import contextlib import os import torch import unittest from torchvision import io from torchvision.datasets.video_utils import VideoClips, unfold from common_utils import get_tmp_dir from _assert_utils import assert_equal @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, 256, (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 class Tester(unittest.TestCase): def test_unfold(self): a = torch.arange(7) r = unfold(a, 3, 3, 1) expected = torch.tensor([ [0, 1, 2], [3, 4, 5], ]) assert_equal(r, expected, check_stride=False) r = unfold(a, 3, 2, 1) expected = torch.tensor([ [0, 1, 2], [2, 3, 4], [4, 5, 6] ]) assert_equal(r, expected, check_stride=False) r = unfold(a, 3, 2, 2) expected = torch.tensor([ [0, 2, 4], [2, 4, 6], ]) assert_equal(r, expected, check_stride=False) @unittest.skipIf(not io.video._av_available(), "this test requires av") def test_video_clips(self): with get_list_of_videos(num_videos=3) as video_list: video_clips = VideoClips(video_list, 5, 5, num_workers=2) assert 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) assert video_idx == v_idx assert clip_idx == c_idx video_clips = VideoClips(video_list, 6, 6) assert 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) assert video_idx == v_idx assert clip_idx == c_idx video_clips = VideoClips(video_list, 6, 1) assert 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) assert video_idx == v_idx assert clip_idx == c_idx @unittest.skipIf(not io.video._av_available(), "this test requires av") def test_video_clips_custom_fps(self): 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, num_workers=2) for i in range(video_clips.num_clips()): video, audio, info, video_idx = video_clips.get_clip(i) assert video.shape[0] == num_frames assert info["video_fps"] == fps # TODO add tests checking that the content is right def test_compute_clips_for_video(self): video_pts = torch.arange(30) # case 1: single clip num_frames = 13 orig_fps = 30 duration = float(len(video_pts)) / orig_fps new_fps = 13 clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps) resampled_idxs = VideoClips._resample_video_idx(int(duration * new_fps), orig_fps, new_fps) assert len(clips) == 1 assert_equal(clips, idxs) assert_equal(idxs[0], resampled_idxs) # case 2: all frames appear only once num_frames = 4 orig_fps = 30 duration = float(len(video_pts)) / orig_fps new_fps = 12 clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps) resampled_idxs = VideoClips._resample_video_idx(int(duration * new_fps), orig_fps, new_fps) assert len(clips) == 3 assert_equal(clips, idxs) assert_equal(idxs.flatten(), resampled_idxs) # case 3: frames aren't enough for a clip num_frames = 32 orig_fps = 30 new_fps = 13 with self.assertWarns(UserWarning): clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps) assert len(clips) == 0 assert len(idxs) == 0 if __name__ == '__main__': unittest.main()