TypeError: can’t convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first (fastai)
I am following the code here:

https://www.kaggle.com/tanlikesmath/diabetic-retinopathy-with-resnet50-oversampling

However, during the metrics calculation, I am getting the following error:

File "main.py", line 50, in &lt;module&gt;
 learn.fit_one_cycle(4,max_lr = 2e-3)
...
File "main.py", line 39, in quadratic_kappa
    return torch.tensor(cohen_kappa_score(torch.argmax(y_hat,1), y, weights='quadratic'),device='cuda:0')
...
File "/pfs/work7/workspace/scratch/ul_dco32-conda-0/conda/envs/resnet50/lib/python3.8/site-packages/torch/tensor.py", line 486, in __array__
    return self.numpy()
TypeError: can't convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.


Here are the metrics and the model:

def quadratic_kappa(y_hat, y):
    return torch.tensor(cohen_kappa_score(torch.argmax(y_hat,1), y, weights='quadratic'),device='cuda:0')

learn = cnn_learner(data, models.resnet50, metrics = [accuracy,quadratic_kappa])
learn.fit_one_cycle(4,max_lr = 2e-3)



As it is being said in the discussion https://discuss.pytorch.org/t/typeerror-can-t-convert-cuda-tensor-to-numpy-use-tensor-cpu-to-copy-the-tensor-to-host-memory-first/32850/6, I have to bring the data back to cpu. But I am slightly lost how to do it.

I tried to add .cpu() all over the metrics but could not solve it so far.

I'm assuming that both y and y_hat are CUDA tensors, that means that you need to bring them both to the CPU for the cohen_kappa_score, not just one.

def quadratic_kappa(y_hat, y):
    return torch.tensor(cohen_kappa_score(torch.argmax(y_hat.cpu(),1), y.cpu(), weights='quadratic'),device='cuda:0')
    #                                                        ^^^         ^^^


Calling .cpu() on a tensor that is already on the CPU has no effect, so it's safe to use in any case.
