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How to resize a tensor in PyTorch?
To resize a PyTorch tensor, we use the .view() method. We can increase or decrease the dimension of the tensor, but we have to make sure that the total number of elements in a tensor must match before and after the resize.
Steps
Import the required library. In all the following Python examples, the required Python library is torch. Make sure you have already installed it.
Create a PyTorch tensor and print it.
Resize the above-created tensor using .view() and assign the value to a variable. .view() does not resize the original tensor; it only gives a view with the new size, as its name suggests.
Finally, print the tensor after the resize.
Example 1
# Python program to resize a tensor in PyTorch
# Import the library
import torch
# Create a tensor
T = torch.Tensor([1, 2, 3, 4, 5, 6])
print(T)
# Resize T to 2x3
x = T.view(2,3)
print("Tensor after resize:\n",x)
# Other way to resize T to 2x3
x = T.view(-1,3)
print("Tensor after resize:\n",x)
# Other way resize T to 2x3
x = T.view(2,-1)
print("Tensor after resize:\n",x)Output
When you run the above Python 3 code, it will produce the following output
tensor([1., 2., 3., 4., 5., 6.]) Tensor after resize: tensor([[1., 2., 3.], [4., 5., 6.]]) Tensor after resize: tensor([[1., 2., 3.], [4., 5., 6.]]) Tensor after resize: tensor([[1., 2., 3.], [4., 5., 6.]])
Example 2
# Import the library
import torch
# Create a tensor shape 4x3
T = torch.Tensor([[1,2,3],[2,1,3],[2,3,5],[5,6,4]])
print(T)
# Resize T to 3x4
x = T.view(-1,4)
print("Tensor after resize:\n",x)
# Other way to esize T to 3x4
x = T.view(3,-1)
print("Tensor after resize:\n",x)
# Resize T to 2x6
x = T.view(2,-1)
print("Tensor after resize:\n",x)Output
When you run the above Python 3 code, it will produce the following output
tensor([[1., 2., 3.], [2., 1., 3.], [2., 3., 5.], [5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2.], [1., 3., 2., 3.], [5., 5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2.], [1., 3., 2., 3.], [5., 5., 6., 4.]]) Tensor after resize: tensor([[1., 2., 3., 2., 1., 3.], [2., 3., 5., 5., 6., 4.]])
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