106 lines
4.0 KiB
Python
106 lines
4.0 KiB
Python
import pytest
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import torch
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from common_utils import assert_equal, get_list_of_videos
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from torchvision import io
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from torchvision.datasets.video_utils import unfold, VideoClips
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class TestVideo:
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def test_unfold(self):
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a = torch.arange(7)
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r = unfold(a, 3, 3, 1)
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expected = torch.tensor(
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[
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[0, 1, 2],
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[3, 4, 5],
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]
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)
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assert_equal(r, expected)
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r = unfold(a, 3, 2, 1)
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expected = torch.tensor([[0, 1, 2], [2, 3, 4], [4, 5, 6]])
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assert_equal(r, expected)
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r = unfold(a, 3, 2, 2)
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expected = torch.tensor(
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[
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[0, 2, 4],
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[2, 4, 6],
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]
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)
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assert_equal(r, expected)
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@pytest.mark.skipif(not io.video._av_available(), reason="this test requires av")
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def test_video_clips(self, tmpdir):
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video_list = get_list_of_videos(tmpdir, num_videos=3)
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video_clips = VideoClips(video_list, 5, 5, num_workers=2)
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assert video_clips.num_clips() == 1 + 2 + 3
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for i, (v_idx, c_idx) in enumerate([(0, 0), (1, 0), (1, 1), (2, 0), (2, 1), (2, 2)]):
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video_idx, clip_idx = video_clips.get_clip_location(i)
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assert video_idx == v_idx
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assert clip_idx == c_idx
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video_clips = VideoClips(video_list, 6, 6)
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assert video_clips.num_clips() == 0 + 1 + 2
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for i, (v_idx, c_idx) in enumerate([(1, 0), (2, 0), (2, 1)]):
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video_idx, clip_idx = video_clips.get_clip_location(i)
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assert video_idx == v_idx
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assert clip_idx == c_idx
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video_clips = VideoClips(video_list, 6, 1)
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assert video_clips.num_clips() == 0 + (10 - 6 + 1) + (15 - 6 + 1)
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for i, v_idx, c_idx in [(0, 1, 0), (4, 1, 4), (5, 2, 0), (6, 2, 1)]:
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video_idx, clip_idx = video_clips.get_clip_location(i)
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assert video_idx == v_idx
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assert clip_idx == c_idx
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@pytest.mark.skipif(not io.video._av_available(), reason="this test requires av")
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def test_video_clips_custom_fps(self, tmpdir):
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video_list = get_list_of_videos(tmpdir, num_videos=3, sizes=[12, 12, 12], fps=[3, 4, 6])
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num_frames = 4
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for fps in [1, 3, 4, 10]:
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video_clips = VideoClips(video_list, num_frames, num_frames, fps)
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for i in range(video_clips.num_clips()):
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video, audio, info, video_idx = video_clips.get_clip(i)
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assert video.shape[0] == num_frames
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assert info["video_fps"] == fps
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# TODO add tests checking that the content is right
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def test_compute_clips_for_video(self):
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video_pts = torch.arange(30)
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# case 1: single clip
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num_frames = 13
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orig_fps = 30
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duration = float(len(video_pts)) / orig_fps
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new_fps = 13
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clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps)
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resampled_idxs = VideoClips._resample_video_idx(int(duration * new_fps), orig_fps, new_fps)
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assert len(clips) == 1
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assert_equal(clips, idxs)
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assert_equal(idxs[0], resampled_idxs)
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# case 2: all frames appear only once
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num_frames = 4
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orig_fps = 30
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duration = float(len(video_pts)) / orig_fps
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new_fps = 12
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clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps)
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resampled_idxs = VideoClips._resample_video_idx(int(duration * new_fps), orig_fps, new_fps)
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assert len(clips) == 3
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assert_equal(clips, idxs)
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assert_equal(idxs.flatten(), resampled_idxs)
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# case 3: frames aren't enough for a clip
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num_frames = 32
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orig_fps = 30
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new_fps = 13
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with pytest.warns(UserWarning):
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clips, idxs = VideoClips.compute_clips_for_video(video_pts, num_frames, num_frames, orig_fps, new_fps)
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assert len(clips) == 0
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assert len(idxs) == 0
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if __name__ == "__main__":
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pytest.main([__file__])
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