isclose in PyTorch

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isclose() can check if the zero or more elements of the 1st 0D or more D tensor are equal or nearly equal to the zero or more elements of the 2nd 0D or more D tensor element-wise, getting the 0D or more D tensor of zero or more elements as shown below:

*Memos:

• isclose() can be used with torch or a tensor.
• The 1st argument(input) with torch or using a tensor(Required-Type:tensor of int, float, complex or bool).
• The 2nd argument with torch or the 1st argument with a tensor is other(Required-Type:tensor of int, float, complex or bool).
• The 3rd argument with torch or the 2nd argument with a tensor is rtol(Optional-Default:1e-05-Type:float).
• The 4th argument with torch or the 3rd argument with a tensor is atol(Optional-Default:1e-08-Type:float).
• The 5th argument with torch or the 4th argument with a tensor is equal_nan(Optional-Default:False-Type:bool): *Memos:
  • If it's True, nan and nan return True.
  • Basically, nan and nan return False.
• The formula is |input - other| <= rtol x |other| atol.

import torch

tensor1 = torch.tensor([1.00001001, 1.00000996, 1.00000995, torch.nan])
tensor2 = torch.tensor([1., 1., 1., torch.nan])

torch.isclose(input=tensor1, other=tensor2)
torch.isclose(input=tensor1, other=tensor2,
              rtol=1e-05, atol=1e-08, equal_nan=False)
            # 0.00001   # 0.00000001
tensor1.isclose(other=tensor2)
torch.isclose(input=tensor2, other=tensor1)
# tensor([False, False, True, False])

torch.isclose(input=tensor1, other=tensor2, equal_nan=True)
# tensor([False, False, True, True])

tensor1 = torch.tensor([[1.00001001, 1.00000996],
                        [1.00000995, torch.nan]])
tensor2 = torch.tensor([[1., 1.],
                        [1., torch.nan]])
torch.isclose(input=tensor1, other=tensor2)
torch.isclose(input=tensor2, other=tensor1)
# tensor([[False, False],
#         [True, False]])

tensor1 = torch.tensor([[[1.00001001],
                         [1.00000996]],
                        [[1.00000995],
                         [torch.nan]]])
tensor2 = torch.tensor([[[1.], [1.]],
                        [[1.], [torch.nan]]])
torch.isclose(input=tensor1, other=tensor2)
torch.isclose(input=tensor2, other=tensor1)
# tensor([[[False], [False]],
#         [[True], [False]]])

tensor1 = torch.tensor([[1.00001001, 1.00000996],
                        [1.00000995, torch.nan]])
tensor2 = torch.tensor([1., 1.])

torch.isclose(input=tensor1, other=tensor2)
torch.isclose(input=tensor2, other=tensor1)
# tensor([[False, False],
#         [True, False]])

tensor1 = torch.tensor([[1.00001001, 1.00000996],
                        [1.00000995, torch.nan]])
tensor2 = torch.tensor(1.)

torch.isclose(input=tensor1, other=tensor2)
torch.isclose(input=tensor2, other=tensor1)
# tensor([[False, False],
#         [True, False]])

tensor1 = torch.tensor([0, 1, 2])
tensor2 = torch.tensor(1)

torch.isclose(input=tensor1, other=tensor2)
# tensor([False, True, False])

tensor1 = torch.tensor([0.+0.j, 1.+0.j, 2.+0.j])
tensor2 = torch.tensor(1.+0.j)

torch.isclose(input=tensor1, other=tensor2)
# tensor([False, True, False])

tensor1 = torch.tensor([False, True, False])
tensor2 = torch.tensor(True)

torch.isclose(input=tensor1, other=tensor2)
# tensor([False, True, False])

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