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- _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
- model = dict(
- rpn_head=dict(
- _delete_=True,
- type='CascadeRPNHead',
- num_stages=2,
- stages=[
- dict(
- type='StageCascadeRPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[1.0],
- strides=[4, 8, 16, 32, 64]),
- adapt_cfg=dict(type='dilation', dilation=3),
- bridged_feature=True,
- sampling=False,
- with_cls=False,
- reg_decoded_bbox=True,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=(.0, .0, .0, .0),
- target_stds=(0.1, 0.1, 0.5, 0.5)),
- loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
- dict(
- type='StageCascadeRPNHead',
- in_channels=256,
- feat_channels=256,
- adapt_cfg=dict(type='offset'),
- bridged_feature=False,
- sampling=True,
- with_cls=True,
- reg_decoded_bbox=True,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=(.0, .0, .0, .0),
- target_stds=(0.05, 0.05, 0.1, 0.1)),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True,
- loss_weight=1.0),
- loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
- ]),
- train_cfg=dict(rpn=[
- dict(
- assigner=dict(
- type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.7,
- min_pos_iou=0.3,
- ignore_iof_thr=-1,
- iou_calculator=dict(type='BboxOverlaps2D')),
- 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)
- ]),
- test_cfg=dict(
- rpn=dict(
- nms_pre=2000,
- max_per_img=2000,
- nms=dict(type='nms', iou_threshold=0.8),
- min_bbox_size=0)))
- optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
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