mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
1b4d3d5748
Summary: These were returning incorrect data before. Now we make a contiguous copy before converting to Java. Exposing raw data to the user might be faster in some cases, but it's not clear that it's worth the complexity and code size. Test Plan: New unit test. Reviewed By: IvanKobzarev Differential Revision: D19221361 fbshipit-source-id: 22ecdad252c8fd968f833a2be5897c5ae483700c
93 lines
2.0 KiB
Plaintext
93 lines
2.0 KiB
Plaintext
def forward(self, input):
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return None
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def eqBool(self, input):
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# type: (bool) -> bool
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return input
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def eqInt(self, input):
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# type: (int) -> int
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return input
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def eqFloat(self, input):
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# type: (float) -> float
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return input
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def eqStr(self, input):
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# type: (str) -> str
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return input
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def eqTensor(self, input):
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# type: (Tensor) -> Tensor
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return input
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def eqDictStrKeyIntValue(self, input):
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# type: (Dict[str, int]) -> Dict[str, int]
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return input
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def eqDictIntKeyIntValue(self, input):
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# type: (Dict[int, int]) -> Dict[int, int]
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return input
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def eqDictFloatKeyIntValue(self, input):
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# type: (Dict[float, int]) -> Dict[float, int]
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return input
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def listIntSumReturnTuple(self, input):
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# type: (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):
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# type: (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):
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# type: (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):
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# type: (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):
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# type: (Optional[int]) -> bool
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return input is None
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def intEq0None(self, input):
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# type: (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):
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# type: (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):
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# type: () -> 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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