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- _base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
- model = dict(
- rpn_head=dict(
- _delete_=True,
- type='GARPNHead',
- in_channels=256,
- feat_channels=256,
- approx_anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=8,
- scales_per_octave=3,
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- square_anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- scales=[8],
- strides=[4, 8, 16, 32, 64]),
- anchor_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.07, 0.07, 0.14, 0.14]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[0.07, 0.07, 0.11, 0.11]),
- loc_filter_thr=0.01,
- loss_loc=dict(
- type='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- ga_assigner=dict(
- type='ApproxMaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- ignore_iof_thr=-1),
- ga_sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- center_ratio=0.2,
- ignore_ratio=0.5)),
- test_cfg=dict(rpn=dict(nms_post=1000)))
- optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
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