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- _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
- # model settings
- model = dict(
- neck=[
- dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5),
- dict(
- type='BFP',
- in_channels=256,
- num_levels=5,
- refine_level=2,
- refine_type='non_local')
- ],
- roi_head=dict(
- bbox_head=dict(
- loss_bbox=dict(
- _delete_=True,
- type='BalancedL1Loss',
- alpha=0.5,
- gamma=1.5,
- beta=1.0,
- loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rcnn=dict(
- sampler=dict(
- _delete_=True,
- type='CombinedSampler',
- num=512,
- pos_fraction=0.25,
- add_gt_as_proposals=True,
- pos_sampler=dict(type='InstanceBalancedPosSampler'),
- neg_sampler=dict(
- type='IoUBalancedNegSampler',
- floor_thr=-1,
- floor_fraction=0,
- num_bins=3)))))
- # MMEngine support the following two ways, users can choose
- # according to convenience
- # _base_.train_dataloader.dataset.proposal_file = 'libra_proposals/rpn_r50_fpn_1x_train2017.pkl' # noqa
- train_dataloader = dict(
- dataset=dict(proposal_file='libra_proposals/rpn_r50_fpn_1x_train2017.pkl'))
- # _base_.val_dataloader.dataset.proposal_file = 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl' # noqa
- # test_dataloader = _base_.val_dataloader
- val_dataloader = dict(
- dataset=dict(proposal_file='libra_proposals/rpn_r50_fpn_1x_val2017.pkl'))
- test_dataloader = val_dataloader
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