add metadata to video dataset classes. bug fix. more robustness

test/test_datasets_video_utils.py

@@ -6,7 +6,6 @@
 
 from torchvision import io
 from torchvision.datasets.video_utils import VideoClips, unfold
-from torchvision import get_video_backend
 
 from common_utils import get_tmp_dir
 
@@ -62,23 +61,22 @@ def test_unfold(self):
     @unittest.skipIf(not io.video._av_available(), "this test requires av")
     @unittest.skipIf('win' in sys.platform, 'temporarily disabled on Windows')
     def test_video_clips(self):
-        _backend = get_video_backend()
         with get_list_of_videos(num_videos=3) as video_list:
-            video_clips = VideoClips(video_list, 5, 5, _backend=_backend)
+            video_clips = VideoClips(video_list, 5, 5)
             self.assertEqual(video_clips.num_clips(), 1 + 2 + 3)
             for i, (v_idx, c_idx) in enumerate([(0, 0), (1, 0), (1, 1), (2, 0), (2, 1), (2, 2)]):
                 video_idx, clip_idx = video_clips.get_clip_location(i)
                 self.assertEqual(video_idx, v_idx)
                 self.assertEqual(clip_idx, c_idx)
 
-            video_clips = VideoClips(video_list, 6, 6, _backend=_backend)
+            video_clips = VideoClips(video_list, 6, 6)
             self.assertEqual(video_clips.num_clips(), 0 + 1 + 2)
             for i, (v_idx, c_idx) in enumerate([(1, 0), (2, 0), (2, 1)]):
                 video_idx, clip_idx = video_clips.get_clip_location(i)
                 self.assertEqual(video_idx, v_idx)
                 self.assertEqual(clip_idx, c_idx)
 
-            video_clips = VideoClips(video_list, 6, 1, _backend=_backend)
+            video_clips = VideoClips(video_list, 6, 1)
             self.assertEqual(video_clips.num_clips(), 0 + (10 - 6 + 1) + (15 - 6 + 1))
             for i, v_idx, c_idx in [(0, 1, 0), (4, 1, 4), (5, 2, 0), (6, 2, 1)]:
                 video_idx, clip_idx = video_clips.get_clip_location(i)
@@ -87,9 +85,8 @@ def test_video_clips(self):
 
     @unittest.skip("Moved to reference scripts for now")
     def test_video_sampler(self):
-        _backend = get_video_backend()
         with get_list_of_videos(num_videos=3, sizes=[25, 25, 25]) as video_list:
-            video_clips = VideoClips(video_list, 5, 5, _backend=_backend)
+            video_clips = VideoClips(video_list, 5, 5)
             sampler = RandomClipSampler(video_clips, 3)  # noqa: F821
             self.assertEqual(len(sampler), 3 * 3)
             indices = torch.tensor(list(iter(sampler)))
@@ -100,9 +97,8 @@ def test_video_sampler(self):
 
     @unittest.skip("Moved to reference scripts for now")
     def test_video_sampler_unequal(self):
-        _backend = get_video_backend()
         with get_list_of_videos(num_videos=3, sizes=[10, 25, 25]) as video_list:
-            video_clips = VideoClips(video_list, 5, 5, _backend=_backend)
+            video_clips = VideoClips(video_list, 5, 5)
             sampler = RandomClipSampler(video_clips, 3)  # noqa: F821
             self.assertEqual(len(sampler), 2 + 3 + 3)
             indices = list(iter(sampler))
@@ -120,11 +116,10 @@ def test_video_sampler_unequal(self):
     @unittest.skipIf(not io.video._av_available(), "this test requires av")
     @unittest.skipIf('win' in sys.platform, 'temporarily disabled on Windows')
     def test_video_clips_custom_fps(self):
-        _backend = get_video_backend()
         with get_list_of_videos(num_videos=3, sizes=[12, 12, 12], fps=[3, 4, 6]) as video_list:
             num_frames = 4
             for fps in [1, 3, 4, 10]:
-                video_clips = VideoClips(video_list, num_frames, num_frames, fps, _backend=_backend)
+                video_clips = VideoClips(video_list, num_frames, num_frames, fps)
                 for i in range(video_clips.num_clips()):
                     video, audio, info, video_idx = video_clips.get_clip(i)
                     self.assertEqual(video.shape[0], num_frames)

torchvision/__init__.py

@@ -1,3 +1,5 @@
+import warnings
+
 from torchvision import models
 from torchvision import datasets
 from torchvision import ops
@@ -57,7 +59,10 @@ def set_video_backend(backend):
         raise ValueError(
             "Invalid video backend '%s'. Options are 'pyav' and 'video_reader'" % backend
         )
-    _video_backend = backend
+    if backend == "video_reader" and not io._HAS_VIDEO_OPT:
+        warnings.warn("video_reader video backend is not available")
+    else:
+        _video_backend = backend
 
 
 def get_video_backend():

torchvision/datasets/hmdb51.py

@@ -1,9 +1,9 @@
 import glob
 import os
 
-from .video_utils import VideoClips
 from .utils import list_dir
 from .folder import make_dataset
+from .video_utils import VideoClips
 from .vision import VisionDataset
 
 
@@ -51,7 +51,8 @@ class HMDB51(VisionDataset):
 
     def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
                  frame_rate=None, fold=1, train=True, transform=None,
-                 _precomputed_metadata=None):
+                 _precomputed_metadata=None, num_workers=1, _video_width=0,
+                 _video_height=0, _video_min_dimension=0, _audio_samples=0):
         super(HMDB51, self).__init__(root)
         if not 1 <= fold <= 3:
             raise ValueError("fold should be between 1 and 3, got {}".format(fold))
@@ -71,11 +72,21 @@ def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
             step_between_clips,
             frame_rate,
             _precomputed_metadata,
+            num_workers=num_workers,
+            _video_width=_video_width,
+            _video_height=_video_height,
+            _video_min_dimension=_video_min_dimension,
+            _audio_samples=_audio_samples,
         )
+        self.video_clips_metadata = video_clips.metadata
         self.indices = self._select_fold(video_list, annotation_path, fold, train)
         self.video_clips = video_clips.subset(self.indices)
         self.transform = transform
 
+    @property
+    def metadata(self):
+        return self.video_clips_metadata
+
     def _select_fold(self, video_list, annotation_path, fold, train):
         target_tag = 1 if train else 2
         name = "*test_split{}.txt".format(fold)

torchvision/datasets/kinetics.py

@@ -1,6 +1,6 @@
-from .video_utils import VideoClips
 from .utils import list_dir
 from .folder import make_dataset
+from .video_utils import VideoClips
 from .vision import VisionDataset
 
 
@@ -37,7 +37,9 @@ class Kinetics400(VisionDataset):
     """
 
     def __init__(self, root, frames_per_clip, step_between_clips=1, frame_rate=None,
-                 extensions=('avi',), transform=None, _precomputed_metadata=None):
+                 extensions=('avi',), transform=None, _precomputed_metadata=None,
+                 num_workers=1, _video_width=0, _video_height=0,
+                 _video_min_dimension=0, _audio_samples=0):
         super(Kinetics400, self).__init__(root)
         extensions = ('avi',)
 
@@ -52,9 +54,18 @@ def __init__(self, root, frames_per_clip, step_between_clips=1, frame_rate=None,
             step_between_clips,
             frame_rate,
             _precomputed_metadata,
+            num_workers=num_workers,
+            _video_width=_video_width,
+            _video_height=_video_height,
+            _video_min_dimension=_video_min_dimension,
+            _audio_samples=_audio_samples,
         )
         self.transform = transform
 
+    @property
+    def metadata(self):
+        return self.video_clips.metadata
+
     def __len__(self):
         return self.video_clips.num_clips()
 

torchvision/datasets/ucf101.py

@@ -1,9 +1,9 @@
 import glob
 import os
 
-from .video_utils import VideoClips
 from .utils import list_dir
 from .folder import make_dataset
+from .video_utils import VideoClips
 from .vision import VisionDataset
 
 
@@ -44,7 +44,8 @@ class UCF101(VisionDataset):
 
     def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
                  frame_rate=None, fold=1, train=True, transform=None,
-                 _precomputed_metadata=None):
+                 _precomputed_metadata=None, num_workers=1, _video_width=0,
+                 _video_height=0, _video_min_dimension=0, _audio_samples=0):
         super(UCF101, self).__init__(root)
         if not 1 <= fold <= 3:
             raise ValueError("fold should be between 1 and 3, got {}".format(fold))
@@ -64,11 +65,21 @@ def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
             step_between_clips,
             frame_rate,
             _precomputed_metadata,
+            num_workers=num_workers,
+            _video_width=_video_width,
+            _video_height=_video_height,
+            _video_min_dimension=_video_min_dimension,
+            _audio_samples=_audio_samples,
         )
+        self.video_clips_metadata = video_clips.metadata
         self.indices = self._select_fold(video_list, annotation_path, fold, train)
         self.video_clips = video_clips.subset(self.indices)
         self.transform = transform
 
+    @property
+    def metadata(self):
+        return self.video_clips_metadata
+
     def _select_fold(self, video_list, annotation_path, fold, train):
         name = "train" if train else "test"
         name = "{}list{:02d}.txt".format(name, fold)

torchvision/datasets/video_utils.py

@@ -68,10 +68,18 @@ class VideoClips(object):
             0 means that the data will be loaded in the main process. (default: 0)
     """
     def __init__(self, video_paths, clip_length_in_frames=16, frames_between_clips=1,
-                 frame_rate=None, _precomputed_metadata=None, num_workers=0, _backend="pyav"):
+                 frame_rate=None, _precomputed_metadata=None, num_workers=0,
+                 _video_width=0, _video_height=0, _video_min_dimension=0,
+                 _audio_samples=0):
+        from torchvision import get_video_backend
+
         self.video_paths = video_paths
         self.num_workers = num_workers
-        self._backend = _backend
+        self._backend = get_video_backend()
+        self._video_width = _video_width
+        self._video_height = _video_height
+        self._video_min_dimension = _video_min_dimension
+        self._audio_samples = _audio_samples
 
         if _precomputed_metadata is None:
             self._compute_frame_pts()
@@ -145,6 +153,7 @@ def metadata(self):
             _metadata.update({"video_fps": self.video_fps})
         else:
             _metadata.update({"info": self.info})
+        return _metadata
 
     def subset(self, indices):
         video_paths = [self.video_paths[i] for i in indices]
@@ -162,7 +171,11 @@ def subset(self, indices):
         else:
             metadata.update({"info": info})
         return type(self)(video_paths, self.num_frames, self.step, self.frame_rate,
-                          _precomputed_metadata=metadata)
+                          _precomputed_metadata=metadata, num_workers=self.num_workers,
+                          _video_width=self._video_width,
+                          _video_height=self._video_height,
+                          _video_min_dimension=self._video_min_dimension,
+                          _audio_samples=self._audio_samples)
 
     @staticmethod
     def compute_clips_for_video(video_pts, num_frames, step, fps, frame_rate):
@@ -206,9 +219,15 @@ def compute_clips(self, num_frames, step, frame_rate=None):
                 self.resampling_idxs.append(idxs)
         else:
             for video_pts, info in zip(self.video_pts, self.info):
-                clips, idxs = self.compute_clips_for_video(video_pts, num_frames, step, info["video_fps"], frame_rate)
-                self.clips.append(clips)
-                self.resampling_idxs.append(idxs)
+                if "video_fps" in info:
+                    clips, idxs = self.compute_clips_for_video(
+                        video_pts, num_frames, step, info["video_fps"], frame_rate)
+                    self.clips.append(clips)
+                    self.resampling_idxs.append(idxs)
+                else:
+                    # properly handle the cases where video decoding fails
+                    self.clips.append(torch.zeros(0, num_frames, dtype=torch.int64))
+                    self.resampling_idxs.append(torch.zeros(0, dtype=torch.int64))
         clip_lengths = torch.as_tensor([len(v) for v in self.clips])
         self.cumulative_sizes = clip_lengths.cumsum(0).tolist()
 
@@ -296,8 +315,12 @@ def get_clip(self, idx):
                 )
             video, audio, info = _read_video_from_file(
                 video_path,
+                video_width=self._video_width,
+                video_height=self._video_height,
+                video_min_dimension=self._video_min_dimension,
                 video_pts_range=(video_start_pts, video_end_pts),
                 video_timebase=info["video_timebase"],
+                audio_samples=self._audio_samples,
                 audio_pts_range=(audio_start_pts, audio_end_pts),
                 audio_timebase=audio_timebase,
             )

torchvision/io/__init__.py

@@ -1,8 +1,8 @@
 from .video import write_video, read_video, read_video_timestamps
-from ._video_opt import _read_video_from_file, _read_video_timestamps_from_file
+from ._video_opt import _read_video_from_file, _read_video_timestamps_from_file, _HAS_VIDEO_OPT
 
 
 __all__ = [
     'write_video', 'read_video', 'read_video_timestamps',
-    '_read_video_from_file', '_read_video_timestamps_from_file',
+    '_read_video_from_file', '_read_video_timestamps_from_file', '_HAS_VIDEO_OPT',
 ]