libra-fast-rcnn_r50_fpn_1x_coco.py

_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