Convert PyTorch Tensor to NumPy: A Step Guide – PyTorch Tutorial

By | May 17, 2022
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In this tutorial, we will introduce you how to convert a pytorch tensor to numpy. It is very easy to understand.

Convert a numpy to tensor

It is easy to create a tensor from a numpy ndarray.

For example:

import torch
import numpy as np

d = np.array([[ 0.0050,  0.8963, -0.7895,  0.1878,  0.9381],
        [ 2.3930,  1.4334,  1.0084, -0.3042, -1.0104],
        [ 0.7383,  1.0310, -0.4101, -0.4086,  0.2880],
        [ 0.0728, -0.6073, -0.0805,  0.3811, -0.0614],
        [-2.4547,  0.7017, -1.1140, -0.3677,  0.8146]])
x = torch.from_numpy(d)

Run this code, we will see:

tensor([[ 0.0050,  0.8963, -0.7895,  0.1878,  0.9381],
        [ 2.3930,  1.4334,  1.0084, -0.3042, -1.0104],
        [ 0.7383,  1.0310, -0.4101, -0.4086,  0.2880],
        [ 0.0728, -0.6073, -0.0805,  0.3811, -0.0614],
        [-2.4547,  0.7017, -1.1140, -0.3677,  0.8146]], dtype=torch.float64)

Here we will use torch.from_numpy() to convert a numpy to tensor.

Convert a tensor to numpy

Here are some methods to convert a pytorch tensor to numpy, for example:

import torch
import numpy as np

x = torch.randn([5,5], requires_grad= True)
print(x)
y = x.data.cpu().numpy()
print(y)

y = x.detach().numpy()
print(y)

Here we can use x.data.cpu().numpy() or x.detach().numpy() to convert tensor x to numpy.

Run this code, we will see:

tensor([[-0.3366, -1.5594,  0.4729, -1.1344, -1.6247],
        [-1.5472, -0.2384,  0.6280,  1.7125, -1.1771],
        [-0.8164, -0.7187, -2.0486, -1.4570,  0.2325],
        [-1.2239, -1.1844, -0.5962,  0.2595, -0.3080],
        [ 0.6933, -0.2938, -0.6494, -0.5251, -0.1348]], requires_grad=True)
[[-0.33663884 -1.5593643   0.47293866 -1.1344287  -1.6246802 ]
 [-1.5471828  -0.23844147  0.6279917   1.712499   -1.177059  ]
 [-0.8164169  -0.7187119  -2.0485656  -1.4570057   0.23254262]
 [-1.223918   -1.184376   -0.5962117   0.2594809  -0.3079657 ]
 [ 0.69329727 -0.2938432  -0.64942586 -0.5251326  -0.13480751]]
[[-0.33663884 -1.5593643   0.47293866 -1.1344287  -1.6246802 ]
 [-1.5471828  -0.23844147  0.6279917   1.712499   -1.177059  ]
 [-0.8164169  -0.7187119  -2.0485656  -1.4570057   0.23254262]
 [-1.223918   -1.184376   -0.5962117   0.2594809  -0.3079657 ]
 [ 0.69329727 -0.2938432  -0.64942586 -0.5251326  -0.13480751]]

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