 (0.330) Remain 05:21:23 loss: 0.7141 Lr: 0.00524 [2023-12-20 15:37:28,164 INFO misc.py line 119 131400] Train: [28/100][40/800] Data 0.004 (0.005) Batch 0.341 (0.331) Remain 05:21:40 loss: 0.2107 Lr: 0.00524 [2023-12-20 15:37:28,532 INFO misc.py line 119 131400] Train: [28/100][41/800] Data 0.004 (0.005) Batch 0.367 (0.332) Remain 05:22:35 loss: 0.2773 Lr: 0.00524 [2023-12-20 15:37:28,885 INFO misc.py line 119 131400] Train: [28/100][42/800] Data 0.005 (0.005) Batch 0.353 (0.332) Remain 05:23:07 loss: 0.5535 Lr: 0.00524 [2023-12-20 15:37:29,214 INFO misc.py line 119 131400] Train: [28/100][43/800] Data 0.005 (0.005) Batch 0.330 (0.332) Remain 05:23:02 loss: 0.4675 Lr: 0.00524 [2023-12-20 15:37:29,549 INFO misc.py line 119 131400] Train: [28/100][44/800] Data 0.003 (0.005) Batch 0.335 (0.332) Remain 05:23:06 loss: 0.6171 Lr: 0.00524 [2023-12-20 15:37:29,898 INFO misc.py line 119 131400] Train: [28/100][45/800] Data 0.004 (0.005) Batch 0.348 (0.333) Remain 05:23:27 loss: 0.5396 Lr: 0.00524 [2023-12-20 15:37:30,222 INFO misc.py line 119 131400] Train: [28/100][46/800] Data 0.006 (0.005) Batch 0.326 (0.332) Remain 05:23:18 loss: 0.5884 Lr: 0.00524 [2023-12-20 15:37:30,576 INFO misc.py line 119 131400] Train: [28/100][47/800] Data 0.003 (0.005) 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line 119 131400] Train: [28/100][782/800] Data 0.004 (0.004) Batch 0.326 (0.334) Remain 05:20:49 loss: 0.7167 Lr: 0.00518 [2023-12-20 15:41:36,546 INFO misc.py line 119 131400] Train: [28/100][783/800] Data 0.005 (0.004) Batch 0.359 (0.334) Remain 05:20:51 loss: 0.6131 Lr: 0.00518 [2023-12-20 15:41:36,937 INFO misc.py line 119 131400] Train: [28/100][784/800] Data 0.005 (0.004) Batch 0.391 (0.334) Remain 05:20:55 loss: 0.3812 Lr: 0.00518 [2023-12-20 15:41:37,254 INFO misc.py line 119 131400] Train: [28/100][785/800] Data 0.005 (0.004) Batch 0.317 (0.334) Remain 05:20:53 loss: 0.4871 Lr: 0.00518 [2023-12-20 15:41:37,617 INFO misc.py line 119 131400] Train: [28/100][786/800] Data 0.005 (0.004) Batch 0.363 (0.334) Remain 05:20:55 loss: 0.5448 Lr: 0.00518 [2023-12-20 15:41:37,983 INFO misc.py line 119 131400] Train: [28/100][787/800] Data 0.004 (0.004) Batch 0.366 (0.334) Remain 05:20:57 loss: 0.7671 Lr: 0.00518 [2023-12-20 15:41:38,339 INFO misc.py line 119 131400] Train: [28/100][788/800] Data 0.005 (0.004) Batch 0.356 (0.334) Remain 05:20:58 loss: 0.3790 Lr: 0.00518 [2023-12-20 15:41:38,678 INFO misc.py line 119 131400] Train: [28/100][789/800] Data 0.005 (0.004) Batch 0.339 (0.334) Remain 05:20:58 loss: 0.2725 Lr: 0.00518 [2023-12-20 15:41:38,984 INFO misc.py line 119 131400] Train: [28/100][790/800] Data 0.004 (0.004) Batch 0.304 (0.334) Remain 05:20:55 loss: 0.6521 Lr: 0.00517 [2023-12-20 15:41:39,284 INFO misc.py line 119 131400] Train: [28/100][791/800] Data 0.005 (0.004) Batch 0.303 (0.334) Remain 05:20:53 loss: 0.2981 Lr: 0.00517 [2023-12-20 15:41:39,606 INFO misc.py line 119 131400] Train: [28/100][792/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 05:20:52 loss: 0.2948 Lr: 0.00517 [2023-12-20 15:41:39,930 INFO misc.py line 119 131400] Train: [28/100][793/800] Data 0.003 (0.004) Batch 0.324 (0.334) Remain 05:20:51 loss: 0.2771 Lr: 0.00517 [2023-12-20 15:41:40,249 INFO misc.py line 119 131400] Train: [28/100][794/800] Data 0.003 (0.004) Batch 0.317 (0.334) Remain 05:20:49 loss: 0.4312 Lr: 0.00517 [2023-12-20 15:41:40,568 INFO misc.py line 119 131400] Train: [28/100][795/800] Data 0.005 (0.004) Batch 0.319 (0.334) Remain 05:20:47 loss: 0.3087 Lr: 0.00517 [2023-12-20 15:41:40,881 INFO misc.py line 119 131400] Train: [28/100][796/800] Data 0.005 (0.004) Batch 0.314 (0.334) Remain 05:20:46 loss: 0.2219 Lr: 0.00517 [2023-12-20 15:41:41,201 INFO misc.py line 119 131400] Train: [28/100][797/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 05:20:44 loss: 0.2759 Lr: 0.00517 [2023-12-20 15:41:41,487 INFO misc.py line 119 131400] Train: [28/100][798/800] Data 0.005 (0.004) Batch 0.287 (0.334) Remain 05:20:41 loss: 0.6762 Lr: 0.00517 [2023-12-20 15:41:41,782 INFO misc.py line 119 131400] Train: [28/100][799/800] Data 0.004 (0.004) Batch 0.296 (0.334) Remain 05:20:37 loss: 0.6753 Lr: 0.00517 [2023-12-20 15:41:42,092 INFO misc.py line 119 131400] Train: [28/100][800/800] Data 0.006 (0.004) Batch 0.309 (0.334) Remain 05:20:35 loss: 0.3837 Lr: 0.00517 [2023-12-20 15:41:42,093 INFO misc.py line 136 131400] Train result: loss: 0.4849 [2023-12-20 15:41:42,100 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:42:04,240 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1087 [2023-12-20 15:42:04,722 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1853 [2023-12-20 15:42:04,816 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3770 [2023-12-20 15:42:04,927 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2392 [2023-12-20 15:42:05,046 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2746 [2023-12-20 15:42:05,150 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5995 [2023-12-20 15:42:05,247 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1586 [2023-12-20 15:42:05,356 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2304 [2023-12-20 15:42:05,441 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3390 [2023-12-20 15:42:05,528 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3274 [2023-12-20 15:42:05,625 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6632 [2023-12-20 15:42:05,763 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5495 [2023-12-20 15:42:05,882 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1940 [2023-12-20 15:42:06,038 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2108 [2023-12-20 15:42:06,130 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.6158 [2023-12-20 15:42:06,264 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7475 [2023-12-20 15:42:06,375 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2974 [2023-12-20 15:42:06,489 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2799 [2023-12-20 15:42:06,599 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3069 [2023-12-20 15:42:06,673 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6316 [2023-12-20 15:42:06,784 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3063 [2023-12-20 15:42:06,939 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1409 [2023-12-20 15:42:07,064 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6761 [2023-12-20 15:42:07,207 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2301 [2023-12-20 15:42:07,352 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2513 [2023-12-20 15:42:07,440 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4435 [2023-12-20 15:42:07,604 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6471 [2023-12-20 15:42:07,730 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5300 [2023-12-20 15:42:07,824 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6199 [2023-12-20 15:42:07,972 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7060 [2023-12-20 15:42:08,082 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6741 [2023-12-20 15:42:08,205 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6287 [2023-12-20 15:42:08,294 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1777 [2023-12-20 15:42:08,365 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2093 [2023-12-20 15:42:08,468 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5781 [2023-12-20 15:42:08,561 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6376 [2023-12-20 15:42:08,693 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7428 [2023-12-20 15:42:08,817 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3327 [2023-12-20 15:42:08,903 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.8490 [2023-12-20 15:42:09,049 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3932 [2023-12-20 15:42:09,194 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1081 [2023-12-20 15:42:09,299 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1438 [2023-12-20 15:42:09,418 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4544 [2023-12-20 15:42:09,562 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8890 [2023-12-20 15:42:09,682 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0779 [2023-12-20 15:42:09,790 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6896 [2023-12-20 15:42:09,978 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5665 [2023-12-20 15:42:10,076 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3851 [2023-12-20 15:42:10,226 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3579 [2023-12-20 15:42:10,325 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8354 [2023-12-20 15:42:10,407 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5564 [2023-12-20 15:42:10,523 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1988 [2023-12-20 15:42:10,672 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2520 [2023-12-20 15:42:10,819 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3140 [2023-12-20 15:42:10,930 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1949 [2023-12-20 15:42:11,021 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9684 [2023-12-20 15:42:11,127 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4808 [2023-12-20 15:42:11,296 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3578 [2023-12-20 15:42:11,404 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4009 [2023-12-20 15:42:11,512 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4126 [2023-12-20 15:42:11,608 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2589 [2023-12-20 15:42:11,702 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2712 [2023-12-20 15:42:11,806 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5727 [2023-12-20 15:42:11,910 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9845 [2023-12-20 15:42:12,042 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3572 [2023-12-20 15:42:12,135 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2915 [2023-12-20 15:42:12,241 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4940 [2023-12-20 15:42:12,334 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0625 [2023-12-20 15:42:12,423 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3457 [2023-12-20 15:42:12,507 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1298 [2023-12-20 15:42:12,600 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9287 [2023-12-20 15:42:12,692 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4831 [2023-12-20 15:42:12,830 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2545 [2023-12-20 15:42:12,929 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6204 [2023-12-20 15:42:13,045 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6997 [2023-12-20 15:42:13,149 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7672 [2023-12-20 15:42:13,239 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3429 [2023-12-20 15:42:13,393 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3181 [2023-12-20 15:42:14,936 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7208/0.8143/0.9020. [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8570/0.9455 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9618/0.9806 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6175/0.8230 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7929/0.8424 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8960/0.9332 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8107/0.8961 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7479/0.8842 [2023-12-20 15:42:14,936 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6370/0.7088 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6620/0.8005 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7955/0.8983 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3519/0.4515 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6382/0.8111 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6855/0.8046 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6942/0.8741 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5973/0.7090 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6463/0.7065 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9508/0.9660 [2023-12-20 15:42:14,937 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6468/0.7143 [2023-12-20 15:42:14,938 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8828/0.9249 [2023-12-20 15:42:14,938 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5440/0.6115 [2023-12-20 15:42:14,938 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:42:14,940 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:42:14,940 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:42:18,877 INFO misc.py line 119 131400] Train: [29/100][1/800] Data 1.152 (1.152) Batch 1.459 (1.459) Remain 23:20:44 loss: 0.5895 Lr: 0.00517 [2023-12-20 15:42:19,200 INFO misc.py line 119 131400] Train: [29/100][2/800] Data 0.005 (0.005) Batch 0.322 (0.322) Remain 05:08:54 loss: 0.3275 Lr: 0.00517 [2023-12-20 15:42:19,561 INFO misc.py line 119 131400] Train: [29/100][3/800] Data 0.006 (0.006) Batch 0.363 (0.363) Remain 05:48:11 loss: 0.5854 Lr: 0.00517 [2023-12-20 15:42:19,893 INFO misc.py line 119 131400] Train: [29/100][4/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 05:19:48 loss: 0.3691 Lr: 0.00517 [2023-12-20 15:42:20,237 INFO misc.py line 119 131400] Train: [29/100][5/800] Data 0.003 (0.003) Batch 0.339 (0.336) Remain 05:22:26 loss: 0.4955 Lr: 0.00517 [2023-12-20 15:42:20,549 INFO misc.py line 119 131400] Train: [29/100][6/800] Data 0.009 (0.005) Batch 0.316 (0.329) Remain 05:15:57 loss: 0.5958 Lr: 0.00517 [2023-12-20 15:42:20,893 INFO misc.py line 119 131400] Train: [29/100][7/800] Data 0.005 (0.005) Batch 0.344 (0.333) Remain 05:19:35 loss: 0.4058 Lr: 0.00517 [2023-12-20 15:42:21,266 INFO misc.py line 119 131400] Train: [29/100][8/800] Data 0.004 (0.005) Batch 0.373 (0.341) Remain 05:27:21 loss: 0.6753 Lr: 0.00517 [2023-12-20 15:42:21,637 INFO misc.py line 119 131400] Train: [29/100][9/800] Data 0.004 (0.005) Batch 0.371 (0.346) Remain 05:32:04 loss: 0.3882 Lr: 0.00517 [2023-12-20 15:42:21,966 INFO misc.py line 119 131400] Train: [29/100][10/800] Data 0.004 (0.005) Batch 0.329 (0.344) Remain 05:29:43 loss: 0.2210 Lr: 0.00517 [2023-12-20 15:42:22,279 INFO misc.py line 119 131400] Train: [29/100][11/800] Data 0.004 (0.005) Batch 0.314 (0.340) Remain 05:26:08 loss: 0.7777 Lr: 0.00517 [2023-12-20 15:42:22,595 INFO misc.py line 119 131400] Train: [29/100][12/800] Data 0.004 (0.004) Batch 0.317 (0.337) Remain 05:23:42 loss: 0.5109 Lr: 0.00517 [2023-12-20 15:42:22,978 INFO misc.py line 119 131400] Train: [29/100][13/800] Data 0.003 (0.004) Batch 0.382 (0.342) Remain 05:27:58 loss: 0.3070 Lr: 0.00517 [2023-12-20 15:42:23,292 INFO misc.py line 119 131400] Train: [29/100][14/800] Data 0.004 (0.004) Batch 0.311 (0.339) Remain 05:25:15 loss: 0.5132 Lr: 0.00517 [2023-12-20 15:42:23,617 INFO misc.py line 119 131400] Train: [29/100][15/800] Data 0.007 (0.005) Batch 0.328 (0.338) Remain 05:24:24 loss: 0.3938 Lr: 0.00517 [2023-12-20 15:42:23,914 INFO misc.py line 119 131400] Train: [29/100][16/800] Data 0.003 (0.004) Batch 0.297 (0.335) Remain 05:21:24 loss: 0.3119 Lr: 0.00517 [2023-12-20 15:42:24,225 INFO misc.py line 119 131400] Train: [29/100][17/800] Data 0.004 (0.004) Batch 0.311 (0.333) Remain 05:19:47 loss: 0.7825 Lr: 0.00517 [2023-12-20 15:42:24,566 INFO misc.py line 119 131400] Train: [29/100][18/800] Data 0.003 (0.004) Batch 0.341 (0.334) Remain 05:20:16 loss: 0.5868 Lr: 0.00517 [2023-12-20 15:42:24,926 INFO misc.py line 119 131400] Train: [29/100][19/800] Data 0.003 (0.004) Batch 0.360 (0.335) Remain 05:21:51 loss: 0.7956 Lr: 0.00517 [2023-12-20 15:42:25,228 INFO misc.py line 119 131400] Train: [29/100][20/800] Data 0.003 (0.004) Batch 0.302 (0.333) Remain 05:19:57 loss: 0.3392 Lr: 0.00517 [2023-12-20 15:42:25,549 INFO misc.py line 119 131400] Train: [29/100][21/800] Data 0.003 (0.004) Batch 0.321 (0.333) Remain 05:19:16 loss: 0.8241 Lr: 0.00517 [2023-12-20 15:42:25,870 INFO misc.py line 119 131400] Train: [29/100][22/800] Data 0.002 (0.004) Batch 0.321 (0.332) Remain 05:18:42 loss: 0.8247 Lr: 0.00517 [2023-12-20 15:42:26,186 INFO misc.py line 119 131400] Train: [29/100][23/800] Data 0.003 (0.004) Batch 0.315 (0.331) Remain 05:17:52 loss: 0.3168 Lr: 0.00517 [2023-12-20 15:42:26,476 INFO misc.py line 119 131400] Train: [29/100][24/800] Data 0.004 (0.004) Batch 0.287 (0.329) Remain 05:15:50 loss: 0.2562 Lr: 0.00517 [2023-12-20 15:42:26,774 INFO misc.py line 119 131400] Train: [29/100][25/800] Data 0.006 (0.004) Batch 0.301 (0.328) Remain 05:14:37 loss: 0.3735 Lr: 0.00517 [2023-12-20 15:42:27,120 INFO misc.py line 119 131400] Train: [29/100][26/800] Data 0.003 (0.004) Batch 0.345 (0.329) Remain 05:15:21 loss: 0.5143 Lr: 0.00517 [2023-12-20 15:42:27,471 INFO misc.py line 119 131400] Train: [29/100][27/800] Data 0.004 (0.004) Batch 0.353 (0.330) Remain 05:16:18 loss: 0.6197 Lr: 0.00517 [2023-12-20 15:42:27,812 INFO misc.py line 119 131400] Train: [29/100][28/800] Data 0.003 (0.004) Batch 0.340 (0.330) Remain 05:16:42 loss: 0.4254 Lr: 0.00517 [2023-12-20 15:42:28,112 INFO misc.py line 119 131400] Train: [29/100][29/800] Data 0.004 (0.004) Batch 0.300 (0.329) Remain 05:15:35 loss: 0.4302 Lr: 0.00517 [2023-12-20 15:42:28,437 INFO misc.py line 119 131400] Train: [29/100][30/800] Data 0.004 (0.004) Batch 0.325 (0.329) Remain 05:15:26 loss: 0.3212 Lr: 0.00517 [2023-12-20 15:42:28,779 INFO misc.py line 119 131400] Train: [29/100][31/800] Data 0.003 (0.004) Batch 0.342 (0.329) Remain 05:15:54 loss: 0.4918 Lr: 0.00517 [2023-12-20 15:42:29,081 INFO misc.py line 119 131400] Train: [29/100][32/800] Data 0.004 (0.004) Batch 0.302 (0.328) Remain 05:15:00 loss: 0.2512 Lr: 0.00517 [2023-12-20 15:42:29,378 INFO misc.py line 119 131400] Train: [29/100][33/800] Data 0.003 (0.004) Batch 0.297 (0.327) Remain 05:13:59 loss: 0.3112 Lr: 0.00517 [2023-12-20 15:42:29,691 INFO misc.py line 119 131400] Train: [29/100][34/800] Data 0.003 (0.004) Batch 0.313 (0.327) Remain 05:13:33 loss: 0.3112 Lr: 0.00517 [2023-12-20 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05:14:35 loss: 0.6353 Lr: 0.00511 [2023-12-20 15:46:34,328 INFO misc.py line 119 131400] Train: [29/100][770/800] Data 0.004 (0.004) Batch 0.344 (0.332) Remain 05:14:36 loss: 0.2926 Lr: 0.00511 [2023-12-20 15:46:34,648 INFO misc.py line 119 131400] Train: [29/100][771/800] Data 0.005 (0.004) Batch 0.321 (0.332) Remain 05:14:35 loss: 0.2690 Lr: 0.00511 [2023-12-20 15:46:34,993 INFO misc.py line 119 131400] Train: [29/100][772/800] Data 0.004 (0.004) Batch 0.347 (0.332) Remain 05:14:36 loss: 0.4693 Lr: 0.00511 [2023-12-20 15:46:35,300 INFO misc.py line 119 131400] Train: [29/100][773/800] Data 0.003 (0.004) Batch 0.306 (0.332) Remain 05:14:33 loss: 0.4622 Lr: 0.00511 [2023-12-20 15:46:35,651 INFO misc.py line 119 131400] Train: [29/100][774/800] Data 0.004 (0.004) Batch 0.352 (0.332) Remain 05:14:34 loss: 0.2813 Lr: 0.00511 [2023-12-20 15:46:35,960 INFO misc.py line 119 131400] Train: [29/100][775/800] Data 0.003 (0.004) Batch 0.309 (0.332) Remain 05:14:32 loss: 0.6537 Lr: 0.00511 [2023-12-20 15:46:36,290 INFO misc.py line 119 131400] Train: [29/100][776/800] Data 0.004 (0.004) Batch 0.330 (0.332) Remain 05:14:32 loss: 0.3417 Lr: 0.00511 [2023-12-20 15:46:36,671 INFO misc.py line 119 131400] Train: [29/100][777/800] Data 0.003 (0.004) Batch 0.382 (0.332) Remain 05:14:35 loss: 0.8972 Lr: 0.00511 [2023-12-20 15:46:37,016 INFO misc.py line 119 131400] Train: [29/100][778/800] Data 0.004 (0.004) Batch 0.344 (0.332) Remain 05:14:36 loss: 0.8087 Lr: 0.00511 [2023-12-20 15:46:37,370 INFO misc.py line 119 131400] Train: [29/100][779/800] Data 0.004 (0.004) Batch 0.353 (0.332) Remain 05:14:37 loss: 0.5571 Lr: 0.00511 [2023-12-20 15:46:37,677 INFO misc.py line 119 131400] Train: [29/100][780/800] Data 0.005 (0.004) Batch 0.307 (0.332) Remain 05:14:35 loss: 0.2865 Lr: 0.00511 [2023-12-20 15:46:38,015 INFO misc.py line 119 131400] Train: [29/100][781/800] Data 0.005 (0.004) Batch 0.338 (0.332) Remain 05:14:35 loss: 0.3490 Lr: 0.00511 [2023-12-20 15:46:38,369 INFO misc.py line 119 131400] Train: [29/100][782/800] Data 0.004 (0.004) Batch 0.353 (0.332) Remain 05:14:36 loss: 0.5043 Lr: 0.00511 [2023-12-20 15:46:38,711 INFO misc.py line 119 131400] Train: [29/100][783/800] Data 0.006 (0.004) Batch 0.344 (0.332) Remain 05:14:36 loss: 0.3168 Lr: 0.00511 [2023-12-20 15:46:39,062 INFO misc.py line 119 131400] Train: [29/100][784/800] Data 0.003 (0.004) Batch 0.350 (0.332) Remain 05:14:37 loss: 0.5193 Lr: 0.00511 [2023-12-20 15:46:39,385 INFO misc.py line 119 131400] Train: [29/100][785/800] Data 0.005 (0.004) Batch 0.324 (0.332) Remain 05:14:37 loss: 0.4695 Lr: 0.00511 [2023-12-20 15:46:39,738 INFO misc.py line 119 131400] Train: [29/100][786/800] Data 0.005 (0.004) Batch 0.352 (0.332) Remain 05:14:38 loss: 0.3165 Lr: 0.00511 [2023-12-20 15:46:40,095 INFO misc.py line 119 131400] Train: [29/100][787/800] Data 0.005 (0.004) Batch 0.358 (0.332) Remain 05:14:39 loss: 0.5049 Lr: 0.00511 [2023-12-20 15:46:40,439 INFO misc.py line 119 131400] Train: [29/100][788/800] Data 0.004 (0.004) Batch 0.344 (0.332) Remain 05:14:40 loss: 0.3622 Lr: 0.00511 [2023-12-20 15:46:40,787 INFO misc.py line 119 131400] Train: [29/100][789/800] Data 0.003 (0.004) Batch 0.348 (0.332) Remain 05:14:40 loss: 0.5741 Lr: 0.00511 [2023-12-20 15:46:41,117 INFO misc.py line 119 131400] Train: [29/100][790/800] Data 0.003 (0.004) Batch 0.330 (0.332) Remain 05:14:40 loss: 0.7079 Lr: 0.00511 [2023-12-20 15:46:41,440 INFO misc.py line 119 131400] Train: [29/100][791/800] Data 0.003 (0.004) Batch 0.324 (0.332) Remain 05:14:39 loss: 0.6517 Lr: 0.00511 [2023-12-20 15:46:41,806 INFO misc.py line 119 131400] Train: [29/100][792/800] Data 0.003 (0.004) Batch 0.365 (0.332) Remain 05:14:41 loss: 0.3394 Lr: 0.00511 [2023-12-20 15:46:42,152 INFO misc.py line 119 131400] Train: [29/100][793/800] Data 0.004 (0.004) Batch 0.346 (0.332) Remain 05:14:42 loss: 0.3764 Lr: 0.00511 [2023-12-20 15:46:42,493 INFO misc.py line 119 131400] Train: [29/100][794/800] Data 0.003 (0.004) Batch 0.341 (0.332) Remain 05:14:42 loss: 0.4433 Lr: 0.00511 [2023-12-20 15:46:42,797 INFO misc.py line 119 131400] Train: [29/100][795/800] Data 0.003 (0.004) Batch 0.304 (0.332) Remain 05:14:40 loss: 0.6226 Lr: 0.00511 [2023-12-20 15:46:43,094 INFO misc.py line 119 131400] Train: [29/100][796/800] Data 0.004 (0.004) Batch 0.294 (0.332) Remain 05:14:37 loss: 0.3112 Lr: 0.00510 [2023-12-20 15:46:43,427 INFO misc.py line 119 131400] Train: [29/100][797/800] Data 0.007 (0.004) Batch 0.335 (0.332) Remain 05:14:36 loss: 0.6826 Lr: 0.00510 [2023-12-20 15:46:43,751 INFO misc.py line 119 131400] Train: [29/100][798/800] Data 0.004 (0.004) Batch 0.325 (0.332) Remain 05:14:36 loss: 0.3725 Lr: 0.00510 [2023-12-20 15:46:44,062 INFO misc.py line 119 131400] Train: [29/100][799/800] Data 0.003 (0.004) Batch 0.311 (0.332) Remain 05:14:34 loss: 0.6828 Lr: 0.00510 [2023-12-20 15:46:44,379 INFO misc.py line 119 131400] Train: [29/100][800/800] Data 0.003 (0.004) Batch 0.317 (0.332) Remain 05:14:32 loss: 0.4325 Lr: 0.00510 [2023-12-20 15:46:44,380 INFO misc.py line 136 131400] Train result: loss: 0.4818 [2023-12-20 15:46:44,380 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:47:05,561 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1371 [2023-12-20 15:47:07,910 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1671 [2023-12-20 15:47:08,010 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4407 [2023-12-20 15:47:08,121 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2306 [2023-12-20 15:47:08,235 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2813 [2023-12-20 15:47:08,341 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.4736 [2023-12-20 15:47:08,434 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0711 [2023-12-20 15:47:08,543 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3223 [2023-12-20 15:47:08,632 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3378 [2023-12-20 15:47:08,718 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4965 [2023-12-20 15:47:08,814 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3784 [2023-12-20 15:47:08,953 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4408 [2023-12-20 15:47:09,074 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5502 [2023-12-20 15:47:09,231 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2353 [2023-12-20 15:47:09,324 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.4420 [2023-12-20 15:47:09,463 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8939 [2023-12-20 15:47:09,571 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2079 [2023-12-20 15:47:09,687 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6560 [2023-12-20 15:47:09,804 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3365 [2023-12-20 15:47:09,927 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4483 [2023-12-20 15:47:10,048 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3990 [2023-12-20 15:47:10,207 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1978 [2023-12-20 15:47:10,339 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.4827 [2023-12-20 15:47:10,485 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2174 [2023-12-20 15:47:10,629 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2062 [2023-12-20 15:47:10,718 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4823 [2023-12-20 15:47:10,876 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6568 [2023-12-20 15:47:11,001 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5005 [2023-12-20 15:47:11,100 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8252 [2023-12-20 15:47:11,251 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3341 [2023-12-20 15:47:11,366 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8678 [2023-12-20 15:47:11,490 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5331 [2023-12-20 15:47:11,576 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5076 [2023-12-20 15:47:11,651 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1988 [2023-12-20 15:47:11,753 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5542 [2023-12-20 15:47:11,847 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4847 [2023-12-20 15:47:11,978 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9037 [2023-12-20 15:47:12,094 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1149 [2023-12-20 15:47:12,183 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6270 [2023-12-20 15:47:12,325 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5947 [2023-12-20 15:47:12,474 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0239 [2023-12-20 15:47:12,588 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1423 [2023-12-20 15:47:12,708 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.6511 [2023-12-20 15:47:12,859 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8590 [2023-12-20 15:47:12,979 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0155 [2023-12-20 15:47:13,086 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6693 [2023-12-20 15:47:13,253 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6640 [2023-12-20 15:47:13,349 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3872 [2023-12-20 15:47:13,497 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5491 [2023-12-20 15:47:13,592 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7722 [2023-12-20 15:47:13,668 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4203 [2023-12-20 15:47:13,776 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4563 [2023-12-20 15:47:13,922 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0393 [2023-12-20 15:47:14,058 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3495 [2023-12-20 15:47:14,159 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.7556 [2023-12-20 15:47:14,249 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8616 [2023-12-20 15:47:14,351 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4020 [2023-12-20 15:47:14,514 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3378 [2023-12-20 15:47:14,614 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3074 [2023-12-20 15:47:14,708 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3132 [2023-12-20 15:47:14,804 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3653 [2023-12-20 15:47:14,901 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3661 [2023-12-20 15:47:15,020 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3892 [2023-12-20 15:47:15,130 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9038 [2023-12-20 15:47:15,257 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3733 [2023-12-20 15:47:15,354 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3652 [2023-12-20 15:47:15,468 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5578 [2023-12-20 15:47:15,565 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0213 [2023-12-20 15:47:15,649 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3287 [2023-12-20 15:47:15,743 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0263 [2023-12-20 15:47:15,840 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7868 [2023-12-20 15:47:15,934 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6696 [2023-12-20 15:47:16,068 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1875 [2023-12-20 15:47:16,164 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6086 [2023-12-20 15:47:16,300 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8563 [2023-12-20 15:47:16,414 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.1211 [2023-12-20 15:47:16,505 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2903 [2023-12-20 15:47:16,665 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2932 [2023-12-20 15:47:18,088 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7252/0.8138/0.9020. [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8411/0.9504 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9656/0.9845 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6243/0.7302 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7987/0.8427 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9050/0.9486 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8541/0.9370 [2023-12-20 15:47:18,088 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7593/0.8903 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6659/0.7731 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6238/0.7339 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7555/0.9192 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3627/0.5301 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6323/0.7122 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7050/0.8384 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7647/0.8714 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5437/0.5927 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7308/0.7928 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9350/0.9644 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6324/0.7888 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8811/0.9269 [2023-12-20 15:47:18,089 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5234/0.5481 [2023-12-20 15:47:18,090 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:47:18,092 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:47:18,093 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:47:22,377 INFO misc.py line 119 131400] Train: [30/100][1/800] Data 1.258 (1.258) Batch 1.572 (1.572) Remain 24:48:27 loss: 0.8138 Lr: 0.00510 [2023-12-20 15:47:22,866 INFO misc.py line 119 131400] Train: [30/100][2/800] Data 0.160 (0.160) Batch 0.489 (0.489) Remain 07:42:42 loss: 0.6081 Lr: 0.00510 [2023-12-20 15:47:23,812 INFO misc.py line 119 131400] Train: [30/100][3/800] Data 0.610 (0.610) Batch 0.945 (0.945) Remain 14:54:59 loss: 0.5731 Lr: 0.00510 [2023-12-20 15:47:24,106 INFO misc.py line 119 131400] Train: [30/100][4/800] Data 0.004 (0.004) Batch 0.295 (0.295) Remain 04:39:07 loss: 0.2157 Lr: 0.00510 [2023-12-20 15:47:24,394 INFO misc.py line 119 131400] Train: [30/100][5/800] Data 0.003 (0.004) Batch 0.288 (0.291) Remain 04:35:39 loss: 0.3140 Lr: 0.00510 [2023-12-20 15:47:24,766 INFO misc.py line 119 131400] Train: [30/100][6/800] Data 0.004 (0.004) Batch 0.372 (0.318) Remain 05:01:10 loss: 0.5329 Lr: 0.00510 [2023-12-20 15:47:25,099 INFO misc.py line 119 131400] Train: [30/100][7/800] Data 0.004 (0.004) Batch 0.333 (0.322) Remain 05:04:35 loss: 0.5562 Lr: 0.00510 [2023-12-20 15:47:25,445 INFO misc.py line 119 131400] Train: [30/100][8/800] Data 0.006 (0.004) Batch 0.347 (0.327) Remain 05:09:21 loss: 0.7388 Lr: 0.00510 [2023-12-20 15:47:25,772 INFO misc.py line 119 131400] Train: [30/100][9/800] Data 0.004 (0.004) Batch 0.327 (0.327) Remain 05:09:26 loss: 0.5463 Lr: 0.00510 [2023-12-20 15:47:26,085 INFO misc.py line 119 131400] Train: [30/100][10/800] Data 0.003 (0.004) Batch 0.313 (0.325) Remain 05:07:33 loss: 0.2973 Lr: 0.00510 [2023-12-20 15:47:26,449 INFO misc.py line 119 131400] Train: [30/100][11/800] Data 0.003 (0.004) Batch 0.362 (0.330) Remain 05:11:58 loss: 0.4412 Lr: 0.00510 [2023-12-20 15:47:26,795 INFO misc.py line 119 131400] Train: [30/100][12/800] Data 0.005 (0.004) Batch 0.347 (0.331) Remain 05:13:44 loss: 0.1490 Lr: 0.00510 [2023-12-20 15:47:27,127 INFO misc.py line 119 131400] Train: [30/100][13/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 05:13:52 loss: 0.2082 Lr: 0.00510 [2023-12-20 15:47:27,480 INFO misc.py line 119 131400] Train: [30/100][14/800] Data 0.003 (0.004) Batch 0.352 (0.333) Remain 05:15:36 loss: 0.5437 Lr: 0.00510 [2023-12-20 15:47:27,829 INFO misc.py line 119 131400] Train: [30/100][15/800] Data 0.004 (0.004) Batch 0.349 (0.335) Remain 05:16:49 loss: 0.6981 Lr: 0.00510 [2023-12-20 15:47:28,147 INFO misc.py line 119 131400] Train: [30/100][16/800] Data 0.004 (0.004) Batch 0.319 (0.334) Remain 05:15:40 loss: 0.3040 Lr: 0.00510 [2023-12-20 15:47:28,495 INFO misc.py line 119 131400] Train: [30/100][17/800] Data 0.003 (0.004) Batch 0.348 (0.335) Remain 05:16:38 loss: 0.3356 Lr: 0.00510 [2023-12-20 15:47:28,997 INFO misc.py line 119 131400] Train: [30/100][18/800] Data 0.004 (0.004) Batch 0.501 (0.346) Remain 05:27:08 loss: 0.5022 Lr: 0.00510 [2023-12-20 15:47:29,333 INFO misc.py line 119 131400] Train: [30/100][19/800] Data 0.006 (0.004) Batch 0.337 (0.345) Remain 05:26:37 loss: 0.5392 Lr: 0.00510 [2023-12-20 15:47:29,651 INFO misc.py line 119 131400] Train: [30/100][20/800] Data 0.003 (0.004) Batch 0.317 (0.343) Remain 05:25:03 loss: 0.5590 Lr: 0.00510 [2023-12-20 15:47:29,947 INFO misc.py line 119 131400] Train: [30/100][21/800] Data 0.004 (0.004) Batch 0.297 (0.341) Remain 05:22:37 loss: 0.2843 Lr: 0.00510 [2023-12-20 15:47:30,285 INFO misc.py line 119 131400] Train: [30/100][22/800] Data 0.003 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131400] Train: [30/100][757/800] Data 0.004 (0.009) Batch 0.352 (0.340) Remain 05:17:50 loss: 0.4761 Lr: 0.00504 [2023-12-20 15:51:40,756 INFO misc.py line 119 131400] Train: [30/100][758/800] Data 0.005 (0.009) Batch 0.363 (0.340) Remain 05:17:52 loss: 0.4050 Lr: 0.00504 [2023-12-20 15:51:41,102 INFO misc.py line 119 131400] Train: [30/100][759/800] Data 0.004 (0.009) Batch 0.346 (0.340) Remain 05:17:52 loss: 0.5249 Lr: 0.00504 [2023-12-20 15:51:41,441 INFO misc.py line 119 131400] Train: [30/100][760/800] Data 0.004 (0.009) Batch 0.338 (0.340) Remain 05:17:51 loss: 0.3607 Lr: 0.00504 [2023-12-20 15:51:41,762 INFO misc.py line 119 131400] Train: [30/100][761/800] Data 0.005 (0.009) Batch 0.321 (0.340) Remain 05:17:50 loss: 0.5131 Lr: 0.00504 [2023-12-20 15:51:42,088 INFO misc.py line 119 131400] Train: [30/100][762/800] Data 0.005 (0.009) Batch 0.326 (0.340) Remain 05:17:48 loss: 0.3743 Lr: 0.00504 [2023-12-20 15:51:42,442 INFO misc.py line 119 131400] Train: [30/100][763/800] Data 0.003 (0.009) Batch 0.355 (0.340) Remain 05:17:49 loss: 0.3474 Lr: 0.00504 [2023-12-20 15:51:42,777 INFO misc.py line 119 131400] Train: [30/100][764/800] Data 0.003 (0.009) Batch 0.335 (0.340) Remain 05:17:48 loss: 0.4226 Lr: 0.00504 [2023-12-20 15:51:43,073 INFO misc.py line 119 131400] Train: [30/100][765/800] Data 0.002 (0.009) Batch 0.294 (0.340) Remain 05:17:45 loss: 0.5406 Lr: 0.00504 [2023-12-20 15:51:43,419 INFO misc.py line 119 131400] Train: [30/100][766/800] Data 0.005 (0.009) Batch 0.346 (0.340) Remain 05:17:45 loss: 0.5279 Lr: 0.00504 [2023-12-20 15:51:43,751 INFO misc.py line 119 131400] Train: [30/100][767/800] Data 0.005 (0.009) Batch 0.332 (0.340) Remain 05:17:44 loss: 0.4740 Lr: 0.00504 [2023-12-20 15:51:44,078 INFO misc.py line 119 131400] Train: [30/100][768/800] Data 0.004 (0.009) Batch 0.327 (0.340) Remain 05:17:42 loss: 0.4913 Lr: 0.00504 [2023-12-20 15:51:44,421 INFO misc.py line 119 131400] Train: [30/100][769/800] Data 0.004 (0.009) Batch 0.343 (0.340) Remain 05:17:42 loss: 0.6104 Lr: 0.00504 [2023-12-20 15:51:44,776 INFO misc.py line 119 131400] Train: [30/100][770/800] Data 0.004 (0.009) Batch 0.356 (0.340) Remain 05:17:43 loss: 0.3605 Lr: 0.00504 [2023-12-20 15:51:45,070 INFO misc.py line 119 131400] Train: [30/100][771/800] Data 0.007 (0.009) Batch 0.294 (0.340) Remain 05:17:39 loss: 0.7084 Lr: 0.00504 [2023-12-20 15:51:45,434 INFO misc.py line 119 131400] Train: [30/100][772/800] Data 0.003 (0.009) Batch 0.364 (0.340) Remain 05:17:41 loss: 0.6014 Lr: 0.00504 [2023-12-20 15:51:45,756 INFO misc.py line 119 131400] Train: [30/100][773/800] Data 0.004 (0.009) Batch 0.322 (0.340) Remain 05:17:39 loss: 0.9036 Lr: 0.00504 [2023-12-20 15:51:46,082 INFO misc.py line 119 131400] Train: [30/100][774/800] Data 0.004 (0.009) Batch 0.326 (0.340) Remain 05:17:38 loss: 0.5239 Lr: 0.00504 [2023-12-20 15:51:46,432 INFO misc.py line 119 131400] Train: [30/100][775/800] Data 0.003 (0.009) Batch 0.349 (0.340) Remain 05:17:38 loss: 0.3222 Lr: 0.00504 [2023-12-20 15:51:46,778 INFO misc.py line 119 131400] Train: [30/100][776/800] Data 0.004 (0.009) Batch 0.346 (0.340) Remain 05:17:38 loss: 0.3269 Lr: 0.00503 [2023-12-20 15:51:47,136 INFO misc.py line 119 131400] Train: [30/100][777/800] Data 0.004 (0.009) Batch 0.359 (0.340) Remain 05:17:39 loss: 0.6365 Lr: 0.00503 [2023-12-20 15:51:47,440 INFO misc.py line 119 131400] Train: [30/100][778/800] Data 0.003 (0.009) Batch 0.303 (0.340) Remain 05:17:36 loss: 0.1704 Lr: 0.00503 [2023-12-20 15:51:47,764 INFO misc.py line 119 131400] Train: [30/100][779/800] Data 0.004 (0.009) Batch 0.324 (0.340) Remain 05:17:35 loss: 0.6329 Lr: 0.00503 [2023-12-20 15:51:48,097 INFO misc.py line 119 131400] Train: [30/100][780/800] Data 0.004 (0.009) Batch 0.333 (0.340) Remain 05:17:34 loss: 0.2734 Lr: 0.00503 [2023-12-20 15:51:48,401 INFO misc.py line 119 131400] Train: [30/100][781/800] Data 0.004 (0.009) Batch 0.305 (0.340) Remain 05:17:31 loss: 0.3306 Lr: 0.00503 [2023-12-20 15:51:48,734 INFO misc.py line 119 131400] Train: [30/100][782/800] Data 0.003 (0.009) Batch 0.333 (0.340) Remain 05:17:30 loss: 0.4467 Lr: 0.00503 [2023-12-20 15:51:49,059 INFO misc.py line 119 131400] Train: [30/100][783/800] Data 0.002 (0.009) Batch 0.324 (0.340) Remain 05:17:29 loss: 0.5480 Lr: 0.00503 [2023-12-20 15:51:49,424 INFO misc.py line 119 131400] Train: [30/100][784/800] Data 0.004 (0.009) Batch 0.364 (0.340) Remain 05:17:30 loss: 0.2885 Lr: 0.00503 [2023-12-20 15:51:49,880 INFO misc.py line 119 131400] Train: [30/100][785/800] Data 0.004 (0.009) Batch 0.457 (0.340) Remain 05:17:38 loss: 0.4150 Lr: 0.00503 [2023-12-20 15:51:50,195 INFO misc.py line 119 131400] Train: [30/100][786/800] Data 0.004 (0.009) Batch 0.315 (0.340) Remain 05:17:36 loss: 0.5935 Lr: 0.00503 [2023-12-20 15:51:50,512 INFO misc.py line 119 131400] Train: [30/100][787/800] Data 0.003 (0.009) Batch 0.317 (0.340) Remain 05:17:34 loss: 0.6521 Lr: 0.00503 [2023-12-20 15:51:50,853 INFO misc.py line 119 131400] Train: [30/100][788/800] Data 0.004 (0.009) Batch 0.341 (0.340) Remain 05:17:34 loss: 0.3856 Lr: 0.00503 [2023-12-20 15:51:51,190 INFO misc.py line 119 131400] Train: [30/100][789/800] Data 0.004 (0.009) Batch 0.338 (0.340) Remain 05:17:33 loss: 0.5470 Lr: 0.00503 [2023-12-20 15:51:51,504 INFO misc.py line 119 131400] Train: [30/100][790/800] Data 0.003 (0.009) Batch 0.314 (0.340) Remain 05:17:31 loss: 0.5734 Lr: 0.00503 [2023-12-20 15:51:51,793 INFO misc.py line 119 131400] Train: [30/100][791/800] Data 0.002 (0.009) Batch 0.289 (0.340) Remain 05:17:27 loss: 0.3028 Lr: 0.00503 [2023-12-20 15:51:52,101 INFO misc.py line 119 131400] Train: [30/100][792/800] Data 0.003 (0.009) Batch 0.308 (0.340) Remain 05:17:24 loss: 0.5481 Lr: 0.00503 [2023-12-20 15:51:52,399 INFO misc.py line 119 131400] Train: [30/100][793/800] Data 0.003 (0.009) Batch 0.299 (0.340) Remain 05:17:21 loss: 0.4206 Lr: 0.00503 [2023-12-20 15:51:52,681 INFO misc.py line 119 131400] Train: [30/100][794/800] Data 0.003 (0.009) Batch 0.281 (0.340) Remain 05:17:16 loss: 0.3323 Lr: 0.00503 [2023-12-20 15:51:52,973 INFO misc.py line 119 131400] Train: [30/100][795/800] Data 0.003 (0.009) Batch 0.292 (0.340) Remain 05:17:13 loss: 0.4777 Lr: 0.00503 [2023-12-20 15:51:53,260 INFO misc.py line 119 131400] Train: [30/100][796/800] Data 0.003 (0.009) Batch 0.287 (0.340) Remain 05:17:09 loss: 0.4028 Lr: 0.00503 [2023-12-20 15:51:53,564 INFO misc.py line 119 131400] Train: [30/100][797/800] Data 0.003 (0.009) Batch 0.304 (0.340) Remain 05:17:06 loss: 0.4716 Lr: 0.00503 [2023-12-20 15:51:53,884 INFO misc.py line 119 131400] Train: [30/100][798/800] Data 0.003 (0.009) Batch 0.319 (0.340) Remain 05:17:04 loss: 0.6389 Lr: 0.00503 [2023-12-20 15:51:54,251 INFO misc.py line 119 131400] Train: [30/100][799/800] Data 0.004 (0.009) Batch 0.327 (0.340) Remain 05:17:03 loss: 0.6077 Lr: 0.00503 [2023-12-20 15:51:54,584 INFO misc.py line 119 131400] Train: [30/100][800/800] Data 0.044 (0.009) Batch 0.374 (0.340) Remain 05:17:05 loss: 0.5201 Lr: 0.00503 [2023-12-20 15:51:54,585 INFO misc.py line 136 131400] Train result: loss: 0.4723 [2023-12-20 15:51:54,585 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:52:18,091 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1350 [2023-12-20 15:52:18,162 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2127 [2023-12-20 15:52:18,252 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3716 [2023-12-20 15:52:18,364 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3580 [2023-12-20 15:52:18,475 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3293 [2023-12-20 15:52:18,576 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.7285 [2023-12-20 15:52:18,664 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.5734 [2023-12-20 15:52:18,770 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1785 [2023-12-20 15:52:18,854 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2858 [2023-12-20 15:52:18,939 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4727 [2023-12-20 15:52:19,031 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4464 [2023-12-20 15:52:19,168 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4024 [2023-12-20 15:52:19,292 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1173 [2023-12-20 15:52:19,447 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2605 [2023-12-20 15:52:19,539 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2300 [2023-12-20 15:52:19,671 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8457 [2023-12-20 15:52:19,784 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2723 [2023-12-20 15:52:19,894 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2297 [2023-12-20 15:52:20,005 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2330 [2023-12-20 15:52:20,079 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6167 [2023-12-20 15:52:20,189 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2669 [2023-12-20 15:52:20,345 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2115 [2023-12-20 15:52:20,466 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.4995 [2023-12-20 15:52:20,609 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2416 [2023-12-20 15:52:20,754 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2882 [2023-12-20 15:52:20,842 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4659 [2023-12-20 15:52:20,998 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7000 [2023-12-20 15:52:21,121 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5992 [2023-12-20 15:52:21,215 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6583 [2023-12-20 15:52:21,360 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7528 [2023-12-20 15:52:21,464 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7684 [2023-12-20 15:52:21,582 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6001 [2023-12-20 15:52:21,671 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.4589 [2023-12-20 15:52:21,747 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1962 [2023-12-20 15:52:21,843 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0925 [2023-12-20 15:52:21,934 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3922 [2023-12-20 15:52:22,063 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9445 [2023-12-20 15:52:22,172 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3009 [2023-12-20 15:52:22,252 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5044 [2023-12-20 15:52:22,396 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4797 [2023-12-20 15:52:22,541 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0216 [2023-12-20 15:52:22,639 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2427 [2023-12-20 15:52:22,758 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4318 [2023-12-20 15:52:22,901 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7683 [2023-12-20 15:52:23,016 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1804 [2023-12-20 15:52:23,116 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4990 [2023-12-20 15:52:23,283 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5175 [2023-12-20 15:52:23,375 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5336 [2023-12-20 15:52:23,522 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3507 [2023-12-20 15:52:23,612 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8979 [2023-12-20 15:52:23,686 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4597 [2023-12-20 15:52:23,792 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.7203 [2023-12-20 15:52:23,938 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1571 [2023-12-20 15:52:24,080 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3586 [2023-12-20 15:52:24,187 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.1457 [2023-12-20 15:52:24,272 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8128 [2023-12-20 15:52:24,374 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4910 [2023-12-20 15:52:24,540 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2528 [2023-12-20 15:52:24,648 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3023 [2023-12-20 15:52:24,741 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1644 [2023-12-20 15:52:24,836 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3589 [2023-12-20 15:52:24,928 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3299 [2023-12-20 15:52:25,015 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4016 [2023-12-20 15:52:25,117 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7084 [2023-12-20 15:52:25,243 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4300 [2023-12-20 15:52:25,332 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3132 [2023-12-20 15:52:25,432 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5413 [2023-12-20 15:52:25,528 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0145 [2023-12-20 15:52:25,615 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3360 [2023-12-20 15:52:25,703 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0152 [2023-12-20 15:52:25,801 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7744 [2023-12-20 15:52:25,893 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7480 [2023-12-20 15:52:26,029 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3149 [2023-12-20 15:52:26,134 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6100 [2023-12-20 15:52:26,253 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6360 [2023-12-20 15:52:26,359 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7686 [2023-12-20 15:52:26,451 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2341 [2023-12-20 15:52:26,607 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3028 [2023-12-20 15:52:27,869 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7283/0.8219/0.9035. [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8533/0.9482 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9633/0.9854 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6593/0.8314 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7851/0.8211 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9038/0.9533 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8258/0.8920 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7331/0.8359 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6118/0.6630 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6536/0.8332 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7862/0.8700 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3863/0.5405 [2023-12-20 15:52:27,870 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6624/0.7771 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6781/0.8926 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7645/0.8505 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5662/0.6532 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7113/0.7796 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9443/0.9736 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6567/0.7989 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8628/0.9253 [2023-12-20 15:52:27,871 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5590/0.6132 [2023-12-20 15:52:27,872 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:52:27,873 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:52:27,873 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:52:33,831 INFO misc.py line 119 131400] Train: [31/100][1/800] Data 2.079 (2.079) Batch 2.448 (2.448) Remain 38:04:19 loss: 0.4752 Lr: 0.00503 [2023-12-20 15:52:34,172 INFO misc.py line 119 131400] Train: [31/100][2/800] Data 0.006 (0.006) Batch 0.343 (0.343) Remain 05:20:31 loss: 0.2860 Lr: 0.00503 [2023-12-20 15:52:34,513 INFO misc.py line 119 131400] Train: [31/100][3/800] Data 0.004 (0.004) Batch 0.341 (0.341) Remain 05:17:56 loss: 0.5750 Lr: 0.00503 [2023-12-20 15:52:34,852 INFO misc.py line 119 131400] Train: [31/100][4/800] Data 0.004 (0.004) Batch 0.338 (0.338) Remain 05:15:45 loss: 0.8484 Lr: 0.00503 [2023-12-20 15:52:35,151 INFO misc.py line 119 131400] Train: [31/100][5/800] Data 0.006 (0.005) Batch 0.300 (0.319) Remain 04:57:43 loss: 0.3088 Lr: 0.00503 [2023-12-20 15:52:35,502 INFO misc.py line 119 131400] Train: [31/100][6/800] Data 0.004 (0.004) Batch 0.350 (0.329) Remain 05:07:29 loss: 0.4832 Lr: 0.00503 [2023-12-20 15:52:35,804 INFO misc.py line 119 131400] Train: [31/100][7/800] Data 0.005 (0.005) Batch 0.302 (0.323) Remain 05:01:09 loss: 0.6573 Lr: 0.00503 [2023-12-20 15:52:36,135 INFO misc.py line 119 131400] Train: [31/100][8/800] Data 0.004 (0.004) Batch 0.331 (0.324) Remain 05:02:44 loss: 0.3537 Lr: 0.00503 [2023-12-20 15:52:36,437 INFO misc.py line 119 131400] Train: [31/100][9/800] Data 0.005 (0.004) Batch 0.302 (0.321) Remain 04:59:13 loss: 0.4136 Lr: 0.00503 [2023-12-20 15:52:36,742 INFO misc.py line 119 131400] Train: [31/100][10/800] Data 0.004 (0.004) Batch 0.306 (0.318) Remain 04:57:11 loss: 0.3367 Lr: 0.00503 [2023-12-20 15:52:37,060 INFO misc.py line 119 131400] Train: [31/100][11/800] Data 0.004 (0.004) Batch 0.317 (0.318) Remain 04:57:02 loss: 0.3857 Lr: 0.00503 [2023-12-20 15:52:37,409 INFO misc.py line 119 131400] Train: [31/100][12/800] Data 0.004 (0.004) Batch 0.350 (0.322) Remain 05:00:18 loss: 0.3521 Lr: 0.00503 [2023-12-20 15:52:37,767 INFO misc.py line 119 131400] Train: [31/100][13/800] Data 0.003 (0.004) Batch 0.358 (0.325) Remain 05:03:37 loss: 0.8566 Lr: 0.00503 [2023-12-20 15:52:38,144 INFO misc.py line 119 131400] Train: [31/100][14/800] Data 0.004 (0.004) Batch 0.378 (0.330) Remain 05:08:02 loss: 0.3669 Lr: 0.00503 [2023-12-20 15:52:38,493 INFO misc.py line 119 131400] Train: [31/100][15/800] Data 0.004 (0.004) Batch 0.348 (0.332) Remain 05:09:23 loss: 0.5191 Lr: 0.00503 [2023-12-20 15:52:38,830 INFO misc.py line 119 131400] Train: [31/100][16/800] Data 0.005 (0.004) Batch 0.338 (0.332) Remain 05:09:52 loss: 0.1941 Lr: 0.00503 [2023-12-20 15:52:39,158 INFO misc.py line 119 131400] Train: [31/100][17/800] Data 0.005 (0.004) Batch 0.327 (0.332) Remain 05:09:31 loss: 0.4015 Lr: 0.00503 [2023-12-20 15:52:39,492 INFO misc.py line 119 131400] Train: [31/100][18/800] Data 0.005 (0.004) Batch 0.323 (0.331) Remain 05:08:58 loss: 0.5461 Lr: 0.00503 [2023-12-20 15:52:39,867 INFO misc.py line 119 131400] Train: [31/100][19/800] Data 0.015 (0.005) Batch 0.386 (0.335) Remain 05:12:09 loss: 0.8985 Lr: 0.00503 [2023-12-20 15:52:40,194 INFO misc.py line 119 131400] Train: [31/100][20/800] Data 0.005 (0.005) Batch 0.328 (0.334) Remain 05:11:47 loss: 0.7397 Lr: 0.00503 [2023-12-20 15:52:40,533 INFO misc.py line 119 131400] Train: [31/100][21/800] Data 0.004 (0.005) Batch 0.339 (0.334) Remain 05:12:00 loss: 0.9618 Lr: 0.00503 [2023-12-20 15:52:40,883 INFO misc.py line 119 131400] Train: [31/100][22/800] Data 0.004 (0.005) Batch 0.351 (0.335) Remain 05:12:47 loss: 0.3745 Lr: 0.00503 [2023-12-20 15:52:41,183 INFO misc.py line 119 131400] Train: [31/100][23/800] Data 0.004 (0.005) Batch 0.299 (0.333) Remain 05:11:07 loss: 0.4287 Lr: 0.00503 [2023-12-20 15:52:41,538 INFO misc.py line 119 131400] Train: [31/100][24/800] Data 0.004 (0.005) Batch 0.355 (0.334) Remain 05:12:03 loss: 0.6007 Lr: 0.00503 [2023-12-20 15:52:41,893 INFO misc.py line 119 131400] Train: [31/100][25/800] Data 0.004 (0.005) Batch 0.356 (0.335) Remain 05:12:57 loss: 0.5790 Lr: 0.00503 [2023-12-20 15:52:42,231 INFO misc.py line 119 131400] Train: [31/100][26/800] Data 0.005 (0.005) Batch 0.338 (0.336) Remain 05:13:02 loss: 0.3584 Lr: 0.00503 [2023-12-20 15:52:42,555 INFO misc.py line 119 131400] Train: [31/100][27/800] Data 0.004 (0.005) Batch 0.325 (0.335) Remain 05:12:37 loss: 0.3238 Lr: 0.00503 [2023-12-20 15:52:42,843 INFO misc.py line 119 131400] Train: [31/100][28/800] Data 0.003 (0.005) Batch 0.288 (0.333) Remain 05:10:52 loss: 0.2379 Lr: 0.00503 [2023-12-20 15:52:43,143 INFO misc.py line 119 131400] Train: [31/100][29/800] Data 0.003 (0.005) Batch 0.299 (0.332) Remain 05:09:37 loss: 0.5252 Lr: 0.00503 [2023-12-20 15:52:43,447 INFO misc.py line 119 131400] Train: [31/100][30/800] Data 0.004 (0.005) Batch 0.305 (0.331) Remain 05:08:41 loss: 0.3761 Lr: 0.00503 [2023-12-20 15:52:43,759 INFO misc.py line 119 131400] Train: [31/100][31/800] Data 0.003 (0.005) Batch 0.311 (0.330) Remain 05:08:01 loss: 0.4131 Lr: 0.00503 [2023-12-20 15:52:44,101 INFO misc.py line 119 131400] Train: [31/100][32/800] Data 0.004 (0.005) Batch 0.342 (0.331) Remain 05:08:23 loss: 0.4893 Lr: 0.00503 [2023-12-20 15:52:44,393 INFO misc.py line 119 131400] Train: [31/100][33/800] Data 0.003 (0.004) Batch 0.293 (0.329) Remain 05:07:12 loss: 0.6356 Lr: 0.00503 [2023-12-20 15:52:44,723 INFO misc.py line 119 131400] Train: [31/100][34/800] Data 0.003 (0.004) Batch 0.330 (0.329) Remain 05:07:13 loss: 0.5858 Lr: 0.00503 [2023-12-20 15:52:45,011 INFO misc.py line 119 131400] Train: [31/100][35/800] Data 0.003 (0.004) Batch 0.287 (0.328) Remain 05:05:59 loss: 0.2951 Lr: 0.00503 [2023-12-20 15:52:45,365 INFO misc.py line 119 131400] Train: [31/100][36/800] Data 0.005 (0.004) Batch 0.354 (0.329) Remain 05:06:43 loss: 0.4072 Lr: 0.00503 [2023-12-20 15:52:45,699 INFO misc.py line 119 131400] Train: [31/100][37/800] Data 0.004 (0.004) Batch 0.334 (0.329) Remain 05:06:50 loss: 0.5263 Lr: 0.00503 [2023-12-20 15:52:46,045 INFO misc.py line 119 131400] Train: [31/100][38/800] Data 0.005 (0.004) Batch 0.347 (0.329) Remain 05:07:19 loss: 0.5539 Lr: 0.00503 [2023-12-20 15:52:46,365 INFO misc.py line 119 131400] Train: [31/100][39/800] Data 0.004 (0.004) Batch 0.320 (0.329) Remain 05:07:04 loss: 0.4123 Lr: 0.00503 [2023-12-20 15:52:46,702 INFO misc.py line 119 131400] Train: [31/100][40/800] Data 0.003 (0.004) Batch 0.337 (0.329) Remain 05:07:14 loss: 0.3018 Lr: 0.00503 [2023-12-20 15:52:47,044 INFO misc.py line 119 131400] Train: [31/100][41/800] Data 0.005 (0.004) Batch 0.343 (0.330) Remain 05:07:33 loss: 0.5385 Lr: 0.00503 [2023-12-20 15:52:47,378 INFO misc.py line 119 131400] Train: [31/100][42/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 05:07:40 loss: 0.6935 Lr: 0.00503 [2023-12-20 15:52:47,686 INFO misc.py line 119 131400] Train: [31/100][43/800] Data 0.003 (0.004) Batch 0.308 (0.329) Remain 05:07:08 loss: 0.4532 Lr: 0.00503 [2023-12-20 15:52:47,999 INFO misc.py line 119 131400] Train: [31/100][44/800] Data 0.004 (0.004) Batch 0.313 (0.329) Remain 05:06:45 loss: 0.4726 Lr: 0.00503 [2023-12-20 15:52:48,313 INFO misc.py line 119 131400] Train: [31/100][45/800] Data 0.005 (0.004) Batch 0.315 (0.329) Remain 05:06:26 loss: 0.3036 Lr: 0.00503 [2023-12-20 15:52:48,640 INFO misc.py line 119 131400] Train: [31/100][46/800] Data 0.003 (0.004) Batch 0.326 (0.329) Remain 05:06:22 loss: 0.3350 Lr: 0.00503 [2023-12-20 15:52:48,948 INFO misc.py line 119 131400] Train: [31/100][47/800] Data 0.004 (0.004) Batch 0.308 (0.328) Remain 05:05:56 loss: 0.5321 Lr: 0.00503 [2023-12-20 15:52:49,301 INFO misc.py line 119 131400] Train: [31/100][48/800] Data 0.004 (0.004) Batch 0.353 (0.329) Remain 05:06:27 loss: 0.5151 Lr: 0.00503 [2023-12-20 15:52:49,628 INFO misc.py line 119 131400] Train: [31/100][49/800] Data 0.003 (0.004) Batch 0.327 (0.329) Remain 05:06:25 loss: 0.5704 Lr: 0.00503 [2023-12-20 15:52:49,940 INFO misc.py line 119 131400] Train: [31/100][50/800] Data 0.003 (0.004) Batch 0.311 (0.328) Remain 05:06:03 loss: 0.5134 Lr: 0.00503 [2023-12-20 15:52:50,282 INFO misc.py line 119 131400] Train: [31/100][51/800] Data 0.005 (0.004) Batch 0.344 (0.329) Remain 05:06:21 loss: 0.3516 Lr: 0.00503 [2023-12-20 15:52:50,604 INFO misc.py line 119 131400] Train: [31/100][52/800] Data 0.004 (0.004) Batch 0.320 (0.328) Remain 05:06:11 loss: 0.4020 Lr: 0.00503 [2023-12-20 15:52:50,980 INFO misc.py line 119 131400] Train: [31/100][53/800] Data 0.005 (0.004) Batch 0.376 (0.329) Remain 05:07:05 loss: 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INFO misc.py line 119 131400] Train: [31/100][60/800] Data 0.003 (0.004) Batch 0.362 (0.332) Remain 05:09:54 loss: 0.4165 Lr: 0.00503 [2023-12-20 15:52:53,781 INFO misc.py line 119 131400] Train: [31/100][61/800] Data 0.004 (0.004) Batch 0.321 (0.332) Remain 05:09:43 loss: 0.6581 Lr: 0.00503 [2023-12-20 15:52:54,136 INFO misc.py line 119 131400] Train: [31/100][62/800] Data 0.004 (0.004) Batch 0.355 (0.333) Remain 05:10:04 loss: 0.3975 Lr: 0.00503 [2023-12-20 15:52:54,473 INFO misc.py line 119 131400] Train: [31/100][63/800] Data 0.004 (0.004) Batch 0.338 (0.333) Remain 05:10:09 loss: 0.5948 Lr: 0.00503 [2023-12-20 15:52:54,811 INFO misc.py line 119 131400] Train: [31/100][64/800] Data 0.004 (0.004) Batch 0.338 (0.333) Remain 05:10:13 loss: 0.5343 Lr: 0.00503 [2023-12-20 15:52:55,124 INFO misc.py line 119 131400] Train: [31/100][65/800] Data 0.004 (0.004) Batch 0.314 (0.332) Remain 05:09:55 loss: 0.4167 Lr: 0.00503 [2023-12-20 15:52:55,456 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [31/100][85/800] Data 0.003 (0.004) Batch 0.305 (0.331) Remain 05:08:55 loss: 0.5361 Lr: 0.00502 [2023-12-20 15:53:02,080 INFO misc.py line 119 131400] Train: [31/100][86/800] Data 0.004 (0.004) Batch 0.377 (0.332) Remain 05:09:25 loss: 0.3174 Lr: 0.00502 [2023-12-20 15:53:02,423 INFO misc.py line 119 131400] Train: [31/100][87/800] Data 0.013 (0.004) Batch 0.351 (0.332) Remain 05:09:37 loss: 0.3887 Lr: 0.00502 [2023-12-20 15:53:02,775 INFO misc.py line 119 131400] Train: [31/100][88/800] Data 0.004 (0.004) Batch 0.351 (0.332) Remain 05:09:49 loss: 0.7156 Lr: 0.00502 [2023-12-20 15:53:03,121 INFO misc.py line 119 131400] Train: [31/100][89/800] Data 0.006 (0.004) Batch 0.347 (0.333) Remain 05:09:58 loss: 0.1703 Lr: 0.00502 [2023-12-20 15:53:03,513 INFO misc.py line 119 131400] Train: [31/100][90/800] Data 0.005 (0.004) Batch 0.393 (0.333) Remain 05:10:36 loss: 0.3329 Lr: 0.00502 [2023-12-20 15:53:03,870 INFO misc.py line 119 131400] Train: [31/100][91/800] Data 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loss: 0.5801 Lr: 0.00496 [2023-12-20 15:56:42,732 INFO misc.py line 119 131400] Train: [31/100][745/800] Data 0.006 (0.004) Batch 0.351 (0.335) Remain 05:08:04 loss: 0.4631 Lr: 0.00496 [2023-12-20 15:56:43,081 INFO misc.py line 119 131400] Train: [31/100][746/800] Data 0.005 (0.004) Batch 0.350 (0.335) Remain 05:08:04 loss: 0.6748 Lr: 0.00496 [2023-12-20 15:56:43,441 INFO misc.py line 119 131400] Train: [31/100][747/800] Data 0.003 (0.004) Batch 0.358 (0.335) Remain 05:08:06 loss: 0.2565 Lr: 0.00496 [2023-12-20 15:56:43,789 INFO misc.py line 119 131400] Train: [31/100][748/800] Data 0.006 (0.004) Batch 0.350 (0.335) Remain 05:08:07 loss: 0.3189 Lr: 0.00496 [2023-12-20 15:56:44,147 INFO misc.py line 119 131400] Train: [31/100][749/800] Data 0.004 (0.004) Batch 0.357 (0.335) Remain 05:08:08 loss: 0.4964 Lr: 0.00496 [2023-12-20 15:56:44,497 INFO misc.py line 119 131400] Train: [31/100][750/800] Data 0.005 (0.004) Batch 0.350 (0.335) Remain 05:08:09 loss: 0.3967 Lr: 0.00496 [2023-12-20 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131400] Train: [31/100][757/800] Data 0.004 (0.004) Batch 0.312 (0.335) Remain 05:08:15 loss: 0.3761 Lr: 0.00496 [2023-12-20 15:56:47,273 INFO misc.py line 119 131400] Train: [31/100][758/800] Data 0.004 (0.004) Batch 0.320 (0.335) Remain 05:08:13 loss: 0.6869 Lr: 0.00496 [2023-12-20 15:56:47,570 INFO misc.py line 119 131400] Train: [31/100][759/800] Data 0.003 (0.004) Batch 0.297 (0.335) Remain 05:08:10 loss: 0.4982 Lr: 0.00496 [2023-12-20 15:56:47,916 INFO misc.py line 119 131400] Train: [31/100][760/800] Data 0.004 (0.004) Batch 0.338 (0.335) Remain 05:08:10 loss: 0.2815 Lr: 0.00496 [2023-12-20 15:56:48,260 INFO misc.py line 119 131400] Train: [31/100][761/800] Data 0.015 (0.004) Batch 0.351 (0.335) Remain 05:08:11 loss: 0.4410 Lr: 0.00496 [2023-12-20 15:56:48,587 INFO misc.py line 119 131400] Train: [31/100][762/800] Data 0.004 (0.004) Batch 0.327 (0.335) Remain 05:08:10 loss: 0.4710 Lr: 0.00496 [2023-12-20 15:56:48,907 INFO misc.py line 119 131400] Train: [31/100][763/800] Data 0.004 (0.004) Batch 0.319 (0.335) Remain 05:08:09 loss: 0.6027 Lr: 0.00496 [2023-12-20 15:56:49,229 INFO misc.py line 119 131400] Train: [31/100][764/800] Data 0.005 (0.004) Batch 0.322 (0.335) Remain 05:08:07 loss: 0.3024 Lr: 0.00496 [2023-12-20 15:56:49,573 INFO misc.py line 119 131400] Train: [31/100][765/800] Data 0.005 (0.004) Batch 0.342 (0.335) Remain 05:08:08 loss: 0.3769 Lr: 0.00496 [2023-12-20 15:56:49,892 INFO misc.py line 119 131400] Train: [31/100][766/800] Data 0.007 (0.004) Batch 0.321 (0.335) Remain 05:08:06 loss: 0.3889 Lr: 0.00496 [2023-12-20 15:56:50,218 INFO misc.py line 119 131400] Train: [31/100][767/800] Data 0.005 (0.004) Batch 0.327 (0.335) Remain 05:08:06 loss: 0.5676 Lr: 0.00496 [2023-12-20 15:56:50,566 INFO misc.py line 119 131400] Train: [31/100][768/800] Data 0.003 (0.004) Batch 0.347 (0.335) Remain 05:08:06 loss: 0.2771 Lr: 0.00496 [2023-12-20 15:56:50,913 INFO misc.py line 119 131400] Train: [31/100][769/800] Data 0.004 (0.004) Batch 0.347 (0.335) Remain 05:08:07 loss: 0.7229 Lr: 0.00496 [2023-12-20 15:56:51,213 INFO misc.py line 119 131400] Train: [31/100][770/800] Data 0.004 (0.004) Batch 0.300 (0.335) Remain 05:08:04 loss: 0.3019 Lr: 0.00496 [2023-12-20 15:56:51,540 INFO misc.py line 119 131400] Train: [31/100][771/800] Data 0.004 (0.004) Batch 0.327 (0.335) Remain 05:08:03 loss: 0.3388 Lr: 0.00496 [2023-12-20 15:56:51,864 INFO misc.py line 119 131400] Train: [31/100][772/800] Data 0.003 (0.004) Batch 0.325 (0.335) Remain 05:08:02 loss: 0.2028 Lr: 0.00496 [2023-12-20 15:56:52,182 INFO misc.py line 119 131400] Train: [31/100][773/800] Data 0.003 (0.004) Batch 0.316 (0.335) Remain 05:08:00 loss: 0.3507 Lr: 0.00496 [2023-12-20 15:56:52,498 INFO misc.py line 119 131400] Train: [31/100][774/800] Data 0.004 (0.004) Batch 0.317 (0.335) Remain 05:07:59 loss: 0.4287 Lr: 0.00496 [2023-12-20 15:56:52,823 INFO misc.py line 119 131400] Train: [31/100][775/800] Data 0.004 (0.004) Batch 0.325 (0.335) Remain 05:07:58 loss: 0.2514 Lr: 0.00496 [2023-12-20 15:56:53,148 INFO misc.py line 119 131400] Train: [31/100][776/800] Data 0.003 (0.004) Batch 0.325 (0.335) Remain 05:07:57 loss: 0.5323 Lr: 0.00496 [2023-12-20 15:56:53,487 INFO misc.py line 119 131400] Train: [31/100][777/800] Data 0.005 (0.004) Batch 0.339 (0.335) Remain 05:07:57 loss: 0.4631 Lr: 0.00496 [2023-12-20 15:56:53,802 INFO misc.py line 119 131400] Train: [31/100][778/800] Data 0.004 (0.004) Batch 0.315 (0.335) Remain 05:07:55 loss: 0.3249 Lr: 0.00496 [2023-12-20 15:56:54,149 INFO misc.py line 119 131400] Train: [31/100][779/800] Data 0.004 (0.004) Batch 0.347 (0.335) Remain 05:07:55 loss: 0.3999 Lr: 0.00496 [2023-12-20 15:56:54,488 INFO misc.py line 119 131400] Train: [31/100][780/800] Data 0.004 (0.004) Batch 0.339 (0.335) Remain 05:07:55 loss: 0.6495 Lr: 0.00496 [2023-12-20 15:56:54,825 INFO misc.py line 119 131400] Train: [31/100][781/800] Data 0.004 (0.004) Batch 0.338 (0.335) Remain 05:07:55 loss: 0.5973 Lr: 0.00496 [2023-12-20 15:56:55,117 INFO misc.py line 119 131400] Train: [31/100][782/800] Data 0.004 (0.004) Batch 0.292 (0.335) Remain 05:07:52 loss: 0.6087 Lr: 0.00496 [2023-12-20 15:56:55,454 INFO misc.py line 119 131400] Train: [31/100][783/800] Data 0.004 (0.004) Batch 0.338 (0.335) Remain 05:07:52 loss: 0.3920 Lr: 0.00496 [2023-12-20 15:56:55,784 INFO misc.py line 119 131400] Train: [31/100][784/800] Data 0.003 (0.004) Batch 0.330 (0.335) Remain 05:07:51 loss: 0.3412 Lr: 0.00496 [2023-12-20 15:56:56,145 INFO misc.py line 119 131400] Train: [31/100][785/800] Data 0.004 (0.004) Batch 0.361 (0.335) Remain 05:07:53 loss: 0.8240 Lr: 0.00496 [2023-12-20 15:56:56,450 INFO misc.py line 119 131400] Train: [31/100][786/800] Data 0.004 (0.004) Batch 0.305 (0.335) Remain 05:07:50 loss: 0.2242 Lr: 0.00496 [2023-12-20 15:56:56,794 INFO misc.py line 119 131400] Train: [31/100][787/800] Data 0.004 (0.004) Batch 0.344 (0.335) Remain 05:07:50 loss: 0.3723 Lr: 0.00496 [2023-12-20 15:56:57,096 INFO misc.py line 119 131400] Train: [31/100][788/800] Data 0.004 (0.004) Batch 0.302 (0.334) Remain 05:07:48 loss: 0.4912 Lr: 0.00496 [2023-12-20 15:56:57,403 INFO misc.py line 119 131400] Train: [31/100][789/800] Data 0.004 (0.004) Batch 0.306 (0.334) Remain 05:07:46 loss: 0.4453 Lr: 0.00496 [2023-12-20 15:56:57,739 INFO misc.py line 119 131400] Train: [31/100][790/800] Data 0.004 (0.004) Batch 0.338 (0.334) Remain 05:07:45 loss: 0.8860 Lr: 0.00496 [2023-12-20 15:56:58,085 INFO misc.py line 119 131400] Train: [31/100][791/800] Data 0.003 (0.004) Batch 0.346 (0.334) Remain 05:07:46 loss: 0.5204 Lr: 0.00496 [2023-12-20 15:56:58,448 INFO misc.py line 119 131400] Train: [31/100][792/800] Data 0.003 (0.004) Batch 0.363 (0.335) Remain 05:07:47 loss: 0.5999 Lr: 0.00496 [2023-12-20 15:56:58,718 INFO misc.py line 119 131400] Train: [31/100][793/800] Data 0.004 (0.004) Batch 0.270 (0.334) Remain 05:07:43 loss: 0.4555 Lr: 0.00496 [2023-12-20 15:56:59,005 INFO misc.py line 119 131400] Train: [31/100][794/800] Data 0.004 (0.004) Batch 0.287 (0.334) Remain 05:07:39 loss: 0.2627 Lr: 0.00496 [2023-12-20 15:56:59,315 INFO misc.py line 119 131400] Train: [31/100][795/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 05:07:37 loss: 0.5882 Lr: 0.00496 [2023-12-20 15:56:59,625 INFO misc.py line 119 131400] Train: [31/100][796/800] Data 0.003 (0.004) Batch 0.310 (0.334) Remain 05:07:35 loss: 0.3284 Lr: 0.00496 [2023-12-20 15:56:59,901 INFO misc.py line 119 131400] Train: [31/100][797/800] Data 0.003 (0.004) Batch 0.273 (0.334) Remain 05:07:30 loss: 0.2586 Lr: 0.00496 [2023-12-20 15:57:00,217 INFO misc.py line 119 131400] Train: [31/100][798/800] Data 0.006 (0.004) Batch 0.319 (0.334) Remain 05:07:29 loss: 0.6122 Lr: 0.00496 [2023-12-20 15:57:00,504 INFO misc.py line 119 131400] Train: [31/100][799/800] Data 0.004 (0.004) Batch 0.288 (0.334) Remain 05:07:25 loss: 0.5005 Lr: 0.00496 [2023-12-20 15:57:00,774 INFO misc.py line 119 131400] Train: [31/100][800/800] Data 0.003 (0.004) Batch 0.267 (0.334) Remain 05:07:20 loss: 0.8231 Lr: 0.00496 [2023-12-20 15:57:00,774 INFO misc.py line 136 131400] Train result: loss: 0.4652 [2023-12-20 15:57:00,775 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 15:57:22,137 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0641 [2023-12-20 15:57:22,229 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1987 [2023-12-20 15:57:22,549 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3977 [2023-12-20 15:57:23,076 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.0896 [2023-12-20 15:57:23,195 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5256 [2023-12-20 15:57:23,301 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.8021 [2023-12-20 15:57:23,394 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7067 [2023-12-20 15:57:23,502 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6205 [2023-12-20 15:57:23,586 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2769 [2023-12-20 15:57:23,679 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5515 [2023-12-20 15:57:23,770 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.9458 [2023-12-20 15:57:23,906 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5577 [2023-12-20 15:57:24,033 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.0832 [2023-12-20 15:57:24,190 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2726 [2023-12-20 15:57:24,282 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1964 [2023-12-20 15:57:24,418 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8054 [2023-12-20 15:57:24,528 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3547 [2023-12-20 15:57:24,640 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8155 [2023-12-20 15:57:24,755 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3123 [2023-12-20 15:57:24,829 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.7222 [2023-12-20 15:57:24,935 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5802 [2023-12-20 15:57:25,097 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2009 [2023-12-20 15:57:25,225 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9824 [2023-12-20 15:57:25,370 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2053 [2023-12-20 15:57:25,515 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.6686 [2023-12-20 15:57:25,600 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5512 [2023-12-20 15:57:25,764 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6073 [2023-12-20 15:57:25,887 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5628 [2023-12-20 15:57:25,983 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7000 [2023-12-20 15:57:26,127 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3305 [2023-12-20 15:57:26,229 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8591 [2023-12-20 15:57:26,348 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6458 [2023-12-20 15:57:26,439 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.6145 [2023-12-20 15:57:26,511 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2075 [2023-12-20 15:57:26,608 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6690 [2023-12-20 15:57:26,701 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4999 [2023-12-20 15:57:26,829 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.1508 [2023-12-20 15:57:26,938 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1283 [2023-12-20 15:57:27,021 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5888 [2023-12-20 15:57:27,162 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5519 [2023-12-20 15:57:27,312 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0327 [2023-12-20 15:57:27,410 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1255 [2023-12-20 15:57:27,528 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5442 [2023-12-20 15:57:27,669 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.2325 [2023-12-20 15:57:27,786 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8627 [2023-12-20 15:57:27,889 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.9058 [2023-12-20 15:57:28,055 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4475 [2023-12-20 15:57:28,148 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5515 [2023-12-20 15:57:28,293 INFO evaluator.py line 159 131400] Test: [49/78] Loss 0.6792 [2023-12-20 15:57:28,384 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8295 [2023-12-20 15:57:28,458 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.9168 [2023-12-20 15:57:28,564 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5135 [2023-12-20 15:57:28,712 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.4123 [2023-12-20 15:57:28,846 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3574 [2023-12-20 15:57:28,949 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3066 [2023-12-20 15:57:29,033 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6760 [2023-12-20 15:57:29,136 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3522 [2023-12-20 15:57:29,296 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2526 [2023-12-20 15:57:29,391 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3306 [2023-12-20 15:57:29,485 INFO evaluator.py line 159 131400] Test: [60/78] Loss 1.1893 [2023-12-20 15:57:29,584 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2435 [2023-12-20 15:57:29,673 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3587 [2023-12-20 15:57:29,758 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6337 [2023-12-20 15:57:29,858 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5081 [2023-12-20 15:57:29,984 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4253 [2023-12-20 15:57:30,069 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5332 [2023-12-20 15:57:30,167 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4835 [2023-12-20 15:57:30,259 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0252 [2023-12-20 15:57:30,341 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2862 [2023-12-20 15:57:30,428 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0275 [2023-12-20 15:57:30,526 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6929 [2023-12-20 15:57:30,615 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4906 [2023-12-20 15:57:30,748 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1842 [2023-12-20 15:57:30,841 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5413 [2023-12-20 15:57:30,955 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8927 [2023-12-20 15:57:31,056 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9148 [2023-12-20 15:57:31,144 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3060 [2023-12-20 15:57:31,302 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4378 [2023-12-20 15:57:32,490 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7106/0.8080/0.8978. [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8543/0.9517 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9613/0.9839 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6125/0.6775 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7637/0.8944 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9069/0.9494 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8204/0.8965 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6584/0.6921 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6651/0.7388 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6647/0.7740 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7948/0.9040 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3676/0.4636 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6325/0.7144 [2023-12-20 15:57:32,490 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5333/0.9308 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7435/0.8227 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5521/0.7474 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6581/0.7381 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9324/0.9548 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6600/0.7194 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8895/0.9406 [2023-12-20 15:57:32,491 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5412/0.6650 [2023-12-20 15:57:32,491 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 15:57:32,492 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 15:57:32,492 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 15:57:36,014 INFO misc.py line 119 131400] Train: [32/100][1/800] Data 1.323 (1.323) Batch 1.670 (1.670) Remain 25:36:09 loss: 0.6938 Lr: 0.00496 [2023-12-20 15:57:36,360 INFO misc.py line 119 131400] Train: [32/100][2/800] Data 0.004 (0.004) Batch 0.346 (0.346) Remain 05:18:10 loss: 0.6986 Lr: 0.00496 [2023-12-20 15:57:36,716 INFO misc.py line 119 131400] Train: [32/100][3/800] Data 0.004 (0.004) Batch 0.356 (0.356) Remain 05:27:20 loss: 0.5714 Lr: 0.00496 [2023-12-20 15:57:37,079 INFO misc.py line 119 131400] Train: [32/100][4/800] Data 0.004 (0.004) Batch 0.364 (0.364) Remain 05:34:33 loss: 0.3322 Lr: 0.00496 [2023-12-20 15:57:37,446 INFO misc.py line 119 131400] Train: [32/100][5/800] Data 0.004 (0.004) Batch 0.367 (0.365) Remain 05:36:08 loss: 0.2978 Lr: 0.00496 [2023-12-20 15:57:37,800 INFO misc.py line 119 131400] Train: [32/100][6/800] Data 0.004 (0.004) Batch 0.353 (0.361) Remain 05:32:28 loss: 0.4523 Lr: 0.00496 [2023-12-20 15:57:38,170 INFO misc.py line 119 131400] Train: [32/100][7/800] Data 0.004 (0.004) Batch 0.371 (0.364) Remain 05:34:42 loss: 0.2994 Lr: 0.00496 [2023-12-20 15:57:38,507 INFO misc.py line 119 131400] Train: [32/100][8/800] Data 0.003 (0.004) Batch 0.336 (0.358) Remain 05:29:37 loss: 0.8412 Lr: 0.00496 [2023-12-20 15:57:38,849 INFO misc.py line 119 131400] Train: [32/100][9/800] Data 0.004 (0.004) Batch 0.342 (0.356) Remain 05:27:04 loss: 0.2550 Lr: 0.00496 [2023-12-20 15:57:39,188 INFO misc.py line 119 131400] Train: [32/100][10/800] Data 0.004 (0.004) Batch 0.339 (0.353) Remain 05:24:50 loss: 0.7816 Lr: 0.00496 [2023-12-20 15:57:39,539 INFO misc.py line 119 131400] Train: [32/100][11/800] Data 0.004 (0.004) Batch 0.352 (0.353) Remain 05:24:40 loss: 0.4073 Lr: 0.00496 [2023-12-20 15:57:39,878 INFO misc.py line 119 131400] Train: [32/100][12/800] Data 0.004 (0.004) Batch 0.339 (0.351) Remain 05:23:15 loss: 0.4249 Lr: 0.00496 [2023-12-20 15:57:40,186 INFO misc.py line 119 131400] Train: [32/100][13/800] Data 0.003 (0.004) Batch 0.308 (0.347) Remain 05:19:13 loss: 0.3857 Lr: 0.00496 [2023-12-20 15:57:40,524 INFO misc.py line 119 131400] Train: [32/100][14/800] Data 0.003 (0.004) Batch 0.338 (0.346) Remain 05:18:28 loss: 0.6298 Lr: 0.00496 [2023-12-20 15:57:40,838 INFO misc.py line 119 131400] Train: [32/100][15/800] Data 0.003 (0.004) Batch 0.313 (0.343) Remain 05:15:54 loss: 0.5411 Lr: 0.00496 [2023-12-20 15:57:41,185 INFO misc.py line 119 131400] Train: [32/100][16/800] Data 0.005 (0.004) Batch 0.348 (0.344) Remain 05:16:13 loss: 0.3774 Lr: 0.00496 [2023-12-20 15:57:41,456 INFO misc.py line 119 131400] Train: [32/100][17/800] Data 0.004 (0.004) Batch 0.271 (0.339) Remain 05:11:26 loss: 0.3064 Lr: 0.00496 [2023-12-20 15:57:41,777 INFO misc.py line 119 131400] Train: [32/100][18/800] Data 0.003 (0.004) Batch 0.320 (0.337) Remain 05:10:17 loss: 0.4766 Lr: 0.00496 [2023-12-20 15:57:42,111 INFO misc.py line 119 131400] Train: [32/100][19/800] Data 0.005 (0.004) Batch 0.335 (0.337) Remain 05:10:08 loss: 0.4803 Lr: 0.00496 [2023-12-20 15:57:42,463 INFO misc.py line 119 131400] Train: [32/100][20/800] Data 0.004 (0.004) Batch 0.346 (0.338) Remain 05:10:37 loss: 0.5362 Lr: 0.00496 [2023-12-20 15:57:42,813 INFO misc.py line 119 131400] Train: [32/100][21/800] Data 0.010 (0.004) Batch 0.356 (0.339) Remain 05:11:31 loss: 0.3225 Lr: 0.00496 [2023-12-20 15:57:43,129 INFO misc.py line 119 131400] Train: [32/100][22/800] Data 0.003 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loss: 0.3328 Lr: 0.00496 [2023-12-20 15:57:45,584 INFO misc.py line 119 131400] Train: [32/100][29/800] Data 0.006 (0.005) Batch 0.339 (0.341) Remain 05:13:39 loss: 0.6443 Lr: 0.00496 [2023-12-20 15:57:45,892 INFO misc.py line 119 131400] Train: [32/100][30/800] Data 0.004 (0.005) Batch 0.308 (0.340) Remain 05:12:30 loss: 1.0796 Lr: 0.00496 [2023-12-20 15:57:46,192 INFO misc.py line 119 131400] Train: [32/100][31/800] Data 0.003 (0.004) Batch 0.300 (0.338) Remain 05:11:10 loss: 0.3542 Lr: 0.00496 [2023-12-20 15:57:46,514 INFO misc.py line 119 131400] Train: [32/100][32/800] Data 0.004 (0.004) Batch 0.323 (0.338) Remain 05:10:40 loss: 0.4633 Lr: 0.00496 [2023-12-20 15:57:46,977 INFO misc.py line 119 131400] Train: [32/100][33/800] Data 0.003 (0.004) Batch 0.462 (0.342) Remain 05:14:29 loss: 0.3571 Lr: 0.00496 [2023-12-20 15:57:47,327 INFO misc.py line 119 131400] Train: [32/100][34/800] Data 0.003 (0.004) Batch 0.347 (0.342) Remain 05:14:38 loss: 0.4782 Lr: 0.00496 [2023-12-20 15:57:47,636 INFO misc.py line 119 131400] Train: [32/100][35/800] Data 0.007 (0.004) Batch 0.312 (0.341) Remain 05:13:46 loss: 0.4783 Lr: 0.00496 [2023-12-20 15:57:48,005 INFO misc.py line 119 131400] Train: [32/100][36/800] Data 0.003 (0.004) Batch 0.369 (0.342) Remain 05:14:32 loss: 0.4207 Lr: 0.00496 [2023-12-20 15:57:48,342 INFO misc.py line 119 131400] Train: [32/100][37/800] Data 0.004 (0.004) Batch 0.335 (0.342) Remain 05:14:20 loss: 0.3216 Lr: 0.00496 [2023-12-20 15:57:48,677 INFO misc.py line 119 131400] Train: [32/100][38/800] Data 0.006 (0.004) Batch 0.336 (0.342) Remain 05:14:11 loss: 0.6998 Lr: 0.00496 [2023-12-20 15:57:49,032 INFO misc.py line 119 131400] Train: [32/100][39/800] Data 0.005 (0.004) Batch 0.355 (0.342) Remain 05:14:32 loss: 0.6844 Lr: 0.00496 [2023-12-20 15:57:49,328 INFO misc.py line 119 131400] Train: [32/100][40/800] Data 0.004 (0.004) Batch 0.295 (0.341) Remain 05:13:21 loss: 0.2776 Lr: 0.00495 [2023-12-20 15:57:49,663 INFO misc.py line 119 131400] Train: [32/100][41/800] Data 0.005 (0.004) Batch 0.336 (0.341) Remain 05:13:14 loss: 0.3324 Lr: 0.00495 [2023-12-20 15:57:49,968 INFO misc.py line 119 131400] Train: [32/100][42/800] Data 0.004 (0.004) Batch 0.305 (0.340) Remain 05:12:23 loss: 0.6221 Lr: 0.00495 [2023-12-20 15:57:50,330 INFO misc.py line 119 131400] Train: [32/100][43/800] Data 0.003 (0.004) Batch 0.350 (0.340) Remain 05:12:36 loss: 0.7583 Lr: 0.00495 [2023-12-20 15:57:50,688 INFO misc.py line 119 131400] Train: [32/100][44/800] Data 0.016 (0.005) Batch 0.370 (0.341) Remain 05:13:16 loss: 0.3637 Lr: 0.00495 [2023-12-20 15:57:51,041 INFO misc.py line 119 131400] Train: [32/100][45/800] Data 0.003 (0.005) Batch 0.353 (0.341) Remain 05:13:32 loss: 0.3408 Lr: 0.00495 [2023-12-20 15:57:51,395 INFO misc.py line 119 131400] Train: [32/100][46/800] Data 0.003 (0.005) Batch 0.353 (0.341) Remain 05:13:47 loss: 0.9338 Lr: 0.00495 [2023-12-20 15:57:51,732 INFO misc.py line 119 131400] Train: [32/100][47/800] Data 0.004 (0.005) Batch 0.337 (0.341) Remain 05:13:42 loss: 0.3946 Lr: 0.00495 [2023-12-20 15:57:52,080 INFO misc.py line 119 131400] Train: [32/100][48/800] Data 0.004 (0.005) Batch 0.348 (0.341) Remain 05:13:50 loss: 0.4901 Lr: 0.00495 [2023-12-20 15:57:52,487 INFO misc.py line 119 131400] Train: [32/100][49/800] Data 0.004 (0.005) Batch 0.407 (0.343) Remain 05:15:08 loss: 0.3601 Lr: 0.00495 [2023-12-20 15:57:52,844 INFO misc.py line 119 131400] Train: [32/100][50/800] Data 0.003 (0.005) Batch 0.357 (0.343) Remain 05:15:25 loss: 0.4241 Lr: 0.00495 [2023-12-20 15:57:53,172 INFO misc.py line 119 131400] Train: [32/100][51/800] Data 0.004 (0.005) Batch 0.329 (0.343) Remain 05:15:08 loss: 0.5169 Lr: 0.00495 [2023-12-20 15:57:53,499 INFO misc.py line 119 131400] Train: [32/100][52/800] Data 0.003 (0.005) Batch 0.323 (0.342) Remain 05:14:46 loss: 0.3713 Lr: 0.00495 [2023-12-20 15:57:53,831 INFO misc.py line 119 131400] Train: [32/100][53/800] Data 0.006 (0.005) Batch 0.334 (0.342) Remain 05:14:36 loss: 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INFO misc.py line 119 131400] Train: [32/100][60/800] Data 0.004 (0.005) Batch 0.363 (0.342) Remain 05:13:50 loss: 0.5787 Lr: 0.00495 [2023-12-20 15:57:56,484 INFO misc.py line 119 131400] Train: [32/100][61/800] Data 0.004 (0.005) Batch 0.303 (0.341) Remain 05:13:13 loss: 0.3250 Lr: 0.00495 [2023-12-20 15:57:56,783 INFO misc.py line 119 131400] Train: [32/100][62/800] Data 0.004 (0.005) Batch 0.296 (0.340) Remain 05:12:31 loss: 0.5406 Lr: 0.00495 [2023-12-20 15:57:57,086 INFO misc.py line 119 131400] Train: [32/100][63/800] Data 0.007 (0.005) Batch 0.306 (0.340) Remain 05:11:59 loss: 0.2880 Lr: 0.00495 [2023-12-20 15:57:57,449 INFO misc.py line 119 131400] Train: [32/100][64/800] Data 0.003 (0.005) Batch 0.363 (0.340) Remain 05:12:20 loss: 0.5879 Lr: 0.00495 [2023-12-20 15:57:57,765 INFO misc.py line 119 131400] Train: [32/100][65/800] Data 0.004 (0.005) Batch 0.315 (0.340) Remain 05:11:58 loss: 0.2995 Lr: 0.00495 [2023-12-20 15:57:58,105 INFO misc.py line 119 131400] Train: 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0.315 (0.338) Remain 05:10:22 loss: 0.4359 Lr: 0.00495 [2023-12-20 15:58:00,363 INFO misc.py line 119 131400] Train: [32/100][73/800] Data 0.005 (0.004) Batch 0.339 (0.338) Remain 05:10:23 loss: 0.3895 Lr: 0.00495 [2023-12-20 15:58:00,706 INFO misc.py line 119 131400] Train: [32/100][74/800] Data 0.004 (0.004) Batch 0.343 (0.338) Remain 05:10:26 loss: 0.5960 Lr: 0.00495 [2023-12-20 15:58:01,048 INFO misc.py line 119 131400] Train: [32/100][75/800] Data 0.004 (0.004) Batch 0.342 (0.338) Remain 05:10:29 loss: 0.2623 Lr: 0.00495 [2023-12-20 15:58:01,363 INFO misc.py line 119 131400] Train: [32/100][76/800] Data 0.003 (0.004) Batch 0.314 (0.338) Remain 05:10:11 loss: 0.3125 Lr: 0.00495 [2023-12-20 15:58:01,654 INFO misc.py line 119 131400] Train: [32/100][77/800] Data 0.004 (0.004) Batch 0.292 (0.337) Remain 05:09:37 loss: 0.2969 Lr: 0.00495 [2023-12-20 15:58:01,989 INFO misc.py line 119 131400] Train: [32/100][78/800] Data 0.003 (0.004) Batch 0.334 (0.337) Remain 05:09:34 loss: 0.5825 Lr: 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Batch 0.348 (0.334) Remain 05:03:18 loss: 0.8004 Lr: 0.00489 [2023-12-20 16:01:42,646 INFO misc.py line 119 131400] Train: [32/100][739/800] Data 0.004 (0.005) Batch 0.324 (0.334) Remain 05:03:17 loss: 0.5166 Lr: 0.00489 [2023-12-20 16:01:42,995 INFO misc.py line 119 131400] Train: [32/100][740/800] Data 0.014 (0.005) Batch 0.359 (0.334) Remain 05:03:18 loss: 0.3236 Lr: 0.00489 [2023-12-20 16:01:43,324 INFO misc.py line 119 131400] Train: [32/100][741/800] Data 0.003 (0.005) Batch 0.328 (0.334) Remain 05:03:17 loss: 0.4441 Lr: 0.00489 [2023-12-20 16:01:43,676 INFO misc.py line 119 131400] Train: [32/100][742/800] Data 0.004 (0.005) Batch 0.350 (0.334) Remain 05:03:18 loss: 0.5695 Lr: 0.00489 [2023-12-20 16:01:44,001 INFO misc.py line 119 131400] Train: [32/100][743/800] Data 0.007 (0.005) Batch 0.329 (0.334) Remain 05:03:17 loss: 0.2804 Lr: 0.00489 [2023-12-20 16:01:44,291 INFO misc.py line 119 131400] Train: [32/100][744/800] Data 0.003 (0.005) Batch 0.290 (0.334) Remain 05:03:14 loss: 0.5239 Lr: 0.00489 [2023-12-20 16:01:44,677 INFO misc.py line 119 131400] Train: [32/100][745/800] Data 0.003 (0.005) Batch 0.384 (0.334) Remain 05:03:17 loss: 0.3732 Lr: 0.00489 [2023-12-20 16:01:45,023 INFO misc.py line 119 131400] Train: [32/100][746/800] Data 0.005 (0.005) Batch 0.348 (0.334) Remain 05:03:18 loss: 0.3169 Lr: 0.00489 [2023-12-20 16:01:45,349 INFO misc.py line 119 131400] Train: [32/100][747/800] Data 0.005 (0.005) Batch 0.326 (0.334) Remain 05:03:17 loss: 0.4947 Lr: 0.00489 [2023-12-20 16:01:45,690 INFO misc.py line 119 131400] Train: [32/100][748/800] Data 0.004 (0.005) Batch 0.341 (0.334) Remain 05:03:17 loss: 0.2262 Lr: 0.00489 [2023-12-20 16:01:46,046 INFO misc.py line 119 131400] Train: [32/100][749/800] Data 0.002 (0.005) Batch 0.356 (0.334) Remain 05:03:18 loss: 0.2950 Lr: 0.00489 [2023-12-20 16:01:46,375 INFO misc.py line 119 131400] Train: [32/100][750/800] Data 0.004 (0.005) Batch 0.329 (0.334) Remain 05:03:18 loss: 0.3394 Lr: 0.00489 [2023-12-20 16:01:46,693 INFO misc.py line 119 131400] Train: [32/100][751/800] Data 0.003 (0.005) Batch 0.317 (0.334) Remain 05:03:16 loss: 0.6449 Lr: 0.00489 [2023-12-20 16:01:47,054 INFO misc.py line 119 131400] Train: [32/100][752/800] Data 0.005 (0.005) Batch 0.361 (0.334) Remain 05:03:18 loss: 0.5746 Lr: 0.00489 [2023-12-20 16:01:47,368 INFO misc.py line 119 131400] Train: [32/100][753/800] Data 0.005 (0.005) Batch 0.315 (0.334) Remain 05:03:16 loss: 0.4214 Lr: 0.00489 [2023-12-20 16:01:47,671 INFO misc.py line 119 131400] Train: [32/100][754/800] Data 0.003 (0.005) Batch 0.303 (0.334) Remain 05:03:13 loss: 0.6360 Lr: 0.00489 [2023-12-20 16:01:47,992 INFO misc.py line 119 131400] Train: [32/100][755/800] Data 0.004 (0.005) Batch 0.321 (0.334) Remain 05:03:12 loss: 0.2164 Lr: 0.00489 [2023-12-20 16:01:48,319 INFO misc.py line 119 131400] Train: [32/100][756/800] Data 0.004 (0.005) Batch 0.326 (0.334) Remain 05:03:11 loss: 0.9431 Lr: 0.00489 [2023-12-20 16:01:48,657 INFO misc.py line 119 131400] Train: [32/100][757/800] Data 0.005 (0.005) Batch 0.339 (0.334) Remain 05:03:11 loss: 0.4979 Lr: 0.00489 [2023-12-20 16:01:48,986 INFO misc.py line 119 131400] Train: [32/100][758/800] Data 0.004 (0.005) Batch 0.326 (0.334) Remain 05:03:10 loss: 0.5530 Lr: 0.00489 [2023-12-20 16:01:49,304 INFO misc.py line 119 131400] Train: [32/100][759/800] Data 0.008 (0.005) Batch 0.321 (0.334) Remain 05:03:09 loss: 0.4317 Lr: 0.00489 [2023-12-20 16:01:49,639 INFO misc.py line 119 131400] Train: [32/100][760/800] Data 0.003 (0.005) Batch 0.333 (0.334) Remain 05:03:08 loss: 0.1545 Lr: 0.00489 [2023-12-20 16:01:49,997 INFO misc.py line 119 131400] Train: [32/100][761/800] Data 0.005 (0.005) Batch 0.352 (0.334) Remain 05:03:09 loss: 0.3907 Lr: 0.00489 [2023-12-20 16:01:50,358 INFO misc.py line 119 131400] Train: [32/100][762/800] Data 0.012 (0.005) Batch 0.369 (0.334) Remain 05:03:12 loss: 0.2355 Lr: 0.00489 [2023-12-20 16:01:50,676 INFO misc.py line 119 131400] Train: [32/100][763/800] Data 0.004 (0.005) Batch 0.313 (0.334) Remain 05:03:10 loss: 0.6334 Lr: 0.00489 [2023-12-20 16:01:50,970 INFO misc.py line 119 131400] Train: [32/100][764/800] Data 0.009 (0.005) Batch 0.297 (0.334) Remain 05:03:07 loss: 0.3080 Lr: 0.00489 [2023-12-20 16:01:51,325 INFO misc.py line 119 131400] Train: [32/100][765/800] Data 0.005 (0.005) Batch 0.356 (0.334) Remain 05:03:08 loss: 0.2338 Lr: 0.00489 [2023-12-20 16:01:51,678 INFO misc.py line 119 131400] Train: [32/100][766/800] Data 0.005 (0.005) Batch 0.354 (0.334) Remain 05:03:09 loss: 0.2867 Lr: 0.00489 [2023-12-20 16:01:52,053 INFO misc.py line 119 131400] Train: [32/100][767/800] Data 0.004 (0.005) Batch 0.374 (0.334) Remain 05:03:12 loss: 0.2941 Lr: 0.00489 [2023-12-20 16:01:52,389 INFO misc.py line 119 131400] Train: [32/100][768/800] Data 0.004 (0.005) Batch 0.335 (0.334) Remain 05:03:11 loss: 0.4172 Lr: 0.00489 [2023-12-20 16:01:52,738 INFO misc.py line 119 131400] Train: [32/100][769/800] Data 0.006 (0.005) Batch 0.351 (0.334) Remain 05:03:12 loss: 0.5134 Lr: 0.00489 [2023-12-20 16:01:53,107 INFO misc.py line 119 131400] Train: [32/100][770/800] Data 0.005 (0.005) Batch 0.368 (0.334) Remain 05:03:14 loss: 0.3723 Lr: 0.00489 [2023-12-20 16:01:53,423 INFO misc.py line 119 131400] Train: [32/100][771/800] Data 0.005 (0.005) Batch 0.317 (0.334) Remain 05:03:13 loss: 0.2206 Lr: 0.00489 [2023-12-20 16:01:53,759 INFO misc.py line 119 131400] Train: [32/100][772/800] Data 0.004 (0.005) Batch 0.336 (0.334) Remain 05:03:12 loss: 0.3569 Lr: 0.00489 [2023-12-20 16:01:54,071 INFO misc.py line 119 131400] Train: [32/100][773/800] Data 0.005 (0.005) Batch 0.312 (0.334) Remain 05:03:10 loss: 0.3666 Lr: 0.00489 [2023-12-20 16:01:54,450 INFO misc.py line 119 131400] Train: [32/100][774/800] Data 0.004 (0.005) Batch 0.379 (0.334) Remain 05:03:13 loss: 0.4473 Lr: 0.00489 [2023-12-20 16:01:54,784 INFO misc.py line 119 131400] Train: [32/100][775/800] Data 0.004 (0.005) Batch 0.335 (0.334) Remain 05:03:13 loss: 0.3015 Lr: 0.00488 [2023-12-20 16:01:55,099 INFO misc.py line 119 131400] Train: [32/100][776/800] Data 0.004 (0.005) Batch 0.315 (0.334) Remain 05:03:11 loss: 0.6794 Lr: 0.00488 [2023-12-20 16:01:55,409 INFO misc.py line 119 131400] Train: [32/100][777/800] Data 0.003 (0.005) Batch 0.299 (0.334) Remain 05:03:08 loss: 0.3315 Lr: 0.00488 [2023-12-20 16:01:55,724 INFO misc.py line 119 131400] Train: [32/100][778/800] Data 0.016 (0.005) Batch 0.326 (0.334) Remain 05:03:07 loss: 0.3608 Lr: 0.00488 [2023-12-20 16:01:56,063 INFO misc.py line 119 131400] Train: [32/100][779/800] Data 0.004 (0.005) Batch 0.339 (0.334) Remain 05:03:07 loss: 0.2183 Lr: 0.00488 [2023-12-20 16:01:56,379 INFO misc.py line 119 131400] Train: [32/100][780/800] Data 0.003 (0.005) Batch 0.313 (0.334) Remain 05:03:06 loss: 0.3901 Lr: 0.00488 [2023-12-20 16:01:56,630 INFO misc.py line 119 131400] Train: [32/100][781/800] Data 0.006 (0.005) Batch 0.254 (0.334) Remain 05:03:00 loss: 0.3540 Lr: 0.00488 [2023-12-20 16:01:56,952 INFO misc.py line 119 131400] Train: [32/100][782/800] Data 0.003 (0.005) Batch 0.321 (0.334) Remain 05:02:58 loss: 0.5782 Lr: 0.00488 [2023-12-20 16:01:57,305 INFO misc.py line 119 131400] Train: [32/100][783/800] Data 0.006 (0.005) Batch 0.354 (0.334) Remain 05:03:00 loss: 0.2502 Lr: 0.00488 [2023-12-20 16:01:57,628 INFO misc.py line 119 131400] Train: [32/100][784/800] Data 0.004 (0.005) Batch 0.323 (0.334) Remain 05:02:58 loss: 0.6477 Lr: 0.00488 [2023-12-20 16:01:57,975 INFO misc.py line 119 131400] Train: [32/100][785/800] Data 0.003 (0.005) Batch 0.345 (0.334) Remain 05:02:59 loss: 0.8199 Lr: 0.00488 [2023-12-20 16:01:58,314 INFO misc.py line 119 131400] Train: [32/100][786/800] Data 0.006 (0.005) Batch 0.341 (0.334) Remain 05:02:59 loss: 0.5501 Lr: 0.00488 [2023-12-20 16:01:58,648 INFO misc.py line 119 131400] Train: [32/100][787/800] Data 0.003 (0.005) Batch 0.333 (0.334) Remain 05:02:59 loss: 0.5838 Lr: 0.00488 [2023-12-20 16:01:58,985 INFO misc.py line 119 131400] Train: [32/100][788/800] Data 0.005 (0.005) Batch 0.339 (0.334) Remain 05:02:59 loss: 0.4139 Lr: 0.00488 [2023-12-20 16:01:59,323 INFO misc.py line 119 131400] Train: [32/100][789/800] Data 0.003 (0.005) Batch 0.337 (0.334) Remain 05:02:58 loss: 0.5885 Lr: 0.00488 [2023-12-20 16:01:59,643 INFO misc.py line 119 131400] Train: [32/100][790/800] Data 0.003 (0.005) Batch 0.320 (0.334) Remain 05:02:57 loss: 0.4440 Lr: 0.00488 [2023-12-20 16:01:59,940 INFO misc.py line 119 131400] Train: [32/100][791/800] Data 0.004 (0.005) Batch 0.297 (0.334) Remain 05:02:54 loss: 0.6720 Lr: 0.00488 [2023-12-20 16:02:00,266 INFO misc.py line 119 131400] Train: [32/100][792/800] Data 0.003 (0.005) Batch 0.326 (0.334) Remain 05:02:53 loss: 0.2326 Lr: 0.00488 [2023-12-20 16:02:00,569 INFO misc.py line 119 131400] Train: [32/100][793/800] Data 0.003 (0.005) Batch 0.304 (0.334) Remain 05:02:51 loss: 0.5044 Lr: 0.00488 [2023-12-20 16:02:00,891 INFO misc.py line 119 131400] Train: [32/100][794/800] Data 0.002 (0.005) Batch 0.321 (0.334) Remain 05:02:50 loss: 0.4633 Lr: 0.00488 [2023-12-20 16:02:01,252 INFO misc.py line 119 131400] Train: [32/100][795/800] Data 0.004 (0.005) Batch 0.361 (0.334) Remain 05:02:51 loss: 0.6789 Lr: 0.00488 [2023-12-20 16:02:01,598 INFO misc.py line 119 131400] Train: [32/100][796/800] Data 0.003 (0.005) Batch 0.346 (0.334) Remain 05:02:52 loss: 0.4769 Lr: 0.00488 [2023-12-20 16:02:01,936 INFO misc.py line 119 131400] Train: [32/100][797/800] Data 0.002 (0.005) Batch 0.338 (0.334) Remain 05:02:52 loss: 0.5166 Lr: 0.00488 [2023-12-20 16:02:02,261 INFO misc.py line 119 131400] Train: [32/100][798/800] Data 0.004 (0.005) Batch 0.325 (0.334) Remain 05:02:51 loss: 0.6769 Lr: 0.00488 [2023-12-20 16:02:02,568 INFO misc.py line 119 131400] Train: [32/100][799/800] Data 0.005 (0.005) Batch 0.307 (0.334) Remain 05:02:49 loss: 0.2295 Lr: 0.00488 [2023-12-20 16:02:02,899 INFO misc.py line 119 131400] Train: [32/100][800/800] Data 0.004 (0.005) Batch 0.330 (0.334) Remain 05:02:48 loss: 0.4179 Lr: 0.00488 [2023-12-20 16:02:02,905 INFO misc.py line 136 131400] Train result: loss: 0.4515 [2023-12-20 16:02:02,910 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:02:24,167 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1562 [2023-12-20 16:02:24,239 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1627 [2023-12-20 16:02:24,333 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4928 [2023-12-20 16:02:24,446 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4128 [2023-12-20 16:02:25,496 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4890 [2023-12-20 16:02:25,604 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9159 [2023-12-20 16:02:25,706 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6191 [2023-12-20 16:02:25,823 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2482 [2023-12-20 16:02:25,930 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2003 [2023-12-20 16:02:26,023 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4753 [2023-12-20 16:02:26,115 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5092 [2023-12-20 16:02:26,254 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3539 [2023-12-20 16:02:26,376 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3671 [2023-12-20 16:02:26,530 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2525 [2023-12-20 16:02:26,630 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.5486 [2023-12-20 16:02:26,763 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5422 [2023-12-20 16:02:26,871 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2849 [2023-12-20 16:02:26,984 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8732 [2023-12-20 16:02:27,096 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3574 [2023-12-20 16:02:27,171 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.7204 [2023-12-20 16:02:27,280 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5397 [2023-12-20 16:02:27,436 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2060 [2023-12-20 16:02:27,557 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9691 [2023-12-20 16:02:27,703 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2450 [2023-12-20 16:02:27,847 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.4970 [2023-12-20 16:02:27,931 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3657 [2023-12-20 16:02:28,091 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7263 [2023-12-20 16:02:28,217 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5536 [2023-12-20 16:02:28,321 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7784 [2023-12-20 16:02:28,468 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2319 [2023-12-20 16:02:28,574 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7023 [2023-12-20 16:02:28,696 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6840 [2023-12-20 16:02:28,781 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5215 [2023-12-20 16:02:28,851 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2292 [2023-12-20 16:02:28,947 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.2279 [2023-12-20 16:02:29,053 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.7166 [2023-12-20 16:02:29,194 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9146 [2023-12-20 16:02:29,313 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1279 [2023-12-20 16:02:29,404 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6122 [2023-12-20 16:02:29,543 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.6366 [2023-12-20 16:02:29,693 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0447 [2023-12-20 16:02:29,799 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0841 [2023-12-20 16:02:29,922 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4847 [2023-12-20 16:02:30,066 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0144 [2023-12-20 16:02:30,188 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0337 [2023-12-20 16:02:30,290 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3348 [2023-12-20 16:02:30,461 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4869 [2023-12-20 16:02:30,556 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4693 [2023-12-20 16:02:30,701 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4977 [2023-12-20 16:02:30,798 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7701 [2023-12-20 16:02:30,897 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.9662 [2023-12-20 16:02:31,007 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3281 [2023-12-20 16:02:31,167 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3751 [2023-12-20 16:02:31,305 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3653 [2023-12-20 16:02:31,427 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0040 [2023-12-20 16:02:31,532 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7459 [2023-12-20 16:02:31,638 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3512 [2023-12-20 16:02:31,807 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2601 [2023-12-20 16:02:31,911 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.0994 [2023-12-20 16:02:32,020 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3447 [2023-12-20 16:02:32,124 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2581 [2023-12-20 16:02:32,220 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3819 [2023-12-20 16:02:32,311 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.9505 [2023-12-20 16:02:32,420 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8476 [2023-12-20 16:02:32,558 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1435 [2023-12-20 16:02:32,650 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2596 [2023-12-20 16:02:32,757 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6125 [2023-12-20 16:02:32,869 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0296 [2023-12-20 16:02:32,966 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2375 [2023-12-20 16:02:33,056 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0376 [2023-12-20 16:02:33,157 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8992 [2023-12-20 16:02:33,247 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7622 [2023-12-20 16:02:33,387 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0955 [2023-12-20 16:02:33,488 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6842 [2023-12-20 16:02:33,609 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7383 [2023-12-20 16:02:33,715 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8915 [2023-12-20 16:02:33,809 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3555 [2023-12-20 16:02:33,963 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.7369 [2023-12-20 16:02:35,322 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7217/0.8146/0.9007. [2023-12-20 16:02:35,322 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8527/0.9484 [2023-12-20 16:02:35,322 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9626/0.9802 [2023-12-20 16:02:35,322 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6527/0.7933 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7797/0.8161 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8956/0.9358 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8000/0.9339 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.6824/0.7263 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6603/0.8316 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6403/0.7003 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8048/0.9025 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3762/0.4884 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6087/0.6594 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5830/0.9240 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7360/0.8567 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6110/0.6403 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7271/0.8082 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9337/0.9805 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6635/0.7795 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8876/0.9325 [2023-12-20 16:02:35,323 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5761/0.6537 [2023-12-20 16:02:35,324 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:02:35,325 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:02:35,325 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:02:40,889 INFO misc.py line 119 131400] Train: [33/100][1/800] Data 0.991 (0.991) Batch 1.315 (1.315) Remain 19:51:52 loss: 0.2423 Lr: 0.00488 [2023-12-20 16:02:41,204 INFO misc.py line 119 131400] Train: [33/100][2/800] Data 0.004 (0.004) Batch 0.315 (0.315) Remain 04:45:34 loss: 0.3622 Lr: 0.00488 [2023-12-20 16:02:41,542 INFO misc.py line 119 131400] Train: [33/100][3/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 05:06:36 loss: 0.5904 Lr: 0.00488 [2023-12-20 16:02:41,893 INFO misc.py line 119 131400] Train: [33/100][4/800] Data 0.003 (0.003) Batch 0.351 (0.351) Remain 05:17:51 loss: 0.4482 Lr: 0.00488 [2023-12-20 16:02:42,382 INFO misc.py line 119 131400] Train: [33/100][5/800] Data 0.004 (0.004) Batch 0.488 (0.419) Remain 06:20:07 loss: 0.4469 Lr: 0.00488 [2023-12-20 16:02:42,752 INFO misc.py line 119 131400] Train: [33/100][6/800] Data 0.005 (0.004) Batch 0.367 (0.402) Remain 06:04:20 loss: 0.5060 Lr: 0.00488 [2023-12-20 16:02:43,116 INFO misc.py line 119 131400] Train: [33/100][7/800] Data 0.007 (0.005) Batch 0.368 (0.393) Remain 05:56:37 loss: 0.4818 Lr: 0.00488 [2023-12-20 16:02:43,444 INFO misc.py line 119 131400] Train: [33/100][8/800] Data 0.005 (0.005) Batch 0.328 (0.380) Remain 05:44:50 loss: 0.2924 Lr: 0.00488 [2023-12-20 16:02:43,778 INFO misc.py line 119 131400] Train: [33/100][9/800] Data 0.003 (0.005) Batch 0.333 (0.372) Remain 05:37:40 loss: 0.5882 Lr: 0.00488 [2023-12-20 16:02:44,103 INFO misc.py line 119 131400] Train: [33/100][10/800] Data 0.004 (0.005) Batch 0.326 (0.366) Remain 05:31:37 loss: 0.1982 Lr: 0.00488 [2023-12-20 16:02:44,445 INFO misc.py line 119 131400] Train: [33/100][11/800] Data 0.003 (0.004) Batch 0.340 (0.363) Remain 05:28:43 loss: 1.5817 Lr: 0.00488 [2023-12-20 16:02:44,858 INFO misc.py line 119 131400] Train: [33/100][12/800] Data 0.005 (0.004) Batch 0.412 (0.368) Remain 05:33:39 loss: 0.3783 Lr: 0.00488 [2023-12-20 16:02:45,180 INFO misc.py line 119 131400] Train: [33/100][13/800] Data 0.006 (0.005) Batch 0.325 (0.364) Remain 05:29:42 loss: 0.7478 Lr: 0.00488 [2023-12-20 16:02:45,506 INFO misc.py line 119 131400] Train: [33/100][14/800] Data 0.003 (0.004) Batch 0.326 (0.360) Remain 05:26:35 loss: 0.7916 Lr: 0.00488 [2023-12-20 16:02:45,823 INFO misc.py line 119 131400] Train: [33/100][15/800] Data 0.004 (0.004) Batch 0.317 (0.357) Remain 05:23:19 loss: 0.2201 Lr: 0.00488 [2023-12-20 16:02:46,181 INFO misc.py line 119 131400] Train: [33/100][16/800] Data 0.004 (0.004) Batch 0.358 (0.357) Remain 05:23:26 loss: 0.5600 Lr: 0.00488 [2023-12-20 16:02:46,532 INFO misc.py line 119 131400] Train: [33/100][17/800] Data 0.003 (0.004) Batch 0.350 (0.356) Remain 05:22:58 loss: 0.2603 Lr: 0.00488 [2023-12-20 16:02:46,851 INFO misc.py line 119 131400] Train: [33/100][18/800] Data 0.004 (0.004) Batch 0.317 (0.354) Remain 05:20:37 loss: 0.6286 Lr: 0.00488 [2023-12-20 16:02:47,195 INFO misc.py line 119 131400] Train: [33/100][19/800] Data 0.006 (0.004) Batch 0.346 (0.353) Remain 05:20:11 loss: 0.3887 Lr: 0.00488 [2023-12-20 16:02:47,528 INFO misc.py line 119 131400] Train: [33/100][20/800] Data 0.003 (0.004) Batch 0.333 (0.352) Remain 05:19:05 loss: 0.6738 Lr: 0.00488 [2023-12-20 16:02:47,846 INFO misc.py line 119 131400] Train: [33/100][21/800] Data 0.004 (0.004) Batch 0.319 (0.350) Remain 05:17:24 loss: 0.5215 Lr: 0.00488 [2023-12-20 16:02:48,152 INFO misc.py line 119 131400] Train: [33/100][22/800] Data 0.003 (0.004) Batch 0.306 (0.348) Remain 05:15:18 loss: 0.1557 Lr: 0.00488 [2023-12-20 16:02:48,467 INFO misc.py line 119 131400] Train: [33/100][23/800] Data 0.003 (0.004) Batch 0.315 (0.346) Remain 05:13:47 loss: 0.4184 Lr: 0.00488 [2023-12-20 16:02:48,803 INFO misc.py line 119 131400] Train: [33/100][24/800] Data 0.003 (0.004) Batch 0.336 (0.346) Remain 05:13:19 loss: 0.3276 Lr: 0.00488 [2023-12-20 16:02:49,108 INFO misc.py line 119 131400] Train: [33/100][25/800] Data 0.003 (0.004) Batch 0.300 (0.344) Remain 05:11:25 loss: 0.3320 Lr: 0.00488 [2023-12-20 16:02:49,445 INFO misc.py line 119 131400] Train: [33/100][26/800] Data 0.008 (0.004) Batch 0.342 (0.344) Remain 05:11:20 loss: 0.2624 Lr: 0.00488 [2023-12-20 16:02:49,772 INFO misc.py line 119 131400] Train: [33/100][27/800] Data 0.004 (0.004) Batch 0.328 (0.343) Remain 05:10:45 loss: 0.5882 Lr: 0.00488 [2023-12-20 16:02:50,121 INFO misc.py line 119 131400] Train: [33/100][28/800] Data 0.004 (0.004) Batch 0.347 (0.343) Remain 05:10:54 loss: 0.3413 Lr: 0.00488 [2023-12-20 16:02:50,468 INFO misc.py line 119 131400] Train: [33/100][29/800] Data 0.005 (0.004) Batch 0.348 (0.343) Remain 05:11:05 loss: 0.3602 Lr: 0.00488 [2023-12-20 16:02:50,817 INFO misc.py line 119 131400] Train: [33/100][30/800] Data 0.003 (0.004) Batch 0.349 (0.344) Remain 05:11:16 loss: 0.2993 Lr: 0.00488 [2023-12-20 16:02:51,159 INFO misc.py line 119 131400] Train: [33/100][31/800] Data 0.003 (0.004) Batch 0.342 (0.343) Remain 05:11:12 loss: 0.6441 Lr: 0.00488 [2023-12-20 16:02:51,444 INFO misc.py line 119 131400] Train: [33/100][32/800] Data 0.004 (0.004) Batch 0.285 (0.341) Remain 05:09:22 loss: 0.3607 Lr: 0.00488 [2023-12-20 16:02:51,768 INFO misc.py line 119 131400] Train: [33/100][33/800] Data 0.004 (0.004) Batch 0.324 (0.341) Remain 05:08:50 loss: 0.6282 Lr: 0.00488 [2023-12-20 16:02:52,132 INFO misc.py line 119 131400] Train: [33/100][34/800] Data 0.004 (0.004) Batch 0.364 (0.342) Remain 05:09:31 loss: 0.3263 Lr: 0.00488 [2023-12-20 16:02:52,479 INFO misc.py line 119 131400] Train: [33/100][35/800] Data 0.004 (0.004) Batch 0.346 (0.342) Remain 05:09:38 loss: 0.5514 Lr: 0.00488 [2023-12-20 16:02:52,827 INFO misc.py line 119 131400] Train: [33/100][36/800] Data 0.004 (0.004) Batch 0.348 (0.342) Remain 05:09:48 loss: 0.5317 Lr: 0.00488 [2023-12-20 16:02:53,158 INFO misc.py line 119 131400] Train: [33/100][37/800] Data 0.004 (0.004) Batch 0.330 (0.342) Remain 05:09:30 loss: 0.5223 Lr: 0.00488 [2023-12-20 16:02:53,623 INFO misc.py line 119 131400] Train: [33/100][38/800] Data 0.004 (0.004) Batch 0.466 (0.345) Remain 05:12:43 loss: 0.6497 Lr: 0.00488 [2023-12-20 16:02:53,950 INFO misc.py line 119 131400] Train: [33/100][39/800] Data 0.004 (0.004) Batch 0.326 (0.345) Remain 05:12:14 loss: 0.3865 Lr: 0.00488 [2023-12-20 16:02:54,294 INFO misc.py line 119 131400] Train: [33/100][40/800] Data 0.004 (0.004) Batch 0.340 (0.345) Remain 05:12:08 loss: 0.5548 Lr: 0.00488 [2023-12-20 16:02:54,627 INFO misc.py line 119 131400] Train: [33/100][41/800] Data 0.008 (0.004) Batch 0.337 (0.344) Remain 05:11:56 loss: 0.6780 Lr: 0.00488 [2023-12-20 16:02:54,958 INFO misc.py line 119 131400] Train: [33/100][42/800] Data 0.005 (0.004) Batch 0.330 (0.344) Remain 05:11:36 loss: 0.6108 Lr: 0.00488 [2023-12-20 16:02:55,268 INFO misc.py line 119 131400] Train: [33/100][43/800] Data 0.004 (0.004) Batch 0.311 (0.343) Remain 05:10:51 loss: 0.4455 Lr: 0.00488 [2023-12-20 16:02:55,643 INFO misc.py line 119 131400] Train: [33/100][44/800] Data 0.004 (0.004) Batch 0.376 (0.344) Remain 05:11:33 loss: 0.6702 Lr: 0.00488 [2023-12-20 16:02:55,944 INFO misc.py line 119 131400] Train: [33/100][45/800] Data 0.004 (0.004) Batch 0.301 (0.343) Remain 05:10:37 loss: 0.2686 Lr: 0.00488 [2023-12-20 16:02:56,270 INFO misc.py line 119 131400] Train: [33/100][46/800] Data 0.003 (0.004) Batch 0.326 (0.342) Remain 05:10:15 loss: 0.2445 Lr: 0.00488 [2023-12-20 16:02:56,563 INFO misc.py line 119 131400] Train: [33/100][47/800] Data 0.011 (0.004) Batch 0.293 (0.341) Remain 05:09:14 loss: 0.1605 Lr: 0.00488 [2023-12-20 16:02:56,888 INFO misc.py line 119 131400] Train: [33/100][48/800] Data 0.004 (0.004) Batch 0.325 (0.341) Remain 05:08:53 loss: 0.3890 Lr: 0.00488 [2023-12-20 16:02:57,243 INFO misc.py line 119 131400] Train: [33/100][49/800] Data 0.004 (0.004) Batch 0.355 (0.341) Remain 05:09:10 loss: 0.3876 Lr: 0.00488 [2023-12-20 16:02:57,560 INFO misc.py line 119 131400] Train: [33/100][50/800] Data 0.004 (0.004) Batch 0.317 (0.341) Remain 05:08:41 loss: 0.2664 Lr: 0.00488 [2023-12-20 16:02:57,906 INFO misc.py line 119 131400] Train: [33/100][51/800] Data 0.005 (0.004) Batch 0.346 (0.341) Remain 05:08:46 loss: 0.5163 Lr: 0.00488 [2023-12-20 16:02:58,241 INFO misc.py line 119 131400] Train: [33/100][52/800] Data 0.005 (0.004) Batch 0.337 (0.341) Remain 05:08:41 loss: 0.4202 Lr: 0.00488 [2023-12-20 16:02:58,582 INFO misc.py line 119 131400] Train: [33/100][53/800] Data 0.003 (0.004) Batch 0.341 (0.341) Remain 05:08:41 loss: 0.4426 Lr: 0.00488 [2023-12-20 16:02:58,896 INFO misc.py line 119 131400] Train: [33/100][54/800] Data 0.003 (0.004) Batch 0.311 (0.340) Remain 05:08:09 loss: 0.3526 Lr: 0.00488 [2023-12-20 16:02:59,243 INFO misc.py line 119 131400] Train: [33/100][55/800] Data 0.006 (0.004) Batch 0.348 (0.340) Remain 05:08:17 loss: 0.3927 Lr: 0.00488 [2023-12-20 16:02:59,579 INFO misc.py line 119 131400] Train: [33/100][56/800] Data 0.005 (0.004) Batch 0.333 (0.340) Remain 05:08:10 loss: 0.5528 Lr: 0.00488 [2023-12-20 16:02:59,905 INFO misc.py line 119 131400] Train: [33/100][57/800] Data 0.008 (0.004) Batch 0.329 (0.340) Remain 05:07:58 loss: 0.4711 Lr: 0.00488 [2023-12-20 16:03:00,250 INFO misc.py line 119 131400] Train: [33/100][58/800] Data 0.004 (0.004) Batch 0.345 (0.340) Remain 05:08:02 loss: 0.4815 Lr: 0.00488 [2023-12-20 16:03:00,584 INFO misc.py line 119 131400] Train: [33/100][59/800] Data 0.006 (0.004) Batch 0.328 (0.340) Remain 05:07:50 loss: 0.6651 Lr: 0.00488 [2023-12-20 16:03:00,909 INFO misc.py line 119 131400] Train: [33/100][60/800] Data 0.011 (0.005) Batch 0.332 (0.340) Remain 05:07:43 loss: 0.3918 Lr: 0.00488 [2023-12-20 16:03:01,250 INFO misc.py line 119 131400] Train: [33/100][61/800] Data 0.003 (0.004) Batch 0.340 (0.340) Remain 05:07:42 loss: 0.3494 Lr: 0.00488 [2023-12-20 16:03:01,598 INFO misc.py line 119 131400] Train: [33/100][62/800] Data 0.005 (0.004) Batch 0.329 (0.340) Remain 05:07:32 loss: 0.5162 Lr: 0.00488 [2023-12-20 16:03:01,905 INFO misc.py line 119 131400] Train: [33/100][63/800] Data 0.024 (0.005) Batch 0.327 (0.339) Remain 05:07:20 loss: 0.5661 Lr: 0.00488 [2023-12-20 16:03:02,240 INFO misc.py line 119 131400] Train: [33/100][64/800] Data 0.003 (0.005) Batch 0.336 (0.339) Remain 05:07:16 loss: 0.3668 Lr: 0.00488 [2023-12-20 16:03:02,582 INFO misc.py line 119 131400] Train: [33/100][65/800] Data 0.003 (0.005) Batch 0.340 (0.339) Remain 05:07:16 loss: 0.2696 Lr: 0.00488 [2023-12-20 16:03:02,924 INFO misc.py line 119 131400] Train: [33/100][66/800] Data 0.005 (0.005) Batch 0.343 (0.339) Remain 05:07:19 loss: 0.1929 Lr: 0.00488 [2023-12-20 16:03:03,302 INFO misc.py line 119 131400] Train: [33/100][67/800] Data 0.005 (0.005) Batch 0.378 (0.340) Remain 05:07:52 loss: 0.4425 Lr: 0.00488 [2023-12-20 16:03:03,626 INFO misc.py line 119 131400] Train: [33/100][68/800] Data 0.004 (0.005) Batch 0.324 (0.340) Remain 05:07:38 loss: 0.1806 Lr: 0.00488 [2023-12-20 16:03:03,955 INFO misc.py line 119 131400] Train: [33/100][69/800] Data 0.003 (0.005) Batch 0.327 (0.340) Remain 05:07:28 loss: 0.6777 Lr: 0.00488 [2023-12-20 16:03:04,290 INFO misc.py line 119 131400] Train: [33/100][70/800] Data 0.006 (0.005) Batch 0.338 (0.340) Remain 05:07:26 loss: 0.3090 Lr: 0.00488 [2023-12-20 16:03:04,616 INFO misc.py line 119 131400] Train: [33/100][71/800] Data 0.003 (0.005) Batch 0.325 (0.339) Remain 05:07:14 loss: 0.5607 Lr: 0.00488 [2023-12-20 16:03:04,941 INFO misc.py line 119 131400] Train: [33/100][72/800] Data 0.003 (0.005) Batch 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line 119 131400] Train: [33/100][782/800] Data 0.015 (0.005) Batch 0.295 (0.336) Remain 05:00:18 loss: 0.4052 Lr: 0.00481 [2023-12-20 16:07:03,693 INFO misc.py line 119 131400] Train: [33/100][783/800] Data 0.003 (0.005) Batch 0.366 (0.336) Remain 05:00:20 loss: 0.3950 Lr: 0.00481 [2023-12-20 16:07:03,964 INFO misc.py line 119 131400] Train: [33/100][784/800] Data 0.004 (0.005) Batch 0.270 (0.336) Remain 05:00:15 loss: 0.2532 Lr: 0.00481 [2023-12-20 16:07:04,260 INFO misc.py line 119 131400] Train: [33/100][785/800] Data 0.005 (0.005) Batch 0.295 (0.336) Remain 05:00:11 loss: 0.4055 Lr: 0.00481 [2023-12-20 16:07:04,565 INFO misc.py line 119 131400] Train: [33/100][786/800] Data 0.007 (0.005) Batch 0.308 (0.336) Remain 05:00:09 loss: 0.4199 Lr: 0.00481 [2023-12-20 16:07:04,870 INFO misc.py line 119 131400] Train: [33/100][787/800] Data 0.003 (0.005) Batch 0.304 (0.336) Remain 05:00:07 loss: 0.3056 Lr: 0.00481 [2023-12-20 16:07:05,176 INFO misc.py line 119 131400] Train: 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Batch 0.286 (0.335) Remain 04:59:44 loss: 0.3683 Lr: 0.00480 [2023-12-20 16:07:07,208 INFO misc.py line 119 131400] Train: [33/100][795/800] Data 0.003 (0.005) Batch 0.288 (0.335) Remain 04:59:40 loss: 0.3160 Lr: 0.00480 [2023-12-20 16:07:07,500 INFO misc.py line 119 131400] Train: [33/100][796/800] Data 0.003 (0.005) Batch 0.291 (0.335) Remain 04:59:37 loss: 0.4410 Lr: 0.00480 [2023-12-20 16:07:07,808 INFO misc.py line 119 131400] Train: [33/100][797/800] Data 0.003 (0.005) Batch 0.308 (0.335) Remain 04:59:35 loss: 0.6069 Lr: 0.00480 [2023-12-20 16:07:08,233 INFO misc.py line 119 131400] Train: [33/100][798/800] Data 0.004 (0.005) Batch 0.423 (0.335) Remain 04:59:41 loss: 0.7572 Lr: 0.00480 [2023-12-20 16:07:08,559 INFO misc.py line 119 131400] Train: [33/100][799/800] Data 0.006 (0.005) Batch 0.329 (0.335) Remain 04:59:40 loss: 0.2502 Lr: 0.00480 [2023-12-20 16:07:08,865 INFO misc.py line 119 131400] Train: [33/100][800/800] Data 0.003 (0.005) Batch 0.305 (0.335) Remain 04:59:37 loss: 0.3460 Lr: 0.00480 [2023-12-20 16:07:08,865 INFO misc.py line 136 131400] Train result: loss: 0.4545 [2023-12-20 16:07:08,866 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:07:31,688 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1501 [2023-12-20 16:07:31,774 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1785 [2023-12-20 16:07:31,865 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3359 [2023-12-20 16:07:31,975 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3179 [2023-12-20 16:07:32,089 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4883 [2023-12-20 16:07:32,193 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.4393 [2023-12-20 16:07:32,285 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0546 [2023-12-20 16:07:32,393 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2814 [2023-12-20 16:07:32,480 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2957 [2023-12-20 16:07:32,563 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5735 [2023-12-20 16:07:32,656 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6207 [2023-12-20 16:07:32,791 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5054 [2023-12-20 16:07:32,912 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4156 [2023-12-20 16:07:33,068 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2402 [2023-12-20 16:07:33,162 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1860 [2023-12-20 16:07:33,304 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0851 [2023-12-20 16:07:33,416 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3275 [2023-12-20 16:07:33,537 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5109 [2023-12-20 16:07:33,660 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2951 [2023-12-20 16:07:33,736 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3114 [2023-12-20 16:07:33,843 INFO evaluator.py line 159 131400] Test: 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0.7757 [2023-12-20 16:07:36,855 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.4359 [2023-12-20 16:07:36,965 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4396 [2023-12-20 16:07:37,150 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5781 [2023-12-20 16:07:37,247 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3615 [2023-12-20 16:07:37,396 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3304 [2023-12-20 16:07:37,487 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8905 [2023-12-20 16:07:37,570 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.2996 [2023-12-20 16:07:37,700 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0428 [2023-12-20 16:07:37,868 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8127 [2023-12-20 16:07:38,016 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3016 [2023-12-20 16:07:38,129 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.8115 [2023-12-20 16:07:38,229 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9818 [2023-12-20 16:07:38,342 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.5114 [2023-12-20 16:07:38,506 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3266 [2023-12-20 16:07:38,607 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1817 [2023-12-20 16:07:38,710 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2951 [2023-12-20 16:07:38,822 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3854 [2023-12-20 16:07:38,922 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3802 [2023-12-20 16:07:39,023 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5505 [2023-12-20 16:07:39,124 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8884 [2023-12-20 16:07:39,252 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3239 [2023-12-20 16:07:39,361 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.5216 [2023-12-20 16:07:39,474 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.8423 [2023-12-20 16:07:39,583 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0351 [2023-12-20 16:07:39,693 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4277 [2023-12-20 16:07:39,777 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0205 [2023-12-20 16:07:39,878 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7830 [2023-12-20 16:07:39,983 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5223 [2023-12-20 16:07:40,126 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.4247 [2023-12-20 16:07:40,223 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6529 [2023-12-20 16:07:40,342 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7424 [2023-12-20 16:07:40,446 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.1566 [2023-12-20 16:07:40,531 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6553 [2023-12-20 16:07:40,690 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2893 [2023-12-20 16:07:42,118 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7154/0.8229/0.8961. [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8374/0.8957 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9638/0.9799 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6487/0.7622 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7908/0.9004 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9113/0.9451 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8119/0.9173 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7314/0.8844 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6507/0.8057 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5305/0.9027 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8025/0.9169 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3650/0.5630 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6820/0.8244 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6504/0.7703 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6596/0.7678 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5656/0.5939 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6305/0.7321 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9188/0.9269 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6548/0.7593 [2023-12-20 16:07:42,119 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8776/0.9311 [2023-12-20 16:07:42,120 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6238/0.6799 [2023-12-20 16:07:42,120 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:07:42,121 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:07:42,121 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:07:46,767 INFO misc.py line 119 131400] Train: [34/100][1/800] Data 1.250 (1.250) Batch 1.574 (1.574) Remain 23:25:49 loss: 0.2681 Lr: 0.00480 [2023-12-20 16:07:47,053 INFO misc.py line 119 131400] Train: [34/100][2/800] Data 0.004 (0.004) Batch 0.287 (0.287) Remain 04:16:25 loss: 0.5305 Lr: 0.00480 [2023-12-20 16:07:47,375 INFO misc.py line 119 131400] Train: [34/100][3/800] Data 0.003 (0.003) Batch 0.321 (0.321) Remain 04:47:03 loss: 0.6217 Lr: 0.00480 [2023-12-20 16:07:47,716 INFO misc.py line 119 131400] Train: [34/100][4/800] Data 0.004 (0.004) Batch 0.337 (0.337) Remain 05:00:53 loss: 0.4659 Lr: 0.00480 [2023-12-20 16:07:47,975 INFO misc.py line 119 131400] Train: [34/100][5/800] Data 0.008 (0.006) Batch 0.263 (0.300) Remain 04:27:44 loss: 0.4702 Lr: 0.00480 [2023-12-20 16:07:48,381 INFO misc.py line 119 131400] Train: [34/100][6/800] Data 0.004 (0.005) Batch 0.405 (0.335) Remain 04:59:01 loss: 0.4291 Lr: 0.00480 [2023-12-20 16:07:48,732 INFO misc.py line 119 131400] Train: [34/100][7/800] Data 0.006 (0.005) Batch 0.345 (0.337) Remain 05:01:18 loss: 0.3408 Lr: 0.00480 [2023-12-20 16:07:49,066 INFO misc.py line 119 131400] Train: [34/100][8/800] Data 0.011 (0.006) Batch 0.341 (0.338) Remain 05:02:03 loss: 0.2151 Lr: 0.00480 [2023-12-20 16:07:49,420 INFO misc.py line 119 131400] Train: [34/100][9/800] Data 0.004 (0.006) Batch 0.354 (0.341) Remain 05:04:26 loss: 0.5790 Lr: 0.00480 [2023-12-20 16:07:49,791 INFO misc.py line 119 131400] Train: [34/100][10/800] Data 0.004 (0.006) Batch 0.370 (0.345) Remain 05:08:11 loss: 0.4461 Lr: 0.00480 [2023-12-20 16:07:50,126 INFO misc.py line 119 131400] Train: [34/100][11/800] Data 0.005 (0.006) Batch 0.336 (0.344) Remain 05:07:10 loss: 0.6832 Lr: 0.00480 [2023-12-20 16:07:50,461 INFO misc.py line 119 131400] Train: [34/100][12/800] Data 0.004 (0.005) Batch 0.334 (0.343) Remain 05:06:11 loss: 0.4515 Lr: 0.00480 [2023-12-20 16:07:50,801 INFO misc.py line 119 131400] Train: [34/100][13/800] Data 0.004 (0.005) Batch 0.341 (0.343) Remain 05:06:03 loss: 0.6589 Lr: 0.00480 [2023-12-20 16:07:51,138 INFO misc.py line 119 131400] Train: [34/100][14/800] Data 0.003 (0.005) Batch 0.336 (0.342) Remain 05:05:30 loss: 0.6098 Lr: 0.00480 [2023-12-20 16:07:51,475 INFO misc.py line 119 131400] Train: [34/100][15/800] Data 0.003 (0.005) Batch 0.337 (0.342) Remain 05:05:05 loss: 0.4639 Lr: 0.00480 [2023-12-20 16:07:51,817 INFO misc.py line 119 131400] Train: [34/100][16/800] Data 0.005 (0.005) Batch 0.340 (0.342) Remain 05:04:59 loss: 0.2516 Lr: 0.00480 [2023-12-20 16:07:52,122 INFO misc.py line 119 131400] Train: [34/100][17/800] Data 0.007 (0.005) Batch 0.308 (0.339) Remain 05:02:48 loss: 0.3409 Lr: 0.00480 [2023-12-20 16:07:52,463 INFO misc.py line 119 131400] Train: [34/100][18/800] Data 0.004 (0.005) Batch 0.340 (0.339) Remain 05:02:51 loss: 0.6142 Lr: 0.00480 [2023-12-20 16:07:52,829 INFO misc.py line 119 131400] Train: [34/100][19/800] Data 0.005 (0.005) Batch 0.363 (0.341) Remain 05:04:11 loss: 0.2896 Lr: 0.00480 [2023-12-20 16:07:53,167 INFO misc.py line 119 131400] Train: [34/100][20/800] Data 0.008 (0.005) Batch 0.343 (0.341) Remain 05:04:17 loss: 0.5923 Lr: 0.00480 [2023-12-20 16:07:53,522 INFO misc.py line 119 131400] Train: [34/100][21/800] Data 0.004 (0.005) Batch 0.355 (0.342) Remain 05:04:58 loss: 0.2667 Lr: 0.00480 [2023-12-20 16:07:53,825 INFO misc.py line 119 131400] Train: [34/100][22/800] Data 0.004 (0.005) Batch 0.303 (0.339) Remain 05:03:08 loss: 0.3253 Lr: 0.00480 [2023-12-20 16:07:54,175 INFO misc.py line 119 131400] Train: [34/100][23/800] Data 0.003 (0.005) Batch 0.350 (0.340) Remain 05:03:35 loss: 0.5828 Lr: 0.00480 [2023-12-20 16:07:54,514 INFO misc.py line 119 131400] Train: [34/100][24/800] Data 0.005 (0.005) Batch 0.339 (0.340) Remain 05:03:32 loss: 0.5289 Lr: 0.00480 [2023-12-20 16:07:54,854 INFO misc.py line 119 131400] Train: [34/100][25/800] Data 0.003 (0.005) Batch 0.337 (0.340) Remain 05:03:24 loss: 0.3482 Lr: 0.00480 [2023-12-20 16:07:55,149 INFO misc.py line 119 131400] Train: [34/100][26/800] Data 0.007 (0.005) Batch 0.299 (0.338) Remain 05:01:49 loss: 0.6927 Lr: 0.00480 [2023-12-20 16:07:55,487 INFO misc.py line 119 131400] Train: [34/100][27/800] Data 0.005 (0.005) Batch 0.337 (0.338) Remain 05:01:47 loss: 0.2257 Lr: 0.00480 [2023-12-20 16:07:55,818 INFO misc.py line 119 131400] Train: [34/100][28/800] Data 0.004 (0.005) Batch 0.332 (0.338) Remain 05:01:33 loss: 0.5333 Lr: 0.00480 [2023-12-20 16:07:56,170 INFO misc.py line 119 131400] Train: [34/100][29/800] Data 0.003 (0.005) Batch 0.351 (0.338) Remain 05:02:01 loss: 0.4012 Lr: 0.00480 [2023-12-20 16:07:56,520 INFO misc.py line 119 131400] Train: [34/100][30/800] Data 0.004 (0.005) Batch 0.351 (0.339) Remain 05:02:25 loss: 0.4658 Lr: 0.00480 [2023-12-20 16:07:56,865 INFO misc.py line 119 131400] Train: [34/100][31/800] Data 0.003 (0.005) Batch 0.345 (0.339) Remain 05:02:37 loss: 0.5885 Lr: 0.00480 [2023-12-20 16:07:57,184 INFO misc.py line 119 131400] Train: [34/100][32/800] Data 0.003 (0.005) Batch 0.319 (0.338) Remain 05:02:00 loss: 0.4819 Lr: 0.00480 [2023-12-20 16:07:57,594 INFO misc.py line 119 131400] Train: [34/100][33/800] Data 0.003 (0.005) Batch 0.410 (0.341) Remain 05:04:07 loss: 0.3746 Lr: 0.00480 [2023-12-20 16:07:57,946 INFO misc.py line 119 131400] Train: [34/100][34/800] Data 0.004 (0.005) Batch 0.352 (0.341) Remain 05:04:26 loss: 0.3434 Lr: 0.00480 [2023-12-20 16:07:58,241 INFO misc.py line 119 131400] Train: [34/100][35/800] Data 0.003 (0.005) Batch 0.296 (0.340) Remain 05:03:10 loss: 0.3631 Lr: 0.00480 [2023-12-20 16:07:58,576 INFO misc.py line 119 131400] Train: [34/100][36/800] Data 0.003 (0.005) Batch 0.333 (0.339) Remain 05:02:59 loss: 0.4317 Lr: 0.00480 [2023-12-20 16:07:58,914 INFO misc.py line 119 131400] Train: [34/100][37/800] Data 0.006 (0.005) Batch 0.337 (0.339) Remain 05:02:56 loss: 0.5364 Lr: 0.00480 [2023-12-20 16:07:59,260 INFO misc.py line 119 131400] Train: [34/100][38/800] Data 0.006 (0.005) Batch 0.347 (0.340) Remain 05:03:08 loss: 0.4965 Lr: 0.00480 [2023-12-20 16:07:59,549 INFO misc.py line 119 131400] Train: [34/100][39/800] Data 0.003 (0.005) Batch 0.289 (0.338) Remain 05:01:52 loss: 0.2502 Lr: 0.00480 [2023-12-20 16:07:59,890 INFO misc.py line 119 131400] Train: [34/100][40/800] Data 0.004 (0.005) Batch 0.341 (0.338) Remain 05:01:57 loss: 0.5603 Lr: 0.00480 [2023-12-20 16:08:00,219 INFO misc.py line 119 131400] Train: [34/100][41/800] Data 0.003 (0.004) Batch 0.325 (0.338) Remain 05:01:37 loss: 0.2683 Lr: 0.00480 [2023-12-20 16:08:00,569 INFO misc.py line 119 131400] Train: [34/100][42/800] Data 0.007 (0.005) Batch 0.353 (0.338) Remain 05:01:58 loss: 1.1068 Lr: 0.00480 [2023-12-20 16:08:00,943 INFO misc.py line 119 131400] Train: [34/100][43/800] Data 0.005 (0.005) Batch 0.375 (0.339) Remain 05:02:46 loss: 0.7915 Lr: 0.00480 [2023-12-20 16:08:01,258 INFO misc.py line 119 131400] Train: [34/100][44/800] Data 0.005 (0.005) Batch 0.313 (0.339) Remain 05:02:11 loss: 0.4594 Lr: 0.00480 [2023-12-20 16:08:01,601 INFO misc.py line 119 131400] Train: [34/100][45/800] Data 0.006 (0.005) Batch 0.345 (0.339) Remain 05:02:19 loss: 0.5198 Lr: 0.00480 [2023-12-20 16:08:01,925 INFO misc.py line 119 131400] Train: [34/100][46/800] Data 0.003 (0.005) Batch 0.324 (0.338) Remain 05:02:00 loss: 0.3323 Lr: 0.00480 [2023-12-20 16:08:02,248 INFO misc.py line 119 131400] Train: [34/100][47/800] Data 0.004 (0.005) Batch 0.324 (0.338) Remain 05:01:42 loss: 0.5459 Lr: 0.00480 [2023-12-20 16:08:02,599 INFO misc.py line 119 131400] Train: [34/100][48/800] Data 0.003 (0.005) Batch 0.350 (0.338) Remain 05:01:56 loss: 0.3382 Lr: 0.00480 [2023-12-20 16:08:02,934 INFO misc.py line 119 131400] Train: [34/100][49/800] Data 0.005 (0.005) Batch 0.336 (0.338) Remain 05:01:53 loss: 0.5872 Lr: 0.00480 [2023-12-20 16:08:03,266 INFO misc.py line 119 131400] Train: [34/100][50/800] Data 0.003 (0.004) Batch 0.332 (0.338) Remain 05:01:46 loss: 0.4573 Lr: 0.00480 [2023-12-20 16:08:03,622 INFO misc.py line 119 131400] Train: [34/100][51/800] Data 0.004 (0.004) Batch 0.354 (0.338) Remain 05:02:03 loss: 0.6618 Lr: 0.00480 [2023-12-20 16:08:03,943 INFO misc.py line 119 131400] Train: [34/100][52/800] Data 0.005 (0.004) Batch 0.322 (0.338) Remain 05:01:44 loss: 0.4180 Lr: 0.00480 [2023-12-20 16:08:04,293 INFO misc.py line 119 131400] Train: [34/100][53/800] Data 0.004 (0.004) Batch 0.352 (0.338) Remain 05:01:59 loss: 0.3495 Lr: 0.00480 [2023-12-20 16:08:04,616 INFO misc.py line 119 131400] Train: [34/100][54/800] Data 0.003 (0.004) Batch 0.323 (0.338) Remain 05:01:42 loss: 0.3705 Lr: 0.00480 [2023-12-20 16:08:04,936 INFO misc.py line 119 131400] Train: [34/100][55/800] Data 0.003 (0.004) Batch 0.319 (0.338) Remain 05:01:22 loss: 0.4034 Lr: 0.00480 [2023-12-20 16:08:05,262 INFO misc.py line 119 131400] Train: [34/100][56/800] Data 0.004 (0.004) Batch 0.327 (0.337) Remain 05:01:10 loss: 0.3167 Lr: 0.00480 [2023-12-20 16:08:05,600 INFO misc.py line 119 131400] Train: [34/100][57/800] Data 0.003 (0.004) Batch 0.338 (0.338) Remain 05:01:10 loss: 0.6041 Lr: 0.00480 [2023-12-20 16:08:05,926 INFO misc.py line 119 131400] Train: [34/100][58/800] Data 0.003 (0.004) Batch 0.325 (0.337) Remain 05:00:58 loss: 0.6002 Lr: 0.00480 [2023-12-20 16:08:06,264 INFO misc.py line 119 131400] Train: [34/100][59/800] Data 0.004 (0.004) Batch 0.339 (0.337) Remain 05:00:59 loss: 0.4696 Lr: 0.00480 [2023-12-20 16:08:06,621 INFO misc.py line 119 131400] Train: [34/100][60/800] Data 0.004 (0.004) Batch 0.355 (0.338) Remain 05:01:15 loss: 1.1291 Lr: 0.00480 [2023-12-20 16:08:06,973 INFO misc.py line 119 131400] Train: [34/100][61/800] Data 0.006 (0.004) Batch 0.352 (0.338) Remain 05:01:28 loss: 0.4426 Lr: 0.00480 [2023-12-20 16:08:07,282 INFO misc.py line 119 131400] Train: [34/100][62/800] Data 0.006 (0.004) Batch 0.311 (0.337) Remain 05:01:04 loss: 0.5056 Lr: 0.00480 [2023-12-20 16:08:07,613 INFO misc.py line 119 131400] Train: [34/100][63/800] Data 0.004 (0.004) Batch 0.331 (0.337) Remain 05:00:57 loss: 0.1858 Lr: 0.00480 [2023-12-20 16:08:07,889 INFO misc.py line 119 131400] Train: [34/100][64/800] Data 0.004 (0.004) Batch 0.276 (0.336) Remain 05:00:04 loss: 0.3548 Lr: 0.00480 [2023-12-20 16:08:08,224 INFO misc.py line 119 131400] Train: [34/100][65/800] Data 0.002 (0.004) Batch 0.335 (0.336) Remain 05:00:02 loss: 0.4930 Lr: 0.00480 [2023-12-20 16:08:08,577 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [34/100][782/800] Data 0.005 (0.005) Batch 0.338 (0.336) Remain 04:55:28 loss: 0.5085 Lr: 0.00473 [2023-12-20 16:12:09,313 INFO misc.py line 119 131400] Train: [34/100][783/800] Data 0.005 (0.005) Batch 0.470 (0.336) Remain 04:55:36 loss: 0.3860 Lr: 0.00473 [2023-12-20 16:12:09,636 INFO misc.py line 119 131400] Train: [34/100][784/800] Data 0.003 (0.005) Batch 0.323 (0.336) Remain 04:55:35 loss: 0.8158 Lr: 0.00473 [2023-12-20 16:12:09,940 INFO misc.py line 119 131400] Train: [34/100][785/800] Data 0.003 (0.005) Batch 0.303 (0.336) Remain 04:55:33 loss: 0.2954 Lr: 0.00473 [2023-12-20 16:12:10,259 INFO misc.py line 119 131400] Train: [34/100][786/800] Data 0.004 (0.005) Batch 0.319 (0.336) Remain 04:55:31 loss: 0.5210 Lr: 0.00473 [2023-12-20 16:12:10,567 INFO misc.py line 119 131400] Train: [34/100][787/800] Data 0.004 (0.005) Batch 0.308 (0.336) Remain 04:55:29 loss: 0.6911 Lr: 0.00473 [2023-12-20 16:12:10,858 INFO misc.py line 119 131400] Train: [34/100][788/800] Data 0.004 (0.005) Batch 0.291 (0.336) Remain 04:55:26 loss: 0.3435 Lr: 0.00473 [2023-12-20 16:12:11,159 INFO misc.py line 119 131400] Train: [34/100][789/800] Data 0.003 (0.005) Batch 0.301 (0.336) Remain 04:55:23 loss: 0.5067 Lr: 0.00473 [2023-12-20 16:12:11,487 INFO misc.py line 119 131400] Train: [34/100][790/800] Data 0.003 (0.005) Batch 0.328 (0.336) Remain 04:55:22 loss: 0.3218 Lr: 0.00473 [2023-12-20 16:12:11,787 INFO misc.py line 119 131400] Train: [34/100][791/800] Data 0.003 (0.005) Batch 0.300 (0.336) Remain 04:55:19 loss: 0.4977 Lr: 0.00473 [2023-12-20 16:12:12,109 INFO misc.py line 119 131400] Train: [34/100][792/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 04:55:18 loss: 0.4504 Lr: 0.00472 [2023-12-20 16:12:12,388 INFO misc.py line 119 131400] Train: [34/100][793/800] Data 0.003 (0.005) Batch 0.280 (0.335) Remain 04:55:14 loss: 0.6879 Lr: 0.00472 [2023-12-20 16:12:12,703 INFO misc.py line 119 131400] Train: [34/100][794/800] Data 0.003 (0.005) Batch 0.314 (0.335) Remain 04:55:12 loss: 0.4138 Lr: 0.00472 [2023-12-20 16:12:13,025 INFO misc.py line 119 131400] Train: [34/100][795/800] Data 0.004 (0.005) Batch 0.323 (0.335) Remain 04:55:11 loss: 0.3040 Lr: 0.00472 [2023-12-20 16:12:13,324 INFO misc.py line 119 131400] Train: [34/100][796/800] Data 0.004 (0.005) Batch 0.298 (0.335) Remain 04:55:08 loss: 0.5009 Lr: 0.00472 [2023-12-20 16:12:13,628 INFO misc.py line 119 131400] Train: [34/100][797/800] Data 0.004 (0.005) Batch 0.304 (0.335) Remain 04:55:06 loss: 0.5355 Lr: 0.00472 [2023-12-20 16:12:13,944 INFO misc.py line 119 131400] Train: [34/100][798/800] Data 0.004 (0.005) Batch 0.317 (0.335) Remain 04:55:04 loss: 0.2075 Lr: 0.00472 [2023-12-20 16:12:14,252 INFO misc.py line 119 131400] Train: [34/100][799/800] Data 0.003 (0.005) Batch 0.304 (0.335) Remain 04:55:02 loss: 0.4188 Lr: 0.00472 [2023-12-20 16:12:14,560 INFO misc.py line 119 131400] Train: [34/100][800/800] Data 0.007 (0.005) Batch 0.312 (0.335) Remain 04:55:00 loss: 0.3383 Lr: 0.00472 [2023-12-20 16:12:14,561 INFO misc.py line 136 131400] Train result: loss: 0.4494 [2023-12-20 16:12:14,561 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:12:35,761 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.3455 [2023-12-20 16:12:35,908 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1497 [2023-12-20 16:12:36,003 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3425 [2023-12-20 16:12:36,116 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1992 [2023-12-20 16:12:36,232 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3611 [2023-12-20 16:12:36,340 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.4993 [2023-12-20 16:12:36,432 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.5319 [2023-12-20 16:12:36,544 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9729 [2023-12-20 16:12:36,627 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2984 [2023-12-20 16:12:36,714 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4593 [2023-12-20 16:12:36,806 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.9540 [2023-12-20 16:12:36,946 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3646 [2023-12-20 16:12:37,072 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2531 [2023-12-20 16:12:37,229 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3004 [2023-12-20 16:12:37,326 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2338 [2023-12-20 16:12:37,463 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9448 [2023-12-20 16:12:37,578 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3170 [2023-12-20 16:12:37,689 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4937 [2023-12-20 16:12:37,802 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1830 [2023-12-20 16:12:37,878 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6555 [2023-12-20 16:12:37,992 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.7528 [2023-12-20 16:12:38,152 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1797 [2023-12-20 16:12:38,273 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7159 [2023-12-20 16:12:38,417 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2086 [2023-12-20 16:12:38,561 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.6791 [2023-12-20 16:12:38,653 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4140 [2023-12-20 16:12:38,813 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4928 [2023-12-20 16:12:38,939 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5941 [2023-12-20 16:12:39,033 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.9534 [2023-12-20 16:12:39,180 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6623 [2023-12-20 16:12:39,285 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6725 [2023-12-20 16:12:39,405 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3907 [2023-12-20 16:12:39,497 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5746 [2023-12-20 16:12:39,572 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1983 [2023-12-20 16:12:39,670 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6862 [2023-12-20 16:12:39,763 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6579 [2023-12-20 16:12:39,892 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.6837 [2023-12-20 16:12:40,004 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1251 [2023-12-20 16:12:40,085 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5702 [2023-12-20 16:12:40,228 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4056 [2023-12-20 16:12:40,373 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0305 [2023-12-20 16:12:40,472 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3974 [2023-12-20 16:12:40,597 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4050 [2023-12-20 16:12:40,754 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8782 [2023-12-20 16:12:40,875 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7951 [2023-12-20 16:12:40,980 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3491 [2023-12-20 16:12:41,153 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4486 [2023-12-20 16:12:41,249 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4270 [2023-12-20 16:12:41,395 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0964 [2023-12-20 16:12:41,488 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8934 [2023-12-20 16:12:41,567 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7004 [2023-12-20 16:12:41,674 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3890 [2023-12-20 16:12:41,824 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8715 [2023-12-20 16:12:41,960 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2852 [2023-12-20 16:12:42,063 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1929 [2023-12-20 16:12:42,152 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7065 [2023-12-20 16:12:42,254 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4090 [2023-12-20 16:12:42,414 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3002 [2023-12-20 16:12:42,510 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3967 [2023-12-20 16:12:42,606 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.6357 [2023-12-20 16:12:42,704 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2650 [2023-12-20 16:12:42,796 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3945 [2023-12-20 16:12:42,883 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7908 [2023-12-20 16:12:42,985 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6636 [2023-12-20 16:12:43,110 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.0157 [2023-12-20 16:12:43,194 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3495 [2023-12-20 16:12:43,294 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4776 [2023-12-20 16:12:43,387 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0117 [2023-12-20 16:12:43,473 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3256 [2023-12-20 16:12:43,558 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0151 [2023-12-20 16:12:43,657 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9585 [2023-12-20 16:12:43,748 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4326 [2023-12-20 16:12:43,885 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1729 [2023-12-20 16:12:43,983 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5915 [2023-12-20 16:12:44,099 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6521 [2023-12-20 16:12:44,201 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8584 [2023-12-20 16:12:44,312 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2920 [2023-12-20 16:12:44,466 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.6041 [2023-12-20 16:12:45,676 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7177/0.8148/0.9011. [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8608/0.9465 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9606/0.9801 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6381/0.7326 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7792/0.8298 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8978/0.9472 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8171/0.8783 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7064/0.7747 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6641/0.8242 [2023-12-20 16:12:45,676 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6790/0.7910 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7412/0.8571 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3609/0.4165 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6708/0.7576 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5352/0.9279 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7841/0.8323 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6313/0.6615 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7143/0.7865 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8990/0.9780 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6044/0.8060 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8469/0.9119 [2023-12-20 16:12:45,677 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5637/0.6568 [2023-12-20 16:12:45,677 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:12:45,679 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:12:45,679 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:12:48,849 INFO misc.py line 119 131400] Train: [35/100][1/800] Data 0.724 (0.724) Batch 0.991 (0.991) Remain 14:31:53 loss: 0.3495 Lr: 0.00472 [2023-12-20 16:12:49,424 INFO misc.py line 119 131400] Train: [35/100][2/800] Data 0.281 (0.281) Batch 0.575 (0.575) Remain 08:25:40 loss: 0.3367 Lr: 0.00472 [2023-12-20 16:12:49,768 INFO misc.py line 119 131400] Train: [35/100][3/800] Data 0.005 (0.005) Batch 0.344 (0.344) Remain 05:02:38 loss: 0.7058 Lr: 0.00472 [2023-12-20 16:12:50,074 INFO misc.py line 119 131400] Train: [35/100][4/800] Data 0.004 (0.004) Batch 0.306 (0.306) Remain 04:29:29 loss: 0.7765 Lr: 0.00472 [2023-12-20 16:12:50,413 INFO misc.py line 119 131400] Train: [35/100][5/800] Data 0.004 (0.004) Batch 0.334 (0.320) Remain 04:41:33 loss: 0.5644 Lr: 0.00472 [2023-12-20 16:12:50,724 INFO misc.py line 119 131400] Train: [35/100][6/800] Data 0.010 (0.006) Batch 0.317 (0.319) Remain 04:40:43 loss: 0.6144 Lr: 0.00472 [2023-12-20 16:12:51,029 INFO misc.py line 119 131400] Train: [35/100][7/800] Data 0.003 (0.005) Batch 0.306 (0.316) Remain 04:37:44 loss: 0.4222 Lr: 0.00472 [2023-12-20 16:12:51,359 INFO misc.py line 119 131400] Train: [35/100][8/800] Data 0.003 (0.005) Batch 0.328 (0.318) Remain 04:39:54 loss: 0.4062 Lr: 0.00472 [2023-12-20 16:12:51,689 INFO misc.py line 119 131400] Train: [35/100][9/800] Data 0.005 (0.005) Batch 0.331 (0.320) Remain 04:41:44 loss: 0.4724 Lr: 0.00472 [2023-12-20 16:12:51,997 INFO misc.py line 119 131400] Train: [35/100][10/800] Data 0.005 (0.005) Batch 0.309 (0.319) Remain 04:40:19 loss: 0.2990 Lr: 0.00472 [2023-12-20 16:12:52,346 INFO misc.py line 119 131400] Train: [35/100][11/800] Data 0.003 (0.005) Batch 0.349 (0.322) Remain 04:43:37 loss: 0.6220 Lr: 0.00472 [2023-12-20 16:12:52,643 INFO misc.py line 119 131400] Train: [35/100][12/800] Data 0.003 (0.004) Batch 0.297 (0.320) Remain 04:41:09 loss: 0.3417 Lr: 0.00472 [2023-12-20 16:12:52,956 INFO misc.py line 119 131400] Train: [35/100][13/800] Data 0.003 (0.004) Batch 0.313 (0.319) Remain 04:40:34 loss: 0.5996 Lr: 0.00472 [2023-12-20 16:12:53,282 INFO misc.py line 119 131400] Train: [35/100][14/800] Data 0.003 (0.004) Batch 0.325 (0.320) Remain 04:41:05 loss: 0.2965 Lr: 0.00472 [2023-12-20 16:12:53,601 INFO misc.py line 119 131400] Train: [35/100][15/800] Data 0.004 (0.004) Batch 0.320 (0.320) Remain 04:41:07 loss: 0.5505 Lr: 0.00472 [2023-12-20 16:12:53,933 INFO misc.py line 119 131400] Train: [35/100][16/800] Data 0.003 (0.004) Batch 0.332 (0.320) Remain 04:41:56 loss: 0.5675 Lr: 0.00472 [2023-12-20 16:12:54,276 INFO misc.py line 119 131400] Train: [35/100][17/800] Data 0.003 (0.004) Batch 0.343 (0.322) Remain 04:43:19 loss: 0.4330 Lr: 0.00472 [2023-12-20 16:12:54,601 INFO misc.py line 119 131400] Train: [35/100][18/800] Data 0.004 (0.004) Batch 0.325 (0.322) Remain 04:43:29 loss: 0.5604 Lr: 0.00472 [2023-12-20 16:12:54,919 INFO misc.py line 119 131400] Train: [35/100][19/800] Data 0.003 (0.004) Batch 0.319 (0.322) Remain 04:43:17 loss: 0.4286 Lr: 0.00472 [2023-12-20 16:12:55,272 INFO misc.py line 119 131400] Train: [35/100][20/800] Data 0.003 (0.004) Batch 0.348 (0.324) Remain 04:44:37 loss: 0.3175 Lr: 0.00472 [2023-12-20 16:12:55,585 INFO misc.py line 119 131400] Train: [35/100][21/800] Data 0.007 (0.004) Batch 0.318 (0.323) Remain 04:44:19 loss: 0.2460 Lr: 0.00472 [2023-12-20 16:12:55,888 INFO misc.py line 119 131400] Train: [35/100][22/800] Data 0.003 (0.004) Batch 0.302 (0.322) Remain 04:43:20 loss: 0.4500 Lr: 0.00472 [2023-12-20 16:12:56,204 INFO misc.py line 119 131400] Train: [35/100][23/800] Data 0.004 (0.004) Batch 0.317 (0.322) Remain 04:43:06 loss: 0.6057 Lr: 0.00472 [2023-12-20 16:12:56,543 INFO misc.py line 119 131400] Train: [35/100][24/800] Data 0.003 (0.004) Batch 0.339 (0.323) Remain 04:43:50 loss: 0.6300 Lr: 0.00472 [2023-12-20 16:12:56,881 INFO misc.py line 119 131400] Train: [35/100][25/800] Data 0.003 (0.004) Batch 0.338 (0.323) Remain 04:44:26 loss: 0.5877 Lr: 0.00472 [2023-12-20 16:12:57,172 INFO misc.py line 119 131400] Train: [35/100][26/800] Data 0.003 (0.004) Batch 0.290 (0.322) Remain 04:43:09 loss: 0.6578 Lr: 0.00472 [2023-12-20 16:12:57,505 INFO misc.py line 119 131400] Train: [35/100][27/800] Data 0.004 (0.004) Batch 0.330 (0.322) Remain 04:43:27 loss: 0.5330 Lr: 0.00472 [2023-12-20 16:12:57,829 INFO misc.py line 119 131400] Train: [35/100][28/800] Data 0.007 (0.004) Batch 0.327 (0.322) Remain 04:43:36 loss: 0.1824 Lr: 0.00472 [2023-12-20 16:12:58,291 INFO misc.py line 119 131400] Train: [35/100][29/800] Data 0.004 (0.004) Batch 0.463 (0.328) Remain 04:48:21 loss: 0.9014 Lr: 0.00472 [2023-12-20 16:12:58,617 INFO misc.py line 119 131400] Train: [35/100][30/800] Data 0.004 (0.004) Batch 0.325 (0.328) Remain 04:48:15 loss: 0.4569 Lr: 0.00472 [2023-12-20 16:12:58,953 INFO misc.py line 119 131400] Train: [35/100][31/800] Data 0.005 (0.004) Batch 0.337 (0.328) Remain 04:48:31 loss: 0.4938 Lr: 0.00472 [2023-12-20 16:12:59,297 INFO misc.py line 119 131400] Train: [35/100][32/800] Data 0.004 (0.004) Batch 0.344 (0.329) Remain 04:48:59 loss: 0.3975 Lr: 0.00472 [2023-12-20 16:12:59,634 INFO misc.py line 119 131400] Train: [35/100][33/800] Data 0.003 (0.004) Batch 0.337 (0.329) Remain 04:49:14 loss: 0.3708 Lr: 0.00472 [2023-12-20 16:12:59,958 INFO misc.py line 119 131400] Train: [35/100][34/800] Data 0.003 (0.004) Batch 0.323 (0.329) Remain 04:49:05 loss: 0.3700 Lr: 0.00472 [2023-12-20 16:13:00,275 INFO misc.py line 119 131400] Train: [35/100][35/800] Data 0.004 (0.004) Batch 0.317 (0.328) Remain 04:48:44 loss: 0.2382 Lr: 0.00472 [2023-12-20 16:13:00,592 INFO misc.py line 119 131400] Train: [35/100][36/800] Data 0.005 (0.004) Batch 0.318 (0.328) Remain 04:48:28 loss: 0.7349 Lr: 0.00472 [2023-12-20 16:13:00,895 INFO misc.py line 119 131400] Train: [35/100][37/800] Data 0.003 (0.004) Batch 0.303 (0.327) Remain 04:47:48 loss: 0.3039 Lr: 0.00472 [2023-12-20 16:13:01,204 INFO misc.py line 119 131400] Train: [35/100][38/800] Data 0.003 (0.004) Batch 0.310 (0.327) Remain 04:47:22 loss: 0.4145 Lr: 0.00472 [2023-12-20 16:13:01,531 INFO misc.py line 119 131400] Train: [35/100][39/800] Data 0.003 (0.004) Batch 0.326 (0.327) Remain 04:47:20 loss: 0.5346 Lr: 0.00472 [2023-12-20 16:13:01,849 INFO misc.py line 119 131400] Train: [35/100][40/800] Data 0.004 (0.004) Batch 0.316 (0.326) Remain 04:47:05 loss: 0.4902 Lr: 0.00472 [2023-12-20 16:13:02,185 INFO misc.py line 119 131400] Train: [35/100][41/800] Data 0.006 (0.004) Batch 0.338 (0.327) Remain 04:47:21 loss: 0.4852 Lr: 0.00472 [2023-12-20 16:13:02,520 INFO misc.py line 119 131400] Train: [35/100][42/800] Data 0.003 (0.004) Batch 0.334 (0.327) Remain 04:47:31 loss: 0.3643 Lr: 0.00472 [2023-12-20 16:13:02,843 INFO misc.py line 119 131400] Train: [35/100][43/800] Data 0.005 (0.004) Batch 0.322 (0.327) Remain 04:47:24 loss: 0.4733 Lr: 0.00472 [2023-12-20 16:13:03,145 INFO misc.py line 119 131400] Train: [35/100][44/800] Data 0.005 (0.004) Batch 0.304 (0.326) Remain 04:46:54 loss: 0.3941 Lr: 0.00472 [2023-12-20 16:13:03,623 INFO misc.py line 119 131400] Train: [35/100][45/800] Data 0.003 (0.004) Batch 0.478 (0.330) Remain 04:50:04 loss: 0.6091 Lr: 0.00472 [2023-12-20 16:13:03,932 INFO misc.py line 119 131400] Train: [35/100][46/800] Data 0.003 (0.004) Batch 0.309 (0.329) Remain 04:49:38 loss: 0.5685 Lr: 0.00472 [2023-12-20 16:13:04,236 INFO misc.py line 119 131400] Train: [35/100][47/800] Data 0.002 (0.004) Batch 0.304 (0.329) Remain 04:49:07 loss: 0.2509 Lr: 0.00472 [2023-12-20 16:13:04,550 INFO misc.py line 119 131400] Train: [35/100][48/800] Data 0.003 (0.004) Batch 0.313 (0.328) Remain 04:48:48 loss: 0.3580 Lr: 0.00472 [2023-12-20 16:13:04,867 INFO misc.py line 119 131400] Train: [35/100][49/800] Data 0.004 (0.004) Batch 0.319 (0.328) Remain 04:48:37 loss: 0.6992 Lr: 0.00472 [2023-12-20 16:13:05,176 INFO misc.py line 119 131400] Train: [35/100][50/800] Data 0.003 (0.004) Batch 0.307 (0.328) Remain 04:48:12 loss: 0.3930 Lr: 0.00472 [2023-12-20 16:13:05,548 INFO misc.py line 119 131400] Train: [35/100][51/800] Data 0.005 (0.004) Batch 0.373 (0.329) Remain 04:49:02 loss: 0.5462 Lr: 0.00472 [2023-12-20 16:13:05,874 INFO misc.py line 119 131400] Train: [35/100][52/800] Data 0.003 (0.004) Batch 0.325 (0.329) Remain 04:48:58 loss: 0.4898 Lr: 0.00472 [2023-12-20 16:13:06,215 INFO misc.py line 119 131400] Train: [35/100][53/800] Data 0.004 (0.004) Batch 0.341 (0.329) Remain 04:49:11 loss: 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line 119 131400] Train: [35/100][782/800] Data 0.004 (0.005) Batch 0.329 (0.336) Remain 04:51:05 loss: 0.2137 Lr: 0.00464 [2023-12-20 16:17:11,650 INFO misc.py line 119 131400] Train: [35/100][783/800] Data 0.004 (0.005) Batch 0.325 (0.336) Remain 04:51:04 loss: 0.4701 Lr: 0.00464 [2023-12-20 16:17:11,971 INFO misc.py line 119 131400] Train: [35/100][784/800] Data 0.005 (0.005) Batch 0.320 (0.336) Remain 04:51:02 loss: 0.6997 Lr: 0.00464 [2023-12-20 16:17:12,310 INFO misc.py line 119 131400] Train: [35/100][785/800] Data 0.005 (0.005) Batch 0.341 (0.336) Remain 04:51:03 loss: 0.4474 Lr: 0.00464 [2023-12-20 16:17:12,647 INFO misc.py line 119 131400] Train: [35/100][786/800] Data 0.003 (0.005) Batch 0.338 (0.336) Remain 04:51:02 loss: 0.5006 Lr: 0.00464 [2023-12-20 16:17:12,948 INFO misc.py line 119 131400] Train: [35/100][787/800] Data 0.003 (0.005) Batch 0.301 (0.336) Remain 04:51:00 loss: 0.5744 Lr: 0.00464 [2023-12-20 16:17:13,248 INFO misc.py line 119 131400] Train: 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Batch 0.282 (0.335) Remain 04:50:40 loss: 0.6611 Lr: 0.00464 [2023-12-20 16:17:15,326 INFO misc.py line 119 131400] Train: [35/100][795/800] Data 0.003 (0.005) Batch 0.284 (0.335) Remain 04:50:37 loss: 0.4353 Lr: 0.00464 [2023-12-20 16:17:15,615 INFO misc.py line 119 131400] Train: [35/100][796/800] Data 0.005 (0.005) Batch 0.291 (0.335) Remain 04:50:33 loss: 0.2277 Lr: 0.00464 [2023-12-20 16:17:16,006 INFO misc.py line 119 131400] Train: [35/100][797/800] Data 0.003 (0.005) Batch 0.390 (0.335) Remain 04:50:37 loss: 0.3193 Lr: 0.00464 [2023-12-20 16:17:16,297 INFO misc.py line 119 131400] Train: [35/100][798/800] Data 0.004 (0.005) Batch 0.292 (0.335) Remain 04:50:34 loss: 0.5558 Lr: 0.00464 [2023-12-20 16:17:16,561 INFO misc.py line 119 131400] Train: [35/100][799/800] Data 0.003 (0.005) Batch 0.264 (0.335) Remain 04:50:28 loss: 0.4087 Lr: 0.00464 [2023-12-20 16:17:16,866 INFO misc.py line 119 131400] Train: [35/100][800/800] Data 0.003 (0.005) Batch 0.305 (0.335) Remain 04:50:26 loss: 0.3488 Lr: 0.00464 [2023-12-20 16:17:16,867 INFO misc.py line 136 131400] Train result: loss: 0.4428 [2023-12-20 16:17:16,867 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:17:39,255 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1435 [2023-12-20 16:17:39,750 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1534 [2023-12-20 16:17:39,839 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.7191 [2023-12-20 16:17:39,962 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2618 [2023-12-20 16:17:40,077 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3791 [2023-12-20 16:17:40,186 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1392 [2023-12-20 16:17:40,291 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.5298 [2023-12-20 16:17:40,406 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9537 [2023-12-20 16:17:40,493 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2980 [2023-12-20 16:17:40,578 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4864 [2023-12-20 16:17:40,676 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7841 [2023-12-20 16:17:40,819 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4108 [2023-12-20 16:17:40,938 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2033 [2023-12-20 16:17:41,097 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2765 [2023-12-20 16:17:41,196 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.9700 [2023-12-20 16:17:41,331 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8467 [2023-12-20 16:17:41,443 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2589 [2023-12-20 16:17:41,560 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7179 [2023-12-20 16:17:41,676 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3271 [2023-12-20 16:17:41,762 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3951 [2023-12-20 16:17:41,880 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4730 [2023-12-20 16:17:42,038 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1741 [2023-12-20 16:17:42,163 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.2655 [2023-12-20 16:17:42,306 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4444 [2023-12-20 16:17:42,452 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1838 [2023-12-20 16:17:42,535 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4517 [2023-12-20 16:17:42,692 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9378 [2023-12-20 16:17:42,815 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5140 [2023-12-20 16:17:42,912 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6274 [2023-12-20 16:17:43,059 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5605 [2023-12-20 16:17:43,165 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5674 [2023-12-20 16:17:43,285 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3872 [2023-12-20 16:17:43,370 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1374 [2023-12-20 16:17:43,440 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3357 [2023-12-20 16:17:43,538 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5074 [2023-12-20 16:17:43,629 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3412 [2023-12-20 16:17:43,759 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9164 [2023-12-20 16:17:43,869 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1174 [2023-12-20 16:17:43,956 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.8800 [2023-12-20 16:17:44,098 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5596 [2023-12-20 16:17:44,243 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0225 [2023-12-20 16:17:44,344 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1446 [2023-12-20 16:17:44,466 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3763 [2023-12-20 16:17:44,607 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9546 [2023-12-20 16:17:44,724 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2266 [2023-12-20 16:17:44,829 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4341 [2023-12-20 16:17:44,996 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5089 [2023-12-20 16:17:45,098 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4097 [2023-12-20 16:17:45,248 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5848 [2023-12-20 16:17:45,346 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9140 [2023-12-20 16:17:45,436 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7160 [2023-12-20 16:17:45,548 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.7023 [2023-12-20 16:17:45,697 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3752 [2023-12-20 16:17:45,834 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2764 [2023-12-20 16:17:45,953 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.7077 [2023-12-20 16:17:46,046 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8830 [2023-12-20 16:17:46,147 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3408 [2023-12-20 16:17:46,310 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2414 [2023-12-20 16:17:46,413 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1691 [2023-12-20 16:17:46,508 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1975 [2023-12-20 16:17:46,608 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4692 [2023-12-20 16:17:46,703 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2777 [2023-12-20 16:17:46,798 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8561 [2023-12-20 16:17:46,905 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9271 [2023-12-20 16:17:47,034 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5325 [2023-12-20 16:17:47,119 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4843 [2023-12-20 16:17:47,217 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4083 [2023-12-20 16:17:47,323 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0195 [2023-12-20 16:17:47,410 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2438 [2023-12-20 16:17:47,495 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0259 [2023-12-20 16:17:47,596 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8535 [2023-12-20 16:17:47,698 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6027 [2023-12-20 16:17:47,839 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1173 [2023-12-20 16:17:47,939 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5471 [2023-12-20 16:17:48,060 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.8951 [2023-12-20 16:17:48,167 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.0209 [2023-12-20 16:17:48,257 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5336 [2023-12-20 16:17:48,411 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1719 [2023-12-20 16:17:50,180 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7316/0.8255/0.9059. [2023-12-20 16:17:50,180 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8565/0.9352 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9642/0.9834 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6517/0.8559 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7803/0.8711 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9043/0.9604 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8416/0.8794 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7410/0.8102 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7009/0.8511 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.5991/0.7015 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8237/0.9499 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3849/0.5193 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6198/0.7210 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6609/0.8581 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7465/0.8610 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5937/0.6521 [2023-12-20 16:17:50,181 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7355/0.8040 [2023-12-20 16:17:50,182 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9347/0.9744 [2023-12-20 16:17:50,182 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6407/0.7802 [2023-12-20 16:17:50,182 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8812/0.9237 [2023-12-20 16:17:50,182 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5713/0.6187 [2023-12-20 16:17:50,182 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:17:50,183 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:17:50,184 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:17:53,526 INFO misc.py line 119 131400] Train: [36/100][1/800] Data 0.692 (0.692) Batch 0.960 (0.960) Remain 13:52:14 loss: 0.7054 Lr: 0.00464 [2023-12-20 16:17:53,894 INFO misc.py line 119 131400] Train: [36/100][2/800] Data 0.005 (0.005) Batch 0.367 (0.367) Remain 05:18:23 loss: 0.2761 Lr: 0.00464 [2023-12-20 16:17:54,210 INFO misc.py line 119 131400] Train: [36/100][3/800] Data 0.004 (0.004) Batch 0.317 (0.317) Remain 04:34:23 loss: 0.3285 Lr: 0.00464 [2023-12-20 16:17:54,500 INFO misc.py line 119 131400] Train: [36/100][4/800] Data 0.004 (0.004) Batch 0.289 (0.289) Remain 04:10:08 loss: 0.3543 Lr: 0.00464 [2023-12-20 16:17:54,849 INFO misc.py line 119 131400] Train: [36/100][5/800] Data 0.006 (0.005) Batch 0.350 (0.319) Remain 04:36:39 loss: 0.2981 Lr: 0.00464 [2023-12-20 16:17:55,234 INFO misc.py line 119 131400] Train: [36/100][6/800] Data 0.004 (0.005) Batch 0.385 (0.341) Remain 04:55:40 loss: 0.2955 Lr: 0.00464 [2023-12-20 16:17:55,594 INFO misc.py line 119 131400] Train: [36/100][7/800] Data 0.004 (0.005) Batch 0.355 (0.345) Remain 04:58:41 loss: 0.2063 Lr: 0.00464 [2023-12-20 16:17:55,892 INFO misc.py line 119 131400] Train: [36/100][8/800] Data 0.009 (0.006) Batch 0.303 (0.336) Remain 04:51:32 loss: 0.3490 Lr: 0.00464 [2023-12-20 16:17:56,257 INFO misc.py line 119 131400] Train: [36/100][9/800] Data 0.004 (0.005) Batch 0.365 (0.341) Remain 04:55:38 loss: 0.2890 Lr: 0.00464 [2023-12-20 16:17:56,606 INFO misc.py line 119 131400] Train: [36/100][10/800] Data 0.004 (0.005) Batch 0.343 (0.341) Remain 04:55:54 loss: 0.3658 Lr: 0.00464 [2023-12-20 16:17:56,927 INFO misc.py line 119 131400] Train: [36/100][11/800] Data 0.010 (0.006) Batch 0.327 (0.340) Remain 04:54:22 loss: 0.6630 Lr: 0.00464 [2023-12-20 16:17:57,259 INFO misc.py line 119 131400] Train: [36/100][12/800] Data 0.003 (0.005) Batch 0.331 (0.339) Remain 04:53:32 loss: 0.5625 Lr: 0.00464 [2023-12-20 16:17:57,595 INFO misc.py line 119 131400] Train: [36/100][13/800] Data 0.004 (0.005) Batch 0.336 (0.339) Remain 04:53:20 loss: 0.4460 Lr: 0.00464 [2023-12-20 16:17:57,926 INFO misc.py line 119 131400] Train: [36/100][14/800] Data 0.003 (0.005) Batch 0.330 (0.338) Remain 04:52:41 loss: 0.6897 Lr: 0.00464 [2023-12-20 16:17:58,268 INFO misc.py line 119 131400] Train: [36/100][15/800] Data 0.004 (0.005) Batch 0.342 (0.338) Remain 04:52:58 loss: 0.2186 Lr: 0.00464 [2023-12-20 16:17:58,577 INFO misc.py line 119 131400] Train: [36/100][16/800] Data 0.004 (0.005) Batch 0.310 (0.336) Remain 04:51:06 loss: 0.2790 Lr: 0.00464 [2023-12-20 16:17:58,871 INFO misc.py line 119 131400] Train: [36/100][17/800] Data 0.003 (0.005) Batch 0.293 (0.333) Remain 04:48:26 loss: 0.3250 Lr: 0.00464 [2023-12-20 16:17:59,252 INFO misc.py line 119 131400] Train: [36/100][18/800] Data 0.004 (0.005) Batch 0.382 (0.336) Remain 04:51:15 loss: 0.4966 Lr: 0.00464 [2023-12-20 16:17:59,624 INFO misc.py line 119 131400] Train: [36/100][19/800] Data 0.004 (0.005) Batch 0.371 (0.338) Remain 04:53:09 loss: 0.3425 Lr: 0.00464 [2023-12-20 16:17:59,933 INFO misc.py line 119 131400] Train: [36/100][20/800] Data 0.004 (0.005) Batch 0.310 (0.337) Remain 04:51:42 loss: 0.3695 Lr: 0.00464 [2023-12-20 16:18:00,255 INFO misc.py line 119 131400] Train: [36/100][21/800] Data 0.003 (0.004) Batch 0.322 (0.336) Remain 04:50:59 loss: 0.5612 Lr: 0.00464 [2023-12-20 16:18:00,582 INFO misc.py line 119 131400] Train: [36/100][22/800] Data 0.003 (0.004) Batch 0.326 (0.335) Remain 04:50:32 loss: 0.4844 Lr: 0.00464 [2023-12-20 16:18:00,884 INFO misc.py line 119 131400] Train: [36/100][23/800] Data 0.004 (0.004) Batch 0.298 (0.334) Remain 04:48:55 loss: 0.3197 Lr: 0.00464 [2023-12-20 16:18:01,226 INFO misc.py line 119 131400] Train: [36/100][24/800] Data 0.007 (0.004) Batch 0.345 (0.334) Remain 04:49:25 loss: 0.6017 Lr: 0.00464 [2023-12-20 16:18:01,549 INFO misc.py line 119 131400] Train: [36/100][25/800] Data 0.004 (0.004) Batch 0.323 (0.334) Remain 04:48:58 loss: 0.5023 Lr: 0.00464 [2023-12-20 16:18:01,857 INFO misc.py line 119 131400] Train: [36/100][26/800] Data 0.003 (0.004) Batch 0.309 (0.333) Remain 04:48:01 loss: 0.3039 Lr: 0.00464 [2023-12-20 16:18:02,173 INFO misc.py line 119 131400] Train: [36/100][27/800] Data 0.003 (0.004) Batch 0.315 (0.332) Remain 04:47:23 loss: 0.6902 Lr: 0.00464 [2023-12-20 16:18:02,514 INFO misc.py line 119 131400] Train: [36/100][28/800] Data 0.003 (0.004) Batch 0.342 (0.332) Remain 04:47:43 loss: 0.6480 Lr: 0.00464 [2023-12-20 16:18:02,834 INFO misc.py line 119 131400] Train: [36/100][29/800] Data 0.003 (0.004) Batch 0.320 (0.332) Remain 04:47:19 loss: 0.4932 Lr: 0.00464 [2023-12-20 16:18:03,175 INFO misc.py line 119 131400] Train: [36/100][30/800] Data 0.003 (0.004) Batch 0.341 (0.332) Remain 04:47:36 loss: 0.4088 Lr: 0.00464 [2023-12-20 16:18:03,474 INFO misc.py line 119 131400] Train: [36/100][31/800] Data 0.003 (0.004) Batch 0.298 (0.331) Remain 04:46:33 loss: 0.3750 Lr: 0.00464 [2023-12-20 16:18:03,804 INFO misc.py line 119 131400] Train: [36/100][32/800] Data 0.004 (0.004) Batch 0.330 (0.331) Remain 04:46:32 loss: 0.4838 Lr: 0.00464 [2023-12-20 16:18:04,126 INFO misc.py line 119 131400] Train: [36/100][33/800] Data 0.005 (0.004) Batch 0.322 (0.331) Remain 04:46:17 loss: 0.4086 Lr: 0.00464 [2023-12-20 16:18:04,465 INFO misc.py line 119 131400] Train: [36/100][34/800] Data 0.004 (0.004) Batch 0.339 (0.331) Remain 04:46:30 loss: 0.3468 Lr: 0.00464 [2023-12-20 16:18:04,799 INFO misc.py line 119 131400] Train: [36/100][35/800] Data 0.003 (0.004) Batch 0.334 (0.331) Remain 04:46:34 loss: 0.5638 Lr: 0.00464 [2023-12-20 16:18:05,084 INFO misc.py line 119 131400] Train: [36/100][36/800] Data 0.004 (0.004) Batch 0.286 (0.330) Remain 04:45:22 loss: 0.2899 Lr: 0.00464 [2023-12-20 16:18:05,426 INFO misc.py line 119 131400] Train: [36/100][37/800] Data 0.004 (0.004) Batch 0.341 (0.330) Remain 04:45:40 loss: 0.3625 Lr: 0.00464 [2023-12-20 16:18:05,741 INFO misc.py line 119 131400] Train: [36/100][38/800] Data 0.006 (0.004) Batch 0.316 (0.329) Remain 04:45:19 loss: 0.2581 Lr: 0.00464 [2023-12-20 16:18:06,072 INFO misc.py line 119 131400] Train: [36/100][39/800] Data 0.003 (0.004) Batch 0.331 (0.330) Remain 04:45:21 loss: 0.6033 Lr: 0.00464 [2023-12-20 16:18:06,426 INFO misc.py line 119 131400] Train: [36/100][40/800] Data 0.005 (0.004) Batch 0.354 (0.330) Remain 04:45:55 loss: 0.4935 Lr: 0.00464 [2023-12-20 16:18:06,764 INFO misc.py line 119 131400] Train: [36/100][41/800] Data 0.004 (0.004) Batch 0.337 (0.330) Remain 04:46:04 loss: 0.4510 Lr: 0.00464 [2023-12-20 16:18:07,090 INFO misc.py line 119 131400] Train: [36/100][42/800] Data 0.005 (0.004) Batch 0.326 (0.330) Remain 04:45:58 loss: 0.4605 Lr: 0.00464 [2023-12-20 16:18:07,421 INFO misc.py line 119 131400] Train: [36/100][43/800] Data 0.004 (0.004) Batch 0.331 (0.330) Remain 04:45:59 loss: 0.3113 Lr: 0.00464 [2023-12-20 16:18:07,726 INFO misc.py line 119 131400] Train: [36/100][44/800] Data 0.004 (0.004) Batch 0.306 (0.330) Remain 04:45:28 loss: 0.5946 Lr: 0.00464 [2023-12-20 16:18:08,046 INFO misc.py line 119 131400] Train: [36/100][45/800] Data 0.003 (0.004) Batch 0.320 (0.329) Remain 04:45:16 loss: 0.5555 Lr: 0.00464 [2023-12-20 16:18:08,381 INFO misc.py line 119 131400] Train: [36/100][46/800] Data 0.003 (0.004) Batch 0.333 (0.330) Remain 04:45:20 loss: 0.8734 Lr: 0.00464 [2023-12-20 16:18:08,724 INFO misc.py line 119 131400] Train: [36/100][47/800] Data 0.005 (0.004) 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line 119 131400] Train: [36/100][782/800] Data 0.003 (0.005) Batch 0.342 (0.333) Remain 04:44:28 loss: 0.3554 Lr: 0.00456 [2023-12-20 16:22:14,135 INFO misc.py line 119 131400] Train: [36/100][783/800] Data 0.004 (0.005) Batch 0.325 (0.333) Remain 04:44:27 loss: 0.3539 Lr: 0.00456 [2023-12-20 16:22:14,478 INFO misc.py line 119 131400] Train: [36/100][784/800] Data 0.004 (0.005) Batch 0.342 (0.333) Remain 04:44:27 loss: 0.5403 Lr: 0.00456 [2023-12-20 16:22:14,800 INFO misc.py line 119 131400] Train: [36/100][785/800] Data 0.004 (0.005) Batch 0.322 (0.333) Remain 04:44:26 loss: 0.2045 Lr: 0.00456 [2023-12-20 16:22:15,150 INFO misc.py line 119 131400] Train: [36/100][786/800] Data 0.003 (0.005) Batch 0.350 (0.333) Remain 04:44:27 loss: 0.8278 Lr: 0.00456 [2023-12-20 16:22:15,440 INFO misc.py line 119 131400] Train: [36/100][787/800] Data 0.004 (0.005) Batch 0.290 (0.333) Remain 04:44:24 loss: 0.2369 Lr: 0.00456 [2023-12-20 16:22:15,742 INFO misc.py line 119 131400] Train: 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Batch 0.316 (0.333) Remain 04:44:19 loss: 0.4583 Lr: 0.00456 [2023-12-20 16:22:18,036 INFO misc.py line 119 131400] Train: [36/100][795/800] Data 0.003 (0.005) Batch 0.304 (0.333) Remain 04:44:16 loss: 0.3137 Lr: 0.00456 [2023-12-20 16:22:18,375 INFO misc.py line 119 131400] Train: [36/100][796/800] Data 0.003 (0.005) Batch 0.338 (0.333) Remain 04:44:16 loss: 0.4348 Lr: 0.00456 [2023-12-20 16:22:18,691 INFO misc.py line 119 131400] Train: [36/100][797/800] Data 0.004 (0.005) Batch 0.316 (0.333) Remain 04:44:15 loss: 0.2664 Lr: 0.00456 [2023-12-20 16:22:19,020 INFO misc.py line 119 131400] Train: [36/100][798/800] Data 0.004 (0.005) Batch 0.329 (0.333) Remain 04:44:14 loss: 0.4620 Lr: 0.00456 [2023-12-20 16:22:19,314 INFO misc.py line 119 131400] Train: [36/100][799/800] Data 0.004 (0.005) Batch 0.291 (0.333) Remain 04:44:11 loss: 0.3817 Lr: 0.00456 [2023-12-20 16:22:19,628 INFO misc.py line 119 131400] Train: [36/100][800/800] Data 0.007 (0.005) Batch 0.317 (0.333) Remain 04:44:10 loss: 0.6799 Lr: 0.00456 [2023-12-20 16:22:19,628 INFO misc.py line 136 131400] Train result: loss: 0.4387 [2023-12-20 16:22:19,629 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:22:41,260 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1405 [2023-12-20 16:22:41,333 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1876 [2023-12-20 16:22:41,423 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2974 [2023-12-20 16:22:41,532 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.0667 [2023-12-20 16:22:41,648 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.7516 [2023-12-20 16:22:41,757 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1791 [2023-12-20 16:22:41,848 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6076 [2023-12-20 16:22:41,962 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.5133 [2023-12-20 16:22:42,043 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3147 [2023-12-20 16:22:42,131 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5778 [2023-12-20 16:22:42,222 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4449 [2023-12-20 16:22:42,362 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3855 [2023-12-20 16:22:42,479 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1249 [2023-12-20 16:22:42,633 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3232 [2023-12-20 16:22:42,727 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2193 [2023-12-20 16:22:42,861 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8822 [2023-12-20 16:22:42,969 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3553 [2023-12-20 16:22:43,080 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5149 [2023-12-20 16:22:43,194 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2307 [2023-12-20 16:22:43,271 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6720 [2023-12-20 16:22:43,382 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1408 [2023-12-20 16:22:43,538 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1332 [2023-12-20 16:22:43,659 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5775 [2023-12-20 16:22:43,800 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4942 [2023-12-20 16:22:43,944 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3970 [2023-12-20 16:22:44,040 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4356 [2023-12-20 16:22:44,201 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9268 [2023-12-20 16:22:44,332 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5101 [2023-12-20 16:22:44,428 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.9499 [2023-12-20 16:22:44,574 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7462 [2023-12-20 16:22:44,683 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5767 [2023-12-20 16:22:44,810 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5619 [2023-12-20 16:22:44,897 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.5192 [2023-12-20 16:22:44,979 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3914 [2023-12-20 16:22:45,079 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7631 [2023-12-20 16:22:45,180 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.7029 [2023-12-20 16:22:45,331 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.1765 [2023-12-20 16:22:45,444 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1145 [2023-12-20 16:22:45,536 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6361 [2023-12-20 16:22:45,679 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5980 [2023-12-20 16:22:45,852 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0277 [2023-12-20 16:22:45,958 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.6409 [2023-12-20 16:22:46,084 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4457 [2023-12-20 16:22:46,239 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0793 [2023-12-20 16:22:46,365 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9725 [2023-12-20 16:22:46,469 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4032 [2023-12-20 16:22:46,643 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4249 [2023-12-20 16:22:46,742 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4707 [2023-12-20 16:22:46,892 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3609 [2023-12-20 16:22:46,985 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9429 [2023-12-20 16:22:47,081 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3173 [2023-12-20 16:22:47,197 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3914 [2023-12-20 16:22:47,344 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8653 [2023-12-20 16:22:47,483 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2783 [2023-12-20 16:22:47,599 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.8352 [2023-12-20 16:22:47,689 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6784 [2023-12-20 16:22:47,806 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4601 [2023-12-20 16:22:47,971 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2837 [2023-12-20 16:22:48,067 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4358 [2023-12-20 16:22:48,160 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5319 [2023-12-20 16:22:48,256 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5561 [2023-12-20 16:22:48,351 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4151 [2023-12-20 16:22:48,439 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8337 [2023-12-20 16:22:48,541 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6728 [2023-12-20 16:22:48,666 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6048 [2023-12-20 16:22:48,749 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2913 [2023-12-20 16:22:48,849 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3319 [2023-12-20 16:22:48,943 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0197 [2023-12-20 16:22:49,030 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3344 [2023-12-20 16:22:49,119 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0363 [2023-12-20 16:22:49,212 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5347 [2023-12-20 16:22:49,305 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4523 [2023-12-20 16:22:49,441 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2253 [2023-12-20 16:22:49,544 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6150 [2023-12-20 16:22:49,662 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6796 [2023-12-20 16:22:49,767 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8086 [2023-12-20 16:22:49,864 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5791 [2023-12-20 16:22:50,033 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3662 [2023-12-20 16:22:51,519 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7314/0.8272/0.9055. [2023-12-20 16:22:51,519 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8548/0.9351 [2023-12-20 16:22:51,519 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9649/0.9825 [2023-12-20 16:22:51,519 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6873/0.7938 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8239/0.8788 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8998/0.9431 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8398/0.9086 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7055/0.8346 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6779/0.7609 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6683/0.8047 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7891/0.9223 [2023-12-20 16:22:51,520 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3741/0.5264 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6600/0.8020 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6271/0.8975 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7003/0.7559 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6111/0.6670 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6788/0.8210 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9432/0.9674 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6569/0.7278 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8758/0.9103 [2023-12-20 16:22:51,521 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5895/0.7040 [2023-12-20 16:22:51,522 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:22:51,523 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:22:51,523 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:22:56,495 INFO misc.py line 119 131400] Train: [37/100][1/800] Data 1.496 (1.496) Batch 1.846 (1.846) Remain 26:15:28 loss: 0.9170 Lr: 0.00456 [2023-12-20 16:22:56,813 INFO misc.py line 119 131400] Train: [37/100][2/800] Data 0.004 (0.004) Batch 0.318 (0.318) Remain 04:31:42 loss: 0.3153 Lr: 0.00456 [2023-12-20 16:22:57,123 INFO misc.py line 119 131400] Train: [37/100][3/800] Data 0.002 (0.002) Batch 0.309 (0.309) Remain 04:23:58 loss: 0.2627 Lr: 0.00456 [2023-12-20 16:22:57,470 INFO misc.py line 119 131400] Train: [37/100][4/800] Data 0.003 (0.003) Batch 0.346 (0.346) Remain 04:54:54 loss: 0.4140 Lr: 0.00456 [2023-12-20 16:22:57,784 INFO misc.py line 119 131400] Train: [37/100][5/800] Data 0.005 (0.004) Batch 0.316 (0.331) Remain 04:42:23 loss: 0.3159 Lr: 0.00456 [2023-12-20 16:22:58,128 INFO misc.py line 119 131400] Train: [37/100][6/800] Data 0.003 (0.004) Batch 0.343 (0.335) Remain 04:45:46 loss: 0.5941 Lr: 0.00456 [2023-12-20 16:22:58,449 INFO misc.py line 119 131400] Train: [37/100][7/800] Data 0.004 (0.004) Batch 0.321 (0.332) Remain 04:42:51 loss: 0.2603 Lr: 0.00456 [2023-12-20 16:22:58,784 INFO misc.py line 119 131400] Train: [37/100][8/800] Data 0.004 (0.004) Batch 0.335 (0.332) Remain 04:43:25 loss: 0.3993 Lr: 0.00456 [2023-12-20 16:22:59,115 INFO misc.py line 119 131400] Train: [37/100][9/800] Data 0.005 (0.004) Batch 0.331 (0.332) Remain 04:43:16 loss: 0.6214 Lr: 0.00456 [2023-12-20 16:22:59,453 INFO misc.py line 119 131400] Train: [37/100][10/800] Data 0.004 (0.004) Batch 0.338 (0.333) Remain 04:43:58 loss: 0.4676 Lr: 0.00456 [2023-12-20 16:22:59,805 INFO misc.py line 119 131400] Train: [37/100][11/800] Data 0.005 (0.004) Batch 0.352 (0.335) Remain 04:46:02 loss: 0.2868 Lr: 0.00456 [2023-12-20 16:23:00,174 INFO misc.py line 119 131400] Train: [37/100][12/800] Data 0.005 (0.004) Batch 0.369 (0.339) Remain 04:49:13 loss: 0.3832 Lr: 0.00456 [2023-12-20 16:23:00,536 INFO misc.py line 119 131400] Train: [37/100][13/800] Data 0.004 (0.004) Batch 0.361 (0.341) Remain 04:51:07 loss: 0.4980 Lr: 0.00456 [2023-12-20 16:23:00,868 INFO misc.py line 119 131400] Train: [37/100][14/800] Data 0.004 (0.004) Batch 0.332 (0.340) Remain 04:50:24 loss: 0.7693 Lr: 0.00456 [2023-12-20 16:23:01,220 INFO misc.py line 119 131400] Train: [37/100][15/800] Data 0.005 (0.004) Batch 0.352 (0.341) Remain 04:51:12 loss: 0.4379 Lr: 0.00456 [2023-12-20 16:23:01,576 INFO misc.py line 119 131400] Train: [37/100][16/800] Data 0.005 (0.004) Batch 0.357 (0.343) Remain 04:52:12 loss: 0.5482 Lr: 0.00456 [2023-12-20 16:23:01,958 INFO misc.py line 119 131400] Train: [37/100][17/800] Data 0.005 (0.004) Batch 0.382 (0.345) Remain 04:54:35 loss: 0.7727 Lr: 0.00456 [2023-12-20 16:23:02,357 INFO misc.py line 119 131400] Train: [37/100][18/800] Data 0.005 (0.004) Batch 0.399 (0.349) Remain 04:57:38 loss: 0.5279 Lr: 0.00456 [2023-12-20 16:23:02,695 INFO misc.py line 119 131400] Train: [37/100][19/800] Data 0.004 (0.004) Batch 0.338 (0.348) Remain 04:57:02 loss: 0.3665 Lr: 0.00456 [2023-12-20 16:23:03,016 INFO misc.py line 119 131400] Train: [37/100][20/800] Data 0.005 (0.004) Batch 0.320 (0.347) Remain 04:55:38 loss: 0.5812 Lr: 0.00456 [2023-12-20 16:23:03,372 INFO misc.py line 119 131400] Train: [37/100][21/800] Data 0.005 (0.004) Batch 0.355 (0.347) Remain 04:56:03 loss: 0.3163 Lr: 0.00456 [2023-12-20 16:23:03,726 INFO misc.py line 119 131400] Train: [37/100][22/800] Data 0.005 (0.004) Batch 0.356 (0.348) Remain 04:56:26 loss: 0.3750 Lr: 0.00456 [2023-12-20 16:23:04,042 INFO misc.py line 119 131400] Train: [37/100][23/800] Data 0.004 (0.004) Batch 0.316 (0.346) Remain 04:55:04 loss: 0.5412 Lr: 0.00456 [2023-12-20 16:23:04,384 INFO misc.py line 119 131400] Train: [37/100][24/800] Data 0.004 (0.004) Batch 0.341 (0.346) Remain 04:54:53 loss: 0.4115 Lr: 0.00456 [2023-12-20 16:23:04,733 INFO misc.py line 119 131400] Train: [37/100][25/800] Data 0.005 (0.004) Batch 0.350 (0.346) Remain 04:55:02 loss: 0.3712 Lr: 0.00456 [2023-12-20 16:23:05,079 INFO misc.py line 119 131400] Train: [37/100][26/800] Data 0.004 (0.004) Batch 0.346 (0.346) Remain 04:55:00 loss: 0.5033 Lr: 0.00456 [2023-12-20 16:23:05,441 INFO misc.py line 119 131400] Train: [37/100][27/800] Data 0.005 (0.004) Batch 0.362 (0.347) Remain 04:55:35 loss: 0.5220 Lr: 0.00456 [2023-12-20 16:23:05,752 INFO misc.py line 119 131400] Train: [37/100][28/800] Data 0.004 (0.004) Batch 0.311 (0.345) Remain 04:54:23 loss: 0.4860 Lr: 0.00456 [2023-12-20 16:23:06,082 INFO misc.py line 119 131400] Train: [37/100][29/800] Data 0.004 (0.004) Batch 0.329 (0.345) Remain 04:53:51 loss: 0.2746 Lr: 0.00456 [2023-12-20 16:23:06,420 INFO misc.py line 119 131400] Train: [37/100][30/800] Data 0.005 (0.004) Batch 0.338 (0.344) Remain 04:53:38 loss: 0.4919 Lr: 0.00456 [2023-12-20 16:23:06,767 INFO misc.py line 119 131400] Train: [37/100][31/800] Data 0.005 (0.004) Batch 0.348 (0.344) Remain 04:53:45 loss: 0.1624 Lr: 0.00455 [2023-12-20 16:23:07,109 INFO misc.py line 119 131400] Train: [37/100][32/800] Data 0.003 (0.004) Batch 0.341 (0.344) Remain 04:53:38 loss: 0.2934 Lr: 0.00455 [2023-12-20 16:23:07,466 INFO misc.py line 119 131400] Train: [37/100][33/800] Data 0.004 (0.004) Batch 0.358 (0.345) Remain 04:54:01 loss: 0.3597 Lr: 0.00455 [2023-12-20 16:23:07,786 INFO misc.py line 119 131400] Train: [37/100][34/800] Data 0.004 (0.004) Batch 0.321 (0.344) Remain 04:53:21 loss: 0.2758 Lr: 0.00455 [2023-12-20 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04:41:57 loss: 0.1896 Lr: 0.00448 [2023-12-20 16:27:14,442 INFO misc.py line 119 131400] Train: [37/100][770/800] Data 0.004 (0.004) Batch 0.359 (0.335) Remain 04:41:58 loss: 0.7295 Lr: 0.00448 [2023-12-20 16:27:14,789 INFO misc.py line 119 131400] Train: [37/100][771/800] Data 0.009 (0.004) Batch 0.347 (0.336) Remain 04:41:58 loss: 0.2772 Lr: 0.00448 [2023-12-20 16:27:15,165 INFO misc.py line 119 131400] Train: [37/100][772/800] Data 0.004 (0.004) Batch 0.373 (0.336) Remain 04:42:01 loss: 0.3618 Lr: 0.00448 [2023-12-20 16:27:15,538 INFO misc.py line 119 131400] Train: [37/100][773/800] Data 0.007 (0.004) Batch 0.375 (0.336) Remain 04:42:03 loss: 0.4164 Lr: 0.00448 [2023-12-20 16:27:15,876 INFO misc.py line 119 131400] Train: [37/100][774/800] Data 0.005 (0.004) Batch 0.338 (0.336) Remain 04:42:03 loss: 0.3330 Lr: 0.00448 [2023-12-20 16:27:16,222 INFO misc.py line 119 131400] Train: [37/100][775/800] Data 0.006 (0.004) Batch 0.346 (0.336) Remain 04:42:03 loss: 0.2170 Lr: 0.00448 [2023-12-20 16:27:16,557 INFO misc.py line 119 131400] Train: [37/100][776/800] Data 0.006 (0.004) Batch 0.334 (0.336) Remain 04:42:02 loss: 0.4304 Lr: 0.00448 [2023-12-20 16:27:16,891 INFO misc.py line 119 131400] Train: [37/100][777/800] Data 0.009 (0.004) Batch 0.337 (0.336) Remain 04:42:02 loss: 0.4100 Lr: 0.00448 [2023-12-20 16:27:17,231 INFO misc.py line 119 131400] Train: [37/100][778/800] Data 0.006 (0.004) Batch 0.341 (0.336) Remain 04:42:02 loss: 0.4912 Lr: 0.00447 [2023-12-20 16:27:17,594 INFO misc.py line 119 131400] Train: [37/100][779/800] Data 0.004 (0.004) Batch 0.363 (0.336) Remain 04:42:04 loss: 0.6070 Lr: 0.00447 [2023-12-20 16:27:17,941 INFO misc.py line 119 131400] Train: [37/100][780/800] Data 0.004 (0.004) Batch 0.347 (0.336) Remain 04:42:04 loss: 0.3768 Lr: 0.00447 [2023-12-20 16:27:18,288 INFO misc.py line 119 131400] Train: [37/100][781/800] Data 0.004 (0.004) Batch 0.347 (0.336) Remain 04:42:04 loss: 0.3174 Lr: 0.00447 [2023-12-20 16:27:18,625 INFO misc.py line 119 131400] Train: [37/100][782/800] Data 0.005 (0.004) Batch 0.333 (0.336) Remain 04:42:04 loss: 0.4176 Lr: 0.00447 [2023-12-20 16:27:18,981 INFO misc.py line 119 131400] Train: [37/100][783/800] Data 0.009 (0.004) Batch 0.362 (0.336) Remain 04:42:05 loss: 0.4379 Lr: 0.00447 [2023-12-20 16:27:19,337 INFO misc.py line 119 131400] Train: [37/100][784/800] Data 0.003 (0.004) Batch 0.355 (0.336) Remain 04:42:06 loss: 0.3477 Lr: 0.00447 [2023-12-20 16:27:19,636 INFO misc.py line 119 131400] Train: [37/100][785/800] Data 0.003 (0.004) Batch 0.297 (0.336) Remain 04:42:03 loss: 0.6515 Lr: 0.00447 [2023-12-20 16:27:19,958 INFO misc.py line 119 131400] Train: [37/100][786/800] Data 0.005 (0.004) Batch 0.325 (0.336) Remain 04:42:02 loss: 0.3272 Lr: 0.00447 [2023-12-20 16:27:20,306 INFO misc.py line 119 131400] Train: [37/100][787/800] Data 0.003 (0.004) Batch 0.348 (0.336) Remain 04:42:03 loss: 0.5466 Lr: 0.00447 [2023-12-20 16:27:20,671 INFO misc.py line 119 131400] Train: [37/100][788/800] Data 0.003 (0.004) Batch 0.360 (0.336) Remain 04:42:04 loss: 0.5321 Lr: 0.00447 [2023-12-20 16:27:21,002 INFO misc.py line 119 131400] Train: [37/100][789/800] Data 0.008 (0.004) Batch 0.335 (0.336) Remain 04:42:04 loss: 0.5016 Lr: 0.00447 [2023-12-20 16:27:21,310 INFO misc.py line 119 131400] Train: [37/100][790/800] Data 0.004 (0.004) Batch 0.309 (0.336) Remain 04:42:02 loss: 0.3113 Lr: 0.00447 [2023-12-20 16:27:21,609 INFO misc.py line 119 131400] Train: [37/100][791/800] Data 0.002 (0.004) Batch 0.299 (0.336) Remain 04:41:59 loss: 0.2729 Lr: 0.00447 [2023-12-20 16:27:21,942 INFO misc.py line 119 131400] Train: [37/100][792/800] Data 0.003 (0.004) Batch 0.333 (0.336) Remain 04:41:58 loss: 0.3083 Lr: 0.00447 [2023-12-20 16:27:22,245 INFO misc.py line 119 131400] Train: [37/100][793/800] Data 0.003 (0.004) Batch 0.302 (0.336) Remain 04:41:56 loss: 0.3897 Lr: 0.00447 [2023-12-20 16:27:22,554 INFO misc.py line 119 131400] Train: [37/100][794/800] Data 0.004 (0.004) Batch 0.310 (0.336) Remain 04:41:54 loss: 0.4502 Lr: 0.00447 [2023-12-20 16:27:22,903 INFO misc.py line 119 131400] Train: [37/100][795/800] Data 0.004 (0.004) Batch 0.349 (0.336) Remain 04:41:54 loss: 0.5061 Lr: 0.00447 [2023-12-20 16:27:23,212 INFO misc.py line 119 131400] Train: [37/100][796/800] Data 0.003 (0.004) Batch 0.308 (0.336) Remain 04:41:52 loss: 0.3316 Lr: 0.00447 [2023-12-20 16:27:23,515 INFO misc.py line 119 131400] Train: [37/100][797/800] Data 0.005 (0.004) Batch 0.305 (0.336) Remain 04:41:50 loss: 0.3666 Lr: 0.00447 [2023-12-20 16:27:23,850 INFO misc.py line 119 131400] Train: [37/100][798/800] Data 0.003 (0.004) Batch 0.331 (0.335) Remain 04:41:49 loss: 0.2923 Lr: 0.00447 [2023-12-20 16:27:24,169 INFO misc.py line 119 131400] Train: [37/100][799/800] Data 0.006 (0.004) Batch 0.323 (0.335) Remain 04:41:48 loss: 0.3475 Lr: 0.00447 [2023-12-20 16:27:24,517 INFO misc.py line 119 131400] Train: [37/100][800/800] Data 0.003 (0.004) Batch 0.348 (0.335) Remain 04:41:49 loss: 0.4641 Lr: 0.00447 [2023-12-20 16:27:24,518 INFO misc.py line 136 131400] Train result: loss: 0.4291 [2023-12-20 16:27:24,518 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:27:45,946 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1947 [2023-12-20 16:27:46,021 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1350 [2023-12-20 16:27:46,550 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3212 [2023-12-20 16:27:46,659 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1878 [2023-12-20 16:27:46,771 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5497 [2023-12-20 16:27:46,870 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1119 [2023-12-20 16:27:46,960 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.3676 [2023-12-20 16:27:47,066 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6805 [2023-12-20 16:27:47,147 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2845 [2023-12-20 16:27:47,234 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3780 [2023-12-20 16:27:47,325 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7281 [2023-12-20 16:27:47,463 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2848 [2023-12-20 16:27:47,580 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3207 [2023-12-20 16:27:47,734 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2823 [2023-12-20 16:27:47,827 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3888 [2023-12-20 16:27:47,964 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9970 [2023-12-20 16:27:48,073 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3834 [2023-12-20 16:27:48,182 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5898 [2023-12-20 16:27:48,293 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1266 [2023-12-20 16:27:48,372 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5292 [2023-12-20 16:27:48,477 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5858 [2023-12-20 16:27:48,633 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1833 [2023-12-20 16:27:48,753 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9407 [2023-12-20 16:27:48,895 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1934 [2023-12-20 16:27:49,037 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1894 [2023-12-20 16:27:49,119 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4432 [2023-12-20 16:27:49,276 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7505 [2023-12-20 16:27:49,399 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4444 [2023-12-20 16:27:49,497 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7408 [2023-12-20 16:27:49,642 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4307 [2023-12-20 16:27:49,743 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6990 [2023-12-20 16:27:49,862 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3625 [2023-12-20 16:27:49,945 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1692 [2023-12-20 16:27:50,013 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1973 [2023-12-20 16:27:50,105 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6570 [2023-12-20 16:27:50,195 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3909 [2023-12-20 16:27:50,322 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8614 [2023-12-20 16:27:50,435 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1190 [2023-12-20 16:27:50,512 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5959 [2023-12-20 16:27:50,655 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3624 [2023-12-20 16:27:50,803 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0247 [2023-12-20 16:27:50,900 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1538 [2023-12-20 16:27:51,017 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5081 [2023-12-20 16:27:51,158 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.2530 [2023-12-20 16:27:54,080 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1133 [2023-12-20 16:27:54,186 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2659 [2023-12-20 16:27:54,498 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4425 [2023-12-20 16:27:54,591 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5424 [2023-12-20 16:27:54,744 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4536 [2023-12-20 16:27:54,836 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.5662 [2023-12-20 16:27:54,924 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8718 [2023-12-20 16:27:55,034 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4050 [2023-12-20 16:27:55,181 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2594 [2023-12-20 16:27:55,318 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3275 [2023-12-20 16:27:55,429 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.9227 [2023-12-20 16:27:55,530 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6604 [2023-12-20 16:27:55,636 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3536 [2023-12-20 16:27:55,804 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2301 [2023-12-20 16:27:55,910 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5403 [2023-12-20 16:27:56,014 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4795 [2023-12-20 16:27:56,129 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3270 [2023-12-20 16:27:56,222 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3016 [2023-12-20 16:27:56,315 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6221 [2023-12-20 16:27:56,418 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6805 [2023-12-20 16:27:56,551 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7247 [2023-12-20 16:27:56,645 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3084 [2023-12-20 16:27:56,751 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6310 [2023-12-20 16:27:56,852 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0124 [2023-12-20 16:27:56,939 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3057 [2023-12-20 16:27:57,025 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0146 [2023-12-20 16:27:57,120 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6302 [2023-12-20 16:27:57,217 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5712 [2023-12-20 16:27:57,350 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1366 [2023-12-20 16:27:57,444 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6423 [2023-12-20 16:27:57,562 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6066 [2023-12-20 16:27:57,664 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7674 [2023-12-20 16:27:57,753 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3199 [2023-12-20 16:27:57,908 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0940 [2023-12-20 16:27:59,494 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7323/0.8315/0.9078. [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8630/0.9353 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9588/0.9886 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6629/0.7854 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8192/0.8872 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9071/0.9475 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8177/0.9547 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7318/0.7730 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6644/0.8564 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6635/0.7924 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8338/0.9199 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3857/0.4803 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6368/0.8559 [2023-12-20 16:27:59,494 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6948/0.9074 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7051/0.7293 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5841/0.7236 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6724/0.7387 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9559/0.9831 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6533/0.7728 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8456/0.9391 [2023-12-20 16:27:59,495 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5908/0.6598 [2023-12-20 16:27:59,495 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:27:59,497 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:27:59,497 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:28:03,778 INFO misc.py line 119 131400] Train: [38/100][1/800] Data 1.128 (1.128) Batch 1.456 (1.456) Remain 20:23:03 loss: 0.4839 Lr: 0.00447 [2023-12-20 16:28:04,170 INFO misc.py line 119 131400] Train: [38/100][2/800] Data 0.005 (0.005) Batch 0.393 (0.393) Remain 05:29:52 loss: 0.3154 Lr: 0.00447 [2023-12-20 16:28:04,506 INFO misc.py line 119 131400] Train: [38/100][3/800] Data 0.003 (0.003) Batch 0.332 (0.332) Remain 04:38:37 loss: 0.5596 Lr: 0.00447 [2023-12-20 16:28:04,893 INFO misc.py line 119 131400] Train: [38/100][4/800] Data 0.008 (0.008) Batch 0.392 (0.392) Remain 05:29:16 loss: 0.5550 Lr: 0.00447 [2023-12-20 16:28:05,246 INFO misc.py line 119 131400] Train: [38/100][5/800] Data 0.003 (0.006) Batch 0.352 (0.372) Remain 05:12:24 loss: 0.2802 Lr: 0.00447 [2023-12-20 16:28:05,564 INFO misc.py line 119 131400] Train: [38/100][6/800] Data 0.003 (0.005) Batch 0.317 (0.354) Remain 04:57:08 loss: 0.5104 Lr: 0.00447 [2023-12-20 16:28:05,883 INFO misc.py line 119 131400] Train: [38/100][7/800] Data 0.004 (0.005) Batch 0.315 (0.344) Remain 04:48:57 loss: 0.3774 Lr: 0.00447 [2023-12-20 16:28:06,247 INFO misc.py line 119 131400] Train: [38/100][8/800] Data 0.008 (0.005) Batch 0.369 (0.349) Remain 04:53:05 loss: 0.4564 Lr: 0.00447 [2023-12-20 16:28:06,568 INFO misc.py line 119 131400] Train: [38/100][9/800] Data 0.003 (0.005) Batch 0.320 (0.344) Remain 04:49:03 loss: 0.4634 Lr: 0.00447 [2023-12-20 16:28:06,869 INFO misc.py line 119 131400] Train: [38/100][10/800] Data 0.004 (0.005) Batch 0.297 (0.337) Remain 04:43:24 loss: 0.5236 Lr: 0.00447 [2023-12-20 16:28:07,212 INFO misc.py line 119 131400] Train: [38/100][11/800] Data 0.009 (0.005) Batch 0.349 (0.339) Remain 04:44:34 loss: 0.3890 Lr: 0.00447 [2023-12-20 16:28:07,538 INFO misc.py line 119 131400] Train: [38/100][12/800] Data 0.002 (0.005) Batch 0.324 (0.337) Remain 04:43:13 loss: 0.3409 Lr: 0.00447 [2023-12-20 16:28:07,878 INFO misc.py line 119 131400] Train: [38/100][13/800] Data 0.004 (0.005) Batch 0.340 (0.338) Remain 04:43:29 loss: 0.5909 Lr: 0.00447 [2023-12-20 16:28:08,248 INFO misc.py line 119 131400] Train: [38/100][14/800] Data 0.004 (0.005) Batch 0.370 (0.341) Remain 04:45:56 loss: 0.1974 Lr: 0.00447 [2023-12-20 16:28:08,618 INFO misc.py line 119 131400] Train: [38/100][15/800] Data 0.004 (0.005) Batch 0.370 (0.343) Remain 04:47:59 loss: 0.3339 Lr: 0.00447 [2023-12-20 16:28:08,978 INFO misc.py line 119 131400] Train: [38/100][16/800] Data 0.004 (0.005) Batch 0.361 (0.344) Remain 04:49:08 loss: 0.3464 Lr: 0.00447 [2023-12-20 16:28:09,333 INFO misc.py line 119 131400] Train: [38/100][17/800] Data 0.004 (0.005) Batch 0.355 (0.345) Remain 04:49:46 loss: 0.3880 Lr: 0.00447 [2023-12-20 16:28:09,734 INFO misc.py line 119 131400] Train: [38/100][18/800] Data 0.004 (0.005) Batch 0.400 (0.349) Remain 04:52:49 loss: 0.4042 Lr: 0.00447 [2023-12-20 16:28:10,080 INFO misc.py line 119 131400] Train: [38/100][19/800] Data 0.006 (0.005) Batch 0.347 (0.349) Remain 04:52:42 loss: 0.2212 Lr: 0.00447 [2023-12-20 16:28:10,424 INFO misc.py line 119 131400] Train: [38/100][20/800] Data 0.004 (0.005) Batch 0.344 (0.348) Remain 04:52:29 loss: 0.3620 Lr: 0.00447 [2023-12-20 16:28:10,759 INFO misc.py line 119 131400] Train: [38/100][21/800] Data 0.004 (0.005) Batch 0.335 (0.348) Remain 04:51:52 loss: 0.2969 Lr: 0.00447 [2023-12-20 16:28:11,081 INFO misc.py line 119 131400] Train: [38/100][22/800] Data 0.003 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131400] Train: [38/100][757/800] Data 0.004 (0.005) Batch 0.339 (0.337) Remain 04:38:32 loss: 0.3177 Lr: 0.00439 [2023-12-20 16:32:18,674 INFO misc.py line 119 131400] Train: [38/100][758/800] Data 0.003 (0.005) Batch 0.328 (0.337) Remain 04:38:32 loss: 0.6089 Lr: 0.00439 [2023-12-20 16:32:18,985 INFO misc.py line 119 131400] Train: [38/100][759/800] Data 0.003 (0.005) Batch 0.310 (0.337) Remain 04:38:29 loss: 0.3187 Lr: 0.00439 [2023-12-20 16:32:19,262 INFO misc.py line 119 131400] Train: [38/100][760/800] Data 0.003 (0.005) Batch 0.276 (0.337) Remain 04:38:25 loss: 0.4419 Lr: 0.00439 [2023-12-20 16:32:19,572 INFO misc.py line 119 131400] Train: [38/100][761/800] Data 0.004 (0.005) Batch 0.311 (0.337) Remain 04:38:23 loss: 0.5709 Lr: 0.00439 [2023-12-20 16:32:19,885 INFO misc.py line 119 131400] Train: [38/100][762/800] Data 0.003 (0.005) Batch 0.313 (0.336) Remain 04:38:21 loss: 0.2973 Lr: 0.00439 [2023-12-20 16:32:20,216 INFO misc.py line 119 131400] Train: [38/100][763/800] Data 0.004 (0.005) Batch 0.332 (0.336) Remain 04:38:21 loss: 0.6249 Lr: 0.00439 [2023-12-20 16:32:20,537 INFO misc.py line 119 131400] Train: [38/100][764/800] Data 0.003 (0.005) Batch 0.320 (0.336) Remain 04:38:19 loss: 0.3736 Lr: 0.00439 [2023-12-20 16:32:20,845 INFO misc.py line 119 131400] Train: [38/100][765/800] Data 0.004 (0.005) Batch 0.309 (0.336) Remain 04:38:17 loss: 0.7955 Lr: 0.00439 [2023-12-20 16:32:21,160 INFO misc.py line 119 131400] Train: [38/100][766/800] Data 0.004 (0.005) Batch 0.315 (0.336) Remain 04:38:15 loss: 0.3825 Lr: 0.00439 [2023-12-20 16:32:21,474 INFO misc.py line 119 131400] Train: [38/100][767/800] Data 0.003 (0.005) Batch 0.313 (0.336) Remain 04:38:13 loss: 0.4434 Lr: 0.00439 [2023-12-20 16:32:21,792 INFO misc.py line 119 131400] Train: [38/100][768/800] Data 0.004 (0.005) Batch 0.317 (0.336) Remain 04:38:12 loss: 0.3222 Lr: 0.00439 [2023-12-20 16:32:22,099 INFO misc.py line 119 131400] Train: [38/100][769/800] Data 0.005 (0.005) Batch 0.308 (0.336) Remain 04:38:10 loss: 0.5858 Lr: 0.00439 [2023-12-20 16:32:22,396 INFO misc.py line 119 131400] Train: [38/100][770/800] Data 0.003 (0.005) Batch 0.296 (0.336) Remain 04:38:07 loss: 0.7466 Lr: 0.00439 [2023-12-20 16:32:22,704 INFO misc.py line 119 131400] Train: [38/100][771/800] Data 0.005 (0.005) Batch 0.309 (0.336) Remain 04:38:05 loss: 0.4237 Lr: 0.00439 [2023-12-20 16:32:23,050 INFO misc.py line 119 131400] Train: [38/100][772/800] Data 0.004 (0.005) Batch 0.337 (0.336) Remain 04:38:04 loss: 0.3390 Lr: 0.00439 [2023-12-20 16:32:23,367 INFO misc.py line 119 131400] Train: [38/100][773/800] Data 0.013 (0.005) Batch 0.325 (0.336) Remain 04:38:03 loss: 0.4850 Lr: 0.00439 [2023-12-20 16:32:23,696 INFO misc.py line 119 131400] Train: [38/100][774/800] Data 0.005 (0.005) Batch 0.330 (0.336) Remain 04:38:03 loss: 0.1939 Lr: 0.00439 [2023-12-20 16:32:24,072 INFO misc.py line 119 131400] Train: [38/100][775/800] Data 0.005 (0.005) Batch 0.372 (0.336) Remain 04:38:05 loss: 0.2035 Lr: 0.00439 [2023-12-20 16:32:24,437 INFO misc.py line 119 131400] Train: [38/100][776/800] Data 0.007 (0.005) Batch 0.369 (0.336) Remain 04:38:06 loss: 0.3988 Lr: 0.00439 [2023-12-20 16:32:24,785 INFO misc.py line 119 131400] Train: [38/100][777/800] Data 0.004 (0.005) Batch 0.348 (0.336) Remain 04:38:07 loss: 0.4754 Lr: 0.00439 [2023-12-20 16:32:25,116 INFO misc.py line 119 131400] Train: [38/100][778/800] Data 0.004 (0.005) Batch 0.320 (0.336) Remain 04:38:05 loss: 0.5058 Lr: 0.00439 [2023-12-20 16:32:25,396 INFO misc.py line 119 131400] Train: [38/100][779/800] Data 0.014 (0.005) Batch 0.291 (0.336) Remain 04:38:02 loss: 0.4586 Lr: 0.00439 [2023-12-20 16:32:25,735 INFO misc.py line 119 131400] Train: [38/100][780/800] Data 0.003 (0.005) Batch 0.338 (0.336) Remain 04:38:02 loss: 0.5364 Lr: 0.00439 [2023-12-20 16:32:26,088 INFO misc.py line 119 131400] Train: [38/100][781/800] Data 0.004 (0.005) Batch 0.352 (0.336) Remain 04:38:03 loss: 0.2758 Lr: 0.00439 [2023-12-20 16:32:26,415 INFO misc.py line 119 131400] Train: [38/100][782/800] Data 0.005 (0.005) Batch 0.329 (0.336) Remain 04:38:02 loss: 0.3620 Lr: 0.00439 [2023-12-20 16:32:26,758 INFO misc.py line 119 131400] Train: [38/100][783/800] Data 0.004 (0.005) Batch 0.343 (0.336) Remain 04:38:02 loss: 0.6749 Lr: 0.00439 [2023-12-20 16:32:27,069 INFO misc.py line 119 131400] Train: [38/100][784/800] Data 0.004 (0.005) Batch 0.311 (0.336) Remain 04:38:00 loss: 0.5217 Lr: 0.00439 [2023-12-20 16:32:27,415 INFO misc.py line 119 131400] Train: [38/100][785/800] Data 0.004 (0.005) Batch 0.347 (0.336) Remain 04:38:00 loss: 0.3757 Lr: 0.00439 [2023-12-20 16:32:27,768 INFO misc.py line 119 131400] Train: [38/100][786/800] Data 0.003 (0.005) Batch 0.352 (0.336) Remain 04:38:01 loss: 0.3034 Lr: 0.00439 [2023-12-20 16:32:28,086 INFO misc.py line 119 131400] Train: [38/100][787/800] Data 0.004 (0.005) Batch 0.319 (0.336) Remain 04:38:00 loss: 0.2511 Lr: 0.00439 [2023-12-20 16:32:28,413 INFO misc.py line 119 131400] Train: [38/100][788/800] Data 0.004 (0.005) Batch 0.327 (0.336) Remain 04:37:59 loss: 0.4681 Lr: 0.00439 [2023-12-20 16:32:28,704 INFO misc.py line 119 131400] Train: [38/100][789/800] Data 0.003 (0.005) Batch 0.291 (0.336) Remain 04:37:55 loss: 0.5163 Lr: 0.00439 [2023-12-20 16:32:29,024 INFO misc.py line 119 131400] Train: [38/100][790/800] Data 0.003 (0.005) Batch 0.320 (0.336) Remain 04:37:54 loss: 0.2856 Lr: 0.00439 [2023-12-20 16:32:29,334 INFO misc.py line 119 131400] Train: [38/100][791/800] Data 0.003 (0.005) Batch 0.309 (0.336) Remain 04:37:52 loss: 0.6488 Lr: 0.00439 [2023-12-20 16:32:29,670 INFO misc.py line 119 131400] Train: [38/100][792/800] Data 0.004 (0.005) Batch 0.337 (0.336) Remain 04:37:52 loss: 0.5735 Lr: 0.00439 [2023-12-20 16:32:29,943 INFO misc.py line 119 131400] Train: [38/100][793/800] Data 0.003 (0.005) Batch 0.272 (0.336) Remain 04:37:47 loss: 0.3841 Lr: 0.00439 [2023-12-20 16:32:30,205 INFO misc.py line 119 131400] Train: [38/100][794/800] Data 0.003 (0.005) Batch 0.262 (0.336) Remain 04:37:42 loss: 0.4905 Lr: 0.00439 [2023-12-20 16:32:30,521 INFO misc.py line 119 131400] Train: [38/100][795/800] Data 0.003 (0.005) Batch 0.312 (0.336) Remain 04:37:41 loss: 0.4609 Lr: 0.00439 [2023-12-20 16:32:30,830 INFO misc.py line 119 131400] Train: [38/100][796/800] Data 0.007 (0.005) Batch 0.313 (0.336) Remain 04:37:39 loss: 0.4114 Lr: 0.00439 [2023-12-20 16:32:31,125 INFO misc.py line 119 131400] Train: [38/100][797/800] Data 0.003 (0.005) Batch 0.295 (0.336) Remain 04:37:36 loss: 0.4216 Lr: 0.00439 [2023-12-20 16:32:31,434 INFO misc.py line 119 131400] Train: [38/100][798/800] Data 0.003 (0.005) Batch 0.310 (0.336) Remain 04:37:34 loss: 0.2411 Lr: 0.00439 [2023-12-20 16:32:31,746 INFO misc.py line 119 131400] Train: [38/100][799/800] Data 0.003 (0.005) Batch 0.311 (0.336) Remain 04:37:32 loss: 0.3605 Lr: 0.00439 [2023-12-20 16:32:32,064 INFO misc.py line 119 131400] Train: [38/100][800/800] Data 0.003 (0.005) Batch 0.318 (0.336) Remain 04:37:31 loss: 0.4871 Lr: 0.00439 [2023-12-20 16:32:32,065 INFO misc.py line 136 131400] Train result: loss: 0.4319 [2023-12-20 16:32:32,065 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:32:55,363 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2527 [2023-12-20 16:32:55,448 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1784 [2023-12-20 16:32:55,551 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4285 [2023-12-20 16:32:55,666 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3677 [2023-12-20 16:32:55,783 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5038 [2023-12-20 16:32:55,889 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3047 [2023-12-20 16:32:55,979 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0329 [2023-12-20 16:32:56,088 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0694 [2023-12-20 16:32:56,170 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2834 [2023-12-20 16:32:56,260 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3085 [2023-12-20 16:32:56,351 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6387 [2023-12-20 16:32:56,489 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3942 [2023-12-20 16:32:56,609 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5145 [2023-12-20 16:32:56,764 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4373 [2023-12-20 16:32:56,858 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2074 [2023-12-20 16:32:56,995 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6995 [2023-12-20 16:32:57,108 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2830 [2023-12-20 16:32:57,225 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3403 [2023-12-20 16:32:57,344 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1284 [2023-12-20 16:32:57,421 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5559 [2023-12-20 16:32:57,534 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2706 [2023-12-20 16:32:57,692 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1567 [2023-12-20 16:32:57,814 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6354 [2023-12-20 16:32:57,956 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4521 [2023-12-20 16:32:58,101 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2587 [2023-12-20 16:32:58,186 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6029 [2023-12-20 16:32:58,353 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6273 [2023-12-20 16:32:58,479 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4773 [2023-12-20 16:32:58,575 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6032 [2023-12-20 16:32:58,720 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.9768 [2023-12-20 16:32:58,824 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8583 [2023-12-20 16:32:58,943 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5380 [2023-12-20 16:32:59,028 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1335 [2023-12-20 16:32:59,100 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.5054 [2023-12-20 16:32:59,199 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5989 [2023-12-20 16:32:59,292 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4658 [2023-12-20 16:32:59,421 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8191 [2023-12-20 16:32:59,532 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2591 [2023-12-20 16:32:59,614 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5326 [2023-12-20 16:32:59,759 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4146 [2023-12-20 16:32:59,907 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0486 [2023-12-20 16:33:00,007 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1142 [2023-12-20 16:33:00,128 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3392 [2023-12-20 16:33:00,270 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8148 [2023-12-20 16:33:00,390 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4178 [2023-12-20 16:33:00,495 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4987 [2023-12-20 16:33:00,669 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5117 [2023-12-20 16:33:00,769 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4293 [2023-12-20 16:33:00,917 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3139 [2023-12-20 16:33:01,010 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.7436 [2023-12-20 16:33:01,087 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4719 [2023-12-20 16:33:01,194 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4232 [2023-12-20 16:33:01,341 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.6278 [2023-12-20 16:33:01,476 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3141 [2023-12-20 16:33:01,585 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0650 [2023-12-20 16:33:01,673 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5612 [2023-12-20 16:33:01,777 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3627 [2023-12-20 16:33:01,947 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2667 [2023-12-20 16:33:02,049 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6070 [2023-12-20 16:33:02,147 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2137 [2023-12-20 16:33:02,243 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.7439 [2023-12-20 16:33:02,338 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2662 [2023-12-20 16:33:02,429 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.1849 [2023-12-20 16:33:02,530 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7292 [2023-12-20 16:33:02,659 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3018 [2023-12-20 16:33:02,748 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3720 [2023-12-20 16:33:02,849 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4449 [2023-12-20 16:33:02,950 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0350 [2023-12-20 16:33:03,039 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4556 [2023-12-20 16:33:03,121 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0308 [2023-12-20 16:33:03,226 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5118 [2023-12-20 16:33:03,316 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7114 [2023-12-20 16:33:03,457 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1158 [2023-12-20 16:33:03,556 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5631 [2023-12-20 16:33:03,674 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7245 [2023-12-20 16:33:03,776 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7592 [2023-12-20 16:33:03,863 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2083 [2023-12-20 16:33:04,017 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2046 [2023-12-20 16:33:05,164 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7131/0.8207/0.8945. [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8379/0.9098 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9611/0.9838 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6392/0.8477 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7861/0.8340 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9149/0.9467 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8374/0.9445 [2023-12-20 16:33:05,164 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7229/0.7788 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6340/0.8222 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6336/0.8390 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8092/0.9137 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.2765/0.5418 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6267/0.7203 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6023/0.9070 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6793/0.7024 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5746/0.7119 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6883/0.7653 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9517/0.9772 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6874/0.7822 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8838/0.9232 [2023-12-20 16:33:05,165 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5153/0.5620 [2023-12-20 16:33:05,166 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:33:05,167 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:33:05,167 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:33:10,037 INFO misc.py line 119 131400] Train: [39/100][1/800] Data 1.957 (1.957) Batch 2.305 (2.305) Remain 31:45:49 loss: 0.5844 Lr: 0.00439 [2023-12-20 16:33:10,339 INFO misc.py line 119 131400] Train: [39/100][2/800] Data 0.003 (0.003) Batch 0.300 (0.300) Remain 04:07:49 loss: 0.6519 Lr: 0.00439 [2023-12-20 16:33:10,671 INFO misc.py line 119 131400] Train: [39/100][3/800] Data 0.006 (0.006) Batch 0.334 (0.334) Remain 04:36:20 loss: 0.4227 Lr: 0.00439 [2023-12-20 16:33:10,973 INFO misc.py line 119 131400] Train: [39/100][4/800] Data 0.003 (0.003) Batch 0.302 (0.302) Remain 04:09:14 loss: 0.2988 Lr: 0.00439 [2023-12-20 16:33:11,308 INFO misc.py line 119 131400] Train: [39/100][5/800] Data 0.003 (0.003) Batch 0.335 (0.318) Remain 04:23:00 loss: 0.2685 Lr: 0.00438 [2023-12-20 16:33:11,653 INFO misc.py line 119 131400] Train: [39/100][6/800] Data 0.004 (0.003) Batch 0.345 (0.327) Remain 04:30:29 loss: 0.3268 Lr: 0.00438 [2023-12-20 16:33:11,974 INFO misc.py line 119 131400] Train: [39/100][7/800] Data 0.004 (0.003) Batch 0.321 (0.326) Remain 04:29:13 loss: 0.5996 Lr: 0.00438 [2023-12-20 16:33:12,301 INFO misc.py line 119 131400] Train: [39/100][8/800] Data 0.003 (0.003) Batch 0.327 (0.326) Remain 04:29:22 loss: 0.3354 Lr: 0.00438 [2023-12-20 16:33:12,605 INFO misc.py line 119 131400] Train: [39/100][9/800] Data 0.003 (0.003) Batch 0.305 (0.322) Remain 04:26:26 loss: 0.4641 Lr: 0.00438 [2023-12-20 16:33:12,913 INFO misc.py line 119 131400] Train: [39/100][10/800] Data 0.003 (0.003) Batch 0.307 (0.320) Remain 04:24:37 loss: 0.3686 Lr: 0.00438 [2023-12-20 16:33:13,233 INFO misc.py line 119 131400] Train: [39/100][11/800] Data 0.004 (0.003) Batch 0.320 (0.320) Remain 04:24:36 loss: 0.3502 Lr: 0.00438 [2023-12-20 16:33:13,572 INFO misc.py line 119 131400] Train: [39/100][12/800] Data 0.003 (0.003) Batch 0.338 (0.322) Remain 04:26:15 loss: 0.1586 Lr: 0.00438 [2023-12-20 16:33:13,918 INFO misc.py line 119 131400] Train: [39/100][13/800] Data 0.005 (0.004) Batch 0.347 (0.325) Remain 04:28:18 loss: 0.6256 Lr: 0.00438 [2023-12-20 16:33:14,269 INFO misc.py line 119 131400] Train: [39/100][14/800] Data 0.004 (0.004) Batch 0.351 (0.327) Remain 04:30:15 loss: 0.3484 Lr: 0.00438 [2023-12-20 16:33:14,627 INFO misc.py line 119 131400] Train: [39/100][15/800] Data 0.004 (0.004) Batch 0.358 (0.330) Remain 04:32:22 loss: 0.4990 Lr: 0.00438 [2023-12-20 16:33:14,964 INFO misc.py line 119 131400] Train: [39/100][16/800] Data 0.004 (0.004) Batch 0.337 (0.330) Remain 04:32:50 loss: 0.1549 Lr: 0.00438 [2023-12-20 16:33:15,301 INFO misc.py line 119 131400] Train: [39/100][17/800] Data 0.004 (0.004) Batch 0.337 (0.331) Remain 04:33:14 loss: 0.6892 Lr: 0.00438 [2023-12-20 16:33:15,617 INFO misc.py line 119 131400] Train: [39/100][18/800] Data 0.004 (0.004) Batch 0.315 (0.330) Remain 04:32:23 loss: 0.3155 Lr: 0.00438 [2023-12-20 16:33:15,969 INFO misc.py line 119 131400] Train: [39/100][19/800] Data 0.005 (0.004) Batch 0.353 (0.331) Remain 04:33:36 loss: 0.6040 Lr: 0.00438 [2023-12-20 16:33:16,312 INFO misc.py line 119 131400] Train: [39/100][20/800] Data 0.004 (0.004) Batch 0.342 (0.332) Remain 04:34:09 loss: 1.0557 Lr: 0.00438 [2023-12-20 16:33:16,685 INFO misc.py line 119 131400] Train: [39/100][21/800] Data 0.005 (0.004) Batch 0.373 (0.334) Remain 04:36:02 loss: 0.3745 Lr: 0.00438 [2023-12-20 16:33:17,056 INFO misc.py line 119 131400] Train: [39/100][22/800] Data 0.004 (0.004) Batch 0.372 (0.336) Remain 04:37:40 loss: 0.5020 Lr: 0.00438 [2023-12-20 16:33:17,420 INFO misc.py line 119 131400] Train: [39/100][23/800] Data 0.004 (0.004) Batch 0.364 (0.337) Remain 04:38:48 loss: 0.3096 Lr: 0.00438 [2023-12-20 16:33:17,747 INFO misc.py line 119 131400] Train: [39/100][24/800] Data 0.004 (0.004) Batch 0.327 (0.337) Remain 04:38:24 loss: 0.4708 Lr: 0.00438 [2023-12-20 16:33:18,081 INFO misc.py line 119 131400] Train: [39/100][25/800] Data 0.003 (0.004) Batch 0.333 (0.337) Remain 04:38:15 loss: 0.4126 Lr: 0.00438 [2023-12-20 16:33:18,455 INFO misc.py line 119 131400] Train: [39/100][26/800] Data 0.005 (0.004) Batch 0.374 (0.338) Remain 04:39:35 loss: 0.5754 Lr: 0.00438 [2023-12-20 16:33:18,814 INFO misc.py line 119 131400] Train: [39/100][27/800] Data 0.004 (0.004) Batch 0.358 (0.339) Remain 04:40:15 loss: 0.3508 Lr: 0.00438 [2023-12-20 16:33:19,141 INFO misc.py line 119 131400] Train: [39/100][28/800] Data 0.006 (0.004) Batch 0.329 (0.339) Remain 04:39:54 loss: 0.4492 Lr: 0.00438 [2023-12-20 16:33:19,477 INFO misc.py line 119 131400] Train: [39/100][29/800] Data 0.003 (0.004) Batch 0.335 (0.339) Remain 04:39:47 loss: 0.3031 Lr: 0.00438 [2023-12-20 16:33:19,760 INFO misc.py line 119 131400] Train: [39/100][30/800] Data 0.003 (0.004) Batch 0.283 (0.337) Remain 04:38:04 loss: 0.4369 Lr: 0.00438 [2023-12-20 16:33:20,090 INFO misc.py line 119 131400] Train: [39/100][31/800] Data 0.004 (0.004) Batch 0.329 (0.336) Remain 04:37:51 loss: 0.4492 Lr: 0.00438 [2023-12-20 16:33:20,412 INFO misc.py line 119 131400] Train: [39/100][32/800] Data 0.005 (0.004) Batch 0.324 (0.336) Remain 04:37:29 loss: 0.4721 Lr: 0.00438 [2023-12-20 16:33:20,732 INFO misc.py line 119 131400] Train: [39/100][33/800] Data 0.003 (0.004) Batch 0.317 (0.335) Remain 04:36:58 loss: 0.5295 Lr: 0.00438 [2023-12-20 16:33:21,087 INFO misc.py line 119 131400] Train: [39/100][34/800] Data 0.006 (0.004) Batch 0.355 (0.336) Remain 04:37:30 loss: 0.1452 Lr: 0.00438 [2023-12-20 16:33:21,413 INFO misc.py line 119 131400] Train: [39/100][35/800] Data 0.006 (0.004) Batch 0.328 (0.336) Remain 04:37:16 loss: 0.3028 Lr: 0.00438 [2023-12-20 16:33:21,734 INFO misc.py line 119 131400] Train: [39/100][36/800] Data 0.004 (0.004) Batch 0.321 (0.335) Remain 04:36:54 loss: 0.3561 Lr: 0.00438 [2023-12-20 16:33:22,047 INFO misc.py line 119 131400] Train: [39/100][37/800] Data 0.005 (0.004) Batch 0.313 (0.335) Remain 04:36:21 loss: 0.3233 Lr: 0.00438 [2023-12-20 16:33:22,333 INFO misc.py line 119 131400] Train: [39/100][38/800] Data 0.004 (0.004) Batch 0.285 (0.333) Remain 04:35:11 loss: 0.2737 Lr: 0.00438 [2023-12-20 16:33:22,676 INFO misc.py line 119 131400] Train: [39/100][39/800] Data 0.005 (0.004) Batch 0.343 (0.333) Remain 04:35:24 loss: 0.5464 Lr: 0.00438 [2023-12-20 16:33:22,980 INFO misc.py line 119 131400] Train: [39/100][40/800] Data 0.011 (0.004) Batch 0.305 (0.333) Remain 04:34:46 loss: 0.5946 Lr: 0.00438 [2023-12-20 16:33:23,305 INFO misc.py line 119 131400] Train: [39/100][41/800] Data 0.003 (0.004) Batch 0.324 (0.332) Remain 04:34:34 loss: 0.2051 Lr: 0.00438 [2023-12-20 16:33:23,631 INFO misc.py line 119 131400] Train: [39/100][42/800] Data 0.005 (0.004) Batch 0.326 (0.332) Remain 04:34:25 loss: 0.3583 Lr: 0.00438 [2023-12-20 16:33:23,973 INFO misc.py line 119 131400] Train: [39/100][43/800] Data 0.007 (0.004) Batch 0.344 (0.333) Remain 04:34:39 loss: 0.4827 Lr: 0.00438 [2023-12-20 16:33:24,269 INFO misc.py line 119 131400] Train: [39/100][44/800] Data 0.003 (0.004) Batch 0.296 (0.332) Remain 04:33:54 loss: 0.2259 Lr: 0.00438 [2023-12-20 16:33:24,588 INFO misc.py line 119 131400] Train: [39/100][45/800] Data 0.003 (0.004) Batch 0.319 (0.331) Remain 04:33:39 loss: 0.3988 Lr: 0.00438 [2023-12-20 16:33:24,898 INFO misc.py line 119 131400] Train: [39/100][46/800] Data 0.005 (0.004) Batch 0.311 (0.331) Remain 04:33:15 loss: 0.6250 Lr: 0.00438 [2023-12-20 16:33:25,246 INFO misc.py line 119 131400] Train: [39/100][47/800] Data 0.003 (0.004) Batch 0.347 (0.331) Remain 04:33:33 loss: 0.3613 Lr: 0.00438 [2023-12-20 16:33:25,570 INFO misc.py line 119 131400] Train: [39/100][48/800] Data 0.003 (0.004) Batch 0.325 (0.331) Remain 04:33:26 loss: 0.3039 Lr: 0.00438 [2023-12-20 16:33:25,908 INFO misc.py line 119 131400] Train: [39/100][49/800] Data 0.007 (0.004) Batch 0.336 (0.331) Remain 04:33:31 loss: 0.4285 Lr: 0.00438 [2023-12-20 16:33:26,246 INFO misc.py line 119 131400] Train: [39/100][50/800] Data 0.005 (0.004) Batch 0.339 (0.331) Remain 04:33:38 loss: 0.3387 Lr: 0.00438 [2023-12-20 16:33:26,608 INFO misc.py line 119 131400] Train: [39/100][51/800] Data 0.004 (0.004) Batch 0.362 (0.332) Remain 04:34:10 loss: 0.4945 Lr: 0.00438 [2023-12-20 16:33:26,947 INFO misc.py line 119 131400] Train: [39/100][52/800] Data 0.004 (0.004) Batch 0.337 (0.332) Remain 04:34:15 loss: 0.5362 Lr: 0.00438 [2023-12-20 16:33:27,291 INFO misc.py line 119 131400] Train: [39/100][53/800] Data 0.006 (0.004) Batch 0.345 (0.332) Remain 04:34:28 loss: 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INFO misc.py line 119 131400] Train: [39/100][60/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 04:35:55 loss: 0.2820 Lr: 0.00438 [2023-12-20 16:33:30,053 INFO misc.py line 119 131400] Train: [39/100][61/800] Data 0.004 (0.004) Batch 0.332 (0.334) Remain 04:35:53 loss: 0.1041 Lr: 0.00438 [2023-12-20 16:33:30,389 INFO misc.py line 119 131400] Train: [39/100][62/800] Data 0.005 (0.004) Batch 0.337 (0.334) Remain 04:35:55 loss: 0.2500 Lr: 0.00438 [2023-12-20 16:33:30,747 INFO misc.py line 119 131400] Train: [39/100][63/800] Data 0.005 (0.004) Batch 0.358 (0.335) Remain 04:36:14 loss: 0.6045 Lr: 0.00438 [2023-12-20 16:33:31,029 INFO misc.py line 119 131400] Train: [39/100][64/800] Data 0.003 (0.004) Batch 0.281 (0.334) Remain 04:35:31 loss: 0.4361 Lr: 0.00438 [2023-12-20 16:33:31,353 INFO misc.py line 119 131400] Train: [39/100][65/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 04:35:23 loss: 0.2355 Lr: 0.00438 [2023-12-20 16:33:31,663 INFO misc.py line 119 131400] Train: 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0.269 (0.333) Remain 04:35:07 loss: 0.3096 Lr: 0.00438 [2023-12-20 16:33:33,996 INFO misc.py line 119 131400] Train: [39/100][73/800] Data 0.003 (0.004) Batch 0.328 (0.333) Remain 04:35:03 loss: 0.2950 Lr: 0.00438 [2023-12-20 16:33:34,292 INFO misc.py line 119 131400] Train: [39/100][74/800] Data 0.003 (0.004) Batch 0.296 (0.333) Remain 04:34:36 loss: 0.5092 Lr: 0.00438 [2023-12-20 16:33:34,633 INFO misc.py line 119 131400] Train: [39/100][75/800] Data 0.004 (0.004) Batch 0.340 (0.333) Remain 04:34:41 loss: 0.2640 Lr: 0.00438 [2023-12-20 16:33:34,959 INFO misc.py line 119 131400] Train: [39/100][76/800] Data 0.004 (0.004) Batch 0.322 (0.333) Remain 04:34:33 loss: 0.6039 Lr: 0.00438 [2023-12-20 16:33:35,281 INFO misc.py line 119 131400] Train: [39/100][77/800] Data 0.008 (0.004) Batch 0.323 (0.333) Remain 04:34:26 loss: 0.4022 Lr: 0.00438 [2023-12-20 16:33:35,632 INFO misc.py line 119 131400] Train: [39/100][78/800] Data 0.009 (0.004) Batch 0.354 (0.333) Remain 04:34:40 loss: 0.2926 Lr: 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line 119 131400] Train: [39/100][85/800] Data 0.003 (0.004) Batch 0.362 (0.333) Remain 04:34:36 loss: 0.3831 Lr: 0.00438 [2023-12-20 16:33:38,327 INFO misc.py line 119 131400] Train: [39/100][86/800] Data 0.006 (0.004) Batch 0.370 (0.333) Remain 04:34:58 loss: 0.3631 Lr: 0.00438 [2023-12-20 16:33:38,661 INFO misc.py line 119 131400] Train: [39/100][87/800] Data 0.003 (0.004) Batch 0.334 (0.333) Remain 04:34:58 loss: 0.4992 Lr: 0.00438 [2023-12-20 16:33:39,001 INFO misc.py line 119 131400] Train: [39/100][88/800] Data 0.003 (0.004) Batch 0.340 (0.333) Remain 04:35:01 loss: 0.3185 Lr: 0.00438 [2023-12-20 16:33:39,363 INFO misc.py line 119 131400] Train: [39/100][89/800] Data 0.003 (0.004) Batch 0.362 (0.334) Remain 04:35:17 loss: 0.6376 Lr: 0.00438 [2023-12-20 16:33:39,715 INFO misc.py line 119 131400] Train: [39/100][90/800] Data 0.004 (0.004) Batch 0.352 (0.334) Remain 04:35:28 loss: 0.3215 Lr: 0.00438 [2023-12-20 16:33:40,050 INFO misc.py line 119 131400] Train: [39/100][91/800] Data 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loss: 0.2763 Lr: 0.00430 [2023-12-20 16:37:21,324 INFO misc.py line 119 131400] Train: [39/100][745/800] Data 0.004 (0.006) Batch 0.339 (0.338) Remain 04:35:03 loss: 0.5446 Lr: 0.00430 [2023-12-20 16:37:21,633 INFO misc.py line 119 131400] Train: [39/100][746/800] Data 0.003 (0.006) Batch 0.309 (0.338) Remain 04:35:01 loss: 0.5533 Lr: 0.00430 [2023-12-20 16:37:21,930 INFO misc.py line 119 131400] Train: [39/100][747/800] Data 0.004 (0.006) Batch 0.295 (0.338) Remain 04:34:58 loss: 0.3375 Lr: 0.00430 [2023-12-20 16:37:22,258 INFO misc.py line 119 131400] Train: [39/100][748/800] Data 0.007 (0.006) Batch 0.329 (0.338) Remain 04:34:57 loss: 0.3981 Lr: 0.00430 [2023-12-20 16:37:22,596 INFO misc.py line 119 131400] Train: [39/100][749/800] Data 0.005 (0.006) Batch 0.338 (0.338) Remain 04:34:56 loss: 0.5007 Lr: 0.00430 [2023-12-20 16:37:22,973 INFO misc.py line 119 131400] Train: [39/100][750/800] Data 0.004 (0.006) Batch 0.377 (0.338) Remain 04:34:59 loss: 0.3024 Lr: 0.00430 [2023-12-20 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131400] Train: [39/100][757/800] Data 0.004 (0.006) Batch 0.331 (0.338) Remain 04:34:58 loss: 0.6899 Lr: 0.00430 [2023-12-20 16:37:25,686 INFO misc.py line 119 131400] Train: [39/100][758/800] Data 0.005 (0.006) Batch 0.328 (0.338) Remain 04:34:57 loss: 0.3623 Lr: 0.00430 [2023-12-20 16:37:25,989 INFO misc.py line 119 131400] Train: [39/100][759/800] Data 0.004 (0.006) Batch 0.303 (0.338) Remain 04:34:54 loss: 0.3272 Lr: 0.00430 [2023-12-20 16:37:26,275 INFO misc.py line 119 131400] Train: [39/100][760/800] Data 0.004 (0.006) Batch 0.286 (0.338) Remain 04:34:50 loss: 0.3110 Lr: 0.00430 [2023-12-20 16:37:26,624 INFO misc.py line 119 131400] Train: [39/100][761/800] Data 0.003 (0.006) Batch 0.349 (0.338) Remain 04:34:51 loss: 0.5159 Lr: 0.00430 [2023-12-20 16:37:26,944 INFO misc.py line 119 131400] Train: [39/100][762/800] Data 0.003 (0.006) Batch 0.320 (0.338) Remain 04:34:49 loss: 0.2781 Lr: 0.00430 [2023-12-20 16:37:27,269 INFO misc.py line 119 131400] Train: [39/100][763/800] Data 0.003 (0.006) Batch 0.325 (0.338) Remain 04:34:48 loss: 0.5974 Lr: 0.00430 [2023-12-20 16:37:27,546 INFO misc.py line 119 131400] Train: [39/100][764/800] Data 0.002 (0.006) Batch 0.277 (0.338) Remain 04:34:44 loss: 0.3429 Lr: 0.00430 [2023-12-20 16:37:27,890 INFO misc.py line 119 131400] Train: [39/100][765/800] Data 0.003 (0.006) Batch 0.343 (0.338) Remain 04:34:44 loss: 0.3072 Lr: 0.00430 [2023-12-20 16:37:28,183 INFO misc.py line 119 131400] Train: [39/100][766/800] Data 0.003 (0.006) Batch 0.294 (0.337) Remain 04:34:41 loss: 0.3391 Lr: 0.00430 [2023-12-20 16:37:28,504 INFO misc.py line 119 131400] Train: [39/100][767/800] Data 0.003 (0.006) Batch 0.318 (0.337) Remain 04:34:39 loss: 0.3796 Lr: 0.00430 [2023-12-20 16:37:28,818 INFO misc.py line 119 131400] Train: [39/100][768/800] Data 0.005 (0.006) Batch 0.316 (0.337) Remain 04:34:38 loss: 0.2394 Lr: 0.00430 [2023-12-20 16:37:29,148 INFO misc.py line 119 131400] Train: [39/100][769/800] Data 0.004 (0.006) Batch 0.330 (0.337) Remain 04:34:37 loss: 0.7095 Lr: 0.00430 [2023-12-20 16:37:29,468 INFO misc.py line 119 131400] Train: [39/100][770/800] Data 0.003 (0.006) Batch 0.320 (0.337) Remain 04:34:35 loss: 0.6463 Lr: 0.00430 [2023-12-20 16:37:29,803 INFO misc.py line 119 131400] Train: [39/100][771/800] Data 0.003 (0.006) Batch 0.335 (0.337) Remain 04:34:35 loss: 0.3205 Lr: 0.00430 [2023-12-20 16:37:30,079 INFO misc.py line 119 131400] Train: [39/100][772/800] Data 0.003 (0.006) Batch 0.275 (0.337) Remain 04:34:31 loss: 0.7643 Lr: 0.00430 [2023-12-20 16:37:30,407 INFO misc.py line 119 131400] Train: [39/100][773/800] Data 0.004 (0.006) Batch 0.328 (0.337) Remain 04:34:30 loss: 0.6031 Lr: 0.00430 [2023-12-20 16:37:30,747 INFO misc.py line 119 131400] Train: [39/100][774/800] Data 0.003 (0.006) Batch 0.340 (0.337) Remain 04:34:29 loss: 0.3274 Lr: 0.00430 [2023-12-20 16:37:31,105 INFO misc.py line 119 131400] Train: [39/100][775/800] Data 0.004 (0.006) Batch 0.356 (0.337) Remain 04:34:30 loss: 0.4848 Lr: 0.00430 [2023-12-20 16:37:31,475 INFO misc.py line 119 131400] Train: [39/100][776/800] Data 0.006 (0.006) Batch 0.372 (0.337) Remain 04:34:32 loss: 0.3693 Lr: 0.00430 [2023-12-20 16:37:31,796 INFO misc.py line 119 131400] Train: [39/100][777/800] Data 0.004 (0.006) Batch 0.321 (0.337) Remain 04:34:31 loss: 0.3793 Lr: 0.00430 [2023-12-20 16:37:32,137 INFO misc.py line 119 131400] Train: [39/100][778/800] Data 0.004 (0.006) Batch 0.342 (0.337) Remain 04:34:31 loss: 0.3121 Lr: 0.00430 [2023-12-20 16:37:32,473 INFO misc.py line 119 131400] Train: [39/100][779/800] Data 0.004 (0.006) Batch 0.335 (0.337) Remain 04:34:30 loss: 0.3931 Lr: 0.00430 [2023-12-20 16:37:32,813 INFO misc.py line 119 131400] Train: [39/100][780/800] Data 0.004 (0.006) Batch 0.340 (0.337) Remain 04:34:30 loss: 0.3863 Lr: 0.00430 [2023-12-20 16:37:33,117 INFO misc.py line 119 131400] Train: [39/100][781/800] Data 0.004 (0.006) Batch 0.304 (0.337) Remain 04:34:28 loss: 0.3695 Lr: 0.00430 [2023-12-20 16:37:33,476 INFO misc.py line 119 131400] Train: [39/100][782/800] Data 0.005 (0.006) Batch 0.360 (0.337) Remain 04:34:29 loss: 0.2751 Lr: 0.00430 [2023-12-20 16:37:33,830 INFO misc.py line 119 131400] Train: [39/100][783/800] Data 0.003 (0.006) Batch 0.352 (0.337) Remain 04:34:29 loss: 0.2593 Lr: 0.00430 [2023-12-20 16:37:34,172 INFO misc.py line 119 131400] Train: [39/100][784/800] Data 0.004 (0.006) Batch 0.343 (0.337) Remain 04:34:29 loss: 0.5266 Lr: 0.00430 [2023-12-20 16:37:34,519 INFO misc.py line 119 131400] Train: [39/100][785/800] Data 0.004 (0.006) Batch 0.348 (0.337) Remain 04:34:30 loss: 0.5162 Lr: 0.00430 [2023-12-20 16:37:34,875 INFO misc.py line 119 131400] Train: [39/100][786/800] Data 0.003 (0.006) Batch 0.356 (0.337) Remain 04:34:30 loss: 0.3098 Lr: 0.00430 [2023-12-20 16:37:35,231 INFO misc.py line 119 131400] Train: [39/100][787/800] Data 0.003 (0.006) Batch 0.356 (0.337) Remain 04:34:31 loss: 1.0740 Lr: 0.00430 [2023-12-20 16:37:35,569 INFO misc.py line 119 131400] Train: [39/100][788/800] Data 0.004 (0.006) Batch 0.337 (0.337) Remain 04:34:31 loss: 0.3636 Lr: 0.00430 [2023-12-20 16:37:35,894 INFO misc.py line 119 131400] Train: [39/100][789/800] Data 0.004 (0.006) Batch 0.326 (0.337) Remain 04:34:30 loss: 0.5167 Lr: 0.00430 [2023-12-20 16:37:36,212 INFO misc.py line 119 131400] Train: [39/100][790/800] Data 0.004 (0.006) Batch 0.318 (0.337) Remain 04:34:28 loss: 0.6143 Lr: 0.00430 [2023-12-20 16:37:36,508 INFO misc.py line 119 131400] Train: [39/100][791/800] Data 0.004 (0.006) Batch 0.296 (0.337) Remain 04:34:25 loss: 0.2796 Lr: 0.00430 [2023-12-20 16:37:36,849 INFO misc.py line 119 131400] Train: [39/100][792/800] Data 0.003 (0.006) Batch 0.341 (0.337) Remain 04:34:25 loss: 0.5660 Lr: 0.00430 [2023-12-20 16:37:37,172 INFO misc.py line 119 131400] Train: [39/100][793/800] Data 0.004 (0.006) Batch 0.324 (0.337) Remain 04:34:24 loss: 0.3226 Lr: 0.00430 [2023-12-20 16:37:37,500 INFO misc.py line 119 131400] Train: [39/100][794/800] Data 0.003 (0.006) Batch 0.328 (0.337) Remain 04:34:23 loss: 0.6045 Lr: 0.00430 [2023-12-20 16:37:37,812 INFO misc.py line 119 131400] Train: [39/100][795/800] Data 0.003 (0.006) Batch 0.311 (0.337) Remain 04:34:21 loss: 0.9587 Lr: 0.00430 [2023-12-20 16:37:38,138 INFO misc.py line 119 131400] Train: [39/100][796/800] Data 0.004 (0.006) Batch 0.324 (0.337) Remain 04:34:20 loss: 0.5847 Lr: 0.00430 [2023-12-20 16:37:38,409 INFO misc.py line 119 131400] Train: [39/100][797/800] Data 0.006 (0.006) Batch 0.273 (0.337) Remain 04:34:16 loss: 0.2944 Lr: 0.00430 [2023-12-20 16:37:38,712 INFO misc.py line 119 131400] Train: [39/100][798/800] Data 0.003 (0.006) Batch 0.303 (0.337) Remain 04:34:13 loss: 0.4602 Lr: 0.00430 [2023-12-20 16:37:39,035 INFO misc.py line 119 131400] Train: [39/100][799/800] Data 0.003 (0.006) Batch 0.323 (0.337) Remain 04:34:12 loss: 0.3233 Lr: 0.00430 [2023-12-20 16:37:39,391 INFO misc.py line 119 131400] Train: [39/100][800/800] Data 0.003 (0.006) Batch 0.353 (0.337) Remain 04:34:13 loss: 0.5151 Lr: 0.00430 [2023-12-20 16:37:39,396 INFO misc.py line 136 131400] Train result: loss: 0.4109 [2023-12-20 16:37:39,397 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:38:02,357 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1419 [2023-12-20 16:38:02,432 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1666 [2023-12-20 16:38:02,537 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4609 [2023-12-20 16:38:02,662 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2409 [2023-12-20 16:38:02,803 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2608 [2023-12-20 16:38:02,918 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5853 [2023-12-20 16:38:03,014 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9512 [2023-12-20 16:38:03,127 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6134 [2023-12-20 16:38:03,207 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3147 [2023-12-20 16:38:03,303 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3797 [2023-12-20 16:38:03,393 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5127 [2023-12-20 16:38:03,548 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5043 [2023-12-20 16:38:03,671 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5267 [2023-12-20 16:38:03,831 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2151 [2023-12-20 16:38:03,926 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3287 [2023-12-20 16:38:04,059 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8192 [2023-12-20 16:38:04,166 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2817 [2023-12-20 16:38:04,278 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6705 [2023-12-20 16:38:04,390 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2251 [2023-12-20 16:38:04,467 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6304 [2023-12-20 16:38:04,575 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1645 [2023-12-20 16:38:04,731 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1953 [2023-12-20 16:38:04,850 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5253 [2023-12-20 16:38:04,992 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1669 [2023-12-20 16:38:05,138 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2172 [2023-12-20 16:38:05,220 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7111 [2023-12-20 16:38:05,376 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4257 [2023-12-20 16:38:05,500 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5689 [2023-12-20 16:38:05,596 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7283 [2023-12-20 16:38:05,740 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3025 [2023-12-20 16:38:05,844 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7199 [2023-12-20 16:38:05,963 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6133 [2023-12-20 16:38:06,047 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1460 [2023-12-20 16:38:06,115 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1860 [2023-12-20 16:38:06,210 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5404 [2023-12-20 16:38:06,305 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5083 [2023-12-20 16:38:06,439 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0357 [2023-12-20 16:38:06,550 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1133 [2023-12-20 16:38:06,630 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7408 [2023-12-20 16:38:06,775 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4757 [2023-12-20 16:38:06,920 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0235 [2023-12-20 16:38:07,018 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2190 [2023-12-20 16:38:07,139 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4088 [2023-12-20 16:38:07,280 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8441 [2023-12-20 16:38:07,399 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4710 [2023-12-20 16:38:07,504 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8396 [2023-12-20 16:38:07,671 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.6435 [2023-12-20 16:38:07,764 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3898 [2023-12-20 16:38:07,916 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3363 [2023-12-20 16:38:08,008 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.8337 [2023-12-20 16:38:08,083 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8473 [2023-12-20 16:38:08,189 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1717 [2023-12-20 16:38:08,336 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3631 [2023-12-20 16:38:08,471 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3396 [2023-12-20 16:38:08,572 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.9599 [2023-12-20 16:38:08,658 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8096 [2023-12-20 16:38:08,761 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4258 [2023-12-20 16:38:08,920 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2612 [2023-12-20 16:38:09,015 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4899 [2023-12-20 16:38:09,111 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2632 [2023-12-20 16:38:09,221 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3565 [2023-12-20 16:38:09,313 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2753 [2023-12-20 16:38:09,399 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8816 [2023-12-20 16:38:09,501 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0120 [2023-12-20 16:38:09,627 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4759 [2023-12-20 16:38:09,713 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4292 [2023-12-20 16:38:09,812 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4039 [2023-12-20 16:38:09,911 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0157 [2023-12-20 16:38:10,001 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3334 [2023-12-20 16:38:10,086 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0159 [2023-12-20 16:38:10,182 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8802 [2023-12-20 16:38:10,272 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7001 [2023-12-20 16:38:10,407 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1666 [2023-12-20 16:38:10,503 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5330 [2023-12-20 16:38:10,618 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7706 [2023-12-20 16:38:10,721 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.9464 [2023-12-20 16:38:10,810 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6199 [2023-12-20 16:38:10,964 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.7917 [2023-12-20 16:38:12,141 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7224/0.8113/0.9040. [2023-12-20 16:38:12,141 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8567/0.9298 [2023-12-20 16:38:12,141 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9613/0.9892 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6189/0.8129 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8064/0.8458 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9048/0.9508 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8526/0.9141 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7508/0.8463 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6659/0.8895 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6581/0.7555 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7981/0.9383 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3721/0.4969 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.5153/0.5657 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6389/0.8144 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7595/0.8433 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5924/0.6176 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6813/0.7549 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.8959/0.9678 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6821/0.7836 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8858/0.9271 [2023-12-20 16:38:12,142 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5507/0.5826 [2023-12-20 16:38:12,143 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:38:12,143 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:38:12,144 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:38:16,422 INFO misc.py line 119 131400] Train: [40/100][1/800] Data 1.881 (1.881) Batch 2.204 (2.204) Remain 29:52:14 loss: 0.3199 Lr: 0.00430 [2023-12-20 16:38:16,778 INFO misc.py line 119 131400] Train: [40/100][2/800] Data 0.004 (0.004) Batch 0.356 (0.356) Remain 04:49:14 loss: 0.2644 Lr: 0.00430 [2023-12-20 16:38:17,100 INFO misc.py line 119 131400] Train: [40/100][3/800] Data 0.004 (0.004) Batch 0.324 (0.324) Remain 04:23:14 loss: 0.6431 Lr: 0.00430 [2023-12-20 16:38:17,444 INFO misc.py line 119 131400] Train: [40/100][4/800] Data 0.003 (0.003) Batch 0.344 (0.344) Remain 04:39:38 loss: 0.3448 Lr: 0.00430 [2023-12-20 16:38:17,786 INFO misc.py line 119 131400] Train: [40/100][5/800] Data 0.003 (0.003) Batch 0.337 (0.340) Remain 04:36:49 loss: 0.4412 Lr: 0.00430 [2023-12-20 16:38:18,099 INFO misc.py line 119 131400] Train: [40/100][6/800] Data 0.010 (0.005) Batch 0.317 (0.333) Remain 04:30:27 loss: 0.2269 Lr: 0.00430 [2023-12-20 16:38:18,441 INFO misc.py line 119 131400] Train: [40/100][7/800] Data 0.005 (0.005) Batch 0.341 (0.335) Remain 04:32:11 loss: 0.5560 Lr: 0.00430 [2023-12-20 16:38:18,783 INFO misc.py line 119 131400] Train: [40/100][8/800] Data 0.006 (0.005) Batch 0.342 (0.336) Remain 04:33:26 loss: 0.4078 Lr: 0.00430 [2023-12-20 16:38:19,086 INFO misc.py line 119 131400] Train: [40/100][9/800] Data 0.004 (0.005) Batch 0.302 (0.331) Remain 04:28:45 loss: 0.3368 Lr: 0.00430 [2023-12-20 16:38:19,387 INFO misc.py line 119 131400] Train: [40/100][10/800] Data 0.005 (0.005) Batch 0.303 (0.327) Remain 04:25:36 loss: 0.5502 Lr: 0.00430 [2023-12-20 16:38:19,714 INFO misc.py line 119 131400] Train: [40/100][11/800] Data 0.003 (0.005) Batch 0.327 (0.327) Remain 04:25:39 loss: 0.4715 Lr: 0.00430 [2023-12-20 16:38:20,023 INFO misc.py line 119 131400] Train: [40/100][12/800] Data 0.003 (0.005) Batch 0.308 (0.325) Remain 04:23:57 loss: 0.3467 Lr: 0.00430 [2023-12-20 16:38:20,376 INFO misc.py line 119 131400] Train: [40/100][13/800] Data 0.004 (0.005) Batch 0.353 (0.327) Remain 04:26:17 loss: 0.3911 Lr: 0.00430 [2023-12-20 16:38:20,666 INFO misc.py line 119 131400] Train: [40/100][14/800] Data 0.004 (0.004) Batch 0.291 (0.324) Remain 04:23:33 loss: 0.3821 Lr: 0.00430 [2023-12-20 16:38:21,002 INFO misc.py line 119 131400] Train: [40/100][15/800] Data 0.003 (0.004) Batch 0.336 (0.325) Remain 04:24:20 loss: 0.2973 Lr: 0.00430 [2023-12-20 16:38:21,348 INFO misc.py line 119 131400] Train: [40/100][16/800] Data 0.003 (0.004) Batch 0.346 (0.327) Remain 04:25:37 loss: 0.4861 Lr: 0.00429 [2023-12-20 16:38:21,687 INFO misc.py line 119 131400] Train: [40/100][17/800] Data 0.005 (0.004) Batch 0.339 (0.328) Remain 04:26:20 loss: 0.2943 Lr: 0.00429 [2023-12-20 16:38:22,047 INFO misc.py line 119 131400] Train: [40/100][18/800] Data 0.003 (0.004) Batch 0.360 (0.330) Remain 04:28:03 loss: 0.3714 Lr: 0.00429 [2023-12-20 16:38:22,378 INFO misc.py line 119 131400] Train: [40/100][19/800] Data 0.004 (0.004) Batch 0.330 (0.330) Remain 04:28:05 loss: 0.4638 Lr: 0.00429 [2023-12-20 16:38:22,721 INFO misc.py line 119 131400] Train: [40/100][20/800] Data 0.005 (0.004) Batch 0.343 (0.331) Remain 04:28:42 loss: 0.2541 Lr: 0.00429 [2023-12-20 16:38:23,058 INFO misc.py line 119 131400] Train: [40/100][21/800] Data 0.005 (0.004) Batch 0.339 (0.331) Remain 04:29:05 loss: 0.5290 Lr: 0.00429 [2023-12-20 16:38:23,392 INFO misc.py line 119 131400] Train: [40/100][22/800] Data 0.003 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loss: 0.3060 Lr: 0.00429 [2023-12-20 16:38:25,710 INFO misc.py line 119 131400] Train: [40/100][29/800] Data 0.003 (0.004) Batch 0.324 (0.331) Remain 04:29:08 loss: 0.3654 Lr: 0.00429 [2023-12-20 16:38:26,051 INFO misc.py line 119 131400] Train: [40/100][30/800] Data 0.005 (0.004) Batch 0.341 (0.331) Remain 04:29:26 loss: 0.3294 Lr: 0.00429 [2023-12-20 16:38:26,391 INFO misc.py line 119 131400] Train: [40/100][31/800] Data 0.004 (0.004) Batch 0.339 (0.332) Remain 04:29:38 loss: 0.2845 Lr: 0.00429 [2023-12-20 16:38:26,722 INFO misc.py line 119 131400] Train: [40/100][32/800] Data 0.005 (0.004) Batch 0.332 (0.332) Remain 04:29:38 loss: 0.5311 Lr: 0.00429 [2023-12-20 16:38:27,038 INFO misc.py line 119 131400] Train: [40/100][33/800] Data 0.004 (0.004) Batch 0.317 (0.331) Remain 04:29:13 loss: 0.2644 Lr: 0.00429 [2023-12-20 16:38:27,345 INFO misc.py line 119 131400] Train: [40/100][34/800] Data 0.003 (0.004) Batch 0.307 (0.330) Remain 04:28:34 loss: 0.7156 Lr: 0.00429 [2023-12-20 16:38:27,697 INFO misc.py line 119 131400] Train: [40/100][35/800] Data 0.004 (0.004) Batch 0.352 (0.331) Remain 04:29:06 loss: 0.2949 Lr: 0.00429 [2023-12-20 16:38:27,995 INFO misc.py line 119 131400] Train: [40/100][36/800] Data 0.004 (0.004) Batch 0.299 (0.330) Remain 04:28:18 loss: 0.2564 Lr: 0.00429 [2023-12-20 16:38:28,327 INFO misc.py line 119 131400] Train: [40/100][37/800] Data 0.003 (0.004) Batch 0.331 (0.330) Remain 04:28:19 loss: 0.3393 Lr: 0.00429 [2023-12-20 16:38:28,649 INFO misc.py line 119 131400] Train: [40/100][38/800] Data 0.005 (0.004) Batch 0.323 (0.330) Remain 04:28:08 loss: 0.1683 Lr: 0.00429 [2023-12-20 16:38:28,999 INFO misc.py line 119 131400] Train: [40/100][39/800] Data 0.003 (0.004) Batch 0.351 (0.331) Remain 04:28:36 loss: 0.2992 Lr: 0.00429 [2023-12-20 16:38:29,305 INFO misc.py line 119 131400] Train: [40/100][40/800] Data 0.003 (0.004) Batch 0.304 (0.330) Remain 04:28:00 loss: 0.1325 Lr: 0.00429 [2023-12-20 16:38:29,669 INFO misc.py line 119 131400] Train: [40/100][41/800] Data 0.006 (0.004) Batch 0.366 (0.331) Remain 04:28:46 loss: 0.5510 Lr: 0.00429 [2023-12-20 16:38:29,999 INFO misc.py line 119 131400] Train: [40/100][42/800] Data 0.003 (0.004) Batch 0.330 (0.331) Remain 04:28:45 loss: 0.2014 Lr: 0.00429 [2023-12-20 16:38:30,275 INFO misc.py line 119 131400] Train: [40/100][43/800] Data 0.004 (0.004) Batch 0.275 (0.329) Remain 04:27:37 loss: 0.2569 Lr: 0.00429 [2023-12-20 16:38:30,576 INFO misc.py line 119 131400] Train: [40/100][44/800] Data 0.004 (0.004) Batch 0.301 (0.329) Remain 04:27:03 loss: 0.5044 Lr: 0.00429 [2023-12-20 16:38:30,906 INFO misc.py line 119 131400] Train: [40/100][45/800] Data 0.004 (0.004) Batch 0.330 (0.329) Remain 04:27:04 loss: 0.4651 Lr: 0.00429 [2023-12-20 16:38:31,243 INFO misc.py line 119 131400] Train: [40/100][46/800] Data 0.004 (0.004) Batch 0.338 (0.329) Remain 04:27:15 loss: 0.5146 Lr: 0.00429 [2023-12-20 16:38:31,571 INFO misc.py line 119 131400] Train: [40/100][47/800] Data 0.003 (0.004) Batch 0.327 (0.329) Remain 04:27:12 loss: 0.4589 Lr: 0.00429 [2023-12-20 16:38:31,929 INFO misc.py line 119 131400] Train: [40/100][48/800] Data 0.003 (0.004) Batch 0.357 (0.329) Remain 04:27:42 loss: 0.3619 Lr: 0.00429 [2023-12-20 16:38:32,263 INFO misc.py line 119 131400] Train: [40/100][49/800] Data 0.006 (0.004) Batch 0.336 (0.330) Remain 04:27:48 loss: 0.4622 Lr: 0.00429 [2023-12-20 16:38:32,615 INFO misc.py line 119 131400] Train: [40/100][50/800] Data 0.003 (0.004) Batch 0.353 (0.330) Remain 04:28:12 loss: 0.3434 Lr: 0.00429 [2023-12-20 16:38:32,963 INFO misc.py line 119 131400] Train: [40/100][51/800] Data 0.003 (0.004) Batch 0.348 (0.330) Remain 04:28:30 loss: 0.5622 Lr: 0.00429 [2023-12-20 16:38:33,307 INFO misc.py line 119 131400] Train: [40/100][52/800] Data 0.003 (0.004) Batch 0.343 (0.331) Remain 04:28:42 loss: 0.4176 Lr: 0.00429 [2023-12-20 16:38:33,638 INFO misc.py line 119 131400] Train: [40/100][53/800] Data 0.004 (0.004) Batch 0.331 (0.331) Remain 04:28:42 loss: 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INFO misc.py line 119 131400] Train: [40/100][60/800] Data 0.004 (0.004) Batch 0.331 (0.332) Remain 04:29:50 loss: 0.3319 Lr: 0.00429 [2023-12-20 16:38:36,382 INFO misc.py line 119 131400] Train: [40/100][61/800] Data 0.004 (0.004) Batch 0.346 (0.332) Remain 04:30:01 loss: 0.5106 Lr: 0.00429 [2023-12-20 16:38:36,714 INFO misc.py line 119 131400] Train: [40/100][62/800] Data 0.004 (0.004) Batch 0.328 (0.332) Remain 04:29:57 loss: 0.2640 Lr: 0.00429 [2023-12-20 16:38:37,029 INFO misc.py line 119 131400] Train: [40/100][63/800] Data 0.009 (0.004) Batch 0.320 (0.332) Remain 04:29:47 loss: 0.1484 Lr: 0.00429 [2023-12-20 16:38:37,376 INFO misc.py line 119 131400] Train: [40/100][64/800] Data 0.003 (0.004) Batch 0.347 (0.332) Remain 04:29:58 loss: 0.4563 Lr: 0.00429 [2023-12-20 16:38:37,720 INFO misc.py line 119 131400] Train: [40/100][65/800] Data 0.003 (0.004) Batch 0.343 (0.333) Remain 04:30:06 loss: 0.5921 Lr: 0.00429 [2023-12-20 16:38:38,058 INFO misc.py line 119 131400] Train: 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0.344 (0.333) Remain 04:30:34 loss: 0.3528 Lr: 0.00429 [2023-12-20 16:38:40,426 INFO misc.py line 119 131400] Train: [40/100][73/800] Data 0.006 (0.004) Batch 0.332 (0.333) Remain 04:30:33 loss: 0.6837 Lr: 0.00429 [2023-12-20 16:38:40,785 INFO misc.py line 119 131400] Train: [40/100][74/800] Data 0.008 (0.004) Batch 0.359 (0.334) Remain 04:30:51 loss: 0.2457 Lr: 0.00429 [2023-12-20 16:38:41,154 INFO misc.py line 119 131400] Train: [40/100][75/800] Data 0.013 (0.004) Batch 0.373 (0.334) Remain 04:31:17 loss: 0.1751 Lr: 0.00429 [2023-12-20 16:38:41,491 INFO misc.py line 119 131400] Train: [40/100][76/800] Data 0.003 (0.004) Batch 0.337 (0.334) Remain 04:31:19 loss: 0.4110 Lr: 0.00429 [2023-12-20 16:38:41,821 INFO misc.py line 119 131400] Train: [40/100][77/800] Data 0.003 (0.004) Batch 0.330 (0.334) Remain 04:31:16 loss: 0.3687 Lr: 0.00429 [2023-12-20 16:38:42,180 INFO misc.py line 119 131400] Train: [40/100][78/800] Data 0.004 (0.004) Batch 0.359 (0.334) Remain 04:31:31 loss: 0.3893 Lr: 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Batch 0.344 (0.334) Remain 04:27:14 loss: 0.3983 Lr: 0.00421 [2023-12-20 16:42:22,654 INFO misc.py line 119 131400] Train: [40/100][739/800] Data 0.004 (0.004) Batch 0.337 (0.334) Remain 04:27:14 loss: 0.3377 Lr: 0.00421 [2023-12-20 16:42:22,959 INFO misc.py line 119 131400] Train: [40/100][740/800] Data 0.003 (0.004) Batch 0.305 (0.334) Remain 04:27:12 loss: 0.3372 Lr: 0.00421 [2023-12-20 16:42:23,230 INFO misc.py line 119 131400] Train: [40/100][741/800] Data 0.003 (0.004) Batch 0.269 (0.334) Remain 04:27:07 loss: 0.2909 Lr: 0.00421 [2023-12-20 16:42:23,542 INFO misc.py line 119 131400] Train: [40/100][742/800] Data 0.005 (0.004) Batch 0.314 (0.333) Remain 04:27:06 loss: 0.4875 Lr: 0.00421 [2023-12-20 16:42:23,863 INFO misc.py line 119 131400] Train: [40/100][743/800] Data 0.003 (0.004) Batch 0.321 (0.333) Remain 04:27:05 loss: 0.3198 Lr: 0.00421 [2023-12-20 16:42:24,188 INFO misc.py line 119 131400] Train: [40/100][744/800] Data 0.004 (0.004) Batch 0.325 (0.333) Remain 04:27:04 loss: 0.3973 Lr: 0.00421 [2023-12-20 16:42:24,497 INFO misc.py line 119 131400] Train: [40/100][745/800] Data 0.003 (0.004) Batch 0.310 (0.333) Remain 04:27:02 loss: 0.4538 Lr: 0.00421 [2023-12-20 16:42:24,768 INFO misc.py line 119 131400] Train: [40/100][746/800] Data 0.003 (0.004) Batch 0.271 (0.333) Remain 04:26:57 loss: 0.4891 Lr: 0.00421 [2023-12-20 16:42:25,086 INFO misc.py line 119 131400] Train: [40/100][747/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 04:26:56 loss: 0.3310 Lr: 0.00421 [2023-12-20 16:42:25,418 INFO misc.py line 119 131400] Train: [40/100][748/800] Data 0.003 (0.004) Batch 0.332 (0.333) Remain 04:26:56 loss: 0.2526 Lr: 0.00421 [2023-12-20 16:42:25,724 INFO misc.py line 119 131400] Train: [40/100][749/800] Data 0.004 (0.004) Batch 0.306 (0.333) Remain 04:26:54 loss: 0.2815 Lr: 0.00421 [2023-12-20 16:42:26,011 INFO misc.py line 119 131400] Train: [40/100][750/800] Data 0.003 (0.004) Batch 0.287 (0.333) Remain 04:26:50 loss: 0.4449 Lr: 0.00421 [2023-12-20 16:42:26,295 INFO misc.py line 119 131400] Train: [40/100][751/800] Data 0.003 (0.004) Batch 0.284 (0.333) Remain 04:26:47 loss: 0.5636 Lr: 0.00421 [2023-12-20 16:42:26,615 INFO misc.py line 119 131400] Train: [40/100][752/800] Data 0.003 (0.004) Batch 0.320 (0.333) Remain 04:26:46 loss: 0.3835 Lr: 0.00421 [2023-12-20 16:42:26,933 INFO misc.py line 119 131400] Train: [40/100][753/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 04:26:44 loss: 0.3931 Lr: 0.00421 [2023-12-20 16:42:27,253 INFO misc.py line 119 131400] Train: [40/100][754/800] Data 0.004 (0.004) Batch 0.320 (0.333) Remain 04:26:43 loss: 0.8426 Lr: 0.00421 [2023-12-20 16:42:27,574 INFO misc.py line 119 131400] Train: [40/100][755/800] Data 0.003 (0.004) Batch 0.321 (0.333) Remain 04:26:42 loss: 0.2658 Lr: 0.00421 [2023-12-20 16:42:27,911 INFO misc.py line 119 131400] Train: [40/100][756/800] Data 0.004 (0.004) Batch 0.337 (0.333) Remain 04:26:42 loss: 0.4153 Lr: 0.00421 [2023-12-20 16:42:28,228 INFO misc.py line 119 131400] Train: [40/100][757/800] Data 0.004 (0.004) Batch 0.313 (0.333) Remain 04:26:40 loss: 0.4068 Lr: 0.00421 [2023-12-20 16:42:28,548 INFO misc.py line 119 131400] Train: [40/100][758/800] Data 0.009 (0.004) Batch 0.325 (0.333) Remain 04:26:39 loss: 0.6493 Lr: 0.00421 [2023-12-20 16:42:28,969 INFO misc.py line 119 131400] Train: [40/100][759/800] Data 0.003 (0.004) Batch 0.420 (0.333) Remain 04:26:45 loss: 0.4064 Lr: 0.00421 [2023-12-20 16:42:29,277 INFO misc.py line 119 131400] Train: [40/100][760/800] Data 0.004 (0.004) Batch 0.306 (0.333) Remain 04:26:43 loss: 0.2977 Lr: 0.00421 [2023-12-20 16:42:29,580 INFO misc.py line 119 131400] Train: [40/100][761/800] Data 0.007 (0.004) Batch 0.306 (0.333) Remain 04:26:41 loss: 0.5644 Lr: 0.00421 [2023-12-20 16:42:29,905 INFO misc.py line 119 131400] Train: [40/100][762/800] Data 0.004 (0.004) Batch 0.325 (0.333) Remain 04:26:40 loss: 0.5913 Lr: 0.00421 [2023-12-20 16:42:30,273 INFO misc.py line 119 131400] Train: [40/100][763/800] Data 0.004 (0.004) Batch 0.368 (0.333) Remain 04:26:42 loss: 0.5710 Lr: 0.00421 [2023-12-20 16:42:30,595 INFO misc.py line 119 131400] Train: [40/100][764/800] Data 0.004 (0.004) Batch 0.322 (0.333) Remain 04:26:41 loss: 0.5348 Lr: 0.00421 [2023-12-20 16:42:30,900 INFO misc.py line 119 131400] Train: [40/100][765/800] Data 0.003 (0.004) Batch 0.304 (0.333) Remain 04:26:38 loss: 0.4322 Lr: 0.00421 [2023-12-20 16:42:31,229 INFO misc.py line 119 131400] Train: [40/100][766/800] Data 0.004 (0.004) Batch 0.329 (0.333) Remain 04:26:38 loss: 0.8397 Lr: 0.00421 [2023-12-20 16:42:31,572 INFO misc.py line 119 131400] Train: [40/100][767/800] Data 0.005 (0.004) Batch 0.344 (0.333) Remain 04:26:38 loss: 0.3262 Lr: 0.00421 [2023-12-20 16:42:31,921 INFO misc.py line 119 131400] Train: [40/100][768/800] Data 0.003 (0.004) Batch 0.348 (0.333) Remain 04:26:39 loss: 0.6071 Lr: 0.00421 [2023-12-20 16:42:32,263 INFO misc.py line 119 131400] Train: [40/100][769/800] Data 0.004 (0.004) Batch 0.343 (0.333) Remain 04:26:39 loss: 0.6034 Lr: 0.00421 [2023-12-20 16:42:32,583 INFO misc.py line 119 131400] Train: [40/100][770/800] Data 0.003 (0.004) Batch 0.320 (0.333) Remain 04:26:38 loss: 0.8571 Lr: 0.00421 [2023-12-20 16:42:32,941 INFO misc.py line 119 131400] Train: [40/100][771/800] Data 0.004 (0.004) Batch 0.355 (0.333) Remain 04:26:39 loss: 0.2904 Lr: 0.00421 [2023-12-20 16:42:33,295 INFO misc.py line 119 131400] Train: [40/100][772/800] Data 0.008 (0.004) Batch 0.356 (0.333) Remain 04:26:40 loss: 0.4397 Lr: 0.00421 [2023-12-20 16:42:33,629 INFO misc.py line 119 131400] Train: [40/100][773/800] Data 0.005 (0.004) Batch 0.336 (0.333) Remain 04:26:40 loss: 0.4777 Lr: 0.00421 [2023-12-20 16:42:34,005 INFO misc.py line 119 131400] Train: [40/100][774/800] Data 0.003 (0.004) Batch 0.375 (0.333) Remain 04:26:42 loss: 0.4386 Lr: 0.00421 [2023-12-20 16:42:34,336 INFO misc.py line 119 131400] Train: [40/100][775/800] Data 0.005 (0.004) Batch 0.331 (0.333) Remain 04:26:42 loss: 0.4187 Lr: 0.00421 [2023-12-20 16:42:34,679 INFO misc.py line 119 131400] Train: [40/100][776/800] Data 0.005 (0.004) Batch 0.342 (0.333) Remain 04:26:42 loss: 0.3261 Lr: 0.00421 [2023-12-20 16:42:35,015 INFO misc.py line 119 131400] Train: [40/100][777/800] Data 0.004 (0.004) Batch 0.337 (0.333) Remain 04:26:42 loss: 0.4295 Lr: 0.00421 [2023-12-20 16:42:35,352 INFO misc.py line 119 131400] Train: [40/100][778/800] Data 0.005 (0.004) Batch 0.338 (0.333) Remain 04:26:42 loss: 0.5440 Lr: 0.00421 [2023-12-20 16:42:35,687 INFO misc.py line 119 131400] Train: [40/100][779/800] Data 0.003 (0.004) Batch 0.333 (0.333) Remain 04:26:41 loss: 0.5121 Lr: 0.00421 [2023-12-20 16:42:35,976 INFO misc.py line 119 131400] Train: [40/100][780/800] Data 0.005 (0.004) Batch 0.290 (0.333) Remain 04:26:38 loss: 0.3494 Lr: 0.00421 [2023-12-20 16:42:36,277 INFO misc.py line 119 131400] Train: [40/100][781/800] Data 0.004 (0.004) Batch 0.302 (0.333) Remain 04:26:36 loss: 0.4059 Lr: 0.00421 [2023-12-20 16:42:36,597 INFO misc.py line 119 131400] Train: [40/100][782/800] Data 0.002 (0.004) Batch 0.316 (0.333) Remain 04:26:35 loss: 0.2033 Lr: 0.00421 [2023-12-20 16:42:36,916 INFO misc.py line 119 131400] Train: [40/100][783/800] Data 0.007 (0.004) Batch 0.323 (0.333) Remain 04:26:34 loss: 0.6029 Lr: 0.00421 [2023-12-20 16:42:37,272 INFO misc.py line 119 131400] Train: [40/100][784/800] Data 0.003 (0.004) Batch 0.356 (0.333) Remain 04:26:35 loss: 0.3487 Lr: 0.00421 [2023-12-20 16:42:37,585 INFO misc.py line 119 131400] Train: [40/100][785/800] Data 0.003 (0.004) Batch 0.313 (0.333) Remain 04:26:33 loss: 0.3100 Lr: 0.00421 [2023-12-20 16:42:37,898 INFO misc.py line 119 131400] Train: [40/100][786/800] Data 0.003 (0.004) Batch 0.313 (0.333) Remain 04:26:32 loss: 0.2519 Lr: 0.00421 [2023-12-20 16:42:38,241 INFO misc.py line 119 131400] Train: [40/100][787/800] Data 0.003 (0.004) Batch 0.342 (0.333) Remain 04:26:32 loss: 0.4992 Lr: 0.00421 [2023-12-20 16:42:38,560 INFO misc.py line 119 131400] Train: [40/100][788/800] Data 0.004 (0.004) Batch 0.319 (0.333) Remain 04:26:31 loss: 0.5785 Lr: 0.00421 [2023-12-20 16:42:38,883 INFO misc.py line 119 131400] Train: [40/100][789/800] Data 0.003 (0.004) Batch 0.323 (0.333) Remain 04:26:30 loss: 0.6836 Lr: 0.00421 [2023-12-20 16:42:39,180 INFO misc.py line 119 131400] Train: [40/100][790/800] Data 0.004 (0.004) Batch 0.298 (0.333) Remain 04:26:27 loss: 0.4640 Lr: 0.00421 [2023-12-20 16:42:39,492 INFO misc.py line 119 131400] Train: [40/100][791/800] Data 0.003 (0.004) Batch 0.312 (0.333) Remain 04:26:26 loss: 0.4430 Lr: 0.00421 [2023-12-20 16:42:39,767 INFO misc.py line 119 131400] Train: [40/100][792/800] Data 0.003 (0.004) Batch 0.272 (0.333) Remain 04:26:22 loss: 0.4409 Lr: 0.00421 [2023-12-20 16:42:40,089 INFO misc.py line 119 131400] Train: [40/100][793/800] Data 0.006 (0.004) Batch 0.325 (0.333) Remain 04:26:21 loss: 0.6713 Lr: 0.00421 [2023-12-20 16:42:40,410 INFO misc.py line 119 131400] Train: [40/100][794/800] Data 0.003 (0.004) Batch 0.304 (0.333) Remain 04:26:19 loss: 0.4226 Lr: 0.00421 [2023-12-20 16:42:40,708 INFO misc.py line 119 131400] Train: [40/100][795/800] Data 0.020 (0.004) Batch 0.314 (0.333) Remain 04:26:17 loss: 0.4005 Lr: 0.00421 [2023-12-20 16:42:41,032 INFO misc.py line 119 131400] Train: [40/100][796/800] Data 0.004 (0.004) Batch 0.324 (0.333) Remain 04:26:16 loss: 0.3961 Lr: 0.00421 [2023-12-20 16:42:41,346 INFO misc.py line 119 131400] Train: [40/100][797/800] Data 0.003 (0.004) Batch 0.313 (0.333) Remain 04:26:15 loss: 0.2772 Lr: 0.00421 [2023-12-20 16:42:41,628 INFO misc.py line 119 131400] Train: [40/100][798/800] Data 0.004 (0.004) Batch 0.282 (0.333) Remain 04:26:11 loss: 0.3529 Lr: 0.00421 [2023-12-20 16:42:41,956 INFO misc.py line 119 131400] Train: [40/100][799/800] Data 0.003 (0.004) Batch 0.329 (0.333) Remain 04:26:11 loss: 0.4249 Lr: 0.00421 [2023-12-20 16:42:42,239 INFO misc.py line 119 131400] Train: [40/100][800/800] Data 0.003 (0.004) Batch 0.284 (0.333) Remain 04:26:08 loss: 0.1717 Lr: 0.00421 [2023-12-20 16:42:42,240 INFO misc.py line 136 131400] Train result: loss: 0.4072 [2023-12-20 16:42:42,240 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:43:06,510 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2041 [2023-12-20 16:43:06,584 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1440 [2023-12-20 16:43:06,676 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4616 [2023-12-20 16:43:06,784 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2824 [2023-12-20 16:43:06,909 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5020 [2023-12-20 16:43:07,010 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2274 [2023-12-20 16:43:07,100 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.4470 [2023-12-20 16:43:07,207 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9912 [2023-12-20 16:43:07,290 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2928 [2023-12-20 16:43:07,380 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4543 [2023-12-20 16:43:07,472 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6930 [2023-12-20 16:43:07,609 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4698 [2023-12-20 16:43:07,738 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.0866 [2023-12-20 16:43:07,900 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3015 [2023-12-20 16:43:07,998 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1573 [2023-12-20 16:43:08,131 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8090 [2023-12-20 16:43:08,238 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3944 [2023-12-20 16:43:08,347 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2155 [2023-12-20 16:43:08,463 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2263 [2023-12-20 16:43:08,538 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.6333 [2023-12-20 16:43:08,644 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4785 [2023-12-20 16:43:08,800 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1715 [2023-12-20 16:43:08,922 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5755 [2023-12-20 16:43:09,069 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1877 [2023-12-20 16:43:09,214 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3969 [2023-12-20 16:43:09,298 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5791 [2023-12-20 16:43:09,474 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.3571 [2023-12-20 16:43:09,601 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6212 [2023-12-20 16:43:09,702 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.9133 [2023-12-20 16:43:09,858 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6025 [2023-12-20 16:43:09,973 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8321 [2023-12-20 16:43:10,105 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5458 [2023-12-20 16:43:10,193 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1845 [2023-12-20 16:43:10,274 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2251 [2023-12-20 16:43:10,376 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7195 [2023-12-20 16:43:10,476 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4272 [2023-12-20 16:43:10,614 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0889 [2023-12-20 16:43:10,732 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1566 [2023-12-20 16:43:10,821 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6112 [2023-12-20 16:43:10,966 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5293 [2023-12-20 16:43:11,112 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0225 [2023-12-20 16:43:11,213 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0927 [2023-12-20 16:43:11,336 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3693 [2023-12-20 16:43:11,480 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0705 [2023-12-20 16:43:11,601 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1426 [2023-12-20 16:43:11,715 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7281 [2023-12-20 16:43:11,882 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4432 [2023-12-20 16:43:11,982 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.6385 [2023-12-20 16:43:12,128 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4916 [2023-12-20 16:43:12,222 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0677 [2023-12-20 16:43:12,302 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4266 [2023-12-20 16:43:12,407 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5208 [2023-12-20 16:43:12,552 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1105 [2023-12-20 16:43:12,691 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3258 [2023-12-20 16:43:12,801 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4492 [2023-12-20 16:43:12,891 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6880 [2023-12-20 16:43:12,994 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.6119 [2023-12-20 16:43:13,154 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2313 [2023-12-20 16:43:13,258 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5914 [2023-12-20 16:43:13,353 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2945 [2023-12-20 16:43:13,453 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3230 [2023-12-20 16:43:13,547 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.5776 [2023-12-20 16:43:13,637 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4735 [2023-12-20 16:43:13,737 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6355 [2023-12-20 16:43:13,866 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4642 [2023-12-20 16:43:13,953 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4164 [2023-12-20 16:43:14,052 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5826 [2023-12-20 16:43:14,147 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0125 [2023-12-20 16:43:14,234 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2879 [2023-12-20 16:43:14,317 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0129 [2023-12-20 16:43:14,410 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7142 [2023-12-20 16:43:14,500 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4120 [2023-12-20 16:43:14,639 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2131 [2023-12-20 16:43:14,738 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5262 [2023-12-20 16:43:14,855 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7019 [2023-12-20 16:43:14,957 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8952 [2023-12-20 16:43:15,049 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2723 [2023-12-20 16:43:15,202 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.5456 [2023-12-20 16:43:16,427 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7239/0.8179/0.9003. [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8501/0.9360 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9650/0.9863 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5639/0.6096 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8022/0.8337 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9031/0.9446 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8517/0.9432 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7422/0.8102 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6637/0.8503 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6398/0.7858 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7476/0.9344 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3746/0.4849 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7044/0.8584 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.5983/0.9102 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7342/0.7711 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5593/0.5826 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7012/0.7880 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9458/0.9757 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6762/0.7681 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8885/0.9202 [2023-12-20 16:43:16,428 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5652/0.6642 [2023-12-20 16:43:16,429 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:43:16,430 INFO misc.py line 165 131400] Currently Best mIoU: 0.7345 [2023-12-20 16:43:16,430 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:43:20,418 INFO misc.py line 119 131400] Train: [41/100][1/800] Data 0.981 (0.981) Batch 1.280 (1.280) Remain 17:03:37 loss: 0.4846 Lr: 0.00421 [2023-12-20 16:43:20,741 INFO misc.py line 119 131400] Train: [41/100][2/800] Data 0.004 (0.004) Batch 0.323 (0.323) Remain 04:18:21 loss: 0.4578 Lr: 0.00421 [2023-12-20 16:43:21,088 INFO misc.py line 119 131400] Train: [41/100][3/800] Data 0.004 (0.004) Batch 0.347 (0.347) Remain 04:37:57 loss: 0.3350 Lr: 0.00421 [2023-12-20 16:43:21,418 INFO misc.py line 119 131400] Train: [41/100][4/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 04:22:56 loss: 0.6517 Lr: 0.00421 [2023-12-20 16:43:21,772 INFO misc.py line 119 131400] Train: [41/100][5/800] Data 0.005 (0.004) Batch 0.355 (0.342) Remain 04:33:33 loss: 0.2495 Lr: 0.00421 [2023-12-20 16:43:22,116 INFO misc.py line 119 131400] Train: [41/100][6/800] Data 0.004 (0.004) Batch 0.344 (0.343) Remain 04:34:03 loss: 0.4543 Lr: 0.00421 [2023-12-20 16:43:22,991 INFO misc.py line 119 131400] Train: [41/100][7/800] Data 0.524 (0.134) Batch 0.864 (0.473) Remain 06:18:24 loss: 0.2715 Lr: 0.00421 [2023-12-20 16:43:23,337 INFO misc.py line 119 131400] Train: [41/100][8/800] Data 0.015 (0.110) Batch 0.357 (0.450) Remain 05:59:46 loss: 0.3692 Lr: 0.00421 [2023-12-20 16:43:23,715 INFO misc.py line 119 131400] Train: [41/100][9/800] Data 0.006 (0.093) Batch 0.378 (0.438) Remain 05:50:09 loss: 0.3645 Lr: 0.00421 [2023-12-20 16:43:24,033 INFO misc.py line 119 131400] Train: [41/100][10/800] Data 0.006 (0.081) Batch 0.315 (0.420) Remain 05:36:08 loss: 0.3555 Lr: 0.00421 [2023-12-20 16:43:24,397 INFO misc.py line 119 131400] Train: [41/100][11/800] Data 0.007 (0.071) Batch 0.367 (0.414) Remain 05:30:47 loss: 0.6357 Lr: 0.00421 [2023-12-20 16:43:24,747 INFO misc.py line 119 131400] Train: [41/100][12/800] Data 0.003 (0.064) Batch 0.351 (0.407) Remain 05:25:10 loss: 0.6077 Lr: 0.00421 [2023-12-20 16:43:25,097 INFO misc.py line 119 131400] Train: [41/100][13/800] Data 0.004 (0.058) Batch 0.337 (0.400) Remain 05:19:35 loss: 0.4997 Lr: 0.00421 [2023-12-20 16:43:25,431 INFO misc.py line 119 131400] Train: [41/100][14/800] Data 0.019 (0.054) Batch 0.348 (0.395) Remain 05:15:48 loss: 0.4113 Lr: 0.00421 [2023-12-20 16:43:25,744 INFO misc.py line 119 131400] Train: [41/100][15/800] Data 0.003 (0.050) Batch 0.312 (0.388) Remain 05:10:17 loss: 0.3758 Lr: 0.00421 [2023-12-20 16:43:26,088 INFO misc.py line 119 131400] Train: [41/100][16/800] Data 0.004 (0.047) Batch 0.344 (0.385) Remain 05:07:36 loss: 0.5596 Lr: 0.00420 [2023-12-20 16:43:26,412 INFO misc.py line 119 131400] Train: [41/100][17/800] Data 0.003 (0.043) Batch 0.324 (0.380) Remain 05:04:08 loss: 0.5678 Lr: 0.00420 [2023-12-20 16:43:26,766 INFO misc.py line 119 131400] Train: [41/100][18/800] Data 0.003 (0.041) Batch 0.354 (0.379) Remain 05:02:43 loss: 0.3510 Lr: 0.00420 [2023-12-20 16:43:27,103 INFO misc.py line 119 131400] Train: [41/100][19/800] Data 0.004 (0.038) Batch 0.333 (0.376) Remain 05:00:25 loss: 0.4351 Lr: 0.00420 [2023-12-20 16:43:27,427 INFO misc.py line 119 131400] Train: [41/100][20/800] Data 0.008 (0.037) Batch 0.329 (0.373) Remain 04:58:13 loss: 0.4278 Lr: 0.00420 [2023-12-20 16:43:27,776 INFO misc.py line 119 131400] Train: [41/100][21/800] Data 0.004 (0.035) Batch 0.349 (0.372) Remain 04:57:08 loss: 0.4324 Lr: 0.00420 [2023-12-20 16:43:28,105 INFO misc.py line 119 131400] Train: [41/100][22/800] Data 0.003 (0.033) Batch 0.327 (0.369) Remain 04:55:16 loss: 0.6560 Lr: 0.00420 [2023-12-20 16:43:28,435 INFO misc.py line 119 131400] Train: [41/100][23/800] Data 0.005 (0.032) Batch 0.331 (0.367) Remain 04:53:44 loss: 0.3504 Lr: 0.00420 [2023-12-20 16:43:28,731 INFO misc.py line 119 131400] Train: [41/100][24/800] Data 0.003 (0.030) Batch 0.296 (0.364) Remain 04:51:01 loss: 0.3410 Lr: 0.00420 [2023-12-20 16:43:29,081 INFO misc.py line 119 131400] Train: [41/100][25/800] Data 0.004 (0.029) Batch 0.350 (0.363) Remain 04:50:29 loss: 0.5754 Lr: 0.00420 [2023-12-20 16:43:29,399 INFO misc.py line 119 131400] Train: [41/100][26/800] Data 0.004 (0.028) Batch 0.317 (0.361) Remain 04:48:52 loss: 0.3386 Lr: 0.00420 [2023-12-20 16:43:29,686 INFO misc.py line 119 131400] Train: [41/100][27/800] Data 0.005 (0.027) Batch 0.289 (0.358) Remain 04:46:27 loss: 0.4476 Lr: 0.00420 [2023-12-20 16:43:29,992 INFO misc.py line 119 131400] Train: [41/100][28/800] Data 0.003 (0.026) Batch 0.305 (0.356) Remain 04:44:45 loss: 0.4405 Lr: 0.00420 [2023-12-20 16:43:30,337 INFO misc.py line 119 131400] Train: [41/100][29/800] Data 0.004 (0.025) Batch 0.341 (0.356) Remain 04:44:16 loss: 0.2658 Lr: 0.00420 [2023-12-20 16:43:30,669 INFO misc.py line 119 131400] Train: [41/100][30/800] Data 0.008 (0.025) Batch 0.337 (0.355) Remain 04:43:42 loss: 0.2000 Lr: 0.00420 [2023-12-20 16:43:31,006 INFO misc.py line 119 131400] Train: [41/100][31/800] Data 0.003 (0.024) Batch 0.336 (0.354) Remain 04:43:10 loss: 0.7631 Lr: 0.00420 [2023-12-20 16:43:31,364 INFO misc.py line 119 131400] Train: [41/100][32/800] Data 0.004 (0.023) Batch 0.355 (0.354) Remain 04:43:12 loss: 0.3750 Lr: 0.00420 [2023-12-20 16:43:31,676 INFO misc.py line 119 131400] Train: [41/100][33/800] Data 0.006 (0.023) Batch 0.315 (0.353) Remain 04:42:09 loss: 0.4556 Lr: 0.00420 [2023-12-20 16:43:32,043 INFO misc.py line 119 131400] Train: [41/100][34/800] Data 0.003 (0.022) Batch 0.367 (0.353) Remain 04:42:30 loss: 0.6442 Lr: 0.00420 [2023-12-20 16:43:32,352 INFO misc.py line 119 131400] Train: [41/100][35/800] Data 0.004 (0.021) Batch 0.309 (0.352) Remain 04:41:24 loss: 0.4283 Lr: 0.00420 [2023-12-20 16:43:32,670 INFO misc.py line 119 131400] Train: [41/100][36/800] Data 0.003 (0.021) Batch 0.318 (0.351) Remain 04:40:34 loss: 0.4212 Lr: 0.00420 [2023-12-20 16:43:33,001 INFO misc.py line 119 131400] Train: [41/100][37/800] Data 0.003 (0.020) Batch 0.329 (0.350) Remain 04:40:03 loss: 0.2781 Lr: 0.00420 [2023-12-20 16:43:33,301 INFO misc.py line 119 131400] Train: [41/100][38/800] Data 0.005 (0.020) Batch 0.301 (0.349) Remain 04:38:55 loss: 0.2729 Lr: 0.00420 [2023-12-20 16:43:33,634 INFO misc.py line 119 131400] Train: [41/100][39/800] Data 0.005 (0.020) Batch 0.326 (0.348) Remain 04:38:23 loss: 0.2939 Lr: 0.00420 [2023-12-20 16:43:33,968 INFO misc.py line 119 131400] Train: [41/100][40/800] Data 0.011 (0.019) Batch 0.341 (0.348) Remain 04:38:14 loss: 0.3610 Lr: 0.00420 [2023-12-20 16:43:34,284 INFO misc.py line 119 131400] Train: [41/100][41/800] Data 0.004 (0.019) Batch 0.317 (0.347) Remain 04:37:35 loss: 0.3205 Lr: 0.00420 [2023-12-20 16:43:34,596 INFO misc.py line 119 131400] Train: [41/100][42/800] Data 0.004 (0.019) Batch 0.311 (0.346) Remain 04:36:50 loss: 0.4598 Lr: 0.00420 [2023-12-20 16:43:34,905 INFO misc.py line 119 131400] Train: [41/100][43/800] Data 0.004 (0.018) Batch 0.310 (0.345) Remain 04:36:05 loss: 0.5981 Lr: 0.00420 [2023-12-20 16:43:35,225 INFO misc.py line 119 131400] Train: [41/100][44/800] Data 0.003 (0.018) Batch 0.321 (0.345) Remain 04:35:36 loss: 0.4263 Lr: 0.00420 [2023-12-20 16:43:35,564 INFO misc.py line 119 131400] Train: [41/100][45/800] Data 0.003 (0.017) Batch 0.338 (0.345) Remain 04:35:27 loss: 0.4374 Lr: 0.00420 [2023-12-20 16:43:35,878 INFO misc.py line 119 131400] Train: [41/100][46/800] Data 0.008 (0.017) Batch 0.314 (0.344) Remain 04:34:53 loss: 0.4251 Lr: 0.00420 [2023-12-20 16:43:36,221 INFO misc.py line 119 131400] Train: [41/100][47/800] Data 0.004 (0.017) Batch 0.343 (0.344) Remain 04:34:52 loss: 0.4304 Lr: 0.00420 [2023-12-20 16:43:36,562 INFO misc.py line 119 131400] Train: [41/100][48/800] Data 0.004 (0.017) Batch 0.341 (0.344) Remain 04:34:49 loss: 0.3405 Lr: 0.00420 [2023-12-20 16:43:36,923 INFO misc.py line 119 131400] Train: [41/100][49/800] Data 0.004 (0.016) Batch 0.361 (0.344) Remain 04:35:06 loss: 0.3712 Lr: 0.00420 [2023-12-20 16:43:37,225 INFO misc.py line 119 131400] Train: [41/100][50/800] Data 0.006 (0.016) Batch 0.303 (0.343) Remain 04:34:23 loss: 0.6523 Lr: 0.00420 [2023-12-20 16:43:37,578 INFO misc.py line 119 131400] Train: [41/100][51/800] Data 0.004 (0.016) Batch 0.353 (0.344) Remain 04:34:33 loss: 0.1168 Lr: 0.00420 [2023-12-20 16:43:37,960 INFO misc.py line 119 131400] Train: [41/100][52/800] Data 0.003 (0.016) Batch 0.379 (0.344) Remain 04:35:07 loss: 0.4069 Lr: 0.00420 [2023-12-20 16:43:38,334 INFO misc.py line 119 131400] Train: [41/100][53/800] Data 0.006 (0.015) Batch 0.377 (0.345) Remain 04:35:38 loss: 0.4032 Lr: 0.00420 [2023-12-20 16:43:38,695 INFO misc.py line 119 131400] Train: [41/100][54/800] Data 0.003 (0.015) Batch 0.361 (0.345) Remain 04:35:53 loss: 0.6853 Lr: 0.00420 [2023-12-20 16:43:39,038 INFO misc.py line 119 131400] Train: [41/100][55/800] Data 0.004 (0.015) Batch 0.342 (0.345) Remain 04:35:49 loss: 0.4276 Lr: 0.00420 [2023-12-20 16:43:39,393 INFO misc.py line 119 131400] Train: [41/100][56/800] Data 0.005 (0.015) Batch 0.356 (0.345) Remain 04:35:58 loss: 0.2642 Lr: 0.00420 [2023-12-20 16:43:39,733 INFO misc.py line 119 131400] Train: [41/100][57/800] Data 0.006 (0.015) Batch 0.340 (0.345) Remain 04:35:53 loss: 0.5707 Lr: 0.00420 [2023-12-20 16:43:40,097 INFO misc.py line 119 131400] Train: [41/100][58/800] Data 0.004 (0.014) Batch 0.363 (0.346) Remain 04:36:08 loss: 0.3402 Lr: 0.00420 [2023-12-20 16:43:40,627 INFO misc.py line 119 131400] Train: [41/100][59/800] Data 0.004 (0.014) Batch 0.531 (0.349) Remain 04:38:46 loss: 0.3339 Lr: 0.00420 [2023-12-20 16:43:41,149 INFO misc.py line 119 131400] Train: [41/100][60/800] Data 0.005 (0.014) Batch 0.521 (0.352) Remain 04:41:11 loss: 0.3788 Lr: 0.00420 [2023-12-20 16:43:41,491 INFO misc.py line 119 131400] Train: [41/100][61/800] Data 0.006 (0.014) Batch 0.343 (0.352) Remain 04:41:03 loss: 0.4130 Lr: 0.00420 [2023-12-20 16:43:41,837 INFO misc.py line 119 131400] Train: [41/100][62/800] Data 0.004 (0.014) Batch 0.346 (0.352) Remain 04:40:58 loss: 0.3051 Lr: 0.00420 [2023-12-20 16:43:42,176 INFO misc.py line 119 131400] Train: [41/100][63/800] Data 0.004 (0.014) Batch 0.339 (0.351) Remain 04:40:48 loss: 0.4790 Lr: 0.00420 [2023-12-20 16:43:42,478 INFO misc.py line 119 131400] Train: [41/100][64/800] Data 0.003 (0.013) Batch 0.301 (0.351) Remain 04:40:08 loss: 0.3727 Lr: 0.00420 [2023-12-20 16:43:42,836 INFO misc.py line 119 131400] Train: [41/100][65/800] Data 0.005 (0.013) Batch 0.359 (0.351) Remain 04:40:14 loss: 0.3437 Lr: 0.00420 [2023-12-20 16:43:43,201 INFO misc.py line 119 131400] Train: [41/100][66/800] Data 0.005 (0.013) Batch 0.364 (0.351) Remain 04:40:24 loss: 0.5348 Lr: 0.00420 [2023-12-20 16:43:43,564 INFO misc.py line 119 131400] Train: [41/100][67/800] Data 0.004 (0.013) Batch 0.364 (0.351) Remain 04:40:33 loss: 0.5327 Lr: 0.00420 [2023-12-20 16:43:43,913 INFO misc.py line 119 131400] Train: [41/100][68/800] Data 0.004 (0.013) Batch 0.348 (0.351) Remain 04:40:31 loss: 0.2728 Lr: 0.00420 [2023-12-20 16:43:44,235 INFO misc.py line 119 131400] Train: [41/100][69/800] Data 0.003 (0.013) Batch 0.323 (0.351) Remain 04:40:10 loss: 0.2888 Lr: 0.00420 [2023-12-20 16:43:44,578 INFO misc.py line 119 131400] Train: [41/100][70/800] Data 0.003 (0.013) Batch 0.341 (0.351) Remain 04:40:03 loss: 0.3609 Lr: 0.00420 [2023-12-20 16:43:44,924 INFO misc.py line 119 131400] Train: [41/100][71/800] Data 0.006 (0.012) Batch 0.347 (0.351) Remain 04:40:00 loss: 0.2100 Lr: 0.00420 [2023-12-20 16:43:45,251 INFO misc.py line 119 131400] Train: [41/100][72/800] Data 0.005 (0.012) Batch 0.327 (0.350) Remain 04:39:43 loss: 0.3542 Lr: 0.00420 [2023-12-20 16:43:45,576 INFO misc.py line 119 131400] Train: [41/100][73/800] Data 0.005 (0.012) Batch 0.326 (0.350) Remain 04:39:26 loss: 0.3155 Lr: 0.00420 [2023-12-20 16:43:45,927 INFO misc.py line 119 131400] Train: [41/100][74/800] Data 0.003 (0.012) Batch 0.351 (0.350) Remain 04:39:26 loss: 0.3369 Lr: 0.00420 [2023-12-20 16:43:46,231 INFO misc.py line 119 131400] Train: [41/100][75/800] Data 0.003 (0.012) Batch 0.303 (0.349) Remain 04:38:55 loss: 0.1932 Lr: 0.00420 [2023-12-20 16:43:46,578 INFO misc.py line 119 131400] Train: [41/100][76/800] Data 0.004 (0.012) Batch 0.348 (0.349) Remain 04:38:54 loss: 0.3373 Lr: 0.00420 [2023-12-20 16:43:46,938 INFO misc.py line 119 131400] Train: [41/100][77/800] Data 0.003 (0.012) Batch 0.360 (0.349) Remain 04:39:00 loss: 0.3865 Lr: 0.00420 [2023-12-20 16:43:47,271 INFO misc.py line 119 131400] Train: [41/100][78/800] Data 0.003 (0.012) Batch 0.332 (0.349) Remain 04:38:49 loss: 0.3789 Lr: 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0.003 (0.005) Batch 0.344 (0.336) Remain 04:24:34 loss: 0.2792 Lr: 0.00412 [2023-12-20 16:47:36,825 INFO misc.py line 119 131400] Train: [41/100][764/800] Data 0.003 (0.005) Batch 0.327 (0.336) Remain 04:24:33 loss: 0.2622 Lr: 0.00412 [2023-12-20 16:47:37,144 INFO misc.py line 119 131400] Train: [41/100][765/800] Data 0.008 (0.005) Batch 0.323 (0.336) Remain 04:24:32 loss: 0.4865 Lr: 0.00412 [2023-12-20 16:47:37,505 INFO misc.py line 119 131400] Train: [41/100][766/800] Data 0.004 (0.005) Batch 0.361 (0.336) Remain 04:24:33 loss: 0.5447 Lr: 0.00412 [2023-12-20 16:47:37,848 INFO misc.py line 119 131400] Train: [41/100][767/800] Data 0.005 (0.005) Batch 0.335 (0.336) Remain 04:24:33 loss: 0.1812 Lr: 0.00412 [2023-12-20 16:47:38,184 INFO misc.py line 119 131400] Train: [41/100][768/800] Data 0.012 (0.005) Batch 0.345 (0.336) Remain 04:24:33 loss: 0.5113 Lr: 0.00412 [2023-12-20 16:47:38,500 INFO misc.py line 119 131400] Train: [41/100][769/800] Data 0.003 (0.005) Batch 0.315 (0.336) Remain 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[2023-12-20 16:47:40,797 INFO misc.py line 119 131400] Train: [41/100][776/800] Data 0.011 (0.005) Batch 0.348 (0.336) Remain 04:24:26 loss: 0.3243 Lr: 0.00412 [2023-12-20 16:47:41,141 INFO misc.py line 119 131400] Train: [41/100][777/800] Data 0.003 (0.005) Batch 0.344 (0.336) Remain 04:24:26 loss: 0.7193 Lr: 0.00412 [2023-12-20 16:47:41,478 INFO misc.py line 119 131400] Train: [41/100][778/800] Data 0.003 (0.005) Batch 0.336 (0.336) Remain 04:24:25 loss: 0.4284 Lr: 0.00412 [2023-12-20 16:47:41,776 INFO misc.py line 119 131400] Train: [41/100][779/800] Data 0.005 (0.005) Batch 0.299 (0.336) Remain 04:24:23 loss: 0.1738 Lr: 0.00412 [2023-12-20 16:47:42,108 INFO misc.py line 119 131400] Train: [41/100][780/800] Data 0.004 (0.005) Batch 0.331 (0.336) Remain 04:24:22 loss: 0.5469 Lr: 0.00412 [2023-12-20 16:47:42,434 INFO misc.py line 119 131400] Train: [41/100][781/800] Data 0.004 (0.005) Batch 0.327 (0.336) Remain 04:24:21 loss: 0.3419 Lr: 0.00412 [2023-12-20 16:47:42,793 INFO misc.py line 119 131400] Train: [41/100][782/800] Data 0.004 (0.005) Batch 0.358 (0.336) Remain 04:24:22 loss: 0.2174 Lr: 0.00412 [2023-12-20 16:47:43,135 INFO misc.py line 119 131400] Train: [41/100][783/800] Data 0.005 (0.005) Batch 0.343 (0.336) Remain 04:24:22 loss: 0.5088 Lr: 0.00412 [2023-12-20 16:47:43,488 INFO misc.py line 119 131400] Train: [41/100][784/800] Data 0.003 (0.005) Batch 0.353 (0.336) Remain 04:24:23 loss: 0.3267 Lr: 0.00412 [2023-12-20 16:47:43,804 INFO misc.py line 119 131400] Train: [41/100][785/800] Data 0.004 (0.005) Batch 0.316 (0.336) Remain 04:24:21 loss: 0.5563 Lr: 0.00412 [2023-12-20 16:47:44,139 INFO misc.py line 119 131400] Train: [41/100][786/800] Data 0.003 (0.005) Batch 0.333 (0.336) Remain 04:24:21 loss: 0.3429 Lr: 0.00412 [2023-12-20 16:47:44,455 INFO misc.py line 119 131400] Train: [41/100][787/800] Data 0.006 (0.005) Batch 0.316 (0.336) Remain 04:24:19 loss: 0.4104 Lr: 0.00412 [2023-12-20 16:47:44,803 INFO misc.py line 119 131400] Train: [41/100][788/800] Data 0.006 (0.005) Batch 0.349 (0.336) Remain 04:24:20 loss: 0.2800 Lr: 0.00412 [2023-12-20 16:47:45,147 INFO misc.py line 119 131400] Train: [41/100][789/800] Data 0.005 (0.005) Batch 0.343 (0.336) Remain 04:24:20 loss: 0.5700 Lr: 0.00412 [2023-12-20 16:47:45,472 INFO misc.py line 119 131400] Train: [41/100][790/800] Data 0.006 (0.005) Batch 0.328 (0.336) Remain 04:24:19 loss: 0.3070 Lr: 0.00412 [2023-12-20 16:47:45,782 INFO misc.py line 119 131400] Train: [41/100][791/800] Data 0.003 (0.005) Batch 0.309 (0.336) Remain 04:24:17 loss: 0.5404 Lr: 0.00412 [2023-12-20 16:47:46,091 INFO misc.py line 119 131400] Train: [41/100][792/800] Data 0.004 (0.005) Batch 0.309 (0.336) Remain 04:24:15 loss: 0.2388 Lr: 0.00412 [2023-12-20 16:47:46,397 INFO misc.py line 119 131400] Train: [41/100][793/800] Data 0.003 (0.005) Batch 0.306 (0.336) Remain 04:24:13 loss: 0.3331 Lr: 0.00412 [2023-12-20 16:47:46,693 INFO misc.py line 119 131400] Train: [41/100][794/800] Data 0.003 (0.005) Batch 0.296 (0.336) Remain 04:24:10 loss: 0.2587 Lr: 0.00412 [2023-12-20 16:47:47,031 INFO misc.py line 119 131400] Train: [41/100][795/800] Data 0.004 (0.005) Batch 0.338 (0.336) Remain 04:24:10 loss: 0.3980 Lr: 0.00412 [2023-12-20 16:47:47,368 INFO misc.py line 119 131400] Train: [41/100][796/800] Data 0.003 (0.005) Batch 0.337 (0.336) Remain 04:24:10 loss: 0.3464 Lr: 0.00412 [2023-12-20 16:47:47,682 INFO misc.py line 119 131400] Train: [41/100][797/800] Data 0.003 (0.005) Batch 0.314 (0.336) Remain 04:24:08 loss: 0.5871 Lr: 0.00412 [2023-12-20 16:47:48,014 INFO misc.py line 119 131400] Train: [41/100][798/800] Data 0.004 (0.005) Batch 0.332 (0.336) Remain 04:24:08 loss: 0.6952 Lr: 0.00412 [2023-12-20 16:47:48,314 INFO misc.py line 119 131400] Train: [41/100][799/800] Data 0.002 (0.005) Batch 0.297 (0.336) Remain 04:24:05 loss: 0.3654 Lr: 0.00412 [2023-12-20 16:47:48,638 INFO misc.py line 119 131400] Train: [41/100][800/800] Data 0.005 (0.005) Batch 0.327 (0.336) Remain 04:24:04 loss: 0.1664 Lr: 0.00412 [2023-12-20 16:47:48,638 INFO misc.py line 136 131400] Train result: loss: 0.4123 [2023-12-20 16:47:48,639 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:48:10,463 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1295 [2023-12-20 16:48:10,540 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1896 [2023-12-20 16:48:10,635 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5300 [2023-12-20 16:48:10,742 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2035 [2023-12-20 16:48:10,856 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3031 [2023-12-20 16:48:10,958 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.0806 [2023-12-20 16:48:11,049 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6759 [2023-12-20 16:48:11,158 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1777 [2023-12-20 16:48:11,238 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2134 [2023-12-20 16:48:11,324 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4018 [2023-12-20 16:48:11,415 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6953 [2023-12-20 16:48:11,550 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.4367 [2023-12-20 16:48:11,669 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.0720 [2023-12-20 16:48:11,826 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2406 [2023-12-20 16:48:11,917 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1855 [2023-12-20 16:48:12,051 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7760 [2023-12-20 16:48:12,168 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3772 [2023-12-20 16:48:12,280 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4952 [2023-12-20 16:48:12,392 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3654 [2023-12-20 16:48:12,465 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4023 [2023-12-20 16:48:12,572 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.1471 [2023-12-20 16:48:14,140 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2058 [2023-12-20 16:48:14,235 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.1880 [2023-12-20 16:48:14,324 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3998 [2023-12-20 16:48:14,452 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9609 [2023-12-20 16:48:14,564 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1115 [2023-12-20 16:48:14,644 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5628 [2023-12-20 16:48:14,786 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4573 [2023-12-20 16:48:14,934 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1001 [2023-12-20 16:48:15,030 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1303 [2023-12-20 16:48:15,159 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4165 [2023-12-20 16:48:15,305 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7645 [2023-12-20 16:48:15,422 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9732 [2023-12-20 16:48:15,523 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3148 [2023-12-20 16:48:15,691 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4404 [2023-12-20 16:48:15,782 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3463 [2023-12-20 16:48:15,938 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3630 [2023-12-20 16:48:16,036 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9401 [2023-12-20 16:48:16,113 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.2244 [2023-12-20 16:48:16,217 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3932 [2023-12-20 16:48:16,363 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0949 [2023-12-20 16:48:16,496 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3311 [2023-12-20 16:48:16,604 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4975 [2023-12-20 16:48:16,689 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5224 [2023-12-20 16:48:16,794 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3163 [2023-12-20 16:48:16,960 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2252 [2023-12-20 16:48:17,057 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.7030 [2023-12-20 16:48:17,149 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3009 [2023-12-20 16:48:17,243 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3390 [2023-12-20 16:48:17,335 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2660 [2023-12-20 16:48:17,423 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6511 [2023-12-20 16:48:17,525 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5408 [2023-12-20 16:48:17,649 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3910 [2023-12-20 16:48:17,731 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3493 [2023-12-20 16:48:17,830 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.7860 [2023-12-20 16:48:17,932 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0883 [2023-12-20 16:48:18,017 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3837 [2023-12-20 16:48:18,099 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1025 [2023-12-20 16:48:18,195 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5000 [2023-12-20 16:48:18,286 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4512 [2023-12-20 16:48:18,421 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1068 [2023-12-20 16:48:18,515 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6092 [2023-12-20 16:48:18,630 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6480 [2023-12-20 16:48:18,731 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7258 [2023-12-20 16:48:18,816 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7680 [2023-12-20 16:48:18,970 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1539 [2023-12-20 16:48:20,589 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7424/0.8370/0.9105. [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8701/0.9444 [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9648/0.9840 [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6617/0.8203 [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8127/0.8641 [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9078/0.9442 [2023-12-20 16:48:20,589 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8618/0.9515 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7495/0.8348 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6727/0.7998 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6449/0.7620 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8063/0.9537 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3918/0.5224 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6919/0.7912 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6830/0.8434 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7681/0.8320 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5670/0.8323 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7042/0.7629 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9541/0.9791 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6878/0.7645 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8696/0.9009 [2023-12-20 16:48:20,590 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5785/0.6523 [2023-12-20 16:48:20,590 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:48:20,591 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7424 [2023-12-20 16:48:20,591 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 16:48:20,592 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:48:28,472 INFO misc.py line 119 131400] Train: [42/100][1/800] Data 1.281 (1.281) Batch 1.607 (1.607) Remain 21:03:57 loss: 0.3331 Lr: 0.00412 [2023-12-20 16:48:28,802 INFO misc.py line 119 131400] Train: [42/100][2/800] Data 0.003 (0.003) Batch 0.330 (0.330) Remain 04:19:31 loss: 0.1954 Lr: 0.00412 [2023-12-20 16:48:29,102 INFO misc.py line 119 131400] Train: [42/100][3/800] Data 0.003 (0.003) Batch 0.300 (0.300) Remain 03:56:07 loss: 0.3034 Lr: 0.00411 [2023-12-20 16:48:29,446 INFO misc.py line 119 131400] Train: [42/100][4/800] Data 0.003 (0.003) Batch 0.337 (0.337) Remain 04:24:50 loss: 0.2594 Lr: 0.00411 [2023-12-20 16:48:29,767 INFO misc.py line 119 131400] Train: [42/100][5/800] Data 0.011 (0.007) Batch 0.327 (0.332) Remain 04:20:52 loss: 0.5248 Lr: 0.00411 [2023-12-20 16:48:30,125 INFO misc.py line 119 131400] Train: [42/100][6/800] Data 0.004 (0.006) Batch 0.359 (0.341) Remain 04:27:55 loss: 0.4238 Lr: 0.00411 [2023-12-20 16:48:30,455 INFO misc.py line 119 131400] Train: [42/100][7/800] Data 0.004 (0.005) Batch 0.330 (0.338) Remain 04:25:53 loss: 0.3800 Lr: 0.00411 [2023-12-20 16:48:30,758 INFO misc.py line 119 131400] Train: [42/100][8/800] Data 0.004 (0.005) Batch 0.303 (0.331) Remain 04:20:21 loss: 0.2005 Lr: 0.00411 [2023-12-20 16:48:31,083 INFO misc.py line 119 131400] Train: [42/100][9/800] Data 0.005 (0.005) Batch 0.326 (0.330) Remain 04:19:40 loss: 0.2662 Lr: 0.00411 [2023-12-20 16:48:31,422 INFO misc.py line 119 131400] Train: [42/100][10/800] Data 0.003 (0.005) Batch 0.338 (0.331) Remain 04:20:31 loss: 0.3607 Lr: 0.00411 [2023-12-20 16:48:31,755 INFO misc.py line 119 131400] Train: [42/100][11/800] Data 0.004 (0.005) Batch 0.333 (0.331) Remain 04:20:41 loss: 0.6356 Lr: 0.00411 [2023-12-20 16:48:32,101 INFO misc.py line 119 131400] Train: [42/100][12/800] Data 0.005 (0.005) Batch 0.345 (0.333) Remain 04:21:55 loss: 0.6945 Lr: 0.00411 [2023-12-20 16:48:32,409 INFO misc.py line 119 131400] Train: [42/100][13/800] Data 0.004 (0.005) Batch 0.309 (0.331) Remain 04:20:01 loss: 0.4084 Lr: 0.00411 [2023-12-20 16:48:32,715 INFO misc.py line 119 131400] Train: [42/100][14/800] Data 0.004 (0.005) Batch 0.302 (0.328) Remain 04:17:56 loss: 0.2347 Lr: 0.00411 [2023-12-20 16:48:33,051 INFO misc.py line 119 131400] Train: [42/100][15/800] Data 0.008 (0.005) Batch 0.340 (0.329) Remain 04:18:41 loss: 0.4020 Lr: 0.00411 [2023-12-20 16:48:33,369 INFO misc.py line 119 131400] Train: [42/100][16/800] Data 0.004 (0.005) Batch 0.319 (0.328) Remain 04:18:05 loss: 0.5143 Lr: 0.00411 [2023-12-20 16:48:33,727 INFO misc.py line 119 131400] Train: [42/100][17/800] Data 0.003 (0.005) Batch 0.356 (0.330) Remain 04:19:38 loss: 0.2538 Lr: 0.00411 [2023-12-20 16:48:34,050 INFO misc.py line 119 131400] Train: [42/100][18/800] Data 0.006 (0.005) Batch 0.325 (0.330) Remain 04:19:21 loss: 0.4748 Lr: 0.00411 [2023-12-20 16:48:34,373 INFO misc.py line 119 131400] Train: [42/100][19/800] Data 0.004 (0.005) Batch 0.323 (0.329) Remain 04:19:01 loss: 0.2951 Lr: 0.00411 [2023-12-20 16:48:34,678 INFO misc.py line 119 131400] Train: [42/100][20/800] Data 0.004 (0.005) Batch 0.305 (0.328) Remain 04:17:53 loss: 0.4186 Lr: 0.00411 [2023-12-20 16:48:35,034 INFO misc.py line 119 131400] Train: [42/100][21/800] Data 0.005 (0.005) Batch 0.354 (0.329) Remain 04:19:02 loss: 0.1382 Lr: 0.00411 [2023-12-20 16:48:35,358 INFO misc.py line 119 131400] Train: [42/100][22/800] Data 0.006 (0.005) Batch 0.325 (0.329) Remain 04:18:50 loss: 0.5724 Lr: 0.00411 [2023-12-20 16:48:35,709 INFO misc.py line 119 131400] Train: [42/100][23/800] Data 0.004 (0.005) Batch 0.352 (0.330) Remain 04:19:43 loss: 0.4987 Lr: 0.00411 [2023-12-20 16:48:36,022 INFO misc.py line 119 131400] Train: [42/100][24/800] Data 0.004 (0.005) Batch 0.313 (0.329) Remain 04:19:03 loss: 0.3043 Lr: 0.00411 [2023-12-20 16:48:36,367 INFO misc.py line 119 131400] Train: [42/100][25/800] Data 0.004 (0.005) Batch 0.346 (0.330) Remain 04:19:37 loss: 0.4109 Lr: 0.00411 [2023-12-20 16:48:36,708 INFO misc.py line 119 131400] Train: [42/100][26/800] Data 0.004 (0.004) Batch 0.341 (0.331) Remain 04:19:58 loss: 0.2721 Lr: 0.00411 [2023-12-20 16:48:37,039 INFO misc.py line 119 131400] Train: [42/100][27/800] Data 0.005 (0.005) Batch 0.331 (0.331) Remain 04:19:58 loss: 0.2583 Lr: 0.00411 [2023-12-20 16:48:37,382 INFO misc.py line 119 131400] Train: [42/100][28/800] Data 0.004 (0.004) Batch 0.343 (0.331) Remain 04:20:21 loss: 0.4572 Lr: 0.00411 [2023-12-20 16:48:37,716 INFO misc.py line 119 131400] Train: [42/100][29/800] Data 0.004 (0.004) Batch 0.334 (0.331) Remain 04:20:26 loss: 0.2000 Lr: 0.00411 [2023-12-20 16:48:38,053 INFO misc.py line 119 131400] Train: [42/100][30/800] Data 0.004 (0.004) Batch 0.337 (0.331) Remain 04:20:36 loss: 0.2083 Lr: 0.00411 [2023-12-20 16:48:38,394 INFO misc.py line 119 131400] Train: [42/100][31/800] Data 0.003 (0.004) Batch 0.339 (0.332) Remain 04:20:48 loss: 0.4068 Lr: 0.00411 [2023-12-20 16:48:38,763 INFO misc.py line 119 131400] Train: [42/100][32/800] Data 0.006 (0.004) Batch 0.370 (0.333) Remain 04:21:50 loss: 0.3809 Lr: 0.00411 [2023-12-20 16:48:39,102 INFO misc.py line 119 131400] Train: [42/100][33/800] Data 0.005 (0.004) Batch 0.339 (0.333) Remain 04:22:00 loss: 0.2982 Lr: 0.00411 [2023-12-20 16:48:39,428 INFO misc.py line 119 131400] Train: [42/100][34/800] Data 0.005 (0.004) Batch 0.326 (0.333) Remain 04:21:49 loss: 0.3243 Lr: 0.00411 [2023-12-20 16:48:39,726 INFO misc.py line 119 131400] Train: [42/100][35/800] Data 0.003 (0.004) Batch 0.299 (0.332) Remain 04:20:58 loss: 0.2985 Lr: 0.00411 [2023-12-20 16:48:40,060 INFO misc.py line 119 131400] Train: [42/100][36/800] Data 0.002 (0.004) Batch 0.319 (0.332) Remain 04:20:39 loss: 0.3127 Lr: 0.00411 [2023-12-20 16:48:40,392 INFO misc.py line 119 131400] Train: [42/100][37/800] Data 0.019 (0.005) Batch 0.346 (0.332) Remain 04:20:59 loss: 0.4283 Lr: 0.00411 [2023-12-20 16:48:40,804 INFO misc.py line 119 131400] Train: [42/100][38/800] Data 0.003 (0.005) Batch 0.413 (0.334) Remain 04:22:47 loss: 0.2728 Lr: 0.00411 [2023-12-20 16:48:41,137 INFO misc.py line 119 131400] Train: [42/100][39/800] Data 0.003 (0.005) Batch 0.327 (0.334) Remain 04:22:37 loss: 0.1470 Lr: 0.00411 [2023-12-20 16:48:41,460 INFO misc.py line 119 131400] Train: [42/100][40/800] Data 0.009 (0.005) Batch 0.329 (0.334) Remain 04:22:30 loss: 0.1975 Lr: 0.00411 [2023-12-20 16:48:41,748 INFO misc.py line 119 131400] Train: [42/100][41/800] Data 0.004 (0.005) Batch 0.288 (0.333) Remain 04:21:32 loss: 0.5921 Lr: 0.00411 [2023-12-20 16:48:42,071 INFO misc.py line 119 131400] Train: [42/100][42/800] Data 0.003 (0.005) Batch 0.323 (0.333) Remain 04:21:21 loss: 0.1991 Lr: 0.00411 [2023-12-20 16:48:42,373 INFO misc.py line 119 131400] Train: [42/100][43/800] Data 0.003 (0.005) Batch 0.302 (0.332) Remain 04:20:44 loss: 0.5484 Lr: 0.00411 [2023-12-20 16:48:42,709 INFO misc.py line 119 131400] Train: [42/100][44/800] Data 0.004 (0.005) Batch 0.337 (0.332) Remain 04:20:50 loss: 0.5005 Lr: 0.00411 [2023-12-20 16:48:43,036 INFO misc.py line 119 131400] Train: [42/100][45/800] Data 0.003 (0.005) Batch 0.326 (0.332) Remain 04:20:43 loss: 0.4325 Lr: 0.00411 [2023-12-20 16:48:43,355 INFO misc.py line 119 131400] Train: [42/100][46/800] Data 0.003 (0.005) Batch 0.317 (0.331) Remain 04:20:26 loss: 0.2638 Lr: 0.00411 [2023-12-20 16:48:43,690 INFO misc.py line 119 131400] Train: [42/100][47/800] Data 0.005 (0.005) Batch 0.338 (0.332) Remain 04:20:32 loss: 0.2508 Lr: 0.00411 [2023-12-20 16:48:44,042 INFO misc.py line 119 131400] Train: [42/100][48/800] Data 0.003 (0.005) Batch 0.352 (0.332) Remain 04:20:53 loss: 0.3496 Lr: 0.00411 [2023-12-20 16:48:44,377 INFO misc.py line 119 131400] Train: [42/100][49/800] Data 0.004 (0.005) Batch 0.329 (0.332) Remain 04:20:50 loss: 0.2363 Lr: 0.00411 [2023-12-20 16:48:44,689 INFO misc.py line 119 131400] Train: [42/100][50/800] Data 0.011 (0.005) Batch 0.318 (0.332) Remain 04:20:36 loss: 0.2669 Lr: 0.00411 [2023-12-20 16:48:45,002 INFO misc.py line 119 131400] Train: [42/100][51/800] Data 0.003 (0.005) Batch 0.312 (0.331) Remain 04:20:16 loss: 0.3131 Lr: 0.00411 [2023-12-20 16:48:45,311 INFO misc.py line 119 131400] Train: [42/100][52/800] Data 0.004 (0.005) Batch 0.308 (0.331) Remain 04:19:54 loss: 0.1959 Lr: 0.00411 [2023-12-20 16:48:45,626 INFO misc.py line 119 131400] Train: [42/100][53/800] Data 0.004 (0.005) Batch 0.315 (0.330) Remain 04:19:39 loss: 0.4434 Lr: 0.00411 [2023-12-20 16:48:45,934 INFO misc.py line 119 131400] Train: [42/100][54/800] Data 0.004 (0.005) Batch 0.309 (0.330) Remain 04:19:19 loss: 0.3575 Lr: 0.00411 [2023-12-20 16:48:46,266 INFO misc.py line 119 131400] Train: [42/100][55/800] Data 0.003 (0.005) Batch 0.331 (0.330) Remain 04:19:20 loss: 0.3214 Lr: 0.00411 [2023-12-20 16:48:46,584 INFO misc.py line 119 131400] Train: [42/100][56/800] Data 0.004 (0.005) Batch 0.318 (0.330) Remain 04:19:09 loss: 0.1655 Lr: 0.00411 [2023-12-20 16:48:46,879 INFO misc.py line 119 131400] Train: [42/100][57/800] Data 0.004 (0.005) Batch 0.294 (0.329) Remain 04:18:38 loss: 0.2068 Lr: 0.00411 [2023-12-20 16:48:47,172 INFO misc.py line 119 131400] Train: [42/100][58/800] Data 0.004 (0.005) Batch 0.294 (0.329) Remain 04:18:08 loss: 0.3254 Lr: 0.00411 [2023-12-20 16:48:47,481 INFO misc.py line 119 131400] Train: [42/100][59/800] Data 0.003 (0.005) Batch 0.308 (0.328) Remain 04:17:50 loss: 0.4859 Lr: 0.00411 [2023-12-20 16:48:47,790 INFO misc.py line 119 131400] Train: [42/100][60/800] Data 0.004 (0.005) Batch 0.309 (0.328) Remain 04:17:34 loss: 0.2573 Lr: 0.00411 [2023-12-20 16:48:48,125 INFO misc.py line 119 131400] Train: [42/100][61/800] Data 0.004 (0.005) Batch 0.335 (0.328) Remain 04:17:40 loss: 0.3177 Lr: 0.00411 [2023-12-20 16:48:48,460 INFO misc.py line 119 131400] Train: [42/100][62/800] Data 0.004 (0.005) Batch 0.334 (0.328) Remain 04:17:44 loss: 0.3914 Lr: 0.00411 [2023-12-20 16:48:48,781 INFO misc.py line 119 131400] Train: [42/100][63/800] Data 0.006 (0.005) Batch 0.322 (0.328) Remain 04:17:39 loss: 0.5186 Lr: 0.00411 [2023-12-20 16:48:49,116 INFO misc.py line 119 131400] Train: [42/100][64/800] Data 0.004 (0.005) Batch 0.334 (0.328) Remain 04:17:43 loss: 0.1962 Lr: 0.00411 [2023-12-20 16:48:49,437 INFO misc.py line 119 131400] Train: [42/100][65/800] Data 0.005 (0.005) Batch 0.322 (0.328) Remain 04:17:39 loss: 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INFO misc.py line 119 131400] Train: [42/100][72/800] Data 0.003 (0.005) Batch 0.377 (0.331) Remain 04:19:37 loss: 0.1423 Lr: 0.00411 [2023-12-20 16:48:52,263 INFO misc.py line 119 131400] Train: [42/100][73/800] Data 0.003 (0.004) Batch 0.353 (0.331) Remain 04:19:52 loss: 0.1639 Lr: 0.00411 [2023-12-20 16:48:52,634 INFO misc.py line 119 131400] Train: [42/100][74/800] Data 0.003 (0.004) Batch 0.371 (0.331) Remain 04:20:18 loss: 0.5481 Lr: 0.00411 [2023-12-20 16:48:52,980 INFO misc.py line 119 131400] Train: [42/100][75/800] Data 0.003 (0.004) Batch 0.346 (0.332) Remain 04:20:27 loss: 0.2739 Lr: 0.00411 [2023-12-20 16:48:53,314 INFO misc.py line 119 131400] Train: [42/100][76/800] Data 0.004 (0.004) Batch 0.335 (0.332) Remain 04:20:29 loss: 0.3573 Lr: 0.00411 [2023-12-20 16:48:53,665 INFO misc.py line 119 131400] Train: [42/100][77/800] Data 0.003 (0.004) Batch 0.350 (0.332) Remain 04:20:40 loss: 0.5484 Lr: 0.00411 [2023-12-20 16:48:53,992 INFO misc.py line 119 131400] Train: 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Batch 0.306 (0.334) Remain 04:18:47 loss: 0.2357 Lr: 0.00403 [2023-12-20 16:52:39,166 INFO misc.py line 119 131400] Train: [42/100][751/800] Data 0.002 (0.004) Batch 0.360 (0.334) Remain 04:18:48 loss: 0.3040 Lr: 0.00403 [2023-12-20 16:52:39,492 INFO misc.py line 119 131400] Train: [42/100][752/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 04:18:47 loss: 0.4178 Lr: 0.00403 [2023-12-20 16:52:39,823 INFO misc.py line 119 131400] Train: [42/100][753/800] Data 0.004 (0.004) Batch 0.331 (0.334) Remain 04:18:46 loss: 0.5595 Lr: 0.00403 [2023-12-20 16:52:40,145 INFO misc.py line 119 131400] Train: [42/100][754/800] Data 0.003 (0.004) Batch 0.322 (0.334) Remain 04:18:45 loss: 0.9761 Lr: 0.00403 [2023-12-20 16:52:40,462 INFO misc.py line 119 131400] Train: [42/100][755/800] Data 0.004 (0.004) Batch 0.318 (0.334) Remain 04:18:44 loss: 0.4242 Lr: 0.00403 [2023-12-20 16:52:40,779 INFO misc.py line 119 131400] Train: [42/100][756/800] Data 0.003 (0.004) Batch 0.317 (0.334) Remain 04:18:42 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131400] Train: [42/100][769/800] Data 0.004 (0.004) Batch 0.293 (0.334) Remain 04:18:28 loss: 0.4091 Lr: 0.00403 [2023-12-20 16:52:45,262 INFO misc.py line 119 131400] Train: [42/100][770/800] Data 0.003 (0.004) Batch 0.297 (0.334) Remain 04:18:26 loss: 0.3548 Lr: 0.00403 [2023-12-20 16:52:45,609 INFO misc.py line 119 131400] Train: [42/100][771/800] Data 0.004 (0.004) Batch 0.348 (0.334) Remain 04:18:26 loss: 0.3371 Lr: 0.00403 [2023-12-20 16:52:45,895 INFO misc.py line 119 131400] Train: [42/100][772/800] Data 0.003 (0.004) Batch 0.282 (0.334) Remain 04:18:23 loss: 0.4883 Lr: 0.00403 [2023-12-20 16:52:46,336 INFO misc.py line 119 131400] Train: [42/100][773/800] Data 0.008 (0.004) Batch 0.444 (0.334) Remain 04:18:29 loss: 0.2932 Lr: 0.00403 [2023-12-20 16:52:46,981 INFO misc.py line 119 131400] Train: [42/100][774/800] Data 0.004 (0.004) Batch 0.646 (0.334) Remain 04:18:48 loss: 0.4104 Lr: 0.00403 [2023-12-20 16:52:47,667 INFO misc.py line 119 131400] Train: [42/100][775/800] Data 0.004 (0.004) Batch 0.685 (0.335) Remain 04:19:08 loss: 0.2147 Lr: 0.00403 [2023-12-20 16:52:47,973 INFO misc.py line 119 131400] Train: [42/100][776/800] Data 0.005 (0.004) Batch 0.307 (0.335) Remain 04:19:06 loss: 0.5213 Lr: 0.00403 [2023-12-20 16:52:48,294 INFO misc.py line 119 131400] Train: [42/100][777/800] Data 0.003 (0.004) Batch 0.319 (0.335) Remain 04:19:05 loss: 0.3925 Lr: 0.00403 [2023-12-20 16:52:48,627 INFO misc.py line 119 131400] Train: [42/100][778/800] Data 0.010 (0.004) Batch 0.335 (0.335) Remain 04:19:05 loss: 0.5112 Lr: 0.00403 [2023-12-20 16:52:48,927 INFO misc.py line 119 131400] Train: [42/100][779/800] Data 0.003 (0.004) Batch 0.300 (0.335) Remain 04:19:02 loss: 0.2722 Lr: 0.00403 [2023-12-20 16:52:49,260 INFO misc.py line 119 131400] Train: [42/100][780/800] Data 0.004 (0.004) Batch 0.333 (0.335) Remain 04:19:02 loss: 0.2652 Lr: 0.00402 [2023-12-20 16:52:49,580 INFO misc.py line 119 131400] Train: [42/100][781/800] Data 0.003 (0.004) Batch 0.320 (0.335) Remain 04:19:01 loss: 0.4516 Lr: 0.00402 [2023-12-20 16:52:49,906 INFO misc.py line 119 131400] Train: [42/100][782/800] Data 0.003 (0.004) Batch 0.325 (0.335) Remain 04:19:00 loss: 0.4919 Lr: 0.00402 [2023-12-20 16:52:50,215 INFO misc.py line 119 131400] Train: [42/100][783/800] Data 0.004 (0.004) Batch 0.311 (0.335) Remain 04:18:58 loss: 0.3612 Lr: 0.00402 [2023-12-20 16:52:50,548 INFO misc.py line 119 131400] Train: [42/100][784/800] Data 0.003 (0.004) Batch 0.331 (0.335) Remain 04:18:57 loss: 0.4809 Lr: 0.00402 [2023-12-20 16:52:50,888 INFO misc.py line 119 131400] Train: [42/100][785/800] Data 0.005 (0.004) Batch 0.341 (0.335) Remain 04:18:57 loss: 0.4177 Lr: 0.00402 [2023-12-20 16:52:51,208 INFO misc.py line 119 131400] Train: [42/100][786/800] Data 0.004 (0.004) Batch 0.320 (0.335) Remain 04:18:56 loss: 0.6160 Lr: 0.00402 [2023-12-20 16:52:51,622 INFO misc.py line 119 131400] Train: [42/100][787/800] Data 0.004 (0.004) Batch 0.415 (0.335) Remain 04:19:01 loss: 0.2808 Lr: 0.00402 [2023-12-20 16:52:51,943 INFO misc.py line 119 131400] Train: [42/100][788/800] Data 0.003 (0.004) Batch 0.320 (0.335) Remain 04:18:59 loss: 0.4540 Lr: 0.00402 [2023-12-20 16:52:52,253 INFO misc.py line 119 131400] Train: [42/100][789/800] Data 0.004 (0.004) Batch 0.310 (0.335) Remain 04:18:58 loss: 0.3517 Lr: 0.00402 [2023-12-20 16:52:52,567 INFO misc.py line 119 131400] Train: [42/100][790/800] Data 0.003 (0.004) Batch 0.314 (0.335) Remain 04:18:56 loss: 0.2609 Lr: 0.00402 [2023-12-20 16:52:52,855 INFO misc.py line 119 131400] Train: [42/100][791/800] Data 0.004 (0.004) Batch 0.288 (0.335) Remain 04:18:53 loss: 0.5163 Lr: 0.00402 [2023-12-20 16:52:53,176 INFO misc.py line 119 131400] Train: [42/100][792/800] Data 0.003 (0.004) Batch 0.321 (0.335) Remain 04:18:52 loss: 0.4142 Lr: 0.00402 [2023-12-20 16:52:53,506 INFO misc.py line 119 131400] Train: [42/100][793/800] Data 0.003 (0.004) Batch 0.329 (0.335) Remain 04:18:51 loss: 0.3214 Lr: 0.00402 [2023-12-20 16:52:53,767 INFO misc.py line 119 131400] Train: [42/100][794/800] Data 0.004 (0.004) Batch 0.262 (0.335) Remain 04:18:47 loss: 0.2994 Lr: 0.00402 [2023-12-20 16:52:54,081 INFO misc.py line 119 131400] Train: [42/100][795/800] Data 0.003 (0.004) Batch 0.314 (0.335) Remain 04:18:45 loss: 0.5506 Lr: 0.00402 [2023-12-20 16:52:54,392 INFO misc.py line 119 131400] Train: [42/100][796/800] Data 0.003 (0.004) Batch 0.310 (0.335) Remain 04:18:43 loss: 0.4553 Lr: 0.00402 [2023-12-20 16:52:54,694 INFO misc.py line 119 131400] Train: [42/100][797/800] Data 0.003 (0.004) Batch 0.301 (0.334) Remain 04:18:41 loss: 0.5485 Lr: 0.00402 [2023-12-20 16:52:54,971 INFO misc.py line 119 131400] Train: [42/100][798/800] Data 0.004 (0.004) Batch 0.278 (0.334) Remain 04:18:37 loss: 0.3126 Lr: 0.00402 [2023-12-20 16:52:55,281 INFO misc.py line 119 131400] Train: [42/100][799/800] Data 0.003 (0.004) Batch 0.310 (0.334) Remain 04:18:36 loss: 0.2499 Lr: 0.00402 [2023-12-20 16:52:55,594 INFO misc.py line 119 131400] Train: [42/100][800/800] Data 0.003 (0.004) Batch 0.313 (0.334) Remain 04:18:34 loss: 0.4362 Lr: 0.00402 [2023-12-20 16:52:55,594 INFO misc.py line 136 131400] Train result: loss: 0.3991 [2023-12-20 16:52:55,594 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:53:16,467 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1333 [2023-12-20 16:53:16,592 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1634 [2023-12-20 16:53:16,680 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3983 [2023-12-20 16:53:16,789 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5575 [2023-12-20 16:53:16,907 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2734 [2023-12-20 16:53:17,007 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.8289 [2023-12-20 16:53:17,095 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.6704 [2023-12-20 16:53:17,201 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2812 [2023-12-20 16:53:17,280 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2984 [2023-12-20 16:53:17,363 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3192 [2023-12-20 16:53:17,460 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.8575 [2023-12-20 16:53:17,595 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3152 [2023-12-20 16:53:17,717 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5446 [2023-12-20 16:53:17,875 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2133 [2023-12-20 16:53:17,973 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1456 [2023-12-20 16:53:18,106 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9248 [2023-12-20 16:53:18,221 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4058 [2023-12-20 16:53:18,332 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2942 [2023-12-20 16:53:18,451 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1373 [2023-12-20 16:53:18,526 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3655 [2023-12-20 16:53:18,640 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.6456 [2023-12-20 16:53:18,800 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1643 [2023-12-20 16:53:18,923 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5670 [2023-12-20 16:53:19,063 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2135 [2023-12-20 16:53:19,206 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1960 [2023-12-20 16:53:19,288 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4898 [2023-12-20 16:53:19,446 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9420 [2023-12-20 16:53:19,569 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5524 [2023-12-20 16:53:19,665 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7150 [2023-12-20 16:53:19,815 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7028 [2023-12-20 16:53:19,919 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8386 [2023-12-20 16:53:20,038 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6119 [2023-12-20 16:53:20,131 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1719 [2023-12-20 16:53:20,206 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.4734 [2023-12-20 16:53:20,303 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9642 [2023-12-20 16:53:20,399 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4040 [2023-12-20 16:53:20,528 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7959 [2023-12-20 16:53:20,640 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1170 [2023-12-20 16:53:20,721 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.9374 [2023-12-20 16:53:20,860 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3978 [2023-12-20 16:53:21,011 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0292 [2023-12-20 16:53:21,109 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0879 [2023-12-20 16:53:21,228 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3363 [2023-12-20 16:53:21,372 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8398 [2023-12-20 16:53:21,499 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.6781 [2023-12-20 16:53:21,609 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4047 [2023-12-20 16:53:21,782 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3690 [2023-12-20 16:53:21,875 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3500 [2023-12-20 16:53:22,023 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4418 [2023-12-20 16:53:22,114 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0236 [2023-12-20 16:53:22,192 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7503 [2023-12-20 16:53:22,301 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4580 [2023-12-20 16:53:22,448 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.6207 [2023-12-20 16:53:22,581 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2871 [2023-12-20 16:53:22,689 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6658 [2023-12-20 16:53:22,776 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8695 [2023-12-20 16:53:22,877 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3834 [2023-12-20 16:53:23,038 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2804 [2023-12-20 16:53:23,133 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.0568 [2023-12-20 16:53:23,227 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2027 [2023-12-20 16:53:23,323 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2420 [2023-12-20 16:53:23,413 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3216 [2023-12-20 16:53:23,500 INFO evaluator.py line 159 131400] Test: [63/78] Loss 1.0431 [2023-12-20 16:53:23,599 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8787 [2023-12-20 16:53:23,726 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4166 [2023-12-20 16:53:23,808 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3677 [2023-12-20 16:53:23,906 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3669 [2023-12-20 16:53:23,999 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0198 [2023-12-20 16:53:24,082 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3723 [2023-12-20 16:53:24,166 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0224 [2023-12-20 16:53:24,261 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6290 [2023-12-20 16:53:24,352 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5384 [2023-12-20 16:53:24,486 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2404 [2023-12-20 16:53:24,586 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6075 [2023-12-20 16:53:24,700 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6158 [2023-12-20 16:53:24,801 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.0124 [2023-12-20 16:53:24,886 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3876 [2023-12-20 16:53:25,039 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1202 [2023-12-20 16:53:26,437 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7356/0.8349/0.9092. [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8673/0.9357 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9634/0.9847 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6483/0.8133 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8201/0.8768 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9015/0.9471 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8287/0.9301 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7375/0.8485 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6624/0.7894 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6563/0.8116 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8014/0.9072 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3804/0.5175 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6256/0.7474 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7091/0.8719 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7343/0.8579 [2023-12-20 16:53:26,438 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5493/0.6350 [2023-12-20 16:53:26,439 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7278/0.8217 [2023-12-20 16:53:26,439 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9332/0.9843 [2023-12-20 16:53:26,439 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6773/0.8107 [2023-12-20 16:53:26,439 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8648/0.9323 [2023-12-20 16:53:26,439 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6231/0.6753 [2023-12-20 16:53:26,439 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:53:26,441 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 16:53:26,441 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:53:29,442 INFO misc.py line 119 131400] Train: [43/100][1/800] Data 0.892 (0.892) Batch 1.226 (1.226) Remain 15:47:54 loss: 0.4208 Lr: 0.00402 [2023-12-20 16:53:29,770 INFO misc.py line 119 131400] Train: [43/100][2/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 04:14:13 loss: 0.3518 Lr: 0.00402 [2023-12-20 16:53:30,080 INFO misc.py line 119 131400] Train: [43/100][3/800] Data 0.003 (0.003) Batch 0.310 (0.310) Remain 03:59:36 loss: 0.2929 Lr: 0.00402 [2023-12-20 16:53:30,420 INFO misc.py line 119 131400] Train: [43/100][4/800] Data 0.003 (0.003) Batch 0.340 (0.340) Remain 04:22:58 loss: 0.2854 Lr: 0.00402 [2023-12-20 16:53:30,696 INFO misc.py line 119 131400] Train: [43/100][5/800] Data 0.004 (0.003) Batch 0.276 (0.308) Remain 03:58:16 loss: 0.3486 Lr: 0.00402 [2023-12-20 16:53:31,041 INFO misc.py line 119 131400] Train: [43/100][6/800] Data 0.003 (0.003) Batch 0.344 (0.320) Remain 04:07:33 loss: 0.1584 Lr: 0.00402 [2023-12-20 16:53:31,351 INFO misc.py line 119 131400] Train: [43/100][7/800] Data 0.003 (0.003) Batch 0.311 (0.318) Remain 04:05:42 loss: 0.3111 Lr: 0.00402 [2023-12-20 16:53:31,667 INFO misc.py line 119 131400] Train: [43/100][8/800] Data 0.003 (0.003) Batch 0.315 (0.317) Remain 04:05:19 loss: 0.2876 Lr: 0.00402 [2023-12-20 16:53:31,993 INFO misc.py line 119 131400] Train: [43/100][9/800] Data 0.003 (0.003) Batch 0.327 (0.319) Remain 04:06:30 loss: 0.3599 Lr: 0.00402 [2023-12-20 16:53:32,279 INFO misc.py line 119 131400] Train: [43/100][10/800] Data 0.004 (0.003) Batch 0.287 (0.314) Remain 04:02:56 loss: 0.3585 Lr: 0.00402 [2023-12-20 16:53:32,597 INFO misc.py line 119 131400] Train: [43/100][11/800] Data 0.003 (0.003) Batch 0.317 (0.315) Remain 04:03:14 loss: 0.3877 Lr: 0.00402 [2023-12-20 16:53:32,917 INFO misc.py line 119 131400] Train: [43/100][12/800] Data 0.004 (0.003) Batch 0.319 (0.315) Remain 04:03:38 loss: 0.3078 Lr: 0.00402 [2023-12-20 16:53:33,277 INFO misc.py line 119 131400] Train: [43/100][13/800] Data 0.003 (0.003) Batch 0.360 (0.320) Remain 04:07:06 loss: 0.5167 Lr: 0.00402 [2023-12-20 16:53:33,671 INFO misc.py line 119 131400] Train: [43/100][14/800] Data 0.003 (0.003) Batch 0.336 (0.321) Remain 04:08:13 loss: 0.3737 Lr: 0.00402 [2023-12-20 16:53:33,999 INFO misc.py line 119 131400] Train: [43/100][15/800] Data 0.062 (0.008) Batch 0.386 (0.326) Remain 04:12:23 loss: 0.4189 Lr: 0.00402 [2023-12-20 16:53:34,313 INFO misc.py line 119 131400] Train: [43/100][16/800] Data 0.005 (0.008) Batch 0.314 (0.326) Remain 04:11:40 loss: 0.3851 Lr: 0.00402 [2023-12-20 16:53:34,715 INFO misc.py line 119 131400] Train: [43/100][17/800] Data 0.005 (0.008) Batch 0.402 (0.331) Remain 04:15:54 loss: 0.6393 Lr: 0.00402 [2023-12-20 16:53:35,067 INFO misc.py line 119 131400] Train: [43/100][18/800] Data 0.004 (0.007) Batch 0.352 (0.332) Remain 04:16:59 loss: 0.3801 Lr: 0.00402 [2023-12-20 16:53:35,422 INFO misc.py line 119 131400] Train: [43/100][19/800] Data 0.003 (0.007) Batch 0.355 (0.334) Remain 04:18:05 loss: 0.3866 Lr: 0.00402 [2023-12-20 16:53:35,750 INFO misc.py line 119 131400] Train: [43/100][20/800] Data 0.003 (0.007) Batch 0.324 (0.333) Remain 04:17:39 loss: 0.2907 Lr: 0.00402 [2023-12-20 16:53:36,117 INFO misc.py line 119 131400] Train: [43/100][21/800] Data 0.006 (0.007) Batch 0.369 (0.335) Remain 04:19:11 loss: 0.4879 Lr: 0.00402 [2023-12-20 16:53:36,438 INFO misc.py line 119 131400] Train: [43/100][22/800] Data 0.004 (0.007) Batch 0.321 (0.335) Remain 04:18:36 loss: 0.6663 Lr: 0.00402 [2023-12-20 16:53:36,825 INFO misc.py line 119 131400] Train: [43/100][23/800] Data 0.004 (0.007) Batch 0.383 (0.337) Remain 04:20:27 loss: 0.2424 Lr: 0.00402 [2023-12-20 16:53:37,147 INFO misc.py line 119 131400] Train: [43/100][24/800] Data 0.011 (0.007) Batch 0.327 (0.336) Remain 04:20:05 loss: 0.1985 Lr: 0.00402 [2023-12-20 16:53:37,473 INFO misc.py line 119 131400] Train: [43/100][25/800] Data 0.004 (0.007) Batch 0.326 (0.336) Remain 04:19:42 loss: 0.3408 Lr: 0.00402 [2023-12-20 16:53:37,801 INFO misc.py line 119 131400] Train: [43/100][26/800] Data 0.004 (0.007) Batch 0.328 (0.336) Remain 04:19:25 loss: 0.3034 Lr: 0.00402 [2023-12-20 16:53:38,234 INFO misc.py line 119 131400] Train: [43/100][27/800] Data 0.004 (0.007) Batch 0.433 (0.340) Remain 04:22:33 loss: 0.3007 Lr: 0.00402 [2023-12-20 16:53:38,590 INFO misc.py line 119 131400] Train: [43/100][28/800] Data 0.004 (0.006) Batch 0.356 (0.340) Remain 04:23:03 loss: 0.3571 Lr: 0.00402 [2023-12-20 16:53:38,909 INFO misc.py line 119 131400] Train: [43/100][29/800] Data 0.004 (0.006) Batch 0.319 (0.340) Remain 04:22:23 loss: 0.3825 Lr: 0.00402 [2023-12-20 16:53:39,279 INFO misc.py line 119 131400] Train: [43/100][30/800] Data 0.004 (0.006) Batch 0.371 (0.341) Remain 04:23:17 loss: 0.3026 Lr: 0.00402 [2023-12-20 16:53:39,621 INFO misc.py line 119 131400] Train: [43/100][31/800] Data 0.004 (0.006) Batch 0.342 (0.341) Remain 04:23:18 loss: 0.2887 Lr: 0.00402 [2023-12-20 16:53:39,991 INFO misc.py line 119 131400] Train: [43/100][32/800] Data 0.004 (0.006) Batch 0.370 (0.342) Remain 04:24:04 loss: 0.3914 Lr: 0.00402 [2023-12-20 16:53:40,308 INFO misc.py line 119 131400] Train: [43/100][33/800] Data 0.005 (0.006) Batch 0.317 (0.341) Remain 04:23:25 loss: 0.3459 Lr: 0.00402 [2023-12-20 16:53:40,627 INFO misc.py line 119 131400] Train: [43/100][34/800] Data 0.004 (0.006) Batch 0.320 (0.340) Remain 04:22:53 loss: 0.7009 Lr: 0.00402 [2023-12-20 16:53:40,941 INFO misc.py line 119 131400] Train: [43/100][35/800] Data 0.004 (0.006) Batch 0.314 (0.339) Remain 04:22:15 loss: 0.3819 Lr: 0.00402 [2023-12-20 16:53:41,286 INFO misc.py line 119 131400] Train: [43/100][36/800] Data 0.004 (0.006) Batch 0.345 (0.340) Remain 04:22:22 loss: 0.3117 Lr: 0.00402 [2023-12-20 16:53:41,647 INFO misc.py line 119 131400] Train: [43/100][37/800] Data 0.005 (0.006) Batch 0.358 (0.340) Remain 04:22:47 loss: 0.4686 Lr: 0.00402 [2023-12-20 16:53:41,965 INFO misc.py line 119 131400] Train: [43/100][38/800] Data 0.007 (0.006) Batch 0.320 (0.340) Remain 04:22:20 loss: 0.5100 Lr: 0.00402 [2023-12-20 16:53:42,302 INFO misc.py line 119 131400] Train: [43/100][39/800] Data 0.006 (0.006) Batch 0.328 (0.339) Remain 04:22:05 loss: 0.3290 Lr: 0.00402 [2023-12-20 16:53:42,629 INFO misc.py line 119 131400] Train: [43/100][40/800] Data 0.015 (0.006) Batch 0.337 (0.339) Remain 04:22:02 loss: 0.2108 Lr: 0.00402 [2023-12-20 16:53:43,000 INFO misc.py line 119 131400] Train: [43/100][41/800] Data 0.005 (0.006) Batch 0.369 (0.340) Remain 04:22:38 loss: 0.2454 Lr: 0.00402 [2023-12-20 16:53:43,346 INFO misc.py line 119 131400] Train: [43/100][42/800] Data 0.006 (0.006) Batch 0.344 (0.340) Remain 04:22:42 loss: 0.4332 Lr: 0.00402 [2023-12-20 16:53:43,679 INFO misc.py line 119 131400] Train: [43/100][43/800] Data 0.008 (0.006) Batch 0.337 (0.340) Remain 04:22:38 loss: 0.4320 Lr: 0.00402 [2023-12-20 16:53:44,014 INFO misc.py line 119 131400] Train: [43/100][44/800] Data 0.005 (0.006) Batch 0.334 (0.340) Remain 04:22:32 loss: 0.5458 Lr: 0.00402 [2023-12-20 16:53:44,356 INFO misc.py line 119 131400] Train: [43/100][45/800] Data 0.005 (0.006) Batch 0.340 (0.340) Remain 04:22:32 loss: 0.2414 Lr: 0.00402 [2023-12-20 16:53:44,714 INFO misc.py line 119 131400] Train: [43/100][46/800] Data 0.007 (0.006) Batch 0.361 (0.340) Remain 04:22:54 loss: 0.4772 Lr: 0.00402 [2023-12-20 16:53:45,064 INFO misc.py line 119 131400] Train: [43/100][47/800] Data 0.003 (0.006) Batch 0.350 (0.341) Remain 04:23:04 loss: 0.3657 Lr: 0.00402 [2023-12-20 16:53:45,426 INFO misc.py line 119 131400] Train: [43/100][48/800] Data 0.005 (0.006) Batch 0.361 (0.341) Remain 04:23:25 loss: 0.4763 Lr: 0.00402 [2023-12-20 16:53:45,794 INFO misc.py line 119 131400] Train: [43/100][49/800] Data 0.004 (0.006) Batch 0.369 (0.342) Remain 04:23:53 loss: 0.5922 Lr: 0.00402 [2023-12-20 16:53:46,175 INFO misc.py line 119 131400] Train: [43/100][50/800] Data 0.003 (0.006) Batch 0.381 (0.342) Remain 04:24:32 loss: 0.4415 Lr: 0.00402 [2023-12-20 16:53:46,519 INFO misc.py line 119 131400] Train: [43/100][51/800] Data 0.004 (0.006) Batch 0.334 (0.342) Remain 04:24:23 loss: 0.4722 Lr: 0.00402 [2023-12-20 16:53:46,853 INFO misc.py line 119 131400] Train: [43/100][52/800] Data 0.013 (0.006) Batch 0.343 (0.342) Remain 04:24:24 loss: 0.2334 Lr: 0.00402 [2023-12-20 16:53:47,193 INFO misc.py line 119 131400] Train: [43/100][53/800] Data 0.005 (0.006) Batch 0.340 (0.342) Remain 04:24:21 loss: 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INFO misc.py line 119 131400] Train: [43/100][60/800] Data 0.004 (0.006) Batch 0.316 (0.341) Remain 04:23:16 loss: 0.2431 Lr: 0.00402 [2023-12-20 16:53:49,866 INFO misc.py line 119 131400] Train: [43/100][61/800] Data 0.003 (0.006) Batch 0.355 (0.341) Remain 04:23:27 loss: 0.3970 Lr: 0.00402 [2023-12-20 16:53:50,223 INFO misc.py line 119 131400] Train: [43/100][62/800] Data 0.004 (0.006) Batch 0.357 (0.341) Remain 04:23:39 loss: 0.9012 Lr: 0.00402 [2023-12-20 16:53:50,554 INFO misc.py line 119 131400] Train: [43/100][63/800] Data 0.004 (0.006) Batch 0.332 (0.341) Remain 04:23:31 loss: 0.4619 Lr: 0.00402 [2023-12-20 16:53:50,874 INFO misc.py line 119 131400] Train: [43/100][64/800] Data 0.003 (0.006) Batch 0.319 (0.341) Remain 04:23:14 loss: 0.4733 Lr: 0.00402 [2023-12-20 16:53:51,168 INFO misc.py line 119 131400] Train: [43/100][65/800] Data 0.004 (0.006) Batch 0.295 (0.340) Remain 04:22:39 loss: 0.3202 Lr: 0.00402 [2023-12-20 16:53:51,526 INFO misc.py line 119 131400] Train: 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[2023-12-20 16:57:47,625 INFO misc.py line 119 131400] Train: [43/100][776/800] Data 0.006 (0.004) Batch 0.327 (0.333) Remain 04:13:20 loss: 0.5664 Lr: 0.00393 [2023-12-20 16:57:47,969 INFO misc.py line 119 131400] Train: [43/100][777/800] Data 0.005 (0.004) Batch 0.345 (0.333) Remain 04:13:20 loss: 0.1922 Lr: 0.00393 [2023-12-20 16:57:48,301 INFO misc.py line 119 131400] Train: [43/100][778/800] Data 0.003 (0.004) Batch 0.331 (0.333) Remain 04:13:20 loss: 0.2279 Lr: 0.00393 [2023-12-20 16:57:48,614 INFO misc.py line 119 131400] Train: [43/100][779/800] Data 0.004 (0.004) Batch 0.314 (0.333) Remain 04:13:19 loss: 0.6381 Lr: 0.00393 [2023-12-20 16:57:48,939 INFO misc.py line 119 131400] Train: [43/100][780/800] Data 0.003 (0.004) Batch 0.324 (0.333) Remain 04:13:18 loss: 0.3211 Lr: 0.00393 [2023-12-20 16:57:49,867 INFO misc.py line 119 131400] Train: [43/100][781/800] Data 0.005 (0.004) Batch 0.929 (0.334) Remain 04:13:52 loss: 0.3480 Lr: 0.00393 [2023-12-20 16:57:50,190 INFO misc.py line 119 131400] Train: [43/100][782/800] Data 0.004 (0.004) Batch 0.323 (0.334) Remain 04:13:51 loss: 0.4400 Lr: 0.00393 [2023-12-20 16:57:50,536 INFO misc.py line 119 131400] Train: [43/100][783/800] Data 0.004 (0.004) Batch 0.347 (0.334) Remain 04:13:52 loss: 0.3594 Lr: 0.00393 [2023-12-20 16:57:50,897 INFO misc.py line 119 131400] Train: [43/100][784/800] Data 0.003 (0.004) Batch 0.356 (0.334) Remain 04:13:53 loss: 0.5375 Lr: 0.00393 [2023-12-20 16:57:51,227 INFO misc.py line 119 131400] Train: [43/100][785/800] Data 0.008 (0.004) Batch 0.335 (0.334) Remain 04:13:52 loss: 0.3145 Lr: 0.00393 [2023-12-20 16:57:51,550 INFO misc.py line 119 131400] Train: [43/100][786/800] Data 0.003 (0.004) Batch 0.323 (0.334) Remain 04:13:51 loss: 0.4252 Lr: 0.00393 [2023-12-20 16:57:51,850 INFO misc.py line 119 131400] Train: [43/100][787/800] Data 0.003 (0.004) Batch 0.300 (0.334) Remain 04:13:49 loss: 0.2708 Lr: 0.00393 [2023-12-20 16:57:52,187 INFO misc.py line 119 131400] Train: [43/100][788/800] Data 0.003 (0.004) Batch 0.336 (0.334) Remain 04:13:49 loss: 0.2752 Lr: 0.00393 [2023-12-20 16:57:52,485 INFO misc.py line 119 131400] Train: [43/100][789/800] Data 0.005 (0.004) Batch 0.298 (0.334) Remain 04:13:46 loss: 0.5502 Lr: 0.00393 [2023-12-20 16:57:52,809 INFO misc.py line 119 131400] Train: [43/100][790/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 04:13:46 loss: 0.4666 Lr: 0.00393 [2023-12-20 16:57:53,163 INFO misc.py line 119 131400] Train: [43/100][791/800] Data 0.004 (0.004) Batch 0.356 (0.334) Remain 04:13:47 loss: 0.1508 Lr: 0.00393 [2023-12-20 16:57:53,501 INFO misc.py line 119 131400] Train: [43/100][792/800] Data 0.003 (0.004) Batch 0.337 (0.334) Remain 04:13:46 loss: 0.6667 Lr: 0.00393 [2023-12-20 16:57:53,966 INFO misc.py line 119 131400] Train: [43/100][793/800] Data 0.004 (0.004) Batch 0.464 (0.334) Remain 04:13:54 loss: 0.3276 Lr: 0.00393 [2023-12-20 16:57:54,295 INFO misc.py line 119 131400] Train: [43/100][794/800] Data 0.005 (0.004) Batch 0.330 (0.334) Remain 04:13:53 loss: 0.2290 Lr: 0.00393 [2023-12-20 16:57:54,636 INFO misc.py line 119 131400] Train: [43/100][795/800] Data 0.004 (0.004) Batch 0.341 (0.334) Remain 04:13:53 loss: 0.5258 Lr: 0.00393 [2023-12-20 16:57:54,951 INFO misc.py line 119 131400] Train: [43/100][796/800] Data 0.004 (0.004) Batch 0.316 (0.334) Remain 04:13:52 loss: 0.7439 Lr: 0.00393 [2023-12-20 16:57:55,280 INFO misc.py line 119 131400] Train: [43/100][797/800] Data 0.003 (0.004) Batch 0.328 (0.334) Remain 04:13:51 loss: 0.5113 Lr: 0.00393 [2023-12-20 16:57:55,601 INFO misc.py line 119 131400] Train: [43/100][798/800] Data 0.004 (0.004) Batch 0.321 (0.334) Remain 04:13:50 loss: 0.3440 Lr: 0.00393 [2023-12-20 16:57:55,924 INFO misc.py line 119 131400] Train: [43/100][799/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 04:13:49 loss: 0.1075 Lr: 0.00393 [2023-12-20 16:57:56,250 INFO misc.py line 119 131400] Train: [43/100][800/800] Data 0.005 (0.004) Batch 0.328 (0.334) Remain 04:13:48 loss: 0.3461 Lr: 0.00393 [2023-12-20 16:57:56,251 INFO misc.py line 136 131400] Train result: loss: 0.3945 [2023-12-20 16:57:56,251 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 16:58:19,238 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1264 [2023-12-20 16:58:19,314 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2089 [2023-12-20 16:58:19,408 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6485 [2023-12-20 16:58:19,608 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1031 [2023-12-20 16:58:19,722 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2464 [2023-12-20 16:58:19,824 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2351 [2023-12-20 16:58:19,914 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0520 [2023-12-20 16:58:20,023 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0321 [2023-12-20 16:58:20,114 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2919 [2023-12-20 16:58:20,201 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3500 [2023-12-20 16:58:20,295 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7619 [2023-12-20 16:58:20,433 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3132 [2023-12-20 16:58:20,551 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1274 [2023-12-20 16:58:20,709 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2722 [2023-12-20 16:58:20,805 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.3499 [2023-12-20 16:58:20,939 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7603 [2023-12-20 16:58:21,050 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4494 [2023-12-20 16:58:21,162 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6265 [2023-12-20 16:58:21,278 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2155 [2023-12-20 16:58:21,362 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3389 [2023-12-20 16:58:21,487 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.6046 [2023-12-20 16:58:21,648 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1512 [2023-12-20 16:58:21,773 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.1370 [2023-12-20 16:58:21,920 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2020 [2023-12-20 16:58:22,068 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2529 [2023-12-20 16:58:22,151 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6740 [2023-12-20 16:58:22,307 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.1953 [2023-12-20 16:58:22,436 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.3774 [2023-12-20 16:58:22,548 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4221 [2023-12-20 16:58:22,696 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4584 [2023-12-20 16:58:22,803 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7136 [2023-12-20 16:58:22,923 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6478 [2023-12-20 16:58:23,022 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2576 [2023-12-20 16:58:23,093 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2631 [2023-12-20 16:58:23,191 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5048 [2023-12-20 16:58:23,287 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4764 [2023-12-20 16:58:23,419 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9714 [2023-12-20 16:58:23,537 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1199 [2023-12-20 16:58:23,620 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6427 [2023-12-20 16:58:23,767 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4581 [2023-12-20 16:58:23,915 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0284 [2023-12-20 16:58:24,021 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2480 [2023-12-20 16:58:24,155 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2574 [2023-12-20 16:58:24,306 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.6919 [2023-12-20 16:58:24,438 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0012 [2023-12-20 16:58:24,543 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2630 [2023-12-20 16:58:24,714 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4154 [2023-12-20 16:58:24,812 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3019 [2023-12-20 16:58:24,960 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1135 [2023-12-20 16:58:25,051 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9251 [2023-12-20 16:58:25,130 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.2804 [2023-12-20 16:58:25,237 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1120 [2023-12-20 16:58:25,384 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.6789 [2023-12-20 16:58:25,536 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3288 [2023-12-20 16:58:25,650 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2912 [2023-12-20 16:58:25,742 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6071 [2023-12-20 16:58:25,845 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3089 [2023-12-20 16:58:26,015 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3494 [2023-12-20 16:58:26,114 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5474 [2023-12-20 16:58:26,207 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1585 [2023-12-20 16:58:26,304 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2961 [2023-12-20 16:58:26,398 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3439 [2023-12-20 16:58:26,488 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6208 [2023-12-20 16:58:26,589 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8169 [2023-12-20 16:58:26,715 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3304 [2023-12-20 16:58:26,798 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3844 [2023-12-20 16:58:26,897 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3206 [2023-12-20 16:58:26,992 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0164 [2023-12-20 16:58:27,077 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2931 [2023-12-20 16:58:27,166 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0170 [2023-12-20 16:58:27,264 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7063 [2023-12-20 16:58:27,355 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5065 [2023-12-20 16:58:27,489 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1441 [2023-12-20 16:58:27,589 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6405 [2023-12-20 16:58:27,706 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7966 [2023-12-20 16:58:27,807 INFO evaluator.py line 159 131400] Test: [76/78] Loss 1.1673 [2023-12-20 16:58:27,893 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4720 [2023-12-20 16:58:28,048 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1729 [2023-12-20 16:58:29,376 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7312/0.8263/0.9067. [2023-12-20 16:58:29,376 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8564/0.9194 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9648/0.9834 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6761/0.7762 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8085/0.8654 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8883/0.9579 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8519/0.9158 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7680/0.8395 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6513/0.7885 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6313/0.8671 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8174/0.9471 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3575/0.5711 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6652/0.7536 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6910/0.8727 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7073/0.8612 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5702/0.5866 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.5556/0.5930 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9551/0.9768 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6851/0.8006 [2023-12-20 16:58:29,377 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8904/0.9212 [2023-12-20 16:58:29,378 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6332/0.7298 [2023-12-20 16:58:29,378 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 16:58:29,379 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 16:58:29,379 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 16:58:33,054 INFO misc.py line 119 131400] Train: [44/100][1/800] Data 0.960 (0.960) Batch 1.283 (1.283) Remain 16:15:03 loss: 0.4673 Lr: 0.00393 [2023-12-20 16:58:33,342 INFO misc.py line 119 131400] Train: [44/100][2/800] Data 0.004 (0.004) Batch 0.289 (0.289) Remain 03:39:30 loss: 0.2775 Lr: 0.00393 [2023-12-20 16:58:33,672 INFO misc.py line 119 131400] Train: [44/100][3/800] Data 0.003 (0.003) Batch 0.329 (0.329) Remain 04:09:46 loss: 0.3360 Lr: 0.00393 [2023-12-20 16:58:38,468 INFO misc.py line 119 131400] Train: [44/100][4/800] Data 0.160 (0.160) Batch 4.797 (4.797) Remain 60:45:15 loss: 0.5130 Lr: 0.00393 [2023-12-20 16:58:38,806 INFO misc.py line 119 131400] Train: [44/100][5/800] Data 0.004 (0.082) Batch 0.338 (2.567) Remain 32:31:04 loss: 0.6020 Lr: 0.00393 [2023-12-20 16:58:39,148 INFO misc.py line 119 131400] Train: [44/100][6/800] Data 0.003 (0.056) Batch 0.342 (1.826) Remain 23:07:12 loss: 0.3984 Lr: 0.00393 [2023-12-20 16:58:39,614 INFO misc.py line 119 131400] Train: [44/100][7/800] Data 0.004 (0.043) Batch 0.465 (1.486) Remain 18:48:48 loss: 0.3199 Lr: 0.00393 [2023-12-20 16:58:39,958 INFO misc.py line 119 131400] Train: [44/100][8/800] Data 0.005 (0.035) Batch 0.344 (1.257) Remain 15:55:21 loss: 0.2650 Lr: 0.00393 [2023-12-20 16:58:40,300 INFO misc.py line 119 131400] Train: [44/100][9/800] Data 0.005 (0.030) Batch 0.335 (1.104) Remain 13:58:35 loss: 0.1583 Lr: 0.00393 [2023-12-20 16:58:40,662 INFO misc.py line 119 131400] Train: [44/100][10/800] Data 0.011 (0.027) Batch 0.350 (0.996) Remain 12:36:46 loss: 0.3963 Lr: 0.00393 [2023-12-20 16:58:41,029 INFO misc.py line 119 131400] Train: [44/100][11/800] Data 0.023 (0.027) Batch 0.385 (0.920) Remain 11:38:43 loss: 0.4947 Lr: 0.00393 [2023-12-20 16:58:41,388 INFO misc.py line 119 131400] Train: [44/100][12/800] Data 0.004 (0.024) Batch 0.358 (0.857) Remain 10:51:20 loss: 0.3935 Lr: 0.00393 [2023-12-20 16:58:41,673 INFO misc.py line 119 131400] Train: [44/100][13/800] Data 0.005 (0.022) Batch 0.286 (0.800) Remain 10:07:57 loss: 0.4908 Lr: 0.00393 [2023-12-20 16:58:41,979 INFO misc.py line 119 131400] Train: [44/100][14/800] Data 0.003 (0.021) Batch 0.306 (0.755) Remain 09:33:47 loss: 0.4281 Lr: 0.00393 [2023-12-20 16:58:42,359 INFO misc.py line 119 131400] Train: [44/100][15/800] Data 0.004 (0.019) Batch 0.379 (0.724) Remain 09:09:59 loss: 0.3877 Lr: 0.00393 [2023-12-20 16:58:42,847 INFO misc.py line 119 131400] Train: [44/100][16/800] Data 0.004 (0.018) Batch 0.490 (0.706) Remain 08:56:17 loss: 0.3122 Lr: 0.00393 [2023-12-20 16:58:43,181 INFO misc.py line 119 131400] Train: [44/100][17/800] Data 0.003 (0.017) Batch 0.332 (0.679) Remain 08:36:00 loss: 0.1596 Lr: 0.00393 [2023-12-20 16:58:43,540 INFO misc.py line 119 131400] Train: [44/100][18/800] Data 0.004 (0.016) Batch 0.359 (0.658) Remain 08:19:47 loss: 0.5921 Lr: 0.00393 [2023-12-20 16:58:43,863 INFO misc.py line 119 131400] Train: [44/100][19/800] Data 0.004 (0.015) Batch 0.323 (0.637) Remain 08:03:54 loss: 0.2589 Lr: 0.00393 [2023-12-20 16:58:44,215 INFO misc.py line 119 131400] Train: [44/100][20/800] Data 0.004 (0.015) Batch 0.352 (0.620) Remain 07:51:09 loss: 0.3233 Lr: 0.00393 [2023-12-20 16:58:44,561 INFO misc.py line 119 131400] Train: [44/100][21/800] Data 0.003 (0.014) Batch 0.346 (0.605) Remain 07:39:34 loss: 0.4637 Lr: 0.00393 [2023-12-20 16:58:44,876 INFO misc.py line 119 131400] Train: [44/100][22/800] Data 0.003 (0.014) Batch 0.313 (0.590) Remain 07:27:53 loss: 0.2600 Lr: 0.00393 [2023-12-20 16:58:45,205 INFO misc.py line 119 131400] Train: [44/100][23/800] Data 0.005 (0.013) Batch 0.330 (0.577) Remain 07:18:02 loss: 0.3416 Lr: 0.00393 [2023-12-20 16:58:45,581 INFO misc.py line 119 131400] Train: [44/100][24/800] Data 0.004 (0.013) Batch 0.375 (0.567) Remain 07:10:44 loss: 0.2232 Lr: 0.00393 [2023-12-20 16:58:45,906 INFO misc.py line 119 131400] Train: [44/100][25/800] Data 0.005 (0.012) Batch 0.326 (0.556) Remain 07:02:24 loss: 0.2877 Lr: 0.00393 [2023-12-20 16:58:46,262 INFO misc.py line 119 131400] Train: [44/100][26/800] Data 0.004 (0.012) Batch 0.356 (0.547) Remain 06:55:48 loss: 0.4128 Lr: 0.00393 [2023-12-20 16:58:46,588 INFO misc.py line 119 131400] Train: [44/100][27/800] Data 0.004 (0.012) Batch 0.326 (0.538) Remain 06:48:48 loss: 0.4769 Lr: 0.00393 [2023-12-20 16:58:47,258 INFO misc.py line 119 131400] Train: [44/100][28/800] Data 0.003 (0.011) Batch 0.297 (0.529) Remain 06:41:27 loss: 0.2059 Lr: 0.00393 [2023-12-20 16:58:47,576 INFO misc.py line 119 131400] Train: [44/100][29/800] Data 0.376 (0.025) Batch 0.691 (0.535) Remain 06:46:10 loss: 0.3448 Lr: 0.00393 [2023-12-20 16:58:47,945 INFO misc.py line 119 131400] Train: [44/100][30/800] Data 0.005 (0.025) Batch 0.369 (0.529) Remain 06:41:29 loss: 0.1937 Lr: 0.00393 [2023-12-20 16:58:48,287 INFO misc.py line 119 131400] Train: [44/100][31/800] Data 0.010 (0.024) Batch 0.342 (0.522) Remain 06:36:26 loss: 0.3280 Lr: 0.00393 [2023-12-20 16:58:48,579 INFO misc.py line 119 131400] Train: [44/100][32/800] Data 0.004 (0.023) Batch 0.292 (0.514) Remain 06:30:24 loss: 0.3477 Lr: 0.00393 [2023-12-20 16:58:48,906 INFO misc.py line 119 131400] Train: [44/100][33/800] Data 0.004 (0.023) Batch 0.327 (0.508) Remain 06:25:40 loss: 0.2232 Lr: 0.00393 [2023-12-20 16:58:49,250 INFO misc.py line 119 131400] Train: [44/100][34/800] Data 0.003 (0.022) Batch 0.343 (0.503) Remain 06:21:38 loss: 0.5206 Lr: 0.00392 [2023-12-20 16:58:49,561 INFO misc.py line 119 131400] Train: [44/100][35/800] Data 0.004 (0.021) Batch 0.311 (0.497) Remain 06:17:05 loss: 0.3332 Lr: 0.00392 [2023-12-20 16:58:49,857 INFO misc.py line 119 131400] Train: [44/100][36/800] Data 0.003 (0.021) Batch 0.297 (0.490) Remain 06:12:28 loss: 0.2784 Lr: 0.00392 [2023-12-20 16:58:50,202 INFO misc.py line 119 131400] Train: [44/100][37/800] Data 0.003 (0.020) Batch 0.345 (0.486) Remain 06:09:13 loss: 0.4524 Lr: 0.00392 [2023-12-20 16:58:50,552 INFO misc.py line 119 131400] Train: [44/100][38/800] Data 0.004 (0.020) Batch 0.349 (0.482) Remain 06:06:13 loss: 0.1871 Lr: 0.00392 [2023-12-20 16:58:50,888 INFO misc.py line 119 131400] Train: [44/100][39/800] Data 0.005 (0.020) Batch 0.337 (0.478) Remain 06:03:09 loss: 0.3593 Lr: 0.00392 [2023-12-20 16:58:51,219 INFO misc.py line 119 131400] Train: [44/100][40/800] Data 0.003 (0.019) Batch 0.331 (0.474) Remain 06:00:07 loss: 0.5987 Lr: 0.00392 [2023-12-20 16:58:51,573 INFO misc.py line 119 131400] Train: [44/100][41/800] Data 0.003 (0.019) Batch 0.352 (0.471) Remain 05:57:41 loss: 0.1562 Lr: 0.00392 [2023-12-20 16:58:51,889 INFO misc.py line 119 131400] Train: [44/100][42/800] Data 0.005 (0.018) Batch 0.318 (0.467) Remain 05:54:41 loss: 0.4965 Lr: 0.00392 [2023-12-20 16:58:52,232 INFO misc.py line 119 131400] Train: [44/100][43/800] Data 0.003 (0.018) Batch 0.343 (0.464) Remain 05:52:19 loss: 0.2028 Lr: 0.00392 [2023-12-20 16:58:52,540 INFO misc.py line 119 131400] Train: [44/100][44/800] Data 0.004 (0.018) Batch 0.307 (0.460) Remain 05:49:24 loss: 0.4821 Lr: 0.00392 [2023-12-20 16:58:52,855 INFO misc.py line 119 131400] Train: [44/100][45/800] Data 0.005 (0.017) Batch 0.316 (0.457) Remain 05:46:47 loss: 0.4373 Lr: 0.00392 [2023-12-20 16:58:53,205 INFO misc.py line 119 131400] Train: [44/100][46/800] Data 0.003 (0.017) Batch 0.350 (0.454) Remain 05:44:53 loss: 0.3662 Lr: 0.00392 [2023-12-20 16:58:53,576 INFO misc.py line 119 131400] Train: [44/100][47/800] Data 0.003 (0.017) Batch 0.371 (0.452) Remain 05:43:27 loss: 0.2846 Lr: 0.00392 [2023-12-20 16:58:53,910 INFO misc.py line 119 131400] Train: [44/100][48/800] Data 0.003 (0.016) Batch 0.332 (0.450) Remain 05:41:25 loss: 0.3054 Lr: 0.00392 [2023-12-20 16:58:54,239 INFO misc.py line 119 131400] Train: [44/100][49/800] Data 0.006 (0.016) Batch 0.329 (0.447) Remain 05:39:25 loss: 0.2618 Lr: 0.00392 [2023-12-20 16:58:54,572 INFO misc.py line 119 131400] Train: [44/100][50/800] Data 0.005 (0.016) Batch 0.334 (0.445) Remain 05:37:35 loss: 0.3404 Lr: 0.00392 [2023-12-20 16:58:54,931 INFO misc.py line 119 131400] Train: [44/100][51/800] Data 0.004 (0.016) Batch 0.360 (0.443) Remain 05:36:14 loss: 0.3331 Lr: 0.00392 [2023-12-20 16:58:55,268 INFO misc.py line 119 131400] Train: [44/100][52/800] Data 0.004 (0.015) Batch 0.337 (0.441) Remain 05:34:35 loss: 0.2386 Lr: 0.00392 [2023-12-20 16:58:55,595 INFO misc.py line 119 131400] Train: [44/100][53/800] Data 0.003 (0.015) Batch 0.325 (0.438) Remain 05:32:49 loss: 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[44/100][788/800] Data 0.005 (0.005) Batch 0.349 (0.341) Remain 04:14:56 loss: 0.5039 Lr: 0.00384 [2023-12-20 17:03:01,974 INFO misc.py line 119 131400] Train: [44/100][789/800] Data 0.004 (0.005) Batch 0.341 (0.341) Remain 04:14:56 loss: 0.5162 Lr: 0.00384 [2023-12-20 17:03:02,303 INFO misc.py line 119 131400] Train: [44/100][790/800] Data 0.003 (0.005) Batch 0.329 (0.341) Remain 04:14:55 loss: 0.4862 Lr: 0.00384 [2023-12-20 17:03:02,634 INFO misc.py line 119 131400] Train: [44/100][791/800] Data 0.004 (0.005) Batch 0.332 (0.341) Remain 04:14:54 loss: 0.2758 Lr: 0.00384 [2023-12-20 17:03:02,972 INFO misc.py line 119 131400] Train: [44/100][792/800] Data 0.003 (0.005) Batch 0.336 (0.341) Remain 04:14:53 loss: 0.3850 Lr: 0.00384 [2023-12-20 17:03:03,264 INFO misc.py line 119 131400] Train: [44/100][793/800] Data 0.005 (0.005) Batch 0.292 (0.341) Remain 04:14:50 loss: 0.3804 Lr: 0.00383 [2023-12-20 17:03:03,583 INFO misc.py line 119 131400] Train: [44/100][794/800] Data 0.004 (0.005) Batch 0.320 (0.341) Remain 04:14:48 loss: 0.2981 Lr: 0.00383 [2023-12-20 17:03:03,885 INFO misc.py line 119 131400] Train: [44/100][795/800] Data 0.003 (0.005) Batch 0.301 (0.341) Remain 04:14:46 loss: 0.3844 Lr: 0.00383 [2023-12-20 17:03:04,207 INFO misc.py line 119 131400] Train: [44/100][796/800] Data 0.005 (0.005) Batch 0.322 (0.341) Remain 04:14:44 loss: 0.3553 Lr: 0.00383 [2023-12-20 17:03:04,524 INFO misc.py line 119 131400] Train: [44/100][797/800] Data 0.005 (0.005) Batch 0.317 (0.341) Remain 04:14:43 loss: 0.4366 Lr: 0.00383 [2023-12-20 17:03:04,829 INFO misc.py line 119 131400] Train: [44/100][798/800] Data 0.005 (0.005) Batch 0.304 (0.341) Remain 04:14:40 loss: 0.4759 Lr: 0.00383 [2023-12-20 17:03:05,147 INFO misc.py line 119 131400] Train: [44/100][799/800] Data 0.005 (0.005) Batch 0.319 (0.341) Remain 04:14:39 loss: 0.5954 Lr: 0.00383 [2023-12-20 17:03:05,449 INFO misc.py line 119 131400] Train: [44/100][800/800] Data 0.003 (0.005) Batch 0.301 (0.341) Remain 04:14:36 loss: 0.3741 Lr: 0.00383 [2023-12-20 17:03:05,450 INFO misc.py line 136 131400] Train result: loss: 0.3827 [2023-12-20 17:03:05,450 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:03:27,557 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1203 [2023-12-20 17:03:27,649 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1853 [2023-12-20 17:03:27,973 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4323 [2023-12-20 17:03:28,086 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2234 [2023-12-20 17:03:28,204 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4700 [2023-12-20 17:03:28,309 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6273 [2023-12-20 17:03:28,402 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8304 [2023-12-20 17:03:28,509 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2986 [2023-12-20 17:03:28,592 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2812 [2023-12-20 17:03:28,687 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3473 [2023-12-20 17:03:28,788 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4185 [2023-12-20 17:03:28,930 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3196 [2023-12-20 17:03:29,054 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1482 [2023-12-20 17:03:29,210 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2309 [2023-12-20 17:03:29,309 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1712 [2023-12-20 17:03:29,446 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7131 [2023-12-20 17:03:29,562 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3823 [2023-12-20 17:03:29,680 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6482 [2023-12-20 17:03:29,794 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3233 [2023-12-20 17:03:29,885 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5537 [2023-12-20 17:03:29,995 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4874 [2023-12-20 17:03:30,153 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1454 [2023-12-20 17:03:30,279 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8428 [2023-12-20 17:03:30,420 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2434 [2023-12-20 17:03:30,566 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1470 [2023-12-20 17:03:30,649 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5989 [2023-12-20 17:03:30,810 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9346 [2023-12-20 17:03:30,940 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5492 [2023-12-20 17:03:31,041 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6142 [2023-12-20 17:03:31,186 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2208 [2023-12-20 17:03:31,289 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8880 [2023-12-20 17:03:31,411 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4922 [2023-12-20 17:03:31,498 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1739 [2023-12-20 17:03:31,570 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1765 [2023-12-20 17:03:31,666 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9440 [2023-12-20 17:03:31,760 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.0790 [2023-12-20 17:03:31,895 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0362 [2023-12-20 17:03:32,006 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1065 [2023-12-20 17:03:32,087 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.9496 [2023-12-20 17:03:32,233 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4438 [2023-12-20 17:03:32,379 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0488 [2023-12-20 17:03:32,479 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1666 [2023-12-20 17:03:32,601 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4515 [2023-12-20 17:03:32,744 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8603 [2023-12-20 17:03:32,863 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3817 [2023-12-20 17:03:32,969 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7125 [2023-12-20 17:03:33,137 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5088 [2023-12-20 17:03:33,234 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4822 [2023-12-20 17:03:33,378 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4563 [2023-12-20 17:03:33,469 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.6900 [2023-12-20 17:03:33,546 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4766 [2023-12-20 17:03:33,654 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.9111 [2023-12-20 17:03:33,800 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.6305 [2023-12-20 17:03:33,935 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2096 [2023-12-20 17:03:34,037 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0984 [2023-12-20 17:03:34,128 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7964 [2023-12-20 17:03:34,231 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4047 [2023-12-20 17:03:34,396 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1985 [2023-12-20 17:03:34,492 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.9303 [2023-12-20 17:03:34,584 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2315 [2023-12-20 17:03:34,684 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2809 [2023-12-20 17:03:34,782 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3159 [2023-12-20 17:03:34,877 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3846 [2023-12-20 17:03:34,979 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6343 [2023-12-20 17:03:35,110 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7477 [2023-12-20 17:03:35,194 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3385 [2023-12-20 17:03:35,295 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5032 [2023-12-20 17:03:35,398 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0172 [2023-12-20 17:03:35,491 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3089 [2023-12-20 17:03:35,576 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1093 [2023-12-20 17:03:35,671 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6825 [2023-12-20 17:03:35,761 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4781 [2023-12-20 17:03:35,904 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2182 [2023-12-20 17:03:35,999 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5591 [2023-12-20 17:03:36,116 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7177 [2023-12-20 17:03:36,222 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8297 [2023-12-20 17:03:36,311 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7478 [2023-12-20 17:03:36,467 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9545 [2023-12-20 17:03:37,704 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7246/0.8086/0.9027. [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8519/0.9563 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9868 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6482/0.7926 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7976/0.8353 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8958/0.9221 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8104/0.9211 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7350/0.8009 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6836/0.8066 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6427/0.7418 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7845/0.8867 [2023-12-20 17:03:37,705 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3662/0.4680 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6850/0.8397 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6532/0.8840 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6307/0.6497 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6343/0.7132 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6395/0.6764 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9538/0.9690 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6830/0.7650 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8842/0.9226 [2023-12-20 17:03:37,706 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5484/0.6348 [2023-12-20 17:03:37,707 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:03:37,708 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 17:03:37,708 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:03:44,618 INFO misc.py line 119 131400] Train: [45/100][1/800] Data 0.692 (0.692) Batch 0.965 (0.965) Remain 12:00:14 loss: 0.1763 Lr: 0.00383 [2023-12-20 17:03:45,293 INFO misc.py line 119 131400] Train: [45/100][2/800] Data 0.377 (0.377) Batch 0.679 (0.679) Remain 08:26:58 loss: 0.2431 Lr: 0.00383 [2023-12-20 17:03:45,599 INFO misc.py line 119 131400] Train: [45/100][3/800] Data 0.002 (0.002) Batch 0.306 (0.306) Remain 03:48:33 loss: 0.1980 Lr: 0.00383 [2023-12-20 17:03:45,911 INFO misc.py line 119 131400] Train: [45/100][4/800] Data 0.003 (0.003) Batch 0.310 (0.310) Remain 03:51:20 loss: 0.2118 Lr: 0.00383 [2023-12-20 17:03:46,228 INFO misc.py line 119 131400] Train: [45/100][5/800] Data 0.005 (0.004) Batch 0.317 (0.313) Remain 03:53:54 loss: 0.5561 Lr: 0.00383 [2023-12-20 17:03:46,579 INFO misc.py line 119 131400] Train: [45/100][6/800] Data 0.005 (0.004) Batch 0.351 (0.326) Remain 04:03:21 loss: 0.2685 Lr: 0.00383 [2023-12-20 17:03:47,047 INFO misc.py line 119 131400] Train: [45/100][7/800] Data 0.123 (0.034) Batch 0.468 (0.361) Remain 04:29:52 loss: 0.3285 Lr: 0.00383 [2023-12-20 17:03:47,384 INFO misc.py line 119 131400] Train: [45/100][8/800] Data 0.005 (0.028) Batch 0.339 (0.357) Remain 04:26:28 loss: 0.2702 Lr: 0.00383 [2023-12-20 17:03:47,702 INFO misc.py line 119 131400] Train: [45/100][9/800] Data 0.002 (0.024) Batch 0.317 (0.350) Remain 04:21:31 loss: 0.8138 Lr: 0.00383 [2023-12-20 17:03:48,000 INFO misc.py line 119 131400] Train: [45/100][10/800] Data 0.004 (0.021) Batch 0.297 (0.343) Remain 04:15:48 loss: 0.2317 Lr: 0.00383 [2023-12-20 17:03:48,337 INFO misc.py line 119 131400] Train: [45/100][11/800] Data 0.006 (0.019) Batch 0.338 (0.342) Remain 04:15:20 loss: 0.3025 Lr: 0.00383 [2023-12-20 17:03:48,701 INFO misc.py line 119 131400] Train: [45/100][12/800] Data 0.005 (0.018) Batch 0.364 (0.345) Remain 04:17:10 loss: 0.2168 Lr: 0.00383 [2023-12-20 17:03:49,050 INFO misc.py line 119 131400] Train: [45/100][13/800] Data 0.004 (0.016) Batch 0.349 (0.345) Remain 04:17:29 loss: 0.2071 Lr: 0.00383 [2023-12-20 17:03:49,392 INFO misc.py line 119 131400] Train: [45/100][14/800] Data 0.004 (0.015) Batch 0.339 (0.344) Remain 04:17:05 loss: 0.2957 Lr: 0.00383 [2023-12-20 17:03:49,713 INFO misc.py line 119 131400] Train: [45/100][15/800] Data 0.006 (0.014) Batch 0.324 (0.343) Remain 04:15:47 loss: 0.3868 Lr: 0.00383 [2023-12-20 17:03:50,065 INFO misc.py line 119 131400] Train: [45/100][16/800] Data 0.004 (0.014) Batch 0.348 (0.343) Remain 04:16:05 loss: 0.3045 Lr: 0.00383 [2023-12-20 17:03:50,429 INFO misc.py line 119 131400] Train: [45/100][17/800] Data 0.009 (0.013) Batch 0.367 (0.345) Remain 04:17:21 loss: 0.4061 Lr: 0.00383 [2023-12-20 17:03:50,745 INFO misc.py line 119 131400] Train: [45/100][18/800] Data 0.006 (0.013) Batch 0.316 (0.343) Remain 04:15:55 loss: 0.3531 Lr: 0.00383 [2023-12-20 17:03:51,061 INFO misc.py line 119 131400] Train: [45/100][19/800] Data 0.005 (0.012) Batch 0.316 (0.341) Remain 04:14:39 loss: 0.1769 Lr: 0.00383 [2023-12-20 17:03:51,346 INFO misc.py line 119 131400] Train: [45/100][20/800] Data 0.006 (0.012) Batch 0.287 (0.338) Remain 04:12:17 loss: 0.3769 Lr: 0.00383 [2023-12-20 17:03:51,691 INFO misc.py line 119 131400] Train: [45/100][21/800] Data 0.003 (0.012) Batch 0.345 (0.338) Remain 04:12:33 loss: 0.5494 Lr: 0.00383 [2023-12-20 17:03:52,051 INFO misc.py line 119 131400] Train: [45/100][22/800] Data 0.004 (0.011) Batch 0.359 (0.339) Remain 04:13:21 loss: 0.3401 Lr: 0.00383 [2023-12-20 17:03:52,391 INFO misc.py line 119 131400] Train: [45/100][23/800] Data 0.005 (0.011) Batch 0.342 (0.340) Remain 04:13:26 loss: 0.2860 Lr: 0.00383 [2023-12-20 17:03:52,728 INFO misc.py line 119 131400] Train: [45/100][24/800] Data 0.003 (0.010) Batch 0.336 (0.339) Remain 04:13:17 loss: 0.2526 Lr: 0.00383 [2023-12-20 17:03:53,087 INFO misc.py line 119 131400] Train: [45/100][25/800] Data 0.004 (0.010) Batch 0.359 (0.340) Remain 04:13:58 loss: 0.4215 Lr: 0.00383 [2023-12-20 17:03:53,415 INFO misc.py line 119 131400] Train: [45/100][26/800] Data 0.004 (0.010) Batch 0.327 (0.340) Remain 04:13:32 loss: 0.5047 Lr: 0.00383 [2023-12-20 17:03:53,758 INFO misc.py line 119 131400] Train: [45/100][27/800] Data 0.004 (0.010) Batch 0.344 (0.340) Remain 04:13:39 loss: 0.3627 Lr: 0.00383 [2023-12-20 17:03:54,104 INFO misc.py line 119 131400] Train: [45/100][28/800] Data 0.004 (0.009) Batch 0.345 (0.340) Remain 04:13:48 loss: 0.5012 Lr: 0.00383 [2023-12-20 17:03:54,447 INFO misc.py line 119 131400] Train: [45/100][29/800] Data 0.004 (0.009) Batch 0.343 (0.340) Remain 04:13:53 loss: 0.5672 Lr: 0.00383 [2023-12-20 17:03:54,789 INFO misc.py line 119 131400] Train: [45/100][30/800] Data 0.005 (0.009) Batch 0.343 (0.340) Remain 04:13:57 loss: 0.1626 Lr: 0.00383 [2023-12-20 17:03:55,119 INFO misc.py line 119 131400] Train: [45/100][31/800] Data 0.004 (0.009) Batch 0.329 (0.340) Remain 04:13:38 loss: 0.2447 Lr: 0.00383 [2023-12-20 17:03:55,472 INFO misc.py line 119 131400] Train: [45/100][32/800] Data 0.005 (0.009) Batch 0.354 (0.340) Remain 04:13:59 loss: 0.3116 Lr: 0.00383 [2023-12-20 17:03:55,791 INFO misc.py line 119 131400] Train: [45/100][33/800] Data 0.003 (0.009) Batch 0.319 (0.340) Remain 04:13:27 loss: 0.2460 Lr: 0.00383 [2023-12-20 17:03:56,121 INFO misc.py line 119 131400] Train: [45/100][34/800] Data 0.004 (0.008) Batch 0.328 (0.339) Remain 04:13:10 loss: 0.5753 Lr: 0.00383 [2023-12-20 17:03:56,475 INFO misc.py line 119 131400] Train: [45/100][35/800] Data 0.006 (0.008) Batch 0.356 (0.340) Remain 04:13:33 loss: 0.4499 Lr: 0.00383 [2023-12-20 17:03:56,843 INFO misc.py line 119 131400] Train: [45/100][36/800] Data 0.004 (0.008) Batch 0.368 (0.341) Remain 04:14:11 loss: 0.4001 Lr: 0.00383 [2023-12-20 17:03:57,217 INFO misc.py line 119 131400] Train: [45/100][37/800] Data 0.004 (0.008) Batch 0.369 (0.342) Remain 04:14:48 loss: 0.4114 Lr: 0.00383 [2023-12-20 17:03:57,542 INFO misc.py line 119 131400] Train: [45/100][38/800] Data 0.008 (0.008) Batch 0.330 (0.341) Remain 04:14:32 loss: 0.5653 Lr: 0.00383 [2023-12-20 17:03:57,866 INFO misc.py line 119 131400] Train: [45/100][39/800] Data 0.004 (0.008) Batch 0.324 (0.341) Remain 04:14:11 loss: 0.3147 Lr: 0.00383 [2023-12-20 17:03:58,168 INFO misc.py line 119 131400] Train: [45/100][40/800] Data 0.004 (0.008) Batch 0.302 (0.340) Remain 04:13:23 loss: 0.2688 Lr: 0.00383 [2023-12-20 17:03:58,520 INFO misc.py line 119 131400] 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[2023-12-20 17:08:03,274 INFO misc.py line 119 131400] Train: [45/100][776/800] Data 0.003 (0.005) Batch 0.332 (0.333) Remain 04:04:34 loss: 0.4117 Lr: 0.00374 [2023-12-20 17:08:03,596 INFO misc.py line 119 131400] Train: [45/100][777/800] Data 0.003 (0.005) Batch 0.323 (0.333) Remain 04:04:34 loss: 0.4185 Lr: 0.00374 [2023-12-20 17:08:03,905 INFO misc.py line 119 131400] Train: [45/100][778/800] Data 0.003 (0.005) Batch 0.308 (0.333) Remain 04:04:32 loss: 0.4942 Lr: 0.00374 [2023-12-20 17:08:04,195 INFO misc.py line 119 131400] Train: [45/100][779/800] Data 0.003 (0.005) Batch 0.291 (0.333) Remain 04:04:29 loss: 0.5307 Lr: 0.00374 [2023-12-20 17:08:04,538 INFO misc.py line 119 131400] Train: [45/100][780/800] Data 0.003 (0.005) Batch 0.342 (0.333) Remain 04:04:29 loss: 0.4863 Lr: 0.00374 [2023-12-20 17:08:04,847 INFO misc.py line 119 131400] Train: [45/100][781/800] Data 0.004 (0.005) Batch 0.310 (0.333) Remain 04:04:28 loss: 0.4493 Lr: 0.00374 [2023-12-20 17:08:05,179 INFO misc.py line 119 131400] Train: [45/100][782/800] Data 0.003 (0.005) Batch 0.332 (0.333) Remain 04:04:27 loss: 0.4038 Lr: 0.00374 [2023-12-20 17:08:05,508 INFO misc.py line 119 131400] Train: [45/100][783/800] Data 0.003 (0.005) Batch 0.328 (0.333) Remain 04:04:27 loss: 0.4318 Lr: 0.00374 [2023-12-20 17:08:05,856 INFO misc.py line 119 131400] Train: [45/100][784/800] Data 0.004 (0.005) Batch 0.341 (0.333) Remain 04:04:27 loss: 0.3044 Lr: 0.00374 [2023-12-20 17:08:06,217 INFO misc.py line 119 131400] Train: [45/100][785/800] Data 0.011 (0.005) Batch 0.368 (0.333) Remain 04:04:28 loss: 0.2072 Lr: 0.00374 [2023-12-20 17:08:06,557 INFO misc.py line 119 131400] Train: [45/100][786/800] Data 0.011 (0.005) Batch 0.341 (0.333) Remain 04:04:28 loss: 0.2415 Lr: 0.00374 [2023-12-20 17:08:06,824 INFO misc.py line 119 131400] Train: [45/100][787/800] Data 0.003 (0.005) Batch 0.262 (0.333) Remain 04:04:24 loss: 0.3744 Lr: 0.00374 [2023-12-20 17:08:07,180 INFO misc.py line 119 131400] Train: [45/100][788/800] Data 0.011 (0.005) Batch 0.360 (0.333) Remain 04:04:25 loss: 0.4333 Lr: 0.00374 [2023-12-20 17:08:07,533 INFO misc.py line 119 131400] Train: [45/100][789/800] Data 0.003 (0.005) Batch 0.353 (0.333) Remain 04:04:26 loss: 0.3095 Lr: 0.00374 [2023-12-20 17:08:07,882 INFO misc.py line 119 131400] Train: [45/100][790/800] Data 0.004 (0.005) Batch 0.350 (0.333) Remain 04:04:27 loss: 0.4229 Lr: 0.00374 [2023-12-20 17:08:08,178 INFO misc.py line 119 131400] Train: [45/100][791/800] Data 0.003 (0.005) Batch 0.296 (0.333) Remain 04:04:24 loss: 0.2093 Lr: 0.00374 [2023-12-20 17:08:08,430 INFO misc.py line 119 131400] Train: [45/100][792/800] Data 0.003 (0.005) Batch 0.252 (0.333) Remain 04:04:19 loss: 0.2011 Lr: 0.00374 [2023-12-20 17:08:08,753 INFO misc.py line 119 131400] Train: [45/100][793/800] Data 0.002 (0.005) Batch 0.323 (0.333) Remain 04:04:18 loss: 0.4905 Lr: 0.00374 [2023-12-20 17:08:09,091 INFO misc.py line 119 131400] Train: [45/100][794/800] Data 0.003 (0.005) Batch 0.333 (0.333) Remain 04:04:18 loss: 0.3300 Lr: 0.00374 [2023-12-20 17:08:09,343 INFO misc.py line 119 131400] Train: [45/100][795/800] Data 0.008 (0.005) Batch 0.258 (0.333) Remain 04:04:14 loss: 0.1897 Lr: 0.00374 [2023-12-20 17:08:09,610 INFO misc.py line 119 131400] Train: [45/100][796/800] Data 0.002 (0.005) Batch 0.265 (0.333) Remain 04:04:09 loss: 0.3168 Lr: 0.00374 [2023-12-20 17:08:09,990 INFO misc.py line 119 131400] Train: [45/100][797/800] Data 0.004 (0.005) Batch 0.382 (0.333) Remain 04:04:12 loss: 0.2844 Lr: 0.00374 [2023-12-20 17:08:10,355 INFO misc.py line 119 131400] Train: [45/100][798/800] Data 0.003 (0.005) Batch 0.364 (0.333) Remain 04:04:13 loss: 0.2029 Lr: 0.00374 [2023-12-20 17:08:10,658 INFO misc.py line 119 131400] Train: [45/100][799/800] Data 0.004 (0.005) Batch 0.303 (0.333) Remain 04:04:11 loss: 0.4791 Lr: 0.00374 [2023-12-20 17:08:11,003 INFO misc.py line 119 131400] Train: [45/100][800/800] Data 0.003 (0.005) Batch 0.345 (0.333) Remain 04:04:11 loss: 0.3782 Lr: 0.00374 [2023-12-20 17:08:11,004 INFO misc.py line 136 131400] Train result: loss: 0.3892 [2023-12-20 17:08:11,004 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:08:33,813 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1828 [2023-12-20 17:08:33,884 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1927 [2023-12-20 17:08:34,439 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4084 [2023-12-20 17:08:35,109 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.6093 [2023-12-20 17:08:35,221 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5062 [2023-12-20 17:08:35,327 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5551 [2023-12-20 17:08:35,417 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.7864 [2023-12-20 17:08:35,522 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0200 [2023-12-20 17:08:35,604 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3096 [2023-12-20 17:08:35,689 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.5419 [2023-12-20 17:08:35,779 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5934 [2023-12-20 17:08:35,915 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2869 [2023-12-20 17:08:36,036 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3491 [2023-12-20 17:08:36,190 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2065 [2023-12-20 17:08:36,288 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2706 [2023-12-20 17:08:36,425 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0520 [2023-12-20 17:08:36,532 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3121 [2023-12-20 17:08:36,640 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5306 [2023-12-20 17:08:36,755 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2115 [2023-12-20 17:08:36,834 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4528 [2023-12-20 17:08:36,941 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.6451 [2023-12-20 17:08:37,101 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2069 [2023-12-20 17:08:37,220 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7665 [2023-12-20 17:08:37,372 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2380 [2023-12-20 17:08:37,517 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2888 [2023-12-20 17:08:37,599 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5971 [2023-12-20 17:08:37,757 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7814 [2023-12-20 17:08:37,881 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5968 [2023-12-20 17:08:37,977 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5942 [2023-12-20 17:08:38,126 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6487 [2023-12-20 17:08:38,232 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7314 [2023-12-20 17:08:38,352 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6431 [2023-12-20 17:08:38,436 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1679 [2023-12-20 17:08:38,509 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1899 [2023-12-20 17:08:38,605 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7625 [2023-12-20 17:08:38,705 INFO evaluator.py line 159 131400] Test: [36/78] Loss 1.0162 [2023-12-20 17:08:38,843 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8517 [2023-12-20 17:08:38,956 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0996 [2023-12-20 17:08:39,040 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6471 [2023-12-20 17:08:39,188 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.5008 [2023-12-20 17:08:39,338 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0527 [2023-12-20 17:08:39,439 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0973 [2023-12-20 17:08:39,572 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5470 [2023-12-20 17:08:39,722 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8080 [2023-12-20 17:08:39,844 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1359 [2023-12-20 17:08:39,965 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.3955 [2023-12-20 17:08:40,136 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3790 [2023-12-20 17:08:40,233 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3331 [2023-12-20 17:08:40,379 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1874 [2023-12-20 17:08:40,473 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0550 [2023-12-20 17:08:40,553 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6445 [2023-12-20 17:08:40,660 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.6563 [2023-12-20 17:08:40,806 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3705 [2023-12-20 17:08:40,945 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2834 [2023-12-20 17:08:41,051 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5773 [2023-12-20 17:08:41,137 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8144 [2023-12-20 17:08:41,239 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3774 [2023-12-20 17:08:41,402 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1903 [2023-12-20 17:08:41,500 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2234 [2023-12-20 17:08:41,591 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2009 [2023-12-20 17:08:41,689 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3613 [2023-12-20 17:08:41,787 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3800 [2023-12-20 17:08:41,877 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5151 [2023-12-20 17:08:41,979 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6295 [2023-12-20 17:08:42,104 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3374 [2023-12-20 17:08:42,190 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2688 [2023-12-20 17:08:42,289 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.7839 [2023-12-20 17:08:42,387 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0218 [2023-12-20 17:08:42,472 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4384 [2023-12-20 17:08:42,560 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0563 [2023-12-20 17:08:42,656 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6052 [2023-12-20 17:08:42,749 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.3813 [2023-12-20 17:08:42,882 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.3721 [2023-12-20 17:08:42,978 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.4714 [2023-12-20 17:08:43,095 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7087 [2023-12-20 17:08:43,197 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7542 [2023-12-20 17:08:43,284 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2881 [2023-12-20 17:08:43,437 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.5155 [2023-12-20 17:08:44,492 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7319/0.8278/0.9053. [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8580/0.9395 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9647/0.9852 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6724/0.7895 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8097/0.8967 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9032/0.9449 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8100/0.9454 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7314/0.8377 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6562/0.8128 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6150/0.7674 [2023-12-20 17:08:44,492 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7988/0.9246 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3565/0.4056 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6657/0.8419 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6908/0.8538 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7261/0.7961 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6508/0.7189 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7171/0.8248 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9335/0.9776 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6759/0.7672 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8570/0.9324 [2023-12-20 17:08:44,493 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5457/0.5939 [2023-12-20 17:08:44,493 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:08:44,494 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 17:08:44,495 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:08:48,494 INFO misc.py line 119 131400] Train: [46/100][1/800] Data 1.387 (1.387) Batch 1.713 (1.713) Remain 20:55:54 loss: 0.3153 Lr: 0.00374 [2023-12-20 17:08:48,809 INFO misc.py line 119 131400] Train: [46/100][2/800] Data 0.004 (0.004) Batch 0.315 (0.315) Remain 03:50:45 loss: 0.4087 Lr: 0.00374 [2023-12-20 17:08:49,132 INFO misc.py line 119 131400] Train: [46/100][3/800] Data 0.005 (0.005) Batch 0.324 (0.324) Remain 03:57:17 loss: 0.3744 Lr: 0.00374 [2023-12-20 17:08:49,429 INFO misc.py line 119 131400] Train: [46/100][4/800] Data 0.004 (0.004) Batch 0.297 (0.297) Remain 03:37:46 loss: 0.3606 Lr: 0.00374 [2023-12-20 17:08:49,760 INFO misc.py line 119 131400] Train: [46/100][5/800] Data 0.004 (0.004) Batch 0.332 (0.314) Remain 03:50:30 loss: 0.3837 Lr: 0.00374 [2023-12-20 17:08:50,118 INFO misc.py line 119 131400] Train: [46/100][6/800] Data 0.003 (0.004) Batch 0.352 (0.327) Remain 03:59:39 loss: 0.3650 Lr: 0.00374 [2023-12-20 17:08:50,420 INFO misc.py line 119 131400] Train: [46/100][7/800] Data 0.009 (0.005) Batch 0.307 (0.322) Remain 03:56:04 loss: 0.2833 Lr: 0.00374 [2023-12-20 17:08:50,763 INFO misc.py line 119 131400] Train: [46/100][8/800] Data 0.004 (0.005) Batch 0.344 (0.326) Remain 03:59:15 loss: 0.3712 Lr: 0.00374 [2023-12-20 17:08:51,130 INFO misc.py line 119 131400] Train: [46/100][9/800] Data 0.002 (0.004) Batch 0.361 (0.332) Remain 04:03:33 loss: 0.4944 Lr: 0.00374 [2023-12-20 17:08:51,475 INFO misc.py line 119 131400] Train: [46/100][10/800] Data 0.008 (0.005) Batch 0.350 (0.335) Remain 04:05:23 loss: 0.2437 Lr: 0.00374 [2023-12-20 17:08:51,814 INFO misc.py line 119 131400] Train: [46/100][11/800] Data 0.004 (0.005) Batch 0.340 (0.335) Remain 04:05:51 loss: 0.5728 Lr: 0.00374 [2023-12-20 17:08:52,250 INFO misc.py line 119 131400] Train: [46/100][12/800] Data 0.003 (0.005) Batch 0.353 (0.337) Remain 04:07:17 loss: 0.4049 Lr: 0.00374 [2023-12-20 17:08:52,567 INFO misc.py line 119 131400] Train: [46/100][13/800] Data 0.085 (0.013) Batch 0.400 (0.344) Remain 04:11:50 loss: 0.2038 Lr: 0.00374 [2023-12-20 17:08:52,916 INFO misc.py line 119 131400] Train: [46/100][14/800] Data 0.003 (0.012) Batch 0.348 (0.344) Remain 04:12:08 loss: 0.2925 Lr: 0.00374 [2023-12-20 17:08:53,287 INFO misc.py line 119 131400] Train: [46/100][15/800] Data 0.004 (0.011) Batch 0.371 (0.346) Remain 04:13:48 loss: 0.2318 Lr: 0.00374 [2023-12-20 17:08:53,628 INFO misc.py line 119 131400] Train: [46/100][16/800] Data 0.004 (0.011) Batch 0.341 (0.346) Remain 04:13:29 loss: 0.4581 Lr: 0.00374 [2023-12-20 17:08:53,977 INFO misc.py line 119 131400] Train: [46/100][17/800] Data 0.004 (0.010) Batch 0.350 (0.346) Remain 04:13:43 loss: 0.3014 Lr: 0.00374 [2023-12-20 17:08:54,284 INFO misc.py line 119 131400] Train: [46/100][18/800] Data 0.003 (0.010) Batch 0.303 (0.343) Remain 04:11:36 loss: 0.3351 Lr: 0.00374 [2023-12-20 17:08:54,609 INFO misc.py line 119 131400] Train: [46/100][19/800] Data 0.007 (0.009) Batch 0.328 (0.342) Remain 04:10:54 loss: 0.6083 Lr: 0.00374 [2023-12-20 17:08:54,950 INFO misc.py line 119 131400] Train: [46/100][20/800] Data 0.003 (0.009) Batch 0.340 (0.342) Remain 04:10:49 loss: 0.6033 Lr: 0.00374 [2023-12-20 17:08:55,278 INFO misc.py line 119 131400] Train: [46/100][21/800] Data 0.004 (0.009) Batch 0.329 (0.341) Remain 04:10:16 loss: 0.2454 Lr: 0.00374 [2023-12-20 17:08:55,609 INFO misc.py line 119 131400] Train: [46/100][22/800] Data 0.004 (0.009) Batch 0.330 (0.341) Remain 04:09:50 loss: 0.4623 Lr: 0.00374 [2023-12-20 17:08:55,906 INFO misc.py line 119 131400] Train: [46/100][23/800] Data 0.005 (0.008) Batch 0.297 (0.339) Remain 04:08:13 loss: 0.2743 Lr: 0.00374 [2023-12-20 17:08:56,251 INFO misc.py line 119 131400] Train: [46/100][24/800] Data 0.004 (0.008) Batch 0.346 (0.339) Remain 04:08:29 loss: 0.5795 Lr: 0.00374 [2023-12-20 17:08:56,540 INFO misc.py line 119 131400] Train: [46/100][25/800] Data 0.003 (0.008) Batch 0.288 (0.337) Remain 04:06:47 loss: 0.3748 Lr: 0.00374 [2023-12-20 17:08:56,849 INFO misc.py line 119 131400] Train: [46/100][26/800] Data 0.003 (0.008) Batch 0.308 (0.335) Remain 04:05:52 loss: 0.6624 Lr: 0.00374 [2023-12-20 17:08:57,169 INFO misc.py line 119 131400] Train: [46/100][27/800] Data 0.004 (0.008) Batch 0.321 (0.335) Remain 04:05:26 loss: 0.2946 Lr: 0.00374 [2023-12-20 17:08:57,518 INFO misc.py line 119 131400] Train: [46/100][28/800] Data 0.003 (0.007) Batch 0.349 (0.335) Remain 04:05:51 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0.008 (0.005) Batch 0.352 (0.334) Remain 04:00:52 loss: 0.6515 Lr: 0.00365 [2023-12-20 17:13:03,511 INFO misc.py line 119 131400] Train: [46/100][764/800] Data 0.004 (0.005) Batch 0.334 (0.334) Remain 04:00:52 loss: 0.4937 Lr: 0.00365 [2023-12-20 17:13:03,818 INFO misc.py line 119 131400] Train: [46/100][765/800] Data 0.004 (0.005) Batch 0.307 (0.334) Remain 04:00:50 loss: 0.3107 Lr: 0.00365 [2023-12-20 17:13:04,150 INFO misc.py line 119 131400] Train: [46/100][766/800] Data 0.004 (0.005) Batch 0.333 (0.334) Remain 04:00:50 loss: 0.4231 Lr: 0.00365 [2023-12-20 17:13:04,482 INFO misc.py line 119 131400] Train: [46/100][767/800] Data 0.004 (0.005) Batch 0.332 (0.334) Remain 04:00:49 loss: 0.3264 Lr: 0.00365 [2023-12-20 17:13:04,817 INFO misc.py line 119 131400] Train: [46/100][768/800] Data 0.003 (0.005) Batch 0.334 (0.334) Remain 04:00:49 loss: 0.4007 Lr: 0.00365 [2023-12-20 17:13:05,164 INFO misc.py line 119 131400] Train: [46/100][769/800] Data 0.004 (0.005) Batch 0.347 (0.334) Remain 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[2023-12-20 17:13:07,552 INFO misc.py line 119 131400] Train: [46/100][776/800] Data 0.004 (0.005) Batch 0.311 (0.334) Remain 04:00:50 loss: 0.2873 Lr: 0.00364 [2023-12-20 17:13:07,898 INFO misc.py line 119 131400] Train: [46/100][777/800] Data 0.004 (0.005) Batch 0.346 (0.334) Remain 04:00:50 loss: 0.3134 Lr: 0.00364 [2023-12-20 17:13:08,246 INFO misc.py line 119 131400] Train: [46/100][778/800] Data 0.003 (0.005) Batch 0.347 (0.334) Remain 04:00:50 loss: 0.5925 Lr: 0.00364 [2023-12-20 17:13:08,605 INFO misc.py line 119 131400] Train: [46/100][779/800] Data 0.004 (0.005) Batch 0.358 (0.334) Remain 04:00:51 loss: 0.2676 Lr: 0.00364 [2023-12-20 17:13:08,953 INFO misc.py line 119 131400] Train: [46/100][780/800] Data 0.005 (0.005) Batch 0.348 (0.334) Remain 04:00:52 loss: 0.4691 Lr: 0.00364 [2023-12-20 17:13:09,269 INFO misc.py line 119 131400] Train: [46/100][781/800] Data 0.005 (0.005) Batch 0.317 (0.334) Remain 04:00:50 loss: 0.2590 Lr: 0.00364 [2023-12-20 17:13:09,614 INFO misc.py line 119 131400] Train: [46/100][782/800] Data 0.003 (0.005) Batch 0.344 (0.334) Remain 04:00:51 loss: 0.5594 Lr: 0.00364 [2023-12-20 17:13:09,971 INFO misc.py line 119 131400] Train: [46/100][783/800] Data 0.006 (0.005) Batch 0.358 (0.334) Remain 04:00:52 loss: 0.2190 Lr: 0.00364 [2023-12-20 17:13:10,303 INFO misc.py line 119 131400] Train: [46/100][784/800] Data 0.004 (0.005) Batch 0.331 (0.334) Remain 04:00:51 loss: 0.1363 Lr: 0.00364 [2023-12-20 17:13:10,642 INFO misc.py line 119 131400] Train: [46/100][785/800] Data 0.003 (0.005) Batch 0.340 (0.334) Remain 04:00:51 loss: 0.2944 Lr: 0.00364 [2023-12-20 17:13:10,982 INFO misc.py line 119 131400] Train: [46/100][786/800] Data 0.003 (0.005) Batch 0.340 (0.334) Remain 04:00:51 loss: 0.3073 Lr: 0.00364 [2023-12-20 17:13:11,283 INFO misc.py line 119 131400] Train: [46/100][787/800] Data 0.003 (0.005) Batch 0.300 (0.334) Remain 04:00:49 loss: 0.2575 Lr: 0.00364 [2023-12-20 17:13:11,642 INFO misc.py line 119 131400] Train: [46/100][788/800] Data 0.004 (0.005) Batch 0.359 (0.334) Remain 04:00:50 loss: 0.4584 Lr: 0.00364 [2023-12-20 17:13:11,968 INFO misc.py line 119 131400] Train: [46/100][789/800] Data 0.003 (0.005) Batch 0.327 (0.334) Remain 04:00:49 loss: 0.4828 Lr: 0.00364 [2023-12-20 17:13:12,289 INFO misc.py line 119 131400] Train: [46/100][790/800] Data 0.002 (0.005) Batch 0.320 (0.334) Remain 04:00:48 loss: 0.6001 Lr: 0.00364 [2023-12-20 17:13:12,601 INFO misc.py line 119 131400] Train: [46/100][791/800] Data 0.004 (0.005) Batch 0.313 (0.334) Remain 04:00:46 loss: 0.3719 Lr: 0.00364 [2023-12-20 17:13:12,961 INFO misc.py line 119 131400] Train: [46/100][792/800] Data 0.003 (0.005) Batch 0.359 (0.334) Remain 04:00:47 loss: 0.4010 Lr: 0.00364 [2023-12-20 17:13:13,255 INFO misc.py line 119 131400] Train: [46/100][793/800] Data 0.004 (0.005) Batch 0.291 (0.334) Remain 04:00:45 loss: 0.2445 Lr: 0.00364 [2023-12-20 17:13:13,585 INFO misc.py line 119 131400] Train: [46/100][794/800] Data 0.006 (0.005) Batch 0.333 (0.334) Remain 04:00:44 loss: 0.3561 Lr: 0.00364 [2023-12-20 17:13:13,896 INFO misc.py line 119 131400] Train: [46/100][795/800] Data 0.003 (0.005) Batch 0.311 (0.334) Remain 04:00:43 loss: 0.7937 Lr: 0.00364 [2023-12-20 17:13:14,200 INFO misc.py line 119 131400] Train: [46/100][796/800] Data 0.002 (0.005) Batch 0.303 (0.334) Remain 04:00:41 loss: 0.5074 Lr: 0.00364 [2023-12-20 17:13:14,497 INFO misc.py line 119 131400] Train: [46/100][797/800] Data 0.004 (0.005) Batch 0.294 (0.334) Remain 04:00:38 loss: 0.2446 Lr: 0.00364 [2023-12-20 17:13:14,763 INFO misc.py line 119 131400] Train: [46/100][798/800] Data 0.008 (0.005) Batch 0.270 (0.334) Remain 04:00:34 loss: 0.5377 Lr: 0.00364 [2023-12-20 17:13:15,074 INFO misc.py line 119 131400] Train: [46/100][799/800] Data 0.003 (0.005) Batch 0.311 (0.334) Remain 04:00:33 loss: 0.3373 Lr: 0.00364 [2023-12-20 17:13:15,410 INFO misc.py line 119 131400] Train: [46/100][800/800] Data 0.003 (0.005) Batch 0.335 (0.334) Remain 04:00:32 loss: 0.4217 Lr: 0.00364 [2023-12-20 17:13:15,411 INFO misc.py line 136 131400] Train result: loss: 0.3877 [2023-12-20 17:13:15,411 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:13:37,382 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1728 [2023-12-20 17:13:37,469 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1942 [2023-12-20 17:13:37,570 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3888 [2023-12-20 17:13:37,683 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1160 [2023-12-20 17:13:37,801 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4423 [2023-12-20 17:13:37,901 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2853 [2023-12-20 17:13:37,992 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1405 [2023-12-20 17:13:38,100 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6747 [2023-12-20 17:13:38,184 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2600 [2023-12-20 17:13:38,271 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3359 [2023-12-20 17:13:38,370 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5228 [2023-12-20 17:13:38,509 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3606 [2023-12-20 17:13:38,629 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4528 [2023-12-20 17:13:38,789 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2316 [2023-12-20 17:13:38,883 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.2133 [2023-12-20 17:13:39,021 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8950 [2023-12-20 17:13:39,134 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4195 [2023-12-20 17:13:39,244 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4403 [2023-12-20 17:13:39,357 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1982 [2023-12-20 17:13:39,434 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3201 [2023-12-20 17:13:39,542 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4191 [2023-12-20 17:13:39,702 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1566 [2023-12-20 17:13:39,827 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.4142 [2023-12-20 17:13:39,980 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1596 [2023-12-20 17:13:40,124 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1923 [2023-12-20 17:13:40,214 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8208 [2023-12-20 17:13:40,378 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4765 [2023-12-20 17:13:40,502 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4383 [2023-12-20 17:13:40,601 INFO evaluator.py line 159 131400] Test: [29/78] Loss 1.0655 [2023-12-20 17:13:40,746 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3854 [2023-12-20 17:13:40,857 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6067 [2023-12-20 17:13:40,975 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4029 [2023-12-20 17:13:41,069 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.2976 [2023-12-20 17:13:41,139 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1787 [2023-12-20 17:13:41,238 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6506 [2023-12-20 17:13:41,332 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.7651 [2023-12-20 17:13:41,463 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0018 [2023-12-20 17:13:41,580 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1601 [2023-12-20 17:13:41,670 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.9291 [2023-12-20 17:13:41,814 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3299 [2023-12-20 17:13:41,961 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.3777 [2023-12-20 17:13:42,074 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.2929 [2023-12-20 17:13:42,202 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3745 [2023-12-20 17:13:42,364 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9529 [2023-12-20 17:13:42,492 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8141 [2023-12-20 17:13:42,605 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5651 [2023-12-20 17:13:42,773 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3548 [2023-12-20 17:13:42,872 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4167 [2023-12-20 17:13:43,022 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4914 [2023-12-20 17:13:43,122 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1385 [2023-12-20 17:13:43,201 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7422 [2023-12-20 17:13:43,312 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1827 [2023-12-20 17:13:43,459 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1334 [2023-12-20 17:13:43,594 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3131 [2023-12-20 17:13:43,699 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.8326 [2023-12-20 17:13:43,786 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6515 [2023-12-20 17:13:43,890 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4704 [2023-12-20 17:13:44,056 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3084 [2023-12-20 17:13:44,153 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1969 [2023-12-20 17:13:44,246 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2243 [2023-12-20 17:13:44,345 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2337 [2023-12-20 17:13:44,436 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3817 [2023-12-20 17:13:44,523 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7329 [2023-12-20 17:13:44,622 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6994 [2023-12-20 17:13:44,750 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.0211 [2023-12-20 17:13:44,835 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.4093 [2023-12-20 17:13:44,934 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3374 [2023-12-20 17:13:45,034 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.1782 [2023-12-20 17:13:45,120 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.9309 [2023-12-20 17:13:45,209 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.5951 [2023-12-20 17:13:45,310 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5210 [2023-12-20 17:13:45,403 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4258 [2023-12-20 17:13:45,536 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2378 [2023-12-20 17:13:45,631 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6439 [2023-12-20 17:13:45,748 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6347 [2023-12-20 17:13:45,851 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7138 [2023-12-20 17:13:45,937 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3132 [2023-12-20 17:13:46,092 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4340 [2023-12-20 17:13:47,516 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7367/0.8402/0.9078. [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8579/0.9300 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9640/0.9869 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6908/0.8146 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7906/0.9052 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8998/0.9365 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7809/0.9552 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7492/0.7913 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6903/0.8226 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6695/0.7862 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8133/0.9250 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3354/0.5054 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7023/0.8323 [2023-12-20 17:13:47,516 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7005/0.8810 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7493/0.8820 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6054/0.6714 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7531/0.8417 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9192/0.9814 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6619/0.7579 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8352/0.9367 [2023-12-20 17:13:47,517 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5656/0.6599 [2023-12-20 17:13:47,517 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:13:47,518 INFO misc.py line 165 131400] Currently Best mIoU: 0.7424 [2023-12-20 17:13:47,519 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:13:50,625 INFO misc.py line 119 131400] Train: [47/100][1/800] Data 0.845 (0.845) Batch 1.113 (1.113) Remain 13:21:24 loss: 0.3051 Lr: 0.00364 [2023-12-20 17:13:50,938 INFO misc.py line 119 131400] Train: [47/100][2/800] Data 0.004 (0.004) Batch 0.314 (0.314) Remain 03:45:45 loss: 0.3170 Lr: 0.00364 [2023-12-20 17:13:51,496 INFO misc.py line 119 131400] Train: [47/100][3/800] Data 0.226 (0.226) Batch 0.553 (0.553) Remain 06:38:01 loss: 0.3299 Lr: 0.00364 [2023-12-20 17:13:51,828 INFO misc.py line 119 131400] Train: [47/100][4/800] Data 0.007 (0.007) Batch 0.337 (0.337) Remain 04:02:47 loss: 0.4218 Lr: 0.00364 [2023-12-20 17:13:52,108 INFO misc.py line 119 131400] Train: [47/100][5/800] Data 0.003 (0.005) Batch 0.279 (0.308) Remain 03:41:54 loss: 0.3424 Lr: 0.00364 [2023-12-20 17:13:52,402 INFO misc.py line 119 131400] Train: [47/100][6/800] Data 0.004 (0.005) Batch 0.294 (0.304) Remain 03:38:31 loss: 0.3193 Lr: 0.00364 [2023-12-20 17:13:52,735 INFO misc.py line 119 131400] Train: [47/100][7/800] Data 0.003 (0.004) Batch 0.333 (0.311) Remain 03:43:53 loss: 0.2294 Lr: 0.00364 [2023-12-20 17:13:53,073 INFO misc.py line 119 131400] Train: [47/100][8/800] Data 0.004 (0.004) Batch 0.338 (0.316) Remain 03:47:42 loss: 0.3334 Lr: 0.00364 [2023-12-20 17:13:53,435 INFO misc.py line 119 131400] Train: [47/100][9/800] Data 0.003 (0.004) Batch 0.362 (0.324) Remain 03:53:12 loss: 0.6125 Lr: 0.00364 [2023-12-20 17:13:53,740 INFO misc.py line 119 131400] Train: [47/100][10/800] Data 0.003 (0.004) Batch 0.305 (0.321) Remain 03:51:14 loss: 0.3250 Lr: 0.00364 [2023-12-20 17:13:54,046 INFO misc.py line 119 131400] Train: [47/100][11/800] Data 0.003 (0.004) Batch 0.306 (0.319) Remain 03:49:52 loss: 0.3054 Lr: 0.00364 [2023-12-20 17:13:54,381 INFO misc.py line 119 131400] Train: [47/100][12/800] Data 0.003 (0.004) Batch 0.334 (0.321) Remain 03:51:01 loss: 0.2856 Lr: 0.00364 [2023-12-20 17:13:54,731 INFO misc.py line 119 131400] Train: [47/100][13/800] Data 0.004 (0.004) Batch 0.348 (0.324) Remain 03:52:59 loss: 0.3541 Lr: 0.00364 [2023-12-20 17:13:55,045 INFO misc.py line 119 131400] Train: [47/100][14/800] Data 0.007 (0.004) Batch 0.317 (0.323) Remain 03:52:33 loss: 0.4035 Lr: 0.00364 [2023-12-20 17:13:55,324 INFO misc.py line 119 131400] Train: [47/100][15/800] Data 0.003 (0.004) Batch 0.277 (0.319) Remain 03:49:47 loss: 0.2594 Lr: 0.00364 [2023-12-20 17:13:55,705 INFO misc.py line 119 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0.005 (0.005) Batch 0.325 (0.336) Remain 03:57:23 loss: 0.2681 Lr: 0.00355 [2023-12-20 17:18:06,910 INFO misc.py line 119 131400] Train: [47/100][764/800] Data 0.004 (0.005) Batch 0.325 (0.336) Remain 03:57:22 loss: 0.3213 Lr: 0.00355 [2023-12-20 17:18:07,204 INFO misc.py line 119 131400] Train: [47/100][765/800] Data 0.004 (0.005) Batch 0.292 (0.336) Remain 03:57:20 loss: 0.3284 Lr: 0.00355 [2023-12-20 17:18:07,560 INFO misc.py line 119 131400] Train: [47/100][766/800] Data 0.005 (0.005) Batch 0.357 (0.336) Remain 03:57:21 loss: 0.5382 Lr: 0.00355 [2023-12-20 17:18:07,881 INFO misc.py line 119 131400] Train: [47/100][767/800] Data 0.004 (0.005) Batch 0.321 (0.336) Remain 03:57:19 loss: 0.3735 Lr: 0.00355 [2023-12-20 17:18:08,230 INFO misc.py line 119 131400] Train: [47/100][768/800] Data 0.006 (0.005) Batch 0.349 (0.336) Remain 03:57:20 loss: 0.2199 Lr: 0.00355 [2023-12-20 17:18:08,568 INFO misc.py line 119 131400] Train: [47/100][769/800] Data 0.004 (0.005) Batch 0.338 (0.336) Remain 03:57:20 loss: 0.3557 Lr: 0.00355 [2023-12-20 17:18:08,876 INFO misc.py line 119 131400] Train: [47/100][770/800] Data 0.004 (0.005) Batch 0.308 (0.336) Remain 03:57:18 loss: 0.5693 Lr: 0.00355 [2023-12-20 17:18:09,193 INFO misc.py line 119 131400] Train: [47/100][771/800] Data 0.004 (0.005) Batch 0.315 (0.336) Remain 03:57:16 loss: 0.2288 Lr: 0.00355 [2023-12-20 17:18:09,527 INFO misc.py line 119 131400] Train: [47/100][772/800] Data 0.006 (0.005) Batch 0.337 (0.336) Remain 03:57:16 loss: 0.3962 Lr: 0.00355 [2023-12-20 17:18:09,876 INFO misc.py line 119 131400] Train: [47/100][773/800] Data 0.003 (0.005) Batch 0.349 (0.336) Remain 03:57:16 loss: 0.3002 Lr: 0.00355 [2023-12-20 17:18:10,239 INFO misc.py line 119 131400] Train: [47/100][774/800] Data 0.003 (0.005) Batch 0.362 (0.336) Remain 03:57:18 loss: 0.6710 Lr: 0.00355 [2023-12-20 17:18:10,568 INFO misc.py line 119 131400] Train: [47/100][775/800] Data 0.004 (0.005) Batch 0.329 (0.336) Remain 03:57:17 loss: 0.3298 Lr: 0.00355 [2023-12-20 17:18:10,861 INFO misc.py line 119 131400] Train: [47/100][776/800] Data 0.004 (0.005) Batch 0.293 (0.336) Remain 03:57:14 loss: 0.4088 Lr: 0.00355 [2023-12-20 17:18:11,223 INFO misc.py line 119 131400] Train: [47/100][777/800] Data 0.004 (0.005) Batch 0.359 (0.336) Remain 03:57:15 loss: 0.2422 Lr: 0.00355 [2023-12-20 17:18:11,567 INFO misc.py line 119 131400] Train: [47/100][778/800] Data 0.007 (0.005) Batch 0.346 (0.336) Remain 03:57:15 loss: 0.5720 Lr: 0.00355 [2023-12-20 17:18:11,929 INFO misc.py line 119 131400] Train: [47/100][779/800] Data 0.006 (0.005) Batch 0.363 (0.336) Remain 03:57:17 loss: 0.7712 Lr: 0.00355 [2023-12-20 17:18:12,314 INFO misc.py line 119 131400] Train: [47/100][780/800] Data 0.005 (0.005) Batch 0.385 (0.336) Remain 03:57:19 loss: 0.2730 Lr: 0.00355 [2023-12-20 17:18:12,656 INFO misc.py line 119 131400] Train: [47/100][781/800] Data 0.004 (0.005) Batch 0.339 (0.336) Remain 03:57:19 loss: 0.3140 Lr: 0.00355 [2023-12-20 17:18:12,971 INFO misc.py line 119 131400] Train: [47/100][782/800] Data 0.010 (0.005) Batch 0.317 (0.336) Remain 03:57:17 loss: 0.2054 Lr: 0.00355 [2023-12-20 17:18:13,309 INFO misc.py line 119 131400] Train: [47/100][783/800] Data 0.005 (0.005) Batch 0.339 (0.336) Remain 03:57:17 loss: 0.5369 Lr: 0.00355 [2023-12-20 17:18:13,628 INFO misc.py line 119 131400] Train: [47/100][784/800] Data 0.004 (0.005) Batch 0.319 (0.336) Remain 03:57:16 loss: 0.4777 Lr: 0.00355 [2023-12-20 17:18:13,937 INFO misc.py line 119 131400] Train: [47/100][785/800] Data 0.005 (0.005) Batch 0.310 (0.336) Remain 03:57:14 loss: 0.4579 Lr: 0.00355 [2023-12-20 17:18:14,296 INFO misc.py line 119 131400] Train: [47/100][786/800] Data 0.004 (0.005) Batch 0.358 (0.336) Remain 03:57:15 loss: 0.2099 Lr: 0.00355 [2023-12-20 17:18:14,625 INFO misc.py line 119 131400] Train: [47/100][787/800] Data 0.005 (0.005) Batch 0.328 (0.336) Remain 03:57:14 loss: 0.3684 Lr: 0.00355 [2023-12-20 17:18:14,964 INFO misc.py line 119 131400] Train: [47/100][788/800] Data 0.006 (0.005) Batch 0.340 (0.336) Remain 03:57:14 loss: 0.4679 Lr: 0.00355 [2023-12-20 17:18:15,313 INFO misc.py line 119 131400] Train: [47/100][789/800] Data 0.005 (0.005) Batch 0.350 (0.336) Remain 03:57:15 loss: 0.3423 Lr: 0.00355 [2023-12-20 17:18:15,659 INFO misc.py line 119 131400] Train: [47/100][790/800] Data 0.003 (0.005) Batch 0.342 (0.336) Remain 03:57:15 loss: 0.2853 Lr: 0.00355 [2023-12-20 17:18:15,984 INFO misc.py line 119 131400] Train: [47/100][791/800] Data 0.008 (0.005) Batch 0.328 (0.336) Remain 03:57:14 loss: 0.4170 Lr: 0.00355 [2023-12-20 17:18:16,285 INFO misc.py line 119 131400] Train: [47/100][792/800] Data 0.005 (0.005) Batch 0.301 (0.336) Remain 03:57:12 loss: 0.2718 Lr: 0.00355 [2023-12-20 17:18:16,607 INFO misc.py line 119 131400] Train: [47/100][793/800] Data 0.006 (0.005) Batch 0.322 (0.336) Remain 03:57:11 loss: 0.3082 Lr: 0.00355 [2023-12-20 17:18:16,919 INFO misc.py line 119 131400] Train: [47/100][794/800] Data 0.005 (0.005) Batch 0.312 (0.336) Remain 03:57:09 loss: 0.3098 Lr: 0.00355 [2023-12-20 17:18:17,204 INFO misc.py line 119 131400] Train: [47/100][795/800] Data 0.003 (0.005) Batch 0.284 (0.335) Remain 03:57:06 loss: 0.2261 Lr: 0.00355 [2023-12-20 17:18:17,530 INFO misc.py line 119 131400] Train: [47/100][796/800] Data 0.005 (0.005) Batch 0.327 (0.335) Remain 03:57:05 loss: 0.2689 Lr: 0.00355 [2023-12-20 17:18:17,842 INFO misc.py line 119 131400] Train: [47/100][797/800] Data 0.004 (0.005) Batch 0.313 (0.335) Remain 03:57:04 loss: 0.5274 Lr: 0.00355 [2023-12-20 17:18:18,095 INFO misc.py line 119 131400] Train: [47/100][798/800] Data 0.003 (0.005) Batch 0.252 (0.335) Remain 03:56:59 loss: 0.3007 Lr: 0.00354 [2023-12-20 17:18:18,432 INFO misc.py line 119 131400] Train: [47/100][799/800] Data 0.003 (0.005) Batch 0.338 (0.335) Remain 03:56:59 loss: 0.2219 Lr: 0.00354 [2023-12-20 17:18:18,734 INFO misc.py line 119 131400] Train: [47/100][800/800] Data 0.003 (0.005) Batch 0.300 (0.335) Remain 03:56:57 loss: 0.3793 Lr: 0.00354 [2023-12-20 17:18:18,735 INFO misc.py line 136 131400] Train result: loss: 0.3688 [2023-12-20 17:18:18,735 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:18:40,093 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1867 [2023-12-20 17:18:40,411 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1875 [2023-12-20 17:18:40,504 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4261 [2023-12-20 17:18:40,613 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3450 [2023-12-20 17:18:40,729 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4073 [2023-12-20 17:18:40,836 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0528 [2023-12-20 17:18:40,939 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0945 [2023-12-20 17:18:41,057 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9474 [2023-12-20 17:18:41,141 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3139 [2023-12-20 17:18:41,230 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3586 [2023-12-20 17:18:41,323 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5371 [2023-12-20 17:18:41,461 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3878 [2023-12-20 17:18:41,583 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5420 [2023-12-20 17:18:41,741 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2333 [2023-12-20 17:18:41,837 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1719 [2023-12-20 17:18:41,975 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0479 [2023-12-20 17:18:42,106 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.4125 [2023-12-20 17:18:42,221 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.1455 [2023-12-20 17:18:42,337 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1314 [2023-12-20 17:18:42,419 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4128 [2023-12-20 17:18:42,532 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4901 [2023-12-20 17:18:42,692 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.2080 [2023-12-20 17:18:42,815 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8080 [2023-12-20 17:18:42,963 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1571 [2023-12-20 17:18:43,117 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2055 [2023-12-20 17:18:43,219 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7780 [2023-12-20 17:18:43,378 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4359 [2023-12-20 17:18:43,507 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5656 [2023-12-20 17:18:43,608 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6915 [2023-12-20 17:18:43,756 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.1992 [2023-12-20 17:18:43,859 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7401 [2023-12-20 17:18:43,985 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5786 [2023-12-20 17:18:44,077 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1384 [2023-12-20 17:18:44,150 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1901 [2023-12-20 17:18:44,247 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9172 [2023-12-20 17:18:44,346 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6825 [2023-12-20 17:18:44,480 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8994 [2023-12-20 17:18:44,590 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1192 [2023-12-20 17:18:44,672 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5819 [2023-12-20 17:18:44,815 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3824 [2023-12-20 17:18:44,966 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0170 [2023-12-20 17:18:45,066 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1333 [2023-12-20 17:18:45,188 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3722 [2023-12-20 17:18:45,331 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9275 [2023-12-20 17:18:45,449 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5588 [2023-12-20 17:18:45,558 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7128 [2023-12-20 17:18:45,727 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3025 [2023-12-20 17:18:45,823 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3532 [2023-12-20 17:18:45,971 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4620 [2023-12-20 17:18:46,065 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2385 [2023-12-20 17:18:46,149 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8900 [2023-12-20 17:18:46,256 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4199 [2023-12-20 17:18:46,410 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9403 [2023-12-20 17:18:46,556 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2618 [2023-12-20 17:18:46,662 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0888 [2023-12-20 17:18:46,756 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5954 [2023-12-20 17:18:46,860 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3960 [2023-12-20 17:18:47,032 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2030 [2023-12-20 17:18:47,131 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5636 [2023-12-20 17:18:47,229 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5900 [2023-12-20 17:18:47,330 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2491 [2023-12-20 17:18:47,428 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3852 [2023-12-20 17:18:47,524 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8055 [2023-12-20 17:18:47,630 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7249 [2023-12-20 17:18:47,757 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4792 [2023-12-20 17:18:47,859 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2467 [2023-12-20 17:18:47,963 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3851 [2023-12-20 17:18:48,055 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0124 [2023-12-20 17:18:48,156 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2919 [2023-12-20 17:18:48,254 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0124 [2023-12-20 17:18:48,363 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7011 [2023-12-20 17:18:48,467 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5682 [2023-12-20 17:18:48,606 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1765 [2023-12-20 17:18:48,702 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6289 [2023-12-20 17:18:48,816 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6128 [2023-12-20 17:18:48,918 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6899 [2023-12-20 17:18:49,006 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2271 [2023-12-20 17:18:49,161 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0807 [2023-12-20 17:18:50,560 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7431/0.8477/0.9104. [2023-12-20 17:18:50,560 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8652/0.9262 [2023-12-20 17:18:50,560 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9648/0.9863 [2023-12-20 17:18:50,560 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6697/0.7557 [2023-12-20 17:18:50,560 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8078/0.8522 [2023-12-20 17:18:50,560 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9116/0.9534 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8284/0.9416 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7567/0.8161 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6658/0.8150 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6746/0.8430 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8116/0.9413 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3626/0.5409 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6883/0.8395 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6835/0.9119 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7136/0.8893 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6521/0.7767 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6924/0.7445 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9389/0.9832 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6692/0.7969 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8881/0.9283 [2023-12-20 17:18:50,561 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6175/0.7122 [2023-12-20 17:18:50,562 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:18:50,563 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7431 [2023-12-20 17:18:50,563 INFO misc.py line 165 131400] Currently Best mIoU: 0.7431 [2023-12-20 17:18:50,563 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:18:59,979 INFO misc.py line 119 131400] Train: [48/100][1/800] Data 1.314 (1.314) Batch 1.662 (1.662) Remain 19:34:30 loss: 0.2587 Lr: 0.00354 [2023-12-20 17:19:00,275 INFO misc.py line 119 131400] Train: [48/100][2/800] Data 0.003 (0.003) Batch 0.296 (0.296) Remain 03:29:29 loss: 0.2854 Lr: 0.00354 [2023-12-20 17:19:00,577 INFO misc.py line 119 131400] Train: [48/100][3/800] Data 0.003 (0.003) Batch 0.302 (0.302) Remain 03:33:39 loss: 0.1957 Lr: 0.00354 [2023-12-20 17:19:00,899 INFO misc.py line 119 131400] Train: [48/100][4/800] Data 0.003 (0.003) Batch 0.321 (0.321) Remain 03:46:51 loss: 0.3900 Lr: 0.00354 [2023-12-20 17:19:01,222 INFO misc.py line 119 131400] Train: [48/100][5/800] Data 0.004 (0.003) Batch 0.324 (0.323) Remain 03:47:53 loss: 0.4501 Lr: 0.00354 [2023-12-20 17:19:01,553 INFO misc.py line 119 131400] Train: [48/100][6/800] Data 0.002 (0.003) Batch 0.330 (0.325) Remain 03:49:44 loss: 0.3129 Lr: 0.00354 [2023-12-20 17:19:01,892 INFO misc.py line 119 131400] Train: [48/100][7/800] Data 0.004 (0.003) Batch 0.339 (0.328) Remain 03:52:05 loss: 0.5271 Lr: 0.00354 [2023-12-20 17:19:02,185 INFO misc.py line 119 131400] Train: [48/100][8/800] Data 0.004 (0.003) Batch 0.293 (0.321) Remain 03:47:07 loss: 0.2451 Lr: 0.00354 [2023-12-20 17:19:02,527 INFO misc.py line 119 131400] Train: 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(0.326) Remain 03:50:00 loss: 0.3338 Lr: 0.00354 [2023-12-20 17:19:04,806 INFO misc.py line 119 131400] Train: [48/100][16/800] Data 0.005 (0.004) Batch 0.321 (0.325) Remain 03:49:45 loss: 0.5290 Lr: 0.00354 [2023-12-20 17:19:05,133 INFO misc.py line 119 131400] Train: [48/100][17/800] Data 0.004 (0.004) Batch 0.326 (0.325) Remain 03:49:47 loss: 0.2346 Lr: 0.00354 [2023-12-20 17:19:05,443 INFO misc.py line 119 131400] Train: [48/100][18/800] Data 0.004 (0.004) Batch 0.312 (0.324) Remain 03:49:08 loss: 0.2081 Lr: 0.00354 [2023-12-20 17:19:05,770 INFO misc.py line 119 131400] Train: [48/100][19/800] Data 0.003 (0.004) Batch 0.322 (0.324) Remain 03:49:03 loss: 0.5480 Lr: 0.00354 [2023-12-20 17:19:06,110 INFO misc.py line 119 131400] Train: [48/100][20/800] Data 0.008 (0.004) Batch 0.344 (0.325) Remain 03:49:50 loss: 0.3824 Lr: 0.00354 [2023-12-20 17:19:06,458 INFO misc.py line 119 131400] Train: [48/100][21/800] Data 0.004 (0.004) Batch 0.348 (0.327) Remain 03:50:43 loss: 0.4650 Lr: 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line 119 131400] Train: [48/100][28/800] Data 0.004 (0.004) Batch 0.346 (0.329) Remain 03:52:13 loss: 0.5500 Lr: 0.00354 [2023-12-20 17:19:09,140 INFO misc.py line 119 131400] Train: [48/100][29/800] Data 0.003 (0.004) Batch 0.340 (0.329) Remain 03:52:32 loss: 0.4729 Lr: 0.00354 [2023-12-20 17:19:09,490 INFO misc.py line 119 131400] Train: [48/100][30/800] Data 0.005 (0.004) Batch 0.351 (0.330) Remain 03:53:05 loss: 0.4786 Lr: 0.00354 [2023-12-20 17:19:09,833 INFO misc.py line 119 131400] Train: [48/100][31/800] Data 0.004 (0.004) Batch 0.343 (0.331) Remain 03:53:23 loss: 0.2609 Lr: 0.00354 [2023-12-20 17:19:10,163 INFO misc.py line 119 131400] Train: [48/100][32/800] Data 0.006 (0.004) Batch 0.330 (0.330) Remain 03:53:22 loss: 0.4301 Lr: 0.00354 [2023-12-20 17:19:10,487 INFO misc.py line 119 131400] Train: [48/100][33/800] Data 0.004 (0.004) Batch 0.324 (0.330) Remain 03:53:12 loss: 0.4418 Lr: 0.00354 [2023-12-20 17:19:10,789 INFO misc.py line 119 131400] Train: [48/100][34/800] Data 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03:54:16 loss: 0.2262 Lr: 0.00354 [2023-12-20 17:19:13,196 INFO misc.py line 119 131400] Train: [48/100][41/800] Data 0.004 (0.004) Batch 0.339 (0.332) Remain 03:54:23 loss: 0.3012 Lr: 0.00354 [2023-12-20 17:19:13,505 INFO misc.py line 119 131400] Train: [48/100][42/800] Data 0.005 (0.004) Batch 0.311 (0.331) Remain 03:54:00 loss: 0.2130 Lr: 0.00354 [2023-12-20 17:19:13,829 INFO misc.py line 119 131400] Train: [48/100][43/800] Data 0.003 (0.004) Batch 0.324 (0.331) Remain 03:53:52 loss: 0.5905 Lr: 0.00354 [2023-12-20 17:19:14,138 INFO misc.py line 119 131400] Train: [48/100][44/800] Data 0.003 (0.004) Batch 0.309 (0.331) Remain 03:53:29 loss: 0.3050 Lr: 0.00354 [2023-12-20 17:19:14,495 INFO misc.py line 119 131400] Train: [48/100][45/800] Data 0.003 (0.004) Batch 0.357 (0.331) Remain 03:53:55 loss: 0.3502 Lr: 0.00354 [2023-12-20 17:19:14,818 INFO misc.py line 119 131400] Train: [48/100][46/800] Data 0.004 (0.004) Batch 0.322 (0.331) Remain 03:53:45 loss: 0.2569 Lr: 0.00354 [2023-12-20 17:19:15,162 INFO misc.py line 119 131400] Train: [48/100][47/800] Data 0.004 (0.004) Batch 0.345 (0.331) Remain 03:53:58 loss: 0.5143 Lr: 0.00354 [2023-12-20 17:19:15,537 INFO misc.py line 119 131400] Train: [48/100][48/800] Data 0.005 (0.004) Batch 0.376 (0.332) Remain 03:54:39 loss: 0.3365 Lr: 0.00354 [2023-12-20 17:19:15,869 INFO misc.py line 119 131400] Train: [48/100][49/800] Data 0.003 (0.004) Batch 0.332 (0.332) Remain 03:54:38 loss: 0.3691 Lr: 0.00354 [2023-12-20 17:19:16,220 INFO misc.py line 119 131400] Train: [48/100][50/800] Data 0.003 (0.004) Batch 0.351 (0.333) Remain 03:54:54 loss: 0.2205 Lr: 0.00354 [2023-12-20 17:19:16,575 INFO misc.py line 119 131400] Train: [48/100][51/800] Data 0.003 (0.004) Batch 0.355 (0.333) Remain 03:55:13 loss: 0.2379 Lr: 0.00354 [2023-12-20 17:19:16,896 INFO misc.py line 119 131400] Train: [48/100][52/800] Data 0.004 (0.004) Batch 0.321 (0.333) Remain 03:55:02 loss: 0.2029 Lr: 0.00354 [2023-12-20 17:19:17,220 INFO misc.py line 119 131400] Train: [48/100][53/800] Data 0.004 (0.004) Batch 0.324 (0.333) Remain 03:54:55 loss: 0.3538 Lr: 0.00354 [2023-12-20 17:19:17,537 INFO misc.py line 119 131400] Train: [48/100][54/800] Data 0.004 (0.004) Batch 0.313 (0.332) Remain 03:54:38 loss: 0.2784 Lr: 0.00354 [2023-12-20 17:19:17,884 INFO misc.py line 119 131400] Train: [48/100][55/800] Data 0.007 (0.004) Batch 0.350 (0.333) Remain 03:54:52 loss: 0.1950 Lr: 0.00354 [2023-12-20 17:19:18,241 INFO misc.py line 119 131400] Train: [48/100][56/800] Data 0.005 (0.004) Batch 0.357 (0.333) Remain 03:55:10 loss: 0.2398 Lr: 0.00354 [2023-12-20 17:19:18,615 INFO misc.py line 119 131400] Train: [48/100][57/800] Data 0.004 (0.004) Batch 0.370 (0.334) Remain 03:55:39 loss: 0.3926 Lr: 0.00354 [2023-12-20 17:19:18,965 INFO misc.py line 119 131400] Train: [48/100][58/800] Data 0.009 (0.004) Batch 0.355 (0.334) Remain 03:55:55 loss: 0.3196 Lr: 0.00354 [2023-12-20 17:19:19,308 INFO misc.py line 119 131400] Train: [48/100][59/800] Data 0.004 (0.004) Batch 0.342 (0.334) Remain 03:56:00 loss: 0.4357 Lr: 0.00354 [2023-12-20 17:19:19,685 INFO misc.py line 119 131400] Train: [48/100][60/800] Data 0.004 (0.004) Batch 0.375 (0.335) Remain 03:56:30 loss: 0.3123 Lr: 0.00354 [2023-12-20 17:19:20,049 INFO misc.py line 119 131400] Train: [48/100][61/800] Data 0.006 (0.004) Batch 0.368 (0.336) Remain 03:56:54 loss: 0.4704 Lr: 0.00354 [2023-12-20 17:19:20,378 INFO misc.py line 119 131400] Train: [48/100][62/800] Data 0.003 (0.004) Batch 0.328 (0.336) Remain 03:56:48 loss: 0.2317 Lr: 0.00354 [2023-12-20 17:19:20,723 INFO misc.py line 119 131400] Train: [48/100][63/800] Data 0.003 (0.004) Batch 0.344 (0.336) Remain 03:56:54 loss: 0.3138 Lr: 0.00354 [2023-12-20 17:19:21,070 INFO misc.py line 119 131400] Train: [48/100][64/800] Data 0.004 (0.004) Batch 0.347 (0.336) Remain 03:57:01 loss: 0.3660 Lr: 0.00354 [2023-12-20 17:19:21,399 INFO misc.py line 119 131400] Train: [48/100][65/800] Data 0.005 (0.004) Batch 0.330 (0.336) Remain 03:56:56 loss: 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INFO misc.py line 119 131400] Train: [48/100][72/800] Data 0.003 (0.004) Batch 0.332 (0.336) Remain 03:56:57 loss: 0.2975 Lr: 0.00354 [2023-12-20 17:19:24,107 INFO misc.py line 119 131400] Train: [48/100][73/800] Data 0.003 (0.004) Batch 0.352 (0.336) Remain 03:57:07 loss: 0.4120 Lr: 0.00354 [2023-12-20 17:19:24,413 INFO misc.py line 119 131400] Train: [48/100][74/800] Data 0.003 (0.004) Batch 0.306 (0.336) Remain 03:56:49 loss: 0.1167 Lr: 0.00354 [2023-12-20 17:19:24,725 INFO misc.py line 119 131400] Train: [48/100][75/800] Data 0.003 (0.004) Batch 0.311 (0.335) Remain 03:56:34 loss: 0.4266 Lr: 0.00354 [2023-12-20 17:19:25,053 INFO misc.py line 119 131400] Train: [48/100][76/800] Data 0.004 (0.004) Batch 0.329 (0.335) Remain 03:56:30 loss: 0.4779 Lr: 0.00354 [2023-12-20 17:19:25,377 INFO misc.py line 119 131400] Train: [48/100][77/800] Data 0.004 (0.004) Batch 0.324 (0.335) Remain 03:56:23 loss: 0.1644 Lr: 0.00354 [2023-12-20 17:19:25,672 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [48/100][738/800] Data 0.003 (0.004) Batch 0.301 (0.333) Remain 03:51:20 loss: 0.2815 Lr: 0.00345 [2023-12-20 17:23:05,793 INFO misc.py line 119 131400] Train: [48/100][739/800] Data 0.003 (0.004) Batch 0.327 (0.333) Remain 03:51:20 loss: 0.3621 Lr: 0.00345 [2023-12-20 17:23:06,155 INFO misc.py line 119 131400] Train: [48/100][740/800] Data 0.005 (0.004) Batch 0.363 (0.333) Remain 03:51:21 loss: 0.5952 Lr: 0.00345 [2023-12-20 17:23:06,493 INFO misc.py line 119 131400] Train: [48/100][741/800] Data 0.005 (0.004) Batch 0.338 (0.333) Remain 03:51:21 loss: 0.2803 Lr: 0.00345 [2023-12-20 17:23:06,847 INFO misc.py line 119 131400] Train: [48/100][742/800] Data 0.004 (0.004) Batch 0.354 (0.333) Remain 03:51:22 loss: 0.2833 Lr: 0.00345 [2023-12-20 17:23:07,178 INFO misc.py line 119 131400] Train: [48/100][743/800] Data 0.004 (0.004) Batch 0.331 (0.333) Remain 03:51:21 loss: 0.3695 Lr: 0.00345 [2023-12-20 17:23:07,466 INFO misc.py line 119 131400] Train: 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Batch 0.359 (0.333) Remain 03:51:17 loss: 0.2907 Lr: 0.00345 [2023-12-20 17:23:09,785 INFO misc.py line 119 131400] Train: [48/100][751/800] Data 0.004 (0.004) Batch 0.308 (0.333) Remain 03:51:15 loss: 0.1824 Lr: 0.00345 [2023-12-20 17:23:10,096 INFO misc.py line 119 131400] Train: [48/100][752/800] Data 0.003 (0.004) Batch 0.311 (0.333) Remain 03:51:14 loss: 0.7298 Lr: 0.00345 [2023-12-20 17:23:10,425 INFO misc.py line 119 131400] Train: [48/100][753/800] Data 0.003 (0.004) Batch 0.327 (0.333) Remain 03:51:13 loss: 0.5016 Lr: 0.00345 [2023-12-20 17:23:10,747 INFO misc.py line 119 131400] Train: [48/100][754/800] Data 0.005 (0.004) Batch 0.323 (0.333) Remain 03:51:12 loss: 0.2291 Lr: 0.00345 [2023-12-20 17:23:11,074 INFO misc.py line 119 131400] Train: [48/100][755/800] Data 0.003 (0.004) Batch 0.327 (0.333) Remain 03:51:12 loss: 0.4728 Lr: 0.00345 [2023-12-20 17:23:11,410 INFO misc.py line 119 131400] Train: [48/100][756/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 03:51:11 loss: 0.2857 Lr: 0.00345 [2023-12-20 17:23:11,735 INFO misc.py line 119 131400] Train: [48/100][757/800] Data 0.007 (0.004) Batch 0.328 (0.333) Remain 03:51:11 loss: 0.3328 Lr: 0.00345 [2023-12-20 17:23:12,058 INFO misc.py line 119 131400] Train: [48/100][758/800] Data 0.003 (0.004) Batch 0.323 (0.333) Remain 03:51:10 loss: 0.1893 Lr: 0.00345 [2023-12-20 17:23:12,390 INFO misc.py line 119 131400] Train: [48/100][759/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 03:51:09 loss: 0.4104 Lr: 0.00345 [2023-12-20 17:23:12,728 INFO misc.py line 119 131400] Train: [48/100][760/800] Data 0.003 (0.004) Batch 0.337 (0.333) Remain 03:51:09 loss: 0.2871 Lr: 0.00345 [2023-12-20 17:23:13,056 INFO misc.py line 119 131400] Train: [48/100][761/800] Data 0.003 (0.004) Batch 0.328 (0.333) Remain 03:51:09 loss: 0.3985 Lr: 0.00345 [2023-12-20 17:23:13,390 INFO misc.py line 119 131400] Train: [48/100][762/800] Data 0.003 (0.004) Batch 0.333 (0.333) Remain 03:51:08 loss: 0.4651 Lr: 0.00345 [2023-12-20 17:23:13,678 INFO misc.py line 119 131400] Train: [48/100][763/800] Data 0.004 (0.004) Batch 0.289 (0.333) Remain 03:51:06 loss: 0.3000 Lr: 0.00345 [2023-12-20 17:23:14,047 INFO misc.py line 119 131400] Train: [48/100][764/800] Data 0.003 (0.004) Batch 0.368 (0.333) Remain 03:51:07 loss: 0.1907 Lr: 0.00345 [2023-12-20 17:23:14,401 INFO misc.py line 119 131400] Train: [48/100][765/800] Data 0.005 (0.004) Batch 0.347 (0.333) Remain 03:51:08 loss: 0.2684 Lr: 0.00345 [2023-12-20 17:23:14,719 INFO misc.py line 119 131400] Train: [48/100][766/800] Data 0.011 (0.004) Batch 0.326 (0.333) Remain 03:51:07 loss: 0.4198 Lr: 0.00345 [2023-12-20 17:23:15,035 INFO misc.py line 119 131400] Train: [48/100][767/800] Data 0.004 (0.004) Batch 0.316 (0.333) Remain 03:51:06 loss: 0.2616 Lr: 0.00345 [2023-12-20 17:23:15,394 INFO misc.py line 119 131400] Train: [48/100][768/800] Data 0.004 (0.004) Batch 0.359 (0.333) Remain 03:51:07 loss: 0.1779 Lr: 0.00345 [2023-12-20 17:23:15,746 INFO misc.py line 119 131400] Train: [48/100][769/800] Data 0.004 (0.004) Batch 0.352 (0.333) Remain 03:51:07 loss: 0.2219 Lr: 0.00345 [2023-12-20 17:23:16,106 INFO misc.py line 119 131400] Train: [48/100][770/800] Data 0.003 (0.004) Batch 0.358 (0.333) Remain 03:51:08 loss: 0.3102 Lr: 0.00345 [2023-12-20 17:23:16,442 INFO misc.py line 119 131400] Train: [48/100][771/800] Data 0.006 (0.004) Batch 0.338 (0.333) Remain 03:51:08 loss: 0.2904 Lr: 0.00345 [2023-12-20 17:23:16,780 INFO misc.py line 119 131400] Train: [48/100][772/800] Data 0.003 (0.004) Batch 0.337 (0.333) Remain 03:51:08 loss: 0.2314 Lr: 0.00345 [2023-12-20 17:23:17,139 INFO misc.py line 119 131400] Train: [48/100][773/800] Data 0.004 (0.004) Batch 0.359 (0.333) Remain 03:51:09 loss: 0.4254 Lr: 0.00345 [2023-12-20 17:23:17,513 INFO misc.py line 119 131400] Train: [48/100][774/800] Data 0.003 (0.004) Batch 0.373 (0.333) Remain 03:51:11 loss: 0.3241 Lr: 0.00345 [2023-12-20 17:23:17,877 INFO misc.py line 119 131400] Train: [48/100][775/800] Data 0.005 (0.004) Batch 0.363 (0.333) Remain 03:51:12 loss: 0.5918 Lr: 0.00345 [2023-12-20 17:23:18,216 INFO misc.py line 119 131400] Train: [48/100][776/800] Data 0.006 (0.004) Batch 0.341 (0.333) Remain 03:51:13 loss: 0.1746 Lr: 0.00345 [2023-12-20 17:23:18,548 INFO misc.py line 119 131400] Train: [48/100][777/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 03:51:12 loss: 0.1720 Lr: 0.00345 [2023-12-20 17:23:18,871 INFO misc.py line 119 131400] Train: [48/100][778/800] Data 0.003 (0.004) Batch 0.323 (0.333) Remain 03:51:11 loss: 0.3783 Lr: 0.00345 [2023-12-20 17:23:19,205 INFO misc.py line 119 131400] Train: [48/100][779/800] Data 0.003 (0.004) Batch 0.334 (0.333) Remain 03:51:11 loss: 0.2915 Lr: 0.00345 [2023-12-20 17:23:19,540 INFO misc.py line 119 131400] Train: [48/100][780/800] Data 0.004 (0.004) Batch 0.335 (0.333) Remain 03:51:11 loss: 0.1911 Lr: 0.00345 [2023-12-20 17:23:19,883 INFO misc.py line 119 131400] Train: [48/100][781/800] Data 0.003 (0.004) Batch 0.343 (0.333) Remain 03:51:11 loss: 0.5811 Lr: 0.00345 [2023-12-20 17:23:20,207 INFO misc.py line 119 131400] Train: [48/100][782/800] Data 0.004 (0.004) Batch 0.325 (0.333) Remain 03:51:10 loss: 0.3458 Lr: 0.00345 [2023-12-20 17:23:20,547 INFO misc.py line 119 131400] Train: [48/100][783/800] Data 0.003 (0.004) Batch 0.339 (0.333) Remain 03:51:10 loss: 0.2041 Lr: 0.00345 [2023-12-20 17:23:20,894 INFO misc.py line 119 131400] Train: [48/100][784/800] Data 0.004 (0.004) Batch 0.347 (0.333) Remain 03:51:10 loss: 0.2490 Lr: 0.00345 [2023-12-20 17:23:21,221 INFO misc.py line 119 131400] Train: [48/100][785/800] Data 0.004 (0.004) Batch 0.327 (0.333) Remain 03:51:10 loss: 0.5488 Lr: 0.00345 [2023-12-20 17:23:21,515 INFO misc.py line 119 131400] Train: [48/100][786/800] Data 0.003 (0.004) Batch 0.295 (0.333) Remain 03:51:07 loss: 0.3051 Lr: 0.00345 [2023-12-20 17:23:21,854 INFO misc.py line 119 131400] Train: [48/100][787/800] Data 0.002 (0.004) Batch 0.339 (0.333) Remain 03:51:07 loss: 0.5676 Lr: 0.00345 [2023-12-20 17:23:22,220 INFO misc.py line 119 131400] Train: [48/100][788/800] Data 0.004 (0.004) Batch 0.365 (0.333) Remain 03:51:09 loss: 0.1805 Lr: 0.00345 [2023-12-20 17:23:22,551 INFO misc.py line 119 131400] Train: [48/100][789/800] Data 0.005 (0.004) Batch 0.332 (0.333) Remain 03:51:08 loss: 0.7057 Lr: 0.00345 [2023-12-20 17:23:22,880 INFO misc.py line 119 131400] Train: [48/100][790/800] Data 0.004 (0.004) Batch 0.329 (0.333) Remain 03:51:08 loss: 0.3148 Lr: 0.00345 [2023-12-20 17:23:23,170 INFO misc.py line 119 131400] Train: [48/100][791/800] Data 0.003 (0.004) Batch 0.291 (0.333) Remain 03:51:05 loss: 0.1808 Lr: 0.00345 [2023-12-20 17:23:23,490 INFO misc.py line 119 131400] Train: [48/100][792/800] Data 0.002 (0.004) Batch 0.320 (0.333) Remain 03:51:04 loss: 0.3452 Lr: 0.00345 [2023-12-20 17:23:23,781 INFO misc.py line 119 131400] Train: [48/100][793/800] Data 0.004 (0.004) Batch 0.291 (0.333) Remain 03:51:02 loss: 0.2033 Lr: 0.00345 [2023-12-20 17:23:24,057 INFO misc.py line 119 131400] Train: [48/100][794/800] Data 0.004 (0.004) Batch 0.276 (0.333) Remain 03:50:58 loss: 0.2488 Lr: 0.00345 [2023-12-20 17:23:24,373 INFO misc.py line 119 131400] Train: [48/100][795/800] Data 0.003 (0.004) Batch 0.316 (0.333) Remain 03:50:57 loss: 0.4309 Lr: 0.00345 [2023-12-20 17:23:24,697 INFO misc.py line 119 131400] Train: [48/100][796/800] Data 0.003 (0.004) Batch 0.323 (0.333) Remain 03:50:56 loss: 0.5289 Lr: 0.00345 [2023-12-20 17:23:25,000 INFO misc.py line 119 131400] Train: [48/100][797/800] Data 0.004 (0.004) Batch 0.304 (0.333) Remain 03:50:54 loss: 0.3187 Lr: 0.00345 [2023-12-20 17:23:25,336 INFO misc.py line 119 131400] Train: [48/100][798/800] Data 0.003 (0.004) Batch 0.336 (0.333) Remain 03:50:54 loss: 0.2232 Lr: 0.00345 [2023-12-20 17:23:25,616 INFO misc.py line 119 131400] Train: [48/100][799/800] Data 0.003 (0.004) Batch 0.280 (0.333) Remain 03:50:51 loss: 0.1667 Lr: 0.00345 [2023-12-20 17:23:25,922 INFO misc.py line 119 131400] Train: [48/100][800/800] Data 0.003 (0.004) Batch 0.306 (0.333) Remain 03:50:49 loss: 0.2978 Lr: 0.00345 [2023-12-20 17:23:25,922 INFO misc.py line 136 131400] Train result: loss: 0.3815 [2023-12-20 17:23:25,923 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:23:49,079 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1831 [2023-12-20 17:23:50,008 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1733 [2023-12-20 17:23:50,103 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5012 [2023-12-20 17:23:50,210 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.0407 [2023-12-20 17:23:50,329 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5926 [2023-12-20 17:23:50,434 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.8918 [2023-12-20 17:23:50,531 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.5605 [2023-12-20 17:23:50,647 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0265 [2023-12-20 17:23:50,742 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2268 [2023-12-20 17:23:50,830 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4039 [2023-12-20 17:23:50,922 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6028 [2023-12-20 17:23:51,059 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3364 [2023-12-20 17:23:51,180 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2821 [2023-12-20 17:23:51,335 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2086 [2023-12-20 17:23:51,438 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1456 [2023-12-20 17:23:51,588 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6349 [2023-12-20 17:23:51,703 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3204 [2023-12-20 17:23:51,818 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5599 [2023-12-20 17:23:51,936 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1239 [2023-12-20 17:23:52,023 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3768 [2023-12-20 17:23:52,130 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.5026 [2023-12-20 17:23:52,293 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1651 [2023-12-20 17:23:52,425 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5200 [2023-12-20 17:23:52,572 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1954 [2023-12-20 17:23:52,717 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1788 [2023-12-20 17:23:52,812 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8850 [2023-12-20 17:23:52,979 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7811 [2023-12-20 17:23:53,114 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6025 [2023-12-20 17:23:53,208 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5184 [2023-12-20 17:23:53,360 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5126 [2023-12-20 17:23:53,468 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7387 [2023-12-20 17:23:53,594 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5294 [2023-12-20 17:23:53,697 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1431 [2023-12-20 17:23:53,775 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1922 [2023-12-20 17:23:53,872 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6887 [2023-12-20 17:23:53,964 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3858 [2023-12-20 17:23:54,092 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9211 [2023-12-20 17:23:54,208 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1136 [2023-12-20 17:23:54,288 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6525 [2023-12-20 17:23:54,429 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4087 [2023-12-20 17:23:54,575 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0202 [2023-12-20 17:23:54,682 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0947 [2023-12-20 17:23:54,800 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3459 [2023-12-20 17:23:54,945 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8490 [2023-12-20 17:23:55,065 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.0025 [2023-12-20 17:23:55,174 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5281 [2023-12-20 17:23:55,344 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3444 [2023-12-20 17:23:55,436 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3098 [2023-12-20 17:23:55,581 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4566 [2023-12-20 17:23:55,673 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1126 [2023-12-20 17:23:55,749 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5702 [2023-12-20 17:23:55,857 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0639 [2023-12-20 17:23:56,002 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9158 [2023-12-20 17:23:56,138 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2858 [2023-12-20 17:23:56,243 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4493 [2023-12-20 17:23:56,329 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6837 [2023-12-20 17:23:56,434 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3503 [2023-12-20 17:23:56,602 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3182 [2023-12-20 17:23:56,703 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.7010 [2023-12-20 17:23:56,798 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1856 [2023-12-20 17:23:56,895 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4351 [2023-12-20 17:23:56,986 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2675 [2023-12-20 17:23:57,073 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7527 [2023-12-20 17:23:57,175 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6595 [2023-12-20 17:23:57,304 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7291 [2023-12-20 17:23:57,388 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3018 [2023-12-20 17:23:57,488 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2756 [2023-12-20 17:23:57,586 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0142 [2023-12-20 17:23:57,673 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2663 [2023-12-20 17:23:57,758 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0136 [2023-12-20 17:23:57,857 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6210 [2023-12-20 17:23:57,950 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7552 [2023-12-20 17:23:58,084 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1634 [2023-12-20 17:23:58,178 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6631 [2023-12-20 17:23:58,297 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5738 [2023-12-20 17:23:58,401 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7613 [2023-12-20 17:23:58,490 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3056 [2023-12-20 17:23:58,643 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0867 [2023-12-20 17:24:00,024 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7511/0.8321/0.9127. [2023-12-20 17:24:00,024 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8668/0.9395 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9628/0.9869 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6811/0.8530 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8102/0.8513 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9160/0.9537 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8634/0.9321 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7413/0.8163 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6615/0.8127 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6769/0.8300 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8502/0.9275 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3818/0.4971 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6827/0.7746 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6985/0.8434 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7606/0.7983 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6340/0.6884 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7247/0.7825 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9433/0.9736 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6736/0.7718 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8843/0.9138 [2023-12-20 17:24:00,025 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6091/0.6957 [2023-12-20 17:24:00,026 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:24:00,027 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7511 [2023-12-20 17:24:00,027 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:24:00,027 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:24:06,150 INFO misc.py line 119 131400] Train: [49/100][1/800] Data 1.519 (1.519) Batch 1.859 (1.859) Remain 21:29:04 loss: 0.2071 Lr: 0.00345 [2023-12-20 17:24:06,602 INFO misc.py line 119 131400] Train: [49/100][2/800] Data 0.003 (0.003) Batch 0.452 (0.452) Remain 05:13:04 loss: 0.5064 Lr: 0.00345 [2023-12-20 17:24:06,906 INFO misc.py line 119 131400] Train: [49/100][3/800] Data 0.003 (0.003) Batch 0.304 (0.304) Remain 03:30:46 loss: 0.2572 Lr: 0.00345 [2023-12-20 17:24:07,235 INFO misc.py line 119 131400] Train: [49/100][4/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 03:48:02 loss: 0.2889 Lr: 0.00345 [2023-12-20 17:24:07,570 INFO misc.py line 119 131400] Train: [49/100][5/800] Data 0.003 (0.003) Batch 0.335 (0.332) Remain 03:50:17 loss: 0.2560 Lr: 0.00345 [2023-12-20 17:24:07,861 INFO misc.py line 119 131400] Train: [49/100][6/800] Data 0.003 (0.003) Batch 0.291 (0.318) Remain 03:40:42 loss: 0.1403 Lr: 0.00345 [2023-12-20 17:24:08,154 INFO misc.py line 119 131400] Train: [49/100][7/800] Data 0.003 (0.003) Batch 0.293 (0.312) Remain 03:36:22 loss: 0.6052 Lr: 0.00345 [2023-12-20 17:24:08,501 INFO misc.py line 119 131400] Train: [49/100][8/800] Data 0.003 (0.003) Batch 0.346 (0.319) Remain 03:41:01 loss: 0.4797 Lr: 0.00345 [2023-12-20 17:24:08,797 INFO misc.py line 119 131400] Train: [49/100][9/800] Data 0.004 (0.003) Batch 0.297 (0.315) Remain 03:38:27 loss: 0.2212 Lr: 0.00345 [2023-12-20 17:24:09,092 INFO misc.py line 119 131400] Train: [49/100][10/800] Data 0.005 (0.004) Batch 0.295 (0.312) Remain 03:36:25 loss: 0.2463 Lr: 0.00345 [2023-12-20 17:24:09,420 INFO misc.py line 119 131400] Train: [49/100][11/800] Data 0.004 (0.004) Batch 0.328 (0.314) Remain 03:37:47 loss: 0.2104 Lr: 0.00345 [2023-12-20 17:24:09,724 INFO misc.py line 119 131400] Train: [49/100][12/800] Data 0.003 (0.004) Batch 0.304 (0.313) Remain 03:37:02 loss: 0.3925 Lr: 0.00345 [2023-12-20 17:24:10,041 INFO misc.py line 119 131400] Train: [49/100][13/800] Data 0.004 (0.004) Batch 0.317 (0.314) Remain 03:37:19 loss: 0.2041 Lr: 0.00345 [2023-12-20 17:24:10,393 INFO misc.py line 119 131400] Train: [49/100][14/800] Data 0.003 (0.004) Batch 0.347 (0.317) Remain 03:39:25 loss: 0.5700 Lr: 0.00345 [2023-12-20 17:24:10,718 INFO misc.py line 119 131400] Train: [49/100][15/800] Data 0.008 (0.004) Batch 0.330 (0.318) Remain 03:40:12 loss: 0.3365 Lr: 0.00345 [2023-12-20 17:24:11,052 INFO misc.py line 119 131400] Train: [49/100][16/800] Data 0.003 (0.004) Batch 0.334 (0.319) Remain 03:41:03 loss: 0.2571 Lr: 0.00344 [2023-12-20 17:24:11,387 INFO misc.py line 119 131400] Train: [49/100][17/800] Data 0.004 (0.004) Batch 0.332 (0.320) Remain 03:41:40 loss: 0.8649 Lr: 0.00344 [2023-12-20 17:24:11,701 INFO misc.py line 119 131400] Train: [49/100][18/800] Data 0.007 (0.004) Batch 0.318 (0.320) Remain 03:41:33 loss: 0.2855 Lr: 0.00344 [2023-12-20 17:24:11,994 INFO misc.py line 119 131400] Train: [49/100][19/800] Data 0.004 (0.004) Batch 0.292 (0.318) Remain 03:40:21 loss: 0.2713 Lr: 0.00344 [2023-12-20 17:24:12,288 INFO misc.py line 119 131400] Train: [49/100][20/800] Data 0.004 (0.004) Batch 0.295 (0.317) Remain 03:39:24 loss: 0.3532 Lr: 0.00344 [2023-12-20 17:24:12,619 INFO misc.py line 119 131400] Train: [49/100][21/800] Data 0.004 (0.004) Batch 0.330 (0.317) Remain 03:39:54 loss: 0.3140 Lr: 0.00344 [2023-12-20 17:24:12,981 INFO misc.py line 119 131400] Train: [49/100][22/800] Data 0.004 (0.004) Batch 0.362 (0.320) Remain 03:41:32 loss: 0.4918 Lr: 0.00344 [2023-12-20 17:24:13,344 INFO misc.py line 119 131400] Train: [49/100][23/800] Data 0.005 (0.004) Batch 0.364 (0.322) Remain 03:43:03 loss: 0.4928 Lr: 0.00344 [2023-12-20 17:24:13,676 INFO misc.py line 119 131400] Train: [49/100][24/800] Data 0.004 (0.004) Batch 0.330 (0.322) Remain 03:43:20 loss: 0.4338 Lr: 0.00344 [2023-12-20 17:24:14,029 INFO misc.py line 119 131400] Train: [49/100][25/800] Data 0.005 (0.004) Batch 0.354 (0.324) Remain 03:44:19 loss: 0.4369 Lr: 0.00344 [2023-12-20 17:24:14,358 INFO misc.py line 119 131400] Train: [49/100][26/800] Data 0.005 (0.004) Batch 0.330 (0.324) Remain 03:44:30 loss: 0.3416 Lr: 0.00344 [2023-12-20 17:24:14,667 INFO misc.py line 119 131400] Train: [49/100][27/800] Data 0.004 (0.004) Batch 0.309 (0.323) Remain 03:44:04 loss: 0.3678 Lr: 0.00344 [2023-12-20 17:24:14,998 INFO misc.py line 119 131400] Train: [49/100][28/800] Data 0.004 (0.004) Batch 0.330 (0.324) Remain 03:44:15 loss: 0.4512 Lr: 0.00344 [2023-12-20 17:24:15,318 INFO misc.py line 119 131400] Train: [49/100][29/800] Data 0.004 (0.004) Batch 0.320 (0.324) Remain 03:44:09 loss: 0.2239 Lr: 0.00344 [2023-12-20 17:24:15,658 INFO misc.py line 119 131400] Train: [49/100][30/800] Data 0.003 (0.004) Batch 0.340 (0.324) Remain 03:44:35 loss: 0.5401 Lr: 0.00344 [2023-12-20 17:24:16,030 INFO misc.py line 119 131400] Train: [49/100][31/800] Data 0.004 (0.004) Batch 0.371 (0.326) Remain 03:45:43 loss: 0.4485 Lr: 0.00344 [2023-12-20 17:24:16,392 INFO misc.py line 119 131400] Train: [49/100][32/800] Data 0.005 (0.004) Batch 0.363 (0.327) Remain 03:46:36 loss: 0.2345 Lr: 0.00344 [2023-12-20 17:24:16,725 INFO misc.py line 119 131400] Train: [49/100][33/800] Data 0.004 (0.004) Batch 0.332 (0.327) Remain 03:46:43 loss: 0.5315 Lr: 0.00344 [2023-12-20 17:24:17,074 INFO misc.py line 119 131400] Train: [49/100][34/800] Data 0.005 (0.004) Batch 0.349 (0.328) Remain 03:47:12 loss: 0.5161 Lr: 0.00344 [2023-12-20 17:24:17,421 INFO misc.py line 119 131400] Train: [49/100][35/800] Data 0.005 (0.004) Batch 0.348 (0.329) Remain 03:47:37 loss: 0.4576 Lr: 0.00344 [2023-12-20 17:24:17,782 INFO misc.py line 119 131400] Train: [49/100][36/800] Data 0.004 (0.004) Batch 0.361 (0.330) Remain 03:48:18 loss: 0.3986 Lr: 0.00344 [2023-12-20 17:24:18,131 INFO misc.py line 119 131400] Train: [49/100][37/800] Data 0.004 (0.004) Batch 0.346 (0.330) Remain 03:48:37 loss: 0.4268 Lr: 0.00344 [2023-12-20 17:24:18,491 INFO misc.py line 119 131400] Train: [49/100][38/800] Data 0.008 (0.004) Batch 0.362 (0.331) Remain 03:49:15 loss: 0.5264 Lr: 0.00344 [2023-12-20 17:24:18,844 INFO misc.py line 119 131400] Train: [49/100][39/800] Data 0.005 (0.004) Batch 0.355 (0.332) Remain 03:49:42 loss: 0.4511 Lr: 0.00344 [2023-12-20 17:24:19,183 INFO misc.py line 119 131400] Train: [49/100][40/800] Data 0.004 (0.004) Batch 0.338 (0.332) Remain 03:49:48 loss: 0.4556 Lr: 0.00344 [2023-12-20 17:24:19,508 INFO misc.py line 119 131400] Train: [49/100][41/800] Data 0.006 (0.004) Batch 0.325 (0.332) Remain 03:49:40 loss: 0.3626 Lr: 0.00344 [2023-12-20 17:24:19,822 INFO misc.py line 119 131400] Train: [49/100][42/800] Data 0.006 (0.004) Batch 0.315 (0.331) Remain 03:49:22 loss: 0.2946 Lr: 0.00344 [2023-12-20 17:24:20,163 INFO misc.py line 119 131400] Train: [49/100][43/800] Data 0.004 (0.004) Batch 0.341 (0.331) Remain 03:49:32 loss: 0.3506 Lr: 0.00344 [2023-12-20 17:24:20,512 INFO misc.py line 119 131400] Train: [49/100][44/800] Data 0.005 (0.004) Batch 0.348 (0.332) Remain 03:49:49 loss: 0.3297 Lr: 0.00344 [2023-12-20 17:24:20,857 INFO misc.py line 119 131400] Train: [49/100][45/800] Data 0.005 (0.004) Batch 0.345 (0.332) Remain 03:50:02 loss: 0.2570 Lr: 0.00344 [2023-12-20 17:24:21,205 INFO misc.py line 119 131400] Train: [49/100][46/800] Data 0.005 (0.004) Batch 0.348 (0.333) Remain 03:50:17 loss: 0.3372 Lr: 0.00344 [2023-12-20 17:24:21,508 INFO misc.py line 119 131400] Train: [49/100][47/800] Data 0.007 (0.005) Batch 0.304 (0.332) Remain 03:49:50 loss: 0.6624 Lr: 0.00344 [2023-12-20 17:24:21,862 INFO misc.py line 119 131400] Train: [49/100][48/800] Data 0.004 (0.004) Batch 0.353 (0.332) Remain 03:50:08 loss: 0.2830 Lr: 0.00344 [2023-12-20 17:24:22,200 INFO misc.py line 119 131400] Train: [49/100][49/800] Data 0.005 (0.005) Batch 0.339 (0.332) Remain 03:50:14 loss: 0.2796 Lr: 0.00344 [2023-12-20 17:24:22,546 INFO misc.py line 119 131400] Train: [49/100][50/800] Data 0.004 (0.005) Batch 0.346 (0.333) Remain 03:50:26 loss: 0.2219 Lr: 0.00344 [2023-12-20 17:24:22,861 INFO misc.py line 119 131400] Train: [49/100][51/800] Data 0.004 (0.004) Batch 0.315 (0.332) Remain 03:50:10 loss: 0.3598 Lr: 0.00344 [2023-12-20 17:24:23,199 INFO misc.py line 119 131400] Train: [49/100][52/800] Data 0.003 (0.004) Batch 0.338 (0.333) Remain 03:50:14 loss: 0.2281 Lr: 0.00344 [2023-12-20 17:24:23,484 INFO misc.py line 119 131400] Train: [49/100][53/800] Data 0.004 (0.004) Batch 0.285 (0.332) Remain 03:49:35 loss: 0.8277 Lr: 0.00344 [2023-12-20 17:24:23,830 INFO misc.py line 119 131400] Train: [49/100][54/800] Data 0.004 (0.004) Batch 0.343 (0.332) Remain 03:49:44 loss: 0.2746 Lr: 0.00344 [2023-12-20 17:24:24,156 INFO misc.py line 119 131400] Train: [49/100][55/800] Data 0.007 (0.004) Batch 0.329 (0.332) Remain 03:49:41 loss: 0.5871 Lr: 0.00344 [2023-12-20 17:24:24,505 INFO misc.py line 119 131400] Train: [49/100][56/800] Data 0.003 (0.004) Batch 0.350 (0.332) Remain 03:49:55 loss: 0.3646 Lr: 0.00344 [2023-12-20 17:24:24,881 INFO misc.py line 119 131400] Train: [49/100][57/800] Data 0.003 (0.004) Batch 0.360 (0.333) Remain 03:50:16 loss: 0.3559 Lr: 0.00344 [2023-12-20 17:24:25,202 INFO misc.py line 119 131400] Train: [49/100][58/800] Data 0.019 (0.005) Batch 0.336 (0.333) Remain 03:50:18 loss: 0.2310 Lr: 0.00344 [2023-12-20 17:24:25,523 INFO misc.py line 119 131400] Train: [49/100][59/800] Data 0.005 (0.005) Batch 0.321 (0.332) Remain 03:50:10 loss: 0.3415 Lr: 0.00344 [2023-12-20 17:24:25,873 INFO misc.py line 119 131400] Train: [49/100][60/800] Data 0.004 (0.005) Batch 0.349 (0.333) Remain 03:50:21 loss: 0.4440 Lr: 0.00344 [2023-12-20 17:24:26,182 INFO misc.py line 119 131400] Train: [49/100][61/800] Data 0.005 (0.005) Batch 0.310 (0.332) Remain 03:50:04 loss: 0.3999 Lr: 0.00344 [2023-12-20 17:24:26,515 INFO misc.py line 119 131400] Train: [49/100][62/800] Data 0.005 (0.005) Batch 0.334 (0.332) Remain 03:50:05 loss: 0.2708 Lr: 0.00344 [2023-12-20 17:24:26,876 INFO misc.py line 119 131400] Train: [49/100][63/800] Data 0.005 (0.005) Batch 0.359 (0.333) Remain 03:50:23 loss: 0.2218 Lr: 0.00344 [2023-12-20 17:24:27,229 INFO misc.py line 119 131400] Train: [49/100][64/800] Data 0.006 (0.005) Batch 0.356 (0.333) Remain 03:50:38 loss: 0.3793 Lr: 0.00344 [2023-12-20 17:24:27,529 INFO misc.py line 119 131400] Train: [49/100][65/800] Data 0.003 (0.005) Batch 0.299 (0.333) Remain 03:50:15 loss: 0.3187 Lr: 0.00344 [2023-12-20 17:24:27,935 INFO misc.py line 119 131400] Train: [49/100][66/800] Data 0.004 (0.005) Batch 0.402 (0.334) Remain 03:51:00 loss: 0.3577 Lr: 0.00344 [2023-12-20 17:24:28,247 INFO misc.py line 119 131400] Train: [49/100][67/800] Data 0.008 (0.005) Batch 0.316 (0.333) Remain 03:50:49 loss: 0.2791 Lr: 0.00344 [2023-12-20 17:24:28,612 INFO misc.py line 119 131400] Train: [49/100][68/800] Data 0.004 (0.005) Batch 0.365 (0.334) Remain 03:51:09 loss: 0.1752 Lr: 0.00344 [2023-12-20 17:24:28,980 INFO misc.py line 119 131400] Train: [49/100][69/800] Data 0.003 (0.005) Batch 0.362 (0.334) Remain 03:51:26 loss: 0.2580 Lr: 0.00344 [2023-12-20 17:24:29,325 INFO misc.py line 119 131400] Train: [49/100][70/800] Data 0.009 (0.005) Batch 0.350 (0.335) Remain 03:51:36 loss: 0.2787 Lr: 0.00344 [2023-12-20 17:24:29,666 INFO misc.py line 119 131400] Train: [49/100][71/800] Data 0.003 (0.005) Batch 0.341 (0.335) Remain 03:51:39 loss: 0.1989 Lr: 0.00344 [2023-12-20 17:24:29,997 INFO misc.py line 119 131400] Train: [49/100][72/800] Data 0.004 (0.005) Batch 0.331 (0.335) Remain 03:51:37 loss: 0.4302 Lr: 0.00344 [2023-12-20 17:24:30,304 INFO misc.py line 119 131400] Train: [49/100][73/800] Data 0.004 (0.005) Batch 0.307 (0.334) Remain 03:51:20 loss: 0.3286 Lr: 0.00344 [2023-12-20 17:24:30,626 INFO misc.py line 119 131400] Train: [49/100][74/800] Data 0.003 (0.005) Batch 0.321 (0.334) Remain 03:51:12 loss: 0.4549 Lr: 0.00344 [2023-12-20 17:24:30,945 INFO misc.py line 119 131400] Train: [49/100][75/800] Data 0.005 (0.005) Batch 0.320 (0.334) Remain 03:51:04 loss: 0.3179 Lr: 0.00344 [2023-12-20 17:24:31,293 INFO misc.py line 119 131400] Train: [49/100][76/800] Data 0.004 (0.005) Batch 0.346 (0.334) Remain 03:51:11 loss: 0.9051 Lr: 0.00344 [2023-12-20 17:24:31,640 INFO misc.py line 119 131400] Train: [49/100][77/800] Data 0.006 (0.005) Batch 0.349 (0.334) Remain 03:51:19 loss: 0.5567 Lr: 0.00344 [2023-12-20 17:24:31,959 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [49/100][738/800] Data 0.003 (0.005) Batch 0.314 (0.335) Remain 03:47:50 loss: 0.3214 Lr: 0.00336 [2023-12-20 17:28:13,126 INFO misc.py line 119 131400] Train: [49/100][739/800] Data 0.003 (0.005) Batch 0.327 (0.335) Remain 03:47:49 loss: 0.4022 Lr: 0.00336 [2023-12-20 17:28:13,448 INFO misc.py line 119 131400] Train: [49/100][740/800] Data 0.003 (0.005) Batch 0.322 (0.335) Remain 03:47:48 loss: 0.2356 Lr: 0.00336 [2023-12-20 17:28:13,756 INFO misc.py line 119 131400] Train: [49/100][741/800] Data 0.004 (0.005) Batch 0.307 (0.334) Remain 03:47:46 loss: 0.5038 Lr: 0.00336 [2023-12-20 17:28:14,080 INFO misc.py line 119 131400] Train: [49/100][742/800] Data 0.004 (0.005) Batch 0.325 (0.334) Remain 03:47:45 loss: 0.2836 Lr: 0.00336 [2023-12-20 17:28:14,379 INFO misc.py line 119 131400] Train: [49/100][743/800] Data 0.003 (0.005) Batch 0.299 (0.334) Remain 03:47:43 loss: 0.4245 Lr: 0.00336 [2023-12-20 17:28:14,693 INFO misc.py line 119 131400] Train: [49/100][744/800] Data 0.005 (0.005) Batch 0.314 (0.334) Remain 03:47:41 loss: 0.2827 Lr: 0.00336 [2023-12-20 17:28:14,984 INFO misc.py line 119 131400] Train: [49/100][745/800] Data 0.004 (0.005) Batch 0.292 (0.334) Remain 03:47:39 loss: 0.2314 Lr: 0.00336 [2023-12-20 17:28:15,314 INFO misc.py line 119 131400] Train: [49/100][746/800] Data 0.004 (0.005) Batch 0.326 (0.334) Remain 03:47:38 loss: 0.4021 Lr: 0.00336 [2023-12-20 17:28:15,622 INFO misc.py line 119 131400] Train: [49/100][747/800] Data 0.007 (0.005) Batch 0.311 (0.334) Remain 03:47:36 loss: 0.2865 Lr: 0.00336 [2023-12-20 17:28:15,954 INFO misc.py line 119 131400] Train: [49/100][748/800] Data 0.005 (0.005) Batch 0.333 (0.334) Remain 03:47:36 loss: 0.2966 Lr: 0.00335 [2023-12-20 17:28:16,330 INFO misc.py line 119 131400] Train: [49/100][749/800] Data 0.004 (0.005) Batch 0.375 (0.334) Remain 03:47:38 loss: 0.3089 Lr: 0.00335 [2023-12-20 17:28:16,643 INFO misc.py line 119 131400] Train: [49/100][750/800] Data 0.005 (0.005) Batch 0.314 (0.334) Remain 03:47:36 loss: 0.3471 Lr: 0.00335 [2023-12-20 17:28:17,016 INFO misc.py line 119 131400] Train: [49/100][751/800] Data 0.004 (0.005) Batch 0.373 (0.334) Remain 03:47:38 loss: 0.2748 Lr: 0.00335 [2023-12-20 17:28:17,329 INFO misc.py line 119 131400] Train: [49/100][752/800] Data 0.005 (0.005) Batch 0.313 (0.334) Remain 03:47:37 loss: 0.2543 Lr: 0.00335 [2023-12-20 17:28:17,654 INFO misc.py line 119 131400] Train: [49/100][753/800] Data 0.004 (0.005) Batch 0.326 (0.334) Remain 03:47:36 loss: 0.4742 Lr: 0.00335 [2023-12-20 17:28:18,018 INFO misc.py line 119 131400] Train: [49/100][754/800] Data 0.003 (0.005) Batch 0.364 (0.334) Remain 03:47:37 loss: 0.4367 Lr: 0.00335 [2023-12-20 17:28:18,361 INFO misc.py line 119 131400] Train: [49/100][755/800] Data 0.005 (0.005) Batch 0.343 (0.334) Remain 03:47:37 loss: 0.2952 Lr: 0.00335 [2023-12-20 17:28:18,688 INFO misc.py line 119 131400] Train: [49/100][756/800] Data 0.004 (0.005) Batch 0.327 (0.334) Remain 03:47:37 loss: 0.1391 Lr: 0.00335 [2023-12-20 17:28:19,065 INFO misc.py line 119 131400] Train: [49/100][757/800] Data 0.003 (0.005) Batch 0.378 (0.334) Remain 03:47:39 loss: 0.5819 Lr: 0.00335 [2023-12-20 17:28:19,428 INFO misc.py line 119 131400] Train: [49/100][758/800] Data 0.003 (0.005) Batch 0.359 (0.334) Remain 03:47:40 loss: 0.4594 Lr: 0.00335 [2023-12-20 17:28:19,771 INFO misc.py line 119 131400] Train: [49/100][759/800] Data 0.006 (0.005) Batch 0.346 (0.334) Remain 03:47:40 loss: 0.3285 Lr: 0.00335 [2023-12-20 17:28:20,117 INFO misc.py line 119 131400] Train: [49/100][760/800] Data 0.004 (0.005) Batch 0.345 (0.334) Remain 03:47:40 loss: 0.2292 Lr: 0.00335 [2023-12-20 17:28:20,465 INFO misc.py line 119 131400] Train: [49/100][761/800] Data 0.005 (0.005) Batch 0.346 (0.335) Remain 03:47:40 loss: 0.3520 Lr: 0.00335 [2023-12-20 17:28:20,823 INFO misc.py line 119 131400] Train: [49/100][762/800] Data 0.007 (0.005) Batch 0.361 (0.335) Remain 03:47:41 loss: 0.3983 Lr: 0.00335 [2023-12-20 17:28:21,160 INFO misc.py line 119 131400] Train: [49/100][763/800] Data 0.003 (0.005) Batch 0.337 (0.335) Remain 03:47:41 loss: 0.2238 Lr: 0.00335 [2023-12-20 17:28:21,478 INFO misc.py line 119 131400] Train: [49/100][764/800] Data 0.004 (0.005) Batch 0.311 (0.335) Remain 03:47:40 loss: 0.6250 Lr: 0.00335 [2023-12-20 17:28:21,811 INFO misc.py line 119 131400] Train: [49/100][765/800] Data 0.011 (0.005) Batch 0.340 (0.335) Remain 03:47:40 loss: 0.1722 Lr: 0.00335 [2023-12-20 17:28:22,150 INFO misc.py line 119 131400] Train: [49/100][766/800] Data 0.005 (0.005) Batch 0.340 (0.335) Remain 03:47:40 loss: 0.2440 Lr: 0.00335 [2023-12-20 17:28:22,460 INFO misc.py line 119 131400] Train: [49/100][767/800] Data 0.003 (0.005) Batch 0.309 (0.334) Remain 03:47:38 loss: 0.2380 Lr: 0.00335 [2023-12-20 17:28:22,801 INFO misc.py line 119 131400] Train: [49/100][768/800] Data 0.004 (0.005) Batch 0.341 (0.335) Remain 03:47:38 loss: 0.4302 Lr: 0.00335 [2023-12-20 17:28:23,179 INFO misc.py line 119 131400] Train: [49/100][769/800] Data 0.004 (0.005) Batch 0.378 (0.335) Remain 03:47:40 loss: 0.4050 Lr: 0.00335 [2023-12-20 17:28:23,546 INFO misc.py line 119 131400] Train: [49/100][770/800] Data 0.004 (0.005) Batch 0.365 (0.335) Remain 03:47:41 loss: 0.2219 Lr: 0.00335 [2023-12-20 17:28:23,917 INFO misc.py line 119 131400] Train: [49/100][771/800] Data 0.009 (0.005) Batch 0.373 (0.335) Remain 03:47:43 loss: 0.5416 Lr: 0.00335 [2023-12-20 17:28:24,255 INFO misc.py line 119 131400] Train: [49/100][772/800] Data 0.005 (0.005) Batch 0.339 (0.335) Remain 03:47:43 loss: 0.1954 Lr: 0.00335 [2023-12-20 17:28:24,606 INFO misc.py line 119 131400] Train: [49/100][773/800] Data 0.004 (0.005) Batch 0.350 (0.335) Remain 03:47:43 loss: 0.4092 Lr: 0.00335 [2023-12-20 17:28:25,130 INFO misc.py line 119 131400] Train: [49/100][774/800] Data 0.006 (0.005) Batch 0.526 (0.335) Remain 03:47:53 loss: 0.3083 Lr: 0.00335 [2023-12-20 17:28:25,406 INFO misc.py line 119 131400] Train: [49/100][775/800] Data 0.003 (0.005) Batch 0.276 (0.335) Remain 03:47:49 loss: 0.2310 Lr: 0.00335 [2023-12-20 17:28:25,720 INFO misc.py line 119 131400] Train: [49/100][776/800] Data 0.003 (0.005) Batch 0.314 (0.335) Remain 03:47:48 loss: 0.3859 Lr: 0.00335 [2023-12-20 17:28:26,053 INFO misc.py line 119 131400] Train: [49/100][777/800] Data 0.003 (0.005) Batch 0.333 (0.335) Remain 03:47:48 loss: 0.6476 Lr: 0.00335 [2023-12-20 17:28:26,394 INFO misc.py line 119 131400] Train: [49/100][778/800] Data 0.004 (0.005) Batch 0.340 (0.335) Remain 03:47:48 loss: 0.2751 Lr: 0.00335 [2023-12-20 17:28:26,729 INFO misc.py line 119 131400] Train: [49/100][779/800] Data 0.004 (0.005) Batch 0.327 (0.335) Remain 03:47:47 loss: 0.3895 Lr: 0.00335 [2023-12-20 17:28:27,051 INFO misc.py line 119 131400] Train: [49/100][780/800] Data 0.013 (0.005) Batch 0.329 (0.335) Remain 03:47:46 loss: 0.2909 Lr: 0.00335 [2023-12-20 17:28:27,392 INFO misc.py line 119 131400] Train: [49/100][781/800] Data 0.006 (0.005) Batch 0.342 (0.335) Remain 03:47:46 loss: 0.4436 Lr: 0.00335 [2023-12-20 17:28:27,710 INFO misc.py line 119 131400] Train: [49/100][782/800] Data 0.005 (0.005) Batch 0.319 (0.335) Remain 03:47:45 loss: 0.4114 Lr: 0.00335 [2023-12-20 17:28:28,069 INFO misc.py line 119 131400] Train: [49/100][783/800] Data 0.004 (0.005) Batch 0.358 (0.335) Remain 03:47:46 loss: 0.1483 Lr: 0.00335 [2023-12-20 17:28:28,409 INFO misc.py line 119 131400] Train: [49/100][784/800] Data 0.005 (0.005) Batch 0.341 (0.335) Remain 03:47:46 loss: 0.2992 Lr: 0.00335 [2023-12-20 17:28:28,758 INFO misc.py line 119 131400] Train: [49/100][785/800] Data 0.004 (0.005) Batch 0.349 (0.335) Remain 03:47:46 loss: 0.1780 Lr: 0.00335 [2023-12-20 17:28:29,099 INFO misc.py line 119 131400] Train: [49/100][786/800] Data 0.003 (0.005) Batch 0.341 (0.335) Remain 03:47:46 loss: 0.3425 Lr: 0.00335 [2023-12-20 17:28:29,447 INFO misc.py line 119 131400] Train: [49/100][787/800] Data 0.005 (0.005) Batch 0.348 (0.335) Remain 03:47:47 loss: 0.2949 Lr: 0.00335 [2023-12-20 17:28:29,806 INFO misc.py line 119 131400] Train: [49/100][788/800] Data 0.003 (0.005) Batch 0.359 (0.335) Remain 03:47:48 loss: 0.3274 Lr: 0.00335 [2023-12-20 17:28:30,127 INFO misc.py line 119 131400] Train: [49/100][789/800] Data 0.003 (0.005) Batch 0.320 (0.335) Remain 03:47:46 loss: 0.3344 Lr: 0.00335 [2023-12-20 17:28:30,458 INFO misc.py line 119 131400] Train: [49/100][790/800] Data 0.004 (0.005) Batch 0.332 (0.335) Remain 03:47:46 loss: 0.3193 Lr: 0.00335 [2023-12-20 17:28:30,807 INFO misc.py line 119 131400] Train: [49/100][791/800] Data 0.003 (0.005) Batch 0.349 (0.335) Remain 03:47:46 loss: 0.2099 Lr: 0.00335 [2023-12-20 17:28:31,142 INFO misc.py line 119 131400] Train: [49/100][792/800] Data 0.004 (0.005) Batch 0.334 (0.335) Remain 03:47:46 loss: 0.1574 Lr: 0.00335 [2023-12-20 17:28:31,421 INFO misc.py line 119 131400] Train: [49/100][793/800] Data 0.004 (0.005) Batch 0.279 (0.335) Remain 03:47:43 loss: 0.3067 Lr: 0.00335 [2023-12-20 17:28:31,761 INFO misc.py line 119 131400] Train: [49/100][794/800] Data 0.004 (0.005) Batch 0.341 (0.335) Remain 03:47:43 loss: 0.1852 Lr: 0.00335 [2023-12-20 17:28:32,107 INFO misc.py line 119 131400] Train: [49/100][795/800] Data 0.002 (0.005) Batch 0.325 (0.335) Remain 03:47:42 loss: 0.1941 Lr: 0.00335 [2023-12-20 17:28:32,418 INFO misc.py line 119 131400] Train: [49/100][796/800] Data 0.024 (0.005) Batch 0.332 (0.335) Remain 03:47:41 loss: 0.3584 Lr: 0.00335 [2023-12-20 17:28:32,737 INFO misc.py line 119 131400] Train: [49/100][797/800] Data 0.002 (0.005) Batch 0.319 (0.335) Remain 03:47:40 loss: 0.5287 Lr: 0.00335 [2023-12-20 17:28:33,047 INFO misc.py line 119 131400] Train: [49/100][798/800] Data 0.003 (0.005) Batch 0.310 (0.335) Remain 03:47:39 loss: 0.2780 Lr: 0.00335 [2023-12-20 17:28:33,359 INFO misc.py line 119 131400] Train: [49/100][799/800] Data 0.003 (0.005) Batch 0.311 (0.335) Remain 03:47:37 loss: 0.2406 Lr: 0.00335 [2023-12-20 17:28:33,655 INFO misc.py line 119 131400] Train: [49/100][800/800] Data 0.003 (0.005) Batch 0.296 (0.335) Remain 03:47:35 loss: 0.4654 Lr: 0.00335 [2023-12-20 17:28:33,655 INFO misc.py line 136 131400] Train result: loss: 0.3619 [2023-12-20 17:28:33,655 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:28:56,688 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2367 [2023-12-20 17:28:56,766 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1522 [2023-12-20 17:28:56,871 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2892 [2023-12-20 17:28:56,981 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3835 [2023-12-20 17:28:57,106 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4851 [2023-12-20 17:28:57,218 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.7107 [2023-12-20 17:28:57,313 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1304 [2023-12-20 17:28:57,424 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9423 [2023-12-20 17:28:57,517 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.1996 [2023-12-20 17:28:57,623 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3060 [2023-12-20 17:28:57,741 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5112 [2023-12-20 17:28:57,894 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.5259 [2023-12-20 17:28:58,015 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1972 [2023-12-20 17:28:58,179 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2225 [2023-12-20 17:28:58,274 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1459 [2023-12-20 17:28:58,410 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0717 [2023-12-20 17:28:58,519 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2927 [2023-12-20 17:28:58,634 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6501 [2023-12-20 17:28:58,753 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1660 [2023-12-20 17:28:58,831 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4855 [2023-12-20 17:28:58,939 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3183 [2023-12-20 17:28:59,100 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1390 [2023-12-20 17:28:59,228 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6668 [2023-12-20 17:28:59,380 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2653 [2023-12-20 17:28:59,535 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1805 [2023-12-20 17:28:59,623 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4234 [2023-12-20 17:28:59,782 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4942 [2023-12-20 17:28:59,908 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4941 [2023-12-20 17:29:00,003 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8857 [2023-12-20 17:29:00,145 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4049 [2023-12-20 17:29:00,247 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5916 [2023-12-20 17:29:00,366 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5109 [2023-12-20 17:29:00,450 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1317 [2023-12-20 17:29:00,519 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1817 [2023-12-20 17:29:00,614 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6467 [2023-12-20 17:29:00,708 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4976 [2023-12-20 17:29:00,840 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9418 [2023-12-20 17:29:00,952 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.2571 [2023-12-20 17:29:01,038 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5558 [2023-12-20 17:29:01,181 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4166 [2023-12-20 17:29:01,331 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0183 [2023-12-20 17:29:01,436 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0942 [2023-12-20 17:29:01,557 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2530 [2023-12-20 17:29:01,700 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1272 [2023-12-20 17:29:01,816 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9956 [2023-12-20 17:29:01,927 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4115 [2023-12-20 17:29:02,094 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4539 [2023-12-20 17:29:02,193 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3420 [2023-12-20 17:29:02,344 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6684 [2023-12-20 17:29:02,447 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9891 [2023-12-20 17:29:02,522 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4808 [2023-12-20 17:29:02,629 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1894 [2023-12-20 17:29:02,776 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.4106 [2023-12-20 17:29:02,911 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3007 [2023-12-20 17:29:03,017 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6875 [2023-12-20 17:29:03,103 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8497 [2023-12-20 17:29:03,205 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3155 [2023-12-20 17:29:03,365 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2629 [2023-12-20 17:29:03,461 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2292 [2023-12-20 17:29:03,558 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4109 [2023-12-20 17:29:03,656 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3608 [2023-12-20 17:29:03,747 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2794 [2023-12-20 17:29:03,833 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3928 [2023-12-20 17:29:03,934 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8681 [2023-12-20 17:29:04,061 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4071 [2023-12-20 17:29:04,144 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3550 [2023-12-20 17:29:04,244 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4066 [2023-12-20 17:29:04,339 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0100 [2023-12-20 17:29:04,426 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3594 [2023-12-20 17:29:04,511 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0104 [2023-12-20 17:29:04,604 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6962 [2023-12-20 17:29:04,697 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6142 [2023-12-20 17:29:04,831 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1869 [2023-12-20 17:29:04,926 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5455 [2023-12-20 17:29:05,041 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7048 [2023-12-20 17:29:05,143 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7631 [2023-12-20 17:29:05,233 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2580 [2023-12-20 17:29:05,387 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.7110 [2023-12-20 17:29:06,699 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7429/0.8320/0.9120. [2023-12-20 17:29:06,699 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8691/0.9482 [2023-12-20 17:29:06,699 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9627/0.9843 [2023-12-20 17:29:06,699 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6779/0.8784 [2023-12-20 17:29:06,699 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8251/0.8777 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9091/0.9628 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8422/0.8930 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7231/0.8327 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7177/0.8174 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6981/0.8218 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8026/0.8603 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3688/0.4581 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6767/0.8237 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6363/0.7819 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7812/0.8702 [2023-12-20 17:29:06,700 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6491/0.6965 [2023-12-20 17:29:06,701 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7483/0.8393 [2023-12-20 17:29:06,701 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9139/0.9794 [2023-12-20 17:29:06,701 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6706/0.7684 [2023-12-20 17:29:06,701 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8181/0.9238 [2023-12-20 17:29:06,701 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5679/0.6211 [2023-12-20 17:29:06,701 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:29:06,702 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:29:06,702 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:29:09,710 INFO misc.py line 119 131400] Train: [50/100][1/800] Data 0.701 (0.701) Batch 1.117 (1.117) Remain 12:39:19 loss: 0.4417 Lr: 0.00335 [2023-12-20 17:29:10,037 INFO misc.py line 119 131400] Train: [50/100][2/800] Data 0.004 (0.004) Batch 0.326 (0.326) Remain 03:41:25 loss: 0.2200 Lr: 0.00335 [2023-12-20 17:29:10,359 INFO misc.py line 119 131400] Train: [50/100][3/800] Data 0.006 (0.006) Batch 0.324 (0.324) Remain 03:40:25 loss: 0.2913 Lr: 0.00335 [2023-12-20 17:29:10,703 INFO misc.py line 119 131400] Train: [50/100][4/800] Data 0.003 (0.003) Batch 0.344 (0.344) Remain 03:53:43 loss: 0.2533 Lr: 0.00335 [2023-12-20 17:29:11,037 INFO misc.py line 119 131400] Train: [50/100][5/800] Data 0.003 (0.003) Batch 0.333 (0.338) Remain 03:50:00 loss: 0.3773 Lr: 0.00335 [2023-12-20 17:29:11,382 INFO misc.py line 119 131400] Train: [50/100][6/800] Data 0.004 (0.004) Batch 0.345 (0.341) Remain 03:51:33 loss: 0.3461 Lr: 0.00335 [2023-12-20 17:29:11,764 INFO misc.py line 119 131400] Train: [50/100][7/800] Data 0.009 (0.005) Batch 0.379 (0.350) Remain 03:58:09 loss: 0.2579 Lr: 0.00335 [2023-12-20 17:29:12,106 INFO misc.py line 119 131400] Train: [50/100][8/800] Data 0.007 (0.005) Batch 0.344 (0.349) Remain 03:57:15 loss: 0.2452 Lr: 0.00335 [2023-12-20 17:29:12,471 INFO misc.py line 119 131400] Train: [50/100][9/800] Data 0.006 (0.006) Batch 0.366 (0.352) Remain 03:59:10 loss: 0.2803 Lr: 0.00335 [2023-12-20 17:29:12,813 INFO misc.py line 119 131400] Train: [50/100][10/800] Data 0.005 (0.006) Batch 0.342 (0.350) Remain 03:58:12 loss: 0.3892 Lr: 0.00335 [2023-12-20 17:29:13,153 INFO misc.py line 119 131400] Train: [50/100][11/800] Data 0.006 (0.006) Batch 0.341 (0.349) Remain 03:57:24 loss: 0.3471 Lr: 0.00335 [2023-12-20 17:29:13,507 INFO misc.py line 119 131400] Train: [50/100][12/800] Data 0.003 (0.005) Batch 0.353 (0.350) Remain 03:57:43 loss: 0.2688 Lr: 0.00335 [2023-12-20 17:29:13,849 INFO misc.py line 119 131400] Train: [50/100][13/800] Data 0.004 (0.005) Batch 0.342 (0.349) Remain 03:57:10 loss: 0.5937 Lr: 0.00335 [2023-12-20 17:29:14,182 INFO misc.py line 119 131400] Train: [50/100][14/800] Data 0.004 (0.005) Batch 0.334 (0.347) Remain 03:56:12 loss: 0.3494 Lr: 0.00335 [2023-12-20 17:29:14,500 INFO misc.py line 119 131400] Train: [50/100][15/800] Data 0.003 (0.005) Batch 0.317 (0.345) Remain 03:54:29 loss: 0.4153 Lr: 0.00335 [2023-12-20 17:29:14,880 INFO misc.py line 119 131400] Train: [50/100][16/800] Data 0.005 (0.005) Batch 0.380 (0.348) Remain 03:56:20 loss: 0.2662 Lr: 0.00335 [2023-12-20 17:29:15,228 INFO misc.py line 119 131400] Train: [50/100][17/800] Data 0.004 (0.005) Batch 0.347 (0.348) Remain 03:56:17 loss: 0.1460 Lr: 0.00335 [2023-12-20 17:29:15,545 INFO misc.py line 119 131400] Train: [50/100][18/800] Data 0.006 (0.005) Batch 0.313 (0.345) Remain 03:54:43 loss: 0.3109 Lr: 0.00335 [2023-12-20 17:29:15,831 INFO misc.py line 119 131400] Train: [50/100][19/800] Data 0.009 (0.005) Batch 0.289 (0.342) Remain 03:52:20 loss: 0.2330 Lr: 0.00335 [2023-12-20 17:29:16,146 INFO misc.py line 119 131400] Train: [50/100][20/800] Data 0.005 (0.005) Batch 0.312 (0.340) Remain 03:51:07 loss: 0.4986 Lr: 0.00335 [2023-12-20 17:29:16,446 INFO misc.py line 119 131400] Train: [50/100][21/800] Data 0.009 (0.005) Batch 0.305 (0.338) Remain 03:49:46 loss: 0.5446 Lr: 0.00335 [2023-12-20 17:29:16,772 INFO misc.py line 119 131400] Train: [50/100][22/800] Data 0.004 (0.005) Batch 0.326 (0.337) Remain 03:49:19 loss: 0.4448 Lr: 0.00335 [2023-12-20 17:29:17,096 INFO misc.py line 119 131400] Train: [50/100][23/800] Data 0.005 (0.005) Batch 0.325 (0.337) Remain 03:48:54 loss: 0.3395 Lr: 0.00335 [2023-12-20 17:29:17,442 INFO misc.py line 119 131400] Train: [50/100][24/800] Data 0.004 (0.005) Batch 0.345 (0.337) Remain 03:49:11 loss: 0.3344 Lr: 0.00335 [2023-12-20 17:29:17,757 INFO misc.py line 119 131400] Train: [50/100][25/800] Data 0.004 (0.005) Batch 0.316 (0.336) Remain 03:48:31 loss: 0.6379 Lr: 0.00335 [2023-12-20 17:29:18,073 INFO misc.py line 119 131400] Train: [50/100][26/800] Data 0.003 (0.005) Batch 0.316 (0.335) Remain 03:47:54 loss: 0.3136 Lr: 0.00335 [2023-12-20 17:29:18,388 INFO misc.py line 119 131400] Train: [50/100][27/800] Data 0.005 (0.005) Batch 0.312 (0.334) Remain 03:47:13 loss: 0.2978 Lr: 0.00335 [2023-12-20 17:29:18,692 INFO misc.py line 119 131400] Train: [50/100][28/800] Data 0.008 (0.005) Batch 0.308 (0.333) Remain 03:46:29 loss: 0.3516 Lr: 0.00335 [2023-12-20 17:29:19,060 INFO misc.py line 119 131400] Train: [50/100][29/800] Data 0.003 (0.005) Batch 0.368 (0.335) Remain 03:47:23 loss: 0.2557 Lr: 0.00334 [2023-12-20 17:29:19,414 INFO misc.py line 119 131400] Train: [50/100][30/800] Data 0.004 (0.005) Batch 0.354 (0.335) Remain 03:47:52 loss: 0.2604 Lr: 0.00334 [2023-12-20 17:29:19,718 INFO misc.py line 119 131400] Train: [50/100][31/800] Data 0.004 (0.005) Batch 0.304 (0.334) Remain 03:47:06 loss: 0.3452 Lr: 0.00334 [2023-12-20 17:29:20,058 INFO misc.py line 119 131400] Train: [50/100][32/800] Data 0.003 (0.005) Batch 0.341 (0.334) Remain 03:47:15 loss: 0.2222 Lr: 0.00334 [2023-12-20 17:29:20,406 INFO misc.py line 119 131400] Train: [50/100][33/800] Data 0.003 (0.005) Batch 0.347 (0.335) Remain 03:47:31 loss: 0.8150 Lr: 0.00334 [2023-12-20 17:29:20,755 INFO misc.py line 119 131400] Train: [50/100][34/800] Data 0.003 (0.005) Batch 0.349 (0.335) Remain 03:47:49 loss: 0.2547 Lr: 0.00334 [2023-12-20 17:29:21,065 INFO misc.py line 119 131400] Train: [50/100][35/800] Data 0.003 (0.005) Batch 0.309 (0.335) Remain 03:47:16 loss: 0.3167 Lr: 0.00334 [2023-12-20 17:29:21,394 INFO misc.py line 119 131400] Train: [50/100][36/800] Data 0.004 (0.005) Batch 0.330 (0.334) Remain 03:47:10 loss: 0.2730 Lr: 0.00334 [2023-12-20 17:29:21,744 INFO misc.py line 119 131400] Train: [50/100][37/800] Data 0.004 (0.005) Batch 0.351 (0.335) Remain 03:47:29 loss: 0.4679 Lr: 0.00334 [2023-12-20 17:29:22,075 INFO misc.py line 119 131400] Train: [50/100][38/800] Data 0.003 (0.005) Batch 0.331 (0.335) Remain 03:47:24 loss: 0.6997 Lr: 0.00334 [2023-12-20 17:29:22,396 INFO misc.py line 119 131400] Train: [50/100][39/800] Data 0.003 (0.005) Batch 0.321 (0.334) Remain 03:47:08 loss: 0.3929 Lr: 0.00334 [2023-12-20 17:29:22,745 INFO misc.py line 119 131400] Train: [50/100][40/800] Data 0.003 (0.005) Batch 0.348 (0.335) Remain 03:47:23 loss: 0.2496 Lr: 0.00334 [2023-12-20 17:29:23,043 INFO misc.py line 119 131400] Train: [50/100][41/800] Data 0.003 (0.005) Batch 0.298 (0.334) Remain 03:46:44 loss: 0.3562 Lr: 0.00334 [2023-12-20 17:29:23,365 INFO misc.py line 119 131400] Train: [50/100][42/800] Data 0.003 (0.004) Batch 0.321 (0.333) Remain 03:46:30 loss: 0.3376 Lr: 0.00334 [2023-12-20 17:29:23,714 INFO misc.py line 119 131400] Train: [50/100][43/800] Data 0.005 (0.004) Batch 0.350 (0.334) Remain 03:46:47 loss: 0.1806 Lr: 0.00334 [2023-12-20 17:29:24,069 INFO misc.py line 119 131400] Train: [50/100][44/800] Data 0.003 (0.004) Batch 0.354 (0.334) Remain 03:47:07 loss: 0.4741 Lr: 0.00334 [2023-12-20 17:29:24,389 INFO misc.py line 119 131400] Train: [50/100][45/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 03:46:52 loss: 0.1488 Lr: 0.00334 [2023-12-20 17:29:24,735 INFO misc.py line 119 131400] Train: [50/100][46/800] Data 0.004 (0.004) Batch 0.341 (0.334) Remain 03:46:58 loss: 0.3657 Lr: 0.00334 [2023-12-20 17:29:25,046 INFO misc.py line 119 131400] Train: [50/100][47/800] Data 0.010 (0.005) Batch 0.317 (0.334) Remain 03:46:42 loss: 0.2511 Lr: 0.00334 [2023-12-20 17:29:25,386 INFO misc.py line 119 131400] Train: [50/100][48/800] Data 0.005 (0.005) Batch 0.339 (0.334) Remain 03:46:46 loss: 0.4984 Lr: 0.00334 [2023-12-20 17:29:25,718 INFO misc.py line 119 131400] Train: [50/100][49/800] Data 0.004 (0.005) Batch 0.333 (0.334) Remain 03:46:45 loss: 0.3191 Lr: 0.00334 [2023-12-20 17:29:26,001 INFO misc.py line 119 131400] Train: [50/100][50/800] Data 0.002 (0.005) Batch 0.283 (0.333) Remain 03:46:01 loss: 0.4966 Lr: 0.00334 [2023-12-20 17:29:26,319 INFO misc.py line 119 131400] Train: [50/100][51/800] Data 0.004 (0.004) Batch 0.317 (0.332) Remain 03:45:48 loss: 0.4410 Lr: 0.00334 [2023-12-20 17:29:26,664 INFO misc.py line 119 131400] Train: [50/100][52/800] Data 0.004 (0.004) Batch 0.345 (0.333) Remain 03:45:58 loss: 0.4817 Lr: 0.00334 [2023-12-20 17:29:27,018 INFO misc.py line 119 131400] Train: [50/100][53/800] Data 0.004 (0.004) Batch 0.349 (0.333) Remain 03:46:11 loss: 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INFO misc.py line 119 131400] Train: [50/100][60/800] Data 0.007 (0.005) Batch 0.343 (0.333) Remain 03:46:12 loss: 0.4959 Lr: 0.00334 [2023-12-20 17:29:29,663 INFO misc.py line 119 131400] Train: [50/100][61/800] Data 0.004 (0.005) Batch 0.314 (0.333) Remain 03:45:58 loss: 0.5613 Lr: 0.00334 [2023-12-20 17:29:30,012 INFO misc.py line 119 131400] Train: [50/100][62/800] Data 0.004 (0.005) Batch 0.349 (0.333) Remain 03:46:09 loss: 0.1440 Lr: 0.00334 [2023-12-20 17:29:30,302 INFO misc.py line 119 131400] Train: [50/100][63/800] Data 0.003 (0.004) Batch 0.289 (0.332) Remain 03:45:39 loss: 0.4219 Lr: 0.00334 [2023-12-20 17:29:30,618 INFO misc.py line 119 131400] Train: [50/100][64/800] Data 0.004 (0.004) Batch 0.316 (0.332) Remain 03:45:28 loss: 0.2872 Lr: 0.00334 [2023-12-20 17:29:30,969 INFO misc.py line 119 131400] Train: [50/100][65/800] Data 0.005 (0.004) Batch 0.350 (0.332) Remain 03:45:39 loss: 0.3037 Lr: 0.00334 [2023-12-20 17:29:31,342 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [50/100][782/800] Data 0.004 (0.004) Batch 0.301 (0.334) Remain 03:42:56 loss: 0.4467 Lr: 0.00325 [2023-12-20 17:33:31,084 INFO misc.py line 119 131400] Train: [50/100][783/800] Data 0.005 (0.004) Batch 0.336 (0.334) Remain 03:42:56 loss: 0.4391 Lr: 0.00325 [2023-12-20 17:33:31,400 INFO misc.py line 119 131400] Train: [50/100][784/800] Data 0.004 (0.004) Batch 0.316 (0.334) Remain 03:42:54 loss: 0.5323 Lr: 0.00325 [2023-12-20 17:33:31,701 INFO misc.py line 119 131400] Train: [50/100][785/800] Data 0.005 (0.004) Batch 0.302 (0.334) Remain 03:42:52 loss: 0.4186 Lr: 0.00325 [2023-12-20 17:33:32,042 INFO misc.py line 119 131400] Train: [50/100][786/800] Data 0.003 (0.004) Batch 0.340 (0.334) Remain 03:42:52 loss: 0.2669 Lr: 0.00325 [2023-12-20 17:33:32,344 INFO misc.py line 119 131400] Train: [50/100][787/800] Data 0.003 (0.004) Batch 0.302 (0.334) Remain 03:42:50 loss: 0.4742 Lr: 0.00325 [2023-12-20 17:33:32,658 INFO misc.py line 119 131400] Train: 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Batch 0.277 (0.334) Remain 03:42:40 loss: 0.1693 Lr: 0.00325 [2023-12-20 17:33:34,822 INFO misc.py line 119 131400] Train: [50/100][795/800] Data 0.003 (0.004) Batch 0.297 (0.334) Remain 03:42:38 loss: 0.6254 Lr: 0.00325 [2023-12-20 17:33:35,165 INFO misc.py line 119 131400] Train: [50/100][796/800] Data 0.003 (0.004) Batch 0.343 (0.334) Remain 03:42:38 loss: 0.4471 Lr: 0.00325 [2023-12-20 17:33:35,449 INFO misc.py line 119 131400] Train: [50/100][797/800] Data 0.004 (0.004) Batch 0.284 (0.334) Remain 03:42:35 loss: 0.2513 Lr: 0.00325 [2023-12-20 17:33:35,744 INFO misc.py line 119 131400] Train: [50/100][798/800] Data 0.003 (0.004) Batch 0.295 (0.334) Remain 03:42:33 loss: 0.2498 Lr: 0.00325 [2023-12-20 17:33:36,028 INFO misc.py line 119 131400] Train: [50/100][799/800] Data 0.003 (0.004) Batch 0.284 (0.334) Remain 03:42:30 loss: 0.2584 Lr: 0.00325 [2023-12-20 17:33:36,354 INFO misc.py line 119 131400] Train: [50/100][800/800] Data 0.003 (0.004) Batch 0.326 (0.334) Remain 03:42:29 loss: 0.2670 Lr: 0.00325 [2023-12-20 17:33:36,355 INFO misc.py line 136 131400] Train result: loss: 0.3599 [2023-12-20 17:33:36,355 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:33:56,929 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2290 [2023-12-20 17:33:56,999 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1580 [2023-12-20 17:33:57,563 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3123 [2023-12-20 17:33:57,673 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1109 [2023-12-20 17:33:57,790 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3966 [2023-12-20 17:33:57,890 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2229 [2023-12-20 17:33:57,983 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7169 [2023-12-20 17:33:58,092 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8794 [2023-12-20 17:33:58,173 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2648 [2023-12-20 17:33:58,258 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4400 [2023-12-20 17:33:58,352 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5455 [2023-12-20 17:33:58,488 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3716 [2023-12-20 17:33:58,606 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3319 [2023-12-20 17:33:58,761 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.4603 [2023-12-20 17:33:58,853 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1425 [2023-12-20 17:33:58,985 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9519 [2023-12-20 17:33:59,093 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2480 [2023-12-20 17:33:59,203 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6056 [2023-12-20 17:33:59,314 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1943 [2023-12-20 17:33:59,387 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3627 [2023-12-20 17:33:59,496 INFO evaluator.py line 159 131400] Test: 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0.9137 [2023-12-20 17:34:02,304 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4256 [2023-12-20 17:34:02,407 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4102 [2023-12-20 17:34:02,574 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2840 [2023-12-20 17:34:02,665 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3372 [2023-12-20 17:34:02,811 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3634 [2023-12-20 17:34:02,903 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0461 [2023-12-20 17:34:02,982 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5491 [2023-12-20 17:34:03,087 INFO evaluator.py line 159 131400] Test: [52/78] Loss 0.9290 [2023-12-20 17:34:03,235 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.6835 [2023-12-20 17:34:03,368 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3193 [2023-12-20 17:34:03,471 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9764 [2023-12-20 17:34:03,558 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6048 [2023-12-20 17:34:03,658 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3554 [2023-12-20 17:34:03,818 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2620 [2023-12-20 17:34:03,912 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3507 [2023-12-20 17:34:04,014 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.9874 [2023-12-20 17:34:04,110 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2487 [2023-12-20 17:34:04,201 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3077 [2023-12-20 17:34:04,310 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7960 [2023-12-20 17:34:04,410 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7701 [2023-12-20 17:34:04,553 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5600 [2023-12-20 17:34:04,642 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2647 [2023-12-20 17:34:04,741 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3083 [2023-12-20 17:34:04,837 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0245 [2023-12-20 17:34:04,922 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3694 [2023-12-20 17:34:05,008 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0277 [2023-12-20 17:34:05,102 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8253 [2023-12-20 17:34:05,192 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6079 [2023-12-20 17:34:05,325 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2463 [2023-12-20 17:34:05,419 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6569 [2023-12-20 17:34:05,533 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6201 [2023-12-20 17:34:05,634 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6498 [2023-12-20 17:34:05,721 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5902 [2023-12-20 17:34:05,873 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3663 [2023-12-20 17:34:07,118 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7500/0.8456/0.9142. [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8701/0.9386 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9620/0.9829 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6592/0.8181 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8122/0.8601 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9122/0.9607 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8575/0.9559 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7599/0.8121 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7137/0.8452 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7022/0.8238 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8066/0.9506 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3906/0.5210 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6896/0.7970 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6853/0.9069 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7577/0.8785 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6127/0.7091 [2023-12-20 17:34:07,118 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7323/0.8321 [2023-12-20 17:34:07,119 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9352/0.9776 [2023-12-20 17:34:07,119 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6589/0.7562 [2023-12-20 17:34:07,119 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8693/0.9321 [2023-12-20 17:34:07,119 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6127/0.6534 [2023-12-20 17:34:07,119 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:34:07,121 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:34:07,121 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:34:10,813 INFO misc.py line 119 131400] Train: [51/100][1/800] Data 0.734 (0.734) Batch 0.999 (0.999) Remain 11:05:41 loss: 0.2708 Lr: 0.00325 [2023-12-20 17:34:11,137 INFO misc.py line 119 131400] Train: [51/100][2/800] Data 0.004 (0.004) Batch 0.324 (0.324) Remain 03:35:41 loss: 0.4857 Lr: 0.00325 [2023-12-20 17:34:11,475 INFO misc.py line 119 131400] Train: [51/100][3/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 03:45:36 loss: 0.2442 Lr: 0.00325 [2023-12-20 17:34:11,825 INFO misc.py line 119 131400] Train: [51/100][4/800] Data 0.006 (0.006) Batch 0.330 (0.330) Remain 03:39:44 loss: 0.2572 Lr: 0.00325 [2023-12-20 17:34:12,226 INFO misc.py line 119 131400] Train: [51/100][5/800] Data 0.024 (0.015) Batch 0.421 (0.375) Remain 04:10:03 loss: 0.6061 Lr: 0.00325 [2023-12-20 17:34:12,565 INFO misc.py line 119 131400] Train: [51/100][6/800] Data 0.005 (0.012) Batch 0.338 (0.363) Remain 04:01:52 loss: 0.2775 Lr: 0.00325 [2023-12-20 17:34:12,954 INFO misc.py line 119 131400] Train: [51/100][7/800] Data 0.005 (0.010) Batch 0.389 (0.370) Remain 04:06:18 loss: 0.3877 Lr: 0.00325 [2023-12-20 17:34:13,285 INFO misc.py line 119 131400] Train: [51/100][8/800] Data 0.004 (0.009) Batch 0.330 (0.362) Remain 04:00:58 loss: 0.3614 Lr: 0.00325 [2023-12-20 17:34:13,637 INFO misc.py line 119 131400] Train: [51/100][9/800] Data 0.006 (0.009) Batch 0.355 (0.360) Remain 04:00:11 loss: 0.2164 Lr: 0.00325 [2023-12-20 17:34:13,976 INFO misc.py line 119 131400] Train: [51/100][10/800] Data 0.003 (0.008) Batch 0.330 (0.356) Remain 03:57:14 loss: 0.4210 Lr: 0.00325 [2023-12-20 17:34:14,322 INFO misc.py line 119 131400] Train: [51/100][11/800] Data 0.013 (0.008) Batch 0.356 (0.356) Remain 03:57:12 loss: 0.2065 Lr: 0.00325 [2023-12-20 17:34:14,670 INFO misc.py line 119 131400] Train: [51/100][12/800] Data 0.003 (0.008) Batch 0.346 (0.355) Remain 03:56:30 loss: 0.4054 Lr: 0.00325 [2023-12-20 17:34:14,955 INFO misc.py line 119 131400] Train: [51/100][13/800] Data 0.004 (0.007) Batch 0.285 (0.348) Remain 03:51:50 loss: 0.3340 Lr: 0.00325 [2023-12-20 17:34:15,286 INFO misc.py line 119 131400] Train: [51/100][14/800] Data 0.005 (0.007) Batch 0.332 (0.346) Remain 03:50:52 loss: 0.5105 Lr: 0.00325 [2023-12-20 17:34:15,628 INFO misc.py line 119 131400] Train: [51/100][15/800] Data 0.004 (0.007) Batch 0.342 (0.346) Remain 03:50:36 loss: 0.4401 Lr: 0.00325 [2023-12-20 17:34:15,952 INFO misc.py line 119 131400] Train: [51/100][16/800] Data 0.003 (0.007) Batch 0.324 (0.344) Remain 03:49:30 loss: 0.1817 Lr: 0.00325 [2023-12-20 17:34:16,287 INFO misc.py line 119 131400] Train: [51/100][17/800] Data 0.003 (0.006) Batch 0.335 (0.344) Remain 03:49:02 loss: 0.4479 Lr: 0.00325 [2023-12-20 17:34:16,628 INFO misc.py line 119 131400] Train: [51/100][18/800] Data 0.003 (0.006) Batch 0.340 (0.343) Remain 03:48:53 loss: 0.5374 Lr: 0.00325 [2023-12-20 17:34:16,933 INFO misc.py line 119 131400] Train: [51/100][19/800] Data 0.004 (0.006) Batch 0.305 (0.341) Remain 03:47:17 loss: 0.2051 Lr: 0.00325 [2023-12-20 17:34:17,237 INFO misc.py line 119 131400] Train: [51/100][20/800] Data 0.003 (0.006) Batch 0.303 (0.339) Remain 03:45:48 loss: 0.5947 Lr: 0.00325 [2023-12-20 17:34:17,598 INFO misc.py line 119 131400] Train: [51/100][21/800] Data 0.005 (0.006) Batch 0.361 (0.340) Remain 03:46:37 loss: 0.1901 Lr: 0.00325 [2023-12-20 17:34:17,897 INFO misc.py line 119 131400] Train: [51/100][22/800] Data 0.004 (0.006) Batch 0.300 (0.338) Remain 03:45:12 loss: 0.3029 Lr: 0.00325 [2023-12-20 17:34:18,226 INFO misc.py line 119 131400] Train: [51/100][23/800] Data 0.004 (0.006) Batch 0.329 (0.338) Remain 03:44:53 loss: 0.4514 Lr: 0.00325 [2023-12-20 17:34:18,553 INFO misc.py line 119 131400] Train: [51/100][24/800] Data 0.003 (0.005) Batch 0.326 (0.337) Remain 03:44:32 loss: 0.3956 Lr: 0.00325 [2023-12-20 17:34:18,887 INFO misc.py line 119 131400] Train: [51/100][25/800] Data 0.004 (0.005) Batch 0.334 (0.337) Remain 03:44:26 loss: 0.5570 Lr: 0.00325 [2023-12-20 17:34:19,335 INFO misc.py line 119 131400] Train: [51/100][26/800] Data 0.005 (0.005) Batch 0.447 (0.342) Remain 03:47:38 loss: 0.2120 Lr: 0.00325 [2023-12-20 17:34:19,658 INFO misc.py line 119 131400] Train: [51/100][27/800] Data 0.005 (0.005) Batch 0.325 (0.341) Remain 03:47:09 loss: 0.5243 Lr: 0.00325 [2023-12-20 17:34:19,984 INFO misc.py line 119 131400] Train: [51/100][28/800] Data 0.003 (0.005) Batch 0.324 (0.340) Remain 03:46:41 loss: 0.2834 Lr: 0.00325 [2023-12-20 17:34:20,287 INFO misc.py line 119 131400] Train: [51/100][29/800] Data 0.005 (0.005) Batch 0.305 (0.339) Remain 03:45:47 loss: 0.2781 Lr: 0.00325 [2023-12-20 17:34:20,615 INFO misc.py line 119 131400] Train: [51/100][30/800] Data 0.003 (0.005) Batch 0.327 (0.338) Remain 03:45:29 loss: 0.3264 Lr: 0.00325 [2023-12-20 17:34:20,947 INFO misc.py line 119 131400] Train: [51/100][31/800] Data 0.003 (0.005) Batch 0.333 (0.338) Remain 03:45:21 loss: 0.6493 Lr: 0.00325 [2023-12-20 17:34:21,260 INFO misc.py line 119 131400] Train: [51/100][32/800] Data 0.003 (0.005) Batch 0.313 (0.337) Remain 03:44:45 loss: 0.2045 Lr: 0.00325 [2023-12-20 17:34:21,564 INFO misc.py line 119 131400] Train: [51/100][33/800] Data 0.003 (0.005) Batch 0.299 (0.336) Remain 03:43:53 loss: 0.5665 Lr: 0.00325 [2023-12-20 17:34:21,867 INFO misc.py line 119 131400] Train: [51/100][34/800] Data 0.008 (0.005) Batch 0.308 (0.335) Remain 03:43:16 loss: 0.3146 Lr: 0.00325 [2023-12-20 17:34:22,211 INFO misc.py line 119 131400] Train: [51/100][35/800] Data 0.003 (0.005) Batch 0.344 (0.335) Remain 03:43:27 loss: 0.3775 Lr: 0.00325 [2023-12-20 17:34:22,521 INFO misc.py line 119 131400] Train: [51/100][36/800] Data 0.004 (0.005) Batch 0.309 (0.335) Remain 03:42:55 loss: 0.4667 Lr: 0.00325 [2023-12-20 17:34:22,832 INFO misc.py line 119 131400] Train: [51/100][37/800] Data 0.005 (0.005) Batch 0.312 (0.334) Remain 03:42:28 loss: 0.4573 Lr: 0.00325 [2023-12-20 17:34:23,159 INFO misc.py line 119 131400] Train: [51/100][38/800] Data 0.003 (0.005) Batch 0.327 (0.334) Remain 03:42:20 loss: 0.2901 Lr: 0.00325 [2023-12-20 17:34:23,481 INFO misc.py line 119 131400] Train: [51/100][39/800] Data 0.003 (0.005) Batch 0.323 (0.334) Remain 03:42:07 loss: 0.2643 Lr: 0.00325 [2023-12-20 17:34:23,806 INFO misc.py line 119 131400] Train: [51/100][40/800] Data 0.002 (0.005) Batch 0.322 (0.333) Remain 03:41:55 loss: 0.4139 Lr: 0.00325 [2023-12-20 17:34:24,143 INFO misc.py line 119 131400] Train: [51/100][41/800] Data 0.006 (0.005) Batch 0.339 (0.333) Remain 03:42:00 loss: 0.1634 Lr: 0.00324 [2023-12-20 17:34:24,460 INFO misc.py line 119 131400] Train: [51/100][42/800] Data 0.003 (0.005) Batch 0.317 (0.333) Remain 03:41:43 loss: 0.3614 Lr: 0.00324 [2023-12-20 17:34:24,782 INFO misc.py line 119 131400] Train: [51/100][43/800] Data 0.003 (0.005) Batch 0.323 (0.333) Remain 03:41:32 loss: 0.3078 Lr: 0.00324 [2023-12-20 17:34:25,101 INFO misc.py line 119 131400] Train: [51/100][44/800] Data 0.003 (0.005) Batch 0.318 (0.332) Remain 03:41:18 loss: 0.2330 Lr: 0.00324 [2023-12-20 17:34:25,400 INFO misc.py line 119 131400] Train: [51/100][45/800] Data 0.004 (0.005) Batch 0.299 (0.332) Remain 03:40:46 loss: 0.2506 Lr: 0.00324 [2023-12-20 17:34:25,732 INFO misc.py line 119 131400] Train: [51/100][46/800] Data 0.003 (0.005) Batch 0.332 (0.332) Remain 03:40:46 loss: 0.3568 Lr: 0.00324 [2023-12-20 17:34:26,047 INFO misc.py line 119 131400] Train: [51/100][47/800] Data 0.004 (0.005) Batch 0.310 (0.331) Remain 03:40:27 loss: 0.2829 Lr: 0.00324 [2023-12-20 17:34:26,376 INFO misc.py line 119 131400] Train: [51/100][48/800] Data 0.008 (0.005) Batch 0.333 (0.331) Remain 03:40:28 loss: 0.3345 Lr: 0.00324 [2023-12-20 17:34:26,627 INFO misc.py line 119 131400] Train: [51/100][49/800] Data 0.003 (0.005) Batch 0.251 (0.329) Remain 03:39:19 loss: 0.1571 Lr: 0.00324 [2023-12-20 17:34:26,932 INFO misc.py line 119 131400] Train: [51/100][50/800] Data 0.003 (0.005) Batch 0.305 (0.329) Remain 03:38:58 loss: 0.3822 Lr: 0.00324 [2023-12-20 17:34:27,276 INFO misc.py line 119 131400] Train: [51/100][51/800] Data 0.003 (0.005) Batch 0.345 (0.329) Remain 03:39:11 loss: 0.2528 Lr: 0.00324 [2023-12-20 17:34:27,585 INFO misc.py line 119 131400] Train: [51/100][52/800] Data 0.003 (0.005) Batch 0.307 (0.329) Remain 03:38:52 loss: 0.4195 Lr: 0.00324 [2023-12-20 17:34:27,899 INFO misc.py line 119 131400] Train: [51/100][53/800] Data 0.004 (0.005) Batch 0.315 (0.328) Remain 03:38:41 loss: 0.1509 Lr: 0.00324 [2023-12-20 17:34:28,211 INFO misc.py line 119 131400] Train: [51/100][54/800] Data 0.004 (0.005) Batch 0.312 (0.328) Remain 03:38:27 loss: 0.1933 Lr: 0.00324 [2023-12-20 17:34:28,547 INFO misc.py line 119 131400] Train: [51/100][55/800] Data 0.004 (0.004) Batch 0.336 (0.328) Remain 03:38:33 loss: 0.3876 Lr: 0.00324 [2023-12-20 17:34:28,848 INFO misc.py line 119 131400] Train: [51/100][56/800] Data 0.004 (0.004) Batch 0.301 (0.328) Remain 03:38:12 loss: 0.2908 Lr: 0.00324 [2023-12-20 17:34:29,179 INFO misc.py line 119 131400] Train: [51/100][57/800] Data 0.004 (0.004) Batch 0.331 (0.328) Remain 03:38:14 loss: 0.3325 Lr: 0.00324 [2023-12-20 17:34:29,526 INFO misc.py line 119 131400] Train: [51/100][58/800] Data 0.006 (0.005) Batch 0.345 (0.328) Remain 03:38:27 loss: 0.2319 Lr: 0.00324 [2023-12-20 17:34:29,860 INFO misc.py line 119 131400] Train: [51/100][59/800] Data 0.007 (0.005) Batch 0.335 (0.328) Remain 03:38:31 loss: 0.2405 Lr: 0.00324 [2023-12-20 17:34:30,239 INFO misc.py line 119 131400] Train: [51/100][60/800] Data 0.005 (0.005) Batch 0.365 (0.329) Remain 03:38:57 loss: 0.2159 Lr: 0.00324 [2023-12-20 17:34:30,572 INFO misc.py line 119 131400] Train: [51/100][61/800] Data 0.021 (0.005) Batch 0.348 (0.329) Remain 03:39:10 loss: 0.2152 Lr: 0.00324 [2023-12-20 17:34:30,942 INFO misc.py line 119 131400] Train: [51/100][62/800] Data 0.004 (0.005) Batch 0.369 (0.330) Remain 03:39:36 loss: 0.2582 Lr: 0.00324 [2023-12-20 17:34:31,295 INFO misc.py line 119 131400] Train: [51/100][63/800] Data 0.004 (0.005) Batch 0.348 (0.330) Remain 03:39:48 loss: 0.6527 Lr: 0.00324 [2023-12-20 17:34:31,642 INFO misc.py line 119 131400] Train: [51/100][64/800] Data 0.010 (0.005) Batch 0.351 (0.331) Remain 03:40:01 loss: 0.4378 Lr: 0.00324 [2023-12-20 17:34:32,009 INFO misc.py line 119 131400] Train: [51/100][65/800] Data 0.006 (0.005) Batch 0.369 (0.331) Remain 03:40:25 loss: 0.7499 Lr: 0.00324 [2023-12-20 17:34:32,367 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [51/100][782/800] Data 0.004 (0.005) Batch 0.333 (0.334) Remain 03:38:15 loss: 0.2348 Lr: 0.00315 [2023-12-20 17:38:31,941 INFO misc.py line 119 131400] Train: [51/100][783/800] Data 0.004 (0.005) Batch 0.340 (0.334) Remain 03:38:15 loss: 0.3627 Lr: 0.00315 [2023-12-20 17:38:32,295 INFO misc.py line 119 131400] Train: [51/100][784/800] Data 0.004 (0.005) Batch 0.355 (0.334) Remain 03:38:16 loss: 0.3762 Lr: 0.00315 [2023-12-20 17:38:32,625 INFO misc.py line 119 131400] Train: [51/100][785/800] Data 0.004 (0.005) Batch 0.331 (0.334) Remain 03:38:15 loss: 0.3752 Lr: 0.00315 [2023-12-20 17:38:32,949 INFO misc.py line 119 131400] Train: [51/100][786/800] Data 0.002 (0.005) Batch 0.323 (0.334) Remain 03:38:14 loss: 0.3816 Lr: 0.00315 [2023-12-20 17:38:33,304 INFO misc.py line 119 131400] Train: [51/100][787/800] Data 0.004 (0.005) Batch 0.355 (0.334) Remain 03:38:15 loss: 0.2684 Lr: 0.00315 [2023-12-20 17:38:33,625 INFO misc.py line 119 131400] Train: 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Batch 0.316 (0.334) Remain 03:38:04 loss: 0.7070 Lr: 0.00315 [2023-12-20 17:38:35,752 INFO misc.py line 119 131400] Train: [51/100][795/800] Data 0.003 (0.005) Batch 0.299 (0.334) Remain 03:38:01 loss: 0.2871 Lr: 0.00315 [2023-12-20 17:38:36,085 INFO misc.py line 119 131400] Train: [51/100][796/800] Data 0.003 (0.005) Batch 0.333 (0.334) Remain 03:38:01 loss: 0.2701 Lr: 0.00315 [2023-12-20 17:38:36,414 INFO misc.py line 119 131400] Train: [51/100][797/800] Data 0.003 (0.005) Batch 0.328 (0.334) Remain 03:38:00 loss: 0.4132 Lr: 0.00315 [2023-12-20 17:38:36,737 INFO misc.py line 119 131400] Train: [51/100][798/800] Data 0.004 (0.005) Batch 0.323 (0.334) Remain 03:38:00 loss: 0.3198 Lr: 0.00315 [2023-12-20 17:38:37,071 INFO misc.py line 119 131400] Train: [51/100][799/800] Data 0.006 (0.005) Batch 0.335 (0.334) Remain 03:37:59 loss: 0.4567 Lr: 0.00315 [2023-12-20 17:38:37,385 INFO misc.py line 119 131400] Train: [51/100][800/800] Data 0.003 (0.005) Batch 0.314 (0.334) Remain 03:37:58 loss: 0.2227 Lr: 0.00315 [2023-12-20 17:38:37,386 INFO misc.py line 136 131400] Train result: loss: 0.3582 [2023-12-20 17:38:37,386 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:39:00,538 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2096 [2023-12-20 17:39:00,612 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1432 [2023-12-20 17:39:00,702 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4049 [2023-12-20 17:39:00,812 INFO evaluator.py line 159 131400] Test: [4/78] Loss 0.9553 [2023-12-20 17:39:00,931 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4137 [2023-12-20 17:39:01,032 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.0359 [2023-12-20 17:39:01,123 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.4286 [2023-12-20 17:39:01,231 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2715 [2023-12-20 17:39:01,319 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3185 [2023-12-20 17:39:01,408 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3306 [2023-12-20 17:39:01,506 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5703 [2023-12-20 17:39:01,645 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3013 [2023-12-20 17:39:01,766 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4396 [2023-12-20 17:39:01,925 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2030 [2023-12-20 17:39:02,021 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1745 [2023-12-20 17:39:02,155 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.1510 [2023-12-20 17:39:02,267 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3604 [2023-12-20 17:39:02,385 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3089 [2023-12-20 17:39:02,522 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2911 [2023-12-20 17:39:02,597 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4874 [2023-12-20 17:39:02,705 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4529 [2023-12-20 17:39:02,872 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1272 [2023-12-20 17:39:03,003 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3437 [2023-12-20 17:39:03,152 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2406 [2023-12-20 17:39:03,301 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1454 [2023-12-20 17:39:03,389 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4599 [2023-12-20 17:39:03,550 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4187 [2023-12-20 17:39:03,678 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4743 [2023-12-20 17:39:03,773 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5516 [2023-12-20 17:39:03,920 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5791 [2023-12-20 17:39:04,023 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5806 [2023-12-20 17:39:04,149 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4519 [2023-12-20 17:39:04,235 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1402 [2023-12-20 17:39:04,316 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1790 [2023-12-20 17:39:04,432 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.3468 [2023-12-20 17:39:04,532 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6158 [2023-12-20 17:39:04,664 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9503 [2023-12-20 17:39:04,788 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1176 [2023-12-20 17:39:04,872 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7088 [2023-12-20 17:39:05,016 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4144 [2023-12-20 17:39:05,167 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1952 [2023-12-20 17:39:05,274 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.4457 [2023-12-20 17:39:05,402 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3512 [2023-12-20 17:39:05,551 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7156 [2023-12-20 17:39:05,676 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4220 [2023-12-20 17:39:05,787 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5104 [2023-12-20 17:39:05,953 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5489 [2023-12-20 17:39:06,067 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4130 [2023-12-20 17:39:06,225 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4903 [2023-12-20 17:39:06,323 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9157 [2023-12-20 17:39:06,410 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4117 [2023-12-20 17:39:06,518 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1071 [2023-12-20 17:39:06,664 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7311 [2023-12-20 17:39:06,798 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3274 [2023-12-20 17:39:06,902 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.0154 [2023-12-20 17:39:06,990 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8092 [2023-12-20 17:39:07,093 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4079 [2023-12-20 17:39:07,257 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1980 [2023-12-20 17:39:07,358 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3052 [2023-12-20 17:39:07,454 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3029 [2023-12-20 17:39:07,551 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4182 [2023-12-20 17:39:07,641 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3398 [2023-12-20 17:39:07,727 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.2707 [2023-12-20 17:39:07,832 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7328 [2023-12-20 17:39:07,961 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.2884 [2023-12-20 17:39:08,054 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2688 [2023-12-20 17:39:08,155 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4745 [2023-12-20 17:39:08,250 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.1938 [2023-12-20 17:39:08,339 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3232 [2023-12-20 17:39:08,424 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.1363 [2023-12-20 17:39:08,519 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8634 [2023-12-20 17:39:08,612 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5127 [2023-12-20 17:39:08,747 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1246 [2023-12-20 17:39:08,845 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6360 [2023-12-20 17:39:08,962 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6155 [2023-12-20 17:39:09,067 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6748 [2023-12-20 17:39:09,154 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3932 [2023-12-20 17:39:09,307 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.8776 [2023-12-20 17:39:10,577 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7502/0.8412/0.9135. [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8672/0.9458 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9645/0.9831 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6837/0.8091 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8359/0.8618 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9176/0.9551 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8396/0.9376 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7574/0.8960 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6817/0.7892 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6634/0.8193 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8010/0.9067 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3726/0.5413 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7101/0.8488 [2023-12-20 17:39:10,578 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7176/0.8365 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7436/0.8239 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6517/0.8105 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7045/0.7547 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9291/0.9570 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6649/0.7719 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8860/0.9198 [2023-12-20 17:39:10,579 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6125/0.6549 [2023-12-20 17:39:10,579 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:39:10,580 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:39:10,580 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:39:14,584 INFO misc.py line 119 131400] Train: [52/100][1/800] Data 1.639 (1.639) Batch 1.977 (1.977) Remain 21:31:40 loss: 0.3718 Lr: 0.00315 [2023-12-20 17:39:14,915 INFO misc.py line 119 131400] Train: [52/100][2/800] Data 0.004 (0.004) Batch 0.332 (0.332) Remain 03:36:38 loss: 0.1468 Lr: 0.00315 [2023-12-20 17:39:15,234 INFO misc.py line 119 131400] Train: [52/100][3/800] Data 0.003 (0.003) Batch 0.318 (0.318) Remain 03:27:51 loss: 0.2407 Lr: 0.00315 [2023-12-20 17:39:15,578 INFO misc.py line 119 131400] Train: [52/100][4/800] Data 0.005 (0.005) Batch 0.344 (0.344) Remain 03:44:32 loss: 0.3797 Lr: 0.00315 [2023-12-20 17:39:15,925 INFO misc.py line 119 131400] Train: [52/100][5/800] Data 0.004 (0.004) Batch 0.348 (0.346) Remain 03:45:54 loss: 0.4002 Lr: 0.00315 [2023-12-20 17:39:16,261 INFO misc.py line 119 131400] Train: [52/100][6/800] Data 0.002 (0.004) Batch 0.336 (0.342) Remain 03:43:41 loss: 0.2877 Lr: 0.00315 [2023-12-20 17:39:16,598 INFO misc.py line 119 131400] Train: [52/100][7/800] Data 0.004 (0.004) Batch 0.337 (0.341) Remain 03:42:45 loss: 0.4684 Lr: 0.00315 [2023-12-20 17:39:17,102 INFO misc.py line 119 131400] Train: [52/100][8/800] Data 0.009 (0.005) Batch 0.504 (0.374) Remain 04:04:05 loss: 0.5441 Lr: 0.00315 [2023-12-20 17:39:17,466 INFO misc.py line 119 131400] Train: [52/100][9/800] Data 0.003 (0.005) Batch 0.363 (0.372) Remain 04:02:58 loss: 0.3818 Lr: 0.00315 [2023-12-20 17:39:17,833 INFO misc.py line 119 131400] Train: [52/100][10/800] Data 0.004 (0.005) Batch 0.367 (0.371) Remain 04:02:29 loss: 0.3610 Lr: 0.00315 [2023-12-20 17:39:18,159 INFO misc.py line 119 131400] Train: [52/100][11/800] Data 0.004 (0.005) Batch 0.326 (0.366) Remain 03:58:49 loss: 0.2376 Lr: 0.00315 [2023-12-20 17:39:18,526 INFO misc.py line 119 131400] Train: [52/100][12/800] Data 0.004 (0.004) Batch 0.357 (0.365) Remain 03:58:12 loss: 0.2139 Lr: 0.00315 [2023-12-20 17:39:18,875 INFO misc.py line 119 131400] Train: [52/100][13/800] Data 0.014 (0.005) Batch 0.359 (0.364) Remain 03:57:49 loss: 0.2522 Lr: 0.00315 [2023-12-20 17:39:19,227 INFO misc.py line 119 131400] Train: [52/100][14/800] Data 0.004 (0.005) Batch 0.351 (0.363) Remain 03:57:03 loss: 0.3038 Lr: 0.00315 [2023-12-20 17:39:19,570 INFO misc.py line 119 131400] Train: [52/100][15/800] Data 0.004 (0.005) Batch 0.340 (0.361) Remain 03:55:48 loss: 0.2623 Lr: 0.00315 [2023-12-20 17:39:19,883 INFO misc.py line 119 131400] Train: [52/100][16/800] Data 0.008 (0.005) Batch 0.316 (0.358) Remain 03:53:32 loss: 0.2863 Lr: 0.00315 [2023-12-20 17:39:20,220 INFO misc.py line 119 131400] Train: [52/100][17/800] Data 0.004 (0.005) Batch 0.338 (0.356) Remain 03:52:36 loss: 0.4011 Lr: 0.00315 [2023-12-20 17:39:20,572 INFO misc.py line 119 131400] Train: [52/100][18/800] Data 0.003 (0.005) Batch 0.351 (0.356) Remain 03:52:22 loss: 0.2398 Lr: 0.00315 [2023-12-20 17:39:20,915 INFO misc.py line 119 131400] Train: [52/100][19/800] Data 0.004 (0.005) Batch 0.343 (0.355) Remain 03:51:52 loss: 0.3545 Lr: 0.00315 [2023-12-20 17:39:21,226 INFO misc.py line 119 131400] Train: [52/100][20/800] Data 0.003 (0.005) Batch 0.311 (0.352) Remain 03:50:10 loss: 0.4166 Lr: 0.00315 [2023-12-20 17:39:21,575 INFO misc.py line 119 131400] Train: [52/100][21/800] Data 0.003 (0.005) Batch 0.344 (0.352) Remain 03:49:51 loss: 0.6400 Lr: 0.00315 [2023-12-20 17:39:21,923 INFO misc.py line 119 131400] Train: [52/100][22/800] Data 0.010 (0.005) Batch 0.353 (0.352) Remain 03:49:52 loss: 0.2164 Lr: 0.00315 [2023-12-20 17:39:22,303 INFO misc.py line 119 131400] Train: [52/100][23/800] Data 0.004 (0.005) Batch 0.380 (0.353) Remain 03:50:47 loss: 0.2845 Lr: 0.00315 [2023-12-20 17:39:22,614 INFO misc.py line 119 131400] Train: [52/100][24/800] Data 0.005 (0.005) Batch 0.312 (0.351) Remain 03:49:28 loss: 0.3767 Lr: 0.00315 [2023-12-20 17:39:22,928 INFO misc.py line 119 131400] Train: [52/100][25/800] Data 0.003 (0.005) Batch 0.314 (0.350) Remain 03:48:21 loss: 0.5120 Lr: 0.00315 [2023-12-20 17:39:23,263 INFO misc.py line 119 131400] Train: [52/100][26/800] Data 0.003 (0.005) Batch 0.333 (0.349) Remain 03:47:53 loss: 0.2305 Lr: 0.00315 [2023-12-20 17:39:23,595 INFO misc.py line 119 131400] Train: [52/100][27/800] Data 0.004 (0.005) Batch 0.333 (0.348) Remain 03:47:27 loss: 0.2584 Lr: 0.00315 [2023-12-20 17:39:23,913 INFO misc.py line 119 131400] Train: [52/100][28/800] Data 0.003 (0.005) Batch 0.318 (0.347) Remain 03:46:39 loss: 0.2160 Lr: 0.00315 [2023-12-20 17:39:24,232 INFO misc.py line 119 131400] Train: [52/100][29/800] Data 0.003 (0.005) Batch 0.318 (0.346) Remain 03:45:55 loss: 0.3465 Lr: 0.00315 [2023-12-20 17:39:24,585 INFO misc.py line 119 131400] Train: [52/100][30/800] Data 0.005 (0.005) Batch 0.353 (0.346) Remain 03:46:05 loss: 0.2356 Lr: 0.00315 [2023-12-20 17:39:24,935 INFO misc.py line 119 131400] Train: [52/100][31/800] Data 0.004 (0.005) Batch 0.351 (0.346) Remain 03:46:11 loss: 0.4419 Lr: 0.00315 [2023-12-20 17:39:25,258 INFO misc.py line 119 131400] Train: [52/100][32/800] Data 0.003 (0.005) Batch 0.323 (0.346) Remain 03:45:39 loss: 0.4020 Lr: 0.00315 [2023-12-20 17:39:25,591 INFO misc.py line 119 131400] Train: [52/100][33/800] Data 0.004 (0.005) Batch 0.333 (0.345) Remain 03:45:22 loss: 0.2166 Lr: 0.00315 [2023-12-20 17:39:25,923 INFO misc.py line 119 131400] Train: [52/100][34/800] Data 0.003 (0.005) Batch 0.331 (0.345) Remain 03:45:03 loss: 0.3836 Lr: 0.00315 [2023-12-20 17:39:26,267 INFO misc.py line 119 131400] Train: [52/100][35/800] Data 0.005 (0.005) Batch 0.344 (0.345) Remain 03:45:02 loss: 0.3939 Lr: 0.00315 [2023-12-20 17:39:26,659 INFO misc.py line 119 131400] Train: [52/100][36/800] Data 0.004 (0.005) Batch 0.393 (0.346) Remain 03:45:59 loss: 0.7261 Lr: 0.00315 [2023-12-20 17:39:26,973 INFO misc.py line 119 131400] Train: [52/100][37/800] Data 0.003 (0.005) Batch 0.314 (0.345) Remain 03:45:22 loss: 0.2654 Lr: 0.00315 [2023-12-20 17:39:27,310 INFO misc.py line 119 131400] Train: [52/100][38/800] Data 0.004 (0.004) Batch 0.336 (0.345) Remain 03:45:10 loss: 0.2526 Lr: 0.00315 [2023-12-20 17:39:27,682 INFO misc.py line 119 131400] Train: [52/100][39/800] Data 0.005 (0.005) Batch 0.371 (0.346) Remain 03:45:38 loss: 0.4451 Lr: 0.00315 [2023-12-20 17:39:28,032 INFO misc.py line 119 131400] Train: [52/100][40/800] Data 0.006 (0.005) Batch 0.353 (0.346) Remain 03:45:45 loss: 0.2422 Lr: 0.00315 [2023-12-20 17:39:28,357 INFO misc.py line 119 131400] Train: [52/100][41/800] Data 0.003 (0.005) Batch 0.325 (0.345) Remain 03:45:23 loss: 0.4881 Lr: 0.00315 [2023-12-20 17:39:28,714 INFO misc.py line 119 131400] Train: [52/100][42/800] Data 0.004 (0.005) Batch 0.357 (0.346) Remain 03:45:35 loss: 0.4341 Lr: 0.00315 [2023-12-20 17:39:29,058 INFO misc.py line 119 131400] Train: [52/100][43/800] Data 0.003 (0.004) Batch 0.344 (0.346) Remain 03:45:33 loss: 0.2165 Lr: 0.00315 [2023-12-20 17:39:29,358 INFO misc.py line 119 131400] Train: [52/100][44/800] Data 0.003 (0.004) Batch 0.300 (0.345) Remain 03:44:49 loss: 0.3695 Lr: 0.00315 [2023-12-20 17:39:29,699 INFO misc.py line 119 131400] Train: [52/100][45/800] Data 0.003 (0.004) Batch 0.341 (0.344) Remain 03:44:45 loss: 0.5504 Lr: 0.00315 [2023-12-20 17:39:30,049 INFO misc.py line 119 131400] Train: [52/100][46/800] Data 0.003 (0.004) Batch 0.350 (0.345) Remain 03:44:50 loss: 0.2467 Lr: 0.00315 [2023-12-20 17:39:30,340 INFO misc.py line 119 131400] Train: [52/100][47/800] Data 0.003 (0.004) Batch 0.291 (0.343) Remain 03:44:02 loss: 0.3555 Lr: 0.00315 [2023-12-20 17:39:30,658 INFO misc.py line 119 131400] Train: [52/100][48/800] Data 0.004 (0.004) Batch 0.318 (0.343) Remain 03:43:40 loss: 0.1466 Lr: 0.00315 [2023-12-20 17:39:30,983 INFO misc.py line 119 131400] Train: [52/100][49/800] Data 0.003 (0.004) Batch 0.324 (0.342) Remain 03:43:23 loss: 0.7921 Lr: 0.00314 [2023-12-20 17:39:31,286 INFO misc.py line 119 131400] Train: [52/100][50/800] Data 0.004 (0.004) Batch 0.304 (0.342) Remain 03:42:51 loss: 0.2549 Lr: 0.00314 [2023-12-20 17:39:31,603 INFO misc.py line 119 131400] Train: [52/100][51/800] Data 0.003 (0.004) Batch 0.317 (0.341) Remain 03:42:31 loss: 0.2871 Lr: 0.00314 [2023-12-20 17:39:31,947 INFO misc.py line 119 131400] Train: [52/100][52/800] Data 0.004 (0.004) Batch 0.343 (0.341) Remain 03:42:32 loss: 0.2988 Lr: 0.00314 [2023-12-20 17:39:32,292 INFO misc.py line 119 131400] Train: [52/100][53/800] Data 0.005 (0.004) Batch 0.345 (0.341) Remain 03:42:35 loss: 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line 119 131400] Train: [52/100][782/800] Data 0.004 (0.004) Batch 0.374 (0.335) Remain 03:34:43 loss: 0.3905 Lr: 0.00305 [2023-12-20 17:43:36,844 INFO misc.py line 119 131400] Train: [52/100][783/800] Data 0.004 (0.004) Batch 0.366 (0.335) Remain 03:34:44 loss: 0.3594 Lr: 0.00305 [2023-12-20 17:43:37,199 INFO misc.py line 119 131400] Train: [52/100][784/800] Data 0.004 (0.004) Batch 0.354 (0.335) Remain 03:34:45 loss: 0.2617 Lr: 0.00305 [2023-12-20 17:43:37,525 INFO misc.py line 119 131400] Train: [52/100][785/800] Data 0.004 (0.004) Batch 0.325 (0.335) Remain 03:34:44 loss: 0.1296 Lr: 0.00305 [2023-12-20 17:43:37,887 INFO misc.py line 119 131400] Train: [52/100][786/800] Data 0.005 (0.004) Batch 0.363 (0.335) Remain 03:34:45 loss: 0.2872 Lr: 0.00305 [2023-12-20 17:43:38,358 INFO misc.py line 119 131400] Train: [52/100][787/800] Data 0.004 (0.004) Batch 0.471 (0.336) Remain 03:34:52 loss: 0.4194 Lr: 0.00305 [2023-12-20 17:43:38,722 INFO misc.py line 119 131400] Train: 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Batch 0.321 (0.335) Remain 03:34:44 loss: 0.3072 Lr: 0.00305 [2023-12-20 17:43:40,919 INFO misc.py line 119 131400] Train: [52/100][795/800] Data 0.003 (0.004) Batch 0.308 (0.335) Remain 03:34:43 loss: 0.6630 Lr: 0.00305 [2023-12-20 17:43:41,191 INFO misc.py line 119 131400] Train: [52/100][796/800] Data 0.003 (0.004) Batch 0.269 (0.335) Remain 03:34:39 loss: 0.4482 Lr: 0.00305 [2023-12-20 17:43:41,474 INFO misc.py line 119 131400] Train: [52/100][797/800] Data 0.007 (0.004) Batch 0.287 (0.335) Remain 03:34:37 loss: 0.4166 Lr: 0.00305 [2023-12-20 17:43:41,785 INFO misc.py line 119 131400] Train: [52/100][798/800] Data 0.003 (0.004) Batch 0.311 (0.335) Remain 03:34:35 loss: 0.3980 Lr: 0.00305 [2023-12-20 17:43:42,067 INFO misc.py line 119 131400] Train: [52/100][799/800] Data 0.003 (0.004) Batch 0.281 (0.335) Remain 03:34:32 loss: 0.3096 Lr: 0.00305 [2023-12-20 17:43:42,388 INFO misc.py line 119 131400] Train: [52/100][800/800] Data 0.003 (0.004) Batch 0.321 (0.335) Remain 03:34:31 loss: 0.4210 Lr: 0.00305 [2023-12-20 17:43:42,389 INFO misc.py line 136 131400] Train result: loss: 0.3475 [2023-12-20 17:43:42,389 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:44:11,687 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.3960 [2023-12-20 17:44:11,790 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2345 [2023-12-20 17:44:11,904 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6079 [2023-12-20 17:44:12,014 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2773 [2023-12-20 17:44:12,133 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3241 [2023-12-20 17:44:12,250 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.8953 [2023-12-20 17:44:12,359 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8623 [2023-12-20 17:44:12,469 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6843 [2023-12-20 17:44:12,552 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2766 [2023-12-20 17:44:12,636 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3105 [2023-12-20 17:44:12,741 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6272 [2023-12-20 17:44:12,909 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3677 [2023-12-20 17:44:13,036 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5342 [2023-12-20 17:44:13,192 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2305 [2023-12-20 17:44:13,295 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1344 [2023-12-20 17:44:13,436 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6767 [2023-12-20 17:44:13,550 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3589 [2023-12-20 17:44:13,668 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3614 [2023-12-20 17:44:13,788 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1365 [2023-12-20 17:44:13,884 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.9087 [2023-12-20 17:44:14,012 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3510 [2023-12-20 17:44:14,170 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1686 [2023-12-20 17:44:14,298 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.0578 [2023-12-20 17:44:14,443 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3606 [2023-12-20 17:44:14,591 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3517 [2023-12-20 17:44:14,691 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4403 [2023-12-20 17:44:14,855 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4498 [2023-12-20 17:44:14,993 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5142 [2023-12-20 17:44:15,100 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7363 [2023-12-20 17:44:15,251 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3710 [2023-12-20 17:44:15,365 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6522 [2023-12-20 17:44:15,490 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5644 [2023-12-20 17:44:15,577 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1453 [2023-12-20 17:44:15,663 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.3074 [2023-12-20 17:44:15,782 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5974 [2023-12-20 17:44:15,885 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3289 [2023-12-20 17:44:16,029 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7919 [2023-12-20 17:44:16,143 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1642 [2023-12-20 17:44:16,225 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.8467 [2023-12-20 17:44:16,372 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3104 [2023-12-20 17:44:16,528 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0319 [2023-12-20 17:44:16,628 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0664 [2023-12-20 17:44:16,758 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3426 [2023-12-20 17:44:16,900 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.4649 [2023-12-20 17:44:17,020 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9313 [2023-12-20 17:44:17,126 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4512 [2023-12-20 17:44:17,293 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3877 [2023-12-20 17:44:17,389 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3574 [2023-12-20 17:44:17,545 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6721 [2023-12-20 17:44:17,646 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2071 [2023-12-20 17:44:17,728 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6967 [2023-12-20 17:44:17,844 INFO evaluator.py line 159 131400] Test: [52/78] Loss 2.0733 [2023-12-20 17:44:17,999 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.6692 [2023-12-20 17:44:18,132 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3117 [2023-12-20 17:44:18,238 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.0549 [2023-12-20 17:44:18,334 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6086 [2023-12-20 17:44:18,444 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.2665 [2023-12-20 17:44:18,605 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2022 [2023-12-20 17:44:18,705 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4526 [2023-12-20 17:44:18,803 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1657 [2023-12-20 17:44:18,898 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4920 [2023-12-20 17:44:18,987 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3225 [2023-12-20 17:44:19,078 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6337 [2023-12-20 17:44:19,181 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6544 [2023-12-20 17:44:19,307 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.2295 [2023-12-20 17:44:19,395 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2207 [2023-12-20 17:44:19,493 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3750 [2023-12-20 17:44:19,590 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0300 [2023-12-20 17:44:19,677 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3190 [2023-12-20 17:44:19,761 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0251 [2023-12-20 17:44:19,859 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5478 [2023-12-20 17:44:19,958 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4274 [2023-12-20 17:44:20,091 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2247 [2023-12-20 17:44:20,185 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6224 [2023-12-20 17:44:20,304 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7334 [2023-12-20 17:44:20,412 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7295 [2023-12-20 17:44:20,499 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3790 [2023-12-20 17:44:20,657 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1928 [2023-12-20 17:44:22,137 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7320/0.8216/0.9045. [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8671/0.9413 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9634/0.9801 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.5701/0.8425 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8245/0.8738 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9111/0.9518 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8256/0.9474 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7899/0.8737 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6637/0.8053 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6540/0.8486 [2023-12-20 17:44:22,137 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.5484/0.5699 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3659/0.4873 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6924/0.7681 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7098/0.8512 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7526/0.8090 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6732/0.7075 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7219/0.7691 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9356/0.9805 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6489/0.8055 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8896/0.9238 [2023-12-20 17:44:22,138 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6329/0.6953 [2023-12-20 17:44:22,139 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:44:22,140 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:44:22,140 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:44:25,942 INFO misc.py line 119 131400] Train: [53/100][1/800] Data 1.037 (1.037) Batch 1.338 (1.338) Remain 14:16:25 loss: 0.2137 Lr: 0.00305 [2023-12-20 17:44:26,265 INFO misc.py line 119 131400] Train: [53/100][2/800] Data 0.005 (0.005) Batch 0.322 (0.322) Remain 03:25:56 loss: 0.2064 Lr: 0.00305 [2023-12-20 17:44:26,616 INFO misc.py line 119 131400] Train: [53/100][3/800] Data 0.006 (0.006) Batch 0.352 (0.352) Remain 03:45:34 loss: 0.5451 Lr: 0.00305 [2023-12-20 17:44:26,960 INFO misc.py line 119 131400] Train: [53/100][4/800] Data 0.004 (0.004) Batch 0.339 (0.339) Remain 03:37:06 loss: 0.2833 Lr: 0.00305 [2023-12-20 17:44:27,296 INFO misc.py line 119 131400] Train: [53/100][5/800] Data 0.009 (0.006) Batch 0.340 (0.340) Remain 03:37:29 loss: 0.3518 Lr: 0.00305 [2023-12-20 17:44:27,699 INFO misc.py line 119 131400] Train: [53/100][6/800] Data 0.005 (0.006) Batch 0.404 (0.361) Remain 03:51:11 loss: 0.3636 Lr: 0.00305 [2023-12-20 17:44:27,999 INFO misc.py line 119 131400] Train: [53/100][7/800] Data 0.003 (0.005) Batch 0.298 (0.346) Remain 03:41:07 loss: 0.3965 Lr: 0.00305 [2023-12-20 17:44:28,309 INFO misc.py line 119 131400] Train: [53/100][8/800] Data 0.005 (0.005) Batch 0.312 (0.339) Remain 03:36:45 loss: 0.4770 Lr: 0.00305 [2023-12-20 17:44:28,665 INFO misc.py line 119 131400] Train: [53/100][9/800] Data 0.003 (0.005) Batch 0.356 (0.342) Remain 03:38:32 loss: 0.3357 Lr: 0.00305 [2023-12-20 17:44:29,005 INFO misc.py line 119 131400] Train: [53/100][10/800] Data 0.004 (0.005) Batch 0.339 (0.341) Remain 03:38:20 loss: 0.1546 Lr: 0.00305 [2023-12-20 17:44:29,273 INFO misc.py line 119 131400] Train: [53/100][11/800] Data 0.006 (0.005) Batch 0.269 (0.332) Remain 03:32:34 loss: 0.5816 Lr: 0.00305 [2023-12-20 17:44:29,562 INFO misc.py line 119 131400] Train: [53/100][12/800] Data 0.003 (0.005) Batch 0.286 (0.327) Remain 03:29:18 loss: 0.2017 Lr: 0.00305 [2023-12-20 17:44:29,882 INFO misc.py line 119 131400] Train: [53/100][13/800] Data 0.007 (0.005) Batch 0.321 (0.326) Remain 03:28:53 loss: 0.5118 Lr: 0.00305 [2023-12-20 17:44:30,197 INFO misc.py line 119 131400] Train: [53/100][14/800] Data 0.005 (0.005) Batch 0.316 (0.326) Remain 03:28:17 loss: 0.1694 Lr: 0.00305 [2023-12-20 17:44:30,506 INFO misc.py line 119 131400] Train: [53/100][15/800] Data 0.004 (0.005) Batch 0.308 (0.324) Remain 03:27:21 loss: 0.1805 Lr: 0.00305 [2023-12-20 17:44:30,874 INFO misc.py line 119 131400] Train: [53/100][16/800] Data 0.005 (0.005) Batch 0.368 (0.328) Remain 03:29:30 loss: 0.6191 Lr: 0.00305 [2023-12-20 17:44:31,250 INFO misc.py line 119 131400] Train: [53/100][17/800] Data 0.005 (0.005) Batch 0.376 (0.331) Remain 03:31:44 loss: 0.3603 Lr: 0.00305 [2023-12-20 17:44:31,605 INFO misc.py line 119 131400] Train: [53/100][18/800] Data 0.005 (0.005) Batch 0.354 (0.333) Remain 03:32:43 loss: 0.5142 Lr: 0.00305 [2023-12-20 17:44:31,928 INFO misc.py line 119 131400] Train: [53/100][19/800] Data 0.006 (0.005) Batch 0.324 (0.332) Remain 03:32:21 loss: 0.2264 Lr: 0.00305 [2023-12-20 17:44:32,249 INFO misc.py line 119 131400] Train: [53/100][20/800] Data 0.006 (0.005) Batch 0.322 (0.331) Remain 03:31:58 loss: 0.3146 Lr: 0.00305 [2023-12-20 17:44:32,585 INFO misc.py line 119 131400] Train: [53/100][21/800] Data 0.004 (0.005) Batch 0.336 (0.332) Remain 03:32:07 loss: 0.4781 Lr: 0.00305 [2023-12-20 17:44:32,919 INFO misc.py line 119 131400] Train: [53/100][22/800] Data 0.004 (0.005) Batch 0.334 (0.332) Remain 03:32:11 loss: 0.2871 Lr: 0.00305 [2023-12-20 17:44:33,254 INFO misc.py line 119 131400] Train: [53/100][23/800] Data 0.005 (0.005) Batch 0.335 (0.332) Remain 03:32:17 loss: 0.3927 Lr: 0.00305 [2023-12-20 17:44:33,606 INFO misc.py line 119 131400] Train: [53/100][24/800] Data 0.006 (0.005) Batch 0.352 (0.333) Remain 03:32:54 loss: 0.3858 Lr: 0.00305 [2023-12-20 17:44:34,005 INFO misc.py line 119 131400] Train: [53/100][25/800] Data 0.004 (0.005) Batch 0.395 (0.336) Remain 03:34:42 loss: 0.5089 Lr: 0.00305 [2023-12-20 17:44:34,361 INFO misc.py line 119 131400] Train: [53/100][26/800] Data 0.009 (0.005) Batch 0.360 (0.337) Remain 03:35:21 loss: 0.6275 Lr: 0.00305 [2023-12-20 17:44:34,698 INFO misc.py line 119 131400] Train: [53/100][27/800] Data 0.004 (0.005) Batch 0.338 (0.337) Remain 03:35:24 loss: 0.5013 Lr: 0.00305 [2023-12-20 17:44:34,995 INFO misc.py line 119 131400] Train: [53/100][28/800] Data 0.003 (0.005) Batch 0.296 (0.335) Remain 03:34:21 loss: 0.2458 Lr: 0.00305 [2023-12-20 17:44:35,319 INFO misc.py line 119 131400] Train: [53/100][29/800] Data 0.004 (0.005) Batch 0.324 (0.335) Remain 03:34:03 loss: 0.2668 Lr: 0.00305 [2023-12-20 17:44:35,639 INFO misc.py line 119 131400] Train: [53/100][30/800] Data 0.004 (0.005) Batch 0.318 (0.334) Remain 03:33:40 loss: 0.1798 Lr: 0.00305 [2023-12-20 17:44:35,991 INFO misc.py line 119 131400] Train: [53/100][31/800] Data 0.007 (0.005) Batch 0.353 (0.335) Remain 03:34:06 loss: 0.1693 Lr: 0.00305 [2023-12-20 17:44:36,393 INFO misc.py line 119 131400] Train: [53/100][32/800] Data 0.006 (0.005) Batch 0.402 (0.337) Remain 03:35:35 loss: 0.5834 Lr: 0.00305 [2023-12-20 17:44:36,721 INFO misc.py line 119 131400] Train: [53/100][33/800] Data 0.005 (0.005) Batch 0.328 (0.337) Remain 03:35:23 loss: 0.1717 Lr: 0.00305 [2023-12-20 17:44:37,055 INFO misc.py line 119 131400] Train: [53/100][34/800] Data 0.005 (0.005) Batch 0.335 (0.337) Remain 03:35:19 loss: 0.4905 Lr: 0.00305 [2023-12-20 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Train: [53/100][41/800] Data 0.005 (0.005) Batch 0.336 (0.336) Remain 03:35:04 loss: 0.3323 Lr: 0.00305 [2023-12-20 17:44:39,765 INFO misc.py line 119 131400] Train: [53/100][42/800] Data 0.006 (0.005) Batch 0.365 (0.337) Remain 03:35:32 loss: 0.3161 Lr: 0.00305 [2023-12-20 17:44:40,086 INFO misc.py line 119 131400] Train: [53/100][43/800] Data 0.004 (0.005) Batch 0.319 (0.337) Remain 03:35:14 loss: 0.2038 Lr: 0.00305 [2023-12-20 17:44:40,475 INFO misc.py line 119 131400] Train: [53/100][44/800] Data 0.007 (0.005) Batch 0.390 (0.338) Remain 03:36:04 loss: 0.7736 Lr: 0.00305 [2023-12-20 17:44:40,814 INFO misc.py line 119 131400] Train: [53/100][45/800] Data 0.006 (0.005) Batch 0.340 (0.338) Remain 03:36:05 loss: 0.2176 Lr: 0.00305 [2023-12-20 17:44:41,286 INFO misc.py line 119 131400] Train: [53/100][46/800] Data 0.004 (0.005) Batch 0.470 (0.341) Remain 03:38:03 loss: 0.3565 Lr: 0.00305 [2023-12-20 17:44:41,643 INFO misc.py line 119 131400] Train: [53/100][47/800] Data 0.007 (0.005) 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line 119 131400] Train: [53/100][782/800] Data 0.003 (0.005) Batch 0.300 (0.333) Remain 03:29:01 loss: 0.3247 Lr: 0.00296 [2023-12-20 17:48:46,625 INFO misc.py line 119 131400] Train: [53/100][783/800] Data 0.003 (0.005) Batch 0.297 (0.333) Remain 03:28:59 loss: 0.3316 Lr: 0.00295 [2023-12-20 17:48:46,923 INFO misc.py line 119 131400] Train: [53/100][784/800] Data 0.004 (0.005) Batch 0.298 (0.333) Remain 03:28:57 loss: 0.1860 Lr: 0.00295 [2023-12-20 17:48:47,206 INFO misc.py line 119 131400] Train: [53/100][785/800] Data 0.003 (0.005) Batch 0.283 (0.333) Remain 03:28:54 loss: 0.2010 Lr: 0.00295 [2023-12-20 17:48:47,524 INFO misc.py line 119 131400] Train: [53/100][786/800] Data 0.003 (0.005) Batch 0.318 (0.333) Remain 03:28:53 loss: 0.2820 Lr: 0.00295 [2023-12-20 17:48:47,864 INFO misc.py line 119 131400] Train: [53/100][787/800] Data 0.003 (0.005) Batch 0.340 (0.333) Remain 03:28:53 loss: 0.3851 Lr: 0.00295 [2023-12-20 17:48:48,211 INFO misc.py line 119 131400] Train: 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Batch 0.328 (0.333) Remain 03:28:49 loss: 0.3426 Lr: 0.00295 [2023-12-20 17:48:50,497 INFO misc.py line 119 131400] Train: [53/100][795/800] Data 0.004 (0.005) Batch 0.330 (0.333) Remain 03:28:49 loss: 0.5276 Lr: 0.00295 [2023-12-20 17:48:50,819 INFO misc.py line 119 131400] Train: [53/100][796/800] Data 0.003 (0.005) Batch 0.322 (0.333) Remain 03:28:48 loss: 0.2332 Lr: 0.00295 [2023-12-20 17:48:51,138 INFO misc.py line 119 131400] Train: [53/100][797/800] Data 0.003 (0.005) Batch 0.319 (0.333) Remain 03:28:47 loss: 0.2612 Lr: 0.00295 [2023-12-20 17:48:51,447 INFO misc.py line 119 131400] Train: [53/100][798/800] Data 0.004 (0.005) Batch 0.309 (0.333) Remain 03:28:45 loss: 0.5611 Lr: 0.00295 [2023-12-20 17:48:51,739 INFO misc.py line 119 131400] Train: [53/100][799/800] Data 0.003 (0.005) Batch 0.292 (0.333) Remain 03:28:43 loss: 0.3080 Lr: 0.00295 [2023-12-20 17:48:52,019 INFO misc.py line 119 131400] Train: [53/100][800/800] Data 0.003 (0.005) Batch 0.277 (0.333) Remain 03:28:40 loss: 0.4629 Lr: 0.00295 [2023-12-20 17:48:52,019 INFO misc.py line 136 131400] Train result: loss: 0.3428 [2023-12-20 17:48:52,020 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:49:14,292 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1564 [2023-12-20 17:49:14,389 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1758 [2023-12-20 17:49:14,481 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3790 [2023-12-20 17:49:14,591 INFO evaluator.py line 159 131400] Test: [4/78] Loss 0.8752 [2023-12-20 17:49:15,170 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4122 [2023-12-20 17:49:15,281 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.9278 [2023-12-20 17:49:15,376 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1334 [2023-12-20 17:49:15,485 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3158 [2023-12-20 17:49:15,570 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3089 [2023-12-20 17:49:15,665 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3325 [2023-12-20 17:49:15,794 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.6258 [2023-12-20 17:49:15,933 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3676 [2023-12-20 17:49:16,066 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4220 [2023-12-20 17:49:16,221 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2003 [2023-12-20 17:49:16,319 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1863 [2023-12-20 17:49:16,455 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9755 [2023-12-20 17:49:16,571 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2695 [2023-12-20 17:49:16,683 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5961 [2023-12-20 17:49:16,801 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1169 [2023-12-20 17:49:16,885 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4531 [2023-12-20 17:49:16,999 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3053 [2023-12-20 17:49:17,160 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1404 [2023-12-20 17:49:17,281 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.2386 [2023-12-20 17:49:17,425 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1525 [2023-12-20 17:49:17,570 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1680 [2023-12-20 17:49:17,654 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4016 [2023-12-20 17:49:17,810 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5609 [2023-12-20 17:49:17,938 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4723 [2023-12-20 17:49:18,037 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6453 [2023-12-20 17:49:18,184 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4701 [2023-12-20 17:49:18,295 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6578 [2023-12-20 17:49:18,414 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4207 [2023-12-20 17:49:18,501 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1675 [2023-12-20 17:49:18,576 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2539 [2023-12-20 17:49:18,681 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8714 [2023-12-20 17:49:18,778 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3692 [2023-12-20 17:49:18,911 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9393 [2023-12-20 17:49:19,023 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1167 [2023-12-20 17:49:19,104 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7076 [2023-12-20 17:49:19,245 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4226 [2023-12-20 17:49:19,392 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0175 [2023-12-20 17:49:19,492 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1428 [2023-12-20 17:49:19,618 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3311 [2023-12-20 17:49:19,766 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.6715 [2023-12-20 17:49:19,884 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9069 [2023-12-20 17:49:19,995 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2905 [2023-12-20 17:49:20,161 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4442 [2023-12-20 17:49:20,257 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4713 [2023-12-20 17:49:20,402 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3975 [2023-12-20 17:49:20,493 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0642 [2023-12-20 17:49:20,573 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5418 [2023-12-20 17:49:20,689 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4690 [2023-12-20 17:49:20,846 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0131 [2023-12-20 17:49:20,980 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3034 [2023-12-20 17:49:21,081 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.0174 [2023-12-20 17:49:21,172 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5884 [2023-12-20 17:49:21,273 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4599 [2023-12-20 17:49:21,434 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2192 [2023-12-20 17:49:21,528 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2605 [2023-12-20 17:49:21,621 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1619 [2023-12-20 17:49:21,715 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2083 [2023-12-20 17:49:21,805 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.4081 [2023-12-20 17:49:21,890 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3814 [2023-12-20 17:49:21,989 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6339 [2023-12-20 17:49:22,121 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3836 [2023-12-20 17:49:22,205 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2807 [2023-12-20 17:49:22,304 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4433 [2023-12-20 17:49:22,397 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0133 [2023-12-20 17:49:22,480 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3086 [2023-12-20 17:49:22,564 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0121 [2023-12-20 17:49:22,659 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5058 [2023-12-20 17:49:22,747 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7884 [2023-12-20 17:49:22,880 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1733 [2023-12-20 17:49:22,974 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6193 [2023-12-20 17:49:23,088 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5873 [2023-12-20 17:49:23,189 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5631 [2023-12-20 17:49:23,275 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2177 [2023-12-20 17:49:23,428 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2927 [2023-12-20 17:49:24,759 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7484/0.8396/0.9139. [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8678/0.9523 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9656/0.9844 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6885/0.7857 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8373/0.8831 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9181/0.9515 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8579/0.9220 [2023-12-20 17:49:24,759 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7488/0.8359 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6838/0.7804 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6687/0.8225 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8228/0.9251 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3882/0.5127 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7092/0.8041 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6764/0.8411 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7715/0.8280 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6405/0.7608 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7462/0.8176 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9147/0.9656 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6305/0.8030 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8332/0.9239 [2023-12-20 17:49:24,760 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5990/0.6918 [2023-12-20 17:49:24,760 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:49:24,762 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:49:24,762 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:49:28,703 INFO misc.py line 119 131400] Train: [54/100][1/800] Data 1.019 (1.019) Batch 1.334 (1.334) Remain 13:55:49 loss: 0.1342 Lr: 0.00295 [2023-12-20 17:49:29,055 INFO misc.py line 119 131400] Train: [54/100][2/800] Data 0.004 (0.004) Batch 0.352 (0.352) Remain 03:40:48 loss: 0.3839 Lr: 0.00295 [2023-12-20 17:49:29,384 INFO misc.py line 119 131400] Train: [54/100][3/800] Data 0.003 (0.003) Batch 0.328 (0.328) Remain 03:25:24 loss: 0.3551 Lr: 0.00295 [2023-12-20 17:49:29,731 INFO misc.py line 119 131400] Train: [54/100][4/800] Data 0.004 (0.004) Batch 0.347 (0.347) Remain 03:37:26 loss: 0.2788 Lr: 0.00295 [2023-12-20 17:49:30,054 INFO misc.py line 119 131400] Train: [54/100][5/800] Data 0.004 (0.004) Batch 0.323 (0.335) Remain 03:29:52 loss: 0.4385 Lr: 0.00295 [2023-12-20 17:49:30,400 INFO misc.py line 119 131400] Train: [54/100][6/800] Data 0.004 (0.004) Batch 0.346 (0.339) Remain 03:32:09 loss: 0.3887 Lr: 0.00295 [2023-12-20 17:49:30,723 INFO misc.py line 119 131400] Train: [54/100][7/800] Data 0.005 (0.004) Batch 0.324 (0.335) Remain 03:29:51 loss: 0.3246 Lr: 0.00295 [2023-12-20 17:49:31,065 INFO misc.py line 119 131400] Train: [54/100][8/800] Data 0.004 (0.004) Batch 0.342 (0.336) Remain 03:30:45 loss: 0.2819 Lr: 0.00295 [2023-12-20 17:49:31,386 INFO misc.py line 119 131400] Train: [54/100][9/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 03:29:08 loss: 0.2420 Lr: 0.00295 [2023-12-20 17:49:31,707 INFO misc.py line 119 131400] Train: [54/100][10/800] Data 0.003 (0.004) Batch 0.318 (0.332) Remain 03:27:44 loss: 0.4238 Lr: 0.00295 [2023-12-20 17:49:32,039 INFO misc.py line 119 131400] Train: [54/100][11/800] Data 0.006 (0.004) Batch 0.334 (0.332) Remain 03:27:56 loss: 0.4992 Lr: 0.00295 [2023-12-20 17:49:32,394 INFO misc.py line 119 131400] Train: [54/100][12/800] Data 0.004 (0.004) Batch 0.354 (0.334) Remain 03:29:30 loss: 0.3606 Lr: 0.00295 [2023-12-20 17:49:32,695 INFO misc.py line 119 131400] Train: [54/100][13/800] Data 0.005 (0.004) Batch 0.302 (0.331) Remain 03:27:28 loss: 0.1598 Lr: 0.00295 [2023-12-20 17:49:33,018 INFO misc.py line 119 131400] Train: [54/100][14/800] Data 0.003 (0.004) Batch 0.322 (0.330) Remain 03:26:56 loss: 0.3391 Lr: 0.00295 [2023-12-20 17:49:33,347 INFO misc.py line 119 131400] Train: [54/100][15/800] Data 0.004 (0.004) Batch 0.329 (0.330) Remain 03:26:52 loss: 0.3653 Lr: 0.00295 [2023-12-20 17:49:33,651 INFO misc.py line 119 131400] Train: [54/100][16/800] Data 0.004 (0.004) Batch 0.304 (0.328) Remain 03:25:36 loss: 0.5678 Lr: 0.00295 [2023-12-20 17:49:33,941 INFO misc.py line 119 131400] Train: [54/100][17/800] Data 0.004 (0.004) Batch 0.291 (0.326) Remain 03:23:54 loss: 0.2771 Lr: 0.00295 [2023-12-20 17:49:34,471 INFO misc.py line 119 131400] Train: [54/100][18/800] Data 0.003 (0.004) Batch 0.530 (0.339) Remain 03:32:27 loss: 0.3931 Lr: 0.00295 [2023-12-20 17:49:34,719 INFO misc.py line 119 131400] Train: [54/100][19/800] Data 0.003 (0.004) Batch 0.247 (0.333) Remain 03:28:50 loss: 0.3237 Lr: 0.00295 [2023-12-20 17:49:35,073 INFO misc.py line 119 131400] Train: [54/100][20/800] Data 0.005 (0.004) Batch 0.356 (0.335) Remain 03:29:39 loss: 0.3984 Lr: 0.00295 [2023-12-20 17:49:35,400 INFO misc.py line 119 131400] Train: [54/100][21/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 03:29:19 loss: 0.5533 Lr: 0.00295 [2023-12-20 17:49:35,751 INFO misc.py line 119 131400] Train: [54/100][22/800] Data 0.005 (0.004) Batch 0.351 (0.335) Remain 03:29:52 loss: 0.2789 Lr: 0.00295 [2023-12-20 17:49:36,104 INFO misc.py line 119 131400] Train: [54/100][23/800] Data 0.004 (0.004) Batch 0.353 (0.336) Remain 03:30:26 loss: 0.3650 Lr: 0.00295 [2023-12-20 17:49:36,445 INFO misc.py line 119 131400] Train: [54/100][24/800] Data 0.004 (0.004) Batch 0.341 (0.336) Remain 03:30:35 loss: 0.2746 Lr: 0.00295 [2023-12-20 17:49:36,784 INFO misc.py line 119 131400] Train: [54/100][25/800] Data 0.004 (0.004) Batch 0.338 (0.336) Remain 03:30:38 loss: 0.6952 Lr: 0.00295 [2023-12-20 17:49:37,109 INFO misc.py line 119 131400] Train: [54/100][26/800] Data 0.004 (0.004) Batch 0.321 (0.336) Remain 03:30:13 loss: 0.3647 Lr: 0.00295 [2023-12-20 17:49:37,413 INFO misc.py line 119 131400] Train: [54/100][27/800] Data 0.009 (0.004) Batch 0.308 (0.335) Remain 03:29:28 loss: 0.5345 Lr: 0.00295 [2023-12-20 17:49:37,741 INFO misc.py line 119 131400] Train: [54/100][28/800] Data 0.005 (0.004) Batch 0.328 (0.334) Remain 03:29:19 loss: 0.2988 Lr: 0.00295 [2023-12-20 17:49:38,111 INFO misc.py line 119 131400] Train: [54/100][29/800] Data 0.004 (0.004) Batch 0.366 (0.335) Remain 03:30:04 loss: 0.2107 Lr: 0.00295 [2023-12-20 17:49:38,425 INFO misc.py line 119 131400] Train: [54/100][30/800] Data 0.008 (0.004) Batch 0.318 (0.335) Remain 03:29:39 loss: 0.5809 Lr: 0.00295 [2023-12-20 17:49:38,779 INFO misc.py line 119 131400] Train: [54/100][31/800] Data 0.005 (0.004) Batch 0.354 (0.336) Remain 03:30:05 loss: 0.2492 Lr: 0.00295 [2023-12-20 17:49:39,115 INFO misc.py line 119 131400] Train: [54/100][32/800] Data 0.004 (0.004) Batch 0.336 (0.336) Remain 03:30:05 loss: 0.3904 Lr: 0.00295 [2023-12-20 17:49:39,393 INFO misc.py line 119 131400] Train: [54/100][33/800] Data 0.004 (0.004) Batch 0.278 (0.334) Remain 03:28:54 loss: 0.3603 Lr: 0.00295 [2023-12-20 17:49:39,726 INFO misc.py line 119 131400] Train: [54/100][34/800] Data 0.003 (0.004) Batch 0.333 (0.334) Remain 03:28:52 loss: 0.2189 Lr: 0.00295 [2023-12-20 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03:27:58 loss: 0.2557 Lr: 0.00286 [2023-12-20 17:53:49,232 INFO misc.py line 119 131400] Train: [54/100][770/800] Data 0.004 (0.005) Batch 0.316 (0.339) Remain 03:27:57 loss: 0.3412 Lr: 0.00286 [2023-12-20 17:53:49,520 INFO misc.py line 119 131400] Train: [54/100][771/800] Data 0.004 (0.005) Batch 0.283 (0.339) Remain 03:27:54 loss: 0.2709 Lr: 0.00286 [2023-12-20 17:53:49,836 INFO misc.py line 119 131400] Train: [54/100][772/800] Data 0.008 (0.005) Batch 0.321 (0.339) Remain 03:27:53 loss: 0.3824 Lr: 0.00286 [2023-12-20 17:53:50,133 INFO misc.py line 119 131400] Train: [54/100][773/800] Data 0.003 (0.005) Batch 0.296 (0.339) Remain 03:27:50 loss: 0.6034 Lr: 0.00286 [2023-12-20 17:53:50,481 INFO misc.py line 119 131400] Train: [54/100][774/800] Data 0.004 (0.005) Batch 0.349 (0.339) Remain 03:27:50 loss: 0.4286 Lr: 0.00286 [2023-12-20 17:53:50,856 INFO misc.py line 119 131400] Train: [54/100][775/800] Data 0.004 (0.005) Batch 0.375 (0.339) Remain 03:27:52 loss: 0.6480 Lr: 0.00286 [2023-12-20 17:53:51,170 INFO misc.py line 119 131400] Train: [54/100][776/800] Data 0.004 (0.005) Batch 0.315 (0.339) Remain 03:27:50 loss: 0.2405 Lr: 0.00286 [2023-12-20 17:53:51,514 INFO misc.py line 119 131400] Train: [54/100][777/800] Data 0.003 (0.005) Batch 0.344 (0.339) Remain 03:27:50 loss: 0.1422 Lr: 0.00286 [2023-12-20 17:53:51,852 INFO misc.py line 119 131400] Train: [54/100][778/800] Data 0.003 (0.005) Batch 0.337 (0.339) Remain 03:27:50 loss: 0.1941 Lr: 0.00286 [2023-12-20 17:53:52,172 INFO misc.py line 119 131400] Train: [54/100][779/800] Data 0.004 (0.005) Batch 0.320 (0.339) Remain 03:27:49 loss: 0.1751 Lr: 0.00286 [2023-12-20 17:53:52,484 INFO misc.py line 119 131400] Train: [54/100][780/800] Data 0.003 (0.005) Batch 0.312 (0.339) Remain 03:27:47 loss: 0.3851 Lr: 0.00286 [2023-12-20 17:53:52,815 INFO misc.py line 119 131400] Train: [54/100][781/800] Data 0.003 (0.005) Batch 0.330 (0.339) Remain 03:27:46 loss: 0.1649 Lr: 0.00286 [2023-12-20 17:53:53,128 INFO misc.py line 119 131400] Train: [54/100][782/800] Data 0.004 (0.005) Batch 0.313 (0.339) Remain 03:27:45 loss: 0.3348 Lr: 0.00286 [2023-12-20 17:53:53,450 INFO misc.py line 119 131400] Train: [54/100][783/800] Data 0.005 (0.005) Batch 0.323 (0.339) Remain 03:27:44 loss: 0.2958 Lr: 0.00286 [2023-12-20 17:53:53,781 INFO misc.py line 119 131400] Train: [54/100][784/800] Data 0.004 (0.005) Batch 0.332 (0.339) Remain 03:27:43 loss: 0.2862 Lr: 0.00286 [2023-12-20 17:53:54,149 INFO misc.py line 119 131400] Train: [54/100][785/800] Data 0.003 (0.005) Batch 0.367 (0.339) Remain 03:27:44 loss: 0.3891 Lr: 0.00286 [2023-12-20 17:53:54,508 INFO misc.py line 119 131400] Train: [54/100][786/800] Data 0.005 (0.005) Batch 0.360 (0.339) Remain 03:27:45 loss: 0.2113 Lr: 0.00286 [2023-12-20 17:53:54,845 INFO misc.py line 119 131400] Train: [54/100][787/800] Data 0.004 (0.005) Batch 0.336 (0.339) Remain 03:27:44 loss: 0.3515 Lr: 0.00286 [2023-12-20 17:53:55,171 INFO misc.py line 119 131400] Train: [54/100][788/800] Data 0.006 (0.005) Batch 0.327 (0.339) Remain 03:27:43 loss: 0.2044 Lr: 0.00286 [2023-12-20 17:53:55,492 INFO misc.py line 119 131400] Train: [54/100][789/800] Data 0.005 (0.005) Batch 0.321 (0.339) Remain 03:27:42 loss: 0.2284 Lr: 0.00286 [2023-12-20 17:53:56,034 INFO misc.py line 119 131400] Train: [54/100][790/800] Data 0.004 (0.005) Batch 0.542 (0.339) Remain 03:27:51 loss: 0.4003 Lr: 0.00285 [2023-12-20 17:53:56,409 INFO misc.py line 119 131400] Train: [54/100][791/800] Data 0.004 (0.005) Batch 0.375 (0.339) Remain 03:27:53 loss: 0.3840 Lr: 0.00285 [2023-12-20 17:53:56,752 INFO misc.py line 119 131400] Train: [54/100][792/800] Data 0.003 (0.005) Batch 0.342 (0.339) Remain 03:27:53 loss: 0.3852 Lr: 0.00285 [2023-12-20 17:53:57,077 INFO misc.py line 119 131400] Train: [54/100][793/800] Data 0.004 (0.005) Batch 0.321 (0.339) Remain 03:27:51 loss: 0.2790 Lr: 0.00285 [2023-12-20 17:53:57,400 INFO misc.py line 119 131400] Train: [54/100][794/800] Data 0.008 (0.005) Batch 0.327 (0.339) Remain 03:27:50 loss: 0.2804 Lr: 0.00285 [2023-12-20 17:53:57,715 INFO misc.py line 119 131400] Train: [54/100][795/800] Data 0.004 (0.005) Batch 0.315 (0.339) Remain 03:27:49 loss: 0.4006 Lr: 0.00285 [2023-12-20 17:53:58,057 INFO misc.py line 119 131400] Train: [54/100][796/800] Data 0.004 (0.005) Batch 0.343 (0.339) Remain 03:27:49 loss: 0.3272 Lr: 0.00285 [2023-12-20 17:53:58,374 INFO misc.py line 119 131400] Train: [54/100][797/800] Data 0.003 (0.005) Batch 0.317 (0.339) Remain 03:27:47 loss: 0.2094 Lr: 0.00285 [2023-12-20 17:53:58,710 INFO misc.py line 119 131400] Train: [54/100][798/800] Data 0.003 (0.005) Batch 0.336 (0.339) Remain 03:27:47 loss: 0.2468 Lr: 0.00285 [2023-12-20 17:53:59,040 INFO misc.py line 119 131400] Train: [54/100][799/800] Data 0.003 (0.005) Batch 0.330 (0.339) Remain 03:27:46 loss: 0.3604 Lr: 0.00285 [2023-12-20 17:53:59,356 INFO misc.py line 119 131400] Train: [54/100][800/800] Data 0.003 (0.005) Batch 0.312 (0.339) Remain 03:27:45 loss: 0.1776 Lr: 0.00285 [2023-12-20 17:53:59,357 INFO misc.py line 136 131400] Train result: loss: 0.3385 [2023-12-20 17:53:59,357 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:54:22,365 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2023 [2023-12-20 17:54:22,473 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1288 [2023-12-20 17:54:22,564 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2966 [2023-12-20 17:54:22,683 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1641 [2023-12-20 17:54:22,807 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5743 [2023-12-20 17:54:22,911 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0599 [2023-12-20 17:54:23,007 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9926 [2023-12-20 17:54:23,125 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1154 [2023-12-20 17:54:23,230 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2697 [2023-12-20 17:54:23,331 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2977 [2023-12-20 17:54:23,432 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3977 [2023-12-20 17:54:23,573 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3185 [2023-12-20 17:54:23,700 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2706 [2023-12-20 17:54:23,858 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2099 [2023-12-20 17:54:23,970 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1437 [2023-12-20 17:54:24,125 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7719 [2023-12-20 17:54:24,236 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2817 [2023-12-20 17:54:24,357 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5243 [2023-12-20 17:54:24,487 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1951 [2023-12-20 17:54:24,574 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3111 [2023-12-20 17:54:24,690 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2491 [2023-12-20 17:54:24,857 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1248 [2023-12-20 17:54:24,979 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8277 [2023-12-20 17:54:25,123 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1530 [2023-12-20 17:54:25,268 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2612 [2023-12-20 17:54:25,355 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3960 [2023-12-20 17:54:25,517 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7612 [2023-12-20 17:54:25,649 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6060 [2023-12-20 17:54:25,749 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6251 [2023-12-20 17:54:25,905 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7514 [2023-12-20 17:54:26,019 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7123 [2023-12-20 17:54:26,139 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.5296 [2023-12-20 17:54:26,232 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1291 [2023-12-20 17:54:26,305 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1965 [2023-12-20 17:54:26,405 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0549 [2023-12-20 17:54:26,499 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.9272 [2023-12-20 17:54:26,628 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9183 [2023-12-20 17:54:26,741 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1467 [2023-12-20 17:54:26,827 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5133 [2023-12-20 17:54:26,971 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3764 [2023-12-20 17:54:27,120 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.4205 [2023-12-20 17:54:27,232 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0910 [2023-12-20 17:54:27,370 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4320 [2023-12-20 17:54:27,512 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7735 [2023-12-20 17:54:27,634 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3623 [2023-12-20 17:54:27,738 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2045 [2023-12-20 17:54:27,905 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4374 [2023-12-20 17:54:27,999 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3380 [2023-12-20 17:54:28,147 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3682 [2023-12-20 17:54:28,245 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0282 [2023-12-20 17:54:28,321 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6855 [2023-12-20 17:54:28,429 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4977 [2023-12-20 17:54:28,576 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9845 [2023-12-20 17:54:28,712 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2072 [2023-12-20 17:54:28,816 INFO evaluator.py line 159 131400] Test: [55/78] Loss 2.0954 [2023-12-20 17:54:28,905 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7351 [2023-12-20 17:54:29,007 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3970 [2023-12-20 17:54:29,175 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2323 [2023-12-20 17:54:29,273 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3875 [2023-12-20 17:54:29,366 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1992 [2023-12-20 17:54:29,464 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2406 [2023-12-20 17:54:29,555 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2880 [2023-12-20 17:54:29,643 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6849 [2023-12-20 17:54:29,742 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6966 [2023-12-20 17:54:29,868 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7302 [2023-12-20 17:54:29,952 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3036 [2023-12-20 17:54:30,051 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2576 [2023-12-20 17:54:30,146 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.3700 [2023-12-20 17:54:30,234 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4343 [2023-12-20 17:54:30,318 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.3684 [2023-12-20 17:54:30,416 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7554 [2023-12-20 17:54:30,508 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.3930 [2023-12-20 17:54:30,642 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1611 [2023-12-20 17:54:30,737 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6074 [2023-12-20 17:54:30,860 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6140 [2023-12-20 17:54:30,975 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6605 [2023-12-20 17:54:31,069 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7190 [2023-12-20 17:54:31,232 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.8033 [2023-12-20 17:54:32,683 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7440/0.8327/0.9110. [2023-12-20 17:54:32,683 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8637/0.9430 [2023-12-20 17:54:32,683 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9616/0.9878 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6849/0.7900 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8225/0.8639 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9017/0.9345 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7944/0.9434 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7654/0.8540 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6858/0.8009 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6607/0.8282 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8188/0.9390 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3663/0.5305 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7009/0.7802 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7005/0.8157 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6986/0.7448 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6193/0.7892 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6994/0.7453 [2023-12-20 17:54:32,684 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9554/0.9642 [2023-12-20 17:54:32,685 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6717/0.7928 [2023-12-20 17:54:32,685 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8859/0.9136 [2023-12-20 17:54:32,685 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6233/0.6930 [2023-12-20 17:54:32,685 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:54:32,687 INFO misc.py line 165 131400] Currently Best mIoU: 0.7511 [2023-12-20 17:54:32,687 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:54:36,207 INFO misc.py line 119 131400] Train: [55/100][1/800] Data 1.090 (1.090) Batch 1.354 (1.354) Remain 13:50:20 loss: 0.1800 Lr: 0.00285 [2023-12-20 17:54:36,554 INFO misc.py line 119 131400] Train: [55/100][2/800] Data 0.003 (0.003) Batch 0.346 (0.346) Remain 03:32:25 loss: 0.2055 Lr: 0.00285 [2023-12-20 17:54:36,878 INFO misc.py line 119 131400] Train: [55/100][3/800] Data 0.003 (0.003) Batch 0.325 (0.325) Remain 03:19:06 loss: 0.3431 Lr: 0.00285 [2023-12-20 17:54:37,175 INFO misc.py line 119 131400] Train: [55/100][4/800] Data 0.003 (0.003) Batch 0.295 (0.295) Remain 03:01:06 loss: 0.2954 Lr: 0.00285 [2023-12-20 17:54:37,519 INFO misc.py line 119 131400] Train: [55/100][5/800] Data 0.005 (0.004) Batch 0.345 (0.320) Remain 03:16:18 loss: 0.3458 Lr: 0.00285 [2023-12-20 17:54:37,831 INFO misc.py line 119 131400] Train: [55/100][6/800] Data 0.004 (0.004) Batch 0.312 (0.317) Remain 03:14:36 loss: 0.3812 Lr: 0.00285 [2023-12-20 17:54:38,162 INFO misc.py line 119 131400] Train: [55/100][7/800] Data 0.004 (0.004) Batch 0.332 (0.321) Remain 03:16:46 loss: 0.3423 Lr: 0.00285 [2023-12-20 17:54:38,468 INFO misc.py line 119 131400] Train: [55/100][8/800] Data 0.003 (0.004) Batch 0.306 (0.318) Remain 03:14:53 loss: 0.3691 Lr: 0.00285 [2023-12-20 17:54:38,794 INFO misc.py line 119 131400] Train: [55/100][9/800] Data 0.003 (0.004) Batch 0.326 (0.319) Remain 03:15:40 loss: 0.2384 Lr: 0.00285 [2023-12-20 17:54:39,131 INFO misc.py line 119 131400] Train: [55/100][10/800] Data 0.004 (0.004) Batch 0.335 (0.321) Remain 03:17:04 loss: 0.2311 Lr: 0.00285 [2023-12-20 17:54:39,466 INFO misc.py line 119 131400] Train: [55/100][11/800] Data 0.006 (0.004) Batch 0.331 (0.323) Remain 03:17:46 loss: 0.7279 Lr: 0.00285 [2023-12-20 17:54:39,819 INFO misc.py line 119 131400] Train: [55/100][12/800] Data 0.010 (0.005) Batch 0.360 (0.327) Remain 03:20:18 loss: 0.4597 Lr: 0.00285 [2023-12-20 17:54:40,179 INFO misc.py line 119 131400] Train: [55/100][13/800] Data 0.004 (0.005) Batch 0.360 (0.330) Remain 03:22:20 loss: 0.2563 Lr: 0.00285 [2023-12-20 17:54:40,512 INFO misc.py line 119 131400] Train: [55/100][14/800] Data 0.004 (0.005) Batch 0.330 (0.330) Remain 03:22:20 loss: 0.4268 Lr: 0.00285 [2023-12-20 17:54:40,843 INFO misc.py line 119 131400] Train: [55/100][15/800] Data 0.007 (0.005) Batch 0.333 (0.330) Remain 03:22:30 loss: 0.3106 Lr: 0.00285 [2023-12-20 17:54:41,181 INFO misc.py line 119 131400] Train: [55/100][16/800] Data 0.004 (0.005) Batch 0.339 (0.331) Remain 03:22:54 loss: 0.2794 Lr: 0.00285 [2023-12-20 17:54:41,525 INFO misc.py line 119 131400] Train: [55/100][17/800] Data 0.003 (0.005) Batch 0.340 (0.332) Remain 03:23:17 loss: 0.3892 Lr: 0.00285 [2023-12-20 17:54:41,861 INFO misc.py line 119 131400] Train: [55/100][18/800] Data 0.007 (0.005) Batch 0.339 (0.332) Remain 03:23:36 loss: 0.4443 Lr: 0.00285 [2023-12-20 17:54:42,174 INFO misc.py line 119 131400] Train: [55/100][19/800] Data 0.003 (0.005) Batch 0.314 (0.331) Remain 03:22:54 loss: 0.1916 Lr: 0.00285 [2023-12-20 17:54:42,503 INFO misc.py line 119 131400] Train: [55/100][20/800] Data 0.003 (0.005) Batch 0.327 (0.331) Remain 03:22:45 loss: 0.1519 Lr: 0.00285 [2023-12-20 17:54:42,852 INFO misc.py line 119 131400] Train: [55/100][21/800] Data 0.004 (0.005) Batch 0.350 (0.332) Remain 03:23:24 loss: 0.3255 Lr: 0.00285 [2023-12-20 17:54:43,219 INFO misc.py line 119 131400] Train: [55/100][22/800] Data 0.004 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0.004 (0.004) Batch 0.340 (0.334) Remain 03:20:44 loss: 0.2135 Lr: 0.00276 [2023-12-20 17:58:51,213 INFO misc.py line 119 131400] Train: [55/100][764/800] Data 0.004 (0.004) Batch 0.311 (0.334) Remain 03:20:43 loss: 0.2127 Lr: 0.00276 [2023-12-20 17:58:51,569 INFO misc.py line 119 131400] Train: [55/100][765/800] Data 0.003 (0.004) Batch 0.356 (0.334) Remain 03:20:44 loss: 0.3254 Lr: 0.00276 [2023-12-20 17:58:51,879 INFO misc.py line 119 131400] Train: [55/100][766/800] Data 0.003 (0.004) Batch 0.310 (0.334) Remain 03:20:42 loss: 0.2321 Lr: 0.00276 [2023-12-20 17:58:52,237 INFO misc.py line 119 131400] Train: [55/100][767/800] Data 0.003 (0.004) Batch 0.358 (0.334) Remain 03:20:43 loss: 0.2529 Lr: 0.00276 [2023-12-20 17:58:52,542 INFO misc.py line 119 131400] Train: [55/100][768/800] Data 0.005 (0.004) Batch 0.306 (0.334) Remain 03:20:41 loss: 0.2476 Lr: 0.00276 [2023-12-20 17:58:52,922 INFO misc.py line 119 131400] Train: [55/100][769/800] Data 0.004 (0.004) Batch 0.366 (0.334) Remain 03:20:42 loss: 0.1995 Lr: 0.00276 [2023-12-20 17:58:53,252 INFO misc.py line 119 131400] Train: [55/100][770/800] Data 0.019 (0.004) Batch 0.344 (0.334) Remain 03:20:43 loss: 0.3321 Lr: 0.00276 [2023-12-20 17:58:53,608 INFO misc.py line 119 131400] Train: [55/100][771/800] Data 0.004 (0.004) Batch 0.356 (0.334) Remain 03:20:43 loss: 0.3341 Lr: 0.00276 [2023-12-20 17:58:53,967 INFO misc.py line 119 131400] Train: [55/100][772/800] Data 0.004 (0.004) Batch 0.355 (0.334) Remain 03:20:44 loss: 0.4290 Lr: 0.00276 [2023-12-20 17:58:54,307 INFO misc.py line 119 131400] Train: [55/100][773/800] Data 0.008 (0.004) Batch 0.345 (0.334) Remain 03:20:44 loss: 0.2163 Lr: 0.00276 [2023-12-20 17:58:54,632 INFO misc.py line 119 131400] Train: [55/100][774/800] Data 0.003 (0.004) Batch 0.324 (0.334) Remain 03:20:43 loss: 0.2443 Lr: 0.00276 [2023-12-20 17:58:54,948 INFO misc.py line 119 131400] Train: [55/100][775/800] Data 0.005 (0.004) Batch 0.317 (0.334) Remain 03:20:42 loss: 0.3192 Lr: 0.00276 [2023-12-20 17:58:55,306 INFO misc.py line 119 131400] Train: [55/100][776/800] Data 0.003 (0.004) Batch 0.358 (0.334) Remain 03:20:43 loss: 0.3021 Lr: 0.00276 [2023-12-20 17:58:55,650 INFO misc.py line 119 131400] Train: [55/100][777/800] Data 0.003 (0.004) Batch 0.344 (0.334) Remain 03:20:43 loss: 0.2688 Lr: 0.00276 [2023-12-20 17:58:55,993 INFO misc.py line 119 131400] Train: [55/100][778/800] Data 0.004 (0.004) Batch 0.343 (0.334) Remain 03:20:43 loss: 0.2685 Lr: 0.00276 [2023-12-20 17:58:56,321 INFO misc.py line 119 131400] Train: [55/100][779/800] Data 0.003 (0.004) Batch 0.327 (0.334) Remain 03:20:42 loss: 0.3667 Lr: 0.00276 [2023-12-20 17:58:56,663 INFO misc.py line 119 131400] Train: [55/100][780/800] Data 0.005 (0.004) Batch 0.342 (0.334) Remain 03:20:42 loss: 0.2923 Lr: 0.00276 [2023-12-20 17:58:57,029 INFO misc.py line 119 131400] Train: [55/100][781/800] Data 0.005 (0.004) Batch 0.360 (0.334) Remain 03:20:43 loss: 0.2836 Lr: 0.00276 [2023-12-20 17:58:57,368 INFO misc.py line 119 131400] Train: [55/100][782/800] Data 0.010 (0.004) Batch 0.345 (0.334) Remain 03:20:43 loss: 0.3460 Lr: 0.00276 [2023-12-20 17:58:57,701 INFO misc.py line 119 131400] Train: [55/100][783/800] Data 0.003 (0.004) Batch 0.332 (0.334) Remain 03:20:43 loss: 0.3013 Lr: 0.00276 [2023-12-20 17:58:58,066 INFO misc.py line 119 131400] Train: [55/100][784/800] Data 0.004 (0.004) Batch 0.350 (0.334) Remain 03:20:43 loss: 0.4617 Lr: 0.00276 [2023-12-20 17:58:58,411 INFO misc.py line 119 131400] Train: [55/100][785/800] Data 0.021 (0.004) Batch 0.360 (0.334) Remain 03:20:44 loss: 0.2304 Lr: 0.00276 [2023-12-20 17:58:58,704 INFO misc.py line 119 131400] Train: [55/100][786/800] Data 0.004 (0.004) Batch 0.293 (0.334) Remain 03:20:42 loss: 0.2762 Lr: 0.00276 [2023-12-20 17:58:59,051 INFO misc.py line 119 131400] Train: [55/100][787/800] Data 0.005 (0.004) Batch 0.346 (0.334) Remain 03:20:42 loss: 0.2571 Lr: 0.00276 [2023-12-20 17:58:59,395 INFO misc.py line 119 131400] Train: [55/100][788/800] Data 0.005 (0.004) Batch 0.346 (0.334) Remain 03:20:42 loss: 0.2797 Lr: 0.00276 [2023-12-20 17:58:59,757 INFO misc.py line 119 131400] Train: [55/100][789/800] Data 0.003 (0.004) Batch 0.362 (0.334) Remain 03:20:43 loss: 0.4413 Lr: 0.00276 [2023-12-20 17:59:00,059 INFO misc.py line 119 131400] Train: [55/100][790/800] Data 0.003 (0.004) Batch 0.302 (0.334) Remain 03:20:42 loss: 0.3265 Lr: 0.00276 [2023-12-20 17:59:00,358 INFO misc.py line 119 131400] Train: [55/100][791/800] Data 0.003 (0.004) Batch 0.298 (0.334) Remain 03:20:40 loss: 0.5064 Lr: 0.00276 [2023-12-20 17:59:00,674 INFO misc.py line 119 131400] Train: [55/100][792/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 03:20:38 loss: 0.3330 Lr: 0.00276 [2023-12-20 17:59:01,002 INFO misc.py line 119 131400] Train: [55/100][793/800] Data 0.003 (0.004) Batch 0.328 (0.334) Remain 03:20:38 loss: 0.2994 Lr: 0.00276 [2023-12-20 17:59:01,323 INFO misc.py line 119 131400] Train: [55/100][794/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 03:20:37 loss: 0.5218 Lr: 0.00276 [2023-12-20 17:59:01,607 INFO misc.py line 119 131400] Train: [55/100][795/800] Data 0.009 (0.004) Batch 0.290 (0.334) Remain 03:20:34 loss: 0.4004 Lr: 0.00276 [2023-12-20 17:59:01,923 INFO misc.py line 119 131400] Train: [55/100][796/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 03:20:33 loss: 0.3744 Lr: 0.00276 [2023-12-20 17:59:02,210 INFO misc.py line 119 131400] Train: [55/100][797/800] Data 0.003 (0.004) Batch 0.287 (0.334) Remain 03:20:31 loss: 0.2916 Lr: 0.00276 [2023-12-20 17:59:02,519 INFO misc.py line 119 131400] Train: [55/100][798/800] Data 0.003 (0.004) Batch 0.309 (0.334) Remain 03:20:29 loss: 0.3943 Lr: 0.00276 [2023-12-20 17:59:02,792 INFO misc.py line 119 131400] Train: [55/100][799/800] Data 0.003 (0.004) Batch 0.273 (0.334) Remain 03:20:26 loss: 0.2145 Lr: 0.00275 [2023-12-20 17:59:03,063 INFO misc.py line 119 131400] Train: [55/100][800/800] Data 0.003 (0.004) Batch 0.270 (0.334) Remain 03:20:23 loss: 0.2365 Lr: 0.00275 [2023-12-20 17:59:03,064 INFO misc.py line 136 131400] Train result: loss: 0.3235 [2023-12-20 17:59:03,064 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 17:59:24,458 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1751 [2023-12-20 17:59:25,213 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1134 [2023-12-20 17:59:25,312 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4936 [2023-12-20 17:59:25,420 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.0173 [2023-12-20 17:59:25,551 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3510 [2023-12-20 17:59:25,652 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0108 [2023-12-20 17:59:25,744 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6813 [2023-12-20 17:59:25,859 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.4226 [2023-12-20 17:59:25,953 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2726 [2023-12-20 17:59:26,045 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2870 [2023-12-20 17:59:26,137 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3824 [2023-12-20 17:59:26,278 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2747 [2023-12-20 17:59:26,395 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4216 [2023-12-20 17:59:26,551 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2012 [2023-12-20 17:59:26,658 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1588 [2023-12-20 17:59:26,796 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.0873 [2023-12-20 17:59:26,910 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3402 [2023-12-20 17:59:27,023 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3586 [2023-12-20 17:59:27,135 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2676 [2023-12-20 17:59:27,220 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5342 [2023-12-20 17:59:27,329 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1796 [2023-12-20 17:59:27,491 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1386 [2023-12-20 17:59:27,612 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6975 [2023-12-20 17:59:27,754 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2566 [2023-12-20 17:59:27,909 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2312 [2023-12-20 17:59:28,000 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7039 [2023-12-20 17:59:28,159 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8691 [2023-12-20 17:59:28,281 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6578 [2023-12-20 17:59:28,375 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5955 [2023-12-20 17:59:28,521 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4319 [2023-12-20 17:59:28,635 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6627 [2023-12-20 17:59:28,758 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4100 [2023-12-20 17:59:28,843 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1716 [2023-12-20 17:59:28,918 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2890 [2023-12-20 17:59:29,014 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8884 [2023-12-20 17:59:29,107 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.6126 [2023-12-20 17:59:29,235 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9419 [2023-12-20 17:59:29,353 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1349 [2023-12-20 17:59:29,449 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.8165 [2023-12-20 17:59:29,594 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2736 [2023-12-20 17:59:29,748 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0227 [2023-12-20 17:59:29,863 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1172 [2023-12-20 17:59:29,990 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5365 [2023-12-20 17:59:30,135 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0852 [2023-12-20 17:59:30,259 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2433 [2023-12-20 17:59:30,366 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5580 [2023-12-20 17:59:30,539 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5350 [2023-12-20 17:59:30,632 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3878 [2023-12-20 17:59:30,777 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3744 [2023-12-20 17:59:30,871 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1113 [2023-12-20 17:59:30,970 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5219 [2023-12-20 17:59:31,080 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4746 [2023-12-20 17:59:31,233 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0138 [2023-12-20 17:59:31,374 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3167 [2023-12-20 17:59:31,478 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0557 [2023-12-20 17:59:31,567 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7385 [2023-12-20 17:59:31,669 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4976 [2023-12-20 17:59:31,837 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2563 [2023-12-20 17:59:31,945 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5608 [2023-12-20 17:59:32,043 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5723 [2023-12-20 17:59:32,143 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3922 [2023-12-20 17:59:32,241 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2384 [2023-12-20 17:59:32,331 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5100 [2023-12-20 17:59:32,434 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8253 [2023-12-20 17:59:32,563 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4405 [2023-12-20 17:59:32,655 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2129 [2023-12-20 17:59:32,758 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5950 [2023-12-20 17:59:32,850 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0159 [2023-12-20 17:59:32,941 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2638 [2023-12-20 17:59:33,024 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0222 [2023-12-20 17:59:33,118 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8340 [2023-12-20 17:59:33,211 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4610 [2023-12-20 17:59:33,347 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0857 [2023-12-20 17:59:33,444 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.7549 [2023-12-20 17:59:33,567 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6414 [2023-12-20 17:59:33,671 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5036 [2023-12-20 17:59:33,758 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3065 [2023-12-20 17:59:33,917 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.8448 [2023-12-20 17:59:35,220 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7526/0.8451/0.9135. [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8712/0.9381 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9647/0.9839 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6619/0.7793 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8116/0.8662 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9178/0.9563 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8611/0.9273 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7546/0.8342 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6743/0.8757 [2023-12-20 17:59:35,220 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6797/0.7989 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8357/0.9329 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4008/0.5128 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6879/0.7974 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6639/0.9191 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7581/0.8266 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6758/0.7934 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7493/0.7977 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9252/0.9736 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6648/0.8041 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8821/0.9098 [2023-12-20 17:59:35,221 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6117/0.6750 [2023-12-20 17:59:35,221 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 17:59:35,223 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7526 [2023-12-20 17:59:35,223 INFO misc.py line 165 131400] Currently Best mIoU: 0.7526 [2023-12-20 17:59:35,223 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 17:59:41,818 INFO misc.py line 119 131400] Train: [56/100][1/800] Data 0.937 (0.937) Batch 1.259 (1.259) Remain 12:35:23 loss: 0.1830 Lr: 0.00275 [2023-12-20 17:59:42,147 INFO misc.py line 119 131400] Train: [56/100][2/800] Data 0.003 (0.003) Batch 0.328 (0.328) Remain 03:17:01 loss: 0.2455 Lr: 0.00275 [2023-12-20 17:59:42,477 INFO misc.py line 119 131400] Train: [56/100][3/800] Data 0.003 (0.003) Batch 0.329 (0.329) Remain 03:17:26 loss: 0.2872 Lr: 0.00275 [2023-12-20 17:59:42,846 INFO misc.py line 119 131400] Train: [56/100][4/800] Data 0.005 (0.005) Batch 0.370 (0.370) Remain 03:41:44 loss: 0.3332 Lr: 0.00275 [2023-12-20 17:59:43,190 INFO misc.py line 119 131400] Train: [56/100][5/800] Data 0.003 (0.004) Batch 0.344 (0.357) Remain 03:34:06 loss: 0.3244 Lr: 0.00275 [2023-12-20 17:59:43,519 INFO misc.py line 119 131400] Train: [56/100][6/800] Data 0.004 (0.004) Batch 0.330 (0.348) Remain 03:28:40 loss: 0.1951 Lr: 0.00275 [2023-12-20 17:59:43,819 INFO misc.py line 119 131400] Train: [56/100][7/800] Data 0.003 (0.004) Batch 0.294 (0.335) Remain 03:20:40 loss: 0.4103 Lr: 0.00275 [2023-12-20 17:59:44,160 INFO misc.py line 119 131400] Train: [56/100][8/800] Data 0.008 (0.005) Batch 0.346 (0.337) Remain 03:22:02 loss: 0.1479 Lr: 0.00275 [2023-12-20 17:59:44,456 INFO misc.py line 119 131400] Train: 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(0.328) Remain 03:16:58 loss: 0.3066 Lr: 0.00275 [2023-12-20 17:59:46,773 INFO misc.py line 119 131400] Train: [56/100][16/800] Data 0.003 (0.004) Batch 0.356 (0.331) Remain 03:18:14 loss: 0.3525 Lr: 0.00275 [2023-12-20 17:59:47,084 INFO misc.py line 119 131400] Train: [56/100][17/800] Data 0.003 (0.004) Batch 0.310 (0.329) Remain 03:17:22 loss: 0.2181 Lr: 0.00275 [2023-12-20 17:59:47,414 INFO misc.py line 119 131400] Train: [56/100][18/800] Data 0.003 (0.004) Batch 0.330 (0.329) Remain 03:17:23 loss: 0.2556 Lr: 0.00275 [2023-12-20 17:59:47,785 INFO misc.py line 119 131400] Train: [56/100][19/800] Data 0.004 (0.004) Batch 0.371 (0.332) Remain 03:18:58 loss: 0.3955 Lr: 0.00275 [2023-12-20 17:59:48,094 INFO misc.py line 119 131400] Train: [56/100][20/800] Data 0.002 (0.004) Batch 0.308 (0.330) Remain 03:18:07 loss: 0.2766 Lr: 0.00275 [2023-12-20 17:59:48,425 INFO misc.py line 119 131400] Train: [56/100][21/800] Data 0.004 (0.004) Batch 0.331 (0.330) Remain 03:18:08 loss: 0.2916 Lr: 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line 119 131400] Train: [56/100][28/800] Data 0.003 (0.004) Batch 0.347 (0.331) Remain 03:18:35 loss: 0.2244 Lr: 0.00275 [2023-12-20 17:59:51,100 INFO misc.py line 119 131400] Train: [56/100][29/800] Data 0.004 (0.004) Batch 0.343 (0.332) Remain 03:18:51 loss: 0.3815 Lr: 0.00275 [2023-12-20 17:59:51,394 INFO misc.py line 119 131400] Train: [56/100][30/800] Data 0.004 (0.004) Batch 0.294 (0.330) Remain 03:18:00 loss: 0.2546 Lr: 0.00275 [2023-12-20 17:59:51,688 INFO misc.py line 119 131400] Train: [56/100][31/800] Data 0.003 (0.004) Batch 0.294 (0.329) Remain 03:17:13 loss: 0.4032 Lr: 0.00275 [2023-12-20 17:59:51,975 INFO misc.py line 119 131400] Train: [56/100][32/800] Data 0.003 (0.004) Batch 0.287 (0.328) Remain 03:16:21 loss: 0.1666 Lr: 0.00275 [2023-12-20 17:59:52,305 INFO misc.py line 119 131400] Train: [56/100][33/800] Data 0.003 (0.004) Batch 0.329 (0.328) Remain 03:16:22 loss: 0.4055 Lr: 0.00275 [2023-12-20 17:59:52,663 INFO misc.py line 119 131400] Train: [56/100][34/800] Data 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03:14:28 loss: 0.3908 Lr: 0.00275 [2023-12-20 17:59:54,793 INFO misc.py line 119 131400] Train: [56/100][41/800] Data 0.004 (0.004) Batch 0.311 (0.324) Remain 03:14:15 loss: 0.4035 Lr: 0.00275 [2023-12-20 17:59:55,121 INFO misc.py line 119 131400] Train: [56/100][42/800] Data 0.004 (0.004) Batch 0.328 (0.324) Remain 03:14:19 loss: 0.3674 Lr: 0.00275 [2023-12-20 17:59:55,444 INFO misc.py line 119 131400] Train: [56/100][43/800] Data 0.003 (0.004) Batch 0.321 (0.324) Remain 03:14:15 loss: 0.3541 Lr: 0.00275 [2023-12-20 17:59:55,762 INFO misc.py line 119 131400] Train: [56/100][44/800] Data 0.005 (0.004) Batch 0.320 (0.324) Remain 03:14:11 loss: 0.2540 Lr: 0.00275 [2023-12-20 17:59:56,046 INFO misc.py line 119 131400] Train: [56/100][45/800] Data 0.003 (0.004) Batch 0.284 (0.323) Remain 03:13:36 loss: 0.1999 Lr: 0.00275 [2023-12-20 17:59:56,336 INFO misc.py line 119 131400] Train: [56/100][46/800] Data 0.003 (0.004) Batch 0.290 (0.322) Remain 03:13:08 loss: 0.2152 Lr: 0.00275 [2023-12-20 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Train: [56/100][53/800] Data 0.003 (0.004) Batch 0.316 (0.325) Remain 03:14:59 loss: 0.3995 Lr: 0.00275 [2023-12-20 17:59:59,041 INFO misc.py line 119 131400] Train: [56/100][54/800] Data 0.003 (0.004) Batch 0.291 (0.325) Remain 03:14:35 loss: 0.3735 Lr: 0.00275 [2023-12-20 17:59:59,377 INFO misc.py line 119 131400] Train: [56/100][55/800] Data 0.004 (0.004) Batch 0.336 (0.325) Remain 03:14:42 loss: 0.4765 Lr: 0.00275 [2023-12-20 17:59:59,692 INFO misc.py line 119 131400] Train: [56/100][56/800] Data 0.003 (0.004) Batch 0.314 (0.325) Remain 03:14:34 loss: 0.3778 Lr: 0.00275 [2023-12-20 18:00:00,011 INFO misc.py line 119 131400] Train: [56/100][57/800] Data 0.004 (0.004) Batch 0.319 (0.325) Remain 03:14:30 loss: 0.2369 Lr: 0.00275 [2023-12-20 18:00:00,332 INFO misc.py line 119 131400] Train: [56/100][58/800] Data 0.005 (0.004) Batch 0.322 (0.325) Remain 03:14:28 loss: 0.3123 Lr: 0.00275 [2023-12-20 18:00:00,626 INFO misc.py line 119 131400] Train: [56/100][59/800] Data 0.004 (0.004) Batch 0.293 (0.324) Remain 03:14:08 loss: 0.2618 Lr: 0.00275 [2023-12-20 18:00:00,928 INFO misc.py line 119 131400] Train: [56/100][60/800] Data 0.004 (0.004) Batch 0.301 (0.324) Remain 03:13:53 loss: 0.1905 Lr: 0.00275 [2023-12-20 18:00:01,298 INFO misc.py line 119 131400] Train: [56/100][61/800] Data 0.017 (0.004) Batch 0.372 (0.325) Remain 03:14:23 loss: 0.1207 Lr: 0.00275 [2023-12-20 18:00:01,618 INFO misc.py line 119 131400] Train: [56/100][62/800] Data 0.002 (0.004) Batch 0.315 (0.324) Remain 03:14:17 loss: 0.3744 Lr: 0.00275 [2023-12-20 18:00:01,936 INFO misc.py line 119 131400] Train: [56/100][63/800] Data 0.007 (0.004) Batch 0.321 (0.324) Remain 03:14:14 loss: 0.3060 Lr: 0.00275 [2023-12-20 18:00:02,273 INFO misc.py line 119 131400] Train: [56/100][64/800] Data 0.006 (0.004) Batch 0.338 (0.325) Remain 03:14:22 loss: 0.3479 Lr: 0.00275 [2023-12-20 18:00:02,601 INFO misc.py line 119 131400] Train: [56/100][65/800] Data 0.004 (0.004) Batch 0.329 (0.325) Remain 03:14:24 loss: 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INFO misc.py line 119 131400] Train: [56/100][72/800] Data 0.004 (0.004) Batch 0.343 (0.326) Remain 03:15:17 loss: 0.3802 Lr: 0.00275 [2023-12-20 18:00:05,334 INFO misc.py line 119 131400] Train: [56/100][73/800] Data 0.003 (0.004) Batch 0.355 (0.327) Remain 03:15:31 loss: 0.1773 Lr: 0.00275 [2023-12-20 18:00:05,695 INFO misc.py line 119 131400] Train: [56/100][74/800] Data 0.003 (0.004) Batch 0.360 (0.327) Remain 03:15:48 loss: 0.2195 Lr: 0.00275 [2023-12-20 18:00:06,072 INFO misc.py line 119 131400] Train: [56/100][75/800] Data 0.003 (0.004) Batch 0.377 (0.328) Remain 03:16:13 loss: 0.6207 Lr: 0.00275 [2023-12-20 18:00:06,403 INFO misc.py line 119 131400] Train: [56/100][76/800] Data 0.004 (0.004) Batch 0.331 (0.328) Remain 03:16:14 loss: 0.4229 Lr: 0.00275 [2023-12-20 18:00:06,730 INFO misc.py line 119 131400] Train: [56/100][77/800] Data 0.003 (0.004) Batch 0.327 (0.328) Remain 03:16:13 loss: 0.4836 Lr: 0.00275 [2023-12-20 18:00:07,063 INFO misc.py line 119 131400] Train: 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Batch 0.328 (0.333) Remain 03:15:51 loss: 0.2616 Lr: 0.00266 [2023-12-20 18:03:51,856 INFO misc.py line 119 131400] Train: [56/100][751/800] Data 0.004 (0.004) Batch 0.336 (0.333) Remain 03:15:51 loss: 0.2316 Lr: 0.00266 [2023-12-20 18:03:52,190 INFO misc.py line 119 131400] Train: [56/100][752/800] Data 0.003 (0.004) Batch 0.334 (0.333) Remain 03:15:51 loss: 0.2893 Lr: 0.00266 [2023-12-20 18:03:52,521 INFO misc.py line 119 131400] Train: [56/100][753/800] Data 0.004 (0.004) Batch 0.331 (0.333) Remain 03:15:51 loss: 0.5001 Lr: 0.00266 [2023-12-20 18:03:52,842 INFO misc.py line 119 131400] Train: [56/100][754/800] Data 0.003 (0.004) Batch 0.319 (0.333) Remain 03:15:50 loss: 0.1796 Lr: 0.00266 [2023-12-20 18:03:53,196 INFO misc.py line 119 131400] Train: [56/100][755/800] Data 0.005 (0.004) Batch 0.356 (0.333) Remain 03:15:50 loss: 0.4251 Lr: 0.00266 [2023-12-20 18:03:53,541 INFO misc.py line 119 131400] Train: [56/100][756/800] Data 0.003 (0.004) Batch 0.344 (0.333) Remain 03:15:50 loss: 0.4458 Lr: 0.00266 [2023-12-20 18:03:53,824 INFO misc.py line 119 131400] Train: [56/100][757/800] Data 0.003 (0.004) Batch 0.283 (0.333) Remain 03:15:48 loss: 0.3402 Lr: 0.00266 [2023-12-20 18:03:54,140 INFO misc.py line 119 131400] Train: [56/100][758/800] Data 0.004 (0.004) Batch 0.316 (0.333) Remain 03:15:47 loss: 0.2339 Lr: 0.00266 [2023-12-20 18:03:54,497 INFO misc.py line 119 131400] Train: [56/100][759/800] Data 0.005 (0.004) Batch 0.357 (0.333) Remain 03:15:47 loss: 0.3112 Lr: 0.00266 [2023-12-20 18:03:54,863 INFO misc.py line 119 131400] Train: [56/100][760/800] Data 0.004 (0.004) Batch 0.365 (0.333) Remain 03:15:49 loss: 0.3345 Lr: 0.00266 [2023-12-20 18:03:55,206 INFO misc.py line 119 131400] Train: [56/100][761/800] Data 0.005 (0.004) Batch 0.342 (0.333) Remain 03:15:49 loss: 0.3064 Lr: 0.00266 [2023-12-20 18:03:55,552 INFO misc.py line 119 131400] Train: [56/100][762/800] Data 0.006 (0.004) Batch 0.348 (0.333) Remain 03:15:49 loss: 0.2138 Lr: 0.00266 [2023-12-20 18:03:55,863 INFO misc.py line 119 131400] Train: [56/100][763/800] Data 0.004 (0.004) Batch 0.309 (0.333) Remain 03:15:48 loss: 0.3123 Lr: 0.00266 [2023-12-20 18:03:56,175 INFO misc.py line 119 131400] Train: [56/100][764/800] Data 0.005 (0.004) Batch 0.311 (0.333) Remain 03:15:46 loss: 0.2517 Lr: 0.00266 [2023-12-20 18:03:56,522 INFO misc.py line 119 131400] Train: [56/100][765/800] Data 0.005 (0.004) Batch 0.349 (0.333) Remain 03:15:47 loss: 0.2225 Lr: 0.00266 [2023-12-20 18:03:56,832 INFO misc.py line 119 131400] Train: [56/100][766/800] Data 0.005 (0.004) Batch 0.311 (0.333) Remain 03:15:45 loss: 0.5655 Lr: 0.00266 [2023-12-20 18:03:57,167 INFO misc.py line 119 131400] Train: [56/100][767/800] Data 0.003 (0.004) Batch 0.334 (0.333) Remain 03:15:45 loss: 0.6206 Lr: 0.00266 [2023-12-20 18:03:57,488 INFO misc.py line 119 131400] Train: [56/100][768/800] Data 0.004 (0.004) Batch 0.314 (0.333) Remain 03:15:44 loss: 0.3288 Lr: 0.00266 [2023-12-20 18:03:57,811 INFO misc.py line 119 131400] Train: [56/100][769/800] Data 0.010 (0.004) Batch 0.330 (0.333) Remain 03:15:43 loss: 0.6008 Lr: 0.00266 [2023-12-20 18:03:58,140 INFO misc.py line 119 131400] Train: [56/100][770/800] Data 0.004 (0.004) Batch 0.330 (0.333) Remain 03:15:43 loss: 0.3476 Lr: 0.00266 [2023-12-20 18:03:58,494 INFO misc.py line 119 131400] Train: [56/100][771/800] Data 0.004 (0.004) Batch 0.350 (0.333) Remain 03:15:43 loss: 0.2813 Lr: 0.00266 [2023-12-20 18:03:58,768 INFO misc.py line 119 131400] Train: [56/100][772/800] Data 0.007 (0.004) Batch 0.278 (0.333) Remain 03:15:40 loss: 0.3180 Lr: 0.00266 [2023-12-20 18:03:59,112 INFO misc.py line 119 131400] Train: [56/100][773/800] Data 0.003 (0.004) Batch 0.343 (0.333) Remain 03:15:40 loss: 0.1113 Lr: 0.00266 [2023-12-20 18:03:59,397 INFO misc.py line 119 131400] Train: [56/100][774/800] Data 0.003 (0.004) Batch 0.285 (0.333) Remain 03:15:38 loss: 0.1816 Lr: 0.00266 [2023-12-20 18:03:59,729 INFO misc.py line 119 131400] Train: [56/100][775/800] Data 0.003 (0.004) Batch 0.333 (0.333) Remain 03:15:37 loss: 0.3398 Lr: 0.00266 [2023-12-20 18:04:00,097 INFO misc.py line 119 131400] Train: [56/100][776/800] Data 0.003 (0.004) Batch 0.368 (0.333) Remain 03:15:39 loss: 0.6318 Lr: 0.00266 [2023-12-20 18:04:00,389 INFO misc.py line 119 131400] Train: [56/100][777/800] Data 0.004 (0.004) Batch 0.292 (0.333) Remain 03:15:36 loss: 0.2026 Lr: 0.00266 [2023-12-20 18:04:00,721 INFO misc.py line 119 131400] Train: [56/100][778/800] Data 0.007 (0.004) Batch 0.331 (0.333) Remain 03:15:36 loss: 0.3410 Lr: 0.00266 [2023-12-20 18:04:01,004 INFO misc.py line 119 131400] Train: [56/100][779/800] Data 0.004 (0.004) Batch 0.284 (0.333) Remain 03:15:34 loss: 0.4275 Lr: 0.00266 [2023-12-20 18:04:01,303 INFO misc.py line 119 131400] Train: [56/100][780/800] Data 0.003 (0.004) Batch 0.297 (0.333) Remain 03:15:32 loss: 0.2259 Lr: 0.00266 [2023-12-20 18:04:01,630 INFO misc.py line 119 131400] Train: [56/100][781/800] Data 0.004 (0.004) Batch 0.327 (0.333) Remain 03:15:31 loss: 0.1725 Lr: 0.00266 [2023-12-20 18:04:01,940 INFO misc.py line 119 131400] Train: [56/100][782/800] Data 0.004 (0.004) Batch 0.311 (0.333) Remain 03:15:30 loss: 0.4359 Lr: 0.00266 [2023-12-20 18:04:02,267 INFO misc.py line 119 131400] Train: [56/100][783/800] Data 0.004 (0.004) Batch 0.327 (0.333) Remain 03:15:29 loss: 0.2215 Lr: 0.00266 [2023-12-20 18:04:02,583 INFO misc.py line 119 131400] Train: [56/100][784/800] Data 0.003 (0.004) Batch 0.316 (0.333) Remain 03:15:28 loss: 0.6503 Lr: 0.00266 [2023-12-20 18:04:02,914 INFO misc.py line 119 131400] Train: [56/100][785/800] Data 0.002 (0.004) Batch 0.330 (0.333) Remain 03:15:27 loss: 0.1862 Lr: 0.00266 [2023-12-20 18:04:03,206 INFO misc.py line 119 131400] Train: [56/100][786/800] Data 0.003 (0.004) Batch 0.293 (0.333) Remain 03:15:25 loss: 0.1847 Lr: 0.00266 [2023-12-20 18:04:03,512 INFO misc.py line 119 131400] Train: [56/100][787/800] Data 0.003 (0.004) Batch 0.306 (0.333) Remain 03:15:24 loss: 0.2241 Lr: 0.00266 [2023-12-20 18:04:03,851 INFO misc.py line 119 131400] Train: [56/100][788/800] Data 0.004 (0.004) Batch 0.340 (0.333) Remain 03:15:24 loss: 0.4016 Lr: 0.00266 [2023-12-20 18:04:04,111 INFO misc.py line 119 131400] Train: [56/100][789/800] Data 0.002 (0.004) Batch 0.260 (0.333) Remain 03:15:20 loss: 0.3907 Lr: 0.00266 [2023-12-20 18:04:04,432 INFO misc.py line 119 131400] Train: [56/100][790/800] Data 0.003 (0.004) Batch 0.320 (0.333) Remain 03:15:19 loss: 0.2149 Lr: 0.00266 [2023-12-20 18:04:04,741 INFO misc.py line 119 131400] Train: [56/100][791/800] Data 0.003 (0.004) Batch 0.309 (0.333) Remain 03:15:18 loss: 0.4091 Lr: 0.00266 [2023-12-20 18:04:05,017 INFO misc.py line 119 131400] Train: [56/100][792/800] Data 0.003 (0.004) Batch 0.276 (0.333) Remain 03:15:15 loss: 0.3381 Lr: 0.00266 [2023-12-20 18:04:05,304 INFO misc.py line 119 131400] Train: [56/100][793/800] Data 0.003 (0.004) Batch 0.287 (0.333) Remain 03:15:13 loss: 0.3960 Lr: 0.00266 [2023-12-20 18:04:05,617 INFO misc.py line 119 131400] Train: [56/100][794/800] Data 0.003 (0.004) Batch 0.313 (0.333) Remain 03:15:11 loss: 0.5772 Lr: 0.00266 [2023-12-20 18:04:05,908 INFO misc.py line 119 131400] Train: [56/100][795/800] Data 0.003 (0.004) Batch 0.290 (0.333) Remain 03:15:09 loss: 0.3622 Lr: 0.00266 [2023-12-20 18:04:06,208 INFO misc.py line 119 131400] Train: [56/100][796/800] Data 0.003 (0.004) Batch 0.300 (0.333) Remain 03:15:07 loss: 0.1618 Lr: 0.00266 [2023-12-20 18:04:06,524 INFO misc.py line 119 131400] Train: [56/100][797/800] Data 0.003 (0.004) Batch 0.316 (0.333) Remain 03:15:06 loss: 0.3649 Lr: 0.00266 [2023-12-20 18:04:06,806 INFO misc.py line 119 131400] Train: [56/100][798/800] Data 0.003 (0.004) Batch 0.282 (0.332) Remain 03:15:04 loss: 0.2786 Lr: 0.00266 [2023-12-20 18:04:07,109 INFO misc.py line 119 131400] Train: [56/100][799/800] Data 0.003 (0.004) Batch 0.300 (0.332) Remain 03:15:02 loss: 0.5025 Lr: 0.00266 [2023-12-20 18:04:07,421 INFO misc.py line 119 131400] Train: [56/100][800/800] Data 0.006 (0.004) Batch 0.315 (0.332) Remain 03:15:01 loss: 0.3244 Lr: 0.00266 [2023-12-20 18:04:07,422 INFO misc.py line 136 131400] Train result: loss: 0.3325 [2023-12-20 18:04:07,422 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:04:30,030 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1909 [2023-12-20 18:04:30,112 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1556 [2023-12-20 18:04:30,210 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3819 [2023-12-20 18:04:30,315 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5535 [2023-12-20 18:04:30,800 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1950 [2023-12-20 18:04:30,900 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.7754 [2023-12-20 18:04:31,003 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0939 [2023-12-20 18:04:31,116 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2148 [2023-12-20 18:04:31,197 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3278 [2023-12-20 18:04:31,285 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3430 [2023-12-20 18:04:31,375 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3762 [2023-12-20 18:04:31,516 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2965 [2023-12-20 18:04:31,640 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2539 [2023-12-20 18:04:31,797 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2269 [2023-12-20 18:04:31,892 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1280 [2023-12-20 18:04:32,029 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7548 [2023-12-20 18:04:32,136 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3352 [2023-12-20 18:04:32,250 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7684 [2023-12-20 18:04:32,363 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.0985 [2023-12-20 18:04:32,438 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.9309 [2023-12-20 18:04:32,542 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1104 [2023-12-20 18:04:32,698 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1361 [2023-12-20 18:04:32,820 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8378 [2023-12-20 18:04:32,962 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2471 [2023-12-20 18:04:33,105 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2057 [2023-12-20 18:04:33,188 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4592 [2023-12-20 18:04:33,349 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.0928 [2023-12-20 18:04:33,473 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4794 [2023-12-20 18:04:33,568 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6018 [2023-12-20 18:04:33,715 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5739 [2023-12-20 18:04:33,825 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8927 [2023-12-20 18:04:33,945 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3798 [2023-12-20 18:04:34,031 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1579 [2023-12-20 18:04:34,100 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2093 [2023-12-20 18:04:34,195 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8527 [2023-12-20 18:04:34,290 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4278 [2023-12-20 18:04:34,421 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0206 [2023-12-20 18:04:34,539 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1106 [2023-12-20 18:04:34,622 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5742 [2023-12-20 18:04:34,764 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3336 [2023-12-20 18:04:34,909 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0220 [2023-12-20 18:04:35,011 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0753 [2023-12-20 18:04:35,136 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5404 [2023-12-20 18:04:35,281 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8306 [2023-12-20 18:04:35,398 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3212 [2023-12-20 18:04:35,500 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5024 [2023-12-20 18:04:35,671 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2926 [2023-12-20 18:04:35,776 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3785 [2023-12-20 18:04:35,923 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4330 [2023-12-20 18:04:36,018 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0475 [2023-12-20 18:04:36,097 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6435 [2023-12-20 18:04:36,204 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5104 [2023-12-20 18:04:36,353 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2191 [2023-12-20 18:04:36,498 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4004 [2023-12-20 18:04:36,604 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6735 [2023-12-20 18:04:36,702 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.9918 [2023-12-20 18:04:36,804 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3911 [2023-12-20 18:04:36,967 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2509 [2023-12-20 18:04:37,067 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3637 [2023-12-20 18:04:37,165 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1810 [2023-12-20 18:04:37,267 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4005 [2023-12-20 18:04:37,374 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2524 [2023-12-20 18:04:37,462 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3480 [2023-12-20 18:04:37,572 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.9175 [2023-12-20 18:04:37,703 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5478 [2023-12-20 18:04:37,789 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2334 [2023-12-20 18:04:37,888 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2365 [2023-12-20 18:04:37,983 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0105 [2023-12-20 18:04:38,075 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4371 [2023-12-20 18:04:38,167 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0152 [2023-12-20 18:04:38,262 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9541 [2023-12-20 18:04:38,358 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5766 [2023-12-20 18:04:38,496 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2285 [2023-12-20 18:04:38,594 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5408 [2023-12-20 18:04:38,711 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5389 [2023-12-20 18:04:38,818 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6605 [2023-12-20 18:04:38,907 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3609 [2023-12-20 18:04:39,065 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2142 [2023-12-20 18:04:40,461 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7524/0.8352/0.9140. [2023-12-20 18:04:40,461 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8673/0.9472 [2023-12-20 18:04:40,461 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9638/0.9841 [2023-12-20 18:04:40,461 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6809/0.8484 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8271/0.8708 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9148/0.9575 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8626/0.9303 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7667/0.8860 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6984/0.8025 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6739/0.8287 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8109/0.9090 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4016/0.5137 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7046/0.8487 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6844/0.8119 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7437/0.7942 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5923/0.6286 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7276/0.7926 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9520/0.9771 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6914/0.8013 [2023-12-20 18:04:40,462 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8903/0.9332 [2023-12-20 18:04:40,463 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5937/0.6377 [2023-12-20 18:04:40,463 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:04:40,465 INFO misc.py line 165 131400] Currently Best mIoU: 0.7526 [2023-12-20 18:04:40,465 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:04:44,253 INFO misc.py line 119 131400] Train: [57/100][1/800] Data 1.259 (1.259) Batch 1.603 (1.603) Remain 15:40:13 loss: 0.3181 Lr: 0.00266 [2023-12-20 18:04:44,586 INFO misc.py line 119 131400] Train: [57/100][2/800] Data 0.003 (0.003) Batch 0.333 (0.333) Remain 03:15:18 loss: 0.2305 Lr: 0.00266 [2023-12-20 18:04:44,937 INFO misc.py line 119 131400] Train: [57/100][3/800] Data 0.003 (0.003) Batch 0.352 (0.352) Remain 03:26:24 loss: 0.2442 Lr: 0.00266 [2023-12-20 18:04:45,247 INFO misc.py line 119 131400] Train: [57/100][4/800] Data 0.003 (0.003) Batch 0.309 (0.309) Remain 03:01:08 loss: 0.2303 Lr: 0.00266 [2023-12-20 18:04:45,585 INFO misc.py line 119 131400] Train: [57/100][5/800] Data 0.003 (0.003) Batch 0.338 (0.324) Remain 03:09:50 loss: 0.5142 Lr: 0.00266 [2023-12-20 18:04:45,921 INFO misc.py line 119 131400] Train: [57/100][6/800] Data 0.004 (0.003) Batch 0.336 (0.328) Remain 03:12:13 loss: 0.1532 Lr: 0.00266 [2023-12-20 18:04:46,227 INFO misc.py line 119 131400] Train: [57/100][7/800] Data 0.004 (0.003) Batch 0.304 (0.322) Remain 03:08:41 loss: 0.2693 Lr: 0.00266 [2023-12-20 18:04:46,581 INFO misc.py line 119 131400] Train: [57/100][8/800] Data 0.006 (0.004) Batch 0.355 (0.328) Remain 03:12:37 loss: 0.2473 Lr: 0.00266 [2023-12-20 18:04:46,897 INFO misc.py line 119 131400] Train: [57/100][9/800] Data 0.006 (0.004) Batch 0.317 (0.327) Remain 03:11:31 loss: 0.3604 Lr: 0.00265 [2023-12-20 18:04:47,239 INFO misc.py line 119 131400] Train: [57/100][10/800] Data 0.004 (0.004) Batch 0.342 (0.329) Remain 03:12:46 loss: 0.2574 Lr: 0.00265 [2023-12-20 18:04:47,579 INFO misc.py line 119 131400] Train: [57/100][11/800] Data 0.004 (0.004) Batch 0.340 (0.330) Remain 03:13:35 loss: 0.1809 Lr: 0.00265 [2023-12-20 18:04:47,928 INFO misc.py line 119 131400] Train: [57/100][12/800] Data 0.003 (0.004) Batch 0.350 (0.332) Remain 03:14:52 loss: 0.4043 Lr: 0.00265 [2023-12-20 18:04:48,285 INFO misc.py line 119 131400] Train: [57/100][13/800] Data 0.004 (0.004) Batch 0.356 (0.335) Remain 03:16:16 loss: 0.3212 Lr: 0.00265 [2023-12-20 18:04:48,635 INFO misc.py line 119 131400] Train: [57/100][14/800] Data 0.004 (0.004) Batch 0.350 (0.336) Remain 03:17:06 loss: 0.3716 Lr: 0.00265 [2023-12-20 18:04:49,008 INFO misc.py line 119 131400] Train: [57/100][15/800] Data 0.004 (0.004) Batch 0.372 (0.339) Remain 03:18:51 loss: 0.2521 Lr: 0.00265 [2023-12-20 18:04:49,375 INFO misc.py line 119 131400] Train: [57/100][16/800] Data 0.005 (0.004) Batch 0.366 (0.341) Remain 03:20:03 loss: 0.3578 Lr: 0.00265 [2023-12-20 18:04:49,721 INFO misc.py line 119 131400] Train: [57/100][17/800] Data 0.009 (0.005) Batch 0.349 (0.342) Remain 03:20:23 loss: 0.1948 Lr: 0.00265 [2023-12-20 18:04:50,021 INFO misc.py line 119 131400] Train: [57/100][18/800] Data 0.003 (0.004) Batch 0.297 (0.339) Remain 03:18:38 loss: 0.5400 Lr: 0.00265 [2023-12-20 18:04:50,393 INFO misc.py line 119 131400] Train: [57/100][19/800] Data 0.006 (0.005) Batch 0.374 (0.341) Remain 03:19:55 loss: 0.3352 Lr: 0.00265 [2023-12-20 18:04:50,729 INFO misc.py line 119 131400] Train: [57/100][20/800] Data 0.004 (0.004) Batch 0.336 (0.341) Remain 03:19:43 loss: 0.2677 Lr: 0.00265 [2023-12-20 18:04:51,086 INFO misc.py line 119 131400] Train: [57/100][21/800] Data 0.004 (0.004) Batch 0.357 (0.342) Remain 03:20:16 loss: 0.4546 Lr: 0.00265 [2023-12-20 18:04:51,437 INFO misc.py line 119 131400] Train: [57/100][22/800] Data 0.004 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loss: 0.1856 Lr: 0.00265 [2023-12-20 18:04:53,899 INFO misc.py line 119 131400] Train: [57/100][29/800] Data 0.003 (0.004) Batch 0.337 (0.345) Remain 03:22:00 loss: 0.3519 Lr: 0.00265 [2023-12-20 18:04:54,214 INFO misc.py line 119 131400] Train: [57/100][30/800] Data 0.004 (0.004) Batch 0.315 (0.344) Remain 03:21:22 loss: 0.2803 Lr: 0.00265 [2023-12-20 18:04:54,529 INFO misc.py line 119 131400] Train: [57/100][31/800] Data 0.004 (0.004) Batch 0.316 (0.343) Remain 03:20:47 loss: 0.4868 Lr: 0.00265 [2023-12-20 18:04:54,854 INFO misc.py line 119 131400] Train: [57/100][32/800] Data 0.003 (0.004) Batch 0.325 (0.342) Remain 03:20:25 loss: 0.2378 Lr: 0.00265 [2023-12-20 18:04:55,139 INFO misc.py line 119 131400] Train: [57/100][33/800] Data 0.003 (0.004) Batch 0.285 (0.340) Remain 03:19:18 loss: 0.2061 Lr: 0.00265 [2023-12-20 18:04:55,482 INFO misc.py line 119 131400] Train: [57/100][34/800] Data 0.003 (0.004) Batch 0.341 (0.340) Remain 03:19:19 loss: 0.3794 Lr: 0.00265 [2023-12-20 18:04:55,792 INFO misc.py line 119 131400] Train: [57/100][35/800] Data 0.005 (0.004) Batch 0.312 (0.339) Remain 03:18:48 loss: 0.4031 Lr: 0.00265 [2023-12-20 18:04:56,118 INFO misc.py line 119 131400] Train: [57/100][36/800] Data 0.003 (0.004) Batch 0.326 (0.339) Remain 03:18:33 loss: 0.4267 Lr: 0.00265 [2023-12-20 18:04:56,420 INFO misc.py line 119 131400] Train: [57/100][37/800] Data 0.003 (0.004) Batch 0.301 (0.338) Remain 03:17:54 loss: 0.3847 Lr: 0.00265 [2023-12-20 18:04:56,720 INFO misc.py line 119 131400] Train: [57/100][38/800] Data 0.005 (0.004) Batch 0.301 (0.337) Remain 03:17:16 loss: 0.5156 Lr: 0.00265 [2023-12-20 18:04:57,051 INFO misc.py line 119 131400] Train: [57/100][39/800] Data 0.003 (0.004) Batch 0.331 (0.336) Remain 03:17:10 loss: 0.3397 Lr: 0.00265 [2023-12-20 18:04:57,368 INFO misc.py line 119 131400] Train: [57/100][40/800] Data 0.004 (0.004) Batch 0.317 (0.336) Remain 03:16:51 loss: 0.2714 Lr: 0.00265 [2023-12-20 18:04:57,687 INFO misc.py line 119 131400] Train: [57/100][41/800] Data 0.003 (0.004) Batch 0.320 (0.336) Remain 03:16:36 loss: 0.6583 Lr: 0.00265 [2023-12-20 18:04:58,016 INFO misc.py line 119 131400] Train: [57/100][42/800] Data 0.003 (0.004) Batch 0.328 (0.335) Remain 03:16:29 loss: 0.3491 Lr: 0.00265 [2023-12-20 18:04:58,333 INFO misc.py line 119 131400] Train: [57/100][43/800] Data 0.003 (0.004) Batch 0.318 (0.335) Remain 03:16:13 loss: 0.3250 Lr: 0.00265 [2023-12-20 18:04:58,636 INFO misc.py line 119 131400] Train: [57/100][44/800] Data 0.003 (0.004) Batch 0.302 (0.334) Remain 03:15:45 loss: 0.7404 Lr: 0.00265 [2023-12-20 18:04:58,958 INFO misc.py line 119 131400] Train: [57/100][45/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 03:15:34 loss: 0.6672 Lr: 0.00265 [2023-12-20 18:04:59,261 INFO misc.py line 119 131400] Train: [57/100][46/800] Data 0.004 (0.004) Batch 0.304 (0.333) Remain 03:15:09 loss: 0.2758 Lr: 0.00265 [2023-12-20 18:04:59,581 INFO misc.py line 119 131400] Train: [57/100][47/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 03:14:57 loss: 0.1512 Lr: 0.00265 [2023-12-20 18:04:59,935 INFO misc.py line 119 131400] Train: [57/100][48/800] Data 0.005 (0.004) Batch 0.355 (0.333) Remain 03:15:14 loss: 0.3675 Lr: 0.00265 [2023-12-20 18:05:00,289 INFO misc.py line 119 131400] Train: [57/100][49/800] Data 0.004 (0.004) Batch 0.353 (0.334) Remain 03:15:28 loss: 0.4415 Lr: 0.00265 [2023-12-20 18:05:00,598 INFO misc.py line 119 131400] Train: [57/100][50/800] Data 0.006 (0.004) Batch 0.309 (0.333) Remain 03:15:10 loss: 0.2223 Lr: 0.00265 [2023-12-20 18:05:00,947 INFO misc.py line 119 131400] Train: [57/100][51/800] Data 0.005 (0.004) Batch 0.344 (0.333) Remain 03:15:18 loss: 0.5751 Lr: 0.00265 [2023-12-20 18:05:01,302 INFO misc.py line 119 131400] Train: [57/100][52/800] Data 0.011 (0.004) Batch 0.360 (0.334) Remain 03:15:37 loss: 0.2103 Lr: 0.00265 [2023-12-20 18:05:01,630 INFO misc.py line 119 131400] Train: [57/100][53/800] Data 0.006 (0.004) Batch 0.329 (0.334) Remain 03:15:33 loss: 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INFO misc.py line 119 131400] Train: [57/100][60/800] Data 0.003 (0.004) Batch 0.314 (0.333) Remain 03:14:52 loss: 0.2620 Lr: 0.00265 [2023-12-20 18:05:04,255 INFO misc.py line 119 131400] Train: [57/100][61/800] Data 0.003 (0.004) Batch 0.351 (0.333) Remain 03:15:03 loss: 0.3172 Lr: 0.00265 [2023-12-20 18:05:04,582 INFO misc.py line 119 131400] Train: [57/100][62/800] Data 0.004 (0.004) Batch 0.327 (0.333) Remain 03:14:58 loss: 0.1837 Lr: 0.00265 [2023-12-20 18:05:04,921 INFO misc.py line 119 131400] Train: [57/100][63/800] Data 0.004 (0.004) Batch 0.341 (0.333) Remain 03:15:02 loss: 0.5802 Lr: 0.00265 [2023-12-20 18:05:05,253 INFO misc.py line 119 131400] Train: [57/100][64/800] Data 0.003 (0.004) Batch 0.331 (0.333) Remain 03:15:01 loss: 0.2022 Lr: 0.00265 [2023-12-20 18:05:05,595 INFO misc.py line 119 131400] Train: [57/100][65/800] Data 0.004 (0.004) Batch 0.342 (0.333) Remain 03:15:06 loss: 0.4749 Lr: 0.00265 [2023-12-20 18:05:05,935 INFO misc.py line 119 131400] Train: 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0.314 (0.333) Remain 03:15:01 loss: 0.1803 Lr: 0.00265 [2023-12-20 18:05:08,221 INFO misc.py line 119 131400] Train: [57/100][73/800] Data 0.004 (0.004) Batch 0.294 (0.333) Remain 03:14:40 loss: 0.3327 Lr: 0.00265 [2023-12-20 18:05:08,540 INFO misc.py line 119 131400] Train: [57/100][74/800] Data 0.010 (0.004) Batch 0.325 (0.332) Remain 03:14:36 loss: 0.4087 Lr: 0.00265 [2023-12-20 18:05:08,855 INFO misc.py line 119 131400] Train: [57/100][75/800] Data 0.003 (0.004) Batch 0.315 (0.332) Remain 03:14:28 loss: 0.2074 Lr: 0.00265 [2023-12-20 18:05:09,179 INFO misc.py line 119 131400] Train: [57/100][76/800] Data 0.003 (0.004) Batch 0.323 (0.332) Remain 03:14:23 loss: 0.3416 Lr: 0.00265 [2023-12-20 18:05:09,497 INFO misc.py line 119 131400] Train: [57/100][77/800] Data 0.004 (0.004) Batch 0.318 (0.332) Remain 03:14:16 loss: 0.1971 Lr: 0.00265 [2023-12-20 18:05:09,819 INFO misc.py line 119 131400] Train: [57/100][78/800] Data 0.004 (0.004) Batch 0.322 (0.332) Remain 03:14:11 loss: 0.3365 Lr: 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Batch 0.296 (0.334) Remain 03:12:01 loss: 0.5852 Lr: 0.00257 [2023-12-20 18:08:51,003 INFO misc.py line 119 131400] Train: [57/100][739/800] Data 0.007 (0.004) Batch 0.325 (0.334) Remain 03:12:01 loss: 0.3388 Lr: 0.00257 [2023-12-20 18:08:51,352 INFO misc.py line 119 131400] Train: [57/100][740/800] Data 0.003 (0.004) Batch 0.349 (0.334) Remain 03:12:01 loss: 0.3667 Lr: 0.00257 [2023-12-20 18:08:51,749 INFO misc.py line 119 131400] Train: [57/100][741/800] Data 0.004 (0.004) Batch 0.395 (0.334) Remain 03:12:04 loss: 0.3266 Lr: 0.00256 [2023-12-20 18:08:52,050 INFO misc.py line 119 131400] Train: [57/100][742/800] Data 0.006 (0.004) Batch 0.302 (0.334) Remain 03:12:02 loss: 0.2348 Lr: 0.00256 [2023-12-20 18:08:52,378 INFO misc.py line 119 131400] Train: [57/100][743/800] Data 0.004 (0.004) Batch 0.329 (0.334) Remain 03:12:01 loss: 0.1286 Lr: 0.00256 [2023-12-20 18:08:52,698 INFO misc.py line 119 131400] Train: [57/100][744/800] Data 0.003 (0.004) Batch 0.319 (0.334) Remain 03:12:00 loss: 0.1793 Lr: 0.00256 [2023-12-20 18:08:53,069 INFO misc.py line 119 131400] Train: [57/100][745/800] Data 0.004 (0.004) Batch 0.362 (0.334) Remain 03:12:01 loss: 0.3296 Lr: 0.00256 [2023-12-20 18:08:53,423 INFO misc.py line 119 131400] Train: [57/100][746/800] Data 0.014 (0.004) Batch 0.364 (0.334) Remain 03:12:02 loss: 0.2438 Lr: 0.00256 [2023-12-20 18:08:53,766 INFO misc.py line 119 131400] Train: [57/100][747/800] Data 0.003 (0.004) Batch 0.342 (0.334) Remain 03:12:02 loss: 0.4488 Lr: 0.00256 [2023-12-20 18:08:54,114 INFO misc.py line 119 131400] Train: [57/100][748/800] Data 0.005 (0.004) Batch 0.341 (0.334) Remain 03:12:02 loss: 0.1827 Lr: 0.00256 [2023-12-20 18:08:54,438 INFO misc.py line 119 131400] Train: [57/100][749/800] Data 0.011 (0.004) Batch 0.332 (0.334) Remain 03:12:02 loss: 0.1613 Lr: 0.00256 [2023-12-20 18:08:54,782 INFO misc.py line 119 131400] Train: [57/100][750/800] Data 0.004 (0.004) Batch 0.343 (0.334) Remain 03:12:02 loss: 0.3143 Lr: 0.00256 [2023-12-20 18:08:55,086 INFO misc.py line 119 131400] Train: [57/100][751/800] Data 0.006 (0.004) Batch 0.305 (0.334) Remain 03:12:00 loss: 0.4302 Lr: 0.00256 [2023-12-20 18:08:55,455 INFO misc.py line 119 131400] Train: [57/100][752/800] Data 0.004 (0.004) Batch 0.368 (0.334) Remain 03:12:01 loss: 0.4446 Lr: 0.00256 [2023-12-20 18:08:55,797 INFO misc.py line 119 131400] Train: [57/100][753/800] Data 0.005 (0.004) Batch 0.342 (0.334) Remain 03:12:01 loss: 0.4276 Lr: 0.00256 [2023-12-20 18:08:56,113 INFO misc.py line 119 131400] Train: [57/100][754/800] Data 0.006 (0.004) Batch 0.318 (0.334) Remain 03:12:00 loss: 0.2015 Lr: 0.00256 [2023-12-20 18:08:56,448 INFO misc.py line 119 131400] Train: [57/100][755/800] Data 0.003 (0.004) Batch 0.335 (0.334) Remain 03:12:00 loss: 0.0951 Lr: 0.00256 [2023-12-20 18:08:56,823 INFO misc.py line 119 131400] Train: [57/100][756/800] Data 0.004 (0.004) Batch 0.374 (0.335) Remain 03:12:01 loss: 0.1847 Lr: 0.00256 [2023-12-20 18:08:57,151 INFO misc.py line 119 131400] Train: [57/100][757/800] Data 0.005 (0.004) Batch 0.328 (0.334) Remain 03:12:01 loss: 0.5438 Lr: 0.00256 [2023-12-20 18:08:57,505 INFO misc.py line 119 131400] Train: [57/100][758/800] Data 0.006 (0.004) Batch 0.356 (0.335) Remain 03:12:01 loss: 0.6383 Lr: 0.00256 [2023-12-20 18:08:57,833 INFO misc.py line 119 131400] Train: [57/100][759/800] Data 0.003 (0.004) Batch 0.327 (0.335) Remain 03:12:01 loss: 0.4483 Lr: 0.00256 [2023-12-20 18:08:58,149 INFO misc.py line 119 131400] Train: [57/100][760/800] Data 0.004 (0.004) Batch 0.315 (0.334) Remain 03:11:59 loss: 0.1765 Lr: 0.00256 [2023-12-20 18:08:58,524 INFO misc.py line 119 131400] Train: [57/100][761/800] Data 0.005 (0.004) Batch 0.376 (0.335) Remain 03:12:01 loss: 0.2204 Lr: 0.00256 [2023-12-20 18:08:58,836 INFO misc.py line 119 131400] Train: [57/100][762/800] Data 0.004 (0.004) Batch 0.312 (0.335) Remain 03:12:00 loss: 0.3275 Lr: 0.00256 [2023-12-20 18:08:59,169 INFO misc.py line 119 131400] Train: [57/100][763/800] Data 0.003 (0.004) Batch 0.334 (0.335) Remain 03:11:59 loss: 0.3880 Lr: 0.00256 [2023-12-20 18:08:59,519 INFO misc.py line 119 131400] Train: [57/100][764/800] Data 0.004 (0.004) Batch 0.348 (0.335) Remain 03:11:59 loss: 0.3803 Lr: 0.00256 [2023-12-20 18:08:59,859 INFO misc.py line 119 131400] Train: [57/100][765/800] Data 0.005 (0.004) Batch 0.342 (0.335) Remain 03:11:59 loss: 0.5565 Lr: 0.00256 [2023-12-20 18:09:00,176 INFO misc.py line 119 131400] Train: [57/100][766/800] Data 0.003 (0.004) Batch 0.317 (0.335) Remain 03:11:58 loss: 0.2060 Lr: 0.00256 [2023-12-20 18:09:00,523 INFO misc.py line 119 131400] Train: [57/100][767/800] Data 0.005 (0.004) Batch 0.345 (0.335) Remain 03:11:58 loss: 0.2975 Lr: 0.00256 [2023-12-20 18:09:00,844 INFO misc.py line 119 131400] Train: [57/100][768/800] Data 0.006 (0.004) Batch 0.323 (0.335) Remain 03:11:58 loss: 0.1783 Lr: 0.00256 [2023-12-20 18:09:01,192 INFO misc.py line 119 131400] Train: [57/100][769/800] Data 0.004 (0.004) Batch 0.348 (0.335) Remain 03:11:58 loss: 0.4659 Lr: 0.00256 [2023-12-20 18:09:01,515 INFO misc.py line 119 131400] Train: [57/100][770/800] Data 0.004 (0.004) Batch 0.323 (0.335) Remain 03:11:57 loss: 0.3490 Lr: 0.00256 [2023-12-20 18:09:01,805 INFO misc.py line 119 131400] Train: [57/100][771/800] Data 0.004 (0.004) Batch 0.290 (0.334) Remain 03:11:55 loss: 0.1876 Lr: 0.00256 [2023-12-20 18:09:02,162 INFO misc.py line 119 131400] Train: [57/100][772/800] Data 0.004 (0.004) Batch 0.356 (0.334) Remain 03:11:55 loss: 0.2877 Lr: 0.00256 [2023-12-20 18:09:02,497 INFO misc.py line 119 131400] Train: [57/100][773/800] Data 0.006 (0.004) Batch 0.334 (0.334) Remain 03:11:55 loss: 0.2039 Lr: 0.00256 [2023-12-20 18:09:02,845 INFO misc.py line 119 131400] Train: [57/100][774/800] Data 0.006 (0.004) Batch 0.349 (0.335) Remain 03:11:55 loss: 0.1535 Lr: 0.00256 [2023-12-20 18:09:03,219 INFO misc.py line 119 131400] Train: [57/100][775/800] Data 0.004 (0.004) Batch 0.375 (0.335) Remain 03:11:57 loss: 0.4455 Lr: 0.00256 [2023-12-20 18:09:03,561 INFO misc.py line 119 131400] Train: [57/100][776/800] Data 0.003 (0.004) Batch 0.341 (0.335) Remain 03:11:57 loss: 0.1096 Lr: 0.00256 [2023-12-20 18:09:03,914 INFO misc.py line 119 131400] Train: [57/100][777/800] Data 0.004 (0.004) Batch 0.353 (0.335) Remain 03:11:57 loss: 0.2503 Lr: 0.00256 [2023-12-20 18:09:04,274 INFO misc.py line 119 131400] Train: [57/100][778/800] Data 0.004 (0.004) Batch 0.361 (0.335) Remain 03:11:58 loss: 0.5859 Lr: 0.00256 [2023-12-20 18:09:04,620 INFO misc.py line 119 131400] Train: [57/100][779/800] Data 0.004 (0.004) Batch 0.343 (0.335) Remain 03:11:58 loss: 0.3062 Lr: 0.00256 [2023-12-20 18:09:04,994 INFO misc.py line 119 131400] Train: [57/100][780/800] Data 0.006 (0.004) Batch 0.377 (0.335) Remain 03:12:00 loss: 0.2958 Lr: 0.00256 [2023-12-20 18:09:05,326 INFO misc.py line 119 131400] Train: [57/100][781/800] Data 0.003 (0.004) Batch 0.332 (0.335) Remain 03:11:59 loss: 0.3698 Lr: 0.00256 [2023-12-20 18:09:05,673 INFO misc.py line 119 131400] Train: [57/100][782/800] Data 0.003 (0.004) Batch 0.342 (0.335) Remain 03:11:59 loss: 0.3135 Lr: 0.00256 [2023-12-20 18:09:05,998 INFO misc.py line 119 131400] Train: [57/100][783/800] Data 0.008 (0.004) Batch 0.330 (0.335) Remain 03:11:59 loss: 0.2626 Lr: 0.00256 [2023-12-20 18:09:06,346 INFO misc.py line 119 131400] Train: [57/100][784/800] Data 0.004 (0.004) Batch 0.348 (0.335) Remain 03:11:59 loss: 0.2457 Lr: 0.00256 [2023-12-20 18:09:06,709 INFO misc.py line 119 131400] Train: [57/100][785/800] Data 0.003 (0.004) Batch 0.363 (0.335) Remain 03:12:00 loss: 0.1649 Lr: 0.00256 [2023-12-20 18:09:07,046 INFO misc.py line 119 131400] Train: [57/100][786/800] Data 0.003 (0.004) Batch 0.336 (0.335) Remain 03:11:59 loss: 0.4478 Lr: 0.00256 [2023-12-20 18:09:07,386 INFO misc.py line 119 131400] Train: [57/100][787/800] Data 0.004 (0.004) Batch 0.340 (0.335) Remain 03:11:59 loss: 0.5971 Lr: 0.00256 [2023-12-20 18:09:07,716 INFO misc.py line 119 131400] Train: [57/100][788/800] Data 0.004 (0.004) Batch 0.329 (0.335) Remain 03:11:59 loss: 0.2390 Lr: 0.00256 [2023-12-20 18:09:08,041 INFO misc.py line 119 131400] Train: [57/100][789/800] Data 0.005 (0.004) Batch 0.326 (0.335) Remain 03:11:58 loss: 0.3097 Lr: 0.00256 [2023-12-20 18:09:08,379 INFO misc.py line 119 131400] Train: [57/100][790/800] Data 0.004 (0.004) Batch 0.337 (0.335) Remain 03:11:58 loss: 0.1429 Lr: 0.00256 [2023-12-20 18:09:08,690 INFO misc.py line 119 131400] Train: [57/100][791/800] Data 0.004 (0.004) Batch 0.312 (0.335) Remain 03:11:57 loss: 0.2909 Lr: 0.00256 [2023-12-20 18:09:08,984 INFO misc.py line 119 131400] Train: [57/100][792/800] Data 0.003 (0.004) Batch 0.294 (0.335) Remain 03:11:54 loss: 0.4314 Lr: 0.00256 [2023-12-20 18:09:09,324 INFO misc.py line 119 131400] Train: [57/100][793/800] Data 0.004 (0.004) Batch 0.340 (0.335) Remain 03:11:54 loss: 0.2257 Lr: 0.00256 [2023-12-20 18:09:09,626 INFO misc.py line 119 131400] Train: [57/100][794/800] Data 0.002 (0.004) Batch 0.301 (0.335) Remain 03:11:53 loss: 0.4399 Lr: 0.00256 [2023-12-20 18:09:09,899 INFO misc.py line 119 131400] Train: [57/100][795/800] Data 0.003 (0.004) Batch 0.274 (0.335) Remain 03:11:50 loss: 0.2194 Lr: 0.00256 [2023-12-20 18:09:10,183 INFO misc.py line 119 131400] Train: [57/100][796/800] Data 0.002 (0.004) Batch 0.284 (0.334) Remain 03:11:47 loss: 0.8413 Lr: 0.00256 [2023-12-20 18:09:10,506 INFO misc.py line 119 131400] Train: [57/100][797/800] Data 0.002 (0.004) Batch 0.323 (0.334) Remain 03:11:46 loss: 0.2596 Lr: 0.00256 [2023-12-20 18:09:10,792 INFO misc.py line 119 131400] Train: [57/100][798/800] Data 0.003 (0.004) Batch 0.286 (0.334) Remain 03:11:44 loss: 0.3665 Lr: 0.00256 [2023-12-20 18:09:11,076 INFO misc.py line 119 131400] Train: [57/100][799/800] Data 0.003 (0.004) Batch 0.284 (0.334) Remain 03:11:41 loss: 0.6717 Lr: 0.00256 [2023-12-20 18:09:11,373 INFO misc.py line 119 131400] Train: [57/100][800/800] Data 0.003 (0.004) Batch 0.297 (0.334) Remain 03:11:39 loss: 0.2127 Lr: 0.00256 [2023-12-20 18:09:11,374 INFO misc.py line 136 131400] Train result: loss: 0.3217 [2023-12-20 18:09:11,374 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:09:36,855 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1139 [2023-12-20 18:09:36,934 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1653 [2023-12-20 18:09:37,025 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3698 [2023-12-20 18:09:37,132 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5301 [2023-12-20 18:09:37,250 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.6106 [2023-12-20 18:09:37,355 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.9974 [2023-12-20 18:09:37,447 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6402 [2023-12-20 18:09:37,556 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1203 [2023-12-20 18:09:37,638 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2755 [2023-12-20 18:09:37,724 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3166 [2023-12-20 18:09:37,821 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5658 [2023-12-20 18:09:37,959 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2803 [2023-12-20 18:09:38,079 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2692 [2023-12-20 18:09:38,234 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2040 [2023-12-20 18:09:38,328 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1950 [2023-12-20 18:09:38,464 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7758 [2023-12-20 18:09:38,579 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2414 [2023-12-20 18:09:38,690 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4098 [2023-12-20 18:09:38,802 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2821 [2023-12-20 18:09:38,880 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5305 [2023-12-20 18:09:38,986 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3030 [2023-12-20 18:09:39,141 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1631 [2023-12-20 18:09:39,263 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.1595 [2023-12-20 18:09:39,404 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1621 [2023-12-20 18:09:39,546 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2421 [2023-12-20 18:09:39,629 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3765 [2023-12-20 18:09:39,786 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6281 [2023-12-20 18:09:39,909 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6484 [2023-12-20 18:09:40,003 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5542 [2023-12-20 18:09:40,147 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6248 [2023-12-20 18:09:40,254 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.8304 [2023-12-20 18:09:40,374 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4401 [2023-12-20 18:09:40,460 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1393 [2023-12-20 18:09:40,529 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2205 [2023-12-20 18:09:40,627 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7567 [2023-12-20 18:09:40,717 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3140 [2023-12-20 18:09:40,848 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9610 [2023-12-20 18:09:40,961 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0953 [2023-12-20 18:09:41,040 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5829 [2023-12-20 18:09:41,181 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3674 [2023-12-20 18:09:41,326 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0284 [2023-12-20 18:09:41,424 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.3430 [2023-12-20 18:09:41,550 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2893 [2023-12-20 18:09:41,695 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7670 [2023-12-20 18:09:41,813 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.7492 [2023-12-20 18:09:41,915 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2394 [2023-12-20 18:09:42,083 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5926 [2023-12-20 18:09:42,183 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3295 [2023-12-20 18:09:42,333 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.0783 [2023-12-20 18:09:42,427 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1118 [2023-12-20 18:09:42,504 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4467 [2023-12-20 18:09:42,610 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.6607 [2023-12-20 18:09:42,757 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1617 [2023-12-20 18:09:42,892 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3195 [2023-12-20 18:09:42,994 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6127 [2023-12-20 18:09:43,081 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.8163 [2023-12-20 18:09:43,186 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3193 [2023-12-20 18:09:43,355 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2447 [2023-12-20 18:09:43,458 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5311 [2023-12-20 18:09:43,556 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2024 [2023-12-20 18:09:43,678 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2104 [2023-12-20 18:09:43,769 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3145 [2023-12-20 18:09:43,854 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3679 [2023-12-20 18:09:43,959 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7463 [2023-12-20 18:09:44,099 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6696 [2023-12-20 18:09:44,195 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2291 [2023-12-20 18:09:44,302 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2751 [2023-12-20 18:09:44,400 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0175 [2023-12-20 18:09:44,487 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2744 [2023-12-20 18:09:44,589 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0181 [2023-12-20 18:09:44,692 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5303 [2023-12-20 18:09:44,799 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5742 [2023-12-20 18:09:44,948 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0919 [2023-12-20 18:09:45,048 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5484 [2023-12-20 18:09:45,174 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6028 [2023-12-20 18:09:45,285 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6685 [2023-12-20 18:09:45,376 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2686 [2023-12-20 18:09:45,537 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3381 [2023-12-20 18:09:46,946 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7483/0.8320/0.9121. [2023-12-20 18:09:46,947 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8598/0.9547 [2023-12-20 18:09:46,947 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9653/0.9828 [2023-12-20 18:09:46,947 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6892/0.7807 [2023-12-20 18:09:46,947 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8395/0.8825 [2023-12-20 18:09:46,947 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9148/0.9564 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8392/0.9659 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7660/0.8261 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6854/0.7762 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6618/0.7967 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8154/0.9064 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3690/0.5222 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7031/0.8324 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7042/0.8808 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6738/0.7104 [2023-12-20 18:09:46,948 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6144/0.7240 [2023-12-20 18:09:46,949 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7489/0.8098 [2023-12-20 18:09:46,949 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9478/0.9614 [2023-12-20 18:09:46,949 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6674/0.7584 [2023-12-20 18:09:46,949 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8948/0.9184 [2023-12-20 18:09:46,949 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6060/0.6930 [2023-12-20 18:09:46,949 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:09:46,950 INFO misc.py line 165 131400] Currently Best mIoU: 0.7526 [2023-12-20 18:09:46,950 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:09:51,332 INFO misc.py line 119 131400] Train: [58/100][1/800] Data 1.265 (1.265) Batch 1.585 (1.585) Remain 15:08:58 loss: 0.3047 Lr: 0.00256 [2023-12-20 18:09:51,645 INFO misc.py line 119 131400] Train: [58/100][2/800] Data 0.005 (0.005) Batch 0.314 (0.314) Remain 03:00:00 loss: 0.4081 Lr: 0.00256 [2023-12-20 18:09:51,950 INFO misc.py line 119 131400] Train: [58/100][3/800] Data 0.005 (0.005) Batch 0.306 (0.306) Remain 02:55:16 loss: 0.2286 Lr: 0.00256 [2023-12-20 18:09:52,283 INFO misc.py line 119 131400] Train: [58/100][4/800] Data 0.003 (0.003) Batch 0.333 (0.333) Remain 03:10:54 loss: 0.2010 Lr: 0.00256 [2023-12-20 18:09:52,588 INFO misc.py line 119 131400] Train: [58/100][5/800] Data 0.003 (0.003) Batch 0.303 (0.318) Remain 03:02:25 loss: 0.4276 Lr: 0.00256 [2023-12-20 18:09:52,925 INFO misc.py line 119 131400] Train: [58/100][6/800] Data 0.006 (0.004) Batch 0.338 (0.325) Remain 03:06:14 loss: 0.3705 Lr: 0.00256 [2023-12-20 18:09:53,259 INFO misc.py line 119 131400] Train: [58/100][7/800] Data 0.005 (0.004) Batch 0.334 (0.327) Remain 03:07:35 loss: 0.3176 Lr: 0.00256 [2023-12-20 18:09:53,612 INFO misc.py line 119 131400] Train: [58/100][8/800] Data 0.003 (0.004) Batch 0.351 (0.332) Remain 03:10:19 loss: 0.3178 Lr: 0.00256 [2023-12-20 18:09:53,944 INFO misc.py line 119 131400] Train: [58/100][9/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 03:10:24 loss: 0.4212 Lr: 0.00256 [2023-12-20 18:09:54,253 INFO misc.py line 119 131400] Train: [58/100][10/800] Data 0.004 (0.004) Batch 0.310 (0.329) Remain 03:08:36 loss: 0.1591 Lr: 0.00256 [2023-12-20 18:09:54,557 INFO misc.py line 119 131400] Train: [58/100][11/800] Data 0.003 (0.004) Batch 0.299 (0.325) Remain 03:06:27 loss: 0.1968 Lr: 0.00256 [2023-12-20 18:09:54,889 INFO misc.py line 119 131400] Train: [58/100][12/800] Data 0.007 (0.004) Batch 0.337 (0.327) Remain 03:07:10 loss: 0.2476 Lr: 0.00256 [2023-12-20 18:09:55,199 INFO misc.py line 119 131400] Train: [58/100][13/800] Data 0.003 (0.004) Batch 0.309 (0.325) Remain 03:06:10 loss: 0.1197 Lr: 0.00256 [2023-12-20 18:09:55,512 INFO misc.py line 119 131400] Train: [58/100][14/800] Data 0.004 (0.004) Batch 0.314 (0.324) Remain 03:05:35 loss: 0.5638 Lr: 0.00256 [2023-12-20 18:09:55,826 INFO misc.py line 119 131400] Train: [58/100][15/800] Data 0.003 (0.004) Batch 0.313 (0.323) Remain 03:05:03 loss: 0.2276 Lr: 0.00256 [2023-12-20 18:09:56,122 INFO misc.py line 119 131400] Train: [58/100][16/800] Data 0.004 (0.004) Batch 0.296 (0.321) Remain 03:03:52 loss: 0.3307 Lr: 0.00256 [2023-12-20 18:09:56,439 INFO misc.py line 119 131400] Train: [58/100][17/800] Data 0.003 (0.004) Batch 0.317 (0.321) Remain 03:03:43 loss: 0.4491 Lr: 0.00256 [2023-12-20 18:09:56,756 INFO misc.py line 119 131400] Train: [58/100][18/800] Data 0.003 (0.004) Batch 0.313 (0.320) Remain 03:03:26 loss: 0.2085 Lr: 0.00256 [2023-12-20 18:09:57,099 INFO misc.py line 119 131400] Train: [58/100][19/800] Data 0.008 (0.004) Batch 0.347 (0.322) Remain 03:04:24 loss: 0.3696 Lr: 0.00256 [2023-12-20 18:09:57,400 INFO misc.py line 119 131400] Train: [58/100][20/800] Data 0.003 (0.004) Batch 0.301 (0.321) Remain 03:03:41 loss: 0.2080 Lr: 0.00256 [2023-12-20 18:09:57,741 INFO misc.py line 119 131400] Train: [58/100][21/800] Data 0.004 (0.004) Batch 0.340 (0.322) Remain 03:04:18 loss: 0.5868 Lr: 0.00256 [2023-12-20 18:09:58,047 INFO misc.py line 119 131400] Train: [58/100][22/800] Data 0.004 (0.004) Batch 0.307 (0.321) Remain 03:03:50 loss: 0.3368 Lr: 0.00256 [2023-12-20 18:09:58,367 INFO misc.py line 119 131400] Train: [58/100][23/800] Data 0.004 (0.004) Batch 0.320 (0.321) Remain 03:03:49 loss: 0.3379 Lr: 0.00255 [2023-12-20 18:09:58,680 INFO misc.py line 119 131400] Train: [58/100][24/800] Data 0.003 (0.004) Batch 0.313 (0.320) Remain 03:03:36 loss: 0.6366 Lr: 0.00255 [2023-12-20 18:09:59,000 INFO misc.py line 119 131400] Train: [58/100][25/800] Data 0.004 (0.004) Batch 0.316 (0.320) Remain 03:03:29 loss: 0.3432 Lr: 0.00255 [2023-12-20 18:09:59,321 INFO misc.py line 119 131400] Train: [58/100][26/800] Data 0.007 (0.004) Batch 0.324 (0.320) Remain 03:03:34 loss: 0.2532 Lr: 0.00255 [2023-12-20 18:09:59,662 INFO misc.py line 119 131400] Train: [58/100][27/800] Data 0.005 (0.004) Batch 0.342 (0.321) Remain 03:04:04 loss: 0.8001 Lr: 0.00255 [2023-12-20 18:09:59,970 INFO misc.py line 119 131400] Train: [58/100][28/800] Data 0.004 (0.004) Batch 0.307 (0.321) Remain 03:03:44 loss: 0.2251 Lr: 0.00255 [2023-12-20 18:10:00,273 INFO misc.py line 119 131400] Train: [58/100][29/800] Data 0.005 (0.004) Batch 0.304 (0.320) Remain 03:03:22 loss: 0.4643 Lr: 0.00255 [2023-12-20 18:10:00,604 INFO misc.py line 119 131400] Train: [58/100][30/800] Data 0.003 (0.004) Batch 0.331 (0.321) Remain 03:03:36 loss: 0.5568 Lr: 0.00255 [2023-12-20 18:10:00,924 INFO misc.py line 119 131400] Train: [58/100][31/800] Data 0.003 (0.004) Batch 0.320 (0.321) Remain 03:03:35 loss: 0.1267 Lr: 0.00255 [2023-12-20 18:10:01,250 INFO misc.py line 119 131400] Train: [58/100][32/800] Data 0.003 (0.004) Batch 0.320 (0.321) Remain 03:03:35 loss: 0.6866 Lr: 0.00255 [2023-12-20 18:10:01,680 INFO misc.py line 119 131400] Train: [58/100][33/800] Data 0.009 (0.004) Batch 0.435 (0.324) Remain 03:05:45 loss: 0.3354 Lr: 0.00255 [2023-12-20 18:10:02,012 INFO misc.py line 119 131400] Train: [58/100][34/800] Data 0.004 (0.004) Batch 0.333 (0.325) Remain 03:05:54 loss: 0.3902 Lr: 0.00255 [2023-12-20 18:10:02,345 INFO misc.py line 119 131400] Train: [58/100][35/800] Data 0.003 (0.004) Batch 0.328 (0.325) Remain 03:05:58 loss: 0.2411 Lr: 0.00255 [2023-12-20 18:10:02,708 INFO misc.py line 119 131400] Train: [58/100][36/800] Data 0.007 (0.004) Batch 0.367 (0.326) Remain 03:06:41 loss: 0.3950 Lr: 0.00255 [2023-12-20 18:10:03,034 INFO misc.py line 119 131400] Train: [58/100][37/800] Data 0.004 (0.004) Batch 0.326 (0.326) Remain 03:06:41 loss: 0.3605 Lr: 0.00255 [2023-12-20 18:10:03,368 INFO misc.py line 119 131400] Train: [58/100][38/800] Data 0.004 (0.004) Batch 0.330 (0.326) Remain 03:06:45 loss: 0.2505 Lr: 0.00255 [2023-12-20 18:10:03,713 INFO misc.py line 119 131400] Train: [58/100][39/800] Data 0.008 (0.004) Batch 0.348 (0.327) Remain 03:07:05 loss: 0.2148 Lr: 0.00255 [2023-12-20 18:10:04,014 INFO misc.py line 119 131400] Train: [58/100][40/800] Data 0.005 (0.004) Batch 0.303 (0.326) Remain 03:06:43 loss: 0.2697 Lr: 0.00255 [2023-12-20 18:10:04,331 INFO misc.py line 119 131400] Train: [58/100][41/800] Data 0.003 (0.004) Batch 0.316 (0.326) Remain 03:06:33 loss: 0.3024 Lr: 0.00255 [2023-12-20 18:10:04,704 INFO misc.py line 119 131400] Train: [58/100][42/800] Data 0.004 (0.004) Batch 0.374 (0.327) Remain 03:07:15 loss: 0.3857 Lr: 0.00255 [2023-12-20 18:10:05,053 INFO misc.py line 119 131400] Train: [58/100][43/800] Data 0.003 (0.004) Batch 0.349 (0.328) Remain 03:07:34 loss: 0.3615 Lr: 0.00255 [2023-12-20 18:10:05,367 INFO misc.py line 119 131400] Train: [58/100][44/800] Data 0.004 (0.004) Batch 0.314 (0.327) Remain 03:07:22 loss: 0.1263 Lr: 0.00255 [2023-12-20 18:10:05,714 INFO misc.py line 119 131400] Train: [58/100][45/800] Data 0.003 (0.004) Batch 0.344 (0.328) Remain 03:07:35 loss: 0.1393 Lr: 0.00255 [2023-12-20 18:10:06,068 INFO misc.py line 119 131400] Train: [58/100][46/800] Data 0.006 (0.004) Batch 0.354 (0.328) Remain 03:07:56 loss: 0.3497 Lr: 0.00255 [2023-12-20 18:10:06,395 INFO misc.py line 119 131400] Train: [58/100][47/800] Data 0.008 (0.004) Batch 0.330 (0.328) Remain 03:07:57 loss: 0.2546 Lr: 0.00255 [2023-12-20 18:10:06,745 INFO misc.py line 119 131400] Train: [58/100][48/800] Data 0.004 (0.004) Batch 0.350 (0.329) Remain 03:08:13 loss: 0.2072 Lr: 0.00255 [2023-12-20 18:10:07,066 INFO misc.py line 119 131400] Train: [58/100][49/800] Data 0.004 (0.004) Batch 0.322 (0.329) Remain 03:08:08 loss: 0.1983 Lr: 0.00255 [2023-12-20 18:10:07,395 INFO misc.py line 119 131400] Train: [58/100][50/800] Data 0.003 (0.004) Batch 0.328 (0.329) Remain 03:08:07 loss: 0.1606 Lr: 0.00255 [2023-12-20 18:10:07,771 INFO misc.py line 119 131400] Train: [58/100][51/800] Data 0.004 (0.004) Batch 0.376 (0.330) Remain 03:08:40 loss: 0.3179 Lr: 0.00255 [2023-12-20 18:10:08,108 INFO misc.py line 119 131400] Train: [58/100][52/800] Data 0.006 (0.004) Batch 0.337 (0.330) Remain 03:08:45 loss: 0.4707 Lr: 0.00255 [2023-12-20 18:10:08,434 INFO misc.py line 119 131400] Train: [58/100][53/800] Data 0.005 (0.004) Batch 0.327 (0.330) Remain 03:08:43 loss: 0.3586 Lr: 0.00255 [2023-12-20 18:10:08,789 INFO misc.py line 119 131400] Train: [58/100][54/800] Data 0.003 (0.004) Batch 0.354 (0.330) Remain 03:08:59 loss: 0.8376 Lr: 0.00255 [2023-12-20 18:10:09,129 INFO misc.py line 119 131400] Train: [58/100][55/800] Data 0.004 (0.004) Batch 0.340 (0.330) Remain 03:09:06 loss: 0.3699 Lr: 0.00255 [2023-12-20 18:10:09,424 INFO misc.py line 119 131400] Train: [58/100][56/800] Data 0.003 (0.004) Batch 0.296 (0.330) Remain 03:08:43 loss: 0.2338 Lr: 0.00255 [2023-12-20 18:10:09,748 INFO misc.py line 119 131400] Train: [58/100][57/800] Data 0.003 (0.004) Batch 0.318 (0.329) Remain 03:08:35 loss: 0.3814 Lr: 0.00255 [2023-12-20 18:10:10,071 INFO misc.py line 119 131400] Train: [58/100][58/800] Data 0.009 (0.004) Batch 0.329 (0.329) Remain 03:08:34 loss: 0.3160 Lr: 0.00255 [2023-12-20 18:10:10,384 INFO misc.py line 119 131400] Train: [58/100][59/800] Data 0.003 (0.004) Batch 0.313 (0.329) Remain 03:08:24 loss: 0.2208 Lr: 0.00255 [2023-12-20 18:10:10,718 INFO misc.py line 119 131400] Train: [58/100][60/800] Data 0.003 (0.004) Batch 0.334 (0.329) Remain 03:08:26 loss: 0.6880 Lr: 0.00255 [2023-12-20 18:10:10,994 INFO misc.py line 119 131400] Train: [58/100][61/800] Data 0.002 (0.004) Batch 0.276 (0.328) Remain 03:07:54 loss: 0.4908 Lr: 0.00255 [2023-12-20 18:10:11,347 INFO misc.py line 119 131400] Train: [58/100][62/800] Data 0.004 (0.004) Batch 0.353 (0.329) Remain 03:08:08 loss: 0.3448 Lr: 0.00255 [2023-12-20 18:10:11,670 INFO misc.py line 119 131400] Train: [58/100][63/800] Data 0.004 (0.004) Batch 0.322 (0.329) Remain 03:08:04 loss: 0.4303 Lr: 0.00255 [2023-12-20 18:10:11,952 INFO misc.py line 119 131400] Train: [58/100][64/800] Data 0.004 (0.004) Batch 0.277 (0.328) Remain 03:07:35 loss: 0.2143 Lr: 0.00255 [2023-12-20 18:10:12,260 INFO misc.py line 119 131400] Train: [58/100][65/800] Data 0.010 (0.004) Batch 0.315 (0.328) Remain 03:07:27 loss: 0.2118 Lr: 0.00255 [2023-12-20 18:10:12,597 INFO misc.py line 119 131400] Train: [58/100][66/800] Data 0.003 (0.004) Batch 0.335 (0.328) Remain 03:07:31 loss: 0.3050 Lr: 0.00255 [2023-12-20 18:10:12,918 INFO misc.py line 119 131400] Train: [58/100][67/800] Data 0.004 (0.004) Batch 0.322 (0.328) Remain 03:07:28 loss: 0.2199 Lr: 0.00255 [2023-12-20 18:10:13,270 INFO misc.py line 119 131400] Train: [58/100][68/800] Data 0.003 (0.004) Batch 0.353 (0.328) Remain 03:07:40 loss: 0.2070 Lr: 0.00255 [2023-12-20 18:10:13,608 INFO misc.py line 119 131400] Train: [58/100][69/800] Data 0.004 (0.004) Batch 0.337 (0.328) Remain 03:07:45 loss: 0.1718 Lr: 0.00255 [2023-12-20 18:10:13,934 INFO misc.py line 119 131400] Train: [58/100][70/800] Data 0.003 (0.004) Batch 0.325 (0.328) Remain 03:07:43 loss: 0.1728 Lr: 0.00255 [2023-12-20 18:10:14,313 INFO misc.py line 119 131400] Train: [58/100][71/800] Data 0.006 (0.004) Batch 0.380 (0.329) Remain 03:08:09 loss: 0.3228 Lr: 0.00255 [2023-12-20 18:10:14,626 INFO misc.py line 119 131400] Train: [58/100][72/800] Data 0.004 (0.004) Batch 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0.006 (0.004) Batch 0.691 (0.335) Remain 03:07:38 loss: 0.1625 Lr: 0.00246 [2023-12-20 18:14:06,924 INFO misc.py line 119 131400] Train: [58/100][764/800] Data 0.005 (0.004) Batch 0.587 (0.335) Remain 03:07:49 loss: 0.1988 Lr: 0.00246 [2023-12-20 18:14:07,236 INFO misc.py line 119 131400] Train: [58/100][765/800] Data 0.004 (0.004) Batch 0.312 (0.335) Remain 03:07:48 loss: 0.2873 Lr: 0.00246 [2023-12-20 18:14:07,582 INFO misc.py line 119 131400] Train: [58/100][766/800] Data 0.004 (0.004) Batch 0.346 (0.335) Remain 03:07:48 loss: 0.3687 Lr: 0.00246 [2023-12-20 18:14:07,922 INFO misc.py line 119 131400] Train: [58/100][767/800] Data 0.008 (0.004) Batch 0.340 (0.335) Remain 03:07:48 loss: 0.3973 Lr: 0.00246 [2023-12-20 18:14:08,249 INFO misc.py line 119 131400] Train: [58/100][768/800] Data 0.003 (0.004) Batch 0.327 (0.335) Remain 03:07:47 loss: 0.6424 Lr: 0.00246 [2023-12-20 18:14:08,584 INFO misc.py line 119 131400] Train: [58/100][769/800] Data 0.004 (0.004) Batch 0.334 (0.335) Remain 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line 119 131400] Train: [58/100][782/800] Data 0.004 (0.004) Batch 0.302 (0.335) Remain 03:07:37 loss: 0.2697 Lr: 0.00246 [2023-12-20 18:14:13,140 INFO misc.py line 119 131400] Train: [58/100][783/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 03:07:36 loss: 0.2751 Lr: 0.00246 [2023-12-20 18:14:13,484 INFO misc.py line 119 131400] Train: [58/100][784/800] Data 0.004 (0.004) Batch 0.344 (0.335) Remain 03:07:36 loss: 0.2008 Lr: 0.00246 [2023-12-20 18:14:13,820 INFO misc.py line 119 131400] Train: [58/100][785/800] Data 0.004 (0.004) Batch 0.336 (0.335) Remain 03:07:36 loss: 0.4381 Lr: 0.00246 [2023-12-20 18:14:14,143 INFO misc.py line 119 131400] Train: [58/100][786/800] Data 0.004 (0.004) Batch 0.323 (0.335) Remain 03:07:35 loss: 0.6370 Lr: 0.00246 [2023-12-20 18:14:14,464 INFO misc.py line 119 131400] Train: [58/100][787/800] Data 0.004 (0.004) Batch 0.323 (0.335) Remain 03:07:34 loss: 0.2598 Lr: 0.00246 [2023-12-20 18:14:14,815 INFO misc.py line 119 131400] Train: 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Batch 0.297 (0.335) Remain 03:07:26 loss: 0.3317 Lr: 0.00246 [2023-12-20 18:14:17,008 INFO misc.py line 119 131400] Train: [58/100][795/800] Data 0.003 (0.004) Batch 0.338 (0.335) Remain 03:07:26 loss: 0.3037 Lr: 0.00246 [2023-12-20 18:14:17,363 INFO misc.py line 119 131400] Train: [58/100][796/800] Data 0.003 (0.004) Batch 0.354 (0.335) Remain 03:07:26 loss: 0.2191 Lr: 0.00246 [2023-12-20 18:14:17,704 INFO misc.py line 119 131400] Train: [58/100][797/800] Data 0.003 (0.004) Batch 0.339 (0.335) Remain 03:07:26 loss: 0.2822 Lr: 0.00246 [2023-12-20 18:14:18,024 INFO misc.py line 119 131400] Train: [58/100][798/800] Data 0.006 (0.004) Batch 0.322 (0.335) Remain 03:07:25 loss: 0.3142 Lr: 0.00246 [2023-12-20 18:14:18,347 INFO misc.py line 119 131400] Train: [58/100][799/800] Data 0.003 (0.004) Batch 0.322 (0.335) Remain 03:07:25 loss: 0.3165 Lr: 0.00246 [2023-12-20 18:14:18,676 INFO misc.py line 119 131400] Train: [58/100][800/800] Data 0.004 (0.004) Batch 0.331 (0.335) Remain 03:07:24 loss: 0.2801 Lr: 0.00246 [2023-12-20 18:14:18,677 INFO misc.py line 136 131400] Train result: loss: 0.3209 [2023-12-20 18:14:18,677 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:14:39,398 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1795 [2023-12-20 18:14:39,479 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1411 [2023-12-20 18:14:40,425 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4590 [2023-12-20 18:14:40,544 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4373 [2023-12-20 18:14:40,664 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4420 [2023-12-20 18:14:40,762 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2624 [2023-12-20 18:14:40,855 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.3753 [2023-12-20 18:14:40,965 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9122 [2023-12-20 18:14:41,051 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2410 [2023-12-20 18:14:41,139 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3489 [2023-12-20 18:14:41,231 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4559 [2023-12-20 18:14:41,367 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2826 [2023-12-20 18:14:41,489 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3875 [2023-12-20 18:14:41,646 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2083 [2023-12-20 18:14:41,743 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1821 [2023-12-20 18:14:41,875 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8603 [2023-12-20 18:14:41,986 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3198 [2023-12-20 18:14:42,100 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3661 [2023-12-20 18:14:42,214 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1073 [2023-12-20 18:14:42,288 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.7159 [2023-12-20 18:14:42,403 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.1303 [2023-12-20 18:14:43,968 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2099 [2023-12-20 18:14:44,064 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.3677 [2023-12-20 18:14:44,155 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3515 [2023-12-20 18:14:44,283 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7710 [2023-12-20 18:14:44,394 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1329 [2023-12-20 18:14:44,477 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5731 [2023-12-20 18:14:44,618 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3113 [2023-12-20 18:14:44,766 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0268 [2023-12-20 18:14:44,869 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1613 [2023-12-20 18:14:44,989 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5126 [2023-12-20 18:14:45,131 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8197 [2023-12-20 18:14:45,249 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3259 [2023-12-20 18:14:45,350 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3236 [2023-12-20 18:14:45,515 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4093 [2023-12-20 18:14:45,610 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4208 [2023-12-20 18:14:45,755 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4482 [2023-12-20 18:14:45,850 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1109 [2023-12-20 18:14:45,925 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4186 [2023-12-20 18:14:46,029 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.6207 [2023-12-20 18:14:46,175 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3632 [2023-12-20 18:14:46,311 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2396 [2023-12-20 18:14:46,417 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4169 [2023-12-20 18:14:46,504 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6934 [2023-12-20 18:14:46,605 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4225 [2023-12-20 18:14:46,766 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2402 [2023-12-20 18:14:46,862 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5077 [2023-12-20 18:14:46,954 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.7419 [2023-12-20 18:14:47,058 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2452 [2023-12-20 18:14:47,148 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2287 [2023-12-20 18:14:47,234 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5651 [2023-12-20 18:14:47,335 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5559 [2023-12-20 18:14:47,460 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1362 [2023-12-20 18:14:47,544 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2525 [2023-12-20 18:14:47,643 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3652 [2023-12-20 18:14:47,738 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0183 [2023-12-20 18:14:47,821 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3319 [2023-12-20 18:14:47,904 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0175 [2023-12-20 18:14:47,998 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8029 [2023-12-20 18:14:48,090 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7733 [2023-12-20 18:14:48,223 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1400 [2023-12-20 18:14:48,316 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6063 [2023-12-20 18:14:48,432 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5695 [2023-12-20 18:14:48,534 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7612 [2023-12-20 18:14:48,619 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6881 [2023-12-20 18:14:48,773 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2079 [2023-12-20 18:14:49,972 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7504/0.8326/0.9114. [2023-12-20 18:14:49,972 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8636/0.9488 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9622/0.9865 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6958/0.8308 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7945/0.8585 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9056/0.9476 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8420/0.9115 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7620/0.8568 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6766/0.7902 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6410/0.7713 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8148/0.8950 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3805/0.5232 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7002/0.8102 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7028/0.8865 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7619/0.8459 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6582/0.6879 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7208/0.7856 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9563/0.9762 [2023-12-20 18:14:49,973 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6772/0.7740 [2023-12-20 18:14:49,974 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8993/0.9200 [2023-12-20 18:14:49,974 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5929/0.6452 [2023-12-20 18:14:49,974 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:14:49,977 INFO misc.py line 165 131400] Currently Best mIoU: 0.7526 [2023-12-20 18:14:49,977 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:14:53,271 INFO misc.py line 119 131400] Train: [59/100][1/800] Data 0.970 (0.970) Batch 1.230 (1.230) Remain 11:28:50 loss: 0.2430 Lr: 0.00246 [2023-12-20 18:14:53,590 INFO misc.py line 119 131400] Train: [59/100][2/800] Data 0.007 (0.007) Batch 0.322 (0.322) Remain 03:00:20 loss: 0.3846 Lr: 0.00246 [2023-12-20 18:14:53,926 INFO misc.py line 119 131400] Train: [59/100][3/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 03:07:27 loss: 0.1296 Lr: 0.00246 [2023-12-20 18:14:54,274 INFO misc.py line 119 131400] Train: [59/100][4/800] Data 0.006 (0.006) Batch 0.344 (0.344) Remain 03:12:42 loss: 0.1436 Lr: 0.00246 [2023-12-20 18:14:54,578 INFO misc.py line 119 131400] Train: [59/100][5/800] Data 0.008 (0.007) Batch 0.308 (0.326) Remain 03:02:26 loss: 0.3781 Lr: 0.00246 [2023-12-20 18:14:54,946 INFO misc.py line 119 131400] Train: [59/100][6/800] Data 0.005 (0.006) Batch 0.369 (0.340) Remain 03:10:28 loss: 0.2888 Lr: 0.00246 [2023-12-20 18:14:55,258 INFO misc.py line 119 131400] Train: [59/100][7/800] Data 0.004 (0.006) Batch 0.313 (0.333) Remain 03:06:35 loss: 0.2663 Lr: 0.00246 [2023-12-20 18:14:55,564 INFO misc.py line 119 131400] Train: [59/100][8/800] Data 0.004 (0.005) Batch 0.307 (0.328) Remain 03:03:35 loss: 0.2445 Lr: 0.00246 [2023-12-20 18:14:56,236 INFO misc.py line 119 131400] Train: [59/100][9/800] Data 0.003 (0.005) Batch 0.671 (0.385) Remain 03:35:36 loss: 0.1865 Lr: 0.00246 [2023-12-20 18:14:56,571 INFO misc.py line 119 131400] Train: [59/100][10/800] Data 0.004 (0.005) Batch 0.334 (0.378) Remain 03:31:30 loss: 0.3490 Lr: 0.00246 [2023-12-20 18:14:57,072 INFO misc.py line 119 131400] Train: [59/100][11/800] Data 0.004 (0.005) Batch 0.502 (0.393) Remain 03:40:13 loss: 0.3624 Lr: 0.00246 [2023-12-20 18:14:57,408 INFO misc.py line 119 131400] Train: [59/100][12/800] Data 0.004 (0.005) Batch 0.328 (0.386) Remain 03:36:08 loss: 0.6015 Lr: 0.00246 [2023-12-20 18:14:57,761 INFO misc.py line 119 131400] Train: [59/100][13/800] Data 0.012 (0.005) Batch 0.360 (0.383) Remain 03:34:40 loss: 0.4158 Lr: 0.00246 [2023-12-20 18:14:58,110 INFO misc.py line 119 131400] Train: [59/100][14/800] Data 0.005 (0.005) Batch 0.349 (0.380) Remain 03:32:55 loss: 0.3950 Lr: 0.00246 [2023-12-20 18:14:58,466 INFO misc.py line 119 131400] Train: [59/100][15/800] Data 0.005 (0.005) Batch 0.345 (0.377) Remain 03:31:16 loss: 0.1478 Lr: 0.00246 [2023-12-20 18:14:58,791 INFO misc.py line 119 131400] Train: [59/100][16/800] Data 0.016 (0.006) Batch 0.337 (0.374) Remain 03:29:31 loss: 0.3722 Lr: 0.00246 [2023-12-20 18:14:59,156 INFO misc.py line 119 131400] Train: [59/100][17/800] Data 0.003 (0.006) Batch 0.364 (0.374) Remain 03:29:06 loss: 0.2932 Lr: 0.00246 [2023-12-20 18:14:59,519 INFO misc.py line 119 131400] Train: [59/100][18/800] Data 0.004 (0.006) Batch 0.362 (0.373) Remain 03:28:40 loss: 0.3385 Lr: 0.00246 [2023-12-20 18:14:59,820 INFO misc.py line 119 131400] Train: [59/100][19/800] Data 0.006 (0.006) Batch 0.300 (0.368) Remain 03:26:07 loss: 0.2761 Lr: 0.00246 [2023-12-20 18:15:00,147 INFO misc.py line 119 131400] Train: [59/100][20/800] Data 0.007 (0.006) Batch 0.328 (0.366) Remain 03:24:47 loss: 0.1917 Lr: 0.00246 [2023-12-20 18:15:00,480 INFO misc.py line 119 131400] Train: [59/100][21/800] Data 0.005 (0.006) Batch 0.335 (0.364) Remain 03:23:49 loss: 0.2289 Lr: 0.00246 [2023-12-20 18:15:00,824 INFO misc.py line 119 131400] Train: [59/100][22/800] Data 0.003 (0.006) Batch 0.338 (0.363) Remain 03:23:02 loss: 0.1834 Lr: 0.00246 [2023-12-20 18:15:01,176 INFO misc.py line 119 131400] Train: [59/100][23/800] Data 0.010 (0.006) Batch 0.357 (0.363) Remain 03:22:52 loss: 0.5180 Lr: 0.00246 [2023-12-20 18:15:01,526 INFO misc.py line 119 131400] Train: [59/100][24/800] Data 0.004 (0.006) Batch 0.351 (0.362) Remain 03:22:32 loss: 0.1789 Lr: 0.00246 [2023-12-20 18:15:01,870 INFO misc.py line 119 131400] Train: [59/100][25/800] Data 0.004 (0.006) Batch 0.343 (0.361) Remain 03:22:03 loss: 0.2504 Lr: 0.00246 [2023-12-20 18:15:02,204 INFO misc.py line 119 131400] Train: [59/100][26/800] Data 0.004 (0.006) Batch 0.333 (0.360) Remain 03:21:22 loss: 0.3037 Lr: 0.00246 [2023-12-20 18:15:02,537 INFO misc.py line 119 131400] Train: [59/100][27/800] Data 0.005 (0.006) Batch 0.334 (0.359) Remain 03:20:46 loss: 0.3995 Lr: 0.00246 [2023-12-20 18:15:02,886 INFO misc.py line 119 131400] Train: [59/100][28/800] Data 0.004 (0.006) Batch 0.348 (0.358) Remain 03:20:31 loss: 0.2575 Lr: 0.00246 [2023-12-20 18:15:03,203 INFO misc.py line 119 131400] Train: [59/100][29/800] Data 0.005 (0.006) Batch 0.318 (0.357) Remain 03:19:39 loss: 0.1867 Lr: 0.00246 [2023-12-20 18:15:03,544 INFO misc.py line 119 131400] Train: [59/100][30/800] Data 0.004 (0.006) Batch 0.342 (0.356) Remain 03:19:20 loss: 0.2480 Lr: 0.00246 [2023-12-20 18:15:03,902 INFO misc.py line 119 131400] Train: [59/100][31/800] Data 0.003 (0.005) Batch 0.356 (0.356) Remain 03:19:20 loss: 0.2282 Lr: 0.00246 [2023-12-20 18:15:04,211 INFO misc.py line 119 131400] Train: [59/100][32/800] Data 0.004 (0.005) Batch 0.310 (0.355) Remain 03:18:26 loss: 0.4781 Lr: 0.00246 [2023-12-20 18:15:04,543 INFO misc.py line 119 131400] Train: [59/100][33/800] Data 0.003 (0.005) Batch 0.330 (0.354) Remain 03:17:57 loss: 0.4158 Lr: 0.00246 [2023-12-20 18:15:04,877 INFO misc.py line 119 131400] Train: [59/100][34/800] Data 0.007 (0.005) Batch 0.320 (0.353) Remain 03:17:20 loss: 0.1840 Lr: 0.00246 [2023-12-20 18:15:05,186 INFO misc.py line 119 131400] Train: [59/100][35/800] Data 0.021 (0.006) Batch 0.325 (0.352) Remain 03:16:51 loss: 0.2198 Lr: 0.00246 [2023-12-20 18:15:05,491 INFO misc.py line 119 131400] Train: [59/100][36/800] Data 0.004 (0.006) Batch 0.305 (0.350) Remain 03:16:03 loss: 0.2705 Lr: 0.00246 [2023-12-20 18:15:05,844 INFO misc.py line 119 131400] Train: [59/100][37/800] Data 0.004 (0.006) Batch 0.354 (0.351) Remain 03:16:05 loss: 0.3850 Lr: 0.00246 [2023-12-20 18:15:06,212 INFO misc.py line 119 131400] Train: [59/100][38/800] Data 0.003 (0.006) Batch 0.368 (0.351) Remain 03:16:22 loss: 0.3015 Lr: 0.00246 [2023-12-20 18:15:06,562 INFO misc.py line 119 131400] Train: [59/100][39/800] Data 0.003 (0.006) Batch 0.350 (0.351) Remain 03:16:20 loss: 0.2800 Lr: 0.00246 [2023-12-20 18:15:06,881 INFO misc.py line 119 131400] Train: [59/100][40/800] Data 0.003 (0.005) Batch 0.318 (0.350) Remain 03:15:50 loss: 0.4870 Lr: 0.00246 [2023-12-20 18:15:07,221 INFO misc.py line 119 131400] Train: [59/100][41/800] Data 0.004 (0.005) Batch 0.340 (0.350) Remain 03:15:41 loss: 0.1685 Lr: 0.00246 [2023-12-20 18:15:07,528 INFO misc.py line 119 131400] Train: [59/100][42/800] Data 0.003 (0.005) Batch 0.308 (0.349) Remain 03:15:05 loss: 0.2451 Lr: 0.00245 [2023-12-20 18:15:07,986 INFO misc.py line 119 131400] Train: [59/100][43/800] Data 0.004 (0.005) Batch 0.457 (0.352) Remain 03:16:35 loss: 0.2324 Lr: 0.00245 [2023-12-20 18:15:08,301 INFO misc.py line 119 131400] Train: [59/100][44/800] Data 0.004 (0.005) Batch 0.316 (0.351) Remain 03:16:06 loss: 0.3526 Lr: 0.00245 [2023-12-20 18:15:08,586 INFO misc.py line 119 131400] Train: [59/100][45/800] Data 0.002 (0.005) Batch 0.285 (0.349) Remain 03:15:13 loss: 0.8583 Lr: 0.00245 [2023-12-20 18:15:08,919 INFO misc.py line 119 131400] Train: [59/100][46/800] Data 0.003 (0.005) Batch 0.332 (0.349) Remain 03:14:59 loss: 0.5002 Lr: 0.00245 [2023-12-20 18:15:09,226 INFO misc.py line 119 131400] Train: [59/100][47/800] Data 0.004 (0.005) Batch 0.307 (0.348) Remain 03:14:27 loss: 0.1024 Lr: 0.00245 [2023-12-20 18:15:09,532 INFO misc.py line 119 131400] Train: [59/100][48/800] Data 0.004 (0.005) Batch 0.307 (0.347) Remain 03:13:56 loss: 0.1564 Lr: 0.00245 [2023-12-20 18:15:09,874 INFO misc.py line 119 131400] Train: [59/100][49/800] Data 0.004 (0.005) Batch 0.342 (0.347) Remain 03:13:52 loss: 0.3172 Lr: 0.00245 [2023-12-20 18:15:10,208 INFO misc.py line 119 131400] Train: [59/100][50/800] Data 0.004 (0.005) Batch 0.334 (0.346) Remain 03:13:43 loss: 0.2224 Lr: 0.00245 [2023-12-20 18:15:10,536 INFO misc.py line 119 131400] Train: [59/100][51/800] Data 0.004 (0.005) Batch 0.329 (0.346) Remain 03:13:30 loss: 0.4509 Lr: 0.00245 [2023-12-20 18:15:10,889 INFO misc.py line 119 131400] Train: [59/100][52/800] Data 0.003 (0.005) Batch 0.352 (0.346) Remain 03:13:34 loss: 0.1091 Lr: 0.00245 [2023-12-20 18:15:11,218 INFO misc.py line 119 131400] Train: [59/100][53/800] Data 0.004 (0.005) Batch 0.328 (0.346) Remain 03:13:21 loss: 0.3133 Lr: 0.00245 [2023-12-20 18:15:11,531 INFO misc.py line 119 131400] Train: [59/100][54/800] Data 0.005 (0.005) Batch 0.314 (0.345) Remain 03:13:00 loss: 0.1601 Lr: 0.00245 [2023-12-20 18:15:11,871 INFO misc.py line 119 131400] Train: [59/100][55/800] Data 0.003 (0.005) Batch 0.339 (0.345) Remain 03:12:56 loss: 0.2801 Lr: 0.00245 [2023-12-20 18:15:12,190 INFO misc.py line 119 131400] Train: [59/100][56/800] Data 0.004 (0.005) Batch 0.319 (0.345) Remain 03:12:40 loss: 0.3424 Lr: 0.00245 [2023-12-20 18:15:12,519 INFO misc.py line 119 131400] Train: [59/100][57/800] Data 0.003 (0.005) Batch 0.323 (0.344) Remain 03:12:26 loss: 0.1514 Lr: 0.00245 [2023-12-20 18:15:12,837 INFO misc.py line 119 131400] Train: [59/100][58/800] Data 0.010 (0.005) Batch 0.324 (0.344) Remain 03:12:13 loss: 0.1451 Lr: 0.00245 [2023-12-20 18:15:13,184 INFO misc.py line 119 131400] Train: [59/100][59/800] Data 0.004 (0.005) Batch 0.347 (0.344) Remain 03:12:14 loss: 0.3599 Lr: 0.00245 [2023-12-20 18:15:13,498 INFO misc.py line 119 131400] Train: [59/100][60/800] Data 0.004 (0.005) Batch 0.311 (0.343) Remain 03:11:55 loss: 0.4565 Lr: 0.00245 [2023-12-20 18:15:13,800 INFO misc.py line 119 131400] Train: [59/100][61/800] Data 0.007 (0.005) Batch 0.306 (0.343) Remain 03:11:32 loss: 0.3499 Lr: 0.00245 [2023-12-20 18:15:14,113 INFO misc.py line 119 131400] Train: [59/100][62/800] Data 0.004 (0.005) Batch 0.312 (0.342) Remain 03:11:15 loss: 0.2921 Lr: 0.00245 [2023-12-20 18:15:14,479 INFO misc.py line 119 131400] Train: [59/100][63/800] Data 0.005 (0.005) Batch 0.366 (0.343) Remain 03:11:28 loss: 0.3543 Lr: 0.00245 [2023-12-20 18:15:14,796 INFO misc.py line 119 131400] Train: [59/100][64/800] Data 0.004 (0.005) Batch 0.317 (0.342) Remain 03:11:14 loss: 0.1243 Lr: 0.00245 [2023-12-20 18:15:15,104 INFO misc.py line 119 131400] Train: [59/100][65/800] Data 0.004 (0.005) Batch 0.307 (0.342) Remain 03:10:54 loss: 0.3238 Lr: 0.00245 [2023-12-20 18:15:15,450 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [59/100][782/800] Data 0.005 (0.004) Batch 0.329 (0.334) Remain 03:02:34 loss: 0.1670 Lr: 0.00236 [2023-12-20 18:19:14,261 INFO misc.py line 119 131400] Train: [59/100][783/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 03:02:33 loss: 0.2117 Lr: 0.00236 [2023-12-20 18:19:14,585 INFO misc.py line 119 131400] Train: [59/100][784/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 03:02:32 loss: 0.1553 Lr: 0.00236 [2023-12-20 18:19:14,889 INFO misc.py line 119 131400] Train: [59/100][785/800] Data 0.003 (0.004) Batch 0.301 (0.334) Remain 03:02:30 loss: 0.1415 Lr: 0.00236 [2023-12-20 18:19:15,191 INFO misc.py line 119 131400] Train: [59/100][786/800] Data 0.007 (0.004) Batch 0.304 (0.334) Remain 03:02:29 loss: 0.2767 Lr: 0.00236 [2023-12-20 18:19:15,539 INFO misc.py line 119 131400] Train: [59/100][787/800] Data 0.006 (0.004) Batch 0.347 (0.334) Remain 03:02:29 loss: 0.3265 Lr: 0.00236 [2023-12-20 18:19:15,861 INFO misc.py line 119 131400] Train: 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Batch 0.293 (0.334) Remain 03:02:21 loss: 0.1621 Lr: 0.00236 [2023-12-20 18:19:18,062 INFO misc.py line 119 131400] Train: [59/100][795/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 03:02:20 loss: 0.3108 Lr: 0.00236 [2023-12-20 18:19:18,419 INFO misc.py line 119 131400] Train: [59/100][796/800] Data 0.004 (0.004) Batch 0.358 (0.334) Remain 03:02:21 loss: 0.2075 Lr: 0.00236 [2023-12-20 18:19:18,725 INFO misc.py line 119 131400] Train: [59/100][797/800] Data 0.004 (0.004) Batch 0.306 (0.333) Remain 03:02:19 loss: 0.3047 Lr: 0.00236 [2023-12-20 18:19:19,034 INFO misc.py line 119 131400] Train: [59/100][798/800] Data 0.003 (0.004) Batch 0.309 (0.333) Remain 03:02:18 loss: 0.3760 Lr: 0.00236 [2023-12-20 18:19:19,351 INFO misc.py line 119 131400] Train: [59/100][799/800] Data 0.003 (0.004) Batch 0.317 (0.333) Remain 03:02:17 loss: 0.2156 Lr: 0.00236 [2023-12-20 18:19:19,653 INFO misc.py line 119 131400] Train: [59/100][800/800] Data 0.004 (0.004) Batch 0.302 (0.333) Remain 03:02:15 loss: 0.4055 Lr: 0.00236 [2023-12-20 18:19:19,653 INFO misc.py line 136 131400] Train result: loss: 0.3156 [2023-12-20 18:19:19,654 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:19:41,969 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1074 [2023-12-20 18:19:42,056 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1779 [2023-12-20 18:19:43,209 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2118 [2023-12-20 18:19:43,315 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1276 [2023-12-20 18:19:43,427 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2702 [2023-12-20 18:19:43,528 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.7382 [2023-12-20 18:19:43,618 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9687 [2023-12-20 18:19:43,725 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1076 [2023-12-20 18:19:43,807 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2619 [2023-12-20 18:19:43,892 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3372 [2023-12-20 18:19:43,983 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3852 [2023-12-20 18:19:44,124 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2640 [2023-12-20 18:19:44,240 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4530 [2023-12-20 18:19:44,394 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2055 [2023-12-20 18:19:44,488 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1657 [2023-12-20 18:19:44,633 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5787 [2023-12-20 18:19:44,750 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3643 [2023-12-20 18:19:44,861 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7310 [2023-12-20 18:19:44,983 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1883 [2023-12-20 18:19:45,061 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4795 [2023-12-20 18:19:45,173 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1320 [2023-12-20 18:19:45,335 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1424 [2023-12-20 18:19:45,465 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7471 [2023-12-20 18:19:45,611 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1654 [2023-12-20 18:19:45,759 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2262 [2023-12-20 18:19:45,855 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5945 [2023-12-20 18:19:46,022 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9119 [2023-12-20 18:19:46,160 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.3767 [2023-12-20 18:19:46,262 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4429 [2023-12-20 18:19:46,413 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8613 [2023-12-20 18:19:46,521 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6692 [2023-12-20 18:19:46,645 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4171 [2023-12-20 18:19:46,732 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1213 [2023-12-20 18:19:46,810 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1863 [2023-12-20 18:19:46,908 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0080 [2023-12-20 18:19:47,003 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3245 [2023-12-20 18:19:47,135 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9915 [2023-12-20 18:19:47,253 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1094 [2023-12-20 18:19:47,356 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6749 [2023-12-20 18:19:47,500 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3299 [2023-12-20 18:19:47,646 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0271 [2023-12-20 18:19:47,746 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1590 [2023-12-20 18:19:47,866 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3226 [2023-12-20 18:19:48,008 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7045 [2023-12-20 18:19:48,126 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2026 [2023-12-20 18:19:48,232 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7723 [2023-12-20 18:19:48,405 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3984 [2023-12-20 18:19:48,502 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3794 [2023-12-20 18:19:48,655 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5921 [2023-12-20 18:19:48,751 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1213 [2023-12-20 18:19:48,830 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4680 [2023-12-20 18:19:48,943 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5500 [2023-12-20 18:19:49,093 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7703 [2023-12-20 18:19:49,229 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2794 [2023-12-20 18:19:49,331 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0007 [2023-12-20 18:19:49,422 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5355 [2023-12-20 18:19:49,534 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3690 [2023-12-20 18:19:49,700 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2635 [2023-12-20 18:19:49,795 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3453 [2023-12-20 18:19:49,891 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5212 [2023-12-20 18:19:49,989 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2702 [2023-12-20 18:19:50,226 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2337 [2023-12-20 18:19:50,316 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7687 [2023-12-20 18:19:50,418 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8625 [2023-12-20 18:19:50,547 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.8898 [2023-12-20 18:19:50,634 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1656 [2023-12-20 18:19:50,740 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4165 [2023-12-20 18:19:50,845 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0211 [2023-12-20 18:19:50,943 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3824 [2023-12-20 18:19:51,028 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0167 [2023-12-20 18:19:51,120 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.4704 [2023-12-20 18:19:51,214 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4511 [2023-12-20 18:19:51,346 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1800 [2023-12-20 18:19:51,439 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6197 [2023-12-20 18:19:51,560 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5915 [2023-12-20 18:19:51,667 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8134 [2023-12-20 18:19:51,754 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2842 [2023-12-20 18:19:51,912 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9975 [2023-12-20 18:19:53,073 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7541/0.8475/0.9148. [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8729/0.9326 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9650/0.9855 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6871/0.8444 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8063/0.8736 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9132/0.9428 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8286/0.9461 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7570/0.8236 [2023-12-20 18:19:53,073 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6908/0.8537 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6705/0.8317 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8125/0.9421 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3905/0.5234 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7257/0.8245 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6869/0.9226 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7511/0.8143 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6172/0.6930 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7260/0.7790 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9481/0.9799 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6931/0.8058 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8926/0.9336 [2023-12-20 18:19:53,074 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6472/0.6968 [2023-12-20 18:19:53,074 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:19:53,075 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7541 [2023-12-20 18:19:53,075 INFO misc.py line 165 131400] Currently Best mIoU: 0.7541 [2023-12-20 18:19:53,075 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:19:59,248 INFO misc.py line 119 131400] Train: [60/100][1/800] Data 1.368 (1.368) Batch 1.691 (1.691) Remain 15:24:19 loss: 0.2179 Lr: 0.00236 [2023-12-20 18:19:59,589 INFO misc.py line 119 131400] Train: [60/100][2/800] Data 0.003 (0.003) Batch 0.341 (0.341) Remain 03:06:21 loss: 0.2072 Lr: 0.00236 [2023-12-20 18:19:59,895 INFO misc.py line 119 131400] Train: [60/100][3/800] Data 0.004 (0.004) Batch 0.306 (0.306) Remain 02:47:23 loss: 0.5652 Lr: 0.00236 [2023-12-20 18:20:00,216 INFO misc.py line 119 131400] Train: [60/100][4/800] Data 0.003 (0.003) Batch 0.316 (0.316) Remain 02:52:45 loss: 0.2055 Lr: 0.00236 [2023-12-20 18:20:00,558 INFO misc.py line 119 131400] Train: [60/100][5/800] Data 0.009 (0.006) Batch 0.347 (0.331) Remain 03:01:05 loss: 0.5628 Lr: 0.00236 [2023-12-20 18:20:00,908 INFO misc.py line 119 131400] Train: [60/100][6/800] Data 0.004 (0.005) Batch 0.350 (0.338) Remain 03:04:29 loss: 0.3201 Lr: 0.00236 [2023-12-20 18:20:01,211 INFO misc.py line 119 131400] Train: [60/100][7/800] Data 0.005 (0.005) Batch 0.303 (0.329) Remain 02:59:47 loss: 0.4270 Lr: 0.00236 [2023-12-20 18:20:01,527 INFO misc.py line 119 131400] Train: [60/100][8/800] Data 0.004 (0.005) Batch 0.317 (0.327) Remain 02:58:27 loss: 0.1635 Lr: 0.00236 [2023-12-20 18:20:01,860 INFO misc.py line 119 131400] Train: [60/100][9/800] Data 0.003 (0.005) Batch 0.332 (0.327) Remain 02:58:58 loss: 0.2916 Lr: 0.00236 [2023-12-20 18:20:02,204 INFO misc.py line 119 131400] Train: [60/100][10/800] Data 0.003 (0.004) Batch 0.344 (0.330) Remain 03:00:16 loss: 0.2518 Lr: 0.00236 [2023-12-20 18:20:02,523 INFO misc.py line 119 131400] Train: [60/100][11/800] Data 0.003 (0.004) Batch 0.319 (0.329) Remain 02:59:33 loss: 0.2409 Lr: 0.00236 [2023-12-20 18:20:02,858 INFO misc.py line 119 131400] Train: [60/100][12/800] Data 0.003 (0.004) Batch 0.333 (0.329) Remain 02:59:48 loss: 0.3765 Lr: 0.00236 [2023-12-20 18:20:03,189 INFO misc.py line 119 131400] Train: [60/100][13/800] Data 0.006 (0.004) Batch 0.333 (0.329) Remain 03:00:01 loss: 0.2716 Lr: 0.00236 [2023-12-20 18:20:03,537 INFO misc.py line 119 131400] Train: [60/100][14/800] Data 0.003 (0.004) Batch 0.347 (0.331) Remain 03:00:54 loss: 0.4524 Lr: 0.00236 [2023-12-20 18:20:03,842 INFO misc.py line 119 131400] Train: [60/100][15/800] Data 0.004 (0.004) Batch 0.305 (0.329) Remain 02:59:44 loss: 0.1143 Lr: 0.00236 [2023-12-20 18:20:04,197 INFO misc.py line 119 131400] Train: [60/100][16/800] Data 0.002 (0.004) Batch 0.354 (0.331) Remain 03:00:47 loss: 0.3981 Lr: 0.00236 [2023-12-20 18:20:04,540 INFO misc.py line 119 131400] Train: [60/100][17/800] Data 0.004 (0.004) Batch 0.343 (0.332) Remain 03:01:15 loss: 0.2326 Lr: 0.00236 [2023-12-20 18:20:04,867 INFO misc.py line 119 131400] Train: [60/100][18/800] Data 0.004 (0.004) Batch 0.328 (0.332) Remain 03:01:07 loss: 0.3375 Lr: 0.00236 [2023-12-20 18:20:05,220 INFO misc.py line 119 131400] Train: [60/100][19/800] Data 0.003 (0.004) Batch 0.353 (0.333) Remain 03:01:51 loss: 0.2265 Lr: 0.00236 [2023-12-20 18:20:05,582 INFO misc.py line 119 131400] Train: [60/100][20/800] Data 0.003 (0.004) Batch 0.360 (0.334) Remain 03:02:43 loss: 0.2138 Lr: 0.00236 [2023-12-20 18:20:05,925 INFO misc.py line 119 131400] Train: [60/100][21/800] Data 0.004 (0.004) Batch 0.343 (0.335) Remain 03:02:59 loss: 0.2236 Lr: 0.00236 [2023-12-20 18:20:06,261 INFO misc.py line 119 131400] Train: [60/100][22/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 03:03:00 loss: 0.3796 Lr: 0.00236 [2023-12-20 18:20:06,569 INFO misc.py line 119 131400] Train: [60/100][23/800] Data 0.005 (0.004) Batch 0.308 (0.334) Remain 03:02:15 loss: 0.2374 Lr: 0.00236 [2023-12-20 18:20:06,894 INFO misc.py line 119 131400] Train: [60/100][24/800] Data 0.005 (0.004) Batch 0.326 (0.333) Remain 03:02:03 loss: 0.3245 Lr: 0.00236 [2023-12-20 18:20:07,191 INFO misc.py line 119 131400] Train: [60/100][25/800] Data 0.004 (0.004) Batch 0.297 (0.332) Remain 03:01:08 loss: 0.2325 Lr: 0.00236 [2023-12-20 18:20:07,498 INFO misc.py line 119 131400] Train: [60/100][26/800] Data 0.003 (0.004) Batch 0.307 (0.331) Remain 03:00:33 loss: 0.2599 Lr: 0.00236 [2023-12-20 18:20:07,826 INFO misc.py line 119 131400] Train: [60/100][27/800] Data 0.003 (0.004) Batch 0.328 (0.330) Remain 03:00:30 loss: 0.3077 Lr: 0.00236 [2023-12-20 18:20:08,128 INFO misc.py line 119 131400] Train: [60/100][28/800] Data 0.003 (0.004) Batch 0.301 (0.329) Remain 02:59:51 loss: 0.6327 Lr: 0.00236 [2023-12-20 18:20:08,480 INFO misc.py line 119 131400] Train: [60/100][29/800] Data 0.004 (0.004) Batch 0.352 (0.330) Remain 03:00:20 loss: 0.5397 Lr: 0.00236 [2023-12-20 18:20:08,834 INFO misc.py line 119 131400] Train: [60/100][30/800] Data 0.004 (0.004) Batch 0.354 (0.331) Remain 03:00:49 loss: 0.2035 Lr: 0.00236 [2023-12-20 18:20:09,159 INFO misc.py line 119 131400] Train: [60/100][31/800] Data 0.004 (0.004) Batch 0.325 (0.331) Remain 03:00:41 loss: 0.1814 Lr: 0.00236 [2023-12-20 18:20:09,480 INFO misc.py line 119 131400] Train: [60/100][32/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 03:00:29 loss: 0.2607 Lr: 0.00236 [2023-12-20 18:20:09,766 INFO misc.py line 119 131400] Train: [60/100][33/800] Data 0.004 (0.004) Batch 0.286 (0.329) Remain 02:59:41 loss: 0.2549 Lr: 0.00236 [2023-12-20 18:20:10,090 INFO misc.py line 119 131400] Train: [60/100][34/800] Data 0.004 (0.004) Batch 0.324 (0.329) Remain 02:59:35 loss: 0.2727 Lr: 0.00236 [2023-12-20 18:20:10,437 INFO misc.py line 119 131400] Train: [60/100][35/800] Data 0.004 (0.004) Batch 0.346 (0.329) Remain 02:59:53 loss: 0.3051 Lr: 0.00236 [2023-12-20 18:20:10,779 INFO misc.py line 119 131400] Train: [60/100][36/800] Data 0.003 (0.004) Batch 0.340 (0.330) Remain 03:00:03 loss: 0.1465 Lr: 0.00236 [2023-12-20 18:20:11,081 INFO misc.py line 119 131400] Train: [60/100][37/800] Data 0.007 (0.004) Batch 0.305 (0.329) Remain 02:59:38 loss: 0.3455 Lr: 0.00236 [2023-12-20 18:20:11,379 INFO misc.py line 119 131400] Train: [60/100][38/800] Data 0.003 (0.004) Batch 0.298 (0.328) Remain 02:59:09 loss: 0.5258 Lr: 0.00236 [2023-12-20 18:20:11,751 INFO misc.py line 119 131400] Train: [60/100][39/800] Data 0.004 (0.004) Batch 0.365 (0.329) Remain 02:59:43 loss: 0.3779 Lr: 0.00236 [2023-12-20 18:20:12,103 INFO misc.py line 119 131400] Train: [60/100][40/800] Data 0.010 (0.004) Batch 0.359 (0.330) Remain 03:00:09 loss: 0.2668 Lr: 0.00236 [2023-12-20 18:20:12,440 INFO misc.py line 119 131400] Train: [60/100][41/800] Data 0.004 (0.004) Batch 0.337 (0.330) Remain 03:00:14 loss: 0.3370 Lr: 0.00236 [2023-12-20 18:20:12,763 INFO misc.py line 119 131400] Train: [60/100][42/800] Data 0.003 (0.004) Batch 0.322 (0.330) Remain 03:00:07 loss: 0.2549 Lr: 0.00236 [2023-12-20 18:20:13,096 INFO misc.py line 119 131400] Train: [60/100][43/800] Data 0.006 (0.004) Batch 0.335 (0.330) Remain 03:00:10 loss: 0.1836 Lr: 0.00236 [2023-12-20 18:20:13,445 INFO misc.py line 119 131400] Train: [60/100][44/800] Data 0.004 (0.004) Batch 0.349 (0.330) Remain 03:00:25 loss: 0.2499 Lr: 0.00236 [2023-12-20 18:20:13,770 INFO misc.py line 119 131400] Train: [60/100][45/800] Data 0.004 (0.004) Batch 0.317 (0.330) Remain 03:00:14 loss: 0.3807 Lr: 0.00236 [2023-12-20 18:20:14,099 INFO misc.py line 119 131400] Train: [60/100][46/800] Data 0.012 (0.004) Batch 0.337 (0.330) Remain 03:00:19 loss: 0.3369 Lr: 0.00236 [2023-12-20 18:20:14,425 INFO misc.py line 119 131400] Train: [60/100][47/800] Data 0.003 (0.004) Batch 0.325 (0.330) Remain 03:00:15 loss: 0.1663 Lr: 0.00236 [2023-12-20 18:20:14,788 INFO misc.py line 119 131400] Train: [60/100][48/800] Data 0.005 (0.004) Batch 0.363 (0.331) Remain 03:00:38 loss: 0.2685 Lr: 0.00236 [2023-12-20 18:20:15,267 INFO misc.py line 119 131400] Train: [60/100][49/800] Data 0.005 (0.004) Batch 0.479 (0.334) Remain 03:02:24 loss: 0.2342 Lr: 0.00236 [2023-12-20 18:20:15,594 INFO misc.py line 119 131400] Train: [60/100][50/800] Data 0.004 (0.004) Batch 0.328 (0.334) Remain 03:02:19 loss: 0.1433 Lr: 0.00236 [2023-12-20 18:20:15,944 INFO misc.py line 119 131400] Train: [60/100][51/800] Data 0.004 (0.004) Batch 0.350 (0.334) Remain 03:02:29 loss: 0.1642 Lr: 0.00236 [2023-12-20 18:20:16,294 INFO misc.py line 119 131400] Train: [60/100][52/800] Data 0.004 (0.004) Batch 0.349 (0.335) Remain 03:02:39 loss: 0.3326 Lr: 0.00236 [2023-12-20 18:20:16,626 INFO misc.py line 119 131400] Train: [60/100][53/800] Data 0.004 (0.004) Batch 0.333 (0.335) Remain 03:02:37 loss: 0.1685 Lr: 0.00236 [2023-12-20 18:20:16,975 INFO misc.py line 119 131400] Train: [60/100][54/800] Data 0.004 (0.004) Batch 0.348 (0.335) Remain 03:02:46 loss: 0.3344 Lr: 0.00236 [2023-12-20 18:20:17,296 INFO misc.py line 119 131400] Train: [60/100][55/800] Data 0.003 (0.004) Batch 0.322 (0.335) Remain 03:02:37 loss: 0.2339 Lr: 0.00236 [2023-12-20 18:20:17,608 INFO misc.py line 119 131400] Train: [60/100][56/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 03:02:22 loss: 0.1667 Lr: 0.00236 [2023-12-20 18:20:17,909 INFO misc.py line 119 131400] Train: [60/100][57/800] Data 0.004 (0.004) Batch 0.301 (0.334) Remain 03:02:02 loss: 0.3950 Lr: 0.00236 [2023-12-20 18:20:18,232 INFO misc.py line 119 131400] Train: [60/100][58/800] Data 0.004 (0.004) Batch 0.324 (0.333) Remain 03:01:56 loss: 0.1695 Lr: 0.00236 [2023-12-20 18:20:18,573 INFO misc.py line 119 131400] Train: [60/100][59/800] Data 0.004 (0.004) 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Batch 0.359 (0.334) Remain 02:58:25 loss: 0.3287 Lr: 0.00227 [2023-12-20 18:24:09,753 INFO misc.py line 119 131400] Train: [60/100][751/800] Data 0.004 (0.004) Batch 0.333 (0.334) Remain 02:58:25 loss: 0.4136 Lr: 0.00227 [2023-12-20 18:24:10,086 INFO misc.py line 119 131400] Train: [60/100][752/800] Data 0.004 (0.004) Batch 0.333 (0.334) Remain 02:58:25 loss: 0.2594 Lr: 0.00227 [2023-12-20 18:24:10,432 INFO misc.py line 119 131400] Train: [60/100][753/800] Data 0.003 (0.004) Batch 0.343 (0.334) Remain 02:58:25 loss: 0.4691 Lr: 0.00227 [2023-12-20 18:24:10,767 INFO misc.py line 119 131400] Train: [60/100][754/800] Data 0.007 (0.004) Batch 0.337 (0.334) Remain 02:58:24 loss: 0.2883 Lr: 0.00227 [2023-12-20 18:24:11,133 INFO misc.py line 119 131400] Train: [60/100][755/800] Data 0.005 (0.004) Batch 0.366 (0.334) Remain 02:58:25 loss: 0.4889 Lr: 0.00227 [2023-12-20 18:24:11,454 INFO misc.py line 119 131400] Train: [60/100][756/800] Data 0.004 (0.004) Batch 0.322 (0.334) Remain 02:58:25 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131400] Train: [60/100][769/800] Data 0.005 (0.004) Batch 0.310 (0.334) Remain 02:58:21 loss: 0.3993 Lr: 0.00227 [2023-12-20 18:24:16,170 INFO misc.py line 119 131400] Train: [60/100][770/800] Data 0.004 (0.004) Batch 0.345 (0.334) Remain 02:58:22 loss: 0.2496 Lr: 0.00227 [2023-12-20 18:24:16,496 INFO misc.py line 119 131400] Train: [60/100][771/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 02:58:21 loss: 0.3499 Lr: 0.00227 [2023-12-20 18:24:16,795 INFO misc.py line 119 131400] Train: [60/100][772/800] Data 0.004 (0.004) Batch 0.299 (0.334) Remain 02:58:19 loss: 0.3886 Lr: 0.00227 [2023-12-20 18:24:17,094 INFO misc.py line 119 131400] Train: [60/100][773/800] Data 0.004 (0.004) Batch 0.299 (0.334) Remain 02:58:17 loss: 0.2726 Lr: 0.00227 [2023-12-20 18:24:17,374 INFO misc.py line 119 131400] Train: [60/100][774/800] Data 0.004 (0.004) Batch 0.281 (0.334) Remain 02:58:15 loss: 0.4370 Lr: 0.00227 [2023-12-20 18:24:17,699 INFO misc.py line 119 131400] Train: [60/100][775/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 02:58:14 loss: 0.1685 Lr: 0.00227 [2023-12-20 18:24:18,046 INFO misc.py line 119 131400] Train: [60/100][776/800] Data 0.008 (0.004) Batch 0.351 (0.334) Remain 02:58:14 loss: 0.1746 Lr: 0.00227 [2023-12-20 18:24:18,394 INFO misc.py line 119 131400] Train: [60/100][777/800] Data 0.003 (0.004) Batch 0.346 (0.334) Remain 02:58:14 loss: 0.2720 Lr: 0.00227 [2023-12-20 18:24:18,742 INFO misc.py line 119 131400] Train: [60/100][778/800] Data 0.005 (0.004) Batch 0.350 (0.334) Remain 02:58:15 loss: 0.3795 Lr: 0.00227 [2023-12-20 18:24:19,081 INFO misc.py line 119 131400] Train: [60/100][779/800] Data 0.004 (0.004) Batch 0.339 (0.334) Remain 02:58:15 loss: 0.2845 Lr: 0.00227 [2023-12-20 18:24:19,387 INFO misc.py line 119 131400] Train: [60/100][780/800] Data 0.003 (0.004) Batch 0.305 (0.334) Remain 02:58:13 loss: 0.3916 Lr: 0.00227 [2023-12-20 18:24:19,664 INFO misc.py line 119 131400] Train: [60/100][781/800] Data 0.003 (0.004) Batch 0.277 (0.334) Remain 02:58:10 loss: 0.3236 Lr: 0.00227 [2023-12-20 18:24:19,987 INFO misc.py line 119 131400] Train: [60/100][782/800] Data 0.003 (0.004) Batch 0.323 (0.334) Remain 02:58:10 loss: 0.2753 Lr: 0.00227 [2023-12-20 18:24:20,335 INFO misc.py line 119 131400] Train: [60/100][783/800] Data 0.003 (0.004) Batch 0.348 (0.334) Remain 02:58:10 loss: 0.2187 Lr: 0.00227 [2023-12-20 18:24:20,668 INFO misc.py line 119 131400] Train: [60/100][784/800] Data 0.003 (0.004) Batch 0.333 (0.334) Remain 02:58:09 loss: 0.3300 Lr: 0.00227 [2023-12-20 18:24:20,959 INFO misc.py line 119 131400] Train: [60/100][785/800] Data 0.004 (0.004) Batch 0.290 (0.334) Remain 02:58:07 loss: 0.2016 Lr: 0.00227 [2023-12-20 18:24:21,329 INFO misc.py line 119 131400] Train: [60/100][786/800] Data 0.004 (0.004) Batch 0.370 (0.334) Remain 02:58:08 loss: 0.5715 Lr: 0.00227 [2023-12-20 18:24:21,645 INFO misc.py line 119 131400] Train: [60/100][787/800] Data 0.006 (0.004) Batch 0.318 (0.334) Remain 02:58:07 loss: 0.3818 Lr: 0.00227 [2023-12-20 18:24:21,940 INFO misc.py line 119 131400] Train: [60/100][788/800] Data 0.003 (0.004) Batch 0.294 (0.334) Remain 02:58:06 loss: 0.2962 Lr: 0.00227 [2023-12-20 18:24:22,208 INFO misc.py line 119 131400] Train: [60/100][789/800] Data 0.003 (0.004) Batch 0.269 (0.334) Remain 02:58:03 loss: 0.1724 Lr: 0.00227 [2023-12-20 18:24:22,558 INFO misc.py line 119 131400] Train: [60/100][790/800] Data 0.003 (0.004) Batch 0.349 (0.334) Remain 02:58:03 loss: 0.1775 Lr: 0.00227 [2023-12-20 18:24:22,861 INFO misc.py line 119 131400] Train: [60/100][791/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 02:58:01 loss: 0.3870 Lr: 0.00227 [2023-12-20 18:24:23,184 INFO misc.py line 119 131400] Train: [60/100][792/800] Data 0.003 (0.004) Batch 0.322 (0.334) Remain 02:58:00 loss: 0.3722 Lr: 0.00227 [2023-12-20 18:24:23,495 INFO misc.py line 119 131400] Train: [60/100][793/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 02:57:59 loss: 0.5902 Lr: 0.00227 [2023-12-20 18:24:23,798 INFO misc.py line 119 131400] Train: [60/100][794/800] Data 0.003 (0.004) Batch 0.301 (0.334) Remain 02:57:58 loss: 0.5054 Lr: 0.00227 [2023-12-20 18:24:24,109 INFO misc.py line 119 131400] Train: [60/100][795/800] Data 0.005 (0.004) Batch 0.311 (0.334) Remain 02:57:56 loss: 0.4127 Lr: 0.00227 [2023-12-20 18:24:24,425 INFO misc.py line 119 131400] Train: [60/100][796/800] Data 0.004 (0.004) Batch 0.315 (0.334) Remain 02:57:55 loss: 0.5115 Lr: 0.00227 [2023-12-20 18:24:24,724 INFO misc.py line 119 131400] Train: [60/100][797/800] Data 0.005 (0.004) Batch 0.301 (0.334) Remain 02:57:54 loss: 0.3577 Lr: 0.00227 [2023-12-20 18:24:25,017 INFO misc.py line 119 131400] Train: [60/100][798/800] Data 0.003 (0.004) Batch 0.292 (0.333) Remain 02:57:52 loss: 0.3146 Lr: 0.00227 [2023-12-20 18:24:25,318 INFO misc.py line 119 131400] Train: [60/100][799/800] Data 0.003 (0.004) Batch 0.301 (0.333) Remain 02:57:50 loss: 0.3647 Lr: 0.00227 [2023-12-20 18:24:25,647 INFO misc.py line 119 131400] Train: [60/100][800/800] Data 0.005 (0.004) Batch 0.328 (0.333) Remain 02:57:49 loss: 0.2792 Lr: 0.00227 [2023-12-20 18:24:25,648 INFO misc.py line 136 131400] Train result: loss: 0.3154 [2023-12-20 18:24:25,648 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:24:49,561 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1909 [2023-12-20 18:24:49,641 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.2311 [2023-12-20 18:24:49,748 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4201 [2023-12-20 18:24:50,271 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2165 [2023-12-20 18:24:50,387 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2978 [2023-12-20 18:24:50,496 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.1783 [2023-12-20 18:24:50,590 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7094 [2023-12-20 18:24:50,701 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8628 [2023-12-20 18:24:50,782 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3031 [2023-12-20 18:24:50,866 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3905 [2023-12-20 18:24:50,962 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3863 [2023-12-20 18:24:51,102 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3012 [2023-12-20 18:24:51,228 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3717 [2023-12-20 18:24:51,385 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1850 [2023-12-20 18:24:51,482 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1676 [2023-12-20 18:24:51,617 INFO evaluator.py line 159 131400] Test: [16/78] Loss 1.2148 [2023-12-20 18:24:51,735 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3958 [2023-12-20 18:24:51,857 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6867 [2023-12-20 18:24:51,983 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1835 [2023-12-20 18:24:52,075 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3733 [2023-12-20 18:24:52,183 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2084 [2023-12-20 18:24:52,349 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1389 [2023-12-20 18:24:52,480 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.4333 [2023-12-20 18:24:52,621 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2691 [2023-12-20 18:24:52,770 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1704 [2023-12-20 18:24:52,855 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4816 [2023-12-20 18:24:53,016 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7534 [2023-12-20 18:24:53,141 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5711 [2023-12-20 18:24:53,239 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5198 [2023-12-20 18:24:53,389 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5739 [2023-12-20 18:24:53,497 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6465 [2023-12-20 18:24:53,617 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4267 [2023-12-20 18:24:53,708 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1577 [2023-12-20 18:24:53,790 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1999 [2023-12-20 18:24:53,891 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6072 [2023-12-20 18:24:53,988 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3435 [2023-12-20 18:24:54,119 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9861 [2023-12-20 18:24:54,240 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1012 [2023-12-20 18:24:54,324 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6825 [2023-12-20 18:24:54,465 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3976 [2023-12-20 18:24:54,618 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0486 [2023-12-20 18:24:54,724 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0945 [2023-12-20 18:24:54,846 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2794 [2023-12-20 18:24:55,001 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1074 [2023-12-20 18:24:55,130 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3971 [2023-12-20 18:24:55,237 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3573 [2023-12-20 18:24:55,405 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.5013 [2023-12-20 18:24:55,506 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3447 [2023-12-20 18:24:55,654 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6026 [2023-12-20 18:24:55,744 INFO evaluator.py line 159 131400] Test: [50/78] Loss 0.9880 [2023-12-20 18:24:55,824 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4523 [2023-12-20 18:24:55,936 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2855 [2023-12-20 18:24:56,086 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1753 [2023-12-20 18:24:56,219 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3042 [2023-12-20 18:24:56,320 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.7625 [2023-12-20 18:24:56,416 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7683 [2023-12-20 18:24:56,532 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4003 [2023-12-20 18:24:56,696 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2246 [2023-12-20 18:24:56,804 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4582 [2023-12-20 18:24:56,903 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3449 [2023-12-20 18:24:57,017 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3594 [2023-12-20 18:24:57,118 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2531 [2023-12-20 18:24:57,215 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5228 [2023-12-20 18:24:57,319 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6820 [2023-12-20 18:24:57,447 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.9332 [2023-12-20 18:24:57,532 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2287 [2023-12-20 18:24:57,634 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3896 [2023-12-20 18:24:57,732 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0489 [2023-12-20 18:24:57,819 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3266 [2023-12-20 18:24:57,910 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0428 [2023-12-20 18:24:58,010 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8020 [2023-12-20 18:24:58,109 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.3624 [2023-12-20 18:24:58,249 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2739 [2023-12-20 18:24:58,345 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5686 [2023-12-20 18:24:58,463 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6701 [2023-12-20 18:24:58,565 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6246 [2023-12-20 18:24:58,668 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2757 [2023-12-20 18:24:58,828 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.8911 [2023-12-20 18:25:00,205 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7464/0.8338/0.9154. [2023-12-20 18:25:00,205 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8700/0.9535 [2023-12-20 18:25:00,205 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9636/0.9840 [2023-12-20 18:25:00,205 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7144/0.8024 [2023-12-20 18:25:00,205 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8303/0.8794 [2023-12-20 18:25:00,205 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9136/0.9524 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8229/0.9271 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7572/0.8828 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7004/0.8100 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6770/0.8141 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8159/0.9098 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3909/0.4590 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7037/0.8279 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7041/0.8664 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7297/0.7999 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6453/0.6907 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6765/0.7571 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9567/0.9715 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6958/0.7817 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7488/0.9424 [2023-12-20 18:25:00,206 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6106/0.6629 [2023-12-20 18:25:00,207 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:25:00,208 INFO misc.py line 165 131400] Currently Best mIoU: 0.7541 [2023-12-20 18:25:00,208 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:25:03,981 INFO misc.py line 119 131400] Train: [61/100][1/800] Data 0.785 (0.785) Batch 1.071 (1.071) Remain 09:31:21 loss: 0.2743 Lr: 0.00227 [2023-12-20 18:25:04,471 INFO misc.py line 119 131400] Train: [61/100][2/800] Data 0.223 (0.223) Batch 0.490 (0.490) Remain 04:21:20 loss: 0.4414 Lr: 0.00227 [2023-12-20 18:25:04,815 INFO misc.py line 119 131400] Train: [61/100][3/800] Data 0.004 (0.004) Batch 0.340 (0.340) Remain 03:01:23 loss: 0.2525 Lr: 0.00227 [2023-12-20 18:25:05,139 INFO misc.py line 119 131400] Train: [61/100][4/800] Data 0.008 (0.008) Batch 0.329 (0.329) Remain 02:55:16 loss: 0.4015 Lr: 0.00227 [2023-12-20 18:25:05,490 INFO misc.py line 119 131400] Train: [61/100][5/800] Data 0.003 (0.005) Batch 0.350 (0.339) Remain 03:01:02 loss: 0.3936 Lr: 0.00227 [2023-12-20 18:25:05,881 INFO misc.py line 119 131400] Train: [61/100][6/800] Data 0.004 (0.005) Batch 0.391 (0.357) Remain 03:10:11 loss: 0.3556 Lr: 0.00227 [2023-12-20 18:25:06,218 INFO misc.py line 119 131400] Train: [61/100][7/800] Data 0.003 (0.004) Batch 0.336 (0.352) Remain 03:07:27 loss: 0.4201 Lr: 0.00227 [2023-12-20 18:25:06,571 INFO misc.py line 119 131400] Train: [61/100][8/800] Data 0.004 (0.004) Batch 0.354 (0.352) Remain 03:07:40 loss: 0.2679 Lr: 0.00227 [2023-12-20 18:25:06,885 INFO misc.py line 119 131400] Train: [61/100][9/800] Data 0.003 (0.004) Batch 0.310 (0.345) Remain 03:03:54 loss: 0.5495 Lr: 0.00227 [2023-12-20 18:25:07,225 INFO misc.py line 119 131400] Train: [61/100][10/800] Data 0.008 (0.005) Batch 0.343 (0.345) Remain 03:03:46 loss: 0.2966 Lr: 0.00226 [2023-12-20 18:25:07,577 INFO misc.py line 119 131400] Train: [61/100][11/800] Data 0.006 (0.005) Batch 0.353 (0.346) Remain 03:04:19 loss: 0.1861 Lr: 0.00226 [2023-12-20 18:25:07,896 INFO misc.py line 119 131400] Train: [61/100][12/800] Data 0.003 (0.005) Batch 0.319 (0.343) Remain 03:02:43 loss: 0.2577 Lr: 0.00226 [2023-12-20 18:25:08,248 INFO misc.py line 119 131400] Train: [61/100][13/800] Data 0.004 (0.005) Batch 0.353 (0.344) Remain 03:03:15 loss: 0.5133 Lr: 0.00226 [2023-12-20 18:25:08,600 INFO misc.py line 119 131400] Train: [61/100][14/800] Data 0.003 (0.004) Batch 0.352 (0.344) Remain 03:03:38 loss: 0.3062 Lr: 0.00226 [2023-12-20 18:25:08,943 INFO misc.py line 119 131400] Train: [61/100][15/800] Data 0.003 (0.004) Batch 0.339 (0.344) Remain 03:03:24 loss: 0.1319 Lr: 0.00226 [2023-12-20 18:25:09,283 INFO misc.py line 119 131400] Train: [61/100][16/800] Data 0.006 (0.004) Batch 0.340 (0.344) Remain 03:03:15 loss: 0.3045 Lr: 0.00226 [2023-12-20 18:25:09,639 INFO misc.py line 119 131400] Train: [61/100][17/800] Data 0.007 (0.005) Batch 0.357 (0.345) Remain 03:03:45 loss: 0.3493 Lr: 0.00226 [2023-12-20 18:25:09,966 INFO misc.py line 119 131400] Train: [61/100][18/800] Data 0.006 (0.005) Batch 0.326 (0.343) Remain 03:03:04 loss: 0.4044 Lr: 0.00226 [2023-12-20 18:25:10,303 INFO misc.py line 119 131400] Train: [61/100][19/800] Data 0.006 (0.005) Batch 0.339 (0.343) Remain 03:02:55 loss: 0.4777 Lr: 0.00226 [2023-12-20 18:25:10,651 INFO misc.py line 119 131400] Train: [61/100][20/800] Data 0.004 (0.005) Batch 0.349 (0.344) Remain 03:03:06 loss: 0.1947 Lr: 0.00226 [2023-12-20 18:25:10,986 INFO misc.py line 119 131400] Train: [61/100][21/800] Data 0.003 (0.005) Batch 0.334 (0.343) Remain 03:02:49 loss: 0.2835 Lr: 0.00226 [2023-12-20 18:25:11,351 INFO misc.py line 119 131400] Train: [61/100][22/800] Data 0.004 (0.005) Batch 0.365 (0.344) Remain 03:03:25 loss: 0.2835 Lr: 0.00226 [2023-12-20 18:25:11,672 INFO misc.py line 119 131400] Train: [61/100][23/800] Data 0.005 (0.005) Batch 0.322 (0.343) Remain 03:02:50 loss: 0.3254 Lr: 0.00226 [2023-12-20 18:25:12,016 INFO misc.py line 119 131400] Train: [61/100][24/800] Data 0.003 (0.005) Batch 0.335 (0.343) Remain 03:02:38 loss: 0.3223 Lr: 0.00226 [2023-12-20 18:25:12,347 INFO misc.py line 119 131400] Train: [61/100][25/800] Data 0.012 (0.005) Batch 0.339 (0.343) Remain 03:02:32 loss: 0.6557 Lr: 0.00226 [2023-12-20 18:25:12,671 INFO misc.py line 119 131400] Train: [61/100][26/800] Data 0.004 (0.005) Batch 0.324 (0.342) Remain 03:02:05 loss: 0.2539 Lr: 0.00226 [2023-12-20 18:25:13,064 INFO misc.py line 119 131400] Train: [61/100][27/800] Data 0.005 (0.005) Batch 0.354 (0.342) Remain 03:02:21 loss: 0.2987 Lr: 0.00226 [2023-12-20 18:25:13,397 INFO misc.py line 119 131400] Train: [61/100][28/800] Data 0.043 (0.006) Batch 0.373 (0.343) Remain 03:03:00 loss: 0.3920 Lr: 0.00226 [2023-12-20 18:25:13,743 INFO misc.py line 119 131400] Train: [61/100][29/800] Data 0.004 (0.006) Batch 0.346 (0.344) Remain 03:03:02 loss: 0.5038 Lr: 0.00226 [2023-12-20 18:25:14,064 INFO misc.py line 119 131400] Train: [61/100][30/800] Data 0.004 (0.006) Batch 0.320 (0.343) Remain 03:02:33 loss: 0.2420 Lr: 0.00226 [2023-12-20 18:25:14,402 INFO misc.py line 119 131400] Train: [61/100][31/800] Data 0.005 (0.006) Batch 0.339 (0.343) Remain 03:02:29 loss: 0.4668 Lr: 0.00226 [2023-12-20 18:25:14,750 INFO misc.py line 119 131400] Train: [61/100][32/800] Data 0.004 (0.006) Batch 0.348 (0.343) Remain 03:02:35 loss: 0.2136 Lr: 0.00226 [2023-12-20 18:25:15,096 INFO misc.py line 119 131400] Train: [61/100][33/800] Data 0.004 (0.006) Batch 0.347 (0.343) Remain 03:02:39 loss: 0.2297 Lr: 0.00226 [2023-12-20 18:25:15,436 INFO misc.py line 119 131400] Train: [61/100][34/800] Data 0.003 (0.006) Batch 0.339 (0.343) Remain 03:02:35 loss: 0.3941 Lr: 0.00226 [2023-12-20 18:25:15,797 INFO misc.py line 119 131400] Train: [61/100][35/800] Data 0.004 (0.006) Batch 0.361 (0.343) Remain 03:02:53 loss: 0.5647 Lr: 0.00226 [2023-12-20 18:25:16,152 INFO misc.py line 119 131400] Train: [61/100][36/800] Data 0.004 (0.006) Batch 0.353 (0.344) Remain 03:03:02 loss: 0.1657 Lr: 0.00226 [2023-12-20 18:25:16,503 INFO misc.py line 119 131400] Train: [61/100][37/800] Data 0.007 (0.006) Batch 0.353 (0.344) Remain 03:03:10 loss: 0.5256 Lr: 0.00226 [2023-12-20 18:25:16,846 INFO misc.py line 119 131400] Train: [61/100][38/800] Data 0.004 (0.006) Batch 0.344 (0.344) Remain 03:03:10 loss: 0.2720 Lr: 0.00226 [2023-12-20 18:25:17,169 INFO misc.py line 119 131400] Train: [61/100][39/800] Data 0.003 (0.006) Batch 0.323 (0.343) Remain 03:02:51 loss: 0.1997 Lr: 0.00226 [2023-12-20 18:25:17,465 INFO misc.py line 119 131400] Train: [61/100][40/800] Data 0.003 (0.006) Batch 0.295 (0.342) Remain 03:02:09 loss: 0.3494 Lr: 0.00226 [2023-12-20 18:25:17,813 INFO misc.py line 119 131400] Train: [61/100][41/800] Data 0.004 (0.006) Batch 0.349 (0.342) Remain 03:02:14 loss: 0.2425 Lr: 0.00226 [2023-12-20 18:25:18,140 INFO misc.py line 119 131400] Train: [61/100][42/800] Data 0.004 (0.006) Batch 0.327 (0.342) Remain 03:02:01 loss: 0.2936 Lr: 0.00226 [2023-12-20 18:25:18,485 INFO misc.py line 119 131400] Train: [61/100][43/800] Data 0.003 (0.005) Batch 0.344 (0.342) Remain 03:02:03 loss: 0.3767 Lr: 0.00226 [2023-12-20 18:25:18,821 INFO misc.py line 119 131400] Train: [61/100][44/800] Data 0.005 (0.005) Batch 0.337 (0.342) Remain 03:01:59 loss: 0.3502 Lr: 0.00226 [2023-12-20 18:25:19,154 INFO misc.py line 119 131400] Train: [61/100][45/800] Data 0.004 (0.005) Batch 0.333 (0.341) Remain 03:01:52 loss: 0.2095 Lr: 0.00226 [2023-12-20 18:25:19,493 INFO misc.py line 119 131400] Train: [61/100][46/800] Data 0.004 (0.005) Batch 0.338 (0.341) Remain 03:01:49 loss: 0.3697 Lr: 0.00226 [2023-12-20 18:25:19,818 INFO misc.py line 119 131400] Train: [61/100][47/800] Data 0.005 (0.005) 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line 119 131400] Train: [61/100][782/800] Data 0.005 (0.004) Batch 0.338 (0.333) Remain 02:53:10 loss: 0.3683 Lr: 0.00217 [2023-12-20 18:29:24,370 INFO misc.py line 119 131400] Train: [61/100][783/800] Data 0.004 (0.004) Batch 0.283 (0.333) Remain 02:53:07 loss: 0.4233 Lr: 0.00217 [2023-12-20 18:29:24,670 INFO misc.py line 119 131400] Train: [61/100][784/800] Data 0.003 (0.004) Batch 0.300 (0.333) Remain 02:53:06 loss: 0.1981 Lr: 0.00217 [2023-12-20 18:29:25,003 INFO misc.py line 119 131400] Train: [61/100][785/800] Data 0.003 (0.004) Batch 0.333 (0.333) Remain 02:53:05 loss: 0.3696 Lr: 0.00217 [2023-12-20 18:29:25,330 INFO misc.py line 119 131400] Train: [61/100][786/800] Data 0.004 (0.004) Batch 0.326 (0.333) Remain 02:53:05 loss: 0.3319 Lr: 0.00217 [2023-12-20 18:29:25,679 INFO misc.py line 119 131400] Train: [61/100][787/800] Data 0.004 (0.004) Batch 0.349 (0.333) Remain 02:53:05 loss: 0.2828 Lr: 0.00217 [2023-12-20 18:29:26,024 INFO misc.py line 119 131400] Train: [61/100][788/800] Data 0.004 (0.004) Batch 0.346 (0.333) Remain 02:53:05 loss: 0.1636 Lr: 0.00217 [2023-12-20 18:29:26,361 INFO misc.py line 119 131400] Train: [61/100][789/800] Data 0.003 (0.004) Batch 0.336 (0.333) Remain 02:53:05 loss: 0.1456 Lr: 0.00217 [2023-12-20 18:29:26,674 INFO misc.py line 119 131400] Train: [61/100][790/800] Data 0.003 (0.004) Batch 0.312 (0.333) Remain 02:53:04 loss: 0.2490 Lr: 0.00217 [2023-12-20 18:29:27,011 INFO misc.py line 119 131400] Train: [61/100][791/800] Data 0.004 (0.004) Batch 0.338 (0.333) Remain 02:53:04 loss: 0.1589 Lr: 0.00217 [2023-12-20 18:29:27,362 INFO misc.py line 119 131400] Train: [61/100][792/800] Data 0.003 (0.004) Batch 0.350 (0.333) Remain 02:53:04 loss: 0.2624 Lr: 0.00217 [2023-12-20 18:29:27,691 INFO misc.py line 119 131400] Train: [61/100][793/800] Data 0.005 (0.004) Batch 0.330 (0.333) Remain 02:53:04 loss: 0.2401 Lr: 0.00217 [2023-12-20 18:29:28,021 INFO misc.py line 119 131400] Train: [61/100][794/800] Data 0.004 (0.004) Batch 0.330 (0.333) Remain 02:53:03 loss: 0.3181 Lr: 0.00217 [2023-12-20 18:29:28,322 INFO misc.py line 119 131400] Train: [61/100][795/800] Data 0.004 (0.004) Batch 0.300 (0.333) Remain 02:53:02 loss: 0.2692 Lr: 0.00217 [2023-12-20 18:29:28,619 INFO misc.py line 119 131400] Train: [61/100][796/800] Data 0.006 (0.004) Batch 0.299 (0.333) Remain 02:53:00 loss: 0.3314 Lr: 0.00217 [2023-12-20 18:29:28,945 INFO misc.py line 119 131400] Train: [61/100][797/800] Data 0.003 (0.004) Batch 0.326 (0.333) Remain 02:52:59 loss: 0.1786 Lr: 0.00217 [2023-12-20 18:29:29,270 INFO misc.py line 119 131400] Train: [61/100][798/800] Data 0.003 (0.004) Batch 0.326 (0.333) Remain 02:52:59 loss: 0.1763 Lr: 0.00217 [2023-12-20 18:29:29,587 INFO misc.py line 119 131400] Train: [61/100][799/800] Data 0.003 (0.004) Batch 0.316 (0.333) Remain 02:52:58 loss: 0.3994 Lr: 0.00217 [2023-12-20 18:29:29,863 INFO misc.py line 119 131400] Train: [61/100][800/800] Data 0.003 (0.004) Batch 0.277 (0.333) Remain 02:52:55 loss: 0.3147 Lr: 0.00217 [2023-12-20 18:29:29,864 INFO misc.py line 136 131400] Train result: loss: 0.3085 [2023-12-20 18:29:29,864 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:29:52,986 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1216 [2023-12-20 18:29:53,078 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1424 [2023-12-20 18:29:53,189 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4453 [2023-12-20 18:29:53,303 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2002 [2023-12-20 18:29:53,416 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2941 [2023-12-20 18:29:53,541 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3229 [2023-12-20 18:29:53,637 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.7846 [2023-12-20 18:29:53,758 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2209 [2023-12-20 18:29:53,843 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3153 [2023-12-20 18:29:53,955 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2929 [2023-12-20 18:29:54,063 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5952 [2023-12-20 18:29:54,213 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2611 [2023-12-20 18:29:54,337 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.1775 [2023-12-20 18:29:54,502 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2277 [2023-12-20 18:29:54,601 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1333 [2023-12-20 18:29:54,734 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7537 [2023-12-20 18:29:54,855 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3176 [2023-12-20 18:29:54,964 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6329 [2023-12-20 18:29:55,080 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.0982 [2023-12-20 18:29:55,158 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5848 [2023-12-20 18:29:55,269 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2155 [2023-12-20 18:29:55,425 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1323 [2023-12-20 18:29:55,549 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6202 [2023-12-20 18:29:55,692 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2177 [2023-12-20 18:29:55,836 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2296 [2023-12-20 18:29:55,919 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4381 [2023-12-20 18:29:56,076 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4159 [2023-12-20 18:29:56,200 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5126 [2023-12-20 18:29:56,300 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8002 [2023-12-20 18:29:56,449 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4942 [2023-12-20 18:29:56,554 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7018 [2023-12-20 18:29:56,678 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3793 [2023-12-20 18:29:56,778 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1847 [2023-12-20 18:29:56,858 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1920 [2023-12-20 18:29:56,958 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8279 [2023-12-20 18:29:57,057 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3643 [2023-12-20 18:29:57,194 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0396 [2023-12-20 18:29:57,311 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0918 [2023-12-20 18:29:57,394 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6032 [2023-12-20 18:29:57,539 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4057 [2023-12-20 18:29:57,693 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0595 [2023-12-20 18:29:57,808 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1530 [2023-12-20 18:29:57,930 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4347 [2023-12-20 18:29:58,082 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0698 [2023-12-20 18:29:58,209 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1672 [2023-12-20 18:29:58,314 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3838 [2023-12-20 18:29:58,488 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3112 [2023-12-20 18:29:58,587 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3693 [2023-12-20 18:29:58,735 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4513 [2023-12-20 18:29:58,841 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1160 [2023-12-20 18:29:58,961 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4380 [2023-12-20 18:29:59,096 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4006 [2023-12-20 18:29:59,257 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7105 [2023-12-20 18:29:59,398 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2507 [2023-12-20 18:29:59,505 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5356 [2023-12-20 18:29:59,599 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6769 [2023-12-20 18:29:59,709 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3379 [2023-12-20 18:29:59,871 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2705 [2023-12-20 18:29:59,978 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1668 [2023-12-20 18:30:00,085 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4233 [2023-12-20 18:30:00,197 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4170 [2023-12-20 18:30:00,300 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3439 [2023-12-20 18:30:00,394 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6030 [2023-12-20 18:30:00,505 INFO evaluator.py line 159 131400] Test: [64/78] Loss 1.0350 [2023-12-20 18:30:00,636 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.1177 [2023-12-20 18:30:00,723 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1816 [2023-12-20 18:30:00,828 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4005 [2023-12-20 18:30:00,927 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0641 [2023-12-20 18:30:01,017 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3574 [2023-12-20 18:30:01,100 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0780 [2023-12-20 18:30:01,202 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9321 [2023-12-20 18:30:01,298 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4078 [2023-12-20 18:30:01,432 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.2409 [2023-12-20 18:30:01,527 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6187 [2023-12-20 18:30:01,649 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.7821 [2023-12-20 18:30:01,768 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.8399 [2023-12-20 18:30:01,857 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3496 [2023-12-20 18:30:02,021 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1806 [2023-12-20 18:30:03,210 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7512/0.8490/0.9139. [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8700/0.9348 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9651/0.9866 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6864/0.8109 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8163/0.8544 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9057/0.9632 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8225/0.8993 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7605/0.8220 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7149/0.8447 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6667/0.8174 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8176/0.9507 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3995/0.5447 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6807/0.8275 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6961/0.9071 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7372/0.8207 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6237/0.7743 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7955/0.8634 [2023-12-20 18:30:03,210 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9349/0.9698 [2023-12-20 18:30:03,211 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6339/0.7901 [2023-12-20 18:30:03,211 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8868/0.9164 [2023-12-20 18:30:03,211 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6095/0.6817 [2023-12-20 18:30:03,211 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:30:03,212 INFO misc.py line 165 131400] Currently Best mIoU: 0.7541 [2023-12-20 18:30:03,212 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:30:07,311 INFO misc.py line 119 131400] Train: [62/100][1/800] Data 1.376 (1.376) Batch 1.768 (1.768) Remain 15:19:09 loss: 0.3197 Lr: 0.00217 [2023-12-20 18:30:07,656 INFO misc.py line 119 131400] Train: [62/100][2/800] Data 0.005 (0.005) Batch 0.347 (0.347) Remain 03:00:13 loss: 0.1387 Lr: 0.00217 [2023-12-20 18:30:07,974 INFO misc.py line 119 131400] Train: [62/100][3/800] Data 0.003 (0.003) Batch 0.318 (0.318) Remain 02:45:25 loss: 0.1645 Lr: 0.00217 [2023-12-20 18:30:08,330 INFO misc.py line 119 131400] Train: [62/100][4/800] Data 0.003 (0.003) Batch 0.355 (0.355) Remain 03:04:41 loss: 0.2391 Lr: 0.00217 [2023-12-20 18:30:08,691 INFO misc.py line 119 131400] Train: [62/100][5/800] Data 0.004 (0.003) Batch 0.362 (0.358) Remain 03:06:22 loss: 0.2593 Lr: 0.00217 [2023-12-20 18:30:09,032 INFO misc.py line 119 131400] Train: [62/100][6/800] Data 0.004 (0.004) Batch 0.341 (0.353) Remain 03:03:20 loss: 0.3872 Lr: 0.00217 [2023-12-20 18:30:09,338 INFO misc.py line 119 131400] Train: [62/100][7/800] Data 0.003 (0.004) Batch 0.305 (0.341) Remain 02:57:09 loss: 0.2110 Lr: 0.00217 [2023-12-20 18:30:09,704 INFO misc.py line 119 131400] Train: [62/100][8/800] Data 0.004 (0.004) Batch 0.367 (0.346) Remain 02:59:50 loss: 0.3844 Lr: 0.00217 [2023-12-20 18:30:10,038 INFO misc.py line 119 131400] Train: [62/100][9/800] Data 0.004 (0.004) Batch 0.334 (0.344) Remain 02:58:48 loss: 0.3608 Lr: 0.00217 [2023-12-20 18:30:10,363 INFO misc.py line 119 131400] Train: [62/100][10/800] Data 0.004 (0.004) Batch 0.324 (0.341) Remain 02:57:20 loss: 0.2784 Lr: 0.00217 [2023-12-20 18:30:10,730 INFO misc.py line 119 131400] Train: [62/100][11/800] Data 0.004 (0.004) Batch 0.368 (0.344) Remain 02:59:04 loss: 0.3799 Lr: 0.00217 [2023-12-20 18:30:11,061 INFO misc.py line 119 131400] Train: [62/100][12/800] Data 0.003 (0.004) Batch 0.330 (0.343) Remain 02:58:15 loss: 0.2452 Lr: 0.00217 [2023-12-20 18:30:11,423 INFO misc.py line 119 131400] Train: [62/100][13/800] Data 0.004 (0.004) Batch 0.361 (0.345) Remain 02:59:10 loss: 0.3572 Lr: 0.00217 [2023-12-20 18:30:11,780 INFO misc.py line 119 131400] Train: [62/100][14/800] Data 0.006 (0.004) Batch 0.358 (0.346) Remain 02:59:48 loss: 0.2505 Lr: 0.00217 [2023-12-20 18:30:12,126 INFO misc.py line 119 131400] Train: [62/100][15/800] Data 0.003 (0.004) Batch 0.346 (0.346) Remain 02:59:48 loss: 0.2620 Lr: 0.00217 [2023-12-20 18:30:12,489 INFO misc.py line 119 131400] Train: [62/100][16/800] Data 0.003 (0.004) Batch 0.363 (0.347) Remain 03:00:27 loss: 0.1467 Lr: 0.00217 [2023-12-20 18:30:12,873 INFO misc.py line 119 131400] Train: [62/100][17/800] Data 0.004 (0.004) Batch 0.384 (0.350) Remain 03:01:49 loss: 0.5353 Lr: 0.00217 [2023-12-20 18:30:13,204 INFO misc.py line 119 131400] Train: [62/100][18/800] Data 0.005 (0.004) Batch 0.329 (0.348) Remain 03:01:06 loss: 0.4181 Lr: 0.00217 [2023-12-20 18:30:13,596 INFO misc.py line 119 131400] Train: [62/100][19/800] Data 0.006 (0.004) Batch 0.393 (0.351) Remain 03:02:32 loss: 0.1739 Lr: 0.00217 [2023-12-20 18:30:13,919 INFO misc.py line 119 131400] Train: [62/100][20/800] Data 0.005 (0.004) Batch 0.324 (0.350) Remain 03:01:42 loss: 0.2547 Lr: 0.00217 [2023-12-20 18:30:14,267 INFO misc.py line 119 131400] Train: [62/100][21/800] Data 0.004 (0.004) Batch 0.348 (0.350) Remain 03:01:38 loss: 0.3738 Lr: 0.00217 [2023-12-20 18:30:14,586 INFO misc.py line 119 131400] Train: [62/100][22/800] Data 0.006 (0.004) Batch 0.320 (0.348) Remain 03:00:49 loss: 0.5751 Lr: 0.00217 [2023-12-20 18:30:14,951 INFO misc.py line 119 131400] Train: [62/100][23/800] Data 0.004 (0.004) Batch 0.365 (0.349) Remain 03:01:14 loss: 0.5428 Lr: 0.00217 [2023-12-20 18:30:15,267 INFO misc.py line 119 131400] Train: [62/100][24/800] Data 0.004 (0.004) Batch 0.316 (0.347) Remain 03:00:25 loss: 0.2387 Lr: 0.00217 [2023-12-20 18:30:15,602 INFO misc.py line 119 131400] Train: [62/100][25/800] Data 0.005 (0.004) Batch 0.335 (0.347) Remain 03:00:08 loss: 0.1701 Lr: 0.00217 [2023-12-20 18:30:15,956 INFO misc.py line 119 131400] Train: [62/100][26/800] Data 0.003 (0.004) Batch 0.354 (0.347) Remain 03:00:18 loss: 0.2846 Lr: 0.00217 [2023-12-20 18:30:16,334 INFO misc.py line 119 131400] Train: [62/100][27/800] Data 0.003 (0.004) Batch 0.378 (0.348) Remain 03:00:57 loss: 0.4469 Lr: 0.00217 [2023-12-20 18:30:16,726 INFO misc.py line 119 131400] Train: [62/100][28/800] Data 0.003 (0.004) Batch 0.391 (0.350) Remain 03:01:50 loss: 0.2002 Lr: 0.00217 [2023-12-20 18:30:17,034 INFO misc.py line 119 131400] Train: [62/100][29/800] Data 0.005 (0.004) Batch 0.309 (0.348) Remain 03:01:01 loss: 0.2351 Lr: 0.00217 [2023-12-20 18:30:17,335 INFO misc.py line 119 131400] Train: [62/100][30/800] Data 0.004 (0.004) Batch 0.301 (0.347) Remain 03:00:05 loss: 0.5687 Lr: 0.00217 [2023-12-20 18:30:17,719 INFO misc.py line 119 131400] Train: [62/100][31/800] Data 0.004 (0.004) Batch 0.382 (0.348) Remain 03:00:45 loss: 0.2559 Lr: 0.00217 [2023-12-20 18:30:18,067 INFO misc.py line 119 131400] Train: [62/100][32/800] Data 0.005 (0.004) Batch 0.350 (0.348) Remain 03:00:46 loss: 0.2445 Lr: 0.00217 [2023-12-20 18:30:18,387 INFO misc.py line 119 131400] Train: [62/100][33/800] Data 0.004 (0.004) Batch 0.321 (0.347) Remain 03:00:18 loss: 0.2813 Lr: 0.00217 [2023-12-20 18:30:18,715 INFO misc.py line 119 131400] Train: [62/100][34/800] Data 0.003 (0.004) Batch 0.327 (0.346) Remain 02:59:57 loss: 0.5048 Lr: 0.00217 [2023-12-20 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02:50:01 loss: 0.5371 Lr: 0.00208 [2023-12-20 18:34:25,100 INFO misc.py line 119 131400] Train: [62/100][770/800] Data 0.004 (0.004) Batch 0.337 (0.335) Remain 02:50:01 loss: 0.3820 Lr: 0.00208 [2023-12-20 18:34:25,413 INFO misc.py line 119 131400] Train: [62/100][771/800] Data 0.004 (0.004) Batch 0.314 (0.335) Remain 02:49:59 loss: 0.2262 Lr: 0.00208 [2023-12-20 18:34:25,772 INFO misc.py line 119 131400] Train: [62/100][772/800] Data 0.003 (0.004) Batch 0.355 (0.335) Remain 02:50:00 loss: 0.3285 Lr: 0.00208 [2023-12-20 18:34:26,079 INFO misc.py line 119 131400] Train: [62/100][773/800] Data 0.008 (0.004) Batch 0.311 (0.335) Remain 02:49:59 loss: 0.3919 Lr: 0.00208 [2023-12-20 18:34:26,409 INFO misc.py line 119 131400] Train: [62/100][774/800] Data 0.003 (0.004) Batch 0.329 (0.335) Remain 02:49:58 loss: 0.3032 Lr: 0.00208 [2023-12-20 18:34:26,737 INFO misc.py line 119 131400] Train: [62/100][775/800] Data 0.004 (0.004) Batch 0.328 (0.335) Remain 02:49:57 loss: 0.3681 Lr: 0.00208 [2023-12-20 18:34:27,052 INFO misc.py line 119 131400] Train: [62/100][776/800] Data 0.004 (0.004) Batch 0.315 (0.335) Remain 02:49:56 loss: 0.3474 Lr: 0.00208 [2023-12-20 18:34:27,390 INFO misc.py line 119 131400] Train: [62/100][777/800] Data 0.004 (0.004) Batch 0.336 (0.335) Remain 02:49:56 loss: 0.3836 Lr: 0.00208 [2023-12-20 18:34:27,756 INFO misc.py line 119 131400] Train: [62/100][778/800] Data 0.007 (0.004) Batch 0.368 (0.335) Remain 02:49:57 loss: 0.4040 Lr: 0.00208 [2023-12-20 18:34:28,076 INFO misc.py line 119 131400] Train: [62/100][779/800] Data 0.003 (0.004) Batch 0.320 (0.335) Remain 02:49:56 loss: 0.4881 Lr: 0.00208 [2023-12-20 18:34:28,409 INFO misc.py line 119 131400] Train: [62/100][780/800] Data 0.003 (0.004) Batch 0.332 (0.335) Remain 02:49:56 loss: 0.3728 Lr: 0.00208 [2023-12-20 18:34:28,753 INFO misc.py line 119 131400] Train: [62/100][781/800] Data 0.004 (0.004) Batch 0.344 (0.335) Remain 02:49:56 loss: 0.3333 Lr: 0.00208 [2023-12-20 18:34:29,094 INFO misc.py line 119 131400] Train: [62/100][782/800] Data 0.004 (0.004) Batch 0.342 (0.335) Remain 02:49:56 loss: 0.2013 Lr: 0.00208 [2023-12-20 18:34:29,406 INFO misc.py line 119 131400] Train: [62/100][783/800] Data 0.003 (0.004) Batch 0.309 (0.335) Remain 02:49:54 loss: 0.4033 Lr: 0.00208 [2023-12-20 18:34:29,710 INFO misc.py line 119 131400] Train: [62/100][784/800] Data 0.004 (0.004) Batch 0.305 (0.335) Remain 02:49:53 loss: 0.1642 Lr: 0.00208 [2023-12-20 18:34:30,011 INFO misc.py line 119 131400] Train: [62/100][785/800] Data 0.004 (0.004) Batch 0.300 (0.335) Remain 02:49:51 loss: 0.2041 Lr: 0.00208 [2023-12-20 18:34:30,349 INFO misc.py line 119 131400] Train: [62/100][786/800] Data 0.005 (0.004) Batch 0.340 (0.335) Remain 02:49:51 loss: 0.2167 Lr: 0.00208 [2023-12-20 18:34:30,662 INFO misc.py line 119 131400] Train: [62/100][787/800] Data 0.003 (0.004) Batch 0.313 (0.335) Remain 02:49:50 loss: 0.2690 Lr: 0.00208 [2023-12-20 18:34:31,028 INFO misc.py line 119 131400] Train: [62/100][788/800] Data 0.003 (0.004) Batch 0.362 (0.335) Remain 02:49:50 loss: 0.4169 Lr: 0.00208 [2023-12-20 18:34:31,372 INFO misc.py line 119 131400] Train: [62/100][789/800] Data 0.006 (0.004) Batch 0.346 (0.335) Remain 02:49:50 loss: 0.2576 Lr: 0.00208 [2023-12-20 18:34:31,700 INFO misc.py line 119 131400] Train: [62/100][790/800] Data 0.004 (0.004) Batch 0.328 (0.335) Remain 02:49:50 loss: 0.3092 Lr: 0.00208 [2023-12-20 18:34:32,002 INFO misc.py line 119 131400] Train: [62/100][791/800] Data 0.003 (0.004) Batch 0.303 (0.335) Remain 02:49:48 loss: 0.1889 Lr: 0.00208 [2023-12-20 18:34:32,292 INFO misc.py line 119 131400] Train: [62/100][792/800] Data 0.003 (0.004) Batch 0.289 (0.335) Remain 02:49:46 loss: 0.4850 Lr: 0.00208 [2023-12-20 18:34:32,724 INFO misc.py line 119 131400] Train: [62/100][793/800] Data 0.004 (0.004) Batch 0.432 (0.335) Remain 02:49:50 loss: 0.3485 Lr: 0.00208 [2023-12-20 18:34:33,014 INFO misc.py line 119 131400] Train: [62/100][794/800] Data 0.004 (0.004) Batch 0.291 (0.335) Remain 02:49:48 loss: 0.2520 Lr: 0.00208 [2023-12-20 18:34:33,325 INFO misc.py line 119 131400] Train: [62/100][795/800] Data 0.003 (0.004) Batch 0.310 (0.335) Remain 02:49:46 loss: 0.3103 Lr: 0.00208 [2023-12-20 18:34:33,634 INFO misc.py line 119 131400] Train: [62/100][796/800] Data 0.003 (0.004) Batch 0.309 (0.335) Remain 02:49:45 loss: 0.6029 Lr: 0.00208 [2023-12-20 18:34:33,943 INFO misc.py line 119 131400] Train: [62/100][797/800] Data 0.004 (0.004) Batch 0.309 (0.335) Remain 02:49:44 loss: 0.2668 Lr: 0.00208 [2023-12-20 18:34:34,265 INFO misc.py line 119 131400] Train: [62/100][798/800] Data 0.005 (0.004) Batch 0.322 (0.335) Remain 02:49:43 loss: 0.4580 Lr: 0.00208 [2023-12-20 18:34:34,611 INFO misc.py line 119 131400] Train: [62/100][799/800] Data 0.004 (0.004) Batch 0.347 (0.335) Remain 02:49:43 loss: 0.4122 Lr: 0.00208 [2023-12-20 18:34:34,919 INFO misc.py line 119 131400] Train: [62/100][800/800] Data 0.004 (0.004) Batch 0.307 (0.335) Remain 02:49:41 loss: 0.5016 Lr: 0.00208 [2023-12-20 18:34:34,919 INFO misc.py line 136 131400] Train result: loss: 0.3023 [2023-12-20 18:34:34,920 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:34:57,913 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1524 [2023-12-20 18:34:57,997 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1552 [2023-12-20 18:34:58,103 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4345 [2023-12-20 18:34:58,214 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2779 [2023-12-20 18:34:58,329 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2503 [2023-12-20 18:34:58,431 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0513 [2023-12-20 18:34:58,526 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2870 [2023-12-20 18:34:58,636 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2567 [2023-12-20 18:34:58,717 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2724 [2023-12-20 18:34:58,801 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3404 [2023-12-20 18:34:58,893 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7792 [2023-12-20 18:34:59,030 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2695 [2023-12-20 18:34:59,147 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4421 [2023-12-20 18:34:59,301 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2015 [2023-12-20 18:34:59,398 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1553 [2023-12-20 18:34:59,537 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8353 [2023-12-20 18:34:59,646 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3440 [2023-12-20 18:34:59,762 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4702 [2023-12-20 18:34:59,876 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1366 [2023-12-20 18:34:59,960 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3678 [2023-12-20 18:35:00,065 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3407 [2023-12-20 18:35:00,233 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1654 [2023-12-20 18:35:00,354 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8267 [2023-12-20 18:35:00,509 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4322 [2023-12-20 18:35:00,656 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.3576 [2023-12-20 18:35:00,738 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.9055 [2023-12-20 18:35:00,895 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.1164 [2023-12-20 18:35:01,030 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.7014 [2023-12-20 18:35:01,128 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5581 [2023-12-20 18:35:01,279 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4068 [2023-12-20 18:35:01,395 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7566 [2023-12-20 18:35:01,525 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4745 [2023-12-20 18:35:01,620 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1378 [2023-12-20 18:35:01,701 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1746 [2023-12-20 18:35:01,806 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5995 [2023-12-20 18:35:01,904 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3775 [2023-12-20 18:35:02,041 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0578 [2023-12-20 18:35:02,157 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1049 [2023-12-20 18:35:02,243 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4865 [2023-12-20 18:35:02,407 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3069 [2023-12-20 18:35:02,560 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0333 [2023-12-20 18:35:02,662 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1285 [2023-12-20 18:35:02,797 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.5365 [2023-12-20 18:35:02,941 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9205 [2023-12-20 18:35:03,059 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.7313 [2023-12-20 18:35:03,163 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6494 [2023-12-20 18:35:03,329 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.4285 [2023-12-20 18:35:03,427 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4224 [2023-12-20 18:35:03,584 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6180 [2023-12-20 18:35:03,678 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0651 [2023-12-20 18:35:03,766 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4551 [2023-12-20 18:35:03,886 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4638 [2023-12-20 18:35:04,041 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.3421 [2023-12-20 18:35:04,174 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2943 [2023-12-20 18:35:04,274 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.9518 [2023-12-20 18:35:04,370 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6396 [2023-12-20 18:35:04,476 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4219 [2023-12-20 18:35:04,636 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2236 [2023-12-20 18:35:04,734 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2520 [2023-12-20 18:35:04,829 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.5060 [2023-12-20 18:35:04,927 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5965 [2023-12-20 18:35:05,022 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2522 [2023-12-20 18:35:05,114 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3329 [2023-12-20 18:35:05,214 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8745 [2023-12-20 18:35:05,352 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7000 [2023-12-20 18:35:05,444 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1660 [2023-12-20 18:35:05,546 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3722 [2023-12-20 18:35:05,640 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0361 [2023-12-20 18:35:05,726 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3576 [2023-12-20 18:35:05,818 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0302 [2023-12-20 18:35:05,911 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8613 [2023-12-20 18:35:06,005 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5481 [2023-12-20 18:35:06,143 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1302 [2023-12-20 18:35:06,241 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5225 [2023-12-20 18:35:06,365 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5390 [2023-12-20 18:35:06,473 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6980 [2023-12-20 18:35:06,559 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2742 [2023-12-20 18:35:06,718 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.5470 [2023-12-20 18:35:07,938 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7436/0.8327/0.9126. [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8648/0.9487 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9657/0.9829 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6855/0.7899 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8204/0.8666 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9120/0.9585 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8523/0.9255 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7647/0.8701 [2023-12-20 18:35:07,938 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6841/0.8049 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6874/0.8202 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8070/0.9646 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3867/0.5090 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6372/0.8436 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6745/0.8144 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7308/0.7901 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.5683/0.5920 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7555/0.8596 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9333/0.9743 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6743/0.7662 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8774/0.9270 [2023-12-20 18:35:07,939 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5910/0.6460 [2023-12-20 18:35:07,939 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:35:07,940 INFO misc.py line 165 131400] Currently Best mIoU: 0.7541 [2023-12-20 18:35:07,941 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:35:12,506 INFO misc.py line 119 131400] Train: [63/100][1/800] Data 1.640 (1.640) Batch 1.958 (1.958) Remain 16:32:09 loss: 0.3320 Lr: 0.00208 [2023-12-20 18:35:12,847 INFO misc.py line 119 131400] Train: [63/100][2/800] Data 0.004 (0.004) Batch 0.341 (0.341) Remain 02:52:52 loss: 0.2949 Lr: 0.00208 [2023-12-20 18:35:13,172 INFO misc.py line 119 131400] Train: [63/100][3/800] Data 0.004 (0.004) Batch 0.326 (0.326) Remain 02:44:56 loss: 0.2781 Lr: 0.00208 [2023-12-20 18:35:13,499 INFO misc.py line 119 131400] Train: [63/100][4/800] Data 0.003 (0.003) Batch 0.327 (0.327) Remain 02:45:32 loss: 0.1598 Lr: 0.00208 [2023-12-20 18:35:13,823 INFO misc.py line 119 131400] Train: [63/100][5/800] Data 0.003 (0.003) Batch 0.324 (0.325) Remain 02:44:52 loss: 0.1432 Lr: 0.00208 [2023-12-20 18:35:14,176 INFO misc.py line 119 131400] Train: [63/100][6/800] Data 0.003 (0.003) Batch 0.352 (0.334) Remain 02:49:17 loss: 0.2180 Lr: 0.00207 [2023-12-20 18:35:14,507 INFO misc.py line 119 131400] Train: [63/100][7/800] Data 0.006 (0.004) Batch 0.333 (0.334) Remain 02:49:04 loss: 0.4156 Lr: 0.00207 [2023-12-20 18:35:14,832 INFO misc.py line 119 131400] Train: [63/100][8/800] Data 0.003 (0.004) Batch 0.325 (0.332) Remain 02:48:09 loss: 0.3670 Lr: 0.00207 [2023-12-20 18:35:15,148 INFO misc.py line 119 131400] Train: [63/100][9/800] Data 0.004 (0.004) Batch 0.317 (0.329) Remain 02:46:52 loss: 0.3181 Lr: 0.00207 [2023-12-20 18:35:15,474 INFO misc.py line 119 131400] Train: [63/100][10/800] Data 0.003 (0.004) Batch 0.326 (0.329) Remain 02:46:36 loss: 0.5228 Lr: 0.00207 [2023-12-20 18:35:15,807 INFO misc.py line 119 131400] Train: [63/100][11/800] Data 0.003 (0.003) Batch 0.332 (0.329) Remain 02:46:45 loss: 0.1445 Lr: 0.00207 [2023-12-20 18:35:16,136 INFO misc.py line 119 131400] Train: [63/100][12/800] Data 0.004 (0.004) Batch 0.330 (0.329) Remain 02:46:47 loss: 0.1617 Lr: 0.00207 [2023-12-20 18:35:16,472 INFO misc.py line 119 131400] Train: [63/100][13/800] Data 0.004 (0.004) Batch 0.333 (0.330) Remain 02:46:59 loss: 0.5517 Lr: 0.00207 [2023-12-20 18:35:16,818 INFO misc.py line 119 131400] Train: [63/100][14/800] Data 0.006 (0.004) Batch 0.347 (0.331) Remain 02:47:48 loss: 0.5727 Lr: 0.00207 [2023-12-20 18:35:17,135 INFO misc.py line 119 131400] Train: [63/100][15/800] Data 0.004 (0.004) Batch 0.318 (0.330) Remain 02:47:15 loss: 0.3552 Lr: 0.00207 [2023-12-20 18:35:17,428 INFO misc.py line 119 131400] Train: [63/100][16/800] Data 0.003 (0.004) Batch 0.292 (0.327) Remain 02:45:46 loss: 0.1782 Lr: 0.00207 [2023-12-20 18:35:17,778 INFO misc.py line 119 131400] Train: [63/100][17/800] Data 0.004 (0.004) Batch 0.348 (0.329) Remain 02:46:29 loss: 0.2460 Lr: 0.00207 [2023-12-20 18:35:18,062 INFO misc.py line 119 131400] Train: [63/100][18/800] Data 0.006 (0.004) Batch 0.286 (0.326) Remain 02:45:02 loss: 0.3532 Lr: 0.00207 [2023-12-20 18:35:18,429 INFO misc.py line 119 131400] Train: [63/100][19/800] Data 0.005 (0.004) Batch 0.368 (0.329) Remain 02:46:22 loss: 0.3633 Lr: 0.00207 [2023-12-20 18:35:18,755 INFO misc.py line 119 131400] Train: [63/100][20/800] Data 0.003 (0.004) Batch 0.326 (0.328) Remain 02:46:17 loss: 0.3691 Lr: 0.00207 [2023-12-20 18:35:19,115 INFO misc.py line 119 131400] Train: [63/100][21/800] Data 0.003 (0.004) Batch 0.360 (0.330) Remain 02:47:09 loss: 0.3327 Lr: 0.00207 [2023-12-20 18:35:19,460 INFO misc.py line 119 131400] Train: [63/100][22/800] Data 0.004 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[2023-12-20 18:36:42,444 INFO misc.py line 119 131400] Train: [63/100][272/800] Data 0.004 (0.006) Batch 0.314 (0.332) Remain 02:46:38 loss: 0.2102 Lr: 0.00204 [2023-12-20 18:36:42,760 INFO misc.py line 119 131400] Train: [63/100][273/800] Data 0.003 (0.006) Batch 0.315 (0.332) Remain 02:46:36 loss: 0.3306 Lr: 0.00204 [2023-12-20 18:36:43,100 INFO misc.py line 119 131400] Train: [63/100][274/800] Data 0.005 (0.006) Batch 0.341 (0.332) Remain 02:46:36 loss: 0.5543 Lr: 0.00204 [2023-12-20 18:36:43,420 INFO misc.py line 119 131400] Train: [63/100][275/800] Data 0.003 (0.006) Batch 0.319 (0.332) Remain 02:46:35 loss: 0.3015 Lr: 0.00204 [2023-12-20 18:36:43,831 INFO misc.py line 119 131400] Train: [63/100][276/800] Data 0.005 (0.006) Batch 0.412 (0.332) Remain 02:46:43 loss: 0.4380 Lr: 0.00204 [2023-12-20 18:36:44,165 INFO misc.py line 119 131400] Train: [63/100][277/800] Data 0.003 (0.005) Batch 0.334 (0.332) Remain 02:46:43 loss: 0.1674 Lr: 0.00204 [2023-12-20 18:36:44,471 INFO misc.py 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Batch 0.287 (0.331) Remain 02:46:20 loss: 0.2506 Lr: 0.00204 [2023-12-20 18:36:48,656 INFO misc.py line 119 131400] Train: [63/100][291/800] Data 0.003 (0.005) Batch 0.351 (0.332) Remain 02:46:22 loss: 0.2094 Lr: 0.00204 [2023-12-20 18:36:48,991 INFO misc.py line 119 131400] Train: [63/100][292/800] Data 0.003 (0.005) Batch 0.334 (0.332) Remain 02:46:22 loss: 0.1005 Lr: 0.00204 [2023-12-20 18:36:49,282 INFO misc.py line 119 131400] Train: [63/100][293/800] Data 0.004 (0.005) Batch 0.292 (0.331) Remain 02:46:17 loss: 0.3754 Lr: 0.00204 [2023-12-20 18:36:49,641 INFO misc.py line 119 131400] Train: [63/100][294/800] Data 0.003 (0.005) Batch 0.358 (0.332) Remain 02:46:20 loss: 0.3537 Lr: 0.00204 [2023-12-20 18:36:49,988 INFO misc.py line 119 131400] Train: [63/100][295/800] Data 0.005 (0.005) Batch 0.346 (0.332) Remain 02:46:21 loss: 0.3473 Lr: 0.00204 [2023-12-20 18:36:50,302 INFO misc.py line 119 131400] Train: [63/100][296/800] Data 0.004 (0.005) Batch 0.315 (0.331) Remain 02:46:19 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131400] Train: [63/100][757/800] Data 0.003 (0.005) Batch 0.333 (0.334) Remain 02:44:46 loss: 0.4265 Lr: 0.00199 [2023-12-20 18:39:24,962 INFO misc.py line 119 131400] Train: [63/100][758/800] Data 0.003 (0.005) Batch 0.303 (0.333) Remain 02:44:45 loss: 0.2605 Lr: 0.00199 [2023-12-20 18:39:25,308 INFO misc.py line 119 131400] Train: [63/100][759/800] Data 0.004 (0.005) Batch 0.345 (0.334) Remain 02:44:45 loss: 0.1386 Lr: 0.00199 [2023-12-20 18:39:25,663 INFO misc.py line 119 131400] Train: [63/100][760/800] Data 0.007 (0.005) Batch 0.356 (0.334) Remain 02:44:46 loss: 0.5391 Lr: 0.00199 [2023-12-20 18:39:26,016 INFO misc.py line 119 131400] Train: [63/100][761/800] Data 0.005 (0.005) Batch 0.353 (0.334) Remain 02:44:46 loss: 0.2736 Lr: 0.00199 [2023-12-20 18:39:26,319 INFO misc.py line 119 131400] Train: [63/100][762/800] Data 0.004 (0.005) Batch 0.302 (0.334) Remain 02:44:44 loss: 0.7696 Lr: 0.00199 [2023-12-20 18:39:26,647 INFO misc.py line 119 131400] Train: [63/100][763/800] Data 0.005 (0.005) Batch 0.330 (0.334) Remain 02:44:44 loss: 0.3371 Lr: 0.00199 [2023-12-20 18:39:26,957 INFO misc.py line 119 131400] Train: [63/100][764/800] Data 0.003 (0.005) Batch 0.310 (0.333) Remain 02:44:43 loss: 0.1579 Lr: 0.00199 [2023-12-20 18:39:27,304 INFO misc.py line 119 131400] Train: [63/100][765/800] Data 0.003 (0.005) Batch 0.347 (0.334) Remain 02:44:43 loss: 0.4222 Lr: 0.00199 [2023-12-20 18:39:27,642 INFO misc.py line 119 131400] Train: [63/100][766/800] Data 0.003 (0.005) Batch 0.334 (0.334) Remain 02:44:43 loss: 0.3297 Lr: 0.00199 [2023-12-20 18:39:27,976 INFO misc.py line 119 131400] Train: [63/100][767/800] Data 0.007 (0.005) Batch 0.337 (0.334) Remain 02:44:42 loss: 0.2644 Lr: 0.00199 [2023-12-20 18:39:28,322 INFO misc.py line 119 131400] Train: [63/100][768/800] Data 0.004 (0.005) Batch 0.347 (0.334) Remain 02:44:43 loss: 0.3223 Lr: 0.00199 [2023-12-20 18:39:28,650 INFO misc.py line 119 131400] Train: [63/100][769/800] Data 0.003 (0.005) Batch 0.326 (0.334) Remain 02:44:42 loss: 0.2188 Lr: 0.00199 [2023-12-20 18:39:29,018 INFO misc.py line 119 131400] Train: [63/100][770/800] Data 0.005 (0.005) Batch 0.369 (0.334) Remain 02:44:43 loss: 0.3152 Lr: 0.00199 [2023-12-20 18:39:29,366 INFO misc.py line 119 131400] Train: [63/100][771/800] Data 0.005 (0.005) Batch 0.349 (0.334) Remain 02:44:43 loss: 0.2677 Lr: 0.00199 [2023-12-20 18:39:29,729 INFO misc.py line 119 131400] Train: [63/100][772/800] Data 0.003 (0.005) Batch 0.363 (0.334) Remain 02:44:44 loss: 0.4281 Lr: 0.00199 [2023-12-20 18:39:30,058 INFO misc.py line 119 131400] Train: [63/100][773/800] Data 0.004 (0.005) Batch 0.328 (0.334) Remain 02:44:44 loss: 0.4034 Lr: 0.00199 [2023-12-20 18:39:30,417 INFO misc.py line 119 131400] Train: [63/100][774/800] Data 0.004 (0.005) Batch 0.359 (0.334) Remain 02:44:44 loss: 0.3150 Lr: 0.00199 [2023-12-20 18:39:30,749 INFO misc.py line 119 131400] Train: [63/100][775/800] Data 0.004 (0.005) Batch 0.332 (0.334) Remain 02:44:44 loss: 0.1706 Lr: 0.00198 [2023-12-20 18:39:31,080 INFO misc.py line 119 131400] Train: [63/100][776/800] Data 0.004 (0.005) Batch 0.331 (0.334) Remain 02:44:43 loss: 0.2335 Lr: 0.00198 [2023-12-20 18:39:31,430 INFO misc.py line 119 131400] Train: [63/100][777/800] Data 0.004 (0.005) Batch 0.350 (0.334) Remain 02:44:44 loss: 0.4159 Lr: 0.00198 [2023-12-20 18:39:31,751 INFO misc.py line 119 131400] Train: [63/100][778/800] Data 0.003 (0.005) Batch 0.321 (0.334) Remain 02:44:43 loss: 0.5428 Lr: 0.00198 [2023-12-20 18:39:32,108 INFO misc.py line 119 131400] Train: [63/100][779/800] Data 0.004 (0.005) Batch 0.357 (0.334) Remain 02:44:43 loss: 0.2693 Lr: 0.00198 [2023-12-20 18:39:32,456 INFO misc.py line 119 131400] Train: [63/100][780/800] Data 0.004 (0.005) Batch 0.348 (0.334) Remain 02:44:44 loss: 0.2457 Lr: 0.00198 [2023-12-20 18:39:32,797 INFO misc.py line 119 131400] Train: [63/100][781/800] Data 0.004 (0.005) Batch 0.341 (0.334) Remain 02:44:43 loss: 0.2144 Lr: 0.00198 [2023-12-20 18:39:33,132 INFO misc.py line 119 131400] Train: [63/100][782/800] Data 0.005 (0.005) Batch 0.334 (0.334) Remain 02:44:43 loss: 0.3738 Lr: 0.00198 [2023-12-20 18:39:33,475 INFO misc.py line 119 131400] Train: [63/100][783/800] Data 0.006 (0.005) Batch 0.345 (0.334) Remain 02:44:43 loss: 0.3867 Lr: 0.00198 [2023-12-20 18:39:33,822 INFO misc.py line 119 131400] Train: [63/100][784/800] Data 0.004 (0.005) Batch 0.347 (0.334) Remain 02:44:43 loss: 0.2169 Lr: 0.00198 [2023-12-20 18:39:34,103 INFO misc.py line 119 131400] Train: [63/100][785/800] Data 0.006 (0.005) Batch 0.282 (0.334) Remain 02:44:41 loss: 0.5032 Lr: 0.00198 [2023-12-20 18:39:34,413 INFO misc.py line 119 131400] Train: [63/100][786/800] Data 0.004 (0.005) Batch 0.310 (0.334) Remain 02:44:40 loss: 0.3495 Lr: 0.00198 [2023-12-20 18:39:34,746 INFO misc.py line 119 131400] Train: [63/100][787/800] Data 0.004 (0.005) Batch 0.333 (0.334) Remain 02:44:40 loss: 0.2565 Lr: 0.00198 [2023-12-20 18:39:35,163 INFO misc.py line 119 131400] Train: [63/100][788/800] Data 0.004 (0.005) Batch 0.417 (0.334) Remain 02:44:42 loss: 0.2817 Lr: 0.00198 [2023-12-20 18:39:35,491 INFO misc.py line 119 131400] Train: [63/100][789/800] Data 0.004 (0.005) Batch 0.328 (0.334) Remain 02:44:42 loss: 0.4216 Lr: 0.00198 [2023-12-20 18:39:35,791 INFO misc.py line 119 131400] Train: [63/100][790/800] Data 0.003 (0.005) Batch 0.301 (0.334) Remain 02:44:40 loss: 0.4029 Lr: 0.00198 [2023-12-20 18:39:36,109 INFO misc.py line 119 131400] Train: [63/100][791/800] Data 0.003 (0.005) Batch 0.317 (0.334) Remain 02:44:39 loss: 0.4153 Lr: 0.00198 [2023-12-20 18:39:36,423 INFO misc.py line 119 131400] Train: [63/100][792/800] Data 0.004 (0.005) Batch 0.314 (0.334) Remain 02:44:38 loss: 0.3243 Lr: 0.00198 [2023-12-20 18:39:36,720 INFO misc.py line 119 131400] Train: [63/100][793/800] Data 0.004 (0.005) Batch 0.298 (0.334) Remain 02:44:36 loss: 0.3837 Lr: 0.00198 [2023-12-20 18:39:37,058 INFO misc.py line 119 131400] Train: [63/100][794/800] Data 0.004 (0.005) Batch 0.337 (0.334) Remain 02:44:36 loss: 0.0919 Lr: 0.00198 [2023-12-20 18:39:37,339 INFO misc.py line 119 131400] Train: [63/100][795/800] Data 0.004 (0.005) Batch 0.280 (0.334) Remain 02:44:34 loss: 0.1597 Lr: 0.00198 [2023-12-20 18:39:37,625 INFO misc.py line 119 131400] Train: [63/100][796/800] Data 0.006 (0.005) Batch 0.287 (0.333) Remain 02:44:32 loss: 0.3627 Lr: 0.00198 [2023-12-20 18:39:37,953 INFO misc.py line 119 131400] Train: [63/100][797/800] Data 0.004 (0.005) Batch 0.325 (0.333) Remain 02:44:31 loss: 0.3008 Lr: 0.00198 [2023-12-20 18:39:38,226 INFO misc.py line 119 131400] Train: [63/100][798/800] Data 0.008 (0.005) Batch 0.276 (0.333) Remain 02:44:29 loss: 0.2213 Lr: 0.00198 [2023-12-20 18:39:38,554 INFO misc.py line 119 131400] Train: [63/100][799/800] Data 0.004 (0.005) Batch 0.329 (0.333) Remain 02:44:28 loss: 0.2240 Lr: 0.00198 [2023-12-20 18:39:38,892 INFO misc.py line 119 131400] Train: [63/100][800/800] Data 0.004 (0.005) Batch 0.332 (0.333) Remain 02:44:28 loss: 0.2971 Lr: 0.00198 [2023-12-20 18:39:38,894 INFO misc.py line 136 131400] Train result: loss: 0.2994 [2023-12-20 18:39:38,894 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:40:01,758 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2068 [2023-12-20 18:40:01,907 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1716 [2023-12-20 18:40:02,003 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3885 [2023-12-20 18:40:02,420 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1992 [2023-12-20 18:40:02,546 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.7052 [2023-12-20 18:40:02,653 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9433 [2023-12-20 18:40:02,756 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.9114 [2023-12-20 18:40:02,867 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.5542 [2023-12-20 18:40:02,950 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2119 [2023-12-20 18:40:03,038 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2924 [2023-12-20 18:40:03,142 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3684 [2023-12-20 18:40:03,290 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3229 [2023-12-20 18:40:03,420 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4622 [2023-12-20 18:40:03,576 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.3028 [2023-12-20 18:40:03,669 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1442 [2023-12-20 18:40:03,813 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9401 [2023-12-20 18:40:03,935 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.5229 [2023-12-20 18:40:04,046 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4181 [2023-12-20 18:40:04,159 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1093 [2023-12-20 18:40:04,234 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5457 [2023-12-20 18:40:04,345 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3804 [2023-12-20 18:40:04,502 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1349 [2023-12-20 18:40:04,623 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3616 [2023-12-20 18:40:04,766 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3681 [2023-12-20 18:40:04,913 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1473 [2023-12-20 18:40:05,007 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5211 [2023-12-20 18:40:05,167 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.7448 [2023-12-20 18:40:05,298 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4673 [2023-12-20 18:40:05,393 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6823 [2023-12-20 18:40:05,537 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.1387 [2023-12-20 18:40:05,641 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.9874 [2023-12-20 18:40:05,761 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.6734 [2023-12-20 18:40:05,853 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1324 [2023-12-20 18:40:05,941 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1782 [2023-12-20 18:40:06,046 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5384 [2023-12-20 18:40:06,144 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4712 [2023-12-20 18:40:06,272 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9699 [2023-12-20 18:40:06,389 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0930 [2023-12-20 18:40:06,476 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6857 [2023-12-20 18:40:06,618 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.4574 [2023-12-20 18:40:06,770 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0728 [2023-12-20 18:40:06,869 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1122 [2023-12-20 18:40:06,994 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3657 [2023-12-20 18:40:07,137 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8097 [2023-12-20 18:40:07,255 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.4936 [2023-12-20 18:40:07,368 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3912 [2023-12-20 18:40:07,537 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3971 [2023-12-20 18:40:07,633 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4023 [2023-12-20 18:40:07,779 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6756 [2023-12-20 18:40:07,872 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0853 [2023-12-20 18:40:07,958 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5663 [2023-12-20 18:40:08,074 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5185 [2023-12-20 18:40:08,226 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2795 [2023-12-20 18:40:08,370 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3004 [2023-12-20 18:40:08,472 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.8280 [2023-12-20 18:40:08,566 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.3767 [2023-12-20 18:40:08,679 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3759 [2023-12-20 18:40:08,842 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2617 [2023-12-20 18:40:08,936 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5767 [2023-12-20 18:40:09,030 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2508 [2023-12-20 18:40:09,125 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5332 [2023-12-20 18:40:09,218 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2328 [2023-12-20 18:40:09,309 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.8132 [2023-12-20 18:40:09,409 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5669 [2023-12-20 18:40:09,538 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7169 [2023-12-20 18:40:09,625 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3308 [2023-12-20 18:40:09,724 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.1898 [2023-12-20 18:40:09,817 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.7019 [2023-12-20 18:40:09,901 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.7066 [2023-12-20 18:40:09,988 INFO evaluator.py line 159 131400] Test: [70/78] Loss 1.6816 [2023-12-20 18:40:10,085 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.3182 [2023-12-20 18:40:10,189 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4641 [2023-12-20 18:40:10,335 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1970 [2023-12-20 18:40:10,433 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6398 [2023-12-20 18:40:10,553 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6791 [2023-12-20 18:40:10,660 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7389 [2023-12-20 18:40:10,748 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.1921 [2023-12-20 18:40:10,902 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0341 [2023-12-20 18:40:12,074 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7370/0.8333/0.9075. [2023-12-20 18:40:12,074 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8604/0.9483 [2023-12-20 18:40:12,074 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9664/0.9850 [2023-12-20 18:40:12,074 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6853/0.8355 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8186/0.8836 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.8764/0.9211 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7945/0.9542 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7534/0.7972 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6875/0.7980 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6573/0.7895 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8209/0.9062 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3935/0.5669 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6632/0.8368 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6830/0.8934 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.6472/0.6906 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6447/0.7713 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7242/0.7688 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9211/0.9824 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6798/0.7600 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8881/0.9361 [2023-12-20 18:40:12,075 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5744/0.6418 [2023-12-20 18:40:12,076 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:40:12,077 INFO misc.py line 165 131400] Currently Best mIoU: 0.7541 [2023-12-20 18:40:12,077 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:40:15,561 INFO misc.py line 119 131400] Train: [64/100][1/800] Data 1.077 (1.077) Batch 1.366 (1.366) Remain 11:13:44 loss: 0.2948 Lr: 0.00198 [2023-12-20 18:40:15,877 INFO misc.py line 119 131400] Train: [64/100][2/800] Data 0.008 (0.008) Batch 0.320 (0.320) Remain 02:37:48 loss: 0.4714 Lr: 0.00198 [2023-12-20 18:40:16,187 INFO misc.py line 119 131400] Train: [64/100][3/800] Data 0.005 (0.005) Batch 0.310 (0.310) Remain 02:32:49 loss: 0.2575 Lr: 0.00198 [2023-12-20 18:40:16,534 INFO misc.py line 119 131400] Train: [64/100][4/800] Data 0.005 (0.005) Batch 0.344 (0.344) Remain 02:49:41 loss: 0.2313 Lr: 0.00198 [2023-12-20 18:40:16,819 INFO misc.py line 119 131400] Train: [64/100][5/800] Data 0.007 (0.006) Batch 0.289 (0.317) Remain 02:36:08 loss: 0.3551 Lr: 0.00198 [2023-12-20 18:40:17,135 INFO misc.py line 119 131400] Train: [64/100][6/800] Data 0.003 (0.005) Batch 0.316 (0.316) Remain 02:36:03 loss: 0.1643 Lr: 0.00198 [2023-12-20 18:40:17,457 INFO misc.py line 119 131400] Train: [64/100][7/800] Data 0.002 (0.004) Batch 0.321 (0.317) Remain 02:36:34 loss: 0.4120 Lr: 0.00198 [2023-12-20 18:40:17,781 INFO misc.py line 119 131400] Train: [64/100][8/800] Data 0.004 (0.004) Batch 0.325 (0.319) Remain 02:37:19 loss: 0.1866 Lr: 0.00198 [2023-12-20 18:40:18,124 INFO misc.py line 119 131400] Train: [64/100][9/800] Data 0.004 (0.004) Batch 0.343 (0.323) Remain 02:39:15 loss: 0.1774 Lr: 0.00198 [2023-12-20 18:40:18,451 INFO misc.py line 119 131400] Train: [64/100][10/800] Data 0.004 (0.004) Batch 0.327 (0.324) Remain 02:39:33 loss: 0.1897 Lr: 0.00198 [2023-12-20 18:40:18,771 INFO misc.py line 119 131400] Train: [64/100][11/800] Data 0.004 (0.004) Batch 0.319 (0.323) Remain 02:39:18 loss: 0.2288 Lr: 0.00198 [2023-12-20 18:40:19,084 INFO misc.py line 119 131400] Train: [64/100][12/800] Data 0.004 (0.004) Batch 0.314 (0.322) Remain 02:38:47 loss: 0.3131 Lr: 0.00198 [2023-12-20 18:40:19,354 INFO misc.py line 119 131400] Train: [64/100][13/800] Data 0.004 (0.004) Batch 0.270 (0.317) Remain 02:36:12 loss: 0.3755 Lr: 0.00198 [2023-12-20 18:40:19,654 INFO misc.py line 119 131400] Train: [64/100][14/800] Data 0.004 (0.004) Batch 0.299 (0.315) Remain 02:35:24 loss: 0.2467 Lr: 0.00198 [2023-12-20 18:40:20,009 INFO misc.py line 119 131400] Train: [64/100][15/800] Data 0.005 (0.004) Batch 0.356 (0.319) Remain 02:37:05 loss: 0.3162 Lr: 0.00198 [2023-12-20 18:40:20,336 INFO misc.py line 119 131400] Train: [64/100][16/800] Data 0.004 (0.004) Batch 0.327 (0.319) Remain 02:37:24 loss: 0.1527 Lr: 0.00198 [2023-12-20 18:40:20,646 INFO misc.py line 119 131400] Train: [64/100][17/800] Data 0.003 (0.004) Batch 0.310 (0.319) Remain 02:37:03 loss: 0.3204 Lr: 0.00198 [2023-12-20 18:40:20,999 INFO misc.py line 119 131400] Train: [64/100][18/800] Data 0.004 (0.004) Batch 0.353 (0.321) Remain 02:38:11 loss: 0.4212 Lr: 0.00198 [2023-12-20 18:40:21,320 INFO misc.py line 119 131400] Train: [64/100][19/800] Data 0.003 (0.004) Batch 0.322 (0.321) Remain 02:38:13 loss: 0.2137 Lr: 0.00198 [2023-12-20 18:40:21,597 INFO misc.py line 119 131400] Train: [64/100][20/800] Data 0.003 (0.004) Batch 0.276 (0.318) Remain 02:36:55 loss: 0.5463 Lr: 0.00198 [2023-12-20 18:40:21,943 INFO misc.py line 119 131400] Train: [64/100][21/800] Data 0.004 (0.004) Batch 0.343 (0.320) Remain 02:37:35 loss: 0.1772 Lr: 0.00198 [2023-12-20 18:40:22,257 INFO misc.py line 119 131400] Train: [64/100][22/800] Data 0.007 (0.004) Batch 0.317 (0.320) Remain 02:37:30 loss: 0.2529 Lr: 0.00198 [2023-12-20 18:40:22,564 INFO misc.py line 119 131400] Train: [64/100][23/800] Data 0.003 (0.004) Batch 0.308 (0.319) Remain 02:37:13 loss: 0.1968 Lr: 0.00198 [2023-12-20 18:40:22,934 INFO misc.py line 119 131400] Train: [64/100][24/800] Data 0.003 (0.004) Batch 0.370 (0.321) Remain 02:38:24 loss: 0.2907 Lr: 0.00198 [2023-12-20 18:40:23,259 INFO misc.py line 119 131400] Train: [64/100][25/800] Data 0.003 (0.004) Batch 0.324 (0.322) Remain 02:38:28 loss: 0.1721 Lr: 0.00198 [2023-12-20 18:40:23,557 INFO misc.py line 119 131400] Train: [64/100][26/800] Data 0.003 (0.004) Batch 0.298 (0.321) Remain 02:37:58 loss: 0.1718 Lr: 0.00198 [2023-12-20 18:40:23,918 INFO misc.py line 119 131400] Train: [64/100][27/800] Data 0.003 (0.004) Batch 0.361 (0.322) Remain 02:38:47 loss: 0.4362 Lr: 0.00198 [2023-12-20 18:40:24,233 INFO misc.py line 119 131400] Train: [64/100][28/800] Data 0.004 (0.004) Batch 0.315 (0.322) Remain 02:38:38 loss: 0.2032 Lr: 0.00198 [2023-12-20 18:40:24,539 INFO misc.py line 119 131400] Train: [64/100][29/800] Data 0.003 (0.004) Batch 0.306 (0.321) Remain 02:38:20 loss: 0.2239 Lr: 0.00198 [2023-12-20 18:40:24,878 INFO misc.py line 119 131400] Train: [64/100][30/800] Data 0.004 (0.004) Batch 0.339 (0.322) Remain 02:38:40 loss: 0.2963 Lr: 0.00198 [2023-12-20 18:40:25,222 INFO misc.py line 119 131400] Train: [64/100][31/800] Data 0.003 (0.004) Batch 0.343 (0.323) Remain 02:39:02 loss: 0.3692 Lr: 0.00198 [2023-12-20 18:40:25,544 INFO misc.py line 119 131400] Train: [64/100][32/800] Data 0.003 (0.004) Batch 0.322 (0.323) Remain 02:39:01 loss: 0.3300 Lr: 0.00198 [2023-12-20 18:40:25,874 INFO misc.py line 119 131400] Train: [64/100][33/800] Data 0.003 (0.004) Batch 0.329 (0.323) Remain 02:39:07 loss: 0.3559 Lr: 0.00198 [2023-12-20 18:40:26,207 INFO misc.py line 119 131400] Train: [64/100][34/800] Data 0.004 (0.004) Batch 0.334 (0.323) Remain 02:39:18 loss: 0.3995 Lr: 0.00198 [2023-12-20 18:40:26,558 INFO misc.py line 119 131400] Train: [64/100][35/800] Data 0.003 (0.004) Batch 0.350 (0.324) Remain 02:39:42 loss: 0.2085 Lr: 0.00198 [2023-12-20 18:40:26,919 INFO misc.py line 119 131400] Train: [64/100][36/800] Data 0.004 (0.004) Batch 0.361 (0.325) Remain 02:40:15 loss: 0.2394 Lr: 0.00198 [2023-12-20 18:40:27,230 INFO misc.py line 119 131400] Train: [64/100][37/800] Data 0.003 (0.004) Batch 0.309 (0.325) Remain 02:40:01 loss: 0.2596 Lr: 0.00198 [2023-12-20 18:40:27,577 INFO misc.py line 119 131400] Train: [64/100][38/800] Data 0.006 (0.004) Batch 0.348 (0.325) Remain 02:40:20 loss: 0.4381 Lr: 0.00198 [2023-12-20 18:40:27,893 INFO misc.py line 119 131400] Train: [64/100][39/800] Data 0.005 (0.004) Batch 0.317 (0.325) Remain 02:40:13 loss: 0.4224 Lr: 0.00198 [2023-12-20 18:40:28,176 INFO misc.py line 119 131400] Train: [64/100][40/800] Data 0.003 (0.004) Batch 0.281 (0.324) Remain 02:39:37 loss: 0.3133 Lr: 0.00198 [2023-12-20 18:40:28,502 INFO misc.py line 119 131400] Train: [64/100][41/800] Data 0.005 (0.004) Batch 0.327 (0.324) Remain 02:39:40 loss: 0.1336 Lr: 0.00198 [2023-12-20 18:40:28,826 INFO misc.py line 119 131400] Train: [64/100][42/800] Data 0.004 (0.004) Batch 0.324 (0.324) Remain 02:39:39 loss: 0.1524 Lr: 0.00198 [2023-12-20 18:40:29,135 INFO misc.py line 119 131400] Train: [64/100][43/800] Data 0.003 (0.004) Batch 0.309 (0.324) Remain 02:39:28 loss: 0.3657 Lr: 0.00198 [2023-12-20 18:40:29,463 INFO misc.py line 119 131400] Train: [64/100][44/800] Data 0.004 (0.004) Batch 0.329 (0.324) Remain 02:39:31 loss: 0.2115 Lr: 0.00198 [2023-12-20 18:40:29,779 INFO misc.py line 119 131400] Train: [64/100][45/800] Data 0.003 (0.004) Batch 0.315 (0.324) Remain 02:39:24 loss: 0.2607 Lr: 0.00198 [2023-12-20 18:40:30,105 INFO misc.py line 119 131400] Train: [64/100][46/800] Data 0.004 (0.004) Batch 0.327 (0.324) Remain 02:39:26 loss: 0.2110 Lr: 0.00198 [2023-12-20 18:40:30,437 INFO misc.py line 119 131400] Train: [64/100][47/800] Data 0.003 (0.004) Batch 0.332 (0.324) Remain 02:39:31 loss: 0.8045 Lr: 0.00198 [2023-12-20 18:40:30,780 INFO misc.py line 119 131400] Train: [64/100][48/800] Data 0.004 (0.004) Batch 0.342 (0.324) Remain 02:39:43 loss: 0.1861 Lr: 0.00198 [2023-12-20 18:40:31,107 INFO misc.py line 119 131400] Train: [64/100][49/800] Data 0.004 (0.004) Batch 0.327 (0.324) Remain 02:39:44 loss: 0.4753 Lr: 0.00198 [2023-12-20 18:40:31,468 INFO misc.py line 119 131400] Train: [64/100][50/800] Data 0.005 (0.004) Batch 0.362 (0.325) Remain 02:40:07 loss: 0.2102 Lr: 0.00198 [2023-12-20 18:40:31,820 INFO misc.py line 119 131400] Train: [64/100][51/800] Data 0.004 (0.004) Batch 0.352 (0.326) Remain 02:40:24 loss: 0.3272 Lr: 0.00198 [2023-12-20 18:40:32,196 INFO misc.py line 119 131400] Train: [64/100][52/800] Data 0.005 (0.004) Batch 0.376 (0.327) Remain 02:40:54 loss: 0.1102 Lr: 0.00198 [2023-12-20 18:40:32,520 INFO misc.py line 119 131400] Train: [64/100][53/800] Data 0.004 (0.004) Batch 0.325 (0.327) Remain 02:40:52 loss: 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INFO misc.py line 119 131400] Train: [64/100][60/800] Data 0.004 (0.004) Batch 0.342 (0.327) Remain 02:41:02 loss: 0.1669 Lr: 0.00197 [2023-12-20 18:40:35,190 INFO misc.py line 119 131400] Train: [64/100][61/800] Data 0.006 (0.004) Batch 0.356 (0.328) Remain 02:41:17 loss: 0.1535 Lr: 0.00197 [2023-12-20 18:40:35,550 INFO misc.py line 119 131400] Train: [64/100][62/800] Data 0.006 (0.004) Batch 0.362 (0.328) Remain 02:41:34 loss: 0.3209 Lr: 0.00197 [2023-12-20 18:40:35,859 INFO misc.py line 119 131400] Train: [64/100][63/800] Data 0.004 (0.004) Batch 0.309 (0.328) Remain 02:41:24 loss: 0.3842 Lr: 0.00197 [2023-12-20 18:40:36,214 INFO misc.py line 119 131400] Train: [64/100][64/800] Data 0.003 (0.004) Batch 0.355 (0.328) Remain 02:41:37 loss: 0.2703 Lr: 0.00197 [2023-12-20 18:40:36,767 INFO misc.py line 119 131400] Train: [64/100][65/800] Data 0.003 (0.004) Batch 0.330 (0.328) Remain 02:41:37 loss: 0.5342 Lr: 0.00197 [2023-12-20 18:40:37,098 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [64/100][85/800] Data 0.005 (0.007) Batch 0.339 (0.332) Remain 02:43:05 loss: 0.2919 Lr: 0.00197 [2023-12-20 18:40:43,697 INFO misc.py line 119 131400] Train: [64/100][86/800] Data 0.003 (0.007) Batch 0.324 (0.331) Remain 02:43:02 loss: 0.1940 Lr: 0.00197 [2023-12-20 18:40:43,998 INFO misc.py line 119 131400] Train: [64/100][87/800] Data 0.003 (0.006) Batch 0.301 (0.331) Remain 02:42:51 loss: 0.3645 Lr: 0.00197 [2023-12-20 18:40:44,340 INFO misc.py line 119 131400] Train: [64/100][88/800] Data 0.003 (0.006) Batch 0.336 (0.331) Remain 02:42:53 loss: 0.3669 Lr: 0.00197 [2023-12-20 18:40:44,632 INFO misc.py line 119 131400] Train: [64/100][89/800] Data 0.009 (0.006) Batch 0.298 (0.331) Remain 02:42:41 loss: 0.1990 Lr: 0.00197 [2023-12-20 18:40:44,951 INFO misc.py line 119 131400] Train: [64/100][90/800] Data 0.003 (0.006) Batch 0.318 (0.331) Remain 02:42:36 loss: 0.2763 Lr: 0.00197 [2023-12-20 18:40:45,278 INFO misc.py line 119 131400] Train: [64/100][91/800] Data 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loss: 0.1698 Lr: 0.00190 [2023-12-20 18:44:22,598 INFO misc.py line 119 131400] Train: [64/100][745/800] Data 0.004 (0.005) Batch 0.348 (0.332) Remain 02:39:41 loss: 0.2484 Lr: 0.00190 [2023-12-20 18:44:22,931 INFO misc.py line 119 131400] Train: [64/100][746/800] Data 0.018 (0.005) Batch 0.346 (0.332) Remain 02:39:42 loss: 0.3221 Lr: 0.00190 [2023-12-20 18:44:23,259 INFO misc.py line 119 131400] Train: [64/100][747/800] Data 0.004 (0.005) Batch 0.328 (0.332) Remain 02:39:41 loss: 0.4852 Lr: 0.00190 [2023-12-20 18:44:23,585 INFO misc.py line 119 131400] Train: [64/100][748/800] Data 0.004 (0.005) Batch 0.326 (0.332) Remain 02:39:41 loss: 0.3423 Lr: 0.00190 [2023-12-20 18:44:23,901 INFO misc.py line 119 131400] Train: [64/100][749/800] Data 0.003 (0.005) Batch 0.315 (0.332) Remain 02:39:40 loss: 0.1827 Lr: 0.00190 [2023-12-20 18:44:24,250 INFO misc.py line 119 131400] Train: [64/100][750/800] Data 0.004 (0.005) Batch 0.348 (0.332) Remain 02:39:40 loss: 0.1935 Lr: 0.00190 [2023-12-20 18:44:24,569 INFO misc.py line 119 131400] Train: [64/100][751/800] Data 0.004 (0.005) Batch 0.320 (0.332) Remain 02:39:39 loss: 0.2520 Lr: 0.00189 [2023-12-20 18:44:24,925 INFO misc.py line 119 131400] Train: [64/100][752/800] Data 0.003 (0.005) Batch 0.356 (0.332) Remain 02:39:40 loss: 0.2231 Lr: 0.00189 [2023-12-20 18:44:25,232 INFO misc.py line 119 131400] Train: [64/100][753/800] Data 0.004 (0.005) Batch 0.308 (0.332) Remain 02:39:38 loss: 0.1737 Lr: 0.00189 [2023-12-20 18:44:25,559 INFO misc.py line 119 131400] Train: [64/100][754/800] Data 0.003 (0.005) Batch 0.327 (0.332) Remain 02:39:38 loss: 0.4315 Lr: 0.00189 [2023-12-20 18:44:25,875 INFO misc.py line 119 131400] Train: [64/100][755/800] Data 0.003 (0.005) Batch 0.315 (0.332) Remain 02:39:37 loss: 0.2812 Lr: 0.00189 [2023-12-20 18:44:26,211 INFO misc.py line 119 131400] Train: [64/100][756/800] Data 0.005 (0.005) Batch 0.336 (0.332) Remain 02:39:37 loss: 0.4164 Lr: 0.00189 [2023-12-20 18:44:26,537 INFO misc.py line 119 131400] Train: [64/100][757/800] Data 0.004 (0.005) Batch 0.326 (0.332) Remain 02:39:36 loss: 0.1547 Lr: 0.00189 [2023-12-20 18:44:26,861 INFO misc.py line 119 131400] Train: [64/100][758/800] Data 0.003 (0.005) Batch 0.324 (0.332) Remain 02:39:36 loss: 0.2400 Lr: 0.00189 [2023-12-20 18:44:27,184 INFO misc.py line 119 131400] Train: [64/100][759/800] Data 0.004 (0.005) Batch 0.323 (0.332) Remain 02:39:35 loss: 0.2428 Lr: 0.00189 [2023-12-20 18:44:27,511 INFO misc.py line 119 131400] Train: [64/100][760/800] Data 0.003 (0.005) Batch 0.327 (0.332) Remain 02:39:34 loss: 0.4045 Lr: 0.00189 [2023-12-20 18:44:27,825 INFO misc.py line 119 131400] Train: [64/100][761/800] Data 0.003 (0.005) Batch 0.313 (0.332) Remain 02:39:33 loss: 0.5942 Lr: 0.00189 [2023-12-20 18:44:28,158 INFO misc.py line 119 131400] Train: [64/100][762/800] Data 0.004 (0.005) Batch 0.333 (0.332) Remain 02:39:33 loss: 0.1847 Lr: 0.00189 [2023-12-20 18:44:28,477 INFO misc.py line 119 131400] Train: [64/100][763/800] Data 0.004 (0.005) Batch 0.319 (0.332) Remain 02:39:32 loss: 0.3572 Lr: 0.00189 [2023-12-20 18:44:28,782 INFO misc.py line 119 131400] Train: [64/100][764/800] Data 0.004 (0.005) Batch 0.306 (0.332) Remain 02:39:31 loss: 0.3312 Lr: 0.00189 [2023-12-20 18:44:29,135 INFO misc.py line 119 131400] Train: [64/100][765/800] Data 0.003 (0.005) Batch 0.349 (0.332) Remain 02:39:31 loss: 0.2738 Lr: 0.00189 [2023-12-20 18:44:29,449 INFO misc.py line 119 131400] Train: [64/100][766/800] Data 0.007 (0.005) Batch 0.318 (0.332) Remain 02:39:30 loss: 0.3527 Lr: 0.00189 [2023-12-20 18:44:29,790 INFO misc.py line 119 131400] Train: [64/100][767/800] Data 0.004 (0.005) Batch 0.341 (0.332) Remain 02:39:30 loss: 0.3303 Lr: 0.00189 [2023-12-20 18:44:30,152 INFO misc.py line 119 131400] Train: [64/100][768/800] Data 0.003 (0.005) Batch 0.362 (0.332) Remain 02:39:31 loss: 0.3887 Lr: 0.00189 [2023-12-20 18:44:30,480 INFO misc.py line 119 131400] Train: [64/100][769/800] Data 0.004 (0.005) Batch 0.327 (0.332) Remain 02:39:31 loss: 0.2250 Lr: 0.00189 [2023-12-20 18:44:30,801 INFO misc.py line 119 131400] Train: [64/100][770/800] Data 0.004 (0.005) Batch 0.322 (0.332) Remain 02:39:30 loss: 0.4919 Lr: 0.00189 [2023-12-20 18:44:31,237 INFO misc.py line 119 131400] Train: [64/100][771/800] Data 0.003 (0.005) Batch 0.431 (0.332) Remain 02:39:33 loss: 0.3935 Lr: 0.00189 [2023-12-20 18:44:31,556 INFO misc.py line 119 131400] Train: [64/100][772/800] Data 0.007 (0.005) Batch 0.323 (0.332) Remain 02:39:33 loss: 0.4772 Lr: 0.00189 [2023-12-20 18:44:31,883 INFO misc.py line 119 131400] Train: [64/100][773/800] Data 0.004 (0.005) Batch 0.327 (0.332) Remain 02:39:32 loss: 0.3988 Lr: 0.00189 [2023-12-20 18:44:32,191 INFO misc.py line 119 131400] Train: [64/100][774/800] Data 0.003 (0.005) Batch 0.309 (0.332) Remain 02:39:31 loss: 0.4758 Lr: 0.00189 [2023-12-20 18:44:32,514 INFO misc.py line 119 131400] Train: [64/100][775/800] Data 0.003 (0.005) Batch 0.323 (0.332) Remain 02:39:30 loss: 0.2659 Lr: 0.00189 [2023-12-20 18:44:32,826 INFO misc.py line 119 131400] Train: [64/100][776/800] Data 0.004 (0.005) Batch 0.313 (0.332) Remain 02:39:29 loss: 0.5520 Lr: 0.00189 [2023-12-20 18:44:33,158 INFO misc.py line 119 131400] Train: [64/100][777/800] Data 0.003 (0.005) Batch 0.331 (0.332) Remain 02:39:29 loss: 0.3269 Lr: 0.00189 [2023-12-20 18:44:33,444 INFO misc.py line 119 131400] Train: [64/100][778/800] Data 0.004 (0.005) Batch 0.285 (0.332) Remain 02:39:27 loss: 0.4521 Lr: 0.00189 [2023-12-20 18:44:33,745 INFO misc.py line 119 131400] Train: [64/100][779/800] Data 0.005 (0.005) Batch 0.302 (0.332) Remain 02:39:25 loss: 0.1561 Lr: 0.00189 [2023-12-20 18:44:34,056 INFO misc.py line 119 131400] Train: [64/100][780/800] Data 0.003 (0.005) Batch 0.312 (0.332) Remain 02:39:24 loss: 0.2286 Lr: 0.00189 [2023-12-20 18:44:34,388 INFO misc.py line 119 131400] Train: [64/100][781/800] Data 0.003 (0.005) Batch 0.332 (0.332) Remain 02:39:24 loss: 0.2660 Lr: 0.00189 [2023-12-20 18:44:34,714 INFO misc.py line 119 131400] Train: [64/100][782/800] Data 0.003 (0.005) Batch 0.325 (0.332) Remain 02:39:23 loss: 0.3584 Lr: 0.00189 [2023-12-20 18:44:35,041 INFO misc.py line 119 131400] Train: [64/100][783/800] Data 0.004 (0.005) Batch 0.328 (0.332) Remain 02:39:23 loss: 0.2189 Lr: 0.00189 [2023-12-20 18:44:35,364 INFO misc.py line 119 131400] Train: [64/100][784/800] Data 0.003 (0.005) Batch 0.323 (0.332) Remain 02:39:22 loss: 0.5521 Lr: 0.00189 [2023-12-20 18:44:35,688 INFO misc.py line 119 131400] Train: [64/100][785/800] Data 0.003 (0.005) Batch 0.323 (0.332) Remain 02:39:22 loss: 0.1937 Lr: 0.00189 [2023-12-20 18:44:36,023 INFO misc.py line 119 131400] Train: [64/100][786/800] Data 0.004 (0.005) Batch 0.336 (0.332) Remain 02:39:21 loss: 0.1927 Lr: 0.00189 [2023-12-20 18:44:36,371 INFO misc.py line 119 131400] Train: [64/100][787/800] Data 0.003 (0.005) Batch 0.348 (0.332) Remain 02:39:22 loss: 0.2451 Lr: 0.00189 [2023-12-20 18:44:36,702 INFO misc.py line 119 131400] Train: [64/100][788/800] Data 0.004 (0.005) Batch 0.332 (0.332) Remain 02:39:21 loss: 0.3631 Lr: 0.00189 [2023-12-20 18:44:36,988 INFO misc.py line 119 131400] Train: [64/100][789/800] Data 0.003 (0.005) Batch 0.284 (0.332) Remain 02:39:19 loss: 0.1544 Lr: 0.00189 [2023-12-20 18:44:37,314 INFO misc.py line 119 131400] Train: [64/100][790/800] Data 0.004 (0.005) Batch 0.327 (0.332) Remain 02:39:19 loss: 0.2797 Lr: 0.00189 [2023-12-20 18:44:37,680 INFO misc.py line 119 131400] Train: [64/100][791/800] Data 0.004 (0.005) Batch 0.366 (0.332) Remain 02:39:20 loss: 0.1708 Lr: 0.00189 [2023-12-20 18:44:37,992 INFO misc.py line 119 131400] Train: [64/100][792/800] Data 0.003 (0.005) Batch 0.312 (0.332) Remain 02:39:19 loss: 0.2237 Lr: 0.00189 [2023-12-20 18:44:38,299 INFO misc.py line 119 131400] Train: [64/100][793/800] Data 0.004 (0.005) Batch 0.306 (0.332) Remain 02:39:17 loss: 0.4765 Lr: 0.00189 [2023-12-20 18:44:38,652 INFO misc.py line 119 131400] Train: [64/100][794/800] Data 0.003 (0.005) Batch 0.354 (0.332) Remain 02:39:18 loss: 0.3682 Lr: 0.00189 [2023-12-20 18:44:38,971 INFO misc.py line 119 131400] Train: [64/100][795/800] Data 0.003 (0.005) Batch 0.319 (0.332) Remain 02:39:17 loss: 0.2236 Lr: 0.00189 [2023-12-20 18:44:39,262 INFO misc.py line 119 131400] Train: [64/100][796/800] Data 0.004 (0.005) Batch 0.291 (0.332) Remain 02:39:15 loss: 0.3282 Lr: 0.00189 [2023-12-20 18:44:39,589 INFO misc.py line 119 131400] Train: [64/100][797/800] Data 0.003 (0.005) Batch 0.326 (0.332) Remain 02:39:15 loss: 0.4688 Lr: 0.00189 [2023-12-20 18:44:39,906 INFO misc.py line 119 131400] Train: [64/100][798/800] Data 0.003 (0.005) Batch 0.318 (0.332) Remain 02:39:14 loss: 0.3290 Lr: 0.00189 [2023-12-20 18:44:40,185 INFO misc.py line 119 131400] Train: [64/100][799/800] Data 0.002 (0.005) Batch 0.279 (0.332) Remain 02:39:12 loss: 0.3175 Lr: 0.00189 [2023-12-20 18:44:40,493 INFO misc.py line 119 131400] Train: [64/100][800/800] Data 0.002 (0.005) Batch 0.308 (0.332) Remain 02:39:10 loss: 0.2009 Lr: 0.00189 [2023-12-20 18:44:40,494 INFO misc.py line 136 131400] Train result: loss: 0.2867 [2023-12-20 18:44:40,494 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:45:02,422 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2218 [2023-12-20 18:45:02,524 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1957 [2023-12-20 18:45:02,639 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.2615 [2023-12-20 18:45:02,756 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1340 [2023-12-20 18:45:02,871 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4062 [2023-12-20 18:45:02,979 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9475 [2023-12-20 18:45:03,068 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8682 [2023-12-20 18:45:03,176 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2707 [2023-12-20 18:45:03,260 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2573 [2023-12-20 18:45:03,349 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4170 [2023-12-20 18:45:03,441 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5154 [2023-12-20 18:45:03,578 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3162 [2023-12-20 18:45:03,698 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3085 [2023-12-20 18:45:03,855 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1938 [2023-12-20 18:45:03,949 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1679 [2023-12-20 18:45:04,082 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5513 [2023-12-20 18:45:04,192 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3314 [2023-12-20 18:45:04,300 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.1095 [2023-12-20 18:45:04,411 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.0951 [2023-12-20 18:45:04,489 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3293 [2023-12-20 18:45:04,598 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1757 [2023-12-20 18:45:04,754 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1328 [2023-12-20 18:45:04,873 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9955 [2023-12-20 18:45:05,015 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1482 [2023-12-20 18:45:05,158 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1572 [2023-12-20 18:45:05,239 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4711 [2023-12-20 18:45:05,394 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.1941 [2023-12-20 18:45:05,518 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5299 [2023-12-20 18:45:05,614 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4597 [2023-12-20 18:45:05,758 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7517 [2023-12-20 18:45:05,861 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5480 [2023-12-20 18:45:05,980 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3926 [2023-12-20 18:45:06,070 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1399 [2023-12-20 18:45:06,141 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1998 [2023-12-20 18:45:06,240 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.4944 [2023-12-20 18:45:06,339 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3143 [2023-12-20 18:45:06,467 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0662 [2023-12-20 18:45:06,577 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0854 [2023-12-20 18:45:06,659 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6074 [2023-12-20 18:45:06,813 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3608 [2023-12-20 18:45:06,959 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0432 [2023-12-20 18:45:07,071 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0541 [2023-12-20 18:45:07,196 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4097 [2023-12-20 18:45:07,339 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9038 [2023-12-20 18:45:07,462 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8183 [2023-12-20 18:45:07,571 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4645 [2023-12-20 18:45:07,739 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3789 [2023-12-20 18:45:07,836 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3927 [2023-12-20 18:45:07,983 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6377 [2023-12-20 18:45:08,076 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1202 [2023-12-20 18:45:08,158 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5204 [2023-12-20 18:45:08,273 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.7565 [2023-12-20 18:45:08,420 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0543 [2023-12-20 18:45:08,556 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3412 [2023-12-20 18:45:08,667 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4856 [2023-12-20 18:45:08,771 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6214 [2023-12-20 18:45:08,880 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3488 [2023-12-20 18:45:09,046 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2325 [2023-12-20 18:45:09,152 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6191 [2023-12-20 18:45:09,259 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1672 [2023-12-20 18:45:09,375 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2133 [2023-12-20 18:45:09,471 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3779 [2023-12-20 18:45:09,567 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3071 [2023-12-20 18:45:09,694 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5938 [2023-12-20 18:45:09,830 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4752 [2023-12-20 18:45:09,921 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2933 [2023-12-20 18:45:10,031 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2901 [2023-12-20 18:45:10,139 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0450 [2023-12-20 18:45:10,238 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3291 [2023-12-20 18:45:10,324 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0278 [2023-12-20 18:45:10,417 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8108 [2023-12-20 18:45:10,519 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8034 [2023-12-20 18:45:10,658 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1503 [2023-12-20 18:45:10,754 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6158 [2023-12-20 18:45:10,880 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.4996 [2023-12-20 18:45:10,992 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4170 [2023-12-20 18:45:11,083 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7075 [2023-12-20 18:45:11,241 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2603 [2023-12-20 18:45:13,141 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7551/0.8383/0.9171. [2023-12-20 18:45:13,141 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8756/0.9552 [2023-12-20 18:45:13,141 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9651/0.9869 [2023-12-20 18:45:13,141 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6767/0.8339 [2023-12-20 18:45:13,141 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8332/0.8713 [2023-12-20 18:45:13,141 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9102/0.9571 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8482/0.9111 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7874/0.8951 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7196/0.8010 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6749/0.8055 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8369/0.9501 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4217/0.5463 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6935/0.8221 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6902/0.7849 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7179/0.7793 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6912/0.8365 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6556/0.7087 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9349/0.9756 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6889/0.7845 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8850/0.9198 [2023-12-20 18:45:13,142 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5961/0.6419 [2023-12-20 18:45:13,143 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:45:13,144 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7551 [2023-12-20 18:45:13,144 INFO misc.py line 165 131400] Currently Best mIoU: 0.7551 [2023-12-20 18:45:13,144 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:45:19,595 INFO misc.py line 119 131400] Train: [65/100][1/800] Data 0.834 (0.834) Batch 1.148 (1.148) Remain 09:11:11 loss: 0.1847 Lr: 0.00189 [2023-12-20 18:45:20,663 INFO misc.py line 119 131400] Train: [65/100][2/800] Data 0.755 (0.755) Batch 1.068 (1.068) Remain 08:32:35 loss: 0.3046 Lr: 0.00189 [2023-12-20 18:45:20,991 INFO misc.py line 119 131400] Train: [65/100][3/800] Data 0.003 (0.003) Batch 0.327 (0.327) Remain 02:37:07 loss: 0.1645 Lr: 0.00189 [2023-12-20 18:45:21,290 INFO misc.py line 119 131400] Train: [65/100][4/800] Data 0.003 (0.003) Batch 0.299 (0.299) Remain 02:23:18 loss: 0.2667 Lr: 0.00189 [2023-12-20 18:45:21,586 INFO misc.py line 119 131400] Train: [65/100][5/800] Data 0.004 (0.004) Batch 0.294 (0.296) Remain 02:22:09 loss: 0.4331 Lr: 0.00189 [2023-12-20 18:45:21,905 INFO misc.py line 119 131400] Train: [65/100][6/800] Data 0.006 (0.004) Batch 0.320 (0.304) Remain 02:26:01 loss: 0.2470 Lr: 0.00189 [2023-12-20 18:45:22,214 INFO misc.py line 119 131400] Train: [65/100][7/800] Data 0.004 (0.004) Batch 0.310 (0.306) Remain 02:26:39 loss: 0.6855 Lr: 0.00189 [2023-12-20 18:45:22,567 INFO misc.py line 119 131400] Train: [65/100][8/800] Data 0.004 (0.004) Batch 0.353 (0.315) Remain 02:31:09 loss: 0.3990 Lr: 0.00189 [2023-12-20 18:45:22,889 INFO misc.py line 119 131400] Train: [65/100][9/800] Data 0.004 (0.004) Batch 0.322 (0.316) Remain 02:31:43 loss: 0.2661 Lr: 0.00189 [2023-12-20 18:45:23,197 INFO misc.py line 119 131400] Train: [65/100][10/800] Data 0.004 (0.004) Batch 0.307 (0.315) Remain 02:31:05 loss: 0.2789 Lr: 0.00189 [2023-12-20 18:45:23,525 INFO misc.py line 119 131400] Train: [65/100][11/800] Data 0.005 (0.004) Batch 0.329 (0.317) Remain 02:31:56 loss: 0.4439 Lr: 0.00189 [2023-12-20 18:45:23,892 INFO misc.py line 119 131400] Train: [65/100][12/800] Data 0.004 (0.004) Batch 0.368 (0.322) Remain 02:34:39 loss: 0.3812 Lr: 0.00189 [2023-12-20 18:45:24,239 INFO misc.py line 119 131400] Train: [65/100][13/800] Data 0.003 (0.004) Batch 0.346 (0.325) Remain 02:35:48 loss: 0.2847 Lr: 0.00189 [2023-12-20 18:45:24,577 INFO misc.py line 119 131400] Train: [65/100][14/800] Data 0.004 (0.004) Batch 0.338 (0.326) Remain 02:36:22 loss: 0.4357 Lr: 0.00189 [2023-12-20 18:45:24,875 INFO misc.py line 119 131400] Train: [65/100][15/800] Data 0.004 (0.004) Batch 0.299 (0.324) Remain 02:35:16 loss: 0.4235 Lr: 0.00189 [2023-12-20 18:45:25,187 INFO misc.py line 119 131400] Train: [65/100][16/800] Data 0.003 (0.004) Batch 0.312 (0.323) Remain 02:34:51 loss: 0.1932 Lr: 0.00189 [2023-12-20 18:45:25,541 INFO misc.py line 119 131400] Train: [65/100][17/800] Data 0.003 (0.004) Batch 0.347 (0.325) Remain 02:35:41 loss: 0.2837 Lr: 0.00189 [2023-12-20 18:45:25,909 INFO misc.py line 119 131400] Train: [65/100][18/800] Data 0.010 (0.004) Batch 0.357 (0.327) Remain 02:36:42 loss: 0.2027 Lr: 0.00189 [2023-12-20 18:45:26,257 INFO misc.py line 119 131400] Train: [65/100][19/800] Data 0.021 (0.005) Batch 0.366 (0.329) Remain 02:37:52 loss: 0.5122 Lr: 0.00189 [2023-12-20 18:45:26,588 INFO misc.py line 119 131400] Train: [65/100][20/800] Data 0.004 (0.005) Batch 0.331 (0.329) Remain 02:37:55 loss: 0.2454 Lr: 0.00189 [2023-12-20 18:45:26,953 INFO misc.py line 119 131400] Train: [65/100][21/800] Data 0.003 (0.005) Batch 0.365 (0.331) Remain 02:38:52 loss: 0.4416 Lr: 0.00189 [2023-12-20 18:45:27,269 INFO misc.py line 119 131400] Train: [65/100][22/800] Data 0.004 (0.005) Batch 0.315 (0.330) Remain 02:38:27 loss: 0.1027 Lr: 0.00189 [2023-12-20 18:45:27,618 INFO misc.py line 119 131400] Train: [65/100][23/800] Data 0.004 (0.005) Batch 0.349 (0.331) Remain 02:38:53 loss: 0.4462 Lr: 0.00189 [2023-12-20 18:45:27,910 INFO misc.py line 119 131400] Train: [65/100][24/800] Data 0.005 (0.005) Batch 0.293 (0.329) Remain 02:38:00 loss: 0.3081 Lr: 0.00189 [2023-12-20 18:45:28,231 INFO misc.py line 119 131400] Train: [65/100][25/800] Data 0.003 (0.005) Batch 0.321 (0.329) Remain 02:37:49 loss: 0.3734 Lr: 0.00189 [2023-12-20 18:45:28,637 INFO misc.py line 119 131400] Train: [65/100][26/800] Data 0.004 (0.005) Batch 0.407 (0.332) Remain 02:39:26 loss: 0.3346 Lr: 0.00189 [2023-12-20 18:45:28,985 INFO misc.py line 119 131400] Train: [65/100][27/800] Data 0.003 (0.005) Batch 0.347 (0.333) Remain 02:39:44 loss: 0.2851 Lr: 0.00189 [2023-12-20 18:45:29,273 INFO misc.py line 119 131400] Train: [65/100][28/800] Data 0.004 (0.005) Batch 0.288 (0.331) Remain 02:38:51 loss: 0.1571 Lr: 0.00189 [2023-12-20 18:45:29,602 INFO misc.py line 119 131400] Train: [65/100][29/800] Data 0.003 (0.005) Batch 0.330 (0.331) Remain 02:38:49 loss: 0.4100 Lr: 0.00189 [2023-12-20 18:45:29,926 INFO misc.py line 119 131400] Train: [65/100][30/800] Data 0.003 (0.005) Batch 0.323 (0.331) Remain 02:38:40 loss: 0.4092 Lr: 0.00189 [2023-12-20 18:45:30,264 INFO misc.py line 119 131400] Train: [65/100][31/800] Data 0.004 (0.005) Batch 0.338 (0.331) Remain 02:38:47 loss: 0.2696 Lr: 0.00189 [2023-12-20 18:45:30,586 INFO misc.py line 119 131400] Train: [65/100][32/800] Data 0.003 (0.005) Batch 0.322 (0.331) Remain 02:38:38 loss: 0.2682 Lr: 0.00189 [2023-12-20 18:45:30,894 INFO misc.py line 119 131400] Train: [65/100][33/800] Data 0.004 (0.005) Batch 0.309 (0.330) Remain 02:38:16 loss: 0.3253 Lr: 0.00189 [2023-12-20 18:45:31,222 INFO misc.py line 119 131400] Train: [65/100][34/800] Data 0.002 (0.004) Batch 0.327 (0.330) Remain 02:38:13 loss: 0.2423 Lr: 0.00189 [2023-12-20 18:45:31,544 INFO misc.py line 119 131400] Train: [65/100][35/800] Data 0.004 (0.004) Batch 0.323 (0.330) Remain 02:38:06 loss: 0.2602 Lr: 0.00189 [2023-12-20 18:45:31,823 INFO misc.py line 119 131400] Train: [65/100][36/800] Data 0.003 (0.004) Batch 0.280 (0.328) Remain 02:37:22 loss: 0.2769 Lr: 0.00189 [2023-12-20 18:45:32,133 INFO misc.py line 119 131400] Train: [65/100][37/800] Data 0.003 (0.004) Batch 0.310 (0.328) Remain 02:37:06 loss: 0.1727 Lr: 0.00189 [2023-12-20 18:45:32,459 INFO misc.py line 119 131400] Train: [65/100][38/800] Data 0.002 (0.004) Batch 0.321 (0.328) Remain 02:37:00 loss: 0.2289 Lr: 0.00188 [2023-12-20 18:45:32,767 INFO misc.py line 119 131400] Train: [65/100][39/800] Data 0.008 (0.004) Batch 0.312 (0.327) Remain 02:36:47 loss: 0.3757 Lr: 0.00188 [2023-12-20 18:45:33,120 INFO misc.py line 119 131400] Train: [65/100][40/800] Data 0.003 (0.004) Batch 0.354 (0.328) Remain 02:37:08 loss: 0.1131 Lr: 0.00188 [2023-12-20 18:45:33,453 INFO misc.py line 119 131400] Train: [65/100][41/800] Data 0.003 (0.004) Batch 0.332 (0.328) Remain 02:37:11 loss: 0.3101 Lr: 0.00188 [2023-12-20 18:45:33,762 INFO misc.py line 119 131400] Train: [65/100][42/800] Data 0.003 (0.004) Batch 0.308 (0.327) Remain 02:36:56 loss: 0.4450 Lr: 0.00188 [2023-12-20 18:45:34,098 INFO misc.py line 119 131400] Train: [65/100][43/800] Data 0.004 (0.004) Batch 0.336 (0.328) Remain 02:37:02 loss: 0.3401 Lr: 0.00188 [2023-12-20 18:45:34,455 INFO misc.py line 119 131400] Train: [65/100][44/800] Data 0.003 (0.004) Batch 0.354 (0.328) Remain 02:37:20 loss: 0.2665 Lr: 0.00188 [2023-12-20 18:45:34,829 INFO misc.py line 119 131400] Train: [65/100][45/800] Data 0.007 (0.004) Batch 0.377 (0.329) Remain 02:37:53 loss: 0.2311 Lr: 0.00188 [2023-12-20 18:45:35,178 INFO misc.py line 119 131400] Train: [65/100][46/800] Data 0.004 (0.004) Batch 0.349 (0.330) Remain 02:38:06 loss: 0.3271 Lr: 0.00188 [2023-12-20 18:45:35,540 INFO misc.py line 119 131400] Train: [65/100][47/800] Data 0.005 (0.004) Batch 0.362 (0.331) Remain 02:38:27 loss: 0.3418 Lr: 0.00188 [2023-12-20 18:45:35,866 INFO misc.py line 119 131400] Train: [65/100][48/800] Data 0.003 (0.004) Batch 0.326 (0.331) Remain 02:38:23 loss: 0.2459 Lr: 0.00188 [2023-12-20 18:45:36,206 INFO misc.py line 119 131400] Train: [65/100][49/800] Data 0.003 (0.004) Batch 0.340 (0.331) Remain 02:38:29 loss: 0.1990 Lr: 0.00188 [2023-12-20 18:45:36,545 INFO misc.py line 119 131400] Train: [65/100][50/800] Data 0.003 (0.004) Batch 0.340 (0.331) Remain 02:38:34 loss: 0.4729 Lr: 0.00188 [2023-12-20 18:45:36,884 INFO misc.py line 119 131400] Train: [65/100][51/800] Data 0.003 (0.004) Batch 0.337 (0.331) Remain 02:38:38 loss: 0.1752 Lr: 0.00188 [2023-12-20 18:45:37,233 INFO misc.py line 119 131400] Train: [65/100][52/800] Data 0.004 (0.004) Batch 0.350 (0.331) Remain 02:38:49 loss: 0.4118 Lr: 0.00188 [2023-12-20 18:45:37,582 INFO misc.py line 119 131400] Train: [65/100][53/800] Data 0.004 (0.004) Batch 0.349 (0.332) Remain 02:38:58 loss: 0.2977 Lr: 0.00188 [2023-12-20 18:45:37,944 INFO misc.py line 119 131400] Train: [65/100][54/800] Data 0.003 (0.004) Batch 0.361 (0.332) Remain 02:39:15 loss: 0.2789 Lr: 0.00188 [2023-12-20 18:45:38,276 INFO misc.py line 119 131400] Train: [65/100][55/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 02:39:15 loss: 0.4691 Lr: 0.00188 [2023-12-20 18:45:38,576 INFO misc.py line 119 131400] Train: [65/100][56/800] Data 0.004 (0.004) Batch 0.299 (0.332) Remain 02:38:56 loss: 0.1886 Lr: 0.00188 [2023-12-20 18:45:38,891 INFO misc.py line 119 131400] Train: [65/100][57/800] Data 0.005 (0.004) Batch 0.316 (0.331) Remain 02:38:47 loss: 0.2138 Lr: 0.00188 [2023-12-20 18:45:39,251 INFO misc.py line 119 131400] Train: [65/100][58/800] Data 0.004 (0.004) Batch 0.360 (0.332) Remain 02:39:02 loss: 0.3007 Lr: 0.00188 [2023-12-20 18:45:39,555 INFO misc.py line 119 131400] Train: [65/100][59/800] Data 0.003 (0.004) Batch 0.304 (0.331) Remain 02:38:47 loss: 0.3279 Lr: 0.00188 [2023-12-20 18:45:39,879 INFO misc.py line 119 131400] Train: [65/100][60/800] Data 0.004 (0.004) Batch 0.323 (0.331) Remain 02:38:42 loss: 0.5121 Lr: 0.00188 [2023-12-20 18:45:40,207 INFO misc.py line 119 131400] Train: [65/100][61/800] Data 0.004 (0.004) Batch 0.329 (0.331) Remain 02:38:41 loss: 0.1053 Lr: 0.00188 [2023-12-20 18:45:40,564 INFO misc.py line 119 131400] Train: [65/100][62/800] Data 0.009 (0.004) Batch 0.357 (0.332) Remain 02:38:53 loss: 0.3118 Lr: 0.00188 [2023-12-20 18:45:40,909 INFO misc.py line 119 131400] Train: [65/100][63/800] Data 0.004 (0.004) Batch 0.343 (0.332) Remain 02:38:58 loss: 0.2048 Lr: 0.00188 [2023-12-20 18:45:41,230 INFO misc.py line 119 131400] Train: [65/100][64/800] Data 0.005 (0.004) Batch 0.323 (0.332) Remain 02:38:54 loss: 0.2529 Lr: 0.00188 [2023-12-20 18:45:41,540 INFO misc.py line 119 131400] Train: [65/100][65/800] Data 0.004 (0.004) Batch 0.310 (0.331) Remain 02:38:43 loss: 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INFO misc.py line 119 131400] Train: [65/100][72/800] Data 0.004 (0.004) Batch 0.323 (0.331) Remain 02:38:29 loss: 0.2179 Lr: 0.00188 [2023-12-20 18:45:44,156 INFO misc.py line 119 131400] Train: [65/100][73/800] Data 0.004 (0.004) Batch 0.325 (0.331) Remain 02:38:26 loss: 0.4114 Lr: 0.00188 [2023-12-20 18:45:44,489 INFO misc.py line 119 131400] Train: [65/100][74/800] Data 0.003 (0.004) Batch 0.331 (0.331) Remain 02:38:26 loss: 0.3213 Lr: 0.00188 [2023-12-20 18:45:44,831 INFO misc.py line 119 131400] Train: [65/100][75/800] Data 0.005 (0.004) Batch 0.343 (0.331) Remain 02:38:30 loss: 0.0972 Lr: 0.00188 [2023-12-20 18:45:45,138 INFO misc.py line 119 131400] Train: [65/100][76/800] Data 0.004 (0.004) Batch 0.308 (0.331) Remain 02:38:21 loss: 0.3031 Lr: 0.00188 [2023-12-20 18:45:45,462 INFO misc.py line 119 131400] Train: [65/100][77/800] Data 0.004 (0.004) Batch 0.324 (0.331) Remain 02:38:18 loss: 0.2614 Lr: 0.00188 [2023-12-20 18:45:45,813 INFO misc.py line 119 131400] Train: 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Batch 0.322 (0.336) Remain 02:37:01 loss: 0.3550 Lr: 0.00180 [2023-12-20 18:49:32,251 INFO misc.py line 119 131400] Train: [65/100][751/800] Data 0.011 (0.004) Batch 0.346 (0.336) Remain 02:37:01 loss: 0.1658 Lr: 0.00180 [2023-12-20 18:49:32,594 INFO misc.py line 119 131400] Train: [65/100][752/800] Data 0.005 (0.004) Batch 0.344 (0.336) Remain 02:37:01 loss: 0.4451 Lr: 0.00180 [2023-12-20 18:49:32,956 INFO misc.py line 119 131400] Train: [65/100][753/800] Data 0.003 (0.004) Batch 0.362 (0.336) Remain 02:37:02 loss: 0.3674 Lr: 0.00180 [2023-12-20 18:49:33,282 INFO misc.py line 119 131400] Train: [65/100][754/800] Data 0.004 (0.004) Batch 0.327 (0.336) Remain 02:37:01 loss: 0.1689 Lr: 0.00180 [2023-12-20 18:49:33,624 INFO misc.py line 119 131400] Train: [65/100][755/800] Data 0.004 (0.004) Batch 0.341 (0.336) Remain 02:37:01 loss: 0.1750 Lr: 0.00180 [2023-12-20 18:49:33,956 INFO misc.py line 119 131400] Train: [65/100][756/800] Data 0.004 (0.004) Batch 0.324 (0.336) Remain 02:37:00 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18:49:36,330 INFO misc.py line 119 131400] Train: [65/100][763/800] Data 0.004 (0.004) Batch 0.319 (0.336) Remain 02:36:59 loss: 0.3338 Lr: 0.00180 [2023-12-20 18:49:36,642 INFO misc.py line 119 131400] Train: [65/100][764/800] Data 0.003 (0.004) Batch 0.312 (0.336) Remain 02:36:58 loss: 0.2499 Lr: 0.00180 [2023-12-20 18:49:36,976 INFO misc.py line 119 131400] Train: [65/100][765/800] Data 0.003 (0.004) Batch 0.334 (0.336) Remain 02:36:57 loss: 0.3886 Lr: 0.00180 [2023-12-20 18:49:37,305 INFO misc.py line 119 131400] Train: [65/100][766/800] Data 0.005 (0.004) Batch 0.329 (0.336) Remain 02:36:57 loss: 0.1334 Lr: 0.00180 [2023-12-20 18:49:37,739 INFO misc.py line 119 131400] Train: [65/100][767/800] Data 0.003 (0.004) Batch 0.434 (0.336) Remain 02:37:00 loss: 0.4390 Lr: 0.00180 [2023-12-20 18:49:38,101 INFO misc.py line 119 131400] Train: [65/100][768/800] Data 0.004 (0.004) Batch 0.359 (0.336) Remain 02:37:01 loss: 0.2126 Lr: 0.00180 [2023-12-20 18:49:38,410 INFO misc.py line 119 131400] Train: [65/100][769/800] Data 0.008 (0.004) Batch 0.311 (0.336) Remain 02:36:59 loss: 0.2733 Lr: 0.00180 [2023-12-20 18:49:38,766 INFO misc.py line 119 131400] Train: [65/100][770/800] Data 0.004 (0.004) Batch 0.355 (0.336) Remain 02:37:00 loss: 0.1945 Lr: 0.00180 [2023-12-20 18:49:39,104 INFO misc.py line 119 131400] Train: [65/100][771/800] Data 0.005 (0.004) Batch 0.335 (0.336) Remain 02:36:59 loss: 0.2501 Lr: 0.00180 [2023-12-20 18:49:39,387 INFO misc.py line 119 131400] Train: [65/100][772/800] Data 0.009 (0.004) Batch 0.287 (0.336) Remain 02:36:57 loss: 0.5856 Lr: 0.00180 [2023-12-20 18:49:39,701 INFO misc.py line 119 131400] Train: [65/100][773/800] Data 0.003 (0.004) Batch 0.315 (0.336) Remain 02:36:56 loss: 0.1385 Lr: 0.00180 [2023-12-20 18:49:39,997 INFO misc.py line 119 131400] Train: [65/100][774/800] Data 0.003 (0.004) Batch 0.295 (0.336) Remain 02:36:54 loss: 0.2627 Lr: 0.00180 [2023-12-20 18:49:40,322 INFO misc.py line 119 131400] Train: [65/100][775/800] Data 0.004 (0.004) Batch 0.323 (0.336) Remain 02:36:54 loss: 0.2025 Lr: 0.00180 [2023-12-20 18:49:40,688 INFO misc.py line 119 131400] Train: [65/100][776/800] Data 0.006 (0.004) Batch 0.367 (0.336) Remain 02:36:54 loss: 0.1643 Lr: 0.00180 [2023-12-20 18:49:41,033 INFO misc.py line 119 131400] Train: [65/100][777/800] Data 0.005 (0.004) Batch 0.345 (0.336) Remain 02:36:54 loss: 0.2800 Lr: 0.00180 [2023-12-20 18:49:41,373 INFO misc.py line 119 131400] Train: [65/100][778/800] Data 0.006 (0.004) Batch 0.341 (0.336) Remain 02:36:54 loss: 0.2544 Lr: 0.00180 [2023-12-20 18:49:41,648 INFO misc.py line 119 131400] Train: [65/100][779/800] Data 0.004 (0.004) Batch 0.276 (0.336) Remain 02:36:52 loss: 0.2229 Lr: 0.00180 [2023-12-20 18:49:41,958 INFO misc.py line 119 131400] Train: [65/100][780/800] Data 0.003 (0.004) Batch 0.311 (0.336) Remain 02:36:50 loss: 0.2571 Lr: 0.00180 [2023-12-20 18:49:42,278 INFO misc.py line 119 131400] Train: [65/100][781/800] Data 0.003 (0.004) Batch 0.316 (0.336) Remain 02:36:49 loss: 0.2452 Lr: 0.00180 [2023-12-20 18:49:42,609 INFO misc.py line 119 131400] Train: [65/100][782/800] Data 0.006 (0.004) Batch 0.334 (0.336) Remain 02:36:49 loss: 0.1436 Lr: 0.00180 [2023-12-20 18:49:42,898 INFO misc.py line 119 131400] Train: [65/100][783/800] Data 0.003 (0.004) Batch 0.289 (0.336) Remain 02:36:47 loss: 0.3117 Lr: 0.00180 [2023-12-20 18:49:43,229 INFO misc.py line 119 131400] Train: [65/100][784/800] Data 0.004 (0.004) Batch 0.331 (0.336) Remain 02:36:46 loss: 0.2239 Lr: 0.00180 [2023-12-20 18:49:43,541 INFO misc.py line 119 131400] Train: [65/100][785/800] Data 0.003 (0.004) Batch 0.312 (0.336) Remain 02:36:45 loss: 0.2409 Lr: 0.00180 [2023-12-20 18:49:43,893 INFO misc.py line 119 131400] Train: [65/100][786/800] Data 0.004 (0.004) Batch 0.352 (0.336) Remain 02:36:45 loss: 0.2886 Lr: 0.00180 [2023-12-20 18:49:44,230 INFO misc.py line 119 131400] Train: [65/100][787/800] Data 0.004 (0.004) Batch 0.336 (0.336) Remain 02:36:45 loss: 0.1625 Lr: 0.00180 [2023-12-20 18:49:44,572 INFO misc.py line 119 131400] Train: [65/100][788/800] Data 0.005 (0.004) Batch 0.343 (0.336) Remain 02:36:45 loss: 0.2347 Lr: 0.00180 [2023-12-20 18:49:44,875 INFO misc.py line 119 131400] Train: [65/100][789/800] Data 0.004 (0.004) Batch 0.302 (0.336) Remain 02:36:44 loss: 0.3018 Lr: 0.00180 [2023-12-20 18:49:45,160 INFO misc.py line 119 131400] Train: [65/100][790/800] Data 0.004 (0.004) Batch 0.285 (0.336) Remain 02:36:41 loss: 0.1965 Lr: 0.00180 [2023-12-20 18:49:45,500 INFO misc.py line 119 131400] Train: [65/100][791/800] Data 0.003 (0.004) Batch 0.340 (0.336) Remain 02:36:41 loss: 0.2187 Lr: 0.00180 [2023-12-20 18:49:45,839 INFO misc.py line 119 131400] Train: [65/100][792/800] Data 0.003 (0.004) Batch 0.340 (0.336) Remain 02:36:41 loss: 0.5174 Lr: 0.00180 [2023-12-20 18:49:46,152 INFO misc.py line 119 131400] Train: [65/100][793/800] Data 0.003 (0.004) Batch 0.312 (0.336) Remain 02:36:40 loss: 0.3591 Lr: 0.00180 [2023-12-20 18:49:46,462 INFO misc.py line 119 131400] Train: [65/100][794/800] Data 0.004 (0.004) Batch 0.310 (0.336) Remain 02:36:39 loss: 0.3055 Lr: 0.00180 [2023-12-20 18:49:46,769 INFO misc.py line 119 131400] Train: [65/100][795/800] Data 0.004 (0.004) Batch 0.308 (0.336) Remain 02:36:37 loss: 0.6513 Lr: 0.00180 [2023-12-20 18:49:47,053 INFO misc.py line 119 131400] Train: [65/100][796/800] Data 0.003 (0.004) Batch 0.285 (0.336) Remain 02:36:35 loss: 0.2348 Lr: 0.00180 [2023-12-20 18:49:47,356 INFO misc.py line 119 131400] Train: [65/100][797/800] Data 0.003 (0.004) Batch 0.302 (0.335) Remain 02:36:34 loss: 0.2829 Lr: 0.00180 [2023-12-20 18:49:47,674 INFO misc.py line 119 131400] Train: [65/100][798/800] Data 0.003 (0.004) Batch 0.311 (0.335) Remain 02:36:32 loss: 0.2904 Lr: 0.00180 [2023-12-20 18:49:48,132 INFO misc.py line 119 131400] Train: [65/100][799/800] Data 0.011 (0.004) Batch 0.465 (0.336) Remain 02:36:37 loss: 0.3071 Lr: 0.00180 [2023-12-20 18:49:48,450 INFO misc.py line 119 131400] Train: [65/100][800/800] Data 0.004 (0.004) Batch 0.319 (0.336) Remain 02:36:36 loss: 0.5413 Lr: 0.00180 [2023-12-20 18:49:48,451 INFO misc.py line 136 131400] Train result: loss: 0.2865 [2023-12-20 18:49:48,451 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:50:12,268 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2097 [2023-12-20 18:50:12,843 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1749 [2023-12-20 18:50:12,937 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5967 [2023-12-20 18:50:13,046 INFO evaluator.py line 159 131400] Test: [4/78] Loss 0.9661 [2023-12-20 18:50:13,160 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.5286 [2023-12-20 18:50:13,262 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6259 [2023-12-20 18:50:13,365 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8111 [2023-12-20 18:50:13,472 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8768 [2023-12-20 18:50:13,555 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3105 [2023-12-20 18:50:13,641 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3800 [2023-12-20 18:50:13,740 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5052 [2023-12-20 18:50:13,876 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2695 [2023-12-20 18:50:13,994 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2948 [2023-12-20 18:50:14,149 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2216 [2023-12-20 18:50:14,242 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1322 [2023-12-20 18:50:14,379 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.4153 [2023-12-20 18:50:14,488 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3540 [2023-12-20 18:50:14,598 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8208 [2023-12-20 18:50:14,716 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1614 [2023-12-20 18:50:14,799 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5221 [2023-12-20 18:50:14,907 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1902 [2023-12-20 18:50:15,069 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1321 [2023-12-20 18:50:15,194 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.2585 [2023-12-20 18:50:15,337 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2091 [2023-12-20 18:50:15,481 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2282 [2023-12-20 18:50:15,566 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3948 [2023-12-20 18:50:15,727 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4383 [2023-12-20 18:50:15,852 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4894 [2023-12-20 18:50:15,947 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4909 [2023-12-20 18:50:16,094 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3657 [2023-12-20 18:50:16,197 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7950 [2023-12-20 18:50:16,322 INFO evaluator.py line 159 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[55/78] Loss 0.9030 [2023-12-20 18:50:19,104 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6621 [2023-12-20 18:50:19,211 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3265 [2023-12-20 18:50:19,373 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.3638 [2023-12-20 18:50:19,472 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2962 [2023-12-20 18:50:19,566 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2292 [2023-12-20 18:50:19,665 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3618 [2023-12-20 18:50:19,760 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2428 [2023-12-20 18:50:19,849 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5398 [2023-12-20 18:50:19,953 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5547 [2023-12-20 18:50:20,080 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3040 [2023-12-20 18:50:20,167 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1884 [2023-12-20 18:50:20,270 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3400 [2023-12-20 18:50:20,368 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0211 [2023-12-20 18:50:20,458 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.5592 [2023-12-20 18:50:20,545 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0178 [2023-12-20 18:50:20,641 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9417 [2023-12-20 18:50:20,733 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6689 [2023-12-20 18:50:20,868 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1250 [2023-12-20 18:50:20,963 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6152 [2023-12-20 18:50:21,079 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5285 [2023-12-20 18:50:21,181 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7357 [2023-12-20 18:50:21,269 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2886 [2023-12-20 18:50:21,423 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1305 [2023-12-20 18:50:22,766 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7555/0.8414/0.9145. [2023-12-20 18:50:22,766 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8679/0.9502 [2023-12-20 18:50:22,766 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9652/0.9834 [2023-12-20 18:50:22,766 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6894/0.8228 [2023-12-20 18:50:22,766 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7997/0.8712 [2023-12-20 18:50:22,766 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9216/0.9573 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8471/0.9188 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7645/0.8431 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7177/0.8424 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6575/0.7622 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8239/0.9219 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3637/0.5708 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7137/0.8482 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6909/0.8504 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7282/0.7653 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6678/0.7484 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7544/0.8128 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9342/0.9792 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7091/0.7861 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8901/0.9150 [2023-12-20 18:50:22,767 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6044/0.6782 [2023-12-20 18:50:22,768 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:50:22,769 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7555 [2023-12-20 18:50:22,769 INFO misc.py line 165 131400] Currently Best mIoU: 0.7555 [2023-12-20 18:50:22,769 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:50:28,567 INFO misc.py line 119 131400] Train: [66/100][1/800] Data 0.687 (0.687) Batch 0.969 (0.969) Remain 07:32:18 loss: 0.3216 Lr: 0.00180 [2023-12-20 18:50:28,887 INFO misc.py line 119 131400] Train: [66/100][2/800] Data 0.004 (0.004) Batch 0.315 (0.315) Remain 02:27:07 loss: 0.1237 Lr: 0.00180 [2023-12-20 18:50:29,239 INFO misc.py line 119 131400] Train: [66/100][3/800] Data 0.009 (0.009) Batch 0.356 (0.356) Remain 02:46:02 loss: 0.2566 Lr: 0.00180 [2023-12-20 18:50:29,577 INFO misc.py line 119 131400] Train: [66/100][4/800] Data 0.006 (0.006) Batch 0.339 (0.339) Remain 02:38:06 loss: 0.2629 Lr: 0.00180 [2023-12-20 18:50:29,913 INFO misc.py line 119 131400] Train: [66/100][5/800] Data 0.005 (0.005) Batch 0.332 (0.335) Remain 02:36:27 loss: 0.2900 Lr: 0.00180 [2023-12-20 18:50:30,250 INFO misc.py line 119 131400] Train: [66/100][6/800] Data 0.009 (0.006) Batch 0.342 (0.338) Remain 02:37:29 loss: 0.4472 Lr: 0.00180 [2023-12-20 18:50:30,598 INFO misc.py line 119 131400] Train: [66/100][7/800] Data 0.004 (0.006) Batch 0.348 (0.340) Remain 02:38:41 loss: 0.3820 Lr: 0.00180 [2023-12-20 18:50:30,941 INFO misc.py line 119 131400] Train: [66/100][8/800] Data 0.005 (0.006) Batch 0.341 (0.340) Remain 02:38:48 loss: 0.6653 Lr: 0.00180 [2023-12-20 18:50:31,294 INFO misc.py line 119 131400] Train: [66/100][9/800] Data 0.006 (0.006) Batch 0.354 (0.343) Remain 02:39:49 loss: 0.3017 Lr: 0.00180 [2023-12-20 18:50:31,632 INFO misc.py line 119 131400] Train: [66/100][10/800] Data 0.005 (0.006) Batch 0.334 (0.341) Remain 02:39:16 loss: 0.3137 Lr: 0.00180 [2023-12-20 18:50:31,966 INFO misc.py line 119 131400] Train: [66/100][11/800] Data 0.009 (0.006) Batch 0.332 (0.340) Remain 02:38:43 loss: 0.1718 Lr: 0.00180 [2023-12-20 18:50:32,318 INFO misc.py line 119 131400] Train: [66/100][12/800] Data 0.010 (0.006) Batch 0.357 (0.342) Remain 02:39:36 loss: 0.1948 Lr: 0.00180 [2023-12-20 18:50:32,641 INFO misc.py line 119 131400] Train: [66/100][13/800] Data 0.005 (0.006) Batch 0.324 (0.340) Remain 02:38:45 loss: 0.1772 Lr: 0.00180 [2023-12-20 18:50:32,987 INFO misc.py line 119 131400] Train: [66/100][14/800] Data 0.003 (0.006) Batch 0.339 (0.340) Remain 02:38:40 loss: 0.2996 Lr: 0.00180 [2023-12-20 18:50:33,336 INFO misc.py line 119 131400] Train: [66/100][15/800] Data 0.011 (0.006) Batch 0.357 (0.342) Remain 02:39:20 loss: 0.2212 Lr: 0.00180 [2023-12-20 18:50:33,671 INFO misc.py line 119 131400] Train: [66/100][16/800] Data 0.003 (0.006) Batch 0.335 (0.341) Remain 02:39:05 loss: 0.2887 Lr: 0.00180 [2023-12-20 18:50:33,977 INFO misc.py line 119 131400] Train: [66/100][17/800] Data 0.003 (0.006) Batch 0.305 (0.339) Remain 02:37:53 loss: 0.1946 Lr: 0.00180 [2023-12-20 18:50:34,309 INFO misc.py line 119 131400] Train: [66/100][18/800] Data 0.004 (0.006) Batch 0.333 (0.338) Remain 02:37:42 loss: 0.2812 Lr: 0.00180 [2023-12-20 18:50:34,653 INFO misc.py line 119 131400] Train: [66/100][19/800] Data 0.003 (0.006) Batch 0.343 (0.338) Remain 02:37:50 loss: 0.5128 Lr: 0.00180 [2023-12-20 18:50:34,989 INFO misc.py line 119 131400] Train: [66/100][20/800] Data 0.004 (0.006) Batch 0.333 (0.338) Remain 02:37:41 loss: 0.2988 Lr: 0.00180 [2023-12-20 18:50:35,332 INFO misc.py line 119 131400] Train: [66/100][21/800] Data 0.008 (0.006) Batch 0.346 (0.339) Remain 02:37:53 loss: 0.2675 Lr: 0.00180 [2023-12-20 18:50:35,661 INFO misc.py line 119 131400] Train: [66/100][22/800] Data 0.003 (0.006) Batch 0.329 (0.338) Remain 02:37:39 loss: 0.3720 Lr: 0.00180 [2023-12-20 18:50:35,978 INFO misc.py line 119 131400] Train: [66/100][23/800] Data 0.003 (0.005) Batch 0.317 (0.337) Remain 02:37:09 loss: 0.1284 Lr: 0.00180 [2023-12-20 18:50:36,307 INFO misc.py line 119 131400] Train: [66/100][24/800] Data 0.004 (0.005) Batch 0.328 (0.337) Remain 02:36:57 loss: 0.4081 Lr: 0.00180 [2023-12-20 18:50:36,665 INFO misc.py line 119 131400] Train: [66/100][25/800] Data 0.005 (0.005) Batch 0.358 (0.338) Remain 02:37:24 loss: 0.3377 Lr: 0.00179 [2023-12-20 18:50:36,999 INFO misc.py line 119 131400] Train: [66/100][26/800] Data 0.003 (0.005) Batch 0.335 (0.337) Remain 02:37:21 loss: 0.2695 Lr: 0.00179 [2023-12-20 18:50:37,342 INFO misc.py line 119 131400] Train: [66/100][27/800] Data 0.003 (0.005) Batch 0.342 (0.338) Remain 02:37:26 loss: 0.3284 Lr: 0.00179 [2023-12-20 18:50:37,673 INFO misc.py line 119 131400] Train: [66/100][28/800] Data 0.005 (0.005) Batch 0.331 (0.337) Remain 02:37:17 loss: 0.3381 Lr: 0.00179 [2023-12-20 18:50:37,984 INFO misc.py line 119 131400] Train: [66/100][29/800] Data 0.005 (0.005) Batch 0.311 (0.336) Remain 02:36:49 loss: 0.1567 Lr: 0.00179 [2023-12-20 18:50:38,306 INFO misc.py line 119 131400] Train: [66/100][30/800] Data 0.004 (0.005) Batch 0.323 (0.336) Remain 02:36:35 loss: 0.1923 Lr: 0.00179 [2023-12-20 18:50:38,669 INFO misc.py line 119 131400] Train: [66/100][31/800] Data 0.003 (0.005) Batch 0.362 (0.337) Remain 02:37:01 loss: 0.2545 Lr: 0.00179 [2023-12-20 18:50:38,999 INFO misc.py line 119 131400] Train: [66/100][32/800] Data 0.005 (0.005) Batch 0.330 (0.337) Remain 02:36:54 loss: 0.3046 Lr: 0.00179 [2023-12-20 18:50:39,341 INFO misc.py line 119 131400] Train: [66/100][33/800] Data 0.004 (0.005) Batch 0.343 (0.337) Remain 02:36:59 loss: 0.4143 Lr: 0.00179 [2023-12-20 18:50:39,709 INFO misc.py line 119 131400] Train: [66/100][34/800] Data 0.005 (0.005) Batch 0.365 (0.338) Remain 02:37:24 loss: 0.2438 Lr: 0.00179 [2023-12-20 18:50:40,031 INFO misc.py line 119 131400] Train: [66/100][35/800] Data 0.007 (0.005) Batch 0.325 (0.337) Remain 02:37:12 loss: 0.4035 Lr: 0.00179 [2023-12-20 18:50:40,342 INFO misc.py line 119 131400] Train: [66/100][36/800] Data 0.004 (0.005) Batch 0.311 (0.337) Remain 02:36:49 loss: 0.2300 Lr: 0.00179 [2023-12-20 18:50:40,649 INFO misc.py line 119 131400] Train: [66/100][37/800] Data 0.004 (0.005) Batch 0.308 (0.336) Remain 02:36:26 loss: 0.2286 Lr: 0.00179 [2023-12-20 18:50:40,984 INFO misc.py line 119 131400] Train: [66/100][38/800] Data 0.003 (0.005) Batch 0.329 (0.335) Remain 02:36:20 loss: 0.1545 Lr: 0.00179 [2023-12-20 18:50:41,315 INFO misc.py line 119 131400] Train: [66/100][39/800] Data 0.009 (0.005) Batch 0.337 (0.336) Remain 02:36:21 loss: 0.2209 Lr: 0.00179 [2023-12-20 18:50:41,634 INFO misc.py line 119 131400] Train: [66/100][40/800] Data 0.003 (0.005) Batch 0.319 (0.335) Remain 02:36:08 loss: 0.4147 Lr: 0.00179 [2023-12-20 18:50:41,941 INFO misc.py line 119 131400] Train: [66/100][41/800] Data 0.003 (0.005) Batch 0.306 (0.334) Remain 02:35:46 loss: 0.3000 Lr: 0.00179 [2023-12-20 18:50:42,309 INFO misc.py line 119 131400] Train: [66/100][42/800] Data 0.004 (0.005) Batch 0.368 (0.335) Remain 02:36:10 loss: 0.5486 Lr: 0.00179 [2023-12-20 18:50:42,623 INFO misc.py line 119 131400] Train: [66/100][43/800] Data 0.004 (0.005) Batch 0.315 (0.335) Remain 02:35:55 loss: 0.2190 Lr: 0.00179 [2023-12-20 18:50:42,953 INFO misc.py line 119 131400] Train: [66/100][44/800] Data 0.003 (0.005) Batch 0.329 (0.335) Remain 02:35:51 loss: 0.3210 Lr: 0.00179 [2023-12-20 18:50:43,273 INFO misc.py line 119 131400] Train: [66/100][45/800] Data 0.004 (0.005) Batch 0.321 (0.334) Remain 02:35:42 loss: 0.2958 Lr: 0.00179 [2023-12-20 18:50:43,610 INFO misc.py line 119 131400] Train: [66/100][46/800] Data 0.004 (0.005) Batch 0.337 (0.334) Remain 02:35:43 loss: 0.1445 Lr: 0.00179 [2023-12-20 18:50:43,917 INFO misc.py line 119 131400] Train: [66/100][47/800] Data 0.003 (0.005) Batch 0.307 (0.334) Remain 02:35:26 loss: 0.4318 Lr: 0.00179 [2023-12-20 18:50:44,261 INFO misc.py line 119 131400] Train: [66/100][48/800] Data 0.003 (0.005) Batch 0.343 (0.334) Remain 02:35:31 loss: 0.3026 Lr: 0.00179 [2023-12-20 18:50:44,640 INFO misc.py line 119 131400] Train: [66/100][49/800] Data 0.005 (0.005) Batch 0.381 (0.335) Remain 02:35:59 loss: 0.1454 Lr: 0.00179 [2023-12-20 18:50:45,020 INFO misc.py line 119 131400] Train: [66/100][50/800] Data 0.003 (0.005) Batch 0.378 (0.336) Remain 02:36:25 loss: 0.3097 Lr: 0.00179 [2023-12-20 18:50:45,368 INFO misc.py line 119 131400] Train: [66/100][51/800] Data 0.004 (0.005) Batch 0.348 (0.336) Remain 02:36:31 loss: 0.4321 Lr: 0.00179 [2023-12-20 18:50:45,692 INFO misc.py line 119 131400] Train: [66/100][52/800] Data 0.005 (0.005) Batch 0.325 (0.336) Remain 02:36:25 loss: 0.2136 Lr: 0.00179 [2023-12-20 18:50:46,025 INFO misc.py line 119 131400] Train: [66/100][53/800] Data 0.004 (0.005) Batch 0.332 (0.336) Remain 02:36:22 loss: 0.6058 Lr: 0.00179 [2023-12-20 18:50:46,390 INFO misc.py line 119 131400] Train: [66/100][54/800] Data 0.005 (0.005) Batch 0.365 (0.336) Remain 02:36:38 loss: 0.5866 Lr: 0.00179 [2023-12-20 18:50:46,747 INFO misc.py line 119 131400] Train: [66/100][55/800] Data 0.006 (0.005) Batch 0.358 (0.337) Remain 02:36:49 loss: 0.3386 Lr: 0.00179 [2023-12-20 18:50:47,099 INFO misc.py line 119 131400] Train: [66/100][56/800] Data 0.003 (0.005) Batch 0.351 (0.337) Remain 02:36:57 loss: 0.2736 Lr: 0.00179 [2023-12-20 18:50:47,418 INFO misc.py line 119 131400] Train: [66/100][57/800] Data 0.005 (0.005) Batch 0.319 (0.337) Remain 02:36:47 loss: 0.3270 Lr: 0.00179 [2023-12-20 18:50:47,742 INFO misc.py line 119 131400] Train: [66/100][58/800] Data 0.005 (0.005) Batch 0.324 (0.336) Remain 02:36:40 loss: 0.1324 Lr: 0.00179 [2023-12-20 18:50:48,079 INFO misc.py line 119 131400] Train: [66/100][59/800] Data 0.004 (0.005) Batch 0.337 (0.336) Remain 02:36:40 loss: 0.3486 Lr: 0.00179 [2023-12-20 18:50:48,426 INFO misc.py line 119 131400] Train: [66/100][60/800] Data 0.004 (0.005) Batch 0.347 (0.337) Remain 02:36:45 loss: 0.2969 Lr: 0.00179 [2023-12-20 18:50:48,737 INFO misc.py line 119 131400] Train: [66/100][61/800] Data 0.004 (0.005) Batch 0.311 (0.336) Remain 02:36:33 loss: 0.2658 Lr: 0.00179 [2023-12-20 18:50:49,108 INFO misc.py line 119 131400] Train: [66/100][62/800] Data 0.004 (0.005) Batch 0.372 (0.337) Remain 02:36:49 loss: 0.2742 Lr: 0.00179 [2023-12-20 18:50:49,435 INFO misc.py line 119 131400] Train: [66/100][63/800] Data 0.003 (0.005) Batch 0.325 (0.337) Remain 02:36:43 loss: 0.2921 Lr: 0.00179 [2023-12-20 18:50:49,796 INFO misc.py line 119 131400] Train: [66/100][64/800] Data 0.006 (0.005) Batch 0.362 (0.337) Remain 02:36:54 loss: 0.1497 Lr: 0.00179 [2023-12-20 18:50:50,155 INFO misc.py line 119 131400] Train: [66/100][65/800] Data 0.005 (0.005) Batch 0.359 (0.337) Remain 02:37:04 loss: 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INFO misc.py line 119 131400] Train: [66/100][72/800] Data 0.004 (0.005) Batch 0.348 (0.337) Remain 02:37:03 loss: 0.2640 Lr: 0.00179 [2023-12-20 18:50:52,870 INFO misc.py line 119 131400] Train: [66/100][73/800] Data 0.004 (0.005) Batch 0.351 (0.338) Remain 02:37:08 loss: 0.4583 Lr: 0.00179 [2023-12-20 18:50:53,181 INFO misc.py line 119 131400] Train: [66/100][74/800] Data 0.003 (0.005) Batch 0.311 (0.337) Remain 02:36:57 loss: 0.1711 Lr: 0.00179 [2023-12-20 18:50:53,515 INFO misc.py line 119 131400] Train: [66/100][75/800] Data 0.003 (0.005) Batch 0.334 (0.337) Remain 02:36:56 loss: 0.3028 Lr: 0.00179 [2023-12-20 18:50:53,876 INFO misc.py line 119 131400] Train: [66/100][76/800] Data 0.004 (0.005) Batch 0.360 (0.338) Remain 02:37:04 loss: 0.1554 Lr: 0.00179 [2023-12-20 18:50:54,187 INFO misc.py line 119 131400] Train: [66/100][77/800] Data 0.005 (0.005) Batch 0.311 (0.337) Remain 02:36:53 loss: 0.2134 Lr: 0.00179 [2023-12-20 18:50:54,524 INFO misc.py line 119 131400] Train: 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0.003 (0.006) Batch 0.326 (0.337) Remain 02:32:49 loss: 0.0972 Lr: 0.00171 [2023-12-20 18:54:49,625 INFO misc.py line 119 131400] Train: [66/100][776/800] Data 0.007 (0.006) Batch 0.362 (0.337) Remain 02:32:50 loss: 0.3788 Lr: 0.00171 [2023-12-20 18:54:49,980 INFO misc.py line 119 131400] Train: [66/100][777/800] Data 0.004 (0.006) Batch 0.355 (0.337) Remain 02:32:50 loss: 0.2098 Lr: 0.00171 [2023-12-20 18:54:50,311 INFO misc.py line 119 131400] Train: [66/100][778/800] Data 0.003 (0.006) Batch 0.331 (0.337) Remain 02:32:50 loss: 0.3695 Lr: 0.00171 [2023-12-20 18:54:50,684 INFO misc.py line 119 131400] Train: [66/100][779/800] Data 0.004 (0.006) Batch 0.373 (0.337) Remain 02:32:51 loss: 0.4835 Lr: 0.00171 [2023-12-20 18:54:50,999 INFO misc.py line 119 131400] Train: [66/100][780/800] Data 0.004 (0.006) Batch 0.315 (0.337) Remain 02:32:50 loss: 0.1549 Lr: 0.00171 [2023-12-20 18:54:51,333 INFO misc.py line 119 131400] Train: [66/100][781/800] Data 0.004 (0.006) Batch 0.334 (0.337) Remain 02:32:49 loss: 0.4425 Lr: 0.00171 [2023-12-20 18:54:51,734 INFO misc.py line 119 131400] Train: [66/100][782/800] Data 0.003 (0.006) Batch 0.400 (0.337) Remain 02:32:51 loss: 0.2133 Lr: 0.00171 [2023-12-20 18:54:52,083 INFO misc.py line 119 131400] Train: [66/100][783/800] Data 0.008 (0.006) Batch 0.345 (0.337) Remain 02:32:51 loss: 0.4593 Lr: 0.00171 [2023-12-20 18:54:52,390 INFO misc.py line 119 131400] Train: [66/100][784/800] Data 0.009 (0.006) Batch 0.312 (0.337) Remain 02:32:50 loss: 0.2043 Lr: 0.00171 [2023-12-20 18:54:52,730 INFO misc.py line 119 131400] Train: [66/100][785/800] Data 0.004 (0.006) Batch 0.342 (0.337) Remain 02:32:50 loss: 0.3651 Lr: 0.00171 [2023-12-20 18:54:53,066 INFO misc.py line 119 131400] Train: [66/100][786/800] Data 0.003 (0.006) Batch 0.335 (0.337) Remain 02:32:49 loss: 0.4229 Lr: 0.00171 [2023-12-20 18:54:53,414 INFO misc.py line 119 131400] Train: [66/100][787/800] Data 0.003 (0.006) Batch 0.348 (0.337) Remain 02:32:49 loss: 0.1984 Lr: 0.00171 [2023-12-20 18:54:53,793 INFO misc.py line 119 131400] Train: [66/100][788/800] Data 0.003 (0.006) Batch 0.379 (0.337) Remain 02:32:50 loss: 0.4380 Lr: 0.00171 [2023-12-20 18:54:54,139 INFO misc.py line 119 131400] Train: [66/100][789/800] Data 0.004 (0.006) Batch 0.331 (0.337) Remain 02:32:50 loss: 0.1621 Lr: 0.00171 [2023-12-20 18:54:54,445 INFO misc.py line 119 131400] Train: [66/100][790/800] Data 0.017 (0.006) Batch 0.320 (0.337) Remain 02:32:49 loss: 0.2500 Lr: 0.00171 [2023-12-20 18:54:54,759 INFO misc.py line 119 131400] Train: [66/100][791/800] Data 0.003 (0.006) Batch 0.313 (0.337) Remain 02:32:48 loss: 0.2156 Lr: 0.00171 [2023-12-20 18:54:55,099 INFO misc.py line 119 131400] Train: [66/100][792/800] Data 0.005 (0.006) Batch 0.339 (0.337) Remain 02:32:47 loss: 0.2587 Lr: 0.00171 [2023-12-20 18:54:55,430 INFO misc.py line 119 131400] Train: [66/100][793/800] Data 0.006 (0.006) Batch 0.332 (0.337) Remain 02:32:47 loss: 0.5605 Lr: 0.00171 [2023-12-20 18:54:55,723 INFO misc.py line 119 131400] Train: [66/100][794/800] Data 0.004 (0.006) Batch 0.294 (0.337) Remain 02:32:45 loss: 0.1298 Lr: 0.00171 [2023-12-20 18:54:56,046 INFO misc.py line 119 131400] Train: [66/100][795/800] Data 0.004 (0.006) Batch 0.323 (0.337) Remain 02:32:44 loss: 0.3457 Lr: 0.00171 [2023-12-20 18:54:56,364 INFO misc.py line 119 131400] Train: [66/100][796/800] Data 0.004 (0.006) Batch 0.318 (0.337) Remain 02:32:43 loss: 0.1763 Lr: 0.00171 [2023-12-20 18:54:56,644 INFO misc.py line 119 131400] Train: [66/100][797/800] Data 0.004 (0.006) Batch 0.280 (0.337) Remain 02:32:41 loss: 0.3448 Lr: 0.00171 [2023-12-20 18:54:56,951 INFO misc.py line 119 131400] Train: [66/100][798/800] Data 0.004 (0.006) Batch 0.308 (0.337) Remain 02:32:40 loss: 0.2222 Lr: 0.00171 [2023-12-20 18:54:57,281 INFO misc.py line 119 131400] Train: [66/100][799/800] Data 0.003 (0.006) Batch 0.330 (0.337) Remain 02:32:39 loss: 0.3498 Lr: 0.00171 [2023-12-20 18:54:57,581 INFO misc.py line 119 131400] Train: [66/100][800/800] Data 0.003 (0.006) Batch 0.300 (0.337) Remain 02:32:37 loss: 0.2039 Lr: 0.00171 [2023-12-20 18:54:57,582 INFO misc.py line 136 131400] Train result: loss: 0.2902 [2023-12-20 18:54:57,583 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 18:55:17,854 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1409 [2023-12-20 18:55:19,478 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1653 [2023-12-20 18:55:19,568 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4185 [2023-12-20 18:55:19,675 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4176 [2023-12-20 18:55:19,788 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2226 [2023-12-20 18:55:19,892 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2215 [2023-12-20 18:55:19,980 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1826 [2023-12-20 18:55:20,086 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9266 [2023-12-20 18:55:20,167 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2581 [2023-12-20 18:55:20,252 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3480 [2023-12-20 18:55:20,342 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3896 [2023-12-20 18:55:20,483 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3830 [2023-12-20 18:55:20,607 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4169 [2023-12-20 18:55:20,761 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2242 [2023-12-20 18:55:20,853 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1226 [2023-12-20 18:55:20,986 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.4481 [2023-12-20 18:55:21,096 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3653 [2023-12-20 18:55:21,204 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2343 [2023-12-20 18:55:21,317 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1239 [2023-12-20 18:55:21,396 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3907 [2023-12-20 18:55:21,505 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1609 [2023-12-20 18:55:21,661 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1216 [2023-12-20 18:55:21,783 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3226 [2023-12-20 18:55:21,924 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2142 [2023-12-20 18:55:22,068 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1578 [2023-12-20 18:55:22,149 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4845 [2023-12-20 18:55:22,306 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.0020 [2023-12-20 18:55:22,429 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4353 [2023-12-20 18:55:22,525 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7074 [2023-12-20 18:55:22,668 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.3789 [2023-12-20 18:55:22,771 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4915 [2023-12-20 18:55:22,889 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3949 [2023-12-20 18:55:22,975 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1247 [2023-12-20 18:55:23,047 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1818 [2023-12-20 18:55:23,143 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6788 [2023-12-20 18:55:23,237 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3626 [2023-12-20 18:55:23,368 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9643 [2023-12-20 18:55:23,480 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1024 [2023-12-20 18:55:23,559 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5512 [2023-12-20 18:55:23,707 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3470 [2023-12-20 18:55:23,852 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0491 [2023-12-20 18:55:23,955 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0926 [2023-12-20 18:55:24,077 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4470 [2023-12-20 18:55:24,217 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0642 [2023-12-20 18:55:24,334 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4743 [2023-12-20 18:55:24,434 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.0494 [2023-12-20 18:55:24,600 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2951 [2023-12-20 18:55:24,692 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3575 [2023-12-20 18:55:24,844 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6643 [2023-12-20 18:55:24,940 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0853 [2023-12-20 18:55:25,014 INFO evaluator.py line 159 131400] Test: [51/78] Loss 1.0048 [2023-12-20 18:55:25,127 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4592 [2023-12-20 18:55:25,273 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.6865 [2023-12-20 18:55:25,407 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2840 [2023-12-20 18:55:25,507 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3937 [2023-12-20 18:55:25,597 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5396 [2023-12-20 18:55:25,702 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3806 [2023-12-20 18:55:25,866 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2091 [2023-12-20 18:55:25,967 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.1427 [2023-12-20 18:55:26,060 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.8365 [2023-12-20 18:55:26,157 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3629 [2023-12-20 18:55:26,251 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2912 [2023-12-20 18:55:26,341 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3301 [2023-12-20 18:55:26,446 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8260 [2023-12-20 18:55:26,575 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4389 [2023-12-20 18:55:26,658 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2418 [2023-12-20 18:55:26,758 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4840 [2023-12-20 18:55:26,851 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0516 [2023-12-20 18:55:26,935 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3540 [2023-12-20 18:55:27,019 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0227 [2023-12-20 18:55:27,113 INFO evaluator.py line 159 131400] Test: [71/78] Loss 1.0247 [2023-12-20 18:55:27,206 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7623 [2023-12-20 18:55:27,338 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1284 [2023-12-20 18:55:27,436 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6420 [2023-12-20 18:55:27,553 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6241 [2023-12-20 18:55:27,654 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7410 [2023-12-20 18:55:27,740 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7245 [2023-12-20 18:55:27,893 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3152 [2023-12-20 18:55:29,850 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7479/0.8467/0.9158. [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8785/0.9424 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9664/0.9852 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7091/0.8288 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.7871/0.8740 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9105/0.9529 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.7973/0.9186 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7623/0.8422 [2023-12-20 18:55:29,850 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6976/0.8170 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6978/0.8318 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.7929/0.9457 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3975/0.5334 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6849/0.8468 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6743/0.8390 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7511/0.8499 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6386/0.6668 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7406/0.8390 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9468/0.9808 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7081/0.8034 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.7914/0.9419 [2023-12-20 18:55:29,851 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6259/0.6944 [2023-12-20 18:55:29,851 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 18:55:29,852 INFO misc.py line 165 131400] Currently Best mIoU: 0.7555 [2023-12-20 18:55:29,853 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 18:55:35,403 INFO misc.py line 119 131400] Train: [67/100][1/800] Data 1.183 (1.183) Batch 1.526 (1.526) Remain 11:31:56 loss: 0.2091 Lr: 0.00171 [2023-12-20 18:55:35,705 INFO misc.py line 119 131400] Train: [67/100][2/800] Data 0.004 (0.004) Batch 0.302 (0.302) Remain 02:16:53 loss: 0.1116 Lr: 0.00171 [2023-12-20 18:55:36,027 INFO misc.py line 119 131400] Train: [67/100][3/800] Data 0.004 (0.004) Batch 0.322 (0.322) Remain 02:26:06 loss: 0.4351 Lr: 0.00171 [2023-12-20 18:55:36,365 INFO misc.py line 119 131400] Train: [67/100][4/800] Data 0.005 (0.005) Batch 0.336 (0.336) Remain 02:32:16 loss: 0.1539 Lr: 0.00171 [2023-12-20 18:55:36,710 INFO misc.py line 119 131400] Train: [67/100][5/800] Data 0.005 (0.005) Batch 0.348 (0.342) Remain 02:34:58 loss: 0.2183 Lr: 0.00171 [2023-12-20 18:55:37,060 INFO misc.py line 119 131400] Train: [67/100][6/800] Data 0.003 (0.004) Batch 0.350 (0.344) Remain 02:36:07 loss: 0.3020 Lr: 0.00171 [2023-12-20 18:55:37,367 INFO misc.py line 119 131400] Train: [67/100][7/800] Data 0.004 (0.004) Batch 0.303 (0.334) Remain 02:31:23 loss: 0.1260 Lr: 0.00171 [2023-12-20 18:55:37,702 INFO misc.py line 119 131400] Train: [67/100][8/800] Data 0.008 (0.005) Batch 0.339 (0.335) Remain 02:31:48 loss: 0.3246 Lr: 0.00171 [2023-12-20 18:55:38,030 INFO misc.py line 119 131400] Train: [67/100][9/800] Data 0.003 (0.005) Batch 0.328 (0.334) Remain 02:31:17 loss: 0.2054 Lr: 0.00171 [2023-12-20 18:55:38,351 INFO misc.py line 119 131400] Train: [67/100][10/800] Data 0.004 (0.005) Batch 0.321 (0.332) Remain 02:30:27 loss: 0.3800 Lr: 0.00171 [2023-12-20 18:55:38,698 INFO misc.py line 119 131400] Train: [67/100][11/800] Data 0.004 (0.004) Batch 0.347 (0.334) Remain 02:31:16 loss: 0.4189 Lr: 0.00171 [2023-12-20 18:55:39,059 INFO misc.py line 119 131400] Train: [67/100][12/800] Data 0.004 (0.004) Batch 0.361 (0.337) Remain 02:32:39 loss: 0.4228 Lr: 0.00171 [2023-12-20 18:55:39,348 INFO misc.py line 119 131400] Train: [67/100][13/800] Data 0.003 (0.004) Batch 0.289 (0.332) Remain 02:30:29 loss: 0.2319 Lr: 0.00171 [2023-12-20 18:55:39,677 INFO misc.py line 119 131400] Train: [67/100][14/800] Data 0.004 (0.004) Batch 0.329 (0.332) Remain 02:30:20 loss: 0.2266 Lr: 0.00171 [2023-12-20 18:55:40,026 INFO misc.py line 119 131400] Train: [67/100][15/800] Data 0.004 (0.004) Batch 0.349 (0.333) Remain 02:30:59 loss: 0.3856 Lr: 0.00171 [2023-12-20 18:55:40,375 INFO misc.py line 119 131400] Train: [67/100][16/800] Data 0.004 (0.004) Batch 0.349 (0.334) Remain 02:31:32 loss: 0.3006 Lr: 0.00171 [2023-12-20 18:55:40,678 INFO misc.py line 119 131400] Train: [67/100][17/800] Data 0.003 (0.004) Batch 0.303 (0.332) Remain 02:30:30 loss: 0.2858 Lr: 0.00171 [2023-12-20 18:55:41,021 INFO misc.py line 119 131400] Train: [67/100][18/800] Data 0.003 (0.004) Batch 0.342 (0.333) Remain 02:30:47 loss: 0.2690 Lr: 0.00171 [2023-12-20 18:55:41,352 INFO misc.py line 119 131400] Train: [67/100][19/800] Data 0.006 (0.004) Batch 0.332 (0.333) Remain 02:30:45 loss: 0.2409 Lr: 0.00171 [2023-12-20 18:55:41,668 INFO misc.py line 119 131400] Train: [67/100][20/800] Data 0.004 (0.004) Batch 0.313 (0.332) Remain 02:30:13 loss: 0.3362 Lr: 0.00171 [2023-12-20 18:55:41,996 INFO misc.py line 119 131400] Train: [67/100][21/800] Data 0.008 (0.004) Batch 0.331 (0.332) Remain 02:30:11 loss: 0.2959 Lr: 0.00171 [2023-12-20 18:55:42,333 INFO misc.py line 119 131400] Train: [67/100][22/800] Data 0.005 (0.004) Batch 0.338 (0.332) Remain 02:30:20 loss: 0.1469 Lr: 0.00171 [2023-12-20 18:55:42,670 INFO misc.py line 119 131400] Train: [67/100][23/800] Data 0.004 (0.004) Batch 0.335 (0.332) Remain 02:30:24 loss: 0.2033 Lr: 0.00171 [2023-12-20 18:55:42,999 INFO misc.py line 119 131400] Train: [67/100][24/800] Data 0.006 (0.004) Batch 0.331 (0.332) Remain 02:30:22 loss: 0.4044 Lr: 0.00170 [2023-12-20 18:55:43,338 INFO misc.py line 119 131400] Train: [67/100][25/800] Data 0.004 (0.004) Batch 0.339 (0.332) Remain 02:30:31 loss: 0.5191 Lr: 0.00170 [2023-12-20 18:55:43,681 INFO misc.py line 119 131400] Train: [67/100][26/800] Data 0.003 (0.004) Batch 0.335 (0.332) Remain 02:30:34 loss: 0.2713 Lr: 0.00170 [2023-12-20 18:55:43,999 INFO misc.py line 119 131400] Train: [67/100][27/800] Data 0.011 (0.005) Batch 0.326 (0.332) Remain 02:30:26 loss: 0.2679 Lr: 0.00170 [2023-12-20 18:55:44,320 INFO misc.py line 119 131400] Train: [67/100][28/800] Data 0.003 (0.005) Batch 0.321 (0.332) Remain 02:30:13 loss: 0.4905 Lr: 0.00170 [2023-12-20 18:55:44,657 INFO misc.py line 119 131400] Train: [67/100][29/800] Data 0.003 (0.005) Batch 0.337 (0.332) Remain 02:30:18 loss: 0.4873 Lr: 0.00170 [2023-12-20 18:55:44,995 INFO misc.py line 119 131400] Train: [67/100][30/800] Data 0.004 (0.005) Batch 0.337 (0.332) Remain 02:30:23 loss: 0.1667 Lr: 0.00170 [2023-12-20 18:55:45,362 INFO misc.py line 119 131400] Train: [67/100][31/800] Data 0.006 (0.005) Batch 0.367 (0.333) Remain 02:30:57 loss: 0.4902 Lr: 0.00170 [2023-12-20 18:55:45,708 INFO misc.py line 119 131400] Train: [67/100][32/800] Data 0.006 (0.005) Batch 0.347 (0.334) Remain 02:31:09 loss: 0.2408 Lr: 0.00170 [2023-12-20 18:55:46,044 INFO misc.py line 119 131400] Train: [67/100][33/800] Data 0.003 (0.005) Batch 0.336 (0.334) Remain 02:31:11 loss: 0.1866 Lr: 0.00170 [2023-12-20 18:55:46,401 INFO misc.py line 119 131400] Train: [67/100][34/800] Data 0.003 (0.004) Batch 0.357 (0.335) Remain 02:31:31 loss: 0.3271 Lr: 0.00170 [2023-12-20 18:55:46,742 INFO misc.py line 119 131400] Train: [67/100][35/800] Data 0.003 (0.004) Batch 0.341 (0.335) Remain 02:31:35 loss: 0.2768 Lr: 0.00170 [2023-12-20 18:55:47,068 INFO misc.py line 119 131400] Train: [67/100][36/800] Data 0.003 (0.004) Batch 0.326 (0.335) Remain 02:31:27 loss: 0.2670 Lr: 0.00170 [2023-12-20 18:55:47,384 INFO misc.py line 119 131400] Train: [67/100][37/800] Data 0.004 (0.004) Batch 0.316 (0.334) Remain 02:31:12 loss: 0.3107 Lr: 0.00170 [2023-12-20 18:55:47,707 INFO misc.py line 119 131400] Train: [67/100][38/800] Data 0.005 (0.004) Batch 0.324 (0.334) Remain 02:31:04 loss: 0.3584 Lr: 0.00170 [2023-12-20 18:55:48,146 INFO misc.py line 119 131400] Train: [67/100][39/800] Data 0.004 (0.004) Batch 0.332 (0.334) Remain 02:31:02 loss: 0.2097 Lr: 0.00170 [2023-12-20 18:55:48,488 INFO misc.py line 119 131400] Train: [67/100][40/800] Data 0.111 (0.007) Batch 0.447 (0.337) Remain 02:32:25 loss: 0.5181 Lr: 0.00170 [2023-12-20 18:55:48,855 INFO misc.py line 119 131400] Train: [67/100][41/800] Data 0.006 (0.007) Batch 0.367 (0.338) Remain 02:32:46 loss: 0.1793 Lr: 0.00170 [2023-12-20 18:55:49,204 INFO misc.py line 119 131400] Train: [67/100][42/800] Data 0.008 (0.007) Batch 0.346 (0.338) Remain 02:32:52 loss: 0.1788 Lr: 0.00170 [2023-12-20 18:55:49,519 INFO misc.py line 119 131400] Train: [67/100][43/800] Data 0.009 (0.007) Batch 0.320 (0.337) Remain 02:32:40 loss: 0.2120 Lr: 0.00170 [2023-12-20 18:55:49,831 INFO misc.py line 119 131400] Train: [67/100][44/800] Data 0.003 (0.007) Batch 0.311 (0.337) Remain 02:32:22 loss: 0.3700 Lr: 0.00170 [2023-12-20 18:55:50,142 INFO misc.py line 119 131400] Train: [67/100][45/800] Data 0.005 (0.007) Batch 0.311 (0.336) Remain 02:32:05 loss: 0.2615 Lr: 0.00170 [2023-12-20 18:55:50,497 INFO misc.py line 119 131400] Train: [67/100][46/800] Data 0.005 (0.007) Batch 0.347 (0.336) Remain 02:32:12 loss: 0.1864 Lr: 0.00170 [2023-12-20 18:55:50,868 INFO misc.py line 119 131400] Train: [67/100][47/800] Data 0.014 (0.007) Batch 0.380 (0.337) Remain 02:32:38 loss: 0.2846 Lr: 0.00170 [2023-12-20 18:55:51,179 INFO misc.py line 119 131400] Train: [67/100][48/800] Data 0.003 (0.007) Batch 0.309 (0.337) Remain 02:32:21 loss: 0.3660 Lr: 0.00170 [2023-12-20 18:55:51,506 INFO misc.py line 119 131400] Train: [67/100][49/800] Data 0.005 (0.007) Batch 0.328 (0.336) Remain 02:32:16 loss: 0.3946 Lr: 0.00170 [2023-12-20 18:55:51,831 INFO misc.py line 119 131400] Train: [67/100][50/800] Data 0.004 (0.007) Batch 0.325 (0.336) Remain 02:32:09 loss: 0.1932 Lr: 0.00170 [2023-12-20 18:55:52,132 INFO misc.py line 119 131400] Train: [67/100][51/800] Data 0.004 (0.007) Batch 0.302 (0.336) Remain 02:31:49 loss: 0.1906 Lr: 0.00170 [2023-12-20 18:55:52,446 INFO misc.py line 119 131400] Train: [67/100][52/800] Data 0.003 (0.007) Batch 0.308 (0.335) Remain 02:31:33 loss: 0.2030 Lr: 0.00170 [2023-12-20 18:55:52,763 INFO misc.py line 119 131400] Train: [67/100][53/800] Data 0.008 (0.007) Batch 0.323 (0.335) Remain 02:31:27 loss: 0.1646 Lr: 0.00170 [2023-12-20 18:55:53,122 INFO misc.py line 119 131400] Train: [67/100][54/800] Data 0.003 (0.007) Batch 0.358 (0.335) Remain 02:31:38 loss: 0.2682 Lr: 0.00170 [2023-12-20 18:55:53,450 INFO misc.py line 119 131400] Train: [67/100][55/800] Data 0.004 (0.007) Batch 0.325 (0.335) Remain 02:31:33 loss: 0.1806 Lr: 0.00170 [2023-12-20 18:55:53,762 INFO misc.py line 119 131400] Train: [67/100][56/800] Data 0.007 (0.007) Batch 0.315 (0.335) Remain 02:31:22 loss: 0.4038 Lr: 0.00170 [2023-12-20 18:55:54,052 INFO misc.py line 119 131400] Train: [67/100][57/800] Data 0.003 (0.007) Batch 0.290 (0.334) Remain 02:31:00 loss: 0.4696 Lr: 0.00170 [2023-12-20 18:55:54,406 INFO misc.py line 119 131400] Train: [67/100][58/800] Data 0.003 (0.007) Batch 0.354 (0.334) Remain 02:31:09 loss: 0.2072 Lr: 0.00170 [2023-12-20 18:55:54,747 INFO misc.py line 119 131400] Train: [67/100][59/800] Data 0.004 (0.007) Batch 0.341 (0.334) Remain 02:31:13 loss: 0.3154 Lr: 0.00170 [2023-12-20 18:55:55,094 INFO misc.py line 119 131400] Train: [67/100][60/800] Data 0.003 (0.007) Batch 0.346 (0.334) Remain 02:31:18 loss: 0.4036 Lr: 0.00170 [2023-12-20 18:55:55,405 INFO misc.py line 119 131400] Train: [67/100][61/800] Data 0.004 (0.006) Batch 0.312 (0.334) Remain 02:31:07 loss: 0.2575 Lr: 0.00170 [2023-12-20 18:55:55,722 INFO misc.py line 119 131400] Train: [67/100][62/800] Data 0.004 (0.006) Batch 0.315 (0.334) Remain 02:30:58 loss: 0.2153 Lr: 0.00170 [2023-12-20 18:55:56,005 INFO misc.py line 119 131400] Train: [67/100][63/800] Data 0.006 (0.006) Batch 0.285 (0.333) Remain 02:30:35 loss: 0.2922 Lr: 0.00170 [2023-12-20 18:55:56,334 INFO misc.py line 119 131400] Train: [67/100][64/800] Data 0.004 (0.006) Batch 0.329 (0.333) Remain 02:30:33 loss: 0.2684 Lr: 0.00170 [2023-12-20 18:55:56,684 INFO misc.py line 119 131400] Train: [67/100][65/800] Data 0.005 (0.006) Batch 0.350 (0.333) Remain 02:30:40 loss: 0.3266 Lr: 0.00170 [2023-12-20 18:55:57,056 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [67/100][782/800] Data 0.003 (0.004) Batch 0.347 (0.334) Remain 02:27:12 loss: 0.2070 Lr: 0.00162 [2023-12-20 18:59:56,820 INFO misc.py line 119 131400] Train: [67/100][783/800] Data 0.006 (0.004) Batch 0.347 (0.334) Remain 02:27:12 loss: 0.1296 Lr: 0.00162 [2023-12-20 18:59:57,116 INFO misc.py line 119 131400] Train: [67/100][784/800] Data 0.003 (0.004) Batch 0.297 (0.334) Remain 02:27:10 loss: 0.1742 Lr: 0.00162 [2023-12-20 18:59:57,424 INFO misc.py line 119 131400] Train: [67/100][785/800] Data 0.003 (0.004) Batch 0.308 (0.334) Remain 02:27:09 loss: 0.3730 Lr: 0.00162 [2023-12-20 18:59:57,753 INFO misc.py line 119 131400] Train: [67/100][786/800] Data 0.003 (0.004) Batch 0.327 (0.334) Remain 02:27:09 loss: 0.2567 Lr: 0.00162 [2023-12-20 18:59:58,098 INFO misc.py line 119 131400] Train: [67/100][787/800] Data 0.005 (0.004) Batch 0.346 (0.334) Remain 02:27:09 loss: 0.3359 Lr: 0.00162 [2023-12-20 18:59:58,434 INFO misc.py line 119 131400] Train: 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Batch 0.295 (0.334) Remain 02:27:02 loss: 0.2886 Lr: 0.00162 [2023-12-20 19:00:00,621 INFO misc.py line 119 131400] Train: [67/100][795/800] Data 0.003 (0.004) Batch 0.298 (0.334) Remain 02:27:01 loss: 0.3613 Lr: 0.00162 [2023-12-20 19:00:00,917 INFO misc.py line 119 131400] Train: [67/100][796/800] Data 0.003 (0.004) Batch 0.296 (0.334) Remain 02:26:59 loss: 0.1805 Lr: 0.00162 [2023-12-20 19:00:01,220 INFO misc.py line 119 131400] Train: [67/100][797/800] Data 0.003 (0.004) Batch 0.303 (0.334) Remain 02:26:58 loss: 0.2157 Lr: 0.00162 [2023-12-20 19:00:01,534 INFO misc.py line 119 131400] Train: [67/100][798/800] Data 0.004 (0.004) Batch 0.315 (0.334) Remain 02:26:57 loss: 0.4636 Lr: 0.00162 [2023-12-20 19:00:01,880 INFO misc.py line 119 131400] Train: [67/100][799/800] Data 0.003 (0.004) Batch 0.346 (0.334) Remain 02:26:57 loss: 0.2554 Lr: 0.00162 [2023-12-20 19:00:02,206 INFO misc.py line 119 131400] Train: [67/100][800/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 02:26:56 loss: 0.2641 Lr: 0.00162 [2023-12-20 19:00:02,211 INFO misc.py line 136 131400] Train result: loss: 0.2810 [2023-12-20 19:00:02,213 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:00:25,228 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1110 [2023-12-20 19:00:25,835 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1875 [2023-12-20 19:00:25,975 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5824 [2023-12-20 19:00:26,099 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3850 [2023-12-20 19:00:26,219 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1854 [2023-12-20 19:00:26,327 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2363 [2023-12-20 19:00:26,427 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1363 [2023-12-20 19:00:26,540 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.7949 [2023-12-20 19:00:26,630 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3119 [2023-12-20 19:00:26,714 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4027 [2023-12-20 19:00:26,807 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.7684 [2023-12-20 19:00:26,942 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3486 [2023-12-20 19:00:27,066 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2075 [2023-12-20 19:00:27,224 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2256 [2023-12-20 19:00:27,318 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1481 [2023-12-20 19:00:27,459 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6874 [2023-12-20 19:00:27,583 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3802 [2023-12-20 19:00:27,692 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2427 [2023-12-20 19:00:27,806 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1342 [2023-12-20 19:00:27,880 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3682 [2023-12-20 19:00:27,990 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.4056 [2023-12-20 19:00:28,151 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1226 [2023-12-20 19:00:28,276 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9424 [2023-12-20 19:00:28,422 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2367 [2023-12-20 19:00:28,576 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2159 [2023-12-20 19:00:28,663 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7855 [2023-12-20 19:00:28,830 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6271 [2023-12-20 19:00:28,954 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4378 [2023-12-20 19:00:29,048 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5422 [2023-12-20 19:00:29,200 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7974 [2023-12-20 19:00:29,309 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5762 [2023-12-20 19:00:29,429 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4562 [2023-12-20 19:00:29,521 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1202 [2023-12-20 19:00:29,601 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1823 [2023-12-20 19:00:29,701 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9038 [2023-12-20 19:00:29,795 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4781 [2023-12-20 19:00:29,926 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0428 [2023-12-20 19:00:30,038 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1183 [2023-12-20 19:00:30,120 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7520 [2023-12-20 19:00:30,262 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2638 [2023-12-20 19:00:30,409 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0262 [2023-12-20 19:00:30,510 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0750 [2023-12-20 19:00:30,633 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2762 [2023-12-20 19:00:30,779 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8789 [2023-12-20 19:00:30,905 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.4116 [2023-12-20 19:00:31,014 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4883 [2023-12-20 19:00:31,193 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3233 [2023-12-20 19:00:31,291 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3238 [2023-12-20 19:00:31,437 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4228 [2023-12-20 19:00:31,530 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2013 [2023-12-20 19:00:31,618 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4751 [2023-12-20 19:00:31,732 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.7489 [2023-12-20 19:00:31,880 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7824 [2023-12-20 19:00:32,015 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3352 [2023-12-20 19:00:32,128 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6090 [2023-12-20 19:00:32,216 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6160 [2023-12-20 19:00:32,320 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3432 [2023-12-20 19:00:32,490 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2033 [2023-12-20 19:00:32,594 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4061 [2023-12-20 19:00:32,690 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1339 [2023-12-20 19:00:32,788 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3881 [2023-12-20 19:00:32,881 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3375 [2023-12-20 19:00:32,971 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7912 [2023-12-20 19:00:33,073 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7387 [2023-12-20 19:00:33,207 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5452 [2023-12-20 19:00:33,290 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2109 [2023-12-20 19:00:33,389 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3810 [2023-12-20 19:00:33,488 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0260 [2023-12-20 19:00:33,573 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2849 [2023-12-20 19:00:33,659 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0170 [2023-12-20 19:00:33,755 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8885 [2023-12-20 19:00:33,844 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7299 [2023-12-20 19:00:33,978 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0949 [2023-12-20 19:00:34,072 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5604 [2023-12-20 19:00:34,188 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6357 [2023-12-20 19:00:34,290 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5799 [2023-12-20 19:00:34,376 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3379 [2023-12-20 19:00:34,531 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3635 [2023-12-20 19:00:35,745 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7566/0.8427/0.9144. [2023-12-20 19:00:35,745 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8659/0.9340 [2023-12-20 19:00:35,745 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9649/0.9855 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6810/0.7993 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8298/0.8995 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9107/0.9533 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8133/0.8938 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7477/0.8911 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.6943/0.8254 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7049/0.8472 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8028/0.9236 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4111/0.5167 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7096/0.8359 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7193/0.8292 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7563/0.8622 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6334/0.7048 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7283/0.7890 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9512/0.9823 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6913/0.7603 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8892/0.9241 [2023-12-20 19:00:35,746 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6279/0.6970 [2023-12-20 19:00:35,747 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:00:35,748 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7566 [2023-12-20 19:00:35,748 INFO misc.py line 165 131400] Currently Best mIoU: 0.7566 [2023-12-20 19:00:35,748 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:00:42,479 INFO misc.py line 119 131400] Train: [68/100][1/800] Data 0.977 (0.977) Batch 1.376 (1.376) Remain 10:05:22 loss: 0.4995 Lr: 0.00162 [2023-12-20 19:00:42,835 INFO misc.py line 119 131400] Train: [68/100][2/800] Data 0.005 (0.005) Batch 0.356 (0.356) Remain 02:36:39 loss: 0.3677 Lr: 0.00162 [2023-12-20 19:00:43,169 INFO misc.py line 119 131400] Train: [68/100][3/800] Data 0.004 (0.004) Batch 0.330 (0.330) Remain 02:25:01 loss: 0.2173 Lr: 0.00162 [2023-12-20 19:00:43,508 INFO misc.py line 119 131400] Train: [68/100][4/800] Data 0.009 (0.009) Batch 0.343 (0.343) Remain 02:31:01 loss: 0.3909 Lr: 0.00162 [2023-12-20 19:00:43,828 INFO misc.py line 119 131400] Train: [68/100][5/800] Data 0.005 (0.007) Batch 0.321 (0.332) Remain 02:26:07 loss: 0.1704 Lr: 0.00162 [2023-12-20 19:00:44,201 INFO misc.py line 119 131400] Train: [68/100][6/800] Data 0.026 (0.013) Batch 0.373 (0.346) Remain 02:32:03 loss: 0.3777 Lr: 0.00162 [2023-12-20 19:00:44,520 INFO misc.py line 119 131400] Train: [68/100][7/800] Data 0.004 (0.011) Batch 0.320 (0.339) Remain 02:29:12 loss: 0.2279 Lr: 0.00162 [2023-12-20 19:00:44,866 INFO misc.py line 119 131400] Train: [68/100][8/800] Data 0.003 (0.009) Batch 0.345 (0.340) Remain 02:29:43 loss: 0.3662 Lr: 0.00162 [2023-12-20 19:00:45,152 INFO misc.py line 119 131400] Train: [68/100][9/800] Data 0.003 (0.008) Batch 0.286 (0.331) Remain 02:25:45 loss: 0.2248 Lr: 0.00162 [2023-12-20 19:00:45,478 INFO misc.py line 119 131400] Train: [68/100][10/800] Data 0.003 (0.008) Batch 0.319 (0.330) Remain 02:24:57 loss: 0.3202 Lr: 0.00162 [2023-12-20 19:00:45,846 INFO misc.py line 119 131400] Train: [68/100][11/800] Data 0.011 (0.008) Batch 0.374 (0.335) Remain 02:27:23 loss: 0.2579 Lr: 0.00162 [2023-12-20 19:00:46,155 INFO misc.py line 119 131400] Train: [68/100][12/800] Data 0.005 (0.008) Batch 0.310 (0.332) Remain 02:26:10 loss: 0.3434 Lr: 0.00162 [2023-12-20 19:00:46,485 INFO misc.py line 119 131400] Train: [68/100][13/800] Data 0.004 (0.007) Batch 0.320 (0.331) Remain 02:25:36 loss: 0.1465 Lr: 0.00162 [2023-12-20 19:00:46,811 INFO misc.py line 119 131400] Train: [68/100][14/800] Data 0.014 (0.008) Batch 0.336 (0.332) Remain 02:25:47 loss: 0.3141 Lr: 0.00162 [2023-12-20 19:00:47,152 INFO misc.py line 119 131400] Train: [68/100][15/800] Data 0.004 (0.007) Batch 0.341 (0.332) Remain 02:26:08 loss: 0.3174 Lr: 0.00162 [2023-12-20 19:00:47,496 INFO misc.py line 119 131400] Train: [68/100][16/800] Data 0.004 (0.007) Batch 0.339 (0.333) Remain 02:26:21 loss: 0.3638 Lr: 0.00162 [2023-12-20 19:00:47,812 INFO misc.py line 119 131400] Train: [68/100][17/800] Data 0.009 (0.007) Batch 0.321 (0.332) Remain 02:26:00 loss: 0.1155 Lr: 0.00162 [2023-12-20 19:00:48,146 INFO misc.py line 119 131400] Train: [68/100][18/800] Data 0.003 (0.007) Batch 0.334 (0.332) Remain 02:26:02 loss: 0.2528 Lr: 0.00162 [2023-12-20 19:00:48,499 INFO misc.py line 119 131400] Train: [68/100][19/800] Data 0.004 (0.007) Batch 0.353 (0.333) Remain 02:26:36 loss: 0.1632 Lr: 0.00162 [2023-12-20 19:00:48,832 INFO misc.py line 119 131400] Train: [68/100][20/800] Data 0.003 (0.007) Batch 0.333 (0.333) Remain 02:26:36 loss: 0.3419 Lr: 0.00162 [2023-12-20 19:00:49,179 INFO misc.py line 119 131400] Train: [68/100][21/800] Data 0.003 (0.007) Batch 0.346 (0.334) Remain 02:26:55 loss: 0.2225 Lr: 0.00162 [2023-12-20 19:00:49,509 INFO misc.py line 119 131400] Train: [68/100][22/800] Data 0.004 (0.006) Batch 0.330 (0.334) Remain 02:26:48 loss: 0.1975 Lr: 0.00162 [2023-12-20 19:00:49,853 INFO misc.py line 119 131400] Train: [68/100][23/800] Data 0.004 (0.006) Batch 0.345 (0.334) Remain 02:27:02 loss: 0.1589 Lr: 0.00162 [2023-12-20 19:00:50,167 INFO misc.py line 119 131400] Train: [68/100][24/800] Data 0.003 (0.006) Batch 0.314 (0.333) Remain 02:26:36 loss: 0.2717 Lr: 0.00162 [2023-12-20 19:00:50,511 INFO misc.py line 119 131400] Train: [68/100][25/800] Data 0.003 (0.006) Batch 0.339 (0.334) Remain 02:26:42 loss: 0.3911 Lr: 0.00162 [2023-12-20 19:00:50,846 INFO misc.py line 119 131400] Train: [68/100][26/800] Data 0.008 (0.006) Batch 0.339 (0.334) Remain 02:26:48 loss: 0.2437 Lr: 0.00162 [2023-12-20 19:00:51,190 INFO misc.py line 119 131400] Train: [68/100][27/800] Data 0.003 (0.006) Batch 0.344 (0.334) Remain 02:26:59 loss: 0.2715 Lr: 0.00162 [2023-12-20 19:00:51,524 INFO misc.py line 119 131400] Train: [68/100][28/800] Data 0.004 (0.006) Batch 0.328 (0.334) Remain 02:26:52 loss: 0.1661 Lr: 0.00162 [2023-12-20 19:00:51,844 INFO misc.py line 119 131400] Train: [68/100][29/800] Data 0.009 (0.006) Batch 0.327 (0.334) Remain 02:26:44 loss: 0.2576 Lr: 0.00162 [2023-12-20 19:00:52,139 INFO misc.py line 119 131400] Train: [68/100][30/800] Data 0.003 (0.006) Batch 0.295 (0.332) Remain 02:26:06 loss: 0.4225 Lr: 0.00162 [2023-12-20 19:00:52,448 INFO misc.py line 119 131400] Train: [68/100][31/800] Data 0.003 (0.006) Batch 0.308 (0.332) Remain 02:25:43 loss: 0.1891 Lr: 0.00162 [2023-12-20 19:00:52,773 INFO misc.py line 119 131400] Train: [68/100][32/800] Data 0.003 (0.006) Batch 0.326 (0.331) Remain 02:25:37 loss: 0.3177 Lr: 0.00162 [2023-12-20 19:00:53,072 INFO misc.py line 119 131400] Train: [68/100][33/800] Data 0.003 (0.006) Batch 0.299 (0.330) Remain 02:25:08 loss: 0.2710 Lr: 0.00162 [2023-12-20 19:00:53,377 INFO misc.py line 119 131400] Train: [68/100][34/800] Data 0.003 (0.006) Batch 0.304 (0.329) Remain 02:24:46 loss: 0.1859 Lr: 0.00162 [2023-12-20 19:00:53,726 INFO misc.py line 119 131400] Train: [68/100][35/800] Data 0.004 (0.005) Batch 0.350 (0.330) Remain 02:25:02 loss: 0.2684 Lr: 0.00161 [2023-12-20 19:00:54,061 INFO misc.py line 119 131400] Train: [68/100][36/800] Data 0.004 (0.005) Batch 0.335 (0.330) Remain 02:25:06 loss: 0.2110 Lr: 0.00161 [2023-12-20 19:00:54,396 INFO misc.py line 119 131400] Train: [68/100][37/800] Data 0.003 (0.005) Batch 0.336 (0.330) Remain 02:25:09 loss: 0.1937 Lr: 0.00161 [2023-12-20 19:00:54,700 INFO misc.py line 119 131400] Train: [68/100][38/800] Data 0.003 (0.005) Batch 0.303 (0.330) Remain 02:24:48 loss: 0.2512 Lr: 0.00161 [2023-12-20 19:00:55,026 INFO misc.py line 119 131400] Train: [68/100][39/800] Data 0.005 (0.005) Batch 0.326 (0.330) Remain 02:24:46 loss: 0.1854 Lr: 0.00161 [2023-12-20 19:00:55,381 INFO misc.py line 119 131400] Train: [68/100][40/800] Data 0.003 (0.005) Batch 0.354 (0.330) Remain 02:25:03 loss: 0.2433 Lr: 0.00161 [2023-12-20 19:00:55,743 INFO misc.py line 119 131400] Train: [68/100][41/800] Data 0.004 (0.005) Batch 0.362 (0.331) Remain 02:25:24 loss: 0.1734 Lr: 0.00161 [2023-12-20 19:00:56,056 INFO misc.py line 119 131400] Train: [68/100][42/800] Data 0.005 (0.005) Batch 0.314 (0.331) Remain 02:25:12 loss: 0.1951 Lr: 0.00161 [2023-12-20 19:00:56,386 INFO misc.py line 119 131400] Train: [68/100][43/800] Data 0.004 (0.005) Batch 0.331 (0.331) Remain 02:25:12 loss: 0.1904 Lr: 0.00161 [2023-12-20 19:00:56,686 INFO misc.py line 119 131400] Train: [68/100][44/800] Data 0.003 (0.005) Batch 0.299 (0.330) Remain 02:24:51 loss: 0.1934 Lr: 0.00161 [2023-12-20 19:00:57,061 INFO misc.py line 119 131400] Train: [68/100][45/800] Data 0.005 (0.005) Batch 0.374 (0.331) Remain 02:25:19 loss: 0.3025 Lr: 0.00161 [2023-12-20 19:00:57,440 INFO misc.py line 119 131400] Train: [68/100][46/800] Data 0.008 (0.005) Batch 0.381 (0.332) Remain 02:25:49 loss: 0.3235 Lr: 0.00161 [2023-12-20 19:00:57,785 INFO misc.py line 119 131400] Train: [68/100][47/800] Data 0.004 (0.005) Batch 0.345 (0.332) Remain 02:25:57 loss: 0.1816 Lr: 0.00161 [2023-12-20 19:00:58,137 INFO misc.py line 119 131400] Train: [68/100][48/800] Data 0.004 (0.005) Batch 0.352 (0.333) Remain 02:26:08 loss: 0.2794 Lr: 0.00161 [2023-12-20 19:00:58,486 INFO misc.py line 119 131400] Train: [68/100][49/800] Data 0.004 (0.005) Batch 0.348 (0.333) Remain 02:26:16 loss: 0.2183 Lr: 0.00161 [2023-12-20 19:00:58,857 INFO misc.py line 119 131400] Train: [68/100][50/800] Data 0.005 (0.005) Batch 0.371 (0.334) Remain 02:26:37 loss: 0.3128 Lr: 0.00161 [2023-12-20 19:00:59,185 INFO misc.py line 119 131400] Train: [68/100][51/800] Data 0.005 (0.005) Batch 0.328 (0.334) Remain 02:26:34 loss: 0.1742 Lr: 0.00161 [2023-12-20 19:00:59,542 INFO misc.py line 119 131400] Train: [68/100][52/800] Data 0.004 (0.005) Batch 0.357 (0.334) Remain 02:26:46 loss: 0.3540 Lr: 0.00161 [2023-12-20 19:00:59,897 INFO misc.py line 119 131400] 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[2023-12-20 19:05:06,963 INFO misc.py line 119 131400] Train: [68/100][788/800] Data 0.003 (0.005) Batch 0.342 (0.336) Remain 02:23:26 loss: 0.2325 Lr: 0.00153 [2023-12-20 19:05:07,335 INFO misc.py line 119 131400] Train: [68/100][789/800] Data 0.004 (0.005) Batch 0.372 (0.336) Remain 02:23:27 loss: 0.2083 Lr: 0.00153 [2023-12-20 19:05:07,646 INFO misc.py line 119 131400] Train: [68/100][790/800] Data 0.004 (0.005) Batch 0.307 (0.336) Remain 02:23:26 loss: 0.1959 Lr: 0.00153 [2023-12-20 19:05:07,930 INFO misc.py line 119 131400] Train: [68/100][791/800] Data 0.008 (0.005) Batch 0.289 (0.336) Remain 02:23:24 loss: 0.4931 Lr: 0.00153 [2023-12-20 19:05:08,209 INFO misc.py line 119 131400] Train: [68/100][792/800] Data 0.003 (0.005) Batch 0.279 (0.336) Remain 02:23:22 loss: 0.2090 Lr: 0.00153 [2023-12-20 19:05:08,478 INFO misc.py line 119 131400] Train: [68/100][793/800] Data 0.003 (0.005) Batch 0.270 (0.336) Remain 02:23:19 loss: 0.2469 Lr: 0.00153 [2023-12-20 19:05:08,803 INFO misc.py line 119 131400] Train: [68/100][794/800] Data 0.002 (0.005) Batch 0.320 (0.336) Remain 02:23:19 loss: 0.1878 Lr: 0.00153 [2023-12-20 19:05:09,070 INFO misc.py line 119 131400] Train: [68/100][795/800] Data 0.007 (0.005) Batch 0.272 (0.336) Remain 02:23:16 loss: 0.2579 Lr: 0.00153 [2023-12-20 19:05:09,387 INFO misc.py line 119 131400] Train: [68/100][796/800] Data 0.003 (0.005) Batch 0.316 (0.336) Remain 02:23:15 loss: 0.1559 Lr: 0.00153 [2023-12-20 19:05:09,678 INFO misc.py line 119 131400] Train: [68/100][797/800] Data 0.003 (0.005) Batch 0.292 (0.336) Remain 02:23:13 loss: 0.2773 Lr: 0.00153 [2023-12-20 19:05:10,006 INFO misc.py line 119 131400] Train: [68/100][798/800] Data 0.003 (0.005) Batch 0.328 (0.336) Remain 02:23:13 loss: 0.2177 Lr: 0.00153 [2023-12-20 19:05:10,353 INFO misc.py line 119 131400] Train: [68/100][799/800] Data 0.003 (0.005) Batch 0.347 (0.336) Remain 02:23:13 loss: 0.3251 Lr: 0.00153 [2023-12-20 19:05:10,682 INFO misc.py line 119 131400] Train: [68/100][800/800] Data 0.003 (0.005) Batch 0.330 (0.336) Remain 02:23:12 loss: 0.1747 Lr: 0.00153 [2023-12-20 19:05:10,683 INFO misc.py line 136 131400] Train result: loss: 0.2699 [2023-12-20 19:05:10,683 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:05:32,391 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.0995 [2023-12-20 19:05:32,479 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1820 [2023-12-20 19:05:32,569 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3866 [2023-12-20 19:05:32,758 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.1726 [2023-12-20 19:05:32,882 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4285 [2023-12-20 19:05:32,999 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.4604 [2023-12-20 19:05:33,096 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0180 [2023-12-20 19:05:33,201 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9088 [2023-12-20 19:05:33,284 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2820 [2023-12-20 19:05:33,369 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3175 [2023-12-20 19:05:33,463 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5878 [2023-12-20 19:05:33,600 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2866 [2023-12-20 19:05:33,719 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3764 [2023-12-20 19:05:33,872 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2221 [2023-12-20 19:05:33,971 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1554 [2023-12-20 19:05:34,107 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8130 [2023-12-20 19:05:34,221 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3347 [2023-12-20 19:05:34,335 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.2355 [2023-12-20 19:05:34,451 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1351 [2023-12-20 19:05:34,531 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3869 [2023-12-20 19:05:34,643 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2496 [2023-12-20 19:05:34,798 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1741 [2023-12-20 19:05:34,918 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6727 [2023-12-20 19:05:35,060 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2290 [2023-12-20 19:05:35,204 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2029 [2023-12-20 19:05:35,287 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4406 [2023-12-20 19:05:35,443 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6440 [2023-12-20 19:05:35,568 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5274 [2023-12-20 19:05:35,669 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5071 [2023-12-20 19:05:35,814 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2193 [2023-12-20 19:05:35,916 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6656 [2023-12-20 19:05:36,036 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3776 [2023-12-20 19:05:36,120 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1168 [2023-12-20 19:05:36,188 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1765 [2023-12-20 19:05:36,282 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7697 [2023-12-20 19:05:36,381 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2890 [2023-12-20 19:05:36,519 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9629 [2023-12-20 19:05:36,636 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1097 [2023-12-20 19:05:36,714 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4548 [2023-12-20 19:05:36,859 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3543 [2023-12-20 19:05:37,018 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0268 [2023-12-20 19:05:37,115 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0656 [2023-12-20 19:05:37,235 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2233 [2023-12-20 19:05:37,377 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9661 [2023-12-20 19:05:37,500 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.8774 [2023-12-20 19:05:37,611 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2665 [2023-12-20 19:05:37,780 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3721 [2023-12-20 19:05:37,886 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5061 [2023-12-20 19:05:38,036 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.1699 [2023-12-20 19:05:38,127 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0690 [2023-12-20 19:05:38,221 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.8005 [2023-12-20 19:05:38,342 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2475 [2023-12-20 19:05:38,501 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7327 [2023-12-20 19:05:38,641 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3078 [2023-12-20 19:05:38,755 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2568 [2023-12-20 19:05:38,852 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.3790 [2023-12-20 19:05:38,959 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3497 [2023-12-20 19:05:39,132 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1941 [2023-12-20 19:05:39,240 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5848 [2023-12-20 19:05:39,339 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3141 [2023-12-20 19:05:39,436 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4428 [2023-12-20 19:05:39,538 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2815 [2023-12-20 19:05:39,629 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3794 [2023-12-20 19:05:39,749 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5083 [2023-12-20 19:05:39,878 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3766 [2023-12-20 19:05:39,970 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2633 [2023-12-20 19:05:40,069 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6608 [2023-12-20 19:05:40,166 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0330 [2023-12-20 19:05:40,254 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3998 [2023-12-20 19:05:40,340 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0147 [2023-12-20 19:05:40,437 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5023 [2023-12-20 19:05:40,528 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6203 [2023-12-20 19:05:40,662 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1686 [2023-12-20 19:05:40,764 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5913 [2023-12-20 19:05:40,889 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5461 [2023-12-20 19:05:41,003 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5844 [2023-12-20 19:05:41,089 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5682 [2023-12-20 19:05:41,260 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1371 [2023-12-20 19:05:42,624 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7580/0.8458/0.9184. [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8731/0.9480 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9666/0.9857 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7140/0.8059 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8265/0.8844 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9144/0.9533 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8140/0.9459 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7688/0.8246 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7038/0.8232 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7082/0.8398 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8256/0.9511 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3939/0.5193 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6939/0.8334 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6833/0.8714 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7634/0.8392 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6680/0.7130 [2023-12-20 19:05:42,624 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6978/0.7480 [2023-12-20 19:05:42,625 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9245/0.9760 [2023-12-20 19:05:42,625 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6821/0.8155 [2023-12-20 19:05:42,625 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8931/0.9284 [2023-12-20 19:05:42,625 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6457/0.7094 [2023-12-20 19:05:42,625 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:05:42,626 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7580 [2023-12-20 19:05:42,626 INFO misc.py line 165 131400] Currently Best mIoU: 0.7580 [2023-12-20 19:05:42,626 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:05:48,432 INFO misc.py line 119 131400] Train: [69/100][1/800] Data 0.799 (0.799) Batch 1.125 (1.125) Remain 07:59:48 loss: 0.4792 Lr: 0.00153 [2023-12-20 19:05:48,782 INFO misc.py line 119 131400] Train: [69/100][2/800] Data 0.027 (0.027) Batch 0.350 (0.350) Remain 02:29:18 loss: 0.2225 Lr: 0.00153 [2023-12-20 19:05:49,117 INFO misc.py line 119 131400] Train: [69/100][3/800] Data 0.003 (0.003) Batch 0.308 (0.308) Remain 02:11:36 loss: 0.2109 Lr: 0.00153 [2023-12-20 19:05:49,428 INFO misc.py line 119 131400] Train: [69/100][4/800] Data 0.030 (0.030) Batch 0.338 (0.338) Remain 02:24:01 loss: 0.2637 Lr: 0.00153 [2023-12-20 19:05:49,803 INFO misc.py line 119 131400] Train: [69/100][5/800] Data 0.004 (0.017) Batch 0.374 (0.356) Remain 02:31:48 loss: 0.2836 Lr: 0.00153 [2023-12-20 19:05:50,155 INFO misc.py line 119 131400] Train: [69/100][6/800] Data 0.005 (0.013) Batch 0.352 (0.355) Remain 02:31:19 loss: 0.3197 Lr: 0.00153 [2023-12-20 19:05:50,480 INFO misc.py line 119 131400] Train: [69/100][7/800] Data 0.004 (0.011) Batch 0.324 (0.347) Remain 02:28:03 loss: 0.1484 Lr: 0.00153 [2023-12-20 19:05:50,844 INFO misc.py line 119 131400] Train: [69/100][8/800] Data 0.007 (0.010) Batch 0.364 (0.350) Remain 02:29:27 loss: 0.1444 Lr: 0.00153 [2023-12-20 19:05:51,194 INFO misc.py line 119 131400] Train: [69/100][9/800] Data 0.005 (0.009) Batch 0.351 (0.351) Remain 02:29:30 loss: 0.3816 Lr: 0.00153 [2023-12-20 19:05:51,535 INFO misc.py line 119 131400] Train: [69/100][10/800] Data 0.005 (0.009) Batch 0.342 (0.349) Remain 02:28:57 loss: 0.2205 Lr: 0.00153 [2023-12-20 19:05:51,848 INFO misc.py line 119 131400] Train: [69/100][11/800] Data 0.004 (0.008) Batch 0.312 (0.345) Remain 02:26:59 loss: 0.3451 Lr: 0.00153 [2023-12-20 19:05:52,179 INFO misc.py line 119 131400] Train: [69/100][12/800] Data 0.005 (0.008) Batch 0.331 (0.343) Remain 02:26:20 loss: 0.3320 Lr: 0.00153 [2023-12-20 19:05:52,502 INFO misc.py line 119 131400] Train: [69/100][13/800] Data 0.005 (0.007) Batch 0.316 (0.340) Remain 02:25:10 loss: 0.2719 Lr: 0.00153 [2023-12-20 19:05:52,808 INFO misc.py line 119 131400] Train: [69/100][14/800] Data 0.011 (0.008) Batch 0.312 (0.338) Remain 02:24:05 loss: 0.3913 Lr: 0.00153 [2023-12-20 19:05:53,148 INFO misc.py line 119 131400] Train: [69/100][15/800] Data 0.006 (0.008) Batch 0.341 (0.338) Remain 02:24:11 loss: 0.3475 Lr: 0.00153 [2023-12-20 19:05:53,442 INFO misc.py line 119 131400] Train: [69/100][16/800] Data 0.003 (0.007) Batch 0.294 (0.335) Remain 02:22:44 loss: 0.1589 Lr: 0.00153 [2023-12-20 19:05:53,800 INFO misc.py line 119 131400] Train: [69/100][17/800] Data 0.003 (0.007) Batch 0.357 (0.336) Remain 02:23:25 loss: 0.2792 Lr: 0.00153 [2023-12-20 19:05:54,148 INFO misc.py line 119 131400] Train: [69/100][18/800] Data 0.004 (0.007) Batch 0.348 (0.337) Remain 02:23:44 loss: 0.2727 Lr: 0.00153 [2023-12-20 19:05:54,454 INFO misc.py line 119 131400] Train: [69/100][19/800] Data 0.004 (0.007) Batch 0.306 (0.335) Remain 02:22:55 loss: 0.4863 Lr: 0.00153 [2023-12-20 19:05:54,797 INFO misc.py line 119 131400] Train: [69/100][20/800] Data 0.003 (0.006) Batch 0.343 (0.336) Remain 02:23:06 loss: 0.3665 Lr: 0.00153 [2023-12-20 19:05:55,099 INFO misc.py line 119 131400] Train: [69/100][21/800] Data 0.004 (0.006) Batch 0.302 (0.334) Remain 02:22:17 loss: 0.2790 Lr: 0.00153 [2023-12-20 19:05:55,450 INFO misc.py line 119 131400] Train: [69/100][22/800] Data 0.004 (0.006) Batch 0.352 (0.335) Remain 02:22:41 loss: 0.8303 Lr: 0.00153 [2023-12-20 19:05:55,821 INFO misc.py line 119 131400] Train: [69/100][23/800] Data 0.003 (0.006) Batch 0.370 (0.336) Remain 02:23:25 loss: 0.4463 Lr: 0.00153 [2023-12-20 19:05:56,187 INFO misc.py line 119 131400] Train: [69/100][24/800] Data 0.005 (0.006) Batch 0.368 (0.338) Remain 02:24:03 loss: 0.1527 Lr: 0.00153 [2023-12-20 19:05:56,508 INFO misc.py line 119 131400] Train: [69/100][25/800] Data 0.003 (0.006) Batch 0.319 (0.337) Remain 02:23:41 loss: 0.3771 Lr: 0.00153 [2023-12-20 19:05:56,845 INFO misc.py line 119 131400] Train: [69/100][26/800] Data 0.008 (0.006) Batch 0.339 (0.337) Remain 02:23:43 loss: 0.3710 Lr: 0.00153 [2023-12-20 19:05:57,201 INFO misc.py line 119 131400] Train: [69/100][27/800] Data 0.004 (0.006) Batch 0.355 (0.338) Remain 02:24:01 loss: 0.1569 Lr: 0.00153 [2023-12-20 19:05:57,561 INFO misc.py line 119 131400] Train: [69/100][28/800] Data 0.004 (0.006) Batch 0.360 (0.339) Remain 02:24:24 loss: 0.3934 Lr: 0.00153 [2023-12-20 19:05:57,908 INFO misc.py line 119 131400] Train: [69/100][29/800] Data 0.005 (0.006) Batch 0.346 (0.339) Remain 02:24:31 loss: 0.2651 Lr: 0.00153 [2023-12-20 19:05:58,240 INFO misc.py line 119 131400] Train: [69/100][30/800] Data 0.005 (0.006) Batch 0.333 (0.339) Remain 02:24:24 loss: 0.4728 Lr: 0.00153 [2023-12-20 19:05:58,622 INFO misc.py line 119 131400] Train: [69/100][31/800] Data 0.004 (0.006) Batch 0.382 (0.340) Remain 02:25:03 loss: 0.2521 Lr: 0.00153 [2023-12-20 19:05:58,966 INFO misc.py line 119 131400] Train: [69/100][32/800] Data 0.005 (0.006) Batch 0.344 (0.341) Remain 02:25:06 loss: 0.1977 Lr: 0.00153 [2023-12-20 19:05:59,329 INFO misc.py line 119 131400] Train: [69/100][33/800] Data 0.005 (0.005) Batch 0.363 (0.341) Remain 02:25:25 loss: 0.1944 Lr: 0.00153 [2023-12-20 19:05:59,677 INFO misc.py line 119 131400] Train: [69/100][34/800] Data 0.004 (0.005) Batch 0.347 (0.341) Remain 02:25:30 loss: 0.2115 Lr: 0.00153 [2023-12-20 19:06:00,026 INFO misc.py line 119 131400] Train: [69/100][35/800] Data 0.005 (0.005) Batch 0.350 (0.342) Remain 02:25:36 loss: 0.3613 Lr: 0.00153 [2023-12-20 19:06:00,387 INFO misc.py line 119 131400] Train: [69/100][36/800] Data 0.005 (0.005) Batch 0.360 (0.342) Remain 02:25:50 loss: 0.2586 Lr: 0.00153 [2023-12-20 19:06:00,752 INFO misc.py line 119 131400] Train: [69/100][37/800] Data 0.005 (0.005) Batch 0.367 (0.343) Remain 02:26:08 loss: 0.1712 Lr: 0.00153 [2023-12-20 19:06:01,078 INFO misc.py line 119 131400] Train: [69/100][38/800] Data 0.004 (0.005) Batch 0.325 (0.342) Remain 02:25:54 loss: 0.1647 Lr: 0.00153 [2023-12-20 19:06:01,381 INFO misc.py line 119 131400] Train: [69/100][39/800] Data 0.004 (0.005) Batch 0.304 (0.341) Remain 02:25:26 loss: 0.2211 Lr: 0.00153 [2023-12-20 19:06:01,718 INFO misc.py line 119 131400] Train: [69/100][40/800] Data 0.004 (0.005) Batch 0.336 (0.341) Remain 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Data 0.003 (0.005) Batch 0.316 (0.338) Remain 02:23:27 loss: 0.4843 Lr: 0.00152 [2023-12-20 19:06:23,242 INFO misc.py line 119 131400] Train: [69/100][104/800] Data 0.004 (0.005) Batch 0.392 (0.338) Remain 02:23:40 loss: 0.2946 Lr: 0.00152 [2023-12-20 19:06:23,573 INFO misc.py line 119 131400] Train: [69/100][105/800] Data 0.004 (0.005) Batch 0.331 (0.338) Remain 02:23:38 loss: 0.3807 Lr: 0.00152 [2023-12-20 19:06:23,926 INFO misc.py line 119 131400] Train: [69/100][106/800] Data 0.004 (0.005) Batch 0.352 (0.338) Remain 02:23:42 loss: 0.3045 Lr: 0.00152 [2023-12-20 19:06:24,265 INFO misc.py line 119 131400] Train: [69/100][107/800] Data 0.004 (0.005) Batch 0.339 (0.338) Remain 02:23:42 loss: 0.2445 Lr: 0.00152 [2023-12-20 19:06:24,603 INFO misc.py line 119 131400] Train: [69/100][108/800] Data 0.004 (0.005) Batch 0.338 (0.338) Remain 02:23:41 loss: 0.4936 Lr: 0.00152 [2023-12-20 19:06:24,903 INFO misc.py line 119 131400] Train: [69/100][109/800] Data 0.003 (0.005) Batch 0.301 (0.338) 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0.003 (0.005) Batch 0.337 (0.333) Remain 02:17:49 loss: 0.1442 Lr: 0.00145 [2023-12-20 19:10:06,629 INFO misc.py line 119 131400] Train: [69/100][776/800] Data 0.005 (0.005) Batch 0.374 (0.333) Remain 02:17:50 loss: 0.2181 Lr: 0.00145 [2023-12-20 19:10:06,988 INFO misc.py line 119 131400] Train: [69/100][777/800] Data 0.005 (0.005) Batch 0.360 (0.333) Remain 02:17:51 loss: 0.4498 Lr: 0.00145 [2023-12-20 19:10:07,334 INFO misc.py line 119 131400] Train: [69/100][778/800] Data 0.004 (0.005) Batch 0.346 (0.333) Remain 02:17:51 loss: 0.2600 Lr: 0.00145 [2023-12-20 19:10:07,693 INFO misc.py line 119 131400] Train: [69/100][779/800] Data 0.004 (0.005) Batch 0.359 (0.333) Remain 02:17:51 loss: 0.2769 Lr: 0.00145 [2023-12-20 19:10:08,032 INFO misc.py line 119 131400] Train: [69/100][780/800] Data 0.005 (0.005) Batch 0.339 (0.333) Remain 02:17:51 loss: 0.1808 Lr: 0.00145 [2023-12-20 19:10:08,347 INFO misc.py line 119 131400] Train: [69/100][781/800] Data 0.004 (0.005) Batch 0.315 (0.333) Remain 02:17:50 loss: 0.1739 Lr: 0.00145 [2023-12-20 19:10:08,706 INFO misc.py line 119 131400] Train: [69/100][782/800] Data 0.004 (0.005) Batch 0.360 (0.333) Remain 02:17:51 loss: 0.3948 Lr: 0.00145 [2023-12-20 19:10:09,100 INFO misc.py line 119 131400] Train: [69/100][783/800] Data 0.004 (0.005) Batch 0.393 (0.333) Remain 02:17:52 loss: 0.2795 Lr: 0.00145 [2023-12-20 19:10:09,444 INFO misc.py line 119 131400] Train: [69/100][784/800] Data 0.004 (0.005) Batch 0.344 (0.333) Remain 02:17:52 loss: 0.1698 Lr: 0.00145 [2023-12-20 19:10:09,780 INFO misc.py line 119 131400] Train: [69/100][785/800] Data 0.003 (0.005) Batch 0.335 (0.333) Remain 02:17:52 loss: 0.2716 Lr: 0.00145 [2023-12-20 19:10:10,104 INFO misc.py line 119 131400] Train: [69/100][786/800] Data 0.004 (0.005) Batch 0.324 (0.333) Remain 02:17:51 loss: 0.1583 Lr: 0.00145 [2023-12-20 19:10:10,455 INFO misc.py line 119 131400] Train: [69/100][787/800] Data 0.004 (0.005) Batch 0.351 (0.333) Remain 02:17:51 loss: 0.3125 Lr: 0.00145 [2023-12-20 19:10:10,760 INFO misc.py line 119 131400] Train: [69/100][788/800] Data 0.004 (0.005) Batch 0.303 (0.333) Remain 02:17:50 loss: 0.3655 Lr: 0.00145 [2023-12-20 19:10:11,109 INFO misc.py line 119 131400] Train: [69/100][789/800] Data 0.006 (0.005) Batch 0.351 (0.333) Remain 02:17:50 loss: 0.3574 Lr: 0.00145 [2023-12-20 19:10:11,423 INFO misc.py line 119 131400] Train: [69/100][790/800] Data 0.003 (0.005) Batch 0.314 (0.333) Remain 02:17:49 loss: 0.4805 Lr: 0.00145 [2023-12-20 19:10:11,756 INFO misc.py line 119 131400] Train: [69/100][791/800] Data 0.003 (0.005) Batch 0.333 (0.333) Remain 02:17:49 loss: 0.4121 Lr: 0.00145 [2023-12-20 19:10:12,095 INFO misc.py line 119 131400] Train: [69/100][792/800] Data 0.004 (0.005) Batch 0.340 (0.333) Remain 02:17:49 loss: 0.3063 Lr: 0.00145 [2023-12-20 19:10:12,423 INFO misc.py line 119 131400] Train: [69/100][793/800] Data 0.003 (0.005) Batch 0.327 (0.333) Remain 02:17:48 loss: 0.6652 Lr: 0.00145 [2023-12-20 19:10:12,743 INFO misc.py line 119 131400] Train: [69/100][794/800] Data 0.003 (0.005) Batch 0.320 (0.333) Remain 02:17:48 loss: 0.5059 Lr: 0.00145 [2023-12-20 19:10:13,063 INFO misc.py line 119 131400] Train: [69/100][795/800] Data 0.004 (0.005) Batch 0.319 (0.333) Remain 02:17:47 loss: 0.2675 Lr: 0.00145 [2023-12-20 19:10:13,415 INFO misc.py line 119 131400] Train: [69/100][796/800] Data 0.005 (0.005) Batch 0.353 (0.333) Remain 02:17:47 loss: 0.2846 Lr: 0.00145 [2023-12-20 19:10:13,782 INFO misc.py line 119 131400] Train: [69/100][797/800] Data 0.004 (0.005) Batch 0.367 (0.333) Remain 02:17:48 loss: 0.1137 Lr: 0.00145 [2023-12-20 19:10:14,089 INFO misc.py line 119 131400] Train: [69/100][798/800] Data 0.003 (0.005) Batch 0.307 (0.333) Remain 02:17:47 loss: 0.2341 Lr: 0.00145 [2023-12-20 19:10:14,404 INFO misc.py line 119 131400] Train: [69/100][799/800] Data 0.002 (0.005) Batch 0.315 (0.333) Remain 02:17:46 loss: 0.3713 Lr: 0.00145 [2023-12-20 19:10:14,691 INFO misc.py line 119 131400] Train: [69/100][800/800] Data 0.003 (0.005) Batch 0.287 (0.333) Remain 02:17:44 loss: 0.1825 Lr: 0.00145 [2023-12-20 19:10:14,691 INFO misc.py line 136 131400] Train result: loss: 0.2748 [2023-12-20 19:10:14,692 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:10:37,838 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1812 [2023-12-20 19:10:37,934 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1390 [2023-12-20 19:10:38,031 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4284 [2023-12-20 19:10:38,152 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3579 [2023-12-20 19:10:38,274 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2061 [2023-12-20 19:10:38,399 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.8809 [2023-12-20 19:10:38,505 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1957 [2023-12-20 19:10:38,618 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9670 [2023-12-20 19:10:38,708 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2307 [2023-12-20 19:10:38,808 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3056 [2023-12-20 19:10:38,926 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5637 [2023-12-20 19:10:39,071 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2577 [2023-12-20 19:10:39,192 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4038 [2023-12-20 19:10:39,349 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2177 [2023-12-20 19:10:39,448 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1498 [2023-12-20 19:10:39,589 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6028 [2023-12-20 19:10:39,701 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2638 [2023-12-20 19:10:39,826 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4433 [2023-12-20 19:10:39,941 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2074 [2023-12-20 19:10:40,023 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3056 [2023-12-20 19:10:40,134 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1447 [2023-12-20 19:10:40,292 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1346 [2023-12-20 19:10:40,425 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.1175 [2023-12-20 19:10:40,567 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1542 [2023-12-20 19:10:40,713 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1694 [2023-12-20 19:10:40,803 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.3641 [2023-12-20 19:10:40,967 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4672 [2023-12-20 19:10:41,099 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5023 [2023-12-20 19:10:41,209 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7743 [2023-12-20 19:10:41,355 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4743 [2023-12-20 19:10:41,471 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6759 [2023-12-20 19:10:41,613 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3870 [2023-12-20 19:10:41,707 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1084 [2023-12-20 19:10:41,788 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1790 [2023-12-20 19:10:41,889 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6293 [2023-12-20 19:10:41,987 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3606 [2023-12-20 19:10:42,129 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.7610 [2023-12-20 19:10:42,248 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0851 [2023-12-20 19:10:42,337 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5406 [2023-12-20 19:10:42,481 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3086 [2023-12-20 19:10:42,643 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0182 [2023-12-20 19:10:42,750 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0663 [2023-12-20 19:10:42,878 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3476 [2023-12-20 19:10:43,021 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0136 [2023-12-20 19:10:43,139 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.5163 [2023-12-20 19:10:43,244 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5878 [2023-12-20 19:10:43,421 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3165 [2023-12-20 19:10:43,524 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3593 [2023-12-20 19:10:43,672 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.2734 [2023-12-20 19:10:43,764 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0430 [2023-12-20 19:10:43,841 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6595 [2023-12-20 19:10:43,949 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1522 [2023-12-20 19:10:44,098 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1058 [2023-12-20 19:10:44,238 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3298 [2023-12-20 19:10:44,340 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.8640 [2023-12-20 19:10:44,430 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6871 [2023-12-20 19:10:44,535 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3597 [2023-12-20 19:10:44,696 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2246 [2023-12-20 19:10:44,797 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4441 [2023-12-20 19:10:44,891 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.6515 [2023-12-20 19:10:44,988 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3980 [2023-12-20 19:10:45,081 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2562 [2023-12-20 19:10:45,169 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5178 [2023-12-20 19:10:45,273 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6207 [2023-12-20 19:10:45,397 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.3880 [2023-12-20 19:10:45,481 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3012 [2023-12-20 19:10:45,580 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6105 [2023-12-20 19:10:45,675 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0133 [2023-12-20 19:10:45,759 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4469 [2023-12-20 19:10:45,847 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0108 [2023-12-20 19:10:45,946 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6648 [2023-12-20 19:10:46,037 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7129 [2023-12-20 19:10:46,173 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0595 [2023-12-20 19:10:46,268 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6278 [2023-12-20 19:10:46,384 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6425 [2023-12-20 19:10:46,486 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6728 [2023-12-20 19:10:46,573 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4120 [2023-12-20 19:10:46,733 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0150 [2023-12-20 19:10:47,937 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7593/0.8418/0.9196. [2023-12-20 19:10:47,937 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8787/0.9528 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9661/0.9856 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7075/0.8145 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8404/0.8811 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9110/0.9614 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8784/0.9411 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7620/0.8196 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7127/0.8293 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6880/0.8090 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8316/0.9500 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3925/0.4908 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7012/0.8250 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7100/0.8896 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7657/0.8763 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6806/0.7143 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6681/0.7139 [2023-12-20 19:10:47,938 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9268/0.9794 [2023-12-20 19:10:47,939 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6769/0.7757 [2023-12-20 19:10:47,939 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8661/0.9293 [2023-12-20 19:10:47,939 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6227/0.6975 [2023-12-20 19:10:47,939 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:10:47,940 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7593 [2023-12-20 19:10:47,940 INFO misc.py line 165 131400] Currently Best mIoU: 0.7593 [2023-12-20 19:10:47,940 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:10:54,326 INFO misc.py line 119 131400] Train: [70/100][1/800] Data 1.131 (1.131) Batch 1.486 (1.486) Remain 10:14:14 loss: 0.3698 Lr: 0.00145 [2023-12-20 19:10:54,668 INFO misc.py line 119 131400] Train: [70/100][2/800] Data 0.005 (0.005) Batch 0.341 (0.341) Remain 02:21:05 loss: 0.2917 Lr: 0.00145 [2023-12-20 19:10:54,987 INFO misc.py line 119 131400] Train: [70/100][3/800] Data 0.005 (0.005) Batch 0.321 (0.321) Remain 02:12:31 loss: 0.2181 Lr: 0.00145 [2023-12-20 19:10:55,286 INFO misc.py line 119 131400] Train: [70/100][4/800] Data 0.003 (0.003) Batch 0.298 (0.298) Remain 02:03:02 loss: 0.2146 Lr: 0.00145 [2023-12-20 19:10:55,627 INFO misc.py line 119 131400] Train: [70/100][5/800] Data 0.004 (0.004) Batch 0.342 (0.320) Remain 02:12:08 loss: 0.1488 Lr: 0.00145 [2023-12-20 19:10:55,985 INFO misc.py line 119 131400] Train: [70/100][6/800] Data 0.004 (0.004) Batch 0.353 (0.331) Remain 02:16:41 loss: 0.4004 Lr: 0.00145 [2023-12-20 19:10:56,344 INFO misc.py line 119 131400] Train: [70/100][7/800] Data 0.010 (0.005) Batch 0.364 (0.339) Remain 02:20:06 loss: 0.2745 Lr: 0.00145 [2023-12-20 19:10:56,663 INFO misc.py line 119 131400] Train: [70/100][8/800] Data 0.005 (0.005) Batch 0.319 (0.335) Remain 02:18:24 loss: 0.3923 Lr: 0.00145 [2023-12-20 19:10:57,006 INFO misc.py line 119 131400] Train: [70/100][9/800] Data 0.005 (0.005) Batch 0.339 (0.336) Remain 02:18:41 loss: 0.1165 Lr: 0.00145 [2023-12-20 19:10:57,331 INFO misc.py line 119 131400] Train: [70/100][10/800] Data 0.009 (0.006) Batch 0.330 (0.335) Remain 02:18:22 loss: 0.2796 Lr: 0.00145 [2023-12-20 19:10:57,679 INFO misc.py line 119 131400] Train: [70/100][11/800] Data 0.003 (0.005) Batch 0.347 (0.336) Remain 02:18:58 loss: 0.1932 Lr: 0.00144 [2023-12-20 19:10:57,986 INFO misc.py line 119 131400] Train: [70/100][12/800] Data 0.006 (0.005) Batch 0.308 (0.333) Remain 02:17:40 loss: 0.1756 Lr: 0.00144 [2023-12-20 19:10:58,308 INFO misc.py line 119 131400] Train: [70/100][13/800] Data 0.003 (0.005) Batch 0.321 (0.332) Remain 02:17:09 loss: 0.1893 Lr: 0.00144 [2023-12-20 19:10:58,628 INFO misc.py line 119 131400] Train: [70/100][14/800] Data 0.004 (0.005) Batch 0.321 (0.331) Remain 02:16:44 loss: 0.1997 Lr: 0.00144 [2023-12-20 19:10:58,924 INFO misc.py line 119 131400] Train: [70/100][15/800] Data 0.003 (0.005) Batch 0.296 (0.328) Remain 02:15:32 loss: 0.3478 Lr: 0.00144 [2023-12-20 19:10:59,262 INFO misc.py line 119 131400] Train: [70/100][16/800] Data 0.003 (0.005) Batch 0.333 (0.329) Remain 02:15:42 loss: 0.2893 Lr: 0.00144 [2023-12-20 19:10:59,580 INFO misc.py line 119 131400] Train: [70/100][17/800] Data 0.007 (0.005) Batch 0.321 (0.328) Remain 02:15:28 loss: 0.1660 Lr: 0.00144 [2023-12-20 19:10:59,909 INFO misc.py line 119 131400] Train: [70/100][18/800] Data 0.003 (0.005) Batch 0.330 (0.328) Remain 02:15:31 loss: 0.1540 Lr: 0.00144 [2023-12-20 19:11:00,198 INFO misc.py line 119 131400] Train: [70/100][19/800] Data 0.004 (0.005) Batch 0.287 (0.326) Remain 02:14:28 loss: 0.4294 Lr: 0.00144 [2023-12-20 19:11:00,567 INFO misc.py line 119 131400] Train: [70/100][20/800] Data 0.005 (0.005) Batch 0.370 (0.328) Remain 02:15:32 loss: 0.6415 Lr: 0.00144 [2023-12-20 19:11:00,898 INFO misc.py line 119 131400] Train: [70/100][21/800] Data 0.004 (0.005) Batch 0.330 (0.328) Remain 02:15:35 loss: 0.4156 Lr: 0.00144 [2023-12-20 19:11:01,243 INFO misc.py line 119 131400] Train: [70/100][22/800] Data 0.005 (0.005) Batch 0.347 (0.329) Remain 02:15:58 loss: 0.2862 Lr: 0.00144 [2023-12-20 19:11:01,581 INFO misc.py line 119 131400] Train: [70/100][23/800] Data 0.003 (0.005) Batch 0.337 (0.330) Remain 02:16:07 loss: 0.1934 Lr: 0.00144 [2023-12-20 19:11:01,942 INFO misc.py line 119 131400] Train: [70/100][24/800] Data 0.004 (0.005) Batch 0.360 (0.331) Remain 02:16:43 loss: 0.2339 Lr: 0.00144 [2023-12-20 19:11:02,264 INFO misc.py line 119 131400] Train: [70/100][25/800] Data 0.006 (0.005) Batch 0.324 (0.331) Remain 02:16:34 loss: 0.3116 Lr: 0.00144 [2023-12-20 19:11:02,604 INFO misc.py line 119 131400] Train: [70/100][26/800] Data 0.004 (0.005) Batch 0.340 (0.331) Remain 02:16:44 loss: 0.3271 Lr: 0.00144 [2023-12-20 19:11:02,937 INFO misc.py line 119 131400] Train: [70/100][27/800] Data 0.003 (0.005) Batch 0.332 (0.331) Remain 02:16:45 loss: 0.1921 Lr: 0.00144 [2023-12-20 19:11:03,284 INFO misc.py 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19:15:09,520 INFO misc.py line 119 131400] Train: [70/100][763/800] Data 0.003 (0.004) Batch 0.338 (0.335) Remain 02:14:10 loss: 0.2053 Lr: 0.00137 [2023-12-20 19:15:09,812 INFO misc.py line 119 131400] Train: [70/100][764/800] Data 0.003 (0.004) Batch 0.292 (0.335) Remain 02:14:08 loss: 0.1664 Lr: 0.00137 [2023-12-20 19:15:10,138 INFO misc.py line 119 131400] Train: [70/100][765/800] Data 0.003 (0.004) Batch 0.318 (0.335) Remain 02:14:07 loss: 0.4422 Lr: 0.00137 [2023-12-20 19:15:10,481 INFO misc.py line 119 131400] Train: [70/100][766/800] Data 0.012 (0.004) Batch 0.352 (0.335) Remain 02:14:07 loss: 0.2452 Lr: 0.00137 [2023-12-20 19:15:10,823 INFO misc.py line 119 131400] Train: [70/100][767/800] Data 0.003 (0.004) Batch 0.340 (0.335) Remain 02:14:07 loss: 0.1324 Lr: 0.00137 [2023-12-20 19:15:11,147 INFO misc.py line 119 131400] Train: [70/100][768/800] Data 0.004 (0.004) Batch 0.325 (0.335) Remain 02:14:07 loss: 0.1816 Lr: 0.00137 [2023-12-20 19:15:11,468 INFO misc.py line 119 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0.004 (0.004) Batch 0.347 (0.335) Remain 02:14:05 loss: 0.1242 Lr: 0.00136 [2023-12-20 19:15:13,861 INFO misc.py line 119 131400] Train: [70/100][776/800] Data 0.005 (0.004) Batch 0.352 (0.335) Remain 02:14:05 loss: 0.2694 Lr: 0.00136 [2023-12-20 19:15:14,195 INFO misc.py line 119 131400] Train: [70/100][777/800] Data 0.004 (0.004) Batch 0.334 (0.335) Remain 02:14:05 loss: 0.2107 Lr: 0.00136 [2023-12-20 19:15:14,532 INFO misc.py line 119 131400] Train: [70/100][778/800] Data 0.004 (0.004) Batch 0.336 (0.335) Remain 02:14:04 loss: 0.2854 Lr: 0.00136 [2023-12-20 19:15:14,878 INFO misc.py line 119 131400] Train: [70/100][779/800] Data 0.005 (0.004) Batch 0.347 (0.335) Remain 02:14:04 loss: 0.1473 Lr: 0.00136 [2023-12-20 19:15:15,197 INFO misc.py line 119 131400] Train: [70/100][780/800] Data 0.003 (0.004) Batch 0.317 (0.335) Remain 02:14:03 loss: 0.2213 Lr: 0.00136 [2023-12-20 19:15:15,531 INFO misc.py line 119 131400] Train: [70/100][781/800] Data 0.007 (0.004) Batch 0.335 (0.335) Remain 02:14:03 loss: 0.1918 Lr: 0.00136 [2023-12-20 19:15:15,925 INFO misc.py line 119 131400] Train: [70/100][782/800] Data 0.006 (0.004) Batch 0.394 (0.335) Remain 02:14:05 loss: 0.2602 Lr: 0.00136 [2023-12-20 19:15:16,274 INFO misc.py line 119 131400] Train: [70/100][783/800] Data 0.009 (0.004) Batch 0.346 (0.335) Remain 02:14:05 loss: 0.1658 Lr: 0.00136 [2023-12-20 19:15:16,636 INFO misc.py line 119 131400] Train: [70/100][784/800] Data 0.007 (0.004) Batch 0.365 (0.335) Remain 02:14:05 loss: 0.1733 Lr: 0.00136 [2023-12-20 19:15:16,998 INFO misc.py line 119 131400] Train: [70/100][785/800] Data 0.005 (0.004) Batch 0.362 (0.335) Remain 02:14:06 loss: 0.3172 Lr: 0.00136 [2023-12-20 19:15:17,333 INFO misc.py line 119 131400] Train: [70/100][786/800] Data 0.004 (0.004) Batch 0.335 (0.335) Remain 02:14:05 loss: 0.3531 Lr: 0.00136 [2023-12-20 19:15:17,671 INFO misc.py line 119 131400] Train: [70/100][787/800] Data 0.004 (0.004) Batch 0.339 (0.335) Remain 02:14:05 loss: 0.2831 Lr: 0.00136 [2023-12-20 19:15:18,000 INFO misc.py line 119 131400] Train: [70/100][788/800] Data 0.004 (0.004) Batch 0.328 (0.335) Remain 02:14:05 loss: 0.1820 Lr: 0.00136 [2023-12-20 19:15:18,363 INFO misc.py line 119 131400] Train: [70/100][789/800] Data 0.005 (0.004) Batch 0.363 (0.335) Remain 02:14:05 loss: 0.2814 Lr: 0.00136 [2023-12-20 19:15:18,670 INFO misc.py line 119 131400] Train: [70/100][790/800] Data 0.004 (0.004) Batch 0.305 (0.335) Remain 02:14:04 loss: 0.2632 Lr: 0.00136 [2023-12-20 19:15:18,990 INFO misc.py line 119 131400] Train: [70/100][791/800] Data 0.006 (0.004) Batch 0.323 (0.335) Remain 02:14:03 loss: 0.5142 Lr: 0.00136 [2023-12-20 19:15:19,327 INFO misc.py line 119 131400] Train: [70/100][792/800] Data 0.003 (0.004) Batch 0.336 (0.335) Remain 02:14:03 loss: 0.2966 Lr: 0.00136 [2023-12-20 19:15:19,628 INFO misc.py line 119 131400] Train: [70/100][793/800] Data 0.005 (0.004) Batch 0.302 (0.335) Remain 02:14:02 loss: 0.1291 Lr: 0.00136 [2023-12-20 19:15:19,949 INFO misc.py line 119 131400] Train: [70/100][794/800] Data 0.004 (0.004) Batch 0.320 (0.335) Remain 02:14:01 loss: 0.4074 Lr: 0.00136 [2023-12-20 19:15:20,274 INFO misc.py line 119 131400] Train: [70/100][795/800] Data 0.004 (0.004) Batch 0.326 (0.335) Remain 02:14:00 loss: 0.2495 Lr: 0.00136 [2023-12-20 19:15:20,586 INFO misc.py line 119 131400] Train: [70/100][796/800] Data 0.003 (0.004) Batch 0.310 (0.335) Remain 02:13:59 loss: 0.2108 Lr: 0.00136 [2023-12-20 19:15:20,882 INFO misc.py line 119 131400] Train: [70/100][797/800] Data 0.005 (0.004) Batch 0.297 (0.335) Remain 02:13:58 loss: 0.2703 Lr: 0.00136 [2023-12-20 19:15:21,210 INFO misc.py line 119 131400] Train: [70/100][798/800] Data 0.004 (0.004) Batch 0.328 (0.335) Remain 02:13:57 loss: 0.2068 Lr: 0.00136 [2023-12-20 19:15:21,508 INFO misc.py line 119 131400] Train: [70/100][799/800] Data 0.003 (0.004) Batch 0.299 (0.335) Remain 02:13:56 loss: 0.2703 Lr: 0.00136 [2023-12-20 19:15:21,805 INFO misc.py line 119 131400] Train: [70/100][800/800] Data 0.002 (0.004) Batch 0.297 (0.335) Remain 02:13:54 loss: 0.1705 Lr: 0.00136 [2023-12-20 19:15:21,806 INFO misc.py line 136 131400] Train result: loss: 0.2618 [2023-12-20 19:15:21,806 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:15:44,676 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2552 [2023-12-20 19:15:44,969 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1576 [2023-12-20 19:15:45,062 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5977 [2023-12-20 19:15:45,168 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2357 [2023-12-20 19:15:45,282 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1813 [2023-12-20 19:15:45,385 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3582 [2023-12-20 19:15:45,478 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.6963 [2023-12-20 19:15:45,586 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0300 [2023-12-20 19:15:45,685 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2553 [2023-12-20 19:15:45,801 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3069 [2023-12-20 19:15:45,896 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4153 [2023-12-20 19:15:46,036 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3349 [2023-12-20 19:15:46,159 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.2565 [2023-12-20 19:15:46,325 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2854 [2023-12-20 19:15:46,421 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1291 [2023-12-20 19:15:46,554 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7712 [2023-12-20 19:15:46,680 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3577 [2023-12-20 19:15:46,789 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5273 [2023-12-20 19:15:46,902 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1764 [2023-12-20 19:15:46,987 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3875 [2023-12-20 19:15:47,115 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1439 [2023-12-20 19:15:47,280 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1132 [2023-12-20 19:15:47,408 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8468 [2023-12-20 19:15:47,557 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1475 [2023-12-20 19:15:47,705 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1512 [2023-12-20 19:15:47,798 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4097 [2023-12-20 19:15:47,973 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4438 [2023-12-20 19:15:48,101 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4891 [2023-12-20 19:15:48,198 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4844 [2023-12-20 19:15:48,347 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8334 [2023-12-20 19:15:48,450 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5868 [2023-12-20 19:15:48,577 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3576 [2023-12-20 19:15:48,678 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1199 [2023-12-20 19:15:48,759 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1762 [2023-12-20 19:15:48,857 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6114 [2023-12-20 19:15:48,951 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3141 [2023-12-20 19:15:49,089 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9320 [2023-12-20 19:15:49,207 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0942 [2023-12-20 19:15:49,287 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5240 [2023-12-20 19:15:49,435 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3690 [2023-12-20 19:15:49,587 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0144 [2023-12-20 19:15:49,686 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0660 [2023-12-20 19:15:49,812 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3329 [2023-12-20 19:15:49,960 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1257 [2023-12-20 19:15:50,079 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3942 [2023-12-20 19:15:50,182 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.2808 [2023-12-20 19:15:50,353 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2918 [2023-12-20 19:15:50,450 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3677 [2023-12-20 19:15:50,599 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7248 [2023-12-20 19:15:50,700 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0783 [2023-12-20 19:15:50,778 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6254 [2023-12-20 19:15:50,894 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2073 [2023-12-20 19:15:51,041 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0874 [2023-12-20 19:15:51,175 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2756 [2023-12-20 19:15:51,278 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2859 [2023-12-20 19:15:51,372 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5892 [2023-12-20 19:15:51,474 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3673 [2023-12-20 19:15:51,637 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2014 [2023-12-20 19:15:51,734 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3011 [2023-12-20 19:15:51,832 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3984 [2023-12-20 19:15:51,940 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2369 [2023-12-20 19:15:52,037 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2816 [2023-12-20 19:15:52,123 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4947 [2023-12-20 19:15:52,222 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5119 [2023-12-20 19:15:52,358 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.4564 [2023-12-20 19:15:52,443 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2163 [2023-12-20 19:15:52,543 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4666 [2023-12-20 19:15:52,638 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0088 [2023-12-20 19:15:52,724 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3215 [2023-12-20 19:15:52,809 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0096 [2023-12-20 19:15:52,902 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6293 [2023-12-20 19:15:52,992 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4570 [2023-12-20 19:15:53,125 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0565 [2023-12-20 19:15:53,219 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6588 [2023-12-20 19:15:53,336 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6828 [2023-12-20 19:15:53,437 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6716 [2023-12-20 19:15:53,523 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2459 [2023-12-20 19:15:53,676 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2539 [2023-12-20 19:15:55,209 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7633/0.8472/0.9184. [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8747/0.9450 [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9614/0.9870 [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7051/0.8193 [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8179/0.8736 [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9114/0.9591 [2023-12-20 19:15:55,209 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8388/0.8952 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7800/0.8366 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7285/0.8535 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7034/0.8125 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8238/0.9160 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4183/0.5640 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7162/0.8099 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7317/0.9030 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7660/0.8720 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6847/0.7514 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6836/0.7427 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9342/0.9764 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6905/0.7912 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8867/0.9340 [2023-12-20 19:15:55,210 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6099/0.7016 [2023-12-20 19:15:55,211 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:15:55,212 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7633 [2023-12-20 19:15:55,212 INFO misc.py line 165 131400] Currently Best mIoU: 0.7633 [2023-12-20 19:15:55,212 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:16:01,779 INFO misc.py line 119 131400] Train: [71/100][1/800] Data 1.092 (1.092) Batch 1.428 (1.428) Remain 09:31:05 loss: 0.2494 Lr: 0.00136 [2023-12-20 19:16:02,099 INFO misc.py line 119 131400] Train: [71/100][2/800] Data 0.006 (0.006) Batch 0.321 (0.321) Remain 02:08:34 loss: 0.3635 Lr: 0.00136 [2023-12-20 19:16:02,444 INFO misc.py line 119 131400] Train: [71/100][3/800] Data 0.004 (0.004) Batch 0.345 (0.345) Remain 02:17:51 loss: 0.1626 Lr: 0.00136 [2023-12-20 19:16:02,797 INFO misc.py line 119 131400] Train: [71/100][4/800] Data 0.006 (0.006) Batch 0.352 (0.352) Remain 02:20:55 loss: 0.2908 Lr: 0.00136 [2023-12-20 19:16:03,145 INFO misc.py line 119 131400] Train: [71/100][5/800] Data 0.005 (0.006) Batch 0.348 (0.350) Remain 02:20:02 loss: 0.2668 Lr: 0.00136 [2023-12-20 19:16:03,487 INFO misc.py line 119 131400] Train: [71/100][6/800] Data 0.008 (0.007) Batch 0.344 (0.348) Remain 02:19:10 loss: 0.2064 Lr: 0.00136 [2023-12-20 19:16:03,857 INFO misc.py line 119 131400] Train: [71/100][7/800] Data 0.003 (0.006) Batch 0.369 (0.353) Remain 02:21:15 loss: 0.0934 Lr: 0.00136 [2023-12-20 19:16:04,210 INFO misc.py line 119 131400] Train: [71/100][8/800] Data 0.005 (0.006) Batch 0.352 (0.353) Remain 02:21:09 loss: 0.4474 Lr: 0.00136 [2023-12-20 19:16:04,536 INFO misc.py line 119 131400] Train: [71/100][9/800] Data 0.006 (0.006) Batch 0.327 (0.349) Remain 02:19:24 loss: 0.2909 Lr: 0.00136 [2023-12-20 19:16:04,901 INFO misc.py line 119 131400] Train: [71/100][10/800] Data 0.004 (0.005) Batch 0.365 (0.351) Remain 02:20:20 loss: 0.1983 Lr: 0.00136 [2023-12-20 19:16:05,247 INFO misc.py line 119 131400] Train: [71/100][11/800] Data 0.004 (0.005) Batch 0.345 (0.350) Remain 02:20:02 loss: 0.2217 Lr: 0.00136 [2023-12-20 19:16:05,627 INFO misc.py line 119 131400] Train: [71/100][12/800] Data 0.005 (0.005) Batch 0.382 (0.354) Remain 02:21:25 loss: 0.4190 Lr: 0.00136 [2023-12-20 19:16:05,978 INFO misc.py line 119 131400] Train: [71/100][13/800] Data 0.003 (0.005) Batch 0.351 (0.353) Remain 02:21:18 loss: 0.3733 Lr: 0.00136 [2023-12-20 19:16:06,273 INFO misc.py line 119 131400] Train: [71/100][14/800] Data 0.004 (0.005) Batch 0.293 (0.348) Remain 02:19:06 loss: 0.1545 Lr: 0.00136 [2023-12-20 19:16:06,621 INFO misc.py line 119 131400] Train: [71/100][15/800] Data 0.005 (0.005) Batch 0.350 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Batch 0.318 (0.340) Remain 02:11:44 loss: 0.1759 Lr: 0.00129 [2023-12-20 19:20:16,728 INFO misc.py line 119 131400] Train: [71/100][751/800] Data 0.003 (0.004) Batch 0.319 (0.340) Remain 02:11:43 loss: 0.1862 Lr: 0.00128 [2023-12-20 19:20:17,086 INFO misc.py line 119 131400] Train: [71/100][752/800] Data 0.003 (0.004) Batch 0.358 (0.340) Remain 02:11:43 loss: 0.2238 Lr: 0.00128 [2023-12-20 19:20:17,419 INFO misc.py line 119 131400] Train: [71/100][753/800] Data 0.003 (0.004) Batch 0.330 (0.340) Remain 02:11:43 loss: 0.2201 Lr: 0.00128 [2023-12-20 19:20:17,742 INFO misc.py line 119 131400] Train: [71/100][754/800] Data 0.006 (0.004) Batch 0.326 (0.340) Remain 02:11:42 loss: 0.3445 Lr: 0.00128 [2023-12-20 19:20:18,051 INFO misc.py line 119 131400] Train: [71/100][755/800] Data 0.003 (0.004) Batch 0.309 (0.340) Remain 02:11:41 loss: 0.3061 Lr: 0.00128 [2023-12-20 19:20:18,363 INFO misc.py line 119 131400] Train: [71/100][756/800] Data 0.003 (0.004) Batch 0.311 (0.340) Remain 02:11:39 loss: 0.1944 Lr: 0.00128 [2023-12-20 19:20:18,647 INFO misc.py line 119 131400] Train: [71/100][757/800] Data 0.004 (0.004) Batch 0.284 (0.340) Remain 02:11:37 loss: 0.1763 Lr: 0.00128 [2023-12-20 19:20:18,973 INFO misc.py line 119 131400] Train: [71/100][758/800] Data 0.003 (0.004) Batch 0.326 (0.340) Remain 02:11:36 loss: 0.1734 Lr: 0.00128 [2023-12-20 19:20:19,273 INFO misc.py line 119 131400] Train: [71/100][759/800] Data 0.004 (0.004) Batch 0.298 (0.340) Remain 02:11:35 loss: 0.2936 Lr: 0.00128 [2023-12-20 19:20:19,627 INFO misc.py line 119 131400] Train: [71/100][760/800] Data 0.006 (0.004) Batch 0.355 (0.340) Remain 02:11:35 loss: 0.2342 Lr: 0.00128 [2023-12-20 19:20:19,898 INFO misc.py line 119 131400] Train: [71/100][761/800] Data 0.004 (0.004) Batch 0.272 (0.340) Remain 02:11:33 loss: 0.1577 Lr: 0.00128 [2023-12-20 19:20:20,234 INFO misc.py line 119 131400] Train: [71/100][762/800] Data 0.004 (0.004) Batch 0.336 (0.340) Remain 02:11:32 loss: 0.2042 Lr: 0.00128 [2023-12-20 19:20:20,536 INFO misc.py line 119 131400] Train: [71/100][763/800] Data 0.003 (0.004) Batch 0.302 (0.340) Remain 02:11:31 loss: 0.3019 Lr: 0.00128 [2023-12-20 19:20:21,136 INFO misc.py line 119 131400] Train: [71/100][764/800] Data 0.003 (0.004) Batch 0.600 (0.340) Remain 02:11:38 loss: 0.1497 Lr: 0.00128 [2023-12-20 19:20:21,433 INFO misc.py line 119 131400] Train: [71/100][765/800] Data 0.004 (0.004) Batch 0.297 (0.340) Remain 02:11:37 loss: 0.5277 Lr: 0.00128 [2023-12-20 19:20:21,788 INFO misc.py line 119 131400] Train: [71/100][766/800] Data 0.003 (0.004) Batch 0.355 (0.340) Remain 02:11:37 loss: 0.1117 Lr: 0.00128 [2023-12-20 19:20:22,128 INFO misc.py line 119 131400] Train: [71/100][767/800] Data 0.004 (0.004) Batch 0.339 (0.340) Remain 02:11:36 loss: 0.1303 Lr: 0.00128 [2023-12-20 19:20:22,445 INFO misc.py line 119 131400] Train: [71/100][768/800] Data 0.004 (0.004) Batch 0.318 (0.340) Remain 02:11:35 loss: 0.5228 Lr: 0.00128 [2023-12-20 19:20:22,782 INFO misc.py line 119 131400] Train: [71/100][769/800] Data 0.003 (0.004) Batch 0.336 (0.340) Remain 02:11:35 loss: 0.2149 Lr: 0.00128 [2023-12-20 19:20:23,154 INFO misc.py line 119 131400] Train: [71/100][770/800] Data 0.005 (0.004) Batch 0.370 (0.340) Remain 02:11:35 loss: 0.3306 Lr: 0.00128 [2023-12-20 19:20:23,491 INFO misc.py line 119 131400] Train: [71/100][771/800] Data 0.017 (0.004) Batch 0.339 (0.340) Remain 02:11:35 loss: 0.2792 Lr: 0.00128 [2023-12-20 19:20:23,849 INFO misc.py line 119 131400] Train: [71/100][772/800] Data 0.005 (0.004) Batch 0.358 (0.340) Remain 02:11:35 loss: 0.1429 Lr: 0.00128 [2023-12-20 19:20:24,218 INFO misc.py line 119 131400] Train: [71/100][773/800] Data 0.004 (0.004) Batch 0.370 (0.340) Remain 02:11:36 loss: 0.2738 Lr: 0.00128 [2023-12-20 19:20:24,582 INFO misc.py line 119 131400] Train: [71/100][774/800] Data 0.003 (0.004) Batch 0.364 (0.340) Remain 02:11:36 loss: 0.2136 Lr: 0.00128 [2023-12-20 19:20:24,867 INFO misc.py line 119 131400] Train: [71/100][775/800] Data 0.004 (0.004) Batch 0.284 (0.340) Remain 02:11:34 loss: 0.1484 Lr: 0.00128 [2023-12-20 19:20:25,191 INFO misc.py line 119 131400] Train: [71/100][776/800] Data 0.005 (0.004) Batch 0.325 (0.340) Remain 02:11:33 loss: 0.5004 Lr: 0.00128 [2023-12-20 19:20:25,516 INFO misc.py line 119 131400] Train: [71/100][777/800] Data 0.005 (0.004) Batch 0.324 (0.340) Remain 02:11:33 loss: 0.2255 Lr: 0.00128 [2023-12-20 19:20:25,899 INFO misc.py line 119 131400] Train: [71/100][778/800] Data 0.005 (0.004) Batch 0.382 (0.340) Remain 02:11:34 loss: 0.1458 Lr: 0.00128 [2023-12-20 19:20:26,270 INFO misc.py line 119 131400] Train: [71/100][779/800] Data 0.005 (0.004) Batch 0.371 (0.340) Remain 02:11:34 loss: 0.1591 Lr: 0.00128 [2023-12-20 19:20:26,604 INFO misc.py line 119 131400] Train: [71/100][780/800] Data 0.005 (0.004) Batch 0.335 (0.340) Remain 02:11:34 loss: 0.3034 Lr: 0.00128 [2023-12-20 19:20:26,926 INFO misc.py line 119 131400] Train: [71/100][781/800] Data 0.004 (0.004) Batch 0.323 (0.340) Remain 02:11:33 loss: 0.2343 Lr: 0.00128 [2023-12-20 19:20:27,226 INFO misc.py line 119 131400] Train: [71/100][782/800] Data 0.003 (0.004) Batch 0.300 (0.340) Remain 02:11:31 loss: 0.1188 Lr: 0.00128 [2023-12-20 19:20:27,542 INFO misc.py line 119 131400] Train: [71/100][783/800] Data 0.003 (0.004) Batch 0.315 (0.340) Remain 02:11:30 loss: 0.2072 Lr: 0.00128 [2023-12-20 19:20:27,870 INFO misc.py line 119 131400] Train: [71/100][784/800] Data 0.004 (0.004) Batch 0.329 (0.340) Remain 02:11:30 loss: 0.4995 Lr: 0.00128 [2023-12-20 19:20:28,193 INFO misc.py line 119 131400] Train: [71/100][785/800] Data 0.003 (0.004) Batch 0.322 (0.340) Remain 02:11:29 loss: 0.3699 Lr: 0.00128 [2023-12-20 19:20:28,503 INFO misc.py line 119 131400] Train: [71/100][786/800] Data 0.004 (0.004) Batch 0.310 (0.340) Remain 02:11:27 loss: 0.1389 Lr: 0.00128 [2023-12-20 19:20:28,779 INFO misc.py line 119 131400] Train: [71/100][787/800] Data 0.004 (0.004) Batch 0.276 (0.340) Remain 02:11:25 loss: 0.2553 Lr: 0.00128 [2023-12-20 19:20:29,104 INFO misc.py line 119 131400] Train: [71/100][788/800] Data 0.003 (0.004) Batch 0.323 (0.340) Remain 02:11:24 loss: 0.1966 Lr: 0.00128 [2023-12-20 19:20:29,432 INFO misc.py line 119 131400] Train: [71/100][789/800] Data 0.006 (0.004) Batch 0.329 (0.340) Remain 02:11:24 loss: 0.1333 Lr: 0.00128 [2023-12-20 19:20:29,778 INFO misc.py line 119 131400] Train: [71/100][790/800] Data 0.005 (0.004) Batch 0.345 (0.340) Remain 02:11:24 loss: 0.2586 Lr: 0.00128 [2023-12-20 19:20:30,085 INFO misc.py line 119 131400] Train: [71/100][791/800] Data 0.006 (0.004) Batch 0.308 (0.340) Remain 02:11:22 loss: 0.2930 Lr: 0.00128 [2023-12-20 19:20:30,437 INFO misc.py line 119 131400] Train: [71/100][792/800] Data 0.004 (0.004) Batch 0.351 (0.340) Remain 02:11:22 loss: 0.3180 Lr: 0.00128 [2023-12-20 19:20:30,744 INFO misc.py line 119 131400] Train: [71/100][793/800] Data 0.006 (0.004) Batch 0.310 (0.340) Remain 02:11:21 loss: 0.2627 Lr: 0.00128 [2023-12-20 19:20:31,062 INFO misc.py line 119 131400] Train: [71/100][794/800] Data 0.003 (0.004) Batch 0.316 (0.340) Remain 02:11:20 loss: 0.1670 Lr: 0.00128 [2023-12-20 19:20:31,389 INFO misc.py line 119 131400] Train: [71/100][795/800] Data 0.005 (0.004) Batch 0.328 (0.340) Remain 02:11:19 loss: 0.2289 Lr: 0.00128 [2023-12-20 19:20:31,760 INFO misc.py line 119 131400] Train: [71/100][796/800] Data 0.004 (0.004) Batch 0.372 (0.340) Remain 02:11:20 loss: 0.4180 Lr: 0.00128 [2023-12-20 19:20:32,124 INFO misc.py line 119 131400] Train: [71/100][797/800] Data 0.004 (0.004) Batch 0.362 (0.340) Remain 02:11:20 loss: 0.4474 Lr: 0.00128 [2023-12-20 19:20:32,446 INFO misc.py line 119 131400] Train: [71/100][798/800] Data 0.006 (0.004) Batch 0.325 (0.340) Remain 02:11:19 loss: 0.1269 Lr: 0.00128 [2023-12-20 19:20:32,750 INFO misc.py line 119 131400] Train: [71/100][799/800] Data 0.004 (0.004) Batch 0.302 (0.340) Remain 02:11:18 loss: 0.2781 Lr: 0.00128 [2023-12-20 19:20:33,045 INFO misc.py line 119 131400] Train: [71/100][800/800] Data 0.005 (0.004) Batch 0.296 (0.340) Remain 02:11:16 loss: 0.1232 Lr: 0.00128 [2023-12-20 19:20:33,059 INFO misc.py line 136 131400] Train result: loss: 0.2582 [2023-12-20 19:20:33,060 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:20:54,243 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2213 [2023-12-20 19:20:55,368 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1325 [2023-12-20 19:20:55,476 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6040 [2023-12-20 19:20:55,585 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2178 [2023-12-20 19:20:55,706 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4057 [2023-12-20 19:20:55,814 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.2811 [2023-12-20 19:20:55,927 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9871 [2023-12-20 19:20:56,035 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9666 [2023-12-20 19:20:56,114 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2019 [2023-12-20 19:20:56,200 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.4577 [2023-12-20 19:20:56,296 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5458 [2023-12-20 19:20:56,433 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2820 [2023-12-20 19:20:56,555 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6555 [2023-12-20 19:20:56,714 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2279 [2023-12-20 19:20:56,811 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1400 [2023-12-20 19:20:56,946 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.4743 [2023-12-20 19:20:57,064 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3224 [2023-12-20 19:20:57,174 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6247 [2023-12-20 19:20:57,292 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1204 [2023-12-20 19:20:57,373 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3029 [2023-12-20 19:20:57,486 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.3535 [2023-12-20 19:20:57,646 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1458 [2023-12-20 19:20:57,781 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5779 [2023-12-20 19:20:57,929 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1374 [2023-12-20 19:20:58,079 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1701 [2023-12-20 19:20:58,163 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4418 [2023-12-20 19:20:58,322 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5428 [2023-12-20 19:20:58,449 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4828 [2023-12-20 19:20:58,544 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.8052 [2023-12-20 19:20:58,692 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8285 [2023-12-20 19:20:58,804 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6014 [2023-12-20 19:20:58,927 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3829 [2023-12-20 19:20:59,020 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1209 [2023-12-20 19:20:59,093 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.2034 [2023-12-20 19:20:59,195 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8548 [2023-12-20 19:20:59,288 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2645 [2023-12-20 19:20:59,424 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8487 [2023-12-20 19:20:59,546 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0999 [2023-12-20 19:20:59,636 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5563 [2023-12-20 19:20:59,778 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3235 [2023-12-20 19:20:59,932 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0190 [2023-12-20 19:21:00,033 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0483 [2023-12-20 19:21:00,154 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2850 [2023-12-20 19:21:00,306 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9271 [2023-12-20 19:21:00,431 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2583 [2023-12-20 19:21:00,543 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.9377 [2023-12-20 19:21:00,719 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3566 [2023-12-20 19:21:00,814 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3184 [2023-12-20 19:21:00,960 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7290 [2023-12-20 19:21:01,052 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1941 [2023-12-20 19:21:01,131 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6063 [2023-12-20 19:21:01,240 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.6915 [2023-12-20 19:21:01,390 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2085 [2023-12-20 19:21:01,535 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3273 [2023-12-20 19:21:01,649 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2102 [2023-12-20 19:21:01,739 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5472 [2023-12-20 19:21:01,843 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3843 [2023-12-20 19:21:02,011 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2242 [2023-12-20 19:21:02,111 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3395 [2023-12-20 19:21:02,209 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2139 [2023-12-20 19:21:02,310 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.2151 [2023-12-20 19:21:02,405 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3176 [2023-12-20 19:21:02,494 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.7528 [2023-12-20 19:21:02,603 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6891 [2023-12-20 19:21:02,747 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5507 [2023-12-20 19:21:02,830 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2935 [2023-12-20 19:21:02,931 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6816 [2023-12-20 19:21:03,036 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0126 [2023-12-20 19:21:03,129 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.7183 [2023-12-20 19:21:03,221 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0111 [2023-12-20 19:21:03,316 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7974 [2023-12-20 19:21:03,408 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6511 [2023-12-20 19:21:03,541 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.4708 [2023-12-20 19:21:03,637 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5541 [2023-12-20 19:21:03,754 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6558 [2023-12-20 19:21:03,856 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7691 [2023-12-20 19:21:03,942 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2467 [2023-12-20 19:21:04,095 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.4094 [2023-12-20 19:21:05,363 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7489/0.8297/0.9134. [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8677/0.9452 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9646/0.9846 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6922/0.7940 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8128/0.8721 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9010/0.9601 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8020/0.8332 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7453/0.8124 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7174/0.8655 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7020/0.8173 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8192/0.9401 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3965/0.4982 [2023-12-20 19:21:05,363 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6624/0.7526 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7036/0.8805 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7245/0.8459 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6493/0.6996 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6822/0.7356 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9583/0.9760 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6860/0.7648 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8937/0.9222 [2023-12-20 19:21:05,364 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.5978/0.6939 [2023-12-20 19:21:05,364 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:21:05,365 INFO misc.py line 165 131400] Currently Best mIoU: 0.7633 [2023-12-20 19:21:05,365 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:21:09,743 INFO misc.py line 119 131400] Train: [72/100][1/800] Data 1.381 (1.381) Batch 1.765 (1.765) Remain 11:22:17 loss: 0.3680 Lr: 0.00128 [2023-12-20 19:21:10,082 INFO misc.py line 119 131400] Train: [72/100][2/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 02:10:45 loss: 0.2772 Lr: 0.00128 [2023-12-20 19:21:10,399 INFO misc.py line 119 131400] Train: [72/100][3/800] Data 0.005 (0.005) Batch 0.317 (0.317) Remain 02:02:44 loss: 0.2105 Lr: 0.00128 [2023-12-20 19:21:10,714 INFO misc.py line 119 131400] Train: [72/100][4/800] Data 0.004 (0.004) Batch 0.316 (0.316) Remain 02:02:02 loss: 0.3142 Lr: 0.00128 [2023-12-20 19:21:11,061 INFO misc.py line 119 131400] Train: [72/100][5/800] Data 0.003 (0.004) Batch 0.347 (0.331) Remain 02:07:59 loss: 0.4376 Lr: 0.00128 [2023-12-20 19:21:11,391 INFO misc.py line 119 131400] Train: [72/100][6/800] Data 0.004 (0.004) Batch 0.330 (0.331) Remain 02:07:49 loss: 0.1342 Lr: 0.00128 [2023-12-20 19:21:11,708 INFO misc.py line 119 131400] Train: [72/100][7/800] Data 0.004 (0.004) Batch 0.317 (0.327) Remain 02:06:27 loss: 0.2721 Lr: 0.00128 [2023-12-20 19:21:12,049 INFO misc.py line 119 131400] Train: [72/100][8/800] Data 0.004 (0.004) Batch 0.339 (0.329) Remain 02:07:20 loss: 0.2037 Lr: 0.00128 [2023-12-20 19:21:12,386 INFO misc.py line 119 131400] Train: [72/100][9/800] Data 0.010 (0.005) Batch 0.340 (0.331) Remain 02:07:59 loss: 0.1357 Lr: 0.00128 [2023-12-20 19:21:12,709 INFO misc.py line 119 131400] Train: [72/100][10/800] Data 0.004 (0.005) Batch 0.323 (0.330) Remain 02:07:33 loss: 0.3634 Lr: 0.00128 [2023-12-20 19:21:13,064 INFO misc.py line 119 131400] Train: [72/100][11/800] Data 0.004 (0.005) Batch 0.355 (0.333) Remain 02:08:44 loss: 0.2619 Lr: 0.00128 [2023-12-20 19:21:13,391 INFO misc.py line 119 131400] Train: [72/100][12/800] Data 0.004 (0.005) Batch 0.327 (0.332) Remain 02:08:29 loss: 0.2671 Lr: 0.00128 [2023-12-20 19:21:13,708 INFO misc.py line 119 131400] Train: [72/100][13/800] Data 0.004 (0.005) Batch 0.318 (0.331) Remain 02:07:55 loss: 0.2389 Lr: 0.00128 [2023-12-20 19:21:14,032 INFO misc.py line 119 131400] Train: [72/100][14/800] Data 0.003 (0.004) Batch 0.317 (0.330) Remain 02:07:24 loss: 0.1821 Lr: 0.00128 [2023-12-20 19:21:14,389 INFO misc.py line 119 131400] Train: [72/100][15/800] Data 0.010 (0.005) Batch 0.363 (0.332) Remain 02:08:28 loss: 0.2554 Lr: 0.00128 [2023-12-20 19:21:14,711 INFO misc.py line 119 131400] Train: [72/100][16/800] Data 0.004 (0.005) Batch 0.322 (0.332) Remain 02:08:08 loss: 0.1357 Lr: 0.00128 [2023-12-20 19:21:15,056 INFO misc.py line 119 131400] Train: [72/100][17/800] Data 0.005 (0.005) Batch 0.335 (0.332) Remain 02:08:14 loss: 0.1234 Lr: 0.00128 [2023-12-20 19:21:15,394 INFO misc.py line 119 131400] Train: [72/100][18/800] Data 0.014 (0.006) Batch 0.349 (0.333) Remain 02:08:39 loss: 0.2338 Lr: 0.00128 [2023-12-20 19:21:15,713 INFO misc.py line 119 131400] Train: [72/100][19/800] Data 0.004 (0.005) Batch 0.319 (0.332) Remain 02:08:19 loss: 0.2122 Lr: 0.00128 [2023-12-20 19:21:16,037 INFO misc.py line 119 131400] Train: [72/100][20/800] Data 0.004 (0.005) Batch 0.319 (0.331) Remain 02:08:01 loss: 0.3149 Lr: 0.00128 [2023-12-20 19:21:16,379 INFO misc.py line 119 131400] Train: [72/100][21/800] Data 0.008 (0.005) Batch 0.347 (0.332) Remain 02:08:21 loss: 0.1334 Lr: 0.00128 [2023-12-20 19:21:16,704 INFO misc.py line 119 131400] Train: [72/100][22/800] Data 0.004 (0.005) Batch 0.320 (0.332) Remain 02:08:06 loss: 0.2849 Lr: 0.00128 [2023-12-20 19:21:17,034 INFO misc.py line 119 131400] Train: [72/100][23/800] Data 0.008 (0.006) Batch 0.333 (0.332) Remain 02:08:07 loss: 0.1621 Lr: 0.00128 [2023-12-20 19:21:17,381 INFO misc.py line 119 131400] Train: [72/100][24/800] Data 0.006 (0.006) Batch 0.348 (0.332) Remain 02:08:25 loss: 0.5498 Lr: 0.00128 [2023-12-20 19:21:17,684 INFO misc.py line 119 131400] Train: [72/100][25/800] Data 0.003 (0.005) Batch 0.304 (0.331) Remain 02:07:54 loss: 0.2373 Lr: 0.00128 [2023-12-20 19:21:18,011 INFO misc.py line 119 131400] Train: [72/100][26/800] Data 0.003 (0.005) Batch 0.326 (0.331) Remain 02:07:49 loss: 0.1246 Lr: 0.00128 [2023-12-20 19:21:18,346 INFO misc.py line 119 131400] Train: [72/100][27/800] Data 0.004 (0.005) Batch 0.336 (0.331) Remain 02:07:53 loss: 0.2767 Lr: 0.00128 [2023-12-20 19:21:18,683 INFO misc.py line 119 131400] Train: [72/100][28/800] Data 0.004 (0.005) Batch 0.337 (0.331) Remain 02:07:58 loss: 0.3505 Lr: 0.00128 [2023-12-20 19:21:19,032 INFO misc.py line 119 131400] Train: [72/100][29/800] Data 0.004 (0.005) Batch 0.349 (0.332) Remain 02:08:13 loss: 0.2863 Lr: 0.00128 [2023-12-20 19:21:19,372 INFO misc.py line 119 131400] Train: [72/100][30/800] Data 0.004 (0.005) Batch 0.340 (0.332) Remain 02:08:20 loss: 0.2976 Lr: 0.00128 [2023-12-20 19:21:19,680 INFO misc.py line 119 131400] Train: [72/100][31/800] Data 0.004 (0.005) Batch 0.308 (0.331) Remain 02:07:59 loss: 0.1247 Lr: 0.00128 [2023-12-20 19:21:20,005 INFO misc.py line 119 131400] Train: [72/100][32/800] Data 0.004 (0.005) Batch 0.325 (0.331) Remain 02:07:54 loss: 0.1232 Lr: 0.00128 [2023-12-20 19:21:20,355 INFO misc.py line 119 131400] Train: [72/100][33/800] Data 0.004 (0.005) Batch 0.350 (0.332) Remain 02:08:08 loss: 0.1644 Lr: 0.00128 [2023-12-20 19:21:20,649 INFO misc.py line 119 131400] Train: [72/100][34/800] Data 0.004 (0.005) Batch 0.294 (0.331) Remain 02:07:40 loss: 0.1889 Lr: 0.00128 [2023-12-20 19:21:20,988 INFO misc.py line 119 131400] Train: [72/100][35/800] Data 0.004 (0.005) Batch 0.339 (0.331) Remain 02:07:45 loss: 0.2699 Lr: 0.00128 [2023-12-20 19:21:21,351 INFO misc.py line 119 131400] Train: [72/100][36/800] Data 0.003 (0.005) Batch 0.352 (0.332) Remain 02:08:00 loss: 0.1785 Lr: 0.00128 [2023-12-20 19:21:21,670 INFO misc.py line 119 131400] Train: [72/100][37/800] Data 0.014 (0.005) Batch 0.328 (0.331) Remain 02:07:57 loss: 0.1353 Lr: 0.00128 [2023-12-20 19:21:22,013 INFO misc.py line 119 131400] Train: [72/100][38/800] Data 0.006 (0.005) Batch 0.344 (0.332) Remain 02:08:05 loss: 0.3651 Lr: 0.00128 [2023-12-20 19:21:22,311 INFO misc.py line 119 131400] Train: [72/100][39/800] Data 0.004 (0.005) Batch 0.298 (0.331) Remain 02:07:43 loss: 0.1781 Lr: 0.00128 [2023-12-20 19:21:22,641 INFO misc.py line 119 131400] Train: [72/100][40/800] Data 0.003 (0.005) Batch 0.330 (0.331) Remain 02:07:43 loss: 0.3617 Lr: 0.00128 [2023-12-20 19:21:22,943 INFO misc.py line 119 131400] Train: [72/100][41/800] Data 0.004 (0.005) Batch 0.302 (0.330) Remain 02:07:25 loss: 0.3238 Lr: 0.00128 [2023-12-20 19:21:23,249 INFO misc.py line 119 131400] Train: [72/100][42/800] Data 0.003 (0.005) Batch 0.305 (0.329) Remain 02:07:10 loss: 0.2846 Lr: 0.00128 [2023-12-20 19:21:23,565 INFO misc.py line 119 131400] Train: [72/100][43/800] Data 0.004 (0.005) Batch 0.317 (0.329) Remain 02:07:02 loss: 0.2184 Lr: 0.00128 [2023-12-20 19:21:23,880 INFO misc.py line 119 131400] Train: [72/100][44/800] Data 0.004 (0.005) Batch 0.315 (0.329) Remain 02:06:54 loss: 0.4296 Lr: 0.00128 [2023-12-20 19:21:24,200 INFO misc.py line 119 131400] Train: [72/100][45/800] Data 0.003 (0.005) Batch 0.320 (0.329) Remain 02:06:49 loss: 0.1819 Lr: 0.00128 [2023-12-20 19:21:24,549 INFO misc.py line 119 131400] Train: [72/100][46/800] Data 0.003 (0.005) Batch 0.349 (0.329) Remain 02:06:59 loss: 0.0867 Lr: 0.00128 [2023-12-20 19:21:24,866 INFO misc.py line 119 131400] Train: [72/100][47/800] Data 0.003 (0.005) Batch 0.317 (0.329) Remain 02:06:52 loss: 0.1438 Lr: 0.00128 [2023-12-20 19:21:25,310 INFO misc.py line 119 131400] Train: [72/100][48/800] Data 0.004 (0.005) Batch 0.444 (0.331) Remain 02:07:51 loss: 0.1938 Lr: 0.00128 [2023-12-20 19:21:25,598 INFO misc.py line 119 131400] Train: [72/100][49/800] Data 0.003 (0.005) Batch 0.288 (0.330) Remain 02:07:29 loss: 0.2098 Lr: 0.00127 [2023-12-20 19:21:25,913 INFO misc.py line 119 131400] Train: [72/100][50/800] Data 0.003 (0.005) Batch 0.315 (0.330) Remain 02:07:21 loss: 0.1760 Lr: 0.00127 [2023-12-20 19:21:26,227 INFO misc.py line 119 131400] Train: [72/100][51/800] Data 0.003 (0.005) Batch 0.313 (0.330) Remain 02:07:13 loss: 0.2137 Lr: 0.00127 [2023-12-20 19:21:26,571 INFO misc.py line 119 131400] Train: [72/100][52/800] Data 0.003 (0.005) Batch 0.345 (0.330) Remain 02:07:20 loss: 0.1067 Lr: 0.00127 [2023-12-20 19:21:26,872 INFO misc.py line 119 131400] Train: [72/100][53/800] Data 0.003 (0.005) Batch 0.301 (0.329) Remain 02:07:06 loss: 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INFO misc.py line 119 131400] Train: [72/100][60/800] Data 0.003 (0.004) Batch 0.290 (0.327) Remain 02:06:02 loss: 0.5713 Lr: 0.00127 [2023-12-20 19:21:29,340 INFO misc.py line 119 131400] Train: [72/100][61/800] Data 0.007 (0.004) Batch 0.314 (0.327) Remain 02:05:56 loss: 0.4080 Lr: 0.00127 [2023-12-20 19:21:29,664 INFO misc.py line 119 131400] Train: [72/100][62/800] Data 0.003 (0.004) Batch 0.323 (0.327) Remain 02:05:55 loss: 0.1391 Lr: 0.00127 [2023-12-20 19:21:29,960 INFO misc.py line 119 131400] Train: [72/100][63/800] Data 0.003 (0.004) Batch 0.296 (0.326) Remain 02:05:43 loss: 0.1822 Lr: 0.00127 [2023-12-20 19:21:30,293 INFO misc.py line 119 131400] Train: [72/100][64/800] Data 0.003 (0.004) Batch 0.332 (0.326) Remain 02:05:45 loss: 0.1804 Lr: 0.00127 [2023-12-20 19:21:30,603 INFO misc.py line 119 131400] Train: [72/100][65/800] Data 0.004 (0.004) Batch 0.310 (0.326) Remain 02:05:38 loss: 0.3061 Lr: 0.00127 [2023-12-20 19:21:30,875 INFO misc.py line 119 131400] Train: 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0.292 (0.325) Remain 02:05:09 loss: 0.2027 Lr: 0.00127 [2023-12-20 19:21:33,080 INFO misc.py line 119 131400] Train: [72/100][73/800] Data 0.004 (0.004) Batch 0.279 (0.324) Remain 02:04:53 loss: 0.2442 Lr: 0.00127 [2023-12-20 19:21:33,405 INFO misc.py line 119 131400] Train: [72/100][74/800] Data 0.003 (0.004) Batch 0.324 (0.324) Remain 02:04:53 loss: 0.2131 Lr: 0.00127 [2023-12-20 19:21:33,735 INFO misc.py line 119 131400] Train: [72/100][75/800] Data 0.003 (0.004) Batch 0.330 (0.324) Remain 02:04:55 loss: 0.3512 Lr: 0.00127 [2023-12-20 19:21:34,045 INFO misc.py line 119 131400] Train: [72/100][76/800] Data 0.003 (0.004) Batch 0.311 (0.324) Remain 02:04:50 loss: 0.1587 Lr: 0.00127 [2023-12-20 19:21:34,367 INFO misc.py line 119 131400] Train: [72/100][77/800] Data 0.003 (0.004) Batch 0.320 (0.324) Remain 02:04:48 loss: 0.3014 Lr: 0.00127 [2023-12-20 19:21:34,703 INFO misc.py line 119 131400] Train: [72/100][78/800] Data 0.005 (0.004) Batch 0.338 (0.324) Remain 02:04:52 loss: 0.2222 Lr: 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Batch 0.275 (0.330) Remain 02:03:43 loss: 0.3539 Lr: 0.00121 [2023-12-20 19:25:13,596 INFO misc.py line 119 131400] Train: [72/100][739/800] Data 0.004 (0.005) Batch 0.297 (0.330) Remain 02:03:41 loss: 0.2510 Lr: 0.00121 [2023-12-20 19:25:13,947 INFO misc.py line 119 131400] Train: [72/100][740/800] Data 0.003 (0.005) Batch 0.350 (0.330) Remain 02:03:42 loss: 0.2701 Lr: 0.00121 [2023-12-20 19:25:14,246 INFO misc.py line 119 131400] Train: [72/100][741/800] Data 0.004 (0.005) Batch 0.299 (0.330) Remain 02:03:40 loss: 0.1270 Lr: 0.00121 [2023-12-20 19:25:14,577 INFO misc.py line 119 131400] Train: [72/100][742/800] Data 0.003 (0.005) Batch 0.331 (0.330) Remain 02:03:40 loss: 0.3975 Lr: 0.00121 [2023-12-20 19:25:14,898 INFO misc.py line 119 131400] Train: [72/100][743/800] Data 0.004 (0.005) Batch 0.322 (0.330) Remain 02:03:39 loss: 0.3495 Lr: 0.00121 [2023-12-20 19:25:15,193 INFO misc.py line 119 131400] Train: [72/100][744/800] Data 0.003 (0.004) Batch 0.293 (0.330) Remain 02:03:38 loss: 0.2529 Lr: 0.00121 [2023-12-20 19:25:15,508 INFO misc.py line 119 131400] Train: [72/100][745/800] Data 0.005 (0.004) Batch 0.317 (0.330) Remain 02:03:37 loss: 0.3074 Lr: 0.00121 [2023-12-20 19:25:15,818 INFO misc.py line 119 131400] Train: [72/100][746/800] Data 0.004 (0.004) Batch 0.310 (0.330) Remain 02:03:36 loss: 0.2629 Lr: 0.00120 [2023-12-20 19:25:16,117 INFO misc.py line 119 131400] Train: [72/100][747/800] Data 0.003 (0.004) Batch 0.299 (0.330) Remain 02:03:35 loss: 0.3950 Lr: 0.00120 [2023-12-20 19:25:16,418 INFO misc.py line 119 131400] Train: [72/100][748/800] Data 0.002 (0.004) Batch 0.300 (0.330) Remain 02:03:34 loss: 0.3752 Lr: 0.00120 [2023-12-20 19:25:16,714 INFO misc.py line 119 131400] Train: [72/100][749/800] Data 0.003 (0.004) Batch 0.296 (0.330) Remain 02:03:32 loss: 0.2611 Lr: 0.00120 [2023-12-20 19:25:17,037 INFO misc.py line 119 131400] Train: [72/100][750/800] Data 0.004 (0.004) Batch 0.322 (0.330) Remain 02:03:32 loss: 0.2124 Lr: 0.00120 [2023-12-20 19:25:17,366 INFO misc.py line 119 131400] Train: [72/100][751/800] Data 0.005 (0.004) Batch 0.329 (0.330) Remain 02:03:31 loss: 0.2189 Lr: 0.00120 [2023-12-20 19:25:17,713 INFO misc.py line 119 131400] Train: [72/100][752/800] Data 0.004 (0.004) Batch 0.348 (0.330) Remain 02:03:32 loss: 0.2053 Lr: 0.00120 [2023-12-20 19:25:18,059 INFO misc.py line 119 131400] Train: [72/100][753/800] Data 0.004 (0.004) Batch 0.345 (0.330) Remain 02:03:32 loss: 0.2156 Lr: 0.00120 [2023-12-20 19:25:18,386 INFO misc.py line 119 131400] Train: [72/100][754/800] Data 0.004 (0.004) Batch 0.327 (0.330) Remain 02:03:31 loss: 0.1397 Lr: 0.00120 [2023-12-20 19:25:18,706 INFO misc.py line 119 131400] Train: [72/100][755/800] Data 0.005 (0.004) Batch 0.321 (0.330) Remain 02:03:31 loss: 0.3142 Lr: 0.00120 [2023-12-20 19:25:19,035 INFO misc.py line 119 131400] Train: [72/100][756/800] Data 0.003 (0.004) Batch 0.328 (0.330) Remain 02:03:30 loss: 0.2919 Lr: 0.00120 [2023-12-20 19:25:19,378 INFO misc.py line 119 131400] Train: [72/100][757/800] Data 0.003 (0.004) Batch 0.343 (0.330) Remain 02:03:30 loss: 0.3651 Lr: 0.00120 [2023-12-20 19:25:19,712 INFO misc.py line 119 131400] Train: [72/100][758/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 02:03:30 loss: 0.1822 Lr: 0.00120 [2023-12-20 19:25:20,018 INFO misc.py line 119 131400] Train: [72/100][759/800] Data 0.005 (0.004) Batch 0.306 (0.330) Remain 02:03:29 loss: 0.4059 Lr: 0.00120 [2023-12-20 19:25:20,329 INFO misc.py line 119 131400] Train: [72/100][760/800] Data 0.004 (0.004) Batch 0.310 (0.330) Remain 02:03:28 loss: 0.2855 Lr: 0.00120 [2023-12-20 19:25:20,652 INFO misc.py line 119 131400] Train: [72/100][761/800] Data 0.004 (0.004) Batch 0.324 (0.330) Remain 02:03:28 loss: 0.1983 Lr: 0.00120 [2023-12-20 19:25:20,944 INFO misc.py line 119 131400] Train: [72/100][762/800] Data 0.004 (0.004) Batch 0.293 (0.330) Remain 02:03:26 loss: 0.1170 Lr: 0.00120 [2023-12-20 19:25:21,257 INFO misc.py line 119 131400] Train: [72/100][763/800] Data 0.003 (0.004) Batch 0.312 (0.330) Remain 02:03:25 loss: 0.2938 Lr: 0.00120 [2023-12-20 19:25:21,550 INFO misc.py line 119 131400] Train: [72/100][764/800] Data 0.005 (0.004) Batch 0.293 (0.330) Remain 02:03:24 loss: 0.2990 Lr: 0.00120 [2023-12-20 19:25:21,874 INFO misc.py line 119 131400] Train: [72/100][765/800] Data 0.003 (0.004) Batch 0.324 (0.330) Remain 02:03:23 loss: 0.1872 Lr: 0.00120 [2023-12-20 19:25:22,187 INFO misc.py line 119 131400] Train: [72/100][766/800] Data 0.004 (0.004) Batch 0.313 (0.330) Remain 02:03:23 loss: 0.1391 Lr: 0.00120 [2023-12-20 19:25:22,523 INFO misc.py line 119 131400] Train: [72/100][767/800] Data 0.004 (0.004) Batch 0.337 (0.330) Remain 02:03:22 loss: 0.2839 Lr: 0.00120 [2023-12-20 19:25:22,851 INFO misc.py line 119 131400] Train: [72/100][768/800] Data 0.003 (0.004) Batch 0.327 (0.330) Remain 02:03:22 loss: 0.2298 Lr: 0.00120 [2023-12-20 19:25:23,146 INFO misc.py line 119 131400] Train: [72/100][769/800] Data 0.004 (0.004) Batch 0.295 (0.330) Remain 02:03:21 loss: 0.2356 Lr: 0.00120 [2023-12-20 19:25:23,510 INFO misc.py line 119 131400] Train: [72/100][770/800] Data 0.004 (0.004) Batch 0.361 (0.330) Remain 02:03:21 loss: 0.2178 Lr: 0.00120 [2023-12-20 19:25:23,849 INFO misc.py line 119 131400] Train: [72/100][771/800] Data 0.008 (0.004) Batch 0.343 (0.330) Remain 02:03:21 loss: 0.2796 Lr: 0.00120 [2023-12-20 19:25:24,144 INFO misc.py line 119 131400] Train: [72/100][772/800] Data 0.004 (0.004) Batch 0.293 (0.330) Remain 02:03:20 loss: 0.1957 Lr: 0.00120 [2023-12-20 19:25:24,485 INFO misc.py line 119 131400] Train: [72/100][773/800] Data 0.005 (0.004) Batch 0.343 (0.330) Remain 02:03:20 loss: 0.1899 Lr: 0.00120 [2023-12-20 19:25:24,805 INFO misc.py line 119 131400] Train: [72/100][774/800] Data 0.004 (0.004) Batch 0.320 (0.330) Remain 02:03:19 loss: 0.1930 Lr: 0.00120 [2023-12-20 19:25:25,150 INFO misc.py line 119 131400] Train: [72/100][775/800] Data 0.005 (0.004) Batch 0.345 (0.330) Remain 02:03:19 loss: 0.1871 Lr: 0.00120 [2023-12-20 19:25:25,482 INFO misc.py line 119 131400] Train: [72/100][776/800] Data 0.004 (0.004) Batch 0.333 (0.330) Remain 02:03:19 loss: 0.2665 Lr: 0.00120 [2023-12-20 19:25:25,801 INFO misc.py line 119 131400] Train: [72/100][777/800] Data 0.004 (0.004) Batch 0.315 (0.330) Remain 02:03:18 loss: 0.1180 Lr: 0.00120 [2023-12-20 19:25:26,138 INFO misc.py line 119 131400] Train: [72/100][778/800] Data 0.007 (0.004) Batch 0.342 (0.330) Remain 02:03:18 loss: 0.3790 Lr: 0.00120 [2023-12-20 19:25:26,459 INFO misc.py line 119 131400] Train: [72/100][779/800] Data 0.003 (0.004) Batch 0.321 (0.330) Remain 02:03:18 loss: 0.1762 Lr: 0.00120 [2023-12-20 19:25:26,830 INFO misc.py line 119 131400] Train: [72/100][780/800] Data 0.003 (0.004) Batch 0.371 (0.330) Remain 02:03:19 loss: 0.2699 Lr: 0.00120 [2023-12-20 19:25:27,191 INFO misc.py line 119 131400] Train: [72/100][781/800] Data 0.003 (0.004) Batch 0.360 (0.330) Remain 02:03:19 loss: 0.3210 Lr: 0.00120 [2023-12-20 19:25:27,544 INFO misc.py line 119 131400] Train: [72/100][782/800] Data 0.005 (0.004) Batch 0.353 (0.330) Remain 02:03:20 loss: 0.3780 Lr: 0.00120 [2023-12-20 19:25:27,876 INFO misc.py line 119 131400] Train: [72/100][783/800] Data 0.005 (0.004) Batch 0.333 (0.330) Remain 02:03:19 loss: 0.1762 Lr: 0.00120 [2023-12-20 19:25:28,237 INFO misc.py line 119 131400] Train: [72/100][784/800] Data 0.004 (0.004) Batch 0.360 (0.330) Remain 02:03:20 loss: 0.2206 Lr: 0.00120 [2023-12-20 19:25:28,600 INFO misc.py line 119 131400] Train: [72/100][785/800] Data 0.004 (0.004) Batch 0.363 (0.330) Remain 02:03:20 loss: 0.1661 Lr: 0.00120 [2023-12-20 19:25:28,943 INFO misc.py line 119 131400] Train: [72/100][786/800] Data 0.004 (0.004) Batch 0.344 (0.330) Remain 02:03:21 loss: 0.3414 Lr: 0.00120 [2023-12-20 19:25:29,297 INFO misc.py line 119 131400] Train: [72/100][787/800] Data 0.004 (0.004) Batch 0.353 (0.330) Remain 02:03:21 loss: 0.3182 Lr: 0.00120 [2023-12-20 19:25:29,641 INFO misc.py line 119 131400] Train: [72/100][788/800] Data 0.004 (0.004) Batch 0.343 (0.330) Remain 02:03:21 loss: 0.2194 Lr: 0.00120 [2023-12-20 19:25:29,960 INFO misc.py line 119 131400] Train: [72/100][789/800] Data 0.005 (0.004) Batch 0.320 (0.330) Remain 02:03:20 loss: 0.2382 Lr: 0.00120 [2023-12-20 19:25:30,269 INFO misc.py line 119 131400] Train: [72/100][790/800] Data 0.004 (0.004) Batch 0.309 (0.330) Remain 02:03:19 loss: 0.1015 Lr: 0.00120 [2023-12-20 19:25:30,619 INFO misc.py line 119 131400] Train: [72/100][791/800] Data 0.003 (0.004) Batch 0.351 (0.330) Remain 02:03:20 loss: 0.3073 Lr: 0.00120 [2023-12-20 19:25:30,943 INFO misc.py line 119 131400] Train: [72/100][792/800] Data 0.002 (0.004) Batch 0.322 (0.330) Remain 02:03:19 loss: 0.2108 Lr: 0.00120 [2023-12-20 19:25:31,280 INFO misc.py line 119 131400] Train: [72/100][793/800] Data 0.005 (0.004) Batch 0.338 (0.330) Remain 02:03:19 loss: 0.1166 Lr: 0.00120 [2023-12-20 19:25:31,588 INFO misc.py line 119 131400] Train: [72/100][794/800] Data 0.004 (0.004) Batch 0.309 (0.330) Remain 02:03:18 loss: 0.1543 Lr: 0.00120 [2023-12-20 19:25:31,922 INFO misc.py line 119 131400] Train: [72/100][795/800] Data 0.003 (0.004) Batch 0.333 (0.330) Remain 02:03:18 loss: 0.2306 Lr: 0.00120 [2023-12-20 19:25:32,239 INFO misc.py line 119 131400] Train: [72/100][796/800] Data 0.004 (0.004) Batch 0.316 (0.330) Remain 02:03:17 loss: 0.2693 Lr: 0.00120 [2023-12-20 19:25:32,532 INFO misc.py line 119 131400] Train: [72/100][797/800] Data 0.005 (0.004) Batch 0.295 (0.330) Remain 02:03:16 loss: 0.3013 Lr: 0.00120 [2023-12-20 19:25:32,834 INFO misc.py line 119 131400] Train: [72/100][798/800] Data 0.003 (0.004) Batch 0.302 (0.330) Remain 02:03:15 loss: 0.2220 Lr: 0.00120 [2023-12-20 19:25:33,117 INFO misc.py line 119 131400] Train: [72/100][799/800] Data 0.003 (0.004) Batch 0.282 (0.330) Remain 02:03:13 loss: 0.2538 Lr: 0.00120 [2023-12-20 19:25:33,403 INFO misc.py line 119 131400] Train: [72/100][800/800] Data 0.003 (0.004) Batch 0.284 (0.330) Remain 02:03:11 loss: 0.1375 Lr: 0.00120 [2023-12-20 19:25:33,403 INFO misc.py line 136 131400] Train result: loss: 0.2562 [2023-12-20 19:25:33,404 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:25:55,069 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2389 [2023-12-20 19:25:55,151 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1457 [2023-12-20 19:25:55,259 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5405 [2023-12-20 19:25:55,374 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4057 [2023-12-20 19:25:55,495 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3567 [2023-12-20 19:25:55,598 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.0497 [2023-12-20 19:25:55,691 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8894 [2023-12-20 19:25:55,805 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0697 [2023-12-20 19:25:55,891 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2878 [2023-12-20 19:25:55,981 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3645 [2023-12-20 19:25:56,098 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3557 [2023-12-20 19:25:56,234 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3119 [2023-12-20 19:25:56,355 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3232 [2023-12-20 19:25:56,519 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2097 [2023-12-20 19:25:56,630 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1498 [2023-12-20 19:25:56,764 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6849 [2023-12-20 19:25:56,881 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2550 [2023-12-20 19:25:57,001 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6144 [2023-12-20 19:25:57,127 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1472 [2023-12-20 19:25:57,204 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3048 [2023-12-20 19:25:57,317 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1730 [2023-12-20 19:25:57,479 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1419 [2023-12-20 19:25:57,604 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9868 [2023-12-20 19:25:57,748 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2688 [2023-12-20 19:25:57,892 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2428 [2023-12-20 19:25:57,986 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4308 [2023-12-20 19:25:58,150 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5669 [2023-12-20 19:25:58,286 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5475 [2023-12-20 19:25:58,381 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7793 [2023-12-20 19:25:58,531 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.2970 [2023-12-20 19:25:58,639 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.6025 [2023-12-20 19:25:58,760 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4301 [2023-12-20 19:25:58,851 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1193 [2023-12-20 19:25:58,941 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1882 [2023-12-20 19:25:59,041 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9240 [2023-12-20 19:25:59,135 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5301 [2023-12-20 19:25:59,270 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9237 [2023-12-20 19:25:59,385 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0856 [2023-12-20 19:25:59,469 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5828 [2023-12-20 19:25:59,622 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2505 [2023-12-20 19:25:59,777 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0182 [2023-12-20 19:25:59,882 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0636 [2023-12-20 19:26:00,002 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2990 [2023-12-20 19:26:00,149 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8618 [2023-12-20 19:26:00,276 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6156 [2023-12-20 19:26:00,395 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5021 [2023-12-20 19:26:00,575 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3348 [2023-12-20 19:26:00,685 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5662 [2023-12-20 19:26:00,836 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7490 [2023-12-20 19:26:00,928 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1944 [2023-12-20 19:26:01,020 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5575 [2023-12-20 19:26:01,127 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4629 [2023-12-20 19:26:01,276 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1920 [2023-12-20 19:26:01,410 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3133 [2023-12-20 19:26:01,512 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.6464 [2023-12-20 19:26:01,600 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6245 [2023-12-20 19:26:01,708 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3465 [2023-12-20 19:26:01,884 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2689 [2023-12-20 19:26:01,991 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.7092 [2023-12-20 19:26:02,101 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2541 [2023-12-20 19:26:02,208 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4767 [2023-12-20 19:26:02,306 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2560 [2023-12-20 19:26:02,398 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3667 [2023-12-20 19:26:02,500 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6106 [2023-12-20 19:26:02,630 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7561 [2023-12-20 19:26:02,724 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.6134 [2023-12-20 19:26:02,825 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6871 [2023-12-20 19:26:02,919 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0100 [2023-12-20 19:26:03,010 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.4062 [2023-12-20 19:26:03,113 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0116 [2023-12-20 19:26:03,215 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5962 [2023-12-20 19:26:03,315 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.3878 [2023-12-20 19:26:03,452 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1167 [2023-12-20 19:26:03,550 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5846 [2023-12-20 19:26:03,674 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6267 [2023-12-20 19:26:03,779 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7037 [2023-12-20 19:26:03,870 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5288 [2023-12-20 19:26:04,026 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0529 [2023-12-20 19:26:05,806 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7568/0.8356/0.9161. [2023-12-20 19:26:05,807 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8648/0.9486 [2023-12-20 19:26:05,807 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9614/0.9887 [2023-12-20 19:26:05,807 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7199/0.8054 [2023-12-20 19:26:05,807 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8116/0.8618 [2023-12-20 19:26:05,807 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9104/0.9588 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8731/0.9544 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7832/0.8586 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7141/0.8359 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6732/0.7853 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8188/0.9353 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3875/0.4491 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7132/0.8424 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6838/0.8345 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7430/0.8397 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6775/0.7447 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6678/0.7096 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9486/0.9773 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6900/0.7930 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8837/0.9266 [2023-12-20 19:26:05,808 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6114/0.6628 [2023-12-20 19:26:05,809 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:26:05,809 INFO misc.py line 165 131400] Currently Best mIoU: 0.7633 [2023-12-20 19:26:05,809 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:26:09,625 INFO misc.py line 119 131400] Train: [73/100][1/800] Data 1.280 (1.280) Batch 1.618 (1.618) Remain 10:04:12 loss: 0.2190 Lr: 0.00120 [2023-12-20 19:26:09,970 INFO misc.py line 119 131400] Train: [73/100][2/800] Data 0.005 (0.005) Batch 0.345 (0.345) Remain 02:08:40 loss: 0.2134 Lr: 0.00120 [2023-12-20 19:26:10,274 INFO misc.py line 119 131400] Train: [73/100][3/800] Data 0.004 (0.004) Batch 0.304 (0.304) Remain 01:53:24 loss: 0.2332 Lr: 0.00120 [2023-12-20 19:26:10,630 INFO misc.py line 119 131400] Train: [73/100][4/800] Data 0.006 (0.006) Batch 0.357 (0.357) Remain 02:13:04 loss: 0.6637 Lr: 0.00120 [2023-12-20 19:26:10,978 INFO misc.py line 119 131400] Train: [73/100][5/800] Data 0.004 (0.005) Batch 0.348 (0.352) Remain 02:11:27 loss: 0.4057 Lr: 0.00120 [2023-12-20 19:26:11,333 INFO misc.py line 119 131400] Train: [73/100][6/800] Data 0.004 (0.005) Batch 0.352 (0.352) Remain 02:11:23 loss: 0.4878 Lr: 0.00120 [2023-12-20 19:26:11,694 INFO misc.py line 119 131400] Train: [73/100][7/800] Data 0.009 (0.006) Batch 0.365 (0.355) Remain 02:12:37 loss: 0.4783 Lr: 0.00120 [2023-12-20 19:26:12,015 INFO misc.py line 119 131400] Train: [73/100][8/800] Data 0.003 (0.005) Batch 0.320 (0.348) Remain 02:10:00 loss: 0.2922 Lr: 0.00120 [2023-12-20 19:26:12,306 INFO misc.py line 119 131400] Train: [73/100][9/800] Data 0.003 (0.005) Batch 0.291 (0.339) Remain 02:06:25 loss: 0.2274 Lr: 0.00120 [2023-12-20 19:26:12,649 INFO misc.py line 119 131400] Train: [73/100][10/800] Data 0.004 (0.005) Batch 0.343 (0.339) Remain 02:06:38 loss: 0.1471 Lr: 0.00120 [2023-12-20 19:26:13,007 INFO misc.py line 119 131400] Train: [73/100][11/800] Data 0.004 (0.005) Batch 0.358 (0.342) Remain 02:07:31 loss: 0.2741 Lr: 0.00120 [2023-12-20 19:26:13,322 INFO misc.py line 119 131400] Train: [73/100][12/800] Data 0.004 (0.004) Batch 0.314 (0.339) Remain 02:06:22 loss: 0.3374 Lr: 0.00120 [2023-12-20 19:26:13,614 INFO misc.py line 119 131400] Train: [73/100][13/800] Data 0.004 (0.004) Batch 0.293 (0.334) Remain 02:04:40 loss: 0.2346 Lr: 0.00120 [2023-12-20 19:26:13,932 INFO misc.py line 119 131400] Train: [73/100][14/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 02:04:06 loss: 0.1352 Lr: 0.00120 [2023-12-20 19:26:14,234 INFO misc.py line 119 131400] Train: [73/100][15/800] Data 0.004 (0.004) Batch 0.302 (0.330) Remain 02:03:08 loss: 0.1446 Lr: 0.00120 [2023-12-20 19:26:14,570 INFO misc.py line 119 131400] Train: [73/100][16/800] Data 0.004 (0.004) Batch 0.336 (0.331) Remain 02:03:18 loss: 0.2124 Lr: 0.00120 [2023-12-20 19:26:14,919 INFO misc.py line 119 131400] Train: [73/100][17/800] Data 0.003 (0.004) Batch 0.348 (0.332) Remain 02:03:45 loss: 0.4852 Lr: 0.00120 [2023-12-20 19:26:15,309 INFO misc.py line 119 131400] Train: [73/100][18/800] Data 0.004 (0.004) Batch 0.389 (0.336) Remain 02:05:10 loss: 0.1393 Lr: 0.00120 [2023-12-20 19:26:15,629 INFO misc.py line 119 131400] Train: [73/100][19/800] Data 0.005 (0.004) Batch 0.322 (0.335) Remain 02:04:52 loss: 0.2800 Lr: 0.00120 [2023-12-20 19:26:15,963 INFO misc.py line 119 131400] Train: [73/100][20/800] Data 0.003 (0.004) Batch 0.334 (0.335) Remain 02:04:50 loss: 0.2416 Lr: 0.00120 [2023-12-20 19:26:16,476 INFO misc.py line 119 131400] Train: [73/100][21/800] Data 0.003 (0.004) Batch 0.513 (0.345) Remain 02:08:31 loss: 0.6028 Lr: 0.00120 [2023-12-20 19:26:16,800 INFO misc.py line 119 131400] Train: [73/100][22/800] Data 0.003 (0.004) Batch 0.324 (0.344) Remain 02:08:07 loss: 0.1865 Lr: 0.00120 [2023-12-20 19:26:17,150 INFO misc.py line 119 131400] Train: [73/100][23/800] Data 0.004 (0.004) Batch 0.349 (0.344) Remain 02:08:13 loss: 0.1071 Lr: 0.00120 [2023-12-20 19:26:17,484 INFO misc.py line 119 131400] Train: [73/100][24/800] Data 0.004 (0.004) Batch 0.331 (0.343) Remain 02:07:58 loss: 0.2630 Lr: 0.00120 [2023-12-20 19:26:17,800 INFO misc.py line 119 131400] Train: [73/100][25/800] Data 0.008 (0.004) Batch 0.321 (0.342) Remain 02:07:35 loss: 0.1522 Lr: 0.00120 [2023-12-20 19:26:18,121 INFO misc.py line 119 131400] Train: [73/100][26/800] Data 0.003 (0.004) Batch 0.321 (0.341) Remain 02:07:14 loss: 0.1438 Lr: 0.00120 [2023-12-20 19:26:18,630 INFO misc.py line 119 131400] Train: [73/100][27/800] Data 0.003 (0.004) Batch 0.503 (0.348) Remain 02:09:45 loss: 0.3217 Lr: 0.00120 [2023-12-20 19:26:18,957 INFO misc.py line 119 131400] Train: [73/100][28/800] Data 0.009 (0.004) Batch 0.332 (0.347) Remain 02:09:31 loss: 0.1748 Lr: 0.00120 [2023-12-20 19:26:19,287 INFO misc.py line 119 131400] Train: [73/100][29/800] Data 0.003 (0.004) Batch 0.330 (0.347) Remain 02:09:15 loss: 0.3403 Lr: 0.00120 [2023-12-20 19:26:19,619 INFO misc.py line 119 131400] Train: [73/100][30/800] Data 0.003 (0.004) Batch 0.332 (0.346) Remain 02:09:03 loss: 0.2687 Lr: 0.00120 [2023-12-20 19:26:19,933 INFO misc.py line 119 131400] Train: [73/100][31/800] Data 0.003 (0.004) Batch 0.313 (0.345) Remain 02:08:36 loss: 0.1406 Lr: 0.00120 [2023-12-20 19:26:20,259 INFO misc.py line 119 131400] Train: [73/100][32/800] Data 0.003 (0.004) Batch 0.326 (0.344) Remain 02:08:21 loss: 0.3813 Lr: 0.00120 [2023-12-20 19:26:20,577 INFO misc.py line 119 131400] Train: [73/100][33/800] Data 0.004 (0.004) Batch 0.318 (0.343) Remain 02:08:02 loss: 0.3647 Lr: 0.00120 [2023-12-20 19:26:20,918 INFO misc.py line 119 131400] Train: [73/100][34/800] Data 0.003 (0.004) Batch 0.341 (0.343) Remain 02:07:59 loss: 0.3153 Lr: 0.00120 [2023-12-20 19:26:21,248 INFO misc.py line 119 131400] Train: [73/100][35/800] Data 0.004 (0.004) Batch 0.330 (0.343) Remain 02:07:50 loss: 0.1892 Lr: 0.00120 [2023-12-20 19:26:21,554 INFO misc.py line 119 131400] Train: [73/100][36/800] Data 0.003 (0.004) Batch 0.306 (0.342) Remain 02:07:24 loss: 0.2232 Lr: 0.00120 [2023-12-20 19:26:21,864 INFO misc.py line 119 131400] Train: [73/100][37/800] Data 0.004 (0.004) Batch 0.311 (0.341) Remain 02:07:03 loss: 0.1508 Lr: 0.00120 [2023-12-20 19:26:22,184 INFO misc.py line 119 131400] Train: [73/100][38/800] Data 0.003 (0.004) Batch 0.320 (0.340) Remain 02:06:50 loss: 0.1564 Lr: 0.00120 [2023-12-20 19:26:22,463 INFO misc.py line 119 131400] Train: [73/100][39/800] Data 0.003 (0.004) Batch 0.279 (0.339) Remain 02:06:11 loss: 0.3273 Lr: 0.00120 [2023-12-20 19:26:22,750 INFO misc.py line 119 131400] Train: [73/100][40/800] Data 0.004 (0.004) Batch 0.287 (0.337) Remain 02:05:40 loss: 0.1679 Lr: 0.00120 [2023-12-20 19:26:23,083 INFO misc.py line 119 131400] Train: [73/100][41/800] Data 0.003 (0.004) Batch 0.332 (0.337) Remain 02:05:36 loss: 0.1862 Lr: 0.00120 [2023-12-20 19:26:23,422 INFO misc.py line 119 131400] Train: [73/100][42/800] Data 0.004 (0.004) Batch 0.339 (0.337) Remain 02:05:37 loss: 0.3436 Lr: 0.00120 [2023-12-20 19:26:23,737 INFO misc.py line 119 131400] Train: [73/100][43/800] Data 0.004 (0.004) Batch 0.316 (0.337) Remain 02:05:25 loss: 0.2441 Lr: 0.00120 [2023-12-20 19:26:24,062 INFO misc.py line 119 131400] Train: [73/100][44/800] Data 0.003 (0.004) Batch 0.324 (0.336) Remain 02:05:18 loss: 0.2146 Lr: 0.00120 [2023-12-20 19:26:24,374 INFO misc.py line 119 131400] Train: [73/100][45/800] Data 0.003 (0.004) Batch 0.312 (0.336) Remain 02:05:05 loss: 0.2375 Lr: 0.00120 [2023-12-20 19:26:24,718 INFO misc.py line 119 131400] Train: [73/100][46/800] Data 0.004 (0.004) Batch 0.343 (0.336) Remain 02:05:08 loss: 0.2243 Lr: 0.00120 [2023-12-20 19:26:25,031 INFO misc.py line 119 131400] Train: [73/100][47/800] Data 0.004 (0.004) Batch 0.314 (0.335) Remain 02:04:57 loss: 0.2630 Lr: 0.00119 [2023-12-20 19:26:25,360 INFO misc.py line 119 131400] Train: [73/100][48/800] Data 0.003 (0.004) Batch 0.326 (0.335) Remain 02:04:52 loss: 0.3027 Lr: 0.00119 [2023-12-20 19:26:25,688 INFO misc.py line 119 131400] Train: [73/100][49/800] Data 0.006 (0.004) Batch 0.332 (0.335) Remain 02:04:50 loss: 0.3017 Lr: 0.00119 [2023-12-20 19:26:26,005 INFO misc.py line 119 131400] Train: [73/100][50/800] Data 0.003 (0.004) Batch 0.316 (0.335) Remain 02:04:40 loss: 0.3718 Lr: 0.00119 [2023-12-20 19:26:26,311 INFO misc.py line 119 131400] Train: [73/100][51/800] Data 0.004 (0.004) Batch 0.306 (0.334) Remain 02:04:27 loss: 0.2033 Lr: 0.00119 [2023-12-20 19:26:26,657 INFO misc.py line 119 131400] Train: [73/100][52/800] Data 0.003 (0.004) Batch 0.345 (0.334) Remain 02:04:31 loss: 0.3359 Lr: 0.00119 [2023-12-20 19:26:27,002 INFO misc.py line 119 131400] Train: [73/100][53/800] Data 0.004 (0.004) Batch 0.346 (0.335) Remain 02:04:36 loss: 0.3711 Lr: 0.00119 [2023-12-20 19:26:27,336 INFO misc.py line 119 131400] Train: [73/100][54/800] Data 0.003 (0.004) Batch 0.334 (0.335) Remain 02:04:36 loss: 0.2166 Lr: 0.00119 [2023-12-20 19:26:27,669 INFO misc.py line 119 131400] Train: [73/100][55/800] Data 0.004 (0.004) Batch 0.332 (0.335) Remain 02:04:34 loss: 0.1983 Lr: 0.00119 [2023-12-20 19:26:27,993 INFO misc.py line 119 131400] Train: [73/100][56/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 02:04:30 loss: 0.2177 Lr: 0.00119 [2023-12-20 19:26:28,319 INFO misc.py line 119 131400] Train: [73/100][57/800] Data 0.005 (0.004) Batch 0.326 (0.334) Remain 02:04:26 loss: 0.1297 Lr: 0.00119 [2023-12-20 19:26:28,673 INFO misc.py line 119 131400] Train: [73/100][58/800] Data 0.004 (0.004) Batch 0.355 (0.335) Remain 02:04:34 loss: 0.3127 Lr: 0.00119 [2023-12-20 19:26:29,019 INFO misc.py line 119 131400] Train: [73/100][59/800] Data 0.003 (0.004) Batch 0.345 (0.335) Remain 02:04:38 loss: 0.3088 Lr: 0.00119 [2023-12-20 19:26:29,375 INFO misc.py line 119 131400] Train: [73/100][60/800] Data 0.004 (0.004) Batch 0.356 (0.335) Remain 02:04:46 loss: 0.2852 Lr: 0.00119 [2023-12-20 19:26:29,722 INFO misc.py line 119 131400] Train: [73/100][61/800] Data 0.006 (0.004) Batch 0.348 (0.335) Remain 02:04:50 loss: 0.3167 Lr: 0.00119 [2023-12-20 19:26:30,062 INFO misc.py line 119 131400] Train: [73/100][62/800] Data 0.004 (0.004) Batch 0.340 (0.335) Remain 02:04:52 loss: 0.1194 Lr: 0.00119 [2023-12-20 19:26:30,427 INFO misc.py line 119 131400] Train: [73/100][63/800] Data 0.005 (0.004) Batch 0.364 (0.336) Remain 02:05:02 loss: 0.2608 Lr: 0.00119 [2023-12-20 19:26:30,765 INFO misc.py line 119 131400] Train: [73/100][64/800] Data 0.004 (0.004) Batch 0.339 (0.336) Remain 02:05:03 loss: 0.3258 Lr: 0.00119 [2023-12-20 19:26:31,092 INFO misc.py line 119 131400] Train: [73/100][65/800] Data 0.004 (0.004) Batch 0.326 (0.336) Remain 02:04:59 loss: 0.2631 Lr: 0.00119 [2023-12-20 19:26:31,420 INFO misc.py line 119 131400] Train: [73/100][66/800] Data 0.004 (0.004) Batch 0.328 (0.336) Remain 02:04:56 loss: 0.2756 Lr: 0.00119 [2023-12-20 19:26:31,777 INFO misc.py line 119 131400] Train: [73/100][67/800] Data 0.004 (0.004) Batch 0.356 (0.336) Remain 02:05:03 loss: 0.2814 Lr: 0.00119 [2023-12-20 19:26:32,134 INFO misc.py line 119 131400] Train: [73/100][68/800] Data 0.004 (0.004) Batch 0.357 (0.336) Remain 02:05:10 loss: 0.1921 Lr: 0.00119 [2023-12-20 19:26:32,468 INFO misc.py line 119 131400] Train: [73/100][69/800] Data 0.004 (0.004) Batch 0.334 (0.336) Remain 02:05:09 loss: 0.2496 Lr: 0.00119 [2023-12-20 19:26:32,838 INFO misc.py line 119 131400] Train: [73/100][70/800] Data 0.004 (0.004) Batch 0.368 (0.337) Remain 02:05:19 loss: 0.1652 Lr: 0.00119 [2023-12-20 19:26:33,197 INFO misc.py line 119 131400] Train: [73/100][71/800] Data 0.006 (0.004) Batch 0.361 (0.337) Remain 02:05:27 loss: 0.2733 Lr: 0.00119 [2023-12-20 19:26:33,532 INFO misc.py line 119 131400] Train: [73/100][72/800] Data 0.004 (0.004) Batch 0.335 (0.337) Remain 02:05:26 loss: 0.2694 Lr: 0.00119 [2023-12-20 19:26:33,841 INFO misc.py line 119 131400] Train: [73/100][73/800] Data 0.003 (0.004) Batch 0.310 (0.337) Remain 02:05:17 loss: 0.3704 Lr: 0.00119 [2023-12-20 19:26:34,199 INFO misc.py line 119 131400] Train: [73/100][74/800] Data 0.003 (0.004) Batch 0.356 (0.337) Remain 02:05:23 loss: 0.2725 Lr: 0.00119 [2023-12-20 19:26:34,592 INFO misc.py line 119 131400] Train: [73/100][75/800] Data 0.005 (0.004) Batch 0.394 (0.338) Remain 02:05:40 loss: 0.2899 Lr: 0.00119 [2023-12-20 19:26:34,945 INFO misc.py line 119 131400] Train: [73/100][76/800] Data 0.003 (0.004) Batch 0.353 (0.338) Remain 02:05:44 loss: 0.3168 Lr: 0.00119 [2023-12-20 19:26:35,285 INFO misc.py line 119 131400] Train: [73/100][77/800] Data 0.004 (0.004) Batch 0.340 (0.338) Remain 02:05:45 loss: 0.3357 Lr: 0.00119 [2023-12-20 19:26:35,651 INFO misc.py line 119 131400] Train: [73/100][78/800] Data 0.004 (0.004) Batch 0.366 (0.338) Remain 02:05:52 loss: 0.2521 Lr: 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Batch 0.365 (0.334) Remain 02:00:34 loss: 0.3534 Lr: 0.00113 [2023-12-20 19:30:16,078 INFO misc.py line 119 131400] Train: [73/100][739/800] Data 0.004 (0.004) Batch 0.326 (0.334) Remain 02:00:34 loss: 0.2211 Lr: 0.00113 [2023-12-20 19:30:16,422 INFO misc.py line 119 131400] Train: [73/100][740/800] Data 0.003 (0.004) Batch 0.342 (0.334) Remain 02:00:34 loss: 0.3029 Lr: 0.00113 [2023-12-20 19:30:16,747 INFO misc.py line 119 131400] Train: [73/100][741/800] Data 0.004 (0.004) Batch 0.326 (0.334) Remain 02:00:33 loss: 0.2446 Lr: 0.00113 [2023-12-20 19:30:17,111 INFO misc.py line 119 131400] Train: [73/100][742/800] Data 0.003 (0.004) Batch 0.364 (0.334) Remain 02:00:34 loss: 0.2196 Lr: 0.00113 [2023-12-20 19:30:17,447 INFO misc.py line 119 131400] Train: [73/100][743/800] Data 0.003 (0.004) Batch 0.335 (0.334) Remain 02:00:33 loss: 0.2930 Lr: 0.00113 [2023-12-20 19:30:17,817 INFO misc.py line 119 131400] Train: [73/100][744/800] Data 0.005 (0.004) Batch 0.370 (0.334) Remain 02:00:34 loss: 0.4988 Lr: 0.00113 [2023-12-20 19:30:18,151 INFO misc.py line 119 131400] Train: [73/100][745/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 02:00:34 loss: 0.2862 Lr: 0.00113 [2023-12-20 19:30:18,469 INFO misc.py line 119 131400] Train: [73/100][746/800] Data 0.004 (0.004) Batch 0.318 (0.334) Remain 02:00:33 loss: 0.3445 Lr: 0.00113 [2023-12-20 19:30:18,798 INFO misc.py line 119 131400] Train: [73/100][747/800] Data 0.004 (0.004) Batch 0.329 (0.334) Remain 02:00:32 loss: 0.2141 Lr: 0.00113 [2023-12-20 19:30:19,117 INFO misc.py line 119 131400] Train: [73/100][748/800] Data 0.005 (0.004) Batch 0.320 (0.334) Remain 02:00:32 loss: 0.3918 Lr: 0.00113 [2023-12-20 19:30:19,452 INFO misc.py line 119 131400] Train: [73/100][749/800] Data 0.004 (0.004) Batch 0.333 (0.334) Remain 02:00:31 loss: 0.1880 Lr: 0.00113 [2023-12-20 19:30:19,827 INFO misc.py line 119 131400] Train: [73/100][750/800] Data 0.006 (0.004) Batch 0.375 (0.334) Remain 02:00:32 loss: 0.2060 Lr: 0.00113 [2023-12-20 19:30:20,196 INFO misc.py line 119 131400] Train: [73/100][751/800] Data 0.005 (0.004) Batch 0.371 (0.334) Remain 02:00:33 loss: 0.3469 Lr: 0.00113 [2023-12-20 19:30:20,558 INFO misc.py line 119 131400] Train: [73/100][752/800] Data 0.003 (0.004) Batch 0.358 (0.334) Remain 02:00:33 loss: 0.2309 Lr: 0.00113 [2023-12-20 19:30:20,873 INFO misc.py line 119 131400] Train: [73/100][753/800] Data 0.009 (0.004) Batch 0.319 (0.334) Remain 02:00:32 loss: 0.1695 Lr: 0.00113 [2023-12-20 19:30:21,215 INFO misc.py line 119 131400] Train: [73/100][754/800] Data 0.003 (0.004) Batch 0.341 (0.334) Remain 02:00:32 loss: 0.2530 Lr: 0.00113 [2023-12-20 19:30:21,559 INFO misc.py line 119 131400] Train: [73/100][755/800] Data 0.005 (0.004) Batch 0.344 (0.334) Remain 02:00:32 loss: 0.2800 Lr: 0.00113 [2023-12-20 19:30:21,884 INFO misc.py line 119 131400] Train: [73/100][756/800] Data 0.006 (0.004) Batch 0.323 (0.334) Remain 02:00:32 loss: 0.3202 Lr: 0.00113 [2023-12-20 19:30:22,274 INFO misc.py line 119 131400] Train: [73/100][757/800] Data 0.006 (0.004) Batch 0.392 (0.334) Remain 02:00:33 loss: 0.1693 Lr: 0.00113 [2023-12-20 19:30:22,655 INFO misc.py line 119 131400] Train: [73/100][758/800] Data 0.006 (0.004) Batch 0.381 (0.334) Remain 02:00:34 loss: 0.1095 Lr: 0.00113 [2023-12-20 19:30:22,987 INFO misc.py line 119 131400] Train: [73/100][759/800] Data 0.004 (0.004) Batch 0.331 (0.334) Remain 02:00:34 loss: 0.1523 Lr: 0.00113 [2023-12-20 19:30:23,297 INFO misc.py line 119 131400] Train: [73/100][760/800] Data 0.004 (0.004) Batch 0.311 (0.334) Remain 02:00:33 loss: 0.1504 Lr: 0.00113 [2023-12-20 19:30:23,614 INFO misc.py line 119 131400] Train: [73/100][761/800] Data 0.003 (0.004) Batch 0.317 (0.334) Remain 02:00:32 loss: 0.1534 Lr: 0.00113 [2023-12-20 19:30:23,993 INFO misc.py line 119 131400] Train: [73/100][762/800] Data 0.004 (0.004) Batch 0.369 (0.334) Remain 02:00:32 loss: 0.1405 Lr: 0.00112 [2023-12-20 19:30:24,329 INFO misc.py line 119 131400] Train: [73/100][763/800] Data 0.014 (0.004) Batch 0.346 (0.334) Remain 02:00:32 loss: 0.3234 Lr: 0.00112 [2023-12-20 19:30:24,687 INFO misc.py line 119 131400] Train: [73/100][764/800] Data 0.005 (0.004) Batch 0.359 (0.334) Remain 02:00:33 loss: 0.1700 Lr: 0.00112 [2023-12-20 19:30:25,025 INFO misc.py line 119 131400] Train: [73/100][765/800] Data 0.004 (0.004) Batch 0.337 (0.334) Remain 02:00:32 loss: 0.2706 Lr: 0.00112 [2023-12-20 19:30:25,344 INFO misc.py line 119 131400] Train: [73/100][766/800] Data 0.004 (0.004) Batch 0.318 (0.334) Remain 02:00:32 loss: 0.1595 Lr: 0.00112 [2023-12-20 19:30:25,681 INFO misc.py line 119 131400] Train: [73/100][767/800] Data 0.006 (0.004) Batch 0.339 (0.334) Remain 02:00:31 loss: 0.2153 Lr: 0.00112 [2023-12-20 19:30:26,004 INFO misc.py line 119 131400] Train: [73/100][768/800] Data 0.003 (0.004) Batch 0.322 (0.334) Remain 02:00:31 loss: 0.3610 Lr: 0.00112 [2023-12-20 19:30:26,338 INFO misc.py line 119 131400] Train: [73/100][769/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 02:00:30 loss: 0.2496 Lr: 0.00112 [2023-12-20 19:30:26,666 INFO misc.py line 119 131400] Train: [73/100][770/800] Data 0.006 (0.004) Batch 0.328 (0.334) Remain 02:00:30 loss: 0.3448 Lr: 0.00112 [2023-12-20 19:30:27,011 INFO misc.py line 119 131400] Train: [73/100][771/800] Data 0.004 (0.004) Batch 0.345 (0.334) Remain 02:00:30 loss: 0.2221 Lr: 0.00112 [2023-12-20 19:30:27,359 INFO misc.py line 119 131400] Train: [73/100][772/800] Data 0.003 (0.004) Batch 0.347 (0.334) Remain 02:00:30 loss: 0.2229 Lr: 0.00112 [2023-12-20 19:30:27,723 INFO misc.py line 119 131400] Train: [73/100][773/800] Data 0.005 (0.004) Batch 0.363 (0.334) Remain 02:00:30 loss: 0.2069 Lr: 0.00112 [2023-12-20 19:30:28,010 INFO misc.py line 119 131400] Train: [73/100][774/800] Data 0.005 (0.004) Batch 0.289 (0.334) Remain 02:00:29 loss: 0.1159 Lr: 0.00112 [2023-12-20 19:30:28,363 INFO misc.py line 119 131400] Train: [73/100][775/800] Data 0.003 (0.004) Batch 0.353 (0.334) Remain 02:00:29 loss: 0.3252 Lr: 0.00112 [2023-12-20 19:30:28,661 INFO misc.py line 119 131400] Train: [73/100][776/800] Data 0.004 (0.004) Batch 0.298 (0.334) Remain 02:00:28 loss: 0.1693 Lr: 0.00112 [2023-12-20 19:30:29,009 INFO misc.py line 119 131400] Train: [73/100][777/800] Data 0.005 (0.004) Batch 0.347 (0.334) Remain 02:00:28 loss: 0.2337 Lr: 0.00112 [2023-12-20 19:30:29,319 INFO misc.py line 119 131400] Train: [73/100][778/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 02:00:27 loss: 0.1071 Lr: 0.00112 [2023-12-20 19:30:29,628 INFO misc.py line 119 131400] Train: [73/100][779/800] Data 0.004 (0.004) Batch 0.307 (0.334) Remain 02:00:26 loss: 0.2240 Lr: 0.00112 [2023-12-20 19:30:29,931 INFO misc.py line 119 131400] Train: [73/100][780/800] Data 0.005 (0.004) Batch 0.305 (0.334) Remain 02:00:24 loss: 0.2216 Lr: 0.00112 [2023-12-20 19:30:30,286 INFO misc.py line 119 131400] Train: [73/100][781/800] Data 0.003 (0.004) Batch 0.354 (0.334) Remain 02:00:25 loss: 0.6259 Lr: 0.00112 [2023-12-20 19:30:30,622 INFO misc.py line 119 131400] Train: [73/100][782/800] Data 0.005 (0.004) Batch 0.337 (0.334) Remain 02:00:24 loss: 0.2509 Lr: 0.00112 [2023-12-20 19:30:30,956 INFO misc.py line 119 131400] Train: [73/100][783/800] Data 0.004 (0.004) Batch 0.332 (0.334) Remain 02:00:24 loss: 0.1851 Lr: 0.00112 [2023-12-20 19:30:31,260 INFO misc.py line 119 131400] Train: [73/100][784/800] Data 0.006 (0.004) Batch 0.306 (0.334) Remain 02:00:23 loss: 0.2578 Lr: 0.00112 [2023-12-20 19:30:31,575 INFO misc.py line 119 131400] Train: [73/100][785/800] Data 0.004 (0.004) Batch 0.315 (0.334) Remain 02:00:22 loss: 0.2165 Lr: 0.00112 [2023-12-20 19:30:31,904 INFO misc.py line 119 131400] Train: [73/100][786/800] Data 0.004 (0.004) Batch 0.329 (0.334) Remain 02:00:22 loss: 0.1923 Lr: 0.00112 [2023-12-20 19:30:32,221 INFO misc.py line 119 131400] Train: [73/100][787/800] Data 0.003 (0.004) Batch 0.317 (0.334) Remain 02:00:21 loss: 0.4381 Lr: 0.00112 [2023-12-20 19:30:32,549 INFO misc.py line 119 131400] Train: [73/100][788/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 02:00:20 loss: 0.2848 Lr: 0.00112 [2023-12-20 19:30:32,877 INFO misc.py line 119 131400] Train: [73/100][789/800] Data 0.007 (0.004) Batch 0.331 (0.334) Remain 02:00:20 loss: 0.2274 Lr: 0.00112 [2023-12-20 19:30:33,191 INFO misc.py line 119 131400] Train: [73/100][790/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 02:00:19 loss: 0.1885 Lr: 0.00112 [2023-12-20 19:30:33,468 INFO misc.py line 119 131400] Train: [73/100][791/800] Data 0.003 (0.004) Batch 0.276 (0.334) Remain 02:00:17 loss: 0.5762 Lr: 0.00112 [2023-12-20 19:30:33,755 INFO misc.py line 119 131400] Train: [73/100][792/800] Data 0.003 (0.004) Batch 0.287 (0.334) Remain 02:00:15 loss: 0.2213 Lr: 0.00112 [2023-12-20 19:30:34,080 INFO misc.py line 119 131400] Train: [73/100][793/800] Data 0.003 (0.004) Batch 0.323 (0.334) Remain 02:00:15 loss: 0.1813 Lr: 0.00112 [2023-12-20 19:30:34,362 INFO misc.py line 119 131400] Train: [73/100][794/800] Data 0.004 (0.004) Batch 0.284 (0.334) Remain 02:00:13 loss: 0.2128 Lr: 0.00112 [2023-12-20 19:30:34,678 INFO misc.py line 119 131400] Train: [73/100][795/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 02:00:12 loss: 0.2943 Lr: 0.00112 [2023-12-20 19:30:35,007 INFO misc.py line 119 131400] Train: [73/100][796/800] Data 0.008 (0.004) Batch 0.334 (0.334) Remain 02:00:12 loss: 0.4159 Lr: 0.00112 [2023-12-20 19:30:35,311 INFO misc.py line 119 131400] Train: [73/100][797/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 02:00:11 loss: 0.1014 Lr: 0.00112 [2023-12-20 19:30:35,618 INFO misc.py line 119 131400] Train: [73/100][798/800] Data 0.003 (0.004) Batch 0.307 (0.334) Remain 02:00:10 loss: 0.2668 Lr: 0.00112 [2023-12-20 19:30:35,954 INFO misc.py line 119 131400] Train: [73/100][799/800] Data 0.003 (0.004) Batch 0.334 (0.334) Remain 02:00:09 loss: 0.2403 Lr: 0.00112 [2023-12-20 19:30:36,208 INFO misc.py line 119 131400] Train: [73/100][800/800] Data 0.005 (0.004) Batch 0.256 (0.334) Remain 02:00:07 loss: 0.1407 Lr: 0.00112 [2023-12-20 19:30:36,209 INFO misc.py line 136 131400] Train result: loss: 0.2514 [2023-12-20 19:30:36,209 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:30:57,965 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1901 [2023-12-20 19:30:59,405 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1474 [2023-12-20 19:30:59,496 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4646 [2023-12-20 19:30:59,601 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.7422 [2023-12-20 19:30:59,713 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1389 [2023-12-20 19:30:59,815 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9783 [2023-12-20 19:30:59,905 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8565 [2023-12-20 19:31:00,013 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9569 [2023-12-20 19:31:00,094 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2960 [2023-12-20 19:31:00,182 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3398 [2023-12-20 19:31:00,274 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5713 [2023-12-20 19:31:00,411 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3045 [2023-12-20 19:31:00,529 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3474 [2023-12-20 19:31:00,689 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2118 [2023-12-20 19:31:00,785 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1432 [2023-12-20 19:31:00,919 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6234 [2023-12-20 19:31:01,028 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2770 [2023-12-20 19:31:01,137 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7270 [2023-12-20 19:31:01,253 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1378 [2023-12-20 19:31:01,330 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4053 [2023-12-20 19:31:01,438 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2084 [2023-12-20 19:31:01,594 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1274 [2023-12-20 19:31:01,713 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7054 [2023-12-20 19:31:01,855 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1516 [2023-12-20 19:31:01,996 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1621 [2023-12-20 19:31:02,080 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7666 [2023-12-20 19:31:02,236 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4563 [2023-12-20 19:31:02,360 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4991 [2023-12-20 19:31:02,462 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6044 [2023-12-20 19:31:02,622 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7599 [2023-12-20 19:31:02,729 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4750 [2023-12-20 19:31:02,853 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3798 [2023-12-20 19:31:02,959 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1253 [2023-12-20 19:31:03,053 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1781 [2023-12-20 19:31:03,148 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7083 [2023-12-20 19:31:03,241 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2717 [2023-12-20 19:31:03,376 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0524 [2023-12-20 19:31:03,497 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0957 [2023-12-20 19:31:03,598 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4258 [2023-12-20 19:31:03,740 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3166 [2023-12-20 19:31:03,889 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0234 [2023-12-20 19:31:03,999 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0460 [2023-12-20 19:31:04,132 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2652 [2023-12-20 19:31:04,285 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0072 [2023-12-20 19:31:04,410 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3043 [2023-12-20 19:31:04,513 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.2151 [2023-12-20 19:31:04,694 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3032 [2023-12-20 19:31:04,797 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.6152 [2023-12-20 19:31:04,945 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6311 [2023-12-20 19:31:05,039 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1613 [2023-12-20 19:31:05,123 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3347 [2023-12-20 19:31:05,234 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1852 [2023-12-20 19:31:05,386 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0416 [2023-12-20 19:31:05,523 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.4436 [2023-12-20 19:31:05,631 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.8811 [2023-12-20 19:31:05,740 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6175 [2023-12-20 19:31:05,845 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3783 [2023-12-20 19:31:06,009 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2455 [2023-12-20 19:31:06,109 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2784 [2023-12-20 19:31:06,204 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4559 [2023-12-20 19:31:06,300 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.3951 [2023-12-20 19:31:06,392 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2295 [2023-12-20 19:31:06,482 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4735 [2023-12-20 19:31:06,588 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7079 [2023-12-20 19:31:06,714 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5236 [2023-12-20 19:31:06,799 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2550 [2023-12-20 19:31:06,897 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5031 [2023-12-20 19:31:06,990 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0211 [2023-12-20 19:31:07,072 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3169 [2023-12-20 19:31:07,160 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0131 [2023-12-20 19:31:07,254 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7362 [2023-12-20 19:31:07,355 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6074 [2023-12-20 19:31:07,489 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0936 [2023-12-20 19:31:07,586 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6139 [2023-12-20 19:31:07,701 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6031 [2023-12-20 19:31:07,803 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5847 [2023-12-20 19:31:07,894 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2254 [2023-12-20 19:31:08,047 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0453 [2023-12-20 19:31:09,227 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7670/0.8457/0.9209. [2023-12-20 19:31:09,227 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8724/0.9521 [2023-12-20 19:31:09,227 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9644/0.9868 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7235/0.8218 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8188/0.8653 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9203/0.9634 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8552/0.9276 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7832/0.8718 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7317/0.8242 [2023-12-20 19:31:09,228 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7157/0.8173 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8432/0.9348 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3987/0.5423 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7165/0.8114 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7242/0.8793 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7488/0.8789 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6739/0.7522 [2023-12-20 19:31:09,229 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7037/0.7594 [2023-12-20 19:31:09,230 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9346/0.9762 [2023-12-20 19:31:09,230 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6838/0.7505 [2023-12-20 19:31:09,230 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8991/0.9189 [2023-12-20 19:31:09,230 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6291/0.6803 [2023-12-20 19:31:09,231 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:31:09,232 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7670 [2023-12-20 19:31:09,233 INFO misc.py line 165 131400] Currently Best mIoU: 0.7670 [2023-12-20 19:31:09,233 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:31:17,810 INFO misc.py line 119 131400] Train: [74/100][1/800] Data 1.430 (1.430) Batch 1.753 (1.753) Remain 10:30:56 loss: 0.2842 Lr: 0.00112 [2023-12-20 19:31:18,155 INFO misc.py line 119 131400] Train: [74/100][2/800] Data 0.006 (0.006) Batch 0.342 (0.342) Remain 02:03:00 loss: 0.1577 Lr: 0.00112 [2023-12-20 19:31:18,448 INFO misc.py line 119 131400] Train: [74/100][3/800] Data 0.008 (0.008) Batch 0.297 (0.297) Remain 01:46:55 loss: 0.1634 Lr: 0.00112 [2023-12-20 19:31:18,797 INFO misc.py line 119 131400] Train: [74/100][4/800] Data 0.003 (0.003) Batch 0.349 (0.349) Remain 02:05:45 loss: 0.2614 Lr: 0.00112 [2023-12-20 19:31:19,107 INFO misc.py line 119 131400] Train: [74/100][5/800] Data 0.004 (0.003) Batch 0.309 (0.329) Remain 01:58:30 loss: 0.1236 Lr: 0.00112 [2023-12-20 19:31:19,455 INFO misc.py line 119 131400] Train: [74/100][6/800] Data 0.005 (0.004) Batch 0.348 (0.336) Remain 02:00:48 loss: 0.1931 Lr: 0.00112 [2023-12-20 19:31:19,772 INFO misc.py line 119 131400] Train: [74/100][7/800] Data 0.003 (0.004) Batch 0.317 (0.331) Remain 01:59:07 loss: 0.1696 Lr: 0.00112 [2023-12-20 19:31:20,176 INFO misc.py line 119 131400] Train: [74/100][8/800] Data 0.003 (0.004) Batch 0.405 (0.346) Remain 02:04:24 loss: 0.2866 Lr: 0.00112 [2023-12-20 19:31:20,547 INFO misc.py line 119 131400] Train: [74/100][9/800] Data 0.004 (0.004) Batch 0.370 (0.350) Remain 02:05:52 loss: 0.2598 Lr: 0.00112 [2023-12-20 19:31:20,885 INFO misc.py line 119 131400] Train: [74/100][10/800] Data 0.004 (0.004) Batch 0.338 (0.348) Remain 02:05:17 loss: 0.2448 Lr: 0.00112 [2023-12-20 19:31:21,198 INFO misc.py line 119 131400] Train: [74/100][11/800] Data 0.003 (0.004) Batch 0.313 (0.344) Remain 02:03:40 loss: 0.1650 Lr: 0.00112 [2023-12-20 19:31:21,721 INFO misc.py line 119 131400] Train: [74/100][12/800] Data 0.005 (0.004) Batch 0.523 (0.364) Remain 02:10:51 loss: 0.3011 Lr: 0.00112 [2023-12-20 19:31:22,071 INFO misc.py line 119 131400] Train: [74/100][13/800] Data 0.003 (0.004) Batch 0.350 (0.362) Remain 02:10:20 loss: 0.3043 Lr: 0.00112 [2023-12-20 19:31:22,430 INFO misc.py line 119 131400] Train: [74/100][14/800] Data 0.004 (0.004) Batch 0.335 (0.360) Remain 02:09:27 loss: 0.2205 Lr: 0.00112 [2023-12-20 19:31:22,781 INFO misc.py line 119 131400] Train: [74/100][15/800] Data 0.028 (0.006) Batch 0.374 (0.361) Remain 02:09:53 loss: 0.2552 Lr: 0.00112 [2023-12-20 19:31:23,125 INFO misc.py line 119 131400] Train: [74/100][16/800] Data 0.004 (0.006) Batch 0.343 (0.360) Remain 02:09:23 loss: 0.3861 Lr: 0.00112 [2023-12-20 19:31:23,481 INFO misc.py line 119 131400] Train: [74/100][17/800] Data 0.004 (0.006) Batch 0.351 (0.359) Remain 02:09:09 loss: 0.1469 Lr: 0.00112 [2023-12-20 19:31:23,803 INFO misc.py line 119 131400] Train: [74/100][18/800] Data 0.011 (0.006) Batch 0.327 (0.357) Remain 02:08:23 loss: 0.1998 Lr: 0.00112 [2023-12-20 19:31:24,139 INFO misc.py line 119 131400] Train: [74/100][19/800] Data 0.005 (0.006) Batch 0.336 (0.356) Remain 02:07:55 loss: 0.5382 Lr: 0.00112 [2023-12-20 19:31:24,477 INFO misc.py line 119 131400] Train: [74/100][20/800] Data 0.005 (0.006) Batch 0.338 (0.355) Remain 02:07:32 loss: 0.1881 Lr: 0.00112 [2023-12-20 19:31:24,844 INFO misc.py line 119 131400] Train: [74/100][21/800] Data 0.004 (0.006) Batch 0.367 (0.355) Remain 02:07:46 loss: 0.3383 Lr: 0.00112 [2023-12-20 19:31:25,185 INFO misc.py line 119 131400] Train: [74/100][22/800] Data 0.004 (0.006) Batch 0.341 (0.355) Remain 02:07:30 loss: 0.1424 Lr: 0.00112 [2023-12-20 19:31:25,479 INFO misc.py line 119 131400] Train: [74/100][23/800] Data 0.008 (0.006) Batch 0.293 (0.351) Remain 02:06:23 loss: 0.2439 Lr: 0.00112 [2023-12-20 19:31:25,791 INFO misc.py line 119 131400] Train: [74/100][24/800] Data 0.005 (0.006) Batch 0.314 (0.350) Remain 02:05:44 loss: 0.2027 Lr: 0.00112 [2023-12-20 19:31:26,159 INFO misc.py line 119 131400] Train: [74/100][25/800] Data 0.003 (0.006) Batch 0.368 (0.351) Remain 02:06:02 loss: 0.2267 Lr: 0.00112 [2023-12-20 19:31:26,512 INFO misc.py line 119 131400] Train: [74/100][26/800] Data 0.003 (0.006) Batch 0.352 (0.351) Remain 02:06:03 loss: 0.2470 Lr: 0.00112 [2023-12-20 19:31:26,860 INFO misc.py line 119 131400] Train: [74/100][27/800] Data 0.004 (0.005) Batch 0.346 (0.350) Remain 02:05:59 loss: 0.2520 Lr: 0.00112 [2023-12-20 19:31:27,154 INFO misc.py line 119 131400] Train: [74/100][28/800] Data 0.005 (0.005) Batch 0.296 (0.348) Remain 02:05:11 loss: 0.2609 Lr: 0.00112 [2023-12-20 19:31:27,483 INFO misc.py line 119 131400] Train: [74/100][29/800] Data 0.004 (0.005) Batch 0.330 (0.348) Remain 02:04:56 loss: 0.1682 Lr: 0.00112 [2023-12-20 19:31:27,831 INFO misc.py line 119 131400] Train: [74/100][30/800] Data 0.003 (0.005) Batch 0.348 (0.348) Remain 02:04:56 loss: 0.2318 Lr: 0.00112 [2023-12-20 19:31:28,146 INFO misc.py line 119 131400] Train: [74/100][31/800] Data 0.004 (0.005) Batch 0.314 (0.346) Remain 02:04:30 loss: 0.6825 Lr: 0.00112 [2023-12-20 19:31:28,493 INFO misc.py line 119 131400] Train: [74/100][32/800] Data 0.004 (0.005) Batch 0.346 (0.346) Remain 02:04:29 loss: 0.1780 Lr: 0.00112 [2023-12-20 19:31:28,815 INFO misc.py line 119 131400] Train: [74/100][33/800] Data 0.006 (0.005) Batch 0.324 (0.346) Remain 02:04:12 loss: 0.1527 Lr: 0.00112 [2023-12-20 19:31:29,135 INFO misc.py line 119 131400] Train: [74/100][34/800] Data 0.003 (0.005) Batch 0.320 (0.345) Remain 02:03:55 loss: 0.1671 Lr: 0.00112 [2023-12-20 19:31:29,458 INFO misc.py line 119 131400] Train: [74/100][35/800] Data 0.003 (0.005) Batch 0.322 (0.344) Remain 02:03:39 loss: 0.4716 Lr: 0.00112 [2023-12-20 19:31:29,775 INFO misc.py line 119 131400] Train: [74/100][36/800] Data 0.003 (0.005) Batch 0.315 (0.343) Remain 02:03:20 loss: 0.1785 Lr: 0.00112 [2023-12-20 19:31:30,082 INFO misc.py line 119 131400] Train: [74/100][37/800] Data 0.008 (0.005) Batch 0.310 (0.342) Remain 02:02:58 loss: 0.1632 Lr: 0.00112 [2023-12-20 19:31:30,421 INFO misc.py line 119 131400] Train: [74/100][38/800] Data 0.004 (0.005) Batch 0.339 (0.342) Remain 02:02:56 loss: 0.2629 Lr: 0.00112 [2023-12-20 19:31:30,713 INFO misc.py line 119 131400] Train: [74/100][39/800] Data 0.004 (0.005) Batch 0.291 (0.341) Remain 02:02:25 loss: 0.5168 Lr: 0.00112 [2023-12-20 19:31:31,051 INFO misc.py line 119 131400] Train: [74/100][40/800] Data 0.004 (0.005) Batch 0.339 (0.341) Remain 02:02:23 loss: 0.2423 Lr: 0.00112 [2023-12-20 19:31:31,388 INFO misc.py line 119 131400] Train: [74/100][41/800] Data 0.004 (0.005) Batch 0.337 (0.341) Remain 02:02:21 loss: 0.3130 Lr: 0.00112 [2023-12-20 19:31:31,703 INFO misc.py line 119 131400] Train: [74/100][42/800] Data 0.003 (0.005) Batch 0.313 (0.340) Remain 02:02:06 loss: 0.2685 Lr: 0.00112 [2023-12-20 19:31:31,983 INFO misc.py line 119 131400] Train: [74/100][43/800] Data 0.006 (0.005) Batch 0.282 (0.338) Remain 02:01:34 loss: 0.1961 Lr: 0.00112 [2023-12-20 19:31:32,316 INFO misc.py line 119 131400] Train: [74/100][44/800] Data 0.003 (0.005) Batch 0.316 (0.338) Remain 02:01:22 loss: 0.2127 Lr: 0.00112 [2023-12-20 19:31:32,637 INFO misc.py line 119 131400] Train: [74/100][45/800] Data 0.022 (0.005) Batch 0.337 (0.338) Remain 02:01:21 loss: 0.1603 Lr: 0.00112 [2023-12-20 19:31:32,979 INFO misc.py line 119 131400] Train: [74/100][46/800] Data 0.007 (0.005) Batch 0.342 (0.338) Remain 02:01:23 loss: 0.2216 Lr: 0.00112 [2023-12-20 19:31:33,291 INFO misc.py line 119 131400] Train: [74/100][47/800] Data 0.004 (0.005) Batch 0.311 (0.337) Remain 02:01:10 loss: 0.2343 Lr: 0.00112 [2023-12-20 19:31:33,652 INFO misc.py line 119 131400] Train: [74/100][48/800] Data 0.004 (0.005) Batch 0.361 (0.338) Remain 02:01:21 loss: 0.1683 Lr: 0.00112 [2023-12-20 19:31:34,004 INFO misc.py line 119 131400] Train: [74/100][49/800] Data 0.005 (0.005) Batch 0.351 (0.338) Remain 02:01:26 loss: 0.2828 Lr: 0.00112 [2023-12-20 19:31:34,323 INFO misc.py line 119 131400] Train: [74/100][50/800] Data 0.007 (0.005) Batch 0.321 (0.338) Remain 02:01:18 loss: 0.1790 Lr: 0.00112 [2023-12-20 19:31:34,697 INFO misc.py line 119 131400] Train: [74/100][51/800] Data 0.003 (0.005) Batch 0.373 (0.338) Remain 02:01:34 loss: 0.2322 Lr: 0.00112 [2023-12-20 19:31:35,025 INFO misc.py line 119 131400] Train: [74/100][52/800] Data 0.005 (0.005) Batch 0.328 (0.338) Remain 02:01:29 loss: 0.2676 Lr: 0.00112 [2023-12-20 19:31:35,368 INFO misc.py line 119 131400] Train: [74/100][53/800] Data 0.005 (0.005) Batch 0.344 (0.338) Remain 02:01:31 loss: 0.3105 Lr: 0.00112 [2023-12-20 19:31:35,729 INFO misc.py line 119 131400] Train: [74/100][54/800] Data 0.004 (0.005) Batch 0.361 (0.339) Remain 02:01:40 loss: 0.0967 Lr: 0.00112 [2023-12-20 19:31:36,083 INFO misc.py line 119 131400] Train: [74/100][55/800] Data 0.005 (0.005) Batch 0.356 (0.339) Remain 02:01:46 loss: 0.2060 Lr: 0.00112 [2023-12-20 19:31:36,448 INFO misc.py line 119 131400] Train: [74/100][56/800] Data 0.003 (0.005) Batch 0.365 (0.340) Remain 02:01:57 loss: 0.1893 Lr: 0.00112 [2023-12-20 19:31:36,774 INFO misc.py line 119 131400] Train: [74/100][57/800] Data 0.003 (0.005) Batch 0.326 (0.339) Remain 02:01:51 loss: 0.2745 Lr: 0.00112 [2023-12-20 19:31:37,114 INFO misc.py line 119 131400] Train: [74/100][58/800] Data 0.003 (0.005) Batch 0.339 (0.339) Remain 02:01:50 loss: 0.2352 Lr: 0.00112 [2023-12-20 19:31:37,437 INFO misc.py line 119 131400] Train: [74/100][59/800] Data 0.003 (0.005) Batch 0.322 (0.339) Remain 02:01:44 loss: 0.2843 Lr: 0.00112 [2023-12-20 19:31:37,778 INFO misc.py line 119 131400] Train: [74/100][60/800] Data 0.005 (0.005) Batch 0.341 (0.339) Remain 02:01:44 loss: 0.4612 Lr: 0.00112 [2023-12-20 19:31:38,115 INFO misc.py line 119 131400] Train: [74/100][61/800] Data 0.005 (0.005) Batch 0.338 (0.339) Remain 02:01:43 loss: 0.2494 Lr: 0.00112 [2023-12-20 19:31:38,465 INFO misc.py line 119 131400] Train: [74/100][62/800] Data 0.004 (0.005) Batch 0.351 (0.339) Remain 02:01:47 loss: 0.2633 Lr: 0.00112 [2023-12-20 19:31:38,805 INFO misc.py line 119 131400] Train: [74/100][63/800] Data 0.003 (0.005) Batch 0.339 (0.339) Remain 02:01:47 loss: 0.2264 Lr: 0.00112 [2023-12-20 19:31:39,145 INFO misc.py line 119 131400] Train: [74/100][64/800] Data 0.003 (0.005) Batch 0.340 (0.339) Remain 02:01:47 loss: 0.1703 Lr: 0.00112 [2023-12-20 19:31:39,491 INFO misc.py line 119 131400] Train: [74/100][65/800] Data 0.003 (0.005) Batch 0.346 (0.339) Remain 02:01:48 loss: 0.2378 Lr: 0.00112 [2023-12-20 19:31:39,843 INFO misc.py line 119 131400] Train: [74/100][66/800] Data 0.004 (0.005) Batch 0.352 (0.340) Remain 02:01:52 loss: 0.2915 Lr: 0.00111 [2023-12-20 19:31:40,198 INFO misc.py line 119 131400] Train: [74/100][67/800] Data 0.004 (0.005) Batch 0.356 (0.340) Remain 02:01:57 loss: 0.1930 Lr: 0.00111 [2023-12-20 19:31:40,521 INFO misc.py line 119 131400] Train: [74/100][68/800] Data 0.003 (0.005) Batch 0.322 (0.340) Remain 02:01:51 loss: 0.2853 Lr: 0.00111 [2023-12-20 19:31:40,868 INFO misc.py line 119 131400] Train: [74/100][69/800] Data 0.007 (0.005) Batch 0.347 (0.340) Remain 02:01:53 loss: 0.4486 Lr: 0.00111 [2023-12-20 19:31:41,214 INFO misc.py line 119 131400] Train: [74/100][70/800] Data 0.005 (0.005) Batch 0.346 (0.340) Remain 02:01:55 loss: 0.3183 Lr: 0.00111 [2023-12-20 19:31:41,557 INFO misc.py line 119 131400] Train: [74/100][71/800] Data 0.004 (0.005) Batch 0.343 (0.340) Remain 02:01:56 loss: 0.1826 Lr: 0.00111 [2023-12-20 19:31:41,907 INFO misc.py line 119 131400] Train: [74/100][72/800] Data 0.003 (0.005) Batch 0.351 (0.340) Remain 02:01:59 loss: 0.1023 Lr: 0.00111 [2023-12-20 19:31:42,251 INFO misc.py line 119 131400] Train: [74/100][73/800] Data 0.003 (0.005) Batch 0.343 (0.340) Remain 02:02:00 loss: 0.1123 Lr: 0.00111 [2023-12-20 19:31:42,565 INFO misc.py line 119 131400] Train: [74/100][74/800] Data 0.003 (0.005) Batch 0.313 (0.340) Remain 02:01:51 loss: 0.4950 Lr: 0.00111 [2023-12-20 19:31:42,880 INFO misc.py line 119 131400] Train: [74/100][75/800] Data 0.003 (0.005) Batch 0.315 (0.339) Remain 02:01:44 loss: 0.1317 Lr: 0.00111 [2023-12-20 19:31:43,221 INFO misc.py line 119 131400] Train: [74/100][76/800] Data 0.003 (0.005) Batch 0.340 (0.339) Remain 02:01:43 loss: 0.2203 Lr: 0.00111 [2023-12-20 19:31:43,537 INFO misc.py line 119 131400] Train: [74/100][77/800] Data 0.004 (0.005) Batch 0.318 (0.339) Remain 02:01:37 loss: 0.1974 Lr: 0.00111 [2023-12-20 19:31:43,863 INFO misc.py line 119 131400] Train: 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Batch 0.298 (0.334) Remain 01:56:10 loss: 0.1351 Lr: 0.00105 [2023-12-20 19:35:28,520 INFO misc.py line 119 131400] Train: [74/100][751/800] Data 0.002 (0.005) Batch 0.325 (0.334) Remain 01:56:10 loss: 0.5219 Lr: 0.00105 [2023-12-20 19:35:28,840 INFO misc.py line 119 131400] Train: [74/100][752/800] Data 0.003 (0.005) Batch 0.320 (0.334) Remain 01:56:09 loss: 0.1603 Lr: 0.00105 [2023-12-20 19:35:29,191 INFO misc.py line 119 131400] Train: [74/100][753/800] Data 0.004 (0.005) Batch 0.350 (0.334) Remain 01:56:09 loss: 0.1993 Lr: 0.00105 [2023-12-20 19:35:29,517 INFO misc.py line 119 131400] Train: [74/100][754/800] Data 0.004 (0.005) Batch 0.327 (0.334) Remain 01:56:09 loss: 0.1768 Lr: 0.00105 [2023-12-20 19:35:29,876 INFO misc.py line 119 131400] Train: [74/100][755/800] Data 0.003 (0.005) Batch 0.358 (0.334) Remain 01:56:09 loss: 0.2138 Lr: 0.00105 [2023-12-20 19:35:30,244 INFO misc.py line 119 131400] Train: [74/100][756/800] Data 0.003 (0.005) Batch 0.367 (0.334) Remain 01:56:09 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0.005 (0.005) Batch 0.370 (0.334) Remain 01:56:02 loss: 0.2425 Lr: 0.00105 [2023-12-20 19:35:36,880 INFO misc.py line 119 131400] Train: [74/100][776/800] Data 0.005 (0.005) Batch 0.340 (0.334) Remain 01:56:01 loss: 0.2262 Lr: 0.00105 [2023-12-20 19:35:37,227 INFO misc.py line 119 131400] Train: [74/100][777/800] Data 0.006 (0.005) Batch 0.347 (0.334) Remain 01:56:01 loss: 0.4176 Lr: 0.00105 [2023-12-20 19:35:37,575 INFO misc.py line 119 131400] Train: [74/100][778/800] Data 0.005 (0.005) Batch 0.349 (0.334) Remain 01:56:01 loss: 0.2266 Lr: 0.00105 [2023-12-20 19:35:37,919 INFO misc.py line 119 131400] Train: [74/100][779/800] Data 0.004 (0.005) Batch 0.344 (0.334) Remain 01:56:01 loss: 0.2267 Lr: 0.00105 [2023-12-20 19:35:38,266 INFO misc.py line 119 131400] Train: [74/100][780/800] Data 0.004 (0.005) Batch 0.346 (0.334) Remain 01:56:01 loss: 0.3068 Lr: 0.00105 [2023-12-20 19:35:38,582 INFO misc.py line 119 131400] Train: [74/100][781/800] Data 0.004 (0.005) Batch 0.317 (0.334) Remain 01:56:01 loss: 0.2235 Lr: 0.00105 [2023-12-20 19:35:38,899 INFO misc.py line 119 131400] Train: [74/100][782/800] Data 0.003 (0.005) Batch 0.317 (0.334) Remain 01:56:00 loss: 0.2804 Lr: 0.00105 [2023-12-20 19:35:39,243 INFO misc.py line 119 131400] Train: [74/100][783/800] Data 0.004 (0.005) Batch 0.344 (0.334) Remain 01:56:00 loss: 0.2180 Lr: 0.00105 [2023-12-20 19:35:39,566 INFO misc.py line 119 131400] Train: [74/100][784/800] Data 0.005 (0.005) Batch 0.324 (0.334) Remain 01:55:59 loss: 0.3792 Lr: 0.00105 [2023-12-20 19:35:39,899 INFO misc.py line 119 131400] Train: [74/100][785/800] Data 0.003 (0.005) Batch 0.333 (0.334) Remain 01:55:59 loss: 0.2247 Lr: 0.00105 [2023-12-20 19:35:40,236 INFO misc.py line 119 131400] Train: [74/100][786/800] Data 0.003 (0.005) Batch 0.336 (0.334) Remain 01:55:58 loss: 0.2487 Lr: 0.00105 [2023-12-20 19:35:40,574 INFO misc.py line 119 131400] Train: [74/100][787/800] Data 0.004 (0.005) Batch 0.339 (0.334) Remain 01:55:58 loss: 0.1407 Lr: 0.00105 [2023-12-20 19:35:40,870 INFO misc.py line 119 131400] Train: [74/100][788/800] Data 0.003 (0.005) Batch 0.296 (0.334) Remain 01:55:57 loss: 0.2071 Lr: 0.00105 [2023-12-20 19:35:41,194 INFO misc.py line 119 131400] Train: [74/100][789/800] Data 0.004 (0.005) Batch 0.322 (0.334) Remain 01:55:56 loss: 0.3695 Lr: 0.00105 [2023-12-20 19:35:41,509 INFO misc.py line 119 131400] Train: [74/100][790/800] Data 0.005 (0.005) Batch 0.317 (0.334) Remain 01:55:55 loss: 0.2307 Lr: 0.00105 [2023-12-20 19:35:41,917 INFO misc.py line 119 131400] Train: [74/100][791/800] Data 0.003 (0.005) Batch 0.408 (0.334) Remain 01:55:57 loss: 0.5220 Lr: 0.00105 [2023-12-20 19:35:42,228 INFO misc.py line 119 131400] Train: [74/100][792/800] Data 0.003 (0.005) Batch 0.312 (0.334) Remain 01:55:56 loss: 0.1450 Lr: 0.00105 [2023-12-20 19:35:42,516 INFO misc.py line 119 131400] Train: [74/100][793/800] Data 0.002 (0.005) Batch 0.287 (0.334) Remain 01:55:54 loss: 0.1933 Lr: 0.00105 [2023-12-20 19:35:42,835 INFO misc.py line 119 131400] Train: [74/100][794/800] Data 0.003 (0.005) Batch 0.319 (0.334) Remain 01:55:54 loss: 0.1851 Lr: 0.00105 [2023-12-20 19:35:43,152 INFO misc.py line 119 131400] Train: [74/100][795/800] Data 0.004 (0.005) Batch 0.316 (0.334) Remain 01:55:53 loss: 0.2850 Lr: 0.00105 [2023-12-20 19:35:43,451 INFO misc.py line 119 131400] Train: [74/100][796/800] Data 0.004 (0.005) Batch 0.299 (0.334) Remain 01:55:52 loss: 0.2804 Lr: 0.00105 [2023-12-20 19:35:43,759 INFO misc.py line 119 131400] Train: [74/100][797/800] Data 0.003 (0.005) Batch 0.309 (0.334) Remain 01:55:51 loss: 0.2803 Lr: 0.00105 [2023-12-20 19:35:44,025 INFO misc.py line 119 131400] Train: [74/100][798/800] Data 0.002 (0.005) Batch 0.265 (0.334) Remain 01:55:49 loss: 0.2479 Lr: 0.00105 [2023-12-20 19:35:44,337 INFO misc.py line 119 131400] Train: [74/100][799/800] Data 0.003 (0.005) Batch 0.312 (0.334) Remain 01:55:48 loss: 0.1901 Lr: 0.00105 [2023-12-20 19:35:44,651 INFO misc.py line 119 131400] Train: [74/100][800/800] Data 0.003 (0.005) Batch 0.315 (0.334) Remain 01:55:47 loss: 0.3515 Lr: 0.00104 [2023-12-20 19:35:44,652 INFO misc.py line 136 131400] Train result: loss: 0.2480 [2023-12-20 19:35:44,652 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:36:05,256 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2034 [2023-12-20 19:36:05,326 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1730 [2023-12-20 19:36:05,427 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4743 [2023-12-20 19:36:05,535 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2970 [2023-12-20 19:36:05,653 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2988 [2023-12-20 19:36:05,753 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.0809 [2023-12-20 19:36:05,845 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8850 [2023-12-20 19:36:05,958 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0255 [2023-12-20 19:36:06,043 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2511 [2023-12-20 19:36:06,128 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3177 [2023-12-20 19:36:06,219 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4890 [2023-12-20 19:36:06,355 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2795 [2023-12-20 19:36:06,475 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6281 [2023-12-20 19:36:06,634 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2131 [2023-12-20 19:36:06,727 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1338 [2023-12-20 19:36:06,868 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7917 [2023-12-20 19:36:06,978 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2399 [2023-12-20 19:36:07,088 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7609 [2023-12-20 19:36:07,201 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1200 [2023-12-20 19:36:07,278 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3578 [2023-12-20 19:36:07,390 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1965 [2023-12-20 19:36:07,547 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1691 [2023-12-20 19:36:07,670 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7217 [2023-12-20 19:36:07,812 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4070 [2023-12-20 19:36:07,955 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1698 [2023-12-20 19:36:08,044 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.1518 [2023-12-20 19:36:08,205 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6724 [2023-12-20 19:36:08,331 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6025 [2023-12-20 19:36:08,429 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7357 [2023-12-20 19:36:08,575 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.4524 [2023-12-20 19:36:08,681 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4930 [2023-12-20 19:36:08,803 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4561 [2023-12-20 19:36:08,889 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1161 [2023-12-20 19:36:08,962 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1772 [2023-12-20 19:36:09,060 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7213 [2023-12-20 19:36:09,154 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3199 [2023-12-20 19:36:09,283 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9880 [2023-12-20 19:36:09,399 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0820 [2023-12-20 19:36:09,482 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6227 [2023-12-20 19:36:09,624 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3162 [2023-12-20 19:36:09,773 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.1743 [2023-12-20 19:36:09,874 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0580 [2023-12-20 19:36:09,994 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3637 [2023-12-20 19:36:10,137 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9470 [2023-12-20 19:36:10,257 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6112 [2023-12-20 19:36:10,365 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.9174 [2023-12-20 19:36:10,531 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3011 [2023-12-20 19:36:10,631 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4349 [2023-12-20 19:36:10,779 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.8139 [2023-12-20 19:36:10,878 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1938 [2023-12-20 19:36:10,955 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.9433 [2023-12-20 19:36:11,062 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4045 [2023-12-20 19:36:11,209 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7461 [2023-12-20 19:36:11,343 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3247 [2023-12-20 19:36:11,447 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9359 [2023-12-20 19:36:11,536 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.4586 [2023-12-20 19:36:11,666 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3080 [2023-12-20 19:36:11,841 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1762 [2023-12-20 19:36:11,962 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4842 [2023-12-20 19:36:12,095 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1995 [2023-12-20 19:36:12,198 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5450 [2023-12-20 19:36:12,323 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3305 [2023-12-20 19:36:12,411 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.3213 [2023-12-20 19:36:12,520 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.4875 [2023-12-20 19:36:12,650 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5380 [2023-12-20 19:36:12,732 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2232 [2023-12-20 19:36:12,831 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5503 [2023-12-20 19:36:12,927 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.2034 [2023-12-20 19:36:13,013 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3958 [2023-12-20 19:36:13,101 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0679 [2023-12-20 19:36:13,199 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5200 [2023-12-20 19:36:13,290 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6317 [2023-12-20 19:36:13,429 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0711 [2023-12-20 19:36:13,526 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6087 [2023-12-20 19:36:13,641 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5726 [2023-12-20 19:36:13,742 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5590 [2023-12-20 19:36:13,834 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2251 [2023-12-20 19:36:13,987 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2311 [2023-12-20 19:36:15,130 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7680/0.8481/0.9209. [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8773/0.9466 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9644/0.9857 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7214/0.8326 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8188/0.8710 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9258/0.9585 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8675/0.9608 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7905/0.8677 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7078/0.8621 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7092/0.8117 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8309/0.9339 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4022/0.5253 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7128/0.8055 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7345/0.8649 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7638/0.8745 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6712/0.7649 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6839/0.7359 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9588/0.9770 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7145/0.7906 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8884/0.9087 [2023-12-20 19:36:15,131 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6159/0.6843 [2023-12-20 19:36:15,132 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:36:15,133 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7680 [2023-12-20 19:36:15,133 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 19:36:15,133 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:36:21,889 INFO misc.py line 119 131400] Train: [75/100][1/800] Data 0.757 (0.757) Batch 1.026 (1.026) Remain 05:55:36 loss: 0.2507 Lr: 0.00104 [2023-12-20 19:36:22,228 INFO misc.py line 119 131400] Train: [75/100][2/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 01:57:17 loss: 0.3542 Lr: 0.00104 [2023-12-20 19:36:22,550 INFO misc.py line 119 131400] Train: [75/100][3/800] Data 0.004 (0.004) Batch 0.323 (0.323) Remain 01:51:51 loss: 0.2318 Lr: 0.00104 [2023-12-20 19:36:22,890 INFO misc.py line 119 131400] Train: [75/100][4/800] Data 0.003 (0.003) Batch 0.340 (0.340) Remain 01:57:40 loss: 0.2753 Lr: 0.00104 [2023-12-20 19:36:23,204 INFO misc.py line 119 131400] Train: [75/100][5/800] Data 0.004 (0.004) Batch 0.314 (0.327) Remain 01:53:15 loss: 0.3461 Lr: 0.00104 [2023-12-20 19:36:23,552 INFO misc.py line 119 131400] Train: [75/100][6/800] Data 0.004 (0.004) Batch 0.348 (0.334) Remain 01:55:44 loss: 0.2257 Lr: 0.00104 [2023-12-20 19:36:23,900 INFO misc.py line 119 131400] Train: [75/100][7/800] Data 0.004 (0.004) Batch 0.349 (0.338) Remain 01:57:00 loss: 0.2202 Lr: 0.00104 [2023-12-20 19:36:24,252 INFO misc.py line 119 131400] Train: [75/100][8/800] Data 0.003 (0.004) Batch 0.352 (0.340) Remain 01:57:57 loss: 0.3186 Lr: 0.00104 [2023-12-20 19:36:24,602 INFO misc.py line 119 131400] Train: [75/100][9/800] Data 0.004 (0.004) Batch 0.350 (0.342) Remain 01:58:30 loss: 0.1736 Lr: 0.00104 [2023-12-20 19:36:24,899 INFO misc.py line 119 131400] Train: [75/100][10/800] Data 0.004 (0.004) Batch 0.297 (0.336) Remain 01:56:16 loss: 0.1597 Lr: 0.00104 [2023-12-20 19:36:25,234 INFO misc.py line 119 131400] Train: [75/100][11/800] Data 0.003 (0.004) Batch 0.335 (0.335) Remain 01:56:14 loss: 0.3239 Lr: 0.00104 [2023-12-20 19:36:25,554 INFO misc.py line 119 131400] Train: [75/100][12/800] Data 0.003 (0.004) Batch 0.320 (0.334) Remain 01:55:39 loss: 0.1808 Lr: 0.00104 [2023-12-20 19:36:25,889 INFO misc.py line 119 131400] Train: [75/100][13/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 01:55:40 loss: 0.2162 Lr: 0.00104 [2023-12-20 19:36:26,236 INFO misc.py line 119 131400] Train: [75/100][14/800] Data 0.005 (0.004) Batch 0.346 (0.335) Remain 01:56:03 loss: 0.1598 Lr: 0.00104 [2023-12-20 19:36:26,515 INFO misc.py line 119 131400] Train: [75/100][15/800] Data 0.005 (0.004) Batch 0.280 (0.330) Remain 01:54:27 loss: 0.1338 Lr: 0.00104 [2023-12-20 19:36:26,855 INFO misc.py line 119 131400] Train: [75/100][16/800] Data 0.004 (0.004) Batch 0.340 (0.331) Remain 01:54:41 loss: 0.3733 Lr: 0.00104 [2023-12-20 19:36:27,163 INFO misc.py line 119 131400] Train: [75/100][17/800] Data 0.004 (0.004) Batch 0.308 (0.329) Remain 01:54:06 loss: 0.1473 Lr: 0.00104 [2023-12-20 19:36:27,472 INFO misc.py line 119 131400] Train: [75/100][18/800] Data 0.004 (0.004) Batch 0.309 (0.328) Remain 01:53:38 loss: 0.1492 Lr: 0.00104 [2023-12-20 19:36:27,823 INFO misc.py line 119 131400] Train: [75/100][19/800] Data 0.003 (0.004) Batch 0.351 (0.330) Remain 01:54:07 loss: 0.1007 Lr: 0.00104 [2023-12-20 19:36:28,145 INFO misc.py line 119 131400] Train: [75/100][20/800] Data 0.005 (0.004) Batch 0.323 (0.329) Remain 01:53:59 loss: 0.1657 Lr: 0.00104 [2023-12-20 19:36:28,490 INFO misc.py line 119 131400] Train: [75/100][21/800] Data 0.002 (0.004) Batch 0.345 (0.330) Remain 01:54:16 loss: 0.1236 Lr: 0.00104 [2023-12-20 19:36:28,824 INFO misc.py line 119 131400] Train: [75/100][22/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 01:54:20 loss: 0.1912 Lr: 0.00104 [2023-12-20 19:36:29,138 INFO misc.py line 119 131400] Train: [75/100][23/800] Data 0.003 (0.004) Batch 0.314 (0.329) Remain 01:54:03 loss: 0.1366 Lr: 0.00104 [2023-12-20 19:36:29,476 INFO misc.py line 119 131400] Train: [75/100][24/800] Data 0.003 (0.004) Batch 0.338 (0.330) Remain 01:54:11 loss: 0.3131 Lr: 0.00104 [2023-12-20 19:36:29,802 INFO misc.py line 119 131400] Train: [75/100][25/800] Data 0.004 (0.004) Batch 0.327 (0.330) Remain 01:54:08 loss: 0.1826 Lr: 0.00104 [2023-12-20 19:36:30,134 INFO misc.py line 119 131400] Train: [75/100][26/800] Data 0.003 (0.004) Batch 0.332 (0.330) Remain 01:54:10 loss: 0.1721 Lr: 0.00104 [2023-12-20 19:36:30,443 INFO misc.py line 119 131400] Train: [75/100][27/800] Data 0.003 (0.004) Batch 0.308 (0.329) Remain 01:53:51 loss: 0.1632 Lr: 0.00104 [2023-12-20 19:36:30,790 INFO misc.py line 119 131400] Train: [75/100][28/800] Data 0.004 (0.004) Batch 0.347 (0.330) Remain 01:54:06 loss: 0.2888 Lr: 0.00104 [2023-12-20 19:36:31,125 INFO misc.py line 119 131400] Train: [75/100][29/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 01:54:09 loss: 0.2275 Lr: 0.00104 [2023-12-20 19:36:31,479 INFO misc.py line 119 131400] Train: [75/100][30/800] Data 0.004 (0.004) Batch 0.351 (0.331) Remain 01:54:25 loss: 0.1046 Lr: 0.00104 [2023-12-20 19:36:31,826 INFO misc.py line 119 131400] Train: [75/100][31/800] Data 0.007 (0.004) Batch 0.351 (0.331) Remain 01:54:40 loss: 0.4353 Lr: 0.00104 [2023-12-20 19:36:32,159 INFO misc.py line 119 131400] Train: [75/100][32/800] Data 0.003 (0.004) Batch 0.333 (0.331) Remain 01:54:41 loss: 0.3125 Lr: 0.00104 [2023-12-20 19:36:32,463 INFO misc.py line 119 131400] Train: [75/100][33/800] Data 0.003 (0.004) Batch 0.304 (0.330) Remain 01:54:22 loss: 0.2919 Lr: 0.00104 [2023-12-20 19:36:32,815 INFO misc.py line 119 131400] Train: [75/100][34/800] Data 0.003 (0.004) Batch 0.351 (0.331) Remain 01:54:35 loss: 0.1438 Lr: 0.00104 [2023-12-20 19:36:33,170 INFO misc.py line 119 131400] Train: [75/100][35/800] Data 0.004 (0.004) Batch 0.356 (0.332) Remain 01:54:51 loss: 0.1420 Lr: 0.00104 [2023-12-20 19:36:33,491 INFO misc.py line 119 131400] Train: [75/100][36/800] Data 0.003 (0.004) Batch 0.320 (0.332) Remain 01:54:44 loss: 0.1994 Lr: 0.00104 [2023-12-20 19:36:33,791 INFO misc.py line 119 131400] Train: [75/100][37/800] Data 0.004 (0.004) Batch 0.300 (0.331) Remain 01:54:24 loss: 0.1219 Lr: 0.00104 [2023-12-20 19:36:34,102 INFO misc.py line 119 131400] Train: [75/100][38/800] Data 0.003 (0.004) Batch 0.310 (0.330) Remain 01:54:12 loss: 0.1851 Lr: 0.00104 [2023-12-20 19:36:34,423 INFO misc.py line 119 131400] Train: [75/100][39/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 01:54:07 loss: 0.2137 Lr: 0.00104 [2023-12-20 19:36:34,732 INFO misc.py line 119 131400] Train: [75/100][40/800] Data 0.003 (0.004) Batch 0.309 (0.329) Remain 01:53:55 loss: 0.3506 Lr: 0.00104 [2023-12-20 19:36:35,031 INFO misc.py line 119 131400] Train: [75/100][41/800] Data 0.002 (0.004) Batch 0.298 (0.328) Remain 01:53:38 loss: 0.4157 Lr: 0.00104 [2023-12-20 19:36:35,355 INFO misc.py line 119 131400] Train: [75/100][42/800] Data 0.003 (0.004) Batch 0.325 (0.328) Remain 01:53:35 loss: 0.2558 Lr: 0.00104 [2023-12-20 19:36:35,669 INFO misc.py line 119 131400] Train: [75/100][43/800] Data 0.003 (0.004) Batch 0.313 (0.328) Remain 01:53:27 loss: 0.3461 Lr: 0.00104 [2023-12-20 19:36:36,014 INFO misc.py line 119 131400] Train: [75/100][44/800] Data 0.004 (0.004) Batch 0.345 (0.328) Remain 01:53:35 loss: 0.2474 Lr: 0.00104 [2023-12-20 19:36:36,331 INFO misc.py line 119 131400] Train: [75/100][45/800] Data 0.004 (0.004) Batch 0.317 (0.328) Remain 01:53:29 loss: 0.4176 Lr: 0.00104 [2023-12-20 19:36:36,657 INFO misc.py line 119 131400] Train: [75/100][46/800] Data 0.004 (0.004) Batch 0.327 (0.328) Remain 01:53:28 loss: 0.1482 Lr: 0.00104 [2023-12-20 19:36:36,983 INFO misc.py line 119 131400] Train: [75/100][47/800] Data 0.003 (0.004) Batch 0.316 (0.328) Remain 01:53:22 loss: 0.2145 Lr: 0.00104 [2023-12-20 19:36:37,323 INFO misc.py line 119 131400] Train: [75/100][48/800] Data 0.013 (0.004) Batch 0.349 (0.328) Remain 01:53:32 loss: 0.1611 Lr: 0.00104 [2023-12-20 19:36:37,632 INFO misc.py line 119 131400] Train: [75/100][49/800] Data 0.004 (0.004) Batch 0.309 (0.328) Remain 01:53:23 loss: 0.3174 Lr: 0.00104 [2023-12-20 19:36:37,978 INFO misc.py line 119 131400] Train: [75/100][50/800] Data 0.003 (0.004) Batch 0.345 (0.328) Remain 01:53:30 loss: 0.1436 Lr: 0.00104 [2023-12-20 19:36:38,332 INFO misc.py line 119 131400] Train: [75/100][51/800] Data 0.005 (0.004) Batch 0.355 (0.329) Remain 01:53:41 loss: 0.5189 Lr: 0.00104 [2023-12-20 19:36:38,654 INFO misc.py line 119 131400] Train: [75/100][52/800] Data 0.004 (0.004) Batch 0.323 (0.329) Remain 01:53:39 loss: 0.1632 Lr: 0.00104 [2023-12-20 19:36:39,005 INFO misc.py line 119 131400] Train: [75/100][53/800] Data 0.003 (0.004) Batch 0.350 (0.329) Remain 01:53:47 loss: 0.4488 Lr: 0.00104 [2023-12-20 19:36:39,348 INFO misc.py line 119 131400] Train: [75/100][54/800] Data 0.003 (0.004) Batch 0.343 (0.329) Remain 01:53:52 loss: 0.2983 Lr: 0.00104 [2023-12-20 19:36:39,658 INFO misc.py line 119 131400] Train: [75/100][55/800] Data 0.004 (0.004) Batch 0.310 (0.329) Remain 01:53:44 loss: 0.2303 Lr: 0.00104 [2023-12-20 19:36:39,983 INFO misc.py line 119 131400] Train: [75/100][56/800] Data 0.003 (0.004) Batch 0.326 (0.329) Remain 01:53:43 loss: 0.2115 Lr: 0.00104 [2023-12-20 19:36:40,296 INFO misc.py line 119 131400] Train: [75/100][57/800] Data 0.003 (0.004) Batch 0.313 (0.329) Remain 01:53:36 loss: 0.1690 Lr: 0.00104 [2023-12-20 19:36:40,641 INFO misc.py line 119 131400] Train: [75/100][58/800] Data 0.003 (0.004) Batch 0.345 (0.329) Remain 01:53:42 loss: 0.7596 Lr: 0.00104 [2023-12-20 19:36:40,980 INFO misc.py line 119 131400] Train: [75/100][59/800] Data 0.003 (0.004) Batch 0.338 (0.329) Remain 01:53:45 loss: 0.2247 Lr: 0.00104 [2023-12-20 19:36:41,308 INFO misc.py line 119 131400] Train: [75/100][60/800] Data 0.003 (0.004) Batch 0.328 (0.329) Remain 01:53:44 loss: 0.2436 Lr: 0.00104 [2023-12-20 19:36:41,620 INFO misc.py line 119 131400] Train: [75/100][61/800] Data 0.004 (0.004) Batch 0.312 (0.329) Remain 01:53:38 loss: 0.1198 Lr: 0.00104 [2023-12-20 19:36:41,945 INFO misc.py line 119 131400] Train: [75/100][62/800] Data 0.003 (0.004) Batch 0.325 (0.329) Remain 01:53:37 loss: 0.3417 Lr: 0.00104 [2023-12-20 19:36:42,237 INFO misc.py line 119 131400] Train: [75/100][63/800] Data 0.003 (0.004) Batch 0.290 (0.328) Remain 01:53:23 loss: 0.3863 Lr: 0.00104 [2023-12-20 19:36:42,545 INFO misc.py line 119 131400] Train: [75/100][64/800] Data 0.005 (0.004) Batch 0.311 (0.328) Remain 01:53:16 loss: 0.1374 Lr: 0.00104 [2023-12-20 19:36:42,882 INFO misc.py line 119 131400] Train: [75/100][65/800] Data 0.003 (0.004) Batch 0.336 (0.328) Remain 01:53:19 loss: 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Batch 0.343 (0.334) Remain 01:51:39 loss: 0.3273 Lr: 0.00098 [2023-12-20 19:40:32,484 INFO misc.py line 119 131400] Train: [75/100][751/800] Data 0.003 (0.005) Batch 0.339 (0.334) Remain 01:51:39 loss: 0.2725 Lr: 0.00098 [2023-12-20 19:40:32,834 INFO misc.py line 119 131400] Train: [75/100][752/800] Data 0.004 (0.005) Batch 0.350 (0.334) Remain 01:51:39 loss: 0.3119 Lr: 0.00098 [2023-12-20 19:40:33,162 INFO misc.py line 119 131400] Train: [75/100][753/800] Data 0.003 (0.005) Batch 0.328 (0.334) Remain 01:51:38 loss: 0.1332 Lr: 0.00098 [2023-12-20 19:40:33,507 INFO misc.py line 119 131400] Train: [75/100][754/800] Data 0.003 (0.005) Batch 0.345 (0.334) Remain 01:51:38 loss: 0.1794 Lr: 0.00098 [2023-12-20 19:40:33,854 INFO misc.py line 119 131400] Train: [75/100][755/800] Data 0.004 (0.005) Batch 0.343 (0.334) Remain 01:51:38 loss: 0.2095 Lr: 0.00098 [2023-12-20 19:40:34,174 INFO misc.py line 119 131400] Train: [75/100][756/800] Data 0.007 (0.005) Batch 0.325 (0.334) Remain 01:51:37 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0.004 (0.005) Batch 0.279 (0.334) Remain 01:51:29 loss: 0.1967 Lr: 0.00097 [2023-12-20 19:40:40,819 INFO misc.py line 119 131400] Train: [75/100][776/800] Data 0.003 (0.005) Batch 0.359 (0.334) Remain 01:51:30 loss: 0.1727 Lr: 0.00097 [2023-12-20 19:40:41,140 INFO misc.py line 119 131400] Train: [75/100][777/800] Data 0.007 (0.005) Batch 0.325 (0.334) Remain 01:51:29 loss: 0.3377 Lr: 0.00097 [2023-12-20 19:40:41,458 INFO misc.py line 119 131400] Train: [75/100][778/800] Data 0.004 (0.005) Batch 0.315 (0.334) Remain 01:51:28 loss: 0.3583 Lr: 0.00097 [2023-12-20 19:40:41,797 INFO misc.py line 119 131400] Train: [75/100][779/800] Data 0.006 (0.005) Batch 0.343 (0.334) Remain 01:51:28 loss: 0.2370 Lr: 0.00097 [2023-12-20 19:40:42,125 INFO misc.py line 119 131400] Train: [75/100][780/800] Data 0.003 (0.005) Batch 0.328 (0.334) Remain 01:51:28 loss: 0.3017 Lr: 0.00097 [2023-12-20 19:40:42,463 INFO misc.py line 119 131400] Train: [75/100][781/800] Data 0.003 (0.005) Batch 0.338 (0.334) Remain 01:51:27 loss: 0.1783 Lr: 0.00097 [2023-12-20 19:40:42,775 INFO misc.py line 119 131400] Train: [75/100][782/800] Data 0.003 (0.005) Batch 0.311 (0.334) Remain 01:51:26 loss: 0.3241 Lr: 0.00097 [2023-12-20 19:40:43,109 INFO misc.py line 119 131400] Train: [75/100][783/800] Data 0.004 (0.005) Batch 0.334 (0.334) Remain 01:51:26 loss: 0.1663 Lr: 0.00097 [2023-12-20 19:40:43,417 INFO misc.py line 119 131400] Train: [75/100][784/800] Data 0.003 (0.005) Batch 0.308 (0.334) Remain 01:51:25 loss: 0.1614 Lr: 0.00097 [2023-12-20 19:40:43,717 INFO misc.py line 119 131400] Train: [75/100][785/800] Data 0.003 (0.005) Batch 0.300 (0.334) Remain 01:51:24 loss: 0.3499 Lr: 0.00097 [2023-12-20 19:40:44,028 INFO misc.py line 119 131400] Train: [75/100][786/800] Data 0.003 (0.005) Batch 0.311 (0.334) Remain 01:51:23 loss: 0.1300 Lr: 0.00097 [2023-12-20 19:40:44,352 INFO misc.py line 119 131400] Train: [75/100][787/800] Data 0.003 (0.005) Batch 0.325 (0.334) Remain 01:51:22 loss: 0.2958 Lr: 0.00097 [2023-12-20 19:40:44,664 INFO misc.py line 119 131400] Train: [75/100][788/800] Data 0.003 (0.005) Batch 0.310 (0.334) Remain 01:51:21 loss: 0.3167 Lr: 0.00097 [2023-12-20 19:40:44,938 INFO misc.py line 119 131400] Train: [75/100][789/800] Data 0.005 (0.005) Batch 0.276 (0.334) Remain 01:51:20 loss: 0.2338 Lr: 0.00097 [2023-12-20 19:40:45,240 INFO misc.py line 119 131400] Train: [75/100][790/800] Data 0.003 (0.005) Batch 0.302 (0.334) Remain 01:51:19 loss: 0.2665 Lr: 0.00097 [2023-12-20 19:40:45,509 INFO misc.py line 119 131400] Train: [75/100][791/800] Data 0.003 (0.005) Batch 0.269 (0.334) Remain 01:51:17 loss: 0.1100 Lr: 0.00097 [2023-12-20 19:40:45,815 INFO misc.py line 119 131400] Train: [75/100][792/800] Data 0.003 (0.005) Batch 0.306 (0.334) Remain 01:51:16 loss: 0.2931 Lr: 0.00097 [2023-12-20 19:40:46,080 INFO misc.py line 119 131400] Train: [75/100][793/800] Data 0.003 (0.005) Batch 0.264 (0.334) Remain 01:51:13 loss: 0.3649 Lr: 0.00097 [2023-12-20 19:40:46,457 INFO misc.py line 119 131400] Train: [75/100][794/800] Data 0.004 (0.005) Batch 0.377 (0.334) Remain 01:51:14 loss: 0.3200 Lr: 0.00097 [2023-12-20 19:40:46,779 INFO misc.py line 119 131400] Train: [75/100][795/800] Data 0.003 (0.005) Batch 0.323 (0.334) Remain 01:51:14 loss: 0.1476 Lr: 0.00097 [2023-12-20 19:40:47,125 INFO misc.py line 119 131400] Train: [75/100][796/800] Data 0.003 (0.005) Batch 0.346 (0.334) Remain 01:51:14 loss: 0.1485 Lr: 0.00097 [2023-12-20 19:40:47,447 INFO misc.py line 119 131400] Train: [75/100][797/800] Data 0.003 (0.005) Batch 0.322 (0.334) Remain 01:51:13 loss: 0.2900 Lr: 0.00097 [2023-12-20 19:40:47,803 INFO misc.py line 119 131400] Train: [75/100][798/800] Data 0.003 (0.005) Batch 0.355 (0.334) Remain 01:51:13 loss: 0.4229 Lr: 0.00097 [2023-12-20 19:40:48,121 INFO misc.py line 119 131400] Train: [75/100][799/800] Data 0.004 (0.005) Batch 0.318 (0.334) Remain 01:51:12 loss: 0.1491 Lr: 0.00097 [2023-12-20 19:40:48,393 INFO misc.py line 119 131400] Train: [75/100][800/800] Data 0.003 (0.005) Batch 0.273 (0.334) Remain 01:51:11 loss: 0.2310 Lr: 0.00097 [2023-12-20 19:40:48,394 INFO misc.py line 136 131400] Train result: loss: 0.2446 [2023-12-20 19:40:48,395 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:41:09,551 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1366 [2023-12-20 19:41:10,907 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1573 [2023-12-20 19:41:11,007 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4570 [2023-12-20 19:41:11,116 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.6936 [2023-12-20 19:41:11,231 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4066 [2023-12-20 19:41:11,333 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.5534 [2023-12-20 19:41:11,423 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9877 [2023-12-20 19:41:11,530 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6269 [2023-12-20 19:41:11,612 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2483 [2023-12-20 19:41:11,699 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3395 [2023-12-20 19:41:11,794 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4350 [2023-12-20 19:41:11,930 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2600 [2023-12-20 19:41:12,049 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5830 [2023-12-20 19:41:12,203 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1865 [2023-12-20 19:41:12,296 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1337 [2023-12-20 19:41:12,431 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.9081 [2023-12-20 19:41:12,541 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2463 [2023-12-20 19:41:12,651 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6059 [2023-12-20 19:41:12,766 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.4423 [2023-12-20 19:41:12,842 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4862 [2023-12-20 19:41:12,958 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1911 [2023-12-20 19:41:13,113 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1398 [2023-12-20 19:41:13,234 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.2518 [2023-12-20 19:41:13,376 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2530 [2023-12-20 19:41:13,521 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1549 [2023-12-20 19:41:13,603 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8518 [2023-12-20 19:41:13,762 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8385 [2023-12-20 19:41:13,885 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6111 [2023-12-20 19:41:13,980 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5260 [2023-12-20 19:41:14,126 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6094 [2023-12-20 19:41:14,229 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5488 [2023-12-20 19:41:14,346 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3960 [2023-12-20 19:41:14,432 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1173 [2023-12-20 19:41:14,502 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1785 [2023-12-20 19:41:14,597 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5879 [2023-12-20 19:41:14,690 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.4086 [2023-12-20 19:41:14,818 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9052 [2023-12-20 19:41:14,927 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0878 [2023-12-20 19:41:15,014 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4311 [2023-12-20 19:41:15,156 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3003 [2023-12-20 19:41:15,302 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0159 [2023-12-20 19:41:15,399 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0603 [2023-12-20 19:41:15,520 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2359 [2023-12-20 19:41:15,662 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8454 [2023-12-20 19:41:15,779 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4383 [2023-12-20 19:41:15,885 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.9109 [2023-12-20 19:41:16,053 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3494 [2023-12-20 19:41:16,145 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5300 [2023-12-20 19:41:16,290 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7460 [2023-12-20 19:41:16,381 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2140 [2023-12-20 19:41:16,457 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4501 [2023-12-20 19:41:16,563 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0700 [2023-12-20 19:41:16,711 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9892 [2023-12-20 19:41:16,851 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3148 [2023-12-20 19:41:16,958 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2210 [2023-12-20 19:41:17,047 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5762 [2023-12-20 19:41:17,150 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3595 [2023-12-20 19:41:17,312 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2044 [2023-12-20 19:41:17,407 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.3050 [2023-12-20 19:41:17,500 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2502 [2023-12-20 19:41:17,597 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4952 [2023-12-20 19:41:17,689 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3210 [2023-12-20 19:41:17,777 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5009 [2023-12-20 19:41:17,877 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.4122 [2023-12-20 19:41:18,004 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6714 [2023-12-20 19:41:18,090 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2825 [2023-12-20 19:41:18,199 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4509 [2023-12-20 19:41:18,312 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0105 [2023-12-20 19:41:18,400 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3537 [2023-12-20 19:41:18,482 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0120 [2023-12-20 19:41:18,590 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.5541 [2023-12-20 19:41:18,718 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.7264 [2023-12-20 19:41:18,851 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1223 [2023-12-20 19:41:18,945 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6040 [2023-12-20 19:41:19,073 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6270 [2023-12-20 19:41:19,185 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.7077 [2023-12-20 19:41:19,275 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6841 [2023-12-20 19:41:19,439 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0478 [2023-12-20 19:41:20,810 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7630/0.8417/0.9198. [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8735/0.9606 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9648/0.9857 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6997/0.8083 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8346/0.8799 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9277/0.9581 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8843/0.9505 [2023-12-20 19:41:20,810 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7630/0.8334 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7221/0.8328 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6870/0.7697 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8463/0.9318 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4020/0.5111 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7065/0.8053 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6831/0.8824 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7575/0.8745 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6985/0.7496 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6817/0.7374 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9401/0.9790 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6740/0.7866 [2023-12-20 19:41:20,811 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8867/0.9222 [2023-12-20 19:41:20,812 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6270/0.6750 [2023-12-20 19:41:20,812 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:41:20,814 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 19:41:20,814 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:41:24,882 INFO misc.py line 119 131400] Train: [76/100][1/800] Data 1.181 (1.181) Batch 1.534 (1.534) Remain 08:31:22 loss: 0.2227 Lr: 0.00097 [2023-12-20 19:41:25,225 INFO misc.py line 119 131400] Train: [76/100][2/800] Data 0.005 (0.005) Batch 0.341 (0.341) Remain 01:53:46 loss: 0.1996 Lr: 0.00097 [2023-12-20 19:41:25,525 INFO misc.py line 119 131400] Train: [76/100][3/800] Data 0.005 (0.005) Batch 0.301 (0.301) Remain 01:40:19 loss: 0.3186 Lr: 0.00097 [2023-12-20 19:41:25,856 INFO misc.py line 119 131400] Train: [76/100][4/800] Data 0.004 (0.004) Batch 0.332 (0.332) Remain 01:50:33 loss: 0.3171 Lr: 0.00097 [2023-12-20 19:41:26,206 INFO misc.py line 119 131400] Train: [76/100][5/800] Data 0.004 (0.004) Batch 0.349 (0.340) Remain 01:53:22 loss: 0.2365 Lr: 0.00097 [2023-12-20 19:41:26,526 INFO misc.py line 119 131400] Train: [76/100][6/800] Data 0.004 (0.004) Batch 0.321 (0.334) Remain 01:51:11 loss: 0.1855 Lr: 0.00097 [2023-12-20 19:41:27,106 INFO misc.py line 119 131400] Train: [76/100][7/800] Data 0.092 (0.026) Batch 0.581 (0.395) Remain 02:11:46 loss: 0.1315 Lr: 0.00097 [2023-12-20 19:41:27,446 INFO misc.py line 119 131400] Train: [76/100][8/800] Data 0.004 (0.022) Batch 0.340 (0.384) Remain 02:08:02 loss: 0.4463 Lr: 0.00097 [2023-12-20 19:41:27,760 INFO misc.py line 119 131400] Train: [76/100][9/800] Data 0.004 (0.019) Batch 0.304 (0.371) Remain 02:03:35 loss: 0.2856 Lr: 0.00097 [2023-12-20 19:41:28,123 INFO misc.py line 119 131400] Train: [76/100][10/800] Data 0.014 (0.018) Batch 0.373 (0.371) Remain 02:03:40 loss: 0.2669 Lr: 0.00097 [2023-12-20 19:41:28,453 INFO misc.py line 119 131400] Train: [76/100][11/800] Data 0.003 (0.016) Batch 0.330 (0.366) Remain 02:01:56 loss: 0.2451 Lr: 0.00097 [2023-12-20 19:41:28,813 INFO misc.py line 119 131400] Train: [76/100][12/800] Data 0.004 (0.015) Batch 0.359 (0.365) Remain 02:01:40 loss: 0.3466 Lr: 0.00097 [2023-12-20 19:41:29,172 INFO misc.py line 119 131400] Train: [76/100][13/800] Data 0.006 (0.014) Batch 0.360 (0.365) Remain 02:01:29 loss: 0.2475 Lr: 0.00097 [2023-12-20 19:41:29,541 INFO misc.py line 119 131400] Train: [76/100][14/800] Data 0.004 (0.013) Batch 0.369 (0.365) Remain 02:01:37 loss: 0.1285 Lr: 0.00097 [2023-12-20 19:41:29,885 INFO misc.py line 119 131400] Train: [76/100][15/800] Data 0.003 (0.012) Batch 0.344 (0.363) Remain 02:01:01 loss: 0.2498 Lr: 0.00097 [2023-12-20 19:41:30,225 INFO misc.py line 119 131400] Train: [76/100][16/800] Data 0.003 (0.011) Batch 0.340 (0.362) Remain 02:00:25 loss: 0.3351 Lr: 0.00097 [2023-12-20 19:41:30,546 INFO misc.py line 119 131400] Train: [76/100][17/800] Data 0.004 (0.011) Batch 0.321 (0.359) Remain 01:59:26 loss: 0.2677 Lr: 0.00097 [2023-12-20 19:41:30,877 INFO misc.py line 119 131400] Train: [76/100][18/800] Data 0.004 (0.010) Batch 0.332 (0.357) Remain 01:58:50 loss: 0.3892 Lr: 0.00097 [2023-12-20 19:41:31,231 INFO misc.py line 119 131400] Train: [76/100][19/800] Data 0.004 (0.010) Batch 0.353 (0.357) Remain 01:58:45 loss: 0.2849 Lr: 0.00097 [2023-12-20 19:41:31,561 INFO misc.py line 119 131400] Train: [76/100][20/800] Data 0.004 (0.010) Batch 0.330 (0.355) Remain 01:58:13 loss: 0.1441 Lr: 0.00097 [2023-12-20 19:41:31,885 INFO misc.py line 119 131400] Train: [76/100][21/800] Data 0.004 (0.009) Batch 0.324 (0.353) Remain 01:57:39 loss: 0.2846 Lr: 0.00097 [2023-12-20 19:41:32,243 INFO misc.py line 119 131400] Train: [76/100][22/800] Data 0.004 (0.009) Batch 0.359 (0.354) Remain 01:57:44 loss: 0.2443 Lr: 0.00097 [2023-12-20 19:41:32,570 INFO misc.py line 119 131400] Train: [76/100][23/800] Data 0.003 (0.009) Batch 0.328 (0.352) Remain 01:57:18 loss: 0.2015 Lr: 0.00097 [2023-12-20 19:41:32,922 INFO misc.py line 119 131400] Train: [76/100][24/800] Data 0.002 (0.008) Batch 0.351 (0.352) Remain 01:57:16 loss: 0.3848 Lr: 0.00097 [2023-12-20 19:41:33,251 INFO misc.py line 119 131400] Train: [76/100][25/800] Data 0.003 (0.008) Batch 0.330 (0.351) Remain 01:56:55 loss: 0.1853 Lr: 0.00097 [2023-12-20 19:41:33,570 INFO misc.py line 119 131400] Train: [76/100][26/800] Data 0.003 (0.008) Batch 0.319 (0.350) Remain 01:56:27 loss: 0.2718 Lr: 0.00097 [2023-12-20 19:41:33,931 INFO misc.py line 119 131400] Train: [76/100][27/800] Data 0.003 (0.008) Batch 0.358 (0.350) Remain 01:56:34 loss: 0.1404 Lr: 0.00097 [2023-12-20 19:41:34,247 INFO misc.py line 119 131400] Train: [76/100][28/800] Data 0.008 (0.008) Batch 0.317 (0.349) Remain 01:56:07 loss: 0.1518 Lr: 0.00097 [2023-12-20 19:41:34,570 INFO misc.py line 119 131400] Train: [76/100][29/800] Data 0.004 (0.008) Batch 0.324 (0.348) Remain 01:55:48 loss: 0.2215 Lr: 0.00097 [2023-12-20 19:41:34,918 INFO misc.py line 119 131400] Train: [76/100][30/800] Data 0.003 (0.007) Batch 0.348 (0.348) Remain 01:55:48 loss: 0.2621 Lr: 0.00097 [2023-12-20 19:41:35,254 INFO misc.py line 119 131400] Train: [76/100][31/800] Data 0.003 (0.007) Batch 0.335 (0.347) Remain 01:55:38 loss: 0.1194 Lr: 0.00097 [2023-12-20 19:41:35,553 INFO misc.py line 119 131400] Train: [76/100][32/800] Data 0.003 (0.007) Batch 0.299 (0.346) Remain 01:55:04 loss: 0.1317 Lr: 0.00097 [2023-12-20 19:41:35,880 INFO misc.py line 119 131400] Train: [76/100][33/800] Data 0.003 (0.007) Batch 0.327 (0.345) Remain 01:54:52 loss: 0.3735 Lr: 0.00097 [2023-12-20 19:41:36,228 INFO misc.py line 119 131400] Train: [76/100][34/800] Data 0.004 (0.007) Batch 0.340 (0.345) Remain 01:54:48 loss: 0.1706 Lr: 0.00097 [2023-12-20 19:41:36,522 INFO misc.py line 119 131400] Train: [76/100][35/800] Data 0.011 (0.007) Batch 0.301 (0.344) Remain 01:54:21 loss: 0.2150 Lr: 0.00097 [2023-12-20 19:41:36,850 INFO misc.py line 119 131400] Train: [76/100][36/800] Data 0.004 (0.007) Batch 0.329 (0.343) Remain 01:54:12 loss: 0.1778 Lr: 0.00097 [2023-12-20 19:41:37,145 INFO misc.py line 119 131400] Train: [76/100][37/800] Data 0.003 (0.007) Batch 0.294 (0.342) Remain 01:53:42 loss: 0.3658 Lr: 0.00097 [2023-12-20 19:41:37,484 INFO misc.py line 119 131400] Train: [76/100][38/800] Data 0.003 (0.007) Batch 0.337 (0.342) Remain 01:53:39 loss: 0.2117 Lr: 0.00097 [2023-12-20 19:41:37,832 INFO misc.py line 119 131400] Train: [76/100][39/800] Data 0.005 (0.007) Batch 0.348 (0.342) Remain 01:53:42 loss: 0.2191 Lr: 0.00097 [2023-12-20 19:41:38,140 INFO misc.py line 119 131400] Train: [76/100][40/800] Data 0.006 (0.007) Batch 0.310 (0.341) Remain 01:53:25 loss: 0.2282 Lr: 0.00097 [2023-12-20 19:41:38,425 INFO misc.py line 119 131400] Train: [76/100][41/800] Data 0.003 (0.007) Batch 0.282 (0.339) Remain 01:52:53 loss: 0.2535 Lr: 0.00097 [2023-12-20 19:41:38,719 INFO misc.py line 119 131400] Train: [76/100][42/800] Data 0.007 (0.007) Batch 0.296 (0.338) Remain 01:52:31 loss: 0.1600 Lr: 0.00097 [2023-12-20 19:41:39,013 INFO misc.py line 119 131400] Train: [76/100][43/800] Data 0.005 (0.007) Batch 0.295 (0.337) Remain 01:52:09 loss: 0.2069 Lr: 0.00097 [2023-12-20 19:41:39,349 INFO misc.py line 119 131400] Train: [76/100][44/800] Data 0.004 (0.006) Batch 0.337 (0.337) Remain 01:52:08 loss: 0.2302 Lr: 0.00097 [2023-12-20 19:41:39,678 INFO misc.py line 119 131400] Train: [76/100][45/800] Data 0.004 (0.006) Batch 0.329 (0.337) Remain 01:52:04 loss: 0.2145 Lr: 0.00097 [2023-12-20 19:41:40,005 INFO misc.py line 119 131400] Train: [76/100][46/800] Data 0.004 (0.006) Batch 0.327 (0.337) Remain 01:51:59 loss: 0.2247 Lr: 0.00097 [2023-12-20 19:41:40,310 INFO misc.py line 119 131400] Train: [76/100][47/800] Data 0.004 (0.006) Batch 0.306 (0.336) Remain 01:51:44 loss: 0.2328 Lr: 0.00097 [2023-12-20 19:41:40,630 INFO misc.py line 119 131400] Train: [76/100][48/800] Data 0.003 (0.006) Batch 0.320 (0.336) Remain 01:51:37 loss: 0.1451 Lr: 0.00097 [2023-12-20 19:41:40,937 INFO misc.py line 119 131400] Train: [76/100][49/800] Data 0.003 (0.006) Batch 0.306 (0.335) Remain 01:51:24 loss: 0.2098 Lr: 0.00097 [2023-12-20 19:41:41,306 INFO misc.py line 119 131400] Train: [76/100][50/800] Data 0.003 (0.006) Batch 0.369 (0.336) Remain 01:51:38 loss: 0.4733 Lr: 0.00097 [2023-12-20 19:41:41,631 INFO misc.py line 119 131400] Train: [76/100][51/800] Data 0.004 (0.006) Batch 0.322 (0.335) Remain 01:51:32 loss: 0.1340 Lr: 0.00097 [2023-12-20 19:41:41,983 INFO misc.py line 119 131400] Train: [76/100][52/800] Data 0.008 (0.006) Batch 0.356 (0.336) Remain 01:51:40 loss: 0.4615 Lr: 0.00097 [2023-12-20 19:41:42,328 INFO misc.py line 119 131400] Train: [76/100][53/800] Data 0.004 (0.006) Batch 0.345 (0.336) Remain 01:51:43 loss: 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[2023-12-20 19:45:45,525 INFO misc.py line 119 131400] Train: [76/100][776/800] Data 0.003 (0.004) Batch 0.320 (0.336) Remain 01:47:46 loss: 0.1453 Lr: 0.00090 [2023-12-20 19:45:45,849 INFO misc.py line 119 131400] Train: [76/100][777/800] Data 0.003 (0.004) Batch 0.324 (0.336) Remain 01:47:45 loss: 0.2490 Lr: 0.00090 [2023-12-20 19:45:46,194 INFO misc.py line 119 131400] Train: [76/100][778/800] Data 0.003 (0.004) Batch 0.345 (0.336) Remain 01:47:45 loss: 0.1376 Lr: 0.00090 [2023-12-20 19:45:46,514 INFO misc.py line 119 131400] Train: [76/100][779/800] Data 0.003 (0.004) Batch 0.318 (0.336) Remain 01:47:44 loss: 0.1110 Lr: 0.00090 [2023-12-20 19:45:46,867 INFO misc.py line 119 131400] Train: [76/100][780/800] Data 0.005 (0.004) Batch 0.354 (0.336) Remain 01:47:44 loss: 0.2157 Lr: 0.00090 [2023-12-20 19:45:47,193 INFO misc.py line 119 131400] Train: [76/100][781/800] Data 0.004 (0.004) Batch 0.326 (0.336) Remain 01:47:43 loss: 0.3367 Lr: 0.00090 [2023-12-20 19:45:47,517 INFO misc.py line 119 131400] Train: [76/100][782/800] Data 0.004 (0.004) Batch 0.325 (0.336) Remain 01:47:43 loss: 0.1932 Lr: 0.00090 [2023-12-20 19:45:47,846 INFO misc.py line 119 131400] Train: [76/100][783/800] Data 0.002 (0.004) Batch 0.327 (0.336) Remain 01:47:42 loss: 0.2196 Lr: 0.00090 [2023-12-20 19:45:48,166 INFO misc.py line 119 131400] Train: [76/100][784/800] Data 0.005 (0.004) Batch 0.321 (0.336) Remain 01:47:42 loss: 0.1727 Lr: 0.00090 [2023-12-20 19:45:48,517 INFO misc.py line 119 131400] Train: [76/100][785/800] Data 0.003 (0.004) Batch 0.347 (0.336) Remain 01:47:42 loss: 0.2919 Lr: 0.00090 [2023-12-20 19:45:48,876 INFO misc.py line 119 131400] Train: [76/100][786/800] Data 0.009 (0.004) Batch 0.362 (0.336) Remain 01:47:42 loss: 0.2766 Lr: 0.00090 [2023-12-20 19:45:49,224 INFO misc.py line 119 131400] Train: [76/100][787/800] Data 0.004 (0.004) Batch 0.348 (0.336) Remain 01:47:42 loss: 0.2578 Lr: 0.00090 [2023-12-20 19:45:49,594 INFO misc.py line 119 131400] Train: 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Batch 0.319 (0.336) Remain 01:47:35 loss: 0.2891 Lr: 0.00090 [2023-12-20 19:45:51,682 INFO misc.py line 119 131400] Train: [76/100][795/800] Data 0.003 (0.004) Batch 0.302 (0.336) Remain 01:47:33 loss: 0.3175 Lr: 0.00090 [2023-12-20 19:45:51,975 INFO misc.py line 119 131400] Train: [76/100][796/800] Data 0.002 (0.004) Batch 0.293 (0.336) Remain 01:47:32 loss: 0.3375 Lr: 0.00090 [2023-12-20 19:45:52,274 INFO misc.py line 119 131400] Train: [76/100][797/800] Data 0.002 (0.004) Batch 0.299 (0.336) Remain 01:47:31 loss: 0.1483 Lr: 0.00090 [2023-12-20 19:45:52,583 INFO misc.py line 119 131400] Train: [76/100][798/800] Data 0.002 (0.004) Batch 0.309 (0.336) Remain 01:47:30 loss: 0.2630 Lr: 0.00090 [2023-12-20 19:45:52,866 INFO misc.py line 119 131400] Train: [76/100][799/800] Data 0.004 (0.004) Batch 0.283 (0.336) Remain 01:47:28 loss: 0.1507 Lr: 0.00090 [2023-12-20 19:45:53,148 INFO misc.py line 119 131400] Train: [76/100][800/800] Data 0.003 (0.004) Batch 0.282 (0.336) Remain 01:47:27 loss: 0.2727 Lr: 0.00090 [2023-12-20 19:45:53,148 INFO misc.py line 136 131400] Train result: loss: 0.2454 [2023-12-20 19:45:53,149 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:46:13,920 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2013 [2023-12-20 19:46:14,731 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1362 [2023-12-20 19:46:15,082 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.6146 [2023-12-20 19:46:15,193 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3265 [2023-12-20 19:46:15,344 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3234 [2023-12-20 19:46:15,454 INFO evaluator.py line 159 131400] Test: [6/78] Loss 0.9828 [2023-12-20 19:46:15,546 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1477 [2023-12-20 19:46:15,653 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.3213 [2023-12-20 19:46:15,737 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3046 [2023-12-20 19:46:15,849 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3245 [2023-12-20 19:46:15,941 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4942 [2023-12-20 19:46:16,080 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2676 [2023-12-20 19:46:16,204 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3855 [2023-12-20 19:46:16,364 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1747 [2023-12-20 19:46:16,461 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1399 [2023-12-20 19:46:16,596 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7877 [2023-12-20 19:46:16,707 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3218 [2023-12-20 19:46:16,818 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.3249 [2023-12-20 19:46:16,941 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2563 [2023-12-20 19:46:17,020 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3903 [2023-12-20 19:46:17,137 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1492 [2023-12-20 19:46:17,305 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1843 [2023-12-20 19:46:17,427 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9919 [2023-12-20 19:46:17,574 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3866 [2023-12-20 19:46:17,721 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1978 [2023-12-20 19:46:17,804 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5575 [2023-12-20 19:46:17,960 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5404 [2023-12-20 19:46:18,092 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5053 [2023-12-20 19:46:18,187 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4752 [2023-12-20 19:46:18,338 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8150 [2023-12-20 19:46:18,448 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.7386 [2023-12-20 19:46:18,573 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4690 [2023-12-20 19:46:18,667 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1157 [2023-12-20 19:46:18,745 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1655 [2023-12-20 19:46:18,845 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8576 [2023-12-20 19:46:18,935 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2961 [2023-12-20 19:46:19,069 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9683 [2023-12-20 19:46:19,183 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0918 [2023-12-20 19:46:19,277 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4745 [2023-12-20 19:46:19,428 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2986 [2023-12-20 19:46:19,578 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0185 [2023-12-20 19:46:19,675 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0501 [2023-12-20 19:46:19,801 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3057 [2023-12-20 19:46:19,945 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8979 [2023-12-20 19:46:20,067 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5549 [2023-12-20 19:46:20,174 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5282 [2023-12-20 19:46:20,344 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3837 [2023-12-20 19:46:20,452 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4699 [2023-12-20 19:46:20,608 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5967 [2023-12-20 19:46:20,708 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0751 [2023-12-20 19:46:20,791 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5001 [2023-12-20 19:46:20,897 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.6512 [2023-12-20 19:46:21,043 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2992 [2023-12-20 19:46:21,176 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3838 [2023-12-20 19:46:21,278 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0234 [2023-12-20 19:46:21,369 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.4992 [2023-12-20 19:46:21,472 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3358 [2023-12-20 19:46:21,633 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1839 [2023-12-20 19:46:21,735 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4668 [2023-12-20 19:46:21,828 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.3281 [2023-12-20 19:46:21,926 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5527 [2023-12-20 19:46:22,019 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2843 [2023-12-20 19:46:22,106 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5056 [2023-12-20 19:46:22,206 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5161 [2023-12-20 19:46:22,336 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7182 [2023-12-20 19:46:22,428 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3314 [2023-12-20 19:46:22,529 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3418 [2023-12-20 19:46:22,643 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0129 [2023-12-20 19:46:22,730 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3270 [2023-12-20 19:46:22,816 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0120 [2023-12-20 19:46:22,919 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.6257 [2023-12-20 19:46:23,010 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6789 [2023-12-20 19:46:23,149 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0643 [2023-12-20 19:46:23,245 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.5906 [2023-12-20 19:46:23,365 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6161 [2023-12-20 19:46:23,474 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6058 [2023-12-20 19:46:23,560 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.1961 [2023-12-20 19:46:23,713 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2648 [2023-12-20 19:46:25,118 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7670/0.8463/0.9179. [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8653/0.9563 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9651/0.9846 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7091/0.8104 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8318/0.8746 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9128/0.9652 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8538/0.9278 [2023-12-20 19:46:25,118 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7756/0.8614 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7206/0.8047 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6827/0.8076 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8362/0.9180 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3986/0.5264 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7121/0.8499 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7271/0.8760 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7627/0.8359 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7106/0.7871 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7338/0.7894 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9427/0.9729 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7166/0.8062 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8777/0.9245 [2023-12-20 19:46:25,119 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6060/0.6469 [2023-12-20 19:46:25,120 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:46:25,121 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 19:46:25,121 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:46:28,330 INFO misc.py line 119 131400] Train: [77/100][1/800] Data 1.027 (1.027) Batch 1.302 (1.302) Remain 06:56:29 loss: 0.1864 Lr: 0.00090 [2023-12-20 19:46:28,713 INFO misc.py line 119 131400] Train: [77/100][2/800] Data 0.004 (0.004) Batch 0.356 (0.356) Remain 01:54:04 loss: 0.1797 Lr: 0.00090 [2023-12-20 19:46:29,059 INFO misc.py line 119 131400] Train: [77/100][3/800] Data 0.031 (0.031) Batch 0.374 (0.374) Remain 01:59:35 loss: 0.4065 Lr: 0.00090 [2023-12-20 19:46:29,414 INFO misc.py line 119 131400] Train: [77/100][4/800] Data 0.002 (0.002) Batch 0.353 (0.353) Remain 01:52:58 loss: 0.1200 Lr: 0.00090 [2023-12-20 19:46:29,747 INFO misc.py line 119 131400] Train: [77/100][5/800] Data 0.005 (0.004) Batch 0.334 (0.344) Remain 01:49:55 loss: 0.2443 Lr: 0.00090 [2023-12-20 19:46:30,061 INFO misc.py line 119 131400] Train: [77/100][6/800] Data 0.005 (0.004) Batch 0.314 (0.334) Remain 01:46:47 loss: 0.1437 Lr: 0.00090 [2023-12-20 19:46:30,386 INFO misc.py line 119 131400] Train: [77/100][7/800] Data 0.003 (0.004) Batch 0.325 (0.332) Remain 01:46:03 loss: 0.3033 Lr: 0.00090 [2023-12-20 19:46:30,720 INFO misc.py line 119 131400] Train: [77/100][8/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 01:46:09 loss: 0.2369 Lr: 0.00090 [2023-12-20 19:46:31,046 INFO misc.py line 119 131400] Train: [77/100][9/800] Data 0.004 (0.004) Batch 0.326 (0.331) Remain 01:45:50 loss: 0.3261 Lr: 0.00090 [2023-12-20 19:46:31,367 INFO misc.py line 119 131400] Train: [77/100][10/800] Data 0.004 (0.004) Batch 0.322 (0.330) Remain 01:45:25 loss: 0.2035 Lr: 0.00090 [2023-12-20 19:46:31,706 INFO misc.py line 119 131400] Train: [77/100][11/800] Data 0.003 (0.004) Batch 0.340 (0.331) Remain 01:45:49 loss: 0.1350 Lr: 0.00090 [2023-12-20 19:46:32,040 INFO misc.py line 119 131400] Train: [77/100][12/800] Data 0.003 (0.004) Batch 0.334 (0.331) Remain 01:45:54 loss: 0.2310 Lr: 0.00090 [2023-12-20 19:46:32,368 INFO misc.py line 119 131400] Train: [77/100][13/800] Data 0.004 (0.004) Batch 0.327 (0.331) Remain 01:45:47 loss: 0.2436 Lr: 0.00090 [2023-12-20 19:46:32,704 INFO misc.py line 119 131400] Train: [77/100][14/800] Data 0.004 (0.004) Batch 0.336 (0.331) Remain 01:45:55 loss: 0.1568 Lr: 0.00090 [2023-12-20 19:46:33,025 INFO misc.py line 119 131400] Train: [77/100][15/800] Data 0.004 (0.004) Batch 0.322 (0.330) Remain 01:45:39 loss: 0.3983 Lr: 0.00090 [2023-12-20 19:46:33,355 INFO misc.py line 119 131400] Train: [77/100][16/800] Data 0.004 (0.004) Batch 0.329 (0.330) Remain 01:45:37 loss: 0.2057 Lr: 0.00090 [2023-12-20 19:46:33,673 INFO misc.py line 119 131400] Train: [77/100][17/800] Data 0.004 (0.004) Batch 0.316 (0.329) Remain 01:45:17 loss: 0.2282 Lr: 0.00090 [2023-12-20 19:46:34,006 INFO misc.py line 119 131400] Train: [77/100][18/800] Data 0.006 (0.004) Batch 0.332 (0.330) Remain 01:45:20 loss: 0.3572 Lr: 0.00090 [2023-12-20 19:46:34,403 INFO misc.py line 119 131400] Train: [77/100][19/800] Data 0.008 (0.004) Batch 0.336 (0.330) Remain 01:45:28 loss: 0.1557 Lr: 0.00090 [2023-12-20 19:46:34,752 INFO misc.py line 119 131400] Train: [77/100][20/800] Data 0.068 (0.008) Batch 0.413 (0.335) Remain 01:47:01 loss: 0.1982 Lr: 0.00090 [2023-12-20 19:46:35,083 INFO misc.py line 119 131400] Train: [77/100][21/800] Data 0.004 (0.008) Batch 0.321 (0.334) Remain 01:46:47 loss: 0.2389 Lr: 0.00090 [2023-12-20 19:46:35,444 INFO misc.py line 119 131400] Train: [77/100][22/800] Data 0.015 (0.008) Batch 0.372 (0.336) Remain 01:47:24 loss: 0.3219 Lr: 0.00090 [2023-12-20 19:46:35,758 INFO misc.py line 119 131400] Train: [77/100][23/800] Data 0.003 (0.008) Batch 0.313 (0.335) Remain 01:47:02 loss: 0.2044 Lr: 0.00090 [2023-12-20 19:46:36,108 INFO misc.py line 119 131400] Train: [77/100][24/800] Data 0.004 (0.008) Batch 0.347 (0.335) Remain 01:47:12 loss: 0.3170 Lr: 0.00090 [2023-12-20 19:46:36,417 INFO misc.py line 119 131400] Train: [77/100][25/800] Data 0.009 (0.008) Batch 0.313 (0.334) Remain 01:46:52 loss: 0.3294 Lr: 0.00090 [2023-12-20 19:46:36,743 INFO misc.py line 119 131400] Train: [77/100][26/800] Data 0.004 (0.008) Batch 0.326 (0.334) Remain 01:46:44 loss: 0.3589 Lr: 0.00090 [2023-12-20 19:46:37,095 INFO misc.py line 119 131400] Train: [77/100][27/800] Data 0.004 (0.007) Batch 0.352 (0.335) Remain 01:46:59 loss: 0.3447 Lr: 0.00090 [2023-12-20 19:46:37,422 INFO misc.py line 119 131400] Train: [77/100][28/800] Data 0.003 (0.007) Batch 0.328 (0.335) Remain 01:46:53 loss: 0.3247 Lr: 0.00090 [2023-12-20 19:46:37,698 INFO misc.py line 119 131400] Train: [77/100][29/800] Data 0.002 (0.007) Batch 0.276 (0.332) Remain 01:46:09 loss: 0.2083 Lr: 0.00090 [2023-12-20 19:46:38,023 INFO misc.py line 119 131400] Train: [77/100][30/800] Data 0.003 (0.007) Batch 0.325 (0.332) Remain 01:46:04 loss: 0.1129 Lr: 0.00090 [2023-12-20 19:46:38,376 INFO misc.py line 119 131400] Train: [77/100][31/800] Data 0.003 (0.007) Batch 0.352 (0.333) Remain 01:46:17 loss: 0.3177 Lr: 0.00090 [2023-12-20 19:46:38,700 INFO misc.py line 119 131400] Train: [77/100][32/800] Data 0.004 (0.007) Batch 0.326 (0.332) Remain 01:46:12 loss: 0.2792 Lr: 0.00090 [2023-12-20 19:46:39,073 INFO misc.py line 119 131400] Train: [77/100][33/800] Data 0.003 (0.007) Batch 0.372 (0.334) Remain 01:46:37 loss: 0.2048 Lr: 0.00090 [2023-12-20 19:46:39,380 INFO misc.py line 119 131400] Train: [77/100][34/800] Data 0.003 (0.006) Batch 0.307 (0.333) Remain 01:46:20 loss: 0.1836 Lr: 0.00090 [2023-12-20 19:46:39,697 INFO misc.py line 119 131400] Train: [77/100][35/800] Data 0.003 (0.006) Batch 0.318 (0.332) Remain 01:46:10 loss: 0.1986 Lr: 0.00090 [2023-12-20 19:46:39,999 INFO misc.py line 119 131400] Train: [77/100][36/800] Data 0.003 (0.006) Batch 0.302 (0.331) Remain 01:45:52 loss: 0.4318 Lr: 0.00090 [2023-12-20 19:46:40,333 INFO misc.py line 119 131400] Train: [77/100][37/800] Data 0.004 (0.006) Batch 0.333 (0.332) Remain 01:45:53 loss: 0.2855 Lr: 0.00090 [2023-12-20 19:46:40,667 INFO misc.py line 119 131400] Train: [77/100][38/800] Data 0.006 (0.006) Batch 0.335 (0.332) Remain 01:45:54 loss: 0.2121 Lr: 0.00090 [2023-12-20 19:46:40,979 INFO misc.py line 119 131400] Train: [77/100][39/800] Data 0.004 (0.006) Batch 0.312 (0.331) Remain 01:45:44 loss: 0.3810 Lr: 0.00090 [2023-12-20 19:46:41,340 INFO misc.py line 119 131400] Train: [77/100][40/800] Data 0.003 (0.006) Batch 0.357 (0.332) Remain 01:45:57 loss: 0.4060 Lr: 0.00090 [2023-12-20 19:46:41,685 INFO misc.py line 119 131400] Train: [77/100][41/800] Data 0.008 (0.006) Batch 0.349 (0.332) Remain 01:46:05 loss: 0.1636 Lr: 0.00090 [2023-12-20 19:46:42,121 INFO misc.py line 119 131400] Train: [77/100][42/800] Data 0.003 (0.006) Batch 0.436 (0.335) Remain 01:46:56 loss: 0.3012 Lr: 0.00090 [2023-12-20 19:46:42,425 INFO misc.py line 119 131400] Train: [77/100][43/800] Data 0.003 (0.006) Batch 0.300 (0.334) Remain 01:46:39 loss: 0.1163 Lr: 0.00090 [2023-12-20 19:46:42,764 INFO misc.py line 119 131400] Train: [77/100][44/800] Data 0.007 (0.006) Batch 0.342 (0.334) Remain 01:46:42 loss: 0.2991 Lr: 0.00090 [2023-12-20 19:46:43,082 INFO misc.py line 119 131400] Train: [77/100][45/800] Data 0.004 (0.006) Batch 0.319 (0.334) Remain 01:46:35 loss: 0.1884 Lr: 0.00090 [2023-12-20 19:46:43,440 INFO misc.py line 119 131400] Train: [77/100][46/800] Data 0.003 (0.006) Batch 0.357 (0.334) Remain 01:46:45 loss: 0.3946 Lr: 0.00089 [2023-12-20 19:46:43,803 INFO misc.py line 119 131400] Train: [77/100][47/800] Data 0.003 (0.006) 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line 119 131400] Train: [77/100][782/800] Data 0.004 (0.005) Batch 0.322 (0.337) Remain 01:43:19 loss: 0.1678 Lr: 0.00083 [2023-12-20 19:50:51,574 INFO misc.py line 119 131400] Train: [77/100][783/800] Data 0.002 (0.005) Batch 0.315 (0.337) Remain 01:43:18 loss: 0.2234 Lr: 0.00083 [2023-12-20 19:50:51,923 INFO misc.py line 119 131400] Train: [77/100][784/800] Data 0.003 (0.005) Batch 0.349 (0.337) Remain 01:43:18 loss: 0.1977 Lr: 0.00083 [2023-12-20 19:50:52,239 INFO misc.py line 119 131400] Train: [77/100][785/800] Data 0.003 (0.005) Batch 0.318 (0.337) Remain 01:43:17 loss: 0.1807 Lr: 0.00083 [2023-12-20 19:50:52,570 INFO misc.py line 119 131400] Train: [77/100][786/800] Data 0.003 (0.005) Batch 0.330 (0.337) Remain 01:43:16 loss: 0.2586 Lr: 0.00083 [2023-12-20 19:50:52,889 INFO misc.py line 119 131400] Train: [77/100][787/800] Data 0.003 (0.005) Batch 0.319 (0.337) Remain 01:43:16 loss: 0.3258 Lr: 0.00083 [2023-12-20 19:50:53,232 INFO misc.py line 119 131400] Train: 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Batch 0.355 (0.336) Remain 01:43:11 loss: 0.1445 Lr: 0.00083 [2023-12-20 19:50:55,429 INFO misc.py line 119 131400] Train: [77/100][795/800] Data 0.004 (0.004) Batch 0.282 (0.336) Remain 01:43:10 loss: 0.1134 Lr: 0.00083 [2023-12-20 19:50:55,767 INFO misc.py line 119 131400] Train: [77/100][796/800] Data 0.003 (0.004) Batch 0.337 (0.336) Remain 01:43:09 loss: 0.2157 Lr: 0.00083 [2023-12-20 19:50:56,093 INFO misc.py line 119 131400] Train: [77/100][797/800] Data 0.004 (0.004) Batch 0.327 (0.336) Remain 01:43:09 loss: 0.1618 Lr: 0.00083 [2023-12-20 19:50:56,419 INFO misc.py line 119 131400] Train: [77/100][798/800] Data 0.003 (0.004) Batch 0.325 (0.336) Remain 01:43:08 loss: 0.2634 Lr: 0.00083 [2023-12-20 19:50:56,710 INFO misc.py line 119 131400] Train: [77/100][799/800] Data 0.005 (0.004) Batch 0.292 (0.336) Remain 01:43:07 loss: 0.2709 Lr: 0.00083 [2023-12-20 19:50:57,043 INFO misc.py line 119 131400] Train: [77/100][800/800] Data 0.003 (0.004) Batch 0.334 (0.336) Remain 01:43:06 loss: 0.2387 Lr: 0.00083 [2023-12-20 19:50:57,044 INFO misc.py line 136 131400] Train result: loss: 0.2388 [2023-12-20 19:50:57,044 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:51:19,472 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1517 [2023-12-20 19:51:19,682 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1576 [2023-12-20 19:51:19,778 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5916 [2023-12-20 19:51:19,897 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3300 [2023-12-20 19:51:20,011 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2340 [2023-12-20 19:51:20,115 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6220 [2023-12-20 19:51:20,209 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8284 [2023-12-20 19:51:20,318 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9825 [2023-12-20 19:51:20,403 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2298 [2023-12-20 19:51:20,491 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3360 [2023-12-20 19:51:20,591 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4673 [2023-12-20 19:51:20,732 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.3256 [2023-12-20 19:51:20,850 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4170 [2023-12-20 19:51:21,006 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1947 [2023-12-20 19:51:21,100 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1399 [2023-12-20 19:51:21,239 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5548 [2023-12-20 19:51:21,355 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3005 [2023-12-20 19:51:21,471 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4811 [2023-12-20 19:51:21,583 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1524 [2023-12-20 19:51:21,659 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4007 [2023-12-20 19:51:21,770 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1379 [2023-12-20 19:51:21,929 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1364 [2023-12-20 19:51:22,051 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.0905 [2023-12-20 19:51:22,196 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2101 [2023-12-20 19:51:22,338 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1619 [2023-12-20 19:51:22,419 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.9314 [2023-12-20 19:51:22,577 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4996 [2023-12-20 19:51:22,701 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5017 [2023-12-20 19:51:22,799 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.7807 [2023-12-20 19:51:22,945 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6158 [2023-12-20 19:51:23,048 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5496 [2023-12-20 19:51:23,166 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3532 [2023-12-20 19:51:23,252 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1090 [2023-12-20 19:51:23,327 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.4421 [2023-12-20 19:51:23,429 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.5819 [2023-12-20 19:51:23,519 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2944 [2023-12-20 19:51:23,647 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9959 [2023-12-20 19:51:23,764 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0882 [2023-12-20 19:51:23,846 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5194 [2023-12-20 19:51:23,986 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3602 [2023-12-20 19:51:24,131 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0168 [2023-12-20 19:51:24,231 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0555 [2023-12-20 19:51:24,350 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.6293 [2023-12-20 19:51:24,499 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1067 [2023-12-20 19:51:24,618 INFO evaluator.py line 159 131400] Test: [45/78] Loss 1.9615 [2023-12-20 19:51:24,724 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7724 [2023-12-20 19:51:24,896 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3862 [2023-12-20 19:51:24,990 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.6012 [2023-12-20 19:51:25,140 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5883 [2023-12-20 19:51:25,230 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2032 [2023-12-20 19:51:25,309 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7542 [2023-12-20 19:51:25,413 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2976 [2023-12-20 19:51:25,558 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.4903 [2023-12-20 19:51:25,695 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3069 [2023-12-20 19:51:25,810 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9611 [2023-12-20 19:51:25,897 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5323 [2023-12-20 19:51:26,001 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.2859 [2023-12-20 19:51:26,163 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1913 [2023-12-20 19:51:26,258 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.2571 [2023-12-20 19:51:26,353 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1530 [2023-12-20 19:51:26,452 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5269 [2023-12-20 19:51:26,542 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3018 [2023-12-20 19:51:26,627 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5935 [2023-12-20 19:51:26,727 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5513 [2023-12-20 19:51:26,854 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6900 [2023-12-20 19:51:26,936 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.1639 [2023-12-20 19:51:27,034 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4130 [2023-12-20 19:51:27,127 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0087 [2023-12-20 19:51:27,211 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3106 [2023-12-20 19:51:27,294 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0111 [2023-12-20 19:51:27,388 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7396 [2023-12-20 19:51:27,478 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5996 [2023-12-20 19:51:27,611 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0754 [2023-12-20 19:51:27,706 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6514 [2023-12-20 19:51:27,820 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6949 [2023-12-20 19:51:27,925 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.6195 [2023-12-20 19:51:28,010 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2726 [2023-12-20 19:51:28,166 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.8162 [2023-12-20 19:51:29,489 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7630/0.8424/0.9184. [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8695/0.9518 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9650/0.9852 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7083/0.8036 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8331/0.8759 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9188/0.9662 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8585/0.9380 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7737/0.8457 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7369/0.8639 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6771/0.7616 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8291/0.9428 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3975/0.5275 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6736/0.7604 [2023-12-20 19:51:29,490 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7183/0.8692 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7581/0.8687 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7120/0.7915 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6791/0.7505 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9478/0.9692 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7074/0.7870 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8952/0.9232 [2023-12-20 19:51:29,491 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6012/0.6658 [2023-12-20 19:51:29,492 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:51:29,493 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 19:51:29,493 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:51:32,680 INFO misc.py line 119 131400] Train: [78/100][1/800] Data 0.695 (0.695) Batch 0.997 (0.997) Remain 05:05:45 loss: 0.1960 Lr: 0.00083 [2023-12-20 19:51:33,004 INFO misc.py line 119 131400] Train: [78/100][2/800] Data 0.005 (0.005) Batch 0.326 (0.326) Remain 01:39:58 loss: 0.1459 Lr: 0.00083 [2023-12-20 19:51:33,340 INFO misc.py line 119 131400] Train: [78/100][3/800] Data 0.003 (0.003) Batch 0.323 (0.323) Remain 01:39:10 loss: 0.3846 Lr: 0.00083 [2023-12-20 19:51:33,681 INFO misc.py line 119 131400] Train: [78/100][4/800] Data 0.016 (0.016) Batch 0.351 (0.351) Remain 01:47:38 loss: 0.2314 Lr: 0.00083 [2023-12-20 19:51:34,011 INFO misc.py line 119 131400] Train: [78/100][5/800] Data 0.006 (0.011) Batch 0.331 (0.341) Remain 01:44:32 loss: 0.1552 Lr: 0.00083 [2023-12-20 19:51:34,670 INFO misc.py line 119 131400] Train: [78/100][6/800] Data 0.340 (0.121) Batch 0.658 (0.447) Remain 02:16:57 loss: 0.4518 Lr: 0.00083 [2023-12-20 19:51:34,990 INFO misc.py line 119 131400] Train: [78/100][7/800] Data 0.007 (0.092) Batch 0.321 (0.415) Remain 02:07:19 loss: 0.3327 Lr: 0.00083 [2023-12-20 19:51:35,327 INFO misc.py line 119 131400] Train: [78/100][8/800] Data 0.004 (0.075) Batch 0.338 (0.400) Remain 02:02:34 loss: 0.2578 Lr: 0.00083 [2023-12-20 19:51:35,688 INFO misc.py line 119 131400] Train: [78/100][9/800] Data 0.003 (0.063) Batch 0.361 (0.393) Remain 02:00:34 loss: 0.3419 Lr: 0.00083 [2023-12-20 19:51:36,019 INFO misc.py line 119 131400] Train: [78/100][10/800] Data 0.003 (0.054) Batch 0.330 (0.384) Remain 01:57:48 loss: 0.2822 Lr: 0.00083 [2023-12-20 19:51:36,348 INFO misc.py line 119 131400] Train: [78/100][11/800] Data 0.004 (0.048) Batch 0.329 (0.377) Remain 01:55:39 loss: 0.3650 Lr: 0.00083 [2023-12-20 19:51:36,687 INFO misc.py line 119 131400] Train: [78/100][12/800] Data 0.004 (0.043) Batch 0.339 (0.373) Remain 01:54:21 loss: 0.1926 Lr: 0.00083 [2023-12-20 19:51:37,012 INFO misc.py line 119 131400] Train: [78/100][13/800] Data 0.004 (0.039) Batch 0.326 (0.368) Remain 01:52:53 loss: 0.1955 Lr: 0.00083 [2023-12-20 19:51:37,349 INFO misc.py line 119 131400] Train: [78/100][14/800] Data 0.003 (0.036) Batch 0.337 (0.365) Remain 01:52:00 loss: 0.3409 Lr: 0.00083 [2023-12-20 19:51:37,703 INFO misc.py line 119 131400] Train: [78/100][15/800] Data 0.004 (0.033) Batch 0.354 (0.365) Remain 01:51:42 loss: 0.2363 Lr: 0.00083 [2023-12-20 19:51:38,050 INFO misc.py line 119 131400] Train: [78/100][16/800] Data 0.010 (0.032) Batch 0.344 (0.363) Remain 01:51:13 loss: 0.2523 Lr: 0.00083 [2023-12-20 19:51:38,390 INFO misc.py line 119 131400] Train: [78/100][17/800] Data 0.008 (0.030) Batch 0.343 (0.362) Remain 01:50:46 loss: 0.2067 Lr: 0.00083 [2023-12-20 19:51:38,733 INFO misc.py line 119 131400] Train: [78/100][18/800] Data 0.003 (0.028) Batch 0.343 (0.360) Remain 01:50:23 loss: 0.2337 Lr: 0.00083 [2023-12-20 19:51:39,056 INFO misc.py line 119 131400] Train: [78/100][19/800] Data 0.003 (0.026) Batch 0.322 (0.358) Remain 01:49:39 loss: 0.1410 Lr: 0.00083 [2023-12-20 19:51:39,423 INFO misc.py line 119 131400] Train: [78/100][20/800] Data 0.004 (0.025) Batch 0.368 (0.359) Remain 01:49:49 loss: 0.1737 Lr: 0.00083 [2023-12-20 19:51:39,794 INFO misc.py line 119 131400] Train: [78/100][21/800] Data 0.004 (0.024) Batch 0.371 (0.359) Remain 01:50:01 loss: 0.2742 Lr: 0.00083 [2023-12-20 19:51:40,133 INFO misc.py line 119 131400] Train: [78/100][22/800] Data 0.004 (0.023) Batch 0.338 (0.358) Remain 01:49:41 loss: 0.2535 Lr: 0.00083 [2023-12-20 19:51:40,475 INFO misc.py line 119 131400] Train: [78/100][23/800] Data 0.005 (0.022) Batch 0.342 (0.357) Remain 01:49:25 loss: 0.3448 Lr: 0.00083 [2023-12-20 19:51:40,801 INFO misc.py line 119 131400] Train: [78/100][24/800] Data 0.005 (0.021) Batch 0.326 (0.356) Remain 01:48:58 loss: 0.0902 Lr: 0.00083 [2023-12-20 19:51:41,158 INFO misc.py line 119 131400] Train: [78/100][25/800] Data 0.005 (0.020) Batch 0.359 (0.356) Remain 01:49:00 loss: 0.3977 Lr: 0.00083 [2023-12-20 19:51:41,493 INFO misc.py line 119 131400] Train: [78/100][26/800] Data 0.002 (0.020) Batch 0.334 (0.355) Remain 01:48:42 loss: 0.1888 Lr: 0.00083 [2023-12-20 19:51:41,778 INFO misc.py line 119 131400] Train: [78/100][27/800] Data 0.005 (0.019) Batch 0.286 (0.352) Remain 01:47:48 loss: 0.2523 Lr: 0.00083 [2023-12-20 19:51:42,131 INFO misc.py line 119 131400] Train: [78/100][28/800] Data 0.003 (0.018) Batch 0.351 (0.352) Remain 01:47:48 loss: 0.1809 Lr: 0.00083 [2023-12-20 19:51:42,451 INFO misc.py line 119 131400] Train: [78/100][29/800] Data 0.005 (0.018) Batch 0.321 (0.351) Remain 01:47:25 loss: 0.2672 Lr: 0.00083 [2023-12-20 19:51:42,834 INFO misc.py line 119 131400] Train: [78/100][30/800] Data 0.005 (0.017) Batch 0.383 (0.352) Remain 01:47:47 loss: 0.2873 Lr: 0.00083 [2023-12-20 19:51:43,188 INFO misc.py line 119 131400] Train: [78/100][31/800] Data 0.004 (0.017) Batch 0.353 (0.352) Remain 01:47:47 loss: 0.2182 Lr: 0.00083 [2023-12-20 19:51:43,553 INFO misc.py line 119 131400] Train: [78/100][32/800] Data 0.004 (0.017) Batch 0.366 (0.353) Remain 01:47:55 loss: 0.4199 Lr: 0.00083 [2023-12-20 19:51:43,894 INFO misc.py line 119 131400] Train: [78/100][33/800] Data 0.004 (0.016) Batch 0.336 (0.352) Remain 01:47:45 loss: 0.1180 Lr: 0.00083 [2023-12-20 19:51:44,220 INFO misc.py line 119 131400] Train: [78/100][34/800] Data 0.013 (0.016) Batch 0.331 (0.351) Remain 01:47:32 loss: 0.1694 Lr: 0.00083 [2023-12-20 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01:38:58 loss: 0.3172 Lr: 0.00076 [2023-12-20 19:55:51,678 INFO misc.py line 119 131400] Train: [78/100][770/800] Data 0.004 (0.005) Batch 0.340 (0.337) Remain 01:38:58 loss: 0.2029 Lr: 0.00076 [2023-12-20 19:55:52,024 INFO misc.py line 119 131400] Train: [78/100][771/800] Data 0.004 (0.005) Batch 0.346 (0.337) Remain 01:38:58 loss: 0.1902 Lr: 0.00076 [2023-12-20 19:55:52,346 INFO misc.py line 119 131400] Train: [78/100][772/800] Data 0.004 (0.005) Batch 0.322 (0.337) Remain 01:38:57 loss: 0.2354 Lr: 0.00076 [2023-12-20 19:55:52,690 INFO misc.py line 119 131400] Train: [78/100][773/800] Data 0.003 (0.005) Batch 0.344 (0.337) Remain 01:38:57 loss: 0.4521 Lr: 0.00076 [2023-12-20 19:55:53,064 INFO misc.py line 119 131400] Train: [78/100][774/800] Data 0.004 (0.005) Batch 0.374 (0.337) Remain 01:38:57 loss: 0.2910 Lr: 0.00076 [2023-12-20 19:55:53,394 INFO misc.py line 119 131400] Train: [78/100][775/800] Data 0.004 (0.005) Batch 0.330 (0.337) Remain 01:38:57 loss: 0.2023 Lr: 0.00076 [2023-12-20 19:55:53,749 INFO misc.py line 119 131400] Train: [78/100][776/800] Data 0.003 (0.005) Batch 0.354 (0.337) Remain 01:38:57 loss: 0.1208 Lr: 0.00076 [2023-12-20 19:55:54,081 INFO misc.py line 119 131400] Train: [78/100][777/800] Data 0.006 (0.005) Batch 0.333 (0.337) Remain 01:38:56 loss: 0.2785 Lr: 0.00076 [2023-12-20 19:55:54,455 INFO misc.py line 119 131400] Train: [78/100][778/800] Data 0.004 (0.005) Batch 0.374 (0.337) Remain 01:38:57 loss: 0.1890 Lr: 0.00076 [2023-12-20 19:55:54,735 INFO misc.py line 119 131400] Train: [78/100][779/800] Data 0.005 (0.005) Batch 0.281 (0.337) Remain 01:38:55 loss: 0.3028 Lr: 0.00076 [2023-12-20 19:55:55,048 INFO misc.py line 119 131400] Train: [78/100][780/800] Data 0.003 (0.005) Batch 0.312 (0.337) Remain 01:38:54 loss: 0.3032 Lr: 0.00076 [2023-12-20 19:55:55,377 INFO misc.py line 119 131400] Train: [78/100][781/800] Data 0.003 (0.005) Batch 0.330 (0.337) Remain 01:38:54 loss: 0.3008 Lr: 0.00076 [2023-12-20 19:55:55,712 INFO misc.py line 119 131400] Train: [78/100][782/800] Data 0.003 (0.005) Batch 0.336 (0.337) Remain 01:38:54 loss: 0.2786 Lr: 0.00076 [2023-12-20 19:55:56,030 INFO misc.py line 119 131400] Train: [78/100][783/800] Data 0.003 (0.005) Batch 0.317 (0.337) Remain 01:38:53 loss: 0.2783 Lr: 0.00076 [2023-12-20 19:55:56,377 INFO misc.py line 119 131400] Train: [78/100][784/800] Data 0.003 (0.005) Batch 0.345 (0.337) Remain 01:38:53 loss: 0.2574 Lr: 0.00076 [2023-12-20 19:55:56,659 INFO misc.py line 119 131400] Train: [78/100][785/800] Data 0.005 (0.005) Batch 0.283 (0.337) Remain 01:38:51 loss: 0.2152 Lr: 0.00076 [2023-12-20 19:55:56,967 INFO misc.py line 119 131400] Train: [78/100][786/800] Data 0.004 (0.005) Batch 0.306 (0.337) Remain 01:38:50 loss: 0.3649 Lr: 0.00076 [2023-12-20 19:55:57,304 INFO misc.py line 119 131400] Train: [78/100][787/800] Data 0.005 (0.005) Batch 0.337 (0.337) Remain 01:38:50 loss: 0.2243 Lr: 0.00076 [2023-12-20 19:55:57,617 INFO misc.py line 119 131400] Train: [78/100][788/800] Data 0.005 (0.005) Batch 0.313 (0.337) Remain 01:38:49 loss: 0.2045 Lr: 0.00076 [2023-12-20 19:55:57,917 INFO misc.py line 119 131400] Train: [78/100][789/800] Data 0.005 (0.005) Batch 0.300 (0.337) Remain 01:38:48 loss: 0.2426 Lr: 0.00076 [2023-12-20 19:55:58,219 INFO misc.py line 119 131400] Train: [78/100][790/800] Data 0.006 (0.005) Batch 0.304 (0.337) Remain 01:38:47 loss: 0.2849 Lr: 0.00076 [2023-12-20 19:55:58,526 INFO misc.py line 119 131400] Train: [78/100][791/800] Data 0.003 (0.005) Batch 0.307 (0.337) Remain 01:38:46 loss: 0.2764 Lr: 0.00076 [2023-12-20 19:55:58,852 INFO misc.py line 119 131400] Train: [78/100][792/800] Data 0.004 (0.005) Batch 0.326 (0.337) Remain 01:38:45 loss: 0.2839 Lr: 0.00076 [2023-12-20 19:55:59,151 INFO misc.py line 119 131400] Train: [78/100][793/800] Data 0.003 (0.005) Batch 0.298 (0.336) Remain 01:38:44 loss: 0.3178 Lr: 0.00076 [2023-12-20 19:55:59,454 INFO misc.py line 119 131400] Train: [78/100][794/800] Data 0.004 (0.005) Batch 0.304 (0.336) Remain 01:38:43 loss: 0.3362 Lr: 0.00076 [2023-12-20 19:55:59,776 INFO misc.py line 119 131400] Train: [78/100][795/800] Data 0.004 (0.005) Batch 0.322 (0.336) Remain 01:38:42 loss: 0.2326 Lr: 0.00076 [2023-12-20 19:56:00,066 INFO misc.py line 119 131400] Train: [78/100][796/800] Data 0.003 (0.005) Batch 0.289 (0.336) Remain 01:38:41 loss: 0.1298 Lr: 0.00076 [2023-12-20 19:56:00,365 INFO misc.py line 119 131400] Train: [78/100][797/800] Data 0.003 (0.005) Batch 0.299 (0.336) Remain 01:38:40 loss: 0.1747 Lr: 0.00076 [2023-12-20 19:56:00,644 INFO misc.py line 119 131400] Train: [78/100][798/800] Data 0.004 (0.005) Batch 0.279 (0.336) Remain 01:38:38 loss: 0.2186 Lr: 0.00076 [2023-12-20 19:56:00,928 INFO misc.py line 119 131400] Train: [78/100][799/800] Data 0.003 (0.005) Batch 0.284 (0.336) Remain 01:38:37 loss: 0.1976 Lr: 0.00076 [2023-12-20 19:56:01,251 INFO misc.py line 119 131400] Train: [78/100][800/800] Data 0.004 (0.005) Batch 0.320 (0.336) Remain 01:38:36 loss: 0.1827 Lr: 0.00076 [2023-12-20 19:56:01,252 INFO misc.py line 136 131400] Train result: loss: 0.2337 [2023-12-20 19:56:01,252 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 19:56:23,498 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2164 [2023-12-20 19:56:23,770 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1749 [2023-12-20 19:56:23,862 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4616 [2023-12-20 19:56:23,974 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2392 [2023-12-20 19:56:24,143 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3512 [2023-12-20 19:56:24,249 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.0257 [2023-12-20 19:56:24,344 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9883 [2023-12-20 19:56:24,452 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8751 [2023-12-20 19:56:24,537 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2556 [2023-12-20 19:56:24,639 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3069 [2023-12-20 19:56:24,738 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3416 [2023-12-20 19:56:24,884 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2618 [2023-12-20 19:56:25,017 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3888 [2023-12-20 19:56:25,175 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1890 [2023-12-20 19:56:25,268 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1483 [2023-12-20 19:56:25,405 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.5796 [2023-12-20 19:56:25,521 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2185 [2023-12-20 19:56:25,631 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4490 [2023-12-20 19:56:25,740 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3643 [2023-12-20 19:56:25,821 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5419 [2023-12-20 19:56:25,938 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1852 [2023-12-20 19:56:26,094 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1569 [2023-12-20 19:56:26,216 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6654 [2023-12-20 19:56:26,360 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4257 [2023-12-20 19:56:26,505 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1626 [2023-12-20 19:56:26,588 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4623 [2023-12-20 19:56:26,745 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8571 [2023-12-20 19:56:26,870 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5668 [2023-12-20 19:56:26,965 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4011 [2023-12-20 19:56:27,109 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7598 [2023-12-20 19:56:27,214 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4700 [2023-12-20 19:56:27,334 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3984 [2023-12-20 19:56:27,423 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1009 [2023-12-20 19:56:27,506 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1716 [2023-12-20 19:56:27,609 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6578 [2023-12-20 19:56:27,702 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5985 [2023-12-20 19:56:27,832 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9587 [2023-12-20 19:56:27,961 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0979 [2023-12-20 19:56:28,039 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5173 [2023-12-20 19:56:28,181 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2670 [2023-12-20 19:56:28,328 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0155 [2023-12-20 19:56:28,429 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0646 [2023-12-20 19:56:28,553 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4867 [2023-12-20 19:56:28,707 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8782 [2023-12-20 19:56:28,835 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4373 [2023-12-20 19:56:28,947 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7843 [2023-12-20 19:56:29,129 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3602 [2023-12-20 19:56:29,227 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3882 [2023-12-20 19:56:29,372 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7073 [2023-12-20 19:56:29,467 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1099 [2023-12-20 19:56:29,549 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4717 [2023-12-20 19:56:29,661 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.0762 [2023-12-20 19:56:29,812 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8482 [2023-12-20 19:56:29,957 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3217 [2023-12-20 19:56:30,062 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0180 [2023-12-20 19:56:30,161 INFO evaluator.py line 159 131400] Test: [56/78] Loss 1.0099 [2023-12-20 19:56:30,267 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3378 [2023-12-20 19:56:30,429 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2129 [2023-12-20 19:56:30,525 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5818 [2023-12-20 19:56:30,619 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1829 [2023-12-20 19:56:30,724 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5046 [2023-12-20 19:56:30,846 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2342 [2023-12-20 19:56:30,938 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.4861 [2023-12-20 19:56:31,040 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7651 [2023-12-20 19:56:31,171 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5585 [2023-12-20 19:56:31,257 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2754 [2023-12-20 19:56:31,357 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5253 [2023-12-20 19:56:31,454 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0106 [2023-12-20 19:56:31,539 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3359 [2023-12-20 19:56:31,625 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0104 [2023-12-20 19:56:31,723 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9809 [2023-12-20 19:56:31,831 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.3468 [2023-12-20 19:56:31,966 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1284 [2023-12-20 19:56:32,060 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6774 [2023-12-20 19:56:32,175 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6324 [2023-12-20 19:56:32,279 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5771 [2023-12-20 19:56:32,368 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2409 [2023-12-20 19:56:32,523 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2284 [2023-12-20 19:56:34,078 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7658/0.8420/0.9181. [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8684/0.9536 [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9640/0.9859 [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7058/0.8343 [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8191/0.8709 [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9247/0.9626 [2023-12-20 19:56:34,078 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8710/0.9501 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7800/0.8748 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7043/0.8418 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6777/0.7589 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8433/0.9107 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4119/0.5579 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7045/0.8098 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6884/0.8204 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7936/0.8576 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6584/0.6995 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7521/0.7958 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9344/0.9780 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7054/0.7947 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8966/0.9260 [2023-12-20 19:56:34,079 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6116/0.6572 [2023-12-20 19:56:34,080 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 19:56:34,081 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 19:56:34,081 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 19:56:37,628 INFO misc.py line 119 131400] Train: [79/100][1/800] Data 1.285 (1.285) Batch 1.626 (1.626) Remain 07:56:52 loss: 0.1803 Lr: 0.00076 [2023-12-20 19:56:37,975 INFO misc.py line 119 131400] Train: [79/100][2/800] Data 0.004 (0.004) Batch 0.348 (0.348) Remain 01:42:00 loss: 0.1016 Lr: 0.00076 [2023-12-20 19:56:38,286 INFO misc.py line 119 131400] Train: [79/100][3/800] Data 0.004 (0.004) Batch 0.310 (0.310) Remain 01:30:56 loss: 0.1874 Lr: 0.00076 [2023-12-20 19:56:38,632 INFO misc.py line 119 131400] Train: [79/100][4/800] Data 0.005 (0.005) Batch 0.348 (0.348) Remain 01:42:01 loss: 0.2555 Lr: 0.00076 [2023-12-20 19:56:38,984 INFO misc.py line 119 131400] Train: [79/100][5/800] Data 0.003 (0.004) Batch 0.352 (0.350) Remain 01:42:36 loss: 0.1398 Lr: 0.00076 [2023-12-20 19:56:39,317 INFO misc.py line 119 131400] Train: [79/100][6/800] Data 0.003 (0.004) Batch 0.332 (0.344) Remain 01:40:52 loss: 0.1215 Lr: 0.00076 [2023-12-20 19:56:39,641 INFO misc.py line 119 131400] Train: [79/100][7/800] Data 0.003 (0.004) Batch 0.325 (0.339) Remain 01:39:26 loss: 0.3122 Lr: 0.00076 [2023-12-20 19:56:39,992 INFO misc.py line 119 131400] Train: [79/100][8/800] Data 0.003 (0.004) Batch 0.351 (0.341) Remain 01:40:06 loss: 0.1570 Lr: 0.00076 [2023-12-20 19:56:40,332 INFO misc.py line 119 131400] Train: [79/100][9/800] Data 0.003 (0.004) Batch 0.339 (0.341) Remain 01:40:00 loss: 0.2543 Lr: 0.00076 [2023-12-20 19:56:40,670 INFO misc.py line 119 131400] Train: [79/100][10/800] Data 0.004 (0.004) Batch 0.339 (0.341) Remain 01:39:54 loss: 0.3437 Lr: 0.00076 [2023-12-20 19:56:41,002 INFO misc.py line 119 131400] Train: [79/100][11/800] Data 0.004 (0.004) Batch 0.332 (0.340) Remain 01:39:35 loss: 0.1613 Lr: 0.00076 [2023-12-20 19:56:41,323 INFO misc.py line 119 131400] Train: [79/100][12/800] Data 0.003 (0.004) Batch 0.321 (0.338) Remain 01:38:57 loss: 0.4177 Lr: 0.00076 [2023-12-20 19:56:41,800 INFO misc.py line 119 131400] Train: [79/100][13/800] Data 0.004 (0.004) Batch 0.477 (0.352) Remain 01:43:02 loss: 0.2054 Lr: 0.00076 [2023-12-20 19:56:42,098 INFO misc.py line 119 131400] Train: [79/100][14/800] Data 0.004 (0.004) Batch 0.299 (0.347) Remain 01:41:38 loss: 0.2521 Lr: 0.00076 [2023-12-20 19:56:42,399 INFO misc.py line 119 131400] Train: [79/100][15/800] Data 0.003 (0.004) Batch 0.300 (0.343) Remain 01:40:29 loss: 0.2105 Lr: 0.00076 [2023-12-20 19:56:42,701 INFO misc.py line 119 131400] Train: [79/100][16/800] Data 0.003 (0.004) Batch 0.302 (0.340) Remain 01:39:33 loss: 0.2296 Lr: 0.00076 [2023-12-20 19:56:43,012 INFO misc.py line 119 131400] Train: [79/100][17/800] Data 0.004 (0.004) Batch 0.311 (0.338) Remain 01:38:56 loss: 0.1612 Lr: 0.00076 [2023-12-20 19:56:43,387 INFO misc.py line 119 131400] Train: [79/100][18/800] Data 0.004 (0.004) Batch 0.376 (0.340) Remain 01:39:41 loss: 0.2231 Lr: 0.00076 [2023-12-20 19:56:43,714 INFO misc.py line 119 131400] Train: [79/100][19/800] Data 0.002 (0.004) Batch 0.327 (0.339) Remain 01:39:26 loss: 0.1977 Lr: 0.00076 [2023-12-20 19:56:44,029 INFO misc.py line 119 131400] Train: [79/100][20/800] Data 0.003 (0.003) Batch 0.314 (0.338) Remain 01:39:00 loss: 0.1976 Lr: 0.00076 [2023-12-20 19:56:44,348 INFO misc.py line 119 131400] Train: [79/100][21/800] Data 0.003 (0.003) Batch 0.319 (0.337) Remain 01:38:41 loss: 0.1647 Lr: 0.00076 [2023-12-20 19:56:44,706 INFO misc.py line 119 131400] Train: [79/100][22/800] Data 0.003 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131400] Train: [79/100][757/800] Data 0.003 (0.004) Batch 0.346 (0.333) Remain 01:33:35 loss: 0.2363 Lr: 0.00070 [2023-12-20 20:00:50,067 INFO misc.py line 119 131400] Train: [79/100][758/800] Data 0.005 (0.004) Batch 0.355 (0.333) Remain 01:33:35 loss: 0.2168 Lr: 0.00070 [2023-12-20 20:00:50,419 INFO misc.py line 119 131400] Train: [79/100][759/800] Data 0.030 (0.004) Batch 0.378 (0.334) Remain 01:33:36 loss: 0.3248 Lr: 0.00070 [2023-12-20 20:00:50,752 INFO misc.py line 119 131400] Train: [79/100][760/800] Data 0.004 (0.004) Batch 0.333 (0.334) Remain 01:33:36 loss: 0.1743 Lr: 0.00070 [2023-12-20 20:00:51,093 INFO misc.py line 119 131400] Train: [79/100][761/800] Data 0.003 (0.004) Batch 0.339 (0.334) Remain 01:33:36 loss: 0.2851 Lr: 0.00070 [2023-12-20 20:00:51,441 INFO misc.py line 119 131400] Train: [79/100][762/800] Data 0.005 (0.004) Batch 0.349 (0.334) Remain 01:33:36 loss: 0.2703 Lr: 0.00070 [2023-12-20 20:00:51,771 INFO misc.py line 119 131400] Train: [79/100][763/800] Data 0.005 (0.004) Batch 0.330 (0.334) Remain 01:33:35 loss: 0.3732 Lr: 0.00070 [2023-12-20 20:00:52,081 INFO misc.py line 119 131400] Train: [79/100][764/800] Data 0.006 (0.004) Batch 0.304 (0.333) Remain 01:33:34 loss: 0.2913 Lr: 0.00070 [2023-12-20 20:00:52,412 INFO misc.py line 119 131400] Train: [79/100][765/800] Data 0.011 (0.004) Batch 0.338 (0.333) Remain 01:33:34 loss: 0.1630 Lr: 0.00070 [2023-12-20 20:00:52,709 INFO misc.py line 119 131400] Train: [79/100][766/800] Data 0.003 (0.004) Batch 0.293 (0.333) Remain 01:33:33 loss: 0.3354 Lr: 0.00070 [2023-12-20 20:00:53,073 INFO misc.py line 119 131400] Train: [79/100][767/800] Data 0.007 (0.004) Batch 0.367 (0.333) Remain 01:33:33 loss: 0.3230 Lr: 0.00070 [2023-12-20 20:00:53,412 INFO misc.py line 119 131400] Train: [79/100][768/800] Data 0.005 (0.004) Batch 0.340 (0.333) Remain 01:33:33 loss: 0.2867 Lr: 0.00070 [2023-12-20 20:00:53,771 INFO misc.py line 119 131400] Train: [79/100][769/800] Data 0.004 (0.004) Batch 0.357 (0.334) Remain 01:33:33 loss: 0.2739 Lr: 0.00070 [2023-12-20 20:00:54,132 INFO misc.py line 119 131400] Train: [79/100][770/800] Data 0.006 (0.004) Batch 0.362 (0.334) Remain 01:33:33 loss: 0.2460 Lr: 0.00070 [2023-12-20 20:00:54,471 INFO misc.py line 119 131400] Train: [79/100][771/800] Data 0.005 (0.004) Batch 0.339 (0.334) Remain 01:33:33 loss: 0.2761 Lr: 0.00070 [2023-12-20 20:00:54,803 INFO misc.py line 119 131400] Train: [79/100][772/800] Data 0.006 (0.004) Batch 0.333 (0.334) Remain 01:33:33 loss: 0.2846 Lr: 0.00070 [2023-12-20 20:00:55,122 INFO misc.py line 119 131400] Train: [79/100][773/800] Data 0.005 (0.004) Batch 0.319 (0.334) Remain 01:33:32 loss: 0.1712 Lr: 0.00070 [2023-12-20 20:00:55,477 INFO misc.py line 119 131400] Train: [79/100][774/800] Data 0.008 (0.004) Batch 0.355 (0.334) Remain 01:33:32 loss: 0.3343 Lr: 0.00070 [2023-12-20 20:00:55,783 INFO misc.py line 119 131400] Train: [79/100][775/800] Data 0.005 (0.004) Batch 0.306 (0.334) Remain 01:33:31 loss: 0.1972 Lr: 0.00070 [2023-12-20 20:00:56,129 INFO misc.py line 119 131400] Train: [79/100][776/800] Data 0.010 (0.004) Batch 0.347 (0.334) Remain 01:33:31 loss: 0.2550 Lr: 0.00070 [2023-12-20 20:00:56,450 INFO misc.py line 119 131400] Train: [79/100][777/800] Data 0.004 (0.004) Batch 0.318 (0.334) Remain 01:33:31 loss: 0.2493 Lr: 0.00070 [2023-12-20 20:00:56,835 INFO misc.py line 119 131400] Train: [79/100][778/800] Data 0.007 (0.004) Batch 0.387 (0.334) Remain 01:33:31 loss: 0.2435 Lr: 0.00070 [2023-12-20 20:00:57,179 INFO misc.py line 119 131400] Train: [79/100][779/800] Data 0.005 (0.004) Batch 0.345 (0.334) Remain 01:33:31 loss: 0.3124 Lr: 0.00070 [2023-12-20 20:00:57,512 INFO misc.py line 119 131400] Train: [79/100][780/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 01:33:31 loss: 0.2650 Lr: 0.00070 [2023-12-20 20:00:57,813 INFO misc.py line 119 131400] Train: [79/100][781/800] Data 0.007 (0.004) Batch 0.305 (0.334) Remain 01:33:30 loss: 0.2198 Lr: 0.00070 [2023-12-20 20:00:58,168 INFO misc.py line 119 131400] Train: [79/100][782/800] Data 0.004 (0.004) Batch 0.354 (0.334) Remain 01:33:30 loss: 0.2364 Lr: 0.00070 [2023-12-20 20:00:58,552 INFO misc.py line 119 131400] Train: [79/100][783/800] Data 0.011 (0.004) Batch 0.378 (0.334) Remain 01:33:31 loss: 0.4517 Lr: 0.00070 [2023-12-20 20:00:58,886 INFO misc.py line 119 131400] Train: [79/100][784/800] Data 0.010 (0.004) Batch 0.341 (0.334) Remain 01:33:31 loss: 0.2112 Lr: 0.00070 [2023-12-20 20:00:59,234 INFO misc.py line 119 131400] Train: [79/100][785/800] Data 0.004 (0.004) Batch 0.348 (0.334) Remain 01:33:31 loss: 0.1489 Lr: 0.00070 [2023-12-20 20:00:59,559 INFO misc.py line 119 131400] Train: [79/100][786/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 01:33:30 loss: 0.4882 Lr: 0.00070 [2023-12-20 20:00:59,887 INFO misc.py line 119 131400] Train: [79/100][787/800] Data 0.013 (0.004) Batch 0.337 (0.334) Remain 01:33:30 loss: 0.2141 Lr: 0.00070 [2023-12-20 20:01:00,228 INFO misc.py line 119 131400] Train: [79/100][788/800] Data 0.004 (0.004) Batch 0.341 (0.334) Remain 01:33:29 loss: 0.1327 Lr: 0.00070 [2023-12-20 20:01:00,566 INFO misc.py line 119 131400] Train: [79/100][789/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 01:33:29 loss: 0.1666 Lr: 0.00070 [2023-12-20 20:01:00,887 INFO misc.py line 119 131400] Train: [79/100][790/800] Data 0.008 (0.004) Batch 0.325 (0.334) Remain 01:33:29 loss: 0.1678 Lr: 0.00070 [2023-12-20 20:01:01,213 INFO misc.py line 119 131400] Train: [79/100][791/800] Data 0.004 (0.004) Batch 0.327 (0.334) Remain 01:33:28 loss: 0.1215 Lr: 0.00070 [2023-12-20 20:01:01,486 INFO misc.py line 119 131400] Train: [79/100][792/800] Data 0.003 (0.004) Batch 0.272 (0.334) Remain 01:33:26 loss: 0.2301 Lr: 0.00070 [2023-12-20 20:01:01,779 INFO misc.py line 119 131400] Train: [79/100][793/800] Data 0.002 (0.004) Batch 0.293 (0.334) Remain 01:33:25 loss: 0.1364 Lr: 0.00070 [2023-12-20 20:01:02,054 INFO misc.py line 119 131400] Train: [79/100][794/800] Data 0.003 (0.004) Batch 0.273 (0.333) Remain 01:33:24 loss: 0.1595 Lr: 0.00070 [2023-12-20 20:01:02,368 INFO misc.py line 119 131400] Train: [79/100][795/800] Data 0.005 (0.004) Batch 0.316 (0.333) Remain 01:33:23 loss: 0.2687 Lr: 0.00070 [2023-12-20 20:01:02,683 INFO misc.py line 119 131400] Train: [79/100][796/800] Data 0.003 (0.004) Batch 0.311 (0.333) Remain 01:33:22 loss: 0.2101 Lr: 0.00070 [2023-12-20 20:01:03,091 INFO misc.py line 119 131400] Train: [79/100][797/800] Data 0.006 (0.004) Batch 0.412 (0.334) Remain 01:33:23 loss: 0.1887 Lr: 0.00070 [2023-12-20 20:01:03,400 INFO misc.py line 119 131400] Train: [79/100][798/800] Data 0.002 (0.004) Batch 0.308 (0.333) Remain 01:33:23 loss: 0.1796 Lr: 0.00070 [2023-12-20 20:01:03,658 INFO misc.py line 119 131400] Train: [79/100][799/800] Data 0.003 (0.004) Batch 0.259 (0.333) Remain 01:33:21 loss: 0.2528 Lr: 0.00070 [2023-12-20 20:01:04,037 INFO misc.py line 119 131400] Train: [79/100][800/800] Data 0.004 (0.004) Batch 0.380 (0.333) Remain 01:33:21 loss: 0.2914 Lr: 0.00070 [2023-12-20 20:01:04,038 INFO misc.py line 136 131400] Train result: loss: 0.2322 [2023-12-20 20:01:04,038 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:01:27,436 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1563 [2023-12-20 20:01:27,539 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1470 [2023-12-20 20:01:27,636 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4466 [2023-12-20 20:01:27,752 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.2959 [2023-12-20 20:01:27,869 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1911 [2023-12-20 20:01:27,997 INFO evaluator.py line 159 131400] Test: [6/78] Loss 2.6431 [2023-12-20 20:01:28,120 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0729 [2023-12-20 20:01:28,239 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.1408 [2023-12-20 20:01:28,321 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2959 [2023-12-20 20:01:28,406 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2851 [2023-12-20 20:01:28,501 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4478 [2023-12-20 20:01:28,644 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2748 [2023-12-20 20:01:28,762 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.6263 [2023-12-20 20:01:28,931 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2153 [2023-12-20 20:01:29,036 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1414 [2023-12-20 20:01:29,170 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7029 [2023-12-20 20:01:29,285 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2386 [2023-12-20 20:01:29,396 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.4000 [2023-12-20 20:01:29,508 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.3281 [2023-12-20 20:01:29,592 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4594 [2023-12-20 20:01:29,711 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1972 [2023-12-20 20:01:29,866 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1389 [2023-12-20 20:01:29,987 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8639 [2023-12-20 20:01:30,133 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1780 [2023-12-20 20:01:30,280 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1417 [2023-12-20 20:01:30,366 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.0838 [2023-12-20 20:01:30,529 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.4266 [2023-12-20 20:01:30,657 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4942 [2023-12-20 20:01:30,755 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5070 [2023-12-20 20:01:30,900 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8470 [2023-12-20 20:01:31,004 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5456 [2023-12-20 20:01:31,123 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3878 [2023-12-20 20:01:31,208 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1137 [2023-12-20 20:01:31,293 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1813 [2023-12-20 20:01:31,391 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.6997 [2023-12-20 20:01:31,486 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3071 [2023-12-20 20:01:31,625 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9095 [2023-12-20 20:01:31,738 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.1013 [2023-12-20 20:01:31,826 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4133 [2023-12-20 20:01:31,974 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2812 [2023-12-20 20:01:32,121 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0259 [2023-12-20 20:01:32,231 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0627 [2023-12-20 20:01:32,355 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2876 [2023-12-20 20:01:32,497 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8745 [2023-12-20 20:01:32,624 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4649 [2023-12-20 20:01:32,731 INFO evaluator.py line 159 131400] Test: [46/78] Loss 1.0687 [2023-12-20 20:01:32,901 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3332 [2023-12-20 20:01:32,995 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3445 [2023-12-20 20:01:33,148 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4928 [2023-12-20 20:01:33,245 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2373 [2023-12-20 20:01:33,319 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4074 [2023-12-20 20:01:33,427 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4411 [2023-12-20 20:01:33,579 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7956 [2023-12-20 20:01:33,713 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3585 [2023-12-20 20:01:33,816 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.5671 [2023-12-20 20:01:33,912 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6226 [2023-12-20 20:01:34,014 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3715 [2023-12-20 20:01:34,175 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2325 [2023-12-20 20:01:34,272 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5019 [2023-12-20 20:01:34,372 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1972 [2023-12-20 20:01:34,478 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5077 [2023-12-20 20:01:34,572 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2212 [2023-12-20 20:01:34,658 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5847 [2023-12-20 20:01:34,757 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6413 [2023-12-20 20:01:34,887 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7178 [2023-12-20 20:01:34,974 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3116 [2023-12-20 20:01:35,074 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5675 [2023-12-20 20:01:35,169 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0253 [2023-12-20 20:01:35,254 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3619 [2023-12-20 20:01:35,344 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0134 [2023-12-20 20:01:35,439 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9707 [2023-12-20 20:01:35,530 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5621 [2023-12-20 20:01:35,664 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1581 [2023-12-20 20:01:35,757 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6209 [2023-12-20 20:01:35,876 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5972 [2023-12-20 20:01:35,982 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5222 [2023-12-20 20:01:36,067 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2271 [2023-12-20 20:01:36,221 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1143 [2023-12-20 20:01:37,358 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7626/0.8423/0.9181. [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8715/0.9555 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9649/0.9850 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6946/0.7924 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8105/0.8574 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9223/0.9604 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8558/0.9070 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7702/0.8809 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7096/0.8338 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6768/0.7909 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8396/0.9287 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4221/0.5353 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6976/0.7836 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6926/0.8889 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7782/0.8399 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6703/0.7739 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7379/0.7931 [2023-12-20 20:01:37,358 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9398/0.9747 [2023-12-20 20:01:37,359 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6822/0.7810 [2023-12-20 20:01:37,359 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8900/0.9190 [2023-12-20 20:01:37,359 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6248/0.6639 [2023-12-20 20:01:37,359 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:01:37,360 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 20:01:37,360 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:01:42,112 INFO misc.py line 119 131400] Train: [80/100][1/800] Data 0.747 (0.747) Batch 1.026 (1.026) Remain 04:47:16 loss: 0.3207 Lr: 0.00070 [2023-12-20 20:01:42,563 INFO misc.py line 119 131400] Train: [80/100][2/800] Data 0.145 (0.145) Batch 0.452 (0.452) Remain 02:06:25 loss: 0.1950 Lr: 0.00070 [2023-12-20 20:01:42,856 INFO misc.py line 119 131400] Train: [80/100][3/800] Data 0.004 (0.004) Batch 0.294 (0.294) Remain 01:22:10 loss: 0.3322 Lr: 0.00070 [2023-12-20 20:01:43,194 INFO misc.py line 119 131400] Train: [80/100][4/800] Data 0.003 (0.003) Batch 0.338 (0.338) Remain 01:34:41 loss: 0.2729 Lr: 0.00070 [2023-12-20 20:01:43,517 INFO misc.py line 119 131400] Train: [80/100][5/800] Data 0.003 (0.003) Batch 0.322 (0.330) Remain 01:32:23 loss: 0.3639 Lr: 0.00070 [2023-12-20 20:01:43,864 INFO misc.py line 119 131400] Train: [80/100][6/800] Data 0.003 (0.003) Batch 0.347 (0.336) Remain 01:33:59 loss: 0.2723 Lr: 0.00070 [2023-12-20 20:01:44,195 INFO misc.py line 119 131400] Train: [80/100][7/800] Data 0.004 (0.003) Batch 0.331 (0.335) Remain 01:33:40 loss: 0.3996 Lr: 0.00070 [2023-12-20 20:01:44,505 INFO misc.py line 119 131400] Train: [80/100][8/800] Data 0.003 (0.003) Batch 0.310 (0.330) Remain 01:32:17 loss: 0.1430 Lr: 0.00070 [2023-12-20 20:01:44,821 INFO misc.py line 119 131400] Train: [80/100][9/800] Data 0.003 (0.003) Batch 0.316 (0.327) Remain 01:31:38 loss: 0.1435 Lr: 0.00070 [2023-12-20 20:01:45,156 INFO misc.py line 119 131400] Train: [80/100][10/800] Data 0.003 (0.003) Batch 0.331 (0.328) Remain 01:31:47 loss: 0.1264 Lr: 0.00070 [2023-12-20 20:01:45,485 INFO misc.py line 119 131400] Train: [80/100][11/800] Data 0.006 (0.004) Batch 0.332 (0.328) Remain 01:31:54 loss: 0.3328 Lr: 0.00070 [2023-12-20 20:01:45,809 INFO misc.py line 119 131400] Train: [80/100][12/800] Data 0.004 (0.004) Batch 0.325 (0.328) Remain 01:31:47 loss: 0.3979 Lr: 0.00070 [2023-12-20 20:01:46,093 INFO misc.py line 119 131400] Train: [80/100][13/800] Data 0.003 (0.004) Batch 0.284 (0.324) Remain 01:30:32 loss: 0.2761 Lr: 0.00070 [2023-12-20 20:01:46,447 INFO misc.py line 119 131400] Train: [80/100][14/800] Data 0.004 (0.004) Batch 0.354 (0.326) Remain 01:31:18 loss: 0.1273 Lr: 0.00070 [2023-12-20 20:01:46,799 INFO misc.py line 119 131400] Train: [80/100][15/800] Data 0.004 (0.004) Batch 0.353 (0.329) Remain 01:31:55 loss: 0.1590 Lr: 0.00070 [2023-12-20 20:01:47,119 INFO misc.py line 119 131400] Train: [80/100][16/800] Data 0.003 (0.004) Batch 0.319 (0.328) Remain 01:31:42 loss: 0.2761 Lr: 0.00070 [2023-12-20 20:01:47,470 INFO misc.py line 119 131400] Train: [80/100][17/800] Data 0.004 (0.004) Batch 0.350 (0.329) Remain 01:32:08 loss: 0.1705 Lr: 0.00070 [2023-12-20 20:01:47,811 INFO misc.py line 119 131400] Train: [80/100][18/800] Data 0.006 (0.004) Batch 0.341 (0.330) Remain 01:32:21 loss: 0.2904 Lr: 0.00070 [2023-12-20 20:01:48,198 INFO misc.py line 119 131400] Train: [80/100][19/800] Data 0.006 (0.004) Batch 0.388 (0.334) Remain 01:33:21 loss: 0.2637 Lr: 0.00070 [2023-12-20 20:01:48,549 INFO misc.py line 119 131400] Train: [80/100][20/800] Data 0.004 (0.004) Batch 0.350 (0.335) Remain 01:33:37 loss: 0.3214 Lr: 0.00070 [2023-12-20 20:01:48,875 INFO misc.py line 119 131400] Train: [80/100][21/800] Data 0.006 (0.004) Batch 0.327 (0.334) Remain 01:33:29 loss: 0.1179 Lr: 0.00070 [2023-12-20 20:01:49,254 INFO misc.py line 119 131400] Train: [80/100][22/800] Data 0.005 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loss: 0.1766 Lr: 0.00070 [2023-12-20 20:01:51,679 INFO misc.py line 119 131400] Train: [80/100][29/800] Data 0.004 (0.004) Batch 0.337 (0.339) Remain 01:34:51 loss: 0.1351 Lr: 0.00070 [2023-12-20 20:01:52,037 INFO misc.py line 119 131400] Train: [80/100][30/800] Data 0.003 (0.004) Batch 0.358 (0.340) Remain 01:35:02 loss: 0.2965 Lr: 0.00070 [2023-12-20 20:01:52,369 INFO misc.py line 119 131400] Train: [80/100][31/800] Data 0.003 (0.004) Batch 0.331 (0.340) Remain 01:34:56 loss: 0.1574 Lr: 0.00069 [2023-12-20 20:01:52,691 INFO misc.py line 119 131400] Train: [80/100][32/800] Data 0.003 (0.004) Batch 0.322 (0.339) Remain 01:34:46 loss: 0.1797 Lr: 0.00069 [2023-12-20 20:01:52,996 INFO misc.py line 119 131400] Train: [80/100][33/800] Data 0.003 (0.004) Batch 0.304 (0.338) Remain 01:34:26 loss: 0.2034 Lr: 0.00069 [2023-12-20 20:01:53,340 INFO misc.py line 119 131400] Train: [80/100][34/800] Data 0.004 (0.004) Batch 0.345 (0.338) Remain 01:34:30 loss: 0.2429 Lr: 0.00069 [2023-12-20 20:01:53,715 INFO misc.py line 119 131400] Train: [80/100][35/800] Data 0.003 (0.004) Batch 0.368 (0.339) Remain 01:34:45 loss: 0.1591 Lr: 0.00069 [2023-12-20 20:01:54,044 INFO misc.py line 119 131400] Train: [80/100][36/800] Data 0.009 (0.004) Batch 0.335 (0.339) Remain 01:34:43 loss: 0.2077 Lr: 0.00069 [2023-12-20 20:01:54,385 INFO misc.py line 119 131400] Train: [80/100][37/800] Data 0.003 (0.004) Batch 0.340 (0.339) Remain 01:34:43 loss: 0.2849 Lr: 0.00069 [2023-12-20 20:01:54,677 INFO misc.py line 119 131400] Train: [80/100][38/800] Data 0.004 (0.004) Batch 0.293 (0.338) Remain 01:34:20 loss: 0.3837 Lr: 0.00069 [2023-12-20 20:01:55,005 INFO misc.py line 119 131400] Train: [80/100][39/800] Data 0.003 (0.004) Batch 0.328 (0.337) Remain 01:34:16 loss: 0.2133 Lr: 0.00069 [2023-12-20 20:01:55,345 INFO misc.py line 119 131400] Train: [80/100][40/800] Data 0.002 (0.004) Batch 0.339 (0.338) Remain 01:34:16 loss: 0.2098 Lr: 0.00069 [2023-12-20 20:01:55,631 INFO misc.py line 119 131400] Train: [80/100][41/800] Data 0.005 (0.004) Batch 0.286 (0.336) Remain 01:33:53 loss: 0.1600 Lr: 0.00069 [2023-12-20 20:01:55,939 INFO misc.py line 119 131400] Train: [80/100][42/800] Data 0.003 (0.004) Batch 0.303 (0.335) Remain 01:33:39 loss: 0.2158 Lr: 0.00069 [2023-12-20 20:01:56,311 INFO misc.py line 119 131400] Train: [80/100][43/800] Data 0.009 (0.004) Batch 0.377 (0.336) Remain 01:33:56 loss: 0.2694 Lr: 0.00069 [2023-12-20 20:01:56,617 INFO misc.py line 119 131400] Train: [80/100][44/800] Data 0.004 (0.004) Batch 0.306 (0.336) Remain 01:33:43 loss: 0.2252 Lr: 0.00069 [2023-12-20 20:01:56,962 INFO misc.py line 119 131400] Train: [80/100][45/800] Data 0.004 (0.004) Batch 0.345 (0.336) Remain 01:33:46 loss: 0.3591 Lr: 0.00069 [2023-12-20 20:01:57,356 INFO misc.py line 119 131400] Train: [80/100][46/800] Data 0.005 (0.004) Batch 0.394 (0.337) Remain 01:34:09 loss: 0.1739 Lr: 0.00069 [2023-12-20 20:01:57,670 INFO misc.py line 119 131400] Train: [80/100][47/800] Data 0.004 (0.004) 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loss: 0.4621 Lr: 0.00064 [2023-12-20 20:05:49,620 INFO misc.py line 119 131400] Train: [80/100][745/800] Data 0.003 (0.004) Batch 0.351 (0.333) Remain 01:28:59 loss: 0.2247 Lr: 0.00064 [2023-12-20 20:05:49,962 INFO misc.py line 119 131400] Train: [80/100][746/800] Data 0.004 (0.004) Batch 0.343 (0.333) Remain 01:28:59 loss: 0.2903 Lr: 0.00064 [2023-12-20 20:05:50,288 INFO misc.py line 119 131400] Train: [80/100][747/800] Data 0.003 (0.004) Batch 0.325 (0.333) Remain 01:28:58 loss: 0.2208 Lr: 0.00064 [2023-12-20 20:05:50,638 INFO misc.py line 119 131400] Train: [80/100][748/800] Data 0.004 (0.004) Batch 0.350 (0.333) Remain 01:28:58 loss: 0.1812 Lr: 0.00064 [2023-12-20 20:05:51,015 INFO misc.py line 119 131400] Train: [80/100][749/800] Data 0.004 (0.004) Batch 0.375 (0.333) Remain 01:28:59 loss: 0.2372 Lr: 0.00064 [2023-12-20 20:05:51,330 INFO misc.py line 119 131400] Train: [80/100][750/800] Data 0.005 (0.004) Batch 0.319 (0.333) Remain 01:28:58 loss: 0.2767 Lr: 0.00064 [2023-12-20 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131400] Train: [80/100][757/800] Data 0.004 (0.004) Batch 0.282 (0.333) Remain 01:28:55 loss: 0.1681 Lr: 0.00064 [2023-12-20 20:05:53,960 INFO misc.py line 119 131400] Train: [80/100][758/800] Data 0.006 (0.004) Batch 0.340 (0.333) Remain 01:28:55 loss: 0.2230 Lr: 0.00064 [2023-12-20 20:05:54,306 INFO misc.py line 119 131400] Train: [80/100][759/800] Data 0.004 (0.004) Batch 0.346 (0.333) Remain 01:28:55 loss: 0.1753 Lr: 0.00064 [2023-12-20 20:05:54,614 INFO misc.py line 119 131400] Train: [80/100][760/800] Data 0.004 (0.004) Batch 0.309 (0.333) Remain 01:28:54 loss: 0.2825 Lr: 0.00064 [2023-12-20 20:05:54,897 INFO misc.py line 119 131400] Train: [80/100][761/800] Data 0.003 (0.004) Batch 0.282 (0.333) Remain 01:28:53 loss: 0.1918 Lr: 0.00064 [2023-12-20 20:05:55,233 INFO misc.py line 119 131400] Train: [80/100][762/800] Data 0.004 (0.004) Batch 0.335 (0.333) Remain 01:28:52 loss: 0.1640 Lr: 0.00064 [2023-12-20 20:05:55,556 INFO misc.py line 119 131400] Train: [80/100][763/800] Data 0.006 (0.004) Batch 0.325 (0.332) Remain 01:28:52 loss: 0.2667 Lr: 0.00064 [2023-12-20 20:05:55,864 INFO misc.py line 119 131400] Train: [80/100][764/800] Data 0.003 (0.004) Batch 0.307 (0.332) Remain 01:28:51 loss: 0.3232 Lr: 0.00064 [2023-12-20 20:05:56,183 INFO misc.py line 119 131400] Train: [80/100][765/800] Data 0.004 (0.004) Batch 0.321 (0.332) Remain 01:28:50 loss: 0.1618 Lr: 0.00064 [2023-12-20 20:05:56,469 INFO misc.py line 119 131400] Train: [80/100][766/800] Data 0.003 (0.004) Batch 0.285 (0.332) Remain 01:28:49 loss: 0.2131 Lr: 0.00064 [2023-12-20 20:05:56,782 INFO misc.py line 119 131400] Train: [80/100][767/800] Data 0.004 (0.004) Batch 0.312 (0.332) Remain 01:28:48 loss: 0.1671 Lr: 0.00064 [2023-12-20 20:05:57,122 INFO misc.py line 119 131400] Train: [80/100][768/800] Data 0.005 (0.004) Batch 0.341 (0.332) Remain 01:28:48 loss: 0.2286 Lr: 0.00064 [2023-12-20 20:05:57,444 INFO misc.py line 119 131400] Train: [80/100][769/800] Data 0.004 (0.004) Batch 0.321 (0.332) Remain 01:28:48 loss: 0.3021 Lr: 0.00064 [2023-12-20 20:05:57,764 INFO misc.py line 119 131400] Train: [80/100][770/800] Data 0.004 (0.004) Batch 0.322 (0.332) Remain 01:28:47 loss: 0.1508 Lr: 0.00064 [2023-12-20 20:05:58,087 INFO misc.py line 119 131400] Train: [80/100][771/800] Data 0.003 (0.004) Batch 0.322 (0.332) Remain 01:28:46 loss: 0.2610 Lr: 0.00064 [2023-12-20 20:05:58,451 INFO misc.py line 119 131400] Train: [80/100][772/800] Data 0.003 (0.004) Batch 0.364 (0.332) Remain 01:28:47 loss: 0.1858 Lr: 0.00064 [2023-12-20 20:05:58,932 INFO misc.py line 119 131400] Train: [80/100][773/800] Data 0.004 (0.004) Batch 0.479 (0.333) Remain 01:28:49 loss: 0.1188 Lr: 0.00064 [2023-12-20 20:05:59,253 INFO misc.py line 119 131400] Train: [80/100][774/800] Data 0.006 (0.004) Batch 0.324 (0.333) Remain 01:28:49 loss: 0.2607 Lr: 0.00064 [2023-12-20 20:05:59,531 INFO misc.py line 119 131400] Train: [80/100][775/800] Data 0.003 (0.004) Batch 0.277 (0.332) Remain 01:28:47 loss: 0.1812 Lr: 0.00064 [2023-12-20 20:05:59,853 INFO misc.py line 119 131400] Train: [80/100][776/800] Data 0.003 (0.004) Batch 0.322 (0.332) Remain 01:28:47 loss: 0.1633 Lr: 0.00064 [2023-12-20 20:06:00,157 INFO misc.py line 119 131400] Train: [80/100][777/800] Data 0.003 (0.004) Batch 0.303 (0.332) Remain 01:28:46 loss: 0.1692 Lr: 0.00064 [2023-12-20 20:06:00,494 INFO misc.py line 119 131400] Train: [80/100][778/800] Data 0.004 (0.004) Batch 0.337 (0.332) Remain 01:28:46 loss: 0.2442 Lr: 0.00064 [2023-12-20 20:06:00,834 INFO misc.py line 119 131400] Train: [80/100][779/800] Data 0.003 (0.004) Batch 0.337 (0.332) Remain 01:28:46 loss: 0.1385 Lr: 0.00064 [2023-12-20 20:06:01,168 INFO misc.py line 119 131400] Train: [80/100][780/800] Data 0.006 (0.004) Batch 0.337 (0.332) Remain 01:28:45 loss: 0.1090 Lr: 0.00064 [2023-12-20 20:06:01,452 INFO misc.py line 119 131400] Train: [80/100][781/800] Data 0.003 (0.004) Batch 0.283 (0.332) Remain 01:28:44 loss: 0.2050 Lr: 0.00064 [2023-12-20 20:06:01,704 INFO misc.py line 119 131400] Train: [80/100][782/800] Data 0.004 (0.004) Batch 0.252 (0.332) Remain 01:28:42 loss: 0.0948 Lr: 0.00064 [2023-12-20 20:06:02,041 INFO misc.py line 119 131400] Train: [80/100][783/800] Data 0.005 (0.004) Batch 0.338 (0.332) Remain 01:28:42 loss: 0.2693 Lr: 0.00064 [2023-12-20 20:06:02,344 INFO misc.py line 119 131400] Train: [80/100][784/800] Data 0.003 (0.004) Batch 0.303 (0.332) Remain 01:28:41 loss: 0.2160 Lr: 0.00064 [2023-12-20 20:06:02,652 INFO misc.py line 119 131400] Train: [80/100][785/800] Data 0.003 (0.004) Batch 0.308 (0.332) Remain 01:28:40 loss: 0.2597 Lr: 0.00064 [2023-12-20 20:06:02,991 INFO misc.py line 119 131400] Train: [80/100][786/800] Data 0.003 (0.004) Batch 0.339 (0.332) Remain 01:28:40 loss: 0.2668 Lr: 0.00064 [2023-12-20 20:06:03,312 INFO misc.py line 119 131400] Train: [80/100][787/800] Data 0.004 (0.004) Batch 0.320 (0.332) Remain 01:28:39 loss: 0.2090 Lr: 0.00064 [2023-12-20 20:06:03,625 INFO misc.py line 119 131400] Train: [80/100][788/800] Data 0.004 (0.004) Batch 0.314 (0.332) Remain 01:28:38 loss: 0.0794 Lr: 0.00064 [2023-12-20 20:06:03,962 INFO misc.py line 119 131400] Train: [80/100][789/800] Data 0.003 (0.004) Batch 0.336 (0.332) Remain 01:28:38 loss: 0.1786 Lr: 0.00064 [2023-12-20 20:06:04,303 INFO misc.py line 119 131400] Train: [80/100][790/800] Data 0.003 (0.004) Batch 0.340 (0.332) Remain 01:28:38 loss: 0.1758 Lr: 0.00064 [2023-12-20 20:06:04,617 INFO misc.py line 119 131400] Train: [80/100][791/800] Data 0.004 (0.004) Batch 0.314 (0.332) Remain 01:28:37 loss: 0.2669 Lr: 0.00064 [2023-12-20 20:06:04,983 INFO misc.py line 119 131400] Train: [80/100][792/800] Data 0.004 (0.004) Batch 0.366 (0.332) Remain 01:28:38 loss: 0.2501 Lr: 0.00064 [2023-12-20 20:06:05,314 INFO misc.py line 119 131400] Train: [80/100][793/800] Data 0.004 (0.004) Batch 0.333 (0.332) Remain 01:28:37 loss: 0.3673 Lr: 0.00064 [2023-12-20 20:06:05,645 INFO misc.py line 119 131400] Train: [80/100][794/800] Data 0.003 (0.004) Batch 0.329 (0.332) Remain 01:28:37 loss: 0.1043 Lr: 0.00064 [2023-12-20 20:06:05,976 INFO misc.py line 119 131400] Train: [80/100][795/800] Data 0.004 (0.004) Batch 0.331 (0.332) Remain 01:28:37 loss: 0.3844 Lr: 0.00064 [2023-12-20 20:06:06,322 INFO misc.py line 119 131400] Train: [80/100][796/800] Data 0.003 (0.004) Batch 0.347 (0.332) Remain 01:28:37 loss: 0.1949 Lr: 0.00064 [2023-12-20 20:06:06,659 INFO misc.py line 119 131400] Train: [80/100][797/800] Data 0.003 (0.004) Batch 0.337 (0.332) Remain 01:28:36 loss: 0.2295 Lr: 0.00064 [2023-12-20 20:06:06,964 INFO misc.py line 119 131400] Train: [80/100][798/800] Data 0.003 (0.004) Batch 0.305 (0.332) Remain 01:28:36 loss: 0.1015 Lr: 0.00064 [2023-12-20 20:06:07,271 INFO misc.py line 119 131400] Train: [80/100][799/800] Data 0.003 (0.004) Batch 0.305 (0.332) Remain 01:28:35 loss: 0.2518 Lr: 0.00064 [2023-12-20 20:06:07,580 INFO misc.py line 119 131400] Train: [80/100][800/800] Data 0.005 (0.004) Batch 0.310 (0.332) Remain 01:28:34 loss: 0.1388 Lr: 0.00064 [2023-12-20 20:06:07,581 INFO misc.py line 136 131400] Train result: loss: 0.2286 [2023-12-20 20:06:07,581 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:06:30,184 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2343 [2023-12-20 20:06:30,368 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1359 [2023-12-20 20:06:30,465 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4750 [2023-12-20 20:06:30,580 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4443 [2023-12-20 20:06:30,693 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3478 [2023-12-20 20:06:30,802 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2714 [2023-12-20 20:06:30,896 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0793 [2023-12-20 20:06:31,004 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.2600 [2023-12-20 20:06:31,085 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3163 [2023-12-20 20:06:31,176 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3087 [2023-12-20 20:06:31,273 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4987 [2023-12-20 20:06:31,418 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2911 [2023-12-20 20:06:31,543 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4655 [2023-12-20 20:06:31,701 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2114 [2023-12-20 20:06:31,796 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1255 [2023-12-20 20:06:31,929 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7951 [2023-12-20 20:06:32,047 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2785 [2023-12-20 20:06:32,160 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6158 [2023-12-20 20:06:32,274 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1337 [2023-12-20 20:06:32,353 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4119 [2023-12-20 20:06:32,467 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1686 [2023-12-20 20:06:32,629 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1493 [2023-12-20 20:06:32,751 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7490 [2023-12-20 20:06:32,895 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2787 [2023-12-20 20:06:33,037 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1602 [2023-12-20 20:06:33,123 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.9168 [2023-12-20 20:06:33,283 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5419 [2023-12-20 20:06:33,406 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.4697 [2023-12-20 20:06:33,501 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5487 [2023-12-20 20:06:33,659 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8524 [2023-12-20 20:06:33,763 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4424 [2023-12-20 20:06:33,886 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4453 [2023-12-20 20:06:33,974 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1095 [2023-12-20 20:06:34,049 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1727 [2023-12-20 20:06:34,149 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8593 [2023-12-20 20:06:34,249 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2915 [2023-12-20 20:06:34,381 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0569 [2023-12-20 20:06:34,492 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.3147 [2023-12-20 20:06:34,574 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4962 [2023-12-20 20:06:34,725 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3208 [2023-12-20 20:06:34,875 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0283 [2023-12-20 20:06:34,977 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0622 [2023-12-20 20:06:35,096 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2859 [2023-12-20 20:06:35,241 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8449 [2023-12-20 20:06:35,358 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3176 [2023-12-20 20:06:35,461 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7359 [2023-12-20 20:06:35,627 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3063 [2023-12-20 20:06:35,720 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4499 [2023-12-20 20:06:35,864 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6562 [2023-12-20 20:06:35,955 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2333 [2023-12-20 20:06:36,031 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4855 [2023-12-20 20:06:36,141 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2535 [2023-12-20 20:06:36,292 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9841 [2023-12-20 20:06:36,426 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3122 [2023-12-20 20:06:36,529 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0624 [2023-12-20 20:06:36,620 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.4790 [2023-12-20 20:06:36,721 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3682 [2023-12-20 20:06:36,880 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2203 [2023-12-20 20:06:36,974 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.7375 [2023-12-20 20:06:37,071 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2195 [2023-12-20 20:06:37,170 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4339 [2023-12-20 20:06:37,263 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2491 [2023-12-20 20:06:37,352 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5497 [2023-12-20 20:06:37,455 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6184 [2023-12-20 20:06:37,579 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5737 [2023-12-20 20:06:37,666 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2977 [2023-12-20 20:06:37,765 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4237 [2023-12-20 20:06:37,858 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0411 [2023-12-20 20:06:37,941 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3843 [2023-12-20 20:06:38,027 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0175 [2023-12-20 20:06:38,135 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8401 [2023-12-20 20:06:38,225 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4742 [2023-12-20 20:06:38,359 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0677 [2023-12-20 20:06:38,455 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6942 [2023-12-20 20:06:38,569 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5907 [2023-12-20 20:06:38,671 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5450 [2023-12-20 20:06:38,763 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2267 [2023-12-20 20:06:38,916 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1757 [2023-12-20 20:06:40,354 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7603/0.8383/0.9185. [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8722/0.9544 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9851 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6982/0.8053 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8195/0.8743 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9274/0.9602 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8466/0.9321 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7600/0.8677 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7222/0.8421 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7025/0.8092 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8220/0.9015 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4052/0.5171 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7193/0.8172 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6955/0.8616 [2023-12-20 20:06:40,355 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7616/0.8495 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6679/0.7238 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6686/0.7186 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9396/0.9792 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7005/0.7721 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8908/0.9222 [2023-12-20 20:06:40,356 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6231/0.6738 [2023-12-20 20:06:40,356 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:06:40,358 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 20:06:40,358 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:06:44,906 INFO misc.py line 119 131400] Train: [81/100][1/800] Data 1.006 (1.006) Batch 1.329 (1.329) Remain 05:54:17 loss: 0.2872 Lr: 0.00064 [2023-12-20 20:06:45,228 INFO misc.py line 119 131400] Train: [81/100][2/800] Data 0.003 (0.003) Batch 0.322 (0.322) Remain 01:25:53 loss: 0.2649 Lr: 0.00064 [2023-12-20 20:06:45,559 INFO misc.py line 119 131400] Train: [81/100][3/800] Data 0.003 (0.003) Batch 0.330 (0.330) Remain 01:28:02 loss: 0.2134 Lr: 0.00063 [2023-12-20 20:06:45,888 INFO misc.py line 119 131400] Train: [81/100][4/800] Data 0.004 (0.004) Batch 0.322 (0.322) Remain 01:25:55 loss: 0.2609 Lr: 0.00063 [2023-12-20 20:06:46,205 INFO misc.py line 119 131400] Train: [81/100][5/800] Data 0.011 (0.007) Batch 0.324 (0.323) Remain 01:26:09 loss: 0.1788 Lr: 0.00063 [2023-12-20 20:06:46,531 INFO misc.py line 119 131400] Train: [81/100][6/800] Data 0.003 (0.006) Batch 0.327 (0.324) Remain 01:26:27 loss: 0.2799 Lr: 0.00063 [2023-12-20 20:06:46,819 INFO misc.py line 119 131400] Train: [81/100][7/800] Data 0.003 (0.005) Batch 0.287 (0.315) Remain 01:23:57 loss: 0.2150 Lr: 0.00063 [2023-12-20 20:06:47,171 INFO misc.py line 119 131400] Train: [81/100][8/800] Data 0.004 (0.005) Batch 0.353 (0.323) Remain 01:25:58 loss: 0.2920 Lr: 0.00063 [2023-12-20 20:06:47,506 INFO misc.py line 119 131400] Train: [81/100][9/800] Data 0.003 (0.005) Batch 0.335 (0.325) Remain 01:26:31 loss: 0.2126 Lr: 0.00063 [2023-12-20 20:06:47,849 INFO misc.py line 119 131400] Train: [81/100][10/800] Data 0.003 (0.004) Batch 0.343 (0.327) Remain 01:27:13 loss: 0.3935 Lr: 0.00063 [2023-12-20 20:06:48,157 INFO misc.py line 119 131400] Train: [81/100][11/800] Data 0.003 (0.004) Batch 0.307 (0.325) Remain 01:26:33 loss: 0.3204 Lr: 0.00063 [2023-12-20 20:06:48,508 INFO misc.py line 119 131400] Train: [81/100][12/800] Data 0.003 (0.004) Batch 0.351 (0.328) Remain 01:27:19 loss: 0.2649 Lr: 0.00063 [2023-12-20 20:06:48,842 INFO misc.py line 119 131400] Train: [81/100][13/800] Data 0.003 (0.004) Batch 0.334 (0.328) Remain 01:27:29 loss: 0.1858 Lr: 0.00063 [2023-12-20 20:06:49,173 INFO misc.py line 119 131400] Train: [81/100][14/800] Data 0.003 (0.004) Batch 0.330 (0.329) Remain 01:27:31 loss: 0.2907 Lr: 0.00063 [2023-12-20 20:06:49,495 INFO misc.py line 119 131400] Train: [81/100][15/800] Data 0.006 (0.004) Batch 0.322 (0.328) Remain 01:27:22 loss: 0.1834 Lr: 0.00063 [2023-12-20 20:06:49,810 INFO misc.py line 119 131400] Train: [81/100][16/800] Data 0.004 (0.004) Batch 0.315 (0.327) Remain 01:27:06 loss: 0.1392 Lr: 0.00063 [2023-12-20 20:06:50,134 INFO misc.py line 119 131400] Train: [81/100][17/800] Data 0.005 (0.004) Batch 0.324 (0.327) Remain 01:27:02 loss: 0.1937 Lr: 0.00063 [2023-12-20 20:06:50,464 INFO misc.py line 119 131400] Train: [81/100][18/800] Data 0.004 (0.004) Batch 0.330 (0.327) Remain 01:27:06 loss: 0.2938 Lr: 0.00063 [2023-12-20 20:06:50,829 INFO misc.py line 119 131400] Train: [81/100][19/800] Data 0.004 (0.004) Batch 0.365 (0.329) Remain 01:27:44 loss: 0.2089 Lr: 0.00063 [2023-12-20 20:06:51,179 INFO misc.py line 119 131400] Train: [81/100][20/800] Data 0.004 (0.004) Batch 0.347 (0.330) Remain 01:28:00 loss: 0.3033 Lr: 0.00063 [2023-12-20 20:06:51,537 INFO misc.py line 119 131400] Train: [81/100][21/800] Data 0.007 (0.004) Batch 0.361 (0.332) Remain 01:28:27 loss: 0.2995 Lr: 0.00063 [2023-12-20 20:06:51,889 INFO misc.py line 119 131400] Train: [81/100][22/800] Data 0.003 (0.004) Batch 0.352 (0.333) Remain 01:28:44 loss: 0.2515 Lr: 0.00063 [2023-12-20 20:06:52,203 INFO misc.py line 119 131400] Train: [81/100][23/800] Data 0.003 (0.004) Batch 0.313 (0.332) Remain 01:28:27 loss: 0.2470 Lr: 0.00063 [2023-12-20 20:06:52,535 INFO misc.py line 119 131400] Train: [81/100][24/800] Data 0.003 (0.004) Batch 0.332 (0.332) Remain 01:28:27 loss: 0.4001 Lr: 0.00063 [2023-12-20 20:06:52,833 INFO misc.py line 119 131400] Train: [81/100][25/800] Data 0.004 (0.004) Batch 0.298 (0.331) Remain 01:28:01 loss: 0.2129 Lr: 0.00063 [2023-12-20 20:06:53,133 INFO misc.py line 119 131400] Train: [81/100][26/800] Data 0.004 (0.004) Batch 0.301 (0.329) Remain 01:27:41 loss: 0.2301 Lr: 0.00063 [2023-12-20 20:06:53,443 INFO misc.py line 119 131400] Train: [81/100][27/800] Data 0.003 (0.004) Batch 0.310 (0.329) Remain 01:27:27 loss: 0.1831 Lr: 0.00063 [2023-12-20 20:06:53,790 INFO misc.py line 119 131400] Train: [81/100][28/800] Data 0.003 (0.004) Batch 0.345 (0.329) Remain 01:27:38 loss: 0.1190 Lr: 0.00063 [2023-12-20 20:06:54,165 INFO misc.py line 119 131400] Train: [81/100][29/800] Data 0.005 (0.004) Batch 0.366 (0.331) Remain 01:28:00 loss: 0.2412 Lr: 0.00063 [2023-12-20 20:06:54,475 INFO misc.py line 119 131400] Train: [81/100][30/800] Data 0.014 (0.004) Batch 0.320 (0.330) Remain 01:27:53 loss: 0.3478 Lr: 0.00063 [2023-12-20 20:06:54,794 INFO misc.py line 119 131400] Train: [81/100][31/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 01:27:47 loss: 0.1948 Lr: 0.00063 [2023-12-20 20:06:55,152 INFO misc.py line 119 131400] Train: [81/100][32/800] Data 0.003 (0.004) Batch 0.357 (0.331) Remain 01:28:02 loss: 0.3346 Lr: 0.00063 [2023-12-20 20:06:55,501 INFO misc.py line 119 131400] Train: [81/100][33/800] Data 0.004 (0.004) Batch 0.348 (0.331) Remain 01:28:11 loss: 0.2678 Lr: 0.00063 [2023-12-20 20:06:55,817 INFO misc.py line 119 131400] Train: [81/100][34/800] Data 0.005 (0.004) Batch 0.315 (0.331) Remain 01:28:03 loss: 0.1102 Lr: 0.00063 [2023-12-20 20:06:56,147 INFO misc.py line 119 131400] Train: [81/100][35/800] Data 0.004 (0.004) Batch 0.331 (0.331) Remain 01:28:02 loss: 0.1891 Lr: 0.00063 [2023-12-20 20:06:56,498 INFO misc.py line 119 131400] Train: [81/100][36/800] Data 0.004 (0.004) Batch 0.351 (0.332) Remain 01:28:12 loss: 0.1218 Lr: 0.00063 [2023-12-20 20:06:56,820 INFO misc.py line 119 131400] Train: [81/100][37/800] Data 0.004 (0.004) Batch 0.322 (0.331) Remain 01:28:07 loss: 0.2961 Lr: 0.00063 [2023-12-20 20:06:57,152 INFO misc.py line 119 131400] Train: [81/100][38/800] Data 0.003 (0.004) Batch 0.331 (0.331) Remain 01:28:06 loss: 0.1289 Lr: 0.00063 [2023-12-20 20:06:57,457 INFO misc.py line 119 131400] Train: [81/100][39/800] Data 0.004 (0.004) Batch 0.304 (0.330) Remain 01:27:54 loss: 0.2156 Lr: 0.00063 [2023-12-20 20:06:57,774 INFO misc.py line 119 131400] Train: [81/100][40/800] Data 0.006 (0.004) Batch 0.318 (0.330) Remain 01:27:48 loss: 0.3060 Lr: 0.00063 [2023-12-20 20:06:58,129 INFO misc.py line 119 131400] Train: [81/100][41/800] Data 0.004 (0.004) Batch 0.356 (0.331) Remain 01:27:59 loss: 0.1640 Lr: 0.00063 [2023-12-20 20:06:58,440 INFO misc.py line 119 131400] Train: [81/100][42/800] Data 0.004 (0.004) Batch 0.311 (0.330) Remain 01:27:50 loss: 0.2985 Lr: 0.00063 [2023-12-20 20:06:58,758 INFO misc.py line 119 131400] Train: [81/100][43/800] Data 0.003 (0.004) Batch 0.318 (0.330) Remain 01:27:45 loss: 0.2417 Lr: 0.00063 [2023-12-20 20:06:59,102 INFO misc.py line 119 131400] Train: [81/100][44/800] Data 0.003 (0.004) Batch 0.344 (0.330) Remain 01:27:50 loss: 0.2443 Lr: 0.00063 [2023-12-20 20:06:59,468 INFO misc.py line 119 131400] Train: [81/100][45/800] Data 0.003 (0.004) Batch 0.362 (0.331) Remain 01:28:02 loss: 0.1607 Lr: 0.00063 [2023-12-20 20:06:59,787 INFO misc.py line 119 131400] Train: [81/100][46/800] Data 0.008 (0.004) Batch 0.323 (0.331) Remain 01:27:59 loss: 0.2274 Lr: 0.00063 [2023-12-20 20:07:00,100 INFO misc.py line 119 131400] Train: [81/100][47/800] Data 0.004 (0.004) Batch 0.313 (0.330) Remain 01:27:52 loss: 0.1824 Lr: 0.00063 [2023-12-20 20:07:00,439 INFO misc.py line 119 131400] Train: [81/100][48/800] Data 0.003 (0.004) Batch 0.339 (0.331) Remain 01:27:54 loss: 0.1924 Lr: 0.00063 [2023-12-20 20:07:00,769 INFO misc.py line 119 131400] Train: [81/100][49/800] Data 0.004 (0.004) Batch 0.330 (0.331) Remain 01:27:54 loss: 0.4550 Lr: 0.00063 [2023-12-20 20:07:01,105 INFO misc.py line 119 131400] Train: [81/100][50/800] Data 0.003 (0.004) Batch 0.336 (0.331) Remain 01:27:55 loss: 0.2498 Lr: 0.00063 [2023-12-20 20:07:01,440 INFO misc.py line 119 131400] Train: [81/100][51/800] Data 0.003 (0.004) Batch 0.334 (0.331) Remain 01:27:56 loss: 0.3298 Lr: 0.00063 [2023-12-20 20:07:01,811 INFO misc.py line 119 131400] Train: [81/100][52/800] Data 0.004 (0.004) Batch 0.372 (0.332) Remain 01:28:09 loss: 0.2427 Lr: 0.00063 [2023-12-20 20:07:02,107 INFO misc.py line 119 131400] Train: [81/100][53/800] Data 0.003 (0.004) Batch 0.296 (0.331) Remain 01:27:58 loss: 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INFO misc.py line 119 131400] Train: [81/100][60/800] Data 0.004 (0.004) Batch 0.433 (0.335) Remain 01:28:59 loss: 0.1599 Lr: 0.00063 [2023-12-20 20:07:04,971 INFO misc.py line 119 131400] Train: [81/100][61/800] Data 0.004 (0.004) Batch 0.311 (0.335) Remain 01:28:53 loss: 0.2580 Lr: 0.00063 [2023-12-20 20:07:05,292 INFO misc.py line 119 131400] Train: [81/100][62/800] Data 0.013 (0.005) Batch 0.327 (0.334) Remain 01:28:50 loss: 0.2959 Lr: 0.00063 [2023-12-20 20:07:05,646 INFO misc.py line 119 131400] Train: [81/100][63/800] Data 0.004 (0.005) Batch 0.353 (0.335) Remain 01:28:55 loss: 0.3706 Lr: 0.00063 [2023-12-20 20:07:06,009 INFO misc.py line 119 131400] Train: [81/100][64/800] Data 0.004 (0.005) Batch 0.363 (0.335) Remain 01:29:02 loss: 0.1949 Lr: 0.00063 [2023-12-20 20:07:06,365 INFO misc.py line 119 131400] Train: [81/100][65/800] Data 0.004 (0.005) Batch 0.356 (0.336) Remain 01:29:07 loss: 0.2263 Lr: 0.00063 [2023-12-20 20:07:06,716 INFO misc.py line 119 131400] Train: [81/100][66/800] Data 0.003 (0.005) Batch 0.350 (0.336) Remain 01:29:10 loss: 0.1504 Lr: 0.00063 [2023-12-20 20:07:07,065 INFO misc.py line 119 131400] Train: [81/100][67/800] Data 0.004 (0.005) Batch 0.351 (0.336) Remain 01:29:14 loss: 0.2157 Lr: 0.00063 [2023-12-20 20:07:07,409 INFO misc.py line 119 131400] Train: [81/100][68/800] Data 0.003 (0.004) Batch 0.343 (0.336) Remain 01:29:15 loss: 0.1673 Lr: 0.00063 [2023-12-20 20:07:07,713 INFO misc.py line 119 131400] Train: [81/100][69/800] Data 0.004 (0.004) Batch 0.305 (0.336) Remain 01:29:07 loss: 0.1887 Lr: 0.00063 [2023-12-20 20:07:08,065 INFO misc.py line 119 131400] Train: [81/100][70/800] Data 0.003 (0.004) Batch 0.346 (0.336) Remain 01:29:09 loss: 0.1577 Lr: 0.00063 [2023-12-20 20:07:08,390 INFO misc.py line 119 131400] Train: [81/100][71/800] Data 0.010 (0.005) Batch 0.330 (0.336) Remain 01:29:08 loss: 0.2434 Lr: 0.00063 [2023-12-20 20:07:08,735 INFO misc.py line 119 131400] Train: [81/100][72/800] Data 0.005 (0.005) Batch 0.346 (0.336) Remain 01:29:10 loss: 0.1824 Lr: 0.00063 [2023-12-20 20:07:09,089 INFO misc.py line 119 131400] Train: [81/100][73/800] Data 0.003 (0.005) Batch 0.353 (0.336) Remain 01:29:13 loss: 0.6230 Lr: 0.00063 [2023-12-20 20:07:09,449 INFO misc.py line 119 131400] Train: [81/100][74/800] Data 0.006 (0.005) Batch 0.361 (0.336) Remain 01:29:18 loss: 0.2937 Lr: 0.00063 [2023-12-20 20:07:09,784 INFO misc.py line 119 131400] Train: [81/100][75/800] Data 0.003 (0.005) Batch 0.334 (0.336) Remain 01:29:18 loss: 0.1848 Lr: 0.00063 [2023-12-20 20:07:10,120 INFO misc.py line 119 131400] Train: [81/100][76/800] Data 0.004 (0.005) Batch 0.334 (0.336) Remain 01:29:17 loss: 0.0816 Lr: 0.00063 [2023-12-20 20:07:10,448 INFO misc.py line 119 131400] Train: [81/100][77/800] Data 0.006 (0.005) Batch 0.331 (0.336) Remain 01:29:15 loss: 0.1416 Lr: 0.00063 [2023-12-20 20:07:10,786 INFO misc.py line 119 131400] Train: [81/100][78/800] Data 0.004 (0.005) Batch 0.338 (0.336) Remain 01:29:15 loss: 0.2890 Lr: 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Batch 0.321 (0.334) Remain 01:24:58 loss: 0.1717 Lr: 0.00058 [2023-12-20 20:10:51,428 INFO misc.py line 119 131400] Train: [81/100][739/800] Data 0.003 (0.004) Batch 0.336 (0.334) Remain 01:24:58 loss: 0.1161 Lr: 0.00058 [2023-12-20 20:10:51,759 INFO misc.py line 119 131400] Train: [81/100][740/800] Data 0.006 (0.004) Batch 0.331 (0.334) Remain 01:24:57 loss: 0.1760 Lr: 0.00058 [2023-12-20 20:10:52,108 INFO misc.py line 119 131400] Train: [81/100][741/800] Data 0.004 (0.004) Batch 0.349 (0.334) Remain 01:24:57 loss: 0.1826 Lr: 0.00058 [2023-12-20 20:10:52,425 INFO misc.py line 119 131400] Train: [81/100][742/800] Data 0.004 (0.004) Batch 0.317 (0.334) Remain 01:24:56 loss: 0.2365 Lr: 0.00058 [2023-12-20 20:10:52,758 INFO misc.py line 119 131400] Train: [81/100][743/800] Data 0.005 (0.004) Batch 0.333 (0.334) Remain 01:24:56 loss: 0.2090 Lr: 0.00058 [2023-12-20 20:10:53,264 INFO misc.py line 119 131400] Train: [81/100][744/800] Data 0.005 (0.004) Batch 0.507 (0.334) Remain 01:24:59 loss: 0.1935 Lr: 0.00058 [2023-12-20 20:10:53,630 INFO misc.py line 119 131400] Train: [81/100][745/800] Data 0.005 (0.004) Batch 0.366 (0.334) Remain 01:25:00 loss: 0.1346 Lr: 0.00058 [2023-12-20 20:10:53,973 INFO misc.py line 119 131400] Train: [81/100][746/800] Data 0.004 (0.004) Batch 0.343 (0.334) Remain 01:24:59 loss: 0.2011 Lr: 0.00058 [2023-12-20 20:10:54,310 INFO misc.py line 119 131400] Train: [81/100][747/800] Data 0.004 (0.004) Batch 0.337 (0.334) Remain 01:24:59 loss: 0.1596 Lr: 0.00058 [2023-12-20 20:10:54,665 INFO misc.py line 119 131400] Train: [81/100][748/800] Data 0.004 (0.004) Batch 0.355 (0.334) Remain 01:24:59 loss: 0.4543 Lr: 0.00058 [2023-12-20 20:10:54,990 INFO misc.py line 119 131400] Train: [81/100][749/800] Data 0.003 (0.004) Batch 0.324 (0.334) Remain 01:24:59 loss: 0.1819 Lr: 0.00058 [2023-12-20 20:10:55,314 INFO misc.py line 119 131400] Train: [81/100][750/800] Data 0.006 (0.004) Batch 0.325 (0.334) Remain 01:24:58 loss: 0.1769 Lr: 0.00058 [2023-12-20 20:10:55,684 INFO misc.py line 119 131400] Train: [81/100][751/800] Data 0.004 (0.004) Batch 0.368 (0.334) Remain 01:24:59 loss: 0.2853 Lr: 0.00058 [2023-12-20 20:10:56,036 INFO misc.py line 119 131400] Train: [81/100][752/800] Data 0.008 (0.004) Batch 0.354 (0.334) Remain 01:24:59 loss: 0.1037 Lr: 0.00058 [2023-12-20 20:10:56,384 INFO misc.py line 119 131400] Train: [81/100][753/800] Data 0.004 (0.004) Batch 0.347 (0.334) Remain 01:24:59 loss: 0.3552 Lr: 0.00058 [2023-12-20 20:10:56,731 INFO misc.py line 119 131400] Train: [81/100][754/800] Data 0.005 (0.004) Batch 0.348 (0.334) Remain 01:24:59 loss: 0.2268 Lr: 0.00058 [2023-12-20 20:10:57,061 INFO misc.py line 119 131400] Train: [81/100][755/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 01:24:58 loss: 0.1840 Lr: 0.00058 [2023-12-20 20:10:57,402 INFO misc.py line 119 131400] Train: [81/100][756/800] Data 0.004 (0.004) Batch 0.342 (0.334) Remain 01:24:58 loss: 0.1463 Lr: 0.00058 [2023-12-20 20:10:57,755 INFO misc.py line 119 131400] Train: [81/100][757/800] Data 0.003 (0.004) Batch 0.351 (0.334) Remain 01:24:58 loss: 0.2304 Lr: 0.00058 [2023-12-20 20:10:58,082 INFO misc.py line 119 131400] Train: [81/100][758/800] Data 0.006 (0.004) Batch 0.329 (0.334) Remain 01:24:57 loss: 0.1022 Lr: 0.00058 [2023-12-20 20:10:58,397 INFO misc.py line 119 131400] Train: [81/100][759/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 01:24:57 loss: 0.2405 Lr: 0.00058 [2023-12-20 20:10:58,746 INFO misc.py line 119 131400] Train: [81/100][760/800] Data 0.003 (0.004) Batch 0.349 (0.334) Remain 01:24:57 loss: 0.2621 Lr: 0.00058 [2023-12-20 20:10:59,042 INFO misc.py line 119 131400] Train: [81/100][761/800] Data 0.003 (0.004) Batch 0.294 (0.334) Remain 01:24:56 loss: 0.2922 Lr: 0.00058 [2023-12-20 20:10:59,398 INFO misc.py line 119 131400] Train: [81/100][762/800] Data 0.005 (0.004) Batch 0.355 (0.334) Remain 01:24:56 loss: 0.1658 Lr: 0.00058 [2023-12-20 20:10:59,750 INFO misc.py line 119 131400] Train: [81/100][763/800] Data 0.005 (0.004) Batch 0.352 (0.334) Remain 01:24:56 loss: 0.2043 Lr: 0.00058 [2023-12-20 20:11:00,105 INFO misc.py line 119 131400] Train: [81/100][764/800] Data 0.005 (0.004) Batch 0.356 (0.334) Remain 01:24:56 loss: 0.1499 Lr: 0.00058 [2023-12-20 20:11:00,455 INFO misc.py line 119 131400] Train: [81/100][765/800] Data 0.004 (0.004) Batch 0.349 (0.335) Remain 01:24:56 loss: 0.2350 Lr: 0.00058 [2023-12-20 20:11:00,778 INFO misc.py line 119 131400] Train: [81/100][766/800] Data 0.006 (0.004) Batch 0.324 (0.334) Remain 01:24:55 loss: 0.1066 Lr: 0.00058 [2023-12-20 20:11:01,102 INFO misc.py line 119 131400] Train: [81/100][767/800] Data 0.004 (0.004) Batch 0.322 (0.334) Remain 01:24:55 loss: 0.1906 Lr: 0.00058 [2023-12-20 20:11:01,470 INFO misc.py line 119 131400] Train: [81/100][768/800] Data 0.006 (0.004) Batch 0.368 (0.335) Remain 01:24:55 loss: 0.2197 Lr: 0.00058 [2023-12-20 20:11:01,800 INFO misc.py line 119 131400] Train: [81/100][769/800] Data 0.006 (0.004) Batch 0.331 (0.335) Remain 01:24:55 loss: 0.1770 Lr: 0.00058 [2023-12-20 20:11:02,094 INFO misc.py line 119 131400] Train: [81/100][770/800] Data 0.004 (0.004) Batch 0.294 (0.334) Remain 01:24:53 loss: 0.1193 Lr: 0.00058 [2023-12-20 20:11:02,427 INFO misc.py line 119 131400] Train: [81/100][771/800] Data 0.004 (0.004) Batch 0.334 (0.334) Remain 01:24:53 loss: 0.2396 Lr: 0.00058 [2023-12-20 20:11:02,762 INFO misc.py line 119 131400] Train: [81/100][772/800] Data 0.003 (0.004) Batch 0.334 (0.334) Remain 01:24:53 loss: 0.0810 Lr: 0.00058 [2023-12-20 20:11:03,073 INFO misc.py line 119 131400] Train: [81/100][773/800] Data 0.004 (0.004) Batch 0.312 (0.334) Remain 01:24:52 loss: 0.2449 Lr: 0.00058 [2023-12-20 20:11:03,466 INFO misc.py line 119 131400] Train: [81/100][774/800] Data 0.003 (0.004) Batch 0.392 (0.335) Remain 01:24:53 loss: 0.3020 Lr: 0.00058 [2023-12-20 20:11:03,804 INFO misc.py line 119 131400] Train: [81/100][775/800] Data 0.004 (0.004) Batch 0.337 (0.335) Remain 01:24:52 loss: 0.4046 Lr: 0.00058 [2023-12-20 20:11:04,138 INFO misc.py line 119 131400] Train: [81/100][776/800] Data 0.005 (0.004) Batch 0.335 (0.335) Remain 01:24:52 loss: 0.3531 Lr: 0.00058 [2023-12-20 20:11:04,468 INFO misc.py line 119 131400] Train: [81/100][777/800] Data 0.003 (0.004) Batch 0.330 (0.335) Remain 01:24:52 loss: 0.1543 Lr: 0.00058 [2023-12-20 20:11:04,811 INFO misc.py line 119 131400] Train: [81/100][778/800] Data 0.003 (0.004) Batch 0.343 (0.335) Remain 01:24:52 loss: 0.1287 Lr: 0.00058 [2023-12-20 20:11:05,137 INFO misc.py line 119 131400] Train: [81/100][779/800] Data 0.004 (0.004) Batch 0.325 (0.335) Remain 01:24:51 loss: 0.2238 Lr: 0.00058 [2023-12-20 20:11:05,486 INFO misc.py line 119 131400] Train: [81/100][780/800] Data 0.005 (0.004) Batch 0.347 (0.335) Remain 01:24:51 loss: 0.1628 Lr: 0.00058 [2023-12-20 20:11:05,851 INFO misc.py line 119 131400] Train: [81/100][781/800] Data 0.006 (0.004) Batch 0.365 (0.335) Remain 01:24:51 loss: 0.2461 Lr: 0.00058 [2023-12-20 20:11:06,193 INFO misc.py line 119 131400] Train: [81/100][782/800] Data 0.007 (0.004) Batch 0.343 (0.335) Remain 01:24:51 loss: 0.1663 Lr: 0.00058 [2023-12-20 20:11:06,540 INFO misc.py line 119 131400] Train: [81/100][783/800] Data 0.004 (0.004) Batch 0.348 (0.335) Remain 01:24:51 loss: 0.2374 Lr: 0.00058 [2023-12-20 20:11:06,882 INFO misc.py line 119 131400] Train: [81/100][784/800] Data 0.004 (0.004) Batch 0.341 (0.335) Remain 01:24:51 loss: 0.3640 Lr: 0.00058 [2023-12-20 20:11:07,215 INFO misc.py line 119 131400] Train: [81/100][785/800] Data 0.005 (0.004) Batch 0.334 (0.335) Remain 01:24:50 loss: 0.1482 Lr: 0.00058 [2023-12-20 20:11:07,582 INFO misc.py line 119 131400] Train: [81/100][786/800] Data 0.005 (0.004) Batch 0.366 (0.335) Remain 01:24:51 loss: 0.1747 Lr: 0.00058 [2023-12-20 20:11:07,891 INFO misc.py line 119 131400] Train: [81/100][787/800] Data 0.006 (0.004) Batch 0.310 (0.335) Remain 01:24:50 loss: 0.3036 Lr: 0.00058 [2023-12-20 20:11:08,224 INFO misc.py line 119 131400] Train: [81/100][788/800] Data 0.004 (0.004) Batch 0.334 (0.335) Remain 01:24:49 loss: 0.1987 Lr: 0.00058 [2023-12-20 20:11:08,578 INFO misc.py line 119 131400] Train: [81/100][789/800] Data 0.003 (0.004) Batch 0.354 (0.335) Remain 01:24:50 loss: 0.1853 Lr: 0.00058 [2023-12-20 20:11:08,922 INFO misc.py line 119 131400] Train: [81/100][790/800] Data 0.003 (0.004) Batch 0.345 (0.335) Remain 01:24:49 loss: 0.1141 Lr: 0.00058 [2023-12-20 20:11:09,227 INFO misc.py line 119 131400] Train: [81/100][791/800] Data 0.003 (0.004) Batch 0.305 (0.335) Remain 01:24:48 loss: 0.2589 Lr: 0.00058 [2023-12-20 20:11:09,518 INFO misc.py line 119 131400] Train: [81/100][792/800] Data 0.003 (0.004) Batch 0.290 (0.335) Remain 01:24:47 loss: 0.2416 Lr: 0.00058 [2023-12-20 20:11:09,828 INFO misc.py line 119 131400] Train: [81/100][793/800] Data 0.003 (0.004) Batch 0.311 (0.335) Remain 01:24:46 loss: 0.2669 Lr: 0.00058 [2023-12-20 20:11:10,085 INFO misc.py line 119 131400] Train: [81/100][794/800] Data 0.003 (0.004) Batch 0.257 (0.334) Remain 01:24:45 loss: 0.1961 Lr: 0.00058 [2023-12-20 20:11:10,360 INFO misc.py line 119 131400] Train: [81/100][795/800] Data 0.003 (0.004) Batch 0.274 (0.334) Remain 01:24:43 loss: 0.1989 Lr: 0.00058 [2023-12-20 20:11:10,664 INFO misc.py line 119 131400] Train: [81/100][796/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 01:24:42 loss: 0.3154 Lr: 0.00058 [2023-12-20 20:11:10,936 INFO misc.py line 119 131400] Train: [81/100][797/800] Data 0.003 (0.004) Batch 0.272 (0.334) Remain 01:24:41 loss: 0.2090 Lr: 0.00058 [2023-12-20 20:11:11,219 INFO misc.py line 119 131400] Train: [81/100][798/800] Data 0.003 (0.004) Batch 0.283 (0.334) Remain 01:24:39 loss: 0.1512 Lr: 0.00058 [2023-12-20 20:11:11,523 INFO misc.py line 119 131400] Train: [81/100][799/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 01:24:39 loss: 0.1993 Lr: 0.00058 [2023-12-20 20:11:11,822 INFO misc.py line 119 131400] Train: [81/100][800/800] Data 0.003 (0.004) Batch 0.299 (0.334) Remain 01:24:38 loss: 0.1173 Lr: 0.00058 [2023-12-20 20:11:11,823 INFO misc.py line 136 131400] Train result: loss: 0.2269 [2023-12-20 20:11:11,823 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:11:34,115 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1971 [2023-12-20 20:11:34,190 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1450 [2023-12-20 20:11:34,295 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4772 [2023-12-20 20:11:34,403 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5205 [2023-12-20 20:11:34,520 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3592 [2023-12-20 20:11:34,621 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1801 [2023-12-20 20:11:34,717 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8508 [2023-12-20 20:11:34,827 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6264 [2023-12-20 20:11:34,918 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3021 [2023-12-20 20:11:35,009 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3297 [2023-12-20 20:11:35,100 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5297 [2023-12-20 20:11:35,237 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2659 [2023-12-20 20:11:35,356 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4472 [2023-12-20 20:11:35,511 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2525 [2023-12-20 20:11:35,610 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1439 [2023-12-20 20:11:35,745 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8428 [2023-12-20 20:11:35,865 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2814 [2023-12-20 20:11:35,986 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6908 [2023-12-20 20:11:36,100 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1024 [2023-12-20 20:11:36,178 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5821 [2023-12-20 20:11:36,284 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1223 [2023-12-20 20:11:36,441 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1316 [2023-12-20 20:11:36,565 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7813 [2023-12-20 20:11:36,709 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2633 [2023-12-20 20:11:36,854 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1475 [2023-12-20 20:11:36,938 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6742 [2023-12-20 20:11:37,100 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8858 [2023-12-20 20:11:37,231 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5051 [2023-12-20 20:11:37,330 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.3171 [2023-12-20 20:11:37,474 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5447 [2023-12-20 20:11:37,577 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5259 [2023-12-20 20:11:37,696 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4119 [2023-12-20 20:11:37,779 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1138 [2023-12-20 20:11:37,848 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1698 [2023-12-20 20:11:37,943 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8853 [2023-12-20 20:11:38,037 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3310 [2023-12-20 20:11:38,166 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8922 [2023-12-20 20:11:38,276 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0939 [2023-12-20 20:11:38,358 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.7942 [2023-12-20 20:11:38,501 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3084 [2023-12-20 20:11:38,648 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0161 [2023-12-20 20:11:38,747 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0748 [2023-12-20 20:11:38,869 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3327 [2023-12-20 20:11:39,011 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0201 [2023-12-20 20:11:39,130 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.2935 [2023-12-20 20:11:39,234 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8700 [2023-12-20 20:11:39,399 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2541 [2023-12-20 20:11:39,494 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3638 [2023-12-20 20:11:39,640 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.3586 [2023-12-20 20:11:39,732 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1800 [2023-12-20 20:11:39,807 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4622 [2023-12-20 20:11:39,912 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2355 [2023-12-20 20:11:40,059 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1737 [2023-12-20 20:11:40,193 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3146 [2023-12-20 20:11:40,297 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.8660 [2023-12-20 20:11:40,384 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6568 [2023-12-20 20:11:40,485 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3332 [2023-12-20 20:11:40,645 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2504 [2023-12-20 20:11:40,742 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.8132 [2023-12-20 20:11:40,836 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2030 [2023-12-20 20:11:40,936 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.6553 [2023-12-20 20:11:41,027 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2068 [2023-12-20 20:11:41,116 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5694 [2023-12-20 20:11:41,221 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6643 [2023-12-20 20:11:41,346 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.8614 [2023-12-20 20:11:41,430 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2206 [2023-12-20 20:11:41,530 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2547 [2023-12-20 20:11:41,622 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0084 [2023-12-20 20:11:41,705 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3656 [2023-12-20 20:11:41,789 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0083 [2023-12-20 20:11:41,883 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8299 [2023-12-20 20:11:41,973 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4612 [2023-12-20 20:11:42,106 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0592 [2023-12-20 20:11:42,200 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6735 [2023-12-20 20:11:42,316 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5950 [2023-12-20 20:11:42,418 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4821 [2023-12-20 20:11:42,504 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3101 [2023-12-20 20:11:42,657 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1022 [2023-12-20 20:11:43,822 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7648/0.8445/0.9199. [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8723/0.9508 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9632/0.9858 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7019/0.8297 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8282/0.8836 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9241/0.9637 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8722/0.9277 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7865/0.8541 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7284/0.8573 [2023-12-20 20:11:43,822 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7077/0.8159 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8370/0.8977 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4108/0.5296 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7303/0.8290 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7030/0.8954 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7720/0.8644 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6827/0.7553 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6524/0.6904 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9358/0.9792 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6941/0.7962 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8798/0.9206 [2023-12-20 20:11:43,823 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6137/0.6625 [2023-12-20 20:11:43,823 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:11:43,824 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 20:11:43,824 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:11:47,463 INFO misc.py line 119 131400] Train: [82/100][1/800] Data 1.252 (1.252) Batch 1.591 (1.591) Remain 06:42:59 loss: 0.2615 Lr: 0.00058 [2023-12-20 20:11:47,774 INFO misc.py line 119 131400] Train: [82/100][2/800] Data 0.003 (0.003) Batch 0.311 (0.311) Remain 01:18:43 loss: 0.2363 Lr: 0.00058 [2023-12-20 20:11:48,095 INFO misc.py line 119 131400] Train: [82/100][3/800] Data 0.003 (0.003) Batch 0.322 (0.322) Remain 01:21:30 loss: 0.1901 Lr: 0.00058 [2023-12-20 20:11:48,437 INFO misc.py line 119 131400] Train: [82/100][4/800] Data 0.003 (0.003) Batch 0.340 (0.340) Remain 01:26:12 loss: 0.1746 Lr: 0.00058 [2023-12-20 20:11:48,759 INFO misc.py line 119 131400] Train: [82/100][5/800] Data 0.003 (0.003) Batch 0.322 (0.331) Remain 01:23:55 loss: 0.2039 Lr: 0.00058 [2023-12-20 20:11:49,086 INFO misc.py line 119 131400] Train: [82/100][6/800] Data 0.003 (0.003) Batch 0.328 (0.330) Remain 01:23:37 loss: 0.1337 Lr: 0.00058 [2023-12-20 20:11:49,537 INFO misc.py line 119 131400] Train: [82/100][7/800] Data 0.004 (0.003) Batch 0.451 (0.360) Remain 01:31:16 loss: 0.1861 Lr: 0.00057 [2023-12-20 20:11:49,849 INFO misc.py line 119 131400] Train: [82/100][8/800] Data 0.003 (0.003) Batch 0.312 (0.351) Remain 01:28:47 loss: 0.2058 Lr: 0.00057 [2023-12-20 20:11:50,174 INFO misc.py line 119 131400] Train: [82/100][9/800] Data 0.003 (0.003) Batch 0.325 (0.346) Remain 01:27:42 loss: 0.3514 Lr: 0.00057 [2023-12-20 20:11:50,520 INFO misc.py line 119 131400] Train: [82/100][10/800] Data 0.002 (0.003) Batch 0.346 (0.346) Remain 01:27:40 loss: 0.2033 Lr: 0.00057 [2023-12-20 20:11:50,828 INFO misc.py line 119 131400] Train: [82/100][11/800] Data 0.003 (0.003) Batch 0.307 (0.341) Remain 01:26:26 loss: 0.3695 Lr: 0.00057 [2023-12-20 20:11:51,167 INFO misc.py line 119 131400] Train: [82/100][12/800] Data 0.004 (0.003) Batch 0.341 (0.341) Remain 01:26:24 loss: 0.1848 Lr: 0.00057 [2023-12-20 20:11:51,488 INFO misc.py line 119 131400] Train: [82/100][13/800] Data 0.003 (0.003) Batch 0.320 (0.339) Remain 01:25:51 loss: 0.1666 Lr: 0.00057 [2023-12-20 20:11:51,799 INFO misc.py line 119 131400] Train: [82/100][14/800] Data 0.004 (0.003) Batch 0.309 (0.336) Remain 01:25:09 loss: 0.1366 Lr: 0.00057 [2023-12-20 20:11:52,118 INFO misc.py line 119 131400] Train: [82/100][15/800] Data 0.006 (0.003) Batch 0.322 (0.335) Remain 01:24:50 loss: 0.2784 Lr: 0.00057 [2023-12-20 20:11:52,431 INFO misc.py line 119 131400] Train: [82/100][16/800] Data 0.002 (0.003) Batch 0.313 (0.333) Remain 01:24:23 loss: 0.2622 Lr: 0.00057 [2023-12-20 20:11:52,748 INFO misc.py line 119 131400] Train: [82/100][17/800] Data 0.003 (0.003) Batch 0.316 (0.332) Remain 01:24:04 loss: 0.1963 Lr: 0.00057 [2023-12-20 20:11:53,079 INFO misc.py line 119 131400] Train: [82/100][18/800] Data 0.004 (0.003) Batch 0.332 (0.332) Remain 01:24:03 loss: 0.2804 Lr: 0.00057 [2023-12-20 20:11:53,367 INFO misc.py line 119 131400] Train: [82/100][19/800] Data 0.003 (0.003) Batch 0.288 (0.329) Remain 01:23:21 loss: 0.1462 Lr: 0.00057 [2023-12-20 20:11:53,689 INFO misc.py line 119 131400] Train: [82/100][20/800] Data 0.003 (0.003) Batch 0.322 (0.329) Remain 01:23:14 loss: 0.3109 Lr: 0.00057 [2023-12-20 20:11:54,001 INFO misc.py line 119 131400] Train: [82/100][21/800] Data 0.003 (0.003) Batch 0.312 (0.328) Remain 01:22:59 loss: 0.1864 Lr: 0.00057 [2023-12-20 20:11:54,294 INFO misc.py line 119 131400] Train: [82/100][22/800] Data 0.004 (0.003) Batch 0.293 (0.326) Remain 01:22:31 loss: 0.3535 Lr: 0.00057 [2023-12-20 20:11:54,629 INFO misc.py line 119 131400] Train: [82/100][23/800] Data 0.003 (0.003) Batch 0.335 (0.327) Remain 01:22:37 loss: 0.2762 Lr: 0.00057 [2023-12-20 20:11:54,985 INFO misc.py line 119 131400] Train: [82/100][24/800] Data 0.004 (0.003) Batch 0.355 (0.328) Remain 01:22:58 loss: 0.1446 Lr: 0.00057 [2023-12-20 20:11:55,289 INFO misc.py line 119 131400] Train: [82/100][25/800] Data 0.004 (0.003) Batch 0.305 (0.327) Remain 01:22:41 loss: 0.0803 Lr: 0.00057 [2023-12-20 20:11:55,608 INFO misc.py line 119 131400] Train: [82/100][26/800] Data 0.003 (0.003) Batch 0.318 (0.327) Remain 01:22:35 loss: 0.2266 Lr: 0.00057 [2023-12-20 20:11:55,904 INFO misc.py line 119 131400] Train: [82/100][27/800] Data 0.004 (0.003) Batch 0.297 (0.325) Remain 01:22:16 loss: 0.2824 Lr: 0.00057 [2023-12-20 20:11:56,241 INFO misc.py line 119 131400] Train: [82/100][28/800] Data 0.003 (0.003) Batch 0.331 (0.326) Remain 01:22:19 loss: 0.3096 Lr: 0.00057 [2023-12-20 20:11:56,525 INFO misc.py line 119 131400] Train: [82/100][29/800] Data 0.008 (0.004) Batch 0.289 (0.324) Remain 01:21:58 loss: 0.1830 Lr: 0.00057 [2023-12-20 20:11:56,810 INFO misc.py line 119 131400] Train: [82/100][30/800] Data 0.004 (0.004) Batch 0.285 (0.323) Remain 01:21:35 loss: 0.2087 Lr: 0.00057 [2023-12-20 20:11:57,117 INFO misc.py line 119 131400] Train: [82/100][31/800] Data 0.004 (0.004) Batch 0.307 (0.322) Remain 01:21:26 loss: 0.1472 Lr: 0.00057 [2023-12-20 20:11:57,433 INFO misc.py line 119 131400] Train: [82/100][32/800] Data 0.004 (0.004) Batch 0.317 (0.322) Remain 01:21:23 loss: 0.1631 Lr: 0.00057 [2023-12-20 20:11:57,740 INFO misc.py line 119 131400] Train: [82/100][33/800] Data 0.003 (0.004) Batch 0.307 (0.321) Remain 01:21:15 loss: 0.2181 Lr: 0.00057 [2023-12-20 20:11:58,072 INFO misc.py line 119 131400] Train: [82/100][34/800] Data 0.003 (0.004) Batch 0.330 (0.322) Remain 01:21:19 loss: 0.1695 Lr: 0.00057 [2023-12-20 20:11:58,423 INFO misc.py line 119 131400] Train: [82/100][35/800] Data 0.005 (0.004) Batch 0.353 (0.323) Remain 01:21:34 loss: 0.1415 Lr: 0.00057 [2023-12-20 20:11:58,750 INFO misc.py line 119 131400] Train: [82/100][36/800] Data 0.003 (0.004) Batch 0.325 (0.323) Remain 01:21:35 loss: 0.1309 Lr: 0.00057 [2023-12-20 20:11:59,085 INFO misc.py line 119 131400] Train: [82/100][37/800] Data 0.004 (0.004) Batch 0.335 (0.323) Remain 01:21:40 loss: 0.2454 Lr: 0.00057 [2023-12-20 20:11:59,400 INFO misc.py line 119 131400] Train: [82/100][38/800] Data 0.005 (0.004) Batch 0.315 (0.323) Remain 01:21:36 loss: 0.2365 Lr: 0.00057 [2023-12-20 20:11:59,707 INFO misc.py line 119 131400] Train: [82/100][39/800] Data 0.004 (0.004) Batch 0.307 (0.323) Remain 01:21:29 loss: 0.2177 Lr: 0.00057 [2023-12-20 20:12:00,011 INFO misc.py line 119 131400] Train: [82/100][40/800] Data 0.003 (0.004) Batch 0.304 (0.322) Remain 01:21:21 loss: 0.1893 Lr: 0.00057 [2023-12-20 20:12:00,454 INFO misc.py line 119 131400] Train: [82/100][41/800] Data 0.003 (0.004) Batch 0.443 (0.325) Remain 01:22:10 loss: 0.2521 Lr: 0.00057 [2023-12-20 20:12:00,789 INFO misc.py line 119 131400] Train: [82/100][42/800] Data 0.003 (0.004) Batch 0.334 (0.325) Remain 01:22:13 loss: 0.2293 Lr: 0.00057 [2023-12-20 20:12:01,138 INFO misc.py line 119 131400] Train: [82/100][43/800] Data 0.004 (0.004) Batch 0.349 (0.326) Remain 01:22:21 loss: 0.1923 Lr: 0.00057 [2023-12-20 20:12:01,479 INFO misc.py line 119 131400] Train: [82/100][44/800] Data 0.003 (0.004) Batch 0.342 (0.326) Remain 01:22:27 loss: 0.2507 Lr: 0.00057 [2023-12-20 20:12:01,782 INFO misc.py line 119 131400] Train: [82/100][45/800] Data 0.003 (0.004) Batch 0.303 (0.326) Remain 01:22:18 loss: 0.2731 Lr: 0.00057 [2023-12-20 20:12:02,096 INFO misc.py line 119 131400] Train: [82/100][46/800] Data 0.003 (0.004) Batch 0.314 (0.326) Remain 01:22:14 loss: 0.1408 Lr: 0.00057 [2023-12-20 20:12:02,408 INFO misc.py line 119 131400] Train: [82/100][47/800] Data 0.003 (0.003) Batch 0.311 (0.325) Remain 01:22:08 loss: 0.2109 Lr: 0.00057 [2023-12-20 20:12:02,697 INFO misc.py line 119 131400] Train: [82/100][48/800] Data 0.004 (0.003) Batch 0.285 (0.324) Remain 01:21:54 loss: 0.1915 Lr: 0.00057 [2023-12-20 20:12:02,961 INFO misc.py line 119 131400] Train: [82/100][49/800] Data 0.008 (0.004) Batch 0.268 (0.323) Remain 01:21:36 loss: 0.2127 Lr: 0.00057 [2023-12-20 20:12:03,304 INFO misc.py line 119 131400] Train: [82/100][50/800] Data 0.004 (0.004) Batch 0.340 (0.324) Remain 01:21:41 loss: 0.0969 Lr: 0.00057 [2023-12-20 20:12:03,608 INFO misc.py line 119 131400] Train: [82/100][51/800] Data 0.006 (0.004) Batch 0.307 (0.323) Remain 01:21:35 loss: 0.4025 Lr: 0.00057 [2023-12-20 20:12:03,920 INFO misc.py line 119 131400] Train: [82/100][52/800] Data 0.003 (0.004) Batch 0.311 (0.323) Remain 01:21:31 loss: 0.3353 Lr: 0.00057 [2023-12-20 20:12:04,266 INFO misc.py line 119 131400] Train: [82/100][53/800] Data 0.004 (0.004) Batch 0.347 (0.323) Remain 01:21:38 loss: 0.1956 Lr: 0.00057 [2023-12-20 20:12:04,616 INFO misc.py line 119 131400] Train: [82/100][54/800] Data 0.005 (0.004) Batch 0.350 (0.324) Remain 01:21:46 loss: 0.6134 Lr: 0.00057 [2023-12-20 20:12:04,944 INFO misc.py line 119 131400] Train: [82/100][55/800] Data 0.003 (0.004) Batch 0.328 (0.324) Remain 01:21:47 loss: 0.1062 Lr: 0.00057 [2023-12-20 20:12:05,299 INFO misc.py line 119 131400] Train: [82/100][56/800] Data 0.003 (0.004) Batch 0.354 (0.325) Remain 01:21:55 loss: 0.2090 Lr: 0.00057 [2023-12-20 20:12:05,643 INFO misc.py line 119 131400] Train: [82/100][57/800] Data 0.004 (0.004) Batch 0.344 (0.325) Remain 01:22:00 loss: 0.3065 Lr: 0.00057 [2023-12-20 20:12:06,008 INFO misc.py line 119 131400] Train: [82/100][58/800] Data 0.005 (0.004) Batch 0.365 (0.326) Remain 01:22:11 loss: 0.5822 Lr: 0.00057 [2023-12-20 20:12:06,372 INFO misc.py line 119 131400] Train: [82/100][59/800] Data 0.004 (0.004) Batch 0.364 (0.326) Remain 01:22:21 loss: 0.3696 Lr: 0.00057 [2023-12-20 20:12:06,719 INFO misc.py line 119 131400] Train: [82/100][60/800] Data 0.004 (0.004) Batch 0.347 (0.327) Remain 01:22:26 loss: 0.3575 Lr: 0.00057 [2023-12-20 20:12:07,101 INFO misc.py line 119 131400] Train: [82/100][61/800] Data 0.004 (0.004) Batch 0.377 (0.328) Remain 01:22:39 loss: 0.2628 Lr: 0.00057 [2023-12-20 20:12:07,443 INFO misc.py line 119 131400] Train: [82/100][62/800] Data 0.009 (0.004) Batch 0.342 (0.328) Remain 01:22:42 loss: 0.1630 Lr: 0.00057 [2023-12-20 20:12:07,860 INFO misc.py line 119 131400] Train: [82/100][63/800] Data 0.008 (0.004) Batch 0.409 (0.329) Remain 01:23:02 loss: 0.2244 Lr: 0.00057 [2023-12-20 20:12:08,224 INFO misc.py line 119 131400] Train: [82/100][64/800] Data 0.016 (0.004) Batch 0.377 (0.330) Remain 01:23:14 loss: 0.1423 Lr: 0.00057 [2023-12-20 20:12:08,531 INFO misc.py line 119 131400] Train: [82/100][65/800] Data 0.005 (0.004) Batch 0.308 (0.330) Remain 01:23:08 loss: 0.2390 Lr: 0.00057 [2023-12-20 20:12:08,877 INFO misc.py line 119 131400] Train: [82/100][66/800] Data 0.003 (0.004) Batch 0.337 (0.330) Remain 01:23:10 loss: 0.1791 Lr: 0.00057 [2023-12-20 20:12:09,180 INFO misc.py line 119 131400] Train: [82/100][67/800] Data 0.011 (0.004) Batch 0.311 (0.329) Remain 01:23:05 loss: 0.3367 Lr: 0.00057 [2023-12-20 20:12:09,527 INFO misc.py line 119 131400] Train: [82/100][68/800] Data 0.003 (0.004) Batch 0.347 (0.330) Remain 01:23:09 loss: 0.2810 Lr: 0.00057 [2023-12-20 20:12:09,865 INFO misc.py line 119 131400] Train: [82/100][69/800] Data 0.004 (0.004) Batch 0.339 (0.330) Remain 01:23:10 loss: 0.2546 Lr: 0.00057 [2023-12-20 20:12:10,204 INFO misc.py line 119 131400] Train: [82/100][70/800] Data 0.004 (0.004) Batch 0.338 (0.330) Remain 01:23:12 loss: 0.2009 Lr: 0.00057 [2023-12-20 20:12:10,540 INFO misc.py line 119 131400] Train: [82/100][71/800] Data 0.004 (0.004) Batch 0.336 (0.330) Remain 01:23:13 loss: 0.4477 Lr: 0.00057 [2023-12-20 20:12:10,909 INFO misc.py line 119 131400] Train: [82/100][72/800] Data 0.004 (0.004) Batch 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line 119 131400] Train: [82/100][782/800] Data 0.003 (0.004) Batch 0.324 (0.335) Remain 01:20:30 loss: 0.2069 Lr: 0.00052 [2023-12-20 20:16:09,386 INFO misc.py line 119 131400] Train: [82/100][783/800] Data 0.003 (0.004) Batch 0.282 (0.335) Remain 01:20:29 loss: 0.1676 Lr: 0.00052 [2023-12-20 20:16:09,719 INFO misc.py line 119 131400] Train: [82/100][784/800] Data 0.003 (0.004) Batch 0.333 (0.335) Remain 01:20:29 loss: 0.1254 Lr: 0.00052 [2023-12-20 20:16:10,040 INFO misc.py line 119 131400] Train: [82/100][785/800] Data 0.004 (0.004) Batch 0.321 (0.335) Remain 01:20:28 loss: 0.1287 Lr: 0.00052 [2023-12-20 20:16:10,370 INFO misc.py line 119 131400] Train: [82/100][786/800] Data 0.003 (0.004) Batch 0.330 (0.335) Remain 01:20:28 loss: 0.2343 Lr: 0.00052 [2023-12-20 20:16:10,704 INFO misc.py line 119 131400] Train: [82/100][787/800] Data 0.004 (0.004) Batch 0.334 (0.335) Remain 01:20:27 loss: 0.2050 Lr: 0.00052 [2023-12-20 20:16:11,001 INFO misc.py line 119 131400] Train: 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Batch 0.280 (0.335) Remain 01:20:21 loss: 0.1034 Lr: 0.00052 [2023-12-20 20:16:13,153 INFO misc.py line 119 131400] Train: [82/100][795/800] Data 0.003 (0.004) Batch 0.319 (0.335) Remain 01:20:20 loss: 0.2252 Lr: 0.00052 [2023-12-20 20:16:13,438 INFO misc.py line 119 131400] Train: [82/100][796/800] Data 0.003 (0.004) Batch 0.284 (0.335) Remain 01:20:19 loss: 0.1128 Lr: 0.00052 [2023-12-20 20:16:13,751 INFO misc.py line 119 131400] Train: [82/100][797/800] Data 0.003 (0.004) Batch 0.313 (0.335) Remain 01:20:18 loss: 0.2545 Lr: 0.00052 [2023-12-20 20:16:14,039 INFO misc.py line 119 131400] Train: [82/100][798/800] Data 0.003 (0.004) Batch 0.288 (0.335) Remain 01:20:17 loss: 0.3048 Lr: 0.00052 [2023-12-20 20:16:14,337 INFO misc.py line 119 131400] Train: [82/100][799/800] Data 0.003 (0.004) Batch 0.299 (0.334) Remain 01:20:16 loss: 0.4912 Lr: 0.00052 [2023-12-20 20:16:14,653 INFO misc.py line 119 131400] Train: [82/100][800/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 01:20:16 loss: 0.2089 Lr: 0.00052 [2023-12-20 20:16:14,654 INFO misc.py line 136 131400] Train result: loss: 0.2275 [2023-12-20 20:16:14,654 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:16:35,144 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1667 [2023-12-20 20:16:37,065 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1342 [2023-12-20 20:16:37,162 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4280 [2023-12-20 20:16:37,292 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3853 [2023-12-20 20:16:37,414 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2658 [2023-12-20 20:16:37,525 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2658 [2023-12-20 20:16:37,622 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2212 [2023-12-20 20:16:37,737 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.7148 [2023-12-20 20:16:37,822 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2170 [2023-12-20 20:16:37,915 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3089 [2023-12-20 20:16:38,013 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.5377 [2023-12-20 20:16:38,155 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2701 [2023-12-20 20:16:38,279 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4032 [2023-12-20 20:16:38,439 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1906 [2023-12-20 20:16:38,537 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1376 [2023-12-20 20:16:38,675 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6306 [2023-12-20 20:16:38,786 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2965 [2023-12-20 20:16:38,898 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7877 [2023-12-20 20:16:39,023 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2192 [2023-12-20 20:16:39,116 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3563 [2023-12-20 20:16:39,221 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.1083 [2023-12-20 20:16:40,921 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1742 [2023-12-20 20:16:41,028 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.7426 [2023-12-20 20:16:41,126 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2760 [2023-12-20 20:16:41,261 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9750 [2023-12-20 20:16:41,373 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0951 [2023-12-20 20:16:41,461 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6238 [2023-12-20 20:16:41,602 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3158 [2023-12-20 20:16:41,747 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0178 [2023-12-20 20:16:41,844 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0594 [2023-12-20 20:16:41,962 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.1631 [2023-12-20 20:16:42,102 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9720 [2023-12-20 20:16:42,219 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.3661 [2023-12-20 20:16:42,323 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5609 [2023-12-20 20:16:42,488 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2874 [2023-12-20 20:16:42,582 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3442 [2023-12-20 20:16:42,725 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.4980 [2023-12-20 20:16:42,815 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1289 [2023-12-20 20:16:42,891 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4577 [2023-12-20 20:16:42,997 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.1183 [2023-12-20 20:16:43,143 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0591 [2023-12-20 20:16:43,276 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.2970 [2023-12-20 20:16:43,377 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0885 [2023-12-20 20:16:43,462 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.7066 [2023-12-20 20:16:43,563 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3617 [2023-12-20 20:16:43,724 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2104 [2023-12-20 20:16:43,819 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4587 [2023-12-20 20:16:43,911 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2750 [2023-12-20 20:16:44,007 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5316 [2023-12-20 20:16:44,097 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2567 [2023-12-20 20:16:44,183 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5829 [2023-12-20 20:16:44,282 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7685 [2023-12-20 20:16:44,411 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6502 [2023-12-20 20:16:44,497 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2176 [2023-12-20 20:16:44,598 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2703 [2023-12-20 20:16:44,698 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0111 [2023-12-20 20:16:44,786 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3554 [2023-12-20 20:16:44,871 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0100 [2023-12-20 20:16:44,976 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9412 [2023-12-20 20:16:45,068 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6046 [2023-12-20 20:16:45,203 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.1017 [2023-12-20 20:16:45,299 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6649 [2023-12-20 20:16:45,416 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5933 [2023-12-20 20:16:45,519 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4476 [2023-12-20 20:16:45,609 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3070 [2023-12-20 20:16:45,764 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0396 [2023-12-20 20:16:46,974 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7611/0.8410/0.9208. [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8773/0.9494 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9646/0.9868 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7108/0.8385 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8402/0.8816 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9221/0.9587 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8660/0.9425 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7821/0.8815 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7286/0.8562 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7071/0.8319 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8230/0.9188 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3782/0.4737 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7064/0.7832 [2023-12-20 20:16:46,975 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6964/0.8222 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7372/0.8488 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6770/0.7656 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6596/0.7045 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9364/0.9794 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6847/0.7880 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8881/0.9283 [2023-12-20 20:16:46,976 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6363/0.6803 [2023-12-20 20:16:46,976 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:16:46,978 INFO misc.py line 165 131400] Currently Best mIoU: 0.7680 [2023-12-20 20:16:46,978 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:16:51,537 INFO misc.py line 119 131400] Train: [83/100][1/800] Data 1.372 (1.372) Batch 1.707 (1.707) Remain 06:49:36 loss: 0.2742 Lr: 0.00052 [2023-12-20 20:16:51,877 INFO misc.py line 119 131400] Train: [83/100][2/800] Data 0.003 (0.003) Batch 0.340 (0.340) Remain 01:21:36 loss: 0.2917 Lr: 0.00052 [2023-12-20 20:16:52,178 INFO misc.py line 119 131400] Train: [83/100][3/800] Data 0.003 (0.003) Batch 0.301 (0.301) Remain 01:12:07 loss: 0.2558 Lr: 0.00052 [2023-12-20 20:16:52,538 INFO misc.py line 119 131400] Train: [83/100][4/800] Data 0.004 (0.004) Batch 0.360 (0.360) Remain 01:26:25 loss: 0.1343 Lr: 0.00052 [2023-12-20 20:16:52,973 INFO misc.py line 119 131400] Train: [83/100][5/800] Data 0.004 (0.004) Batch 0.436 (0.398) Remain 01:35:27 loss: 0.1342 Lr: 0.00052 [2023-12-20 20:16:53,302 INFO misc.py line 119 131400] Train: [83/100][6/800] Data 0.003 (0.004) Batch 0.328 (0.375) Remain 01:29:53 loss: 0.3939 Lr: 0.00052 [2023-12-20 20:16:53,617 INFO misc.py line 119 131400] Train: [83/100][7/800] Data 0.005 (0.004) Batch 0.315 (0.360) Remain 01:26:17 loss: 0.2662 Lr: 0.00052 [2023-12-20 20:16:53,945 INFO misc.py line 119 131400] Train: [83/100][8/800] Data 0.005 (0.004) Batch 0.328 (0.353) Remain 01:24:46 loss: 0.2142 Lr: 0.00052 [2023-12-20 20:16:54,265 INFO misc.py line 119 131400] Train: [83/100][9/800] Data 0.004 (0.004) Batch 0.319 (0.348) Remain 01:23:23 loss: 0.2189 Lr: 0.00052 [2023-12-20 20:16:54,599 INFO misc.py line 119 131400] Train: [83/100][10/800] Data 0.005 (0.004) Batch 0.335 (0.346) Remain 01:22:56 loss: 0.2085 Lr: 0.00052 [2023-12-20 20:16:54,899 INFO misc.py line 119 131400] Train: [83/100][11/800] Data 0.004 (0.004) Batch 0.300 (0.340) Remain 01:21:32 loss: 0.1988 Lr: 0.00052 [2023-12-20 20:16:55,224 INFO misc.py line 119 131400] Train: [83/100][12/800] Data 0.003 (0.004) Batch 0.326 (0.339) Remain 01:21:10 loss: 0.1710 Lr: 0.00052 [2023-12-20 20:16:55,571 INFO misc.py line 119 131400] Train: [83/100][13/800] Data 0.003 (0.004) Batch 0.347 (0.339) Remain 01:21:22 loss: 0.1929 Lr: 0.00052 [2023-12-20 20:16:55,945 INFO misc.py line 119 131400] Train: [83/100][14/800] Data 0.004 (0.004) Batch 0.373 (0.342) Remain 01:22:06 loss: 0.1102 Lr: 0.00052 [2023-12-20 20:16:56,280 INFO misc.py line 119 131400] Train: [83/100][15/800] Data 0.004 (0.004) Batch 0.336 (0.342) Remain 01:21:58 loss: 0.1656 Lr: 0.00052 [2023-12-20 20:16:56,599 INFO misc.py line 119 131400] Train: [83/100][16/800] Data 0.003 (0.004) Batch 0.319 (0.340) Remain 01:21:31 loss: 0.1550 Lr: 0.00052 [2023-12-20 20:16:56,955 INFO misc.py line 119 131400] Train: [83/100][17/800] Data 0.005 (0.004) Batch 0.355 (0.341) Remain 01:21:47 loss: 0.1907 Lr: 0.00052 [2023-12-20 20:16:57,261 INFO misc.py line 119 131400] Train: [83/100][18/800] Data 0.004 (0.004) Batch 0.305 (0.339) Remain 01:21:12 loss: 0.1904 Lr: 0.00052 [2023-12-20 20:16:57,597 INFO misc.py line 119 131400] Train: [83/100][19/800] Data 0.005 (0.004) Batch 0.336 (0.339) Remain 01:21:10 loss: 0.3516 Lr: 0.00052 [2023-12-20 20:16:57,924 INFO misc.py line 119 131400] Train: [83/100][20/800] Data 0.004 (0.004) Batch 0.327 (0.338) Remain 01:20:59 loss: 0.2531 Lr: 0.00052 [2023-12-20 20:16:58,264 INFO misc.py line 119 131400] Train: [83/100][21/800] Data 0.004 (0.004) Batch 0.340 (0.338) Remain 01:21:01 loss: 0.2448 Lr: 0.00052 [2023-12-20 20:16:58,575 INFO misc.py line 119 131400] Train: [83/100][22/800] Data 0.004 (0.004) Batch 0.311 (0.337) Remain 01:20:40 loss: 0.3980 Lr: 0.00052 [2023-12-20 20:16:58,898 INFO misc.py line 119 131400] Train: [83/100][23/800] Data 0.004 (0.004) Batch 0.319 (0.336) Remain 01:20:27 loss: 0.1864 Lr: 0.00052 [2023-12-20 20:16:59,217 INFO misc.py line 119 131400] Train: [83/100][24/800] Data 0.009 (0.004) Batch 0.321 (0.335) Remain 01:20:17 loss: 0.1770 Lr: 0.00052 [2023-12-20 20:16:59,513 INFO misc.py line 119 131400] Train: [83/100][25/800] Data 0.006 (0.004) Batch 0.298 (0.333) Remain 01:19:52 loss: 0.2937 Lr: 0.00052 [2023-12-20 20:16:59,846 INFO misc.py line 119 131400] Train: [83/100][26/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 01:19:52 loss: 0.2860 Lr: 0.00052 [2023-12-20 20:17:00,152 INFO misc.py line 119 131400] Train: [83/100][27/800] Data 0.004 (0.004) Batch 0.307 (0.332) Remain 01:19:35 loss: 0.2081 Lr: 0.00052 [2023-12-20 20:17:00,495 INFO misc.py line 119 131400] Train: [83/100][28/800] Data 0.003 (0.004) Batch 0.342 (0.333) Remain 01:19:41 loss: 0.1772 Lr: 0.00052 [2023-12-20 20:17:00,818 INFO misc.py line 119 131400] Train: [83/100][29/800] Data 0.004 (0.004) Batch 0.324 (0.332) Remain 01:19:36 loss: 0.3406 Lr: 0.00052 [2023-12-20 20:17:01,120 INFO misc.py line 119 131400] Train: [83/100][30/800] Data 0.003 (0.004) Batch 0.302 (0.331) Remain 01:19:19 loss: 0.2466 Lr: 0.00052 [2023-12-20 20:17:01,456 INFO misc.py line 119 131400] Train: [83/100][31/800] Data 0.003 (0.004) Batch 0.333 (0.331) Remain 01:19:19 loss: 0.1971 Lr: 0.00052 [2023-12-20 20:17:01,770 INFO misc.py line 119 131400] Train: [83/100][32/800] Data 0.007 (0.004) Batch 0.316 (0.331) Remain 01:19:12 loss: 0.1709 Lr: 0.00052 [2023-12-20 20:17:02,068 INFO misc.py line 119 131400] Train: [83/100][33/800] Data 0.003 (0.004) Batch 0.298 (0.330) Remain 01:18:56 loss: 0.1913 Lr: 0.00052 [2023-12-20 20:17:02,362 INFO misc.py line 119 131400] Train: [83/100][34/800] Data 0.004 (0.004) Batch 0.294 (0.328) Remain 01:18:39 loss: 0.1283 Lr: 0.00052 [2023-12-20 20:17:02,695 INFO misc.py line 119 131400] Train: [83/100][35/800] Data 0.004 (0.004) Batch 0.333 (0.329) Remain 01:18:41 loss: 0.1413 Lr: 0.00052 [2023-12-20 20:17:03,041 INFO misc.py line 119 131400] Train: [83/100][36/800] Data 0.004 (0.004) Batch 0.346 (0.329) Remain 01:18:48 loss: 0.2240 Lr: 0.00052 [2023-12-20 20:17:03,353 INFO misc.py line 119 131400] Train: [83/100][37/800] Data 0.004 (0.004) Batch 0.312 (0.329) Remain 01:18:40 loss: 0.1896 Lr: 0.00052 [2023-12-20 20:17:03,698 INFO misc.py line 119 131400] Train: [83/100][38/800] Data 0.005 (0.004) Batch 0.342 (0.329) Remain 01:18:45 loss: 0.4006 Lr: 0.00052 [2023-12-20 20:17:04,021 INFO misc.py line 119 131400] Train: [83/100][39/800] Data 0.008 (0.004) Batch 0.325 (0.329) Remain 01:18:43 loss: 0.2406 Lr: 0.00052 [2023-12-20 20:17:04,344 INFO misc.py line 119 131400] Train: [83/100][40/800] Data 0.006 (0.004) Batch 0.323 (0.329) Remain 01:18:41 loss: 0.3455 Lr: 0.00052 [2023-12-20 20:17:04,687 INFO misc.py line 119 131400] Train: [83/100][41/800] Data 0.006 (0.004) Batch 0.344 (0.329) Remain 01:18:46 loss: 0.1369 Lr: 0.00052 [2023-12-20 20:17:05,017 INFO misc.py line 119 131400] Train: [83/100][42/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 01:18:46 loss: 0.1708 Lr: 0.00052 [2023-12-20 20:17:05,321 INFO misc.py line 119 131400] Train: [83/100][43/800] Data 0.005 (0.004) Batch 0.305 (0.329) Remain 01:18:37 loss: 0.2385 Lr: 0.00052 [2023-12-20 20:17:05,629 INFO misc.py line 119 131400] Train: [83/100][44/800] Data 0.003 (0.004) Batch 0.308 (0.328) Remain 01:18:29 loss: 0.1367 Lr: 0.00052 [2023-12-20 20:17:05,939 INFO misc.py line 119 131400] Train: [83/100][45/800] Data 0.004 (0.004) Batch 0.310 (0.328) Remain 01:18:23 loss: 0.3015 Lr: 0.00052 [2023-12-20 20:17:06,260 INFO misc.py line 119 131400] Train: [83/100][46/800] Data 0.004 (0.004) Batch 0.320 (0.327) Remain 01:18:20 loss: 0.3328 Lr: 0.00052 [2023-12-20 20:17:06,591 INFO misc.py line 119 131400] Train: [83/100][47/800] Data 0.005 (0.004) Batch 0.332 (0.328) Remain 01:18:21 loss: 0.1643 Lr: 0.00052 [2023-12-20 20:17:06,934 INFO misc.py line 119 131400] Train: [83/100][48/800] Data 0.005 (0.004) Batch 0.343 (0.328) Remain 01:18:26 loss: 0.2457 Lr: 0.00052 [2023-12-20 20:17:07,240 INFO misc.py line 119 131400] Train: [83/100][49/800] Data 0.004 (0.004) Batch 0.306 (0.327) Remain 01:18:18 loss: 0.2026 Lr: 0.00051 [2023-12-20 20:17:07,538 INFO misc.py line 119 131400] Train: [83/100][50/800] Data 0.004 (0.004) Batch 0.297 (0.327) Remain 01:18:09 loss: 0.0582 Lr: 0.00051 [2023-12-20 20:17:07,866 INFO misc.py line 119 131400] Train: [83/100][51/800] Data 0.005 (0.004) Batch 0.330 (0.327) Remain 01:18:09 loss: 0.3530 Lr: 0.00051 [2023-12-20 20:17:08,216 INFO misc.py line 119 131400] Train: [83/100][52/800] Data 0.003 (0.004) Batch 0.348 (0.327) Remain 01:18:15 loss: 0.1352 Lr: 0.00051 [2023-12-20 20:17:08,546 INFO misc.py line 119 131400] Train: [83/100][53/800] Data 0.005 (0.004) Batch 0.331 (0.327) Remain 01:18:16 loss: 0.1376 Lr: 0.00051 [2023-12-20 20:17:08,826 INFO misc.py line 119 131400] Train: [83/100][54/800] Data 0.004 (0.004) Batch 0.280 (0.326) Remain 01:18:03 loss: 0.1733 Lr: 0.00051 [2023-12-20 20:17:09,148 INFO misc.py line 119 131400] Train: [83/100][55/800] Data 0.003 (0.004) Batch 0.322 (0.326) Remain 01:18:01 loss: 0.2872 Lr: 0.00051 [2023-12-20 20:17:09,474 INFO misc.py line 119 131400] Train: [83/100][56/800] Data 0.003 (0.004) Batch 0.326 (0.326) Remain 01:18:01 loss: 0.2336 Lr: 0.00051 [2023-12-20 20:17:09,798 INFO misc.py line 119 131400] Train: [83/100][57/800] Data 0.003 (0.004) Batch 0.323 (0.326) Remain 01:17:59 loss: 0.1832 Lr: 0.00051 [2023-12-20 20:17:10,119 INFO misc.py line 119 131400] Train: [83/100][58/800] Data 0.004 (0.004) Batch 0.320 (0.326) Remain 01:17:57 loss: 0.1528 Lr: 0.00051 [2023-12-20 20:17:10,389 INFO misc.py line 119 131400] Train: [83/100][59/800] Data 0.005 (0.004) Batch 0.271 (0.325) Remain 01:17:43 loss: 0.1546 Lr: 0.00051 [2023-12-20 20:17:10,739 INFO misc.py line 119 131400] Train: [83/100][60/800] Data 0.004 (0.004) Batch 0.351 (0.326) Remain 01:17:49 loss: 0.3341 Lr: 0.00051 [2023-12-20 20:17:11,057 INFO misc.py line 119 131400] Train: [83/100][61/800] Data 0.003 (0.004) Batch 0.317 (0.325) Remain 01:17:47 loss: 0.1901 Lr: 0.00051 [2023-12-20 20:17:11,398 INFO misc.py line 119 131400] Train: [83/100][62/800] Data 0.004 (0.004) Batch 0.340 (0.326) Remain 01:17:50 loss: 0.1238 Lr: 0.00051 [2023-12-20 20:17:11,743 INFO misc.py line 119 131400] Train: [83/100][63/800] Data 0.004 (0.004) Batch 0.348 (0.326) Remain 01:17:55 loss: 0.3945 Lr: 0.00051 [2023-12-20 20:17:12,047 INFO misc.py line 119 131400] Train: [83/100][64/800] Data 0.002 (0.004) Batch 0.304 (0.326) Remain 01:17:49 loss: 0.3108 Lr: 0.00051 [2023-12-20 20:17:12,363 INFO misc.py line 119 131400] Train: [83/100][65/800] Data 0.002 (0.004) Batch 0.316 (0.326) Remain 01:17:47 loss: 0.2979 Lr: 0.00051 [2023-12-20 20:17:12,693 INFO misc.py line 119 131400] Train: 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Batch 0.344 (0.333) Remain 01:15:35 loss: 0.2822 Lr: 0.00046 [2023-12-20 20:21:16,171 INFO misc.py line 119 131400] Train: [83/100][795/800] Data 0.003 (0.004) Batch 0.315 (0.333) Remain 01:15:34 loss: 0.3238 Lr: 0.00046 [2023-12-20 20:21:16,482 INFO misc.py line 119 131400] Train: [83/100][796/800] Data 0.004 (0.004) Batch 0.311 (0.333) Remain 01:15:34 loss: 0.1820 Lr: 0.00046 [2023-12-20 20:21:16,815 INFO misc.py line 119 131400] Train: [83/100][797/800] Data 0.003 (0.004) Batch 0.334 (0.333) Remain 01:15:33 loss: 0.2125 Lr: 0.00046 [2023-12-20 20:21:17,142 INFO misc.py line 119 131400] Train: [83/100][798/800] Data 0.003 (0.004) Batch 0.327 (0.333) Remain 01:15:33 loss: 0.3196 Lr: 0.00046 [2023-12-20 20:21:17,464 INFO misc.py line 119 131400] Train: [83/100][799/800] Data 0.003 (0.004) Batch 0.322 (0.333) Remain 01:15:32 loss: 0.1307 Lr: 0.00046 [2023-12-20 20:21:17,801 INFO misc.py line 119 131400] Train: [83/100][800/800] Data 0.003 (0.004) Batch 0.337 (0.333) Remain 01:15:32 loss: 0.1592 Lr: 0.00046 [2023-12-20 20:21:17,802 INFO misc.py line 136 131400] Train result: loss: 0.2190 [2023-12-20 20:21:17,802 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:21:40,013 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1945 [2023-12-20 20:21:40,229 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1493 [2023-12-20 20:21:40,679 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4764 [2023-12-20 20:21:40,786 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5607 [2023-12-20 20:21:40,902 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3136 [2023-12-20 20:21:41,007 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1908 [2023-12-20 20:21:41,097 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0262 [2023-12-20 20:21:41,208 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9086 [2023-12-20 20:21:41,287 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2500 [2023-12-20 20:21:41,371 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3081 [2023-12-20 20:21:41,463 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4494 [2023-12-20 20:21:41,602 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2941 [2023-12-20 20:21:41,726 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5002 [2023-12-20 20:21:41,882 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1857 [2023-12-20 20:21:41,975 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1870 [2023-12-20 20:21:42,108 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6684 [2023-12-20 20:21:42,217 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2847 [2023-12-20 20:21:42,326 INFO evaluator.py line 159 131400] Test: [18/78] Loss 2.0749 [2023-12-20 20:21:42,441 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1024 [2023-12-20 20:21:42,521 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3991 [2023-12-20 20:21:42,635 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1919 [2023-12-20 20:21:42,796 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1259 [2023-12-20 20:21:42,917 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8860 [2023-12-20 20:21:43,061 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3579 [2023-12-20 20:21:43,207 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1780 [2023-12-20 20:21:43,295 INFO evaluator.py line 159 131400] Test: [26/78] Loss 1.0303 [2023-12-20 20:21:43,457 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5506 [2023-12-20 20:21:43,588 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5069 [2023-12-20 20:21:43,685 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.6366 [2023-12-20 20:21:43,835 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.9187 [2023-12-20 20:21:43,947 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5208 [2023-12-20 20:21:44,087 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3276 [2023-12-20 20:21:44,175 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1145 [2023-12-20 20:21:44,257 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1763 [2023-12-20 20:21:44,358 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8518 [2023-12-20 20:21:44,453 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2684 [2023-12-20 20:21:44,590 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9573 [2023-12-20 20:21:44,706 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0881 [2023-12-20 20:21:44,792 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5548 [2023-12-20 20:21:44,940 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3329 [2023-12-20 20:21:45,090 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0172 [2023-12-20 20:21:45,198 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0925 [2023-12-20 20:21:45,321 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.1599 [2023-12-20 20:21:45,463 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.8757 [2023-12-20 20:21:45,587 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6958 [2023-12-20 20:21:45,695 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8553 [2023-12-20 20:21:45,861 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3047 [2023-12-20 20:21:45,959 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4146 [2023-12-20 20:21:46,108 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.5443 [2023-12-20 20:21:46,211 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2175 [2023-12-20 20:21:46,296 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3937 [2023-12-20 20:21:46,402 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2795 [2023-12-20 20:21:46,548 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8100 [2023-12-20 20:21:46,688 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3116 [2023-12-20 20:21:46,790 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.7649 [2023-12-20 20:21:46,882 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6507 [2023-12-20 20:21:46,986 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.4033 [2023-12-20 20:21:47,149 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2279 [2023-12-20 20:21:47,247 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4641 [2023-12-20 20:21:47,344 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1465 [2023-12-20 20:21:47,450 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5541 [2023-12-20 20:21:47,550 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.3375 [2023-12-20 20:21:47,638 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5185 [2023-12-20 20:21:47,748 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6019 [2023-12-20 20:21:47,872 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6312 [2023-12-20 20:21:47,956 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2136 [2023-12-20 20:21:48,055 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3132 [2023-12-20 20:21:48,148 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0065 [2023-12-20 20:21:48,232 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3506 [2023-12-20 20:21:48,316 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0078 [2023-12-20 20:21:48,411 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9875 [2023-12-20 20:21:48,505 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4357 [2023-12-20 20:21:48,641 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0914 [2023-12-20 20:21:48,735 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6837 [2023-12-20 20:21:48,852 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6431 [2023-12-20 20:21:48,955 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4147 [2023-12-20 20:21:49,041 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6092 [2023-12-20 20:21:49,194 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1186 [2023-12-20 20:21:50,769 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7712/0.8469/0.9221. [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8794/0.9491 [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9631/0.9868 [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7011/0.8349 [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8255/0.8617 [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9263/0.9615 [2023-12-20 20:21:50,769 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8470/0.9333 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7811/0.8955 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7305/0.8660 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7160/0.8211 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8538/0.9211 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4029/0.5036 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7114/0.8081 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7135/0.8205 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7821/0.8627 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7053/0.7710 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7211/0.7676 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9400/0.9780 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7017/0.7820 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8863/0.9203 [2023-12-20 20:21:50,770 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6352/0.6935 [2023-12-20 20:21:50,771 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:21:50,772 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7712 [2023-12-20 20:21:50,772 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:21:50,772 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:21:59,413 INFO misc.py line 119 131400] Train: [84/100][1/800] Data 1.172 (1.172) Batch 1.518 (1.518) Remain 05:44:07 loss: 0.4205 Lr: 0.00046 [2023-12-20 20:21:59,758 INFO misc.py line 119 131400] Train: [84/100][2/800] Data 0.003 (0.003) Batch 0.345 (0.345) Remain 01:18:11 loss: 0.3001 Lr: 0.00046 [2023-12-20 20:22:00,115 INFO misc.py line 119 131400] Train: [84/100][3/800] Data 0.004 (0.004) Batch 0.357 (0.357) Remain 01:20:58 loss: 0.3088 Lr: 0.00046 [2023-12-20 20:22:00,481 INFO misc.py line 119 131400] Train: [84/100][4/800] Data 0.004 (0.004) Batch 0.365 (0.365) Remain 01:22:45 loss: 0.2459 Lr: 0.00046 [2023-12-20 20:22:00,811 INFO misc.py line 119 131400] Train: [84/100][5/800] Data 0.004 (0.004) Batch 0.329 (0.347) Remain 01:18:41 loss: 0.2218 Lr: 0.00046 [2023-12-20 20:22:01,164 INFO misc.py line 119 131400] Train: [84/100][6/800] Data 0.005 (0.004) Batch 0.353 (0.349) Remain 01:19:09 loss: 0.3575 Lr: 0.00046 [2023-12-20 20:22:01,515 INFO misc.py line 119 131400] Train: [84/100][7/800] Data 0.004 (0.004) Batch 0.351 (0.350) Remain 01:19:14 loss: 0.2175 Lr: 0.00046 [2023-12-20 20:22:01,850 INFO misc.py line 119 131400] Train: [84/100][8/800] Data 0.004 (0.004) Batch 0.335 (0.347) Remain 01:18:33 loss: 0.1795 Lr: 0.00046 [2023-12-20 20:22:02,193 INFO misc.py line 119 131400] Train: [84/100][9/800] Data 0.005 (0.004) Batch 0.343 (0.346) Remain 01:18:25 loss: 0.2261 Lr: 0.00046 [2023-12-20 20:22:02,526 INFO misc.py line 119 131400] Train: [84/100][10/800] Data 0.003 (0.004) Batch 0.333 (0.344) Remain 01:17:59 loss: 0.2772 Lr: 0.00046 [2023-12-20 20:22:02,872 INFO misc.py line 119 131400] Train: [84/100][11/800] Data 0.005 (0.004) Batch 0.346 (0.345) Remain 01:18:02 loss: 0.1499 Lr: 0.00046 [2023-12-20 20:22:03,179 INFO misc.py line 119 131400] Train: [84/100][12/800] Data 0.004 (0.004) Batch 0.307 (0.340) Remain 01:17:05 loss: 0.2581 Lr: 0.00046 [2023-12-20 20:22:03,543 INFO misc.py line 119 131400] Train: [84/100][13/800] Data 0.004 (0.004) Batch 0.364 (0.343) Remain 01:17:37 loss: 0.1965 Lr: 0.00046 [2023-12-20 20:22:03,900 INFO misc.py line 119 131400] Train: [84/100][14/800] Data 0.003 (0.004) Batch 0.356 (0.344) Remain 01:17:53 loss: 0.2478 Lr: 0.00046 [2023-12-20 20:22:04,235 INFO misc.py line 119 131400] Train: [84/100][15/800] Data 0.005 (0.004) Batch 0.335 (0.343) Remain 01:17:42 loss: 0.1641 Lr: 0.00046 [2023-12-20 20:22:04,603 INFO misc.py line 119 131400] Train: [84/100][16/800] Data 0.005 (0.004) Batch 0.369 (0.345) Remain 01:18:09 loss: 0.1771 Lr: 0.00046 [2023-12-20 20:22:04,922 INFO misc.py line 119 131400] Train: [84/100][17/800] Data 0.004 (0.004) Batch 0.319 (0.343) Remain 01:17:43 loss: 0.2100 Lr: 0.00046 [2023-12-20 20:22:05,236 INFO misc.py line 119 131400] Train: [84/100][18/800] Data 0.003 (0.004) Batch 0.314 (0.341) Remain 01:17:16 loss: 0.2378 Lr: 0.00046 [2023-12-20 20:22:05,591 INFO misc.py line 119 131400] Train: [84/100][19/800] Data 0.004 (0.004) Batch 0.354 (0.342) Remain 01:17:26 loss: 0.2104 Lr: 0.00046 [2023-12-20 20:22:05,947 INFO misc.py line 119 131400] Train: [84/100][20/800] Data 0.005 (0.004) Batch 0.357 (0.343) Remain 01:17:38 loss: 0.2295 Lr: 0.00046 [2023-12-20 20:22:06,285 INFO misc.py line 119 131400] Train: [84/100][21/800] Data 0.003 (0.004) Batch 0.338 (0.343) Remain 01:17:34 loss: 0.2388 Lr: 0.00046 [2023-12-20 20:22:06,631 INFO misc.py line 119 131400] Train: [84/100][22/800] Data 0.004 (0.004) Batch 0.347 (0.343) Remain 01:17:36 loss: 0.2579 Lr: 0.00046 [2023-12-20 20:22:06,963 INFO misc.py line 119 131400] Train: [84/100][23/800] Data 0.003 (0.004) Batch 0.332 (0.342) Remain 01:17:28 loss: 0.1680 Lr: 0.00046 [2023-12-20 20:22:07,324 INFO misc.py line 119 131400] Train: [84/100][24/800] Data 0.005 (0.004) Batch 0.361 (0.343) Remain 01:17:40 loss: 0.4282 Lr: 0.00046 [2023-12-20 20:22:07,636 INFO misc.py line 119 131400] Train: [84/100][25/800] Data 0.003 (0.004) Batch 0.306 (0.342) Remain 01:17:16 loss: 0.2436 Lr: 0.00046 [2023-12-20 20:22:07,970 INFO misc.py line 119 131400] Train: [84/100][26/800] Data 0.012 (0.004) Batch 0.340 (0.342) Remain 01:17:15 loss: 0.1348 Lr: 0.00046 [2023-12-20 20:22:08,309 INFO misc.py line 119 131400] Train: [84/100][27/800] Data 0.005 (0.004) Batch 0.338 (0.341) Remain 01:17:13 loss: 0.2354 Lr: 0.00046 [2023-12-20 20:22:08,627 INFO misc.py line 119 131400] Train: [84/100][28/800] Data 0.005 (0.004) Batch 0.319 (0.340) Remain 01:17:00 loss: 0.2832 Lr: 0.00046 [2023-12-20 20:22:08,971 INFO misc.py line 119 131400] Train: [84/100][29/800] Data 0.004 (0.004) Batch 0.344 (0.341) Remain 01:17:02 loss: 0.1914 Lr: 0.00046 [2023-12-20 20:22:09,329 INFO misc.py line 119 131400] Train: [84/100][30/800] Data 0.005 (0.004) Batch 0.357 (0.341) Remain 01:17:10 loss: 0.1787 Lr: 0.00046 [2023-12-20 20:22:09,675 INFO misc.py line 119 131400] Train: [84/100][31/800] Data 0.005 (0.005) Batch 0.347 (0.341) Remain 01:17:12 loss: 0.1460 Lr: 0.00046 [2023-12-20 20:22:09,999 INFO misc.py line 119 131400] Train: [84/100][32/800] Data 0.005 (0.005) Batch 0.325 (0.341) Remain 01:17:04 loss: 0.2687 Lr: 0.00046 [2023-12-20 20:22:10,319 INFO misc.py line 119 131400] Train: [84/100][33/800] Data 0.003 (0.004) Batch 0.320 (0.340) Remain 01:16:54 loss: 0.1327 Lr: 0.00046 [2023-12-20 20:22:10,642 INFO misc.py line 119 131400] Train: [84/100][34/800] Data 0.003 (0.004) Batch 0.319 (0.339) Remain 01:16:44 loss: 0.2094 Lr: 0.00046 [2023-12-20 20:22:10,955 INFO misc.py line 119 131400] Train: [84/100][35/800] Data 0.007 (0.004) Batch 0.317 (0.339) Remain 01:16:35 loss: 0.3203 Lr: 0.00046 [2023-12-20 20:22:11,297 INFO misc.py line 119 131400] Train: [84/100][36/800] Data 0.003 (0.004) Batch 0.342 (0.339) Remain 01:16:36 loss: 0.3115 Lr: 0.00046 [2023-12-20 20:22:11,644 INFO misc.py line 119 131400] Train: [84/100][37/800] Data 0.004 (0.004) Batch 0.345 (0.339) Remain 01:16:38 loss: 0.1874 Lr: 0.00046 [2023-12-20 20:22:11,974 INFO misc.py line 119 131400] Train: [84/100][38/800] Data 0.005 (0.004) Batch 0.332 (0.339) Remain 01:16:35 loss: 0.1553 Lr: 0.00046 [2023-12-20 20:22:12,310 INFO misc.py line 119 131400] Train: [84/100][39/800] Data 0.003 (0.004) Batch 0.335 (0.339) Remain 01:16:33 loss: 0.1840 Lr: 0.00046 [2023-12-20 20:22:12,654 INFO misc.py line 119 131400] Train: [84/100][40/800] Data 0.004 (0.004) Batch 0.344 (0.339) Remain 01:16:35 loss: 0.2481 Lr: 0.00046 [2023-12-20 20:22:12,988 INFO misc.py line 119 131400] Train: [84/100][41/800] Data 0.003 (0.004) Batch 0.334 (0.339) Remain 01:16:33 loss: 0.1961 Lr: 0.00046 [2023-12-20 20:22:13,312 INFO misc.py line 119 131400] Train: [84/100][42/800] Data 0.003 (0.004) Batch 0.324 (0.338) Remain 01:16:27 loss: 0.1787 Lr: 0.00046 [2023-12-20 20:22:13,607 INFO misc.py line 119 131400] Train: [84/100][43/800] Data 0.003 (0.004) Batch 0.294 (0.337) Remain 01:16:12 loss: 0.2400 Lr: 0.00046 [2023-12-20 20:22:13,923 INFO misc.py line 119 131400] Train: [84/100][44/800] Data 0.003 (0.004) Batch 0.316 (0.337) Remain 01:16:05 loss: 0.1721 Lr: 0.00046 [2023-12-20 20:22:14,247 INFO misc.py line 119 131400] Train: [84/100][45/800] Data 0.003 (0.004) Batch 0.325 (0.336) Remain 01:16:01 loss: 0.1494 Lr: 0.00046 [2023-12-20 20:22:14,566 INFO misc.py line 119 131400] Train: [84/100][46/800] Data 0.003 (0.004) Batch 0.318 (0.336) Remain 01:15:54 loss: 0.2296 Lr: 0.00046 [2023-12-20 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Train: [84/100][53/800] Data 0.003 (0.004) Batch 0.333 (0.334) Remain 01:15:22 loss: 0.3114 Lr: 0.00046 [2023-12-20 20:22:17,145 INFO misc.py line 119 131400] Train: [84/100][54/800] Data 0.003 (0.004) Batch 0.336 (0.334) Remain 01:15:23 loss: 0.1733 Lr: 0.00046 [2023-12-20 20:22:17,452 INFO misc.py line 119 131400] Train: [84/100][55/800] Data 0.003 (0.004) Batch 0.308 (0.333) Remain 01:15:15 loss: 0.4013 Lr: 0.00046 [2023-12-20 20:22:17,785 INFO misc.py line 119 131400] Train: [84/100][56/800] Data 0.004 (0.004) Batch 0.331 (0.333) Remain 01:15:15 loss: 0.2994 Lr: 0.00046 [2023-12-20 20:22:18,109 INFO misc.py line 119 131400] Train: [84/100][57/800] Data 0.006 (0.004) Batch 0.326 (0.333) Remain 01:15:12 loss: 0.2699 Lr: 0.00046 [2023-12-20 20:22:18,452 INFO misc.py line 119 131400] Train: [84/100][58/800] Data 0.003 (0.004) Batch 0.343 (0.333) Remain 01:15:14 loss: 0.1473 Lr: 0.00046 [2023-12-20 20:22:18,755 INFO misc.py line 119 131400] Train: [84/100][59/800] Data 0.003 (0.004) 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0.004 (0.009) Batch 0.332 (0.339) Remain 01:12:30 loss: 0.2770 Lr: 0.00041 [2023-12-20 20:26:22,322 INFO misc.py line 119 131400] Train: [84/100][776/800] Data 0.005 (0.009) Batch 0.303 (0.339) Remain 01:12:29 loss: 0.1650 Lr: 0.00041 [2023-12-20 20:26:22,650 INFO misc.py line 119 131400] Train: [84/100][777/800] Data 0.005 (0.009) Batch 0.330 (0.339) Remain 01:12:29 loss: 0.2498 Lr: 0.00041 [2023-12-20 20:26:22,973 INFO misc.py line 119 131400] Train: [84/100][778/800] Data 0.004 (0.009) Batch 0.323 (0.339) Remain 01:12:28 loss: 0.1342 Lr: 0.00041 [2023-12-20 20:26:23,302 INFO misc.py line 119 131400] Train: [84/100][779/800] Data 0.005 (0.009) Batch 0.328 (0.339) Remain 01:12:28 loss: 0.2155 Lr: 0.00041 [2023-12-20 20:26:23,616 INFO misc.py line 119 131400] Train: [84/100][780/800] Data 0.004 (0.009) Batch 0.315 (0.339) Remain 01:12:27 loss: 0.1406 Lr: 0.00041 [2023-12-20 20:26:23,960 INFO misc.py line 119 131400] Train: [84/100][781/800] Data 0.003 (0.009) Batch 0.343 (0.339) Remain 01:12:27 loss: 0.0702 Lr: 0.00041 [2023-12-20 20:26:24,333 INFO misc.py line 119 131400] Train: [84/100][782/800] Data 0.004 (0.009) Batch 0.374 (0.339) Remain 01:12:27 loss: 0.2842 Lr: 0.00041 [2023-12-20 20:26:24,656 INFO misc.py line 119 131400] Train: [84/100][783/800] Data 0.004 (0.009) Batch 0.322 (0.339) Remain 01:12:26 loss: 0.2566 Lr: 0.00041 [2023-12-20 20:26:25,025 INFO misc.py line 119 131400] Train: [84/100][784/800] Data 0.004 (0.009) Batch 0.366 (0.339) Remain 01:12:27 loss: 0.1237 Lr: 0.00041 [2023-12-20 20:26:25,356 INFO misc.py line 119 131400] Train: [84/100][785/800] Data 0.007 (0.009) Batch 0.334 (0.339) Remain 01:12:26 loss: 0.3048 Lr: 0.00041 [2023-12-20 20:26:25,693 INFO misc.py line 119 131400] Train: [84/100][786/800] Data 0.004 (0.009) Batch 0.337 (0.339) Remain 01:12:26 loss: 0.2522 Lr: 0.00041 [2023-12-20 20:26:26,044 INFO misc.py line 119 131400] Train: [84/100][787/800] Data 0.004 (0.009) Batch 0.351 (0.339) Remain 01:12:26 loss: 0.2119 Lr: 0.00041 [2023-12-20 20:26:26,372 INFO misc.py line 119 131400] Train: [84/100][788/800] Data 0.003 (0.009) Batch 0.327 (0.339) Remain 01:12:25 loss: 0.1121 Lr: 0.00041 [2023-12-20 20:26:26,719 INFO misc.py line 119 131400] Train: [84/100][789/800] Data 0.005 (0.009) Batch 0.348 (0.339) Remain 01:12:25 loss: 0.1456 Lr: 0.00041 [2023-12-20 20:26:27,015 INFO misc.py line 119 131400] Train: [84/100][790/800] Data 0.004 (0.008) Batch 0.295 (0.339) Remain 01:12:24 loss: 0.1742 Lr: 0.00041 [2023-12-20 20:26:27,309 INFO misc.py line 119 131400] Train: [84/100][791/800] Data 0.004 (0.008) Batch 0.295 (0.339) Remain 01:12:23 loss: 0.2510 Lr: 0.00041 [2023-12-20 20:26:27,617 INFO misc.py line 119 131400] Train: [84/100][792/800] Data 0.003 (0.008) Batch 0.307 (0.339) Remain 01:12:22 loss: 0.0984 Lr: 0.00041 [2023-12-20 20:26:27,957 INFO misc.py line 119 131400] Train: [84/100][793/800] Data 0.004 (0.008) Batch 0.341 (0.339) Remain 01:12:22 loss: 0.1999 Lr: 0.00041 [2023-12-20 20:26:28,280 INFO misc.py line 119 131400] Train: [84/100][794/800] Data 0.004 (0.008) Batch 0.323 (0.339) Remain 01:12:21 loss: 0.1964 Lr: 0.00041 [2023-12-20 20:26:28,568 INFO misc.py line 119 131400] Train: [84/100][795/800] Data 0.005 (0.008) Batch 0.287 (0.339) Remain 01:12:20 loss: 0.1681 Lr: 0.00041 [2023-12-20 20:26:28,888 INFO misc.py line 119 131400] Train: [84/100][796/800] Data 0.005 (0.008) Batch 0.322 (0.339) Remain 01:12:19 loss: 0.3893 Lr: 0.00041 [2023-12-20 20:26:29,170 INFO misc.py line 119 131400] Train: [84/100][797/800] Data 0.003 (0.008) Batch 0.281 (0.339) Remain 01:12:18 loss: 0.2237 Lr: 0.00041 [2023-12-20 20:26:29,493 INFO misc.py line 119 131400] Train: [84/100][798/800] Data 0.004 (0.008) Batch 0.324 (0.339) Remain 01:12:17 loss: 0.1779 Lr: 0.00041 [2023-12-20 20:26:29,809 INFO misc.py line 119 131400] Train: [84/100][799/800] Data 0.003 (0.008) Batch 0.313 (0.339) Remain 01:12:17 loss: 0.2156 Lr: 0.00041 [2023-12-20 20:26:30,077 INFO misc.py line 119 131400] Train: [84/100][800/800] Data 0.005 (0.008) Batch 0.270 (0.339) Remain 01:12:15 loss: 0.1470 Lr: 0.00041 [2023-12-20 20:26:30,086 INFO misc.py line 136 131400] Train result: loss: 0.2148 [2023-12-20 20:26:30,087 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:26:52,867 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2276 [2023-12-20 20:26:52,937 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1474 [2023-12-20 20:26:53,027 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4834 [2023-12-20 20:26:53,137 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4524 [2023-12-20 20:26:53,251 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2989 [2023-12-20 20:26:53,355 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1953 [2023-12-20 20:26:53,444 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2999 [2023-12-20 20:26:53,551 INFO evaluator.py line 159 131400] Test: [8/78] Loss 1.0877 [2023-12-20 20:26:53,636 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2948 [2023-12-20 20:26:53,720 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3237 [2023-12-20 20:26:53,815 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3899 [2023-12-20 20:26:53,954 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2639 [2023-12-20 20:26:54,073 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4709 [2023-12-20 20:26:54,228 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2462 [2023-12-20 20:26:54,327 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1301 [2023-12-20 20:26:54,460 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6047 [2023-12-20 20:26:54,572 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2746 [2023-12-20 20:26:54,681 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7894 [2023-12-20 20:26:54,793 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.0954 [2023-12-20 20:26:54,876 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4749 [2023-12-20 20:26:54,985 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1763 [2023-12-20 20:26:55,141 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1360 [2023-12-20 20:26:55,261 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8147 [2023-12-20 20:26:55,403 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1249 [2023-12-20 20:26:55,546 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1479 [2023-12-20 20:26:55,627 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6370 [2023-12-20 20:26:55,787 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5368 [2023-12-20 20:26:55,914 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5137 [2023-12-20 20:26:56,015 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5048 [2023-12-20 20:26:56,163 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6923 [2023-12-20 20:26:56,272 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4684 [2023-12-20 20:26:56,390 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3576 [2023-12-20 20:26:56,483 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1125 [2023-12-20 20:26:56,555 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1705 [2023-12-20 20:26:56,655 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9095 [2023-12-20 20:26:56,748 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2626 [2023-12-20 20:26:56,877 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9448 [2023-12-20 20:26:56,987 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0769 [2023-12-20 20:26:57,066 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5383 [2023-12-20 20:26:57,210 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.3024 [2023-12-20 20:26:57,358 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0160 [2023-12-20 20:26:57,457 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0594 [2023-12-20 20:26:57,596 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.1811 [2023-12-20 20:26:57,738 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.7825 [2023-12-20 20:26:57,867 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.1269 [2023-12-20 20:26:57,984 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5450 [2023-12-20 20:26:58,150 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2981 [2023-12-20 20:26:58,249 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3291 [2023-12-20 20:26:58,396 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6133 [2023-12-20 20:26:58,488 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.0642 [2023-12-20 20:26:58,568 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3930 [2023-12-20 20:26:58,678 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3674 [2023-12-20 20:26:58,831 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.7708 [2023-12-20 20:26:58,973 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3290 [2023-12-20 20:26:59,082 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.4118 [2023-12-20 20:26:59,172 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5181 [2023-12-20 20:26:59,277 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3997 [2023-12-20 20:26:59,440 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2384 [2023-12-20 20:26:59,541 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6453 [2023-12-20 20:26:59,636 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.4887 [2023-12-20 20:26:59,740 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5011 [2023-12-20 20:26:59,838 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2437 [2023-12-20 20:26:59,931 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5967 [2023-12-20 20:27:00,032 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7410 [2023-12-20 20:27:00,161 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7688 [2023-12-20 20:27:00,250 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2532 [2023-12-20 20:27:00,355 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3123 [2023-12-20 20:27:00,455 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0083 [2023-12-20 20:27:00,555 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3195 [2023-12-20 20:27:00,640 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0089 [2023-12-20 20:27:00,744 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7941 [2023-12-20 20:27:00,840 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.8465 [2023-12-20 20:27:00,979 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0923 [2023-12-20 20:27:01,078 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6736 [2023-12-20 20:27:01,207 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6921 [2023-12-20 20:27:01,315 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4833 [2023-12-20 20:27:01,402 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.1792 [2023-12-20 20:27:01,565 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9929 [2023-12-20 20:27:02,986 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7649/0.8405/0.9209. [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8760/0.9564 [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9657/0.9859 [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6911/0.8244 [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8488/0.8952 [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9284/0.9652 [2023-12-20 20:27:02,986 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8722/0.9220 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7858/0.8807 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7337/0.8419 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6887/0.8105 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8175/0.9160 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4095/0.5295 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.6988/0.7859 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7114/0.8559 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7444/0.7988 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6771/0.7492 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6865/0.7284 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9385/0.9798 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6981/0.7894 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8924/0.9223 [2023-12-20 20:27:02,987 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6335/0.6727 [2023-12-20 20:27:02,988 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:27:02,989 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:27:02,989 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:27:07,052 INFO misc.py line 119 131400] Train: [85/100][1/800] Data 0.754 (0.754) Batch 1.051 (1.051) Remain 03:44:12 loss: 0.1728 Lr: 0.00041 [2023-12-20 20:27:07,367 INFO misc.py line 119 131400] Train: [85/100][2/800] Data 0.005 (0.005) Batch 0.315 (0.315) Remain 01:07:16 loss: 0.1072 Lr: 0.00041 [2023-12-20 20:27:07,679 INFO misc.py line 119 131400] Train: [85/100][3/800] Data 0.004 (0.004) Batch 0.312 (0.312) Remain 01:06:31 loss: 0.1086 Lr: 0.00041 [2023-12-20 20:27:08,031 INFO misc.py line 119 131400] Train: [85/100][4/800] Data 0.004 (0.004) Batch 0.353 (0.353) Remain 01:15:14 loss: 0.1908 Lr: 0.00041 [2023-12-20 20:27:08,339 INFO misc.py line 119 131400] Train: [85/100][5/800] Data 0.004 (0.004) Batch 0.308 (0.331) Remain 01:10:29 loss: 0.1712 Lr: 0.00041 [2023-12-20 20:27:08,666 INFO misc.py line 119 131400] Train: [85/100][6/800] Data 0.003 (0.004) Batch 0.326 (0.329) Remain 01:10:11 loss: 0.3291 Lr: 0.00041 [2023-12-20 20:27:09,041 INFO misc.py line 119 131400] Train: [85/100][7/800] Data 0.005 (0.004) Batch 0.372 (0.340) Remain 01:12:27 loss: 0.2054 Lr: 0.00041 [2023-12-20 20:27:09,333 INFO misc.py line 119 131400] Train: [85/100][8/800] Data 0.007 (0.005) Batch 0.296 (0.331) Remain 01:10:34 loss: 0.1302 Lr: 0.00041 [2023-12-20 20:27:09,627 INFO misc.py line 119 131400] Train: [85/100][9/800] Data 0.004 (0.004) Batch 0.294 (0.325) Remain 01:09:14 loss: 0.4001 Lr: 0.00041 [2023-12-20 20:27:09,974 INFO misc.py line 119 131400] Train: [85/100][10/800] Data 0.003 (0.004) Batch 0.347 (0.328) Remain 01:09:55 loss: 0.4436 Lr: 0.00041 [2023-12-20 20:27:10,345 INFO misc.py line 119 131400] Train: [85/100][11/800] Data 0.003 (0.004) Batch 0.371 (0.333) Remain 01:11:03 loss: 0.3310 Lr: 0.00041 [2023-12-20 20:27:10,674 INFO misc.py line 119 131400] Train: [85/100][12/800] Data 0.004 (0.004) Batch 0.329 (0.333) Remain 01:10:57 loss: 0.2066 Lr: 0.00041 [2023-12-20 20:27:11,016 INFO misc.py line 119 131400] Train: [85/100][13/800] Data 0.003 (0.004) Batch 0.342 (0.334) Remain 01:11:08 loss: 0.1631 Lr: 0.00041 [2023-12-20 20:27:11,354 INFO misc.py line 119 131400] Train: [85/100][14/800] Data 0.004 (0.004) Batch 0.338 (0.334) Remain 01:11:13 loss: 0.2918 Lr: 0.00041 [2023-12-20 20:27:11,741 INFO misc.py line 119 131400] Train: [85/100][15/800] Data 0.004 (0.004) Batch 0.387 (0.339) Remain 01:12:08 loss: 0.1047 Lr: 0.00041 [2023-12-20 20:27:12,104 INFO misc.py line 119 131400] Train: [85/100][16/800] Data 0.004 (0.004) Batch 0.363 (0.340) Remain 01:12:32 loss: 0.1570 Lr: 0.00041 [2023-12-20 20:27:12,449 INFO misc.py line 119 131400] Train: [85/100][17/800] Data 0.005 (0.004) Batch 0.344 (0.341) Remain 01:12:35 loss: 0.1747 Lr: 0.00041 [2023-12-20 20:27:12,779 INFO misc.py line 119 131400] Train: [85/100][18/800] Data 0.004 (0.004) Batch 0.331 (0.340) Remain 01:12:27 loss: 0.2952 Lr: 0.00041 [2023-12-20 20:27:13,112 INFO misc.py line 119 131400] Train: [85/100][19/800] Data 0.003 (0.004) Batch 0.325 (0.339) Remain 01:12:15 loss: 0.4147 Lr: 0.00041 [2023-12-20 20:27:13,449 INFO misc.py line 119 131400] Train: [85/100][20/800] Data 0.010 (0.004) Batch 0.343 (0.339) Remain 01:12:17 loss: 0.1981 Lr: 0.00041 [2023-12-20 20:27:13,819 INFO misc.py line 119 131400] Train: [85/100][21/800] Data 0.007 (0.004) Batch 0.371 (0.341) Remain 01:12:39 loss: 0.2143 Lr: 0.00041 [2023-12-20 20:27:14,148 INFO misc.py line 119 131400] Train: [85/100][22/800] Data 0.005 (0.005) Batch 0.322 (0.340) Remain 01:12:26 loss: 0.2544 Lr: 0.00041 [2023-12-20 20:27:14,458 INFO misc.py line 119 131400] Train: [85/100][23/800] Data 0.011 (0.005) Batch 0.317 (0.339) Remain 01:12:11 loss: 0.1231 Lr: 0.00041 [2023-12-20 20:27:14,782 INFO misc.py line 119 131400] Train: [85/100][24/800] Data 0.003 (0.005) Batch 0.323 (0.338) Remain 01:12:01 loss: 0.1768 Lr: 0.00041 [2023-12-20 20:27:15,115 INFO misc.py line 119 131400] Train: [85/100][25/800] Data 0.003 (0.005) Batch 0.333 (0.338) Remain 01:11:57 loss: 0.2079 Lr: 0.00041 [2023-12-20 20:27:15,411 INFO misc.py line 119 131400] Train: [85/100][26/800] Data 0.004 (0.005) Batch 0.296 (0.336) Remain 01:11:34 loss: 0.1363 Lr: 0.00041 [2023-12-20 20:27:15,700 INFO misc.py line 119 131400] Train: [85/100][27/800] Data 0.004 (0.005) Batch 0.290 (0.334) Remain 01:11:09 loss: 0.2129 Lr: 0.00041 [2023-12-20 20:27:16,000 INFO misc.py line 119 131400] Train: [85/100][28/800] Data 0.003 (0.005) Batch 0.299 (0.333) Remain 01:10:51 loss: 0.1004 Lr: 0.00041 [2023-12-20 20:27:16,343 INFO misc.py line 119 131400] Train: [85/100][29/800] Data 0.004 (0.005) Batch 0.343 (0.333) Remain 01:10:55 loss: 0.2033 Lr: 0.00041 [2023-12-20 20:27:16,668 INFO misc.py line 119 131400] Train: [85/100][30/800] Data 0.004 (0.005) Batch 0.325 (0.333) Remain 01:10:51 loss: 0.2076 Lr: 0.00041 [2023-12-20 20:27:16,984 INFO misc.py line 119 131400] Train: [85/100][31/800] Data 0.004 (0.005) Batch 0.317 (0.332) Remain 01:10:43 loss: 0.1538 Lr: 0.00041 [2023-12-20 20:27:17,306 INFO misc.py line 119 131400] Train: [85/100][32/800] Data 0.003 (0.004) Batch 0.321 (0.332) Remain 01:10:38 loss: 0.1768 Lr: 0.00041 [2023-12-20 20:27:17,651 INFO misc.py line 119 131400] Train: [85/100][33/800] Data 0.004 (0.004) Batch 0.346 (0.332) Remain 01:10:44 loss: 0.1891 Lr: 0.00041 [2023-12-20 20:27:17,988 INFO misc.py line 119 131400] Train: [85/100][34/800] Data 0.003 (0.004) Batch 0.336 (0.333) Remain 01:10:45 loss: 0.1611 Lr: 0.00041 [2023-12-20 20:27:18,317 INFO misc.py line 119 131400] Train: [85/100][35/800] Data 0.004 (0.004) Batch 0.330 (0.332) Remain 01:10:43 loss: 0.2445 Lr: 0.00041 [2023-12-20 20:27:18,649 INFO misc.py line 119 131400] Train: [85/100][36/800] Data 0.004 (0.004) Batch 0.331 (0.332) Remain 01:10:43 loss: 0.1853 Lr: 0.00041 [2023-12-20 20:27:18,988 INFO misc.py line 119 131400] Train: [85/100][37/800] Data 0.005 (0.004) Batch 0.340 (0.333) Remain 01:10:45 loss: 0.1527 Lr: 0.00041 [2023-12-20 20:27:19,283 INFO misc.py line 119 131400] Train: [85/100][38/800] Data 0.002 (0.004) Batch 0.295 (0.332) Remain 01:10:31 loss: 0.1674 Lr: 0.00041 [2023-12-20 20:27:19,650 INFO misc.py line 119 131400] Train: [85/100][39/800] Data 0.003 (0.004) Batch 0.365 (0.332) Remain 01:10:42 loss: 0.2181 Lr: 0.00041 [2023-12-20 20:27:19,964 INFO misc.py line 119 131400] Train: [85/100][40/800] Data 0.007 (0.004) Batch 0.317 (0.332) Remain 01:10:37 loss: 0.1637 Lr: 0.00041 [2023-12-20 20:27:20,293 INFO misc.py line 119 131400] Train: [85/100][41/800] Data 0.003 (0.004) Batch 0.328 (0.332) Remain 01:10:35 loss: 0.1804 Lr: 0.00041 [2023-12-20 20:27:20,668 INFO misc.py line 119 131400] Train: [85/100][42/800] Data 0.004 (0.004) Batch 0.374 (0.333) Remain 01:10:48 loss: 0.2107 Lr: 0.00041 [2023-12-20 20:27:21,006 INFO misc.py line 119 131400] Train: [85/100][43/800] Data 0.012 (0.005) Batch 0.339 (0.333) Remain 01:10:50 loss: 0.2381 Lr: 0.00041 [2023-12-20 20:27:21,348 INFO misc.py line 119 131400] Train: [85/100][44/800] Data 0.005 (0.005) Batch 0.343 (0.333) Remain 01:10:53 loss: 0.2104 Lr: 0.00041 [2023-12-20 20:27:21,694 INFO misc.py line 119 131400] Train: [85/100][45/800] Data 0.007 (0.005) Batch 0.344 (0.334) Remain 01:10:56 loss: 0.1489 Lr: 0.00041 [2023-12-20 20:27:22,040 INFO misc.py line 119 131400] Train: [85/100][46/800] Data 0.004 (0.005) Batch 0.346 (0.334) Remain 01:10:59 loss: 0.2573 Lr: 0.00041 [2023-12-20 20:27:22,377 INFO misc.py line 119 131400] Train: [85/100][47/800] Data 0.005 (0.005) 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line 119 131400] Train: [85/100][782/800] Data 0.004 (0.005) Batch 0.307 (0.334) Remain 01:06:58 loss: 0.1565 Lr: 0.00036 [2023-12-20 20:31:28,462 INFO misc.py line 119 131400] Train: [85/100][783/800] Data 0.003 (0.005) Batch 0.314 (0.334) Remain 01:06:57 loss: 0.2382 Lr: 0.00036 [2023-12-20 20:31:28,823 INFO misc.py line 119 131400] Train: [85/100][784/800] Data 0.004 (0.005) Batch 0.361 (0.334) Remain 01:06:57 loss: 0.2508 Lr: 0.00036 [2023-12-20 20:31:29,127 INFO misc.py line 119 131400] Train: [85/100][785/800] Data 0.004 (0.005) Batch 0.306 (0.334) Remain 01:06:57 loss: 0.2361 Lr: 0.00036 [2023-12-20 20:31:29,454 INFO misc.py line 119 131400] Train: [85/100][786/800] Data 0.003 (0.005) Batch 0.327 (0.334) Remain 01:06:56 loss: 0.2606 Lr: 0.00036 [2023-12-20 20:31:29,766 INFO misc.py line 119 131400] Train: [85/100][787/800] Data 0.003 (0.005) Batch 0.310 (0.334) Remain 01:06:55 loss: 0.2410 Lr: 0.00036 [2023-12-20 20:31:30,083 INFO misc.py line 119 131400] Train: 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Batch 0.306 (0.334) Remain 01:06:51 loss: 0.1238 Lr: 0.00036 [2023-12-20 20:31:32,272 INFO misc.py line 119 131400] Train: [85/100][795/800] Data 0.007 (0.005) Batch 0.311 (0.334) Remain 01:06:50 loss: 0.3125 Lr: 0.00036 [2023-12-20 20:31:32,613 INFO misc.py line 119 131400] Train: [85/100][796/800] Data 0.003 (0.005) Batch 0.341 (0.334) Remain 01:06:50 loss: 0.3144 Lr: 0.00036 [2023-12-20 20:31:32,938 INFO misc.py line 119 131400] Train: [85/100][797/800] Data 0.003 (0.005) Batch 0.325 (0.334) Remain 01:06:49 loss: 0.2738 Lr: 0.00036 [2023-12-20 20:31:33,259 INFO misc.py line 119 131400] Train: [85/100][798/800] Data 0.003 (0.005) Batch 0.320 (0.334) Remain 01:06:49 loss: 0.2054 Lr: 0.00036 [2023-12-20 20:31:33,576 INFO misc.py line 119 131400] Train: [85/100][799/800] Data 0.004 (0.005) Batch 0.318 (0.334) Remain 01:06:48 loss: 0.3133 Lr: 0.00036 [2023-12-20 20:31:33,867 INFO misc.py line 119 131400] Train: [85/100][800/800] Data 0.003 (0.005) Batch 0.291 (0.334) Remain 01:06:47 loss: 0.1786 Lr: 0.00036 [2023-12-20 20:31:33,868 INFO misc.py line 136 131400] Train result: loss: 0.2168 [2023-12-20 20:31:33,868 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:31:56,363 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1256 [2023-12-20 20:31:56,445 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1346 [2023-12-20 20:31:56,542 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3824 [2023-12-20 20:31:56,656 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4377 [2023-12-20 20:31:56,770 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.1583 [2023-12-20 20:31:56,887 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5843 [2023-12-20 20:31:56,983 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2159 [2023-12-20 20:31:57,091 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9573 [2023-12-20 20:31:57,176 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2819 [2023-12-20 20:31:57,266 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3102 [2023-12-20 20:31:57,365 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3717 [2023-12-20 20:31:57,506 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2963 [2023-12-20 20:31:57,641 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.3465 [2023-12-20 20:31:57,804 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2509 [2023-12-20 20:31:57,911 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1407 [2023-12-20 20:31:58,053 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6120 [2023-12-20 20:31:58,168 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2336 [2023-12-20 20:31:58,278 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8071 [2023-12-20 20:31:58,391 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1262 [2023-12-20 20:31:58,469 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4329 [2023-12-20 20:31:58,576 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1616 [2023-12-20 20:31:58,734 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1247 [2023-12-20 20:31:58,855 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8727 [2023-12-20 20:31:59,000 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3677 [2023-12-20 20:31:59,142 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1677 [2023-12-20 20:31:59,224 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5955 [2023-12-20 20:31:59,380 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6930 [2023-12-20 20:31:59,505 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5381 [2023-12-20 20:31:59,601 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4988 [2023-12-20 20:31:59,746 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7302 [2023-12-20 20:31:59,851 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5046 [2023-12-20 20:31:59,971 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3734 [2023-12-20 20:32:00,059 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1142 [2023-12-20 20:32:00,130 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1677 [2023-12-20 20:32:00,227 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8819 [2023-12-20 20:32:00,322 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2902 [2023-12-20 20:32:00,453 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9496 [2023-12-20 20:32:00,565 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0837 [2023-12-20 20:32:00,645 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.4821 [2023-12-20 20:32:00,787 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2854 [2023-12-20 20:32:00,934 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0182 [2023-12-20 20:32:01,033 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0766 [2023-12-20 20:32:01,155 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2289 [2023-12-20 20:32:01,298 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0171 [2023-12-20 20:32:01,416 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5623 [2023-12-20 20:32:01,521 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.3327 [2023-12-20 20:32:01,689 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3393 [2023-12-20 20:32:01,790 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4975 [2023-12-20 20:32:01,937 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7015 [2023-12-20 20:32:02,031 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1332 [2023-12-20 20:32:02,110 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4904 [2023-12-20 20:32:02,215 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3019 [2023-12-20 20:32:02,373 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8677 [2023-12-20 20:32:02,507 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3356 [2023-12-20 20:32:02,610 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9903 [2023-12-20 20:32:02,697 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5464 [2023-12-20 20:32:02,800 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3694 [2023-12-20 20:32:02,961 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2458 [2023-12-20 20:32:03,058 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5913 [2023-12-20 20:32:03,155 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2516 [2023-12-20 20:32:03,253 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5579 [2023-12-20 20:32:03,343 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2019 [2023-12-20 20:32:03,429 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6181 [2023-12-20 20:32:03,530 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6874 [2023-12-20 20:32:03,660 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6888 [2023-12-20 20:32:03,745 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2133 [2023-12-20 20:32:03,845 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3469 [2023-12-20 20:32:03,939 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0085 [2023-12-20 20:32:04,027 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3228 [2023-12-20 20:32:04,111 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0102 [2023-12-20 20:32:04,206 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8893 [2023-12-20 20:32:04,296 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4255 [2023-12-20 20:32:04,430 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0669 [2023-12-20 20:32:04,525 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6262 [2023-12-20 20:32:04,640 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6290 [2023-12-20 20:32:04,741 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4568 [2023-12-20 20:32:04,829 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2376 [2023-12-20 20:32:04,982 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0132 [2023-12-20 20:32:06,028 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7681/0.8414/0.9208. [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8738/0.9598 [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9637/0.9860 [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6912/0.8023 [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8193/0.8661 [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9256/0.9643 [2023-12-20 20:32:06,028 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8734/0.9374 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7789/0.8686 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7304/0.8333 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7144/0.8088 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8361/0.9148 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4115/0.5227 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7271/0.8309 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7094/0.8565 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7668/0.8582 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6868/0.7282 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6896/0.7353 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9462/0.9805 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6992/0.7762 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8900/0.9251 [2023-12-20 20:32:06,029 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6284/0.6734 [2023-12-20 20:32:06,030 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:32:06,031 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:32:06,031 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:32:09,520 INFO misc.py line 119 131400] Train: [86/100][1/800] Data 0.642 (0.642) Batch 0.889 (0.889) Remain 02:57:42 loss: 0.0964 Lr: 0.00036 [2023-12-20 20:32:10,284 INFO misc.py line 119 131400] Train: [86/100][2/800] Data 0.462 (0.462) Batch 0.763 (0.763) Remain 02:32:38 loss: 0.1863 Lr: 0.00036 [2023-12-20 20:32:10,601 INFO misc.py line 119 131400] Train: [86/100][3/800] Data 0.004 (0.004) Batch 0.317 (0.317) Remain 01:03:28 loss: 0.5308 Lr: 0.00036 [2023-12-20 20:32:10,935 INFO misc.py line 119 131400] Train: [86/100][4/800] Data 0.003 (0.003) Batch 0.332 (0.332) Remain 01:06:27 loss: 0.2242 Lr: 0.00036 [2023-12-20 20:32:11,254 INFO misc.py line 119 131400] Train: [86/100][5/800] Data 0.004 (0.004) Batch 0.320 (0.326) Remain 01:05:14 loss: 0.2039 Lr: 0.00036 [2023-12-20 20:32:11,578 INFO misc.py line 119 131400] Train: [86/100][6/800] Data 0.003 (0.003) Batch 0.324 (0.325) Remain 01:05:03 loss: 0.2708 Lr: 0.00036 [2023-12-20 20:32:11,875 INFO misc.py line 119 131400] Train: [86/100][7/800] Data 0.003 (0.003) Batch 0.297 (0.318) Remain 01:03:37 loss: 0.1748 Lr: 0.00036 [2023-12-20 20:32:12,204 INFO misc.py line 119 131400] Train: [86/100][8/800] Data 0.004 (0.003) Batch 0.329 (0.320) Remain 01:04:02 loss: 0.1493 Lr: 0.00036 [2023-12-20 20:32:12,527 INFO misc.py line 119 131400] Train: [86/100][9/800] Data 0.005 (0.004) Batch 0.320 (0.320) Remain 01:04:00 loss: 0.1928 Lr: 0.00036 [2023-12-20 20:32:12,854 INFO misc.py line 119 131400] Train: [86/100][10/800] Data 0.007 (0.004) Batch 0.331 (0.322) Remain 01:04:18 loss: 0.1903 Lr: 0.00036 [2023-12-20 20:32:13,197 INFO misc.py line 119 131400] Train: [86/100][11/800] Data 0.003 (0.004) Batch 0.342 (0.324) Remain 01:04:49 loss: 0.2738 Lr: 0.00036 [2023-12-20 20:32:13,572 INFO misc.py line 119 131400] Train: [86/100][12/800] Data 0.005 (0.004) Batch 0.358 (0.328) Remain 01:05:33 loss: 0.2336 Lr: 0.00036 [2023-12-20 20:32:13,901 INFO misc.py line 119 131400] Train: [86/100][13/800] Data 0.022 (0.006) Batch 0.346 (0.330) Remain 01:05:55 loss: 0.3560 Lr: 0.00036 [2023-12-20 20:32:14,258 INFO misc.py line 119 131400] Train: [86/100][14/800] Data 0.004 (0.006) Batch 0.356 (0.332) Remain 01:06:23 loss: 0.2249 Lr: 0.00036 [2023-12-20 20:32:14,618 INFO misc.py line 119 131400] Train: [86/100][15/800] Data 0.004 (0.006) Batch 0.361 (0.335) Remain 01:06:51 loss: 0.1402 Lr: 0.00036 [2023-12-20 20:32:14,946 INFO misc.py line 119 131400] Train: [86/100][16/800] Data 0.004 (0.006) Batch 0.328 (0.334) Remain 01:06:45 loss: 0.1407 Lr: 0.00036 [2023-12-20 20:32:15,258 INFO misc.py line 119 131400] Train: [86/100][17/800] Data 0.003 (0.005) Batch 0.311 (0.333) Remain 01:06:24 loss: 0.2556 Lr: 0.00036 [2023-12-20 20:32:15,640 INFO misc.py line 119 131400] Train: [86/100][18/800] Data 0.005 (0.005) Batch 0.378 (0.336) Remain 01:07:00 loss: 0.1036 Lr: 0.00036 [2023-12-20 20:32:15,976 INFO misc.py line 119 131400] Train: [86/100][19/800] Data 0.009 (0.006) Batch 0.341 (0.336) Remain 01:07:04 loss: 0.1695 Lr: 0.00036 [2023-12-20 20:32:16,332 INFO misc.py line 119 131400] Train: [86/100][20/800] Data 0.003 (0.005) Batch 0.356 (0.337) Remain 01:07:18 loss: 0.1677 Lr: 0.00036 [2023-12-20 20:32:16,703 INFO misc.py line 119 131400] Train: [86/100][21/800] Data 0.005 (0.005) Batch 0.371 (0.339) Remain 01:07:40 loss: 0.2098 Lr: 0.00036 [2023-12-20 20:32:17,040 INFO misc.py line 119 131400] Train: [86/100][22/800] Data 0.004 (0.005) Batch 0.338 (0.339) Remain 01:07:39 loss: 0.2737 Lr: 0.00036 [2023-12-20 20:32:17,375 INFO misc.py line 119 131400] Train: [86/100][23/800] Data 0.003 (0.005) Batch 0.335 (0.339) Remain 01:07:36 loss: 0.2379 Lr: 0.00036 [2023-12-20 20:32:17,705 INFO misc.py line 119 131400] Train: [86/100][24/800] Data 0.003 (0.005) Batch 0.329 (0.338) Remain 01:07:31 loss: 0.3378 Lr: 0.00036 [2023-12-20 20:32:18,063 INFO misc.py line 119 131400] Train: [86/100][25/800] Data 0.005 (0.005) Batch 0.358 (0.339) Remain 01:07:41 loss: 0.0957 Lr: 0.00036 [2023-12-20 20:32:18,414 INFO misc.py line 119 131400] Train: [86/100][26/800] Data 0.003 (0.005) Batch 0.349 (0.340) Remain 01:07:46 loss: 0.1587 Lr: 0.00036 [2023-12-20 20:32:18,736 INFO misc.py line 119 131400] Train: [86/100][27/800] Data 0.006 (0.005) Batch 0.324 (0.339) Remain 01:07:38 loss: 0.1340 Lr: 0.00036 [2023-12-20 20:32:19,096 INFO misc.py line 119 131400] Train: [86/100][28/800] Data 0.003 (0.005) Batch 0.359 (0.340) Remain 01:07:47 loss: 0.1849 Lr: 0.00036 [2023-12-20 20:32:19,432 INFO misc.py line 119 131400] Train: [86/100][29/800] Data 0.005 (0.005) Batch 0.336 (0.340) Remain 01:07:45 loss: 0.3252 Lr: 0.00036 [2023-12-20 20:32:19,758 INFO misc.py line 119 131400] Train: [86/100][30/800] Data 0.004 (0.005) Batch 0.325 (0.339) Remain 01:07:39 loss: 0.1193 Lr: 0.00036 [2023-12-20 20:32:20,083 INFO misc.py line 119 131400] Train: [86/100][31/800] Data 0.004 (0.005) Batch 0.326 (0.339) Remain 01:07:32 loss: 0.1493 Lr: 0.00036 [2023-12-20 20:32:20,396 INFO misc.py line 119 131400] Train: [86/100][32/800] Data 0.003 (0.005) Batch 0.313 (0.338) Remain 01:07:22 loss: 0.1419 Lr: 0.00036 [2023-12-20 20:32:20,724 INFO misc.py line 119 131400] Train: [86/100][33/800] Data 0.003 (0.005) Batch 0.328 (0.337) Remain 01:07:17 loss: 0.2075 Lr: 0.00036 [2023-12-20 20:32:21,062 INFO misc.py line 119 131400] Train: [86/100][34/800] Data 0.004 (0.005) Batch 0.338 (0.337) Remain 01:07:17 loss: 0.1404 Lr: 0.00036 [2023-12-20 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01:02:35 loss: 0.2719 Lr: 0.00032 [2023-12-20 20:36:27,093 INFO misc.py line 119 131400] Train: [86/100][770/800] Data 0.004 (0.005) Batch 0.371 (0.334) Remain 01:02:35 loss: 0.1753 Lr: 0.00032 [2023-12-20 20:36:27,416 INFO misc.py line 119 131400] Train: [86/100][771/800] Data 0.003 (0.005) Batch 0.319 (0.334) Remain 01:02:34 loss: 0.1780 Lr: 0.00032 [2023-12-20 20:36:27,738 INFO misc.py line 119 131400] Train: [86/100][772/800] Data 0.008 (0.005) Batch 0.326 (0.334) Remain 01:02:34 loss: 0.2632 Lr: 0.00032 [2023-12-20 20:36:28,037 INFO misc.py line 119 131400] Train: [86/100][773/800] Data 0.004 (0.005) Batch 0.300 (0.334) Remain 01:02:33 loss: 0.2545 Lr: 0.00032 [2023-12-20 20:36:28,357 INFO misc.py line 119 131400] Train: [86/100][774/800] Data 0.003 (0.005) Batch 0.319 (0.334) Remain 01:02:32 loss: 0.2572 Lr: 0.00032 [2023-12-20 20:36:28,648 INFO misc.py line 119 131400] Train: [86/100][775/800] Data 0.003 (0.005) Batch 0.291 (0.334) Remain 01:02:32 loss: 0.1849 Lr: 0.00032 [2023-12-20 20:36:28,920 INFO misc.py line 119 131400] Train: [86/100][776/800] Data 0.003 (0.005) Batch 0.272 (0.334) Remain 01:02:30 loss: 0.2648 Lr: 0.00032 [2023-12-20 20:36:29,255 INFO misc.py line 119 131400] Train: [86/100][777/800] Data 0.003 (0.005) Batch 0.335 (0.334) Remain 01:02:30 loss: 0.2348 Lr: 0.00032 [2023-12-20 20:36:29,588 INFO misc.py line 119 131400] Train: [86/100][778/800] Data 0.003 (0.005) Batch 0.331 (0.334) Remain 01:02:30 loss: 0.0858 Lr: 0.00032 [2023-12-20 20:36:29,892 INFO misc.py line 119 131400] Train: [86/100][779/800] Data 0.005 (0.005) Batch 0.306 (0.334) Remain 01:02:29 loss: 0.1566 Lr: 0.00032 [2023-12-20 20:36:30,187 INFO misc.py line 119 131400] Train: [86/100][780/800] Data 0.003 (0.005) Batch 0.296 (0.334) Remain 01:02:28 loss: 0.1302 Lr: 0.00032 [2023-12-20 20:36:30,509 INFO misc.py line 119 131400] Train: [86/100][781/800] Data 0.003 (0.005) Batch 0.320 (0.334) Remain 01:02:27 loss: 0.1789 Lr: 0.00032 [2023-12-20 20:36:30,845 INFO misc.py line 119 131400] Train: [86/100][782/800] Data 0.004 (0.005) Batch 0.337 (0.334) Remain 01:02:27 loss: 0.2471 Lr: 0.00032 [2023-12-20 20:36:31,170 INFO misc.py line 119 131400] Train: [86/100][783/800] Data 0.004 (0.005) Batch 0.324 (0.334) Remain 01:02:27 loss: 0.1931 Lr: 0.00032 [2023-12-20 20:36:31,516 INFO misc.py line 119 131400] Train: [86/100][784/800] Data 0.004 (0.005) Batch 0.346 (0.334) Remain 01:02:26 loss: 0.1371 Lr: 0.00032 [2023-12-20 20:36:31,873 INFO misc.py line 119 131400] Train: [86/100][785/800] Data 0.004 (0.005) Batch 0.356 (0.334) Remain 01:02:26 loss: 0.3156 Lr: 0.00032 [2023-12-20 20:36:32,212 INFO misc.py line 119 131400] Train: [86/100][786/800] Data 0.006 (0.005) Batch 0.340 (0.334) Remain 01:02:26 loss: 0.1197 Lr: 0.00032 [2023-12-20 20:36:32,540 INFO misc.py line 119 131400] Train: [86/100][787/800] Data 0.004 (0.005) Batch 0.327 (0.334) Remain 01:02:26 loss: 0.1162 Lr: 0.00032 [2023-12-20 20:36:32,907 INFO misc.py line 119 131400] Train: [86/100][788/800] Data 0.004 (0.005) Batch 0.365 (0.334) Remain 01:02:26 loss: 0.2557 Lr: 0.00032 [2023-12-20 20:36:33,286 INFO misc.py line 119 131400] Train: [86/100][789/800] Data 0.008 (0.005) Batch 0.382 (0.334) Remain 01:02:26 loss: 0.1904 Lr: 0.00032 [2023-12-20 20:36:33,617 INFO misc.py line 119 131400] Train: [86/100][790/800] Data 0.003 (0.005) Batch 0.330 (0.334) Remain 01:02:26 loss: 0.2645 Lr: 0.00032 [2023-12-20 20:36:33,954 INFO misc.py line 119 131400] Train: [86/100][791/800] Data 0.003 (0.005) Batch 0.338 (0.334) Remain 01:02:26 loss: 0.2102 Lr: 0.00032 [2023-12-20 20:36:34,296 INFO misc.py line 119 131400] Train: [86/100][792/800] Data 0.002 (0.005) Batch 0.341 (0.334) Remain 01:02:25 loss: 0.2515 Lr: 0.00032 [2023-12-20 20:36:34,640 INFO misc.py line 119 131400] Train: [86/100][793/800] Data 0.004 (0.005) Batch 0.345 (0.334) Remain 01:02:25 loss: 0.3732 Lr: 0.00032 [2023-12-20 20:36:34,958 INFO misc.py line 119 131400] Train: [86/100][794/800] Data 0.003 (0.005) Batch 0.318 (0.334) Remain 01:02:25 loss: 0.1850 Lr: 0.00032 [2023-12-20 20:36:35,281 INFO misc.py line 119 131400] Train: [86/100][795/800] Data 0.003 (0.005) Batch 0.323 (0.334) Remain 01:02:24 loss: 0.2052 Lr: 0.00032 [2023-12-20 20:36:35,573 INFO misc.py line 119 131400] Train: [86/100][796/800] Data 0.003 (0.005) Batch 0.292 (0.334) Remain 01:02:23 loss: 0.0867 Lr: 0.00032 [2023-12-20 20:36:35,850 INFO misc.py line 119 131400] Train: [86/100][797/800] Data 0.002 (0.005) Batch 0.276 (0.334) Remain 01:02:22 loss: 0.1402 Lr: 0.00032 [2023-12-20 20:36:36,152 INFO misc.py line 119 131400] Train: [86/100][798/800] Data 0.002 (0.005) Batch 0.301 (0.334) Remain 01:02:21 loss: 0.1578 Lr: 0.00032 [2023-12-20 20:36:36,458 INFO misc.py line 119 131400] Train: [86/100][799/800] Data 0.003 (0.005) Batch 0.306 (0.334) Remain 01:02:21 loss: 0.1877 Lr: 0.00032 [2023-12-20 20:36:36,740 INFO misc.py line 119 131400] Train: [86/100][800/800] Data 0.003 (0.005) Batch 0.282 (0.334) Remain 01:02:19 loss: 0.1655 Lr: 0.00032 [2023-12-20 20:36:36,741 INFO misc.py line 136 131400] Train result: loss: 0.2144 [2023-12-20 20:36:36,741 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:36:58,984 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1801 [2023-12-20 20:36:59,159 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1381 [2023-12-20 20:36:59,252 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.3934 [2023-12-20 20:36:59,365 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3857 [2023-12-20 20:36:59,479 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4523 [2023-12-20 20:36:59,588 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1553 [2023-12-20 20:36:59,683 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.2516 [2023-12-20 20:36:59,792 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8784 [2023-12-20 20:36:59,877 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.3050 [2023-12-20 20:36:59,971 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3262 [2023-12-20 20:37:00,097 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4705 [2023-12-20 20:37:00,242 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2889 [2023-12-20 20:37:00,360 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5752 [2023-12-20 20:37:00,514 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2375 [2023-12-20 20:37:00,609 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1996 [2023-12-20 20:37:00,743 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7422 [2023-12-20 20:37:00,862 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2389 [2023-12-20 20:37:00,977 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.9229 [2023-12-20 20:37:01,091 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1758 [2023-12-20 20:37:01,168 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.5217 [2023-12-20 20:37:01,278 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.2275 [2023-12-20 20:37:01,443 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1401 [2023-12-20 20:37:01,564 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8792 [2023-12-20 20:37:01,708 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3115 [2023-12-20 20:37:01,856 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1586 [2023-12-20 20:37:01,945 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8756 [2023-12-20 20:37:02,104 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.0566 [2023-12-20 20:37:02,233 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6356 [2023-12-20 20:37:02,339 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5642 [2023-12-20 20:37:02,488 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5802 [2023-12-20 20:37:02,601 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5026 [2023-12-20 20:37:02,730 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3787 [2023-12-20 20:37:02,816 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1196 [2023-12-20 20:37:02,898 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1718 [2023-12-20 20:37:02,998 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8572 [2023-12-20 20:37:03,092 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2958 [2023-12-20 20:37:03,234 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9085 [2023-12-20 20:37:03,363 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0808 [2023-12-20 20:37:03,465 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5240 [2023-12-20 20:37:03,616 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2602 [2023-12-20 20:37:03,764 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0171 [2023-12-20 20:37:03,871 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.1195 [2023-12-20 20:37:03,995 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3010 [2023-12-20 20:37:04,141 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9780 [2023-12-20 20:37:04,258 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4987 [2023-12-20 20:37:04,362 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6822 [2023-12-20 20:37:04,531 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3068 [2023-12-20 20:37:04,639 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5072 [2023-12-20 20:37:04,784 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6846 [2023-12-20 20:37:04,877 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1664 [2023-12-20 20:37:04,960 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5995 [2023-12-20 20:37:05,077 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5589 [2023-12-20 20:37:05,223 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2548 [2023-12-20 20:37:05,364 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3694 [2023-12-20 20:37:05,466 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1157 [2023-12-20 20:37:05,557 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6299 [2023-12-20 20:37:05,663 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3518 [2023-12-20 20:37:05,823 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2223 [2023-12-20 20:37:05,925 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4542 [2023-12-20 20:37:06,018 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.6133 [2023-12-20 20:37:06,116 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5086 [2023-12-20 20:37:06,210 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2399 [2023-12-20 20:37:06,296 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6704 [2023-12-20 20:37:06,396 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.4288 [2023-12-20 20:37:06,526 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5800 [2023-12-20 20:37:06,610 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2938 [2023-12-20 20:37:06,709 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5419 [2023-12-20 20:37:06,807 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0099 [2023-12-20 20:37:06,899 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2957 [2023-12-20 20:37:06,988 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0095 [2023-12-20 20:37:07,081 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8384 [2023-12-20 20:37:07,174 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5044 [2023-12-20 20:37:07,310 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0873 [2023-12-20 20:37:07,409 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6565 [2023-12-20 20:37:07,529 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6650 [2023-12-20 20:37:07,642 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4234 [2023-12-20 20:37:07,733 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.5593 [2023-12-20 20:37:07,887 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.3209 [2023-12-20 20:37:09,345 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7647/0.8447/0.9188. [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8720/0.9558 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9644/0.9857 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7053/0.8184 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8286/0.8731 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9232/0.9657 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8710/0.9215 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7537/0.8305 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7205/0.8290 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7069/0.8068 [2023-12-20 20:37:09,345 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8314/0.9188 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4062/0.5032 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7113/0.8131 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6722/0.8717 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7731/0.8668 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6946/0.7830 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7147/0.7658 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9509/0.9786 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6960/0.8135 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8817/0.9261 [2023-12-20 20:37:09,346 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6162/0.6676 [2023-12-20 20:37:09,346 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:37:09,347 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:37:09,347 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:37:13,729 INFO misc.py line 119 131400] Train: [87/100][1/800] Data 1.785 (1.785) Batch 2.138 (2.138) Remain 06:39:07 loss: 0.2746 Lr: 0.00032 [2023-12-20 20:37:14,078 INFO misc.py line 119 131400] Train: [87/100][2/800] Data 0.004 (0.004) Batch 0.350 (0.350) Remain 01:05:20 loss: 0.2031 Lr: 0.00032 [2023-12-20 20:37:14,430 INFO misc.py line 119 131400] Train: [87/100][3/800] Data 0.005 (0.005) Batch 0.351 (0.351) Remain 01:05:32 loss: 0.3083 Lr: 0.00032 [2023-12-20 20:37:14,775 INFO misc.py line 119 131400] Train: [87/100][4/800] Data 0.004 (0.004) Batch 0.345 (0.345) Remain 01:04:24 loss: 0.1727 Lr: 0.00032 [2023-12-20 20:37:15,085 INFO misc.py line 119 131400] Train: [87/100][5/800] Data 0.004 (0.004) Batch 0.310 (0.328) Remain 01:01:09 loss: 0.2233 Lr: 0.00032 [2023-12-20 20:37:15,419 INFO misc.py line 119 131400] Train: [87/100][6/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 01:01:31 loss: 0.1939 Lr: 0.00032 [2023-12-20 20:37:15,787 INFO misc.py line 119 131400] Train: [87/100][7/800] Data 0.004 (0.004) Batch 0.368 (0.339) Remain 01:03:17 loss: 0.2482 Lr: 0.00032 [2023-12-20 20:37:16,077 INFO misc.py line 119 131400] Train: [87/100][8/800] Data 0.004 (0.004) Batch 0.290 (0.330) Remain 01:01:27 loss: 0.2144 Lr: 0.00032 [2023-12-20 20:37:16,428 INFO misc.py line 119 131400] Train: [87/100][9/800] Data 0.005 (0.004) Batch 0.350 (0.333) Remain 01:02:06 loss: 0.1574 Lr: 0.00032 [2023-12-20 20:37:16,769 INFO misc.py line 119 131400] Train: [87/100][10/800] Data 0.005 (0.004) Batch 0.343 (0.334) Remain 01:02:21 loss: 0.2260 Lr: 0.00032 [2023-12-20 20:37:17,081 INFO misc.py line 119 131400] Train: [87/100][11/800] Data 0.003 (0.004) Batch 0.311 (0.331) Remain 01:01:49 loss: 0.2878 Lr: 0.00032 [2023-12-20 20:37:17,437 INFO misc.py line 119 131400] Train: [87/100][12/800] Data 0.003 (0.004) Batch 0.355 (0.334) Remain 01:02:18 loss: 0.1125 Lr: 0.00032 [2023-12-20 20:37:17,772 INFO misc.py line 119 131400] Train: [87/100][13/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 01:02:19 loss: 0.2048 Lr: 0.00032 [2023-12-20 20:37:18,092 INFO misc.py line 119 131400] Train: [87/100][14/800] Data 0.003 (0.004) Batch 0.320 (0.333) Remain 01:02:04 loss: 0.1478 Lr: 0.00032 [2023-12-20 20:37:18,430 INFO misc.py line 119 131400] Train: [87/100][15/800] Data 0.004 (0.004) Batch 0.337 (0.333) Remain 01:02:08 loss: 0.1669 Lr: 0.00032 [2023-12-20 20:37:18,772 INFO misc.py line 119 131400] Train: [87/100][16/800] Data 0.005 (0.004) Batch 0.342 (0.334) Remain 01:02:15 loss: 0.1897 Lr: 0.00032 [2023-12-20 20:37:19,093 INFO misc.py line 119 131400] Train: [87/100][17/800] Data 0.004 (0.004) Batch 0.321 (0.333) Remain 01:02:05 loss: 0.4236 Lr: 0.00032 [2023-12-20 20:37:19,426 INFO misc.py line 119 131400] Train: [87/100][18/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 01:02:04 loss: 0.1242 Lr: 0.00032 [2023-12-20 20:37:19,737 INFO misc.py line 119 131400] Train: [87/100][19/800] Data 0.004 (0.004) Batch 0.312 (0.332) Remain 01:01:49 loss: 0.1203 Lr: 0.00032 [2023-12-20 20:37:20,075 INFO misc.py line 119 131400] Train: [87/100][20/800] Data 0.003 (0.004) Batch 0.337 (0.332) Remain 01:01:52 loss: 0.1835 Lr: 0.00032 [2023-12-20 20:37:20,447 INFO misc.py line 119 131400] Train: [87/100][21/800] Data 0.004 (0.004) Batch 0.373 (0.334) Remain 01:02:17 loss: 0.2483 Lr: 0.00032 [2023-12-20 20:37:20,772 INFO misc.py line 119 131400] Train: [87/100][22/800] Data 0.004 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0.004 (0.004) Batch 0.370 (0.334) Remain 00:58:10 loss: 0.1942 Lr: 0.00028 [2023-12-20 20:41:28,971 INFO misc.py line 119 131400] Train: [87/100][764/800] Data 0.004 (0.004) Batch 0.348 (0.334) Remain 00:58:10 loss: 0.0786 Lr: 0.00028 [2023-12-20 20:41:29,325 INFO misc.py line 119 131400] Train: [87/100][765/800] Data 0.004 (0.004) Batch 0.353 (0.335) Remain 00:58:10 loss: 0.1836 Lr: 0.00028 [2023-12-20 20:41:29,693 INFO misc.py line 119 131400] Train: [87/100][766/800] Data 0.004 (0.004) Batch 0.369 (0.335) Remain 00:58:10 loss: 0.2255 Lr: 0.00028 [2023-12-20 20:41:30,023 INFO misc.py line 119 131400] Train: [87/100][767/800] Data 0.004 (0.004) Batch 0.330 (0.335) Remain 00:58:10 loss: 0.2453 Lr: 0.00028 [2023-12-20 20:41:30,377 INFO misc.py line 119 131400] Train: [87/100][768/800] Data 0.003 (0.004) Batch 0.354 (0.335) Remain 00:58:10 loss: 0.2102 Lr: 0.00028 [2023-12-20 20:41:30,714 INFO misc.py line 119 131400] Train: [87/100][769/800] Data 0.004 (0.004) Batch 0.336 (0.335) Remain 00:58:09 loss: 0.3226 Lr: 0.00028 [2023-12-20 20:41:31,051 INFO misc.py line 119 131400] Train: [87/100][770/800] Data 0.004 (0.004) Batch 0.338 (0.335) Remain 00:58:09 loss: 0.1797 Lr: 0.00028 [2023-12-20 20:41:31,409 INFO misc.py line 119 131400] Train: [87/100][771/800] Data 0.003 (0.004) Batch 0.357 (0.335) Remain 00:58:09 loss: 0.3287 Lr: 0.00028 [2023-12-20 20:41:31,770 INFO misc.py line 119 131400] Train: [87/100][772/800] Data 0.004 (0.004) Batch 0.362 (0.335) Remain 00:58:09 loss: 0.1142 Lr: 0.00028 [2023-12-20 20:41:32,079 INFO misc.py line 119 131400] Train: [87/100][773/800] Data 0.004 (0.004) Batch 0.309 (0.335) Remain 00:58:08 loss: 0.1824 Lr: 0.00028 [2023-12-20 20:41:32,405 INFO misc.py line 119 131400] Train: [87/100][774/800] Data 0.004 (0.004) Batch 0.326 (0.335) Remain 00:58:08 loss: 0.1426 Lr: 0.00028 [2023-12-20 20:41:32,744 INFO misc.py line 119 131400] Train: [87/100][775/800] Data 0.005 (0.004) Batch 0.339 (0.335) Remain 00:58:08 loss: 0.2639 Lr: 0.00028 [2023-12-20 20:41:33,093 INFO misc.py line 119 131400] Train: [87/100][776/800] Data 0.004 (0.004) Batch 0.339 (0.335) Remain 00:58:07 loss: 0.1839 Lr: 0.00028 [2023-12-20 20:41:33,419 INFO misc.py line 119 131400] Train: [87/100][777/800] Data 0.014 (0.004) Batch 0.336 (0.335) Remain 00:58:07 loss: 0.1317 Lr: 0.00028 [2023-12-20 20:41:33,771 INFO misc.py line 119 131400] Train: [87/100][778/800] Data 0.006 (0.004) Batch 0.352 (0.335) Remain 00:58:07 loss: 0.3584 Lr: 0.00028 [2023-12-20 20:41:34,112 INFO misc.py line 119 131400] Train: [87/100][779/800] Data 0.006 (0.004) Batch 0.341 (0.335) Remain 00:58:07 loss: 0.1724 Lr: 0.00028 [2023-12-20 20:41:34,436 INFO misc.py line 119 131400] Train: [87/100][780/800] Data 0.005 (0.004) Batch 0.325 (0.335) Remain 00:58:06 loss: 0.2218 Lr: 0.00028 [2023-12-20 20:41:34,753 INFO misc.py line 119 131400] Train: [87/100][781/800] Data 0.003 (0.004) Batch 0.318 (0.335) Remain 00:58:06 loss: 0.1196 Lr: 0.00028 [2023-12-20 20:41:35,110 INFO misc.py line 119 131400] Train: [87/100][782/800] Data 0.003 (0.004) Batch 0.354 (0.335) Remain 00:58:06 loss: 0.1563 Lr: 0.00028 [2023-12-20 20:41:35,448 INFO misc.py line 119 131400] Train: [87/100][783/800] Data 0.006 (0.004) Batch 0.339 (0.335) Remain 00:58:05 loss: 0.2170 Lr: 0.00028 [2023-12-20 20:41:35,790 INFO misc.py line 119 131400] Train: [87/100][784/800] Data 0.004 (0.004) Batch 0.343 (0.335) Remain 00:58:05 loss: 0.2127 Lr: 0.00028 [2023-12-20 20:41:36,162 INFO misc.py line 119 131400] Train: [87/100][785/800] Data 0.003 (0.004) Batch 0.371 (0.335) Remain 00:58:05 loss: 0.4308 Lr: 0.00028 [2023-12-20 20:41:36,484 INFO misc.py line 119 131400] Train: [87/100][786/800] Data 0.006 (0.004) Batch 0.323 (0.335) Remain 00:58:05 loss: 0.1924 Lr: 0.00028 [2023-12-20 20:41:36,815 INFO misc.py line 119 131400] Train: [87/100][787/800] Data 0.004 (0.004) Batch 0.331 (0.335) Remain 00:58:04 loss: 0.2736 Lr: 0.00028 [2023-12-20 20:41:37,133 INFO misc.py line 119 131400] Train: [87/100][788/800] Data 0.004 (0.004) Batch 0.319 (0.335) Remain 00:58:04 loss: 0.1920 Lr: 0.00028 [2023-12-20 20:41:37,430 INFO misc.py line 119 131400] Train: [87/100][789/800] Data 0.003 (0.004) Batch 0.297 (0.335) Remain 00:58:03 loss: 0.2256 Lr: 0.00028 [2023-12-20 20:41:37,732 INFO misc.py line 119 131400] Train: [87/100][790/800] Data 0.003 (0.004) Batch 0.302 (0.335) Remain 00:58:02 loss: 0.1657 Lr: 0.00028 [2023-12-20 20:41:38,010 INFO misc.py line 119 131400] Train: [87/100][791/800] Data 0.002 (0.004) Batch 0.278 (0.334) Remain 00:58:01 loss: 0.1200 Lr: 0.00028 [2023-12-20 20:41:38,315 INFO misc.py line 119 131400] Train: [87/100][792/800] Data 0.003 (0.004) Batch 0.305 (0.334) Remain 00:58:00 loss: 0.2375 Lr: 0.00028 [2023-12-20 20:41:38,627 INFO misc.py line 119 131400] Train: [87/100][793/800] Data 0.003 (0.004) Batch 0.313 (0.334) Remain 00:58:00 loss: 0.2306 Lr: 0.00028 [2023-12-20 20:41:38,930 INFO misc.py line 119 131400] Train: [87/100][794/800] Data 0.003 (0.004) Batch 0.302 (0.334) Remain 00:57:59 loss: 0.1778 Lr: 0.00028 [2023-12-20 20:41:39,233 INFO misc.py line 119 131400] Train: [87/100][795/800] Data 0.003 (0.004) Batch 0.303 (0.334) Remain 00:57:58 loss: 0.2796 Lr: 0.00028 [2023-12-20 20:41:39,535 INFO misc.py line 119 131400] Train: [87/100][796/800] Data 0.003 (0.004) Batch 0.302 (0.334) Remain 00:57:58 loss: 0.2643 Lr: 0.00028 [2023-12-20 20:41:39,814 INFO misc.py line 119 131400] Train: [87/100][797/800] Data 0.004 (0.004) Batch 0.279 (0.334) Remain 00:57:57 loss: 0.2257 Lr: 0.00028 [2023-12-20 20:41:40,120 INFO misc.py line 119 131400] Train: [87/100][798/800] Data 0.003 (0.004) Batch 0.306 (0.334) Remain 00:57:56 loss: 0.2213 Lr: 0.00028 [2023-12-20 20:41:40,430 INFO misc.py line 119 131400] Train: [87/100][799/800] Data 0.003 (0.004) Batch 0.306 (0.334) Remain 00:57:55 loss: 0.2414 Lr: 0.00028 [2023-12-20 20:41:40,728 INFO misc.py line 119 131400] Train: [87/100][800/800] Data 0.009 (0.004) Batch 0.303 (0.334) Remain 00:57:54 loss: 0.1464 Lr: 0.00028 [2023-12-20 20:41:40,729 INFO misc.py line 136 131400] Train result: loss: 0.2096 [2023-12-20 20:41:40,729 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:42:03,781 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2471 [2023-12-20 20:42:03,940 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1362 [2023-12-20 20:42:04,052 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5499 [2023-12-20 20:42:04,248 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4320 [2023-12-20 20:42:04,370 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2447 [2023-12-20 20:42:04,481 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5256 [2023-12-20 20:42:04,578 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0882 [2023-12-20 20:42:04,689 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6053 [2023-12-20 20:42:04,777 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2955 [2023-12-20 20:42:04,867 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3088 [2023-12-20 20:42:04,970 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4327 [2023-12-20 20:42:05,109 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2574 [2023-12-20 20:42:05,235 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4285 [2023-12-20 20:42:05,404 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2074 [2023-12-20 20:42:05,515 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1418 [2023-12-20 20:42:05,651 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7827 [2023-12-20 20:42:05,762 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2533 [2023-12-20 20:42:05,886 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5125 [2023-12-20 20:42:06,026 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1448 [2023-12-20 20:42:06,109 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4064 [2023-12-20 20:42:06,223 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1818 [2023-12-20 20:42:06,390 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1101 [2023-12-20 20:42:06,514 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.5318 [2023-12-20 20:42:06,666 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.3243 [2023-12-20 20:42:06,812 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1746 [2023-12-20 20:42:06,905 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5602 [2023-12-20 20:42:07,071 INFO evaluator.py line 159 131400] Test: [27/78] Loss 2.0407 [2023-12-20 20:42:07,197 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5719 [2023-12-20 20:42:07,300 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4835 [2023-12-20 20:42:07,448 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8592 [2023-12-20 20:42:07,554 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4783 [2023-12-20 20:42:07,678 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3673 [2023-12-20 20:42:07,769 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1115 [2023-12-20 20:42:07,863 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1725 [2023-12-20 20:42:07,980 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8327 [2023-12-20 20:42:08,075 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.5772 [2023-12-20 20:42:08,210 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9193 [2023-12-20 20:42:08,328 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0893 [2023-12-20 20:42:08,418 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5277 [2023-12-20 20:42:08,564 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2855 [2023-12-20 20:42:08,725 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0187 [2023-12-20 20:42:08,840 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0720 [2023-12-20 20:42:08,963 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3715 [2023-12-20 20:42:09,108 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9993 [2023-12-20 20:42:09,235 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5317 [2023-12-20 20:42:09,342 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8282 [2023-12-20 20:42:09,513 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3430 [2023-12-20 20:42:09,614 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3565 [2023-12-20 20:42:09,761 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7424 [2023-12-20 20:42:09,854 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2195 [2023-12-20 20:42:09,936 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5097 [2023-12-20 20:42:10,051 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5185 [2023-12-20 20:42:10,201 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1802 [2023-12-20 20:42:10,342 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3991 [2023-12-20 20:42:10,448 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3390 [2023-12-20 20:42:10,544 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5760 [2023-12-20 20:42:10,646 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3469 [2023-12-20 20:42:10,805 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.1961 [2023-12-20 20:42:10,907 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5619 [2023-12-20 20:42:11,013 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2204 [2023-12-20 20:42:11,128 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5406 [2023-12-20 20:42:11,222 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2265 [2023-12-20 20:42:11,314 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6194 [2023-12-20 20:42:11,424 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6310 [2023-12-20 20:42:11,565 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6505 [2023-12-20 20:42:11,657 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.3241 [2023-12-20 20:42:11,758 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3915 [2023-12-20 20:42:11,852 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0101 [2023-12-20 20:42:11,940 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3292 [2023-12-20 20:42:12,024 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0099 [2023-12-20 20:42:12,119 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.7356 [2023-12-20 20:42:12,213 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5743 [2023-12-20 20:42:12,349 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0527 [2023-12-20 20:42:12,445 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6573 [2023-12-20 20:42:12,562 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6596 [2023-12-20 20:42:12,663 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5278 [2023-12-20 20:42:12,751 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4417 [2023-12-20 20:42:12,906 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.2274 [2023-12-20 20:42:14,333 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7665/0.8404/0.9208. [2023-12-20 20:42:14,333 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8756/0.9599 [2023-12-20 20:42:14,333 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9649/0.9851 [2023-12-20 20:42:14,333 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6911/0.8127 [2023-12-20 20:42:14,333 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8443/0.8989 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9231/0.9631 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8735/0.9285 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7761/0.8787 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7388/0.8446 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.6871/0.7835 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8385/0.9120 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4054/0.5018 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7252/0.8079 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7049/0.8488 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7570/0.8464 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7057/0.7521 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6691/0.7176 [2023-12-20 20:42:14,334 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9372/0.9806 [2023-12-20 20:42:14,335 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6960/0.7939 [2023-12-20 20:42:14,335 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8920/0.9247 [2023-12-20 20:42:14,335 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6235/0.6678 [2023-12-20 20:42:14,335 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:42:14,337 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:42:14,337 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:42:18,093 INFO misc.py line 119 131400] Train: [88/100][1/800] Data 0.813 (0.813) Batch 1.113 (1.113) Remain 03:12:58 loss: 0.2371 Lr: 0.00028 [2023-12-20 20:42:18,533 INFO misc.py line 119 131400] Train: [88/100][2/800] Data 0.132 (0.132) Batch 0.440 (0.440) Remain 01:16:20 loss: 0.1171 Lr: 0.00027 [2023-12-20 20:42:18,874 INFO misc.py line 119 131400] Train: [88/100][3/800] Data 0.004 (0.004) Batch 0.341 (0.341) Remain 00:59:03 loss: 0.2203 Lr: 0.00027 [2023-12-20 20:42:19,211 INFO misc.py line 119 131400] Train: [88/100][4/800] Data 0.003 (0.003) Batch 0.337 (0.337) Remain 00:58:25 loss: 0.2457 Lr: 0.00027 [2023-12-20 20:42:19,536 INFO misc.py line 119 131400] Train: [88/100][5/800] Data 0.005 (0.004) Batch 0.325 (0.331) Remain 00:57:22 loss: 0.1597 Lr: 0.00027 [2023-12-20 20:42:19,936 INFO misc.py line 119 131400] Train: [88/100][6/800] Data 0.003 (0.004) Batch 0.400 (0.354) Remain 01:01:20 loss: 0.1171 Lr: 0.00027 [2023-12-20 20:42:20,271 INFO misc.py line 119 131400] Train: [88/100][7/800] Data 0.003 (0.004) Batch 0.335 (0.349) Remain 01:00:29 loss: 0.1382 Lr: 0.00027 [2023-12-20 20:42:20,660 INFO misc.py line 119 131400] Train: [88/100][8/800] Data 0.011 (0.005) Batch 0.380 (0.355) Remain 01:01:33 loss: 0.1744 Lr: 0.00027 [2023-12-20 20:42:21,005 INFO misc.py line 119 131400] Train: [88/100][9/800] Data 0.013 (0.006) Batch 0.355 (0.355) Remain 01:01:32 loss: 0.2864 Lr: 0.00027 [2023-12-20 20:42:21,357 INFO misc.py line 119 131400] Train: [88/100][10/800] Data 0.003 (0.006) Batch 0.351 (0.355) Remain 01:01:25 loss: 0.3403 Lr: 0.00027 [2023-12-20 20:42:21,668 INFO misc.py line 119 131400] Train: [88/100][11/800] Data 0.003 (0.006) Batch 0.311 (0.349) Remain 01:00:28 loss: 0.4048 Lr: 0.00027 [2023-12-20 20:42:21,977 INFO misc.py line 119 131400] Train: [88/100][12/800] Data 0.004 (0.005) Batch 0.309 (0.345) Remain 00:59:41 loss: 0.1379 Lr: 0.00027 [2023-12-20 20:42:22,314 INFO misc.py line 119 131400] Train: [88/100][13/800] Data 0.004 (0.005) Batch 0.337 (0.344) Remain 00:59:33 loss: 0.3158 Lr: 0.00027 [2023-12-20 20:42:22,656 INFO misc.py line 119 131400] Train: [88/100][14/800] Data 0.004 (0.005) Batch 0.341 (0.344) Remain 00:59:30 loss: 0.1129 Lr: 0.00027 [2023-12-20 20:42:23,013 INFO misc.py line 119 131400] Train: [88/100][15/800] Data 0.005 (0.005) Batch 0.358 (0.345) Remain 00:59:42 loss: 0.2647 Lr: 0.00027 [2023-12-20 20:42:23,314 INFO misc.py line 119 131400] Train: [88/100][16/800] Data 0.003 (0.005) Batch 0.299 (0.341) Remain 00:59:05 loss: 0.1357 Lr: 0.00027 [2023-12-20 20:42:23,653 INFO misc.py line 119 131400] Train: [88/100][17/800] Data 0.005 (0.005) Batch 0.340 (0.341) Remain 00:59:04 loss: 0.3491 Lr: 0.00027 [2023-12-20 20:42:24,034 INFO misc.py line 119 131400] Train: [88/100][18/800] Data 0.004 (0.005) Batch 0.381 (0.344) Remain 00:59:31 loss: 0.2403 Lr: 0.00027 [2023-12-20 20:42:24,548 INFO misc.py line 119 131400] Train: [88/100][19/800] Data 0.004 (0.005) Batch 0.513 (0.355) Remain 01:01:20 loss: 0.3205 Lr: 0.00027 [2023-12-20 20:42:24,870 INFO misc.py line 119 131400] Train: [88/100][20/800] Data 0.005 (0.005) Batch 0.324 (0.353) Remain 01:01:01 loss: 0.1636 Lr: 0.00027 [2023-12-20 20:42:25,229 INFO misc.py line 119 131400] Train: [88/100][21/800] Data 0.003 (0.005) Batch 0.355 (0.353) Remain 01:01:02 loss: 0.1512 Lr: 0.00027 [2023-12-20 20:42:25,534 INFO misc.py line 119 131400] Train: [88/100][22/800] Data 0.008 (0.005) Batch 0.309 (0.351) Remain 01:00:37 loss: 0.1672 Lr: 0.00027 [2023-12-20 20:42:25,859 INFO misc.py line 119 131400] Train: [88/100][23/800] Data 0.003 (0.005) Batch 0.325 (0.349) Remain 01:00:24 loss: 0.2745 Lr: 0.00027 [2023-12-20 20:42:26,194 INFO misc.py line 119 131400] Train: [88/100][24/800] Data 0.003 (0.005) Batch 0.336 (0.349) Remain 01:00:17 loss: 0.1910 Lr: 0.00027 [2023-12-20 20:42:26,523 INFO misc.py line 119 131400] Train: [88/100][25/800] Data 0.003 (0.005) Batch 0.328 (0.348) Remain 01:00:07 loss: 0.1762 Lr: 0.00027 [2023-12-20 20:42:26,878 INFO misc.py line 119 131400] Train: [88/100][26/800] Data 0.004 (0.005) Batch 0.355 (0.348) Remain 01:00:10 loss: 0.3242 Lr: 0.00027 [2023-12-20 20:42:27,196 INFO misc.py line 119 131400] Train: [88/100][27/800] Data 0.003 (0.005) Batch 0.318 (0.347) Remain 00:59:56 loss: 0.1467 Lr: 0.00027 [2023-12-20 20:42:27,525 INFO misc.py line 119 131400] Train: [88/100][28/800] Data 0.004 (0.005) Batch 0.329 (0.346) Remain 00:59:49 loss: 0.1991 Lr: 0.00027 [2023-12-20 20:42:27,830 INFO misc.py line 119 131400] Train: [88/100][29/800] Data 0.004 (0.005) Batch 0.306 (0.344) Remain 00:59:32 loss: 0.1760 Lr: 0.00027 [2023-12-20 20:42:28,176 INFO misc.py line 119 131400] Train: [88/100][30/800] Data 0.003 (0.005) Batch 0.346 (0.345) Remain 00:59:32 loss: 0.2679 Lr: 0.00027 [2023-12-20 20:42:28,513 INFO misc.py line 119 131400] Train: [88/100][31/800] Data 0.003 (0.004) Batch 0.337 (0.344) Remain 00:59:29 loss: 0.1622 Lr: 0.00027 [2023-12-20 20:42:28,831 INFO misc.py line 119 131400] Train: [88/100][32/800] Data 0.003 (0.004) Batch 0.318 (0.343) Remain 00:59:20 loss: 0.2406 Lr: 0.00027 [2023-12-20 20:42:29,158 INFO misc.py line 119 131400] Train: [88/100][33/800] Data 0.003 (0.004) Batch 0.327 (0.343) Remain 00:59:14 loss: 0.1526 Lr: 0.00027 [2023-12-20 20:42:29,465 INFO misc.py line 119 131400] Train: [88/100][34/800] Data 0.003 (0.004) Batch 0.303 (0.342) Remain 00:59:00 loss: 0.1316 Lr: 0.00027 [2023-12-20 20:42:29,784 INFO misc.py line 119 131400] Train: [88/100][35/800] Data 0.007 (0.004) Batch 0.322 (0.341) Remain 00:58:53 loss: 0.3107 Lr: 0.00027 [2023-12-20 20:42:30,093 INFO misc.py line 119 131400] Train: [88/100][36/800] Data 0.003 (0.004) Batch 0.309 (0.340) Remain 00:58:43 loss: 0.1409 Lr: 0.00027 [2023-12-20 20:42:30,410 INFO misc.py line 119 131400] Train: [88/100][37/800] Data 0.003 (0.004) Batch 0.317 (0.339) Remain 00:58:36 loss: 0.1765 Lr: 0.00027 [2023-12-20 20:42:30,721 INFO misc.py line 119 131400] Train: [88/100][38/800] Data 0.004 (0.004) Batch 0.312 (0.338) Remain 00:58:27 loss: 0.2329 Lr: 0.00027 [2023-12-20 20:42:31,049 INFO misc.py line 119 131400] Train: [88/100][39/800] Data 0.003 (0.004) Batch 0.327 (0.338) Remain 00:58:23 loss: 0.3201 Lr: 0.00027 [2023-12-20 20:42:31,410 INFO misc.py line 119 131400] Train: [88/100][40/800] Data 0.005 (0.004) Batch 0.362 (0.339) Remain 00:58:30 loss: 0.2681 Lr: 0.00027 [2023-12-20 20:42:31,704 INFO misc.py line 119 131400] Train: [88/100][41/800] Data 0.003 (0.004) Batch 0.294 (0.338) Remain 00:58:17 loss: 0.1715 Lr: 0.00027 [2023-12-20 20:42:32,025 INFO misc.py line 119 131400] Train: [88/100][42/800] Data 0.003 (0.004) Batch 0.322 (0.337) Remain 00:58:13 loss: 0.3068 Lr: 0.00027 [2023-12-20 20:42:32,314 INFO misc.py line 119 131400] Train: [88/100][43/800] Data 0.003 (0.004) Batch 0.289 (0.336) Remain 00:58:00 loss: 0.1242 Lr: 0.00027 [2023-12-20 20:42:32,649 INFO misc.py line 119 131400] Train: [88/100][44/800] Data 0.003 (0.004) Batch 0.332 (0.336) Remain 00:57:58 loss: 0.2699 Lr: 0.00027 [2023-12-20 20:42:32,977 INFO misc.py line 119 131400] Train: [88/100][45/800] Data 0.006 (0.004) Batch 0.330 (0.336) Remain 00:57:56 loss: 0.1673 Lr: 0.00027 [2023-12-20 20:42:33,361 INFO misc.py line 119 131400] Train: [88/100][46/800] Data 0.005 (0.004) Batch 0.379 (0.337) Remain 00:58:06 loss: 0.1723 Lr: 0.00027 [2023-12-20 20:42:33,693 INFO misc.py line 119 131400] Train: [88/100][47/800] Data 0.009 (0.004) Batch 0.338 (0.337) Remain 00:58:07 loss: 0.2873 Lr: 0.00027 [2023-12-20 20:42:34,026 INFO misc.py line 119 131400] Train: [88/100][48/800] Data 0.003 (0.004) Batch 0.332 (0.337) Remain 00:58:05 loss: 0.2737 Lr: 0.00027 [2023-12-20 20:42:34,334 INFO misc.py line 119 131400] Train: [88/100][49/800] Data 0.004 (0.004) Batch 0.308 (0.336) Remain 00:57:58 loss: 0.1446 Lr: 0.00027 [2023-12-20 20:42:34,684 INFO misc.py line 119 131400] Train: [88/100][50/800] Data 0.003 (0.004) Batch 0.350 (0.336) Remain 00:58:01 loss: 0.3963 Lr: 0.00027 [2023-12-20 20:42:35,023 INFO misc.py line 119 131400] Train: [88/100][51/800] Data 0.004 (0.004) Batch 0.339 (0.336) Remain 00:58:01 loss: 0.1582 Lr: 0.00027 [2023-12-20 20:42:35,320 INFO misc.py line 119 131400] Train: [88/100][52/800] Data 0.004 (0.004) Batch 0.297 (0.336) Remain 00:57:53 loss: 0.1700 Lr: 0.00027 [2023-12-20 20:42:35,656 INFO misc.py line 119 131400] Train: [88/100][53/800] Data 0.005 (0.004) Batch 0.336 (0.336) Remain 00:57:52 loss: 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INFO misc.py line 119 131400] Train: [88/100][60/800] Data 0.005 (0.004) Batch 0.315 (0.336) Remain 00:57:49 loss: 0.1275 Lr: 0.00027 [2023-12-20 20:42:38,351 INFO misc.py line 119 131400] Train: [88/100][61/800] Data 0.005 (0.004) Batch 0.349 (0.336) Remain 00:57:52 loss: 0.2179 Lr: 0.00027 [2023-12-20 20:42:38,689 INFO misc.py line 119 131400] Train: [88/100][62/800] Data 0.004 (0.004) Batch 0.337 (0.336) Remain 00:57:51 loss: 0.1411 Lr: 0.00027 [2023-12-20 20:42:39,030 INFO misc.py line 119 131400] Train: [88/100][63/800] Data 0.005 (0.004) Batch 0.341 (0.336) Remain 00:57:52 loss: 0.2302 Lr: 0.00027 [2023-12-20 20:42:39,395 INFO misc.py line 119 131400] Train: [88/100][64/800] Data 0.005 (0.004) Batch 0.366 (0.336) Remain 00:57:57 loss: 0.1928 Lr: 0.00027 [2023-12-20 20:42:39,744 INFO misc.py line 119 131400] Train: [88/100][65/800] Data 0.004 (0.004) Batch 0.348 (0.337) Remain 00:57:58 loss: 0.2303 Lr: 0.00027 [2023-12-20 20:42:40,072 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [88/100][85/800] Data 0.003 (0.004) Batch 0.343 (0.335) Remain 00:57:37 loss: 0.1633 Lr: 0.00027 [2023-12-20 20:42:46,687 INFO misc.py line 119 131400] Train: [88/100][86/800] Data 0.003 (0.004) Batch 0.326 (0.335) Remain 00:57:36 loss: 0.1879 Lr: 0.00027 [2023-12-20 20:42:47,017 INFO misc.py line 119 131400] Train: [88/100][87/800] Data 0.004 (0.004) Batch 0.330 (0.335) Remain 00:57:35 loss: 0.1172 Lr: 0.00027 [2023-12-20 20:42:47,390 INFO misc.py line 119 131400] Train: [88/100][88/800] Data 0.004 (0.004) Batch 0.373 (0.335) Remain 00:57:39 loss: 0.2845 Lr: 0.00027 [2023-12-20 20:42:47,714 INFO misc.py line 119 131400] Train: [88/100][89/800] Data 0.004 (0.004) Batch 0.324 (0.335) Remain 00:57:37 loss: 0.1483 Lr: 0.00027 [2023-12-20 20:42:48,046 INFO misc.py line 119 131400] Train: [88/100][90/800] Data 0.005 (0.004) Batch 0.332 (0.335) Remain 00:57:37 loss: 0.3259 Lr: 0.00027 [2023-12-20 20:42:48,407 INFO misc.py line 119 131400] Train: [88/100][91/800] Data 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loss: 0.2548 Lr: 0.00024 [2023-12-20 20:46:26,984 INFO misc.py line 119 131400] Train: [88/100][745/800] Data 0.003 (0.004) Batch 0.332 (0.334) Remain 00:53:48 loss: 0.2929 Lr: 0.00024 [2023-12-20 20:46:27,328 INFO misc.py line 119 131400] Train: [88/100][746/800] Data 0.003 (0.004) Batch 0.342 (0.334) Remain 00:53:48 loss: 0.1457 Lr: 0.00024 [2023-12-20 20:46:27,673 INFO misc.py line 119 131400] Train: [88/100][747/800] Data 0.005 (0.004) Batch 0.346 (0.334) Remain 00:53:48 loss: 0.2017 Lr: 0.00024 [2023-12-20 20:46:28,044 INFO misc.py line 119 131400] Train: [88/100][748/800] Data 0.004 (0.004) Batch 0.371 (0.334) Remain 00:53:48 loss: 0.3465 Lr: 0.00024 [2023-12-20 20:46:28,359 INFO misc.py line 119 131400] Train: [88/100][749/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:53:47 loss: 0.1594 Lr: 0.00024 [2023-12-20 20:46:28,684 INFO misc.py line 119 131400] Train: [88/100][750/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 00:53:47 loss: 0.2029 Lr: 0.00024 [2023-12-20 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131400] Train: [88/100][757/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:53:45 loss: 0.1368 Lr: 0.00024 [2023-12-20 20:46:31,390 INFO misc.py line 119 131400] Train: [88/100][758/800] Data 0.003 (0.004) Batch 0.320 (0.334) Remain 00:53:44 loss: 0.2849 Lr: 0.00024 [2023-12-20 20:46:31,709 INFO misc.py line 119 131400] Train: [88/100][759/800] Data 0.003 (0.004) Batch 0.319 (0.334) Remain 00:53:44 loss: 0.2544 Lr: 0.00024 [2023-12-20 20:46:32,031 INFO misc.py line 119 131400] Train: [88/100][760/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 00:53:43 loss: 0.1751 Lr: 0.00024 [2023-12-20 20:46:32,340 INFO misc.py line 119 131400] Train: [88/100][761/800] Data 0.004 (0.004) Batch 0.310 (0.334) Remain 00:53:43 loss: 0.1567 Lr: 0.00024 [2023-12-20 20:46:32,659 INFO misc.py line 119 131400] Train: [88/100][762/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:53:42 loss: 0.1250 Lr: 0.00024 [2023-12-20 20:46:32,953 INFO misc.py line 119 131400] Train: [88/100][763/800] Data 0.007 (0.004) Batch 0.298 (0.334) Remain 00:53:41 loss: 0.1750 Lr: 0.00024 [2023-12-20 20:46:33,254 INFO misc.py line 119 131400] Train: [88/100][764/800] Data 0.004 (0.004) Batch 0.301 (0.334) Remain 00:53:41 loss: 0.3318 Lr: 0.00024 [2023-12-20 20:46:33,596 INFO misc.py line 119 131400] Train: [88/100][765/800] Data 0.004 (0.004) Batch 0.342 (0.334) Remain 00:53:40 loss: 0.1237 Lr: 0.00024 [2023-12-20 20:46:33,915 INFO misc.py line 119 131400] Train: [88/100][766/800] Data 0.003 (0.004) Batch 0.319 (0.334) Remain 00:53:40 loss: 0.2568 Lr: 0.00024 [2023-12-20 20:46:34,220 INFO misc.py line 119 131400] Train: [88/100][767/800] Data 0.004 (0.004) Batch 0.306 (0.334) Remain 00:53:39 loss: 0.1502 Lr: 0.00024 [2023-12-20 20:46:34,529 INFO misc.py line 119 131400] Train: [88/100][768/800] Data 0.003 (0.004) Batch 0.308 (0.334) Remain 00:53:38 loss: 0.2214 Lr: 0.00024 [2023-12-20 20:46:34,846 INFO misc.py line 119 131400] Train: [88/100][769/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:53:38 loss: 0.1068 Lr: 0.00024 [2023-12-20 20:46:35,165 INFO misc.py line 119 131400] Train: [88/100][770/800] Data 0.005 (0.004) Batch 0.321 (0.334) Remain 00:53:37 loss: 0.2146 Lr: 0.00024 [2023-12-20 20:46:35,478 INFO misc.py line 119 131400] Train: [88/100][771/800] Data 0.002 (0.004) Batch 0.313 (0.334) Remain 00:53:37 loss: 0.1630 Lr: 0.00024 [2023-12-20 20:46:35,753 INFO misc.py line 119 131400] Train: [88/100][772/800] Data 0.003 (0.004) Batch 0.275 (0.334) Remain 00:53:36 loss: 0.0998 Lr: 0.00024 [2023-12-20 20:46:36,068 INFO misc.py line 119 131400] Train: [88/100][773/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:53:35 loss: 0.1598 Lr: 0.00024 [2023-12-20 20:46:36,384 INFO misc.py line 119 131400] Train: [88/100][774/800] Data 0.004 (0.004) Batch 0.316 (0.334) Remain 00:53:35 loss: 0.3118 Lr: 0.00024 [2023-12-20 20:46:36,703 INFO misc.py line 119 131400] Train: [88/100][775/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:53:34 loss: 0.4402 Lr: 0.00024 [2023-12-20 20:46:37,008 INFO misc.py line 119 131400] Train: [88/100][776/800] Data 0.004 (0.004) Batch 0.305 (0.334) Remain 00:53:33 loss: 0.1926 Lr: 0.00024 [2023-12-20 20:46:37,373 INFO misc.py line 119 131400] Train: [88/100][777/800] Data 0.004 (0.004) Batch 0.365 (0.334) Remain 00:53:33 loss: 0.2216 Lr: 0.00024 [2023-12-20 20:46:37,687 INFO misc.py line 119 131400] Train: [88/100][778/800] Data 0.005 (0.004) Batch 0.314 (0.334) Remain 00:53:33 loss: 0.2475 Lr: 0.00024 [2023-12-20 20:46:38,167 INFO misc.py line 119 131400] Train: [88/100][779/800] Data 0.003 (0.004) Batch 0.481 (0.334) Remain 00:53:34 loss: 0.1438 Lr: 0.00024 [2023-12-20 20:46:38,463 INFO misc.py line 119 131400] Train: [88/100][780/800] Data 0.003 (0.004) Batch 0.296 (0.334) Remain 00:53:33 loss: 0.2072 Lr: 0.00024 [2023-12-20 20:46:38,808 INFO misc.py line 119 131400] Train: [88/100][781/800] Data 0.003 (0.004) Batch 0.344 (0.334) Remain 00:53:33 loss: 0.2747 Lr: 0.00024 [2023-12-20 20:46:39,153 INFO misc.py line 119 131400] Train: [88/100][782/800] Data 0.005 (0.004) Batch 0.346 (0.334) Remain 00:53:33 loss: 0.1839 Lr: 0.00024 [2023-12-20 20:46:39,448 INFO misc.py line 119 131400] Train: [88/100][783/800] Data 0.004 (0.004) Batch 0.294 (0.334) Remain 00:53:32 loss: 0.3023 Lr: 0.00024 [2023-12-20 20:46:39,787 INFO misc.py line 119 131400] Train: [88/100][784/800] Data 0.004 (0.004) Batch 0.339 (0.334) Remain 00:53:32 loss: 0.1759 Lr: 0.00024 [2023-12-20 20:46:40,135 INFO misc.py line 119 131400] Train: [88/100][785/800] Data 0.005 (0.004) Batch 0.349 (0.334) Remain 00:53:32 loss: 0.1798 Lr: 0.00024 [2023-12-20 20:46:40,487 INFO misc.py line 119 131400] Train: [88/100][786/800] Data 0.003 (0.004) Batch 0.352 (0.334) Remain 00:53:32 loss: 0.2115 Lr: 0.00024 [2023-12-20 20:46:40,823 INFO misc.py line 119 131400] Train: [88/100][787/800] Data 0.003 (0.004) Batch 0.336 (0.334) Remain 00:53:31 loss: 0.2116 Lr: 0.00024 [2023-12-20 20:46:41,164 INFO misc.py line 119 131400] Train: [88/100][788/800] Data 0.003 (0.004) Batch 0.340 (0.334) Remain 00:53:31 loss: 0.1356 Lr: 0.00024 [2023-12-20 20:46:41,490 INFO misc.py line 119 131400] Train: [88/100][789/800] Data 0.003 (0.004) Batch 0.326 (0.334) Remain 00:53:31 loss: 0.1452 Lr: 0.00024 [2023-12-20 20:46:41,795 INFO misc.py line 119 131400] Train: [88/100][790/800] Data 0.003 (0.004) Batch 0.305 (0.334) Remain 00:53:30 loss: 0.2138 Lr: 0.00024 [2023-12-20 20:46:42,113 INFO misc.py line 119 131400] Train: [88/100][791/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:53:29 loss: 0.2822 Lr: 0.00024 [2023-12-20 20:46:42,400 INFO misc.py line 119 131400] Train: [88/100][792/800] Data 0.003 (0.004) Batch 0.283 (0.334) Remain 00:53:29 loss: 0.1028 Lr: 0.00024 [2023-12-20 20:46:42,701 INFO misc.py line 119 131400] Train: [88/100][793/800] Data 0.007 (0.004) Batch 0.304 (0.334) Remain 00:53:28 loss: 0.2456 Lr: 0.00024 [2023-12-20 20:46:43,018 INFO misc.py line 119 131400] Train: [88/100][794/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:53:27 loss: 0.1166 Lr: 0.00024 [2023-12-20 20:46:43,339 INFO misc.py line 119 131400] Train: [88/100][795/800] Data 0.003 (0.004) Batch 0.321 (0.334) Remain 00:53:27 loss: 0.2332 Lr: 0.00024 [2023-12-20 20:46:43,648 INFO misc.py line 119 131400] Train: [88/100][796/800] Data 0.003 (0.004) Batch 0.309 (0.334) Remain 00:53:26 loss: 0.1801 Lr: 0.00024 [2023-12-20 20:46:43,923 INFO misc.py line 119 131400] Train: [88/100][797/800] Data 0.002 (0.004) Batch 0.274 (0.334) Remain 00:53:25 loss: 0.1496 Lr: 0.00024 [2023-12-20 20:46:44,203 INFO misc.py line 119 131400] Train: [88/100][798/800] Data 0.003 (0.004) Batch 0.279 (0.334) Remain 00:53:24 loss: 0.2115 Lr: 0.00024 [2023-12-20 20:46:44,506 INFO misc.py line 119 131400] Train: [88/100][799/800] Data 0.005 (0.004) Batch 0.302 (0.334) Remain 00:53:23 loss: 0.1307 Lr: 0.00024 [2023-12-20 20:46:44,829 INFO misc.py line 119 131400] Train: [88/100][800/800] Data 0.004 (0.004) Batch 0.325 (0.334) Remain 00:53:23 loss: 0.2395 Lr: 0.00024 [2023-12-20 20:46:44,830 INFO misc.py line 136 131400] Train result: loss: 0.2089 [2023-12-20 20:46:44,830 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:47:07,247 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2231 [2023-12-20 20:47:07,325 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1754 [2023-12-20 20:47:07,424 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5272 [2023-12-20 20:47:07,532 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4376 [2023-12-20 20:47:07,647 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4030 [2023-12-20 20:47:07,748 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5021 [2023-12-20 20:47:07,838 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9688 [2023-12-20 20:47:07,946 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6335 [2023-12-20 20:47:08,029 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2804 [2023-12-20 20:47:08,117 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3134 [2023-12-20 20:47:08,210 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3880 [2023-12-20 20:47:08,347 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2794 [2023-12-20 20:47:08,469 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5379 [2023-12-20 20:47:08,626 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1825 [2023-12-20 20:47:08,720 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1459 [2023-12-20 20:47:08,854 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7015 [2023-12-20 20:47:08,965 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2703 [2023-12-20 20:47:09,076 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6684 [2023-12-20 20:47:09,191 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2172 [2023-12-20 20:47:09,269 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3809 [2023-12-20 20:47:09,376 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1464 [2023-12-20 20:47:09,550 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1497 [2023-12-20 20:47:09,677 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.5179 [2023-12-20 20:47:09,819 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1366 [2023-12-20 20:47:09,966 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1524 [2023-12-20 20:47:10,059 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7077 [2023-12-20 20:47:10,217 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8073 [2023-12-20 20:47:10,348 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6396 [2023-12-20 20:47:10,443 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5658 [2023-12-20 20:47:10,592 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7063 [2023-12-20 20:47:10,705 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4592 [2023-12-20 20:47:10,842 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3565 [2023-12-20 20:47:10,936 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1089 [2023-12-20 20:47:11,020 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1726 [2023-12-20 20:47:11,132 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8187 [2023-12-20 20:47:11,233 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2935 [2023-12-20 20:47:11,366 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9802 [2023-12-20 20:47:11,483 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0861 [2023-12-20 20:47:11,562 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5476 [2023-12-20 20:47:11,705 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2662 [2023-12-20 20:47:11,859 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0182 [2023-12-20 20:47:11,963 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0650 [2023-12-20 20:47:12,092 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3147 [2023-12-20 20:47:12,236 INFO evaluator.py line 159 131400] Test: [44/78] Loss 0.9921 [2023-12-20 20:47:12,360 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4862 [2023-12-20 20:47:12,465 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.4388 [2023-12-20 20:47:12,637 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3004 [2023-12-20 20:47:12,730 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5633 [2023-12-20 20:47:12,876 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7442 [2023-12-20 20:47:12,966 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2145 [2023-12-20 20:47:13,043 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6442 [2023-12-20 20:47:13,148 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.2586 [2023-12-20 20:47:13,295 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2691 [2023-12-20 20:47:13,429 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3639 [2023-12-20 20:47:13,532 INFO evaluator.py line 159 131400] Test: [55/78] Loss 0.9931 [2023-12-20 20:47:13,618 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6863 [2023-12-20 20:47:13,721 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.2724 [2023-12-20 20:47:13,888 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2243 [2023-12-20 20:47:13,984 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5271 [2023-12-20 20:47:14,079 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1679 [2023-12-20 20:47:14,175 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4676 [2023-12-20 20:47:14,265 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2268 [2023-12-20 20:47:14,352 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6423 [2023-12-20 20:47:14,453 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6253 [2023-12-20 20:47:14,577 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7209 [2023-12-20 20:47:14,661 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2573 [2023-12-20 20:47:14,760 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.5192 [2023-12-20 20:47:14,856 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0087 [2023-12-20 20:47:14,941 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3261 [2023-12-20 20:47:15,030 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0082 [2023-12-20 20:47:15,127 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8549 [2023-12-20 20:47:15,227 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.4948 [2023-12-20 20:47:15,368 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0568 [2023-12-20 20:47:15,463 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6598 [2023-12-20 20:47:15,586 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6134 [2023-12-20 20:47:15,690 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5814 [2023-12-20 20:47:15,776 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.2336 [2023-12-20 20:47:15,933 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9979 [2023-12-20 20:47:17,306 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7696/0.8449/0.9215. [2023-12-20 20:47:17,306 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8720/0.9579 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9853 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7158/0.8119 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8416/0.8904 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9261/0.9640 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8608/0.9313 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7788/0.8588 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7319/0.8365 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7109/0.8160 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8434/0.9232 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4101/0.5052 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7108/0.7994 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6987/0.8592 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7914/0.8607 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6833/0.7497 [2023-12-20 20:47:17,307 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7179/0.7735 [2023-12-20 20:47:17,308 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9347/0.9796 [2023-12-20 20:47:17,308 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6827/0.7906 [2023-12-20 20:47:17,308 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8888/0.9245 [2023-12-20 20:47:17,308 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6284/0.6804 [2023-12-20 20:47:17,308 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:47:17,309 INFO misc.py line 165 131400] Currently Best mIoU: 0.7712 [2023-12-20 20:47:17,310 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:47:21,440 INFO misc.py line 119 131400] Train: [89/100][1/800] Data 1.306 (1.306) Batch 1.641 (1.641) Remain 04:22:36 loss: 0.1144 Lr: 0.00024 [2023-12-20 20:47:21,777 INFO misc.py line 119 131400] Train: [89/100][2/800] Data 0.006 (0.006) Batch 0.339 (0.339) Remain 00:54:10 loss: 0.2636 Lr: 0.00024 [2023-12-20 20:47:22,129 INFO misc.py line 119 131400] Train: [89/100][3/800] Data 0.003 (0.003) Batch 0.351 (0.351) Remain 00:56:12 loss: 0.1687 Lr: 0.00023 [2023-12-20 20:47:22,487 INFO misc.py line 119 131400] Train: [89/100][4/800] Data 0.004 (0.004) Batch 0.359 (0.359) Remain 00:57:24 loss: 0.1364 Lr: 0.00023 [2023-12-20 20:47:22,817 INFO misc.py line 119 131400] Train: [89/100][5/800] Data 0.003 (0.004) Batch 0.329 (0.344) Remain 00:55:02 loss: 0.2178 Lr: 0.00023 [2023-12-20 20:47:23,177 INFO misc.py line 119 131400] Train: [89/100][6/800] Data 0.003 (0.004) Batch 0.360 (0.350) Remain 00:55:53 loss: 0.1600 Lr: 0.00023 [2023-12-20 20:47:23,518 INFO misc.py line 119 131400] Train: [89/100][7/800] Data 0.004 (0.004) Batch 0.340 (0.347) Remain 00:55:31 loss: 0.2017 Lr: 0.00023 [2023-12-20 20:47:23,884 INFO misc.py line 119 131400] Train: [89/100][8/800] Data 0.004 (0.004) Batch 0.367 (0.351) Remain 00:56:08 loss: 0.1674 Lr: 0.00023 [2023-12-20 20:47:24,208 INFO misc.py line 119 131400] Train: [89/100][9/800] Data 0.004 (0.004) Batch 0.324 (0.347) Remain 00:55:24 loss: 0.1401 Lr: 0.00023 [2023-12-20 20:47:24,548 INFO misc.py line 119 131400] Train: [89/100][10/800] Data 0.004 (0.004) Batch 0.340 (0.346) Remain 00:55:14 loss: 0.2414 Lr: 0.00023 [2023-12-20 20:47:24,873 INFO misc.py line 119 131400] Train: [89/100][11/800] Data 0.004 (0.004) Batch 0.325 (0.343) Remain 00:54:50 loss: 0.1687 Lr: 0.00023 [2023-12-20 20:47:25,207 INFO misc.py line 119 131400] Train: [89/100][12/800] Data 0.003 (0.004) Batch 0.334 (0.342) Remain 00:54:40 loss: 0.2225 Lr: 0.00023 [2023-12-20 20:47:25,542 INFO misc.py line 119 131400] Train: [89/100][13/800] Data 0.004 (0.004) Batch 0.335 (0.341) Remain 00:54:33 loss: 0.1409 Lr: 0.00023 [2023-12-20 20:47:25,865 INFO misc.py line 119 131400] Train: [89/100][14/800] Data 0.003 (0.004) Batch 0.322 (0.340) Remain 00:54:16 loss: 0.2404 Lr: 0.00023 [2023-12-20 20:47:26,179 INFO misc.py line 119 131400] Train: [89/100][15/800] Data 0.003 (0.004) Batch 0.313 (0.337) Remain 00:53:54 loss: 0.1514 Lr: 0.00023 [2023-12-20 20:47:26,503 INFO misc.py line 119 131400] Train: [89/100][16/800] Data 0.004 (0.004) Batch 0.324 (0.336) Remain 00:53:44 loss: 0.4492 Lr: 0.00023 [2023-12-20 20:47:26,829 INFO misc.py line 119 131400] Train: [89/100][17/800] Data 0.005 (0.004) Batch 0.326 (0.336) Remain 00:53:36 loss: 0.1328 Lr: 0.00023 [2023-12-20 20:47:27,154 INFO misc.py line 119 131400] Train: [89/100][18/800] Data 0.004 (0.004) Batch 0.325 (0.335) Remain 00:53:29 loss: 0.1664 Lr: 0.00023 [2023-12-20 20:47:27,489 INFO misc.py line 119 131400] Train: [89/100][19/800] Data 0.005 (0.004) Batch 0.333 (0.335) Remain 00:53:28 loss: 0.1787 Lr: 0.00023 [2023-12-20 20:47:27,803 INFO misc.py line 119 131400] Train: [89/100][20/800] Data 0.007 (0.004) Batch 0.317 (0.334) Remain 00:53:17 loss: 0.2401 Lr: 0.00023 [2023-12-20 20:47:28,130 INFO misc.py line 119 131400] Train: [89/100][21/800] Data 0.003 (0.004) Batch 0.326 (0.333) Remain 00:53:13 loss: 0.3140 Lr: 0.00023 [2023-12-20 20:47:28,478 INFO misc.py line 119 131400] Train: [89/100][22/800] Data 0.005 (0.004) Batch 0.349 (0.334) Remain 00:53:20 loss: 0.2003 Lr: 0.00023 [2023-12-20 20:47:28,813 INFO misc.py line 119 131400] Train: [89/100][23/800] Data 0.004 (0.004) Batch 0.336 (0.334) Remain 00:53:21 loss: 0.1278 Lr: 0.00023 [2023-12-20 20:47:29,150 INFO misc.py line 119 131400] Train: [89/100][24/800] Data 0.003 (0.004) Batch 0.335 (0.334) Remain 00:53:21 loss: 0.1410 Lr: 0.00023 [2023-12-20 20:47:29,467 INFO misc.py line 119 131400] Train: [89/100][25/800] Data 0.005 (0.004) Batch 0.314 (0.333) Remain 00:53:12 loss: 0.2022 Lr: 0.00023 [2023-12-20 20:47:29,820 INFO misc.py line 119 131400] Train: [89/100][26/800] Data 0.008 (0.004) Batch 0.357 (0.334) Remain 00:53:21 loss: 0.1660 Lr: 0.00023 [2023-12-20 20:47:30,108 INFO misc.py line 119 131400] Train: [89/100][27/800] Data 0.003 (0.004) Batch 0.287 (0.332) Remain 00:53:02 loss: 0.1472 Lr: 0.00023 [2023-12-20 20:47:30,428 INFO misc.py line 119 131400] Train: [89/100][28/800] Data 0.003 (0.004) Batch 0.320 (0.332) Remain 00:52:57 loss: 0.0955 Lr: 0.00023 [2023-12-20 20:47:30,733 INFO misc.py line 119 131400] Train: [89/100][29/800] Data 0.003 (0.004) Batch 0.300 (0.331) Remain 00:52:45 loss: 0.2104 Lr: 0.00023 [2023-12-20 20:47:31,085 INFO misc.py line 119 131400] Train: [89/100][30/800] Data 0.009 (0.004) Batch 0.355 (0.332) Remain 00:52:53 loss: 0.1486 Lr: 0.00023 [2023-12-20 20:47:31,394 INFO misc.py line 119 131400] Train: [89/100][31/800] Data 0.005 (0.004) Batch 0.310 (0.331) Remain 00:52:46 loss: 0.1953 Lr: 0.00023 [2023-12-20 20:47:31,769 INFO misc.py line 119 131400] Train: [89/100][32/800] Data 0.005 (0.004) Batch 0.369 (0.332) Remain 00:52:58 loss: 0.2680 Lr: 0.00023 [2023-12-20 20:47:32,093 INFO misc.py line 119 131400] Train: [89/100][33/800] Data 0.011 (0.005) Batch 0.330 (0.332) Remain 00:52:57 loss: 0.1979 Lr: 0.00023 [2023-12-20 20:47:32,410 INFO misc.py line 119 131400] Train: [89/100][34/800] Data 0.005 (0.005) Batch 0.318 (0.332) Remain 00:52:52 loss: 0.1790 Lr: 0.00023 [2023-12-20 20:47:32,754 INFO misc.py line 119 131400] Train: [89/100][35/800] Data 0.003 (0.005) Batch 0.344 (0.332) Remain 00:52:55 loss: 0.1353 Lr: 0.00023 [2023-12-20 20:47:33,088 INFO misc.py line 119 131400] Train: [89/100][36/800] Data 0.004 (0.005) Batch 0.332 (0.332) Remain 00:52:55 loss: 0.1551 Lr: 0.00023 [2023-12-20 20:47:33,455 INFO misc.py line 119 131400] Train: [89/100][37/800] Data 0.006 (0.005) Batch 0.368 (0.333) Remain 00:53:05 loss: 0.3057 Lr: 0.00023 [2023-12-20 20:47:33,777 INFO misc.py line 119 131400] Train: [89/100][38/800] Data 0.004 (0.005) Batch 0.320 (0.333) Remain 00:53:01 loss: 0.0801 Lr: 0.00023 [2023-12-20 20:47:34,114 INFO misc.py line 119 131400] Train: [89/100][39/800] Data 0.006 (0.005) Batch 0.338 (0.333) Remain 00:53:02 loss: 0.2525 Lr: 0.00023 [2023-12-20 20:47:34,451 INFO misc.py line 119 131400] Train: [89/100][40/800] Data 0.007 (0.005) Batch 0.338 (0.333) Remain 00:53:03 loss: 0.1401 Lr: 0.00023 [2023-12-20 20:47:34,783 INFO misc.py line 119 131400] Train: [89/100][41/800] Data 0.005 (0.005) Batch 0.333 (0.333) Remain 00:53:03 loss: 0.2350 Lr: 0.00023 [2023-12-20 20:47:35,101 INFO misc.py line 119 131400] Train: [89/100][42/800] Data 0.005 (0.005) Batch 0.318 (0.333) Remain 00:52:59 loss: 0.3003 Lr: 0.00023 [2023-12-20 20:47:35,406 INFO misc.py line 119 131400] Train: [89/100][43/800] Data 0.005 (0.005) Batch 0.306 (0.332) Remain 00:52:52 loss: 0.1154 Lr: 0.00023 [2023-12-20 20:47:35,753 INFO misc.py line 119 131400] Train: [89/100][44/800] Data 0.004 (0.005) Batch 0.347 (0.332) Remain 00:52:55 loss: 0.1667 Lr: 0.00023 [2023-12-20 20:47:36,084 INFO misc.py line 119 131400] Train: [89/100][45/800] Data 0.003 (0.005) Batch 0.328 (0.332) Remain 00:52:54 loss: 0.1449 Lr: 0.00023 [2023-12-20 20:47:36,400 INFO misc.py line 119 131400] Train: [89/100][46/800] Data 0.007 (0.005) Batch 0.318 (0.332) Remain 00:52:50 loss: 0.1994 Lr: 0.00023 [2023-12-20 20:47:36,752 INFO misc.py line 119 131400] Train: [89/100][47/800] Data 0.004 (0.005) Batch 0.352 (0.332) Remain 00:52:54 loss: 0.3126 Lr: 0.00023 [2023-12-20 20:47:37,057 INFO misc.py line 119 131400] Train: [89/100][48/800] Data 0.004 (0.005) Batch 0.306 (0.332) Remain 00:52:48 loss: 0.1347 Lr: 0.00023 [2023-12-20 20:47:37,387 INFO misc.py line 119 131400] Train: [89/100][49/800] Data 0.004 (0.005) Batch 0.329 (0.332) Remain 00:52:47 loss: 0.1674 Lr: 0.00023 [2023-12-20 20:47:37,708 INFO misc.py line 119 131400] Train: [89/100][50/800] Data 0.004 (0.005) Batch 0.323 (0.331) Remain 00:52:45 loss: 0.3341 Lr: 0.00023 [2023-12-20 20:47:38,019 INFO misc.py line 119 131400] Train: [89/100][51/800] Data 0.003 (0.005) Batch 0.308 (0.331) Remain 00:52:40 loss: 0.2556 Lr: 0.00023 [2023-12-20 20:47:38,343 INFO misc.py line 119 131400] Train: [89/100][52/800] Data 0.005 (0.005) Batch 0.325 (0.331) Remain 00:52:39 loss: 0.1276 Lr: 0.00023 [2023-12-20 20:47:38,656 INFO misc.py line 119 131400] Train: [89/100][53/800] Data 0.005 (0.005) Batch 0.315 (0.331) Remain 00:52:35 loss: 0.1888 Lr: 0.00023 [2023-12-20 20:47:38,963 INFO misc.py line 119 131400] Train: [89/100][54/800] Data 0.003 (0.005) Batch 0.307 (0.330) Remain 00:52:31 loss: 0.1765 Lr: 0.00023 [2023-12-20 20:47:39,300 INFO misc.py line 119 131400] Train: [89/100][55/800] Data 0.004 (0.005) Batch 0.336 (0.330) Remain 00:52:31 loss: 0.1849 Lr: 0.00023 [2023-12-20 20:47:39,651 INFO misc.py line 119 131400] Train: [89/100][56/800] Data 0.004 (0.005) Batch 0.352 (0.331) Remain 00:52:35 loss: 0.1468 Lr: 0.00023 [2023-12-20 20:47:39,976 INFO misc.py line 119 131400] Train: [89/100][57/800] Data 0.004 (0.005) Batch 0.325 (0.331) Remain 00:52:33 loss: 0.1743 Lr: 0.00023 [2023-12-20 20:47:40,286 INFO misc.py line 119 131400] Train: [89/100][58/800] Data 0.005 (0.005) Batch 0.310 (0.330) Remain 00:52:30 loss: 0.1076 Lr: 0.00023 [2023-12-20 20:47:40,615 INFO misc.py line 119 131400] Train: [89/100][59/800] Data 0.005 (0.005) Batch 0.329 (0.330) Remain 00:52:29 loss: 0.1555 Lr: 0.00023 [2023-12-20 20:47:40,892 INFO misc.py line 119 131400] Train: [89/100][60/800] Data 0.004 (0.005) Batch 0.277 (0.329) Remain 00:52:20 loss: 0.2085 Lr: 0.00023 [2023-12-20 20:47:41,241 INFO misc.py line 119 131400] Train: [89/100][61/800] Data 0.004 (0.005) Batch 0.348 (0.330) Remain 00:52:23 loss: 0.1488 Lr: 0.00023 [2023-12-20 20:47:41,604 INFO misc.py line 119 131400] Train: [89/100][62/800] Data 0.004 (0.005) Batch 0.363 (0.330) Remain 00:52:28 loss: 0.2085 Lr: 0.00023 [2023-12-20 20:47:41,956 INFO misc.py line 119 131400] Train: [89/100][63/800] Data 0.005 (0.005) Batch 0.353 (0.330) Remain 00:52:31 loss: 0.1100 Lr: 0.00023 [2023-12-20 20:47:42,315 INFO misc.py line 119 131400] Train: [89/100][64/800] Data 0.004 (0.005) Batch 0.359 (0.331) Remain 00:52:35 loss: 0.1487 Lr: 0.00023 [2023-12-20 20:47:42,662 INFO misc.py line 119 131400] Train: [89/100][65/800] Data 0.004 (0.005) Batch 0.345 (0.331) Remain 00:52:37 loss: 0.2763 Lr: 0.00023 [2023-12-20 20:47:42,984 INFO misc.py line 119 131400] Train: 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0.336 (0.333) Remain 00:52:50 loss: 0.1251 Lr: 0.00023 [2023-12-20 20:47:45,426 INFO misc.py line 119 131400] Train: [89/100][73/800] Data 0.003 (0.005) Batch 0.339 (0.333) Remain 00:52:50 loss: 0.2190 Lr: 0.00023 [2023-12-20 20:47:45,731 INFO misc.py line 119 131400] Train: [89/100][74/800] Data 0.004 (0.005) Batch 0.305 (0.332) Remain 00:52:46 loss: 0.2056 Lr: 0.00023 [2023-12-20 20:47:46,096 INFO misc.py line 119 131400] Train: [89/100][75/800] Data 0.005 (0.005) Batch 0.365 (0.333) Remain 00:52:50 loss: 0.6208 Lr: 0.00023 [2023-12-20 20:47:46,458 INFO misc.py line 119 131400] Train: [89/100][76/800] Data 0.004 (0.005) Batch 0.362 (0.333) Remain 00:52:54 loss: 0.4210 Lr: 0.00023 [2023-12-20 20:47:46,828 INFO misc.py line 119 131400] Train: [89/100][77/800] Data 0.004 (0.005) Batch 0.370 (0.334) Remain 00:52:58 loss: 0.1779 Lr: 0.00023 [2023-12-20 20:47:47,153 INFO misc.py line 119 131400] Train: [89/100][78/800] Data 0.003 (0.005) Batch 0.323 (0.334) Remain 00:52:56 loss: 0.2238 Lr: 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Batch 0.350 (0.337) Remain 00:49:43 loss: 0.2444 Lr: 0.00020 [2023-12-20 20:51:29,909 INFO misc.py line 119 131400] Train: [89/100][739/800] Data 0.004 (0.005) Batch 0.350 (0.337) Remain 00:49:43 loss: 0.2398 Lr: 0.00020 [2023-12-20 20:51:30,226 INFO misc.py line 119 131400] Train: [89/100][740/800] Data 0.004 (0.005) Batch 0.316 (0.337) Remain 00:49:42 loss: 0.2280 Lr: 0.00020 [2023-12-20 20:51:30,559 INFO misc.py line 119 131400] Train: [89/100][741/800] Data 0.005 (0.005) Batch 0.334 (0.337) Remain 00:49:42 loss: 0.2545 Lr: 0.00020 [2023-12-20 20:51:30,887 INFO misc.py line 119 131400] Train: [89/100][742/800] Data 0.004 (0.005) Batch 0.327 (0.337) Remain 00:49:41 loss: 0.6010 Lr: 0.00020 [2023-12-20 20:51:31,186 INFO misc.py line 119 131400] Train: [89/100][743/800] Data 0.004 (0.005) Batch 0.300 (0.337) Remain 00:49:40 loss: 0.1790 Lr: 0.00020 [2023-12-20 20:51:31,532 INFO misc.py line 119 131400] Train: [89/100][744/800] Data 0.004 (0.005) Batch 0.346 (0.337) Remain 00:49:40 loss: 0.2719 Lr: 0.00020 [2023-12-20 20:51:31,857 INFO misc.py line 119 131400] Train: [89/100][745/800] Data 0.004 (0.005) Batch 0.325 (0.337) Remain 00:49:40 loss: 0.2610 Lr: 0.00020 [2023-12-20 20:51:32,203 INFO misc.py line 119 131400] Train: [89/100][746/800] Data 0.004 (0.005) Batch 0.337 (0.337) Remain 00:49:39 loss: 0.3037 Lr: 0.00020 [2023-12-20 20:51:32,503 INFO misc.py line 119 131400] Train: [89/100][747/800] Data 0.012 (0.005) Batch 0.309 (0.337) Remain 00:49:39 loss: 0.1779 Lr: 0.00020 [2023-12-20 20:51:32,818 INFO misc.py line 119 131400] Train: [89/100][748/800] Data 0.004 (0.005) Batch 0.316 (0.336) Remain 00:49:38 loss: 0.3543 Lr: 0.00020 [2023-12-20 20:51:33,155 INFO misc.py line 119 131400] Train: [89/100][749/800] Data 0.004 (0.005) Batch 0.337 (0.336) Remain 00:49:38 loss: 0.2365 Lr: 0.00020 [2023-12-20 20:51:33,479 INFO misc.py line 119 131400] Train: [89/100][750/800] Data 0.004 (0.005) Batch 0.323 (0.336) Remain 00:49:37 loss: 0.1531 Lr: 0.00020 [2023-12-20 20:51:33,817 INFO misc.py line 119 131400] Train: [89/100][751/800] Data 0.004 (0.005) Batch 0.339 (0.336) Remain 00:49:37 loss: 0.1755 Lr: 0.00020 [2023-12-20 20:51:34,138 INFO misc.py line 119 131400] Train: [89/100][752/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 00:49:36 loss: 0.1790 Lr: 0.00020 [2023-12-20 20:51:34,461 INFO misc.py line 119 131400] Train: [89/100][753/800] Data 0.003 (0.005) Batch 0.323 (0.336) Remain 00:49:36 loss: 0.2005 Lr: 0.00020 [2023-12-20 20:51:34,802 INFO misc.py line 119 131400] Train: [89/100][754/800] Data 0.004 (0.005) Batch 0.342 (0.336) Remain 00:49:36 loss: 0.2033 Lr: 0.00020 [2023-12-20 20:51:35,102 INFO misc.py line 119 131400] Train: [89/100][755/800] Data 0.003 (0.005) Batch 0.299 (0.336) Remain 00:49:35 loss: 0.1737 Lr: 0.00020 [2023-12-20 20:51:35,427 INFO misc.py line 119 131400] Train: [89/100][756/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 00:49:34 loss: 0.2036 Lr: 0.00020 [2023-12-20 20:51:35,717 INFO misc.py line 119 131400] Train: [89/100][757/800] Data 0.008 (0.005) Batch 0.293 (0.336) Remain 00:49:34 loss: 0.1914 Lr: 0.00020 [2023-12-20 20:51:36,073 INFO misc.py line 119 131400] Train: [89/100][758/800] Data 0.004 (0.005) Batch 0.356 (0.336) Remain 00:49:33 loss: 0.2913 Lr: 0.00020 [2023-12-20 20:51:36,396 INFO misc.py line 119 131400] Train: [89/100][759/800] Data 0.004 (0.005) Batch 0.324 (0.336) Remain 00:49:33 loss: 0.1296 Lr: 0.00020 [2023-12-20 20:51:36,702 INFO misc.py line 119 131400] Train: [89/100][760/800] Data 0.003 (0.005) Batch 0.307 (0.336) Remain 00:49:32 loss: 0.1736 Lr: 0.00020 [2023-12-20 20:51:37,021 INFO misc.py line 119 131400] Train: [89/100][761/800] Data 0.003 (0.005) Batch 0.318 (0.336) Remain 00:49:32 loss: 0.2670 Lr: 0.00020 [2023-12-20 20:51:37,351 INFO misc.py line 119 131400] Train: [89/100][762/800] Data 0.004 (0.005) Batch 0.331 (0.336) Remain 00:49:31 loss: 0.2028 Lr: 0.00020 [2023-12-20 20:51:37,686 INFO misc.py line 119 131400] Train: [89/100][763/800] Data 0.004 (0.005) Batch 0.335 (0.336) Remain 00:49:31 loss: 0.1740 Lr: 0.00020 [2023-12-20 20:51:37,994 INFO misc.py line 119 131400] Train: [89/100][764/800] Data 0.002 (0.005) Batch 0.307 (0.336) Remain 00:49:30 loss: 0.1656 Lr: 0.00020 [2023-12-20 20:51:38,330 INFO misc.py line 119 131400] Train: [89/100][765/800] Data 0.003 (0.005) Batch 0.336 (0.336) Remain 00:49:30 loss: 0.3026 Lr: 0.00020 [2023-12-20 20:51:38,663 INFO misc.py line 119 131400] Train: [89/100][766/800] Data 0.004 (0.005) Batch 0.333 (0.336) Remain 00:49:30 loss: 0.2055 Lr: 0.00020 [2023-12-20 20:51:38,988 INFO misc.py line 119 131400] Train: [89/100][767/800] Data 0.003 (0.005) Batch 0.325 (0.336) Remain 00:49:29 loss: 0.1095 Lr: 0.00020 [2023-12-20 20:51:39,323 INFO misc.py line 119 131400] Train: [89/100][768/800] Data 0.004 (0.005) Batch 0.335 (0.336) Remain 00:49:29 loss: 0.2575 Lr: 0.00020 [2023-12-20 20:51:39,606 INFO misc.py line 119 131400] Train: [89/100][769/800] Data 0.004 (0.005) Batch 0.283 (0.336) Remain 00:49:28 loss: 0.1742 Lr: 0.00020 [2023-12-20 20:51:39,938 INFO misc.py line 119 131400] Train: [89/100][770/800] Data 0.003 (0.005) Batch 0.325 (0.336) Remain 00:49:27 loss: 0.1330 Lr: 0.00020 [2023-12-20 20:51:40,214 INFO misc.py line 119 131400] Train: [89/100][771/800] Data 0.010 (0.005) Batch 0.283 (0.336) Remain 00:49:26 loss: 0.1778 Lr: 0.00020 [2023-12-20 20:51:40,544 INFO misc.py line 119 131400] Train: [89/100][772/800] Data 0.003 (0.005) Batch 0.329 (0.336) Remain 00:49:26 loss: 0.1912 Lr: 0.00020 [2023-12-20 20:51:40,840 INFO misc.py line 119 131400] Train: [89/100][773/800] Data 0.003 (0.005) Batch 0.296 (0.336) Remain 00:49:25 loss: 0.2605 Lr: 0.00020 [2023-12-20 20:51:41,177 INFO misc.py line 119 131400] Train: [89/100][774/800] Data 0.002 (0.005) Batch 0.337 (0.336) Remain 00:49:25 loss: 0.3137 Lr: 0.00020 [2023-12-20 20:51:41,525 INFO misc.py line 119 131400] Train: [89/100][775/800] Data 0.003 (0.005) Batch 0.347 (0.336) Remain 00:49:25 loss: 0.2265 Lr: 0.00020 [2023-12-20 20:51:41,824 INFO misc.py line 119 131400] Train: [89/100][776/800] Data 0.005 (0.005) Batch 0.300 (0.336) Remain 00:49:24 loss: 0.1471 Lr: 0.00020 [2023-12-20 20:51:42,160 INFO misc.py line 119 131400] Train: [89/100][777/800] Data 0.003 (0.005) Batch 0.336 (0.336) Remain 00:49:24 loss: 0.2453 Lr: 0.00020 [2023-12-20 20:51:42,490 INFO misc.py line 119 131400] Train: [89/100][778/800] Data 0.004 (0.005) Batch 0.330 (0.336) Remain 00:49:23 loss: 0.1900 Lr: 0.00020 [2023-12-20 20:51:42,813 INFO misc.py line 119 131400] Train: [89/100][779/800] Data 0.004 (0.005) Batch 0.324 (0.336) Remain 00:49:23 loss: 0.1679 Lr: 0.00020 [2023-12-20 20:51:43,177 INFO misc.py line 119 131400] Train: [89/100][780/800] Data 0.003 (0.005) Batch 0.360 (0.336) Remain 00:49:23 loss: 0.0698 Lr: 0.00020 [2023-12-20 20:51:43,474 INFO misc.py line 119 131400] Train: [89/100][781/800] Data 0.007 (0.005) Batch 0.301 (0.336) Remain 00:49:22 loss: 0.1543 Lr: 0.00020 [2023-12-20 20:51:43,782 INFO misc.py line 119 131400] Train: [89/100][782/800] Data 0.002 (0.005) Batch 0.308 (0.336) Remain 00:49:21 loss: 0.2684 Lr: 0.00020 [2023-12-20 20:51:44,109 INFO misc.py line 119 131400] Train: [89/100][783/800] Data 0.003 (0.005) Batch 0.327 (0.336) Remain 00:49:21 loss: 0.2629 Lr: 0.00020 [2023-12-20 20:51:44,469 INFO misc.py line 119 131400] Train: [89/100][784/800] Data 0.003 (0.005) Batch 0.358 (0.336) Remain 00:49:21 loss: 0.1594 Lr: 0.00020 [2023-12-20 20:51:44,769 INFO misc.py line 119 131400] Train: [89/100][785/800] Data 0.005 (0.005) Batch 0.302 (0.336) Remain 00:49:20 loss: 0.3318 Lr: 0.00020 [2023-12-20 20:51:45,083 INFO misc.py line 119 131400] Train: [89/100][786/800] Data 0.003 (0.005) Batch 0.313 (0.336) Remain 00:49:19 loss: 0.3167 Lr: 0.00020 [2023-12-20 20:51:45,414 INFO misc.py line 119 131400] Train: [89/100][787/800] Data 0.004 (0.005) Batch 0.332 (0.336) Remain 00:49:19 loss: 0.4207 Lr: 0.00020 [2023-12-20 20:51:45,737 INFO misc.py line 119 131400] Train: [89/100][788/800] Data 0.004 (0.005) Batch 0.322 (0.336) Remain 00:49:19 loss: 0.2337 Lr: 0.00020 [2023-12-20 20:51:46,064 INFO misc.py line 119 131400] Train: [89/100][789/800] Data 0.005 (0.005) Batch 0.327 (0.336) Remain 00:49:18 loss: 0.2052 Lr: 0.00020 [2023-12-20 20:51:46,398 INFO misc.py line 119 131400] Train: [89/100][790/800] Data 0.004 (0.005) Batch 0.335 (0.336) Remain 00:49:18 loss: 0.3209 Lr: 0.00020 [2023-12-20 20:51:46,733 INFO misc.py line 119 131400] Train: [89/100][791/800] Data 0.004 (0.005) Batch 0.336 (0.336) Remain 00:49:17 loss: 0.1705 Lr: 0.00020 [2023-12-20 20:51:47,029 INFO misc.py line 119 131400] Train: [89/100][792/800] Data 0.003 (0.005) Batch 0.296 (0.336) Remain 00:49:17 loss: 0.2153 Lr: 0.00020 [2023-12-20 20:51:47,347 INFO misc.py line 119 131400] Train: [89/100][793/800] Data 0.003 (0.005) Batch 0.316 (0.336) Remain 00:49:16 loss: 0.1763 Lr: 0.00020 [2023-12-20 20:51:47,640 INFO misc.py line 119 131400] Train: [89/100][794/800] Data 0.005 (0.005) Batch 0.295 (0.336) Remain 00:49:15 loss: 0.2739 Lr: 0.00020 [2023-12-20 20:51:47,936 INFO misc.py line 119 131400] Train: [89/100][795/800] Data 0.003 (0.005) Batch 0.297 (0.336) Remain 00:49:15 loss: 0.2268 Lr: 0.00020 [2023-12-20 20:51:48,277 INFO misc.py line 119 131400] Train: [89/100][796/800] Data 0.002 (0.005) Batch 0.340 (0.336) Remain 00:49:14 loss: 0.2442 Lr: 0.00020 [2023-12-20 20:51:48,588 INFO misc.py line 119 131400] Train: [89/100][797/800] Data 0.004 (0.005) Batch 0.311 (0.336) Remain 00:49:14 loss: 0.2698 Lr: 0.00020 [2023-12-20 20:51:48,917 INFO misc.py line 119 131400] Train: [89/100][798/800] Data 0.004 (0.005) Batch 0.329 (0.336) Remain 00:49:13 loss: 0.1496 Lr: 0.00020 [2023-12-20 20:51:49,287 INFO misc.py line 119 131400] Train: [89/100][799/800] Data 0.004 (0.005) Batch 0.369 (0.336) Remain 00:49:13 loss: 0.2280 Lr: 0.00020 [2023-12-20 20:51:49,612 INFO misc.py line 119 131400] Train: [89/100][800/800] Data 0.003 (0.005) Batch 0.326 (0.336) Remain 00:49:13 loss: 0.2614 Lr: 0.00020 [2023-12-20 20:51:49,612 INFO misc.py line 136 131400] Train result: loss: 0.2108 [2023-12-20 20:51:49,613 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:52:10,712 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2229 [2023-12-20 20:52:11,491 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1520 [2023-12-20 20:52:11,587 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5949 [2023-12-20 20:52:11,698 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5005 [2023-12-20 20:52:11,817 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4077 [2023-12-20 20:52:11,921 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3845 [2023-12-20 20:52:12,017 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9679 [2023-12-20 20:52:12,137 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.5327 [2023-12-20 20:52:12,225 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2897 [2023-12-20 20:52:12,322 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2991 [2023-12-20 20:52:12,420 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4229 [2023-12-20 20:52:12,558 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2715 [2023-12-20 20:52:12,685 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4258 [2023-12-20 20:52:12,842 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1887 [2023-12-20 20:52:12,945 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1371 [2023-12-20 20:52:13,089 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6494 [2023-12-20 20:52:13,209 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2907 [2023-12-20 20:52:13,332 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8361 [2023-12-20 20:52:13,449 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1943 [2023-12-20 20:52:13,525 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3323 [2023-12-20 20:52:13,639 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1716 [2023-12-20 20:52:13,799 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1209 [2023-12-20 20:52:13,922 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7744 [2023-12-20 20:52:14,068 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2373 [2023-12-20 20:52:14,218 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.2224 [2023-12-20 20:52:14,305 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.8623 [2023-12-20 20:52:14,469 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5262 [2023-12-20 20:52:14,598 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5287 [2023-12-20 20:52:14,707 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5162 [2023-12-20 20:52:14,863 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6205 [2023-12-20 20:52:14,969 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4874 [2023-12-20 20:52:15,092 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3841 [2023-12-20 20:52:15,181 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1296 [2023-12-20 20:52:15,266 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1731 [2023-12-20 20:52:15,373 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.9297 [2023-12-20 20:52:15,472 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2804 [2023-12-20 20:52:15,604 INFO evaluator.py line 159 131400] Test: [37/78] Loss 1.0407 [2023-12-20 20:52:15,732 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0800 [2023-12-20 20:52:15,816 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5927 [2023-12-20 20:52:15,964 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2343 [2023-12-20 20:52:16,119 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0158 [2023-12-20 20:52:16,231 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0612 [2023-12-20 20:52:16,352 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3301 [2023-12-20 20:52:16,497 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0559 [2023-12-20 20:52:16,614 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5449 [2023-12-20 20:52:16,717 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6937 [2023-12-20 20:52:16,890 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3222 [2023-12-20 20:52:16,992 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3527 [2023-12-20 20:52:17,138 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6602 [2023-12-20 20:52:17,229 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2084 [2023-12-20 20:52:17,315 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3694 [2023-12-20 20:52:17,421 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3908 [2023-12-20 20:52:17,568 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9579 [2023-12-20 20:52:17,714 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3324 [2023-12-20 20:52:17,825 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3052 [2023-12-20 20:52:17,913 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6042 [2023-12-20 20:52:18,026 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3557 [2023-12-20 20:52:18,197 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2409 [2023-12-20 20:52:18,296 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5994 [2023-12-20 20:52:18,389 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1450 [2023-12-20 20:52:18,485 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4484 [2023-12-20 20:52:18,579 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2357 [2023-12-20 20:52:18,670 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5946 [2023-12-20 20:52:18,779 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.7244 [2023-12-20 20:52:18,907 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5229 [2023-12-20 20:52:18,994 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2411 [2023-12-20 20:52:19,095 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3693 [2023-12-20 20:52:19,190 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0074 [2023-12-20 20:52:19,277 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3191 [2023-12-20 20:52:19,365 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0079 [2023-12-20 20:52:19,460 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9670 [2023-12-20 20:52:19,556 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6540 [2023-12-20 20:52:19,694 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0599 [2023-12-20 20:52:19,793 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6920 [2023-12-20 20:52:19,908 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5809 [2023-12-20 20:52:20,012 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4659 [2023-12-20 20:52:20,107 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3562 [2023-12-20 20:52:20,261 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1265 [2023-12-20 20:52:21,589 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7737/0.8510/0.9226. [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8775/0.9514 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9642/0.9868 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6998/0.8312 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8455/0.8900 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9242/0.9628 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8701/0.9301 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7829/0.8686 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7388/0.8490 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7239/0.8190 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8400/0.9275 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4089/0.5505 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7231/0.8275 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7113/0.8601 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7826/0.8695 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7054/0.7578 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7009/0.7539 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9470/0.9792 [2023-12-20 20:52:21,590 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7044/0.7894 [2023-12-20 20:52:21,591 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8888/0.9275 [2023-12-20 20:52:21,591 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6337/0.6884 [2023-12-20 20:52:21,591 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:52:21,592 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7737 [2023-12-20 20:52:21,592 INFO misc.py line 165 131400] Currently Best mIoU: 0.7737 [2023-12-20 20:52:21,592 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:52:27,927 INFO misc.py line 119 131400] Train: [90/100][1/800] Data 0.889 (0.889) Batch 1.186 (1.186) Remain 02:53:54 loss: 0.1554 Lr: 0.00020 [2023-12-20 20:52:28,251 INFO misc.py line 119 131400] Train: [90/100][2/800] Data 0.003 (0.003) Batch 0.324 (0.324) Remain 00:47:29 loss: 0.1663 Lr: 0.00020 [2023-12-20 20:52:28,551 INFO misc.py line 119 131400] Train: [90/100][3/800] Data 0.004 (0.004) Batch 0.300 (0.300) Remain 00:44:00 loss: 0.2932 Lr: 0.00020 [2023-12-20 20:52:28,869 INFO misc.py line 119 131400] Train: [90/100][4/800] Data 0.004 (0.004) Batch 0.314 (0.314) Remain 00:46:03 loss: 0.1512 Lr: 0.00020 [2023-12-20 20:52:29,207 INFO misc.py line 119 131400] Train: [90/100][5/800] Data 0.008 (0.006) Batch 0.342 (0.328) Remain 00:48:05 loss: 0.1545 Lr: 0.00020 [2023-12-20 20:52:29,540 INFO misc.py line 119 131400] Train: [90/100][6/800] Data 0.003 (0.005) Batch 0.333 (0.330) Remain 00:48:19 loss: 0.1779 Lr: 0.00020 [2023-12-20 20:52:29,880 INFO misc.py line 119 131400] Train: [90/100][7/800] Data 0.005 (0.005) Batch 0.339 (0.332) Remain 00:48:40 loss: 0.2550 Lr: 0.00020 [2023-12-20 20:52:30,223 INFO misc.py line 119 131400] Train: [90/100][8/800] Data 0.004 (0.005) Batch 0.343 (0.334) Remain 00:48:59 loss: 0.2354 Lr: 0.00020 [2023-12-20 20:52:30,569 INFO misc.py line 119 131400] Train: [90/100][9/800] Data 0.004 (0.005) Batch 0.347 (0.336) Remain 00:49:17 loss: 0.1353 Lr: 0.00020 [2023-12-20 20:52:30,861 INFO misc.py line 119 131400] Train: [90/100][10/800] Data 0.003 (0.004) Batch 0.291 (0.330) Remain 00:48:19 loss: 0.2283 Lr: 0.00020 [2023-12-20 20:52:31,185 INFO misc.py line 119 131400] Train: [90/100][11/800] Data 0.005 (0.004) Batch 0.325 (0.329) Remain 00:48:13 loss: 0.1986 Lr: 0.00020 [2023-12-20 20:52:31,517 INFO misc.py line 119 131400] Train: [90/100][12/800] Data 0.004 (0.004) Batch 0.331 (0.329) Remain 00:48:15 loss: 0.2294 Lr: 0.00020 [2023-12-20 20:52:31,877 INFO misc.py line 119 131400] Train: [90/100][13/800] Data 0.005 (0.004) Batch 0.361 (0.333) Remain 00:48:42 loss: 0.3072 Lr: 0.00020 [2023-12-20 20:52:32,193 INFO misc.py line 119 131400] Train: [90/100][14/800] Data 0.004 (0.004) Batch 0.317 (0.331) Remain 00:48:29 loss: 0.1443 Lr: 0.00020 [2023-12-20 20:52:32,552 INFO misc.py line 119 131400] Train: [90/100][15/800] Data 0.003 (0.004) Batch 0.358 (0.333) Remain 00:48:49 loss: 0.2231 Lr: 0.00020 [2023-12-20 20:52:32,877 INFO misc.py line 119 131400] Train: [90/100][16/800] Data 0.003 (0.004) Batch 0.324 (0.333) Remain 00:48:42 loss: 0.6915 Lr: 0.00020 [2023-12-20 20:52:33,182 INFO misc.py line 119 131400] Train: [90/100][17/800] Data 0.004 (0.004) Batch 0.302 (0.331) Remain 00:48:22 loss: 0.1339 Lr: 0.00020 [2023-12-20 20:52:33,491 INFO misc.py line 119 131400] Train: [90/100][18/800] Data 0.007 (0.004) Batch 0.313 (0.329) Remain 00:48:12 loss: 0.1750 Lr: 0.00020 [2023-12-20 20:52:33,815 INFO misc.py line 119 131400] Train: [90/100][19/800] Data 0.003 (0.004) Batch 0.323 (0.329) Remain 00:48:08 loss: 0.2138 Lr: 0.00020 [2023-12-20 20:52:34,193 INFO misc.py line 119 131400] Train: [90/100][20/800] Data 0.004 (0.004) Batch 0.375 (0.332) Remain 00:48:31 loss: 0.3356 Lr: 0.00020 [2023-12-20 20:52:34,507 INFO misc.py line 119 131400] Train: [90/100][21/800] Data 0.007 (0.004) Batch 0.319 (0.331) Remain 00:48:25 loss: 0.1155 Lr: 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line 119 131400] Train: [90/100][28/800] Data 0.003 (0.004) Batch 0.331 (0.330) Remain 00:48:13 loss: 0.1514 Lr: 0.00020 [2023-12-20 20:52:37,121 INFO misc.py line 119 131400] Train: [90/100][29/800] Data 0.004 (0.004) Batch 0.324 (0.330) Remain 00:48:10 loss: 0.1478 Lr: 0.00020 [2023-12-20 20:52:37,462 INFO misc.py line 119 131400] Train: [90/100][30/800] Data 0.003 (0.004) Batch 0.341 (0.330) Remain 00:48:14 loss: 0.1040 Lr: 0.00020 [2023-12-20 20:52:37,811 INFO misc.py line 119 131400] Train: [90/100][31/800] Data 0.004 (0.004) Batch 0.349 (0.331) Remain 00:48:20 loss: 0.2200 Lr: 0.00020 [2023-12-20 20:52:38,128 INFO misc.py line 119 131400] Train: [90/100][32/800] Data 0.004 (0.004) Batch 0.317 (0.330) Remain 00:48:15 loss: 0.3135 Lr: 0.00020 [2023-12-20 20:52:38,458 INFO misc.py line 119 131400] Train: [90/100][33/800] Data 0.003 (0.004) Batch 0.329 (0.330) Remain 00:48:14 loss: 0.2338 Lr: 0.00020 [2023-12-20 20:52:38,778 INFO misc.py line 119 131400] Train: [90/100][34/800] Data 0.004 (0.004) Batch 0.321 (0.330) Remain 00:48:11 loss: 0.1271 Lr: 0.00020 [2023-12-20 20:52:39,112 INFO misc.py line 119 131400] Train: [90/100][35/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 00:48:12 loss: 0.3385 Lr: 0.00020 [2023-12-20 20:52:39,479 INFO misc.py line 119 131400] Train: [90/100][36/800] Data 0.004 (0.004) Batch 0.363 (0.331) Remain 00:48:21 loss: 0.3166 Lr: 0.00020 [2023-12-20 20:52:39,798 INFO misc.py line 119 131400] Train: [90/100][37/800] Data 0.008 (0.004) Batch 0.322 (0.331) Remain 00:48:18 loss: 0.2539 Lr: 0.00020 [2023-12-20 20:52:40,142 INFO misc.py line 119 131400] Train: [90/100][38/800] Data 0.004 (0.004) Batch 0.345 (0.331) Remain 00:48:21 loss: 0.1765 Lr: 0.00020 [2023-12-20 20:52:40,450 INFO misc.py line 119 131400] Train: [90/100][39/800] Data 0.004 (0.004) Batch 0.308 (0.331) Remain 00:48:15 loss: 0.2608 Lr: 0.00020 [2023-12-20 20:52:40,785 INFO misc.py line 119 131400] Train: [90/100][40/800] Data 0.004 (0.004) Batch 0.335 (0.331) Remain 00:48:16 loss: 0.2504 Lr: 0.00020 [2023-12-20 20:52:41,142 INFO misc.py line 119 131400] Train: [90/100][41/800] Data 0.003 (0.004) Batch 0.356 (0.331) Remain 00:48:22 loss: 0.1727 Lr: 0.00020 [2023-12-20 20:52:41,445 INFO misc.py line 119 131400] Train: [90/100][42/800] Data 0.005 (0.004) Batch 0.303 (0.331) Remain 00:48:15 loss: 0.1621 Lr: 0.00020 [2023-12-20 20:52:41,794 INFO misc.py line 119 131400] Train: [90/100][43/800] Data 0.004 (0.004) Batch 0.348 (0.331) Remain 00:48:18 loss: 0.2863 Lr: 0.00020 [2023-12-20 20:52:42,109 INFO misc.py line 119 131400] Train: [90/100][44/800] Data 0.005 (0.004) Batch 0.317 (0.331) Remain 00:48:15 loss: 0.3245 Lr: 0.00020 [2023-12-20 20:52:42,440 INFO misc.py line 119 131400] Train: [90/100][45/800] Data 0.003 (0.004) Batch 0.330 (0.331) Remain 00:48:15 loss: 0.2847 Lr: 0.00020 [2023-12-20 20:52:42,856 INFO misc.py line 119 131400] Train: [90/100][46/800] Data 0.004 (0.004) Batch 0.416 (0.333) Remain 00:48:32 loss: 0.3029 Lr: 0.00020 [2023-12-20 20:52:43,179 INFO misc.py line 119 131400] Train: [90/100][47/800] Data 0.006 (0.004) Batch 0.323 (0.332) Remain 00:48:29 loss: 0.3914 Lr: 0.00020 [2023-12-20 20:52:43,517 INFO misc.py line 119 131400] Train: [90/100][48/800] Data 0.006 (0.004) Batch 0.339 (0.333) Remain 00:48:30 loss: 0.3169 Lr: 0.00020 [2023-12-20 20:52:43,856 INFO misc.py line 119 131400] Train: [90/100][49/800] Data 0.003 (0.004) Batch 0.337 (0.333) Remain 00:48:31 loss: 0.3521 Lr: 0.00020 [2023-12-20 20:52:44,193 INFO misc.py line 119 131400] Train: [90/100][50/800] Data 0.005 (0.004) Batch 0.339 (0.333) Remain 00:48:32 loss: 0.1306 Lr: 0.00020 [2023-12-20 20:52:44,540 INFO misc.py line 119 131400] Train: [90/100][51/800] Data 0.003 (0.004) Batch 0.347 (0.333) Remain 00:48:34 loss: 0.1587 Lr: 0.00020 [2023-12-20 20:52:44,915 INFO misc.py line 119 131400] Train: [90/100][52/800] Data 0.003 (0.004) Batch 0.375 (0.334) Remain 00:48:41 loss: 0.1515 Lr: 0.00020 [2023-12-20 20:52:45,258 INFO misc.py line 119 131400] Train: [90/100][53/800] Data 0.003 (0.004) Batch 0.344 (0.334) Remain 00:48:42 loss: 0.2306 Lr: 0.00020 [2023-12-20 20:52:45,570 INFO misc.py line 119 131400] Train: [90/100][54/800] Data 0.004 (0.004) Batch 0.311 (0.334) Remain 00:48:38 loss: 0.1163 Lr: 0.00020 [2023-12-20 20:52:45,902 INFO misc.py line 119 131400] Train: [90/100][55/800] Data 0.006 (0.004) Batch 0.333 (0.334) Remain 00:48:38 loss: 0.1456 Lr: 0.00020 [2023-12-20 20:52:46,246 INFO misc.py line 119 131400] Train: [90/100][56/800] Data 0.003 (0.004) Batch 0.344 (0.334) Remain 00:48:39 loss: 0.1269 Lr: 0.00020 [2023-12-20 20:52:46,587 INFO misc.py line 119 131400] Train: [90/100][57/800] Data 0.003 (0.004) Batch 0.341 (0.334) Remain 00:48:40 loss: 0.1984 Lr: 0.00020 [2023-12-20 20:52:46,947 INFO misc.py line 119 131400] Train: [90/100][58/800] Data 0.003 (0.004) Batch 0.359 (0.334) Remain 00:48:43 loss: 0.2975 Lr: 0.00020 [2023-12-20 20:52:47,276 INFO misc.py line 119 131400] Train: [90/100][59/800] Data 0.005 (0.004) Batch 0.329 (0.334) Remain 00:48:42 loss: 0.1283 Lr: 0.00020 [2023-12-20 20:52:47,603 INFO misc.py line 119 131400] Train: [90/100][60/800] Data 0.005 (0.004) Batch 0.326 (0.334) Remain 00:48:41 loss: 0.3442 Lr: 0.00020 [2023-12-20 20:52:47,945 INFO misc.py line 119 131400] Train: [90/100][61/800] Data 0.005 (0.004) Batch 0.344 (0.334) Remain 00:48:42 loss: 0.1164 Lr: 0.00020 [2023-12-20 20:52:48,299 INFO misc.py line 119 131400] Train: [90/100][62/800] Data 0.004 (0.004) Batch 0.354 (0.335) Remain 00:48:44 loss: 0.2348 Lr: 0.00020 [2023-12-20 20:52:48,648 INFO misc.py line 119 131400] Train: [90/100][63/800] Data 0.004 (0.004) Batch 0.350 (0.335) Remain 00:48:46 loss: 0.3524 Lr: 0.00020 [2023-12-20 20:52:48,995 INFO misc.py line 119 131400] Train: [90/100][64/800] Data 0.003 (0.004) Batch 0.345 (0.335) Remain 00:48:47 loss: 0.2492 Lr: 0.00020 [2023-12-20 20:52:49,317 INFO misc.py line 119 131400] Train: [90/100][65/800] Data 0.005 (0.004) Batch 0.323 (0.335) Remain 00:48:45 loss: 0.2917 Lr: 0.00020 [2023-12-20 20:52:49,635 INFO misc.py line 119 131400] Train: [90/100][66/800] Data 0.006 (0.004) Batch 0.318 (0.335) Remain 00:48:42 loss: 0.2272 Lr: 0.00020 [2023-12-20 20:52:49,989 INFO misc.py line 119 131400] Train: [90/100][67/800] Data 0.005 (0.004) Batch 0.355 (0.335) Remain 00:48:45 loss: 0.3578 Lr: 0.00020 [2023-12-20 20:52:50,312 INFO misc.py line 119 131400] Train: [90/100][68/800] Data 0.003 (0.004) Batch 0.323 (0.335) Remain 00:48:43 loss: 0.2603 Lr: 0.00020 [2023-12-20 20:52:50,652 INFO misc.py line 119 131400] Train: [90/100][69/800] Data 0.003 (0.004) Batch 0.339 (0.335) Remain 00:48:43 loss: 0.2046 Lr: 0.00020 [2023-12-20 20:52:51,012 INFO misc.py line 119 131400] Train: [90/100][70/800] Data 0.005 (0.004) Batch 0.360 (0.335) Remain 00:48:46 loss: 0.2067 Lr: 0.00020 [2023-12-20 20:52:51,395 INFO misc.py line 119 131400] Train: [90/100][71/800] Data 0.005 (0.004) Batch 0.383 (0.336) Remain 00:48:52 loss: 0.2610 Lr: 0.00020 [2023-12-20 20:52:51,739 INFO misc.py line 119 131400] Train: [90/100][72/800] Data 0.004 (0.004) Batch 0.344 (0.336) Remain 00:48:53 loss: 0.2748 Lr: 0.00020 [2023-12-20 20:52:52,066 INFO misc.py line 119 131400] Train: [90/100][73/800] Data 0.004 (0.004) Batch 0.326 (0.336) Remain 00:48:51 loss: 0.1385 Lr: 0.00019 [2023-12-20 20:52:52,414 INFO misc.py line 119 131400] Train: [90/100][74/800] Data 0.006 (0.004) Batch 0.346 (0.336) Remain 00:48:52 loss: 0.1972 Lr: 0.00019 [2023-12-20 20:52:52,748 INFO misc.py line 119 131400] Train: [90/100][75/800] Data 0.006 (0.004) Batch 0.336 (0.336) Remain 00:48:52 loss: 0.1321 Lr: 0.00019 [2023-12-20 20:52:53,070 INFO misc.py line 119 131400] Train: [90/100][76/800] Data 0.004 (0.004) Batch 0.322 (0.336) Remain 00:48:50 loss: 0.1684 Lr: 0.00019 [2023-12-20 20:52:53,445 INFO misc.py line 119 131400] Train: [90/100][77/800] Data 0.004 (0.004) Batch 0.375 (0.336) Remain 00:48:54 loss: 0.1390 Lr: 0.00019 [2023-12-20 20:52:53,796 INFO misc.py line 119 131400] Train: 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Batch 0.352 (0.334) Remain 00:44:45 loss: 0.2548 Lr: 0.00017 [2023-12-20 20:56:38,119 INFO misc.py line 119 131400] Train: [90/100][751/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 00:44:45 loss: 0.1526 Lr: 0.00017 [2023-12-20 20:56:38,505 INFO misc.py line 119 131400] Train: [90/100][752/800] Data 0.007 (0.004) Batch 0.387 (0.334) Remain 00:44:45 loss: 0.1232 Lr: 0.00017 [2023-12-20 20:56:38,840 INFO misc.py line 119 131400] Train: [90/100][753/800] Data 0.005 (0.004) Batch 0.336 (0.334) Remain 00:44:45 loss: 0.2394 Lr: 0.00017 [2023-12-20 20:56:39,204 INFO misc.py line 119 131400] Train: [90/100][754/800] Data 0.004 (0.004) Batch 0.364 (0.334) Remain 00:44:45 loss: 0.2452 Lr: 0.00017 [2023-12-20 20:56:39,558 INFO misc.py line 119 131400] Train: [90/100][755/800] Data 0.005 (0.004) Batch 0.356 (0.334) Remain 00:44:45 loss: 0.1978 Lr: 0.00017 [2023-12-20 20:56:39,875 INFO misc.py line 119 131400] Train: [90/100][756/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 00:44:44 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0.004 (0.004) Batch 0.342 (0.334) Remain 00:44:40 loss: 0.3427 Lr: 0.00017 [2023-12-20 20:56:46,751 INFO misc.py line 119 131400] Train: [90/100][776/800] Data 0.004 (0.004) Batch 0.331 (0.334) Remain 00:44:40 loss: 0.2086 Lr: 0.00017 [2023-12-20 20:56:47,044 INFO misc.py line 119 131400] Train: [90/100][777/800] Data 0.004 (0.004) Batch 0.293 (0.334) Remain 00:44:39 loss: 0.2203 Lr: 0.00017 [2023-12-20 20:56:47,349 INFO misc.py line 119 131400] Train: [90/100][778/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 00:44:38 loss: 0.2571 Lr: 0.00017 [2023-12-20 20:56:47,619 INFO misc.py line 119 131400] Train: [90/100][779/800] Data 0.003 (0.004) Batch 0.265 (0.334) Remain 00:44:37 loss: 0.1917 Lr: 0.00017 [2023-12-20 20:56:47,991 INFO misc.py line 119 131400] Train: [90/100][780/800] Data 0.009 (0.004) Batch 0.378 (0.334) Remain 00:44:37 loss: 0.1612 Lr: 0.00017 [2023-12-20 20:56:48,309 INFO misc.py line 119 131400] Train: [90/100][781/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 00:44:37 loss: 0.1113 Lr: 0.00017 [2023-12-20 20:56:48,627 INFO misc.py line 119 131400] Train: [90/100][782/800] Data 0.005 (0.004) Batch 0.317 (0.334) Remain 00:44:36 loss: 0.1335 Lr: 0.00017 [2023-12-20 20:56:48,949 INFO misc.py line 119 131400] Train: [90/100][783/800] Data 0.006 (0.004) Batch 0.324 (0.334) Remain 00:44:36 loss: 0.3181 Lr: 0.00017 [2023-12-20 20:56:49,270 INFO misc.py line 119 131400] Train: [90/100][784/800] Data 0.004 (0.004) Batch 0.322 (0.334) Remain 00:44:35 loss: 0.2008 Lr: 0.00016 [2023-12-20 20:56:49,578 INFO misc.py line 119 131400] Train: [90/100][785/800] Data 0.003 (0.004) Batch 0.308 (0.334) Remain 00:44:35 loss: 0.1114 Lr: 0.00016 [2023-12-20 20:56:49,917 INFO misc.py line 119 131400] Train: [90/100][786/800] Data 0.003 (0.004) Batch 0.339 (0.334) Remain 00:44:35 loss: 0.0955 Lr: 0.00016 [2023-12-20 20:56:50,235 INFO misc.py line 119 131400] Train: [90/100][787/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:44:34 loss: 0.2074 Lr: 0.00016 [2023-12-20 20:56:50,552 INFO misc.py line 119 131400] Train: [90/100][788/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 00:44:34 loss: 0.2399 Lr: 0.00016 [2023-12-20 20:56:50,866 INFO misc.py line 119 131400] Train: [90/100][789/800] Data 0.004 (0.004) Batch 0.315 (0.334) Remain 00:44:33 loss: 0.3264 Lr: 0.00016 [2023-12-20 20:56:51,191 INFO misc.py line 119 131400] Train: [90/100][790/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 00:44:33 loss: 0.1596 Lr: 0.00016 [2023-12-20 20:56:51,490 INFO misc.py line 119 131400] Train: [90/100][791/800] Data 0.003 (0.004) Batch 0.298 (0.334) Remain 00:44:32 loss: 0.2137 Lr: 0.00016 [2023-12-20 20:56:51,809 INFO misc.py line 119 131400] Train: [90/100][792/800] Data 0.003 (0.004) Batch 0.318 (0.334) Remain 00:44:31 loss: 0.1266 Lr: 0.00016 [2023-12-20 20:56:52,112 INFO misc.py line 119 131400] Train: [90/100][793/800] Data 0.004 (0.004) Batch 0.303 (0.334) Remain 00:44:31 loss: 0.2406 Lr: 0.00016 [2023-12-20 20:56:52,424 INFO misc.py line 119 131400] Train: [90/100][794/800] Data 0.004 (0.004) Batch 0.313 (0.334) Remain 00:44:30 loss: 0.3585 Lr: 0.00016 [2023-12-20 20:56:52,730 INFO misc.py line 119 131400] Train: [90/100][795/800] Data 0.003 (0.004) Batch 0.305 (0.334) Remain 00:44:30 loss: 0.1430 Lr: 0.00016 [2023-12-20 20:56:53,025 INFO misc.py line 119 131400] Train: [90/100][796/800] Data 0.004 (0.004) Batch 0.296 (0.334) Remain 00:44:29 loss: 0.2092 Lr: 0.00016 [2023-12-20 20:56:53,339 INFO misc.py line 119 131400] Train: [90/100][797/800] Data 0.003 (0.004) Batch 0.314 (0.333) Remain 00:44:28 loss: 0.0852 Lr: 0.00016 [2023-12-20 20:56:53,644 INFO misc.py line 119 131400] Train: [90/100][798/800] Data 0.003 (0.004) Batch 0.305 (0.333) Remain 00:44:28 loss: 0.1968 Lr: 0.00016 [2023-12-20 20:56:53,962 INFO misc.py line 119 131400] Train: [90/100][799/800] Data 0.003 (0.004) Batch 0.318 (0.333) Remain 00:44:27 loss: 0.2274 Lr: 0.00016 [2023-12-20 20:56:54,262 INFO misc.py line 119 131400] Train: [90/100][800/800] Data 0.003 (0.004) Batch 0.300 (0.333) Remain 00:44:27 loss: 0.1112 Lr: 0.00016 [2023-12-20 20:56:54,263 INFO misc.py line 136 131400] Train result: loss: 0.2085 [2023-12-20 20:56:54,263 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 20:57:16,527 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1597 [2023-12-20 20:57:16,700 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1503 [2023-12-20 20:57:17,351 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5709 [2023-12-20 20:57:17,459 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4641 [2023-12-20 20:57:17,572 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3705 [2023-12-20 20:57:17,675 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.1219 [2023-12-20 20:57:17,766 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.8725 [2023-12-20 20:57:17,874 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9182 [2023-12-20 20:57:17,959 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2849 [2023-12-20 20:57:18,047 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3335 [2023-12-20 20:57:18,140 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4284 [2023-12-20 20:57:18,280 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2751 [2023-12-20 20:57:18,408 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4381 [2023-12-20 20:57:18,568 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1964 [2023-12-20 20:57:18,662 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1356 [2023-12-20 20:57:18,800 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7629 [2023-12-20 20:57:18,928 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2529 [2023-12-20 20:57:19,038 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8755 [2023-12-20 20:57:19,156 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1727 [2023-12-20 20:57:19,233 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3429 [2023-12-20 20:57:19,340 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1501 [2023-12-20 20:57:19,497 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1279 [2023-12-20 20:57:19,617 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8059 [2023-12-20 20:57:19,773 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.1835 [2023-12-20 20:57:19,918 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1847 [2023-12-20 20:57:20,000 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7093 [2023-12-20 20:57:20,157 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.8245 [2023-12-20 20:57:20,285 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5239 [2023-12-20 20:57:20,379 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4460 [2023-12-20 20:57:20,523 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.6077 [2023-12-20 20:57:20,639 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4981 [2023-12-20 20:57:20,759 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.4254 [2023-12-20 20:57:20,845 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1224 [2023-12-20 20:57:20,914 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1735 [2023-12-20 20:57:21,010 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8698 [2023-12-20 20:57:21,103 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.3003 [2023-12-20 20:57:21,233 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9726 [2023-12-20 20:57:21,344 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0932 [2023-12-20 20:57:21,426 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5895 [2023-12-20 20:57:21,572 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2658 [2023-12-20 20:57:21,719 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0149 [2023-12-20 20:57:21,818 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0491 [2023-12-20 20:57:21,938 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3393 [2023-12-20 20:57:22,080 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.2114 [2023-12-20 20:57:22,200 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4916 [2023-12-20 20:57:22,302 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.7897 [2023-12-20 20:57:22,470 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2887 [2023-12-20 20:57:22,565 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.3918 [2023-12-20 20:57:22,711 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7013 [2023-12-20 20:57:22,809 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2281 [2023-12-20 20:57:22,886 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5068 [2023-12-20 20:57:22,993 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4909 [2023-12-20 20:57:23,139 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.8276 [2023-12-20 20:57:23,274 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3786 [2023-12-20 20:57:23,374 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0928 [2023-12-20 20:57:23,461 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6736 [2023-12-20 20:57:23,571 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3232 [2023-12-20 20:57:23,732 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2558 [2023-12-20 20:57:23,829 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5425 [2023-12-20 20:57:23,921 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1780 [2023-12-20 20:57:24,018 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5562 [2023-12-20 20:57:24,113 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2281 [2023-12-20 20:57:24,208 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6163 [2023-12-20 20:57:24,310 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.8249 [2023-12-20 20:57:24,436 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.7032 [2023-12-20 20:57:24,526 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2277 [2023-12-20 20:57:24,624 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3470 [2023-12-20 20:57:24,716 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0075 [2023-12-20 20:57:24,800 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.2944 [2023-12-20 20:57:24,886 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0082 [2023-12-20 20:57:24,978 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9783 [2023-12-20 20:57:25,067 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6305 [2023-12-20 20:57:25,202 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0518 [2023-12-20 20:57:25,295 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6597 [2023-12-20 20:57:25,411 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5954 [2023-12-20 20:57:25,513 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5209 [2023-12-20 20:57:25,602 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4501 [2023-12-20 20:57:25,757 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0257 [2023-12-20 20:57:27,010 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7700/0.8449/0.9216. [2023-12-20 20:57:27,010 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8744/0.9542 [2023-12-20 20:57:27,010 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9640/0.9863 [2023-12-20 20:57:27,010 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7061/0.8206 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8445/0.8973 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9252/0.9636 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8547/0.9218 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7713/0.8664 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7374/0.8467 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7235/0.8198 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8413/0.9241 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4135/0.5220 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7275/0.8276 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.6932/0.8424 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7659/0.8549 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7209/0.7742 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6589/0.7055 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9541/0.9811 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6970/0.7837 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8898/0.9229 [2023-12-20 20:57:27,011 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6367/0.6836 [2023-12-20 20:57:27,012 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 20:57:27,013 INFO misc.py line 165 131400] Currently Best mIoU: 0.7737 [2023-12-20 20:57:27,013 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 20:57:30,017 INFO misc.py line 119 131400] Train: [91/100][1/800] Data 0.911 (0.911) Batch 1.215 (1.215) Remain 02:41:54 loss: 0.2486 Lr: 0.00016 [2023-12-20 20:57:30,354 INFO misc.py line 119 131400] Train: [91/100][2/800] Data 0.016 (0.016) Batch 0.348 (0.348) Remain 00:46:19 loss: 0.2305 Lr: 0.00016 [2023-12-20 20:57:30,709 INFO misc.py line 119 131400] Train: [91/100][3/800] Data 0.004 (0.004) Batch 0.355 (0.355) Remain 00:47:20 loss: 0.4319 Lr: 0.00016 [2023-12-20 20:57:31,045 INFO misc.py line 119 131400] Train: [91/100][4/800] Data 0.004 (0.004) Batch 0.336 (0.336) Remain 00:44:48 loss: 0.2834 Lr: 0.00016 [2023-12-20 20:57:31,396 INFO misc.py line 119 131400] Train: [91/100][5/800] Data 0.005 (0.004) Batch 0.350 (0.343) Remain 00:45:44 loss: 0.2397 Lr: 0.00016 [2023-12-20 20:57:31,669 INFO misc.py line 119 131400] Train: [91/100][6/800] Data 0.004 (0.004) Batch 0.274 (0.320) Remain 00:42:40 loss: 0.0970 Lr: 0.00016 [2023-12-20 20:57:32,104 INFO misc.py line 119 131400] Train: [91/100][7/800] Data 0.017 (0.007) Batch 0.434 (0.349) Remain 00:46:27 loss: 0.2089 Lr: 0.00016 [2023-12-20 20:57:32,932 INFO misc.py line 119 131400] Train: [91/100][8/800] Data 0.007 (0.007) Batch 0.824 (0.444) Remain 00:59:07 loss: 0.1861 Lr: 0.00016 [2023-12-20 20:57:33,464 INFO misc.py line 119 131400] Train: [91/100][9/800] Data 0.008 (0.007) Batch 0.536 (0.459) Remain 01:01:10 loss: 0.2859 Lr: 0.00016 [2023-12-20 20:57:33,767 INFO misc.py line 119 131400] Train: [91/100][10/800] Data 0.004 (0.007) Batch 0.302 (0.437) Remain 00:58:10 loss: 0.2473 Lr: 0.00016 [2023-12-20 20:57:34,050 INFO misc.py line 119 131400] Train: [91/100][11/800] Data 0.004 (0.007) Batch 0.284 (0.418) Remain 00:55:37 loss: 0.1672 Lr: 0.00016 [2023-12-20 20:57:34,384 INFO misc.py line 119 131400] Train: [91/100][12/800] Data 0.004 (0.006) Batch 0.334 (0.408) Remain 00:54:22 loss: 0.1421 Lr: 0.00016 [2023-12-20 20:57:34,687 INFO misc.py line 119 131400] Train: [91/100][13/800] Data 0.003 (0.006) Batch 0.302 (0.398) Remain 00:52:57 loss: 0.2774 Lr: 0.00016 [2023-12-20 20:57:35,048 INFO misc.py line 119 131400] Train: [91/100][14/800] Data 0.003 (0.006) Batch 0.361 (0.394) Remain 00:52:30 loss: 0.1823 Lr: 0.00016 [2023-12-20 20:57:35,346 INFO misc.py line 119 131400] Train: [91/100][15/800] Data 0.004 (0.006) Batch 0.298 (0.386) Remain 00:51:25 loss: 0.3181 Lr: 0.00016 [2023-12-20 20:57:35,684 INFO misc.py line 119 131400] Train: [91/100][16/800] Data 0.003 (0.005) Batch 0.338 (0.383) Remain 00:50:55 loss: 0.3351 Lr: 0.00016 [2023-12-20 20:57:36,003 INFO misc.py line 119 131400] Train: [91/100][17/800] Data 0.003 (0.005) Batch 0.319 (0.378) Remain 00:50:19 loss: 0.3632 Lr: 0.00016 [2023-12-20 20:57:36,341 INFO misc.py line 119 131400] Train: [91/100][18/800] Data 0.004 (0.005) Batch 0.337 (0.375) Remain 00:49:56 loss: 0.1389 Lr: 0.00016 [2023-12-20 20:57:36,689 INFO misc.py line 119 131400] Train: [91/100][19/800] Data 0.004 (0.005) Batch 0.348 (0.374) Remain 00:49:42 loss: 0.2650 Lr: 0.00016 [2023-12-20 20:57:36,982 INFO misc.py line 119 131400] Train: [91/100][20/800] Data 0.004 (0.005) Batch 0.293 (0.369) Remain 00:49:04 loss: 0.3136 Lr: 0.00016 [2023-12-20 20:57:37,286 INFO misc.py line 119 131400] Train: [91/100][21/800] Data 0.004 (0.005) Batch 0.304 (0.365) Remain 00:48:35 loss: 0.2145 Lr: 0.00016 [2023-12-20 20:57:37,611 INFO misc.py line 119 131400] Train: [91/100][22/800] Data 0.003 (0.005) Batch 0.325 (0.363) Remain 00:48:18 loss: 0.1856 Lr: 0.00016 [2023-12-20 20:57:37,935 INFO misc.py line 119 131400] Train: [91/100][23/800] Data 0.004 (0.005) Batch 0.323 (0.361) Remain 00:48:01 loss: 0.3223 Lr: 0.00016 [2023-12-20 20:57:38,283 INFO misc.py line 119 131400] Train: [91/100][24/800] Data 0.005 (0.005) Batch 0.350 (0.361) Remain 00:47:57 loss: 0.1511 Lr: 0.00016 [2023-12-20 20:57:38,643 INFO misc.py line 119 131400] Train: [91/100][25/800] Data 0.004 (0.005) Batch 0.359 (0.361) Remain 00:47:56 loss: 0.2115 Lr: 0.00016 [2023-12-20 20:57:39,013 INFO misc.py line 119 131400] Train: [91/100][26/800] Data 0.004 (0.005) Batch 0.370 (0.361) Remain 00:47:59 loss: 0.2564 Lr: 0.00016 [2023-12-20 20:57:39,328 INFO misc.py line 119 131400] Train: [91/100][27/800] Data 0.003 (0.005) Batch 0.315 (0.359) Remain 00:47:43 loss: 0.2258 Lr: 0.00016 [2023-12-20 20:57:39,672 INFO misc.py line 119 131400] Train: [91/100][28/800] Data 0.004 (0.005) Batch 0.344 (0.359) Remain 00:47:38 loss: 0.2262 Lr: 0.00016 [2023-12-20 20:57:40,005 INFO misc.py line 119 131400] Train: [91/100][29/800] Data 0.003 (0.005) Batch 0.332 (0.358) Remain 00:47:30 loss: 0.2180 Lr: 0.00016 [2023-12-20 20:57:40,324 INFO misc.py line 119 131400] Train: [91/100][30/800] Data 0.003 (0.005) Batch 0.319 (0.356) Remain 00:47:18 loss: 0.2956 Lr: 0.00016 [2023-12-20 20:57:40,658 INFO misc.py line 119 131400] Train: [91/100][31/800] Data 0.003 (0.004) Batch 0.333 (0.355) Remain 00:47:11 loss: 0.2031 Lr: 0.00016 [2023-12-20 20:57:40,987 INFO misc.py line 119 131400] Train: [91/100][32/800] Data 0.004 (0.004) Batch 0.331 (0.354) Remain 00:47:04 loss: 0.1321 Lr: 0.00016 [2023-12-20 20:57:41,323 INFO misc.py line 119 131400] Train: [91/100][33/800] Data 0.003 (0.004) Batch 0.336 (0.354) Remain 00:46:58 loss: 0.1964 Lr: 0.00016 [2023-12-20 20:57:41,624 INFO misc.py line 119 131400] Train: [91/100][34/800] Data 0.004 (0.004) Batch 0.301 (0.352) Remain 00:46:44 loss: 0.1649 Lr: 0.00016 [2023-12-20 20:57:41,983 INFO misc.py line 119 131400] Train: [91/100][35/800] Data 0.004 (0.004) Batch 0.359 (0.352) Remain 00:46:46 loss: 0.2306 Lr: 0.00016 [2023-12-20 20:57:42,313 INFO misc.py line 119 131400] Train: [91/100][36/800] Data 0.004 (0.004) Batch 0.330 (0.352) Remain 00:46:40 loss: 0.1628 Lr: 0.00016 [2023-12-20 20:57:42,598 INFO misc.py line 119 131400] Train: [91/100][37/800] Data 0.004 (0.004) Batch 0.285 (0.350) Remain 00:46:24 loss: 0.3215 Lr: 0.00016 [2023-12-20 20:57:42,897 INFO misc.py line 119 131400] Train: [91/100][38/800] Data 0.003 (0.004) Batch 0.300 (0.348) Remain 00:46:12 loss: 0.1501 Lr: 0.00016 [2023-12-20 20:57:43,231 INFO misc.py line 119 131400] Train: [91/100][39/800] Data 0.003 (0.004) Batch 0.334 (0.348) Remain 00:46:09 loss: 0.2832 Lr: 0.00016 [2023-12-20 20:57:43,601 INFO misc.py line 119 131400] Train: [91/100][40/800] Data 0.003 (0.004) Batch 0.369 (0.348) Remain 00:46:13 loss: 0.2097 Lr: 0.00016 [2023-12-20 20:57:43,979 INFO misc.py line 119 131400] Train: [91/100][41/800] Data 0.004 (0.004) Batch 0.379 (0.349) Remain 00:46:19 loss: 0.2105 Lr: 0.00016 [2023-12-20 20:57:44,386 INFO misc.py line 119 131400] Train: [91/100][42/800] Data 0.003 (0.004) Batch 0.406 (0.351) Remain 00:46:30 loss: 0.1658 Lr: 0.00016 [2023-12-20 20:57:44,731 INFO misc.py line 119 131400] Train: [91/100][43/800] Data 0.004 (0.004) Batch 0.346 (0.351) Remain 00:46:29 loss: 0.3649 Lr: 0.00016 [2023-12-20 20:57:45,053 INFO misc.py line 119 131400] Train: [91/100][44/800] Data 0.003 (0.004) Batch 0.322 (0.350) Remain 00:46:23 loss: 0.3171 Lr: 0.00016 [2023-12-20 20:57:45,353 INFO misc.py line 119 131400] Train: [91/100][45/800] Data 0.003 (0.004) Batch 0.300 (0.349) Remain 00:46:13 loss: 0.1483 Lr: 0.00016 [2023-12-20 20:57:45,716 INFO misc.py line 119 131400] Train: [91/100][46/800] Data 0.004 (0.004) Batch 0.362 (0.349) Remain 00:46:15 loss: 0.1101 Lr: 0.00016 [2023-12-20 20:57:46,032 INFO misc.py line 119 131400] Train: [91/100][47/800] Data 0.004 (0.004) Batch 0.317 (0.348) Remain 00:46:09 loss: 0.1090 Lr: 0.00016 [2023-12-20 20:57:46,364 INFO misc.py line 119 131400] Train: [91/100][48/800] Data 0.003 (0.004) Batch 0.332 (0.348) Remain 00:46:06 loss: 0.2510 Lr: 0.00016 [2023-12-20 20:57:46,700 INFO misc.py line 119 131400] Train: [91/100][49/800] Data 0.004 (0.004) Batch 0.334 (0.348) Remain 00:46:03 loss: 0.1111 Lr: 0.00016 [2023-12-20 20:57:47,006 INFO misc.py line 119 131400] Train: [91/100][50/800] Data 0.006 (0.004) Batch 0.308 (0.347) Remain 00:45:56 loss: 0.2740 Lr: 0.00016 [2023-12-20 20:57:47,371 INFO misc.py line 119 131400] Train: [91/100][51/800] Data 0.003 (0.004) Batch 0.364 (0.347) Remain 00:45:59 loss: 0.2142 Lr: 0.00016 [2023-12-20 20:57:47,726 INFO misc.py line 119 131400] Train: [91/100][52/800] Data 0.003 (0.004) Batch 0.355 (0.347) Remain 00:46:00 loss: 0.1536 Lr: 0.00016 [2023-12-20 20:57:48,035 INFO misc.py line 119 131400] Train: [91/100][53/800] Data 0.004 (0.004) Batch 0.310 (0.347) Remain 00:45:53 loss: 0.2216 Lr: 0.00016 [2023-12-20 20:57:48,349 INFO misc.py line 119 131400] Train: [91/100][54/800] Data 0.003 (0.004) Batch 0.313 (0.346) Remain 00:45:48 loss: 0.1536 Lr: 0.00016 [2023-12-20 20:57:48,678 INFO misc.py line 119 131400] Train: [91/100][55/800] Data 0.005 (0.004) Batch 0.329 (0.346) Remain 00:45:45 loss: 0.3524 Lr: 0.00016 [2023-12-20 20:57:49,021 INFO misc.py line 119 131400] Train: [91/100][56/800] Data 0.005 (0.004) Batch 0.344 (0.346) Remain 00:45:44 loss: 0.2521 Lr: 0.00016 [2023-12-20 20:57:49,373 INFO misc.py line 119 131400] Train: [91/100][57/800] Data 0.004 (0.004) Batch 0.352 (0.346) Remain 00:45:45 loss: 0.1105 Lr: 0.00016 [2023-12-20 20:57:49,710 INFO misc.py line 119 131400] Train: [91/100][58/800] Data 0.003 (0.004) Batch 0.337 (0.345) Remain 00:45:43 loss: 0.0896 Lr: 0.00016 [2023-12-20 20:57:50,030 INFO misc.py line 119 131400] Train: [91/100][59/800] Data 0.004 (0.004) Batch 0.321 (0.345) Remain 00:45:39 loss: 0.1406 Lr: 0.00016 [2023-12-20 20:57:50,374 INFO misc.py line 119 131400] Train: [91/100][60/800] Data 0.003 (0.004) Batch 0.345 (0.345) Remain 00:45:39 loss: 0.2982 Lr: 0.00016 [2023-12-20 20:57:50,725 INFO misc.py line 119 131400] Train: [91/100][61/800] Data 0.003 (0.004) Batch 0.349 (0.345) Remain 00:45:39 loss: 0.2946 Lr: 0.00016 [2023-12-20 20:57:51,064 INFO misc.py line 119 131400] Train: [91/100][62/800] Data 0.004 (0.004) Batch 0.340 (0.345) Remain 00:45:38 loss: 0.3398 Lr: 0.00016 [2023-12-20 20:57:51,377 INFO misc.py line 119 131400] Train: [91/100][63/800] Data 0.004 (0.004) Batch 0.314 (0.344) Remain 00:45:34 loss: 0.1288 Lr: 0.00016 [2023-12-20 20:57:51,706 INFO misc.py line 119 131400] Train: [91/100][64/800] Data 0.003 (0.004) Batch 0.328 (0.344) Remain 00:45:31 loss: 0.1424 Lr: 0.00016 [2023-12-20 20:57:52,038 INFO misc.py line 119 131400] Train: [91/100][65/800] Data 0.004 (0.004) Batch 0.333 (0.344) Remain 00:45:29 loss: 0.1775 Lr: 0.00016 [2023-12-20 20:57:52,384 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [91/100][782/800] Data 0.003 (0.004) Batch 0.291 (0.332) Remain 00:39:57 loss: 0.1535 Lr: 0.00013 [2023-12-20 21:01:49,825 INFO misc.py line 119 131400] Train: [91/100][783/800] Data 0.004 (0.004) Batch 0.344 (0.332) Remain 00:39:57 loss: 0.1532 Lr: 0.00013 [2023-12-20 21:01:50,117 INFO misc.py line 119 131400] Train: [91/100][784/800] Data 0.003 (0.004) Batch 0.288 (0.332) Remain 00:39:56 loss: 0.1779 Lr: 0.00013 [2023-12-20 21:01:50,425 INFO misc.py line 119 131400] Train: [91/100][785/800] Data 0.007 (0.004) Batch 0.312 (0.332) Remain 00:39:56 loss: 0.2129 Lr: 0.00013 [2023-12-20 21:01:50,748 INFO misc.py line 119 131400] Train: [91/100][786/800] Data 0.004 (0.004) Batch 0.324 (0.332) Remain 00:39:55 loss: 0.2136 Lr: 0.00013 [2023-12-20 21:01:51,093 INFO misc.py line 119 131400] Train: [91/100][787/800] Data 0.003 (0.004) Batch 0.343 (0.332) Remain 00:39:55 loss: 0.0968 Lr: 0.00013 [2023-12-20 21:01:51,393 INFO misc.py line 119 131400] Train: 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Batch 0.312 (0.332) Remain 00:39:51 loss: 0.2133 Lr: 0.00013 [2023-12-20 21:01:53,579 INFO misc.py line 119 131400] Train: [91/100][795/800] Data 0.003 (0.004) Batch 0.301 (0.332) Remain 00:39:51 loss: 0.1638 Lr: 0.00013 [2023-12-20 21:01:53,927 INFO misc.py line 119 131400] Train: [91/100][796/800] Data 0.005 (0.004) Batch 0.348 (0.332) Remain 00:39:51 loss: 0.3745 Lr: 0.00013 [2023-12-20 21:01:54,262 INFO misc.py line 119 131400] Train: [91/100][797/800] Data 0.004 (0.004) Batch 0.335 (0.332) Remain 00:39:50 loss: 0.1509 Lr: 0.00013 [2023-12-20 21:01:54,580 INFO misc.py line 119 131400] Train: [91/100][798/800] Data 0.003 (0.004) Batch 0.318 (0.332) Remain 00:39:50 loss: 0.2031 Lr: 0.00013 [2023-12-20 21:01:54,916 INFO misc.py line 119 131400] Train: [91/100][799/800] Data 0.004 (0.004) Batch 0.337 (0.332) Remain 00:39:50 loss: 0.2157 Lr: 0.00013 [2023-12-20 21:01:55,261 INFO misc.py line 119 131400] Train: [91/100][800/800] Data 0.003 (0.004) Batch 0.342 (0.332) Remain 00:39:49 loss: 0.1566 Lr: 0.00013 [2023-12-20 21:01:55,261 INFO misc.py line 136 131400] Train result: loss: 0.2039 [2023-12-20 21:01:55,261 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:02:17,266 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1943 [2023-12-20 21:02:17,347 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1637 [2023-12-20 21:02:17,442 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4697 [2023-12-20 21:02:17,550 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4405 [2023-12-20 21:02:17,666 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3599 [2023-12-20 21:02:17,771 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5441 [2023-12-20 21:02:17,862 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9239 [2023-12-20 21:02:17,971 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8573 [2023-12-20 21:02:18,059 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2713 [2023-12-20 21:02:18,145 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3451 [2023-12-20 21:02:18,240 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3658 [2023-12-20 21:02:18,379 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2753 [2023-12-20 21:02:18,504 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4863 [2023-12-20 21:02:18,661 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1803 [2023-12-20 21:02:18,756 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1508 [2023-12-20 21:02:18,896 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7560 [2023-12-20 21:02:19,007 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2773 [2023-12-20 21:02:19,119 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7898 [2023-12-20 21:02:19,232 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.2031 [2023-12-20 21:02:19,309 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3393 [2023-12-20 21:02:19,417 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1462 [2023-12-20 21:02:19,576 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1390 [2023-12-20 21:02:19,698 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.3573 [2023-12-20 21:02:19,842 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2024 [2023-12-20 21:02:19,990 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1407 [2023-12-20 21:02:20,075 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.9106 [2023-12-20 21:02:20,234 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5262 [2023-12-20 21:02:20,360 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5671 [2023-12-20 21:02:20,458 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5816 [2023-12-20 21:02:20,604 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8371 [2023-12-20 21:02:20,712 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5056 [2023-12-20 21:02:20,832 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3720 [2023-12-20 21:02:20,923 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1128 [2023-12-20 21:02:20,993 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1699 [2023-12-20 21:02:21,094 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8343 [2023-12-20 21:02:21,185 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2724 [2023-12-20 21:02:21,316 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9925 [2023-12-20 21:02:21,428 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0870 [2023-12-20 21:02:21,512 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6517 [2023-12-20 21:02:21,658 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2439 [2023-12-20 21:02:21,805 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0167 [2023-12-20 21:02:21,909 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0586 [2023-12-20 21:02:22,031 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2081 [2023-12-20 21:02:22,173 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1074 [2023-12-20 21:02:22,296 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5743 [2023-12-20 21:02:22,425 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5703 [2023-12-20 21:02:22,599 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3078 [2023-12-20 21:02:22,700 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4017 [2023-12-20 21:02:22,856 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6978 [2023-12-20 21:02:22,957 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2440 [2023-12-20 21:02:23,034 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.4826 [2023-12-20 21:02:23,141 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3404 [2023-12-20 21:02:23,289 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9657 [2023-12-20 21:02:23,431 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3538 [2023-12-20 21:02:23,547 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.3932 [2023-12-20 21:02:23,645 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5766 [2023-12-20 21:02:23,752 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3473 [2023-12-20 21:02:23,913 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2143 [2023-12-20 21:02:24,011 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4946 [2023-12-20 21:02:24,112 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1840 [2023-12-20 21:02:24,208 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5436 [2023-12-20 21:02:24,311 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2185 [2023-12-20 21:02:24,414 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6333 [2023-12-20 21:02:24,520 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.5179 [2023-12-20 21:02:24,650 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6113 [2023-12-20 21:02:24,743 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2645 [2023-12-20 21:02:24,856 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.6398 [2023-12-20 21:02:24,949 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0105 [2023-12-20 21:02:25,032 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3306 [2023-12-20 21:02:25,123 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0088 [2023-12-20 21:02:25,221 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9202 [2023-12-20 21:02:25,312 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5952 [2023-12-20 21:02:25,452 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0436 [2023-12-20 21:02:25,552 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6826 [2023-12-20 21:02:25,676 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.5871 [2023-12-20 21:02:25,788 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4849 [2023-12-20 21:02:25,899 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.6274 [2023-12-20 21:02:26,061 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0025 [2023-12-20 21:02:27,308 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7719/0.8484/0.9214. [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8743/0.9536 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9642/0.9864 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7063/0.8097 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8280/0.8739 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9226/0.9583 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8324/0.9368 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7744/0.8765 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7395/0.8617 [2023-12-20 21:02:27,308 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7247/0.8075 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8310/0.9246 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4143/0.5077 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7157/0.8126 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7039/0.8296 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7908/0.8716 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7282/0.8159 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7237/0.7798 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9395/0.9798 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.7011/0.7780 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8919/0.9214 [2023-12-20 21:02:27,309 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6316/0.6830 [2023-12-20 21:02:27,309 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:02:27,310 INFO misc.py line 165 131400] Currently Best mIoU: 0.7737 [2023-12-20 21:02:27,310 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:02:31,808 INFO misc.py line 119 131400] Train: [92/100][1/800] Data 1.510 (1.510) Batch 1.802 (1.802) Remain 03:36:13 loss: 0.2938 Lr: 0.00013 [2023-12-20 21:02:32,136 INFO misc.py line 119 131400] Train: [92/100][2/800] Data 0.004 (0.004) Batch 0.329 (0.329) Remain 00:39:25 loss: 0.2254 Lr: 0.00013 [2023-12-20 21:02:32,477 INFO misc.py line 119 131400] Train: [92/100][3/800] Data 0.003 (0.003) Batch 0.341 (0.341) Remain 00:40:53 loss: 0.1460 Lr: 0.00013 [2023-12-20 21:02:32,830 INFO misc.py line 119 131400] Train: [92/100][4/800] Data 0.004 (0.004) Batch 0.352 (0.352) Remain 00:42:15 loss: 0.2742 Lr: 0.00013 [2023-12-20 21:02:33,171 INFO misc.py line 119 131400] Train: [92/100][5/800] Data 0.004 (0.004) Batch 0.341 (0.347) Remain 00:41:33 loss: 0.2772 Lr: 0.00013 [2023-12-20 21:02:33,548 INFO misc.py line 119 131400] Train: [92/100][6/800] Data 0.004 (0.004) Batch 0.377 (0.357) Remain 00:42:46 loss: 0.1179 Lr: 0.00013 [2023-12-20 21:02:33,878 INFO misc.py line 119 131400] Train: [92/100][7/800] Data 0.004 (0.004) Batch 0.329 (0.350) Remain 00:41:56 loss: 0.2761 Lr: 0.00013 [2023-12-20 21:02:34,186 INFO misc.py line 119 131400] Train: [92/100][8/800] Data 0.005 (0.004) Batch 0.310 (0.342) Remain 00:40:58 loss: 0.3399 Lr: 0.00013 [2023-12-20 21:02:34,534 INFO misc.py line 119 131400] Train: [92/100][9/800] Data 0.004 (0.004) Batch 0.346 (0.343) Remain 00:41:03 loss: 0.3025 Lr: 0.00013 [2023-12-20 21:02:34,859 INFO misc.py line 119 131400] Train: [92/100][10/800] Data 0.005 (0.004) Batch 0.326 (0.340) Remain 00:40:46 loss: 0.2203 Lr: 0.00013 [2023-12-20 21:02:35,228 INFO misc.py line 119 131400] Train: [92/100][11/800] Data 0.005 (0.004) Batch 0.370 (0.344) Remain 00:41:12 loss: 0.2349 Lr: 0.00013 [2023-12-20 21:02:35,553 INFO misc.py line 119 131400] Train: [92/100][12/800] Data 0.003 (0.004) Batch 0.323 (0.342) Remain 00:40:55 loss: 0.2625 Lr: 0.00013 [2023-12-20 21:02:35,913 INFO misc.py line 119 131400] Train: [92/100][13/800] Data 0.006 (0.004) Batch 0.361 (0.344) Remain 00:41:09 loss: 0.1885 Lr: 0.00013 [2023-12-20 21:02:36,225 INFO misc.py line 119 131400] Train: [92/100][14/800] Data 0.005 (0.004) Batch 0.312 (0.341) Remain 00:40:48 loss: 0.1985 Lr: 0.00013 [2023-12-20 21:02:36,570 INFO misc.py line 119 131400] Train: [92/100][15/800] Data 0.004 (0.004) Batch 0.344 (0.341) Remain 00:40:49 loss: 0.1893 Lr: 0.00013 [2023-12-20 21:02:36,921 INFO misc.py line 119 131400] Train: [92/100][16/800] Data 0.004 (0.004) Batch 0.351 (0.342) Remain 00:40:55 loss: 0.2910 Lr: 0.00013 [2023-12-20 21:02:37,248 INFO misc.py line 119 131400] Train: [92/100][17/800] Data 0.003 (0.004) Batch 0.327 (0.341) Remain 00:40:47 loss: 0.1185 Lr: 0.00013 [2023-12-20 21:02:37,550 INFO misc.py line 119 131400] Train: [92/100][18/800] Data 0.004 (0.004) Batch 0.301 (0.338) Remain 00:40:28 loss: 0.1280 Lr: 0.00013 [2023-12-20 21:02:37,879 INFO misc.py line 119 131400] Train: [92/100][19/800] Data 0.004 (0.004) Batch 0.331 (0.338) Remain 00:40:24 loss: 0.1672 Lr: 0.00013 [2023-12-20 21:02:38,234 INFO misc.py line 119 131400] Train: [92/100][20/800] Data 0.003 (0.004) Batch 0.354 (0.339) Remain 00:40:31 loss: 0.1963 Lr: 0.00013 [2023-12-20 21:02:38,547 INFO misc.py line 119 131400] Train: [92/100][21/800] Data 0.005 (0.004) Batch 0.313 (0.337) Remain 00:40:20 loss: 0.3817 Lr: 0.00013 [2023-12-20 21:02:38,875 INFO misc.py line 119 131400] Train: [92/100][22/800] Data 0.004 (0.004) Batch 0.327 (0.337) Remain 00:40:16 loss: 0.1415 Lr: 0.00013 [2023-12-20 21:02:39,208 INFO misc.py line 119 131400] Train: [92/100][23/800] Data 0.005 (0.004) Batch 0.334 (0.337) Remain 00:40:15 loss: 0.1360 Lr: 0.00013 [2023-12-20 21:02:39,553 INFO misc.py line 119 131400] Train: [92/100][24/800] Data 0.003 (0.004) Batch 0.342 (0.337) Remain 00:40:16 loss: 0.2131 Lr: 0.00013 [2023-12-20 21:02:39,881 INFO misc.py line 119 131400] Train: [92/100][25/800] Data 0.007 (0.004) Batch 0.331 (0.337) Remain 00:40:14 loss: 0.1717 Lr: 0.00013 [2023-12-20 21:02:40,216 INFO misc.py line 119 131400] Train: [92/100][26/800] Data 0.004 (0.004) Batch 0.335 (0.336) Remain 00:40:13 loss: 0.2027 Lr: 0.00013 [2023-12-20 21:02:40,554 INFO misc.py line 119 131400] Train: [92/100][27/800] Data 0.003 (0.004) Batch 0.335 (0.336) Remain 00:40:13 loss: 0.1515 Lr: 0.00013 [2023-12-20 21:02:40,894 INFO misc.py line 119 131400] Train: [92/100][28/800] Data 0.006 (0.004) Batch 0.342 (0.337) Remain 00:40:14 loss: 0.2835 Lr: 0.00013 [2023-12-20 21:02:41,208 INFO misc.py line 119 131400] Train: [92/100][29/800] Data 0.003 (0.004) Batch 0.315 (0.336) Remain 00:40:08 loss: 0.1908 Lr: 0.00013 [2023-12-20 21:02:41,558 INFO misc.py line 119 131400] Train: [92/100][30/800] Data 0.004 (0.004) Batch 0.346 (0.336) Remain 00:40:10 loss: 0.0914 Lr: 0.00013 [2023-12-20 21:02:41,897 INFO misc.py line 119 131400] Train: [92/100][31/800] Data 0.007 (0.004) Batch 0.343 (0.336) Remain 00:40:11 loss: 0.1578 Lr: 0.00013 [2023-12-20 21:02:42,220 INFO misc.py line 119 131400] Train: [92/100][32/800] Data 0.004 (0.004) Batch 0.322 (0.336) Remain 00:40:07 loss: 0.2421 Lr: 0.00013 [2023-12-20 21:02:42,521 INFO misc.py line 119 131400] Train: [92/100][33/800] Data 0.005 (0.004) Batch 0.302 (0.335) Remain 00:39:59 loss: 0.1576 Lr: 0.00013 [2023-12-20 21:02:42,874 INFO misc.py line 119 131400] Train: [92/100][34/800] Data 0.004 (0.004) Batch 0.353 (0.335) Remain 00:40:03 loss: 0.3645 Lr: 0.00013 [2023-12-20 21:02:43,195 INFO misc.py line 119 131400] Train: [92/100][35/800] Data 0.004 (0.004) Batch 0.314 (0.335) Remain 00:39:58 loss: 0.2738 Lr: 0.00013 [2023-12-20 21:02:43,514 INFO misc.py line 119 131400] Train: [92/100][36/800] Data 0.010 (0.004) Batch 0.326 (0.334) Remain 00:39:55 loss: 0.1695 Lr: 0.00013 [2023-12-20 21:02:43,868 INFO misc.py line 119 131400] Train: [92/100][37/800] Data 0.003 (0.004) Batch 0.354 (0.335) Remain 00:39:59 loss: 0.1662 Lr: 0.00013 [2023-12-20 21:02:44,233 INFO misc.py line 119 131400] Train: [92/100][38/800] Data 0.005 (0.004) Batch 0.365 (0.336) Remain 00:40:05 loss: 0.2627 Lr: 0.00013 [2023-12-20 21:02:44,608 INFO misc.py line 119 131400] Train: [92/100][39/800] Data 0.004 (0.004) Batch 0.370 (0.337) Remain 00:40:12 loss: 0.2769 Lr: 0.00013 [2023-12-20 21:02:44,929 INFO misc.py line 119 131400] Train: [92/100][40/800] Data 0.009 (0.005) Batch 0.325 (0.336) Remain 00:40:09 loss: 0.3068 Lr: 0.00013 [2023-12-20 21:02:45,245 INFO misc.py line 119 131400] Train: [92/100][41/800] Data 0.005 (0.005) Batch 0.317 (0.336) Remain 00:40:05 loss: 0.1341 Lr: 0.00013 [2023-12-20 21:02:45,564 INFO misc.py line 119 131400] Train: [92/100][42/800] Data 0.004 (0.005) Batch 0.319 (0.336) Remain 00:40:01 loss: 0.4089 Lr: 0.00013 [2023-12-20 21:02:45,904 INFO misc.py line 119 131400] Train: [92/100][43/800] Data 0.003 (0.005) Batch 0.339 (0.336) Remain 00:40:02 loss: 0.2521 Lr: 0.00013 [2023-12-20 21:02:46,246 INFO misc.py line 119 131400] Train: [92/100][44/800] Data 0.005 (0.005) Batch 0.341 (0.336) Remain 00:40:02 loss: 0.1021 Lr: 0.00013 [2023-12-20 21:02:46,592 INFO misc.py line 119 131400] Train: [92/100][45/800] Data 0.007 (0.005) Batch 0.348 (0.336) Remain 00:40:04 loss: 0.3180 Lr: 0.00013 [2023-12-20 21:02:46,930 INFO misc.py line 119 131400] Train: [92/100][46/800] Data 0.003 (0.005) Batch 0.339 (0.336) Remain 00:40:04 loss: 0.2832 Lr: 0.00013 [2023-12-20 21:02:47,251 INFO misc.py line 119 131400] Train: [92/100][47/800] Data 0.003 (0.005) Batch 0.321 (0.336) Remain 00:40:01 loss: 0.1809 Lr: 0.00013 [2023-12-20 21:02:47,589 INFO misc.py line 119 131400] Train: [92/100][48/800] Data 0.004 (0.005) Batch 0.338 (0.336) Remain 00:40:01 loss: 0.1315 Lr: 0.00013 [2023-12-20 21:02:47,952 INFO misc.py line 119 131400] Train: [92/100][49/800] Data 0.004 (0.005) Batch 0.362 (0.336) Remain 00:40:05 loss: 0.1462 Lr: 0.00013 [2023-12-20 21:02:48,277 INFO misc.py line 119 131400] Train: [92/100][50/800] Data 0.005 (0.005) Batch 0.327 (0.336) Remain 00:40:03 loss: 0.1312 Lr: 0.00013 [2023-12-20 21:02:48,592 INFO misc.py line 119 131400] Train: [92/100][51/800] Data 0.003 (0.005) Batch 0.315 (0.336) Remain 00:40:00 loss: 0.1223 Lr: 0.00013 [2023-12-20 21:02:48,931 INFO misc.py line 119 131400] Train: [92/100][52/800] Data 0.004 (0.005) Batch 0.337 (0.336) Remain 00:39:59 loss: 0.1864 Lr: 0.00013 [2023-12-20 21:02:49,285 INFO misc.py line 119 131400] Train: [92/100][53/800] Data 0.006 (0.005) Batch 0.356 (0.336) Remain 00:40:02 loss: 0.1168 Lr: 0.00013 [2023-12-20 21:02:49,653 INFO misc.py line 119 131400] Train: [92/100][54/800] Data 0.005 (0.005) Batch 0.368 (0.337) Remain 00:40:06 loss: 0.1809 Lr: 0.00013 [2023-12-20 21:02:50,097 INFO misc.py line 119 131400] Train: [92/100][55/800] Data 0.003 (0.005) Batch 0.443 (0.339) Remain 00:40:20 loss: 0.2200 Lr: 0.00013 [2023-12-20 21:02:50,397 INFO misc.py line 119 131400] Train: [92/100][56/800] Data 0.005 (0.005) Batch 0.301 (0.338) Remain 00:40:15 loss: 0.3560 Lr: 0.00013 [2023-12-20 21:02:50,772 INFO misc.py line 119 131400] Train: [92/100][57/800] Data 0.004 (0.004) Batch 0.374 (0.339) Remain 00:40:19 loss: 0.1817 Lr: 0.00013 [2023-12-20 21:02:51,115 INFO misc.py line 119 131400] Train: [92/100][58/800] Data 0.004 (0.004) Batch 0.342 (0.339) Remain 00:40:19 loss: 0.1643 Lr: 0.00013 [2023-12-20 21:02:51,450 INFO misc.py line 119 131400] Train: [92/100][59/800] Data 0.006 (0.005) Batch 0.337 (0.339) Remain 00:40:19 loss: 0.1443 Lr: 0.00013 [2023-12-20 21:02:51,782 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Batch 0.303 (0.334) Remain 00:35:40 loss: 0.2440 Lr: 0.00011 [2023-12-20 21:06:57,460 INFO misc.py line 119 131400] Train: [92/100][795/800] Data 0.344 (0.005) Batch 0.644 (0.335) Remain 00:35:42 loss: 0.2778 Lr: 0.00011 [2023-12-20 21:06:57,750 INFO misc.py line 119 131400] Train: [92/100][796/800] Data 0.003 (0.005) Batch 0.289 (0.335) Remain 00:35:42 loss: 0.2343 Lr: 0.00011 [2023-12-20 21:06:58,054 INFO misc.py line 119 131400] Train: [92/100][797/800] Data 0.004 (0.005) Batch 0.304 (0.334) Remain 00:35:41 loss: 0.1223 Lr: 0.00011 [2023-12-20 21:06:58,377 INFO misc.py line 119 131400] Train: [92/100][798/800] Data 0.004 (0.005) Batch 0.323 (0.334) Remain 00:35:41 loss: 0.1807 Lr: 0.00011 [2023-12-20 21:06:58,696 INFO misc.py line 119 131400] Train: [92/100][799/800] Data 0.004 (0.005) Batch 0.318 (0.334) Remain 00:35:40 loss: 0.1420 Lr: 0.00011 [2023-12-20 21:06:58,975 INFO misc.py line 119 131400] Train: [92/100][800/800] Data 0.004 (0.005) Batch 0.280 (0.334) Remain 00:35:39 loss: 0.1759 Lr: 0.00011 [2023-12-20 21:06:58,976 INFO misc.py line 136 131400] Train result: loss: 0.2016 [2023-12-20 21:06:58,976 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:07:21,785 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1942 [2023-12-20 21:07:21,883 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1571 [2023-12-20 21:07:21,980 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5063 [2023-12-20 21:07:22,094 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5825 [2023-12-20 21:07:22,211 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3506 [2023-12-20 21:07:22,316 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.7889 [2023-12-20 21:07:22,416 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0042 [2023-12-20 21:07:22,524 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6902 [2023-12-20 21:07:22,617 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2840 [2023-12-20 21:07:22,717 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3159 [2023-12-20 21:07:22,813 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4116 [2023-12-20 21:07:22,950 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2817 [2023-12-20 21:07:23,074 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4397 [2023-12-20 21:07:23,226 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1726 [2023-12-20 21:07:23,321 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1302 [2023-12-20 21:07:23,464 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6635 [2023-12-20 21:07:23,577 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2975 [2023-12-20 21:07:23,690 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6903 [2023-12-20 21:07:23,802 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1708 [2023-12-20 21:07:23,888 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4563 [2023-12-20 21:07:24,003 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1522 [2023-12-20 21:07:24,160 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1176 [2023-12-20 21:07:24,285 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8444 [2023-12-20 21:07:24,428 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2620 [2023-12-20 21:07:24,578 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1824 [2023-12-20 21:07:24,668 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4958 [2023-12-20 21:07:24,826 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.9054 [2023-12-20 21:07:24,951 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5502 [2023-12-20 21:07:25,047 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.5186 [2023-12-20 21:07:25,194 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8050 [2023-12-20 21:07:25,297 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5329 [2023-12-20 21:07:25,418 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3611 [2023-12-20 21:07:25,504 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1071 [2023-12-20 21:07:25,577 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1723 [2023-12-20 21:07:25,673 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8661 [2023-12-20 21:07:25,770 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2707 [2023-12-20 21:07:25,903 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9833 [2023-12-20 21:07:26,014 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0834 [2023-12-20 21:07:26,105 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5983 [2023-12-20 21:07:26,254 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2479 [2023-12-20 21:07:26,407 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0148 [2023-12-20 21:07:26,506 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0510 [2023-12-20 21:07:26,639 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3414 [2023-12-20 21:07:26,786 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1272 [2023-12-20 21:07:26,903 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.4965 [2023-12-20 21:07:27,005 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5006 [2023-12-20 21:07:27,171 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2835 [2023-12-20 21:07:27,263 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5135 [2023-12-20 21:07:27,416 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6947 [2023-12-20 21:07:27,511 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2225 [2023-12-20 21:07:27,602 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.3905 [2023-12-20 21:07:27,711 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5096 [2023-12-20 21:07:27,865 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2514 [2023-12-20 21:07:28,000 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3503 [2023-12-20 21:07:28,104 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.0995 [2023-12-20 21:07:28,203 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6565 [2023-12-20 21:07:28,306 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3660 [2023-12-20 21:07:28,466 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2229 [2023-12-20 21:07:28,562 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5448 [2023-12-20 21:07:28,655 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1928 [2023-12-20 21:07:28,756 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4916 [2023-12-20 21:07:28,853 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2323 [2023-12-20 21:07:28,946 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.5894 [2023-12-20 21:07:29,045 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6315 [2023-12-20 21:07:29,171 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6938 [2023-12-20 21:07:29,257 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2674 [2023-12-20 21:07:29,360 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4191 [2023-12-20 21:07:29,454 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0089 [2023-12-20 21:07:29,538 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3500 [2023-12-20 21:07:29,624 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0086 [2023-12-20 21:07:29,719 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9191 [2023-12-20 21:07:29,808 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5816 [2023-12-20 21:07:29,946 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0446 [2023-12-20 21:07:30,052 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6581 [2023-12-20 21:07:30,168 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6247 [2023-12-20 21:07:30,270 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4930 [2023-12-20 21:07:30,357 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3630 [2023-12-20 21:07:30,510 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0707 [2023-12-20 21:07:31,705 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7682/0.8447/0.9213. [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8739/0.9576 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9639/0.9866 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7044/0.8067 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8372/0.8815 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9258/0.9627 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8631/0.9340 [2023-12-20 21:07:31,706 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7799/0.8722 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7299/0.8391 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7175/0.8061 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8329/0.9136 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3992/0.5042 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7183/0.8182 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7102/0.8642 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7709/0.8562 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.6935/0.7780 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6936/0.7411 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9382/0.9782 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6874/0.7933 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8939/0.9250 [2023-12-20 21:07:31,707 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6303/0.6763 [2023-12-20 21:07:31,708 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:07:31,709 INFO misc.py line 165 131400] Currently Best mIoU: 0.7737 [2023-12-20 21:07:31,709 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:07:36,431 INFO misc.py line 119 131400] Train: [93/100][1/800] Data 1.716 (1.716) Batch 2.046 (2.046) Remain 03:38:10 loss: 0.2183 Lr: 0.00011 [2023-12-20 21:07:36,745 INFO misc.py line 119 131400] Train: [93/100][2/800] Data 0.004 (0.004) Batch 0.314 (0.314) Remain 00:33:29 loss: 0.3489 Lr: 0.00011 [2023-12-20 21:07:37,066 INFO misc.py line 119 131400] Train: [93/100][3/800] Data 0.003 (0.003) Batch 0.321 (0.321) Remain 00:34:14 loss: 0.1260 Lr: 0.00011 [2023-12-20 21:07:37,393 INFO misc.py line 119 131400] Train: [93/100][4/800] Data 0.003 (0.003) Batch 0.326 (0.326) Remain 00:34:46 loss: 0.1561 Lr: 0.00011 [2023-12-20 21:07:37,732 INFO misc.py line 119 131400] Train: [93/100][5/800] Data 0.004 (0.004) Batch 0.339 (0.333) Remain 00:35:26 loss: 0.0795 Lr: 0.00011 [2023-12-20 21:07:38,078 INFO misc.py line 119 131400] Train: [93/100][6/800] Data 0.005 (0.004) Batch 0.347 (0.337) Remain 00:35:57 loss: 0.1807 Lr: 0.00011 [2023-12-20 21:07:38,404 INFO misc.py line 119 131400] Train: [93/100][7/800] Data 0.003 (0.004) Batch 0.325 (0.334) Remain 00:35:37 loss: 0.1910 Lr: 0.00011 [2023-12-20 21:07:38,727 INFO misc.py line 119 131400] Train: [93/100][8/800] Data 0.004 (0.004) Batch 0.324 (0.332) Remain 00:35:23 loss: 0.2792 Lr: 0.00011 [2023-12-20 21:07:39,060 INFO misc.py line 119 131400] Train: [93/100][9/800] Data 0.003 (0.004) Batch 0.333 (0.332) Remain 00:35:23 loss: 0.2213 Lr: 0.00011 [2023-12-20 21:07:39,392 INFO misc.py line 119 131400] Train: [93/100][10/800] Data 0.003 (0.004) Batch 0.331 (0.332) Remain 00:35:22 loss: 0.3226 Lr: 0.00011 [2023-12-20 21:07:39,744 INFO misc.py line 119 131400] Train: [93/100][11/800] Data 0.004 (0.004) Batch 0.352 (0.335) Remain 00:35:38 loss: 0.1553 Lr: 0.00011 [2023-12-20 21:07:40,063 INFO misc.py line 119 131400] Train: [93/100][12/800] Data 0.003 (0.004) Batch 0.319 (0.333) Remain 00:35:26 loss: 0.1848 Lr: 0.00011 [2023-12-20 21:07:40,378 INFO misc.py line 119 131400] Train: [93/100][13/800] Data 0.004 (0.004) Batch 0.316 (0.331) Remain 00:35:15 loss: 0.2807 Lr: 0.00011 [2023-12-20 21:07:40,699 INFO misc.py line 119 131400] Train: [93/100][14/800] Data 0.003 (0.004) Batch 0.307 (0.329) Remain 00:35:00 loss: 0.2791 Lr: 0.00011 [2023-12-20 21:07:41,053 INFO misc.py line 119 131400] Train: [93/100][15/800] Data 0.019 (0.005) Batch 0.369 (0.332) Remain 00:35:21 loss: 0.2321 Lr: 0.00011 [2023-12-20 21:07:41,414 INFO misc.py line 119 131400] Train: [93/100][16/800] Data 0.004 (0.005) Batch 0.361 (0.334) Remain 00:35:35 loss: 0.2003 Lr: 0.00011 [2023-12-20 21:07:41,694 INFO misc.py line 119 131400] Train: [93/100][17/800] Data 0.003 (0.005) Batch 0.279 (0.330) Remain 00:35:09 loss: 0.2552 Lr: 0.00011 [2023-12-20 21:07:42,006 INFO misc.py line 119 131400] Train: [93/100][18/800] Data 0.005 (0.005) Batch 0.313 (0.329) Remain 00:35:01 loss: 0.1475 Lr: 0.00011 [2023-12-20 21:07:42,355 INFO misc.py line 119 131400] Train: [93/100][19/800] Data 0.004 (0.005) Batch 0.349 (0.331) Remain 00:35:09 loss: 0.2640 Lr: 0.00011 [2023-12-20 21:07:42,673 INFO misc.py line 119 131400] Train: [93/100][20/800] Data 0.004 (0.005) Batch 0.317 (0.330) Remain 00:35:03 loss: 0.2730 Lr: 0.00011 [2023-12-20 21:07:42,991 INFO misc.py line 119 131400] Train: [93/100][21/800] Data 0.004 (0.005) Batch 0.319 (0.329) Remain 00:34:59 loss: 0.1991 Lr: 0.00011 [2023-12-20 21:07:43,342 INFO misc.py line 119 131400] Train: [93/100][22/800] Data 0.004 (0.005) Batch 0.351 (0.330) Remain 00:35:06 loss: 0.1127 Lr: 0.00011 [2023-12-20 21:07:43,685 INFO misc.py line 119 131400] Train: [93/100][23/800] Data 0.003 (0.005) Batch 0.343 (0.331) Remain 00:35:10 loss: 0.1549 Lr: 0.00011 [2023-12-20 21:07:44,009 INFO misc.py line 119 131400] Train: [93/100][24/800] Data 0.003 (0.004) Batch 0.323 (0.331) Remain 00:35:07 loss: 0.1256 Lr: 0.00011 [2023-12-20 21:07:44,337 INFO misc.py line 119 131400] Train: [93/100][25/800] Data 0.004 (0.004) Batch 0.329 (0.331) Remain 00:35:06 loss: 0.1930 Lr: 0.00011 [2023-12-20 21:07:44,657 INFO misc.py line 119 131400] Train: [93/100][26/800] Data 0.003 (0.004) Batch 0.319 (0.330) Remain 00:35:03 loss: 0.2649 Lr: 0.00011 [2023-12-20 21:07:44,991 INFO misc.py line 119 131400] Train: [93/100][27/800] Data 0.004 (0.004) Batch 0.334 (0.330) Remain 00:35:04 loss: 0.3111 Lr: 0.00011 [2023-12-20 21:07:45,299 INFO misc.py line 119 131400] Train: [93/100][28/800] Data 0.003 (0.004) Batch 0.308 (0.329) Remain 00:34:58 loss: 0.1179 Lr: 0.00011 [2023-12-20 21:07:45,616 INFO misc.py line 119 131400] Train: [93/100][29/800] Data 0.003 (0.004) Batch 0.316 (0.329) Remain 00:34:54 loss: 0.2641 Lr: 0.00011 [2023-12-20 21:07:45,951 INFO misc.py line 119 131400] Train: [93/100][30/800] Data 0.005 (0.004) Batch 0.336 (0.329) Remain 00:34:56 loss: 0.1423 Lr: 0.00010 [2023-12-20 21:07:46,257 INFO misc.py line 119 131400] Train: [93/100][31/800] Data 0.004 (0.004) Batch 0.306 (0.328) Remain 00:34:50 loss: 0.2446 Lr: 0.00010 [2023-12-20 21:07:46,585 INFO misc.py line 119 131400] Train: [93/100][32/800] Data 0.003 (0.004) Batch 0.328 (0.328) Remain 00:34:50 loss: 0.2235 Lr: 0.00010 [2023-12-20 21:07:46,930 INFO misc.py line 119 131400] Train: [93/100][33/800] Data 0.003 (0.004) Batch 0.345 (0.329) Remain 00:34:53 loss: 0.1110 Lr: 0.00010 [2023-12-20 21:07:47,376 INFO misc.py line 119 131400] Train: [93/100][34/800] Data 0.003 (0.004) Batch 0.446 (0.333) Remain 00:35:17 loss: 0.1359 Lr: 0.00010 [2023-12-20 21:07:47,642 INFO misc.py line 119 131400] Train: [93/100][35/800] Data 0.003 (0.004) Batch 0.266 (0.330) Remain 00:35:03 loss: 0.1335 Lr: 0.00010 [2023-12-20 21:07:47,997 INFO misc.py line 119 131400] Train: [93/100][36/800] Data 0.003 (0.004) Batch 0.354 (0.331) Remain 00:35:07 loss: 0.2335 Lr: 0.00010 [2023-12-20 21:07:48,316 INFO misc.py line 119 131400] Train: [93/100][37/800] Data 0.004 (0.004) Batch 0.319 (0.331) Remain 00:35:05 loss: 0.1323 Lr: 0.00010 [2023-12-20 21:07:48,641 INFO misc.py line 119 131400] Train: [93/100][38/800] Data 0.004 (0.004) Batch 0.325 (0.331) Remain 00:35:03 loss: 0.2092 Lr: 0.00010 [2023-12-20 21:07:48,956 INFO misc.py line 119 131400] Train: [93/100][39/800] Data 0.004 (0.004) Batch 0.314 (0.330) Remain 00:35:00 loss: 0.0998 Lr: 0.00010 [2023-12-20 21:07:49,253 INFO misc.py line 119 131400] Train: [93/100][40/800] Data 0.005 (0.004) Batch 0.298 (0.329) Remain 00:34:54 loss: 0.1548 Lr: 0.00010 [2023-12-20 21:07:49,585 INFO misc.py line 119 131400] Train: [93/100][41/800] Data 0.003 (0.004) Batch 0.331 (0.329) Remain 00:34:54 loss: 0.1222 Lr: 0.00010 [2023-12-20 21:07:49,905 INFO misc.py line 119 131400] Train: [93/100][42/800] Data 0.004 (0.004) Batch 0.321 (0.329) Remain 00:34:53 loss: 0.1521 Lr: 0.00010 [2023-12-20 21:07:50,218 INFO misc.py line 119 131400] Train: [93/100][43/800] Data 0.003 (0.004) Batch 0.312 (0.329) Remain 00:34:50 loss: 0.1742 Lr: 0.00010 [2023-12-20 21:07:50,571 INFO misc.py line 119 131400] Train: [93/100][44/800] Data 0.004 (0.004) Batch 0.353 (0.329) Remain 00:34:53 loss: 0.1361 Lr: 0.00010 [2023-12-20 21:07:50,899 INFO misc.py line 119 131400] Train: [93/100][45/800] Data 0.004 (0.004) Batch 0.328 (0.329) Remain 00:34:52 loss: 0.1524 Lr: 0.00010 [2023-12-20 21:07:51,240 INFO misc.py line 119 131400] Train: [93/100][46/800] Data 0.003 (0.004) Batch 0.341 (0.330) Remain 00:34:54 loss: 0.4414 Lr: 0.00010 [2023-12-20 21:07:51,557 INFO misc.py line 119 131400] Train: [93/100][47/800] Data 0.004 (0.004) 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line 119 131400] Train: [93/100][782/800] Data 0.004 (0.005) Batch 0.344 (0.336) Remain 00:31:29 loss: 0.2572 Lr: 0.00008 [2023-12-20 21:11:59,365 INFO misc.py line 119 131400] Train: [93/100][783/800] Data 0.003 (0.005) Batch 0.346 (0.336) Remain 00:31:28 loss: 0.1751 Lr: 0.00008 [2023-12-20 21:11:59,710 INFO misc.py line 119 131400] Train: [93/100][784/800] Data 0.004 (0.005) Batch 0.345 (0.336) Remain 00:31:28 loss: 0.1908 Lr: 0.00008 [2023-12-20 21:12:00,030 INFO misc.py line 119 131400] Train: [93/100][785/800] Data 0.003 (0.005) Batch 0.320 (0.336) Remain 00:31:28 loss: 0.2723 Lr: 0.00008 [2023-12-20 21:12:00,340 INFO misc.py line 119 131400] Train: [93/100][786/800] Data 0.003 (0.005) Batch 0.309 (0.336) Remain 00:31:27 loss: 0.1858 Lr: 0.00008 [2023-12-20 21:12:00,654 INFO misc.py line 119 131400] Train: [93/100][787/800] Data 0.003 (0.005) Batch 0.314 (0.336) Remain 00:31:27 loss: 0.2137 Lr: 0.00008 [2023-12-20 21:12:00,981 INFO misc.py line 119 131400] Train: 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Batch 0.305 (0.336) Remain 00:31:23 loss: 0.1207 Lr: 0.00008 [2023-12-20 21:12:03,080 INFO misc.py line 119 131400] Train: [93/100][795/800] Data 0.003 (0.005) Batch 0.284 (0.336) Remain 00:31:22 loss: 0.3809 Lr: 0.00008 [2023-12-20 21:12:03,390 INFO misc.py line 119 131400] Train: [93/100][796/800] Data 0.003 (0.005) Batch 0.311 (0.336) Remain 00:31:22 loss: 0.2813 Lr: 0.00008 [2023-12-20 21:12:03,702 INFO misc.py line 119 131400] Train: [93/100][797/800] Data 0.003 (0.005) Batch 0.312 (0.336) Remain 00:31:21 loss: 0.2170 Lr: 0.00008 [2023-12-20 21:12:04,024 INFO misc.py line 119 131400] Train: [93/100][798/800] Data 0.003 (0.005) Batch 0.320 (0.336) Remain 00:31:21 loss: 0.1525 Lr: 0.00008 [2023-12-20 21:12:04,389 INFO misc.py line 119 131400] Train: [93/100][799/800] Data 0.006 (0.005) Batch 0.366 (0.336) Remain 00:31:20 loss: 0.1516 Lr: 0.00008 [2023-12-20 21:12:04,718 INFO misc.py line 119 131400] Train: [93/100][800/800] Data 0.004 (0.005) Batch 0.329 (0.336) Remain 00:31:20 loss: 0.3068 Lr: 0.00008 [2023-12-20 21:12:04,719 INFO misc.py line 136 131400] Train result: loss: 0.2057 [2023-12-20 21:12:04,720 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:12:25,757 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2154 [2023-12-20 21:12:25,829 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1654 [2023-12-20 21:12:25,923 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5342 [2023-12-20 21:12:26,033 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5330 [2023-12-20 21:12:26,147 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.4031 [2023-12-20 21:12:26,250 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5918 [2023-12-20 21:12:26,342 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1006 [2023-12-20 21:12:26,449 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.6658 [2023-12-20 21:12:26,531 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2889 [2023-12-20 21:12:26,618 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3171 [2023-12-20 21:12:26,711 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3781 [2023-12-20 21:12:26,848 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2807 [2023-12-20 21:12:26,970 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5262 [2023-12-20 21:12:27,126 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2023 [2023-12-20 21:12:27,224 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1221 [2023-12-20 21:12:27,365 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7346 [2023-12-20 21:12:27,478 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2678 [2023-12-20 21:12:27,599 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8619 [2023-12-20 21:12:27,711 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1277 [2023-12-20 21:12:27,789 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4185 [2023-12-20 21:12:27,942 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1341 [2023-12-20 21:12:28,121 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1129 [2023-12-20 21:12:28,241 INFO evaluator.py line 159 131400] Test: [23/78] Loss 2.0676 [2023-12-20 21:12:28,384 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.4054 [2023-12-20 21:12:28,533 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1795 [2023-12-20 21:12:28,626 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7564 [2023-12-20 21:12:28,797 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6532 [2023-12-20 21:12:28,930 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.6003 [2023-12-20 21:12:29,027 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4757 [2023-12-20 21:12:29,174 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.5649 [2023-12-20 21:12:29,277 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5059 [2023-12-20 21:12:29,399 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3642 [2023-12-20 21:12:29,486 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1069 [2023-12-20 21:12:29,562 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1747 [2023-12-20 21:12:29,661 INFO evaluator.py line 159 131400] Test: [35/78] Loss 1.0430 [2023-12-20 21:12:29,754 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2614 [2023-12-20 21:12:29,884 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9721 [2023-12-20 21:12:29,993 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0838 [2023-12-20 21:12:30,075 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5761 [2023-12-20 21:12:30,222 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2586 [2023-12-20 21:12:30,379 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0160 [2023-12-20 21:12:30,484 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0580 [2023-12-20 21:12:30,604 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2480 [2023-12-20 21:12:30,747 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0674 [2023-12-20 21:12:30,869 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6053 [2023-12-20 21:12:30,983 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6069 [2023-12-20 21:12:31,158 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2871 [2023-12-20 21:12:31,267 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4833 [2023-12-20 21:12:31,435 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6919 [2023-12-20 21:12:31,547 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1952 [2023-12-20 21:12:31,636 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6105 [2023-12-20 21:12:31,746 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4756 [2023-12-20 21:12:31,908 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.0885 [2023-12-20 21:12:32,049 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3426 [2023-12-20 21:12:32,157 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2797 [2023-12-20 21:12:32,247 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6079 [2023-12-20 21:12:32,358 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3373 [2023-12-20 21:12:32,522 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2638 [2023-12-20 21:12:32,622 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5036 [2023-12-20 21:12:32,725 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2065 [2023-12-20 21:12:32,826 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5856 [2023-12-20 21:12:32,920 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2466 [2023-12-20 21:12:33,020 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6209 [2023-12-20 21:12:33,126 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6268 [2023-12-20 21:12:33,264 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.5911 [2023-12-20 21:12:33,358 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2293 [2023-12-20 21:12:33,465 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3988 [2023-12-20 21:12:33,564 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0100 [2023-12-20 21:12:33,663 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3236 [2023-12-20 21:12:33,759 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0090 [2023-12-20 21:12:33,860 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8529 [2023-12-20 21:12:33,959 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6253 [2023-12-20 21:12:34,094 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0614 [2023-12-20 21:12:34,189 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6752 [2023-12-20 21:12:34,313 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6433 [2023-12-20 21:12:34,425 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5451 [2023-12-20 21:12:34,512 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4747 [2023-12-20 21:12:34,671 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0755 [2023-12-20 21:12:36,122 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7688/0.8473/0.9207. [2023-12-20 21:12:36,122 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8732/0.9548 [2023-12-20 21:12:36,122 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9862 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7090/0.8089 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8438/0.8920 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9225/0.9616 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8652/0.9302 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7778/0.8533 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7257/0.8373 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7124/0.8178 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8242/0.9237 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3875/0.4863 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7237/0.8244 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7069/0.8785 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7774/0.8759 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7016/0.7887 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7167/0.7684 [2023-12-20 21:12:36,123 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9366/0.9806 [2023-12-20 21:12:36,124 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6897/0.7840 [2023-12-20 21:12:36,124 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8947/0.9236 [2023-12-20 21:12:36,124 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6222/0.6691 [2023-12-20 21:12:36,124 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:12:36,125 INFO misc.py line 165 131400] Currently Best mIoU: 0.7737 [2023-12-20 21:12:36,125 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:12:40,408 INFO misc.py line 119 131400] Train: [94/100][1/800] Data 1.387 (1.387) Batch 1.727 (1.727) Remain 02:41:08 loss: 0.2137 Lr: 0.00008 [2023-12-20 21:12:40,720 INFO misc.py line 119 131400] Train: [94/100][2/800] Data 0.006 (0.006) Batch 0.312 (0.312) Remain 00:29:09 loss: 0.1921 Lr: 0.00008 [2023-12-20 21:12:41,066 INFO misc.py line 119 131400] Train: [94/100][3/800] Data 0.006 (0.006) Batch 0.348 (0.348) Remain 00:32:28 loss: 0.1659 Lr: 0.00008 [2023-12-20 21:12:41,390 INFO misc.py line 119 131400] Train: [94/100][4/800] Data 0.003 (0.003) Batch 0.323 (0.323) Remain 00:30:08 loss: 0.1546 Lr: 0.00008 [2023-12-20 21:12:41,699 INFO misc.py line 119 131400] Train: [94/100][5/800] Data 0.005 (0.004) Batch 0.309 (0.316) Remain 00:29:29 loss: 0.1798 Lr: 0.00008 [2023-12-20 21:12:42,019 INFO misc.py line 119 131400] Train: [94/100][6/800] Data 0.004 (0.004) Batch 0.321 (0.318) Remain 00:29:37 loss: 0.1741 Lr: 0.00008 [2023-12-20 21:12:42,516 INFO misc.py line 119 131400] Train: [94/100][7/800] Data 0.003 (0.004) Batch 0.492 (0.361) Remain 00:33:40 loss: 0.2326 Lr: 0.00008 [2023-12-20 21:12:42,848 INFO misc.py line 119 131400] Train: [94/100][8/800] Data 0.009 (0.005) Batch 0.336 (0.356) Remain 00:33:12 loss: 0.3234 Lr: 0.00008 [2023-12-20 21:12:43,186 INFO misc.py line 119 131400] Train: [94/100][9/800] Data 0.004 (0.005) Batch 0.338 (0.353) Remain 00:32:54 loss: 0.1529 Lr: 0.00008 [2023-12-20 21:12:43,555 INFO misc.py line 119 131400] Train: [94/100][10/800] Data 0.004 (0.005) Batch 0.371 (0.356) Remain 00:33:08 loss: 0.2167 Lr: 0.00008 [2023-12-20 21:12:43,874 INFO misc.py line 119 131400] Train: [94/100][11/800] Data 0.003 (0.004) Batch 0.318 (0.351) Remain 00:32:41 loss: 0.2262 Lr: 0.00008 [2023-12-20 21:12:44,209 INFO misc.py line 119 131400] Train: [94/100][12/800] Data 0.004 (0.004) Batch 0.335 (0.349) Remain 00:32:31 loss: 0.2185 Lr: 0.00008 [2023-12-20 21:12:44,543 INFO misc.py line 119 131400] Train: [94/100][13/800] Data 0.004 (0.004) Batch 0.334 (0.348) Remain 00:32:22 loss: 0.1731 Lr: 0.00008 [2023-12-20 21:12:44,896 INFO misc.py line 119 131400] Train: [94/100][14/800] Data 0.004 (0.004) Batch 0.353 (0.348) Remain 00:32:24 loss: 0.2556 Lr: 0.00008 [2023-12-20 21:12:45,209 INFO misc.py line 119 131400] Train: [94/100][15/800] Data 0.003 (0.004) Batch 0.313 (0.345) Remain 00:32:08 loss: 0.1086 Lr: 0.00008 [2023-12-20 21:12:45,543 INFO misc.py line 119 131400] Train: [94/100][16/800] Data 0.004 (0.004) Batch 0.335 (0.344) Remain 00:32:03 loss: 0.1458 Lr: 0.00008 [2023-12-20 21:12:45,888 INFO misc.py line 119 131400] Train: [94/100][17/800] Data 0.003 (0.004) Batch 0.346 (0.344) Remain 00:32:03 loss: 0.2038 Lr: 0.00008 [2023-12-20 21:12:46,210 INFO misc.py line 119 131400] Train: [94/100][18/800] Data 0.003 (0.004) Batch 0.321 (0.343) Remain 00:31:54 loss: 0.1223 Lr: 0.00008 [2023-12-20 21:12:46,552 INFO misc.py line 119 131400] Train: [94/100][19/800] Data 0.003 (0.004) Batch 0.342 (0.343) Remain 00:31:53 loss: 0.1996 Lr: 0.00008 [2023-12-20 21:12:46,891 INFO misc.py line 119 131400] Train: [94/100][20/800] Data 0.003 (0.004) Batch 0.340 (0.343) Remain 00:31:52 loss: 0.1333 Lr: 0.00008 [2023-12-20 21:12:47,302 INFO misc.py line 119 131400] Train: [94/100][21/800] Data 0.003 (0.004) Batch 0.410 (0.346) Remain 00:32:12 loss: 0.4299 Lr: 0.00008 [2023-12-20 21:12:47,618 INFO misc.py line 119 131400] Train: [94/100][22/800] Data 0.004 (0.004) Batch 0.316 (0.345) Remain 00:32:03 loss: 0.1241 Lr: 0.00008 [2023-12-20 21:12:47,949 INFO misc.py line 119 131400] Train: [94/100][23/800] Data 0.004 (0.004) Batch 0.332 (0.344) Remain 00:31:59 loss: 0.1182 Lr: 0.00008 [2023-12-20 21:12:48,432 INFO misc.py line 119 131400] Train: [94/100][24/800] Data 0.004 (0.004) Batch 0.481 (0.351) Remain 00:32:35 loss: 0.1235 Lr: 0.00008 [2023-12-20 21:12:48,764 INFO misc.py line 119 131400] Train: [94/100][25/800] Data 0.005 (0.004) Batch 0.330 (0.350) Remain 00:32:29 loss: 0.1607 Lr: 0.00008 [2023-12-20 21:12:49,098 INFO misc.py line 119 131400] Train: [94/100][26/800] Data 0.007 (0.004) Batch 0.337 (0.349) Remain 00:32:26 loss: 0.2110 Lr: 0.00008 [2023-12-20 21:12:49,423 INFO misc.py line 119 131400] Train: [94/100][27/800] Data 0.004 (0.004) Batch 0.325 (0.348) Remain 00:32:20 loss: 0.1370 Lr: 0.00008 [2023-12-20 21:12:49,740 INFO misc.py line 119 131400] Train: [94/100][28/800] Data 0.004 (0.004) Batch 0.316 (0.347) Remain 00:32:13 loss: 0.1206 Lr: 0.00008 [2023-12-20 21:12:50,048 INFO misc.py line 119 131400] Train: [94/100][29/800] Data 0.005 (0.004) Batch 0.308 (0.345) Remain 00:32:04 loss: 0.1217 Lr: 0.00008 [2023-12-20 21:12:50,340 INFO misc.py line 119 131400] Train: [94/100][30/800] Data 0.004 (0.004) Batch 0.293 (0.344) Remain 00:31:53 loss: 0.2256 Lr: 0.00008 [2023-12-20 21:12:50,708 INFO misc.py line 119 131400] Train: [94/100][31/800] Data 0.003 (0.004) Batch 0.367 (0.344) Remain 00:31:57 loss: 0.1491 Lr: 0.00008 [2023-12-20 21:12:51,044 INFO misc.py line 119 131400] Train: [94/100][32/800] Data 0.003 (0.004) Batch 0.337 (0.344) Remain 00:31:55 loss: 0.1719 Lr: 0.00008 [2023-12-20 21:12:51,344 INFO misc.py line 119 131400] Train: [94/100][33/800] Data 0.003 (0.004) Batch 0.300 (0.343) Remain 00:31:47 loss: 0.2911 Lr: 0.00008 [2023-12-20 21:12:51,641 INFO misc.py line 119 131400] Train: [94/100][34/800] Data 0.004 (0.004) Batch 0.296 (0.341) Remain 00:31:38 loss: 0.2207 Lr: 0.00008 [2023-12-20 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line 119 131400] Train: [94/100][782/800] Data 0.003 (0.004) Batch 0.315 (0.336) Remain 00:26:57 loss: 0.1779 Lr: 0.00006 [2023-12-20 21:17:02,848 INFO misc.py line 119 131400] Train: [94/100][783/800] Data 0.004 (0.004) Batch 0.329 (0.336) Remain 00:26:56 loss: 0.1956 Lr: 0.00006 [2023-12-20 21:17:03,290 INFO misc.py line 119 131400] Train: [94/100][784/800] Data 0.003 (0.004) Batch 0.440 (0.336) Remain 00:26:56 loss: 0.1549 Lr: 0.00006 [2023-12-20 21:17:03,628 INFO misc.py line 119 131400] Train: [94/100][785/800] Data 0.004 (0.004) Batch 0.339 (0.336) Remain 00:26:56 loss: 0.1872 Lr: 0.00006 [2023-12-20 21:17:04,063 INFO misc.py line 119 131400] Train: [94/100][786/800] Data 0.004 (0.004) Batch 0.436 (0.336) Remain 00:26:56 loss: 0.1966 Lr: 0.00006 [2023-12-20 21:17:04,398 INFO misc.py line 119 131400] Train: [94/100][787/800] Data 0.003 (0.004) Batch 0.334 (0.336) Remain 00:26:56 loss: 0.1301 Lr: 0.00006 [2023-12-20 21:17:04,701 INFO misc.py line 119 131400] Train: [94/100][788/800] Data 0.003 (0.004) Batch 0.304 (0.336) Remain 00:26:56 loss: 0.1339 Lr: 0.00006 [2023-12-20 21:17:05,004 INFO misc.py line 119 131400] Train: [94/100][789/800] Data 0.003 (0.004) Batch 0.303 (0.336) Remain 00:26:55 loss: 0.2206 Lr: 0.00006 [2023-12-20 21:17:05,330 INFO misc.py line 119 131400] Train: [94/100][790/800] Data 0.003 (0.004) Batch 0.325 (0.336) Remain 00:26:55 loss: 0.1910 Lr: 0.00006 [2023-12-20 21:17:05,609 INFO misc.py line 119 131400] Train: [94/100][791/800] Data 0.003 (0.004) Batch 0.279 (0.336) Remain 00:26:54 loss: 0.2746 Lr: 0.00006 [2023-12-20 21:17:05,932 INFO misc.py line 119 131400] Train: [94/100][792/800] Data 0.003 (0.004) Batch 0.324 (0.336) Remain 00:26:54 loss: 0.1242 Lr: 0.00006 [2023-12-20 21:17:06,215 INFO misc.py line 119 131400] Train: [94/100][793/800] Data 0.003 (0.004) Batch 0.281 (0.336) Remain 00:26:53 loss: 0.1739 Lr: 0.00006 [2023-12-20 21:17:06,526 INFO misc.py line 119 131400] Train: [94/100][794/800] Data 0.004 (0.004) Batch 0.313 (0.336) Remain 00:26:52 loss: 0.1788 Lr: 0.00006 [2023-12-20 21:17:06,789 INFO misc.py line 119 131400] Train: [94/100][795/800] Data 0.003 (0.004) Batch 0.263 (0.336) Remain 00:26:52 loss: 0.3201 Lr: 0.00006 [2023-12-20 21:17:07,072 INFO misc.py line 119 131400] Train: [94/100][796/800] Data 0.003 (0.004) Batch 0.282 (0.335) Remain 00:26:51 loss: 0.0996 Lr: 0.00006 [2023-12-20 21:17:07,380 INFO misc.py line 119 131400] Train: [94/100][797/800] Data 0.003 (0.004) Batch 0.309 (0.335) Remain 00:26:50 loss: 0.2499 Lr: 0.00006 [2023-12-20 21:17:07,692 INFO misc.py line 119 131400] Train: [94/100][798/800] Data 0.002 (0.004) Batch 0.312 (0.335) Remain 00:26:50 loss: 0.1631 Lr: 0.00006 [2023-12-20 21:17:07,966 INFO misc.py line 119 131400] Train: [94/100][799/800] Data 0.003 (0.004) Batch 0.275 (0.335) Remain 00:26:49 loss: 0.1692 Lr: 0.00006 [2023-12-20 21:17:08,249 INFO misc.py line 119 131400] Train: [94/100][800/800] Data 0.003 (0.004) Batch 0.283 (0.335) Remain 00:26:49 loss: 0.1837 Lr: 0.00006 [2023-12-20 21:17:08,250 INFO misc.py line 136 131400] Train result: loss: 0.1973 [2023-12-20 21:17:08,250 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:17:30,200 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2299 [2023-12-20 21:17:30,274 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1533 [2023-12-20 21:17:30,370 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5190 [2023-12-20 21:17:30,985 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4471 [2023-12-20 21:17:31,100 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.2736 [2023-12-20 21:17:31,205 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.2920 [2023-12-20 21:17:31,300 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9198 [2023-12-20 21:17:31,416 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8750 [2023-12-20 21:17:31,500 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2902 [2023-12-20 21:17:31,586 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3200 [2023-12-20 21:17:31,677 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4051 [2023-12-20 21:17:31,818 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2755 [2023-12-20 21:17:31,935 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5976 [2023-12-20 21:17:32,089 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1874 [2023-12-20 21:17:32,183 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1385 [2023-12-20 21:17:32,326 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7228 [2023-12-20 21:17:32,464 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2810 [2023-12-20 21:17:32,578 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8395 [2023-12-20 21:17:32,697 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1340 [2023-12-20 21:17:32,807 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4374 [2023-12-20 21:17:32,930 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1369 [2023-12-20 21:17:33,087 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1253 [2023-12-20 21:17:33,222 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7312 [2023-12-20 21:17:33,367 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2715 [2023-12-20 21:17:33,517 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1801 [2023-12-20 21:17:33,606 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6879 [2023-12-20 21:17:33,769 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5404 [2023-12-20 21:17:33,893 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5590 [2023-12-20 21:17:33,992 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4348 [2023-12-20 21:17:34,142 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.7807 [2023-12-20 21:17:34,247 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.5056 [2023-12-20 21:17:34,369 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3296 [2023-12-20 21:17:34,468 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1077 [2023-12-20 21:17:34,551 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1735 [2023-12-20 21:17:34,654 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8927 [2023-12-20 21:17:34,752 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2709 [2023-12-20 21:17:34,888 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9855 [2023-12-20 21:17:35,010 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0825 [2023-12-20 21:17:35,096 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6747 [2023-12-20 21:17:35,240 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2572 [2023-12-20 21:17:35,390 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0158 [2023-12-20 21:17:35,498 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0561 [2023-12-20 21:17:35,625 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3355 [2023-12-20 21:17:35,769 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1084 [2023-12-20 21:17:35,888 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5372 [2023-12-20 21:17:35,996 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8671 [2023-12-20 21:17:36,169 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2756 [2023-12-20 21:17:36,261 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5537 [2023-12-20 21:17:36,412 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6881 [2023-12-20 21:17:36,504 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.1712 [2023-12-20 21:17:36,586 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5020 [2023-12-20 21:17:36,700 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3753 [2023-12-20 21:17:36,853 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9683 [2023-12-20 21:17:36,987 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3447 [2023-12-20 21:17:37,093 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2421 [2023-12-20 21:17:37,190 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5696 [2023-12-20 21:17:37,299 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3643 [2023-12-20 21:17:37,459 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2249 [2023-12-20 21:17:37,577 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.4997 [2023-12-20 21:17:37,677 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1998 [2023-12-20 21:17:37,778 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5372 [2023-12-20 21:17:37,887 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2448 [2023-12-20 21:17:37,996 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6017 [2023-12-20 21:17:38,098 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6829 [2023-12-20 21:17:38,228 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6077 [2023-12-20 21:17:38,322 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2475 [2023-12-20 21:17:38,427 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3071 [2023-12-20 21:17:38,530 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0084 [2023-12-20 21:17:38,623 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3152 [2023-12-20 21:17:38,708 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0094 [2023-12-20 21:17:38,815 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9203 [2023-12-20 21:17:38,917 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5940 [2023-12-20 21:17:39,051 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0503 [2023-12-20 21:17:39,147 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6766 [2023-12-20 21:17:39,270 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6031 [2023-12-20 21:17:39,373 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4523 [2023-12-20 21:17:39,471 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4569 [2023-12-20 21:17:39,632 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9797 [2023-12-20 21:17:41,252 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7751/0.8516/0.9224. [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8740/0.9534 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9638/0.9866 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7108/0.8245 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8382/0.8804 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9263/0.9636 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8728/0.9371 [2023-12-20 21:17:41,252 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7840/0.8710 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7430/0.8573 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7158/0.8150 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8305/0.9129 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3965/0.5101 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7228/0.8195 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7178/0.8692 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7938/0.8781 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7108/0.7955 [2023-12-20 21:17:41,253 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7406/0.7948 [2023-12-20 21:17:41,254 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9469/0.9799 [2023-12-20 21:17:41,254 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6966/0.7883 [2023-12-20 21:17:41,254 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8896/0.9241 [2023-12-20 21:17:41,254 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6283/0.6706 [2023-12-20 21:17:41,255 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:17:41,256 INFO misc.py line 160 131400] Best validation mIoU updated to: 0.7751 [2023-12-20 21:17:41,257 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:17:41,257 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:17:47,836 INFO misc.py line 119 131400] Train: [95/100][1/800] Data 1.265 (1.265) Batch 1.574 (1.574) Remain 02:05:51 loss: 0.2325 Lr: 0.00006 [2023-12-20 21:17:48,123 INFO misc.py line 119 131400] Train: [95/100][2/800] Data 0.004 (0.004) Batch 0.288 (0.288) Remain 00:23:02 loss: 0.1937 Lr: 0.00006 [2023-12-20 21:17:48,471 INFO misc.py line 119 131400] Train: [95/100][3/800] Data 0.003 (0.003) Batch 0.347 (0.347) Remain 00:27:46 loss: 0.2332 Lr: 0.00006 [2023-12-20 21:17:48,790 INFO misc.py line 119 131400] Train: [95/100][4/800] Data 0.004 (0.004) Batch 0.315 (0.315) Remain 00:25:10 loss: 0.1678 Lr: 0.00006 [2023-12-20 21:17:49,119 INFO misc.py line 119 131400] Train: [95/100][5/800] Data 0.008 (0.006) Batch 0.333 (0.324) Remain 00:25:53 loss: 0.1388 Lr: 0.00006 [2023-12-20 21:17:49,441 INFO misc.py line 119 131400] Train: [95/100][6/800] Data 0.004 (0.005) Batch 0.322 (0.323) Remain 00:25:49 loss: 0.1615 Lr: 0.00006 [2023-12-20 21:17:49,767 INFO misc.py line 119 131400] Train: [95/100][7/800] Data 0.004 (0.005) Batch 0.327 (0.324) Remain 00:25:53 loss: 0.1881 Lr: 0.00006 [2023-12-20 21:17:50,120 INFO misc.py line 119 131400] Train: [95/100][8/800] Data 0.003 (0.005) Batch 0.352 (0.330) Remain 00:26:20 loss: 0.1741 Lr: 0.00006 [2023-12-20 21:17:50,427 INFO misc.py line 119 131400] Train: 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(0.332) Remain 00:26:29 loss: 0.1708 Lr: 0.00006 [2023-12-20 21:17:52,819 INFO misc.py line 119 131400] Train: [95/100][16/800] Data 0.004 (0.004) Batch 0.359 (0.334) Remain 00:26:39 loss: 0.3170 Lr: 0.00006 [2023-12-20 21:17:53,222 INFO misc.py line 119 131400] Train: [95/100][17/800] Data 0.007 (0.005) Batch 0.405 (0.339) Remain 00:27:02 loss: 0.1824 Lr: 0.00006 [2023-12-20 21:17:53,578 INFO misc.py line 119 131400] Train: [95/100][18/800] Data 0.005 (0.005) Batch 0.357 (0.340) Remain 00:27:08 loss: 0.1627 Lr: 0.00006 [2023-12-20 21:17:53,937 INFO misc.py line 119 131400] Train: [95/100][19/800] Data 0.004 (0.005) Batch 0.359 (0.342) Remain 00:27:13 loss: 0.0882 Lr: 0.00006 [2023-12-20 21:17:54,235 INFO misc.py line 119 131400] Train: [95/100][20/800] Data 0.003 (0.005) Batch 0.298 (0.339) Remain 00:27:00 loss: 0.1478 Lr: 0.00006 [2023-12-20 21:17:54,561 INFO misc.py line 119 131400] Train: [95/100][21/800] Data 0.003 (0.004) Batch 0.325 (0.338) Remain 00:26:56 loss: 0.1252 Lr: 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loss: 0.2438 Lr: 0.00004 [2023-12-20 21:22:03,429 INFO misc.py line 119 131400] Train: [95/100][757/800] Data 0.004 (0.005) Batch 0.330 (0.338) Remain 00:22:47 loss: 0.1972 Lr: 0.00004 [2023-12-20 21:22:03,752 INFO misc.py line 119 131400] Train: [95/100][758/800] Data 0.007 (0.005) Batch 0.326 (0.338) Remain 00:22:46 loss: 0.2031 Lr: 0.00004 [2023-12-20 21:22:04,100 INFO misc.py line 119 131400] Train: [95/100][759/800] Data 0.004 (0.005) Batch 0.349 (0.338) Remain 00:22:46 loss: 0.1261 Lr: 0.00004 [2023-12-20 21:22:04,393 INFO misc.py line 119 131400] Train: [95/100][760/800] Data 0.003 (0.005) Batch 0.292 (0.338) Remain 00:22:45 loss: 0.1332 Lr: 0.00004 [2023-12-20 21:22:04,764 INFO misc.py line 119 131400] Train: [95/100][761/800] Data 0.004 (0.005) Batch 0.372 (0.338) Remain 00:22:45 loss: 0.1271 Lr: 0.00004 [2023-12-20 21:22:05,106 INFO misc.py line 119 131400] Train: [95/100][762/800] Data 0.004 (0.005) Batch 0.342 (0.338) Remain 00:22:45 loss: 0.2627 Lr: 0.00004 [2023-12-20 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131400] Train: [95/100][769/800] Data 0.004 (0.005) Batch 0.356 (0.338) Remain 00:22:42 loss: 0.1730 Lr: 0.00004 [2023-12-20 21:22:07,790 INFO misc.py line 119 131400] Train: [95/100][770/800] Data 0.006 (0.005) Batch 0.348 (0.338) Remain 00:22:42 loss: 0.2193 Lr: 0.00004 [2023-12-20 21:22:08,107 INFO misc.py line 119 131400] Train: [95/100][771/800] Data 0.003 (0.005) Batch 0.317 (0.338) Remain 00:22:42 loss: 0.1549 Lr: 0.00004 [2023-12-20 21:22:08,432 INFO misc.py line 119 131400] Train: [95/100][772/800] Data 0.003 (0.005) Batch 0.325 (0.338) Remain 00:22:41 loss: 0.3659 Lr: 0.00004 [2023-12-20 21:22:08,771 INFO misc.py line 119 131400] Train: [95/100][773/800] Data 0.004 (0.005) Batch 0.336 (0.338) Remain 00:22:41 loss: 0.1957 Lr: 0.00004 [2023-12-20 21:22:09,099 INFO misc.py line 119 131400] Train: [95/100][774/800] Data 0.006 (0.005) Batch 0.331 (0.338) Remain 00:22:40 loss: 0.1763 Lr: 0.00004 [2023-12-20 21:22:09,440 INFO misc.py line 119 131400] Train: [95/100][775/800] Data 0.003 (0.005) Batch 0.341 (0.338) Remain 00:22:40 loss: 0.1931 Lr: 0.00004 [2023-12-20 21:22:09,779 INFO misc.py line 119 131400] Train: [95/100][776/800] Data 0.004 (0.005) Batch 0.335 (0.338) Remain 00:22:40 loss: 0.2578 Lr: 0.00004 [2023-12-20 21:22:10,117 INFO misc.py line 119 131400] Train: [95/100][777/800] Data 0.007 (0.005) Batch 0.342 (0.338) Remain 00:22:39 loss: 0.1076 Lr: 0.00004 [2023-12-20 21:22:10,450 INFO misc.py line 119 131400] Train: [95/100][778/800] Data 0.004 (0.005) Batch 0.333 (0.338) Remain 00:22:39 loss: 0.1167 Lr: 0.00004 [2023-12-20 21:22:10,760 INFO misc.py line 119 131400] Train: [95/100][779/800] Data 0.003 (0.005) Batch 0.309 (0.338) Remain 00:22:39 loss: 0.1561 Lr: 0.00004 [2023-12-20 21:22:11,086 INFO misc.py line 119 131400] Train: [95/100][780/800] Data 0.004 (0.005) Batch 0.326 (0.338) Remain 00:22:38 loss: 0.3067 Lr: 0.00004 [2023-12-20 21:22:11,406 INFO misc.py line 119 131400] Train: [95/100][781/800] Data 0.004 (0.005) Batch 0.320 (0.338) Remain 00:22:38 loss: 0.2835 Lr: 0.00004 [2023-12-20 21:22:11,740 INFO misc.py line 119 131400] Train: [95/100][782/800] Data 0.003 (0.005) Batch 0.334 (0.338) Remain 00:22:37 loss: 0.2290 Lr: 0.00004 [2023-12-20 21:22:12,032 INFO misc.py line 119 131400] Train: [95/100][783/800] Data 0.003 (0.005) Batch 0.291 (0.338) Remain 00:22:37 loss: 0.1061 Lr: 0.00004 [2023-12-20 21:22:12,371 INFO misc.py line 119 131400] Train: [95/100][784/800] Data 0.004 (0.005) Batch 0.340 (0.338) Remain 00:22:36 loss: 0.3404 Lr: 0.00004 [2023-12-20 21:22:12,695 INFO misc.py line 119 131400] Train: [95/100][785/800] Data 0.004 (0.005) Batch 0.324 (0.338) Remain 00:22:36 loss: 0.1125 Lr: 0.00004 [2023-12-20 21:22:12,994 INFO misc.py line 119 131400] Train: [95/100][786/800] Data 0.003 (0.005) Batch 0.299 (0.338) Remain 00:22:36 loss: 0.1452 Lr: 0.00004 [2023-12-20 21:22:13,335 INFO misc.py line 119 131400] Train: [95/100][787/800] Data 0.004 (0.005) Batch 0.342 (0.338) Remain 00:22:35 loss: 0.2152 Lr: 0.00004 [2023-12-20 21:22:13,635 INFO misc.py line 119 131400] Train: [95/100][788/800] Data 0.004 (0.005) Batch 0.300 (0.338) Remain 00:22:35 loss: 0.1408 Lr: 0.00004 [2023-12-20 21:22:13,941 INFO misc.py line 119 131400] Train: [95/100][789/800] Data 0.003 (0.005) Batch 0.306 (0.338) Remain 00:22:34 loss: 0.1741 Lr: 0.00004 [2023-12-20 21:22:14,246 INFO misc.py line 119 131400] Train: [95/100][790/800] Data 0.003 (0.005) Batch 0.302 (0.338) Remain 00:22:34 loss: 0.3272 Lr: 0.00004 [2023-12-20 21:22:14,548 INFO misc.py line 119 131400] Train: [95/100][791/800] Data 0.006 (0.005) Batch 0.304 (0.338) Remain 00:22:33 loss: 0.0742 Lr: 0.00004 [2023-12-20 21:22:14,880 INFO misc.py line 119 131400] Train: [95/100][792/800] Data 0.004 (0.005) Batch 0.333 (0.338) Remain 00:22:33 loss: 0.1344 Lr: 0.00004 [2023-12-20 21:22:15,200 INFO misc.py line 119 131400] Train: [95/100][793/800] Data 0.003 (0.005) Batch 0.317 (0.338) Remain 00:22:32 loss: 0.1622 Lr: 0.00004 [2023-12-20 21:22:15,523 INFO misc.py line 119 131400] Train: [95/100][794/800] Data 0.006 (0.005) Batch 0.326 (0.338) Remain 00:22:32 loss: 0.3075 Lr: 0.00004 [2023-12-20 21:22:15,849 INFO misc.py line 119 131400] Train: [95/100][795/800] Data 0.003 (0.005) Batch 0.325 (0.338) Remain 00:22:32 loss: 0.3024 Lr: 0.00004 [2023-12-20 21:22:16,171 INFO misc.py line 119 131400] Train: [95/100][796/800] Data 0.004 (0.005) Batch 0.323 (0.338) Remain 00:22:31 loss: 0.1189 Lr: 0.00004 [2023-12-20 21:22:16,498 INFO misc.py line 119 131400] Train: [95/100][797/800] Data 0.003 (0.005) Batch 0.328 (0.338) Remain 00:22:31 loss: 0.1688 Lr: 0.00004 [2023-12-20 21:22:16,837 INFO misc.py line 119 131400] Train: [95/100][798/800] Data 0.003 (0.005) Batch 0.339 (0.338) Remain 00:22:30 loss: 0.2699 Lr: 0.00004 [2023-12-20 21:22:17,142 INFO misc.py line 119 131400] Train: [95/100][799/800] Data 0.003 (0.005) Batch 0.305 (0.338) Remain 00:22:30 loss: 0.3186 Lr: 0.00004 [2023-12-20 21:22:17,479 INFO misc.py line 119 131400] Train: [95/100][800/800] Data 0.004 (0.005) Batch 0.336 (0.338) Remain 00:22:30 loss: 0.1697 Lr: 0.00004 [2023-12-20 21:22:17,479 INFO misc.py line 136 131400] Train result: loss: 0.2015 [2023-12-20 21:22:17,480 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:22:40,163 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2245 [2023-12-20 21:22:40,283 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1703 [2023-12-20 21:22:40,380 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4920 [2023-12-20 21:22:40,490 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5462 [2023-12-20 21:22:40,604 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3571 [2023-12-20 21:22:40,741 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.5256 [2023-12-20 21:22:40,833 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0055 [2023-12-20 21:22:40,950 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8496 [2023-12-20 21:22:41,052 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2505 [2023-12-20 21:22:41,148 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3426 [2023-12-20 21:22:41,247 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3730 [2023-12-20 21:22:41,388 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2701 [2023-12-20 21:22:41,531 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5084 [2023-12-20 21:22:41,714 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1831 [2023-12-20 21:22:41,829 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1293 [2023-12-20 21:22:41,964 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.8093 [2023-12-20 21:22:42,076 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2918 [2023-12-20 21:22:42,187 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8742 [2023-12-20 21:22:42,305 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1220 [2023-12-20 21:22:42,399 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3757 [2023-12-20 21:22:42,514 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1506 [2023-12-20 21:22:42,676 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1125 [2023-12-20 21:22:42,799 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.7778 [2023-12-20 21:22:42,944 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2672 [2023-12-20 21:22:43,089 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1808 [2023-12-20 21:22:43,181 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.5311 [2023-12-20 21:22:43,341 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6107 [2023-12-20 21:22:43,472 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5439 [2023-12-20 21:22:43,569 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4741 [2023-12-20 21:22:43,717 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8868 [2023-12-20 21:22:43,827 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4883 [2023-12-20 21:22:43,947 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3380 [2023-12-20 21:22:44,037 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1074 [2023-12-20 21:22:44,108 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1702 [2023-12-20 21:22:44,206 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8091 [2023-12-20 21:22:44,300 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2752 [2023-12-20 21:22:44,428 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9029 [2023-12-20 21:22:44,545 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0854 [2023-12-20 21:22:44,626 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5894 [2023-12-20 21:22:44,769 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2515 [2023-12-20 21:22:44,921 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0155 [2023-12-20 21:22:45,031 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0588 [2023-12-20 21:22:45,150 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.4028 [2023-12-20 21:22:45,291 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1459 [2023-12-20 21:22:45,415 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5919 [2023-12-20 21:22:45,522 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8559 [2023-12-20 21:22:45,690 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3135 [2023-12-20 21:22:45,785 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5755 [2023-12-20 21:22:45,930 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7284 [2023-12-20 21:22:46,021 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2272 [2023-12-20 21:22:46,096 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.7097 [2023-12-20 21:22:46,204 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3151 [2023-12-20 21:22:46,350 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9564 [2023-12-20 21:22:46,486 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3468 [2023-12-20 21:22:46,589 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1936 [2023-12-20 21:22:46,676 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5819 [2023-12-20 21:22:46,788 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3353 [2023-12-20 21:22:46,949 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2443 [2023-12-20 21:22:47,050 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6401 [2023-12-20 21:22:47,147 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2579 [2023-12-20 21:22:47,241 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5017 [2023-12-20 21:22:47,331 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2263 [2023-12-20 21:22:47,416 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6495 [2023-12-20 21:22:47,515 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6567 [2023-12-20 21:22:47,641 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6681 [2023-12-20 21:22:47,726 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2196 [2023-12-20 21:22:47,824 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.2704 [2023-12-20 21:22:47,918 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0076 [2023-12-20 21:22:48,002 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3417 [2023-12-20 21:22:48,084 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0075 [2023-12-20 21:22:48,179 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9247 [2023-12-20 21:22:48,275 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6390 [2023-12-20 21:22:48,408 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0518 [2023-12-20 21:22:48,501 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6780 [2023-12-20 21:22:48,627 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6307 [2023-12-20 21:22:48,737 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4707 [2023-12-20 21:22:48,836 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4844 [2023-12-20 21:22:48,998 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.1696 [2023-12-20 21:22:50,457 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7707/0.8445/0.9224. [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8761/0.9578 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9864 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7110/0.8254 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8410/0.8850 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9266/0.9641 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8713/0.9394 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7769/0.8577 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7425/0.8535 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7186/0.8035 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8376/0.9186 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3973/0.4899 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7135/0.8043 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7005/0.8668 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7737/0.8621 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7130/0.7687 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6890/0.7344 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9419/0.9804 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6962/0.7901 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8939/0.9239 [2023-12-20 21:22:50,458 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6287/0.6779 [2023-12-20 21:22:50,459 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:22:50,460 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:22:50,460 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:22:55,046 INFO misc.py line 119 131400] Train: [96/100][1/800] Data 1.177 (1.177) Batch 1.506 (1.506) Remain 01:40:23 loss: 0.1629 Lr: 0.00004 [2023-12-20 21:22:55,359 INFO misc.py line 119 131400] Train: [96/100][2/800] Data 0.004 (0.004) Batch 0.313 (0.313) Remain 00:20:52 loss: 0.2647 Lr: 0.00004 [2023-12-20 21:22:55,683 INFO misc.py line 119 131400] Train: [96/100][3/800] Data 0.003 (0.003) Batch 0.324 (0.324) Remain 00:21:34 loss: 0.1225 Lr: 0.00004 [2023-12-20 21:22:56,051 INFO misc.py line 119 131400] Train: [96/100][4/800] Data 0.004 (0.004) Batch 0.368 (0.368) Remain 00:24:29 loss: 0.1619 Lr: 0.00004 [2023-12-20 21:22:56,374 INFO misc.py line 119 131400] Train: [96/100][5/800] Data 0.004 (0.004) Batch 0.323 (0.345) Remain 00:22:58 loss: 0.1491 Lr: 0.00004 [2023-12-20 21:22:56,735 INFO misc.py line 119 131400] Train: [96/100][6/800] Data 0.004 (0.004) Batch 0.362 (0.351) Remain 00:23:20 loss: 0.1745 Lr: 0.00004 [2023-12-20 21:22:57,197 INFO misc.py line 119 131400] Train: [96/100][7/800] Data 0.137 (0.038) Batch 0.462 (0.378) Remain 00:25:10 loss: 0.3490 Lr: 0.00004 [2023-12-20 21:22:57,483 INFO misc.py line 119 131400] Train: [96/100][8/800] Data 0.004 (0.031) Batch 0.286 (0.360) Remain 00:23:57 loss: 0.1481 Lr: 0.00004 [2023-12-20 21:22:57,810 INFO misc.py line 119 131400] Train: [96/100][9/800] Data 0.003 (0.026) Batch 0.327 (0.354) Remain 00:23:34 loss: 0.2634 Lr: 0.00004 [2023-12-20 21:22:58,153 INFO misc.py line 119 131400] Train: [96/100][10/800] Data 0.004 (0.023) Batch 0.343 (0.353) Remain 00:23:27 loss: 0.2123 Lr: 0.00004 [2023-12-20 21:22:58,646 INFO misc.py line 119 131400] Train: [96/100][11/800] Data 0.003 (0.021) Batch 0.492 (0.370) Remain 00:24:36 loss: 0.2687 Lr: 0.00004 [2023-12-20 21:22:58,972 INFO misc.py line 119 131400] Train: [96/100][12/800] Data 0.005 (0.019) Batch 0.326 (0.365) Remain 00:24:16 loss: 0.1308 Lr: 0.00004 [2023-12-20 21:22:59,318 INFO misc.py line 119 131400] Train: [96/100][13/800] Data 0.005 (0.018) Batch 0.346 (0.363) Remain 00:24:08 loss: 0.2219 Lr: 0.00004 [2023-12-20 21:22:59,646 INFO misc.py line 119 131400] Train: [96/100][14/800] Data 0.005 (0.016) Batch 0.327 (0.360) Remain 00:23:55 loss: 0.2258 Lr: 0.00004 [2023-12-20 21:22:59,972 INFO misc.py line 119 131400] Train: [96/100][15/800] Data 0.006 (0.016) Batch 0.329 (0.357) Remain 00:23:44 loss: 0.0995 Lr: 0.00004 [2023-12-20 21:23:00,280 INFO misc.py line 119 131400] Train: [96/100][16/800] Data 0.003 (0.015) Batch 0.308 (0.354) Remain 00:23:28 loss: 0.1587 Lr: 0.00004 [2023-12-20 21:23:00,606 INFO misc.py line 119 131400] Train: [96/100][17/800] Data 0.004 (0.014) Batch 0.327 (0.352) Remain 00:23:20 loss: 0.2216 Lr: 0.00004 [2023-12-20 21:23:00,937 INFO misc.py line 119 131400] Train: [96/100][18/800] Data 0.003 (0.013) Batch 0.330 (0.350) Remain 00:23:14 loss: 0.2652 Lr: 0.00004 [2023-12-20 21:23:01,259 INFO misc.py line 119 131400] Train: [96/100][19/800] Data 0.003 (0.012) Batch 0.321 (0.348) Remain 00:23:07 loss: 0.1952 Lr: 0.00004 [2023-12-20 21:23:01,580 INFO misc.py line 119 131400] Train: [96/100][20/800] Data 0.005 (0.012) Batch 0.323 (0.347) Remain 00:23:00 loss: 0.1330 Lr: 0.00004 [2023-12-20 21:23:01,913 INFO misc.py line 119 131400] Train: [96/100][21/800] Data 0.003 (0.011) Batch 0.328 (0.346) Remain 00:22:56 loss: 0.3402 Lr: 0.00004 [2023-12-20 21:23:02,220 INFO misc.py line 119 131400] Train: [96/100][22/800] Data 0.007 (0.011) Batch 0.311 (0.344) Remain 00:22:48 loss: 0.1605 Lr: 0.00004 [2023-12-20 21:23:02,546 INFO misc.py line 119 131400] Train: [96/100][23/800] Data 0.004 (0.011) Batch 0.327 (0.343) Remain 00:22:44 loss: 0.1731 Lr: 0.00004 [2023-12-20 21:23:02,887 INFO misc.py line 119 131400] Train: [96/100][24/800] Data 0.003 (0.011) Batch 0.340 (0.343) Remain 00:22:43 loss: 0.1626 Lr: 0.00004 [2023-12-20 21:23:03,215 INFO misc.py line 119 131400] Train: [96/100][25/800] Data 0.004 (0.010) Batch 0.328 (0.342) Remain 00:22:40 loss: 0.2009 Lr: 0.00004 [2023-12-20 21:23:03,542 INFO misc.py line 119 131400] Train: [96/100][26/800] Data 0.004 (0.010) Batch 0.328 (0.342) Remain 00:22:37 loss: 0.3288 Lr: 0.00004 [2023-12-20 21:23:03,881 INFO misc.py line 119 131400] Train: [96/100][27/800] Data 0.004 (0.010) Batch 0.339 (0.342) Remain 00:22:37 loss: 0.1779 Lr: 0.00004 [2023-12-20 21:23:04,178 INFO misc.py line 119 131400] Train: [96/100][28/800] Data 0.003 (0.009) Batch 0.294 (0.340) Remain 00:22:29 loss: 0.1415 Lr: 0.00004 [2023-12-20 21:23:04,515 INFO misc.py line 119 131400] Train: [96/100][29/800] Data 0.007 (0.009) Batch 0.341 (0.340) Remain 00:22:29 loss: 0.2246 Lr: 0.00004 [2023-12-20 21:23:04,824 INFO misc.py line 119 131400] Train: [96/100][30/800] Data 0.003 (0.009) Batch 0.307 (0.339) Remain 00:22:23 loss: 0.1278 Lr: 0.00004 [2023-12-20 21:23:05,187 INFO misc.py line 119 131400] Train: [96/100][31/800] Data 0.004 (0.009) Batch 0.365 (0.339) Remain 00:22:27 loss: 0.1885 Lr: 0.00004 [2023-12-20 21:23:05,516 INFO misc.py line 119 131400] Train: [96/100][32/800] Data 0.003 (0.009) Batch 0.329 (0.339) Remain 00:22:25 loss: 0.1686 Lr: 0.00004 [2023-12-20 21:23:05,837 INFO misc.py line 119 131400] Train: [96/100][33/800] Data 0.003 (0.009) Batch 0.320 (0.338) Remain 00:22:22 loss: 0.3800 Lr: 0.00004 [2023-12-20 21:23:06,174 INFO misc.py line 119 131400] Train: [96/100][34/800] Data 0.004 (0.008) Batch 0.337 (0.338) Remain 00:22:22 loss: 0.2863 Lr: 0.00004 [2023-12-20 21:23:06,492 INFO misc.py line 119 131400] Train: [96/100][35/800] Data 0.003 (0.008) Batch 0.318 (0.338) Remain 00:22:19 loss: 0.2112 Lr: 0.00004 [2023-12-20 21:23:06,825 INFO misc.py line 119 131400] Train: [96/100][36/800] Data 0.004 (0.008) Batch 0.332 (0.338) Remain 00:22:18 loss: 0.2578 Lr: 0.00004 [2023-12-20 21:23:07,125 INFO misc.py line 119 131400] Train: [96/100][37/800] Data 0.004 (0.008) Batch 0.300 (0.337) Remain 00:22:13 loss: 0.1150 Lr: 0.00004 [2023-12-20 21:23:07,481 INFO misc.py line 119 131400] Train: [96/100][38/800] Data 0.004 (0.008) Batch 0.356 (0.337) Remain 00:22:15 loss: 0.1592 Lr: 0.00004 [2023-12-20 21:23:07,857 INFO misc.py line 119 131400] Train: [96/100][39/800] Data 0.004 (0.008) Batch 0.377 (0.338) Remain 00:22:19 loss: 0.1472 Lr: 0.00004 [2023-12-20 21:23:08,191 INFO misc.py line 119 131400] Train: [96/100][40/800] Data 0.004 (0.008) Batch 0.333 (0.338) Remain 00:22:18 loss: 0.1484 Lr: 0.00004 [2023-12-20 21:23:08,554 INFO misc.py line 119 131400] Train: [96/100][41/800] Data 0.005 (0.008) Batch 0.363 (0.339) Remain 00:22:20 loss: 0.1988 Lr: 0.00004 [2023-12-20 21:23:08,902 INFO misc.py line 119 131400] Train: [96/100][42/800] Data 0.004 (0.007) Batch 0.349 (0.339) Remain 00:22:21 loss: 0.1453 Lr: 0.00004 [2023-12-20 21:23:09,261 INFO misc.py line 119 131400] Train: [96/100][43/800] Data 0.003 (0.007) Batch 0.358 (0.339) Remain 00:22:23 loss: 0.1417 Lr: 0.00004 [2023-12-20 21:23:09,589 INFO misc.py line 119 131400] Train: [96/100][44/800] Data 0.004 (0.007) Batch 0.328 (0.339) Remain 00:22:21 loss: 0.2288 Lr: 0.00004 [2023-12-20 21:23:09,917 INFO misc.py line 119 131400] Train: [96/100][45/800] Data 0.004 (0.007) Batch 0.329 (0.339) Remain 00:22:20 loss: 0.1978 Lr: 0.00004 [2023-12-20 21:23:10,235 INFO misc.py line 119 131400] Train: [96/100][46/800] Data 0.003 (0.007) Batch 0.318 (0.338) Remain 00:22:18 loss: 0.2139 Lr: 0.00004 [2023-12-20 21:23:10,572 INFO misc.py line 119 131400] Train: [96/100][47/800] Data 0.003 (0.007) Batch 0.336 (0.338) Remain 00:22:17 loss: 0.2824 Lr: 0.00004 [2023-12-20 21:23:10,882 INFO misc.py line 119 131400] Train: [96/100][48/800] Data 0.003 (0.007) Batch 0.310 (0.338) Remain 00:22:14 loss: 0.2765 Lr: 0.00004 [2023-12-20 21:23:11,210 INFO misc.py line 119 131400] Train: [96/100][49/800] Data 0.003 (0.007) Batch 0.327 (0.338) Remain 00:22:13 loss: 0.2120 Lr: 0.00004 [2023-12-20 21:23:11,511 INFO misc.py line 119 131400] Train: [96/100][50/800] Data 0.003 (0.007) Batch 0.301 (0.337) Remain 00:22:10 loss: 0.1868 Lr: 0.00004 [2023-12-20 21:23:11,867 INFO misc.py line 119 131400] Train: [96/100][51/800] Data 0.003 (0.007) Batch 0.356 (0.337) Remain 00:22:11 loss: 0.1436 Lr: 0.00004 [2023-12-20 21:23:12,213 INFO misc.py line 119 131400] Train: [96/100][52/800] Data 0.003 (0.007) Batch 0.346 (0.337) Remain 00:22:11 loss: 0.1017 Lr: 0.00004 [2023-12-20 21:23:12,544 INFO misc.py line 119 131400] Train: [96/100][53/800] Data 0.003 (0.007) Batch 0.331 (0.337) Remain 00:22:11 loss: 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INFO misc.py line 119 131400] Train: [96/100][60/800] Data 0.003 (0.006) Batch 0.313 (0.339) Remain 00:22:14 loss: 0.2189 Lr: 0.00004 [2023-12-20 21:23:15,309 INFO misc.py line 119 131400] Train: [96/100][61/800] Data 0.002 (0.006) Batch 0.323 (0.338) Remain 00:22:12 loss: 0.1244 Lr: 0.00004 [2023-12-20 21:23:15,643 INFO misc.py line 119 131400] Train: [96/100][62/800] Data 0.006 (0.006) Batch 0.335 (0.338) Remain 00:22:12 loss: 0.0945 Lr: 0.00004 [2023-12-20 21:23:15,987 INFO misc.py line 119 131400] Train: [96/100][63/800] Data 0.004 (0.006) Batch 0.346 (0.338) Remain 00:22:12 loss: 0.1387 Lr: 0.00004 [2023-12-20 21:23:16,331 INFO misc.py line 119 131400] Train: [96/100][64/800] Data 0.003 (0.006) Batch 0.342 (0.338) Remain 00:22:12 loss: 0.1850 Lr: 0.00004 [2023-12-20 21:23:16,851 INFO misc.py line 119 131400] Train: [96/100][65/800] Data 0.005 (0.006) Batch 0.517 (0.341) Remain 00:22:23 loss: 0.2274 Lr: 0.00004 [2023-12-20 21:23:17,184 INFO misc.py line 119 131400] Train: 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line 119 131400] Train: [96/100][85/800] Data 0.004 (0.006) Batch 0.346 (0.342) Remain 00:22:18 loss: 0.2005 Lr: 0.00004 [2023-12-20 21:23:24,057 INFO misc.py line 119 131400] Train: [96/100][86/800] Data 0.004 (0.006) Batch 0.349 (0.342) Remain 00:22:18 loss: 0.5125 Lr: 0.00004 [2023-12-20 21:23:24,384 INFO misc.py line 119 131400] Train: [96/100][87/800] Data 0.004 (0.006) Batch 0.327 (0.342) Remain 00:22:16 loss: 0.1206 Lr: 0.00004 [2023-12-20 21:23:24,722 INFO misc.py line 119 131400] Train: [96/100][88/800] Data 0.004 (0.006) Batch 0.338 (0.342) Remain 00:22:16 loss: 0.2390 Lr: 0.00004 [2023-12-20 21:23:25,063 INFO misc.py line 119 131400] Train: [96/100][89/800] Data 0.004 (0.006) Batch 0.340 (0.342) Remain 00:22:16 loss: 0.1291 Lr: 0.00004 [2023-12-20 21:23:25,437 INFO misc.py line 119 131400] Train: [96/100][90/800] Data 0.005 (0.006) Batch 0.373 (0.342) Remain 00:22:17 loss: 0.2373 Lr: 0.00004 [2023-12-20 21:23:25,773 INFO misc.py line 119 131400] Train: [96/100][91/800] Data 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loss: 0.1200 Lr: 0.00003 [2023-12-20 21:27:04,087 INFO misc.py line 119 131400] Train: [96/100][745/800] Data 0.005 (0.005) Batch 0.357 (0.335) Remain 00:18:09 loss: 0.2383 Lr: 0.00003 [2023-12-20 21:27:04,404 INFO misc.py line 119 131400] Train: [96/100][746/800] Data 0.004 (0.005) Batch 0.317 (0.335) Remain 00:18:09 loss: 0.1846 Lr: 0.00003 [2023-12-20 21:27:04,716 INFO misc.py line 119 131400] Train: [96/100][747/800] Data 0.003 (0.005) Batch 0.312 (0.335) Remain 00:18:08 loss: 0.3139 Lr: 0.00003 [2023-12-20 21:27:05,033 INFO misc.py line 119 131400] Train: [96/100][748/800] Data 0.003 (0.005) Batch 0.316 (0.335) Remain 00:18:08 loss: 0.2194 Lr: 0.00003 [2023-12-20 21:27:05,368 INFO misc.py line 119 131400] Train: [96/100][749/800] Data 0.004 (0.005) Batch 0.332 (0.335) Remain 00:18:08 loss: 0.1960 Lr: 0.00003 [2023-12-20 21:27:05,711 INFO misc.py line 119 131400] Train: [96/100][750/800] Data 0.007 (0.005) Batch 0.347 (0.335) Remain 00:18:07 loss: 0.2041 Lr: 0.00003 [2023-12-20 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131400] Train: [96/100][757/800] Data 0.004 (0.005) Batch 0.337 (0.335) Remain 00:18:05 loss: 0.2519 Lr: 0.00003 [2023-12-20 21:27:08,376 INFO misc.py line 119 131400] Train: [96/100][758/800] Data 0.004 (0.005) Batch 0.349 (0.335) Remain 00:18:05 loss: 0.2378 Lr: 0.00003 [2023-12-20 21:27:08,715 INFO misc.py line 119 131400] Train: [96/100][759/800] Data 0.005 (0.005) Batch 0.340 (0.335) Remain 00:18:04 loss: 0.2819 Lr: 0.00003 [2023-12-20 21:27:09,043 INFO misc.py line 119 131400] Train: [96/100][760/800] Data 0.004 (0.005) Batch 0.327 (0.335) Remain 00:18:04 loss: 0.1123 Lr: 0.00003 [2023-12-20 21:27:09,362 INFO misc.py line 119 131400] Train: [96/100][761/800] Data 0.005 (0.005) Batch 0.320 (0.335) Remain 00:18:03 loss: 0.1423 Lr: 0.00003 [2023-12-20 21:27:09,725 INFO misc.py line 119 131400] Train: [96/100][762/800] Data 0.004 (0.005) Batch 0.363 (0.335) Remain 00:18:03 loss: 0.1581 Lr: 0.00003 [2023-12-20 21:27:10,066 INFO misc.py line 119 131400] Train: [96/100][763/800] Data 0.005 (0.005) Batch 0.342 (0.335) Remain 00:18:03 loss: 0.1780 Lr: 0.00003 [2023-12-20 21:27:10,427 INFO misc.py line 119 131400] Train: [96/100][764/800] Data 0.005 (0.005) Batch 0.352 (0.335) Remain 00:18:03 loss: 0.2176 Lr: 0.00003 [2023-12-20 21:27:10,780 INFO misc.py line 119 131400] Train: [96/100][765/800] Data 0.013 (0.005) Batch 0.362 (0.335) Remain 00:18:02 loss: 0.1423 Lr: 0.00003 [2023-12-20 21:27:11,108 INFO misc.py line 119 131400] Train: [96/100][766/800] Data 0.003 (0.005) Batch 0.325 (0.335) Remain 00:18:02 loss: 0.4021 Lr: 0.00003 [2023-12-20 21:27:11,438 INFO misc.py line 119 131400] Train: [96/100][767/800] Data 0.007 (0.005) Batch 0.331 (0.335) Remain 00:18:02 loss: 0.1661 Lr: 0.00003 [2023-12-20 21:27:11,798 INFO misc.py line 119 131400] Train: [96/100][768/800] Data 0.006 (0.005) Batch 0.363 (0.335) Remain 00:18:02 loss: 0.1426 Lr: 0.00003 [2023-12-20 21:27:12,123 INFO misc.py line 119 131400] Train: [96/100][769/800] Data 0.003 (0.005) Batch 0.324 (0.335) Remain 00:18:01 loss: 0.2837 Lr: 0.00003 [2023-12-20 21:27:12,471 INFO misc.py line 119 131400] Train: [96/100][770/800] Data 0.004 (0.005) Batch 0.348 (0.335) Remain 00:18:01 loss: 0.1825 Lr: 0.00003 [2023-12-20 21:27:12,793 INFO misc.py line 119 131400] Train: [96/100][771/800] Data 0.005 (0.005) Batch 0.324 (0.335) Remain 00:18:00 loss: 0.2148 Lr: 0.00003 [2023-12-20 21:27:13,115 INFO misc.py line 119 131400] Train: [96/100][772/800] Data 0.003 (0.005) Batch 0.320 (0.335) Remain 00:18:00 loss: 0.3109 Lr: 0.00003 [2023-12-20 21:27:13,477 INFO misc.py line 119 131400] Train: [96/100][773/800] Data 0.005 (0.005) Batch 0.361 (0.335) Remain 00:18:00 loss: 0.3394 Lr: 0.00003 [2023-12-20 21:27:13,815 INFO misc.py line 119 131400] Train: [96/100][774/800] Data 0.005 (0.005) Batch 0.339 (0.335) Remain 00:18:00 loss: 0.2137 Lr: 0.00003 [2023-12-20 21:27:14,169 INFO misc.py line 119 131400] Train: [96/100][775/800] Data 0.004 (0.005) Batch 0.353 (0.335) Remain 00:17:59 loss: 0.1805 Lr: 0.00003 [2023-12-20 21:27:14,467 INFO misc.py line 119 131400] Train: [96/100][776/800] Data 0.004 (0.005) Batch 0.299 (0.335) Remain 00:17:59 loss: 0.1706 Lr: 0.00003 [2023-12-20 21:27:14,796 INFO misc.py line 119 131400] Train: [96/100][777/800] Data 0.004 (0.005) Batch 0.330 (0.335) Remain 00:17:58 loss: 0.2664 Lr: 0.00003 [2023-12-20 21:27:15,149 INFO misc.py line 119 131400] Train: [96/100][778/800] Data 0.004 (0.005) Batch 0.352 (0.335) Remain 00:17:58 loss: 0.1101 Lr: 0.00003 [2023-12-20 21:27:15,481 INFO misc.py line 119 131400] Train: [96/100][779/800] Data 0.004 (0.005) Batch 0.332 (0.335) Remain 00:17:58 loss: 0.3315 Lr: 0.00003 [2023-12-20 21:27:15,786 INFO misc.py line 119 131400] Train: [96/100][780/800] Data 0.004 (0.005) Batch 0.301 (0.335) Remain 00:17:57 loss: 0.1908 Lr: 0.00003 [2023-12-20 21:27:16,141 INFO misc.py line 119 131400] Train: [96/100][781/800] Data 0.008 (0.005) Batch 0.359 (0.335) Remain 00:17:57 loss: 0.2121 Lr: 0.00003 [2023-12-20 21:27:16,475 INFO misc.py line 119 131400] Train: [96/100][782/800] Data 0.004 (0.005) Batch 0.335 (0.335) Remain 00:17:57 loss: 0.2152 Lr: 0.00003 [2023-12-20 21:27:16,758 INFO misc.py line 119 131400] Train: [96/100][783/800] Data 0.004 (0.005) Batch 0.283 (0.335) Remain 00:17:56 loss: 0.1005 Lr: 0.00003 [2023-12-20 21:27:17,105 INFO misc.py line 119 131400] Train: [96/100][784/800] Data 0.003 (0.005) Batch 0.346 (0.335) Remain 00:17:56 loss: 0.2436 Lr: 0.00003 [2023-12-20 21:27:17,544 INFO misc.py line 119 131400] Train: [96/100][785/800] Data 0.004 (0.005) Batch 0.440 (0.335) Remain 00:17:56 loss: 0.1469 Lr: 0.00003 [2023-12-20 21:27:17,888 INFO misc.py line 119 131400] Train: [96/100][786/800] Data 0.004 (0.005) Batch 0.342 (0.335) Remain 00:17:56 loss: 0.1162 Lr: 0.00003 [2023-12-20 21:27:18,215 INFO misc.py line 119 131400] Train: [96/100][787/800] Data 0.006 (0.005) Batch 0.326 (0.335) Remain 00:17:55 loss: 0.2935 Lr: 0.00003 [2023-12-20 21:27:18,568 INFO misc.py line 119 131400] Train: [96/100][788/800] Data 0.008 (0.005) Batch 0.355 (0.335) Remain 00:17:55 loss: 0.1683 Lr: 0.00003 [2023-12-20 21:27:18,863 INFO misc.py line 119 131400] Train: [96/100][789/800] Data 0.005 (0.005) Batch 0.296 (0.335) Remain 00:17:55 loss: 0.2344 Lr: 0.00003 [2023-12-20 21:27:19,140 INFO misc.py line 119 131400] Train: [96/100][790/800] Data 0.003 (0.005) Batch 0.278 (0.335) Remain 00:17:54 loss: 0.1957 Lr: 0.00003 [2023-12-20 21:27:19,464 INFO misc.py line 119 131400] Train: [96/100][791/800] Data 0.003 (0.005) Batch 0.324 (0.335) Remain 00:17:54 loss: 0.1672 Lr: 0.00003 [2023-12-20 21:27:19,780 INFO misc.py line 119 131400] Train: [96/100][792/800] Data 0.003 (0.005) Batch 0.316 (0.335) Remain 00:17:53 loss: 0.2244 Lr: 0.00003 [2023-12-20 21:27:20,072 INFO misc.py line 119 131400] Train: [96/100][793/800] Data 0.003 (0.005) Batch 0.290 (0.335) Remain 00:17:53 loss: 0.2913 Lr: 0.00003 [2023-12-20 21:27:20,382 INFO misc.py line 119 131400] Train: [96/100][794/800] Data 0.005 (0.005) Batch 0.312 (0.335) Remain 00:17:52 loss: 0.1885 Lr: 0.00003 [2023-12-20 21:27:20,703 INFO misc.py line 119 131400] Train: [96/100][795/800] Data 0.003 (0.005) Batch 0.321 (0.335) Remain 00:17:52 loss: 0.1767 Lr: 0.00003 [2023-12-20 21:27:20,983 INFO misc.py line 119 131400] Train: [96/100][796/800] Data 0.003 (0.005) Batch 0.281 (0.335) Remain 00:17:51 loss: 0.1425 Lr: 0.00003 [2023-12-20 21:27:21,275 INFO misc.py line 119 131400] Train: [96/100][797/800] Data 0.003 (0.005) Batch 0.291 (0.334) Remain 00:17:51 loss: 0.1920 Lr: 0.00003 [2023-12-20 21:27:21,579 INFO misc.py line 119 131400] Train: [96/100][798/800] Data 0.004 (0.005) Batch 0.305 (0.334) Remain 00:17:50 loss: 0.3114 Lr: 0.00003 [2023-12-20 21:27:21,903 INFO misc.py line 119 131400] Train: [96/100][799/800] Data 0.003 (0.005) Batch 0.325 (0.334) Remain 00:17:50 loss: 0.1981 Lr: 0.00003 [2023-12-20 21:27:22,176 INFO misc.py line 119 131400] Train: [96/100][800/800] Data 0.002 (0.005) Batch 0.273 (0.334) Remain 00:17:49 loss: 0.1807 Lr: 0.00003 [2023-12-20 21:27:22,177 INFO misc.py line 136 131400] Train result: loss: 0.1993 [2023-12-20 21:27:22,177 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:27:44,308 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1612 [2023-12-20 21:27:44,378 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1711 [2023-12-20 21:27:44,469 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5160 [2023-12-20 21:27:44,575 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5749 [2023-12-20 21:27:44,689 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3884 [2023-12-20 21:27:44,789 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.6318 [2023-12-20 21:27:44,881 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.1446 [2023-12-20 21:27:44,988 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8302 [2023-12-20 21:27:45,072 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2854 [2023-12-20 21:27:45,157 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3057 [2023-12-20 21:27:45,248 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3589 [2023-12-20 21:27:45,384 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2716 [2023-12-20 21:27:45,505 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5112 [2023-12-20 21:27:45,660 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2449 [2023-12-20 21:27:45,753 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1307 [2023-12-20 21:27:45,887 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7854 [2023-12-20 21:27:45,995 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2707 [2023-12-20 21:27:46,105 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8227 [2023-12-20 21:27:46,219 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1442 [2023-12-20 21:27:46,295 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4095 [2023-12-20 21:27:46,400 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1451 [2023-12-20 21:27:46,562 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1095 [2023-12-20 21:27:46,683 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.6927 [2023-12-20 21:27:46,829 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2303 [2023-12-20 21:27:46,972 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1709 [2023-12-20 21:27:47,054 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.4768 [2023-12-20 21:27:47,211 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5095 [2023-12-20 21:27:47,336 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5298 [2023-12-20 21:27:47,432 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4748 [2023-12-20 21:27:47,580 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8346 [2023-12-20 21:27:47,690 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4736 [2023-12-20 21:27:47,821 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3317 [2023-12-20 21:27:47,908 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1099 [2023-12-20 21:27:47,977 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1725 [2023-12-20 21:27:48,078 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8364 [2023-12-20 21:27:48,188 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2865 [2023-12-20 21:27:48,330 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9089 [2023-12-20 21:27:48,440 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0837 [2023-12-20 21:27:48,524 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6532 [2023-12-20 21:27:48,684 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2803 [2023-12-20 21:27:48,832 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0168 [2023-12-20 21:27:48,933 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0498 [2023-12-20 21:27:49,070 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3040 [2023-12-20 21:27:49,222 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1356 [2023-12-20 21:27:49,354 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5699 [2023-12-20 21:27:49,469 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.6758 [2023-12-20 21:27:49,642 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2792 [2023-12-20 21:27:49,754 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5365 [2023-12-20 21:27:49,909 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.6789 [2023-12-20 21:27:50,017 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2232 [2023-12-20 21:27:50,092 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6233 [2023-12-20 21:27:50,197 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.5049 [2023-12-20 21:27:50,350 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2035 [2023-12-20 21:27:50,497 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3406 [2023-12-20 21:27:50,607 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1500 [2023-12-20 21:27:50,696 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6136 [2023-12-20 21:27:50,802 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3806 [2023-12-20 21:27:50,963 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2291 [2023-12-20 21:27:51,067 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6246 [2023-12-20 21:27:51,162 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2238 [2023-12-20 21:27:51,258 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5023 [2023-12-20 21:27:51,355 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2378 [2023-12-20 21:27:51,446 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6252 [2023-12-20 21:27:51,572 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6582 [2023-12-20 21:27:51,708 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6639 [2023-12-20 21:27:51,791 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2357 [2023-12-20 21:27:51,903 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3127 [2023-12-20 21:27:51,999 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0102 [2023-12-20 21:27:52,087 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3375 [2023-12-20 21:27:52,173 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0082 [2023-12-20 21:27:52,292 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8374 [2023-12-20 21:27:52,392 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.5360 [2023-12-20 21:27:52,531 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0588 [2023-12-20 21:27:52,630 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6855 [2023-12-20 21:27:52,752 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6128 [2023-12-20 21:27:52,857 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4899 [2023-12-20 21:27:52,948 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.3437 [2023-12-20 21:27:53,108 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0362 [2023-12-20 21:27:54,540 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7715/0.8481/0.9221. [2023-12-20 21:27:54,540 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8748/0.9572 [2023-12-20 21:27:54,540 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9644/0.9863 [2023-12-20 21:27:54,540 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7130/0.8171 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8311/0.8799 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9253/0.9618 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8576/0.9337 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7757/0.8603 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7395/0.8492 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7251/0.8193 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8350/0.9192 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4174/0.5278 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7098/0.8022 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7146/0.8656 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7748/0.8644 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7196/0.7992 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7010/0.7516 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9439/0.9795 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6867/0.7881 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8938/0.9277 [2023-12-20 21:27:54,541 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6271/0.6712 [2023-12-20 21:27:54,542 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:27:54,542 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:27:54,542 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:27:57,963 INFO misc.py line 119 131400] Train: [97/100][1/800] Data 0.905 (0.905) Batch 1.199 (1.199) Remain 01:03:57 loss: 0.3522 Lr: 0.00003 [2023-12-20 21:27:58,299 INFO misc.py line 119 131400] Train: [97/100][2/800] Data 0.005 (0.005) Batch 0.337 (0.337) Remain 00:17:57 loss: 0.2408 Lr: 0.00003 [2023-12-20 21:27:58,635 INFO misc.py line 119 131400] Train: [97/100][3/800] Data 0.003 (0.003) Batch 0.336 (0.336) Remain 00:17:55 loss: 0.1579 Lr: 0.00003 [2023-12-20 21:27:59,003 INFO misc.py line 119 131400] Train: [97/100][4/800] Data 0.003 (0.003) Batch 0.367 (0.367) Remain 00:19:33 loss: 0.2159 Lr: 0.00003 [2023-12-20 21:27:59,349 INFO misc.py line 119 131400] Train: [97/100][5/800] Data 0.004 (0.004) Batch 0.346 (0.357) Remain 00:18:59 loss: 0.2302 Lr: 0.00003 [2023-12-20 21:27:59,681 INFO misc.py line 119 131400] Train: [97/100][6/800] Data 0.004 (0.004) Batch 0.332 (0.348) Remain 00:18:33 loss: 0.1914 Lr: 0.00003 [2023-12-20 21:27:59,999 INFO misc.py line 119 131400] Train: [97/100][7/800] Data 0.004 (0.004) Batch 0.319 (0.341) Remain 00:18:08 loss: 0.1221 Lr: 0.00003 [2023-12-20 21:28:00,354 INFO misc.py line 119 131400] Train: [97/100][8/800] Data 0.003 (0.004) Batch 0.355 (0.344) Remain 00:18:17 loss: 0.1013 Lr: 0.00003 [2023-12-20 21:28:00,690 INFO misc.py line 119 131400] Train: [97/100][9/800] Data 0.003 (0.004) Batch 0.335 (0.342) Remain 00:18:12 loss: 0.3755 Lr: 0.00003 [2023-12-20 21:28:01,063 INFO misc.py line 119 131400] Train: [97/100][10/800] Data 0.004 (0.004) Batch 0.373 (0.347) Remain 00:18:26 loss: 0.1018 Lr: 0.00003 [2023-12-20 21:28:01,360 INFO misc.py line 119 131400] Train: [97/100][11/800] Data 0.004 (0.004) Batch 0.298 (0.341) Remain 00:18:06 loss: 0.1729 Lr: 0.00003 [2023-12-20 21:28:01,715 INFO misc.py line 119 131400] Train: [97/100][12/800] Data 0.003 (0.004) Batch 0.355 (0.342) Remain 00:18:10 loss: 0.1661 Lr: 0.00003 [2023-12-20 21:28:02,073 INFO misc.py line 119 131400] Train: [97/100][13/800] Data 0.003 (0.004) Batch 0.356 (0.344) Remain 00:18:15 loss: 0.1557 Lr: 0.00003 [2023-12-20 21:28:02,449 INFO misc.py line 119 131400] Train: [97/100][14/800] Data 0.005 (0.004) Batch 0.378 (0.347) Remain 00:18:24 loss: 0.1880 Lr: 0.00003 [2023-12-20 21:28:02,788 INFO misc.py line 119 131400] Train: [97/100][15/800] Data 0.003 (0.004) Batch 0.338 (0.346) Remain 00:18:22 loss: 0.1639 Lr: 0.00003 [2023-12-20 21:28:03,118 INFO misc.py line 119 131400] Train: [97/100][16/800] Data 0.003 (0.004) Batch 0.329 (0.345) Remain 00:18:17 loss: 0.0758 Lr: 0.00003 [2023-12-20 21:28:03,439 INFO misc.py line 119 131400] Train: [97/100][17/800] Data 0.004 (0.004) Batch 0.322 (0.343) Remain 00:18:12 loss: 0.1740 Lr: 0.00003 [2023-12-20 21:28:03,788 INFO misc.py line 119 131400] Train: [97/100][18/800] Data 0.003 (0.004) Batch 0.348 (0.343) Remain 00:18:12 loss: 0.3212 Lr: 0.00003 [2023-12-20 21:28:04,127 INFO misc.py line 119 131400] Train: [97/100][19/800] Data 0.005 (0.004) Batch 0.339 (0.343) Remain 00:18:11 loss: 0.3520 Lr: 0.00003 [2023-12-20 21:28:04,460 INFO misc.py line 119 131400] Train: [97/100][20/800] Data 0.004 (0.004) Batch 0.333 (0.343) Remain 00:18:09 loss: 0.3303 Lr: 0.00003 [2023-12-20 21:28:04,776 INFO misc.py line 119 131400] Train: [97/100][21/800] Data 0.005 (0.004) Batch 0.317 (0.341) Remain 00:18:04 loss: 0.4413 Lr: 0.00003 [2023-12-20 21:28:05,102 INFO misc.py line 119 131400] Train: [97/100][22/800] Data 0.003 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loss: 0.1247 Lr: 0.00003 [2023-12-20 21:28:07,489 INFO misc.py line 119 131400] Train: [97/100][29/800] Data 0.004 (0.004) Batch 0.316 (0.340) Remain 00:17:59 loss: 0.1239 Lr: 0.00003 [2023-12-20 21:28:07,853 INFO misc.py line 119 131400] Train: [97/100][30/800] Data 0.004 (0.004) Batch 0.365 (0.341) Remain 00:18:02 loss: 0.1815 Lr: 0.00003 [2023-12-20 21:28:08,162 INFO misc.py line 119 131400] Train: [97/100][31/800] Data 0.003 (0.004) Batch 0.308 (0.340) Remain 00:17:58 loss: 0.1208 Lr: 0.00003 [2023-12-20 21:28:08,482 INFO misc.py line 119 131400] Train: [97/100][32/800] Data 0.004 (0.004) Batch 0.320 (0.340) Remain 00:17:55 loss: 0.2420 Lr: 0.00003 [2023-12-20 21:28:08,796 INFO misc.py line 119 131400] Train: [97/100][33/800] Data 0.003 (0.004) Batch 0.315 (0.339) Remain 00:17:52 loss: 0.2917 Lr: 0.00003 [2023-12-20 21:28:09,125 INFO misc.py line 119 131400] Train: [97/100][34/800] Data 0.003 (0.004) Batch 0.327 (0.338) Remain 00:17:51 loss: 0.1368 Lr: 0.00003 [2023-12-20 21:28:09,424 INFO misc.py line 119 131400] Train: [97/100][35/800] Data 0.004 (0.004) Batch 0.300 (0.337) Remain 00:17:47 loss: 0.1634 Lr: 0.00003 [2023-12-20 21:28:09,748 INFO misc.py line 119 131400] Train: [97/100][36/800] Data 0.005 (0.004) Batch 0.325 (0.337) Remain 00:17:45 loss: 0.1840 Lr: 0.00003 [2023-12-20 21:28:10,087 INFO misc.py line 119 131400] Train: [97/100][37/800] Data 0.003 (0.004) Batch 0.337 (0.337) Remain 00:17:45 loss: 0.1088 Lr: 0.00003 [2023-12-20 21:28:10,567 INFO misc.py line 119 131400] Train: [97/100][38/800] Data 0.005 (0.004) Batch 0.481 (0.341) Remain 00:17:57 loss: 0.2634 Lr: 0.00003 [2023-12-20 21:28:10,841 INFO misc.py line 119 131400] Train: [97/100][39/800] Data 0.004 (0.004) Batch 0.274 (0.339) Remain 00:17:51 loss: 0.1092 Lr: 0.00003 [2023-12-20 21:28:11,139 INFO misc.py line 119 131400] Train: [97/100][40/800] Data 0.004 (0.004) Batch 0.298 (0.338) Remain 00:17:47 loss: 0.1220 Lr: 0.00003 [2023-12-20 21:28:11,464 INFO misc.py line 119 131400] Train: [97/100][41/800] Data 0.003 (0.004) Batch 0.326 (0.338) Remain 00:17:46 loss: 0.2284 Lr: 0.00003 [2023-12-20 21:28:11,814 INFO misc.py line 119 131400] Train: [97/100][42/800] Data 0.003 (0.004) Batch 0.350 (0.338) Remain 00:17:47 loss: 0.1659 Lr: 0.00003 [2023-12-20 21:28:12,150 INFO misc.py line 119 131400] Train: [97/100][43/800] Data 0.003 (0.004) Batch 0.335 (0.338) Remain 00:17:46 loss: 0.2542 Lr: 0.00003 [2023-12-20 21:28:12,468 INFO misc.py line 119 131400] Train: [97/100][44/800] Data 0.004 (0.004) Batch 0.319 (0.337) Remain 00:17:44 loss: 0.3069 Lr: 0.00003 [2023-12-20 21:28:12,776 INFO misc.py line 119 131400] Train: [97/100][45/800] Data 0.003 (0.004) Batch 0.308 (0.337) Remain 00:17:42 loss: 0.2515 Lr: 0.00003 [2023-12-20 21:28:13,056 INFO misc.py line 119 131400] Train: [97/100][46/800] Data 0.003 (0.004) Batch 0.280 (0.335) Remain 00:17:37 loss: 0.1995 Lr: 0.00003 [2023-12-20 21:28:13,388 INFO misc.py line 119 131400] Train: [97/100][47/800] Data 0.003 (0.004) Batch 0.333 (0.335) Remain 00:17:37 loss: 0.1004 Lr: 0.00003 [2023-12-20 21:28:13,715 INFO misc.py line 119 131400] Train: [97/100][48/800] Data 0.003 (0.004) Batch 0.326 (0.335) Remain 00:17:36 loss: 0.2380 Lr: 0.00003 [2023-12-20 21:28:14,063 INFO misc.py line 119 131400] Train: [97/100][49/800] Data 0.004 (0.004) Batch 0.349 (0.335) Remain 00:17:36 loss: 0.1342 Lr: 0.00003 [2023-12-20 21:28:14,406 INFO misc.py line 119 131400] Train: [97/100][50/800] Data 0.004 (0.004) Batch 0.342 (0.336) Remain 00:17:36 loss: 0.1365 Lr: 0.00003 [2023-12-20 21:28:14,751 INFO misc.py line 119 131400] Train: [97/100][51/800] Data 0.004 (0.004) Batch 0.345 (0.336) Remain 00:17:37 loss: 0.1183 Lr: 0.00003 [2023-12-20 21:28:15,123 INFO misc.py line 119 131400] Train: [97/100][52/800] Data 0.004 (0.004) Batch 0.371 (0.336) Remain 00:17:39 loss: 0.1726 Lr: 0.00003 [2023-12-20 21:28:15,477 INFO misc.py line 119 131400] Train: [97/100][53/800] Data 0.005 (0.004) Batch 0.354 (0.337) Remain 00:17:39 loss: 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INFO misc.py line 119 131400] Train: [97/100][60/800] Data 0.007 (0.004) Batch 0.383 (0.338) Remain 00:17:41 loss: 0.2960 Lr: 0.00003 [2023-12-20 21:28:18,240 INFO misc.py line 119 131400] Train: [97/100][61/800] Data 0.004 (0.004) Batch 0.332 (0.338) Remain 00:17:40 loss: 0.1388 Lr: 0.00003 [2023-12-20 21:28:18,576 INFO misc.py line 119 131400] Train: [97/100][62/800] Data 0.004 (0.004) Batch 0.335 (0.338) Remain 00:17:40 loss: 0.1363 Lr: 0.00003 [2023-12-20 21:28:18,932 INFO misc.py line 119 131400] Train: [97/100][63/800] Data 0.005 (0.004) Batch 0.356 (0.338) Remain 00:17:41 loss: 0.2720 Lr: 0.00003 [2023-12-20 21:28:19,278 INFO misc.py line 119 131400] Train: [97/100][64/800] Data 0.004 (0.004) Batch 0.345 (0.338) Remain 00:17:41 loss: 0.2681 Lr: 0.00003 [2023-12-20 21:28:19,588 INFO misc.py line 119 131400] Train: [97/100][65/800] Data 0.005 (0.004) Batch 0.308 (0.338) Remain 00:17:39 loss: 0.0879 Lr: 0.00003 [2023-12-20 21:28:19,963 INFO misc.py line 119 131400] Train: 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Batch 0.338 (0.334) Remain 00:13:41 loss: 0.1877 Lr: 0.00002 [2023-12-20 21:32:04,335 INFO misc.py line 119 131400] Train: [97/100][739/800] Data 0.004 (0.004) Batch 0.321 (0.334) Remain 00:13:41 loss: 0.0841 Lr: 0.00002 [2023-12-20 21:32:04,653 INFO misc.py line 119 131400] Train: [97/100][740/800] Data 0.004 (0.004) Batch 0.316 (0.334) Remain 00:13:41 loss: 0.2322 Lr: 0.00002 [2023-12-20 21:32:04,990 INFO misc.py line 119 131400] Train: [97/100][741/800] Data 0.005 (0.004) Batch 0.339 (0.334) Remain 00:13:40 loss: 0.3309 Lr: 0.00002 [2023-12-20 21:32:05,307 INFO misc.py line 119 131400] Train: [97/100][742/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:13:40 loss: 0.2136 Lr: 0.00002 [2023-12-20 21:32:05,641 INFO misc.py line 119 131400] Train: [97/100][743/800] Data 0.004 (0.004) Batch 0.332 (0.334) Remain 00:13:40 loss: 0.2490 Lr: 0.00002 [2023-12-20 21:32:05,985 INFO misc.py line 119 131400] Train: [97/100][744/800] Data 0.007 (0.004) Batch 0.346 (0.334) Remain 00:13:39 loss: 0.1925 Lr: 0.00002 [2023-12-20 21:32:06,306 INFO misc.py line 119 131400] Train: [97/100][745/800] Data 0.004 (0.004) Batch 0.320 (0.334) Remain 00:13:39 loss: 0.1184 Lr: 0.00002 [2023-12-20 21:32:06,659 INFO misc.py line 119 131400] Train: [97/100][746/800] Data 0.006 (0.004) Batch 0.355 (0.334) Remain 00:13:39 loss: 0.1958 Lr: 0.00002 [2023-12-20 21:32:06,963 INFO misc.py line 119 131400] Train: [97/100][747/800] Data 0.003 (0.004) Batch 0.303 (0.334) Remain 00:13:38 loss: 0.2287 Lr: 0.00002 [2023-12-20 21:32:07,323 INFO misc.py line 119 131400] Train: [97/100][748/800] Data 0.004 (0.004) Batch 0.360 (0.334) Remain 00:13:38 loss: 0.2017 Lr: 0.00002 [2023-12-20 21:32:07,673 INFO misc.py line 119 131400] Train: [97/100][749/800] Data 0.005 (0.004) Batch 0.347 (0.334) Remain 00:13:38 loss: 0.1451 Lr: 0.00002 [2023-12-20 21:32:08,011 INFO misc.py line 119 131400] Train: [97/100][750/800] Data 0.008 (0.004) Batch 0.339 (0.334) Remain 00:13:37 loss: 0.2236 Lr: 0.00002 [2023-12-20 21:32:08,358 INFO misc.py line 119 131400] Train: [97/100][751/800] Data 0.007 (0.004) Batch 0.349 (0.334) Remain 00:13:37 loss: 0.4603 Lr: 0.00002 [2023-12-20 21:32:08,698 INFO misc.py line 119 131400] Train: [97/100][752/800] Data 0.005 (0.004) Batch 0.340 (0.334) Remain 00:13:37 loss: 0.2073 Lr: 0.00002 [2023-12-20 21:32:09,035 INFO misc.py line 119 131400] Train: [97/100][753/800] Data 0.005 (0.004) Batch 0.337 (0.334) Remain 00:13:36 loss: 0.1648 Lr: 0.00002 [2023-12-20 21:32:09,382 INFO misc.py line 119 131400] Train: [97/100][754/800] Data 0.006 (0.004) Batch 0.348 (0.334) Remain 00:13:36 loss: 0.1464 Lr: 0.00002 [2023-12-20 21:32:09,735 INFO misc.py line 119 131400] Train: [97/100][755/800] Data 0.003 (0.004) Batch 0.352 (0.334) Remain 00:13:36 loss: 0.2838 Lr: 0.00002 [2023-12-20 21:32:10,064 INFO misc.py line 119 131400] Train: [97/100][756/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 00:13:36 loss: 0.1139 Lr: 0.00002 [2023-12-20 21:32:10,420 INFO misc.py line 119 131400] Train: [97/100][757/800] Data 0.003 (0.004) Batch 0.354 (0.334) Remain 00:13:35 loss: 0.2646 Lr: 0.00002 [2023-12-20 21:32:10,776 INFO misc.py line 119 131400] Train: [97/100][758/800] Data 0.004 (0.004) Batch 0.351 (0.334) Remain 00:13:35 loss: 0.1486 Lr: 0.00002 [2023-12-20 21:32:11,108 INFO misc.py line 119 131400] Train: [97/100][759/800] Data 0.009 (0.004) Batch 0.337 (0.334) Remain 00:13:35 loss: 0.1341 Lr: 0.00002 [2023-12-20 21:32:11,462 INFO misc.py line 119 131400] Train: [97/100][760/800] Data 0.004 (0.004) Batch 0.354 (0.334) Remain 00:13:34 loss: 0.3128 Lr: 0.00002 [2023-12-20 21:32:11,788 INFO misc.py line 119 131400] Train: [97/100][761/800] Data 0.003 (0.004) Batch 0.319 (0.334) Remain 00:13:34 loss: 0.2036 Lr: 0.00002 [2023-12-20 21:32:12,169 INFO misc.py line 119 131400] Train: [97/100][762/800] Data 0.011 (0.004) Batch 0.387 (0.334) Remain 00:13:34 loss: 0.2090 Lr: 0.00002 [2023-12-20 21:32:12,512 INFO misc.py line 119 131400] Train: [97/100][763/800] Data 0.004 (0.004) Batch 0.343 (0.334) Remain 00:13:34 loss: 0.0801 Lr: 0.00002 [2023-12-20 21:32:12,849 INFO misc.py line 119 131400] Train: [97/100][764/800] Data 0.004 (0.004) Batch 0.332 (0.334) Remain 00:13:33 loss: 0.2779 Lr: 0.00002 [2023-12-20 21:32:13,155 INFO misc.py line 119 131400] Train: [97/100][765/800] Data 0.010 (0.004) Batch 0.312 (0.334) Remain 00:13:33 loss: 0.0805 Lr: 0.00002 [2023-12-20 21:32:13,484 INFO misc.py line 119 131400] Train: [97/100][766/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 00:13:32 loss: 0.2280 Lr: 0.00002 [2023-12-20 21:32:13,801 INFO misc.py line 119 131400] Train: [97/100][767/800] Data 0.003 (0.004) Batch 0.316 (0.334) Remain 00:13:32 loss: 0.1246 Lr: 0.00002 [2023-12-20 21:32:14,151 INFO misc.py line 119 131400] Train: [97/100][768/800] Data 0.004 (0.004) Batch 0.350 (0.334) Remain 00:13:32 loss: 0.3160 Lr: 0.00002 [2023-12-20 21:32:14,473 INFO misc.py line 119 131400] Train: [97/100][769/800] Data 0.004 (0.004) Batch 0.322 (0.334) Remain 00:13:31 loss: 0.2179 Lr: 0.00002 [2023-12-20 21:32:14,839 INFO misc.py line 119 131400] Train: [97/100][770/800] Data 0.004 (0.004) Batch 0.362 (0.334) Remain 00:13:31 loss: 0.3616 Lr: 0.00002 [2023-12-20 21:32:15,159 INFO misc.py line 119 131400] Train: [97/100][771/800] Data 0.009 (0.004) Batch 0.325 (0.334) Remain 00:13:31 loss: 0.1433 Lr: 0.00002 [2023-12-20 21:32:15,478 INFO misc.py line 119 131400] Train: [97/100][772/800] Data 0.003 (0.004) Batch 0.319 (0.334) Remain 00:13:30 loss: 0.2995 Lr: 0.00002 [2023-12-20 21:32:15,778 INFO misc.py line 119 131400] Train: [97/100][773/800] Data 0.004 (0.004) Batch 0.299 (0.334) Remain 00:13:30 loss: 0.2534 Lr: 0.00002 [2023-12-20 21:32:16,123 INFO misc.py line 119 131400] Train: [97/100][774/800] Data 0.004 (0.004) Batch 0.341 (0.334) Remain 00:13:30 loss: 0.2447 Lr: 0.00002 [2023-12-20 21:32:16,475 INFO misc.py line 119 131400] Train: [97/100][775/800] Data 0.009 (0.004) Batch 0.356 (0.334) Remain 00:13:29 loss: 0.1583 Lr: 0.00002 [2023-12-20 21:32:16,804 INFO misc.py line 119 131400] Train: [97/100][776/800] Data 0.004 (0.004) Batch 0.330 (0.334) Remain 00:13:29 loss: 0.2003 Lr: 0.00002 [2023-12-20 21:32:17,123 INFO misc.py line 119 131400] Train: [97/100][777/800] Data 0.004 (0.004) Batch 0.319 (0.334) Remain 00:13:29 loss: 0.2675 Lr: 0.00002 [2023-12-20 21:32:17,450 INFO misc.py line 119 131400] Train: [97/100][778/800] Data 0.004 (0.004) Batch 0.328 (0.334) Remain 00:13:28 loss: 0.1723 Lr: 0.00002 [2023-12-20 21:32:17,802 INFO misc.py line 119 131400] Train: [97/100][779/800] Data 0.003 (0.004) Batch 0.346 (0.334) Remain 00:13:28 loss: 0.1652 Lr: 0.00002 [2023-12-20 21:32:18,137 INFO misc.py line 119 131400] Train: [97/100][780/800] Data 0.009 (0.004) Batch 0.333 (0.334) Remain 00:13:28 loss: 0.2392 Lr: 0.00002 [2023-12-20 21:32:18,450 INFO misc.py line 119 131400] Train: [97/100][781/800] Data 0.012 (0.004) Batch 0.320 (0.334) Remain 00:13:27 loss: 0.2310 Lr: 0.00002 [2023-12-20 21:32:18,786 INFO misc.py line 119 131400] Train: [97/100][782/800] Data 0.003 (0.004) Batch 0.336 (0.334) Remain 00:13:27 loss: 0.2693 Lr: 0.00002 [2023-12-20 21:32:19,088 INFO misc.py line 119 131400] Train: [97/100][783/800] Data 0.005 (0.004) Batch 0.302 (0.334) Remain 00:13:27 loss: 0.2359 Lr: 0.00002 [2023-12-20 21:32:19,382 INFO misc.py line 119 131400] Train: [97/100][784/800] Data 0.004 (0.004) Batch 0.294 (0.334) Remain 00:13:26 loss: 0.1237 Lr: 0.00002 [2023-12-20 21:32:19,726 INFO misc.py line 119 131400] Train: [97/100][785/800] Data 0.005 (0.004) Batch 0.344 (0.334) Remain 00:13:26 loss: 0.2233 Lr: 0.00002 [2023-12-20 21:32:20,045 INFO misc.py line 119 131400] Train: [97/100][786/800] Data 0.004 (0.004) Batch 0.319 (0.334) Remain 00:13:25 loss: 0.2229 Lr: 0.00002 [2023-12-20 21:32:20,370 INFO misc.py line 119 131400] Train: [97/100][787/800] Data 0.004 (0.004) Batch 0.324 (0.334) Remain 00:13:25 loss: 0.1800 Lr: 0.00002 [2023-12-20 21:32:20,690 INFO misc.py line 119 131400] Train: [97/100][788/800] Data 0.006 (0.004) Batch 0.322 (0.334) Remain 00:13:25 loss: 0.1259 Lr: 0.00002 [2023-12-20 21:32:21,007 INFO misc.py line 119 131400] Train: [97/100][789/800] Data 0.005 (0.004) Batch 0.318 (0.334) Remain 00:13:24 loss: 0.0736 Lr: 0.00002 [2023-12-20 21:32:21,318 INFO misc.py line 119 131400] Train: [97/100][790/800] Data 0.003 (0.004) Batch 0.311 (0.334) Remain 00:13:24 loss: 0.2444 Lr: 0.00002 [2023-12-20 21:32:21,592 INFO misc.py line 119 131400] Train: [97/100][791/800] Data 0.003 (0.004) Batch 0.273 (0.334) Remain 00:13:23 loss: 0.1081 Lr: 0.00002 [2023-12-20 21:32:22,056 INFO misc.py line 119 131400] Train: [97/100][792/800] Data 0.003 (0.004) Batch 0.465 (0.334) Remain 00:13:23 loss: 0.1126 Lr: 0.00002 [2023-12-20 21:32:22,349 INFO misc.py line 119 131400] Train: [97/100][793/800] Data 0.003 (0.004) Batch 0.292 (0.334) Remain 00:13:23 loss: 0.2556 Lr: 0.00002 [2023-12-20 21:32:22,641 INFO misc.py line 119 131400] Train: [97/100][794/800] Data 0.003 (0.004) Batch 0.292 (0.334) Remain 00:13:23 loss: 0.3372 Lr: 0.00002 [2023-12-20 21:32:22,979 INFO misc.py line 119 131400] Train: [97/100][795/800] Data 0.004 (0.004) Batch 0.337 (0.334) Remain 00:13:22 loss: 0.2820 Lr: 0.00002 [2023-12-20 21:32:23,312 INFO misc.py line 119 131400] Train: [97/100][796/800] Data 0.005 (0.004) Batch 0.335 (0.334) Remain 00:13:22 loss: 0.1935 Lr: 0.00002 [2023-12-20 21:32:23,784 INFO misc.py line 119 131400] Train: [97/100][797/800] Data 0.003 (0.004) Batch 0.287 (0.334) Remain 00:13:21 loss: 0.1883 Lr: 0.00002 [2023-12-20 21:32:24,085 INFO misc.py line 119 131400] Train: [97/100][798/800] Data 0.188 (0.005) Batch 0.486 (0.334) Remain 00:13:22 loss: 0.1500 Lr: 0.00002 [2023-12-20 21:32:24,402 INFO misc.py line 119 131400] Train: [97/100][799/800] Data 0.003 (0.005) Batch 0.317 (0.334) Remain 00:13:21 loss: 0.2076 Lr: 0.00002 [2023-12-20 21:32:24,751 INFO misc.py line 119 131400] Train: [97/100][800/800] Data 0.005 (0.005) Batch 0.349 (0.334) Remain 00:13:21 loss: 0.2504 Lr: 0.00002 [2023-12-20 21:32:24,752 INFO misc.py line 136 131400] Train result: loss: 0.2009 [2023-12-20 21:32:24,753 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:32:47,895 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1412 [2023-12-20 21:32:48,717 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1572 [2023-12-20 21:32:48,806 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4779 [2023-12-20 21:32:48,915 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.5174 [2023-12-20 21:32:49,032 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3533 [2023-12-20 21:32:49,140 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.7440 [2023-12-20 21:32:49,238 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9419 [2023-12-20 21:32:49,357 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8031 [2023-12-20 21:32:49,441 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2823 [2023-12-20 21:32:49,528 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3212 [2023-12-20 21:32:49,622 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3860 [2023-12-20 21:32:49,773 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2815 [2023-12-20 21:32:49,910 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4829 [2023-12-20 21:32:50,073 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2021 [2023-12-20 21:32:50,182 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1298 [2023-12-20 21:32:50,318 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7382 [2023-12-20 21:32:50,434 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.3027 [2023-12-20 21:32:50,552 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.6564 [2023-12-20 21:32:50,675 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1216 [2023-12-20 21:32:50,763 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4440 [2023-12-20 21:32:50,875 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1568 [2023-12-20 21:32:51,036 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1220 [2023-12-20 21:32:51,164 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.8860 [2023-12-20 21:32:51,312 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2704 [2023-12-20 21:32:51,455 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1797 [2023-12-20 21:32:51,544 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.7945 [2023-12-20 21:32:51,708 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.6047 [2023-12-20 21:32:51,837 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5414 [2023-12-20 21:32:51,941 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4963 [2023-12-20 21:32:52,089 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8642 [2023-12-20 21:32:52,220 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4849 [2023-12-20 21:32:52,348 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3493 [2023-12-20 21:32:52,450 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1191 [2023-12-20 21:32:52,525 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1712 [2023-12-20 21:32:52,629 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8293 [2023-12-20 21:32:52,720 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2910 [2023-12-20 21:32:52,849 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8858 [2023-12-20 21:32:52,967 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0872 [2023-12-20 21:32:53,056 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6310 [2023-12-20 21:32:53,201 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2875 [2023-12-20 21:32:53,354 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0177 [2023-12-20 21:32:53,462 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0493 [2023-12-20 21:32:53,592 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.3191 [2023-12-20 21:32:53,741 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1063 [2023-12-20 21:32:53,863 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.5848 [2023-12-20 21:32:53,968 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8402 [2023-12-20 21:32:54,140 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2863 [2023-12-20 21:32:54,235 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5021 [2023-12-20 21:32:54,380 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7156 [2023-12-20 21:32:54,469 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2288 [2023-12-20 21:32:54,544 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.6222 [2023-12-20 21:32:54,655 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4989 [2023-12-20 21:32:54,802 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9832 [2023-12-20 21:32:54,936 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3529 [2023-12-20 21:32:55,041 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2791 [2023-12-20 21:32:55,129 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.5146 [2023-12-20 21:32:55,237 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3860 [2023-12-20 21:32:55,401 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2476 [2023-12-20 21:32:55,502 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.6177 [2023-12-20 21:32:55,600 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.2154 [2023-12-20 21:32:55,699 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.4308 [2023-12-20 21:32:55,791 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2331 [2023-12-20 21:32:55,877 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6510 [2023-12-20 21:32:55,981 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6862 [2023-12-20 21:32:56,106 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6166 [2023-12-20 21:32:56,190 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2082 [2023-12-20 21:32:56,288 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3499 [2023-12-20 21:32:56,381 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0077 [2023-12-20 21:32:56,464 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3417 [2023-12-20 21:32:56,552 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0077 [2023-12-20 21:32:56,645 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8698 [2023-12-20 21:32:56,736 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6138 [2023-12-20 21:32:56,869 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0531 [2023-12-20 21:32:56,965 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6761 [2023-12-20 21:32:57,080 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6178 [2023-12-20 21:32:57,183 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.5087 [2023-12-20 21:32:57,269 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4372 [2023-12-20 21:32:57,423 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9781 [2023-12-20 21:32:58,670 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7724/0.8481/0.9222. [2023-12-20 21:32:58,670 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8735/0.9554 [2023-12-20 21:32:58,670 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9640/0.9861 [2023-12-20 21:32:58,670 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7209/0.8256 [2023-12-20 21:32:58,670 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8381/0.8813 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9251/0.9625 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8638/0.9384 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7862/0.8710 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7355/0.8484 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7108/0.8018 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8410/0.9281 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3951/0.5051 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7258/0.8169 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7166/0.8679 [2023-12-20 21:32:58,671 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7650/0.8555 [2023-12-20 21:32:58,673 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7334/0.8046 [2023-12-20 21:32:58,673 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6885/0.7370 [2023-12-20 21:32:58,674 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9397/0.9791 [2023-12-20 21:32:58,674 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6977/0.7947 [2023-12-20 21:32:58,674 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8936/0.9247 [2023-12-20 21:32:58,674 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6335/0.6771 [2023-12-20 21:32:58,675 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:32:58,677 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:32:58,677 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:33:02,342 INFO misc.py line 119 131400] Train: [98/100][1/800] Data 0.632 (0.632) Batch 0.907 (0.907) Remain 00:36:16 loss: 0.0836 Lr: 0.00002 [2023-12-20 21:33:02,681 INFO misc.py line 119 131400] Train: [98/100][2/800] Data 0.037 (0.037) Batch 0.339 (0.339) Remain 00:13:33 loss: 0.1476 Lr: 0.00002 [2023-12-20 21:33:03,015 INFO misc.py line 119 131400] Train: [98/100][3/800] Data 0.003 (0.003) Batch 0.334 (0.334) Remain 00:13:21 loss: 0.2198 Lr: 0.00002 [2023-12-20 21:33:03,366 INFO misc.py line 119 131400] Train: [98/100][4/800] Data 0.004 (0.004) Batch 0.351 (0.351) Remain 00:13:59 loss: 0.1630 Lr: 0.00002 [2023-12-20 21:33:03,730 INFO misc.py line 119 131400] Train: [98/100][5/800] Data 0.005 (0.004) Batch 0.364 (0.357) Remain 00:14:15 loss: 0.1576 Lr: 0.00002 [2023-12-20 21:33:04,087 INFO misc.py line 119 131400] Train: [98/100][6/800] Data 0.004 (0.004) Batch 0.357 (0.357) Remain 00:14:14 loss: 0.2160 Lr: 0.00002 [2023-12-20 21:33:04,450 INFO misc.py line 119 131400] Train: [98/100][7/800] Data 0.012 (0.006) Batch 0.363 (0.358) Remain 00:14:17 loss: 0.1966 Lr: 0.00002 [2023-12-20 21:33:04,789 INFO misc.py line 119 131400] Train: [98/100][8/800] Data 0.005 (0.006) Batch 0.340 (0.355) Remain 00:14:08 loss: 0.2497 Lr: 0.00002 [2023-12-20 21:33:05,139 INFO misc.py line 119 131400] Train: [98/100][9/800] Data 0.005 (0.005) Batch 0.351 (0.354) Remain 00:14:06 loss: 0.1275 Lr: 0.00002 [2023-12-20 21:33:05,452 INFO misc.py line 119 131400] Train: [98/100][10/800] Data 0.003 (0.005) Batch 0.312 (0.348) Remain 00:13:51 loss: 0.1537 Lr: 0.00002 [2023-12-20 21:33:05,782 INFO misc.py line 119 131400] Train: [98/100][11/800] Data 0.004 (0.005) Batch 0.330 (0.346) Remain 00:13:46 loss: 0.1587 Lr: 0.00002 [2023-12-20 21:33:06,102 INFO misc.py line 119 131400] Train: [98/100][12/800] Data 0.002 (0.005) Batch 0.319 (0.343) Remain 00:13:38 loss: 0.1659 Lr: 0.00002 [2023-12-20 21:33:06,407 INFO misc.py line 119 131400] Train: [98/100][13/800] Data 0.004 (0.005) Batch 0.305 (0.339) Remain 00:13:29 loss: 0.2834 Lr: 0.00002 [2023-12-20 21:33:06,734 INFO misc.py line 119 131400] Train: [98/100][14/800] Data 0.003 (0.004) Batch 0.326 (0.338) Remain 00:13:26 loss: 0.3308 Lr: 0.00002 [2023-12-20 21:33:07,074 INFO misc.py line 119 131400] Train: [98/100][15/800] Data 0.004 (0.004) Batch 0.338 (0.338) Remain 00:13:26 loss: 0.2127 Lr: 0.00002 [2023-12-20 21:33:07,400 INFO misc.py line 119 131400] Train: [98/100][16/800] Data 0.006 (0.005) Batch 0.328 (0.337) Remain 00:13:24 loss: 0.1824 Lr: 0.00002 [2023-12-20 21:33:07,753 INFO misc.py line 119 131400] Train: [98/100][17/800] Data 0.003 (0.004) Batch 0.353 (0.338) Remain 00:13:26 loss: 0.1303 Lr: 0.00002 [2023-12-20 21:33:08,078 INFO misc.py line 119 131400] Train: [98/100][18/800] Data 0.004 (0.004) Batch 0.325 (0.337) Remain 00:13:23 loss: 0.2038 Lr: 0.00002 [2023-12-20 21:33:08,407 INFO misc.py line 119 131400] Train: [98/100][19/800] Data 0.004 (0.004) Batch 0.329 (0.337) Remain 00:13:22 loss: 0.2168 Lr: 0.00002 [2023-12-20 21:33:08,750 INFO misc.py line 119 131400] Train: [98/100][20/800] Data 0.004 (0.004) Batch 0.343 (0.337) Remain 00:13:22 loss: 0.1014 Lr: 0.00002 [2023-12-20 21:33:09,093 INFO misc.py line 119 131400] Train: [98/100][21/800] Data 0.004 (0.004) Batch 0.343 (0.338) Remain 00:13:23 loss: 0.2471 Lr: 0.00002 [2023-12-20 21:33:09,469 INFO misc.py line 119 131400] Train: [98/100][22/800] Data 0.004 (0.004) Batch 0.375 (0.340) Remain 00:13:27 loss: 0.2602 Lr: 0.00002 [2023-12-20 21:33:09,873 INFO misc.py line 119 131400] Train: [98/100][23/800] Data 0.005 (0.004) Batch 0.405 (0.343) Remain 00:13:34 loss: 0.2682 Lr: 0.00002 [2023-12-20 21:33:10,231 INFO misc.py line 119 131400] Train: [98/100][24/800] Data 0.005 (0.004) Batch 0.359 (0.344) Remain 00:13:36 loss: 0.1894 Lr: 0.00002 [2023-12-20 21:33:10,607 INFO misc.py line 119 131400] Train: [98/100][25/800] Data 0.003 (0.004) Batch 0.375 (0.345) Remain 00:13:39 loss: 0.1078 Lr: 0.00002 [2023-12-20 21:33:10,938 INFO misc.py line 119 131400] Train: [98/100][26/800] Data 0.005 (0.004) Batch 0.331 (0.344) Remain 00:13:37 loss: 0.1489 Lr: 0.00002 [2023-12-20 21:33:11,281 INFO misc.py line 119 131400] Train: [98/100][27/800] Data 0.005 (0.004) Batch 0.343 (0.344) Remain 00:13:37 loss: 0.2712 Lr: 0.00002 [2023-12-20 21:33:11,592 INFO misc.py line 119 131400] Train: [98/100][28/800] Data 0.004 (0.004) Batch 0.310 (0.343) Remain 00:13:33 loss: 0.1542 Lr: 0.00002 [2023-12-20 21:33:11,918 INFO misc.py line 119 131400] Train: [98/100][29/800] Data 0.005 (0.004) Batch 0.325 (0.342) Remain 00:13:31 loss: 0.3774 Lr: 0.00002 [2023-12-20 21:33:12,279 INFO misc.py line 119 131400] Train: [98/100][30/800] Data 0.005 (0.004) Batch 0.362 (0.343) Remain 00:13:33 loss: 0.1287 Lr: 0.00002 [2023-12-20 21:33:12,616 INFO misc.py line 119 131400] Train: [98/100][31/800] Data 0.004 (0.004) Batch 0.331 (0.343) Remain 00:13:31 loss: 0.2016 Lr: 0.00002 [2023-12-20 21:33:12,948 INFO misc.py line 119 131400] Train: [98/100][32/800] Data 0.010 (0.005) Batch 0.338 (0.342) Remain 00:13:31 loss: 0.1748 Lr: 0.00002 [2023-12-20 21:33:13,314 INFO misc.py line 119 131400] Train: [98/100][33/800] Data 0.003 (0.005) Batch 0.366 (0.343) Remain 00:13:32 loss: 0.1910 Lr: 0.00002 [2023-12-20 21:33:13,657 INFO misc.py line 119 131400] Train: [98/100][34/800] Data 0.004 (0.005) Batch 0.344 (0.343) Remain 00:13:32 loss: 0.2041 Lr: 0.00002 [2023-12-20 21:33:13,995 INFO misc.py line 119 131400] Train: [98/100][35/800] Data 0.004 (0.005) Batch 0.338 (0.343) Remain 00:13:31 loss: 0.3186 Lr: 0.00002 [2023-12-20 21:33:14,343 INFO misc.py line 119 131400] Train: [98/100][36/800] Data 0.003 (0.004) Batch 0.345 (0.343) Remain 00:13:31 loss: 0.3698 Lr: 0.00002 [2023-12-20 21:33:14,685 INFO misc.py line 119 131400] Train: [98/100][37/800] Data 0.008 (0.005) Batch 0.346 (0.343) Remain 00:13:31 loss: 0.3871 Lr: 0.00002 [2023-12-20 21:33:15,001 INFO misc.py line 119 131400] Train: [98/100][38/800] Data 0.003 (0.005) Batch 0.315 (0.342) Remain 00:13:28 loss: 0.2132 Lr: 0.00002 [2023-12-20 21:33:15,385 INFO misc.py line 119 131400] Train: [98/100][39/800] Data 0.005 (0.005) Batch 0.383 (0.344) Remain 00:13:31 loss: 0.3168 Lr: 0.00002 [2023-12-20 21:33:15,712 INFO misc.py line 119 131400] Train: [98/100][40/800] Data 0.006 (0.005) Batch 0.328 (0.343) Remain 00:13:29 loss: 0.0549 Lr: 0.00002 [2023-12-20 21:33:16,071 INFO misc.py line 119 131400] Train: [98/100][41/800] Data 0.004 (0.005) Batch 0.359 (0.344) Remain 00:13:30 loss: 0.1875 Lr: 0.00002 [2023-12-20 21:33:16,405 INFO misc.py line 119 131400] Train: [98/100][42/800] Data 0.005 (0.005) Batch 0.335 (0.343) Remain 00:13:29 loss: 0.1468 Lr: 0.00002 [2023-12-20 21:33:16,766 INFO misc.py line 119 131400] Train: [98/100][43/800] Data 0.003 (0.005) Batch 0.361 (0.344) Remain 00:13:30 loss: 0.1413 Lr: 0.00002 [2023-12-20 21:33:17,088 INFO misc.py line 119 131400] Train: [98/100][44/800] Data 0.004 (0.005) Batch 0.322 (0.343) Remain 00:13:28 loss: 0.1308 Lr: 0.00002 [2023-12-20 21:33:17,430 INFO misc.py line 119 131400] Train: [98/100][45/800] Data 0.004 (0.004) Batch 0.341 (0.343) Remain 00:13:28 loss: 0.2308 Lr: 0.00002 [2023-12-20 21:33:17,761 INFO misc.py line 119 131400] Train: [98/100][46/800] Data 0.005 (0.005) Batch 0.318 (0.343) Remain 00:13:26 loss: 0.2039 Lr: 0.00002 [2023-12-20 21:33:18,112 INFO misc.py line 119 131400] Train: [98/100][47/800] Data 0.020 (0.005) Batch 0.366 (0.343) Remain 00:13:27 loss: 0.2295 Lr: 0.00002 [2023-12-20 21:33:18,442 INFO misc.py line 119 131400] Train: [98/100][48/800] Data 0.003 (0.005) Batch 0.329 (0.343) Remain 00:13:26 loss: 0.0902 Lr: 0.00002 [2023-12-20 21:33:18,808 INFO misc.py line 119 131400] Train: [98/100][49/800] Data 0.005 (0.005) Batch 0.365 (0.343) Remain 00:13:27 loss: 0.2230 Lr: 0.00002 [2023-12-20 21:33:19,134 INFO misc.py line 119 131400] Train: [98/100][50/800] Data 0.005 (0.005) Batch 0.328 (0.343) Remain 00:13:25 loss: 0.2490 Lr: 0.00002 [2023-12-20 21:33:19,468 INFO misc.py line 119 131400] Train: [98/100][51/800] Data 0.003 (0.005) Batch 0.334 (0.343) Remain 00:13:25 loss: 0.4130 Lr: 0.00002 [2023-12-20 21:33:19,795 INFO misc.py line 119 131400] Train: [98/100][52/800] Data 0.004 (0.005) Batch 0.322 (0.342) Remain 00:13:23 loss: 0.1158 Lr: 0.00002 [2023-12-20 21:33:20,161 INFO misc.py line 119 131400] Train: [98/100][53/800] Data 0.008 (0.005) Batch 0.370 (0.343) Remain 00:13:24 loss: 0.1453 Lr: 0.00002 [2023-12-20 21:33:20,479 INFO misc.py line 119 131400] Train: [98/100][54/800] Data 0.004 (0.005) Batch 0.318 (0.342) Remain 00:13:23 loss: 0.1897 Lr: 0.00002 [2023-12-20 21:33:20,799 INFO misc.py line 119 131400] Train: [98/100][55/800] Data 0.004 (0.005) Batch 0.316 (0.342) Remain 00:13:21 loss: 0.2080 Lr: 0.00002 [2023-12-20 21:33:21,101 INFO misc.py line 119 131400] Train: [98/100][56/800] Data 0.008 (0.005) Batch 0.307 (0.341) Remain 00:13:19 loss: 0.1274 Lr: 0.00002 [2023-12-20 21:33:21,434 INFO misc.py line 119 131400] Train: [98/100][57/800] Data 0.003 (0.005) Batch 0.332 (0.341) Remain 00:13:19 loss: 0.3525 Lr: 0.00002 [2023-12-20 21:33:21,757 INFO misc.py line 119 131400] Train: [98/100][58/800] Data 0.004 (0.005) Batch 0.323 (0.341) Remain 00:13:18 loss: 0.1343 Lr: 0.00002 [2023-12-20 21:33:22,101 INFO misc.py line 119 131400] Train: [98/100][59/800] Data 0.004 (0.005) Batch 0.345 (0.341) Remain 00:13:17 loss: 0.1643 Lr: 0.00002 [2023-12-20 21:33:22,439 INFO misc.py line 119 131400] Train: [98/100][60/800] Data 0.003 (0.005) Batch 0.337 (0.341) Remain 00:13:17 loss: 0.1266 Lr: 0.00001 [2023-12-20 21:33:22,757 INFO misc.py line 119 131400] Train: [98/100][61/800] Data 0.005 (0.005) Batch 0.316 (0.340) Remain 00:13:15 loss: 0.3141 Lr: 0.00001 [2023-12-20 21:33:23,071 INFO misc.py line 119 131400] Train: [98/100][62/800] Data 0.007 (0.005) Batch 0.317 (0.340) Remain 00:13:14 loss: 0.1426 Lr: 0.00001 [2023-12-20 21:33:23,414 INFO misc.py line 119 131400] Train: [98/100][63/800] Data 0.004 (0.005) Batch 0.344 (0.340) Remain 00:13:14 loss: 0.1984 Lr: 0.00001 [2023-12-20 21:33:23,701 INFO misc.py line 119 131400] Train: [98/100][64/800] Data 0.003 (0.005) Batch 0.285 (0.339) Remain 00:13:12 loss: 0.1641 Lr: 0.00001 [2023-12-20 21:33:24,044 INFO misc.py line 119 131400] Train: [98/100][65/800] Data 0.005 (0.005) Batch 0.342 (0.339) Remain 00:13:11 loss: 0.2039 Lr: 0.00001 [2023-12-20 21:33:24,366 INFO misc.py line 119 131400] Train: [98/100][66/800] Data 0.005 (0.005) Batch 0.324 (0.339) Remain 00:13:10 loss: 0.2528 Lr: 0.00001 [2023-12-20 21:33:24,709 INFO misc.py line 119 131400] Train: [98/100][67/800] Data 0.005 (0.005) Batch 0.342 (0.339) Remain 00:13:10 loss: 0.1346 Lr: 0.00001 [2023-12-20 21:33:25,013 INFO misc.py line 119 131400] Train: [98/100][68/800] Data 0.004 (0.005) Batch 0.306 (0.338) Remain 00:13:09 loss: 0.1408 Lr: 0.00001 [2023-12-20 21:33:25,297 INFO misc.py line 119 131400] Train: [98/100][69/800] Data 0.003 (0.005) Batch 0.283 (0.338) Remain 00:13:06 loss: 0.2129 Lr: 0.00001 [2023-12-20 21:33:25,649 INFO misc.py line 119 131400] Train: [98/100][70/800] Data 0.004 (0.005) Batch 0.353 (0.338) Remain 00:13:07 loss: 0.1813 Lr: 0.00001 [2023-12-20 21:33:25,927 INFO misc.py line 119 131400] Train: [98/100][71/800] Data 0.003 (0.005) Batch 0.279 (0.337) Remain 00:13:04 loss: 0.1439 Lr: 0.00001 [2023-12-20 21:33:26,266 INFO misc.py line 119 131400] Train: [98/100][72/800] Data 0.003 (0.005) Batch 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[2023-12-20 21:37:26,148 INFO misc.py line 119 131400] Train: [98/100][776/800] Data 0.004 (0.010) Batch 0.326 (0.340) Remain 00:09:12 loss: 0.1625 Lr: 0.00001 [2023-12-20 21:37:26,469 INFO misc.py line 119 131400] Train: [98/100][777/800] Data 0.004 (0.010) Batch 0.322 (0.340) Remain 00:09:12 loss: 0.2704 Lr: 0.00001 [2023-12-20 21:37:26,827 INFO misc.py line 119 131400] Train: [98/100][778/800] Data 0.003 (0.010) Batch 0.357 (0.340) Remain 00:09:12 loss: 0.2225 Lr: 0.00001 [2023-12-20 21:37:27,181 INFO misc.py line 119 131400] Train: [98/100][779/800] Data 0.005 (0.010) Batch 0.354 (0.340) Remain 00:09:11 loss: 0.1792 Lr: 0.00001 [2023-12-20 21:37:27,524 INFO misc.py line 119 131400] Train: [98/100][780/800] Data 0.005 (0.010) Batch 0.343 (0.340) Remain 00:09:11 loss: 0.3449 Lr: 0.00001 [2023-12-20 21:37:27,912 INFO misc.py line 119 131400] Train: [98/100][781/800] Data 0.005 (0.010) Batch 0.390 (0.340) Remain 00:09:11 loss: 0.2344 Lr: 0.00001 [2023-12-20 21:37:28,230 INFO misc.py line 119 131400] Train: [98/100][782/800] Data 0.004 (0.010) Batch 0.316 (0.340) Remain 00:09:10 loss: 0.1685 Lr: 0.00001 [2023-12-20 21:37:28,578 INFO misc.py line 119 131400] Train: [98/100][783/800] Data 0.005 (0.010) Batch 0.349 (0.340) Remain 00:09:10 loss: 0.1127 Lr: 0.00001 [2023-12-20 21:37:28,892 INFO misc.py line 119 131400] Train: [98/100][784/800] Data 0.003 (0.010) Batch 0.312 (0.340) Remain 00:09:10 loss: 0.1627 Lr: 0.00001 [2023-12-20 21:37:29,212 INFO misc.py line 119 131400] Train: [98/100][785/800] Data 0.005 (0.010) Batch 0.321 (0.340) Remain 00:09:09 loss: 0.1759 Lr: 0.00001 [2023-12-20 21:37:29,544 INFO misc.py line 119 131400] Train: [98/100][786/800] Data 0.005 (0.010) Batch 0.332 (0.340) Remain 00:09:09 loss: 0.1221 Lr: 0.00001 [2023-12-20 21:37:29,884 INFO misc.py line 119 131400] Train: [98/100][787/800] Data 0.004 (0.010) Batch 0.340 (0.340) Remain 00:09:09 loss: 0.1800 Lr: 0.00001 [2023-12-20 21:37:30,191 INFO misc.py line 119 131400] Train: [98/100][788/800] Data 0.004 (0.010) Batch 0.307 (0.340) Remain 00:09:08 loss: 0.1425 Lr: 0.00001 [2023-12-20 21:37:30,550 INFO misc.py line 119 131400] Train: [98/100][789/800] Data 0.003 (0.010) Batch 0.356 (0.340) Remain 00:09:08 loss: 0.1720 Lr: 0.00001 [2023-12-20 21:37:30,906 INFO misc.py line 119 131400] Train: [98/100][790/800] Data 0.007 (0.010) Batch 0.358 (0.340) Remain 00:09:08 loss: 0.1770 Lr: 0.00001 [2023-12-20 21:37:31,211 INFO misc.py line 119 131400] Train: [98/100][791/800] Data 0.005 (0.010) Batch 0.307 (0.340) Remain 00:09:07 loss: 0.1398 Lr: 0.00001 [2023-12-20 21:37:31,489 INFO misc.py line 119 131400] Train: [98/100][792/800] Data 0.003 (0.010) Batch 0.278 (0.340) Remain 00:09:07 loss: 0.1880 Lr: 0.00001 [2023-12-20 21:37:31,889 INFO misc.py line 119 131400] Train: [98/100][793/800] Data 0.004 (0.010) Batch 0.401 (0.340) Remain 00:09:06 loss: 0.2413 Lr: 0.00001 [2023-12-20 21:37:32,202 INFO misc.py line 119 131400] Train: [98/100][794/800] Data 0.003 (0.010) Batch 0.313 (0.340) Remain 00:09:06 loss: 0.0963 Lr: 0.00001 [2023-12-20 21:37:32,487 INFO misc.py line 119 131400] Train: [98/100][795/800] Data 0.003 (0.010) Batch 0.286 (0.340) Remain 00:09:06 loss: 0.2545 Lr: 0.00001 [2023-12-20 21:37:32,766 INFO misc.py line 119 131400] Train: [98/100][796/800] Data 0.003 (0.010) Batch 0.279 (0.340) Remain 00:09:05 loss: 0.0847 Lr: 0.00001 [2023-12-20 21:37:33,080 INFO misc.py line 119 131400] Train: [98/100][797/800] Data 0.003 (0.010) Batch 0.314 (0.340) Remain 00:09:05 loss: 0.2104 Lr: 0.00001 [2023-12-20 21:37:33,373 INFO misc.py line 119 131400] Train: [98/100][798/800] Data 0.003 (0.010) Batch 0.293 (0.340) Remain 00:09:04 loss: 0.1944 Lr: 0.00001 [2023-12-20 21:37:33,698 INFO misc.py line 119 131400] Train: [98/100][799/800] Data 0.003 (0.010) Batch 0.324 (0.340) Remain 00:09:04 loss: 0.1617 Lr: 0.00001 [2023-12-20 21:37:34,021 INFO misc.py line 119 131400] Train: [98/100][800/800] Data 0.003 (0.010) Batch 0.324 (0.340) Remain 00:09:04 loss: 0.1518 Lr: 0.00001 [2023-12-20 21:37:34,021 INFO misc.py line 136 131400] Train result: loss: 0.1942 [2023-12-20 21:37:34,022 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:37:56,580 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1712 [2023-12-20 21:37:56,654 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1689 [2023-12-20 21:37:56,761 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5073 [2023-12-20 21:37:56,874 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.4061 [2023-12-20 21:37:56,988 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3569 [2023-12-20 21:37:57,092 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.3462 [2023-12-20 21:37:57,183 INFO evaluator.py line 159 131400] Test: [7/78] Loss 1.0330 [2023-12-20 21:37:57,294 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8185 [2023-12-20 21:37:57,377 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2723 [2023-12-20 21:37:57,462 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.2926 [2023-12-20 21:37:57,555 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.4094 [2023-12-20 21:37:57,694 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2708 [2023-12-20 21:37:57,822 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.4879 [2023-12-20 21:37:57,978 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1879 [2023-12-20 21:37:58,074 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1291 [2023-12-20 21:37:58,214 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.6814 [2023-12-20 21:37:58,325 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2894 [2023-12-20 21:37:58,436 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.7139 [2023-12-20 21:37:58,548 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1362 [2023-12-20 21:37:58,628 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.3887 [2023-12-20 21:37:58,750 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.0998 [2023-12-20 21:38:00,374 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1714 [2023-12-20 21:38:00,476 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8460 [2023-12-20 21:38:00,567 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2824 [2023-12-20 21:38:00,697 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9187 [2023-12-20 21:38:00,809 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0815 [2023-12-20 21:38:00,889 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.5904 [2023-12-20 21:38:01,030 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2934 [2023-12-20 21:38:01,176 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0163 [2023-12-20 21:38:01,275 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0552 [2023-12-20 21:38:01,404 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2475 [2023-12-20 21:38:01,545 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.1468 [2023-12-20 21:38:01,662 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6429 [2023-12-20 21:38:01,764 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.5810 [2023-12-20 21:38:01,935 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.2692 [2023-12-20 21:38:02,032 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.4813 [2023-12-20 21:38:02,178 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7305 [2023-12-20 21:38:02,268 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2319 [2023-12-20 21:38:02,351 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5619 [2023-12-20 21:38:02,458 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4190 [2023-12-20 21:38:02,611 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.1075 [2023-12-20 21:38:02,745 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3692 [2023-12-20 21:38:02,849 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2343 [2023-12-20 21:38:02,943 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.4963 [2023-12-20 21:38:03,059 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3428 [2023-12-20 21:38:03,227 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2100 [2023-12-20 21:38:03,332 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5885 [2023-12-20 21:38:03,429 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1934 [2023-12-20 21:38:03,531 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5444 [2023-12-20 21:38:03,626 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2476 [2023-12-20 21:38:03,713 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6393 [2023-12-20 21:38:03,823 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6783 [2023-12-20 21:38:03,970 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6562 [2023-12-20 21:38:04,061 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2647 [2023-12-20 21:38:04,160 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3998 [2023-12-20 21:38:04,252 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0080 [2023-12-20 21:38:04,336 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3393 [2023-12-20 21:38:04,419 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0085 [2023-12-20 21:38:04,513 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9536 [2023-12-20 21:38:04,609 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6769 [2023-12-20 21:38:04,744 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0490 [2023-12-20 21:38:04,838 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6824 [2023-12-20 21:38:04,952 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6495 [2023-12-20 21:38:05,055 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4713 [2023-12-20 21:38:05,147 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.7111 [2023-12-20 21:38:05,302 INFO evaluator.py line 159 131400] Test: [78/78] Loss 0.9298 [2023-12-20 21:38:06,832 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7706/0.8474/0.9223. [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8752/0.9560 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9642/0.9861 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7119/0.8140 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8304/0.8814 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9260/0.9617 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8589/0.9350 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7841/0.8722 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7422/0.8526 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7287/0.8228 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8370/0.9275 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.3949/0.4966 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7234/0.8183 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7036/0.8506 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7724/0.8640 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7173/0.7979 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.7002/0.7467 [2023-12-20 21:38:06,833 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9392/0.9801 [2023-12-20 21:38:06,834 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6891/0.7888 [2023-12-20 21:38:06,834 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8871/0.9233 [2023-12-20 21:38:06,834 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6260/0.6728 [2023-12-20 21:38:06,834 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:38:06,835 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:38:06,835 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:38:10,737 INFO misc.py line 119 131400] Train: [99/100][1/800] Data 0.848 (0.848) Batch 1.139 (1.139) Remain 00:30:20 loss: 0.0899 Lr: 0.00001 [2023-12-20 21:38:11,189 INFO misc.py line 119 131400] Train: [99/100][2/800] Data 0.179 (0.179) Batch 0.470 (0.470) Remain 00:12:30 loss: 0.1418 Lr: 0.00001 [2023-12-20 21:38:11,530 INFO misc.py line 119 131400] Train: [99/100][3/800] Data 0.003 (0.003) Batch 0.341 (0.341) Remain 00:09:03 loss: 0.2875 Lr: 0.00001 [2023-12-20 21:38:11,953 INFO misc.py line 119 131400] Train: [99/100][4/800] Data 0.080 (0.080) Batch 0.423 (0.423) Remain 00:11:15 loss: 0.2910 Lr: 0.00001 [2023-12-20 21:38:12,268 INFO misc.py line 119 131400] Train: [99/100][5/800] Data 0.003 (0.041) Batch 0.315 (0.369) Remain 00:09:48 loss: 0.2974 Lr: 0.00001 [2023-12-20 21:38:12,605 INFO misc.py line 119 131400] Train: [99/100][6/800] Data 0.004 (0.029) Batch 0.336 (0.358) Remain 00:09:30 loss: 0.1480 Lr: 0.00001 [2023-12-20 21:38:12,945 INFO misc.py line 119 131400] Train: [99/100][7/800] Data 0.004 (0.023) Batch 0.309 (0.346) Remain 00:09:10 loss: 0.2610 Lr: 0.00001 [2023-12-20 21:38:13,282 INFO misc.py line 119 131400] Train: [99/100][8/800] Data 0.036 (0.025) Batch 0.366 (0.350) Remain 00:09:16 loss: 0.1307 Lr: 0.00001 [2023-12-20 21:38:13,595 INFO misc.py line 119 131400] Train: [99/100][9/800] Data 0.007 (0.022) Batch 0.315 (0.344) Remain 00:09:07 loss: 0.1707 Lr: 0.00001 [2023-12-20 21:38:13,877 INFO misc.py line 119 131400] Train: [99/100][10/800] Data 0.004 (0.020) Batch 0.282 (0.335) Remain 00:08:53 loss: 0.1421 Lr: 0.00001 [2023-12-20 21:38:14,236 INFO misc.py line 119 131400] Train: [99/100][11/800] Data 0.004 (0.018) Batch 0.359 (0.338) Remain 00:08:57 loss: 0.1319 Lr: 0.00001 [2023-12-20 21:38:14,596 INFO misc.py line 119 131400] Train: [99/100][12/800] Data 0.004 (0.016) Batch 0.361 (0.341) Remain 00:09:01 loss: 0.1430 Lr: 0.00001 [2023-12-20 21:38:14,909 INFO misc.py line 119 131400] Train: [99/100][13/800] Data 0.003 (0.015) Batch 0.312 (0.338) Remain 00:08:56 loss: 0.2261 Lr: 0.00001 [2023-12-20 21:38:15,259 INFO misc.py line 119 131400] Train: [99/100][14/800] Data 0.004 (0.014) Batch 0.351 (0.339) Remain 00:08:57 loss: 0.1403 Lr: 0.00001 [2023-12-20 21:38:15,581 INFO misc.py line 119 131400] Train: [99/100][15/800] Data 0.003 (0.013) Batch 0.322 (0.338) Remain 00:08:55 loss: 0.1449 Lr: 0.00001 [2023-12-20 21:38:15,922 INFO misc.py line 119 131400] Train: [99/100][16/800] Data 0.003 (0.012) Batch 0.339 (0.338) Remain 00:08:54 loss: 0.1202 Lr: 0.00001 [2023-12-20 21:38:16,237 INFO misc.py line 119 131400] Train: [99/100][17/800] Data 0.005 (0.012) Batch 0.317 (0.336) Remain 00:08:52 loss: 0.2249 Lr: 0.00001 [2023-12-20 21:38:16,575 INFO misc.py line 119 131400] Train: [99/100][18/800] Data 0.004 (0.011) Batch 0.335 (0.336) Remain 00:08:51 loss: 0.1854 Lr: 0.00001 [2023-12-20 21:38:16,923 INFO misc.py line 119 131400] Train: [99/100][19/800] Data 0.007 (0.011) Batch 0.349 (0.337) Remain 00:08:52 loss: 0.1727 Lr: 0.00001 [2023-12-20 21:38:17,268 INFO misc.py line 119 131400] Train: [99/100][20/800] Data 0.007 (0.011) Batch 0.346 (0.338) Remain 00:08:53 loss: 0.1936 Lr: 0.00001 [2023-12-20 21:38:17,617 INFO misc.py line 119 131400] Train: [99/100][21/800] Data 0.004 (0.010) Batch 0.349 (0.338) Remain 00:08:53 loss: 0.2414 Lr: 0.00001 [2023-12-20 21:38:17,930 INFO misc.py line 119 131400] Train: [99/100][22/800] Data 0.004 (0.010) Batch 0.314 (0.337) Remain 00:08:51 loss: 0.1560 Lr: 0.00001 [2023-12-20 21:38:18,255 INFO misc.py line 119 131400] Train: [99/100][23/800] Data 0.004 (0.010) Batch 0.323 (0.336) Remain 00:08:50 loss: 0.1258 Lr: 0.00001 [2023-12-20 21:38:18,562 INFO misc.py line 119 131400] Train: [99/100][24/800] Data 0.005 (0.010) Batch 0.307 (0.335) Remain 00:08:47 loss: 0.1829 Lr: 0.00001 [2023-12-20 21:38:18,881 INFO misc.py line 119 131400] Train: [99/100][25/800] Data 0.004 (0.009) Batch 0.321 (0.334) Remain 00:08:46 loss: 0.2283 Lr: 0.00001 [2023-12-20 21:38:19,207 INFO misc.py line 119 131400] Train: [99/100][26/800] Data 0.003 (0.009) Batch 0.323 (0.334) Remain 00:08:45 loss: 0.3048 Lr: 0.00001 [2023-12-20 21:38:19,544 INFO misc.py line 119 131400] Train: [99/100][27/800] Data 0.006 (0.009) Batch 0.339 (0.334) Remain 00:08:45 loss: 0.2023 Lr: 0.00001 [2023-12-20 21:38:19,859 INFO misc.py line 119 131400] Train: [99/100][28/800] Data 0.005 (0.009) Batch 0.316 (0.333) Remain 00:08:43 loss: 0.1317 Lr: 0.00001 [2023-12-20 21:38:20,176 INFO misc.py line 119 131400] Train: [99/100][29/800] Data 0.003 (0.008) Batch 0.316 (0.333) Remain 00:08:42 loss: 0.1181 Lr: 0.00001 [2023-12-20 21:38:20,468 INFO misc.py line 119 131400] Train: [99/100][30/800] Data 0.004 (0.008) Batch 0.292 (0.331) Remain 00:08:39 loss: 0.1098 Lr: 0.00001 [2023-12-20 21:38:20,951 INFO misc.py line 119 131400] Train: [99/100][31/800] Data 0.004 (0.008) Batch 0.481 (0.336) Remain 00:08:47 loss: 0.1275 Lr: 0.00001 [2023-12-20 21:38:21,773 INFO misc.py line 119 131400] Train: [99/100][32/800] Data 0.010 (0.008) Batch 0.821 (0.353) Remain 00:09:13 loss: 0.2414 Lr: 0.00001 [2023-12-20 21:38:22,340 INFO misc.py line 119 131400] Train: [99/100][33/800] Data 0.007 (0.008) Batch 0.570 (0.360) Remain 00:09:24 loss: 0.2488 Lr: 0.00001 [2023-12-20 21:38:22,669 INFO misc.py line 119 131400] Train: [99/100][34/800] Data 0.004 (0.008) Batch 0.329 (0.359) Remain 00:09:22 loss: 0.3288 Lr: 0.00001 [2023-12-20 21:38:22,976 INFO misc.py line 119 131400] Train: [99/100][35/800] Data 0.004 (0.008) Batch 0.307 (0.358) Remain 00:09:19 loss: 0.3557 Lr: 0.00001 [2023-12-20 21:38:23,345 INFO misc.py line 119 131400] Train: [99/100][36/800] Data 0.004 (0.008) Batch 0.369 (0.358) Remain 00:09:19 loss: 0.1878 Lr: 0.00001 [2023-12-20 21:38:23,646 INFO misc.py line 119 131400] Train: [99/100][37/800] Data 0.005 (0.008) Batch 0.301 (0.356) Remain 00:09:16 loss: 0.1642 Lr: 0.00001 [2023-12-20 21:38:23,962 INFO misc.py line 119 131400] Train: [99/100][38/800] Data 0.004 (0.008) Batch 0.316 (0.355) Remain 00:09:14 loss: 0.1602 Lr: 0.00001 [2023-12-20 21:38:24,283 INFO misc.py line 119 131400] Train: [99/100][39/800] Data 0.003 (0.007) Batch 0.321 (0.354) Remain 00:09:12 loss: 0.1207 Lr: 0.00001 [2023-12-20 21:38:24,610 INFO misc.py line 119 131400] Train: [99/100][40/800] Data 0.004 (0.007) Batch 0.324 (0.353) Remain 00:09:11 loss: 0.2190 Lr: 0.00001 [2023-12-20 21:38:24,906 INFO misc.py line 119 131400] Train: [99/100][41/800] Data 0.006 (0.007) Batch 0.300 (0.352) Remain 00:09:08 loss: 0.2042 Lr: 0.00001 [2023-12-20 21:38:25,206 INFO misc.py line 119 131400] Train: [99/100][42/800] Data 0.002 (0.007) Batch 0.299 (0.351) Remain 00:09:06 loss: 0.2910 Lr: 0.00001 [2023-12-20 21:38:25,547 INFO misc.py line 119 131400] Train: [99/100][43/800] Data 0.003 (0.007) Batch 0.341 (0.350) Remain 00:09:05 loss: 0.1364 Lr: 0.00001 [2023-12-20 21:38:25,884 INFO misc.py line 119 131400] Train: [99/100][44/800] Data 0.004 (0.007) Batch 0.337 (0.350) Remain 00:09:04 loss: 0.2874 Lr: 0.00001 [2023-12-20 21:38:26,219 INFO misc.py line 119 131400] Train: [99/100][45/800] Data 0.003 (0.007) Batch 0.335 (0.350) Remain 00:09:03 loss: 0.2133 Lr: 0.00001 [2023-12-20 21:38:26,558 INFO misc.py line 119 131400] Train: [99/100][46/800] Data 0.003 (0.007) Batch 0.339 (0.349) Remain 00:09:03 loss: 0.1545 Lr: 0.00001 [2023-12-20 21:38:26,890 INFO misc.py line 119 131400] Train: [99/100][47/800] Data 0.003 (0.007) Batch 0.332 (0.349) Remain 00:09:02 loss: 0.2367 Lr: 0.00001 [2023-12-20 21:38:27,244 INFO misc.py line 119 131400] Train: [99/100][48/800] Data 0.003 (0.007) Batch 0.354 (0.349) Remain 00:09:01 loss: 0.1571 Lr: 0.00001 [2023-12-20 21:38:27,575 INFO misc.py line 119 131400] Train: [99/100][49/800] Data 0.004 (0.007) Batch 0.332 (0.349) Remain 00:09:01 loss: 0.2035 Lr: 0.00001 [2023-12-20 21:38:27,902 INFO misc.py line 119 131400] Train: [99/100][50/800] Data 0.004 (0.007) Batch 0.327 (0.348) Remain 00:08:59 loss: 0.1769 Lr: 0.00001 [2023-12-20 21:38:28,203 INFO misc.py line 119 131400] Train: [99/100][51/800] Data 0.003 (0.006) Batch 0.301 (0.347) Remain 00:08:58 loss: 0.3008 Lr: 0.00001 [2023-12-20 21:38:28,543 INFO misc.py line 119 131400] Train: [99/100][52/800] Data 0.003 (0.006) Batch 0.341 (0.347) Remain 00:08:57 loss: 0.1748 Lr: 0.00001 [2023-12-20 21:38:28,906 INFO misc.py line 119 131400] Train: [99/100][53/800] Data 0.004 (0.006) Batch 0.362 (0.348) Remain 00:08:57 loss: 0.1339 Lr: 0.00001 [2023-12-20 21:38:29,263 INFO misc.py line 119 131400] Train: [99/100][54/800] Data 0.004 (0.006) Batch 0.357 (0.348) Remain 00:08:57 loss: 0.2279 Lr: 0.00001 [2023-12-20 21:38:29,608 INFO misc.py line 119 131400] Train: [99/100][55/800] Data 0.004 (0.006) Batch 0.346 (0.348) Remain 00:08:57 loss: 0.1729 Lr: 0.00001 [2023-12-20 21:38:29,909 INFO misc.py line 119 131400] Train: [99/100][56/800] Data 0.003 (0.006) Batch 0.300 (0.347) Remain 00:08:55 loss: 0.1821 Lr: 0.00001 [2023-12-20 21:38:30,247 INFO misc.py line 119 131400] Train: [99/100][57/800] Data 0.004 (0.006) Batch 0.339 (0.347) Remain 00:08:54 loss: 0.2259 Lr: 0.00001 [2023-12-20 21:38:30,559 INFO misc.py line 119 131400] Train: [99/100][58/800] Data 0.002 (0.006) Batch 0.311 (0.346) Remain 00:08:53 loss: 0.1765 Lr: 0.00001 [2023-12-20 21:38:30,896 INFO misc.py line 119 131400] Train: [99/100][59/800] Data 0.004 (0.006) Batch 0.337 (0.346) Remain 00:08:52 loss: 0.1929 Lr: 0.00001 [2023-12-20 21:38:31,229 INFO misc.py line 119 131400] Train: [99/100][60/800] Data 0.004 (0.006) Batch 0.333 (0.346) Remain 00:08:52 loss: 0.1733 Lr: 0.00001 [2023-12-20 21:38:31,541 INFO misc.py line 119 131400] Train: [99/100][61/800] Data 0.004 (0.006) Batch 0.310 (0.345) Remain 00:08:50 loss: 0.1206 Lr: 0.00001 [2023-12-20 21:38:31,867 INFO misc.py line 119 131400] Train: [99/100][62/800] Data 0.005 (0.006) Batch 0.328 (0.345) Remain 00:08:50 loss: 0.1355 Lr: 0.00001 [2023-12-20 21:38:32,221 INFO misc.py line 119 131400] Train: [99/100][63/800] Data 0.004 (0.006) Batch 0.354 (0.345) Remain 00:08:50 loss: 0.2943 Lr: 0.00001 [2023-12-20 21:38:32,658 INFO misc.py line 119 131400] Train: [99/100][64/800] Data 0.003 (0.006) Batch 0.438 (0.346) Remain 00:08:52 loss: 0.2724 Lr: 0.00001 [2023-12-20 21:38:32,990 INFO misc.py line 119 131400] Train: [99/100][65/800] Data 0.002 (0.006) Batch 0.331 (0.346) Remain 00:08:51 loss: 0.2524 Lr: 0.00001 [2023-12-20 21:38:33,296 INFO misc.py line 119 131400] Train: 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[2023-12-20 21:42:29,890 INFO misc.py line 119 131400] Train: [99/100][776/800] Data 0.005 (0.004) Batch 0.365 (0.334) Remain 00:04:35 loss: 0.1406 Lr: 0.00000 [2023-12-20 21:42:30,218 INFO misc.py line 119 131400] Train: [99/100][777/800] Data 0.004 (0.004) Batch 0.328 (0.334) Remain 00:04:35 loss: 0.0846 Lr: 0.00000 [2023-12-20 21:42:30,566 INFO misc.py line 119 131400] Train: [99/100][778/800] Data 0.004 (0.004) Batch 0.347 (0.334) Remain 00:04:34 loss: 0.2187 Lr: 0.00000 [2023-12-20 21:42:30,920 INFO misc.py line 119 131400] Train: [99/100][779/800] Data 0.006 (0.004) Batch 0.354 (0.334) Remain 00:04:34 loss: 0.2919 Lr: 0.00000 [2023-12-20 21:42:31,266 INFO misc.py line 119 131400] Train: [99/100][780/800] Data 0.005 (0.004) Batch 0.345 (0.334) Remain 00:04:34 loss: 0.1590 Lr: 0.00000 [2023-12-20 21:42:31,640 INFO misc.py line 119 131400] Train: [99/100][781/800] Data 0.005 (0.004) Batch 0.369 (0.334) Remain 00:04:33 loss: 0.1459 Lr: 0.00000 [2023-12-20 21:42:32,002 INFO misc.py line 119 131400] Train: [99/100][782/800] Data 0.011 (0.004) Batch 0.369 (0.334) Remain 00:04:33 loss: 0.3161 Lr: 0.00000 [2023-12-20 21:42:32,336 INFO misc.py line 119 131400] Train: [99/100][783/800] Data 0.004 (0.004) Batch 0.333 (0.334) Remain 00:04:33 loss: 0.1470 Lr: 0.00000 [2023-12-20 21:42:32,645 INFO misc.py line 119 131400] Train: [99/100][784/800] Data 0.004 (0.004) Batch 0.310 (0.334) Remain 00:04:32 loss: 0.2389 Lr: 0.00000 [2023-12-20 21:42:32,961 INFO misc.py line 119 131400] Train: [99/100][785/800] Data 0.005 (0.004) Batch 0.318 (0.334) Remain 00:04:32 loss: 0.1102 Lr: 0.00000 [2023-12-20 21:42:33,324 INFO misc.py line 119 131400] Train: [99/100][786/800] Data 0.003 (0.004) Batch 0.361 (0.334) Remain 00:04:32 loss: 0.2675 Lr: 0.00000 [2023-12-20 21:42:33,656 INFO misc.py line 119 131400] Train: [99/100][787/800] Data 0.005 (0.004) Batch 0.333 (0.334) Remain 00:04:31 loss: 0.1249 Lr: 0.00000 [2023-12-20 21:42:33,987 INFO misc.py line 119 131400] Train: 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Batch 0.336 (0.334) Remain 00:04:29 loss: 0.3009 Lr: 0.00000 [2023-12-20 21:42:36,246 INFO misc.py line 119 131400] Train: [99/100][795/800] Data 0.003 (0.004) Batch 0.309 (0.334) Remain 00:04:29 loss: 0.1625 Lr: 0.00000 [2023-12-20 21:42:36,553 INFO misc.py line 119 131400] Train: [99/100][796/800] Data 0.003 (0.004) Batch 0.307 (0.334) Remain 00:04:28 loss: 0.2022 Lr: 0.00000 [2023-12-20 21:42:36,858 INFO misc.py line 119 131400] Train: [99/100][797/800] Data 0.003 (0.004) Batch 0.304 (0.334) Remain 00:04:28 loss: 0.2266 Lr: 0.00000 [2023-12-20 21:42:37,166 INFO misc.py line 119 131400] Train: [99/100][798/800] Data 0.003 (0.004) Batch 0.308 (0.334) Remain 00:04:27 loss: 0.1312 Lr: 0.00000 [2023-12-20 21:42:37,474 INFO misc.py line 119 131400] Train: [99/100][799/800] Data 0.004 (0.004) Batch 0.309 (0.334) Remain 00:04:27 loss: 0.1095 Lr: 0.00000 [2023-12-20 21:42:37,790 INFO misc.py line 119 131400] Train: [99/100][800/800] Data 0.003 (0.004) Batch 0.315 (0.334) Remain 00:04:27 loss: 0.2149 Lr: 0.00000 [2023-12-20 21:42:37,790 INFO misc.py line 136 131400] Train result: loss: 0.1967 [2023-12-20 21:42:37,791 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:42:59,744 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.2161 [2023-12-20 21:43:00,809 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1743 [2023-12-20 21:43:01,131 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.4852 [2023-12-20 21:43:01,443 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3539 [2023-12-20 21:43:01,558 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3572 [2023-12-20 21:43:01,661 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.7315 [2023-12-20 21:43:01,753 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9111 [2023-12-20 21:43:01,863 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.8131 [2023-12-20 21:43:01,943 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2693 [2023-12-20 21:43:02,027 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3407 [2023-12-20 21:43:02,116 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3649 [2023-12-20 21:43:02,252 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2686 [2023-12-20 21:43:02,374 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5012 [2023-12-20 21:43:02,529 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.1967 [2023-12-20 21:43:02,627 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1401 [2023-12-20 21:43:02,769 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7262 [2023-12-20 21:43:02,876 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2835 [2023-12-20 21:43:02,990 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.5806 [2023-12-20 21:43:03,102 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1441 [2023-12-20 21:43:03,177 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4019 [2023-12-20 21:43:03,282 INFO evaluator.py line 159 131400] Test: 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evaluator.py line 159 131400] Test: [33/78] Loss 0.1040 [2023-12-20 21:43:04,866 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1701 [2023-12-20 21:43:04,964 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8459 [2023-12-20 21:43:05,058 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2768 [2023-12-20 21:43:05,187 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.9198 [2023-12-20 21:43:05,297 INFO evaluator.py line 159 131400] Test: [38/78] Loss 0.0808 [2023-12-20 21:43:05,377 INFO evaluator.py line 159 131400] Test: [39/78] Loss 0.6230 [2023-12-20 21:43:05,518 INFO evaluator.py line 159 131400] Test: [40/78] Loss 0.2522 [2023-12-20 21:43:05,664 INFO evaluator.py line 159 131400] Test: [41/78] Loss 0.0165 [2023-12-20 21:43:05,768 INFO evaluator.py line 159 131400] Test: [42/78] Loss 0.0538 [2023-12-20 21:43:05,889 INFO evaluator.py line 159 131400] Test: [43/78] Loss 0.2359 [2023-12-20 21:43:06,038 INFO evaluator.py line 159 131400] Test: [44/78] Loss 1.0555 [2023-12-20 21:43:06,157 INFO evaluator.py line 159 131400] Test: [45/78] Loss 2.6973 [2023-12-20 21:43:06,264 INFO evaluator.py line 159 131400] Test: [46/78] Loss 0.8704 [2023-12-20 21:43:06,434 INFO evaluator.py line 159 131400] Test: [47/78] Loss 0.3154 [2023-12-20 21:43:06,530 INFO evaluator.py line 159 131400] Test: [48/78] Loss 0.5263 [2023-12-20 21:43:06,681 INFO evaluator.py line 159 131400] Test: [49/78] Loss 1.7247 [2023-12-20 21:43:06,774 INFO evaluator.py line 159 131400] Test: [50/78] Loss 1.2310 [2023-12-20 21:43:06,851 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5467 [2023-12-20 21:43:06,955 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.3431 [2023-12-20 21:43:07,101 INFO evaluator.py line 159 131400] Test: [53/78] Loss 0.9414 [2023-12-20 21:43:07,238 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3296 [2023-12-20 21:43:07,339 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.2959 [2023-12-20 21:43:07,427 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6496 [2023-12-20 21:43:07,529 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3707 [2023-12-20 21:43:07,690 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2405 [2023-12-20 21:43:07,786 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5499 [2023-12-20 21:43:07,881 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1927 [2023-12-20 21:43:07,985 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5651 [2023-12-20 21:43:08,075 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2258 [2023-12-20 21:43:08,160 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6291 [2023-12-20 21:43:08,259 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6744 [2023-12-20 21:43:08,386 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6534 [2023-12-20 21:43:08,469 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2172 [2023-12-20 21:43:08,567 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.3746 [2023-12-20 21:43:08,660 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0087 [2023-12-20 21:43:08,741 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3377 [2023-12-20 21:43:08,825 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0089 [2023-12-20 21:43:08,917 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.8274 [2023-12-20 21:43:09,006 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6839 [2023-12-20 21:43:09,139 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0530 [2023-12-20 21:43:09,232 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6839 [2023-12-20 21:43:09,347 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6163 [2023-12-20 21:43:09,448 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4977 [2023-12-20 21:43:09,534 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4207 [2023-12-20 21:43:09,687 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0291 [2023-12-20 21:43:11,067 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7736/0.8477/0.9230. [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8773/0.9575 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9643/0.9862 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.7119/0.8208 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8346/0.8819 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9271/0.9622 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8561/0.9347 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7842/0.8720 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7438/0.8558 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7221/0.8146 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8304/0.9208 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4112/0.5077 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7210/0.8158 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7206/0.8688 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7821/0.8600 [2023-12-20 21:43:11,068 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7332/0.7816 [2023-12-20 21:43:11,069 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6984/0.7461 [2023-12-20 21:43:11,069 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9408/0.9811 [2023-12-20 21:43:11,069 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6908/0.7875 [2023-12-20 21:43:11,069 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8960/0.9256 [2023-12-20 21:43:11,069 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6257/0.6735 [2023-12-20 21:43:11,069 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:43:11,070 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:43:11,071 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:43:15,304 INFO misc.py line 119 131400] Train: [100/100][1/800] Data 1.338 (1.338) Batch 1.684 (1.684) Remain 00:22:25 loss: 0.2931 Lr: 0.00000 [2023-12-20 21:43:15,615 INFO misc.py line 119 131400] Train: [100/100][2/800] Data 0.011 (0.011) Batch 0.317 (0.317) Remain 00:04:12 loss: 0.2641 Lr: 0.00000 [2023-12-20 21:43:15,969 INFO misc.py line 119 131400] Train: [100/100][3/800] Data 0.004 (0.004) Batch 0.353 (0.353) Remain 00:04:41 loss: 0.1592 Lr: 0.00000 [2023-12-20 21:43:16,288 INFO misc.py line 119 131400] Train: [100/100][4/800] Data 0.005 (0.005) Batch 0.319 (0.319) Remain 00:04:14 loss: 0.1366 Lr: 0.00000 [2023-12-20 21:43:16,606 INFO misc.py line 119 131400] Train: [100/100][5/800] Data 0.004 (0.005) Batch 0.318 (0.319) Remain 00:04:13 loss: 0.1770 Lr: 0.00000 [2023-12-20 21:43:16,948 INFO misc.py line 119 131400] Train: [100/100][6/800] Data 0.003 (0.004) Batch 0.343 (0.327) Remain 00:04:19 loss: 0.2630 Lr: 0.00000 [2023-12-20 21:43:17,296 INFO misc.py line 119 131400] Train: [100/100][7/800] Data 0.003 (0.004) Batch 0.346 (0.332) Remain 00:04:23 loss: 0.2106 Lr: 0.00000 [2023-12-20 21:43:17,623 INFO misc.py line 119 131400] Train: [100/100][8/800] Data 0.006 (0.004) Batch 0.329 (0.331) Remain 00:04:22 loss: 0.2213 Lr: 0.00000 [2023-12-20 21:43:17,961 INFO misc.py line 119 131400] Train: [100/100][9/800] Data 0.003 (0.004) Batch 0.337 (0.332) Remain 00:04:22 loss: 0.1500 Lr: 0.00000 [2023-12-20 21:43:18,313 INFO misc.py line 119 131400] Train: [100/100][10/800] Data 0.004 (0.004) Batch 0.350 (0.335) Remain 00:04:24 loss: 0.1488 Lr: 0.00000 [2023-12-20 21:43:18,671 INFO misc.py line 119 131400] Train: [100/100][11/800] Data 0.006 (0.004) Batch 0.359 (0.338) Remain 00:04:26 loss: 0.1639 Lr: 0.00000 [2023-12-20 21:43:19,034 INFO misc.py line 119 131400] Train: [100/100][12/800] Data 0.005 (0.004) Batch 0.364 (0.341) Remain 00:04:28 loss: 0.1546 Lr: 0.00000 [2023-12-20 21:43:19,390 INFO misc.py line 119 131400] Train: [100/100][13/800] Data 0.004 (0.004) Batch 0.351 (0.342) Remain 00:04:28 loss: 0.1692 Lr: 0.00000 [2023-12-20 21:43:19,750 INFO misc.py line 119 131400] Train: [100/100][14/800] Data 0.010 (0.005) Batch 0.365 (0.344) Remain 00:04:30 loss: 0.2116 Lr: 0.00000 [2023-12-20 21:43:20,093 INFO misc.py line 119 131400] Train: [100/100][15/800] Data 0.003 (0.005) Batch 0.343 (0.344) Remain 00:04:29 loss: 0.1717 Lr: 0.00000 [2023-12-20 21:43:20,411 INFO misc.py line 119 131400] Train: [100/100][16/800] Data 0.004 (0.005) Batch 0.318 (0.342) Remain 00:04:27 loss: 0.1760 Lr: 0.00000 [2023-12-20 21:43:20,762 INFO misc.py line 119 131400] Train: [100/100][17/800] Data 0.005 (0.005) Batch 0.351 (0.342) Remain 00:04:28 loss: 0.2715 Lr: 0.00000 [2023-12-20 21:43:21,114 INFO misc.py line 119 131400] Train: [100/100][18/800] Data 0.004 (0.005) Batch 0.351 (0.343) Remain 00:04:28 loss: 0.1360 Lr: 0.00000 [2023-12-20 21:43:21,432 INFO misc.py line 119 131400] Train: [100/100][19/800] Data 0.006 (0.005) Batch 0.314 (0.341) Remain 00:04:26 loss: 0.1418 Lr: 0.00000 [2023-12-20 21:43:21,779 INFO misc.py line 119 131400] Train: [100/100][20/800] Data 0.009 (0.005) Batch 0.352 (0.342) Remain 00:04:26 loss: 0.2458 Lr: 0.00000 [2023-12-20 21:43:22,134 INFO misc.py line 119 131400] Train: [100/100][21/800] Data 0.003 (0.005) Batch 0.355 (0.343) Remain 00:04:26 loss: 0.1045 Lr: 0.00000 [2023-12-20 21:43:22,500 INFO misc.py line 119 131400] Train: [100/100][22/800] Data 0.003 (0.005) Batch 0.365 (0.344) Remain 00:04:27 loss: 0.2862 Lr: 0.00000 [2023-12-20 21:43:22,879 INFO misc.py line 119 131400] Train: [100/100][23/800] Data 0.004 (0.005) Batch 0.380 (0.346) Remain 00:04:28 loss: 0.2635 Lr: 0.00000 [2023-12-20 21:43:23,232 INFO misc.py line 119 131400] Train: [100/100][24/800] Data 0.003 (0.005) Batch 0.353 (0.346) Remain 00:04:28 loss: 0.1731 Lr: 0.00000 [2023-12-20 21:43:23,576 INFO misc.py line 119 131400] Train: [100/100][25/800] Data 0.003 (0.005) Batch 0.331 (0.345) Remain 00:04:27 loss: 0.2627 Lr: 0.00000 [2023-12-20 21:43:23,914 INFO misc.py line 119 131400] Train: [100/100][26/800] Data 0.017 (0.005) Batch 0.351 (0.345) Remain 00:04:27 loss: 0.1390 Lr: 0.00000 [2023-12-20 21:43:24,228 INFO misc.py line 119 131400] Train: [100/100][27/800] Data 0.004 (0.005) Batch 0.314 (0.344) Remain 00:04:26 loss: 0.2554 Lr: 0.00000 [2023-12-20 21:43:24,563 INFO misc.py line 119 131400] Train: [100/100][28/800] Data 0.004 (0.005) Batch 0.335 (0.344) Remain 00:04:25 loss: 0.2306 Lr: 0.00000 [2023-12-20 21:43:24,909 INFO misc.py line 119 131400] Train: [100/100][29/800] Data 0.006 (0.005) Batch 0.346 (0.344) Remain 00:04:25 loss: 0.3709 Lr: 0.00000 [2023-12-20 21:43:25,247 INFO misc.py line 119 131400] Train: [100/100][30/800] Data 0.004 (0.005) Batch 0.339 (0.344) Remain 00:04:24 loss: 0.0872 Lr: 0.00000 [2023-12-20 21:43:25,586 INFO misc.py line 119 131400] Train: [100/100][31/800] Data 0.003 (0.005) Batch 0.338 (0.343) Remain 00:04:24 loss: 0.1396 Lr: 0.00000 [2023-12-20 21:43:25,943 INFO misc.py line 119 131400] Train: [100/100][32/800] Data 0.005 (0.005) Batch 0.358 (0.344) Remain 00:04:24 loss: 0.2134 Lr: 0.00000 [2023-12-20 21:43:26,271 INFO misc.py line 119 131400] Train: [100/100][33/800] Data 0.003 (0.005) Batch 0.328 (0.343) Remain 00:04:23 loss: 0.2428 Lr: 0.00000 [2023-12-20 21:43:26,605 INFO misc.py line 119 131400] Train: [100/100][34/800] Data 0.003 (0.005) Batch 0.334 (0.343) Remain 00:04:22 loss: 0.3196 Lr: 0.00000 [2023-12-20 21:43:26,915 INFO misc.py line 119 131400] Train: [100/100][35/800] Data 0.004 (0.005) Batch 0.310 (0.342) Remain 00:04:21 loss: 0.1654 Lr: 0.00000 [2023-12-20 21:43:27,205 INFO misc.py line 119 131400] Train: [100/100][36/800] Data 0.005 (0.005) Batch 0.291 (0.341) Remain 00:04:20 loss: 0.1888 Lr: 0.00000 [2023-12-20 21:43:27,496 INFO misc.py line 119 131400] Train: [100/100][37/800] Data 0.003 (0.005) Batch 0.289 (0.339) Remain 00:04:18 loss: 0.2283 Lr: 0.00000 [2023-12-20 21:43:27,801 INFO misc.py line 119 131400] Train: [100/100][38/800] Data 0.004 (0.005) Batch 0.300 (0.338) Remain 00:04:17 loss: 0.1446 Lr: 0.00000 [2023-12-20 21:43:28,131 INFO misc.py line 119 131400] Train: [100/100][39/800] Data 0.010 (0.005) Batch 0.336 (0.338) Remain 00:04:17 loss: 0.2240 Lr: 0.00000 [2023-12-20 21:43:28,484 INFO misc.py line 119 131400] Train: [100/100][40/800] Data 0.003 (0.005) Batch 0.354 (0.338) Remain 00:04:17 loss: 0.2023 Lr: 0.00000 [2023-12-20 21:43:28,838 INFO misc.py line 119 131400] Train: [100/100][41/800] Data 0.003 (0.005) Batch 0.354 (0.339) Remain 00:04:17 loss: 0.2273 Lr: 0.00000 [2023-12-20 21:43:29,176 INFO misc.py line 119 131400] Train: [100/100][42/800] Data 0.003 (0.005) Batch 0.337 (0.339) Remain 00:04:16 loss: 0.2211 Lr: 0.00000 [2023-12-20 21:43:29,506 INFO misc.py line 119 131400] Train: [100/100][43/800] Data 0.004 (0.005) Batch 0.330 (0.338) Remain 00:04:16 loss: 0.1301 Lr: 0.00000 [2023-12-20 21:43:29,838 INFO misc.py line 119 131400] Train: [100/100][44/800] Data 0.004 (0.005) Batch 0.329 (0.338) Remain 00:04:15 loss: 0.2974 Lr: 0.00000 [2023-12-20 21:43:30,188 INFO misc.py line 119 131400] Train: [100/100][45/800] Data 0.007 (0.005) Batch 0.351 (0.339) Remain 00:04:15 loss: 0.1985 Lr: 0.00000 [2023-12-20 21:43:30,481 INFO misc.py line 119 131400] Train: [100/100][46/800] Data 0.005 (0.005) Batch 0.293 (0.337) Remain 00:04:14 loss: 0.1796 Lr: 0.00000 [2023-12-20 21:43:30,819 INFO misc.py line 119 131400] Train: [100/100][47/800] Data 0.007 (0.005) Batch 0.340 (0.338) Remain 00:04:14 loss: 0.1709 Lr: 0.00000 [2023-12-20 21:43:31,135 INFO misc.py line 119 131400] Train: [100/100][48/800] Data 0.003 (0.005) Batch 0.316 (0.337) Remain 00:04:13 loss: 0.1833 Lr: 0.00000 [2023-12-20 21:43:31,463 INFO misc.py line 119 131400] Train: [100/100][49/800] Data 0.003 (0.005) Batch 0.327 (0.337) Remain 00:04:12 loss: 0.2543 Lr: 0.00000 [2023-12-20 21:43:31,823 INFO misc.py line 119 131400] Train: [100/100][50/800] Data 0.004 (0.005) Batch 0.361 (0.337) Remain 00:04:13 loss: 0.2047 Lr: 0.00000 [2023-12-20 21:43:32,154 INFO misc.py line 119 131400] Train: [100/100][51/800] Data 0.003 (0.005) Batch 0.330 (0.337) Remain 00:04:12 loss: 0.2967 Lr: 0.00000 [2023-12-20 21:43:32,457 INFO misc.py line 119 131400] Train: [100/100][52/800] Data 0.003 (0.005) Batch 0.303 (0.337) Remain 00:04:11 loss: 0.1210 Lr: 0.00000 [2023-12-20 21:43:32,747 INFO misc.py line 119 131400] Train: [100/100][53/800] Data 0.003 (0.005) Batch 0.290 (0.336) Remain 00:04:10 loss: 0.1264 Lr: 0.00000 [2023-12-20 21:43:33,051 INFO misc.py line 119 131400] Train: [100/100][54/800] Data 0.004 (0.005) Batch 0.304 (0.335) Remain 00:04:09 loss: 0.1326 Lr: 0.00000 [2023-12-20 21:43:33,383 INFO misc.py line 119 131400] Train: [100/100][55/800] Data 0.004 (0.005) Batch 0.331 (0.335) Remain 00:04:09 loss: 0.2371 Lr: 0.00000 [2023-12-20 21:43:33,716 INFO misc.py line 119 131400] Train: [100/100][56/800] Data 0.004 (0.005) Batch 0.333 (0.335) Remain 00:04:09 loss: 0.3484 Lr: 0.00000 [2023-12-20 21:43:34,057 INFO misc.py line 119 131400] Train: [100/100][57/800] Data 0.004 (0.005) Batch 0.342 (0.335) Remain 00:04:08 loss: 0.2839 Lr: 0.00000 [2023-12-20 21:43:34,369 INFO misc.py line 119 131400] Train: [100/100][58/800] Data 0.003 (0.005) Batch 0.312 (0.335) Remain 00:04:08 loss: 0.1190 Lr: 0.00000 [2023-12-20 21:43:34,684 INFO misc.py line 119 131400] Train: [100/100][59/800] Data 0.003 (0.005) Batch 0.313 (0.334) Remain 00:04:07 loss: 0.1222 Lr: 0.00000 [2023-12-20 21:43:34,988 INFO misc.py line 119 131400] Train: [100/100][60/800] Data 0.004 (0.005) Batch 0.304 (0.334) Remain 00:04:06 loss: 0.1298 Lr: 0.00000 [2023-12-20 21:43:35,295 INFO misc.py line 119 131400] Train: [100/100][61/800] Data 0.004 (0.005) Batch 0.308 (0.333) Remain 00:04:06 loss: 0.1138 Lr: 0.00000 [2023-12-20 21:43:35,657 INFO misc.py line 119 131400] Train: [100/100][62/800] Data 0.003 (0.004) Batch 0.362 (0.334) Remain 00:04:06 loss: 0.1303 Lr: 0.00000 [2023-12-20 21:43:36,010 INFO misc.py line 119 131400] Train: [100/100][63/800] Data 0.003 (0.004) Batch 0.352 (0.334) Remain 00:04:06 loss: 0.2448 Lr: 0.00000 [2023-12-20 21:43:36,345 INFO misc.py line 119 131400] Train: [100/100][64/800] Data 0.004 (0.004) Batch 0.335 (0.334) Remain 00:04:05 loss: 0.2763 Lr: 0.00000 [2023-12-20 21:43:36,705 INFO misc.py line 119 131400] Train: [100/100][65/800] Data 0.004 (0.004) Batch 0.360 (0.334) Remain 00:04:05 loss: 0.1205 Lr: 0.00000 [2023-12-20 21:43:37,058 INFO misc.py line 119 131400] Train: [100/100][66/800] Data 0.004 (0.004) Batch 0.353 (0.335) Remain 00:04:05 loss: 0.1729 Lr: 0.00000 [2023-12-20 21:43:37,381 INFO misc.py line 119 131400] Train: [100/100][67/800] Data 0.004 (0.004) Batch 0.318 (0.335) Remain 00:04:05 loss: 0.2567 Lr: 0.00000 [2023-12-20 21:43:37,660 INFO misc.py line 119 131400] Train: [100/100][68/800] Data 0.009 (0.005) Batch 0.283 (0.334) Remain 00:04:04 loss: 0.2150 Lr: 0.00000 [2023-12-20 21:43:37,982 INFO misc.py line 119 131400] Train: [100/100][69/800] Data 0.005 (0.005) Batch 0.324 (0.334) Remain 00:04:03 loss: 0.1658 Lr: 0.00000 [2023-12-20 21:43:38,321 INFO misc.py line 119 131400] Train: [100/100][70/800] Data 0.003 (0.004) Batch 0.324 (0.333) Remain 00:04:03 loss: 0.2070 Lr: 0.00000 [2023-12-20 21:43:38,629 INFO misc.py line 119 131400] Train: [100/100][71/800] Data 0.018 (0.005) Batch 0.323 (0.333) Remain 00:04:02 loss: 0.1996 Lr: 0.00000 [2023-12-20 21:43:38,933 INFO misc.py 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21:47:24,435 INFO misc.py line 119 131400] Train: [100/100][748/800] Data 0.004 (0.004) Batch 0.429 (0.334) Remain 00:00:17 loss: 0.2667 Lr: 0.00000 [2023-12-20 21:47:24,769 INFO misc.py line 119 131400] Train: [100/100][749/800] Data 0.004 (0.004) Batch 0.334 (0.334) Remain 00:00:17 loss: 0.2748 Lr: 0.00000 [2023-12-20 21:47:25,083 INFO misc.py line 119 131400] Train: [100/100][750/800] Data 0.004 (0.004) Batch 0.313 (0.333) Remain 00:00:16 loss: 0.2371 Lr: 0.00000 [2023-12-20 21:47:25,419 INFO misc.py line 119 131400] Train: [100/100][751/800] Data 0.005 (0.004) Batch 0.337 (0.333) Remain 00:00:16 loss: 0.2881 Lr: 0.00000 [2023-12-20 21:47:25,713 INFO misc.py line 119 131400] Train: [100/100][752/800] Data 0.004 (0.004) Batch 0.295 (0.333) Remain 00:00:16 loss: 0.2689 Lr: 0.00000 [2023-12-20 21:47:26,053 INFO misc.py line 119 131400] Train: [100/100][753/800] Data 0.003 (0.004) Batch 0.339 (0.333) Remain 00:00:15 loss: 0.3005 Lr: 0.00000 [2023-12-20 21:47:26,337 INFO misc.py line 119 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Data 0.003 (0.004) Batch 0.296 (0.333) Remain 00:00:13 loss: 0.1099 Lr: 0.00000 [2023-12-20 21:47:28,546 INFO misc.py line 119 131400] Train: [100/100][761/800] Data 0.004 (0.004) Batch 0.318 (0.333) Remain 00:00:12 loss: 0.2772 Lr: 0.00000 [2023-12-20 21:47:28,849 INFO misc.py line 119 131400] Train: [100/100][762/800] Data 0.004 (0.004) Batch 0.303 (0.333) Remain 00:00:12 loss: 0.2122 Lr: 0.00000 [2023-12-20 21:47:29,206 INFO misc.py line 119 131400] Train: [100/100][763/800] Data 0.003 (0.004) Batch 0.357 (0.333) Remain 00:00:12 loss: 0.2256 Lr: 0.00000 [2023-12-20 21:47:29,514 INFO misc.py line 119 131400] Train: [100/100][764/800] Data 0.004 (0.004) Batch 0.308 (0.333) Remain 00:00:11 loss: 0.1912 Lr: 0.00000 [2023-12-20 21:47:29,854 INFO misc.py line 119 131400] Train: [100/100][765/800] Data 0.003 (0.004) Batch 0.339 (0.333) Remain 00:00:11 loss: 0.2050 Lr: 0.00000 [2023-12-20 21:47:30,180 INFO misc.py line 119 131400] Train: [100/100][766/800] Data 0.004 (0.004) Batch 0.326 (0.333) Remain 00:00:11 loss: 0.1405 Lr: 0.00000 [2023-12-20 21:47:30,504 INFO misc.py line 119 131400] Train: [100/100][767/800] Data 0.004 (0.004) Batch 0.324 (0.333) Remain 00:00:10 loss: 0.1987 Lr: 0.00000 [2023-12-20 21:47:30,849 INFO misc.py line 119 131400] Train: [100/100][768/800] Data 0.004 (0.004) Batch 0.345 (0.333) Remain 00:00:10 loss: 0.1185 Lr: 0.00000 [2023-12-20 21:47:31,208 INFO misc.py line 119 131400] Train: [100/100][769/800] Data 0.004 (0.004) Batch 0.359 (0.333) Remain 00:00:10 loss: 0.2748 Lr: 0.00000 [2023-12-20 21:47:31,559 INFO misc.py line 119 131400] Train: [100/100][770/800] Data 0.004 (0.004) Batch 0.351 (0.333) Remain 00:00:09 loss: 0.2471 Lr: 0.00000 [2023-12-20 21:47:31,894 INFO misc.py line 119 131400] Train: [100/100][771/800] Data 0.003 (0.004) Batch 0.336 (0.333) Remain 00:00:09 loss: 0.1503 Lr: 0.00000 [2023-12-20 21:47:32,254 INFO misc.py line 119 131400] Train: [100/100][772/800] Data 0.004 (0.004) Batch 0.359 (0.333) Remain 00:00:09 loss: 0.1960 Lr: 0.00000 [2023-12-20 21:47:32,594 INFO misc.py line 119 131400] Train: [100/100][773/800] Data 0.005 (0.004) Batch 0.341 (0.333) Remain 00:00:08 loss: 0.1651 Lr: 0.00000 [2023-12-20 21:47:32,927 INFO misc.py line 119 131400] Train: [100/100][774/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 00:00:08 loss: 0.1588 Lr: 0.00000 [2023-12-20 21:47:33,279 INFO misc.py line 119 131400] Train: [100/100][775/800] Data 0.005 (0.004) Batch 0.353 (0.333) Remain 00:00:08 loss: 0.1952 Lr: 0.00000 [2023-12-20 21:47:33,640 INFO misc.py line 119 131400] Train: [100/100][776/800] Data 0.004 (0.004) Batch 0.361 (0.333) Remain 00:00:08 loss: 0.2487 Lr: 0.00000 [2023-12-20 21:47:33,955 INFO misc.py line 119 131400] Train: [100/100][777/800] Data 0.004 (0.004) Batch 0.315 (0.333) Remain 00:00:07 loss: 0.1965 Lr: 0.00000 [2023-12-20 21:47:34,288 INFO misc.py line 119 131400] Train: [100/100][778/800] Data 0.004 (0.004) Batch 0.333 (0.333) Remain 00:00:07 loss: 0.2608 Lr: 0.00000 [2023-12-20 21:47:34,663 INFO misc.py line 119 131400] Train: [100/100][779/800] Data 0.003 (0.004) Batch 0.375 (0.333) Remain 00:00:07 loss: 0.1805 Lr: 0.00000 [2023-12-20 21:47:35,002 INFO misc.py line 119 131400] Train: [100/100][780/800] Data 0.004 (0.004) Batch 0.338 (0.333) Remain 00:00:06 loss: 0.1747 Lr: 0.00000 [2023-12-20 21:47:35,311 INFO misc.py line 119 131400] Train: [100/100][781/800] Data 0.004 (0.004) Batch 0.309 (0.333) Remain 00:00:06 loss: 0.3097 Lr: 0.00000 [2023-12-20 21:47:35,658 INFO misc.py line 119 131400] Train: [100/100][782/800] Data 0.005 (0.004) Batch 0.347 (0.333) Remain 00:00:06 loss: 0.2145 Lr: 0.00000 [2023-12-20 21:47:35,993 INFO misc.py line 119 131400] Train: [100/100][783/800] Data 0.004 (0.004) Batch 0.335 (0.333) Remain 00:00:05 loss: 0.1368 Lr: 0.00000 [2023-12-20 21:47:36,338 INFO misc.py line 119 131400] Train: [100/100][784/800] Data 0.004 (0.004) Batch 0.345 (0.333) Remain 00:00:05 loss: 0.2729 Lr: 0.00000 [2023-12-20 21:47:36,658 INFO misc.py line 119 131400] Train: [100/100][785/800] Data 0.005 (0.004) Batch 0.319 (0.333) Remain 00:00:05 loss: 0.1079 Lr: 0.00000 [2023-12-20 21:47:37,015 INFO misc.py line 119 131400] Train: [100/100][786/800] Data 0.004 (0.004) Batch 0.358 (0.333) Remain 00:00:04 loss: 0.2714 Lr: 0.00000 [2023-12-20 21:47:37,345 INFO misc.py line 119 131400] Train: [100/100][787/800] Data 0.004 (0.004) Batch 0.331 (0.333) Remain 00:00:04 loss: 0.1869 Lr: 0.00000 [2023-12-20 21:47:37,659 INFO misc.py line 119 131400] Train: [100/100][788/800] Data 0.003 (0.004) Batch 0.314 (0.333) Remain 00:00:04 loss: 0.1896 Lr: 0.00000 [2023-12-20 21:47:37,941 INFO misc.py line 119 131400] Train: [100/100][789/800] Data 0.004 (0.004) Batch 0.282 (0.333) Remain 00:00:03 loss: 0.1786 Lr: 0.00000 [2023-12-20 21:47:38,257 INFO misc.py line 119 131400] Train: [100/100][790/800] Data 0.003 (0.004) Batch 0.316 (0.333) Remain 00:00:03 loss: 0.4049 Lr: 0.00000 [2023-12-20 21:47:38,572 INFO misc.py line 119 131400] Train: [100/100][791/800] Data 0.003 (0.004) Batch 0.314 (0.333) Remain 00:00:02 loss: 0.1527 Lr: 0.00000 [2023-12-20 21:47:38,879 INFO misc.py line 119 131400] Train: [100/100][792/800] Data 0.004 (0.004) Batch 0.307 (0.333) Remain 00:00:02 loss: 0.1441 Lr: 0.00000 [2023-12-20 21:47:39,184 INFO misc.py line 119 131400] Train: [100/100][793/800] Data 0.004 (0.004) Batch 0.307 (0.333) Remain 00:00:02 loss: 0.1358 Lr: 0.00000 [2023-12-20 21:47:39,475 INFO misc.py line 119 131400] Train: [100/100][794/800] Data 0.003 (0.004) Batch 0.290 (0.333) Remain 00:00:01 loss: 0.2020 Lr: 0.00000 [2023-12-20 21:47:39,765 INFO misc.py line 119 131400] Train: [100/100][795/800] Data 0.003 (0.004) Batch 0.289 (0.333) Remain 00:00:01 loss: 0.4803 Lr: 0.00000 [2023-12-20 21:47:40,082 INFO misc.py line 119 131400] Train: [100/100][796/800] Data 0.004 (0.004) Batch 0.318 (0.333) Remain 00:00:01 loss: 0.1476 Lr: 0.00000 [2023-12-20 21:47:40,385 INFO misc.py line 119 131400] Train: [100/100][797/800] Data 0.003 (0.004) Batch 0.304 (0.333) Remain 00:00:00 loss: 0.2547 Lr: 0.00000 [2023-12-20 21:47:40,694 INFO misc.py line 119 131400] Train: [100/100][798/800] Data 0.003 (0.004) Batch 0.309 (0.333) Remain 00:00:00 loss: 0.2280 Lr: 0.00000 [2023-12-20 21:47:41,020 INFO misc.py line 119 131400] Train: [100/100][799/800] Data 0.003 (0.004) Batch 0.325 (0.333) Remain 00:00:00 loss: 0.2349 Lr: 0.00000 [2023-12-20 21:47:41,324 INFO misc.py line 119 131400] Train: [100/100][800/800] Data 0.004 (0.004) Batch 0.305 (0.333) Remain 00:00:00 loss: 0.1347 Lr: 0.00000 [2023-12-20 21:47:41,325 INFO misc.py line 136 131400] Train result: loss: 0.1993 [2023-12-20 21:47:41,325 INFO evaluator.py line 112 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:48:04,465 INFO evaluator.py line 159 131400] Test: [1/78] Loss 0.1570 [2023-12-20 21:48:04,534 INFO evaluator.py line 159 131400] Test: [2/78] Loss 0.1705 [2023-12-20 21:48:04,623 INFO evaluator.py line 159 131400] Test: [3/78] Loss 0.5018 [2023-12-20 21:48:04,730 INFO evaluator.py line 159 131400] Test: [4/78] Loss 1.3172 [2023-12-20 21:48:04,851 INFO evaluator.py line 159 131400] Test: [5/78] Loss 0.3031 [2023-12-20 21:48:04,953 INFO evaluator.py line 159 131400] Test: [6/78] Loss 1.9170 [2023-12-20 21:48:05,045 INFO evaluator.py line 159 131400] Test: [7/78] Loss 0.9437 [2023-12-20 21:48:05,156 INFO evaluator.py line 159 131400] Test: [8/78] Loss 0.9085 [2023-12-20 21:48:05,238 INFO evaluator.py line 159 131400] Test: [9/78] Loss 0.2743 [2023-12-20 21:48:05,330 INFO evaluator.py line 159 131400] Test: [10/78] Loss 0.3144 [2023-12-20 21:48:05,423 INFO evaluator.py line 159 131400] Test: [11/78] Loss 0.3832 [2023-12-20 21:48:05,561 INFO evaluator.py line 159 131400] Test: [12/78] Loss 0.2689 [2023-12-20 21:48:05,681 INFO evaluator.py line 159 131400] Test: [13/78] Loss 1.5878 [2023-12-20 21:48:05,838 INFO evaluator.py line 159 131400] Test: [14/78] Loss 0.2000 [2023-12-20 21:48:05,940 INFO evaluator.py line 159 131400] Test: [15/78] Loss 0.1396 [2023-12-20 21:48:06,075 INFO evaluator.py line 159 131400] Test: [16/78] Loss 0.7580 [2023-12-20 21:48:06,189 INFO evaluator.py line 159 131400] Test: [17/78] Loss 0.2753 [2023-12-20 21:48:06,297 INFO evaluator.py line 159 131400] Test: [18/78] Loss 1.8955 [2023-12-20 21:48:06,416 INFO evaluator.py line 159 131400] Test: [19/78] Loss 0.1544 [2023-12-20 21:48:06,492 INFO evaluator.py line 159 131400] Test: [20/78] Loss 0.4055 [2023-12-20 21:48:06,602 INFO evaluator.py line 159 131400] Test: [21/78] Loss 0.1280 [2023-12-20 21:48:06,761 INFO evaluator.py line 159 131400] Test: [22/78] Loss 0.1301 [2023-12-20 21:48:06,887 INFO evaluator.py line 159 131400] Test: [23/78] Loss 1.9275 [2023-12-20 21:48:07,035 INFO evaluator.py line 159 131400] Test: [24/78] Loss 0.2186 [2023-12-20 21:48:07,182 INFO evaluator.py line 159 131400] Test: [25/78] Loss 0.1770 [2023-12-20 21:48:07,271 INFO evaluator.py line 159 131400] Test: [26/78] Loss 0.6900 [2023-12-20 21:48:07,438 INFO evaluator.py line 159 131400] Test: [27/78] Loss 1.5463 [2023-12-20 21:48:07,565 INFO evaluator.py line 159 131400] Test: [28/78] Loss 0.5372 [2023-12-20 21:48:07,664 INFO evaluator.py line 159 131400] Test: [29/78] Loss 0.4698 [2023-12-20 21:48:07,817 INFO evaluator.py line 159 131400] Test: [30/78] Loss 0.8116 [2023-12-20 21:48:07,926 INFO evaluator.py line 159 131400] Test: [31/78] Loss 0.4749 [2023-12-20 21:48:08,048 INFO evaluator.py line 159 131400] Test: [32/78] Loss 0.3324 [2023-12-20 21:48:08,135 INFO evaluator.py line 159 131400] Test: [33/78] Loss 0.1115 [2023-12-20 21:48:08,210 INFO evaluator.py line 159 131400] Test: [34/78] Loss 0.1692 [2023-12-20 21:48:08,307 INFO evaluator.py line 159 131400] Test: [35/78] Loss 0.8754 [2023-12-20 21:48:08,399 INFO evaluator.py line 159 131400] Test: [36/78] Loss 0.2833 [2023-12-20 21:48:08,528 INFO evaluator.py line 159 131400] Test: [37/78] Loss 0.8922 [2023-12-20 21:48:08,638 INFO evaluator.py line 159 131400] Test: [38/78] Loss 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159 131400] Test: [50/78] Loss 1.2371 [2023-12-20 21:48:10,226 INFO evaluator.py line 159 131400] Test: [51/78] Loss 0.5149 [2023-12-20 21:48:10,337 INFO evaluator.py line 159 131400] Test: [52/78] Loss 1.4083 [2023-12-20 21:48:10,485 INFO evaluator.py line 159 131400] Test: [53/78] Loss 1.2333 [2023-12-20 21:48:10,620 INFO evaluator.py line 159 131400] Test: [54/78] Loss 0.3288 [2023-12-20 21:48:10,722 INFO evaluator.py line 159 131400] Test: [55/78] Loss 1.1636 [2023-12-20 21:48:10,810 INFO evaluator.py line 159 131400] Test: [56/78] Loss 0.6448 [2023-12-20 21:48:10,911 INFO evaluator.py line 159 131400] Test: [57/78] Loss 0.3591 [2023-12-20 21:48:11,074 INFO evaluator.py line 159 131400] Test: [58/78] Loss 0.2339 [2023-12-20 21:48:11,169 INFO evaluator.py line 159 131400] Test: [59/78] Loss 1.5176 [2023-12-20 21:48:11,262 INFO evaluator.py line 159 131400] Test: [60/78] Loss 0.1820 [2023-12-20 21:48:11,359 INFO evaluator.py line 159 131400] Test: [61/78] Loss 0.5184 [2023-12-20 21:48:11,452 INFO evaluator.py line 159 131400] Test: [62/78] Loss 0.2375 [2023-12-20 21:48:11,540 INFO evaluator.py line 159 131400] Test: [63/78] Loss 0.6295 [2023-12-20 21:48:11,642 INFO evaluator.py line 159 131400] Test: [64/78] Loss 0.6819 [2023-12-20 21:48:11,767 INFO evaluator.py line 159 131400] Test: [65/78] Loss 1.6308 [2023-12-20 21:48:11,853 INFO evaluator.py line 159 131400] Test: [66/78] Loss 0.2433 [2023-12-20 21:48:11,954 INFO evaluator.py line 159 131400] Test: [67/78] Loss 0.4561 [2023-12-20 21:48:12,048 INFO evaluator.py line 159 131400] Test: [68/78] Loss 0.0083 [2023-12-20 21:48:12,134 INFO evaluator.py line 159 131400] Test: [69/78] Loss 0.3329 [2023-12-20 21:48:12,219 INFO evaluator.py line 159 131400] Test: [70/78] Loss 0.0085 [2023-12-20 21:48:12,312 INFO evaluator.py line 159 131400] Test: [71/78] Loss 0.9696 [2023-12-20 21:48:12,408 INFO evaluator.py line 159 131400] Test: [72/78] Loss 0.6253 [2023-12-20 21:48:12,540 INFO evaluator.py line 159 131400] Test: [73/78] Loss 0.0467 [2023-12-20 21:48:12,634 INFO evaluator.py line 159 131400] Test: [74/78] Loss 0.6834 [2023-12-20 21:48:12,753 INFO evaluator.py line 159 131400] Test: [75/78] Loss 0.6039 [2023-12-20 21:48:12,856 INFO evaluator.py line 159 131400] Test: [76/78] Loss 0.4742 [2023-12-20 21:48:12,942 INFO evaluator.py line 159 131400] Test: [77/78] Loss 0.4107 [2023-12-20 21:48:13,098 INFO evaluator.py line 159 131400] Test: [78/78] Loss 1.0292 [2023-12-20 21:48:14,332 INFO evaluator.py line 174 131400] Val result: mIoU/mAcc/allAcc 0.7703/0.8471/0.9215. [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_0-wall Result: iou/accuracy 0.8743/0.9545 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_1-floor Result: iou/accuracy 0.9640/0.9863 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_2-cabinet Result: iou/accuracy 0.6956/0.8060 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_3-bed Result: iou/accuracy 0.8298/0.8768 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_4-chair Result: iou/accuracy 0.9253/0.9622 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_5-sofa Result: iou/accuracy 0.8556/0.9379 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_6-table Result: iou/accuracy 0.7832/0.8640 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_7-door Result: iou/accuracy 0.7340/0.8561 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_8-window Result: iou/accuracy 0.7193/0.8117 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_9-bookshelf Result: iou/accuracy 0.8428/0.9277 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_10-picture Result: iou/accuracy 0.4041/0.5101 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_11-counter Result: iou/accuracy 0.7227/0.8164 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_12-desk Result: iou/accuracy 0.7115/0.8623 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_13-curtain Result: iou/accuracy 0.7715/0.8624 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_14-refridgerator Result: iou/accuracy 0.7266/0.7981 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_15-shower curtain Result: iou/accuracy 0.6839/0.7308 [2023-12-20 21:48:14,332 INFO evaluator.py line 180 131400] Class_16-toilet Result: iou/accuracy 0.9382/0.9798 [2023-12-20 21:48:14,333 INFO evaluator.py line 180 131400] Class_17-sink Result: iou/accuracy 0.6949/0.7942 [2023-12-20 21:48:14,333 INFO evaluator.py line 180 131400] Class_18-bathtub Result: iou/accuracy 0.8940/0.9246 [2023-12-20 21:48:14,333 INFO evaluator.py line 180 131400] Class_19-otherfurniture Result: iou/accuracy 0.6344/0.6811 [2023-12-20 21:48:14,333 INFO evaluator.py line 194 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2023-12-20 21:48:14,334 INFO misc.py line 165 131400] Currently Best mIoU: 0.7751 [2023-12-20 21:48:14,334 INFO misc.py line 174 131400] Saving checkpoint to: exp/scannet/semseg-pt-v3m1-0-base/model/model_last.pth [2023-12-20 21:48:16,047 INFO evaluator.py line 199 131400] Best mIoU: 0.7751 [2023-12-20 21:48:16,047 INFO misc.py line 259 131400] >>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:48:16,233 INFO test.py line 41 131400] => Loading config ... [2023-12-20 21:48:16,233 INFO test.py line 53 131400] => Building test dataset & dataloader ... [2023-12-20 21:48:16,239 INFO scannet.py line 72 131400] Totally 312 x 1 samples in val set. [2023-12-20 21:48:16,239 INFO misc.py line 270 131400] => Testing on model_best ... [2023-12-20 21:48:17,389 INFO test.py line 119 131400] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2023-12-20 21:48:54,415 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 0/127 [2023-12-20 21:48:54,537 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 1/127 [2023-12-20 21:48:54,722 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 2/127 [2023-12-20 21:48:54,924 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 3/127 [2023-12-20 21:48:55,116 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 4/127 [2023-12-20 21:48:55,310 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 5/127 [2023-12-20 21:48:55,404 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 6/127 [2023-12-20 21:48:55,493 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 7/127 [2023-12-20 21:48:55,579 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 8/127 [2023-12-20 21:48:55,659 INFO test.py line 196 131400] 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Batch: 125/127 [2023-12-20 21:49:06,452 INFO test.py line 196 131400] Test: 1/78-scene0598_02, Batch: 126/127 [2023-12-20 21:49:06,479 INFO test.py line 230 131400] Test: scene0598_02 [1/78]-176139 Batch 12.206 (12.206) Accuracy 0.9649 (0.1461) mIoU 0.9413 (0.1412) [2023-12-20 21:49:06,802 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 0/131 [2023-12-20 21:49:06,853 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 1/131 [2023-12-20 21:49:06,904 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 2/131 [2023-12-20 21:49:06,958 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 3/131 [2023-12-20 21:49:07,012 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 4/131 [2023-12-20 21:49:07,065 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 5/131 [2023-12-20 21:49:07,123 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 6/131 [2023-12-20 21:49:07,179 INFO test.py line 196 131400] Test: 2/78-scene0494_00, 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131400] Test: 2/78-scene0494_00, Batch: 18/131 [2023-12-20 21:49:07,831 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 19/131 [2023-12-20 21:49:07,881 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 20/131 [2023-12-20 21:49:07,931 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 21/131 [2023-12-20 21:49:07,982 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 22/131 [2023-12-20 21:49:08,031 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 23/131 [2023-12-20 21:49:08,085 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 24/131 [2023-12-20 21:49:08,138 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 25/131 [2023-12-20 21:49:08,199 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 26/131 [2023-12-20 21:49:08,259 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 27/131 [2023-12-20 21:49:08,321 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 28/131 [2023-12-20 21:49:08,371 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 29/131 [2023-12-20 21:49:08,423 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 30/131 [2023-12-20 21:49:08,477 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 31/131 [2023-12-20 21:49:08,533 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 32/131 [2023-12-20 21:49:08,589 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 33/131 [2023-12-20 21:49:08,645 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 34/131 [2023-12-20 21:49:08,700 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 35/131 [2023-12-20 21:49:08,754 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 36/131 [2023-12-20 21:49:08,807 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 37/131 [2023-12-20 21:49:08,857 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 38/131 [2023-12-20 21:49:08,908 INFO test.py line 196 131400] Test: 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INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 50/131 [2023-12-20 21:49:09,539 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 51/131 [2023-12-20 21:49:09,593 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 52/131 [2023-12-20 21:49:09,644 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 53/131 [2023-12-20 21:49:09,698 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 54/131 [2023-12-20 21:49:09,752 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 55/131 [2023-12-20 21:49:09,803 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 56/131 [2023-12-20 21:49:09,853 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 57/131 [2023-12-20 21:49:09,903 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 58/131 [2023-12-20 21:49:09,953 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 59/131 [2023-12-20 21:49:10,003 INFO test.py line 196 131400] Test: 2/78-scene0494_00, 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131400] Test: 2/78-scene0494_00, Batch: 71/131 [2023-12-20 21:49:10,610 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 72/131 [2023-12-20 21:49:10,672 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 73/131 [2023-12-20 21:49:10,728 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 74/131 [2023-12-20 21:49:10,779 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 75/131 [2023-12-20 21:49:10,831 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 76/131 [2023-12-20 21:49:10,883 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 77/131 [2023-12-20 21:49:10,933 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 78/131 [2023-12-20 21:49:10,986 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 79/131 [2023-12-20 21:49:11,039 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 80/131 [2023-12-20 21:49:11,091 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 81/131 [2023-12-20 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INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 103/131 [2023-12-20 21:49:12,336 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 104/131 [2023-12-20 21:49:12,391 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 105/131 [2023-12-20 21:49:12,444 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 106/131 [2023-12-20 21:49:12,498 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 107/131 [2023-12-20 21:49:12,552 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 108/131 [2023-12-20 21:49:12,605 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 109/131 [2023-12-20 21:49:12,658 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 110/131 [2023-12-20 21:49:12,711 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 111/131 [2023-12-20 21:49:12,765 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 112/131 [2023-12-20 21:49:12,818 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 113/131 [2023-12-20 21:49:12,871 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 114/131 [2023-12-20 21:49:12,925 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 115/131 [2023-12-20 21:49:12,978 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 116/131 [2023-12-20 21:49:13,032 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 117/131 [2023-12-20 21:49:13,086 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 118/131 [2023-12-20 21:49:13,139 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 119/131 [2023-12-20 21:49:13,222 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 120/131 [2023-12-20 21:49:13,306 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 121/131 [2023-12-20 21:49:13,391 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 122/131 [2023-12-20 21:49:13,470 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 123/131 [2023-12-20 21:49:13,523 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 124/131 [2023-12-20 21:49:13,577 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 125/131 [2023-12-20 21:49:13,631 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 126/131 [2023-12-20 21:49:13,684 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 127/131 [2023-12-20 21:49:13,738 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 128/131 [2023-12-20 21:49:13,792 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 129/131 [2023-12-20 21:49:13,849 INFO test.py line 196 131400] Test: 2/78-scene0494_00, Batch: 130/131 [2023-12-20 21:49:13,863 INFO test.py line 230 131400] Test: scene0494_00 [2/78]-62048 Batch 7.119 (9.663) Accuracy 0.9821 (0.3825) mIoU 0.9025 (0.3597) [2023-12-20 21:49:14,146 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 0/125 [2023-12-20 21:49:14,261 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 1/125 [2023-12-20 21:49:14,342 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 2/125 [2023-12-20 21:49:14,424 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 3/125 [2023-12-20 21:49:14,509 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 4/125 [2023-12-20 21:49:14,591 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 5/125 [2023-12-20 21:49:14,673 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 6/125 [2023-12-20 21:49:14,754 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 7/125 [2023-12-20 21:49:14,835 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 8/125 [2023-12-20 21:49:14,916 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 9/125 [2023-12-20 21:49:14,996 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 10/125 [2023-12-20 21:49:15,077 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 11/125 [2023-12-20 21:49:15,158 INFO test.py line 196 131400] Test: 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131400] Test: 3/78-scene0474_00, Batch: 97/125 [2023-12-20 21:49:24,196 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 98/125 [2023-12-20 21:49:24,288 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 99/125 [2023-12-20 21:49:24,389 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 100/125 [2023-12-20 21:49:24,479 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 101/125 [2023-12-20 21:49:24,566 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 102/125 [2023-12-20 21:49:24,654 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 103/125 [2023-12-20 21:49:24,746 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 104/125 [2023-12-20 21:49:24,837 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 105/125 [2023-12-20 21:49:24,925 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 106/125 [2023-12-20 21:49:25,015 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 107/125 [2023-12-20 21:49:25,110 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 108/125 [2023-12-20 21:49:25,204 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 109/125 [2023-12-20 21:49:25,290 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 110/125 [2023-12-20 21:49:25,375 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 111/125 [2023-12-20 21:49:25,468 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 112/125 [2023-12-20 21:49:25,559 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 113/125 [2023-12-20 21:49:25,651 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 114/125 [2023-12-20 21:49:25,738 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 115/125 [2023-12-20 21:49:25,826 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 116/125 [2023-12-20 21:49:25,910 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 117/125 [2023-12-20 21:49:25,994 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 118/125 [2023-12-20 21:49:26,080 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 119/125 [2023-12-20 21:49:26,168 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 120/125 [2023-12-20 21:49:26,256 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 121/125 [2023-12-20 21:49:26,342 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 122/125 [2023-12-20 21:49:26,435 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 123/125 [2023-12-20 21:49:26,530 INFO test.py line 196 131400] Test: 3/78-scene0474_00, Batch: 124/125 [2023-12-20 21:49:26,554 INFO test.py line 230 131400] Test: scene0474_00 [3/78]-182281 Batch 12.566 (10.631) Accuracy 0.9355 (0.4768) mIoU 0.8773 (0.4535) [2023-12-20 21:49:26,985 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 0/125 [2023-12-20 21:49:27,088 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 1/125 [2023-12-20 21:49:27,199 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 2/125 [2023-12-20 21:49:27,298 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 3/125 [2023-12-20 21:49:27,392 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 4/125 [2023-12-20 21:49:27,487 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 5/125 [2023-12-20 21:49:27,584 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 6/125 [2023-12-20 21:49:27,682 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 7/125 [2023-12-20 21:49:27,780 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 8/125 [2023-12-20 21:49:27,877 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 9/125 [2023-12-20 21:49:27,972 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 10/125 [2023-12-20 21:49:28,068 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 11/125 [2023-12-20 21:49:28,165 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 12/125 [2023-12-20 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line 196 131400] Test: 4/78-scene0608_02, Batch: 108/125 [2023-12-20 21:49:37,520 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 109/125 [2023-12-20 21:49:37,626 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 110/125 [2023-12-20 21:49:37,727 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 111/125 [2023-12-20 21:49:37,828 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 112/125 [2023-12-20 21:49:37,929 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 113/125 [2023-12-20 21:49:38,029 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 114/125 [2023-12-20 21:49:38,129 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 115/125 [2023-12-20 21:49:38,224 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 116/125 [2023-12-20 21:49:38,319 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 117/125 [2023-12-20 21:49:38,414 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 118/125 [2023-12-20 21:49:38,509 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 119/125 [2023-12-20 21:49:38,604 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 120/125 [2023-12-20 21:49:38,703 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 121/125 [2023-12-20 21:49:38,800 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 122/125 [2023-12-20 21:49:38,896 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 123/125 [2023-12-20 21:49:38,991 INFO test.py line 196 131400] Test: 4/78-scene0608_02, Batch: 124/125 [2023-12-20 21:49:39,020 INFO test.py line 230 131400] Test: scene0608_02 [4/78]-235030 Batch 12.157 (11.012) Accuracy 0.8100 (0.4701) mIoU 0.5317 (0.4392) [2023-12-20 21:49:39,476 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 0/138 [2023-12-20 21:49:39,570 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 1/138 [2023-12-20 21:49:39,663 INFO test.py line 196 131400] Test: 5/78-scene0568_00, 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131400] Test: 5/78-scene0568_00, Batch: 66/138 [2023-12-20 21:49:45,760 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 67/138 [2023-12-20 21:49:45,849 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 68/138 [2023-12-20 21:49:45,938 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 69/138 [2023-12-20 21:49:46,027 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 70/138 [2023-12-20 21:49:46,116 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 71/138 [2023-12-20 21:49:46,205 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 72/138 [2023-12-20 21:49:46,295 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 73/138 [2023-12-20 21:49:46,385 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 74/138 [2023-12-20 21:49:46,475 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 75/138 [2023-12-20 21:49:46,563 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 76/138 [2023-12-20 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INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 98/138 [2023-12-20 21:49:48,811 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 99/138 [2023-12-20 21:49:48,917 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 100/138 [2023-12-20 21:49:49,024 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 101/138 [2023-12-20 21:49:49,123 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 102/138 [2023-12-20 21:49:49,222 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 103/138 [2023-12-20 21:49:49,324 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 104/138 [2023-12-20 21:49:49,430 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 105/138 [2023-12-20 21:49:49,528 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 106/138 [2023-12-20 21:49:49,625 INFO test.py line 196 131400] Test: 5/78-scene0568_00, Batch: 107/138 [2023-12-20 21:49:49,724 INFO test.py line 196 131400] Test: 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131400] Test: 6/78-scene0690_01, Batch: 0/134 [2023-12-20 21:49:53,181 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 1/134 [2023-12-20 21:49:53,245 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 2/134 [2023-12-20 21:49:53,309 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 3/134 [2023-12-20 21:49:53,373 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 4/134 [2023-12-20 21:49:53,438 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 5/134 [2023-12-20 21:49:53,504 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 6/134 [2023-12-20 21:49:53,567 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 7/134 [2023-12-20 21:49:53,631 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 8/134 [2023-12-20 21:49:53,695 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 9/134 [2023-12-20 21:49:53,758 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 10/134 [2023-12-20 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INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 85/134 [2023-12-20 21:49:58,856 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 86/134 [2023-12-20 21:49:58,920 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 87/134 [2023-12-20 21:49:58,987 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 88/134 [2023-12-20 21:49:59,050 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 89/134 [2023-12-20 21:49:59,114 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 90/134 [2023-12-20 21:49:59,177 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 91/134 [2023-12-20 21:49:59,241 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 92/134 [2023-12-20 21:49:59,317 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 93/134 [2023-12-20 21:49:59,387 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 94/134 [2023-12-20 21:49:59,452 INFO test.py line 196 131400] Test: 6/78-scene0690_01, 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line 196 131400] Test: 6/78-scene0690_01, Batch: 106/134 [2023-12-20 21:50:00,289 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 107/134 [2023-12-20 21:50:00,400 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 108/134 [2023-12-20 21:50:00,494 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 109/134 [2023-12-20 21:50:00,583 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 110/134 [2023-12-20 21:50:00,652 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 111/134 [2023-12-20 21:50:00,723 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 112/134 [2023-12-20 21:50:00,788 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 113/134 [2023-12-20 21:50:00,857 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 114/134 [2023-12-20 21:50:00,936 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 115/134 [2023-12-20 21:50:01,004 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 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196 131400] Test: 6/78-scene0690_01, Batch: 127/134 [2023-12-20 21:50:01,819 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 128/134 [2023-12-20 21:50:01,884 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 129/134 [2023-12-20 21:50:01,950 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 130/134 [2023-12-20 21:50:02,015 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 131/134 [2023-12-20 21:50:02,078 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 132/134 [2023-12-20 21:50:02,144 INFO test.py line 196 131400] Test: 6/78-scene0690_01, Batch: 133/134 [2023-12-20 21:50:02,182 INFO test.py line 230 131400] Test: scene0690_01 [6/78]-109935 Batch 9.132 (11.085) Accuracy 0.7808 (0.5383) mIoU 0.5511 (0.4732) [2023-12-20 21:50:02,468 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 0/129 [2023-12-20 21:50:02,555 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 1/129 [2023-12-20 21:50:02,642 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 2/129 [2023-12-20 21:50:02,728 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 3/129 [2023-12-20 21:50:02,814 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 4/129 [2023-12-20 21:50:02,900 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 5/129 [2023-12-20 21:50:02,995 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 6/129 [2023-12-20 21:50:03,081 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 7/129 [2023-12-20 21:50:03,165 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 8/129 [2023-12-20 21:50:03,247 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 9/129 [2023-12-20 21:50:03,333 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 10/129 [2023-12-20 21:50:03,418 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 11/129 [2023-12-20 21:50:03,501 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 12/129 [2023-12-20 21:50:03,583 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 13/129 [2023-12-20 21:50:03,666 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 14/129 [2023-12-20 21:50:03,749 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 15/129 [2023-12-20 21:50:03,832 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 16/129 [2023-12-20 21:50:03,915 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 17/129 [2023-12-20 21:50:03,997 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 18/129 [2023-12-20 21:50:04,082 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 19/129 [2023-12-20 21:50:04,167 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 20/129 [2023-12-20 21:50:04,252 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 21/129 [2023-12-20 21:50:04,335 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 22/129 [2023-12-20 21:50:04,416 INFO test.py line 196 131400] Test: 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INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 34/129 [2023-12-20 21:50:05,449 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 35/129 [2023-12-20 21:50:05,529 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 36/129 [2023-12-20 21:50:05,607 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 37/129 [2023-12-20 21:50:05,685 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 38/129 [2023-12-20 21:50:05,764 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 39/129 [2023-12-20 21:50:05,841 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 40/129 [2023-12-20 21:50:05,919 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 41/129 [2023-12-20 21:50:06,000 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 42/129 [2023-12-20 21:50:06,080 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 43/129 [2023-12-20 21:50:06,160 INFO test.py line 196 131400] Test: 7/78-scene0050_01, 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131400] Test: 7/78-scene0050_01, Batch: 55/129 [2023-12-20 21:50:07,122 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 56/129 [2023-12-20 21:50:07,204 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 57/129 [2023-12-20 21:50:07,284 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 58/129 [2023-12-20 21:50:07,365 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 59/129 [2023-12-20 21:50:07,445 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 60/129 [2023-12-20 21:50:07,524 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 61/129 [2023-12-20 21:50:07,603 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 62/129 [2023-12-20 21:50:07,682 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 63/129 [2023-12-20 21:50:07,761 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 64/129 [2023-12-20 21:50:07,840 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 65/129 [2023-12-20 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INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 87/129 [2023-12-20 21:50:09,726 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 88/129 [2023-12-20 21:50:09,810 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 89/129 [2023-12-20 21:50:09,894 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 90/129 [2023-12-20 21:50:09,979 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 91/129 [2023-12-20 21:50:10,063 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 92/129 [2023-12-20 21:50:10,148 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 93/129 [2023-12-20 21:50:10,233 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 94/129 [2023-12-20 21:50:10,317 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 95/129 [2023-12-20 21:50:10,401 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 96/129 [2023-12-20 21:50:10,485 INFO test.py line 196 131400] Test: 7/78-scene0050_01, 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line 196 131400] Test: 7/78-scene0050_01, Batch: 108/129 [2023-12-20 21:50:11,503 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 109/129 [2023-12-20 21:50:11,588 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 110/129 [2023-12-20 21:50:11,672 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 111/129 [2023-12-20 21:50:11,756 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 112/129 [2023-12-20 21:50:11,841 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 113/129 [2023-12-20 21:50:11,925 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 114/129 [2023-12-20 21:50:12,008 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 115/129 [2023-12-20 21:50:12,093 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 116/129 [2023-12-20 21:50:12,177 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 117/129 [2023-12-20 21:50:12,261 INFO test.py line 196 131400] Test: 7/78-scene0050_01, Batch: 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230 131400] Test: scene0050_01 [7/78]-199034 Batch 10.834 (11.049) Accuracy 0.8439 (0.5319) mIoU 0.4300 (0.4594) [2023-12-20 21:50:13,577 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 0/143 [2023-12-20 21:50:13,670 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 1/143 [2023-12-20 21:50:13,762 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 2/143 [2023-12-20 21:50:13,854 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 3/143 [2023-12-20 21:50:13,946 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 4/143 [2023-12-20 21:50:14,038 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 5/143 [2023-12-20 21:50:14,130 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 6/143 [2023-12-20 21:50:14,222 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 7/143 [2023-12-20 21:50:14,314 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 8/143 [2023-12-20 21:50:14,406 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 9/143 [2023-12-20 21:50:14,497 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 10/143 [2023-12-20 21:50:14,590 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 11/143 [2023-12-20 21:50:14,683 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 12/143 [2023-12-20 21:50:14,775 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 13/143 [2023-12-20 21:50:14,868 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 14/143 [2023-12-20 21:50:14,960 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 15/143 [2023-12-20 21:50:15,053 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 16/143 [2023-12-20 21:50:15,146 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 17/143 [2023-12-20 21:50:15,239 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 18/143 [2023-12-20 21:50:15,331 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 19/143 [2023-12-20 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test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 136/143 [2023-12-20 21:50:26,480 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 137/143 [2023-12-20 21:50:26,585 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 138/143 [2023-12-20 21:50:26,689 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 139/143 [2023-12-20 21:50:26,792 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 140/143 [2023-12-20 21:50:26,895 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 141/143 [2023-12-20 21:50:26,996 INFO test.py line 196 131400] Test: 8/78-scene0629_02, Batch: 142/143 [2023-12-20 21:50:27,016 INFO test.py line 230 131400] Test: scene0629_02 [8/78]-225983 Batch 13.536 (11.360) Accuracy 0.8539 (0.5397) mIoU 0.4926 (0.4630) [2023-12-20 21:50:27,396 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 0/113 [2023-12-20 21:50:27,460 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 1/113 [2023-12-20 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131400] Test: 9/78-scene0338_00, Batch: 76/113 [2023-12-20 21:50:32,673 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 77/113 [2023-12-20 21:50:32,741 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 78/113 [2023-12-20 21:50:32,809 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 79/113 [2023-12-20 21:50:32,876 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 80/113 [2023-12-20 21:50:32,943 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 81/113 [2023-12-20 21:50:33,012 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 82/113 [2023-12-20 21:50:33,080 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 83/113 [2023-12-20 21:50:33,147 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 84/113 [2023-12-20 21:50:33,211 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 85/113 [2023-12-20 21:50:33,284 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 86/113 [2023-12-20 21:50:33,358 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 87/113 [2023-12-20 21:50:33,430 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 88/113 [2023-12-20 21:50:33,504 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 89/113 [2023-12-20 21:50:33,573 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 90/113 [2023-12-20 21:50:33,642 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 91/113 [2023-12-20 21:50:33,721 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 92/113 [2023-12-20 21:50:33,795 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 93/113 [2023-12-20 21:50:33,867 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 94/113 [2023-12-20 21:50:33,943 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 95/113 [2023-12-20 21:50:34,013 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 96/113 [2023-12-20 21:50:34,085 INFO test.py line 196 131400] Test: 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21:50:34,856 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 108/113 [2023-12-20 21:50:34,919 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 109/113 [2023-12-20 21:50:34,983 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 110/113 [2023-12-20 21:50:35,046 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 111/113 [2023-12-20 21:50:35,109 INFO test.py line 196 131400] Test: 9/78-scene0338_00, Batch: 112/113 [2023-12-20 21:50:35,128 INFO test.py line 230 131400] Test: scene0338_00 [9/78]-106339 Batch 7.806 (10.965) Accuracy 0.9563 (0.5923) mIoU 0.6757 (0.5061) [2023-12-20 21:50:35,413 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 0/135 [2023-12-20 21:50:35,494 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 1/135 [2023-12-20 21:50:35,574 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 2/135 [2023-12-20 21:50:35,662 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 3/135 [2023-12-20 21:50:35,743 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 4/135 [2023-12-20 21:50:35,825 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 5/135 [2023-12-20 21:50:35,904 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 6/135 [2023-12-20 21:50:35,988 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 7/135 [2023-12-20 21:50:36,068 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 8/135 [2023-12-20 21:50:36,146 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 9/135 [2023-12-20 21:50:36,225 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 10/135 [2023-12-20 21:50:36,305 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 11/135 [2023-12-20 21:50:36,384 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 12/135 [2023-12-20 21:50:36,462 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 13/135 [2023-12-20 21:50:36,540 INFO test.py line 196 131400] 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[2023-12-20 21:50:37,477 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 25/135 [2023-12-20 21:50:37,556 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 26/135 [2023-12-20 21:50:37,640 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 27/135 [2023-12-20 21:50:37,721 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 28/135 [2023-12-20 21:50:37,800 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 29/135 [2023-12-20 21:50:37,879 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 30/135 [2023-12-20 21:50:37,960 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 31/135 [2023-12-20 21:50:38,039 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 32/135 [2023-12-20 21:50:38,128 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 33/135 [2023-12-20 21:50:38,210 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 34/135 [2023-12-20 21:50:38,295 INFO test.py line 196 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[2023-12-20 21:50:39,148 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 46/135 [2023-12-20 21:50:39,225 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 47/135 [2023-12-20 21:50:39,300 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 48/135 [2023-12-20 21:50:39,380 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 49/135 [2023-12-20 21:50:39,455 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 50/135 [2023-12-20 21:50:39,530 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 51/135 [2023-12-20 21:50:39,605 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 52/135 [2023-12-20 21:50:39,680 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 53/135 [2023-12-20 21:50:39,761 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 54/135 [2023-12-20 21:50:39,843 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 55/135 [2023-12-20 21:50:39,923 INFO test.py line 196 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[2023-12-20 21:50:40,774 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 67/135 [2023-12-20 21:50:40,850 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 68/135 [2023-12-20 21:50:40,926 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 69/135 [2023-12-20 21:50:41,002 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 70/135 [2023-12-20 21:50:41,085 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 71/135 [2023-12-20 21:50:41,165 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 72/135 [2023-12-20 21:50:41,243 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 73/135 [2023-12-20 21:50:41,322 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 74/135 [2023-12-20 21:50:41,397 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 75/135 [2023-12-20 21:50:41,471 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 76/135 [2023-12-20 21:50:41,544 INFO test.py line 196 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[2023-12-20 21:50:42,395 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 88/135 [2023-12-20 21:50:42,476 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 89/135 [2023-12-20 21:50:42,558 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 90/135 [2023-12-20 21:50:42,640 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 91/135 [2023-12-20 21:50:42,720 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 92/135 [2023-12-20 21:50:42,801 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 93/135 [2023-12-20 21:50:42,882 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 94/135 [2023-12-20 21:50:42,962 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 95/135 [2023-12-20 21:50:43,042 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 96/135 [2023-12-20 21:50:43,126 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 97/135 [2023-12-20 21:50:43,212 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 98/135 [2023-12-20 21:50:43,296 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 99/135 [2023-12-20 21:50:43,377 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 100/135 [2023-12-20 21:50:43,457 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 101/135 [2023-12-20 21:50:43,539 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 102/135 [2023-12-20 21:50:43,623 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 103/135 [2023-12-20 21:50:43,709 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 104/135 [2023-12-20 21:50:43,795 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 105/135 [2023-12-20 21:50:43,881 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 106/135 [2023-12-20 21:50:43,963 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 107/135 [2023-12-20 21:50:44,046 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 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10/78-scene0088_02, Batch: 129/135 [2023-12-20 21:50:45,810 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 130/135 [2023-12-20 21:50:45,888 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 131/135 [2023-12-20 21:50:45,972 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 132/135 [2023-12-20 21:50:46,051 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 133/135 [2023-12-20 21:50:46,134 INFO test.py line 196 131400] Test: 10/78-scene0088_02, Batch: 134/135 [2023-12-20 21:50:46,158 INFO test.py line 230 131400] Test: scene0088_02 [10/78]-170685 Batch 10.829 (10.952) Accuracy 0.9596 (0.5982) mIoU 0.7927 (0.5143) [2023-12-20 21:50:46,472 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 0/134 [2023-12-20 21:50:46,542 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 1/134 [2023-12-20 21:50:46,631 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 2/134 [2023-12-20 21:50:46,723 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 3/134 [2023-12-20 21:50:46,813 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 4/134 [2023-12-20 21:50:46,893 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 5/134 [2023-12-20 21:50:46,995 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 6/134 [2023-12-20 21:50:47,173 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 7/134 [2023-12-20 21:50:47,378 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 8/134 [2023-12-20 21:50:47,587 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 9/134 [2023-12-20 21:50:47,811 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 10/134 [2023-12-20 21:50:48,074 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 11/134 [2023-12-20 21:50:48,244 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 12/134 [2023-12-20 21:50:48,417 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 13/134 [2023-12-20 21:50:48,587 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 14/134 [2023-12-20 21:50:48,740 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 15/134 [2023-12-20 21:50:48,911 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 16/134 [2023-12-20 21:50:49,081 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 17/134 [2023-12-20 21:50:49,249 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 18/134 [2023-12-20 21:50:49,427 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 19/134 [2023-12-20 21:50:49,521 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 20/134 [2023-12-20 21:50:49,585 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 21/134 [2023-12-20 21:50:49,647 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 22/134 [2023-12-20 21:50:49,713 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 23/134 [2023-12-20 21:50:49,780 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 24/134 [2023-12-20 21:50:49,842 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 25/134 [2023-12-20 21:50:49,901 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 26/134 [2023-12-20 21:50:49,960 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 27/134 [2023-12-20 21:50:50,018 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 28/134 [2023-12-20 21:50:50,079 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 29/134 [2023-12-20 21:50:50,137 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 30/134 [2023-12-20 21:50:50,195 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 31/134 [2023-12-20 21:50:50,284 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 32/134 [2023-12-20 21:50:50,373 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 33/134 [2023-12-20 21:50:50,449 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 34/134 [2023-12-20 21:50:50,509 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 35/134 [2023-12-20 21:50:50,568 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 36/134 [2023-12-20 21:50:50,628 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 37/134 [2023-12-20 21:50:50,695 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 38/134 [2023-12-20 21:50:50,758 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 39/134 [2023-12-20 21:50:50,819 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 40/134 [2023-12-20 21:50:50,876 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 41/134 [2023-12-20 21:50:50,936 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 42/134 [2023-12-20 21:50:50,992 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 43/134 [2023-12-20 21:50:51,048 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 44/134 [2023-12-20 21:50:51,104 INFO test.py line 196 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131400] Test: 11/78-scene0558_00, Batch: 66/134 [2023-12-20 21:50:52,347 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 67/134 [2023-12-20 21:50:52,401 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 68/134 [2023-12-20 21:50:52,456 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 69/134 [2023-12-20 21:50:52,511 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 70/134 [2023-12-20 21:50:52,566 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 71/134 [2023-12-20 21:50:52,621 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 72/134 [2023-12-20 21:50:52,676 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 73/134 [2023-12-20 21:50:52,731 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 74/134 [2023-12-20 21:50:52,786 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 75/134 [2023-12-20 21:50:52,840 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 76/134 [2023-12-20 21:50:52,895 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 77/134 [2023-12-20 21:50:52,950 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 78/134 [2023-12-20 21:50:53,005 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 79/134 [2023-12-20 21:50:53,060 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 80/134 [2023-12-20 21:50:53,114 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 81/134 [2023-12-20 21:50:53,169 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 82/134 [2023-12-20 21:50:53,224 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 83/134 [2023-12-20 21:50:53,284 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 84/134 [2023-12-20 21:50:53,343 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 85/134 [2023-12-20 21:50:53,402 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 86/134 [2023-12-20 21:50:53,460 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 87/134 [2023-12-20 21:50:53,520 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 88/134 [2023-12-20 21:50:53,579 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 89/134 [2023-12-20 21:50:53,638 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 90/134 [2023-12-20 21:50:53,697 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 91/134 [2023-12-20 21:50:53,756 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 92/134 [2023-12-20 21:50:53,815 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 93/134 [2023-12-20 21:50:53,875 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 94/134 [2023-12-20 21:50:53,935 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 95/134 [2023-12-20 21:50:53,994 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 96/134 [2023-12-20 21:50:54,053 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 97/134 [2023-12-20 21:50:54,113 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 98/134 [2023-12-20 21:50:54,172 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 99/134 [2023-12-20 21:50:54,231 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 100/134 [2023-12-20 21:50:54,290 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 101/134 [2023-12-20 21:50:54,349 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 102/134 [2023-12-20 21:50:54,408 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 103/134 [2023-12-20 21:50:54,467 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 104/134 [2023-12-20 21:50:54,526 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 105/134 [2023-12-20 21:50:54,585 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 106/134 [2023-12-20 21:50:54,645 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 107/134 [2023-12-20 21:50:54,704 INFO test.py line 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Batch: 118/134 [2023-12-20 21:50:55,353 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 119/134 [2023-12-20 21:50:55,412 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 120/134 [2023-12-20 21:50:55,471 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 121/134 [2023-12-20 21:50:55,530 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 122/134 [2023-12-20 21:50:55,589 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 123/134 [2023-12-20 21:50:55,646 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 124/134 [2023-12-20 21:50:55,705 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 125/134 [2023-12-20 21:50:55,763 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 126/134 [2023-12-20 21:50:55,821 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 127/134 [2023-12-20 21:50:55,880 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 128/134 [2023-12-20 21:50:55,939 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 129/134 [2023-12-20 21:50:55,998 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 130/134 [2023-12-20 21:50:56,056 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 131/134 [2023-12-20 21:50:56,115 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 132/134 [2023-12-20 21:50:56,172 INFO test.py line 196 131400] Test: 11/78-scene0558_00, Batch: 133/134 [2023-12-20 21:50:56,186 INFO test.py line 230 131400] Test: scene0558_00 [11/78]-92953 Batch 9.779 (10.845) Accuracy 0.9718 (0.6001) mIoU 0.7775 (0.5170) [2023-12-20 21:50:56,453 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 0/152 [2023-12-20 21:50:56,577 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 1/152 [2023-12-20 21:50:56,702 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 2/152 [2023-12-20 21:50:56,826 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 3/152 [2023-12-20 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[2023-12-20 21:51:14,301 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 140/152 [2023-12-20 21:51:14,427 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 141/152 [2023-12-20 21:51:14,554 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 142/152 [2023-12-20 21:51:14,677 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 143/152 [2023-12-20 21:51:14,801 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 144/152 [2023-12-20 21:51:14,925 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 145/152 [2023-12-20 21:51:15,048 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 146/152 [2023-12-20 21:51:15,171 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 147/152 [2023-12-20 21:51:15,296 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 148/152 [2023-12-20 21:51:15,421 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 149/152 [2023-12-20 21:51:15,545 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 150/152 [2023-12-20 21:51:15,669 INFO test.py line 196 131400] Test: 12/78-scene0645_01, Batch: 151/152 [2023-12-20 21:51:15,704 INFO test.py line 230 131400] Test: scene0645_01 [12/78]-330321 Batch 19.390 (11.557) Accuracy 0.9637 (0.8352) mIoU 0.8770 (0.7506) [2023-12-20 21:51:16,246 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 0/108 [2023-12-20 21:51:16,326 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 1/108 [2023-12-20 21:51:16,407 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 2/108 [2023-12-20 21:51:16,490 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 3/108 [2023-12-20 21:51:16,572 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 4/108 [2023-12-20 21:51:16,651 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 5/108 [2023-12-20 21:51:16,728 INFO test.py line 196 131400] Test: 13/78-scene0648_01, Batch: 6/108 [2023-12-20 21:51:16,806 INFO 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[2023-12-20 21:51:30,842 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 76/134 [2023-12-20 21:51:30,907 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 77/134 [2023-12-20 21:51:30,974 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 78/134 [2023-12-20 21:51:31,039 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 79/134 [2023-12-20 21:51:31,106 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 80/134 [2023-12-20 21:51:31,171 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 81/134 [2023-12-20 21:51:31,237 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 82/134 [2023-12-20 21:51:31,302 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 83/134 [2023-12-20 21:51:31,368 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 84/134 [2023-12-20 21:51:31,434 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 85/134 [2023-12-20 21:51:31,500 INFO test.py line 196 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[2023-12-20 21:51:32,290 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 97/134 [2023-12-20 21:51:32,365 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 98/134 [2023-12-20 21:51:32,436 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 99/134 [2023-12-20 21:51:32,508 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 100/134 [2023-12-20 21:51:32,579 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 101/134 [2023-12-20 21:51:32,651 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 102/134 [2023-12-20 21:51:32,722 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 103/134 [2023-12-20 21:51:32,794 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 104/134 [2023-12-20 21:51:32,865 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 105/134 [2023-12-20 21:51:32,937 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 106/134 [2023-12-20 21:51:33,008 INFO test.py line 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Batch: 117/134 [2023-12-20 21:51:33,807 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 118/134 [2023-12-20 21:51:33,886 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 119/134 [2023-12-20 21:51:33,964 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 120/134 [2023-12-20 21:51:34,042 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 121/134 [2023-12-20 21:51:34,121 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 122/134 [2023-12-20 21:51:34,197 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 123/134 [2023-12-20 21:51:34,267 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 124/134 [2023-12-20 21:51:34,336 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 125/134 [2023-12-20 21:51:34,404 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 126/134 [2023-12-20 21:51:34,472 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 127/134 [2023-12-20 21:51:34,540 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 128/134 [2023-12-20 21:51:34,608 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 129/134 [2023-12-20 21:51:34,676 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 130/134 [2023-12-20 21:51:34,744 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 131/134 [2023-12-20 21:51:34,812 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 132/134 [2023-12-20 21:51:34,880 INFO test.py line 196 131400] Test: 14/78-scene0578_00, Batch: 133/134 [2023-12-20 21:51:34,902 INFO test.py line 230 131400] Test: scene0578_00 [14/78]-147607 Batch 9.437 (11.223) Accuracy 0.9718 (0.8312) mIoU 0.9095 (0.7459) [2023-12-20 21:51:35,190 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 0/143 [2023-12-20 21:51:35,276 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 1/143 [2023-12-20 21:51:35,361 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 2/143 [2023-12-20 21:51:35,447 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 3/143 [2023-12-20 21:51:35,537 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 4/143 [2023-12-20 21:51:35,625 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 5/143 [2023-12-20 21:51:35,710 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 6/143 [2023-12-20 21:51:35,796 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 7/143 [2023-12-20 21:51:35,881 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 8/143 [2023-12-20 21:51:35,966 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 9/143 [2023-12-20 21:51:36,052 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 10/143 [2023-12-20 21:51:36,137 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 11/143 [2023-12-20 21:51:36,222 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 12/143 [2023-12-20 21:51:36,307 INFO test.py line 196 131400] Test: 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[2023-12-20 21:51:44,378 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 108/143 [2023-12-20 21:51:44,467 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 109/143 [2023-12-20 21:51:44,556 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 110/143 [2023-12-20 21:51:44,646 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 111/143 [2023-12-20 21:51:44,735 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 112/143 [2023-12-20 21:51:44,828 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 113/143 [2023-12-20 21:51:44,921 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 114/143 [2023-12-20 21:51:45,013 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 115/143 [2023-12-20 21:51:45,104 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 116/143 [2023-12-20 21:51:45,194 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 117/143 [2023-12-20 21:51:45,284 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 118/143 [2023-12-20 21:51:45,374 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 119/143 [2023-12-20 21:51:45,464 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 120/143 [2023-12-20 21:51:45,554 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 121/143 [2023-12-20 21:51:45,644 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 122/143 [2023-12-20 21:51:45,734 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 123/143 [2023-12-20 21:51:45,824 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 124/143 [2023-12-20 21:51:45,913 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 125/143 [2023-12-20 21:51:46,004 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 126/143 [2023-12-20 21:51:46,093 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 127/143 [2023-12-20 21:51:46,183 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 128/143 [2023-12-20 21:51:46,272 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 129/143 [2023-12-20 21:51:46,362 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 130/143 [2023-12-20 21:51:46,452 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 131/143 [2023-12-20 21:51:46,537 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 132/143 [2023-12-20 21:51:46,623 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 133/143 [2023-12-20 21:51:46,711 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 134/143 [2023-12-20 21:51:46,796 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 135/143 [2023-12-20 21:51:46,882 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 136/143 [2023-12-20 21:51:46,968 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 137/143 [2023-12-20 21:51:47,054 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 138/143 [2023-12-20 21:51:47,139 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 139/143 [2023-12-20 21:51:47,225 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 140/143 [2023-12-20 21:51:47,310 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 141/143 [2023-12-20 21:51:47,396 INFO test.py line 196 131400] Test: 15/78-scene0575_02, Batch: 142/143 [2023-12-20 21:51:47,414 INFO test.py line 230 131400] Test: scene0575_02 [15/78]-202882 Batch 12.315 (11.296) Accuracy 0.9736 (0.8334) mIoU 0.9392 (0.7507) [2023-12-20 21:51:47,743 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 0/130 [2023-12-20 21:51:47,793 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 1/130 [2023-12-20 21:51:47,842 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 2/130 [2023-12-20 21:51:47,891 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 3/130 [2023-12-20 21:51:47,940 INFO test.py line 196 131400] Test: 16/78-scene0702_01, 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INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 120/130 [2023-12-20 21:51:55,147 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 121/130 [2023-12-20 21:51:55,198 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 122/130 [2023-12-20 21:51:55,248 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 123/130 [2023-12-20 21:51:55,299 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 124/130 [2023-12-20 21:51:55,351 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 125/130 [2023-12-20 21:51:55,402 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 126/130 [2023-12-20 21:51:55,452 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 127/130 [2023-12-20 21:51:55,504 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 128/130 [2023-12-20 21:51:55,555 INFO test.py line 196 131400] Test: 16/78-scene0702_01, Batch: 129/130 [2023-12-20 21:51:55,642 INFO test.py line 230 131400] Test: scene0702_01 [16/78]-56603 Batch 7.952 (11.087) Accuracy 0.8639 (0.8331) mIoU 0.6059 (0.7500) [2023-12-20 21:51:55,825 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 0/139 [2023-12-20 21:51:55,921 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 1/139 [2023-12-20 21:51:56,013 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 2/139 [2023-12-20 21:51:56,104 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 3/139 [2023-12-20 21:51:56,195 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 4/139 [2023-12-20 21:51:56,286 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 5/139 [2023-12-20 21:51:56,376 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 6/139 [2023-12-20 21:51:56,467 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 7/139 [2023-12-20 21:51:56,558 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 8/139 [2023-12-20 21:51:56,651 INFO test.py line 196 131400] Test: 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21:51:57,671 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 20/139 [2023-12-20 21:51:57,761 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 21/139 [2023-12-20 21:51:57,854 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 22/139 [2023-12-20 21:51:57,948 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 23/139 [2023-12-20 21:51:58,038 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 24/139 [2023-12-20 21:51:58,149 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 25/139 [2023-12-20 21:51:58,239 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 26/139 [2023-12-20 21:51:58,333 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 27/139 [2023-12-20 21:51:58,431 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 28/139 [2023-12-20 21:51:58,527 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 29/139 [2023-12-20 21:51:58,623 INFO test.py line 196 131400] Test: 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21:51:59,810 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 41/139 [2023-12-20 21:51:59,901 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 42/139 [2023-12-20 21:51:59,993 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 43/139 [2023-12-20 21:52:00,082 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 44/139 [2023-12-20 21:52:00,169 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 45/139 [2023-12-20 21:52:00,258 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 46/139 [2023-12-20 21:52:00,346 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 47/139 [2023-12-20 21:52:00,433 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 48/139 [2023-12-20 21:52:00,520 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 49/139 [2023-12-20 21:52:00,608 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 50/139 [2023-12-20 21:52:00,696 INFO test.py line 196 131400] Test: 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131400] Test: 17/78-scene0015_00, Batch: 114/139 [2023-12-20 21:52:06,537 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 115/139 [2023-12-20 21:52:06,632 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 116/139 [2023-12-20 21:52:06,728 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 117/139 [2023-12-20 21:52:06,824 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 118/139 [2023-12-20 21:52:06,919 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 119/139 [2023-12-20 21:52:07,015 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 120/139 [2023-12-20 21:52:07,109 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 121/139 [2023-12-20 21:52:07,205 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 122/139 [2023-12-20 21:52:07,301 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 123/139 [2023-12-20 21:52:07,398 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 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test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 135/139 [2023-12-20 21:52:08,507 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 136/139 [2023-12-20 21:52:08,600 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 137/139 [2023-12-20 21:52:08,693 INFO test.py line 196 131400] Test: 17/78-scene0015_00, Batch: 138/139 [2023-12-20 21:52:08,712 INFO test.py line 230 131400] Test: scene0015_00 [17/78]-217086 Batch 12.989 (11.199) Accuracy 0.9483 (0.8340) mIoU 0.7357 (0.7507) [2023-12-20 21:52:09,086 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 0/117 [2023-12-20 21:52:09,155 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 1/117 [2023-12-20 21:52:09,223 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 2/117 [2023-12-20 21:52:09,293 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 3/117 [2023-12-20 21:52:09,363 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 4/117 [2023-12-20 21:52:09,431 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 5/117 [2023-12-20 21:52:09,500 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 6/117 [2023-12-20 21:52:09,569 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 7/117 [2023-12-20 21:52:09,638 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 8/117 [2023-12-20 21:52:09,708 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 9/117 [2023-12-20 21:52:09,776 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 10/117 [2023-12-20 21:52:09,845 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 11/117 [2023-12-20 21:52:09,913 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 12/117 [2023-12-20 21:52:09,980 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 13/117 [2023-12-20 21:52:10,049 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 14/117 [2023-12-20 21:52:10,117 INFO test.py line 196 131400] Test: 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21:52:15,138 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 89/117 [2023-12-20 21:52:15,208 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 90/117 [2023-12-20 21:52:15,279 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 91/117 [2023-12-20 21:52:15,350 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 92/117 [2023-12-20 21:52:15,421 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 93/117 [2023-12-20 21:52:15,492 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 94/117 [2023-12-20 21:52:15,563 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 95/117 [2023-12-20 21:52:15,633 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 96/117 [2023-12-20 21:52:15,704 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 97/117 [2023-12-20 21:52:15,775 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 98/117 [2023-12-20 21:52:15,845 INFO test.py line 196 131400] Test: 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[2023-12-20 21:52:16,611 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 110/117 [2023-12-20 21:52:16,678 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 111/117 [2023-12-20 21:52:16,745 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 112/117 [2023-12-20 21:52:16,812 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 113/117 [2023-12-20 21:52:16,879 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 114/117 [2023-12-20 21:52:16,947 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 115/117 [2023-12-20 21:52:17,014 INFO test.py line 196 131400] Test: 18/78-scene0412_01, Batch: 116/117 [2023-12-20 21:52:17,030 INFO test.py line 230 131400] Test: scene0412_01 [18/78]-133337 Batch 8.017 (11.022) Accuracy 0.7831 (0.8084) mIoU 0.4572 (0.7290) [2023-12-20 21:52:17,283 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 0/142 [2023-12-20 21:52:17,350 INFO test.py line 196 131400] Test: 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21:52:23,631 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 96/142 [2023-12-20 21:52:23,699 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 97/142 [2023-12-20 21:52:23,770 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 98/142 [2023-12-20 21:52:23,838 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 99/142 [2023-12-20 21:52:23,906 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 100/142 [2023-12-20 21:52:23,974 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 101/142 [2023-12-20 21:52:24,041 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 102/142 [2023-12-20 21:52:24,109 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 103/142 [2023-12-20 21:52:24,177 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 104/142 [2023-12-20 21:52:24,245 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 105/142 [2023-12-20 21:52:24,313 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 106/142 [2023-12-20 21:52:24,380 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 107/142 [2023-12-20 21:52:24,449 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 108/142 [2023-12-20 21:52:24,517 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 109/142 [2023-12-20 21:52:24,584 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 110/142 [2023-12-20 21:52:24,653 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 111/142 [2023-12-20 21:52:24,720 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 112/142 [2023-12-20 21:52:24,788 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 113/142 [2023-12-20 21:52:24,856 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 114/142 [2023-12-20 21:52:24,923 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 115/142 [2023-12-20 21:52:24,994 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 116/142 [2023-12-20 21:52:25,062 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 117/142 [2023-12-20 21:52:25,130 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 118/142 [2023-12-20 21:52:25,198 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 119/142 [2023-12-20 21:52:25,266 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 120/142 [2023-12-20 21:52:25,334 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 121/142 [2023-12-20 21:52:25,401 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 122/142 [2023-12-20 21:52:25,469 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 123/142 [2023-12-20 21:52:25,537 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 124/142 [2023-12-20 21:52:25,605 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 125/142 [2023-12-20 21:52:25,673 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 126/142 [2023-12-20 21:52:25,741 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 127/142 [2023-12-20 21:52:25,809 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 128/142 [2023-12-20 21:52:25,877 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 129/142 [2023-12-20 21:52:25,945 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 130/142 [2023-12-20 21:52:26,013 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 131/142 [2023-12-20 21:52:26,078 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 132/142 [2023-12-20 21:52:26,145 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 133/142 [2023-12-20 21:52:26,211 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 134/142 [2023-12-20 21:52:26,276 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 135/142 [2023-12-20 21:52:26,342 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 136/142 [2023-12-20 21:52:26,408 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 137/142 [2023-12-20 21:52:26,474 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 138/142 [2023-12-20 21:52:26,540 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 139/142 [2023-12-20 21:52:26,606 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 140/142 [2023-12-20 21:52:26,673 INFO test.py line 196 131400] Test: 19/78-scene0086_02, Batch: 141/142 [2023-12-20 21:52:26,685 INFO test.py line 230 131400] Test: scene0086_02 [19/78]-124091 Batch 9.475 (10.940) Accuracy 0.9731 (0.8189) mIoU 0.8336 (0.7407) [2023-12-20 21:52:26,924 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 0/112 [2023-12-20 21:52:26,992 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 1/112 [2023-12-20 21:52:27,059 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 2/112 [2023-12-20 21:52:27,126 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 3/112 [2023-12-20 21:52:27,194 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 4/112 [2023-12-20 21:52:27,261 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 5/112 [2023-12-20 21:52:27,328 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 6/112 [2023-12-20 21:52:27,396 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 7/112 [2023-12-20 21:52:27,466 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 8/112 [2023-12-20 21:52:27,533 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 9/112 [2023-12-20 21:52:27,602 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 10/112 [2023-12-20 21:52:27,669 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 11/112 [2023-12-20 21:52:27,737 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 12/112 [2023-12-20 21:52:27,804 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 13/112 [2023-12-20 21:52:27,872 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 14/112 [2023-12-20 21:52:27,939 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 15/112 [2023-12-20 21:52:28,007 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 16/112 [2023-12-20 21:52:28,074 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 17/112 [2023-12-20 21:52:28,141 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 18/112 [2023-12-20 21:52:28,209 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 19/112 [2023-12-20 21:52:28,276 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 20/112 [2023-12-20 21:52:28,349 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 21/112 [2023-12-20 21:52:28,417 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 22/112 [2023-12-20 21:52:28,484 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 23/112 [2023-12-20 21:52:28,562 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 24/112 [2023-12-20 21:52:28,653 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 25/112 [2023-12-20 21:52:28,745 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 26/112 [2023-12-20 21:52:28,827 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 27/112 [2023-12-20 21:52:28,896 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 28/112 [2023-12-20 21:52:28,964 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 29/112 [2023-12-20 21:52:29,035 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 30/112 [2023-12-20 21:52:29,103 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 31/112 [2023-12-20 21:52:29,169 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 32/112 [2023-12-20 21:52:29,234 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 33/112 [2023-12-20 21:52:29,300 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 34/112 [2023-12-20 21:52:29,365 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 35/112 [2023-12-20 21:52:29,430 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 36/112 [2023-12-20 21:52:29,495 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 37/112 [2023-12-20 21:52:29,560 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 38/112 [2023-12-20 21:52:29,624 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 39/112 [2023-12-20 21:52:29,689 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 40/112 [2023-12-20 21:52:29,754 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 41/112 [2023-12-20 21:52:29,819 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 42/112 [2023-12-20 21:52:29,885 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 43/112 [2023-12-20 21:52:29,950 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 44/112 [2023-12-20 21:52:30,016 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 45/112 [2023-12-20 21:52:30,081 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 46/112 [2023-12-20 21:52:30,146 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 47/112 [2023-12-20 21:52:30,211 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 48/112 [2023-12-20 21:52:30,276 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 49/112 [2023-12-20 21:52:30,341 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 50/112 [2023-12-20 21:52:30,407 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 51/112 [2023-12-20 21:52:30,473 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 52/112 [2023-12-20 21:52:30,538 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 53/112 [2023-12-20 21:52:30,604 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 54/112 [2023-12-20 21:52:30,669 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 55/112 [2023-12-20 21:52:30,734 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 56/112 [2023-12-20 21:52:30,800 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 57/112 [2023-12-20 21:52:30,867 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 58/112 [2023-12-20 21:52:30,933 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 59/112 [2023-12-20 21:52:30,998 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 60/112 [2023-12-20 21:52:31,063 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 61/112 [2023-12-20 21:52:31,128 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 62/112 [2023-12-20 21:52:31,193 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 63/112 [2023-12-20 21:52:31,259 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 64/112 [2023-12-20 21:52:31,323 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 65/112 [2023-12-20 21:52:31,388 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 66/112 [2023-12-20 21:52:31,453 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 67/112 [2023-12-20 21:52:31,518 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 68/112 [2023-12-20 21:52:31,584 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 69/112 [2023-12-20 21:52:31,649 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 70/112 [2023-12-20 21:52:31,714 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 71/112 [2023-12-20 21:52:31,785 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 72/112 [2023-12-20 21:52:31,854 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 73/112 [2023-12-20 21:52:31,923 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 74/112 [2023-12-20 21:52:31,993 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 75/112 [2023-12-20 21:52:32,062 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 76/112 [2023-12-20 21:52:32,132 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 77/112 [2023-12-20 21:52:32,201 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 78/112 [2023-12-20 21:52:32,271 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 79/112 [2023-12-20 21:52:32,340 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 80/112 [2023-12-20 21:52:32,410 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 81/112 [2023-12-20 21:52:32,479 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 82/112 [2023-12-20 21:52:32,549 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 83/112 [2023-12-20 21:52:32,619 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 84/112 [2023-12-20 21:52:32,689 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 85/112 [2023-12-20 21:52:32,759 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 86/112 [2023-12-20 21:52:32,828 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 87/112 [2023-12-20 21:52:32,898 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 88/112 [2023-12-20 21:52:32,968 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 89/112 [2023-12-20 21:52:33,037 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 90/112 [2023-12-20 21:52:33,106 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 91/112 [2023-12-20 21:52:33,176 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 92/112 [2023-12-20 21:52:33,245 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 93/112 [2023-12-20 21:52:33,315 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 94/112 [2023-12-20 21:52:33,384 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 95/112 [2023-12-20 21:52:33,454 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 96/112 [2023-12-20 21:52:33,524 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 97/112 [2023-12-20 21:52:33,593 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 98/112 [2023-12-20 21:52:33,664 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 99/112 [2023-12-20 21:52:33,733 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 100/112 [2023-12-20 21:52:33,803 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 101/112 [2023-12-20 21:52:33,873 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 102/112 [2023-12-20 21:52:33,944 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 103/112 [2023-12-20 21:52:34,025 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 104/112 [2023-12-20 21:52:34,100 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 105/112 [2023-12-20 21:52:34,172 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 106/112 [2023-12-20 21:52:34,242 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 107/112 [2023-12-20 21:52:34,311 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 108/112 [2023-12-20 21:52:34,380 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 109/112 [2023-12-20 21:52:34,452 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 110/112 [2023-12-20 21:52:34,523 INFO test.py line 196 131400] Test: 20/78-scene0695_01, Batch: 111/112 [2023-12-20 21:52:34,578 INFO test.py line 230 131400] Test: scene0695_01 [20/78]-143776 Batch 7.726 (10.780) Accuracy 0.9380 (0.8191) mIoU 0.7495 (0.7444) [2023-12-20 21:52:34,921 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 0/125 [2023-12-20 21:52:35,001 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 1/125 [2023-12-20 21:52:35,079 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 2/125 [2023-12-20 21:52:35,157 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 3/125 [2023-12-20 21:52:35,237 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 4/125 [2023-12-20 21:52:35,317 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 5/125 [2023-12-20 21:52:35,396 INFO 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Batch: 100/125 [2023-12-20 21:52:43,254 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 101/125 [2023-12-20 21:52:43,334 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 102/125 [2023-12-20 21:52:43,414 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 103/125 [2023-12-20 21:52:43,496 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 104/125 [2023-12-20 21:52:43,578 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 105/125 [2023-12-20 21:52:43,657 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 106/125 [2023-12-20 21:52:43,738 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 107/125 [2023-12-20 21:52:43,818 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 108/125 [2023-12-20 21:52:43,898 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 109/125 [2023-12-20 21:52:43,978 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 110/125 [2023-12-20 21:52:44,058 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 111/125 [2023-12-20 21:52:44,137 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 112/125 [2023-12-20 21:52:44,221 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 113/125 [2023-12-20 21:52:44,304 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 114/125 [2023-12-20 21:52:44,387 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 115/125 [2023-12-20 21:52:44,466 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 116/125 [2023-12-20 21:52:44,546 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 117/125 [2023-12-20 21:52:44,630 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 118/125 [2023-12-20 21:52:44,710 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 119/125 [2023-12-20 21:52:44,788 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 120/125 [2023-12-20 21:52:44,866 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 121/125 [2023-12-20 21:52:44,942 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 122/125 [2023-12-20 21:52:45,020 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 123/125 [2023-12-20 21:52:45,099 INFO test.py line 196 131400] Test: 21/78-scene0558_02, Batch: 124/125 [2023-12-20 21:52:45,120 INFO test.py line 230 131400] Test: scene0558_02 [21/78]-173904 Batch 10.283 (10.756) Accuracy 0.9808 (0.8202) mIoU 0.9608 (0.7462) [2023-12-20 21:52:45,428 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 0/138 [2023-12-20 21:52:45,489 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 1/138 [2023-12-20 21:52:45,561 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 2/138 [2023-12-20 21:52:45,622 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 3/138 [2023-12-20 21:52:45,694 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 4/138 [2023-12-20 21:52:45,768 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 5/138 [2023-12-20 21:52:45,825 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 6/138 [2023-12-20 21:52:45,884 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 7/138 [2023-12-20 21:52:45,941 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 8/138 [2023-12-20 21:52:45,994 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 9/138 [2023-12-20 21:52:46,047 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 10/138 [2023-12-20 21:52:46,100 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 11/138 [2023-12-20 21:52:46,152 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 12/138 [2023-12-20 21:52:46,204 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 13/138 [2023-12-20 21:52:46,256 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 14/138 [2023-12-20 21:52:46,308 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 15/138 [2023-12-20 21:52:46,360 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 16/138 [2023-12-20 21:52:46,412 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 17/138 [2023-12-20 21:52:46,464 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 18/138 [2023-12-20 21:52:46,517 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 19/138 [2023-12-20 21:52:46,571 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 20/138 [2023-12-20 21:52:46,624 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 21/138 [2023-12-20 21:52:46,677 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 22/138 [2023-12-20 21:52:46,730 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 23/138 [2023-12-20 21:52:46,783 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 24/138 [2023-12-20 21:52:46,836 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 25/138 [2023-12-20 21:52:46,889 INFO test.py line 196 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[2023-12-20 21:52:47,518 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 37/138 [2023-12-20 21:52:47,570 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 38/138 [2023-12-20 21:52:47,623 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 39/138 [2023-12-20 21:52:47,677 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 40/138 [2023-12-20 21:52:47,730 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 41/138 [2023-12-20 21:52:47,784 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 42/138 [2023-12-20 21:52:47,837 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 43/138 [2023-12-20 21:52:47,890 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 44/138 [2023-12-20 21:52:47,942 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 45/138 [2023-12-20 21:52:47,995 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 46/138 [2023-12-20 21:52:48,048 INFO test.py line 196 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[2023-12-20 21:52:48,654 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 58/138 [2023-12-20 21:52:48,711 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 59/138 [2023-12-20 21:52:48,767 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 60/138 [2023-12-20 21:52:48,822 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 61/138 [2023-12-20 21:52:48,876 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 62/138 [2023-12-20 21:52:48,929 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 63/138 [2023-12-20 21:52:48,987 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 64/138 [2023-12-20 21:52:49,044 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 65/138 [2023-12-20 21:52:49,104 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 66/138 [2023-12-20 21:52:49,160 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 67/138 [2023-12-20 21:52:49,216 INFO test.py line 196 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[2023-12-20 21:52:49,830 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 79/138 [2023-12-20 21:52:49,885 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 80/138 [2023-12-20 21:52:49,940 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 81/138 [2023-12-20 21:52:49,996 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 82/138 [2023-12-20 21:52:50,054 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 83/138 [2023-12-20 21:52:50,113 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 84/138 [2023-12-20 21:52:50,174 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 85/138 [2023-12-20 21:52:50,230 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 86/138 [2023-12-20 21:52:50,288 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 87/138 [2023-12-20 21:52:50,350 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 88/138 [2023-12-20 21:52:50,432 INFO test.py line 196 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[2023-12-20 21:52:51,160 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 100/138 [2023-12-20 21:52:51,213 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 101/138 [2023-12-20 21:52:51,277 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 102/138 [2023-12-20 21:52:51,341 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 103/138 [2023-12-20 21:52:51,406 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 104/138 [2023-12-20 21:52:51,476 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 105/138 [2023-12-20 21:52:51,550 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 106/138 [2023-12-20 21:52:51,641 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 107/138 [2023-12-20 21:52:51,741 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 108/138 [2023-12-20 21:52:51,844 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 109/138 [2023-12-20 21:52:51,927 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 110/138 [2023-12-20 21:52:51,985 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 111/138 [2023-12-20 21:52:52,049 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 112/138 [2023-12-20 21:52:52,105 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 113/138 [2023-12-20 21:52:52,159 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 114/138 [2023-12-20 21:52:52,214 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 115/138 [2023-12-20 21:52:52,271 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 116/138 [2023-12-20 21:52:52,328 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 117/138 [2023-12-20 21:52:52,389 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 118/138 [2023-12-20 21:52:52,446 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 119/138 [2023-12-20 21:52:52,509 INFO test.py line 196 131400] Test: 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[2023-12-20 21:52:53,141 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 131/138 [2023-12-20 21:52:53,201 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 132/138 [2023-12-20 21:52:53,256 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 133/138 [2023-12-20 21:52:53,309 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 134/138 [2023-12-20 21:52:53,363 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 135/138 [2023-12-20 21:52:53,416 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 136/138 [2023-12-20 21:52:53,473 INFO test.py line 196 131400] Test: 22/78-scene0553_02, Batch: 137/138 [2023-12-20 21:52:53,485 INFO test.py line 230 131400] Test: scene0553_02 [22/78]-67632 Batch 8.121 (10.636) Accuracy 0.9877 (0.8227) mIoU 0.8057 (0.7490) [2023-12-20 21:52:53,698 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 0/126 [2023-12-20 21:52:53,779 INFO test.py line 196 131400] Test: 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21:52:59,464 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 75/126 [2023-12-20 21:52:59,533 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 76/126 [2023-12-20 21:52:59,602 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 77/126 [2023-12-20 21:52:59,673 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 78/126 [2023-12-20 21:52:59,745 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 79/126 [2023-12-20 21:52:59,822 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 80/126 [2023-12-20 21:52:59,896 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 81/126 [2023-12-20 21:52:59,973 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 82/126 [2023-12-20 21:53:00,051 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 83/126 [2023-12-20 21:53:00,127 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 84/126 [2023-12-20 21:53:00,205 INFO test.py line 196 131400] Test: 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21:53:01,038 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 96/126 [2023-12-20 21:53:01,114 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 97/126 [2023-12-20 21:53:01,191 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 98/126 [2023-12-20 21:53:01,269 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 99/126 [2023-12-20 21:53:01,346 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 100/126 [2023-12-20 21:53:01,428 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 101/126 [2023-12-20 21:53:01,507 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 102/126 [2023-12-20 21:53:01,584 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 103/126 [2023-12-20 21:53:01,660 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 104/126 [2023-12-20 21:53:01,736 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 105/126 [2023-12-20 21:53:01,814 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 106/126 [2023-12-20 21:53:01,890 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 107/126 [2023-12-20 21:53:01,966 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 108/126 [2023-12-20 21:53:02,043 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 109/126 [2023-12-20 21:53:02,126 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 110/126 [2023-12-20 21:53:02,203 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 111/126 [2023-12-20 21:53:02,276 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 112/126 [2023-12-20 21:53:02,349 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 113/126 [2023-12-20 21:53:02,422 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 114/126 [2023-12-20 21:53:02,496 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 115/126 [2023-12-20 21:53:02,566 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 116/126 [2023-12-20 21:53:02,644 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 117/126 [2023-12-20 21:53:02,732 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 118/126 [2023-12-20 21:53:02,806 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 119/126 [2023-12-20 21:53:02,884 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 120/126 [2023-12-20 21:53:02,959 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 121/126 [2023-12-20 21:53:03,031 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 122/126 [2023-12-20 21:53:03,116 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 123/126 [2023-12-20 21:53:03,204 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 124/126 [2023-12-20 21:53:03,288 INFO test.py line 196 131400] Test: 23/78-scene0598_00, Batch: 125/126 [2023-12-20 21:53:03,302 INFO test.py line 230 131400] Test: scene0598_00 [23/78]-145562 Batch 9.702 (10.596) Accuracy 0.7742 (0.8169) mIoU 0.5185 (0.7426) [2023-12-20 21:53:03,596 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 0/139 [2023-12-20 21:53:03,692 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 1/139 [2023-12-20 21:53:03,789 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 2/139 [2023-12-20 21:53:03,884 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 3/139 [2023-12-20 21:53:03,976 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 4/139 [2023-12-20 21:53:04,069 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 5/139 [2023-12-20 21:53:04,160 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 6/139 [2023-12-20 21:53:04,255 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 7/139 [2023-12-20 21:53:04,348 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 8/139 [2023-12-20 21:53:04,439 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 9/139 [2023-12-20 21:53:04,526 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 10/139 [2023-12-20 21:53:04,617 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 11/139 [2023-12-20 21:53:04,706 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 12/139 [2023-12-20 21:53:04,820 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 13/139 [2023-12-20 21:53:04,935 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 14/139 [2023-12-20 21:53:05,024 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 15/139 [2023-12-20 21:53:05,120 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 16/139 [2023-12-20 21:53:05,214 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 17/139 [2023-12-20 21:53:05,306 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 18/139 [2023-12-20 21:53:05,400 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 19/139 [2023-12-20 21:53:05,491 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 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[2023-12-20 21:53:16,082 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 136/139 [2023-12-20 21:53:16,168 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 137/139 [2023-12-20 21:53:16,256 INFO test.py line 196 131400] Test: 24/78-scene0435_02, Batch: 138/139 [2023-12-20 21:53:16,281 INFO test.py line 230 131400] Test: scene0435_02 [24/78]-210308 Batch 12.786 (10.687) Accuracy 0.9340 (0.8686) mIoU 0.7764 (0.7936) [2023-12-20 21:53:16,630 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 0/113 [2023-12-20 21:53:16,681 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 1/113 [2023-12-20 21:53:16,732 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 2/113 [2023-12-20 21:53:16,782 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 3/113 [2023-12-20 21:53:16,839 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 4/113 [2023-12-20 21:53:16,890 INFO test.py line 196 131400] Test: 25/78-scene0684_00, 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line 196 131400] Test: 25/78-scene0684_00, Batch: 16/113 [2023-12-20 21:53:17,534 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 17/113 [2023-12-20 21:53:17,585 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 18/113 [2023-12-20 21:53:17,635 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 19/113 [2023-12-20 21:53:17,685 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 20/113 [2023-12-20 21:53:17,735 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 21/113 [2023-12-20 21:53:17,785 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 22/113 [2023-12-20 21:53:17,834 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 23/113 [2023-12-20 21:53:17,884 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 24/113 [2023-12-20 21:53:17,934 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 25/113 [2023-12-20 21:53:17,985 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 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196 131400] Test: 25/78-scene0684_00, Batch: 100/113 [2023-12-20 21:53:22,086 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 101/113 [2023-12-20 21:53:22,136 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 102/113 [2023-12-20 21:53:22,187 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 103/113 [2023-12-20 21:53:22,237 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 104/113 [2023-12-20 21:53:22,286 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 105/113 [2023-12-20 21:53:22,342 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 106/113 [2023-12-20 21:53:22,397 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 107/113 [2023-12-20 21:53:22,458 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 108/113 [2023-12-20 21:53:22,516 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 109/113 [2023-12-20 21:53:22,571 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 110/113 [2023-12-20 21:53:22,627 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 111/113 [2023-12-20 21:53:22,680 INFO test.py line 196 131400] Test: 25/78-scene0684_00, Batch: 112/113 [2023-12-20 21:53:22,687 INFO test.py line 230 131400] Test: scene0684_00 [25/78]-41364 Batch 6.111 (10.504) Accuracy 0.9765 (0.8690) mIoU 0.7805 (0.7942) [2023-12-20 21:53:22,824 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 0/108 [2023-12-20 21:53:22,877 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 1/108 [2023-12-20 21:53:22,932 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 2/108 [2023-12-20 21:53:22,987 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 3/108 [2023-12-20 21:53:23,049 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 4/108 [2023-12-20 21:53:23,107 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 5/108 [2023-12-20 21:53:23,165 INFO test.py line 196 131400] Test: 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21:53:23,782 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 17/108 [2023-12-20 21:53:23,835 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 18/108 [2023-12-20 21:53:23,889 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 19/108 [2023-12-20 21:53:23,942 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 20/108 [2023-12-20 21:53:24,004 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 21/108 [2023-12-20 21:53:24,064 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 22/108 [2023-12-20 21:53:24,126 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 23/108 [2023-12-20 21:53:24,183 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 24/108 [2023-12-20 21:53:24,236 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 25/108 [2023-12-20 21:53:24,289 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 26/108 [2023-12-20 21:53:24,342 INFO test.py line 196 131400] Test: 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21:53:24,934 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 38/108 [2023-12-20 21:53:24,987 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 39/108 [2023-12-20 21:53:25,040 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 40/108 [2023-12-20 21:53:25,091 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 41/108 [2023-12-20 21:53:25,144 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 42/108 [2023-12-20 21:53:25,196 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 43/108 [2023-12-20 21:53:25,248 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 44/108 [2023-12-20 21:53:25,300 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 45/108 [2023-12-20 21:53:25,352 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 46/108 [2023-12-20 21:53:25,404 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 47/108 [2023-12-20 21:53:25,456 INFO test.py line 196 131400] Test: 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21:53:26,178 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 59/108 [2023-12-20 21:53:26,232 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 60/108 [2023-12-20 21:53:26,285 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 61/108 [2023-12-20 21:53:26,337 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 62/108 [2023-12-20 21:53:26,392 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 63/108 [2023-12-20 21:53:26,455 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 64/108 [2023-12-20 21:53:26,522 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 65/108 [2023-12-20 21:53:26,588 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 66/108 [2023-12-20 21:53:26,653 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 67/108 [2023-12-20 21:53:26,708 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 68/108 [2023-12-20 21:53:26,762 INFO test.py line 196 131400] Test: 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21:53:27,369 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 80/108 [2023-12-20 21:53:27,424 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 81/108 [2023-12-20 21:53:27,477 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 82/108 [2023-12-20 21:53:27,531 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 83/108 [2023-12-20 21:53:27,585 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 84/108 [2023-12-20 21:53:27,638 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 85/108 [2023-12-20 21:53:27,692 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 86/108 [2023-12-20 21:53:27,747 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 87/108 [2023-12-20 21:53:27,803 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 88/108 [2023-12-20 21:53:27,857 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 89/108 [2023-12-20 21:53:27,911 INFO test.py line 196 131400] Test: 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21:53:28,505 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 101/108 [2023-12-20 21:53:28,558 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 102/108 [2023-12-20 21:53:28,613 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 103/108 [2023-12-20 21:53:28,667 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 104/108 [2023-12-20 21:53:28,720 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 105/108 [2023-12-20 21:53:28,774 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 106/108 [2023-12-20 21:53:28,827 INFO test.py line 196 131400] Test: 26/78-scene0574_02, Batch: 107/108 [2023-12-20 21:53:28,840 INFO test.py line 230 131400] Test: scene0574_02 [26/78]-62475 Batch 6.074 (10.333) Accuracy 0.9389 (0.8705) mIoU 0.8701 (0.7956) [2023-12-20 21:53:29,061 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 0/138 [2023-12-20 21:53:29,178 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 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131400] Test: 27/78-scene0616_00, Batch: 12/138 [2023-12-20 21:53:30,524 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 13/138 [2023-12-20 21:53:30,637 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 14/138 [2023-12-20 21:53:30,749 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 15/138 [2023-12-20 21:53:30,865 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 16/138 [2023-12-20 21:53:30,978 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 17/138 [2023-12-20 21:53:31,090 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 18/138 [2023-12-20 21:53:31,202 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 19/138 [2023-12-20 21:53:31,322 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 20/138 [2023-12-20 21:53:31,442 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 21/138 [2023-12-20 21:53:31,582 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 22/138 [2023-12-20 21:53:31,738 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 23/138 [2023-12-20 21:53:31,868 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 24/138 [2023-12-20 21:53:31,980 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 25/138 [2023-12-20 21:53:32,090 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 26/138 [2023-12-20 21:53:32,200 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 27/138 [2023-12-20 21:53:32,313 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 28/138 [2023-12-20 21:53:32,425 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 29/138 [2023-12-20 21:53:32,538 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 30/138 [2023-12-20 21:53:32,648 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 31/138 [2023-12-20 21:53:32,757 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 32/138 [2023-12-20 21:53:32,867 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 33/138 [2023-12-20 21:53:32,976 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 34/138 [2023-12-20 21:53:33,087 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 35/138 [2023-12-20 21:53:33,198 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 36/138 [2023-12-20 21:53:33,307 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 37/138 [2023-12-20 21:53:33,416 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 38/138 [2023-12-20 21:53:33,526 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 39/138 [2023-12-20 21:53:33,630 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 40/138 [2023-12-20 21:53:33,734 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 41/138 [2023-12-20 21:53:33,844 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 42/138 [2023-12-20 21:53:33,948 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 43/138 [2023-12-20 21:53:34,051 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 44/138 [2023-12-20 21:53:34,155 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 45/138 [2023-12-20 21:53:34,258 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 46/138 [2023-12-20 21:53:34,361 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 47/138 [2023-12-20 21:53:34,465 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 48/138 [2023-12-20 21:53:34,569 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 49/138 [2023-12-20 21:53:34,673 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 50/138 [2023-12-20 21:53:34,777 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 51/138 [2023-12-20 21:53:34,880 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 52/138 [2023-12-20 21:53:34,984 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 53/138 [2023-12-20 21:53:35,087 INFO test.py line 196 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[2023-12-20 21:53:36,219 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 65/138 [2023-12-20 21:53:36,323 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 66/138 [2023-12-20 21:53:36,427 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 67/138 [2023-12-20 21:53:36,542 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 68/138 [2023-12-20 21:53:36,684 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 69/138 [2023-12-20 21:53:36,800 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 70/138 [2023-12-20 21:53:36,903 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 71/138 [2023-12-20 21:53:37,006 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 72/138 [2023-12-20 21:53:37,110 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 73/138 [2023-12-20 21:53:37,214 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 74/138 [2023-12-20 21:53:37,319 INFO test.py line 196 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[2023-12-20 21:53:38,510 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 86/138 [2023-12-20 21:53:38,630 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 87/138 [2023-12-20 21:53:38,757 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 88/138 [2023-12-20 21:53:38,877 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 89/138 [2023-12-20 21:53:38,996 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 90/138 [2023-12-20 21:53:39,111 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 91/138 [2023-12-20 21:53:39,229 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 92/138 [2023-12-20 21:53:39,346 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 93/138 [2023-12-20 21:53:39,461 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 94/138 [2023-12-20 21:53:39,582 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 95/138 [2023-12-20 21:53:39,704 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 96/138 [2023-12-20 21:53:39,823 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 97/138 [2023-12-20 21:53:39,943 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 98/138 [2023-12-20 21:53:40,060 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 99/138 [2023-12-20 21:53:40,176 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 100/138 [2023-12-20 21:53:40,291 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 101/138 [2023-12-20 21:53:40,407 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 102/138 [2023-12-20 21:53:40,522 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 103/138 [2023-12-20 21:53:40,641 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 104/138 [2023-12-20 21:53:40,761 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 105/138 [2023-12-20 21:53:40,884 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 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test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 117/138 [2023-12-20 21:53:42,273 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 118/138 [2023-12-20 21:53:42,389 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 119/138 [2023-12-20 21:53:42,504 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 120/138 [2023-12-20 21:53:42,619 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 121/138 [2023-12-20 21:53:42,734 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 122/138 [2023-12-20 21:53:42,848 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 123/138 [2023-12-20 21:53:42,963 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 124/138 [2023-12-20 21:53:43,078 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 125/138 [2023-12-20 21:53:43,193 INFO test.py line 196 131400] Test: 27/78-scene0616_00, Batch: 126/138 [2023-12-20 21:53:43,308 INFO test.py line 196 131400] Test: 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[2023-12-20 21:53:44,446 INFO test.py line 230 131400] Test: scene0616_00 [27/78]-310248 Batch 15.513 (10.525) Accuracy 0.7836 (0.8522) mIoU 0.7060 (0.7829) [2023-12-20 21:53:45,008 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 0/139 [2023-12-20 21:53:45,106 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 1/139 [2023-12-20 21:53:45,203 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 2/139 [2023-12-20 21:53:45,300 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 3/139 [2023-12-20 21:53:45,396 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 4/139 [2023-12-20 21:53:45,491 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 5/139 [2023-12-20 21:53:45,585 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 6/139 [2023-12-20 21:53:45,681 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 7/139 [2023-12-20 21:53:45,778 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 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line 196 131400] Test: 28/78-scene0169_00, Batch: 103/139 [2023-12-20 21:53:55,236 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 104/139 [2023-12-20 21:53:55,334 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 105/139 [2023-12-20 21:53:55,433 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 106/139 [2023-12-20 21:53:55,531 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 107/139 [2023-12-20 21:53:55,631 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 108/139 [2023-12-20 21:53:55,728 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 109/139 [2023-12-20 21:53:55,826 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 110/139 [2023-12-20 21:53:55,924 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 111/139 [2023-12-20 21:53:56,023 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 112/139 [2023-12-20 21:53:56,121 INFO test.py line 196 131400] Test: 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[2023-12-20 21:53:57,208 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 124/139 [2023-12-20 21:53:57,306 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 125/139 [2023-12-20 21:53:57,404 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 126/139 [2023-12-20 21:53:57,502 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 127/139 [2023-12-20 21:53:57,596 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 128/139 [2023-12-20 21:53:57,690 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 129/139 [2023-12-20 21:53:57,783 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 130/139 [2023-12-20 21:53:57,877 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 131/139 [2023-12-20 21:53:57,971 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 132/139 [2023-12-20 21:53:58,065 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 133/139 [2023-12-20 21:53:58,159 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 134/139 [2023-12-20 21:53:58,253 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 135/139 [2023-12-20 21:53:58,348 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 136/139 [2023-12-20 21:53:58,442 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 137/139 [2023-12-20 21:53:58,536 INFO test.py line 196 131400] Test: 28/78-scene0169_00, Batch: 138/139 [2023-12-20 21:53:58,557 INFO test.py line 230 131400] Test: scene0169_00 [28/78]-239354 Batch 13.650 (10.637) Accuracy 0.9405 (0.8552) mIoU 0.8185 (0.7866) [2023-12-20 21:53:58,993 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 0/146 [2023-12-20 21:53:59,080 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 1/146 [2023-12-20 21:53:59,166 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 2/146 [2023-12-20 21:53:59,253 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 3/146 [2023-12-20 21:53:59,339 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[2023-12-20 21:54:10,687 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 130/146 [2023-12-20 21:54:10,777 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 131/146 [2023-12-20 21:54:10,868 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 132/146 [2023-12-20 21:54:10,958 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 133/146 [2023-12-20 21:54:11,049 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 134/146 [2023-12-20 21:54:11,139 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 135/146 [2023-12-20 21:54:11,225 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 136/146 [2023-12-20 21:54:11,311 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 137/146 [2023-12-20 21:54:11,397 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 138/146 [2023-12-20 21:54:11,483 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 139/146 [2023-12-20 21:54:11,569 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 140/146 [2023-12-20 21:54:11,655 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 141/146 [2023-12-20 21:54:11,741 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 142/146 [2023-12-20 21:54:11,827 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 143/146 [2023-12-20 21:54:11,914 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 144/146 [2023-12-20 21:54:12,006 INFO test.py line 196 131400] Test: 29/78-scene0046_02, Batch: 145/146 [2023-12-20 21:54:12,049 INFO test.py line 230 131400] Test: scene0046_02 [29/78]-199774 Batch 13.151 (10.724) Accuracy 0.9197 (0.8562) mIoU 0.7527 (0.7678) [2023-12-20 21:54:12,425 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 0/126 [2023-12-20 21:54:12,527 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 1/126 [2023-12-20 21:54:12,626 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 2/126 [2023-12-20 21:54:12,721 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 3/126 [2023-12-20 21:54:12,816 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 4/126 [2023-12-20 21:54:12,910 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 5/126 [2023-12-20 21:54:13,003 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 6/126 [2023-12-20 21:54:13,097 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 7/126 [2023-12-20 21:54:13,191 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 8/126 [2023-12-20 21:54:13,284 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 9/126 [2023-12-20 21:54:13,376 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 10/126 [2023-12-20 21:54:13,469 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 11/126 [2023-12-20 21:54:13,560 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 12/126 [2023-12-20 21:54:13,652 INFO test.py line 196 131400] Test: 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21:54:14,677 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 24/126 [2023-12-20 21:54:14,772 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 25/126 [2023-12-20 21:54:14,863 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 26/126 [2023-12-20 21:54:14,958 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 27/126 [2023-12-20 21:54:15,052 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 28/126 [2023-12-20 21:54:15,144 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 29/126 [2023-12-20 21:54:15,238 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 30/126 [2023-12-20 21:54:15,333 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 31/126 [2023-12-20 21:54:15,432 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 32/126 [2023-12-20 21:54:15,533 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 33/126 [2023-12-20 21:54:15,627 INFO test.py line 196 131400] Test: 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21:54:18,655 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 66/126 [2023-12-20 21:54:18,743 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 67/126 [2023-12-20 21:54:18,832 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 68/126 [2023-12-20 21:54:18,921 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 69/126 [2023-12-20 21:54:19,015 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 70/126 [2023-12-20 21:54:19,111 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 71/126 [2023-12-20 21:54:19,205 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 72/126 [2023-12-20 21:54:19,294 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 73/126 [2023-12-20 21:54:19,382 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 74/126 [2023-12-20 21:54:19,469 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 75/126 [2023-12-20 21:54:19,556 INFO test.py line 196 131400] Test: 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21:54:20,615 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 87/126 [2023-12-20 21:54:20,714 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 88/126 [2023-12-20 21:54:20,811 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 89/126 [2023-12-20 21:54:20,908 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 90/126 [2023-12-20 21:54:21,005 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 91/126 [2023-12-20 21:54:21,106 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 92/126 [2023-12-20 21:54:21,204 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 93/126 [2023-12-20 21:54:21,300 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 94/126 [2023-12-20 21:54:21,398 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 95/126 [2023-12-20 21:54:21,498 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 96/126 [2023-12-20 21:54:21,597 INFO test.py line 196 131400] Test: 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[2023-12-20 21:54:22,702 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 108/126 [2023-12-20 21:54:22,808 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 109/126 [2023-12-20 21:54:22,910 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 110/126 [2023-12-20 21:54:23,010 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 111/126 [2023-12-20 21:54:23,116 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 112/126 [2023-12-20 21:54:23,216 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 113/126 [2023-12-20 21:54:23,313 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 114/126 [2023-12-20 21:54:23,410 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 115/126 [2023-12-20 21:54:23,508 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 116/126 [2023-12-20 21:54:23,605 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 117/126 [2023-12-20 21:54:23,698 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 118/126 [2023-12-20 21:54:23,790 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 119/126 [2023-12-20 21:54:23,892 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 120/126 [2023-12-20 21:54:23,994 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 121/126 [2023-12-20 21:54:24,087 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 122/126 [2023-12-20 21:54:24,180 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 123/126 [2023-12-20 21:54:24,273 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 124/126 [2023-12-20 21:54:24,365 INFO test.py line 196 131400] Test: 30/78-scene0334_01, Batch: 125/126 [2023-12-20 21:54:24,384 INFO test.py line 230 131400] Test: scene0334_01 [30/78]-226580 Batch 12.062 (10.768) Accuracy 0.9405 (0.8534) mIoU 0.6951 (0.7652) [2023-12-20 21:54:24,795 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 0/139 [2023-12-20 21:54:24,889 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 1/139 [2023-12-20 21:54:24,979 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 2/139 [2023-12-20 21:54:25,065 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 3/139 [2023-12-20 21:54:25,150 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 4/139 [2023-12-20 21:54:25,234 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 5/139 [2023-12-20 21:54:25,320 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 6/139 [2023-12-20 21:54:25,404 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 7/139 [2023-12-20 21:54:25,487 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 8/139 [2023-12-20 21:54:25,570 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 9/139 [2023-12-20 21:54:25,653 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 10/139 [2023-12-20 21:54:25,741 INFO test.py line 196 131400] Test: 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21:54:26,877 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 22/139 [2023-12-20 21:54:26,961 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 23/139 [2023-12-20 21:54:27,045 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 24/139 [2023-12-20 21:54:27,129 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 25/139 [2023-12-20 21:54:27,212 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 26/139 [2023-12-20 21:54:27,296 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 27/139 [2023-12-20 21:54:27,380 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 28/139 [2023-12-20 21:54:27,464 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 29/139 [2023-12-20 21:54:27,548 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 30/139 [2023-12-20 21:54:27,632 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 31/139 [2023-12-20 21:54:27,718 INFO test.py line 196 131400] Test: 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[2023-12-20 21:54:33,831 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 106/139 [2023-12-20 21:54:33,919 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 107/139 [2023-12-20 21:54:34,008 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 108/139 [2023-12-20 21:54:34,096 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 109/139 [2023-12-20 21:54:34,184 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 110/139 [2023-12-20 21:54:34,272 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 111/139 [2023-12-20 21:54:34,360 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 112/139 [2023-12-20 21:54:34,448 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 113/139 [2023-12-20 21:54:34,537 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 114/139 [2023-12-20 21:54:34,626 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 115/139 [2023-12-20 21:54:34,716 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 116/139 [2023-12-20 21:54:34,804 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 117/139 [2023-12-20 21:54:34,892 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 118/139 [2023-12-20 21:54:34,980 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 119/139 [2023-12-20 21:54:35,069 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 120/139 [2023-12-20 21:54:35,157 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 121/139 [2023-12-20 21:54:35,245 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 122/139 [2023-12-20 21:54:35,333 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 123/139 [2023-12-20 21:54:35,420 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 124/139 [2023-12-20 21:54:35,509 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 125/139 [2023-12-20 21:54:35,597 INFO test.py line 196 131400] Test: 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[2023-12-20 21:54:36,533 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 137/139 [2023-12-20 21:54:36,617 INFO test.py line 196 131400] Test: 31/78-scene0221_00, Batch: 138/139 [2023-12-20 21:54:36,639 INFO test.py line 230 131400] Test: scene0221_00 [31/78]-185828 Batch 11.941 (10.806) Accuracy 0.9396 (0.8538) mIoU 0.6350 (0.7617) [2023-12-20 21:54:37,016 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 0/118 [2023-12-20 21:54:37,098 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 1/118 [2023-12-20 21:54:37,176 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 2/118 [2023-12-20 21:54:37,254 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 3/118 [2023-12-20 21:54:37,334 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 4/118 [2023-12-20 21:54:37,417 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 5/118 [2023-12-20 21:54:37,495 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 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131400] Test: 32/78-scene0221_01, Batch: 17/118 [2023-12-20 21:54:38,450 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 18/118 [2023-12-20 21:54:38,529 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 19/118 [2023-12-20 21:54:38,608 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 20/118 [2023-12-20 21:54:38,686 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 21/118 [2023-12-20 21:54:38,764 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 22/118 [2023-12-20 21:54:38,843 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 23/118 [2023-12-20 21:54:38,922 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 24/118 [2023-12-20 21:54:39,002 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 25/118 [2023-12-20 21:54:39,084 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 26/118 [2023-12-20 21:54:39,164 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 27/118 [2023-12-20 21:54:39,242 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 28/118 [2023-12-20 21:54:39,322 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 29/118 [2023-12-20 21:54:39,401 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 30/118 [2023-12-20 21:54:39,479 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 31/118 [2023-12-20 21:54:39,564 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 32/118 [2023-12-20 21:54:39,686 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 33/118 [2023-12-20 21:54:39,826 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 34/118 [2023-12-20 21:54:39,906 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 35/118 [2023-12-20 21:54:39,990 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 36/118 [2023-12-20 21:54:40,083 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 37/118 [2023-12-20 21:54:40,179 INFO test.py line 196 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[2023-12-20 21:54:41,056 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 49/118 [2023-12-20 21:54:41,134 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 50/118 [2023-12-20 21:54:41,211 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 51/118 [2023-12-20 21:54:41,288 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 52/118 [2023-12-20 21:54:41,365 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 53/118 [2023-12-20 21:54:41,442 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 54/118 [2023-12-20 21:54:41,520 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 55/118 [2023-12-20 21:54:41,599 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 56/118 [2023-12-20 21:54:41,678 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 57/118 [2023-12-20 21:54:41,760 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 58/118 [2023-12-20 21:54:41,847 INFO test.py line 196 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[2023-12-20 21:54:42,732 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 70/118 [2023-12-20 21:54:42,813 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 71/118 [2023-12-20 21:54:42,890 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 72/118 [2023-12-20 21:54:42,967 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 73/118 [2023-12-20 21:54:43,045 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 74/118 [2023-12-20 21:54:43,120 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 75/118 [2023-12-20 21:54:43,204 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 76/118 [2023-12-20 21:54:43,286 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 77/118 [2023-12-20 21:54:43,368 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 78/118 [2023-12-20 21:54:43,460 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 79/118 [2023-12-20 21:54:43,548 INFO test.py line 196 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[2023-12-20 21:54:44,494 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 91/118 [2023-12-20 21:54:44,578 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 92/118 [2023-12-20 21:54:44,662 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 93/118 [2023-12-20 21:54:44,748 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 94/118 [2023-12-20 21:54:44,833 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 95/118 [2023-12-20 21:54:44,918 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 96/118 [2023-12-20 21:54:45,004 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 97/118 [2023-12-20 21:54:45,086 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 98/118 [2023-12-20 21:54:45,172 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 99/118 [2023-12-20 21:54:45,258 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 100/118 [2023-12-20 21:54:45,344 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 101/118 [2023-12-20 21:54:45,429 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 102/118 [2023-12-20 21:54:45,512 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 103/118 [2023-12-20 21:54:45,595 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 104/118 [2023-12-20 21:54:45,677 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 105/118 [2023-12-20 21:54:45,762 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 106/118 [2023-12-20 21:54:45,851 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 107/118 [2023-12-20 21:54:45,937 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 108/118 [2023-12-20 21:54:46,021 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 109/118 [2023-12-20 21:54:46,109 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 110/118 [2023-12-20 21:54:46,192 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 111/118 [2023-12-20 21:54:46,278 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 112/118 [2023-12-20 21:54:46,359 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 113/118 [2023-12-20 21:54:46,440 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 114/118 [2023-12-20 21:54:46,524 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 115/118 [2023-12-20 21:54:46,609 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 116/118 [2023-12-20 21:54:46,690 INFO test.py line 196 131400] Test: 32/78-scene0221_01, Batch: 117/118 [2023-12-20 21:54:46,707 INFO test.py line 230 131400] Test: scene0221_01 [32/78]-170654 Batch 9.775 (10.774) Accuracy 0.9486 (0.8566) mIoU 0.6579 (0.7630) [2023-12-20 21:54:47,089 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 0/136 [2023-12-20 21:54:47,150 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 1/136 [2023-12-20 21:54:47,205 INFO test.py line 196 131400] Test: 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Test: 33/78-scene0684_01, Batch: 107/136 [2023-12-20 21:54:53,547 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 108/136 [2023-12-20 21:54:53,597 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 109/136 [2023-12-20 21:54:53,644 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 110/136 [2023-12-20 21:54:53,691 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 111/136 [2023-12-20 21:54:53,739 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 112/136 [2023-12-20 21:54:53,795 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 113/136 [2023-12-20 21:54:53,858 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 114/136 [2023-12-20 21:54:53,911 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 115/136 [2023-12-20 21:54:53,961 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 116/136 [2023-12-20 21:54:54,010 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 117/136 [2023-12-20 21:54:54,058 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 118/136 [2023-12-20 21:54:54,109 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 119/136 [2023-12-20 21:54:54,161 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 120/136 [2023-12-20 21:54:54,214 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 121/136 [2023-12-20 21:54:54,271 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 122/136 [2023-12-20 21:54:54,327 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 123/136 [2023-12-20 21:54:54,379 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 124/136 [2023-12-20 21:54:54,429 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 125/136 [2023-12-20 21:54:54,481 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 126/136 [2023-12-20 21:54:54,536 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 127/136 [2023-12-20 21:54:54,592 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 128/136 [2023-12-20 21:54:54,646 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 129/136 [2023-12-20 21:54:54,698 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 130/136 [2023-12-20 21:54:54,749 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 131/136 [2023-12-20 21:54:54,800 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 132/136 [2023-12-20 21:54:54,851 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 133/136 [2023-12-20 21:54:54,902 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 134/136 [2023-12-20 21:54:54,952 INFO test.py line 196 131400] Test: 33/78-scene0684_01, Batch: 135/136 [2023-12-20 21:54:54,960 INFO test.py line 230 131400] Test: scene0684_01 [33/78]-39474 Batch 7.942 (10.688) Accuracy 0.9832 (0.8568) mIoU 0.6986 (0.7633) [2023-12-20 21:54:55,090 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 0/123 [2023-12-20 21:54:55,150 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 1/123 [2023-12-20 21:54:55,211 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 2/123 [2023-12-20 21:54:55,271 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 3/123 [2023-12-20 21:54:55,329 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 4/123 [2023-12-20 21:54:55,386 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 5/123 [2023-12-20 21:54:55,446 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 6/123 [2023-12-20 21:54:55,507 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 7/123 [2023-12-20 21:54:55,567 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 8/123 [2023-12-20 21:54:55,625 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 9/123 [2023-12-20 21:54:55,683 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 10/123 [2023-12-20 21:54:55,741 INFO test.py line 196 131400] Test: 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21:54:56,444 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 22/123 [2023-12-20 21:54:56,507 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 23/123 [2023-12-20 21:54:56,567 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 24/123 [2023-12-20 21:54:56,623 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 25/123 [2023-12-20 21:54:56,680 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 26/123 [2023-12-20 21:54:56,738 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 27/123 [2023-12-20 21:54:56,797 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 28/123 [2023-12-20 21:54:56,853 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 29/123 [2023-12-20 21:54:56,908 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 30/123 [2023-12-20 21:54:56,964 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 31/123 [2023-12-20 21:54:57,020 INFO test.py line 196 131400] Test: 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21:54:57,635 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 43/123 [2023-12-20 21:54:57,691 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 44/123 [2023-12-20 21:54:57,745 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 45/123 [2023-12-20 21:54:57,800 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 46/123 [2023-12-20 21:54:57,854 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 47/123 [2023-12-20 21:54:57,909 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 48/123 [2023-12-20 21:54:57,964 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 49/123 [2023-12-20 21:54:58,023 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 50/123 [2023-12-20 21:54:58,078 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 51/123 [2023-12-20 21:54:58,134 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 52/123 [2023-12-20 21:54:58,189 INFO test.py line 196 131400] Test: 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21:54:58,800 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 64/123 [2023-12-20 21:54:58,855 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 65/123 [2023-12-20 21:54:58,910 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 66/123 [2023-12-20 21:54:58,965 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 67/123 [2023-12-20 21:54:59,021 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 68/123 [2023-12-20 21:54:59,076 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 69/123 [2023-12-20 21:54:59,132 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 70/123 [2023-12-20 21:54:59,186 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 71/123 [2023-12-20 21:54:59,240 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 72/123 [2023-12-20 21:54:59,295 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 73/123 [2023-12-20 21:54:59,350 INFO test.py line 196 131400] Test: 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21:54:59,969 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 85/123 [2023-12-20 21:55:00,052 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 86/123 [2023-12-20 21:55:00,139 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 87/123 [2023-12-20 21:55:00,222 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 88/123 [2023-12-20 21:55:00,288 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 89/123 [2023-12-20 21:55:00,346 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 90/123 [2023-12-20 21:55:00,403 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 91/123 [2023-12-20 21:55:00,460 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 92/123 [2023-12-20 21:55:00,521 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 93/123 [2023-12-20 21:55:00,579 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 94/123 [2023-12-20 21:55:00,637 INFO test.py line 196 131400] Test: 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[2023-12-20 21:55:01,281 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 106/123 [2023-12-20 21:55:01,338 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 107/123 [2023-12-20 21:55:01,395 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 108/123 [2023-12-20 21:55:01,452 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 109/123 [2023-12-20 21:55:01,510 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 110/123 [2023-12-20 21:55:01,567 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 111/123 [2023-12-20 21:55:01,627 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 112/123 [2023-12-20 21:55:01,683 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 113/123 [2023-12-20 21:55:01,738 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 114/123 [2023-12-20 21:55:01,794 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 115/123 [2023-12-20 21:55:01,850 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 116/123 [2023-12-20 21:55:01,905 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 117/123 [2023-12-20 21:55:01,961 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 118/123 [2023-12-20 21:55:02,017 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 119/123 [2023-12-20 21:55:02,072 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 120/123 [2023-12-20 21:55:02,128 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 121/123 [2023-12-20 21:55:02,184 INFO test.py line 196 131400] Test: 34/78-scene0338_01, Batch: 122/123 [2023-12-20 21:55:02,195 INFO test.py line 230 131400] Test: scene0338_01 [34/78]-85459 Batch 7.164 (10.584) Accuracy 0.9711 (0.8632) mIoU 0.8002 (0.7710) [2023-12-20 21:55:02,374 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 0/138 [2023-12-20 21:55:02,433 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 1/138 [2023-12-20 21:55:02,492 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 2/138 [2023-12-20 21:55:02,552 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 3/138 [2023-12-20 21:55:02,611 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 4/138 [2023-12-20 21:55:02,669 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 5/138 [2023-12-20 21:55:02,727 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 6/138 [2023-12-20 21:55:02,785 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 7/138 [2023-12-20 21:55:02,842 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 8/138 [2023-12-20 21:55:02,900 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 9/138 [2023-12-20 21:55:02,958 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 10/138 [2023-12-20 21:55:03,015 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 11/138 [2023-12-20 21:55:03,073 INFO test.py line 196 131400] Test: 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21:55:03,744 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 23/138 [2023-12-20 21:55:03,806 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 24/138 [2023-12-20 21:55:03,868 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 25/138 [2023-12-20 21:55:03,935 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 26/138 [2023-12-20 21:55:04,007 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 27/138 [2023-12-20 21:55:04,067 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 28/138 [2023-12-20 21:55:04,129 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 29/138 [2023-12-20 21:55:04,189 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 30/138 [2023-12-20 21:55:04,248 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 31/138 [2023-12-20 21:55:04,310 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 32/138 [2023-12-20 21:55:04,368 INFO test.py line 196 131400] Test: 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21:55:05,044 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 44/138 [2023-12-20 21:55:05,116 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 45/138 [2023-12-20 21:55:05,197 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 46/138 [2023-12-20 21:55:05,278 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 47/138 [2023-12-20 21:55:05,359 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 48/138 [2023-12-20 21:55:05,443 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 49/138 [2023-12-20 21:55:05,527 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 50/138 [2023-12-20 21:55:05,608 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 51/138 [2023-12-20 21:55:05,695 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 52/138 [2023-12-20 21:55:05,777 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 53/138 [2023-12-20 21:55:05,859 INFO test.py line 196 131400] Test: 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21:55:06,555 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 65/138 [2023-12-20 21:55:06,618 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 66/138 [2023-12-20 21:55:06,681 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 67/138 [2023-12-20 21:55:06,743 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 68/138 [2023-12-20 21:55:06,818 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 69/138 [2023-12-20 21:55:06,880 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 70/138 [2023-12-20 21:55:06,938 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 71/138 [2023-12-20 21:55:06,996 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 72/138 [2023-12-20 21:55:07,061 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 73/138 [2023-12-20 21:55:07,140 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 74/138 [2023-12-20 21:55:07,227 INFO test.py line 196 131400] Test: 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21:55:07,916 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 86/138 [2023-12-20 21:55:07,986 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 87/138 [2023-12-20 21:55:08,048 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 88/138 [2023-12-20 21:55:08,111 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 89/138 [2023-12-20 21:55:08,172 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 90/138 [2023-12-20 21:55:08,230 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 91/138 [2023-12-20 21:55:08,301 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 92/138 [2023-12-20 21:55:08,363 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 93/138 [2023-12-20 21:55:08,424 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 94/138 [2023-12-20 21:55:08,484 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 95/138 [2023-12-20 21:55:08,542 INFO test.py line 196 131400] Test: 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[2023-12-20 21:55:09,262 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 107/138 [2023-12-20 21:55:09,325 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 108/138 [2023-12-20 21:55:09,386 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 109/138 [2023-12-20 21:55:09,445 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 110/138 [2023-12-20 21:55:09,506 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 111/138 [2023-12-20 21:55:09,568 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 112/138 [2023-12-20 21:55:09,632 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 113/138 [2023-12-20 21:55:09,692 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 114/138 [2023-12-20 21:55:09,753 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 115/138 [2023-12-20 21:55:09,820 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 116/138 [2023-12-20 21:55:09,887 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 117/138 [2023-12-20 21:55:09,953 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 118/138 [2023-12-20 21:55:10,018 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 119/138 [2023-12-20 21:55:10,091 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 120/138 [2023-12-20 21:55:10,167 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 121/138 [2023-12-20 21:55:10,234 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 122/138 [2023-12-20 21:55:10,291 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 123/138 [2023-12-20 21:55:10,357 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 124/138 [2023-12-20 21:55:10,423 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 125/138 [2023-12-20 21:55:10,490 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 126/138 [2023-12-20 21:55:10,554 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 127/138 [2023-12-20 21:55:10,623 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 128/138 [2023-12-20 21:55:10,694 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 129/138 [2023-12-20 21:55:10,759 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 130/138 [2023-12-20 21:55:10,824 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 131/138 [2023-12-20 21:55:10,888 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 132/138 [2023-12-20 21:55:10,950 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 133/138 [2023-12-20 21:55:11,018 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 134/138 [2023-12-20 21:55:11,090 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 135/138 [2023-12-20 21:55:11,150 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 136/138 [2023-12-20 21:55:11,208 INFO test.py line 196 131400] Test: 35/78-scene0278_01, Batch: 137/138 [2023-12-20 21:55:11,222 INFO test.py line 230 131400] Test: scene0278_01 [35/78]-83269 Batch 8.911 (10.537) Accuracy 0.8643 (0.8635) mIoU 0.6281 (0.7664) [2023-12-20 21:55:11,463 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 0/138 [2023-12-20 21:55:11,556 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 1/138 [2023-12-20 21:55:11,643 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 2/138 [2023-12-20 21:55:11,729 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 3/138 [2023-12-20 21:55:11,815 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 4/138 [2023-12-20 21:55:11,914 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 5/138 [2023-12-20 21:55:12,029 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 6/138 [2023-12-20 21:55:12,147 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 7/138 [2023-12-20 21:55:12,237 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 8/138 [2023-12-20 21:55:12,326 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 9/138 [2023-12-20 21:55:12,411 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 10/138 [2023-12-20 21:55:12,500 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 11/138 [2023-12-20 21:55:12,590 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 12/138 [2023-12-20 21:55:12,678 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 13/138 [2023-12-20 21:55:12,764 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 14/138 [2023-12-20 21:55:12,853 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 15/138 [2023-12-20 21:55:12,940 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 16/138 [2023-12-20 21:55:13,026 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 17/138 [2023-12-20 21:55:13,114 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 18/138 [2023-12-20 21:55:13,206 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 19/138 [2023-12-20 21:55:13,297 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 20/138 [2023-12-20 21:55:13,385 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 21/138 [2023-12-20 21:55:13,472 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 22/138 [2023-12-20 21:55:13,560 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 23/138 [2023-12-20 21:55:13,651 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 24/138 [2023-12-20 21:55:13,739 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 25/138 [2023-12-20 21:55:13,830 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 26/138 [2023-12-20 21:55:13,922 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 27/138 [2023-12-20 21:55:14,017 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 28/138 [2023-12-20 21:55:14,110 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 29/138 [2023-12-20 21:55:14,201 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 30/138 [2023-12-20 21:55:14,296 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 31/138 [2023-12-20 21:55:14,390 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 32/138 [2023-12-20 21:55:14,478 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 33/138 [2023-12-20 21:55:14,565 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 34/138 [2023-12-20 21:55:14,650 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 35/138 [2023-12-20 21:55:14,735 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 36/138 [2023-12-20 21:55:14,821 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 37/138 [2023-12-20 21:55:14,922 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 38/138 [2023-12-20 21:55:15,018 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 39/138 [2023-12-20 21:55:15,102 INFO test.py line 196 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[2023-12-20 21:55:16,057 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 51/138 [2023-12-20 21:55:16,141 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 52/138 [2023-12-20 21:55:16,225 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 53/138 [2023-12-20 21:55:16,307 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 54/138 [2023-12-20 21:55:16,388 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 55/138 [2023-12-20 21:55:16,468 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 56/138 [2023-12-20 21:55:16,560 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 57/138 [2023-12-20 21:55:16,650 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 58/138 [2023-12-20 21:55:16,731 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 59/138 [2023-12-20 21:55:16,812 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 60/138 [2023-12-20 21:55:16,899 INFO test.py line 196 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[2023-12-20 21:55:17,785 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 72/138 [2023-12-20 21:55:17,868 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 73/138 [2023-12-20 21:55:17,953 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 74/138 [2023-12-20 21:55:18,034 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 75/138 [2023-12-20 21:55:18,112 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 76/138 [2023-12-20 21:55:18,194 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 77/138 [2023-12-20 21:55:18,275 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 78/138 [2023-12-20 21:55:18,353 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 79/138 [2023-12-20 21:55:18,431 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 80/138 [2023-12-20 21:55:18,510 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 81/138 [2023-12-20 21:55:18,588 INFO test.py line 196 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[2023-12-20 21:55:19,690 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 93/138 [2023-12-20 21:55:19,778 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 94/138 [2023-12-20 21:55:19,866 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 95/138 [2023-12-20 21:55:19,955 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 96/138 [2023-12-20 21:55:20,055 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 97/138 [2023-12-20 21:55:20,150 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 98/138 [2023-12-20 21:55:20,240 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 99/138 [2023-12-20 21:55:20,330 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 100/138 [2023-12-20 21:55:20,417 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 101/138 [2023-12-20 21:55:20,508 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 102/138 [2023-12-20 21:55:20,611 INFO test.py line 196 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test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 124/138 [2023-12-20 21:55:22,574 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 125/138 [2023-12-20 21:55:22,661 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 126/138 [2023-12-20 21:55:22,748 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 127/138 [2023-12-20 21:55:22,835 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 128/138 [2023-12-20 21:55:22,924 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 129/138 [2023-12-20 21:55:23,010 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 130/138 [2023-12-20 21:55:23,095 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 131/138 [2023-12-20 21:55:23,180 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 132/138 [2023-12-20 21:55:23,267 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 133/138 [2023-12-20 21:55:23,352 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 134/138 [2023-12-20 21:55:23,437 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 135/138 [2023-12-20 21:55:23,523 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 136/138 [2023-12-20 21:55:23,608 INFO test.py line 196 131400] Test: 36/78-scene0414_00, Batch: 137/138 [2023-12-20 21:55:23,624 INFO test.py line 230 131400] Test: scene0414_00 [36/78]-192685 Batch 12.261 (10.584) Accuracy 0.9495 (0.8654) mIoU 0.8991 (0.7681) [2023-12-20 21:55:24,028 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 0/126 [2023-12-20 21:55:24,146 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 1/126 [2023-12-20 21:55:24,268 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 2/126 [2023-12-20 21:55:24,387 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 3/126 [2023-12-20 21:55:24,506 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 4/126 [2023-12-20 21:55:24,625 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 5/126 [2023-12-20 21:55:24,744 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 6/126 [2023-12-20 21:55:24,862 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 7/126 [2023-12-20 21:55:24,980 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 8/126 [2023-12-20 21:55:25,099 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 9/126 [2023-12-20 21:55:25,217 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 10/126 [2023-12-20 21:55:25,334 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 11/126 [2023-12-20 21:55:25,454 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 12/126 [2023-12-20 21:55:25,573 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 13/126 [2023-12-20 21:55:25,692 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 14/126 [2023-12-20 21:55:25,809 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 15/126 [2023-12-20 21:55:25,926 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 16/126 [2023-12-20 21:55:26,043 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 17/126 [2023-12-20 21:55:26,161 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 18/126 [2023-12-20 21:55:26,278 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 19/126 [2023-12-20 21:55:26,395 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 20/126 [2023-12-20 21:55:26,512 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 21/126 [2023-12-20 21:55:26,630 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 22/126 [2023-12-20 21:55:26,748 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 23/126 [2023-12-20 21:55:26,865 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 24/126 [2023-12-20 21:55:26,983 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 25/126 [2023-12-20 21:55:27,101 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 26/126 [2023-12-20 21:55:27,218 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 27/126 [2023-12-20 21:55:27,359 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 28/126 [2023-12-20 21:55:27,491 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 29/126 [2023-12-20 21:55:27,609 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 30/126 [2023-12-20 21:55:27,726 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 31/126 [2023-12-20 21:55:27,845 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 32/126 [2023-12-20 21:55:27,963 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 33/126 [2023-12-20 21:55:28,080 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 34/126 [2023-12-20 21:55:28,197 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 35/126 [2023-12-20 21:55:28,314 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 36/126 [2023-12-20 21:55:28,431 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 37/126 [2023-12-20 21:55:28,550 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 38/126 [2023-12-20 21:55:28,669 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 39/126 [2023-12-20 21:55:28,782 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 40/126 [2023-12-20 21:55:28,893 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 41/126 [2023-12-20 21:55:29,007 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 42/126 [2023-12-20 21:55:29,117 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 43/126 [2023-12-20 21:55:29,227 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 44/126 [2023-12-20 21:55:29,337 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 45/126 [2023-12-20 21:55:29,446 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 46/126 [2023-12-20 21:55:29,555 INFO test.py line 196 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[2023-12-20 21:55:30,770 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 58/126 [2023-12-20 21:55:30,878 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 59/126 [2023-12-20 21:55:30,987 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 60/126 [2023-12-20 21:55:31,095 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 61/126 [2023-12-20 21:55:31,206 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 62/126 [2023-12-20 21:55:31,320 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 63/126 [2023-12-20 21:55:31,434 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 64/126 [2023-12-20 21:55:31,548 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 65/126 [2023-12-20 21:55:31,659 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 66/126 [2023-12-20 21:55:31,768 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 67/126 [2023-12-20 21:55:31,881 INFO test.py line 196 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[2023-12-20 21:55:33,129 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 79/126 [2023-12-20 21:55:33,253 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 80/126 [2023-12-20 21:55:33,377 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 81/126 [2023-12-20 21:55:33,500 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 82/126 [2023-12-20 21:55:33,623 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 83/126 [2023-12-20 21:55:33,748 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 84/126 [2023-12-20 21:55:33,874 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 85/126 [2023-12-20 21:55:34,002 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 86/126 [2023-12-20 21:55:34,132 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 87/126 [2023-12-20 21:55:34,255 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 88/126 [2023-12-20 21:55:34,380 INFO test.py line 196 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[2023-12-20 21:55:35,759 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 100/126 [2023-12-20 21:55:35,883 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 101/126 [2023-12-20 21:55:36,008 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 102/126 [2023-12-20 21:55:36,134 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 103/126 [2023-12-20 21:55:36,262 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 104/126 [2023-12-20 21:55:36,393 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 105/126 [2023-12-20 21:55:36,521 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 106/126 [2023-12-20 21:55:36,644 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 107/126 [2023-12-20 21:55:36,773 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 108/126 [2023-12-20 21:55:36,905 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 109/126 [2023-12-20 21:55:37,030 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 110/126 [2023-12-20 21:55:37,158 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 111/126 [2023-12-20 21:55:37,283 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 112/126 [2023-12-20 21:55:37,409 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 113/126 [2023-12-20 21:55:37,531 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 114/126 [2023-12-20 21:55:37,653 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 115/126 [2023-12-20 21:55:37,770 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 116/126 [2023-12-20 21:55:37,887 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 117/126 [2023-12-20 21:55:38,007 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 118/126 [2023-12-20 21:55:38,125 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 119/126 [2023-12-20 21:55:38,245 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 120/126 [2023-12-20 21:55:38,364 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 121/126 [2023-12-20 21:55:38,481 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 122/126 [2023-12-20 21:55:38,601 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 123/126 [2023-12-20 21:55:38,719 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 124/126 [2023-12-20 21:55:38,837 INFO test.py line 196 131400] Test: 37/78-scene0207_00, Batch: 125/126 [2023-12-20 21:55:38,862 INFO test.py line 230 131400] Test: scene0207_00 [37/78]-304396 Batch 14.969 (10.703) Accuracy 0.8798 (0.8652) mIoU 0.7486 (0.7762) [2023-12-20 21:55:39,469 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 0/135 [2023-12-20 21:55:39,571 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 1/135 [2023-12-20 21:55:39,669 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 2/135 [2023-12-20 21:55:39,767 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 3/135 [2023-12-20 21:55:39,866 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 4/135 [2023-12-20 21:55:39,965 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 5/135 [2023-12-20 21:55:40,064 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 6/135 [2023-12-20 21:55:40,163 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 7/135 [2023-12-20 21:55:40,261 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 8/135 [2023-12-20 21:55:40,360 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 9/135 [2023-12-20 21:55:40,462 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 10/135 [2023-12-20 21:55:40,564 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 11/135 [2023-12-20 21:55:40,666 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 12/135 [2023-12-20 21:55:40,771 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 13/135 [2023-12-20 21:55:40,877 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 14/135 [2023-12-20 21:55:40,975 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 15/135 [2023-12-20 21:55:41,073 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 16/135 [2023-12-20 21:55:41,172 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 17/135 [2023-12-20 21:55:41,271 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 18/135 [2023-12-20 21:55:41,371 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 19/135 [2023-12-20 21:55:41,469 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 20/135 [2023-12-20 21:55:41,572 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 21/135 [2023-12-20 21:55:41,671 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 22/135 [2023-12-20 21:55:41,772 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 23/135 [2023-12-20 21:55:41,881 INFO test.py line 196 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[2023-12-20 21:55:43,028 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 35/135 [2023-12-20 21:55:43,131 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 36/135 [2023-12-20 21:55:43,241 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 37/135 [2023-12-20 21:55:43,350 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 38/135 [2023-12-20 21:55:43,453 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 39/135 [2023-12-20 21:55:43,556 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 40/135 [2023-12-20 21:55:43,665 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 41/135 [2023-12-20 21:55:43,769 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 42/135 [2023-12-20 21:55:43,874 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 43/135 [2023-12-20 21:55:43,975 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 44/135 [2023-12-20 21:55:44,076 INFO test.py line 196 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[2023-12-20 21:55:45,136 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 56/135 [2023-12-20 21:55:45,231 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 57/135 [2023-12-20 21:55:45,328 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 58/135 [2023-12-20 21:55:45,428 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 59/135 [2023-12-20 21:55:45,523 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 60/135 [2023-12-20 21:55:45,617 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 61/135 [2023-12-20 21:55:45,710 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 62/135 [2023-12-20 21:55:45,803 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 63/135 [2023-12-20 21:55:45,897 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 64/135 [2023-12-20 21:55:45,990 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 65/135 [2023-12-20 21:55:46,083 INFO test.py line 196 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[2023-12-20 21:55:47,126 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 77/135 [2023-12-20 21:55:47,220 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 78/135 [2023-12-20 21:55:47,313 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 79/135 [2023-12-20 21:55:47,410 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 80/135 [2023-12-20 21:55:47,506 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 81/135 [2023-12-20 21:55:47,601 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 82/135 [2023-12-20 21:55:47,696 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 83/135 [2023-12-20 21:55:47,791 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 84/135 [2023-12-20 21:55:47,895 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 85/135 [2023-12-20 21:55:47,997 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 86/135 [2023-12-20 21:55:48,092 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 87/135 [2023-12-20 21:55:48,196 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 88/135 [2023-12-20 21:55:48,302 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 89/135 [2023-12-20 21:55:48,407 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 90/135 [2023-12-20 21:55:48,512 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 91/135 [2023-12-20 21:55:48,620 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 92/135 [2023-12-20 21:55:48,726 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 93/135 [2023-12-20 21:55:48,836 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 94/135 [2023-12-20 21:55:48,943 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 95/135 [2023-12-20 21:55:49,045 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 96/135 [2023-12-20 21:55:49,150 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 97/135 [2023-12-20 21:55:49,255 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 98/135 [2023-12-20 21:55:49,359 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 99/135 [2023-12-20 21:55:49,463 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 100/135 [2023-12-20 21:55:49,565 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 101/135 [2023-12-20 21:55:49,668 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 102/135 [2023-12-20 21:55:49,778 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 103/135 [2023-12-20 21:55:49,883 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 104/135 [2023-12-20 21:55:49,988 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 105/135 [2023-12-20 21:55:50,093 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 106/135 [2023-12-20 21:55:50,196 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 107/135 [2023-12-20 21:55:50,301 INFO test.py line 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Batch: 118/135 [2023-12-20 21:55:51,499 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 119/135 [2023-12-20 21:55:51,601 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 120/135 [2023-12-20 21:55:51,773 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 121/135 [2023-12-20 21:55:51,994 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 122/135 [2023-12-20 21:55:52,142 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 123/135 [2023-12-20 21:55:52,243 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 124/135 [2023-12-20 21:55:52,343 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 125/135 [2023-12-20 21:55:52,442 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 126/135 [2023-12-20 21:55:52,543 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 127/135 [2023-12-20 21:55:52,643 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 128/135 [2023-12-20 21:55:52,748 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 129/135 [2023-12-20 21:55:52,849 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 130/135 [2023-12-20 21:55:52,947 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 131/135 [2023-12-20 21:55:53,046 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 132/135 [2023-12-20 21:55:53,146 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 133/135 [2023-12-20 21:55:53,245 INFO test.py line 196 131400] Test: 38/78-scene0621_00, Batch: 134/135 [2023-12-20 21:55:53,277 INFO test.py line 230 131400] Test: scene0621_00 [38/78]-250821 Batch 13.921 (10.788) Accuracy 0.9853 (0.8663) mIoU 0.9696 (0.7782) [2023-12-20 21:55:53,695 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 0/122 [2023-12-20 21:55:53,768 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 1/122 [2023-12-20 21:55:53,843 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 2/122 [2023-12-20 21:55:53,911 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 3/122 [2023-12-20 21:55:53,979 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 4/122 [2023-12-20 21:55:54,046 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 5/122 [2023-12-20 21:55:54,114 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 6/122 [2023-12-20 21:55:54,182 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 7/122 [2023-12-20 21:55:54,255 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 8/122 [2023-12-20 21:55:54,328 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 9/122 [2023-12-20 21:55:54,400 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 10/122 [2023-12-20 21:55:54,472 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 11/122 [2023-12-20 21:55:54,547 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 12/122 [2023-12-20 21:55:54,627 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 13/122 [2023-12-20 21:55:54,700 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 14/122 [2023-12-20 21:55:54,775 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 15/122 [2023-12-20 21:55:54,848 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 16/122 [2023-12-20 21:55:54,932 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 17/122 [2023-12-20 21:55:55,015 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 18/122 [2023-12-20 21:55:55,089 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 19/122 [2023-12-20 21:55:55,172 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 20/122 [2023-12-20 21:55:55,293 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 21/122 [2023-12-20 21:55:55,397 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 22/122 [2023-12-20 21:55:55,480 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 23/122 [2023-12-20 21:55:55,551 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 24/122 [2023-12-20 21:55:55,623 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 25/122 [2023-12-20 21:55:55,694 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 26/122 [2023-12-20 21:55:55,763 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 27/122 [2023-12-20 21:55:55,833 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 28/122 [2023-12-20 21:55:55,900 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 29/122 [2023-12-20 21:55:55,969 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 30/122 [2023-12-20 21:55:56,038 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 31/122 [2023-12-20 21:55:56,111 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 32/122 [2023-12-20 21:55:56,180 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 33/122 [2023-12-20 21:55:56,247 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 34/122 [2023-12-20 21:55:56,316 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 35/122 [2023-12-20 21:55:56,390 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 36/122 [2023-12-20 21:55:56,469 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 37/122 [2023-12-20 21:55:56,547 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 38/122 [2023-12-20 21:55:56,619 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 39/122 [2023-12-20 21:55:56,687 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 40/122 [2023-12-20 21:55:56,753 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 41/122 [2023-12-20 21:55:56,821 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 42/122 [2023-12-20 21:55:56,890 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 43/122 [2023-12-20 21:55:56,962 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 44/122 [2023-12-20 21:55:57,036 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 45/122 [2023-12-20 21:55:57,104 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 46/122 [2023-12-20 21:55:57,174 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 47/122 [2023-12-20 21:55:57,243 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 48/122 [2023-12-20 21:55:57,310 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 49/122 [2023-12-20 21:55:57,377 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 50/122 [2023-12-20 21:55:57,444 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 51/122 [2023-12-20 21:55:57,509 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 52/122 [2023-12-20 21:55:57,582 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 53/122 [2023-12-20 21:55:57,651 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 54/122 [2023-12-20 21:55:57,716 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 55/122 [2023-12-20 21:55:57,782 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 56/122 [2023-12-20 21:55:57,846 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 57/122 [2023-12-20 21:55:57,919 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 58/122 [2023-12-20 21:55:57,996 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 59/122 [2023-12-20 21:55:58,071 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 60/122 [2023-12-20 21:55:58,146 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 61/122 [2023-12-20 21:55:58,216 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 62/122 [2023-12-20 21:55:58,282 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 63/122 [2023-12-20 21:55:58,347 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 64/122 [2023-12-20 21:55:58,413 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 65/122 [2023-12-20 21:55:58,481 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 66/122 [2023-12-20 21:55:58,550 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 67/122 [2023-12-20 21:55:58,617 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 68/122 [2023-12-20 21:55:58,683 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 69/122 [2023-12-20 21:55:58,751 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 70/122 [2023-12-20 21:55:58,816 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 71/122 [2023-12-20 21:55:58,884 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 72/122 [2023-12-20 21:55:58,955 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 73/122 [2023-12-20 21:55:59,025 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 74/122 [2023-12-20 21:55:59,092 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 75/122 [2023-12-20 21:55:59,165 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 76/122 [2023-12-20 21:55:59,243 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 77/122 [2023-12-20 21:55:59,318 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 78/122 [2023-12-20 21:55:59,389 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 79/122 [2023-12-20 21:55:59,461 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 80/122 [2023-12-20 21:55:59,535 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 81/122 [2023-12-20 21:55:59,605 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 82/122 [2023-12-20 21:55:59,675 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 83/122 [2023-12-20 21:55:59,745 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 84/122 [2023-12-20 21:55:59,813 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 85/122 [2023-12-20 21:55:59,882 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 86/122 [2023-12-20 21:55:59,953 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 87/122 [2023-12-20 21:56:00,027 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 88/122 [2023-12-20 21:56:00,098 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 89/122 [2023-12-20 21:56:00,167 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 90/122 [2023-12-20 21:56:00,238 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 91/122 [2023-12-20 21:56:00,308 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 92/122 [2023-12-20 21:56:00,378 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 93/122 [2023-12-20 21:56:00,447 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 94/122 [2023-12-20 21:56:00,516 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 95/122 [2023-12-20 21:56:00,587 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 96/122 [2023-12-20 21:56:00,658 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 97/122 [2023-12-20 21:56:00,728 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 98/122 [2023-12-20 21:56:00,799 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 99/122 [2023-12-20 21:56:00,870 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 100/122 [2023-12-20 21:56:00,942 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 101/122 [2023-12-20 21:56:01,013 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 102/122 [2023-12-20 21:56:01,085 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 103/122 [2023-12-20 21:56:01,156 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 104/122 [2023-12-20 21:56:01,224 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 105/122 [2023-12-20 21:56:01,294 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 106/122 [2023-12-20 21:56:01,364 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 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test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 118/122 [2023-12-20 21:56:02,226 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 119/122 [2023-12-20 21:56:02,295 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 120/122 [2023-12-20 21:56:02,364 INFO test.py line 196 131400] Test: 39/78-scene0377_01, Batch: 121/122 [2023-12-20 21:56:02,378 INFO test.py line 230 131400] Test: scene0377_01 [39/78]-131297 Batch 8.766 (10.736) Accuracy 0.9390 (0.8662) mIoU 0.5406 (0.7780) [2023-12-20 21:56:02,697 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 0/148 [2023-12-20 21:56:02,825 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 1/148 [2023-12-20 21:56:02,953 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 2/148 [2023-12-20 21:56:03,081 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 3/148 [2023-12-20 21:56:03,210 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 4/148 [2023-12-20 21:56:03,341 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 5/148 [2023-12-20 21:56:03,478 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 6/148 [2023-12-20 21:56:03,606 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 7/148 [2023-12-20 21:56:03,737 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 8/148 [2023-12-20 21:56:03,867 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 9/148 [2023-12-20 21:56:03,996 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 10/148 [2023-12-20 21:56:04,128 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 11/148 [2023-12-20 21:56:04,265 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 12/148 [2023-12-20 21:56:04,395 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 13/148 [2023-12-20 21:56:04,534 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 14/148 [2023-12-20 21:56:04,664 INFO test.py line 196 131400] Test: 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21:56:06,116 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 26/148 [2023-12-20 21:56:06,244 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 27/148 [2023-12-20 21:56:06,372 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 28/148 [2023-12-20 21:56:06,500 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 29/148 [2023-12-20 21:56:06,629 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 30/148 [2023-12-20 21:56:06,757 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 31/148 [2023-12-20 21:56:06,885 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 32/148 [2023-12-20 21:56:07,013 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 33/148 [2023-12-20 21:56:07,141 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 34/148 [2023-12-20 21:56:07,272 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 35/148 [2023-12-20 21:56:07,402 INFO test.py line 196 131400] Test: 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21:56:08,817 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 47/148 [2023-12-20 21:56:08,942 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 48/148 [2023-12-20 21:56:09,065 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 49/148 [2023-12-20 21:56:09,186 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 50/148 [2023-12-20 21:56:09,312 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 51/148 [2023-12-20 21:56:09,434 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 52/148 [2023-12-20 21:56:09,555 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 53/148 [2023-12-20 21:56:09,682 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 54/148 [2023-12-20 21:56:09,804 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 55/148 [2023-12-20 21:56:09,928 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 56/148 [2023-12-20 21:56:10,048 INFO test.py line 196 131400] Test: 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21:56:11,409 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 68/148 [2023-12-20 21:56:11,538 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 69/148 [2023-12-20 21:56:11,665 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 70/148 [2023-12-20 21:56:11,788 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 71/148 [2023-12-20 21:56:11,921 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 72/148 [2023-12-20 21:56:12,053 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 73/148 [2023-12-20 21:56:12,179 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 74/148 [2023-12-20 21:56:12,305 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 75/148 [2023-12-20 21:56:12,432 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 76/148 [2023-12-20 21:56:12,561 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 77/148 [2023-12-20 21:56:12,688 INFO test.py line 196 131400] Test: 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21:56:14,082 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 89/148 [2023-12-20 21:56:14,209 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 90/148 [2023-12-20 21:56:14,330 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 91/148 [2023-12-20 21:56:14,452 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 92/148 [2023-12-20 21:56:14,574 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 93/148 [2023-12-20 21:56:14,703 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 94/148 [2023-12-20 21:56:14,830 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 95/148 [2023-12-20 21:56:14,971 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 96/148 [2023-12-20 21:56:15,112 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 97/148 [2023-12-20 21:56:15,249 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 98/148 [2023-12-20 21:56:15,395 INFO test.py line 196 131400] Test: 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[2023-12-20 21:56:16,923 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 110/148 [2023-12-20 21:56:17,061 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 111/148 [2023-12-20 21:56:17,198 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 112/148 [2023-12-20 21:56:17,337 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 113/148 [2023-12-20 21:56:17,472 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 114/148 [2023-12-20 21:56:17,608 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 115/148 [2023-12-20 21:56:17,745 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 116/148 [2023-12-20 21:56:17,883 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 117/148 [2023-12-20 21:56:18,023 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 118/148 [2023-12-20 21:56:18,163 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 119/148 [2023-12-20 21:56:18,299 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 120/148 [2023-12-20 21:56:18,444 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 121/148 [2023-12-20 21:56:18,580 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 122/148 [2023-12-20 21:56:18,716 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 123/148 [2023-12-20 21:56:18,851 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 124/148 [2023-12-20 21:56:18,987 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 125/148 [2023-12-20 21:56:19,125 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 126/148 [2023-12-20 21:56:19,261 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 127/148 [2023-12-20 21:56:19,398 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 128/148 [2023-12-20 21:56:19,534 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 129/148 [2023-12-20 21:56:19,670 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 130/148 [2023-12-20 21:56:19,806 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 131/148 [2023-12-20 21:56:19,942 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 132/148 [2023-12-20 21:56:20,078 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 133/148 [2023-12-20 21:56:20,214 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 134/148 [2023-12-20 21:56:20,349 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 135/148 [2023-12-20 21:56:20,480 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 136/148 [2023-12-20 21:56:20,609 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 137/148 [2023-12-20 21:56:20,737 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 138/148 [2023-12-20 21:56:20,868 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 139/148 [2023-12-20 21:56:20,999 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 140/148 [2023-12-20 21:56:21,127 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 141/148 [2023-12-20 21:56:21,255 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 142/148 [2023-12-20 21:56:21,384 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 143/148 [2023-12-20 21:56:21,513 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 144/148 [2023-12-20 21:56:21,642 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 145/148 [2023-12-20 21:56:21,771 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 146/148 [2023-12-20 21:56:21,903 INFO test.py line 196 131400] Test: 40/78-scene0207_02, Batch: 147/148 [2023-12-20 21:56:21,994 INFO test.py line 230 131400] Test: scene0207_02 [40/78]-347322 Batch 19.435 (10.953) Accuracy 0.9515 (0.8690) mIoU 0.7880 (0.7846) [2023-12-20 21:56:22,534 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 0/116 [2023-12-20 21:56:22,587 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 1/116 [2023-12-20 21:56:22,639 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 2/116 [2023-12-20 21:56:22,691 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 3/116 [2023-12-20 21:56:22,742 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 4/116 [2023-12-20 21:56:22,792 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 5/116 [2023-12-20 21:56:22,844 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 6/116 [2023-12-20 21:56:22,897 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 7/116 [2023-12-20 21:56:22,958 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 8/116 [2023-12-20 21:56:23,016 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 9/116 [2023-12-20 21:56:23,068 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 10/116 [2023-12-20 21:56:23,120 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 11/116 [2023-12-20 21:56:23,171 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 12/116 [2023-12-20 21:56:23,223 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 13/116 [2023-12-20 21:56:23,274 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 14/116 [2023-12-20 21:56:23,326 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 15/116 [2023-12-20 21:56:23,381 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 16/116 [2023-12-20 21:56:23,432 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 17/116 [2023-12-20 21:56:23,482 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 18/116 [2023-12-20 21:56:23,531 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 19/116 [2023-12-20 21:56:23,581 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 20/116 [2023-12-20 21:56:23,634 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 21/116 [2023-12-20 21:56:23,695 INFO test.py line 196 131400] Test: 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21:56:26,499 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 75/116 [2023-12-20 21:56:26,551 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 76/116 [2023-12-20 21:56:26,602 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 77/116 [2023-12-20 21:56:26,654 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 78/116 [2023-12-20 21:56:26,704 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 79/116 [2023-12-20 21:56:26,755 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 80/116 [2023-12-20 21:56:26,806 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 81/116 [2023-12-20 21:56:26,857 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 82/116 [2023-12-20 21:56:26,908 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 83/116 [2023-12-20 21:56:26,959 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 84/116 [2023-12-20 21:56:27,009 INFO test.py line 196 131400] Test: 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21:56:27,572 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 96/116 [2023-12-20 21:56:27,624 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 97/116 [2023-12-20 21:56:27,675 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 98/116 [2023-12-20 21:56:27,726 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 99/116 [2023-12-20 21:56:27,777 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 100/116 [2023-12-20 21:56:27,829 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 101/116 [2023-12-20 21:56:27,880 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 102/116 [2023-12-20 21:56:27,931 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 103/116 [2023-12-20 21:56:27,982 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 104/116 [2023-12-20 21:56:28,033 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 105/116 [2023-12-20 21:56:28,085 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 106/116 [2023-12-20 21:56:28,138 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 107/116 [2023-12-20 21:56:28,189 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 108/116 [2023-12-20 21:56:28,242 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 109/116 [2023-12-20 21:56:28,293 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 110/116 [2023-12-20 21:56:28,343 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 111/116 [2023-12-20 21:56:28,393 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 112/116 [2023-12-20 21:56:28,443 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 113/116 [2023-12-20 21:56:28,493 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 114/116 [2023-12-20 21:56:28,543 INFO test.py line 196 131400] Test: 41/78-scene0423_02, Batch: 115/116 [2023-12-20 21:56:28,553 INFO test.py line 230 131400] Test: scene0423_02 [41/78]-60071 Batch 6.083 (10.834) Accuracy 0.9981 (0.8691) mIoU 0.7420 (0.7849) [2023-12-20 21:56:28,699 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 0/122 [2023-12-20 21:56:28,767 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 1/122 [2023-12-20 21:56:28,825 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 2/122 [2023-12-20 21:56:28,883 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 3/122 [2023-12-20 21:56:28,939 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 4/122 [2023-12-20 21:56:29,003 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 5/122 [2023-12-20 21:56:29,072 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 6/122 [2023-12-20 21:56:29,134 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 7/122 [2023-12-20 21:56:29,197 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 8/122 [2023-12-20 21:56:29,256 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 9/122 [2023-12-20 21:56:29,314 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 10/122 [2023-12-20 21:56:29,370 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 11/122 [2023-12-20 21:56:29,425 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 12/122 [2023-12-20 21:56:29,481 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 13/122 [2023-12-20 21:56:29,537 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 14/122 [2023-12-20 21:56:29,595 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 15/122 [2023-12-20 21:56:29,664 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 16/122 [2023-12-20 21:56:29,722 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 17/122 [2023-12-20 21:56:29,778 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 18/122 [2023-12-20 21:56:29,835 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 19/122 [2023-12-20 21:56:29,891 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 20/122 [2023-12-20 21:56:29,948 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 21/122 [2023-12-20 21:56:30,006 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 22/122 [2023-12-20 21:56:30,066 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 23/122 [2023-12-20 21:56:30,128 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 24/122 [2023-12-20 21:56:30,196 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 25/122 [2023-12-20 21:56:30,265 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 26/122 [2023-12-20 21:56:30,332 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 27/122 [2023-12-20 21:56:30,397 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 28/122 [2023-12-20 21:56:30,454 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 29/122 [2023-12-20 21:56:30,510 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 30/122 [2023-12-20 21:56:30,567 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 31/122 [2023-12-20 21:56:30,641 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 32/122 [2023-12-20 21:56:30,728 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 33/122 [2023-12-20 21:56:30,816 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 34/122 [2023-12-20 21:56:30,907 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 35/122 [2023-12-20 21:56:30,970 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 36/122 [2023-12-20 21:56:31,027 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 37/122 [2023-12-20 21:56:31,084 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 38/122 [2023-12-20 21:56:31,144 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 39/122 [2023-12-20 21:56:31,205 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 40/122 [2023-12-20 21:56:31,265 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 41/122 [2023-12-20 21:56:31,329 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 42/122 [2023-12-20 21:56:31,393 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 43/122 [2023-12-20 21:56:31,457 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 44/122 [2023-12-20 21:56:31,524 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 45/122 [2023-12-20 21:56:31,586 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 46/122 [2023-12-20 21:56:31,643 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 47/122 [2023-12-20 21:56:31,700 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 48/122 [2023-12-20 21:56:31,756 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 49/122 [2023-12-20 21:56:31,822 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 50/122 [2023-12-20 21:56:31,879 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 51/122 [2023-12-20 21:56:31,937 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 52/122 [2023-12-20 21:56:31,994 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 53/122 [2023-12-20 21:56:32,051 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 54/122 [2023-12-20 21:56:32,105 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 55/122 [2023-12-20 21:56:32,160 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 56/122 [2023-12-20 21:56:32,215 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 57/122 [2023-12-20 21:56:32,269 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 58/122 [2023-12-20 21:56:32,323 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 59/122 [2023-12-20 21:56:32,378 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 60/122 [2023-12-20 21:56:32,433 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 61/122 [2023-12-20 21:56:32,487 INFO test.py line 196 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[2023-12-20 21:56:33,096 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 73/122 [2023-12-20 21:56:33,152 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 74/122 [2023-12-20 21:56:33,207 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 75/122 [2023-12-20 21:56:33,266 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 76/122 [2023-12-20 21:56:33,324 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 77/122 [2023-12-20 21:56:33,382 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 78/122 [2023-12-20 21:56:33,440 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 79/122 [2023-12-20 21:56:33,498 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 80/122 [2023-12-20 21:56:33,555 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 81/122 [2023-12-20 21:56:33,614 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 82/122 [2023-12-20 21:56:33,672 INFO test.py line 196 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[2023-12-20 21:56:34,322 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 94/122 [2023-12-20 21:56:34,380 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 95/122 [2023-12-20 21:56:34,438 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 96/122 [2023-12-20 21:56:34,496 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 97/122 [2023-12-20 21:56:34,555 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 98/122 [2023-12-20 21:56:34,636 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 99/122 [2023-12-20 21:56:34,719 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 100/122 [2023-12-20 21:56:34,807 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 101/122 [2023-12-20 21:56:34,890 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 102/122 [2023-12-20 21:56:34,948 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 103/122 [2023-12-20 21:56:35,007 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 104/122 [2023-12-20 21:56:35,065 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 105/122 [2023-12-20 21:56:35,124 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 106/122 [2023-12-20 21:56:35,182 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 107/122 [2023-12-20 21:56:35,241 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 108/122 [2023-12-20 21:56:35,299 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 109/122 [2023-12-20 21:56:35,358 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 110/122 [2023-12-20 21:56:35,416 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 111/122 [2023-12-20 21:56:35,473 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 112/122 [2023-12-20 21:56:35,531 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 113/122 [2023-12-20 21:56:35,587 INFO test.py line 196 131400] Test: 42/78-scene0686_01, Batch: 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Test: 43/78-scene0030_00, Batch: 106/130 [2023-12-20 21:56:48,127 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 107/130 [2023-12-20 21:56:48,244 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 108/130 [2023-12-20 21:56:48,361 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 109/130 [2023-12-20 21:56:48,478 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 110/130 [2023-12-20 21:56:48,595 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 111/130 [2023-12-20 21:56:48,712 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 112/130 [2023-12-20 21:56:48,829 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 113/130 [2023-12-20 21:56:48,946 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 114/130 [2023-12-20 21:56:49,063 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 115/130 [2023-12-20 21:56:49,179 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 116/130 [2023-12-20 21:56:49,300 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 117/130 [2023-12-20 21:56:49,417 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 118/130 [2023-12-20 21:56:49,535 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 119/130 [2023-12-20 21:56:49,648 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 120/130 [2023-12-20 21:56:49,758 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 121/130 [2023-12-20 21:56:49,868 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 122/130 [2023-12-20 21:56:49,978 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 123/130 [2023-12-20 21:56:50,089 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 124/130 [2023-12-20 21:56:50,199 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 125/130 [2023-12-20 21:56:50,311 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 126/130 [2023-12-20 21:56:50,425 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 127/130 [2023-12-20 21:56:50,535 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 128/130 [2023-12-20 21:56:50,645 INFO test.py line 196 131400] Test: 43/78-scene0030_00, Batch: 129/130 [2023-12-20 21:56:50,674 INFO test.py line 230 131400] Test: scene0030_00 [43/78]-293811 Batch 14.551 (10.840) Accuracy 0.9365 (0.8694) mIoU 0.7732 (0.7845) [2023-12-20 21:56:51,159 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 0/151 [2023-12-20 21:56:51,243 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 1/151 [2023-12-20 21:56:51,325 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 2/151 [2023-12-20 21:56:51,407 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 3/151 [2023-12-20 21:56:51,487 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 4/151 [2023-12-20 21:56:51,569 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 5/151 [2023-12-20 21:56:51,645 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 6/151 [2023-12-20 21:56:51,725 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 7/151 [2023-12-20 21:56:51,801 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 8/151 [2023-12-20 21:56:51,881 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 9/151 [2023-12-20 21:56:51,961 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 10/151 [2023-12-20 21:56:52,049 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 11/151 [2023-12-20 21:56:52,140 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 12/151 [2023-12-20 21:56:52,228 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 13/151 [2023-12-20 21:56:52,318 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 14/151 [2023-12-20 21:56:52,397 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 15/151 [2023-12-20 21:56:52,472 INFO test.py line 196 131400] Test: 44/78-scene0580_01, 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test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 48/151 [2023-12-20 21:56:55,112 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 49/151 [2023-12-20 21:56:55,182 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 50/151 [2023-12-20 21:56:55,254 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 51/151 [2023-12-20 21:56:55,325 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 52/151 [2023-12-20 21:56:55,396 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 53/151 [2023-12-20 21:56:55,467 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 54/151 [2023-12-20 21:56:55,538 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 55/151 [2023-12-20 21:56:55,609 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 56/151 [2023-12-20 21:56:55,680 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 57/151 [2023-12-20 21:56:55,751 INFO test.py line 196 131400] Test: 44/78-scene0580_01, 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Batch: 100/151 [2023-12-20 21:56:59,142 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 101/151 [2023-12-20 21:56:59,221 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 102/151 [2023-12-20 21:56:59,299 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 103/151 [2023-12-20 21:56:59,378 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 104/151 [2023-12-20 21:56:59,456 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 105/151 [2023-12-20 21:56:59,532 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 106/151 [2023-12-20 21:56:59,611 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 107/151 [2023-12-20 21:56:59,689 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 108/151 [2023-12-20 21:56:59,767 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 109/151 [2023-12-20 21:56:59,849 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 110/151 [2023-12-20 21:56:59,931 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 111/151 [2023-12-20 21:57:00,010 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 112/151 [2023-12-20 21:57:00,092 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 113/151 [2023-12-20 21:57:00,178 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 114/151 [2023-12-20 21:57:00,259 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 115/151 [2023-12-20 21:57:00,337 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 116/151 [2023-12-20 21:57:00,414 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 117/151 [2023-12-20 21:57:00,492 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 118/151 [2023-12-20 21:57:00,574 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 119/151 [2023-12-20 21:57:00,654 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 120/151 [2023-12-20 21:57:00,737 INFO test.py line 196 131400] Test: 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[2023-12-20 21:57:01,650 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 132/151 [2023-12-20 21:57:01,728 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 133/151 [2023-12-20 21:57:01,807 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 134/151 [2023-12-20 21:57:01,887 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 135/151 [2023-12-20 21:57:01,964 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 136/151 [2023-12-20 21:57:02,041 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 137/151 [2023-12-20 21:57:02,119 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 138/151 [2023-12-20 21:57:02,196 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 139/151 [2023-12-20 21:57:02,271 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 140/151 [2023-12-20 21:57:02,346 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 141/151 [2023-12-20 21:57:02,422 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 142/151 [2023-12-20 21:57:02,498 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 143/151 [2023-12-20 21:57:02,574 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 144/151 [2023-12-20 21:57:02,657 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 145/151 [2023-12-20 21:57:02,745 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 146/151 [2023-12-20 21:57:02,827 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 147/151 [2023-12-20 21:57:02,914 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 148/151 [2023-12-20 21:57:02,995 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 149/151 [2023-12-20 21:57:03,073 INFO test.py line 196 131400] Test: 44/78-scene0580_01, Batch: 150/151 [2023-12-20 21:57:03,092 INFO test.py line 230 131400] Test: scene0580_01 [44/78]-165009 Batch 12.017 (10.867) Accuracy 0.8244 (0.8670) mIoU 0.4983 (0.7795) [2023-12-20 21:57:03,450 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 0/155 [2023-12-20 21:57:03,531 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 1/155 [2023-12-20 21:57:03,613 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 2/155 [2023-12-20 21:57:03,694 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 3/155 [2023-12-20 21:57:03,774 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 4/155 [2023-12-20 21:57:03,854 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 5/155 [2023-12-20 21:57:03,935 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 6/155 [2023-12-20 21:57:04,012 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 7/155 [2023-12-20 21:57:04,090 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 8/155 [2023-12-20 21:57:04,171 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 9/155 [2023-12-20 21:57:04,257 INFO test.py line 196 131400] Test: 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21:57:05,212 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 21/155 [2023-12-20 21:57:05,296 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 22/155 [2023-12-20 21:57:05,373 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 23/155 [2023-12-20 21:57:05,453 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 24/155 [2023-12-20 21:57:05,530 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 25/155 [2023-12-20 21:57:05,613 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 26/155 [2023-12-20 21:57:05,697 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 27/155 [2023-12-20 21:57:05,778 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 28/155 [2023-12-20 21:57:05,863 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 29/155 [2023-12-20 21:57:05,940 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 30/155 [2023-12-20 21:57:06,023 INFO test.py line 196 131400] Test: 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21:57:06,891 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 42/155 [2023-12-20 21:57:06,971 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 43/155 [2023-12-20 21:57:07,074 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 44/155 [2023-12-20 21:57:07,163 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 45/155 [2023-12-20 21:57:07,251 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 46/155 [2023-12-20 21:57:07,326 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 47/155 [2023-12-20 21:57:07,407 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 48/155 [2023-12-20 21:57:07,484 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 49/155 [2023-12-20 21:57:07,557 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 50/155 [2023-12-20 21:57:07,629 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 51/155 [2023-12-20 21:57:07,703 INFO test.py line 196 131400] Test: 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21:57:08,504 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 63/155 [2023-12-20 21:57:08,577 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 64/155 [2023-12-20 21:57:08,649 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 65/155 [2023-12-20 21:57:08,721 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 66/155 [2023-12-20 21:57:08,795 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 67/155 [2023-12-20 21:57:08,874 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 68/155 [2023-12-20 21:57:08,962 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 69/155 [2023-12-20 21:57:09,039 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 70/155 [2023-12-20 21:57:09,116 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 71/155 [2023-12-20 21:57:09,189 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 72/155 [2023-12-20 21:57:09,260 INFO test.py line 196 131400] Test: 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21:57:10,073 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 84/155 [2023-12-20 21:57:10,146 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 85/155 [2023-12-20 21:57:10,218 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 86/155 [2023-12-20 21:57:10,292 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 87/155 [2023-12-20 21:57:10,364 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 88/155 [2023-12-20 21:57:10,436 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 89/155 [2023-12-20 21:57:10,507 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 90/155 [2023-12-20 21:57:10,579 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 91/155 [2023-12-20 21:57:10,651 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 92/155 [2023-12-20 21:57:10,723 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 93/155 [2023-12-20 21:57:10,797 INFO test.py line 196 131400] Test: 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21:57:11,627 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 105/155 [2023-12-20 21:57:11,705 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 106/155 [2023-12-20 21:57:11,783 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 107/155 [2023-12-20 21:57:11,861 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 108/155 [2023-12-20 21:57:11,940 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 109/155 [2023-12-20 21:57:12,018 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 110/155 [2023-12-20 21:57:12,096 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 111/155 [2023-12-20 21:57:12,174 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 112/155 [2023-12-20 21:57:12,252 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 113/155 [2023-12-20 21:57:12,331 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 114/155 [2023-12-20 21:57:12,409 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 115/155 [2023-12-20 21:57:12,488 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 116/155 [2023-12-20 21:57:12,566 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 117/155 [2023-12-20 21:57:12,645 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 118/155 [2023-12-20 21:57:12,723 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 119/155 [2023-12-20 21:57:12,800 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 120/155 [2023-12-20 21:57:12,878 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 121/155 [2023-12-20 21:57:12,955 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 122/155 [2023-12-20 21:57:13,033 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 123/155 [2023-12-20 21:57:13,110 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 124/155 [2023-12-20 21:57:13,189 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 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test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 136/155 [2023-12-20 21:57:14,131 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 137/155 [2023-12-20 21:57:14,209 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 138/155 [2023-12-20 21:57:14,288 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 139/155 [2023-12-20 21:57:14,368 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 140/155 [2023-12-20 21:57:14,447 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 141/155 [2023-12-20 21:57:14,525 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 142/155 [2023-12-20 21:57:14,603 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 143/155 [2023-12-20 21:57:14,679 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 144/155 [2023-12-20 21:57:14,755 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 145/155 [2023-12-20 21:57:14,831 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 146/155 [2023-12-20 21:57:14,908 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 147/155 [2023-12-20 21:57:14,986 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 148/155 [2023-12-20 21:57:15,070 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 149/155 [2023-12-20 21:57:15,154 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 150/155 [2023-12-20 21:57:15,236 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 151/155 [2023-12-20 21:57:15,323 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 152/155 [2023-12-20 21:57:15,400 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 153/155 [2023-12-20 21:57:15,476 INFO test.py line 196 131400] Test: 45/78-scene0697_03, Batch: 154/155 [2023-12-20 21:57:15,494 INFO test.py line 230 131400] Test: scene0697_03 [45/78]-165912 Batch 12.129 (10.895) Accuracy 0.7574 (0.8644) mIoU 0.3048 (0.7760) [2023-12-20 21:57:15,779 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 0/116 [2023-12-20 21:57:15,831 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 1/116 [2023-12-20 21:57:15,883 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 2/116 [2023-12-20 21:57:15,935 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 3/116 [2023-12-20 21:57:15,988 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 4/116 [2023-12-20 21:57:16,042 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 5/116 [2023-12-20 21:57:16,098 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 6/116 [2023-12-20 21:57:16,154 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 7/116 [2023-12-20 21:57:16,207 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 8/116 [2023-12-20 21:57:16,258 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 9/116 [2023-12-20 21:57:16,309 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 10/116 [2023-12-20 21:57:16,359 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 11/116 [2023-12-20 21:57:16,410 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 12/116 [2023-12-20 21:57:16,462 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 13/116 [2023-12-20 21:57:16,543 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 14/116 [2023-12-20 21:57:16,651 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 15/116 [2023-12-20 21:57:16,753 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 16/116 [2023-12-20 21:57:16,840 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 17/116 [2023-12-20 21:57:16,924 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 18/116 [2023-12-20 21:57:16,996 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 19/116 [2023-12-20 21:57:17,048 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 20/116 [2023-12-20 21:57:17,100 INFO test.py line 196 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[2023-12-20 21:57:17,726 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 32/116 [2023-12-20 21:57:17,781 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 33/116 [2023-12-20 21:57:17,833 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 34/116 [2023-12-20 21:57:17,884 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 35/116 [2023-12-20 21:57:17,934 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 36/116 [2023-12-20 21:57:17,985 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 37/116 [2023-12-20 21:57:18,038 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 38/116 [2023-12-20 21:57:18,093 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 39/116 [2023-12-20 21:57:18,152 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 40/116 [2023-12-20 21:57:18,212 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 41/116 [2023-12-20 21:57:18,265 INFO test.py line 196 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[2023-12-20 21:57:18,854 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 53/116 [2023-12-20 21:57:18,927 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 54/116 [2023-12-20 21:57:19,008 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 55/116 [2023-12-20 21:57:19,094 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 56/116 [2023-12-20 21:57:19,155 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 57/116 [2023-12-20 21:57:19,209 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 58/116 [2023-12-20 21:57:19,262 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 59/116 [2023-12-20 21:57:19,315 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 60/116 [2023-12-20 21:57:19,374 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 61/116 [2023-12-20 21:57:19,430 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 62/116 [2023-12-20 21:57:19,487 INFO test.py line 196 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[2023-12-20 21:57:20,043 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 74/116 [2023-12-20 21:57:20,093 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 75/116 [2023-12-20 21:57:20,146 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 76/116 [2023-12-20 21:57:20,195 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 77/116 [2023-12-20 21:57:20,245 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 78/116 [2023-12-20 21:57:20,296 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 79/116 [2023-12-20 21:57:20,346 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 80/116 [2023-12-20 21:57:20,396 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 81/116 [2023-12-20 21:57:20,446 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 82/116 [2023-12-20 21:57:20,496 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 83/116 [2023-12-20 21:57:20,546 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 84/116 [2023-12-20 21:57:20,596 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 85/116 [2023-12-20 21:57:20,647 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 86/116 [2023-12-20 21:57:20,697 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 87/116 [2023-12-20 21:57:20,750 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 88/116 [2023-12-20 21:57:20,800 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 89/116 [2023-12-20 21:57:20,850 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 90/116 [2023-12-20 21:57:20,900 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 91/116 [2023-12-20 21:57:20,949 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 92/116 [2023-12-20 21:57:20,999 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 93/116 [2023-12-20 21:57:21,048 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 94/116 [2023-12-20 21:57:21,098 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 95/116 [2023-12-20 21:57:21,147 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 96/116 [2023-12-20 21:57:21,197 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 97/116 [2023-12-20 21:57:21,249 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 98/116 [2023-12-20 21:57:21,299 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 99/116 [2023-12-20 21:57:21,347 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 100/116 [2023-12-20 21:57:21,395 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 101/116 [2023-12-20 21:57:21,447 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 102/116 [2023-12-20 21:57:21,501 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 103/116 [2023-12-20 21:57:21,559 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 104/116 [2023-12-20 21:57:21,611 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 105/116 [2023-12-20 21:57:21,664 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 106/116 [2023-12-20 21:57:21,715 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 107/116 [2023-12-20 21:57:21,765 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 108/116 [2023-12-20 21:57:21,816 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 109/116 [2023-12-20 21:57:21,867 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 110/116 [2023-12-20 21:57:21,917 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 111/116 [2023-12-20 21:57:21,969 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 112/116 [2023-12-20 21:57:22,019 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 113/116 [2023-12-20 21:57:22,070 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 114/116 [2023-12-20 21:57:22,121 INFO test.py line 196 131400] Test: 46/78-scene0574_01, Batch: 115/116 [2023-12-20 21:57:22,129 INFO test.py line 230 131400] Test: scene0574_01 [46/78]-53641 Batch 6.404 (10.797) Accuracy 0.9078 (0.8648) mIoU 0.7139 (0.7765) [2023-12-20 21:57:22,352 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 0/173 [2023-12-20 21:57:22,485 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 1/173 [2023-12-20 21:57:22,618 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 2/173 [2023-12-20 21:57:22,749 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 3/173 [2023-12-20 21:57:22,880 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 4/173 [2023-12-20 21:57:23,015 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 5/173 [2023-12-20 21:57:23,153 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 6/173 [2023-12-20 21:57:23,290 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 7/173 [2023-12-20 21:57:23,424 INFO test.py line 196 131400] Test: 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131400] Test: 47/78-scene0500_01, Batch: 113/173 [2023-12-20 21:57:37,282 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 114/173 [2023-12-20 21:57:37,434 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 115/173 [2023-12-20 21:57:37,577 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 116/173 [2023-12-20 21:57:37,719 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 117/173 [2023-12-20 21:57:37,859 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 118/173 [2023-12-20 21:57:38,000 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 119/173 [2023-12-20 21:57:38,141 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 120/173 [2023-12-20 21:57:38,286 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 121/173 [2023-12-20 21:57:38,429 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 122/173 [2023-12-20 21:57:38,569 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 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[2023-12-20 21:57:43,159 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 155/173 [2023-12-20 21:57:43,302 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 156/173 [2023-12-20 21:57:43,444 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 157/173 [2023-12-20 21:57:43,585 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 158/173 [2023-12-20 21:57:43,729 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 159/173 [2023-12-20 21:57:43,865 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 160/173 [2023-12-20 21:57:43,998 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 161/173 [2023-12-20 21:57:44,133 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 162/173 [2023-12-20 21:57:44,268 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 163/173 [2023-12-20 21:57:44,404 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 164/173 [2023-12-20 21:57:44,541 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 165/173 [2023-12-20 21:57:44,678 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 166/173 [2023-12-20 21:57:44,809 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 167/173 [2023-12-20 21:57:44,940 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 168/173 [2023-12-20 21:57:45,071 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 169/173 [2023-12-20 21:57:45,203 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 170/173 [2023-12-20 21:57:45,333 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 171/173 [2023-12-20 21:57:45,464 INFO test.py line 196 131400] Test: 47/78-scene0500_01, Batch: 172/173 [2023-12-20 21:57:45,497 INFO test.py line 230 131400] Test: scene0500_01 [47/78]-352674 Batch 23.285 (11.063) Accuracy 0.9533 (0.8658) mIoU 0.7808 (0.7779) [2023-12-20 21:57:46,083 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 0/126 [2023-12-20 21:57:46,154 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 1/126 [2023-12-20 21:57:46,221 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 2/126 [2023-12-20 21:57:46,285 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 3/126 [2023-12-20 21:57:46,344 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 4/126 [2023-12-20 21:57:46,402 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 5/126 [2023-12-20 21:57:46,462 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 6/126 [2023-12-20 21:57:46,531 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 7/126 [2023-12-20 21:57:46,610 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 8/126 [2023-12-20 21:57:46,673 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 9/126 [2023-12-20 21:57:46,733 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 10/126 [2023-12-20 21:57:46,792 INFO test.py line 196 131400] Test: 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[2023-12-20 21:57:52,749 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 106/126 [2023-12-20 21:57:52,811 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 107/126 [2023-12-20 21:57:52,870 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 108/126 [2023-12-20 21:57:52,930 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 109/126 [2023-12-20 21:57:52,989 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 110/126 [2023-12-20 21:57:53,048 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 111/126 [2023-12-20 21:57:53,107 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 112/126 [2023-12-20 21:57:53,168 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 113/126 [2023-12-20 21:57:53,228 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 114/126 [2023-12-20 21:57:53,287 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 115/126 [2023-12-20 21:57:53,345 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 116/126 [2023-12-20 21:57:53,402 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 117/126 [2023-12-20 21:57:53,460 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 118/126 [2023-12-20 21:57:53,517 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 119/126 [2023-12-20 21:57:53,575 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 120/126 [2023-12-20 21:57:53,636 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 121/126 [2023-12-20 21:57:53,698 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 122/126 [2023-12-20 21:57:53,762 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 123/126 [2023-12-20 21:57:53,822 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 124/126 [2023-12-20 21:57:53,880 INFO test.py line 196 131400] Test: 48/78-scene0552_01, Batch: 125/126 [2023-12-20 21:57:53,892 INFO test.py line 230 131400] Test: scene0552_01 [48/78]-91978 Batch 7.876 (10.996) Accuracy 0.9284 (0.8660) mIoU 0.6632 (0.7790) [2023-12-20 21:57:54,080 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 0/122 [2023-12-20 21:57:54,135 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 1/122 [2023-12-20 21:57:54,189 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 2/122 [2023-12-20 21:57:54,245 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 3/122 [2023-12-20 21:57:54,300 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 4/122 [2023-12-20 21:57:54,354 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 5/122 [2023-12-20 21:57:54,408 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 6/122 [2023-12-20 21:57:54,463 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 7/122 [2023-12-20 21:57:54,519 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 8/122 [2023-12-20 21:57:54,576 INFO test.py line 196 131400] Test: 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21:57:59,980 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 104/122 [2023-12-20 21:58:00,034 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 105/122 [2023-12-20 21:58:00,089 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 106/122 [2023-12-20 21:58:00,144 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 107/122 [2023-12-20 21:58:00,199 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 108/122 [2023-12-20 21:58:00,254 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 109/122 [2023-12-20 21:58:00,308 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 110/122 [2023-12-20 21:58:00,363 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 111/122 [2023-12-20 21:58:00,417 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 112/122 [2023-12-20 21:58:00,471 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 113/122 [2023-12-20 21:58:00,526 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 114/122 [2023-12-20 21:58:00,580 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 115/122 [2023-12-20 21:58:00,634 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 116/122 [2023-12-20 21:58:00,688 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 117/122 [2023-12-20 21:58:00,742 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 118/122 [2023-12-20 21:58:00,796 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 119/122 [2023-12-20 21:58:00,850 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 120/122 [2023-12-20 21:58:00,904 INFO test.py line 196 131400] Test: 49/78-scene0609_00, Batch: 121/122 [2023-12-20 21:58:00,914 INFO test.py line 230 131400] Test: scene0609_00 [49/78]-78836 Batch 6.897 (10.913) Accuracy 0.8074 (0.8644) mIoU 0.5434 (0.7763) [2023-12-20 21:58:01,084 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 0/140 [2023-12-20 21:58:01,140 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 1/140 [2023-12-20 21:58:01,195 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 2/140 [2023-12-20 21:58:01,249 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 3/140 [2023-12-20 21:58:01,303 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 4/140 [2023-12-20 21:58:01,358 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 5/140 [2023-12-20 21:58:01,413 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 6/140 [2023-12-20 21:58:01,469 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 7/140 [2023-12-20 21:58:01,524 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 8/140 [2023-12-20 21:58:01,578 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 9/140 [2023-12-20 21:58:01,636 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 10/140 [2023-12-20 21:58:01,690 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 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line 196 131400] Test: 50/78-scene0277_02, Batch: 106/140 [2023-12-20 21:58:07,399 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 107/140 [2023-12-20 21:58:07,463 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 108/140 [2023-12-20 21:58:07,525 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 109/140 [2023-12-20 21:58:07,599 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 110/140 [2023-12-20 21:58:07,676 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 111/140 [2023-12-20 21:58:07,746 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 112/140 [2023-12-20 21:58:07,810 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 113/140 [2023-12-20 21:58:07,869 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 114/140 [2023-12-20 21:58:07,933 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 115/140 [2023-12-20 21:58:07,997 INFO test.py line 196 131400] Test: 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[2023-12-20 21:58:08,657 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 127/140 [2023-12-20 21:58:08,715 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 128/140 [2023-12-20 21:58:08,773 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 129/140 [2023-12-20 21:58:08,831 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 130/140 [2023-12-20 21:58:08,888 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 131/140 [2023-12-20 21:58:08,945 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 132/140 [2023-12-20 21:58:09,002 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 133/140 [2023-12-20 21:58:09,059 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 134/140 [2023-12-20 21:58:09,114 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 135/140 [2023-12-20 21:58:09,168 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 136/140 [2023-12-20 21:58:09,222 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 137/140 [2023-12-20 21:58:09,276 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 138/140 [2023-12-20 21:58:09,330 INFO test.py line 196 131400] Test: 50/78-scene0277_02, Batch: 139/140 [2023-12-20 21:58:09,341 INFO test.py line 230 131400] Test: scene0277_02 [50/78]-82251 Batch 8.316 (10.861) Accuracy 0.8900 (0.8642) mIoU 0.7390 (0.7759) [2023-12-20 21:58:09,526 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 0/117 [2023-12-20 21:58:09,594 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 1/117 [2023-12-20 21:58:09,663 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 2/117 [2023-12-20 21:58:09,732 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 3/117 [2023-12-20 21:58:09,801 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 4/117 [2023-12-20 21:58:09,872 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 5/117 [2023-12-20 21:58:09,941 INFO 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Batch: 100/117 [2023-12-20 21:58:16,723 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 101/117 [2023-12-20 21:58:16,796 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 102/117 [2023-12-20 21:58:16,869 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 103/117 [2023-12-20 21:58:16,943 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 104/117 [2023-12-20 21:58:17,015 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 105/117 [2023-12-20 21:58:17,088 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 106/117 [2023-12-20 21:58:17,160 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 107/117 [2023-12-20 21:58:17,230 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 108/117 [2023-12-20 21:58:17,302 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 109/117 [2023-12-20 21:58:17,372 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 110/117 [2023-12-20 21:58:17,441 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 111/117 [2023-12-20 21:58:17,513 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 112/117 [2023-12-20 21:58:17,596 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 113/117 [2023-12-20 21:58:17,669 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 114/117 [2023-12-20 21:58:17,739 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 115/117 [2023-12-20 21:58:17,810 INFO test.py line 196 131400] Test: 51/78-scene0685_01, Batch: 116/117 [2023-12-20 21:58:17,824 INFO test.py line 230 131400] Test: scene0685_01 [51/78]-138130 Batch 8.371 (10.812) Accuracy 0.9587 (0.8641) mIoU 0.5247 (0.7761) [2023-12-20 21:58:18,128 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 0/113 [2023-12-20 21:58:18,232 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 1/113 [2023-12-20 21:58:18,339 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 2/113 [2023-12-20 21:58:18,439 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 3/113 [2023-12-20 21:58:18,539 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 4/113 [2023-12-20 21:58:18,639 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 5/113 [2023-12-20 21:58:18,739 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 6/113 [2023-12-20 21:58:18,846 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 7/113 [2023-12-20 21:58:18,942 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 8/113 [2023-12-20 21:58:19,039 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 9/113 [2023-12-20 21:58:19,136 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 10/113 [2023-12-20 21:58:19,239 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 11/113 [2023-12-20 21:58:19,336 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 12/113 [2023-12-20 21:58:19,434 INFO test.py line 196 131400] Test: 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[2023-12-20 21:58:29,135 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 108/113 [2023-12-20 21:58:29,233 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 109/113 [2023-12-20 21:58:29,331 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 110/113 [2023-12-20 21:58:29,439 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 111/113 [2023-12-20 21:58:29,542 INFO test.py line 196 131400] Test: 52/78-scene0203_02, Batch: 112/113 [2023-12-20 21:58:29,571 INFO test.py line 230 131400] Test: scene0203_02 [52/78]-238392 Batch 11.548 (10.826) Accuracy 0.8155 (0.8565) mIoU 0.5544 (0.7693) [2023-12-20 21:58:30,061 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 0/157 [2023-12-20 21:58:30,201 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 1/157 [2023-12-20 21:58:30,341 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 2/157 [2023-12-20 21:58:30,481 INFO test.py line 196 131400] Test: 53/78-scene0678_01, 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[2023-12-20 21:58:49,476 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 140/157 [2023-12-20 21:58:49,624 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 141/157 [2023-12-20 21:58:49,778 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 142/157 [2023-12-20 21:58:49,925 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 143/157 [2023-12-20 21:58:50,063 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 144/157 [2023-12-20 21:58:50,199 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 145/157 [2023-12-20 21:58:50,336 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 146/157 [2023-12-20 21:58:50,476 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 147/157 [2023-12-20 21:58:50,613 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 148/157 [2023-12-20 21:58:50,749 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 149/157 [2023-12-20 21:58:50,886 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 150/157 [2023-12-20 21:58:51,022 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 151/157 [2023-12-20 21:58:51,159 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 152/157 [2023-12-20 21:58:51,296 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 153/157 [2023-12-20 21:58:51,433 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 154/157 [2023-12-20 21:58:51,573 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 155/157 [2023-12-20 21:58:51,709 INFO test.py line 196 131400] Test: 53/78-scene0678_01, Batch: 156/157 [2023-12-20 21:58:51,737 INFO test.py line 230 131400] Test: scene0678_01 [53/78]-377683 Batch 21.833 (11.034) Accuracy 0.8464 (0.8575) mIoU 0.4722 (0.7707) [2023-12-20 21:58:52,308 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 0/152 [2023-12-20 21:58:52,366 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 1/152 [2023-12-20 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[2023-12-20 21:58:58,484 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 107/152 [2023-12-20 21:58:58,543 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 108/152 [2023-12-20 21:58:58,600 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 109/152 [2023-12-20 21:58:58,657 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 110/152 [2023-12-20 21:58:58,714 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 111/152 [2023-12-20 21:58:58,772 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 112/152 [2023-12-20 21:58:58,828 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 113/152 [2023-12-20 21:58:58,885 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 114/152 [2023-12-20 21:58:58,943 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 115/152 [2023-12-20 21:58:59,000 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 116/152 [2023-12-20 21:58:59,058 INFO test.py 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[2023-12-20 21:59:00,333 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 138/152 [2023-12-20 21:59:00,392 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 139/152 [2023-12-20 21:59:00,450 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 140/152 [2023-12-20 21:59:00,509 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 141/152 [2023-12-20 21:59:00,566 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 142/152 [2023-12-20 21:59:00,623 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 143/152 [2023-12-20 21:59:00,683 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 144/152 [2023-12-20 21:59:00,745 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 145/152 [2023-12-20 21:59:00,807 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 146/152 [2023-12-20 21:59:00,881 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 147/152 [2023-12-20 21:59:00,963 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 148/152 [2023-12-20 21:59:01,023 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 149/152 [2023-12-20 21:59:01,082 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 150/152 [2023-12-20 21:59:01,140 INFO test.py line 196 131400] Test: 54/78-scene0356_00, Batch: 151/152 [2023-12-20 21:59:01,154 INFO test.py line 230 131400] Test: scene0356_00 [54/78]-81172 Batch 8.907 (10.994) Accuracy 0.9584 (0.8581) mIoU 0.6625 (0.7716) [2023-12-20 21:59:01,379 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 0/125 [2023-12-20 21:59:01,443 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 1/125 [2023-12-20 21:59:01,515 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 2/125 [2023-12-20 21:59:01,597 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 3/125 [2023-12-20 21:59:01,676 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 4/125 [2023-12-20 21:59:01,756 INFO 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INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 110/125 [2023-12-20 21:59:08,639 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 111/125 [2023-12-20 21:59:08,699 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 112/125 [2023-12-20 21:59:08,758 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 113/125 [2023-12-20 21:59:08,818 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 114/125 [2023-12-20 21:59:08,878 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 115/125 [2023-12-20 21:59:08,936 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 116/125 [2023-12-20 21:59:08,993 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 117/125 [2023-12-20 21:59:09,051 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 118/125 [2023-12-20 21:59:09,108 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 119/125 [2023-12-20 21:59:09,166 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 120/125 [2023-12-20 21:59:09,223 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 121/125 [2023-12-20 21:59:09,280 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 122/125 [2023-12-20 21:59:09,338 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 123/125 [2023-12-20 21:59:09,395 INFO test.py line 196 131400] Test: 55/78-scene0535_00, Batch: 124/125 [2023-12-20 21:59:09,437 INFO test.py line 230 131400] Test: scene0535_00 [55/78]-90268 Batch 8.138 (10.943) Accuracy 0.8249 (0.8546) mIoU 0.5402 (0.7686) [2023-12-20 21:59:09,669 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 0/144 [2023-12-20 21:59:09,768 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 1/144 [2023-12-20 21:59:09,863 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 2/144 [2023-12-20 21:59:09,946 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 3/144 [2023-12-20 21:59:10,027 INFO test.py line 196 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[2023-12-20 21:59:10,912 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 15/144 [2023-12-20 21:59:10,991 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 16/144 [2023-12-20 21:59:11,071 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 17/144 [2023-12-20 21:59:11,154 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 18/144 [2023-12-20 21:59:11,233 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 19/144 [2023-12-20 21:59:11,313 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 20/144 [2023-12-20 21:59:11,392 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 21/144 [2023-12-20 21:59:11,472 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 22/144 [2023-12-20 21:59:11,552 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 23/144 [2023-12-20 21:59:11,631 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 24/144 [2023-12-20 21:59:11,711 INFO test.py line 196 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[2023-12-20 21:59:12,679 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 36/144 [2023-12-20 21:59:12,760 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 37/144 [2023-12-20 21:59:12,840 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 38/144 [2023-12-20 21:59:12,921 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 39/144 [2023-12-20 21:59:13,001 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 40/144 [2023-12-20 21:59:13,080 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 41/144 [2023-12-20 21:59:13,160 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 42/144 [2023-12-20 21:59:13,239 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 43/144 [2023-12-20 21:59:13,319 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 44/144 [2023-12-20 21:59:13,398 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 45/144 [2023-12-20 21:59:13,478 INFO test.py line 196 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[2023-12-20 21:59:14,331 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 57/144 [2023-12-20 21:59:14,410 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 58/144 [2023-12-20 21:59:14,487 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 59/144 [2023-12-20 21:59:14,565 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 60/144 [2023-12-20 21:59:14,642 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 61/144 [2023-12-20 21:59:14,721 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 62/144 [2023-12-20 21:59:14,799 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 63/144 [2023-12-20 21:59:14,876 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 64/144 [2023-12-20 21:59:14,952 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 65/144 [2023-12-20 21:59:15,029 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 66/144 [2023-12-20 21:59:15,106 INFO test.py line 196 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[2023-12-20 21:59:15,946 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 78/144 [2023-12-20 21:59:16,022 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 79/144 [2023-12-20 21:59:16,099 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 80/144 [2023-12-20 21:59:16,175 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 81/144 [2023-12-20 21:59:16,251 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 82/144 [2023-12-20 21:59:16,328 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 83/144 [2023-12-20 21:59:16,404 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 84/144 [2023-12-20 21:59:16,480 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 85/144 [2023-12-20 21:59:16,557 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 86/144 [2023-12-20 21:59:16,633 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 87/144 [2023-12-20 21:59:16,709 INFO test.py line 196 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[2023-12-20 21:59:17,614 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 99/144 [2023-12-20 21:59:17,698 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 100/144 [2023-12-20 21:59:17,781 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 101/144 [2023-12-20 21:59:17,864 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 102/144 [2023-12-20 21:59:17,947 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 103/144 [2023-12-20 21:59:18,030 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 104/144 [2023-12-20 21:59:18,113 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 105/144 [2023-12-20 21:59:18,197 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 106/144 [2023-12-20 21:59:18,279 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 107/144 [2023-12-20 21:59:18,362 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 108/144 [2023-12-20 21:59:18,445 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 109/144 [2023-12-20 21:59:18,528 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 110/144 [2023-12-20 21:59:18,611 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 111/144 [2023-12-20 21:59:18,697 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 112/144 [2023-12-20 21:59:18,783 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 113/144 [2023-12-20 21:59:18,868 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 114/144 [2023-12-20 21:59:18,951 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 115/144 [2023-12-20 21:59:19,035 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 116/144 [2023-12-20 21:59:19,121 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 117/144 [2023-12-20 21:59:19,210 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 118/144 [2023-12-20 21:59:19,295 INFO test.py line 196 131400] Test: 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[2023-12-20 21:59:20,234 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 130/144 [2023-12-20 21:59:20,319 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 131/144 [2023-12-20 21:59:20,400 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 132/144 [2023-12-20 21:59:20,481 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 133/144 [2023-12-20 21:59:20,563 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 134/144 [2023-12-20 21:59:20,647 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 135/144 [2023-12-20 21:59:20,736 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 136/144 [2023-12-20 21:59:20,824 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 137/144 [2023-12-20 21:59:20,915 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 138/144 [2023-12-20 21:59:21,001 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 139/144 [2023-12-20 21:59:21,083 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 140/144 [2023-12-20 21:59:21,166 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 141/144 [2023-12-20 21:59:21,248 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 142/144 [2023-12-20 21:59:21,330 INFO test.py line 196 131400] Test: 56/78-scene0131_00, Batch: 143/144 [2023-12-20 21:59:21,355 INFO test.py line 230 131400] Test: scene0131_00 [56/78]-177091 Batch 11.784 (10.958) Accuracy 0.9464 (0.8455) mIoU 0.3955 (0.7608) [2023-12-20 21:59:21,729 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 0/139 [2023-12-20 21:59:21,800 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 1/139 [2023-12-20 21:59:21,864 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 2/139 [2023-12-20 21:59:21,933 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 3/139 [2023-12-20 21:59:22,003 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 4/139 [2023-12-20 21:59:22,075 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[2023-12-20 21:59:30,422 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 131/139 [2023-12-20 21:59:30,482 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 132/139 [2023-12-20 21:59:30,541 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 133/139 [2023-12-20 21:59:30,604 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 134/139 [2023-12-20 21:59:30,670 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 135/139 [2023-12-20 21:59:30,732 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 136/139 [2023-12-20 21:59:30,791 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 137/139 [2023-12-20 21:59:30,851 INFO test.py line 196 131400] Test: 57/78-scene0084_02, Batch: 138/139 [2023-12-20 21:59:30,864 INFO test.py line 230 131400] Test: scene0084_02 [57/78]-98346 Batch 9.228 (10.927) Accuracy 0.9579 (0.8444) mIoU 0.7474 (0.7627) [2023-12-20 21:59:31,083 INFO test.py line 196 131400] Test: 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Test: 58/78-scene0651_02, Batch: 105/121 [2023-12-20 21:59:38,387 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 106/121 [2023-12-20 21:59:38,454 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 107/121 [2023-12-20 21:59:38,521 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 108/121 [2023-12-20 21:59:38,599 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 109/121 [2023-12-20 21:59:38,671 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 110/121 [2023-12-20 21:59:38,742 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 111/121 [2023-12-20 21:59:38,806 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 112/121 [2023-12-20 21:59:38,872 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 113/121 [2023-12-20 21:59:38,937 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 114/121 [2023-12-20 21:59:39,003 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 115/121 [2023-12-20 21:59:39,067 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 116/121 [2023-12-20 21:59:39,133 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 117/121 [2023-12-20 21:59:39,203 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 118/121 [2023-12-20 21:59:39,278 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 119/121 [2023-12-20 21:59:39,353 INFO test.py line 196 131400] Test: 58/78-scene0651_02, Batch: 120/121 [2023-12-20 21:59:39,368 INFO test.py line 230 131400] Test: scene0651_02 [58/78]-124048 Batch 8.355 (10.883) Accuracy 0.9526 (0.8481) mIoU 0.8174 (0.7674) [2023-12-20 21:59:39,616 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 0/125 [2023-12-20 21:59:39,674 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 1/125 [2023-12-20 21:59:39,732 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 2/125 [2023-12-20 21:59:39,794 INFO test.py line 196 131400] Test: 59/78-scene0583_02, 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INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 119/125 [2023-12-20 21:59:47,365 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 120/125 [2023-12-20 21:59:47,451 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 121/125 [2023-12-20 21:59:47,515 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 122/125 [2023-12-20 21:59:47,573 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 123/125 [2023-12-20 21:59:47,630 INFO test.py line 196 131400] Test: 59/78-scene0583_02, Batch: 124/125 [2023-12-20 21:59:47,644 INFO test.py line 230 131400] Test: scene0583_02 [59/78]-89810 Batch 8.091 (10.836) Accuracy 0.8157 (0.8306) mIoU 0.4738 (0.7549) [2023-12-20 21:59:47,840 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 0/121 [2023-12-20 21:59:47,899 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 1/121 [2023-12-20 21:59:47,959 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 2/121 [2023-12-20 21:59:48,019 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 3/121 [2023-12-20 21:59:48,080 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 4/121 [2023-12-20 21:59:48,138 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 5/121 [2023-12-20 21:59:48,207 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 6/121 [2023-12-20 21:59:48,274 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 7/121 [2023-12-20 21:59:48,335 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 8/121 [2023-12-20 21:59:48,394 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 9/121 [2023-12-20 21:59:48,472 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 10/121 [2023-12-20 21:59:48,547 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 11/121 [2023-12-20 21:59:48,629 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 12/121 [2023-12-20 21:59:48,702 INFO test.py line 196 131400] Test: 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21:59:53,152 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 87/121 [2023-12-20 21:59:53,215 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 88/121 [2023-12-20 21:59:53,283 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 89/121 [2023-12-20 21:59:53,352 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 90/121 [2023-12-20 21:59:53,418 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 91/121 [2023-12-20 21:59:53,479 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 92/121 [2023-12-20 21:59:53,541 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 93/121 [2023-12-20 21:59:53,605 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 94/121 [2023-12-20 21:59:53,700 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 95/121 [2023-12-20 21:59:53,794 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 96/121 [2023-12-20 21:59:53,909 INFO test.py line 196 131400] Test: 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[2023-12-20 21:59:54,646 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 108/121 [2023-12-20 21:59:54,711 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 109/121 [2023-12-20 21:59:54,776 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 110/121 [2023-12-20 21:59:54,836 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 111/121 [2023-12-20 21:59:54,893 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 112/121 [2023-12-20 21:59:54,949 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 113/121 [2023-12-20 21:59:55,006 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 114/121 [2023-12-20 21:59:55,063 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 115/121 [2023-12-20 21:59:55,119 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 116/121 [2023-12-20 21:59:55,176 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 117/121 [2023-12-20 21:59:55,232 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 118/121 [2023-12-20 21:59:55,289 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 119/121 [2023-12-20 21:59:55,346 INFO test.py line 196 131400] Test: 60/78-scene0598_01, Batch: 120/121 [2023-12-20 21:59:55,357 INFO test.py line 230 131400] Test: scene0598_01 [60/78]-91924 Batch 7.579 (10.781) Accuracy 0.9683 (0.8308) mIoU 0.6327 (0.7553) [2023-12-20 21:59:55,603 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 0/125 [2023-12-20 21:59:55,692 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 1/125 [2023-12-20 21:59:55,776 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 2/125 [2023-12-20 21:59:55,864 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 3/125 [2023-12-20 21:59:55,948 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 4/125 [2023-12-20 21:59:56,032 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 5/125 [2023-12-20 21:59:56,117 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 6/125 [2023-12-20 21:59:56,201 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 7/125 [2023-12-20 21:59:56,287 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 8/125 [2023-12-20 21:59:56,372 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 9/125 [2023-12-20 21:59:56,458 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 10/125 [2023-12-20 21:59:56,543 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 11/125 [2023-12-20 21:59:56,628 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 12/125 [2023-12-20 21:59:56,712 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 13/125 [2023-12-20 21:59:56,798 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 14/125 [2023-12-20 21:59:56,883 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 15/125 [2023-12-20 21:59:56,967 INFO test.py line 196 131400] Test: 61/78-scene0435_03, 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test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 27/125 [2023-12-20 21:59:57,991 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 28/125 [2023-12-20 21:59:58,075 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 29/125 [2023-12-20 21:59:58,161 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 30/125 [2023-12-20 21:59:58,245 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 31/125 [2023-12-20 21:59:58,330 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 32/125 [2023-12-20 21:59:58,415 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 33/125 [2023-12-20 21:59:58,500 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 34/125 [2023-12-20 21:59:58,585 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 35/125 [2023-12-20 21:59:58,668 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 36/125 [2023-12-20 21:59:58,749 INFO test.py line 196 131400] Test: 61/78-scene0435_03, 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test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 48/125 [2023-12-20 21:59:59,727 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 49/125 [2023-12-20 21:59:59,808 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 50/125 [2023-12-20 21:59:59,889 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 51/125 [2023-12-20 21:59:59,973 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 52/125 [2023-12-20 22:00:00,054 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 53/125 [2023-12-20 22:00:00,135 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 54/125 [2023-12-20 22:00:00,218 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 55/125 [2023-12-20 22:00:00,311 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 56/125 [2023-12-20 22:00:00,419 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 57/125 [2023-12-20 22:00:00,526 INFO test.py line 196 131400] Test: 61/78-scene0435_03, 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test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 90/125 [2023-12-20 22:00:03,578 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 91/125 [2023-12-20 22:00:03,673 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 92/125 [2023-12-20 22:00:03,763 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 93/125 [2023-12-20 22:00:03,857 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 94/125 [2023-12-20 22:00:03,955 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 95/125 [2023-12-20 22:00:04,051 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 96/125 [2023-12-20 22:00:04,144 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 97/125 [2023-12-20 22:00:04,236 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 98/125 [2023-12-20 22:00:04,326 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 99/125 [2023-12-20 22:00:04,417 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 100/125 [2023-12-20 22:00:04,507 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 101/125 [2023-12-20 22:00:04,599 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 102/125 [2023-12-20 22:00:04,690 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 103/125 [2023-12-20 22:00:04,780 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 104/125 [2023-12-20 22:00:04,870 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 105/125 [2023-12-20 22:00:04,962 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 106/125 [2023-12-20 22:00:05,053 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 107/125 [2023-12-20 22:00:05,143 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 108/125 [2023-12-20 22:00:05,234 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 109/125 [2023-12-20 22:00:05,324 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 110/125 [2023-12-20 22:00:05,413 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 111/125 [2023-12-20 22:00:05,503 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 112/125 [2023-12-20 22:00:05,594 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 113/125 [2023-12-20 22:00:05,683 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 114/125 [2023-12-20 22:00:05,772 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 115/125 [2023-12-20 22:00:05,856 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 116/125 [2023-12-20 22:00:05,941 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 117/125 [2023-12-20 22:00:06,026 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 118/125 [2023-12-20 22:00:06,110 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 119/125 [2023-12-20 22:00:06,194 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 120/125 [2023-12-20 22:00:06,278 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 121/125 [2023-12-20 22:00:06,363 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 122/125 [2023-12-20 22:00:06,450 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 123/125 [2023-12-20 22:00:06,535 INFO test.py line 196 131400] Test: 61/78-scene0435_03, Batch: 124/125 [2023-12-20 22:00:06,558 INFO test.py line 230 131400] Test: scene0435_03 [61/78]-203722 Batch 11.044 (10.786) Accuracy 0.9317 (0.8331) mIoU 0.7595 (0.7574) [2023-12-20 22:00:06,950 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 0/139 [2023-12-20 22:00:07,036 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 1/139 [2023-12-20 22:00:07,126 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 2/139 [2023-12-20 22:00:07,210 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 3/139 [2023-12-20 22:00:07,293 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 4/139 [2023-12-20 22:00:07,378 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 5/139 [2023-12-20 22:00:07,463 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 6/139 [2023-12-20 22:00:07,546 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 7/139 [2023-12-20 22:00:07,631 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 8/139 [2023-12-20 22:00:07,714 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 9/139 [2023-12-20 22:00:07,797 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 10/139 [2023-12-20 22:00:07,880 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 11/139 [2023-12-20 22:00:07,963 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 12/139 [2023-12-20 22:00:08,046 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 13/139 [2023-12-20 22:00:08,130 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 14/139 [2023-12-20 22:00:08,214 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 15/139 [2023-12-20 22:00:08,297 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 16/139 [2023-12-20 22:00:08,380 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 17/139 [2023-12-20 22:00:08,464 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 18/139 [2023-12-20 22:00:08,548 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 19/139 [2023-12-20 22:00:08,631 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 20/139 [2023-12-20 22:00:08,715 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 21/139 [2023-12-20 22:00:08,798 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 22/139 [2023-12-20 22:00:08,882 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 23/139 [2023-12-20 22:00:08,965 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 24/139 [2023-12-20 22:00:09,049 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 25/139 [2023-12-20 22:00:09,133 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 26/139 [2023-12-20 22:00:09,216 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 27/139 [2023-12-20 22:00:09,300 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 28/139 [2023-12-20 22:00:09,383 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 29/139 [2023-12-20 22:00:09,467 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 30/139 [2023-12-20 22:00:09,549 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 31/139 [2023-12-20 22:00:09,631 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 32/139 [2023-12-20 22:00:09,714 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 33/139 [2023-12-20 22:00:09,798 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 34/139 [2023-12-20 22:00:09,881 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 35/139 [2023-12-20 22:00:09,964 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 36/139 [2023-12-20 22:00:10,047 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 37/139 [2023-12-20 22:00:10,130 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 38/139 [2023-12-20 22:00:10,213 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 39/139 [2023-12-20 22:00:10,296 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 40/139 [2023-12-20 22:00:10,385 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 41/139 [2023-12-20 22:00:10,469 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 42/139 [2023-12-20 22:00:10,553 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 43/139 [2023-12-20 22:00:10,634 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 44/139 [2023-12-20 22:00:10,715 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 45/139 [2023-12-20 22:00:10,796 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 46/139 [2023-12-20 22:00:10,899 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 47/139 [2023-12-20 22:00:11,006 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 48/139 [2023-12-20 22:00:11,099 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 49/139 [2023-12-20 22:00:11,180 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 50/139 [2023-12-20 22:00:11,261 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 51/139 [2023-12-20 22:00:11,342 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 52/139 [2023-12-20 22:00:11,423 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 53/139 [2023-12-20 22:00:11,504 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 54/139 [2023-12-20 22:00:11,588 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 55/139 [2023-12-20 22:00:11,675 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 56/139 [2023-12-20 22:00:11,760 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 57/139 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[2023-12-20 22:00:13,542 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 79/139 [2023-12-20 22:00:13,622 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 80/139 [2023-12-20 22:00:13,709 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 81/139 [2023-12-20 22:00:13,795 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 82/139 [2023-12-20 22:00:13,881 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 83/139 [2023-12-20 22:00:13,966 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 84/139 [2023-12-20 22:00:14,050 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 85/139 [2023-12-20 22:00:14,134 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 86/139 [2023-12-20 22:00:14,216 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 87/139 [2023-12-20 22:00:14,323 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 88/139 [2023-12-20 22:00:14,415 INFO test.py line 196 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[2023-12-20 22:00:15,422 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 100/139 [2023-12-20 22:00:15,511 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 101/139 [2023-12-20 22:00:15,601 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 102/139 [2023-12-20 22:00:15,689 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 103/139 [2023-12-20 22:00:15,778 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 104/139 [2023-12-20 22:00:15,873 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 105/139 [2023-12-20 22:00:15,964 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 106/139 [2023-12-20 22:00:16,054 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 107/139 [2023-12-20 22:00:16,143 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 108/139 [2023-12-20 22:00:16,234 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 109/139 [2023-12-20 22:00:16,324 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 110/139 [2023-12-20 22:00:16,437 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 111/139 [2023-12-20 22:00:16,539 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 112/139 [2023-12-20 22:00:16,636 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 113/139 [2023-12-20 22:00:16,733 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 114/139 [2023-12-20 22:00:16,826 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 115/139 [2023-12-20 22:00:16,927 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 116/139 [2023-12-20 22:00:17,018 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 117/139 [2023-12-20 22:00:17,110 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 118/139 [2023-12-20 22:00:17,207 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 119/139 [2023-12-20 22:00:17,304 INFO test.py line 196 131400] Test: 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[2023-12-20 22:00:18,318 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 131/139 [2023-12-20 22:00:18,402 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 132/139 [2023-12-20 22:00:18,488 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 133/139 [2023-12-20 22:00:18,575 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 134/139 [2023-12-20 22:00:18,674 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 135/139 [2023-12-20 22:00:18,770 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 136/139 [2023-12-20 22:00:18,870 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 137/139 [2023-12-20 22:00:18,961 INFO test.py line 196 131400] Test: 62/78-scene0378_01, Batch: 138/139 [2023-12-20 22:00:18,979 INFO test.py line 230 131400] Test: scene0378_01 [62/78]-196608 Batch 12.132 (10.807) Accuracy 0.9492 (0.8345) mIoU 0.7511 (0.7591) [2023-12-20 22:00:19,377 INFO test.py line 196 131400] Test: 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[2023-12-20 22:00:29,098 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 116/126 [2023-12-20 22:00:29,183 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 117/126 [2023-12-20 22:00:29,269 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 118/126 [2023-12-20 22:00:29,353 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 119/126 [2023-12-20 22:00:29,437 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 120/126 [2023-12-20 22:00:29,521 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 121/126 [2023-12-20 22:00:29,603 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 122/126 [2023-12-20 22:00:29,685 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 123/126 [2023-12-20 22:00:29,768 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 124/126 [2023-12-20 22:00:29,852 INFO test.py line 196 131400] Test: 63/78-scene0704_00, Batch: 125/126 [2023-12-20 22:00:29,935 INFO test.py line 230 131400] Test: scene0704_00 [63/78]-183029 Batch 10.642 (10.805) Accuracy 0.9217 (0.8355) mIoU 0.6025 (0.7602) [2023-12-20 22:00:30,281 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 0/144 [2023-12-20 22:00:30,353 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 1/144 [2023-12-20 22:00:30,438 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 2/144 [2023-12-20 22:00:30,528 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 3/144 [2023-12-20 22:00:30,609 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 4/144 [2023-12-20 22:00:30,687 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 5/144 [2023-12-20 22:00:30,760 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 6/144 [2023-12-20 22:00:30,833 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 7/144 [2023-12-20 22:00:30,905 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 8/144 [2023-12-20 22:00:30,977 INFO 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[2023-12-20 22:00:38,673 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 114/144 [2023-12-20 22:00:38,756 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 115/144 [2023-12-20 22:00:38,839 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 116/144 [2023-12-20 22:00:38,914 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 117/144 [2023-12-20 22:00:38,989 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 118/144 [2023-12-20 22:00:39,077 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 119/144 [2023-12-20 22:00:39,156 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 120/144 [2023-12-20 22:00:39,236 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 121/144 [2023-12-20 22:00:39,311 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 122/144 [2023-12-20 22:00:39,384 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 123/144 [2023-12-20 22:00:39,462 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 124/144 [2023-12-20 22:00:39,537 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 125/144 [2023-12-20 22:00:39,613 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 126/144 [2023-12-20 22:00:39,686 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 127/144 [2023-12-20 22:00:39,763 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 128/144 [2023-12-20 22:00:39,836 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 129/144 [2023-12-20 22:00:39,909 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 130/144 [2023-12-20 22:00:39,983 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 131/144 [2023-12-20 22:00:40,057 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 132/144 [2023-12-20 22:00:40,131 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 133/144 [2023-12-20 22:00:40,203 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 134/144 [2023-12-20 22:00:40,280 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 135/144 [2023-12-20 22:00:40,357 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 136/144 [2023-12-20 22:00:40,429 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 137/144 [2023-12-20 22:00:40,500 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 138/144 [2023-12-20 22:00:40,574 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 139/144 [2023-12-20 22:00:40,645 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 140/144 [2023-12-20 22:00:40,715 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 141/144 [2023-12-20 22:00:40,786 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 142/144 [2023-12-20 22:00:40,855 INFO test.py line 196 131400] Test: 64/78-scene0131_02, Batch: 143/144 [2023-12-20 22:00:40,873 INFO test.py line 230 131400] Test: scene0131_02 [64/78]-140599 Batch 10.682 (10.803) Accuracy 0.9395 (0.8324) mIoU 0.4599 (0.7571) [2023-12-20 22:00:41,187 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 0/121 [2023-12-20 22:00:41,270 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 1/121 [2023-12-20 22:00:41,347 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 2/121 [2023-12-20 22:00:41,408 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 3/121 [2023-12-20 22:00:41,479 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 4/121 [2023-12-20 22:00:41,554 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 5/121 [2023-12-20 22:00:41,631 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 6/121 [2023-12-20 22:00:41,701 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 7/121 [2023-12-20 22:00:41,790 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 8/121 [2023-12-20 22:00:41,871 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 9/121 [2023-12-20 22:00:41,943 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 10/121 [2023-12-20 22:00:42,010 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 11/121 [2023-12-20 22:00:42,086 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 12/121 [2023-12-20 22:00:42,155 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 13/121 [2023-12-20 22:00:42,220 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 14/121 [2023-12-20 22:00:42,288 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 15/121 [2023-12-20 22:00:42,359 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 16/121 [2023-12-20 22:00:42,426 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 17/121 [2023-12-20 22:00:42,494 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 18/121 [2023-12-20 22:00:42,563 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 19/121 [2023-12-20 22:00:42,624 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 20/121 [2023-12-20 22:00:42,687 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 21/121 [2023-12-20 22:00:42,753 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 22/121 [2023-12-20 22:00:42,817 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 23/121 [2023-12-20 22:00:42,877 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 24/121 [2023-12-20 22:00:42,937 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 25/121 [2023-12-20 22:00:42,995 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 26/121 [2023-12-20 22:00:43,052 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 27/121 [2023-12-20 22:00:43,109 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 28/121 [2023-12-20 22:00:43,166 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 29/121 [2023-12-20 22:00:43,223 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 30/121 [2023-12-20 22:00:43,280 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 31/121 [2023-12-20 22:00:43,337 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 32/121 [2023-12-20 22:00:43,394 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 33/121 [2023-12-20 22:00:43,451 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 34/121 [2023-12-20 22:00:43,508 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 35/121 [2023-12-20 22:00:43,567 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 36/121 [2023-12-20 22:00:43,623 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 37/121 [2023-12-20 22:00:43,679 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 38/121 [2023-12-20 22:00:43,735 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 39/121 [2023-12-20 22:00:43,790 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 40/121 [2023-12-20 22:00:43,846 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 41/121 [2023-12-20 22:00:43,903 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 42/121 [2023-12-20 22:00:43,959 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 43/121 [2023-12-20 22:00:44,014 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 44/121 [2023-12-20 22:00:44,071 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 45/121 [2023-12-20 22:00:44,127 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 46/121 [2023-12-20 22:00:44,183 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 47/121 [2023-12-20 22:00:44,239 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 48/121 [2023-12-20 22:00:44,296 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 49/121 [2023-12-20 22:00:44,351 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 50/121 [2023-12-20 22:00:44,407 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 51/121 [2023-12-20 22:00:44,463 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 52/121 [2023-12-20 22:00:44,518 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 53/121 [2023-12-20 22:00:44,574 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 54/121 [2023-12-20 22:00:44,629 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 55/121 [2023-12-20 22:00:44,686 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 56/121 [2023-12-20 22:00:44,741 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 57/121 [2023-12-20 22:00:44,798 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 58/121 [2023-12-20 22:00:44,854 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 59/121 [2023-12-20 22:00:44,911 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 60/121 [2023-12-20 22:00:44,967 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 61/121 [2023-12-20 22:00:45,023 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 62/121 [2023-12-20 22:00:45,079 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 63/121 [2023-12-20 22:00:45,135 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 64/121 [2023-12-20 22:00:45,191 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 65/121 [2023-12-20 22:00:45,246 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 66/121 [2023-12-20 22:00:45,302 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 67/121 [2023-12-20 22:00:45,358 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 68/121 [2023-12-20 22:00:45,415 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 69/121 [2023-12-20 22:00:45,473 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 70/121 [2023-12-20 22:00:45,529 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 71/121 [2023-12-20 22:00:45,585 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 72/121 [2023-12-20 22:00:45,641 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 73/121 [2023-12-20 22:00:45,705 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 74/121 [2023-12-20 22:00:45,766 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 75/121 [2023-12-20 22:00:45,827 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 76/121 [2023-12-20 22:00:45,884 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 77/121 [2023-12-20 22:00:45,942 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 78/121 [2023-12-20 22:00:45,998 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 79/121 [2023-12-20 22:00:46,060 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 80/121 [2023-12-20 22:00:46,121 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 81/121 [2023-12-20 22:00:46,182 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 82/121 [2023-12-20 22:00:46,250 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 83/121 [2023-12-20 22:00:46,326 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 84/121 [2023-12-20 22:00:46,395 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 85/121 [2023-12-20 22:00:46,456 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 86/121 [2023-12-20 22:00:46,517 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 87/121 [2023-12-20 22:00:46,578 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 88/121 [2023-12-20 22:00:46,639 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 89/121 [2023-12-20 22:00:46,701 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 90/121 [2023-12-20 22:00:46,763 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 91/121 [2023-12-20 22:00:46,832 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 92/121 [2023-12-20 22:00:46,911 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 93/121 [2023-12-20 22:00:46,991 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 94/121 [2023-12-20 22:00:47,058 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 95/121 [2023-12-20 22:00:47,128 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 96/121 [2023-12-20 22:00:47,196 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 97/121 [2023-12-20 22:00:47,262 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 98/121 [2023-12-20 22:00:47,329 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 99/121 [2023-12-20 22:00:47,399 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 100/121 [2023-12-20 22:00:47,471 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 101/121 [2023-12-20 22:00:47,542 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 102/121 [2023-12-20 22:00:47,610 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 103/121 [2023-12-20 22:00:47,677 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 104/121 [2023-12-20 22:00:47,751 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 105/121 [2023-12-20 22:00:47,824 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 106/121 [2023-12-20 22:00:47,896 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 107/121 [2023-12-20 22:00:47,959 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 108/121 [2023-12-20 22:00:48,022 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 109/121 [2023-12-20 22:00:48,085 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 110/121 [2023-12-20 22:00:48,153 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 111/121 [2023-12-20 22:00:48,214 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 112/121 [2023-12-20 22:00:48,297 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 113/121 [2023-12-20 22:00:48,370 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 114/121 [2023-12-20 22:00:48,431 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 115/121 [2023-12-20 22:00:48,499 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 116/121 [2023-12-20 22:00:48,565 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 117/121 [2023-12-20 22:00:48,629 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 118/121 [2023-12-20 22:00:48,690 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 119/121 [2023-12-20 22:00:48,755 INFO test.py line 196 131400] Test: 65/78-scene0382_01, Batch: 120/121 [2023-12-20 22:00:48,769 INFO test.py line 230 131400] Test: scene0382_01 [65/78]-96933 Batch 7.669 (10.755) Accuracy 0.8594 (0.8315) mIoU 0.5378 (0.7560) [2023-12-20 22:00:49,001 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 0/121 [2023-12-20 22:00:49,090 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 1/121 [2023-12-20 22:00:49,202 INFO test.py line 196 131400] Test: 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Test: 66/78-scene0351_01, Batch: 107/121 [2023-12-20 22:00:58,131 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 108/121 [2023-12-20 22:00:58,213 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 109/121 [2023-12-20 22:00:58,294 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 110/121 [2023-12-20 22:00:58,374 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 111/121 [2023-12-20 22:00:58,450 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 112/121 [2023-12-20 22:00:58,527 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 113/121 [2023-12-20 22:00:58,604 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 114/121 [2023-12-20 22:00:58,680 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 115/121 [2023-12-20 22:00:58,757 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 116/121 [2023-12-20 22:00:58,834 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 117/121 [2023-12-20 22:00:58,910 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 118/121 [2023-12-20 22:00:58,987 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 119/121 [2023-12-20 22:00:59,063 INFO test.py line 196 131400] Test: 66/78-scene0351_01, Batch: 120/121 [2023-12-20 22:00:59,081 INFO test.py line 230 131400] Test: scene0351_01 [66/78]-170840 Batch 10.163 (10.746) Accuracy 0.9576 (0.8326) mIoU 0.9340 (0.7578) [2023-12-20 22:00:59,422 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 0/198 [2023-12-20 22:00:59,516 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 1/198 [2023-12-20 22:00:59,608 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 2/198 [2023-12-20 22:00:59,702 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 3/198 [2023-12-20 22:00:59,795 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 4/198 [2023-12-20 22:00:59,887 INFO test.py line 196 131400] Test: 67/78-scene0251_00, 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[2023-12-20 22:01:15,778 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 173/198 [2023-12-20 22:01:15,884 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 174/198 [2023-12-20 22:01:15,986 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 175/198 [2023-12-20 22:01:16,083 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 176/198 [2023-12-20 22:01:16,180 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 177/198 [2023-12-20 22:01:16,286 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 178/198 [2023-12-20 22:01:16,387 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 179/198 [2023-12-20 22:01:16,484 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 180/198 [2023-12-20 22:01:16,586 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 181/198 [2023-12-20 22:01:16,685 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 182/198 [2023-12-20 22:01:16,782 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 183/198 [2023-12-20 22:01:16,878 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 184/198 [2023-12-20 22:01:16,985 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 185/198 [2023-12-20 22:01:17,084 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 186/198 [2023-12-20 22:01:17,180 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 187/198 [2023-12-20 22:01:17,281 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 188/198 [2023-12-20 22:01:17,385 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 189/198 [2023-12-20 22:01:17,483 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 190/198 [2023-12-20 22:01:17,581 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 191/198 [2023-12-20 22:01:17,684 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 192/198 [2023-12-20 22:01:17,780 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 193/198 [2023-12-20 22:01:17,873 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 194/198 [2023-12-20 22:01:17,967 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 195/198 [2023-12-20 22:01:18,062 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 196/198 [2023-12-20 22:01:18,159 INFO test.py line 196 131400] Test: 67/78-scene0251_00, Batch: 197/198 [2023-12-20 22:01:18,181 INFO test.py line 230 131400] Test: scene0251_00 [67/78]-226765 Batch 18.863 (10.867) Accuracy 0.9583 (0.8339) mIoU 0.8082 (0.7592) [2023-12-20 22:01:18,577 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 0/116 [2023-12-20 22:01:18,635 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 1/116 [2023-12-20 22:01:18,704 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 2/116 [2023-12-20 22:01:18,770 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 3/116 [2023-12-20 22:01:18,833 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 4/116 [2023-12-20 22:01:18,892 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 5/116 [2023-12-20 22:01:18,954 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 6/116 [2023-12-20 22:01:19,012 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 7/116 [2023-12-20 22:01:19,071 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 8/116 [2023-12-20 22:01:19,130 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 9/116 [2023-12-20 22:01:19,188 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 10/116 [2023-12-20 22:01:19,246 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 11/116 [2023-12-20 22:01:19,305 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 12/116 [2023-12-20 22:01:19,364 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 13/116 [2023-12-20 22:01:19,422 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 14/116 [2023-12-20 22:01:19,481 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 15/116 [2023-12-20 22:01:19,540 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 16/116 [2023-12-20 22:01:19,612 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 17/116 [2023-12-20 22:01:19,680 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 18/116 [2023-12-20 22:01:19,747 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 19/116 [2023-12-20 22:01:19,803 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 20/116 [2023-12-20 22:01:19,865 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 21/116 [2023-12-20 22:01:19,959 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 22/116 [2023-12-20 22:01:20,068 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 23/116 [2023-12-20 22:01:20,150 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 24/116 [2023-12-20 22:01:20,204 INFO test.py line 196 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[2023-12-20 22:01:20,775 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 36/116 [2023-12-20 22:01:20,827 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 37/116 [2023-12-20 22:01:20,878 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 38/116 [2023-12-20 22:01:20,926 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 39/116 [2023-12-20 22:01:20,973 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 40/116 [2023-12-20 22:01:21,020 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 41/116 [2023-12-20 22:01:21,067 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 42/116 [2023-12-20 22:01:21,115 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 43/116 [2023-12-20 22:01:21,163 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 44/116 [2023-12-20 22:01:21,210 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 45/116 [2023-12-20 22:01:21,256 INFO test.py line 196 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[2023-12-20 22:01:21,792 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 57/116 [2023-12-20 22:01:21,840 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 58/116 [2023-12-20 22:01:21,888 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 59/116 [2023-12-20 22:01:21,937 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 60/116 [2023-12-20 22:01:21,984 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 61/116 [2023-12-20 22:01:22,031 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 62/116 [2023-12-20 22:01:22,079 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 63/116 [2023-12-20 22:01:22,127 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 64/116 [2023-12-20 22:01:22,175 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 65/116 [2023-12-20 22:01:22,224 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 66/116 [2023-12-20 22:01:22,272 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 67/116 [2023-12-20 22:01:22,323 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 68/116 [2023-12-20 22:01:22,374 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 69/116 [2023-12-20 22:01:22,433 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 70/116 [2023-12-20 22:01:22,488 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 71/116 [2023-12-20 22:01:22,550 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 72/116 [2023-12-20 22:01:22,608 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 73/116 [2023-12-20 22:01:22,675 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 74/116 [2023-12-20 22:01:22,748 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 75/116 [2023-12-20 22:01:22,824 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 76/116 [2023-12-20 22:01:22,885 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 77/116 [2023-12-20 22:01:22,937 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 78/116 [2023-12-20 22:01:22,990 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 79/116 [2023-12-20 22:01:23,043 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 80/116 [2023-12-20 22:01:23,097 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 81/116 [2023-12-20 22:01:23,151 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 82/116 [2023-12-20 22:01:23,206 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 83/116 [2023-12-20 22:01:23,259 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 84/116 [2023-12-20 22:01:23,312 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 85/116 [2023-12-20 22:01:23,365 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 86/116 [2023-12-20 22:01:23,417 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 87/116 [2023-12-20 22:01:23,471 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 88/116 [2023-12-20 22:01:23,523 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 89/116 [2023-12-20 22:01:23,577 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 90/116 [2023-12-20 22:01:23,631 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 91/116 [2023-12-20 22:01:23,686 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 92/116 [2023-12-20 22:01:23,742 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 93/116 [2023-12-20 22:01:23,801 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 94/116 [2023-12-20 22:01:23,862 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 95/116 [2023-12-20 22:01:23,936 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 96/116 [2023-12-20 22:01:23,998 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 97/116 [2023-12-20 22:01:24,056 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 98/116 [2023-12-20 22:01:24,132 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 99/116 [2023-12-20 22:01:24,191 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 100/116 [2023-12-20 22:01:24,241 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 101/116 [2023-12-20 22:01:24,292 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 102/116 [2023-12-20 22:01:24,345 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 103/116 [2023-12-20 22:01:24,407 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 104/116 [2023-12-20 22:01:24,470 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 105/116 [2023-12-20 22:01:24,527 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 106/116 [2023-12-20 22:01:24,581 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 107/116 [2023-12-20 22:01:24,653 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 108/116 [2023-12-20 22:01:24,724 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 109/116 [2023-12-20 22:01:24,783 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 110/116 [2023-12-20 22:01:24,845 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 111/116 [2023-12-20 22:01:24,909 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 112/116 [2023-12-20 22:01:24,965 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 113/116 [2023-12-20 22:01:25,017 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 114/116 [2023-12-20 22:01:25,065 INFO test.py line 196 131400] Test: 68/78-scene0423_01, Batch: 115/116 [2023-12-20 22:01:25,074 INFO test.py line 230 131400] Test: scene0423_01 [68/78]-41518 Batch 6.565 (10.803) Accuracy 0.9994 (0.8339) mIoU 0.9945 (0.7593) [2023-12-20 22:01:25,223 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 0/148 [2023-12-20 22:01:25,303 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 1/148 [2023-12-20 22:01:25,381 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 2/148 [2023-12-20 22:01:25,463 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 3/148 [2023-12-20 22:01:25,556 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 4/148 [2023-12-20 22:01:25,641 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 5/148 [2023-12-20 22:01:25,726 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 6/148 [2023-12-20 22:01:25,812 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 7/148 [2023-12-20 22:01:25,891 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 8/148 [2023-12-20 22:01:25,969 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 9/148 [2023-12-20 22:01:26,048 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 10/148 [2023-12-20 22:01:26,131 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 11/148 [2023-12-20 22:01:26,208 INFO test.py line 196 131400] Test: 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[2023-12-20 22:01:36,091 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 138/148 [2023-12-20 22:01:36,171 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 139/148 [2023-12-20 22:01:36,253 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 140/148 [2023-12-20 22:01:36,335 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 141/148 [2023-12-20 22:01:36,421 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 142/148 [2023-12-20 22:01:36,500 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 143/148 [2023-12-20 22:01:36,582 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 144/148 [2023-12-20 22:01:36,664 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 145/148 [2023-12-20 22:01:36,744 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 146/148 [2023-12-20 22:01:36,825 INFO test.py line 196 131400] Test: 69/78-scene0187_00, Batch: 147/148 [2023-12-20 22:01:36,840 INFO test.py line 230 131400] Test: scene0187_00 [69/78]-173752 Batch 11.705 (10.817) Accuracy 0.9687 (0.8342) mIoU 0.6974 (0.7566) [2023-12-20 22:01:37,181 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 0/112 [2023-12-20 22:01:37,234 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 1/112 [2023-12-20 22:01:37,298 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 2/112 [2023-12-20 22:01:37,369 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 3/112 [2023-12-20 22:01:37,434 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 4/112 [2023-12-20 22:01:37,495 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 5/112 [2023-12-20 22:01:37,554 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 6/112 [2023-12-20 22:01:37,605 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 7/112 [2023-12-20 22:01:37,656 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 8/112 [2023-12-20 22:01:37,708 INFO 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70/78-scene0423_00, Batch: 103/112 [2023-12-20 22:01:43,035 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 104/112 [2023-12-20 22:01:43,089 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 105/112 [2023-12-20 22:01:43,156 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 106/112 [2023-12-20 22:01:43,241 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 107/112 [2023-12-20 22:01:43,326 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 108/112 [2023-12-20 22:01:43,404 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 109/112 [2023-12-20 22:01:43,472 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 110/112 [2023-12-20 22:01:43,524 INFO test.py line 196 131400] Test: 70/78-scene0423_00, Batch: 111/112 [2023-12-20 22:01:43,565 INFO test.py line 230 131400] Test: scene0423_00 [70/78]-53074 Batch 6.440 (10.754) Accuracy 0.9992 (0.8343) mIoU 0.9954 (0.7568) [2023-12-20 22:01:43,728 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 0/134 [2023-12-20 22:01:43,807 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 1/134 [2023-12-20 22:01:43,886 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 2/134 [2023-12-20 22:01:43,966 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 3/134 [2023-12-20 22:01:44,046 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 4/134 [2023-12-20 22:01:44,127 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 5/134 [2023-12-20 22:01:44,208 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 6/134 [2023-12-20 22:01:44,287 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 7/134 [2023-12-20 22:01:44,366 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 8/134 [2023-12-20 22:01:44,452 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 9/134 [2023-12-20 22:01:44,531 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 10/134 [2023-12-20 22:01:44,620 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 11/134 [2023-12-20 22:01:44,718 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 12/134 [2023-12-20 22:01:44,809 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 13/134 [2023-12-20 22:01:44,889 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 14/134 [2023-12-20 22:01:44,967 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 15/134 [2023-12-20 22:01:45,045 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 16/134 [2023-12-20 22:01:45,123 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 17/134 [2023-12-20 22:01:45,201 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 18/134 [2023-12-20 22:01:45,279 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 19/134 [2023-12-20 22:01:45,358 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 20/134 [2023-12-20 22:01:45,436 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 21/134 [2023-12-20 22:01:45,513 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 22/134 [2023-12-20 22:01:45,592 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 23/134 [2023-12-20 22:01:45,676 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 24/134 [2023-12-20 22:01:45,760 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 25/134 [2023-12-20 22:01:45,843 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 26/134 [2023-12-20 22:01:45,930 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 27/134 [2023-12-20 22:01:46,011 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 28/134 [2023-12-20 22:01:46,094 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 29/134 [2023-12-20 22:01:46,178 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 30/134 [2023-12-20 22:01:46,259 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 31/134 [2023-12-20 22:01:46,338 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 32/134 [2023-12-20 22:01:46,417 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 33/134 [2023-12-20 22:01:46,496 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 34/134 [2023-12-20 22:01:46,576 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 35/134 [2023-12-20 22:01:46,656 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 36/134 [2023-12-20 22:01:46,734 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 37/134 [2023-12-20 22:01:46,812 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 38/134 [2023-12-20 22:01:46,889 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 39/134 [2023-12-20 22:01:46,965 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 40/134 [2023-12-20 22:01:47,041 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 41/134 [2023-12-20 22:01:47,116 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 42/134 [2023-12-20 22:01:47,190 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 43/134 [2023-12-20 22:01:47,265 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 44/134 [2023-12-20 22:01:47,340 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 45/134 [2023-12-20 22:01:47,415 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 46/134 [2023-12-20 22:01:47,490 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 47/134 [2023-12-20 22:01:47,566 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 48/134 [2023-12-20 22:01:47,641 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 49/134 [2023-12-20 22:01:47,716 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 50/134 [2023-12-20 22:01:47,790 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 51/134 [2023-12-20 22:01:47,865 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 52/134 [2023-12-20 22:01:47,940 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 53/134 [2023-12-20 22:01:48,015 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 54/134 [2023-12-20 22:01:48,090 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 55/134 [2023-12-20 22:01:48,164 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 56/134 [2023-12-20 22:01:48,239 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 57/134 [2023-12-20 22:01:48,314 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 58/134 [2023-12-20 22:01:48,389 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 59/134 [2023-12-20 22:01:48,463 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 60/134 [2023-12-20 22:01:48,539 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 61/134 [2023-12-20 22:01:48,614 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 62/134 [2023-12-20 22:01:48,693 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 63/134 [2023-12-20 22:01:48,768 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 64/134 [2023-12-20 22:01:48,843 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 65/134 [2023-12-20 22:01:48,918 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 66/134 [2023-12-20 22:01:48,992 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 67/134 [2023-12-20 22:01:49,067 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 68/134 [2023-12-20 22:01:49,142 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 69/134 [2023-12-20 22:01:49,217 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 70/134 [2023-12-20 22:01:49,291 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 71/134 [2023-12-20 22:01:49,367 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 72/134 [2023-12-20 22:01:49,442 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 73/134 [2023-12-20 22:01:49,517 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 74/134 [2023-12-20 22:01:49,593 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 75/134 [2023-12-20 22:01:49,670 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 76/134 [2023-12-20 22:01:49,746 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 77/134 [2023-12-20 22:01:49,821 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 78/134 [2023-12-20 22:01:49,896 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 79/134 [2023-12-20 22:01:49,971 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 80/134 [2023-12-20 22:01:50,045 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 81/134 [2023-12-20 22:01:50,120 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 82/134 [2023-12-20 22:01:50,194 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 83/134 [2023-12-20 22:01:50,269 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 84/134 [2023-12-20 22:01:50,344 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 85/134 [2023-12-20 22:01:50,418 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 86/134 [2023-12-20 22:01:50,493 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 87/134 [2023-12-20 22:01:50,574 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 88/134 [2023-12-20 22:01:50,658 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 89/134 [2023-12-20 22:01:50,742 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 90/134 [2023-12-20 22:01:50,823 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 91/134 [2023-12-20 22:01:50,903 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 92/134 [2023-12-20 22:01:50,982 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 93/134 [2023-12-20 22:01:51,062 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 94/134 [2023-12-20 22:01:51,142 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 95/134 [2023-12-20 22:01:51,221 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 96/134 [2023-12-20 22:01:51,301 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 97/134 [2023-12-20 22:01:51,381 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 98/134 [2023-12-20 22:01:51,461 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 99/134 [2023-12-20 22:01:51,540 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 100/134 [2023-12-20 22:01:51,621 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 101/134 [2023-12-20 22:01:51,702 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 102/134 [2023-12-20 22:01:51,782 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 103/134 [2023-12-20 22:01:51,862 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 104/134 [2023-12-20 22:01:51,942 INFO test.py line 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Batch: 115/134 [2023-12-20 22:01:52,854 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 116/134 [2023-12-20 22:01:52,938 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 117/134 [2023-12-20 22:01:53,020 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 118/134 [2023-12-20 22:01:53,100 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 119/134 [2023-12-20 22:01:53,183 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 120/134 [2023-12-20 22:01:53,267 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 121/134 [2023-12-20 22:01:53,348 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 122/134 [2023-12-20 22:01:53,428 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 123/134 [2023-12-20 22:01:53,510 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 124/134 [2023-12-20 22:01:53,594 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 125/134 [2023-12-20 22:01:53,683 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 126/134 [2023-12-20 22:01:53,766 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 127/134 [2023-12-20 22:01:53,846 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 128/134 [2023-12-20 22:01:53,926 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 129/134 [2023-12-20 22:01:54,007 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 130/134 [2023-12-20 22:01:54,087 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 131/134 [2023-12-20 22:01:54,169 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 132/134 [2023-12-20 22:01:54,250 INFO test.py line 196 131400] Test: 71/78-scene0131_01, Batch: 133/134 [2023-12-20 22:01:54,272 INFO test.py line 230 131400] Test: scene0131_01 [71/78]-172221 Batch 10.627 (10.752) Accuracy 0.8973 (0.8286) mIoU 0.3224 (0.7507) [2023-12-20 22:01:54,607 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 0/138 [2023-12-20 22:01:54,690 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 1/138 [2023-12-20 22:01:54,780 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 2/138 [2023-12-20 22:01:54,865 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 3/138 [2023-12-20 22:01:54,952 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 4/138 [2023-12-20 22:01:55,041 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 5/138 [2023-12-20 22:01:55,129 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 6/138 [2023-12-20 22:01:55,215 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 7/138 [2023-12-20 22:01:55,299 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 8/138 [2023-12-20 22:01:55,382 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 9/138 [2023-12-20 22:01:55,469 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 10/138 [2023-12-20 22:01:55,557 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 11/138 [2023-12-20 22:01:55,667 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 12/138 [2023-12-20 22:01:55,765 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 13/138 [2023-12-20 22:01:55,851 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 14/138 [2023-12-20 22:01:55,959 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 15/138 [2023-12-20 22:01:56,097 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 16/138 [2023-12-20 22:01:56,214 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 17/138 [2023-12-20 22:01:56,301 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 18/138 [2023-12-20 22:01:56,395 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 19/138 [2023-12-20 22:01:56,482 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 20/138 [2023-12-20 22:01:56,573 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 21/138 [2023-12-20 22:01:56,665 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 22/138 [2023-12-20 22:01:56,752 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 23/138 [2023-12-20 22:01:56,841 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 24/138 [2023-12-20 22:01:56,933 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 25/138 [2023-12-20 22:01:57,024 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 26/138 [2023-12-20 22:01:57,112 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 27/138 [2023-12-20 22:01:57,195 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 28/138 [2023-12-20 22:01:57,298 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 29/138 [2023-12-20 22:01:57,403 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 30/138 [2023-12-20 22:01:57,508 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 31/138 [2023-12-20 22:01:57,603 INFO test.py line 196 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[2023-12-20 22:01:58,541 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 43/138 [2023-12-20 22:01:58,625 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 44/138 [2023-12-20 22:01:58,711 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 45/138 [2023-12-20 22:01:58,797 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 46/138 [2023-12-20 22:01:58,884 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 47/138 [2023-12-20 22:01:58,986 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 48/138 [2023-12-20 22:01:59,074 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 49/138 [2023-12-20 22:01:59,158 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 50/138 [2023-12-20 22:01:59,238 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 51/138 [2023-12-20 22:01:59,320 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 52/138 [2023-12-20 22:01:59,406 INFO test.py line 196 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[2023-12-20 22:02:00,300 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 64/138 [2023-12-20 22:02:00,382 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 65/138 [2023-12-20 22:02:00,465 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 66/138 [2023-12-20 22:02:00,546 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 67/138 [2023-12-20 22:02:00,627 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 68/138 [2023-12-20 22:02:00,709 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 69/138 [2023-12-20 22:02:00,792 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 70/138 [2023-12-20 22:02:00,870 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 71/138 [2023-12-20 22:02:00,949 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 72/138 [2023-12-20 22:02:01,029 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 73/138 [2023-12-20 22:02:01,111 INFO test.py line 196 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[2023-12-20 22:02:02,042 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 85/138 [2023-12-20 22:02:02,132 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 86/138 [2023-12-20 22:02:02,230 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 87/138 [2023-12-20 22:02:02,319 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 88/138 [2023-12-20 22:02:02,408 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 89/138 [2023-12-20 22:02:02,495 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 90/138 [2023-12-20 22:02:02,584 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 91/138 [2023-12-20 22:02:02,671 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 92/138 [2023-12-20 22:02:02,758 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 93/138 [2023-12-20 22:02:02,848 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 94/138 [2023-12-20 22:02:02,941 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 95/138 [2023-12-20 22:02:03,029 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 96/138 [2023-12-20 22:02:03,120 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 97/138 [2023-12-20 22:02:03,209 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 98/138 [2023-12-20 22:02:03,301 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 99/138 [2023-12-20 22:02:03,390 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 100/138 [2023-12-20 22:02:03,486 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 101/138 [2023-12-20 22:02:03,576 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 102/138 [2023-12-20 22:02:03,665 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 103/138 [2023-12-20 22:02:03,753 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 104/138 [2023-12-20 22:02:03,841 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 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[2023-12-20 22:02:06,634 INFO test.py line 196 131400] Test: 72/78-scene0046_00, Batch: 137/138 [2023-12-20 22:02:06,656 INFO test.py line 230 131400] Test: scene0046_00 [72/78]-186857 Batch 12.137 (10.771) Accuracy 0.9226 (0.8330) mIoU 0.7864 (0.7557) [2023-12-20 22:02:07,055 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 0/100 [2023-12-20 22:02:07,126 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 1/100 [2023-12-20 22:02:07,183 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 2/100 [2023-12-20 22:02:07,245 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 3/100 [2023-12-20 22:02:07,301 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 4/100 [2023-12-20 22:02:07,366 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 5/100 [2023-12-20 22:02:07,424 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 6/100 [2023-12-20 22:02:07,478 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 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131400] Test: 73/78-scene0432_01, Batch: 18/100 [2023-12-20 22:02:08,145 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 19/100 [2023-12-20 22:02:08,198 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 20/100 [2023-12-20 22:02:08,250 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 21/100 [2023-12-20 22:02:08,305 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 22/100 [2023-12-20 22:02:08,360 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 23/100 [2023-12-20 22:02:08,413 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 24/100 [2023-12-20 22:02:08,466 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 25/100 [2023-12-20 22:02:08,523 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 26/100 [2023-12-20 22:02:08,581 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 27/100 [2023-12-20 22:02:08,642 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 28/100 [2023-12-20 22:02:08,696 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 29/100 [2023-12-20 22:02:08,749 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 30/100 [2023-12-20 22:02:08,802 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 31/100 [2023-12-20 22:02:08,856 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 32/100 [2023-12-20 22:02:08,911 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 33/100 [2023-12-20 22:02:08,965 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 34/100 [2023-12-20 22:02:09,015 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 35/100 [2023-12-20 22:02:09,068 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 36/100 [2023-12-20 22:02:09,123 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 37/100 [2023-12-20 22:02:09,184 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 38/100 [2023-12-20 22:02:09,241 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 39/100 [2023-12-20 22:02:09,295 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 40/100 [2023-12-20 22:02:09,356 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 41/100 [2023-12-20 22:02:09,410 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 42/100 [2023-12-20 22:02:09,464 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 43/100 [2023-12-20 22:02:09,519 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 44/100 [2023-12-20 22:02:09,585 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 45/100 [2023-12-20 22:02:09,648 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 46/100 [2023-12-20 22:02:09,707 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 47/100 [2023-12-20 22:02:09,762 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 48/100 [2023-12-20 22:02:09,818 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 49/100 [2023-12-20 22:02:09,874 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 50/100 [2023-12-20 22:02:09,930 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 51/100 [2023-12-20 22:02:09,979 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 52/100 [2023-12-20 22:02:10,028 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 53/100 [2023-12-20 22:02:10,076 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 54/100 [2023-12-20 22:02:10,125 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 55/100 [2023-12-20 22:02:10,175 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 56/100 [2023-12-20 22:02:10,226 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 57/100 [2023-12-20 22:02:10,279 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 58/100 [2023-12-20 22:02:10,330 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 59/100 [2023-12-20 22:02:10,382 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 60/100 [2023-12-20 22:02:10,434 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 61/100 [2023-12-20 22:02:10,487 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 62/100 [2023-12-20 22:02:10,542 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 63/100 [2023-12-20 22:02:10,599 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 64/100 [2023-12-20 22:02:10,653 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 65/100 [2023-12-20 22:02:10,713 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 66/100 [2023-12-20 22:02:10,764 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 67/100 [2023-12-20 22:02:10,816 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 68/100 [2023-12-20 22:02:10,869 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 69/100 [2023-12-20 22:02:10,920 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 70/100 [2023-12-20 22:02:10,972 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 71/100 [2023-12-20 22:02:11,023 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 72/100 [2023-12-20 22:02:11,075 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 73/100 [2023-12-20 22:02:11,125 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 74/100 [2023-12-20 22:02:11,176 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 75/100 [2023-12-20 22:02:11,230 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 76/100 [2023-12-20 22:02:11,284 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 77/100 [2023-12-20 22:02:11,337 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 78/100 [2023-12-20 22:02:11,390 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 79/100 [2023-12-20 22:02:11,443 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 80/100 [2023-12-20 22:02:11,497 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 81/100 [2023-12-20 22:02:11,553 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 82/100 [2023-12-20 22:02:11,607 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 83/100 [2023-12-20 22:02:11,658 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 84/100 [2023-12-20 22:02:11,711 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 85/100 [2023-12-20 22:02:11,764 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 86/100 [2023-12-20 22:02:11,826 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 87/100 [2023-12-20 22:02:11,889 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 88/100 [2023-12-20 22:02:11,947 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 89/100 [2023-12-20 22:02:12,007 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 90/100 [2023-12-20 22:02:12,064 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 91/100 [2023-12-20 22:02:12,119 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 92/100 [2023-12-20 22:02:12,174 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 93/100 [2023-12-20 22:02:12,224 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 94/100 [2023-12-20 22:02:12,276 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 95/100 [2023-12-20 22:02:12,326 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 96/100 [2023-12-20 22:02:12,380 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 97/100 [2023-12-20 22:02:12,436 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 98/100 [2023-12-20 22:02:12,492 INFO test.py line 196 131400] Test: 73/78-scene0432_01, Batch: 99/100 [2023-12-20 22:02:12,537 INFO test.py line 230 131400] Test: scene0432_01 [73/78]-37547 Batch 5.563 (10.700) Accuracy 0.9908 (0.8331) mIoU 0.7862 (0.7558) [2023-12-20 22:02:12,655 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 0/127 [2023-12-20 22:02:12,705 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 1/127 [2023-12-20 22:02:12,756 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 2/127 [2023-12-20 22:02:12,805 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 3/127 [2023-12-20 22:02:12,855 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 4/127 [2023-12-20 22:02:12,905 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 5/127 [2023-12-20 22:02:12,956 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 6/127 [2023-12-20 22:02:13,006 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 7/127 [2023-12-20 22:02:13,056 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 8/127 [2023-12-20 22:02:13,105 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 9/127 [2023-12-20 22:02:13,155 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 10/127 [2023-12-20 22:02:13,204 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 11/127 [2023-12-20 22:02:13,254 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 12/127 [2023-12-20 22:02:13,305 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 13/127 [2023-12-20 22:02:13,357 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 14/127 [2023-12-20 22:02:13,410 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 15/127 [2023-12-20 22:02:13,460 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 16/127 [2023-12-20 22:02:13,511 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 17/127 [2023-12-20 22:02:13,561 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 18/127 [2023-12-20 22:02:13,611 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 19/127 [2023-12-20 22:02:13,661 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 20/127 [2023-12-20 22:02:13,710 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 21/127 [2023-12-20 22:02:13,760 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 22/127 [2023-12-20 22:02:13,810 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 23/127 [2023-12-20 22:02:13,880 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 24/127 [2023-12-20 22:02:13,943 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 25/127 [2023-12-20 22:02:13,992 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 26/127 [2023-12-20 22:02:14,042 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 27/127 [2023-12-20 22:02:14,091 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 28/127 [2023-12-20 22:02:14,153 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 29/127 [2023-12-20 22:02:14,210 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 30/127 [2023-12-20 22:02:14,267 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 31/127 [2023-12-20 22:02:14,319 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 32/127 [2023-12-20 22:02:14,374 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 33/127 [2023-12-20 22:02:14,424 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 34/127 [2023-12-20 22:02:14,478 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 35/127 [2023-12-20 22:02:14,534 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 36/127 [2023-12-20 22:02:14,588 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 37/127 [2023-12-20 22:02:14,642 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 38/127 [2023-12-20 22:02:14,695 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 39/127 [2023-12-20 22:02:14,747 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 40/127 [2023-12-20 22:02:14,814 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 41/127 [2023-12-20 22:02:14,873 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 42/127 [2023-12-20 22:02:14,921 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 43/127 [2023-12-20 22:02:14,971 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 44/127 [2023-12-20 22:02:15,019 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 45/127 [2023-12-20 22:02:15,077 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 46/127 [2023-12-20 22:02:15,126 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 47/127 [2023-12-20 22:02:15,179 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 48/127 [2023-12-20 22:02:15,230 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 49/127 [2023-12-20 22:02:15,279 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 50/127 [2023-12-20 22:02:15,328 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 51/127 [2023-12-20 22:02:15,376 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 52/127 [2023-12-20 22:02:15,434 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 53/127 [2023-12-20 22:02:15,516 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 54/127 [2023-12-20 22:02:15,592 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 55/127 [2023-12-20 22:02:15,668 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 56/127 [2023-12-20 22:02:15,741 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 57/127 [2023-12-20 22:02:15,790 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 58/127 [2023-12-20 22:02:15,840 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 59/127 [2023-12-20 22:02:15,890 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 60/127 [2023-12-20 22:02:15,940 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 61/127 [2023-12-20 22:02:15,989 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 62/127 [2023-12-20 22:02:16,039 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 63/127 [2023-12-20 22:02:16,089 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 64/127 [2023-12-20 22:02:16,138 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 65/127 [2023-12-20 22:02:16,187 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 66/127 [2023-12-20 22:02:16,237 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 67/127 [2023-12-20 22:02:16,287 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 68/127 [2023-12-20 22:02:16,336 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 69/127 [2023-12-20 22:02:16,386 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 70/127 [2023-12-20 22:02:16,435 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 71/127 [2023-12-20 22:02:16,484 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 72/127 [2023-12-20 22:02:16,534 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 73/127 [2023-12-20 22:02:16,583 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 74/127 [2023-12-20 22:02:16,632 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 75/127 [2023-12-20 22:02:16,681 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 76/127 [2023-12-20 22:02:16,730 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 77/127 [2023-12-20 22:02:16,779 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 78/127 [2023-12-20 22:02:16,828 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 79/127 [2023-12-20 22:02:16,879 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 80/127 [2023-12-20 22:02:16,927 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 81/127 [2023-12-20 22:02:16,976 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 82/127 [2023-12-20 22:02:17,025 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 83/127 [2023-12-20 22:02:17,074 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 84/127 [2023-12-20 22:02:17,122 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 85/127 [2023-12-20 22:02:17,171 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 86/127 [2023-12-20 22:02:17,220 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 87/127 [2023-12-20 22:02:17,268 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 88/127 [2023-12-20 22:02:17,318 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 89/127 [2023-12-20 22:02:17,367 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 90/127 [2023-12-20 22:02:17,416 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 91/127 [2023-12-20 22:02:17,465 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 92/127 [2023-12-20 22:02:17,513 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 93/127 [2023-12-20 22:02:17,562 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 94/127 [2023-12-20 22:02:17,611 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 95/127 [2023-12-20 22:02:17,660 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 96/127 [2023-12-20 22:02:17,709 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 97/127 [2023-12-20 22:02:17,759 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 98/127 [2023-12-20 22:02:17,808 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 99/127 [2023-12-20 22:02:17,857 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 100/127 [2023-12-20 22:02:17,906 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 101/127 [2023-12-20 22:02:17,954 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 102/127 [2023-12-20 22:02:18,003 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 103/127 [2023-12-20 22:02:18,051 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 104/127 [2023-12-20 22:02:18,100 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 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test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 116/127 [2023-12-20 22:02:18,694 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 117/127 [2023-12-20 22:02:18,745 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 118/127 [2023-12-20 22:02:18,797 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 119/127 [2023-12-20 22:02:18,848 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 120/127 [2023-12-20 22:02:18,898 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 121/127 [2023-12-20 22:02:18,951 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 122/127 [2023-12-20 22:02:19,002 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 123/127 [2023-12-20 22:02:19,050 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 124/127 [2023-12-20 22:02:19,100 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 125/127 [2023-12-20 22:02:19,148 INFO test.py line 196 131400] Test: 74/78-scene0664_02, Batch: 126/127 [2023-12-20 22:02:19,158 INFO test.py line 230 131400] Test: scene0664_02 [74/78]-46804 Batch 6.557 (10.644) Accuracy 0.9326 (0.8332) mIoU 0.7989 (0.7577) [2023-12-20 22:02:19,354 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 0/138 [2023-12-20 22:02:19,465 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 1/138 [2023-12-20 22:02:19,571 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 2/138 [2023-12-20 22:02:19,677 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 3/138 [2023-12-20 22:02:19,782 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 4/138 [2023-12-20 22:02:19,888 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 5/138 [2023-12-20 22:02:19,994 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 6/138 [2023-12-20 22:02:20,099 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 7/138 [2023-12-20 22:02:20,204 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 8/138 [2023-12-20 22:02:20,310 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 9/138 [2023-12-20 22:02:20,416 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 10/138 [2023-12-20 22:02:20,521 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 11/138 [2023-12-20 22:02:20,627 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 12/138 [2023-12-20 22:02:20,734 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 13/138 [2023-12-20 22:02:20,839 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 14/138 [2023-12-20 22:02:20,944 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 15/138 [2023-12-20 22:02:21,055 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 16/138 [2023-12-20 22:02:21,160 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 17/138 [2023-12-20 22:02:21,271 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 18/138 [2023-12-20 22:02:21,378 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 19/138 [2023-12-20 22:02:21,490 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 20/138 [2023-12-20 22:02:21,599 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 21/138 [2023-12-20 22:02:21,710 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 22/138 [2023-12-20 22:02:21,820 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 23/138 [2023-12-20 22:02:21,932 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 24/138 [2023-12-20 22:02:22,038 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 25/138 [2023-12-20 22:02:22,157 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 26/138 [2023-12-20 22:02:22,303 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 27/138 [2023-12-20 22:02:22,450 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 28/138 [2023-12-20 22:02:22,562 INFO test.py line 196 131400] Test: 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22:02:23,840 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 40/138 [2023-12-20 22:02:23,976 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 41/138 [2023-12-20 22:02:24,095 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 42/138 [2023-12-20 22:02:24,212 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 43/138 [2023-12-20 22:02:24,324 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 44/138 [2023-12-20 22:02:24,430 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 45/138 [2023-12-20 22:02:24,537 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 46/138 [2023-12-20 22:02:24,645 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 47/138 [2023-12-20 22:02:24,757 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 48/138 [2023-12-20 22:02:24,874 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 49/138 [2023-12-20 22:02:24,981 INFO test.py line 196 131400] Test: 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22:02:26,132 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 61/138 [2023-12-20 22:02:26,236 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 62/138 [2023-12-20 22:02:26,338 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 63/138 [2023-12-20 22:02:26,447 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 64/138 [2023-12-20 22:02:26,552 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 65/138 [2023-12-20 22:02:26,659 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 66/138 [2023-12-20 22:02:26,770 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 67/138 [2023-12-20 22:02:26,876 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 68/138 [2023-12-20 22:02:26,983 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 69/138 [2023-12-20 22:02:27,090 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 70/138 [2023-12-20 22:02:27,193 INFO test.py line 196 131400] Test: 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22:02:28,329 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 82/138 [2023-12-20 22:02:28,440 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 83/138 [2023-12-20 22:02:28,549 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 84/138 [2023-12-20 22:02:28,658 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 85/138 [2023-12-20 22:02:28,766 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 86/138 [2023-12-20 22:02:28,869 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 87/138 [2023-12-20 22:02:28,981 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 88/138 [2023-12-20 22:02:29,092 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 89/138 [2023-12-20 22:02:29,203 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 90/138 [2023-12-20 22:02:29,317 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 91/138 [2023-12-20 22:02:29,428 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 92/138 [2023-12-20 22:02:29,538 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 93/138 [2023-12-20 22:02:29,649 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 94/138 [2023-12-20 22:02:29,760 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 95/138 [2023-12-20 22:02:29,870 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 96/138 [2023-12-20 22:02:29,981 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 97/138 [2023-12-20 22:02:30,091 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 98/138 [2023-12-20 22:02:30,202 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 99/138 [2023-12-20 22:02:30,313 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 100/138 [2023-12-20 22:02:30,425 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 101/138 [2023-12-20 22:02:30,535 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 102/138 [2023-12-20 22:02:30,647 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 103/138 [2023-12-20 22:02:30,758 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 104/138 [2023-12-20 22:02:30,869 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 105/138 [2023-12-20 22:02:30,979 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 106/138 [2023-12-20 22:02:31,090 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 107/138 [2023-12-20 22:02:31,201 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 108/138 [2023-12-20 22:02:31,317 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 109/138 [2023-12-20 22:02:31,428 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 110/138 [2023-12-20 22:02:31,538 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 111/138 [2023-12-20 22:02:31,649 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 112/138 [2023-12-20 22:02:31,760 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 113/138 [2023-12-20 22:02:31,870 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 114/138 [2023-12-20 22:02:31,981 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 115/138 [2023-12-20 22:02:32,093 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 116/138 [2023-12-20 22:02:32,204 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 117/138 [2023-12-20 22:02:32,316 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 118/138 [2023-12-20 22:02:32,427 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 119/138 [2023-12-20 22:02:32,538 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 120/138 [2023-12-20 22:02:32,649 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 121/138 [2023-12-20 22:02:32,762 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 122/138 [2023-12-20 22:02:32,872 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 123/138 [2023-12-20 22:02:32,983 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 124/138 [2023-12-20 22:02:33,097 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 125/138 [2023-12-20 22:02:33,214 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 126/138 [2023-12-20 22:02:33,336 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 127/138 [2023-12-20 22:02:33,443 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 128/138 [2023-12-20 22:02:33,557 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 129/138 [2023-12-20 22:02:33,674 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 130/138 [2023-12-20 22:02:33,784 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 131/138 [2023-12-20 22:02:33,892 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 132/138 [2023-12-20 22:02:33,999 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 133/138 [2023-12-20 22:02:34,104 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 134/138 [2023-12-20 22:02:34,209 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 135/138 [2023-12-20 22:02:34,317 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 136/138 [2023-12-20 22:02:34,424 INFO test.py line 196 131400] Test: 75/78-scene0207_01, Batch: 137/138 [2023-12-20 22:02:34,446 INFO test.py line 230 131400] Test: scene0207_01 [75/78]-269274 Batch 15.211 (10.705) Accuracy 0.9202 (0.8356) mIoU 0.7952 (0.7598) [2023-12-20 22:02:34,933 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 0/159 [2023-12-20 22:02:35,029 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 1/159 [2023-12-20 22:02:35,125 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 2/159 [2023-12-20 22:02:35,219 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 3/159 [2023-12-20 22:02:35,313 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 4/159 [2023-12-20 22:02:35,407 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 5/159 [2023-12-20 22:02:35,502 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 6/159 [2023-12-20 22:02:35,596 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 7/159 [2023-12-20 22:02:35,690 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 8/159 [2023-12-20 22:02:35,784 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 9/159 [2023-12-20 22:02:35,878 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 10/159 [2023-12-20 22:02:35,973 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 11/159 [2023-12-20 22:02:36,066 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 12/159 [2023-12-20 22:02:36,160 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 13/159 [2023-12-20 22:02:36,255 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 14/159 [2023-12-20 22:02:36,349 INFO test.py line 196 131400] Test: 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22:02:37,391 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 26/159 [2023-12-20 22:02:37,486 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 27/159 [2023-12-20 22:02:37,586 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 28/159 [2023-12-20 22:02:37,681 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 29/159 [2023-12-20 22:02:37,777 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 30/159 [2023-12-20 22:02:37,873 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 31/159 [2023-12-20 22:02:37,969 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 32/159 [2023-12-20 22:02:38,064 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 33/159 [2023-12-20 22:02:38,159 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 34/159 [2023-12-20 22:02:38,253 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 35/159 [2023-12-20 22:02:38,348 INFO test.py line 196 131400] Test: 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22:02:39,376 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 47/159 [2023-12-20 22:02:39,467 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 48/159 [2023-12-20 22:02:39,557 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 49/159 [2023-12-20 22:02:39,648 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 50/159 [2023-12-20 22:02:39,739 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 51/159 [2023-12-20 22:02:39,830 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 52/159 [2023-12-20 22:02:39,921 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 53/159 [2023-12-20 22:02:40,012 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 54/159 [2023-12-20 22:02:40,103 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 55/159 [2023-12-20 22:02:40,197 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 56/159 [2023-12-20 22:02:40,293 INFO test.py line 196 131400] Test: 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22:02:41,309 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 68/159 [2023-12-20 22:02:41,399 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 69/159 [2023-12-20 22:02:41,490 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 70/159 [2023-12-20 22:02:41,581 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 71/159 [2023-12-20 22:02:41,671 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 72/159 [2023-12-20 22:02:41,762 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 73/159 [2023-12-20 22:02:41,852 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 74/159 [2023-12-20 22:02:41,943 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 75/159 [2023-12-20 22:02:42,033 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 76/159 [2023-12-20 22:02:42,123 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 77/159 [2023-12-20 22:02:42,213 INFO test.py line 196 131400] Test: 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22:02:43,210 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 89/159 [2023-12-20 22:02:43,300 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 90/159 [2023-12-20 22:02:43,389 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 91/159 [2023-12-20 22:02:43,480 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 92/159 [2023-12-20 22:02:43,569 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 93/159 [2023-12-20 22:02:43,658 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 94/159 [2023-12-20 22:02:43,748 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 95/159 [2023-12-20 22:02:43,838 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 96/159 [2023-12-20 22:02:43,927 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 97/159 [2023-12-20 22:02:44,017 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 98/159 [2023-12-20 22:02:44,107 INFO test.py line 196 131400] Test: 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[2023-12-20 22:02:45,174 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 110/159 [2023-12-20 22:02:45,273 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 111/159 [2023-12-20 22:02:45,372 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 112/159 [2023-12-20 22:02:45,470 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 113/159 [2023-12-20 22:02:45,569 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 114/159 [2023-12-20 22:02:45,668 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 115/159 [2023-12-20 22:02:45,768 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 116/159 [2023-12-20 22:02:45,867 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 117/159 [2023-12-20 22:02:45,968 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 118/159 [2023-12-20 22:02:46,070 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 119/159 [2023-12-20 22:02:46,170 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 120/159 [2023-12-20 22:02:46,269 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 121/159 [2023-12-20 22:02:46,368 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 122/159 [2023-12-20 22:02:46,469 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 123/159 [2023-12-20 22:02:46,568 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 124/159 [2023-12-20 22:02:46,668 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 125/159 [2023-12-20 22:02:46,767 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 126/159 [2023-12-20 22:02:46,866 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 127/159 [2023-12-20 22:02:46,964 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 128/159 [2023-12-20 22:02:47,063 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 129/159 [2023-12-20 22:02:47,162 INFO test.py line 196 131400] Test: 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[2023-12-20 22:02:48,298 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 141/159 [2023-12-20 22:02:48,397 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 142/159 [2023-12-20 22:02:48,497 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 143/159 [2023-12-20 22:02:48,597 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 144/159 [2023-12-20 22:02:48,697 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 145/159 [2023-12-20 22:02:48,796 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 146/159 [2023-12-20 22:02:48,896 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 147/159 [2023-12-20 22:02:48,992 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 148/159 [2023-12-20 22:02:49,086 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 149/159 [2023-12-20 22:02:49,181 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 150/159 [2023-12-20 22:02:49,274 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 151/159 [2023-12-20 22:02:49,369 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 152/159 [2023-12-20 22:02:49,463 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 153/159 [2023-12-20 22:02:49,556 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 154/159 [2023-12-20 22:02:49,652 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 155/159 [2023-12-20 22:02:49,748 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 156/159 [2023-12-20 22:02:49,842 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 157/159 [2023-12-20 22:02:49,936 INFO test.py line 196 131400] Test: 76/78-scene0011_00, Batch: 158/159 [2023-12-20 22:02:49,956 INFO test.py line 230 131400] Test: scene0011_00 [76/78]-237360 Batch 15.127 (10.763) Accuracy 0.9106 (0.8349) mIoU 0.7421 (0.7592) [2023-12-20 22:02:50,377 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 0/117 [2023-12-20 22:02:50,437 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 1/117 [2023-12-20 22:02:50,498 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 2/117 [2023-12-20 22:02:50,556 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 3/117 [2023-12-20 22:02:50,615 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 4/117 [2023-12-20 22:02:50,677 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 5/117 [2023-12-20 22:02:50,737 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 6/117 [2023-12-20 22:02:50,794 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 7/117 [2023-12-20 22:02:50,852 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 8/117 [2023-12-20 22:02:50,910 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 9/117 [2023-12-20 22:02:50,972 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 10/117 [2023-12-20 22:02:51,039 INFO test.py line 196 131400] Test: 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[2023-12-20 22:02:57,282 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 106/117 [2023-12-20 22:02:57,349 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 107/117 [2023-12-20 22:02:57,408 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 108/117 [2023-12-20 22:02:57,468 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 109/117 [2023-12-20 22:02:57,528 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 110/117 [2023-12-20 22:02:57,594 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 111/117 [2023-12-20 22:02:57,659 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 112/117 [2023-12-20 22:02:57,724 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 113/117 [2023-12-20 22:02:57,786 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 114/117 [2023-12-20 22:02:57,847 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 115/117 [2023-12-20 22:02:57,906 INFO test.py line 196 131400] Test: 77/78-scene0193_00, Batch: 116/117 [2023-12-20 22:02:57,919 INFO test.py line 230 131400] Test: scene0193_00 [77/78]-101783 Batch 7.610 (10.722) Accuracy 0.9199 (0.8348) mIoU 0.7887 (0.7590) [2023-12-20 22:02:58,233 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 0/144 [2023-12-20 22:02:58,384 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 1/144 [2023-12-20 22:02:58,530 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 2/144 [2023-12-20 22:02:58,673 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 3/144 [2023-12-20 22:02:58,817 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 4/144 [2023-12-20 22:02:58,968 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 5/144 [2023-12-20 22:02:59,116 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 6/144 [2023-12-20 22:02:59,268 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 7/144 [2023-12-20 22:02:59,418 INFO 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line 196 131400] Test: 78/78-scene0307_01, Batch: 123/144 [2023-12-20 22:03:16,205 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 124/144 [2023-12-20 22:03:16,357 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 125/144 [2023-12-20 22:03:16,509 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 126/144 [2023-12-20 22:03:16,661 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 127/144 [2023-12-20 22:03:16,815 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 128/144 [2023-12-20 22:03:16,968 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 129/144 [2023-12-20 22:03:17,121 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 130/144 [2023-12-20 22:03:17,274 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 131/144 [2023-12-20 22:03:17,420 INFO test.py line 196 131400] Test: 78/78-scene0307_01, Batch: 132/144 [2023-12-20 22:03:17,566 INFO test.py line 196 131400] Test: 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[2023-12-20 22:03:19,052 INFO test.py line 230 131400] Test: scene0307_01 [78/78]-412511 Batch 20.980 (10.854) Accuracy 0.8544 (0.8343) mIoU 0.4272 (0.7564) [2023-12-20 22:03:20,936 INFO test.py line 289 131400] Syncing ... [2023-12-20 22:03:35,921 INFO test.py line 317 131400] Val result: mIoU/mAcc/allAcc 0.7757/0.8497/0.9204 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_0 - wall Result: iou/accuracy 0.8727/0.9572 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_1 - floor Result: iou/accuracy 0.9568/0.9832 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_2 - cabinet Result: iou/accuracy 0.7114/0.8304 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_3 - bed Result: iou/accuracy 0.8339/0.8787 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_4 - chair Result: iou/accuracy 0.9295/0.9645 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_5 - sofa Result: iou/accuracy 0.8635/0.9362 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_6 - table Result: iou/accuracy 0.7908/0.8660 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_7 - door Result: iou/accuracy 0.7502/0.8455 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_8 - window Result: iou/accuracy 0.7393/0.8411 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_9 - bookshelf Result: iou/accuracy 0.8304/0.9165 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_10 - picture Result: iou/accuracy 0.3962/0.4840 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_11 - counter Result: iou/accuracy 0.7168/0.8172 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_12 - desk Result: iou/accuracy 0.7400/0.8916 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_13 - curtain Result: iou/accuracy 0.7907/0.8750 [2023-12-20 22:03:35,922 INFO test.py line 323 131400] Class_14 - refridgerator Result: iou/accuracy 0.7129/0.7571 [2023-12-20 22:03:35,923 INFO test.py line 323 131400] Class_15 - shower curtain Result: iou/accuracy 0.7233/0.7747 [2023-12-20 22:03:35,923 INFO test.py line 323 131400] Class_16 - toilet Result: iou/accuracy 0.9336/0.9790 [2023-12-20 22:03:35,923 INFO test.py line 323 131400] Class_17 - sink Result: iou/accuracy 0.7097/0.8146 [2023-12-20 22:03:35,923 INFO test.py line 323 131400] Class_18 - bathtub Result: iou/accuracy 0.8825/0.9140 [2023-12-20 22:03:35,923 INFO test.py line 323 131400] Class_19 - otherfurniture Result: iou/accuracy 0.6303/0.6677 [2023-12-20 22:03:35,923 INFO test.py line 331 131400] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<