How to Use ‘collate_fn’ with dataloaders in Pytorch

The collate_fn is a function used to merge a list of samples into a mini-batch of Tensor(s) that can be loaded into the model. It is used in conjunction with DataLoader class in PyTorch.

How to Use ‘collate_fn’ with dataloaders

Step 1: Define a custom ‘collate_fn’ function

Define a function that takes a list of samples from your dataset as input and returns a batched data version. The function should perform any necessary preprocessing and padding and convert the data to tensors if necessary.

import torch


def custom_collate_fn(batch):
  # Separate the input data and targets from the batch
  data, targets = zip(*batch)

  # Perform any preprocessing, padding, or other operations

  # Convert the data and targets to tensors
  data = torch.stack(data)
  targets = torch.tensor(targets)

  # Return the batched data and targets
  return data, targets

Step 2: Pass the custom ‘collate_fn’ to the DataLoader

When creating a DataLoader instance, you can provide the ‘collate_fn’ parameter with your custom function.

from torch.utils.data import DataLoader

train_dataset = ... # Your dataset instance
batch_size = 64

train_loader = DataLoader(
  train_dataset,
  batch_size=batch_size,
  shuffle=True,
  collate_fn=custom_collate_fn
)

Step 3: Use the DataLoader in your training loop

Now that you’ve created a DataLoader with your custom ‘collate_fn’, use it in your training loop to iterate through the data.

See the complete code here.

import torch
from torch.utils.data import DataLoader


def custom_collate_fn(batch):
  # Separate the input data and targets from the batch
  data, targets = zip(*batch)

  # Perform any preprocessing, padding, or other operations

  # Convert the data and targets to tensors
  data = torch.stack(data)
  targets = torch.tensor(targets)

  # Return the batched data and targets
  return data, targets


train_dataset = ... # Your dataset instance
batch_size = 64

train_loader = DataLoader(
  train_dataset,
  batch_size=batch_size,
  shuffle=True,
  collate_fn=custom_collate_fn
)

for epoch in range(num_epochs):
  for batch_idx, (data, targets) in enumerate(train_loader):
    # do whatever you want

That’s it! By defining a custom ‘collate_fn’ and passing it to the DataLoader, you can preprocess and combine your dataset samples into batches to tailor your specific use case.

Conclusion

The ‘collate_fn’ is a function you can define and pass to the DataLoader in PyTorch. It is responsible for preprocessing, combining, and formatting the data samples from the dataset into a batch.

By default, DataLoader uses a default ‘collate_fn’, which converts the data into a batched format. However, you may need a custom ‘collate_fn’ in cases where your dataset has varying data shapes, custom data structures, or requires specific preprocessing.

That’s it.