CODEWITHSUNDEEP
pythonpytorch
Rani
Rani

Reputation: 503

TypeError although same shape: if not (target.size() == input.size()): 'int' object is not callable

This is the error message I get. In the first line, I output the shapes of predicted and target. From my understanding, the error arises from those shapes not being the same but here they clearly are.

torch.Size([6890, 3]) torch.Size([6890, 3])

Traceback (most recent call last):
  File "train.py", line 251, in <module>
    main()
  File "train.py", line 230, in main
    train(net, training_dataset, targets, device, criterion, optimizer, epoch, args.epochs)
  File "train.py", line 101, in train
    loss = criterion(predicted, target.detach().cpu().numpy())
  File "/home/hb119056/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/hb119056/.local/lib/python3.6/site-packages/torch/nn/modules/loss.py", line 443, in forward
    return F.mse_loss(input, target, reduction=self.reduction)
  File "/home/hb119056/.local/lib/python3.6/site-packages/torch/nn/functional.py", line 2244, in mse_loss
    if not (target.size() == input.size()):
TypeError: 'int' object is not callable

I hope all the relevant context information is provided and if not, please let me know. Thanks for any suggestions!

EDIT: This is the part of the code where this error occurs:

        target = torch.from_numpy(np.load(file_dir + '/points/points{:03}.npy'.format(i))).to(device)
        rv = torch.zeros(12 * outputs.shape[0])

        for j in [x for x in range(10) if x != i]:
            source = torch.from_numpy(np.load(file_dir + '/points/points{:03}.npy'.format(j))).to(device)
            rv = factor.ransac(source, target, prob, n_iter, tol, device) # some self-written RANSAC-like method

        predicted = factor.predict(source, rv, outputs)
        print(target.shape, predicted.shape)
        loss = criterion(predicted, target.detach().cpu().numpy()) ## error occurs here

criterion is nn.MSELoss().

Upvotes: 2

Views: 4111

Answers (3)

Alena
Alena

Reputation: 135

A little bit late but maybe it will help someone else. Just solved the same problem for myself.

As Alpha said in his answer we cannot call .size() for a numpy array. But we can call .size() for a tensor. Therefore, we need to make our target a tensor. You can do it like this:

target = torch.from_numpy(target)

I'm using GPU, so I also needed to send my target to GPU. You can do it like this:

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
target = target.to(device)

And then the loss function must work perfectly.

Upvotes: 4

one
one

Reputation: 2585

that's because your target is a numpy object

File "train.py", line 101, in train:
target.detach().cpu().numpy()

in your code change the target type to numpy.

TLDR try change

loss = criterion(predicted, target.detach().cpu().numpy()) ## error occurs here

to 

loss = criterion(predicted, target) ## error occurs here

for example:

In [6]: b = np.ones(3)                                                                                                                                                                                      

In [7]: b.size                                                                                                                                                                                              
Out[7]: 3

In [8]: b.size()                                                                                                                                                                                            
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-8-365705555409> in <module>
----> 1 b.size()

TypeError: 'int' object is not callable

Upvotes: 1

Harshvardhan Arora
Harshvardhan Arora

Reputation: 103

It probably means that you are trying to call a method when a property with the same name is available. If this is indeed the problem, the solution is easy. Simply change the method call into a property access.

If you are comparing in the following way:

compare = (X.method() == Y.method())

Change it to:

compare = (X.method == Y.method)

If this does not answer your question, kindly share the code which you have used to compare the shapes.

Upvotes: 2

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