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Convert generator in Sampler back to lazy construction #63646

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Convert generator in Sampler back to lazy construction #63646

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8b4ddf7
Convert Sampler back to lazily construction
ejguan Aug 20, 2021
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Update on "Convert Sampler back to lazily construction"
ejguan Aug 20, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
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Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
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13 changes: 13 additions & 0 deletions test/test_dataloader.py
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Expand Up @@ -1495,6 +1495,19 @@ def test_sampler_reproducibility(self):
):
self.assertEqual(list(fn()), list(fn()))

for sampler in (
RandomSampler(self.dataset, num_samples=5, replacement=True),
RandomSampler(self.dataset, replacement=False),
WeightedRandomSampler(weights, num_samples=5, replacement=True),
WeightedRandomSampler(weights, num_samples=5, replacement=False),
SubsetRandomSampler(range(10)),
):
torch.manual_seed(0)
l1 = list(sampler) + list(sampler)

torch.manual_seed(0)
l2 = list(sampler) + list(sampler)
self.assertEqual(l1, l2)

def _test_sampler(self, **kwargs):
indices = range(2, 12) # using a regular iterable
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15 changes: 10 additions & 5 deletions torch/utils/data/sampler.py
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Expand Up @@ -89,6 +89,7 @@ def __init__(self, data_source: Sized, replacement: bool = False,
self.replacement = replacement
self._num_samples = num_samples
self.generator = generator
self.gen: Optional[torch.Generator] = None
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if not isinstance(self.replacement, bool):
raise TypeError("replacement should be a boolean value, but got "
Expand All @@ -112,15 +113,19 @@ def num_samples(self) -> int:
def __iter__(self) -> Iterator[int]:
n = len(self.data_source)
if self.generator is None:
self.generator = torch.Generator()
self.generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
if self.gen is None:
self.gen = torch.Generator()
self.gen.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
else:
self.gen = self.generator

if self.replacement:
for _ in range(self.num_samples // 32):
yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=self.generator).tolist()
yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=self.generator).tolist()
yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=self.gen).tolist()
yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=self.gen).tolist()
else:
yield from torch.randperm(n, generator=self.generator).tolist()
yield from torch.randperm(n, generator=self.gen).tolist()
self.gen = None

def __len__(self) -> int:
return self.num_samples
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