[fbsync] Updated gpu decoder test to use parametrize (#5328)

Summary:
* Updated gpu decoder test to use parametrize

* Remove list comprehension

Reviewed By: kazhang

Differential Revision: D33927498

fbshipit-source-id: 731b998f24ac12f6f68e73109f27cf07ece388e2

Author: yiwen-song

test/test_video_gpu_decoder.py

@@ -12,30 +12,31 @@
 
 VIDEO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "videos")
 
-test_videos = [
-    "RATRACE_wave_f_nm_np1_fr_goo_37.avi",
-    "TrumanShow_wave_f_nm_np1_fr_med_26.avi",
-    "v_SoccerJuggling_g23_c01.avi",
-    "v_SoccerJuggling_g24_c01.avi",
-    "R6llTwEh07w.mp4",
-    "SOX5yA1l24A.mp4",
-    "WUzgd7C1pWA.mp4",
-]
-
 
 @pytest.mark.skipif(_HAS_VIDEO_DECODER is False, reason="Didn't compile with support for gpu decoder")
 class TestVideoGPUDecoder:
     @pytest.mark.skipif(av is None, reason="PyAV unavailable")
-    def test_frame_reading(self):
-        for test_video in test_videos:
-            full_path = os.path.join(VIDEO_DIR, test_video)
-            decoder = VideoReader(full_path, device="cuda:0")
-            with av.open(full_path) as container:
-                for av_frame in container.decode(container.streams.video[0]):
-                    av_frames = torch.tensor(av_frame.to_rgb(src_colorspace="ITU709").to_ndarray())
-                    vision_frames = next(decoder)["data"]
-                    mean_delta = torch.mean(torch.abs(av_frames.float() - vision_frames.cpu().float()))
-                    assert mean_delta < 0.75
+    @pytest.mark.parametrize(
+        "video_file",
+        [
+            "RATRACE_wave_f_nm_np1_fr_goo_37.avi",
+            "TrumanShow_wave_f_nm_np1_fr_med_26.avi",
+            "v_SoccerJuggling_g23_c01.avi",
+            "v_SoccerJuggling_g24_c01.avi",
+            "R6llTwEh07w.mp4",
+            "SOX5yA1l24A.mp4",
+            "WUzgd7C1pWA.mp4",
+        ],
+    )
+    def test_frame_reading(self, video_file):
+        full_path = os.path.join(VIDEO_DIR, video_file)
+        decoder = VideoReader(full_path, device="cuda:0")
+        with av.open(full_path) as container:
+            for av_frame in container.decode(container.streams.video[0]):
+                av_frames = torch.tensor(av_frame.to_rgb(src_colorspace="ITU709").to_ndarray())
+                vision_frames = next(decoder)["data"]
+                mean_delta = torch.mean(torch.abs(av_frames.float() - vision_frames.cpu().float()))
+                assert mean_delta < 0.75
 
     @pytest.mark.skipif(av is None, reason="PyAV unavailable")
     @pytest.mark.parametrize("keyframes", [True, False])
@@ -65,16 +66,27 @@ def test_seek_reading(self, keyframes, full_path, duration):
                 assert mean_delta < 0.75
 
     @pytest.mark.skipif(av is None, reason="PyAV unavailable")
-    def test_metadata(self):
-        for test_video in test_videos:
-            full_path = os.path.join(VIDEO_DIR, test_video)
-            decoder = VideoReader(full_path, device="cuda:0")
-            video_metadata = decoder.get_metadata()["video"]
-            with av.open(full_path) as container:
-                video = container.streams.video[0]
-                av_duration = float(video.duration * video.time_base)
-                assert math.isclose(video_metadata["duration"], av_duration, rel_tol=1e-2)
-                assert math.isclose(video_metadata["fps"], video.base_rate, rel_tol=1e-2)
+    @pytest.mark.parametrize(
+        "video_file",
+        [
+            "RATRACE_wave_f_nm_np1_fr_goo_37.avi",
+            "TrumanShow_wave_f_nm_np1_fr_med_26.avi",
+            "v_SoccerJuggling_g23_c01.avi",
+            "v_SoccerJuggling_g24_c01.avi",
+            "R6llTwEh07w.mp4",
+            "SOX5yA1l24A.mp4",
+            "WUzgd7C1pWA.mp4",
+        ],
+    )
+    def test_metadata(self, video_file):
+        full_path = os.path.join(VIDEO_DIR, video_file)
+        decoder = VideoReader(full_path, device="cuda:0")
+        video_metadata = decoder.get_metadata()["video"]
+        with av.open(full_path) as container:
+            video = container.streams.video[0]
+            av_duration = float(video.duration * video.time_base)
+            assert math.isclose(video_metadata["duration"], av_duration, rel_tol=1e-2)
+            assert math.isclose(video_metadata["fps"], video.base_rate, rel_tol=1e-2)
 
 
 if __name__ == "__main__":