# model settings
model = dict(
    type="RPN",
    pretrained="open-mmlab://detectron2/resnet50_caffe",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=3,
        strides=(1, 2, 2),
        dilations=(1, 1, 1),
        out_indices=(2,),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=False),
        norm_eval=True,
        style="caffe",
    ),
    neck=None,
    rpn_head=dict(
        type="RPNHead",
        in_channels=1024,
        feat_channels=1024,
        anchor_generator=dict(
            type="AnchorGenerator",
            scales=[2, 4, 8, 16, 32],
            ratios=[0.5, 1.0, 2.0],
            strides=[16],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0],
        ),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="L1Loss", loss_weight=1.0),
    ),
)
# model training and testing settings
train_cfg = dict(
    rpn=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            ignore_iof_thr=-1,
        ),
        sampler=dict(
            type="RandomSampler",
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False,
        ),
        allowed_border=0,
        pos_weight=-1,
        debug=False,
    )
)
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=12000,
        nms_post=2000,
        max_num=2000,
        nms_thr=0.7,
        min_bbox_size=0,
    )
)
