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Fix make_batched_videos and add tests (#36143)

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* add support for initial shift in video processing and other fixes

* revert modifications video loading functions
This commit is contained in:
Yoni Gozlan
2025-02-13 17:14:30 -05:00
committed by GitHub
parent 8fd4bc7d1d
commit e6a7981711
2 changed files with 29 additions and 11 deletions
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2
src/transformers/image_utils.py
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@@ -314,7 +314,7 @@ def make_batched_videos(videos) -> VideoInput:
if isinstance(videos, (list, tuple)) and isinstance(videos[0], (list, tuple)) and is_valid_image(videos[0][0]):
# case 1: nested batch of videos so we flatten it
if not is_pil_image(videos[0][0]) and videos[0][0].ndim == 4:
videos = [video for batch_list in videos for video in batch_list]
videos = [[video for batch_list in batched_videos for video in batch_list] for batched_videos in videos]
# case 2: list of videos represented as list of video frames
return videos

38
tests/utils/test_image_utils.py
View File

@@ -424,14 +424,14 @@ class ImageFeatureExtractionTester(unittest.TestCase):
def test_make_batched_videos_numpy(self):
# Test a single image is converted to a list of 1 video with 1 frame
images = np.random.randint(0, 256, (16, 32, 3))
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list), 1)
self.assertTrue(np.array_equal(videos_list[0][0], images))
# Test a 4d array of images is converted to a a list of 1 video
images = np.random.randint(0, 256, (4, 16, 32, 3))
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], np.ndarray)
self.assertEqual(len(videos_list), 1)
@@ -440,7 +440,7 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a list of images is converted to a list of videos
images = [np.random.randint(0, 256, (16, 32, 3)) for _ in range(4)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list), 1)
self.assertEqual(len(videos_list[0]), 4)
@@ -448,7 +448,7 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a nested list of images is left unchanged
images = [[np.random.randint(0, 256, (16, 32, 3)) for _ in range(2)] for _ in range(2)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 2)
@@ -456,25 +456,34 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a list of 4d array images is converted to a list of videos
images = [np.random.randint(0, 256, (4, 16, 32, 3)) for _ in range(2)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], np.ndarray)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 4)
self.assertTrue(np.array_equal(videos_list[0][0], images[0][0]))
# Test a batch of list of 4d array images is converted to a list of videos
images = [[np.random.randint(0, 256, (4, 16, 32, 3)) for _ in range(2)] for _ in range(2)]
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], np.ndarray)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 8)
self.assertTrue(np.array_equal(videos_list[0][0], images[0][0][0]))
@require_torch
def test_make_batched_videos_torch(self):
# Test a single image is converted to a list of 1 video with 1 frame
images = torch.randint(0, 256, (16, 32, 3))
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list[0]), 1)
self.assertTrue(np.array_equal(videos_list[0][0], images))
# Test a 4d tensor of images is converted to a list of 1 video
images = torch.randint(0, 256, (4, 16, 32, 3))
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], torch.Tensor)
self.assertEqual(len(videos_list), 1)
@@ -483,7 +492,7 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a list of images is converted to a list of videos
images = [torch.randint(0, 256, (16, 32, 3)) for _ in range(4)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list), 1)
self.assertEqual(len(videos_list[0]), 4)
@@ -491,7 +500,7 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a nested list of images is left unchanged
images = [[torch.randint(0, 256, (16, 32, 3)) for _ in range(2)] for _ in range(2)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 2)
@@ -499,13 +508,22 @@ class ImageFeatureExtractionTester(unittest.TestCase):
# Test a list of 4d tensor images is converted to a list of videos
images = [torch.randint(0, 256, (4, 16, 32, 3)) for _ in range(2)]
videos_list = make_nested_list_of_images(images)
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], torch.Tensor)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 4)
self.assertTrue(np.array_equal(videos_list[0][0], images[0][0]))
# Test a batch of list of 4d tensor images is converted to a list of videos
images = [[torch.randint(0, 256, (4, 16, 32, 3)) for _ in range(2)] for _ in range(2)]
videos_list = make_batched_videos(images)
self.assertIsInstance(videos_list[0], list)
self.assertIsInstance(videos_list[0][0], torch.Tensor)
self.assertEqual(len(videos_list), 2)
self.assertEqual(len(videos_list[0]), 8)
self.assertTrue(np.array_equal(videos_list[0][0], images[0][0][0]))
@require_torch
def test_conversion_torch_to_array(self):
feature_extractor = ImageFeatureExtractionMixin()