Port test_datasets_samplers.py to pytest

test/test_datasets_samplers.py

@@ -2,7 +2,7 @@
 import sys
 import os
 import torch
-import unittest
+import pytest
 
 from torchvision import io
 from torchvision.datasets.samplers import (
@@ -38,13 +38,13 @@ def get_list_of_videos(num_videos=5, sizes=None, fps=None):
         yield names
 
 
-@unittest.skipIf(not io.video._av_available(), "this test requires av")
-class Tester(unittest.TestCase):
+@pytest.mark.skipif(not io.video._av_available(), reason="this test requires av")
+class TestDatasetsSamplers:
     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)
+            assert len(sampler) == 3 * 3
             indices = torch.tensor(list(iter(sampler)))
             videos = torch.div(indices, 5, rounding_mode='floor')
             v_idxs, count = torch.unique(videos, return_counts=True)
@@ -55,10 +55,10 @@ 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)
+            assert len(sampler) == 2 + 3 + 3
             indices = list(iter(sampler))
-            self.assertIn(0, indices)
-            self.assertIn(1, indices)
+            assert 0 in indices
+            assert 1 in indices
             # remove elements of the first video, to simplify testing
             indices.remove(0)
             indices.remove(1)
@@ -72,7 +72,7 @@ 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)
+            assert len(sampler) == 3 * 3
             indices = torch.tensor(list(iter(sampler)))
             videos = torch.div(indices, 5, rounding_mode='floor')
             v_idxs, count = torch.unique(videos, return_counts=True)
@@ -84,7 +84,7 @@ 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)
+            assert len(sampler) == 3 * 3
             indices = torch.tensor(list(iter(sampler)))
             assert_equal(indices, torch.tensor([0, 0, 1, 2, 4, 6, 7, 9, 11]))
 
@@ -100,7 +100,7 @@ def test_distributed_sampler_and_uniform_clip_sampler(self):
                 group_size=3,
             )
             indices = torch.tensor(list(iter(distributed_sampler_rank0)))
-            self.assertEqual(len(distributed_sampler_rank0), 6)
+            assert len(distributed_sampler_rank0) == 6
             assert_equal(indices, torch.tensor([0, 2, 4, 10, 12, 14]))
 
             distributed_sampler_rank1 = DistributedSampler(
@@ -110,9 +110,9 @@ def test_distributed_sampler_and_uniform_clip_sampler(self):
                 group_size=3,
             )
             indices = torch.tensor(list(iter(distributed_sampler_rank1)))
-            self.assertEqual(len(distributed_sampler_rank1), 6)
+            assert len(distributed_sampler_rank1) == 6
             assert_equal(indices, torch.tensor([5, 7, 9, 0, 2, 4]))
 
 
 if __name__ == '__main__':
-    unittest.main()
+    pytest.main([__file__])