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- checkpoint_config = dict(interval=1)
- # yapf:disable
- log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook'),
- # dict(type='TensorboardLoggerHook')
- ])
- # yapf:enable
- dist_params = dict(backend='nccl')
- log_level = 'INFO'
- load_from = None
- resume_from = None
- workflow = [('train', 1)]
- # optimizer
- optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
- optimizer_config = dict(grad_clip=None)
- # learning policy
- lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
- total_epochs = 12
- model = dict(
- type='FasterRCNN',
- pretrained='torchvision://resnet50',
- 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=True),
- norm_eval=True,
- style='pytorch'),
- 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],
- 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=1,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[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))),
- # model training and testing settings
- 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=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- 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)
- # soft-nms is also supported for rcnn testing
- # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
- ))
- dataset_type = 'CocoDataset'
- data_root = 'data/coco'
- img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
- train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
- ]
- test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img']),
- ])
- ]
- data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_train2017.json',
- img_prefix=f'{data_root}/train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_val2017.json',
- img_prefix=f'{data_root}/val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_val2017.json',
- img_prefix=f'{data_root}/val2017/',
- pipeline=test_pipeline))
- evaluation = dict(interval=1, metric='bbox')
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