model = dict(
    type='MultiViewDfM',
    data_preprocessor=dict(
        type='Det3DDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32),
    backbone=dict(
        type='mmdet.ResNet',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet101'),
        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, False, True, True)),
    neck=dict(
        type='mmdet.FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=64,
        num_outs=4),
    neck_2d=None,
    bbox_head_2d=None,
    backbone_stereo=None,
    depth_head=None,
    backbone_3d=None,
    neck_3d=dict(type='OutdoorImVoxelNeck', in_channels=64, out_channels=256),
    valid_sample=True,
    voxel_size=(0.5, 0.5, 0.5),  # n_voxels=[240, 300, 12]
    anchor_generator=dict(
        type='AlignedAnchor3DRangeGenerator',
        ranges=[[-35.0, -75.0, -2, 75.0, 75.0, 4]],
        rotations=[.0]),
    bbox_head_3d=dict(
        type='Anchor3DHead',
        num_classes=3,
        in_channels=256,
        feat_channels=256,
        use_direction_classifier=True,
        anchor_generator=dict(
            type='AlignedAnchor3DRangeGenerator',
            ranges=[[-35.0, -75.0, 0, 75.0, 75.0, 0],
                    [-35.0, -75.0, -0.1188, 75.0, 75.0, -0.1188],
                    [-35.0, -75.0, -0.0345, 75.0, 75.0, -0.0345]],
            sizes=[
                [0.91, 0.84, 1.74],  # pedestrian
                [1.81, 0.84, 1.77],  # cyclist
                [4.73, 2.08, 1.77],  # car
            ],
            rotations=[0, 1.57],
            reshape_out=False),
        diff_rad_by_sin=True,
        dir_offset=-0.7854,  # -pi / 4
        bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
        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=2.0),
        loss_dir=dict(
            type='mmdet.CrossEntropyLoss', use_sigmoid=False,
            loss_weight=0.2)),
    train_cfg=dict(
        assigner=[
            dict(  # for Pedestrian
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                ignore_iof_thr=-1),
            dict(  # for Cyclist
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.5,
                neg_iou_thr=0.35,
                min_pos_iou=0.35,
                ignore_iof_thr=-1),
            dict(  # for Car
                type='Max3DIoUAssigner',
                iou_calculator=dict(type='BboxOverlapsNearest3D'),
                pos_iou_thr=0.6,
                neg_iou_thr=0.45,
                min_pos_iou=0.45,
                ignore_iof_thr=-1)
        ],
        allowed_border=0,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        use_rotate_nms=True,
        nms_across_levels=False,
        nms_thr=0.05,
        score_thr=0.001,
        min_bbox_size=0,
        nms_pre=4096,
        max_num=500))