mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
99dbc5b0e2
See #145101 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145176 Approved by: https://github.com/bobrenjc93
135 lines
3.7 KiB
Python
135 lines
3.7 KiB
Python
from typing import Optional
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import torch
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from torch import Tensor
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class AndroidAPIModule(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, input):
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return None
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@torch.jit.script_method
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def eqBool(self, input: bool) -> bool:
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return input
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@torch.jit.script_method
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def eqInt(self, input: int) -> int:
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return input
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@torch.jit.script_method
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def eqFloat(self, input: float) -> float:
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return input
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@torch.jit.script_method
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def eqStr(self, input: str) -> str:
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return input
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@torch.jit.script_method
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def eqTensor(self, input: Tensor) -> Tensor:
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return input
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@torch.jit.script_method
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def eqDictStrKeyIntValue(self, input: dict[str, int]) -> dict[str, int]:
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return input
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@torch.jit.script_method
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def eqDictIntKeyIntValue(self, input: dict[int, int]) -> dict[int, int]:
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return input
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@torch.jit.script_method
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def eqDictFloatKeyIntValue(self, input: dict[float, int]) -> dict[float, int]:
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return input
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@torch.jit.script_method
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def listIntSumReturnTuple(self, input: list[int]) -> tuple[list[int], int]:
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sum = 0
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for x in input:
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sum += x
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return (input, sum)
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@torch.jit.script_method
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def listBoolConjunction(self, input: list[bool]) -> bool:
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res = True
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for x in input:
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res = res and x
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return res
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@torch.jit.script_method
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def listBoolDisjunction(self, input: list[bool]) -> bool:
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res = False
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for x in input:
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res = res or x
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return res
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@torch.jit.script_method
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def tupleIntSumReturnTuple(
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self, input: tuple[int, int, int]
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) -> tuple[tuple[int, int, int], int]:
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sum = 0
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for x in input:
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sum += x
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return (input, sum)
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@torch.jit.script_method
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def optionalIntIsNone(self, input: Optional[int]) -> bool:
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return input is None
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@torch.jit.script_method
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def intEq0None(self, input: int) -> Optional[int]:
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if input == 0:
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return None
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return input
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@torch.jit.script_method
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def str3Concat(self, input: str) -> str:
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return input + input + input
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@torch.jit.script_method
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def newEmptyShapeWithItem(self, input):
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return torch.tensor([int(input.item())])[0]
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@torch.jit.script_method
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def testAliasWithOffset(self) -> list[Tensor]:
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x = torch.tensor([100, 200])
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a = [x[0], x[1]]
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return a
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@torch.jit.script_method
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def testNonContiguous(self):
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x = torch.tensor([100, 200, 300])[::2]
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assert not x.is_contiguous()
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assert x[0] == 100
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assert x[1] == 300
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return x
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@torch.jit.script_method
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def conv2d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
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r = torch.nn.functional.conv2d(x, w)
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if toChannelsLast:
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r = r.contiguous(memory_format=torch.channels_last)
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else:
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r = r.contiguous()
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return r
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@torch.jit.script_method
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def conv3d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
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r = torch.nn.functional.conv3d(x, w)
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if toChannelsLast:
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r = r.contiguous(memory_format=torch.channels_last_3d)
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else:
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r = r.contiguous()
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return r
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@torch.jit.script_method
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def contiguous(self, x: Tensor) -> Tensor:
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return x.contiguous()
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@torch.jit.script_method
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def contiguousChannelsLast(self, x: Tensor) -> Tensor:
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return x.contiguous(memory_format=torch.channels_last)
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@torch.jit.script_method
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def contiguousChannelsLast3d(self, x: Tensor) -> Tensor:
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return x.contiguous(memory_format=torch.channels_last_3d)
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