Resize PyTorch Tensor

Question

I am currently using the tensor.resize() function to resize a tensor to a new shape t = t.resize(1, 2, 3).

This gives me a deprecation warning:

Answer 1

Simply use t = t.contiguous().view(1, 2, 3) if you don't really want to change its data.

If not the case, the in-place resize_ operation will break the grad computation graph of t.
If it doesn't matter to you, just use t = t.data.resize_(1,2,3).

Answer 2

You can instead choose to go with tensor.reshape(new_shape) or torch.reshape(tensor, new_shape) as in:

# a `Variable` tensor
In [15]: ten = torch.randn(6, requires_grad=True)

# this would throw RuntimeError error
In [16]: ten.resize_(2, 3)
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-16-094491c46baa> in <module>()
----> 1 ten.resize_(2, 3)

RuntimeError: cannot resize variables that require grad

---

The above RuntimeError can be resolved or avoided by using tensor.reshape(new_shape)

In [17]: ten.reshape(2, 3)
Out[17]: 
tensor([[-0.2185, -0.6335, -0.0041],
        [-1.0147, -1.6359,  0.6965]])

# yet another way of changing tensor shape
In [18]: torch.reshape(ten, (2, 3))
Out[18]: 
tensor([[-0.2185, -0.6335, -0.0041],
        [-1.0147, -1.6359,  0.6965]])

Answer 3

Please can you try something like:

import torch
x = torch.tensor([[1, 2], [3, 4], [5, 6]])
print(":::",x.resize_(2, 2))
print("::::",x.resize_(3, 3))