mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
09eefec627
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49944 Upgrades type annotations from Python2 to Python3 Test Plan: Sandcastle tests Reviewed By: xush6528 Differential Revision: D25717539 fbshipit-source-id: c621e2712e87eaed08cda48eb0fb224f6b0570c9
97 lines
2.4 KiB
Plaintext
97 lines
2.4 KiB
Plaintext
def forward(self, input):
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return None
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def eqBool(self, input: bool) -> bool:
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return input
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def eqInt(self, input: int) -> int:
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return input
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def eqFloat(self, input: float) -> float:
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return input
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def eqStr(self, input: str) -> str:
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return input
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def eqTensor(self, input: Tensor) -> Tensor:
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return input
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def eqDictStrKeyIntValue(self, input: Dict[str, int]) -> Dict[str, int]:
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return input
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def eqDictIntKeyIntValue(self, input: Dict[int, int]) -> Dict[int, int]:
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return input
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def eqDictFloatKeyIntValue(self, input: Dict[float, int]) -> Dict[float, int]:
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return input
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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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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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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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def tupleIntSumReturnTuple(self, input: Tuple[int, int, int]) -> 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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def optionalIntIsNone(self, input: Optional[int]) -> bool:
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return input is None
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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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def str3Concat(self, input: str) -> str:
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return input + input + input
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def newEmptyShapeWithItem(self, input):
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return torch.tensor([int(input.item())])[0]
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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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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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def conv2d(self, x: Tensor, w: Tensor, toChannelsLast: bool) -> Tensor:
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r = torch.conv2d(x, w)
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if (toChannelsLast):
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# memory_format=torch.channels_last
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r = r.contiguous(memory_format=2)
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else:
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r = r.contiguous()
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return r
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def contiguous(self, x: Tensor) -> Tensor:
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return x.contiguous()
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def contiguousChannelsLast(self, x: Tensor) -> Tensor:
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# memory_format=torch.channels_last
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return x.contiguous(memory_format=2)
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def contiguousChannelsLast3d(self, x: Tensor) -> Tensor:
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# memory_format=torch.channels_last_3d
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return x.contiguous(memory_format=3)
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