Is it possible to shuffle two 2D tensors in PyTorch by their rows, but maintain the same order for both? I know you can shuffle a 2D tensor by rows with the following code:
a=a[torch.randperm(a.size()[0])]
To elaborate: If I had 2 tensors
a = torch.tensor([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]]) b = torch.tensor([[4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6]])
And ran them through some function/block of code to shuffle randomly but maintain correlation and produce something like the following
a = torch.tensor([[2, 2, 2, 2, 2], [1, 1, 1, 1, 1], [3, 3, 3, 3, 3]]) b = torch.tensor([[5, 5, 5, 5, 5], [4, 4, 4, 4, 4], [6, 6, 6, 6, 6]])
My current solution is converting to a list, using the random.shuffle() function like below.
a_list = a.tolist() b_list = b.tolist() temp_list = list(zip(a_list , b_list )) random.shuffle(temp_list) # Shuffle a_temp, b_temp = zip(*temp_list) a_list, b_list = list(a_temp), list(b_temp) # Convert back to tensors a = torch.tensor(a_list) b = torch.tensor(b_list)
This takes quite a while and was wondering if there is a better way.
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Answer
You mean
indices = torch.randperm(a.size()[0]) a=a[indices] b=b[indices]
?