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0565141728
Summary: I haven't had a chance to rigorously try these out yet so don't merge yet. Closes #18725. Pull Request resolved: https://github.com/pytorch/pytorch/pull/18963 Differential Revision: D14832897 Pulled By: ezyang fbshipit-source-id: 4780e7a34126bc66ddbfd9d808dfc9e0edd77e68
25 lines
886 B
Python
25 lines
886 B
Python
from typing import Iterator, Optional, Sequence, List, TypeVar, Generic, Sized
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T_co = TypeVar('T_co', covariant=True)
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class Sampler(Generic[T_co]):
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def __init__(self, data_source: Sized) -> None: ...
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def __iter__(self) -> Iterator[T_co]: ...
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def __len__(self) -> int: ...
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class SequentialSampler(Sampler[int]):
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pass
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class RandomSampler(Sampler[int]):
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num_samples: int
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def __init__(self, data_source: Sized, replacement: bool=..., num_samples: Optional[int]=...) -> None: ...
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class SubsetRandomSampler(Sampler[int]):
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def __init__(self, indices: Sequence[int]) -> None: ...
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class WeightedRandomSampler(Sampler[int]):
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def __init__(self, weights: Sequence[float], num_samples: int, replacement: bool=...) -> None: ...
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class BatchSampler(Sampler[List[int]]):
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def __init__(self, sampler: Sampler[int], batch_size: int, drop_last: bool) -> None: ...
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