cascade-rpn_r50-caffe_fpn_1x_coco.py

_base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
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,
                sampling=False,
                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)),
            dict(
                type='StageCascadeRPNHead',
                in_channels=256,
                feat_channels=256,
                adapt_cfg=dict(type='offset'),
                bridged_feature=False,
                sampling=True,
                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),
                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
        ]),
    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,
                iou_calculator=dict(type='BboxOverlaps2D')),
            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)
    ]),
    test_cfg=dict(
        rpn=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type='nms', iou_threshold=0.8),
            min_bbox_size=0)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))