Suppose i have a tensor t consisting only zeros and ones: t = torch.Tensor([1, 0, 0, 1]) And a dict with the weights: weights = {0: 0.1, 1: 0.9} I want to form a new tensor new_t, such that every element in tensor t is mapped to the corresponding value in the dict weights: new_t = torch.Tensor([0.9, 0.1, 0.1, 0.9]) Is there an elegant way to do this without iterating over tensor t? I've heard about torch.apply, but it only works if tensor t is on the CPU, is there any other options?
If you convert your weights dict into a tensor, you can index directly t = torch.tensor([1, 0, 0, 1]) weights = torch.tensor([0.1, 0.9]) new_t = weights[t] new_t >tensor([0.9000, 0.1000, 0.1000, 0.9000]) 