*Memos:
- My post explains isreal(), isnan() and isfinite().
- My post explains isinf(), isposinf() and isneginf().
- My post explains is_floating_point(), is_complex() and is_nonzero().
isin() can check if the zero or more elements of the 1st 0D or more D tensor contain the same zero or more elements of the 2nd 0D or more D tensor, getting the 0D or more D tensor of zero or more boolean values as shown below:
*Memos:
-
isin()can be used with torch but not with a tensor. - The 1st argument with
torchiselements(Required-Type:tensororscalarofintorfloat). *You must use a scalar withoutelements=. - The 2nd argument with
torchistest_elements(Required-Type:tensororscalarofintorfloat). *You must use a scalar withouttest_elements=. - The 3rd argument with
torchisassume_unique(Optional-Default=False-Type:bool). *IfTrue, assumes bothelementsandtest_elementscontain unique elements, which can speed up the calculation. - The 4th argument with
torchisinvert(Optional-Default=False-Type:bool). *IfTrue, inverts the boolean return tensor, resulting inTruevalues for elements not intest_elements. - The combination of a scalar(
elements) and ascalar(test_elements) cannot be used.
import torch tensor1 = torch.tensor([0, 1, 2, 3]) tensor2 = torch.tensor(2) torch.isin(elements=tensor1, test_elements=tensor2) torch.isin(tensor1, 2) # tensor([False, False, True, False]) torch.isin(elements=tensor1, test_elements=tensor2, assume_unique=True, invert=True) torch.isin(tensor1, 2, assume_unique=True, invert=True) # tensor([True, True, False, True]) torch.isin(elements=tensor2, test_elements=tensor1) torch.isin(tensor2, 2) torch.isin(2, test_elements=tensor1) torch.isin(2, test_elements=tensor2) # tensor(True) torch.isin(elements=tensor2, test_elements=tensor1, assume_unique=True, invert=True) torch.isin(tensor2, 2, assume_unique=True, invert=True) torch.isin(2, test_elements=tensor1, assume_unique=True, invert=True) torch.isin(2, test_elements=tensor2, assume_unique=True, invert=True) # tensor(False) tensor1 = torch.tensor([[[0., 1., 2.], [3., 4., 5.]], [[6., 7., 8.], [9., 10., 11.]]]) tensor2 = torch.tensor([[3., 5.], [7., 11.]]) torch.isin(elements=tensor1, test_elements=tensor2) # tensor([[[False, False, False], # [True, False, True]], # [[False, True, False], # [False, False, True]]]) torch.isin(elements=tensor1, test_elements=tensor2, assume_unique=True, invert=True) # tensor([[[True, True, True], # [False, True, False]], # [[True, False, True], # [True, True, False]]]) torch.isin(elements=tensor2, test_elements=tensor1) # tensor([[True, True], # [True, True]]) torch.isin(elements=tensor2, test_elements=tensor1, assume_unique=True, invert=True) # tensor([[False, False], # [False, False]]) torch.isin(tensor1, 3.) # tensor([[[False, False, False], # [True, False, False]], # [[False, False, False], # [False, False, False]]]) torch.isin(tensor1, 3., assume_unique=True, invert=True) # tensor([[[True, True, True], # [False, True, True]], # [[True, True, True], # [True, True, True]]]) torch.isin(tensor2, 3.) # tensor([[True, False], # [False, False]]) torch.isin(tensor2, 3., assume_unique=True, invert=True) # tensor([[False, True], # [True, True]]) torch.isin(3., test_elements=tensor1) torch.isin(3., test_elements=tensor2) # tensor(True) torch.isin(3., test_elements=tensor1, assume_unique=True, invert=True) torch.isin(3., test_elements=tensor2, assume_unique=True, invert=True) # tensor(False) 
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