关于Pytorch中accuracy和loss的计算

def train(train_loader, model, criteon, optimizer, epoch):
    train_loss = 0
    train_acc = 0
    num_correct= 0
    for step, (x,y) in enumerate(train_loader):

        # x: [b, 3, 224, 224], y: [b]
        x, y = x.to(device), y.to(device)

        model.train()
        logits = model(x)
        loss = criteon(logits, y)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        train_loss += float(loss.item())
        train_losses.append(train_loss)
        pred = logits.argmax(dim=1)
        num_correct += torch.eq(pred, y).sum().float().item()
    logger.info("Train Epoch: {}\t Loss: {:.6f}\t Acc: {:.6f}".format(epoch,train_loss/len(train_loader),num_correct/len(train_loader.dataset)))
    return num_correct/len(train_loader.dataset), train_loss/len(train_loader)