test_heads.py

# Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import pytest
import random
import torch
from os.path import dirname, exists, join

from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes,
                               DepthInstance3DBoxes, LiDARInstance3DBoxes)
from mmdet3d.models.builder import build_head
from mmdet.apis import set_random_seed


def _setup_seed(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.backends.cudnn.deterministic = True


def _get_config_directory():
    """Find the predefined detector config directory."""
    try:
        # Assume we are running in the source mmdetection3d repo
        repo_dpath = dirname(dirname(dirname(dirname(__file__))))
    except NameError:
        # For IPython development when this __file__ is not defined
        import mmdet3d
        repo_dpath = dirname(dirname(mmdet3d.__file__))
    config_dpath = join(repo_dpath, 'configs')
    if not exists(config_dpath):
        raise Exception('Cannot find config path')
    return config_dpath


def _get_config_module(fname):
    """Load a configuration as a python module."""
    from mmcv import Config
    config_dpath = _get_config_directory()
    config_fpath = join(config_dpath, fname)
    config_mod = Config.fromfile(config_fpath)
    return config_mod


def _get_head_cfg(fname):
    """Grab configs necessary to create a bbox_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    import mmcv
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)
    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg))
    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg))

    bbox_head = model.bbox_head
    bbox_head.update(train_cfg=train_cfg)
    bbox_head.update(test_cfg=test_cfg)
    return bbox_head


def _get_rpn_head_cfg(fname):
    """Grab configs necessary to create a rpn_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    import mmcv
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)
    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg))
    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg))

    rpn_head = model.rpn_head
    rpn_head.update(train_cfg=train_cfg.rpn)
    rpn_head.update(test_cfg=test_cfg.rpn)
    return rpn_head, train_cfg.rpn_proposal


def _get_roi_head_cfg(fname):
    """Grab configs necessary to create a roi_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    import mmcv
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)
    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg))
    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg))

    roi_head = model.roi_head
    roi_head.update(train_cfg=train_cfg.rcnn)
    roi_head.update(test_cfg=test_cfg.rcnn)
    return roi_head


def _get_pts_bbox_head_cfg(fname):
    """Grab configs necessary to create a pts_bbox_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    import mmcv
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)
    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg.pts))
    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg.pts))

    pts_bbox_head = model.pts_bbox_head
    pts_bbox_head.update(train_cfg=train_cfg)
    pts_bbox_head.update(test_cfg=test_cfg)
    return pts_bbox_head


def _get_vote_head_cfg(fname):
    """Grab configs necessary to create a vote_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    import mmcv
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)
    train_cfg = mmcv.Config(copy.deepcopy(config.model.train_cfg))
    test_cfg = mmcv.Config(copy.deepcopy(config.model.test_cfg))

    vote_head = model.bbox_head
    vote_head.update(train_cfg=train_cfg)
    vote_head.update(test_cfg=test_cfg)
    return vote_head


def _get_parta2_bbox_head_cfg(fname):
    """Grab configs necessary to create a parta2_bbox_head.

    These are deep copied to allow for safe modification of parameters without
    influencing other tests.
    """
    config = _get_config_module(fname)
    model = copy.deepcopy(config.model)

    vote_head = model.roi_head.bbox_head
    return vote_head


def test_anchor3d_head_loss():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    bbox_head_cfg = _get_head_cfg(
        'second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py')

    from mmdet3d.models.builder import build_head
    self = build_head(bbox_head_cfg)
    self.cuda()
    assert isinstance(self.conv_cls, torch.nn.modules.conv.Conv2d)
    assert self.conv_cls.in_channels == 512
    assert self.conv_cls.out_channels == 18
    assert self.conv_reg.out_channels == 42
    assert self.conv_dir_cls.out_channels == 12

    # test forward
    feats = list()
    feats.append(torch.rand([2, 512, 200, 176], dtype=torch.float32).cuda())
    (cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)
    assert cls_score[0].shape == torch.Size([2, 18, 200, 176])
    assert bbox_pred[0].shape == torch.Size([2, 42, 200, 176])
    assert dir_cls_preds[0].shape == torch.Size([2, 12, 200, 176])

    # test loss
    gt_bboxes = list(
        torch.tensor(
            [[[6.4118, -3.4305, -1.7291, 1.7033, 3.4693, 1.6197, -0.9091]],
             [[16.9107, 9.7925, -1.9201, 1.6097, 3.2786, 1.5307, -2.4056]]],
            dtype=torch.float32).cuda())
    gt_labels = list(torch.tensor([[0], [1]], dtype=torch.int64).cuda())
    input_metas = [{
        'sample_idx': 1234
    }, {
        'sample_idx': 2345
    }]  # fake input_metas

    losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
                       gt_labels, input_metas)
    assert losses['loss_cls'][0] > 0
    assert losses['loss_bbox'][0] > 0
    assert losses['loss_dir'][0] > 0

    # test empty ground truth case
    gt_bboxes = list(torch.empty((2, 0, 7)).cuda())
    gt_labels = list(torch.empty((2, 0)).cuda())
    empty_gt_losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
                                gt_labels, input_metas)
    assert empty_gt_losses['loss_cls'][0] > 0
    assert empty_gt_losses['loss_bbox'][0] == 0
    assert empty_gt_losses['loss_dir'][0] == 0


def test_anchor3d_head_getboxes():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    bbox_head_cfg = _get_head_cfg(
        'second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py')

    from mmdet3d.models.builder import build_head
    self = build_head(bbox_head_cfg)
    self.cuda()

    feats = list()
    feats.append(torch.rand([2, 512, 200, 176], dtype=torch.float32).cuda())
    # fake input_metas
    input_metas = [{
        'sample_idx': 1234,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }, {
        'sample_idx': 2345,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }]
    (cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)

    # test get_boxes
    cls_score[0] -= 1.5  # too many positive samples may cause cuda oom
    result_list = self.get_bboxes(cls_score, bbox_pred, dir_cls_preds,
                                  input_metas)
    assert (result_list[0][1] > 0.3).all()


def test_parta2_rpnhead_getboxes():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    rpn_head_cfg, proposal_cfg = _get_rpn_head_cfg(
        'parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')

    self = build_head(rpn_head_cfg)
    self.cuda()

    feats = list()
    feats.append(torch.rand([2, 512, 200, 176], dtype=torch.float32).cuda())
    # fake input_metas
    input_metas = [{
        'sample_idx': 1234,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }, {
        'sample_idx': 2345,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }]
    (cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)

    # test get_boxes
    cls_score[0] -= 1.5  # too many positive samples may cause cuda oom
    result_list = self.get_bboxes(cls_score, bbox_pred, dir_cls_preds,
                                  input_metas, proposal_cfg)
    assert result_list[0]['scores_3d'].shape == torch.Size([512])
    assert result_list[0]['labels_3d'].shape == torch.Size([512])
    assert result_list[0]['cls_preds'].shape == torch.Size([512, 3])
    assert result_list[0]['boxes_3d'].tensor.shape == torch.Size([512, 7])


def test_vote_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)
    vote_head_cfg = _get_vote_head_cfg(
        'votenet/votenet_8x8_scannet-3d-18class.py')
    self = build_head(vote_head_cfg).cuda()
    fp_xyz = [torch.rand([2, 256, 3], dtype=torch.float32).cuda()]
    fp_features = [torch.rand([2, 256, 256], dtype=torch.float32).cuda()]
    fp_indices = [torch.randint(0, 128, [2, 256]).cuda()]

    input_dict = dict(
        fp_xyz=fp_xyz, fp_features=fp_features, fp_indices=fp_indices)

    # test forward
    ret_dict = self(input_dict, 'vote')
    assert ret_dict['center'].shape == torch.Size([2, 256, 3])
    assert ret_dict['obj_scores'].shape == torch.Size([2, 256, 2])
    assert ret_dict['size_res'].shape == torch.Size([2, 256, 18, 3])
    assert ret_dict['dir_res'].shape == torch.Size([2, 256, 1])

    # test loss
    points = [torch.rand([40000, 4], device='cuda') for i in range(2)]
    gt_bbox1 = LiDARInstance3DBoxes(torch.rand([10, 7], device='cuda'))
    gt_bbox2 = LiDARInstance3DBoxes(torch.rand([10, 7], device='cuda'))
    gt_bboxes = [gt_bbox1, gt_bbox2]
    gt_labels = [torch.randint(0, 18, [10], device='cuda') for i in range(2)]
    pts_semantic_mask = [
        torch.randint(0, 18, [40000], device='cuda') for i in range(2)
    ]
    pts_instance_mask = [
        torch.randint(0, 10, [40000], device='cuda') for i in range(2)
    ]
    losses = self.loss(ret_dict, points, gt_bboxes, gt_labels,
                       pts_semantic_mask, pts_instance_mask)
    assert losses['vote_loss'] >= 0
    assert losses['objectness_loss'] >= 0
    assert losses['semantic_loss'] >= 0
    assert losses['center_loss'] >= 0
    assert losses['dir_class_loss'] >= 0
    assert losses['dir_res_loss'] >= 0
    assert losses['size_class_loss'] >= 0
    assert losses['size_res_loss'] >= 0

    # test multiclass_nms_single
    obj_scores = torch.rand([256], device='cuda')
    sem_scores = torch.rand([256, 18], device='cuda')
    points = torch.rand([40000, 3], device='cuda')
    bbox = torch.rand([256, 7], device='cuda')
    input_meta = dict(box_type_3d=DepthInstance3DBoxes)
    bbox_selected, score_selected, labels = self.multiclass_nms_single(
        obj_scores, sem_scores, bbox, points, input_meta)
    assert bbox_selected.shape[0] >= 0
    assert bbox_selected.shape[1] == 7
    assert score_selected.shape[0] >= 0
    assert labels.shape[0] >= 0

    # test get_boxes
    points = torch.rand([1, 40000, 4], device='cuda')
    seed_points = torch.rand([1, 1024, 3], device='cuda')
    seed_indices = torch.randint(0, 40000, [1, 1024], device='cuda')
    vote_points = torch.rand([1, 1024, 3], device='cuda')
    vote_features = torch.rand([1, 256, 1024], device='cuda')
    aggregated_points = torch.rand([1, 256, 3], device='cuda')
    aggregated_indices = torch.range(0, 256, device='cuda')
    obj_scores = torch.rand([1, 256, 2], device='cuda')
    center = torch.rand([1, 256, 3], device='cuda')
    dir_class = torch.rand([1, 256, 1], device='cuda')
    dir_res_norm = torch.rand([1, 256, 1], device='cuda')
    dir_res = torch.rand([1, 256, 1], device='cuda')
    size_class = torch.rand([1, 256, 18], device='cuda')
    size_res = torch.rand([1, 256, 18, 3], device='cuda')
    sem_scores = torch.rand([1, 256, 18], device='cuda')
    bbox_preds = dict(
        seed_points=seed_points,
        seed_indices=seed_indices,
        vote_points=vote_points,
        vote_features=vote_features,
        aggregated_points=aggregated_points,
        aggregated_indices=aggregated_indices,
        obj_scores=obj_scores,
        center=center,
        dir_class=dir_class,
        dir_res_norm=dir_res_norm,
        dir_res=dir_res,
        size_class=size_class,
        size_res=size_res,
        sem_scores=sem_scores)
    results = self.get_bboxes(points, bbox_preds, [input_meta])
    assert results[0][0].tensor.shape[0] >= 0
    assert results[0][0].tensor.shape[1] == 7
    assert results[0][1].shape[0] >= 0
    assert results[0][2].shape[0] >= 0


def test_parta2_bbox_head():
    parta2_bbox_head_cfg = _get_parta2_bbox_head_cfg(
        './parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
    self = build_head(parta2_bbox_head_cfg)
    seg_feats = torch.rand([256, 14, 14, 14, 16])
    part_feats = torch.rand([256, 14, 14, 14, 4])

    cls_score, bbox_pred = self.forward(seg_feats, part_feats)
    assert cls_score.shape == (256, 1)
    assert bbox_pred.shape == (256, 7)


def test_part_aggregation_ROI_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')

    roi_head_cfg = _get_roi_head_cfg(
        'parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
    self = build_head(roi_head_cfg).cuda()

    features = np.load('./tests/test_samples/parta2_roihead_inputs.npz')
    seg_features = torch.tensor(
        features['seg_features'], dtype=torch.float32, device='cuda')
    feats_dict = dict(seg_features=seg_features)

    voxels = torch.tensor(
        features['voxels'], dtype=torch.float32, device='cuda')
    num_points = torch.ones([500], device='cuda')
    coors = torch.zeros([500, 4], device='cuda')
    voxel_centers = torch.zeros([500, 3], device='cuda')
    box_type_3d = LiDARInstance3DBoxes
    img_metas = [dict(box_type_3d=box_type_3d)]
    voxels_dict = dict(
        voxels=voxels,
        num_points=num_points,
        coors=coors,
        voxel_centers=voxel_centers)

    pred_bboxes = LiDARInstance3DBoxes(
        torch.tensor(
            [[0.3990, 0.5167, 0.0249, 0.9401, 0.9459, 0.7967, 0.4150],
             [0.8203, 0.2290, 0.9096, 0.1183, 0.0752, 0.4092, 0.9601],
             [0.2093, 0.1940, 0.8909, 0.4387, 0.3570, 0.5454, 0.8299],
             [0.2099, 0.7684, 0.4290, 0.2117, 0.6606, 0.1654, 0.4250],
             [0.9927, 0.6964, 0.2472, 0.7028, 0.7494, 0.9303, 0.0494]],
            dtype=torch.float32,
            device='cuda'))
    pred_scores = torch.tensor([0.9722, 0.7910, 0.4690, 0.3300, 0.3345],
                               dtype=torch.float32,
                               device='cuda')
    pred_labels = torch.tensor([0, 1, 0, 2, 1],
                               dtype=torch.int64,
                               device='cuda')
    pred_clses = torch.tensor(
        [[0.7874, 0.1344, 0.2190], [0.8193, 0.6969, 0.7304],
         [0.2328, 0.9028, 0.3900], [0.6177, 0.5012, 0.2330],
         [0.8985, 0.4894, 0.7152]],
        dtype=torch.float32,
        device='cuda')
    proposal = dict(
        boxes_3d=pred_bboxes,
        scores_3d=pred_scores,
        labels_3d=pred_labels,
        cls_preds=pred_clses)
    proposal_list = [proposal]
    gt_bboxes_3d = [LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))]
    gt_labels_3d = [torch.randint(0, 3, [5], device='cuda')]

    losses = self.forward_train(feats_dict, voxels_dict, {}, proposal_list,
                                gt_bboxes_3d, gt_labels_3d)
    assert losses['loss_seg'] >= 0
    assert losses['loss_part'] >= 0
    assert losses['loss_cls'] >= 0
    assert losses['loss_bbox'] >= 0
    assert losses['loss_corner'] >= 0

    bbox_results = self.simple_test(feats_dict, voxels_dict, img_metas,
                                    proposal_list)
    boxes_3d = bbox_results[0]['boxes_3d']
    scores_3d = bbox_results[0]['scores_3d']
    labels_3d = bbox_results[0]['labels_3d']
    assert boxes_3d.tensor.shape == (12, 7)
    assert scores_3d.shape == (12, )
    assert labels_3d.shape == (12, )


def test_free_anchor_3D_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)
    pts_bbox_head_cfg = _get_pts_bbox_head_cfg(
        './free_anchor/hv_pointpillars_fpn_sbn-all_'
        'free-anchor_4x8_2x_nus-3d.py')
    self = build_head(pts_bbox_head_cfg)
    cls_scores = [
        torch.rand([4, 80, 200, 200], device='cuda') for i in range(3)
    ]
    bbox_preds = [
        torch.rand([4, 72, 200, 200], device='cuda') for i in range(3)
    ]
    dir_cls_preds = [
        torch.rand([4, 16, 200, 200], device='cuda') for i in range(3)
    ]
    gt_bboxes = [
        LiDARInstance3DBoxes(torch.rand([8, 9], device='cuda'), box_dim=9)
        for i in range(4)
    ]
    gt_labels = [
        torch.randint(0, 10, [8], device='cuda', dtype=torch.long)
        for i in range(4)
    ]
    input_metas = [0]
    losses = self.loss(cls_scores, bbox_preds, dir_cls_preds, gt_bboxes,
                       gt_labels, input_metas, None)
    assert losses['positive_bag_loss'] >= 0
    assert losses['negative_bag_loss'] >= 0


def test_primitive_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)

    primitive_head_cfg = dict(
        type='PrimitiveHead',
        num_dims=2,
        num_classes=18,
        primitive_mode='z',
        vote_module_cfg=dict(
            in_channels=256,
            vote_per_seed=1,
            gt_per_seed=1,
            conv_channels=(256, 256),
            conv_cfg=dict(type='Conv1d'),
            norm_cfg=dict(type='BN1d'),
            norm_feats=True,
            vote_loss=dict(
                type='ChamferDistance',
                mode='l1',
                reduction='none',
                loss_dst_weight=10.0)),
        vote_aggregation_cfg=dict(
            type='PointSAModule',
            num_point=64,
            radius=0.3,
            num_sample=16,
            mlp_channels=[256, 128, 128, 128],
            use_xyz=True,
            normalize_xyz=True),
        feat_channels=(128, 128),
        conv_cfg=dict(type='Conv1d'),
        norm_cfg=dict(type='BN1d'),
        objectness_loss=dict(
            type='CrossEntropyLoss',
            class_weight=[0.4, 0.6],
            reduction='mean',
            loss_weight=1.0),
        center_loss=dict(
            type='ChamferDistance',
            mode='l1',
            reduction='sum',
            loss_src_weight=1.0,
            loss_dst_weight=1.0),
        semantic_reg_loss=dict(
            type='ChamferDistance',
            mode='l1',
            reduction='sum',
            loss_src_weight=1.0,
            loss_dst_weight=1.0),
        semantic_cls_loss=dict(
            type='CrossEntropyLoss', reduction='sum', loss_weight=1.0),
        train_cfg=dict(
            dist_thresh=0.2,
            var_thresh=1e-2,
            lower_thresh=1e-6,
            num_point=100,
            num_point_line=10,
            line_thresh=0.2))

    self = build_head(primitive_head_cfg).cuda()
    fp_xyz = [torch.rand([2, 64, 3], dtype=torch.float32).cuda()]
    hd_features = torch.rand([2, 256, 64], dtype=torch.float32).cuda()
    fp_indices = [torch.randint(0, 64, [2, 64]).cuda()]
    input_dict = dict(
        fp_xyz_net0=fp_xyz, hd_feature=hd_features, fp_indices_net0=fp_indices)

    # test forward
    ret_dict = self(input_dict, 'vote')
    assert ret_dict['center_z'].shape == torch.Size([2, 64, 3])
    assert ret_dict['size_residuals_z'].shape == torch.Size([2, 64, 2])
    assert ret_dict['sem_cls_scores_z'].shape == torch.Size([2, 64, 18])
    assert ret_dict['aggregated_points_z'].shape == torch.Size([2, 64, 3])

    # test loss
    points = torch.rand([2, 1024, 3], dtype=torch.float32).cuda()
    ret_dict['seed_points'] = fp_xyz[0]
    ret_dict['seed_indices'] = fp_indices[0]

    from mmdet3d.core.bbox import DepthInstance3DBoxes
    gt_bboxes_3d = [
        DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda()),
        DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda())
    ]
    gt_labels_3d = torch.randint(0, 18, [2, 4]).cuda()
    gt_labels_3d = [gt_labels_3d[0], gt_labels_3d[1]]
    pts_semantic_mask = torch.randint(0, 19, [2, 1024]).cuda()
    pts_semantic_mask = [pts_semantic_mask[0], pts_semantic_mask[1]]
    pts_instance_mask = torch.randint(0, 4, [2, 1024]).cuda()
    pts_instance_mask = [pts_instance_mask[0], pts_instance_mask[1]]

    loss_input_dict = dict(
        bbox_preds=ret_dict,
        points=points,
        gt_bboxes_3d=gt_bboxes_3d,
        gt_labels_3d=gt_labels_3d,
        pts_semantic_mask=pts_semantic_mask,
        pts_instance_mask=pts_instance_mask)
    losses_dict = self.loss(**loss_input_dict)

    assert losses_dict['flag_loss_z'] >= 0
    assert losses_dict['vote_loss_z'] >= 0
    assert losses_dict['center_loss_z'] >= 0
    assert losses_dict['size_loss_z'] >= 0
    assert losses_dict['sem_loss_z'] >= 0

    # 'Primitive_mode' should be one of ['z', 'xy', 'line']
    with pytest.raises(AssertionError):
        primitive_head_cfg['vote_module_cfg']['in_channels'] = 'xyz'
        build_head(primitive_head_cfg)


def test_h3d_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)

    h3d_head_cfg = _get_roi_head_cfg('h3dnet/h3dnet_3x8_scannet-3d-18class.py')

    num_point = 128
    num_proposal = 64
    h3d_head_cfg.primitive_list[0].vote_aggregation_cfg.num_point = num_point
    h3d_head_cfg.primitive_list[1].vote_aggregation_cfg.num_point = num_point
    h3d_head_cfg.primitive_list[2].vote_aggregation_cfg.num_point = num_point
    h3d_head_cfg.bbox_head.num_proposal = num_proposal
    self = build_head(h3d_head_cfg).cuda()

    # prepare roi outputs
    fp_xyz = [torch.rand([1, num_point, 3], dtype=torch.float32).cuda()]
    hd_features = torch.rand([1, 256, num_point], dtype=torch.float32).cuda()
    fp_indices = [torch.randint(0, 128, [1, num_point]).cuda()]
    aggregated_points = torch.rand([1, num_proposal, 3],
                                   dtype=torch.float32).cuda()
    aggregated_features = torch.rand([1, 128, num_proposal],
                                     dtype=torch.float32).cuda()
    proposal_list = torch.cat([
        torch.rand([1, num_proposal, 3], dtype=torch.float32).cuda() * 4 - 2,
        torch.rand([1, num_proposal, 3], dtype=torch.float32).cuda() * 4,
        torch.zeros([1, num_proposal, 1]).cuda()
    ],
                              dim=-1)

    input_dict = dict(
        fp_xyz_net0=fp_xyz,
        hd_feature=hd_features,
        aggregated_points=aggregated_points,
        aggregated_features=aggregated_features,
        seed_points=fp_xyz[0],
        seed_indices=fp_indices[0],
        proposal_list=proposal_list)

    # prepare gt label
    from mmdet3d.core.bbox import DepthInstance3DBoxes
    gt_bboxes_3d = [
        DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda()),
        DepthInstance3DBoxes(torch.rand([4, 7], dtype=torch.float32).cuda())
    ]
    gt_labels_3d = torch.randint(0, 18, [1, 4]).cuda()
    gt_labels_3d = [gt_labels_3d[0]]
    pts_semantic_mask = torch.randint(0, 19, [1, num_point]).cuda()
    pts_semantic_mask = [pts_semantic_mask[0]]
    pts_instance_mask = torch.randint(0, 4, [1, num_point]).cuda()
    pts_instance_mask = [pts_instance_mask[0]]
    points = torch.rand([1, num_point, 3], dtype=torch.float32).cuda()

    # prepare rpn targets
    vote_targets = torch.rand([1, num_point, 9], dtype=torch.float32).cuda()
    vote_target_masks = torch.rand([1, num_point], dtype=torch.float32).cuda()
    size_class_targets = torch.rand([1, num_proposal],
                                    dtype=torch.float32).cuda().long()
    size_res_targets = torch.rand([1, num_proposal, 3],
                                  dtype=torch.float32).cuda()
    dir_class_targets = torch.rand([1, num_proposal],
                                   dtype=torch.float32).cuda().long()
    dir_res_targets = torch.rand([1, num_proposal], dtype=torch.float32).cuda()
    center_targets = torch.rand([1, 4, 3], dtype=torch.float32).cuda()
    mask_targets = torch.rand([1, num_proposal],
                              dtype=torch.float32).cuda().long()
    valid_gt_masks = torch.rand([1, 4], dtype=torch.float32).cuda()
    objectness_targets = torch.rand([1, num_proposal],
                                    dtype=torch.float32).cuda().long()
    objectness_weights = torch.rand([1, num_proposal],
                                    dtype=torch.float32).cuda()
    box_loss_weights = torch.rand([1, num_proposal],
                                  dtype=torch.float32).cuda()
    valid_gt_weights = torch.rand([1, 4], dtype=torch.float32).cuda()

    targets = (vote_targets, vote_target_masks, size_class_targets,
               size_res_targets, dir_class_targets, dir_res_targets,
               center_targets, None, mask_targets, valid_gt_masks,
               objectness_targets, objectness_weights, box_loss_weights,
               valid_gt_weights)

    input_dict['targets'] = targets

    # train forward
    ret_dict = self.forward_train(
        input_dict,
        points=points,
        gt_bboxes_3d=gt_bboxes_3d,
        gt_labels_3d=gt_labels_3d,
        pts_semantic_mask=pts_semantic_mask,
        pts_instance_mask=pts_instance_mask,
        img_metas=None)

    assert ret_dict['flag_loss_z'] >= 0
    assert ret_dict['vote_loss_z'] >= 0
    assert ret_dict['center_loss_z'] >= 0
    assert ret_dict['size_loss_z'] >= 0
    assert ret_dict['sem_loss_z'] >= 0
    assert ret_dict['objectness_loss_optimized'] >= 0
    assert ret_dict['primitive_sem_matching_loss'] >= 0


def test_center_head():
    tasks = [
        dict(num_class=1, class_names=['car']),
        dict(num_class=2, class_names=['truck', 'construction_vehicle']),
        dict(num_class=2, class_names=['bus', 'trailer']),
        dict(num_class=1, class_names=['barrier']),
        dict(num_class=2, class_names=['motorcycle', 'bicycle']),
        dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
    ]
    bbox_cfg = dict(
        type='CenterPointBBoxCoder',
        post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
        max_num=500,
        score_threshold=0.1,
        pc_range=[-51.2, -51.2],
        out_size_factor=8,
        voxel_size=[0.2, 0.2])
    train_cfg = dict(
        grid_size=[1024, 1024, 40],
        point_cloud_range=[-51.2, -51.2, -5., 51.2, 51.2, 3.],
        voxel_size=[0.1, 0.1, 0.2],
        out_size_factor=8,
        dense_reg=1,
        gaussian_overlap=0.1,
        max_objs=500,
        code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0],
        min_radius=2)
    test_cfg = dict(
        post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
        max_per_img=500,
        max_pool_nms=False,
        min_radius=[4, 12, 10, 1, 0.85, 0.175],
        post_max_size=83,
        score_threshold=0.1,
        pc_range=[-51.2, -51.2],
        out_size_factor=8,
        voxel_size=[0.2, 0.2],
        nms_type='circle')
    center_head_cfg = dict(
        type='CenterHead',
        in_channels=sum([256, 256]),
        tasks=tasks,
        train_cfg=train_cfg,
        test_cfg=test_cfg,
        bbox_coder=bbox_cfg,
        common_heads=dict(
            reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
        share_conv_channel=64,
        norm_bbox=True)

    center_head = build_head(center_head_cfg)

    x = torch.rand([2, 512, 128, 128])
    output = center_head([x])
    for i in range(6):
        assert output[i][0]['reg'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['height'].shape == torch.Size([2, 1, 128, 128])
        assert output[i][0]['dim'].shape == torch.Size([2, 3, 128, 128])
        assert output[i][0]['rot'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['vel'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['heatmap'].shape == torch.Size(
            [2, tasks[i]['num_class'], 128, 128])

    # test get_bboxes
    img_metas = [
        dict(box_type_3d=LiDARInstance3DBoxes),
        dict(box_type_3d=LiDARInstance3DBoxes)
    ]
    ret_lists = center_head.get_bboxes(output, img_metas)
    for ret_list in ret_lists:
        assert ret_list[0].tensor.shape[0] <= 500
        assert ret_list[1].shape[0] <= 500
        assert ret_list[2].shape[0] <= 500


def test_dcn_center_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and CUDA')
    set_random_seed(0)
    tasks = [
        dict(num_class=1, class_names=['car']),
        dict(num_class=2, class_names=['truck', 'construction_vehicle']),
        dict(num_class=2, class_names=['bus', 'trailer']),
        dict(num_class=1, class_names=['barrier']),
        dict(num_class=2, class_names=['motorcycle', 'bicycle']),
        dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
    ]
    voxel_size = [0.2, 0.2, 8]
    dcn_center_head_cfg = dict(
        type='CenterHead',
        in_channels=sum([128, 128, 128]),
        tasks=[
            dict(num_class=1, class_names=['car']),
            dict(num_class=2, class_names=['truck', 'construction_vehicle']),
            dict(num_class=2, class_names=['bus', 'trailer']),
            dict(num_class=1, class_names=['barrier']),
            dict(num_class=2, class_names=['motorcycle', 'bicycle']),
            dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
        ],
        common_heads={
            'reg': (2, 2),
            'height': (1, 2),
            'dim': (3, 2),
            'rot': (2, 2),
            'vel': (2, 2)
        },
        share_conv_channel=64,
        bbox_coder=dict(
            type='CenterPointBBoxCoder',
            post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
            max_num=500,
            score_threshold=0.1,
            pc_range=[-51.2, -51.2],
            out_size_factor=4,
            voxel_size=voxel_size[:2],
            code_size=9),
        separate_head=dict(
            type='DCNSeparateHead',
            dcn_config=dict(
                type='DCN',
                in_channels=64,
                out_channels=64,
                kernel_size=3,
                padding=1,
                groups=4,
                bias=False),  # mmcv 1.2.6 doesn't support bias=True anymore
            init_bias=-2.19,
            final_kernel=3),
        loss_cls=dict(type='GaussianFocalLoss', reduction='mean'),
        loss_bbox=dict(type='L1Loss', reduction='none', loss_weight=0.25),
        norm_bbox=True)
    # model training and testing settings
    train_cfg = dict(
        grid_size=[512, 512, 1],
        point_cloud_range=[-51.2, -51.2, -5., 51.2, 51.2, 3.],
        voxel_size=voxel_size,
        out_size_factor=4,
        dense_reg=1,
        gaussian_overlap=0.1,
        max_objs=500,
        min_radius=2,
        code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2, 1.0, 1.0])

    test_cfg = dict(
        post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
        max_per_img=500,
        max_pool_nms=False,
        min_radius=[4, 12, 10, 1, 0.85, 0.175],
        post_max_size=83,
        score_threshold=0.1,
        pc_range=[-51.2, -51.2],
        out_size_factor=4,
        voxel_size=voxel_size[:2],
        nms_type='circle')
    dcn_center_head_cfg.update(train_cfg=train_cfg, test_cfg=test_cfg)

    dcn_center_head = build_head(dcn_center_head_cfg).cuda()

    x = torch.ones([2, 384, 128, 128]).cuda()
    output = dcn_center_head([x])
    for i in range(6):
        assert output[i][0]['reg'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['height'].shape == torch.Size([2, 1, 128, 128])
        assert output[i][0]['dim'].shape == torch.Size([2, 3, 128, 128])
        assert output[i][0]['rot'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['vel'].shape == torch.Size([2, 2, 128, 128])
        assert output[i][0]['heatmap'].shape == torch.Size(
            [2, tasks[i]['num_class'], 128, 128])

    # Test loss.
    gt_bboxes_0 = LiDARInstance3DBoxes(torch.rand([10, 9]).cuda(), box_dim=9)
    gt_bboxes_1 = LiDARInstance3DBoxes(torch.rand([20, 9]).cuda(), box_dim=9)
    gt_labels_0 = torch.randint(1, 11, [10]).cuda()
    gt_labels_1 = torch.randint(1, 11, [20]).cuda()
    gt_bboxes_3d = [gt_bboxes_0, gt_bboxes_1]
    gt_labels_3d = [gt_labels_0, gt_labels_1]
    loss = dcn_center_head.loss(gt_bboxes_3d, gt_labels_3d, output)
    for key, item in loss.items():
        if 'heatmap' in key:
            assert item >= 0
        else:
            assert torch.sum(item) >= 0

    # test get_bboxes
    img_metas = [
        dict(box_type_3d=LiDARInstance3DBoxes),
        dict(box_type_3d=LiDARInstance3DBoxes)
    ]
    ret_lists = dcn_center_head.get_bboxes(output, img_metas)
    for ret_list in ret_lists:
        assert ret_list[0].tensor.shape[0] <= 500
        assert ret_list[1].shape[0] <= 500
        assert ret_list[2].shape[0] <= 500


def test_ssd3d_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)
    ssd3d_head_cfg = _get_vote_head_cfg('3dssd/3dssd_4x4_kitti-3d-car.py')
    ssd3d_head_cfg.vote_module_cfg.num_points = 64
    self = build_head(ssd3d_head_cfg).cuda()
    sa_xyz = [torch.rand([2, 128, 3], dtype=torch.float32).cuda()]
    sa_features = [torch.rand([2, 256, 128], dtype=torch.float32).cuda()]
    sa_indices = [torch.randint(0, 64, [2, 128]).cuda()]

    input_dict = dict(
        sa_xyz=sa_xyz, sa_features=sa_features, sa_indices=sa_indices)

    # test forward
    ret_dict = self(input_dict, 'spec')
    assert ret_dict['center'].shape == torch.Size([2, 64, 3])
    assert ret_dict['obj_scores'].shape == torch.Size([2, 1, 64])
    assert ret_dict['size'].shape == torch.Size([2, 64, 3])
    assert ret_dict['dir_res'].shape == torch.Size([2, 64, 12])

    # test loss
    points = [torch.rand([4000, 3], device='cuda') for i in range(2)]
    gt_bbox1 = LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))
    gt_bbox2 = LiDARInstance3DBoxes(torch.rand([5, 7], device='cuda'))
    gt_bboxes = [gt_bbox1, gt_bbox2]
    gt_labels = [
        torch.zeros([5], dtype=torch.long, device='cuda') for i in range(2)
    ]
    img_metas = [dict(box_type_3d=LiDARInstance3DBoxes) for i in range(2)]
    losses = self.loss(
        ret_dict, points, gt_bboxes, gt_labels, img_metas=img_metas)

    assert losses['centerness_loss'] >= 0
    assert losses['center_loss'] >= 0
    assert losses['dir_class_loss'] >= 0
    assert losses['dir_res_loss'] >= 0
    assert losses['size_res_loss'] >= 0
    assert losses['corner_loss'] >= 0
    assert losses['vote_loss'] >= 0

    # test multiclass_nms_single
    sem_scores = ret_dict['obj_scores'].transpose(1, 2)[0]
    obj_scores = sem_scores.max(-1)[0]
    bbox = self.bbox_coder.decode(ret_dict)[0]
    input_meta = img_metas[0]
    bbox_selected, score_selected, labels = self.multiclass_nms_single(
        obj_scores, sem_scores, bbox, points[0], input_meta)
    assert bbox_selected.shape[0] >= 0
    assert bbox_selected.shape[1] == 7
    assert score_selected.shape[0] >= 0
    assert labels.shape[0] >= 0

    # test get_boxes
    points = torch.stack(points, 0)
    results = self.get_bboxes(points, ret_dict, img_metas)
    assert results[0][0].tensor.shape[0] >= 0
    assert results[0][0].tensor.shape[1] == 7
    assert results[0][1].shape[0] >= 0
    assert results[0][2].shape[0] >= 0


def test_shape_aware_head_loss():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    bbox_head_cfg = _get_pts_bbox_head_cfg(
        'ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d.py')
    # modify bn config to avoid bugs caused by syncbn
    for task in bbox_head_cfg['tasks']:
        task['norm_cfg'] = dict(type='BN2d')

    from mmdet3d.models.builder import build_head
    self = build_head(bbox_head_cfg)
    self.cuda()
    assert len(self.heads) == 4
    assert isinstance(self.heads[0].conv_cls, torch.nn.modules.conv.Conv2d)
    assert self.heads[0].conv_cls.in_channels == 64
    assert self.heads[0].conv_cls.out_channels == 36
    assert self.heads[0].conv_reg.out_channels == 28
    assert self.heads[0].conv_dir_cls.out_channels == 8

    # test forward
    feats = list()
    feats.append(torch.rand([2, 384, 200, 200], dtype=torch.float32).cuda())
    (cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)
    assert cls_score[0].shape == torch.Size([2, 420000, 9])
    assert bbox_pred[0].shape == torch.Size([2, 420000, 7])
    assert dir_cls_preds[0].shape == torch.Size([2, 420000, 2])

    # test loss
    gt_bboxes = [
        LiDARInstance3DBoxes(
            torch.tensor(
                [[-14.5695, -6.4169, -2.1054, 1.8830, 4.6720, 1.4840, 1.5587],
                 [25.7215, 3.4581, -1.3456, 1.6720, 4.4090, 1.5830, 1.5301]],
                dtype=torch.float32).cuda()),
        LiDARInstance3DBoxes(
            torch.tensor(
                [[-50.763, -3.5517, -0.99658, 1.7430, 4.4020, 1.6990, 1.7874],
                 [-68.720, 0.033, -0.75276, 1.7860, 4.9100, 1.6610, 1.7525]],
                dtype=torch.float32).cuda())
    ]
    gt_labels = list(torch.tensor([[4, 4], [4, 4]], dtype=torch.int64).cuda())
    input_metas = [{
        'sample_idx': 1234
    }, {
        'sample_idx': 2345
    }]  # fake input_metas

    losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
                       gt_labels, input_metas)

    assert losses['loss_cls'][0] > 0
    assert losses['loss_bbox'][0] > 0
    assert losses['loss_dir'][0] > 0

    # test empty ground truth case
    gt_bboxes = list(torch.empty((2, 0, 7)).cuda())
    gt_labels = list(torch.empty((2, 0)).cuda())
    empty_gt_losses = self.loss(cls_score, bbox_pred, dir_cls_preds, gt_bboxes,
                                gt_labels, input_metas)
    assert empty_gt_losses['loss_cls'][0] > 0
    assert empty_gt_losses['loss_bbox'][0] == 0
    assert empty_gt_losses['loss_dir'][0] == 0


def test_shape_aware_head_getboxes():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    bbox_head_cfg = _get_pts_bbox_head_cfg(
        'ssn/hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d.py')
    # modify bn config to avoid bugs caused by syncbn
    for task in bbox_head_cfg['tasks']:
        task['norm_cfg'] = dict(type='BN2d')

    from mmdet3d.models.builder import build_head
    self = build_head(bbox_head_cfg)
    self.cuda()

    feats = list()
    feats.append(torch.rand([2, 384, 200, 200], dtype=torch.float32).cuda())
    # fake input_metas
    input_metas = [{
        'sample_idx': 1234,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }, {
        'sample_idx': 2345,
        'box_type_3d': LiDARInstance3DBoxes,
        'box_mode_3d': Box3DMode.LIDAR
    }]
    (cls_score, bbox_pred, dir_cls_preds) = self.forward(feats)

    # test get_bboxes
    cls_score[0] -= 1.5  # too many positive samples may cause cuda oom
    result_list = self.get_bboxes(cls_score, bbox_pred, dir_cls_preds,
                                  input_metas)
    assert len(result_list[0][1]) > 0  # ensure not all boxes are filtered
    assert (result_list[0][1] > 0.3).all()


def test_fcos_mono3d_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)
    fcos3d_head_cfg = _get_head_cfg(
        'fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d.py')
    self = build_head(fcos3d_head_cfg).cuda()

    feats = [
        torch.rand([2, 256, 116, 200], dtype=torch.float32).cuda(),
        torch.rand([2, 256, 58, 100], dtype=torch.float32).cuda(),
        torch.rand([2, 256, 29, 50], dtype=torch.float32).cuda(),
        torch.rand([2, 256, 15, 25], dtype=torch.float32).cuda(),
        torch.rand([2, 256, 8, 13], dtype=torch.float32).cuda()
    ]

    # test forward
    ret_dict = self(feats)
    assert len(ret_dict) == 5
    assert len(ret_dict[0]) == 5
    assert ret_dict[0][0].shape == torch.Size([2, 10, 116, 200])

    # test loss
    gt_bboxes = [
        torch.rand([3, 4], dtype=torch.float32).cuda(),
        torch.rand([3, 4], dtype=torch.float32).cuda()
    ]
    gt_bboxes_3d = CameraInstance3DBoxes(
        torch.rand([3, 9], device='cuda'), box_dim=9)
    gt_labels = [torch.randint(0, 10, [3], device='cuda') for i in range(2)]
    gt_labels_3d = gt_labels
    centers2d = [
        torch.rand([3, 2], dtype=torch.float32).cuda(),
        torch.rand([3, 2], dtype=torch.float32).cuda()
    ]
    depths = [
        torch.rand([3], dtype=torch.float32).cuda(),
        torch.rand([3], dtype=torch.float32).cuda()
    ]
    attr_labels = [torch.randint(0, 9, [3], device='cuda') for i in range(2)]
    img_metas = [
        dict(
            cam2img=[[1260.8474446004698, 0.0, 807.968244525554],
                     [0.0, 1260.8474446004698, 495.3344268742088],
                     [0.0, 0.0, 1.0]],
            scale_factor=np.array([1., 1., 1., 1.], dtype=np.float32),
            box_type_3d=CameraInstance3DBoxes) for i in range(2)
    ]
    losses = self.loss(*ret_dict, gt_bboxes, gt_labels, gt_bboxes_3d,
                       gt_labels_3d, centers2d, depths, attr_labels, img_metas)
    assert losses['loss_cls'] >= 0
    assert losses['loss_offset'] >= 0
    assert losses['loss_depth'] >= 0
    assert losses['loss_size'] >= 0
    assert losses['loss_rotsin'] >= 0
    assert losses['loss_centerness'] >= 0
    assert losses['loss_velo'] >= 0
    assert losses['loss_dir'] >= 0
    assert losses['loss_attr'] >= 0

    # test get_boxes
    results = self.get_bboxes(*ret_dict, img_metas)
    assert len(results) == 2
    assert len(results[0]) == 4
    assert results[0][0].tensor.shape == torch.Size([200, 9])
    assert results[0][1].shape == torch.Size([200])
    assert results[0][2].shape == torch.Size([200])
    assert results[0][3].shape == torch.Size([200])


def test_groupfree3d_head():
    if not torch.cuda.is_available():
        pytest.skip('test requires GPU and torch+cuda')
    _setup_seed(0)
    vote_head_cfg = _get_vote_head_cfg(
        'groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256.py')
    self = build_head(vote_head_cfg).cuda()

    fp_xyz = [torch.rand([2, 256, 3], dtype=torch.float32).cuda()]
    fp_features = [torch.rand([2, 288, 256], dtype=torch.float32).cuda()]
    fp_indices = [torch.randint(0, 128, [2, 256]).cuda()]

    input_dict = dict(
        fp_xyz=fp_xyz, fp_features=fp_features, fp_indices=fp_indices)

    # test forward
    ret_dict = self(input_dict, 'kps')
    assert ret_dict['seeds_obj_cls_logits'].shape == torch.Size([2, 1, 256])
    assert ret_dict['s5.center'].shape == torch.Size([2, 256, 3])
    assert ret_dict['s5.dir_class'].shape == torch.Size([2, 256, 1])
    assert ret_dict['s5.dir_res'].shape == torch.Size([2, 256, 1])
    assert ret_dict['s5.size_class'].shape == torch.Size([2, 256, 18])
    assert ret_dict['s5.size_res'].shape == torch.Size([2, 256, 18, 3])
    assert ret_dict['s5.obj_scores'].shape == torch.Size([2, 256, 1])
    assert ret_dict['s5.sem_scores'].shape == torch.Size([2, 256, 18])

    # test losses
    points = [torch.rand([5000, 4], device='cuda') for i in range(2)]
    gt_bbox1 = torch.rand([10, 7], dtype=torch.float32).cuda()
    gt_bbox2 = torch.rand([10, 7], dtype=torch.float32).cuda()

    gt_bbox1 = DepthInstance3DBoxes(gt_bbox1)
    gt_bbox2 = DepthInstance3DBoxes(gt_bbox2)
    gt_bboxes = [gt_bbox1, gt_bbox2]

    pts_instance_mask_1 = torch.randint(0, 10, [5000], device='cuda')
    pts_instance_mask_2 = torch.randint(0, 10, [5000], device='cuda')
    pts_instance_mask = [pts_instance_mask_1, pts_instance_mask_2]

    pts_semantic_mask_1 = torch.randint(0, 19, [5000], device='cuda')
    pts_semantic_mask_2 = torch.randint(0, 19, [5000], device='cuda')
    pts_semantic_mask = [pts_semantic_mask_1, pts_semantic_mask_2]

    labels_1 = torch.randint(0, 18, [10], device='cuda')
    labels_2 = torch.randint(0, 18, [10], device='cuda')
    gt_labels = [labels_1, labels_2]

    losses = self.loss(ret_dict, points, gt_bboxes, gt_labels,
                       pts_semantic_mask, pts_instance_mask)

    assert losses['s5.objectness_loss'] >= 0
    assert losses['s5.semantic_loss'] >= 0
    assert losses['s5.center_loss'] >= 0
    assert losses['s5.dir_class_loss'] >= 0
    assert losses['s5.dir_res_loss'] >= 0
    assert losses['s5.size_class_loss'] >= 0
    assert losses['s5.size_res_loss'] >= 0

    # test multiclass_nms_single
    obj_scores = torch.rand([256], device='cuda')
    sem_scores = torch.rand([256, 18], device='cuda')
    points = torch.rand([5000, 3], device='cuda')
    bbox = torch.rand([256, 7], device='cuda')
    input_meta = dict(box_type_3d=DepthInstance3DBoxes)
    bbox_selected, score_selected, labels = \
        self.multiclass_nms_single(obj_scores,
                                   sem_scores,
                                   bbox,
                                   points,
                                   input_meta)
    assert bbox_selected.shape[0] >= 0
    assert bbox_selected.shape[1] == 7
    assert score_selected.shape[0] >= 0
    assert labels.shape[0] >= 0

    # test get_boxes
    points = torch.rand([1, 5000, 3], device='cuda')
    seed_points = torch.rand([1, 1024, 3], device='cuda')
    seed_indices = torch.randint(0, 5000, [1, 1024], device='cuda')
    obj_scores = torch.rand([1, 256, 1], device='cuda')
    center = torch.rand([1, 256, 3], device='cuda')
    dir_class = torch.rand([1, 256, 1], device='cuda')
    dir_res_norm = torch.rand([1, 256, 1], device='cuda')
    dir_res = torch.rand([1, 256, 1], device='cuda')
    size_class = torch.rand([1, 256, 18], device='cuda')
    size_res = torch.rand([1, 256, 18, 3], device='cuda')
    sem_scores = torch.rand([1, 256, 18], device='cuda')
    bbox_preds = dict()
    bbox_preds['seed_points'] = seed_points
    bbox_preds['seed_indices'] = seed_indices
    bbox_preds['s5.obj_scores'] = obj_scores
    bbox_preds['s5.center'] = center
    bbox_preds['s5.dir_class'] = dir_class
    bbox_preds['s5.dir_res_norm'] = dir_res_norm
    bbox_preds['s5.dir_res'] = dir_res
    bbox_preds['s5.size_class'] = size_class
    bbox_preds['s5.size_res'] = size_res
    bbox_preds['s5.sem_scores'] = sem_scores

    self.test_cfg['prediction_stages'] = 'last'
    results = self.get_bboxes(points, bbox_preds, [input_meta])
    assert results[0][0].tensor.shape[0] >= 0
    assert results[0][0].tensor.shape[1] == 7
    assert results[0][1].shape[0] >= 0
    assert results[0][2].shape[0] >= 0