Try to use Matlab CNN Resnet50 pre-trained model to convert ONNX to use in Pytorch

[icui4cu0118]

I first use onnx2pytorch to convert onnx to pytorch, and then use it to use the test set to get the accuracy.
This is my part of code :

model = torch.load(" .pkl")
model.eval()
for epoch in range(1) :
    testing_loss = 0.0
    testing_correct = 0.0
    zpred, ztrue = [], []
    for test_inputs, test_labels in test_data :
        test_inputs, test_labels = test_inputs.to(device), test_labels.to(device)
        output = model(test_inputs)    #error poit
        loss = criterion(output, test_labels)
        _, pred = torch.max(output.data, 1)
        testing_loss += loss.item() 
        testing_correct += torch.sum(pred == test_labels.data)
        output = (torch.max(torch.exp(output), 1)[1]).data.cpu().numpy()
        zpred.extend(output)
        testlabel = test_labels.data.cpu().numpy()
        ztrue.extend(testlabel)
    test_loss = testing_loss / len(test_loader)
    test_acc = 100 * testing_correct.cpu().numpy() / len(test_loader)

    print('Epoch is : {}, Test Loss is : {:.4f} Test Accuracy is :{:.4f}%'.format(epoch + 1, test_loss, test_acc))

But I got this error :

sum() received an invalid combination of arguments - got (Tensor, Tensor), but expected one of :
 * (Tensor input, *, torch.dtype dtype)
 * (Tensor input, tuple of ints dim, bool keepdim, *, torch.dtype dtype, Tensor out)
 * (Tensor input, tuple of names dim, bool keepdim, *, torch.dtype dtype, Tensor out)

How can I change the second tensor to the expected attribute?