Use two separate dataloaders

❓ Questions and Help

Before asking:

1. search the issues.
2. search the docs.

Here is the relevant issue that I have looked up.

What is your question?

I have a labeled and an unlabeled dataset that I am using for a semi-supervised segmentation problem.

Here is what I want to implement in the training loop:

1. Train an epoch on the labeled dataset. (alternatively, a batch)
2. Train an epoch on the unlabeled dataset. (alternatively, a batch)

What is the best way to do it? I modified the code in the issue listed here.

Code

Please see below.

What have you tried?

    def train_dataloader(self):
        labeled_dataloader = DataLoader(self.labeled_dataset,
                                        batch_size=self.batch_size,
                                        num_workers=4,
                                        shuffle=True, drop_last=True)

        unlabeled_dataloader = DataLoader(self.unlabeled_dataset,
                                          batch_size=self.batch_size,
                                          num_workers=4,
                                          shuffle=True, drop_last=True)
        
        return {'labeled':labeled_dataloader, 'unlabeled': unlabeled_dataloader}

What’s your environment?

• OS: [e.g. iOS, Linux, Win]
• Packaging [e.g. pip, conda]
• Version [e.g. 0.5.2.1]

[nazim1021]

Hi @awaelchli thanks a lot for this solution, I have a similar problem where i am trying to use unlabeled dataset for semi-supervised classifcation, however i m having trouble on how can we add up the 2 losses and return them as one after each epoch. ? since here the losses are being calculated alternatively

[awaelchli]

I created an issue for this here: #8435