Use two separate dataloaders
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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:

Train an epoch on the labeled dataset. (alternatively, a batch)
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]