Using more than 1 metric in pytorch-CodePudding

I have some experience in Tenserflow but I'm new to pytorch. Sometimes I need more than 1 metric to check the accuracy of training. In Tenserflow, I used to do as shown below. But I wonder how could list more than 1 metric in pytorch.

LR = 0.0001
optim = keras.optimizers.Adam(LR)

dice_loss_se2 = sm.losses.DiceLoss()
mae = tf.keras.losses.MeanAbsoluteError( )
metrics = [ mae,sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5) , dice_loss_se2]

model.compile(optimizer=optim,loss= dice_loss_se2,metrics= metrics)

CodePudding user response：

In pytorch training is done mostly through loops so you have define via each step, there are packages like torchmetrics which you can run each metric heres an example:

import torchmetrics

for step, (test_image, test_labels) in tqdm(enumerate(test_dataloader), total=len(test_dataloader)):
        test_batch_image = test_image.to('cuda')
        test_batch_label = test_labels.to('cuda')
        targets.append(test_labels)
        
        with torch.no_grad():
            logits = model(test_batch_image)
        
        loss = criterion(logits, test_batch_label)
        test_loss  = loss.item()
        
        preds.append(logits.detach().cpu().numpy().argmax(axis=1))
    
    preds = torch.tensor(np.concatenate(preds))
    targets = torch.tensor(np.concatenate(targets))
    print('[Epoch %d] Test loss: %.3f' %(epoch   1, test_loss/ len(test_dataloader)))
    print('Accuracy: {}%'.format(round(torchmetrics.functional.accuracy(target=targets, preds=preds).item() * 100), 2))