Pytorch: Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead

Question

I have an error in my code which is not getting fixed any which way I try.

The Error is simple, I return a value:

torch.exp(-LL_total/T_total)

and get the error later in the pipeline:

RuntimeError: Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead.

Solutions such as cpu().detach().numpy() give the same error.

How could I fix it? Thanks.

Solution 1

 Error reproduced

import torch

tensor1 = torch.tensor([1.0,2.0],requires_grad=True)

print(tensor1)
print(type(tensor1))

tensor1 = tensor1.numpy()

print(tensor1)
print(type(tensor1))

which leads to the exact same error for the line tensor1 = tensor1.numpy():

tensor([1., 2.], requires_grad=True)
<class 'torch.Tensor'>
Traceback (most recent call last):
  File "/home/badScript.py", line 8, in <module>
    tensor1 = tensor1.numpy()
RuntimeError: Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead.

Process finished with exit code 1

Generic solution

this was suggested to you in your error message, just replace var with your variable name

import torch

tensor1 = torch.tensor([1.0,2.0],requires_grad=True)

print(tensor1)
print(type(tensor1))

tensor1 = tensor1.detach().numpy()

print(tensor1)
print(type(tensor1))

which returns as expected

tensor([1., 2.], requires_grad=True)
<class 'torch.Tensor'>
[1. 2.]
<class 'numpy.ndarray'>

Process finished with exit code 0

Some explanation

You need to convert your tensor to another tensor that isn't requiring a gradient in addition to its actual value definition. This other tensor can be converted to a numpy array. Cf. this discuss.pytorch post. (I think, more precisely, that one needs to do that in order to get the actual tensor out of its pytorch Variable wrapper, cf. this other discuss.pytorch post).

Solution 2

I had the same error message but it was for drawing a scatter plot on matplotlib.

There is 2 steps I could get out of this error message :

1. import the fastai.basics library with : from fastai.basics import *

2. If you only use the torch library, remember to take off the requires_grad with :

   with torch.no_grad():
       (your code)
   

Solution 3

For existing tensor

from torch.autograd import Variable

type(y)  # <class 'torch.Tensor'>

y = Variable(y, requires_grad=True)
y = y.detach().numpy()

type(y)  #<class 'numpy.ndarray'>

Solution 4

I just ran into this problem when running through epochs, I recorded the loss to a list

final_losses.append(loss)

Once I ran through all the epochs I wanted to graph the output

plt.plot(range(epochs), final_loss)
plt.ylabel('RMSE Loss')
plt.xlabel('Epoch');

I was running this on my Mac, with no problem, but, I needed to run this on a windows PC and it produced the error noted above. So, I checked the type of each variable.

Type(range(epochs)), type(final_losses)

range, list

Seems like it should be OK.

It took a little bit of fidgeting to realize that the final_losses list was a list of tensors. I then converted them to an actual list with a new list variable fi_los.

fi_los = [fl.item() for fl in final_losses ]
plt.plot(range(epochs), fi_los)
plt.ylabel('RMSE Loss')
plt.xlabel('Epoch');

Success!

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