Dimension error in Learn.TTA output


(John May) #1

I following the instructions here DeepLearning-LecNotes2 to run resnet34 on the dogbreeds dataset. When running these lines
log_preds,y = learn.TTA()
probs = np.exp(log_preds)
accuracy(log_preds,y), metrics.log_loss(y, probs)
I get a dimensions error when the last two functions are called.Should this be happening? I could reshape my probs data to the same dimension of the y variable, but how could I call the TTA function so that I would not have to do this. Can anyone give an explaination of what exactly learn.TTA() does?
The error I get is this:
TypeError Traceback (most recent call last)
in ()
----> 1 accuracy(log_preds,y), metrics.log_loss(y, probs)
2

~/fastai/courses/dl1/fastai/metrics.py in accuracy(preds, targs)
7
8 def accuracy(preds, targs):
----> 9 preds = torch.max(preds, dim=1)[1]
10 return (preds==targs).float().mean()
11

TypeError: torch.max received an invalid combination of arguments - got (numpy.ndarray, dim=int), but expected one of:

  • (torch.FloatTensor source)
  • (torch.FloatTensor source, torch.FloatTensor other)
    didn’t match because some of the keywords were incorrect: dim
  • (torch.FloatTensor source, int dim)
  • (torch.FloatTensor source, int dim, bool keepdim)

(William Horton) #2

Your error is because the accuracy function expects torch Tensors and your preds are a np array. You’ll want to call torch.from_numpy(preds).cuda(), that should fix it.


(John May) #3

That doesn’t seem to be working for me I get the following error.

RuntimeError Traceback (most recent call last)
in ()
----> 1 accuracy(torch.from_numpy(log_preds).y), metrics.log_loss(torch.from_numpy(probs).cuda(),y)
2

RuntimeError: from_numpy expects an np.ndarray but got torch.cuda.FloatTensor


(William Horton) #4

I don’t think probs is an np array, so you don’t have to call torch.from_numpy on it. Just on log_preds.


(Michael) #5

I’m having the same issue as the OP but haven’t managed to find a working solution yet from following the help here. I had the same original error and have now tried changing to

log_preds,y = learn.TTA()
probs = np.exp(log_preds)
accuracy(torch.from_numpy(log_preds),y), metrics.log_loss(y, probs)

but this gives the error

TypeError: eq received an invalid combination of arguments - got (numpy.ndarray), but expected one of:
* (int value)
didn't match because some of the arguments have invalid types: (numpy.ndarray)
* (torch.LongTensor other)
didn't match because some of the arguments have invalid types: (numpy.ndarray)