move sampler into TV core. Update UniformClipSampler

references/video_classification/train.py

@@ -11,9 +11,10 @@
 import torchvision
 import torchvision.datasets.video_utils
 from torchvision import transforms
+from torchvision.datasets.samplers import DistributedSampler, UniformClipSampler, RandomClipSampler
 
 import utils
-from sampler import DistributedSampler, UniformClipSampler, RandomClipSampler
+
 from scheduler import WarmupMultiStepLR
 import transforms as T
 

test/test_datasets_samplers.py

@@ -0,0 +1,88 @@
+import contextlib
+import sys
+import os
+import torch
+import unittest
+
+from torchvision import io
+from torchvision.datasets.samplers import RandomClipSampler, UniformClipSampler
+from torchvision.datasets.video_utils import VideoClips, unfold
+from torchvision import get_video_backend
+
+from common_utils import get_tmp_dir
+
+
+@contextlib.contextmanager
+def get_list_of_videos(num_videos=5, sizes=None, fps=None):
+    with get_tmp_dir() as tmp_dir:
+        names = []
+        for i in range(num_videos):
+            if sizes is None:
+                size = 5 * (i + 1)
+            else:
+                size = sizes[i]
+            if fps is None:
+                f = 5
+            else:
+                f = fps[i]
+            data = torch.randint(0, 255, (size, 300, 400, 3), dtype=torch.uint8)
+            name = os.path.join(tmp_dir, "{}.mp4".format(i))
+            names.append(name)
+            io.write_video(name, data, fps=f)
+
+        yield names
+
+
+@unittest.skipIf(not io.video._av_available(), "this test requires av")
+class Tester(unittest.TestCase):
+    def test_random_clip_sampler(self):
+        with get_list_of_videos(num_videos=3, sizes=[25, 25, 25]) as video_list:
+            video_clips = VideoClips(video_list, 5, 5)
+            sampler = RandomClipSampler(video_clips, 3)
+            self.assertEqual(len(sampler), 3 * 3)
+            indices = torch.tensor(list(iter(sampler)))
+            videos = indices // 5
+            v_idxs, count = torch.unique(videos, return_counts=True)
+            self.assertTrue(v_idxs.equal(torch.tensor([0, 1, 2])))
+            self.assertTrue(count.equal(torch.tensor([3, 3, 3])))
+
+    def test_random_clip_sampler_unequal(self):
+        with get_list_of_videos(num_videos=3, sizes=[10, 25, 25]) as video_list:
+            video_clips = VideoClips(video_list, 5, 5)
+            sampler = RandomClipSampler(video_clips, 3)
+            self.assertEqual(len(sampler), 2 + 3 + 3)
+            indices = list(iter(sampler))
+            self.assertIn(0, indices)
+            self.assertIn(1, indices)
+            # remove elements of the first video, to simplify testing
+            indices.remove(0)
+            indices.remove(1)
+            indices = torch.tensor(indices) - 2
+            videos = indices // 5
+            v_idxs, count = torch.unique(videos, return_counts=True)
+            self.assertTrue(v_idxs.equal(torch.tensor([0, 1])))
+            self.assertTrue(count.equal(torch.tensor([3, 3])))
+
+    def test_uniform_clip_sampler(self):
+        with get_list_of_videos(num_videos=3, sizes=[25, 25, 25]) as video_list:
+            video_clips = VideoClips(video_list, 5, 5)
+            sampler = UniformClipSampler(video_clips, 3)
+            self.assertEqual(len(sampler), 3 * 3)
+            indices = torch.tensor(list(iter(sampler)))
+            videos = indices // 5
+            v_idxs, count = torch.unique(videos, return_counts=True)
+            self.assertTrue(v_idxs.equal(torch.tensor([0, 1, 2])))
+            self.assertTrue(count.equal(torch.tensor([3, 3, 3])))
+            self.assertTrue(indices.equal(torch.tensor([0, 2, 4, 5, 7, 9, 10, 12, 14])))
+
+    def test_uniform_clip_sampler_insufficient_clips(self):
+        with get_list_of_videos(num_videos=3, sizes=[10, 25, 25]) as video_list:
+            video_clips = VideoClips(video_list, 5, 5)
+            sampler = UniformClipSampler(video_clips, 3)
+            self.assertEqual(len(sampler), 3 * 3)
+            indices = torch.tensor(list(iter(sampler)))
+            self.assertTrue(indices.equal(torch.tensor([0, 0, 1, 2, 4, 6, 7, 9, 11])))
+
+
+if __name__ == '__main__':
+    unittest.main()

test/test_datasets_video_utils.py

@@ -83,36 +83,6 @@ def test_video_clips(self):
                 self.assertEqual(video_idx, v_idx)
                 self.assertEqual(clip_idx, c_idx)
 
-    @unittest.skip("Moved to reference scripts for now")
-    def test_video_sampler(self):
-        with get_list_of_videos(num_videos=3, sizes=[25, 25, 25]) as video_list:
-            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)))
-            videos = indices // 5
-            v_idxs, count = torch.unique(videos, return_counts=True)
-            self.assertTrue(v_idxs.equal(torch.tensor([0, 1, 2])))
-            self.assertTrue(count.equal(torch.tensor([3, 3, 3])))
-
-    @unittest.skip("Moved to reference scripts for now")
-    def test_video_sampler_unequal(self):
-        with get_list_of_videos(num_videos=3, sizes=[10, 25, 25]) as video_list:
-            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))
-            self.assertIn(0, indices)
-            self.assertIn(1, indices)
-            # remove elements of the first video, to simplify testing
-            indices.remove(0)
-            indices.remove(1)
-            indices = torch.tensor(indices) - 2
-            videos = indices // 5
-            v_idxs, count = torch.unique(videos, return_counts=True)
-            self.assertTrue(v_idxs.equal(torch.tensor([0, 1])))
-            self.assertTrue(count.equal(torch.tensor([3, 3])))
-
     @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):

torchvision/datasets/samplers/__init__.py

@@ -0,0 +1,3 @@
+from .clip_sampler import DistributedSampler, UniformClipSampler, RandomClipSampler
+
+__all__ = ('DistributedSampler', 'UniformClipSampler', 'RandomClipSampler')

references/video_classification/sampler.py → torchvision/datasets/samplers/clip_sampler.py

@@ -60,33 +60,45 @@ def set_epoch(self, epoch):
 
 class UniformClipSampler(torch.utils.data.Sampler):
     """
-    Samples at most `max_video_clips_per_video` clips for each video, equally spaced
+    Sample `num_video_clips_per_video` clips for each video, equally spaced.
+    When number of unique clips in the video is fewer than num_video_clips_per_video,
+    repeat the clips until `num_video_clips_per_video` clips are collected
+
     Arguments:
         video_clips (VideoClips): video clips to sample from
-        max_clips_per_video (int): maximum number of clips to be sampled per video
+        num_clips_per_video (int): number of clips to be sampled per video
     """
-    def __init__(self, video_clips, max_clips_per_video):
+    def __init__(self, video_clips, num_clips_per_video):
         if not isinstance(video_clips, torchvision.datasets.video_utils.VideoClips):
             raise TypeError("Expected video_clips to be an instance of VideoClips, "
                             "got {}".format(type(video_clips)))
         self.video_clips = video_clips
-        self.max_clips_per_video = max_clips_per_video
+        self.num_clips_per_video = num_clips_per_video
 
     def __iter__(self):
         idxs = []
         s = 0
-        # select at most max_clips_per_video for each video, uniformly spaced
+        # select num_clips_per_video for each video, uniformly spaced
         for c in self.video_clips.clips:
             length = len(c)
-            step = max(length // self.max_clips_per_video, 1)
-            sampled = torch.arange(length)[::step] + s
+            if length == 0:
+                # corner case where video decoding fails
+                continue
+
+            sampled = (
+                torch.linspace(s, s + length - 1, steps=self.num_clips_per_video)
+                .floor()
+                .to(torch.int64)
+            )
             s += length
             idxs.append(sampled)
         idxs = torch.cat(idxs).tolist()
         return iter(idxs)
 
     def __len__(self):
-        return sum(min(len(c), self.max_clips_per_video) for c in self.video_clips.clips)
+        return sum(
+            self.num_clips_per_video for c in self.video_clips.clips if len(c) > 0
+        )
 
 
 class RandomClipSampler(torch.utils.data.Sampler):