Make prediction

inference.py

test_acc = 0.0
for samples, labels in loaders['test']:
    with torch.no_grad():
        samples, labels = samples.cuda(), labels.cuda()
        output = trained_model(samples)
        # calculate accuracy
        pred = torch.argmax(output, dim=1)
        correct = pred.eq(labels)
        test_acc += torch.mean(correct.float())
print('Accuracy of the network on {} test images: {}%'.format(len(testset), round(test_acc.item()*100.0/len(loaders['test']), 2)))