[Enhance]: Check dtype in transform unit tests. (#5969)

* add dtype to bbox unit test

* add dtype to unit test

* add docstring

* add label and check dtype

tests/test_data/test_pipelines/test_transform/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import check_result_same, construct_toy_data, create_random_bboxes
+
+__all__ = ['create_random_bboxes', 'construct_toy_data', 'check_result_same']

tests/test_data/test_pipelines/test_transform/test_img_augment.py

@@ -6,38 +6,8 @@ import numpy as np
 from mmcv.utils import build_from_cfg
 from numpy.testing import assert_array_equal
 
-from mmdet.core.mask import BitmapMasks, PolygonMasks
 from mmdet.datasets.builder import PIPELINES
-
-
-def construct_toy_data(poly2mask=True):
-    img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
-    img = np.stack([img, img, img], axis=-1)
-    results = dict()
-    # image
-    results['img'] = img
-    results['img_shape'] = img.shape
-    results['img_fields'] = ['img']
-    # bboxes
-    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
-    results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
-    results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
-                                           dtype=np.float32)
-    # labels
-    results['gt_labels'] = np.array([1], dtype=np.int64)
-    # masks
-    results['mask_fields'] = ['gt_masks']
-    if poly2mask:
-        gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
-                            dtype=np.uint8)[None, :, :]
-        results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
-    else:
-        raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]]
-        results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
-    # segmentations
-    results['seg_fields'] = ['gt_semantic_seg']
-    results['gt_semantic_seg'] = img[..., 0]
-    return results
+from .utils import construct_toy_data
 
 
 def test_adjust_color():

tests/test_data/test_pipelines/test_transform/test_rotate.py

@@ -7,60 +7,7 @@ from mmcv.utils import build_from_cfg
 
 from mmdet.core.mask import BitmapMasks, PolygonMasks
 from mmdet.datasets.builder import PIPELINES
-
-
-def construct_toy_data(poly2mask=True):
-    img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
-    img = np.stack([img, img, img], axis=-1)
-    results = dict()
-    # image
-    results['img'] = img
-    results['img_shape'] = img.shape
-    results['img_fields'] = ['img']
-    # bboxes
-    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
-    results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
-    results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
-                                           dtype=np.float32)
-    # labels
-    results['gt_labels'] = np.array([1], dtype=np.int64)
-    # masks
-    results['mask_fields'] = ['gt_masks']
-    if poly2mask:
-        gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
-                            dtype=np.uint8)[None, :, :]
-        results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
-    else:
-        raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]]
-        results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
-    # segmentations
-    results['seg_fields'] = ['gt_semantic_seg']
-    results['gt_semantic_seg'] = img[..., 0]
-    return results
-
-
-def _check_fields(results, results_rotated, keys):
-    for key in keys:
-        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
-            assert np.equal(results[key].to_ndarray(),
-                            results_rotated[key].to_ndarray()).all()
-        else:
-            assert np.equal(results[key], results_rotated[key]).all()
-
-
-def check_rotate(results, results_rotated):
-    # check image
-    _check_fields(results, results_rotated, results.get('img_fields', ['img']))
-    # check bboxes
-    _check_fields(results, results_rotated, results.get('bbox_fields', []))
-    # check masks
-    _check_fields(results, results_rotated, results.get('mask_fields', []))
-    # check segmentations
-    _check_fields(results, results_rotated, results.get('seg_fields', []))
-    # _check gt_labels
-    if 'gt_labels' in results:
-        assert np.equal(results['gt_labels'],
-                        results_rotated['gt_labels']).all()
+from .utils import check_result_same, construct_toy_data
 
 
 def test_rotate():
@@ -105,14 +52,14 @@ def test_rotate():
     )
     rotate_module = build_from_cfg(transform, PIPELINES)
     results_wo_rotate = rotate_module(copy.deepcopy(results))
-    check_rotate(results, results_wo_rotate)
+    check_result_same(results, results_wo_rotate)
 
     # test case when no rotate aug (prob<=0)
     transform = dict(
         type='Rotate', level=10, prob=0., img_fill_val=img_fill_val, scale=0.6)
     rotate_module = build_from_cfg(transform, PIPELINES)
     results_wo_rotate = rotate_module(copy.deepcopy(results))
-    check_rotate(results, results_wo_rotate)
+    check_result_same(results, results_wo_rotate)
 
     # test clockwise rotation with angle 90
     results = construct_toy_data()
@@ -140,14 +87,14 @@ def test_rotate():
     results_gt['gt_semantic_seg'] = np.array(
         [[255, 6, 2, 255], [255, 7, 3,
                             255]]).astype(results['gt_semantic_seg'].dtype)
-    check_rotate(results_gt, results_rotated)
+    check_result_same(results_gt, results_rotated)
 
     # test clockwise rotation with angle 90, PolygonMasks
     results = construct_toy_data(poly2mask=False)
     results_rotated = rotate_module(copy.deepcopy(results))
     gt_masks = [[np.array([2, 0, 2, 1, 1, 1, 1, 0], dtype=np.float)]]
     results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
-    check_rotate(results_gt, results_rotated)
+    check_result_same(results_gt, results_rotated)
 
     # test counter-clockwise roatation with angle 90,
     # and specify the ratation center
@@ -183,7 +130,7 @@ def test_rotate():
     gt_seg = (np.ones((h, w)) * 255).astype(results['gt_semantic_seg'].dtype)
     gt_seg[0, 0], gt_seg[0, 1] = 1, 5
     results_gt['gt_semantic_seg'] = gt_seg
-    check_rotate(results_gt, results_rotated)
+    check_result_same(results_gt, results_rotated)
 
     transform = dict(
         type='Rotate',
@@ -195,7 +142,7 @@ def test_rotate():
         prob=1.)
     rotate_module = build_from_cfg(transform, PIPELINES)
     results_rotated = rotate_module(copy.deepcopy(results))
-    check_rotate(results_gt, results_rotated)
+    check_result_same(results_gt, results_rotated)
 
     # test counter-clockwise roatation with angle 90,
     # and specify the ratation center, PolygonMasks
@@ -203,7 +150,7 @@ def test_rotate():
     results_rotated = rotate_module(copy.deepcopy(results))
     gt_masks = [[np.array([0, 0, 0, 0, 1, 0, 1, 0], dtype=np.float)]]
     results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
-    check_rotate(results_gt, results_rotated)
+    check_result_same(results_gt, results_rotated)
 
     # test AutoAugment equipped with Rotate
     policies = [[dict(type='Rotate', level=10, prob=1.)]]

tests/test_data/test_pipelines/test_transform/test_shear.py

@@ -7,62 +7,7 @@ from mmcv.utils import build_from_cfg
 
 from mmdet.core.mask import BitmapMasks, PolygonMasks
 from mmdet.datasets.builder import PIPELINES
-
-
-def construct_toy_data(poly2mask=True):
-    img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
-    img = np.stack([img, img, img], axis=-1)
-    results = dict()
-    # image
-    results['img'] = img
-    results['img_shape'] = img.shape
-    results['img_fields'] = ['img']
-    # bboxes
-    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
-    results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
-    results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
-                                           dtype=np.float32)
-    # labels
-    results['gt_labels'] = np.array([1], dtype=np.int64)
-    # masks
-    results['mask_fields'] = ['gt_masks']
-    if poly2mask:
-        gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
-                            dtype=np.uint8)[None, :, :]
-        results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
-    else:
-        raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]]
-        results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
-
-    # segmentations
-    results['seg_fields'] = ['gt_semantic_seg']
-    results['gt_semantic_seg'] = img[..., 0]
-    return results
-
-
-def _check_fields(results, results_sheared, keys):
-    for key in keys:
-        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
-            assert np.equal(results[key].to_ndarray(),
-                            results_sheared[key].to_ndarray()).all()
-        else:
-            assert np.equal(results[key], results_sheared[key]).all()
-
-
-def check_shear(results, results_sheared):
-    # _check_keys(results, results_sheared)
-    # check image
-    _check_fields(results, results_sheared, results.get('img_fields', ['img']))
-    # check bboxes
-    _check_fields(results, results_sheared, results.get('bbox_fields', []))
-    # check masks
-    _check_fields(results, results_sheared, results.get('mask_fields', []))
-    # check segmentations
-    _check_fields(results, results_sheared, results.get('seg_fields', []))
-    # check gt_labels
-    if 'gt_labels' in results:
-        assert np.equal(results['gt_labels'],
-                        results_sheared['gt_labels']).all()
+from .utils import check_result_same, construct_toy_data
 
 
 def test_shear():
@@ -94,7 +39,7 @@ def test_shear():
         direction='horizontal')
     shear_module = build_from_cfg(transform, PIPELINES)
     results_wo_shear = shear_module(copy.deepcopy(results))
-    check_shear(results, results_wo_shear)
+    check_result_same(results, results_wo_shear)
 
     # test case when no shear aug (level=0, direction='vertical')
     transform = dict(
@@ -106,7 +51,7 @@ def test_shear():
         direction='vertical')
     shear_module = build_from_cfg(transform, PIPELINES)
     results_wo_shear = shear_module(copy.deepcopy(results))
-    check_shear(results, results_wo_shear)
+    check_result_same(results, results_wo_shear)
 
     # test case when no shear aug (prob<=0)
     transform = dict(
@@ -117,7 +62,7 @@ def test_shear():
         direction='vertical')
     shear_module = build_from_cfg(transform, PIPELINES)
     results_wo_shear = shear_module(copy.deepcopy(results))
-    check_shear(results, results_wo_shear)
+    check_result_same(results, results_wo_shear)
 
     # test shear horizontally, magnitude=1
     transform = dict(
@@ -143,14 +88,15 @@ def test_shear():
     results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
     results_gt['gt_semantic_seg'] = np.array(
         [[1, 2, 3, 4], [255, 5, 6, 7]], dtype=results['gt_semantic_seg'].dtype)
-    check_shear(results_gt, results_sheared)
+    check_result_same(results_gt, results_sheared)
 
     # test PolygonMasks with shear horizontally, magnitude=1
     results = construct_toy_data(poly2mask=False)
     results_sheared = shear_module(copy.deepcopy(results))
-    gt_masks = [[np.array([1, 0, 2, 0, 3, 1, 2, 1], dtype=np.float)]]
+    print(results_sheared['gt_masks'])
+    gt_masks = [[np.array([0, 0, 2, 0, 3, 1, 1, 1], dtype=np.float)]]
     results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
-    check_shear(results_gt, results_sheared)
+    check_result_same(results_gt, results_sheared)
 
     # test shear vertically, magnitude=-1
     img_fill_val = 128
@@ -180,14 +126,14 @@ def test_shear():
     results_gt['gt_semantic_seg'] = np.array(
         [[1, 6, 255, 255], [5, 255, 255, 255]],
         dtype=results['gt_semantic_seg'].dtype)
-    check_shear(results_gt, results_sheared)
+    check_result_same(results_gt, results_sheared)
 
     # test PolygonMasks with shear vertically, magnitude=-1
     results = construct_toy_data(poly2mask=False)
     results_sheared = shear_module(copy.deepcopy(results))
-    gt_masks = [[np.array([1, 0, 2, 0, 2, 0, 1, 0], dtype=np.float)]]
+    gt_masks = [[np.array([0, 0, 2, 0, 2, 0, 0, 1], dtype=np.float)]]
     results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
-    check_shear(results_gt, results_sheared)
+    check_result_same(results_gt, results_sheared)
 
     results = construct_toy_data()
     # same mask for BitmapMasks and PolygonMasks
@@ -196,7 +142,7 @@ def test_shear():
         4)
     results['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32)
     results_sheared_bitmap = shear_module(copy.deepcopy(results))
-    check_shear(results_sheared_bitmap, results_sheared)
+    check_result_same(results_sheared_bitmap, results_sheared)
 
     # test AutoAugment equipped with Shear
     policies = [[dict(type='Shear', level=10, prob=1.)]]

tests/test_data/test_pipelines/test_transform/test_transform.py

@@ -10,6 +10,7 @@ from mmcv.utils import build_from_cfg
 
 from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
 from mmdet.datasets.builder import PIPELINES
+from .utils import create_random_bboxes
 
 
 def test_resize():
@@ -78,6 +79,7 @@ def test_resize():
     results = resize_module(results)
     assert np.equal(results['img'], results['img2']).all()
     assert results['img_shape'] == (800, 1280, 3)
+    assert results['img'].dtype == results['img'].dtype == np.uint8
 
 
 def test_flip():
@@ -200,17 +202,10 @@ def test_random_crop():
     results['pad_shape'] = img.shape
     results['scale_factor'] = 1.0
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20))
@@ -219,6 +214,9 @@ def test_random_crop():
     assert results['img'].shape[:2] == (h - 20, w - 20)
     # All bboxes should be reserved after crop
     assert results['img_shape'][:2] == (h - 20, w - 20)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
     assert results['gt_bboxes'].shape[0] == 8
     assert results['gt_bboxes_ignore'].shape[0] == 2
 
@@ -227,6 +225,8 @@ def test_random_crop():
 
     assert (area(results['gt_bboxes']) <= area(gt_bboxes)).all()
     assert (area(results['gt_bboxes_ignore']) <= area(gt_bboxes_ignore)).all()
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
 
     # test assertion for invalid crop_type
     with pytest.raises(ValueError):
@@ -269,6 +269,8 @@ def test_random_crop():
     h, w = results_transformed['img_shape'][:2]
     assert int(2 * 0.3 + 0.5) <= h <= int(2 * 1 + 0.5)
     assert int(4 * 0.7 + 0.5) <= w <= int(4 * 1 + 0.5)
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
 
     # test crop_type "relative"
     transform = dict(
@@ -280,6 +282,8 @@ def test_random_crop():
     results_transformed = transform_module(copy.deepcopy(results))
     h, w = results_transformed['img_shape'][:2]
     assert h == int(2 * 0.3 + 0.5) and w == int(4 * 0.7 + 0.5)
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
 
     # test crop_type "absolute"
     transform = dict(
@@ -291,6 +295,8 @@ def test_random_crop():
     results_transformed = transform_module(copy.deepcopy(results))
     h, w = results_transformed['img_shape'][:2]
     assert h == 1 and w == 2
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
 
     # test crop_type "absolute_range"
     transform = dict(
@@ -302,18 +308,11 @@ def test_random_crop():
     results_transformed = transform_module(copy.deepcopy(results))
     h, w = results_transformed['img_shape'][:2]
     assert 1 <= h <= 2 and 1 <= w <= 4
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
 
 
 def test_min_iou_random_crop():
-
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     results = dict()
     img = mmcv.imread(
         osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
@@ -328,6 +327,7 @@ def test_min_iou_random_crop():
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(1, w, h)
     gt_bboxes_ignore = create_random_bboxes(1, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='MinIoURandomCrop')
@@ -340,6 +340,11 @@ def test_min_iou_random_crop():
     with pytest.raises(AssertionError):
         crop_module(results_test)
     results = crop_module(results)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
     patch = np.array([0, 0, results['img_shape'][1], results['img_shape'][0]])
     ious = bbox_overlaps(patch.reshape(-1, 4),
                          results['gt_bboxes']).reshape(-1)
@@ -555,14 +560,6 @@ def test_random_center_crop_pad():
     results = load(results)
     test_results = copy.deepcopy(results)
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = results['img_shape']
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
@@ -585,6 +582,8 @@ def test_random_center_crop_pad():
     assert train_results['pad_shape'][:2] == (h - 20, w - 20)
     assert train_results['gt_bboxes'].shape[0] == 8
     assert train_results['gt_bboxes_ignore'].shape[0] == 2
+    assert train_results['gt_bboxes'].dtype == np.float32
+    assert train_results['gt_bboxes_ignore'].dtype == np.float32
 
     test_transform = dict(
         type='RandomCenterCropPad',
@@ -782,18 +781,10 @@ def test_random_shift():
     # TODO: add img_fields test
     results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
-    results['gt_labels'] = torch.ones(gt_bboxes.shape[0])
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='RandomShift', shift_ratio=1.0)
@@ -802,6 +793,9 @@ def test_random_shift():
 
     assert results['img'].shape[:2] == (h, w)
     assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
 
 
 def test_random_affine():
@@ -825,18 +819,10 @@ def test_random_affine():
     results['img'] = img
     results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
-    results['gt_labels'] = torch.ones(gt_bboxes.shape[0])
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='RandomAffine')
@@ -845,10 +831,13 @@ def test_random_affine():
 
     assert results['img'].shape[:2] == (h, w)
     assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
 
     # test filter bbox
-    gt_bboxes = np.array([[0, 0, 1, 1], [0, 0, 3, 100]])
-    results['gt_labels'] = torch.ones(gt_bboxes.shape[0])
+    gt_bboxes = np.array([[0, 0, 1, 1], [0, 0, 3, 100]], dtype=np.float32)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     transform = dict(
         type='RandomAffine',
@@ -865,6 +854,10 @@ def test_random_affine():
 
     assert results['gt_bboxes'].shape[0] == 0
     assert results['gt_labels'].shape[0] == 0
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
 
 
 def test_mosaic():
@@ -880,18 +873,10 @@ def test_mosaic():
     # TODO: add img_fields test
     results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
-    results['gt_labels'] = torch.ones(gt_bboxes.shape[0])
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='Mosaic', img_scale=(10, 12))
@@ -904,6 +889,10 @@ def test_mosaic():
     results['mix_results'] = [copy.deepcopy(results)] * 3
     results = mosaic_module(results)
     assert results['img'].shape[:2] == (20, 24)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
 
 
 def test_mixup():
@@ -919,18 +908,10 @@ def test_mixup():
     # TODO: add img_fields test
     results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
 
-    def create_random_bboxes(num_bboxes, img_w, img_h):
-        bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
-        bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
-        bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
-        bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
-            np.int)
-        return bboxes
-
     h, w, _ = img.shape
     gt_bboxes = create_random_bboxes(8, w, h)
     gt_bboxes_ignore = create_random_bboxes(2, w, h)
-    results['gt_labels'] = torch.ones(gt_bboxes.shape[0])
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
     results['gt_bboxes'] = gt_bboxes
     results['gt_bboxes_ignore'] = gt_bboxes_ignore
     transform = dict(type='MixUp', img_scale=(10, 12))
@@ -947,3 +928,7 @@ def test_mixup():
     results['mix_results'] = [copy.deepcopy(results)]
     results = mixup_module(results)
     assert results['img'].shape[:2] == (288, 512)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32

tests/test_data/test_pipelines/test_transform/utils.py

@@ -0,0 +1,78 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+
+
+def _check_fields(results, pipeline_results, keys):
+    """Check data in fields from two results are same."""
+    for key in keys:
+        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
+            assert np.equal(results[key].to_ndarray(),
+                            pipeline_results[key].to_ndarray()).all()
+        else:
+            assert np.equal(results[key], pipeline_results[key]).all()
+            assert results[key].dtype == pipeline_results[key].dtype
+
+
+def check_result_same(results, pipeline_results):
+    """Check whether the `pipeline_results` is the same with the predefined
+    `results`.
+
+    Args:
+        results (dict): Predefined results which should be the standard output
+            of the transform pipeline.
+        pipeline_results (dict): Results processed by the transform pipeline.
+    """
+    # check image
+    _check_fields(results, pipeline_results,
+                  results.get('img_fields', ['img']))
+    # check bboxes
+    _check_fields(results, pipeline_results, results.get('bbox_fields', []))
+    # check masks
+    _check_fields(results, pipeline_results, results.get('mask_fields', []))
+    # check segmentations
+    _check_fields(results, pipeline_results, results.get('seg_fields', []))
+    # check gt_labels
+    if 'gt_labels' in results:
+        assert np.equal(results['gt_labels'],
+                        pipeline_results['gt_labels']).all()
+
+
+def construct_toy_data(poly2mask=True):
+    img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
+    img = np.stack([img, img, img], axis=-1)
+    results = dict()
+    # image
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['img_fields'] = ['img']
+    # bboxes
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+    results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
+    results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
+                                           dtype=np.float32)
+    # labels
+    results['gt_labels'] = np.array([1], dtype=np.int64)
+    # masks
+    results['mask_fields'] = ['gt_masks']
+    if poly2mask:
+        gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
+                            dtype=np.uint8)[None, :, :]
+        results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
+    else:
+        raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]]
+        results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
+    # segmentations
+    results['seg_fields'] = ['gt_semantic_seg']
+    results['gt_semantic_seg'] = img[..., 0]
+    return results
+
+
+def create_random_bboxes(num_bboxes, img_w, img_h):
+    bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
+    bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
+    bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
+    bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
+        np.float32)
+    return bboxes