_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
    type='RepPointsDetector',
    pretrained='torchvision://resnet50',
    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'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5),
    bbox_head=dict(
        type='RepPointsHead',
        num_classes=80,
        in_channels=256,
        feat_channels=256,
        point_feat_channels=256,
        stacked_convs=3,
        num_points=9,
        gradient_mul=0.1,
        point_strides=[8, 16, 32, 64, 128],
        point_base_scale=4,
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5),
        loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0),
        transform_method='moment'),
    # training and testing settings
    train_cfg=dict(
        init=dict(
            assigner=dict(type='PointAssigner', scale=4, pos_num=1),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        refine=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.4,
                min_pos_iou=0,
                ignore_iof_thr=-1),
            allowed_border=-1,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
optimizer = dict(lr=0.01)
