If you want to squeeze a tensor, you should use the torch.squeeze() method. This method returns the new tensor dimension of the input tensor. It does, however, eliminate size 1.
If you want to unsqueeze a tensor, you should use the touch.unsqueeze() function. This function returns the new tensor dimension of the size 1 placed at the specified point.
How to squeeze and unsqueeze a tensor in PyTorch
To squeeze and unsqueeze a tensor in PyTorch, follow the steps below:
Step 1: Importing the torch library is the initial step.
Step 2: Construct and print a tensor.
Step 3: Ascertain torch.squeeze (input). It squeezes (removes) the size 1 and returns a tensor with all of the remaining dimensions of the input tensor.
Step 4: Select torch.unsqueeze (input, dim). After adding a new dimension of size 1 at the supplied dim, it returns the tensor.
Step 5: Squeezed and/or unsqueezed tensors should be printed.
Example
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import torch T = torch.ones(2,1,2) # size 2x1x2 print("Original Tensor T:\n", T ) print("Size of T:", T.size()) squeezed_T = torch.squeeze(T) print("Squeezed_T\n:", squeezed_T ) print("Size of Squeezed_T:", squeezed_T.size())
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Output:
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Original Tensor T: tensor([[[1., 1.]], [[1., 1.]]]) Size of T: torch.Size([2, 1, 2]) Squeezed_T : tensor([[1., 1.], [1., 1.]]) Size of Squeezed_T: torch.Size([2, 2])
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