import copy
import numpy as np
import pytest
import torch

from mmdet3d.datasets import ScanNetDataset, ScanNetSegDataset


def test_getitem():
    np.random.seed(0)
    root_path = './tests/data/scannet/'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                   'window', 'bookshelf', 'picture', 'counter', 'desk',
                   'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                   'sink', 'bathtub', 'garbagebin')
    pipelines = [
        dict(
            type='LoadPointsFromFile',
            coord_type='DEPTH',
            shift_height=True,
            load_dim=6,
            use_dim=[0, 1, 2]),
        dict(
            type='LoadAnnotations3D',
            with_bbox_3d=True,
            with_label_3d=True,
            with_mask_3d=True,
            with_seg_3d=True),
        dict(type='IndoorPointSample', num_points=5),
        dict(
            type='RandomFlip3D',
            sync_2d=False,
            flip_ratio_bev_horizontal=1.0,
            flip_ratio_bev_vertical=1.0),
        dict(
            type='GlobalRotScaleTrans',
            rot_range=[-0.087266, 0.087266],
            scale_ratio_range=[1.0, 1.0],
            shift_height=True),
        dict(type='DefaultFormatBundle3D', class_names=class_names),
        dict(
            type='Collect3D',
            keys=[
                'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
                'pts_instance_mask'
            ],
            meta_keys=['file_name', 'sample_idx', 'pcd_rotation']),
    ]

    scannet_dataset = ScanNetDataset(root_path, ann_file, pipelines)
    data = scannet_dataset[0]
    points = data['points']._data
    gt_bboxes_3d = data['gt_bboxes_3d']._data
    gt_labels = data['gt_labels_3d']._data
    pts_semantic_mask = data['pts_semantic_mask']._data
    pts_instance_mask = data['pts_instance_mask']._data
    file_name = data['img_metas']._data['file_name']
    pcd_rotation = data['img_metas']._data['pcd_rotation']
    sample_idx = data['img_metas']._data['sample_idx']
    expected_rotation = np.array([[0.99654, 0.08311407, 0.],
                                  [-0.08311407, 0.99654, 0.], [0., 0., 1.]])
    assert file_name == './tests/data/scannet/points/scene0000_00.bin'
    assert np.allclose(pcd_rotation, expected_rotation, 1e-3)
    assert sample_idx == 'scene0000_00'
    expected_points = torch.tensor([[-2.7231, -2.2068, 2.3543, 2.3895],
                                    [-0.4065, -3.4857, 2.1330, 2.1682],
                                    [-1.4578, 1.3510, -0.0441, -0.0089],
                                    [2.2428, -1.1323, -0.0288, 0.0064],
                                    [0.7052, -2.9752, 1.5560, 1.5912]])
    expected_gt_bboxes_3d = torch.tensor(
        [[-1.1835, -3.6317, 1.5704, 1.7577, 0.3761, 0.5724, 0.0000],
         [-3.1832, 3.2269, 1.1911, 0.6727, 0.2251, 0.6715, 0.0000],
         [-0.9598, -2.2864, 0.0093, 0.7506, 2.5709, 1.2145, 0.0000],
         [-2.6988, -2.7354, 0.8288, 0.7680, 1.8877, 0.2870, 0.0000],
         [3.2989, 0.2885, -0.0090, 0.7600, 3.8814, 2.1603, 0.0000]])
    expected_gt_labels = np.array([
        6, 6, 4, 9, 11, 11, 10, 0, 15, 17, 17, 17, 3, 12, 4, 4, 14, 1, 0, 0, 0,
        0, 0, 0, 5, 5, 5
    ])
    expected_pts_semantic_mask = np.array([3, 1, 2, 2, 15])
    expected_pts_instance_mask = np.array([44, 22, 10, 10, 57])
    original_classes = scannet_dataset.CLASSES

    assert scannet_dataset.CLASSES == class_names
    assert torch.allclose(points, expected_points, 1e-2)
    assert gt_bboxes_3d.tensor[:5].shape == (5, 7)
    assert torch.allclose(gt_bboxes_3d.tensor[:5], expected_gt_bboxes_3d, 1e-2)
    assert np.all(gt_labels.numpy() == expected_gt_labels)
    assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
    assert np.all(pts_instance_mask.numpy() == expected_pts_instance_mask)
    assert original_classes == class_names

    scannet_dataset = ScanNetDataset(
        root_path, ann_file, pipeline=None, classes=['cabinet', 'bed'])
    assert scannet_dataset.CLASSES != original_classes
    assert scannet_dataset.CLASSES == ['cabinet', 'bed']

    scannet_dataset = ScanNetDataset(
        root_path, ann_file, pipeline=None, classes=('cabinet', 'bed'))
    assert scannet_dataset.CLASSES != original_classes
    assert scannet_dataset.CLASSES == ('cabinet', 'bed')

    # Test load classes from file
    import tempfile
    tmp_file = tempfile.NamedTemporaryFile()
    with open(tmp_file.name, 'w') as f:
        f.write('cabinet\nbed\n')

    scannet_dataset = ScanNetDataset(
        root_path, ann_file, pipeline=None, classes=tmp_file.name)
    assert scannet_dataset.CLASSES != original_classes
    assert scannet_dataset.CLASSES == ['cabinet', 'bed']


def test_evaluate():
    if not torch.cuda.is_available():
        pytest.skip()
    from mmdet3d.core.bbox.structures import DepthInstance3DBoxes
    root_path = './tests/data/scannet'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    scannet_dataset = ScanNetDataset(root_path, ann_file)
    results = []
    pred_boxes = dict()
    pred_boxes['boxes_3d'] = DepthInstance3DBoxes(
        torch.tensor([[
            1.4813e+00, 3.5207e+00, 1.5704e+00, 1.7445e+00, 2.3196e-01,
            5.7235e-01, 0.0000e+00
        ],
                      [
                          2.9040e+00, -3.4803e+00, 1.1911e+00, 6.6078e-01,
                          1.7072e-01, 6.7154e-01, 0.0000e+00
                      ],
                      [
                          1.1466e+00, 2.1987e+00, 9.2576e-03, 5.4184e-01,
                          2.5346e+00, 1.2145e+00, 0.0000e+00
                      ],
                      [
                          2.9168e+00, 2.5016e+00, 8.2875e-01, 6.1697e-01,
                          1.8428e+00, 2.8697e-01, 0.0000e+00
                      ],
                      [
                          -3.3114e+00, -1.3351e-02, -8.9524e-03, 4.4082e-01,
                          3.8582e+00, 2.1603e+00, 0.0000e+00
                      ],
                      [
                          -2.0135e+00, -3.4857e+00, 9.3848e-01, 1.9911e+00,
                          2.1603e-01, 1.2767e+00, 0.0000e+00
                      ],
                      [
                          -2.1945e+00, -3.1402e+00, -3.8165e-02, 1.4801e+00,
                          6.8676e-01, 1.0586e+00, 0.0000e+00
                      ],
                      [
                          -2.7553e+00, 2.4055e+00, -2.9972e-02, 1.4764e+00,
                          1.4927e+00, 2.3380e+00, 0.0000e+00
                      ]]))
    pred_boxes['labels_3d'] = torch.tensor([6, 6, 4, 9, 11, 11])
    pred_boxes['scores_3d'] = torch.tensor([0.5, 1.0, 1.0, 1.0, 1.0, 0.5])
    results.append(pred_boxes)
    metric = [0.25, 0.5]
    ret_dict = scannet_dataset.evaluate(results, metric)
    assert abs(ret_dict['table_AP_0.25'] - 0.3333) < 0.01
    assert abs(ret_dict['window_AP_0.25'] - 1.0) < 0.01
    assert abs(ret_dict['counter_AP_0.25'] - 1.0) < 0.01
    assert abs(ret_dict['curtain_AP_0.25'] - 1.0) < 0.01


def test_show():
    import mmcv
    import tempfile
    from os import path as osp

    from mmdet3d.core.bbox import DepthInstance3DBoxes
    tmp_dir = tempfile.TemporaryDirectory()
    temp_dir = tmp_dir.name
    root_path = './tests/data/scannet'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    scannet_dataset = ScanNetDataset(root_path, ann_file)
    boxes_3d = DepthInstance3DBoxes(
        torch.tensor([[
            -2.4053e+00, 9.2295e-01, 8.0661e-02, 2.4054e+00, 2.1468e+00,
            8.5990e-01, 0.0000e+00
        ],
                      [
                          -1.9341e+00, -2.0741e+00, 3.0698e-03, 3.2206e-01,
                          2.5322e-01, 3.5144e-01, 0.0000e+00
                      ],
                      [
                          -3.6908e+00, 8.0684e-03, 2.6201e-01, 4.1515e-01,
                          7.6489e-01, 5.3585e-01, 0.0000e+00
                      ],
                      [
                          2.6332e+00, 8.5143e-01, -4.9964e-03, 3.0367e-01,
                          1.3448e+00, 1.8329e+00, 0.0000e+00
                      ],
                      [
                          2.0221e-02, 2.6153e+00, 1.5109e-02, 7.3335e-01,
                          1.0429e+00, 1.0251e+00, 0.0000e+00
                      ]]))
    scores_3d = torch.tensor(
        [1.2058e-04, 2.3012e-03, 6.2324e-06, 6.6139e-06, 6.7965e-05])
    labels_3d = torch.tensor([0, 0, 0, 0, 0])
    result = dict(boxes_3d=boxes_3d, scores_3d=scores_3d, labels_3d=labels_3d)
    results = [result]
    scannet_dataset.show(results, temp_dir, show=False)
    pts_file_path = osp.join(temp_dir, 'scene0000_00',
                             'scene0000_00_points.obj')
    gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
    pred_file_path = osp.join(temp_dir, 'scene0000_00',
                              'scene0000_00_pred.obj')
    mmcv.check_file_exist(pts_file_path)
    mmcv.check_file_exist(gt_file_path)
    mmcv.check_file_exist(pred_file_path)
    tmp_dir.cleanup()


def test_seg_getitem():
    np.random.seed(0)
    root_path = './tests/data/scannet/'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
                   'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
                   'curtain', 'refrigerator', 'showercurtrain', 'toilet',
                   'sink', 'bathtub', 'otherfurniture')
    palette = [
        [174, 199, 232],
        [152, 223, 138],
        [31, 119, 180],
        [255, 187, 120],
        [188, 189, 34],
        [140, 86, 75],
        [255, 152, 150],
        [214, 39, 40],
        [197, 176, 213],
        [148, 103, 189],
        [196, 156, 148],
        [23, 190, 207],
        [247, 182, 210],
        [219, 219, 141],
        [255, 127, 14],
        [158, 218, 229],
        [44, 160, 44],
        [112, 128, 144],
        [227, 119, 194],
        [82, 84, 163],
    ]
    scene_idxs = [0 for _ in range(20)]
    label_weight = [
        2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
        4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071, 5.019636,
        4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416, 5.3954206,
        4.6971426
    ]

    # test network inputs are (xyz, rgb, normalized_xyz)
    pipelines = [
        dict(
            type='LoadPointsFromFile',
            coord_type='DEPTH',
            shift_height=False,
            use_color=True,
            load_dim=6,
            use_dim=[0, 1, 2, 3, 4, 5]),
        dict(
            type='LoadAnnotations3D',
            with_bbox_3d=False,
            with_label_3d=False,
            with_mask_3d=False,
            with_seg_3d=True),
        dict(
            type='PointSegClassMapping',
            valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24,
                           28, 33, 34, 36, 39)),
        dict(
            type='IndoorPatchPointSample',
            num_points=5,
            block_size=1.5,
            sample_rate=1.0,
            ignore_index=len(class_names),
            use_normalized_coord=True),
        dict(type='NormalizePointsColor', color_mean=None),
        dict(type='DefaultFormatBundle3D', class_names=class_names),
        dict(
            type='Collect3D',
            keys=['points', 'pts_semantic_mask'],
            meta_keys=['file_name', 'sample_idx'])
    ]

    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=pipelines,
        classes=None,
        palette=None,
        modality=None,
        test_mode=False,
        ignore_index=None,
        scene_idxs=scene_idxs,
        label_weight=label_weight)

    data = scannet_dataset[0]
    points = data['points']._data
    pts_semantic_mask = data['pts_semantic_mask']._data
    file_name = data['img_metas']._data['file_name']
    sample_idx = data['img_metas']._data['sample_idx']

    assert file_name == './tests/data/scannet/points/scene0000_00.bin'
    assert sample_idx == 'scene0000_00'
    expected_points = torch.tensor([[
        0.0000, 0.0000, 1.2427, 0.6118, 0.5529, 0.4471, -0.6462, -1.0046,
        0.4280
    ],
                                    [
                                        0.1553, -0.0074, 1.6077, 0.5882,
                                        0.6157, 0.5569, -0.6001, -1.0068,
                                        0.5537
                                    ],
                                    [
                                        0.1518, 0.6016, 0.6548, 0.1490, 0.1059,
                                        0.0431, -0.6012, -0.8309, 0.2255
                                    ],
                                    [
                                        -0.7494, 0.1033, 0.6756, 0.5216,
                                        0.4353, 0.3333, -0.8687, -0.9748,
                                        0.2327
                                    ],
                                    [
                                        -0.6836, -0.0203, 0.5884, 0.5765,
                                        0.5020, 0.4510, -0.8491, -1.0105,
                                        0.2027
                                    ]])
    expected_pts_semantic_mask = np.array([13, 13, 12, 2, 0])
    original_classes = scannet_dataset.CLASSES
    original_palette = scannet_dataset.PALETTE

    assert scannet_dataset.CLASSES == class_names
    assert scannet_dataset.ignore_index == 20
    assert torch.allclose(points, expected_points, 1e-2)
    assert np.all(pts_semantic_mask.numpy() == expected_pts_semantic_mask)
    assert original_classes == class_names
    assert original_palette == palette
    assert scannet_dataset.scene_idxs.dtype == np.int32
    assert np.all(scannet_dataset.scene_idxs == np.array(scene_idxs))
    assert np.allclose(scannet_dataset.label_weight, np.array(label_weight),
                       1e-5)

    # test network inputs are (xyz, rgb)
    np.random.seed(0)
    new_pipelines = copy.deepcopy(pipelines)
    new_pipelines[3] = dict(
        type='IndoorPatchPointSample',
        num_points=5,
        block_size=1.5,
        sample_rate=1.0,
        ignore_index=len(class_names),
        use_normalized_coord=False)
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=new_pipelines,
        scene_idxs=scene_idxs)

    data = scannet_dataset[0]
    points = data['points']._data
    assert torch.allclose(points, expected_points[:, :6], 1e-2)

    # test network inputs are (xyz, normalized_xyz)
    np.random.seed(0)
    new_pipelines = copy.deepcopy(pipelines)
    new_pipelines[0] = dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=False,
        load_dim=6,
        use_dim=[0, 1, 2])
    new_pipelines.remove(new_pipelines[4])
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=new_pipelines,
        scene_idxs=scene_idxs)

    data = scannet_dataset[0]
    points = data['points']._data
    assert torch.allclose(points, expected_points[:, [0, 1, 2, 6, 7, 8]], 1e-2)

    # test network inputs are (xyz,)
    np.random.seed(0)
    new_pipelines = copy.deepcopy(pipelines)
    new_pipelines[0] = dict(
        type='LoadPointsFromFile',
        coord_type='DEPTH',
        shift_height=False,
        use_color=False,
        load_dim=6,
        use_dim=[0, 1, 2])
    new_pipelines[3] = dict(
        type='IndoorPatchPointSample',
        num_points=5,
        block_size=1.5,
        sample_rate=1.0,
        ignore_index=len(class_names),
        use_normalized_coord=False)
    new_pipelines.remove(new_pipelines[4])
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=new_pipelines,
        scene_idxs=scene_idxs)

    data = scannet_dataset[0]
    points = data['points']._data
    assert torch.allclose(points, expected_points[:, :3], 1e-2)

    # test dataset with selected classes
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=None,
        classes=['cabinet', 'chair'],
        scene_idxs=scene_idxs)

    label_map = {i: 20 for i in range(41)}
    label_map.update({3: 0, 5: 1})

    assert scannet_dataset.CLASSES != original_classes
    assert scannet_dataset.CLASSES == ['cabinet', 'chair']
    assert scannet_dataset.PALETTE == [palette[2], palette[4]]
    assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
    assert scannet_dataset.label_map == label_map
    assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}
    assert np.all(scannet_dataset.label_weight == np.ones(2))

    # test load classes from file
    import tempfile
    tmp_file = tempfile.NamedTemporaryFile()
    with open(tmp_file.name, 'w') as f:
        f.write('cabinet\nchair\n')

    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=None,
        classes=tmp_file.name,
        scene_idxs=scene_idxs)
    assert scannet_dataset.CLASSES != original_classes
    assert scannet_dataset.CLASSES == ['cabinet', 'chair']
    assert scannet_dataset.PALETTE == [palette[2], palette[4]]
    assert scannet_dataset.VALID_CLASS_IDS == [3, 5]
    assert scannet_dataset.label_map == label_map
    assert scannet_dataset.label2cat == {0: 'cabinet', 1: 'chair'}

    # test scene_idxs in dataset
    # we should input scene_idxs in train mode
    with pytest.raises(NotImplementedError):
        scannet_dataset = ScanNetSegDataset(
            data_root=root_path,
            ann_file=ann_file,
            pipeline=None,
            scene_idxs=None)

    # test mode
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path,
        ann_file=ann_file,
        pipeline=None,
        test_mode=True,
        scene_idxs=scene_idxs)
    assert np.all(scannet_dataset.scene_idxs == np.array([0]))
    assert np.all(scannet_dataset.label_weight == np.ones(len(class_names)))


def test_seg_evaluate():
    if not torch.cuda.is_available():
        pytest.skip()
    root_path = './tests/data/scannet'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path, ann_file=ann_file, test_mode=True)
    results = []
    pred_sem_mask = dict(
        semantic_mask=torch.tensor([
            13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
            18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
            3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
            17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
            13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
        ]).long())
    results.append(pred_sem_mask)
    ret_dict = scannet_dataset.evaluate(results)
    assert abs(ret_dict['miou'] - 0.5308) < 0.01
    assert abs(ret_dict['acc'] - 0.8219) < 0.01
    assert abs(ret_dict['acc_cls'] - 0.7649) < 0.01


def test_seg_show():
    import mmcv
    import tempfile
    from os import path as osp

    tmp_dir = tempfile.TemporaryDirectory()
    temp_dir = tmp_dir.name
    root_path = './tests/data/scannet'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path, ann_file=ann_file, scene_idxs=[0])
    result = dict(
        semantic_mask=torch.tensor([
            13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
            18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
            3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
            17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
            13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
        ]).long())
    results = [result]
    scannet_dataset.show(results, temp_dir, show=False)
    pts_file_path = osp.join(temp_dir, 'scene0000_00',
                             'scene0000_00_points.obj')
    gt_file_path = osp.join(temp_dir, 'scene0000_00', 'scene0000_00_gt.obj')
    pred_file_path = osp.join(temp_dir, 'scene0000_00',
                              'scene0000_00_pred.obj')
    mmcv.check_file_exist(pts_file_path)
    mmcv.check_file_exist(gt_file_path)
    mmcv.check_file_exist(pred_file_path)
    tmp_dir.cleanup()


def test_seg_format_results():
    import mmcv
    from os import path as osp

    root_path = './tests/data/scannet'
    ann_file = './tests/data/scannet/scannet_infos.pkl'
    scannet_dataset = ScanNetSegDataset(
        data_root=root_path, ann_file=ann_file, test_mode=True)
    results = []
    pred_sem_mask = dict(
        semantic_mask=torch.tensor([
            13, 5, 1, 2, 6, 2, 13, 1, 14, 2, 0, 0, 5, 5, 3, 0, 1, 14, 0, 0, 0,
            18, 6, 15, 13, 0, 2, 4, 0, 3, 16, 6, 13, 5, 13, 0, 0, 0, 0, 1, 7,
            3, 19, 12, 8, 0, 11, 0, 0, 1, 2, 13, 17, 1, 1, 1, 6, 2, 13, 19, 4,
            17, 0, 14, 1, 7, 2, 1, 7, 2, 0, 5, 17, 5, 0, 0, 3, 6, 5, 11, 1, 13,
            13, 2, 3, 1, 0, 13, 19, 1, 14, 5, 3, 1, 13, 1, 2, 3, 2, 1
        ]).long())
    results.append(pred_sem_mask)
    result_files, tmp_dir = scannet_dataset.format_results(results)

    expected_label = np.array([
        16, 6, 2, 3, 7, 3, 16, 2, 24, 3, 1, 1, 6, 6, 4, 1, 2, 24, 1, 1, 1, 36,
        7, 28, 16, 1, 3, 5, 1, 4, 33, 7, 16, 6, 16, 1, 1, 1, 1, 2, 8, 4, 39,
        14, 9, 1, 12, 1, 1, 2, 3, 16, 34, 2, 2, 2, 7, 3, 16, 39, 5, 34, 1, 24,
        2, 8, 3, 2, 8, 3, 1, 6, 34, 6, 1, 1, 4, 7, 6, 12, 2, 16, 16, 3, 4, 2,
        1, 16, 39, 2, 24, 6, 4, 2, 16, 2, 3, 4, 3, 2
    ])
    expected_txt_path = osp.join(tmp_dir.name, 'results', 'scene0000_00.txt')
    assert np.all(result_files[0]['seg_mask'] == expected_label)
    mmcv.check_file_exist(expected_txt_path)
    tmp_dir.cleanup()
