Hi,
I need some help to do cross validation for my code. I am implementing federated learning for cancer prediction. But don’t know to how to implement cross validation in pytorch. Here is my code
federated_train_loader = sy.FederatedDataLoader(train_data.federate((hospital_1, hospital_2)), batch_size=args.batch_size, shuffle=True)
dataloaders['train'] = federated_train_loader

def train_model_federated(model, criterion, optimizer, scheduler, num_epochs=10):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{} at {}'.format(epoch, num_epochs - 1, datetime.now(my_timezone).strftime('%I:%M:%S %p (%d %b %Y)')))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'valid']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode
            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                if phase == 'valid':
                    inputs = inputs.to(device)
                    labels = labels.to(device)
                else:
                    inputs = inputs.to(device).get()
                    labels = labels.to(device).get()
                
                # zero the parameter gradients
                optimizer.zero_grad()
                #print("Enter 2st loop")
                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()
#                         scheduler.step()


                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                phase, epoch_loss, epoch_acc))

            # deep copy the model
            if phase == 'valid' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
                

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model