Number of batches in training and validation

[ajinkyaambatwar]

Hi I have a custom map-style dataLoader function for my application. Please excuse the indentation below.

class data(object):
    def __init__(self, train):
            self.train = train
           <some other init>
   def __len__(self):
           if self.train:
                  return 640
           else:
                 return 160
  def __getitem__(self, batchIdx):
         <do something>
         return something

Now as it can be seen, in the __len__, I have used dummy numbers. This is for my own experimentation. I am using a batch size of 32.
This is my training and validation dataloader build inside LightningModule class

def prepare_data(self):
        self.train_dset = data(
            train=True
        )
        self.val_dset = data(
            train=False
        )

    def _build_dataloader(self, dset, mode):
        return DataLoader(
            dset,
            batch_size =32,
            drop_last=mode == "train",
            collate_fn=collate_fn,
            pin_memory=True,
            num_workers=8,
            prefetch_factor=1
        )

    def train_dataloader(self):
        return self._build_dataloader(self.train_dset, mode="train")

    def val_dataloader(self):
        return self._build_dataloader(self.val_dset, mode="val")

I am expecting 20 batches for training(return len of 640 for batch size of 32) and 5 for validation(return len of 160 for batch size of 32). But during training, I prints

Epoch 0: 100%|██████████████| 25/25
Validating: 100%|██████████████| 5/5

The validation size is correct. But I can't understand the reason behind 25 training batches here.
Am I missing something?

Thank you!!

[s-rog]

The top progress bar is actually train + val (the whole epoch loop) so your output is correct!