Upload mmdetection-config.py

mmdetection-config.py

@@ -0,0 +1,235 @@
+num_batch_size = 4
+num_epochs = 15
+num_frozen_stages = 2
+
+auto_scale_lr = dict(base_batch_size=2, enable=False)
+backend_args = None
+data_root = 'C:/vs_code_workspaces/mmdetection/mmdetection/data/ins/v9'
+dataset_type = 'CocoDataset'
+default_hooks = dict(
+    checkpoint=dict(interval=1, type='CheckpointHook'),
+    logger=dict(interval=50, type='LoggerHook'),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    timer=dict(type='IterTimerHook'),
+    visualization=dict(type='DetVisualizationHook'))
+default_scope = 'mmdet'
+env_cfg = dict(cudnn_benchmark=False, dist_cfg=dict(backend='nccl'), mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0))
+launcher = 'none'
+load_from = 'C:/vs_code_workspaces/mmdetection/mmdetection/ins_development/resources/add300_frozen2/epoch_9.pth'
+log_level = 'INFO'
+log_processor = dict(by_epoch=True, type='LogProcessor', window_size=50)
+metainfo = dict(classes=('waste', ), palette=[ (220, 20, 60, ),])
+model = dict(
+    backbone=dict(
+        depth=101,
+        frozen_stages=num_frozen_stages,
+        init_cfg=dict(checkpoint='C:/Users/INS/.cache/torch/hub/checkpoints/resnet101-63fe2227.pth', type='Pretrained'),
+        norm_cfg=dict(requires_grad=True, type='BN'),
+        norm_eval=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3, ),
+        style='pytorch',
+        type='ResNet'),
+    data_preprocessor=dict(
+        bgr_to_rgb=True,
+        mean=[123.675, 116.28, 103.53, ],
+        pad_size_divisor=32,
+        std=[58.395, 57.12, 57.375, ],
+        type='DetDataPreprocessor'),
+    neck=dict(in_channels=[256, 512, 1024, 2048, ],
+        num_outs=5,
+        out_channels=256,
+        type='FPN'),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(
+                target_means=[0.0, 0.0, 0.0, 0.0,],
+                target_stds=[0.1, 0.1, 0.2, 0.2,],
+                type='DeltaXYWHBBoxCoder'),
+            fc_out_channels=1024,
+            in_channels=256,
+            loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+            loss_cls=dict(
+                loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False),
+            num_classes=1,
+            reg_class_agnostic=False,
+            roi_feat_size=7,
+            type='Shared2FCBBoxHead'),
+        bbox_roi_extractor=dict(
+            featmap_strides=[4, 8, 16, 32, ],
+            out_channels=256,
+            roi_layer=dict(output_size=7, sampling_ratio=0, type='RoIAlign'),
+            type='SingleRoIExtractor'),
+        type='StandardRoIHead'),
+    rpn_head=dict(
+        anchor_generator=dict(
+            ratios=[0.5, 1.0, 2.0, ],
+            scales=[8,],
+            strides=[4, 8, 16, 32, 64, ],
+            type='AnchorGenerator'),
+        bbox_coder=dict(
+            target_means=[0.0, 0.0, 0.0, 0.0, ],
+            target_stds=[1.0, 1.0, 1.0, 1.0, ],
+            type='DeltaXYWHBBoxCoder'),
+        feat_channels=256,
+        in_channels=256,
+        loss_bbox=dict(loss_weight=1.0, type='L1Loss'),
+        loss_cls=dict(loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=True),
+        type='RPNHead'),
+    test_cfg=dict(
+        rcnn=dict(
+            max_per_img=100,
+            nms=dict(iou_threshold=0.5, type='nms'),
+            score_thr=0.05),
+        rpn=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=1000)),
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=False,
+                min_pos_iou=0.5,
+                neg_iou_thr=0.5,
+                pos_iou_thr=0.5,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=True,
+                neg_pos_ub=-1,
+                num=512,
+                pos_fraction=0.25,
+                type='RandomSampler')),
+        rpn=dict(
+            allowed_border=-1,
+            assigner=dict(
+                ignore_iof_thr=-1,
+                match_low_quality=True,
+                min_pos_iou=0.3,
+                neg_iou_thr=0.3,
+                pos_iou_thr=0.7,
+                type='MaxIoUAssigner'),
+            debug=False,
+            pos_weight=-1,
+            sampler=dict(
+                add_gt_as_proposals=False,
+                neg_pos_ub=-1,
+                num=256,
+                pos_fraction=0.5,
+                type='RandomSampler')),
+        rpn_proposal=dict(
+            max_per_img=1000,
+            min_bbox_size=0,
+            nms=dict(iou_threshold=0.7, type='nms'),
+            nms_pre=2000)),
+    type='FasterRCNN')
+optim_wrapper = dict(
+    optimizer=dict(lr=0.02, momentum=0.9, type='SGD', weight_decay=0.0001),
+    type='OptimWrapper')
+param_scheduler = [
+    dict(begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'),
+    dict(begin=0, by_epoch=True, end=12, gamma=0.1, milestones=[8, 11, ], type='MultiStepLR'),
+]
+resume = False
+test_cfg = dict(type='TestLoop')
+test_dataloader = dict(
+    batch_size=num_batch_size,
+    dataset=dict(
+        ann_file='test/annotations_coco.json',
+        backend_args=None,
+        data_prefix=dict(img='test/'),
+        data_root=data_root,
+        metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ), ]),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(meta_keys=( 'img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+test_evaluator = dict(
+    ann_file='data/ins_annotated_v9/test/annotations_coco.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+test_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',),type='PackDetInputs'),
+]
+train_cfg = dict(max_epochs=num_epochs, type='EpochBasedTrainLoop', val_interval=1)
+train_dataloader = dict(
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    batch_size=num_batch_size,
+    dataset=dict(
+        ann_file='train/annotations_coco.json',
+        backend_args=None,
+        data_prefix=dict(img='train/'),
+        data_root=data_root,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
+            dict(prob=0.5, type='RandomFlip'),
+            dict(type='PackDetInputs'),
+        ],
+        type='CocoDataset'),
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(shuffle=True, type='DefaultSampler'))
+train_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
+    dict(prob=0.5, type='RandomFlip'),
+    dict(type='PackDetInputs'),
+]
+val_cfg = dict(type='ValLoop')
+val_dataloader = dict(
+    batch_size=num_batch_size,
+    dataset=dict(
+        ann_file='valid/annotations_coco.json',
+        backend_args=None,
+        data_prefix=dict(img='valid/'),
+        data_root=data_root,
+        metainfo=dict(classes=('waste', ), palette=[(220, 20, 60, ),]),
+        pipeline=[
+            dict(backend_args=None, type='LoadImageFromFile'),
+            dict(keep_ratio=True, scale=(1280, 1280,), type='Resize'),
+            dict(type='LoadAnnotations', with_bbox=True),
+            dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', ),type='PackDetInputs'),
+        ],
+        test_mode=True,
+        type='CocoDataset'),
+    drop_last=False,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(shuffle=False, type='DefaultSampler'))
+val_evaluator = dict(
+    ann_file='data/ins_annotated_v9/valid/annotations_coco.json',
+    backend_args=None,
+    format_only=False,
+    metric='bbox',
+    type='CocoMetric')
+val_pipeline = [
+    dict(backend_args=None, type='LoadImageFromFile'),
+    dict(keep_ratio=True, scale=(1280, 1280, ), type='Resize'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor',), type='PackDetInputs'),
+]
+vis_backends = [dict(type='LocalVisBackend'), ]
+visualizer = dict(name='visualizer', type='DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend'), ])
+work_dir = './ins_development/training/ins_annotated_v9/pretrained/add300/faster/2frozen/e9\\'