[Solved] Pytorch tensor to numpy array

I have a pytorch Tensor of size torch.Size([4, 3, 966, 1296])

I want to convert it to numpy array using the following code:

imgs = imgs.numpy()[:, ::-1, :, :]

Can anyone please explain what this code is doing ?

Solution #1:

There are 4 dimensions of the tensor you want to convert.

[:, ::-1, :, :] 

: means that the first dimension should be copied as it is and converted, same goes for the third and fourth dimension.

::-1 means that for the second axes it reverses the the axes

Respondent: Maaz Bin Musa

Solution #2:

I believe you also have to use .detach(). I had to convert my Tensor to a numpy array on Colab which uses CUDA and GPU. I did it like the following:

# this is just my embedding matrix which is a Torch tensor object
embedding = learn.model.u_weight

embedding_list = list(range(0, 64382))

input = torch.cuda.LongTensor(embedding_list)
tensor_array = embedding(input)
# the output of the line below is a numpy array
tensor_array.cpu().detach().numpy()
Respondent: Azizbro

Solution #3:

This worked for me:

np_arr = torch_tensor.cpu().detach().numpy()
Respondent: Scott

Solution #4:

While other answers perfectly explained the question I will add some real life examples converting tensors to numpy array:

Example: Shared storage

PyTorch tensor residing on CPU shares the same storage as numpy array na

import torch
a = torch.ones((1,2))
print(a)
na = a.numpy()
na[0][0]=10
print(na)
print(a)

Output:

tensor([[1., 1.]])
[[10.  1.]]
tensor([[10.,  1.]])

Example: Eliminate effect of shared storage, copy numpy array first

To avoid the effect of shared storage we need to copy() the numpy array na to a new numpy array nac. Numpy copy() method creates the new separate storage.

import torch
a = torch.ones((1,2))
print(a)
na = a.numpy()
nac = na.copy()
nac[0][0]=10
?print(nac)
print(na)
print(a)

Output:

tensor([[1., 1.]])
[[10.  1.]]
[[1. 1.]]
tensor([[1., 1.]])

Now, just the nac numpy array will be altered with the line nac[0][0]=10, na and a will remain as is.

Example: CPU tensor with requires_grad=True

import torch
a = torch.ones((1,2), requires_grad=True)
print(a)
na = a.detach().numpy()
na[0][0]=10
print(na)
print(a)

Output:

tensor([[1., 1.]], requires_grad=True)
[[10.  1.]]
tensor([[10.,  1.]], requires_grad=True)

In here we call:

na = a.numpy() 

This would cause: RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead., because tensors that require_grad=True are recorded by PyTorch AD. Note that tensor.detach() is the new way for tensor.data.

This explains why we need to detach() them first before converting using numpy().

Example: CUDA tensor with requires_grad=False

a = torch.ones((1,2), device='cuda')
print(a)
na = a.to('cpu').numpy()
na[0][0]=10
print(na)
print(a)

Output:

tensor([[1., 1.]], device='cuda:0')
[[10.  1.]]
tensor([[1., 1.]], device='cuda:0')

?

Example: CUDA tensor with requires_grad=True

a = torch.ones((1,2), device='cuda', requires_grad=True)
print(a)
na = a.detach().to('cpu').numpy()
na[0][0]=10
?print(na)
print(a)

Output:

tensor([[1., 1.]], device='cuda:0', requires_grad=True)
[[10.  1.]]
tensor([[1., 1.]], device='cuda:0', requires_grad=True)

Without detach() method the error RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead. will be set.

Without .to('cpu') method TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first. will be set.

You could use cpu() but instead of to('cpu') but I prefer the newer to('cpu').

Respondent: prosti

Solution #5:

You can use this syntax if some grads are attached with your variables.

y=torch.Tensor.cpu(x).detach().numpy()[:,:,:,-1]

Respondent: Muhammad Bilal

Solution #6:

Your question is very poorly worded. Your code (sort of) already does what you want. What exactly are you confused about? x.numpy() answer the original title of your question:

Pytorch tensor to numpy array

you need improve your question starting with your title.

Anyway, just in case this is useful to others. You might need to call detach for your code to work. e.g.

RuntimeError: Can't call numpy() on Variable that requires grad.

So call .detach(). Sample code:

# creating data and running through a nn and saving it

import torch
import torch.nn as nn

from pathlib import Path
from collections import OrderedDict

import numpy as np

path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)

num_samples = 3
Din, Dout = 1, 1
lb, ub = -1, 1

x = torch.torch.distributions.Uniform(low=lb, high=ub).sample((num_samples, Din))

f = nn.Sequential(OrderedDict([
    ('f1', nn.Linear(Din,Dout)),
    ('out', nn.SELU())
]))
y = f(x)

# save data
y.numpy()
x_np, y_np = x.detach().cpu().numpy(), y.detach().cpu().numpy()
np.savez(path / 'db', x=x_np, y=y_np)

print(x_np)

cpu goes after detach. See: https://discuss.pytorch.org/t/should-it-really-be-necessary-to-do-var-detach-cpu-numpy/35489/5


Also I won’t make any comments on the slicking since that is off topic and that should not be the focus of your question. See this:

Understanding slice notation

Respondent: Charlie Parker

The answers/resolutions are collected from stackoverflow, are licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 .

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