| model = dict( | |
| type='MaskRCNN', | |
| data_preprocessor=dict( | |
| type='DetDataPreprocessor', | |
| mean=[103.53, 116.28, 123.675], | |
| std=[1.0, 1.0, 1.0], | |
| bgr_to_rgb=False, | |
| pad_mask=True, | |
| pad_size_divisor=32), | |
| backbone=dict( | |
| type='ResNet', | |
| depth=50, | |
| num_stages=4, | |
| out_indices=(0, 1, 2, 3), | |
| frozen_stages=1, | |
| norm_cfg=dict(type='BN', requires_grad=False), | |
| norm_eval=True, | |
| style='caffe', | |
| init_cfg=dict( | |
| type='Pretrained', | |
| checkpoint='open-mmlab://detectron2/resnet50_caffe')), | |
| neck=dict( | |
| type='FPN', | |
| in_channels=[256, 512, 1024, 2048], | |
| out_channels=256, | |
| num_outs=5), | |
| rpn_head=dict( | |
| type='RPNHead', | |
| in_channels=256, | |
| feat_channels=256, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8], | |
| ratios=[0.5, 1.0, 2.0], | |
| strides=[4, 8, 16, 32, 64]), | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[1.0, 1.0, 1.0, 1.0]), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)), | |
| roi_head=dict( | |
| type='StandardRoIHead', | |
| bbox_roi_extractor=dict( | |
| type='SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
| out_channels=256, | |
| featmap_strides=[4, 8, 16, 32]), | |
| bbox_head=dict( | |
| type='Shared2FCBBoxHead', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| roi_feat_size=7, | |
| num_classes=80, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0.0, 0.0, 0.0, 0.0], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]), | |
| reg_class_agnostic=False, | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0)), | |
| mask_roi_extractor=dict( | |
| type='SingleRoIExtractor', | |
| roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), | |
| out_channels=256, | |
| featmap_strides=[4, 8, 16, 32]), | |
| mask_head=dict( | |
| type='FCNMaskHead', | |
| num_convs=4, | |
| in_channels=256, | |
| conv_out_channels=256, | |
| num_classes=80, | |
| loss_mask=dict( | |
| type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), | |
| train_cfg=dict( | |
| rpn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.3, | |
| match_low_quality=True, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.5, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=False), | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=2000, | |
| max_per_img=1000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| match_low_quality=True, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| mask_size=28, | |
| pos_weight=-1, | |
| debug=False)), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=1000, | |
| max_per_img=1000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| score_thr=0.05, | |
| nms=dict(type='nms', iou_threshold=0.5), | |
| max_per_img=100, | |
| mask_thr_binary=0.5))) | |
| dataset_type = 'CocoDataset' | |
| data_root = 'data/coco/' | |
| backend_args = None | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict( | |
| type='LoadAnnotations', | |
| with_bbox=True, | |
| with_mask=True, | |
| poly2mask=False), | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), | |
| (1333, 768), (1333, 800)], | |
| keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ] | |
| test_pipeline = [ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ] | |
| train_dataloader = dict( | |
| batch_size=2, | |
| num_workers=2, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| batch_sampler=dict(type='AspectRatioBatchSampler'), | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/instances_train2017.json', | |
| data_prefix=dict(img='train2017/'), | |
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict( | |
| type='LoadAnnotations', | |
| with_bbox=True, | |
| with_mask=True, | |
| poly2mask=False), | |
| dict( | |
| type='RandomChoiceResize', | |
| scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), | |
| (1333, 768), (1333, 800)], | |
| keep_ratio=True), | |
| dict(type='RandomFlip', prob=0.5), | |
| dict(type='PackDetInputs') | |
| ], | |
| backend_args=None)) | |
| val_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/instances_val2017.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ], | |
| backend_args=None)) | |
| test_dataloader = dict( | |
| batch_size=1, | |
| num_workers=2, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False), | |
| dataset=dict( | |
| type='CocoDataset', | |
| data_root='data/coco/', | |
| ann_file='annotations/instances_val2017.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImageFromFile', backend_args=None), | |
| dict(type='Resize', scale=(1333, 800), keep_ratio=True), | |
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True), | |
| dict( | |
| type='PackDetInputs', | |
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | |
| 'scale_factor')) | |
| ], | |
| backend_args=None)) | |
| val_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file='data/coco/annotations/instances_val2017.json', | |
| metric=['bbox', 'segm'], | |
| format_only=False, | |
| backend_args=None) | |
| test_evaluator = dict( | |
| type='CocoMetric', | |
| ann_file='data/coco/annotations/instances_val2017.json', | |
| metric=['bbox', 'segm'], | |
| format_only=False, | |
| backend_args=None) | |
| train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=36, val_interval=1) | |
| val_cfg = dict(type='ValLoop') | |
| test_cfg = dict(type='TestLoop') | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), | |
| dict( | |
| type='MultiStepLR', | |
| begin=0, | |
| end=24, | |
| by_epoch=True, | |
| milestones=[28, 34], | |
| gamma=0.1) | |
| ] | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)) | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| default_scope = 'mmdet' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='DetVisualizationHook')) | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='DetLocalVisualizer', | |
| vis_backends=[dict(type='LocalVisBackend')], | |
| name='visualizer') | |
| log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |