[video utils] group and reorder by number of frames (#38374)

fix

Author: zucchini-nlp

src/transformers/video_utils.py

@@ -696,11 +696,13 @@ def group_videos_by_shape(
     grouped_videos_index = {}
     for i, video in enumerate(videos):
         shape = video.shape[-2::]
+        num_frames = video.shape[-4]  # video format BTCHW
+        shape = (num_frames, *shape)
         if shape not in grouped_videos:
             grouped_videos[shape] = []
         grouped_videos[shape].append(video)
         grouped_videos_index[i] = (shape, len(grouped_videos[shape]) - 1)
-    # stack videos with the same shape
+    # stack videos with the same size and number of frames
     grouped_videos = {shape: torch.stack(videos, dim=0) for shape, videos in grouped_videos.items()}
     return grouped_videos, grouped_videos_index
 

tests/utils/test_video_utils.py

@@ -30,7 +30,7 @@
     require_torchvision,
     require_vision,
 )
-from transformers.video_utils import make_batched_videos
+from transformers.video_utils import group_videos_by_shape, make_batched_videos, reorder_videos
 
 
 if is_torch_available():
@@ -43,9 +43,9 @@
     from transformers.video_utils import VideoMetadata, load_video
 
 
-def get_random_video(height, width, return_torch=False):
+def get_random_video(height, width, num_frames=8, return_torch=False):
     random_frame = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
-    video = np.array(([random_frame] * 8))
+    video = np.array(([random_frame] * num_frames))
     if return_torch:
         # move channel first
         return torch.from_numpy(video).permute(0, 3, 1, 2)
@@ -189,6 +189,53 @@ def test_convert_to_rgb(self):
         rgb_video = video_processor.convert_to_rgb(torch.cat([video, video[:, :1]], dim=1))
         self.assertEqual(rgb_video.shape, (8, 3, 20, 20))
 
+    def test_group_and_reorder_videos(self):
+        """Tests that videos can be grouped by frame size and number of frames"""
+        video_1 = get_random_video(20, 20, num_frames=3, return_torch=True)
+        video_2 = get_random_video(20, 20, num_frames=5, return_torch=True)
+
+        # Group two videos of same size but different number of frames
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_2])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group two videos of different size but same number of frames
+        video_3 = get_random_video(15, 20, num_frames=3, return_torch=True)
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_3])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group all three videos where some have same size or same frame count
+        # But since none have frames and sizes identical, we'll have 3 groups
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_2, video_3])
+        self.assertEqual(len(grouped_videos), 3)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 3)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group if we had some videos with identical shapes
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_1, video_3])
+        self.assertEqual(len(grouped_videos), 2)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 2)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
+        # Group if we had all videos with identical shapes
+        grouped_videos, grouped_videos_index = group_videos_by_shape([video_1, video_1, video_1])
+        self.assertEqual(len(grouped_videos), 1)
+
+        regrouped_videos = reorder_videos(grouped_videos, grouped_videos_index)
+        self.assertTrue(len(regrouped_videos), 1)
+        self.assertEqual(video_1.shape, regrouped_videos[0].shape)
+
 
 @require_vision
 @require_av