TypeError: can’t convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first

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I am using a modified predict.py for testing a pruned SqueezeNet Model

[phung@archlinux SqueezeNet-Pruning]$ python predict.py --image 3_100.jpg --model model_prunned --num_class 2
prediction in progress
Traceback (most recent call last):
File “predict.py”, line 66, in
prediction = predict_image(imagepath)
File “predict.py”, line 52, in predict_image
index = output.data.numpy().argmax()
TypeError: can’t convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
[phung@archlinux SqueezeNet-Pruning]$

I understand that numpy does not support GPU yet.

How shall I modify the code to get away from this error without invoking tensor copy data operation, tensor.cpu() ?

pruning pytorch neural-network numpy
3 Answers

Change

index = output.data.numpy().argmax()

to

index = output.cpu().data.numpy().argmax()

This means data is first moved to cpu and then converted to numpy array.

user_32369250

I found out that I can just use

output.argmax()
user_87761670

You can use torch.max function like follows:

value, index = torch.max(output,1)
user_51365500
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