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- _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
- model = dict(
- bbox_head=dict(
- _delete_=True,
- type='GARetinaHead',
- num_classes=80,
- in_channels=256,
- stacked_convs=4,
- feat_channels=256,
- approx_anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=4,
- scales_per_octave=3,
- ratios=[0.5, 1.0, 2.0],
- strides=[8, 16, 32, 64, 128]),
- square_anchor_generator=dict(
- type='AnchorGenerator',
- ratios=[1.0],
- scales=[4],
- strides=[8, 16, 32, 64, 128]),
- anchor_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- 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='FocalLoss',
- use_sigmoid=True,
- gamma=2.0,
- alpha=0.25,
- loss_weight=1.0),
- loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
- # training and testing settings
- train_cfg=dict(
- ga_assigner=dict(
- type='ApproxMaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.4,
- min_pos_iou=0.4,
- ignore_iof_thr=-1),
- ga_sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
- center_ratio=0.2,
- ignore_ratio=0.5))
- optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
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