def test_distributed_sampler_and_uniform_clip_sampler(self, tmpdir):
video_list = get_list_of_videos(tmpdir,
num_videos=3,
sizes=[25, 25, 25])
video_clips = VideoClips(video_list, 5, 5)
clip_sampler = UniformClipSampler(video_clips, 3)
distributed_sampler_rank0 = DistributedSampler(
clip_sampler,
num_replicas=2,
rank=0,
group_size=3,
)
indices = torch.tensor(list(iter(distributed_sampler_rank0)))
assert len(distributed_sampler_rank0) == 6
assert_equal(indices, torch.tensor([0, 2, 4, 10, 12, 14]))
distributed_sampler_rank1 = DistributedSampler(
clip_sampler,
num_replicas=2,
rank=1,
group_size=3,
)
indices = torch.tensor(list(iter(distributed_sampler_rank1)))
assert len(distributed_sampler_rank1) == 6
assert_equal(indices, torch.tensor([5, 7, 9, 0, 2, 4]))
def test_uniform_clip_sampler_insufficient_clips(self, tmpdir):
video_list = get_list_of_videos(tmpdir, num_videos=3, sizes=[10, 25, 25])
video_clips = VideoClips(video_list, 5, 5)
sampler = UniformClipSampler(video_clips, 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]))
def test_video_clips_custom_fps(self, tmpdir):
video_list = get_list_of_videos(tmpdir, num_videos=3, sizes=[12, 12, 12], fps=[3, 4, 6])
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
def test_random_clip_sampler(self, tmpdir):
video_list = get_list_of_videos(tmpdir, num_videos=3, sizes=[25, 25, 25])
video_clips = VideoClips(video_list, 5, 5)
sampler = RandomClipSampler(video_clips, 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)
assert_equal(v_idxs, torch.tensor([0, 1, 2]))
assert_equal(count, torch.tensor([3, 3, 3]))
def test_random_clip_sampler_unequal(self, tmpdir):
video_list = get_list_of_videos(tmpdir, num_videos=3, sizes=[10, 25, 25])
video_clips = VideoClips(video_list, 5, 5)
sampler = RandomClipSampler(video_clips, 3)
assert len(sampler) == 2 + 3 + 3
indices = list(iter(sampler))
assert 0 in indices
assert 1 in indices
# remove elements of the first video, to simplify testing
indices.remove(0)
indices.remove(1)
indices = torch.tensor(indices) - 2
videos = torch.div(indices, 5, rounding_mode="floor")
v_idxs, count = torch.unique(videos, return_counts=True)
assert_equal(v_idxs, torch.tensor([0, 1]))
assert_equal(count, torch.tensor([3, 3]))
def test_video_clips(self, tmpdir):
video_list = get_list_of_videos(tmpdir, num_videos=3)
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
