Exemplo n.º 1
0
def test_dataset_wrapper():
    CustomDataset.load_annotations = MagicMock()
    CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
    dataset_a = CustomDataset(ann_file=MagicMock(),
                              pipeline=[],
                              test_mode=True,
                              img_prefix='')
    len_a = 10
    cat_ids_list_a = [
        np.random.randint(0, 80, num).tolist()
        for num in np.random.randint(1, 20, len_a)
    ]
    dataset_a.data_infos = MagicMock()
    dataset_a.data_infos.__len__.return_value = len_a
    dataset_a.get_cat_ids = MagicMock(
        side_effect=lambda idx: cat_ids_list_a[idx])
    dataset_b = CustomDataset(ann_file=MagicMock(),
                              pipeline=[],
                              test_mode=True,
                              img_prefix='')
    len_b = 20
    cat_ids_list_b = [
        np.random.randint(0, 80, num).tolist()
        for num in np.random.randint(1, 20, len_b)
    ]
    dataset_b.data_infos = MagicMock()
    dataset_b.data_infos.__len__.return_value = len_b
    dataset_b.get_cat_ids = MagicMock(
        side_effect=lambda idx: cat_ids_list_b[idx])

    concat_dataset = ConcatDataset([dataset_a, dataset_b])
    assert concat_dataset[5] == 5
    assert concat_dataset[25] == 15
    assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
    assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15]
    assert len(concat_dataset) == len(dataset_a) + len(dataset_b)

    repeat_dataset = RepeatDataset(dataset_a, 10)
    assert repeat_dataset[5] == 5
    assert repeat_dataset[15] == 5
    assert repeat_dataset[27] == 7
    assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
    assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5]
    assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7]
    assert len(repeat_dataset) == 10 * len(dataset_a)

    category_freq = defaultdict(int)
    for cat_ids in cat_ids_list_a:
        cat_ids = set(cat_ids)
        for cat_id in cat_ids:
            category_freq[cat_id] += 1
    for k, v in category_freq.items():
        category_freq[k] = v / len(cat_ids_list_a)

    mean_freq = np.mean(list(category_freq.values()))
    repeat_thr = mean_freq

    category_repeat = {
        cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
        for cat_id, cat_freq in category_freq.items()
    }

    repeat_factors = []
    for cat_ids in cat_ids_list_a:
        cat_ids = set(cat_ids)
        repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids})
        repeat_factors.append(math.ceil(repeat_factor))
    repeat_factors_cumsum = np.cumsum(repeat_factors)
    repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)
    assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1]
    for idx in np.random.randint(0, len(repeat_factor_dataset), 3):
        assert repeat_factor_dataset[idx] == bisect.bisect_right(
            repeat_factors_cumsum, idx)
def test_dataset_wrapper():
    CustomDataset.load_annotations = MagicMock()
    CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
    dataset_a = CustomDataset(
        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
    len_a = 10
    cat_ids_list_a = [
        np.random.randint(0, 80, num).tolist()
        for num in np.random.randint(1, 20, len_a)
    ]
    dataset_a.data_infos = MagicMock()
    dataset_a.data_infos.__len__.return_value = len_a
    dataset_a.get_cat_ids = MagicMock(
        side_effect=lambda idx: cat_ids_list_a[idx])
    dataset_b = CustomDataset(
        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
    len_b = 20
    cat_ids_list_b = [
        np.random.randint(0, 80, num).tolist()
        for num in np.random.randint(1, 20, len_b)
    ]
    dataset_b.data_infos = MagicMock()
    dataset_b.data_infos.__len__.return_value = len_b
    dataset_b.get_cat_ids = MagicMock(
        side_effect=lambda idx: cat_ids_list_b[idx])

    concat_dataset = ConcatDataset([dataset_a, dataset_b])
    assert concat_dataset[5] == 5
    assert concat_dataset[25] == 15
    assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
    assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15]
    assert len(concat_dataset) == len(dataset_a) + len(dataset_b)

    repeat_dataset = RepeatDataset(dataset_a, 10)
    assert repeat_dataset[5] == 5
    assert repeat_dataset[15] == 5
    assert repeat_dataset[27] == 7
    assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
    assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5]
    assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7]
    assert len(repeat_dataset) == 10 * len(dataset_a)

    category_freq = defaultdict(int)
    for cat_ids in cat_ids_list_a:
        cat_ids = set(cat_ids)
        for cat_id in cat_ids:
            category_freq[cat_id] += 1
    for k, v in category_freq.items():
        category_freq[k] = v / len(cat_ids_list_a)

    mean_freq = np.mean(list(category_freq.values()))
    repeat_thr = mean_freq

    category_repeat = {
        cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
        for cat_id, cat_freq in category_freq.items()
    }

    repeat_factors = []
    for cat_ids in cat_ids_list_a:
        cat_ids = set(cat_ids)
        repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids})
        repeat_factors.append(math.ceil(repeat_factor))
    repeat_factors_cumsum = np.cumsum(repeat_factors)
    repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)
    assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1]
    for idx in np.random.randint(0, len(repeat_factor_dataset), 3):
        assert repeat_factor_dataset[idx] == bisect.bisect_right(
            repeat_factors_cumsum, idx)

    img_scale = (60, 60)
    dynamic_scale = (80, 80)
    pipeline = [
        dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
        dict(
            type='RandomAffine',
            scaling_ratio_range=(0.1, 2),
            border=(-img_scale[0] // 2, -img_scale[1] // 2)),
        dict(
            type='MixUp',
            img_scale=img_scale,
            ratio_range=(0.8, 1.6),
            pad_val=114.0),
        dict(type='RandomFlip', flip_ratio=0.5),
        dict(type='Resize', keep_ratio=True),
        dict(type='Pad', pad_to_square=True, pad_val=114.0),
    ]

    CustomDataset.load_annotations = MagicMock()
    results = []
    for _ in range(2):
        height = np.random.randint(10, 30)
        weight = np.random.randint(10, 30)
        img = np.ones((height, weight, 3))
        gt_bbox = np.concatenate([
            np.random.randint(1, 5, (2, 2)),
            np.random.randint(1, 5, (2, 2)) + 5
        ],
                                 axis=1)
        gt_labels = np.random.randint(0, 80, 2)
        results.append(dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img))

    CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx])
    dataset_a = CustomDataset(
        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
    len_a = 2
    cat_ids_list_a = [
        np.random.randint(0, 80, num).tolist()
        for num in np.random.randint(1, 20, len_a)
    ]
    dataset_a.data_infos = MagicMock()
    dataset_a.data_infos.__len__.return_value = len_a
    dataset_a.get_cat_ids = MagicMock(
        side_effect=lambda idx: cat_ids_list_a[idx])

    multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline,
                                                   dynamic_scale)
    for idx in range(len_a):
        results_ = multi_image_mix_dataset[idx]
        assert results_['img'].shape == (dynamic_scale[0], dynamic_scale[1], 3)

    # test skip_type_keys
    multi_image_mix_dataset = MultiImageMixDataset(
        dataset_a,
        pipeline,
        dynamic_scale,
        skip_type_keys=('MixUp', 'RandomFlip', 'Resize', 'Pad'))
    for idx in range(len_a):
        results_ = multi_image_mix_dataset[idx]
        assert results_['img'].shape == (img_scale[0], img_scale[1], 3)