_base_ = './fcos3d.py'
# model settings
model = dict(
    bbox_head=dict(
        _delete_=True,
        type='PGDHead',
        num_classes=10,
        in_channels=256,
        stacked_convs=2,
        feat_channels=256,
        use_direction_classifier=True,
        diff_rad_by_sin=True,
        pred_attrs=True,
        pred_velo=True,
        pred_bbox2d=True,
        pred_keypoints=False,
        dir_offset=0.7854,  # pi/4
        strides=[8, 16, 32, 64, 128],
        group_reg_dims=(2, 1, 3, 1, 2),  # offset, depth, size, rot, velo
        cls_branch=(256, ),
        reg_branch=(
            (256, ),  # offset
            (256, ),  # depth
            (256, ),  # size
            (256, ),  # rot
            ()  # velo
        ),
        dir_branch=(256, ),
        attr_branch=(256, ),
        loss_cls=dict(
            type='mmdet.FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(
            type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
        loss_dir=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_attr=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        loss_centerness=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        norm_on_bbox=True,
        centerness_on_reg=True,
        center_sampling=True,
        conv_bias=True,
        dcn_on_last_conv=True,
        use_depth_classifier=True,
        depth_branch=(256, ),
        depth_range=(0, 50),
        depth_unit=10,
        division='uniform',
        depth_bins=6,
        bbox_coder=dict(type='PGDBBoxCoder', code_size=9)),
    test_cfg=dict(nms_pre=1000, nms_thr=0.8, score_thr=0.01, max_per_img=200))