27.11.2019 · I have two tensors of dimension 1000 * 1. I want to check how many of the 1000 elements are equal in the two tensors. I think I should be …
08.03.2021 · How might one compare two tensors in Libtorch (C++) to check if they are equal? I want to see if they are equal for an unit test, and understand in python one might: torch.all(tens_a.eq(tens_b)) However torch::all ret…
06.10.2015 · At some point may be important to check element wise how many elements are equal, comparing to the full number of elements. If you have two tensors dt1 and dt2 you get number of elements of dt1 as dt1.nelement () And with this formula you get the percentage: print (torch.sum (torch.eq (dt1, dt2)).item ()/dt1.nelement ()) Share Improve this answer
I need a Torch command that checks if two tensors have the same content, and returns TRUE if they have the same content.For example:local tens_a = torch.
18.11.2021 · We can divide it into 2 steps: a == b returns a boolean tensor (a mask) where values are True if the both a and b has the same value. The good thing is that with PyTorch, this operation is performed element-wise. So it checks each item at each channel, column, row and performs this operation. Once we have this mask, we can use it to select ...
torch.eq(input, other, *, out=None) → Tensor. Computes element-wise equality. The second argument can be a number or a tensor whose shape is broadcastable with the first argument. Parameters. input ( Tensor) – the tensor to compare. other ( Tensor or float) – the tensor or value to compare. Keyword Arguments.
If the dimensions of the input tensor and batches multiplication do not match ... This function performs an element-wise equality check between two tensors.
torch. equal (input, other) → bool. True if two tensors have the same size and elements, False otherwise. Example: >>> torch.equal(torch.tensor([1, 2]), ...
Learn about PyTorch’s features and capabilities. Community. Join the PyTorch developer community to contribute, learn, and get your questions answered. Developer Resources. Find resources and get questions answered. Forums. A place to discuss PyTorch code, issues, install, research. Models (Beta) Discover, publish, and reuse pre-trained models