How can I make a FloatTensor with requires_grad=True from a numpy array using PyTorch 0.4.0?

Question

Pytorch 0.4.0 introduced the merging on the Tensor and Variable classes.

Before this version, when I wanted to create a Variable with autograd from a numpy array I would do the following (where x is a numpy array):

x = Variable(torch.from_numpy(x).float(), requires_grad=True)

With PyTorch version 0.4.0, the migration guide shows how we can create Tensors with autograd enabled, examples show you can do things such as

x = torch.ones(3, 4, requires_grad=True) 

and also set requires_grad to existing tensors

existing_tensor.requires_grad_()

I have tried the following three things to try and create a Tensor with requires_grad=True which give errors (where x is a numpy array):

The first is

x = FloatTensor(x, requires_grad=True)

which gives the error

TypeError: new() received an invalid combination of arguments - got 
(numpy.ndarray, requires_grad=bool), but expected one of:
 * (torch.device device)
 * (tuple of ints size, torch.device device)
      didn't match because some of the keywords were incorrect: 
requires_grad
 * (torch.Storage storage)
 * (Tensor other)
 * (object data, torch.device device)
      didn't match because some of the keywords were incorrect: 
requires_grad

The second is to do

x = FloatTensor(x)
x.requires_grad()

And the third is

x = torch.from_numpy(x).single()
x.requires_grad()

Which both throw the following error on the second line:

TypeError: 'bool' object is not callable

These errors give me little hint at what I'm doing wrong, and since the latest version is so new its hard to find content online to help. How can I make a FloatTensor with requires_grad=True from a numpy array using PyTorch 0.4.0, preferably in a single line?

Answer 1

How can I make a FloatTensor with requires_grad=True from a numpy array using PyTorch 0.4.0, preferably in a single line?

If x is your numpy array this line should do the trick:

torch.tensor(x, requires_grad=True)

Here is a full example tested with PyTorch 0.4.0:

import numpy as np
import torch

x = np.array([1.3, 0.5, 1.9, 2.45])
print('np.array:', x)
t = torch.tensor(x, requires_grad=True)
print('tensor:', t)
print('requires_grad:', t.requires_grad)

This gives the following output:

np.array: [1.3  0.5  1.9  2.45]
tensor: tensor([ 1.3000,  0.5000,  1.9000,  2.4500], dtype=torch.float64)
requires_grad: True

Edit: dtype should be determined by the given dtype of your numpy array x.

I hope this helps.