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- _base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
- rpn_weight = 0.7
- 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,
- 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 * rpn_weight)),
- dict(
- type='StageCascadeRPNHead',
- in_channels=256,
- feat_channels=256,
- adapt_cfg=dict(type='offset'),
- bridged_feature=False,
- 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 * rpn_weight),
- loss_bbox=dict(
- type='IoULoss', linear=True,
- loss_weight=10.0 * rpn_weight))
- ]),
- roi_head=dict(
- bbox_head=dict(
- bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
- # model training and testing settings
- 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),
- 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(max_per_img=300, nms=dict(iou_threshold=0.8)),
- rcnn=dict(
- assigner=dict(
- pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
- sampler=dict(type='RandomSampler', num=256))),
- test_cfg=dict(
- rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
- rcnn=dict(score_thr=1e-3)))
- optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
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