# model settings
model = dict(
    type="FasterRCNN",
    backbone=dict(
        type="ResNet",
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type="BN", requires_grad=True),
        norm_eval=True,
        style="pytorch",
        init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
    ),
    neck=dict(type="FPN", in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5),
    rpn_head=dict(
        type="RPNHead",
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(type="AnchorGenerator", scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]),
        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),
    ),
    roi_head=dict(
        type="StandardRoIHead",
        bbox_roi_extractor=dict(
            type="SingleRoIExtractor",
            roi_layer=dict(type="RoIAlign", output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32],
        ),
        bbox_head=dict(
            type="Shared2FCBBoxHead",
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=80,
            bbox_coder=dict(
                type="DeltaXYWHBBoxCoder", target_means=[0.0, 0.0, 0.0, 0.0], target_stds=[0.1, 0.1, 0.2, 0.2]
            ),
            reg_class_agnostic=False,
            loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, 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,
                match_low_quality=True,
                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=-1,
            pos_weight=-1,
            debug=False,
        ),
        rpn_proposal=dict(nms_pre=2000, max_per_img=1000, nms=dict(type="nms", iou_threshold=0.7), min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type="MaxIoUAssigner",
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=False,
                ignore_iof_thr=-1,
            ),
            sampler=dict(type="RandomSampler", num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True),
            pos_weight=-1,
            debug=False,
        ),
    ),
    test_cfg=dict(
        rpn=dict(nms_pre=1000, max_per_img=1000, nms=dict(type="nms", iou_threshold=0.7), min_bbox_size=0),
        rcnn=dict(score_thr=0.05, nms=dict(type="nms", iou_threshold=0.5), max_per_img=100)
        # soft-nms is also supported for rcnn testing
        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
    ),
)
