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import pytest
import torch
from common_utils import assert_equal, get_list_of_videos
from torchvision import io
from torchvision.datasets.samplers import DistributedSampler, RandomClipSampler, UniformClipSampler
from torchvision.datasets.video_utils import VideoClips
@pytest.mark.skipif(not io.video._av_available(), reason="this test requires av")
class TestDatasetsSamplers:
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_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)
sampler = UniformClipSampler(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]))
assert_equal(indices, torch.tensor([0, 2, 4, 5, 7, 9, 10, 12, 14]))
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_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]))
if __name__ == "__main__":
pytest.main([__file__])
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