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I have a pytorch
Tensor
of shape
[4, 3, 966, 1296]
. I want to convert it to
numpy
array using the following code:
imgs = imgs.numpy()[:, ::-1, :, :]
How does that code work?
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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()
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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').
: 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
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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
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