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fix type hints for interpolation functions (#157202)

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Fixes #129053

Previously interpolate had a bad signature and not correct type hints.
This fixes this issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/157202
Approved by: https://github.com/ezyang, https://github.com/albanD
This commit is contained in:
florian
2025-07-09 03:11:37 +00:00
committed by PyTorch MergeBot
parent c515385b0a
commit 8d070187e3
2 changed files with 74 additions and 6 deletions
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68
torch/_C/_nn.pyi.in
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@ -67,5 +67,73 @@ def pad_sequence(
padding_value: float = 0.0, padding_value: float = 0.0,
padding_side: Literal["left", "right"] = "right", padding_side: Literal["left", "right"] = "right",
) -> Tensor: ... ) -> Tensor: ...
# Upsample functions used by torch.nn.functional.interpolate
def upsample_nearest1d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_nearest2d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_nearest3d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def _upsample_nearest_exact1d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def _upsample_nearest_exact2d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def _upsample_nearest_exact3d(
input: Tensor,
output_size: Sequence[int] | None,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_linear1d(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def _upsample_bilinear2d_aa(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_bilinear2d(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_trilinear3d(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def _upsample_bicubic2d_aa(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def upsample_bicubic2d(
input: Tensor,
output_size: Sequence[int] | None,
align_corners: bool,
scale_factors: Sequence[float] | None,
) -> Tensor: ...
def flatten_dense_tensors(tensors: list[Tensor]) -> Tensor: ... def flatten_dense_tensors(tensors: list[Tensor]) -> Tensor: ...
def unflatten_dense_tensors(flat: Tensor, tensors: list[Tensor]) -> list[Tensor]: ... def unflatten_dense_tensors(flat: Tensor, tensors: list[Tensor]) -> list[Tensor]: ...

12
torch/nn/functional.pyi.in
View File

@ -396,14 +396,14 @@ def instance_norm(
__all__ += ["instance_norm"] __all__ += ["instance_norm"]
def interpolate( def interpolate(
input: Any, input: Tensor,
size: Any | None = ..., size: int | Sequence[int] | None = ...,
scale_factor: Any | None = ..., scale_factor: float | Sequence[float] | None = ...,
mode: str = ..., mode: str = ...,
align_corners: Any | None = ..., align_corners: bool | None = ...,
recompute_scale_factor: Any | None = ..., recompute_scale_factor: bool | None = ...,
antialias: bool = ..., antialias: bool = ...,
): ... ) -> Tensor: ...
__all__ += ["interpolate"] __all__ += ["interpolate"]